Repository: TheAlgorithms/Python
Branch: master
Commit: 68473afc4b22
Files: 1489
Total size: 8.4 MB

Directory structure:
gitextract_lj21xc5n/

├── .devcontainer/
│   ├── Dockerfile
│   ├── README.md
│   ├── devcontainer.json
│   └── post_install
├── .gitattributes
├── .github/
│   ├── CODEOWNERS
│   ├── ISSUE_TEMPLATE/
│   │   ├── bug_report.yml
│   │   ├── config.yml
│   │   ├── feature_request.yml
│   │   └── other.yml
│   ├── dependabot.yml
│   ├── pull_request_template.md
│   ├── stale.yml
│   └── workflows/
│       ├── build.yml
│       ├── devcontainer_ci.yml
│       ├── directory_writer.yml
│       ├── project_euler.yml
│       ├── ruff.yml
│       └── sphinx.yml
├── .gitignore
├── .gitpod.yml
├── .pre-commit-config.yaml
├── CONTRIBUTING.md
├── DIRECTORY.md
├── LICENSE.md
├── README.md
├── audio_filters/
│   ├── README.md
│   ├── __init__.py
│   ├── butterworth_filter.py
│   ├── equal_loudness_filter.py.broken.txt
│   ├── iir_filter.py
│   ├── loudness_curve.json
│   └── show_response.py
├── backtracking/
│   ├── README.md
│   ├── __init__.py
│   ├── all_combinations.py
│   ├── all_permutations.py
│   ├── all_subsequences.py
│   ├── coloring.py
│   ├── combination_sum.py
│   ├── crossword_puzzle_solver.py
│   ├── generate_parentheses.py
│   ├── generate_parentheses_iterative.py
│   ├── hamiltonian_cycle.py
│   ├── knight_tour.py
│   ├── match_word_pattern.py
│   ├── minimax.py
│   ├── n_queens.py
│   ├── n_queens_math.py
│   ├── power_sum.py
│   ├── rat_in_maze.py
│   ├── sudoku.py
│   ├── sum_of_subsets.py
│   ├── word_break.py
│   ├── word_ladder.py
│   └── word_search.py
├── bit_manipulation/
│   ├── README.md
│   ├── __init__.py
│   ├── binary_and_operator.py
│   ├── binary_coded_decimal.py
│   ├── binary_count_setbits.py
│   ├── binary_count_trailing_zeros.py
│   ├── binary_or_operator.py
│   ├── binary_shifts.py
│   ├── binary_twos_complement.py
│   ├── binary_xor_operator.py
│   ├── bitwise_addition_recursive.py
│   ├── count_1s_brian_kernighan_method.py
│   ├── count_number_of_one_bits.py
│   ├── excess_3_code.py
│   ├── find_previous_power_of_two.py
│   ├── find_unique_number.py
│   ├── gray_code_sequence.py
│   ├── highest_set_bit.py
│   ├── index_of_rightmost_set_bit.py
│   ├── is_even.py
│   ├── is_power_of_two.py
│   ├── largest_pow_of_two_le_num.py
│   ├── missing_number.py
│   ├── numbers_different_signs.py
│   ├── power_of_4.py
│   ├── reverse_bits.py
│   ├── single_bit_manipulation_operations.py
│   └── swap_all_odd_and_even_bits.py
├── blockchain/
│   ├── README.md
│   ├── __init__.py
│   └── diophantine_equation.py
├── boolean_algebra/
│   ├── README.md
│   ├── __init__.py
│   ├── and_gate.py
│   ├── imply_gate.py
│   ├── karnaugh_map_simplification.py
│   ├── multiplexer.py
│   ├── nand_gate.py
│   ├── nimply_gate.py
│   ├── nor_gate.py
│   ├── not_gate.py
│   ├── or_gate.py
│   ├── quine_mc_cluskey.py
│   ├── xnor_gate.py
│   └── xor_gate.py
├── cellular_automata/
│   ├── README.md
│   ├── __init__.py
│   ├── conways_game_of_life.py
│   ├── game_of_life.py
│   ├── langtons_ant.py
│   ├── nagel_schrekenberg.py
│   ├── one_dimensional.py
│   └── wa_tor.py
├── ciphers/
│   ├── README.md
│   ├── __init__.py
│   ├── a1z26.py
│   ├── affine_cipher.py
│   ├── atbash.py
│   ├── autokey.py
│   ├── baconian_cipher.py
│   ├── base16.py
│   ├── base32.py
│   ├── base64_cipher.py
│   ├── base85.py
│   ├── beaufort_cipher.py
│   ├── bifid.py
│   ├── brute_force_caesar_cipher.py
│   ├── caesar_cipher.py
│   ├── cryptomath_module.py
│   ├── decrypt_caesar_with_chi_squared.py
│   ├── deterministic_miller_rabin.py
│   ├── diffie.py
│   ├── diffie_hellman.py
│   ├── elgamal_key_generator.py
│   ├── enigma_machine2.py
│   ├── fractionated_morse_cipher.py
│   ├── gronsfeld_cipher.py
│   ├── hill_cipher.py
│   ├── mixed_keyword_cypher.py
│   ├── mono_alphabetic_ciphers.py
│   ├── morse_code.py
│   ├── onepad_cipher.py
│   ├── permutation_cipher.py
│   ├── playfair_cipher.py
│   ├── polybius.py
│   ├── porta_cipher.py
│   ├── prehistoric_men.txt
│   ├── rabin_miller.py
│   ├── rail_fence_cipher.py
│   ├── rot13.py
│   ├── rsa_cipher.py
│   ├── rsa_factorization.py
│   ├── rsa_key_generator.py
│   ├── running_key_cipher.py
│   ├── shuffled_shift_cipher.py
│   ├── simple_keyword_cypher.py
│   ├── simple_substitution_cipher.py
│   ├── transposition_cipher.py
│   ├── transposition_cipher_encrypt_decrypt_file.py
│   ├── trifid_cipher.py
│   ├── vernam_cipher.py
│   ├── vigenere_cipher.py
│   └── xor_cipher.py
├── computer_vision/
│   ├── README.md
│   ├── __init__.py
│   ├── cnn_classification.py
│   ├── flip_augmentation.py
│   ├── haralick_descriptors.py
│   ├── harris_corner.py
│   ├── horn_schunck.py
│   ├── intensity_based_segmentation.py
│   ├── mean_threshold.py
│   ├── mosaic_augmentation.py
│   └── pooling_functions.py
├── conversions/
│   ├── README.md
│   ├── __init__.py
│   ├── astronomical_length_scale_conversion.py
│   ├── binary_to_decimal.py
│   ├── binary_to_hexadecimal.py
│   ├── binary_to_octal.py
│   ├── convert_number_to_words.py
│   ├── decimal_to_any.py
│   ├── decimal_to_binary.py
│   ├── decimal_to_hexadecimal.py
│   ├── decimal_to_octal.py
│   ├── energy_conversions.py
│   ├── excel_title_to_column.py
│   ├── hex_to_bin.py
│   ├── hexadecimal_to_decimal.py
│   ├── ipv4_conversion.py
│   ├── length_conversion.py
│   ├── molecular_chemistry.py
│   ├── octal_to_binary.py
│   ├── octal_to_decimal.py
│   ├── octal_to_hexadecimal.py
│   ├── prefix_conversions.py
│   ├── prefix_conversions_string.py
│   ├── pressure_conversions.py
│   ├── rectangular_to_polar.py
│   ├── rgb_cmyk_conversion.py
│   ├── rgb_hsv_conversion.py
│   ├── roman_numerals.py
│   ├── speed_conversions.py
│   ├── temperature_conversions.py
│   ├── time_conversions.py
│   ├── volume_conversions.py
│   └── weight_conversion.py
├── data_compression/
│   ├── README.md
│   ├── __init__.py
│   ├── burrows_wheeler.py
│   ├── coordinate_compression.py
│   ├── huffman.py
│   ├── lempel_ziv.py
│   ├── lempel_ziv_decompress.py
│   ├── lz77.py
│   ├── peak_signal_to_noise_ratio.py
│   └── run_length_encoding.py
├── data_structures/
│   ├── __init__.py
│   ├── arrays/
│   │   ├── __init__.py
│   │   ├── equilibrium_index_in_array.py
│   │   ├── find_triplets_with_0_sum.py
│   │   ├── index_2d_array_in_1d.py
│   │   ├── kth_largest_element.py
│   │   ├── median_two_array.py
│   │   ├── monotonic_array.py
│   │   ├── pairs_with_given_sum.py
│   │   ├── permutations.py
│   │   ├── prefix_sum.py
│   │   ├── product_sum.py
│   │   ├── rotate_array.py
│   │   ├── sparse_table.py
│   │   └── sudoku_solver.py
│   ├── binary_tree/
│   │   ├── README.md
│   │   ├── __init__.py
│   │   ├── avl_tree.py
│   │   ├── basic_binary_tree.py
│   │   ├── binary_search_tree.py
│   │   ├── binary_search_tree_recursive.py
│   │   ├── binary_tree_mirror.py
│   │   ├── binary_tree_node_sum.py
│   │   ├── binary_tree_path_sum.py
│   │   ├── binary_tree_traversals.py
│   │   ├── diameter_of_binary_tree.py
│   │   ├── diff_views_of_binary_tree.py
│   │   ├── distribute_coins.py
│   │   ├── fenwick_tree.py
│   │   ├── flatten_binarytree_to_linkedlist.py
│   │   ├── floor_and_ceiling.py
│   │   ├── inorder_tree_traversal_2022.py
│   │   ├── is_sorted.py
│   │   ├── is_sum_tree.py
│   │   ├── lazy_segment_tree.py
│   │   ├── lowest_common_ancestor.py
│   │   ├── maximum_fenwick_tree.py
│   │   ├── maximum_sum_bst.py
│   │   ├── merge_two_binary_trees.py
│   │   ├── mirror_binary_tree.py
│   │   ├── non_recursive_segment_tree.py
│   │   ├── number_of_possible_binary_trees.py
│   │   ├── red_black_tree.py
│   │   ├── segment_tree.py
│   │   ├── segment_tree_other.py
│   │   ├── serialize_deserialize_binary_tree.py
│   │   ├── symmetric_tree.py
│   │   ├── treap.py
│   │   └── wavelet_tree.py
│   ├── disjoint_set/
│   │   ├── __init__.py
│   │   ├── alternate_disjoint_set.py
│   │   └── disjoint_set.py
│   ├── hashing/
│   │   ├── __init__.py
│   │   ├── bloom_filter.py
│   │   ├── double_hash.py
│   │   ├── hash_map.py
│   │   ├── hash_table.py
│   │   ├── hash_table_with_linked_list.py
│   │   ├── number_theory/
│   │   │   ├── __init__.py
│   │   │   └── prime_numbers.py
│   │   ├── quadratic_probing.py
│   │   └── tests/
│   │       ├── __init__.py
│   │       └── test_hash_map.py
│   ├── heap/
│   │   ├── __init__.py
│   │   ├── binomial_heap.py
│   │   ├── heap.py
│   │   ├── heap_generic.py
│   │   ├── max_heap.py
│   │   ├── min_heap.py
│   │   ├── randomized_heap.py
│   │   └── skew_heap.py
│   ├── kd_tree/
│   │   ├── __init__.py
│   │   ├── build_kdtree.py
│   │   ├── example/
│   │   │   ├── __init__.py
│   │   │   ├── example_usage.py
│   │   │   └── hypercube_points.py
│   │   ├── kd_node.py
│   │   ├── nearest_neighbour_search.py
│   │   └── tests/
│   │       ├── __init__.py
│   │       └── test_kdtree.py
│   ├── linked_list/
│   │   ├── __init__.py
│   │   ├── circular_linked_list.py
│   │   ├── deque_doubly.py
│   │   ├── doubly_linked_list.py
│   │   ├── doubly_linked_list_two.py
│   │   ├── floyds_cycle_detection.py
│   │   ├── from_sequence.py
│   │   ├── has_loop.py
│   │   ├── is_palindrome.py
│   │   ├── merge_two_lists.py
│   │   ├── middle_element_of_linked_list.py
│   │   ├── print_reverse.py
│   │   ├── reverse_k_group.py
│   │   ├── rotate_to_the_right.py
│   │   ├── singly_linked_list.py
│   │   ├── skip_list.py
│   │   └── swap_nodes.py
│   ├── queues/
│   │   ├── __init__.py
│   │   ├── circular_queue.py
│   │   ├── circular_queue_linked_list.py
│   │   ├── double_ended_queue.py
│   │   ├── linked_queue.py
│   │   ├── priority_queue_using_list.py
│   │   ├── queue_by_list.py
│   │   ├── queue_by_two_stacks.py
│   │   └── queue_on_pseudo_stack.py
│   ├── stacks/
│   │   ├── __init__.py
│   │   ├── balanced_parentheses.py
│   │   ├── dijkstras_two_stack_algorithm.py
│   │   ├── infix_to_postfix_conversion.py
│   │   ├── infix_to_prefix_conversion.py
│   │   ├── largest_rectangle_histogram.py
│   │   ├── lexicographical_numbers.py
│   │   ├── next_greater_element.py
│   │   ├── postfix_evaluation.py
│   │   ├── prefix_evaluation.py
│   │   ├── stack.py
│   │   ├── stack_using_two_queues.py
│   │   ├── stack_with_doubly_linked_list.py
│   │   ├── stack_with_singly_linked_list.py
│   │   └── stock_span_problem.py
│   ├── suffix_tree/
│   │   ├── __init__.py
│   │   ├── example/
│   │   │   ├── __init__.py
│   │   │   └── example_usage.py
│   │   ├── suffix_tree.py
│   │   ├── suffix_tree_node.py
│   │   └── tests/
│   │       ├── __init__.py
│   │       └── test_suffix_tree.py
│   └── trie/
│       ├── __init__.py
│       ├── radix_tree.py
│       └── trie.py
├── digital_image_processing/
│   ├── __init__.py
│   ├── change_brightness.py
│   ├── change_contrast.py
│   ├── convert_to_negative.py
│   ├── dithering/
│   │   ├── __init__.py
│   │   └── burkes.py
│   ├── edge_detection/
│   │   ├── __init__.py
│   │   └── canny.py
│   ├── filters/
│   │   ├── __init__.py
│   │   ├── bilateral_filter.py
│   │   ├── convolve.py
│   │   ├── gabor_filter.py
│   │   ├── gaussian_filter.py
│   │   ├── laplacian_filter.py
│   │   ├── local_binary_pattern.py
│   │   ├── median_filter.py
│   │   └── sobel_filter.py
│   ├── histogram_equalization/
│   │   ├── __init__.py
│   │   ├── histogram_stretch.py
│   │   ├── image_data/
│   │   │   └── __init__.py
│   │   └── output_data/
│   │       └── __init__.py
│   ├── image_data/
│   │   └── __init__.py
│   ├── index_calculation.py
│   ├── morphological_operations/
│   │   ├── __init__.py
│   │   ├── dilation_operation.py
│   │   └── erosion_operation.py
│   ├── resize/
│   │   ├── __init__.py
│   │   └── resize.py
│   ├── rotation/
│   │   ├── __init__.py
│   │   └── rotation.py
│   ├── sepia.py
│   └── test_digital_image_processing.py
├── divide_and_conquer/
│   ├── __init__.py
│   ├── closest_pair_of_points.py
│   ├── convex_hull.py
│   ├── heaps_algorithm.py
│   ├── heaps_algorithm_iterative.py
│   ├── inversions.py
│   ├── kth_order_statistic.py
│   ├── max_difference_pair.py
│   ├── max_subarray.py
│   ├── mergesort.py
│   ├── peak.py
│   ├── power.py
│   └── strassen_matrix_multiplication.py
├── docs/
│   ├── __init__.py
│   ├── conf.py
│   └── source/
│       └── __init__.py
├── dynamic_programming/
│   ├── __init__.py
│   ├── abbreviation.py
│   ├── all_construct.py
│   ├── bitmask.py
│   ├── catalan_numbers.py
│   ├── climbing_stairs.py
│   ├── combination_sum_iv.py
│   ├── edit_distance.py
│   ├── factorial.py
│   ├── fast_fibonacci.py
│   ├── fibonacci.py
│   ├── fizz_buzz.py
│   ├── floyd_warshall.py
│   ├── integer_partition.py
│   ├── iterating_through_submasks.py
│   ├── k_means_clustering_tensorflow.py
│   ├── knapsack.py
│   ├── largest_divisible_subset.py
│   ├── longest_common_subsequence.py
│   ├── longest_common_substring.py
│   ├── longest_increasing_subsequence.py
│   ├── longest_increasing_subsequence_iterative.py
│   ├── longest_increasing_subsequence_o_nlogn.py
│   ├── longest_palindromic_subsequence.py
│   ├── matrix_chain_multiplication.py
│   ├── matrix_chain_order.py
│   ├── max_non_adjacent_sum.py
│   ├── max_product_subarray.py
│   ├── max_subarray_sum.py
│   ├── min_distance_up_bottom.py
│   ├── minimum_coin_change.py
│   ├── minimum_cost_path.py
│   ├── minimum_partition.py
│   ├── minimum_size_subarray_sum.py
│   ├── minimum_squares_to_represent_a_number.py
│   ├── minimum_steps_to_one.py
│   ├── minimum_tickets_cost.py
│   ├── narcissistic_number.py
│   ├── optimal_binary_search_tree.py
│   ├── palindrome_partitioning.py
│   ├── range_sum_query.py
│   ├── regex_match.py
│   ├── rod_cutting.py
│   ├── smith_waterman.py
│   ├── subset_generation.py
│   ├── sum_of_subset.py
│   ├── trapped_water.py
│   ├── tribonacci.py
│   ├── viterbi.py
│   ├── wildcard_matching.py
│   └── word_break.py
├── electronics/
│   ├── __init__.py
│   ├── apparent_power.py
│   ├── builtin_voltage.py
│   ├── capacitor_equivalence.py
│   ├── carrier_concentration.py
│   ├── charging_capacitor.py
│   ├── charging_inductor.py
│   ├── circular_convolution.py
│   ├── coulombs_law.py
│   ├── electric_conductivity.py
│   ├── electric_power.py
│   ├── electrical_impedance.py
│   ├── ic_555_timer.py
│   ├── ind_reactance.py
│   ├── ohms_law.py
│   ├── real_and_reactive_power.py
│   ├── resistor_color_code.py
│   ├── resistor_equivalence.py
│   ├── resonant_frequency.py
│   └── wheatstone_bridge.py
├── file_transfer/
│   ├── __init__.py
│   ├── mytext.txt
│   ├── receive_file.py
│   ├── send_file.py
│   └── tests/
│       ├── __init__.py
│       └── test_send_file.py
├── financial/
│   ├── README.md
│   ├── __init__.py
│   ├── equated_monthly_installments.py
│   ├── exponential_moving_average.py
│   ├── interest.py
│   ├── present_value.py
│   ├── price_plus_tax.py
│   ├── simple_moving_average.py
│   ├── straight_line_depreciation.py
│   └── time_and_half_pay.py
├── fractals/
│   ├── __init__.py
│   ├── julia_sets.py
│   ├── koch_snowflake.py
│   ├── mandelbrot.py
│   ├── sierpinski_triangle.py
│   └── vicsek.py
├── fuzzy_logic/
│   ├── __init__.py
│   ├── fuzzy_operations.py
│   └── fuzzy_operations.py.DISABLED.txt
├── genetic_algorithm/
│   ├── __init__.py
│   └── basic_string.py
├── geodesy/
│   ├── __init__.py
│   ├── haversine_distance.py
│   └── lamberts_ellipsoidal_distance.py
├── geometry/
│   ├── __init__.py
│   ├── geometry.py
│   ├── graham_scan.py
│   ├── jarvis_march.py
│   └── tests/
│       ├── __init__.py
│       ├── test_graham_scan.py
│       └── test_jarvis_march.py
├── graphics/
│   ├── __init__.py
│   ├── bezier_curve.py
│   ├── butterfly_pattern.py
│   ├── digital_differential_analyzer_line.py
│   └── vector3_for_2d_rendering.py
├── graphs/
│   ├── __init__.py
│   ├── a_star.py
│   ├── ant_colony_optimization_algorithms.py
│   ├── articulation_points.py
│   ├── basic_graphs.py
│   ├── bellman_ford.py
│   ├── bi_directional_dijkstra.py
│   ├── bidirectional_a_star.py
│   ├── bidirectional_breadth_first_search.py
│   ├── bidirectional_search.py
│   ├── boruvka.py
│   ├── breadth_first_search.py
│   ├── breadth_first_search_2.py
│   ├── breadth_first_search_shortest_path.py
│   ├── breadth_first_search_shortest_path_2.py
│   ├── breadth_first_search_zero_one_shortest_path.py
│   ├── check_bipatrite.py
│   ├── check_cycle.py
│   ├── connected_components.py
│   ├── deep_clone_graph.py
│   ├── depth_first_search.py
│   ├── depth_first_search_2.py
│   ├── dijkstra.py
│   ├── dijkstra_2.py
│   ├── dijkstra_algorithm.py
│   ├── dijkstra_alternate.py
│   ├── dijkstra_binary_grid.py
│   ├── dinic.py
│   ├── directed_and_undirected_weighted_graph.py
│   ├── edmonds_karp_multiple_source_and_sink.py
│   ├── eulerian_path_and_circuit_for_undirected_graph.py
│   ├── even_tree.py
│   ├── finding_bridges.py
│   ├── frequent_pattern_graph_miner.py
│   ├── g_topological_sort.py
│   ├── gale_shapley_bigraph.py
│   ├── graph_adjacency_list.py
│   ├── graph_adjacency_matrix.py
│   ├── graph_list.py
│   ├── graphs_floyd_warshall.py
│   ├── greedy_best_first.py
│   ├── greedy_min_vertex_cover.py
│   ├── kahns_algorithm_long.py
│   ├── kahns_algorithm_topo.py
│   ├── karger.py
│   ├── lanczos_eigenvectors.py
│   ├── markov_chain.py
│   ├── matching_min_vertex_cover.py
│   ├── minimum_path_sum.py
│   ├── minimum_spanning_tree_boruvka.py
│   ├── minimum_spanning_tree_kruskal.py
│   ├── minimum_spanning_tree_kruskal2.py
│   ├── minimum_spanning_tree_prims.py
│   ├── minimum_spanning_tree_prims2.py
│   ├── multi_heuristic_astar.py
│   ├── page_rank.py
│   ├── prim.py
│   ├── random_graph_generator.py
│   ├── scc_kosaraju.py
│   ├── strongly_connected_components.py
│   ├── tarjans_scc.py
│   └── tests/
│       ├── __init__.py
│       ├── test_min_spanning_tree_kruskal.py
│       └── test_min_spanning_tree_prim.py
├── greedy_methods/
│   ├── __init__.py
│   ├── best_time_to_buy_and_sell_stock.py
│   ├── fractional_cover_problem.py
│   ├── fractional_knapsack.py
│   ├── fractional_knapsack_2.py
│   ├── gas_station.py
│   ├── minimum_coin_change.py
│   ├── minimum_waiting_time.py
│   ├── optimal_merge_pattern.py
│   └── smallest_range.py
├── hashes/
│   ├── README.md
│   ├── __init__.py
│   ├── adler32.py
│   ├── chaos_machine.py
│   ├── djb2.py
│   ├── elf.py
│   ├── enigma_machine.py
│   ├── fletcher16.py
│   ├── hamming_code.py
│   ├── luhn.py
│   ├── md5.py
│   ├── sdbm.py
│   ├── sha1.py
│   └── sha256.py
├── index.md
├── knapsack/
│   ├── README.md
│   ├── __init__.py
│   ├── greedy_knapsack.py
│   ├── knapsack.py
│   ├── recursive_approach_knapsack.py
│   └── tests/
│       ├── __init__.py
│       ├── test_greedy_knapsack.py
│       └── test_knapsack.py
├── linear_algebra/
│   ├── README.md
│   ├── __init__.py
│   ├── gaussian_elimination.py
│   ├── jacobi_iteration_method.py
│   ├── lu_decomposition.py
│   ├── matrix_inversion.py
│   └── src/
│       ├── __init__.py
│       ├── conjugate_gradient.py
│       ├── gaussian_elimination_pivoting.py
│       ├── lib.py
│       ├── polynom_for_points.py
│       ├── power_iteration.py
│       ├── rank_of_matrix.py
│       ├── rayleigh_quotient.py
│       ├── schur_complement.py
│       ├── test_linear_algebra.py
│       └── transformations_2d.py
├── linear_programming/
│   ├── __init__.py
│   └── simplex.py
├── machine_learning/
│   ├── __init__.py
│   ├── apriori_algorithm.py
│   ├── astar.py
│   ├── automatic_differentiation.py
│   ├── data_transformations.py
│   ├── decision_tree.py
│   ├── dimensionality_reduction.py
│   ├── forecasting/
│   │   ├── __init__.py
│   │   ├── ex_data.csv
│   │   └── run.py
│   ├── frequent_pattern_growth.py
│   ├── gaussian_naive_bayes.py.broken.txt
│   ├── gradient_boosting_classifier.py
│   ├── gradient_boosting_regressor.py.broken.txt
│   ├── gradient_descent.py
│   ├── k_means_clust.py
│   ├── k_nearest_neighbours.py
│   ├── linear_discriminant_analysis.py
│   ├── linear_regression.py
│   ├── local_weighted_learning/
│   │   ├── README.md
│   │   ├── __init__.py
│   │   └── local_weighted_learning.py
│   ├── logistic_regression.py
│   ├── loss_functions.py
│   ├── lstm/
│   │   ├── __init__.py
│   │   ├── lstm_prediction.py
│   │   └── sample_data.csv
│   ├── mfcc.py
│   ├── multilayer_perceptron_classifier.py
│   ├── polynomial_regression.py
│   ├── principle_component_analysis.py
│   ├── random_forest_classifier.py.broken.txt
│   ├── random_forest_regressor.py.broken.txt
│   ├── scoring_functions.py
│   ├── self_organizing_map.py
│   ├── sequential_minimum_optimization.py
│   ├── similarity_search.py
│   ├── support_vector_machines.py
│   ├── t_stochastic_neighbour_embedding.py
│   ├── word_frequency_functions.py
│   ├── xgboost_classifier.py
│   └── xgboost_regressor.py
├── maths/
│   ├── __init__.py
│   ├── abs.py
│   ├── addition_without_arithmetic.py
│   ├── aliquot_sum.py
│   ├── allocation_number.py
│   ├── arc_length.py
│   ├── area.py
│   ├── area_under_curve.py
│   ├── average_absolute_deviation.py
│   ├── average_mean.py
│   ├── average_median.py
│   ├── average_mode.py
│   ├── bailey_borwein_plouffe.py
│   ├── base_neg2_conversion.py
│   ├── basic_maths.py
│   ├── binary_exponentiation.py
│   ├── binary_multiplication.py
│   ├── binomial_coefficient.py
│   ├── binomial_distribution.py
│   ├── ceil.py
│   ├── chebyshev_distance.py
│   ├── check_polygon.py
│   ├── chinese_remainder_theorem.py
│   ├── chudnovsky_algorithm.py
│   ├── collatz_sequence.py
│   ├── combinations.py
│   ├── continued_fraction.py
│   ├── decimal_isolate.py
│   ├── decimal_to_fraction.py
│   ├── dodecahedron.py
│   ├── double_factorial.py
│   ├── dual_number_automatic_differentiation.py
│   ├── entropy.py
│   ├── euclidean_distance.py
│   ├── euler_method.py
│   ├── euler_modified.py
│   ├── eulers_totient.py
│   ├── extended_euclidean_algorithm.py
│   ├── factorial.py
│   ├── factors.py
│   ├── fast_inverse_sqrt.py
│   ├── fermat_little_theorem.py
│   ├── fibonacci.py
│   ├── find_max.py
│   ├── find_min.py
│   ├── floor.py
│   ├── gamma.py
│   ├── gaussian.py
│   ├── gcd_of_n_numbers.py
│   ├── geometric_mean.py
│   ├── germain_primes.py
│   ├── greatest_common_divisor.py
│   ├── hardy_ramanujanalgo.py
│   ├── images/
│   │   └── __init__.py
│   ├── integer_square_root.py
│   ├── interquartile_range.py
│   ├── is_int_palindrome.py
│   ├── is_ip_v4_address_valid.py
│   ├── is_square_free.py
│   ├── jaccard_similarity.py
│   ├── joint_probability_distribution.py
│   ├── josephus_problem.py
│   ├── juggler_sequence.py
│   ├── karatsuba.py
│   ├── kth_lexicographic_permutation.py
│   ├── largest_of_very_large_numbers.py
│   ├── least_common_multiple.py
│   ├── line_length.py
│   ├── liouville_lambda.py
│   ├── lucas_lehmer_primality_test.py
│   ├── lucas_series.py
│   ├── maclaurin_series.py
│   ├── manhattan_distance.py
│   ├── matrix_exponentiation.py
│   ├── max_sum_sliding_window.py
│   ├── minkowski_distance.py
│   ├── mobius_function.py
│   ├── modular_division.py
│   ├── modular_exponential.py
│   ├── monte_carlo.py
│   ├── monte_carlo_dice.py
│   ├── number_of_digits.py
│   ├── numerical_analysis/
│   │   ├── __init__.py
│   │   ├── adams_bashforth.py
│   │   ├── bisection.py
│   │   ├── bisection_2.py
│   │   ├── integration_by_simpson_approx.py
│   │   ├── intersection.py
│   │   ├── nevilles_method.py
│   │   ├── newton_forward_interpolation.py
│   │   ├── newton_raphson.py
│   │   ├── numerical_integration.py
│   │   ├── proper_fractions.py
│   │   ├── runge_kutta.py
│   │   ├── runge_kutta_fehlberg_45.py
│   │   ├── runge_kutta_gills.py
│   │   ├── secant_method.py
│   │   ├── simpson_rule.py
│   │   ├── square_root.py
│   │   └── weierstrass_method.py
│   ├── odd_sieve.py
│   ├── perfect_cube.py
│   ├── perfect_number.py
│   ├── perfect_square.py
│   ├── persistence.py
│   ├── pi_generator.py
│   ├── pi_monte_carlo_estimation.py
│   ├── points_are_collinear_3d.py
│   ├── pollard_rho.py
│   ├── polynomial_evaluation.py
│   ├── polynomials/
│   │   ├── __init__.py
│   │   └── single_indeterminate_operations.py
│   ├── power_using_recursion.py
│   ├── prime_check.py
│   ├── prime_factors.py
│   ├── prime_numbers.py
│   ├── prime_sieve_eratosthenes.py
│   ├── primelib.py
│   ├── print_multiplication_table.py
│   ├── pythagoras.py
│   ├── qr_decomposition.py
│   ├── quadratic_equations_complex_numbers.py
│   ├── radians.py
│   ├── radix2_fft.py
│   ├── remove_digit.py
│   ├── segmented_sieve.py
│   ├── series/
│   │   ├── __init__.py
│   │   ├── arithmetic.py
│   │   ├── geometric.py
│   │   ├── geometric_series.py
│   │   ├── harmonic.py
│   │   ├── harmonic_series.py
│   │   ├── hexagonal_numbers.py
│   │   └── p_series.py
│   ├── sieve_of_eratosthenes.py
│   ├── sigmoid.py
│   ├── signum.py
│   ├── simultaneous_linear_equation_solver.py
│   ├── sin.py
│   ├── sock_merchant.py
│   ├── softmax.py
│   ├── solovay_strassen_primality_test.py
│   ├── spearman_rank_correlation_coefficient.py
│   ├── special_numbers/
│   │   ├── __init__.py
│   │   ├── armstrong_numbers.py
│   │   ├── automorphic_number.py
│   │   ├── bell_numbers.py
│   │   ├── carmichael_number.py
│   │   ├── catalan_number.py
│   │   ├── hamming_numbers.py
│   │   ├── happy_number.py
│   │   ├── harshad_numbers.py
│   │   ├── hexagonal_number.py
│   │   ├── krishnamurthy_number.py
│   │   ├── perfect_number.py
│   │   ├── polygonal_numbers.py
│   │   ├── pronic_number.py
│   │   ├── proth_number.py
│   │   ├── triangular_numbers.py
│   │   ├── ugly_numbers.py
│   │   └── weird_number.py
│   ├── sum_of_arithmetic_series.py
│   ├── sum_of_digits.py
│   ├── sum_of_geometric_progression.py
│   ├── sum_of_harmonic_series.py
│   ├── sumset.py
│   ├── sylvester_sequence.py
│   ├── tanh.py
│   ├── test_factorial.py
│   ├── test_prime_check.py
│   ├── three_sum.py
│   ├── trapezoidal_rule.py
│   ├── triplet_sum.py
│   ├── twin_prime.py
│   ├── two_pointer.py
│   ├── two_sum.py
│   ├── volume.py
│   └── zellers_congruence.py
├── matrix/
│   ├── __init__.py
│   ├── binary_search_matrix.py
│   ├── count_islands_in_matrix.py
│   ├── count_negative_numbers_in_sorted_matrix.py
│   ├── count_paths.py
│   ├── cramers_rule_2x2.py
│   ├── inverse_of_matrix.py
│   ├── largest_square_area_in_matrix.py
│   ├── matrix_based_game.py
│   ├── matrix_class.py
│   ├── matrix_equalization.py
│   ├── matrix_multiplication_recursion.py
│   ├── matrix_operation.py
│   ├── max_area_of_island.py
│   ├── median_matrix.py
│   ├── nth_fibonacci_using_matrix_exponentiation.py
│   ├── pascal_triangle.py
│   ├── rotate_matrix.py
│   ├── searching_in_sorted_matrix.py
│   ├── sherman_morrison.py
│   ├── spiral_print.py
│   ├── tests/
│   │   ├── __init__.py
│   │   ├── pytest.ini
│   │   └── test_matrix_operation.py
│   └── validate_sudoku_board.py
├── networking_flow/
│   ├── __init__.py
│   ├── ford_fulkerson.py
│   └── minimum_cut.py
├── neural_network/
│   ├── __init__.py
│   ├── activation_functions/
│   │   ├── __init__.py
│   │   ├── binary_step.py
│   │   ├── exponential_linear_unit.py
│   │   ├── gaussian_error_linear_unit.py
│   │   ├── leaky_rectified_linear_unit.py
│   │   ├── mish.py
│   │   ├── rectified_linear_unit.py
│   │   ├── scaled_exponential_linear_unit.py
│   │   ├── soboleva_modified_hyperbolic_tangent.py
│   │   ├── softplus.py
│   │   ├── squareplus.py
│   │   └── swish.py
│   ├── back_propagation_neural_network.py
│   ├── convolution_neural_network.py
│   ├── gan.py_tf
│   ├── input_data.py
│   ├── perceptron.py.DISABLED
│   ├── simple_neural_network.py
│   └── two_hidden_layers_neural_network.py
├── other/
│   ├── __init__.py
│   ├── activity_selection.py
│   ├── alternative_list_arrange.py
│   ├── bankers_algorithm.py
│   ├── davis_putnam_logemann_loveland.py
│   ├── doomsday.py
│   ├── fischer_yates_shuffle.py
│   ├── gauss_easter.py
│   ├── graham_scan.py
│   ├── greedy.py
│   ├── guess_the_number_search.py
│   ├── h_index.py
│   ├── least_recently_used.py
│   ├── lfu_cache.py
│   ├── linear_congruential_generator.py
│   ├── lru_cache.py
│   ├── magicdiamondpattern.py
│   ├── majority_vote_algorithm.py
│   ├── maximum_subsequence.py
│   ├── nested_brackets.py
│   ├── number_container_system.py
│   ├── password.py
│   ├── quine.py
│   ├── scoring_algorithm.py
│   ├── sdes.py
│   ├── sliding_window_maximum.py
│   ├── tower_of_hanoi.py
│   └── word_search.py
├── physics/
│   ├── __init__.py
│   ├── altitude_pressure.py
│   ├── archimedes_principle_of_buoyant_force.py
│   ├── basic_orbital_capture.py
│   ├── casimir_effect.py
│   ├── center_of_mass.py
│   ├── centripetal_force.py
│   ├── coulombs_law.py
│   ├── doppler_frequency.py
│   ├── escape_velocity.py
│   ├── grahams_law.py
│   ├── horizontal_projectile_motion.py
│   ├── hubble_parameter.py
│   ├── ideal_gas_law.py
│   ├── image_data/
│   │   └── __init__.py
│   ├── in_static_equilibrium.py
│   ├── kinetic_energy.py
│   ├── lens_formulae.py
│   ├── lorentz_transformation_four_vector.py
│   ├── malus_law.py
│   ├── mass_energy_equivalence.py
│   ├── mirror_formulae.py
│   ├── n_body_simulation.py
│   ├── newtons_law_of_gravitation.py
│   ├── newtons_second_law_of_motion.py
│   ├── orbital_transfer_work.py
│   ├── period_of_pendulum.py
│   ├── photoelectric_effect.py
│   ├── potential_energy.py
│   ├── rainfall_intensity.py
│   ├── reynolds_number.py
│   ├── rms_speed_of_molecule.py
│   ├── shear_stress.py
│   ├── speed_of_sound.py
│   ├── speeds_of_gas_molecules.py
│   └── terminal_velocity.py
├── project_euler/
│   ├── README.md
│   ├── __init__.py
│   ├── problem_001/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   ├── sol2.py
│   │   ├── sol3.py
│   │   ├── sol4.py
│   │   ├── sol5.py
│   │   ├── sol6.py
│   │   └── sol7.py
│   ├── problem_002/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   ├── sol2.py
│   │   ├── sol3.py
│   │   ├── sol4.py
│   │   └── sol5.py
│   ├── problem_003/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   ├── sol2.py
│   │   └── sol3.py
│   ├── problem_004/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   └── sol2.py
│   ├── problem_005/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   └── sol2.py
│   ├── problem_006/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   ├── sol2.py
│   │   ├── sol3.py
│   │   └── sol4.py
│   ├── problem_007/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   ├── sol2.py
│   │   └── sol3.py
│   ├── problem_008/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   ├── sol2.py
│   │   └── sol3.py
│   ├── problem_009/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   ├── sol2.py
│   │   ├── sol3.py
│   │   └── sol4.py
│   ├── problem_010/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   ├── sol2.py
│   │   └── sol3.py
│   ├── problem_011/
│   │   ├── __init__.py
│   │   ├── grid.txt
│   │   ├── sol1.py
│   │   └── sol2.py
│   ├── problem_012/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   └── sol2.py
│   ├── problem_013/
│   │   ├── __init__.py
│   │   ├── num.txt
│   │   └── sol1.py
│   ├── problem_014/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   └── sol2.py
│   ├── problem_015/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   └── sol2.py
│   ├── problem_016/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   └── sol2.py
│   ├── problem_017/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_018/
│   │   ├── __init__.py
│   │   ├── solution.py
│   │   └── triangle.txt
│   ├── problem_019/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_020/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   ├── sol2.py
│   │   ├── sol3.py
│   │   └── sol4.py
│   ├── problem_021/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_022/
│   │   ├── __init__.py
│   │   ├── p022_names.txt
│   │   ├── sol1.py
│   │   └── sol2.py
│   ├── problem_023/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_024/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_025/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   ├── sol2.py
│   │   └── sol3.py
│   ├── problem_026/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_027/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_028/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_029/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_030/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_031/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   └── sol2.py
│   ├── problem_032/
│   │   ├── __init__.py
│   │   └── sol32.py
│   ├── problem_033/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_034/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_035/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_036/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_037/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_038/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_039/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_040/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_041/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_042/
│   │   ├── __init__.py
│   │   ├── solution42.py
│   │   └── words.txt
│   ├── problem_043/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_044/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_045/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_046/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_047/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_048/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_049/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_050/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_051/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_052/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_053/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_054/
│   │   ├── __init__.py
│   │   ├── poker_hands.txt
│   │   ├── sol1.py
│   │   └── test_poker_hand.py
│   ├── problem_055/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_056/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_057/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_058/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_059/
│   │   ├── __init__.py
│   │   ├── p059_cipher.txt
│   │   ├── sol1.py
│   │   └── test_cipher.txt
│   ├── problem_062/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_063/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_064/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_065/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_067/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   ├── sol2.py
│   │   └── triangle.txt
│   ├── problem_068/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_069/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_070/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_071/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_072/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   └── sol2.py
│   ├── problem_073/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_074/
│   │   ├── __init__.py
│   │   ├── sol1.py
│   │   └── sol2.py
│   ├── problem_075/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_076/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_077/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_078/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_079/
│   │   ├── __init__.py
│   │   ├── keylog.txt
│   │   ├── keylog_test.txt
│   │   └── sol1.py
│   ├── problem_080/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_081/
│   │   ├── __init__.py
│   │   ├── matrix.txt
│   │   └── sol1.py
│   ├── problem_082/
│   │   ├── __init__.py
│   │   ├── input.txt
│   │   ├── sol1.py
│   │   └── test_matrix.txt
│   ├── problem_085/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_086/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_087/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_089/
│   │   ├── __init__.py
│   │   ├── numeralcleanup_test.txt
│   │   ├── p089_roman.txt
│   │   └── sol1.py
│   ├── problem_091/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_092/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_094/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_095/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_097/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_099/
│   │   ├── __init__.py
│   │   ├── base_exp.txt
│   │   └── sol1.py
│   ├── problem_100/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_101/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_102/
│   │   ├── __init__.py
│   │   ├── p102_triangles.txt
│   │   ├── sol1.py
│   │   └── test_triangles.txt
│   ├── problem_104/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_107/
│   │   ├── __init__.py
│   │   ├── p107_network.txt
│   │   ├── sol1.py
│   │   └── test_network.txt
│   ├── problem_109/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_112/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_113/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_114/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_115/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_116/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_117/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_119/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_120/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_121/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_122/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_123/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_125/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_129/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_131/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_135/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_136/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_144/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_145/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_164/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_173/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_174/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_180/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_187/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_188/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_190/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_191/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_203/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_205/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_206/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_207/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_234/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_301/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_345/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_493/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_551/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_587/
│   │   ├── __init__.py
│   │   └── sol1.py
│   ├── problem_686/
│   │   ├── __init__.py
│   │   └── sol1.py
│   └── problem_800/
│       ├── __init__.py
│       └── sol1.py
├── pyproject.toml
├── quantum/
│   ├── README.md
│   ├── __init__.py
│   ├── bb84.py.DISABLED.txt
│   ├── deutsch_jozsa.py.DISABLED.txt
│   ├── half_adder.py.DISABLED.txt
│   ├── not_gate.py.DISABLED.txt
│   ├── q_fourier_transform.py
│   ├── q_full_adder.py.DISABLED.txt
│   ├── quantum_entanglement.py.DISABLED.txt
│   ├── quantum_random.py.DISABLED.txt
│   ├── quantum_teleportation.py.DISABLED.txt
│   ├── ripple_adder_classic.py.DISABLED.txt
│   ├── single_qubit_measure.py.DISABLED.txt
│   └── superdense_coding.py.DISABLED.txt
├── scheduling/
│   ├── __init__.py
│   ├── first_come_first_served.py
│   ├── highest_response_ratio_next.py
│   ├── job_sequence_with_deadline.py
│   ├── job_sequencing_with_deadline.py
│   ├── multi_level_feedback_queue.py
│   ├── non_preemptive_shortest_job_first.py
│   ├── round_robin.py
│   └── shortest_job_first.py
├── scripts/
│   ├── README.md
│   ├── __init__.py
│   ├── build_directory_md.py
│   ├── close_pull_requests_with_awaiting_changes.sh
│   ├── close_pull_requests_with_failing_tests.sh
│   ├── close_pull_requests_with_require_descriptive_names.sh
│   ├── close_pull_requests_with_require_tests.sh
│   ├── close_pull_requests_with_require_type_hints.sh
│   ├── find_git_conflicts.sh
│   ├── project_euler_answers.json
│   ├── validate_filenames.py
│   └── validate_solutions.py
├── searches/
│   ├── __init__.py
│   ├── binary_search.py
│   ├── binary_tree_traversal.py
│   ├── double_linear_search.py
│   ├── double_linear_search_recursion.py
│   ├── exponential_search.py
│   ├── fibonacci_search.py
│   ├── hill_climbing.py
│   ├── interpolation_search.py
│   ├── jump_search.py
│   ├── linear_search.py
│   ├── median_of_medians.py
│   ├── quick_select.py
│   ├── sentinel_linear_search.py
│   ├── simple_binary_search.py
│   ├── simulated_annealing.py
│   ├── tabu_search.py
│   ├── tabu_test_data.txt
│   └── ternary_search.py
├── sorts/
│   ├── README.md
│   ├── __init__.py
│   ├── bead_sort.py
│   ├── binary_insertion_sort.py
│   ├── bitonic_sort.py
│   ├── bogo_sort.py
│   ├── bubble_sort.py
│   ├── bucket_sort.py
│   ├── circle_sort.py
│   ├── cocktail_shaker_sort.py
│   ├── comb_sort.py
│   ├── counting_sort.py
│   ├── cycle_sort.py
│   ├── cyclic_sort.py
│   ├── double_sort.py
│   ├── dutch_national_flag_sort.py
│   ├── exchange_sort.py
│   ├── external_sort.py
│   ├── gnome_sort.py
│   ├── heap_sort.py
│   ├── insertion_sort.py
│   ├── intro_sort.py
│   ├── iterative_merge_sort.py
│   ├── merge_insertion_sort.py
│   ├── merge_sort.py
│   ├── msd_radix_sort.py
│   ├── natural_sort.py
│   ├── normal_distribution_quick_sort.md
│   ├── odd_even_sort.py
│   ├── odd_even_transposition_parallel.py
│   ├── odd_even_transposition_single_threaded.py
│   ├── pancake_sort.py
│   ├── patience_sort.py
│   ├── pigeon_sort.py
│   ├── pigeonhole_sort.py
│   ├── quick_sort.py
│   ├── quick_sort_3_partition.py
│   ├── radix_sort.py
│   ├── recursive_insertion_sort.py
│   ├── recursive_mergesort_array.py
│   ├── recursive_quick_sort.py
│   ├── selection_sort.py
│   ├── shell_sort.py
│   ├── shrink_shell_sort.py
│   ├── slowsort.py
│   ├── stalin_sort.py
│   ├── stooge_sort.py
│   ├── strand_sort.py
│   ├── tim_sort.py
│   ├── topological_sort.py
│   ├── tree_sort.py
│   ├── unknown_sort.py
│   └── wiggle_sort.py
├── strings/
│   ├── __init__.py
│   ├── aho_corasick.py
│   ├── alternative_string_arrange.py
│   ├── anagrams.py
│   ├── anagrams.txt
│   ├── autocomplete_using_trie.py
│   ├── barcode_validator.py
│   ├── bitap_string_match.py
│   ├── boyer_moore_search.py
│   ├── camel_case_to_snake_case.py
│   ├── can_string_be_rearranged_as_palindrome.py
│   ├── capitalize.py
│   ├── check_anagrams.py
│   ├── count_vowels.py
│   ├── credit_card_validator.py
│   ├── damerau_levenshtein_distance.py
│   ├── detecting_english_programmatically.py
│   ├── dictionary.txt
│   ├── dna.py
│   ├── edit_distance.py
│   ├── frequency_finder.py
│   ├── hamming_distance.py
│   ├── indian_phone_validator.py
│   ├── is_contains_unique_chars.py
│   ├── is_isogram.py
│   ├── is_pangram.py
│   ├── is_polish_national_id.py
│   ├── is_spain_national_id.py
│   ├── is_srilankan_phone_number.py
│   ├── is_valid_email_address.py
│   ├── jaro_winkler.py
│   ├── join.py
│   ├── knuth_morris_pratt.py
│   ├── levenshtein_distance.py
│   ├── lower.py
│   ├── manacher.py
│   ├── min_cost_string_conversion.py
│   ├── naive_string_search.py
│   ├── ngram.py
│   ├── palindrome.py
│   ├── pig_latin.py
│   ├── prefix_function.py
│   ├── rabin_karp.py
│   ├── remove_duplicate.py
│   ├── reverse_letters.py
│   ├── reverse_words.py
│   ├── snake_case_to_camel_pascal_case.py
│   ├── split.py
│   ├── string_switch_case.py
│   ├── strip.py
│   ├── text_justification.py
│   ├── title.py
│   ├── top_k_frequent_words.py
│   ├── upper.py
│   ├── wave_string.py
│   ├── wildcard_pattern_matching.py
│   ├── word_occurrence.py
│   ├── word_patterns.py
│   ├── words.txt
│   └── z_function.py
└── web_programming/
    ├── __init__.py
    ├── co2_emission.py
    ├── covid_stats_via_xpath.py
    ├── crawl_google_results.py
    ├── crawl_google_scholar_citation.py
    ├── currency_converter.py
    ├── current_stock_price.py
    ├── current_weather.py
    ├── daily_horoscope.py
    ├── download_images_from_google_query.py
    ├── emails_from_url.py
    ├── fetch_anime_and_play.py
    ├── fetch_bbc_news.py
    ├── fetch_github_info.py
    ├── fetch_jobs.py
    ├── fetch_quotes.py
    ├── fetch_well_rx_price.py
    ├── get_amazon_product_data.py
    ├── get_imdb_top_250_movies_csv.py
    ├── get_imdbtop.py.DISABLED
    ├── get_ip_geolocation.py
    ├── get_top_billionaires.py
    ├── get_top_hn_posts.py
    ├── get_user_tweets.py.DISABLED
    ├── giphy.py
    ├── instagram_crawler.py
    ├── instagram_pic.py
    ├── instagram_video.py
    ├── nasa_data.py
    ├── open_google_results.py
    ├── random_anime_character.py
    ├── recaptcha_verification.py
    ├── reddit.py
    ├── search_books_by_isbn.py
    ├── slack_message.py
    ├── test_fetch_github_info.py
    └── world_covid19_stats.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .devcontainer/Dockerfile
================================================
# https://github.com/microsoft/vscode-dev-containers/blob/main/containers/python-3/README.md
ARG VARIANT=3.13-bookworm
FROM mcr.microsoft.com/vscode/devcontainers/python:${VARIANT}
COPY requirements.txt /tmp/pip-tmp/
RUN python3 -m pip install --upgrade pip \
  && python3 -m pip install --no-cache-dir -r /tmp/pip-tmp/requirements.txt \
  && pipx install pre-commit ruff


================================================
FILE: .devcontainer/README.md
================================================
# Development Container

This is **Devcontainer** configuration to provide a consistent development environment for all contributors.

## Features

- [x] Pre-configured **Python environment**
- [x] Automatic installation of **pre-commit hooks**
- [x] **Ruff** linter ready to check your code
- [x] **Oh My Zsh** with plugins:
- `zsh-autosuggestions`
- `zsh-syntax-highlighting`

## Usage

1. Install [**Docker** ](https://www.docker.com/get-started/) and [**Visual Studio Code**](https://code.visualstudio.com/)
2. Install the **Remote - Containers** extension in VS Code

    - Do `CTRL+P`, paste this command and press `Enter`

        ```shell
        ext install ms-vscode-remote.remote-containers
        ```
3. Open this repository in VS Code
4. When prompted, click **"Reopen in Container"**
5. Wait for the environment to build and initialize

After setup:

- `pre-commit` hooks are installed
- `ruff` and other tools are available
- The shell uses Zsh by default

## Tips

To manually run checks on all files:

```bash
pre-commit run --all-files
```

> For further information here's [Microsoft tutorial about devcontainers.](https://code.visualstudio.com/docs/devcontainers/tutorial)


================================================
FILE: .devcontainer/devcontainer.json
================================================
{
	"name": "Python 3",
	"build": {
		"dockerfile": "Dockerfile",
		"context": "..",
		"args": {
			// Update 'VARIANT' to pick a Python version: 3, 3.11, 3.10, 3.9, 3.8
			// Append -bullseye or -buster to pin to an OS version.
			// Use -bullseye variants on local on arm64/Apple Silicon.
			"VARIANT": "3.13-bookworm"
		}
	},

	"postCreateCommand": "zsh .devcontainer/post_install",

	// Configure tool-specific properties.
	"customizations": {
		// Configure properties specific to VS Code.
		"vscode": {
			// Set *default* container specific settings.json values on container create.
			"settings": {
				"python.defaultInterpreterPath": "/usr/local/bin/python",
				"python.linting.enabled": true,
				"python.formatting.blackPath": "/usr/local/py-utils/bin/black",
				"python.linting.mypyPath": "/usr/local/py-utils/bin/mypy",
				"terminal.integrated.defaultProfile.linux": "zsh"
			},

			// Add the IDs of extensions you want installed when the container is created.
			"extensions": [
				"ms-python.python",
				"ms-python.vscode-pylance"
			]
		}
	},

	// Use 'forwardPorts' to make a list of ports inside the container available locally.
	// "forwardPorts": [],

	// Use 'postCreateCommand' to run commands after the container is created.
	// "postCreateCommand": "pip3 install --user -r requirements.txt",

	// Comment out to connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root.
	"remoteUser": "vscode"
}


================================================
FILE: .devcontainer/post_install
================================================
#!/usr/bin/env bash

echo "Begin post-installation steps..."

set -e

echo "Installing pre-commit hooks..."
pre-commit install

echo "Installing Oh My Zsh plugins..."

# Install zsh-autosuggestions if not present
if [ ! -d "${ZSH_CUSTOM:-$HOME/.oh-my-zsh/custom}/plugins/zsh-autosuggestions" ]; then
  echo "Cloning zsh-autosuggestions..."
  git clone https://github.com/zsh-users/zsh-autosuggestions \
    "${ZSH_CUSTOM:-$HOME/.oh-my-zsh/custom}/plugins/zsh-autosuggestions"
fi

# Install zsh-syntax-highlighting if not present
if [ ! -d "${ZSH_CUSTOM:-$HOME/.oh-my-zsh/custom}/plugins/zsh-syntax-highlighting" ]; then
  echo "Cloning zsh-syntax-highlighting..."
  git clone https://github.com/zsh-users/zsh-syntax-highlighting.git \
    "${ZSH_CUSTOM:-$HOME/.oh-my-zsh/custom}/plugins/zsh-syntax-highlighting"
fi

echo "Configuring plugins in ~/.zshrc..."
sed -i '/^plugins=/c\plugins=(git zsh-autosuggestions zsh-syntax-highlighting)' ~/.zshrc

echo "Post-installation steps completed successfully. Enjoy!"


================================================
FILE: .gitattributes
================================================
* text=auto


================================================
FILE: .github/CODEOWNERS
================================================
# This is a comment.
# Each line is a file pattern followed by one or more owners.

# More details are here: https://help.github.com/articles/about-codeowners/

# The '*' pattern is global owners.

# Order is important. The last matching pattern has the most precedence.

/.*  @cclauss

# /backtracking/

# /bit_manipulation/

# /blockchain/

# /boolean_algebra/

# /cellular_automata/

# /ciphers/

# /compression/

# /computer_vision/

# /conversions/

# /data_structures/

# /digital_image_processing/

# /divide_and_conquer/

# /dynamic_programming/

# /file_transfer/

# /fuzzy_logic/

# /genetic_algorithm/

# /geodesy/

# /graphics/

# /graphs/

# /greedy_method/

# /hashes/

# /images/

# /linear_algebra/

# /machine_learning/

# /maths/

# /matrix/

# /networking_flow/

# /neural_network/

# /other/

# /project_euler/

# /quantum/

# /scheduling/

# /scripts/

# /searches/

# /sorts/

# /strings/

# /traversals/

/web_programming/  @cclauss


================================================
FILE: .github/ISSUE_TEMPLATE/bug_report.yml
================================================
name: Bug report
description: Create a bug report to help us address errors in the repository
labels: [bug]
body:
  - type: markdown
    attributes:
      value: >
        Before requesting please search [existing issues](https://github.com/TheAlgorithms/Python/labels/bug).
        Usage questions such as "How do I...?"  belong on the
        [Discord](https://discord.gg/c7MnfGFGa6) and will be closed.

  - type: input
    attributes:
      label: "Repository commit"
      description: >
        The commit hash for `TheAlgorithms/Python` repository. You can get this
        by running the command `git rev-parse HEAD` locally.
      placeholder: "a0b0f414ae134aa1772d33bb930e5a960f9979e8"
    validations:
      required: true

  - type: input
    attributes:
      label: "Python version (python --version)"
      placeholder: "Python 3.10.7"
    validations:
      required: true

  - type: textarea
    attributes:
      label: "Dependencies version (pip freeze)"
      description: >
        This is the output of the command `pip freeze --all`. Note that the
        actual output might be different as compared to the placeholder text.
      placeholder: |
        appnope==0.1.3
        asttokens==2.0.8
        backcall==0.2.0
        ...
    validations:
      required: true

  - type: textarea
    attributes:
      label: "Expected behavior"
      description: "Describe the behavior you expect. May include images or videos."
    validations:
      required: true

  - type: textarea
    attributes:
      label: "Actual behavior"
    validations:
      required: true


================================================
FILE: .github/ISSUE_TEMPLATE/config.yml
================================================
blank_issues_enabled: false
contact_links:
  - name: Discord community
    url: https://discord.gg/c7MnfGFGa6
    about: Have any questions or need any help? Please contact us via Discord


================================================
FILE: .github/ISSUE_TEMPLATE/feature_request.yml
================================================
name: Feature request
description: Suggest features, propose improvements, discuss new ideas.
labels: [enhancement]
body:
  - type: markdown
    attributes:
      value: >
        Before requesting please search [existing issues](https://github.com/TheAlgorithms/Python/labels/enhancement).
        Do not create issues to implement new algorithms as these will be closed.
        Usage questions such as "How do I...?"  belong on the
        [Discord](https://discord.gg/c7MnfGFGa6) and will be closed.

  - type: textarea
    attributes:
      label: "Feature description"
      description: >
        This could include new topics or improving any existing implementations.
    validations:
      required: true


================================================
FILE: .github/ISSUE_TEMPLATE/other.yml
================================================
name: Other
description: Use this for any other issues. PLEASE do not create blank issues
labels: ["awaiting triage"]
body:
  - type: textarea
    id: issuedescription
    attributes:
      label: What would you like to share?
      description: Provide a clear and concise explanation of your issue.
    validations:
      required: true

  - type: textarea
    id: extrainfo
    attributes:
      label: Additional information
      description: Is there anything else we should know about this issue?
    validations:
      required: false


================================================
FILE: .github/dependabot.yml
================================================
# Keep GitHub Actions up to date with Dependabot...
# https://docs.github.com/en/code-security/dependabot/working-with-dependabot/keeping-your-actions-up-to-date-with-dependabot
version: 2
updates:
  - package-ecosystem: "github-actions"
    directory: "/"
    schedule:
      interval: "daily"


================================================
FILE: .github/pull_request_template.md
================================================
### Describe your change:



* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
* [ ] Documentation change?

### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file.  To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".


================================================
FILE: .github/stale.yml
================================================
# Configuration for probot-stale - https://github.com/probot/stale

# Number of days of inactivity before an Issue or Pull Request becomes stale
daysUntilStale: 30

# Number of days of inactivity before an Issue or Pull Request with the stale label is closed.
# Set to false to disable. If disabled, issues still need to be closed manually, but will remain marked as stale.
daysUntilClose: 7

# Only issues or pull requests with all of these labels are check if stale. Defaults to `[]` (disabled)
onlyLabels: []

# Issues or Pull Requests with these labels will never be considered stale. Set to `[]` to disable
exemptLabels:
  - "Status: on hold"

# Set to true to ignore issues in a project (defaults to false)
exemptProjects: false

# Set to true to ignore issues in a milestone (defaults to false)
exemptMilestones: false

# Set to true to ignore issues with an assignee (defaults to false)
exemptAssignees: false

# Label to use when marking as stale
staleLabel: stale

# Limit the number of actions per hour, from 1-30. Default is 30
limitPerRun: 5

# Comment to post when removing the stale label.
# unmarkComment: >
#   Your comment here.

# Optionally, specify configuration settings that are specific to just 'issues' or 'pulls':
pulls:
  # Comment to post when marking as stale. Set to `false` to disable
  markComment: >
    This pull request has been automatically marked as stale because it has not had
    recent activity. It will be closed if no further activity occurs. Thank you
    for your contributions.

  # Comment to post when closing a stale Pull Request.
  closeComment: >
    Please reopen this pull request once you commit the changes requested
    or make improvements on the code. If this is not the case and you need
    some help, feel free to seek help from our [Gitter](https://gitter.im/TheAlgorithms/community)
    or ping one of the reviewers. Thank you for your contributions!

issues:
  # Comment to post when marking as stale. Set to `false` to disable
  markComment: >
    This issue has been automatically marked as stale because it has not had
    recent activity. It will be closed if no further activity occurs. Thank you
    for your contributions.

  # Comment to post when closing a stale Issue.
  closeComment: >
    Please reopen this issue once you add more information and updates here.
    If this is not the case and you need some help, feel free to seek help
    from our [Gitter](https://gitter.im/TheAlgorithms/community) or ping one of the
    reviewers. Thank you for your contributions!


================================================
FILE: .github/workflows/build.yml
================================================
name: "build"

on:
  pull_request:
  schedule:
    - cron: "0 0 * * *" # Run everyday

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - run: sudo apt-get update && sudo apt-get install -y libhdf5-dev
      - uses: actions/checkout@v6
      - uses: astral-sh/setup-uv@v7
        with:
          enable-cache: true
          cache-dependency-glob: uv.lock
      - uses: actions/setup-python@v6
        with:
          python-version: 3.14
          allow-prereleases: true
      - run: uv sync --group=test
      - name: Run tests
        # TODO: #8818 Re-enable quantum tests
        run: uv run --with=pytest-run-parallel pytest
          --iterations=8 --parallel-threads=auto
          --ignore=computer_vision/cnn_classification.py
          --ignore=docs/conf.py
          --ignore=dynamic_programming/k_means_clustering_tensorflow.py
          --ignore=machine_learning/local_weighted_learning/local_weighted_learning.py
          --ignore=machine_learning/lstm/lstm_prediction.py
          --ignore=neural_network/input_data.py
          --ignore=project_euler/
          --ignore=quantum/q_fourier_transform.py
          --ignore=scripts/validate_solutions.py
          --ignore=web_programming/current_stock_price.py
          --ignore=web_programming/fetch_anime_and_play.py
          --cov-report=term-missing:skip-covered
          --cov=. .
      - if: ${{ success() }}
        run: scripts/build_directory_md.py 2>&1 | tee DIRECTORY.md


================================================
FILE: .github/workflows/devcontainer_ci.yml
================================================
name: Test DevContainer Build

on:
  push:
    paths:
      - ".devcontainer/**"
  pull_request:
    paths:
      - ".devcontainer/**"

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v6
      - uses: devcontainers/ci@v0.3
        with:
          push: never
          runCmd: "true"


================================================
FILE: .github/workflows/directory_writer.yml
================================================
# The objective of this GitHub Action is to update the DIRECTORY.md file (if needed)
# when doing a git push
name: directory_writer
on: [push]
jobs:
  directory_writer:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v6
        with:
          fetch-depth: 0
      - uses: actions/setup-python@v6
        with:
          python-version: 3.14
          allow-prereleases: true
      - name: Write DIRECTORY.md
        run: |
          scripts/build_directory_md.py 2>&1 | tee DIRECTORY.md
          git config --global user.name "$GITHUB_ACTOR"
          git config --global user.email "$GITHUB_ACTOR@users.noreply.github.com"
          git remote set-url origin https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/$GITHUB_REPOSITORY
      - name: Update DIRECTORY.md
        run: |
          git add DIRECTORY.md
          git commit -am "updating DIRECTORY.md" ||  true
          git push --force origin HEAD:$GITHUB_REF || true


================================================
FILE: .github/workflows/project_euler.yml
================================================
on:
  pull_request:
    # Run only if a file is changed within the project_euler directory and related files
    paths:
      - "project_euler/**"
      - ".github/workflows/project_euler.yml"
      - "scripts/validate_solutions.py"
  schedule:
    - cron: "0 0 * * *" # Run everyday

name: "Project Euler"

jobs:
  project-euler:
    runs-on: ubuntu-latest
    steps:
      - run:
          sudo apt-get update && sudo apt-get install -y libtiff5-dev libjpeg8-dev libopenjp2-7-dev
          zlib1g-dev libfreetype6-dev liblcms2-dev libwebp-dev tcl8.6-dev tk8.6-dev python3-tk
          libharfbuzz-dev libfribidi-dev libxcb1-dev
          libxml2-dev libxslt-dev
          libhdf5-dev
          libopenblas-dev
      - uses: actions/checkout@v6
      - uses: astral-sh/setup-uv@v7
      - uses: actions/setup-python@v6
        with:
          python-version: 3.14
          allow-prereleases: true
      - run: uv sync --group=euler-validate --group=test
      - run: uv run pytest --doctest-modules --cov-report=term-missing:skip-covered --cov=project_euler/ project_euler/
  validate-solutions:
    runs-on: ubuntu-latest
    steps:
      - run:
          sudo apt-get update && sudo apt-get install -y libtiff5-dev libjpeg8-dev libopenjp2-7-dev
          zlib1g-dev libfreetype6-dev liblcms2-dev libwebp-dev tcl8.6-dev tk8.6-dev python3-tk
          libharfbuzz-dev libfribidi-dev libxcb1-dev
          libxml2-dev libxslt-dev
          libhdf5-dev
          libopenblas-dev
      - uses: actions/checkout@v6
      - uses: astral-sh/setup-uv@v7
      - uses: actions/setup-python@v6
        with:
          python-version: 3.14
          allow-prereleases: true
      - run: uv sync --group=euler-validate --group=test
      - run: uv run pytest scripts/validate_solutions.py
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}


================================================
FILE: .github/workflows/ruff.yml
================================================
# https://beta.ruff.rs
name: ruff
on:
  push:
    branches:
      - master
  pull_request:
    branches:
      - master
jobs:
  ruff:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v6
      - uses: astral-sh/setup-uv@v7
      - run: uvx ruff check --output-format=github .


================================================
FILE: .github/workflows/sphinx.yml
================================================
name: sphinx

on:
  # Triggers the workflow on push or pull request events but only for the "master" branch
  push:
    branches: ["master"]
  pull_request:
    branches: ["master"]
  # Or manually from the Actions tab
  workflow_dispatch:

# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
permissions:
  contents: read
  pages: write
  id-token: write

# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
concurrency:
  group: "pages"
  cancel-in-progress: false

jobs:
  build_docs:
    runs-on: ubuntu-24.04-arm
    steps:
      - run:
          sudo apt-get update && sudo apt-get install -y libtiff5-dev libjpeg8-dev libopenjp2-7-dev
          zlib1g-dev libfreetype6-dev liblcms2-dev libwebp-dev tcl8.6-dev tk8.6-dev python3-tk
          libharfbuzz-dev libfribidi-dev libxcb1-dev
          libxml2-dev libxslt-dev
          libhdf5-dev
          libopenblas-dev
      - uses: actions/checkout@v6
      - uses: astral-sh/setup-uv@v7
      - uses: actions/setup-python@v6
        with:
          python-version: 3.14
          allow-prereleases: true
      - run: uv sync --group=docs
      - uses: actions/configure-pages@v5
      - run: uv run sphinx-build -c docs . docs/_build/html
      - uses: actions/upload-pages-artifact@v4
        with:
          path: docs/_build/html

  deploy_docs:
    environment:
      name: github-pages
      url: ${{ steps.deployment.outputs.page_url }}
    if: github.event_name != 'pull_request'
    needs: build_docs
    runs-on: ubuntu-latest
    steps:
      - uses: actions/deploy-pages@v4
        id: deployment


================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
#  Usually these files are written by a Python script from a template
#  before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
target/

# Jupyter Notebook
.ipynb_checkpoints

# pyenv
.python-version

# celery beat schedule file
celerybeat-schedule

# SageMath parsed files
*.sage.py

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

# Spyder project settings
.spyderproject
.spyproject

# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/

.DS_Store
.idea
.try
.vscode/
.vs/


================================================
FILE: .gitpod.yml
================================================
tasks:
  - init: pip3 install -r ./requirements.txt


================================================
FILE: .pre-commit-config.yaml
================================================
ci:
  autoupdate_schedule: monthly

repos:
  - repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v6.0.0
    hooks:
      - id: check-executables-have-shebangs
      - id: check-toml
      - id: check-yaml
      - id: end-of-file-fixer
        types: [python]
      - id: trailing-whitespace
      - id: requirements-txt-fixer

  - repo: https://github.com/MarcoGorelli/auto-walrus
    rev: 0.4.1
    hooks:
      - id: auto-walrus

  - repo: https://github.com/astral-sh/ruff-pre-commit
    rev: v0.14.14
    hooks:
      - id: ruff-check
      - id: ruff-format

  - repo: https://github.com/codespell-project/codespell
    rev: v2.4.1
    hooks:
      - id: codespell
        additional_dependencies:
          - tomli

  - repo: https://github.com/tox-dev/pyproject-fmt
    rev: v2.12.1
    hooks:
      - id: pyproject-fmt

  - repo: local
    hooks:
      - id: validate-filenames
        name: Validate filenames
        entry: ./scripts/validate_filenames.py
        language: script
        pass_filenames: false

  - repo: https://github.com/abravalheri/validate-pyproject
    rev: v0.24.1
    hooks:
      - id: validate-pyproject

  - repo: https://github.com/pre-commit/mirrors-mypy
    rev: v1.19.1
    hooks:
      - id: mypy
        args:
          - --explicit-package-bases
          - --ignore-missing-imports
          - --install-types
          - --non-interactive

  - repo: https://github.com/pre-commit/mirrors-prettier
    rev: v4.0.0-alpha.8
    hooks:
      - id: prettier
        types_or: [toml, yaml]


================================================
FILE: CONTRIBUTING.md
================================================
# Contributing guidelines

## Before contributing

Welcome to [TheAlgorithms/Python](https://github.com/TheAlgorithms/Python)! Before submitting your pull requests, please ensure that you __read the whole guidelines__. If you have any doubts about the contributing guide, please feel free to [state it clearly in an issue](https://github.com/TheAlgorithms/Python/issues/new) or ask the community on [Gitter](https://gitter.im/TheAlgorithms/community).

## Contributing

### Contributor

We are delighted that you are considering implementing algorithms and data structures for others! This repository is referenced and used by learners from all over the globe. By being one of our contributors, you agree and confirm that:

- You did your work - no plagiarism allowed.
  - Any plagiarized work will not be merged.
- Your work will be distributed under [MIT License](LICENSE.md) once your pull request is merged.
- Your submitted work fulfills or mostly fulfills our styles and standards.

__New implementation__ is welcome! For example, new solutions for a problem, different representations for a graph data structure or algorithm designs with different complexity, but __identical implementation__ of an existing implementation is not allowed. Please check whether the solution is already implemented or not before submitting your pull request.

__Improving comments__ and __writing proper tests__ are also highly welcome.

### Contribution

We appreciate any contribution, from fixing a grammar mistake in a comment to implementing complex algorithms. Please read this section if you are contributing your work.

Your contribution will be tested by our [automated testing on GitHub Actions](https://github.com/TheAlgorithms/Python/actions) to save time and mental energy.  After you have submitted your pull request, you should see the GitHub Actions tests start to run at the bottom of your submission page. If those tests fail, then click on the ___details___ button to read through the GitHub Actions output to understand the failure.  If you do not understand, please leave a comment on your submission page and a community member will try to help.

#### Issues

If you are interested in resolving an [open issue](https://github.com/TheAlgorithms/Python/issues), simply make a pull request with your proposed fix. __We do not assign issues in this repo__ so please do not ask for permission to work on an issue.

__Do not__ create an issue to contribute an algorithm. Please submit a pull request instead.

Please help us keep our issue list small by adding `Fixes #{$ISSUE_NUMBER}` to the description of pull requests that resolve open issues.
For example, if your pull request fixes issue #10, then please add the following to its description:
```
Fixes #10
```
GitHub will use this tag to [auto-close the issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue) if and when the PR is merged.

#### What is an Algorithm?

An Algorithm is one or more functions (or classes) that:
* take one or more inputs,
* perform some internal calculations or data manipulations,
* return one or more outputs,
* have minimal side effects (Ex. `print()`, `plot()`, `read()`, `write()`).

Algorithms should be packaged in a way that would make it easy for readers to put them into larger programs.

Algorithms should:
* have intuitive class and function names that make their purpose clear to readers
* use Python naming conventions and intuitive variable names to ease comprehension
* be flexible to take different input values
* have Python type hints for their input parameters and return values
* raise Python exceptions (`ValueError`, etc.) on erroneous input values
* have docstrings with clear explanations and/or URLs to source materials
* contain doctests that test both valid and erroneous input values
* return all calculation results instead of printing or plotting them

Algorithms in this repo should not be how-to examples for existing Python packages. Instead, they should perform internal calculations or manipulations to convert input values into different output values. Those calculations or manipulations can use data types, classes, or functions of existing Python packages but each algorithm in this repo should add unique value.

#### Pre-commit plugin
Use [pre-commit](https://pre-commit.com/#installation) to automatically format your code to match our coding style:

```bash
python3 -m pip install pre-commit  # only required the first time
pre-commit install
```
That's it! The plugin will run every time you commit any changes. If there are any errors found during the run, fix them and commit those changes. You can even run the plugin manually on all files:

```bash
pre-commit run --all-files --show-diff-on-failure
```

#### Coding Style

We want your work to be readable by others; therefore, we encourage you to note the following:

- Please write in Python 3.13+. For instance: `print()` is a function in Python 3 so `print "Hello"` will *not* work but `print("Hello")` will.
- Please focus hard on the naming of functions, classes, and variables.  Help your reader by using __descriptive names__ that can help you to remove redundant comments.
  - Single letter variable names are *old school* so please avoid them unless their life only spans a few lines.
  - Expand acronyms because `gcd()` is hard to understand but `greatest_common_divisor()` is not.
  - Please follow the [Python Naming Conventions](https://pep8.org/#prescriptive-naming-conventions) so variable_names and function_names should be lower_case, CONSTANTS in UPPERCASE, ClassNames should be CamelCase, etc.

- We encourage the use of Python [f-strings](https://realpython.com/python-f-strings/#f-strings-a-new-and-improved-way-to-format-strings-in-python) where they make the code easier to read.

- Please consider running [__psf/black__](https://github.com/python/black) on your Python file(s) before submitting your pull request.  This is not yet a requirement but it does make your code more readable and automatically aligns it with much of [PEP 8](https://www.python.org/dev/peps/pep-0008/). There are other code formatters (autopep8, yapf) but the __black__ formatter is now hosted by the Python Software Foundation. To use it,

  ```bash
  python3 -m pip install black  # only required the first time
  black .
  ```

- All submissions will need to pass the test `ruff .` before they will be accepted so if possible, try this test locally on your Python file(s) before submitting your pull request.

  ```bash
  python3 -m pip install ruff  # only required the first time
  ruff check
  ```

- Original code submissions require docstrings or comments to describe your work.

- More on docstrings and comments:

  If you used a Wikipedia article or some other source material to create your algorithm, please add the URL in a docstring or comment to help your reader.

  The following are considered to be bad and may be requested to be improved:

  ```python
  x = x + 2	# increased by 2
  ```

  This is too trivial. Comments are expected to be explanatory. For comments, you can write them above, on or below a line of code, as long as you are consistent within the same piece of code.

  We encourage you to put docstrings inside your functions but please pay attention to the indentation of docstrings. The following is a good example:

  ```python
  def sum_ab(a, b):
      """
      Return the sum of two integers a and b.
      """
      return a + b
  ```

- Write tests (especially [__doctests__](https://docs.python.org/3/library/doctest.html)) to illustrate and verify your work.  We highly encourage the use of _doctests on all functions_.

  ```python
  def sum_ab(a, b):
      """
      Return the sum of two integers a and b
      >>> sum_ab(2, 2)
      4
      >>> sum_ab(-2, 3)
      1
      >>> sum_ab(4.9, 5.1)
      10.0
      """
      return a + b
  ```

  These doctests will be run by pytest as part of our automated testing so please try to run your doctests locally and make sure that they are found and pass:

  ```bash
  python3 -m doctest -v my_submission.py
  ```

  The use of the Python built-in `input()` function is __not__ encouraged:

  ```python
  input('Enter your input:')
  # Or even worse...
  input = eval(input("Enter your input: "))
  ```

  However, if your code uses `input()` then we encourage you to gracefully deal with leading and trailing whitespace in user input by adding `.strip()` as in:

  ```python
  starting_value = int(input("Please enter a starting value: ").strip())
  ```

  The use of [Python type hints](https://docs.python.org/3/library/typing.html) is encouraged for function parameters and return values.  Our automated testing will run [mypy](https://mypy-lang.org) so run that locally before making your submission.

  ```python
  def sum_ab(a: int, b: int) -> int:
      return a + b
  ```

  Instructions on how to install mypy can be found [here](https://github.com/python/mypy). Please use the command `mypy --ignore-missing-imports .` to test all files or `mypy --ignore-missing-imports path/to/file.py` to test a specific file.

- [__List comprehensions and generators__](https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions) are preferred over the use of `lambda`, `map`, `filter`, `reduce` but the important thing is to demonstrate the power of Python in code that is easy to read and maintain.

- Avoid importing external libraries for basic algorithms. Only use those libraries for complicated algorithms.
- If you need a third-party module that is not in the file __requirements.txt__, please add it to that file as part of your submission.

#### Other Requirements for Submissions
- If you are submitting code in the `project_euler/` directory, please also read [the dedicated Guideline](https://github.com/TheAlgorithms/Python/blob/master/project_euler/README.md) before contributing to our Project Euler library.
- The file extension for code files should be `.py`. Jupyter Notebooks should be submitted to [TheAlgorithms/Jupyter](https://github.com/TheAlgorithms/Jupyter).
- Strictly use snake_case (underscore_separated) in your file_name, as it will be easy to parse in future using scripts.
- Please avoid creating new directories if at all possible. Try to fit your work into the existing directory structure.
- If possible, follow the standard *within* the folder you are submitting to.
- If you have modified/added code work, make sure the code compiles before submitting.
- If you have modified/added documentation work, ensure your language is concise and contains no grammar errors.
- Do not update the README.md or DIRECTORY.md file which will be periodically autogenerated by our GitHub Actions processes.
- Add a corresponding explanation to [Algorithms-Explanation](https://github.com/TheAlgorithms/Algorithms-Explanation) (Optional but recommended).
- All submissions will be tested with [__mypy__](http://www.mypy-lang.org) so we encourage you to add [__Python type hints__](https://docs.python.org/3/library/typing.html) where it makes sense to do so.

- Most importantly,
  - __Be consistent in the use of these guidelines when submitting.__
  - __Join__ us on [Discord](https://discord.com/invite/c7MnfGFGa6) and [Gitter](https://gitter.im/TheAlgorithms/community) __now!__
  - Happy coding!

Writer [@poyea](https://github.com/poyea), Jun 2019.


================================================
FILE: DIRECTORY.md
================================================

## Audio Filters
  * [Butterworth Filter](audio_filters/butterworth_filter.py)
  * [Iir Filter](audio_filters/iir_filter.py)
  * [Show Response](audio_filters/show_response.py)

## Backtracking
  * [All Combinations](backtracking/all_combinations.py)
  * [All Permutations](backtracking/all_permutations.py)
  * [All Subsequences](backtracking/all_subsequences.py)
  * [Coloring](backtracking/coloring.py)
  * [Combination Sum](backtracking/combination_sum.py)
  * [Crossword Puzzle Solver](backtracking/crossword_puzzle_solver.py)
  * [Generate Parentheses](backtracking/generate_parentheses.py)
  * [Generate Parentheses Iterative](backtracking/generate_parentheses_iterative.py)
  * [Hamiltonian Cycle](backtracking/hamiltonian_cycle.py)
  * [Knight Tour](backtracking/knight_tour.py)
  * [Match Word Pattern](backtracking/match_word_pattern.py)
  * [Minimax](backtracking/minimax.py)
  * [N Queens](backtracking/n_queens.py)
  * [N Queens Math](backtracking/n_queens_math.py)
  * [Power Sum](backtracking/power_sum.py)
  * [Rat In Maze](backtracking/rat_in_maze.py)
  * [Sudoku](backtracking/sudoku.py)
  * [Sum Of Subsets](backtracking/sum_of_subsets.py)
  * [Word Break](backtracking/word_break.py)
  * [Word Ladder](backtracking/word_ladder.py)
  * [Word Search](backtracking/word_search.py)

## Bit Manipulation
  * [Binary And Operator](bit_manipulation/binary_and_operator.py)
  * [Binary Coded Decimal](bit_manipulation/binary_coded_decimal.py)
  * [Binary Count Setbits](bit_manipulation/binary_count_setbits.py)
  * [Binary Count Trailing Zeros](bit_manipulation/binary_count_trailing_zeros.py)
  * [Binary Or Operator](bit_manipulation/binary_or_operator.py)
  * [Binary Shifts](bit_manipulation/binary_shifts.py)
  * [Binary Twos Complement](bit_manipulation/binary_twos_complement.py)
  * [Binary Xor Operator](bit_manipulation/binary_xor_operator.py)
  * [Bitwise Addition Recursive](bit_manipulation/bitwise_addition_recursive.py)
  * [Count 1S Brian Kernighan Method](bit_manipulation/count_1s_brian_kernighan_method.py)
  * [Count Number Of One Bits](bit_manipulation/count_number_of_one_bits.py)
  * [Excess 3 Code](bit_manipulation/excess_3_code.py)
  * [Find Previous Power Of Two](bit_manipulation/find_previous_power_of_two.py)
  * [Find Unique Number](bit_manipulation/find_unique_number.py)
  * [Gray Code Sequence](bit_manipulation/gray_code_sequence.py)
  * [Highest Set Bit](bit_manipulation/highest_set_bit.py)
  * [Index Of Rightmost Set Bit](bit_manipulation/index_of_rightmost_set_bit.py)
  * [Is Even](bit_manipulation/is_even.py)
  * [Is Power Of Two](bit_manipulation/is_power_of_two.py)
  * [Largest Pow Of Two Le Num](bit_manipulation/largest_pow_of_two_le_num.py)
  * [Missing Number](bit_manipulation/missing_number.py)
  * [Numbers Different Signs](bit_manipulation/numbers_different_signs.py)
  * [Power Of 4](bit_manipulation/power_of_4.py)
  * [Reverse Bits](bit_manipulation/reverse_bits.py)
  * [Single Bit Manipulation Operations](bit_manipulation/single_bit_manipulation_operations.py)
  * [Swap All Odd And Even Bits](bit_manipulation/swap_all_odd_and_even_bits.py)

## Blockchain
  * [Diophantine Equation](blockchain/diophantine_equation.py)

## Boolean Algebra
  * [And Gate](boolean_algebra/and_gate.py)
  * [Imply Gate](boolean_algebra/imply_gate.py)
  * [Karnaugh Map Simplification](boolean_algebra/karnaugh_map_simplification.py)
  * [Multiplexer](boolean_algebra/multiplexer.py)
  * [Nand Gate](boolean_algebra/nand_gate.py)
  * [Nimply Gate](boolean_algebra/nimply_gate.py)
  * [Nor Gate](boolean_algebra/nor_gate.py)
  * [Not Gate](boolean_algebra/not_gate.py)
  * [Or Gate](boolean_algebra/or_gate.py)
  * [Quine Mc Cluskey](boolean_algebra/quine_mc_cluskey.py)
  * [Xnor Gate](boolean_algebra/xnor_gate.py)
  * [Xor Gate](boolean_algebra/xor_gate.py)

## Cellular Automata
  * [Conways Game Of Life](cellular_automata/conways_game_of_life.py)
  * [Game Of Life](cellular_automata/game_of_life.py)
  * [Langtons Ant](cellular_automata/langtons_ant.py)
  * [Nagel Schrekenberg](cellular_automata/nagel_schrekenberg.py)
  * [One Dimensional](cellular_automata/one_dimensional.py)
  * [Wa Tor](cellular_automata/wa_tor.py)

## Ciphers
  * [A1Z26](ciphers/a1z26.py)
  * [Affine Cipher](ciphers/affine_cipher.py)
  * [Atbash](ciphers/atbash.py)
  * [Autokey](ciphers/autokey.py)
  * [Baconian Cipher](ciphers/baconian_cipher.py)
  * [Base16](ciphers/base16.py)
  * [Base32](ciphers/base32.py)
  * [Base64 Cipher](ciphers/base64_cipher.py)
  * [Base85](ciphers/base85.py)
  * [Beaufort Cipher](ciphers/beaufort_cipher.py)
  * [Bifid](ciphers/bifid.py)
  * [Brute Force Caesar Cipher](ciphers/brute_force_caesar_cipher.py)
  * [Caesar Cipher](ciphers/caesar_cipher.py)
  * [Cryptomath Module](ciphers/cryptomath_module.py)
  * [Decrypt Caesar With Chi Squared](ciphers/decrypt_caesar_with_chi_squared.py)
  * [Deterministic Miller Rabin](ciphers/deterministic_miller_rabin.py)
  * [Diffie](ciphers/diffie.py)
  * [Diffie Hellman](ciphers/diffie_hellman.py)
  * [Elgamal Key Generator](ciphers/elgamal_key_generator.py)
  * [Enigma Machine2](ciphers/enigma_machine2.py)
  * [Fractionated Morse Cipher](ciphers/fractionated_morse_cipher.py)
  * [Gronsfeld Cipher](ciphers/gronsfeld_cipher.py)
  * [Hill Cipher](ciphers/hill_cipher.py)
  * [Mixed Keyword Cypher](ciphers/mixed_keyword_cypher.py)
  * [Mono Alphabetic Ciphers](ciphers/mono_alphabetic_ciphers.py)
  * [Morse Code](ciphers/morse_code.py)
  * [Onepad Cipher](ciphers/onepad_cipher.py)
  * [Permutation Cipher](ciphers/permutation_cipher.py)
  * [Playfair Cipher](ciphers/playfair_cipher.py)
  * [Polybius](ciphers/polybius.py)
  * [Porta Cipher](ciphers/porta_cipher.py)
  * [Rabin Miller](ciphers/rabin_miller.py)
  * [Rail Fence Cipher](ciphers/rail_fence_cipher.py)
  * [Rot13](ciphers/rot13.py)
  * [Rsa Cipher](ciphers/rsa_cipher.py)
  * [Rsa Factorization](ciphers/rsa_factorization.py)
  * [Rsa Key Generator](ciphers/rsa_key_generator.py)
  * [Running Key Cipher](ciphers/running_key_cipher.py)
  * [Shuffled Shift Cipher](ciphers/shuffled_shift_cipher.py)
  * [Simple Keyword Cypher](ciphers/simple_keyword_cypher.py)
  * [Simple Substitution Cipher](ciphers/simple_substitution_cipher.py)
  * [Transposition Cipher](ciphers/transposition_cipher.py)
  * [Transposition Cipher Encrypt Decrypt File](ciphers/transposition_cipher_encrypt_decrypt_file.py)
  * [Trifid Cipher](ciphers/trifid_cipher.py)
  * [Vernam Cipher](ciphers/vernam_cipher.py)
  * [Vigenere Cipher](ciphers/vigenere_cipher.py)
  * [Xor Cipher](ciphers/xor_cipher.py)

## Computer Vision
  * [Cnn Classification](computer_vision/cnn_classification.py)
  * [Flip Augmentation](computer_vision/flip_augmentation.py)
  * [Haralick Descriptors](computer_vision/haralick_descriptors.py)
  * [Harris Corner](computer_vision/harris_corner.py)
  * [Horn Schunck](computer_vision/horn_schunck.py)
  * [Intensity Based Segmentation](computer_vision/intensity_based_segmentation.py)
  * [Mean Threshold](computer_vision/mean_threshold.py)
  * [Mosaic Augmentation](computer_vision/mosaic_augmentation.py)
  * [Pooling Functions](computer_vision/pooling_functions.py)

## Conversions
  * [Astronomical Length Scale Conversion](conversions/astronomical_length_scale_conversion.py)
  * [Binary To Decimal](conversions/binary_to_decimal.py)
  * [Binary To Hexadecimal](conversions/binary_to_hexadecimal.py)
  * [Binary To Octal](conversions/binary_to_octal.py)
  * [Convert Number To Words](conversions/convert_number_to_words.py)
  * [Decimal To Any](conversions/decimal_to_any.py)
  * [Decimal To Binary](conversions/decimal_to_binary.py)
  * [Decimal To Hexadecimal](conversions/decimal_to_hexadecimal.py)
  * [Decimal To Octal](conversions/decimal_to_octal.py)
  * [Energy Conversions](conversions/energy_conversions.py)
  * [Excel Title To Column](conversions/excel_title_to_column.py)
  * [Hex To Bin](conversions/hex_to_bin.py)
  * [Hexadecimal To Decimal](conversions/hexadecimal_to_decimal.py)
  * [Ipv4 Conversion](conversions/ipv4_conversion.py)
  * [Length Conversion](conversions/length_conversion.py)
  * [Molecular Chemistry](conversions/molecular_chemistry.py)
  * [Octal To Binary](conversions/octal_to_binary.py)
  * [Octal To Decimal](conversions/octal_to_decimal.py)
  * [Octal To Hexadecimal](conversions/octal_to_hexadecimal.py)
  * [Prefix Conversions](conversions/prefix_conversions.py)
  * [Prefix Conversions String](conversions/prefix_conversions_string.py)
  * [Pressure Conversions](conversions/pressure_conversions.py)
  * [Rectangular To Polar](conversions/rectangular_to_polar.py)
  * [Rgb Cmyk Conversion](conversions/rgb_cmyk_conversion.py)
  * [Rgb Hsv Conversion](conversions/rgb_hsv_conversion.py)
  * [Roman Numerals](conversions/roman_numerals.py)
  * [Speed Conversions](conversions/speed_conversions.py)
  * [Temperature Conversions](conversions/temperature_conversions.py)
  * [Time Conversions](conversions/time_conversions.py)
  * [Volume Conversions](conversions/volume_conversions.py)
  * [Weight Conversion](conversions/weight_conversion.py)

## Data Compression
  * [Burrows Wheeler](data_compression/burrows_wheeler.py)
  * [Coordinate Compression](data_compression/coordinate_compression.py)
  * [Huffman](data_compression/huffman.py)
  * [Lempel Ziv](data_compression/lempel_ziv.py)
  * [Lempel Ziv Decompress](data_compression/lempel_ziv_decompress.py)
  * [Lz77](data_compression/lz77.py)
  * [Peak Signal To Noise Ratio](data_compression/peak_signal_to_noise_ratio.py)
  * [Run Length Encoding](data_compression/run_length_encoding.py)

## Data Structures
  * Arrays
    * [Equilibrium Index In Array](data_structures/arrays/equilibrium_index_in_array.py)
    * [Find Triplets With 0 Sum](data_structures/arrays/find_triplets_with_0_sum.py)
    * [Index 2D Array In 1D](data_structures/arrays/index_2d_array_in_1d.py)
    * [Kth Largest Element](data_structures/arrays/kth_largest_element.py)
    * [Median Two Array](data_structures/arrays/median_two_array.py)
    * [Monotonic Array](data_structures/arrays/monotonic_array.py)
    * [Pairs With Given Sum](data_structures/arrays/pairs_with_given_sum.py)
    * [Permutations](data_structures/arrays/permutations.py)
    * [Prefix Sum](data_structures/arrays/prefix_sum.py)
    * [Product Sum](data_structures/arrays/product_sum.py)
    * [Rotate Array](data_structures/arrays/rotate_array.py)
    * [Sparse Table](data_structures/arrays/sparse_table.py)
    * [Sudoku Solver](data_structures/arrays/sudoku_solver.py)
  * Binary Tree
    * [Avl Tree](data_structures/binary_tree/avl_tree.py)
    * [Basic Binary Tree](data_structures/binary_tree/basic_binary_tree.py)
    * [Binary Search Tree](data_structures/binary_tree/binary_search_tree.py)
    * [Binary Search Tree Recursive](data_structures/binary_tree/binary_search_tree_recursive.py)
    * [Binary Tree Mirror](data_structures/binary_tree/binary_tree_mirror.py)
    * [Binary Tree Node Sum](data_structures/binary_tree/binary_tree_node_sum.py)
    * [Binary Tree Path Sum](data_structures/binary_tree/binary_tree_path_sum.py)
    * [Binary Tree Traversals](data_structures/binary_tree/binary_tree_traversals.py)
    * [Diameter Of Binary Tree](data_structures/binary_tree/diameter_of_binary_tree.py)
    * [Diff Views Of Binary Tree](data_structures/binary_tree/diff_views_of_binary_tree.py)
    * [Distribute Coins](data_structures/binary_tree/distribute_coins.py)
    * [Fenwick Tree](data_structures/binary_tree/fenwick_tree.py)
    * [Flatten Binarytree To Linkedlist](data_structures/binary_tree/flatten_binarytree_to_linkedlist.py)
    * [Floor And Ceiling](data_structures/binary_tree/floor_and_ceiling.py)
    * [Inorder Tree Traversal 2022](data_structures/binary_tree/inorder_tree_traversal_2022.py)
    * [Is Sorted](data_structures/binary_tree/is_sorted.py)
    * [Is Sum Tree](data_structures/binary_tree/is_sum_tree.py)
    * [Lazy Segment Tree](data_structures/binary_tree/lazy_segment_tree.py)
    * [Lowest Common Ancestor](data_structures/binary_tree/lowest_common_ancestor.py)
    * [Maximum Fenwick Tree](data_structures/binary_tree/maximum_fenwick_tree.py)
    * [Maximum Sum Bst](data_structures/binary_tree/maximum_sum_bst.py)
    * [Merge Two Binary Trees](data_structures/binary_tree/merge_two_binary_trees.py)
    * [Mirror Binary Tree](data_structures/binary_tree/mirror_binary_tree.py)
    * [Non Recursive Segment Tree](data_structures/binary_tree/non_recursive_segment_tree.py)
    * [Number Of Possible Binary Trees](data_structures/binary_tree/number_of_possible_binary_trees.py)
    * [Red Black Tree](data_structures/binary_tree/red_black_tree.py)
    * [Segment Tree](data_structures/binary_tree/segment_tree.py)
    * [Segment Tree Other](data_structures/binary_tree/segment_tree_other.py)
    * [Serialize Deserialize Binary Tree](data_structures/binary_tree/serialize_deserialize_binary_tree.py)
    * [Symmetric Tree](data_structures/binary_tree/symmetric_tree.py)
    * [Treap](data_structures/binary_tree/treap.py)
    * [Wavelet Tree](data_structures/binary_tree/wavelet_tree.py)
  * Disjoint Set
    * [Alternate Disjoint Set](data_structures/disjoint_set/alternate_disjoint_set.py)
    * [Disjoint Set](data_structures/disjoint_set/disjoint_set.py)
  * Hashing
    * [Bloom Filter](data_structures/hashing/bloom_filter.py)
    * [Double Hash](data_structures/hashing/double_hash.py)
    * [Hash Map](data_structures/hashing/hash_map.py)
    * [Hash Table](data_structures/hashing/hash_table.py)
    * [Hash Table With Linked List](data_structures/hashing/hash_table_with_linked_list.py)
    * Number Theory
      * [Prime Numbers](data_structures/hashing/number_theory/prime_numbers.py)
    * [Quadratic Probing](data_structures/hashing/quadratic_probing.py)
    * Tests
      * [Test Hash Map](data_structures/hashing/tests/test_hash_map.py)
  * Heap
    * [Binomial Heap](data_structures/heap/binomial_heap.py)
    * [Heap](data_structures/heap/heap.py)
    * [Heap Generic](data_structures/heap/heap_generic.py)
    * [Max Heap](data_structures/heap/max_heap.py)
    * [Min Heap](data_structures/heap/min_heap.py)
    * [Randomized Heap](data_structures/heap/randomized_heap.py)
    * [Skew Heap](data_structures/heap/skew_heap.py)
  * Kd Tree
    * [Build Kdtree](data_structures/kd_tree/build_kdtree.py)
    * Example
      * [Example Usage](data_structures/kd_tree/example/example_usage.py)
      * [Hypercube Points](data_structures/kd_tree/example/hypercube_points.py)
    * [Kd Node](data_structures/kd_tree/kd_node.py)
    * [Nearest Neighbour Search](data_structures/kd_tree/nearest_neighbour_search.py)
    * Tests
      * [Test Kdtree](data_structures/kd_tree/tests/test_kdtree.py)
  * Linked List
    * [Circular Linked List](data_structures/linked_list/circular_linked_list.py)
    * [Deque Doubly](data_structures/linked_list/deque_doubly.py)
    * [Doubly Linked List](data_structures/linked_list/doubly_linked_list.py)
    * [Doubly Linked List Two](data_structures/linked_list/doubly_linked_list_two.py)
    * [Floyds Cycle Detection](data_structures/linked_list/floyds_cycle_detection.py)
    * [From Sequence](data_structures/linked_list/from_sequence.py)
    * [Has Loop](data_structures/linked_list/has_loop.py)
    * [Is Palindrome](data_structures/linked_list/is_palindrome.py)
    * [Merge Two Lists](data_structures/linked_list/merge_two_lists.py)
    * [Middle Element Of Linked List](data_structures/linked_list/middle_element_of_linked_list.py)
    * [Print Reverse](data_structures/linked_list/print_reverse.py)
    * [Reverse K Group](data_structures/linked_list/reverse_k_group.py)
    * [Rotate To The Right](data_structures/linked_list/rotate_to_the_right.py)
    * [Singly Linked List](data_structures/linked_list/singly_linked_list.py)
    * [Skip List](data_structures/linked_list/skip_list.py)
    * [Swap Nodes](data_structures/linked_list/swap_nodes.py)
  * Queues
    * [Circular Queue](data_structures/queues/circular_queue.py)
    * [Circular Queue Linked List](data_structures/queues/circular_queue_linked_list.py)
    * [Double Ended Queue](data_structures/queues/double_ended_queue.py)
    * [Linked Queue](data_structures/queues/linked_queue.py)
    * [Priority Queue Using List](data_structures/queues/priority_queue_using_list.py)
    * [Queue By List](data_structures/queues/queue_by_list.py)
    * [Queue By Two Stacks](data_structures/queues/queue_by_two_stacks.py)
    * [Queue On Pseudo Stack](data_structures/queues/queue_on_pseudo_stack.py)
  * Stacks
    * [Balanced Parentheses](data_structures/stacks/balanced_parentheses.py)
    * [Dijkstras Two Stack Algorithm](data_structures/stacks/dijkstras_two_stack_algorithm.py)
    * [Infix To Postfix Conversion](data_structures/stacks/infix_to_postfix_conversion.py)
    * [Infix To Prefix Conversion](data_structures/stacks/infix_to_prefix_conversion.py)
    * [Largest Rectangle Histogram](data_structures/stacks/largest_rectangle_histogram.py)
    * [Lexicographical Numbers](data_structures/stacks/lexicographical_numbers.py)
    * [Next Greater Element](data_structures/stacks/next_greater_element.py)
    * [Postfix Evaluation](data_structures/stacks/postfix_evaluation.py)
    * [Prefix Evaluation](data_structures/stacks/prefix_evaluation.py)
    * [Stack](data_structures/stacks/stack.py)
    * [Stack Using Two Queues](data_structures/stacks/stack_using_two_queues.py)
    * [Stack With Doubly Linked List](data_structures/stacks/stack_with_doubly_linked_list.py)
    * [Stack With Singly Linked List](data_structures/stacks/stack_with_singly_linked_list.py)
    * [Stock Span Problem](data_structures/stacks/stock_span_problem.py)
  * Suffix Tree
    * Example
      * [Example Usage](data_structures/suffix_tree/example/example_usage.py)
    * [Suffix Tree](data_structures/suffix_tree/suffix_tree.py)
    * [Suffix Tree Node](data_structures/suffix_tree/suffix_tree_node.py)
    * Tests
      * [Test Suffix Tree](data_structures/suffix_tree/tests/test_suffix_tree.py)
  * Trie
    * [Radix Tree](data_structures/trie/radix_tree.py)
    * [Trie](data_structures/trie/trie.py)

## Digital Image Processing
  * [Change Brightness](digital_image_processing/change_brightness.py)
  * [Change Contrast](digital_image_processing/change_contrast.py)
  * [Convert To Negative](digital_image_processing/convert_to_negative.py)
  * Dithering
    * [Burkes](digital_image_processing/dithering/burkes.py)
  * Edge Detection
    * [Canny](digital_image_processing/edge_detection/canny.py)
  * Filters
    * [Bilateral Filter](digital_image_processing/filters/bilateral_filter.py)
    * [Convolve](digital_image_processing/filters/convolve.py)
    * [Gabor Filter](digital_image_processing/filters/gabor_filter.py)
    * [Gaussian Filter](digital_image_processing/filters/gaussian_filter.py)
    * [Laplacian Filter](digital_image_processing/filters/laplacian_filter.py)
    * [Local Binary Pattern](digital_image_processing/filters/local_binary_pattern.py)
    * [Median Filter](digital_image_processing/filters/median_filter.py)
    * [Sobel Filter](digital_image_processing/filters/sobel_filter.py)
  * Histogram Equalization
    * [Histogram Stretch](digital_image_processing/histogram_equalization/histogram_stretch.py)
  * [Index Calculation](digital_image_processing/index_calculation.py)
  * Morphological Operations
    * [Dilation Operation](digital_image_processing/morphological_operations/dilation_operation.py)
    * [Erosion Operation](digital_image_processing/morphological_operations/erosion_operation.py)
  * Resize
    * [Resize](digital_image_processing/resize/resize.py)
  * Rotation
    * [Rotation](digital_image_processing/rotation/rotation.py)
  * [Sepia](digital_image_processing/sepia.py)
  * [Test Digital Image Processing](digital_image_processing/test_digital_image_processing.py)

## Divide And Conquer
  * [Closest Pair Of Points](divide_and_conquer/closest_pair_of_points.py)
  * [Convex Hull](divide_and_conquer/convex_hull.py)
  * [Heaps Algorithm](divide_and_conquer/heaps_algorithm.py)
  * [Heaps Algorithm Iterative](divide_and_conquer/heaps_algorithm_iterative.py)
  * [Inversions](divide_and_conquer/inversions.py)
  * [Kth Order Statistic](divide_and_conquer/kth_order_statistic.py)
  * [Max Difference Pair](divide_and_conquer/max_difference_pair.py)
  * [Max Subarray](divide_and_conquer/max_subarray.py)
  * [Mergesort](divide_and_conquer/mergesort.py)
  * [Peak](divide_and_conquer/peak.py)
  * [Power](divide_and_conquer/power.py)
  * [Strassen Matrix Multiplication](divide_and_conquer/strassen_matrix_multiplication.py)

## Docs
  * [Conf](docs/conf.py)

## Dynamic Programming
  * [Abbreviation](dynamic_programming/abbreviation.py)
  * [All Construct](dynamic_programming/all_construct.py)
  * [Bitmask](dynamic_programming/bitmask.py)
  * [Catalan Numbers](dynamic_programming/catalan_numbers.py)
  * [Climbing Stairs](dynamic_programming/climbing_stairs.py)
  * [Combination Sum Iv](dynamic_programming/combination_sum_iv.py)
  * [Edit Distance](dynamic_programming/edit_distance.py)
  * [Factorial](dynamic_programming/factorial.py)
  * [Fast Fibonacci](dynamic_programming/fast_fibonacci.py)
  * [Fibonacci](dynamic_programming/fibonacci.py)
  * [Fizz Buzz](dynamic_programming/fizz_buzz.py)
  * [Floyd Warshall](dynamic_programming/floyd_warshall.py)
  * [Integer Partition](dynamic_programming/integer_partition.py)
  * [Iterating Through Submasks](dynamic_programming/iterating_through_submasks.py)
  * [K Means Clustering Tensorflow](dynamic_programming/k_means_clustering_tensorflow.py)
  * [Knapsack](dynamic_programming/knapsack.py)
  * [Largest Divisible Subset](dynamic_programming/largest_divisible_subset.py)
  * [Longest Common Subsequence](dynamic_programming/longest_common_subsequence.py)
  * [Longest Common Substring](dynamic_programming/longest_common_substring.py)
  * [Longest Increasing Subsequence](dynamic_programming/longest_increasing_subsequence.py)
  * [Longest Increasing Subsequence Iterative](dynamic_programming/longest_increasing_subsequence_iterative.py)
  * [Longest Increasing Subsequence O Nlogn](dynamic_programming/longest_increasing_subsequence_o_nlogn.py)
  * [Longest Palindromic Subsequence](dynamic_programming/longest_palindromic_subsequence.py)
  * [Matrix Chain Multiplication](dynamic_programming/matrix_chain_multiplication.py)
  * [Matrix Chain Order](dynamic_programming/matrix_chain_order.py)
  * [Max Non Adjacent Sum](dynamic_programming/max_non_adjacent_sum.py)
  * [Max Product Subarray](dynamic_programming/max_product_subarray.py)
  * [Max Subarray Sum](dynamic_programming/max_subarray_sum.py)
  * [Min Distance Up Bottom](dynamic_programming/min_distance_up_bottom.py)
  * [Minimum Coin Change](dynamic_programming/minimum_coin_change.py)
  * [Minimum Cost Path](dynamic_programming/minimum_cost_path.py)
  * [Minimum Partition](dynamic_programming/minimum_partition.py)
  * [Minimum Size Subarray Sum](dynamic_programming/minimum_size_subarray_sum.py)
  * [Minimum Squares To Represent A Number](dynamic_programming/minimum_squares_to_represent_a_number.py)
  * [Minimum Steps To One](dynamic_programming/minimum_steps_to_one.py)
  * [Minimum Tickets Cost](dynamic_programming/minimum_tickets_cost.py)
  * [Narcissistic Number](dynamic_programming/narcissistic_number.py)
  * [Optimal Binary Search Tree](dynamic_programming/optimal_binary_search_tree.py)
  * [Palindrome Partitioning](dynamic_programming/palindrome_partitioning.py)
  * [Range Sum Query](dynamic_programming/range_sum_query.py)
  * [Regex Match](dynamic_programming/regex_match.py)
  * [Rod Cutting](dynamic_programming/rod_cutting.py)
  * [Smith Waterman](dynamic_programming/smith_waterman.py)
  * [Subset Generation](dynamic_programming/subset_generation.py)
  * [Sum Of Subset](dynamic_programming/sum_of_subset.py)
  * [Trapped Water](dynamic_programming/trapped_water.py)
  * [Tribonacci](dynamic_programming/tribonacci.py)
  * [Viterbi](dynamic_programming/viterbi.py)
  * [Wildcard Matching](dynamic_programming/wildcard_matching.py)
  * [Word Break](dynamic_programming/word_break.py)

## Electronics
  * [Apparent Power](electronics/apparent_power.py)
  * [Builtin Voltage](electronics/builtin_voltage.py)
  * [Capacitor Equivalence](electronics/capacitor_equivalence.py)
  * [Carrier Concentration](electronics/carrier_concentration.py)
  * [Charging Capacitor](electronics/charging_capacitor.py)
  * [Charging Inductor](electronics/charging_inductor.py)
  * [Circular Convolution](electronics/circular_convolution.py)
  * [Coulombs Law](electronics/coulombs_law.py)
  * [Electric Conductivity](electronics/electric_conductivity.py)
  * [Electric Power](electronics/electric_power.py)
  * [Electrical Impedance](electronics/electrical_impedance.py)
  * [Ic 555 Timer](electronics/ic_555_timer.py)
  * [Ind Reactance](electronics/ind_reactance.py)
  * [Ohms Law](electronics/ohms_law.py)
  * [Real And Reactive Power](electronics/real_and_reactive_power.py)
  * [Resistor Color Code](electronics/resistor_color_code.py)
  * [Resistor Equivalence](electronics/resistor_equivalence.py)
  * [Resonant Frequency](electronics/resonant_frequency.py)
  * [Wheatstone Bridge](electronics/wheatstone_bridge.py)

## File Transfer
  * [Receive File](file_transfer/receive_file.py)
  * [Send File](file_transfer/send_file.py)
  * Tests
    * [Test Send File](file_transfer/tests/test_send_file.py)

## Financial
  * [Equated Monthly Installments](financial/equated_monthly_installments.py)
  * [Exponential Moving Average](financial/exponential_moving_average.py)
  * [Interest](financial/interest.py)
  * [Present Value](financial/present_value.py)
  * [Price Plus Tax](financial/price_plus_tax.py)
  * [Simple Moving Average](financial/simple_moving_average.py)
  * [Straight Line Depreciation](financial/straight_line_depreciation.py)
  * [Time And Half Pay](financial/time_and_half_pay.py)

## Fractals
  * [Julia Sets](fractals/julia_sets.py)
  * [Koch Snowflake](fractals/koch_snowflake.py)
  * [Mandelbrot](fractals/mandelbrot.py)
  * [Sierpinski Triangle](fractals/sierpinski_triangle.py)
  * [Vicsek](fractals/vicsek.py)

## Fuzzy Logic
  * [Fuzzy Operations](fuzzy_logic/fuzzy_operations.py)

## Genetic Algorithm
  * [Basic String](genetic_algorithm/basic_string.py)

## Geodesy
  * [Haversine Distance](geodesy/haversine_distance.py)
  * [Lamberts Ellipsoidal Distance](geodesy/lamberts_ellipsoidal_distance.py)

## Geometry
  * [Geometry](geometry/geometry.py)
  * [Graham Scan](geometry/graham_scan.py)
  * [Jarvis March](geometry/jarvis_march.py)
  * Tests
    * [Test Graham Scan](geometry/tests/test_graham_scan.py)
    * [Test Jarvis March](geometry/tests/test_jarvis_march.py)

## Graphics
  * [Bezier Curve](graphics/bezier_curve.py)
  * [Butterfly Pattern](graphics/butterfly_pattern.py)
  * [Digital Differential Analyzer Line](graphics/digital_differential_analyzer_line.py)
  * [Vector3 For 2D Rendering](graphics/vector3_for_2d_rendering.py)

## Graphs
  * [A Star](graphs/a_star.py)
  * [Ant Colony Optimization Algorithms](graphs/ant_colony_optimization_algorithms.py)
  * [Articulation Points](graphs/articulation_points.py)
  * [Basic Graphs](graphs/basic_graphs.py)
  * [Bellman Ford](graphs/bellman_ford.py)
  * [Bi Directional Dijkstra](graphs/bi_directional_dijkstra.py)
  * [Bidirectional A Star](graphs/bidirectional_a_star.py)
  * [Bidirectional Breadth First Search](graphs/bidirectional_breadth_first_search.py)
  * [Bidirectional Search](graphs/bidirectional_search.py)
  * [Boruvka](graphs/boruvka.py)
  * [Breadth First Search](graphs/breadth_first_search.py)
  * [Breadth First Search 2](graphs/breadth_first_search_2.py)
  * [Breadth First Search Shortest Path](graphs/breadth_first_search_shortest_path.py)
  * [Breadth First Search Shortest Path 2](graphs/breadth_first_search_shortest_path_2.py)
  * [Breadth First Search Zero One Shortest Path](graphs/breadth_first_search_zero_one_shortest_path.py)
  * [Check Bipatrite](graphs/check_bipatrite.py)
  * [Check Cycle](graphs/check_cycle.py)
  * [Connected Components](graphs/connected_components.py)
  * [Deep Clone Graph](graphs/deep_clone_graph.py)
  * [Depth First Search](graphs/depth_first_search.py)
  * [Depth First Search 2](graphs/depth_first_search_2.py)
  * [Dijkstra](graphs/dijkstra.py)
  * [Dijkstra 2](graphs/dijkstra_2.py)
  * [Dijkstra Algorithm](graphs/dijkstra_algorithm.py)
  * [Dijkstra Alternate](graphs/dijkstra_alternate.py)
  * [Dijkstra Binary Grid](graphs/dijkstra_binary_grid.py)
  * [Dinic](graphs/dinic.py)
  * [Directed And Undirected Weighted Graph](graphs/directed_and_undirected_weighted_graph.py)
  * [Edmonds Karp Multiple Source And Sink](graphs/edmonds_karp_multiple_source_and_sink.py)
  * [Eulerian Path And Circuit For Undirected Graph](graphs/eulerian_path_and_circuit_for_undirected_graph.py)
  * [Even Tree](graphs/even_tree.py)
  * [Finding Bridges](graphs/finding_bridges.py)
  * [Frequent Pattern Graph Miner](graphs/frequent_pattern_graph_miner.py)
  * [G Topological Sort](graphs/g_topological_sort.py)
  * [Gale Shapley Bigraph](graphs/gale_shapley_bigraph.py)
  * [Graph Adjacency List](graphs/graph_adjacency_list.py)
  * [Graph Adjacency Matrix](graphs/graph_adjacency_matrix.py)
  * [Graph List](graphs/graph_list.py)
  * [Graphs Floyd Warshall](graphs/graphs_floyd_warshall.py)
  * [Greedy Best First](graphs/greedy_best_first.py)
  * [Greedy Min Vertex Cover](graphs/greedy_min_vertex_cover.py)
  * [Kahns Algorithm Long](graphs/kahns_algorithm_long.py)
  * [Kahns Algorithm Topo](graphs/kahns_algorithm_topo.py)
  * [Karger](graphs/karger.py)
  * [Lanczos Eigenvectors](graphs/lanczos_eigenvectors.py)
  * [Markov Chain](graphs/markov_chain.py)
  * [Matching Min Vertex Cover](graphs/matching_min_vertex_cover.py)
  * [Minimum Path Sum](graphs/minimum_path_sum.py)
  * [Minimum Spanning Tree Boruvka](graphs/minimum_spanning_tree_boruvka.py)
  * [Minimum Spanning Tree Kruskal](graphs/minimum_spanning_tree_kruskal.py)
  * [Minimum Spanning Tree Kruskal2](graphs/minimum_spanning_tree_kruskal2.py)
  * [Minimum Spanning Tree Prims](graphs/minimum_spanning_tree_prims.py)
  * [Minimum Spanning Tree Prims2](graphs/minimum_spanning_tree_prims2.py)
  * [Multi Heuristic Astar](graphs/multi_heuristic_astar.py)
  * [Page Rank](graphs/page_rank.py)
  * [Prim](graphs/prim.py)
  * [Random Graph Generator](graphs/random_graph_generator.py)
  * [Scc Kosaraju](graphs/scc_kosaraju.py)
  * [Strongly Connected Components](graphs/strongly_connected_components.py)
  * [Tarjans Scc](graphs/tarjans_scc.py)
  * Tests
    * [Test Min Spanning Tree Kruskal](graphs/tests/test_min_spanning_tree_kruskal.py)
    * [Test Min Spanning Tree Prim](graphs/tests/test_min_spanning_tree_prim.py)

## Greedy Methods
  * [Best Time To Buy And Sell Stock](greedy_methods/best_time_to_buy_and_sell_stock.py)
  * [Fractional Cover Problem](greedy_methods/fractional_cover_problem.py)
  * [Fractional Knapsack](greedy_methods/fractional_knapsack.py)
  * [Fractional Knapsack 2](greedy_methods/fractional_knapsack_2.py)
  * [Gas Station](greedy_methods/gas_station.py)
  * [Minimum Coin Change](greedy_methods/minimum_coin_change.py)
  * [Minimum Waiting Time](greedy_methods/minimum_waiting_time.py)
  * [Optimal Merge Pattern](greedy_methods/optimal_merge_pattern.py)
  * [Smallest Range](greedy_methods/smallest_range.py)

## Hashes
  * [Adler32](hashes/adler32.py)
  * [Chaos Machine](hashes/chaos_machine.py)
  * [Djb2](hashes/djb2.py)
  * [Elf](hashes/elf.py)
  * [Enigma Machine](hashes/enigma_machine.py)
  * [Fletcher16](hashes/fletcher16.py)
  * [Hamming Code](hashes/hamming_code.py)
  * [Luhn](hashes/luhn.py)
  * [Md5](hashes/md5.py)
  * [Sdbm](hashes/sdbm.py)
  * [Sha1](hashes/sha1.py)
  * [Sha256](hashes/sha256.py)

## Knapsack
  * [Greedy Knapsack](knapsack/greedy_knapsack.py)
  * [Knapsack](knapsack/knapsack.py)
  * [Recursive Approach Knapsack](knapsack/recursive_approach_knapsack.py)
  * Tests
    * [Test Greedy Knapsack](knapsack/tests/test_greedy_knapsack.py)
    * [Test Knapsack](knapsack/tests/test_knapsack.py)

## Linear Algebra
  * [Gaussian Elimination](linear_algebra/gaussian_elimination.py)
  * [Jacobi Iteration Method](linear_algebra/jacobi_iteration_method.py)
  * [Lu Decomposition](linear_algebra/lu_decomposition.py)
  * [Matrix Inversion](linear_algebra/matrix_inversion.py)
  * Src
    * [Conjugate Gradient](linear_algebra/src/conjugate_gradient.py)
    * [Gaussian Elimination Pivoting](linear_algebra/src/gaussian_elimination_pivoting.py)
    * [Lib](linear_algebra/src/lib.py)
    * [Polynom For Points](linear_algebra/src/polynom_for_points.py)
    * [Power Iteration](linear_algebra/src/power_iteration.py)
    * [Rank Of Matrix](linear_algebra/src/rank_of_matrix.py)
    * [Rayleigh Quotient](linear_algebra/src/rayleigh_quotient.py)
    * [Schur Complement](linear_algebra/src/schur_complement.py)
    * [Test Linear Algebra](linear_algebra/src/test_linear_algebra.py)
    * [Transformations 2D](linear_algebra/src/transformations_2d.py)

## Linear Programming
  * [Simplex](linear_programming/simplex.py)

## Machine Learning
  * [Apriori Algorithm](machine_learning/apriori_algorithm.py)
  * [Astar](machine_learning/astar.py)
  * [Automatic Differentiation](machine_learning/automatic_differentiation.py)
  * [Data Transformations](machine_learning/data_transformations.py)
  * [Decision Tree](machine_learning/decision_tree.py)
  * [Dimensionality Reduction](machine_learning/dimensionality_reduction.py)
  * Forecasting
    * [Run](machine_learning/forecasting/run.py)
  * [Frequent Pattern Growth](machine_learning/frequent_pattern_growth.py)
  * [Gradient Boosting Classifier](machine_learning/gradient_boosting_classifier.py)
  * [Gradient Descent](machine_learning/gradient_descent.py)
  * [K Means Clust](machine_learning/k_means_clust.py)
  * [K Nearest Neighbours](machine_learning/k_nearest_neighbours.py)
  * [Linear Discriminant Analysis](machine_learning/linear_discriminant_analysis.py)
  * [Linear Regression](machine_learning/linear_regression.py)
  * Local Weighted Learning
    * [Local Weighted Learning](machine_learning/local_weighted_learning/local_weighted_learning.py)
  * [Logistic Regression](machine_learning/logistic_regression.py)
  * [Loss Functions](machine_learning/loss_functions.py)
  * Lstm
    * [Lstm Prediction](machine_learning/lstm/lstm_prediction.py)
  * [Mfcc](machine_learning/mfcc.py)
  * [Multilayer Perceptron Classifier](machine_learning/multilayer_perceptron_classifier.py)
  * [Polynomial Regression](machine_learning/polynomial_regression.py)
  * [Principle Component Analysis](machine_learning/principle_component_analysis.py)
  * [Scoring Functions](machine_learning/scoring_functions.py)
  * [Self Organizing Map](machine_learning/self_organizing_map.py)
  * [Sequential Minimum Optimization](machine_learning/sequential_minimum_optimization.py)
  * [Similarity Search](machine_learning/similarity_search.py)
  * [Support Vector Machines](machine_learning/support_vector_machines.py)
  * [T Stochastic Neighbour Embedding](machine_learning/t_stochastic_neighbour_embedding.py)
  * [Word Frequency Functions](machine_learning/word_frequency_functions.py)
  * [Xgboost Classifier](machine_learning/xgboost_classifier.py)
  * [Xgboost Regressor](machine_learning/xgboost_regressor.py)

## Maths
  * [Abs](maths/abs.py)
  * [Addition Without Arithmetic](maths/addition_without_arithmetic.py)
  * [Aliquot Sum](maths/aliquot_sum.py)
  * [Allocation Number](maths/allocation_number.py)
  * [Arc Length](maths/arc_length.py)
  * [Area](maths/area.py)
  * [Area Under Curve](maths/area_under_curve.py)
  * [Average Absolute Deviation](maths/average_absolute_deviation.py)
  * [Average Mean](maths/average_mean.py)
  * [Average Median](maths/average_median.py)
  * [Average Mode](maths/average_mode.py)
  * [Bailey Borwein Plouffe](maths/bailey_borwein_plouffe.py)
  * [Base Neg2 Conversion](maths/base_neg2_conversion.py)
  * [Basic Maths](maths/basic_maths.py)
  * [Binary Exponentiation](maths/binary_exponentiation.py)
  * [Binary Multiplication](maths/binary_multiplication.py)
  * [Binomial Coefficient](maths/binomial_coefficient.py)
  * [Binomial Distribution](maths/binomial_distribution.py)
  * [Ceil](maths/ceil.py)
  * [Chebyshev Distance](maths/chebyshev_distance.py)
  * [Check Polygon](maths/check_polygon.py)
  * [Chinese Remainder Theorem](maths/chinese_remainder_theorem.py)
  * [Chudnovsky Algorithm](maths/chudnovsky_algorithm.py)
  * [Collatz Sequence](maths/collatz_sequence.py)
  * [Combinations](maths/combinations.py)
  * [Continued Fraction](maths/continued_fraction.py)
  * [Decimal Isolate](maths/decimal_isolate.py)
  * [Decimal To Fraction](maths/decimal_to_fraction.py)
  * [Dodecahedron](maths/dodecahedron.py)
  * [Double Factorial](maths/double_factorial.py)
  * [Dual Number Automatic Differentiation](maths/dual_number_automatic_differentiation.py)
  * [Entropy](maths/entropy.py)
  * [Euclidean Distance](maths/euclidean_distance.py)
  * [Euler Method](maths/euler_method.py)
  * [Euler Modified](maths/euler_modified.py)
  * [Eulers Totient](maths/eulers_totient.py)
  * [Extended Euclidean Algorithm](maths/extended_euclidean_algorithm.py)
  * [Factorial](maths/factorial.py)
  * [Factors](maths/factors.py)
  * [Fast Inverse Sqrt](maths/fast_inverse_sqrt.py)
  * [Fermat Little Theorem](maths/fermat_little_theorem.py)
  * [Fibonacci](maths/fibonacci.py)
  * [Find Max](maths/find_max.py)
  * [Find Min](maths/find_min.py)
  * [Floor](maths/floor.py)
  * [Gamma](maths/gamma.py)
  * [Gaussian](maths/gaussian.py)
  * [Gcd Of N Numbers](maths/gcd_of_n_numbers.py)
  * [Geometric Mean](maths/geometric_mean.py)
  * [Germain Primes](maths/germain_primes.py)
  * [Greatest Common Divisor](maths/greatest_common_divisor.py)
  * [Hardy Ramanujanalgo](maths/hardy_ramanujanalgo.py)
  * [Integer Square Root](maths/integer_square_root.py)
  * [Interquartile Range](maths/interquartile_range.py)
  * [Is Int Palindrome](maths/is_int_palindrome.py)
  * [Is Ip V4 Address Valid](maths/is_ip_v4_address_valid.py)
  * [Is Square Free](maths/is_square_free.py)
  * [Jaccard Similarity](maths/jaccard_similarity.py)
  * [Joint Probability Distribution](maths/joint_probability_distribution.py)
  * [Josephus Problem](maths/josephus_problem.py)
  * [Juggler Sequence](maths/juggler_sequence.py)
  * [Karatsuba](maths/karatsuba.py)
  * [Kth Lexicographic Permutation](maths/kth_lexicographic_permutation.py)
  * [Largest Of Very Large Numbers](maths/largest_of_very_large_numbers.py)
  * [Least Common Multiple](maths/least_common_multiple.py)
  * [Line Length](maths/line_length.py)
  * [Liouville Lambda](maths/liouville_lambda.py)
  * [Lucas Lehmer Primality Test](maths/lucas_lehmer_primality_test.py)
  * [Lucas Series](maths/lucas_series.py)
  * [Maclaurin Series](maths/maclaurin_series.py)
  * [Manhattan Distance](maths/manhattan_distance.py)
  * [Matrix Exponentiation](maths/matrix_exponentiation.py)
  * [Max Sum Sliding Window](maths/max_sum_sliding_window.py)
  * [Minkowski Distance](maths/minkowski_distance.py)
  * [Mobius Function](maths/mobius_function.py)
  * [Modular Division](maths/modular_division.py)
  * [Modular Exponential](maths/modular_exponential.py)
  * [Monte Carlo](maths/monte_carlo.py)
  * [Monte Carlo Dice](maths/monte_carlo_dice.py)
  * [Number Of Digits](maths/number_of_digits.py)
  * Numerical Analysis
    * [Adams Bashforth](maths/numerical_analysis/adams_bashforth.py)
    * [Bisection](maths/numerical_analysis/bisection.py)
    * [Bisection 2](maths/numerical_analysis/bisection_2.py)
    * [Integration By Simpson Approx](maths/numerical_analysis/integration_by_simpson_approx.py)
    * [Intersection](maths/numerical_analysis/intersection.py)
    * [Nevilles Method](maths/numerical_analysis/nevilles_method.py)
    * [Newton Forward Interpolation](maths/numerical_analysis/newton_forward_interpolation.py)
    * [Newton Raphson](maths/numerical_analysis/newton_raphson.py)
    * [Numerical Integration](maths/numerical_analysis/numerical_integration.py)
    * [Proper Fractions](maths/numerical_analysis/proper_fractions.py)
    * [Runge Kutta](maths/numerical_analysis/runge_kutta.py)
    * [Runge Kutta Fehlberg 45](maths/numerical_analysis/runge_kutta_fehlberg_45.py)
    * [Runge Kutta Gills](maths/numerical_analysis/runge_kutta_gills.py)
    * [Secant Method](maths/numerical_analysis/secant_method.py)
    * [Simpson Rule](maths/numerical_analysis/simpson_rule.py)
    * [Square Root](maths/numerical_analysis/square_root.py)
    * [Weierstrass Method](maths/numerical_analysis/weierstrass_method.py)
  * [Odd Sieve](maths/odd_sieve.py)
  * [Perfect Cube](maths/perfect_cube.py)
  * [Perfect Number](maths/perfect_number.py)
  * [Perfect Square](maths/perfect_square.py)
  * [Persistence](maths/persistence.py)
  * [Pi Generator](maths/pi_generator.py)
  * [Pi Monte Carlo Estimation](maths/pi_monte_carlo_estimation.py)
  * [Points Are Collinear 3D](maths/points_are_collinear_3d.py)
  * [Pollard Rho](maths/pollard_rho.py)
  * [Polynomial Evaluation](maths/polynomial_evaluation.py)
  * Polynomials
    * [Single Indeterminate Operations](maths/polynomials/single_indeterminate_operations.py)
  * [Power Using Recursion](maths/power_using_recursion.py)
  * [Prime Check](maths/prime_check.py)
  * [Prime Factors](maths/prime_factors.py)
  * [Prime Numbers](maths/prime_numbers.py)
  * [Prime Sieve Eratosthenes](maths/prime_sieve_eratosthenes.py)
  * [Primelib](maths/primelib.py)
  * [Print Multiplication Table](maths/print_multiplication_table.py)
  * [Pythagoras](maths/pythagoras.py)
  * [Qr Decomposition](maths/qr_decomposition.py)
  * [Quadratic Equations Complex Numbers](maths/quadratic_equations_complex_numbers.py)
  * [Radians](maths/radians.py)
  * [Radix2 Fft](maths/radix2_fft.py)
  * [Remove Digit](maths/remove_digit.py)
  * [Segmented Sieve](maths/segmented_sieve.py)
  * Series
    * [Arithmetic](maths/series/arithmetic.py)
    * [Geometric](maths/series/geometric.py)
    * [Geometric Series](maths/series/geometric_series.py)
    * [Harmonic](maths/series/harmonic.py)
    * [Harmonic Series](maths/series/harmonic_series.py)
    * [Hexagonal Numbers](maths/series/hexagonal_numbers.py)
    * [P Series](maths/series/p_series.py)
  * [Sieve Of Eratosthenes](maths/sieve_of_eratosthenes.py)
  * [Sigmoid](maths/sigmoid.py)
  * [Signum](maths/signum.py)
  * [Simultaneous Linear Equation Solver](maths/simultaneous_linear_equation_solver.py)
  * [Sin](maths/sin.py)
  * [Sock Merchant](maths/sock_merchant.py)
  * [Softmax](maths/softmax.py)
  * [Solovay Strassen Primality Test](maths/solovay_strassen_primality_test.py)
  * [Spearman Rank Correlation Coefficient](maths/spearman_rank_correlation_coefficient.py)
  * Special Numbers
    * [Armstrong Numbers](maths/special_numbers/armstrong_numbers.py)
    * [Automorphic Number](maths/special_numbers/automorphic_number.py)
    * [Bell Numbers](maths/special_numbers/bell_numbers.py)
    * [Carmichael Number](maths/special_numbers/carmichael_number.py)
    * [Catalan Number](maths/special_numbers/catalan_number.py)
    * [Hamming Numbers](maths/special_numbers/hamming_numbers.py)
    * [Happy Number](maths/special_numbers/happy_number.py)
    * [Harshad Numbers](maths/special_numbers/harshad_numbers.py)
    * [Hexagonal Number](maths/special_numbers/hexagonal_number.py)
    * [Krishnamurthy Number](maths/special_numbers/krishnamurthy_number.py)
    * [Perfect Number](maths/special_numbers/perfect_number.py)
    * [Polygonal Numbers](maths/special_numbers/polygonal_numbers.py)
    * [Pronic Number](maths/special_numbers/pronic_number.py)
    * [Proth Number](maths/special_numbers/proth_number.py)
    * [Triangular Numbers](maths/special_numbers/triangular_numbers.py)
    * [Ugly Numbers](maths/special_numbers/ugly_numbers.py)
    * [Weird Number](maths/special_numbers/weird_number.py)
  * [Sum Of Arithmetic Series](maths/sum_of_arithmetic_series.py)
  * [Sum Of Digits](maths/sum_of_digits.py)
  * [Sum Of Geometric Progression](maths/sum_of_geometric_progression.py)
  * [Sum Of Harmonic Series](maths/sum_of_harmonic_series.py)
  * [Sumset](maths/sumset.py)
  * [Sylvester Sequence](maths/sylvester_sequence.py)
  * [Tanh](maths/tanh.py)
  * [Test Factorial](maths/test_factorial.py)
  * [Test Prime Check](maths/test_prime_check.py)
  * [Three Sum](maths/three_sum.py)
  * [Trapezoidal Rule](maths/trapezoidal_rule.py)
  * [Triplet Sum](maths/triplet_sum.py)
  * [Twin Prime](maths/twin_prime.py)
  * [Two Pointer](maths/two_pointer.py)
  * [Two Sum](maths/two_sum.py)
  * [Volume](maths/volume.py)
  * [Zellers Congruence](maths/zellers_congruence.py)

## Matrix
  * [Binary Search Matrix](matrix/binary_search_matrix.py)
  * [Count Islands In Matrix](matrix/count_islands_in_matrix.py)
  * [Count Negative Numbers In Sorted Matrix](matrix/count_negative_numbers_in_sorted_matrix.py)
  * [Count Paths](matrix/count_paths.py)
  * [Cramers Rule 2X2](matrix/cramers_rule_2x2.py)
  * [Inverse Of Matrix](matrix/inverse_of_matrix.py)
  * [Largest Square Area In Matrix](matrix/largest_square_area_in_matrix.py)
  * [Matrix Based Game](matrix/matrix_based_game.py)
  * [Matrix Class](matrix/matrix_class.py)
  * [Matrix Equalization](matrix/matrix_equalization.py)
  * [Matrix Multiplication Recursion](matrix/matrix_multiplication_recursion.py)
  * [Matrix Operation](matrix/matrix_operation.py)
  * [Max Area Of Island](matrix/max_area_of_island.py)
  * [Median Matrix](matrix/median_matrix.py)
  * [Nth Fibonacci Using Matrix Exponentiation](matrix/nth_fibonacci_using_matrix_exponentiation.py)
  * [Pascal Triangle](matrix/pascal_triangle.py)
  * [Rotate Matrix](matrix/rotate_matrix.py)
  * [Searching In Sorted Matrix](matrix/searching_in_sorted_matrix.py)
  * [Sherman Morrison](matrix/sherman_morrison.py)
  * [Spiral Print](matrix/spiral_print.py)
  * Tests
    * [Test Matrix Operation](matrix/tests/test_matrix_operation.py)
  * [Validate Sudoku Board](matrix/validate_sudoku_board.py)

## Networking Flow
  * [Ford Fulkerson](networking_flow/ford_fulkerson.py)
  * [Minimum Cut](networking_flow/minimum_cut.py)

## Neural Network
  * Activation Functions
    * [Binary Step](neural_network/activation_functions/binary_step.py)
    * [Exponential Linear Unit](neural_network/activation_functions/exponential_linear_unit.py)
    * [Gaussian Error Linear Unit](neural_network/activation_functions/gaussian_error_linear_unit.py)
    * [Leaky Rectified Linear Unit](neural_network/activation_functions/leaky_rectified_linear_unit.py)
    * [Mish](neural_network/activation_functions/mish.py)
    * [Rectified Linear Unit](neural_network/activation_functions/rectified_linear_unit.py)
    * [Scaled Exponential Linear Unit](neural_network/activation_functions/scaled_exponential_linear_unit.py)
    * [Soboleva Modified Hyperbolic Tangent](neural_network/activation_functions/soboleva_modified_hyperbolic_tangent.py)
    * [Softplus](neural_network/activation_functions/softplus.py)
    * [Squareplus](neural_network/activation_functions/squareplus.py)
    * [Swish](neural_network/activation_functions/swish.py)
  * [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py)
  * [Convolution Neural Network](neural_network/convolution_neural_network.py)
  * [Input Data](neural_network/input_data.py)
  * [Simple Neural Network](neural_network/simple_neural_network.py)
  * [Two Hidden Layers Neural Network](neural_network/two_hidden_layers_neural_network.py)

## Other
  * [Activity Selection](other/activity_selection.py)
  * [Alternative List Arrange](other/alternative_list_arrange.py)
  * [Bankers Algorithm](other/bankers_algorithm.py)
  * [Davis Putnam Logemann Loveland](other/davis_putnam_logemann_loveland.py)
  * [Doomsday](other/doomsday.py)
  * [Fischer Yates Shuffle](other/fischer_yates_shuffle.py)
  * [Gauss Easter](other/gauss_easter.py)
  * [Graham Scan](other/graham_scan.py)
  * [Greedy](other/greedy.py)
  * [Guess The Number Search](other/guess_the_number_search.py)
  * [H Index](other/h_index.py)
  * [Least Recently Used](other/least_recently_used.py)
  * [Lfu Cache](other/lfu_cache.py)
  * [Linear Congruential Generator](other/linear_congruential_generator.py)
  * [Lru Cache](other/lru_cache.py)
  * [Magicdiamondpattern](other/magicdiamondpattern.py)
  * [Majority Vote Algorithm](other/majority_vote_algorithm.py)
  * [Maximum Subsequence](other/maximum_subsequence.py)
  * [Nested Brackets](other/nested_brackets.py)
  * [Number Container System](other/number_container_system.py)
  * [Password](other/password.py)
  * [Quine](other/quine.py)
  * [Scoring Algorithm](other/scoring_algorithm.py)
  * [Sdes](other/sdes.py)
  * [Sliding Window Maximum](other/sliding_window_maximum.py)
  * [Tower Of Hanoi](other/tower_of_hanoi.py)
  * [Word Search](other/word_search.py)

## Physics
  * [Altitude Pressure](physics/altitude_pressure.py)
  * [Archimedes Principle Of Buoyant Force](physics/archimedes_principle_of_buoyant_force.py)
  * [Basic Orbital Capture](physics/basic_orbital_capture.py)
  * [Casimir Effect](physics/casimir_effect.py)
  * [Center Of Mass](physics/center_of_mass.py)
  * [Centripetal Force](physics/centripetal_force.py)
  * [Coulombs Law](physics/coulombs_law.py)
  * [Doppler Frequency](physics/doppler_frequency.py)
  * [Escape Velocity](physics/escape_velocity.py)
  * [Grahams Law](physics/grahams_law.py)
  * [Horizontal Projectile Motion](physics/horizontal_projectile_motion.py)
  * [Hubble Parameter](physics/hubble_parameter.py)
  * [Ideal Gas Law](physics/ideal_gas_law.py)
  * [In Static Equilibrium](physics/in_static_equilibrium.py)
  * [Kinetic Energy](physics/kinetic_energy.py)
  * [Lens Formulae](physics/lens_formulae.py)
  * [Lorentz Transformation Four Vector](physics/lorentz_transformation_four_vector.py)
  * [Malus Law](physics/malus_law.py)
  * [Mass Energy Equivalence](physics/mass_energy_equivalence.py)
  * [Mirror Formulae](physics/mirror_formulae.py)
  * [N Body Simulation](physics/n_body_simulation.py)
  * [Newtons Law Of Gravitation](physics/newtons_law_of_gravitation.py)
  * [Newtons Second Law Of Motion](physics/newtons_second_law_of_motion.py)
  * [Orbital Transfer Work](physics/orbital_transfer_work.py)
  * [Period Of Pendulum](physics/period_of_pendulum.py)
  * [Photoelectric Effect](physics/photoelectric_effect.py)
  * [Potential Energy](physics/potential_energy.py)
  * [Rainfall Intensity](physics/rainfall_intensity.py)
  * [Reynolds Number](physics/reynolds_number.py)
  * [Rms Speed Of Molecule](physics/rms_speed_of_molecule.py)
  * [Shear Stress](physics/shear_stress.py)
  * [Speed Of Sound](physics/speed_of_sound.py)
  * [Speeds Of Gas Molecules](physics/speeds_of_gas_molecules.py)
  * [Terminal Velocity](physics/terminal_velocity.py)

## Project Euler
  * Problem 001
    * [Sol1](project_euler/problem_001/sol1.py)
    * [Sol2](project_euler/problem_001/sol2.py)
    * [Sol3](project_euler/problem_001/sol3.py)
    * [Sol4](project_euler/problem_001/sol4.py)
    * [Sol5](project_euler/problem_001/sol5.py)
    * [Sol6](project_euler/problem_001/sol6.py)
    * [Sol7](project_euler/problem_001/sol7.py)
  * Problem 002
    * [Sol1](project_euler/problem_002/sol1.py)
    * [Sol2](project_euler/problem_002/sol2.py)
    * [Sol3](project_euler/problem_002/sol3.py)
    * [Sol4](project_euler/problem_002/sol4.py)
    * [Sol5](project_euler/problem_002/sol5.py)
  * Problem 003
    * [Sol1](project_euler/problem_003/sol1.py)
    * [Sol2](project_euler/problem_003/sol2.py)
    * [Sol3](project_euler/problem_003/sol3.py)
  * Problem 004
    * [Sol1](project_euler/problem_004/sol1.py)
    * [Sol2](project_euler/problem_004/sol2.py)
  * Problem 005
    * [Sol1](project_euler/problem_005/sol1.py)
    * [Sol2](project_euler/problem_005/sol2.py)
  * Problem 006
    * [Sol1](project_euler/problem_006/sol1.py)
    * [Sol2](project_euler/problem_006/sol2.py)
    * [Sol3](project_euler/problem_006/sol3.py)
    * [Sol4](project_euler/problem_006/sol4.py)
  * Problem 007
    * [Sol1](project_euler/problem_007/sol1.py)
    * [Sol2](project_euler/problem_007/sol2.py)
    * [Sol3](project_euler/problem_007/sol3.py)
  * Problem 008
    * [Sol1](project_euler/problem_008/sol1.py)
    * [Sol2](project_euler/problem_008/sol2.py)
    * [Sol3](project_euler/problem_008/sol3.py)
  * Problem 009
    * [Sol1](project_euler/problem_009/sol1.py)
    * [Sol2](project_euler/problem_009/sol2.py)
    * [Sol3](project_euler/problem_009/sol3.py)
    * [Sol4](project_euler/problem_009/sol4.py)
  * Problem 010
    * [Sol1](project_euler/problem_010/sol1.py)
    * [Sol2](project_euler/problem_010/sol2.py)
    * [Sol3](project_euler/problem_010/sol3.py)
  * Problem 011
    * [Sol1](project_euler/problem_011/sol1.py)
    * [Sol2](project_euler/problem_011/sol2.py)
  * Problem 012
    * [Sol1](project_euler/problem_012/sol1.py)
    * [Sol2](project_euler/problem_012/sol2.py)
  * Problem 013
    * [Sol1](project_euler/problem_013/sol1.py)
  * Problem 014
    * [Sol1](project_euler/problem_014/sol1.py)
    * [Sol2](project_euler/problem_014/sol2.py)
  * Problem 015
    * [Sol1](project_euler/problem_015/sol1.py)
    * [Sol2](project_euler/problem_015/sol2.py)
  * Problem 016
    * [Sol1](project_euler/problem_016/sol1.py)
    * [Sol2](project_euler/problem_016/sol2.py)
  * Problem 017
    * [Sol1](project_euler/problem_017/sol1.py)
  * Problem 018
    * [Solution](project_euler/problem_018/solution.py)
  * Problem 019
    * [Sol1](project_euler/problem_019/sol1.py)
  * Problem 020
    * [Sol1](project_euler/problem_020/sol1.py)
    * [Sol2](project_euler/problem_020/sol2.py)
    * [Sol3](project_euler/problem_020/sol3.py)
    * [Sol4](project_euler/problem_020/sol4.py)
  * Problem 021
    * [Sol1](project_euler/problem_021/sol1.py)
  * Problem 022
    * [Sol1](project_euler/problem_022/sol1.py)
    * [Sol2](project_euler/problem_022/sol2.py)
  * Problem 023
    * [Sol1](project_euler/problem_023/sol1.py)
  * Problem 024
    * [Sol1](project_euler/problem_024/sol1.py)
  * Problem 025
    * [Sol1](project_euler/problem_025/sol1.py)
    * [Sol2](project_euler/problem_025/sol2.py)
    * [Sol3](project_euler/problem_025/sol3.py)
  * Problem 026
    * [Sol1](project_euler/problem_026/sol1.py)
  * Problem 027
    * [Sol1](project_euler/problem_027/sol1.py)
  * Problem 028
    * [Sol1](project_euler/problem_028/sol1.py)
  * Problem 029
    * [Sol1](project_euler/problem_029/sol1.py)
  * Problem 030
    * [Sol1](project_euler/problem_030/sol1.py)
  * Problem 031
    * [Sol1](project_euler/problem_031/sol1.py)
    * [Sol2](project_euler/problem_031/sol2.py)
  * Problem 032
    * [Sol32](project_euler/problem_032/sol32.py)
  * Problem 033
    * [Sol1](project_euler/problem_033/sol1.py)
  * Problem 034
    * [Sol1](project_euler/problem_034/sol1.py)
  * Problem 035
    * [Sol1](project_euler/problem_035/sol1.py)
  * Problem 036
    * [Sol1](project_euler/problem_036/sol1.py)
  * Problem 037
    * [Sol1](project_euler/problem_037/sol1.py)
  * Problem 038
    * [Sol1](project_euler/problem_038/sol1.py)
  * Problem 039
    * [Sol1](project_euler/problem_039/sol1.py)
  * Problem 040
    * [Sol1](project_euler/problem_040/sol1.py)
  * Problem 041
    * [Sol1](project_euler/problem_041/sol1.py)
  * Problem 042
    * [Solution42](project_euler/problem_042/solution42.py)
  * Problem 043
    * [Sol1](project_euler/problem_043/sol1.py)
  * Problem 044
    * [Sol1](project_euler/problem_044/sol1.py)
  * Problem 045
    * [Sol1](project_euler/problem_045/sol1.py)
  * Problem 046
    * [Sol1](project_euler/problem_046/sol1.py)
  * Problem 047
    * [Sol1](project_euler/problem_047/sol1.py)
  * Problem 048
    * [Sol1](project_euler/problem_048/sol1.py)
  * Problem 049
    * [Sol1](project_euler/problem_049/sol1.py)
  * Problem 050
    * [Sol1](project_euler/problem_050/sol1.py)
  * Problem 051
    * [Sol1](project_euler/problem_051/sol1.py)
  * Problem 052
    * [Sol1](project_euler/problem_052/sol1.py)
  * Problem 053
    * [Sol1](project_euler/problem_053/sol1.py)
  * Problem 054
    * [Sol1](project_euler/problem_054/sol1.py)
    * [Test Poker Hand](project_euler/problem_054/test_poker_hand.py)
  * Problem 055
    * [Sol1](project_euler/problem_055/sol1.py)
  * Problem 056
    * [Sol1](project_euler/problem_056/sol1.py)
  * Problem 057
    * [Sol1](project_euler/problem_057/sol1.py)
  * Problem 058
    * [Sol1](project_euler/problem_058/sol1.py)
  * Problem 059
    * [Sol1](project_euler/problem_059/sol1.py)
  * Problem 062
    * [Sol1](project_euler/problem_062/sol1.py)
  * Problem 063
    * [Sol1](project_euler/problem_063/sol1.py)
  * Problem 064
    * [Sol1](project_euler/problem_064/sol1.py)
  * Problem 065
    * [Sol1](project_euler/problem_065/sol1.py)
  * Problem 067
    * [Sol1](project_euler/problem_067/sol1.py)
    * [Sol2](project_euler/problem_067/sol2.py)
  * Problem 068
    * [Sol1](project_euler/problem_068/sol1.py)
  * Problem 069
    * [Sol1](project_euler/problem_069/sol1.py)
  * Problem 070
    * [Sol1](project_euler/problem_070/sol1.py)
  * Problem 071
    * [Sol1](project_euler/problem_071/sol1.py)
  * Problem 072
    * [Sol1](project_euler/problem_072/sol1.py)
    * [Sol2](project_euler/problem_072/sol2.py)
  * Problem 073
    * [Sol1](project_euler/problem_073/sol1.py)
  * Problem 074
    * [Sol1](project_euler/problem_074/sol1.py)
    * [Sol2](project_euler/problem_074/sol2.py)
  * Problem 075
    * [Sol1](project_euler/problem_075/sol1.py)
  * Problem 076
    * [Sol1](project_euler/problem_076/sol1.py)
  * Problem 077
    * [Sol1](project_euler/problem_077/sol1.py)
  * Problem 078
    * [Sol1](project_euler/problem_078/sol1.py)
  * Problem 079
    * [Sol1](project_euler/problem_079/sol1.py)
  * Problem 080
    * [Sol1](project_euler/problem_080/sol1.py)
  * Problem 081
    * [Sol1](project_euler/problem_081/sol1.py)
  * Problem 082
    * [Sol1](project_euler/problem_082/sol1.py)
  * Problem 085
    * [Sol1](project_euler/problem_085/sol1.py)
  * Problem 086
    * [Sol1](project_euler/problem_086/sol1.py)
  * Problem 087
    * [Sol1](project_euler/problem_087/sol1.py)
  * Problem 089
    * [Sol1](project_euler/problem_089/sol1.py)
  * Problem 091
    * [Sol1](project_euler/problem_091/sol1.py)
  * Problem 092
    * [Sol1](project_euler/problem_092/sol1.py)
  * Problem 094
    * [Sol1](project_euler/problem_094/sol1.py)
  * Problem 095
    * [Sol1](project_euler/problem_095/sol1.py)
  * Problem 097
    * [Sol1](project_euler/problem_097/sol1.py)
  * Problem 099
    * [Sol1](project_euler/problem_099/sol1.py)
  * Problem 100
    * [Sol1](project_euler/problem_100/sol1.py)
  * Problem 101
    * [Sol1](project_euler/problem_101/sol1.py)
  * Problem 102
    * [Sol1](project_euler/problem_102/sol1.py)
  * Problem 104
    * [Sol1](project_euler/problem_104/sol1.py)
  * Problem 107
    * [Sol1](project_euler/problem_107/sol1.py)
  * Problem 109
    * [Sol1](project_euler/problem_109/sol1.py)
  * Problem 112
    * [Sol1](project_euler/problem_112/sol1.py)
  * Problem 113
    * [Sol1](project_euler/problem_113/sol1.py)
  * Problem 114
    * [Sol1](project_euler/problem_114/sol1.py)
  * Problem 115
    * [Sol1](project_euler/problem_115/sol1.py)
  * Problem 116
    * [Sol1](project_euler/problem_116/sol1.py)
  * Problem 117
    * [Sol1](project_euler/problem_117/sol1.py)
  * Problem 119
    * [Sol1](project_euler/problem_119/sol1.py)
  * Problem 120
    * [Sol1](project_euler/problem_120/sol1.py)
  * Problem 121
    * [Sol1](project_euler/problem_121/sol1.py)
  * Problem 122
    * [Sol1](project_euler/problem_122/sol1.py)
  * Problem 123
    * [Sol1](project_euler/problem_123/sol1.py)
  * Problem 125
    * [Sol1](project_euler/problem_125/sol1.py)
  * Problem 129
    * [Sol1](project_euler/problem_129/sol1.py)
  * Problem 131
    * [Sol1](project_euler/problem_131/sol1.py)
  * Problem 135
    * [Sol1](project_euler/problem_135/sol1.py)
  * Problem 136
    * [Sol1](project_euler/problem_136/sol1.py)
  * Problem 144
    * [Sol1](project_euler/problem_144/sol1.py)
  * Problem 145
    * [Sol1](project_euler/problem_145/sol1.py)
  * Problem 164
    * [Sol1](project_euler/problem_164/sol1.py)
  * Problem 173
    * [Sol1](project_euler/problem_173/sol1.py)
  * Problem 174
    * [Sol1](project_euler/problem_174/sol1.py)
  * Problem 180
    * [Sol1](project_euler/problem_180/sol1.py)
  * Problem 187
    * [Sol1](project_euler/problem_187/sol1.py)
  * Problem 188
    * [Sol1](project_euler/problem_188/sol1.py)
  * Problem 190
    * [Sol1](project_euler/problem_190/sol1.py)
  * Problem 191
    * [Sol1](project_euler/problem_191/sol1.py)
  * Problem 203
    * [Sol1](project_euler/problem_203/sol1.py)
  * Problem 205
    * [Sol1](project_euler/problem_205/sol1.py)
  * Problem 206
    * [Sol1](project_euler/problem_206/sol1.py)
  * Problem 207
    * [Sol1](project_euler/problem_207/sol1.py)
  * Problem 234
    * [Sol1](project_euler/problem_234/sol1.py)
  * Problem 301
    * [Sol1](project_euler/problem_301/sol1.py)
  * Problem 345
    * [Sol1](project_euler/problem_345/sol1.py)
  * Problem 493
    * [Sol1](project_euler/problem_493/sol1.py)
  * Problem 551
    * [Sol1](project_euler/problem_551/sol1.py)
  * Problem 587
    * [Sol1](project_euler/problem_587/sol1.py)
  * Problem 686
    * [Sol1](project_euler/problem_686/sol1.py)
  * Problem 800
    * [Sol1](project_euler/problem_800/sol1.py)

## Quantum
  * [Q Fourier Transform](quantum/q_fourier_transform.py)

## Scheduling
  * [First Come First Served](scheduling/first_come_first_served.py)
  * [Highest Response Ratio Next](scheduling/highest_response_ratio_next.py)
  * [Job Sequence With Deadline](scheduling/job_sequence_with_deadline.py)
  * [Job Sequencing With Deadline](scheduling/job_sequencing_with_deadline.py)
  * [Multi Level Feedback Queue](scheduling/multi_level_feedback_queue.py)
  * [Non Preemptive Shortest Job First](scheduling/non_preemptive_shortest_job_first.py)
  * [Round Robin](scheduling/round_robin.py)
  * [Shortest Job First](scheduling/shortest_job_first.py)

## Searches
  * [Binary Search](searches/binary_search.py)
  * [Binary Tree Traversal](searches/binary_tree_traversal.py)
  * [Double Linear Search](searches/double_linear_search.py)
  * [Double Linear Search Recursion](searches/double_linear_search_recursion.py)
  * [Exponential Search](searches/exponential_search.py)
  * [Fibonacci Search](searches/fibonacci_search.py)
  * [Hill Climbing](searches/hill_climbing.py)
  * [Interpolation Search](searches/interpolation_search.py)
  * [Jump Search](searches/jump_search.py)
  * [Linear Search](searches/linear_search.py)
  * [Median Of Medians](searches/median_of_medians.py)
  * [Quick Select](searches/quick_select.py)
  * [Sentinel Linear Search](searches/sentinel_linear_search.py)
  * [Simple Binary Search](searches/simple_binary_search.py)
  * [Simulated Annealing](searches/simulated_annealing.py)
  * [Tabu Search](searches/tabu_search.py)
  * [Ternary Search](searches/ternary_search.py)

## Sorts
  * [Bead Sort](sorts/bead_sort.py)
  * [Binary Insertion Sort](sorts/binary_insertion_sort.py)
  * [Bitonic Sort](sorts/bitonic_sort.py)
  * [Bogo Sort](sorts/bogo_sort.py)
  * [Bubble Sort](sorts/bubble_sort.py)
  * [Bucket Sort](sorts/bucket_sort.py)
  * [Circle Sort](sorts/circle_sort.py)
  * [Cocktail Shaker Sort](sorts/cocktail_shaker_sort.py)
  * [Comb Sort](sorts/comb_sort.py)
  * [Counting Sort](sorts/counting_sort.py)
  * [Cycle Sort](sorts/cycle_sort.py)
  * [Cyclic Sort](sorts/cyclic_sort.py)
  * [Double Sort](sorts/double_sort.py)
  * [Dutch National Flag Sort](sorts/dutch_national_flag_sort.py)
  * [Exchange Sort](sorts/exchange_sort.py)
  * [External Sort](sorts/external_sort.py)
  * [Gnome Sort](sorts/gnome_sort.py)
  * [Heap Sort](sorts/heap_sort.py)
  * [Insertion Sort](sorts/insertion_sort.py)
  * [Intro Sort](sorts/intro_sort.py)
  * [Iterative Merge Sort](sorts/iterative_merge_sort.py)
  * [Merge Insertion Sort](sorts/merge_insertion_sort.py)
  * [Merge Sort](sorts/merge_sort.py)
  * [Msd Radix Sort](sorts/msd_radix_sort.py)
  * [Natural Sort](sorts/natural_sort.py)
  * [Odd Even Sort](sorts/odd_even_sort.py)
  * [Odd Even Transposition Parallel](sorts/odd_even_transposition_parallel.py)
  * [Odd Even Transposition Single Threaded](sorts/odd_even_transposition_single_threaded.py)
  * [Pancake Sort](sorts/pancake_sort.py)
  * [Patience Sort](sorts/patience_sort.py)
  * [Pigeon Sort](sorts/pigeon_sort.py)
  * [Pigeonhole Sort](sorts/pigeonhole_sort.py)
  * [Quick Sort](sorts/quick_sort.py)
  * [Quick Sort 3 Partition](sorts/quick_sort_3_partition.py)
  * [Radix Sort](sorts/radix_sort.py)
  * [Recursive Insertion Sort](sorts/recursive_insertion_sort.py)
  * [Recursive Mergesort Array](sorts/recursive_mergesort_array.py)
  * [Recursive Quick Sort](sorts/recursive_quick_sort.py)
  * [Selection Sort](sorts/selection_sort.py)
  * [Shell Sort](sorts/shell_sort.py)
  * [Shrink Shell Sort](sorts/shrink_shell_sort.py)
  * [Slowsort](sorts/slowsort.py)
  * [Stalin Sort](sorts/stalin_sort.py)
  * [Stooge Sort](sorts/stooge_sort.py)
  * [Strand Sort](sorts/strand_sort.py)
  * [Tim Sort](sorts/tim_sort.py)
  * [Topological Sort](sorts/topological_sort.py)
  * [Tree Sort](sorts/tree_sort.py)
  * [Unknown Sort](sorts/unknown_sort.py)
  * [Wiggle Sort](sorts/wiggle_sort.py)

## Strings
  * [Aho Corasick](strings/aho_corasick.py)
  * [Alternative String Arrange](strings/alternative_string_arrange.py)
  * [Anagrams](strings/anagrams.py)
  * [Autocomplete Using Trie](strings/autocomplete_using_trie.py)
  * [Barcode Validator](strings/barcode_validator.py)
  * [Bitap String Match](strings/bitap_string_match.py)
  * [Boyer Moore Search](strings/boyer_moore_search.py)
  * [Camel Case To Snake Case](strings/camel_case_to_snake_case.py)
  * [Can String Be Rearranged As Palindrome](strings/can_string_be_rearranged_as_palindrome.py)
  * [Capitalize](strings/capitalize.py)
  * [Check Anagrams](strings/check_anagrams.py)
  * [Count Vowels](strings/count_vowels.py)
  * [Credit Card Validator](strings/credit_card_validator.py)
  * [Damerau Levenshtein Distance](strings/damerau_levenshtein_distance.py)
  * [Detecting English Programmatically](strings/detecting_english_programmatically.py)
  * [Dna](strings/dna.py)
  * [Edit Distance](strings/edit_distance.py)
  * [Frequency Finder](strings/frequency_finder.py)
  * [Hamming Distance](strings/hamming_distance.py)
  * [Indian Phone Validator](strings/indian_phone_validator.py)
  * [Is Contains Unique Chars](strings/is_contains_unique_chars.py)
  * [Is Isogram](strings/is_isogram.py)
  * [Is Pangram](strings/is_pangram.py)
  * [Is Polish National Id](strings/is_polish_national_id.py)
  * [Is Spain National Id](strings/is_spain_national_id.py)
  * [Is Srilankan Phone Number](strings/is_srilankan_phone_number.py)
  * [Is Valid Email Address](strings/is_valid_email_address.py)
  * [Jaro Winkler](strings/jaro_winkler.py)
  * [Join](strings/join.py)
  * [Knuth Morris Pratt](strings/knuth_morris_pratt.py)
  * [Levenshtein Distance](strings/levenshtein_distance.py)
  * [Lower](strings/lower.py)
  * [Manacher](strings/manacher.py)
  * [Min Cost String Conversion](strings/min_cost_string_conversion.py)
  * [Naive String Search](strings/naive_string_search.py)
  * [Ngram](strings/ngram.py)
  * [Palindrome](strings/palindrome.py)
  * [Pig Latin](strings/pig_latin.py)
  * [Prefix Function](strings/prefix_function.py)
  * [Rabin Karp](strings/rabin_karp.py)
  * [Remove Duplicate](strings/remove_duplicate.py)
  * [Reverse Letters](strings/reverse_letters.py)
  * [Reverse Words](strings/reverse_words.py)
  * [Snake Case To Camel Pascal Case](strings/snake_case_to_camel_pascal_case.py)
  * [Split](strings/split.py)
  * [String Switch Case](strings/string_switch_case.py)
  * [Strip](strings/strip.py)
  * [Text Justification](strings/text_justification.py)
  * [Title](strings/title.py)
  * [Top K Frequent Words](strings/top_k_frequent_words.py)
  * [Upper](strings/upper.py)
  * [Wave String](strings/wave_string.py)
  * [Wildcard Pattern Matching](strings/wildcard_pattern_matching.py)
  * [Word Occurrence](strings/word_occurrence.py)
  * [Word Patterns](strings/word_patterns.py)
  * [Z Function](strings/z_function.py)

## Web Programming
  * [Co2 Emission](web_programming/co2_emission.py)
  * [Covid Stats Via Xpath](web_programming/covid_stats_via_xpath.py)
  * [Crawl Google Results](web_programming/crawl_google_results.py)
  * [Crawl Google Scholar Citation](web_programming/crawl_google_scholar_citation.py)
  * [Currency Converter](web_programming/currency_converter.py)
  * [Current Stock Price](web_programming/current_stock_price.py)
  * [Current Weather](web_programming/current_weather.py)
  * [Daily Horoscope](web_programming/daily_horoscope.py)
  * [Download Images From Google Query](web_programming/download_images_from_google_query.py)
  * [Emails From Url](web_programming/emails_from_url.py)
  * [Fetch Anime And Play](web_programming/fetch_anime_and_play.py)
  * [Fetch Bbc News](web_programming/fetch_bbc_news.py)
  * [Fetch Github Info](web_programming/fetch_github_info.py)
  * [Fetch Jobs](web_programming/fetch_jobs.py)
  * [Fetch Quotes](web_programming/fetch_quotes.py)
  * [Fetch Well Rx Price](web_programming/fetch_well_rx_price.py)
  * [Get Amazon Product Data](web_programming/get_amazon_product_data.py)
  * [Get Imdb Top 250 Movies Csv](web_programming/get_imdb_top_250_movies_csv.py)
  * [Get Ip Geolocation](web_programming/get_ip_geolocation.py)
  * [Get Top Billionaires](web_programming/get_top_billionaires.py)
  * [Get Top Hn Posts](web_programming/get_top_hn_posts.py)
  * [Giphy](web_programming/giphy.py)
  * [Instagram Crawler](web_programming/instagram_crawler.py)
  * [Instagram Pic](web_programming/instagram_pic.py)
  * [Instagram Video](web_programming/instagram_video.py)
  * [Nasa Data](web_programming/nasa_data.py)
  * [Open Google Results](web_programming/open_google_results.py)
  * [Random Anime Character](web_programming/random_anime_character.py)
  * [Recaptcha Verification](web_programming/recaptcha_verification.py)
  * [Reddit](web_programming/reddit.py)
  * [Search Books By Isbn](web_programming/search_books_by_isbn.py)
  * [Slack Message](web_programming/slack_message.py)
  * [Test Fetch Github Info](web_programming/test_fetch_github_info.py)
  * [World Covid19 Stats](web_programming/world_covid19_stats.py)


================================================
FILE: LICENSE.md
================================================
## MIT License

Copyright (c) 2016-2022 TheAlgorithms and contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: README.md
================================================
<div align="center">
<!-- Title: -->
  <a href="https://github.com/TheAlgorithms/">
    <img src="https://raw.githubusercontent.com/TheAlgorithms/website/1cd824df116b27029f17c2d1b42d81731f28a920/public/logo.svg" height="100">
  </a>
  <h1><a href="https://github.com/TheAlgorithms/">The Algorithms</a> - Python</h1>

<!-- Labels: -->
  <!-- First row: -->
  <a href="https://gitpod.io/#https://github.com/TheAlgorithms/Python">
    <img src="https://img.shields.io/badge/Gitpod-Ready--to--Code-blue?logo=gitpod&style=flat-square" height="20" alt="Gitpod Ready-to-Code">
  </a>
  <a href="https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md">
    <img src="https://img.shields.io/static/v1.svg?label=Contributions&message=Welcome&color=0059b3&style=flat-square" height="20" alt="Contributions Welcome">
  </a>
  <img src="https://img.shields.io/github/repo-size/TheAlgorithms/Python.svg?label=Repo%20size&style=flat-square" height="20">
  <a href="https://the-algorithms.com/discord">
    <img src="https://img.shields.io/discord/808045925556682782.svg?logo=discord&colorB=7289DA&style=flat-square" height="20" alt="Discord chat">
  </a>
  <a href="https://gitter.im/TheAlgorithms/community">
    <img src="https://img.shields.io/badge/Chat-Gitter-ff69b4.svg?label=Chat&logo=gitter&style=flat-square" height="20" alt="Gitter chat">
  </a>

  <!-- Second row: -->
  <br>
  <a href="https://github.com/TheAlgorithms/Python/actions">
    <img src="https://img.shields.io/github/actions/workflow/status/TheAlgorithms/Python/build.yml?branch=master&label=CI&logo=github&style=flat-square" height="20" alt="GitHub Workflow Status">
  </a>
  <a href="https://github.com/pre-commit/pre-commit">
    <img src="https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white&style=flat-square" height="20" alt="pre-commit">
  </a>
  <a href="https://docs.astral.sh/ruff/formatter/">
    <img src="https://img.shields.io/static/v1?label=code%20style&message=ruff&color=black&style=flat-square" height="20" alt="code style: black">
  </a>

<!-- Short description: -->
  <h3>All algorithms implemented in Python - for education 📚</h3>
</div>

Implementations are for learning purposes only. They may be less efficient than the implementations in the Python standard library. Use them at your discretion.

## 🚀 Getting Started

📋 Read through our [Contribution Guidelines](CONTRIBUTING.md) before you contribute.

## 🌐 Community Channels

We are on [Discord](https://the-algorithms.com/discord) and [Gitter](https://gitter.im/TheAlgorithms/community)! Community channels are a great way for you to ask questions and get help. Please join us!

## 📜 List of Algorithms

See our [directory](DIRECTORY.md) for easier navigation and a better overview of the project.


================================================
FILE: audio_filters/README.md
================================================
# Audio Filter

Audio filters work on the frequency of an audio signal to attenuate unwanted frequency and amplify wanted ones.
They are used within anything related to sound, whether it is radio communication or a hi-fi system.

* <https://www.masteringbox.com/filter-types/>
* <http://ethanwiner.com/filters.html>
* <https://en.wikipedia.org/wiki/Audio_filter>
* <https://en.wikipedia.org/wiki/Electronic_filter>


================================================
FILE: audio_filters/__init__.py
================================================


================================================
FILE: audio_filters/butterworth_filter.py
================================================
from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter

"""
Create 2nd-order IIR filters with Butterworth design.

Code based on https://webaudio.github.io/Audio-EQ-Cookbook/audio-eq-cookbook.html
Alternatively you can use scipy.signal.butter, which should yield the same results.
"""


def make_lowpass(
    frequency: int,
    samplerate: int,
    q_factor: float = 1 / sqrt(2),
) -> IIRFilter:
    """
    Creates a low-pass filter

    >>> filter = make_lowpass(1000, 48000)
    >>> filter.a_coeffs + filter.b_coeffs  # doctest: +NORMALIZE_WHITESPACE
    [1.0922959556412573, -1.9828897227476208, 0.9077040443587427, 0.004277569313094809,
     0.008555138626189618, 0.004277569313094809]
    """
    w0 = tau * frequency / samplerate
    _sin = sin(w0)
    _cos = cos(w0)
    alpha = _sin / (2 * q_factor)

    b0 = (1 - _cos) / 2
    b1 = 1 - _cos

    a0 = 1 + alpha
    a1 = -2 * _cos
    a2 = 1 - alpha

    filt = IIRFilter(2)
    filt.set_coefficients([a0, a1, a2], [b0, b1, b0])
    return filt


def make_highpass(
    frequency: int,
    samplerate: int,
    q_factor: float = 1 / sqrt(2),
) -> IIRFilter:
    """
    Creates a high-pass filter

    >>> filter = make_highpass(1000, 48000)
    >>> filter.a_coeffs + filter.b_coeffs  # doctest: +NORMALIZE_WHITESPACE
    [1.0922959556412573, -1.9828897227476208, 0.9077040443587427, 0.9957224306869052,
     -1.9914448613738105, 0.9957224306869052]
    """
    w0 = tau * frequency / samplerate
    _sin = sin(w0)
    _cos = cos(w0)
    alpha = _sin / (2 * q_factor)

    b0 = (1 + _cos) / 2
    b1 = -1 - _cos

    a0 = 1 + alpha
    a1 = -2 * _cos
    a2 = 1 - alpha

    filt = IIRFilter(2)
    filt.set_coefficients([a0, a1, a2], [b0, b1, b0])
    return filt


def make_bandpass(
    frequency: int,
    samplerate: int,
    q_factor: float = 1 / sqrt(2),
) -> IIRFilter:
    """
    Creates a band-pass filter

    >>> filter = make_bandpass(1000, 48000)
    >>> filter.a_coeffs + filter.b_coeffs  # doctest: +NORMALIZE_WHITESPACE
    [1.0922959556412573, -1.9828897227476208, 0.9077040443587427, 0.06526309611002579,
     0, -0.06526309611002579]
    """
    w0 = tau * frequency / samplerate
    _sin = sin(w0)
    _cos = cos(w0)
    alpha = _sin / (2 * q_factor)

    b0 = _sin / 2
    b1 = 0
    b2 = -b0

    a0 = 1 + alpha
    a1 = -2 * _cos
    a2 = 1 - alpha

    filt = IIRFilter(2)
    filt.set_coefficients([a0, a1, a2], [b0, b1, b2])
    return filt


def make_allpass(
    frequency: int,
    samplerate: int,
    q_factor: float = 1 / sqrt(2),
) -> IIRFilter:
    """
    Creates an all-pass filter

    >>> filter = make_allpass(1000, 48000)
    >>> filter.a_coeffs + filter.b_coeffs  # doctest: +NORMALIZE_WHITESPACE
    [1.0922959556412573, -1.9828897227476208, 0.9077040443587427, 0.9077040443587427,
     -1.9828897227476208, 1.0922959556412573]
    """
    w0 = tau * frequency / samplerate
    _sin = sin(w0)
    _cos = cos(w0)
    alpha = _sin / (2 * q_factor)

    b0 = 1 - alpha
    b1 = -2 * _cos
    b2 = 1 + alpha

    filt = IIRFilter(2)
    filt.set_coefficients([b2, b1, b0], [b0, b1, b2])
    return filt


def make_peak(
    frequency: int,
    samplerate: int,
    gain_db: float,
    q_factor: float = 1 / sqrt(2),
) -> IIRFilter:
    """
    Creates a peak filter

    >>> filter = make_peak(1000, 48000, 6)
    >>> filter.a_coeffs + filter.b_coeffs  # doctest: +NORMALIZE_WHITESPACE
    [1.0653405327119334, -1.9828897227476208, 0.9346594672880666, 1.1303715025601122,
     -1.9828897227476208, 0.8696284974398878]
    """
    w0 = tau * frequency / samplerate
    _sin = sin(w0)
    _cos = cos(w0)
    alpha = _sin / (2 * q_factor)
    big_a = 10 ** (gain_db / 40)

    b0 = 1 + alpha * big_a
    b1 = -2 * _cos
    b2 = 1 - alpha * big_a
    a0 = 1 + alpha / big_a
    a1 = -2 * _cos
    a2 = 1 - alpha / big_a

    filt = IIRFilter(2)
    filt.set_coefficients([a0, a1, a2], [b0, b1, b2])
    return filt


def make_lowshelf(
    frequency: int,
    samplerate: int,
    gain_db: float,
    q_factor: float = 1 / sqrt(2),
) -> IIRFilter:
    """
    Creates a low-shelf filter

    >>> filter = make_lowshelf(1000, 48000, 6)
    >>> filter.a_coeffs + filter.b_coeffs  # doctest: +NORMALIZE_WHITESPACE
    [3.0409336710888786, -5.608870992220748, 2.602157875636628, 3.139954022810743,
     -5.591841778072785, 2.5201667380627257]
    """
    w0 = tau * frequency / samplerate
    _sin = sin(w0)
    _cos = cos(w0)
    alpha = _sin / (2 * q_factor)
    big_a = 10 ** (gain_db / 40)
    pmc = (big_a + 1) - (big_a - 1) * _cos
    ppmc = (big_a + 1) + (big_a - 1) * _cos
    mpc = (big_a - 1) - (big_a + 1) * _cos
    pmpc = (big_a - 1) + (big_a + 1) * _cos
    aa2 = 2 * sqrt(big_a) * alpha

    b0 = big_a * (pmc + aa2)
    b1 = 2 * big_a * mpc
    b2 = big_a * (pmc - aa2)
    a0 = ppmc + aa2
    a1 = -2 * pmpc
    a2 = ppmc - aa2

    filt = IIRFilter(2)
    filt.set_coefficients([a0, a1, a2], [b0, b1, b2])
    return filt


def make_highshelf(
    frequency: int,
    samplerate: int,
    gain_db: float,
    q_factor: float = 1 / sqrt(2),
) -> IIRFilter:
    """
    Creates a high-shelf filter

    >>> filter = make_highshelf(1000, 48000, 6)
    >>> filter.a_coeffs + filter.b_coeffs  # doctest: +NORMALIZE_WHITESPACE
    [2.2229172136088806, -3.9587208137297303, 1.7841414181566304, 4.295432981120543,
     -7.922740859457287, 3.6756456963725253]
    """
    w0 = tau * frequency / samplerate
    _sin = sin(w0)
    _cos = cos(w0)
    alpha = _sin / (2 * q_factor)
    big_a = 10 ** (gain_db / 40)
    pmc = (big_a + 1) - (big_a - 1) * _cos
    ppmc = (big_a + 1) + (big_a - 1) * _cos
    mpc = (big_a - 1) - (big_a + 1) * _cos
    pmpc = (big_a - 1) + (big_a + 1) * _cos
    aa2 = 2 * sqrt(big_a) * alpha

    b0 = big_a * (ppmc + aa2)
    b1 = -2 * big_a * pmpc
    b2 = big_a * (ppmc - aa2)
    a0 = pmc + aa2
    a1 = 2 * mpc
    a2 = pmc - aa2

    filt = IIRFilter(2)
    filt.set_coefficients([a0, a1, a2], [b0, b1, b2])
    return filt


================================================
FILE: audio_filters/equal_loudness_filter.py.broken.txt
================================================
from json import loads
from pathlib import Path

import numpy as np
from yulewalker import yulewalk

from audio_filters.butterworth_filter import make_highpass
from audio_filters.iir_filter import IIRFilter

data = loads((Path(__file__).resolve().parent / "loudness_curve.json").read_text())


class EqualLoudnessFilter:
    r"""
    An equal-loudness filter which compensates for the human ear's non-linear response
     to sound.
    This filter corrects this by cascading a yulewalk filter and a butterworth filter.

    Designed for use with samplerate of 44.1kHz and above. If you're using a lower
     samplerate, use with caution.

    Code based on matlab implementation at https://bit.ly/3eqh2HU
    (url shortened for ruff)

    Target curve: https://i.imgur.com/3g2VfaM.png
    Yulewalk response: https://i.imgur.com/J9LnJ4C.png
    Butterworth and overall response: https://i.imgur.com/3g2VfaM.png

    Images and original matlab implementation by David Robinson, 2001
    """

    def __init__(self, samplerate: int = 44100) -> None:
        self.yulewalk_filter = IIRFilter(10)
        self.butterworth_filter = make_highpass(150, samplerate)

        # pad the data to nyquist
        curve_freqs = np.array(data["frequencies"] + [max(20000.0, samplerate / 2)])
        curve_gains = np.array(data["gains"] + [140])

        # Convert to angular frequency
        freqs_normalized = curve_freqs / samplerate * 2
        # Invert the curve and normalize to 0dB
        gains_normalized = np.power(10, (np.min(curve_gains) - curve_gains) / 20)

        # Scipy's `yulewalk` function is a stub, so we're using the
        #  `yulewalker` library instead.
        # This function computes the coefficients using a least-squares
        #  fit to the specified curve.
        ya, yb = yulewalk(10, freqs_normalized, gains_normalized)
        self.yulewalk_filter.set_coefficients(ya, yb)

    def process(self, sample: float) -> float:
        """
        Process a single sample through both filters

        >>> filt = EqualLoudnessFilter()
        >>> filt.process(0.0)
        0.0
        """
        tmp = self.yulewalk_filter.process(sample)
        return self.butterworth_filter.process(tmp)


================================================
FILE: audio_filters/iir_filter.py
================================================
from __future__ import annotations


class IIRFilter:
    r"""
    N-Order IIR filter
    Assumes working with float samples normalized on [-1, 1]

    ---

    Implementation details:
    Based on the 2nd-order function from
    https://en.wikipedia.org/wiki/Digital_biquad_filter,
    this generalized N-order function was made.

    Using the following transfer function
        .. math:: H(z)=\frac{b_{0}+b_{1}z^{-1}+b_{2}z^{-2}+...+b_{k}z^{-k}}
                  {a_{0}+a_{1}z^{-1}+a_{2}z^{-2}+...+a_{k}z^{-k}}

    we can rewrite this to
        .. math:: y[n]={\frac{1}{a_{0}}}
                  \left(\left(b_{0}x[n]+b_{1}x[n-1]+b_{2}x[n-2]+...+b_{k}x[n-k]\right)-
                  \left(a_{1}y[n-1]+a_{2}y[n-2]+...+a_{k}y[n-k]\right)\right)
    """

    def __init__(self, order: int) -> None:
        self.order = order

        # a_{0} ... a_{k}
        self.a_coeffs = [1.0] + [0.0] * order
        # b_{0} ... b_{k}
        self.b_coeffs = [1.0] + [0.0] * order

        # x[n-1] ... x[n-k]
        self.input_history = [0.0] * self.order
        # y[n-1] ... y[n-k]
        self.output_history = [0.0] * self.order

    def set_coefficients(self, a_coeffs: list[float], b_coeffs: list[float]) -> None:
        """
        Set the coefficients for the IIR filter.
        These should both be of size `order` + 1.
        :math:`a_0` may be left out, and it will use 1.0 as default value.

        This method works well with scipy's filter design functions

        >>> # Make a 2nd-order 1000Hz butterworth lowpass filter
        >>> import scipy.signal
        >>> b_coeffs, a_coeffs = scipy.signal.butter(2, 1000,
        ...                                          btype='lowpass',
        ...                                          fs=48000)
        >>> filt = IIRFilter(2)
        >>> filt.set_coefficients(a_coeffs, b_coeffs)
        """
        if len(a_coeffs) < self.order:
            a_coeffs = [1.0, *a_coeffs]

        if len(a_coeffs) != self.order + 1:
            msg = (
                f"Expected a_coeffs to have {self.order + 1} elements "
                f"for {self.order}-order filter, got {len(a_coeffs)}"
            )
            raise ValueError(msg)

        if len(b_coeffs) != self.order + 1:
            msg = (
                f"Expected b_coeffs to have {self.order + 1} elements "
                f"for {self.order}-order filter, got {len(a_coeffs)}"
            )
            raise ValueError(msg)

        self.a_coeffs = a_coeffs
        self.b_coeffs = b_coeffs

    def process(self, sample: float) -> float:
        """
        Calculate :math:`y[n]`

        >>> filt = IIRFilter(2)
        >>> filt.process(0)
        0.0
        """
        result = 0.0

        # Start at index 1 and do index 0 at the end.
        for i in range(1, self.order + 1):
            result += (
                self.b_coeffs[i] * self.input_history[i - 1]
                - self.a_coeffs[i] * self.output_history[i - 1]
            )

        result = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

        self.input_history[1:] = self.input_history[:-1]
        self.output_history[1:] = self.output_history[:-1]

        self.input_history[0] = sample
        self.output_history[0] = result

        return result


================================================
FILE: audio_filters/loudness_curve.json
================================================
{
  "_comment": "The following is a representative average of the Equal Loudness Contours as measured by Robinson and Dadson, 1956",
  "_doi": "10.1088/0508-3443/7/5/302",
  "frequencies": [
    0,
    20,
    30,
    40,
    50,
    60,
    70,
    80,
    90,
    100,
    200,
    300,
    400,
    500,
    600,
    700,
    800,
    900,
    1000,
    1500,
    2000,
    2500,
    3000,
    3700,
    4000,
    5000,
    6000,
    7000,
    8000,
    9000,
    10000,
    12000,
    15000,
    20000
  ],
  "gains": [
    120,
    113,
    103,
    97,
    93,
    91,
    89,
    87,
    86,
    85,
    78,
    76,
    76,
    76,
    76,
    77,
    78,
    79.5,
    80,
    79,
    77,
    74,
    71.5,
    70,
    70.5,
    74,
    79,
    84,
    86,
    86,
    85,
    95,
    110,
    125
  ]
}


================================================
FILE: audio_filters/show_response.py
================================================
from __future__ import annotations

from abc import abstractmethod
from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np


class FilterType(Protocol):
    @abstractmethod
    def process(self, sample: float) -> float:
        """
        Calculate y[n]

        >>> issubclass(FilterType, Protocol)
        True
        """


def get_bounds(
    fft_results: np.ndarray, samplerate: int
) -> tuple[int | float, int | float]:
    """
    Get bounds for printing fft results

    >>> import numpy
    >>> array = numpy.linspace(-20.0, 20.0, 1000)
    >>> get_bounds(array, 1000)
    (-20, 20)
    """
    lowest = min([-20, np.min(fft_results[1 : samplerate // 2 - 1])])
    highest = max([20, np.max(fft_results[1 : samplerate // 2 - 1])])
    return lowest, highest


def show_frequency_response(filter_type: FilterType, samplerate: int) -> None:
    """
    Show frequency response of a filter

    >>> from audio_filters.iir_filter import IIRFilter
    >>> filt = IIRFilter(4)
    >>> show_frequency_response(filt, 48000)
    """

    size = 512
    inputs = [1] + [0] * (size - 1)
    outputs = [filter_type.process(item) for item in inputs]

    filler = [0] * (samplerate - size)  # zero-padding
    outputs += filler
    fft_out = np.abs(np.fft.fft(outputs))
    fft_db = 20 * np.log10(fft_out)

    # Frequencies on log scale from 24 to nyquist frequency
    plt.xlim(24, samplerate / 2 - 1)
    plt.xlabel("Frequency (Hz)")
    plt.xscale("log")

    # Display within reasonable bounds
    bounds = get_bounds(fft_db, samplerate)
    plt.ylim(max([-80, bounds[0]]), min([80, bounds[1]]))
    plt.ylabel("Gain (dB)")

    plt.plot(fft_db)
    plt.show()


def show_phase_response(filter_type: FilterType, samplerate: int) -> None:
    """
    Show phase response of a filter

    >>> from audio_filters.iir_filter import IIRFilter
    >>> filt = IIRFilter(4)
    >>> show_phase_response(filt, 48000)
    """

    size = 512
    inputs = [1] + [0] * (size - 1)
    outputs = [filter_type.process(item) for item in inputs]

    filler = [0] * (samplerate - size)  # zero-padding
    outputs += filler
    fft_out = np.angle(np.fft.fft(outputs))

    # Frequencies on log scale from 24 to nyquist frequency
    plt.xlim(24, samplerate / 2 - 1)
    plt.xlabel("Frequency (Hz)")
    plt.xscale("log")

    plt.ylim(-2 * pi, 2 * pi)
    plt.ylabel("Phase shift (Radians)")
    plt.plot(np.unwrap(fft_out, -2 * pi))
    plt.show()


================================================
FILE: backtracking/README.md
================================================
# Backtracking

Backtracking is a way to speed up the search process by removing candidates when they can't be the solution of a problem.

* <https://en.wikipedia.org/wiki/Backtracking>
* <https://en.wikipedia.org/wiki/Decision_tree_pruning>
* <https://medium.com/@priyankmistry1999/backtracking-sudoku-6e4439e4825c>
* <https://www.geeksforgeeks.org/sudoku-backtracking-7/>


================================================
FILE: backtracking/__init__.py
================================================


================================================
FILE: backtracking/all_combinations.py
================================================
"""
In this problem, we want to determine all possible combinations of k
numbers out of 1 ... n. We use backtracking to solve this problem.

Time complexity: O(C(n,k)) which is O(n choose k) = O((n!/(k! * (n - k)!))),
"""

from __future__ import annotations

from itertools import combinations


def combination_lists(n: int, k: int) -> list[list[int]]:
    """
    Generates all possible combinations of k numbers out of 1 ... n using itertools.

    >>> combination_lists(n=4, k=2)
    [[1, 2], [1, 3], [1, 4], [2, 3], [2, 4], [3, 4]]
    """
    return [list(x) for x in combinations(range(1, n + 1), k)]


def generate_all_combinations(n: int, k: int) -> list[list[int]]:
    """
    Generates all possible combinations of k numbers out of 1 ... n using backtracking.

    >>> generate_all_combinations(n=4, k=2)
    [[1, 2], [1, 3], [1, 4], [2, 3], [2, 4], [3, 4]]
    >>> generate_all_combinations(n=0, k=0)
    [[]]
    >>> generate_all_combinations(n=10, k=-1)
    Traceback (most recent call last):
        ...
    ValueError: k must not be negative
    >>> generate_all_combinations(n=-1, k=10)
    Traceback (most recent call last):
        ...
    ValueError: n must not be negative
    >>> generate_all_combinations(n=5, k=4)
    [[1, 2, 3, 4], [1, 2, 3, 5], [1, 2, 4, 5], [1, 3, 4, 5], [2, 3, 4, 5]]
    >>> generate_all_combinations(n=3, k=3)
    [[1, 2, 3]]
    >>> generate_all_combinations(n=3, k=1)
    [[1], [2], [3]]
    >>> generate_all_combinations(n=1, k=0)
    [[]]
    >>> generate_all_combinations(n=1, k=1)
    [[1]]
    >>> from itertools import combinations
    >>> all(generate_all_combinations(n, k) == combination_lists(n, k)
    ...     for n in range(1, 6) for k in range(1, 6))
    True
    """
    if k < 0:
        raise ValueError("k must not be negative")
    if n < 0:
        raise ValueError("n must not be negative")

    result: list[list[int]] = []
    create_all_state(1, n, k, [], result)
    return result


def create_all_state(
    increment: int,
    total_number: int,
    level: int,
    current_list: list[int],
    total_list: list[list[int]],
) -> None:
    """
    Helper function to recursively build all combinations.

    >>> create_all_state(1, 4, 2, [], result := [])
    >>> result
    [[1, 2], [1, 3], [1, 4], [2, 3], [2, 4], [3, 4]]
    >>> create_all_state(1, 3, 3, [], result := [])
    >>> result
    [[1, 2, 3]]
    >>> create_all_state(2, 2, 1, [1], result := [])
    >>> result
    [[1, 2]]
    >>> create_all_state(1, 0, 0, [], result := [])
    >>> result
    [[]]
    >>> create_all_state(1, 4, 0, [1, 2], result := [])
    >>> result
    [[1, 2]]
    >>> create_all_state(5, 4, 2, [1, 2], result := [])
    >>> result
    []
    """
    if level == 0:
        total_list.append(current_list[:])
        return

    for i in range(increment, total_number - level + 2):
        current_list.append(i)
        create_all_state(i + 1, total_number, level - 1, current_list, total_list)
        current_list.pop()


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    print(generate_all_combinations(n=4, k=2))
    tests = ((n, k) for n in range(1, 5) for k in range(1, 5))
    for n, k in tests:
        print(n, k, generate_all_combinations(n, k) == combination_lists(n, k))

    print("Benchmark:")
    from timeit import timeit

    for func in ("combination_lists", "generate_all_combinations"):
        print(f"{func:>25}(): {timeit(f'{func}(n=4, k = 2)', globals=globals())}")


================================================
FILE: backtracking/all_permutations.py
================================================
"""
In this problem, we want to determine all possible permutations
of the given sequence. We use backtracking to solve this problem.

Time complexity: O(n! * n),
where n denotes the length of the given sequence.
"""

from __future__ import annotations


def generate_all_permutations(sequence: list[int | str]) -> None:
    create_state_space_tree(sequence, [], 0, [0 for i in range(len(sequence))])


def create_state_space_tree(
    sequence: list[int | str],
    current_sequence: list[int | str],
    index: int,
    index_used: list[int],
) -> None:
    """
    Creates a state space tree to iterate through each branch using DFS.
    We know that each state has exactly len(sequence) - index children.
    It terminates when it reaches the end of the given sequence.

    :param sequence: The input sequence for which permutations are generated.
    :param current_sequence: The current permutation being built.
    :param index: The current index in the sequence.
    :param index_used: list to track which elements are used in permutation.

    Example 1:
    >>> sequence = [1, 2, 3]
    >>> current_sequence = []
    >>> index_used = [False, False, False]
    >>> create_state_space_tree(sequence, current_sequence, 0, index_used)
    [1, 2, 3]
    [1, 3, 2]
    [2, 1, 3]
    [2, 3, 1]
    [3, 1, 2]
    [3, 2, 1]

    Example 2:
    >>> sequence = ["A", "B", "C"]
    >>> current_sequence = []
    >>> index_used = [False, False, False]
    >>> create_state_space_tree(sequence, current_sequence, 0, index_used)
    ['A', 'B', 'C']
    ['A', 'C', 'B']
    ['B', 'A', 'C']
    ['B', 'C', 'A']
    ['C', 'A', 'B']
    ['C', 'B', 'A']

    Example 3:
    >>> sequence = [1]
    >>> current_sequence = []
    >>> index_used = [False]
    >>> create_state_space_tree(sequence, current_sequence, 0, index_used)
    [1]
    """

    if index == len(sequence):
        print(current_sequence)
        return

    for i in range(len(sequence)):
        if not index_used[i]:
            current_sequence.append(sequence[i])
            index_used[i] = True
            create_state_space_tree(sequence, current_sequence, index + 1, index_used)
            current_sequence.pop()
            index_used[i] = False


"""
remove the comment to take an input from the user

print("Enter the elements")
sequence = list(map(int, input().split()))
"""

sequence: list[int | str] = [3, 1, 2, 4]
generate_all_permutations(sequence)

sequence_2: list[int | str] = ["A", "B", "C"]
generate_all_permutations(sequence_2)


================================================
FILE: backtracking/all_subsequences.py
================================================
"""
In this problem, we want to determine all possible subsequences
of the given sequence. We use backtracking to solve this problem.

Time complexity: O(2^n),
where n denotes the length of the given sequence.
"""

from __future__ import annotations

from typing import Any


def generate_all_subsequences(sequence: list[Any]) -> None:
    create_state_space_tree(sequence, [], 0)


def create_state_space_tree(
    sequence: list[Any], current_subsequence: list[Any], index: int
) -> None:
    """
    Creates a state space tree to iterate through each branch using DFS.
    We know that each state has exactly two children.
    It terminates when it reaches the end of the given sequence.

    :param sequence: The input sequence for which subsequences are generated.
    :param current_subsequence: The current subsequence being built.
    :param index: The current index in the sequence.

    Example:
    >>> sequence = [3, 2, 1]
    >>> current_subsequence = []
    >>> create_state_space_tree(sequence, current_subsequence, 0)
    []
    [1]
    [2]
    [2, 1]
    [3]
    [3, 1]
    [3, 2]
    [3, 2, 1]

    >>> sequence = ["A", "B"]
    >>> current_subsequence = []
    >>> create_state_space_tree(sequence, current_subsequence, 0)
    []
    ['B']
    ['A']
    ['A', 'B']

    >>> sequence = []
    >>> current_subsequence = []
    >>> create_state_space_tree(sequence, current_subsequence, 0)
    []

    >>> sequence = [1, 2, 3, 4]
    >>> current_subsequence = []
    >>> create_state_space_tree(sequence, current_subsequence, 0)
    []
    [4]
    [3]
    [3, 4]
    [2]
    [2, 4]
    [2, 3]
    [2, 3, 4]
    [1]
    [1, 4]
    [1, 3]
    [1, 3, 4]
    [1, 2]
    [1, 2, 4]
    [1, 2, 3]
    [1, 2, 3, 4]
    """

    if index == len(sequence):
        print(current_subsequence)
        return

    create_state_space_tree(sequence, current_subsequence, index + 1)
    current_subsequence.append(sequence[index])
    create_state_space_tree(sequence, current_subsequence, index + 1)
    current_subsequence.pop()


if __name__ == "__main__":
    seq: list[Any] = [1, 2, 3]
    generate_all_subsequences(seq)

    seq.clear()
    seq.extend(["A", "B", "C"])
    generate_all_subsequences(seq)


================================================
FILE: backtracking/coloring.py
================================================
"""
Graph Coloring also called "m coloring problem"
consists of coloring a given graph with at most m colors
such that no adjacent vertices are assigned the same color

Wikipedia: https://en.wikipedia.org/wiki/Graph_coloring
"""


def valid_coloring(
    neighbours: list[int], colored_vertices: list[int], color: int
) -> bool:
    """
    For each neighbour check if the coloring constraint is satisfied
    If any of the neighbours fail the constraint return False
    If all neighbours validate the constraint return True

    >>> neighbours = [0,1,0,1,0]
    >>> colored_vertices = [0, 2, 1, 2, 0]

    >>> color = 1
    >>> valid_coloring(neighbours, colored_vertices, color)
    True

    >>> color = 2
    >>> valid_coloring(neighbours, colored_vertices, color)
    False
    """
    # Does any neighbour not satisfy the constraints
    return not any(
        neighbour == 1 and colored_vertices[i] == color
        for i, neighbour in enumerate(neighbours)
    )


def util_color(
    graph: list[list[int]], max_colors: int, colored_vertices: list[int], index: int
) -> bool:
    """
    Pseudo-Code

    Base Case:
    1. Check if coloring is complete
        1.1 If complete return True (meaning that we successfully colored the graph)

    Recursive Step:
    2. Iterates over each color:
        Check if the current coloring is valid:
            2.1. Color given vertex
            2.2. Do recursive call, check if this coloring leads to a solution
            2.4. if current coloring leads to a solution return
            2.5. Uncolor given vertex

    >>> graph = [[0, 1, 0, 0, 0],
    ...          [1, 0, 1, 0, 1],
    ...          [0, 1, 0, 1, 0],
    ...          [0, 1, 1, 0, 0],
    ...          [0, 1, 0, 0, 0]]
    >>> max_colors = 3
    >>> colored_vertices = [0, 1, 0, 0, 0]
    >>> index = 3

    >>> util_color(graph, max_colors, colored_vertices, index)
    True

    >>> max_colors = 2
    >>> util_color(graph, max_colors, colored_vertices, index)
    False
    """

    # Base Case
    if index == len(graph):
        return True

    # Recursive Step
    for i in range(max_colors):
        if valid_coloring(graph[index], colored_vertices, i):
            # Color current vertex
            colored_vertices[index] = i
            # Validate coloring
            if util_color(graph, max_colors, colored_vertices, index + 1):
                return True
            # Backtrack
            colored_vertices[index] = -1
    return False


def color(graph: list[list[int]], max_colors: int) -> list[int]:
    """
    Wrapper function to call subroutine called util_color
    which will either return True or False.
    If True is returned colored_vertices list is filled with correct colorings

    >>> graph = [[0, 1, 0, 0, 0],
    ...          [1, 0, 1, 0, 1],
    ...          [0, 1, 0, 1, 0],
    ...          [0, 1, 1, 0, 0],
    ...          [0, 1, 0, 0, 0]]

    >>> max_colors = 3
    >>> color(graph, max_colors)
    [0, 1, 0, 2, 0]

    >>> max_colors = 2
    >>> color(graph, max_colors)
    []
    >>> color([], 2)  # empty graph
    []
    >>> color([[0]], 1)  # single node, 1 color
    [0]
    >>> color([[0, 1], [1, 0]], 1)  # 2 nodes, 1 color (impossible)
    []
    >>> color([[0, 1], [1, 0]], 2)  # 2 nodes, 2 colors (possible)
    [0, 1]
    """
    colored_vertices = [-1] * len(graph)

    if util_color(graph, max_colors, colored_vertices, 0):
        return colored_vertices

    return []


================================================
FILE: backtracking/combination_sum.py
================================================
"""
In the Combination Sum problem, we are given a list consisting of distinct integers.
We need to find all the combinations whose sum equals to target given.
We can use an element more than one.

Time complexity(Average Case): O(n!)

Constraints:
1 <= candidates.length <= 30
2 <= candidates[i] <= 40
All elements of candidates are distinct.
1 <= target <= 40
"""


def backtrack(
    candidates: list, path: list, answer: list, target: int, previous_index: int
) -> None:
    """
    A recursive function that searches for possible combinations. Backtracks in case
    of a bigger current combination value than the target value.

    Parameters
    ----------
    previous_index: Last index from the previous search
    target: The value we need to obtain by summing our integers in the path list.
    answer: A list of possible combinations
    path: Current combination
    candidates: A list of integers we can use.
    """
    if target == 0:
        answer.append(path.copy())
    else:
        for index in range(previous_index, len(candidates)):
            if target >= candidates[index]:
                path.append(candidates[index])
                backtrack(candidates, path, answer, target - candidates[index], index)
                path.pop(len(path) - 1)


def combination_sum(candidates: list, target: int) -> list:
    """
    >>> combination_sum([2, 3, 5], 8)
    [[2, 2, 2, 2], [2, 3, 3], [3, 5]]
    >>> combination_sum([2, 3, 6, 7], 7)
    [[2, 2, 3], [7]]
    >>> combination_sum([-8, 2.3, 0], 1)
    Traceback (most recent call last):
        ...
    ValueError: All elements in candidates must be non-negative
    >>> combination_sum([], 1)
    Traceback (most recent call last):
        ...
    ValueError: Candidates list should not be empty
    """
    if not candidates:
        raise ValueError("Candidates list should not be empty")

    if any(x < 0 for x in candidates):
        raise ValueError("All elements in candidates must be non-negative")

    path = []  # type: list[int]
    answer = []  # type: list[int]
    backtrack(candidates, path, answer, target, 0)
    return answer


def main() -> None:
    print(combination_sum([-8, 2.3, 0], 1))


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: backtracking/crossword_puzzle_solver.py
================================================
# https://www.geeksforgeeks.org/solve-crossword-puzzle/


def is_valid(
    puzzle: list[list[str]], word: str, row: int, col: int, vertical: bool
) -> bool:
    """
    Check if a word can be placed at the given position.

    >>> puzzle = [
    ...     ['', '', '', ''],
    ...     ['', '', '', ''],
    ...     ['', '', '', ''],
    ...     ['', '', '', '']
    ... ]
    >>> is_valid(puzzle, 'word', 0, 0, True)
    True
    >>> puzzle = [
    ...     ['', '', '', ''],
    ...     ['', '', '', ''],
    ...     ['', '', '', ''],
    ...     ['', '', '', '']
    ... ]
    >>> is_valid(puzzle, 'word', 0, 0, False)
    True
    """
    for i in range(len(word)):
        if vertical:
            if row + i >= len(puzzle) or puzzle[row + i][col] != "":
                return False
        elif col + i >= len(puzzle[0]) or puzzle[row][col + i] != "":
            return False
    return True


def place_word(
    puzzle: list[list[str]], word: str, row: int, col: int, vertical: bool
) -> None:
    """
    Place a word at the given position.

    >>> puzzle = [
    ...     ['', '', '', ''],
    ...     ['', '', '', ''],
    ...     ['', '', '', ''],
    ...     ['', '', '', '']
    ... ]
    >>> place_word(puzzle, 'word', 0, 0, True)
    >>> puzzle
    [['w', '', '', ''], ['o', '', '', ''], ['r', '', '', ''], ['d', '', '', '']]
    """
    for i, char in enumerate(word):
        if vertical:
            puzzle[row + i][col] = char
        else:
            puzzle[row][col + i] = char


def remove_word(
    puzzle: list[list[str]], word: str, row: int, col: int, vertical: bool
) -> None:
    """
    Remove a word from the given position.

    >>> puzzle = [
    ...     ['w', '', '', ''],
    ...     ['o', '', '', ''],
    ...     ['r', '', '', ''],
    ...     ['d', '', '', '']
    ... ]
    >>> remove_word(puzzle, 'word', 0, 0, True)
    >>> puzzle
    [['', '', '', ''], ['', '', '', ''], ['', '', '', ''], ['', '', '', '']]
    """
    for i in range(len(word)):
        if vertical:
            puzzle[row + i][col] = ""
        else:
            puzzle[row][col + i] = ""


def solve_crossword(puzzle: list[list[str]], words: list[str]) -> bool:
    """
    Solve the crossword puzzle using backtracking.

    >>> puzzle = [
    ...     ['', '', '', ''],
    ...     ['', '', '', ''],
    ...     ['', '', '', ''],
    ...     ['', '', '', '']
    ... ]

    >>> words = ['word', 'four', 'more', 'last']
    >>> solve_crossword(puzzle, words)
    True
    >>> puzzle = [
    ...     ['', '', '', ''],
    ...     ['', '', '', ''],
    ...     ['', '', '', ''],
    ...     ['', '', '', '']
    ... ]
    >>> words = ['word', 'four', 'more', 'paragraphs']
    >>> solve_crossword(puzzle, words)
    False
    """
    for row in range(len(puzzle)):
        for col in range(len(puzzle[0])):
            if puzzle[row][col] == "":
                for word in words:
                    for vertical in [True, False]:
                        if is_valid(puzzle, word, row, col, vertical):
                            place_word(puzzle, word, row, col, vertical)
                            words.remove(word)
                            if solve_crossword(puzzle, words):
                                return True
                            words.append(word)
                            remove_word(puzzle, word, row, col, vertical)
                return False
    return True


if __name__ == "__main__":
    PUZZLE = [[""] * 3 for _ in range(3)]
    WORDS = ["cat", "dog", "car"]

    if solve_crossword(PUZZLE, WORDS):
        print("Solution found:")
        for row in PUZZLE:
            print(" ".join(row))
    else:
        print("No solution found:")


================================================
FILE: backtracking/generate_parentheses.py
================================================
"""
author: Aayush Soni
Given n pairs of parentheses, write a function to generate all
combinations of well-formed parentheses.
Input: n = 2
Output: ["(())","()()"]
Leetcode link: https://leetcode.com/problems/generate-parentheses/description/
"""


def backtrack(
    partial: str, open_count: int, close_count: int, n: int, result: list[str]
) -> None:
    """
    Generate valid combinations of balanced parentheses using recursion.

    :param partial: A string representing the current combination.
    :param open_count: An integer representing the count of open parentheses.
    :param close_count: An integer representing the count of close parentheses.
    :param n: An integer representing the total number of pairs.
    :param result: A list to store valid combinations.
    :return: None

    This function uses recursion to explore all possible combinations,
    ensuring that at each step, the parentheses remain balanced.

    Example:
    >>> result = []
    >>> backtrack("", 0, 0, 2, result)
    >>> result
    ['(())', '()()']
    """
    if len(partial) == 2 * n:
        # When the combination is complete, add it to the result.
        result.append(partial)
        return

    if open_count < n:
        # If we can add an open parenthesis, do so, and recurse.
        backtrack(partial + "(", open_count + 1, close_count, n, result)

    if close_count < open_count:
        # If we can add a close parenthesis (it won't make the combination invalid),
        # do so, and recurse.
        backtrack(partial + ")", open_count, close_count + 1, n, result)


def generate_parenthesis(n: int) -> list[str]:
    """
    Generate valid combinations of balanced parentheses for a given n.

    :param n: An integer representing the number of pairs of parentheses.
    :return: A list of strings with valid combinations.

    This function uses a recursive approach to generate the combinations.

    Time Complexity: O(2^(2n)) - In the worst case, we have 2^(2n) combinations.
    Space Complexity: O(n) - where 'n' is the number of pairs.

    Example 1:
    >>> generate_parenthesis(3)
    ['((()))', '(()())', '(())()', '()(())', '()()()']

    Example 2:
    >>> generate_parenthesis(1)
    ['()']

    Example 3:
    >>> generate_parenthesis(0)
    ['']
    """

    result: list[str] = []
    backtrack("", 0, 0, n, result)
    return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: backtracking/generate_parentheses_iterative.py
================================================
def generate_parentheses_iterative(length: int) -> list[str]:
    """
    Generate all valid combinations of parentheses (Iterative Approach).

    The algorithm works as follows:
    1. Initialize an empty list to store the combinations.
    2. Initialize a stack to keep track of partial combinations.
    3. Start with empty string and push it on stack along with
       the counts of '(' and ')'.
    4. While the stack is not empty:
        a. Pop a partial combination and its open and close counts from the stack.
        b. If the combination length is equal to 2*length, add it to the result.
        c. If open count < length, push new combination with added '(' on stack.
        d. If close count < open count, push new combination with added ')' on stack.
    5. Return the result containing all valid combinations.

    Args:
        length: The desired length of the parentheses combinations

    Returns:
        A list of strings representing valid combinations of parentheses

    Time Complexity:
        O(2^(2*length))

    Space Complexity:
        O(2^(2*length))

    >>> generate_parentheses_iterative(3)
    ['()()()', '()(())', '(())()', '(()())', '((()))']
    >>> generate_parentheses_iterative(2)
    ['()()', '(())']
    >>> generate_parentheses_iterative(1)
    ['()']
    >>> generate_parentheses_iterative(0)
    ['']
    """
    if length == 0:
        return [""]

    result: list[str] = []
    stack: list[tuple[str, int, int]] = []

    # Each element in stack is a tuple (current_combination, open_count, close_count)
    stack.append(("", 0, 0))

    while stack:
        current_combination, open_count, close_count = stack.pop()

        if len(current_combination) == 2 * length:
            result.append(current_combination)
            continue

        if open_count < length:
            stack.append((current_combination + "(", open_count + 1, close_count))

        if close_count < open_count:
            stack.append((current_combination + ")", open_count, close_count + 1))

    return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(generate_parentheses_iterative(3))


================================================
FILE: backtracking/hamiltonian_cycle.py
================================================
"""
A Hamiltonian cycle (Hamiltonian circuit) is a graph cycle
through a graph that visits each node exactly once.
Determining whether such paths and cycles exist in graphs
is the 'Hamiltonian path problem', which is NP-complete.

Wikipedia: https://en.wikipedia.org/wiki/Hamiltonian_path
"""


def valid_connection(
    graph: list[list[int]], next_ver: int, curr_ind: int, path: list[int]
) -> bool:
    """
    Checks whether it is possible to add next into path by validating 2 statements
    1. There should be path between current and next vertex
    2. Next vertex should not be in path
    If both validations succeed we return True, saying that it is possible to connect
    this vertices, otherwise we return False

    Case 1:Use exact graph as in main function, with initialized values
    >>> graph = [[0, 1, 0, 1, 0],
    ...          [1, 0, 1, 1, 1],
    ...          [0, 1, 0, 0, 1],
    ...          [1, 1, 0, 0, 1],
    ...          [0, 1, 1, 1, 0]]
    >>> path = [0, -1, -1, -1, -1, 0]
    >>> curr_ind = 1
    >>> next_ver = 1
    >>> valid_connection(graph, next_ver, curr_ind, path)
    True

    Case 2: Same graph, but trying to connect to node that is already in path
    >>> path = [0, 1, 2, 4, -1, 0]
    >>> curr_ind = 4
    >>> next_ver = 1
    >>> valid_connection(graph, next_ver, curr_ind, path)
    False
    """

    # 1. Validate that path exists between current and next vertices
    if graph[path[curr_ind - 1]][next_ver] == 0:
        return False

    # 2. Validate that next vertex is not already in path
    return not any(vertex == next_ver for vertex in path)


def util_hamilton_cycle(graph: list[list[int]], path: list[int], curr_ind: int) -> bool:
    """
    Pseudo-Code
    Base Case:
    1. Check if we visited all of vertices
        1.1 If last visited vertex has path to starting vertex return True either
            return False
    Recursive Step:
    2. Iterate over each vertex
        Check if next vertex is valid for transiting from current vertex
            2.1 Remember next vertex as next transition
            2.2 Do recursive call and check if going to this vertex solves problem
            2.3 If next vertex leads to solution return True
            2.4 Else backtrack, delete remembered vertex

    Case 1: Use exact graph as in main function, with initialized values
    >>> graph = [[0, 1, 0, 1, 0],
    ...          [1, 0, 1, 1, 1],
    ...          [0, 1, 0, 0, 1],
    ...          [1, 1, 0, 0, 1],
    ...          [0, 1, 1, 1, 0]]
    >>> path = [0, -1, -1, -1, -1, 0]
    >>> curr_ind = 1
    >>> util_hamilton_cycle(graph, path, curr_ind)
    True
    >>> path
    [0, 1, 2, 4, 3, 0]

    Case 2: Use exact graph as in previous case, but in the properties taken from
        middle of calculation
    >>> graph = [[0, 1, 0, 1, 0],
    ...          [1, 0, 1, 1, 1],
    ...          [0, 1, 0, 0, 1],
    ...          [1, 1, 0, 0, 1],
    ...          [0, 1, 1, 1, 0]]
    >>> path = [0, 1, 2, -1, -1, 0]
    >>> curr_ind = 3
    >>> util_hamilton_cycle(graph, path, curr_ind)
    True
    >>> path
    [0, 1, 2, 4, 3, 0]
    """

    # Base Case
    if curr_ind == len(graph):
        # return whether path exists between current and starting vertices
        return graph[path[curr_ind - 1]][path[0]] == 1

    # Recursive Step
    for next_ver in range(len(graph)):
        if valid_connection(graph, next_ver, curr_ind, path):
            # Insert current vertex  into path as next transition
            path[curr_ind] = next_ver
            # Validate created path
            if util_hamilton_cycle(graph, path, curr_ind + 1):
                return True
            # Backtrack
            path[curr_ind] = -1
    return False


def hamilton_cycle(graph: list[list[int]], start_index: int = 0) -> list[int]:
    r"""
    Wrapper function to call subroutine called util_hamilton_cycle,
    which will either return array of vertices indicating hamiltonian cycle
    or an empty list indicating that hamiltonian cycle was not found.
    Case 1:
    Following graph consists of 5 edges.
    If we look closely, we can see that there are multiple Hamiltonian cycles.
    For example one result is when we iterate like:
    (0)->(1)->(2)->(4)->(3)->(0)

    (0)---(1)---(2)
     |   /   \   |
     |  /     \  |
     | /       \ |
     |/         \|
    (3)---------(4)
    >>> graph = [[0, 1, 0, 1, 0],
    ...          [1, 0, 1, 1, 1],
    ...          [0, 1, 0, 0, 1],
    ...          [1, 1, 0, 0, 1],
    ...          [0, 1, 1, 1, 0]]
    >>> hamilton_cycle(graph)
    [0, 1, 2, 4, 3, 0]

    Case 2:
    Same Graph as it was in Case 1, changed starting index from default to 3

    (0)---(1)---(2)
     |   /   \   |
     |  /     \  |
     | /       \ |
     |/         \|
    (3)---------(4)
    >>> graph = [[0, 1, 0, 1, 0],
    ...          [1, 0, 1, 1, 1],
    ...          [0, 1, 0, 0, 1],
    ...          [1, 1, 0, 0, 1],
    ...          [0, 1, 1, 1, 0]]
    >>> hamilton_cycle(graph, 3)
    [3, 0, 1, 2, 4, 3]

    Case 3:
    Following Graph is exactly what it was before, but edge 3-4 is removed.
    Result is that there is no Hamiltonian Cycle anymore.

    (0)---(1)---(2)
     |   /   \   |
     |  /     \  |
     | /       \ |
     |/         \|
    (3)         (4)
    >>> graph = [[0, 1, 0, 1, 0],
    ...          [1, 0, 1, 1, 1],
    ...          [0, 1, 0, 0, 1],
    ...          [1, 1, 0, 0, 0],
    ...          [0, 1, 1, 0, 0]]
    >>> hamilton_cycle(graph,4)
    []
    """

    # Initialize path with -1, indicating that we have not visited them yet
    path = [-1] * (len(graph) + 1)
    # initialize start and end of path with starting index
    path[0] = path[-1] = start_index
    # evaluate and if we find answer return path either return empty array
    return path if util_hamilton_cycle(graph, path, 1) else []


================================================
FILE: backtracking/knight_tour.py
================================================
# Knight Tour Intro: https://www.youtube.com/watch?v=ab_dY3dZFHM

from __future__ import annotations


def get_valid_pos(position: tuple[int, int], n: int) -> list[tuple[int, int]]:
    """
    Find all the valid positions a knight can move to from the current position.

    >>> get_valid_pos((1, 3), 4)
    [(2, 1), (0, 1), (3, 2)]
    """

    y, x = position
    positions = [
        (y + 1, x + 2),
        (y - 1, x + 2),
        (y + 1, x - 2),
        (y - 1, x - 2),
        (y + 2, x + 1),
        (y + 2, x - 1),
        (y - 2, x + 1),
        (y - 2, x - 1),
    ]
    permissible_positions = []

    for inner_position in positions:
        y_test, x_test = inner_position
        if 0 <= y_test < n and 0 <= x_test < n:
            permissible_positions.append(inner_position)

    return permissible_positions


def is_complete(board: list[list[int]]) -> bool:
    """
    Check if the board (matrix) has been completely filled with non-zero values.

    >>> is_complete([[1]])
    True

    >>> is_complete([[1, 2], [3, 0]])
    False
    """

    return not any(elem == 0 for row in board for elem in row)


def open_knight_tour_helper(
    board: list[list[int]], pos: tuple[int, int], curr: int
) -> bool:
    """
    Helper function to solve knight tour problem.
    """

    if is_complete(board):
        return True

    for position in get_valid_pos(pos, len(board)):
        y, x = position

        if board[y][x] == 0:
            board[y][x] = curr + 1
            if open_knight_tour_helper(board, position, curr + 1):
                return True
            board[y][x] = 0

    return False


def open_knight_tour(n: int) -> list[list[int]]:
    """
    Find the solution for the knight tour problem for a board of size n. Raises
    ValueError if the tour cannot be performed for the given size.

    >>> open_knight_tour(1)
    [[1]]

    >>> open_knight_tour(2)
    Traceback (most recent call last):
        ...
    ValueError: Open Knight Tour cannot be performed on a board of size 2
    """

    board = [[0 for i in range(n)] for j in range(n)]

    for i in range(n):
        for j in range(n):
            board[i][j] = 1
            if open_knight_tour_helper(board, (i, j), 1):
                return board
            board[i][j] = 0

    msg = f"Open Knight Tour cannot be performed on a board of size {n}"
    raise ValueError(msg)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: backtracking/match_word_pattern.py
================================================
def match_word_pattern(pattern: str, input_string: str) -> bool:
    """
    Determine if a given pattern matches a string using backtracking.

    pattern: The pattern to match.
    input_string: The string to match against the pattern.
    return: True if the pattern matches the string, False otherwise.

    >>> match_word_pattern("aba", "GraphTreesGraph")
    True

    >>> match_word_pattern("xyx", "PythonRubyPython")
    True

    >>> match_word_pattern("GG", "PythonJavaPython")
    False
    """

    def backtrack(pattern_index: int, str_index: int) -> bool:
        """
        >>> backtrack(0, 0)
        True

        >>> backtrack(0, 1)
        True

        >>> backtrack(0, 4)
        False
        """
        if pattern_index == len(pattern) and str_index == len(input_string):
            return True
        if pattern_index == len(pattern) or str_index == len(input_string):
            return False
        char = pattern[pattern_index]
        if char in pattern_map:
            mapped_str = pattern_map[char]
            if input_string.startswith(mapped_str, str_index):
                return backtrack(pattern_index + 1, str_index + len(mapped_str))
            else:
                return False
        for end in range(str_index + 1, len(input_string) + 1):
            substr = input_string[str_index:end]
            if substr in str_map:
                continue
            pattern_map[char] = substr
            str_map[substr] = char
            if backtrack(pattern_index + 1, end):
                return True
            del pattern_map[char]
            del str_map[substr]
        return False

    pattern_map: dict[str, str] = {}
    str_map: dict[str, str] = {}
    return backtrack(0, 0)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: backtracking/minimax.py
================================================
"""
Minimax helps to achieve maximum score in a game by checking all possible moves
depth is current depth in game tree.

nodeIndex is index of current node in scores[].
if move is of maximizer return true else false
leaves of game tree is stored in scores[]
height is maximum height of Game tree
"""

from __future__ import annotations

import math


def minimax(
    depth: int, node_index: int, is_max: bool, scores: list[int], height: float
) -> int:
    """
    This function implements the minimax algorithm, which helps achieve the optimal
    score for a player in a two-player game by checking all possible moves.
    If the player is the maximizer, then the score is maximized.
    If the player is the minimizer, then the score is minimized.

    Parameters:
    - depth: Current depth in the game tree.
    - node_index: Index of the current node in the scores list.
    - is_max: A boolean indicating whether the current move
              is for the maximizer (True) or minimizer (False).
    - scores: A list containing the scores of the leaves of the game tree.
    - height: The maximum height of the game tree.

    Returns:
    - An integer representing the optimal score for the current player.

    >>> import math
    >>> scores = [90, 23, 6, 33, 21, 65, 123, 34423]
    >>> height = math.log(len(scores), 2)
    >>> minimax(0, 0, True, scores, height)
    65
    >>> minimax(-1, 0, True, scores, height)
    Traceback (most recent call last):
        ...
    ValueError: Depth cannot be less than 0
    >>> minimax(0, 0, True, [], 2)
    Traceback (most recent call last):
        ...
    ValueError: Scores cannot be empty
    >>> scores = [3, 5, 2, 9, 12, 5, 23, 23]
    >>> height = math.log(len(scores), 2)
    >>> minimax(0, 0, True, scores, height)
    12
    """

    if depth < 0:
        raise ValueError("Depth cannot be less than 0")
    if len(scores) == 0:
        raise ValueError("Scores cannot be empty")

    # Base case: If the current depth equals the height of the tree,
    # return the score of the current node.
    if depth == height:
        return scores[node_index]

    # If it's the maximizer's turn, choose the maximum score
    # between the two possible moves.
    if is_max:
        return max(
            minimax(depth + 1, node_index * 2, False, scores, height),
            minimax(depth + 1, node_index * 2 + 1, False, scores, height),
        )

    # If it's the minimizer's turn, choose the minimum score
    # between the two possible moves.
    return min(
        minimax(depth + 1, node_index * 2, True, scores, height),
        minimax(depth + 1, node_index * 2 + 1, True, scores, height),
    )


def main() -> None:
    # Sample scores and height calculation
    scores = [90, 23, 6, 33, 21, 65, 123, 34423]
    height = math.log(len(scores), 2)

    # Calculate and print the optimal value using the minimax algorithm
    print("Optimal value : ", end="")
    print(minimax(0, 0, True, scores, height))


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: backtracking/n_queens.py
================================================
"""

The nqueens problem is of placing N queens on a N * N
chess board such that no queen can attack any other queens placed
on that chess board.
This means that one queen cannot have any other queen on its horizontal, vertical and
diagonal lines.

"""

from __future__ import annotations

solution = []


def is_safe(board: list[list[int]], row: int, column: int) -> bool:
    """
    This function returns a boolean value True if it is safe to place a queen there
    considering the current state of the board.

    Parameters:
    board (2D matrix): The chessboard
    row, column: Coordinates of the cell on the board

    Returns:
    Boolean Value

    >>> is_safe([[0, 0, 0], [0, 0, 0], [0, 0, 0]], 1, 1)
    True
    >>> is_safe([[0, 1, 0], [0, 0, 0], [0, 0, 0]], 1, 1)
    False
    >>> is_safe([[1, 0, 0], [0, 0, 0], [0, 0, 0]], 1, 1)
    False
    >>> is_safe([[0, 0, 1], [0, 0, 0], [0, 0, 0]], 1, 1)
    False
    >>> is_safe([[1, 0, 0], [0, 0, 0], [0, 0, 0]], 1, 2)
    True
    >>> is_safe([[1, 0, 0], [0, 0, 0], [0, 0, 0]], 2, 1)
    True
    >>> is_safe([[0, 0, 0], [1, 0, 0], [0, 0, 0]], 0, 2)
    True
    >>> is_safe([[0, 0, 0], [1, 0, 0], [0, 0, 0]], 2, 2)
    True
    """

    n = len(board)  # Size of the board

    # Check if there is any queen in the same upper column,
    # left upper diagonal and right upper diagonal
    return (
        all(board[i][j] != 1 for i, j in zip(range(row), [column] * row))
        and all(
            board[i][j] != 1
            for i, j in zip(range(row - 1, -1, -1), range(column - 1, -1, -1))
        )
        and all(
            board[i][j] != 1
            for i, j in zip(range(row - 1, -1, -1), range(column + 1, n))
        )
    )


def solve(board: list[list[int]], row: int) -> bool:
    """
    This function creates a state space tree and calls the safe function until it
    receives a False Boolean and terminates that branch and backtracks to the next
    possible solution branch.
    """
    if row >= len(board):
        """
        If the row number exceeds N, we have a board with a successful combination
        and that combination is appended to the solution list and the board is printed.
        """
        solution.append(board)
        printboard(board)
        print()
        return True
    for i in range(len(board)):
        """
        For every row, it iterates through each column to check if it is feasible to
        place a queen there.
        If all the combinations for that particular branch are successful, the board is
        reinitialized for the next possible combination.
        """
        if is_safe(board, row, i):
            board[row][i] = 1
            solve(board, row + 1)
            board[row][i] = 0
    return False


def printboard(board: list[list[int]]) -> None:
    """
    Prints the boards that have a successful combination.
    """
    for i in range(len(board)):
        for j in range(len(board)):
            if board[i][j] == 1:
                print("Q", end=" ")  # Queen is present
            else:
                print(".", end=" ")  # Empty cell
        print()


# Number of queens (e.g., n=8 for an 8x8 board)
n = 8
board = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total number of solutions are:", len(solution))


================================================
FILE: backtracking/n_queens_math.py
================================================
r"""
Problem:

The n queens problem is: placing N queens on a N * N chess board such that no queen
can attack any other queens placed on that chess board.  This means that one queen
cannot have any other queen on its horizontal, vertical and diagonal lines.

Solution:

To solve this problem we will use simple math. First we know the queen can move in all
the possible ways, we can simplify it in this: vertical, horizontal, diagonal left and
 diagonal right.

We can visualize it like this:

left diagonal = \
right diagonal = /

On a chessboard vertical movement could be the rows and horizontal movement could be
the columns.

In programming we can use an array, and in this array each index could be the rows and
each value in the array could be the column. For example:

    . Q . .     We have this chessboard with one queen in each column and each queen
    . . . Q     can't attack to each other.
    Q . . .     The array for this example would look like this: [1, 3, 0, 2]
    . . Q .

So if we use an array and we verify that each value in the array is different to each
other we know that at least the queens can't attack each other in horizontal and
vertical.

At this point we have it halfway completed and we will treat the chessboard as a
Cartesian plane.  Hereinafter we are going to remember basic math, so in the school we
learned this formula:

    Slope of a line:

           y2 - y1
     m = ----------
          x2 - x1

This formula allow us to get the slope. For the angles 45º (right diagonal) and 135º
(left diagonal) this formula gives us m = 1, and m = -1 respectively.

See::
https://www.enotes.com/homework-help/write-equation-line-that-hits-origin-45-degree-1474860

Then we have this other formula:

Slope intercept:

y = mx + b

b is where the line crosses the Y axis (to get more information see:
https://www.mathsisfun.com/y_intercept.html), if we change the formula to solve for b
we would have:

y - mx = b

And since we already have the m values for the angles 45º and 135º, this formula would
look like this:

45º: y - (1)x = b
45º: y - x = b

135º: y - (-1)x = b
135º: y + x = b

y = row
x = column

Applying these two formulas we can check if a queen in some position is being attacked
for another one or vice versa.

"""

from __future__ import annotations


def depth_first_search(
    possible_board: list[int],
    diagonal_right_collisions: list[int],
    diagonal_left_collisions: list[int],
    boards: list[list[str]],
    n: int,
) -> None:
    """
    >>> boards = []
    >>> depth_first_search([], [], [], boards, 4)
    >>> for board in boards:
    ...     print(board)
    ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
    ['. . Q . ', 'Q . . . ', '. . . Q ', '. Q . . ']
    """

    # Get next row in the current board (possible_board) to fill it with a queen
    row = len(possible_board)

    # If row is equal to the size of the board it means there are a queen in each row in
    # the current board (possible_board)
    if row == n:
        # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
        # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
        boards.append([". " * i + "Q " + ". " * (n - 1 - i) for i in possible_board])
        return

    # We iterate each column in the row to find all possible results in each row
    for col in range(n):
        # We apply that we learned previously. First we check that in the current board
        # (possible_board) there are not other same value because if there is it means
        # that there are a collision in vertical. Then we apply the two formulas we
        # learned before:
        #
        # 45º: y - x = b or 45: row - col = b
        # 135º: y + x = b or row + col = b.
        #
        # And we verify if the results of this two formulas not exist in their variables
        # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
        #
        # If any or these are True it means there is a collision so we continue to the
        # next value in the for loop.
        if (
            col in possible_board
            or row - col in diagonal_right_collisions
            or row + col in diagonal_left_collisions
        ):
            continue

        # If it is False we call dfs function again and we update the inputs
        depth_first_search(
            [*possible_board, col],
            [*diagonal_right_collisions, row - col],
            [*diagonal_left_collisions, row + col],
            boards,
            n,
        )


def n_queens_solution(n: int) -> None:
    boards: list[list[str]] = []
    depth_first_search([], [], [], boards, n)

    # Print all the boards
    for board in boards:
        for column in board:
            print(column)
        print("")

    print(len(boards), "solutions were found.")


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    n_queens_solution(4)


================================================
FILE: backtracking/power_sum.py
================================================
"""
Problem source: https://www.hackerrank.com/challenges/the-power-sum/problem
Find the number of ways that a given integer X, can be expressed as the sum
of the Nth powers of unique, natural numbers. For example, if X=13 and N=2.
We have to find all combinations of unique squares adding up to 13.
The only solution is 2^2+3^2. Constraints: 1<=X<=1000, 2<=N<=10.
"""


def backtrack(
    needed_sum: int,
    power: int,
    current_number: int,
    current_sum: int,
    solutions_count: int,
) -> tuple[int, int]:
    """
    >>> backtrack(13, 2, 1, 0, 0)
    (0, 1)
    >>> backtrack(10, 2, 1, 0, 0)
    (0, 1)
    >>> backtrack(10, 3, 1, 0, 0)
    (0, 0)
    >>> backtrack(20, 2, 1, 0, 0)
    (0, 1)
    >>> backtrack(15, 10, 1, 0, 0)
    (0, 0)
    >>> backtrack(16, 2, 1, 0, 0)
    (0, 1)
    >>> backtrack(20, 1, 1, 0, 0)
    (0, 64)
    """
    if current_sum == needed_sum:
        # If the sum of the powers is equal to needed_sum, then we have a solution.
        solutions_count += 1
        return current_sum, solutions_count

    i_to_n = current_number**power
    if current_sum + i_to_n <= needed_sum:
        # If the sum of the powers is less than needed_sum, then continue adding powers.
        current_sum += i_to_n
        current_sum, solutions_count = backtrack(
            needed_sum, power, current_number + 1, current_sum, solutions_count
        )
        current_sum -= i_to_n
    if i_to_n < needed_sum:
        # If the power of i is less than needed_sum, then try with the next power.
        current_sum, solutions_count = backtrack(
            needed_sum, power, current_number + 1, current_sum, solutions_count
        )
    return current_sum, solutions_count


def solve(needed_sum: int, power: int) -> int:
    """
    >>> solve(13, 2)
    1
    >>> solve(10, 2)
    1
    >>> solve(10, 3)
    0
    >>> solve(20, 2)
    1
    >>> solve(15, 10)
    0
    >>> solve(16, 2)
    1
    >>> solve(20, 1)
    Traceback (most recent call last):
        ...
    ValueError: Invalid input
    needed_sum must be between 1 and 1000, power between 2 and 10.
    >>> solve(-10, 5)
    Traceback (most recent call last):
        ...
    ValueError: Invalid input
    needed_sum must be between 1 and 1000, power between 2 and 10.
    """
    if not (1 <= needed_sum <= 1000 and 2 <= power <= 10):
        raise ValueError(
            "Invalid input\n"
            "needed_sum must be between 1 and 1000, power between 2 and 10."
        )

    return backtrack(needed_sum, power, 1, 0, 0)[1]  # Return the solutions_count


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: backtracking/rat_in_maze.py
================================================
from __future__ import annotations


def solve_maze(
    maze: list[list[int]],
    source_row: int,
    source_column: int,
    destination_row: int,
    destination_column: int,
) -> list[list[int]]:
    """
    This method solves the "rat in maze" problem.
    Parameters :
        - maze: A two dimensional matrix of zeros and ones.
        - source_row: The row index of the starting point.
        - source_column: The column index of the starting point.
        - destination_row: The row index of the destination point.
        - destination_column: The column index of the destination point.
    Returns:
        - solution: A 2D matrix representing the solution path if it exists.
    Raises:
        - ValueError: If no solution exists or if the source or
            destination coordinates are invalid.
    Description:
        This method navigates through a maze represented as an n by n matrix,
        starting from a specified source cell and
        aiming to reach a destination cell.
        The maze consists of walls (1s) and open paths (0s).
        By providing custom row and column values, the source and destination
        cells can be adjusted.
    >>> maze = [[0, 1, 0, 1, 1],
    ...         [0, 0, 0, 0, 0],
    ...         [1, 0, 1, 0, 1],
    ...         [0, 0, 1, 0, 0],
    ...         [1, 0, 0, 1, 0]]
    >>> solve_maze(maze,0,0,len(maze)-1,len(maze)-1)    # doctest: +NORMALIZE_WHITESPACE
    [[0, 1, 1, 1, 1],
    [0, 0, 0, 0, 1],
    [1, 1, 1, 0, 1],
    [1, 1, 1, 0, 0],
    [1, 1, 1, 1, 0]]

    Note:
        In the output maze, the zeros (0s) represent one of the possible
        paths from the source to the destination.

    >>> maze = [[0, 1, 0, 1, 1],
    ...         [0, 0, 0, 0, 0],
    ...         [0, 0, 0, 0, 1],
    ...         [0, 0, 0, 0, 0],
    ...         [0, 0, 0, 0, 0]]
    >>> solve_maze(maze,0,0,len(maze)-1,len(maze)-1)    # doctest: +NORMALIZE_WHITESPACE
    [[0, 1, 1, 1, 1],
    [0, 1, 1, 1, 1],
    [0, 1, 1, 1, 1],
    [0, 1, 1, 1, 1],
    [0, 0, 0, 0, 0]]

    >>> maze = [[0, 0, 0],
    ...         [0, 1, 0],
    ...         [1, 0, 0]]
    >>> solve_maze(maze,0,0,len(maze)-1,len(maze)-1)    # doctest: +NORMALIZE_WHITESPACE
    [[0, 0, 0],
    [1, 1, 0],
    [1, 1, 0]]

    >>> maze = [[1, 0, 0],
    ...         [0, 1, 0],
    ...         [1, 0, 0]]
    >>> solve_maze(maze,0,1,len(maze)-1,len(maze)-1)    # doctest: +NORMALIZE_WHITESPACE
    [[1, 0, 0],
    [1, 1, 0],
    [1, 1, 0]]

    >>> maze = [[1, 1, 0, 0, 1, 0, 0, 1],
    ...         [1, 0, 1, 0, 0, 1, 1, 1],
    ...         [0, 1, 0, 1, 0, 0, 1, 0],
    ...         [1, 1, 1, 0, 0, 1, 0, 1],
    ...         [0, 1, 0, 0, 1, 0, 1, 1],
    ...         [0, 0, 0, 1, 1, 1, 0, 1],
    ...         [0, 1, 0, 1, 0, 1, 1, 1],
    ...         [1, 1, 0, 0, 0, 0, 0, 1]]
    >>> solve_maze(maze,0,2,len(maze)-1,2)  # doctest: +NORMALIZE_WHITESPACE
    [[1, 1, 0, 0, 1, 1, 1, 1],
    [1, 1, 1, 0, 0, 1, 1, 1],
    [1, 1, 1, 1, 0, 1, 1, 1],
    [1, 1, 1, 0, 0, 1, 1, 1],
    [1, 1, 0, 0, 1, 1, 1, 1],
    [1, 1, 0, 1, 1, 1, 1, 1],
    [1, 1, 0, 1, 1, 1, 1, 1],
    [1, 1, 0, 1, 1, 1, 1, 1]]
    >>> maze = [[1, 0, 0],
    ...         [0, 1, 1],
    ...         [1, 0, 1]]
    >>> solve_maze(maze,0,1,len(maze)-1,len(maze)-1)
    Traceback (most recent call last):
        ...
    ValueError: No solution exists!

    >>> maze = [[0, 0],
    ...         [1, 1]]
    >>> solve_maze(maze,0,0,len(maze)-1,len(maze)-1)
    Traceback (most recent call last):
        ...
    ValueError: No solution exists!

    >>> maze = [[0, 1],
    ...         [1, 0]]
    >>> solve_maze(maze,2,0,len(maze)-1,len(maze)-1)
    Traceback (most recent call last):
        ...
    ValueError: Invalid source or destination coordinates

    >>> maze = [[1, 0, 0],
    ...         [0, 1, 0],
    ...         [1, 0, 0]]
    >>> solve_maze(maze,0,1,len(maze),len(maze)-1)
    Traceback (most recent call last):
        ...
    ValueError: Invalid source or destination coordinates
    """
    size = len(maze)
    # Check if source and destination coordinates are Invalid.
    if not (0 <= source_row <= size - 1 and 0 <= source_column <= size - 1) or (
        not (0 <= destination_row <= size - 1 and 0 <= destination_column <= size - 1)
    ):
        raise ValueError("Invalid source or destination coordinates")
    # We need to create solution object to save path.
    solutions = [[1 for _ in range(size)] for _ in range(size)]
    solved = run_maze(
        maze, source_row, source_column, destination_row, destination_column, solutions
    )
    if solved:
        return solutions
    else:
        raise ValueError("No solution exists!")


def run_maze(
    maze: list[list[int]],
    i: int,
    j: int,
    destination_row: int,
    destination_column: int,
    solutions: list[list[int]],
) -> bool:
    """
    This method is recursive starting from (i, j) and going in one of four directions:
    up, down, left, right.
    If a path is found to destination it returns True otherwise it returns False.
    Parameters
        maze: A two dimensional matrix of zeros and ones.
        i, j : coordinates of matrix
        solutions: A two dimensional matrix of solutions.
    Returns:
        Boolean if path is found True, Otherwise False.
    """
    size = len(maze)
    # Final check point.
    if i == destination_row and j == destination_column and maze[i][j] == 0:
        solutions[i][j] = 0
        return True

    lower_flag = (not i < 0) and (not j < 0)  # Check lower bounds
    upper_flag = (i < size) and (j < size)  # Check upper bounds

    if lower_flag and upper_flag:
        # check for already visited and block points.
        block_flag = (solutions[i][j]) and (not maze[i][j])
        if block_flag:
            # check visited
            solutions[i][j] = 0

            # check for directions
            if (
                run_maze(maze, i + 1, j, destination_row, destination_column, solutions)
                or run_maze(
                    maze, i, j + 1, destination_row, destination_column, solutions
                )
                or run_maze(
                    maze, i - 1, j, destination_row, destination_column, solutions
                )
                or run_maze(
                    maze, i, j - 1, destination_row, destination_column, solutions
                )
            ):
                return True

            solutions[i][j] = 1
            return False
    return False


if __name__ == "__main__":
    import doctest

    doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)


================================================
FILE: backtracking/sudoku.py
================================================
"""
Given a partially filled 9x9 2D array, the objective is to fill a 9x9
square grid with digits numbered 1 to 9, so that every row, column, and
and each of the nine 3x3 sub-grids contains all of the digits.

This can be solved using Backtracking and is similar to n-queens.
We check to see if a cell is safe or not and recursively call the
function on the next column to see if it returns True. if yes, we
have solved the puzzle. else, we backtrack and place another number
in that cell and repeat this process.
"""

from __future__ import annotations

Matrix = list[list[int]]

# assigning initial values to the grid
initial_grid: Matrix = [
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
no_solution: Matrix = [
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]


def is_safe(grid: Matrix, row: int, column: int, n: int) -> bool:
    """
    This function checks the grid to see if each row,
    column, and the 3x3 subgrids contain the digit 'n'.
    It returns False if it is not 'safe' (a duplicate digit
    is found) else returns True if it is 'safe'
    """
    for i in range(9):
        if n in {grid[row][i], grid[i][column]}:
            return False

    for i in range(3):
        for j in range(3):
            if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
                return False

    return True


def find_empty_location(grid: Matrix) -> tuple[int, int] | None:
    """
    This function finds an empty location so that we can assign a number
    for that particular row and column.
    """
    for i in range(9):
        for j in range(9):
            if grid[i][j] == 0:
                return i, j
    return None


def sudoku(grid: Matrix) -> Matrix | None:
    """
    Takes a partially filled-in grid and attempts to assign values to
    all unassigned locations in such a way to meet the requirements
    for Sudoku solution (non-duplication across rows, columns, and boxes)

    >>> sudoku(initial_grid)  # doctest: +NORMALIZE_WHITESPACE
    [[3, 1, 6, 5, 7, 8, 4, 9, 2],
     [5, 2, 9, 1, 3, 4, 7, 6, 8],
     [4, 8, 7, 6, 2, 9, 5, 3, 1],
     [2, 6, 3, 4, 1, 5, 9, 8, 7],
     [9, 7, 4, 8, 6, 3, 1, 2, 5],
     [8, 5, 1, 7, 9, 2, 6, 4, 3],
     [1, 3, 8, 9, 4, 7, 2, 5, 6],
     [6, 9, 2, 3, 5, 1, 8, 7, 4],
     [7, 4, 5, 2, 8, 6, 3, 1, 9]]
     >>> sudoku(no_solution) is None
     True
    """
    if location := find_empty_location(grid):
        row, column = location
    else:
        # If the location is ``None``, then the grid is solved.
        return grid

    for digit in range(1, 10):
        if is_safe(grid, row, column, digit):
            grid[row][column] = digit

            if sudoku(grid) is not None:
                return grid

            grid[row][column] = 0

    return None


def print_solution(grid: Matrix) -> None:
    """
    A function to print the solution in the form
    of a 9x9 grid
    """
    for row in grid:
        for cell in row:
            print(cell, end=" ")
        print()


if __name__ == "__main__":
    # make a copy of grid so that you can compare with the unmodified grid
    for example_grid in (initial_grid, no_solution):
        print("\nExample grid:\n" + "=" * 20)
        print_solution(example_grid)
        print("\nExample grid solution:")
        solution = sudoku(example_grid)
        if solution is not None:
            print_solution(solution)
        else:
            print("Cannot find a solution.")


================================================
FILE: backtracking/sum_of_subsets.py
================================================
"""
The sum-of-subsets problem states that a set of non-negative integers, and a
value M, determine all possible subsets of the given set whose summation sum
equal to given M.

Summation of the chosen numbers must be equal to given number M and one number
can be used only once.
"""


def generate_sum_of_subsets_solutions(nums: list[int], max_sum: int) -> list[list[int]]:
    """
    The main function. For list of numbers 'nums' find the subsets with sum
    equal to 'max_sum'

    >>> generate_sum_of_subsets_solutions(nums=[3, 34, 4, 12, 5, 2], max_sum=9)
    [[3, 4, 2], [4, 5]]
    >>> generate_sum_of_subsets_solutions(nums=[3, 34, 4, 12, 5, 2], max_sum=3)
    [[3]]
    >>> generate_sum_of_subsets_solutions(nums=[3, 34, 4, 12, 5, 2], max_sum=1)
    []
    """

    result: list[list[int]] = []
    path: list[int] = []
    num_index = 0
    remaining_nums_sum = sum(nums)
    create_state_space_tree(nums, max_sum, num_index, path, result, remaining_nums_sum)
    return result


def create_state_space_tree(
    nums: list[int],
    max_sum: int,
    num_index: int,
    path: list[int],
    result: list[list[int]],
    remaining_nums_sum: int,
) -> None:
    """
    Creates a state space tree to iterate through each branch using DFS.
    It terminates the branching of a node when any of the two conditions
    given below satisfy.
    This algorithm follows depth-fist-search and backtracks when the node is not
    branchable.

    >>> path = []
    >>> result = []
    >>> create_state_space_tree(
    ...     nums=[1],
    ...     max_sum=1,
    ...     num_index=0,
    ...     path=path,
    ...     result=result,
    ...     remaining_nums_sum=1)
    >>> path
    []
    >>> result
    [[1]]
    """

    if sum(path) > max_sum or (remaining_nums_sum + sum(path)) < max_sum:
        return
    if sum(path) == max_sum:
        result.append(path)
        return
    for index in range(num_index, len(nums)):
        create_state_space_tree(
            nums,
            max_sum,
            index + 1,
            [*path, nums[index]],
            result,
            remaining_nums_sum - nums[index],
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: backtracking/word_break.py
================================================
"""
Word Break Problem is a well-known problem in computer science.
Given a string and a dictionary of words, the task is to determine if
the string can be segmented into a sequence of one or more dictionary words.

Wikipedia: https://en.wikipedia.org/wiki/Word_break_problem
"""


def backtrack(input_string: str, word_dict: set[str], start: int) -> bool:
    """
    Helper function that uses backtracking to determine if a valid
    word segmentation is possible starting from index 'start'.

    Parameters:
    input_string (str): The input string to be segmented.
    word_dict (set[str]): A set of valid dictionary words.
    start (int): The starting index of the substring to be checked.

    Returns:
    bool: True if a valid segmentation is possible, otherwise False.

    Example:
    >>> backtrack("leetcode", {"leet", "code"}, 0)
    True

    >>> backtrack("applepenapple", {"apple", "pen"}, 0)
    True

    >>> backtrack("catsandog", {"cats", "dog", "sand", "and", "cat"}, 0)
    False
    """

    # Base case: if the starting index has reached the end of the string
    if start == len(input_string):
        return True

    # Try every possible substring from 'start' to 'end'
    for end in range(start + 1, len(input_string) + 1):
        if input_string[start:end] in word_dict and backtrack(
            input_string, word_dict, end
        ):
            return True

    return False


def word_break(input_string: str, word_dict: set[str]) -> bool:
    """
    Determines if the input string can be segmented into a sequence of
    valid dictionary words using backtracking.

    Parameters:
    input_string (str): The input string to segment.
    word_dict (set[str]): The set of valid words.

    Returns:
    bool: True if the string can be segmented into valid words, otherwise False.

    Example:
    >>> word_break("leetcode", {"leet", "code"})
    True

    >>> word_break("applepenapple", {"apple", "pen"})
    True

    >>> word_break("catsandog", {"cats", "dog", "sand", "and", "cat"})
    False

    >>> word_break("applepenapple", {})
    False
    """

    return backtrack(input_string, word_dict, 0)


================================================
FILE: backtracking/word_ladder.py
================================================
"""
Word Ladder is a classic problem in computer science.
The problem is to transform a start word into an end word
by changing one letter at a time.
Each intermediate word must be a valid word from a given list of words.
The goal is to find a transformation sequence
from the start word to the end word.

Wikipedia: https://en.wikipedia.org/wiki/Word_ladder
"""

import string


def backtrack(
    current_word: str, path: list[str], end_word: str, word_set: set[str]
) -> list[str]:
    """
    Helper function to perform backtracking to find the transformation
    from the current_word to the end_word.

    Parameters:
    current_word (str): The current word in the transformation sequence.
    path (list[str]): The list of transformations from begin_word to current_word.
    end_word (str): The target word for transformation.
    word_set (set[str]): The set of valid words for transformation.

    Returns:
    list[str]: The list of transformations from begin_word to end_word.
               Returns an empty list if there is no valid
                transformation from current_word to end_word.

    Example:
    >>> backtrack("hit", ["hit"], "cog", {"hot", "dot", "dog", "lot", "log", "cog"})
    ['hit', 'hot', 'dot', 'lot', 'log', 'cog']

    >>> backtrack("hit", ["hit"], "cog", {"hot", "dot", "dog", "lot", "log"})
    []

    >>> backtrack("lead", ["lead"], "gold", {"load", "goad", "gold", "lead", "lord"})
    ['lead', 'lead', 'load', 'goad', 'gold']

    >>> backtrack("game", ["game"], "code", {"came", "cage", "code", "cade", "gave"})
    ['game', 'came', 'cade', 'code']
    """

    # Base case: If the current word is the end word, return the path
    if current_word == end_word:
        return path

    # Try all possible single-letter transformations
    for i in range(len(current_word)):
        for c in string.ascii_lowercase:  # Try changing each letter
            transformed_word = current_word[:i] + c + current_word[i + 1 :]
            if transformed_word in word_set:
                word_set.remove(transformed_word)
                # Recur with the new word added to the path
                result = backtrack(
                    transformed_word, [*path, transformed_word], end_word, word_set
                )
                if result:  # valid transformation found
                    return result
                word_set.add(transformed_word)  # backtrack

    return []  # No valid transformation found


def word_ladder(begin_word: str, end_word: str, word_set: set[str]) -> list[str]:
    """
    Solve the Word Ladder problem using Backtracking and return
    the list of transformations from begin_word to end_word.

    Parameters:
    begin_word (str): The word from which the transformation starts.
    end_word (str): The target word for transformation.
    word_list (list[str]): The list of valid words for transformation.

    Returns:
    list[str]: The list of transformations from begin_word to end_word.
               Returns an empty list if there is no valid transformation.

    Example:
    >>> word_ladder("hit", "cog", ["hot", "dot", "dog", "lot", "log", "cog"])
    ['hit', 'hot', 'dot', 'lot', 'log', 'cog']

    >>> word_ladder("hit", "cog", ["hot", "dot", "dog", "lot", "log"])
    []

    >>> word_ladder("lead", "gold", ["load", "goad", "gold", "lead", "lord"])
    ['lead', 'lead', 'load', 'goad', 'gold']

    >>> word_ladder("game", "code", ["came", "cage", "code", "cade", "gave"])
    ['game', 'came', 'cade', 'code']
    """

    if end_word not in word_set:  # no valid transformation possible
        return []

    # Perform backtracking starting from the begin_word
    return backtrack(begin_word, [begin_word], end_word, word_set)


================================================
FILE: backtracking/word_search.py
================================================
"""
Author  : Alexander Pantyukhin
Date    : November 24, 2022

Task:
Given an m x n grid of characters board and a string word,
return true if word exists in the grid.

The word can be constructed from letters of sequentially adjacent cells,
where adjacent cells are horizontally or vertically neighboring.
The same letter cell may not be used more than once.

Example:

Matrix:
---------
|A|B|C|E|
|S|F|C|S|
|A|D|E|E|
---------

Word:
"ABCCED"

Result:
True

Implementation notes: Use backtracking approach.
At each point, check all neighbors to try to find the next letter of the word.

leetcode: https://leetcode.com/problems/word-search/

"""


def get_point_key(len_board: int, len_board_column: int, row: int, column: int) -> int:
    """
    Returns the hash key of matrix indexes.

    >>> get_point_key(10, 20, 1, 0)
    200
    """

    return len_board * len_board_column * row + column


def exits_word(
    board: list[list[str]],
    word: str,
    row: int,
    column: int,
    word_index: int,
    visited_points_set: set[int],
) -> bool:
    """
    Return True if it's possible to search the word suffix
    starting from the word_index.

    >>> exits_word([["A"]], "B", 0, 0, 0, set())
    False
    """

    if board[row][column] != word[word_index]:
        return False

    if word_index == len(word) - 1:
        return True

    traverts_directions = [(0, 1), (0, -1), (-1, 0), (1, 0)]
    len_board = len(board)
    len_board_column = len(board[0])
    for direction in traverts_directions:
        next_i = row + direction[0]
        next_j = column + direction[1]
        if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
            continue

        key = get_point_key(len_board, len_board_column, next_i, next_j)
        if key in visited_points_set:
            continue

        visited_points_set.add(key)
        if exits_word(board, word, next_i, next_j, word_index + 1, visited_points_set):
            return True

        visited_points_set.remove(key)

    return False


def word_exists(board: list[list[str]], word: str) -> bool:
    """
    >>> word_exists([["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], "ABCCED")
    True
    >>> word_exists([["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], "SEE")
    True
    >>> word_exists([["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], "ABCB")
    False
    >>> word_exists([["A"]], "A")
    True
    >>> word_exists([["B", "A", "A"], ["A", "A", "A"], ["A", "B", "A"]], "ABB")
    False
    >>> word_exists([["A"]], 123)
    Traceback (most recent call last):
        ...
    ValueError: The word parameter should be a string of length greater than 0.
    >>> word_exists([["A"]], "")
    Traceback (most recent call last):
        ...
    ValueError: The word parameter should be a string of length greater than 0.
    >>> word_exists([[]], "AB")
    Traceback (most recent call last):
        ...
    ValueError: The board should be a non empty matrix of single chars strings.
    >>> word_exists([], "AB")
    Traceback (most recent call last):
        ...
    ValueError: The board should be a non empty matrix of single chars strings.
    >>> word_exists([["A"], [21]], "AB")
    Traceback (most recent call last):
        ...
    ValueError: The board should be a non empty matrix of single chars strings.
    """

    # Validate board
    board_error_message = (
        "The board should be a non empty matrix of single chars strings."
    )

    len_board = len(board)
    if not isinstance(board, list) or len(board) == 0:
        raise ValueError(board_error_message)

    for row in board:
        if not isinstance(row, list) or len(row) == 0:
            raise ValueError(board_error_message)

        for item in row:
            if not isinstance(item, str) or len(item) != 1:
                raise ValueError(board_error_message)

    # Validate word
    if not isinstance(word, str) or len(word) == 0:
        raise ValueError(
            "The word parameter should be a string of length greater than 0."
        )

    len_board_column = len(board[0])
    for i in range(len_board):
        for j in range(len_board_column):
            if exits_word(
                board, word, i, j, 0, {get_point_key(len_board, len_board_column, i, j)}
            ):
                return True

    return False


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/README.md
================================================
# Bit manipulation

Bit manipulation is the act of manipulating bits to detect errors (hamming code), encrypts and decrypts messages (more on that in the 'ciphers' folder) or just do anything at the lowest level of your computer.

* <https://en.wikipedia.org/wiki/Bit_manipulation>
* <https://docs.python.org/3/reference/expressions.html#binary-bitwise-operations>
* <https://docs.python.org/3/reference/expressions.html#unary-arithmetic-and-bitwise-operations>
* <https://docs.python.org/3/library/stdtypes.html#bitwise-operations-on-integer-types>
* <https://wiki.python.org/moin/BitManipulation>
* <https://wiki.python.org/moin/BitwiseOperators>
* <https://www.tutorialspoint.com/python3/bitwise_operators_example.htm>


================================================
FILE: bit_manipulation/__init__.py
================================================


================================================
FILE: bit_manipulation/binary_and_operator.py
================================================
# https://www.tutorialspoint.com/python3/bitwise_operators_example.htm


def binary_and(a: int, b: int) -> str:
    """
    Take in 2 integers, convert them to binary,
    return a binary number that is the
    result of a binary and operation on the integers provided.

    >>> binary_and(25, 32)
    '0b000000'
    >>> binary_and(37, 50)
    '0b100000'
    >>> binary_and(21, 30)
    '0b10100'
    >>> binary_and(58, 73)
    '0b0001000'
    >>> binary_and(0, 255)
    '0b00000000'
    >>> binary_and(256, 256)
    '0b100000000'
    >>> binary_and(0, -1)
    Traceback (most recent call last):
        ...
    ValueError: the value of both inputs must be positive
    >>> binary_and(0, 1.1)
    Traceback (most recent call last):
        ...
    ValueError: Unknown format code 'b' for object of type 'float'
    >>> binary_and("0", "1")
    Traceback (most recent call last):
        ...
    TypeError: '<' not supported between instances of 'str' and 'int'
    """
    if a < 0 or b < 0:
        raise ValueError("the value of both inputs must be positive")

    a_binary = format(a, "b")
    b_binary = format(b, "b")

    max_len = max(len(a_binary), len(b_binary))

    return "0b" + "".join(
        str(int(char_a == "1" and char_b == "1"))
        for char_a, char_b in zip(a_binary.zfill(max_len), b_binary.zfill(max_len))
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/binary_coded_decimal.py
================================================
def binary_coded_decimal(number: int) -> str:
    """
    Find binary coded decimal (bcd) of integer base 10.
    Each digit of the number is represented by a 4-bit binary.
    Example:
    >>> binary_coded_decimal(-2)
    '0b0000'
    >>> binary_coded_decimal(-1)
    '0b0000'
    >>> binary_coded_decimal(0)
    '0b0000'
    >>> binary_coded_decimal(3)
    '0b0011'
    >>> binary_coded_decimal(2)
    '0b0010'
    >>> binary_coded_decimal(12)
    '0b00010010'
    >>> binary_coded_decimal(987)
    '0b100110000111'
    """
    return "0b" + "".join(
        str(bin(int(digit)))[2:].zfill(4) for digit in str(max(0, number))
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/binary_count_setbits.py
================================================
def binary_count_setbits(a: int) -> int:
    """
    Take in 1 integer, return a number that is
    the number of 1's in binary representation of that number.

    >>> binary_count_setbits(25)
    3
    >>> binary_count_setbits(36)
    2
    >>> binary_count_setbits(16)
    1
    >>> binary_count_setbits(58)
    4
    >>> binary_count_setbits(4294967295)
    32
    >>> binary_count_setbits(0)
    0
    >>> binary_count_setbits(-10)
    Traceback (most recent call last):
        ...
    ValueError: Input value must be a positive integer
    >>> binary_count_setbits(0.8)
    Traceback (most recent call last):
        ...
    TypeError: Input value must be a 'int' type
    >>> binary_count_setbits("0")
    Traceback (most recent call last):
        ...
    TypeError: '<' not supported between instances of 'str' and 'int'
    """
    if a < 0:
        raise ValueError("Input value must be a positive integer")
    elif isinstance(a, float):
        raise TypeError("Input value must be a 'int' type")
    return bin(a).count("1")


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/binary_count_trailing_zeros.py
================================================
from math import log2


def binary_count_trailing_zeros(a: int) -> int:
    """
    Take in 1 integer, return a number that is
    the number of trailing zeros in binary representation of that number.

    >>> binary_count_trailing_zeros(25)
    0
    >>> binary_count_trailing_zeros(36)
    2
    >>> binary_count_trailing_zeros(16)
    4
    >>> binary_count_trailing_zeros(58)
    1
    >>> binary_count_trailing_zeros(4294967296)
    32
    >>> binary_count_trailing_zeros(0)
    0
    >>> binary_count_trailing_zeros(-10)
    Traceback (most recent call last):
        ...
    ValueError: Input value must be a positive integer
    >>> binary_count_trailing_zeros(0.8)
    Traceback (most recent call last):
        ...
    TypeError: Input value must be a 'int' type
    >>> binary_count_trailing_zeros("0")
    Traceback (most recent call last):
        ...
    TypeError: '<' not supported between instances of 'str' and 'int'
    """
    if a < 0:
        raise ValueError("Input value must be a positive integer")
    elif isinstance(a, float):
        raise TypeError("Input value must be a 'int' type")
    return 0 if (a == 0) else int(log2(a & -a))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/binary_or_operator.py
================================================
# https://www.tutorialspoint.com/python3/bitwise_operators_example.htm


def binary_or(a: int, b: int) -> str:
    """
    Take in 2 integers, convert them to binary, and return a binary number that is the
    result of a binary or operation on the integers provided.

    >>> binary_or(25, 32)
    '0b111001'
    >>> binary_or(37, 50)
    '0b110111'
    >>> binary_or(21, 30)
    '0b11111'
    >>> binary_or(58, 73)
    '0b1111011'
    >>> binary_or(0, 255)
    '0b11111111'
    >>> binary_or(0, 256)
    '0b100000000'
    >>> binary_or(0, -1)
    Traceback (most recent call last):
        ...
    ValueError: the value of both inputs must be positive
    >>> binary_or(0, 1.1)
    Traceback (most recent call last):
        ...
    TypeError: 'float' object cannot be interpreted as an integer
    >>> binary_or("0", "1")
    Traceback (most recent call last):
        ...
    TypeError: '<' not supported between instances of 'str' and 'int'
    """
    if a < 0 or b < 0:
        raise ValueError("the value of both inputs must be positive")
    a_binary = str(bin(a))[2:]  # remove the leading "0b"
    b_binary = str(bin(b))[2:]
    max_len = max(len(a_binary), len(b_binary))
    return "0b" + "".join(
        str(int("1" in (char_a, char_b)))
        for char_a, char_b in zip(a_binary.zfill(max_len), b_binary.zfill(max_len))
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/binary_shifts.py
================================================
# Information on binary shifts:
# https://docs.python.org/3/library/stdtypes.html#bitwise-operations-on-integer-types
# https://www.interviewcake.com/concept/java/bit-shift


def logical_left_shift(number: int, shift_amount: int) -> str:
    """
    Take in 2 positive integers.
    'number' is the integer to be logically left shifted 'shift_amount' times.
    i.e. (number << shift_amount)
    Return the shifted binary representation.

    >>> logical_left_shift(0, 1)
    '0b00'
    >>> logical_left_shift(1, 1)
    '0b10'
    >>> logical_left_shift(1, 5)
    '0b100000'
    >>> logical_left_shift(17, 2)
    '0b1000100'
    >>> logical_left_shift(1983, 4)
    '0b111101111110000'
    >>> logical_left_shift(1, -1)
    Traceback (most recent call last):
        ...
    ValueError: both inputs must be positive integers
    """
    if number < 0 or shift_amount < 0:
        raise ValueError("both inputs must be positive integers")

    binary_number = str(bin(number))
    binary_number += "0" * shift_amount
    return binary_number


def logical_right_shift(number: int, shift_amount: int) -> str:
    """
    Take in positive 2 integers.
    'number' is the integer to be logically right shifted 'shift_amount' times.
    i.e. (number >>> shift_amount)
    Return the shifted binary representation.

    >>> logical_right_shift(0, 1)
    '0b0'
    >>> logical_right_shift(1, 1)
    '0b0'
    >>> logical_right_shift(1, 5)
    '0b0'
    >>> logical_right_shift(17, 2)
    '0b100'
    >>> logical_right_shift(1983, 4)
    '0b1111011'
    >>> logical_right_shift(1, -1)
    Traceback (most recent call last):
        ...
    ValueError: both inputs must be positive integers
    """
    if number < 0 or shift_amount < 0:
        raise ValueError("both inputs must be positive integers")

    binary_number = str(bin(number))[2:]
    if shift_amount >= len(binary_number):
        return "0b0"
    shifted_binary_number = binary_number[: len(binary_number) - shift_amount]
    return "0b" + shifted_binary_number


def arithmetic_right_shift(number: int, shift_amount: int) -> str:
    """
    Take in 2 integers.
    'number' is the integer to be arithmetically right shifted 'shift_amount' times.
    i.e. (number >> shift_amount)
    Return the shifted binary representation.

    >>> arithmetic_right_shift(0, 1)
    '0b00'
    >>> arithmetic_right_shift(1, 1)
    '0b00'
    >>> arithmetic_right_shift(-1, 1)
    '0b11'
    >>> arithmetic_right_shift(17, 2)
    '0b000100'
    >>> arithmetic_right_shift(-17, 2)
    '0b111011'
    >>> arithmetic_right_shift(-1983, 4)
    '0b111110000100'
    """
    if number >= 0:  # Get binary representation of positive number
        binary_number = "0" + str(bin(number)).strip("-")[2:]
    else:  # Get binary (2's complement) representation of negative number
        binary_number_length = len(bin(number)[3:])  # Find 2's complement of number
        binary_number = bin(abs(number) - (1 << binary_number_length))[3:]
        binary_number = (
            "1" + "0" * (binary_number_length - len(binary_number)) + binary_number
        )

    if shift_amount >= len(binary_number):
        return "0b" + binary_number[0] * len(binary_number)
    return (
        "0b"
        + binary_number[0] * shift_amount
        + binary_number[: len(binary_number) - shift_amount]
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/binary_twos_complement.py
================================================
# Information on 2's complement: https://en.wikipedia.org/wiki/Two%27s_complement


def twos_complement(number: int) -> str:
    """
    Take in a negative integer 'number'.
    Return the two's complement representation of 'number'.

    >>> twos_complement(0)
    '0b0'
    >>> twos_complement(-1)
    '0b11'
    >>> twos_complement(-5)
    '0b1011'
    >>> twos_complement(-17)
    '0b101111'
    >>> twos_complement(-207)
    '0b100110001'
    >>> twos_complement(1)
    Traceback (most recent call last):
        ...
    ValueError: input must be a negative integer
    """
    if number > 0:
        raise ValueError("input must be a negative integer")
    binary_number_length = len(bin(number)[3:])
    twos_complement_number = bin(abs(number) - (1 << binary_number_length))[3:]
    twos_complement_number = (
        (
            "1"
            + "0" * (binary_number_length - len(twos_complement_number))
            + twos_complement_number
        )
        if number < 0
        else "0"
    )
    return "0b" + twos_complement_number


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/binary_xor_operator.py
================================================
# https://www.tutorialspoint.com/python3/bitwise_operators_example.htm


def binary_xor(a: int, b: int) -> str:
    """
    Take in 2 integers, convert them to binary,
    return a binary number that is the
    result of a binary xor operation on the integers provided.

    >>> binary_xor(25, 32)
    '0b111001'
    >>> binary_xor(37, 50)
    '0b010111'
    >>> binary_xor(21, 30)
    '0b01011'
    >>> binary_xor(58, 73)
    '0b1110011'
    >>> binary_xor(0, 255)
    '0b11111111'
    >>> binary_xor(256, 256)
    '0b000000000'
    >>> binary_xor(0, -1)
    Traceback (most recent call last):
        ...
    ValueError: the value of both inputs must be positive
    >>> binary_xor(0, 1.1)
    Traceback (most recent call last):
        ...
    TypeError: 'float' object cannot be interpreted as an integer
    >>> binary_xor("0", "1")
    Traceback (most recent call last):
        ...
    TypeError: '<' not supported between instances of 'str' and 'int'
    """
    if a < 0 or b < 0:
        raise ValueError("the value of both inputs must be positive")

    a_binary = str(bin(a))[2:]  # remove the leading "0b"
    b_binary = str(bin(b))[2:]  # remove the leading "0b"

    max_len = max(len(a_binary), len(b_binary))

    return "0b" + "".join(
        str(int(char_a != char_b))
        for char_a, char_b in zip(a_binary.zfill(max_len), b_binary.zfill(max_len))
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/bitwise_addition_recursive.py
================================================
"""
Calculates the sum of two non-negative integers using bitwise operators
Wikipedia explanation: https://en.wikipedia.org/wiki/Binary_number
"""


def bitwise_addition_recursive(number: int, other_number: int) -> int:
    """
    >>> bitwise_addition_recursive(4, 5)
    9
    >>> bitwise_addition_recursive(8, 9)
    17
    >>> bitwise_addition_recursive(0, 4)
    4
    >>> bitwise_addition_recursive(4.5, 9)
    Traceback (most recent call last):
        ...
    TypeError: Both arguments MUST be integers!
    >>> bitwise_addition_recursive('4', 9)
    Traceback (most recent call last):
        ...
    TypeError: Both arguments MUST be integers!
    >>> bitwise_addition_recursive('4.5', 9)
    Traceback (most recent call last):
        ...
    TypeError: Both arguments MUST be integers!
    >>> bitwise_addition_recursive(-1, 9)
    Traceback (most recent call last):
        ...
    ValueError: Both arguments MUST be non-negative!
    >>> bitwise_addition_recursive(1, -9)
    Traceback (most recent call last):
        ...
    ValueError: Both arguments MUST be non-negative!
    """

    if not isinstance(number, int) or not isinstance(other_number, int):
        raise TypeError("Both arguments MUST be integers!")

    if number < 0 or other_number < 0:
        raise ValueError("Both arguments MUST be non-negative!")

    bitwise_sum = number ^ other_number
    carry = number & other_number

    if carry == 0:
        return bitwise_sum

    return bitwise_addition_recursive(bitwise_sum, carry << 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/count_1s_brian_kernighan_method.py
================================================
def get_1s_count(number: int) -> int:
    """
    Count the number of set bits in a 32 bit integer using Brian Kernighan's way.
    Ref - https://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetKernighan
    >>> get_1s_count(25)
    3
    >>> get_1s_count(37)
    3
    >>> get_1s_count(21)
    3
    >>> get_1s_count(58)
    4
    >>> get_1s_count(0)
    0
    >>> get_1s_count(256)
    1
    >>> get_1s_count(-1)
    Traceback (most recent call last):
        ...
    ValueError: Input must be a non-negative integer
    >>> get_1s_count(0.8)
    Traceback (most recent call last):
        ...
    ValueError: Input must be a non-negative integer
    >>> get_1s_count("25")
    Traceback (most recent call last):
        ...
    ValueError: Input must be a non-negative integer
    """
    if not isinstance(number, int) or number < 0:
        raise ValueError("Input must be a non-negative integer")

    count = 0
    while number:
        # This way we arrive at next set bit (next 1) instead of looping
        # through each bit and checking for 1s hence the
        # loop won't run 32 times it will only run the number of `1` times
        number &= number - 1
        count += 1
    return count


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/count_number_of_one_bits.py
================================================
from timeit import timeit


def get_set_bits_count_using_brian_kernighans_algorithm(number: int) -> int:
    """
    Count the number of set bits in a 32 bit integer
    >>> get_set_bits_count_using_brian_kernighans_algorithm(25)
    3
    >>> get_set_bits_count_using_brian_kernighans_algorithm(37)
    3
    >>> get_set_bits_count_using_brian_kernighans_algorithm(21)
    3
    >>> get_set_bits_count_using_brian_kernighans_algorithm(58)
    4
    >>> get_set_bits_count_using_brian_kernighans_algorithm(0)
    0
    >>> get_set_bits_count_using_brian_kernighans_algorithm(256)
    1
    >>> get_set_bits_count_using_brian_kernighans_algorithm(-1)
    Traceback (most recent call last):
        ...
    ValueError: the value of input must not be negative
    """
    if number < 0:
        raise ValueError("the value of input must not be negative")
    result = 0
    while number:
        number &= number - 1
        result += 1
    return result


def get_set_bits_count_using_modulo_operator(number: int) -> int:
    """
    Count the number of set bits in a 32 bit integer
    >>> get_set_bits_count_using_modulo_operator(25)
    3
    >>> get_set_bits_count_using_modulo_operator(37)
    3
    >>> get_set_bits_count_using_modulo_operator(21)
    3
    >>> get_set_bits_count_using_modulo_operator(58)
    4
    >>> get_set_bits_count_using_modulo_operator(0)
    0
    >>> get_set_bits_count_using_modulo_operator(256)
    1
    >>> get_set_bits_count_using_modulo_operator(-1)
    Traceback (most recent call last):
        ...
    ValueError: the value of input must not be negative
    """
    if number < 0:
        raise ValueError("the value of input must not be negative")
    result = 0
    while number:
        if number % 2 == 1:
            result += 1
        number >>= 1
    return result


def benchmark() -> None:
    """
    Benchmark code for comparing 2 functions, with different length int values.
    Brian Kernighan's algorithm is consistently faster than using modulo_operator.
    """

    def do_benchmark(number: int) -> None:
        setup = "import __main__ as z"
        print(f"Benchmark when {number = }:")
        print(f"{get_set_bits_count_using_modulo_operator(number) = }")
        timing = timeit(
            f"z.get_set_bits_count_using_modulo_operator({number})", setup=setup
        )
        print(f"timeit() runs in {timing} seconds")
        print(f"{get_set_bits_count_using_brian_kernighans_algorithm(number) = }")
        timing = timeit(
            f"z.get_set_bits_count_using_brian_kernighans_algorithm({number})",
            setup=setup,
        )
        print(f"timeit() runs in {timing} seconds")

    for number in (25, 37, 58, 0):
        do_benchmark(number)
        print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()


================================================
FILE: bit_manipulation/excess_3_code.py
================================================
def excess_3_code(number: int) -> str:
    """
    Find excess-3 code of integer base 10.
    Add 3 to all digits in a decimal number then convert to a binary-coded decimal.
    https://en.wikipedia.org/wiki/Excess-3

    >>> excess_3_code(0)
    '0b0011'
    >>> excess_3_code(3)
    '0b0110'
    >>> excess_3_code(2)
    '0b0101'
    >>> excess_3_code(20)
    '0b01010011'
    >>> excess_3_code(120)
    '0b010001010011'
    """
    num = ""
    for digit in str(max(0, number)):
        num += str(bin(int(digit) + 3))[2:].zfill(4)
    return "0b" + num


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/find_previous_power_of_two.py
================================================
def find_previous_power_of_two(number: int) -> int:
    """
    Find the largest power of two that is less than or equal to a given integer.
    https://stackoverflow.com/questions/1322510

    >>> [find_previous_power_of_two(i) for i in range(18)]
    [0, 1, 2, 2, 4, 4, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, 16, 16]
    >>> find_previous_power_of_two(-5)
    Traceback (most recent call last):
        ...
    ValueError: Input must be a non-negative integer
    >>> find_previous_power_of_two(10.5)
    Traceback (most recent call last):
        ...
    ValueError: Input must be a non-negative integer
    """
    if not isinstance(number, int) or number < 0:
        raise ValueError("Input must be a non-negative integer")
    if number == 0:
        return 0
    power = 1
    while power <= number:
        power <<= 1  # Equivalent to multiplying by 2
    return power >> 1 if number > 1 else 1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/find_unique_number.py
================================================
def find_unique_number(arr: list[int]) -> int:
    """
    Given a list of integers where every element appears twice except for one,
    this function returns the element that appears only once using bitwise XOR.

    >>> find_unique_number([1, 1, 2, 2, 3])
    3
    >>> find_unique_number([4, 5, 4, 6, 6])
    5
    >>> find_unique_number([7])
    7
    >>> find_unique_number([10, 20, 10])
    20
    >>> find_unique_number([])
    Traceback (most recent call last):
        ...
    ValueError: input list must not be empty
    >>> find_unique_number([1, 'a', 1])
    Traceback (most recent call last):
        ...
    TypeError: all elements must be integers
    """
    if not arr:
        raise ValueError("input list must not be empty")
    if not all(isinstance(x, int) for x in arr):
        raise TypeError("all elements must be integers")

    result = 0
    for num in arr:
        result ^= num
    return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/gray_code_sequence.py
================================================
def gray_code(bit_count: int) -> list:
    """
    Takes in an integer n and returns a n-bit
    gray code sequence
    An n-bit gray code sequence is a sequence of 2^n
    integers where:

    a) Every integer is between [0,2^n -1] inclusive
    b) The sequence begins with 0
    c) An integer appears at most one times in the sequence
    d)The binary representation of every pair of integers differ
       by exactly one bit
    e) The binary representation of first and last bit also
       differ by exactly one bit

    >>> gray_code(2)
    [0, 1, 3, 2]

    >>> gray_code(1)
    [0, 1]

    >>> gray_code(3)
    [0, 1, 3, 2, 6, 7, 5, 4]

    >>> gray_code(-1)
    Traceback (most recent call last):
        ...
    ValueError: The given input must be positive

    >>> gray_code(10.6)
    Traceback (most recent call last):
        ...
    TypeError: unsupported operand type(s) for <<: 'int' and 'float'
    """

    # bit count represents no. of bits in the gray code
    if bit_count < 0:
        raise ValueError("The given input must be positive")

    # get the generated string sequence
    sequence = gray_code_sequence_string(bit_count)
    #
    # convert them to integers
    for i in range(len(sequence)):
        sequence[i] = int(sequence[i], 2)

    return sequence


def gray_code_sequence_string(bit_count: int) -> list:
    """
    Will output the n-bit grey sequence as a
    string of bits

    >>> gray_code_sequence_string(2)
    ['00', '01', '11', '10']

    >>> gray_code_sequence_string(1)
    ['0', '1']
    """

    # The approach is a recursive one
    # Base case achieved when either n = 0 or n=1
    if bit_count == 0:
        return ["0"]

    if bit_count == 1:
        return ["0", "1"]

    seq_len = 1 << bit_count  # defines the length of the sequence
    # 1<< n is equivalent to 2^n

    # recursive answer will generate answer for n-1 bits
    smaller_sequence = gray_code_sequence_string(bit_count - 1)

    sequence = []

    # append 0 to first half of the smaller sequence generated
    for i in range(seq_len // 2):
        generated_no = "0" + smaller_sequence[i]
        sequence.append(generated_no)

    # append 1 to second half ... start from the end of the list
    for i in reversed(range(seq_len // 2)):
        generated_no = "1" + smaller_sequence[i]
        sequence.append(generated_no)

    return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/highest_set_bit.py
================================================
def get_highest_set_bit_position(number: int) -> int:
    """
    Returns position of the highest set bit of a number.
    Ref - https://graphics.stanford.edu/~seander/bithacks.html#IntegerLogObvious
    >>> get_highest_set_bit_position(25)
    5
    >>> get_highest_set_bit_position(37)
    6
    >>> get_highest_set_bit_position(1)
    1
    >>> get_highest_set_bit_position(4)
    3
    >>> get_highest_set_bit_position(0)
    0
    >>> get_highest_set_bit_position(0.8)
    Traceback (most recent call last):
        ...
    TypeError: Input value must be an 'int' type
    """
    if not isinstance(number, int):
        raise TypeError("Input value must be an 'int' type")

    position = 0
    while number:
        position += 1
        number >>= 1

    return position


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/index_of_rightmost_set_bit.py
================================================
# Reference: https://www.geeksforgeeks.org/position-of-rightmost-set-bit/


def get_index_of_rightmost_set_bit(number: int) -> int:
    """
    Take in a positive integer 'number'.
    Returns the zero-based index of first set bit in that 'number' from right.
    Returns -1, If no set bit found.

    >>> get_index_of_rightmost_set_bit(0)
    -1
    >>> get_index_of_rightmost_set_bit(5)
    0
    >>> get_index_of_rightmost_set_bit(36)
    2
    >>> get_index_of_rightmost_set_bit(8)
    3
    >>> get_index_of_rightmost_set_bit(-18)
    Traceback (most recent call last):
        ...
    ValueError: Input must be a non-negative integer
    >>> get_index_of_rightmost_set_bit('test')
    Traceback (most recent call last):
        ...
    ValueError: Input must be a non-negative integer
    >>> get_index_of_rightmost_set_bit(1.25)
    Traceback (most recent call last):
        ...
    ValueError: Input must be a non-negative integer
    """

    if not isinstance(number, int) or number < 0:
        raise ValueError("Input must be a non-negative integer")

    intermediate = number & ~(number - 1)
    index = 0
    while intermediate:
        intermediate >>= 1
        index += 1
    return index - 1


if __name__ == "__main__":
    """
    Finding the index of rightmost set bit has some very peculiar use-cases,
    especially in finding missing or/and repeating numbers in a list of
    positive integers.
    """
    import doctest

    doctest.testmod(verbose=True)


================================================
FILE: bit_manipulation/is_even.py
================================================
def is_even(number: int) -> bool:
    """
    return true if the input integer is even
    Explanation: Lets take a look at the following decimal to binary conversions
    2 => 10
    14 => 1110
    100 => 1100100
    3 => 11
    13 => 1101
    101 => 1100101
    from the above examples we can observe that
    for all the odd integers there is always 1 set bit at the end
    also, 1 in binary can be represented as 001, 00001, or 0000001
    so for any odd integer n => n&1 is always equals 1 else the integer is even

    >>> is_even(1)
    False
    >>> is_even(4)
    True
    >>> is_even(9)
    False
    >>> is_even(15)
    False
    >>> is_even(40)
    True
    >>> is_even(100)
    True
    >>> is_even(101)
    False
    """
    return number & 1 == 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/is_power_of_two.py
================================================
"""
Author  : Alexander Pantyukhin
Date    : November 1, 2022

Task:
Given a positive int number. Return True if this number is power of 2
or False otherwise.

Implementation notes: Use bit manipulation.
For example if the number is the power of two it's bits representation:
n     = 0..100..00
n - 1 = 0..011..11

n & (n - 1) - no intersections = 0
"""


def is_power_of_two(number: int) -> bool:
    """
    Return True if this number is power of 2 or False otherwise.

    >>> is_power_of_two(0)
    True
    >>> is_power_of_two(1)
    True
    >>> is_power_of_two(2)
    True
    >>> is_power_of_two(4)
    True
    >>> is_power_of_two(6)
    False
    >>> is_power_of_two(8)
    True
    >>> is_power_of_two(17)
    False
    >>> is_power_of_two(-1)
    Traceback (most recent call last):
        ...
    ValueError: number must not be negative
    >>> is_power_of_two(1.2)
    Traceback (most recent call last):
        ...
    TypeError: unsupported operand type(s) for &: 'float' and 'float'

    # Test all powers of 2 from 0 to 10,000
    >>> all(is_power_of_two(int(2 ** i)) for i in range(10000))
    True
    """
    if number < 0:
        raise ValueError("number must not be negative")
    return number & (number - 1) == 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/largest_pow_of_two_le_num.py
================================================
"""
Author  : Naman Sharma
Date    : October 2, 2023

Task:
To Find the largest power of 2 less than or equal to a given number.

Implementation notes: Use bit manipulation.
We start from 1 & left shift the set bit to check if (res<<1)<=number.
Each left bit shift represents a pow of 2.

For example:
number: 15
res:    1   0b1
        2   0b10
        4   0b100
        8   0b1000
        16  0b10000 (Exit)
"""


def largest_pow_of_two_le_num(number: int) -> int:
    """
    Return the largest power of two less than or equal to a number.

    >>> largest_pow_of_two_le_num(0)
    0
    >>> largest_pow_of_two_le_num(1)
    1
    >>> largest_pow_of_two_le_num(-1)
    0
    >>> largest_pow_of_two_le_num(3)
    2
    >>> largest_pow_of_two_le_num(15)
    8
    >>> largest_pow_of_two_le_num(99)
    64
    >>> largest_pow_of_two_le_num(178)
    128
    >>> largest_pow_of_two_le_num(999999)
    524288
    >>> largest_pow_of_two_le_num(99.9)
    Traceback (most recent call last):
        ...
    TypeError: Input value must be a 'int' type
    """
    if isinstance(number, float):
        raise TypeError("Input value must be a 'int' type")
    if number <= 0:
        return 0
    res = 1
    while (res << 1) <= number:
        res <<= 1
    return res


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/missing_number.py
================================================
def find_missing_number(nums: list[int]) -> int:
    """
    Finds the missing number in a list of consecutive integers.

    Args:
        nums: A list of integers.

    Returns:
        The missing number.

    Example:
        >>> find_missing_number([0, 1, 3, 4])
        2
        >>> find_missing_number([4, 3, 1, 0])
        2
        >>> find_missing_number([-4, -3, -1, 0])
        -2
        >>> find_missing_number([-2, 2, 1, 3, 0])
        -1
        >>> find_missing_number([1, 3, 4, 5, 6])
        2
        >>> find_missing_number([6, 5, 4, 2, 1])
        3
        >>> find_missing_number([6, 1, 5, 3, 4])
        2
    """
    low = min(nums)
    high = max(nums)
    missing_number = high

    for i in range(low, high):
        missing_number ^= i ^ nums[i - low]

    return missing_number


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/numbers_different_signs.py
================================================
"""
Author  : Alexander Pantyukhin
Date    : November 30, 2022

Task:
Given two int numbers. Return True these numbers have opposite signs
or False otherwise.

Implementation notes: Use bit manipulation.
Use XOR for two numbers.
"""


def different_signs(num1: int, num2: int) -> bool:
    """
    Return True if numbers have opposite signs False otherwise.

    >>> different_signs(1, -1)
    True
    >>> different_signs(1, 1)
    False
    >>> different_signs(1000000000000000000000000000, -1000000000000000000000000000)
    True
    >>> different_signs(-1000000000000000000000000000, 1000000000000000000000000000)
    True
    >>> different_signs(50, 278)
    False
    >>> different_signs(0, 2)
    False
    >>> different_signs(2, 0)
    False
    """
    return num1 ^ num2 < 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/power_of_4.py
================================================
"""

Task:
Given a positive int number. Return True if this number is power of 4
or False otherwise.

Implementation notes: Use bit manipulation.
For example if the number is the power of 2 it's bits representation:
n     = 0..100..00
n - 1 = 0..011..11

n & (n - 1) - no intersections = 0
If the number is a power of 4 then it should be a power of 2
and the set bit should be at an odd position.
"""


def power_of_4(number: int) -> bool:
    """
    Return True if this number is power of 4 or False otherwise.

    >>> power_of_4(0)
    Traceback (most recent call last):
        ...
    ValueError: number must be positive
    >>> power_of_4(1)
    True
    >>> power_of_4(2)
    False
    >>> power_of_4(4)
    True
    >>> power_of_4(6)
    False
    >>> power_of_4(8)
    False
    >>> power_of_4(17)
    False
    >>> power_of_4(64)
    True
    >>> power_of_4(-1)
    Traceback (most recent call last):
        ...
    ValueError: number must be positive
    >>> power_of_4(1.2)
    Traceback (most recent call last):
        ...
    TypeError: number must be an integer

    """
    if not isinstance(number, int):
        raise TypeError("number must be an integer")
    if number <= 0:
        raise ValueError("number must be positive")
    if number & (number - 1) == 0:
        c = 0
        while number:
            c += 1
            number >>= 1
        return c % 2 == 1
    else:
        return False


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/reverse_bits.py
================================================
def get_reverse_bit_string(number: int) -> str:
    """
    Return the reverse bit string of a 32 bit integer

    >>> get_reverse_bit_string(9)
    '10010000000000000000000000000000'
    >>> get_reverse_bit_string(43)
    '11010100000000000000000000000000'
    >>> get_reverse_bit_string(2873)
    '10011100110100000000000000000000'
    >>> get_reverse_bit_string(2550136832)
    '00000000000000000000000000011001'
    >>> get_reverse_bit_string("this is not a number")
    Traceback (most recent call last):
        ...
    TypeError: operation can not be conducted on an object of type str
    """
    if not isinstance(number, int):
        msg = (
            "operation can not be conducted on an object of type "
            f"{type(number).__name__}"
        )
        raise TypeError(msg)
    bit_string = ""
    for _ in range(32):
        bit_string += str(number % 2)
        number >>= 1
    return bit_string


def reverse_bit(number: int) -> int:
    """
    Take in a 32 bit integer, reverse its bits, return a 32 bit integer result

    >>> reverse_bit(25)
    2550136832
    >>> reverse_bit(37)
    2751463424
    >>> reverse_bit(21)
    2818572288
    >>> reverse_bit(58)
    1543503872
    >>> reverse_bit(0)
    0
    >>> reverse_bit(256)
    8388608
    >>> reverse_bit(2550136832)
    25
    >>> reverse_bit(-1)
    Traceback (most recent call last):
        ...
    ValueError: The value of input must be non-negative

    >>> reverse_bit(1.1)
    Traceback (most recent call last):
        ...
    TypeError: Input value must be an 'int' type

    >>> reverse_bit("0")
    Traceback (most recent call last):
        ...
    TypeError: Input value must be an 'int' type
    """
    if not isinstance(number, int):
        raise TypeError("Input value must be an 'int' type")
    if number < 0:
        raise ValueError("The value of input must be non-negative")

    result = 0
    # iterator over [0 to 31], since we are dealing with a 32 bit integer
    for _ in range(32):
        # left shift the bits by unity
        result <<= 1
        # get the end bit
        end_bit = number & 1
        # right shift the bits by unity
        number >>= 1
        # add that bit to our answer
        result |= end_bit
    return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/single_bit_manipulation_operations.py
================================================
#!/usr/bin/env python3

"""Provide the functionality to manipulate a single bit."""


def set_bit(number: int, position: int) -> int:
    """
    Set the bit at position to 1.

    Details: perform bitwise or for given number and X.
    Where X is a number with all the bits - zeroes and bit on given
    position - one.

    >>> set_bit(0b1101, 1) # 0b1111
    15
    >>> set_bit(0b0, 5) # 0b100000
    32
    >>> set_bit(0b1111, 1) # 0b1111
    15
    """
    return number | (1 << position)


def clear_bit(number: int, position: int) -> int:
    """
    Set the bit at position to 0.

    Details: perform bitwise and for given number and X.
    Where X is a number with all the bits - ones and bit on given
    position - zero.

    >>> clear_bit(0b10010, 1) # 0b10000
    16
    >>> clear_bit(0b0, 5) # 0b0
    0
    """
    return number & ~(1 << position)


def flip_bit(number: int, position: int) -> int:
    """
    Flip the bit at position.

    Details: perform bitwise xor for given number and X.
    Where X is a number with all the bits - zeroes and bit on given
    position - one.

    >>> flip_bit(0b101, 1) # 0b111
    7
    >>> flip_bit(0b101, 0) # 0b100
    4
    """
    return number ^ (1 << position)


def is_bit_set(number: int, position: int) -> bool:
    """
    Is the bit at position set?

    Details: Shift the bit at position to be the first (smallest) bit.
    Then check if the first bit is set by anding the shifted number with 1.

    >>> is_bit_set(0b1010, 0)
    False
    >>> is_bit_set(0b1010, 1)
    True
    >>> is_bit_set(0b1010, 2)
    False
    >>> is_bit_set(0b1010, 3)
    True
    >>> is_bit_set(0b0, 17)
    False
    """
    return ((number >> position) & 1) == 1


def get_bit(number: int, position: int) -> int:
    """
    Get the bit at the given position

    Details: perform bitwise and for the given number and X,
    Where X is a number with all the bits - zeroes and bit on given position - one.
    If the result is not equal to 0, then the bit on the given position is 1, else 0.

    >>> get_bit(0b1010, 0)
    0
    >>> get_bit(0b1010, 1)
    1
    >>> get_bit(0b1010, 2)
    0
    >>> get_bit(0b1010, 3)
    1
    """
    return int((number & (1 << position)) != 0)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: bit_manipulation/swap_all_odd_and_even_bits.py
================================================
def show_bits(before: int, after: int) -> str:
    """
    >>> print(show_bits(0, 0xFFFF))
        0: 00000000
    65535: 1111111111111111
    """
    return f"{before:>5}: {before:08b}\n{after:>5}: {after:08b}"


def swap_odd_even_bits(num: int) -> int:
    """
    1. We use bitwise AND operations to separate the even bits (0, 2, 4, 6, etc.) and
       odd bits (1, 3, 5, 7, etc.) in the input number.
    2. We then right-shift the even bits by 1 position and left-shift the odd bits by
       1 position to swap them.
    3. Finally, we combine the swapped even and odd bits using a bitwise OR operation
       to obtain the final result.
    >>> print(show_bits(0, swap_odd_even_bits(0)))
        0: 00000000
        0: 00000000
    >>> print(show_bits(1, swap_odd_even_bits(1)))
        1: 00000001
        2: 00000010
    >>> print(show_bits(2, swap_odd_even_bits(2)))
        2: 00000010
        1: 00000001
    >>> print(show_bits(3, swap_odd_even_bits(3)))
        3: 00000011
        3: 00000011
    >>> print(show_bits(4, swap_odd_even_bits(4)))
        4: 00000100
        8: 00001000
    >>> print(show_bits(5, swap_odd_even_bits(5)))
        5: 00000101
       10: 00001010
    >>> print(show_bits(6, swap_odd_even_bits(6)))
        6: 00000110
        9: 00001001
    >>> print(show_bits(23, swap_odd_even_bits(23)))
       23: 00010111
       43: 00101011
    """
    # Get all even bits - 0xAAAAAAAA is a 32-bit number with all even bits set to 1
    even_bits = num & 0xAAAAAAAA

    # Get all odd bits - 0x55555555 is a 32-bit number with all odd bits set to 1
    odd_bits = num & 0x55555555

    # Right shift even bits and left shift odd bits and swap them
    return even_bits >> 1 | odd_bits << 1


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    for i in (-1, 0, 1, 2, 3, 4, 23, 24):
        print(show_bits(i, swap_odd_even_bits(i)), "\n")


================================================
FILE: blockchain/README.md
================================================
# Blockchain

A Blockchain is a type of **distributed ledger** technology (DLT) that consists of a growing list of records, called **blocks**, that are securely linked together using **cryptography**.

Let's break down the terminologies in the above definition. We find below terminologies,

- Digital Ledger Technology (DLT)
- Blocks
- Cryptography

## Digital Ledger Technology

Blockchain is also called distributed ledger technology. It is simply the opposite of a centralized database. Firstly, what is a **ledger**? A ledger is a book or collection of accounts that records account transactions.

*Why is Blockchain addressed as a digital ledger if it can record more than account transactions? What other transaction details and information can it hold?*

Digital Ledger Technology is just a ledger that is shared among multiple nodes. This way there exists no need for a central authority to hold the info. Okay, how is it differentiated from a central database and what are their benefits?

Suppose that there is an organization that has 4 branches whose data are stored in a centralized database. So even if one branch needs any data from the ledger it needs approval from the database in charge. And if one hacks the central database he gets to tamper and control all the data.

Now let's assume every branch has a copy of the ledger and then once anything is added to the ledger by any branch it is gonna automatically reflect in all other ledgers available in other branches. This is done using a peer-to-peer network.

This means that even if information is tampered with in one branch we can find out. If one branch is hacked we can be alerted, so we can safeguard other branches. Now, assume these branches as computers or nodes and the ledger is a transaction record or digital receipt. If one ledger is hacked in a node we can detect since there will be a mismatch in comparison with other node information. So this is the concept of Digital Ledger Technology.

*Is it required for all nodes to have access to all information in other nodes? Wouldn't this require enormous storage space in each node?*

## Blocks

In short, a block is nothing but a collection of records with a labelled header. These are connected cryptographically. Once a new block is added to a chain, the previous block is connected, more precisely said as locked, and hence will remain unaltered. We can understand this concept once we get a clear understanding of the working mechanism of blockchain.

## Cryptography

Cryptography is the practice and study of secure communication techniques amid adversarial behavior. More broadly, cryptography is the creation and analysis of protocols that prevent third parties or the general public from accessing private messages.

*Which cryptography technology is most widely used in blockchain and why?*

So, in general, blockchain technology is a distributed record holder that records the information about ownership of an asset. To define precisely,
> Blockchain is a distributed, immutable ledger that makes it easier to record transactions and track assets in a corporate network.
An asset could be tangible (such as a house, car, cash, or land) or intangible (such as a business) (intellectual property, patents, copyrights, branding). A blockchain network can track and sell almost anything of value, lowering risk and costs for everyone involved.

So this is all about the introduction to blockchain technology. To learn more about the topic refer below links....
* <https://en.wikipedia.org/wiki/Blockchain>
* <https://en.wikipedia.org/wiki/Chinese_remainder_theorem>
* <https://en.wikipedia.org/wiki/Diophantine_equation>
* <https://www.geeksforgeeks.org/modular-division/>


================================================
FILE: blockchain/__init__.py
================================================


================================================
FILE: blockchain/diophantine_equation.py
================================================
from __future__ import annotations

from maths.greatest_common_divisor import greatest_common_divisor


def diophantine(a: int, b: int, c: int) -> tuple[float, float]:
    """
    Diophantine Equation : Given integers a,b,c ( at least one of a and b != 0), the
    diophantine equation a*x + b*y = c has a solution (where x and y are integers)
    iff greatest_common_divisor(a,b) divides c.

    GCD ( Greatest Common Divisor ) or HCF ( Highest Common Factor )

    >>> diophantine(10,6,14)
    (-7.0, 14.0)

    >>> diophantine(391,299,-69)
    (9.0, -12.0)

    But above equation has one more solution i.e., x = -4, y = 5.
    That's why we need diophantine all solution function.

    """

    assert (
        c % greatest_common_divisor(a, b) == 0
    )  # greatest_common_divisor(a,b) is in maths directory
    (d, x, y) = extended_gcd(a, b)  # extended_gcd(a,b) function implemented below
    r = c / d
    return (r * x, r * y)


def diophantine_all_soln(a: int, b: int, c: int, n: int = 2) -> None:
    """
    Lemma : if n|ab and gcd(a,n) = 1, then n|b.

    Finding All solutions of Diophantine Equations:

    Theorem : Let gcd(a,b) = d, a = d*p, b = d*q. If (x0,y0) is a solution of
    Diophantine Equation a*x + b*y = c.  a*x0 + b*y0 = c, then all the
    solutions have the form a(x0 + t*q) + b(y0 - t*p) = c,
    where t is an arbitrary integer.

    n is the number of solution you want, n = 2 by default

    >>> diophantine_all_soln(10, 6, 14)
    -7.0 14.0
    -4.0 9.0

    >>> diophantine_all_soln(10, 6, 14, 4)
    -7.0 14.0
    -4.0 9.0
    -1.0 4.0
    2.0 -1.0

    >>> diophantine_all_soln(391, 299, -69, n = 4)
    9.0 -12.0
    22.0 -29.0
    35.0 -46.0
    48.0 -63.0

    """
    (x0, y0) = diophantine(a, b, c)  # Initial value
    d = greatest_common_divisor(a, b)
    p = a // d
    q = b // d

    for i in range(n):
        x = x0 + i * q
        y = y0 - i * p
        print(x, y)


def extended_gcd(a: int, b: int) -> tuple[int, int, int]:
    """
    Extended Euclid's Algorithm : If d divides a and b and d = a*x + b*y for integers
    x and y, then d = gcd(a,b)

    >>> extended_gcd(10, 6)
    (2, -1, 2)

    >>> extended_gcd(7, 5)
    (1, -2, 3)

    """
    assert a >= 0
    assert b >= 0

    if b == 0:
        d, x, y = a, 1, 0
    else:
        (d, p, q) = extended_gcd(b, a % b)
        x = q
        y = p - q * (a // b)

    assert a % d == 0
    assert b % d == 0
    assert d == a * x + b * y

    return (d, x, y)


if __name__ == "__main__":
    from doctest import testmod

    testmod(name="diophantine", verbose=True)
    testmod(name="diophantine_all_soln", verbose=True)
    testmod(name="extended_gcd", verbose=True)
    testmod(name="greatest_common_divisor", verbose=True)


================================================
FILE: boolean_algebra/README.md
================================================
# Boolean Algebra

Boolean algebra is used to do arithmetic with bits of values True (1) or False (0).
There are three basic operations: 'and', 'or' and 'not'.

* <https://en.wikipedia.org/wiki/Boolean_algebra>
* <https://plato.stanford.edu/entries/boolalg-math/>


================================================
FILE: boolean_algebra/__init__.py
================================================


================================================
FILE: boolean_algebra/and_gate.py
================================================
"""
An AND Gate is a logic gate in boolean algebra which results to 1 (True) if all the
inputs are 1 (True), and 0 (False) otherwise.

Following is the truth table of a Two Input AND Gate:
    ------------------------------
    | Input 1 | Input 2 | Output |
    ------------------------------
    |    0    |    0    |    0   |
    |    0    |    1    |    0   |
    |    1    |    0    |    0   |
    |    1    |    1    |    1   |
    ------------------------------

Refer - https://www.geeksforgeeks.org/logic-gates/
"""


def and_gate(input_1: int, input_2: int) -> int:
    """
    Calculate AND of the input values

    >>> and_gate(0, 0)
    0
    >>> and_gate(0, 1)
    0
    >>> and_gate(1, 0)
    0
    >>> and_gate(1, 1)
    1
    """
    return int(input_1 and input_2)


def n_input_and_gate(inputs: list[int]) -> int:
    """
    Calculate AND of a list of input values

    >>> n_input_and_gate([1, 0, 1, 1, 0])
    0
    >>> n_input_and_gate([1, 1, 1, 1, 1])
    1
    """
    return int(all(inputs))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: boolean_algebra/imply_gate.py
================================================
"""
An IMPLY Gate is a logic gate in boolean algebra which results to 1 if
either input 1 is 0, or if input 1 is 1, then the output is 1 only if input 2 is 1.
It is true if input 1 implies input 2.

Following is the truth table of an IMPLY Gate:
    ------------------------------
    | Input 1 | Input 2 | Output |
    ------------------------------
    |    0    |    0    |    1   |
    |    0    |    1    |    1   |
    |    1    |    0    |    0   |
    |    1    |    1    |    1   |
    ------------------------------

Refer - https://en.wikipedia.org/wiki/IMPLY_gate
"""


def imply_gate(input_1: int, input_2: int) -> int:
    """
    Calculate IMPLY of the input values

    >>> imply_gate(0, 0)
    1
    >>> imply_gate(0, 1)
    1
    >>> imply_gate(1, 0)
    0
    >>> imply_gate(1, 1)
    1
    """
    return int(input_1 == 0 or input_2 == 1)


def recursive_imply_list(input_list: list[int]) -> int:
    """
    Recursively calculates the implication of a list.
    Strictly the implication is applied consecutively left to right:
    ( (a -> b) -> c ) -> d ...

    >>> recursive_imply_list([])
    Traceback (most recent call last):
        ...
    ValueError: Input list must contain at least two elements
    >>> recursive_imply_list([0])
    Traceback (most recent call last):
        ...
    ValueError: Input list must contain at least two elements
    >>> recursive_imply_list([1])
    Traceback (most recent call last):
        ...
    ValueError: Input list must contain at least two elements
    >>> recursive_imply_list([0, 0])
    1
    >>> recursive_imply_list([0, 1])
    1
    >>> recursive_imply_list([1, 0])
    0
    >>> recursive_imply_list([1, 1])
    1
    >>> recursive_imply_list([0, 0, 0])
    0
    >>> recursive_imply_list([0, 0, 1])
    1
    >>> recursive_imply_list([0, 1, 0])
    0
    >>> recursive_imply_list([0, 1, 1])
    1
    >>> recursive_imply_list([1, 0, 0])
    1
    >>> recursive_imply_list([1, 0, 1])
    1
    >>> recursive_imply_list([1, 1, 0])
    0
    >>> recursive_imply_list([1, 1, 1])
    1
    """
    if len(input_list) < 2:
        raise ValueError("Input list must contain at least two elements")
    first_implication = imply_gate(input_list[0], input_list[1])
    if len(input_list) == 2:
        return first_implication
    new_list = [first_implication, *input_list[2:]]
    return recursive_imply_list(new_list)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: boolean_algebra/karnaugh_map_simplification.py
================================================
"""
https://en.wikipedia.org/wiki/Karnaugh_map
https://www.allaboutcircuits.com/technical-articles/karnaugh-map-boolean-algebraic-simplification-technique
"""


def simplify_kmap(kmap: list[list[int]]) -> str:
    """
    Simplify the Karnaugh map.
    >>> simplify_kmap(kmap=[[0, 1], [1, 1]])
    "A'B + AB' + AB"
    >>> simplify_kmap(kmap=[[0, 0], [0, 0]])
    ''
    >>> simplify_kmap(kmap=[[0, 1], [1, -1]])
    "A'B + AB' + AB"
    >>> simplify_kmap(kmap=[[0, 1], [1, 2]])
    "A'B + AB' + AB"
    >>> simplify_kmap(kmap=[[0, 1], [1, 1.1]])
    "A'B + AB' + AB"
    >>> simplify_kmap(kmap=[[0, 1], [1, 'a']])
    "A'B + AB' + AB"
    """
    simplified_f = []
    for a, row in enumerate(kmap):
        for b, item in enumerate(row):
            if item:
                term = ("A" if a else "A'") + ("B" if b else "B'")
                simplified_f.append(term)
    return " + ".join(simplified_f)


def main() -> None:
    """
    Main function to create and simplify a K-Map.

    >>> main()
    [0, 1]
    [1, 1]
    Simplified Expression:
    A'B + AB' + AB
    """
    kmap = [[0, 1], [1, 1]]

    # Manually generate the product of [0, 1] and [0, 1]

    for row in kmap:
        print(row)

    print("Simplified Expression:")
    print(simplify_kmap(kmap))


if __name__ == "__main__":
    main()
    print(f"{simplify_kmap(kmap=[[0, 1], [1, 1]]) = }")


================================================
FILE: boolean_algebra/multiplexer.py
================================================
def mux(input0: int, input1: int, select: int) -> int:
    """
    Implement a 2-to-1 Multiplexer.

    :param input0: The first input value (0 or 1).
    :param input1: The second input value (0 or 1).
    :param select: The select signal (0 or 1) to choose between input0 and input1.
    :return: The output based on the select signal.  input1 if select else input0.

    https://www.electrically4u.com/solved-problems-on-multiplexer
    https://en.wikipedia.org/wiki/Multiplexer

    >>> mux(0, 1, 0)
    0
    >>> mux(0, 1, 1)
    1
    >>> mux(1, 0, 0)
    1
    >>> mux(1, 0, 1)
    0
    >>> mux(2, 1, 0)
    Traceback (most recent call last):
        ...
    ValueError: Inputs and select signal must be 0 or 1
    >>> mux(0, -1, 0)
    Traceback (most recent call last):
        ...
    ValueError: Inputs and select signal must be 0 or 1
    >>> mux(0, 1, 1.1)
    Traceback (most recent call last):
        ...
    ValueError: Inputs and select signal must be 0 or 1
    """
    if all(i in (0, 1) for i in (input0, input1, select)):
        return input1 if select else input0
    raise ValueError("Inputs and select signal must be 0 or 1")


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: boolean_algebra/nand_gate.py
================================================
"""
A NAND Gate is a logic gate in boolean algebra which results to 0 (False) if both
the inputs are 1, and 1 (True) otherwise. It's similar to adding
a NOT gate along with an AND gate.
Following is the truth table of a NAND Gate:
    ------------------------------
    | Input 1 | Input 2 | Output |
    ------------------------------
    |    0    |    0    |    1   |
    |    0    |    1    |    1   |
    |    1    |    0    |    1   |
    |    1    |    1    |    0   |
    ------------------------------
Refer - https://www.geeksforgeeks.org/logic-gates-in-python/
"""


def nand_gate(input_1: int, input_2: int) -> int:
    """
    Calculate NAND of the input values
    >>> nand_gate(0, 0)
    1
    >>> nand_gate(0, 1)
    1
    >>> nand_gate(1, 0)
    1
    >>> nand_gate(1, 1)
    0
    """
    return int(not (input_1 and input_2))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: boolean_algebra/nimply_gate.py
================================================
"""
An NIMPLY Gate is a logic gate in boolean algebra which results to 0 if
either input 1 is 0, or if input 1 is 1, then it is 0 only if input 2 is 1.
It is false if input 1 implies input 2. It is the negated form of imply

Following is the truth table of an NIMPLY Gate:
    ------------------------------
    | Input 1 | Input 2 | Output |
    ------------------------------
    |    0    |    0    |    0   |
    |    0    |    1    |    0   |
    |    1    |    0    |    1   |
    |    1    |    1    |    0   |
    ------------------------------

Refer - https://en.wikipedia.org/wiki/NIMPLY_gate
"""


def nimply_gate(input_1: int, input_2: int) -> int:
    """
    Calculate NIMPLY of the input values

    >>> nimply_gate(0, 0)
    0
    >>> nimply_gate(0, 1)
    0
    >>> nimply_gate(1, 0)
    1
    >>> nimply_gate(1, 1)
    0
    """
    return int(input_1 == 1 and input_2 == 0)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: boolean_algebra/nor_gate.py
================================================
"""
A NOR Gate is a logic gate in boolean algebra which results in false(0) if any of the
inputs is 1, and True(1) if all inputs are 0.
Following is the truth table of a NOR Gate:
    Truth Table of NOR Gate:
    | Input 1  | Input 2  |  Output  |
    |    0     |    0     |    1     |
    |    0     |    1     |    0     |
    |    1     |    0     |    0     |
    |    1     |    1     |    0     |

    Code provided by Akshaj Vishwanathan
https://www.geeksforgeeks.org/logic-gates-in-python
"""

from collections.abc import Callable


def nor_gate(input_1: int, input_2: int) -> int:
    """
    >>> nor_gate(0, 0)
    1
    >>> nor_gate(0, 1)
    0
    >>> nor_gate(1, 0)
    0
    >>> nor_gate(1, 1)
    0
    >>> nor_gate(0.0, 0.0)
    1
    >>> nor_gate(0, -7)
    0
    """
    return int(input_1 == input_2 == 0)


def truth_table(func: Callable) -> str:
    """
    >>> print(truth_table(nor_gate))
    Truth Table of NOR Gate:
    | Input 1  | Input 2  |  Output  |
    |    0     |    0     |    1     |
    |    0     |    1     |    0     |
    |    1     |    0     |    0     |
    |    1     |    1     |    0     |
    """

    def make_table_row(items: list | tuple) -> str:
        """
        >>> make_table_row(("One", "Two", "Three"))
        '|   One    |   Two    |  Three   |'
        """
        return f"| {' | '.join(f'{item:^8}' for item in items)} |"

    return "\n".join(
        (
            "Truth Table of NOR Gate:",
            make_table_row(("Input 1", "Input 2", "Output")),
            *[make_table_row((i, j, func(i, j))) for i in (0, 1) for j in (0, 1)],
        )
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(truth_table(nor_gate))


================================================
FILE: boolean_algebra/not_gate.py
================================================
"""
A NOT Gate is a logic gate in boolean algebra which results to 0 (False) if the
input is high, and 1 (True) if the input is low.
Following is the truth table of a XOR Gate:
    ------------------------------
    | Input   |  Output |
    ------------------------------
    |    0    |    1    |
    |    1    |    0    |
    ------------------------------
Refer - https://www.geeksforgeeks.org/logic-gates-in-python/
"""


def not_gate(input_1: int) -> int:
    """
    Calculate NOT of the input values
    >>> not_gate(0)
    1
    >>> not_gate(1)
    0
    """

    return 1 if input_1 == 0 else 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: boolean_algebra/or_gate.py
================================================
"""
An OR Gate is a logic gate in boolean algebra which results to 0 (False) if both the
inputs are 0, and 1 (True) otherwise.
Following is the truth table of an AND Gate:
    ------------------------------
    | Input 1 | Input 2 | Output |
    ------------------------------
    |    0    |    0    |    0   |
    |    0    |    1    |    1   |
    |    1    |    0    |    1   |
    |    1    |    1    |    1   |
    ------------------------------
Refer - https://www.geeksforgeeks.org/logic-gates-in-python/
"""


def or_gate(input_1: int, input_2: int) -> int:
    """
    Calculate OR of the input values
    >>> or_gate(0, 0)
    0
    >>> or_gate(0, 1)
    1
    >>> or_gate(1, 0)
    1
    >>> or_gate(1, 1)
    1
    """
    return int((input_1, input_2).count(1) != 0)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: boolean_algebra/quine_mc_cluskey.py
================================================
from __future__ import annotations

from collections.abc import Sequence
from typing import Literal


def compare_string(string1: str, string2: str) -> str | Literal[False]:
    """
    >>> compare_string('0010','0110')
    '0_10'

    >>> compare_string('0110','1101')
    False
    """
    list1 = list(string1)
    list2 = list(string2)
    count = 0
    for i in range(len(list1)):
        if list1[i] != list2[i]:
            count += 1
            list1[i] = "_"
    if count > 1:
        return False
    else:
        return "".join(list1)


def check(binary: list[str]) -> list[str]:
    """
    >>> check(['0.00.01.5'])
    ['0.00.01.5']
    """
    pi = []
    while True:
        check1 = ["$"] * len(binary)
        temp = []
        for i in range(len(binary)):
            for j in range(i + 1, len(binary)):
                k = compare_string(binary[i], binary[j])
                if k is False:
                    check1[i] = "*"
                    check1[j] = "*"
                    temp.append("X")
        for i in range(len(binary)):
            if check1[i] == "$":
                pi.append(binary[i])
        if len(temp) == 0:
            return pi
        binary = list(set(temp))


def decimal_to_binary(no_of_variable: int, minterms: Sequence[float]) -> list[str]:
    """
    >>> decimal_to_binary(3,[1.5])
    ['0.00.01.5']
    """
    temp = []
    for minterm in minterms:
        string = ""
        for _ in range(no_of_variable):
            string = str(minterm % 2) + string
            minterm //= 2
        temp.append(string)
    return temp


def is_for_table(string1: str, string2: str, count: int) -> bool:
    """
    >>> is_for_table('__1','011',2)
    True

    >>> is_for_table('01_','001',1)
    False
    """
    list1 = list(string1)
    list2 = list(string2)
    count_n = sum(item1 != item2 for item1, item2 in zip(list1, list2))
    return count_n == count


def selection(chart: list[list[int]], prime_implicants: list[str]) -> list[str]:
    """
    >>> selection([[1]],['0.00.01.5'])
    ['0.00.01.5']

    >>> selection([[1]],['0.00.01.5'])
    ['0.00.01.5']
    """
    temp = []
    select = [0] * len(chart)
    for i in range(len(chart[0])):
        count = sum(row[i] == 1 for row in chart)
        if count == 1:
            rem = max(j for j, row in enumerate(chart) if row[i] == 1)
            select[rem] = 1
    for i, item in enumerate(select):
        if item != 1:
            continue
        for j in range(len(chart[0])):
            if chart[i][j] != 1:
                continue
            for row in chart:
                row[j] = 0
        temp.append(prime_implicants[i])
    while True:
        counts = [chart[i].count(1) for i in range(len(chart))]
        max_n = max(counts)
        rem = counts.index(max_n)

        if max_n == 0:
            return temp

        temp.append(prime_implicants[rem])

        for j in range(len(chart[0])):
            if chart[rem][j] != 1:
                continue
            for i in range(len(chart)):
                chart[i][j] = 0


def prime_implicant_chart(
    prime_implicants: list[str], binary: list[str]
) -> list[list[int]]:
    """
    >>> prime_implicant_chart(['0.00.01.5'],['0.00.01.5'])
    [[1]]
    """
    chart = [[0 for x in range(len(binary))] for x in range(len(prime_implicants))]
    for i in range(len(prime_implicants)):
        count = prime_implicants[i].count("_")
        for j in range(len(binary)):
            if is_for_table(prime_implicants[i], binary[j], count):
                chart[i][j] = 1

    return chart


def main() -> None:
    no_of_variable = int(input("Enter the no. of variables\n"))
    minterms = [
        float(x)
        for x in input(
            "Enter the decimal representation of Minterms 'Spaces Separated'\n"
        ).split()
    ]
    binary = decimal_to_binary(no_of_variable, minterms)

    prime_implicants = check(binary)
    print("Prime Implicants are:")
    print(prime_implicants)
    chart = prime_implicant_chart(prime_implicants, binary)

    essential_prime_implicants = selection(chart, prime_implicants)
    print("Essential Prime Implicants are:")
    print(essential_prime_implicants)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: boolean_algebra/xnor_gate.py
================================================
"""
A XNOR Gate is a logic gate in boolean algebra which results to 0 (False) if both the
inputs are different, and 1 (True), if the inputs are same.
It's similar to adding a NOT gate to an XOR gate

Following is the truth table of a XNOR Gate:
    ------------------------------
    | Input 1 | Input 2 | Output |
    ------------------------------
    |    0    |    0    |    1   |
    |    0    |    1    |    0   |
    |    1    |    0    |    0   |
    |    1    |    1    |    1   |
    ------------------------------
Refer - https://www.geeksforgeeks.org/logic-gates-in-python/
"""


def xnor_gate(input_1: int, input_2: int) -> int:
    """
    Calculate XOR of the input values
    >>> xnor_gate(0, 0)
    1
    >>> xnor_gate(0, 1)
    0
    >>> xnor_gate(1, 0)
    0
    >>> xnor_gate(1, 1)
    1
    """
    return 1 if input_1 == input_2 else 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: boolean_algebra/xor_gate.py
================================================
"""
A XOR Gate is a logic gate in boolean algebra which results to 1 (True) if only one of
the two inputs is 1, and 0 (False) if an even number of inputs are 1.
Following is the truth table of a XOR Gate:
    ------------------------------
    | Input 1 | Input 2 | Output |
    ------------------------------
    |    0    |    0    |    0   |
    |    0    |    1    |    1   |
    |    1    |    0    |    1   |
    |    1    |    1    |    0   |
    ------------------------------

Refer - https://www.geeksforgeeks.org/logic-gates-in-python/
"""


def xor_gate(input_1: int, input_2: int) -> int:
    """
    calculate xor of the input values

    >>> xor_gate(0, 0)
    0
    >>> xor_gate(0, 1)
    1
    >>> xor_gate(1, 0)
    1
    >>> xor_gate(1, 1)
    0
    """
    return (input_1, input_2).count(0) % 2


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: cellular_automata/README.md
================================================
# Cellular Automata

Cellular automata are a way to simulate the behavior of "life", no matter if it is a robot or cell.
They usually follow simple rules but can lead to the creation of complex forms.
The most popular cellular automaton is Conway's [Game of Life](https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life).

* <https://en.wikipedia.org/wiki/Cellular_automaton>
* <https://mathworld.wolfram.com/ElementaryCellularAutomaton.html>


================================================
FILE: cellular_automata/__init__.py
================================================


================================================
FILE: cellular_automata/conways_game_of_life.py
================================================
"""
Conway's Game of Life implemented in Python.
https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life
"""

from __future__ import annotations

from PIL import Image

# Define glider example
GLIDER = [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
BLINKER = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]


def new_generation(cells: list[list[int]]) -> list[list[int]]:
    """
    Generates the next generation for a given state of Conway's Game of Life.
    >>> new_generation(BLINKER)
    [[0, 0, 0], [1, 1, 1], [0, 0, 0]]
    """
    next_generation = []
    for i in range(len(cells)):
        next_generation_row = []
        for j in range(len(cells[i])):
            # Get the number of live neighbours
            neighbour_count = 0
            if i > 0 and j > 0:
                neighbour_count += cells[i - 1][j - 1]
            if i > 0:
                neighbour_count += cells[i - 1][j]
            if i > 0 and j < len(cells[i]) - 1:
                neighbour_count += cells[i - 1][j + 1]
            if j > 0:
                neighbour_count += cells[i][j - 1]
            if j < len(cells[i]) - 1:
                neighbour_count += cells[i][j + 1]
            if i < len(cells) - 1 and j > 0:
                neighbour_count += cells[i + 1][j - 1]
            if i < len(cells) - 1:
                neighbour_count += cells[i + 1][j]
            if i < len(cells) - 1 and j < len(cells[i]) - 1:
                neighbour_count += cells[i + 1][j + 1]

            # Rules of the game of life (excerpt from Wikipedia):
            # 1. Any live cell with two or three live neighbours survives.
            # 2. Any dead cell with three live neighbours becomes a live cell.
            # 3. All other live cells die in the next generation.
            #    Similarly, all other dead cells stay dead.
            alive = cells[i][j] == 1
            if (alive and 2 <= neighbour_count <= 3) or (
                not alive and neighbour_count == 3
            ):
                next_generation_row.append(1)
            else:
                next_generation_row.append(0)

        next_generation.append(next_generation_row)
    return next_generation


def generate_images(cells: list[list[int]], frames: int) -> list[Image.Image]:
    """
    Generates a list of images of subsequent Game of Life states.
    """
    images = []
    for _ in range(frames):
        # Create output image
        img = Image.new("RGB", (len(cells[0]), len(cells)))
        pixels = img.load()

        # Save cells to image
        for x in range(len(cells)):
            for y in range(len(cells[0])):
                colour = 255 - cells[y][x] * 255
                pixels[x, y] = (colour, colour, colour)

        # Save image
        images.append(img)
        cells = new_generation(cells)
    return images


if __name__ == "__main__":
    images = generate_images(GLIDER, 16)
    images[0].save("out.gif", save_all=True, append_images=images[1:])


================================================
FILE: cellular_automata/game_of_life.py
================================================
"""Conway's Game Of Life, Author Anurag Kumar(mailto:anuragkumarak95@gmail.com)

Requirements:
  - numpy
  - random
  - time
  - matplotlib

Python:
  - 3.5

Usage:
  - $python3 game_of_life <canvas_size:int>

Game-Of-Life Rules:

 1.
 Any live cell with fewer than two live neighbours
 dies, as if caused by under-population.
 2.
 Any live cell with two or three live neighbours lives
 on to the next generation.
 3.
 Any live cell with more than three live neighbours
 dies, as if by over-population.
 4.
 Any dead cell with exactly three live neighbours be-
 comes a live cell, as if by reproduction.
"""

import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

usage_doc = "Usage of script: script_name <size_of_canvas:int>"

choice = [0] * 100 + [1] * 10
random.shuffle(choice)


def create_canvas(size: int) -> list[list[bool]]:
    canvas = [[False for i in range(size)] for j in range(size)]
    return canvas


def seed(canvas: list[list[bool]]) -> None:
    for i, row in enumerate(canvas):
        for j, _ in enumerate(row):
            canvas[i][j] = bool(random.getrandbits(1))


def run(canvas: list[list[bool]]) -> list[list[bool]]:
    """
    This function runs the rules of game through all points, and changes their
    status accordingly.(in the same canvas)
    @Args:
    --
    canvas : canvas of population to run the rules on.

    @returns:
    --
    canvas of population after one step
    """
    current_canvas = np.array(canvas)
    next_gen_canvas = np.array(create_canvas(current_canvas.shape[0]))
    for r, row in enumerate(current_canvas):
        for c, pt in enumerate(row):
            next_gen_canvas[r][c] = __judge_point(
                pt, current_canvas[r - 1 : r + 2, c - 1 : c + 2]
            )

    return next_gen_canvas.tolist()


def __judge_point(pt: bool, neighbours: list[list[bool]]) -> bool:
    dead = 0
    alive = 0
    # finding dead or alive neighbours count.
    for i in neighbours:
        for status in i:
            if status:
                alive += 1
            else:
                dead += 1

    # handling duplicate entry for focus pt.
    if pt:
        alive -= 1
    else:
        dead -= 1

    # running the rules of game here.
    state = pt
    if pt:
        if alive < 2:
            state = False
        elif alive in {2, 3}:
            state = True
        elif alive > 3:
            state = False
    elif alive == 3:
        state = True

    return state


if __name__ == "__main__":
    if len(sys.argv) != 2:
        raise Exception(usage_doc)

    canvas_size = int(sys.argv[1])
    # main working structure of this module.
    c = create_canvas(canvas_size)
    seed(c)
    fig, ax = plt.subplots()
    fig.show()
    cmap = ListedColormap(["w", "k"])
    try:
        while True:
            c = run(c)
            ax.matshow(c, cmap=cmap)
            fig.canvas.draw()
            ax.cla()
    except KeyboardInterrupt:
        # do nothing.
        pass


================================================
FILE: cellular_automata/langtons_ant.py
================================================
"""
Langton's ant

@ https://en.wikipedia.org/wiki/Langton%27s_ant
@ https://upload.wikimedia.org/wikipedia/commons/0/09/LangtonsAntAnimated.gif
"""

from functools import partial

from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation

WIDTH = 80
HEIGHT = 80


class LangtonsAnt:
    """
    Represents the main LangonsAnt algorithm.

    >>> la = LangtonsAnt(2, 2)
    >>> la.board
    [[True, True], [True, True]]
    >>> la.ant_position
    (1, 1)
    """

    def __init__(self, width: int, height: int) -> None:
        # Each square is either True or False where True is white and False is black
        self.board = [[True] * width for _ in range(height)]
        self.ant_position: tuple[int, int] = (width // 2, height // 2)

        # Initially pointing left (similar to the wikipedia image)
        # (0 = 0° | 1 = 90° | 2 = 180 ° | 3 = 270°)
        self.ant_direction: int = 3

    def move_ant(self, axes: plt.Axes | None, display: bool, _frame: int) -> None:
        """
        Performs three tasks:
            1. The ant turns either clockwise or anti-clockwise according to the colour
            of the square that it is currently on. If the square is white, the ant
            turns clockwise, and if the square is black the ant turns anti-clockwise
            2. The ant moves one square in the direction that it is currently facing
            3. The square the ant was previously on is inverted (White -> Black and
            Black -> White)

        If display is True, the board will also be displayed on the axes

        >>> la = LangtonsAnt(2, 2)
        >>> la.move_ant(None, True, 0)
        >>> la.board
        [[True, True], [True, False]]
        >>> la.move_ant(None, True, 0)
        >>> la.board
        [[True, False], [True, False]]
        """
        directions = {
            0: (-1, 0),  # 0°
            1: (0, 1),  # 90°
            2: (1, 0),  # 180°
            3: (0, -1),  # 270°
        }
        x, y = self.ant_position

        # Turn clockwise or anti-clockwise according to colour of square
        if self.board[x][y] is True:
            # The square is white so turn 90° clockwise
            self.ant_direction = (self.ant_direction + 1) % 4
        else:
            # The square is black so turn 90° anti-clockwise
            self.ant_direction = (self.ant_direction - 1) % 4

        # Move ant
        move_x, move_y = directions[self.ant_direction]
        self.ant_position = (x + move_x, y + move_y)

        # Flip colour of square
        self.board[x][y] = not self.board[x][y]

        if display and axes:
            # Display the board on the axes
            axes.get_xaxis().set_ticks([])
            axes.get_yaxis().set_ticks([])
            axes.imshow(self.board, cmap="gray", interpolation="nearest")

    def display(self, frames: int = 100_000) -> None:
        """
        Displays the board without delay in a matplotlib plot
        to visually understand and track the ant.

        >>> _ = LangtonsAnt(WIDTH, HEIGHT)
        """
        fig, ax = plt.subplots()
        # Assign animation to a variable to prevent it from getting garbage collected
        self.animation = FuncAnimation(
            fig, partial(self.move_ant, ax, True), frames=frames, interval=1
        )
        plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    LangtonsAnt(WIDTH, HEIGHT).display()


================================================
FILE: cellular_automata/nagel_schrekenberg.py
================================================
"""
Simulate the evolution of a highway with only one road that is a loop.
The highway is divided in cells, each cell can have at most one car in it.
The highway is a loop so when a car comes to one end, it will come out on the other.
Each car is represented by its speed (from 0 to 5).

Some information about speed:
    -1 means that the cell on the highway is empty
    0 to 5 are the speed of the cars with 0 being the lowest and 5 the highest

highway: list[int]  Where every position and speed of every car will be stored
probability         The probability that a driver will slow down
initial_speed       The speed of the cars a the start
frequency           How many cells there are between two cars at the start
max_speed           The maximum speed a car can go to
number_of_cells     How many cell are there in the highway
number_of_update    How many times will the position be updated

More information here: https://en.wikipedia.org/wiki/Nagel%E2%80%93Schreckenberg_model

Examples for doctest:
>>> simulate(construct_highway(6, 3, 0), 2, 0, 2)
[[0, -1, -1, 0, -1, -1], [-1, 1, -1, -1, 1, -1], [-1, -1, 1, -1, -1, 1]]
>>> simulate(construct_highway(5, 2, -2), 3, 0, 2)
[[0, -1, 0, -1, 0], [0, -1, 0, -1, -1], [0, -1, -1, 1, -1], [-1, 1, -1, 0, -1]]
"""

from random import randint, random


def construct_highway(
    number_of_cells: int,
    frequency: int,
    initial_speed: int,
    random_frequency: bool = False,
    random_speed: bool = False,
    max_speed: int = 5,
) -> list:
    """
    Build the highway following the parameters given
    >>> construct_highway(10, 2, 6)
    [[6, -1, 6, -1, 6, -1, 6, -1, 6, -1]]
    >>> construct_highway(10, 10, 2)
    [[2, -1, -1, -1, -1, -1, -1, -1, -1, -1]]
    """

    highway = [[-1] * number_of_cells]  # Create a highway without any car
    i = 0
    initial_speed = max(initial_speed, 0)
    while i < number_of_cells:
        highway[0][i] = (
            randint(0, max_speed) if random_speed else initial_speed
        )  # Place the cars
        i += (
            randint(1, max_speed * 2) if random_frequency else frequency
        )  # Arbitrary number, may need tuning
    return highway


def get_distance(highway_now: list, car_index: int) -> int:
    """
    Get the distance between a car (at index car_index) and the next car
    >>> get_distance([6, -1, 6, -1, 6], 2)
    1
    >>> get_distance([2, -1, -1, -1, 3, 1, 0, 1, 3, 2], 0)
    3
    >>> get_distance([-1, -1, -1, -1, 2, -1, -1, -1, 3], -1)
    4
    """

    distance = 0
    cells = highway_now[car_index + 1 :]
    for cell in range(len(cells)):  # May need a better name for this
        if cells[cell] != -1:  # If the cell is not empty then
            return distance  # we have the distance we wanted
        distance += 1
    # Here if the car is near the end of the highway
    return distance + get_distance(highway_now, -1)


def update(highway_now: list, probability: float, max_speed: int) -> list:
    """
    Update the speed of the cars
    >>> update([-1, -1, -1, -1, -1, 2, -1, -1, -1, -1, 3], 0.0, 5)
    [-1, -1, -1, -1, -1, 3, -1, -1, -1, -1, 4]
    >>> update([-1, -1, 2, -1, -1, -1, -1, 3], 0.0, 5)
    [-1, -1, 3, -1, -1, -1, -1, 1]
    """

    number_of_cells = len(highway_now)
    # Beforce calculations, the highway is empty
    next_highway = [-1] * number_of_cells

    for car_index in range(number_of_cells):
        if highway_now[car_index] != -1:
            # Add 1 to the current speed of the car and cap the speed
            next_highway[car_index] = min(highway_now[car_index] + 1, max_speed)
            # Number of empty cell before the next car
            dn = get_distance(highway_now, car_index) - 1
            # We can't have the car causing an accident
            next_highway[car_index] = min(next_highway[car_index], dn)
            if random() < probability:
                # Randomly, a driver will slow down
                next_highway[car_index] = max(next_highway[car_index] - 1, 0)
    return next_highway


def simulate(
    highway: list, number_of_update: int, probability: float, max_speed: int
) -> list:
    """
    The main function, it will simulate the evolution of the highway
    >>> simulate([[-1, 2, -1, -1, -1, 3]], 2, 0.0, 3)
    [[-1, 2, -1, -1, -1, 3], [-1, -1, -1, 2, -1, 0], [1, -1, -1, 0, -1, -1]]
    >>> simulate([[-1, 2, -1, 3]], 4, 0.0, 3)
    [[-1, 2, -1, 3], [-1, 0, -1, 0], [-1, 0, -1, 0], [-1, 0, -1, 0], [-1, 0, -1, 0]]
    """

    number_of_cells = len(highway[0])

    for i in range(number_of_update):
        next_speeds_calculated = update(highway[i], probability, max_speed)
        real_next_speeds = [-1] * number_of_cells

        for car_index in range(number_of_cells):
            speed = next_speeds_calculated[car_index]
            if speed != -1:
                # Change the position based on the speed (with % to create the loop)
                index = (car_index + speed) % number_of_cells
                # Commit the change of position
                real_next_speeds[index] = speed
        highway.append(real_next_speeds)

    return highway


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: cellular_automata/one_dimensional.py
================================================
"""
Return an image of 16 generations of one-dimensional cellular automata based on a given
ruleset number
https://mathworld.wolfram.com/ElementaryCellularAutomaton.html
"""

from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
CELLS = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
# fmt: on


def format_ruleset(ruleset: int) -> list[int]:
    """
    >>> format_ruleset(11100)
    [0, 0, 0, 1, 1, 1, 0, 0]
    >>> format_ruleset(0)
    [0, 0, 0, 0, 0, 0, 0, 0]
    >>> format_ruleset(11111111)
    [1, 1, 1, 1, 1, 1, 1, 1]
    """
    return [int(c) for c in f"{ruleset:08}"[:8]]


def new_generation(cells: list[list[int]], rule: list[int], time: int) -> list[int]:
    population = len(cells[0])  # 31
    next_generation = []
    for i in range(population):
        # Get the neighbors of each cell
        # Handle neighbours outside bounds by using 0 as their value
        left_neighbor = 0 if i == 0 else cells[time][i - 1]
        right_neighbor = 0 if i == population - 1 else cells[time][i + 1]
        # Define a new cell and add it to the new generation
        situation = 7 - int(f"{left_neighbor}{cells[time][i]}{right_neighbor}", 2)
        next_generation.append(rule[situation])
    return next_generation


def generate_image(cells: list[list[int]]) -> Image.Image:
    """
    Convert the cells into a greyscale PIL.Image.Image and return it to the caller.
    >>> from random import random
    >>> cells = [[random() for w in range(31)] for h in range(16)]
    >>> img = generate_image(cells)
    >>> isinstance(img, Image.Image)
    True
    >>> img.width, img.height
    (31, 16)
    """
    # Create the output image
    img = Image.new("RGB", (len(cells[0]), len(cells)))
    pixels = img.load()
    # Generates image
    for w in range(img.width):
        for h in range(img.height):
            color = 255 - int(255 * cells[h][w])
            pixels[w, h] = (color, color, color)
    return img


if __name__ == "__main__":
    rule_num = bin(int(input("Rule:\n").strip()))[2:]
    rule = format_ruleset(int(rule_num))
    for time in range(16):
        CELLS.append(new_generation(CELLS, rule, time))
    img = generate_image(CELLS)
    # Uncomment to save the image
    # img.save(f"rule_{rule_num}.png")
    img.show()


================================================
FILE: cellular_automata/wa_tor.py
================================================
"""
Wa-Tor algorithm (1984)

| @ https://en.wikipedia.org/wiki/Wa-Tor
| @ https://beltoforion.de/en/wator/
| @ https://beltoforion.de/en/wator/images/wator_medium.webm

This solution aims to completely remove any systematic approach
to the Wa-Tor planet, and utilise fully random methods.

The constants are a working set that allows the Wa-Tor planet
to result in one of the three possible results.
"""

from collections.abc import Callable
from random import randint, shuffle
from time import sleep
from typing import Literal

WIDTH = 50  # Width of the Wa-Tor planet
HEIGHT = 50  # Height of the Wa-Tor planet

PREY_INITIAL_COUNT = 30  # The initial number of prey entities
PREY_REPRODUCTION_TIME = 5  # The chronons before reproducing

PREDATOR_INITIAL_COUNT = 50  # The initial number of predator entities
# The initial energy value of predator entities
PREDATOR_INITIAL_ENERGY_VALUE = 15
# The energy value provided when consuming prey
PREDATOR_FOOD_VALUE = 5
PREDATOR_REPRODUCTION_TIME = 20  # The chronons before reproducing

MAX_ENTITIES = 500  # The max number of organisms on the board
# The number of entities to delete from the unbalanced side
DELETE_UNBALANCED_ENTITIES = 50


class Entity:
    """
    Represents an entity (either prey or predator).

    >>> e = Entity(True, coords=(0, 0))
    >>> e.prey
    True
    >>> e.coords
    (0, 0)
    >>> e.alive
    True
    """

    def __init__(self, prey: bool, coords: tuple[int, int]) -> None:
        self.prey = prey
        # The (row, col) pos of the entity
        self.coords = coords

        self.remaining_reproduction_time = (
            PREY_REPRODUCTION_TIME if prey else PREDATOR_REPRODUCTION_TIME
        )
        self.energy_value = None if prey is True else PREDATOR_INITIAL_ENERGY_VALUE
        self.alive = True

    def reset_reproduction_time(self) -> None:
        """
        >>> e = Entity(True, coords=(0, 0))
        >>> e.reset_reproduction_time()
        >>> e.remaining_reproduction_time == PREY_REPRODUCTION_TIME
        True
        >>> e = Entity(False, coords=(0, 0))
        >>> e.reset_reproduction_time()
        >>> e.remaining_reproduction_time == PREDATOR_REPRODUCTION_TIME
        True
        """
        self.remaining_reproduction_time = (
            PREY_REPRODUCTION_TIME if self.prey is True else PREDATOR_REPRODUCTION_TIME
        )

    def __repr__(self) -> str:
        """
        >>> Entity(prey=True, coords=(1, 1))
        Entity(prey=True, coords=(1, 1), remaining_reproduction_time=5)
        >>> Entity(prey=False, coords=(2, 1))  # doctest: +NORMALIZE_WHITESPACE
        Entity(prey=False, coords=(2, 1),
        remaining_reproduction_time=20, energy_value=15)
        """
        repr_ = (
            f"Entity(prey={self.prey}, coords={self.coords}, "
            f"remaining_reproduction_time={self.remaining_reproduction_time}"
        )
        if self.energy_value is not None:
            repr_ += f", energy_value={self.energy_value}"
        return f"{repr_})"


class WaTor:
    """
    Represents the main Wa-Tor algorithm.

    :attr time_passed: A function that is called every time
        time passes (a chronon) in order to visually display
        the new Wa-Tor planet. The `time_passed` function can block
        using ``time.sleep`` to slow the algorithm progression.

    >>> wt = WaTor(10, 15)
    >>> wt.width
    10
    >>> wt.height
    15
    >>> len(wt.planet)
    15
    >>> len(wt.planet[0])
    10
    >>> len(wt.get_entities()) == PREDATOR_INITIAL_COUNT + PREY_INITIAL_COUNT
    True
    """

    time_passed: Callable[["WaTor", int], None] | None

    def __init__(self, width: int, height: int) -> None:
        self.width = width
        self.height = height
        self.time_passed = None

        self.planet: list[list[Entity | None]] = [[None] * width for _ in range(height)]

        # Populate planet with predators and prey randomly
        for _ in range(PREY_INITIAL_COUNT):
            self.add_entity(prey=True)
        for _ in range(PREDATOR_INITIAL_COUNT):
            self.add_entity(prey=False)
        self.set_planet(self.planet)

    def set_planet(self, planet: list[list[Entity | None]]) -> None:
        """
        Ease of access for testing

        >>> wt = WaTor(WIDTH, HEIGHT)
        >>> planet = [
        ... [None, None, None],
        ... [None, Entity(True, coords=(1, 1)), None]
        ... ]
        >>> wt.set_planet(planet)
        >>> wt.planet == planet
        True
        >>> wt.width
        3
        >>> wt.height
        2
        """
        self.planet = planet
        self.width = len(planet[0])
        self.height = len(planet)

    def add_entity(self, prey: bool) -> None:
        """
        Adds an entity, making sure the entity does
        not override another entity

        >>> wt = WaTor(WIDTH, HEIGHT)
        >>> wt.set_planet([[None, None], [None, None]])
        >>> wt.add_entity(True)
        >>> len(wt.get_entities())
        1
        >>> wt.add_entity(False)
        >>> len(wt.get_entities())
        2
        """
        while True:
            row, col = randint(0, self.height - 1), randint(0, self.width - 1)
            if self.planet[row][col] is None:
                self.planet[row][col] = Entity(prey=prey, coords=(row, col))
                return

    def get_entities(self) -> list[Entity]:
        """
        Returns a list of all the entities within the planet.

        >>> wt = WaTor(WIDTH, HEIGHT)
        >>> len(wt.get_entities()) == PREDATOR_INITIAL_COUNT + PREY_INITIAL_COUNT
        True
        """
        return [entity for column in self.planet for entity in column if entity]

    def balance_predators_and_prey(self) -> None:
        """
        Balances predators and preys so that prey
        can not dominate the predators, blocking up
        space for them to reproduce.

        >>> wt = WaTor(WIDTH, HEIGHT)
        >>> for i in range(2000):
        ...     row, col = i // HEIGHT, i % WIDTH
        ...     wt.planet[row][col] = Entity(True, coords=(row, col))
        >>> entities = len(wt.get_entities())
        >>> wt.balance_predators_and_prey()
        >>> len(wt.get_entities()) == entities
        False
        """
        entities = self.get_entities()
        shuffle(entities)

        if len(entities) >= MAX_ENTITIES - MAX_ENTITIES / 10:
            prey = [entity for entity in entities if entity.prey]
            predators = [entity for entity in entities if not entity.prey]

            prey_count, predator_count = len(prey), len(predators)

            entities_to_purge = (
                prey[:DELETE_UNBALANCED_ENTITIES]
                if prey_count > predator_count
                else predators[:DELETE_UNBALANCED_ENTITIES]
            )
            for entity in entities_to_purge:
                self.planet[entity.coords[0]][entity.coords[1]] = None

    def get_surrounding_prey(self, entity: Entity) -> list[Entity]:
        """
        Returns all the prey entities around (N, S, E, W) a predator entity.

        Subtly different to the `move_and_reproduce`.

        >>> wt = WaTor(WIDTH, HEIGHT)
        >>> wt.set_planet([
        ... [None, Entity(True, (0, 1)), None],
        ... [None, Entity(False, (1, 1)), None],
        ... [None, Entity(True, (2, 1)), None]])
        >>> wt.get_surrounding_prey(
        ... Entity(False, (1, 1)))  # doctest: +NORMALIZE_WHITESPACE
        [Entity(prey=True, coords=(0, 1), remaining_reproduction_time=5),
        Entity(prey=True, coords=(2, 1), remaining_reproduction_time=5)]
        >>> wt.set_planet([[Entity(False, (0, 0))]])
        >>> wt.get_surrounding_prey(Entity(False, (0, 0)))
        []
        >>> wt.set_planet([
        ... [Entity(True, (0, 0)), Entity(False, (1, 0)), Entity(False, (2, 0))],
        ... [None, Entity(False, (1, 1)), Entity(True, (2, 1))],
        ... [None, None, None]])
        >>> wt.get_surrounding_prey(Entity(False, (1, 0)))
        [Entity(prey=True, coords=(0, 0), remaining_reproduction_time=5)]
        """
        row, col = entity.coords
        adjacent: list[tuple[int, int]] = [
            (row - 1, col),  # North
            (row + 1, col),  # South
            (row, col - 1),  # West
            (row, col + 1),  # East
        ]

        return [
            ent
            for r, c in adjacent
            if 0 <= r < self.height
            and 0 <= c < self.width
            and (ent := self.planet[r][c]) is not None
            and ent.prey
        ]

    def move_and_reproduce(
        self, entity: Entity, direction_orders: list[Literal["N", "E", "S", "W"]]
    ) -> None:
        """
        Attempts to move to an unoccupied neighbouring square
        in either of the four directions (North, South, East, West).
        If the move was successful and the `remaining_reproduction_time` is
        equal to 0, then a new prey or predator can also be created
        in the previous square.

        :param direction_orders: Ordered list (like priority queue) depicting
                            order to attempt to move. Removes any systematic
                            approach of checking neighbouring squares.

        >>> planet = [
        ... [None, None, None],
        ... [None, Entity(True, coords=(1, 1)), None],
        ... [None, None, None]
        ... ]
        >>> wt = WaTor(WIDTH, HEIGHT)
        >>> wt.set_planet(planet)
        >>> wt.move_and_reproduce(Entity(True, coords=(1, 1)), direction_orders=["N"])
        >>> wt.planet  # doctest: +NORMALIZE_WHITESPACE
        [[None, Entity(prey=True, coords=(0, 1), remaining_reproduction_time=4), None],
        [None, None, None],
        [None, None, None]]
        >>> wt.planet[0][0] = Entity(True, coords=(0, 0))
        >>> wt.move_and_reproduce(Entity(True, coords=(0, 1)),
        ... direction_orders=["N", "W", "E", "S"])
        >>> wt.planet  # doctest: +NORMALIZE_WHITESPACE
        [[Entity(prey=True, coords=(0, 0), remaining_reproduction_time=5), None,
        Entity(prey=True, coords=(0, 2), remaining_reproduction_time=4)],
        [None, None, None],
        [None, None, None]]
        >>> wt.planet[0][1] = wt.planet[0][2]
        >>> wt.planet[0][2] = None
        >>> wt.move_and_reproduce(Entity(True, coords=(0, 1)),
        ... direction_orders=["N", "W", "S", "E"])
        >>> wt.planet  # doctest: +NORMALIZE_WHITESPACE
        [[Entity(prey=True, coords=(0, 0), remaining_reproduction_time=5), None, None],
        [None, Entity(prey=True, coords=(1, 1), remaining_reproduction_time=4), None],
        [None, None, None]]

        >>> wt = WaTor(WIDTH, HEIGHT)
        >>> reproducable_entity = Entity(False, coords=(0, 1))
        >>> reproducable_entity.remaining_reproduction_time = 0
        >>> wt.planet = [[None, reproducable_entity]]
        >>> wt.move_and_reproduce(reproducable_entity,
        ... direction_orders=["N", "W", "S", "E"])
        >>> wt.planet  # doctest: +NORMALIZE_WHITESPACE
        [[Entity(prey=False, coords=(0, 0),
        remaining_reproduction_time=20, energy_value=15),
        Entity(prey=False, coords=(0, 1), remaining_reproduction_time=20,
        energy_value=15)]]
        """
        row, col = coords = entity.coords

        adjacent_squares: dict[Literal["N", "E", "S", "W"], tuple[int, int]] = {
            "N": (row - 1, col),  # North
            "S": (row + 1, col),  # South
            "W": (row, col - 1),  # West
            "E": (row, col + 1),  # East
        }
        # Weight adjacent locations
        adjacent: list[tuple[int, int]] = []
        for order in direction_orders:
            adjacent.append(adjacent_squares[order])

        for r, c in adjacent:
            if (
                0 <= r < self.height
                and 0 <= c < self.width
                and self.planet[r][c] is None
            ):
                # Move entity to empty adjacent square
                self.planet[r][c] = entity
                self.planet[row][col] = None
                entity.coords = (r, c)
                break

        # (2.) See if it possible to reproduce in previous square
        if coords != entity.coords and entity.remaining_reproduction_time <= 0:
            # Check if the entities on the planet is less than the max limit
            if len(self.get_entities()) < MAX_ENTITIES:
                # Reproduce in previous square
                self.planet[row][col] = Entity(prey=entity.prey, coords=coords)
                entity.reset_reproduction_time()
        else:
            entity.remaining_reproduction_time -= 1

    def perform_prey_actions(
        self, entity: Entity, direction_orders: list[Literal["N", "E", "S", "W"]]
    ) -> None:
        """
        Performs the actions for a prey entity

        For prey the rules are:
            1. At each chronon, a prey moves randomly to one of the adjacent unoccupied
               squares. If there are no free squares, no movement takes place.
            2. Once a prey has survived a certain number of chronons it may reproduce.
               This is done as it moves to a neighbouring square,
               leaving behind a new prey in its old position.
               Its reproduction time is also reset to zero.

        >>> wt = WaTor(WIDTH, HEIGHT)
        >>> reproducable_entity = Entity(True, coords=(0, 1))
        >>> reproducable_entity.remaining_reproduction_time = 0
        >>> wt.planet = [[None, reproducable_entity]]
        >>> wt.perform_prey_actions(reproducable_entity,
        ... direction_orders=["N", "W", "S", "E"])
        >>> wt.planet  # doctest: +NORMALIZE_WHITESPACE
        [[Entity(prey=True, coords=(0, 0), remaining_reproduction_time=5),
        Entity(prey=True, coords=(0, 1), remaining_reproduction_time=5)]]
        """
        self.move_and_reproduce(entity, direction_orders)

    def perform_predator_actions(
        self,
        entity: Entity,
        occupied_by_prey_coords: tuple[int, int] | None,
        direction_orders: list[Literal["N", "E", "S", "W"]],
    ) -> None:
        """
        Performs the actions for a predator entity

        :param occupied_by_prey_coords: Move to this location if there is prey there

        For predators the rules are:
            1. At each chronon, a predator moves randomly to an adjacent square occupied
               by a prey. If there is none, the predator moves to a random adjacent
               unoccupied square. If there are no free squares, no movement takes place.
            2. At each chronon, each predator is deprived of a unit of energy.
            3. Upon reaching zero energy, a predator dies.
            4. If a predator moves to a square occupied by a prey,
               it eats the prey and earns a certain amount of energy.
            5. Once a predator has survived a certain number of chronons
               it may reproduce in exactly the same way as the prey.

        >>> wt = WaTor(WIDTH, HEIGHT)
        >>> wt.set_planet([[Entity(True, coords=(0, 0)), Entity(False, coords=(0, 1))]])
        >>> wt.perform_predator_actions(Entity(False, coords=(0, 1)), (0, 0), [])
        >>> wt.planet  # doctest: +NORMALIZE_WHITESPACE
        [[Entity(prey=False, coords=(0, 0),
        remaining_reproduction_time=20, energy_value=19), None]]
        """
        assert entity.energy_value is not None  # [type checking]

        # (3.) If the entity has 0 energy, it will die
        if entity.energy_value == 0:
            self.planet[entity.coords[0]][entity.coords[1]] = None
            return

        # (1.) Move to entity if possible
        if occupied_by_prey_coords is not None:
            # Kill the prey
            prey = self.planet[occupied_by_prey_coords[0]][occupied_by_prey_coords[1]]
            assert prey is not None
            prey.alive = False

            # Move onto prey
            self.planet[occupied_by_prey_coords[0]][occupied_by_prey_coords[1]] = entity
            self.planet[entity.coords[0]][entity.coords[1]] = None

            entity.coords = occupied_by_prey_coords
            # (4.) Eats the prey and earns energy
            entity.energy_value += PREDATOR_FOOD_VALUE
        else:
            # (5.) If it has survived the certain number of chronons it will also
            # reproduce in this function
            self.move_and_reproduce(entity, direction_orders)

        # (2.) Each chronon, the predator is deprived of a unit of energy
        entity.energy_value -= 1

    def run(self, *, iteration_count: int) -> None:
        """
        Emulate time passing by looping `iteration_count` times

        >>> wt = WaTor(WIDTH, HEIGHT)
        >>> wt.run(iteration_count=PREDATOR_INITIAL_ENERGY_VALUE - 1)
        >>> len(list(filter(lambda entity: entity.prey is False,
        ... wt.get_entities()))) >= PREDATOR_INITIAL_COUNT
        True
        """
        for iter_num in range(iteration_count):
            # Generate list of all entities in order to randomly
            # pop an entity at a time to simulate true randomness
            # This removes the systematic approach of iterating
            # through each entity width by height
            all_entities = self.get_entities()

            for __ in range(len(all_entities)):
                entity = all_entities.pop(randint(0, len(all_entities) - 1))
                if entity.alive is False:
                    continue

                directions: list[Literal["N", "E", "S", "W"]] = ["N", "E", "S", "W"]
                shuffle(directions)  # Randomly shuffle directions

                if entity.prey:
                    self.perform_prey_actions(entity, directions)
                else:
                    # Create list of surrounding prey
                    surrounding_prey = self.get_surrounding_prey(entity)
                    surrounding_prey_coords = None

                    if surrounding_prey:
                        # Again, randomly shuffle directions
                        shuffle(surrounding_prey)
                        surrounding_prey_coords = surrounding_prey[0].coords

                    self.perform_predator_actions(
                        entity, surrounding_prey_coords, directions
                    )

            # Balance out the predators and prey
            self.balance_predators_and_prey()

            if self.time_passed is not None:
                # Call time_passed function for Wa-Tor planet
                # visualisation in a terminal or a graph.
                self.time_passed(self, iter_num)


def visualise(wt: WaTor, iter_number: int, *, colour: bool = True) -> None:
    """
    Visually displays the Wa-Tor planet using
    an ascii code in terminal to clear and re-print
    the Wa-Tor planet at intervals.

    Uses ascii colour codes to colourfully display the predators and prey:
        * (0x60f197) Prey = ``#``
        * (0xfffff) Predator = ``x``

    >>> wt = WaTor(30, 30)
    >>> wt.set_planet([
    ... [Entity(True, coords=(0, 0)), Entity(False, coords=(0, 1)), None],
    ... [Entity(False, coords=(1, 0)), None, Entity(False, coords=(1, 2))],
    ... [None, Entity(True, coords=(2, 1)), None]
    ... ])
    >>> visualise(wt, 0, colour=False)  # doctest: +NORMALIZE_WHITESPACE
    #  x  .
    x  .  x
    .  #  .
    <BLANKLINE>
    Iteration: 0 | Prey count: 2 | Predator count: 3 |
    """
    if colour:
        __import__("os").system("")
        print("\x1b[0;0H\x1b[2J\x1b[?25l")

    reprint = "\x1b[0;0H" if colour else ""
    ansi_colour_end = "\x1b[0m " if colour else " "

    planet = wt.planet
    output = ""

    # Iterate over every entity in the planet
    for row in planet:
        for entity in row:
            if entity is None:
                output += " . "
            else:
                if colour is True:
                    output += (
                        "\x1b[38;2;96;241;151m"
                        if entity.prey
                        else "\x1b[38;2;255;255;15m"
                    )
                output += f" {'#' if entity.prey else 'x'}{ansi_colour_end}"

        output += "\n"

    entities = wt.get_entities()
    prey_count = sum(entity.prey for entity in entities)

    print(
        f"{output}\n Iteration: {iter_number} | Prey count: {prey_count} | "
        f"Predator count: {len(entities) - prey_count} | {reprint}"
    )
    # Block the thread to be able to visualise seeing the algorithm
    sleep(0.05)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    wt = WaTor(WIDTH, HEIGHT)
    wt.time_passed = visualise
    wt.run(iteration_count=100_000)


================================================
FILE: ciphers/README.md
================================================
# Ciphers

Ciphers are used to protect data from people that are not allowed to have it. They are everywhere on the internet to protect your connections.

* <https://en.wikipedia.org/wiki/Cipher>
* <http://practicalcryptography.com/ciphers/>
* <https://practicalcryptography.com/ciphers/classical-era/>


================================================
FILE: ciphers/__init__.py
================================================


================================================
FILE: ciphers/a1z26.py
================================================
"""
Convert a string of characters to a sequence of numbers
corresponding to the character's position in the alphabet.

https://www.dcode.fr/letter-number-cipher
http://bestcodes.weebly.com/a1z26.html
"""

from __future__ import annotations


def encode(plain: str) -> list[int]:
    """
    >>> encode("myname")
    [13, 25, 14, 1, 13, 5]
    """
    return [ord(elem) - 96 for elem in plain]


def decode(encoded: list[int]) -> str:
    """
    >>> decode([13, 25, 14, 1, 13, 5])
    'myname'
    """
    return "".join(chr(elem + 96) for elem in encoded)


def main() -> None:
    encoded = encode(input("-> ").strip().lower())
    print("Encoded: ", encoded)
    print("Decoded:", decode(encoded))


if __name__ == "__main__":
    main()


================================================
FILE: ciphers/affine_cipher.py
================================================
import random
import sys

from maths.greatest_common_divisor import gcd_by_iterative

from . import cryptomath_module as cryptomath

SYMBOLS = (
    r""" !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`"""
    r"""abcdefghijklmnopqrstuvwxyz{|}~"""
)


def check_keys(key_a: int, key_b: int, mode: str) -> None:
    if mode == "encrypt":
        if key_a == 1:
            sys.exit(
                "The affine cipher becomes weak when key "
                "A is set to 1. Choose different key"
            )
        if key_b == 0:
            sys.exit(
                "The affine cipher becomes weak when key "
                "B is set to 0. Choose different key"
            )
    if key_a < 0 or key_b < 0 or key_b > len(SYMBOLS) - 1:
        sys.exit(
            "Key A must be greater than 0 and key B must "
            f"be between 0 and {len(SYMBOLS) - 1}."
        )
    if gcd_by_iterative(key_a, len(SYMBOLS)) != 1:
        sys.exit(
            f"Key A {key_a} and the symbol set size {len(SYMBOLS)} "
            "are not relatively prime. Choose a different key."
        )


def encrypt_message(key: int, message: str) -> str:
    """
    >>> encrypt_message(4545, 'The affine cipher is a type of monoalphabetic '
    ...                       'substitution cipher.')
    'VL}p MM{I}p~{HL}Gp{vp pFsH}pxMpyxIx JHL O}F{~pvuOvF{FuF{xIp~{HL}Gi'
    """
    key_a, key_b = divmod(key, len(SYMBOLS))
    check_keys(key_a, key_b, "encrypt")
    cipher_text = ""
    for symbol in message:
        if symbol in SYMBOLS:
            sym_index = SYMBOLS.find(symbol)
            cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(SYMBOLS)]
        else:
            cipher_text += symbol
    return cipher_text


def decrypt_message(key: int, message: str) -> str:
    """
    >>> decrypt_message(4545, 'VL}p MM{I}p~{HL}Gp{vp pFsH}pxMpyxIx JHL O}F{~pvuOvF{FuF'
    ...                       '{xIp~{HL}Gi')
    'The affine cipher is a type of monoalphabetic substitution cipher.'
    """
    key_a, key_b = divmod(key, len(SYMBOLS))
    check_keys(key_a, key_b, "decrypt")
    plain_text = ""
    mod_inverse_of_key_a = cryptomath.find_mod_inverse(key_a, len(SYMBOLS))
    for symbol in message:
        if symbol in SYMBOLS:
            sym_index = SYMBOLS.find(symbol)
            plain_text += SYMBOLS[
                (sym_index - key_b) * mod_inverse_of_key_a % len(SYMBOLS)
            ]
        else:
            plain_text += symbol
    return plain_text


def get_random_key() -> int:
    while True:
        key_b = random.randint(2, len(SYMBOLS))
        key_b = random.randint(2, len(SYMBOLS))
        if gcd_by_iterative(key_b, len(SYMBOLS)) == 1 and key_b % len(SYMBOLS) != 0:
            return key_b * len(SYMBOLS) + key_b


def main() -> None:
    """
    >>> key = get_random_key()
    >>> msg = "This is a test!"
    >>> decrypt_message(key, encrypt_message(key, msg)) == msg
    True
    """
    message = input("Enter message: ").strip()
    key = int(input("Enter key [2000 - 9000]: ").strip())
    mode = input("Encrypt/Decrypt [E/D]: ").strip().lower()

    if mode.startswith("e"):
        mode = "encrypt"
        translated = encrypt_message(key, message)
    elif mode.startswith("d"):
        mode = "decrypt"
        translated = decrypt_message(key, message)
    print(f"\n{mode.title()}ed text: \n{translated}")


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    # main()


================================================
FILE: ciphers/atbash.py
================================================
"""https://en.wikipedia.org/wiki/Atbash"""

import string


def atbash_slow(sequence: str) -> str:
    """
    >>> atbash_slow("ABCDEFG")
    'ZYXWVUT'

    >>> atbash_slow("aW;;123BX")
    'zD;;123YC'
    """
    output = ""
    for i in sequence:
        extract = ord(i)
        if 65 <= extract <= 90:
            output += chr(155 - extract)
        elif 97 <= extract <= 122:
            output += chr(219 - extract)
        else:
            output += i
    return output


def atbash(sequence: str) -> str:
    """
    >>> atbash("ABCDEFG")
    'ZYXWVUT'

    >>> atbash("aW;;123BX")
    'zD;;123YC'
    """
    letters = string.ascii_letters
    letters_reversed = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
    return "".join(
        letters_reversed[letters.index(c)] if c in letters else c for c in sequence
    )


def benchmark() -> None:
    """Let's benchmark our functions side-by-side..."""
    from timeit import timeit

    print("Running performance benchmarks...")
    setup = "from string import printable ; from __main__ import atbash, atbash_slow"
    print(f"> atbash_slow(): {timeit('atbash_slow(printable)', setup=setup)} seconds")
    print(f">      atbash(): {timeit('atbash(printable)', setup=setup)} seconds")


if __name__ == "__main__":
    for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
        print(f"{example} encrypted in atbash: {atbash(example)}")
    benchmark()


================================================
FILE: ciphers/autokey.py
================================================
"""
https://en.wikipedia.org/wiki/Autokey_cipher

An autokey cipher (also known as the autoclave cipher) is a cipher that
incorporates the message (the plaintext) into the key.
The key is generated from the message in some automated fashion,
sometimes by selecting certain letters from the text or, more commonly,
by adding a short primer key to the front of the message.
"""


def encrypt(plaintext: str, key: str) -> str:
    """
    Encrypt a given `plaintext` (string) and `key` (string), returning the
    encrypted ciphertext.

    >>> encrypt("hello world", "coffee")
    'jsqqs avvwo'
    >>> encrypt("coffee is good as python", "TheAlgorithms")
    'vvjfpk wj ohvp su ddylsv'
    >>> encrypt("coffee is good as python", 2)
    Traceback (most recent call last):
        ...
    TypeError: key must be a string
    >>> encrypt("", "TheAlgorithms")
    Traceback (most recent call last):
        ...
    ValueError: plaintext is empty
    >>> encrypt("coffee is good as python", "")
    Traceback (most recent call last):
        ...
    ValueError: key is empty
    >>> encrypt(527.26, "TheAlgorithms")
    Traceback (most recent call last):
        ...
    TypeError: plaintext must be a string
    """
    if not isinstance(plaintext, str):
        raise TypeError("plaintext must be a string")
    if not isinstance(key, str):
        raise TypeError("key must be a string")

    if not plaintext:
        raise ValueError("plaintext is empty")
    if not key:
        raise ValueError("key is empty")

    key += plaintext
    plaintext = plaintext.lower()
    key = key.lower()
    plaintext_iterator = 0
    key_iterator = 0
    ciphertext = ""
    while plaintext_iterator < len(plaintext):
        if (
            ord(plaintext[plaintext_iterator]) < 97
            or ord(plaintext[plaintext_iterator]) > 122
        ):
            ciphertext += plaintext[plaintext_iterator]
            plaintext_iterator += 1
        elif ord(key[key_iterator]) < 97 or ord(key[key_iterator]) > 122:
            key_iterator += 1
        else:
            ciphertext += chr(
                (
                    (ord(plaintext[plaintext_iterator]) - 97 + ord(key[key_iterator]))
                    - 97
                )
                % 26
                + 97
            )
            key_iterator += 1
            plaintext_iterator += 1
    return ciphertext


def decrypt(ciphertext: str, key: str) -> str:
    """
    Decrypt a given `ciphertext` (string) and `key` (string), returning the decrypted
    ciphertext.

    >>> decrypt("jsqqs avvwo", "coffee")
    'hello world'
    >>> decrypt("vvjfpk wj ohvp su ddylsv", "TheAlgorithms")
    'coffee is good as python'
    >>> decrypt("vvjfpk wj ohvp su ddylsv", "")
    Traceback (most recent call last):
        ...
    ValueError: key is empty
    >>> decrypt(527.26, "TheAlgorithms")
    Traceback (most recent call last):
        ...
    TypeError: ciphertext must be a string
    >>> decrypt("", "TheAlgorithms")
    Traceback (most recent call last):
        ...
    ValueError: ciphertext is empty
    >>> decrypt("vvjfpk wj ohvp su ddylsv", 2)
    Traceback (most recent call last):
        ...
    TypeError: key must be a string
    """
    if not isinstance(ciphertext, str):
        raise TypeError("ciphertext must be a string")
    if not isinstance(key, str):
        raise TypeError("key must be a string")

    if not ciphertext:
        raise ValueError("ciphertext is empty")
    if not key:
        raise ValueError("key is empty")

    key = key.lower()
    ciphertext_iterator = 0
    key_iterator = 0
    plaintext = ""
    while ciphertext_iterator < len(ciphertext):
        if (
            ord(ciphertext[ciphertext_iterator]) < 97
            or ord(ciphertext[ciphertext_iterator]) > 122
        ):
            plaintext += ciphertext[ciphertext_iterator]
        else:
            plaintext += chr(
                (ord(ciphertext[ciphertext_iterator]) - ord(key[key_iterator])) % 26
                + 97
            )
            key += chr(
                (ord(ciphertext[ciphertext_iterator]) - ord(key[key_iterator])) % 26
                + 97
            )
            key_iterator += 1
        ciphertext_iterator += 1
    return plaintext


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    operation = int(input("Type 1 to encrypt or 2 to decrypt:"))
    if operation == 1:
        plaintext = input("Typeplaintext to be encrypted:\n")
        key = input("Type the key:\n")
        print(encrypt(plaintext, key))
    elif operation == 2:
        ciphertext = input("Type the ciphertext to be decrypted:\n")
        key = input("Type the key:\n")
        print(decrypt(ciphertext, key))
    decrypt("jsqqs avvwo", "coffee")


================================================
FILE: ciphers/baconian_cipher.py
================================================
"""
Program to encode and decode Baconian or Bacon's Cipher
Wikipedia reference : https://en.wikipedia.org/wiki/Bacon%27s_cipher
"""

encode_dict = {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


decode_dict = {value: key for key, value in encode_dict.items()}


def encode(word: str) -> str:
    """
    Encodes to Baconian cipher

    >>> encode("hello")
    'AABBBAABAAABABAABABAABBAB'
    >>> encode("hello world")
    'AABBBAABAAABABAABABAABBAB BABAAABBABBAAAAABABAAAABB'
    >>> encode("hello world!")
    Traceback (most recent call last):
        ...
    Exception: encode() accepts only letters of the alphabet and spaces
    """
    encoded = ""
    for letter in word.lower():
        if letter.isalpha() or letter == " ":
            encoded += encode_dict[letter]
        else:
            raise Exception("encode() accepts only letters of the alphabet and spaces")
    return encoded


def decode(coded: str) -> str:
    """
    Decodes from Baconian cipher

    >>> decode("AABBBAABAAABABAABABAABBAB BABAAABBABBAAAAABABAAAABB")
    'hello world'
    >>> decode("AABBBAABAAABABAABABAABBAB")
    'hello'
    >>> decode("AABBBAABAAABABAABABAABBAB BABAAABBABBAAAAABABAAAABB!")
    Traceback (most recent call last):
        ...
    Exception: decode() accepts only 'A', 'B' and spaces
    """
    if set(coded) - {"A", "B", " "} != set():
        raise Exception("decode() accepts only 'A', 'B' and spaces")
    decoded = ""
    for word in coded.split():
        while len(word) != 0:
            decoded += decode_dict[word[:5]]
            word = word[5:]
        decoded += " "
    return decoded.strip()


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: ciphers/base16.py
================================================
def base16_encode(data: bytes) -> str:
    """
    Encodes the given bytes into base16.

    >>> base16_encode(b'Hello World!')
    '48656C6C6F20576F726C6421'
    >>> base16_encode(b'HELLO WORLD!')
    '48454C4C4F20574F524C4421'
    >>> base16_encode(b'')
    ''
    """
    # Turn the data into a list of integers (where each integer is a byte),
    # Then turn each byte into its hexadecimal representation, make sure
    # it is uppercase, and then join everything together and return it.
    return "".join([hex(byte)[2:].zfill(2).upper() for byte in list(data)])


def base16_decode(data: str) -> bytes:
    """
    Decodes the given base16 encoded data into bytes.

    >>> base16_decode('48656C6C6F20576F726C6421')
    b'Hello World!'
    >>> base16_decode('48454C4C4F20574F524C4421')
    b'HELLO WORLD!'
    >>> base16_decode('')
    b''
    >>> base16_decode('486')
    Traceback (most recent call last):
      ...
    ValueError: Base16 encoded data is invalid:
    Data does not have an even number of hex digits.
    >>> base16_decode('48656c6c6f20576f726c6421')
    Traceback (most recent call last):
      ...
    ValueError: Base16 encoded data is invalid:
    Data is not uppercase hex or it contains invalid characters.
    >>> base16_decode('This is not base64 encoded data.')
    Traceback (most recent call last):
      ...
    ValueError: Base16 encoded data is invalid:
    Data is not uppercase hex or it contains invalid characters.
    """
    # Check data validity, following RFC3548
    # https://www.ietf.org/rfc/rfc3548.txt
    if (len(data) % 2) != 0:
        raise ValueError(
            """Base16 encoded data is invalid:
Data does not have an even number of hex digits."""
        )
    # Check the character set - the standard base16 alphabet
    # is uppercase according to RFC3548 section 6
    if not set(data) <= set("0123456789ABCDEF"):
        raise ValueError(
            """Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters."""
        )
    # For every two hexadecimal digits (= a byte), turn it into an integer.
    # Then, string the result together into bytes, and return it.
    return bytes(int(data[i] + data[i + 1], 16) for i in range(0, len(data), 2))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: ciphers/base32.py
================================================
"""
Base32 encoding and decoding

https://en.wikipedia.org/wiki/Base32
"""

B32_CHARSET = "ABCDEFGHIJKLMNOPQRSTUVWXYZ234567"


def base32_encode(data: bytes) -> bytes:
    """
    >>> base32_encode(b"Hello World!")
    b'JBSWY3DPEBLW64TMMQQQ===='
    >>> base32_encode(b"123456")
    b'GEZDGNBVGY======'
    >>> base32_encode(b"some long complex string")
    b'ONXW2ZJANRXW4ZZAMNXW24DMMV4CA43UOJUW4ZY='
    """
    binary_data = "".join(bin(ord(d))[2:].zfill(8) for d in data.decode("utf-8"))
    binary_data = binary_data.ljust(5 * ((len(binary_data) // 5) + 1), "0")
    b32_chunks = map("".join, zip(*[iter(binary_data)] * 5))
    b32_result = "".join(B32_CHARSET[int(chunk, 2)] for chunk in b32_chunks)
    return bytes(b32_result.ljust(8 * ((len(b32_result) // 8) + 1), "="), "utf-8")


def base32_decode(data: bytes) -> bytes:
    """
    >>> base32_decode(b'JBSWY3DPEBLW64TMMQQQ====')
    b'Hello World!'
    >>> base32_decode(b'GEZDGNBVGY======')
    b'123456'
    >>> base32_decode(b'ONXW2ZJANRXW4ZZAMNXW24DMMV4CA43UOJUW4ZY=')
    b'some long complex string'
    """
    binary_chunks = "".join(
        bin(B32_CHARSET.index(_d))[2:].zfill(5)
        for _d in data.decode("utf-8").strip("=")
    )
    binary_data = list(map("".join, zip(*[iter(binary_chunks)] * 8)))
    return bytes("".join([chr(int(_d, 2)) for _d in binary_data]), "utf-8")


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: ciphers/base64_cipher.py
================================================
B64_CHARSET = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"


def base64_encode(data: bytes) -> bytes:
    """Encodes data according to RFC4648.

    The data is first transformed to binary and appended with binary digits so that its
    length becomes a multiple of 6, then each 6 binary digits will match a character in
    the B64_CHARSET string. The number of appended binary digits would later determine
    how many "=" signs should be added, the padding.
    For every 2 binary digits added, a "=" sign is added in the output.
    We can add any binary digits to make it a multiple of 6, for instance, consider the
    following example:
    "AA" -> 0010100100101001 -> 001010 010010 1001
    As can be seen above, 2 more binary digits should be added, so there's 4
    possibilities here: 00, 01, 10 or 11.
    That being said, Base64 encoding can be used in Steganography to hide data in these
    appended digits.

    >>> from base64 import b64encode
    >>> a = b"This pull request is part of Hacktoberfest20!"
    >>> b = b"https://tools.ietf.org/html/rfc4648"
    >>> c = b"A"
    >>> base64_encode(a) == b64encode(a)
    True
    >>> base64_encode(b) == b64encode(b)
    True
    >>> base64_encode(c) == b64encode(c)
    True
    >>> base64_encode("abc")
    Traceback (most recent call last):
      ...
    TypeError: a bytes-like object is required, not 'str'
    """
    # Make sure the supplied data is a bytes-like object
    if not isinstance(data, bytes):
        msg = f"a bytes-like object is required, not '{data.__class__.__name__}'"
        raise TypeError(msg)

    binary_stream = "".join(bin(byte)[2:].zfill(8) for byte in data)

    padding_needed = len(binary_stream) % 6 != 0

    if padding_needed:
        # The padding that will be added later
        padding = b"=" * ((6 - len(binary_stream) % 6) // 2)

        # Append binary_stream with arbitrary binary digits (0's by default) to make its
        # length a multiple of 6.
        binary_stream += "0" * (6 - len(binary_stream) % 6)
    else:
        padding = b""

    # Encode every 6 binary digits to their corresponding Base64 character
    return (
        "".join(
            B64_CHARSET[int(binary_stream[index : index + 6], 2)]
            for index in range(0, len(binary_stream), 6)
        ).encode()
        + padding
    )


def base64_decode(encoded_data: str) -> bytes:
    """Decodes data according to RFC4648.

    This does the reverse operation of base64_encode.
    We first transform the encoded data back to a binary stream, take off the
    previously appended binary digits according to the padding, at this point we
    would have a binary stream whose length is multiple of 8, the last step is
    to convert every 8 bits to a byte.

    >>> from base64 import b64decode
    >>> a = "VGhpcyBwdWxsIHJlcXVlc3QgaXMgcGFydCBvZiBIYWNrdG9iZXJmZXN0MjAh"
    >>> b = "aHR0cHM6Ly90b29scy5pZXRmLm9yZy9odG1sL3JmYzQ2NDg="
    >>> c = "QQ=="
    >>> base64_decode(a) == b64decode(a)
    True
    >>> base64_decode(b) == b64decode(b)
    True
    >>> base64_decode(c) == b64decode(c)
    True
    >>> base64_decode("abc")
    Traceback (most recent call last):
      ...
    AssertionError: Incorrect padding
    """
    # Make sure encoded_data is either a string or a bytes-like object
    if not isinstance(encoded_data, bytes) and not isinstance(encoded_data, str):
        msg = (
            "argument should be a bytes-like object or ASCII string, "
            f"not '{encoded_data.__class__.__name__}'"
        )
        raise TypeError(msg)

    # In case encoded_data is a bytes-like object, make sure it contains only
    # ASCII characters so we convert it to a string object
    if isinstance(encoded_data, bytes):
        try:
            encoded_data = encoded_data.decode("utf-8")
        except UnicodeDecodeError:
            raise ValueError("base64 encoded data should only contain ASCII characters")

    padding = encoded_data.count("=")

    # Check if the encoded string contains non base64 characters
    if padding:
        assert all(char in B64_CHARSET for char in encoded_data[:-padding]), (
            "Invalid base64 character(s) found."
        )
    else:
        assert all(char in B64_CHARSET for char in encoded_data), (
            "Invalid base64 character(s) found."
        )

    # Check the padding
    assert len(encoded_data) % 4 == 0 and padding < 3, "Incorrect padding"

    if padding:
        # Remove padding if there is one
        encoded_data = encoded_data[:-padding]

        binary_stream = "".join(
            bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
        )[: -padding * 2]
    else:
        binary_stream = "".join(
            bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
        )

    data = [
        int(binary_stream[index : index + 8], 2)
        for index in range(0, len(binary_stream), 8)
    ]

    return bytes(data)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: ciphers/base85.py
================================================
"""
Base85 (Ascii85) encoding and decoding

https://en.wikipedia.org/wiki/Ascii85
"""


def _base10_to_85(d: int) -> str:
    return "".join(chr(d % 85 + 33)) + _base10_to_85(d // 85) if d > 0 else ""


def _base85_to_10(digits: list) -> int:
    return sum(char * 85**i for i, char in enumerate(reversed(digits)))


def ascii85_encode(data: bytes) -> bytes:
    """
    >>> ascii85_encode(b"")
    b''
    >>> ascii85_encode(b"12345")
    b'0etOA2#'
    >>> ascii85_encode(b"base 85")
    b'@UX=h+?24'
    """
    binary_data = "".join(bin(ord(d))[2:].zfill(8) for d in data.decode("utf-8"))
    null_values = (32 * ((len(binary_data) // 32) + 1) - len(binary_data)) // 8
    binary_data = binary_data.ljust(32 * ((len(binary_data) // 32) + 1), "0")
    b85_chunks = [int(_s, 2) for _s in map("".join, zip(*[iter(binary_data)] * 32))]
    result = "".join(_base10_to_85(chunk)[::-1] for chunk in b85_chunks)
    return bytes(result[:-null_values] if null_values % 4 != 0 else result, "utf-8")


def ascii85_decode(data: bytes) -> bytes:
    """
    >>> ascii85_decode(b"")
    b''
    >>> ascii85_decode(b"0etOA2#")
    b'12345'
    >>> ascii85_decode(b"@UX=h+?24")
    b'base 85'
    """
    null_values = 5 * ((len(data) // 5) + 1) - len(data)
    binary_data = data.decode("utf-8") + "u" * null_values
    b85_chunks = map("".join, zip(*[iter(binary_data)] * 5))
    b85_segments = [[ord(_s) - 33 for _s in chunk] for chunk in b85_chunks]
    results = [bin(_base85_to_10(chunk))[2::].zfill(32) for chunk in b85_segments]
    char_chunks = [
        [chr(int(_s, 2)) for _s in map("".join, zip(*[iter(r)] * 8))] for r in results
    ]
    result = "".join("".join(char) for char in char_chunks)
    offset = int(null_values % 5 == 0)
    return bytes(result[: offset - null_values], "utf-8")


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: ciphers/beaufort_cipher.py
================================================
"""
Author: Mohit Radadiya
"""

from string import ascii_uppercase

dict1 = {char: i for i, char in enumerate(ascii_uppercase)}
dict2 = dict(enumerate(ascii_uppercase))


# This function generates the key in
# a cyclic manner until it's length isn't
# equal to the length of original text
def generate_key(message: str, key: str) -> str:
    """
    >>> generate_key("THE GERMAN ATTACK","SECRET")
    'SECRETSECRETSECRE'
    """
    x = len(message)
    i = 0
    while True:
        if x == i:
            i = 0
        if len(key) == len(message):
            break
        key += key[i]
        i += 1
    return key


# This function returns the encrypted text
# generated with the help of the key
def cipher_text(message: str, key_new: str) -> str:
    """
    >>> cipher_text("THE GERMAN ATTACK","SECRETSECRETSECRE")
    'BDC PAYUWL JPAIYI'
    """
    cipher_text = ""
    i = 0
    for letter in message:
        if letter == " ":
            cipher_text += " "
        else:
            x = (dict1[letter] - dict1[key_new[i]]) % 26
            i += 1
            cipher_text += dict2[x]
    return cipher_text


# This function decrypts the encrypted text
# and returns the original text
def original_text(cipher_text: str, key_new: str) -> str:
    """
    >>> original_text("BDC PAYUWL JPAIYI","SECRETSECRETSECRE")
    'THE GERMAN ATTACK'
    """
    or_txt = ""
    i = 0
    for letter in cipher_text:
        if letter == " ":
            or_txt += " "
        else:
            x = (dict1[letter] + dict1[key_new[i]] + 26) % 26
            i += 1
            or_txt += dict2[x]
    return or_txt


def main() -> None:
    message = "THE GERMAN ATTACK"
    key = "SECRET"
    key_new = generate_key(message, key)
    s = cipher_text(message, key_new)
    print(f"Encrypted Text = {s}")
    print(f"Original Text = {original_text(s, key_new)}")


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: ciphers/bifid.py
================================================
#!/usr/bin/env python3

"""
The Bifid Cipher uses a Polybius Square to encipher a message in a way that
makes it fairly difficult to decipher without knowing the secret.

https://www.braingle.com/brainteasers/codes/bifid.php
"""

import numpy as np

SQUARE = [
    ["a", "b", "c", "d", "e"],
    ["f", "g", "h", "i", "k"],
    ["l", "m", "n", "o", "p"],
    ["q", "r", "s", "t", "u"],
    ["v", "w", "x", "y", "z"],
]


class BifidCipher:
    def __init__(self) -> None:
        self.SQUARE = np.array(SQUARE)

    def letter_to_numbers(self, letter: str) -> np.ndarray:
        """
        Return the pair of numbers that represents the given letter in the
        polybius square

        >>> np.array_equal(BifidCipher().letter_to_numbers('a'), [1,1])
        True

        >>> np.array_equal(BifidCipher().letter_to_numbers('u'), [4,5])
        True
        """
        index1, index2 = np.where(letter == self.SQUARE)
        indexes = np.concatenate([index1 + 1, index2 + 1])
        return indexes

    def numbers_to_letter(self, index1: int, index2: int) -> str:
        """
        Return the letter corresponding to the position [index1, index2] in
        the polybius square

        >>> BifidCipher().numbers_to_letter(4, 5) == "u"
        True

        >>> BifidCipher().numbers_to_letter(1, 1) == "a"
        True
        """
        letter = self.SQUARE[index1 - 1, index2 - 1]
        return letter

    def encode(self, message: str) -> str:
        """
        Return the encoded version of message according to the polybius cipher

        >>> BifidCipher().encode('testmessage') == 'qtltbdxrxlk'
        True

        >>> BifidCipher().encode('Test Message') == 'qtltbdxrxlk'
        True

        >>> BifidCipher().encode('test j') == BifidCipher().encode('test i')
        True
        """
        message = message.lower()
        message = message.replace(" ", "")
        message = message.replace("j", "i")

        first_step = np.empty((2, len(message)))
        for letter_index in range(len(message)):
            numbers = self.letter_to_numbers(message[letter_index])

            first_step[0, letter_index] = numbers[0]
            first_step[1, letter_index] = numbers[1]

        second_step = first_step.reshape(2 * len(message))
        encoded_message = ""
        for numbers_index in range(len(message)):
            index1 = int(second_step[numbers_index * 2])
            index2 = int(second_step[(numbers_index * 2) + 1])
            letter = self.numbers_to_letter(index1, index2)
            encoded_message = encoded_message + letter

        return encoded_message

    def decode(self, message: str) -> str:
        """
        Return the decoded version of message according to the polybius cipher

        >>> BifidCipher().decode('qtltbdxrxlk') == 'testmessage'
        True
        """
        message = message.lower()
        message.replace(" ", "")
        first_step = np.empty(2 * len(message))
        for letter_index in range(len(message)):
            numbers = self.letter_to_numbers(message[letter_index])
            first_step[letter_index * 2] = numbers[0]
            first_step[letter_index * 2 + 1] = numbers[1]

        second_step = first_step.reshape((2, len(message)))
        decoded_message = ""
        for numbers_index in range(len(message)):
            index1 = int(second_step[0, numbers_index])
            index2 = int(second_step[1, numbers_index])
            letter = self.numbers_to_letter(index1, index2)
            decoded_message = decoded_message + letter

        return decoded_message


================================================
FILE: ciphers/brute_force_caesar_cipher.py
================================================
import string


def decrypt(message: str) -> None:
    """
    >>> decrypt('TMDETUX PMDVU')
    Decryption using Key #0: TMDETUX PMDVU
    Decryption using Key #1: SLCDSTW OLCUT
    Decryption using Key #2: RKBCRSV NKBTS
    Decryption using Key #3: QJABQRU MJASR
    Decryption using Key #4: PIZAPQT LIZRQ
    Decryption using Key #5: OHYZOPS KHYQP
    Decryption using Key #6: NGXYNOR JGXPO
    Decryption using Key #7: MFWXMNQ IFWON
    Decryption using Key #8: LEVWLMP HEVNM
    Decryption using Key #9: KDUVKLO GDUML
    Decryption using Key #10: JCTUJKN FCTLK
    Decryption using Key #11: IBSTIJM EBSKJ
    Decryption using Key #12: HARSHIL DARJI
    Decryption using Key #13: GZQRGHK CZQIH
    Decryption using Key #14: FYPQFGJ BYPHG
    Decryption using Key #15: EXOPEFI AXOGF
    Decryption using Key #16: DWNODEH ZWNFE
    Decryption using Key #17: CVMNCDG YVMED
    Decryption using Key #18: BULMBCF XULDC
    Decryption using Key #19: ATKLABE WTKCB
    Decryption using Key #20: ZSJKZAD VSJBA
    Decryption using Key #21: YRIJYZC URIAZ
    Decryption using Key #22: XQHIXYB TQHZY
    Decryption using Key #23: WPGHWXA SPGYX
    Decryption using Key #24: VOFGVWZ ROFXW
    Decryption using Key #25: UNEFUVY QNEWV
    """
    for key in range(len(string.ascii_uppercase)):
        translated = ""
        for symbol in message:
            if symbol in string.ascii_uppercase:
                num = string.ascii_uppercase.find(symbol)
                num = num - key
                if num < 0:
                    num = num + len(string.ascii_uppercase)
                translated = translated + string.ascii_uppercase[num]
            else:
                translated = translated + symbol
        print(f"Decryption using Key #{key}: {translated}")


def main() -> None:
    message = input("Encrypted message: ")
    message = message.upper()
    decrypt(message)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: ciphers/caesar_cipher.py
================================================
from __future__ import annotations

from string import ascii_letters


def encrypt(input_string: str, key: int, alphabet: str | None = None) -> str:
    """
    encrypt
    =======

    Encodes a given string with the caesar cipher and returns the encoded
    message

    Parameters:
    -----------

    *   `input_string`: the plain-text that needs to be encoded
    *   `key`: the number of letters to shift the message by

    Optional:

    *   `alphabet` (``None``): the alphabet used to encode the cipher, if not
        specified, the standard english alphabet with upper and lowercase
        letters is used

    Returns:

    *   A string containing the encoded cipher-text

    More on the caesar cipher
    =========================

    The caesar cipher is named after Julius Caesar who used it when sending
    secret military messages to his troops. This is a simple substitution cipher
    where every character in the plain-text is shifted by a certain number known
    as the "key" or "shift".

    Example:
    Say we have the following message:
    ``Hello, captain``

    And our alphabet is made up of lower and uppercase letters:
    ``abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ``

    And our shift is ``2``

    We can then encode the message, one letter at a time. ``H`` would become ``J``,
    since ``J`` is two letters away, and so on. If the shift is ever too large, or
    our letter is at the end of the alphabet, we just start at the beginning
    (``Z`` would shift to ``a`` then ``b`` and so on).

    Our final message would be ``Jgnnq, ecrvckp``

    Further reading
    ===============

    *   https://en.m.wikipedia.org/wiki/Caesar_cipher

    Doctests
    ========

    >>> encrypt('The quick brown fox jumps over the lazy dog', 8)
    'bpm yCqks jzwEv nwF rCuxA wDmz Bpm tiHG lwo'

    >>> encrypt('A very large key', 8000)
    's nWjq dSjYW cWq'

    >>> encrypt('a lowercase alphabet', 5, 'abcdefghijklmnopqrstuvwxyz')
    'f qtbjwhfxj fqumfgjy'
    """
    # Set default alphabet to lower and upper case english chars
    alpha = alphabet or ascii_letters

    # The final result string
    result = ""

    for character in input_string:
        if character not in alpha:
            # Append without encryption if character is not in the alphabet
            result += character
        else:
            # Get the index of the new key and make sure it isn't too large
            new_key = (alpha.index(character) + key) % len(alpha)

            # Append the encoded character to the alphabet
            result += alpha[new_key]

    return result


def decrypt(input_string: str, key: int, alphabet: str | None = None) -> str:
    """
    decrypt
    =======

    Decodes a given string of cipher-text and returns the decoded plain-text

    Parameters:
    -----------

    *   `input_string`: the cipher-text that needs to be decoded
    *   `key`: the number of letters to shift the message backwards by to decode

    Optional:

    *   `alphabet` (``None``): the alphabet used to decode the cipher, if not
        specified, the standard english alphabet with upper and lowercase
        letters is used

    Returns:

    *   A string containing the decoded plain-text

    More on the caesar cipher
    =========================

    The caesar cipher is named after Julius Caesar who used it when sending
    secret military messages to his troops. This is a simple substitution cipher
    where very character in the plain-text is shifted by a certain number known
    as the "key" or "shift". Please keep in mind, here we will be focused on
    decryption.

    Example:
    Say we have the following cipher-text:
    ``Jgnnq, ecrvckp``

    And our alphabet is made up of lower and uppercase letters:
    ``abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ``

    And our shift is ``2``

    To decode the message, we would do the same thing as encoding, but in
    reverse. The first letter, ``J`` would become ``H`` (remember: we are decoding)
    because ``H`` is two letters in reverse (to the left) of ``J``. We would
    continue doing this. A letter like ``a`` would shift back to the end of
    the alphabet, and would become ``Z`` or ``Y`` and so on.

    Our final message would be ``Hello, captain``

    Further reading
    ===============

    *   https://en.m.wikipedia.org/wiki/Caesar_cipher

    Doctests
    ========

    >>> decrypt('bpm yCqks jzwEv nwF rCuxA wDmz Bpm tiHG lwo', 8)
    'The quick brown fox jumps over the lazy dog'

    >>> decrypt('s nWjq dSjYW cWq', 8000)
    'A very large key'

    >>> decrypt('f qtbjwhfxj fqumfgjy', 5, 'abcdefghijklmnopqrstuvwxyz')
    'a lowercase alphabet'
    """
    # Turn on decode mode by making the key negative
    key *= -1

    return encrypt(input_string, key, alphabet)


def brute_force(input_string: str, alphabet: str | None = None) -> dict[int, str]:
    """
    brute_force
    ===========

    Returns all the possible combinations of keys and the decoded strings in the
    form of a dictionary

    Parameters:
    -----------

    *   `input_string`: the cipher-text that needs to be used during brute-force

    Optional:

    *   `alphabet` (``None``): the alphabet used to decode the cipher, if not
        specified, the standard english alphabet with upper and lowercase
        letters is used

    More about brute force
    ======================

    Brute force is when a person intercepts a message or password, not knowing
    the key and tries every single combination. This is easy with the caesar
    cipher since there are only all the letters in the alphabet. The more
    complex the cipher, the larger amount of time it will take to do brute force

    Ex:
    Say we have a ``5`` letter alphabet (``abcde``), for simplicity and we intercepted
    the following message: ``dbc``,
    we could then just write out every combination:
    ``ecd``... and so on, until we reach a combination that makes sense:
    ``cab``

    Further reading
    ===============

    *   https://en.wikipedia.org/wiki/Brute_force

    Doctests
    ========

    >>> brute_force("jFyuMy xIH'N vLONy zILwy Gy!")[20]
    "Please don't brute force me!"

    >>> brute_force(1)
    Traceback (most recent call last):
    TypeError: 'int' object is not iterable
    """
    # Set default alphabet to lower and upper case english chars
    alpha = alphabet or ascii_letters

    # To store data on all the combinations
    brute_force_data = {}

    # Cycle through each combination
    for key in range(1, len(alpha) + 1):
        # Decrypt the message and store the result in the data
        brute_force_data[key] = decrypt(input_string, key, alpha)

    return brute_force_data


if __name__ == "__main__":
    while True:
        print(f"\n{'-' * 10}\n Menu\n{'-' * 10}")
        print(*["1.Encrypt", "2.Decrypt", "3.BruteForce", "4.Quit"], sep="\n")

        # get user input
        choice = input("\nWhat would you like to do?: ").strip() or "4"

        # run functions based on what the user chose
        if choice not in ("1", "2", "3", "4"):
            print("Invalid choice, please enter a valid choice")
        elif choice == "1":
            input_string = input("Please enter the string to be encrypted: ")
            key = int(input("Please enter off-set: ").strip())

            print(encrypt(input_string, key))
        elif choice == "2":
            input_string = input("Please enter the string to be decrypted: ")
            key = int(input("Please enter off-set: ").strip())

            print(decrypt(input_string, key))
        elif choice == "3":
            input_string = input("Please enter the string to be decrypted: ")
            brute_force_data = brute_force(input_string)

            for key, value in brute_force_data.items():
                print(f"Key: {key} | Message: {value}")

        elif choice == "4":
            print("Goodbye.")
            break


================================================
FILE: ciphers/cryptomath_module.py
================================================
from maths.greatest_common_divisor import gcd_by_iterative


def find_mod_inverse(a: int, m: int) -> int:
    if gcd_by_iterative(a, m) != 1:
        msg = f"mod inverse of {a!r} and {m!r} does not exist"
        raise ValueError(msg)
    u1, u2, u3 = 1, 0, a
    v1, v2, v3 = 0, 1, m
    while v3 != 0:
        q = u3 // v3
        v1, v2, v3, u1, u2, u3 = (u1 - q * v1), (u2 - q * v2), (u3 - q * v3), v1, v2, v3
    return u1 % m


================================================
FILE: ciphers/decrypt_caesar_with_chi_squared.py
================================================
#!/usr/bin/env python3
from __future__ import annotations


def decrypt_caesar_with_chi_squared(
    ciphertext: str,
    cipher_alphabet: list[str] | None = None,
    frequencies_dict: dict[str, float] | None = None,
    case_sensitive: bool = False,
) -> tuple[int, float, str]:
    """
    Basic Usage
    ===========

    Arguments:
      * `ciphertext` (str): the text to decode (encoded with the caesar cipher)

    Optional Arguments:
      * `cipher_alphabet` (list): the alphabet used for the cipher (each letter is
        a string separated by commas)
      * `frequencies_dict` (dict): a dictionary of word frequencies where keys are
        the letters and values are a percentage representation of the frequency as
        a decimal/float
      * `case_sensitive` (bool): a boolean value: ``True`` if the case matters during
        decryption, ``False`` if it doesn't

    Returns:
      * A tuple in the form of:
        (`most_likely_cipher`, `most_likely_cipher_chi_squared_value`,
        `decoded_most_likely_cipher`)

        where...
          - `most_likely_cipher` is an integer representing the shift of the smallest
            chi-squared statistic (most likely key)
          - `most_likely_cipher_chi_squared_value` is a float representing the
            chi-squared statistic of the most likely shift
          - `decoded_most_likely_cipher` is a string with the decoded cipher
            (decoded by the most_likely_cipher key)


    The Chi-squared test
    ====================

    The caesar cipher
    -----------------

    The caesar cipher is a very insecure encryption algorithm, however it has
    been used since Julius Caesar. The cipher is a simple substitution cipher
    where each character in the plain text is replaced by a character in the
    alphabet a certain number of characters after the original character. The
    number of characters away is called the shift or key. For example:

    | Plain text: ``hello``
    | Key: ``1``
    | Cipher text: ``ifmmp``
    | (each letter in ``hello`` has been shifted one to the right in the eng. alphabet)

    As you can imagine, this doesn't provide lots of security. In fact
    decrypting ciphertext by brute-force is extremely easy even by hand. However
    one way to do that is the chi-squared test.

    The chi-squared test
    --------------------

    Each letter in the english alphabet has a frequency, or the amount of times
    it shows up compared to other letters (usually expressed as a decimal
    representing the percentage likelihood). The most common letter in the
    english language is ``e`` with a frequency of ``0.11162`` or ``11.162%``.
    The test is completed in the following fashion.

    1. The ciphertext is decoded in a brute force way (every combination of the
       ``26`` possible combinations)
    2. For every combination, for each letter in the combination, the average
       amount of times the letter should appear the message is calculated by
       multiplying the total number of characters by the frequency of the letter.

       | For example:
       | In a message of ``100`` characters, ``e`` should appear around ``11.162``
         times.

    3. Then, to calculate the margin of error (the amount of times the letter
       SHOULD appear with the amount of times the letter DOES appear), we use
       the chi-squared test. The following formula is used:

       Let:
         - n be the number of times the letter actually appears
         - p be the predicted value of the number of times the letter should
           appear (see item ``2``)
         - let v be the chi-squared test result (referred to here as chi-squared
           value/statistic)

         ::

           (n - p)^2
           --------- = v
              p

    4. Each chi squared value for each letter is then added up to the total.
       The total is the chi-squared statistic for that encryption key.
    5. The encryption key with the lowest chi-squared value is the most likely
       to be the decoded answer.

    Further Reading
    ===============

    * http://practicalcryptography.com/cryptanalysis/text-characterisation/chi-squared-statistic/
    * https://en.wikipedia.org/wiki/Letter_frequency
    * https://en.wikipedia.org/wiki/Chi-squared_test
    * https://en.m.wikipedia.org/wiki/Caesar_cipher

    Doctests
    ========

    >>> decrypt_caesar_with_chi_squared(
    ...    'dof pz aol jhlzhy jpwoly zv wvwbshy? pa pz avv lhzf av jyhjr!'
    ... )  # doctest: +NORMALIZE_WHITESPACE
    (7, 3129.228005747531,
     'why is the caesar cipher so popular? it is too easy to crack!')

    >>> decrypt_caesar_with_chi_squared('crybd cdbsxq')
    (10, 233.35343938980898, 'short string')

    >>> decrypt_caesar_with_chi_squared('Crybd Cdbsxq', case_sensitive=True)
    (10, 233.35343938980898, 'Short String')

    >>> decrypt_caesar_with_chi_squared(12)
    Traceback (most recent call last):
    AttributeError: 'int' object has no attribute 'lower'
    """
    alphabet_letters = cipher_alphabet or [chr(i) for i in range(97, 123)]

    # If the argument is None or the user provided an empty dictionary
    if not frequencies_dict:
        # Frequencies of letters in the english language (how much they show up)
        frequencies = {
            "a": 0.08497,
            "b": 0.01492,
            "c": 0.02202,
            "d": 0.04253,
            "e": 0.11162,
            "f": 0.02228,
            "g": 0.02015,
            "h": 0.06094,
            "i": 0.07546,
            "j": 0.00153,
            "k": 0.01292,
            "l": 0.04025,
            "m": 0.02406,
            "n": 0.06749,
            "o": 0.07507,
            "p": 0.01929,
            "q": 0.00095,
            "r": 0.07587,
            "s": 0.06327,
            "t": 0.09356,
            "u": 0.02758,
            "v": 0.00978,
            "w": 0.02560,
            "x": 0.00150,
            "y": 0.01994,
            "z": 0.00077,
        }
    else:
        # Custom frequencies dictionary
        frequencies = frequencies_dict

    if not case_sensitive:
        ciphertext = ciphertext.lower()

    # Chi squared statistic values
    chi_squared_statistic_values: dict[int, tuple[float, str]] = {}

    # cycle through all of the shifts
    for shift in range(len(alphabet_letters)):
        decrypted_with_shift = ""

        # decrypt the message with the shift
        for letter in ciphertext:
            try:
                # Try to index the letter in the alphabet
                new_key = (alphabet_letters.index(letter.lower()) - shift) % len(
                    alphabet_letters
                )
                decrypted_with_shift += (
                    alphabet_letters[new_key].upper()
                    if case_sensitive and letter.isupper()
                    else alphabet_letters[new_key]
                )
            except ValueError:
                # Append the character if it isn't in the alphabet
                decrypted_with_shift += letter

        chi_squared_statistic = 0.0

        # Loop through each letter in the decoded message with the shift
        for letter in decrypted_with_shift:
            if case_sensitive:
                letter = letter.lower()
                if letter in frequencies:
                    # Get the amount of times the letter occurs in the message
                    occurrences = decrypted_with_shift.lower().count(letter)

                    # Get the excepcted amount of times the letter should appear based
                    # on letter frequencies
                    expected = frequencies[letter] * occurrences

                    # Complete the chi squared statistic formula
                    chi_letter_value = ((occurrences - expected) ** 2) / expected

                    # Add the margin of error to the total chi squared statistic
                    chi_squared_statistic += chi_letter_value
            elif letter.lower() in frequencies:
                # Get the amount of times the letter occurs in the message
                occurrences = decrypted_with_shift.count(letter)

                # Get the excepcted amount of times the letter should appear based
                # on letter frequencies
                expected = frequencies[letter] * occurrences

                # Complete the chi squared statistic formula
                chi_letter_value = ((occurrences - expected) ** 2) / expected

                # Add the margin of error to the total chi squared statistic
                chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
        chi_squared_statistic_values[shift] = (
            chi_squared_statistic,
            decrypted_with_shift,
        )

    # Get the most likely cipher by finding the cipher with the smallest chi squared
    # statistic
    def chi_squared_statistic_values_sorting_key(key: int) -> tuple[float, str]:
        return chi_squared_statistic_values[key]

    most_likely_cipher: int = min(
        chi_squared_statistic_values,
        key=chi_squared_statistic_values_sorting_key,
    )

    # Get all the data from the most likely cipher (key, decoded message)
    (
        most_likely_cipher_chi_squared_value,
        decoded_most_likely_cipher,
    ) = chi_squared_statistic_values[most_likely_cipher]

    # Return the data on the most likely shift
    return (
        most_likely_cipher,
        most_likely_cipher_chi_squared_value,
        decoded_most_likely_cipher,
    )


================================================
FILE: ciphers/deterministic_miller_rabin.py
================================================
"""Created by Nathan Damon, @bizzfitch on github
>>> test_miller_rabin()
"""


def miller_rabin(n: int, allow_probable: bool = False) -> bool:
    """Deterministic Miller-Rabin algorithm for primes ~< 3.32e24.

    Uses numerical analysis results to return whether or not the passed number
    is prime. If the passed number is above the upper limit, and
    allow_probable is True, then a return value of True indicates that n is
    probably prime. This test does not allow False negatives- a return value
    of False is ALWAYS composite.

    Parameters
    ----------
    n : int
        The integer to be tested. Since we usually care if a number is prime,
        n < 2 returns False instead of raising a ValueError.
    allow_probable: bool, default False
        Whether or not to test n above the upper bound of the deterministic test.

    Raises
    ------
    ValueError

    Reference
    ---------
    https://en.wikipedia.org/wiki/Miller%E2%80%93Rabin_primality_test
    """
    if n == 2:
        return True
    if not n % 2 or n < 2:
        return False
    if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
        return False
    if n > 3_317_044_064_679_887_385_961_981 and not allow_probable:
        raise ValueError(
            "Warning: upper bound of deterministic test is exceeded. "
            "Pass allow_probable=True to allow probabilistic test. "
            "A return value of True indicates a probable prime."
        )
    # array bounds provided by analysis
    bounds = [
        2_047,
        1_373_653,
        25_326_001,
        3_215_031_751,
        2_152_302_898_747,
        3_474_749_660_383,
        341_550_071_728_321,
        1,
        3_825_123_056_546_413_051,
        1,
        1,
        318_665_857_834_031_151_167_461,
        3_317_044_064_679_887_385_961_981,
    ]

    primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
    for idx, _p in enumerate(bounds, 1):
        if n < _p:
            # then we have our last prime to check
            plist = primes[:idx]
            break
    d, s = n - 1, 0
    # break up n -1 into a power of 2 (s) and
    # remaining odd component
    # essentially, solve for d * 2 ** s == n - 1
    while d % 2 == 0:
        d //= 2
        s += 1
    for prime in plist:
        pr = False
        for r in range(s):
            m = pow(prime, d * 2**r, n)
            # see article for analysis explanation for m
            if (r == 0 and m == 1) or ((m + 1) % n == 0):
                pr = True
                # this loop will not determine compositeness
                break
        if pr:
            continue
        # if pr is False, then the above loop never evaluated to true,
        # and the n MUST be composite
        return False
    return True


def test_miller_rabin() -> None:
    """Testing a nontrivial (ends in 1, 3, 7, 9) composite
    and a prime in each range.
    """
    assert not miller_rabin(561)
    assert miller_rabin(563)
    # 2047

    assert not miller_rabin(838_201)
    assert miller_rabin(838_207)
    # 1_373_653

    assert not miller_rabin(17_316_001)
    assert miller_rabin(17_316_017)
    # 25_326_001

    assert not miller_rabin(3_078_386_641)
    assert miller_rabin(3_078_386_653)
    # 3_215_031_751

    assert not miller_rabin(1_713_045_574_801)
    assert miller_rabin(1_713_045_574_819)
    # 2_152_302_898_747

    assert not miller_rabin(2_779_799_728_307)
    assert miller_rabin(2_779_799_728_327)
    # 3_474_749_660_383

    assert not miller_rabin(113_850_023_909_441)
    assert miller_rabin(113_850_023_909_527)
    # 341_550_071_728_321

    assert not miller_rabin(1_275_041_018_848_804_351)
    assert miller_rabin(1_275_041_018_848_804_391)
    # 3_825_123_056_546_413_051

    assert not miller_rabin(79_666_464_458_507_787_791_867)
    assert miller_rabin(79_666_464_458_507_787_791_951)
    # 318_665_857_834_031_151_167_461

    assert not miller_rabin(552_840_677_446_647_897_660_333)
    assert miller_rabin(552_840_677_446_647_897_660_359)
    # 3_317_044_064_679_887_385_961_981
    # upper limit for probabilistic test


if __name__ == "__main__":
    test_miller_rabin()


================================================
FILE: ciphers/diffie.py
================================================
from __future__ import annotations


def find_primitive(modulus: int) -> int | None:
    """
    Find a primitive root modulo modulus, if one exists.

    Args:
        modulus : The modulus for which to find a primitive root.

    Returns:
        The primitive root if one exists, or None if there is none.

    Examples:
    >>> find_primitive(7)  # Modulo 7 has primitive root 3
    3
    >>> find_primitive(11)  # Modulo 11 has primitive root 2
    2
    >>> find_primitive(8) == None # Modulo 8 has no primitive root
    True
    """
    for r in range(1, modulus):
        li = []
        for x in range(modulus - 1):
            val = pow(r, x, modulus)
            if val in li:
                break
            li.append(val)
        else:
            return r
    return None


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    prime = int(input("Enter a prime number q: "))
    primitive_root = find_primitive(prime)
    if primitive_root is None:
        print(f"Cannot find the primitive for the value: {primitive_root!r}")
    else:
        a_private = int(input("Enter private key of A: "))
        a_public = pow(primitive_root, a_private, prime)
        b_private = int(input("Enter private key of B: "))
        b_public = pow(primitive_root, b_private, prime)

        a_secret = pow(b_public, a_private, prime)
        b_secret = pow(a_public, b_private, prime)

        print("The key value generated by A is: ", a_secret)
        print("The key value generated by B is: ", b_secret)


================================================
FILE: ciphers/diffie_hellman.py
================================================
from binascii import hexlify
from hashlib import sha256
from os import urandom

# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526

primes = {
    # 1536-bit
    5: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 2048-bit
    14: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 3072-bit
    15: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 4096-bit
    16: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
            "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
            "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
            "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
            "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
            "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
            "FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 6144-bit
    17: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
            "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
            "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
            "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
            "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
            "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
            "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
            "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
            "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
            "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
            "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
            "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
            "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
            "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
            "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
            "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
            "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
            "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
            "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
            "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
            "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
            "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
            "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
            "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
            "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
            "6DCC4024FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 8192-bit
    18: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
            "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
            "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
            "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
            "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
            "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
            "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
            "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
            "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
            "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
            "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
            "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
            "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
            "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
            "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
            "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
            "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
            "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
            "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
            "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
            "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
            "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
            "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
            "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
            "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
            "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
            "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
            "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
}


class DiffieHellman:
    """
    Class to represent the Diffie-Hellman key exchange protocol


    >>> alice = DiffieHellman()
    >>> bob = DiffieHellman()

    >>> alice_private = alice.get_private_key()
    >>> alice_public = alice.generate_public_key()

    >>> bob_private = bob.get_private_key()
    >>> bob_public = bob.generate_public_key()

    >>> # generating shared key using the DH object
    >>> alice_shared = alice.generate_shared_key(bob_public)
    >>> bob_shared = bob.generate_shared_key(alice_public)

    >>> assert alice_shared == bob_shared

    >>> # generating shared key using static methods
    >>> alice_shared = DiffieHellman.generate_shared_key_static(
    ...     alice_private, bob_public
    ... )
    >>> bob_shared = DiffieHellman.generate_shared_key_static(
    ...     bob_private, alice_public
    ... )

    >>> assert alice_shared == bob_shared
    """

    # Current minimum recommendation is 2048 bit (group 14)
    def __init__(self, group: int = 14) -> None:
        if group not in primes:
            raise ValueError("Unsupported Group")
        self.prime = primes[group]["prime"]
        self.generator = primes[group]["generator"]

        self.__private_key = int(hexlify(urandom(32)), base=16)

    def get_private_key(self) -> str:
        return hex(self.__private_key)[2:]

    def generate_public_key(self) -> str:
        public_key = pow(self.generator, self.__private_key, self.prime)
        return hex(public_key)[2:]

    def is_valid_public_key(self, key: int) -> bool:
        # check if the other public key is valid based on NIST SP800-56
        return (
            2 <= key <= self.prime - 2
            and pow(key, (self.prime - 1) // 2, self.prime) == 1
        )

    def generate_shared_key(self, other_key_str: str) -> str:
        other_key = int(other_key_str, base=16)
        if not self.is_valid_public_key(other_key):
            raise ValueError("Invalid public key")
        shared_key = pow(other_key, self.__private_key, self.prime)
        return sha256(str(shared_key).encode()).hexdigest()

    @staticmethod
    def is_valid_public_key_static(remote_public_key_str: int, prime: int) -> bool:
        # check if the other public key is valid based on NIST SP800-56
        return (
            2 <= remote_public_key_str <= prime - 2
            and pow(remote_public_key_str, (prime - 1) // 2, prime) == 1
        )

    @staticmethod
    def generate_shared_key_static(
        local_private_key_str: str, remote_public_key_str: str, group: int = 14
    ) -> str:
        local_private_key = int(local_private_key_str, base=16)
        remote_public_key = int(remote_public_key_str, base=16)
        prime = primes[group]["prime"]
        if not DiffieHellman.is_valid_public_key_static(remote_public_key, prime):
            raise ValueError("Invalid public key")
        shared_key = pow(remote_public_key, local_private_key, prime)
        return sha256(str(shared_key).encode()).hexdigest()


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: ciphers/elgamal_key_generator.py
================================================
import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

min_primitive_root = 3


# I have written my code naively same as definition of primitive root
# however every time I run this program, memory exceeded...
# so I used 4.80 Algorithm in
# Handbook of Applied Cryptography(CRC Press, ISBN : 0-8493-8523-7, October 1996)
# and it seems to run nicely!
def primitive_root(p_val: int) -> int:
    print("Generating primitive root of p")
    while True:
        g = random.randrange(3, p_val)
        if pow(g, 2, p_val) == 1:
            continue
        if pow(g, p_val, p_val) == 1:
            continue
        return g


def generate_key(key_size: int) -> tuple[tuple[int, int, int, int], tuple[int, int]]:
    print("Generating prime p...")
    p = rabin_miller.generate_large_prime(key_size)  # select large prime number.
    e_1 = primitive_root(p)  # one primitive root on modulo p.
    d = random.randrange(3, p)  # private_key -> have to be greater than 2 for safety.
    e_2 = cryptomath.find_mod_inverse(pow(e_1, d, p), p)

    public_key = (key_size, e_1, e_2, p)
    private_key = (key_size, d)

    return public_key, private_key


def make_key_files(name: str, key_size: int) -> None:
    if os.path.exists(f"{name}_pubkey.txt") or os.path.exists(f"{name}_privkey.txt"):
        print("\nWARNING:")
        print(
            f'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
            "Use a different name or delete these files and re-run this program."
        )
        sys.exit()

    public_key, private_key = generate_key(key_size)
    print(f"\nWriting public key to file {name}_pubkey.txt...")
    with open(f"{name}_pubkey.txt", "w") as fo:
        fo.write(f"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}")

    print(f"Writing private key to file {name}_privkey.txt...")
    with open(f"{name}_privkey.txt", "w") as fo:
        fo.write(f"{private_key[0]},{private_key[1]}")


def main() -> None:
    print("Making key files...")
    make_key_files("elgamal", 2048)
    print("Key files generation successful")


if __name__ == "__main__":
    main()


================================================
FILE: ciphers/enigma_machine2.py
================================================
"""
| Wikipedia: https://en.wikipedia.org/wiki/Enigma_machine
| Video explanation: https://youtu.be/QwQVMqfoB2E
| Also check out Numberphile's and Computerphile's videos on this topic

This module contains function ``enigma`` which emulates
the famous Enigma machine from WWII.

Module includes:

- ``enigma`` function
- showcase of function usage
- ``9`` randomly generated rotors
- reflector (aka static rotor)
- original alphabet

Created by TrapinchO
"""

from __future__ import annotations

RotorPositionT = tuple[int, int, int]
RotorSelectionT = tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
abc = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"

# -------------------------- default selection --------------------------
# rotors --------------------------
rotor1 = "EGZWVONAHDCLFQMSIPJBYUKXTR"
rotor2 = "FOBHMDKEXQNRAULPGSJVTYICZW"
rotor3 = "ZJXESIUQLHAVRMDOYGTNFWPBKC"
# reflector --------------------------
reflector = {
    "A": "N",
    "N": "A",
    "B": "O",
    "O": "B",
    "C": "P",
    "P": "C",
    "D": "Q",
    "Q": "D",
    "E": "R",
    "R": "E",
    "F": "S",
    "S": "F",
    "G": "T",
    "T": "G",
    "H": "U",
    "U": "H",
    "I": "V",
    "V": "I",
    "J": "W",
    "W": "J",
    "K": "X",
    "X": "K",
    "L": "Y",
    "Y": "L",
    "M": "Z",
    "Z": "M",
}

# -------------------------- extra rotors --------------------------
rotor4 = "RMDJXFUWGISLHVTCQNKYPBEZOA"
rotor5 = "SGLCPQWZHKXAREONTFBVIYJUDM"
rotor6 = "HVSICLTYKQUBXDWAJZOMFGPREN"
rotor7 = "RZWQHFMVDBKICJLNTUXAGYPSOE"
rotor8 = "LFKIJODBEGAMQPXVUHYSTCZRWN"
rotor9 = "KOAEGVDHXPQZMLFTYWJNBRCIUS"


def _validator(
    rotpos: RotorPositionT, rotsel: RotorSelectionT, pb: str
) -> tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
    """
    Checks if the values can be used for the ``enigma`` function

    >>> _validator((1,1,1), (rotor1, rotor2, rotor3), 'POLAND')
    ((1, 1, 1), ('EGZWVONAHDCLFQMSIPJBYUKXTR', 'FOBHMDKEXQNRAULPGSJVTYICZW', \
'ZJXESIUQLHAVRMDOYGTNFWPBKC'), \
{'P': 'O', 'O': 'P', 'L': 'A', 'A': 'L', 'N': 'D', 'D': 'N'})

    :param rotpos: rotor_positon
    :param rotsel: rotor_selection
    :param pb: plugb -> validated and transformed
    :return: (`rotpos`, `rotsel`, `pb`)
    """
    # Checks if there are 3 unique rotors

    if (unique_rotsel := len(set(rotsel))) < 3:
        msg = f"Please use 3 unique rotors (not {unique_rotsel})"
        raise Exception(msg)

    # Checks if rotor positions are valid
    rotorpos1, rotorpos2, rotorpos3 = rotpos
    if not 0 < rotorpos1 <= len(abc):
        msg = f"First rotor position is not within range of 1..26 ({rotorpos1}"
        raise ValueError(msg)
    if not 0 < rotorpos2 <= len(abc):
        msg = f"Second rotor position is not within range of 1..26 ({rotorpos2})"
        raise ValueError(msg)
    if not 0 < rotorpos3 <= len(abc):
        msg = f"Third rotor position is not within range of 1..26 ({rotorpos3})"
        raise ValueError(msg)

    # Validates string and returns dict
    pbdict = _plugboard(pb)

    return rotpos, rotsel, pbdict


def _plugboard(pbstring: str) -> dict[str, str]:
    """
    https://en.wikipedia.org/wiki/Enigma_machine#Plugboard

    >>> _plugboard('PICTURES')
    {'P': 'I', 'I': 'P', 'C': 'T', 'T': 'C', 'U': 'R', 'R': 'U', 'E': 'S', 'S': 'E'}
    >>> _plugboard('POLAND')
    {'P': 'O', 'O': 'P', 'L': 'A', 'A': 'L', 'N': 'D', 'D': 'N'}

    In the code, ``pb`` stands for ``plugboard``

    Pairs can be separated by spaces

    :param pbstring: string containing plugboard setting for the Enigma machine
    :return: dictionary containing converted pairs
    """

    # tests the input string if it
    # a) is type string
    # b) has even length (so pairs can be made)
    if not isinstance(pbstring, str):
        msg = f"Plugboard setting isn't type string ({type(pbstring)})"
        raise TypeError(msg)
    elif len(pbstring) % 2 != 0:
        msg = f"Odd number of symbols ({len(pbstring)})"
        raise Exception(msg)
    elif pbstring == "":
        return {}

    pbstring.replace(" ", "")

    # Checks if all characters are unique
    tmppbl = set()
    for i in pbstring:
        if i not in abc:
            msg = f"'{i}' not in list of symbols"
            raise Exception(msg)
        elif i in tmppbl:
            msg = f"Duplicate symbol ({i})"
            raise Exception(msg)
        else:
            tmppbl.add(i)
    del tmppbl

    # Created the dictionary
    pb = {}
    for j in range(0, len(pbstring) - 1, 2):
        pb[pbstring[j]] = pbstring[j + 1]
        pb[pbstring[j + 1]] = pbstring[j]

    return pb


def enigma(
    text: str,
    rotor_position: RotorPositionT,
    rotor_selection: RotorSelectionT = (rotor1, rotor2, rotor3),
    plugb: str = "",
) -> str:
    """
    The only difference with real-world enigma is that ``I`` allowed string input.
    All characters are converted to uppercase. (non-letter symbol are ignored)

    | How it works:
    | (for every letter in the message)

    - Input letter goes into the plugboard.
      If it is connected to another one, switch it.

    - Letter goes through ``3`` rotors.
      Each rotor can be represented as ``2`` sets of symbol, where one is shuffled.
      Each symbol from the first set has corresponding symbol in
      the second set and vice versa.

      example::

      | ABCDEFGHIJKLMNOPQRSTUVWXYZ | e.g. F=D and D=F
      | VKLEPDBGRNWTFCJOHQAMUZYIXS |

    - Symbol then goes through reflector (static rotor).
      There it is switched with paired symbol.
      The reflector can be represented as ``2`` sets, each with half of the alphanet.
      There are usually ``10`` pairs of letters.

      Example::

      | ABCDEFGHIJKLM | e.g. E is paired to X
      | ZYXWVUTSRQPON | so when E goes in X goes out and vice versa

    - Letter then goes through the rotors again

    - If the letter is connected to plugboard, it is switched.

    - Return the letter

    >>> enigma('Hello World!', (1, 2, 1), plugb='pictures')
    'KORYH JUHHI!'
    >>> enigma('KORYH, juhhi!', (1, 2, 1), plugb='pictures')
    'HELLO, WORLD!'
    >>> enigma('hello world!', (1, 1, 1), plugb='pictures')
    'FPNCZ QWOBU!'
    >>> enigma('FPNCZ QWOBU', (1, 1, 1), plugb='pictures')
    'HELLO WORLD'


    :param text: input message
    :param rotor_position: tuple with ``3`` values in range ``1``.. ``26``
    :param rotor_selection: tuple with ``3`` rotors
    :param plugb: string containing plugboard configuration (default ``''``)
    :return: en/decrypted string
    """

    text = text.upper()
    rotor_position, rotor_selection, plugboard = _validator(
        rotor_position, rotor_selection, plugb.upper()
    )

    rotorpos1, rotorpos2, rotorpos3 = rotor_position
    rotor1, rotor2, rotor3 = rotor_selection
    rotorpos1 -= 1
    rotorpos2 -= 1
    rotorpos3 -= 1

    result = []

    # encryption/decryption process --------------------------
    for symbol in text:
        if symbol in abc:
            # 1st plugboard --------------------------
            if symbol in plugboard:
                symbol = plugboard[symbol]

            # rotor ra --------------------------
            index = abc.index(symbol) + rotorpos1
            symbol = rotor1[index % len(abc)]

            # rotor rb --------------------------
            index = abc.index(symbol) + rotorpos2
            symbol = rotor2[index % len(abc)]

            # rotor rc --------------------------
            index = abc.index(symbol) + rotorpos3
            symbol = rotor3[index % len(abc)]

            # reflector --------------------------
            # this is the reason you don't need another machine to decipher

            symbol = reflector[symbol]

            # 2nd rotors
            symbol = abc[rotor3.index(symbol) - rotorpos3]
            symbol = abc[rotor2.index(symbol) - rotorpos2]
            symbol = abc[rotor1.index(symbol) - rotorpos1]

            # 2nd plugboard
            if symbol in plugboard:
                symbol = plugboard[symbol]

            # moves/resets rotor positions
            rotorpos1 += 1
            if rotorpos1 >= len(abc):
                rotorpos1 = 0
                rotorpos2 += 1
            if rotorpos2 >= len(abc):
                rotorpos2 = 0
                rotorpos3 += 1
            if rotorpos3 >= len(abc):
                rotorpos3 = 0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
        result.append(symbol)

    return "".join(result)


if __name__ == "__main__":
    message = "This is my Python script that emulates the Enigma machine from WWII."
    rotor_pos = (1, 1, 1)
    pb = "pictures"
    rotor_sel = (rotor2, rotor4, rotor8)
    en = enigma(message, rotor_pos, rotor_sel, pb)

    print("Encrypted message:", en)
    print("Decrypted message:", enigma(en, rotor_pos, rotor_sel, pb))


================================================
FILE: ciphers/fractionated_morse_cipher.py
================================================
"""
Python program for the Fractionated Morse Cipher.

The Fractionated Morse cipher first converts the plaintext to Morse code,
then enciphers fixed-size blocks of Morse code back to letters.
This procedure means plaintext letters are mixed into the ciphertext letters,
making it more secure than substitution ciphers.

http://practicalcryptography.com/ciphers/fractionated-morse-cipher/
"""

import string

MORSE_CODE_DICT = {
    "A": ".-",
    "B": "-...",
    "C": "-.-.",
    "D": "-..",
    "E": ".",
    "F": "..-.",
    "G": "--.",
    "H": "....",
    "I": "..",
    "J": ".---",
    "K": "-.-",
    "L": ".-..",
    "M": "--",
    "N": "-.",
    "O": "---",
    "P": ".--.",
    "Q": "--.-",
    "R": ".-.",
    "S": "...",
    "T": "-",
    "U": "..-",
    "V": "...-",
    "W": ".--",
    "X": "-..-",
    "Y": "-.--",
    "Z": "--..",
    " ": "",
}

# Define possible trigrams of Morse code
MORSE_COMBINATIONS = [
    "...",
    "..-",
    "..x",
    ".-.",
    ".--",
    ".-x",
    ".x.",
    ".x-",
    ".xx",
    "-..",
    "-.-",
    "-.x",
    "--.",
    "---",
    "--x",
    "-x.",
    "-x-",
    "-xx",
    "x..",
    "x.-",
    "x.x",
    "x-.",
    "x--",
    "x-x",
    "xx.",
    "xx-",
    "xxx",
]

# Create a reverse dictionary for Morse code
REVERSE_DICT = {value: key for key, value in MORSE_CODE_DICT.items()}


def encode_to_morse(plaintext: str) -> str:
    """Encode a plaintext message into Morse code.

    Args:
        plaintext: The plaintext message to encode.

    Returns:
        The Morse code representation of the plaintext message.

    Example:
        >>> encode_to_morse("defend the east")
        '-..x.x..-.x.x-.x-..xx-x....x.xx.x.-x...x-'
    """
    return "x".join([MORSE_CODE_DICT.get(letter.upper(), "") for letter in plaintext])


def encrypt_fractionated_morse(plaintext: str, key: str) -> str:
    """Encrypt a plaintext message using Fractionated Morse Cipher.

    Args:
        plaintext: The plaintext message to encrypt.
        key: The encryption key.

    Returns:
        The encrypted ciphertext.

    Example:
        >>> encrypt_fractionated_morse("defend the east","Roundtable")
        'ESOAVVLJRSSTRX'

    """
    morse_code = encode_to_morse(plaintext)
    key = key.upper() + string.ascii_uppercase
    key = "".join(sorted(set(key), key=key.find))

    # Ensure morse_code length is a multiple of 3
    padding_length = 3 - (len(morse_code) % 3)
    morse_code += "x" * padding_length

    fractionated_morse_dict = {v: k for k, v in zip(key, MORSE_COMBINATIONS)}
    fractionated_morse_dict["xxx"] = ""
    encrypted_text = "".join(
        [
            fractionated_morse_dict[morse_code[i : i + 3]]
            for i in range(0, len(morse_code), 3)
        ]
    )
    return encrypted_text


def decrypt_fractionated_morse(ciphertext: str, key: str) -> str:
    """Decrypt a ciphertext message encrypted with Fractionated Morse Cipher.

    Args:
        ciphertext: The ciphertext message to decrypt.
        key: The decryption key.

    Returns:
        The decrypted plaintext message.

    Example:
        >>> decrypt_fractionated_morse("ESOAVVLJRSSTRX","Roundtable")
        'DEFEND THE EAST'
    """
    key = key.upper() + string.ascii_uppercase
    key = "".join(sorted(set(key), key=key.find))

    inverse_fractionated_morse_dict = dict(zip(key, MORSE_COMBINATIONS))
    morse_code = "".join(
        [inverse_fractionated_morse_dict.get(letter, "") for letter in ciphertext]
    )
    decrypted_text = "".join(
        [REVERSE_DICT[code] for code in morse_code.split("x")]
    ).strip()
    return decrypted_text


if __name__ == "__main__":
    """
    Example usage of Fractionated Morse Cipher.
    """
    plaintext = "defend the east"
    print("Plain Text:", plaintext)
    key = "ROUNDTABLE"

    ciphertext = encrypt_fractionated_morse(plaintext, key)
    print("Encrypted:", ciphertext)

    decrypted_text = decrypt_fractionated_morse(ciphertext, key)
    print("Decrypted:", decrypted_text)


================================================
FILE: ciphers/gronsfeld_cipher.py
================================================
from string import ascii_uppercase


def gronsfeld(text: str, key: str) -> str:
    """
    Encrypt plaintext with the Gronsfeld cipher

    >>> gronsfeld('hello', '412')
    'LFNPP'
    >>> gronsfeld('hello', '123')
    'IGOMQ'
    >>> gronsfeld('', '123')
    ''
    >>> gronsfeld('yes, ¥€$ - _!@#%?', '0')
    'YES, ¥€$ - _!@#%?'
    >>> gronsfeld('yes, ¥€$ - _!@#%?', '01')
    'YFS, ¥€$ - _!@#%?'
    >>> gronsfeld('yes, ¥€$ - _!@#%?', '012')
    'YFU, ¥€$ - _!@#%?'
    >>> gronsfeld('yes, ¥€$ - _!@#%?', '')
    Traceback (most recent call last):
      ...
    ZeroDivisionError: division by zero
    """
    ascii_len = len(ascii_uppercase)
    key_len = len(key)
    encrypted_text = ""
    keys = [int(char) for char in key]
    upper_case_text = text.upper()

    for i, char in enumerate(upper_case_text):
        if char in ascii_uppercase:
            new_position = (ascii_uppercase.index(char) + keys[i % key_len]) % ascii_len
            shifted_letter = ascii_uppercase[new_position]
            encrypted_text += shifted_letter
        else:
            encrypted_text += char

    return encrypted_text


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: ciphers/hill_cipher.py
================================================
"""

Hill Cipher:
The 'HillCipher' class below implements the Hill Cipher algorithm which uses
modern linear algebra techniques to encode and decode text using an encryption
key matrix.

Algorithm:
Let the order of the encryption key be N (as it is a square matrix).
Your text is divided into batches of length N and converted to numerical vectors
by a simple mapping starting with A=0 and so on.

The key is then multiplied with the newly created batch vector to obtain the
encoded vector. After each multiplication modular 36 calculations are performed
on the vectors so as to bring the numbers between 0 and 36 and then mapped with
their corresponding alphanumerics.

While decrypting, the decrypting key is found which is the inverse of the
encrypting key modular 36. The same process is repeated for decrypting to get
the original message back.

Constraints:
The determinant of the encryption key matrix must be relatively prime w.r.t 36.

Note:
This implementation only considers alphanumerics in the text.  If the length of
the text to be encrypted is not a multiple of the break key(the length of one
batch of letters), the last character of the text is added to the text until the
length of the text reaches a multiple of the break_key. So the text after
decrypting might be a little different than the original text.

References:
https://apprendre-en-ligne.net/crypto/hill/Hillciph.pdf
https://www.youtube.com/watch?v=kfmNeskzs2o
https://www.youtube.com/watch?v=4RhLNDqcjpA

"""

import string

import numpy as np

from maths.greatest_common_divisor import greatest_common_divisor


class HillCipher:
    key_string = string.ascii_uppercase + string.digits
    # This cipher takes alphanumerics into account
    # i.e. a total of 36 characters

    # take x and return x % len(key_string)
    modulus = np.vectorize(lambda x: x % 36)

    to_int = np.vectorize(round)

    def __init__(self, encrypt_key: np.ndarray) -> None:
        """
        encrypt_key is an NxN numpy array
        """
        self.encrypt_key = self.modulus(encrypt_key)  # mod36 calc's on the encrypt key
        self.check_determinant()  # validate the determinant of the encryption key
        self.break_key = encrypt_key.shape[0]

    def replace_letters(self, letter: str) -> int:
        """
        >>> hill_cipher = HillCipher(np.array([[2, 5], [1, 6]]))
        >>> hill_cipher.replace_letters('T')
        19
        >>> hill_cipher.replace_letters('0')
        26
        """
        return self.key_string.index(letter)

    def replace_digits(self, num: int) -> str:
        """
        >>> hill_cipher = HillCipher(np.array([[2, 5], [1, 6]]))
        >>> hill_cipher.replace_digits(19)
        'T'
        >>> hill_cipher.replace_digits(26)
        '0'
        >>> hill_cipher.replace_digits(26.1)
        '0'
        """
        return self.key_string[int(num)]

    def check_determinant(self) -> None:
        """
        >>> hill_cipher = HillCipher(np.array([[2, 5], [1, 6]]))
        >>> hill_cipher.check_determinant()
        """
        det = round(np.linalg.det(self.encrypt_key))

        if det < 0:
            det = det % len(self.key_string)

        req_l = len(self.key_string)
        if greatest_common_divisor(det, len(self.key_string)) != 1:
            msg = (
                f"determinant modular {req_l} of encryption key({det}) "
                f"is not co prime w.r.t {req_l}.\nTry another key."
            )
            raise ValueError(msg)

    def process_text(self, text: str) -> str:
        """
        >>> hill_cipher = HillCipher(np.array([[2, 5], [1, 6]]))
        >>> hill_cipher.process_text('Testing Hill Cipher')
        'TESTINGHILLCIPHERR'
        >>> hill_cipher.process_text('hello')
        'HELLOO'
        """
        chars = [char for char in text.upper() if char in self.key_string]

        last = chars[-1]
        while len(chars) % self.break_key != 0:
            chars.append(last)

        return "".join(chars)

    def encrypt(self, text: str) -> str:
        """
        >>> hill_cipher = HillCipher(np.array([[2, 5], [1, 6]]))
        >>> hill_cipher.encrypt('testing hill cipher')
        'WHXYJOLM9C6XT085LL'
        >>> hill_cipher.encrypt('hello')
        '85FF00'
        """
        text = self.process_text(text.upper())
        encrypted = ""

        for i in range(0, len(text) - self.break_key + 1, self.break_key):
            batch = text[i : i + self.break_key]
            vec = [self.replace_letters(char) for char in batch]
            batch_vec = np.array([vec]).T
            batch_encrypted = self.modulus(self.encrypt_key.dot(batch_vec)).T.tolist()[
                0
            ]
            encrypted_batch = "".join(
                self.replace_digits(num) for num in batch_encrypted
            )
            encrypted += encrypted_batch

        return encrypted

    def make_decrypt_key(self) -> np.ndarray:
        """
        >>> hill_cipher = HillCipher(np.array([[2, 5], [1, 6]]))
        >>> hill_cipher.make_decrypt_key()
        array([[ 6, 25],
               [ 5, 26]])
        """
        det = round(np.linalg.det(self.encrypt_key))

        if det < 0:
            det = det % len(self.key_string)
        det_inv = None
        for i in range(len(self.key_string)):
            if (det * i) % len(self.key_string) == 1:
                det_inv = i
                break

        inv_key = (
            det_inv * np.linalg.det(self.encrypt_key) * np.linalg.inv(self.encrypt_key)
        )

        return self.to_int(self.modulus(inv_key))

    def decrypt(self, text: str) -> str:
        """
        >>> hill_cipher = HillCipher(np.array([[2, 5], [1, 6]]))
        >>> hill_cipher.decrypt('WHXYJOLM9C6XT085LL')
        'TESTINGHILLCIPHERR'
        >>> hill_cipher.decrypt('85FF00')
        'HELLOO'
        """
        decrypt_key = self.make_decrypt_key()
        text = self.process_text(text.upper())
        decrypted = ""

        for i in range(0, len(text) - self.break_key + 1, self.break_key):
            batch = text[i : i + self.break_key]
            vec = [self.replace_letters(char) for char in batch]
            batch_vec = np.array([vec]).T
            batch_decrypted = self.modulus(decrypt_key.dot(batch_vec)).T.tolist()[0]
            decrypted_batch = "".join(
                self.replace_digits(num) for num in batch_decrypted
            )
            decrypted += decrypted_batch

        return decrypted


def main() -> None:
    n = int(input("Enter the order of the encryption key: "))
    hill_matrix = []

    print("Enter each row of the encryption key with space separated integers")
    for _ in range(n):
        row = [int(x) for x in input().split()]
        hill_matrix.append(row)

    hc = HillCipher(np.array(hill_matrix))

    print("Would you like to encrypt or decrypt some text? (1 or 2)")
    option = input("\n1. Encrypt\n2. Decrypt\n")
    if option == "1":
        text_e = input("What text would you like to encrypt?: ")
        print("Your encrypted text is:")
        print(hc.encrypt(text_e))
    elif option == "2":
        text_d = input("What text would you like to decrypt?: ")
        print("Your decrypted text is:")
        print(hc.decrypt(text_d))


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    main()


================================================
FILE: ciphers/mixed_keyword_cypher.py
================================================
from string import ascii_uppercase


def mixed_keyword(
    keyword: str, plaintext: str, verbose: bool = False, alphabet: str = ascii_uppercase
) -> str:
    """
    For keyword: hello

    H E L O
    A B C D
    F G I J
    K M N P
    Q R S T
    U V W X
    Y Z
    and map vertically

    >>> mixed_keyword("college", "UNIVERSITY", True)  # doctest: +NORMALIZE_WHITESPACE
    {'A': 'C', 'B': 'A', 'C': 'I', 'D': 'P', 'E': 'U', 'F': 'Z', 'G': 'O', 'H': 'B',
     'I': 'J', 'J': 'Q', 'K': 'V', 'L': 'L', 'M': 'D', 'N': 'K', 'O': 'R', 'P': 'W',
     'Q': 'E', 'R': 'F', 'S': 'M', 'T': 'S', 'U': 'X', 'V': 'G', 'W': 'H', 'X': 'N',
     'Y': 'T', 'Z': 'Y'}
    'XKJGUFMJST'

    >>> mixed_keyword("college", "UNIVERSITY", False)  # doctest: +NORMALIZE_WHITESPACE
    'XKJGUFMJST'
    """
    keyword = keyword.upper()
    plaintext = plaintext.upper()
    alphabet_set = set(alphabet)

    # create a list of unique characters in the keyword - their order matters
    # it determines how we will map plaintext characters to the ciphertext
    unique_chars = []
    for char in keyword:
        if char in alphabet_set and char not in unique_chars:
            unique_chars.append(char)
    # the number of those unique characters will determine the number of rows
    num_unique_chars_in_keyword = len(unique_chars)

    # create a shifted version of the alphabet
    shifted_alphabet = unique_chars + [
        char for char in alphabet if char not in unique_chars
    ]

    # create a modified alphabet by splitting the shifted alphabet into rows
    modified_alphabet = [
        shifted_alphabet[k : k + num_unique_chars_in_keyword]
        for k in range(0, 26, num_unique_chars_in_keyword)
    ]

    # map the alphabet characters to the modified alphabet characters
    # going 'vertically' through the modified alphabet - consider columns first
    mapping = {}
    letter_index = 0
    for column in range(num_unique_chars_in_keyword):
        for row in modified_alphabet:
            # if current row (the last one) is too short, break out of loop
            if len(row) <= column:
                break

            # map current letter to letter in modified alphabet
            mapping[alphabet[letter_index]] = row[column]
            letter_index += 1

    if verbose:
        print(mapping)
    # create the encrypted text by mapping the plaintext to the modified alphabet
    return "".join(mapping.get(char, char) for char in plaintext)


if __name__ == "__main__":
    # example use
    print(mixed_keyword("college", "UNIVERSITY"))


================================================
FILE: ciphers/mono_alphabetic_ciphers.py
================================================
from typing import Literal

LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"


def translate_message(
    key: str, message: str, mode: Literal["encrypt", "decrypt"]
) -> str:
    """
    >>> translate_message("QWERTYUIOPASDFGHJKLZXCVBNM","Hello World","encrypt")
    'Pcssi Bidsm'
    """
    chars_a = LETTERS if mode == "decrypt" else key
    chars_b = key if mode == "decrypt" else LETTERS
    translated = ""
    # loop through each symbol in the message
    for symbol in message:
        if symbol.upper() in chars_a:
            # encrypt/decrypt the symbol
            sym_index = chars_a.find(symbol.upper())
            if symbol.isupper():
                translated += chars_b[sym_index].upper()
            else:
                translated += chars_b[sym_index].lower()
        else:
            # symbol is not in LETTERS, just add it
            translated += symbol
    return translated


def encrypt_message(key: str, message: str) -> str:
    """
    >>> encrypt_message("QWERTYUIOPASDFGHJKLZXCVBNM", "Hello World")
    'Pcssi Bidsm'
    """
    return translate_message(key, message, "encrypt")


def decrypt_message(key: str, message: str) -> str:
    """
    >>> decrypt_message("QWERTYUIOPASDFGHJKLZXCVBNM", "Hello World")
    'Itssg Vgksr'
    """
    return translate_message(key, message, "decrypt")


def main() -> None:
    message = "Hello World"
    key = "QWERTYUIOPASDFGHJKLZXCVBNM"
    mode = "decrypt"  # set to 'encrypt' or 'decrypt'

    if mode == "encrypt":
        translated = encrypt_message(key, message)
    elif mode == "decrypt":
        translated = decrypt_message(key, message)
    print(f"Using the key {key}, the {mode}ed message is: {translated}")


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: ciphers/morse_code.py
================================================
#!/usr/bin/env python3

"""
Python program to translate to and from Morse code.

https://en.wikipedia.org/wiki/Morse_code
"""

# fmt: off
MORSE_CODE_DICT = {
    "A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
    "H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
    "O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
    "V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
    "2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
    "8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
    ":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", '"': ".-..-.",
    "?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
    "(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
REVERSE_DICT = {value: key for key, value in MORSE_CODE_DICT.items()}


def encrypt(message: str) -> str:
    """
    >>> encrypt("Sos!")
    '... --- ... -.-.--'
    >>> encrypt("SOS!") == encrypt("sos!")
    True
    """
    return " ".join(MORSE_CODE_DICT[char] for char in message.upper())


def decrypt(message: str) -> str:
    """
    >>> decrypt('... --- ... -.-.--')
    'SOS!'
    """
    return "".join(REVERSE_DICT[char] for char in message.split())


def main() -> None:
    """
    >>> s = "".join(MORSE_CODE_DICT)
    >>> decrypt(encrypt(s)) == s
    True
    """
    message = "Morse code here!"
    print(message)
    message = encrypt(message)
    print(message)
    message = decrypt(message)
    print(message)


if __name__ == "__main__":
    main()


================================================
FILE: ciphers/onepad_cipher.py
================================================
import random


class Onepad:
    @staticmethod
    def encrypt(text: str) -> tuple[list[int], list[int]]:
        """
        Function to encrypt text using pseudo-random numbers
        >>> Onepad().encrypt("")
        ([], [])
        >>> Onepad().encrypt([])
        ([], [])
        >>> random.seed(1)
        >>> Onepad().encrypt(" ")
        ([6969], [69])
        >>> random.seed(1)
        >>> Onepad().encrypt("Hello")
        ([9729, 114756, 4653, 31309, 10492], [69, 292, 33, 131, 61])
        >>> Onepad().encrypt(1)
        Traceback (most recent call last):
        ...
        TypeError: 'int' object is not iterable
        >>> Onepad().encrypt(1.1)
        Traceback (most recent call last):
        ...
        TypeError: 'float' object is not iterable
        """
        plain = [ord(i) for i in text]
        key = []
        cipher = []
        for i in plain:
            k = random.randint(1, 300)
            c = (i + k) * k
            cipher.append(c)
            key.append(k)
        return cipher, key

    @staticmethod
    def decrypt(cipher: list[int], key: list[int]) -> str:
        """
        Function to decrypt text using pseudo-random numbers.
        >>> Onepad().decrypt([], [])
        ''
        >>> Onepad().decrypt([35], [])
        ''
        >>> Onepad().decrypt([], [35])
        Traceback (most recent call last):
        ...
        IndexError: list index out of range
        >>> random.seed(1)
        >>> Onepad().decrypt([9729, 114756, 4653, 31309, 10492], [69, 292, 33, 131, 61])
        'Hello'
        """
        plain = []
        for i in range(len(key)):
            p = int((cipher[i] - (key[i]) ** 2) / key[i])
            plain.append(chr(p))
        return "".join(plain)


if __name__ == "__main__":
    c, k = Onepad().encrypt("Hello")
    print(c, k)
    print(Onepad().decrypt(c, k))


================================================
FILE: ciphers/permutation_cipher.py
================================================
"""
The permutation cipher, also called the transposition cipher, is a simple encryption
technique that rearranges the characters in a message based on a secret key. It
divides the message into blocks and applies a permutation to the characters within
each block according to the key. The key is a sequence of unique integers that
determine the order of character rearrangement.

For more info: https://www.nku.edu/~christensen/1402%20permutation%20ciphers.pdf
"""

import random


def generate_valid_block_size(message_length: int) -> int:
    """
    Generate a valid block size that is a factor of the message length.

    Args:
        message_length (int): The length of the message.

    Returns:
        int: A valid block size.

    Example:
        >>> random.seed(1)
        >>> generate_valid_block_size(12)
        3
    """
    block_sizes = [
        block_size
        for block_size in range(2, message_length + 1)
        if message_length % block_size == 0
    ]
    return random.choice(block_sizes)


def generate_permutation_key(block_size: int) -> list[int]:
    """
    Generate a random permutation key of a specified block size.

    Args:
        block_size (int): The size of each permutation block.

    Returns:
        list[int]: A list containing a random permutation of digits.

    Example:
        >>> random.seed(0)
        >>> generate_permutation_key(4)
        [2, 0, 1, 3]
    """
    digits = list(range(block_size))
    random.shuffle(digits)
    return digits


def encrypt(
    message: str, key: list[int] | None = None, block_size: int | None = None
) -> tuple[str, list[int]]:
    """
    Encrypt a message using a permutation cipher with block rearrangement using a key.

    Args:
        message (str): The plaintext message to be encrypted.
        key (list[int]): The permutation key for decryption.
        block_size (int): The size of each permutation block.

    Returns:
        tuple: A tuple containing the encrypted message and the encryption key.

    Example:
        >>> encrypted_message, key = encrypt("HELLO WORLD")
        >>> decrypted_message = decrypt(encrypted_message, key)
        >>> decrypted_message
        'HELLO WORLD'
    """
    message = message.upper()
    message_length = len(message)

    if key is None or block_size is None:
        block_size = generate_valid_block_size(message_length)
        key = generate_permutation_key(block_size)

    encrypted_message = ""

    for i in range(0, message_length, block_size):
        block = message[i : i + block_size]
        rearranged_block = [block[digit] for digit in key]
        encrypted_message += "".join(rearranged_block)

    return encrypted_message, key


def decrypt(encrypted_message: str, key: list[int]) -> str:
    """
    Decrypt an encrypted message using a permutation cipher with block rearrangement.

    Args:
        encrypted_message (str): The encrypted message.
        key (list[int]): The permutation key for decryption.

    Returns:
        str: The decrypted plaintext message.

    Example:
        >>> encrypted_message, key = encrypt("HELLO WORLD")
        >>> decrypted_message = decrypt(encrypted_message, key)
        >>> decrypted_message
        'HELLO WORLD'
    """
    key_length = len(key)
    decrypted_message = ""

    for i in range(0, len(encrypted_message), key_length):
        block = encrypted_message[i : i + key_length]
        original_block = [""] * key_length
        for j, digit in enumerate(key):
            original_block[digit] = block[j]
        decrypted_message += "".join(original_block)

    return decrypted_message


def main() -> None:
    """
    Driver function to pass message to get encrypted, then decrypted.

    Example:
    >>> main()
    Decrypted message: HELLO WORLD
    """
    message = "HELLO WORLD"
    encrypted_message, key = encrypt(message)

    decrypted_message = decrypt(encrypted_message, key)
    print(f"Decrypted message: {decrypted_message}")


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: ciphers/playfair_cipher.py
================================================
"""
https://en.wikipedia.org/wiki/Playfair_cipher#Description

The Playfair cipher was developed by Charles Wheatstone in 1854
It's use was heavily promotedby Lord Playfair, hence its name

Some features of the Playfair cipher are:

1) It was the first literal diagram substitution cipher
2) It is a manual symmetric encryption technique
3) It is a multiple letter encryption cipher

The implementation in the code below encodes alphabets only.
It removes spaces, special characters and numbers from the
code.

Playfair is no longer used by military forces because of known
insecurities and of the advent of automated encryption devices.
This cipher is regarded as insecure since before World War I.
"""

import itertools
import string
from collections.abc import Generator, Iterable


def chunker(seq: Iterable[str], size: int) -> Generator[tuple[str, ...]]:
    it = iter(seq)
    while True:
        chunk = tuple(itertools.islice(it, size))
        if not chunk:
            return
        yield chunk


def prepare_input(dirty: str) -> str:
    """
    Prepare the plaintext by up-casing it
    and separating repeated letters with X's
    """

    dirty = "".join([c.upper() for c in dirty if c in string.ascii_letters])
    clean = ""

    if len(dirty) < 2:
        return dirty

    for i in range(len(dirty) - 1):
        clean += dirty[i]

        if dirty[i] == dirty[i + 1]:
            clean += "X"

    clean += dirty[-1]

    if len(clean) & 1:
        clean += "X"

    return clean


def generate_table(key: str) -> list[str]:
    # I and J are used interchangeably to allow
    # us to use a 5x5 table (25 letters)
    alphabet = "ABCDEFGHIKLMNOPQRSTUVWXYZ"
    # we're using a list instead of a '2d' array because it makes the math
    # for setting up the table and doing the actual encoding/decoding simpler
    table = []

    # copy key chars into the table if they are in `alphabet` ignoring duplicates
    for char in key.upper():
        if char not in table and char in alphabet:
            table.append(char)

    # fill the rest of the table in with the remaining alphabet chars
    for char in alphabet:
        if char not in table:
            table.append(char)

    return table


def encode(plaintext: str, key: str) -> str:
    """
    Encode the given plaintext using the Playfair cipher.
    Takes the plaintext and the key as input and returns the encoded string.

    >>> encode("Hello", "MONARCHY")
    'CFSUPM'
    >>> encode("attack on the left flank", "EMERGENCY")
    'DQZSBYFSDZFMFNLOHFDRSG'
    >>> encode("Sorry!", "SPECIAL")
    'AVXETX'
    >>> encode("Number 1", "NUMBER")
    'UMBENF'
    >>> encode("Photosynthesis!", "THE SUN")
    'OEMHQHVCHESUKE'
    """

    table = generate_table(key)
    plaintext = prepare_input(plaintext)
    ciphertext = ""

    for char1, char2 in chunker(plaintext, 2):
        row1, col1 = divmod(table.index(char1), 5)
        row2, col2 = divmod(table.index(char2), 5)

        if row1 == row2:
            ciphertext += table[row1 * 5 + (col1 + 1) % 5]
            ciphertext += table[row2 * 5 + (col2 + 1) % 5]
        elif col1 == col2:
            ciphertext += table[((row1 + 1) % 5) * 5 + col1]
            ciphertext += table[((row2 + 1) % 5) * 5 + col2]
        else:  # rectangle
            ciphertext += table[row1 * 5 + col2]
            ciphertext += table[row2 * 5 + col1]

    return ciphertext


def decode(ciphertext: str, key: str) -> str:
    """
    Decode the input string using the provided key.

    >>> decode("BMZFAZRZDH", "HAZARD")
    'FIREHAZARD'
    >>> decode("HNBWBPQT", "AUTOMOBILE")
    'DRIVINGX'
    >>> decode("SLYSSAQS", "CASTLE")
    'ATXTACKX'
    """

    table = generate_table(key)
    plaintext = ""

    for char1, char2 in chunker(ciphertext, 2):
        row1, col1 = divmod(table.index(char1), 5)
        row2, col2 = divmod(table.index(char2), 5)

        if row1 == row2:
            plaintext += table[row1 * 5 + (col1 - 1) % 5]
            plaintext += table[row2 * 5 + (col2 - 1) % 5]
        elif col1 == col2:
            plaintext += table[((row1 - 1) % 5) * 5 + col1]
            plaintext += table[((row2 - 1) % 5) * 5 + col2]
        else:  # rectangle
            plaintext += table[row1 * 5 + col2]
            plaintext += table[row2 * 5 + col1]

    return plaintext


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print("Encoded:", encode("BYE AND THANKS", "GREETING"))
    print("Decoded:", decode("CXRBANRLBALQ", "GREETING"))


================================================
FILE: ciphers/polybius.py
================================================
#!/usr/bin/env python3

"""
A Polybius Square is a table that allows someone to translate letters into numbers.

https://www.braingle.com/brainteasers/codes/polybius.php
"""

import numpy as np

SQUARE = [
    ["a", "b", "c", "d", "e"],
    ["f", "g", "h", "i", "k"],
    ["l", "m", "n", "o", "p"],
    ["q", "r", "s", "t", "u"],
    ["v", "w", "x", "y", "z"],
]


class PolybiusCipher:
    def __init__(self) -> None:
        self.SQUARE = np.array(SQUARE)

    def letter_to_numbers(self, letter: str) -> np.ndarray:
        """
        Return the pair of numbers that represents the given letter in the
        polybius square
        >>> np.array_equal(PolybiusCipher().letter_to_numbers('a'), [1,1])
        True

        >>> np.array_equal(PolybiusCipher().letter_to_numbers('u'), [4,5])
        True
        """
        index1, index2 = np.where(letter == self.SQUARE)
        indexes = np.concatenate([index1 + 1, index2 + 1])
        return indexes

    def numbers_to_letter(self, index1: int, index2: int) -> str:
        """
        Return the letter corresponding to the position [index1, index2] in
        the polybius square

        >>> PolybiusCipher().numbers_to_letter(4, 5) == "u"
        True

        >>> PolybiusCipher().numbers_to_letter(1, 1) == "a"
        True
        """
        return self.SQUARE[index1 - 1, index2 - 1]

    def encode(self, message: str) -> str:
        """
        Return the encoded version of message according to the polybius cipher

        >>> PolybiusCipher().encode("test message") == "44154344 32154343112215"
        True

        >>> PolybiusCipher().encode("Test Message") == "44154344 32154343112215"
        True
        """
        message = message.lower()
        message = message.replace("j", "i")

        encoded_message = ""
        for letter_index in range(len(message)):
            if message[letter_index] != " ":
                numbers = self.letter_to_numbers(message[letter_index])
                encoded_message = encoded_message + str(numbers[0]) + str(numbers[1])
            elif message[letter_index] == " ":
                encoded_message = encoded_message + " "

        return encoded_message

    def decode(self, message: str) -> str:
        """
        Return the decoded version of message according to the polybius cipher

        >>> PolybiusCipher().decode("44154344 32154343112215") == "test message"
        True

        >>> PolybiusCipher().decode("4415434432154343112215") == "testmessage"
        True
        """
        message = message.replace(" ", "  ")
        decoded_message = ""
        for numbers_index in range(int(len(message) / 2)):
            if message[numbers_index * 2] != " ":
                index1 = message[numbers_index * 2]
                index2 = message[numbers_index * 2 + 1]

                letter = self.numbers_to_letter(int(index1), int(index2))
                decoded_message = decoded_message + letter
            elif message[numbers_index * 2] == " ":
                decoded_message = decoded_message + " "

        return decoded_message


================================================
FILE: ciphers/porta_cipher.py
================================================
alphabet = {
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}


def generate_table(key: str) -> list[tuple[str, str]]:
    """
    >>> generate_table('marvin')  # doctest: +NORMALIZE_WHITESPACE
    [('ABCDEFGHIJKLM', 'UVWXYZNOPQRST'), ('ABCDEFGHIJKLM', 'NOPQRSTUVWXYZ'),
     ('ABCDEFGHIJKLM', 'STUVWXYZNOPQR'), ('ABCDEFGHIJKLM', 'QRSTUVWXYZNOP'),
     ('ABCDEFGHIJKLM', 'WXYZNOPQRSTUV'), ('ABCDEFGHIJKLM', 'UVWXYZNOPQRST')]
    """
    return [alphabet[char] for char in key.upper()]


def encrypt(key: str, words: str) -> str:
    """
    >>> encrypt('marvin', 'jessica')
    'QRACRWU'
    """
    cipher = ""
    count = 0
    table = generate_table(key)
    for char in words.upper():
        cipher += get_opponent(table[count], char)
        count = (count + 1) % len(table)
    return cipher


def decrypt(key: str, words: str) -> str:
    """
    >>> decrypt('marvin', 'QRACRWU')
    'JESSICA'
    """
    return encrypt(key, words)


def get_position(table: tuple[str, str], char: str) -> tuple[int, int]:
    """
    >>> get_position(generate_table('marvin')[0], 'M')
    (0, 12)
    """
    # `char` is either in the 0th row or the 1st row
    row = 0 if char in table[0] else 1
    col = table[row].index(char)
    return row, col


def get_opponent(table: tuple[str, str], char: str) -> str:
    """
    >>> get_opponent(generate_table('marvin')[0], 'M')
    'T'
    """
    row, col = get_position(table, char.upper())
    if row == 1:
        return table[0][col]
    else:
        return table[1][col] if row == 0 else char


if __name__ == "__main__":
    import doctest

    doctest.testmod()  # Fist ensure that all our tests are passing...
    """
    Demo:

    Enter key: marvin
    Enter text to encrypt: jessica
    Encrypted: QRACRWU
    Decrypted with key: JESSICA
    """
    key = input("Enter key: ").strip()
    text = input("Enter text to encrypt: ").strip()
    cipher_text = encrypt(key, text)

    print(f"Encrypted: {cipher_text}")
    print(f"Decrypted with key: {decrypt(key, cipher_text)}")


================================================
FILE: ciphers/prehistoric_men.txt
================================================
The Project Gutenberg eBook, Prehistoric Men, by Robert J. (Robert John)
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PREHISTORIC MEN

by

ROBERT J. BRAIDWOOD

Research Associate, Old World Prehistory

Professor
Oriental Institute and Department of Anthropology
University of Chicago

Drawings by Susan T. Richert


[Illustration]

Chicago Natural History Museum
Popular Series
Anthropology, Number 37

Third Edition Issued in Co-operation with
The Oriental Institute, The University of Chicago

Edited by Lillian A. Ross

Printed in the United States of America
by Chicago Natural History Museum Press

Copyright 1948, 1951, and 1957 by Chicago Natural History Museum

First edition 1948
Second edition 1951
Third edition 1957
Fourth edition 1959


Preface

[Illustration]


Like the writing of most professional archeologists, mine has been
confined to so-called learned papers. Good, bad, or indifferent, these
papers were in a jargon that only my colleagues and a few advanced
students could understand. Hence, when I was asked to do this little
book, I soon found it extremely difficult to say what I meant in simple
fashion. The style is new to me, but I hope the reader will not find it
forced or pedantic; at least I have done my very best to tell the story
simply and clearly.

Many friends have aided in the preparation of the book. The whimsical
charm of Miss Susan Richert�s illustrations add enormously to the
spirit I wanted. She gave freely of her own time on the drawings and
in planning the book with me. My colleagues at the University of
Chicago, especially Professor Wilton M. Krogman (now of the University
of Pennsylvania), and also Mrs. Linda Braidwood, Associate of the
Oriental Institute, and Professors Fay-Cooper Cole and Sol Tax, of
the Department of Anthropology, gave me counsel in matters bearing on
their special fields, and the Department of Anthropology bore some of
the expense of the illustrations. From Mrs. Irma Hunter and Mr. Arnold
Maremont, who are not archeologists at all and have only an intelligent
layman�s notion of archeology, I had sound advice on how best to tell
the story. I am deeply indebted to all these friends.

While I was preparing the second edition, I had the great fortune
to be able to rework the third chapter with Professor Sherwood L.
Washburn, now of the Department of Anthropology of the University of
California, and the fourth, fifth, and sixth chapters with Professor
Hallum L. Movius, Jr., of the Peabody Museum, Harvard University. The
book has gained greatly in accuracy thereby. In matters of dating,
Professor Movius and the indications of Professor W. F. Libby�s Carbon
14 chronology project have both encouraged me to choose the lowest
dates now current for the events of the Pleistocene Ice Age. There is
still no certain way of fixing a direct chronology for most of the
Pleistocene, but Professor Libby�s method appears very promising for
its end range and for proto-historic dates. In any case, this book
names �periods,� and new dates may be written in against mine, if new
and better dating systems appear.

I wish to thank Dr. Clifford C. Gregg, Director of Chicago Natural
History Museum, for the opportunity to publish this book. My old
friend, Dr. Paul S. Martin, Chief Curator in the Department of
Anthropology, asked me to undertake the job and inspired me to complete
it. I am also indebted to Miss Lillian A. Ross, Associate Editor of
Scientific Publications, and to Mr. George I. Quimby, Curator of
Exhibits in Anthropology, for all the time they have given me in
getting the manuscript into proper shape.

            ROBERT J. BRAIDWOOD
  _June 15, 1950_




Preface to the Third Edition


In preparing the enlarged third edition, many of the above mentioned
friends have again helped me. I have picked the brains of Professor F.
Clark Howell of the Department of Anthropology of the University of
Chicago in reworking the earlier chapters, and he was very patient in
the matter, which I sincerely appreciate.

All of Mrs. Susan Richert Allen�s original drawings appear, but a few
necessary corrections have been made in some of the charts and some new
drawings have been added by Mr. John Pfiffner, Staff Artist, Chicago
Natural History Museum.

            ROBERT J. BRAIDWOOD
  _March 1, 1959_




Contents


                                                       PAGE
  How We Learn about Prehistoric Men                      7

  The Changing World in Which Prehistoric Men Lived      17

  Prehistoric Men Themselves                             22

  Cultural Beginnings                                    38

  More Evidence of Culture                               56

  Early Moderns                                          70

  End and Prelude                                        92

  The First Revolution                                  121

  The Conquest of Civilization                          144

  End of Prehistory                                     162

  Summary                                               176

  List of Books                                         180

  Index                                                 184




HOW WE LEARN about Prehistoric Men

[Illustration]


Prehistory means the time before written history began. Actually, more
than 99 per cent of man�s story is prehistory. Man is at least half a
million years old, but he did not begin to write history (or to write
anything) until about 5,000 years ago.

The men who lived in prehistoric times left us no history books, but
they did unintentionally leave a record of their presence and their way
of life. This record is studied and interpreted by different kinds of
scientists.


SCIENTISTS WHO FIND OUT ABOUT PREHISTORIC MEN

The scientists who study the bones and teeth and any other parts
they find of the bodies of prehistoric men, are called _physical
anthropologists_. Physical anthropologists are trained, much like
doctors, to know all about the human body. They study living people,
too; they know more about the biological facts of human �races� than
anybody else. If the police find a badly decayed body in a trunk,
they ask a physical anthropologist to tell them what the person
originally looked like. The physical anthropologists who specialize in
prehistoric men work with fossils, so they are sometimes called _human
paleontologists_.


ARCHEOLOGISTS

There is a kind of scientist who studies the things that prehistoric
men made and did. Such a scientist is called an _archeologist_. It is
the archeologist�s business to look for the stone and metal tools, the
pottery, the graves, and the caves or huts of the men who lived before
history began.

But there is more to archeology than just looking for things. In
Professor V. Gordon Childe�s words, archeology �furnishes a sort of
history of human activity, provided always that the actions have
produced concrete results and left recognizable material traces.� You
will see that there are at least three points in what Childe says:

  1. The archeologists have to find the traces of things left behind by
     ancient man, and

  2. Only a few objects may be found, for most of these were probably
     too soft or too breakable to last through the years. However,

  3. The archeologist must use whatever he can find to tell a story--to
     make a �sort of history�--from the objects and living-places and
     graves that have escaped destruction.

What I mean is this: Let us say you are walking through a dump yard,
and you find a rusty old spark plug. If you want to think about what
the spark plug means, you quickly remember that it is a part of an
automobile motor. This tells you something about the man who threw
the spark plug on the dump. He either had an automobile, or he knew
or lived near someone who did. He can�t have lived so very long ago,
you�ll remember, because spark plugs and automobiles are only about
sixty years old.

When you think about the old spark plug in this way you have
just been making the beginnings of what we call an archeological
_interpretation_; you have been making the spark plug tell a story.
It is the same way with the man-made things we archeologists find
and put in museums. Usually, only a few of these objects are pretty
to look at; but each of them has some sort of story to tell. Making
the interpretation of his finds is the most important part of the
archeologist�s job. It is the way he gets at the �sort of history of
human activity� which is expected of archeology.


SOME OTHER SCIENTISTS

There are many other scientists who help the archeologist and the
physical anthropologist find out about prehistoric men. The geologists
help us tell the age of the rocks or caves or gravel beds in which
human bones or man-made objects are found. There are other scientists
with names which all begin with �paleo� (the Greek word for �old�). The
_paleontologists_ study fossil animals. There are also, for example,
such scientists as _paleobotanists_ and _paleoclimatologists_, who
study ancient plants and climates. These scientists help us to know
the kinds of animals and plants that were living in prehistoric times
and so could be used for food by ancient man; what the weather was
like; and whether there were glaciers. Also, when I tell you that
prehistoric men did not appear until long after the great dinosaurs had
disappeared, I go on the say-so of the paleontologists. They know that
fossils of men and of dinosaurs are not found in the same geological
period. The dinosaur fossils come in early periods, the fossils of men
much later.

Since World War II even the atomic scientists have been helping the
archeologists. By testing the amount of radioactivity left in charcoal,
wood, or other vegetable matter obtained from archeological sites, they
have been able to date the sites. Shell has been used also, and even
the hair of Egyptian mummies. The dates of geological and climatic
events have also been discovered. Some of this work has been done from
drillings taken from the bottom of the sea.

This dating by radioactivity has considerably shortened the dates which
the archeologists used to give. If you find that some of the dates
I give here are more recent than the dates you see in other books
on prehistory, it is because I am using one of the new lower dating
systems.

[Illustration: RADIOCARBON CHART

The rate of disappearance of radioactivity as time passes.[1]]

  [1] It is important that the limitations of the radioactive carbon
      �dating� system be held in mind. As the statistics involved in
      the system are used, there are two chances in three that the
      �date� of the sample falls within the range given as plus or
      minus an added number of years. For example, the �date� for the
      Jarmo village (see chart), given as 6750 � 200 B.C., really
      means that there are only two chances in three that the real
      date of the charcoal sampled fell between 6950 and 6550 B.C.
      We have also begun to suspect that there are ways in which the
      samples themselves may have become �contaminated,� either on
      the early or on the late side. We now tend to be suspicious of
      single radioactive carbon determinations, or of determinations
      from one site alone. But as a fabric of consistent
      determinations for several or more sites of one archeological
      period, we gain confidence in the dates.


HOW THE SCIENTISTS FIND OUT

So far, this chapter has been mainly about the people who find out
about prehistoric men. We also need a word about _how_ they find out.

All our finds came by accident until about a hundred years ago. Men
digging wells, or digging in caves for fertilizer, often turned up
ancient swords or pots or stone arrowheads. People also found some odd
pieces of stone that didn�t look like natural forms, but they also
didn�t look like any known tool. As a result, the people who found them
gave them queer names; for example, �thunderbolts.� The people thought
the strange stones came to earth as bolts of lightning. We know now
that these strange stones were prehistoric stone tools.

Many important finds still come to us by accident. In 1935, a British
dentist, A. T. Marston, found the first of two fragments of a very
important fossil human skull, in a gravel pit at Swanscombe, on the
River Thames, England. He had to wait nine months, until the face of
the gravel pit had been dug eight yards farther back, before the second
fragment appeared. They fitted! Then, twenty years later, still another
piece appeared. In 1928 workmen who were blasting out rock for the
breakwater in the port of Haifa began to notice flint tools. Thus the
story of cave men on Mount Carmel, in Palestine, began to be known.

Planned archeological digging is only about a century old. Even before
this, however, a few men realized the significance of objects they dug
from the ground; one of these early archeologists was our own Thomas
Jefferson. The first real mound-digger was a German grocer�s clerk,
Heinrich Schliemann. Schliemann made a fortune as a merchant, first
in Europe and then in the California gold-rush of 1849. He became an
American citizen. Then he retired and had both money and time to test
an old idea of his. He believed that the heroes of ancient Troy and
Mycenae were once real Trojans and Greeks. He proved it by going to
Turkey and Greece and digging up the remains of both cities.

Schliemann had the great good fortune to find rich and spectacular
treasures, and he also had the common sense to keep notes and make
descriptions of what he found. He proved beyond doubt that many ancient
city mounds can be _stratified_. This means that there may be the
remains of many towns in a mound, one above another, like layers in a
cake.

You might like to have an idea of how mounds come to be in layers.
The original settlers may have chosen the spot because it had a good
spring and there were good fertile lands nearby, or perhaps because
it was close to some road or river or harbor. These settlers probably
built their town of stone and mud-brick. Finally, something would have
happened to the town--a flood, or a burning, or a raid by enemies--and
the walls of the houses would have fallen in or would have melted down
as mud in the rain. Nothing would have remained but the mud and debris
of a low mound of _one_ layer.

The second settlers would have wanted the spot for the same reasons
the first settlers did--good water, land, and roads. Also, the second
settlers would have found a nice low mound to build their houses on,
a protection from floods. But again, something would finally have
happened to the second town, and the walls of _its_ houses would have
come tumbling down. This makes the _second_ layer. And so on....

In Syria I once had the good fortune to dig on a large mound that had
no less than fifteen layers. Also, most of the layers were thick, and
there were signs of rebuilding and repairs within each layer. The mound
was more than a hundred feet high. In each layer, the building material
used had been a soft, unbaked mud-brick, and most of the debris
consisted of fallen or rain-melted mud from these mud-bricks.

This idea of _stratification_, like the cake layers, was already a
familiar one to the geologists by Schliemann�s time. They could show
that their lowest layer of rock was oldest or earliest, and that the
overlying layers became more recent as one moved upward. Schliemann�s
digging proved the same thing at Troy. His first (lowest and earliest)
city had at least nine layers above it; he thought that the second
layer contained the remains of Homer�s Troy. We now know that Homeric
Troy was layer VIIa from the bottom; also, we count eleven layers or
sub-layers in total.

Schliemann�s work marks the beginnings of modern archeology. Scholars
soon set out to dig on ancient sites, from Egypt to Central America.


ARCHEOLOGICAL INFORMATION

As time went on, the study of archeological materials--found either
by accident or by digging on purpose--began to show certain things.
Archeologists began to get ideas as to the kinds of objects that
belonged together. If you compared a mail-order catalogue of 1890 with
one of today, you would see a lot of differences. If you really studied
the two catalogues hard, you would also begin to see that certain
objects �go together.� Horseshoes and metal buggy tires and pieces of
harness would begin to fit into a picture with certain kinds of coal
stoves and furniture and china dishes and kerosene lamps. Our friend
the spark plug, and radios and electric refrigerators and light bulbs
would fit into a picture with different kinds of furniture and dishes
and tools. You won�t be old enough to remember the kind of hats that
women wore in 1890, but you�ve probably seen pictures of them, and you
know very well they couldn�t be worn with the fashions of today.

This is one of the ways that archeologists study their materials.
The various tools and weapons and jewelry, the pottery, the kinds
of houses, and even the ways of burying the dead tend to fit into
pictures. Some archeologists call all of the things that go together to
make such a picture an _assemblage_. The assemblage of the first layer
of Schliemann�s Troy was as different from that of the seventh layer as
our 1900 mail-order catalogue is from the one of today.

The archeologists who came after Schliemann began to notice other
things and to compare them with occurrences in modern times. The
idea that people will buy better mousetraps goes back into very
ancient times. Today, if we make good automobiles or radios, we can
sell some of them in Turkey or even in Timbuktu. This means that a
few present-day types of American automobiles and radios form part
of present-day �assemblages� in both Turkey and Timbuktu. The total
present-day �assemblage� of Turkey is quite different from that of
Timbuktu or that of America, but they have at least some automobiles
and some radios in common.

Now these automobiles and radios will eventually wear out. Let us
suppose we could go to some remote part of Turkey or to Timbuktu in a
dream. We don�t know what the date is, in our dream, but we see all
sorts of strange things and ways of living in both places. Nobody
tells us what the date is. But suddenly we see a 1936 Ford; so we
know that in our dream it has to be at least the year 1936, and only
as many years after that as we could reasonably expect a Ford to keep
in running order. The Ford would probably break down in twenty years�
time, so the Turkish or Timbuktu �assemblage� we�re seeing in our dream
has to date at about A.D. 1936-56.

Archeologists not only �date� their ancient materials in this way; they
also see over what distances and between which peoples trading was
done. It turns out that there was a good deal of trading in ancient
times, probably all on a barter and exchange basis.


EVERYTHING BEGINS TO FIT TOGETHER

Now we need to pull these ideas all together and see the complicated
structure the archeologists can build with their materials.

Even the earliest archeologists soon found that there was a very long
range of prehistoric time which would yield only very simple things.
For this very long early part of prehistory, there was little to be
found but the flint tools which wandering, hunting and gathering
people made, and the bones of the wild animals they ate. Toward the
end of prehistoric time there was a general settling down with the
coming of agriculture, and all sorts of new things began to be made.
Archeologists soon got a general notion of what ought to appear with
what. Thus, it would upset a French prehistorian digging at the bottom
of a very early cave if he found a fine bronze sword, just as much as
it would upset him if he found a beer bottle. The people of his very
early cave layer simply could not have made bronze swords, which came
later, just as do beer bottles. Some accidental disturbance of the
layers of his cave must have happened.

With any luck, archeologists do their digging in a layered, stratified
site. They find the remains of everything that would last through
time, in several different layers. They know that the assemblage in
the bottom layer was laid down earlier than the assemblage in the next
layer above, and so on up to the topmost layer, which is the latest.
They look at the results of other �digs� and find that some other
archeologist 900 miles away has found ax-heads in his lowest layer,
exactly like the ax-heads of their fifth layer. This means that their
fifth layer must have been lived in at about the same time as was the
first layer in the site 200 miles away. It also may mean that the
people who lived in the two layers knew and traded with each other. Or
it could mean that they didn�t necessarily know each other, but simply
that both traded with a third group at about the same time.

You can see that the more we dig and find, the more clearly the main
facts begin to stand out. We begin to be more sure of which people
lived at the same time, which earlier and which later. We begin to
know who traded with whom, and which peoples seemed to live off by
themselves. We begin to find enough skeletons in burials so that the
physical anthropologists can tell us what the people looked like. We
get animal bones, and a paleontologist may tell us they are all bones
of wild animals; or he may tell us that some or most of the bones are
those of domesticated animals, for instance, sheep or cattle, and
therefore the people must have kept herds.

More important than anything else--as our structure grows more
complicated and our materials increase--is the fact that �a sort
of history of human activity� does begin to appear. The habits or
traditions that men formed in the making of their tools and in the
ways they did things, begin to stand out for us. How characteristic
were these habits and traditions? What areas did they spread over?
How long did they last? We watch the different tools and the traces
of the way things were done--how the burials were arranged, what
the living-places were like, and so on. We wonder about the people
themselves, for the traces of habits and traditions are useful to us
only as clues to the men who once had them. So we ask the physical
anthropologists about the skeletons that we found in the burials. The
physical anthropologists tell us about the anatomy and the similarities
and differences which the skeletons show when compared with other
skeletons. The physical anthropologists are even working on a
method--chemical tests of the bones--that will enable them to discover
what the blood-type may have been. One thing is sure. We have never
found a group of skeletons so absolutely similar among themselves--so
cast from a single mould, so to speak--that we could claim to have a
�pure� race. I am sure we never shall.

We become particularly interested in any signs of change--when new
materials and tool types and ways of doing things replace old ones. We
watch for signs of social change and progress in one way or another.

We must do all this without one word of written history to aid us.
Everything we are concerned with goes back to the time _before_ men
learned to write. That is the prehistorian�s job--to find out what
happened before history began.




THE CHANGING WORLD in which Prehistoric Men Lived

[Illustration]


Mankind, we�ll say, is at least a half million years old. It is very
hard to understand how long a time half a million years really is.
If we were to compare this whole length of time to one day, we�d get
something like this: The present time is midnight, and Jesus was
born just five minutes and thirty-six seconds ago. Earliest history
began less than fifteen minutes ago. Everything before 11:45 was in
prehistoric time.

Or maybe we can grasp the length of time better in terms of
generations. As you know, primitive peoples tend to marry and have
children rather early in life. So suppose we say that twenty years
will make an average generation. At this rate there would be 25,000
generations in a half-million years. But our United States is much less
than ten generations old, twenty-five generations take us back before
the time of Columbus, Julius Caesar was alive just 100 generations ago,
David was king of Israel less than 150 generations ago, 250 generations
take us back to the beginning of written history. And there were 24,750
generations of men before written history began!

I should probably tell you that there is a new method of prehistoric
dating which would cut the earliest dates in my reckoning almost
in half. Dr. Cesare Emiliani, combining radioactive (C14) and
chemical (oxygen isotope) methods in the study of deep-sea borings,
has developed a system which would lower the total range of human
prehistory to about 300,000 years. The system is still too new to have
had general examination and testing. Hence, I have not used it in this
book; it would mainly affect the dates earlier than 25,000 years ago.


CHANGES IN ENVIRONMENT

The earth probably hasn�t changed much in the last 5,000 years (250
generations). Men have built things on its surface and dug into it and
drawn boundaries on maps of it, but the places where rivers, lakes,
seas, and mountains now stand have changed very little.

In earlier times the earth looked very different. Geologists call the
last great geological period the _Pleistocene_. It began somewhere
between a half million and a million years ago, and was a time of great
changes. Sometimes we call it the Ice Age, for in the Pleistocene
there were at least three or four times when large areas of earth
were covered with glaciers. The reason for my uncertainty is that
while there seem to have been four major mountain or alpine phases of
glaciation, there may only have been three general continental phases
in the Old World.[2]

  [2] This is a complicated affair and I do not want to bother you
      with its details. Both the alpine and the continental ice sheets
      seem to have had minor fluctuations during their _main_ phases,
      and the advances of the later phases destroyed many of the
      traces of the earlier phases. The general textbooks have tended
      to follow the names and numbers established for the Alps early
      in this century by two German geologists. I will not bother you
      with the names, but there were _four_ major phases. It is the
      second of these alpine phases which seems to fit the traces of
      the earliest of the great continental glaciations. In this book,
      I will use the four-part system, since it is the most familiar,
      but will add the word _alpine_ so you may remember to make the
      transition to the continental system if you wish to do so.

Glaciers are great sheets of ice, sometimes over a thousand feet
thick, which are now known only in Greenland and Antarctica and in
high mountains. During several of the glacial periods in the Ice Age,
the glaciers covered most of Canada and the northern United States and
reached down to southern England and France in Europe. Smaller ice
sheets sat like caps on the Rockies, the Alps, and the Himalayas. The
continental glaciation only happened north of the equator, however, so
remember that �Ice Age� is only half true.

As you know, the amount of water on and about the earth does not vary.
These large glaciers contained millions of tons of water frozen into
ice. Because so much water was frozen and contained in the glaciers,
the water level of lakes and oceans was lowered. Flooded areas were
drained and appeared as dry land. There were times in the Ice Age when
there was no English Channel, so that England was not an island, and a
land bridge at the Dardanelles probably divided the Mediterranean from
the Black Sea.

A very important thing for people living during the time of a
glaciation was the region adjacent to the glacier. They could not, of
course, live on the ice itself. The questions would be how close could
they live to it, and how would they have had to change their way of
life to do so.


GLACIERS CHANGE THE WEATHER

Great sheets of ice change the weather. When the front of a glacier
stood at Milwaukee, the weather must have been bitterly cold in
Chicago. The climate of the whole world would have been different, and
you can see how animals and men would have been forced to move from one
place to another in search of food and warmth.

On the other hand, it looks as if only a minor proportion of the whole
Ice Age was really taken up by times of glaciation. In between came
the _interglacial_ periods. During these times the climate around
Chicago was as warm as it is now, and sometimes even warmer. It may
interest you to know that the last great glacier melted away less than
10,000 years ago. Professor Ernst Antevs thinks we may be living in an
interglacial period and that the Ice Age may not be over yet. So if you
want to make a killing in real estate for your several hundred times
great-grandchildren, you might buy some land in the Arizona desert or
the Sahara.

We do not yet know just why the glaciers appeared and disappeared, as
they did. It surely had something to do with an increase in rainfall
and a fall in temperature. It probably also had to do with a general
tendency for the land to rise at the beginning of the Pleistocene. We
know there was some mountain-building at that time. Hence, rain-bearing
winds nourished the rising and cooler uplands with snow. An increase
in all three of these factors--if they came together--would only have
needed to be slight. But exactly why this happened we do not know.

The reason I tell you about the glaciers is simply to remind you of the
changing world in which prehistoric men lived. Their surroundings--the
animals and plants they used for food, and the weather they had to
protect themselves from--were always changing. On the other hand, this
change happened over so long a period of time and was so slow that
individual people could not have noticed it. Glaciers, about which they
probably knew nothing, moved in hundreds of miles to the north of them.
The people must simply have wandered ever more southward in search
of the plants and animals on which they lived. Or some men may have
stayed where they were and learned to hunt different animals and eat
different foods. Prehistoric men had to keep adapting themselves to new
environments and those who were most adaptive were most successful.


OTHER CHANGES

Changes took place in the men themselves as well as in the ways they
lived. As time went on, they made better tools and weapons. Then, too,
we begin to find signs of how they started thinking of other things
than food and the tools to get it with. We find that they painted on
the walls of caves, and decorated their tools; we find that they buried
their dead.

At about the time when the last great glacier was finally melting away,
men in the Near East made the first basic change in human economy.
They began to plant grain, and they learned to raise and herd certain
animals. This meant that they could store food in granaries and �on the
hoof� against the bad times of the year. This first really basic change
in man�s way of living has been called the �food-producing revolution.�
By the time it happened, a modern kind of climate was beginning. Men
had already grown to look as they do now. Know-how in ways of living
had developed and progressed, slowly but surely, up to a point. It was
impossible for men to go beyond that point if they only hunted and
fished and gathered wild foods. Once the basic change was made--once
the food-producing revolution became effective--technology leaped ahead
and civilization and written history soon began.




Prehistoric Men THEMSELVES

[Illustration]


DO WE KNOW WHERE MAN ORIGINATED?

For a long time some scientists thought the �cradle of mankind� was in
central Asia. Other scientists insisted it was in Africa, and still
others said it might have been in Europe. Actually, we don�t know
where it was. We don�t even know that there was only _one_ �cradle.�
If we had to choose a �cradle� at this moment, we would probably say
Africa. But the southern portions of Asia and Europe may also have been
included in the general area. The scene of the early development of
mankind was certainly the Old World. It is pretty certain men didn�t
reach North or South America until almost the end of the Ice Age--had
they done so earlier we would certainly have found some trace of them
by now.

The earliest tools we have yet found come from central and south
Africa. By the dating system I�m using, these tools must be over
500,000 years old. There are now reports that a few such early tools
have been found--at the Sterkfontein cave in South Africa--along with
the bones of small fossil men called �australopithecines.�

Not all scientists would agree that the australopithecines were �men,�
or would agree that the tools were made by the australopithecines
themselves. For these sticklers, the earliest bones of men come from
the island of Java. The date would be about 450,000 years ago. So far,
we have not yet found the tools which we suppose these earliest men in
the Far East must have made.

Let me say it another way. How old are the earliest traces of men we
now have? Over half a million years. This was a time when the first
alpine glaciation was happening in the north. What has been found so
far? The tools which the men of those times made, in different parts
of Africa. It is now fairly generally agreed that the �men� who made
the tools were the australopithecines. There is also a more �man-like�
jawbone at Kanam in Kenya, but its find-spot has been questioned. The
next earliest bones we have were found in Java, and they may be almost
a hundred thousand years younger than the earliest African finds. We
haven�t yet found the tools of these early Javanese. Our knowledge of
tool-using in Africa spreads quickly as time goes on: soon after the
appearance of tools in the south we shall have them from as far north
as Algeria.

Very soon after the earliest Javanese come the bones of slightly more
developed people in Java, and the jawbone of a man who once lived in
what is now Germany. The same general glacial beds which yielded the
later Javanese bones and the German jawbone also include tools. These
finds come from the time of the second alpine glaciation.

So this is the situation. By the time of the end of the second alpine
or first continental glaciation (say 400,000 years ago) we have traces
of men from the extremes of the more southerly portions of the Old
World--South Africa, eastern Asia, and western Europe. There are also
some traces of men in the middle ground. In fact, Professor Franz
Weidenreich believed that creatures who were the immediate ancestors
of men had already spread over Europe, Africa, and Asia by the time
the Ice Age began. We certainly have no reason to disbelieve this, but
fortunate accidents of discovery have not yet given us the evidence to
prove it.


MEN AND APES

Many people used to get extremely upset at the ill-formed notion
that �man descended from the apes.� Such words were much more likely
to start fights or �monkey trials� than the correct notion that all
living animals, including man, ascended or evolved from a single-celled
organism which lived in the primeval seas hundreds of millions of years
ago. Men are mammals, of the order called Primates, and man�s living
relatives are the great apes. Men didn�t �descend� from the apes or
apes from men, and mankind must have had much closer relatives who have
since become extinct.

Men stand erect. They also walk and run on their two feet. Apes are
happiest in trees, swinging with their arms from branch to branch.
Few branches of trees will hold the mighty gorilla, although he still
manages to sleep in trees. Apes can�t stand really erect in our sense,
and when they have to run on the ground, they use the knuckles of their
hands as well as their feet.

A key group of fossil bones here are the south African
australopithecines. These are called the _Australopithecinae_ or
�man-apes� or sometimes even �ape-men.� We do not _know_ that they were
directly ancestral to men but they can hardly have been so to apes.
Presently I�ll describe them a bit more. The reason I mention them
here is that while they had brains no larger than those of apes, their
hipbones were enough like ours so that they must have stood erect.
There is no good reason to think they couldn�t have walked as we do.


BRAINS, HANDS, AND TOOLS

Whether the australopithecines were our ancestors or not, the proper
ancestors of men must have been able to stand erect and to walk on
their two feet. Three further important things probably were involved,
next, before they could become men proper. These are:

  1. The increasing size and development of the brain.

  2. The increasing usefulness (specialization) of the thumb and hand.

  3. The use of tools.

Nobody knows which of these three is most important, or which came
first. Most probably the growth of all three things was very much
blended together. If you think about each of the things, you will see
what I mean. Unless your hand is more flexible than a paw, and your
thumb will work against (or oppose) your fingers, you can�t hold a tool
very well. But you wouldn�t get the idea of using a tool unless you had
enough brain to help you see cause and effect. And it is rather hard to
see how your hand and brain would develop unless they had something to
practice on--like using tools. In Professor Krogman�s words, �the hand
must become the obedient servant of the eye and the brain.� It is the
_co-ordination_ of these things that counts.

Many other things must have been happening to the bodies of the
creatures who were the ancestors of men. Our ancestors had to develop
organs of speech. More than that, they had to get the idea of letting
_certain sounds_ made with these speech organs have _certain meanings_.

All this must have gone very slowly. Probably everything was developing
little by little, all together. Men became men very slowly.


WHEN SHALL WE CALL MEN MEN?

What do I mean when I say �men�? People who looked pretty much as we
do, and who used different tools to do different things, are men to me.
We�ll probably never know whether the earliest ones talked or not. They
probably had vocal cords, so they could make sounds, but did they know
how to make sounds work as symbols to carry meanings? But if the fossil
bones look like our skeletons, and if we find tools which we�ll agree
couldn�t have been made by nature or by animals, then I�d say we had
traces of _men_.

The australopithecine finds of the Transvaal and Bechuanaland, in
south Africa, are bound to come into the discussion here. I�ve already
told you that the australopithecines could have stood upright and
walked on their two hind legs. They come from the very base of the
Pleistocene or Ice Age, and a few coarse stone tools have been found
with the australopithecine fossils. But there are three varieties
of the australopithecines and they last on until a time equal to
that of the second alpine glaciation. They are the best suggestion
we have yet as to what the ancestors of men _may_ have looked like.
They were certainly closer to men than to apes. Although their brain
size was no larger than the brains of modern apes their body size and
stature were quite small; hence, relative to their small size, their
brains were large. We have not been able to prove without doubt that
the australopithecines were _tool-making_ creatures, even though the
recent news has it that tools have been found with australopithecine
bones. The doubt as to whether the australopithecines used the tools
themselves goes like this--just suppose some man-like creature (whose
bones we have not yet found) made the tools and used them to kill
and butcher australopithecines. Hence a few experts tend to let
australopithecines still hang in limbo as �man-apes.�


THE EARLIEST MEN WE KNOW

I�ll postpone talking about the tools of early men until the next
chapter. The men whose bones were the earliest of the Java lot have
been given the name _Meganthropus_. The bones are very fragmentary. We
would not understand them very well unless we had the somewhat later
Javanese lot--the more commonly known _Pithecanthropus_ or �Java
man�--against which to refer them for study. One of the less well-known
and earliest fragments, a piece of lower jaw and some teeth, rather
strongly resembles the lower jaws and teeth of the australopithecine
type. Was _Meganthropus_ a sort of half-way point between the
australopithecines and _Pithecanthropus_? It is still too early to say.
We shall need more finds before we can be definite one way or the other.

Java man, _Pithecanthropus_, comes from geological beds equal in age
to the latter part of the second alpine glaciation; the _Meganthropus_
finds refer to beds of the beginning of this glaciation. The first
finds of Java man were made in 1891-92 by Dr. Eugene Dubois, a Dutch
doctor in the colonial service. Finds have continued to be made. There
are now bones enough to account for four skulls. There are also four
jaws and some odd teeth and thigh bones. Java man, generally speaking,
was about five feet six inches tall, and didn�t hold his head very
erect. His skull was very thick and heavy and had room for little more
than two-thirds as large a brain as we have. He had big teeth and a big
jaw and enormous eyebrow ridges.

No tools were found in the geological deposits where bones of Java man
appeared. There are some tools in the same general area, but they come
a bit later in time. One reason we accept the Java man as man--aside
from his general anatomical appearance--is that these tools probably
belonged to his near descendants.

Remember that there are several varieties of men in the whole early
Java lot, at least two of which are earlier than the _Pithecanthropus_,
�Java man.� Some of the earlier ones seem to have gone in for
bigness, in tooth-size at least. _Meganthropus_ is one of these
earlier varieties. As we said, he _may_ turn out to be a link to
the australopithecines, who _may_ or _may not_ be ancestral to men.
_Meganthropus_ is best understandable in terms of _Pithecanthropus_,
who appeared later in the same general area. _Pithecanthropus_ is
pretty well understandable from the bones he left us, and also because
of his strong resemblance to the fully tool-using cave-dwelling �Peking
man,� _Sinanthropus_, about whom we shall talk next. But you can see
that the physical anthropologists and prehistoric archeologists still
have a lot of work to do on the problem of earliest men.


PEKING MEN AND SOME EARLY WESTERNERS

The earliest known Chinese are called _Sinanthropus_, or �Peking man,�
because the finds were made near that city. In World War II, the United
States Marine guard at our Embassy in Peking tried to help get the
bones out of the city before the Japanese attack. Nobody knows where
these bones are now. The Red Chinese accuse us of having stolen them.
They were last seen on a dock-side at a Chinese port. But should you
catch a Marine with a sack of old bones, perhaps we could achieve peace
in Asia by returning them! Fortunately, there is a complete set of
casts of the bones.

Peking man lived in a cave in a limestone hill, made tools, cracked
animal bones to get the marrow out, and used fire. Incidentally, the
bones of Peking man were found because Chinese dig for what they call
�dragon bones� and �dragon teeth.� Uneducated Chinese buy these things
in their drug stores and grind them into powder for medicine. The
�dragon teeth� and �bones� are really fossils of ancient animals, and
sometimes of men. The people who supply the drug stores have learned
where to dig for strange bones and teeth. Paleontologists who get to
China go to the drug stores to buy fossils. In a roundabout way, this
is how the fallen-in cave of Peking man at Choukoutien was discovered.

Peking man was not quite as tall as Java man but he probably stood
straighter. His skull looked very much like that of the Java skull
except that it had room for a slightly larger brain. His face was less
brutish than was Java man�s face, but this isn�t saying much.

Peking man dates from early in the interglacial period following the
second alpine glaciation. He probably lived close to 350,000 years
ago. There are several finds to account for in Europe by about this
time, and one from northwest Africa. The very large jawbone found
near Heidelberg in Germany is doubtless even earlier than Peking man.
The beds where it was found are of second alpine glacial times, and
recently some tools have been said to have come from the same beds.
There is not much I need tell you about the Heidelberg jaw save that it
seems certainly to have belonged to an early man, and that it is very
big.

Another find in Germany was made at Steinheim. It consists of the
fragmentary skull of a man. It is very important because of its
relative completeness, but it has not yet been fully studied. The bone
is thick, but the back of the head is neither very low nor primitive,
and the face is also not primitive. The forehead does, however, have
big ridges over the eyes. The more fragmentary skull from Swanscombe in
England (p. 11) has been much more carefully studied. Only the top and
back of that skull have been found. Since the skull rounds up nicely,
it has been assumed that the face and forehead must have been quite
�modern.� Careful comparison with Steinheim shows that this was not
necessarily so. This is important because it bears on the question of
how early truly �modern� man appeared.

Recently two fragmentary jaws were found at Ternafine in Algeria,
northwest Africa. They look like the jaws of Peking man. Tools were
found with them. Since no jaws have yet been found at Steinheim or
Swanscombe, but the time is the same, one wonders if these people had
jaws like those of Ternafine.


WHAT HAPPENED TO JAVA AND PEKING MEN

Professor Weidenreich thought that there were at least a dozen ways in
which the Peking man resembled the modern Mongoloids. This would seem
to indicate that Peking man was really just a very early Chinese.

Several later fossil men have been found in the Java-Australian area.
The best known of these is the so-called Solo man. There are some finds
from Australia itself which we now know to be quite late. But it looks
as if we may assume a line of evolution from Java man down to the
modern Australian natives. During parts of the Ice Age there was a land
bridge all the way from Java to Australia.


TWO ENGLISHMEN WHO WEREN�T OLD

The older textbooks contain descriptions of two English finds which
were thought to be very old. These were called Piltdown (_Eoanthropus
dawsoni_) and Galley Hill. The skulls were very modern in appearance.
In 1948-49, British scientists began making chemical tests which proved
that neither of these finds is very old. It is now known that both
�Piltdown man� and the tools which were said to have been found with
him were part of an elaborate fake!


TYPICAL �CAVE MEN�

The next men we have to talk about are all members of a related group.
These are the Neanderthal group. �Neanderthal man� himself was found in
the Neander Valley, near D�sseldorf, Germany, in 1856. He was the first
human fossil to be recognized as such.

[Illustration: PRINCIPAL KNOWN TYPES OF FOSSIL MEN

  CRO-MAGNON
  NEANDERTHAL
  MODERN SKULL
  COMBE-CAPELLE
  SINANTHROPUS
  PITHECANTHROPUS]

Some of us think that the neanderthaloids proper are only those people
of western Europe who didn�t get out before the beginning of the last
great glaciation, and who found themselves hemmed in by the glaciers
in the Alps and northern Europe. Being hemmed in, they intermarried
a bit too much and developed into a special type. Professor F. Clark
Howell sees it this way. In Europe, the earliest trace of men we
now know is the Heidelberg jaw. Evolution continued in Europe, from
Heidelberg through the Swanscombe and Steinheim types to a group of
pre-neanderthaloids. There are traces of these pre-neanderthaloids
pretty much throughout Europe during the third interglacial period--say
100,000 years ago. The pre-neanderthaloids are represented by such
finds as the ones at Ehringsdorf in Germany and Saccopastore in Italy.
I won�t describe them for you, since they are simply less extreme than
the neanderthaloids proper--about half way between Steinheim and the
classic Neanderthal people.

Professor Howell believes that the pre-neanderthaloids who happened to
get caught in the pocket of the southwest corner of Europe at the onset
of the last great glaciation became the classic Neanderthalers. Out in
the Near East, Howell thinks, it is possible to see traces of people
evolving from the pre-neanderthaloid type toward that of fully modern
man. Certainly, we don�t see such extreme cases of �neanderthaloidism�
outside of western Europe.

There are at least a dozen good examples in the main or classic
Neanderthal group in Europe. They date to just before and in the
earlier part of the last great glaciation (85,000 to 40,000 years ago).
Many of the finds have been made in caves. The �cave men� the movies
and the cartoonists show you are probably meant to be Neanderthalers.
I�m not at all sure they dragged their women by the hair; the women
were probably pretty tough, too!

Neanderthal men had large bony heads, but plenty of room for brains.
Some had brain cases even larger than the average for modern man. Their
faces were heavy, and they had eyebrow ridges of bone, but the ridges
were not as big as those of Java man. Their foreheads were very low,
and they didn�t have much chin. They were about five feet three inches
tall, but were heavy and barrel-chested. But the Neanderthalers didn�t
slouch as much as they�ve been blamed for, either.

One important thing about the Neanderthal group is that there is a fair
number of them to study. Just as important is the fact that we know
something about how they lived, and about some of the tools they made.


OTHER MEN CONTEMPORARY WITH THE NEANDERTHALOIDS

We have seen that the neanderthaloids seem to be a specialization
in a corner of Europe. What was going on elsewhere? We think that
the pre-neanderthaloid type was a generally widespread form of men.
From this type evolved other more or less extreme although generally
related men. The Solo finds in Java form one such case. Another was the
Rhodesian man of Africa, and the more recent Hopefield finds show more
of the general Rhodesian type. It is more confusing than it needs to be
if these cases outside western Europe are called neanderthaloids. They
lived during the same approximate time range but they were all somewhat
different-looking people.


EARLY MODERN MEN

How early is modern man (_Homo sapiens_), the �wise man�? Some people
have thought that he was very early, a few still think so. Piltdown
and Galley Hill, which were quite modern in anatomical appearance and
_supposedly_ very early in date, were the best �evidence� for very
early modern men. Now that Piltdown has been liquidated and Galley Hill
is known to be very late, what is left of the idea?

The backs of the skulls of the Swanscombe and Steinheim finds look
rather modern. Unless you pay attention to the face and forehead of the
Steinheim find--which not many people have--and perhaps also consider
the Ternafine jaws, you might come to the conclusion that the crown of
the Swanscombe head was that of a modern-like man.

Two more skulls, again without faces, are available from a French
cave site, Font�chevade. They come from the time of the last great
interglacial, as did the pre-neanderthaloids. The crowns of the
Font�chevade skulls also look quite modern. There is a bit of the
forehead preserved on one of these skulls and the brow-ridge is not
heavy. Nevertheless, there is a suggestion that the bones belonged to
an immature individual. In this case, his (or even more so, if _her_)
brow-ridges would have been weak anyway. The case for the Font�chevade
fossils, as modern type men, is little stronger than that for
Swanscombe, although Professor Vallois believes it a good case.

It seems to add up to the fact that there were people living in
Europe--before the classic neanderthaloids--who looked more modern,
in some features, than the classic western neanderthaloids did. Our
best suggestion of what men looked like--just before they became fully
modern--comes from a cave on Mount Carmel in Palestine.


THE FIRST MODERNS

Professor T. D. McCown and the late Sir Arthur Keith, who studied the
Mount Carmel bones, figured out that one of the two groups involved
was as much as 70 per cent modern. There were, in fact, two groups or
varieties of men in the Mount Carmel caves and in at least two other
Palestinian caves of about the same time. The time would be about that
of the onset of colder weather, when the last glaciation was beginning
in the north--say 75,000 years ago.

The 70 per cent modern group came from only one cave, Mugharet es-Skhul
(�cave of the kids�). The other group, from several caves, had bones of
men of the type we�ve been calling pre-neanderthaloid which we noted
were widespread in Europe and beyond. The tools which came with each
of these finds were generally similar, and McCown and Keith, and other
scholars since their study, have tended to assume that both the Skhul
group and the pre-neanderthaloid group came from exactly the same time.
The conclusion was quite natural: here was a population of men in the
act of evolving in two different directions. But the time may not be
exactly the same. It is very difficult to be precise, within say 10,000
years, for a time some 75,000 years ago. If the Skhul men are in fact
later than the pre-neanderthaloid group of Palestine, as some of us
think, then they show how relatively modern some men were--men who
lived at the same time as the classic Neanderthalers of the European
pocket.

Soon after the first extremely cold phase of the last glaciation, we
begin to get a number of bones of completely modern men in Europe.
We also get great numbers of the tools they made, and their living
places in caves. Completely modern skeletons begin turning up in caves
dating back to toward 40,000 years ago. The time is about that of the
beginning of the second phase of the last glaciation. These skeletons
belonged to people no different from many people we see today. Like
people today, not everybody looked alike. (The positions of the more
important fossil men of later Europe are shown in the chart on page
72.)


DIFFERENCES IN THE EARLY MODERNS

The main early European moderns have been divided into two groups, the
Cro-Magnon group and the Combe Capelle-Br�nn group. Cro-Magnon people
were tall and big-boned, with large, long, and rugged heads. They
must have been built like many present-day Scandinavians. The Combe
Capelle-Br�nn people were shorter; they had narrow heads and faces, and
big eyebrow-ridges. Of course we don�t find the skin or hair of these
people. But there is little doubt they were Caucasoids (�Whites�).

Another important find came in the Italian Riviera, near Monte Carlo.
Here, in a cave near Grimaldi, there was a grave containing a woman
and a young boy, buried together. The two skeletons were first called
�Negroid� because some features of their bones were thought to resemble
certain features of modern African Negro bones. But more recently,
Professor E. A. Hooton and other experts questioned the use of the word
�Negroid� in describing the Grimaldi skeletons. It is true that nothing
is known of the skin color, hair form, or any other fleshy feature of
the Grimaldi people, so that the word �Negroid� in its usual meaning is
not proper here. It is also not clear whether the features of the bones
claimed to be �Negroid� are really so at all.

From a place called Wadjak, in Java, we have �proto-Australoid� skulls
which closely resemble those of modern Australian natives. Some of
the skulls found in South Africa, especially the Boskop skull, look
like those of modern Bushmen, but are much bigger. The ancestors of
the Bushmen seem to have once been very widespread south of the Sahara
Desert. True African Negroes were forest people who apparently expanded
out of the west central African area only in the last several thousand
years. Although dark in skin color, neither the Australians nor the
Bushmen are Negroes; neither the Wadjak nor the Boskop skulls are
�Negroid.�

As we�ve already mentioned, Professor Weidenreich believed that Peking
man was already on the way to becoming a Mongoloid. Anyway, the
Mongoloids would seem to have been present by the time of the �Upper
Cave� at Choukoutien, the _Sinanthropus_ find-spot.


WHAT THE DIFFERENCES MEAN

What does all this difference mean? It means that, at one moment in
time, within each different area, men tended to look somewhat alike.
From area to area, men tended to look somewhat different, just as
they do today. This is all quite natural. People _tended_ to mate
near home; in the anthropological jargon, they made up geographically
localized breeding populations. The simple continental division of
�stocks�--black = Africa, yellow = Asia, white = Europe--is too simple
a picture to fit the facts. People became accustomed to life in some
particular area within a continent (we might call it a �natural area�).
As they went on living there, they evolved towards some particular
physical variety. It would, of course, have been difficult to draw
a clear boundary between two adjacent areas. There must always have
been some mating across the boundaries in every case. One thing human
beings don�t do, and never have done, is to mate for �purity.� It is
self-righteous nonsense when we try to kid ourselves into thinking that
they do.

I am not going to struggle with the whole business of modern stocks and
races. This is a book about prehistoric men, not recent historic or
modern men. My physical anthropologist friends have been very patient
in helping me to write and rewrite this chapter--I am not going to
break their patience completely. Races are their business, not mine,
and they must do the writing about races. I shall, however, give two
modern definitions of race, and then make one comment.

  Dr. William G. Boyd, professor of Immunochemistry, School of
    Medicine, Boston University: �We may define a human race as a
    population which differs significantly from other human populations
    in regard to the frequency of one or more of the genes it
    possesses.�

  Professor Sherwood L. Washburn, professor of Physical Anthropology,
    Department of Anthropology, the University of California: �A �race�
    is a group of genetically similar populations, and races intergrade
    because there are always intermediate populations.�

My comment is that the ideas involved here are all biological: they
concern groups, _not_ individuals. Boyd and Washburn may differ a bit
on what they want to consider a �population,� but a population is a
group nevertheless, and genetics is biology to the hilt. Now a lot of
people still think of race in terms of how people dress or fix their
food or of other habits or customs they have. The next step is to talk
about racial �purity.� None of this has anything whatever to do with
race proper, which is a matter of the biology of groups.

Incidentally, I�m told that if man very carefully _controls_
the breeding of certain animals over generations--dogs, cattle,
chickens--he might achieve a �pure� race of animals. But he doesn�t do
it. Some unfortunate genetic trait soon turns up, so this has just as
carefully to be bred out again, and so on.


SUMMARY OF PRESENT KNOWLEDGE OF FOSSIL MEN

The earliest bones of men we now have--upon which all the experts
would probably agree--are those of _Meganthropus_, from Java, of about
450,000 years ago. The earlier australopithecines of Africa were
possibly not tool-users and may not have been ancestral to men at all.
But there is an alternate and evidently increasingly stronger chance
that some of them may have been. The Kanam jaw from Kenya, another
early possibility, is not only very incomplete but its find-spot is
very questionable.

Java man proper, _Pithecanthropus_, comes next, at about 400,000 years
ago, and the big Heidelberg jaw in Germany must be of about the same
date. Next comes Swanscombe in England, Steinheim in Germany, the
Ternafine jaws in Algeria, and Peking man, _Sinanthropus_. They all
date to the second great interglacial period, about 350,000 years ago.

Piltdown and Galley Hill are out, and with them, much of the starch
in the old idea that there were two distinct lines of development
in human evolution: (1) a line of �paleoanthropic� development from
Heidelberg to the Neanderthalers where it became extinct, and (2) a
very early �modern� line, through Piltdown, Galley Hill, Swanscombe, to
us. Swanscombe, Steinheim, and Ternafine are just as easily cases of
very early pre-neanderthaloids.

The pre-neanderthaloids were very widespread during the third
interglacial: Ehringsdorf, Saccopastore, some of the Mount Carmel
people, and probably Font�chevade are cases in point. A variety of
their descendants can be seen, from Java (Solo), Africa (Rhodesian
man), and about the Mediterranean and in western Europe. As the acute
cold of the last glaciation set in, the western Europeans found
themselves surrounded by water, ice, or bitter cold tundra. To vastly
over-simplify it, they �bred in� and became classic neanderthaloids.
But on Mount Carmel, the Skhul cave-find with its 70 per cent modern
features shows what could happen elsewhere at the same time.

Lastly, from about 40,000 or 35,000 years ago--the time of the onset
of the second phase of the last glaciation--we begin to find the fully
modern skeletons of men. The modern skeletons differ from place to
place, just as different groups of men living in different places still
look different.

What became of the Neanderthalers? Nobody can tell me for sure. I�ve a
hunch they were simply �bred out� again when the cold weather was over.
Many Americans, as the years go by, are no longer ashamed to claim they
have �Indian blood in their veins.� Give us a few more generations
and there will not be very many other Americans left to whom we can
brag about it. It certainly isn�t inconceivable to me to imagine a
little Cro-Magnon boy bragging to his friends about his tough, strong,
Neanderthaler great-great-great-great-grandfather!




Cultural BEGINNINGS

[Illustration]


Men, unlike the lower animals, are made up of much more than flesh and
blood and bones; for men have �culture.�


WHAT IS CULTURE?

�Culture� is a word with many meanings. The doctors speak of making a
�culture� of a certain kind of bacteria, and ants are said to have a
�culture.� Then there is the Emily Post kind of �culture�--you say a
person is �cultured,� or that he isn�t, depending on such things as
whether or not he eats peas with his knife.

The anthropologists use the word too, and argue heatedly over its finer
meanings; but they all agree that every human being is part of or has
some kind of culture. Each particular human group has a particular
culture; that is one of the ways in which we can tell one group of
men from another. In this sense, a CULTURE means the way the members
of a group of people think and believe and live, the tools they make,
and the way they do things. Professor Robert Redfield says a culture
is an organized or formalized body of conventional understandings.
�Conventional understandings� means the whole set of rules, beliefs,
and standards which a group of people lives by. These understandings
show themselves in art, and in the other things a people may make and
do. The understandings continue to last, through tradition, from one
generation to another. They are what really characterize different
human groups.


SOME CHARACTERISTICS OF CULTURE

A culture lasts, although individual men in the group die off. On
the other hand, a culture changes as the different conventions and
understandings change. You could almost say that a culture lives in the
minds of the men who have it. But people are not born with it; they
get it as they grow up. Suppose a day-old Hungarian baby is adopted by
a family in Oshkosh, Wisconsin, and the child is not told that he is
Hungarian. He will grow up with no more idea of Hungarian culture than
anyone else in Oshkosh.

So when I speak of ancient Egyptian culture, I mean the whole body
of understandings and beliefs and knowledge possessed by the ancient
Egyptians. I mean their beliefs as to why grain grew, as well as their
ability to make tools with which to reap the grain. I mean their
beliefs about life after death. What I am thinking about as culture is
a thing which lasted in time. If any one Egyptian, even the Pharaoh,
died, it didn�t affect the Egyptian culture of that particular moment.


PREHISTORIC CULTURES

For that long period of man�s history that is all prehistory, we have
no written descriptions of cultures. We find only the tools men made,
the places where they lived, the graves in which they buried their
dead. Fortunately for us, these tools and living places and graves all
tell us something about the ways these men lived and the things they
believed. But the story we learn of the very early cultures must be
only a very small part of the whole, for we find so few things. The
rest of the story is gone forever. We have to do what we can with what
we find.

For all of the time up to about 75,000 years ago, which was the time
of the classic European Neanderthal group of men, we have found few
cave-dwelling places of very early prehistoric men. First, there is the
fallen-in cave where Peking man was found, near Peking. Then there are
two or three other _early_, but not _very early_, possibilities. The
finds at the base of the French cave of Font�chevade, those in one of
the Makapan caves in South Africa, and several open sites such as Dr.
L. S. B. Leakey�s Olorgesailie in Kenya doubtless all lie earlier than
the time of the main European Neanderthal group, but none are so early
as the Peking finds.

You can see that we know very little about the home life of earlier
prehistoric men. We find different kinds of early stone tools, but we
can�t even be really sure which tools may have been used together.


WHY LITTLE HAS LASTED FROM EARLY TIMES

Except for the rare find-spots mentioned above, all our very early
finds come from geological deposits, or from the wind-blown surfaces
of deserts. Here is what the business of geological deposits really
means. Let us say that a group of people was living in England about
300,000 years ago. They made the tools they needed, lived in some sort
of camp, almost certainly built fires, and perhaps buried their dead.
While the climate was still warm, many generations may have lived in
the same place, hunting, and gathering nuts and berries; but after some
few thousand years, the weather began very gradually to grow colder.
These early Englishmen would not have known that a glacier was forming
over northern Europe. They would only have noticed that the animals
they hunted seemed to be moving south, and that the berries grew larger
toward the south. So they would have moved south, too.

The camp site they left is the place we archeologists would really have
liked to find. All of the different tools the people used would have
been there together--many broken, some whole. The graves, and traces
of fire, and the tools would have been there. But the glacier got
there first! The front of this enormous sheet of ice moved down over
the country, crushing and breaking and plowing up everything, like a
gigantic bulldozer. You can see what happened to our camp site.

Everything the glacier couldn�t break, it pushed along in front of it
or plowed beneath it. Rocks were ground to gravel, and soil was caught
into the ice, which afterwards melted and ran off as muddy water. Hard
tools of flint sometimes remained whole. Human bones weren�t so hard;
it�s a wonder _any_ of them lasted. Gushing streams of melt water
flushed out the debris from underneath the glacier, and water flowed
off the surface and through great crevasses. The hard materials these
waters carried were even more rolled and ground up. Finally, such
materials were dropped by the rushing waters as gravels, miles from
the front of the glacier. At last the glacier reached its greatest
extent; then it melted backward toward the north. Debris held in the
ice was dropped where the ice melted, or was flushed off by more melt
water. When the glacier, leaving the land, had withdrawn to the sea,
great hunks of ice were broken off as icebergs. These icebergs probably
dropped the materials held in their ice wherever they floated and
melted. There must be many tools and fragmentary bones of prehistoric
men on the bottom of the Atlantic Ocean and the North Sea.

Remember, too, that these glaciers came and went at least three or four
times during the Ice Age. Then you will realize why the earlier things
we find are all mixed up. Stone tools from one camp site got mixed up
with stone tools from many other camp sites--tools which may have been
made tens of thousands or more years apart. The glaciers mixed them
all up, and so we cannot say which particular sets of tools belonged
together in the first place.


�EOLITHS�

But what sort of tools do we find earliest? For almost a century,
people have been picking up odd bits of flint and other stone in the
oldest Ice Age gravels in England and France. It is now thought these
odd bits of stone weren�t actually worked by prehistoric men. The
stones were given a name, _eoliths_, or �dawn stones.� You can see them
in many museums; but you can be pretty sure that very few of them were
actually fashioned by men.

It is impossible to pick out �eoliths� that seem to be made in any
one _tradition_. By �tradition� I mean a set of habits for making one
kind of tool for some particular job. No two �eoliths� look very much
alike: tools made as part of some one tradition all look much alike.
Now it�s easy to suppose that the very earliest prehistoric men picked
up and used almost any sort of stone. This wouldn�t be surprising; you
and I do it when we go camping. In other words, some of these �eoliths�
may actually have been used by prehistoric men. They must have used
anything that might be handy when they needed it. We could have figured
that out without the �eoliths.�


THE ROAD TO STANDARDIZATION

Reasoning from what we know or can easily imagine, there should have
been three major steps in the prehistory of tool-making. The first step
would have been simple _utilization_ of what was at hand. This is the
step into which the �eoliths� would fall. The second step would have
been _fashioning_--the haphazard preparation of a tool when there was a
need for it. Probably many of the earlier pebble tools, which I shall
describe next, fall into this group. The third step would have been
_standardization_. Here, men began to make tools according to certain
set traditions. Counting the better-made pebble tools, there are four
such traditions or sets of habits for the production of stone tools in
earliest prehistoric times. Toward the end of the Pleistocene, a fifth
tradition appears.


PEBBLE TOOLS

At the beginning of the last chapter, you�ll remember that I said there
were tools from very early geological beds. The earliest bones of men
have not yet been found in such early beds although the Sterkfontein
australopithecine cave approaches this early date. The earliest tools
come from Africa. They date back to the time of the first great
alpine glaciation and are at least 500,000 years old. The earliest
ones are made of split pebbles, about the size of your fist or a bit
bigger. They go under the name of pebble tools. There are many natural
exposures of early Pleistocene geological beds in Africa, and the
prehistoric archeologists of south and central Africa have concentrated
on searching for early tools. Other finds of early pebble tools have
recently been made in Algeria and Morocco.

[Illustration: SOUTH AFRICAN PEBBLE TOOL]

There are probably early pebble tools to be found in areas of the
Old World besides Africa; in fact, some prehistorians already claim
to have identified a few. Since the forms and the distinct ways of
making the earlier pebble tools had not yet sufficiently jelled into
a set tradition, they are difficult for us to recognize. It is not
so difficult, however, if there are great numbers of �possibles�
available. A little later in time the tradition becomes more clearly
set, and pebble tools are easier to recognize. So far, really large
collections of pebble tools have only been found and examined in Africa.


CORE-BIFACE TOOLS

The next tradition we�ll look at is the _core_ or biface one. The tools
are large pear-shaped pieces of stone trimmed flat on the two opposite
sides or �faces.� Hence �biface� has been used to describe these tools.
The front view is like that of a pear with a rather pointed top, and
the back view looks almost exactly the same. Look at them side on, and
you can see that the front and back faces are the same and have been
trimmed to a thin tip. The real purpose in trimming down the two faces
was to get a good cutting edge all around. You can see all this in the
illustration.

[Illustration: ABBEVILLIAN BIFACE]

We have very little idea of the way in which these core-bifaces were
used. They have been called �hand axes,� but this probably gives the
wrong idea, for an ax, to us, is not a pointed tool. All of these early
tools must have been used for a number of jobs--chopping, scraping,
cutting, hitting, picking, and prying. Since the core-bifaces tend to
be pointed, it seems likely that they were used for hitting, picking,
and prying. But they have rough cutting edges, so they could have been
used for chopping, scraping, and cutting.


FLAKE TOOLS

The third tradition is the _flake_ tradition. The idea was to get a
tool with a good cutting edge by simply knocking a nice large flake off
a big block of stone. You had to break off the flake in such a way that
it was broad and thin, and also had a good sharp cutting edge. Once you
really got on to the trick of doing it, this was probably a simpler way
to make a good cutting tool than preparing a biface. You have to know
how, though; I�ve tried it and have mashed my fingers more than once.

The flake tools look as if they were meant mainly for chopping,
scraping, and cutting jobs. When one made a flake tool, the idea seems
to have been to produce a broad, sharp, cutting edge.

[Illustration: CLACTONIAN FLAKE]

The core-biface and the flake traditions were spread, from earliest
times, over much of Europe, Africa, and western Asia. The map on page
52 shows the general area. Over much of this great region there was
flint. Both of these traditions seem well adapted to flint, although
good core-bifaces and flakes were made from other kinds of stone,
especially in Africa south of the Sahara.


CHOPPERS AND ADZE-LIKE TOOLS

The fourth early tradition is found in southern and eastern Asia, from
northwestern India through Java and Burma into China. Father Maringer
recently reported an early group of tools in Japan, which most resemble
those of Java, called Patjitanian. The prehistoric men in this general
area mostly used quartz and tuff and even petrified wood for their
stone tools (see illustration, p. 46).

This fourth early tradition is called the _chopper-chopping tool_
tradition. It probably has its earliest roots in the pebble tool
tradition of African type. There are several kinds of tools in this
tradition, but all differ from the western core-bifaces and flakes.
There are broad, heavy scrapers or cleavers, and tools with an
adze-like cutting edge. These last-named tools are called �hand adzes,�
just as the core-bifaces of the west have often been called �hand
axes.� The section of an adze cutting edge is ? shaped; the section of
an ax is < shaped.

[Illustration: ANYATHIAN ADZE-LIKE TOOL]

There are also pointed pebble tools. Thus the tool kit of these early
south and east Asiatic peoples seems to have included tools for doing
as many different jobs as did the tools of the Western traditions.

Dr. H. L. Movius has emphasized that the tools which were found in the
Peking cave with Peking man belong to the chopper-tool tradition. This
is the only case as yet where the tools and the man have been found
together from very earliest times--if we except Sterkfontein.


DIFFERENCES WITHIN THE TOOL-MAKING TRADITIONS

The latter three great traditions in the manufacture of stone
tools--and the less clear-cut pebble tools before them--are all we have
to show of the cultures of the men of those times. Changes happened in
each of the traditions. As time went on, the tools in each tradition
were better made. There could also be slight regional differences in
the tools within one tradition. Thus, tools with small differences, but
all belonging to one tradition, can be given special group (facies)
names.

This naming of special groups has been going on for some time. Here are
some of these names, since you may see them used in museum displays
of flint tools, or in books. Within each tradition of tool-making
(save the chopper tools), the earliest tool type is at the bottom
of the list, just as it appears in the lowest beds of a geological
stratification.[3]

  [3] Archeologists usually make their charts and lists with the
      earliest materials at the bottom and the latest on top, since
      this is the way they find them in the ground.

  Chopper tool (all about equally early):
    Anyathian (Burma)
    Choukoutienian (China)
    Patjitanian (Java)
    Soan (India)

  Flake:
    �Typical Mousterian�
    Levalloiso-Mousterian
    Levalloisian
    Tayacian
    Clactonian (localized in England)

  Core-biface:
    Some blended elements in �Mousterian�
    Micoquian (= Acheulean 6 and 7)
    Acheulean
    Abbevillian (once called �Chellean�)

  Pebble tool:
    Oldowan
    Ain Hanech
    pre-Stellenbosch
    Kafuan

The core-biface and the flake traditions appear in the chart (p. 65).

The early archeologists had many of the tool groups named before they
ever realized that there were broader tool preparation traditions. This
was understandable, for in dealing with the mixture of things that come
out of glacial gravels the easiest thing to do first is to isolate
individual types of tools into groups. First you put a bushel-basketful
of tools on a table and begin matching up types. Then you give names to
the groups of each type. The groups and the types are really matters of
the archeologists� choice; in real life, they were probably less exact
than the archeologists� lists of them. We now know pretty well in which
of the early traditions the various early groups belong.


THE MEANING OF THE DIFFERENT TRADITIONS

What do the traditions really mean? I see them as the standardization
of ways to make tools for particular jobs. We may not know exactly what
job the maker of a particular core-biface or flake tool had in mind. We
can easily see, however, that he already enjoyed a know-how, a set of
persistent habits of tool preparation, which would always give him the
same type of tool when he wanted to make it. Therefore, the traditions
show us that persistent habits already existed for the preparation of
one type of tool or another.

This tells us that one of the characteristic aspects of human culture
was already present. There must have been, in the minds of these
early men, a notion of the ideal type of tool for a particular job.
Furthermore, since we find so many thousands upon thousands of tools
of one type or another, the notion of the ideal types of tools _and_
the know-how for the making of each type must have been held in common
by many men. The notions of the ideal types and the know-how for their
production must have been passed on from one generation to another.

I could even guess that the notions of the ideal type of one or the
other of these tools stood out in the minds of men of those times
somewhat like a symbol of �perfect tool for good job.� If this were
so--remember it�s only a wild guess of mine--then men were already
symbol users. Now let�s go on a further step to the fact that the words
men speak are simply sounds, each different sound being a symbol for a
different meaning. If standardized tool-making suggests symbol-making,
is it also possible that crude word-symbols were also being made? I
suppose that it is not impossible.

There may, of course, be a real question whether tool-utilizing
creatures--our first step, on page 42--were actually men. Other
animals utilize things at hand as tools. The tool-fashioning creature
of our second step is more suggestive, although we may not yet feel
sure that many of the earlier pebble tools were man-made products. But
with the step to standardization and the appearance of the traditions,
I believe we must surely be dealing with the traces of culture-bearing
_men_. The �conventional understandings� which Professor Redfield�s
definition of culture suggests are now evidenced for us in the
persistent habits for the preparation of stone tools. Were we able to
see the other things these prehistoric men must have made--in materials
no longer preserved for the archeologist to find--I believe there would
be clear signs of further conventional understandings. The men may have
been physically primitive and pretty shaggy in appearance, but I think
we must surely call them men.


AN OLDER INTERPRETATION OF THE WESTERN TRADITIONS

In the last chapter, I told you that many of the older archeologists
and human paleontologists used to think that modern man was very old.
The supposed ages of Piltdown and Galley Hill were given as evidence
of the great age of anatomically modern man, and some interpretations
of the Swanscombe and Font�chevade fossils were taken to support
this view. The conclusion was that there were two parallel lines or
�phyla� of men already present well back in the Pleistocene. The
first of these, the more primitive or �paleoanthropic� line, was
said to include Heidelberg, the proto-neanderthaloids and classic
Neanderthal. The more anatomically modern or �neanthropic� line was
thought to consist of Piltdown and the others mentioned above. The
Neanderthaler or paleoanthropic line was thought to have become extinct
after the first phase of the last great glaciation. Of course, the
modern or neanthropic line was believed to have persisted into the
present, as the basis for the world�s population today. But with
Piltdown liquidated, Galley Hill known to be very late, and Swanscombe
and Font�chevade otherwise interpreted, there is little left of the
so-called parallel phyla theory.

While the theory was in vogue, however, and as long as the European
archeological evidence was looked at in one short-sighted way, the
archeological materials _seemed_ to fit the parallel phyla theory. It
was simply necessary to believe that the flake tools were made only
by the paleoanthropic Neanderthaler line, and that the more handsome
core-biface tools were the product of the neanthropic modern-man line.

Remember that _almost_ all of the early prehistoric European tools
came only from the redeposited gravel beds. This means that the tools
were not normally found in the remains of camp sites or work shops
where they had actually been dropped by the men who made and used
them. The tools came, rather, from the secondary hodge-podge of the
glacial gravels. I tried to give you a picture of the bulldozing action
of glaciers (p. 40) and of the erosion and weathering that were
side-effects of a glacially conditioned climate on the earth�s surface.
As we said above, if one simply plucks tools out of the redeposited
gravels, his natural tendency is to �type� the tools by groups, and to
think that the groups stand for something _on their own_.

In 1906, M. Victor Commont actually made a rare find of what seems
to have been a kind of workshop site, on a terrace above the Somme
river in France. Here, Commont realized, flake tools appeared clearly
in direct association with core-biface tools. Few prehistorians paid
attention to Commont or his site, however. It was easier to believe
that flake tools represented a distinct �culture� and that this
�culture� was that of the Neanderthaler or paleoanthropic line, and
that the core-bifaces stood for another �culture� which was that of the
supposed early modern or neanthropic line. Of course, I am obviously
skipping many details here. Some later sites with Neanderthal fossils
do seem to have only flake tools, but other such sites have both types
of tools. The flake tools which appeared _with_ the core-bifaces
in the Swanscombe gravels were never made much of, although it
was embarrassing for the parallel phyla people that Font�chevade
ran heavily to flake tools. All in all, the parallel phyla theory
flourished because it seemed so neat and easy to understand.


TRADITIONS ARE TOOL-MAKING HABITS, NOT CULTURES

In case you think I simply enjoy beating a dead horse, look in any
standard book on prehistory written twenty (or even ten) years ago, or
in most encyclopedias. You�ll find that each of the individual tool
types, of the West, at least, was supposed to represent a �culture.�
The �cultures� were believed to correspond to parallel lines of human
evolution.

In 1937, Mr. Harper Kelley strongly re-emphasized the importance
of Commont�s workshop site and the presence of flake tools with
core-bifaces. Next followed Dr. Movius� clear delineation of the
chopper-chopping tool tradition of the Far East. This spoiled the nice
symmetry of the flake-tool = paleoanthropic, core-biface = neanthropic
equations. Then came increasing understanding of the importance of
the pebble tools in Africa, and the location of several more workshop
sites there, especially at Olorgesailie in Kenya. Finally came the
liquidation of Piltdown and the deflation of Galley Hill�s date. So it
is at last possible to picture an individual prehistoric man making a
flake tool to do one job and a core-biface tool to do another. Commont
showed us this picture in 1906, but few believed him.

[Illustration: DISTRIBUTION OF TOOL-PREPARATION TRADITIONS

Time approximately 100,000 years ago]

There are certainly a few cases in which flake tools did appear with
few or no core-bifaces. The flake-tool group called Clactonian in
England is such a case. Another good, but certainly later case is
that of the cave on Mount Carmel in Palestine, where the blended
pre-neanderthaloid, 70 per cent modern-type skulls were found. Here, in
the same level with the skulls, were 9,784 flint tools. Of these, only
three--doubtless strays--were core-bifaces; all the rest were flake
tools or flake chips. We noted above how the Font�chevade cave ran to
flake tools. The only conclusion I would draw from this is that times
and circumstances did exist in which prehistoric men needed only flake
tools. So they only made flake tools for those particular times and
circumstances.


LIFE IN EARLIEST TIMES

What do we actually know of life in these earliest times? In the
glacial gravels, or in the terrace gravels of rivers once swollen by
floods of melt water or heavy rains, or on the windswept deserts, we
find stone tools. The earliest and coarsest of these are the pebble
tools. We do not yet know what the men who made them looked like,
although the Sterkfontein australopithecines probably give us a good
hint. Then begin the more formal tool preparation traditions of the
west--the core-bifaces and the flake tools--and the chopper-chopping
tool series of the farther east. There is an occasional roughly worked
piece of bone. From the gravels which yield the Clactonian flakes of
England comes the fire-hardened point of a wooden spear. There are
also the chance finds of the fossil human bones themselves, of which
we spoke in the last chapter. Aside from the cave of Peking man, none
of the earliest tools have been found in caves. Open air or �workshop�
sites which do not seem to have been disturbed later by some geological
agency are very rare.

The chart on page 65 shows graphically what the situation in
west-central Europe seems to have been. It is not yet certain whether
there were pebble tools there or not. The Font�chevade cave comes
into the picture about 100,000 years ago or more. But for the earlier
hundreds of thousands of years--below the red-dotted line on the
chart--the tools we find come almost entirely from the haphazard
mixture within the geological contexts.

The stone tools of each of the earlier traditions are the simplest
kinds of all-purpose tools. Almost any one of them could be used for
hacking, chopping, cutting, and scraping; so the men who used them must
have been living in a rough and ready sort of way. They found or hunted
their food wherever they could. In the anthropological jargon, they
were �food-gatherers,� pure and simple.

Because of the mixture in the gravels and in the materials they
carried, we can�t be sure which animals these men hunted. Bones of
the larger animals turn up in the gravels, but they could just as
well belong to the animals who hunted the men, rather than the other
way about. We don�t know. This is why camp sites like Commont�s and
Olorgesailie in Kenya are so important when we do find them. The animal
bones at Olorgesailie belonged to various mammals of extremely large
size. Probably they were taken in pit-traps, but there are a number of
groups of three round stones on the site which suggest that the people
used bolas. The South American Indians used three-ball bolas, with the
stones in separate leather bags connected by thongs. These were whirled
and then thrown through the air so as to entangle the feet of a fleeing
animal.

Professor F. Clark Howell recently returned from excavating another
important open air site at Isimila in Tanganyika. The site yielded
the bones of many fossil animals and also thousands of core-bifaces,
flakes, and choppers. But Howell�s reconstruction of the food-getting
habits of the Isimila people certainly suggests that the word �hunting�
is too dignified for what they did; �scavenging� would be much nearer
the mark.

During a great part of this time the climate was warm and pleasant. The
second interglacial period (the time between the second and third great
alpine glaciations) lasted a long time, and during much of this time
the climate may have been even better than ours is now. We don�t know
that earlier prehistoric men in Europe or Africa lived in caves. They
may not have needed to; much of the weather may have been so nice that
they lived in the open. Perhaps they didn�t wear clothes, either.


WHAT THE PEKING CAVE-FINDS TELL US

The one early cave-dwelling we have found is that of Peking man, in
China. Peking man had fire. He probably cooked his meat, or used
the fire to keep dangerous animals away from his den. In the cave
were bones of dangerous animals, members of the wolf, bear, and cat
families. Some of the cat bones belonged to beasts larger than tigers.
There were also bones of other wild animals: buffalo, camel, deer,
elephants, horses, sheep, and even ostriches. Seventy per cent of the
animals Peking man killed were fallow deer. It�s much too cold and dry
in north China for all these animals to live there today. So this list
helps us know that the weather was reasonably warm, and that there was
enough rain to grow grass for the grazing animals. The list also helps
the paleontologists to date the find.

Peking man also seems to have eaten plant food, for there are hackberry
seeds in the debris of the cave. His tools were made of sandstone and
quartz and sometimes of a rather bad flint. As we�ve already seen, they
belong in the chopper-tool tradition. It seems fairly clear that some
of the edges were chipped by right-handed people. There are also many
split pieces of heavy bone. Peking man probably split them so he could
eat the bone marrow, but he may have used some of them as tools.

Many of these split bones were the bones of Peking men. Each one of the
skulls had already had the base broken out of it. In no case were any
of the bones resting together in their natural relation to one another.
There is nothing like a burial; all of the bones are scattered. Now
it�s true that animals could have scattered bodies that were not cared
for or buried. But splitting bones lengthwise and carefully removing
the base of a skull call for both the tools and the people to use them.
It�s pretty clear who the people were. Peking man was a cannibal.

       *       *       *       *       *

This rounds out about all we can say of the life and times of early
prehistoric men. In those days life was rough. You evidently had to
watch out not only for dangerous animals but also for your fellow men.
You ate whatever you could catch or find growing. But you had sense
enough to build fires, and you had already formed certain habits for
making the kinds of stone tools you needed. That�s about all we know.
But I think we�ll have to admit that cultural beginnings had been made,
and that these early people were really _men_.




MORE EVIDENCE of Culture

[Illustration]


While the dating is not yet sure, the material that we get from caves
in Europe must go back to about 100,000 years ago; the time of the
classic Neanderthal group followed soon afterwards. We don�t know why
there is no earlier material in the caves; apparently they were not
used before the last interglacial phase (the period just before the
last great glaciation). We know that men of the classic Neanderthal
group were living in caves from about 75,000 to 45,000 years ago.
New radioactive carbon dates even suggest that some of the traces of
culture we�ll describe in this chapter may have lasted to about 35,000
years ago. Probably some of the pre-neanderthaloid types of men had
also lived in caves. But we have so far found their bones in caves only
in Palestine and at Font�chevade.


THE CAVE LAYERS

In parts of France, some peasants still live in caves. In prehistoric
time, many generations of people lived in them. As a result, many
caves have deep layers of debris. The first people moved in and lived
on the rock floor. They threw on the floor whatever they didn�t want,
and they tracked in mud; nobody bothered to clean house in those days.
Their debris--junk and mud and garbage and what not--became packed
into a layer. As time went on, and generations passed, the layer grew
thicker. Then there might have been a break in the occupation of the
cave for a while. Perhaps the game animals got scarce and the people
moved away; or maybe the cave became flooded. Later on, other people
moved in and began making a new layer of their own on top of the first
layer. Perhaps this process of layering went on in the same cave for a
hundred thousand years; you can see what happened. The drawing on this
page shows a section through such a cave. The earliest layer is on the
bottom, the latest one on top. They go in order from bottom to top,
earliest to latest. This is the _stratification_ we talked about (p.
12).

[Illustration: SECTION OF SHELTER ON LOWER TERRACE, LE MOUSTIER]

While we may find a mix-up in caves, it�s not nearly as bad as the
mixing up that was done by glaciers. The animal bones and shells, the
fireplaces, the bones of men, and the tools the men made all belong
together, if they come from one layer. That�s the reason why the cave
of Peking man is so important. It is also the reason why the caves in
Europe and the Near East are so important. We can get an idea of which
things belong together and which lot came earliest and which latest.

In most cases, prehistoric men lived only in the mouths of caves.
They didn�t like the dark inner chambers as places to live in. They
preferred rock-shelters, at the bases of overhanging cliffs, if there
was enough overhang to give shelter. When the weather was good, they no
doubt lived in the open air as well. I�ll go on using the term �cave�
since it�s more familiar, but remember that I really mean rock-shelter,
as a place in which people actually lived.

The most important European cave sites are in Spain, France, and
central Europe; there are also sites in England and Italy. A few caves
are known in the Near East and Africa, and no doubt more sites will be
found when the out-of-the-way parts of Europe, Africa, and Asia are
studied.


AN �INDUSTRY� DEFINED

We have already seen that the earliest European cave materials are
those from the cave of Font�chevade. Movius feels certain that the
lowest materials here date back well into the third interglacial stage,
that which lay between the Riss (next to the last) and the W�rm I
(first stage of the last) alpine glaciations. This material consists
of an _industry_ of stone tools, apparently all made in the flake
tradition. This is the first time we have used the word �industry.�
It is useful to call all of the different tools found together in one
layer and made of _one kind of material_ an industry; that is, the
tools must be found together as men left them. Tools taken from the
glacial gravels (or from windswept desert surfaces or river gravels
or any geological deposit) are not �together� in this sense. We might
say the latter have only �geological,� not �archeological� context.
Archeological context means finding things just as men left them. We
can tell what tools go together in an �industrial� sense only if we
have archeological context.

Up to now, the only things we could have called �industries� were the
worked stone industry and perhaps the worked (?) bone industry of the
Peking cave. We could add some of the very clear cases of open air
sites, like Olorgesailie. We couldn�t use the term for the stone tools
from the glacial gravels, because we do not know which tools belonged
together. But when the cave materials begin to appear in Europe, we can
begin to speak of industries. Most of the European caves of this time
contain industries of flint tools alone.


THE EARLIEST EUROPEAN CAVE LAYERS

We�ve just mentioned the industry from what is said to be the oldest
inhabited cave in Europe; that is, the industry from the deepest layer
of the site at Font�chevade. Apparently it doesn�t amount to much. The
tools are made of stone, in the flake tradition, and are very poorly
worked. This industry is called _Tayacian_. Its type tool seems to be
a smallish flake tool, but there are also larger flakes which seem to
have been fashioned for hacking. In fact, the type tool seems to be
simply a smaller edition of the Clactonian tool (pictured on p. 45).

None of the Font�chevade tools are really good. There are scrapers,
and more or less pointed tools, and tools that may have been used
for hacking and chopping. Many of the tools from the earlier glacial
gravels are better made than those of this first industry we see in
a European cave. There is so little of this material available that
we do not know which is really typical and which is not. You would
probably find it hard to see much difference between this industry and
a collection of tools of the type called Clactonian, taken from the
glacial gravels, especially if the Clactonian tools were small-sized.

The stone industry of the bottommost layer of the Mount Carmel cave,
in Palestine, where somewhat similar tools were found, has also been
called Tayacian.

I shall have to bring in many unfamiliar words for the names of the
industries. The industries are usually named after the places where
they were first found, and since these were in most cases in France,
most of the names which follow will be of French origin. However,
the names have simply become handles and are in use far beyond the
boundaries of France. It would be better if we had a non-place-name
terminology, but archeologists have not yet been able to agree on such
a terminology.


THE ACHEULEAN INDUSTRY

Both in France and in Palestine, as well as in some African cave
sites, the next layers in the deep caves have an industry in both the
core-biface and the flake traditions. The core-biface tools usually
make up less than half of all the tools in the industry. However,
the name of the biface type of tool is generally given to the whole
industry. It is called the _Acheulean_, actually a late form of it, as
�Acheulean� is also used for earlier core-biface tools taken from the
glacial gravels. In western Europe, the name used is _Upper Acheulean_
or _Micoquian_. The same terms have been borrowed to name layers E and
F in the Tabun cave, on Mount Carmel in Palestine.

The Acheulean core-biface type of tool is worked on two faces so as
to give a cutting edge all around. The outline of its front view may
be oval, or egg-shaped, or a quite pointed pear shape. The large
chip-scars of the Acheulean core-bifaces are shallow and flat. It is
suspected that this resulted from the removal of the chips with a
wooden club; the deep chip-scars of the earlier Abbevillian core-biface
came from beating the tool against a stone anvil. These tools are
really the best and also the final products of the core-biface
tradition. We first noticed the tradition in the early glacial gravels
(p. 43); now we see its end, but also its finest examples, in the
deeper cave levels.

The flake tools, which really make up the greater bulk of this
industry, are simple scrapers and chips with sharp cutting edges. The
habits used to prepare them must have been pretty much the same as
those used for at least one of the flake industries we shall mention
presently.

There is very little else in these early cave layers. We do not have
a proper �industry� of bone tools. There are traces of fire, and of
animal bones, and a few shells. In Palestine, there are many more
bones of deer than of gazelle in these layers; the deer lives in a
wetter climate than does the gazelle. In the European cave layers, the
animal bones are those of beasts that live in a warm climate. They
belonged in the last interglacial period. We have not yet found the
bones of fossil men definitely in place with this industry.

[Illustration: ACHEULEAN BIFACE]


FLAKE INDUSTRIES FROM THE CAVES

Two more stone industries--the _Levalloisian_ and the
�_Mousterian_�--turn up at approximately the same time in the European
cave layers. Their tools seem to be mainly in the flake tradition,
but according to some of the authorities their preparation also shows
some combination with the habits by which the core-biface tools were
prepared.

Now notice that I don�t tell you the Levalloisian and the �Mousterian�
layers are both above the late Acheulean layers. Look at the cave
section (p. 57) and you�ll find that some �Mousterian of Acheulean
tradition� appears above some �typical Mousterian.� This means that
there may be some kinds of Acheulean industries that are later than
some kinds of �Mousterian.� The same is true of the Levalloisian.

There were now several different kinds of habits that men used in
making stone tools. These habits were based on either one or the other
of the two traditions--core-biface or flake--or on combinations of
the habits used in the preparation techniques of both traditions. All
were popular at about the same time. So we find that people who made
one kind of stone tool industry lived in a cave for a while. Then they
gave up the cave for some reason, and people with another industry
moved in. Then the first people came back--or at least somebody with
the same tool-making habits as the first people. Or maybe a third group
of tool-makers moved in. The people who had these different habits for
making their stone tools seem to have moved around a good deal. They no
doubt borrowed and exchanged tricks of the trade with each other. There
were no patent laws in those days.

The extremely complicated interrelationships of the different habits
used by the tool-makers of this range of time are at last being
systematically studied. M. Fran�ois Bordes has developed a statistical
method of great importance for understanding these tool preparation
habits.


THE LEVALLOISIAN AND MOUSTERIAN

The easiest Levalloisian tool to spot is a big flake tool. The trick
in making it was to fashion carefully a big chunk of stone (called
the Levalloisian �tortoise core,� because it resembles the shape of
a turtle-shell) and then to whack this in such a way that a large
flake flew off. This large thin flake, with sharp cutting edges, is
the finished Levalloisian tool. There were various other tools in a
Levalloisian industry, but this is the characteristic _Levalloisian_
tool.

There are several �typical Mousterian� stone tools. Different from
the tools of the Levalloisian type, these were made from �disc-like
cores.� There are medium-sized flake �side scrapers.� There are also
some small pointed tools and some small �hand axes.� The last of these
tool types is often a flake worked on both of the flat sides (that
is, bifacially). There are also pieces of flint worked into the form
of crude balls. The pointed tools may have been fixed on shafts to
make short jabbing spears; the round flint balls may have been used as
bolas. Actually, we don�t _know_ what either tool was used for. The
points and side scrapers are illustrated (pp. 64 and 66).

[Illustration: LEVALLOIS FLAKE]


THE MIXING OF TRADITIONS

Nowadays the archeologists are less and less sure of the importance
of any one specific tool type and name. Twenty years ago, they used
to speak simply of Acheulean or Levalloisian or Mousterian tools.
Now, more and more, _all_ of the tools from some one layer in a
cave are called an �industry,� which is given a mixed name. Thus we
have �Levalloiso-Mousterian,� and �Acheuleo-Levalloisian,� and even
�Acheuleo-Mousterian� (or �Mousterian of Acheulean tradition�). Bordes�
systematic work is beginning to clear up some of our confusion.

The time of these late Acheuleo-Levalloiso-Mousterioid industries
is from perhaps as early as 100,000 years ago. It may have lasted
until well past 50,000 years ago. This was the time of the first
phase of the last great glaciation. It was also the time that the
classic group of Neanderthal men was living in Europe. A number of
the Neanderthal fossil finds come from these cave layers. Before the
different habits of tool preparation were understood it used to be
popular to say Neanderthal man was �Mousterian man.� I think this is
wrong. What used to be called �Mousterian� is now known to be a variety
of industries with tools of both core-biface and flake habits, and
so mixed that the word �Mousterian� used alone really doesn�t mean
anything. The Neanderthalers doubtless understood the tool preparation
habits by means of which Acheulean, Levalloisian and Mousterian type
tools were produced. We also have the more modern-like Mount Carmel
people, found in a cave layer of Palestine with tools almost entirely
in the flake tradition, called �Levalloiso-Mousterian,� and the
Font�chevade-Tayacian (p. 59).

[Illustration: MOUSTERIAN POINT]


OTHER SUGGESTIONS OF LIFE IN THE EARLY CAVE LAYERS

Except for the stone tools, what do we know of the way men lived in the
time range after 100,000 to perhaps 40,000 years ago or even later?
We know that in the area from Europe to Palestine, at least some of
the people (some of the time) lived in the fronts of caves and warmed
themselves over fires. In Europe, in the cave layers of these times,
we find the bones of different animals; the bones in the lowest layers
belong to animals that lived in a warm climate; above them are the
bones of those who could stand the cold, like the reindeer and mammoth.
Thus, the meat diet must have been changing, as the glacier crept
farther south. Shells and possibly fish bones have lasted in these
cave layers, but there is not a trace of the vegetable foods and the
nuts and berries and other wild fruits that must have been eaten when
they could be found.

[Illustration: CHART SHOWING PRESENT UNDERSTANDING OF RELATIONSHIPS AND
SUCCESSION OF TOOL-PREPARATION TRADITIONS, INDUSTRIES, AND ASSEMBLAGES
OF WEST-CENTRAL EUROPE

Wavy lines indicate transitions in industrial habits. These transitions
are not yet understood in detail. The glacial and climatic scheme shown
is the alpine one.]

Bone tools have also been found from this period. Some are called
scrapers, and there are also long chisel-like leg-bone fragments
believed to have been used for skinning animals. Larger hunks of bone,
which seem to have served as anvils or chopping blocks, are fairly
common.

Bits of mineral, used as coloring matter, have also been found. We
don�t know what the color was used for.

[Illustration: MOUSTERIAN SIDE SCRAPER]

There is a small but certain number of cases of intentional burials.
These burials have been found on the floors of the caves; in other
words, the people dug graves in the places where they lived. The holes
made for the graves were small. For this reason (or perhaps for some
other?) the bodies were in a curled-up or contracted position. Flint or
bone tools or pieces of meat seem to have been put in with some of the
bodies. In several cases, flat stones had been laid over the graves.


TOOLS FROM AFRICA AND ASIA ABOUT 100,000 YEARS AGO

Professor Movius characterizes early prehistoric Africa as a continent
showing a variety of stone industries. Some of these industries were
purely local developments and some were practically identical with
industries found in Europe at the same time. From northwest Africa
to Capetown--excepting the tropical rain forest region of the west
center--tools of developed Acheulean, Levalloisian, and Mousterian
types have been recognized. Often they are named after African place
names.

In east and south Africa lived people whose industries show a
development of the Levalloisian technique. Such industries are
called Stillbay. Another industry, developed on the basis of the
Acheulean technique, is called Fauresmith. From the northwest comes
an industry with tanged points and flake-blades; this is called the
Aterian. The tropical rain forest region contained people whose stone
tools apparently show adjustment to this peculiar environment; the
so-called Sangoan industry includes stone picks, adzes, core-bifaces
of specialized Acheulean type, and bifacial points which were probably
spearheads.

In western Asia, even as far as the east coast of India, the tools of
the Eurafrican core-biface and flake tool traditions continued to be
used. But in the Far East, as we noted in the last chapter, men had
developed characteristic stone chopper and chopping tools. This tool
preparation tradition--basically a pebble tool tradition--lasted to the
very end of the Ice Age.

When more intact open air sites such as that of an earlier time at
Olorgesailie, and more stratified cave sites are found and excavated
in Asia and Africa, we shall be able to get a more complete picture.
So far, our picture of the general cultural level of the Old World at
about 100,000 years ago--and soon afterwards--is best from Europe, but
it is still far from complete there, too.


CULTURE AT THE BEGINNING OF THE LAST GREAT GLACIAL PERIOD

The few things we have found must indicate only a very small part
of the total activities of the people who lived at the time. All of
the things they made of wood and bark, of skins, of anything soft,
are gone. The fact that burials were made, at least in Europe and
Palestine, is pretty clear proof that the people had some notion of a
life after death. But what this notion really was, or what gods (if
any) men believed in, we cannot know. Dr. Movius has also reminded me
of the so-called bear cults--cases in which caves have been found which
contain the skulls of bears in apparently purposeful arrangement. This
might suggest some notion of hoarding up the spirits or the strength of
bears killed in the hunt. Probably the people lived in small groups,
as hunting and food-gathering seldom provide enough food for large
groups of people. These groups probably had some kind of leader or
�chief.� Very likely the rude beginnings of rules for community life
and politics, and even law, were being made. But what these were, we
do not know. We can only guess about such things, as we can only guess
about many others; for example, how the idea of a family must have been
growing, and how there may have been witch doctors who made beginnings
in medicine or in art, in the materials they gathered for their trade.

The stone tools help us most. They have lasted, and we can find
them. As they come to us, from this cave or that, and from this
layer or that, the tool industries show a variety of combinations
of the different basic habits or traditions of tool preparation.
This seems only natural, as the groups of people must have been very
small. The mixtures and blendings of the habits used in making stone
tools must mean that there were also mixtures and blends in many of
the other ideas and beliefs of these small groups. And what this
probably means is that there was no one _culture_ of the time. It is
certainly unlikely that there were simply three cultures, �Acheulean,�
�Levalloisian,� and �Mousterian,� as has been thought in the past.
Rather there must have been a great variety of loosely related cultures
at about the same stage of advancement. We could say, too, that here
we really begin to see, for the first time, that remarkable ability
of men to adapt themselves to a variety of conditions. We shall see
this adaptive ability even more clearly as time goes on and the record
becomes more complete.

Over how great an area did these loosely related cultures reach in
the time 75,000 to 45,000 or even as late as 35,000 years ago? We
have described stone tools made in one or another of the flake and
core-biface habits, for an enormous area. It covers all of Europe, all
of Africa, the Near East, and parts of India. It is perfectly possible
that the flake and core-biface habits lasted on after 35,000 years ago,
in some places outside of Europe. In northern Africa, for example, we
are certain that they did (see chart, p. 72).

On the other hand, in the Far East (China, Burma, Java) and in northern
India, the tools of the old chopper-tool tradition were still being
made. Out there, we must assume, there was a different set of loosely
related cultures. At least, there was a different set of loosely
related habits for the making of tools. But the men who made them must
have looked much like the men of the West. Their tools were different,
but just as useful.

As to what the men of the West looked like, I�ve already hinted at all
we know so far (pp. 29 ff.). The Neanderthalers were present at
the time. Some more modern-like men must have been about, too, since
fossils of them have turned up at Mount Carmel in Palestine, and at
Teshik Tash, in Trans-caspian Russia. It is still too soon to know
whether certain combinations of tools within industries were made
only by certain physical types of men. But since tools of both the
core-biface and the flake traditions, and their blends, turn up from
South Africa to England to India, it is most unlikely that only one
type of man used only one particular habit in the preparation of tools.
What seems perfectly clear is that men in Africa and men in India were
making just as good tools as the men who lived in western Europe.




EARLY MODERNS

[Illustration]


From some time during the first inter-stadial of the last great
glaciation (say some time after about 40,000 years ago), we have
more accurate dates for the European-Mediterranean area and less
accurate ones for the rest of the Old World. This is probably
because the effects of the last glaciation have been studied in the
European-Mediterranean area more than they have been elsewhere.


A NEW TRADITION APPEARS

Something new was probably beginning to happen in the
European-Mediterranean area about 40,000 years ago, though all the
rest of the Old World seems to have been going on as it had been. I
can�t be sure of this because the information we are using as a basis
for dates is very inaccurate for the areas outside of Europe and the
Mediterranean.

We can at least make a guess. In Egypt and north Africa, men were still
using the old methods of making stone tools. This was especially true
of flake tools of the Levalloisian type, save that they were growing
smaller and smaller as time went on. But at the same time, a new
tradition was becoming popular in westernmost Asia and in Europe. This
was the blade-tool tradition.


BLADE TOOLS

A stone blade is really just a long parallel-sided flake, as the
drawing shows. It has sharp cutting edges, and makes a very useful
knife. The real trick is to be able to make one. It is almost
impossible to make a blade out of any stone but flint or a natural
volcanic glass called obsidian. And even if you have flint or obsidian,
you first have to work up a special cone-shaped �blade-core,� from
which to whack off blades.

[Illustration: PLAIN BLADE]

You whack with a hammer stone against a bone or antler punch which is
directed at the proper place on the blade-core. The blade-core has to
be well supported or gripped while this is going on. To get a good
flint blade tool takes a great deal of know-how.

Remember that a tradition in stone tools means no more than that some
particular way of making the tools got started and lasted a long time.
Men who made some tools in one tradition or set of habits would also
make other tools for different purposes by means of another tradition
or set of habits. It was even possible for the two sets of habits to
become combined.


THE EARLIEST BLADE TOOLS

The oldest blade tools we have found were deep down in the layers of
the Mount Carmel caves, in Tabun Eb and Ea. Similar tools have been
found in equally early cave levels in Syria; their popularity there
seems to fluctuate a bit. Some more or less parallel-sided flakes are
known in the Levalloisian industry in France, but they are probably
no earlier than Tabun E. The Tabun blades are part of a local late
�Acheulean� industry, which is characterized by core-biface �hand
axes,� but which has many flake tools as well. Professor F. E.
Zeuner believes that this industry may be more than 120,000 years old;
actually its date has not yet been fixed, but it is very old--older
than the fossil finds of modern-like men in the same caves.

[Illustration: SUCCESSION OF ICE AGE FLINT TYPES, INDUSTRIES, AND
ASSEMBLAGES, AND OF FOSSIL MEN, IN NORTHWESTERN EURAFRASIA]

For some reason, the habit of making blades in Palestine and Syria was
interrupted. Blades only reappeared there at about the same time they
were first made in Europe, some time after 45,000 years ago; that is,
after the first phase of the last glaciation was ended.

[Illustration: BACKED BLADE]

We are not sure just where the earliest _persisting_ habits for the
production of blade tools developed. Impressed by the very early
momentary appearance of blades at Tabun on Mount Carmel, Professor
Dorothy A. Garrod first favored the Near East as a center of origin.
She spoke of �some as yet unidentified Asiatic centre,� which she
thought might be in the highlands of Iran or just beyond. But more
recent work has been done in this area, especially by Professor Coon,
and the blade tools do not seem to have an early appearance there. When
the blade tools reappear in the Syro-Palestinian area, they do so in
industries which also include Levalloiso-Mousterian flake tools. From
the point of view of form and workmanship, the blade tools themselves
are not so fine as those which seem to be making their appearance
in western Europe about the same time. There is a characteristic
Syro-Palestinian flake point, possibly a projectile tip, called the
Emiran, which is not known from Europe. The appearance of blade tools,
together with Levalloiso-Mousterian flakes, continues even after the
Emiran point has gone out of use.

It seems clear that the production of blade tools did not immediately
swamp the set of older habits in Europe, too; the use of flake
tools also continued there. This was not so apparent to the older
archeologists, whose attention was focused on individual tool types. It
is not, in fact, impossible--although it is certainly not proved--that
the technique developed in the preparation of the Levalloisian tortoise
core (and the striking of the Levalloisian flake from it) might have
followed through to the conical core and punch technique for the
production of blades. Professor Garrod is much impressed with the speed
of change during the later phases of the last glaciation, and its
probable consequences. She speaks of �the greater number of industries
having enough individual character to be classified as distinct ...
since evolution now starts to outstrip diffusion.� Her �evolution� here
is of course an industrial evolution rather than a biological one.
Certainly the people of Europe had begun to make blade tools during
the warm spell after the first phase of the last glaciation. By about
40,000 years ago blades were well established. The bones of the blade
tool makers we�ve found so far indicate that anatomically modern men
had now certainly appeared. Unfortunately, only a few fossil men have
so far been found from the very beginning of the blade tool range in
Europe (or elsewhere). What I certainly shall _not_ tell you is that
conquering bands of fine, strong, anatomically modern men, armed with
superior blade tools, came sweeping out of the East to exterminate the
lowly Neanderthalers. Even if we don�t know exactly what happened, I�d
lay a good bet it wasn�t that simple.

We do know a good deal about different blade industries in Europe.
Almost all of them come from cave layers. There is a great deal of
complication in what we find. The chart (p. 72) tries to simplify
this complication; in fact, it doubtless simplifies it too much. But
it may suggest all the complication of industries which is going
on at this time. You will note that the upper portion of my much
simpler chart (p. 65) covers the same material (in the section
marked �Various Blade-Tool Industries�). That chart is certainly too
simplified.

You will realize that all this complication comes not only from
the fact that we are finding more material. It is due also to the
increasing ability of men to adapt themselves to a great variety of
situations. Their tools indicate this adaptiveness. We know there was
a good deal of climatic change at this time. The plants and animals
that men used for food were changing, too. The great variety of tools
and industries we now find reflect these changes and the ability of men
to keep up with the times. Now, for example, is the first time we are
sure that there are tools to _make_ other tools. They also show men�s
increasing ability to adapt themselves.


SPECIAL TYPES OF BLADE TOOLS

The most useful tools that appear at this time were made from blades.

  1. The �backed� blade. This is a knife made of a flint blade, with
     one edge purposely blunted, probably to save the user�s fingers
     from being cut. There are several shapes of backed blades (p.
     73).

  [Illustration: TWO BURINS]

  2. The _burin_ or �graver.� The burin was the original chisel. Its
     cutting edge is _transverse_, like a chisel�s. Some burins are
     made like a screw-driver, save that burins are sharp. Others have
     edges more like the blade of a chisel or a push plane, with
     only one bevel. Burins were probably used to make slots in wood
     and bone; that is, to make handles or shafts for other tools.
     They must also be the tools with which much of the engraving on
     bone (see p. 83) was done. There is a bewildering variety of
     different kinds of burins.

[Illustration: TANGED POINT]

  3. The �tanged� point. These stone points were used to tip arrows or
     light spears. They were made from blades, and they had a long tang
     at the bottom where they were fixed to the shaft. At the place
     where the tang met the main body of the stone point, there was
     a marked �shoulder,� the beginnings of a barb. Such points had
     either one or two shoulders.

[Illustration: NOTCHED BLADE]

  4. The �notched� or �strangulated� blade. Along with the points for
     arrows or light spears must go a tool to prepare the arrow or
     spear shaft. Today, such a tool would be called a �draw-knife� or
     a �spoke-shave,� and this is what the notched blades probably are.
     Our spoke-shaves have sharp straight cutting blades and really
     �shave.� Notched blades of flint probably scraped rather than cut.

  5. The �awl,� �drill,� or �borer.� These blade tools are worked out
     to a spike-like point. They must have been used for making holes
     in wood, bone, shell, skin, or other things.

[Illustration: DRILL OR AWL]

  6. The �end-scraper on a blade� is a tool with one or both ends
     worked so as to give a good scraping edge. It could have been used
     to hollow out wood or bone, scrape hides, remove bark from trees,
     and a number of other things (p. 78).

There is one very special type of flint tool, which is best known from
western Europe in an industry called the Solutrean. These tools were
usually made of blades, but the best examples are so carefully worked
on both sides (bifacially) that it is impossible to see the original
blade. This tool is

  7. The �laurel leaf� point. Some of these tools were long and
     dagger-like, and must have been used as knives or daggers. Others
     were small, called �willow leaf,� and must have been mounted on
     spear or arrow shafts. Another typical Solutrean tool is the
     �shouldered� point. Both the �laurel leaf� and �shouldered� point
     types are illustrated (see above and p. 79).

[Illustration: END-SCRAPER ON A BLADE]

[Illustration: LAUREL LEAF POINT]

The industries characterized by tools in the blade tradition also
yield some flake and core tools. We will end this list with two types
of tools that appear at this time. The first is made of a flake; the
second is a core tool.

[Illustration: SHOULDERED POINT]

  8. The �keel-shaped round scraper� is usually small and quite round,
     and has had chips removed up to a peak in the center. It is called
     �keel-shaped� because it is supposed to look (when upside down)
     like a section through a boat. Actually, it looks more like a tent
     or an umbrella. Its outer edges are sharp all the way around, and
     it was probably a general purpose scraping tool (see illustration,
     p. 81).

  9. The �keel-shaped nosed scraper� is a much larger and heavier tool
     than the round scraper. It was made on a core with a flat bottom,
     and has one nicely worked end or �nose.� Such tools are usually
     large enough to be easily grasped, and probably were used like
     push planes (see illustration, p. 81).

[Illustration: KEEL-SHAPED ROUND SCRAPER]

[Illustration: KEEL-SHAPED NOSED SCRAPER]

The stone tools (usually made of flint) we have just listed are among
the most easily recognized blade tools, although they show differences
in detail at different times. There are also many other kinds. Not
all of these tools appear in any one industry at one time. Thus the
different industries shown in the chart (p. 72) each have only some
of the blade tools we�ve just listed, and also a few flake tools. Some
industries even have a few core tools. The particular types of blade
tools appearing in one cave layer or another, and the frequency of
appearance of the different types, tell which industry we have in each
layer.


OTHER KINDS OF TOOLS

By this time in Europe--say from about 40,000 to about 10,000 years
ago--we begin to find other kinds of material too. Bone tools begin
to appear. There are knives, pins, needles with eyes, and little
double-pointed straight bars of bone that were probably fish-hooks. The
fish-line would have been fastened in the center of the bar; when the
fish swallowed the bait, the bar would have caught cross-wise in the
fish�s mouth.

One quite special kind of bone tool is a long flat point for a light
spear. It has a deep notch cut up into the breadth of its base, and is
called a �split-based bone point� (p. 82). We know examples of bone
beads from these times, and of bone handles for flint tools. Pierced
teeth of some animals were worn as beads or pendants, but I am not sure
that elks� teeth were worn this early. There are even spool-shaped
�buttons� or toggles.

[Illustration: SPLIT-BASED BONE POINT]

[Illustration: SPEAR-THROWER]

[Illustration: BONE HARPOON]

Antler came into use for tools, especially in central and western
Europe. We do not know the use of one particular antler tool that
has a large hole bored in one end. One suggestion is that it was
a thong-stropper used to strop or work up hide thongs (see
illustration, below); another suggestion is that it was an arrow-shaft
straightener.

Another interesting tool, usually of antler, is the spear-thrower,
which is little more than a stick with a notch or hook on one end.
The hook fits into the butt end of the spear, and the length of the
spear-thrower allows you to put much more power into the throw (p.
82). It works on pretty much the same principle as the sling.

Very fancy harpoons of antler were also made in the latter half of
the period in western Europe. These harpoons had barbs on one or both
sides and a base which would slip out of the shaft (p. 82). Some have
engraved decoration.


THE BEGINNING OF ART

[Illustration: THONG-STROPPER]

In western Europe, at least, the period saw the beginning of several
kinds of art work. It is handy to break the art down into two great
groups: the movable art, and the cave paintings and sculpture. The
movable art group includes the scratchings, engravings, and modeling
which decorate tools and weapons. Knives, stroppers, spear-throwers,
harpoons, and sometimes just plain fragments of bone or antler are
often carved. There is also a group of large flat pebbles which seem
almost to have served as sketch blocks. The surfaces of these various
objects may show animals, or rather abstract floral designs, or
geometric designs.

[Illustration: �VENUS� FIGURINE FROM WILLENDORF]

Some of the movable art is not done on tools. The most remarkable
examples of this class are little figures of women. These women seem to
be pregnant, and their most female characteristics are much emphasized.
It is thought that these �Venus� or �Mother-goddess� figurines may be
meant to show the great forces of nature--fertility and the birth of
life.


CAVE PAINTINGS

In the paintings on walls and ceilings of caves we have some examples
that compare with the best art of any time. The subjects were usually
animals, the great cold-weather beasts of the end of the Ice Age: the
mammoth, the wooly rhinoceros, the bison, the reindeer, the wild horse,
the bear, the wild boar, and wild cattle. As in the movable art, there
are different styles in the cave art. The really great cave art is
pretty well restricted to southern France and Cantabrian (northwestern)
Spain.

There are several interesting things about the �Franco-Cantabrian� cave
art. It was done deep down in the darkest and most dangerous parts of
the caves, although the men lived only in the openings of caves. If you
think what they must have had for lights--crude lamps of hollowed stone
have been found, which must have burned some kind of oil or grease,
with a matted hair or fiber wick--and of the animals that may have
lurked in the caves, you�ll understand the part about danger. Then,
too, we�re sure the pictures these people painted were not simply to be
looked at and admired, for they painted one picture right over other
pictures which had been done earlier. Clearly, it was the _act_ of
_painting_ that counted. The painter had to go way down into the most
mysterious depths of the earth and create an animal in paint. Possibly
he believed that by doing this he gained some sort of magic power over
the same kind of animal when he hunted it in the open air. It certainly
doesn�t look as if he cared very much about the picture he painted--as
a finished product to be admired--for he or somebody else soon went
down and painted another animal right over the one he had done.

The cave art of the Franco-Cantabrian style is one of the great
artistic achievements of all time. The subjects drawn are almost always
the larger animals of the time: the bison, wild cattle and horses, the
wooly rhinoceros, the mammoth, the wild boar, and the bear. In some of
the best examples, the beasts are drawn in full color and the paintings
are remarkably alive and charged with energy. They come from the hands
of men who knew the great animals well--knew the feel of their fur, the
tremendous drive of their muscles, and the danger one faced when he
hunted them.

Another artistic style has been found in eastern Spain. It includes
lively drawings, often of people hunting with bow and arrow. The East
Spanish art is found on open rock faces and in rock-shelters. It is
less spectacular and apparently more recent than the Franco-Cantabrian
cave art.


LIFE AT THE END OF THE ICE AGE IN EUROPE

Life in these times was probably as good as a hunter could expect it
to be. Game and fish seem to have been plentiful; berries and wild
fruits probably were, too. From France to Russia, great pits or
piles of animal bones have been found. Some of this killing was done
as our Plains Indians killed the buffalo--by stampeding them over
steep river banks or cliffs. There were also good tools for hunting,
however. In western Europe, people lived in the openings of caves and
under overhanging rocks. On the great plains of eastern Europe, very
crude huts were being built, half underground. The first part of this
time must have been cold, for it was the middle and end phases of the
last great glaciation. Northern Europe from Scotland to Scandinavia,
northern Germany and Russia, and also the higher mountains to the
south, were certainly covered with ice. But people had fire, and the
needles and tools that were used for scraping hides must mean that they
wore clothing.

It is clear that men were thinking of a great variety of things beside
the tools that helped them get food and shelter. Such burials as we
find have more grave-gifts than before. Beads and ornaments and often
flint, bone, or antler tools are included in the grave, and sometimes
the body is sprinkled with red ochre. Red is the color of blood, which
means life, and of fire, which means heat. Professor Childe wonders if
the red ochre was a pathetic attempt at magic--to give back to the body
the heat that had gone from it. But pathetic or not, it is sure proof
that these people were already moved by death as men still are moved by
it.

Their art is another example of the direction the human mind was
taking. And when I say human, I mean it in the fullest sense, for this
is the time in which fully modern man has appeared. On page 34, we
spoke of the Cro-Magnon group and of the Combe Capelle-Br�nn group of
Caucasoids and of the Grimaldi �Negroids,� who are no longer believed
to be Negroid. I doubt that any one of these groups produced most of
the achievements of the times. It�s not yet absolutely sure which
particular group produced the great cave art. The artists were almost
certainly a blend of several (no doubt already mixed) groups. The pair
of Grimaldians were buried in a grave with a sprinkling of red ochre,
and were provided with shell beads and ornaments and with some blade
tools of flint. Regardless of the different names once given them by
the human paleontologists, each of these groups seems to have shared
equally in the cultural achievements of the times, for all that the
archeologists can say.


MICROLITHS

One peculiar set of tools seems to serve as a marker for the very last
phase of the Ice Age in southwestern Europe. This tool-making habit is
also found about the shore of the Mediterranean basin, and it moved
into northern Europe as the last glaciation pulled northward. People
began making blade tools of very small size. They learned how to chip
very slender and tiny blades from a prepared core. Then they made these
little blades into tiny triangles, half-moons (�lunates�), trapezoids,
and several other geometric forms. These little tools are called
�microliths.� They are so small that most of them must have been fixed
in handles or shafts.

[Illustration: MICROLITHS

  BLADE FRAGMENT
  BURIN
  LUNATE
  TRAPEZOID
  SCALENE TRIANGLE
  ARROWHEAD]

We have found several examples of microliths mounted in shafts. In
northern Europe, where their use soon spread, the microlithic triangles
or lunates were set in rows down each side of a bone or wood point.
One corner of each little triangle stuck out, and the whole thing
made a fine barbed harpoon. In historic times in Egypt, geometric
trapezoidal microliths were still in use as arrowheads. They were
fastened--broad end out--on the end of an arrow shaft. It seems queer
to give an arrow a point shaped like a �T.� Actually, the little points
were very sharp, and must have pierced the hides of animals very
easily. We also think that the broader cutting edge of the point may
have caused more bleeding than a pointed arrowhead would. In hunting
fleet-footed animals like the gazelle, which might run for miles after
being shot with an arrow, it was an advantage to cause as much bleeding
as possible, for the animal would drop sooner.

We are not really sure where the microliths were first invented. There
is some evidence that they appear early in the Near East. Their use
was very common in northwest Africa but this came later. The microlith
makers who reached south Russia and central Europe possibly moved up
out of the Near East. Or it may have been the other way around; we
simply don�t yet know.

Remember that the microliths we are talking about here were made from
carefully prepared little blades, and are often geometric in outline.
Each microlithic industry proper was made up, in good part, of such
tiny blade tools. But there were also some normal-sized blade tools and
even some flake scrapers, in most microlithic industries. I emphasize
this bladelet and the geometric character of the microlithic industries
of the western Old World, since there has sometimes been confusion in
the matter. Sometimes small flake chips, utilized as minute pointed
tools, have been called �microliths.� They may be _microlithic_ in size
in terms of the general meaning of the word, but they do not seem to
belong to the sub-tradition of the blade tool preparation habits which
we have been discussing here.


LATER BLADE-TOOL INDUSTRIES OF THE NEAR EAST AND AFRICA

The blade-tool industries of normal size we talked about earlier spread
from Europe to central Siberia. We noted that blade tools were made
in western Asia too, and early, although Professor Garrod is no longer
sure that the whole tradition originated in the Near East. If you look
again at my chart (p. 72) you will note that in western Asia I list
some of the names of the western European industries, but with the
qualification �-like� (for example, �Gravettian-like�). The western
Asiatic blade-tool industries do vaguely recall some aspects of those
of western Europe, but we would probably be better off if we used
completely local names for them. The �Emiran� of my chart is such an
example; its industry includes a long spike-like blade point which has
no western European counterpart.

When we last spoke of Africa (p. 66), I told you that stone tools
there were continuing in the Levalloisian flake tradition, and were
becoming smaller. At some time during this process, two new tool
types appeared in northern Africa: one was the Aterian point with
a tang (p. 67), and the other was a sort of �laurel leaf� point,
called the �Sbaikian.� These two tool types were both produced from
flakes. The Sbaikian points, especially, are roughly similar to some
of the Solutrean points of Europe. It has been suggested that both the
Sbaikian and Aterian points may be seen on their way to France through
their appearance in the Spanish cave deposits of Parpallo, but there is
also a rival �pre-Solutrean� in central Europe. We still do not know
whether there was any contact between the makers of these north African
tools and the Solutrean tool-makers. What does seem clear is that the
blade-tool tradition itself arrived late in northern Africa.


NETHER AFRICA

Blade tools and �laurel leaf� points and some other probably late
stone tool types also appear in central and southern Africa. There
are geometric microliths on bladelets and even some coarse pottery in
east Africa. There is as yet no good way of telling just where these
items belong in time; in broad geological terms they are �late.�
Some people have guessed that they are as early as similar European
and Near Eastern examples, but I doubt it. The makers of small-sized
Levalloisian flake tools occupied much of Africa until very late in
time.


THE FAR EAST

India and the Far East still seem to be going their own way. In India,
some blade tools have been found. These are not well dated, save that
we believe they must be post-Pleistocene. In the Far East it looks as
if the old chopper-tool tradition was still continuing. For Burma,
Dr. Movius feels this is fairly certain; for China he feels even more
certain. Actually, we know very little about the Far East at about the
time of the last glaciation. This is a shame, too, as you will soon
agree.


THE NEW WORLD BECOMES INHABITED

At some time toward the end of the last great glaciation--almost
certainly after 20,000 years ago--people began to move over Bering
Strait, from Asia into America. As you know, the American Indians have
been assumed to be basically Mongoloids. New studies of blood group
types make this somewhat uncertain, but there is no doubt that the
ancestors of the American Indians came from Asia.

The stone-tool traditions of Europe, Africa, the Near and Middle East,
and central Siberia, did _not_ move into the New World. With only a
very few special or late exceptions, there are _no_ core-bifaces,
flakes, or blade tools of the Old World. Such things just haven�t been
found here.

This is why I say it�s a shame we don�t know more of the end of the
chopper-tool tradition in the Far East. According to Weidenreich,
the Mongoloids were in the Far East long before the end of the last
glaciation. If the genetics of the blood group types do demand a
non-Mongoloid ancestry for the American Indians, who else may have been
in the Far East 25,000 years ago? We know a little about the habits
for making stone tools which these first people brought with them,
and these habits don�t conform with those of the western Old World.
We�d better keep our eyes open for whatever happened to the end of
the chopper-tool tradition in northern China; already there are hints
that it lasted late there. Also we should watch future excavations
in eastern Siberia. Perhaps we shall find the chopper-tool tradition
spreading up that far.


THE NEW ERA

Perhaps it comes in part from the way I read the evidence and perhaps
in part it is only intuition, but I feel that the materials of this
chapter suggest a new era in the ways of life. Before about 40,000
years ago, people simply �gathered� their food, wandering over large
areas to scavenge or to hunt in a simple sort of way. But here we
have seen them �settling-in� more, perhaps restricting themselves in
their wanderings and adapting themselves to a given locality in more
intensive ways. This intensification might be suggested by the word
�collecting.� The ways of life we described in the earlier chapters
were �food-gathering� ways, but now an era of �food-collecting� has
begun. We shall see further intensifications of it in the next chapter.




End and PRELUDE

[Illustration]


Up to the end of the last glaciation, we prehistorians have a
relatively comfortable time schedule. The farther back we go the less
exact we can be about time and details. Elbow-room of five, ten,
even fifty or more thousands of years becomes available for us to
maneuver in as we work backward in time. But now our story has come
forward to the point where more exact methods of dating are at hand.
The radioactive carbon method reaches back into the span of the last
glaciation. There are other methods, developed by the geologists and
paleobotanists, which supplement and extend the usefulness of the
radioactive carbon dates. And, happily, as our means of being more
exact increases, our story grows more exciting. There are also more
details of culture for us to deal with, which add to the interest.


CHANGES AT THE END OF THE ICE AGE

The last great glaciation of the Ice Age was a two-part affair, with a
sub-phase at the end of the second part. In Europe the last sub-phase
of this glaciation commenced somewhere around 15,000 years ago. Then
the glaciers began to melt back, for the last time. Remember that
Professor Antevs (p. 19) isn�t sure the Ice Age is over yet! This
melting sometimes went by fits and starts, and the weather wasn�t
always changing for the better; but there was at least one time when
European weather was even better than it is now.

The melting back of the glaciers and the weather fluctuations caused
other changes, too. We know a fair amount about these changes in
Europe. In an earlier chapter, we said that the whole Ice Age was a
matter of continual change over long periods of time. As the last
glaciers began to melt back some interesting things happened to mankind.

In Europe, along with the melting of the last glaciers, geography
itself was changing. Britain and Ireland had certainly become islands
by 5000 B.C. The Baltic was sometimes a salt sea, sometimes a large
fresh-water lake. Forests began to grow where the glaciers had been,
and in what had once been the cold tundra areas in front of the
glaciers. The great cold-weather animals--the mammoth and the wooly
rhinoceros--retreated northward and finally died out. It is probable
that the efficient hunting of the earlier people of 20,000 or 25,000
to about 12,000 years ago had helped this process along (see p. 86).
Europeans, especially those of the post-glacial period, had to keep
changing to keep up with the times.

The archeological materials for the time from 10,000 to 6000 B.C. seem
simpler than those of the previous five thousand years. The great cave
art of France and Spain had gone; so had the fine carving in bone and
antler. Smaller, speedier animals were moving into the new forests. New
ways of hunting them, or ways of getting other food, had to be found.
Hence, new tools and weapons were necessary. Some of the people who
moved into northern Germany were successful reindeer hunters. Then the
reindeer moved off to the north, and again new sources of food had to
be found.


THE READJUSTMENTS COMPLETED IN EUROPE

After a few thousand years, things began to look better. Or at least
we can say this: By about 6000 B.C. we again get hotter archeological
materials. The best of these come from the north European area:
Britain, Belgium, Holland, Denmark, north Germany, southern Norway and
Sweden. Much of this north European material comes from bogs and swamps
where it had become water-logged and has kept very well. Thus we have
much more complete _assemblages_[4] than for any time earlier.

  [4] �Assemblage� is a useful word when there are different kinds of
      archeological materials belonging together, from one area and of
      one time. An assemblage is made up of a number of �industries�
      (that is, all the tools in chipped stone, all the tools in
      bone, all the tools in wood, the traces of houses, etc.) and
      everything else that manages to survive, such as the art, the
      burials, the bones of the animals used as food, and the traces
      of plant foods; in fact, everything that has been left to us
      and can be used to help reconstruct the lives of the people to
      whom it once belonged. Our own present-day �assemblage� would be
      the sum total of all the objects in our mail-order catalogues,
      department stores and supply houses of every sort, our churches,
      our art galleries and other buildings, together with our roads,
      canals, dams, irrigation ditches, and any other traces we might
      leave of ourselves, from graves to garbage dumps. Not everything
      would last, so that an archeologist digging us up--say 2,000
      years from now--would find only the most durable items in our
      assemblage.

The best known of these assemblages is the _Maglemosian_, named after a
great Danish peat-swamp where much has been found.

[Illustration: SKETCH OF MAGLEMOSIAN ASSEMBLAGE

  CHIPPED STONE
  HEMP
  GROUND STONE
  BONE AND ANTLER
  WOOD]

In the Maglemosian assemblage the flint industry was still very
important. Blade tools, tanged arrow points, and burins were still
made, but there were also axes for cutting the trees in the new
forests. Moreover, the tiny microlithic blades, in a variety of
geometric forms, are also found. Thus, a specialized tradition that
possibly began east of the Mediterranean had reached northern Europe.
There was also a ground stone industry; some axes and club-heads were
made by grinding and polishing rather than by chipping. The industries
in bone and antler show a great variety of tools: axes, fish-hooks,
fish spears, handles and hafts for other tools, harpoons, and clubs.
A remarkable industry in wood has been preserved. Paddles, sled
runners, handles for tools, and bark floats for fish-nets have been
found. There are even fish-nets made of plant fibers. Canoes of some
kind were no doubt made. Bone and antler tools were decorated with
simple patterns, and amber was collected. Wooden bows and arrows are
found.

It seems likely that the Maglemosian bog finds are remains of summer
camps, and that in winter the people moved to higher and drier regions.
Childe calls them the �Forest folk�; they probably lived much the
same sort of life as did our pre-agricultural Indians of the north
central states. They hunted small game or deer; they did a great deal
of fishing; they collected what plant food they could find. In fact,
their assemblage shows us again that remarkable ability of men to adapt
themselves to change. They had succeeded in domesticating the dog; he
was still a very wolf-like dog, but his long association with mankind
had now begun. Professor Coon believes that these people were direct
descendants of the men of the glacial age and that they had much the
same appearance. He believes that most of the Ice Age survivors still
extant are living today in the northwestern European area.


SOUTH AND CENTRAL EUROPE PERHAPS AS READJUSTED AS THE NORTH

There is always one trouble with things that come from areas where
preservation is exceptionally good: The very quantity of materials in
such an assemblage tends to make things from other areas look poor
and simple, although they may not have been so originally at all. The
assemblages of the people who lived to the south of the Maglemosian
area may also have been quite large and varied; but, unfortunately,
relatively little of the southern assemblages has lasted. The
water-logged sites of the Maglemosian area preserved a great deal
more. Hence the Maglemosian itself _looks_ quite advanced to us, when
we compare it with the few things that have happened to last in other
areas. If we could go back and wander over the Europe of eight thousand
years ago, we would probably find that the peoples of France, central
Europe, and south central Russia were just as advanced as those of the
north European-Baltic belt.

South of the north European belt the hunting-food-collecting peoples
were living on as best they could during this time. One interesting
group, which seems to have kept to the regions of sandy soil and scrub
forest, made great quantities of geometric microliths. These are the
materials called _Tardenoisian_. The materials of the �Forest folk� of
France and central Europe generally are called _Azilian_; Dr. Movius
believes the term might best be restricted to the area south of the
Loire River.


HOW MUCH REAL CHANGE WAS THERE?

You can see that no really _basic_ change in the way of life has yet
been described. Childe sees the problem that faced the Europeans of
10,000 to 3000 B.C. as a problem in readaptation to the post-glacial
forest environment. By 6000 B.C. some quite successful solutions of
the problem--like the Maglemosian--had been made. The upsets that came
with the melting of the last ice gradually brought about all sorts of
changes in the tools and food-getting habits, but the people themselves
were still just as much simple hunters, fishers, and food-collectors as
they had been in 25,000 B.C. It could be said that they changed just
enough so that they would not have to change. But there is a bit more
to it than this.

Professor Mathiassen of Copenhagen, who knows the archeological remains
of this time very well, poses a question. He speaks of the material
as being neither rich nor progressive, in fact �rather stagnant,� but
he goes on to add that the people had a certain �receptiveness� and
were able to adapt themselves quickly when the next change did come.
My own understanding of the situation is that the �Forest folk� made
nothing as spectacular as had the producers of the earlier Magdalenian
assemblage and the Franco-Cantabrian art. On the other hand, they
_seem_ to have been making many more different kinds of tools for many
more different kinds of tasks than had their Ice Age forerunners. I
emphasize �seem� because the preservation in the Maglemosian bogs
is very complete; certainly we cannot list anywhere near as many
different things for earlier times as we did for the Maglemosians
(p. 94). I believe this experimentation with all kinds of new tools
and gadgets, this intensification of adaptiveness (p. 91), this
�receptiveness,� even if it is still only pointed toward hunting,
fishing, and food-collecting, is an important thing.

Remember that the only marker we have handy for the _beginning_ of
this tendency toward �receptiveness� and experimentation is the
little microlithic blade tools of various geometric forms. These, we
saw, began before the last ice had melted away, and they lasted on
in use for a very long time. I wish there were a better marker than
the microliths but I do not know of one. Remember, too, that as yet
we can only use the microliths as a marker in Europe and about the
Mediterranean.


CHANGES IN OTHER AREAS?

All this last section was about Europe. How about the rest of the world
when the last glaciers were melting away?

We simply don�t know much about this particular time in other parts
of the world except in Europe, the Mediterranean basin and the Middle
East. People were certainly continuing to move into the New World by
way of Siberia and the Bering Strait about this time. But for the
greater part of Africa and Asia, we do not know exactly what was
happening. Some day, we shall no doubt find out; today we are without
clear information.


REAL CHANGE AND PRELUDE IN THE NEAR EAST

The appearance of the microliths and the developments made by the
�Forest folk� of northwestern Europe also mark an end. They show us
the terminal phase of the old food-collecting way of life. It grows
increasingly clear that at about the same time that the Maglemosian and
other �Forest folk� were adapting themselves to hunting, fishing, and
collecting in new ways to fit the post-glacial environment, something
completely new was being made ready in western Asia.

Unfortunately, we do not have as much understanding of the climate and
environment of the late Ice Age in western Asia as we have for most
of Europe. Probably the weather was never so violent or life quite
so rugged as it was in northern Europe. We know that the microliths
made their appearance in western Asia at least by 10,000 B.C. and
possibly earlier, marking the beginning of the terminal phase of
food-collecting. Then, gradually, we begin to see the build-up towards
the first _basic change_ in human life.

This change amounted to a revolution just as important as the
Industrial Revolution. In it, men first learned to domesticate
plants and animals. They began _producing_ their food instead of
simply gathering or collecting it. When their food-production
became reasonably effective, people could and did settle down in
village-farming communities. With the appearance of the little farming
villages, a new way of life was actually under way. Professor Childe
has good reason to speak of the �food-producing revolution,� for it was
indeed a revolution.


QUESTIONS ABOUT CAUSE

We do not yet know _how_ and _why_ this great revolution took place. We
are only just beginning to put the questions properly. I suspect the
answers will concern some delicate and subtle interplay between man and
nature. Clearly, both the level of culture and the natural condition of
the environment must have been ready for the great change, before the
change itself could come about.

It is going to take years of co-operative field work by both
archeologists and the natural scientists who are most helpful to them
before the _how_ and _why_ answers begin to appear. Anthropologically
trained archeologists are fascinated with the cultures of men in times
of great change. About ten or twelve thousand years ago, the general
level of culture in many parts of the world seems to have been ready
for change. In northwestern Europe, we saw that cultures �changed
just enough so that they would not have to change.� We linked this to
environmental changes with the coming of post-glacial times.

In western Asia, we archeologists can prove that the food-producing
revolution actually took place. We can see _the_ important consequence
of effective domestication of plants and animals in the appearance of
the settled village-farming community. And within the village-farming
community was the seed of civilization. The way in which effective
domestication of plants and animals came about, however, must also be
linked closely with the natural environment. Thus the archeologists
will not solve the _how_ and _why_ questions alone--they will need the
help of interested natural scientists in the field itself.


PRECONDITIONS FOR THE REVOLUTION

Especially at this point in our story, we must remember how culture and
environment go hand in hand. Neither plants nor animals domesticate
themselves; men domesticate them. Furthermore, men usually domesticate
only those plants and animals which are useful. There is a good
question here: What is cultural usefulness? But I shall side-step it to
save time. Men cannot domesticate plants and animals that do not exist
in the environment where the men live. Also, there are certainly some
animals and probably some plants that resist domestication, although
they might be useful.

This brings me back again to the point that _both_ the level of culture
and the natural condition of the environment--with the proper plants
and animals in it--must have been ready before domestication could
have happened. But this is precondition, not cause. Why did effective
food-production happen first in the Near East? Why did it happen
independently in the New World slightly later? Why also in the Far
East? Why did it happen at all? Why are all human beings not still
living as the Maglemosians did? These are the questions we still have
to face.


CULTURAL �RECEPTIVENESS� AND PROMISING ENVIRONMENTS

Until the archeologists and the natural scientists--botanists,
geologists, zoologists, and general ecologists--have spent many more
years on the problem, we shall not have full _how_ and _why_ answers. I
do think, however, that we are beginning to understand what to look for.

We shall have to learn much more of what makes the cultures of men
�receptive� and experimental. Did change in the environment alone
force it? Was it simply a case of Professor Toynbee�s �challenge and
response?� I cannot believe the answer is quite that simple. Were it
so simple, we should want to know why the change hadn�t come earlier,
along with earlier environmental changes. We shall not know the answer,
however, until we have excavated the traces of many more cultures of
the time in question. We shall doubtless also have to learn more about,
and think imaginatively about, the simpler cultures still left today.
The �mechanics� of culture in general will be bound to interest us.

It will also be necessary to learn much more of the environments of
10,000 to 12,000 years ago. In which regions of the world were the
natural conditions most promising? Did this promise include plants and
animals which could be domesticated, or did it only offer new ways of
food-collecting? There is much work to do on this problem, but we are
beginning to get some general hints.

Before I begin to detail the hints we now have from western Asia, I
want to do two things. First, I shall tell you of an old theory as to
how food-production might have appeared. Second, I will bother you with
some definitions which should help us in our thinking as the story goes
on.


AN OLD THEORY AS TO THE CAUSE OF THE REVOLUTION

The idea that change would result, if the balance between nature
and culture became upset, is of course not a new one. For at least
twenty-five years, there has been a general theory as to _how_ the
food-producing revolution happened. This theory depends directly on the
idea of natural change in the environment.

The five thousand years following about 10,000 B.C. must have been
very difficult ones, the theory begins. These were the years when
the most marked melting of the last glaciers was going on. While the
glaciers were in place, the climate to the south of them must have been
different from the climate in those areas today. You have no doubt read
that people once lived in regions now covered by the Sahara Desert.
This is true; just when is not entirely clear. The theory is that
during the time of the glaciers, there was a broad belt of rain winds
south of the glaciers. These rain winds would have kept north Africa,
the Nile Valley, and the Middle East green and fertile. But when the
glaciers melted back to the north, the belt of rain winds is supposed
to have moved north too. Then the people living south and east of the
Mediterranean would have found that their water supply was drying up,
that the animals they hunted were dying or moving away, and that the
plant foods they collected were dried up and scarce.

According to the theory, all this would have been true except in the
valleys of rivers and in oases in the growing deserts. Here, in the
only places where water was left, the men and animals and plants would
have clustered. They would have been forced to live close to one
another, in order to live at all. Presently the men would have seen
that some animals were more useful or made better food than others,
and so they would have begun to protect these animals from their
natural enemies. The men would also have been forced to try new plant
foods--foods which possibly had to be prepared before they could be
eaten. Thus, with trials and errors, but by being forced to live close
to plants and animals, men would have learned to domesticate them.


THE OLD THEORY TOO SIMPLE FOR THE FACTS

This theory was set up before we really knew anything in detail about
the later prehistory of the Near and Middle East. We now know that
the facts which have been found don�t fit the old theory at all well.
Also, I have yet to find an American meteorologist who feels that we
know enough about the changes in the weather pattern to say that it can
have been so simple and direct. And, of course, the glacial ice which
began melting after 12,000 years ago was merely the last sub-phase of
the last great glaciation. There had also been three earlier periods
of great alpine glaciers, and long periods of warm weather in between.
If the rain belt moved north as the glaciers melted for the last time,
it must have moved in the same direction in earlier times. Thus, the
forced neighborliness of men, plants, and animals in river valleys and
oases must also have happened earlier. Why didn�t domestication happen
earlier, then?

Furthermore, it does not seem to be in the oases and river valleys
that we have our first or only traces of either food-production
or the earliest farming villages. These traces are also in the
hill-flanks of the mountains of western Asia. Our earliest sites of the
village-farmers do not seem to indicate a greatly different climate
from that which the same region now shows. In fact, everything we now
know suggests that the old theory was just too simple an explanation to
have been the true one. The only reason I mention it--beyond correcting
the ideas you may get in the general texts--is that it illustrates the
kind of thinking we shall have to do, even if it is doubtless wrong in
detail.

We archeologists shall have to depend much more than we ever have on
the natural scientists who can really help us. I can tell you this from
experience. I had the great good fortune to have on my expedition staff
in Iraq in 1954-55, a geologist, a botanist, and a zoologist. Their
studies added whole new bands of color to my spectrum of thinking about
_how_ and _why_ the revolution took place and how the village-farming
community began. But it was only a beginning; as I said earlier, we are
just now learning to ask the proper questions.


ABOUT STAGES AND ERAS

Now come some definitions, so I may describe my material more easily.
Archeologists have always loved to make divisions and subdivisions
within the long range of materials which they have found. They often
disagree violently about which particular assemblage of material
goes into which subdivision, about what the subdivisions should be
named, about what the subdivisions really mean culturally. Some
archeologists, probably through habit, favor an old scheme of Grecized
names for the subdivisions: paleolithic, mesolithic, neolithic. I
refuse to use these words myself. They have meant too many different
things to too many different people and have tended to hide some pretty
fuzzy thinking. Probably you haven�t even noticed my own scheme of
subdivision up to now, but I�d better tell you in general what it is.

I think of the earliest great group of archeological materials, from
which we can deduce only a food-gathering way of culture, as the
_food-gathering stage_. I say �stage� rather than �age,� because it
is not quite over yet; there are still a few primitive people in
out-of-the-way parts of the world who remain in the _food-gathering
stage_. In fact, Professor Julian Steward would probably prefer to call
it a food-gathering _level_ of existence, rather than a stage. This
would be perfectly acceptable to me. I also tend to find myself using
_collecting_, rather than _gathering_, for the more recent aspects or
era of the stage, as the word �collecting� appears to have more sense
of purposefulness and specialization than does �gathering� (see p.
91).

Now, while I think we could make several possible subdivisions of the
food-gathering stage--I call my subdivisions of stages _eras_[5]--I
believe the only one which means much to us here is the last or
_terminal sub-era of food-collecting_ of the whole food-gathering
stage. The microliths seem to mark its approach in the northwestern
part of the Old World. It is really shown best in the Old World by
the materials of the �Forest folk,� the cultural adaptation to the
post-glacial environment in northwestern Europe. We talked about
the �Forest folk� at the beginning of this chapter, and I used the
Maglemosian assemblage of Denmark as an example.

  [5] It is difficult to find words which have a sequence or gradation
      of meaning with respect to both development and a range of time
      in the past, or with a range of time from somewhere in the past
      which is perhaps not yet ended. One standard Webster definition
      of _stage_ is: �One of the steps into which the material
      development of man ... is divided.� I cannot find any dictionary
      definition that suggests which of the words, _stage_ or _era_,
      has the meaning of a longer span of time. Therefore, I have
      chosen to let my eras be shorter, and to subdivide my stages
      into eras. Webster gives _era_ as: �A signal stage of history,
      an epoch.� When I want to subdivide my eras, I find myself using
      _sub-eras_. Thus I speak of the _eras_ within a _stage_ and of
      the _sub-eras_ within an _era_; that is, I do so when I feel
      that I really have to, and when the evidence is clear enough to
      allow it.

The food-producing revolution ushers in the _food-producing stage_.
This stage began to be replaced by the _industrial stage_ only about
two hundred years ago. Now notice that my stage divisions are in terms
of technology and economics. We must think sharply to be sure that the
subdivisions of the stages, the eras, are in the same terms. This does
not mean that I think technology and economics are the only important
realms of culture. It is rather that for most of prehistoric time the
materials left to the archeologists tend to limit our deductions to
technology and economics.

I�m so soon out of my competence, as conventional ancient history
begins, that I shall only suggest the earlier eras of the
food-producing stage to you. This book is about prehistory, and I�m not
a universal historian.


THE TWO EARLIEST ERAS OF THE FOOD-PRODUCING STAGE

The food-producing stage seems to appear in western Asia with really
revolutionary suddenness. It is seen by the relative speed with which
the traces of new crafts appear in the earliest village-farming
community sites we�ve dug. It is seen by the spread and multiplication
of these sites themselves, and the remarkable growth in human
population we deduce from this increase in sites. We�ll look at some
of these sites and the archeological traces they yield in the next
chapter. When such village sites begin to appear, I believe we are in
the _era of the primary village-farming community_. I also believe this
is the second era of the food-producing stage.

The first era of the food-producing stage, I believe, was an _era of
incipient cultivation and animal domestication_. I keep saying �I
believe� because the actual evidence for this earlier era is so slight
that one has to set it up mainly by playing a hunch for it. The reason
for playing the hunch goes about as follows.

One thing we seem to be able to see, in the food-collecting era in
general, is a tendency for people to begin to settle down. This
settling down seemed to become further intensified in the terminal
era. How this is connected with Professor Mathiassen�s �receptiveness�
and the tendency to be experimental, we do not exactly know. The
evidence from the New World comes into play here as well as that from
the Old World. With this settling down in one place, the people of the
terminal era--especially the �Forest folk� whom we know best--began
making a great variety of new things. I remarked about this earlier in
the chapter. Dr. Robert M. Adams is of the opinion that this atmosphere
of experimentation with new tools--with new ways of collecting food--is
the kind of atmosphere in which one might expect trials at planting
and at animal domestication to have been made. We first begin to find
traces of more permanent life in outdoor camp sites, although caves
were still inhabited at the beginning of the terminal era. It is not
surprising at all that the �Forest folk� had already domesticated the
dog. In this sense, the whole era of food-collecting was becoming ready
and almost �incipient� for cultivation and animal domestication.

Northwestern Europe was not the place for really effective beginnings
in agriculture and animal domestication. These would have had to take
place in one of those natural environments of promise, where a variety
of plants and animals, each possible of domestication, was available in
the wild state. Let me spell this out. Really effective food-production
must include a variety of items to make up a reasonably well-rounded
diet. The food-supply so produced must be trustworthy, even though
the food-producing peoples themselves might be happy to supplement
it with fish and wild strawberries, just as we do when such things
are available. So, as we said earlier, part of our problem is that
of finding a region with a natural environment which includes--and
did include, some ten thousand years ago--a variety of possibly
domesticable wild plants and animals.


NUCLEAR AREAS

Now comes the last of my definitions. A region with a natural
environment which included a variety of wild plants and animals,
both possible and ready for domestication, would be a central
or core or _nuclear area_, that is, it would be when and _if_
food-production took place within it. It is pretty hard for me to
imagine food-production having ever made an independent start outside
such a nuclear area, although there may be some possible nuclear areas
in which food-production never took place (possibly in parts of Africa,
for example).

We know of several such nuclear areas. In the New World, Middle America
and the Andean highlands make up one or two; it is my understanding
that the evidence is not yet clear as to which. There seems to have
been a nuclear area somewhere in southeastern Asia, in the Malay
peninsula or Burma perhaps, connected with the early cultivation of
taro, breadfruit, the banana and the mango. Possibly the cultivation
of rice and the domestication of the chicken and of zebu cattle and
the water buffalo belong to this southeast Asiatic nuclear area. We
know relatively little about it archeologically, as yet. The nuclear
area which was the scene of the earliest experiment in effective
food-production was in western Asia. Since I know it best, I shall use
it as my example.


THE NUCLEAR NEAR EAST

The nuclear area of western Asia is naturally the one of greatest
interest to people of the western cultural tradition. Our cultural
heritage began within it. The area itself is the region of the hilly
flanks of rain-watered grass-land which build up to the high mountain
ridges of Iran, Iraq, Turkey, Syria, and Palestine. The map on page
125 indicates the region. If you have a good atlas, try to locate the
zone which surrounds the drainage basin of the Tigris and Euphrates
Rivers at elevations of from approximately 2,000 to 5,000 feet. The
lower alluvial land of the Tigris-Euphrates basin itself has very
little rainfall. Some years ago Professor James Henry Breasted called
the alluvial lands of the Tigris-Euphrates a part of the �fertile
crescent.� These alluvial lands are very fertile if irrigated. Breasted
was most interested in the oriental civilizations of conventional
ancient history, and irrigation had been discovered before they
appeared.

The country of hilly flanks above Breasted�s crescent receives from
10 to 20 or more inches of winter rainfall each year, which is about
what Kansas has. Above the hilly-flanks zone tower the peaks and ridges
of the Lebanon-Amanus chain bordering the coast-line from Palestine
to Turkey, the Taurus Mountains of southern Turkey, and the Zagros
range of the Iraq-Iran borderland. This rugged mountain frame for our
hilly-flanks zone rises to some magnificent alpine scenery, with peaks
of from ten to fifteen thousand feet in elevation. There are several
gaps in the Mediterranean coastal portion of the frame, through which
the winter�s rain-bearing winds from the sea may break so as to carry
rain to the foothills of the Taurus and the Zagros.

The picture I hope you will have from this description is that of an
intermediate hilly-flanks zone lying between two regions of extremes.
The lower Tigris-Euphrates basin land is low and far too dry and hot
for agriculture based on rainfall alone; to the south and southwest, it
merges directly into the great desert of Arabia. The mountains which
lie above the hilly-flanks zone are much too high and rugged to have
encouraged farmers.


THE NATURAL ENVIRONMENT OF THE NUCLEAR NEAR EAST

The more we learn of this hilly-flanks zone that I describe, the
more it seems surely to have been a nuclear area. This is where we
archeologists need, and are beginning to get, the help of natural
scientists. They are coming to the conclusion that the natural
environment of the hilly-flanks zone today is much as it was some eight
to ten thousand years ago. There are still two kinds of wild wheat and
a wild barley, and the wild sheep, goat, and pig. We have discovered
traces of each of these at about nine thousand years ago, also traces
of wild ox, horse, and dog, each of which appears to be the probable
ancestor of the domesticated form. In fact, at about nine thousand
years ago, the two wheats, the barley, and at least the goat, were
already well on the road to domestication.

The wild wheats give us an interesting clue. They are only available
together with the wild barley within the hilly-flanks zone. While the
wild barley grows in a variety of elevations and beyond the zone,
at least one of the wild wheats does not seem to grow below the hill
country. As things look at the moment, the domestication of both the
wheats together could _only_ have taken place within the hilly-flanks
zone. Barley seems to have first come into cultivation due to its
presence as a weed in already cultivated wheat fields. There is also
a suggestion--there is still much more to learn in the matter--that
the animals which were first domesticated were most at home up in the
hilly-flanks zone in their wild state.

With a single exception--that of the dog--the earliest positive
evidence of domestication includes the two forms of wheat, the barley,
and the goat. The evidence comes from within the hilly-flanks zone.
However, it comes from a settled village proper, Jarmo (which I�ll
describe in the next chapter), and is thus from the era of the primary
village-farming community. We are still without positive evidence of
domesticated grain and animals in the first era of the food-producing
stage, that of incipient cultivation and animal domestication.


THE ERA OF INCIPIENT CULTIVATION AND ANIMAL DOMESTICATION

I said above (p. 105) that my era of incipient cultivation and animal
domestication is mainly set up by playing a hunch. Although we cannot
really demonstrate it--and certainly not in the Near East--it would
be very strange for food-collectors not to have known a great deal
about the plants and animals most useful to them. They do seem to have
domesticated the dog. We can easily imagine them remembering to go
back, season after season, to a particular patch of ground where seeds
or acorns or berries grew particularly well. Most human beings, unless
they are extremely hungry, are attracted to baby animals, and many wild
pups or fawns or piglets must have been brought back alive by hunting
parties.

In this last sense, man has probably always been an incipient
cultivator and domesticator. But I believe that Adams is right in
suggesting that this would be doubly true with the experimenters of
the terminal era of food-collecting. We noticed that they also seem
to have had a tendency to settle down. Now my hunch goes that _when_
this experimentation and settling down took place within a potential
nuclear area--where a whole constellation of plants and animals
possible of domestication was available--the change was easily made.
Professor Charles A. Reed, our field colleague in zoology, agrees that
year-round settlement with plant domestication probably came before
there were important animal domestications.


INCIPIENT ERAS AND NUCLEAR AREAS

I have put this scheme into a simple chart (p. 111) with the names
of a few of the sites we are going to talk about. You will see that my
hunch means that there are eras of incipient cultivation _only_ within
nuclear areas. In a nuclear area, the terminal era of food-collecting
would probably have been quite short. I do not know for how long a time
the era of incipient cultivation and domestication would have lasted,
but perhaps for several thousand years. Then it passed on into the era
of the primary village-farming community.

Outside a nuclear area, the terminal era of food-collecting would last
for a long time; in a few out-of-the-way parts of the world, it still
hangs on. It would end in any particular place through contact with
and the spread of ideas of people who had passed on into one of the
more developed eras. In many cases, the terminal era of food-collecting
was ended by the incoming of the food-producing peoples themselves.
For example, the practices of food-production were carried into Europe
by the actual movement of some numbers of peoples (we don�t know how
many) who had reached at least the level of the primary village-farming
community. The �Forest folk� learned food-production from them. There
was never an era of incipient cultivation and domestication proper in
Europe, if my hunch is right.


ARCHEOLOGICAL DIFFICULTIES IN SEEING THE INCIPIENT ERA

The way I see it, two things were required in order that an era of
incipient cultivation and domestication could begin. First, there had
to be the natural environment of a nuclear area, with its whole group
of plants and animals capable of domestication. This is the aspect of
the matter which we�ve said is directly given by nature. But it is
quite possible that such an environment with such a group of plants
and animals in it may have existed well before ten thousand years ago
in the Near East. It is also quite possible that the same promising
condition may have existed in regions which never developed into
nuclear areas proper. Here, again, we come back to the cultural factor.
I think it was that �atmosphere of experimentation� we�ve talked about
once or twice before. I can�t define it for you, other than to say that
by the end of the Ice Age, the general level of many cultures was ready
for change. Ask me how and why this was so, and I�ll tell you we don�t
know yet, and that if we did understand this kind of question, there
would be no need for me to go on being a prehistorian!

[Illustration: POSSIBLE RELATIONSHIPS OF STAGES AND ERAS IN WESTERN
ASIA AND NORTHEASTERN AFRICA]

Now since this was an era of incipience, of the birth of new ideas,
and of experimentation, it is very difficult to see its traces
archeologically. New tools having to do with the new ways of getting
and, in fact, producing food would have taken some time to develop.
It need not surprise us too much if we cannot find hoes for planting
and sickles for reaping grain at the very beginning. We might expect
a time of making-do with some of the older tools, or with make-shift
tools, for some of the new jobs. The present-day wild cousin of the
domesticated sheep still lives in the mountains of western Asia. It has
no wool, only a fine down under hair like that of a deer, so it need
not surprise us to find neither the whorls used for spinning nor traces
of woolen cloth. It must have taken some time for a wool-bearing sheep
to develop and also time for the invention of the new tools which go
with weaving. It would have been the same with other kinds of tools for
the new way of life.

It is difficult even for an experienced comparative zoologist to tell
which are the bones of domesticated animals and which are those of
their wild cousins. This is especially so because the animal bones the
archeologists find are usually fragmentary. Furthermore, we do not have
a sort of library collection of the skeletons of the animals or an
herbarium of the plants of those times, against which the traces which
the archeologists find may be checked. We are only beginning to get
such collections for the modern wild forms of animals and plants from
some of our nuclear areas. In the nuclear area in the Near East, some
of the wild animals, at least, have already become extinct. There are
no longer wild cattle or wild horses in western Asia. We know they were
there from the finds we�ve made in caves of late Ice Age times, and
from some slightly later sites.


SITES WITH ANTIQUITIES OF THE INCIPIENT ERA

So far, we know only a very few sites which would suit my notion of the
incipient era of cultivation and animal domestication. I am closing
this chapter with descriptions of two of the best Near Eastern examples
I know of. You may not be satisfied that what I am able to describe
makes a full-bodied era of development at all. Remember, however, that
I�ve told you I�m largely playing a kind of a hunch, and also that the
archeological materials of this era will always be extremely difficult
to interpret. At the beginning of any new way of life, there will be a
great tendency for people to make-do, at first, with tools and habits
they are already used to. I would suspect that a great deal of this
making-do went on almost to the end of this era.


THE NATUFIAN, AN ASSEMBLAGE OF THE INCIPIENT ERA

The assemblage called the Natufian comes from the upper layers of a
number of caves in Palestine. Traces of its flint industry have also
turned up in Syria and Lebanon. We don�t know just how old it is. I
guess that it probably falls within five hundred years either way of
about 5000 B.C.

Until recently, the people who produced the Natufian assemblage were
thought to have been only cave dwellers, but now at least three open
air Natufian sites have been briefly described. In their best-known
dwelling place, on Mount Carmel, the Natufian folk lived in the open
mouth of a large rock-shelter and on the terrace in front of it. On the
terrace, they had set at least two short curving lines of stones; but
these were hardly architecture; they seem more like benches or perhaps
the low walls of open pens. There were also one or two small clusters
of stones laid like paving, and a ring of stones around a hearth or
fireplace. One very round and regular basin-shaped depression had been
cut into the rocky floor of the terrace, and there were other less
regular basin-like depressions. In the newly reported open air sites,
there seem to have been huts with rounded corners.

Most of the finds in the Natufian layer of the Mount Carmel cave were
flints. About 80 per cent of these flint tools were microliths made
by the regular working of tiny blades into various tools, some having
geometric forms. The larger flint tools included backed blades, burins,
scrapers, a few arrow points, some larger hacking or picking tools, and
one special type. This last was the sickle blade.

We know a sickle blade of flint when we see one, because of a strange
polish or sheen which seems to develop on the cutting edge when the
blade has been used to cut grasses or grain, or--perhaps--reeds. In
the Natufian, we have even found the straight bone handles in which a
number of flint sickle blades were set in a line.

There was a small industry in ground or pecked stone (that is, abraded
not chipped) in the Natufian. This included some pestle and mortar
fragments. The mortars are said to have a deep and narrow hole,
and some of the pestles show traces of red ochre. We are not sure
that these mortars and pestles were also used for grinding food. In
addition, there were one or two bits of carving in stone.


NATUFIAN ANTIQUITIES IN OTHER MATERIALS; BURIALS AND PEOPLE

The Natufian industry in bone was quite rich. It included, beside the
sickle hafts mentioned above, points and harpoons, straight and curved
types of fish-hooks, awls, pins and needles, and a variety of beads and
pendants. There were also beads and pendants of pierced teeth and shell.

A number of Natufian burials have been found in the caves; some burials
were grouped together in one grave. The people who were buried within
the Mount Carmel cave were laid on their backs in an extended position,
while those on the terrace seem to have been �flexed� (placed in their
graves in a curled-up position). This may mean no more than that it was
easier to dig a long hole in cave dirt than in the hard-packed dirt of
the terrace. The people often had some kind of object buried with them,
and several of the best collections of beads come from the burials. On
two of the skulls there were traces of elaborate head-dresses of shell
beads.

[Illustration: SKETCH OF NATUFIAN ASSEMBLAGE

  MICROLITHS
  ARCHITECTURE?
  BURIAL
  CHIPPED STONE
  GROUND STONE
  BONE]

The animal bones of the Natufian layers show beasts of a �modern� type,
but with some differences from those of present-day Palestine. The
bones of the gazelle far outnumber those of the deer; since gazelles
like a much drier climate than deer, Palestine must then have had much
the same climate that it has today. Some of the animal bones were those
of large or dangerous beasts: the hyena, the bear, the wild boar,
and the leopard. But the Natufian people may have had the help of a
large domesticated dog. If our guess at a date for the Natufian is
right (about 7750 B.C.), this is an earlier dog than was that in the
Maglemosian of northern Europe. More recently, it has been reported
that a domesticated goat is also part of the Natufian finds.

The study of the human bones from the Natufian burials is not yet
complete. Until Professor McCown�s study becomes available, we may note
Professor Coon�s assessment that these people were of a �basically
Mediterranean type.�


THE KARIM SHAHIR ASSEMBLAGE

Karim Shahir differs from the Natufian sites in that it shows traces
of a temporary open site or encampment. It lies on the top of a bluff
in the Kurdish hill-country of northeastern Iraq. It was dug by Dr.
Bruce Howe of the expedition I directed in 1950-51 for the Oriental
Institute and the American Schools of Oriental Research. In 1954-55,
our expedition located another site, M�lefaat, with general resemblance
to Karim Shahir, but about a hundred miles north of it. In 1956, Dr.
Ralph Solecki located still another Karim Shahir type of site called
Zawi Chemi Shanidar. The Zawi Chemi site has a radiocarbon date of 8900
� 300 B.C.

Karim Shahir has evidence of only one very shallow level of occupation.
It was probably not lived on very long, although the people who lived
on it spread out over about three acres of area. In spots, the single
layer yielded great numbers of fist-sized cracked pieces of limestone,
which had been carried up from the bed of a stream at the bottom of the
bluff. We think these cracked stones had something to do with a kind of
architecture, but we were unable to find positive traces of hut plans.
At M�lefaat and Zawi Chemi, there were traces of rounded hut plans.

As in the Natufian, the great bulk of small objects of the Karim Shahir
assemblage was in chipped flint. A large proportion of the flint tools
were microlithic bladelets and geometric forms. The flint sickle blade
was almost non-existent, being far scarcer than in the Natufian. The
people of Karim Shahir did a modest amount of work in the grinding of
stone; there were milling stone fragments of both the mortar and the
quern type, and stone hoes or axes with polished bits. Beads, pendants,
rings, and bracelets were made of finer quality stone. We found a few
simple points and needles of bone, and even two rather formless unbaked
clay figurines which seemed to be of animal form.

[Illustration: SKETCH OF KARIM SHAHIR ASSEMBLAGE

  CHIPPED STONE
  GROUND STONE
  UNBAKED CLAY
  SHELL
  BONE
  �ARCHITECTURE�]

Karim Shahir did not yield direct evidence of the kind of vegetable
food its people ate. The animal bones showed a considerable
increase in the proportion of the bones of the species capable of
domestication--sheep, goat, cattle, horse, dog--as compared with animal
bones from the earlier cave sites of the area, which have a high
proportion of bones of wild forms like deer and gazelle. But we do not
know that any of the Karim Shahir animals were actually domesticated.
Some of them may have been, in an �incipient� way, but we have no means
at the moment that will tell us from the bones alone.


WERE THE NATUFIAN AND KARIM SHAHIR PEOPLES FOOD-PRODUCERS?

It is clear that a great part of the food of the Natufian people
must have been hunted or collected. Shells of land, fresh-water, and
sea animals occur in their cave layers. The same is true as regards
Karim Shahir, save for sea shells. But on the other hand, we have
the sickles, the milling stones, the possible Natufian dog, and the
goat, and the general animal situation at Karim Shahir to hint at an
incipient approach to food-production. At Karim Shahir, there was the
tendency to settle down out in the open; this is echoed by the new
reports of open air Natufian sites. The large number of cracked stones
certainly indicates that it was worth the peoples� while to have some
kind of structure, even if the site as a whole was short-lived.

It is a part of my hunch that these things all point toward
food-production--that the hints we seek are there. But in the sense
that the peoples of the era of the primary village-farming community,
which we shall look at next, are fully food-producing, the Natufian
and Karim Shahir folk had not yet arrived. I think they were part of
a general build-up to full scale food-production. They were possibly
controlling a few animals of several kinds and perhaps one or two
plants, without realizing the full possibilities of this �control� as a
new way of life.

This is why I think of the Karim Shahir and Natufian folk as being at
a level, or in an era, of incipient cultivation and domestication. But
we shall have to do a great deal more excavation in this range of time
before we�ll get the kind of positive information we need.


SUMMARY

I am sorry that this chapter has had to be so much more about ideas
than about the archeological traces of prehistoric men themselves.
But the antiquities of the incipient era of cultivation and animal
domestication will not be spectacular, even when we do have them
excavated in quantity. Few museums will be interested in these
antiquities for exhibition purposes. The charred bits or impressions
of plants, the fragments of animal bone and shell, and the varied
clues to climate and environment will be as important as the artifacts
themselves. It will be the ideas to which these traces lead us that
will be important. I am sure that this unspectacular material--when we
have much more of it, and learn how to understand what it says--will
lead us to how and why answers about the first great change in human
history.

We know the earliest village-farming communities appeared in western
Asia, in a nuclear area. We do not yet know why the Near Eastern
experiment came first, or why it didn�t happen earlier in some other
nuclear area. Apparently, the level of culture and the promise of the
natural environment were ready first in western Asia. The next sites
we look at will show a simple but effective food-production already
in existence. Without effective food-production and the settled
village-farming communities, civilization never could have followed.
How effective food-production came into being by the end of the
incipient era, is, I believe, one of the most fascinating questions any
archeologist could face.

It now seems probable--from possibly two of the Palestinian sites with
varieties of the Natufian (Jericho and Nahal Oren)--that there were
one or more local Palestinian developments out of the Natufian into
later times. In the same way, what followed after the Karim Shahir type
of assemblage in northeastern Iraq was in some ways a reflection of
beginnings made at Karim Shahir and Zawi Chemi.




THE First Revolution

[Illustration]


As the incipient era of cultivation and animal domestication passed
onward into the era of the primary village-farming community, the first
basic change in human economy was fully achieved. In southwestern Asia,
this seems to have taken place about nine thousand years ago. I am
going to restrict my description to this earliest Near Eastern case--I
do not know enough about the later comparable experiments in the Far
East and in the New World. Let us first, once again, think of the
contrast between food-collecting and food-producing as ways of life.


THE DIFFERENCE BETWEEN FOOD-COLLECTORS AND FOOD-PRODUCERS

Childe used the word �revolution� because of the radical change that
took place in the habits and customs of man. Food-collectors--that is,
hunters, fishers, berry- and nut-gatherers--had to live in small groups
or bands, for they had to be ready to move wherever their food supply
moved. Not many people can be fed in this way in one area, and small
children and old folks are a burden. There is not enough food to store,
and it is not the kind that can be stored for long.

Do you see how this all fits into a picture? Small groups of people
living now in this cave, now in that--or out in the open--as they moved
after the animals they hunted; no permanent villages, a few half-buried
huts at best; no breakable utensils; no pottery; no signs of anything
for clothing beyond the tools that were probably used to dress the
skins of animals; no time to think of much of anything but food and
protection and disposal of the dead when death did come: an existence
which takes nature as it finds it, which does little or nothing to
modify nature--all in all, a savage�s existence, and a very tough one.
A man who spends his whole life following animals just to kill them to
eat, or moving from one berry patch to another, is really living just
like an animal himself.


THE FOOD-PRODUCING ECONOMY

Against this picture let me try to draw another--that of man�s life
after food-production had begun. His meat was stored �on the hoof,�
his grain in silos or great pottery jars. He lived in a house: it was
worth his while to build one, because he couldn�t move far from his
fields and flocks. In his neighborhood enough food could be grown
and enough animals bred so that many people were kept busy. They all
lived close to their flocks and fields, in a village. The village was
already of a fair size, and it was growing, too. Everybody had more to
eat; they were presumably all stronger, and there were more children.
Children and old men could shepherd the animals by day or help with
the lighter work in the fields. After the crops had been harvested the
younger men might go hunting and some of them would fish, but the food
they brought in was only an addition to the food in the village; the
villagers wouldn�t starve, even if the hunters and fishermen came home
empty-handed.

There was more time to do different things, too. They began to modify
nature. They made pottery out of raw clay, and textiles out of hair
or fiber. People who became good at pottery-making traded their pots
for food and spent all of their time on pottery alone. Other people
were learning to weave cloth or to make new tools. There were already
people in the village who were becoming full-time craftsmen.

Other things were changing, too. The villagers must have had
to agree on new rules for living together. The head man of the
village had problems different from those of the chief of the small
food-collectors� band. If somebody�s flock of sheep spoiled a wheat
field, the owner wanted payment for the grain he lost. The chief of
the hunters was never bothered with such questions. Even the gods
had changed. The spirits and the magic that had been used by hunters
weren�t of any use to the villagers. They needed gods who would watch
over the fields and the flocks, and they eventually began to erect
buildings where their gods might dwell, and where the men who knew most
about the gods might live.


WAS FOOD-PRODUCTION A �REVOLUTION�?

If you can see the difference between these two pictures--between
life in the food-collecting stage and life after food-production
had begun--you�ll see why Professor Childe speaks of a revolution.
By revolution, he doesn�t mean that it happened over night or that
it happened only once. We don�t know exactly how long it took. Some
people think that all these changes may have occurred in less than
500 years, but I doubt that. The incipient era was probably an affair
of some duration. Once the level of the village-farming community had
been established, however, things did begin to move very fast. By
six thousand years ago, the descendants of the first villagers had
developed irrigation and plow agriculture in the relatively rainless
Mesopotamian alluvium and were living in towns with temples. Relative
to the half million years of food-gathering which lay behind, this had
been achieved with truly revolutionary suddenness.


GAPS IN OUR KNOWLEDGE OF THE NEAR EAST

If you�ll look again at the chart (p. 111) you�ll see that I have
very few sites and assemblages to name in the incipient era of
cultivation and domestication, and not many in the earlier part of
the primary village-farming level either. Thanks in no small part
to the intelligent co-operation given foreign excavators by the
Iraq Directorate General of Antiquities, our understanding of the
sequence in Iraq is growing more complete. I shall use Iraq as my main
yard-stick here. But I am far from being able to show you a series of
Sears Roebuck catalogues, even century by century, for any part of
the nuclear area. There is still a great deal of earth to move, and a
great mass of material to recover and interpret before we even begin to
understand �how� and �why.�

Perhaps here, because this kind of archeology is really my specialty,
you�ll excuse it if I become personal for a moment. I very much look
forward to having further part in closing some of the gaps in knowledge
of the Near East. This is not, as I�ve told you, the spectacular
range of Near Eastern archeology. There are no royal tombs, no gold,
no great buildings or sculpture, no writing, in fact nothing to
excite the normal museum at all. Nevertheless it is a range which,
idea-wise, gives the archeologist tremendous satisfaction. The country
of the hilly flanks is an exciting combination of green grasslands
and mountainous ridges. The Kurds, who inhabit the part of the area
in which I�ve worked most recently, are an extremely interesting and
hospitable people. Archeologists don�t become rich, but I�ll forego
the Cadillac for any bright spring morning in the Kurdish hills, on a
good site with a happy crew of workmen and an interested and efficient
staff. It is probably impossible to convey the full feeling which life
on such a dig holds--halcyon days for the body and acute pleasurable
stimulation for the mind. Old things coming newly out of the good dirt,
and the pieces of the human puzzle fitting into place! I think I am
an honest man; I cannot tell you that I am sorry the job is not yet
finished and that there are still gaps in this part of the Near Eastern
archeological sequence.


EARLIEST SITES OF THE VILLAGE FARMERS

So far, the Karim Shahir type of assemblage, which we looked at in the
last chapter, is the earliest material available in what I take to
be the nuclear area. We do not believe that Karim Shahir was a village
site proper: it looks more like the traces of a temporary encampment.
Two caves, called Belt and Hotu, which are outside the nuclear area
and down on the foreshore of the Caspian Sea, have been excavated
by Professor Coon. These probably belong in the later extension of
the terminal era of food-gathering; in their upper layers are traits
like the use of pottery borrowed from the more developed era of the
same time in the nuclear area. The same general explanation doubtless
holds true for certain materials in Egypt, along the upper Nile and in
the Kharga oasis: these materials, called Sebilian III, the Khartoum
�neolithic,� and the Khargan microlithic, are from surface sites,
not from caves. The chart (p. 111) shows where I would place these
materials in era and time.

[Illustration: THE HILLY FLANKS OF THE CRESCENT AND EARLY SITES OF THE
NEAR EAST]

Both M�lefaat and Dr. Solecki�s Zawi Chemi Shanidar site appear to have
been slightly more �settled in� than was Karim Shahir itself. But I do
not think they belong to the era of farming-villages proper. The first
site of this era, in the hills of Iraqi Kurdistan, is Jarmo, on which
we have spent three seasons of work. Following Jarmo comes a variety of
sites and assemblages which lie along the hilly flanks of the crescent
and just below it. I am going to describe and illustrate some of these
for you.

Since not very much archeological excavation has yet been done on sites
of this range of time, I shall have to mention the names of certain
single sites which now alone stand for an assemblage. This does not
mean that I think the individual sites I mention were unique. In the
times when their various cultures flourished, there must have been
many little villages which shared the same general assemblage. We are
only now beginning to locate them again. Thus, if I speak of Jarmo,
or Jericho, or Sialk as single examples of their particular kinds of
assemblages, I don�t mean that they were unique at all. I think I could
take you to the sites of at least three more Jarmos, within twenty
miles of the original one. They are there, but they simply haven�t yet
been excavated. In 1956, a Danish expedition discovered material of
Jarmo type at Shimshara, only two dozen miles northeast of Jarmo, and
below an assemblage of Hassunan type (which I shall describe presently).


THE GAP BETWEEN KARIM SHAHIR AND JARMO

As we see the matter now, there is probably still a gap in the
available archeological record between the Karim Shahir-M�lefaat-Zawi
Chemi group (of the incipient era) and that of Jarmo (of the
village-farming era). Although some items of the Jarmo type materials
do reflect the beginnings of traditions set in the Karim Shahir group
(see p. 120), there is not a clear continuity. Moreover--to the
degree that we may trust a few radiocarbon dates--there would appear
to be around two thousand years of difference in time. The single
available Zawi Chemi �date� is 8900 � 300 B.C.; the most reasonable
group of �dates� from Jarmo average to about 6750 � 200 B.C. I am
uncertain about this two thousand years--I do not think it can have
been so long.

This suggests that we still have much work to do in Iraq. You can
imagine how earnestly we await the return of political stability in the
Republic of Iraq.


JARMO, IN THE KURDISH HILLS, IRAQ

The site of Jarmo has a depth of deposit of about twenty-seven feet,
and approximately a dozen layers of architectural renovation and
change. Nevertheless it is a �one period� site: its assemblage remains
essentially the same throughout, although one or two new items are
added in later levels. It covers about four acres of the top of a
bluff, below which runs a small stream. Jarmo lies in the hill country
east of the modern oil town of Kirkuk. The Iraq Directorate General of
Antiquities suggested that we look at it in 1948, and we have had three
seasons of digging on it since.

The people of Jarmo grew the barley plant and two different kinds of
wheat. They made flint sickles with which to reap their grain, mortars
or querns on which to crack it, ovens in which it might be parched, and
stone bowls out of which they might eat their porridge. We are sure
that they had the domesticated goat, but Professor Reed (the staff
zoologist) is not convinced that the bones of the other potentially
domesticable animals of Jarmo--sheep, cattle, pig, horse, dog--show
sure signs of domestication. We had first thought that all of these
animals were domesticated ones, but Reed feels he must find out much
more before he can be sure. As well as their grain and the meat from
their animals, the people of Jarmo consumed great quantities of land
snails. Botanically, the Jarmo wheat stands about half way between
fully bred wheat and the wild forms.


ARCHITECTURE: HALL-MARK OF THE VILLAGE

The sure sign of the village proper is in its traces of architectural
permanence. The houses of Jarmo were only the size of a small cottage
by our standards, but each was provided with several rectangular rooms.
The walls of the houses were made of puddled mud, often set on crude
foundations of stone. (The puddled mud wall, which the Arabs call
_touf_, is built by laying a three to six inch course of soft mud,
letting this sun-dry for a day or two, then adding the next course,
etc.) The village probably looked much like the simple Kurdish farming
village of today, with its mud-walled houses and low mud-on-brush
roofs. I doubt that the Jarmo village had more than twenty houses at
any one moment of its existence. Today, an average of about seven
people live in a comparable Kurdish house; probably the population of
Jarmo was about 150 people.

[Illustration: SKETCH OF JARMO ASSEMBLAGE

  CHIPPED STONE
  UNBAKED CLAY
  GROUND STONE
  POTTERY _UPPER THIRD OF SITE ONLY._
  REED MATTING
  BONE
  ARCHITECTURE]

It is interesting that portable pottery does not appear until the
last third of the life of the Jarmo village. Throughout the duration
of the village, however, its people had experimented with the plastic
qualities of clay. They modeled little figurines of animals and of
human beings in clay; one type of human figurine they favored was that
of a markedly pregnant woman, probably the expression of some sort of
fertility spirit. They provided their house floors with baked-in-place
depressions, either as basins or hearths, and later with domed ovens of
clay. As we�ve noted, the houses themselves were of clay or mud; one
could almost say they were built up like a house-sized pot. Then,
finally, the idea of making portable pottery itself appeared, although
I very much doubt that the people of the Jarmo village discovered the
art.

On the other hand, the old tradition of making flint blades and
microlithic tools was still very strong at Jarmo. The sickle-blade was
made in quantities, but so also were many of the much older tool types.
Strangely enough, it is within this age-old category of chipped stone
tools that we see one of the clearest pointers to a newer age. Many of
the Jarmo chipped stone tools--microliths--were made of obsidian, a
black volcanic natural glass. The obsidian beds nearest to Jarmo are
over three hundred miles to the north. Already a bulk carrying trade
had been established--the forerunner of commerce--and the routes were
set by which, in later times, the metal trade was to move.

There are now twelve radioactive carbon �dates� from Jarmo. The most
reasonable cluster of determinations averages to about 6750 � 200
B.C., although there is a completely unreasonable range of �dates�
running from 3250 to 9250 B.C.! _If_ I am right in what I take to be
�reasonable,� the first flush of the food-producing revolution had been
achieved almost nine thousand years ago.


HASSUNA, IN UPPER MESOPOTAMIAN IRAQ

We are not sure just how soon after Jarmo the next assemblage of Iraqi
material is to be placed. I do not think the time was long, and there
are a few hints that detailed habits in the making of pottery and
ground stone tools were actually continued from Jarmo times into the
time of the next full assemblage. This is called after a site named
Hassuna, a few miles to the south and west of modern Mosul. We also
have Hassunan type materials from several other sites in the same
general region. It is probably too soon to make generalizations about
it, but the Hassunan sites seem to cluster at slightly lower elevations
than those we have been talking about so far.

The catalogue of the Hassuna assemblage is of course more full and
elaborate than that of Jarmo. The Iraqi government�s archeologists
who dug Hassuna itself, exposed evidence of increasing architectural
know-how. The walls of houses were still formed of puddled mud;
sun-dried bricks appear only in later periods. There were now several
different ways of making and decorating pottery vessels. One style of
pottery painting, called the Samarran style, is an extremely handsome
one and must have required a great deal of concentration and excellence
of draftsmanship. On the other hand, the old habits for the preparation
of good chipped stone tools--still apparent at Jarmo--seem to have
largely disappeared by Hassunan times. The flint work of the Hassunan
catalogue is, by and large, a wretched affair. We might guess that the
kinaesthetic concentration of the Hassuna craftsmen now went into other
categories; that is, they suddenly discovered they might have more fun
working with the newer materials. It�s a shame, for example, that none
of their weaving is preserved for us.

The two available radiocarbon determinations from Hassunan contexts
stand at about 5100 and 5600 B.C. � 250 years.


OTHER EARLY VILLAGE SITES IN THE NUCLEAR AREA

I�ll now name and very briefly describe a few of the other early
village assemblages either in or adjacent to the hilly flanks of the
crescent. Unfortunately, we do not have radioactive carbon dates for
many of these materials. We may guess that some particular assemblage,
roughly comparable to that of Hassuna, for example, must reflect a
culture which lived at just about the same time as that of Hassuna. We
do this guessing on the basis of the general similarity and degree of
complexity of the Sears Roebuck catalogues of the particular assemblage
and that of Hassuna. We suppose that for sites near at hand and of a
comparable cultural level, as indicated by their generally similar
assemblages, the dating must be about the same. We may also know that
in a general stratigraphic sense, the sites in question may both appear
at the bottom of the ascending village sequence in their respective
areas. Without a number of consistent radioactive carbon dates, we
cannot be precise about priorities.

[Illustration: SKETCH OF HASSUNA ASSEMBLAGE

  POTTERY
  POTTERY OBJECTS
  CHIPPED STONE
  BONE
  GROUND STONE
  ARCHITECTURE
  REED MATTING
  BURIAL]

The ancient mound at Jericho, in the Dead Sea valley in Palestine,
yields some very interesting material. Its catalogue somewhat resembles
that of Jarmo, especially in the sense that there is a fair depth
of deposit without portable pottery vessels. On the other hand, the
architecture of Jericho is surprisingly complex, with traces of massive
stone fortification walls and the general use of formed sun-dried
mud brick. Jericho lies in a somewhat strange and tropically lush
ecological niche, some seven hundred feet below sea level; it is
geographically within the hilly-flanks zone but environmentally not
part of it.

Several radiocarbon �dates� for Jericho fall within the range of those
I find reasonable for Jarmo, and their internal statistical consistency
is far better than that for the Jarmo determinations. It is not yet
clear exactly what this means.

The mound at Jericho (Tell es-Sultan) contains a remarkably
fine sequence, which perhaps does not have the gap we noted in
Iraqi-Kurdistan between the Karim Shahir group and Jarmo. While I am
not sure that the Jericho sequence will prove valid for those parts
of Palestine outside the special Dead Sea environmental niche, the
sequence does appear to proceed from the local variety of Natufian into
that of a very well settled community. So far, we have little direct
evidence for the food-production basis upon which the Jericho people
subsisted.

There is an early village assemblage with strong characteristics of its
own in the land bordering the northeast corner of the Mediterranean
Sea, where Syria and the Cilician province of Turkey join. This early
Syro-Cilician assemblage must represent a general cultural pattern
which was at least in part contemporary with that of the Hassuna
assemblage. These materials from the bases of the mounds at Mersin, and
from Judaidah in the Amouq plain, as well as from a few other sites,
represent the remains of true villages. The walls of their houses were
built of puddled mud, but some of the house foundations were of stone.
Several different kinds of pottery were made by the people of these
villages. None of it resembles the pottery from Hassuna or from the
upper levels of Jarmo or Jericho. The Syro-Cilician people had not
lost their touch at working flint. An important southern variation of
the Syro-Cilician assemblage has been cleared recently at Byblos, a
port town famous in later Phoenician times. There are three radiocarbon
determinations which suggest that the time range for these developments
was in the sixth or early fifth millennium B.C.

It would be fascinating to search for traces of even earlier
village-farming communities and for the remains of the incipient
cultivation era, in the Syro-Cilician region.


THE IRANIAN PLATEAU AND THE NILE VALLEY

The map on page 125 shows some sites which lie either outside or in
an extension of the hilly-flanks zone proper. From the base of the
great mound at Sialk on the Iranian plateau came an assemblage of
early village material, generally similar, in the kinds of things it
contained, to the catalogues of Hassuna and Judaidah. The details of
how things were made are different; the Sialk assemblage represents
still another cultural pattern. I suspect it appeared a bit later
in time than did that of Hassuna. There is an important new item in
the Sialk catalogue. The Sialk people made small drills or pins of
hammered copper. Thus the metallurgist�s specialized craft had made its
appearance.

There is at least one very early Iranian site on the inward slopes
of the hilly-flanks zone. It is the earlier of two mounds at a place
called Bakun, in southwestern Iran; the results of the excavations
there are not yet published and we only know of its coarse and
primitive pottery. I only mention Bakun because it helps us to plot the
extent of the hilly-flanks zone villages on the map.

The Nile Valley lies beyond the peculiar environmental zone of the
hilly flanks of the crescent, and it is probable that the earliest
village-farming communities in Egypt were established by a few people
who wandered into the Nile delta area from the nuclear area. The
assemblage which is most closely comparable to the catalogue of Hassuna
or Judaidah, for example, is that from little settlements along the
shore of the Fayum lake. The Fayum materials come mainly from grain
bins or silos. Another site, Merimde, in the western part of the Nile
delta, shows the remains of a true village, but it may be slightly
later than the settlement of the Fayum. There are radioactive carbon
�dates� for the Fayum materials at about 4275 B.C. � 320 years, which
is almost fifteen hundred years later than the determinations suggested
for the Hassunan or Syro-Cilician assemblages. I suspect that this
is a somewhat over-extended indication of the time it took for the
generalized cultural pattern of village-farming community life to
spread from the nuclear area down into Egypt, but as yet we have no way
of testing these matters.

In this same vein, we have two radioactive carbon dates for an
assemblage from sites near Khartoum in the Sudan, best represented by
the mound called Shaheinab. The Shaheinab catalogue roughly corresponds
to that of the Fayum; the distance between the two places, as the Nile
flows, is roughly 1,500 miles. Thus it took almost a thousand years for
the new way of life to be carried as far south into Africa as Khartoum;
the two Shaheinab �dates� average about 3300 B.C. � 400 years.

If the movement was up the Nile (southward), as these dates suggest,
then I suspect that the earliest available village material of middle
Egypt, the so-called Tasian, is also later than that of the Fayum. The
Tasian materials come from a few graves near a village called Deir
Tasa, and I have an uncomfortable feeling that the Tasian �assemblage�
may be mainly an artificial selection of poor examples of objects which
belong in the following range of time.


SPREAD IN TIME AND SPACE

There are now two things we can do; in fact, we have already begun to
do them. We can watch the spread of the new way of life upward through
time in the nuclear area. We can also see how the new way of life
spread outward in space from the nuclear area, as time went on. There
is good archeological evidence that both these processes took place.
For the hill country of northeastern Iraq, in the nuclear area, we
have already noticed how the succession (still with gaps) from Karim
Shahir, through M�lefaat and Jarmo, to Hassuna can be charted (see
chart, p. 111). In the next chapter, we shall continue this charting
and description of what happened in Iraq upward through time. We also
watched traces of the new way of life move through space up the Nile
into Africa, to reach Khartoum in the Sudan some thirty-five hundred
years later than we had seen it at Jarmo or Jericho. We caught glimpses
of it in the Fayum and perhaps at Tasa along the way.

For the remainder of this chapter, I shall try to suggest briefly for
you the directions taken by the spread of the new way of life from the
nuclear area in the Near East. First, let me make clear again that
I _do not_ believe that the village-farming community way of life
was invented only once and in the Near East. It seems to me that the
evidence is very clear that a separate experiment arose in the New
World. For China, the question of independence or borrowing--in the
appearance of the village-farming community there--is still an open
one. In the last chapter, we noted the probability of an independent
nuclear area in southeastern Asia. Professor Carl Sauer strongly
champions the great importance of this area as _the_ original center
of agricultural pursuits, as a kind of �cradle� of all incipient eras
of the Old World at least. While there is certainly not the slightest
archeological evidence to allow us to go that far, we may easily expect
that an early southeast Asian development would have been felt in
China. However, the appearance of the village-farming community in the
northwest of India, at least, seems to have depended on the earlier
development in the Near East. It is also probable that ideas of the new
way of life moved well beyond Khartoum in Africa.


THE SPREAD OF THE VILLAGE-FARMING COMMUNITY WAY OF LIFE INTO EUROPE

How about Europe? I won�t give you many details. You can easily imagine
that the late prehistoric prelude to European history is a complicated
affair. We all know very well how complicated an area Europe is now,
with its welter of different languages and cultures. Remember, however,
that a great deal of archeology has been done on the late prehistory of
Europe, and very little on that of further Asia and Africa. If we knew
as much about these areas as we do of Europe, I expect we�d find them
just as complicated.

This much is clear for Europe, as far as the spread of the
village-community way of life is concerned. The general idea and much
of the know-how and the basic tools of food-production moved from the
Near East to Europe. So did the plants and animals which had been
domesticated; they were not naturally at home in Europe, as they were
in western Asia. I do not, of course, mean that there were traveling
salesmen who carried these ideas and things to Europe with a commercial
gleam in their eyes. The process took time, and the ideas and things
must have been passed on from one group of people to the next. There
was also some actual movement of peoples, but we don�t know the size of
the groups that moved.

The story of the �colonization� of Europe by the first farmers is
thus one of (1) the movement from the eastern Mediterranean lands
of some people who were farmers; (2) the spread of ideas and things
beyond the Near East itself and beyond the paths along which the
�colonists� moved; and (3) the adaptations of the ideas and things
by the indigenous �Forest folk�, about whose �receptiveness� Professor
Mathiassen speaks (p. 97). It is important to note that the resulting
cultures in the new European environment were European, not Near
Eastern. The late Professor Childe remarked that �the peoples of the
West were not slavish imitators; they adapted the gifts from the East
... into a new and organic whole capable of developing on its own
original lines.�


THE WAYS TO EUROPE

Suppose we want to follow the traces of those earliest village-farmers
who did travel from western Asia into Europe. Let us start from
Syro-Cilicia, that part of the hilly-flanks zone proper which lies in
the very northeastern corner of the Mediterranean. Three ways would be
open to us (of course we could not be worried about permission from the
Soviet authorities!). We would go north, or north and slightly east,
across Anatolian Turkey, and skirt along either shore of the Black Sea
or even to the east of the Caucasus Mountains along the Caspian Sea,
to reach the plains of Ukrainian Russia. From here, we could march
across eastern Europe to the Baltic and Scandinavia, or even hook back
southwestward to Atlantic Europe.

Our second way from Syro-Cilicia would also lie over Anatolia, to the
northwest, where we would have to swim or raft ourselves over the
Dardanelles or the Bosphorus to the European shore. Then we would bear
left toward Greece, but some of us might turn right again in Macedonia,
going up the valley of the Vardar River to its divide and on down
the valley of the Morava beyond, to reach the Danube near Belgrade
in Jugoslavia. Here we would turn left, following the great river
valley of the Danube up into central Europe. We would have a number of
tributary valleys to explore, or we could cross the divide and go down
the valley of the Rhine to the North Sea.

Our third way from Syro-Cilicia would be by sea. We would coast along
southern Anatolia and visit Cyprus, Crete, and the Aegean islands on
our way to Greece, where, in the north, we might meet some of those who
had taken the second route. From Greece, we would sail on to Italy and
the western isles, to reach southern France and the coasts of Spain.
Eventually a few of us would sail up the Atlantic coast of Europe, to
reach western Britain and even Ireland.

[Illustration: PROBABLE ROUTES AND TIMING IN THE SPREAD OF THE
VILLAGE-FARMING COMMUNITY WAY OF LIFE FROM THE NEAR EAST TO EUROPE]

Of course none of us could ever take these journeys as the first
farmers took them, since the whole course of each journey must have
lasted many lifetimes. The date given to the assemblage called Windmill
Hill, the earliest known trace of village-farming communities in
England, is about 2500 B.C. I would expect about 5500 B.C. to be a
safe date to give for the well-developed early village communities of
Syro-Cilicia. We suspect that the spread throughout Europe did not
proceed at an even rate. Professor Piggott writes that �at a date
probably about 2600 B.C., simple agricultural communities were being
established in Spain and southern France, and from the latter region a
spread northwards can be traced ... from points on the French seaboard
of the [English] Channel ... there were emigrations of a certain number
of these tribes by boat, across to the chalk lands of Wessex and Sussex
[in England], probably not more than three or four generations later
than the formation of the south French colonies.�

New radiocarbon determinations are becoming available all the
time--already several suggest that the food-producing way of life
had reached the lower Rhine and Holland by 4000 B.C. But not all
prehistorians accept these �dates,� so I do not show them on my map
(p. 139).


THE EARLIEST FARMERS OF ENGLAND

To describe the later prehistory of all Europe for you would take
another book and a much larger one than this is. Therefore, I have
decided to give you only a few impressions of the later prehistory of
Britain. Of course the British Isles lie at the other end of Europe
from our base-line in western Asia. Also, they received influences
along at least two of the three ways in which the new way of life
moved into Europe. We will look at more of their late prehistory in a
following chapter: here, I shall speak only of the first farmers.

The assemblage called Windmill Hill, which appears in the south of
England, exhibits three different kinds of structures, evidence of
grain-growing and of stock-breeding, and some distinctive types of
pottery and stone implements. The most remarkable type of structure
is the earthwork enclosures which seem to have served as seasonal
cattle corrals. These enclosures were roughly circular, reached over
a thousand feet in diameter, and sometimes included two or three
concentric sets of banks and ditches. Traces of oblong timber houses
have been found, but not within the enclosures. The second type of
structure is mine-shafts, dug down into the chalk beds where good
flint for the making of axes or hoes could be found. The third type
of structure is long simple mounds or �unchambered barrows,� in one
end of which burials were made. It has been commonly believed that the
Windmill Hill assemblage belonged entirely to the cultural tradition
which moved up through France to the Channel. Professor Piggott is now
convinced, however, that important elements of Windmill Hill stem from
northern Germany and Denmark--products of the first way into Europe
from the east.

The archeological traces of a second early culture are to be found
in the west of England, western and northern Scotland, and most of
Ireland. The bearers of this culture had come up the Atlantic coast
by sea from southern France and Spain. The evidence they have left us
consists mainly of tombs and the contents of tombs, with only very
rare settlement sites. The tombs were of some size and received the
bodies of many people. The tombs themselves were built of stone, heaped
over with earth; the stones enclosed a passage to a central chamber
(�passage graves�), or to a simple long gallery, along the sides of
which the bodies were laid (�gallery graves�). The general type of
construction is called �megalithic� (= great stone), and the whole
earth-mounded structure is often called a _barrow_. Since many have
proper chambers, in one sense or another, we used the term �unchambered
barrow� above to distinguish those of the Windmill Hill type from these
megalithic structures. There is some evidence for sacrifice, libations,
and ceremonial fires, and it is clear that some form of community
ritual was focused on the megalithic tombs.

The cultures of the people who produced the Windmill Hill assemblage
and of those who made the megalithic tombs flourished, at least in
part, at the same time. Although the distributions of the two different
types of archeological traces are in quite different parts of the
country, there is Windmill Hill pottery in some of the megalithic
tombs. But the tombs also contain pottery which seems to have arrived
with the tomb builders themselves.

The third early British group of antiquities of this general time
(following 2500 B.C.) comes from sites in southern and eastern England.
It is not so certain that the people who made this assemblage, called
Peterborough, were actually farmers. While they may on occasion have
practiced a simple agriculture, many items of their assemblage link
them closely with that of the �Forest folk� of earlier times in
England and in the Baltic countries. Their pottery is decorated with
impressions of cords and is quite different from that of Windmill Hill
and the megalithic builders. In addition, the distribution of their
finds extends into eastern Britain, where the other cultures have left
no trace. The Peterborough people had villages with semi-subterranean
huts, and the bones of oxen, pigs, and sheep have been found in a few
of these. On the whole, however, hunting and fishing seem to have been
their vital occupations. They also established trade routes especially
to acquire the raw material for stone axes.

A probably slightly later culture, whose traces are best known from
Skara Brae on Orkney, also had its roots in those cultures of the
Baltic area which fused out of the meeting of the �Forest folk� and
the peoples who took the eastern way into Europe. Skara Brae is very
well preserved, having been built of thin stone slabs about which
dune-sand drifted after the village died. The individual houses, the
bedsteads, the shelves, the chests for clothes and oddments--all built
of thin stone-slabs--may still be seen in place. But the Skara Brae
people lived entirely by sheep- and cattle-breeding, and by catching
shellfish. Neither grain nor the instruments of agriculture appeared at
Skara Brae.


THE EUROPEAN ACHIEVEMENT

The above is only a very brief description of what went on in Britain
with the arrival of the first farmers. There are many interesting
details which I have omitted in order to shorten the story.

I believe some of the difficulty we have in understanding the
establishment of the first farming communities in Europe is with
the word �colonization.� We have a natural tendency to think of
�colonization� as it has happened within the last few centuries. In the
case of the colonization of the Americas, for example, the colonists
came relatively quickly, and in increasingly vast numbers. They had
vastly superior technical, political, and war-making skills, compared
with those of the Indians. There was not much mixing with the Indians.
The case in Europe five or six thousand years ago must have been very
different. I wonder if it is even proper to call people �colonists�
who move some miles to a new region, settle down and farm it for some
years, then move on again, generation after generation? The ideas and
the things which these new people carried were only _potentially_
superior. The ideas and things and the people had to prove themselves
in their adaptation to each new environment. Once this was done another
link to the chain would be added, and then the forest-dwellers and
other indigenous folk of Europe along the way might accept the new
ideas and things. It is quite reasonable to expect that there must have
been much mixture of the migrants and the indigenes along the way; the
Peterborough and Skara Brae assemblages we mentioned above would seem
to be clear traces of such fused cultures. Sometimes, especially if the
migrants were moving by boat, long distances may have been covered in
a short time. Remember, however, we seem to have about three thousand
years between the early Syro-Cilician villages and Windmill Hill.

Let me repeat Professor Childe again. �The peoples of the West were
not slavish imitators: they adapted the gifts from the East ... into
a new and organic whole capable of developing on its own original
lines.� Childe is of course completely conscious of the fact that his
�peoples of the West� were in part the descendants of migrants who came
originally from the �East,� bringing their �gifts� with them. This
was the late prehistoric achievement of Europe--to take new ideas and
things and some migrant peoples and, by mixing them with the old in its
own environments, to forge a new and unique series of cultures.

What we know of the ways of men suggests to us that when the details
of the later prehistory of further Asia and Africa are learned, their
stories will be just as exciting.




THE Conquest of Civilization

[Illustration]


Now we must return to the Near East again. We are coming to the point
where history is about to begin. I am going to stick pretty close
to Iraq and Egypt in this chapter. These countries will perhaps be
the most interesting to most of us, for the foundations of western
civilization were laid in the river lands of the Tigris and Euphrates
and of the Nile. I shall probably stick closest of all to Iraq, because
things first happened there and also because I know it best.

There is another interesting thing, too. We have seen that the first
experiment in village-farming took place in the Near East. So did
the first experiment in civilization. Both experiments �took.� The
traditions we live by today are based, ultimately, on those ancient
beginnings in food-production and civilization in the Near East.


WHAT �CIVILIZATION� MEANS

I shall not try to define �civilization� for you; rather, I shall
tell you what the word brings to my mind. To me civilization means
urbanization: the fact that there are cities. It means a formal
political set-up--that there are kings or governing bodies that the
people have set up. It means formal laws--rules of conduct--which the
government (if not the people) believes are necessary. It probably
means that there are formalized projects--roads, harbors, irrigation
canals, and the like--and also some sort of army or police force
to protect them. It means quite new and different art forms. It
also usually means there is writing. (The people of the Andes--the
Incas--had everything which goes to make up a civilization but formal
writing. I can see no reason to say they were not civilized.) Finally,
as the late Professor Redfield reminded us, civilization seems to bring
with it the dawn of a new kind of moral order.

In different civilizations, there may be important differences in the
way such things as the above are managed. In early civilizations, it is
usual to find religion very closely tied in with government, law, and
so forth. The king may also be a high priest, or he may even be thought
of as a god. The laws are usually thought to have been given to the
people by the gods. The temples are protected just as carefully as the
other projects.


CIVILIZATION IMPOSSIBLE WITHOUT FOOD-PRODUCTION

Civilizations have to be made up of many people. Some of the people
live in the country; some live in very large towns or cities. Classes
of society have begun. There are officials and government people; there
are priests or religious officials; there are merchants and traders;
there are craftsmen, metal-workers, potters, builders, and so on; there
are also farmers, and these are the people who produce the food for the
whole population. It must be obvious that civilization cannot exist
without food-production and that food-production must also be at a
pretty efficient level of village-farming before civilization can even
begin.

But people can be food-producing without being civilized. In many
parts of the world this is still the case. When the white men first
came to America, the Indians in most parts of this hemisphere were
food-producers. They grew corn, potatoes, tomatoes, squash, and many
other things the white men had never eaten before. But only the Aztecs
of Mexico, the Mayas of Yucatan and Guatemala, and the Incas of the
Andes were civilized.


WHY DIDN�T CIVILIZATION COME TO ALL FOOD-PRODUCERS?

Once you have food-production, even at the well-advanced level of
the village-farming community, what else has to happen before you
get civilization? Many men have asked this question and have failed
to give a full and satisfactory answer. There is probably no _one_
answer. I shall give you my own idea about how civilization _may_ have
come about in the Near East alone. Remember, it is only a guess--a
putting together of hunches from incomplete evidence. It is _not_ meant
to explain how civilization began in any of the other areas--China,
southeast Asia, the Americas--where other early experiments in
civilization went on. The details in those areas are quite different.
Whether certain general principles hold, for the appearance of any
early civilization, is still an open and very interesting question.


WHERE CIVILIZATION FIRST APPEARED IN THE NEAR EAST

You remember that our earliest village-farming communities lay along
the hilly flanks of a great �crescent.� (See map on p. 125.)
Professor Breasted�s �fertile crescent� emphasized the rich river
valleys of the Nile and the Tigris-Euphrates Rivers. Our hilly-flanks
area of the crescent zone arches up from Egypt through Palestine and
Syria, along southern Turkey into northern Iraq, and down along the
southwestern fringe of Iran. The earliest food-producing villages we
know already existed in this area by about 6750 B.C. (� 200 years).

Now notice that this hilly-flanks zone does not include southern
Mesopotamia, the alluvial land of the lower Tigris and Euphrates in
Iraq, or the Nile Valley proper. The earliest known villages of classic
Mesopotamia and Egypt seem to appear fifteen hundred or more years
after those of the hilly-flanks zone. For example, the early Fayum
village which lies near a lake west of the Nile Valley proper (see p.
135) has a radiocarbon date of 4275 B.C. � 320 years. It was in the
river lands, however, that the immediate beginnings of civilization
were made.

We know that by about 3200 B.C. the Early Dynastic period had begun
in southern Mesopotamia. The beginnings of writing go back several
hundred years earlier, but we can safely say that civilization had
begun in Mesopotamia by 3200 B.C. In Egypt, the beginning of the First
Dynasty is slightly later, at about 3100 B.C., and writing probably
did not appear much earlier. There is no question but that history and
civilization were well under way in both Mesopotamia and Egypt by 3000
B.C.--about five thousand years ago.


THE HILLY-FLANKS ZONE VERSUS THE RIVER LANDS

Why did these two civilizations spring up in these two river
lands which apparently were not even part of the area where the
village-farming community began? Why didn�t we have the first
civilizations in Palestine, Syria, north Iraq, or Iran, where we�re
sure food-production had had a long time to develop? I think the
probable answer gives a clue to the ways in which civilization began in
Egypt and Mesopotamia.

The land in the hilly flanks is of a sort which people can farm without
too much trouble. There is a fairly fertile coastal strip in Palestine
and Syria. There are pleasant mountain slopes, streams running out to
the sea, and rain, at least in the winter months. The rain belt and the
foothills of the Turkish mountains also extend to northern Iraq and on
to the Iranian plateau. The Iranian plateau has its mountain valleys,
streams, and some rain. These hilly flanks of the �crescent,� through
most of its arc, are almost made-to-order for beginning farmers. The
grassy slopes of the higher hills would be pasture for their herds
and flocks. As soon as the earliest experiments with agriculture and
domestic animals had been successful, a pleasant living could be
made--and without too much trouble.

I should add here again, that our evidence points increasingly to a
climate for those times which is very little different from that for
the area today. Now look at Egypt and southern Mesopotamia. Both are
lands without rain, for all intents and purposes. Both are lands with
rivers that have laid down very fertile soil--soil perhaps superior to
that in the hilly flanks. But in both lands, the rivers are of no great
aid without some control.

The Nile floods its banks once a year, in late September or early
October. It not only soaks the narrow fertile strip of land on either
side; it lays down a fresh layer of new soil each year. Beyond the
fertile strip on either side rise great cliffs, and behind them is the
desert. In its natural, uncontrolled state, the yearly flood of the
Nile must have caused short-lived swamps that were full of crocodiles.
After a short time, the flood level would have dropped, the water and
the crocodiles would have run back into the river, and the swamp plants
would have become parched and dry.

The Tigris and the Euphrates of Mesopotamia are less likely to flood
regularly than the Nile. The Tigris has a shorter and straighter course
than the Euphrates; it is also the more violent river. Its banks are
high, and when the snows melt and flow into all of its tributary rivers
it is swift and dangerous. The Euphrates has a much longer and more
curving course and few important tributaries. Its banks are lower and
it is less likely to flood dangerously. The land on either side and
between the two rivers is very fertile, south of the modern city of
Baghdad. Unlike the Nile Valley, neither the Tigris nor the Euphrates
is flanked by cliffs. The land on either side of the rivers stretches
out for miles and is not much rougher than a poor tennis court.


THE RIVERS MUST BE CONTROLLED

The real trick in both Egypt and Mesopotamia is to make the rivers work
for you. In Egypt, this is a matter of building dikes and reservoirs
that will catch and hold the Nile flood. In this way, the water is held
and allowed to run off over the fields as it is needed. In Mesopotamia,
it is a matter of taking advantage of natural river channels and branch
channels, and of leading ditches from these onto the fields.

Obviously, we can no longer find the first dikes or reservoirs of
the Nile Valley, or the first canals or ditches of Mesopotamia. The
same land has been lived on far too long for any traces of the first
attempts to be left; or, especially in Egypt, it has been covered by
the yearly deposits of silt, dropped by the river floods. But we�re
pretty sure the first food-producers of Egypt and southern Mesopotamia
must have made such dikes, canals, and ditches. In the first place,
there can�t have been enough rain for them to grow things otherwise.
In the second place, the patterns for such projects seem to have been
pretty well set by historic times.


CONTROL OF THE RIVERS THE BUSINESS OF EVERYONE

Here, then, is a _part_ of the reason why civilization grew in Egypt
and Mesopotamia first--not in Palestine, Syria, or Iran. In the latter
areas, people could manage to produce their food as individuals. It
wasn�t too hard; there were rain and some streams, and good pasturage
for the animals even if a crop or two went wrong. In Egypt and
Mesopotamia, people had to put in a much greater amount of work, and
this work couldn�t be individual work. Whole villages or groups of
people had to turn out to fix dikes or dig ditches. The dikes had to be
repaired and the ditches carefully cleared of silt each year, or they
would become useless.

There also had to be hard and fast rules. The person who lived nearest
the ditch or the reservoir must not be allowed to take all the water
and leave none for his neighbors. It was not only a business of
learning to control the rivers and of making their waters do the
farmer�s work. It also meant controlling men. But once these men had
managed both kinds of controls, what a wonderful yield they had! The
soil was already fertile, and the silt which came in the floods and
ditches kept adding fertile soil.


THE GERM OF CIVILIZATION IN EGYPT AND MESOPOTAMIA

This learning to work together for the common good was the real germ of
the Egyptian and the Mesopotamian civilizations. The bare elements of
civilization were already there: the need for a governing hand and for
laws to see that the communities� work was done and that the water was
justly shared. You may object that there is a sort of chicken and egg
paradox in this idea. How could the people set up the rules until they
had managed to get a way to live, and how could they manage to get a
way to live until they had set up the rules? I think that small groups
must have moved down along the mud-flats of the river banks quite
early, making use of naturally favorable spots, and that the rules grew
out of such cases. It would have been like the hand-in-hand growth of
automobiles and paved highways in the United States.

Once the rules and the know-how did get going, there must have been a
constant interplay of the two. Thus, the more the crops yielded, the
richer and better-fed the people would have been, and the more the
population would have grown. As the population grew, more land would
have needed to be flooded or irrigated, and more complex systems of
dikes, reservoirs, canals, and ditches would have been built. The more
complex the system, the more necessity for work on new projects and for
the control of their use.... And so on....

What I have just put down for you is a guess at the manner of growth of
some of the formalized systems that go to make up a civilized society.
My explanation has been pointed particularly at Egypt and Mesopotamia.
I have already told you that the irrigation and water-control part of
it does not apply to the development of the Aztecs or the Mayas, or
perhaps anybody else. But I think that a fair part of the story of
Egypt and Mesopotamia must be as I�ve just told you.

I am particularly anxious that you do _not_ understand me to mean that
irrigation _caused_ civilization. I am sure it was not that simple at
all. For, in fact, a complex and highly engineered irrigation system
proper did not come until later times. Let�s say rather that the simple
beginnings of irrigation allowed and in fact encouraged a great number
of things in the technological, political, social, and moral realms of
culture. We do not yet understand what all these things were or how
they worked. But without these other aspects of culture, I do not
think that urbanization and civilization itself could have come into
being.


THE ARCHEOLOGICAL SEQUENCE TO CIVILIZATION IN IRAQ

We last spoke of the archeological materials of Iraq on page 130,
where I described the village-farming community of Hassunan type. The
Hassunan type villages appear in the hilly-flanks zone and in the
rolling land adjacent to the Tigris in northern Iraq. It is probable
that even before the Hassuna pattern of culture lived its course, a
new assemblage had been established in northern Iraq and Syria. This
assemblage is called Halaf, after a site high on a tributary of the
Euphrates, on the Syro-Turkish border.

[Illustration: SKETCH OF SELECTED ITEMS OF HALAFIAN ASSEMBLAGE

  BEADS AND PENDANTS
  POTTERY MOTIFS
  POTTERY]

The Halafian assemblage is incompletely known. The culture it
represents included a remarkably handsome painted pottery.
Archeologists have tended to be so fascinated with this pottery that
they have bothered little with the rest of the Halafian assemblage. We
do know that strange stone-founded houses, with plans like those of the
popular notion of an Eskimo igloo, were built. Like the pottery of the
Samarran style, which appears as part of the Hassunan assemblage (see
p. 131), the Halafian painted pottery implies great concentration and
excellence of draftsmanship on the part of the people who painted it.

We must mention two very interesting sites adjacent to the mud-flats of
the rivers, half way down from northern Iraq to the classic alluvial
Mesopotamian area. One is Baghouz on the Euphrates; the other is
Samarra on the Tigris (see map, p. 125). Both these sites yield the
handsome painted pottery of the style called Samarran: in fact it
is Samarra which gives its name to the pottery. Neither Baghouz nor
Samarra have completely Hassunan types of assemblages, and at Samarra
there are a few pots of proper Halafian style. I suppose that Samarra
and Baghouz give us glimpses of those early farmers who had begun to
finger their way down the mud-flats of the river banks toward the
fertile but yet untilled southland.


CLASSIC SOUTHERN MESOPOTAMIA FIRST OCCUPIED

Our next step is into the southland proper. Here, deep in the core of
the mound which later became the holy Sumerian city of Eridu, Iraqi
archeologists uncovered a handsome painted pottery. Pottery of the same
type had been noticed earlier by German archeologists on the surface
of a small mound, awash in the spring floods, near the remains of the
Biblical city of Erich (Sumerian = Uruk; Arabic = Warka). This �Eridu�
pottery, which is about all we have of the assemblage of the people who
once produced it, may be seen as a blend of the Samarran and Halafian
painted pottery styles. This may over-simplify the case, but as yet we
do not have much evidence to go on. The idea does at least fit with my
interpretation of the meaning of Baghouz and Samarra as way-points on
the mud-flats of the rivers half way down from the north.

My colleague, Robert Adams, believes that there were certainly
riverine-adapted food-collectors living in lower Mesopotamia. The
presence of such would explain why the Eridu assemblage is not simply
the sum of the Halafian and Samarran assemblages. But the domesticated
plants and animals and the basic ways of food-production must have
come from the hilly-flanks country in the north.

Above the basal Eridu levels, and at a number of other sites in the
south, comes a full-fledged assemblage called Ubaid. Incidentally,
there is an aspect of the Ubaidian assemblage in the north as well. It
seems to move into place before the Halaf manifestation is finished,
and to blend with it. The Ubaidian assemblage in the south is by far
the more spectacular. The development of the temple has been traced
at Eridu from a simple little structure to a monumental building some
62 feet long, with a pilaster-decorated fa�ade and an altar in its
central chamber. There is painted Ubaidian pottery, but the style is
hurried and somewhat careless and gives the _impression_ of having been
a cheap mass-production means of decoration when compared with the
carefully drafted styles of Samarra and Halaf. The Ubaidian people made
other items of baked clay: sickles and axes of very hard-baked clay
are found. The northern Ubaidian sites have yielded tools of copper,
but metal tools of unquestionable Ubaidian find-spots are not yet
available from the south. Clay figurines of human beings with monstrous
turtle-like faces are another item in the southern Ubaidian assemblage.

[Illustration: SKETCH OF SELECTED ITEMS OF UBAIDIAN ASSEMBLAGE]

There is a large Ubaid cemetery at Eridu, much of it still awaiting
excavation. The few skeletons so far tentatively studied reveal a
completely modern type of �Mediterraneanoid�; the individuals whom the
skeletons represent would undoubtedly blend perfectly into the modern
population of southern Iraq. What the Ubaidian assemblage says to us is
that these people had already adapted themselves and their culture to
the peculiar riverine environment of classic southern Mesopotamia. For
example, hard-baked clay axes will chop bundles of reeds very well, or
help a mason dress his unbaked mud bricks, and there were only a few
soft and pithy species of trees available. The Ubaidian levels of Eridu
yield quantities of date pits; that excellent and characteristically
Iraqi fruit was already in use. The excavators also found the clay
model of a ship, with the stepping-point for a mast, so that Sinbad the
Sailor must have had his antecedents as early as the time of Ubaid.
The bones of fish, which must have flourished in the larger canals as
well as in the rivers, are common in the Ubaidian levels and thereafter.


THE UBAIDIAN ACHIEVEMENT

On present evidence, my tendency is to see the Ubaidian assemblage
in southern Iraq as the trace of a new era. I wish there were more
evidence, but what we have suggests this to me. The culture of southern
Ubaid soon became a culture of towns--of centrally located towns with
some rural villages about them. The town had a temple and there must
have been priests. These priests probably had political and economic
functions as well as religious ones, if the somewhat later history of
Mesopotamia may suggest a pattern for us. Presently the temple and its
priesthood were possibly the focus of the market; the temple received
its due, and may already have had its own lands and herds and flocks.
The people of the town, undoubtedly at least in consultation with the
temple administration, planned and maintained the simple irrigation
ditches. As the system flourished, the community of rural farmers would
have produced more than sufficient food. The tendency for specialized
crafts to develop--tentative at best at the cultural level of the
earlier village-farming community era--would now have been achieved,
and probably many other specialists in temple administration, water
control, architecture, and trade would also have appeared, as the
surplus food-supply was assured.

Southern Mesopotamia is not a land rich in natural resources other
than its fertile soil. Stone, good wood for construction, metal, and
innumerable other things would have had to be imported. Grain and
dates--although both are bulky and difficult to transport--and wool and
woven stuffs must have been the mediums of exchange. Over what area did
the trading net-work of Ubaid extend? We start with the idea that the
Ubaidian assemblage is most richly developed in the south. We assume, I
think, correctly, that it represents a cultural flowering of the south.
On the basis of the pottery of the still elusive �Eridu� immigrants
who had first followed the rivers into alluvial Mesopotamia, we get
the notion that the characteristic painted pottery style of Ubaid
was developed in the southland. If this reconstruction is correct
then we may watch with interest where the Ubaid pottery-painting
tradition spread. We have already mentioned that there is a substantial
assemblage of (and from the southern point of view, _fairly_ pure)
Ubaidian material in northern Iraq. The pottery appears all along the
Iranian flanks, even well east of the head of the Persian Gulf, and
ends in a later and spectacular flourish in an extremely handsome
painted style called the �Susa� style. Ubaidian pottery has been noted
up the valleys of both of the great rivers, well north of the Iraqi
and Syrian borders on the southern flanks of the Anatolian plateau.
It reaches the Mediterranean Sea and the valley of the Orontes in
Syria, and it may be faintly reflected in the painted style of a
site called Ghassul, on the east bank of the Jordan in the Dead Sea
Valley. Over this vast area--certainly in all of the great basin of
the Tigris-Euphrates drainage system and its natural extensions--I
believe we may lay our fingers on the traces of a peculiar way of
decorating pottery, which we call Ubaidian. This cursive and even
slap-dash decoration, it appears to me, was part of a new cultural
tradition which arose from the adjustments which immigrant northern
farmers first made to the new and challenging environment of southern
Mesopotamia. But exciting as the idea of the spread of influences of
the Ubaid tradition in space may be, I believe you will agree that the
consequences of the growth of that tradition in southern Mesopotamia
itself, as time passed, are even more important.


THE WARKA PHASE IN THE SOUTH

So far, there are only two radiocarbon determinations for the Ubaidian
assemblage, one from Tepe Gawra in the north and one from Warka in the
south. My hunch would be to use the dates 4500 to 3750 B.C., with a
plus or more probably a minus factor of about two hundred years for
each, as the time duration of the Ubaidian assemblage in southern
Mesopotamia.

Next, much to our annoyance, we have what is almost a temporary
black-out. According to the system of terminology I favor, our next
�assemblage� after that of Ubaid is called the _Warka_ phase, from
the Arabic name for the site of Uruk or Erich. We know it only from
six or seven levels in a narrow test-pit at Warka, and from an even
smaller hole at another site. This �assemblage,� so far, is known only
by its pottery, some of which still bears Ubaidian style painting. The
characteristic Warkan pottery is unpainted, with smoothed red or gray
surfaces and peculiar shapes. Unquestionably, there must be a great
deal more to say about the Warkan assemblage, but someone will first
have to excavate it!


THE DAWN OF CIVILIZATION

After our exasperation with the almost unknown Warka interlude,
following the brilliant �false dawn� of Ubaid, we move next to an
assemblage which yields traces of a preponderance of those elements
which we noted (p. 144) as meaning civilization. This assemblage
is that called _Proto-Literate_; it already contains writing. On
the somewhat shaky principle that writing, however early, means
history--and no longer prehistory--the assemblage is named for the
historical implications of its content, and no longer after the name of
the site where it was first found. Since some of the older books used
site-names for this assemblage, I will tell you that the Proto-Literate
includes the latter half of what used to be called the �Uruk period�
_plus_ all of what used to be called the �Jemdet Nasr period.� It shows
a consistent development from beginning to end.

I shall, in fact, leave much of the description and the historic
implications of the Proto-Literate assemblage to the conventional
historians. Professor T. J. Jacobsen, reaching backward from the
legends he finds in the cuneiform writings of slightly later times, can
in fact tell you a more complete story of Proto-Literate culture than
I can. It should be enough here if I sum up briefly what the excavated
archeological evidence shows.

We have yet to dig a Proto-Literate site in its entirety, but the
indications are that the sites cover areas the size of small cities.
In architecture, we know of large and monumental temple structures,
which were built on elaborate high terraces. The plans and decoration
of these temples follow the pattern set in the Ubaid phase: the chief
difference is one of size. The German excavators at the site of Warka
reckoned that the construction of only one of the Proto-Literate temple
complexes there must have taken 1,500 men, each working a ten-hour day,
five years to build.


ART AND WRITING

If the architecture, even in its monumental forms, can be seen to
stem from Ubaidian developments, this is not so with our other
evidence of Proto-Literate artistic expression. In relief and applied
sculpture, in sculpture in the round, and on the engraved cylinder
seals--all of which now make their appearance--several completely
new artistic principles are apparent. These include the composition
of subject-matter in groups, commemorative scenes, and especially
the ability and apparent desire to render the human form and face.
Excellent as the animals of the Franco-Cantabrian art may have been
(see p. 85), and however handsome were the carefully drafted
geometric designs and conventionalized figures on the pottery of the
early farmers, there seems to have been, up to this time, a mental
block about the drawing of the human figure and especially the human
face. We do not yet know what caused this self-consciousness about
picturing themselves which seems characteristic of men before the
appearance of civilization. We do know that with civilization, the
mental block seems to have been removed.

Clay tablets bearing pictographic signs are the Proto-Literate
forerunners of cuneiform writing. The earliest examples are not well
understood but they seem to be �devices for making accounts and
for remembering accounts.� Different from the later case in Egypt,
where writing appears fully formed in the earliest examples, the
development from simple pictographic signs to proper cuneiform writing
may be traced, step by step, in Mesopotamia. It is most probable
that the development of writing was connected with the temple and
the need for keeping account of the temple�s possessions. Professor
Jacobsen sees writing as a means for overcoming space, time, and the
increasing complications of human affairs: �Literacy, which began
with ... civilization, enhanced mightily those very tendencies in its
development which characterize it as a civilization and mark it off as
such from other types of culture.�

[Illustration: RELIEF ON A PROTO-LITERATE STONE VASE, WARKA

Unrolled drawing, with restoration suggested by figures from
contemporary cylinder seals]

While the new principles in art and the idea of writing are not
foreshadowed in the Ubaid phase, or in what little we know of the
Warkan, I do not think we need to look outside southern Mesopotamia
for their beginnings. We do know something of the adjacent areas,
too, and these beginnings are not there. I think we must accept them
as completely new discoveries, made by the people who were developing
the whole new culture pattern of classic southern Mesopotamia. Full
description of the art, architecture, and writing of the Proto-Literate
phase would call for many details. Men like Professor Jacobsen and Dr.
Adams can give you these details much better than I can. Nor shall I do
more than tell you that the common pottery of the Proto-Literate phase
was so well standardized that it looks factory made. There was also
some handsome painted pottery, and there were stone bowls with inlaid
decoration. Well-made tools in metal had by now become fairly common,
and the metallurgist was experimenting with the casting process. Signs
for plows have been identified in the early pictographs, and a wheeled
chariot is shown on a cylinder seal engraving. But if I were forced to
a guess in the matter, I would say that the development of plows and
draft-animals probably began in the Ubaid period and was another of the
great innovations of that time.

The Proto-Literate assemblage clearly suggests a highly developed and
sophisticated culture. While perhaps not yet fully urban, it is on
the threshold of urbanization. There seems to have been a very dense
settlement of Proto-Literate sites in classic southern Mesopotamia,
many of them newly founded on virgin soil where no earlier settlements
had been. When we think for a moment of what all this implies, of the
growth of an irrigation system which must have existed to allow the
flourish of this culture, and of the social and political organization
necessary to maintain the irrigation system, I think we will agree that
at last we are dealing with civilization proper.


FROM PREHISTORY TO HISTORY

Now it is time for the conventional ancient historians to take over
the story from me. Remember this when you read what they write. Their
real base-line is with cultures ruled over by later kings and emperors,
whose writings describe military campaigns and the administration of
laws and fully organized trading ventures. To these historians, the
Proto-Literate phase is still a simple beginning for what is to follow.
If they mention the Ubaid assemblage at all--the one I was so lyrical
about--it will be as some dim and fumbling step on the path to the
civilized way of life.

I suppose you could say that the difference in the approach is that as
a prehistorian I have been looking forward or upward in time, while the
historians look backward to glimpse what I�ve been describing here. My
base-line was half a million years ago with a being who had little more
than the capacity to make tools and fire to distinguish him from the
animals about him. Thus my point of view and that of the conventional
historian are bound to be different. You will need both if you want to
understand all of the story of men, as they lived through time to the
present.




End of PREHISTORY

[Illustration]


You�ll doubtless easily recall your general course in ancient history:
how the Sumerian dynasties of Mesopotamia were supplanted by those of
Babylonia, how the Hittite kingdom appeared in Anatolian Turkey, and
about the three great phases of Egyptian history. The literate kingdom
of Crete arose, and by 1500 B.C. there were splendid fortified Mycenean
towns on the mainland of Greece. This was the time--about the whole
eastern end of the Mediterranean--of what Professor Breasted called the
�first great internationalism,� with flourishing trade, international
treaties, and royal marriages between Egyptians, Babylonians, and
Hittites. By 1200 B.C., the whole thing had fragmented: �the peoples of
the sea were restless in their isles,� and the great ancient centers in
Egypt, Mesopotamia, and Anatolia were eclipsed. Numerous smaller states
arose--Assyria, Phoenicia, Israel--and the Trojan war was fought.
Finally Assyria became the paramount power of all the Near East,
presently to be replaced by Persia.

A new culture, partaking of older west Asiatic and Egyptian elements,
but casting them with its own tradition into a new mould, arose in
mainland Greece.

I once shocked my Classical colleagues to the core by referring to
Greece as �a second degree derived civilization,� but there is much
truth in this. The principles of bronze- and then of iron-working, of
the alphabet, and of many other elements in Greek culture were borrowed
from western Asia. Our debt to the Greeks is too well known for me even
to mention it, beyond recalling to you that it is to Greece we owe the
beginnings of rational or empirical science and thought in general. But
Greece fell in its turn to Rome, and in 55 B.C. Caesar invaded Britain.

I last spoke of Britain on page 142; I had chosen it as my single
example for telling you something of how the earliest farming
communities were established in Europe. Now I will continue with
Britain�s later prehistory, so you may sense something of the end of
prehistory itself. Remember that Britain is simply a single example
we select; the same thing could be done for all the other countries
of Europe, and will be possible also, some day, for further Asia and
Africa. Remember, too, that prehistory in most of Europe runs on for
three thousand or more years _after_ conventional ancient history
begins in the Near East. Britain is a good example to use in showing
how prehistory ended in Europe. As we said earlier, it lies at the
opposite end of Europe from the area of highest cultural achievement in
those times, and should you care to read more of the story in detail,
you may do so in the English language.


METAL USERS REACH ENGLAND

We left the story of Britain with the peoples who made three different
assemblages--the Windmill Hill, the megalith-builders, and the
Peterborough--making adjustments to their environments, to the original
inhabitants of the island, and to each other. They had first arrived
about 2500 B.C., and were simple pastoralists and hoe cultivators who
lived in little village communities. Some of them planted little if any
grain. By 2000 B.C., they were well settled in. Then, somewhere in the
range from about 1900 to 1800 B.C., the traces of the invasion of a new
series of peoples began to appear.

The first newcomers are called the Beaker folk, after the name of a
peculiar form of pottery they made. The beaker type of pottery seems
oldest in Spain, where it occurs with great collective tombs of
megalithic construction and with copper tools. But the Beaker folk who
reached England seem already to have moved first from Spain(?) to the
Rhineland and Holland. While in the Rhineland, and before leaving for
England, the Beaker folk seem to have mixed with the local population
and also with incomers from northeastern Europe whose culture included
elements brought originally from the Near East by the eastern way
through the steppes. This last group has also been named for a peculiar
article in its assemblage; the group is called the Battle-axe folk. A
few Battle-axe folk elements, including, in fact, stone battle-axes,
reached England with the earliest Beaker folk,[6] coming from the
Rhineland.

  [6] The British authors use the term �Beaker folk� to mean both
      archeological assemblage and human physical type. They speak
      of a �... tall, heavy-boned, rugged, and round-headed� strain
      which they take to have developed, apparently in the Rhineland,
      by a mixture of the original (Spanish?) beaker-makers and
      the northeast European battle-axe makers. However, since the
      science of physical anthropology is very much in flux at the
      moment, and since I am not able to assess the evidence for these
      physical types, I _do not_ use the term �folk� in this book with
      its usual meaning of standardized physical type. When I use
      �folk� here, I mean simply _the makers of a given archeological
      assemblage_. The difficulty only comes when assemblages are
      named for some item in them; it is too clumsy to make an
      adjective of the item and refer to a �beakerian� assemblage.

The Beaker folk settled earliest in the agriculturally fertile south
and east. There seem to have been several phases of Beaker folk
invasions, and it is not clear whether these all came strictly from the
Rhineland or Holland. We do know that their copper daggers and awls
and armlets are more of Irish or Atlantic European than of Rhineland
origin. A few simple habitation sites and many burials of the Beaker
folk are known. They buried their dead singly, sometimes in conspicuous
individual barrows with the dead warrior in his full trappings. The
spectacular element in the assemblage of the Beaker folk is a group
of large circular monuments with ditches and with uprights of wood or
stone. These �henges� became truly monumental several hundred years
later; while they were occasionally dedicated with a burial, they were
not primarily tombs. The effect of the invasion of the Beaker folk
seems to cut across the whole fabric of life in Britain.

[Illustration: BEAKER]

There was, however, a second major element in British life at this
time. It shows itself in the less well understood traces of a group
again called after one of the items in their catalogue, the Food-vessel
folk. There are many burials in these �food-vessel� pots in northern
England, Scotland, and Ireland, and the pottery itself seems to
link back to that of the Peterborough assemblage. Like the earlier
Peterborough people in the highland zone before them, the makers of
the food-vessels seem to have been heavily involved in trade. It is
quite proper to wonder whether the food-vessel pottery itself was made
by local women who were married to traders who were middlemen in the
transmission of Irish metal objects to north Germany and Scandinavia.
The belt of high, relatively woodless country, from southwest to
northeast, was already established as a natural route for inland trade.


MORE INVASIONS

About 1500 B.C., the situation became further complicated by the
arrival of new people in the region of southern England anciently
called Wessex. The traces suggest the Brittany coast of France as a
source, and the people seem at first to have been a small but �heroic�
group of aristocrats. Their �heroes� are buried with wealth and
ceremony, surrounded by their axes and daggers of bronze, their gold
ornaments, and amber and jet beads. These rich finds show that the
trade-linkage these warriors patronized spread from the Baltic sources
of amber to Mycenean Greece or even Egypt, as evidenced by glazed blue
beads.

The great visual trace of Wessex achievement is the final form of
the spectacular sanctuary at Stonehenge. A wooden henge or circular
monument was first made several hundred years earlier, but the site
now received its great circles of stone uprights and lintels. The
diameter of the surrounding ditch at Stonehenge is about 350 feet, the
diameter of the inner circle of large stones is about 100 feet, and
the tallest stone of the innermost horseshoe-shaped enclosure is 29
feet 8 inches high. One circle is made of blue stones which must have
been transported from Pembrokeshire, 145 miles away as the crow flies.
Recently, many carvings representing the profile of a standard type of
bronze axe of the time, and several profiles of bronze daggers--one of
which has been called Mycenean in type--have been found carved in the
stones. We cannot, of course, describe the details of the religious
ceremonies which must have been staged in Stonehenge, but we can
certainly imagine the well-integrated and smoothly working culture
which must have been necessary before such a great monument could have
been built.


�THIS ENGLAND�

The range from 1900 to about 1400 B.C. includes the time of development
of the archeological features usually called the �Early Bronze Age�
in Britain. In fact, traces of the Wessex warriors persisted down to
about 1200 B.C. The main regions of the island were populated, and the
adjustments to the highland and lowland zones were distinct and well
marked. The different aspects of the assemblages of the Beaker folk and
the clearly expressed activities of the Food-vessel folk and the Wessex
warriors show that Britain was already taking on her characteristic
trading role, separated from the European continent but conveniently
adjacent to it. The tin of Cornwall--so important in the production
of good bronze--as well as the copper of the west and of Ireland,
taken with the gold of Ireland and the general excellence of Irish
metal work, assured Britain a trader�s place in the then known world.
Contacts with the eastern Mediterranean may have been by sea, with
Cornish tin as the attraction, or may have been made by the Food-vessel
middlemen on their trips to the Baltic coast. There they would have
encountered traders who traveled the great north-south European road,
by which Baltic amber moved southward to Greece and the Levant, and
ideas and things moved northward again.

There was, however, the Channel between England and Europe, and this
relative isolation gave some peace and also gave time for a leveling
and further fusion of culture. The separate cultural traditions began
to have more in common. The growing of barley, the herding of sheep and
cattle, and the production of woolen garments were already features
common to all Britain�s inhabitants save a few in the remote highlands,
the far north, and the distant islands not yet fully touched by
food-production. The �personality of Britain� was being formed.


CREMATION BURIALS BEGIN

Along with people of certain religious faiths, archeologists are
against cremation (for other people!). Individuals to be cremated seem
in past times to have been dressed in their trappings and put upon a
large pyre: it takes a lot of wood and a very hot fire for a thorough
cremation. When the burning had been completed, the few fragile scraps
of bone and such odd beads of stone or other rare items as had resisted
the great heat seem to have been whisked into a pot and the pot buried.
The archeologist is left with the pot and the unsatisfactory scraps in
it.

Tentatively, after about 1400 B.C. and almost completely over the whole
island by 1200 B.C., Britain became the scene of cremation burials
in urns. We know very little of the people themselves. None of their
settlements have been identified, although there is evidence that they
grew barley and made enclosures for cattle. The urns used for the
burials seem to have antecedents in the pottery of the Food-vessel
folk, and there are some other links with earlier British traditions.
In Lancashire, a wooden circle seems to have been built about a grave
with cremated burials in urns. Even occasional instances of cremation
may be noticed earlier in Britain, and it is not clear what, if any,
connection the British cremation burials in urns have with the classic
_Urnfields_ which were now beginning in the east Mediterranean and
which we shall mention below.

The British cremation-burial-in-urns folk survived a long time in the
highland zone. In the general British scheme, they make up what is
called the �Middle Bronze Age,� but in the highland zone they last
until after 900 B.C. and are considered to be a specialized highland
�Late Bronze Age.� In the highland zone, these later cremation-burial
folk seem to have continued the older Food-vessel tradition of being
middlemen in the metal market.

Granting that our knowledge of this phase of British prehistory is
very restricted because the cremations have left so little for the
archeologist, it does not appear that the cremation-burial-urn folk can
be sharply set off from their immediate predecessors. But change on a
grander scale was on the way.


REVERBERATIONS FROM CENTRAL EUROPE

In the centuries immediately following 1000 B.C., we see with fair
clarity two phases of a cultural process which must have been going
on for some time. Certainly several of the invasions we have already
described in this chapter were due to earlier phases of the same
cultural process, but we could not see the details.

[Illustration: SLASHING SWORD]

Around 1200 B.C. central Europe was upset by the spread of the
so-called Urnfield folk, who practiced cremation burial in urns and
whom we also know to have been possessors of long, slashing swords and
the horse. I told you above that we have no idea that the Urnfield
folk proper were in any way connected with the people who made
cremation-burial-urn cemeteries a century or so earlier in Britain. It
has been supposed that the Urnfield folk themselves may have shared
ideas with the people who sacked Troy. We know that the Urnfield
pressure from central Europe displaced other people in northern France,
and perhaps in northwestern Germany, and that this reverberated into
Britain about 1000 B.C.

Soon after 750 B.C., the same thing happened again. This time, the
pressure from central Europe came from the Hallstatt folk who were iron
tool makers: the reverberation brought people from the western Alpine
region across the Channel into Britain.

At first it is possible to see the separate results of these folk
movements, but the developing cultures soon fused with each other and
with earlier British elements. Presently there were also strains of
other northern and western European pottery and traces of Urnfield
practices themselves which appeared in the finished British product. I
hope you will sense that I am vastly over-simplifying the details.

The result seems to have been--among other things--a new kind of
agricultural system. The land was marked off by ditched divisions.
Rectangular fields imply the plow rather than hoe cultivation. We seem
to get a picture of estate or tribal boundaries which included village
communities; we find a variety of tools in bronze, and even whetstones
which show that iron has been honed on them (although the scarce iron
has not been found). Let me give you the picture in Professor S.
Piggott�s words: �The ... Late Bronze Age of southern England was but
the forerunner of the earliest Iron Age in the same region, not only in
the techniques of agriculture, but almost certainly in terms of ethnic
kinship ... we can with some assurance talk of the Celts ... the great
early Celtic expansion of the Continent is recognized to be that of the
Urnfield people.�

Thus, certainly by 500 B.C., there were people in Britain, some of
whose descendants we may recognize today in name or language in remote
parts of Wales, Scotland, and the Hebrides.


THE COMING OF IRON

Iron--once the know-how of reducing it from its ore in a very hot,
closed fire has been achieved--produces a far cheaper and much more
efficient set of tools than does bronze. Iron tools seem first to
have been made in quantity in Hittite Anatolia about 1500 B.C. In
continental Europe, the earliest, so-called Hallstatt, iron-using
cultures appeared in Germany soon after 750 B.C. Somewhat later,
Greek and especially Etruscan exports of _objets d�art_--which moved
with a flourishing trans-Alpine wine trade--influenced the Hallstatt
iron-working tradition. Still later new classical motifs, together with
older Hallstatt, oriental, and northern nomad motifs, gave rise to a
new style in metal decoration which characterizes the so-called La T�ne
phase.

A few iron users reached Britain a little before 400 B.C. Not long
after that, a number of allied groups appeared in southern and
southeastern England. They came over the Channel from France and must
have been Celts with dialects related to those already in England. A
second wave of Celts arrived from the Marne district in France about
250 B.C. Finally, in the second quarter of the first century B.C.,
there were several groups of newcomers, some of whom were Belgae of
a mixed Teutonic-Celtic confederacy of tribes in northern France and
Belgium. The Belgae preceded the Romans by only a few years.


HILL-FORTS AND FARMS

The earliest iron-users seem to have entrenched themselves temporarily
within hill-top forts, mainly in the south. Gradually, they moved
inland, establishing _individual_ farm sites with extensive systems
of rectangular fields. We recognize these fields by the �lynchets� or
lines of soil-creep which plowing left on the slopes of hills. New
crops appeared; there were now bread wheat, oats, and rye, as well as
barley.

At Little Woodbury, near the town of Salisbury, a farmstead has been
rather completely excavated. The rustic buildings were within a
palisade, the round house itself was built of wood, and there were
various outbuildings and pits for the storage of grain. Weaving was
done on the farm, but not blacksmithing, which must have been a
specialized trade. Save for the lack of firearms, the place might
almost be taken for a farmstead on the American frontier in the early
1800�s.

Toward 250 B.C. there seems to have been a hasty attempt to repair the
hill-forts and to build new ones, evidently in response to signs of
restlessness being shown by remote relatives in France.


THE SECOND PHASE

Perhaps the hill-forts were not entirely effective or perhaps a
compromise was reached. In any case, the newcomers from the Marne
district did establish themselves, first in the southeast and then to
the north and west. They brought iron with decoration of the La T�ne
type and also the two-wheeled chariot. Like the Wessex warriors of
over a thousand years earlier, they made �heroes�� graves, with their
warriors buried in the war-chariots and dressed in full trappings.

[Illustration: CELTIC BUCKLE]

The metal work of these Marnian newcomers is excellent. The peculiar
Celtic art style, based originally on the classic tendril motif,
is colorful and virile, and fits with Greek and Roman descriptions
of Celtic love of color in dress. There is a strong trace of these
newcomers northward in Yorkshire, linked by Ptolemy�s description to
the Parisii, doubtless part of the Celtic tribe which originally gave
its name to Paris on the Seine. Near Glastonbury, in Somerset, two
villages in swamps have been excavated. They seem to date toward the
middle of the first century B.C., which was a troubled time in Britain.
The circular houses were built on timber platforms surrounded with
palisades. The preservation of antiquities by the water-logged peat of
the swamp has yielded us a long catalogue of the materials of these
villagers.

In Scotland, which yields its first iron tools at a date of about 100
B.C., and in northern Ireland even slightly earlier, the effects of the
two phases of newcomers tend especially to blend. Hill-forts, �brochs�
(stone-built round towers) and a variety of other strange structures
seem to appear as the new ideas develop in the comparative isolation of
northern Britain.


THE THIRD PHASE

For the time of about the middle of the first century B.C., we again
see traces of frantic hill-fort construction. This simple military
architecture now took some new forms. Its multiple ramparts must
reflect the use of slings as missiles, rather than spears. We probably
know the reason. In 56 B.C., Julius Caesar chastised the Veneti of
Brittany for outraging the dignity of Roman ambassadors. The Veneti
were famous slingers, and doubtless the reverberations of escaping
Veneti were felt across the Channel. The military architecture suggests
that some Veneti did escape to Britain.

Also, through Caesar, we learn the names of newcomers who arrived in
two waves, about 75 B.C. and about 50 B.C. These were the Belgae. Now,
at last, we can even begin to speak of dynasties and individuals.
Some time before 55 B.C., the Catuvellauni, originally from the Marne
district in France, had possessed themselves of a large part of
southeastern England. They evidently sailed up the Thames and built a
town of over a hundred acres in area. Here ruled Cassivellaunus, �the
first man in England whose name we know,� and whose town Caesar sacked.
The town sprang up elsewhere again, however.


THE END OF PREHISTORY

Prehistory, strictly speaking, is now over in southern Britain.
Claudius� effective invasion took place in 43 A.D.; by 83 A.D., a raid
had been made as far north as Aberdeen in Scotland. But by 127 A.D.,
Hadrian had completed his wall from the Solway to the Tyne, and the
Romans settled behind it. In Scotland, Romanization can have affected
the countryside very little. Professor Piggott adds that �... it is
when the pressure of Romanization is relaxed by the break-up of the
Dark Ages that we see again the Celtic metal-smiths handling their
material with the same consummate skill as they had before the Roman
Conquest, and with traditional styles that had not even then forgotten
their Marnian and Belgic heritage.�

In fact, many centuries go by, in Britain as well as in the rest of
Europe, before the archeologist�s task is complete and the historian on
his own is able to describe the ways of men in the past.


BRITAIN AS A SAMPLE OF THE GENERAL COURSE OF PREHISTORY IN EUROPE

In giving this very brief outline of the later prehistory of Britain,
you will have noticed how often I had to refer to the European
continent itself. Britain, beyond the English Channel for all of her
later prehistory, had a much simpler course of events than did most of
the rest of Europe in later prehistoric times. This holds, in spite
of all the �invasions� and �reverberations� from the continent. Most
of Europe was the scene of an even more complicated ebb and flow of
cultural change, save in some of its more remote mountain valleys and
peninsulas.

The whole course of later prehistory in Europe is, in fact, so very
complicated that there is no single good book to cover it all;
certainly there is none in English. There are some good regional
accounts and some good general accounts of part of the range from about
3000 B.C. to A.D. 1. I suspect that the difficulty of making a good
book that covers all of its later prehistory is another aspect of what
makes Europe so very complicated a continent today. The prehistoric
foundations for Europe�s very complicated set of civilizations,
cultures, and sub-cultures--which begin to appear as history
proceeds--were in themselves very complicated.

Hence, I selected the case of Britain as a single example of how
prehistory ends in Europe. It could have been more complicated than we
found it to be. Even in the subject matter on Britain in the chapter
before the last, we did not see direct traces of the effect on Britain
of the very important developments which took place in the Danubian
way from the Near East. Apparently Britain was not affected. Britain
received the impulses which brought copper, bronze, and iron tools from
an original east Mediterranean homeland into Europe, almost at the ends
of their journeys. But by the same token, they had had time en route to
take on their characteristic European aspects.

Some time ago, Sir Cyril Fox wrote a famous book called _The
Personality of Britain_, sub-titled �Its Influence on Inhabitant and
Invader in Prehistoric and Early Historic Times.� We have not gone
into the post-Roman early historic period here; there are still the
Anglo-Saxons and Normans to account for as well as the effects of
the Romans. But what I have tried to do was to begin the story of
how the personality of Britain was formed. The principles that Fox
used, in trying to balance cultural and environmental factors and
interrelationships would not be greatly different for other lands.




Summary

[Illustration]


In the pages you have read so far, you have been brought through the
earliest 99 per cent of the story of man�s life on this planet. I have
left only 1 per cent of the story for the historians to tell.


THE DRAMA OF THE PAST

Men first became men when evolution had carried them to a certain
point. This was the point where the eye-hand-brain co-ordination was
good enough so that tools could be made. When tools began to be made
according to sets of lasting habits, we know that men had appeared.
This happened over a half million years ago. The stage for the play
may have been as broad as all of Europe, Africa, and Asia. At least,
it seems unlikely that it was only one little region that saw the
beginning of the drama.

Glaciers and different climates came and went, to change the settings.
But the play went on in the same first act for a very long time. The
men who were the players had simple roles. They had to feed themselves
and protect themselves as best they could. They did this by hunting,
catching, and finding food wherever they could, and by taking such
protection as caves, fire, and their simple tools would give them.
Before the first act was over, the last of the glaciers was melting
away, and the players had added the New World to their stage. If
we want a special name for the first act, we could call it _The
Food-Gatherers_.

There were not many climaxes in the first act, so far as we can see.
But I think there may have been a few. Certainly the pace of the
first act accelerated with the swing from simple gathering to more
intensified collecting. The great cave art of France and Spain was
probably an expression of a climax. Even the ideas of burying the dead
and of the �Venus� figurines must also point to levels of human thought
and activity that were over and above pure food-getting.


THE SECOND ACT

The second act began only about ten thousand years ago. A few of the
players started it by themselves near the center of the Old World part
of the stage, in the Near East. It began as a plant and animal act, but
it soon became much more complicated.

But the players in this one part of the stage--in the Near East--were
not the only ones to start off on the second act by themselves. Other
players, possibly in several places in the Far East, and certainly in
the New World, also started second acts that began as plant and animal
acts, and then became complicated. We can call the whole second act
_The Food-Producers_.


THE FIRST GREAT CLIMAX OF THE SECOND ACT

In the Near East, the first marked climax of the second act happened
in Mesopotamia and Egypt. The play and the players reached that great
climax that we call civilization. This seems to have come less than
five thousand years after the second act began. But it could never have
happened in the first act at all.

There is another curious thing about the first act. Many of the players
didn�t know it was over and they kept on with their roles long after
the second act had begun. On the edges of the stage there are today
some players who are still going on with the first act. The Eskimos,
and the native Australians, and certain tribes in the Amazon jungle are
some of these players. They seem perfectly happy to keep on with the
first act.

The second act moved from climax to climax. The civilizations of
Mesopotamia and Egypt were only the earliest of these climaxes. The
players to the west caught the spirit of the thing, and climaxes
followed there. So also did climaxes come in the Far Eastern and New
World portions of the stage.

The greater part of the second act should really be described to you
by a historian. Although it was a very short act when compared to the
first one, the climaxes complicate it a great deal. I, a prehistorian,
have told you about only the first act, and the very beginning of the
second.


THE THIRD ACT

Also, as a prehistorian I probably should not even mention the third
act--it began so recently. The third act is _The Industrialization_.
It is the one in which we ourselves are players. If the pace of the
second act was so much faster than that of the first, the pace of the
third act is terrific. The danger is that it may wear down the players
completely.

What sort of climaxes will the third act have, and are we already in
one? You have seen by now that the acts of my play are given in terms
of modes or basic patterns of human economy--ways in which people
get food and protection and safety. The climaxes involve more than
human economy. Economics and technological factors may be part of the
climaxes, but they are not all. The climaxes may be revolutions in
their own way, intellectual and social revolutions if you like.

If the third act follows the pattern of the second act, a climax should
come soon after the act begins. We may be due for one soon if we are
not already in it. Remember the terrific pace of this third act.


WHY BOTHER WITH PREHISTORY?

Why do we bother about prehistory? The main reason is that we think it
may point to useful ideas for the present. We are in the troublesome
beginnings of the third act of the play. The beginnings of the second
act may have lessons for us and give depth to our thinking. I know
there are at least _some_ lessons, even in the present incomplete
state of our knowledge. The players who began the second act--that of
food-production--separately, in different parts of the world, were not
all of one �pure race� nor did they have �pure� cultural traditions.
Some apparently quite mixed Mediterraneans got off to the first start
on the second act and brought it to its first two climaxes as well.
Peoples of quite different physical type achieved the first climaxes in
China and in the New World.

In our British example of how the late prehistory of Europe worked, we
listed a continuous series of �invasions� and �reverberations.� After
each of these came fusion. Even though the Channel protected Britain
from some of the extreme complications of the mixture and fusion of
continental Europe, you can see how silly it would be to refer to a
�pure� British race or a �pure� British culture. We speak of the United
States as a �melting pot.� But this is nothing new. Actually, Britain
and all the rest of the world have been �melting pots� at one time or
another.

By the time the written records of Mesopotamia and Egypt begin to turn
up in number, the climaxes there are well under way. To understand the
beginnings of the climaxes, and the real beginnings of the second act
itself, we are thrown back on prehistoric archeology. And this is as
true for China, India, Middle America, and the Andes, as it is for the
Near East.

There are lessons to be learned from all of man�s past, not simply
lessons of how to fight battles or win peace conferences, but of how
human society evolves from one stage to another. Many of these lessons
can only be looked for in the prehistoric past. So far, we have only
made a beginning. There is much still to do, and many gaps in the story
are yet to be filled. The prehistorian�s job is to find the evidence,
to fill the gaps, and to discover the lessons men have learned in the
past. As I see it, this is not only an exciting but a very practical
goal for which to strive.




List of Books


BOOKS OF GENERAL INTEREST

(Chosen from a variety of the increasingly useful list of cheap
paperbound books.)

  Childe, V. Gordon
    _What Happened in History._ 1954. Penguin.
    _Man Makes Himself._ 1955. Mentor.
    _The Prehistory of European Society._ 1958. Penguin.

  Dunn, L. C., and Dobzhansky, Th.
    _Heredity, Race, and Society._ 1952. Mentor.

  Frankfort, Henri, Frankfort, H. A., Jacobsen, Thorkild, and Wilson,
        John A.
    _Before Philosophy._ 1954. Penguin.

  Simpson, George G.
    _The Meaning of Evolution._ 1955. Mentor.

  Wheeler, Sir Mortimer
    _Archaeology from the Earth._ 1956. Penguin.


GEOCHRONOLOGY AND THE ICE AGE

(Two general books. Some Pleistocene geologists disagree with Zeuner�s
interpretation of the dating evidence, but their points of view appear
in professional journals, in articles too cumbersome to list here.)

  Flint, R. F.
    _Glacial Geology and the Pleistocene Epoch._ 1947. John Wiley
        and Sons.

  Zeuner, F. E.
    _Dating the Past._ 1952 (3rd ed.). Methuen and Co.


FOSSIL MEN AND RACE

(The points of view of physical anthropologists and human
paleontologists are changing very quickly. Two of the different points
of view are listed here.)

  Clark, W. E. Le Gros
    _History of the Primates._ 1956 (5th ed.). British Museum
       (Natural History). (Also in Phoenix edition, 1957.)

  Howells, W. W.
    _Mankind So Far._ 1944. Doubleday, Doran.


GENERAL ANTHROPOLOGY

(These are standard texts not absolutely up to date in every detail, or
interpretative essays concerned with cultural change through time as
well as in space.)

  Kroeber, A. L.
    _Anthropology._ 1948. Harcourt, Brace.

  Linton, Ralph
    _The Tree of Culture._ 1955. Alfred A. Knopf, Inc.

  Redfield, Robert
    _The Primitive World and Its Transformations._ 1953. Cornell
        University Press.

  Steward, Julian H.
    _Theory of Culture Change._ 1955. University of Illinois Press.

  White, Leslie
    _The Science of Culture._ 1949. Farrar, Strauss.


GENERAL PREHISTORY

(A sampling of the more useful and current standard works in English.)

  Childe, V. Gordon
    _The Dawn of European Civilization._ 1957. Kegan Paul, Trench,
        Trubner.
    _Prehistoric Migrations in Europe._ 1950. Instituttet for
        Sammenlignende Kulturforskning.

  Clark, Grahame
    _Archaeology and Society._ 1957. Harvard University Press.

  Clark, J. G. D.
    _Prehistoric Europe: The Economic Basis._ 1952. Methuen and Co.

  Garrod, D. A. E.
    _Environment, Tools, and Man._ 1946. Cambridge University
        Press.

  Movius, Hallam L., Jr.
    �Old World Prehistory: Paleolithic� in _Anthropology Today_.
        Kroeber, A. L., ed. 1953. University of Chicago Press.

  Oakley, Kenneth P.
    _Man the Tool-Maker._ 1956. British Museum (Natural History).
        (Also in Phoenix edition, 1957.)

  Piggott, Stuart
    _British Prehistory._ 1949. Oxford University Press.

  Pittioni, Richard
    _Die Urgeschichtlichen Grundlagen der Europ�ischen Kultur._
        1949. Deuticke. (A single book which does attempt to cover the
        whole range of European prehistory to ca. 1 A.D.)


THE NEAR EAST

  Adams, Robert M.
    �Developmental Stages in Ancient Mesopotamia,� _in_ Steward,
        Julian, _et al_, _Irrigation Civilizations: A Comparative
        Study_. 1955. Pan American Union.

  Braidwood, Robert J.
    _The Near East and the Foundations for Civilization._ 1952.
        University of Oregon.

  Childe, V. Gordon
    _New Light on the Most Ancient East._ 1952. Oriental Dept.,
        Routledge and Kegan Paul.

  Frankfort, Henri
    _The Birth of Civilization in the Near East._ 1951. University
        of Indiana Press. (Also in Anchor edition, 1956.)

  Pallis, Svend A.
    _The Antiquity of Iraq._ 1956. Munksgaard.

  Wilson, John A.
    _The Burden of Egypt._ 1951. University of Chicago Press. (Also
        in Phoenix edition, called _The Culture of Ancient Egypt_,
        1956.)


HOW DIGGING IS DONE

  Braidwood, Linda
    _Digging beyond the Tigris._ 1953. Schuman, New York.

  Wheeler, Sir Mortimer
    _Archaeology from the Earth._ 1954. Oxford, London.




Index


  Abbevillian, 48;
    core-biface tool, 44, 48

  Acheulean, 48, 60

  Acheuleo-Levalloisian, 63

  Acheuleo-Mousterian, 63

  Adams, R. M., 106

  Adzes, 45

  Africa, east, 67, 89;
    north, 70, 89;
    south, 22, 25, 34, 40, 67

  Agriculture, incipient, in England, 140;
    in Near East, 123

  Ain Hanech, 48

  Amber, taken from Baltic to Greece, 167

  American Indians, 90, 142

  Anatolia, used as route to Europe, 138

  Animals, in caves, 54, 64;
    in cave art, 85

  Antevs, Ernst, 19

  Anyathian, 47

  Archeological interpretation, 8

  Archeology, defined, 8

  Architecture, at Jarmo, 128;
    at Jericho, 133

  Arrow, points, 94;
    shaft straightener, 83

  Art, in caves, 84;
    East Spanish, 85;
    figurines, 84;
    Franco-Cantabrian, 84, 85;
    movable (engravings, modeling, scratchings), 83;
    painting, 83;
    sculpture, 83

  Asia, western, 67

  Assemblage, defined, 13, 14;
    European, 94;
    Jarmo, 129;
    Maglemosian, 94;
    Natufian, 113

  Aterian, industry, 67;
    point, 89

  Australopithecinae, 24

  Australopithecine, 25, 26

  Awls, 77

  Axes, 62, 94

  Ax-heads, 15

  Azilian, 97

  Aztecs, 145


  Baghouz, 152

  Bakun, 134

  Baltic sea, 93

  Banana, 107

  Barley, wild, 108

  Barrow, 141

  Battle-axe folk, 164;
    assemblage, 164

  Beads, 80;
    bone, 114

  Beaker folk, 164;
    assemblage, 164-165

  Bear, in cave art, 85;
    cult, 68

  Belgium, 94

  Belt cave, 126

  Bering Strait, used as route to New World, 98

  Bison, in cave art, 85

  Blade, awl, 77;
    backed, 75;
    blade-core, 71;
    end-scraper, 77;
    stone, defined, 71;
    strangulated (notched), 76;
    tanged point, 76;
    tools, 71, 75-80, 90;
    tool tradition, 70

  Boar, wild, in cave art, 85

  Bogs, source of archeological materials, 94

  Bolas, 54

  Bordes, Fran�ois, 62

  Borer, 77

  Boskop skull, 34

  Boyd, William C., 35

  Bracelets, 118

  Brain, development of, 24

  Breadfruit, 107

  Breasted, James H., 107

  Brick, at Jericho, 133

  Britain, 94;
    late prehistory, 163-175;
    invaders, 173

  Broch, 172

  Buffalo, in China, 54;
    killed by stampede, 86

  Burials, 66, 86;
    in �henges,� 164;
    in urns, 168

  Burins, 75

  Burma, 90

  Byblos, 134


  Camel, 54

  Cannibalism, 55

  Cattle, wild, 85, 112;
    in cave art, 85;
    domesticated, 15;
    at Skara Brae, 142

  Caucasoids, 34

  Cave men, 29

  Caves, 62;
    art in, 84

  Celts, 170

  Chariot, 160

  Chicken, domestication of, 107

  Chiefs, in food-gathering groups, 68

  Childe, V. Gordon, 8

  China, 136

  Choukoutien, 28, 35

  Choukoutienian, 47

  Civilization, beginnings, 144, 149, 157;
    meaning of, 144

  Clactonian, 45, 47

  Clay, used in modeling, 128;
    baked, used for tools, 153

  Club-heads, 82, 94

  Colonization, in America, 142;
    in Europe, 142

  Combe Capelle, 30

  Combe Capelle-Br�nn group, 34

  Commont, Victor, 51

  Coon, Carlton S., 73

  Copper, 134

  Corn, in America, 145

  Corrals for cattle, 140

  �Cradle of mankind,� 136

  Cremation, 167

  Crete, 162

  Cro-Magnon, 30, 34

  Cultivation, incipient, 105, 109, 111

  Culture, change, 99;
    characteristics, defined, 38, 49;
    prehistoric, 39


  Danube Valley, used as route from Asia, 138

  Dates, 153

  Deer, 54, 96

  Dog, domesticated, 96

  Domestication, of animals, 100, 105, 107;
    of plants, 100

  �Dragon teeth� fossils in China, 28

  Drill, 77

  Dubois, Eugene, 26


  Early Dynastic Period, Mesopotamia, 147

  East Spanish art, 72, 85

  Egypt, 70, 126

  Ehringsdorf, 31

  Elephant, 54

  Emiliani, Cesare, 18

  Emiran flake point, 73

  England, 163-168;
    prehistoric, 19, 40;
    farmers in, 140

  Eoanthropus dawsoni, 29

  Eoliths, 41

  Erich, 152

  Eridu, 152

  Euphrates River, floods in, 148

  Europe, cave dwellings, 58;
    at end of Ice Age, 93;
    early farmers, 140;
    glaciers in, 40;
    huts in, 86;
    routes into, 137-140;
    spread of food-production to, 136


  Far East, 69, 90

  Farmers, 103

  Fauresmith industry, 67

  Fayum, 135;
    radiocarbon date, 146

  �Fertile Crescent,� 107, 146

  Figurines, �Venus,� 84;
    at Jarmo, 128;
    at Ubaid, 153

  Fire, used by Peking man, 54

  First Dynasty, Egypt, 147

  Fish-hooks, 80, 94

  Fishing, 80;
    by food-producers, 122

  Fish-lines, 80

  Fish spears, 94

  Flint industry, 127

  Font�chevade, 32, 56, 58

  Food-collecting, 104, 121;
    end of, 104

  Food-gatherers, 53, 176

  Food-gathering, 99, 104;
    in Old World, 104;
    stages of, 104

  Food-producers, 176

  Food-producing economy, 122;
    in America, 145;
    in Asia, 105

  Food-producing revolution, 99, 105;
    causes of, 101;
    preconditions for, 100

  Food-production, beginnings of, 99;
    carried to Europe, 110

  Food-vessel folk, 164

  �Forest folk,� 97, 98, 104, 110

  Fox, Sir Cyril, 174

  France, caves in, 56


  Galley Hill (fossil type), 29

  Garrod, D. A., 73

  Gazelle, 114

  Germany, 94

  Ghassul, 156

  Glaciers, 18, 30;
    destruction by, 40

  Goat, wild, 108;
    domesticated, 128

  Grain, first planted, 20

  Graves, passage, 141;
    gallery, 141

  Greece, civilization in, 163;
    as route to western Europe, 138;
    towns in, 162

  Grimaldi skeletons, 34


  Hackberry seeds used as food, 55

  Halaf, 151;
    assemblage, 151

  Hallstatt, tradition, 169

  Hand, development of, 24, 25

  Hand adzes, 46

  Hand axes, 44

  Harpoons, antler, 83, 94;
    bone, 82, 94

  Hassuna, 131;
    assemblage, 131, 132

  Heidelberg, fossil type, 28

  Hill-forts, in England, 171;
    in Scotland, 172

  Hilly flanks of Near East, 107, 108, 125, 131, 146, 147

  History, beginning of, 7, 17

  Hoes, 112

  Holland, 164

  Homo sapiens, 32

  Hooton, E. A., 34

  Horse, 112;
    wild, in cave art, 85;
    in China, 54

  Hotu cave, 126

  Houses, 122;
    at Jarmo, 128;
    at Halaf, 151

  Howe, Bruce, 116

  Howell, F. Clark, 30

  Hunting, 93


  Ice Age, in Asia, 99;
    beginning of, 18;
    glaciers in, 41;
    last glaciation, 93

  Incas, 145

  India, 90, 136

  Industrialization, 178

  Industry, blade-tool, 88;
    defined, 58;
    ground stone, 94

  Internationalism, 162

  Iran, 107, 147

  Iraq, 107, 124, 127, 136, 147

  Iron, introduction of, 170

  Irrigation, 123, 149, 155

  Italy, 138


  Jacobsen, T. J., 157

  Jarmo, 109, 126, 128, 130;
    assemblage, 129

  Java, 23, 29

  Java man, 26, 27, 29

  Jefferson, Thomas, 11

  Jericho, 119, 133

  Judaidah, 134


  Kafuan, 48

  Kanam, 23, 36

  Karim Shahir, 116-119, 124;
    assemblage, 116, 117

  Keith, Sir Arthur, 33

  Kelley, Harper, 51

  Kharga, 126

  Khartoum, 136

  Knives, 80

  Krogman, W. M., 3, 25


  Lamps, 85

  Land bridges in Mediterranean, 19

  La T�ne phase, 170

  Laurel leaf point, 78, 89

  Leakey, L. S. B., 40

  Le Moustier, 57

  Levalloisian, 47, 61, 62

  Levalloiso-Mousterian, 47, 63

  Little Woodbury, 170


  Magic, used by hunters, 123

  Maglemosian, assemblage, 94, 95;
    folk, 98

  Makapan, 40

  Mammoth, 93;
    in cave art, 85

  �Man-apes,� 26

  Mango, 107

  Mankind, age, 17

  Maringer, J., 45

  Markets, 155

  Marston, A. T., 11

  Mathiassen, T., 97

  McCown, T. D., 33

  Meganthropus, 26, 27, 36

  Men, defined, 25;
    modern, 32

  Merimde, 135

  Mersin, 133

  Metal-workers, 160, 163, 167, 172

  Micoquian, 48, 60

  Microliths, 87;
    at Jarmo, 130;
    �lunates,� 87;
    trapezoids, 87;
    triangles, 87

  Minerals used as coloring matter, 66

  Mine-shafts, 140

  M�lefaat, 126, 127

  Mongoloids, 29, 90

  Mortars, 114, 118, 127

  Mounds, how formed, 12

  Mount Carmel, 11, 33, 52, 59, 64, 69, 113, 114

  �Mousterian man,� 64

  �Mousterian� tools, 61, 62;
    of Acheulean tradition, 62

  Movius, H. L., 47


  Natufian, animals in, 114;
    assemblage, 113, 114, 115;
    burials, 114;
    date of, 113

  Neanderthal man, 29, 30, 31, 56

  Near East, beginnings of civilization in, 20, 144;
    cave sites, 58;
    climate in Ice Age, 99;
    �Fertile Crescent,� 107, 146;
    food-production in, 99;
    Natufian assemblage in, 113-115;
    stone tools, 114

  Needles, 80

  Negroid, 34

  New World, 90

  Nile River valley, 102, 134;
    floods in, 148

  Nuclear area, 106, 110;
    in Near East, 107


  Obsidian, used for blade tools, 71;
    at Jarmo, 130

  Ochre, red, with burials, 86

  Oldowan, 48

  Old World, 67, 70, 90;
    continental phases in, 18

  Olorgesailie, 40, 51

  Ostrich, in China, 54

  Ovens, 128

  Oxygen isotopes, 18


  Paintings in caves, 83

  Paleoanthropic man, 50

  Palestine, burials, 56;
    cave sites, 52;
    types of man, 69

  Parpallo, 89

  Patjitanian, 45, 47

  Pebble tools, 42

  Peking cave, 54;
    animals in, 54

  Peking man, 27, 28, 29, 54, 58

  Pendants, 80;
    bone, 114

  Pestle, 114

  Peterborough, 141;
    assemblage, 141

  Pictographic signs, 158

  Pig, wild, 108

  �Piltdown man,� 29

  Pins, 80

  Pithecanthropus, 26, 27, 30, 36

  Pleistocene, 18, 25

  Plows developed, 123

  Points, arrow, 76;
    laurel leaf, 78;
    shouldered, 78, 79;
    split-based bone, 80, 82;
    tanged, 76;
    willow leaf, 78

  Potatoes, in America, 145

  Pottery, 122, 130, 156;
    decorated, 142;
    painted, 131, 151, 152;
    Susa style, 156;
    in tombs, 141

  Prehistory, defined, 7;
    range of, 18

  Pre-neanderthaloids, 30, 31, 37

  Pre-Solutrean point, 89

  Pre-Stellenbosch, 48

  Proto-Literate assemblage, 157-160


  Race, 35;
    biological, 36;
    �pure,� 16

  Radioactivity, 9, 10

  Radioactive carbon dates, 18, 92, 120, 130, 135, 156

  Redfield, Robert, 38, 49

  Reed, C. A., 128

  Reindeer, 94

  Rhinoceros, 93;
    in cave art, 85

  Rhodesian man, 32

  Riss glaciation, 58

  Rock-shelters, 58;
    art in, 85


  Saccopastore, 31

  Sahara Desert, 34, 102

  Samarra, 152;
    pottery, 131, 152

  Sangoan industry, 67

  Sauer, Carl, 136

  Sbaikian point, 89

  Schliemann, H., 11, 12

  Scotland, 171

  Scraper, flake, 79;
    end-scraper on blade, 77, 78;
    keel-shaped, 79, 80, 81

  Sculpture in caves, 83

  Sebilian III, 126

  Shaheinab, 135

  Sheep, wild, 108;
    at Skara Brae, 142;
    in China, 54

  Shellfish, 142

  Ship, Ubaidian, 153

  Sialk, 126, 134;
    assemblage, 134

  Siberia, 88;
    pathway to New World, 98

  Sickle, 112, 153;
    blade, 113, 130

  Silo, 122

  Sinanthropus, 27, 30, 35

  Skara Brae, 142

  Snails used as food, 128

  Soan, 47

  Solecki, R., 116

  Solo (fossil type), 29, 32

  Solutrean industry, 77

  Spear, shaft, 78;
    thrower, 82, 83

  Speech, development of organs of, 25

  Squash, in America, 145

  Steinheim fossil skull, 28

  Stillbay industry, 67

  Stonehenge, 166

  Stratification, in caves, 12, 57;
    in sites, 12

  Swanscombe (fossil type), 11, 28

  Syria, 107


  Tabun, 60, 71

  Tardenoisian, 97

  Taro, 107

  Tasa, 135

  Tayacian, 47, 59

  Teeth, pierced, in beads and pendants, 114

  Temples, 123, 155

  Tepe Gawra, 156

  Ternafine, 29

  Teshik Tash, 69

  Textiles, 122

  Thong-stropper, 80

  Tigris River, floods in, 148

  Toggle, 80

  Tomatoes, in America, 145

  Tombs, megalithic, 141

  Tool-making, 42, 49

  Tool-preparation traditions, 65

  Tools, 62;
    antler, 80;
    blade, 70, 71, 75;
    bone, 66;
    chopper, 47;
    core-biface, 43, 48, 60, 61;
    flake, 44, 47, 51, 60, 64;
    flint, 80, 127;
    ground stone, 68, 127;
    handles, 94;
    pebble, 42, 43, 48, 53;
    use of, 24

  Touf (mud wall), 128

  Toynbee, A. J., 101

  Trade, 130, 155, 162

  Traders, 167

  Traditions, 15;
    blade tool, 70;
    definition of, 51;
    interpretation of, 49;
    tool-making, 42, 48;
    chopper-tool, 47;
    chopper-chopping tool, 45;
    core-biface, 43, 48;
    flake, 44, 47;
    pebble tool, 42, 48

  Tool-making, prehistory of, 42

  Turkey, 107, 108


  Ubaid, 153;
    assemblage, 153-155

  Urnfields, 168, 169


  Village-farming community era, 105, 119


  Wad B, 72

  Wadjak, 34

  Warka phase, 156;
    assemblage, 156

  Washburn, Sherwood L., 36

  Water buffalo, domestication of, 107

  Weidenreich, F., 29, 34

  Wessex, 166, 167

  Wheat, wild, 108;
    partially domesticated, 127

  Willow leaf point, 78

  Windmill Hill, 138;
    assemblage, 138, 140

  Witch doctors, 68

  Wool, 112;
    in garments, 167

  Writing, 158;
    cuneiform, 158

  W�rm I glaciation, 58


  Zebu cattle, domestication of, 107

  Zeuner, F. E., 73




      *      *      *      *      *      *




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================================================
FILE: ciphers/rabin_miller.py
================================================
# Primality Testing with the Rabin-Miller Algorithm

import random


def rabin_miller(num: int) -> bool:
    s = num - 1
    t = 0

    while s % 2 == 0:
        s = s // 2
        t += 1

    for _ in range(5):
        a = random.randrange(2, num - 1)
        v = pow(a, s, num)
        if v != 1:
            i = 0
            while v != (num - 1):
                if i == t - 1:
                    return False
                else:
                    i = i + 1
                    v = (v**2) % num
    return True


def is_prime_low_num(num: int) -> bool:
    if num < 2:
        return False

    low_primes = [
        2,
        3,
        5,
        7,
        11,
        13,
        17,
        19,
        23,
        29,
        31,
        37,
        41,
        43,
        47,
        53,
        59,
        61,
        67,
        71,
        73,
        79,
        83,
        89,
        97,
        101,
        103,
        107,
        109,
        113,
        127,
        131,
        137,
        139,
        149,
        151,
        157,
        163,
        167,
        173,
        179,
        181,
        191,
        193,
        197,
        199,
        211,
        223,
        227,
        229,
        233,
        239,
        241,
        251,
        257,
        263,
        269,
        271,
        277,
        281,
        283,
        293,
        307,
        311,
        313,
        317,
        331,
        337,
        347,
        349,
        353,
        359,
        367,
        373,
        379,
        383,
        389,
        397,
        401,
        409,
        419,
        421,
        431,
        433,
        439,
        443,
        449,
        457,
        461,
        463,
        467,
        479,
        487,
        491,
        499,
        503,
        509,
        521,
        523,
        541,
        547,
        557,
        563,
        569,
        571,
        577,
        587,
        593,
        599,
        601,
        607,
        613,
        617,
        619,
        631,
        641,
        643,
        647,
        653,
        659,
        661,
        673,
        677,
        683,
        691,
        701,
        709,
        719,
        727,
        733,
        739,
        743,
        751,
        757,
        761,
        769,
        773,
        787,
        797,
        809,
        811,
        821,
        823,
        827,
        829,
        839,
        853,
        857,
        859,
        863,
        877,
        881,
        883,
        887,
        907,
        911,
        919,
        929,
        937,
        941,
        947,
        953,
        967,
        971,
        977,
        983,
        991,
        997,
    ]

    if num in low_primes:
        return True

    for prime in low_primes:
        if (num % prime) == 0:
            return False

    return rabin_miller(num)


def generate_large_prime(keysize: int = 1024) -> int:
    while True:
        num = random.randrange(2 ** (keysize - 1), 2 ** (keysize))
        if is_prime_low_num(num):
            return num


if __name__ == "__main__":
    num = generate_large_prime()
    print(("Prime number:", num))
    print(("is_prime_low_num:", is_prime_low_num(num)))


================================================
FILE: ciphers/rail_fence_cipher.py
================================================
"""https://en.wikipedia.org/wiki/Rail_fence_cipher"""


def encrypt(input_string: str, key: int) -> str:
    """
    Shuffles the character of a string by placing each of them
    in a grid (the height is dependent on the key) in a zigzag
    formation and reading it left to right.

    >>> encrypt("Hello World", 4)
    'HWe olordll'

    >>> encrypt("This is a message", 0)
    Traceback (most recent call last):
        ...
    ValueError: Height of grid can't be 0 or negative

    >>> encrypt(b"This is a byte string", 5)
    Traceback (most recent call last):
        ...
    TypeError: sequence item 0: expected str instance, int found
    """
    temp_grid: list[list[str]] = [[] for _ in range(key)]
    lowest = key - 1

    if key <= 0:
        raise ValueError("Height of grid can't be 0 or negative")
    if key == 1 or len(input_string) <= key:
        return input_string

    for position, character in enumerate(input_string):
        num = position % (lowest * 2)  # puts it in bounds
        num = min(num, lowest * 2 - num)  # creates zigzag pattern
        temp_grid[num].append(character)
    grid = ["".join(row) for row in temp_grid]
    output_string = "".join(grid)

    return output_string


def decrypt(input_string: str, key: int) -> str:
    """
    Generates a template based on the key and fills it in with
    the characters of the input string and then reading it in
    a zigzag formation.

    >>> decrypt("HWe olordll", 4)
    'Hello World'

    >>> decrypt("This is a message", -10)
    Traceback (most recent call last):
        ...
    ValueError: Height of grid can't be 0 or negative

    >>> decrypt("My key is very big", 100)
    'My key is very big'
    """
    grid = []
    lowest = key - 1

    if key <= 0:
        raise ValueError("Height of grid can't be 0 or negative")
    if key == 1:
        return input_string

    temp_grid: list[list[str]] = [[] for _ in range(key)]  # generates template
    for position in range(len(input_string)):
        num = position % (lowest * 2)  # puts it in bounds
        num = min(num, lowest * 2 - num)  # creates zigzag pattern
        temp_grid[num].append("*")

    counter = 0
    for row in temp_grid:  # fills in the characters
        splice = input_string[counter : counter + len(row)]
        grid.append(list(splice))
        counter += len(row)

    output_string = ""  # reads as zigzag
    for position in range(len(input_string)):
        num = position % (lowest * 2)  # puts it in bounds
        num = min(num, lowest * 2 - num)  # creates zigzag pattern
        output_string += grid[num][0]
        grid[num].pop(0)
    return output_string


def bruteforce(input_string: str) -> dict[int, str]:
    """Uses decrypt function by guessing every key

    >>> bruteforce("HWe olordll")[4]
    'Hello World'
    """
    results = {}
    for key_guess in range(1, len(input_string)):  # tries every key
        results[key_guess] = decrypt(input_string, key_guess)
    return results


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: ciphers/rot13.py
================================================
def dencrypt(s: str, n: int = 13) -> str:
    """
    https://en.wikipedia.org/wiki/ROT13

    >>> msg = "My secret bank account number is 173-52946 so don't tell anyone!!"
    >>> s = dencrypt(msg)
    >>> s
    "Zl frperg onax nppbhag ahzore vf 173-52946 fb qba'g gryy nalbar!!"
    >>> dencrypt(s) == msg
    True
    """
    out = ""
    for c in s:
        if "A" <= c <= "Z":
            out += chr(ord("A") + (ord(c) - ord("A") + n) % 26)
        elif "a" <= c <= "z":
            out += chr(ord("a") + (ord(c) - ord("a") + n) % 26)
        else:
            out += c
    return out


def main() -> None:
    s0 = input("Enter message: ")

    s1 = dencrypt(s0, 13)
    print("Encryption:", s1)

    s2 = dencrypt(s1, 13)
    print("Decryption: ", s2)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: ciphers/rsa_cipher.py
================================================
import os
import sys

from . import rsa_key_generator as rkg

DEFAULT_BLOCK_SIZE = 128
BYTE_SIZE = 256


def get_blocks_from_text(
    message: str, block_size: int = DEFAULT_BLOCK_SIZE
) -> list[int]:
    message_bytes = message.encode("ascii")

    block_ints = []
    for block_start in range(0, len(message_bytes), block_size):
        block_int = 0
        for i in range(block_start, min(block_start + block_size, len(message_bytes))):
            block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
        block_ints.append(block_int)
    return block_ints


def get_text_from_blocks(
    block_ints: list[int], message_length: int, block_size: int = DEFAULT_BLOCK_SIZE
) -> str:
    message: list[str] = []
    for block_int in block_ints:
        block_message: list[str] = []
        for i in range(block_size - 1, -1, -1):
            if len(message) + i < message_length:
                ascii_number = block_int // (BYTE_SIZE**i)
                block_int = block_int % (BYTE_SIZE**i)
                block_message.insert(0, chr(ascii_number))
        message.extend(block_message)
    return "".join(message)


def encrypt_message(
    message: str, key: tuple[int, int], block_size: int = DEFAULT_BLOCK_SIZE
) -> list[int]:
    encrypted_blocks = []
    n, e = key

    for block in get_blocks_from_text(message, block_size):
        encrypted_blocks.append(pow(block, e, n))
    return encrypted_blocks


def decrypt_message(
    encrypted_blocks: list[int],
    message_length: int,
    key: tuple[int, int],
    block_size: int = DEFAULT_BLOCK_SIZE,
) -> str:
    decrypted_blocks = []
    n, d = key
    for block in encrypted_blocks:
        decrypted_blocks.append(pow(block, d, n))
    return get_text_from_blocks(decrypted_blocks, message_length, block_size)


def read_key_file(key_filename: str) -> tuple[int, int, int]:
    with open(key_filename) as fo:
        content = fo.read()
    key_size, n, eor_d = content.split(",")
    return (int(key_size), int(n), int(eor_d))


def encrypt_and_write_to_file(
    message_filename: str,
    key_filename: str,
    message: str,
    block_size: int = DEFAULT_BLOCK_SIZE,
) -> str:
    key_size, n, e = read_key_file(key_filename)
    if key_size < block_size * 8:
        sys.exit(
            f"ERROR: Block size is {block_size * 8} bits and key size is {key_size} "
            "bits. The RSA cipher requires the block size to be equal to or greater "
            "than the key size. Either decrease the block size or use different keys."
        )

    encrypted_blocks = [str(i) for i in encrypt_message(message, (n, e), block_size)]

    encrypted_content = ",".join(encrypted_blocks)
    encrypted_content = f"{len(message)}_{block_size}_{encrypted_content}"
    with open(message_filename, "w") as fo:
        fo.write(encrypted_content)
    return encrypted_content


def read_from_file_and_decrypt(message_filename: str, key_filename: str) -> str:
    key_size, n, d = read_key_file(key_filename)
    with open(message_filename) as fo:
        content = fo.read()
    message_length_str, block_size_str, encrypted_message = content.split("_")
    message_length = int(message_length_str)
    block_size = int(block_size_str)

    if key_size < block_size * 8:
        sys.exit(
            f"ERROR: Block size is {block_size * 8} bits and key size is {key_size} "
            "bits. The RSA cipher requires the block size to be equal to or greater "
            "than the key size. Were the correct key file and encrypted file specified?"
        )

    encrypted_blocks = []
    for block in encrypted_message.split(","):
        encrypted_blocks.append(int(block))

    return decrypt_message(encrypted_blocks, message_length, (n, d), block_size)


def main() -> None:
    filename = "encrypted_file.txt"
    response = input(r"Encrypt\Decrypt [e\d]: ")

    if response.lower().startswith("e"):
        mode = "encrypt"
    elif response.lower().startswith("d"):
        mode = "decrypt"

    if mode == "encrypt":
        if not os.path.exists("rsa_pubkey.txt"):
            rkg.make_key_files("rsa", 1024)

        message = input("\nEnter message: ")
        pubkey_filename = "rsa_pubkey.txt"
        print(f"Encrypting and writing to {filename}...")
        encrypted_text = encrypt_and_write_to_file(filename, pubkey_filename, message)

        print("\nEncrypted text:")
        print(encrypted_text)

    elif mode == "decrypt":
        privkey_filename = "rsa_privkey.txt"
        print(f"Reading from {filename} and decrypting...")
        decrypted_text = read_from_file_and_decrypt(filename, privkey_filename)
        print("writing decryption to rsa_decryption.txt...")
        with open("rsa_decryption.txt", "w") as dec:
            dec.write(decrypted_text)

        print("\nDecryption:")
        print(decrypted_text)


if __name__ == "__main__":
    main()


================================================
FILE: ciphers/rsa_factorization.py
================================================
"""
An RSA prime factor algorithm.

The program can efficiently factor RSA prime number given the private key d and
public key e.

| Source: on page ``3`` of https://crypto.stanford.edu/~dabo/papers/RSA-survey.pdf
| More readable source: https://www.di-mgt.com.au/rsa_factorize_n.html

large number can take minutes to factor, therefore are not included in doctest.
"""

from __future__ import annotations

import math
import random


def rsafactor(d: int, e: int, n: int) -> list[int]:
    """
    This function returns the factors of N, where p*q=N

    Return: [p, q]

    We call N the RSA modulus, e the encryption exponent, and d the decryption exponent.
    The pair (N, e) is the public key. As its name suggests, it is public and is used to
    encrypt messages.
    The pair (N, d) is the secret key or private key and is known only to the recipient
    of encrypted messages.

    >>> rsafactor(3, 16971, 25777)
    [149, 173]
    >>> rsafactor(7331, 11, 27233)
    [113, 241]
    >>> rsafactor(4021, 13, 17711)
    [89, 199]
    """
    k = d * e - 1
    p = 0
    q = 0
    while p == 0:
        g = random.randint(2, n - 1)
        t = k
        while True:
            if t % 2 == 0:
                t = t // 2
                x = (g**t) % n
                y = math.gcd(x - 1, n)
                if x > 1 and y > 1:
                    p = y
                    q = n // y
                    break  # find the correct factors
            else:
                break  # t is not divisible by 2, break and choose another g
    return sorted([p, q])


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: ciphers/rsa_key_generator.py
================================================
import os
import random
import sys

from maths.greatest_common_divisor import gcd_by_iterative

from . import cryptomath_module, rabin_miller


def main() -> None:
    print("Making key files...")
    make_key_files("rsa", 1024)
    print("Key files generation successful.")


def generate_key(key_size: int) -> tuple[tuple[int, int], tuple[int, int]]:
    """
    >>> random.seed(0) # for repeatability
    >>> public_key, private_key = generate_key(8)
    >>> public_key
    (26569, 239)
    >>> private_key
    (26569, 2855)
    """
    p = rabin_miller.generate_large_prime(key_size)
    q = rabin_miller.generate_large_prime(key_size)
    n = p * q

    # Generate e that is relatively prime to (p - 1) * (q - 1)
    while True:
        e = random.randrange(2 ** (key_size - 1), 2 ** (key_size))
        if gcd_by_iterative(e, (p - 1) * (q - 1)) == 1:
            break

    # Calculate d that is mod inverse of e
    d = cryptomath_module.find_mod_inverse(e, (p - 1) * (q - 1))

    public_key = (n, e)
    private_key = (n, d)
    return (public_key, private_key)


def make_key_files(name: str, key_size: int) -> None:
    if os.path.exists(f"{name}_pubkey.txt") or os.path.exists(f"{name}_privkey.txt"):
        print("\nWARNING:")
        print(
            f'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
            "Use a different name or delete these files and re-run this program."
        )
        sys.exit()

    public_key, private_key = generate_key(key_size)
    print(f"\nWriting public key to file {name}_pubkey.txt...")
    with open(f"{name}_pubkey.txt", "w") as out_file:
        out_file.write(f"{key_size},{public_key[0]},{public_key[1]}")

    print(f"Writing private key to file {name}_privkey.txt...")
    with open(f"{name}_privkey.txt", "w") as out_file:
        out_file.write(f"{key_size},{private_key[0]},{private_key[1]}")


if __name__ == "__main__":
    main()


================================================
FILE: ciphers/running_key_cipher.py
================================================
"""
https://en.wikipedia.org/wiki/Running_key_cipher
"""


def running_key_encrypt(key: str, plaintext: str) -> str:
    """
    Encrypts the plaintext using the Running Key Cipher.

    :param key: The running key (long piece of text).
    :param plaintext: The plaintext to be encrypted.
    :return: The ciphertext.
    """
    plaintext = plaintext.replace(" ", "").upper()
    key = key.replace(" ", "").upper()
    key_length = len(key)
    ciphertext = []
    ord_a = ord("A")

    for i, char in enumerate(plaintext):
        p = ord(char) - ord_a
        k = ord(key[i % key_length]) - ord_a
        c = (p + k) % 26
        ciphertext.append(chr(c + ord_a))

    return "".join(ciphertext)


def running_key_decrypt(key: str, ciphertext: str) -> str:
    """
    Decrypts the ciphertext using the Running Key Cipher.

    :param key: The running key (long piece of text).
    :param ciphertext: The ciphertext to be decrypted.
    :return: The plaintext.
    """
    ciphertext = ciphertext.replace(" ", "").upper()
    key = key.replace(" ", "").upper()
    key_length = len(key)
    plaintext = []
    ord_a = ord("A")

    for i, char in enumerate(ciphertext):
        c = ord(char) - ord_a
        k = ord(key[i % key_length]) - ord_a
        p = (c - k) % 26
        plaintext.append(chr(p + ord_a))

    return "".join(plaintext)


def test_running_key_encrypt() -> None:
    """
    >>> key = "How does the duck know that? said Victor"
    >>> ciphertext = running_key_encrypt(key, "DEFEND THIS")
    >>> running_key_decrypt(key, ciphertext) == "DEFENDTHIS"
    True
    """


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    test_running_key_encrypt()

    plaintext = input("Enter the plaintext: ").upper()
    print(f"\n{plaintext = }")

    key = "How does the duck know that? said Victor"
    encrypted_text = running_key_encrypt(key, plaintext)
    print(f"{encrypted_text = }")

    decrypted_text = running_key_decrypt(key, encrypted_text)
    print(f"{decrypted_text = }")


================================================
FILE: ciphers/shuffled_shift_cipher.py
================================================
from __future__ import annotations

import random
import string


class ShuffledShiftCipher:
    """
    This algorithm uses the Caesar Cipher algorithm but removes the option to
    use brute force to decrypt the message.

    The passcode is a random password from the selection buffer of
    1. uppercase letters of the English alphabet
    2. lowercase letters of the English alphabet
    3. digits from 0 to 9

    Using unique characters from the passcode, the normal list of characters,
    that can be allowed in the plaintext, is pivoted and shuffled. Refer to docstring
    of __make_key_list() to learn more about the shuffling.

    Then, using the passcode, a number is calculated which is used to encrypt the
    plaintext message with the normal shift cipher method, only in this case, the
    reference, to look back at while decrypting, is shuffled.

    Each cipher object can possess an optional argument as passcode, without which a
    new passcode is generated for that object automatically.
    cip1 = ShuffledShiftCipher('d4usr9TWxw9wMD')
    cip2 = ShuffledShiftCipher()
    """

    def __init__(self, passcode: str | None = None) -> None:
        """
        Initializes a cipher object with a passcode as it's entity
        Note: No new passcode is generated if user provides a passcode
        while creating the object
        """
        self.__passcode = passcode or self.__passcode_creator()
        self.__key_list = self.__make_key_list()
        self.__shift_key = self.__make_shift_key()

    def __str__(self) -> str:
        """
        :return: passcode of the cipher object
        """
        return "".join(self.__passcode)

    def __neg_pos(self, iterlist: list[int]) -> list[int]:
        """
        Mutates the list by changing the sign of each alternate element

        :param iterlist: takes a list iterable
        :return: the mutated list

        """
        for i in range(1, len(iterlist), 2):
            iterlist[i] *= -1
        return iterlist

    def __passcode_creator(self) -> list[str]:
        """
        Creates a random password from the selection buffer of
        1. uppercase letters of the English alphabet
        2. lowercase letters of the English alphabet
        3. digits from 0 to 9

        :rtype: list
        :return: a password of a random length between 10 to 20
        """
        choices = string.ascii_letters + string.digits
        password = [random.choice(choices) for _ in range(random.randint(10, 20))]
        return password

    def __make_key_list(self) -> list[str]:
        """
        Shuffles the ordered character choices by pivoting at breakpoints
        Breakpoints are the set of characters in the passcode

        eg:
            if, ABCDEFGHIJKLMNOPQRSTUVWXYZ are the possible characters
            and CAMERA is the passcode
            then, breakpoints = [A,C,E,M,R] # sorted set of characters from passcode
            shuffled parts: [A,CB,ED,MLKJIHGF,RQPON,ZYXWVUTS]
            shuffled __key_list : ACBEDMLKJIHGFRQPONZYXWVUTS

        Shuffling only 26 letters of the english alphabet can generate 26!
        combinations for the shuffled list. In the program we consider, a set of
        97 characters (including letters, digits, punctuation and whitespaces),
        thereby creating a possibility of 97! combinations (which is a 152 digit number
        in itself), thus diminishing the possibility of a brute force approach.
        Moreover, shift keys even introduce a multiple of 26 for a brute force approach
        for each of the already 97! combinations.
        """
        # key_list_options contain nearly all printable except few elements from
        # string.whitespace
        key_list_options = (
            string.ascii_letters + string.digits + string.punctuation + " \t\n"
        )

        keys_l = []

        # creates points known as breakpoints to break the key_list_options at those
        # points and pivot each substring
        breakpoints = sorted(set(self.__passcode))
        temp_list: list[str] = []

        # algorithm for creating a new shuffled list, keys_l, out of key_list_options
        for i in key_list_options:
            temp_list.extend(i)

            # checking breakpoints at which to pivot temporary sublist and add it into
            # keys_l
            if i in breakpoints or i == key_list_options[-1]:
                keys_l.extend(temp_list[::-1])
                temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
        return keys_l

    def __make_shift_key(self) -> int:
        """
        sum() of the mutated list of ascii values of all characters where the
        mutated list is the one returned by __neg_pos()
        """
        num = sum(self.__neg_pos([ord(x) for x in self.__passcode]))
        return num if num > 0 else len(self.__passcode)

    def decrypt(self, encoded_message: str) -> str:
        """
        Performs shifting of the encoded_message w.r.t. the shuffled __key_list
        to create the decoded_message

        >>> ssc = ShuffledShiftCipher('4PYIXyqeQZr44')
        >>> ssc.decrypt("d>**-1z6&'5z'5z:z+-='$'>=zp:>5:#z<'.&>#")
        'Hello, this is a modified Caesar cipher'

        """
        decoded_message = ""

        # decoding shift like Caesar cipher algorithm implementing negative shift or
        # reverse shift or left shift
        for i in encoded_message:
            position = self.__key_list.index(i)
            decoded_message += self.__key_list[
                (position - self.__shift_key) % -len(self.__key_list)
            ]

        return decoded_message

    def encrypt(self, plaintext: str) -> str:
        """
        Performs shifting of the plaintext w.r.t. the shuffled __key_list
        to create the encoded_message

        >>> ssc = ShuffledShiftCipher('4PYIXyqeQZr44')
        >>> ssc.encrypt('Hello, this is a modified Caesar cipher')
        "d>**-1z6&'5z'5z:z+-='$'>=zp:>5:#z<'.&>#"

        """
        encoded_message = ""

        # encoding shift like Caesar cipher algorithm implementing positive shift or
        # forward shift or right shift
        for i in plaintext:
            position = self.__key_list.index(i)
            encoded_message += self.__key_list[
                (position + self.__shift_key) % len(self.__key_list)
            ]

        return encoded_message


def test_end_to_end(msg: str = "Hello, this is a modified Caesar cipher") -> str:
    """
    >>> test_end_to_end()
    'Hello, this is a modified Caesar cipher'
    """
    cip1 = ShuffledShiftCipher()
    return cip1.decrypt(cip1.encrypt(msg))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: ciphers/simple_keyword_cypher.py
================================================
def remove_duplicates(key: str) -> str:
    """
    Removes duplicate alphabetic characters in a keyword (letter is ignored after its
    first appearance).

    :param key: Keyword to use
    :return: String with duplicates removed

    >>> remove_duplicates('Hello World!!')
    'Helo Wrd'
    """

    key_no_dups = ""
    for ch in key:
        if ch == " " or (ch not in key_no_dups and ch.isalpha()):
            key_no_dups += ch
    return key_no_dups


def create_cipher_map(key: str) -> dict[str, str]:
    """
    Returns a cipher map given a keyword.

    :param key: keyword to use
    :return: dictionary cipher map
    """
    # Create a list of the letters in the alphabet
    alphabet = [chr(i + 65) for i in range(26)]
    # Remove duplicate characters from key
    key = remove_duplicates(key.upper())
    offset = len(key)
    # First fill cipher with key characters
    cipher_alphabet = {alphabet[i]: char for i, char in enumerate(key)}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(cipher_alphabet), 26):
        char = alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            char = alphabet[i - offset]
        cipher_alphabet[alphabet[i]] = char
    return cipher_alphabet


def encipher(message: str, cipher_map: dict[str, str]) -> str:
    """
    Enciphers a message given a cipher map.

    :param message: Message to encipher
    :param cipher_map: Cipher map
    :return: enciphered string

    >>> encipher('Hello World!!', create_cipher_map('Goodbye!!'))
    'CYJJM VMQJB!!'
    """
    return "".join(cipher_map.get(ch, ch) for ch in message.upper())


def decipher(message: str, cipher_map: dict[str, str]) -> str:
    """
    Deciphers a message given a cipher map

    :param message: Message to decipher
    :param cipher_map: Dictionary mapping to use
    :return: Deciphered string

    >>> cipher_map = create_cipher_map('Goodbye!!')
    >>> decipher(encipher('Hello World!!', cipher_map), cipher_map)
    'HELLO WORLD!!'
    """
    # Reverse our cipher mappings
    rev_cipher_map = {v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(ch, ch) for ch in message.upper())


def main() -> None:
    """
    Handles I/O

    :return: void
    """
    message = input("Enter message to encode or decode: ").strip()
    key = input("Enter keyword: ").strip()
    option = input("Encipher or decipher? E/D:").strip()[0].lower()
    try:
        func = {"e": encipher, "d": decipher}[option]
    except KeyError:
        raise KeyError("invalid input option")
    cipher_map = create_cipher_map(key)
    print(func(message, cipher_map))


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: ciphers/simple_substitution_cipher.py
================================================
import random
import sys

LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"


def main() -> None:
    message = input("Enter message: ")
    key = "LFWOAYUISVKMNXPBDCRJTQEGHZ"
    resp = input("Encrypt/Decrypt [e/d]: ")

    check_valid_key(key)

    if resp.lower().startswith("e"):
        mode = "encrypt"
        translated = encrypt_message(key, message)
    elif resp.lower().startswith("d"):
        mode = "decrypt"
        translated = decrypt_message(key, message)

    print(f"\n{mode.title()}ion: \n{translated}")


def check_valid_key(key: str) -> None:
    key_list = list(key)
    letters_list = list(LETTERS)
    key_list.sort()
    letters_list.sort()

    if key_list != letters_list:
        sys.exit("Error in the key or symbol set.")


def encrypt_message(key: str, message: str) -> str:
    """
    >>> encrypt_message('LFWOAYUISVKMNXPBDCRJTQEGHZ', 'Harshil Darji')
    'Ilcrism Olcvs'
    """
    return translate_message(key, message, "encrypt")


def decrypt_message(key: str, message: str) -> str:
    """
    >>> decrypt_message('LFWOAYUISVKMNXPBDCRJTQEGHZ', 'Ilcrism Olcvs')
    'Harshil Darji'
    """
    return translate_message(key, message, "decrypt")


def translate_message(key: str, message: str, mode: str) -> str:
    translated = ""
    chars_a = LETTERS
    chars_b = key

    if mode == "decrypt":
        chars_a, chars_b = chars_b, chars_a

    for symbol in message:
        if symbol.upper() in chars_a:
            sym_index = chars_a.find(symbol.upper())
            if symbol.isupper():
                translated += chars_b[sym_index].upper()
            else:
                translated += chars_b[sym_index].lower()
        else:
            translated += symbol

    return translated


def get_random_key() -> str:
    key = list(LETTERS)
    random.shuffle(key)
    return "".join(key)


if __name__ == "__main__":
    main()


================================================
FILE: ciphers/transposition_cipher.py
================================================
import math

"""
In cryptography, the TRANSPOSITION cipher is a method of encryption where the
positions of plaintext are shifted a certain number(determined by the key) that
follows a regular system that results in the permuted text, known as the encrypted
text. The type of transposition cipher demonstrated under is the ROUTE cipher.
"""


def main() -> None:
    message = input("Enter message: ")
    key = int(input(f"Enter key [2-{len(message) - 1}]: "))
    mode = input("Encryption/Decryption [e/d]: ")

    if mode.lower().startswith("e"):
        text = encrypt_message(key, message)
    elif mode.lower().startswith("d"):
        text = decrypt_message(key, message)

    # Append pipe symbol (vertical bar) to identify spaces at the end.
    print(f"Output:\n{text + '|'}")


def encrypt_message(key: int, message: str) -> str:
    """
    >>> encrypt_message(6, 'Harshil Darji')
    'Hlia rDsahrij'
    """
    cipher_text = [""] * key
    for col in range(key):
        pointer = col
        while pointer < len(message):
            cipher_text[col] += message[pointer]
            pointer += key
    return "".join(cipher_text)


def decrypt_message(key: int, message: str) -> str:
    """
    >>> decrypt_message(6, 'Hlia rDsahrij')
    'Harshil Darji'
    """
    num_cols = math.ceil(len(message) / key)
    num_rows = key
    num_shaded_boxes = (num_cols * num_rows) - len(message)
    plain_text = [""] * num_cols
    col = 0
    row = 0

    for symbol in message:
        plain_text[col] += symbol
        col += 1

        if (col == num_cols) or (
            (col == num_cols - 1) and (row >= num_rows - num_shaded_boxes)
        ):
            col = 0
            row += 1

    return "".join(plain_text)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: ciphers/transposition_cipher_encrypt_decrypt_file.py
================================================
import os
import sys
import time

from . import transposition_cipher as trans_cipher


def main() -> None:
    input_file = "./prehistoric_men.txt"
    output_file = "./Output.txt"
    key = int(input("Enter key: "))
    mode = input("Encrypt/Decrypt [e/d]: ")

    if not os.path.exists(input_file):
        print(f"File {input_file} does not exist. Quitting...")
        sys.exit()
    if os.path.exists(output_file):
        print(f"Overwrite {output_file}? [y/n]")
        response = input("> ")
        if not response.lower().startswith("y"):
            sys.exit()

    start_time = time.time()
    if mode.lower().startswith("e"):
        with open(input_file) as f:
            content = f.read()
        translated = trans_cipher.encrypt_message(key, content)
    elif mode.lower().startswith("d"):
        with open(output_file) as f:
            content = f.read()
        translated = trans_cipher.decrypt_message(key, content)

    with open(output_file, "w") as output_obj:
        output_obj.write(translated)

    total_time = round(time.time() - start_time, 2)
    print(("Done (", total_time, "seconds )"))


if __name__ == "__main__":
    main()


================================================
FILE: ciphers/trifid_cipher.py
================================================
"""
The trifid cipher uses a table to fractionate each plaintext letter into a trigram,
mixes the constituents of the trigrams, and then applies the table in reverse to turn
these mixed trigrams into ciphertext letters.

https://en.wikipedia.org/wiki/Trifid_cipher
"""

from __future__ import annotations

# fmt: off
TEST_CHARACTER_TO_NUMBER = {
    "A": "111", "B": "112", "C": "113", "D": "121", "E": "122", "F": "123", "G": "131",
    "H": "132", "I": "133", "J": "211", "K": "212", "L": "213", "M": "221", "N": "222",
    "O": "223", "P": "231", "Q": "232", "R": "233", "S": "311", "T": "312", "U": "313",
    "V": "321", "W": "322", "X": "323", "Y": "331", "Z": "332", "+": "333",
}
# fmt: off

TEST_NUMBER_TO_CHARACTER = {val: key for key, val in TEST_CHARACTER_TO_NUMBER.items()}


def __encrypt_part(message_part: str, character_to_number: dict[str, str]) -> str:
    """
    Arrange the triagram value of each letter of `message_part` vertically and join
    them horizontally.

    >>> __encrypt_part('ASK', TEST_CHARACTER_TO_NUMBER)
    '132111112'
    """
    one, two, three = "", "", ""
    for each in (character_to_number[character] for character in message_part):
        one += each[0]
        two += each[1]
        three += each[2]

    return one + two + three


def __decrypt_part(
    message_part: str, character_to_number: dict[str, str]
) -> tuple[str, str, str]:
    """
    Convert each letter of the input string into their respective trigram values, join
    them and split them into three equal groups of strings which are returned.

    >>> __decrypt_part('ABCDE', TEST_CHARACTER_TO_NUMBER)
    ('11111', '21131', '21122')
    """
    this_part = "".join(character_to_number[character] for character in message_part)
    result = []
    tmp = ""
    for digit in this_part:
        tmp += digit
        if len(tmp) == len(message_part):
            result.append(tmp)
            tmp = ""

    return result[0], result[1], result[2]


def __prepare(
    message: str, alphabet: str
) -> tuple[str, str, dict[str, str], dict[str, str]]:
    """
    A helper function that generates the triagrams and assigns each letter of the
    alphabet to its corresponding triagram and stores this in a dictionary
    (`character_to_number` and `number_to_character`) after confirming if the
    alphabet's length is ``27``.

    >>> test = __prepare('I aM a BOy','abCdeFghijkLmnopqrStuVwxYZ+')
    >>> expected = ('IAMABOY','ABCDEFGHIJKLMNOPQRSTUVWXYZ+',
    ... TEST_CHARACTER_TO_NUMBER, TEST_NUMBER_TO_CHARACTER)
    >>> test == expected
    True

    Testing with incomplete alphabet

    >>> __prepare('I aM a BOy','abCdeFghijkLmnopqrStuVw')
    Traceback (most recent call last):
        ...
    KeyError: 'Length of alphabet has to be 27.'

    Testing with extra long alphabets

    >>> __prepare('I aM a BOy','abCdeFghijkLmnopqrStuVwxyzzwwtyyujjgfd')
    Traceback (most recent call last):
        ...
    KeyError: 'Length of alphabet has to be 27.'

    Testing with punctuation not in the given alphabet

    >>> __prepare('am i a boy?','abCdeFghijkLmnopqrStuVwxYZ+')
    Traceback (most recent call last):
        ...
    ValueError: Each message character has to be included in alphabet!

    Testing with numbers

    >>> __prepare(500,'abCdeFghijkLmnopqrStuVwxYZ+')
    Traceback (most recent call last):
        ...
    AttributeError: 'int' object has no attribute 'replace'
    """
    # Validate message and alphabet, set to upper and remove spaces
    alphabet = alphabet.replace(" ", "").upper()
    message = message.replace(" ", "").upper()

    # Check length and characters
    if len(alphabet) != 27:
        raise KeyError("Length of alphabet has to be 27.")
    if any(char not in alphabet for char in message):
        raise ValueError("Each message character has to be included in alphabet!")

    # Generate dictionares
    character_to_number = dict(zip(alphabet, TEST_CHARACTER_TO_NUMBER.values()))
    number_to_character = {
        number: letter for letter, number in character_to_number.items()
    }

    return message, alphabet, character_to_number, number_to_character


def encrypt_message(
    message: str, alphabet: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period: int = 5
) -> str:
    """
    encrypt_message
    ===============

    Encrypts a message using the trifid_cipher. Any punctuatuion chars that
    would be used should be added to the alphabet.

    PARAMETERS
    ----------

    *   `message`: The message you want to encrypt.
    *   `alphabet` (optional): The characters to be used for the cipher .
    *   `period` (optional): The number of characters you want in a group whilst
        encrypting.

    >>> encrypt_message('I am a boy')
    'BCDGBQY'

    >>> encrypt_message(' ')
    ''

    >>> encrypt_message('   aide toi le c  iel      ta id  era    ',
    ... 'FELIXMARDSTBCGHJKNOPQUVWYZ+',5)
    'FMJFVOISSUFTFPUFEQQC'

    """
    message, alphabet, character_to_number, number_to_character = __prepare(
        message, alphabet
    )

    encrypted_numeric = ""
    for i in range(0, len(message) + 1, period):
        encrypted_numeric += __encrypt_part(
            message[i : i + period], character_to_number
        )

    encrypted = ""
    for i in range(0, len(encrypted_numeric), 3):
        encrypted += number_to_character[encrypted_numeric[i : i + 3]]
    return encrypted


def decrypt_message(
    message: str, alphabet: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period: int = 5
) -> str:
    """
    decrypt_message
    ===============

    Decrypts a trifid_cipher encrypted message.

    PARAMETERS
    ----------

    *   `message`: The message you want to decrypt.
    *   `alphabet` (optional): The characters used for the cipher.
    *   `period` (optional): The number of characters used in grouping when it
        was encrypted.

    >>> decrypt_message('BCDGBQY')
    'IAMABOY'

    Decrypting with your own alphabet and period

    >>> decrypt_message('FMJFVOISSUFTFPUFEQQC','FELIXMARDSTBCGHJKNOPQUVWYZ+',5)
    'AIDETOILECIELTAIDERA'
    """
    message, alphabet, character_to_number, number_to_character = __prepare(
        message, alphabet
    )

    decrypted_numeric = []
    for i in range(0, len(message), period):
        a, b, c = __decrypt_part(message[i : i + period], character_to_number)

        for j in range(len(a)):
            decrypted_numeric.append(a[j] + b[j] + c[j])

    return "".join(number_to_character[each] for each in decrypted_numeric)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    msg = "DEFEND THE EAST WALL OF THE CASTLE."
    encrypted = encrypt_message(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
    decrypted = decrypt_message(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
    print(f"Encrypted: {encrypted}\nDecrypted: {decrypted}")


================================================
FILE: ciphers/vernam_cipher.py
================================================
def vernam_encrypt(plaintext: str, key: str) -> str:
    """
    >>> vernam_encrypt("HELLO","KEY")
    'RIJVS'
    """
    ciphertext = ""
    for i in range(len(plaintext)):
        ct = ord(key[i % len(key)]) - 65 + ord(plaintext[i]) - 65
        while ct > 25:
            ct = ct - 26
        ciphertext += chr(65 + ct)
    return ciphertext


def vernam_decrypt(ciphertext: str, key: str) -> str:
    """
    >>> vernam_decrypt("RIJVS","KEY")
    'HELLO'
    """
    decrypted_text = ""
    for i in range(len(ciphertext)):
        ct = ord(ciphertext[i]) - ord(key[i % len(key)])
        while ct < 0:
            ct = 26 + ct
        decrypted_text += chr(65 + ct)
    return decrypted_text


if __name__ == "__main__":
    from doctest import testmod

    testmod()

    # Example usage
    plaintext = "HELLO"
    key = "KEY"
    encrypted_text = vernam_encrypt(plaintext, key)
    decrypted_text = vernam_decrypt(encrypted_text, key)
    print("\n\n")
    print("Plaintext:", plaintext)
    print("Encrypted:", encrypted_text)
    print("Decrypted:", decrypted_text)


================================================
FILE: ciphers/vigenere_cipher.py
================================================
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"


def main() -> None:
    message = input("Enter message: ")
    key = input("Enter key [alphanumeric]: ")
    mode = input("Encrypt/Decrypt [e/d]: ")

    if mode.lower().startswith("e"):
        mode = "encrypt"
        translated = encrypt_message(key, message)
    elif mode.lower().startswith("d"):
        mode = "decrypt"
        translated = decrypt_message(key, message)

    print(f"\n{mode.title()}ed message:")
    print(translated)


def encrypt_message(key: str, message: str) -> str:
    """
    >>> encrypt_message('HDarji', 'This is Harshil Darji from Dharmaj.')
    'Akij ra Odrjqqs Gaisq muod Mphumrs.'
    """
    return translate_message(key, message, "encrypt")


def decrypt_message(key: str, message: str) -> str:
    """
    >>> decrypt_message('HDarji', 'Akij ra Odrjqqs Gaisq muod Mphumrs.')
    'This is Harshil Darji from Dharmaj.'
    """
    return translate_message(key, message, "decrypt")


def translate_message(key: str, message: str, mode: str) -> str:
    translated = []
    key_index = 0
    key = key.upper()

    for symbol in message:
        num = LETTERS.find(symbol.upper())
        if num != -1:
            if mode == "encrypt":
                num += LETTERS.find(key[key_index])
            elif mode == "decrypt":
                num -= LETTERS.find(key[key_index])

            num %= len(LETTERS)

            if symbol.isupper():
                translated.append(LETTERS[num])
            elif symbol.islower():
                translated.append(LETTERS[num].lower())

            key_index += 1
            if key_index == len(key):
                key_index = 0
        else:
            translated.append(symbol)
    return "".join(translated)


if __name__ == "__main__":
    main()


================================================
FILE: ciphers/xor_cipher.py
================================================
"""
author: Christian Bender
date: 21.12.2017
class: XORCipher

This class implements the XOR-cipher algorithm and provides
some useful methods for encrypting and decrypting strings and
files.

Overview about methods

- encrypt : list of char
- decrypt : list of char
- encrypt_string : str
- decrypt_string : str
- encrypt_file : boolean
- decrypt_file : boolean
"""

from __future__ import annotations


class XORCipher:
    def __init__(self, key: int = 0):
        """
        simple constructor that receives a key or uses
        default key = 0
        """

        # private field
        self.__key = key

    def encrypt(self, content: str, key: int) -> list[str]:
        """
        input: 'content' of type string and 'key' of type int
        output: encrypted string 'content' as a list of chars
        if key not passed the method uses the key by the constructor.
        otherwise key = 1

        Empty list
        >>> XORCipher().encrypt("", 5)
        []

        One key
        >>> XORCipher().encrypt("hallo welt", 1)
        ['i', '`', 'm', 'm', 'n', '!', 'v', 'd', 'm', 'u']

        Normal key
        >>> XORCipher().encrypt("HALLO WELT", 32)
        ['h', 'a', 'l', 'l', 'o', '\\x00', 'w', 'e', 'l', 't']

        Key greater than 255
        >>> XORCipher().encrypt("hallo welt", 256)
        ['h', 'a', 'l', 'l', 'o', ' ', 'w', 'e', 'l', 't']
        """

        # precondition
        assert isinstance(key, int)
        assert isinstance(content, str)

        key = key or self.__key or 1

        # make sure key is an appropriate size
        key %= 256

        return [chr(ord(ch) ^ key) for ch in content]

    def decrypt(self, content: str, key: int) -> list[str]:
        """
        input: 'content' of type list and 'key' of type int
        output: decrypted string 'content' as a list of chars
        if key not passed the method uses the key by the constructor.
        otherwise key = 1

        Empty list
        >>> XORCipher().decrypt("", 5)
        []

        One key
        >>> XORCipher().decrypt("hallo welt", 1)
        ['i', '`', 'm', 'm', 'n', '!', 'v', 'd', 'm', 'u']

        Normal key
        >>> XORCipher().decrypt("HALLO WELT", 32)
        ['h', 'a', 'l', 'l', 'o', '\\x00', 'w', 'e', 'l', 't']

        Key greater than 255
        >>> XORCipher().decrypt("hallo welt", 256)
        ['h', 'a', 'l', 'l', 'o', ' ', 'w', 'e', 'l', 't']
        """

        # precondition
        assert isinstance(key, int)
        assert isinstance(content, str)

        key = key or self.__key or 1

        # make sure key is an appropriate size
        key %= 256

        return [chr(ord(ch) ^ key) for ch in content]

    def encrypt_string(self, content: str, key: int = 0) -> str:
        """
        input: 'content' of type string and 'key' of type int
        output: encrypted string 'content'
        if key not passed the method uses the key by the constructor.
        otherwise key = 1

        Empty list
        >>> XORCipher().encrypt_string("", 5)
        ''

        One key
        >>> XORCipher().encrypt_string("hallo welt", 1)
        'i`mmn!vdmu'

        Normal key
        >>> XORCipher().encrypt_string("HALLO WELT", 32)
        'hallo\\x00welt'

        Key greater than 255
        >>> XORCipher().encrypt_string("hallo welt", 256)
        'hallo welt'
        """

        # precondition
        assert isinstance(key, int)
        assert isinstance(content, str)

        key = key or self.__key or 1

        # make sure key is an appropriate size
        key %= 256

        # This will be returned
        ans = ""

        for ch in content:
            ans += chr(ord(ch) ^ key)

        return ans

    def decrypt_string(self, content: str, key: int = 0) -> str:
        """
        input: 'content' of type string and 'key' of type int
        output: decrypted string 'content'
        if key not passed the method uses the key by the constructor.
        otherwise key = 1

        Empty list
        >>> XORCipher().decrypt_string("", 5)
        ''

        One key
        >>> XORCipher().decrypt_string("hallo welt", 1)
        'i`mmn!vdmu'

        Normal key
        >>> XORCipher().decrypt_string("HALLO WELT", 32)
        'hallo\\x00welt'

        Key greater than 255
        >>> XORCipher().decrypt_string("hallo welt", 256)
        'hallo welt'
        """

        # precondition
        assert isinstance(key, int)
        assert isinstance(content, str)

        key = key or self.__key or 1

        # make sure key is an appropriate size
        key %= 256

        # This will be returned
        ans = ""

        for ch in content:
            ans += chr(ord(ch) ^ key)

        return ans

    def encrypt_file(self, file: str, key: int = 0) -> bool:
        """
        input: filename (str) and a key (int)
        output: returns true if encrypt process was
        successful otherwise false
        if key not passed the method uses the key by the constructor.
        otherwise key = 1
        """

        # precondition
        assert isinstance(file, str)
        assert isinstance(key, int)

        # make sure key is an appropriate size
        key %= 256

        try:
            with open(file) as fin, open("encrypt.out", "w+") as fout:
                # actual encrypt-process
                for line in fin:
                    fout.write(self.encrypt_string(line, key))

        except OSError:
            return False

        return True

    def decrypt_file(self, file: str, key: int) -> bool:
        """
        input: filename (str) and a key (int)
        output: returns true if decrypt process was
        successful otherwise false
        if key not passed the method uses the key by the constructor.
        otherwise key = 1
        """

        # precondition
        assert isinstance(file, str)
        assert isinstance(key, int)

        # make sure key is an appropriate size
        key %= 256

        try:
            with open(file) as fin, open("decrypt.out", "w+") as fout:
                # actual encrypt-process
                for line in fin:
                    fout.write(self.decrypt_string(line, key))

        except OSError:
            return False

        return True


if __name__ == "__main__":
    from doctest import testmod

    testmod()

# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")


================================================
FILE: computer_vision/README.md
================================================
# Computer Vision

Computer vision is a field of computer science that works on enabling computers to see, identify and process images in the same way that human does, and provide appropriate output.

It is like imparting human intelligence and instincts to a computer.
Image processing and computer vision are a little different from each other. Image processing means applying some algorithms for transforming image from one form to the other like smoothing, contrasting, stretching, etc.

While computer vision comes from modelling image processing using the techniques of machine learning, computer vision applies machine learning to recognize patterns for interpretation of images (much like the process of visual reasoning of human vision).

* <https://en.wikipedia.org/wiki/Computer_vision>


================================================
FILE: computer_vision/__init__.py
================================================


================================================
FILE: computer_vision/cnn_classification.py
================================================
"""
Convolutional Neural Network

Objective : To train a CNN model detect if TB is present in Lung X-ray or not.

Resources CNN Theory :
    https://en.wikipedia.org/wiki/Convolutional_neural_network
Resources Tensorflow : https://www.tensorflow.org/tutorials/images/cnn

Download dataset from :
https://lhncbc.nlm.nih.gov/LHC-publications/pubs/TuberculosisChestXrayImageDataSets.html

1. Download the dataset folder and create two folder training set and test set
in the parent dataset folder
2. Move 30-40 image from both TB positive and TB Negative folder
in the test set folder
3. The labels of the images will be extracted from the folder name
the image is present in.

"""

# Part 1 - Building the CNN

import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from keras import layers, models

if __name__ == "__main__":
    # Initialising the CNN
    # (Sequential- Building the model layer by layer)
    classifier = models.Sequential()

    # Step 1 - Convolution
    # Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
    # (3,3) is the kernel size (filter matrix)
    classifier.add(
        layers.Conv2D(32, (3, 3), input_shape=(64, 64, 3), activation="relu")
    )

    # Step 2 - Pooling
    classifier.add(layers.MaxPooling2D(pool_size=(2, 2)))

    # Adding a second convolutional layer
    classifier.add(layers.Conv2D(32, (3, 3), activation="relu"))
    classifier.add(layers.MaxPooling2D(pool_size=(2, 2)))

    # Step 3 - Flattening
    classifier.add(layers.Flatten())

    # Step 4 - Full connection
    classifier.add(layers.Dense(units=128, activation="relu"))
    classifier.add(layers.Dense(units=1, activation="sigmoid"))

    # Compiling the CNN
    classifier.compile(
        optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
    )

    # Part 2 - Fitting the CNN to the images

    # Load Trained model weights

    # from keras.models import load_model
    # regressor=load_model('cnn.h5')

    train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
        rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
    )

    test_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

    training_set = train_datagen.flow_from_directory(
        "dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
    )

    test_set = test_datagen.flow_from_directory(
        "dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"
    )

    classifier.fit_generator(
        training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
    )

    classifier.save("cnn.h5")

    # Part 3 - Making new predictions

    test_image = tf.keras.preprocessing.image.load_img(
        "dataset/single_prediction/image.png", target_size=(64, 64)
    )
    test_image = tf.keras.preprocessing.image.img_to_array(test_image)
    test_image = np.expand_dims(test_image, axis=0)
    result = classifier.predict(test_image)
    # training_set.class_indices
    if result[0][0] == 0:
        prediction = "Normal"
    if result[0][0] == 1:
        prediction = "Abnormality detected"


================================================
FILE: computer_vision/flip_augmentation.py
================================================
import glob
import os
import random
from string import ascii_lowercase, digits

import cv2

"""
Flip image and bounding box for computer vision task
https://paperswithcode.com/method/randomhorizontalflip
"""

# Params
LABEL_DIR = ""
IMAGE_DIR = ""
OUTPUT_DIR = ""
FLIP_TYPE = 1  # (0 is vertical, 1 is horizontal)


def main() -> None:
    """
    Get images list and annotations list from input dir.
    Update new images and annotations.
    Save images and annotations in output dir.
    """
    img_paths, annos = get_dataset(LABEL_DIR, IMAGE_DIR)
    print("Processing...")
    new_images, new_annos, paths = update_image_and_anno(img_paths, annos, FLIP_TYPE)

    for index, image in enumerate(new_images):
        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        letter_code = random_chars(32)
        file_name = paths[index].split(os.sep)[-1].rsplit(".", 1)[0]
        file_root = f"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
        cv2.imwrite(f"{file_root}.jpg", image, [cv2.IMWRITE_JPEG_QUALITY, 85])
        print(f"Success {index + 1}/{len(new_images)} with {file_name}")
        annos_list = []
        for anno in new_annos[index]:
            obj = f"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
            annos_list.append(obj)
        with open(f"{file_root}.txt", "w") as outfile:
            outfile.write("\n".join(line for line in annos_list))


def get_dataset(label_dir: str, img_dir: str) -> tuple[list, list]:
    """
    - label_dir <type: str>: Path to label include annotation of images
    - img_dir <type: str>: Path to folder contain images
    Return <type: list>: List of images path and labels
    """
    img_paths = []
    labels = []
    for label_file in glob.glob(os.path.join(label_dir, "*.txt")):
        label_name = label_file.split(os.sep)[-1].rsplit(".", 1)[0]
        with open(label_file) as in_file:
            obj_lists = in_file.readlines()
        img_path = os.path.join(img_dir, f"{label_name}.jpg")

        boxes = []
        for obj_list in obj_lists:
            obj = obj_list.rstrip("\n").split(" ")
            boxes.append(
                [
                    int(obj[0]),
                    float(obj[1]),
                    float(obj[2]),
                    float(obj[3]),
                    float(obj[4]),
                ]
            )
        if not boxes:
            continue
        img_paths.append(img_path)
        labels.append(boxes)
    return img_paths, labels


def update_image_and_anno(
    img_list: list, anno_list: list, flip_type: int = 1
) -> tuple[list, list, list]:
    """
    - img_list <type: list>: list of all images
    - anno_list <type: list>: list of all annotations of specific image
    - flip_type <type: int>: 0 is vertical, 1 is horizontal
    Return:
        - new_imgs_list <type: narray>: image after resize
        - new_annos_lists <type: list>: list of new annotation after scale
        - path_list <type: list>: list the name of image file
    """
    new_annos_lists = []
    path_list = []
    new_imgs_list = []
    for idx in range(len(img_list)):
        new_annos = []
        path = img_list[idx]
        path_list.append(path)
        img_annos = anno_list[idx]
        img = cv2.imread(path)
        if flip_type == 1:
            new_img = cv2.flip(img, flip_type)
            for bbox in img_annos:
                x_center_new = 1 - bbox[1]
                new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]])
        elif flip_type == 0:
            new_img = cv2.flip(img, flip_type)
            for bbox in img_annos:
                y_center_new = 1 - bbox[2]
                new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]])
        new_annos_lists.append(new_annos)
        new_imgs_list.append(new_img)
    return new_imgs_list, new_annos_lists, path_list


def random_chars(number_char: int = 32) -> str:
    """
    Automatic generate random 32 characters.
    Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    >>> len(random_chars(32))
    32
    """
    assert number_char > 1, "The number of character should greater than 1"
    letter_code = ascii_lowercase + digits
    return "".join(random.choice(letter_code) for _ in range(number_char))


if __name__ == "__main__":
    main()
    print("DONE ✅")


================================================
FILE: computer_vision/haralick_descriptors.py
================================================
"""
https://en.wikipedia.org/wiki/Image_texture
https://en.wikipedia.org/wiki/Co-occurrence_matrix#Application_to_image_analysis
"""

import imageio.v2 as imageio
import numpy as np


def root_mean_square_error(original: np.ndarray, reference: np.ndarray) -> float:
    """Simple implementation of Root Mean Squared Error
    for two N dimensional numpy arrays.

    Examples:
        >>> root_mean_square_error(np.array([1, 2, 3]), np.array([1, 2, 3]))
        0.0
        >>> root_mean_square_error(np.array([1, 2, 3]), np.array([2, 2, 2]))
        0.816496580927726
        >>> root_mean_square_error(np.array([1, 2, 3]), np.array([6, 4, 2]))
        3.1622776601683795
    """
    return float(np.sqrt(((original - reference) ** 2).mean()))


def normalize_image(
    image: np.ndarray, cap: float = 255.0, data_type: np.dtype = np.uint8
) -> np.ndarray:
    """
    Normalizes image in Numpy 2D array format, between ranges 0-cap,
    as to fit uint8 type.

    Args:
        image: 2D numpy array representing image as matrix, with values in any range
        cap: Maximum cap amount for normalization
        data_type: numpy data type to set output variable to
    Returns:
        return 2D numpy array of type uint8, corresponding to limited range matrix

    Examples:
        >>> normalize_image(np.array([[1, 2, 3], [4, 5, 10]]),
        ...                 cap=1.0, data_type=np.float64)
        array([[0.        , 0.11111111, 0.22222222],
               [0.33333333, 0.44444444, 1.        ]])
        >>> normalize_image(np.array([[4, 4, 3], [1, 7, 2]]))
        array([[127, 127,  85],
               [  0, 255,  42]], dtype=uint8)
    """
    normalized = (image - np.min(image)) / (np.max(image) - np.min(image)) * cap
    return normalized.astype(data_type)


def normalize_array(array: np.ndarray, cap: float = 1) -> np.ndarray:
    """Normalizes a 1D array, between ranges 0-cap.

    Args:
        array: List containing values to be normalized between cap range.
        cap: Maximum cap amount for normalization.
    Returns:
        return 1D numpy array, corresponding to limited range array

    Examples:
        >>> normalize_array(np.array([2, 3, 5, 7]))
        array([0. , 0.2, 0.6, 1. ])
        >>> normalize_array(np.array([[5], [7], [11], [13]]))
        array([[0.  ],
               [0.25],
               [0.75],
               [1.  ]])
    """
    diff = np.max(array) - np.min(array)
    return (array - np.min(array)) / (1 if diff == 0 else diff) * cap


def grayscale(image: np.ndarray) -> np.ndarray:
    """
    Uses luminance weights to transform RGB channel to greyscale, by
    taking the dot product between the channel and the weights.

    Example:
        >>> grayscale(np.array([[[108, 201, 72], [255, 11,  127]],
        ...                     [[56,  56,  56], [128, 255, 107]]]))
        array([[158,  97],
               [ 56, 200]], dtype=uint8)
    """
    return np.dot(image[:, :, 0:3], [0.299, 0.587, 0.114]).astype(np.uint8)


def binarize(image: np.ndarray, threshold: float = 127.0) -> np.ndarray:
    """
    Binarizes a grayscale image based on a given threshold value,
    setting values to 1 or 0 accordingly.

    Examples:
        >>> binarize(np.array([[128, 255], [101, 156]]))
        array([[1, 1],
               [0, 1]])
        >>> binarize(np.array([[0.07, 1], [0.51, 0.3]]), threshold=0.5)
        array([[0, 1],
               [1, 0]])
    """
    return np.where(image > threshold, 1, 0)


def transform(
    image: np.ndarray, kind: str, kernel: np.ndarray | None = None
) -> np.ndarray:
    """
    Simple image transformation using one of two available filter functions:
    Erosion and Dilation.

    Args:
        image: binarized input image, onto which to apply transformation
        kind: Can be either 'erosion', in which case the :func:np.max
              function is called, or 'dilation', when :func:np.min is used instead.
        kernel: n x n kernel with shape < :attr:image.shape,
              to be used when applying convolution to original image

    Returns:
        returns a numpy array with same shape as input image,
        corresponding to applied binary transformation.

    Examples:
        >>> img = np.array([[1, 0.5], [0.2, 0.7]])
        >>> img = binarize(img, threshold=0.5)
        >>> transform(img, 'erosion')
        array([[1, 1],
               [1, 1]], dtype=uint8)
        >>> transform(img, 'dilation')
        array([[0, 0],
               [0, 0]], dtype=uint8)
    """
    if kernel is None:
        kernel = np.ones((3, 3))

    if kind == "erosion":
        constant = 1
        apply = np.max
    else:
        constant = 0
        apply = np.min

    center_x, center_y = (x // 2 for x in kernel.shape)

    # Use padded image when applying convolution
    # to not go out of bounds of the original the image
    transformed = np.zeros(image.shape, dtype=np.uint8)
    padded = np.pad(image, 1, "constant", constant_values=constant)

    for x in range(center_x, padded.shape[0] - center_x):
        for y in range(center_y, padded.shape[1] - center_y):
            center = padded[
                x - center_x : x + center_x + 1, y - center_y : y + center_y + 1
            ]
            # Apply transformation method to the centered section of the image
            transformed[x - center_x, y - center_y] = apply(center[kernel == 1])

    return transformed


def opening_filter(image: np.ndarray, kernel: np.ndarray | None = None) -> np.ndarray:
    """
    Opening filter, defined as the sequence of
    erosion and then a dilation filter on the same image.

    Examples:
        >>> img = np.array([[1, 0.5], [0.2, 0.7]])
        >>> img = binarize(img, threshold=0.5)
        >>> opening_filter(img)
        array([[1, 1],
               [1, 1]], dtype=uint8)
    """
    if kernel is None:
        np.ones((3, 3))

    return transform(transform(image, "dilation", kernel), "erosion", kernel)


def closing_filter(image: np.ndarray, kernel: np.ndarray | None = None) -> np.ndarray:
    """
    Opening filter, defined as the sequence of
    dilation and then erosion filter on the same image.

    Examples:
        >>> img = np.array([[1, 0.5], [0.2, 0.7]])
        >>> img = binarize(img, threshold=0.5)
        >>> closing_filter(img)
        array([[0, 0],
               [0, 0]], dtype=uint8)
    """
    if kernel is None:
        kernel = np.ones((3, 3))
    return transform(transform(image, "erosion", kernel), "dilation", kernel)


def binary_mask(
    image_gray: np.ndarray, image_map: np.ndarray
) -> tuple[np.ndarray, np.ndarray]:
    """
    Apply binary mask, or thresholding based
    on bit mask value (mapping mask is binary).

    Returns the mapped true value mask and its complementary false value mask.

    Example:
        >>> img = np.array([[[108, 201, 72], [255, 11,  127]],
        ...                 [[56,  56,  56], [128, 255, 107]]])
        >>> gray = grayscale(img)
        >>> binary = binarize(gray)
        >>> morphological = opening_filter(binary)
        >>> binary_mask(gray, morphological)
        (array([[1, 1],
               [1, 1]], dtype=uint8), array([[158,  97],
               [ 56, 200]], dtype=uint8))
    """
    true_mask, false_mask = image_gray.copy(), image_gray.copy()
    true_mask[image_map == 1] = 1
    false_mask[image_map == 0] = 0

    return true_mask, false_mask


def matrix_concurrency(image: np.ndarray, coordinate: tuple[int, int]) -> np.ndarray:
    """
    Calculate sample co-occurrence matrix based on input image
    as well as selected coordinates on image.

    Implementation is made using basic iteration,
    as function to be performed (np.max) is non-linear and therefore
    not callable on the frequency domain.

    Example:
        >>> img = np.array([[[108, 201, 72], [255, 11,  127]],
        ...                 [[56,  56,  56], [128, 255, 107]]])
        >>> gray = grayscale(img)
        >>> binary = binarize(gray)
        >>> morphological = opening_filter(binary)
        >>> mask_1 = binary_mask(gray, morphological)[0]
        >>> matrix_concurrency(mask_1, (0, 1))
        array([[0., 0.],
               [0., 0.]])
    """
    matrix = np.zeros([np.max(image) + 1, np.max(image) + 1])

    offset_x, offset_y = coordinate

    for x in range(1, image.shape[0] - 1):
        for y in range(1, image.shape[1] - 1):
            base_pixel = image[x, y]
            offset_pixel = image[x + offset_x, y + offset_y]

            matrix[base_pixel, offset_pixel] += 1
    matrix_sum = np.sum(matrix)
    return matrix / (1 if matrix_sum == 0 else matrix_sum)


def haralick_descriptors(matrix: np.ndarray) -> list[float]:
    """Calculates all 8 Haralick descriptors based on co-occurrence input matrix.
    All descriptors are as follows:
    Maximum probability, Inverse Difference, Homogeneity, Entropy,
    Energy, Dissimilarity, Contrast and Correlation

    Args:
        matrix: Co-occurrence matrix to use as base for calculating descriptors.

    Returns:
        Reverse ordered list of resulting descriptors

    Example:
        >>> img = np.array([[[108, 201, 72], [255, 11,  127]],
        ...                 [[56,  56,  56], [128, 255, 107]]])
        >>> gray = grayscale(img)
        >>> binary = binarize(gray)
        >>> morphological = opening_filter(binary)
        >>> mask_1 = binary_mask(gray, morphological)[0]
        >>> concurrency = matrix_concurrency(mask_1, (0, 1))
        >>> [float(f) for f in haralick_descriptors(concurrency)]
        [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
    """
    # Function np.indices could be used for bigger input types,
    # but np.ogrid works just fine
    i, j = np.ogrid[0 : matrix.shape[0], 0 : matrix.shape[1]]  # np.indices()

    # Pre-calculate frequent multiplication and subtraction
    prod = np.multiply(i, j)
    sub = np.subtract(i, j)

    # Calculate numerical value of Maximum Probability
    maximum_prob = np.max(matrix)
    # Using the definition for each descriptor individually to calculate its matrix
    correlation = prod * matrix
    energy = np.power(matrix, 2)
    contrast = matrix * np.power(sub, 2)

    dissimilarity = matrix * np.abs(sub)
    inverse_difference = matrix / (1 + np.abs(sub))
    homogeneity = matrix / (1 + np.power(sub, 2))
    entropy = -(matrix[matrix > 0] * np.log(matrix[matrix > 0]))

    # Sum values for descriptors ranging from the first one to the last,
    # as all are their respective origin matrix and not the resulting value yet.
    return [
        maximum_prob,
        correlation.sum(),
        energy.sum(),
        contrast.sum(),
        dissimilarity.sum(),
        inverse_difference.sum(),
        homogeneity.sum(),
        entropy.sum(),
    ]


def get_descriptors(
    masks: tuple[np.ndarray, np.ndarray], coordinate: tuple[int, int]
) -> np.ndarray:
    """
    Calculate all Haralick descriptors for a sequence of
    different co-occurrence matrices, given input masks and coordinates.

    Example:
        >>> img = np.array([[[108, 201, 72], [255, 11,  127]],
        ...                 [[56,  56,  56], [128, 255, 107]]])
        >>> gray = grayscale(img)
        >>> binary = binarize(gray)
        >>> morphological = opening_filter(binary)
        >>> get_descriptors(binary_mask(gray, morphological), (0, 1))
        array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
    """
    descriptors = np.array(
        [haralick_descriptors(matrix_concurrency(mask, coordinate)) for mask in masks]
    )

    # Concatenate each individual descriptor into
    # one single list containing sequence of descriptors
    return np.concatenate(descriptors, axis=None)


def euclidean(point_1: np.ndarray, point_2: np.ndarray) -> float:
    """
    Simple method for calculating the euclidean distance between two points,
    with type np.ndarray.

    Example:
        >>> a = np.array([1, 0, -2])
        >>> b = np.array([2, -1, 1])
        >>> euclidean(a, b)
        3.3166247903554
    """
    return float(np.sqrt(np.sum(np.square(point_1 - point_2))))


def get_distances(descriptors: np.ndarray, base: int) -> list[tuple[int, float]]:
    """
    Calculate all Euclidean distances between a selected base descriptor
    and all other Haralick descriptors
    The resulting comparison is return in decreasing order,
    showing which descriptor is the most similar to the selected base.

    Args:
        descriptors: Haralick descriptors to compare with base index
        base: Haralick descriptor index to use as base when calculating respective
        euclidean distance to other descriptors.

    Returns:
        Ordered distances between descriptors

    Example:
        >>> index = 1
        >>> img = np.array([[[108, 201, 72], [255, 11,  127]],
        ...                 [[56,  56,  56], [128, 255, 107]]])
        >>> gray = grayscale(img)
        >>> binary = binarize(gray)
        >>> morphological = opening_filter(binary)
        >>> get_distances(get_descriptors(
        ...                 binary_mask(gray, morphological), (0, 1)),
        ...               index)
        [(0, 0.0), (1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), \
(6, 0.0), (7, 0.0), (8, 0.0), (9, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), \
(13, 0.0), (14, 0.0), (15, 0.0)]
    """
    distances = np.array(
        [euclidean(descriptor, descriptors[base]) for descriptor in descriptors]
    )
    # Normalize distances between range [0, 1]
    normalized_distances: list[float] = normalize_array(distances, 1).tolist()
    enum_distances = list(enumerate(normalized_distances))
    enum_distances.sort(key=lambda tup: tup[1], reverse=True)
    return enum_distances


if __name__ == "__main__":
    # Index to compare haralick descriptors to
    index = int(input())
    q_value_list = [int(value) for value in input().split()]
    q_value = (q_value_list[0], q_value_list[1])

    # Format is the respective filter to apply,
    # can be either 1 for the opening filter or else for the closing
    parameters = {"format": int(input()), "threshold": int(input())}

    # Number of images to perform methods on
    b_number = int(input())

    files, descriptors = [], []

    for _ in range(b_number):
        file = input().rstrip()
        files.append(file)

        # Open given image and calculate morphological filter,
        # respective masks and correspondent Harralick Descriptors.
        image = imageio.imread(file).astype(np.float32)
        gray = grayscale(image)
        threshold = binarize(gray, parameters["threshold"])

        morphological = (
            opening_filter(threshold)
            if parameters["format"] == 1
            else closing_filter(threshold)
        )
        masks = binary_mask(gray, morphological)
        descriptors.append(get_descriptors(masks, q_value))

    # Transform ordered distances array into a sequence of indexes
    # corresponding to original file position
    distances = get_distances(np.array(descriptors), index)
    indexed_distances = np.array(distances).astype(np.uint8)[:, 0]

    # Finally, print distances considering the Haralick descriptions from the base
    # file to all other images using the morphology method of choice.
    print(f"Query: {files[index]}")
    print("Ranking:")
    for idx, file_idx in enumerate(indexed_distances):
        print(f"({idx}) {files[file_idx]}", end="\n")


================================================
FILE: computer_vision/harris_corner.py
================================================
import cv2
import numpy as np

"""
Harris Corner Detector
https://en.wikipedia.org/wiki/Harris_Corner_Detector
"""


class HarrisCorner:
    def __init__(self, k: float, window_size: int):
        """
        k : is an empirically determined constant in [0.04,0.06]
        window_size : neighbourhoods considered
        """

        if k in (0.04, 0.06):
            self.k = k
            self.window_size = window_size
        else:
            raise ValueError("invalid k value")

    def __str__(self) -> str:
        return str(self.k)

    def detect(self, img_path: str) -> tuple[cv2.Mat, list[list[int]]]:
        """
        Returns the image with corners identified
        img_path  : path of the image
        output : list of the corner positions, image
        """

        img = cv2.imread(img_path, 0)
        h, w = img.shape
        corner_list: list[list[int]] = []
        color_img = img.copy()
        color_img = cv2.cvtColor(color_img, cv2.COLOR_GRAY2RGB)
        dy, dx = np.gradient(img)
        ixx = dx**2
        iyy = dy**2
        ixy = dx * dy
        k = 0.04
        offset = self.window_size // 2
        for y in range(offset, h - offset):
            for x in range(offset, w - offset):
                wxx = ixx[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                wyy = iyy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                wxy = ixy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()

                det = (wxx * wyy) - (wxy**2)
                trace = wxx + wyy
                r = det - k * (trace**2)
                # Can change the value
                if r > 0.5:
                    corner_list.append([x, y, r])
                    color_img.itemset((y, x, 0), 0)
                    color_img.itemset((y, x, 1), 0)
                    color_img.itemset((y, x, 2), 255)
        return color_img, corner_list


if __name__ == "__main__":
    edge_detect = HarrisCorner(0.04, 3)
    color_img, _ = edge_detect.detect("path_to_image")
    cv2.imwrite("detect.png", color_img)


================================================
FILE: computer_vision/horn_schunck.py
================================================
"""
The Horn-Schunck method estimates the optical flow for every single pixel of
a sequence of images.
It works by assuming brightness constancy between two consecutive frames
and smoothness in the optical flow.

Useful resources:
Wikipedia: https://en.wikipedia.org/wiki/Horn%E2%80%93Schunck_method
Paper: http://image.diku.dk/imagecanon/material/HornSchunckOptical_Flow.pdf
"""

from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve


def warp(
    image: np.ndarray, horizontal_flow: np.ndarray, vertical_flow: np.ndarray
) -> np.ndarray:
    """
    Warps the pixels of an image into a new image using the horizontal and vertical
    flows.
    Pixels that are warped from an invalid location are set to 0.

    Parameters:
        image: Grayscale image
        horizontal_flow: Horizontal flow
        vertical_flow: Vertical flow

    Returns: Warped image

    >>> warp(np.array([[0, 1, 2], [0, 3, 0], [2, 2, 2]]), \
    np.array([[0, 1, -1], [-1, 0, 0], [1, 1, 1]]), \
    np.array([[0, 0, 0], [0, 1, 0], [0, 0, 1]]))
    array([[0, 0, 0],
           [3, 1, 0],
           [0, 2, 3]])
    """
    flow = np.stack((horizontal_flow, vertical_flow), 2)

    # Create a grid of all pixel coordinates and subtract the flow to get the
    # target pixels coordinates
    grid = np.stack(
        np.meshgrid(np.arange(0, image.shape[1]), np.arange(0, image.shape[0])), 2
    )
    grid = np.round(grid - flow).astype(np.int32)

    # Find the locations outside of the original image
    invalid = (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]))
    grid[invalid] = 0

    warped = image[grid[:, :, 1], grid[:, :, 0]]

    # Set pixels at invalid locations to 0
    warped[invalid[:, :, 0] | invalid[:, :, 1]] = 0

    return warped


def horn_schunck(
    image0: np.ndarray,
    image1: np.ndarray,
    num_iter: SupportsIndex,
    alpha: float | None = None,
) -> tuple[np.ndarray, np.ndarray]:
    """
    This function performs the Horn-Schunck algorithm and returns the estimated
    optical flow. It is assumed that the input images are grayscale and
    normalized to be in [0, 1].

    Parameters:
        image0: First image of the sequence
        image1: Second image of the sequence
        alpha: Regularization constant
        num_iter: Number of iterations performed

    Returns: estimated horizontal & vertical flow

    >>> np.round(horn_schunck(np.array([[0, 0, 2], [0, 0, 2]]), \
    np.array([[0, 2, 0], [0, 2, 0]]), alpha=0.1, num_iter=110)).\
    astype(np.int32)
    array([[[ 0, -1, -1],
            [ 0, -1, -1]],
    <BLANKLINE>
           [[ 0,  0,  0],
            [ 0,  0,  0]]], dtype=int32)
    """
    if alpha is None:
        alpha = 0.1

    # Initialize flow
    horizontal_flow = np.zeros_like(image0)
    vertical_flow = np.zeros_like(image0)

    # Prepare kernels for the calculation of the derivatives and the average velocity
    kernel_x = np.array([[-1, 1], [-1, 1]]) * 0.25
    kernel_y = np.array([[-1, -1], [1, 1]]) * 0.25
    kernel_t = np.array([[1, 1], [1, 1]]) * 0.25
    kernel_laplacian = np.array(
        [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]
    )

    # Iteratively refine the flow
    for _ in range(num_iter):
        warped_image = warp(image0, horizontal_flow, vertical_flow)
        derivative_x = convolve(warped_image, kernel_x) + convolve(image1, kernel_x)
        derivative_y = convolve(warped_image, kernel_y) + convolve(image1, kernel_y)
        derivative_t = convolve(warped_image, kernel_t) + convolve(image1, -kernel_t)

        avg_horizontal_velocity = convolve(horizontal_flow, kernel_laplacian)
        avg_vertical_velocity = convolve(vertical_flow, kernel_laplacian)

        # This updates the flow as proposed in the paper (Step 12)
        update = (
            derivative_x * avg_horizontal_velocity
            + derivative_y * avg_vertical_velocity
            + derivative_t
        )
        update = update / (alpha**2 + derivative_x**2 + derivative_y**2)

        horizontal_flow = avg_horizontal_velocity - derivative_x * update
        vertical_flow = avg_vertical_velocity - derivative_y * update

    return horizontal_flow, vertical_flow


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: computer_vision/intensity_based_segmentation.py
================================================
# Source: "https://www.ijcse.com/docs/IJCSE11-02-03-117.pdf"

# Importing necessary libraries
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image


def segment_image(image: np.ndarray, thresholds: list[int]) -> np.ndarray:
    """
    Performs image segmentation based on intensity thresholds.

    Args:
        image: Input grayscale image as a 2D array.
        thresholds: Intensity thresholds to define segments.

    Returns:
        A labeled 2D array where each region corresponds to a threshold range.

    Example:
        >>> img = np.array([[80, 120, 180], [40, 90, 150], [20, 60, 100]])
        >>> segment_image(img, [50, 100, 150])
        array([[1, 2, 3],
               [0, 1, 2],
               [0, 1, 1]], dtype=int32)
    """
    # Initialize segmented array with zeros
    segmented = np.zeros_like(image, dtype=np.int32)

    # Assign labels based on thresholds
    for i, threshold in enumerate(thresholds):
        segmented[image > threshold] = i + 1

    return segmented


if __name__ == "__main__":
    # Load the image
    image_path = "path_to_image"  # Replace with your image path
    original_image = Image.open(image_path).convert("L")
    image_array = np.array(original_image)

    # Define thresholds
    thresholds = [50, 100, 150, 200]

    # Perform segmentation
    segmented_image = segment_image(image_array, thresholds)

    # Display the results
    plt.figure(figsize=(10, 5))

    plt.subplot(1, 2, 1)
    plt.title("Original Image")
    plt.imshow(image_array, cmap="gray")
    plt.axis("off")

    plt.subplot(1, 2, 2)
    plt.title("Segmented Image")
    plt.imshow(segmented_image, cmap="tab20")
    plt.axis("off")

    plt.show()


================================================
FILE: computer_vision/mean_threshold.py
================================================
from PIL import Image

"""
Mean thresholding algorithm for image processing
https://en.wikipedia.org/wiki/Thresholding_(image_processing)
"""


def mean_threshold(image: Image) -> Image:
    """
    image: is a grayscale PIL image object
    """
    height, width = image.size
    mean = 0
    pixels = image.load()
    for i in range(width):
        for j in range(height):
            pixel = pixels[j, i]
            mean += pixel
    mean //= width * height

    for j in range(width):
        for i in range(height):
            pixels[i, j] = 255 if pixels[i, j] > mean else 0
    return image


if __name__ == "__main__":
    image = mean_threshold(Image.open("path_to_image").convert("L"))
    image.save("output_image_path")


================================================
FILE: computer_vision/mosaic_augmentation.py
================================================
"""Source: https://github.com/jason9075/opencv-mosaic-data-aug"""

import glob
import os
import random
from string import ascii_lowercase, digits

import cv2
import numpy as np

# Parameters
OUTPUT_SIZE = (720, 1280)  # Height, Width
SCALE_RANGE = (0.4, 0.6)  # if height or width lower than this scale, drop it.
FILTER_TINY_SCALE = 1 / 100
LABEL_DIR = ""
IMG_DIR = ""
OUTPUT_DIR = ""
NUMBER_IMAGES = 250


def main() -> None:
    """
    Get images list and annotations list from input dir.
    Update new images and annotations.
    Save images and annotations in output dir.
    """
    img_paths, annos = get_dataset(LABEL_DIR, IMG_DIR)
    for index in range(NUMBER_IMAGES):
        idxs = random.sample(range(len(annos)), 4)
        new_image, new_annos, path = update_image_and_anno(
            img_paths,
            annos,
            idxs,
            OUTPUT_SIZE,
            SCALE_RANGE,
            filter_scale=FILTER_TINY_SCALE,
        )

        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        letter_code = random_chars(32)
        file_name = path.split(os.sep)[-1].rsplit(".", 1)[0]
        file_root = f"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
        cv2.imwrite(f"{file_root}.jpg", new_image, [cv2.IMWRITE_JPEG_QUALITY, 85])
        print(f"Succeeded {index + 1}/{NUMBER_IMAGES} with {file_name}")
        annos_list = []
        for anno in new_annos:
            width = anno[3] - anno[1]
            height = anno[4] - anno[2]
            x_center = anno[1] + width / 2
            y_center = anno[2] + height / 2
            obj = f"{anno[0]} {x_center} {y_center} {width} {height}"
            annos_list.append(obj)
        with open(f"{file_root}.txt", "w") as outfile:
            outfile.write("\n".join(line for line in annos_list))


def get_dataset(label_dir: str, img_dir: str) -> tuple[list, list]:
    """
    - label_dir <type: str>: Path to label include annotation of images
    - img_dir <type: str>: Path to folder contain images
    Return <type: list>: List of images path and labels
    """
    img_paths = []
    labels = []
    for label_file in glob.glob(os.path.join(label_dir, "*.txt")):
        label_name = label_file.split(os.sep)[-1].rsplit(".", 1)[0]
        with open(label_file) as in_file:
            obj_lists = in_file.readlines()
        img_path = os.path.join(img_dir, f"{label_name}.jpg")

        boxes = []
        for obj_list in obj_lists:
            obj = obj_list.rstrip("\n").split(" ")
            xmin = float(obj[1]) - float(obj[3]) / 2
            ymin = float(obj[2]) - float(obj[4]) / 2
            xmax = float(obj[1]) + float(obj[3]) / 2
            ymax = float(obj[2]) + float(obj[4]) / 2

            boxes.append([int(obj[0]), xmin, ymin, xmax, ymax])
        if not boxes:
            continue
        img_paths.append(img_path)
        labels.append(boxes)
    return img_paths, labels


def update_image_and_anno(
    all_img_list: list,
    all_annos: list,
    idxs: list[int],
    output_size: tuple[int, int],
    scale_range: tuple[float, float],
    filter_scale: float = 0.0,
) -> tuple[list, list, str]:
    """
    - all_img_list <type: list>: list of all images
    - all_annos <type: list>: list of all annotations of specific image
    - idxs <type: list>: index of image in list
    - output_size <type: tuple>: size of output image (Height, Width)
    - scale_range <type: tuple>: range of scale image
    - filter_scale <type: float>: the condition of downscale image and bounding box
    Return:
        - output_img <type: narray>: image after resize
        - new_anno <type: list>: list of new annotation after scale
        - path[0] <type: string>: get the name of image file
    """
    output_img = np.zeros([output_size[0], output_size[1], 3], dtype=np.uint8)
    scale_x = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    scale_y = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    divid_point_x = int(scale_x * output_size[1])
    divid_point_y = int(scale_y * output_size[0])

    new_anno = []
    path_list = []
    for i, index in enumerate(idxs):
        path = all_img_list[index]
        path_list.append(path)
        img_annos = all_annos[index]
        img = cv2.imread(path)
        if i == 0:  # top-left
            img = cv2.resize(img, (divid_point_x, divid_point_y))
            output_img[:divid_point_y, :divid_point_x, :] = img
            for bbox in img_annos:
                xmin = bbox[1] * scale_x
                ymin = bbox[2] * scale_y
                xmax = bbox[3] * scale_x
                ymax = bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
        elif i == 1:  # top-right
            img = cv2.resize(img, (output_size[1] - divid_point_x, divid_point_y))
            output_img[:divid_point_y, divid_point_x : output_size[1], :] = img
            for bbox in img_annos:
                xmin = scale_x + bbox[1] * (1 - scale_x)
                ymin = bbox[2] * scale_y
                xmax = scale_x + bbox[3] * (1 - scale_x)
                ymax = bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
        elif i == 2:  # bottom-left
            img = cv2.resize(img, (divid_point_x, output_size[0] - divid_point_y))
            output_img[divid_point_y : output_size[0], :divid_point_x, :] = img
            for bbox in img_annos:
                xmin = bbox[1] * scale_x
                ymin = scale_y + bbox[2] * (1 - scale_y)
                xmax = bbox[3] * scale_x
                ymax = scale_y + bbox[4] * (1 - scale_y)
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
        else:  # bottom-right
            img = cv2.resize(
                img, (output_size[1] - divid_point_x, output_size[0] - divid_point_y)
            )
            output_img[
                divid_point_y : output_size[0], divid_point_x : output_size[1], :
            ] = img
            for bbox in img_annos:
                xmin = scale_x + bbox[1] * (1 - scale_x)
                ymin = scale_y + bbox[2] * (1 - scale_y)
                xmax = scale_x + bbox[3] * (1 - scale_x)
                ymax = scale_y + bbox[4] * (1 - scale_y)
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax])

    # Remove bounding box small than scale of filter
    if filter_scale > 0:
        new_anno = [
            anno
            for anno in new_anno
            if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
        ]

    return output_img, new_anno, path_list[0]


def random_chars(number_char: int) -> str:
    """
    Automatic generate random 32 characters.
    Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    >>> len(random_chars(32))
    32
    """
    assert number_char > 1, "The number of character should greater than 1"
    letter_code = ascii_lowercase + digits
    return "".join(random.choice(letter_code) for _ in range(number_char))


if __name__ == "__main__":
    main()
    print("DONE ✅")


================================================
FILE: computer_vision/pooling_functions.py
================================================
# Source : https://computersciencewiki.org/index.php/Max-pooling_/_Pooling
# Importing the libraries
import numpy as np
from PIL import Image


# Maxpooling Function
def maxpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
    """
    This function is used to perform maxpooling on the input array of 2D matrix(image)
    Args:
        arr: numpy array
        size: size of pooling matrix
        stride: the number of pixels shifts over the input matrix
    Returns:
        numpy array of maxpooled matrix
    Sample Input Output:
    >>> maxpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)
    array([[ 6.,  8.],
           [14., 16.]])
    >>> maxpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
    array([[241., 180.],
           [241., 157.]])
    """
    arr = np.array(arr)
    if arr.shape[0] != arr.shape[1]:
        raise ValueError("The input array is not a square matrix")
    i = 0
    j = 0
    mat_i = 0
    mat_j = 0

    # compute the shape of the output matrix
    maxpool_shape = (arr.shape[0] - size) // stride + 1
    # initialize the output matrix with zeros of shape maxpool_shape
    updated_arr = np.zeros((maxpool_shape, maxpool_shape))

    while i < arr.shape[0]:
        if i + size > arr.shape[0]:
            # if the end of the matrix is reached, break
            break
        while j < arr.shape[1]:
            # if the end of the matrix is reached, break
            if j + size > arr.shape[1]:
                break
            # compute the maximum of the pooling matrix
            updated_arr[mat_i][mat_j] = np.max(arr[i : i + size, j : j + size])
            # shift the pooling matrix by stride of column pixels
            j += stride
            mat_j += 1

        # shift the pooling matrix by stride of row pixels
        i += stride
        mat_i += 1

        # reset the column index to 0
        j = 0
        mat_j = 0

    return updated_arr


# Averagepooling Function
def avgpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
    """
    This function is used to perform avgpooling on the input array of 2D matrix(image)
    Args:
        arr: numpy array
        size: size of pooling matrix
        stride: the number of pixels shifts over the input matrix
    Returns:
        numpy array of avgpooled matrix
    Sample Input Output:
    >>> avgpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)
    array([[ 3.,  5.],
           [11., 13.]])
    >>> avgpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
    array([[161., 102.],
           [114.,  69.]])
    """
    arr = np.array(arr)
    if arr.shape[0] != arr.shape[1]:
        raise ValueError("The input array is not a square matrix")
    i = 0
    j = 0
    mat_i = 0
    mat_j = 0

    # compute the shape of the output matrix
    avgpool_shape = (arr.shape[0] - size) // stride + 1
    # initialize the output matrix with zeros of shape avgpool_shape
    updated_arr = np.zeros((avgpool_shape, avgpool_shape))

    while i < arr.shape[0]:
        # if the end of the matrix is reached, break
        if i + size > arr.shape[0]:
            break
        while j < arr.shape[1]:
            # if the end of the matrix is reached, break
            if j + size > arr.shape[1]:
                break
            # compute the average of the pooling matrix
            updated_arr[mat_i][mat_j] = int(np.average(arr[i : i + size, j : j + size]))
            # shift the pooling matrix by stride of column pixels
            j += stride
            mat_j += 1

        # shift the pooling matrix by stride of row pixels
        i += stride
        mat_i += 1
        # reset the column index to 0
        j = 0
        mat_j = 0

    return updated_arr


# Main Function
if __name__ == "__main__":
    from doctest import testmod

    testmod(name="avgpooling", verbose=True)

    # Loading the image
    image = Image.open("path_to_image")

    # Converting the image to numpy array and maxpooling, displaying the result
    # Ensure that the image is a square matrix

    Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

    # Converting the image to numpy array and averagepooling, displaying the result
    # Ensure that the image is a square matrix

    Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()


================================================
FILE: conversions/README.md
================================================
# Conversion

Conversion programs convert a type of data, a number from a numerical base or unit into one of another type, base or unit, e.g. binary to decimal, integer to string or foot to meters.

* <https://en.wikipedia.org/wiki/Data_conversion>
* <https://en.wikipedia.org/wiki/Transcoding>


================================================
FILE: conversions/__init__.py
================================================


================================================
FILE: conversions/astronomical_length_scale_conversion.py
================================================
"""
Conversion of length units.
Available Units:
Metre, Kilometre, Megametre, Gigametre,
Terametre, Petametre, Exametre, Zettametre, Yottametre

USAGE :
-> Import this file into their respective project.
-> Use the function length_conversion() for conversion of length units.
-> Parameters :
    -> value : The number of from units you want to convert
    -> from_type : From which type you want to convert
    -> to_type : To which type you want to convert

REFERENCES :
-> Wikipedia reference: https://en.wikipedia.org/wiki/Meter
-> Wikipedia reference: https://en.wikipedia.org/wiki/Kilometer
-> Wikipedia reference: https://en.wikipedia.org/wiki/Orders_of_magnitude_(length)
"""

UNIT_SYMBOL = {
    "meter": "m",
    "kilometer": "km",
    "megametre": "Mm",
    "gigametre": "Gm",
    "terametre": "Tm",
    "petametre": "Pm",
    "exametre": "Em",
    "zettametre": "Zm",
    "yottametre": "Ym",
}
# Exponent of the factor(meter)
METRIC_CONVERSION = {
    "m": 0,
    "km": 3,
    "Mm": 6,
    "Gm": 9,
    "Tm": 12,
    "Pm": 15,
    "Em": 18,
    "Zm": 21,
    "Ym": 24,
}


def length_conversion(value: float, from_type: str, to_type: str) -> float:
    """
    Conversion between astronomical length units.

    >>> length_conversion(1, "meter", "kilometer")
    0.001
    >>> length_conversion(1, "meter", "megametre")
    1e-06
    >>> length_conversion(1, "gigametre", "meter")
    1000000000
    >>> length_conversion(1, "gigametre", "terametre")
    0.001
    >>> length_conversion(1, "petametre", "terametre")
    1000
    >>> length_conversion(1, "petametre", "exametre")
    0.001
    >>> length_conversion(1, "terametre", "zettametre")
    1e-09
    >>> length_conversion(1, "yottametre", "zettametre")
    1000
    >>> length_conversion(4, "wrongUnit", "inch")
    Traceback (most recent call last):
      ...
    ValueError: Invalid 'from_type' value: 'wrongUnit'.
    Conversion abbreviations are: m, km, Mm, Gm, Tm, Pm, Em, Zm, Ym
    """

    from_sanitized = from_type.lower().strip("s")
    to_sanitized = to_type.lower().strip("s")

    from_sanitized = UNIT_SYMBOL.get(from_sanitized, from_sanitized)
    to_sanitized = UNIT_SYMBOL.get(to_sanitized, to_sanitized)

    if from_sanitized not in METRIC_CONVERSION:
        msg = (
            f"Invalid 'from_type' value: {from_type!r}.\n"
            f"Conversion abbreviations are: {', '.join(METRIC_CONVERSION)}"
        )
        raise ValueError(msg)
    if to_sanitized not in METRIC_CONVERSION:
        msg = (
            f"Invalid 'to_type' value: {to_type!r}.\n"
            f"Conversion abbreviations are: {', '.join(METRIC_CONVERSION)}"
        )
        raise ValueError(msg)
    from_exponent = METRIC_CONVERSION[from_sanitized]
    to_exponent = METRIC_CONVERSION[to_sanitized]
    exponent = 1

    if from_exponent > to_exponent:
        exponent = from_exponent - to_exponent
    else:
        exponent = -(to_exponent - from_exponent)

    return value * pow(10, exponent)


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: conversions/binary_to_decimal.py
================================================
def bin_to_decimal(bin_string: str) -> int:
    """
    Convert a binary value to its decimal equivalent

    >>> bin_to_decimal("101")
    5
    >>> bin_to_decimal(" 1010   ")
    10
    >>> bin_to_decimal("-11101")
    -29
    >>> bin_to_decimal("0")
    0
    >>> bin_to_decimal("a")
    Traceback (most recent call last):
        ...
    ValueError: Non-binary value was passed to the function
    >>> bin_to_decimal("")
    Traceback (most recent call last):
        ...
    ValueError: Empty string was passed to the function
    >>> bin_to_decimal("39")
    Traceback (most recent call last):
        ...
    ValueError: Non-binary value was passed to the function
    """
    bin_string = str(bin_string).strip()
    if not bin_string:
        raise ValueError("Empty string was passed to the function")
    is_negative = bin_string[0] == "-"
    if is_negative:
        bin_string = bin_string[1:]
    if not all(char in "01" for char in bin_string):
        raise ValueError("Non-binary value was passed to the function")
    decimal_number = 0
    for char in bin_string:
        decimal_number = 2 * decimal_number + int(char)
    return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: conversions/binary_to_hexadecimal.py
================================================
BITS_TO_HEX = {
    "0000": "0",
    "0001": "1",
    "0010": "2",
    "0011": "3",
    "0100": "4",
    "0101": "5",
    "0110": "6",
    "0111": "7",
    "1000": "8",
    "1001": "9",
    "1010": "a",
    "1011": "b",
    "1100": "c",
    "1101": "d",
    "1110": "e",
    "1111": "f",
}


def bin_to_hexadecimal(binary_str: str) -> str:
    """
    Converting a binary string into hexadecimal using Grouping Method

    >>> bin_to_hexadecimal('101011111')
    '0x15f'
    >>> bin_to_hexadecimal(' 1010   ')
    '0x0a'
    >>> bin_to_hexadecimal('-11101')
    '-0x1d'
    >>> bin_to_hexadecimal('a')
    Traceback (most recent call last):
        ...
    ValueError: Non-binary value was passed to the function
    >>> bin_to_hexadecimal('')
    Traceback (most recent call last):
        ...
    ValueError: Empty string was passed to the function
    """
    # Sanitising parameter
    binary_str = str(binary_str).strip()

    # Exceptions
    if not binary_str:
        raise ValueError("Empty string was passed to the function")
    is_negative = binary_str[0] == "-"
    binary_str = binary_str[1:] if is_negative else binary_str
    if not all(char in "01" for char in binary_str):
        raise ValueError("Non-binary value was passed to the function")

    binary_str = (
        "0" * (4 * (divmod(len(binary_str), 4)[0] + 1) - len(binary_str)) + binary_str
    )

    hexadecimal = []
    for x in range(0, len(binary_str), 4):
        hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]])
    hexadecimal_str = "0x" + "".join(hexadecimal)

    return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: conversions/binary_to_octal.py
================================================
"""
The function below will convert any binary string to the octal equivalent.

>>> bin_to_octal("1111")
'17'

>>> bin_to_octal("101010101010011")
'52523'

>>> bin_to_octal("")
Traceback (most recent call last):
    ...
ValueError: Empty string was passed to the function
>>> bin_to_octal("a-1")
Traceback (most recent call last):
    ...
ValueError: Non-binary value was passed to the function
"""


def bin_to_octal(bin_string: str) -> str:
    if not all(char in "01" for char in bin_string):
        raise ValueError("Non-binary value was passed to the function")
    if not bin_string:
        raise ValueError("Empty string was passed to the function")
    oct_string = ""
    while len(bin_string) % 3 != 0:
        bin_string = "0" + bin_string
    bin_string_in_3_list = [
        bin_string[index : index + 3]
        for index in range(len(bin_string))
        if index % 3 == 0
    ]
    for bin_group in bin_string_in_3_list:
        oct_val = 0
        for index, val in enumerate(bin_group):
            oct_val += int(2 ** (2 - index) * int(val))
        oct_string += str(oct_val)
    return oct_string


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: conversions/convert_number_to_words.py
================================================
from enum import Enum
from typing import Literal


class NumberingSystem(Enum):
    SHORT = (
        (15, "quadrillion"),
        (12, "trillion"),
        (9, "billion"),
        (6, "million"),
        (3, "thousand"),
        (2, "hundred"),
    )

    LONG = (
        (15, "billiard"),
        (9, "milliard"),
        (6, "million"),
        (3, "thousand"),
        (2, "hundred"),
    )

    INDIAN = (
        (14, "crore crore"),
        (12, "lakh crore"),
        (7, "crore"),
        (5, "lakh"),
        (3, "thousand"),
        (2, "hundred"),
    )

    @classmethod
    def max_value(cls, system: str) -> int:
        """
        Gets the max value supported by the given number system.

        >>> NumberingSystem.max_value("short") == 10**18 - 1
        True
        >>> NumberingSystem.max_value("long") == 10**21 - 1
        True
        >>> NumberingSystem.max_value("indian") == 10**19 - 1
        True
        """
        match system_enum := cls[system.upper()]:
            case cls.SHORT:
                max_exp = system_enum.value[0][0] + 3
            case cls.LONG:
                max_exp = system_enum.value[0][0] + 6
            case cls.INDIAN:
                max_exp = 19
            case _:
                raise ValueError("Invalid numbering system")
        return 10**max_exp - 1


class NumberWords(Enum):
    ONES = {  # noqa: RUF012
        0: "",
        1: "one",
        2: "two",
        3: "three",
        4: "four",
        5: "five",
        6: "six",
        7: "seven",
        8: "eight",
        9: "nine",
    }

    TEENS = {  # noqa: RUF012
        0: "ten",
        1: "eleven",
        2: "twelve",
        3: "thirteen",
        4: "fourteen",
        5: "fifteen",
        6: "sixteen",
        7: "seventeen",
        8: "eighteen",
        9: "nineteen",
    }

    TENS = {  # noqa: RUF012
        2: "twenty",
        3: "thirty",
        4: "forty",
        5: "fifty",
        6: "sixty",
        7: "seventy",
        8: "eighty",
        9: "ninety",
    }


def convert_small_number(num: int) -> str:
    """
    Converts small, non-negative integers with irregular constructions in English (i.e.,
    numbers under 100) into words.

    >>> convert_small_number(0)
    'zero'
    >>> convert_small_number(5)
    'five'
    >>> convert_small_number(10)
    'ten'
    >>> convert_small_number(15)
    'fifteen'
    >>> convert_small_number(20)
    'twenty'
    >>> convert_small_number(25)
    'twenty-five'
    >>> convert_small_number(-1)
    Traceback (most recent call last):
    ...
    ValueError: This function only accepts non-negative integers
    >>> convert_small_number(123)
    Traceback (most recent call last):
    ...
    ValueError: This function only converts numbers less than 100
    """
    if num < 0:
        raise ValueError("This function only accepts non-negative integers")
    if num >= 100:
        raise ValueError("This function only converts numbers less than 100")
    tens, ones = divmod(num, 10)
    if tens == 0:
        return NumberWords.ONES.value[ones] or "zero"
    if tens == 1:
        return NumberWords.TEENS.value[ones]
    return (
        NumberWords.TENS.value[tens]
        + ("-" if NumberWords.ONES.value[ones] else "")
        + NumberWords.ONES.value[ones]
    )


def convert_number(
    num: int, system: Literal["short", "long", "indian"] = "short"
) -> str:
    """
    Converts an integer to English words.

    :param num: The integer to be converted
    :param system: The numbering system (short, long, or Indian)

    >>> convert_number(0)
    'zero'
    >>> convert_number(1)
    'one'
    >>> convert_number(100)
    'one hundred'
    >>> convert_number(-100)
    'negative one hundred'
    >>> convert_number(123_456_789_012_345) # doctest: +NORMALIZE_WHITESPACE
    'one hundred twenty-three trillion four hundred fifty-six billion
    seven hundred eighty-nine million twelve thousand three hundred forty-five'
    >>> convert_number(123_456_789_012_345, "long") # doctest: +NORMALIZE_WHITESPACE
    'one hundred twenty-three thousand four hundred fifty-six milliard
    seven hundred eighty-nine million twelve thousand three hundred forty-five'
    >>> convert_number(12_34_56_78_90_12_345, "indian") # doctest: +NORMALIZE_WHITESPACE
    'one crore crore twenty-three lakh crore
    forty-five thousand six hundred seventy-eight crore
    ninety lakh twelve thousand three hundred forty-five'
    >>> convert_number(10**18)
    Traceback (most recent call last):
    ...
    ValueError: Input number is too large
    >>> convert_number(10**21, "long")
    Traceback (most recent call last):
    ...
    ValueError: Input number is too large
    >>> convert_number(10**19, "indian")
    Traceback (most recent call last):
    ...
    ValueError: Input number is too large
    """
    word_groups = []

    if num < 0:
        word_groups.append("negative")
        num *= -1

    if num > NumberingSystem.max_value(system):
        raise ValueError("Input number is too large")

    for power, unit in NumberingSystem[system.upper()].value:
        digit_group, num = divmod(num, 10**power)
        if digit_group > 0:
            word_group = (
                convert_number(digit_group, system)
                if digit_group >= 100
                else convert_small_number(digit_group)
            )
            word_groups.append(f"{word_group} {unit}")
    if num > 0 or not word_groups:  # word_groups is only empty if input num was 0
        word_groups.append(convert_small_number(num))
    return " ".join(word_groups)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print(f"{convert_number(123456789) = }")


================================================
FILE: conversions/decimal_to_any.py
================================================
"""Convert a positive Decimal Number to Any Other Representation"""

from string import ascii_uppercase

ALPHABET_VALUES = {str(ord(c) - 55): c for c in ascii_uppercase}


def decimal_to_any(num: int, base: int) -> str:
    """
    Convert a positive integer to another base as str.
    >>> decimal_to_any(0, 2)
    '0'
    >>> decimal_to_any(5, 4)
    '11'
    >>> decimal_to_any(20, 3)
    '202'
    >>> decimal_to_any(58, 16)
    '3A'
    >>> decimal_to_any(243, 17)
    'E5'
    >>> decimal_to_any(34923, 36)
    'QY3'
    >>> decimal_to_any(10, 11)
    'A'
    >>> decimal_to_any(16, 16)
    '10'
    >>> decimal_to_any(36, 36)
    '10'
    >>> # negatives will error
    >>> decimal_to_any(-45, 8)  # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    ValueError: parameter must be positive int
    >>> # floats will error
    >>> decimal_to_any(34.4, 6) # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    TypeError: int() can't convert non-string with explicit base
    >>> # a float base will error
    >>> decimal_to_any(5, 2.5) # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    TypeError: 'float' object cannot be interpreted as an integer
    >>> # a str base will error
    >>> decimal_to_any(10, '16') # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    TypeError: 'str' object cannot be interpreted as an integer
    >>> # a base less than 2 will error
    >>> decimal_to_any(7, 0) # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    ValueError: base must be >= 2
    >>> # a base greater than 36 will error
    >>> decimal_to_any(34, 37) # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    ValueError: base must be <= 36
    """
    if isinstance(num, float):
        raise TypeError("int() can't convert non-string with explicit base")
    if num < 0:
        raise ValueError("parameter must be positive int")
    if isinstance(base, str):
        raise TypeError("'str' object cannot be interpreted as an integer")
    if isinstance(base, float):
        raise TypeError("'float' object cannot be interpreted as an integer")
    if base in (0, 1):
        raise ValueError("base must be >= 2")
    if base > 36:
        raise ValueError("base must be <= 36")
    new_value = ""
    mod = 0
    div = 0
    while div != 1:
        div, mod = divmod(num, base)
        if base >= 11 and 9 < mod < 36:
            actual_value = ALPHABET_VALUES[str(mod)]
        else:
            actual_value = str(mod)
        new_value += actual_value
        div = num // base
        num = div
        if div == 0:
            return str(new_value[::-1])
        elif div == 1:
            new_value += str(div)
            return str(new_value[::-1])

    return new_value[::-1]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    for base in range(2, 37):
        for num in range(1000):
            assert int(decimal_to_any(num, base), base) == num, (
                num,
                base,
                decimal_to_any(num, base),
                int(decimal_to_any(num, base), base),
            )


================================================
FILE: conversions/decimal_to_binary.py
================================================
"""Convert a Decimal Number to a Binary Number."""


def decimal_to_binary_iterative(num: int) -> str:
    """
    Convert an Integer Decimal Number to a Binary Number as str.
    >>> decimal_to_binary_iterative(0)
    '0b0'
    >>> decimal_to_binary_iterative(2)
    '0b10'
    >>> decimal_to_binary_iterative(7)
    '0b111'
    >>> decimal_to_binary_iterative(35)
    '0b100011'
    >>> # negatives work too
    >>> decimal_to_binary_iterative(-2)
    '-0b10'
    >>> # other floats will error
    >>> decimal_to_binary_iterative(16.16) # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    TypeError: 'float' object cannot be interpreted as an integer
    >>> # strings will error as well
    >>> decimal_to_binary_iterative('0xfffff') # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    TypeError: 'str' object cannot be interpreted as an integer
    """

    if isinstance(num, float):
        raise TypeError("'float' object cannot be interpreted as an integer")
    if isinstance(num, str):
        raise TypeError("'str' object cannot be interpreted as an integer")

    if num == 0:
        return "0b0"

    negative = False

    if num < 0:
        negative = True
        num = -num

    binary: list[int] = []
    while num > 0:
        binary.insert(0, num % 2)
        num >>= 1

    if negative:
        return "-0b" + "".join(str(e) for e in binary)

    return "0b" + "".join(str(e) for e in binary)


def decimal_to_binary_recursive_helper(decimal: int) -> str:
    """
    Take a positive integer value and return its binary equivalent.
    >>> decimal_to_binary_recursive_helper(1000)
    '1111101000'
    >>> decimal_to_binary_recursive_helper("72")
    '1001000'
    >>> decimal_to_binary_recursive_helper("number")
    Traceback (most recent call last):
        ...
    ValueError: invalid literal for int() with base 10: 'number'
    """
    decimal = int(decimal)
    if decimal in (0, 1):  # Exit cases for the recursion
        return str(decimal)
    div, mod = divmod(decimal, 2)
    return decimal_to_binary_recursive_helper(div) + str(mod)


def decimal_to_binary_recursive(number: str) -> str:
    """
    Take an integer value and raise ValueError for wrong inputs,
    call the function above and return the output with prefix "0b" & "-0b"
    for positive and negative integers respectively.
    >>> decimal_to_binary_recursive(0)
    '0b0'
    >>> decimal_to_binary_recursive(40)
    '0b101000'
    >>> decimal_to_binary_recursive(-40)
    '-0b101000'
    >>> decimal_to_binary_recursive(40.8)
    Traceback (most recent call last):
        ...
    ValueError: Input value is not an integer
    >>> decimal_to_binary_recursive("forty")
    Traceback (most recent call last):
        ...
    ValueError: Input value is not an integer
    """
    number = str(number).strip()
    if not number:
        raise ValueError("No input value was provided")
    negative = "-" if number.startswith("-") else ""
    number = number.lstrip("-")
    if not number.isnumeric():
        raise ValueError("Input value is not an integer")
    return f"{negative}0b{decimal_to_binary_recursive_helper(int(number))}"


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print(decimal_to_binary_recursive(input("Input a decimal number: ")))


================================================
FILE: conversions/decimal_to_hexadecimal.py
================================================
"""Convert Base 10 (Decimal) Values to Hexadecimal Representations"""

# set decimal value for each hexadecimal digit
values = {
    0: "0",
    1: "1",
    2: "2",
    3: "3",
    4: "4",
    5: "5",
    6: "6",
    7: "7",
    8: "8",
    9: "9",
    10: "a",
    11: "b",
    12: "c",
    13: "d",
    14: "e",
    15: "f",
}


def decimal_to_hexadecimal(decimal: float) -> str:
    """
    take integer decimal value, return hexadecimal representation as str beginning
    with 0x
    >>> decimal_to_hexadecimal(5)
    '0x5'
    >>> decimal_to_hexadecimal(15)
    '0xf'
    >>> decimal_to_hexadecimal(37)
    '0x25'
    >>> decimal_to_hexadecimal(255)
    '0xff'
    >>> decimal_to_hexadecimal(4096)
    '0x1000'
    >>> decimal_to_hexadecimal(999098)
    '0xf3eba'
    >>> # negatives work too
    >>> decimal_to_hexadecimal(-256)
    '-0x100'
    >>> # floats are acceptable if equivalent to an int
    >>> decimal_to_hexadecimal(17.0)
    '0x11'
    >>> # other floats will error
    >>> decimal_to_hexadecimal(16.16) # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    AssertionError
    >>> # strings will error as well
    >>> decimal_to_hexadecimal('0xfffff') # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    AssertionError
    >>> # results are the same when compared to Python's default hex function
    >>> decimal_to_hexadecimal(-256) == hex(-256)
    True
    """
    assert isinstance(decimal, (int, float))
    assert decimal == int(decimal)
    decimal = int(decimal)
    hexadecimal = ""
    negative = False
    if decimal < 0:
        negative = True
        decimal *= -1
    while decimal > 0:
        decimal, remainder = divmod(decimal, 16)
        hexadecimal = values[remainder] + hexadecimal
    hexadecimal = "0x" + hexadecimal
    if negative:
        hexadecimal = "-" + hexadecimal
    return hexadecimal


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: conversions/decimal_to_octal.py
================================================
"""Convert a Decimal Number to an Octal Number."""

import math

# Modified from:
# https://github.com/TheAlgorithms/Javascript/blob/master/Conversions/DecimalToOctal.js


def decimal_to_octal(num: int) -> str:
    """Convert a Decimal Number to an Octal Number.

    >>> all(decimal_to_octal(i) == oct(i) for i
    ...     in (0, 2, 8, 64, 65, 216, 255, 256, 512))
    True
    """
    octal = 0
    counter = 0
    while num > 0:
        remainder = num % 8
        octal = octal + (remainder * math.floor(math.pow(10, counter)))
        counter += 1
        num = math.floor(num / 8)  # basically /= 8 without remainder if any
        # This formatting removes trailing '.0' from `octal`.
    return f"0o{int(octal)}"


def main() -> None:
    """Print octal equivalents of decimal numbers."""
    print("\n2 in octal is:")
    print(decimal_to_octal(2))  # = 2
    print("\n8 in octal is:")
    print(decimal_to_octal(8))  # = 10
    print("\n65 in octal is:")
    print(decimal_to_octal(65))  # = 101
    print("\n216 in octal is:")
    print(decimal_to_octal(216))  # = 330
    print("\n512 in octal is:")
    print(decimal_to_octal(512))  # = 1000
    print("\n")


if __name__ == "__main__":
    main()


================================================
FILE: conversions/energy_conversions.py
================================================
"""
Conversion of energy units.

Available units: joule, kilojoule, megajoule, gigajoule,\
      wattsecond, watthour, kilowatthour, newtonmeter, calorie_nutr,\
          kilocalorie_nutr, electronvolt, britishthermalunit_it, footpound

USAGE :
-> Import this file into their respective project.
-> Use the function energy_conversion() for conversion of energy units.
-> Parameters :
    -> from_type : From which type you want to convert
    -> to_type : To which type you want to convert
    -> value : the value which you want to convert

REFERENCES :
-> Wikipedia reference: https://en.wikipedia.org/wiki/Units_of_energy
-> Wikipedia reference: https://en.wikipedia.org/wiki/Joule
-> Wikipedia reference: https://en.wikipedia.org/wiki/Kilowatt-hour
-> Wikipedia reference: https://en.wikipedia.org/wiki/Newton-metre
-> Wikipedia reference: https://en.wikipedia.org/wiki/Calorie
-> Wikipedia reference: https://en.wikipedia.org/wiki/Electronvolt
-> Wikipedia reference: https://en.wikipedia.org/wiki/British_thermal_unit
-> Wikipedia reference: https://en.wikipedia.org/wiki/Foot-pound_(energy)
-> Unit converter reference: https://www.unitconverters.net/energy-converter.html
"""

ENERGY_CONVERSION: dict[str, float] = {
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.602_176_634e-19,
    "britishthermalunit_it": 1_055.055_85,
    "footpound": 1.355_818,
}


def energy_conversion(from_type: str, to_type: str, value: float) -> float:
    """
    Conversion of energy units.
    >>> energy_conversion("joule", "joule", 1)
    1.0
    >>> energy_conversion("joule", "kilojoule", 1)
    0.001
    >>> energy_conversion("joule", "megajoule", 1)
    1e-06
    >>> energy_conversion("joule", "gigajoule", 1)
    1e-09
    >>> energy_conversion("joule", "wattsecond", 1)
    1.0
    >>> energy_conversion("joule", "watthour", 1)
    0.0002777777777777778
    >>> energy_conversion("joule", "kilowatthour", 1)
    2.7777777777777776e-07
    >>> energy_conversion("joule", "newtonmeter", 1)
    1.0
    >>> energy_conversion("joule", "calorie_nutr", 1)
    0.00023884589662749592
    >>> energy_conversion("joule", "kilocalorie_nutr", 1)
    2.388458966274959e-07
    >>> energy_conversion("joule", "electronvolt", 1)
    6.241509074460763e+18
    >>> energy_conversion("joule", "britishthermalunit_it", 1)
    0.0009478171226670134
    >>> energy_conversion("joule", "footpound", 1)
    0.7375621211696556
    >>> energy_conversion("joule", "megajoule", 1000)
    0.001
    >>> energy_conversion("calorie_nutr", "kilocalorie_nutr", 1000)
    1.0
    >>> energy_conversion("kilowatthour", "joule", 10)
    36000000.0
    >>> energy_conversion("britishthermalunit_it", "footpound", 1)
    778.1692306784539
    >>> energy_conversion("watthour", "joule", "a") # doctest: +ELLIPSIS
    Traceback (most recent call last):
      ...
    TypeError: unsupported operand type(s) for /: 'str' and 'float'
    >>> energy_conversion("wrongunit", "joule", 1) # doctest: +ELLIPSIS
    Traceback (most recent call last):
      ...
    ValueError: Incorrect 'from_type' or 'to_type' value: 'wrongunit', 'joule'
    Valid values are: joule, ... footpound
    >>> energy_conversion("joule", "wrongunit", 1) # doctest: +ELLIPSIS
    Traceback (most recent call last):
      ...
    ValueError: Incorrect 'from_type' or 'to_type' value: 'joule', 'wrongunit'
    Valid values are: joule, ... footpound
    >>> energy_conversion("123", "abc", 1) # doctest: +ELLIPSIS
    Traceback (most recent call last):
      ...
    ValueError: Incorrect 'from_type' or 'to_type' value: '123', 'abc'
    Valid values are: joule, ... footpound
    """
    if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
        msg = (
            f"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
            f"Valid values are: {', '.join(ENERGY_CONVERSION)}"
        )
        raise ValueError(msg)
    return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: conversions/excel_title_to_column.py
================================================
def excel_title_to_column(column_title: str) -> int:
    """
    Given a string column_title that represents
    the column title in an Excel sheet, return
    its corresponding column number.

    >>> excel_title_to_column("A")
    1
    >>> excel_title_to_column("B")
    2
    >>> excel_title_to_column("AB")
    28
    >>> excel_title_to_column("Z")
    26
    """
    assert column_title.isupper()
    answer = 0
    index = len(column_title) - 1
    power = 0

    while index >= 0:
        value = (ord(column_title[index]) - 64) * pow(26, power)
        answer += value
        power += 1
        index -= 1

    return answer


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: conversions/hex_to_bin.py
================================================
def hex_to_bin(hex_num: str) -> int:
    """
    Convert a hexadecimal value to its binary equivalent
    #https://stackoverflow.com/questions/1425493/convert-hex-to-binary
    Here, we have used the bitwise right shift operator: >>
    Shifts the bits of the number to the right and fills 0 on voids left as a result.
    Similar effect as of dividing the number with some power of two.
    Example:
    a = 10
    a >> 1 = 5

    >>> hex_to_bin("AC")
    10101100
    >>> hex_to_bin("9A4")
    100110100100
    >>> hex_to_bin("   12f   ")
    100101111
    >>> hex_to_bin("FfFf")
    1111111111111111
    >>> hex_to_bin("-fFfF")
    -1111111111111111
    >>> hex_to_bin("F-f")
    Traceback (most recent call last):
        ...
    ValueError: Invalid value was passed to the function
    >>> hex_to_bin("")
    Traceback (most recent call last):
        ...
    ValueError: No value was passed to the function
    """

    hex_num = hex_num.strip()
    if not hex_num:
        raise ValueError("No value was passed to the function")

    is_negative = hex_num[0] == "-"
    if is_negative:
        hex_num = hex_num[1:]

    try:
        int_num = int(hex_num, 16)
    except ValueError:
        raise ValueError("Invalid value was passed to the function")

    bin_str = ""
    while int_num > 0:
        bin_str = str(int_num % 2) + bin_str
        int_num >>= 1

    return int(("-" + bin_str) if is_negative else bin_str)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: conversions/hexadecimal_to_decimal.py
================================================
hex_table = {hex(i)[2:]: i for i in range(16)}  # Use [:2] to strip off the leading '0x'


def hex_to_decimal(hex_string: str) -> int:
    """
    Convert a hexadecimal value to its decimal equivalent
    #https://www.programiz.com/python-programming/methods/built-in/hex

    >>> hex_to_decimal("a")
    10
    >>> hex_to_decimal("12f")
    303
    >>> hex_to_decimal("   12f   ")
    303
    >>> hex_to_decimal("FfFf")
    65535
    >>> hex_to_decimal("-Ff")
    -255
    >>> hex_to_decimal("F-f")
    Traceback (most recent call last):
        ...
    ValueError: Non-hexadecimal value was passed to the function
    >>> hex_to_decimal("")
    Traceback (most recent call last):
        ...
    ValueError: Empty string was passed to the function
    >>> hex_to_decimal("12m")
    Traceback (most recent call last):
        ...
    ValueError: Non-hexadecimal value was passed to the function
    """
    hex_string = hex_string.strip().lower()
    if not hex_string:
        raise ValueError("Empty string was passed to the function")
    is_negative = hex_string[0] == "-"
    if is_negative:
        hex_string = hex_string[1:]
    if not all(char in hex_table for char in hex_string):
        raise ValueError("Non-hexadecimal value was passed to the function")
    decimal_number = 0
    for char in hex_string:
        decimal_number = 16 * decimal_number + hex_table[char]
    return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: conversions/ipv4_conversion.py
================================================
# https://www.geeksforgeeks.org/convert-ip-address-to-integer-and-vice-versa/


def ipv4_to_decimal(ipv4_address: str) -> int:
    """
    Convert an IPv4 address to its decimal representation.

    Args:
        ip_address: A string representing an IPv4 address (e.g., "192.168.0.1").

    Returns:
        int: The decimal representation of the IP address.

    >>> ipv4_to_decimal("192.168.0.1")
    3232235521
    >>> ipv4_to_decimal("10.0.0.255")
    167772415
    >>> ipv4_to_decimal("10.0.255")
    Traceback (most recent call last):
        ...
    ValueError: Invalid IPv4 address format
    >>> ipv4_to_decimal("10.0.0.256")
    Traceback (most recent call last):
        ...
    ValueError: Invalid IPv4 octet 256
    """

    octets = [int(octet) for octet in ipv4_address.split(".")]
    if len(octets) != 4:
        raise ValueError("Invalid IPv4 address format")

    decimal_ipv4 = 0
    for octet in octets:
        if not 0 <= octet <= 255:
            raise ValueError(f"Invalid IPv4 octet {octet}")  # noqa: EM102
        decimal_ipv4 = (decimal_ipv4 << 8) + int(octet)

    return decimal_ipv4


def alt_ipv4_to_decimal(ipv4_address: str) -> int:
    """
    >>> alt_ipv4_to_decimal("192.168.0.1")
    3232235521
    >>> alt_ipv4_to_decimal("10.0.0.255")
    167772415
    """
    return int("0x" + "".join(f"{int(i):02x}" for i in ipv4_address.split(".")), 16)


def decimal_to_ipv4(decimal_ipv4: int) -> str:
    """
    Convert a decimal representation of an IP address to its IPv4 format.

    Args:
        decimal_ipv4: An integer representing the decimal IP address.

    Returns:
        The IPv4 representation of the decimal IP address.

    >>> decimal_to_ipv4(3232235521)
    '192.168.0.1'
    >>> decimal_to_ipv4(167772415)
    '10.0.0.255'
    >>> decimal_to_ipv4(-1)
    Traceback (most recent call last):
        ...
    ValueError: Invalid decimal IPv4 address
    """

    if not (0 <= decimal_ipv4 <= 4294967295):
        raise ValueError("Invalid decimal IPv4 address")

    ip_parts = []
    for _ in range(4):
        ip_parts.append(str(decimal_ipv4 & 255))
        decimal_ipv4 >>= 8

    return ".".join(reversed(ip_parts))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: conversions/length_conversion.py
================================================
"""
Conversion of length units.
Available Units:- Metre,Kilometre,Feet,Inch,Centimeter,Yard,Foot,Mile,Millimeter

USAGE :
-> Import this file into their respective project.
-> Use the function length_conversion() for conversion of length units.
-> Parameters :
    -> value : The number of from units you want to convert
    -> from_type : From which type you want to convert
    -> to_type : To which type you want to convert

REFERENCES :
-> Wikipedia reference: https://en.wikipedia.org/wiki/Meter
-> Wikipedia reference: https://en.wikipedia.org/wiki/Kilometer
-> Wikipedia reference: https://en.wikipedia.org/wiki/Feet
-> Wikipedia reference: https://en.wikipedia.org/wiki/Inch
-> Wikipedia reference: https://en.wikipedia.org/wiki/Centimeter
-> Wikipedia reference: https://en.wikipedia.org/wiki/Yard
-> Wikipedia reference: https://en.wikipedia.org/wiki/Foot
-> Wikipedia reference: https://en.wikipedia.org/wiki/Mile
-> Wikipedia reference: https://en.wikipedia.org/wiki/Millimeter
"""

from typing import NamedTuple


class FromTo(NamedTuple):
    from_factor: float
    to_factor: float


TYPE_CONVERSION = {
    "millimeter": "mm",
    "centimeter": "cm",
    "meter": "m",
    "kilometer": "km",
    "inch": "in",
    "inche": "in",  # Trailing 's' has been stripped off
    "feet": "ft",
    "foot": "ft",
    "yard": "yd",
    "mile": "mi",
}

METRIC_CONVERSION = {
    "mm": FromTo(0.001, 1000),
    "cm": FromTo(0.01, 100),
    "m": FromTo(1, 1),
    "km": FromTo(1000, 0.001),
    "in": FromTo(0.0254, 39.3701),
    "ft": FromTo(0.3048, 3.28084),
    "yd": FromTo(0.9144, 1.09361),
    "mi": FromTo(1609.34, 0.000621371),
}


def length_conversion(value: float, from_type: str, to_type: str) -> float:
    """
    Conversion between length units.

    >>> length_conversion(4, "METER", "FEET")
    13.12336
    >>> length_conversion(4, "M", "FT")
    13.12336
    >>> length_conversion(1, "meter", "kilometer")
    0.001
    >>> length_conversion(1, "kilometer", "inch")
    39370.1
    >>> length_conversion(3, "kilometer", "mile")
    1.8641130000000001
    >>> length_conversion(2, "feet", "meter")
    0.6096
    >>> length_conversion(4, "feet", "yard")
    1.333329312
    >>> length_conversion(1, "inch", "meter")
    0.0254
    >>> length_conversion(2, "inch", "mile")
    3.15656468e-05
    >>> length_conversion(2, "centimeter", "millimeter")
    20.0
    >>> length_conversion(2, "centimeter", "yard")
    0.0218722
    >>> length_conversion(4, "yard", "meter")
    3.6576
    >>> length_conversion(4, "yard", "kilometer")
    0.0036576
    >>> length_conversion(3, "foot", "meter")
    0.9144000000000001
    >>> length_conversion(3, "foot", "inch")
    36.00001944
    >>> length_conversion(4, "mile", "kilometer")
    6.43736
    >>> length_conversion(2, "miles", "InChEs")
    126719.753468
    >>> length_conversion(3, "millimeter", "centimeter")
    0.3
    >>> length_conversion(3, "mm", "in")
    0.1181103
    >>> length_conversion(4, "wrongUnit", "inch")
    Traceback (most recent call last):
      ...
    ValueError: Invalid 'from_type' value: 'wrongUnit'.
    Conversion abbreviations are: mm, cm, m, km, in, ft, yd, mi
    """
    new_from = from_type.lower().rstrip("s")
    new_from = TYPE_CONVERSION.get(new_from, new_from)
    new_to = to_type.lower().rstrip("s")
    new_to = TYPE_CONVERSION.get(new_to, new_to)
    if new_from not in METRIC_CONVERSION:
        msg = (
            f"Invalid 'from_type' value: {from_type!r}.\n"
            f"Conversion abbreviations are: {', '.join(METRIC_CONVERSION)}"
        )
        raise ValueError(msg)
    if new_to not in METRIC_CONVERSION:
        msg = (
            f"Invalid 'to_type' value: {to_type!r}.\n"
            f"Conversion abbreviations are: {', '.join(METRIC_CONVERSION)}"
        )
        raise ValueError(msg)
    return (
        value
        * METRIC_CONVERSION[new_from].from_factor
        * METRIC_CONVERSION[new_to].to_factor
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: conversions/molecular_chemistry.py
================================================
"""
Functions useful for doing molecular chemistry:
* molarity_to_normality
* moles_to_pressure
* moles_to_volume
* pressure_and_volume_to_temperature
"""


def molarity_to_normality(nfactor: int, moles: float, volume: float) -> float:
    """
    Convert molarity to normality.
      Volume is taken in litres.

      Wikipedia reference: https://en.wikipedia.org/wiki/Equivalent_concentration
      Wikipedia reference: https://en.wikipedia.org/wiki/Molar_concentration

      >>> molarity_to_normality(2, 3.1, 0.31)
      20
      >>> molarity_to_normality(4, 11.4, 5.7)
      8
    """
    return round(float(moles / volume) * nfactor)


def moles_to_pressure(volume: float, moles: float, temperature: float) -> float:
    """
    Convert moles to pressure.
      Ideal gas laws are used.
      Temperature is taken in kelvin.
      Volume is taken in litres.
      Pressure has atm as SI unit.

      Wikipedia reference: https://en.wikipedia.org/wiki/Gas_laws
      Wikipedia reference: https://en.wikipedia.org/wiki/Pressure
      Wikipedia reference: https://en.wikipedia.org/wiki/Temperature

      >>> moles_to_pressure(0.82, 3, 300)
      90
      >>> moles_to_pressure(8.2, 5, 200)
      10
    """
    return round(float((moles * 0.0821 * temperature) / (volume)))


def moles_to_volume(pressure: float, moles: float, temperature: float) -> float:
    """
    Convert moles to volume.
      Ideal gas laws are used.
      Temperature is taken in kelvin.
      Volume is taken in litres.
      Pressure has atm as SI unit.

      Wikipedia reference: https://en.wikipedia.org/wiki/Gas_laws
      Wikipedia reference: https://en.wikipedia.org/wiki/Pressure
      Wikipedia reference: https://en.wikipedia.org/wiki/Temperature

      >>> moles_to_volume(0.82, 3, 300)
      90
      >>> moles_to_volume(8.2, 5, 200)
      10
    """
    return round(float((moles * 0.0821 * temperature) / (pressure)))


def pressure_and_volume_to_temperature(
    pressure: float, moles: float, volume: float
) -> float:
    """
    Convert pressure and volume to temperature.
      Ideal gas laws are used.
      Temperature is taken in kelvin.
      Volume is taken in litres.
      Pressure has atm as SI unit.

      Wikipedia reference: https://en.wikipedia.org/wiki/Gas_laws
      Wikipedia reference: https://en.wikipedia.org/wiki/Pressure
      Wikipedia reference: https://en.wikipedia.org/wiki/Temperature

      >>> pressure_and_volume_to_temperature(0.82, 1, 2)
      20
      >>> pressure_and_volume_to_temperature(8.2, 5, 3)
      60
    """
    return round(float((pressure * volume) / (0.0821 * moles)))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: conversions/octal_to_binary.py
================================================
"""
* Author: Bama Charan Chhandogi (https://github.com/BamaCharanChhandogi)
* Description: Convert a Octal number to Binary.

References for better understanding:
https://en.wikipedia.org/wiki/Binary_number
https://en.wikipedia.org/wiki/Octal
"""


def octal_to_binary(octal_number: str) -> str:
    """
    Convert an Octal number to Binary.

    >>> octal_to_binary("17")
    '001111'
    >>> octal_to_binary("7")
    '111'
    >>> octal_to_binary("Av")
    Traceback (most recent call last):
        ...
    ValueError: Non-octal value was passed to the function
    >>> octal_to_binary("@#")
    Traceback (most recent call last):
        ...
    ValueError: Non-octal value was passed to the function
    >>> octal_to_binary("")
    Traceback (most recent call last):
        ...
    ValueError: Empty string was passed to the function
    """
    if not octal_number:
        raise ValueError("Empty string was passed to the function")

    binary_number = ""
    octal_digits = "01234567"
    for digit in octal_number:
        if digit not in octal_digits:
            raise ValueError("Non-octal value was passed to the function")

        binary_digit = ""
        value = int(digit)
        for _ in range(3):
            binary_digit = str(value % 2) + binary_digit
            value //= 2
        binary_number += binary_digit

    return binary_number


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: conversions/octal_to_decimal.py
================================================
def oct_to_decimal(oct_string: str) -> int:
    """
    Convert a octal value to its decimal equivalent

    >>> oct_to_decimal("")
    Traceback (most recent call last):
        ...
    ValueError: Empty string was passed to the function
    >>> oct_to_decimal("-")
    Traceback (most recent call last):
        ...
    ValueError: Non-octal value was passed to the function
    >>> oct_to_decimal("e")
    Traceback (most recent call last):
        ...
    ValueError: Non-octal value was passed to the function
    >>> oct_to_decimal("8")
    Traceback (most recent call last):
        ...
    ValueError: Non-octal value was passed to the function
    >>> oct_to_decimal("-e")
    Traceback (most recent call last):
        ...
    ValueError: Non-octal value was passed to the function
    >>> oct_to_decimal("-8")
    Traceback (most recent call last):
        ...
    ValueError: Non-octal value was passed to the function
    >>> oct_to_decimal("1")
    1
    >>> oct_to_decimal("-1")
    -1
    >>> oct_to_decimal("12")
    10
    >>> oct_to_decimal(" 12   ")
    10
    >>> oct_to_decimal("-45")
    -37
    >>> oct_to_decimal("-")
    Traceback (most recent call last):
        ...
    ValueError: Non-octal value was passed to the function
    >>> oct_to_decimal("0")
    0
    >>> oct_to_decimal("-4055")
    -2093
    >>> oct_to_decimal("2-0Fm")
    Traceback (most recent call last):
        ...
    ValueError: Non-octal value was passed to the function
    >>> oct_to_decimal("")
    Traceback (most recent call last):
        ...
    ValueError: Empty string was passed to the function
    >>> oct_to_decimal("19")
    Traceback (most recent call last):
        ...
    ValueError: Non-octal value was passed to the function
    """
    oct_string = str(oct_string).strip()
    if not oct_string:
        raise ValueError("Empty string was passed to the function")
    is_negative = oct_string[0] == "-"
    if is_negative:
        oct_string = oct_string[1:]
    if not oct_string.isdigit() or not all(0 <= int(char) <= 7 for char in oct_string):
        raise ValueError("Non-octal value was passed to the function")
    decimal_number = 0
    for char in oct_string:
        decimal_number = 8 * decimal_number + int(char)
    if is_negative:
        decimal_number = -decimal_number
    return decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: conversions/octal_to_hexadecimal.py
================================================
def octal_to_hex(octal: str) -> str:
    """
    Convert an Octal number to Hexadecimal number.
    For more information: https://en.wikipedia.org/wiki/Octal

    >>> octal_to_hex("100")
    '0x40'
    >>> octal_to_hex("235")
    '0x9D'
    >>> octal_to_hex(17)
    Traceback (most recent call last):
        ...
    TypeError: Expected a string as input
    >>> octal_to_hex("Av")
    Traceback (most recent call last):
        ...
    ValueError: Not a Valid Octal Number
    >>> octal_to_hex("")
    Traceback (most recent call last):
        ...
    ValueError: Empty string was passed to the function
    """

    if not isinstance(octal, str):
        raise TypeError("Expected a string as input")
    if octal.startswith("0o"):
        octal = octal[2:]
    if octal == "":
        raise ValueError("Empty string was passed to the function")
    if any(char not in "01234567" for char in octal):
        raise ValueError("Not a Valid Octal Number")

    decimal = 0
    for char in octal:
        decimal <<= 3
        decimal |= int(char)

    hex_char = "0123456789ABCDEF"

    revhex = ""
    while decimal:
        revhex += hex_char[decimal & 15]
        decimal >>= 4

    return "0x" + revhex[::-1]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    nums = ["030", "100", "247", "235", "007"]

    ## Main Tests

    for num in nums:
        hexadecimal = octal_to_hex(num)
        expected = "0x" + hex(int(num, 8))[2:].upper()

        assert hexadecimal == expected

        print(f"Hex of '0o{num}' is: {hexadecimal}")
        print(f"Expected was: {expected}")
        print("---")


================================================
FILE: conversions/prefix_conversions.py
================================================
"""
Convert International System of Units (SI) and Binary prefixes
"""

from __future__ import annotations

from enum import Enum


class SIUnit(Enum):
    yotta = 24
    zetta = 21
    exa = 18
    peta = 15
    tera = 12
    giga = 9
    mega = 6
    kilo = 3
    hecto = 2
    deca = 1
    deci = -1
    centi = -2
    milli = -3
    micro = -6
    nano = -9
    pico = -12
    femto = -15
    atto = -18
    zepto = -21
    yocto = -24


class BinaryUnit(Enum):
    yotta = 8
    zetta = 7
    exa = 6
    peta = 5
    tera = 4
    giga = 3
    mega = 2
    kilo = 1


def convert_si_prefix(
    known_amount: float,
    known_prefix: str | SIUnit,
    unknown_prefix: str | SIUnit,
) -> float:
    """
    Wikipedia reference: https://en.wikipedia.org/wiki/Binary_prefix
    Wikipedia reference: https://en.wikipedia.org/wiki/International_System_of_Units
    >>> convert_si_prefix(1, SIUnit.giga, SIUnit.mega)
    1000
    >>> convert_si_prefix(1, SIUnit.mega, SIUnit.giga)
    0.001
    >>> convert_si_prefix(1, SIUnit.kilo, SIUnit.kilo)
    1
    >>> convert_si_prefix(1, 'giga', 'mega')
    1000
    >>> convert_si_prefix(1, 'gIGa', 'mEGa')
    1000
    """
    if isinstance(known_prefix, str):
        known_prefix = SIUnit[known_prefix.lower()]
    if isinstance(unknown_prefix, str):
        unknown_prefix = SIUnit[unknown_prefix.lower()]
    unknown_amount: float = known_amount * (
        10 ** (known_prefix.value - unknown_prefix.value)
    )
    return unknown_amount


def convert_binary_prefix(
    known_amount: float,
    known_prefix: str | BinaryUnit,
    unknown_prefix: str | BinaryUnit,
) -> float:
    """
    Wikipedia reference: https://en.wikipedia.org/wiki/Metric_prefix
    >>> convert_binary_prefix(1, BinaryUnit.giga, BinaryUnit.mega)
    1024
    >>> convert_binary_prefix(1, BinaryUnit.mega, BinaryUnit.giga)
    0.0009765625
    >>> convert_binary_prefix(1, BinaryUnit.kilo, BinaryUnit.kilo)
    1
    >>> convert_binary_prefix(1, 'giga', 'mega')
    1024
    >>> convert_binary_prefix(1, 'gIGa', 'mEGa')
    1024
    """
    if isinstance(known_prefix, str):
        known_prefix = BinaryUnit[known_prefix.lower()]
    if isinstance(unknown_prefix, str):
        unknown_prefix = BinaryUnit[unknown_prefix.lower()]
    unknown_amount: float = known_amount * (
        2 ** ((known_prefix.value - unknown_prefix.value) * 10)
    )
    return unknown_amount


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: conversions/prefix_conversions_string.py
================================================
"""
* Author: Manuel Di Lullo (https://github.com/manueldilullo)
* Description: Convert a number to use the correct SI or Binary unit prefix.

Inspired by prefix_conversion.py file in this repository by lance-pyles

URL: https://en.wikipedia.org/wiki/Metric_prefix#List_of_SI_prefixes
URL: https://en.wikipedia.org/wiki/Binary_prefix
"""

from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
T = TypeVar("T", bound="Enum")


@unique
class BinaryUnit(Enum):
    yotta = 80
    zetta = 70
    exa = 60
    peta = 50
    tera = 40
    giga = 30
    mega = 20
    kilo = 10


@unique
class SIUnit(Enum):
    yotta = 24
    zetta = 21
    exa = 18
    peta = 15
    tera = 12
    giga = 9
    mega = 6
    kilo = 3
    hecto = 2
    deca = 1
    deci = -1
    centi = -2
    milli = -3
    micro = -6
    nano = -9
    pico = -12
    femto = -15
    atto = -18
    zepto = -21
    yocto = -24

    @classmethod
    def get_positive(cls) -> dict:
        """
        Returns a dictionary with only the elements of this enum
        that has a positive value
        >>> from itertools import islice
        >>> positive = SIUnit.get_positive()
        >>> inc = iter(positive.items())
        >>> dict(islice(inc, len(positive) // 2))
        {'yotta': 24, 'zetta': 21, 'exa': 18, 'peta': 15, 'tera': 12}
        >>> dict(inc)
        {'giga': 9, 'mega': 6, 'kilo': 3, 'hecto': 2, 'deca': 1}
        """
        return {unit.name: unit.value for unit in cls if unit.value > 0}

    @classmethod
    def get_negative(cls) -> dict:
        """
        Returns a dictionary with only the elements of this enum
        that has a negative value
        @example
        >>> from itertools import islice
        >>> negative = SIUnit.get_negative()
        >>> inc = iter(negative.items())
        >>> dict(islice(inc, len(negative) // 2))
        {'deci': -1, 'centi': -2, 'milli': -3, 'micro': -6, 'nano': -9}
        >>> dict(inc)
        {'pico': -12, 'femto': -15, 'atto': -18, 'zepto': -21, 'yocto': -24}
        """
        return {unit.name: unit.value for unit in cls if unit.value < 0}


def add_si_prefix(value: float) -> str:
    """
    Function that converts a number to his version with SI prefix
    @input value (an integer)
    @example:
    >>> add_si_prefix(10000)
    '10.0 kilo'
    """
    prefixes = SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
    for name_prefix, value_prefix in prefixes.items():
        numerical_part = value / (10**value_prefix)
        if numerical_part > 1:
            return f"{numerical_part!s} {name_prefix}"
    return str(value)


def add_binary_prefix(value: float) -> str:
    """
    Function that converts a number to his version with Binary prefix
    @input value (an integer)
    @example:
    >>> add_binary_prefix(65536)
    '64.0 kilo'
    """
    for prefix in BinaryUnit:
        numerical_part = value / (2**prefix.value)
        if numerical_part > 1:
            return f"{numerical_part!s} {prefix.name}"
    return str(value)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: conversions/pressure_conversions.py
================================================
"""
Conversion of pressure units.
Available Units:- Pascal,Bar,Kilopascal,Megapascal,psi(pound per square inch),
inHg(in mercury column),torr,atm
USAGE :
-> Import this file into their respective project.
-> Use the function pressure_conversion() for conversion of pressure units.
-> Parameters :
    -> value : The number of from units you want to convert
    -> from_type : From which type you want to convert
    -> to_type : To which type you want to convert
REFERENCES :
-> Wikipedia reference: https://en.wikipedia.org/wiki/Pascal_(unit)
-> Wikipedia reference: https://en.wikipedia.org/wiki/Pound_per_square_inch
-> Wikipedia reference: https://en.wikipedia.org/wiki/Inch_of_mercury
-> Wikipedia reference: https://en.wikipedia.org/wiki/Torr
-> https://en.wikipedia.org/wiki/Standard_atmosphere_(unit)
-> https://msestudent.com/what-are-the-units-of-pressure/
-> https://www.unitconverters.net/pressure-converter.html
"""

from typing import NamedTuple


class FromTo(NamedTuple):
    from_factor: float
    to_factor: float


PRESSURE_CONVERSION = {
    "atm": FromTo(1, 1),
    "pascal": FromTo(0.0000098, 101325),
    "bar": FromTo(0.986923, 1.01325),
    "kilopascal": FromTo(0.00986923, 101.325),
    "megapascal": FromTo(9.86923, 0.101325),
    "psi": FromTo(0.068046, 14.6959),
    "inHg": FromTo(0.0334211, 29.9213),
    "torr": FromTo(0.00131579, 760),
}


def pressure_conversion(value: float, from_type: str, to_type: str) -> float:
    """
    Conversion between pressure units.
    >>> pressure_conversion(4, "atm", "pascal")
    405300
    >>> pressure_conversion(1, "pascal", "psi")
    0.00014401981999999998
    >>> pressure_conversion(1, "bar", "atm")
    0.986923
    >>> pressure_conversion(3, "kilopascal", "bar")
    0.029999991892499998
    >>> pressure_conversion(2, "megapascal", "psi")
    290.074434314
    >>> pressure_conversion(4, "psi", "torr")
    206.85984
    >>> pressure_conversion(1, "inHg", "atm")
    0.0334211
    >>> pressure_conversion(1, "torr", "psi")
    0.019336718261000002
    >>> pressure_conversion(4, "wrongUnit", "atm")
    Traceback (most recent call last):
        ...
    ValueError: Invalid 'from_type' value: 'wrongUnit'  Supported values are:
    atm, pascal, bar, kilopascal, megapascal, psi, inHg, torr
    """
    if from_type not in PRESSURE_CONVERSION:
        raise ValueError(
            f"Invalid 'from_type' value: {from_type!r}  Supported values are:\n"
            + ", ".join(PRESSURE_CONVERSION)
        )
    if to_type not in PRESSURE_CONVERSION:
        raise ValueError(
            f"Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"
            + ", ".join(PRESSURE_CONVERSION)
        )
    return (
        value
        * PRESSURE_CONVERSION[from_type].from_factor
        * PRESSURE_CONVERSION[to_type].to_factor
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: conversions/rectangular_to_polar.py
================================================
import math


def rectangular_to_polar(real: float, img: float) -> tuple[float, float]:
    """
    https://en.wikipedia.org/wiki/Polar_coordinate_system

    >>> rectangular_to_polar(5,-5)
    (7.07, -45.0)
    >>> rectangular_to_polar(-1,1)
    (1.41, 135.0)
    >>> rectangular_to_polar(-1,-1)
    (1.41, -135.0)
    >>> rectangular_to_polar(1e-10,1e-10)
    (0.0, 45.0)
    >>> rectangular_to_polar(-1e-10,1e-10)
    (0.0, 135.0)
    >>> rectangular_to_polar(9.75,5.93)
    (11.41, 31.31)
    >>> rectangular_to_polar(10000,99999)
    (100497.76, 84.29)
    """

    mod = round(math.sqrt((real**2) + (img**2)), 2)
    ang = round(math.degrees(math.atan2(img, real)), 2)
    return (mod, ang)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: conversions/rgb_cmyk_conversion.py
================================================
def rgb_to_cmyk(r_input: int, g_input: int, b_input: int) -> tuple[int, int, int, int]:
    """
    Simple RGB to CMYK conversion. Returns percentages of CMYK paint.
    https://www.programmingalgorithms.com/algorithm/rgb-to-cmyk/

    Note: this is a very popular algorithm that converts colors linearly and gives
    only approximate results. Actual preparation for printing requires advanced color
    conversion considering the color profiles and parameters of the target device.

    >>> rgb_to_cmyk(255, 200, "a")
    Traceback (most recent call last):
        ...
    ValueError: Expected int, found (<class 'int'>, <class 'int'>, <class 'str'>)

    >>> rgb_to_cmyk(255, 255, 999)
    Traceback (most recent call last):
        ...
    ValueError: Expected int of the range 0..255

    >>> rgb_to_cmyk(255, 255, 255)  # white
    (0, 0, 0, 0)

    >>> rgb_to_cmyk(128, 128, 128)  # gray
    (0, 0, 0, 50)

    >>> rgb_to_cmyk(0, 0, 0)    # black
    (0, 0, 0, 100)

    >>> rgb_to_cmyk(255, 0, 0)  # red
    (0, 100, 100, 0)

    >>> rgb_to_cmyk(0, 255, 0)  # green
    (100, 0, 100, 0)

    >>> rgb_to_cmyk(0, 0, 255)    # blue
    (100, 100, 0, 0)
    """

    if (
        not isinstance(r_input, int)
        or not isinstance(g_input, int)
        or not isinstance(b_input, int)
    ):
        msg = f"Expected int, found {type(r_input), type(g_input), type(b_input)}"
        raise ValueError(msg)

    if not 0 <= r_input < 256 or not 0 <= g_input < 256 or not 0 <= b_input < 256:
        raise ValueError("Expected int of the range 0..255")

    # changing range from 0..255 to 0..1
    r = r_input / 255
    g = g_input / 255
    b = b_input / 255

    k = 1 - max(r, g, b)

    if k == 1:  # pure black
        return 0, 0, 0, 100

    c = round(100 * (1 - r - k) / (1 - k))
    m = round(100 * (1 - g - k) / (1 - k))
    y = round(100 * (1 - b - k) / (1 - k))
    k = round(100 * k)

    return c, m, y, k


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: conversions/rgb_hsv_conversion.py
================================================
"""
The RGB color model is an additive color model in which red, green, and blue light
are added together in various ways to reproduce a broad array of colors. The name
of the model comes from the initials of the three additive primary colors, red,
green, and blue. Meanwhile, the HSV representation models how colors appear under
light. In it, colors are represented using three components: hue, saturation and
(brightness-)value. This file provides functions for converting colors from one
representation to the other.

(description adapted from https://en.wikipedia.org/wiki/RGB_color_model and
https://en.wikipedia.org/wiki/HSL_and_HSV).
"""


def hsv_to_rgb(hue: float, saturation: float, value: float) -> list[int]:
    """
    Conversion from the HSV-representation to the RGB-representation.
    Expected RGB-values taken from
    https://www.rapidtables.com/convert/color/hsv-to-rgb.html

    >>> hsv_to_rgb(0, 0, 0)
    [0, 0, 0]
    >>> hsv_to_rgb(0, 0, 1)
    [255, 255, 255]
    >>> hsv_to_rgb(0, 1, 1)
    [255, 0, 0]
    >>> hsv_to_rgb(60, 1, 1)
    [255, 255, 0]
    >>> hsv_to_rgb(120, 1, 1)
    [0, 255, 0]
    >>> hsv_to_rgb(240, 1, 1)
    [0, 0, 255]
    >>> hsv_to_rgb(300, 1, 1)
    [255, 0, 255]
    >>> hsv_to_rgb(180, 0.5, 0.5)
    [64, 128, 128]
    >>> hsv_to_rgb(234, 0.14, 0.88)
    [193, 196, 224]
    >>> hsv_to_rgb(330, 0.75, 0.5)
    [128, 32, 80]
    """
    if hue < 0 or hue > 360:
        raise Exception("hue should be between 0 and 360")

    if saturation < 0 or saturation > 1:
        raise Exception("saturation should be between 0 and 1")

    if value < 0 or value > 1:
        raise Exception("value should be between 0 and 1")

    chroma = value * saturation
    hue_section = hue / 60
    second_largest_component = chroma * (1 - abs(hue_section % 2 - 1))
    match_value = value - chroma

    if hue_section >= 0 and hue_section <= 1:
        red = round(255 * (chroma + match_value))
        green = round(255 * (second_largest_component + match_value))
        blue = round(255 * (match_value))
    elif hue_section > 1 and hue_section <= 2:
        red = round(255 * (second_largest_component + match_value))
        green = round(255 * (chroma + match_value))
        blue = round(255 * (match_value))
    elif hue_section > 2 and hue_section <= 3:
        red = round(255 * (match_value))
        green = round(255 * (chroma + match_value))
        blue = round(255 * (second_largest_component + match_value))
    elif hue_section > 3 and hue_section <= 4:
        red = round(255 * (match_value))
        green = round(255 * (second_largest_component + match_value))
        blue = round(255 * (chroma + match_value))
    elif hue_section > 4 and hue_section <= 5:
        red = round(255 * (second_largest_component + match_value))
        green = round(255 * (match_value))
        blue = round(255 * (chroma + match_value))
    else:
        red = round(255 * (chroma + match_value))
        green = round(255 * (match_value))
        blue = round(255 * (second_largest_component + match_value))

    return [red, green, blue]


def rgb_to_hsv(red: int, green: int, blue: int) -> list[float]:
    """
    Conversion from the RGB-representation to the HSV-representation.
    The tested values are the reverse values from the hsv_to_rgb-doctests.
    Function "approximately_equal_hsv" is needed because of small deviations due to
    rounding for the RGB-values.

    >>> approximately_equal_hsv(rgb_to_hsv(0, 0, 0), [0, 0, 0])
    True
    >>> approximately_equal_hsv(rgb_to_hsv(255, 255, 255), [0, 0, 1])
    True
    >>> approximately_equal_hsv(rgb_to_hsv(255, 0, 0), [0, 1, 1])
    True
    >>> approximately_equal_hsv(rgb_to_hsv(255, 255, 0), [60, 1, 1])
    True
    >>> approximately_equal_hsv(rgb_to_hsv(0, 255, 0), [120, 1, 1])
    True
    >>> approximately_equal_hsv(rgb_to_hsv(0, 0, 255), [240, 1, 1])
    True
    >>> approximately_equal_hsv(rgb_to_hsv(255, 0, 255), [300, 1, 1])
    True
    >>> approximately_equal_hsv(rgb_to_hsv(64, 128, 128), [180, 0.5, 0.5])
    True
    >>> approximately_equal_hsv(rgb_to_hsv(193, 196, 224), [234, 0.14, 0.88])
    True
    >>> approximately_equal_hsv(rgb_to_hsv(128, 32, 80), [330, 0.75, 0.5])
    True
    """
    if red < 0 or red > 255:
        raise Exception("red should be between 0 and 255")

    if green < 0 or green > 255:
        raise Exception("green should be between 0 and 255")

    if blue < 0 or blue > 255:
        raise Exception("blue should be between 0 and 255")

    float_red = red / 255
    float_green = green / 255
    float_blue = blue / 255
    value = max(float_red, float_green, float_blue)
    chroma = value - min(float_red, float_green, float_blue)
    saturation = 0 if value == 0 else chroma / value

    if chroma == 0:
        hue = 0.0
    elif value == float_red:
        hue = 60 * (0 + (float_green - float_blue) / chroma)
    elif value == float_green:
        hue = 60 * (2 + (float_blue - float_red) / chroma)
    else:
        hue = 60 * (4 + (float_red - float_green) / chroma)

    hue = (hue + 360) % 360

    return [hue, saturation, value]


def approximately_equal_hsv(hsv_1: list[float], hsv_2: list[float]) -> bool:
    """
    Utility-function to check that two hsv-colors are approximately equal

    >>> approximately_equal_hsv([0, 0, 0], [0, 0, 0])
    True
    >>> approximately_equal_hsv([180, 0.5, 0.3], [179.9999, 0.500001, 0.30001])
    True
    >>> approximately_equal_hsv([0, 0, 0], [1, 0, 0])
    False
    >>> approximately_equal_hsv([180, 0.5, 0.3], [179.9999, 0.6, 0.30001])
    False
    """
    check_hue = abs(hsv_1[0] - hsv_2[0]) < 0.2
    check_saturation = abs(hsv_1[1] - hsv_2[1]) < 0.002
    check_value = abs(hsv_1[2] - hsv_2[2]) < 0.002

    return check_hue and check_saturation and check_value


================================================
FILE: conversions/roman_numerals.py
================================================
ROMAN = [
    (1000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]


def roman_to_int(roman: str) -> int:
    """
    LeetCode No. 13 Roman to Integer
    Given a roman numeral, convert it to an integer.
    Input is guaranteed to be within the range from 1 to 3999.
    https://en.wikipedia.org/wiki/Roman_numerals
    >>> tests = {"III": 3, "CLIV": 154, "MIX": 1009, "MMD": 2500, "MMMCMXCIX": 3999}
    >>> all(roman_to_int(key) == value for key, value in tests.items())
    True
    """
    vals = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
    total = 0
    place = 0
    while place < len(roman):
        if (place + 1 < len(roman)) and (vals[roman[place]] < vals[roman[place + 1]]):
            total += vals[roman[place + 1]] - vals[roman[place]]
            place += 2
        else:
            total += vals[roman[place]]
            place += 1
    return total


def int_to_roman(number: int) -> str:
    """
    Given a integer, convert it to an roman numeral.
    https://en.wikipedia.org/wiki/Roman_numerals
    >>> tests = {"III": 3, "CLIV": 154, "MIX": 1009, "MMD": 2500, "MMMCMXCIX": 3999}
    >>> all(int_to_roman(value) == key for key, value in tests.items())
    True
    """
    result = []
    for arabic, roman in ROMAN:
        (factor, number) = divmod(number, arabic)
        result.append(roman * factor)
        if number == 0:
            break
    return "".join(result)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: conversions/speed_conversions.py
================================================
"""
Convert speed units

https://en.wikipedia.org/wiki/Kilometres_per_hour
https://en.wikipedia.org/wiki/Miles_per_hour
https://en.wikipedia.org/wiki/Knot_(unit)
https://en.wikipedia.org/wiki/Metre_per_second
"""

speed_chart: dict[str, float] = {
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.609344,
    "knot": 1.852,
}

speed_chart_inverse: dict[str, float] = {
    "km/h": 1.0,
    "m/s": 0.277777778,
    "mph": 0.621371192,
    "knot": 0.539956803,
}


def convert_speed(speed: float, unit_from: str, unit_to: str) -> float:
    """
    Convert speed from one unit to another using the speed_chart above.

    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.609344,
    "knot": 1.852,

    >>> convert_speed(100, "km/h", "m/s")
    27.778
    >>> convert_speed(100, "km/h", "mph")
    62.137
    >>> convert_speed(100, "km/h", "knot")
    53.996
    >>> convert_speed(100, "m/s", "km/h")
    360.0
    >>> convert_speed(100, "m/s", "mph")
    223.694
    >>> convert_speed(100, "m/s", "knot")
    194.384
    >>> convert_speed(100, "mph", "km/h")
    160.934
    >>> convert_speed(100, "mph", "m/s")
    44.704
    >>> convert_speed(100, "mph", "knot")
    86.898
    >>> convert_speed(100, "knot", "km/h")
    185.2
    >>> convert_speed(100, "knot", "m/s")
    51.444
    >>> convert_speed(100, "knot", "mph")
    115.078
    """
    if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
        msg = (
            f"Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"
            f"Valid values are: {', '.join(speed_chart_inverse)}"
        )
        raise ValueError(msg)
    return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to], 3)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: conversions/temperature_conversions.py
================================================
"""Convert between different units of temperature"""


def celsius_to_fahrenheit(celsius: float, ndigits: int = 2) -> float:
    """
    Convert a given value from Celsius to Fahrenheit and round it to 2 decimal places.
    Wikipedia reference: https://en.wikipedia.org/wiki/Celsius
    Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit

    >>> celsius_to_fahrenheit(273.354, 3)
    524.037
    >>> celsius_to_fahrenheit(273.354, 0)
    524.0
    >>> celsius_to_fahrenheit(-40.0)
    -40.0
    >>> celsius_to_fahrenheit(-20.0)
    -4.0
    >>> celsius_to_fahrenheit(0)
    32.0
    >>> celsius_to_fahrenheit(20)
    68.0
    >>> celsius_to_fahrenheit("40")
    104.0
    >>> celsius_to_fahrenheit("celsius")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'celsius'
    """
    return round((float(celsius) * 9 / 5) + 32, ndigits)


def celsius_to_kelvin(celsius: float, ndigits: int = 2) -> float:
    """
    Convert a given value from Celsius to Kelvin and round it to 2 decimal places.
    Wikipedia reference: https://en.wikipedia.org/wiki/Celsius
    Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin

    >>> celsius_to_kelvin(273.354, 3)
    546.504
    >>> celsius_to_kelvin(273.354, 0)
    547.0
    >>> celsius_to_kelvin(0)
    273.15
    >>> celsius_to_kelvin(20.0)
    293.15
    >>> celsius_to_kelvin("40")
    313.15
    >>> celsius_to_kelvin("celsius")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'celsius'
    """
    return round(float(celsius) + 273.15, ndigits)


def celsius_to_rankine(celsius: float, ndigits: int = 2) -> float:
    """
    Convert a given value from Celsius to Rankine and round it to 2 decimal places.
    Wikipedia reference: https://en.wikipedia.org/wiki/Celsius
    Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale

    >>> celsius_to_rankine(273.354, 3)
    983.707
    >>> celsius_to_rankine(273.354, 0)
    984.0
    >>> celsius_to_rankine(0)
    491.67
    >>> celsius_to_rankine(20.0)
    527.67
    >>> celsius_to_rankine("40")
    563.67
    >>> celsius_to_rankine("celsius")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'celsius'
    """
    return round((float(celsius) * 9 / 5) + 491.67, ndigits)


def fahrenheit_to_celsius(fahrenheit: float, ndigits: int = 2) -> float:
    """
    Convert a given value from Fahrenheit to Celsius and round it to 2 decimal places.
    Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit
    Wikipedia reference: https://en.wikipedia.org/wiki/Celsius

    >>> fahrenheit_to_celsius(273.354, 3)
    134.086
    >>> fahrenheit_to_celsius(273.354, 0)
    134.0
    >>> fahrenheit_to_celsius(0)
    -17.78
    >>> fahrenheit_to_celsius(20.0)
    -6.67
    >>> fahrenheit_to_celsius(40.0)
    4.44
    >>> fahrenheit_to_celsius(60)
    15.56
    >>> fahrenheit_to_celsius(80)
    26.67
    >>> fahrenheit_to_celsius("100")
    37.78
    >>> fahrenheit_to_celsius("fahrenheit")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'fahrenheit'
    """
    return round((float(fahrenheit) - 32) * 5 / 9, ndigits)


def fahrenheit_to_kelvin(fahrenheit: float, ndigits: int = 2) -> float:
    """
    Convert a given value from Fahrenheit to Kelvin and round it to 2 decimal places.
    Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit
    Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin

    >>> fahrenheit_to_kelvin(273.354, 3)
    407.236
    >>> fahrenheit_to_kelvin(273.354, 0)
    407.0
    >>> fahrenheit_to_kelvin(0)
    255.37
    >>> fahrenheit_to_kelvin(20.0)
    266.48
    >>> fahrenheit_to_kelvin(40.0)
    277.59
    >>> fahrenheit_to_kelvin(60)
    288.71
    >>> fahrenheit_to_kelvin(80)
    299.82
    >>> fahrenheit_to_kelvin("100")
    310.93
    >>> fahrenheit_to_kelvin("fahrenheit")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'fahrenheit'
    """
    return round(((float(fahrenheit) - 32) * 5 / 9) + 273.15, ndigits)


def fahrenheit_to_rankine(fahrenheit: float, ndigits: int = 2) -> float:
    """
    Convert a given value from Fahrenheit to Rankine and round it to 2 decimal places.
    Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit
    Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale

    >>> fahrenheit_to_rankine(273.354, 3)
    733.024
    >>> fahrenheit_to_rankine(273.354, 0)
    733.0
    >>> fahrenheit_to_rankine(0)
    459.67
    >>> fahrenheit_to_rankine(20.0)
    479.67
    >>> fahrenheit_to_rankine(40.0)
    499.67
    >>> fahrenheit_to_rankine(60)
    519.67
    >>> fahrenheit_to_rankine(80)
    539.67
    >>> fahrenheit_to_rankine("100")
    559.67
    >>> fahrenheit_to_rankine("fahrenheit")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'fahrenheit'
    """
    return round(float(fahrenheit) + 459.67, ndigits)


def kelvin_to_celsius(kelvin: float, ndigits: int = 2) -> float:
    """
    Convert a given value from Kelvin to Celsius and round it to 2 decimal places.
    Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin
    Wikipedia reference: https://en.wikipedia.org/wiki/Celsius

    >>> kelvin_to_celsius(273.354, 3)
    0.204
    >>> kelvin_to_celsius(273.354, 0)
    0.0
    >>> kelvin_to_celsius(273.15)
    0.0
    >>> kelvin_to_celsius(300)
    26.85
    >>> kelvin_to_celsius("315.5")
    42.35
    >>> kelvin_to_celsius("kelvin")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'kelvin'
    """
    return round(float(kelvin) - 273.15, ndigits)


def kelvin_to_fahrenheit(kelvin: float, ndigits: int = 2) -> float:
    """
    Convert a given value from Kelvin to Fahrenheit and round it to 2 decimal places.
    Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin
    Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit

    >>> kelvin_to_fahrenheit(273.354, 3)
    32.367
    >>> kelvin_to_fahrenheit(273.354, 0)
    32.0
    >>> kelvin_to_fahrenheit(273.15)
    32.0
    >>> kelvin_to_fahrenheit(300)
    80.33
    >>> kelvin_to_fahrenheit("315.5")
    108.23
    >>> kelvin_to_fahrenheit("kelvin")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'kelvin'
    """
    return round(((float(kelvin) - 273.15) * 9 / 5) + 32, ndigits)


def kelvin_to_rankine(kelvin: float, ndigits: int = 2) -> float:
    """
    Convert a given value from Kelvin to Rankine and round it to 2 decimal places.
    Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin
    Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale

    >>> kelvin_to_rankine(273.354, 3)
    492.037
    >>> kelvin_to_rankine(273.354, 0)
    492.0
    >>> kelvin_to_rankine(0)
    0.0
    >>> kelvin_to_rankine(20.0)
    36.0
    >>> kelvin_to_rankine("40")
    72.0
    >>> kelvin_to_rankine("kelvin")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'kelvin'
    """
    return round((float(kelvin) * 9 / 5), ndigits)


def rankine_to_celsius(rankine: float, ndigits: int = 2) -> float:
    """
    Convert a given value from Rankine to Celsius and round it to 2 decimal places.
    Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale
    Wikipedia reference: https://en.wikipedia.org/wiki/Celsius

    >>> rankine_to_celsius(273.354, 3)
    -121.287
    >>> rankine_to_celsius(273.354, 0)
    -121.0
    >>> rankine_to_celsius(273.15)
    -121.4
    >>> rankine_to_celsius(300)
    -106.48
    >>> rankine_to_celsius("315.5")
    -97.87
    >>> rankine_to_celsius("rankine")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'rankine'
    """
    return round((float(rankine) - 491.67) * 5 / 9, ndigits)


def rankine_to_fahrenheit(rankine: float, ndigits: int = 2) -> float:
    """
    Convert a given value from Rankine to Fahrenheit and round it to 2 decimal places.
    Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale
    Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit

    >>> rankine_to_fahrenheit(273.15)
    -186.52
    >>> rankine_to_fahrenheit(300)
    -159.67
    >>> rankine_to_fahrenheit("315.5")
    -144.17
    >>> rankine_to_fahrenheit("rankine")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'rankine'
    """
    return round(float(rankine) - 459.67, ndigits)


def rankine_to_kelvin(rankine: float, ndigits: int = 2) -> float:
    """
    Convert a given value from Rankine to Kelvin and round it to 2 decimal places.
    Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale
    Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin

    >>> rankine_to_kelvin(0)
    0.0
    >>> rankine_to_kelvin(20.0)
    11.11
    >>> rankine_to_kelvin("40")
    22.22
    >>> rankine_to_kelvin("rankine")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'rankine'
    """
    return round((float(rankine) * 5 / 9), ndigits)


def reaumur_to_kelvin(reaumur: float, ndigits: int = 2) -> float:
    """
    Convert a given value from reaumur to Kelvin and round it to 2 decimal places.
    Reference:- http://www.csgnetwork.com/temp2conv.html

    >>> reaumur_to_kelvin(0)
    273.15
    >>> reaumur_to_kelvin(20.0)
    298.15
    >>> reaumur_to_kelvin(40)
    323.15
    >>> reaumur_to_kelvin("reaumur")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'reaumur'
    """
    return round((float(reaumur) * 1.25 + 273.15), ndigits)


def reaumur_to_fahrenheit(reaumur: float, ndigits: int = 2) -> float:
    """
    Convert a given value from reaumur to fahrenheit and round it to 2 decimal places.
    Reference:- http://www.csgnetwork.com/temp2conv.html

    >>> reaumur_to_fahrenheit(0)
    32.0
    >>> reaumur_to_fahrenheit(20.0)
    77.0
    >>> reaumur_to_fahrenheit(40)
    122.0
    >>> reaumur_to_fahrenheit("reaumur")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'reaumur'
    """
    return round((float(reaumur) * 2.25 + 32), ndigits)


def reaumur_to_celsius(reaumur: float, ndigits: int = 2) -> float:
    """
    Convert a given value from reaumur to celsius and round it to 2 decimal places.
    Reference:- http://www.csgnetwork.com/temp2conv.html

    >>> reaumur_to_celsius(0)
    0.0
    >>> reaumur_to_celsius(20.0)
    25.0
    >>> reaumur_to_celsius(40)
    50.0
    >>> reaumur_to_celsius("reaumur")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'reaumur'
    """
    return round((float(reaumur) * 1.25), ndigits)


def reaumur_to_rankine(reaumur: float, ndigits: int = 2) -> float:
    """
    Convert a given value from reaumur to rankine and round it to 2 decimal places.
    Reference:- http://www.csgnetwork.com/temp2conv.html

    >>> reaumur_to_rankine(0)
    491.67
    >>> reaumur_to_rankine(20.0)
    536.67
    >>> reaumur_to_rankine(40)
    581.67
    >>> reaumur_to_rankine("reaumur")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'reaumur'
    """
    return round((float(reaumur) * 2.25 + 32 + 459.67), ndigits)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: conversions/time_conversions.py
================================================
"""
A unit of time is any particular time interval, used as a standard way of measuring or
expressing duration.  The base unit of time in the International System of Units (SI),
and by extension most of the Western world, is the second, defined as about 9 billion
oscillations of the caesium atom.

https://en.wikipedia.org/wiki/Unit_of_time
"""

time_chart: dict[str, float] = {
    "seconds": 1.0,
    "minutes": 60.0,  # 1 minute = 60 sec
    "hours": 3600.0,  # 1 hour = 60 minutes = 3600 seconds
    "days": 86400.0,  # 1 day = 24 hours = 1440 min = 86400 sec
    "weeks": 604800.0,  # 1 week=7d=168hr=10080min = 604800 sec
    "months": 2629800.0,  # Approximate value for a month in seconds
    "years": 31557600.0,  # Approximate value for a year in seconds
}

time_chart_inverse: dict[str, float] = {
    key: 1 / value for key, value in time_chart.items()
}


def convert_time(time_value: float, unit_from: str, unit_to: str) -> float:
    """
    Convert time from one unit to another using the time_chart above.

    >>> convert_time(3600, "seconds", "hours")
    1.0
    >>> convert_time(3500, "Seconds", "Hours")
    0.972
    >>> convert_time(1, "DaYs", "hours")
    24.0
    >>> convert_time(120, "minutes", "SeCoNdS")
    7200.0
    >>> convert_time(2, "WEEKS", "days")
    14.0
    >>> convert_time(0.5, "hours", "MINUTES")
    30.0
    >>> convert_time(-3600, "seconds", "hours")
    Traceback (most recent call last):
        ...
    ValueError: 'time_value' must be a non-negative number.
    >>> convert_time("Hello", "hours", "minutes")
    Traceback (most recent call last):
        ...
    ValueError: 'time_value' must be a non-negative number.
    >>> convert_time([0, 1, 2], "weeks", "days")
    Traceback (most recent call last):
        ...
    ValueError: 'time_value' must be a non-negative number.
    >>> convert_time(1, "cool", "century")  # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    ValueError: Invalid unit cool is not in seconds, minutes, hours, days, weeks, ...
    >>> convert_time(1, "seconds", "hot")  # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    ValueError: Invalid unit hot is not in seconds, minutes, hours, days, weeks, ...
    """
    if not isinstance(time_value, (int, float)) or time_value < 0:
        msg = "'time_value' must be a non-negative number."
        raise ValueError(msg)

    unit_from = unit_from.lower()
    unit_to = unit_to.lower()
    if unit_from not in time_chart or unit_to not in time_chart:
        invalid_unit = unit_from if unit_from not in time_chart else unit_to
        msg = f"Invalid unit {invalid_unit} is not in {', '.join(time_chart)}."
        raise ValueError(msg)

    return round(
        time_value * time_chart[unit_from] * time_chart_inverse[unit_to],
        3,
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(f"{convert_time(3600,'seconds', 'hours') = :,}")
    print(f"{convert_time(360, 'days', 'months') = :,}")
    print(f"{convert_time(360, 'months', 'years') = :,}")
    print(f"{convert_time(1, 'years', 'seconds') = :,}")


================================================
FILE: conversions/volume_conversions.py
================================================
"""
Conversion of volume units.
Available Units:- Cubic metre,Litre,KiloLitre,Gallon,Cubic yard,Cubic foot,cup
USAGE :
-> Import this file into their respective project.
-> Use the function length_conversion() for conversion of volume units.
-> Parameters :
    -> value : The number of from units you want to convert
    -> from_type : From which type you want to convert
    -> to_type : To which type you want to convert
REFERENCES :
-> Wikipedia reference: https://en.wikipedia.org/wiki/Cubic_metre
-> Wikipedia reference: https://en.wikipedia.org/wiki/Litre
-> Wikipedia reference: https://en.wiktionary.org/wiki/kilolitre
-> Wikipedia reference: https://en.wikipedia.org/wiki/Gallon
-> Wikipedia reference: https://en.wikipedia.org/wiki/Cubic_yard
-> Wikipedia reference: https://en.wikipedia.org/wiki/Cubic_foot
-> Wikipedia reference: https://en.wikipedia.org/wiki/Cup_(unit)
"""

from typing import NamedTuple


class FromTo(NamedTuple):
    from_factor: float
    to_factor: float


METRIC_CONVERSION = {
    "cubic meter": FromTo(1, 1),
    "litre": FromTo(0.001, 1000),
    "kilolitre": FromTo(1, 1),
    "gallon": FromTo(0.00454, 264.172),
    "cubic yard": FromTo(0.76455, 1.30795),
    "cubic foot": FromTo(0.028, 35.3147),
    "cup": FromTo(0.000236588, 4226.75),
}


def volume_conversion(value: float, from_type: str, to_type: str) -> float:
    """
    Conversion between volume units.
    >>> volume_conversion(4, "cubic meter", "litre")
    4000
    >>> volume_conversion(1, "litre", "gallon")
    0.264172
    >>> volume_conversion(1, "kilolitre", "cubic meter")
    1
    >>> volume_conversion(3, "gallon", "cubic yard")
    0.017814279
    >>> volume_conversion(2, "cubic yard", "litre")
    1529.1
    >>> volume_conversion(4, "cubic foot", "cup")
    473.396
    >>> volume_conversion(1, "cup", "kilolitre")
    0.000236588
    >>> volume_conversion(4, "wrongUnit", "litre")
    Traceback (most recent call last):
        ...
    ValueError: Invalid 'from_type' value: 'wrongUnit'  Supported values are:
    cubic meter, litre, kilolitre, gallon, cubic yard, cubic foot, cup
    """
    if from_type not in METRIC_CONVERSION:
        raise ValueError(
            f"Invalid 'from_type' value: {from_type!r}  Supported values are:\n"
            + ", ".join(METRIC_CONVERSION)
        )
    if to_type not in METRIC_CONVERSION:
        raise ValueError(
            f"Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"
            + ", ".join(METRIC_CONVERSION)
        )
    return (
        value
        * METRIC_CONVERSION[from_type].from_factor
        * METRIC_CONVERSION[to_type].to_factor
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: conversions/weight_conversion.py
================================================
"""
Conversion of weight units.

__author__ = "Anubhav Solanki"
__license__ = "MIT"
__version__ = "1.1.0"
__maintainer__ = "Anubhav Solanki"
__email__ = "anubhavsolanki0@gmail.com"

USAGE :
-> Import this file into their respective project.
-> Use the function weight_conversion() for conversion of weight units.
-> Parameters :
    -> from_type : From which type you want to convert
    -> to_type : To which type you want to convert
    -> value : the value which you want to convert

REFERENCES :

-> Wikipedia reference: https://en.wikipedia.org/wiki/Kilogram
-> Wikipedia reference: https://en.wikipedia.org/wiki/Gram
-> Wikipedia reference: https://en.wikipedia.org/wiki/Millimetre
-> Wikipedia reference: https://en.wikipedia.org/wiki/Tonne
-> Wikipedia reference: https://en.wikipedia.org/wiki/Long_ton
-> Wikipedia reference: https://en.wikipedia.org/wiki/Short_ton
-> Wikipedia reference: https://en.wikipedia.org/wiki/Pound
-> Wikipedia reference: https://en.wikipedia.org/wiki/Ounce
-> Wikipedia reference: https://en.wikipedia.org/wiki/Fineness#Karat
-> Wikipedia reference: https://en.wikipedia.org/wiki/Dalton_(unit)
-> Wikipedia reference: https://en.wikipedia.org/wiki/Stone_(unit)
"""

KILOGRAM_CHART: dict[str, float] = {
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0009842073,
    "short-ton": 0.0011023122,
    "pound": 2.2046244202,
    "stone": 0.1574731728,
    "ounce": 35.273990723,
    "carrat": 5000,
    "atomic-mass-unit": 6.022136652e26,
}

WEIGHT_TYPE_CHART: dict[str, float] = {
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1016.04608,
    "short-ton": 907.184,
    "pound": 0.453592,
    "stone": 6.35029,
    "ounce": 0.0283495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.660540199e-27,
}


def weight_conversion(from_type: str, to_type: str, value: float) -> float:
    """
    Conversion of weight unit with the help of KILOGRAM_CHART

    "kilogram" : 1,
    "gram" : pow(10, 3),
    "milligram" : pow(10, 6),
    "metric-ton" : pow(10, -3),
    "long-ton" : 0.0009842073,
    "short-ton" : 0.0011023122,
    "pound" : 2.2046244202,
    "stone": 0.1574731728,
    "ounce" : 35.273990723,
    "carrat" : 5000,
    "atomic-mass-unit" : 6.022136652E+26

    >>> weight_conversion("kilogram","kilogram",4)
    4
    >>> weight_conversion("kilogram","gram",1)
    1000
    >>> weight_conversion("kilogram","milligram",4)
    4000000
    >>> weight_conversion("kilogram","metric-ton",4)
    0.004
    >>> weight_conversion("kilogram","long-ton",3)
    0.0029526219
    >>> weight_conversion("kilogram","short-ton",1)
    0.0011023122
    >>> weight_conversion("kilogram","pound",4)
    8.8184976808
    >>> weight_conversion("kilogram","stone",5)
    0.7873658640000001
    >>> weight_conversion("kilogram","ounce",4)
    141.095962892
    >>> weight_conversion("kilogram","carrat",3)
    15000
    >>> weight_conversion("kilogram","atomic-mass-unit",1)
    6.022136652e+26
    >>> weight_conversion("gram","kilogram",1)
    0.001
    >>> weight_conversion("gram","gram",3)
    3.0
    >>> weight_conversion("gram","milligram",2)
    2000.0
    >>> weight_conversion("gram","metric-ton",4)
    4e-06
    >>> weight_conversion("gram","long-ton",3)
    2.9526219e-06
    >>> weight_conversion("gram","short-ton",3)
    3.3069366000000003e-06
    >>> weight_conversion("gram","pound",3)
    0.0066138732606
    >>> weight_conversion("gram","stone",4)
    0.0006298926912000001
    >>> weight_conversion("gram","ounce",1)
    0.035273990723
    >>> weight_conversion("gram","carrat",2)
    10.0
    >>> weight_conversion("gram","atomic-mass-unit",1)
    6.022136652e+23
    >>> weight_conversion("milligram","kilogram",1)
    1e-06
    >>> weight_conversion("milligram","gram",2)
    0.002
    >>> weight_conversion("milligram","milligram",3)
    3.0
    >>> weight_conversion("milligram","metric-ton",3)
    3e-09
    >>> weight_conversion("milligram","long-ton",3)
    2.9526219e-09
    >>> weight_conversion("milligram","short-ton",1)
    1.1023122e-09
    >>> weight_conversion("milligram","pound",3)
    6.6138732605999995e-06
    >>> weight_conversion("milligram","ounce",2)
    7.054798144599999e-05
    >>> weight_conversion("milligram","carrat",1)
    0.005
    >>> weight_conversion("milligram","atomic-mass-unit",1)
    6.022136652e+20
    >>> weight_conversion("metric-ton","kilogram",2)
    2000
    >>> weight_conversion("metric-ton","gram",2)
    2000000
    >>> weight_conversion("metric-ton","milligram",3)
    3000000000
    >>> weight_conversion("metric-ton","metric-ton",2)
    2.0
    >>> weight_conversion("metric-ton","long-ton",3)
    2.9526219
    >>> weight_conversion("metric-ton","short-ton",2)
    2.2046244
    >>> weight_conversion("metric-ton","pound",3)
    6613.8732606
    >>> weight_conversion("metric-ton","ounce",4)
    141095.96289199998
    >>> weight_conversion("metric-ton","carrat",4)
    20000000
    >>> weight_conversion("metric-ton","atomic-mass-unit",1)
    6.022136652e+29
    >>> weight_conversion("long-ton","kilogram",4)
    4064.18432
    >>> weight_conversion("long-ton","gram",4)
    4064184.32
    >>> weight_conversion("long-ton","milligram",3)
    3048138240.0
    >>> weight_conversion("long-ton","metric-ton",4)
    4.06418432
    >>> weight_conversion("long-ton","long-ton",3)
    2.999999907217152
    >>> weight_conversion("long-ton","short-ton",1)
    1.119999989746176
    >>> weight_conversion("long-ton","pound",3)
    6720.000000049448
    >>> weight_conversion("long-ton","ounce",1)
    35840.000000060514
    >>> weight_conversion("long-ton","carrat",4)
    20320921.599999998
    >>> weight_conversion("long-ton","atomic-mass-unit",4)
    2.4475073353955697e+30
    >>> weight_conversion("short-ton","kilogram",3)
    2721.5519999999997
    >>> weight_conversion("short-ton","gram",3)
    2721552.0
    >>> weight_conversion("short-ton","milligram",1)
    907184000.0
    >>> weight_conversion("short-ton","metric-ton",4)
    3.628736
    >>> weight_conversion("short-ton","long-ton",3)
    2.6785713457296
    >>> weight_conversion("short-ton","short-ton",3)
    2.9999999725344
    >>> weight_conversion("short-ton","pound",2)
    4000.0000000294335
    >>> weight_conversion("short-ton","ounce",4)
    128000.00000021611
    >>> weight_conversion("short-ton","carrat",4)
    18143680.0
    >>> weight_conversion("short-ton","atomic-mass-unit",1)
    5.463186016507968e+29
    >>> weight_conversion("pound","kilogram",4)
    1.814368
    >>> weight_conversion("pound","gram",2)
    907.184
    >>> weight_conversion("pound","milligram",3)
    1360776.0
    >>> weight_conversion("pound","metric-ton",3)
    0.001360776
    >>> weight_conversion("pound","long-ton",2)
    0.0008928571152432
    >>> weight_conversion("pound","short-ton",1)
    0.0004999999954224
    >>> weight_conversion("pound","pound",3)
    3.0000000000220752
    >>> weight_conversion("pound","ounce",1)
    16.000000000027015
    >>> weight_conversion("pound","carrat",1)
    2267.96
    >>> weight_conversion("pound","atomic-mass-unit",4)
    1.0926372033015936e+27
    >>> weight_conversion("stone","kilogram",5)
    31.751450000000002
    >>> weight_conversion("stone","gram",2)
    12700.58
    >>> weight_conversion("stone","milligram",3)
    19050870.0
    >>> weight_conversion("stone","metric-ton",3)
    0.01905087
    >>> weight_conversion("stone","long-ton",3)
    0.018750005325351003
    >>> weight_conversion("stone","short-ton",3)
    0.021000006421614002
    >>> weight_conversion("stone","pound",2)
    28.00000881870372
    >>> weight_conversion("stone","ounce",1)
    224.00007054835967
    >>> weight_conversion("stone","carrat",2)
    63502.9
    >>> weight_conversion("ounce","kilogram",3)
    0.0850485
    >>> weight_conversion("ounce","gram",3)
    85.0485
    >>> weight_conversion("ounce","milligram",4)
    113398.0
    >>> weight_conversion("ounce","metric-ton",4)
    0.000113398
    >>> weight_conversion("ounce","long-ton",4)
    0.0001116071394054
    >>> weight_conversion("ounce","short-ton",4)
    0.0001249999988556
    >>> weight_conversion("ounce","pound",1)
    0.0625000000004599
    >>> weight_conversion("ounce","ounce",2)
    2.000000000003377
    >>> weight_conversion("ounce","carrat",1)
    141.7475
    >>> weight_conversion("ounce","atomic-mass-unit",1)
    1.70724563015874e+25
    >>> weight_conversion("carrat","kilogram",1)
    0.0002
    >>> weight_conversion("carrat","gram",4)
    0.8
    >>> weight_conversion("carrat","milligram",2)
    400.0
    >>> weight_conversion("carrat","metric-ton",2)
    4.0000000000000003e-07
    >>> weight_conversion("carrat","long-ton",3)
    5.9052438e-07
    >>> weight_conversion("carrat","short-ton",4)
    8.818497600000002e-07
    >>> weight_conversion("carrat","pound",1)
    0.00044092488404000004
    >>> weight_conversion("carrat","ounce",2)
    0.0141095962892
    >>> weight_conversion("carrat","carrat",4)
    4.0
    >>> weight_conversion("carrat","atomic-mass-unit",4)
    4.8177093216e+23
    >>> weight_conversion("atomic-mass-unit","kilogram",4)
    6.642160796e-27
    >>> weight_conversion("atomic-mass-unit","gram",2)
    3.321080398e-24
    >>> weight_conversion("atomic-mass-unit","milligram",2)
    3.3210803980000002e-21
    >>> weight_conversion("atomic-mass-unit","metric-ton",3)
    4.9816205970000004e-30
    >>> weight_conversion("atomic-mass-unit","long-ton",3)
    4.9029473573977584e-30
    >>> weight_conversion("atomic-mass-unit","short-ton",1)
    1.830433719948128e-30
    >>> weight_conversion("atomic-mass-unit","pound",3)
    1.0982602420317504e-26
    >>> weight_conversion("atomic-mass-unit","ounce",2)
    1.1714775914938915e-25
    >>> weight_conversion("atomic-mass-unit","carrat",2)
    1.660540199e-23
    >>> weight_conversion("atomic-mass-unit","atomic-mass-unit",2)
    1.999999998903455
    >>> weight_conversion("slug", "kilogram", 1)
    Traceback (most recent call last):
    ...
    ValueError: Invalid 'from_type' or 'to_type' value: 'slug', 'kilogram'
    Supported values are: kilogram, gram, milligram, metric-ton, long-ton, short-ton, \
pound, stone, ounce, carrat, atomic-mass-unit
    """
    if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
        msg = (
            f"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
            f"Supported values are: {', '.join(WEIGHT_TYPE_CHART)}"
        )
        raise ValueError(msg)
    return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_compression/README.md
================================================
# Compression

Data compression is everywhere, you need it to store data without taking too much space.
Either the compression loses some data (then we talk about lossy compression, such as .jpg) or it does not (and then it is lossless compression, such as .png)

Lossless compression is mainly used for archive purpose as it allows storing data without losing information about the file archived. On the other hand, lossy compression is used for transfer of file where quality isn't necessarily what is required (i.e: images on Twitter).

* <https://www.sciencedirect.com/topics/computer-science/compression-algorithm>
* <https://en.wikipedia.org/wiki/Data_compression>
* <https://en.wikipedia.org/wiki/Pigeonhole_principle>


================================================
FILE: data_compression/__init__.py
================================================


================================================
FILE: data_compression/burrows_wheeler.py
================================================
"""
https://en.wikipedia.org/wiki/Burrows%E2%80%93Wheeler_transform

The Burrows-Wheeler transform (BWT, also called block-sorting compression)
rearranges a character string into runs of similar characters. This is useful
for compression, since it tends to be easy to compress a string that has runs
of repeated characters by techniques such as move-to-front transform and
run-length encoding. More importantly, the transformation is reversible,
without needing to store any additional data except the position of the first
original character. The BWT is thus a "free" method of improving the efficiency
of text compression algorithms, costing only some extra computation.
"""

from __future__ import annotations

from typing import TypedDict


class BWTTransformDict(TypedDict):
    bwt_string: str
    idx_original_string: int


def all_rotations(s: str) -> list[str]:
    """
    :param s: The string that will be rotated len(s) times.
    :return: A list with the rotations.
    :raises TypeError: If s is not an instance of str.
    Examples:

    >>> all_rotations("^BANANA|") # doctest: +NORMALIZE_WHITESPACE
    ['^BANANA|', 'BANANA|^', 'ANANA|^B', 'NANA|^BA', 'ANA|^BAN', 'NA|^BANA',
    'A|^BANAN', '|^BANANA']
    >>> all_rotations("a_asa_da_casa") # doctest: +NORMALIZE_WHITESPACE
    ['a_asa_da_casa', '_asa_da_casaa', 'asa_da_casaa_', 'sa_da_casaa_a',
    'a_da_casaa_as', '_da_casaa_asa', 'da_casaa_asa_', 'a_casaa_asa_d',
    '_casaa_asa_da', 'casaa_asa_da_', 'asaa_asa_da_c', 'saa_asa_da_ca',
    'aa_asa_da_cas']
    >>> all_rotations("panamabanana") # doctest: +NORMALIZE_WHITESPACE
    ['panamabanana', 'anamabananap', 'namabananapa', 'amabananapan',
    'mabananapana', 'abananapanam', 'bananapanama', 'ananapanamab',
    'nanapanamaba', 'anapanamaban', 'napanamabana', 'apanamabanan']
    >>> all_rotations(5)
    Traceback (most recent call last):
        ...
    TypeError: The parameter s type must be str.
    """
    if not isinstance(s, str):
        raise TypeError("The parameter s type must be str.")

    return [s[i:] + s[:i] for i in range(len(s))]


def bwt_transform(s: str) -> BWTTransformDict:
    """
    :param s: The string that will be used at bwt algorithm
    :return: the string composed of the last char of each row of the ordered
    rotations and the index of the original string at ordered rotations list
    :raises TypeError: If the s parameter type is not str
    :raises ValueError: If the s parameter is empty
    Examples:

    >>> bwt_transform("^BANANA")
    {'bwt_string': 'BNN^AAA', 'idx_original_string': 6}
    >>> bwt_transform("a_asa_da_casa")
    {'bwt_string': 'aaaadss_c__aa', 'idx_original_string': 3}
    >>> bwt_transform("panamabanana")
    {'bwt_string': 'mnpbnnaaaaaa', 'idx_original_string': 11}
    >>> bwt_transform(4)
    Traceback (most recent call last):
        ...
    TypeError: The parameter s type must be str.
    >>> bwt_transform('')
    Traceback (most recent call last):
        ...
    ValueError: The parameter s must not be empty.
    """
    if not isinstance(s, str):
        raise TypeError("The parameter s type must be str.")
    if not s:
        raise ValueError("The parameter s must not be empty.")

    rotations = all_rotations(s)
    rotations.sort()  # sort the list of rotations in alphabetically order
    # make a string composed of the last char of each rotation
    response: BWTTransformDict = {
        "bwt_string": "".join([word[-1] for word in rotations]),
        "idx_original_string": rotations.index(s),
    }
    return response


def reverse_bwt(bwt_string: str, idx_original_string: int) -> str:
    """
    :param bwt_string: The string returned from bwt algorithm execution
    :param idx_original_string: A 0-based index of the string that was used to
    generate bwt_string at ordered rotations list
    :return: The string used to generate bwt_string when bwt was executed
    :raises TypeError: If the bwt_string parameter type is not str
    :raises ValueError: If the bwt_string parameter is empty
    :raises TypeError: If the idx_original_string type is not int or if not
    possible to cast it to int
    :raises ValueError: If the idx_original_string value is lower than 0 or
    greater than len(bwt_string) - 1

    >>> reverse_bwt("BNN^AAA", 6)
    '^BANANA'
    >>> reverse_bwt("aaaadss_c__aa", 3)
    'a_asa_da_casa'
    >>> reverse_bwt("mnpbnnaaaaaa", 11)
    'panamabanana'
    >>> reverse_bwt(4, 11)
    Traceback (most recent call last):
        ...
    TypeError: The parameter bwt_string type must be str.
    >>> reverse_bwt("", 11)
    Traceback (most recent call last):
        ...
    ValueError: The parameter bwt_string must not be empty.
    >>> reverse_bwt("mnpbnnaaaaaa", "asd") # doctest: +NORMALIZE_WHITESPACE
    Traceback (most recent call last):
        ...
    TypeError: The parameter idx_original_string type must be int or passive
    of cast to int.
    >>> reverse_bwt("mnpbnnaaaaaa", -1)
    Traceback (most recent call last):
        ...
    ValueError: The parameter idx_original_string must not be lower than 0.
    >>> reverse_bwt("mnpbnnaaaaaa", 12) # doctest: +NORMALIZE_WHITESPACE
    Traceback (most recent call last):
        ...
    ValueError: The parameter idx_original_string must be lower than
    len(bwt_string).
    >>> reverse_bwt("mnpbnnaaaaaa", 11.0)
    'panamabanana'
    >>> reverse_bwt("mnpbnnaaaaaa", 11.4)
    'panamabanana'
    """
    if not isinstance(bwt_string, str):
        raise TypeError("The parameter bwt_string type must be str.")
    if not bwt_string:
        raise ValueError("The parameter bwt_string must not be empty.")
    try:
        idx_original_string = int(idx_original_string)
    except ValueError:
        raise TypeError(
            "The parameter idx_original_string type must be int or passive"
            " of cast to int."
        )
    if idx_original_string < 0:
        raise ValueError("The parameter idx_original_string must not be lower than 0.")
    if idx_original_string >= len(bwt_string):
        raise ValueError(
            "The parameter idx_original_string must be lower than len(bwt_string)."
        )

    ordered_rotations = [""] * len(bwt_string)
    for _ in range(len(bwt_string)):
        for i in range(len(bwt_string)):
            ordered_rotations[i] = bwt_string[i] + ordered_rotations[i]
        ordered_rotations.sort()
    return ordered_rotations[idx_original_string]


if __name__ == "__main__":
    entry_msg = "Provide a string that I will generate its BWT transform: "
    s = input(entry_msg).strip()
    result = bwt_transform(s)
    print(
        f"Burrows Wheeler transform for string '{s}' results "
        f"in '{result['bwt_string']}'"
    )
    original_string = reverse_bwt(result["bwt_string"], result["idx_original_string"])
    print(
        f"Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' "
        f"we get original string '{original_string}'"
    )


================================================
FILE: data_compression/coordinate_compression.py
================================================
"""
Assumption:
    - The values to compress are assumed to be comparable,
      values can be sorted and compared with '<' and '>' operators.
"""


class CoordinateCompressor:
    """
    A class for coordinate compression.

    This class allows you to compress and decompress a list of values.

    Mapping:
    In addition to compression and decompression, this class maintains a mapping
    between original values and their compressed counterparts using two data
    structures: a dictionary `coordinate_map` and a list `reverse_map`:
    - `coordinate_map`: A dictionary that maps original values to their compressed
      coordinates. Keys are original values, and values are compressed coordinates.
    - `reverse_map`: A list used for reverse mapping, where each index corresponds
      to a compressed coordinate, and the value at that index is the original value.

    Example of mapping:
    Original: 10, Compressed: 0
    Original: 52, Compressed: 1
    Original: 83, Compressed: 2
    Original: 100, Compressed: 3

    This mapping allows for efficient compression and decompression of values within
    the list.
    """

    def __init__(self, arr: list[int | float | str]) -> None:
        """
        Initialize the CoordinateCompressor with a list.

        Args:
        arr: The list of values to be compressed.

        >>> arr = [100, 10, 52, 83]
        >>> cc = CoordinateCompressor(arr)
        >>> cc.compress(100)
        3
        >>> cc.compress(52)
        1
        >>> cc.decompress(1)
        52
        """

        # A dictionary to store compressed coordinates
        self.coordinate_map: dict[int | float | str, int] = {}

        # A list to store reverse mapping
        self.reverse_map: list[int | float | str] = [-1] * len(arr)

        self.arr = sorted(arr)  # The input list
        self.n = len(arr)  # The length of the input list
        self.compress_coordinates()

    def compress_coordinates(self) -> None:
        """
        Compress the coordinates in the input list.

        >>> arr = [100, 10, 52, 83]
        >>> cc = CoordinateCompressor(arr)
        >>> cc.coordinate_map[83]
        2
        >>> cc.coordinate_map[80]  # Value not in the original list
        Traceback (most recent call last):
            ...
        KeyError: 80
        >>> cc.reverse_map[2]
        83
        """
        key = 0
        for val in self.arr:
            if val not in self.coordinate_map:
                self.coordinate_map[val] = key
                self.reverse_map[key] = val
                key += 1

    def compress(self, original: float | str) -> int:
        """
        Compress a single value.

        Args:
        original: The value to compress.

        Returns:
        The compressed integer, or -1 if not found in the original list.

        >>> arr = [100, 10, 52, 83]
        >>> cc = CoordinateCompressor(arr)
        >>> cc.compress(100)
        3
        >>> cc.compress(7)  # Value not in the original list
        -1
        """
        return self.coordinate_map.get(original, -1)

    def decompress(self, num: int) -> int | float | str:
        """
        Decompress a single integer.

        Args:
        num: The compressed integer to decompress.

        Returns:
        The original value.

        >>> arr = [100, 10, 52, 83]
        >>> cc = CoordinateCompressor(arr)
        >>> cc.decompress(0)
        10
        >>> cc.decompress(5)  # Compressed coordinate out of range
        -1
        """
        return self.reverse_map[num] if 0 <= num < len(self.reverse_map) else -1


if __name__ == "__main__":
    from doctest import testmod

    testmod()

    arr: list[int | float | str] = [100, 10, 52, 83]
    cc = CoordinateCompressor(arr)

    for original in arr:
        compressed = cc.compress(original)
        decompressed = cc.decompress(compressed)
        print(f"Original: {decompressed}, Compressed: {compressed}")


================================================
FILE: data_compression/huffman.py
================================================
from __future__ import annotations

import sys


class Letter:
    def __init__(self, letter: str, freq: int):
        self.letter: str = letter
        self.freq: int = freq
        self.bitstring: dict[str, str] = {}

    def __repr__(self) -> str:
        return f"{self.letter}:{self.freq}"


class TreeNode:
    def __init__(self, freq: int, left: Letter | TreeNode, right: Letter | TreeNode):
        self.freq: int = freq
        self.left: Letter | TreeNode = left
        self.right: Letter | TreeNode = right


def parse_file(file_path: str) -> list[Letter]:
    """
    Read the file and build a dict of all letters and their
    frequencies, then convert the dict into a list of Letters.
    """
    chars: dict[str, int] = {}
    with open(file_path) as f:
        while True:
            c = f.read(1)
            if not c:
                break
            chars[c] = chars[c] + 1 if c in chars else 1
    return sorted((Letter(c, f) for c, f in chars.items()), key=lambda x: x.freq)


def build_tree(letters: list[Letter]) -> Letter | TreeNode:
    """
    Run through the list of Letters and build the min heap
    for the Huffman Tree.
    """
    response: list[Letter | TreeNode] = list(letters)
    while len(response) > 1:
        left = response.pop(0)
        right = response.pop(0)
        total_freq = left.freq + right.freq
        node = TreeNode(total_freq, left, right)
        response.append(node)
        response.sort(key=lambda x: x.freq)
    return response[0]


def traverse_tree(root: Letter | TreeNode, bitstring: str) -> list[Letter]:
    """
    Recursively traverse the Huffman Tree to set each
    Letter's bitstring dictionary, and return the list of Letters
    """
    if isinstance(root, Letter):
        root.bitstring[root.letter] = bitstring
        return [root]
    treenode: TreeNode = root
    letters = []
    letters += traverse_tree(treenode.left, bitstring + "0")
    letters += traverse_tree(treenode.right, bitstring + "1")
    return letters


def huffman(file_path: str) -> None:
    """
    Parse the file, build the tree, then run through the file
    again, using the letters dictionary to find and print out the
    bitstring for each letter.
    """
    letters_list = parse_file(file_path)
    root = build_tree(letters_list)
    letters = {
        k: v for letter in traverse_tree(root, "") for k, v in letter.bitstring.items()
    }
    print(f"Huffman Coding  of {file_path}: ")
    with open(file_path) as f:
        while True:
            c = f.read(1)
            if not c:
                break
            print(letters[c], end=" ")
    print()


if __name__ == "__main__":
    # pass the file path to the huffman function
    huffman(sys.argv[1])


================================================
FILE: data_compression/lempel_ziv.py
================================================
"""
One of the several implementations of Lempel-Ziv-Welch compression algorithm
https://en.wikipedia.org/wiki/Lempel%E2%80%93Ziv%E2%80%93Welch
"""

import math
import os
import sys


def read_file_binary(file_path: str) -> str:
    """
    Reads given file as bytes and returns them as a long string
    """
    result = ""
    try:
        with open(file_path, "rb") as binary_file:
            data = binary_file.read()
        for dat in data:
            curr_byte = f"{dat:08b}"
            result += curr_byte
        return result
    except OSError:
        print("File not accessible")
        sys.exit()


def add_key_to_lexicon(
    lexicon: dict[str, str], curr_string: str, index: int, last_match_id: str
) -> None:
    """
    Adds new strings (curr_string + "0",  curr_string + "1") to the lexicon
    """
    lexicon.pop(curr_string)
    lexicon[curr_string + "0"] = last_match_id

    if math.log2(index).is_integer():
        for curr_key, value in lexicon.items():
            lexicon[curr_key] = f"0{value}"

    lexicon[curr_string + "1"] = bin(index)[2:]


def compress_data(data_bits: str) -> str:
    """
    Compresses given data_bits using Lempel-Ziv-Welch compression algorithm
    and returns the result as a string
    """
    lexicon = {"0": "0", "1": "1"}
    result, curr_string = "", ""
    index = len(lexicon)

    for i in range(len(data_bits)):
        curr_string += data_bits[i]
        if curr_string not in lexicon:
            continue

        last_match_id = lexicon[curr_string]
        result += last_match_id
        add_key_to_lexicon(lexicon, curr_string, index, last_match_id)
        index += 1
        curr_string = ""

    while curr_string != "" and curr_string not in lexicon:
        curr_string += "0"

    if curr_string != "":
        last_match_id = lexicon[curr_string]
        result += last_match_id

    return result


def add_file_length(source_path: str, compressed: str) -> str:
    """
    Adds given file's length in front (using Elias  gamma coding) of the compressed
    string
    """
    file_length = os.path.getsize(source_path)
    file_length_binary = bin(file_length)[2:]
    length_length = len(file_length_binary)

    return "0" * (length_length - 1) + file_length_binary + compressed


def write_file_binary(file_path: str, to_write: str) -> None:
    """
    Writes given to_write string (should only consist of 0's and 1's) as bytes in the
    file
    """
    byte_length = 8
    try:
        with open(file_path, "wb") as opened_file:
            result_byte_array = [
                to_write[i : i + byte_length]
                for i in range(0, len(to_write), byte_length)
            ]

            if len(result_byte_array[-1]) % byte_length == 0:
                result_byte_array.append("10000000")
            else:
                result_byte_array[-1] += "1" + "0" * (
                    byte_length - len(result_byte_array[-1]) - 1
                )

            for elem in result_byte_array:
                opened_file.write(int(elem, 2).to_bytes(1, byteorder="big"))
    except OSError:
        print("File not accessible")
        sys.exit()


def compress(source_path: str, destination_path: str) -> None:
    """
    Reads source file, compresses it and writes the compressed result in destination
    file
    """
    data_bits = read_file_binary(source_path)
    compressed = compress_data(data_bits)
    compressed = add_file_length(source_path, compressed)
    write_file_binary(destination_path, compressed)


if __name__ == "__main__":
    compress(sys.argv[1], sys.argv[2])


================================================
FILE: data_compression/lempel_ziv_decompress.py
================================================
"""
One of the several implementations of Lempel-Ziv-Welch decompression algorithm
https://en.wikipedia.org/wiki/Lempel%E2%80%93Ziv%E2%80%93Welch
"""

import math
import sys


def read_file_binary(file_path: str) -> str:
    """
    Reads given file as bytes and returns them as a long string
    """
    result = ""
    try:
        with open(file_path, "rb") as binary_file:
            data = binary_file.read()
        for dat in data:
            curr_byte = f"{dat:08b}"
            result += curr_byte
        return result
    except OSError:
        print("File not accessible")
        sys.exit()


def decompress_data(data_bits: str) -> str:
    """
    Decompresses given data_bits using Lempel-Ziv-Welch compression algorithm
    and returns the result as a string
    """
    lexicon = {"0": "0", "1": "1"}
    result, curr_string = "", ""
    index = len(lexicon)

    for i in range(len(data_bits)):
        curr_string += data_bits[i]
        if curr_string not in lexicon:
            continue

        last_match_id = lexicon[curr_string]
        result += last_match_id
        lexicon[curr_string] = last_match_id + "0"

        if math.log2(index).is_integer():
            new_lex = {}
            for curr_key in list(lexicon):
                new_lex["0" + curr_key] = lexicon.pop(curr_key)
            lexicon = new_lex

        lexicon[bin(index)[2:]] = last_match_id + "1"
        index += 1
        curr_string = ""
    return result


def write_file_binary(file_path: str, to_write: str) -> None:
    """
    Writes given to_write string (should only consist of 0's and 1's) as bytes in the
    file
    """
    byte_length = 8
    try:
        with open(file_path, "wb") as opened_file:
            result_byte_array = [
                to_write[i : i + byte_length]
                for i in range(0, len(to_write), byte_length)
            ]

            if len(result_byte_array[-1]) % byte_length == 0:
                result_byte_array.append("10000000")
            else:
                result_byte_array[-1] += "1" + "0" * (
                    byte_length - len(result_byte_array[-1]) - 1
                )

            for elem in result_byte_array[:-1]:
                opened_file.write(int(elem, 2).to_bytes(1, byteorder="big"))
    except OSError:
        print("File not accessible")
        sys.exit()


def remove_prefix(data_bits: str) -> str:
    """
    Removes size prefix, that compressed file should have
    Returns the result
    """
    counter = 0
    for letter in data_bits:
        if letter == "1":
            break
        counter += 1

    data_bits = data_bits[counter:]
    data_bits = data_bits[counter + 1 :]
    return data_bits


def compress(source_path: str, destination_path: str) -> None:
    """
    Reads source file, decompresses it and writes the result in destination file
    """
    data_bits = read_file_binary(source_path)
    data_bits = remove_prefix(data_bits)
    decompressed = decompress_data(data_bits)
    write_file_binary(destination_path, decompressed)


if __name__ == "__main__":
    compress(sys.argv[1], sys.argv[2])


================================================
FILE: data_compression/lz77.py
================================================
"""
LZ77 compression algorithm
- lossless data compression published in papers by Abraham Lempel and Jacob Ziv in 1977
- also known as LZ1 or sliding-window compression
- form the basis for many variations including LZW, LZSS, LZMA and others

It uses a “sliding window” method. Within the sliding window we have:
  - search buffer
  - look ahead buffer
len(sliding_window) = len(search_buffer) + len(look_ahead_buffer)

LZ77 manages a dictionary that uses triples composed of:
    - Offset into search buffer, it's the distance between the start of a phrase and
      the beginning of a file.
    - Length of the match, it's the number of characters that make up a phrase.
    - The indicator is represented by a character that is going to be encoded next.

As a file is parsed, the dictionary is dynamically updated to reflect the compressed
data contents and size.

Examples:
"cabracadabrarrarrad" <-> [(0, 0, 'c'), (0, 0, 'a'), (0, 0, 'b'), (0, 0, 'r'),
                           (3, 1, 'c'), (2, 1, 'd'), (7, 4, 'r'), (3, 5, 'd')]
"ababcbababaa" <-> [(0, 0, 'a'), (0, 0, 'b'), (2, 2, 'c'), (4, 3, 'a'), (2, 2, 'a')]
"aacaacabcabaaac" <-> [(0, 0, 'a'), (1, 1, 'c'), (3, 4, 'b'), (3, 3, 'a'), (1, 2, 'c')]

Sources:
en.wikipedia.org/wiki/LZ77_and_LZ78
"""

from dataclasses import dataclass

__version__ = "0.1"
__author__ = "Lucia Harcekova"


@dataclass
class Token:
    """
    Dataclass representing triplet called token consisting of length, offset
    and indicator. This triplet is used during LZ77 compression.
    """

    offset: int
    length: int
    indicator: str

    def __repr__(self) -> str:
        """
        >>> token = Token(1, 2, "c")
        >>> repr(token)
        '(1, 2, c)'
        >>> str(token)
        '(1, 2, c)'
        """
        return f"({self.offset}, {self.length}, {self.indicator})"


class LZ77Compressor:
    """
    Class containing compress and decompress methods using LZ77 compression algorithm.
    """

    def __init__(self, window_size: int = 13, lookahead_buffer_size: int = 6) -> None:
        self.window_size = window_size
        self.lookahead_buffer_size = lookahead_buffer_size
        self.search_buffer_size = self.window_size - self.lookahead_buffer_size

    def compress(self, text: str) -> list[Token]:
        """
        Compress the given string text using LZ77 compression algorithm.

        Args:
            text: string to be compressed

        Returns:
            output: the compressed text as a list of Tokens

        >>> lz77_compressor = LZ77Compressor()
        >>> str(lz77_compressor.compress("ababcbababaa"))
        '[(0, 0, a), (0, 0, b), (2, 2, c), (4, 3, a), (2, 2, a)]'
        >>> str(lz77_compressor.compress("aacaacabcabaaac"))
        '[(0, 0, a), (1, 1, c), (3, 4, b), (3, 3, a), (1, 2, c)]'
        """

        output = []
        search_buffer = ""

        # while there are still characters in text to compress
        while text:
            # find the next encoding phrase
            # - triplet with offset, length, indicator (the next encoding character)
            token = self._find_encoding_token(text, search_buffer)

            # update the search buffer:
            # - add new characters from text into it
            # - check if size exceed the max search buffer size, if so, drop the
            #   oldest elements
            search_buffer += text[: token.length + 1]
            if len(search_buffer) > self.search_buffer_size:
                search_buffer = search_buffer[-self.search_buffer_size :]

            # update the text
            text = text[token.length + 1 :]

            # append the token to output
            output.append(token)

        return output

    def decompress(self, tokens: list[Token]) -> str:
        """
        Convert the list of tokens into an output string.

        Args:
            tokens: list containing triplets (offset, length, char)

        Returns:
            output: decompressed text

        Tests:
            >>> lz77_compressor = LZ77Compressor()
            >>> lz77_compressor.decompress([Token(0, 0, 'c'), Token(0, 0, 'a'),
            ... Token(0, 0, 'b'), Token(0, 0, 'r'), Token(3, 1, 'c'),
            ... Token(2, 1, 'd'), Token(7, 4, 'r'), Token(3, 5, 'd')])
            'cabracadabrarrarrad'
            >>> lz77_compressor.decompress([Token(0, 0, 'a'), Token(0, 0, 'b'),
            ... Token(2, 2, 'c'), Token(4, 3, 'a'), Token(2, 2, 'a')])
            'ababcbababaa'
            >>> lz77_compressor.decompress([Token(0, 0, 'a'), Token(1, 1, 'c'),
            ... Token(3, 4, 'b'), Token(3, 3, 'a'), Token(1, 2, 'c')])
            'aacaacabcabaaac'
        """

        output = ""

        for token in tokens:
            for _ in range(token.length):
                output += output[-token.offset]
            output += token.indicator

        return output

    def _find_encoding_token(self, text: str, search_buffer: str) -> Token:
        """Finds the encoding token for the first character in the text.

        Tests:
            >>> lz77_compressor = LZ77Compressor()
            >>> lz77_compressor._find_encoding_token("abrarrarrad", "abracad").offset
            7
            >>> lz77_compressor._find_encoding_token("adabrarrarrad", "cabrac").length
            1
            >>> lz77_compressor._find_encoding_token("abc", "xyz").offset
            0
            >>> lz77_compressor._find_encoding_token("", "xyz").offset
            Traceback (most recent call last):
                ...
            ValueError: We need some text to work with.
            >>> lz77_compressor._find_encoding_token("abc", "").offset
            0
        """

        if not text:
            raise ValueError("We need some text to work with.")

        # Initialise result parameters to default values
        length, offset = 0, 0

        if not search_buffer:
            return Token(offset, length, text[length])

        for i, character in enumerate(search_buffer):
            found_offset = len(search_buffer) - i
            if character == text[0]:
                found_length = self._match_length_from_index(text, search_buffer, 0, i)
                # if the found length is bigger than the current or if it's equal,
                # which means it's offset is smaller: update offset and length
                if found_length >= length:
                    offset, length = found_offset, found_length

        return Token(offset, length, text[length])

    def _match_length_from_index(
        self, text: str, window: str, text_index: int, window_index: int
    ) -> int:
        """Calculate the longest possible match of text and window characters from
        text_index in text and window_index in window.

        Args:
            text: _description_
            window: sliding window
            text_index: index of character in text
            window_index: index of character in sliding window

        Returns:
            The maximum match between text and window, from given indexes.

        Tests:
            >>> lz77_compressor = LZ77Compressor(13, 6)
            >>> lz77_compressor._match_length_from_index("rarrad", "adabrar", 0, 4)
            5
            >>> lz77_compressor._match_length_from_index("adabrarrarrad",
            ...     "cabrac", 0, 1)
            1
        """
        if not text or text[text_index] != window[window_index]:
            return 0
        return 1 + self._match_length_from_index(
            text, window + text[text_index], text_index + 1, window_index + 1
        )


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    # Initialize compressor class
    lz77_compressor = LZ77Compressor(window_size=13, lookahead_buffer_size=6)

    # Example
    TEXT = "cabracadabrarrarrad"
    compressed_text = lz77_compressor.compress(TEXT)
    print(lz77_compressor.compress("ababcbababaa"))
    decompressed_text = lz77_compressor.decompress(compressed_text)
    assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."


================================================
FILE: data_compression/peak_signal_to_noise_ratio.py
================================================
"""
Peak signal-to-noise ratio - PSNR
    https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
Source:
https://tutorials.techonical.com/how-to-calculate-psnr-value-of-two-images-using-python
"""

import math
import os

import cv2
import numpy as np

PIXEL_MAX = 255.0


def peak_signal_to_noise_ratio(original: float, contrast: float) -> float:
    mse = np.mean((original - contrast) ** 2)
    if mse == 0:
        return 100

    return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))


def main() -> None:
    dir_path = os.path.dirname(os.path.realpath(__file__))
    # Loading images (original image and compressed image)
    original = cv2.imread(os.path.join(dir_path, "image_data/original_image.png"))
    contrast = cv2.imread(os.path.join(dir_path, "image_data/compressed_image.png"), 1)

    original2 = cv2.imread(os.path.join(dir_path, "image_data/PSNR-example-base.png"))
    contrast2 = cv2.imread(
        os.path.join(dir_path, "image_data/PSNR-example-comp-10.jpg"), 1
    )

    # Value expected: 29.73dB
    print("-- First Test --")
    print(f"PSNR value is {peak_signal_to_noise_ratio(original, contrast)} dB")

    # # Value expected: 31.53dB (Wikipedia Example)
    print("\n-- Second Test --")
    print(f"PSNR value is {peak_signal_to_noise_ratio(original2, contrast2)} dB")


if __name__ == "__main__":
    main()


================================================
FILE: data_compression/run_length_encoding.py
================================================
# https://en.wikipedia.org/wiki/Run-length_encoding


def run_length_encode(text: str) -> list:
    """
    Performs Run Length Encoding
    >>> run_length_encode("AAAABBBCCDAA")
    [('A', 4), ('B', 3), ('C', 2), ('D', 1), ('A', 2)]
    >>> run_length_encode("A")
    [('A', 1)]
    >>> run_length_encode("AA")
    [('A', 2)]
    >>> run_length_encode("AAADDDDDDFFFCCCAAVVVV")
    [('A', 3), ('D', 6), ('F', 3), ('C', 3), ('A', 2), ('V', 4)]
    """
    encoded = []
    count = 1

    for i in range(len(text)):
        if i + 1 < len(text) and text[i] == text[i + 1]:
            count += 1
        else:
            encoded.append((text[i], count))
            count = 1

    return encoded


def run_length_decode(encoded: list) -> str:
    """
    Performs Run Length Decoding
    >>> run_length_decode([('A', 4), ('B', 3), ('C', 2), ('D', 1), ('A', 2)])
    'AAAABBBCCDAA'
    >>> run_length_decode([('A', 1)])
    'A'
    >>> run_length_decode([('A', 2)])
    'AA'
    >>> run_length_decode([('A', 3), ('D', 6), ('F', 3), ('C', 3), ('A', 2), ('V', 4)])
    'AAADDDDDDFFFCCCAAVVVV'
    """
    return "".join(char * length for char, length in encoded)


if __name__ == "__main__":
    from doctest import testmod

    testmod(name="run_length_encode", verbose=True)
    testmod(name="run_length_decode", verbose=True)


================================================
FILE: data_structures/__init__.py
================================================


================================================
FILE: data_structures/arrays/__init__.py
================================================


================================================
FILE: data_structures/arrays/equilibrium_index_in_array.py
================================================
"""
Find the Equilibrium Index of an Array.
Reference: https://www.geeksforgeeks.org/equilibrium-index-of-an-array/

Python doctest can be run with the following command:
python -m doctest -v equilibrium_index_in_array.py

Given a sequence arr[] of size n, this function returns
an equilibrium index (if any) or -1 if no equilibrium index exists.

The equilibrium index of an array is an index such that the sum of
elements at lower indexes is equal to the sum of elements at higher indexes.



Example Input:
arr = [-7, 1, 5, 2, -4, 3, 0]
Output: 3

"""


def equilibrium_index(arr: list[int]) -> int:
    """
    Find the equilibrium index of an array.

    Args:
        arr (list[int]): The input array of integers.

    Returns:
        int: The equilibrium index or -1 if no equilibrium index exists.

    Examples:
        >>> equilibrium_index([-7, 1, 5, 2, -4, 3, 0])
        3
        >>> equilibrium_index([1, 2, 3, 4, 5])
        -1
        >>> equilibrium_index([1, 1, 1, 1, 1])
        2
        >>> equilibrium_index([2, 4, 6, 8, 10, 3])
        -1
    """
    total_sum = sum(arr)
    left_sum = 0

    for i, value in enumerate(arr):
        total_sum -= value
        if left_sum == total_sum:
            return i
        left_sum += value

    return -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/arrays/find_triplets_with_0_sum.py
================================================
from itertools import combinations


def find_triplets_with_0_sum(nums: list[int]) -> list[list[int]]:
    """
    Given a list of integers, return elements a, b, c such that a + b + c = 0.
    Args:
        nums: list of integers
    Returns:
        list of lists of integers where sum(each_list) == 0
    Examples:
        >>> find_triplets_with_0_sum([-1, 0, 1, 2, -1, -4])
        [[-1, -1, 2], [-1, 0, 1]]
        >>> find_triplets_with_0_sum([])
        []
        >>> find_triplets_with_0_sum([0, 0, 0])
        [[0, 0, 0]]
        >>> find_triplets_with_0_sum([1, 2, 3, 0, -1, -2, -3])
        [[-3, 0, 3], [-3, 1, 2], [-2, -1, 3], [-2, 0, 2], [-1, 0, 1]]
    """
    return [
        list(x)
        for x in sorted({abc for abc in combinations(sorted(nums), 3) if not sum(abc)})
    ]


def find_triplets_with_0_sum_hashing(arr: list[int]) -> list[list[int]]:
    """
    Function for finding the triplets with a given sum in the array using hashing.

    Given a list of integers, return elements a, b, c such that a + b + c = 0.

    Args:
        nums: list of integers
    Returns:
        list of lists of integers where sum(each_list) == 0
    Examples:
        >>> find_triplets_with_0_sum_hashing([-1, 0, 1, 2, -1, -4])
        [[-1, 0, 1], [-1, -1, 2]]
        >>> find_triplets_with_0_sum_hashing([])
        []
        >>> find_triplets_with_0_sum_hashing([0, 0, 0])
        [[0, 0, 0]]
        >>> find_triplets_with_0_sum_hashing([1, 2, 3, 0, -1, -2, -3])
        [[-1, 0, 1], [-3, 1, 2], [-2, 0, 2], [-2, -1, 3], [-3, 0, 3]]

    Time complexity: O(N^2)
    Auxiliary Space: O(N)

    """
    target_sum = 0

    # Initialize the final output array with blank.
    output_arr = []

    # Set the initial element as arr[i].
    for index, item in enumerate(arr[:-2]):
        # to store second elements that can complement the final sum.
        set_initialize = set()

        # current sum needed for reaching the target sum
        current_sum = target_sum - item

        # Traverse the subarray arr[i+1:].
        for other_item in arr[index + 1 :]:
            # required value for the second element
            required_value = current_sum - other_item

            # Verify if the desired value exists in the set.
            if required_value in set_initialize:
                # finding triplet elements combination.
                combination_array = sorted([item, other_item, required_value])
                if combination_array not in output_arr:
                    output_arr.append(combination_array)

            # Include the current element in the set
            # for subsequent complement verification.
            set_initialize.add(other_item)

    # Return all the triplet combinations.
    return output_arr


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: data_structures/arrays/index_2d_array_in_1d.py
================================================
"""
Retrieves the value of an 0-indexed 1D index from a 2D array.
There are two ways to retrieve value(s):

1. Index2DArrayIterator(matrix) -> Iterator[int]
This iterator allows you to iterate through a 2D array by passing in the matrix and
calling next(your_iterator). You can also use the iterator in a loop.
Examples:
list(Index2DArrayIterator(matrix))
set(Index2DArrayIterator(matrix))
tuple(Index2DArrayIterator(matrix))
sum(Index2DArrayIterator(matrix))
-5 in Index2DArrayIterator(matrix)

2. index_2d_array_in_1d(array: list[int], index: int) -> int
This function allows you to provide a 2D array and a 0-indexed 1D integer index,
and retrieves the integer value at that index.

Python doctests can be run using this command:
python3 -m doctest -v index_2d_array_in_1d.py
"""

from collections.abc import Iterator
from dataclasses import dataclass


@dataclass
class Index2DArrayIterator:
    matrix: list[list[int]]

    def __iter__(self) -> Iterator[int]:
        """
        >>> tuple(Index2DArrayIterator([[5], [-523], [-1], [34], [0]]))
        (5, -523, -1, 34, 0)
        >>> tuple(Index2DArrayIterator([[5, -523, -1], [34, 0]]))
        (5, -523, -1, 34, 0)
        >>> tuple(Index2DArrayIterator([[5, -523, -1, 34, 0]]))
        (5, -523, -1, 34, 0)
        >>> t = Index2DArrayIterator([[5, 2, 25], [23, 14, 5], [324, -1, 0]])
        >>> tuple(t)
        (5, 2, 25, 23, 14, 5, 324, -1, 0)
        >>> list(t)
        [5, 2, 25, 23, 14, 5, 324, -1, 0]
        >>> sorted(t)
        [-1, 0, 2, 5, 5, 14, 23, 25, 324]
        >>> tuple(t)[3]
        23
        >>> sum(t)
        397
        >>> -1 in t
        True
        >>> t = iter(Index2DArrayIterator([[5], [-523], [-1], [34], [0]]))
        >>> next(t)
        5
        >>> next(t)
        -523
        """
        for row in self.matrix:
            yield from row


def index_2d_array_in_1d(array: list[list[int]], index: int) -> int:
    """
    Retrieves the value of the one-dimensional index from a two-dimensional array.

    Args:
        array: A 2D array of integers where all rows are the same size and all
               columns are the same size.
        index: A 1D index.

    Returns:
        int: The 0-indexed value of the 1D index in the array.

    Examples:
    >>> index_2d_array_in_1d([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], 5)
    5
    >>> index_2d_array_in_1d([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], -1)
    Traceback (most recent call last):
        ...
    ValueError: index out of range
    >>> index_2d_array_in_1d([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], 12)
    Traceback (most recent call last):
        ...
    ValueError: index out of range
    >>> index_2d_array_in_1d([[]], 0)
    Traceback (most recent call last):
        ...
    ValueError: no items in array
    """
    rows = len(array)
    cols = len(array[0])

    if rows == 0 or cols == 0:
        raise ValueError("no items in array")

    if index < 0 or index >= rows * cols:
        raise ValueError("index out of range")

    return array[index // cols][index % cols]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/arrays/kth_largest_element.py
================================================
"""
Given an array of integers and an integer k, find the kth largest element in the array.

https://stackoverflow.com/questions/251781
"""


def partition(arr: list[int], low: int, high: int) -> int:
    """
    Partitions list based on the pivot element.

    This function rearranges the elements in the input list 'elements' such that
    all elements greater than or equal to the chosen pivot are on the right side
    of the pivot, and all elements smaller than the pivot are on the left side.

    Args:
        arr: The list to be partitioned
        low: The lower index of the list
        high: The higher index of the list

    Returns:
        int: The index of pivot element after partitioning

        Examples:
        >>> partition([3, 1, 4, 5, 9, 2, 6, 5, 3, 5], 0, 9)
        4
        >>> partition([7, 1, 4, 5, 9, 2, 6, 5, 8], 0, 8)
        1
        >>> partition(['apple', 'cherry', 'date', 'banana'], 0, 3)
        2
        >>> partition([3.1, 1.2, 5.6, 4.7], 0, 3)
        1
    """
    pivot = arr[high]
    i = low - 1
    for j in range(low, high):
        if arr[j] >= pivot:
            i += 1
            arr[i], arr[j] = arr[j], arr[i]
    arr[i + 1], arr[high] = arr[high], arr[i + 1]
    return i + 1


def kth_largest_element(arr: list[int], position: int) -> int:
    """
    Finds the kth largest element in a list.
    Should deliver similar results to:
    ```python
    def kth_largest_element(arr, position):
        return sorted(arr)[-position]
    ```

    Args:
        nums: The list of numbers.
        k: The position of the desired kth largest element.

    Returns:
        int: The kth largest element.

    Examples:
        >>> kth_largest_element([3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5], 3)
        5
        >>> kth_largest_element([2, 5, 6, 1, 9, 3, 8, 4, 7, 3, 5], 1)
        9
        >>> kth_largest_element([2, 5, 6, 1, 9, 3, 8, 4, 7, 3, 5], -2)
        Traceback (most recent call last):
        ...
        ValueError: Invalid value of 'position'
        >>> kth_largest_element([9, 1, 3, 6, 7, 9, 8, 4, 2, 4, 9], 110)
        Traceback (most recent call last):
        ...
        ValueError: Invalid value of 'position'
        >>> kth_largest_element([1, 2, 4, 3, 5, 9, 7, 6, 5, 9, 3], 0)
        Traceback (most recent call last):
        ...
        ValueError: Invalid value of 'position'
        >>> kth_largest_element(['apple', 'cherry', 'date', 'banana'], 2)
        'cherry'
        >>> kth_largest_element([3.1, 1.2, 5.6, 4.7,7.9,5,0], 2)
        5.6
        >>> kth_largest_element([-2, -5, -4, -1], 1)
        -1
        >>> kth_largest_element([], 1)
        -1
        >>> kth_largest_element([3.1, 1.2, 5.6, 4.7, 7.9, 5, 0], 1.5)
        Traceback (most recent call last):
        ...
        ValueError: The position should be an integer
        >>> kth_largest_element((4, 6, 1, 2), 4)
        Traceback (most recent call last):
        ...
        TypeError: 'tuple' object does not support item assignment
    """
    if not arr:
        return -1
    if not isinstance(position, int):
        raise ValueError("The position should be an integer")
    if not 1 <= position <= len(arr):
        raise ValueError("Invalid value of 'position'")
    low, high = 0, len(arr) - 1
    while low <= high:
        if low > len(arr) - 1 or high < 0:
            return -1
        pivot_index = partition(arr, low, high)
        if pivot_index == position - 1:
            return arr[pivot_index]
        elif pivot_index > position - 1:
            high = pivot_index - 1
        else:
            low = pivot_index + 1
    return -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/arrays/median_two_array.py
================================================
"""
https://www.enjoyalgorithms.com/blog/median-of-two-sorted-arrays
"""


def find_median_sorted_arrays(nums1: list[int], nums2: list[int]) -> float:
    """
    Find the median of two arrays.

    Args:
        nums1: The first array.
        nums2: The second array.

    Returns:
    The median of the two arrays.

    Examples:
        >>> find_median_sorted_arrays([1, 3], [2])
        2.0

        >>> find_median_sorted_arrays([1, 2], [3, 4])
        2.5

        >>> find_median_sorted_arrays([0, 0], [0, 0])
        0.0

        >>> find_median_sorted_arrays([], [])
        Traceback (most recent call last):
            ...
        ValueError: Both input arrays are empty.

        >>> find_median_sorted_arrays([], [1])
        1.0

        >>> find_median_sorted_arrays([-1000], [1000])
        0.0

        >>> find_median_sorted_arrays([-1.1, -2.2], [-3.3, -4.4])
        -2.75
    """
    if not nums1 and not nums2:
        raise ValueError("Both input arrays are empty.")

    # Merge the arrays into a single sorted array.
    merged = sorted(nums1 + nums2)
    total = len(merged)

    if total % 2 == 1:  # If the total number of elements is odd
        return float(merged[total // 2])  # then return the middle element

    # If the total number of elements is even, calculate
    # the average of the two middle elements as the median.
    middle1 = merged[total // 2 - 1]
    middle2 = merged[total // 2]
    return (float(middle1) + float(middle2)) / 2.0


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/arrays/monotonic_array.py
================================================
# https://leetcode.com/problems/monotonic-array/
def is_monotonic(nums: list[int]) -> bool:
    """
    Check if a list is monotonic.

    >>> is_monotonic([1, 2, 2, 3])
    True
    >>> is_monotonic([6, 5, 4, 4])
    True
    >>> is_monotonic([1, 3, 2])
    False
    >>> is_monotonic([1,2,3,4,5,6,5])
    False
    >>> is_monotonic([-3,-2,-1])
    True
    >>> is_monotonic([-5,-6,-7])
    True
    >>> is_monotonic([0,0,0])
    True
    >>> is_monotonic([-100,0,100])
    True
    """
    return all(nums[i] <= nums[i + 1] for i in range(len(nums) - 1)) or all(
        nums[i] >= nums[i + 1] for i in range(len(nums) - 1)
    )


# Test the function with your examples
if __name__ == "__main__":
    # Test the function with your examples
    print(is_monotonic([1, 2, 2, 3]))  # Output: True
    print(is_monotonic([6, 5, 4, 4]))  # Output: True
    print(is_monotonic([1, 3, 2]))  # Output: False

    import doctest

    doctest.testmod()


================================================
FILE: data_structures/arrays/pairs_with_given_sum.py
================================================
#!/usr/bin/env python3

"""
Given an array of integers and an integer req_sum, find the number of pairs of array
elements whose sum is equal to req_sum.

https://practice.geeksforgeeks.org/problems/count-pairs-with-given-sum5022/0
"""

from itertools import combinations


def pairs_with_sum(arr: list, req_sum: int) -> int:
    """
    Return the no. of pairs with sum "sum"
    >>> pairs_with_sum([1, 5, 7, 1], 6)
    2
    >>> pairs_with_sum([1, 1, 1, 1, 1, 1, 1, 1], 2)
    28
    >>> pairs_with_sum([1, 7, 6, 2, 5, 4, 3, 1, 9, 8], 7)
    4
    """
    return len([1 for a, b in combinations(arr, 2) if a + b == req_sum])


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: data_structures/arrays/permutations.py
================================================
def permute_recursive(nums: list[int]) -> list[list[int]]:
    """
    Return all permutations.

    >>> permute_recursive([1, 2, 3])
    [[3, 2, 1], [2, 3, 1], [1, 3, 2], [3, 1, 2], [2, 1, 3], [1, 2, 3]]
    """
    result: list[list[int]] = []
    if len(nums) == 0:
        return [[]]
    for _ in range(len(nums)):
        n = nums.pop(0)
        permutations = permute_recursive(nums.copy())
        for perm in permutations:
            perm.append(n)
        result.extend(permutations)
        nums.append(n)
    return result


def permute_backtrack(nums: list[int]) -> list[list[int]]:
    """
    Return all permutations of the given list.

    >>> permute_backtrack([1, 2, 3])
    [[1, 2, 3], [1, 3, 2], [2, 1, 3], [2, 3, 1], [3, 2, 1], [3, 1, 2]]
    """

    def backtrack(start: int) -> None:
        if start == len(nums) - 1:
            output.append(nums[:])
        else:
            for i in range(start, len(nums)):
                nums[start], nums[i] = nums[i], nums[start]
                backtrack(start + 1)
                nums[start], nums[i] = nums[i], nums[start]  # backtrack

    output: list[list[int]] = []
    backtrack(0)
    return output


if __name__ == "__main__":
    import doctest

    result = permute_backtrack([1, 2, 3])
    print(result)
    doctest.testmod()


================================================
FILE: data_structures/arrays/prefix_sum.py
================================================
"""
Author  : Alexander Pantyukhin
Date    : November 3, 2022

Implement the class of prefix sum with useful functions based on it.

"""


class PrefixSum:
    def __init__(self, array: list[int]) -> None:
        len_array = len(array)
        self.prefix_sum = [0] * len_array

        if len_array > 0:
            self.prefix_sum[0] = array[0]

        for i in range(1, len_array):
            self.prefix_sum[i] = self.prefix_sum[i - 1] + array[i]

    def get_sum(self, start: int, end: int) -> int:
        """
        The function returns the sum of array from the start to the end indexes.
        Runtime : O(1)
        Space: O(1)

        >>> PrefixSum([1,2,3]).get_sum(0, 2)
        6
        >>> PrefixSum([1,2,3]).get_sum(1, 2)
        5
        >>> PrefixSum([1,2,3]).get_sum(2, 2)
        3
        >>> PrefixSum([]).get_sum(0, 0)
        Traceback (most recent call last):
        ...
        ValueError: The array is empty.
        >>> PrefixSum([1,2,3]).get_sum(-1, 2)
        Traceback (most recent call last):
        ...
        ValueError: Invalid range specified.
        >>> PrefixSum([1,2,3]).get_sum(2, 3)
        Traceback (most recent call last):
        ...
        ValueError: Invalid range specified.
        >>> PrefixSum([1,2,3]).get_sum(2, 1)
        Traceback (most recent call last):
        ...
        ValueError: Invalid range specified.
        """
        if not self.prefix_sum:
            raise ValueError("The array is empty.")

        if start < 0 or end >= len(self.prefix_sum) or start > end:
            raise ValueError("Invalid range specified.")

        if start == 0:
            return self.prefix_sum[end]

        return self.prefix_sum[end] - self.prefix_sum[start - 1]

    def contains_sum(self, target_sum: int) -> bool:
        """
        The function returns True if array contains the target_sum,
        False otherwise.

        Runtime : O(n)
        Space: O(n)

        >>> PrefixSum([1,2,3]).contains_sum(6)
        True
        >>> PrefixSum([1,2,3]).contains_sum(5)
        True
        >>> PrefixSum([1,2,3]).contains_sum(3)
        True
        >>> PrefixSum([1,2,3]).contains_sum(4)
        False
        >>> PrefixSum([1,2,3]).contains_sum(7)
        False
        >>> PrefixSum([1,-2,3]).contains_sum(2)
        True
        """

        sums = {0}
        for sum_item in self.prefix_sum:
            if sum_item - target_sum in sums:
                return True

            sums.add(sum_item)

        return False


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/arrays/product_sum.py
================================================
"""
Calculate the Product Sum from a Special Array.
reference: https://dev.to/sfrasica/algorithms-product-sum-from-an-array-dc6

Python doctests can be run with the following command:
python -m doctest -v product_sum.py

Calculate the product sum of a "special" array which can contain integers or nested
arrays. The product sum is obtained by adding all elements and multiplying by their
respective depths.

For example, in the array [x, y], the product sum is (x + y). In the array [x, [y, z]],
the product sum is x + 2 * (y + z). In the array [x, [y, [z]]],
the product sum is x + 2 * (y + 3z).

Example Input:
[5, 2, [-7, 1], 3, [6, [-13, 8], 4]]
Output: 12

"""


def product_sum(arr: list[int | list], depth: int) -> int:
    """
    Recursively calculates the product sum of an array.

    The product sum of an array is defined as the sum of its elements multiplied by
    their respective depths.  If an element is a list, its product sum is calculated
    recursively by multiplying the sum of its elements with its depth plus one.

    Args:
        arr: The array of integers and nested lists.
        depth: The current depth level.

    Returns:
        int: The product sum of the array.

    Examples:
        >>> product_sum([1, 2, 3], 1)
        6
        >>> product_sum([-1, 2, [-3, 4]], 2)
        8
        >>> product_sum([1, 2, 3], -1)
        -6
        >>> product_sum([1, 2, 3], 0)
        0
        >>> product_sum([1, 2, 3], 7)
        42
        >>> product_sum((1, 2, 3), 7)
        42
        >>> product_sum({1, 2, 3}, 7)
        42
        >>> product_sum([1, -1], 1)
        0
        >>> product_sum([1, -2], 1)
        -1
        >>> product_sum([-3.5, [1, [0.5]]], 1)
        1.5

    """
    total_sum = 0
    for ele in arr:
        total_sum += product_sum(ele, depth + 1) if isinstance(ele, list) else ele
    return total_sum * depth


def product_sum_array(array: list[int | list]) -> int:
    """
    Calculates the product sum of an array.

    Args:
        array (List[Union[int, List]]): The array of integers and nested lists.

    Returns:
        int: The product sum of the array.

    Examples:
        >>> product_sum_array([1, 2, 3])
        6
        >>> product_sum_array([1, [2, 3]])
        11
        >>> product_sum_array([1, [2, [3, 4]]])
        47
        >>> product_sum_array([0])
        0
        >>> product_sum_array([-3.5, [1, [0.5]]])
        1.5
        >>> product_sum_array([1, -2])
        -1

    """
    return product_sum(array, 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/arrays/rotate_array.py
================================================
def rotate_array(arr: list[int], steps: int) -> list[int]:
    """
    Rotates a list to the right by steps positions.

    Parameters:
    arr (List[int]): The list of integers to rotate.
    steps (int): Number of positions to rotate. Can be negative for left rotation.

    Returns:
    List[int]: Rotated list.

    Examples:
    >>> rotate_array([1, 2, 3, 4, 5], 2)
    [4, 5, 1, 2, 3]
    >>> rotate_array([1, 2, 3, 4, 5], -2)
    [3, 4, 5, 1, 2]
    >>> rotate_array([1, 2, 3, 4, 5], 7)
    [4, 5, 1, 2, 3]
    >>> rotate_array([], 3)
    []
    """

    n = len(arr)
    if n == 0:
        return arr

    steps = steps % n

    if steps < 0:
        steps += n

    def reverse(start: int, end: int) -> None:
        """
        Reverses a portion of the list in place from index start to end.

        Parameters:
        start (int): Starting index of the portion to reverse.
        end (int): Ending index of the portion to reverse.

        Returns:
        None

        Examples:
        >>> example = [1, 2, 3, 4, 5]
        >>> def reverse_test(arr, start, end):
        ...     while start < end:
        ...         arr[start], arr[end] = arr[end], arr[start]
        ...         start += 1
        ...         end -= 1
        >>> reverse_test(example, 0, 2)
        >>> example
        [3, 2, 1, 4, 5]
        >>> reverse_test(example, 2, 4)
        >>> example
        [3, 2, 5, 4, 1]
        """

        while start < end:
            arr[start], arr[end] = arr[end], arr[start]
            start += 1
            end -= 1

    reverse(0, n - 1)
    reverse(0, steps - 1)
    reverse(steps, n - 1)

    return arr


if __name__ == "__main__":
    examples = [
        ([1, 2, 3, 4, 5], 2),
        ([1, 2, 3, 4, 5], -2),
        ([1, 2, 3, 4, 5], 7),
        ([], 3),
    ]

    for arr, steps in examples:
        rotated = rotate_array(arr.copy(), steps)
        print(f"Rotate {arr} by {steps}: {rotated}")


================================================
FILE: data_structures/arrays/sparse_table.py
================================================
"""
Sparse table is a data structure that allows answering range queries on
a static number list, i.e. the elements do not change throughout all the queries.

The implementation below will solve the problem of Range Minimum Query:
Finding the minimum value of a subset [L..R] of a static number list.

Overall time complexity: O(nlogn)
Overall space complexity: O(nlogn)

Wikipedia link: https://en.wikipedia.org/wiki/Range_minimum_query
"""

from math import log2


def build_sparse_table(number_list: list[int]) -> list[list[int]]:
    """
    Precompute range minimum queries with power of two length and store the precomputed
    values in a table.

    >>> build_sparse_table([8, 1, 0, 3, 4, 9, 3])
    [[8, 1, 0, 3, 4, 9, 3], [1, 0, 0, 3, 4, 3, 0], [0, 0, 0, 3, 0, 0, 0]]
    >>> build_sparse_table([3, 1, 9])
    [[3, 1, 9], [1, 1, 0]]
    >>> build_sparse_table([])
    Traceback (most recent call last):
    ...
    ValueError: empty number list not allowed
    """
    if not number_list:
        raise ValueError("empty number list not allowed")

    length = len(number_list)
    # Initialise sparse_table -- sparse_table[j][i] represents the minimum value of the
    # subset of length (2 ** j) of number_list, starting from index i.

    # smallest power of 2 subset length that fully covers number_list
    row = int(log2(length)) + 1
    sparse_table = [[0 for i in range(length)] for j in range(row)]

    # minimum of subset of length 1 is that value itself
    for i, value in enumerate(number_list):
        sparse_table[0][i] = value
    j = 1

    # compute the minimum value for all intervals with size (2 ** j)
    while (1 << j) <= length:
        i = 0
        # while subset starting from i still have at least (2 ** j) elements
        while (i + (1 << j) - 1) < length:
            # split range [i, i + 2 ** j] and find minimum of 2 halves
            sparse_table[j][i] = min(
                sparse_table[j - 1][i + (1 << (j - 1))], sparse_table[j - 1][i]
            )
            i += 1
        j += 1
    return sparse_table


def query(sparse_table: list[list[int]], left_bound: int, right_bound: int) -> int:
    """
    >>> query(build_sparse_table([8, 1, 0, 3, 4, 9, 3]), 0, 4)
    0
    >>> query(build_sparse_table([8, 1, 0, 3, 4, 9, 3]), 4, 6)
    3
    >>> query(build_sparse_table([3, 1, 9]), 2, 2)
    9
    >>> query(build_sparse_table([3, 1, 9]), 0, 1)
    1
    >>> query(build_sparse_table([8, 1, 0, 3, 4, 9, 3]), 0, 11)
    Traceback (most recent call last):
    ...
    IndexError: list index out of range
    >>> query(build_sparse_table([]), 0, 0)
    Traceback (most recent call last):
    ...
    ValueError: empty number list not allowed
    """
    if left_bound < 0 or right_bound >= len(sparse_table[0]):
        raise IndexError("list index out of range")

    # highest subset length of power of 2 that is within range [left_bound, right_bound]
    j = int(log2(right_bound - left_bound + 1))

    # minimum of 2 overlapping smaller subsets:
    # [left_bound, left_bound + 2 ** j - 1] and [right_bound - 2 ** j + 1, right_bound]
    return min(sparse_table[j][right_bound - (1 << j) + 1], sparse_table[j][left_bound])


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    print(f"{query(build_sparse_table([3, 1, 9]), 2, 2) = }")


================================================
FILE: data_structures/arrays/sudoku_solver.py
================================================
"""
Please do not modify this file!  It is published at https://norvig.com/sudoku.html with
only minimal changes to work with modern versions of Python.  If you have improvements,
please make them in a separate file.
"""

import random
import time


def cross(items_a, items_b):
    """
    Cross product of elements in A and elements in B.

    >>> cross('AB', '12')
    ['A1', 'A2', 'B1', 'B2']
    >>> cross('ABC', '123')
    ['A1', 'A2', 'A3', 'B1', 'B2', 'B3', 'C1', 'C2', 'C3']
    >>> cross('ABC', '1234')
    ['A1', 'A2', 'A3', 'A4', 'B1', 'B2', 'B3', 'B4', 'C1', 'C2', 'C3', 'C4']
    >>> cross('', '12')
    []
    >>> cross('A', '')
    []
    >>> cross('', '')
    []
    """
    return [a + b for a in items_a for b in items_b]


digits = "123456789"
rows = "ABCDEFGHI"
cols = digits
squares = cross(rows, cols)
unitlist = (
    [cross(rows, c) for c in cols]
    + [cross(r, cols) for r in rows]
    + [cross(rs, cs) for rs in ("ABC", "DEF", "GHI") for cs in ("123", "456", "789")]
)
units = {s: [u for u in unitlist if s in u] for s in squares}
peers = {s: {x for u in units[s] for x in u} - {s} for s in squares}


def test():
    """A set of unit tests."""
    assert len(squares) == 81
    assert len(unitlist) == 27
    assert all(len(units[s]) == 3 for s in squares)
    assert all(len(peers[s]) == 20 for s in squares)
    assert units["C2"] == [
        ["A2", "B2", "C2", "D2", "E2", "F2", "G2", "H2", "I2"],
        ["C1", "C2", "C3", "C4", "C5", "C6", "C7", "C8", "C9"],
        ["A1", "A2", "A3", "B1", "B2", "B3", "C1", "C2", "C3"],
    ]
    # fmt: off
    assert peers["C2"] == {
        "A2", "B2", "D2", "E2", "F2", "G2", "H2", "I2", "C1", "C3",
        "C4", "C5", "C6", "C7", "C8", "C9", "A1", "A3", "B1", "B3"
    }
    # fmt: on
    print("All tests pass.")


def parse_grid(grid):
    """
    Convert grid to a dict of possible values, {square: digits}, or
    return False if a contradiction is detected.
    """
    ## To start, every square can be any digit; then assign values from the grid.
    values = dict.fromkeys(squares, digits)
    for s, d in grid_values(grid).items():
        if d in digits and not assign(values, s, d):
            return False  ## (Fail if we can't assign d to square s.)
    return values


def grid_values(grid):
    """
    Convert grid into a dict of {square: char} with '0' or '.' for empties.
    """
    chars = [c for c in grid if c in digits or c in "0."]
    assert len(chars) == 81
    return dict(zip(squares, chars))


def assign(values, s, d):
    """
    Eliminate all the other values (except d) from values[s] and propagate.
    Return values, except return False if a contradiction is detected.
    """
    other_values = values[s].replace(d, "")
    if all(eliminate(values, s, d2) for d2 in other_values):
        return values
    else:
        return False


def eliminate(values, s, d):
    """
    Eliminate d from values[s]; propagate when values or places <= 2.
    Return values, except return False if a contradiction is detected.
    """
    if d not in values[s]:
        return values  ## Already eliminated
    values[s] = values[s].replace(d, "")
    ## (1) If a square s is reduced to one value d2, then eliminate d2 from the peers.
    if len(values[s]) == 0:
        return False  ## Contradiction: removed last value
    elif len(values[s]) == 1:
        d2 = values[s]
        if not all(eliminate(values, s2, d2) for s2 in peers[s]):
            return False
    ## (2) If a unit u is reduced to only one place for a value d, then put it there.
    for u in units[s]:
        dplaces = [s for s in u if d in values[s]]
        if len(dplaces) == 0:
            return False  ## Contradiction: no place for this value
        # d can only be in one place in unit; assign it there
        elif len(dplaces) == 1 and not assign(values, dplaces[0], d):
            return False
    return values


def display(values):
    """
    Display these values as a 2-D grid.
    """
    width = 1 + max(len(values[s]) for s in squares)
    line = "+".join(["-" * (width * 3)] * 3)
    for r in rows:
        print(
            "".join(
                values[r + c].center(width) + ("|" if c in "36" else "") for c in cols
            )
        )
        if r in "CF":
            print(line)
    print()


def solve(grid):
    """
    Solve the grid.
    """
    return search(parse_grid(grid))


def some(seq):
    """Return some element of seq that is true."""
    for e in seq:
        if e:
            return e
    return False


def search(values):
    """
    Using depth-first search and propagation, try all possible values.
    """
    if values is False:
        return False  ## Failed earlier
    if all(len(values[s]) == 1 for s in squares):
        return values  ## Solved!
    ## Chose the unfilled square s with the fewest possibilities
    _n, s = min((len(values[s]), s) for s in squares if len(values[s]) > 1)
    return some(search(assign(values.copy(), s, d)) for d in values[s])


def solve_all(grids, name="", showif=0.0):
    """
    Attempt to solve a sequence of grids. Report results.
    When showif is a number of seconds, display puzzles that take longer.
    When showif is None, don't display any puzzles.
    """

    def time_solve(grid):
        start = time.monotonic()
        values = solve(grid)
        t = time.monotonic() - start
        ## Display puzzles that take long enough
        if showif is not None and t > showif:
            display(grid_values(grid))
            if values:
                display(values)
            print(f"({t:.5f} seconds)\n")
        return (t, solved(values))

    times, results = zip(*[time_solve(grid) for grid in grids])
    if (n := len(grids)) > 1:
        print(
            "Solved %d of %d %s puzzles (avg %.2f secs (%d Hz), max %.2f secs)."  # noqa: UP031
            % (sum(results), n, name, sum(times) / n, n / sum(times), max(times))
        )


def solved(values):
    """
    A puzzle is solved if each unit is a permutation of the digits 1 to 9.
    """

    def unitsolved(unit):
        return {values[s] for s in unit} == set(digits)

    return values is not False and all(unitsolved(unit) for unit in unitlist)


def from_file(filename, sep="\n"):
    "Parse a file into a list of strings, separated by sep."
    with open(filename) as file:
        return file.read().strip().split(sep)


def random_puzzle(assignments=17):
    """
    Make a random puzzle with N or more assignments. Restart on contradictions.
    Note the resulting puzzle is not guaranteed to be solvable, but empirically
    about 99.8% of them are solvable. Some have multiple solutions.
    """
    values = dict.fromkeys(squares, digits)
    for s in shuffled(squares):
        if not assign(values, s, random.choice(values[s])):
            break
        ds = [values[s] for s in squares if len(values[s]) == 1]
        if len(ds) >= assignments and len(set(ds)) >= 8:
            return "".join(values[s] if len(values[s]) == 1 else "." for s in squares)
    return random_puzzle(assignments)  ## Give up and make a new puzzle


def shuffled(seq):
    """
    Return a randomly shuffled copy of the input sequence.
    """
    seq = list(seq)
    random.shuffle(seq)
    return seq


grid1 = (
    "003020600900305001001806400008102900700000008006708200002609500800203009005010300"
)
grid2 = (
    "4.....8.5.3..........7......2.....6.....8.4......1.......6.3.7.5..2.....1.4......"
)
hard1 = (
    ".....6....59.....82....8....45........3........6..3.54...325..6.................."
)

if __name__ == "__main__":
    test()
    # solve_all(from_file("easy50.txt", '========'), "easy", None)
    # solve_all(from_file("top95.txt"), "hard", None)
    # solve_all(from_file("hardest.txt"), "hardest", None)
    solve_all([random_puzzle() for _ in range(99)], "random", 100.0)
    for puzzle in (grid1, grid2):  # , hard1):  # Takes 22 sec to solve on my M1 Mac.
        display(parse_grid(puzzle))
        start = time.monotonic()
        solve(puzzle)
        t = time.monotonic() - start
        print(f"Solved: {t:.5f} sec")


================================================
FILE: data_structures/binary_tree/README.md
================================================
# Binary Tree Traversal

## Overview

The combination of binary trees being data structures and traversal being an algorithm relates to classic problems, either directly or indirectly.

> If you can grasp the traversal of binary trees, the traversal of other complicated trees will be easy for you.

The following are some common ways to traverse trees.

- Depth First Traversals (DFS): In-order, Pre-order, Post-order

- Level Order Traversal or Breadth First or Traversal (BFS)

There are applications for both DFS and BFS.

Stack can be used to simplify the process of DFS traversal. Besides, since tree is a recursive data structure, recursion and stack are two key points for DFS.

Graph for DFS:

![binary-tree-traversal-dfs](https://tva1.sinaimg.cn/large/007S8ZIlly1ghluhzhynsg30dw0dw3yl.gif)

The key point of BFS is how to determine whether the traversal of each level has been completed. The answer is to use a variable as a flag to represent the end of the traversal of current level.

## Pre-order Traversal

The traversal order of pre-order traversal is `root-left-right`.

Algorithm Pre-order

1. Visit the root node and push it into a stack.

2. Pop a node from the stack, and push its right and left child node into the stack respectively.

3. Repeat step 2.

Conclusion: This problem involves the classic recursive data structure (i.e. a binary tree), and the algorithm above demonstrates how a simplified solution can be reached by using a stack.

If you look at the bigger picture, you'll find that the process of traversal is as followed. `Visit the left subtrees respectively from top to bottom, and visit the right subtrees respectively from bottom to top`. If we are to implement it from this perspective, things will be somewhat different. For the `top to bottom` part we can simply use recursion, and for the `bottom to top` part we can turn to stack.

## In-order Traversal

The traversal order of in-order traversal is `left-root-right`.

So the root node is not printed first. Things are getting a bit complicated here.

Algorithm In-order

1. Visit the root and push it into a stack.

2. If there is a left child node, push it into the stack. Repeat this process until a leaf node reached.

    > At this point the root node and all the left nodes are in the stack.

3. Start popping nodes from the stack. If a node has a right child node, push the child node into the stack. Repeat step 2.

It's worth pointing out that the in-order traversal of a binary search tree (BST) is a sorted array, which is helpful for coming up simplified solutions for some problems.

## Post-order Traversal

The traversal order of post-order traversal is `left-right-root`.

This one is a bit of a challenge. It deserves the `hard` tag of LeetCode.

In this case, the root node is printed not as the first but the last one. A cunning way to do it is to:

Record whether the current node has been visited. If 1) it's a leaf node or 2) both its left and right subtrees have been traversed, then it can be popped from the stack.

As for `1) it's a leaf node`, you can easily tell whether a node is a leaf if both its left and right are `null`.

As for `2) both its left and right subtrees have been traversed`, we only need a variable to record whether a node has been visited or not. In the worst case, we need to record the status for every single node and the space complexity is `O(n)`. But if you come to think about it, as we are using a stack and start printing the result from the leaf nodes, it makes sense that we only record the status for the current node popping from the stack, reducing the space complexity to `O(1)`.

## Level Order Traversal

The key point of level order traversal is how do we know whether the traversal of each level is done. The answer is that we use a variable as a flag representing the end of the traversal of the current level.

![binary-tree-traversal-bfs](https://tva1.sinaimg.cn/large/007S8ZIlly1ghlui1tpoug30dw0dw3yl.gif)

Algorithm Level-order

1. Visit the root node, put it in a FIFO queue, put in the queue a special flag (we are using `null` here).

2. Dequeue a node.

3. If the node equals `null`, it means that all nodes of the current level have been visited. If the queue is empty, we do nothing. Or else we put in another `null`.

4. If the node is not `null`, meaning the traversal of current level has not finished yet, we enqueue its left subtree and right subtree respectively.

## Bi-color marking

We know that there is a tri-color marking in garbage collection algorithm, which works as described below.

- The white color represents "not visited".

- The gray color represents "not all child nodes visited".

- The black color represents "all child nodes visited".

Enlightened by tri-color marking, a bi-color marking method can be invented to solve all three traversal problems with one solution.

The core idea is as follow.

- Use a color to mark whether a node has been visited or not. Nodes yet to be visited are marked as white and visited nodes are marked as gray.

- If we are visiting a white node, turn it into gray, and push its right child node, itself, and it's left child node into the stack respectively.

- If we are visiting a gray node, print it.

Implementation of pre-order and post-order traversal algorithms can be easily done by changing the order of pushing the child nodes into the stack.

Reference: [LeetCode](https://github.com/azl397985856/leetcode/blob/master/thinkings/binary-tree-traversal.en.md)


================================================
FILE: data_structures/binary_tree/__init__.py
================================================


================================================
FILE: data_structures/binary_tree/avl_tree.py
================================================
"""
Implementation of an auto-balanced binary tree!
For doctests run following command:
python3 -m doctest -v avl_tree.py
For testing run:
python avl_tree.py
"""

from __future__ import annotations

import math
import random
from typing import Any


class MyQueue:
    def __init__(self) -> None:
        self.data: list[Any] = []
        self.head: int = 0
        self.tail: int = 0

    def is_empty(self) -> bool:
        return self.head == self.tail

    def push(self, data: Any) -> None:
        self.data.append(data)
        self.tail = self.tail + 1

    def pop(self) -> Any:
        ret = self.data[self.head]
        self.head = self.head + 1
        return ret

    def count(self) -> int:
        return self.tail - self.head

    def print_queue(self) -> None:
        print(self.data)
        print("**************")
        print(self.data[self.head : self.tail])


class MyNode:
    def __init__(self, data: Any) -> None:
        self.data = data
        self.left: MyNode | None = None
        self.right: MyNode | None = None
        self.height: int = 1

    def get_data(self) -> Any:
        return self.data

    def get_left(self) -> MyNode | None:
        return self.left

    def get_right(self) -> MyNode | None:
        return self.right

    def get_height(self) -> int:
        return self.height

    def set_data(self, data: Any) -> None:
        self.data = data

    def set_left(self, node: MyNode | None) -> None:
        self.left = node

    def set_right(self, node: MyNode | None) -> None:
        self.right = node

    def set_height(self, height: int) -> None:
        self.height = height


def get_height(node: MyNode | None) -> int:
    if node is None:
        return 0
    return node.get_height()


def my_max(a: int, b: int) -> int:
    if a > b:
        return a
    return b


def right_rotation(node: MyNode) -> MyNode:
    r"""
            A                      B
           / \                    / \
          B   C                  Bl  A
         / \       -->          /   / \
        Bl  Br                 UB Br  C
       /
     UB
    UB = unbalanced node
    """
    print("left rotation node:", node.get_data())
    ret = node.get_left()
    assert ret is not None
    node.set_left(ret.get_right())
    ret.set_right(node)
    h1 = my_max(get_height(node.get_right()), get_height(node.get_left())) + 1
    node.set_height(h1)
    h2 = my_max(get_height(ret.get_right()), get_height(ret.get_left())) + 1
    ret.set_height(h2)
    return ret


def left_rotation(node: MyNode) -> MyNode:
    """
    a mirror symmetry rotation of the left_rotation
    """
    print("right rotation node:", node.get_data())
    ret = node.get_right()
    assert ret is not None
    node.set_right(ret.get_left())
    ret.set_left(node)
    h1 = my_max(get_height(node.get_right()), get_height(node.get_left())) + 1
    node.set_height(h1)
    h2 = my_max(get_height(ret.get_right()), get_height(ret.get_left())) + 1
    ret.set_height(h2)
    return ret


def lr_rotation(node: MyNode) -> MyNode:
    r"""
            A              A                    Br
           / \            / \                  /  \
          B   C    LR    Br  C       RR       B    A
         / \       -->  /  \         -->    /     / \
        Bl  Br         B   UB              Bl    UB  C
             \        /
             UB     Bl
    RR = right_rotation   LR = left_rotation
    """
    left_child = node.get_left()
    assert left_child is not None
    node.set_left(left_rotation(left_child))
    return right_rotation(node)


def rl_rotation(node: MyNode) -> MyNode:
    right_child = node.get_right()
    assert right_child is not None
    node.set_right(right_rotation(right_child))
    return left_rotation(node)


def insert_node(node: MyNode | None, data: Any) -> MyNode | None:
    if node is None:
        return MyNode(data)
    if data < node.get_data():
        node.set_left(insert_node(node.get_left(), data))
        if (
            get_height(node.get_left()) - get_height(node.get_right()) == 2
        ):  # an unbalance detected
            left_child = node.get_left()
            assert left_child is not None
            if (
                data < left_child.get_data()
            ):  # new node is the left child of the left child
                node = right_rotation(node)
            else:
                node = lr_rotation(node)
    else:
        node.set_right(insert_node(node.get_right(), data))
        if get_height(node.get_right()) - get_height(node.get_left()) == 2:
            right_child = node.get_right()
            assert right_child is not None
            if data < right_child.get_data():
                node = rl_rotation(node)
            else:
                node = left_rotation(node)
    h1 = my_max(get_height(node.get_right()), get_height(node.get_left())) + 1
    node.set_height(h1)
    return node


def get_right_most(root: MyNode) -> Any:
    while True:
        right_child = root.get_right()
        if right_child is None:
            break
        root = right_child
    return root.get_data()


def get_left_most(root: MyNode) -> Any:
    while True:
        left_child = root.get_left()
        if left_child is None:
            break
        root = left_child
    return root.get_data()


def del_node(root: MyNode, data: Any) -> MyNode | None:
    left_child = root.get_left()
    right_child = root.get_right()
    if root.get_data() == data:
        if left_child is not None and right_child is not None:
            temp_data = get_left_most(right_child)
            root.set_data(temp_data)
            root.set_right(del_node(right_child, temp_data))
        elif left_child is not None:
            root = left_child
        elif right_child is not None:
            root = right_child
        else:
            return None
    elif root.get_data() > data:
        if left_child is None:
            print("No such data")
            return root
        else:
            root.set_left(del_node(left_child, data))
    # root.get_data() < data
    elif right_child is None:
        return root
    else:
        root.set_right(del_node(right_child, data))

    # Re-fetch left_child and right_child references
    left_child = root.get_left()
    right_child = root.get_right()

    if get_height(right_child) - get_height(left_child) == 2:
        assert right_child is not None
        if get_height(right_child.get_right()) > get_height(right_child.get_left()):
            root = left_rotation(root)
        else:
            root = rl_rotation(root)
    elif get_height(right_child) - get_height(left_child) == -2:
        assert left_child is not None
        if get_height(left_child.get_left()) > get_height(left_child.get_right()):
            root = right_rotation(root)
        else:
            root = lr_rotation(root)
    height = my_max(get_height(root.get_right()), get_height(root.get_left())) + 1
    root.set_height(height)
    return root


class AVLtree:
    """
    An AVL tree doctest
    Examples:
    >>> t = AVLtree()
    >>> t.insert(4)
    insert:4
    >>> print(str(t).replace(" \\n","\\n"))
     4
    *************************************
    >>> t.insert(2)
    insert:2
    >>> print(str(t).replace(" \\n","\\n").replace(" \\n","\\n"))
      4
     2  *
    *************************************
    >>> t.insert(3)
    insert:3
    right rotation node: 2
    left rotation node: 4
    >>> print(str(t).replace(" \\n","\\n").replace(" \\n","\\n"))
      3
     2  4
    *************************************
    >>> t.get_height()
    2
    >>> t.del_node(3)
    delete:3
    >>> print(str(t).replace(" \\n","\\n").replace(" \\n","\\n"))
      4
     2  *
    *************************************
    """

    def __init__(self) -> None:
        self.root: MyNode | None = None

    def get_height(self) -> int:
        return get_height(self.root)

    def insert(self, data: Any) -> None:
        print("insert:" + str(data))
        self.root = insert_node(self.root, data)

    def del_node(self, data: Any) -> None:
        print("delete:" + str(data))
        if self.root is None:
            print("Tree is empty!")
            return
        self.root = del_node(self.root, data)

    def __str__(
        self,
    ) -> str:  # a level traversale, gives a more intuitive look on the tree
        output = ""
        q = MyQueue()
        q.push(self.root)
        layer = self.get_height()
        if layer == 0:
            return output
        cnt = 0
        while not q.is_empty():
            node = q.pop()
            space = " " * int(math.pow(2, layer - 1))
            output += space
            if node is None:
                output += "*"
                q.push(None)
                q.push(None)
            else:
                output += str(node.get_data())
                q.push(node.get_left())
                q.push(node.get_right())
            output += space
            cnt = cnt + 1
            for i in range(100):
                if cnt == math.pow(2, i) - 1:
                    layer = layer - 1
                    if layer == 0:
                        output += "\n*************************************"
                        return output
                    output += "\n"
                    break
        output += "\n*************************************"
        return output


def _test() -> None:
    import doctest

    doctest.testmod()


if __name__ == "__main__":
    _test()
    t = AVLtree()
    lst = list(range(10))
    random.shuffle(lst)
    for i in lst:
        t.insert(i)
        print(str(t))
    random.shuffle(lst)
    for i in lst:
        t.del_node(i)
        print(str(t))


================================================
FILE: data_structures/binary_tree/basic_binary_tree.py
================================================
from __future__ import annotations

from collections.abc import Iterator
from dataclasses import dataclass


@dataclass
class Node:
    data: int
    left: Node | None = None
    right: Node | None = None

    def __iter__(self) -> Iterator[int]:
        if self.left:
            yield from self.left
        yield self.data
        if self.right:
            yield from self.right

    def __len__(self) -> int:
        return sum(1 for _ in self)

    def is_full(self) -> bool:
        if not self or (not self.left and not self.right):
            return True
        if self.left and self.right:
            return self.left.is_full() and self.right.is_full()
        return False


@dataclass
class BinaryTree:
    root: Node

    def __iter__(self) -> Iterator[int]:
        return iter(self.root)

    def __len__(self) -> int:
        return len(self.root)

    @classmethod
    def small_tree(cls) -> BinaryTree:
        """
        Return a small binary tree with 3 nodes.
        >>> binary_tree = BinaryTree.small_tree()
        >>> len(binary_tree)
        3
        >>> list(binary_tree)
        [1, 2, 3]
        """
        binary_tree = BinaryTree(Node(2))
        binary_tree.root.left = Node(1)
        binary_tree.root.right = Node(3)
        return binary_tree

    @classmethod
    def medium_tree(cls) -> BinaryTree:
        """
        Return a medium binary tree with 3 nodes.
        >>> binary_tree = BinaryTree.medium_tree()
        >>> len(binary_tree)
        7
        >>> list(binary_tree)
        [1, 2, 3, 4, 5, 6, 7]
        """
        binary_tree = BinaryTree(Node(4))
        binary_tree.root.left = two = Node(2)
        two.left = Node(1)
        two.right = Node(3)
        binary_tree.root.right = five = Node(5)
        five.right = six = Node(6)
        six.right = Node(7)
        return binary_tree

    def depth(self) -> int:
        """
        Returns the depth of the tree

        >>> BinaryTree(Node(1)).depth()
        1
        >>> BinaryTree.small_tree().depth()
        2
        >>> BinaryTree.medium_tree().depth()
        4
        """
        return self._depth(self.root)

    def _depth(self, node: Node | None) -> int:
        if not node:
            return 0
        return 1 + max(self._depth(node.left), self._depth(node.right))

    def is_full(self) -> bool:
        """
        Returns True if the tree is full

        >>> BinaryTree(Node(1)).is_full()
        True
        >>> BinaryTree.small_tree().is_full()
        True
        >>> BinaryTree.medium_tree().is_full()
        False
        """
        return self.root.is_full()


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/binary_tree/binary_search_tree.py
================================================
r"""
A binary search Tree

Example
              8
             / \
            3   10
           / \    \
          1   6    14
             / \   /
            4   7 13

>>> t = BinarySearchTree().insert(8, 3, 6, 1, 10, 14, 13, 4, 7)
>>> print(" ".join(repr(i.value) for i in t.traversal_tree()))
8 3 1 6 4 7 10 14 13

>>> tuple(i.value for i in t.traversal_tree(inorder))
(1, 3, 4, 6, 7, 8, 10, 13, 14)
>>> tuple(t)
(1, 3, 4, 6, 7, 8, 10, 13, 14)
>>> t.find_kth_smallest(3, t.root)
4
>>> tuple(t)[3-1]
4

>>> print(" ".join(repr(i.value) for i in t.traversal_tree(postorder)))
1 4 7 6 3 13 14 10 8
>>> t.remove(20)
Traceback (most recent call last):
    ...
ValueError: Value 20 not found
>>> BinarySearchTree().search(6)
Traceback (most recent call last):
    ...
IndexError: Warning: Tree is empty! please use another.

Other example:

>>> testlist = (8, 3, 6, 1, 10, 14, 13, 4, 7)
>>> t = BinarySearchTree()
>>> for i in testlist:
...     t.insert(i)  # doctest: +ELLIPSIS
BinarySearchTree(root=8)
BinarySearchTree(root={'8': (3, None)})
BinarySearchTree(root={'8': ({'3': (None, 6)}, None)})
BinarySearchTree(root={'8': ({'3': (1, 6)}, None)})
BinarySearchTree(root={'8': ({'3': (1, 6)}, 10)})
BinarySearchTree(root={'8': ({'3': (1, 6)}, {'10': (None, 14)})})
BinarySearchTree(root={'8': ({'3': (1, 6)}, {'10': (None, {'14': (13, None)})})})
BinarySearchTree(root={'8': ({'3': (1, {'6': (4, None)})}, {'10': (None, {'14': ...
BinarySearchTree(root={'8': ({'3': (1, {'6': (4, 7)})}, {'10': (None, {'14': (13, ...

Prints all the elements of the list in order traversal
>>> print(t)
{'8': ({'3': (1, {'6': (4, 7)})}, {'10': (None, {'14': (13, None)})})}

Test existence
>>> t.search(6) is not None
True
>>> 6 in t
True
>>> t.search(-1) is not None
False
>>> -1 in t
False

>>> t.search(6).is_right
True
>>> t.search(1).is_right
False

>>> t.get_max().value
14
>>> max(t)
14
>>> t.get_min().value
1
>>> min(t)
1
>>> t.empty()
False
>>> not t
False
>>> for i in testlist:
...     t.remove(i)
>>> t.empty()
True
>>> not t
True
"""

from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass
from typing import Any, Self


@dataclass
class Node:
    value: int
    left: Node | None = None
    right: Node | None = None
    parent: Node | None = None  # Added in order to delete a node easier

    def __iter__(self) -> Iterator[int]:
        """
        >>> list(Node(0))
        [0]
        >>> list(Node(0, Node(-1), Node(1), None))
        [-1, 0, 1]
        """
        yield from self.left or []
        yield self.value
        yield from self.right or []

    def __repr__(self) -> str:
        from pprint import pformat

        if self.left is None and self.right is None:
            return str(self.value)
        return pformat({f"{self.value}": (self.left, self.right)}, indent=1)

    @property
    def is_right(self) -> bool:
        return bool(self.parent and self is self.parent.right)


@dataclass
class BinarySearchTree:
    root: Node | None = None

    def __bool__(self) -> bool:
        return bool(self.root)

    def __iter__(self) -> Iterator[int]:
        yield from self.root or []

    def __str__(self) -> str:
        """
        Return a string of all the Nodes using in order traversal
        """
        return str(self.root)

    def __reassign_nodes(self, node: Node, new_children: Node | None) -> None:
        if new_children is not None:  # reset its kids
            new_children.parent = node.parent
        if node.parent is not None:  # reset its parent
            if node.is_right:  # If it is the right child
                node.parent.right = new_children
            else:
                node.parent.left = new_children
        else:
            self.root = new_children

    def empty(self) -> bool:
        """
        Returns True if the tree does not have any element(s).
        False if the tree has element(s).

        >>> BinarySearchTree().empty()
        True
        >>> BinarySearchTree().insert(1).empty()
        False
        >>> BinarySearchTree().insert(8, 3, 6, 1, 10, 14, 13, 4, 7).empty()
        False
        """
        return not self.root

    def __insert(self, value) -> None:
        """
        Insert a new node in Binary Search Tree with value label
        """
        new_node = Node(value)  # create a new Node
        if self.empty():  # if Tree is empty
            self.root = new_node  # set its root
        else:  # Tree is not empty
            parent_node = self.root  # from root
            if parent_node is None:
                return
            while True:  # While we don't get to a leaf
                if value < parent_node.value:  # We go left
                    if parent_node.left is None:
                        parent_node.left = new_node  # We insert the new node in a leaf
                        break
                    else:
                        parent_node = parent_node.left
                elif parent_node.right is None:
                    parent_node.right = new_node
                    break
                else:
                    parent_node = parent_node.right
            new_node.parent = parent_node

    def insert(self, *values) -> Self:
        for value in values:
            self.__insert(value)
        return self

    def search(self, value) -> Node | None:
        """
        >>> tree = BinarySearchTree().insert(10, 20, 30, 40, 50)
        >>> tree.search(10)
        {'10': (None, {'20': (None, {'30': (None, {'40': (None, 50)})})})}
        >>> tree.search(20)
        {'20': (None, {'30': (None, {'40': (None, 50)})})}
        >>> tree.search(30)
        {'30': (None, {'40': (None, 50)})}
        >>> tree.search(40)
        {'40': (None, 50)}
        >>> tree.search(50)
        50
        >>> tree.search(5) is None  # element not present
        True
        >>> tree.search(0) is None  # element not present
        True
        >>> tree.search(-5) is None  # element not present
        True
        >>> BinarySearchTree().search(10)
        Traceback (most recent call last):
            ...
        IndexError: Warning: Tree is empty! please use another.
        """

        if self.empty():
            raise IndexError("Warning: Tree is empty! please use another.")
        else:
            node = self.root
            # use lazy evaluation here to avoid NoneType Attribute error
            while node is not None and node.value is not value:
                node = node.left if value < node.value else node.right
            return node

    def get_max(self, node: Node | None = None) -> Node | None:
        """
        We go deep on the right branch

        >>> BinarySearchTree().insert(10, 20, 30, 40, 50).get_max()
        50
        >>> BinarySearchTree().insert(-5, -1, 0.1, -0.3, -4.5).get_max()
        {'0.1': (-0.3, None)}
        >>> BinarySearchTree().insert(1, 78.3, 30, 74.0, 1).get_max()
        {'78.3': ({'30': (1, 74.0)}, None)}
        >>> BinarySearchTree().insert(1, 783, 30, 740, 1).get_max()
        {'783': ({'30': (1, 740)}, None)}
        """
        if node is None:
            if self.root is None:
                return None
            node = self.root

        if not self.empty():
            while node.right is not None:
                node = node.right
        return node

    def get_min(self, node: Node | None = None) -> Node | None:
        """
        We go deep on the left branch

        >>> BinarySearchTree().insert(10, 20, 30, 40, 50).get_min()
        {'10': (None, {'20': (None, {'30': (None, {'40': (None, 50)})})})}
        >>> BinarySearchTree().insert(-5, -1, 0, -0.3, -4.5).get_min()
        {'-5': (None, {'-1': (-4.5, {'0': (-0.3, None)})})}
        >>> BinarySearchTree().insert(1, 78.3, 30, 74.0, 1).get_min()
        {'1': (None, {'78.3': ({'30': (1, 74.0)}, None)})}
        >>> BinarySearchTree().insert(1, 783, 30, 740, 1).get_min()
        {'1': (None, {'783': ({'30': (1, 740)}, None)})}
        """
        if node is None:
            node = self.root
        if self.root is None:
            return None
        if not self.empty():
            node = self.root
            while node.left is not None:
                node = node.left
        return node

    def remove(self, value: int) -> None:
        # Look for the node with that label
        node = self.search(value)
        if node is None:
            msg = f"Value {value} not found"
            raise ValueError(msg)

        if node.left is None and node.right is None:  # If it has no children
            self.__reassign_nodes(node, None)
        elif node.left is None:  # Has only right children
            self.__reassign_nodes(node, node.right)
        elif node.right is None:  # Has only left children
            self.__reassign_nodes(node, node.left)
        else:
            predecessor = self.get_max(
                node.left
            )  # Gets the max value of the left branch
            self.remove(predecessor.value)  # type: ignore[union-attr]
            node.value = (
                predecessor.value  # type: ignore[union-attr]
            )  # Assigns the value to the node to delete and keep tree structure

    def preorder_traverse(self, node: Node | None) -> Iterable:
        if node is not None:
            yield node  # Preorder Traversal
            yield from self.preorder_traverse(node.left)
            yield from self.preorder_traverse(node.right)

    def traversal_tree(self, traversal_function=None) -> Any:
        """
        This function traversal the tree.
        You can pass a function to traversal the tree as needed by client code
        """
        if traversal_function is None:
            return self.preorder_traverse(self.root)
        else:
            return traversal_function(self.root)

    def inorder(self, arr: list, node: Node | None) -> None:
        """Perform an inorder traversal and append values of the nodes to
        a list named arr"""
        if node:
            self.inorder(arr, node.left)
            arr.append(node.value)
            self.inorder(arr, node.right)

    def find_kth_smallest(self, k: int, node: Node) -> int:
        """Return the kth smallest element in a binary search tree"""
        arr: list[int] = []
        self.inorder(arr, node)  # append all values to list using inorder traversal
        return arr[k - 1]


def inorder(curr_node: Node | None) -> list[Node]:
    """
    inorder (left, self, right)
    """
    node_list = []
    if curr_node is not None:
        node_list = [*inorder(curr_node.left), curr_node, *inorder(curr_node.right)]
    return node_list


def postorder(curr_node: Node | None) -> list[Node]:
    """
    postOrder (left, right, self)
    """
    node_list = []
    if curr_node is not None:
        node_list = postorder(curr_node.left) + postorder(curr_node.right) + [curr_node]
    return node_list


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)


================================================
FILE: data_structures/binary_tree/binary_search_tree_recursive.py
================================================
"""
This is a python3 implementation of binary search tree using recursion

To run tests:
python -m unittest binary_search_tree_recursive.py

To run an example:
python binary_search_tree_recursive.py
"""

from __future__ import annotations

import unittest
from collections.abc import Iterator

import pytest


class Node:
    def __init__(self, label: int, parent: Node | None) -> None:
        self.label = label
        self.parent = parent
        self.left: Node | None = None
        self.right: Node | None = None


class BinarySearchTree:
    def __init__(self) -> None:
        self.root: Node | None = None

    def empty(self) -> None:
        """
        Empties the tree

        >>> t = BinarySearchTree()
        >>> assert t.root is None
        >>> t.put(8)
        >>> assert t.root is not None
        """
        self.root = None

    def is_empty(self) -> bool:
        """
        Checks if the tree is empty

        >>> t = BinarySearchTree()
        >>> t.is_empty()
        True
        >>> t.put(8)
        >>> t.is_empty()
        False
        """
        return self.root is None

    def put(self, label: int) -> None:
        """
        Put a new node in the tree

        >>> t = BinarySearchTree()
        >>> t.put(8)
        >>> assert t.root.parent is None
        >>> assert t.root.label == 8

        >>> t.put(10)
        >>> assert t.root.right.parent == t.root
        >>> assert t.root.right.label == 10

        >>> t.put(3)
        >>> assert t.root.left.parent == t.root
        >>> assert t.root.left.label == 3
        """
        self.root = self._put(self.root, label)

    def _put(self, node: Node | None, label: int, parent: Node | None = None) -> Node:
        if node is None:
            node = Node(label, parent)
        elif label < node.label:
            node.left = self._put(node.left, label, node)
        elif label > node.label:
            node.right = self._put(node.right, label, node)
        else:
            msg = f"Node with label {label} already exists"
            raise ValueError(msg)

        return node

    def search(self, label: int) -> Node:
        """
        Searches a node in the tree

        >>> t = BinarySearchTree()
        >>> t.put(8)
        >>> t.put(10)
        >>> node = t.search(8)
        >>> assert node.label == 8

        >>> node = t.search(3)
        Traceback (most recent call last):
            ...
        ValueError: Node with label 3 does not exist
        """
        return self._search(self.root, label)

    def _search(self, node: Node | None, label: int) -> Node:
        if node is None:
            msg = f"Node with label {label} does not exist"
            raise ValueError(msg)
        elif label < node.label:
            node = self._search(node.left, label)
        elif label > node.label:
            node = self._search(node.right, label)

        return node

    def remove(self, label: int) -> None:
        """
        Removes a node in the tree

        >>> t = BinarySearchTree()
        >>> t.put(8)
        >>> t.put(10)
        >>> t.remove(8)
        >>> assert t.root.label == 10

        >>> t.remove(3)
        Traceback (most recent call last):
            ...
        ValueError: Node with label 3 does not exist
        """
        node = self.search(label)
        if node.right and node.left:
            lowest_node = self._get_lowest_node(node.right)
            lowest_node.left = node.left
            lowest_node.right = node.right
            node.left.parent = lowest_node
            if node.right:
                node.right.parent = lowest_node
            self._reassign_nodes(node, lowest_node)
        elif not node.right and node.left:
            self._reassign_nodes(node, node.left)
        elif node.right and not node.left:
            self._reassign_nodes(node, node.right)
        else:
            self._reassign_nodes(node, None)

    def _reassign_nodes(self, node: Node, new_children: Node | None) -> None:
        if new_children:
            new_children.parent = node.parent

        if node.parent:
            if node.parent.right == node:
                node.parent.right = new_children
            else:
                node.parent.left = new_children
        else:
            self.root = new_children

    def _get_lowest_node(self, node: Node) -> Node:
        if node.left:
            lowest_node = self._get_lowest_node(node.left)
        else:
            lowest_node = node
            self._reassign_nodes(node, node.right)

        return lowest_node

    def exists(self, label: int) -> bool:
        """
        Checks if a node exists in the tree

        >>> t = BinarySearchTree()
        >>> t.put(8)
        >>> t.put(10)
        >>> t.exists(8)
        True

        >>> t.exists(3)
        False
        """
        try:
            self.search(label)
            return True
        except ValueError:
            return False

    def get_max_label(self) -> int:
        """
        Gets the max label inserted in the tree

        >>> t = BinarySearchTree()
        >>> t.get_max_label()
        Traceback (most recent call last):
            ...
        ValueError: Binary search tree is empty

        >>> t.put(8)
        >>> t.put(10)
        >>> t.get_max_label()
        10
        """
        if self.root is None:
            raise ValueError("Binary search tree is empty")

        node = self.root
        while node.right is not None:
            node = node.right

        return node.label

    def get_min_label(self) -> int:
        """
        Gets the min label inserted in the tree

        >>> t = BinarySearchTree()
        >>> t.get_min_label()
        Traceback (most recent call last):
            ...
        ValueError: Binary search tree is empty

        >>> t.put(8)
        >>> t.put(10)
        >>> t.get_min_label()
        8
        """
        if self.root is None:
            raise ValueError("Binary search tree is empty")

        node = self.root
        while node.left is not None:
            node = node.left

        return node.label

    def inorder_traversal(self) -> Iterator[Node]:
        """
        Return the inorder traversal of the tree

        >>> t = BinarySearchTree()
        >>> [i.label for i in t.inorder_traversal()]
        []

        >>> t.put(8)
        >>> t.put(10)
        >>> t.put(9)
        >>> [i.label for i in t.inorder_traversal()]
        [8, 9, 10]
        """
        return self._inorder_traversal(self.root)

    def _inorder_traversal(self, node: Node | None) -> Iterator[Node]:
        if node is not None:
            yield from self._inorder_traversal(node.left)
            yield node
            yield from self._inorder_traversal(node.right)

    def preorder_traversal(self) -> Iterator[Node]:
        """
        Return the preorder traversal of the tree

        >>> t = BinarySearchTree()
        >>> [i.label for i in t.preorder_traversal()]
        []

        >>> t.put(8)
        >>> t.put(10)
        >>> t.put(9)
        >>> [i.label for i in t.preorder_traversal()]
        [8, 10, 9]
        """
        return self._preorder_traversal(self.root)

    def _preorder_traversal(self, node: Node | None) -> Iterator[Node]:
        if node is not None:
            yield node
            yield from self._preorder_traversal(node.left)
            yield from self._preorder_traversal(node.right)


class BinarySearchTreeTest(unittest.TestCase):
    @staticmethod
    def _get_binary_search_tree() -> BinarySearchTree:
        r"""
              8
             / \
            3   10
           / \    \
          1   6    14
             / \   /
            4   7 13
             \
              5
        """
        t = BinarySearchTree()
        t.put(8)
        t.put(3)
        t.put(6)
        t.put(1)
        t.put(10)
        t.put(14)
        t.put(13)
        t.put(4)
        t.put(7)
        t.put(5)

        return t

    def test_put(self) -> None:
        t = BinarySearchTree()
        assert t.is_empty()

        t.put(8)
        r"""
              8
        """
        assert t.root is not None
        assert t.root.parent is None
        assert t.root.label == 8

        t.put(10)
        r"""
              8
               \
                10
        """
        assert t.root.right is not None
        assert t.root.right.parent == t.root
        assert t.root.right.label == 10

        t.put(3)
        r"""
              8
             / \
            3   10
        """
        assert t.root.left is not None
        assert t.root.left.parent == t.root
        assert t.root.left.label == 3

        t.put(6)
        r"""
              8
             / \
            3   10
             \
              6
        """
        assert t.root.left.right is not None
        assert t.root.left.right.parent == t.root.left
        assert t.root.left.right.label == 6

        t.put(1)
        r"""
              8
             / \
            3   10
           / \
          1   6
        """
        assert t.root.left.left is not None
        assert t.root.left.left.parent == t.root.left
        assert t.root.left.left.label == 1

        with pytest.raises(ValueError):
            t.put(1)

    def test_search(self) -> None:
        t = self._get_binary_search_tree()

        node = t.search(6)
        assert node.label == 6

        node = t.search(13)
        assert node.label == 13

        with pytest.raises(ValueError):
            t.search(2)

    def test_remove(self) -> None:
        t = self._get_binary_search_tree()

        t.remove(13)
        r"""
              8
             / \
            3   10
           / \    \
          1   6    14
             / \
            4   7
             \
              5
        """
        assert t.root is not None
        assert t.root.right is not None
        assert t.root.right.right is not None
        assert t.root.right.right.right is None
        assert t.root.right.right.left is None

        t.remove(7)
        r"""
              8
             / \
            3   10
           / \    \
          1   6    14
             /
            4
             \
              5
        """
        assert t.root.left is not None
        assert t.root.left.right is not None
        assert t.root.left.right.left is not None
        assert t.root.left.right.right is None
        assert t.root.left.right.left.label == 4

        t.remove(6)
        r"""
              8
             / \
            3   10
           / \    \
          1   4    14
               \
                5
        """
        assert t.root.left.left is not None
        assert t.root.left.right.right is not None
        assert t.root.left.left.label == 1
        assert t.root.left.right.label == 4
        assert t.root.left.right.right.label == 5
        assert t.root.left.right.left is None
        assert t.root.left.left.parent == t.root.left
        assert t.root.left.right.parent == t.root.left

        t.remove(3)
        r"""
              8
             / \
            4   10
           / \    \
          1   5    14
        """
        assert t.root is not None
        assert t.root.left.label == 4
        assert t.root.left.right.label == 5
        assert t.root.left.left.label == 1
        assert t.root.left.parent == t.root
        assert t.root.left.left.parent == t.root.left
        assert t.root.left.right.parent == t.root.left

        t.remove(4)
        r"""
              8
             / \
            5   10
           /      \
          1        14
        """
        assert t.root.left is not None
        assert t.root.left.left is not None
        assert t.root.left.label == 5
        assert t.root.left.right is None
        assert t.root.left.left.label == 1
        assert t.root.left.parent == t.root
        assert t.root.left.left.parent == t.root.left

    def test_remove_2(self) -> None:
        t = self._get_binary_search_tree()

        t.remove(3)
        r"""
              8
             / \
            4   10
           / \    \
          1   6    14
             / \   /
            5   7 13
        """
        assert t.root is not None
        assert t.root.left is not None
        assert t.root.left.left is not None
        assert t.root.left.right is not None
        assert t.root.left.right.left is not None
        assert t.root.left.right.right is not None
        assert t.root.left.label == 4
        assert t.root.left.right.label == 6
        assert t.root.left.left.label == 1
        assert t.root.left.right.right.label == 7
        assert t.root.left.right.left.label == 5
        assert t.root.left.parent == t.root
        assert t.root.left.right.parent == t.root.left
        assert t.root.left.left.parent == t.root.left
        assert t.root.left.right.left.parent == t.root.left.right

    def test_empty(self) -> None:
        t = self._get_binary_search_tree()
        t.empty()
        assert t.root is None

    def test_is_empty(self) -> None:
        t = self._get_binary_search_tree()
        assert not t.is_empty()

        t.empty()
        assert t.is_empty()

    def test_exists(self) -> None:
        t = self._get_binary_search_tree()

        assert t.exists(6)
        assert not t.exists(-1)

    def test_get_max_label(self) -> None:
        t = self._get_binary_search_tree()

        assert t.get_max_label() == 14

        t.empty()
        with pytest.raises(ValueError):
            t.get_max_label()

    def test_get_min_label(self) -> None:
        t = self._get_binary_search_tree()

        assert t.get_min_label() == 1

        t.empty()
        with pytest.raises(ValueError):
            t.get_min_label()

    def test_inorder_traversal(self) -> None:
        t = self._get_binary_search_tree()

        inorder_traversal_nodes = [i.label for i in t.inorder_traversal()]
        assert inorder_traversal_nodes == [1, 3, 4, 5, 6, 7, 8, 10, 13, 14]

    def test_preorder_traversal(self) -> None:
        t = self._get_binary_search_tree()

        preorder_traversal_nodes = [i.label for i in t.preorder_traversal()]
        assert preorder_traversal_nodes == [8, 3, 1, 6, 4, 5, 7, 10, 14, 13]


def binary_search_tree_example() -> None:
    r"""
    Example
                  8
                 / \
                3   10
               / \    \
              1   6    14
                 / \   /
                4   7 13
                \
                5

    Example After Deletion
                  4
                 / \
                1   7
                     \
                      5

    """

    t = BinarySearchTree()
    t.put(8)
    t.put(3)
    t.put(6)
    t.put(1)
    t.put(10)
    t.put(14)
    t.put(13)
    t.put(4)
    t.put(7)
    t.put(5)

    print(
        """
            8
           / \\
          3   10
         / \\    \\
        1   6    14
           / \\   /
          4   7 13
           \\
            5
        """
    )

    print("Label 6 exists:", t.exists(6))
    print("Label 13 exists:", t.exists(13))
    print("Label -1 exists:", t.exists(-1))
    print("Label 12 exists:", t.exists(12))

    # Prints all the elements of the list in inorder traversal
    inorder_traversal_nodes = [i.label for i in t.inorder_traversal()]
    print("Inorder traversal:", inorder_traversal_nodes)

    # Prints all the elements of the list in preorder traversal
    preorder_traversal_nodes = [i.label for i in t.preorder_traversal()]
    print("Preorder traversal:", preorder_traversal_nodes)

    print("Max. label:", t.get_max_label())
    print("Min. label:", t.get_min_label())

    # Delete elements
    print("\nDeleting elements 13, 10, 8, 3, 6, 14")
    print(
        """
          4
         / \\
        1   7
             \\
              5
        """
    )
    t.remove(13)
    t.remove(10)
    t.remove(8)
    t.remove(3)
    t.remove(6)
    t.remove(14)

    # Prints all the elements of the list in inorder traversal after delete
    inorder_traversal_nodes = [i.label for i in t.inorder_traversal()]
    print("Inorder traversal after delete:", inorder_traversal_nodes)

    # Prints all the elements of the list in preorder traversal after delete
    preorder_traversal_nodes = [i.label for i in t.preorder_traversal()]
    print("Preorder traversal after delete:", preorder_traversal_nodes)

    print("Max. label:", t.get_max_label())
    print("Min. label:", t.get_min_label())


if __name__ == "__main__":
    binary_search_tree_example()


================================================
FILE: data_structures/binary_tree/binary_tree_mirror.py
================================================
"""
Problem Description:
Given a binary tree, return its mirror.
"""


def binary_tree_mirror_dict(binary_tree_mirror_dictionary: dict, root: int):
    if not root or root not in binary_tree_mirror_dictionary:
        return
    left_child, right_child = binary_tree_mirror_dictionary[root][:2]
    binary_tree_mirror_dictionary[root] = [right_child, left_child]
    binary_tree_mirror_dict(binary_tree_mirror_dictionary, left_child)
    binary_tree_mirror_dict(binary_tree_mirror_dictionary, right_child)


def binary_tree_mirror(binary_tree: dict, root: int = 1) -> dict:
    """
    >>> binary_tree_mirror({ 1: [2,3], 2: [4,5], 3: [6,7], 7: [8,9]}, 1)
    {1: [3, 2], 2: [5, 4], 3: [7, 6], 7: [9, 8]}
    >>> binary_tree_mirror({ 1: [2,3], 2: [4,5], 3: [6,7], 4: [10,11]}, 1)
    {1: [3, 2], 2: [5, 4], 3: [7, 6], 4: [11, 10]}
    >>> binary_tree_mirror({ 1: [2,3], 2: [4,5], 3: [6,7], 4: [10,11]}, 5)
    Traceback (most recent call last):
        ...
    ValueError: root 5 is not present in the binary_tree
    >>> binary_tree_mirror({}, 5)
    Traceback (most recent call last):
        ...
    ValueError: binary tree cannot be empty
    """
    if not binary_tree:
        raise ValueError("binary tree cannot be empty")
    if root not in binary_tree:
        msg = f"root {root} is not present in the binary_tree"
        raise ValueError(msg)
    binary_tree_mirror_dictionary = dict(binary_tree)
    binary_tree_mirror_dict(binary_tree_mirror_dictionary, root)
    return binary_tree_mirror_dictionary


if __name__ == "__main__":
    binary_tree = {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
    print(f"Binary tree: {binary_tree}")
    binary_tree_mirror_dictionary = binary_tree_mirror(binary_tree, 5)
    print(f"Binary tree mirror: {binary_tree_mirror_dictionary}")


================================================
FILE: data_structures/binary_tree/binary_tree_node_sum.py
================================================
"""
Sum of all nodes in a binary tree.

Python implementation:
    O(n) time complexity - Recurses through :meth:`depth_first_search`
                            with each element.
    O(n) space complexity - At any point in time maximum number of stack
                            frames that could be in memory is `n`
"""

from __future__ import annotations

from collections.abc import Iterator


class Node:
    """
    A Node has a value variable and pointers to Nodes to its left and right.
    """

    def __init__(self, value: int) -> None:
        self.value = value
        self.left: Node | None = None
        self.right: Node | None = None


class BinaryTreeNodeSum:
    r"""
    The below tree looks like this
        10
       /  \
      5   -3
     /    / \
    12   8  0

    >>> tree = Node(10)
    >>> sum(BinaryTreeNodeSum(tree))
    10

    >>> tree.left = Node(5)
    >>> sum(BinaryTreeNodeSum(tree))
    15

    >>> tree.right = Node(-3)
    >>> sum(BinaryTreeNodeSum(tree))
    12

    >>> tree.left.left = Node(12)
    >>> sum(BinaryTreeNodeSum(tree))
    24

    >>> tree.right.left = Node(8)
    >>> tree.right.right = Node(0)
    >>> sum(BinaryTreeNodeSum(tree))
    32
    """

    def __init__(self, tree: Node) -> None:
        self.tree = tree

    def depth_first_search(self, node: Node | None) -> int:
        if node is None:
            return 0
        return node.value + (
            self.depth_first_search(node.left) + self.depth_first_search(node.right)
        )

    def __iter__(self) -> Iterator[int]:
        yield self.depth_first_search(self.tree)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/binary_tree/binary_tree_path_sum.py
================================================
"""
Given the root of a binary tree and an integer target,
find the number of paths where the sum of the values
along the path equals target.


Leetcode reference: https://leetcode.com/problems/path-sum-iii/
"""

from __future__ import annotations


class Node:
    """
    A Node has value variable and pointers to Nodes to its left and right.
    """

    def __init__(self, value: int) -> None:
        self.value = value
        self.left: Node | None = None
        self.right: Node | None = None


class BinaryTreePathSum:
    r"""
    The below tree looks like this
          10
         /  \
        5   -3
       / \    \
      3   2    11
     / \   \
    3  -2   1


    >>> tree = Node(10)
    >>> tree.left = Node(5)
    >>> tree.right = Node(-3)
    >>> tree.left.left = Node(3)
    >>> tree.left.right = Node(2)
    >>> tree.right.right = Node(11)
    >>> tree.left.left.left = Node(3)
    >>> tree.left.left.right = Node(-2)
    >>> tree.left.right.right = Node(1)

    >>> BinaryTreePathSum().path_sum(tree, 8)
    3
    >>> BinaryTreePathSum().path_sum(tree, 7)
    2
    >>> tree.right.right = Node(10)
    >>> BinaryTreePathSum().path_sum(tree, 8)
    2
    >>> BinaryTreePathSum().path_sum(None, 0)
    0
    >>> BinaryTreePathSum().path_sum(tree, 0)
    0

    The second tree looks like this
          0
         / \
        5   5

    >>> tree2 = Node(0)
    >>> tree2.left = Node(5)
    >>> tree2.right = Node(5)

    >>> BinaryTreePathSum().path_sum(tree2, 5)
    4
    >>> BinaryTreePathSum().path_sum(tree2, -1)
    0
    >>> BinaryTreePathSum().path_sum(tree2, 0)
    1
    """

    target: int

    def __init__(self) -> None:
        self.paths = 0

    def depth_first_search(self, node: Node | None, path_sum: int) -> None:
        if node is None:
            return

        if path_sum == self.target:
            self.paths += 1

        if node.left:
            self.depth_first_search(node.left, path_sum + node.left.value)
        if node.right:
            self.depth_first_search(node.right, path_sum + node.right.value)

    def path_sum(self, node: Node | None, target: int | None = None) -> int:
        if node is None:
            return 0
        if target is not None:
            self.target = target

        self.depth_first_search(node, node.value)
        self.path_sum(node.left)
        self.path_sum(node.right)

        return self.paths


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/binary_tree/binary_tree_traversals.py
================================================
from __future__ import annotations

from collections import deque
from collections.abc import Generator
from dataclasses import dataclass


# https://en.wikipedia.org/wiki/Tree_traversal
@dataclass
class Node:
    data: int
    left: Node | None = None
    right: Node | None = None


def make_tree() -> Node | None:
    r"""
    The below tree
        1
       / \
      2   3
     / \
    4   5
    """
    tree = Node(1)
    tree.left = Node(2)
    tree.right = Node(3)
    tree.left.left = Node(4)
    tree.left.right = Node(5)
    return tree


def preorder(root: Node | None) -> Generator[int]:
    """
    Pre-order traversal visits root node, left subtree, right subtree.
    >>> list(preorder(make_tree()))
    [1, 2, 4, 5, 3]
    """
    if not root:
        return
    yield root.data
    yield from preorder(root.left)
    yield from preorder(root.right)


def postorder(root: Node | None) -> Generator[int]:
    """
    Post-order traversal visits left subtree, right subtree, root node.
    >>> list(postorder(make_tree()))
    [4, 5, 2, 3, 1]
    """
    if not root:
        return
    yield from postorder(root.left)
    yield from postorder(root.right)
    yield root.data


def inorder(root: Node | None) -> Generator[int]:
    """
    In-order traversal visits left subtree, root node, right subtree.
    >>> list(inorder(make_tree()))
    [4, 2, 5, 1, 3]
    """
    if not root:
        return
    yield from inorder(root.left)
    yield root.data
    yield from inorder(root.right)


def reverse_inorder(root: Node | None) -> Generator[int]:
    """
    Reverse in-order traversal visits right subtree, root node, left subtree.
    >>> list(reverse_inorder(make_tree()))
    [3, 1, 5, 2, 4]
    """
    if not root:
        return
    yield from reverse_inorder(root.right)
    yield root.data
    yield from reverse_inorder(root.left)


def height(root: Node | None) -> int:
    """
    Recursive function for calculating the height of the binary tree.
    >>> height(None)
    0
    >>> height(make_tree())
    3
    """
    return (max(height(root.left), height(root.right)) + 1) if root else 0


def level_order(root: Node | None) -> Generator[int]:
    """
    Returns a list of nodes value from a whole binary tree in Level Order Traverse.
    Level Order traverse: Visit nodes of the tree level-by-level.
    >>> list(level_order(make_tree()))
    [1, 2, 3, 4, 5]
    """

    if root is None:
        return

    process_queue = deque([root])

    while process_queue:
        node = process_queue.popleft()
        yield node.data

        if node.left:
            process_queue.append(node.left)
        if node.right:
            process_queue.append(node.right)


def get_nodes_from_left_to_right(root: Node | None, level: int) -> Generator[int]:
    """
    Returns a list of nodes value from a particular level:
    Left to right direction of the binary tree.
    >>> list(get_nodes_from_left_to_right(make_tree(), 1))
    [1]
    >>> list(get_nodes_from_left_to_right(make_tree(), 2))
    [2, 3]
    """

    def populate_output(root: Node | None, level: int) -> Generator[int]:
        if not root:
            return
        if level == 1:
            yield root.data
        elif level > 1:
            yield from populate_output(root.left, level - 1)
            yield from populate_output(root.right, level - 1)

    yield from populate_output(root, level)


def get_nodes_from_right_to_left(root: Node | None, level: int) -> Generator[int]:
    """
    Returns a list of nodes value from a particular level:
    Right to left direction of the binary tree.
    >>> list(get_nodes_from_right_to_left(make_tree(), 1))
    [1]
    >>> list(get_nodes_from_right_to_left(make_tree(), 2))
    [3, 2]
    """

    def populate_output(root: Node | None, level: int) -> Generator[int]:
        if not root:
            return
        if level == 1:
            yield root.data
        elif level > 1:
            yield from populate_output(root.right, level - 1)
            yield from populate_output(root.left, level - 1)

    yield from populate_output(root, level)


def zigzag(root: Node | None) -> Generator[int]:
    """
    ZigZag traverse:
    Returns a list of nodes value from left to right and right to left, alternatively.
    >>> list(zigzag(make_tree()))
    [1, 3, 2, 4, 5]
    """
    if root is None:
        return

    flag = 0
    height_tree = height(root)

    for h in range(1, height_tree + 1):
        if not flag:
            yield from get_nodes_from_left_to_right(root, h)
            flag = 1
        else:
            yield from get_nodes_from_right_to_left(root, h)
            flag = 0


def main() -> None:  # Main function for testing.
    # Create binary tree.
    root = make_tree()

    # All Traversals of the binary are as follows:
    print(f"In-order Traversal: {list(inorder(root))}")
    print(f"Reverse In-order Traversal: {list(reverse_inorder(root))}")
    print(f"Pre-order Traversal: {list(preorder(root))}")
    print(f"Post-order Traversal: {list(postorder(root))}", "\n")

    print(f"Height of Tree: {height(root)}", "\n")

    print("Complete Level Order Traversal: ")
    print(f"{list(level_order(root))} \n")

    print("Level-wise order Traversal: ")

    for level in range(1, height(root) + 1):
        print(f"Level {level}:", list(get_nodes_from_left_to_right(root, level=level)))

    print("\nZigZag order Traversal: ")
    print(f"{list(zigzag(root))}")


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: data_structures/binary_tree/diameter_of_binary_tree.py
================================================
"""
The diameter/width of a tree is defined as the number of nodes on the longest path
between two end nodes.
"""

from __future__ import annotations

from dataclasses import dataclass


@dataclass
class Node:
    data: int
    left: Node | None = None
    right: Node | None = None

    def depth(self) -> int:
        """
        >>> root = Node(1)
        >>> root.depth()
        1
        >>> root.left = Node(2)
        >>> root.depth()
        2
        >>> root.left.depth()
        1
        >>> root.right = Node(3)
        >>> root.depth()
        2
        """
        left_depth = self.left.depth() if self.left else 0
        right_depth = self.right.depth() if self.right else 0
        return max(left_depth, right_depth) + 1

    def diameter(self) -> int:
        """
        >>> root = Node(1)
        >>> root.diameter()
        1
        >>> root.left = Node(2)
        >>> root.diameter()
        2
        >>> root.left.diameter()
        1
        >>> root.right = Node(3)
        >>> root.diameter()
        3
        """
        left_depth = self.left.depth() if self.left else 0
        right_depth = self.right.depth() if self.right else 0
        return left_depth + right_depth + 1


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    root = Node(1)
    root.left = Node(2)
    root.right = Node(3)
    root.left.left = Node(4)
    root.left.right = Node(5)
    r"""
    Constructed binary tree is
        1
       / \
      2	  3
     / \
    4	 5
    """
    print(f"{root.diameter() = }")  # 4
    print(f"{root.left.diameter() = }")  # 3
    print(f"{root.right.diameter() = }")  # 1


================================================
FILE: data_structures/binary_tree/diff_views_of_binary_tree.py
================================================
r"""
Problem: Given root of a binary tree, return the:
1. binary-tree-right-side-view
2. binary-tree-left-side-view
3. binary-tree-top-side-view
4. binary-tree-bottom-side-view
"""

from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass


@dataclass
class TreeNode:
    val: int
    left: TreeNode | None = None
    right: TreeNode | None = None


def make_tree() -> TreeNode:
    """
    >>> make_tree().val
    3
    """
    return TreeNode(3, TreeNode(9), TreeNode(20, TreeNode(15), TreeNode(7)))


def binary_tree_right_side_view(root: TreeNode) -> list[int]:
    r"""
    Function returns the right side view of binary tree.

       3       <-  3
     / \
    9   20    <-  20
       /  \
      15   7  <-  7

    >>> binary_tree_right_side_view(make_tree())
    [3, 20, 7]
    >>> binary_tree_right_side_view(None)
    []
    """

    def depth_first_search(
        root: TreeNode | None, depth: int, right_view: list[int]
    ) -> None:
        """
        A depth first search preorder traversal to append the values at
        right side of tree.
        """
        if not root:
            return

        if depth == len(right_view):
            right_view.append(root.val)

        depth_first_search(root.right, depth + 1, right_view)
        depth_first_search(root.left, depth + 1, right_view)

    right_view: list = []
    if not root:
        return right_view

    depth_first_search(root, 0, right_view)
    return right_view


def binary_tree_left_side_view(root: TreeNode) -> list[int]:
    r"""
    Function returns the left side view of binary tree.

    3  ->    3
            / \
    9  ->  9   20
              /  \
    15 ->    15   7

    >>> binary_tree_left_side_view(make_tree())
    [3, 9, 15]
    >>> binary_tree_left_side_view(None)
    []
    """

    def depth_first_search(
        root: TreeNode | None, depth: int, left_view: list[int]
    ) -> None:
        """
        A depth first search preorder traversal to append the values
        at left side of tree.
        """
        if not root:
            return

        if depth == len(left_view):
            left_view.append(root.val)

        depth_first_search(root.left, depth + 1, left_view)
        depth_first_search(root.right, depth + 1, left_view)

    left_view: list = []
    if not root:
        return left_view

    depth_first_search(root, 0, left_view)
    return left_view


def binary_tree_top_side_view(root: TreeNode) -> list[int]:
    r"""
    Function returns the top side view of binary tree.

    9 3 20 7
    ⬇ ⬇ ⬇  ⬇

      3
     / \
    9   20
       /  \
      15   7

    >>> binary_tree_top_side_view(make_tree())
    [9, 3, 20, 7]
    >>> binary_tree_top_side_view(None)
    []
    """

    def breadth_first_search(root: TreeNode, top_view: list[int]) -> None:
        """
        A breadth first search traversal with defaultdict ds to append
        the values of tree from top view
        """
        queue = [(root, 0)]
        lookup = defaultdict(list)

        while queue:
            first = queue.pop(0)
            node, hd = first

            lookup[hd].append(node.val)

            if node.left:
                queue.append((node.left, hd - 1))
            if node.right:
                queue.append((node.right, hd + 1))

        for pair in sorted(lookup.items(), key=lambda each: each[0]):
            top_view.append(pair[1][0])

    top_view: list = []
    if not root:
        return top_view

    breadth_first_search(root, top_view)
    return top_view


def binary_tree_bottom_side_view(root: TreeNode) -> list[int]:
    r"""
    Function returns the bottom side view of binary tree

      3
     / \
    9   20
       /  \
      15   7
    ↑  ↑ ↑  ↑
    9 15 20 7

    >>> binary_tree_bottom_side_view(make_tree())
    [9, 15, 20, 7]
    >>> binary_tree_bottom_side_view(None)
    []
    """
    from collections import defaultdict

    def breadth_first_search(root: TreeNode, bottom_view: list[int]) -> None:
        """
        A breadth first search traversal with defaultdict ds to append
        the values of tree from bottom view
        """
        queue = [(root, 0)]
        lookup = defaultdict(list)

        while queue:
            first = queue.pop(0)
            node, hd = first
            lookup[hd].append(node.val)

            if node.left:
                queue.append((node.left, hd - 1))
            if node.right:
                queue.append((node.right, hd + 1))

        for pair in sorted(lookup.items(), key=lambda each: each[0]):
            bottom_view.append(pair[1][-1])

    bottom_view: list = []
    if not root:
        return bottom_view

    breadth_first_search(root, bottom_view)
    return bottom_view


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/binary_tree/distribute_coins.py
================================================
"""
Author  : Alexander Pantyukhin
Date    : November 7, 2022

Task:
You are given a tree root of a binary tree with n nodes, where each node has
node.data coins. There are exactly n coins in whole tree.

In one move, we may choose two adjacent nodes and move one coin from one node
to another. A move may be from parent to child, or from child to parent.

Return the minimum number of moves required to make every node have exactly one coin.

Example 1:

   3
  / \
 0   0

Result: 2

Example 2:

   0
  / \
 3   0

Result 3

leetcode: https://leetcode.com/problems/distribute-coins-in-binary-tree/

Implementation notes:
User depth-first search approach.

Let n is the number of nodes in tree
Runtime: O(n)
Space: O(1)
"""

from __future__ import annotations

from dataclasses import dataclass
from typing import NamedTuple


@dataclass
class TreeNode:
    data: int
    left: TreeNode | None = None
    right: TreeNode | None = None


class CoinsDistribResult(NamedTuple):
    moves: int
    excess: int


def distribute_coins(root: TreeNode | None) -> int:
    """
    >>> distribute_coins(TreeNode(3, TreeNode(0), TreeNode(0)))
    2
    >>> distribute_coins(TreeNode(0, TreeNode(3), TreeNode(0)))
    3
    >>> distribute_coins(TreeNode(0, TreeNode(0), TreeNode(3)))
    3
    >>> distribute_coins(None)
    0
    >>> distribute_coins(TreeNode(0, TreeNode(0), TreeNode(0)))
    Traceback (most recent call last):
     ...
    ValueError: The nodes number should be same as the number of coins
    >>> distribute_coins(TreeNode(0, TreeNode(1), TreeNode(1)))
    Traceback (most recent call last):
     ...
    ValueError: The nodes number should be same as the number of coins
    """

    if root is None:
        return 0

    # Validation
    def count_nodes(node: TreeNode | None) -> int:
        """
        >>> count_nodes(None)
        0
        """
        if node is None:
            return 0

        return count_nodes(node.left) + count_nodes(node.right) + 1

    def count_coins(node: TreeNode | None) -> int:
        """
        >>> count_coins(None)
        0
        """
        if node is None:
            return 0

        return count_coins(node.left) + count_coins(node.right) + node.data

    if count_nodes(root) != count_coins(root):
        raise ValueError("The nodes number should be same as the number of coins")

    # Main calculation
    def get_distrib(node: TreeNode | None) -> CoinsDistribResult:
        """
        >>> get_distrib(None)
        namedtuple("CoinsDistribResult", "0 2")
        """

        if node is None:
            return CoinsDistribResult(0, 1)

        left_distrib_moves, left_distrib_excess = get_distrib(node.left)
        right_distrib_moves, right_distrib_excess = get_distrib(node.right)

        coins_to_left = 1 - left_distrib_excess
        coins_to_right = 1 - right_distrib_excess

        result_moves = (
            left_distrib_moves
            + right_distrib_moves
            + abs(coins_to_left)
            + abs(coins_to_right)
        )
        result_excess = node.data - coins_to_left - coins_to_right

        return CoinsDistribResult(result_moves, result_excess)

    return get_distrib(root)[0]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/binary_tree/fenwick_tree.py
================================================
from copy import deepcopy


class FenwickTree:
    """
    Fenwick Tree

    More info: https://en.wikipedia.org/wiki/Fenwick_tree
    """

    def __init__(self, arr: list[int] | None = None, size: int | None = None) -> None:
        """
        Constructor for the Fenwick tree

        Parameters:
            arr (list): list of elements to initialize the tree with (optional)
            size (int): size of the Fenwick tree (if arr is None)
        """

        if arr is None and size is not None:
            self.size = size
            self.tree = [0] * size
        elif arr is not None:
            self.init(arr)
        else:
            raise ValueError("Either arr or size must be specified")

    def init(self, arr: list[int]) -> None:
        """
        Initialize the Fenwick tree with arr in O(N)

        Parameters:
            arr (list): list of elements to initialize the tree with

        Returns:
            None

        >>> a = [1, 2, 3, 4, 5]
        >>> f1 = FenwickTree(a)
        >>> f2 = FenwickTree(size=len(a))
        >>> for index, value in enumerate(a):
        ...     f2.add(index, value)
        >>> f1.tree == f2.tree
        True
        """
        self.size = len(arr)
        self.tree = deepcopy(arr)
        for i in range(1, self.size):
            j = self.next_(i)
            if j < self.size:
                self.tree[j] += self.tree[i]

    def get_array(self) -> list[int]:
        """
        Get the Normal Array of the Fenwick tree in O(N)

        Returns:
            list: Normal Array of the Fenwick tree

        >>> a = [i for i in range(128)]
        >>> f = FenwickTree(a)
        >>> f.get_array() == a
        True
        """
        arr = self.tree[:]
        for i in range(self.size - 1, 0, -1):
            j = self.next_(i)
            if j < self.size:
                arr[j] -= arr[i]
        return arr

    @staticmethod
    def next_(index: int) -> int:
        return index + (index & (-index))

    @staticmethod
    def prev(index: int) -> int:
        return index - (index & (-index))

    def add(self, index: int, value: int) -> None:
        """
        Add a value to index in O(lg N)

        Parameters:
            index (int): index to add value to
            value (int): value to add to index

        Returns:
            None

        >>> f = FenwickTree([1, 2, 3, 4, 5])
        >>> f.add(0, 1)
        >>> f.add(1, 2)
        >>> f.add(2, 3)
        >>> f.add(3, 4)
        >>> f.add(4, 5)
        >>> f.get_array()
        [2, 4, 6, 8, 10]
        """
        if index == 0:
            self.tree[0] += value
            return
        while index < self.size:
            self.tree[index] += value
            index = self.next_(index)

    def update(self, index: int, value: int) -> None:
        """
        Set the value of index in O(lg N)

        Parameters:
            index (int): index to set value to
            value (int): value to set in index

        Returns:
            None

        >>> f = FenwickTree([5, 4, 3, 2, 1])
        >>> f.update(0, 1)
        >>> f.update(1, 2)
        >>> f.update(2, 3)
        >>> f.update(3, 4)
        >>> f.update(4, 5)
        >>> f.get_array()
        [1, 2, 3, 4, 5]
        """
        self.add(index, value - self.get(index))

    def prefix(self, right: int) -> int:
        """
        Prefix sum of all elements in [0, right) in O(lg N)

        Parameters:
            right (int): right bound of the query (exclusive)

        Returns:
            int: sum of all elements in [0, right)

        >>> a = [i for i in range(128)]
        >>> f = FenwickTree(a)
        >>> res = True
        >>> for i in range(len(a)):
        ...     res = res and f.prefix(i) == sum(a[:i])
        >>> res
        True
        """
        if right == 0:
            return 0
        result = self.tree[0]
        right -= 1  # make right inclusive
        while right > 0:
            result += self.tree[right]
            right = self.prev(right)
        return result

    def query(self, left: int, right: int) -> int:
        """
        Query the sum of all elements in [left, right) in O(lg N)

        Parameters:
            left (int): left bound of the query (inclusive)
            right (int): right bound of the query (exclusive)

        Returns:
            int: sum of all elements in [left, right)

        >>> a = [i for i in range(128)]
        >>> f = FenwickTree(a)
        >>> res = True
        >>> for i in range(len(a)):
        ...     for j in range(i + 1, len(a)):
        ...         res = res and f.query(i, j) == sum(a[i:j])
        >>> res
        True
        """
        return self.prefix(right) - self.prefix(left)

    def get(self, index: int) -> int:
        """
        Get value at index in O(lg N)

        Parameters:
            index (int): index to get the value

        Returns:
            int: Value of element at index

        >>> a = [i for i in range(128)]
        >>> f = FenwickTree(a)
        >>> res = True
        >>> for i in range(len(a)):
        ...     res = res and f.get(i) == a[i]
        >>> res
        True
        """
        return self.query(index, index + 1)

    def rank_query(self, value: int) -> int:
        """
        Find the largest index with prefix(i) <= value in O(lg N)
        NOTE: Requires that all values are non-negative!

        Parameters:
            value (int): value to find the largest index of

        Returns:
            -1: if value is smaller than all elements in prefix sum
            int: largest index with prefix(i) <= value

        >>> f = FenwickTree([1, 2, 0, 3, 0, 5])
        >>> f.rank_query(0)
        -1
        >>> f.rank_query(2)
        0
        >>> f.rank_query(1)
        0
        >>> f.rank_query(3)
        2
        >>> f.rank_query(5)
        2
        >>> f.rank_query(6)
        4
        >>> f.rank_query(11)
        5
        """
        value -= self.tree[0]
        if value < 0:
            return -1

        j = 1  # Largest power of 2 <= size
        while j * 2 < self.size:
            j *= 2

        i = 0

        while j > 0:
            if i + j < self.size and self.tree[i + j] <= value:
                value -= self.tree[i + j]
                i += j
            j //= 2
        return i


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/binary_tree/flatten_binarytree_to_linkedlist.py
================================================
"""
Binary Tree Flattening Algorithm

This code defines an algorithm to flatten a binary tree into a linked list
represented using the right pointers of the tree nodes. It uses in-place
flattening and demonstrates the flattening process along with a display
function to visualize the flattened linked list.
https://www.geeksforgeeks.org/flatten-a-binary-tree-into-linked-list

Author: Arunkumar A
Date: 04/09/2023
"""

from __future__ import annotations


class TreeNode:
    """
    A TreeNode has data variable and pointers to TreeNode objects
    for its left and right children.
    """

    def __init__(self, data: int) -> None:
        self.data = data
        self.left: TreeNode | None = None
        self.right: TreeNode | None = None


def build_tree() -> TreeNode:
    """
    Build and return a sample binary tree.

    Returns:
        TreeNode: The root of the binary tree.

    Examples:
        >>> root = build_tree()
        >>> root.data
        1
        >>> root.left.data
        2
        >>> root.right.data
        5
        >>> root.left.left.data
        3
        >>> root.left.right.data
        4
        >>> root.right.right.data
        6
    """
    root = TreeNode(1)
    root.left = TreeNode(2)
    root.right = TreeNode(5)
    root.left.left = TreeNode(3)
    root.left.right = TreeNode(4)
    root.right.right = TreeNode(6)
    return root


def flatten(root: TreeNode | None) -> None:
    """
    Flatten a binary tree into a linked list in-place, where the linked list is
    represented using the right pointers of the tree nodes.

    Args:
        root (TreeNode): The root of the binary tree to be flattened.

    Examples:
        >>> root = TreeNode(1)
        >>> root.left = TreeNode(2)
        >>> root.right = TreeNode(5)
        >>> root.left.left = TreeNode(3)
        >>> root.left.right = TreeNode(4)
        >>> root.right.right = TreeNode(6)
        >>> flatten(root)
        >>> root.data
        1
        >>> root.right.right is None
        False
        >>> root.right.right = TreeNode(3)
        >>> root.right.right.right is None
        True
    """
    if not root:
        return

    # Flatten the left subtree
    flatten(root.left)

    # Save the right subtree
    right_subtree = root.right

    # Make the left subtree the new right subtree
    root.right = root.left
    root.left = None

    # Find the end of the new right subtree
    current = root
    while current.right:
        current = current.right

    # Append the original right subtree to the end
    current.right = right_subtree

    # Flatten the updated right subtree
    flatten(right_subtree)


def display_linked_list(root: TreeNode | None) -> None:
    """
    Display the flattened linked list.

    Args:
        root (TreeNode | None): The root of the flattened linked list.

    Examples:
        >>> root = TreeNode(1)
        >>> root.right = TreeNode(2)
        >>> root.right.right = TreeNode(3)
        >>> display_linked_list(root)
        1 2 3
        >>> root = None
        >>> display_linked_list(root)

    """
    current = root
    while current:
        if current.right is None:
            print(current.data, end="")
            break
        print(current.data, end=" ")
        current = current.right


if __name__ == "__main__":
    print("Flattened Linked List:")
    root = build_tree()
    flatten(root)
    display_linked_list(root)


================================================
FILE: data_structures/binary_tree/floor_and_ceiling.py
================================================
"""
In a binary search tree (BST):
* The floor of key 'k' is the maximum value that is smaller than or equal to 'k'.
* The ceiling of key 'k' is the minimum value that is greater than or equal to 'k'.

Reference:
https://bit.ly/46uB0a2

Author : Arunkumar
Date : 14th October 2023
"""

from __future__ import annotations

from collections.abc import Iterator
from dataclasses import dataclass


@dataclass
class Node:
    key: int
    left: Node | None = None
    right: Node | None = None

    def __iter__(self) -> Iterator[int]:
        if self.left:
            yield from self.left
        yield self.key
        if self.right:
            yield from self.right

    def __len__(self) -> int:
        return sum(1 for _ in self)


def floor_ceiling(root: Node | None, key: int) -> tuple[int | None, int | None]:
    """
    Find the floor and ceiling values for a given key in a Binary Search Tree (BST).

    Args:
        root: The root of the binary search tree.
        key: The key for which to find the floor and ceiling.

    Returns:
        A tuple containing the floor and ceiling values, respectively.

    Examples:
        >>> root = Node(10)
        >>> root.left = Node(5)
        >>> root.right = Node(20)
        >>> root.left.left = Node(3)
        >>> root.left.right = Node(7)
        >>> root.right.left = Node(15)
        >>> root.right.right = Node(25)
        >>> tuple(root)
        (3, 5, 7, 10, 15, 20, 25)
        >>> floor_ceiling(root, 8)
        (7, 10)
        >>> floor_ceiling(root, 14)
        (10, 15)
        >>> floor_ceiling(root, -1)
        (None, 3)
        >>> floor_ceiling(root, 30)
        (25, None)
    """
    floor_val = None
    ceiling_val = None

    while root:
        if root.key == key:
            floor_val = root.key
            ceiling_val = root.key
            break

        if key < root.key:
            ceiling_val = root.key
            root = root.left
        else:
            floor_val = root.key
            root = root.right

    return floor_val, ceiling_val


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/binary_tree/inorder_tree_traversal_2022.py
================================================
"""
Illustrate how to implement inorder traversal in binary search tree.
Author: Gurneet Singh
https://www.geeksforgeeks.org/tree-traversals-inorder-preorder-and-postorder/
"""


class BinaryTreeNode:
    """Defining the structure of BinaryTreeNode"""

    def __init__(self, data: int) -> None:
        self.data = data
        self.left_child: BinaryTreeNode | None = None
        self.right_child: BinaryTreeNode | None = None


def insert(node: BinaryTreeNode | None, new_value: int) -> BinaryTreeNode | None:
    """
    If the binary search tree is empty, make a new node and declare it as root.
    >>> node_a = BinaryTreeNode(12345)
    >>> node_b = insert(node_a, 67890)
    >>> node_a.left_child == node_b.left_child
    True
    >>> node_a.right_child == node_b.right_child
    True
    >>> node_a.data == node_b.data
    True
    """
    if node is None:
        node = BinaryTreeNode(new_value)
        return node

    # binary search tree is not empty,
    # so we will insert it into the tree
    # if new_value is less than value of data in node,
    #  add it to left subtree and proceed recursively
    if new_value < node.data:
        node.left_child = insert(node.left_child, new_value)
    else:
        # if new_value is greater than value of data in node,
        #  add it to right subtree and proceed recursively
        node.right_child = insert(node.right_child, new_value)
    return node


def inorder(node: None | BinaryTreeNode) -> list[int]:  # if node is None,return
    """
    >>> inorder(make_tree())
    [6, 10, 14, 15, 20, 25, 60]
    """
    if node:
        inorder_array = inorder(node.left_child)
        inorder_array = [*inorder_array, node.data]
        inorder_array = inorder_array + inorder(node.right_child)
    else:
        inorder_array = []
    return inorder_array


def make_tree() -> BinaryTreeNode | None:
    root = insert(None, 15)
    insert(root, 10)
    insert(root, 25)
    insert(root, 6)
    insert(root, 14)
    insert(root, 20)
    insert(root, 60)
    return root


def main() -> None:
    # main function
    root = make_tree()
    print("Printing values of binary search tree in Inorder Traversal.")
    inorder(root)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: data_structures/binary_tree/is_sorted.py
================================================
"""
Given the root of a binary tree, determine if it is a valid binary search tree (BST).

A valid binary search tree is defined as follows:
- The left subtree of a node contains only nodes with keys less than the node's key.
- The right subtree of a node contains only nodes with keys greater than the node's key.
- Both the left and right subtrees must also be binary search trees.

In effect, a binary tree is a valid BST if its nodes are sorted in ascending order.
leetcode: https://leetcode.com/problems/validate-binary-search-tree/

If n is the number of nodes in the tree then:
Runtime: O(n)
Space: O(1)
"""

from __future__ import annotations

from collections.abc import Iterator
from dataclasses import dataclass


@dataclass
class Node:
    data: float
    left: Node | None = None
    right: Node | None = None

    def __iter__(self) -> Iterator[float]:
        """
        >>> root = Node(data=2.1)
        >>> list(root)
        [2.1]
        >>> root.left=Node(data=2.0)
        >>> list(root)
        [2.0, 2.1]
        >>> root.right=Node(data=2.2)
        >>> list(root)
        [2.0, 2.1, 2.2]
        """
        if self.left:
            yield from self.left
        yield self.data
        if self.right:
            yield from self.right

    @property
    def is_sorted(self) -> bool:
        """
        >>> Node(data='abc').is_sorted
        True
        >>> Node(data=2,
        ...      left=Node(data=1.999),
        ...      right=Node(data=3)).is_sorted
        True
        >>> Node(data=0,
        ...      left=Node(data=0),
        ...      right=Node(data=0)).is_sorted
        True
        >>> Node(data=0,
        ...      left=Node(data=-11),
        ...      right=Node(data=3)).is_sorted
        True
        >>> Node(data=5,
        ...      left=Node(data=1),
        ...      right=Node(data=4, left=Node(data=3))).is_sorted
        False
        >>> Node(data='a',
        ...      left=Node(data=1),
        ...      right=Node(data=4, left=Node(data=3))).is_sorted
        Traceback (most recent call last):
            ...
        TypeError: '<' not supported between instances of 'str' and 'int'
        >>> Node(data=2,
        ...      left=Node([]),
        ...      right=Node(data=4, left=Node(data=3))).is_sorted
        Traceback (most recent call last):
            ...
        TypeError: '<' not supported between instances of 'int' and 'list'
        """
        if self.left and (self.data < self.left.data or not self.left.is_sorted):
            return False
        return not (
            self.right and (self.data > self.right.data or not self.right.is_sorted)
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    tree = Node(data=2.1, left=Node(data=2.0), right=Node(data=2.2))
    print(f"Tree {list(tree)} is sorted: {tree.is_sorted = }.")
    assert tree.right
    tree.right.data = 2.0
    print(f"Tree {list(tree)} is sorted: {tree.is_sorted = }.")
    tree.right.data = 2.1
    print(f"Tree {list(tree)} is sorted: {tree.is_sorted = }.")


================================================
FILE: data_structures/binary_tree/is_sum_tree.py
================================================
"""
Is a binary tree a sum tree where the value of every non-leaf node is equal to the sum
of the values of its left and right subtrees?
https://www.geeksforgeeks.org/check-if-a-given-binary-tree-is-sumtree
"""

from __future__ import annotations

from collections.abc import Iterator
from dataclasses import dataclass


@dataclass
class Node:
    data: int
    left: Node | None = None
    right: Node | None = None

    def __iter__(self) -> Iterator[int]:
        """
        >>> root = Node(2)
        >>> list(root)
        [2]
        >>> root.left = Node(1)
        >>> tuple(root)
        (1, 2)
        """
        if self.left:
            yield from self.left
        yield self.data
        if self.right:
            yield from self.right

    def __len__(self) -> int:
        """
        >>> root = Node(2)
        >>> len(root)
        1
        >>> root.left = Node(1)
        >>> len(root)
        2
        """
        return sum(1 for _ in self)

    @property
    def is_sum_node(self) -> bool:
        """
        >>> root = Node(3)
        >>> root.is_sum_node
        True
        >>> root.left = Node(1)
        >>> root.is_sum_node
        False
        >>> root.right = Node(2)
        >>> root.is_sum_node
        True
        """
        if not self.left and not self.right:
            return True  # leaf nodes are considered sum nodes
        left_sum = sum(self.left) if self.left else 0
        right_sum = sum(self.right) if self.right else 0
        return all(
            (
                self.data == left_sum + right_sum,
                self.left.is_sum_node if self.left else True,
                self.right.is_sum_node if self.right else True,
            )
        )


@dataclass
class BinaryTree:
    root: Node

    def __iter__(self) -> Iterator[int]:
        """
        >>> list(BinaryTree.build_a_tree())
        [1, 2, 7, 11, 15, 29, 35, 40]
        """
        return iter(self.root)

    def __len__(self) -> int:
        """
        >>> len(BinaryTree.build_a_tree())
        8
        """
        return len(self.root)

    def __str__(self) -> str:
        """
        Returns a string representation of the inorder traversal of the binary tree.

        >>> str(list(BinaryTree.build_a_tree()))
        '[1, 2, 7, 11, 15, 29, 35, 40]'
        """
        return str(list(self))

    @property
    def is_sum_tree(self) -> bool:
        """
        >>> BinaryTree.build_a_tree().is_sum_tree
        False
        >>> BinaryTree.build_a_sum_tree().is_sum_tree
        True
        """
        return self.root.is_sum_node

    @classmethod
    def build_a_tree(cls) -> BinaryTree:
        r"""
        Create a binary tree with the specified structure:
              11
           /     \
          2       29
         / \     /  \
        1   7  15    40
                       \
                        35
        >>> list(BinaryTree.build_a_tree())
        [1, 2, 7, 11, 15, 29, 35, 40]
        """
        tree = BinaryTree(Node(11))
        root = tree.root
        root.left = Node(2)
        root.right = Node(29)
        root.left.left = Node(1)
        root.left.right = Node(7)
        root.right.left = Node(15)
        root.right.right = Node(40)
        root.right.right.left = Node(35)
        return tree

    @classmethod
    def build_a_sum_tree(cls) -> BinaryTree:
        r"""
        Create a binary tree with the specified structure:
             26
            /  \
          10    3
         /  \    \
        4    6    3
        >>> list(BinaryTree.build_a_sum_tree())
        [4, 10, 6, 26, 3, 3]
        """
        tree = BinaryTree(Node(26))
        root = tree.root
        root.left = Node(10)
        root.right = Node(3)
        root.left.left = Node(4)
        root.left.right = Node(6)
        root.right.right = Node(3)
        return tree


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    tree = BinaryTree.build_a_tree()
    print(f"{tree} has {len(tree)} nodes and {tree.is_sum_tree = }.")
    tree = BinaryTree.build_a_sum_tree()
    print(f"{tree} has {len(tree)} nodes and {tree.is_sum_tree = }.")


================================================
FILE: data_structures/binary_tree/lazy_segment_tree.py
================================================
from __future__ import annotations

import math


class SegmentTree:
    def __init__(self, size: int) -> None:
        self.size = size
        # approximate the overall size of segment tree with given value
        self.segment_tree = [0 for i in range(4 * size)]
        # create array to store lazy update
        self.lazy = [0 for i in range(4 * size)]
        self.flag = [0 for i in range(4 * size)]  # flag for lazy update

    def left(self, idx: int) -> int:
        """
        >>> segment_tree = SegmentTree(15)
        >>> segment_tree.left(1)
        2
        >>> segment_tree.left(2)
        4
        >>> segment_tree.left(12)
        24
        """
        return idx * 2

    def right(self, idx: int) -> int:
        """
        >>> segment_tree = SegmentTree(15)
        >>> segment_tree.right(1)
        3
        >>> segment_tree.right(2)
        5
        >>> segment_tree.right(12)
        25
        """
        return idx * 2 + 1

    def build(
        self, idx: int, left_element: int, right_element: int, a: list[int]
    ) -> None:
        if left_element == right_element:
            self.segment_tree[idx] = a[left_element - 1]
        else:
            mid = (left_element + right_element) // 2
            self.build(self.left(idx), left_element, mid, a)
            self.build(self.right(idx), mid + 1, right_element, a)
            self.segment_tree[idx] = max(
                self.segment_tree[self.left(idx)], self.segment_tree[self.right(idx)]
            )

    def update(
        self, idx: int, left_element: int, right_element: int, a: int, b: int, val: int
    ) -> bool:
        """
        update with O(lg n) (Normal segment tree without lazy update will take O(nlg n)
        for each update)

        update(1, 1, size, a, b, v) for update val v to [a,b]
        """
        if self.flag[idx] is True:
            self.segment_tree[idx] = self.lazy[idx]
            self.flag[idx] = False
            if left_element != right_element:
                self.lazy[self.left(idx)] = self.lazy[idx]
                self.lazy[self.right(idx)] = self.lazy[idx]
                self.flag[self.left(idx)] = True
                self.flag[self.right(idx)] = True

        if right_element < a or left_element > b:
            return True
        if left_element >= a and right_element <= b:
            self.segment_tree[idx] = val
            if left_element != right_element:
                self.lazy[self.left(idx)] = val
                self.lazy[self.right(idx)] = val
                self.flag[self.left(idx)] = True
                self.flag[self.right(idx)] = True
            return True
        mid = (left_element + right_element) // 2
        self.update(self.left(idx), left_element, mid, a, b, val)
        self.update(self.right(idx), mid + 1, right_element, a, b, val)
        self.segment_tree[idx] = max(
            self.segment_tree[self.left(idx)], self.segment_tree[self.right(idx)]
        )
        return True

    # query with O(lg n)
    def query(
        self, idx: int, left_element: int, right_element: int, a: int, b: int
    ) -> int | float:
        """
        query(1, 1, size, a, b) for query max of [a,b]
        >>> A = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
        >>> segment_tree = SegmentTree(15)
        >>> segment_tree.build(1, 1, 15, A)
        >>> segment_tree.query(1, 1, 15, 4, 6)
        7
        >>> segment_tree.query(1, 1, 15, 7, 11)
        14
        >>> segment_tree.query(1, 1, 15, 7, 12)
        15
        """
        if self.flag[idx] is True:
            self.segment_tree[idx] = self.lazy[idx]
            self.flag[idx] = False
            if left_element != right_element:
                self.lazy[self.left(idx)] = self.lazy[idx]
                self.lazy[self.right(idx)] = self.lazy[idx]
                self.flag[self.left(idx)] = True
                self.flag[self.right(idx)] = True
        if right_element < a or left_element > b:
            return -math.inf
        if left_element >= a and right_element <= b:
            return self.segment_tree[idx]
        mid = (left_element + right_element) // 2
        q1 = self.query(self.left(idx), left_element, mid, a, b)
        q2 = self.query(self.right(idx), mid + 1, right_element, a, b)
        return max(q1, q2)

    def __str__(self) -> str:
        return str([self.query(1, 1, self.size, i, i) for i in range(1, self.size + 1)])


if __name__ == "__main__":
    A = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
    size = 15
    segt = SegmentTree(size)
    segt.build(1, 1, size, A)
    print(segt.query(1, 1, size, 4, 6))
    print(segt.query(1, 1, size, 7, 11))
    print(segt.query(1, 1, size, 7, 12))
    segt.update(1, 1, size, 1, 3, 111)
    print(segt.query(1, 1, size, 1, 15))
    segt.update(1, 1, size, 7, 8, 235)
    print(segt)


================================================
FILE: data_structures/binary_tree/lowest_common_ancestor.py
================================================
# https://en.wikipedia.org/wiki/Lowest_common_ancestor
# https://en.wikipedia.org/wiki/Breadth-first_search

from __future__ import annotations

from queue import Queue


def swap(a: int, b: int) -> tuple[int, int]:
    """
    Return a tuple (b, a) when given two integers a and b
    >>> swap(2,3)
    (3, 2)
    >>> swap(3,4)
    (4, 3)
    >>> swap(67, 12)
    (12, 67)
    >>> swap(3,-4)
    (-4, 3)
    """
    a ^= b
    b ^= a
    a ^= b
    return a, b


def create_sparse(max_node: int, parent: list[list[int]]) -> list[list[int]]:
    """
    creating sparse table which saves each nodes 2^i-th parent
    >>> max_node = 6
    >>> parent = [[0, 0, 1, 1, 2, 2, 3]] + [[0] * 7 for _ in range(19)]
    >>> parent = create_sparse(max_node=max_node, parent=parent)
    >>> parent[0]
    [0, 0, 1, 1, 2, 2, 3]
    >>> parent[1]
    [0, 0, 0, 0, 1, 1, 1]
    >>> parent[2]
    [0, 0, 0, 0, 0, 0, 0]

    >>> max_node = 1
    >>> parent = [[0, 0]] + [[0] * 2 for _ in range(19)]
    >>> parent = create_sparse(max_node=max_node, parent=parent)
    >>> parent[0]
    [0, 0]
    >>> parent[1]
    [0, 0]
    """
    j = 1
    while (1 << j) < max_node:
        for i in range(1, max_node + 1):
            parent[j][i] = parent[j - 1][parent[j - 1][i]]
        j += 1
    return parent


# returns lca of node u,v
def lowest_common_ancestor(
    u: int, v: int, level: list[int], parent: list[list[int]]
) -> int:
    """
    Return the lowest common ancestor between u and v

    >>> level = [-1, 0, 1, 1, 2, 2, 2]
    >>> parent = [[0, 0, 1, 1, 2, 2, 3],[0, 0, 0, 0, 1, 1, 1]] + \
                    [[0] * 7 for _ in range(17)]
    >>> lowest_common_ancestor(u=4, v=5, level=level, parent=parent)
    2
    >>> lowest_common_ancestor(u=4, v=6, level=level, parent=parent)
    1
    >>> lowest_common_ancestor(u=2, v=3, level=level, parent=parent)
    1
    >>> lowest_common_ancestor(u=6, v=6, level=level, parent=parent)
    6
    """
    # u must be deeper in the tree than v
    if level[u] < level[v]:
        u, v = swap(u, v)
    # making depth of u same as depth of v
    for i in range(18, -1, -1):
        if level[u] - (1 << i) >= level[v]:
            u = parent[i][u]
    # at the same depth if u==v that mean lca is found
    if u == v:
        return u
    # moving both nodes upwards till lca in found
    for i in range(18, -1, -1):
        if parent[i][u] not in [0, parent[i][v]]:
            u, v = parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
    return parent[0][u]


# runs a breadth first search from root node of the tree
def breadth_first_search(
    level: list[int],
    parent: list[list[int]],
    max_node: int,
    graph: dict[int, list[int]],
    root: int = 1,
) -> tuple[list[int], list[list[int]]]:
    """
    sets every nodes direct parent
    parent of root node is set to 0
    calculates depth of each node from root node
    >>> level = [-1] * 7
    >>> parent = [[0] * 7 for _ in range(20)]
    >>> graph = {1: [2, 3], 2: [4, 5], 3: [6], 4: [], 5: [], 6: []}
    >>> level, parent = breadth_first_search(
    ...     level=level, parent=parent, max_node=6, graph=graph, root=1)
    >>> level
    [-1, 0, 1, 1, 2, 2, 2]
    >>> parent[0]
    [0, 0, 1, 1, 2, 2, 3]


    >>> level = [-1] * 2
    >>> parent = [[0] * 2 for _ in range(20)]
    >>> graph = {1: []}
    >>> level, parent = breadth_first_search(
    ...     level=level, parent=parent, max_node=1, graph=graph, root=1)
    >>> level
    [-1, 0]
    >>> parent[0]
    [0, 0]
    """
    level[root] = 0
    q: Queue[int] = Queue(maxsize=max_node)
    q.put(root)
    while q.qsize() != 0:
        u = q.get()
        for v in graph[u]:
            if level[v] == -1:
                level[v] = level[u] + 1
                q.put(v)
                parent[0][v] = u
    return level, parent


def main() -> None:
    max_node = 13
    # initializing with 0
    parent = [[0 for _ in range(max_node + 10)] for _ in range(20)]
    # initializing with -1 which means every node is unvisited
    level = [-1 for _ in range(max_node + 10)]
    graph: dict[int, list[int]] = {
        1: [2, 3, 4],
        2: [5],
        3: [6, 7],
        4: [8],
        5: [9, 10],
        6: [11],
        7: [],
        8: [12, 13],
        9: [],
        10: [],
        11: [],
        12: [],
        13: [],
    }
    level, parent = breadth_first_search(level, parent, max_node, graph, 1)
    parent = create_sparse(max_node, parent)
    print("LCA of node 1 and 3 is: ", lowest_common_ancestor(1, 3, level, parent))
    print("LCA of node 5 and 6 is: ", lowest_common_ancestor(5, 6, level, parent))
    print("LCA of node 7 and 11 is: ", lowest_common_ancestor(7, 11, level, parent))
    print("LCA of node 6 and 7 is: ", lowest_common_ancestor(6, 7, level, parent))
    print("LCA of node 4 and 12 is: ", lowest_common_ancestor(4, 12, level, parent))
    print("LCA of node 8 and 8 is: ", lowest_common_ancestor(8, 8, level, parent))


if __name__ == "__main__":
    main()


================================================
FILE: data_structures/binary_tree/maximum_fenwick_tree.py
================================================
class MaxFenwickTree:
    """
    Maximum Fenwick Tree

    More info: https://cp-algorithms.com/data_structures/fenwick.html
    ---------
    >>> ft = MaxFenwickTree(5)
    >>> ft.query(0, 5)
    0
    >>> ft.update(4, 100)
    >>> ft.query(0, 5)
    100
    >>> ft.update(4, 0)
    >>> ft.update(2, 20)
    >>> ft.query(0, 5)
    20
    >>> ft.update(4, 10)
    >>> ft.query(2, 5)
    20
    >>> ft.query(1, 5)
    20
    >>> ft.update(2, 0)
    >>> ft.query(0, 5)
    10
    >>> ft = MaxFenwickTree(10000)
    >>> ft.update(255, 30)
    >>> ft.query(0, 10000)
    30
    >>> ft = MaxFenwickTree(6)
    >>> ft.update(5, 1)
    >>> ft.query(5, 6)
    1
    >>> ft = MaxFenwickTree(6)
    >>> ft.update(0, 1000)
    >>> ft.query(0, 1)
    1000
    """

    def __init__(self, size: int) -> None:
        """
        Create empty Maximum Fenwick Tree with specified size

        Parameters:
            size: size of Array

        Returns:
            None
        """
        self.size = size
        self.arr = [0] * size
        self.tree = [0] * size

    @staticmethod
    def get_next(index: int) -> int:
        """
        Get next index in O(1)
        """
        return index | (index + 1)

    @staticmethod
    def get_prev(index: int) -> int:
        """
        Get previous index in O(1)
        """
        return (index & (index + 1)) - 1

    def update(self, index: int, value: int) -> None:
        """
        Set index to value in O(lg^2 N)

        Parameters:
            index: index to update
            value: value to set

        Returns:
            None
        """
        self.arr[index] = value
        while index < self.size:
            current_left_border = self.get_prev(index) + 1
            if current_left_border == index:
                self.tree[index] = value
            else:
                self.tree[index] = max(value, current_left_border, index)
            index = self.get_next(index)

    def query(self, left: int, right: int) -> int:
        """
        Answer the query of maximum range [l, r) in O(lg^2 N)

        Parameters:
            left: left index of query range (inclusive)
            right: right index of query range (exclusive)

        Returns:
            Maximum value of range [left, right)
        """
        right -= 1  # Because of right is exclusive
        result = 0
        while left <= right:
            current_left = self.get_prev(right)
            if left <= current_left:
                result = max(result, self.tree[right])
                right = current_left
            else:
                result = max(result, self.arr[right])
                right -= 1
        return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/binary_tree/maximum_sum_bst.py
================================================
from __future__ import annotations

import sys
from dataclasses import dataclass

INT_MIN = -sys.maxsize + 1
INT_MAX = sys.maxsize - 1


@dataclass
class TreeNode:
    val: int = 0
    left: TreeNode | None = None
    right: TreeNode | None = None


def max_sum_bst(root: TreeNode | None) -> int:
    """
    The solution traverses a binary tree to find the maximum sum of
    keys in any subtree that is a Binary Search Tree (BST). It uses
    recursion to validate BST properties and calculates sums, returning
    the highest sum found among all valid BST subtrees.

    >>> t1 = TreeNode(4)
    >>> t1.left = TreeNode(3)
    >>> t1.left.left = TreeNode(1)
    >>> t1.left.right = TreeNode(2)
    >>> print(max_sum_bst(t1))
    2
    >>> t2 = TreeNode(-4)
    >>> t2.left = TreeNode(-2)
    >>> t2.right = TreeNode(-5)
    >>> print(max_sum_bst(t2))
    0
    >>> t3 = TreeNode(1)
    >>> t3.left = TreeNode(4)
    >>> t3.left.left = TreeNode(2)
    >>> t3.left.right = TreeNode(4)
    >>> t3.right = TreeNode(3)
    >>> t3.right.left = TreeNode(2)
    >>> t3.right.right = TreeNode(5)
    >>> t3.right.right.left = TreeNode(4)
    >>> t3.right.right.right = TreeNode(6)
    >>> print(max_sum_bst(t3))
    20
    """
    ans: int = 0

    def solver(node: TreeNode | None) -> tuple[bool, int, int, int]:
        """
        Returns the maximum sum by making recursive calls
        >>> t1 = TreeNode(1)
        >>> print(solver(t1))
        1
        """
        nonlocal ans

        if not node:
            return True, INT_MAX, INT_MIN, 0  # Valid BST, min, max, sum

        is_left_valid, min_left, max_left, sum_left = solver(node.left)
        is_right_valid, min_right, max_right, sum_right = solver(node.right)

        if is_left_valid and is_right_valid and max_left < node.val < min_right:
            total_sum = sum_left + sum_right + node.val
            ans = max(ans, total_sum)
            return True, min(min_left, node.val), max(max_right, node.val), total_sum

        return False, -1, -1, -1  # Not a valid BST

    solver(root)
    return ans


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/binary_tree/merge_two_binary_trees.py
================================================
#!/usr/local/bin/python3
"""
Problem Description: Given two binary tree, return the merged tree.
The rule for merging is that if two nodes overlap, then put the value sum of
both nodes to the new value of the merged node. Otherwise, the NOT null node
will be used as the node of new tree.
"""

from __future__ import annotations


class Node:
    """
    A binary node has value variable and pointers to its left and right node.
    """

    def __init__(self, value: int = 0) -> None:
        self.value = value
        self.left: Node | None = None
        self.right: Node | None = None


def merge_two_binary_trees(tree1: Node | None, tree2: Node | None) -> Node | None:
    """
    Returns root node of the merged tree.

    >>> tree1 = Node(5)
    >>> tree1.left = Node(6)
    >>> tree1.right = Node(7)
    >>> tree1.left.left = Node(2)
    >>> tree2 = Node(4)
    >>> tree2.left = Node(5)
    >>> tree2.right = Node(8)
    >>> tree2.left.right = Node(1)
    >>> tree2.right.right = Node(4)
    >>> merged_tree = merge_two_binary_trees(tree1, tree2)
    >>> print_preorder(merged_tree)
    9
    11
    2
    1
    15
    4
    """
    if tree1 is None:
        return tree2
    if tree2 is None:
        return tree1

    tree1.value = tree1.value + tree2.value
    tree1.left = merge_two_binary_trees(tree1.left, tree2.left)
    tree1.right = merge_two_binary_trees(tree1.right, tree2.right)
    return tree1


def print_preorder(root: Node | None) -> None:
    """
    Print pre-order traversal of the tree.

    >>> root = Node(1)
    >>> root.left = Node(2)
    >>> root.right = Node(3)
    >>> print_preorder(root)
    1
    2
    3
    >>> print_preorder(root.right)
    3
    """
    if root:
        print(root.value)
        print_preorder(root.left)
        print_preorder(root.right)


if __name__ == "__main__":
    tree1 = Node(1)
    tree1.left = Node(2)
    tree1.right = Node(3)
    tree1.left.left = Node(4)

    tree2 = Node(2)
    tree2.left = Node(4)
    tree2.right = Node(6)
    tree2.left.right = Node(9)
    tree2.right.right = Node(5)

    print("Tree1 is: ")
    print_preorder(tree1)
    print("Tree2 is: ")
    print_preorder(tree2)
    merged_tree = merge_two_binary_trees(tree1, tree2)
    print("Merged Tree is: ")
    print_preorder(merged_tree)


================================================
FILE: data_structures/binary_tree/mirror_binary_tree.py
================================================
"""
Given the root of a binary tree, mirror the tree, and return its root.

Leetcode problem reference: https://leetcode.com/problems/mirror-binary-tree/
"""

from __future__ import annotations

from collections.abc import Iterator
from dataclasses import dataclass


@dataclass
class Node:
    """
    A Node has value variable and pointers to Nodes to its left and right.
    """

    value: int
    left: Node | None = None
    right: Node | None = None

    def __iter__(self) -> Iterator[int]:
        if self.left:
            yield from self.left
        yield self.value
        if self.right:
            yield from self.right

    def __len__(self) -> int:
        return sum(1 for _ in self)

    def mirror(self) -> Node:
        """
        Mirror the binary tree rooted at this node by swapping left and right children.

        >>> tree = Node(0)
        >>> list(tree)
        [0]
        >>> list(tree.mirror())
        [0]
        >>> tree = Node(1, Node(0), Node(3, Node(2), Node(4, None, Node(5))))
        >>> tuple(tree)
        (0, 1, 2, 3, 4, 5)
        >>> tuple(tree.mirror())
        (5, 4, 3, 2, 1, 0)
        """
        self.left, self.right = self.right, self.left
        if self.left:
            self.left.mirror()
        if self.right:
            self.right.mirror()
        return self


def make_tree_seven() -> Node:
    r"""
    Return a binary tree with 7 nodes that looks like this:
    ::

           1
         /   \
        2     3
       / \   / \
      4   5 6   7

    >>> tree_seven = make_tree_seven()
    >>> len(tree_seven)
    7
    >>> list(tree_seven)
    [4, 2, 5, 1, 6, 3, 7]
    """
    tree = Node(1)
    tree.left = Node(2)
    tree.right = Node(3)
    tree.left.left = Node(4)
    tree.left.right = Node(5)
    tree.right.left = Node(6)
    tree.right.right = Node(7)
    return tree


def make_tree_nine() -> Node:
    r"""
    Return a binary tree with 9 nodes that looks like this:
    ::

            1
           / \
          2   3
         / \   \
        4   5   6
       / \   \
      7   8   9

    >>> tree_nine = make_tree_nine()
    >>> len(tree_nine)
    9
    >>> list(tree_nine)
    [7, 4, 8, 2, 5, 9, 1, 3, 6]
    """
    tree = Node(1)
    tree.left = Node(2)
    tree.right = Node(3)
    tree.left.left = Node(4)
    tree.left.right = Node(5)
    tree.right.right = Node(6)
    tree.left.left.left = Node(7)
    tree.left.left.right = Node(8)
    tree.left.right.right = Node(9)
    return tree


def main() -> None:
    r"""
    Mirror binary trees with the given root and returns the root

    >>> tree = make_tree_nine()
    >>> tuple(tree)
    (7, 4, 8, 2, 5, 9, 1, 3, 6)
    >>> tuple(tree.mirror())
    (6, 3, 1, 9, 5, 2, 8, 4, 7)

    nine_tree::

            1
           / \
          2   3
         / \   \
        4   5   6
       / \   \
      7   8   9

    The mirrored tree looks like this::

          1
         / \
        3   2
       /   / \
      6   5   4
         /   / \
        9   8   7
    """
    trees = {"zero": Node(0), "seven": make_tree_seven(), "nine": make_tree_nine()}
    for name, tree in trees.items():
        print(f"      The {name} tree: {tuple(tree)}")
        # (0,)
        # (4, 2, 5, 1, 6, 3, 7)
        # (7, 4, 8, 2, 5, 9, 1, 3, 6)
        print(f"Mirror of {name} tree: {tuple(tree.mirror())}")
        # (0,)
        # (7, 3, 6, 1, 5, 2, 4)
        # (6, 3, 1, 9, 5, 2, 8, 4, 7)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: data_structures/binary_tree/non_recursive_segment_tree.py
================================================
"""
A non-recursive Segment Tree implementation with range query and single element update,
works virtually with any list of the same type of elements with a "commutative"
combiner.

Explanation:
https://www.geeksforgeeks.org/iterative-segment-tree-range-minimum-query/
https://www.geeksforgeeks.org/segment-tree-efficient-implementation/

>>> SegmentTree([1, 2, 3], lambda a, b: a + b).query(0, 2)
6
>>> SegmentTree([3, 1, 2], min).query(0, 2)
1
>>> SegmentTree([2, 3, 1], max).query(0, 2)
3
>>> st = SegmentTree([1, 5, 7, -1, 6], lambda a, b: a + b)
>>> st.update(1, -1)
>>> st.update(2, 3)
>>> st.query(1, 2)
2
>>> st.query(1, 1)
-1
>>> st.update(4, 1)
>>> st.query(3, 4)
0
>>> st = SegmentTree([[1, 2, 3], [3, 2, 1], [1, 1, 1]], lambda a, b: [a[i] + b[i] for i
...                                                                   in range(len(a))])
>>> st.query(0, 1)
[4, 4, 4]
>>> st.query(1, 2)
[4, 3, 2]
>>> st.update(1, [-1, -1, -1])
>>> st.query(1, 2)
[0, 0, 0]
>>> st.query(0, 2)
[1, 2, 3]
"""

from __future__ import annotations

from collections.abc import Callable
from typing import Any, TypeVar

T = TypeVar("T")


class SegmentTree[T]:
    def __init__(self, arr: list[T], fnc: Callable[[T, T], T]) -> None:
        """
        Segment Tree constructor, it works just with commutative combiner.
        :param arr: list of elements for the segment tree
        :param fnc: commutative function for combine two elements

        >>> SegmentTree(['a', 'b', 'c'], lambda a, b: f'{a}{b}').query(0, 2)
        'abc'
        >>> SegmentTree([(1, 2), (2, 3), (3, 4)],
        ...             lambda a, b: (a[0] + b[0], a[1] + b[1])).query(0, 2)
        (6, 9)
        """
        any_type: Any | T = None

        self.N: int = len(arr)
        self.st: list[T] = [any_type for _ in range(self.N)] + arr
        self.fn = fnc
        self.build()

    def build(self) -> None:
        for p in range(self.N - 1, 0, -1):
            self.st[p] = self.fn(self.st[p * 2], self.st[p * 2 + 1])

    def update(self, p: int, v: T) -> None:
        """
        Update an element in log(N) time
        :param p: position to be update
        :param v: new value

        >>> st = SegmentTree([3, 1, 2, 4], min)
        >>> st.query(0, 3)
        1
        >>> st.update(2, -1)
        >>> st.query(0, 3)
        -1
        """
        p += self.N
        self.st[p] = v
        while p > 1:
            p = p // 2
            self.st[p] = self.fn(self.st[p * 2], self.st[p * 2 + 1])

    def query(self, left: int, right: int) -> T | None:
        """
        Get range query value in log(N) time
        :param left: left element index
        :param right: right element index
        :return: element combined in the range [left, right]

        >>> st = SegmentTree([1, 2, 3, 4], lambda a, b: a + b)
        >>> st.query(0, 2)
        6
        >>> st.query(1, 2)
        5
        >>> st.query(0, 3)
        10
        >>> st.query(2, 3)
        7
        """
        left, right = left + self.N, right + self.N

        res: T | None = None
        while left <= right:
            if left % 2 == 1:
                res = self.st[left] if res is None else self.fn(res, self.st[left])
            if right % 2 == 0:
                res = self.st[right] if res is None else self.fn(res, self.st[right])
            left, right = (left + 1) // 2, (right - 1) // 2
        return res


if __name__ == "__main__":
    from functools import reduce

    test_array = [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

    test_updates = {
        0: 7,
        1: 2,
        2: 6,
        3: -14,
        4: 5,
        5: 4,
        6: 7,
        7: -10,
        8: 9,
        9: 10,
        10: 12,
        11: 1,
    }

    min_segment_tree = SegmentTree(test_array, min)
    max_segment_tree = SegmentTree(test_array, max)
    sum_segment_tree = SegmentTree(test_array, lambda a, b: a + b)

    def test_all_segments() -> None:
        """
        Test all possible segments
        """
        for i in range(len(test_array)):
            for j in range(i, len(test_array)):
                min_range = reduce(min, test_array[i : j + 1])
                max_range = reduce(max, test_array[i : j + 1])
                sum_range = reduce(lambda a, b: a + b, test_array[i : j + 1])
                assert min_range == min_segment_tree.query(i, j)
                assert max_range == max_segment_tree.query(i, j)
                assert sum_range == sum_segment_tree.query(i, j)

    test_all_segments()

    for index, value in test_updates.items():
        test_array[index] = value
        min_segment_tree.update(index, value)
        max_segment_tree.update(index, value)
        sum_segment_tree.update(index, value)
        test_all_segments()


================================================
FILE: data_structures/binary_tree/number_of_possible_binary_trees.py
================================================
"""
Hey, we are going to find an exciting number called Catalan number which is use to find
the number of possible binary search trees from tree of a given number of nodes.

We will use the formula: t(n) = SUMMATION(i = 1 to n)t(i-1)t(n-i)

Further details at Wikipedia: https://en.wikipedia.org/wiki/Catalan_number
"""

"""
Our Contribution:
Basically we Create the 2 function:
    1. catalan_number(node_count: int) -> int
        Returns the number of possible binary search trees for n nodes.
    2. binary_tree_count(node_count: int) -> int
        Returns the number of possible binary trees for n nodes.
"""


def binomial_coefficient(n: int, k: int) -> int:
    """
    Since Here we Find the Binomial Coefficient:
    https://en.wikipedia.org/wiki/Binomial_coefficient
    C(n,k) = n! / k!(n-k)!
    :param n: 2 times of Number of nodes
    :param k: Number of nodes
    :return:  Integer Value

    >>> binomial_coefficient(4, 2)
    6
    """
    result = 1  # To kept the Calculated Value
    # Since C(n, k) = C(n, n-k)
    k = min(k, n - k)
    # Calculate C(n,k)
    for i in range(k):
        result *= n - i
        result //= i + 1
    return result


def catalan_number(node_count: int) -> int:
    """
    We can find Catalan number many ways but here we use Binomial Coefficient because it
    does the job in O(n)

    return the Catalan number of n using 2nCn/(n+1).
    :param n: number of nodes
    :return: Catalan number of n nodes

    >>> catalan_number(5)
    42
    >>> catalan_number(6)
    132
    """
    return binomial_coefficient(2 * node_count, node_count) // (node_count + 1)


def factorial(n: int) -> int:
    """
    Return the factorial of a number.
    :param n: Number to find the Factorial of.
    :return: Factorial of n.

    >>> import math
    >>> all(factorial(i) == math.factorial(i) for i in range(10))
    True
    >>> factorial(-5)  # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    ValueError: factorial() not defined for negative values
    """
    if n < 0:
        raise ValueError("factorial() not defined for negative values")
    result = 1
    for i in range(1, n + 1):
        result *= i
    return result


def binary_tree_count(node_count: int) -> int:
    """
    Return the number of possible of binary trees.
    :param n: number of nodes
    :return: Number of possible binary trees

    >>> binary_tree_count(5)
    5040
    >>> binary_tree_count(6)
    95040
    """
    return catalan_number(node_count) * factorial(node_count)


if __name__ == "__main__":
    node_count = int(input("Enter the number of nodes: ").strip() or 0)
    if node_count <= 0:
        raise ValueError("We need some nodes to work with.")
    print(
        f"Given {node_count} nodes, there are {binary_tree_count(node_count)} "
        f"binary trees and {catalan_number(node_count)} binary search trees."
    )


================================================
FILE: data_structures/binary_tree/red_black_tree.py
================================================
from __future__ import annotations

from collections.abc import Iterator


class RedBlackTree:
    """
    A Red-Black tree, which is a self-balancing BST (binary search
    tree).
    This tree has similar performance to AVL trees, but the balancing is
    less strict, so it will perform faster for writing/deleting nodes
    and slower for reading in the average case, though, because they're
    both balanced binary search trees, both will get the same asymptotic
    performance.
    To read more about them, https://en.wikipedia.org/wiki/Red-black_tree
    Unless otherwise specified, all asymptotic runtimes are specified in
    terms of the size of the tree.
    """

    def __init__(
        self,
        label: int | None = None,
        color: int = 0,
        parent: RedBlackTree | None = None,
        left: RedBlackTree | None = None,
        right: RedBlackTree | None = None,
    ) -> None:
        """Initialize a new Red-Black Tree node with the given values:
        label: The value associated with this node
        color: 0 if black, 1 if red
        parent: The parent to this node
        left: This node's left child
        right: This node's right child
        """
        self.label = label
        self.parent = parent
        self.left = left
        self.right = right
        self.color = color

    # Here are functions which are specific to red-black trees

    def rotate_left(self) -> RedBlackTree:
        """Rotate the subtree rooted at this node to the left and
        returns the new root to this subtree.
        Performing one rotation can be done in O(1).
        """
        parent = self.parent
        right = self.right
        if right is None:
            return self
        self.right = right.left
        if self.right:
            self.right.parent = self
        self.parent = right
        right.left = self
        if parent is not None:
            if parent.left == self:
                parent.left = right
            else:
                parent.right = right
        right.parent = parent
        return right

    def rotate_right(self) -> RedBlackTree:
        """Rotate the subtree rooted at this node to the right and
        returns the new root to this subtree.
        Performing one rotation can be done in O(1).
        """
        if self.left is None:
            return self
        parent = self.parent
        left = self.left
        self.left = left.right
        if self.left:
            self.left.parent = self
        self.parent = left
        left.right = self
        if parent is not None:
            if parent.right is self:
                parent.right = left
            else:
                parent.left = left
        left.parent = parent
        return left

    def insert(self, label: int) -> RedBlackTree:
        """Inserts label into the subtree rooted at self, performs any
        rotations necessary to maintain balance, and then returns the
        new root to this subtree (likely self).
        This is guaranteed to run in O(log(n)) time.
        """
        if self.label is None:
            # Only possible with an empty tree
            self.label = label
            return self
        if self.label == label:
            return self
        elif self.label > label:
            if self.left:
                self.left.insert(label)
            else:
                self.left = RedBlackTree(label, 1, self)
                self.left._insert_repair()
        elif self.right:
            self.right.insert(label)
        else:
            self.right = RedBlackTree(label, 1, self)
            self.right._insert_repair()
        return self.parent or self

    def _insert_repair(self) -> None:
        """Repair the coloring from inserting into a tree."""
        if self.parent is None:
            # This node is the root, so it just needs to be black
            self.color = 0
        elif color(self.parent) == 0:
            # If the parent is black, then it just needs to be red
            self.color = 1
        else:
            uncle = self.parent.sibling
            if color(uncle) == 0:
                if self.is_left() and self.parent.is_right():
                    self.parent.rotate_right()
                    if self.right:
                        self.right._insert_repair()
                elif self.is_right() and self.parent.is_left():
                    self.parent.rotate_left()
                    if self.left:
                        self.left._insert_repair()
                elif self.is_left():
                    if self.grandparent:
                        self.grandparent.rotate_right()
                        self.parent.color = 0
                    if self.parent.right:
                        self.parent.right.color = 1
                else:
                    if self.grandparent:
                        self.grandparent.rotate_left()
                        self.parent.color = 0
                    if self.parent.left:
                        self.parent.left.color = 1
            else:
                self.parent.color = 0
                if uncle and self.grandparent:
                    uncle.color = 0
                    self.grandparent.color = 1
                    self.grandparent._insert_repair()

    def remove(self, label: int) -> RedBlackTree:
        """Remove label from this tree."""
        if self.label == label:
            if self.left and self.right:
                # It's easier to balance a node with at most one child,
                # so we replace this node with the greatest one less than
                # it and remove that.
                value = self.left.get_max()
                if value is not None:
                    self.label = value
                    self.left.remove(value)
            else:
                # This node has at most one non-None child, so we don't
                # need to replace
                child = self.left or self.right
                if self.color == 1:
                    # This node is red, and its child is black
                    # The only way this happens to a node with one child
                    # is if both children are None leaves.
                    # We can just remove this node and call it a day.
                    if self.parent:
                        if self.is_left():
                            self.parent.left = None
                        else:
                            self.parent.right = None
                # The node is black
                elif child is None:
                    # This node and its child are black
                    if self.parent is None:
                        # The tree is now empty
                        return RedBlackTree(None)
                    else:
                        self._remove_repair()
                        if self.is_left():
                            self.parent.left = None
                        else:
                            self.parent.right = None
                        self.parent = None
                else:
                    # This node is black and its child is red
                    # Move the child node here and make it black
                    self.label = child.label
                    self.left = child.left
                    self.right = child.right
                    if self.left:
                        self.left.parent = self
                    if self.right:
                        self.right.parent = self
        elif self.label is not None and self.label > label:
            if self.left:
                self.left.remove(label)
        elif self.right:
            self.right.remove(label)
        return self.parent or self

    def _remove_repair(self) -> None:
        """Repair the coloring of the tree that may have been messed up."""
        if (
            self.parent is None
            or self.sibling is None
            or self.parent.sibling is None
            or self.grandparent is None
        ):
            return
        if color(self.sibling) == 1:
            self.sibling.color = 0
            self.parent.color = 1
            if self.is_left():
                self.parent.rotate_left()
            else:
                self.parent.rotate_right()
        if (
            color(self.parent) == 0
            and color(self.sibling) == 0
            and color(self.sibling.left) == 0
            and color(self.sibling.right) == 0
        ):
            self.sibling.color = 1
            self.parent._remove_repair()
            return
        if (
            color(self.parent) == 1
            and color(self.sibling) == 0
            and color(self.sibling.left) == 0
            and color(self.sibling.right) == 0
        ):
            self.sibling.color = 1
            self.parent.color = 0
            return
        if (
            self.is_left()
            and color(self.sibling) == 0
            and color(self.sibling.right) == 0
            and color(self.sibling.left) == 1
        ):
            self.sibling.rotate_right()
            self.sibling.color = 0
            if self.sibling.right:
                self.sibling.right.color = 1
        if (
            self.is_right()
            and color(self.sibling) == 0
            and color(self.sibling.right) == 1
            and color(self.sibling.left) == 0
        ):
            self.sibling.rotate_left()
            self.sibling.color = 0
            if self.sibling.left:
                self.sibling.left.color = 1
        if (
            self.is_left()
            and color(self.sibling) == 0
            and color(self.sibling.right) == 1
        ):
            self.parent.rotate_left()
            self.grandparent.color = self.parent.color
            self.parent.color = 0
            self.parent.sibling.color = 0
        if (
            self.is_right()
            and color(self.sibling) == 0
            and color(self.sibling.left) == 1
        ):
            self.parent.rotate_right()
            self.grandparent.color = self.parent.color
            self.parent.color = 0
            self.parent.sibling.color = 0

    def check_color_properties(self) -> bool:
        """Check the coloring of the tree, and return True iff the tree
        is colored in a way which matches these five properties:
        (wording stolen from wikipedia article)
         1. Each node is either red or black.
         2. The root node is black.
         3. All leaves are black.
         4. If a node is red, then both its children are black.
         5. Every path from any node to all of its descendent NIL nodes
            has the same number of black nodes.
        This function runs in O(n) time, because properties 4 and 5 take
        that long to check.
        """
        # I assume property 1 to hold because there is nothing that can
        # make the color be anything other than 0 or 1.
        # Property 2
        if self.color:
            # The root was red
            print("Property 2")
            return False
        # Property 3 does not need to be checked, because None is assumed
        # to be black and is all the leaves.
        # Property 4
        if not self.check_coloring():
            print("Property 4")
            return False
        # Property 5
        if self.black_height() is None:
            print("Property 5")
            return False
        # All properties were met
        return True

    def check_coloring(self) -> bool:
        """A helper function to recursively check Property 4 of a
        Red-Black Tree. See check_color_properties for more info.
        """
        if self.color == 1 and 1 in (color(self.left), color(self.right)):
            return False
        if self.left and not self.left.check_coloring():
            return False
        return not (self.right and not self.right.check_coloring())

    def black_height(self) -> int | None:
        """Returns the number of black nodes from this node to the
        leaves of the tree, or None if there isn't one such value (the
        tree is color incorrectly).
        """
        if self is None or self.left is None or self.right is None:
            # If we're already at a leaf, there is no path
            return 1
        left = RedBlackTree.black_height(self.left)
        right = RedBlackTree.black_height(self.right)
        if left is None or right is None:
            # There are issues with coloring below children nodes
            return None
        if left != right:
            # The two children have unequal depths
            return None
        # Return the black depth of children, plus one if this node is
        # black
        return left + (1 - self.color)

    # Here are functions which are general to all binary search trees

    def __contains__(self, label: int) -> bool:
        """Search through the tree for label, returning True iff it is
        found somewhere in the tree.
        Guaranteed to run in O(log(n)) time.
        """
        return self.search(label) is not None

    def search(self, label: int) -> RedBlackTree | None:
        """Search through the tree for label, returning its node if
        it's found, and None otherwise.
        This method is guaranteed to run in O(log(n)) time.
        """
        if self.label == label:
            return self
        elif self.label is not None and label > self.label:
            if self.right is None:
                return None
            else:
                return self.right.search(label)
        elif self.left is None:
            return None
        else:
            return self.left.search(label)

    def floor(self, label: int) -> int | None:
        """Returns the largest element in this tree which is at most label.
        This method is guaranteed to run in O(log(n)) time."""
        if self.label == label:
            return self.label
        elif self.label is not None and self.label > label:
            if self.left:
                return self.left.floor(label)
            else:
                return None
        else:
            if self.right:
                attempt = self.right.floor(label)
                if attempt is not None:
                    return attempt
            return self.label

    def ceil(self, label: int) -> int | None:
        """Returns the smallest element in this tree which is at least label.
        This method is guaranteed to run in O(log(n)) time.
        """
        if self.label == label:
            return self.label
        elif self.label is not None and self.label < label:
            if self.right:
                return self.right.ceil(label)
            else:
                return None
        else:
            if self.left:
                attempt = self.left.ceil(label)
                if attempt is not None:
                    return attempt
            return self.label

    def get_max(self) -> int | None:
        """Returns the largest element in this tree.
        This method is guaranteed to run in O(log(n)) time.
        """
        if self.right:
            # Go as far right as possible
            return self.right.get_max()
        else:
            return self.label

    def get_min(self) -> int | None:
        """Returns the smallest element in this tree.
        This method is guaranteed to run in O(log(n)) time.
        """
        if self.left:
            # Go as far left as possible
            return self.left.get_min()
        else:
            return self.label

    @property
    def grandparent(self) -> RedBlackTree | None:
        """Get the current node's grandparent, or None if it doesn't exist."""
        if self.parent is None:
            return None
        else:
            return self.parent.parent

    @property
    def sibling(self) -> RedBlackTree | None:
        """Get the current node's sibling, or None if it doesn't exist."""
        if self.parent is None:
            return None
        elif self.parent.left is self:
            return self.parent.right
        else:
            return self.parent.left

    def is_left(self) -> bool:
        """Returns true iff this node is the left child of its parent."""
        if self.parent is None:
            return False
        return self.parent.left is self

    def is_right(self) -> bool:
        """Returns true iff this node is the right child of its parent."""
        if self.parent is None:
            return False
        return self.parent.right is self

    def __bool__(self) -> bool:
        return True

    def __len__(self) -> int:
        """
        Return the number of nodes in this tree.
        """
        ln = 1
        if self.left:
            ln += len(self.left)
        if self.right:
            ln += len(self.right)
        return ln

    def preorder_traverse(self) -> Iterator[int | None]:
        yield self.label
        if self.left:
            yield from self.left.preorder_traverse()
        if self.right:
            yield from self.right.preorder_traverse()

    def inorder_traverse(self) -> Iterator[int | None]:
        if self.left:
            yield from self.left.inorder_traverse()
        yield self.label
        if self.right:
            yield from self.right.inorder_traverse()

    def postorder_traverse(self) -> Iterator[int | None]:
        if self.left:
            yield from self.left.postorder_traverse()
        if self.right:
            yield from self.right.postorder_traverse()
        yield self.label

    def __repr__(self) -> str:
        from pprint import pformat

        if self.left is None and self.right is None:
            return f"'{self.label} {(self.color and 'red') or 'blk'}'"
        return pformat(
            {
                f"{self.label} {(self.color and 'red') or 'blk'}": (
                    self.left,
                    self.right,
                )
            },
            indent=1,
        )

    def __eq__(self, other: object) -> bool:
        """Test if two trees are equal."""
        if not isinstance(other, RedBlackTree):
            return NotImplemented
        if self.label == other.label:
            return self.left == other.left and self.right == other.right
        else:
            return False


def color(node: RedBlackTree | None) -> int:
    """Returns the color of a node, allowing for None leaves."""
    if node is None:
        return 0
    else:
        return node.color


"""
Code for testing the various
functions of the red-black tree.
"""


def test_rotations() -> bool:
    """Test that the rotate_left and rotate_right functions work."""
    # Make a tree to test on
    tree = RedBlackTree(0)
    tree.left = RedBlackTree(-10, parent=tree)
    tree.right = RedBlackTree(10, parent=tree)
    tree.left.left = RedBlackTree(-20, parent=tree.left)
    tree.left.right = RedBlackTree(-5, parent=tree.left)
    tree.right.left = RedBlackTree(5, parent=tree.right)
    tree.right.right = RedBlackTree(20, parent=tree.right)
    # Make the right rotation
    left_rot = RedBlackTree(10)
    left_rot.left = RedBlackTree(0, parent=left_rot)
    left_rot.left.left = RedBlackTree(-10, parent=left_rot.left)
    left_rot.left.right = RedBlackTree(5, parent=left_rot.left)
    left_rot.left.left.left = RedBlackTree(-20, parent=left_rot.left.left)
    left_rot.left.left.right = RedBlackTree(-5, parent=left_rot.left.left)
    left_rot.right = RedBlackTree(20, parent=left_rot)
    tree = tree.rotate_left()
    if tree != left_rot:
        return False
    tree = tree.rotate_right()
    tree = tree.rotate_right()
    # Make the left rotation
    right_rot = RedBlackTree(-10)
    right_rot.left = RedBlackTree(-20, parent=right_rot)
    right_rot.right = RedBlackTree(0, parent=right_rot)
    right_rot.right.left = RedBlackTree(-5, parent=right_rot.right)
    right_rot.right.right = RedBlackTree(10, parent=right_rot.right)
    right_rot.right.right.left = RedBlackTree(5, parent=right_rot.right.right)
    right_rot.right.right.right = RedBlackTree(20, parent=right_rot.right.right)
    return tree == right_rot


def test_insertion_speed() -> bool:
    """Test that the tree balances inserts to O(log(n)) by doing a lot
    of them.
    """
    tree = RedBlackTree(-1)
    for i in range(300000):
        tree = tree.insert(i)
    return True


def test_insert() -> bool:
    """Test the insert() method of the tree correctly balances, colors,
    and inserts.
    """
    tree = RedBlackTree(0)
    tree.insert(8)
    tree.insert(-8)
    tree.insert(4)
    tree.insert(12)
    tree.insert(10)
    tree.insert(11)
    ans = RedBlackTree(0, 0)
    ans.left = RedBlackTree(-8, 0, ans)
    ans.right = RedBlackTree(8, 1, ans)
    ans.right.left = RedBlackTree(4, 0, ans.right)
    ans.right.right = RedBlackTree(11, 0, ans.right)
    ans.right.right.left = RedBlackTree(10, 1, ans.right.right)
    ans.right.right.right = RedBlackTree(12, 1, ans.right.right)
    return tree == ans


def test_insert_and_search() -> bool:
    """Tests searching through the tree for values."""
    tree = RedBlackTree(0)
    tree.insert(8)
    tree.insert(-8)
    tree.insert(4)
    tree.insert(12)
    tree.insert(10)
    tree.insert(11)
    if any(i in tree for i in (5, -6, -10, 13)):
        # Found something not in there
        return False
    # Find all these things in there
    return all(i in tree for i in (11, 12, -8, 0))


def test_insert_delete() -> bool:
    """Test the insert() and delete() method of the tree, verifying the
    insertion and removal of elements, and the balancing of the tree.
    """
    tree = RedBlackTree(0)
    tree = tree.insert(-12)
    tree = tree.insert(8)
    tree = tree.insert(-8)
    tree = tree.insert(15)
    tree = tree.insert(4)
    tree = tree.insert(12)
    tree = tree.insert(10)
    tree = tree.insert(9)
    tree = tree.insert(11)
    tree = tree.remove(15)
    tree = tree.remove(-12)
    tree = tree.remove(9)
    if not tree.check_color_properties():
        return False
    return list(tree.inorder_traverse()) == [-8, 0, 4, 8, 10, 11, 12]


def test_floor_ceil() -> bool:
    """Tests the floor and ceiling functions in the tree."""
    tree = RedBlackTree(0)
    tree.insert(-16)
    tree.insert(16)
    tree.insert(8)
    tree.insert(24)
    tree.insert(20)
    tree.insert(22)
    tuples = [(-20, None, -16), (-10, -16, 0), (8, 8, 8), (50, 24, None)]
    for val, floor, ceil in tuples:
        if tree.floor(val) != floor or tree.ceil(val) != ceil:
            return False
    return True


def test_min_max() -> bool:
    """Tests the min and max functions in the tree."""
    tree = RedBlackTree(0)
    tree.insert(-16)
    tree.insert(16)
    tree.insert(8)
    tree.insert(24)
    tree.insert(20)
    tree.insert(22)
    return not (tree.get_max() != 22 or tree.get_min() != -16)


def test_tree_traversal() -> bool:
    """Tests the three different tree traversal functions."""
    tree = RedBlackTree(0)
    tree = tree.insert(-16)
    tree.insert(16)
    tree.insert(8)
    tree.insert(24)
    tree.insert(20)
    tree.insert(22)
    if list(tree.inorder_traverse()) != [-16, 0, 8, 16, 20, 22, 24]:
        return False
    if list(tree.preorder_traverse()) != [0, -16, 16, 8, 22, 20, 24]:
        return False
    return list(tree.postorder_traverse()) == [-16, 8, 20, 24, 22, 16, 0]


def test_tree_chaining() -> bool:
    """Tests the three different tree chaining functions."""
    tree = RedBlackTree(0)
    tree = tree.insert(-16).insert(16).insert(8).insert(24).insert(20).insert(22)
    if list(tree.inorder_traverse()) != [-16, 0, 8, 16, 20, 22, 24]:
        return False
    if list(tree.preorder_traverse()) != [0, -16, 16, 8, 22, 20, 24]:
        return False
    return list(tree.postorder_traverse()) == [-16, 8, 20, 24, 22, 16, 0]


def print_results(msg: str, passes: bool) -> None:
    print(str(msg), "works!" if passes else "doesn't work :(")


def pytests() -> None:
    assert test_rotations()
    assert test_insert()
    assert test_insert_and_search()
    assert test_insert_delete()
    assert test_floor_ceil()
    assert test_tree_traversal()
    assert test_tree_chaining()


def main() -> None:
    """
    >>> pytests()
    """
    print_results("Rotating right and left", test_rotations())
    print_results("Inserting", test_insert())
    print_results("Searching", test_insert_and_search())
    print_results("Deleting", test_insert_delete())
    print_results("Floor and ceil", test_floor_ceil())
    print_results("Tree traversal", test_tree_traversal())
    print_results("Tree traversal", test_tree_chaining())
    print("Testing tree balancing...")
    print("This should only be a few seconds.")
    test_insertion_speed()
    print("Done!")


if __name__ == "__main__":
    main()


================================================
FILE: data_structures/binary_tree/segment_tree.py
================================================
import math


class SegmentTree:
    def __init__(self, a):
        self.A = a
        self.N = len(self.A)
        self.st = [0] * (
            4 * self.N
        )  # approximate the overall size of segment tree with array N
        if self.N:
            self.build(1, 0, self.N - 1)

    def left(self, idx):
        """
        Returns the left child index for a given index in a binary tree.

        >>> s = SegmentTree([1, 2, 3])
        >>> s.left(1)
        2
        >>> s.left(2)
        4
        """
        return idx * 2

    def right(self, idx):
        """
        Returns the right child index for a given index in a binary tree.

        >>> s = SegmentTree([1, 2, 3])
        >>> s.right(1)
        3
        >>> s.right(2)
        5
        """
        return idx * 2 + 1

    def build(self, idx, left, right):
        if left == right:
            self.st[idx] = self.A[left]
        else:
            mid = (left + right) // 2
            self.build(self.left(idx), left, mid)
            self.build(self.right(idx), mid + 1, right)
            self.st[idx] = max(self.st[self.left(idx)], self.st[self.right(idx)])

    def update(self, a, b, val):
        """
        Update the values in the segment tree in the range [a,b] with the given value.

        >>> s = SegmentTree([1, 2, 3, 4, 5])
        >>> s.update(2, 4, 10)
        True
        >>> s.query(1, 5)
        10
        """
        return self.update_recursive(1, 0, self.N - 1, a - 1, b - 1, val)

    def update_recursive(self, idx, left, right, a, b, val):
        """
        update(1, 1, N, a, b, v) for update val v to [a,b]
        """
        if right < a or left > b:
            return True
        if left == right:
            self.st[idx] = val
            return True
        mid = (left + right) // 2
        self.update_recursive(self.left(idx), left, mid, a, b, val)
        self.update_recursive(self.right(idx), mid + 1, right, a, b, val)
        self.st[idx] = max(self.st[self.left(idx)], self.st[self.right(idx)])
        return True

    def query(self, a, b):
        """
        Query the maximum value in the range [a,b].

        >>> s = SegmentTree([1, 2, 3, 4, 5])
        >>> s.query(1, 3)
        3
        >>> s.query(1, 5)
        5
        """
        return self.query_recursive(1, 0, self.N - 1, a - 1, b - 1)

    def query_recursive(self, idx, left, right, a, b):
        """
        query(1, 1, N, a, b) for query max of [a,b]
        """
        if right < a or left > b:
            return -math.inf
        if left >= a and right <= b:
            return self.st[idx]
        mid = (left + right) // 2
        q1 = self.query_recursive(self.left(idx), left, mid, a, b)
        q2 = self.query_recursive(self.right(idx), mid + 1, right, a, b)
        return max(q1, q2)

    def show_data(self):
        show_list = []
        for i in range(1, self.N + 1):
            show_list += [self.query(i, i)]
        print(show_list)


if __name__ == "__main__":
    A = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
    N = 15
    segt = SegmentTree(A)
    print(segt.query(4, 6))
    print(segt.query(7, 11))
    print(segt.query(7, 12))
    segt.update(1, 3, 111)
    print(segt.query(1, 15))
    segt.update(7, 8, 235)
    segt.show_data()


================================================
FILE: data_structures/binary_tree/segment_tree_other.py
================================================
"""
Segment_tree creates a segment tree with a given array and function,
allowing queries to be done later in log(N) time
function takes 2 values and returns a same type value
"""

from collections.abc import Sequence
from queue import Queue


class SegmentTreeNode:
    def __init__(self, start, end, val, left=None, right=None):
        self.start = start
        self.end = end
        self.val = val
        self.mid = (start + end) // 2
        self.left = left
        self.right = right

    def __repr__(self):
        return f"SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"


class SegmentTree:
    """
    >>> import operator
    >>> num_arr = SegmentTree([2, 1, 5, 3, 4], operator.add)
    >>> tuple(num_arr.traverse())  # doctest: +NORMALIZE_WHITESPACE
    (SegmentTreeNode(start=0, end=4, val=15),
        SegmentTreeNode(start=0, end=2, val=8),
        SegmentTreeNode(start=3, end=4, val=7),
        SegmentTreeNode(start=0, end=1, val=3),
        SegmentTreeNode(start=2, end=2, val=5),
        SegmentTreeNode(start=3, end=3, val=3),
        SegmentTreeNode(start=4, end=4, val=4),
        SegmentTreeNode(start=0, end=0, val=2),
        SegmentTreeNode(start=1, end=1, val=1))
    >>>
    >>> num_arr.update(1, 5)
    >>> tuple(num_arr.traverse())  # doctest: +NORMALIZE_WHITESPACE
    (SegmentTreeNode(start=0, end=4, val=19),
        SegmentTreeNode(start=0, end=2, val=12),
        SegmentTreeNode(start=3, end=4, val=7),
        SegmentTreeNode(start=0, end=1, val=7),
        SegmentTreeNode(start=2, end=2, val=5),
        SegmentTreeNode(start=3, end=3, val=3),
        SegmentTreeNode(start=4, end=4, val=4),
        SegmentTreeNode(start=0, end=0, val=2),
        SegmentTreeNode(start=1, end=1, val=5))
    >>>
    >>> num_arr.query_range(3, 4)
    7
    >>> num_arr.query_range(2, 2)
    5
    >>> num_arr.query_range(1, 3)
    13
    >>>
    >>> max_arr = SegmentTree([2, 1, 5, 3, 4], max)
    >>> for node in max_arr.traverse():
    ...     print(node)
    ...
    SegmentTreeNode(start=0, end=4, val=5)
    SegmentTreeNode(start=0, end=2, val=5)
    SegmentTreeNode(start=3, end=4, val=4)
    SegmentTreeNode(start=0, end=1, val=2)
    SegmentTreeNode(start=2, end=2, val=5)
    SegmentTreeNode(start=3, end=3, val=3)
    SegmentTreeNode(start=4, end=4, val=4)
    SegmentTreeNode(start=0, end=0, val=2)
    SegmentTreeNode(start=1, end=1, val=1)
    >>>
    >>> max_arr.update(1, 5)
    >>> for node in max_arr.traverse():
    ...     print(node)
    ...
    SegmentTreeNode(start=0, end=4, val=5)
    SegmentTreeNode(start=0, end=2, val=5)
    SegmentTreeNode(start=3, end=4, val=4)
    SegmentTreeNode(start=0, end=1, val=5)
    SegmentTreeNode(start=2, end=2, val=5)
    SegmentTreeNode(start=3, end=3, val=3)
    SegmentTreeNode(start=4, end=4, val=4)
    SegmentTreeNode(start=0, end=0, val=2)
    SegmentTreeNode(start=1, end=1, val=5)
    >>>
    >>> max_arr.query_range(3, 4)
    4
    >>> max_arr.query_range(2, 2)
    5
    >>> max_arr.query_range(1, 3)
    5
    >>>
    >>> min_arr = SegmentTree([2, 1, 5, 3, 4], min)
    >>> for node in min_arr.traverse():
    ...     print(node)
    ...
    SegmentTreeNode(start=0, end=4, val=1)
    SegmentTreeNode(start=0, end=2, val=1)
    SegmentTreeNode(start=3, end=4, val=3)
    SegmentTreeNode(start=0, end=1, val=1)
    SegmentTreeNode(start=2, end=2, val=5)
    SegmentTreeNode(start=3, end=3, val=3)
    SegmentTreeNode(start=4, end=4, val=4)
    SegmentTreeNode(start=0, end=0, val=2)
    SegmentTreeNode(start=1, end=1, val=1)
    >>>
    >>> min_arr.update(1, 5)
    >>> for node in min_arr.traverse():
    ...     print(node)
    ...
    SegmentTreeNode(start=0, end=4, val=2)
    SegmentTreeNode(start=0, end=2, val=2)
    SegmentTreeNode(start=3, end=4, val=3)
    SegmentTreeNode(start=0, end=1, val=2)
    SegmentTreeNode(start=2, end=2, val=5)
    SegmentTreeNode(start=3, end=3, val=3)
    SegmentTreeNode(start=4, end=4, val=4)
    SegmentTreeNode(start=0, end=0, val=2)
    SegmentTreeNode(start=1, end=1, val=5)
    >>>
    >>> min_arr.query_range(3, 4)
    3
    >>> min_arr.query_range(2, 2)
    5
    >>> min_arr.query_range(1, 3)
    3
    >>>
    """

    def __init__(self, collection: Sequence, function):
        self.collection = collection
        self.fn = function
        if self.collection:
            self.root = self._build_tree(0, len(collection) - 1)

    def update(self, i, val):
        """
        Update an element in log(N) time
        :param i: position to be update
        :param val: new value
        >>> import operator
        >>> num_arr = SegmentTree([2, 1, 5, 3, 4], operator.add)
        >>> num_arr.update(1, 5)
        >>> num_arr.query_range(1, 3)
        13
        """
        self._update_tree(self.root, i, val)

    def query_range(self, i, j):
        """
        Get range query value in log(N) time
        :param i: left element index
        :param j: right element index
        :return: element combined in the range [i, j]
        >>> import operator
        >>> num_arr = SegmentTree([2, 1, 5, 3, 4], operator.add)
        >>> num_arr.update(1, 5)
        >>> num_arr.query_range(3, 4)
        7
        >>> num_arr.query_range(2, 2)
        5
        >>> num_arr.query_range(1, 3)
        13
        >>>
        """
        return self._query_range(self.root, i, j)

    def _build_tree(self, start, end):
        if start == end:
            return SegmentTreeNode(start, end, self.collection[start])
        mid = (start + end) // 2
        left = self._build_tree(start, mid)
        right = self._build_tree(mid + 1, end)
        return SegmentTreeNode(start, end, self.fn(left.val, right.val), left, right)

    def _update_tree(self, node, i, val):
        if node.start == i and node.end == i:
            node.val = val
            return
        if i <= node.mid:
            self._update_tree(node.left, i, val)
        else:
            self._update_tree(node.right, i, val)
        node.val = self.fn(node.left.val, node.right.val)

    def _query_range(self, node, i, j):
        if node.start == i and node.end == j:
            return node.val

        if i <= node.mid:
            if j <= node.mid:
                # range in left child tree
                return self._query_range(node.left, i, j)
            else:
                # range in left child tree and right child tree
                return self.fn(
                    self._query_range(node.left, i, node.mid),
                    self._query_range(node.right, node.mid + 1, j),
                )
        else:
            # range in right child tree
            return self._query_range(node.right, i, j)

    def traverse(self):
        if self.root is not None:
            queue = Queue()
            queue.put(self.root)
            while not queue.empty():
                node = queue.get()
                yield node

                if node.left is not None:
                    queue.put(node.left)

                if node.right is not None:
                    queue.put(node.right)


if __name__ == "__main__":
    import operator

    for fn in [operator.add, max, min]:
        print("*" * 50)
        arr = SegmentTree([2, 1, 5, 3, 4], fn)
        for node in arr.traverse():
            print(node)
        print()

        arr.update(1, 5)
        for node in arr.traverse():
            print(node)
        print()

        print(arr.query_range(3, 4))  # 7
        print(arr.query_range(2, 2))  # 5
        print(arr.query_range(1, 3))  # 13
        print()


================================================
FILE: data_structures/binary_tree/serialize_deserialize_binary_tree.py
================================================
from __future__ import annotations

from collections.abc import Iterator
from dataclasses import dataclass


@dataclass
class TreeNode:
    """
    A binary tree node has a value, left child, and right child.

    Props:
        value: The value of the node.
        left: The left child of the node.
        right: The right child of the node.
    """

    value: int = 0
    left: TreeNode | None = None
    right: TreeNode | None = None

    def __post_init__(self):
        if not isinstance(self.value, int):
            raise TypeError("Value must be an integer.")

    def __iter__(self) -> Iterator[TreeNode]:
        """
        Iterate through the tree in preorder.

        Returns:
            An iterator of the tree nodes.

        >>> list(TreeNode(1))
        [1,null,null]
        >>> tuple(TreeNode(1, TreeNode(2), TreeNode(3)))
        (1,2,null,null,3,null,null, 2,null,null, 3,null,null)
        """
        yield self
        yield from self.left or ()
        yield from self.right or ()

    def __len__(self) -> int:
        """
        Count the number of nodes in the tree.

        Returns:
            The number of nodes in the tree.

        >>> len(TreeNode(1))
        1
        >>> len(TreeNode(1, TreeNode(2), TreeNode(3)))
        3
        """
        return sum(1 for _ in self)

    def __repr__(self) -> str:
        """
        Represent the tree as a string.

        Returns:
            A string representation of the tree.

        >>> repr(TreeNode(1))
        '1,null,null'
        >>> repr(TreeNode(1, TreeNode(2), TreeNode(3)))
        '1,2,null,null,3,null,null'
        >>> repr(TreeNode(1, TreeNode(2), TreeNode(3, TreeNode(4), TreeNode(5))))
        '1,2,null,null,3,4,null,null,5,null,null'
        """
        return f"{self.value},{self.left!r},{self.right!r}".replace("None", "null")

    @classmethod
    def five_tree(cls) -> TreeNode:
        """
        >>> repr(TreeNode.five_tree())
        '1,2,null,null,3,4,null,null,5,null,null'
        """
        root = TreeNode(1)
        root.left = TreeNode(2)
        root.right = TreeNode(3)
        root.right.left = TreeNode(4)
        root.right.right = TreeNode(5)
        return root


def deserialize(data: str) -> TreeNode | None:
    """
    Deserialize a string to a binary tree.

    Args:
        data(str): The serialized string.

    Returns:
        The root of the binary tree.

    >>> root = TreeNode.five_tree()
    >>> serialzed_data = repr(root)
    >>> deserialized = deserialize(serialzed_data)
    >>> root == deserialized
    True
    >>> root is deserialized  # two separate trees
    False
    >>> root.right.right.value = 6
    >>> root == deserialized
    False
    >>> serialzed_data = repr(root)
    >>> deserialized = deserialize(serialzed_data)
    >>> root == deserialized
    True
    >>> deserialize("")
    Traceback (most recent call last):
        ...
    ValueError: Data cannot be empty.
    """

    if not data:
        raise ValueError("Data cannot be empty.")

    # Split the serialized string by a comma to get node values
    nodes = data.split(",")

    def build_tree() -> TreeNode | None:
        # Get the next value from the list
        value = nodes.pop(0)

        if value == "null":
            return None

        node = TreeNode(int(value))
        node.left = build_tree()  # Recursively build left subtree
        node.right = build_tree()  # Recursively build right subtree
        return node

    return build_tree()


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/binary_tree/symmetric_tree.py
================================================
"""
Given the root of a binary tree, check whether it is a mirror of itself
(i.e., symmetric around its center).

Leetcode reference: https://leetcode.com/problems/symmetric-tree/
"""

from __future__ import annotations

from dataclasses import dataclass


@dataclass
class Node:
    """
    A Node represents an element of a binary tree, which contains:

    Attributes:
    data: The value stored in the node (int).
    left: Pointer to the left child node (Node or None).
    right: Pointer to the right child node (Node or None).

    Example:
    >>> node = Node(1, Node(2), Node(3))
    >>> node.data
    1
    >>> node.left.data
    2
    >>> node.right.data
    3
    """

    data: int
    left: Node | None = None
    right: Node | None = None


def make_symmetric_tree() -> Node:
    r"""
    Create a symmetric tree for testing.

    The tree looks like this:
           1
         /   \
        2     2
      / \    / \
     3   4   4  3

    Returns:
    Node: Root node of a symmetric tree.

    Example:
    >>> tree = make_symmetric_tree()
    >>> tree.data
    1
    >>> tree.left.data == tree.right.data
    True
    >>> tree.left.left.data == tree.right.right.data
    True
    """
    root = Node(1)
    root.left = Node(2)
    root.right = Node(2)
    root.left.left = Node(3)
    root.left.right = Node(4)
    root.right.left = Node(4)
    root.right.right = Node(3)
    return root


def make_asymmetric_tree() -> Node:
    r"""
    Create an asymmetric tree for testing.

    The tree looks like this:
           1
         /   \
        2     2
      / \    / \
     3   4   3  4

    Returns:
    Node: Root node of an asymmetric tree.

    Example:
    >>> tree = make_asymmetric_tree()
    >>> tree.data
    1
    >>> tree.left.data == tree.right.data
    True
    >>> tree.left.left.data == tree.right.right.data
    False
    """
    root = Node(1)
    root.left = Node(2)
    root.right = Node(2)
    root.left.left = Node(3)
    root.left.right = Node(4)
    root.right.left = Node(3)
    root.right.right = Node(4)
    return root


def is_symmetric_tree(tree: Node) -> bool:
    """
    Check if a binary tree is symmetric (i.e., a mirror of itself).

    Parameters:
    tree: The root node of the binary tree.

    Returns:
    bool: True if the tree is symmetric, False otherwise.

    Example:
    >>> is_symmetric_tree(make_symmetric_tree())
    True
    >>> is_symmetric_tree(make_asymmetric_tree())
    False
    """
    if tree:
        return is_mirror(tree.left, tree.right)
    return True  # An empty tree is considered symmetric.


def is_mirror(left: Node | None, right: Node | None) -> bool:
    """
    Check if two subtrees are mirror images of each other.

    Parameters:
    left: The root node of the left subtree.
    right: The root node of the right subtree.

    Returns:
    bool: True if the two subtrees are mirrors of each other, False otherwise.

    Example:
    >>> tree1 = make_symmetric_tree()
    >>> is_mirror(tree1.left, tree1.right)
    True
    >>> tree2 = make_asymmetric_tree()
    >>> is_mirror(tree2.left, tree2.right)
    False
    """
    if left is None and right is None:
        # Both sides are empty, which is symmetric.
        return True
    if left is None or right is None:
        # One side is empty while the other is not, which is not symmetric.
        return False
    if left.data == right.data:
        # The values match, so check the subtrees recursively.
        return is_mirror(left.left, right.right) and is_mirror(left.right, right.left)
    return False


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: data_structures/binary_tree/treap.py
================================================
from __future__ import annotations

from random import random


class Node:
    """
    Treap's node
    Treap is a binary tree by value and heap by priority
    """

    def __init__(self, value: int | None = None):
        self.value = value
        self.prior = random()
        self.left: Node | None = None
        self.right: Node | None = None

    def __repr__(self) -> str:
        from pprint import pformat

        if self.left is None and self.right is None:
            return f"'{self.value}: {self.prior:.5}'"
        else:
            return pformat(
                {f"{self.value}: {self.prior:.5}": (self.left, self.right)}, indent=1
            )

    def __str__(self) -> str:
        value = str(self.value) + " "
        left = str(self.left or "")
        right = str(self.right or "")
        return value + left + right


def split(root: Node | None, value: int) -> tuple[Node | None, Node | None]:
    """
    We split current tree into 2 trees with value:

    Left tree contains all values less than split value.
    Right tree contains all values greater or equal, than split value
    """
    if root is None or root.value is None:  # None tree is split into 2 Nones
        return None, None
    elif value < root.value:
        """
        Right tree's root will be current node.
        Now we split(with the same value) current node's left son
        Left tree: left part of that split
        Right tree's left son: right part of that split
        """
        left, root.left = split(root.left, value)
        return left, root
    else:
        """
        Just symmetric to previous case
        """
        root.right, right = split(root.right, value)
        return root, right


def merge(left: Node | None, right: Node | None) -> Node | None:
    """
    We merge 2 trees into one.
    Note: all left tree's values must be less than all right tree's
    """
    if (not left) or (not right):  # If one node is None, return the other
        return left or right
    elif left.prior < right.prior:
        """
        Left will be root because it has more priority
        Now we need to merge left's right son and right tree
        """
        left.right = merge(left.right, right)
        return left
    else:
        """
        Symmetric as well
        """
        right.left = merge(left, right.left)
        return right


def insert(root: Node | None, value: int) -> Node | None:
    """
    Insert element

    Split current tree with a value into left, right,
    Insert new node into the middle
    Merge left, node, right into root
    """
    node = Node(value)
    left, right = split(root, value)
    return merge(merge(left, node), right)


def erase(root: Node | None, value: int) -> Node | None:
    """
    Erase element

    Split all nodes with values less into left,
    Split all nodes with values greater into right.
    Merge left, right
    """
    left, right = split(root, value - 1)
    _, right = split(right, value)
    return merge(left, right)


def inorder(root: Node | None) -> None:
    """
    Just recursive print of a tree
    """
    if not root:  # None
        return
    else:
        inorder(root.left)
        print(root.value, end=",")
        inorder(root.right)


def interact_treap(root: Node | None, args: str) -> Node | None:
    """
    Commands:
    + value to add value into treap
    - value to erase all nodes with value

        >>> root = interact_treap(None, "+1")
        >>> inorder(root)
        1,
        >>> root = interact_treap(root, "+3 +5 +17 +19 +2 +16 +4 +0")
        >>> inorder(root)
        0,1,2,3,4,5,16,17,19,
        >>> root = interact_treap(root, "+4 +4 +4")
        >>> inorder(root)
        0,1,2,3,4,4,4,4,5,16,17,19,
        >>> root = interact_treap(root, "-0")
        >>> inorder(root)
        1,2,3,4,4,4,4,5,16,17,19,
        >>> root = interact_treap(root, "-4")
        >>> inorder(root)
        1,2,3,5,16,17,19,
        >>> root = interact_treap(root, "=0")
        Unknown command
    """
    for arg in args.split():
        if arg[0] == "+":
            root = insert(root, int(arg[1:]))

        elif arg[0] == "-":
            root = erase(root, int(arg[1:]))

        else:
            print("Unknown command")

    return root


def main() -> None:
    """After each command, program prints treap"""
    root = None
    print(
        "enter numbers to create a tree, + value to add value into treap, "
        "- value to erase all nodes with value. 'q' to quit. "
    )

    args = input()
    while args != "q":
        root = interact_treap(root, args)
        print(root)
        args = input()

    print("good by!")


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: data_structures/binary_tree/wavelet_tree.py
================================================
"""
Wavelet tree is a data-structure designed to efficiently answer various range queries
for arrays. Wavelets trees are different from other binary trees in the sense that
the nodes are split based on the actual values of the elements and not on indices,
such as the with segment trees or fenwick trees. You can read more about them here:
1. https://users.dcc.uchile.cl/~jperez/papers/ioiconf16.pdf
2. https://www.youtube.com/watch?v=4aSv9PcecDw&t=811s
3. https://www.youtube.com/watch?v=CybAgVF-MMc&t=1178s
"""

from __future__ import annotations

test_array = [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]


class Node:
    def __init__(self, length: int) -> None:
        self.minn: int = -1
        self.maxx: int = -1
        self.map_left: list[int] = [-1] * length
        self.left: Node | None = None
        self.right: Node | None = None

    def __repr__(self) -> str:
        """
        >>> node = Node(length=27)
        >>> repr(node)
        'Node(min_value=-1 max_value=-1)'
        >>> repr(node) == str(node)
        True
        """
        return f"Node(min_value={self.minn} max_value={self.maxx})"


def build_tree(arr: list[int]) -> Node | None:
    """
    Builds the tree for arr and returns the root
    of the constructed tree

    >>> build_tree(test_array)
    Node(min_value=0 max_value=9)
    """
    root = Node(len(arr))
    root.minn, root.maxx = min(arr), max(arr)
    # Leaf node case where the node contains only one unique value
    if root.minn == root.maxx:
        return root
    """
    Take the mean of min and max element of arr as the pivot and
    partition arr into left_arr and right_arr with all elements <= pivot in the
    left_arr and the rest in right_arr, maintaining the order of the elements,
    then recursively build trees for left_arr and right_arr
    """
    pivot = (root.minn + root.maxx) // 2

    left_arr: list[int] = []
    right_arr: list[int] = []

    for index, num in enumerate(arr):
        if num <= pivot:
            left_arr.append(num)
        else:
            right_arr.append(num)
        root.map_left[index] = len(left_arr)
    root.left = build_tree(left_arr)
    root.right = build_tree(right_arr)
    return root


def rank_till_index(node: Node | None, num: int, index: int) -> int:
    """
    Returns the number of occurrences of num in interval [0, index] in the list

    >>> root = build_tree(test_array)
    >>> rank_till_index(root, 6, 6)
    1
    >>> rank_till_index(root, 2, 0)
    1
    >>> rank_till_index(root, 1, 10)
    2
    >>> rank_till_index(root, 17, 7)
    0
    >>> rank_till_index(root, 0, 9)
    1
    """
    if index < 0 or node is None:
        return 0
    # Leaf node cases
    if node.minn == node.maxx:
        return index + 1 if node.minn == num else 0
    pivot = (node.minn + node.maxx) // 2
    if num <= pivot:
        # go the left subtree and map index to the left subtree
        return rank_till_index(node.left, num, node.map_left[index] - 1)
    else:
        # go to the right subtree and map index to the right subtree
        return rank_till_index(node.right, num, index - node.map_left[index])


def rank(node: Node | None, num: int, start: int, end: int) -> int:
    """
    Returns the number of occurrences of num in interval [start, end] in the list

    >>> root = build_tree(test_array)
    >>> rank(root, 6, 3, 13)
    2
    >>> rank(root, 2, 0, 19)
    4
    >>> rank(root, 9, 2 ,2)
    0
    >>> rank(root, 0, 5, 10)
    2
    """
    if start > end:
        return 0
    rank_till_end = rank_till_index(node, num, end)
    rank_before_start = rank_till_index(node, num, start - 1)
    return rank_till_end - rank_before_start


def quantile(node: Node | None, index: int, start: int, end: int) -> int:
    """
    Returns the index'th smallest element in interval [start, end] in the list
    index is 0-indexed

    >>> root = build_tree(test_array)
    >>> quantile(root, 2, 2, 5)
    5
    >>> quantile(root, 5, 2, 13)
    4
    >>> quantile(root, 0, 6, 6)
    8
    >>> quantile(root, 4, 2, 5)
    -1
    """
    if index > (end - start) or start > end or node is None:
        return -1
    # Leaf node case
    if node.minn == node.maxx:
        return node.minn
    # Number of elements in the left subtree in interval [start, end]
    num_elements_in_left_tree = node.map_left[end] - (
        node.map_left[start - 1] if start else 0
    )
    if num_elements_in_left_tree > index:
        return quantile(
            node.left,
            index,
            (node.map_left[start - 1] if start else 0),
            node.map_left[end] - 1,
        )
    else:
        return quantile(
            node.right,
            index - num_elements_in_left_tree,
            start - (node.map_left[start - 1] if start else 0),
            end - node.map_left[end],
        )


def range_counting(
    node: Node | None, start: int, end: int, start_num: int, end_num: int
) -> int:
    """
    Returns the number of elements in range [start_num, end_num]
    in interval [start, end] in the list

    >>> root = build_tree(test_array)
    >>> range_counting(root, 1, 10, 3, 7)
    3
    >>> range_counting(root, 2, 2, 1, 4)
    1
    >>> range_counting(root, 0, 19, 0, 100)
    20
    >>> range_counting(root, 1, 0, 1, 100)
    0
    >>> range_counting(root, 0, 17, 100, 1)
    0
    """
    if (
        start > end
        or node is None
        or start_num > end_num
        or node.minn > end_num
        or node.maxx < start_num
    ):
        return 0
    if start_num <= node.minn and node.maxx <= end_num:
        return end - start + 1
    left = range_counting(
        node.left,
        (node.map_left[start - 1] if start else 0),
        node.map_left[end] - 1,
        start_num,
        end_num,
    )
    right = range_counting(
        node.right,
        start - (node.map_left[start - 1] if start else 0),
        end - node.map_left[end],
        start_num,
        end_num,
    )
    return left + right


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/disjoint_set/__init__.py
================================================


================================================
FILE: data_structures/disjoint_set/alternate_disjoint_set.py
================================================
"""
Implements a disjoint set using Lists and some added heuristics for efficiency
Union by Rank Heuristic and Path Compression
"""


class DisjointSet:
    def __init__(self, set_counts: list) -> None:
        """
        Initialize with a list of the number of items in each set
        and with rank = 1 for each set
        """
        self.set_counts = set_counts
        self.max_set = max(set_counts)
        num_sets = len(set_counts)
        self.ranks = [1] * num_sets
        self.parents = list(range(num_sets))

    def merge(self, src: int, dst: int) -> bool:
        """
        Merge two sets together using Union by rank heuristic
        Return True if successful
        Merge two disjoint sets
        >>> A = DisjointSet([1, 1, 1])
        >>> A.merge(1, 2)
        True
        >>> A.merge(0, 2)
        True
        >>> A.merge(0, 1)
        False
        """
        src_parent = self.get_parent(src)
        dst_parent = self.get_parent(dst)

        if src_parent == dst_parent:
            return False

        if self.ranks[dst_parent] >= self.ranks[src_parent]:
            self.set_counts[dst_parent] += self.set_counts[src_parent]
            self.set_counts[src_parent] = 0
            self.parents[src_parent] = dst_parent
            if self.ranks[dst_parent] == self.ranks[src_parent]:
                self.ranks[dst_parent] += 1
            joined_set_size = self.set_counts[dst_parent]
        else:
            self.set_counts[src_parent] += self.set_counts[dst_parent]
            self.set_counts[dst_parent] = 0
            self.parents[dst_parent] = src_parent
            joined_set_size = self.set_counts[src_parent]

        self.max_set = max(self.max_set, joined_set_size)
        return True

    def get_parent(self, disj_set: int) -> int:
        """
        Find the Parent of a given set
        >>> A = DisjointSet([1, 1, 1])
        >>> A.merge(1, 2)
        True
        >>> A.get_parent(0)
        0
        >>> A.get_parent(1)
        2
        """
        if self.parents[disj_set] == disj_set:
            return disj_set
        self.parents[disj_set] = self.get_parent(self.parents[disj_set])
        return self.parents[disj_set]


================================================
FILE: data_structures/disjoint_set/disjoint_set.py
================================================
"""
Disjoint set.
Reference: https://en.wikipedia.org/wiki/Disjoint-set_data_structure
"""


class Node:
    def __init__(self, data: int) -> None:
        self.data = data
        self.rank: int
        self.parent: Node


def make_set(x: Node) -> None:
    """
    Make x as a set.
    """
    # rank is the distance from x to its' parent
    # root's rank is 0
    x.rank = 0
    x.parent = x


def union_set(x: Node, y: Node) -> None:
    """
    Union of two sets.
    set with bigger rank should be parent, so that the
    disjoint set tree will be more flat.
    """
    x, y = find_set(x), find_set(y)
    if x == y:
        return

    elif x.rank > y.rank:
        y.parent = x
    else:
        x.parent = y
        if x.rank == y.rank:
            y.rank += 1


def find_set(x: Node) -> Node:
    """
    Return the parent of x
    """
    if x != x.parent:
        x.parent = find_set(x.parent)
    return x.parent


def find_python_set(node: Node) -> set:
    """
    Return a Python Standard Library set that contains i.
    """
    sets = ({0, 1, 2}, {3, 4, 5})
    for s in sets:
        if node.data in s:
            return s
    msg = f"{node.data} is not in {sets}"
    raise ValueError(msg)


def test_disjoint_set() -> None:
    """
    >>> test_disjoint_set()
    """
    vertex = [Node(i) for i in range(6)]
    for v in vertex:
        make_set(v)

    union_set(vertex[0], vertex[1])
    union_set(vertex[1], vertex[2])
    union_set(vertex[3], vertex[4])
    union_set(vertex[3], vertex[5])

    for node0 in vertex:
        for node1 in vertex:
            if find_python_set(node0).isdisjoint(find_python_set(node1)):
                assert find_set(node0) != find_set(node1)
            else:
                assert find_set(node0) == find_set(node1)


if __name__ == "__main__":
    test_disjoint_set()


================================================
FILE: data_structures/hashing/__init__.py
================================================


================================================
FILE: data_structures/hashing/bloom_filter.py
================================================
"""
See https://en.wikipedia.org/wiki/Bloom_filter

The use of this data structure is to test membership in a set.
Compared to Python's built-in set() it is more space-efficient.
In the following example, only 8 bits of memory will be used:
>>> bloom = Bloom(size=8)

Initially, the filter contains all zeros:
>>> bloom.bitstring
'00000000'

When an element is added, two bits are set to 1
since there are 2 hash functions in this implementation:
>>> "Titanic" in bloom
False
>>> bloom.add("Titanic")
>>> bloom.bitstring
'01100000'
>>> "Titanic" in bloom
True

However, sometimes only one bit is added
because both hash functions return the same value
>>> bloom.add("Avatar")
>>> "Avatar" in bloom
True
>>> bloom.format_hash("Avatar")
'00000100'
>>> bloom.bitstring
'01100100'

Not added elements should return False ...
>>> not_present_films = ("The Godfather", "Interstellar", "Parasite", "Pulp Fiction")
>>> {
...   film: bloom.format_hash(film) for film in not_present_films
... } # doctest: +NORMALIZE_WHITESPACE
{'The Godfather': '00000101',
 'Interstellar': '00000011',
 'Parasite': '00010010',
 'Pulp Fiction': '10000100'}
>>> any(film in bloom for film in not_present_films)
False

but sometimes there are false positives:
>>> "Ratatouille" in bloom
True
>>> bloom.format_hash("Ratatouille")
'01100000'

The probability increases with the number of elements added.
The probability decreases with the number of bits in the bitarray.
>>> bloom.estimated_error_rate
0.140625
>>> bloom.add("The Godfather")
>>> bloom.estimated_error_rate
0.25
>>> bloom.bitstring
'01100101'
"""

from hashlib import md5, sha256

HASH_FUNCTIONS = (sha256, md5)


class Bloom:
    def __init__(self, size: int = 8) -> None:
        self.bitarray = 0b0
        self.size = size

    def add(self, value: str) -> None:
        h = self.hash_(value)
        self.bitarray |= h

    def exists(self, value: str) -> bool:
        h = self.hash_(value)
        return (h & self.bitarray) == h

    def __contains__(self, other: str) -> bool:
        return self.exists(other)

    def format_bin(self, bitarray: int) -> str:
        res = bin(bitarray)[2:]
        return res.zfill(self.size)

    @property
    def bitstring(self) -> str:
        return self.format_bin(self.bitarray)

    def hash_(self, value: str) -> int:
        res = 0b0
        for func in HASH_FUNCTIONS:
            position = (
                int.from_bytes(func(value.encode()).digest(), "little") % self.size
            )
            res |= 2**position
        return res

    def format_hash(self, value: str) -> str:
        return self.format_bin(self.hash_(value))

    @property
    def estimated_error_rate(self) -> float:
        n_ones = bin(self.bitarray).count("1")
        return (n_ones / self.size) ** len(HASH_FUNCTIONS)


================================================
FILE: data_structures/hashing/double_hash.py
================================================
#!/usr/bin/env python3
"""
Double hashing is a collision resolving technique in Open Addressed Hash tables.
Double hashing uses the idea of applying a second hash function to key when a collision
occurs. The advantage of Double hashing is that it is one of the best form of  probing,
producing a uniform distribution of records throughout a hash table. This technique
does not yield any clusters. It is one of effective method for resolving collisions.

Double hashing can be done using: (hash1(key) + i * hash2(key)) % TABLE_SIZE
Where hash1() and hash2() are hash functions and TABLE_SIZE is size of hash table.

Reference: https://en.wikipedia.org/wiki/Double_hashing
"""

from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime


class DoubleHash(HashTable):
    """
    Hash Table example with open addressing and Double Hash
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def __hash_function_2(self, value, data):
        next_prime_gt = (
            next_prime(value % self.size_table)
            if not is_prime(value % self.size_table)
            else value % self.size_table
        )  # gt = bigger than
        return next_prime_gt - (data % next_prime_gt)

    def __hash_double_function(self, key, data, increment):
        return (increment * self.__hash_function_2(key, data)) % self.size_table

    def _collision_resolution(self, key, data=None):
        """
        Examples:

        1. Try to add three data elements when the size is three
        >>> dh = DoubleHash(3)
        >>> dh.insert_data(10)
        >>> dh.insert_data(20)
        >>> dh.insert_data(30)
        >>> dh.keys()
        {1: 10, 2: 20, 0: 30}

        2. Try to add three data elements when the size is two
        >>> dh = DoubleHash(2)
        >>> dh.insert_data(10)
        >>> dh.insert_data(20)
        >>> dh.insert_data(30)
        >>> dh.keys()
        {10: 10, 9: 20, 8: 30}

        3. Try to add three data elements when the size is four
        >>> dh = DoubleHash(4)
        >>> dh.insert_data(10)
        >>> dh.insert_data(20)
        >>> dh.insert_data(30)
        >>> dh.keys()
        {9: 20, 10: 10, 8: 30}
        """
        i = 1
        new_key = self.hash_function(data)

        while self.values[new_key] is not None and self.values[new_key] != key:
            new_key = (
                self.__hash_double_function(key, data, i)
                if self.balanced_factor() >= self.lim_charge
                else None
            )
            if new_key is None:
                break
            else:
                i += 1

        return new_key


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/hashing/hash_map.py
================================================
"""
Hash map with open addressing.

https://en.wikipedia.org/wiki/Hash_table

Another hash map implementation, with a good explanation.
Modern Dictionaries by Raymond Hettinger
https://www.youtube.com/watch?v=p33CVV29OG8
"""

from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import TypeVar

KEY = TypeVar("KEY")
VAL = TypeVar("VAL")


@dataclass(slots=True)
class _Item[KEY, VAL]:
    key: KEY
    val: VAL


class _DeletedItem(_Item):
    def __init__(self) -> None:
        super().__init__(None, None)

    def __bool__(self) -> bool:
        return False


_deleted = _DeletedItem()


class HashMap(MutableMapping[KEY, VAL]):
    """
    Hash map with open addressing.
    """

    def __init__(
        self, initial_block_size: int = 8, capacity_factor: float = 0.75
    ) -> None:
        self._initial_block_size = initial_block_size
        self._buckets: list[_Item | None] = [None] * initial_block_size
        assert 0.0 < capacity_factor < 1.0
        self._capacity_factor = capacity_factor
        self._len = 0

    def _get_bucket_index(self, key: KEY) -> int:
        return hash(key) % len(self._buckets)

    def _get_next_ind(self, ind: int) -> int:
        """
        Get next index.

        Implements linear open addressing.
        >>> HashMap(5)._get_next_ind(3)
        4
        >>> HashMap(5)._get_next_ind(5)
        1
        >>> HashMap(5)._get_next_ind(6)
        2
        >>> HashMap(5)._get_next_ind(9)
        0
        """
        return (ind + 1) % len(self._buckets)

    def _try_set(self, ind: int, key: KEY, val: VAL) -> bool:
        """
        Try to add value to the bucket.

        If bucket is empty or key is the same, does insert and return True.

        If bucket has another key that means that we need to check next bucket.
        """
        stored = self._buckets[ind]
        if not stored:
            # A falsy item means that bucket was never used (None)
            # or was deleted (_deleted).
            self._buckets[ind] = _Item(key, val)
            self._len += 1
            return True
        elif stored.key == key:
            stored.val = val
            return True
        else:
            return False

    def _is_full(self) -> bool:
        """
        Return true if we have reached safe capacity.

        So we need to increase the number of buckets to avoid collisions.

        >>> hm = HashMap(2)
        >>> hm._add_item(1, 10)
        >>> hm._add_item(2, 20)
        >>> hm._is_full()
        True
        >>> HashMap(2)._is_full()
        False
        """
        limit = len(self._buckets) * self._capacity_factor
        return len(self) >= int(limit)

    def _is_sparse(self) -> bool:
        """Return true if we need twice fewer buckets when we have now."""
        if len(self._buckets) <= self._initial_block_size:
            return False
        limit = len(self._buckets) * self._capacity_factor / 2
        return len(self) < limit

    def _resize(self, new_size: int) -> None:
        old_buckets = self._buckets
        self._buckets = [None] * new_size
        self._len = 0
        for item in old_buckets:
            if item:
                self._add_item(item.key, item.val)

    def _size_up(self) -> None:
        self._resize(len(self._buckets) * 2)

    def _size_down(self) -> None:
        self._resize(len(self._buckets) // 2)

    def _iterate_buckets(self, key: KEY) -> Iterator[int]:
        ind = self._get_bucket_index(key)
        for _ in range(len(self._buckets)):
            yield ind
            ind = self._get_next_ind(ind)

    def _add_item(self, key: KEY, val: VAL) -> None:
        """
        Try to add 3 elements when the size is 5
        >>> hm = HashMap(5)
        >>> hm._add_item(1, 10)
        >>> hm._add_item(2, 20)
        >>> hm._add_item(3, 30)
        >>> hm
        HashMap(1: 10, 2: 20, 3: 30)

        Try to add 3 elements when the size is 5
        >>> hm = HashMap(5)
        >>> hm._add_item(-5, 10)
        >>> hm._add_item(6, 30)
        >>> hm._add_item(-7, 20)
        >>> hm
        HashMap(-5: 10, 6: 30, -7: 20)

        Try to add 3 elements when size is 1
        >>> hm = HashMap(1)
        >>> hm._add_item(10, 13.2)
        >>> hm._add_item(6, 5.26)
        >>> hm._add_item(7, 5.155)
        >>> hm
        HashMap(10: 13.2)

        Trying to add an element with a key that is a floating point value
        >>> hm = HashMap(5)
        >>> hm._add_item(1.5, 10)
        >>> hm
        HashMap(1.5: 10)

        5. Trying to add an item with the same key
        >>> hm = HashMap(5)
        >>> hm._add_item(1, 10)
        >>> hm._add_item(1, 20)
        >>> hm
        HashMap(1: 20)
        """
        for ind in self._iterate_buckets(key):
            if self._try_set(ind, key, val):
                break

    def __setitem__(self, key: KEY, val: VAL) -> None:
        """
        1. Changing value of item whose key is present
        >>> hm = HashMap(5)
        >>> hm._add_item(1, 10)
        >>> hm.__setitem__(1, 20)
        >>> hm
        HashMap(1: 20)

        2. Changing value of item whose key is not present
        >>> hm = HashMap(5)
        >>> hm._add_item(1, 10)
        >>> hm.__setitem__(0, 20)
        >>> hm
        HashMap(0: 20, 1: 10)

        3. Changing the value of the same item multiple times
        >>> hm = HashMap(5)
        >>> hm._add_item(1, 10)
        >>> hm.__setitem__(1, 20)
        >>> hm.__setitem__(1, 30)
        >>> hm
        HashMap(1: 30)
        """
        if self._is_full():
            self._size_up()

        self._add_item(key, val)

    def __delitem__(self, key: KEY) -> None:
        """
        >>> hm = HashMap(5)
        >>> hm._add_item(1, 10)
        >>> hm._add_item(2, 20)
        >>> hm._add_item(3, 30)
        >>> hm.__delitem__(3)
        >>> hm
        HashMap(1: 10, 2: 20)
        >>> hm = HashMap(5)
        >>> hm._add_item(-5, 10)
        >>> hm._add_item(6, 30)
        >>> hm._add_item(-7, 20)
        >>> hm.__delitem__(-5)
        >>> hm
        HashMap(6: 30, -7: 20)

        # Trying to remove a non-existing item
        >>> hm = HashMap(5)
        >>> hm._add_item(1, 10)
        >>> hm._add_item(2, 20)
        >>> hm._add_item(3, 30)
        >>> hm.__delitem__(4)
        Traceback (most recent call last):
        ...
        KeyError: 4

        # Test resize down when sparse
        ## Setup: resize up
        >>> hm = HashMap(initial_block_size=100, capacity_factor=0.75)
        >>> len(hm._buckets)
        100
        >>> for i in range(75):
        ...     hm[i] = i
        >>> len(hm._buckets)
        100
        >>> hm[75] = 75
        >>> len(hm._buckets)
        200

        ## Resize down
        >>> del hm[75]
        >>> len(hm._buckets)
        200
        >>> del hm[74]
        >>> len(hm._buckets)
        100
        """
        for ind in self._iterate_buckets(key):
            item = self._buckets[ind]
            if item is None:
                raise KeyError(key)
            if item is _deleted:
                continue
            if item.key == key:
                self._buckets[ind] = _deleted
                self._len -= 1
                break
        if self._is_sparse():
            self._size_down()

    def __getitem__(self, key: KEY) -> VAL:
        """
        Returns the item at the given key

        >>> hm = HashMap(5)
        >>> hm._add_item(1, 10)
        >>> hm.__getitem__(1)
        10

        >>> hm = HashMap(5)
        >>> hm._add_item(10, -10)
        >>> hm._add_item(20, -20)
        >>> hm.__getitem__(20)
        -20

        >>> hm = HashMap(5)
        >>> hm._add_item(-1, 10)
        >>> hm.__getitem__(-1)
        10
        """
        for ind in self._iterate_buckets(key):
            item = self._buckets[ind]
            if item is None:
                break
            if item is _deleted:
                continue
            if item.key == key:
                return item.val
        raise KeyError(key)

    def __len__(self) -> int:
        """
        Returns the number of items present in hashmap

        >>> hm = HashMap(5)
        >>> hm._add_item(1, 10)
        >>> hm._add_item(2, 20)
        >>> hm._add_item(3, 30)
        >>> hm.__len__()
        3

        >>> hm = HashMap(5)
        >>> hm.__len__()
        0
        """
        return self._len

    def __iter__(self) -> Iterator[KEY]:
        yield from (item.key for item in self._buckets if item)

    def __repr__(self) -> str:
        val_string = ", ".join(
            f"{item.key}: {item.val}" for item in self._buckets if item
        )
        return f"HashMap({val_string})"


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/hashing/hash_table.py
================================================
#!/usr/bin/env python3
from abc import abstractmethod

from .number_theory.prime_numbers import next_prime


class HashTable:
    """
    Basic Hash Table example with open addressing and linear probing
    """

    def __init__(
        self,
        size_table: int,
        charge_factor: int | None = None,
        lim_charge: float | None = None,
    ) -> None:
        self.size_table = size_table
        self.values = [None] * self.size_table
        self.lim_charge = 0.75 if lim_charge is None else lim_charge
        self.charge_factor = 1 if charge_factor is None else charge_factor
        self.__aux_list: list = []
        self._keys: dict = {}

    def keys(self):
        """
        The keys function returns a dictionary containing the key value pairs.
        key being the index number in hash table and value being the data value.

        Examples:
        1. creating HashTable with size 10 and inserting 3 elements
        >>> ht = HashTable(10)
        >>> ht.insert_data(10)
        >>> ht.insert_data(20)
        >>> ht.insert_data(30)
        >>> ht.keys()
        {0: 10, 1: 20, 2: 30}

        2. creating HashTable with size 5 and inserting 5 elements
        >>> ht = HashTable(5)
        >>> ht.insert_data(5)
        >>> ht.insert_data(4)
        >>> ht.insert_data(3)
        >>> ht.insert_data(2)
        >>> ht.insert_data(1)
        >>> ht.keys()
        {0: 5, 4: 4, 3: 3, 2: 2, 1: 1}
        """
        return self._keys

    def balanced_factor(self):
        return sum(1 for slot in self.values if slot is not None) / (
            self.size_table * self.charge_factor
        )

    def hash_function(self, key):
        """
        Generates hash for the given key value

        Examples:

        Creating HashTable with size 5
        >>> ht = HashTable(5)
        >>> ht.hash_function(10)
        0
        >>> ht.hash_function(20)
        0
        >>> ht.hash_function(4)
        4
        >>> ht.hash_function(18)
        3
        >>> ht.hash_function(-18)
        2
        >>> ht.hash_function(18.5)
        3.5
        >>> ht.hash_function(0)
        0
        >>> ht.hash_function(-0)
        0
        """
        return key % self.size_table

    def _step_by_step(self, step_ord):
        print(f"step {step_ord}")
        print(list(range(len(self.values))))
        print(self.values)

    def bulk_insert(self, values):
        """
        bulk_insert is used for entering more than one element at a time
        in the HashTable.

        Examples:
        1.
        >>> ht = HashTable(5)
        >>> ht.bulk_insert((10,20,30))
        step 1
        [0, 1, 2, 3, 4]
        [10, None, None, None, None]
        step 2
        [0, 1, 2, 3, 4]
        [10, 20, None, None, None]
        step 3
        [0, 1, 2, 3, 4]
        [10, 20, 30, None, None]

        2.
        >>> ht = HashTable(5)
        >>> ht.bulk_insert([5,4,3,2,1])
        step 1
        [0, 1, 2, 3, 4]
        [5, None, None, None, None]
        step 2
        [0, 1, 2, 3, 4]
        [5, None, None, None, 4]
        step 3
        [0, 1, 2, 3, 4]
        [5, None, None, 3, 4]
        step 4
        [0, 1, 2, 3, 4]
        [5, None, 2, 3, 4]
        step 5
        [0, 1, 2, 3, 4]
        [5, 1, 2, 3, 4]
        """
        i = 1
        self.__aux_list = values
        for value in values:
            self.insert_data(value)
            self._step_by_step(i)
            i += 1

    def _set_value(self, key, data):
        """
        _set_value functions allows to update value at a particular hash

        Examples:
        1. _set_value in HashTable of size 5
        >>> ht = HashTable(5)
        >>> ht.insert_data(10)
        >>> ht.insert_data(20)
        >>> ht.insert_data(30)
        >>> ht._set_value(0,15)
        >>> ht.keys()
        {0: 15, 1: 20, 2: 30}

        2. _set_value in HashTable of size 2
        >>> ht = HashTable(2)
        >>> ht.insert_data(17)
        >>> ht.insert_data(18)
        >>> ht.insert_data(99)
        >>> ht._set_value(3,15)
        >>> ht.keys()
        {3: 15, 2: 17, 4: 99}

        3. _set_value in HashTable when hash is not present
        >>> ht = HashTable(2)
        >>> ht.insert_data(17)
        >>> ht.insert_data(18)
        >>> ht.insert_data(99)
        >>> ht._set_value(0,15)
        >>> ht.keys()
        {3: 18, 2: 17, 4: 99, 0: 15}

        4. _set_value in HashTable when multiple hash are not present
        >>> ht = HashTable(2)
        >>> ht.insert_data(17)
        >>> ht.insert_data(18)
        >>> ht.insert_data(99)
        >>> ht._set_value(0,15)
        >>> ht._set_value(1,20)
        >>> ht.keys()
        {3: 18, 2: 17, 4: 99, 0: 15, 1: 20}
        """
        self.values[key] = data
        self._keys[key] = data

    @abstractmethod
    def _collision_resolution(self, key, data=None):
        """
        This method is a type of open addressing which is used for handling collision.

        In this implementation the concept of linear probing has been used.

        The hash table is searched sequentially from the original location of the
        hash, if the new hash/location we get is already occupied we check for the next
        hash/location.

        references:
            - https://en.wikipedia.org/wiki/Linear_probing

        Examples:
        1. The collision will be with keys 18 & 99, so new hash will be created for 99
        >>> ht = HashTable(3)
        >>> ht.insert_data(17)
        >>> ht.insert_data(18)
        >>> ht.insert_data(99)
        >>> ht.keys()
        {2: 17, 0: 18, 1: 99}

        2. The collision will be with keys 17 & 101, so new hash
        will be created for 101
        >>> ht = HashTable(4)
        >>> ht.insert_data(17)
        >>> ht.insert_data(18)
        >>> ht.insert_data(99)
        >>> ht.insert_data(101)
        >>> ht.keys()
        {1: 17, 2: 18, 3: 99, 0: 101}

        2. The collision will be with all keys, so new hash will be created for all
        >>> ht = HashTable(1)
        >>> ht.insert_data(17)
        >>> ht.insert_data(18)
        >>> ht.insert_data(99)
        >>> ht.keys()
        {2: 17, 3: 18, 4: 99}

        3. Trying to insert float key in hash
        >>> ht = HashTable(1)
        >>> ht.insert_data(17)
        >>> ht.insert_data(18)
        >>> ht.insert_data(99.99)
        Traceback (most recent call last):
        ...
        TypeError: list indices must be integers or slices, not float
        """
        new_key = self.hash_function(key + 1)

        while self.values[new_key] is not None and self.values[new_key] != key:
            if self.values.count(None) > 0:
                new_key = self.hash_function(new_key + 1)
            else:
                new_key = None
                break

        return new_key

    def rehashing(self):
        survivor_values = [value for value in self.values if value is not None]
        self.size_table = next_prime(self.size_table, factor=2)
        self._keys.clear()
        self.values = [None] * self.size_table  # hell's pointers D: don't DRY ;/
        for value in survivor_values:
            self.insert_data(value)

    def insert_data(self, data):
        """
        insert_data is used for inserting a single element at a time in the HashTable.

        Examples:

        >>> ht = HashTable(3)
        >>> ht.insert_data(5)
        >>> ht.keys()
        {2: 5}
        >>> ht = HashTable(5)
        >>> ht.insert_data(30)
        >>> ht.insert_data(50)
        >>> ht.keys()
        {0: 30, 1: 50}
        """
        key = self.hash_function(data)

        if self.values[key] is None:
            self._set_value(key, data)

        elif self.values[key] == data:
            pass

        else:
            collision_resolution = self._collision_resolution(key, data)
            if collision_resolution is not None:
                self._set_value(collision_resolution, data)
            else:
                self.rehashing()
                self.insert_data(data)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/hashing/hash_table_with_linked_list.py
================================================
from collections import deque

from .hash_table import HashTable


class HashTableWithLinkedList(HashTable):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def _set_value(self, key, data):
        self.values[key] = deque() if self.values[key] is None else self.values[key]
        self.values[key].appendleft(data)
        self._keys[key] = self.values[key]

    def balanced_factor(self):
        return (
            sum(self.charge_factor - len(slot) for slot in self.values)
            / self.size_table
            * self.charge_factor
        )

    def _collision_resolution(self, key, data=None):
        if not (
            len(self.values[key]) == self.charge_factor and self.values.count(None) == 0
        ):
            return key
        return super()._collision_resolution(key, data)


================================================
FILE: data_structures/hashing/number_theory/__init__.py
================================================


================================================
FILE: data_structures/hashing/number_theory/prime_numbers.py
================================================
#!/usr/bin/env python3
"""
module to operations with prime numbers
"""

import math


def is_prime(number: int) -> bool:
    """Checks to see if a number is a prime in O(sqrt(n)).

    A number is prime if it has exactly two factors: 1 and itself.

    >>> is_prime(0)
    False
    >>> is_prime(1)
    False
    >>> is_prime(2)
    True
    >>> is_prime(3)
    True
    >>> is_prime(27)
    False
    >>> is_prime(87)
    False
    >>> is_prime(563)
    True
    >>> is_prime(2999)
    True
    >>> is_prime(67483)
    False
    """

    # precondition
    assert isinstance(number, int) and (number >= 0), (
        "'number' must been an int and positive"
    )

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or not number % 2:
        # Negatives, 0, 1 and all even numbers are not primes
        return False

    odd_numbers = range(3, int(math.sqrt(number) + 1), 2)
    return not any(not number % i for i in odd_numbers)


def next_prime(value, factor=1, **kwargs):
    value = factor * value
    first_value_val = value

    while not is_prime(value):
        value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

    if value == first_value_val:
        return next_prime(value + 1, **kwargs)
    return value


================================================
FILE: data_structures/hashing/quadratic_probing.py
================================================
#!/usr/bin/env python3

from .hash_table import HashTable


class QuadraticProbing(HashTable):
    """
    Basic Hash Table example with open addressing using Quadratic Probing
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def _collision_resolution(self, key, data=None):  # noqa: ARG002
        """
        Quadratic probing is an open addressing scheme used for resolving
        collisions in hash table.

        It works by taking the original hash index and adding successive
        values of an arbitrary quadratic polynomial until open slot is found.

        Hash + 1², Hash + 2², Hash + 3² .... Hash + n²

        reference:
            - https://en.wikipedia.org/wiki/Quadratic_probing
        e.g:
        1. Create hash table with size 7
        >>> qp = QuadraticProbing(7)
        >>> qp.insert_data(90)
        >>> qp.insert_data(340)
        >>> qp.insert_data(24)
        >>> qp.insert_data(45)
        >>> qp.insert_data(99)
        >>> qp.insert_data(73)
        >>> qp.insert_data(7)
        >>> qp.keys()
        {11: 45, 14: 99, 7: 24, 0: 340, 5: 73, 6: 90, 8: 7}

        2. Create hash table with size 8
        >>> qp = QuadraticProbing(8)
        >>> qp.insert_data(0)
        >>> qp.insert_data(999)
        >>> qp.insert_data(111)
        >>> qp.keys()
        {0: 0, 7: 999, 3: 111}

        3. Try to add three data elements when the size is two
        >>> qp =  QuadraticProbing(2)
        >>> qp.insert_data(0)
        >>> qp.insert_data(999)
        >>> qp.insert_data(111)
        >>> qp.keys()
        {0: 0, 4: 999, 1: 111}

        4. Try to add three data elements when the size is one
        >>> qp =  QuadraticProbing(1)
        >>> qp.insert_data(0)
        >>> qp.insert_data(999)
        >>> qp.insert_data(111)
        >>> qp.keys()
        {4: 999, 1: 111}
        """

        i = 1
        new_key = self.hash_function(key + i * i)

        while self.values[new_key] is not None and self.values[new_key] != key:
            i += 1
            new_key = (
                self.hash_function(key + i * i)
                if not self.balanced_factor() >= self.lim_charge
                else None
            )

            if new_key is None:
                break

        return new_key


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/hashing/tests/__init__.py
================================================


================================================
FILE: data_structures/hashing/tests/test_hash_map.py
================================================
from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap


def _get(k):
    return getitem, k


def _set(k, v):
    return setitem, k, v


def _del(k):
    return delitem, k


def _run_operation(obj, fun, *args):
    try:
        return fun(obj, *args), None
    except Exception as e:
        return None, e


_add_items = (
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
)

_overwrite_items = [
    _set("key_a", "val_a"),
    _set("key_a", "val_b"),
]

_delete_items = [
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
    _del("key_a"),
    _del("key_b"),
    _set("key_a", "val_a"),
    _del("key_a"),
]

_access_absent_items = [
    _get("key_a"),
    _del("key_a"),
    _set("key_a", "val_a"),
    _del("key_a"),
    _del("key_a"),
    _get("key_a"),
]

_add_with_resize_up = [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

_add_with_resize_down = [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("key_a", "val_b"),
]


@pytest.mark.parametrize(
    "operations",
    [
        pytest.param(_add_items, id="add items"),
        pytest.param(_overwrite_items, id="overwrite items"),
        pytest.param(_delete_items, id="delete items"),
        pytest.param(_access_absent_items, id="access absent items"),
        pytest.param(_add_with_resize_up, id="add with resize up"),
        pytest.param(_add_with_resize_down, id="add with resize down"),
    ],
)
def test_hash_map_is_the_same_as_dict(operations):
    my = HashMap(initial_block_size=4)
    py = {}
    for _, (fun, *args) in enumerate(operations):
        my_res, my_exc = _run_operation(my, fun, *args)
        py_res, py_exc = _run_operation(py, fun, *args)
        assert my_res == py_res
        assert str(my_exc) == str(py_exc)
        assert set(py) == set(my)
        assert len(py) == len(my)
        assert set(my.items()) == set(py.items())


def test_no_new_methods_was_added_to_api():
    def is_public(name: str) -> bool:
        return not name.startswith("_")

    dict_public_names = {name for name in dir({}) if is_public(name)}
    hash_public_names = {name for name in dir(HashMap()) if is_public(name)}

    assert dict_public_names > hash_public_names


================================================
FILE: data_structures/heap/__init__.py
================================================


================================================
FILE: data_structures/heap/binomial_heap.py
================================================
"""
Binomial Heap
Reference: Advanced Data Structures, Peter Brass
"""


class Node:
    """
    Node in a doubly-linked binomial tree, containing:
        - value
        - size of left subtree
        - link to left, right and parent nodes
    """

    def __init__(self, val):
        self.val = val
        # Number of nodes in left subtree
        self.left_tree_size = 0
        self.left = None
        self.right = None
        self.parent = None

    def merge_trees(self, other):
        """
        In-place merge of two binomial trees of equal size.
        Returns the root of the resulting tree
        """
        assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

        if self.val < other.val:
            other.left = self.right
            other.parent = None
            if self.right:
                self.right.parent = other
            self.right = other
            self.left_tree_size = self.left_tree_size * 2 + 1
            return self
        else:
            self.left = other.right
            self.parent = None
            if other.right:
                other.right.parent = self
            other.right = self
            other.left_tree_size = other.left_tree_size * 2 + 1
            return other


class BinomialHeap:
    r"""
    Min-oriented priority queue implemented with the Binomial Heap data
    structure implemented with the BinomialHeap class. It supports:
        - Insert element in a heap with n elements: Guaranteed logn, amoratized 1
        - Merge (meld) heaps of size m and n: O(logn + logm)
        - Delete Min: O(logn)
        - Peek (return min without deleting it): O(1)

    Example:

    Create a random permutation of 30 integers to be inserted and 19 of them deleted
    >>> import numpy as np
    >>> permutation = np.random.permutation(list(range(30)))

    Create a Heap and insert the 30 integers
    __init__() test
    >>> first_heap = BinomialHeap()

    30 inserts - insert() test
    >>> for number in permutation:
    ...     first_heap.insert(number)

    Size test
    >>> first_heap.size
    30

    Deleting - delete() test
    >>> [int(first_heap.delete_min()) for _ in range(20)]
    [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]

    Create a new Heap
    >>> second_heap = BinomialHeap()
    >>> vals = [17, 20, 31, 34]
    >>> for value in vals:
    ...     second_heap.insert(value)


    The heap should have the following structure:

                    17
                   /  \
                  #    31
                      /  \
                    20    34
                   /  \  /  \
                  #    # #   #

    preOrder() test
    >>> " ".join(str(x) for x in second_heap.pre_order())
    "(17, 0) ('#', 1) (31, 1) (20, 2) ('#', 3) ('#', 3) (34, 2) ('#', 3) ('#', 3)"

    printing Heap - __str__() test
    >>> print(second_heap)
    17
    -#
    -31
    --20
    ---#
    ---#
    --34
    ---#
    ---#

    mergeHeaps() test
    >>>
    >>> merged = second_heap.merge_heaps(first_heap)
    >>> merged.peek()
    17

    values in merged heap; (merge is inplace)
    >>> results = []
    >>> while not first_heap.is_empty():
    ...     results.append(int(first_heap.delete_min()))
    >>> results
    [17, 20, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 34]
    """

    def __init__(self, bottom_root=None, min_node=None, heap_size=0):
        self.size = heap_size
        self.bottom_root = bottom_root
        self.min_node = min_node

    def merge_heaps(self, other):
        """
        In-place merge of two binomial heaps.
        Both of them become the resulting merged heap
        """

        # Empty heaps corner cases
        if other.size == 0:
            return None
        if self.size == 0:
            self.size = other.size
            self.bottom_root = other.bottom_root
            self.min_node = other.min_node
            return None
        # Update size
        self.size = self.size + other.size

        # Update min.node
        if self.min_node.val > other.min_node.val:
            self.min_node = other.min_node
        # Merge

        # Order roots by left_subtree_size
        combined_roots_list = []
        i, j = self.bottom_root, other.bottom_root
        while i or j:
            if i and ((not j) or i.left_tree_size < j.left_tree_size):
                combined_roots_list.append((i, True))
                i = i.parent
            else:
                combined_roots_list.append((j, False))
                j = j.parent
        # Insert links between them
        for i in range(len(combined_roots_list) - 1):
            if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
                combined_roots_list[i][0].parent = combined_roots_list[i + 1][0]
                combined_roots_list[i + 1][0].left = combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
        i = combined_roots_list[0][0]
        while i.parent:
            if (
                (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
            ) or (
                i.left_tree_size == i.parent.left_tree_size
                and i.left_tree_size != i.parent.parent.left_tree_size
            ):
                # Neighbouring Nodes
                previous_node = i.left
                next_node = i.parent.parent

                # Merging trees
                i = i.merge_trees(i.parent)

                # Updating links
                i.left = previous_node
                i.parent = next_node
                if previous_node:
                    previous_node.parent = i
                if next_node:
                    next_node.left = i
            else:
                i = i.parent
        # Updating self.bottom_root
        while i.left:
            i = i.left
        self.bottom_root = i

        # Update other
        other.size = self.size
        other.bottom_root = self.bottom_root
        other.min_node = self.min_node

        # Return the merged heap
        return self

    def insert(self, val):
        """
        insert a value in the heap
        """
        if self.size == 0:
            self.bottom_root = Node(val)
            self.size = 1
            self.min_node = self.bottom_root
        else:
            # Create new node
            new_node = Node(val)

            # Update size
            self.size += 1

            # update min_node
            if val < self.min_node.val:
                self.min_node = new_node
            # Put new_node as a bottom_root in heap
            self.bottom_root.left = new_node
            new_node.parent = self.bottom_root
            self.bottom_root = new_node

            # Consecutively merge roots with same left_tree_size
            while (
                self.bottom_root.parent
                and self.bottom_root.left_tree_size
                == self.bottom_root.parent.left_tree_size
            ):
                # Next node
                next_node = self.bottom_root.parent.parent

                # Merge
                self.bottom_root = self.bottom_root.merge_trees(self.bottom_root.parent)

                # Update Links
                self.bottom_root.parent = next_node
                self.bottom_root.left = None
                if next_node:
                    next_node.left = self.bottom_root

    def peek(self):
        """
        return min element without deleting it
        """
        return self.min_node.val

    def is_empty(self):
        return self.size == 0

    def delete_min(self):
        """
        delete min element and return it
        """
        # assert not self.isEmpty(), "Empty Heap"

        # Save minimal value
        min_value = self.min_node.val

        # Last element in heap corner case
        if self.size == 1:
            # Update size
            self.size = 0

            # Update bottom root
            self.bottom_root = None

            # Update min_node
            self.min_node = None

            return min_value
        # No right subtree corner case
        # The structure of the tree implies that this should be the bottom root
        # and there is at least one other root
        if self.min_node.right is None:
            # Update size
            self.size -= 1

            # Update bottom root
            self.bottom_root = self.bottom_root.parent
            self.bottom_root.left = None

            # Update min_node
            self.min_node = self.bottom_root
            i = self.bottom_root.parent
            while i:
                if i.val < self.min_node.val:
                    self.min_node = i
                i = i.parent
            return min_value
        # General case
        # Find the BinomialHeap of the right subtree of min_node
        bottom_of_new = self.min_node.right
        bottom_of_new.parent = None
        min_of_new = bottom_of_new
        size_of_new = 1

        # Size, min_node and bottom_root
        while bottom_of_new.left:
            size_of_new = size_of_new * 2 + 1
            bottom_of_new = bottom_of_new.left
            if bottom_of_new.val < min_of_new.val:
                min_of_new = bottom_of_new
        # Corner case of single root on top left path
        if (not self.min_node.left) and (not self.min_node.parent):
            self.size = size_of_new
            self.bottom_root = bottom_of_new
            self.min_node = min_of_new
            # print("Single root, multiple nodes case")
            return min_value
        # Remaining cases
        # Construct heap of right subtree
        new_heap = BinomialHeap(
            bottom_root=bottom_of_new, min_node=min_of_new, heap_size=size_of_new
        )

        # Update size
        self.size = self.size - 1 - size_of_new

        # Neighbour nodes
        previous_node = self.min_node.left
        next_node = self.min_node.parent

        # Initialize new bottom_root and min_node
        self.min_node = previous_node or next_node
        self.bottom_root = next_node

        # Update links of previous_node and search below for new min_node and
        # bottom_root
        if previous_node:
            previous_node.parent = next_node

            # Update bottom_root and search for min_node below
            self.bottom_root = previous_node
            self.min_node = previous_node
            while self.bottom_root.left:
                self.bottom_root = self.bottom_root.left
                if self.bottom_root.val < self.min_node.val:
                    self.min_node = self.bottom_root
        if next_node:
            next_node.left = previous_node

            # Search for new min_node above min_node
            i = next_node
            while i:
                if i.val < self.min_node.val:
                    self.min_node = i
                i = i.parent
        # Merge heaps
        self.merge_heaps(new_heap)

        return int(min_value)

    def pre_order(self):
        """
        Returns the Pre-order representation of the heap including
        values of nodes plus their level distance from the root;
        Empty nodes appear as #
        """
        # Find top root
        top_root = self.bottom_root
        while top_root.parent:
            top_root = top_root.parent
        # preorder
        heap_pre_order = []
        self.__traversal(top_root, heap_pre_order)
        return heap_pre_order

    def __traversal(self, curr_node, preorder, level=0):
        """
        Pre-order traversal of nodes
        """
        if curr_node:
            preorder.append((curr_node.val, level))
            self.__traversal(curr_node.left, preorder, level + 1)
            self.__traversal(curr_node.right, preorder, level + 1)
        else:
            preorder.append(("#", level))

    def __str__(self):
        """
        Overwriting str for a pre-order print of nodes in heap;
        Performance is poor, so use only for small examples
        """
        if self.is_empty():
            return ""
        preorder_heap = self.pre_order()

        return "\n".join(("-" * level + str(value)) for value, level in preorder_heap)


# Unit Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/heap/heap.py
================================================
from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Protocol, TypeVar


class Comparable(Protocol):
    @abstractmethod
    def __lt__(self: T, other: T) -> bool:
        pass

    @abstractmethod
    def __gt__(self: T, other: T) -> bool:
        pass

    @abstractmethod
    def __eq__(self: T, other: object) -> bool:
        pass


T = TypeVar("T", bound=Comparable)


class Heap[T: Comparable]:
    """A Max Heap Implementation

    >>> unsorted = [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5]
    >>> h = Heap()
    >>> h.build_max_heap(unsorted)
    >>> h
    [209, 201, 25, 103, 107, 15, 1, 9, 7, 11, 5]
    >>>
    >>> h.extract_max()
    209
    >>> h
    [201, 107, 25, 103, 11, 15, 1, 9, 7, 5]
    >>>
    >>> h.insert(100)
    >>> h
    [201, 107, 25, 103, 100, 15, 1, 9, 7, 5, 11]
    >>>
    >>> h.heap_sort()
    >>> h
    [1, 5, 7, 9, 11, 15, 25, 100, 103, 107, 201]
    """

    def __init__(self) -> None:
        self.h: list[T] = []
        self.heap_size: int = 0

    def __repr__(self) -> str:
        return str(self.h)

    def parent_index(self, child_idx: int) -> int | None:
        """
        returns the parent index based on the given child index

        >>> h = Heap()
        >>> h.build_max_heap([103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5])
        >>> h
        [209, 201, 25, 103, 107, 15, 1, 9, 7, 11, 5]

        >>> h.parent_index(-1)  # returns none if index is <=0

        >>> h.parent_index(0)   # returns none if index is <=0

        >>> h.parent_index(1)
        0
        >>> h.parent_index(2)
        0
        >>> h.parent_index(3)
        1
        >>> h.parent_index(4)
        1
        >>> h.parent_index(5)
        2
        >>> h.parent_index(10.5)
        4.0
        >>> h.parent_index(209.0)
        104.0
        >>> h.parent_index("Test")
        Traceback (most recent call last):
        ...
        TypeError: '>' not supported between instances of 'str' and 'int'
        """
        if child_idx > 0:
            return (child_idx - 1) // 2
        return None

    def left_child_idx(self, parent_idx: int) -> int | None:
        """
        return the left child index if the left child exists.
        if not, return None.
        """
        left_child_index = 2 * parent_idx + 1
        if left_child_index < self.heap_size:
            return left_child_index
        return None

    def right_child_idx(self, parent_idx: int) -> int | None:
        """
        return the right child index if the right child exists.
        if not, return None.
        """
        right_child_index = 2 * parent_idx + 2
        if right_child_index < self.heap_size:
            return right_child_index
        return None

    def max_heapify(self, index: int) -> None:
        """
        correct a single violation of the heap property in a subtree's root.

        It is the function that is responsible for restoring the property
        of Max heap i.e the maximum element is always at top.
        """
        if index < self.heap_size:
            violation: int = index
            left_child = self.left_child_idx(index)
            right_child = self.right_child_idx(index)
            # check which child is larger than its parent
            if left_child is not None and self.h[left_child] > self.h[violation]:
                violation = left_child
            if right_child is not None and self.h[right_child] > self.h[violation]:
                violation = right_child
            # if violation indeed exists
            if violation != index:
                # swap to fix the violation
                self.h[violation], self.h[index] = self.h[index], self.h[violation]
                # fix the subsequent violation recursively if any
                self.max_heapify(violation)

    def build_max_heap(self, collection: Iterable[T]) -> None:
        """
        build max heap from an unsorted array

        >>> h = Heap()
        >>> h.build_max_heap([20,40,50,20,10])
        >>> h
        [50, 40, 20, 20, 10]

        >>> h = Heap()
        >>> h.build_max_heap([1,2,3,4,5,6,7,8,9,0])
        >>> h
        [9, 8, 7, 4, 5, 6, 3, 2, 1, 0]

        >>> h = Heap()
        >>> h.build_max_heap([514,5,61,57,8,99,105])
        >>> h
        [514, 57, 105, 5, 8, 99, 61]

        >>> h = Heap()
        >>> h.build_max_heap([514,5,61.6,57,8,9.9,105])
        >>> h
        [514, 57, 105, 5, 8, 9.9, 61.6]
        """
        self.h = list(collection)
        self.heap_size = len(self.h)
        if self.heap_size > 1:
            # max_heapify from right to left but exclude leaves (last level)
            for i in range(self.heap_size // 2 - 1, -1, -1):
                self.max_heapify(i)

    def extract_max(self) -> T:
        """
        get and remove max from heap

        >>> h = Heap()
        >>> h.build_max_heap([20,40,50,20,10])
        >>> h.extract_max()
        50

        >>> h = Heap()
        >>> h.build_max_heap([514,5,61,57,8,99,105])
        >>> h.extract_max()
        514

        >>> h = Heap()
        >>> h.build_max_heap([1,2,3,4,5,6,7,8,9,0])
        >>> h.extract_max()
        9
        """
        if self.heap_size >= 2:
            me = self.h[0]
            self.h[0] = self.h.pop(-1)
            self.heap_size -= 1
            self.max_heapify(0)
            return me
        elif self.heap_size == 1:
            self.heap_size -= 1
            return self.h.pop(-1)
        else:
            raise Exception("Empty heap")

    def insert(self, value: T) -> None:
        """
        insert a new value into the max heap

        >>> h = Heap()
        >>> h.insert(10)
        >>> h
        [10]

        >>> h = Heap()
        >>> h.insert(10)
        >>> h.insert(10)
        >>> h
        [10, 10]

        >>> h = Heap()
        >>> h.insert(10)
        >>> h.insert(10.1)
        >>> h
        [10.1, 10]

        >>> h = Heap()
        >>> h.insert(0.1)
        >>> h.insert(0)
        >>> h.insert(9)
        >>> h.insert(5)
        >>> h
        [9, 5, 0.1, 0]
        """
        self.h.append(value)
        idx = (self.heap_size - 1) // 2
        self.heap_size += 1
        while idx >= 0:
            self.max_heapify(idx)
            idx = (idx - 1) // 2

    def heap_sort(self) -> None:
        size = self.heap_size
        for j in range(size - 1, 0, -1):
            self.h[0], self.h[j] = self.h[j], self.h[0]
            self.heap_size -= 1
            self.max_heapify(0)
        self.heap_size = size


if __name__ == "__main__":
    import doctest

    # run doc test
    doctest.testmod()

    # demo
    for unsorted in [
        [0],
        [2],
        [3, 5],
        [5, 3],
        [5, 5],
        [0, 0, 0, 0],
        [1, 1, 1, 1],
        [2, 2, 3, 5],
        [0, 2, 2, 3, 5],
        [2, 5, 3, 0, 2, 3, 0, 3],
        [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
        [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
        [-45, -2, -5],
    ]:
        print(f"unsorted array: {unsorted}")

        heap: Heap[int] = Heap()
        heap.build_max_heap(unsorted)
        print(f"after build heap: {heap}")

        print(f"max value: {heap.extract_max()}")
        print(f"after max value removed: {heap}")

        heap.insert(100)
        print(f"after new value 100 inserted: {heap}")

        heap.heap_sort()
        print(f"heap-sorted array: {heap}\n")


================================================
FILE: data_structures/heap/heap_generic.py
================================================
from collections.abc import Callable


class Heap:
    """
    A generic Heap class, can be used as min or max by passing the key function
    accordingly.
    """

    def __init__(self, key: Callable | None = None) -> None:
        # Stores actual heap items.
        self.arr: list = []
        # Stores indexes of each item for supporting updates and deletion.
        self.pos_map: dict = {}
        # Stores current size of heap.
        self.size = 0
        # Stores function used to evaluate the score of an item on which basis ordering
        # will be done.
        self.key = key or (lambda x: x)

    def _parent(self, i: int) -> int | None:
        """Returns parent index of given index if exists else None"""
        return int((i - 1) / 2) if i > 0 else None

    def _left(self, i: int) -> int | None:
        """Returns left-child-index of given index if exists else None"""
        left = int(2 * i + 1)
        return left if 0 < left < self.size else None

    def _right(self, i: int) -> int | None:
        """Returns right-child-index of given index if exists else None"""
        right = int(2 * i + 2)
        return right if 0 < right < self.size else None

    def _swap(self, i: int, j: int) -> None:
        """Performs changes required for swapping two elements in the heap"""
        # First update the indexes of the items in index map.
        self.pos_map[self.arr[i][0]], self.pos_map[self.arr[j][0]] = (
            self.pos_map[self.arr[j][0]],
            self.pos_map[self.arr[i][0]],
        )
        # Then swap the items in the list.
        self.arr[i], self.arr[j] = self.arr[j], self.arr[i]

    def _cmp(self, i: int, j: int) -> bool:
        """Compares the two items using default comparison"""
        return self.arr[i][1] < self.arr[j][1]

    def _get_valid_parent(self, i: int) -> int:
        """
        Returns index of valid parent as per desired ordering among given index and
        both it's children
        """
        left = self._left(i)
        right = self._right(i)
        valid_parent = i

        if left is not None and not self._cmp(left, valid_parent):
            valid_parent = left
        if right is not None and not self._cmp(right, valid_parent):
            valid_parent = right

        return valid_parent

    def _heapify_up(self, index: int) -> None:
        """Fixes the heap in upward direction of given index"""
        parent = self._parent(index)
        while parent is not None and not self._cmp(index, parent):
            self._swap(index, parent)
            index, parent = parent, self._parent(parent)

    def _heapify_down(self, index: int) -> None:
        """Fixes the heap in downward direction of given index"""
        valid_parent = self._get_valid_parent(index)
        while valid_parent != index:
            self._swap(index, valid_parent)
            index, valid_parent = valid_parent, self._get_valid_parent(valid_parent)

    def update_item(self, item: int, item_value: int) -> None:
        """Updates given item value in heap if present"""
        if item not in self.pos_map:
            return
        index = self.pos_map[item]
        self.arr[index] = [item, self.key(item_value)]
        # Make sure heap is right in both up and down direction.
        # Ideally only one of them will make any change.
        self._heapify_up(index)
        self._heapify_down(index)

    def delete_item(self, item: int) -> None:
        """Deletes given item from heap if present"""
        if item not in self.pos_map:
            return
        index = self.pos_map[item]
        del self.pos_map[item]
        self.arr[index] = self.arr[self.size - 1]
        self.pos_map[self.arr[self.size - 1][0]] = index
        self.size -= 1
        # Make sure heap is right in both up and down direction. Ideally only one
        # of them will make any change- so no performance loss in calling both.
        if self.size > index:
            self._heapify_up(index)
            self._heapify_down(index)

    def insert_item(self, item: int, item_value: int) -> None:
        """Inserts given item with given value in heap"""
        arr_len = len(self.arr)
        if arr_len == self.size:
            self.arr.append([item, self.key(item_value)])
        else:
            self.arr[self.size] = [item, self.key(item_value)]
        self.pos_map[item] = self.size
        self.size += 1
        self._heapify_up(self.size - 1)

    def get_top(self) -> tuple | None:
        """Returns top item tuple (Calculated value, item) from heap if present"""
        return self.arr[0] if self.size else None

    def extract_top(self) -> tuple | None:
        """
        Return top item tuple (Calculated value, item) from heap and removes it as well
        if present
        """
        top_item_tuple = self.get_top()
        if top_item_tuple:
            self.delete_item(top_item_tuple[0])
        return top_item_tuple


def test_heap() -> None:
    """
    >>> h = Heap()  # Max-heap
    >>> h.insert_item(5, 34)
    >>> h.insert_item(6, 31)
    >>> h.insert_item(7, 37)
    >>> h.get_top()
    [7, 37]
    >>> h.extract_top()
    [7, 37]
    >>> h.extract_top()
    [5, 34]
    >>> h.extract_top()
    [6, 31]
    >>> h = Heap(key=lambda x: -x)  # Min heap
    >>> h.insert_item(5, 34)
    >>> h.insert_item(6, 31)
    >>> h.insert_item(7, 37)
    >>> h.get_top()
    [6, -31]
    >>> h.extract_top()
    [6, -31]
    >>> h.extract_top()
    [5, -34]
    >>> h.extract_top()
    [7, -37]
    >>> h.insert_item(8, 45)
    >>> h.insert_item(9, 40)
    >>> h.insert_item(10, 50)
    >>> h.get_top()
    [9, -40]
    >>> h.update_item(10, 30)
    >>> h.get_top()
    [10, -30]
    >>> h.delete_item(10)
    >>> h.get_top()
    [9, -40]
    """


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/heap/max_heap.py
================================================
class BinaryHeap:
    """
    A max-heap implementation in Python
    >>> binary_heap = BinaryHeap()
    >>> binary_heap.insert(6)
    >>> binary_heap.insert(10)
    >>> binary_heap.insert(15)
    >>> binary_heap.insert(12)
    >>> binary_heap.pop()
    15
    >>> binary_heap.pop()
    12
    >>> binary_heap.get_list
    [10, 6]
    >>> len(binary_heap)
    2
    """

    def __init__(self):
        self.__heap = [0]
        self.__size = 0

    def __swap_up(self, i: int) -> None:
        """Swap the element up"""
        temporary = self.__heap[i]
        while i // 2 > 0:
            if self.__heap[i] > self.__heap[i // 2]:
                self.__heap[i] = self.__heap[i // 2]
                self.__heap[i // 2] = temporary
            i //= 2

    def insert(self, value: int) -> None:
        """Insert new element"""
        self.__heap.append(value)
        self.__size += 1
        self.__swap_up(self.__size)

    def __swap_down(self, i: int) -> None:
        """Swap the element down"""
        while self.__size >= 2 * i:
            if 2 * i + 1 > self.__size:  # noqa: SIM114
                bigger_child = 2 * i
            elif self.__heap[2 * i] > self.__heap[2 * i + 1]:
                bigger_child = 2 * i
            else:
                bigger_child = 2 * i + 1
            temporary = self.__heap[i]
            if self.__heap[i] < self.__heap[bigger_child]:
                self.__heap[i] = self.__heap[bigger_child]
                self.__heap[bigger_child] = temporary
            i = bigger_child

    def pop(self) -> int:
        """Pop the root element"""
        max_value = self.__heap[1]
        self.__heap[1] = self.__heap[self.__size]
        self.__size -= 1
        self.__heap.pop()
        self.__swap_down(1)
        return max_value

    @property
    def get_list(self):
        return self.__heap[1:]

    def __len__(self):
        """Length of the array"""
        return self.__size


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    # create an instance of BinaryHeap
    binary_heap = BinaryHeap()
    binary_heap.insert(6)
    binary_heap.insert(10)
    binary_heap.insert(15)
    binary_heap.insert(12)
    # pop root(max-values because it is max heap)
    print(binary_heap.pop())  # 15
    print(binary_heap.pop())  # 12
    # get the list and size after operations
    print(binary_heap.get_list)
    print(len(binary_heap))


================================================
FILE: data_structures/heap/min_heap.py
================================================
# Min heap data structure
# with decrease key functionality - in O(log(n)) time


class Node:
    def __init__(self, name, val):
        self.name = name
        self.val = val

    def __str__(self):
        return f"{self.__class__.__name__}({self.name}, {self.val})"

    def __lt__(self, other):
        return self.val < other.val


class MinHeap:
    """
    >>> r = Node("R", -1)
    >>> b = Node("B", 6)
    >>> a = Node("A", 3)
    >>> x = Node("X", 1)
    >>> e = Node("E", 4)
    >>> print(b)
    Node(B, 6)
    >>> myMinHeap = MinHeap([r, b, a, x, e])
    >>> myMinHeap.decrease_key(b, -17)
    >>> print(b)
    Node(B, -17)
    >>> myMinHeap["B"]
    -17
    """

    def __init__(self, array):
        self.idx_of_element = {}
        self.heap_dict = {}
        self.heap = self.build_heap(array)

    def __getitem__(self, key):
        return self.get_value(key)

    def get_parent_idx(self, idx):
        return (idx - 1) // 2

    def get_left_child_idx(self, idx):
        return idx * 2 + 1

    def get_right_child_idx(self, idx):
        return idx * 2 + 2

    def get_value(self, key):
        return self.heap_dict[key]

    def build_heap(self, array):
        last_idx = len(array) - 1
        start_from = self.get_parent_idx(last_idx)

        for idx, i in enumerate(array):
            self.idx_of_element[i] = idx
            self.heap_dict[i.name] = i.val

        for i in range(start_from, -1, -1):
            self.sift_down(i, array)
        return array

    # this is min-heapify method
    def sift_down(self, idx, array):
        while True:
            left = self.get_left_child_idx(idx)
            right = self.get_right_child_idx(idx)

            smallest = idx
            if left < len(array) and array[left] < array[idx]:
                smallest = left
            if right < len(array) and array[right] < array[smallest]:
                smallest = right

            if smallest != idx:
                array[idx], array[smallest] = array[smallest], array[idx]
                (
                    self.idx_of_element[array[idx]],
                    self.idx_of_element[array[smallest]],
                ) = (
                    self.idx_of_element[array[smallest]],
                    self.idx_of_element[array[idx]],
                )
                idx = smallest
            else:
                break

    def sift_up(self, idx):
        p = self.get_parent_idx(idx)
        while p >= 0 and self.heap[p] > self.heap[idx]:
            self.heap[p], self.heap[idx] = self.heap[idx], self.heap[p]
            self.idx_of_element[self.heap[p]], self.idx_of_element[self.heap[idx]] = (
                self.idx_of_element[self.heap[idx]],
                self.idx_of_element[self.heap[p]],
            )
            idx = p
            p = self.get_parent_idx(idx)

    def peek(self):
        return self.heap[0]

    def remove(self):
        self.heap[0], self.heap[-1] = self.heap[-1], self.heap[0]
        self.idx_of_element[self.heap[0]], self.idx_of_element[self.heap[-1]] = (
            self.idx_of_element[self.heap[-1]],
            self.idx_of_element[self.heap[0]],
        )

        x = self.heap.pop()
        del self.idx_of_element[x]
        self.sift_down(0, self.heap)
        return x

    def insert(self, node):
        self.heap.append(node)
        self.idx_of_element[node] = len(self.heap) - 1
        self.heap_dict[node.name] = node.val
        self.sift_up(len(self.heap) - 1)

    def is_empty(self):
        return len(self.heap) == 0

    def decrease_key(self, node, new_value):
        assert self.heap[self.idx_of_element[node]].val > new_value, (
            "newValue must be less that current value"
        )
        node.val = new_value
        self.heap_dict[node.name] = new_value
        self.sift_up(self.idx_of_element[node])


# USAGE

r = Node("R", -1)
b = Node("B", 6)
a = Node("A", 3)
x = Node("X", 1)
e = Node("E", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
my_min_heap = MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("Min Heap - before decrease key")
for i in my_min_heap.heap:
    print(i)

print("Min Heap - After decrease key of node [B -> -17]")
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
    print(i)

if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/heap/randomized_heap.py
================================================
#!/usr/bin/env python3

from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, TypeVar

T = TypeVar("T", bound=bool)


class RandomizedHeapNode[T: bool]:
    """
    One node of the randomized heap. Contains the value and references to
    two children.
    """

    def __init__(self, value: T) -> None:
        self._value: T = value
        self.left: RandomizedHeapNode[T] | None = None
        self.right: RandomizedHeapNode[T] | None = None

    @property
    def value(self) -> T:
        """
        Return the value of the node.

        >>> rhn = RandomizedHeapNode(10)
        >>> rhn.value
        10
        >>> rhn = RandomizedHeapNode(-10)
        >>> rhn.value
        -10
        """
        return self._value

    @staticmethod
    def merge(
        root1: RandomizedHeapNode[T] | None, root2: RandomizedHeapNode[T] | None
    ) -> RandomizedHeapNode[T] | None:
        """
        Merge 2 nodes together.

        >>> rhn1 = RandomizedHeapNode(10)
        >>> rhn2 = RandomizedHeapNode(20)
        >>> RandomizedHeapNode.merge(rhn1, rhn2).value
        10

        >>> rhn1 = RandomizedHeapNode(20)
        >>> rhn2 = RandomizedHeapNode(10)
        >>> RandomizedHeapNode.merge(rhn1, rhn2).value
        10

        >>> rhn1 = RandomizedHeapNode(5)
        >>> rhn2 = RandomizedHeapNode(0)
        >>> RandomizedHeapNode.merge(rhn1, rhn2).value
        0
        """
        if not root1:
            return root2

        if not root2:
            return root1

        if root1.value > root2.value:
            root1, root2 = root2, root1

        if random.choice([True, False]):
            root1.left, root1.right = root1.right, root1.left

        root1.left = RandomizedHeapNode.merge(root1.left, root2)

        return root1


class RandomizedHeap[T: bool]:
    """
    A data structure that allows inserting a new value and to pop the smallest
    values. Both operations take O(logN) time where N is the size of the
    structure.
    Wiki: https://en.wikipedia.org/wiki/Randomized_meldable_heap

    >>> RandomizedHeap([2, 3, 1, 5, 1, 7]).to_sorted_list()
    [1, 1, 2, 3, 5, 7]

    >>> rh = RandomizedHeap()
    >>> rh.pop()
    Traceback (most recent call last):
        ...
    IndexError: Can't get top element for the empty heap.

    >>> rh.insert(1)
    >>> rh.insert(-1)
    >>> rh.insert(0)
    >>> rh.to_sorted_list()
    [-1, 0, 1]
    """

    def __init__(self, data: Iterable[T] | None = ()) -> None:
        """
        >>> rh = RandomizedHeap([3, 1, 3, 7])
        >>> rh.to_sorted_list()
        [1, 3, 3, 7]
        """
        self._root: RandomizedHeapNode[T] | None = None

        if data:
            for item in data:
                self.insert(item)

    def insert(self, value: T) -> None:
        """
        Insert the value into the heap.

        >>> rh = RandomizedHeap()
        >>> rh.insert(3)
        >>> rh.insert(1)
        >>> rh.insert(3)
        >>> rh.insert(7)
        >>> rh.to_sorted_list()
        [1, 3, 3, 7]
        """
        self._root = RandomizedHeapNode.merge(self._root, RandomizedHeapNode(value))

    def pop(self) -> T | None:
        """
        Pop the smallest value from the heap and return it.

        >>> rh = RandomizedHeap([3, 1, 3, 7])
        >>> rh.pop()
        1
        >>> rh.pop()
        3
        >>> rh.pop()
        3
        >>> rh.pop()
        7
        >>> rh.pop()
        Traceback (most recent call last):
            ...
        IndexError: Can't get top element for the empty heap.
        """

        result = self.top()

        if self._root is None:
            return None

        self._root = RandomizedHeapNode.merge(self._root.left, self._root.right)

        return result

    def top(self) -> T:
        """
        Return the smallest value from the heap.

        >>> rh = RandomizedHeap()
        >>> rh.insert(3)
        >>> rh.top()
        3
        >>> rh.insert(1)
        >>> rh.top()
        1
        >>> rh.insert(3)
        >>> rh.top()
        1
        >>> rh.insert(7)
        >>> rh.top()
        1
        """
        if not self._root:
            raise IndexError("Can't get top element for the empty heap.")
        return self._root.value

    def clear(self) -> None:
        """
        Clear the heap.

        >>> rh = RandomizedHeap([3, 1, 3, 7])
        >>> rh.clear()
        >>> rh.pop()
        Traceback (most recent call last):
            ...
        IndexError: Can't get top element for the empty heap.
        """
        self._root = None

    def to_sorted_list(self) -> list[Any]:
        """
        Returns sorted list containing all the values in the heap.

        >>> rh = RandomizedHeap([3, 1, 3, 7])
        >>> rh.to_sorted_list()
        [1, 3, 3, 7]
        """
        result = []
        while self:
            result.append(self.pop())

        return result

    def __bool__(self) -> bool:
        """
        Check if the heap is not empty.

        >>> rh = RandomizedHeap()
        >>> bool(rh)
        False
        >>> rh.insert(1)
        >>> bool(rh)
        True
        >>> rh.clear()
        >>> bool(rh)
        False
        """
        return self._root is not None


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/heap/skew_heap.py
================================================
#!/usr/bin/env python3

from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, TypeVar

T = TypeVar("T", bound=bool)


class SkewNode[T: bool]:
    """
    One node of the skew heap. Contains the value and references to
    two children.
    """

    def __init__(self, value: T) -> None:
        self._value: T = value
        self.left: SkewNode[T] | None = None
        self.right: SkewNode[T] | None = None

    @property
    def value(self) -> T:
        """
        Return the value of the node.

        >>> SkewNode(0).value
        0
        >>> SkewNode(3.14159).value
        3.14159
        >>> SkewNode("hello").value
        'hello'
        >>> SkewNode(None).value

        >>> SkewNode(True).value
        True
        >>> SkewNode([]).value
        []
        >>> SkewNode({}).value
        {}
        >>> SkewNode(set()).value
        set()
        >>> SkewNode(0.0).value
        0.0
        >>> SkewNode(-1e-10).value
        -1e-10
        >>> SkewNode(10).value
        10
        >>> SkewNode(-10.5).value
        -10.5
        >>> SkewNode().value
        Traceback (most recent call last):
        ...
        TypeError: SkewNode.__init__() missing 1 required positional argument: 'value'
        """
        return self._value

    @staticmethod
    def merge(
        root1: SkewNode[T] | None, root2: SkewNode[T] | None
    ) -> SkewNode[T] | None:
        """
        Merge 2 nodes together.
        >>> SkewNode.merge(SkewNode(10),SkewNode(-10.5)).value
        -10.5
        >>> SkewNode.merge(SkewNode(10),SkewNode(10.5)).value
        10
        >>> SkewNode.merge(SkewNode(10),SkewNode(10)).value
        10
        >>> SkewNode.merge(SkewNode(-100),SkewNode(-10.5)).value
        -100
        """
        if not root1:
            return root2

        if not root2:
            return root1

        if root1.value > root2.value:
            root1, root2 = root2, root1

        result = root1
        temp = root1.right
        result.right = root1.left
        result.left = SkewNode.merge(temp, root2)

        return result


class SkewHeap[T: bool]:
    """
    A data structure that allows inserting a new value and to pop the smallest
    values. Both operations take O(logN) time where N is the size of the
    structure.
    Wiki: https://en.wikipedia.org/wiki/Skew_heap
    Visualization: https://www.cs.usfca.edu/~galles/visualization/SkewHeap.html

    >>> list(SkewHeap([2, 3, 1, 5, 1, 7]))
    [1, 1, 2, 3, 5, 7]

    >>> sh = SkewHeap()
    >>> sh.pop()
    Traceback (most recent call last):
        ...
    IndexError: Can't get top element for the empty heap.

    >>> sh.insert(1)
    >>> sh.insert(-1)
    >>> sh.insert(0)
    >>> list(sh)
    [-1, 0, 1]
    """

    def __init__(self, data: Iterable[T] | None = ()) -> None:
        """
        >>> sh = SkewHeap([3, 1, 3, 7])
        >>> list(sh)
        [1, 3, 3, 7]
        """
        self._root: SkewNode[T] | None = None
        if data:
            for item in data:
                self.insert(item)

    def __bool__(self) -> bool:
        """
        Check if the heap is not empty.

        >>> sh = SkewHeap()
        >>> bool(sh)
        False
        >>> sh.insert(1)
        >>> bool(sh)
        True
        >>> sh.clear()
        >>> bool(sh)
        False
        """
        return self._root is not None

    def __iter__(self) -> Iterator[T]:
        """
        Returns sorted list containing all the values in the heap.

        >>> sh = SkewHeap([3, 1, 3, 7])
        >>> list(sh)
        [1, 3, 3, 7]
        """
        result: list[Any] = []
        while self:
            result.append(self.pop())

        # Pushing items back to the heap not to clear it.
        for item in result:
            self.insert(item)

        return iter(result)

    def insert(self, value: T) -> None:
        """
        Insert the value into the heap.

        >>> sh = SkewHeap()
        >>> sh.insert(3)
        >>> sh.insert(1)
        >>> sh.insert(3)
        >>> sh.insert(7)
        >>> list(sh)
        [1, 3, 3, 7]
        """
        self._root = SkewNode.merge(self._root, SkewNode(value))

    def pop(self) -> T | None:
        """
        Pop the smallest value from the heap and return it.

        >>> sh = SkewHeap([3, 1, 3, 7])
        >>> sh.pop()
        1
        >>> sh.pop()
        3
        >>> sh.pop()
        3
        >>> sh.pop()
        7
        >>> sh.pop()
        Traceback (most recent call last):
            ...
        IndexError: Can't get top element for the empty heap.
        """
        result = self.top()
        self._root = (
            SkewNode.merge(self._root.left, self._root.right) if self._root else None
        )

        return result

    def top(self) -> T:
        """
        Return the smallest value from the heap.

        >>> sh = SkewHeap()
        >>> sh.insert(3)
        >>> sh.top()
        3
        >>> sh.insert(1)
        >>> sh.top()
        1
        >>> sh.insert(3)
        >>> sh.top()
        1
        >>> sh.insert(7)
        >>> sh.top()
        1
        """
        if not self._root:
            raise IndexError("Can't get top element for the empty heap.")
        return self._root.value

    def clear(self) -> None:
        """
        Clear the heap.

        >>> sh = SkewHeap([3, 1, 3, 7])
        >>> sh.clear()
        >>> sh.pop()
        Traceback (most recent call last):
            ...
        IndexError: Can't get top element for the empty heap.
        """
        self._root = None


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/kd_tree/__init__.py
================================================


================================================
FILE: data_structures/kd_tree/build_kdtree.py
================================================
#  Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
#  in Pull Request: #11532
#  https://github.com/TheAlgorithms/Python/pull/11532
#
#  Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
#  addressing bugs/corrections to this file.
#  Thank you!

from data_structures.kd_tree.kd_node import KDNode


def build_kdtree(points: list[list[float]], depth: int = 0) -> KDNode | None:
    """
    Builds a KD-Tree from a list of points.

    Args:
        points: The list of points to build the KD-Tree from.
        depth: The current depth in the tree
                     (used to determine axis for splitting).

    Returns:
        The root node of the KD-Tree,
                       or None if no points are provided.
    """
    if not points:
        return None

    k = len(points[0])  # Dimensionality of the points
    axis = depth % k

    # Sort point list and choose median as pivot element
    points.sort(key=lambda point: point[axis])
    median_idx = len(points) // 2

    # Create node and construct subtrees
    left_points = points[:median_idx]
    right_points = points[median_idx + 1 :]

    return KDNode(
        point=points[median_idx],
        left=build_kdtree(left_points, depth + 1),
        right=build_kdtree(right_points, depth + 1),
    )


================================================
FILE: data_structures/kd_tree/example/__init__.py
================================================


================================================
FILE: data_structures/kd_tree/example/example_usage.py
================================================
#  Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
#  in Pull Request: #11532
#  https://github.com/TheAlgorithms/Python/pull/11532
#
#  Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
#  addressing bugs/corrections to this file.
#  Thank you!

import numpy as np

from data_structures.kd_tree.build_kdtree import build_kdtree
from data_structures.kd_tree.example.hypercube_points import hypercube_points
from data_structures.kd_tree.nearest_neighbour_search import nearest_neighbour_search


def main() -> None:
    """
    Demonstrates the use of KD-Tree by building it from random points
    in a 10-dimensional hypercube and performing a nearest neighbor search.
    """
    num_points: int = 5000
    cube_size: float = 10.0  # Size of the hypercube (edge length)
    num_dimensions: int = 10

    # Generate random points within the hypercube
    points: np.ndarray = hypercube_points(num_points, cube_size, num_dimensions)
    hypercube_kdtree = build_kdtree(points.tolist())

    # Generate a random query point within the same space
    rng = np.random.default_rng()
    query_point: list[float] = rng.random(num_dimensions).tolist()

    # Perform nearest neighbor search
    nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(
        hypercube_kdtree, query_point
    )

    # Print the results
    print(f"Query point: {query_point}")
    print(f"Nearest point: {nearest_point}")
    print(f"Distance: {nearest_dist:.4f}")
    print(f"Nodes visited: {nodes_visited}")


if __name__ == "__main__":
    main()


================================================
FILE: data_structures/kd_tree/example/hypercube_points.py
================================================
#  Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
#  in Pull Request: #11532
#  https://github.com/TheAlgorithms/Python/pull/11532
#
#  Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
#  addressing bugs/corrections to this file.
#  Thank you!

import numpy as np


def hypercube_points(
    num_points: int, hypercube_size: float, num_dimensions: int
) -> np.ndarray:
    """
    Generates random points uniformly distributed within an n-dimensional hypercube.

    Args:
        num_points: Number of points to generate.
        hypercube_size: Size of the hypercube.
        num_dimensions: Number of dimensions of the hypercube.

    Returns:
        An array of shape (num_points, num_dimensions)
                    with generated points.
    """
    rng = np.random.default_rng()
    shape = (num_points, num_dimensions)
    return hypercube_size * rng.random(shape)


================================================
FILE: data_structures/kd_tree/kd_node.py
================================================
#  Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
#  in Pull Request: #11532
#  https://github.com/TheAlgorithms/Python/pull/11532
#
#  Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
#  addressing bugs/corrections to this file.
#  Thank you!

from __future__ import annotations


class KDNode:
    """
    Represents a node in a KD-Tree.

    Attributes:
        point: The point stored in this node.
        left: The left child node.
        right: The right child node.
    """

    def __init__(
        self,
        point: list[float],
        left: KDNode | None = None,
        right: KDNode | None = None,
    ) -> None:
        """
        Initializes a KDNode with the given point and child nodes.

        Args:
            point (list[float]): The point stored in this node.
            left (Optional[KDNode]): The left child node.
            right (Optional[KDNode]): The right child node.
        """
        self.point = point
        self.left = left
        self.right = right


================================================
FILE: data_structures/kd_tree/nearest_neighbour_search.py
================================================
#  Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
#  in Pull Request: #11532
#  https://github.com/TheAlgorithms/Python/pull/11532
#
#  Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
#  addressing bugs/corrections to this file.
#  Thank you!

from data_structures.kd_tree.kd_node import KDNode


def nearest_neighbour_search(
    root: KDNode | None, query_point: list[float]
) -> tuple[list[float] | None, float, int]:
    """
    Performs a nearest neighbor search in a KD-Tree for a given query point.

    Args:
        root (KDNode | None): The root node of the KD-Tree.
        query_point (list[float]): The point for which the nearest neighbor
                                    is being searched.

    Returns:
        tuple[list[float] | None, float, int]:
            - The nearest point found in the KD-Tree to the query point,
              or None if no point is found.
            - The squared distance to the nearest point.
            - The number of nodes visited during the search.
    """
    nearest_point: list[float] | None = None
    nearest_dist: float = float("inf")
    nodes_visited: int = 0

    def search(node: KDNode | None, depth: int = 0) -> None:
        """
        Recursively searches for the nearest neighbor in the KD-Tree.

        Args:
            node: The current node in the KD-Tree.
            depth: The current depth in the KD-Tree.
        """
        nonlocal nearest_point, nearest_dist, nodes_visited
        if node is None:
            return

        nodes_visited += 1

        # Calculate the current distance (squared distance)
        current_point = node.point
        current_dist = sum(
            (query_coord - point_coord) ** 2
            for query_coord, point_coord in zip(query_point, current_point)
        )

        # Update nearest point if the current node is closer
        if nearest_point is None or current_dist < nearest_dist:
            nearest_point = current_point
            nearest_dist = current_dist

        # Determine which subtree to search first (based on axis and query point)
        k = len(query_point)  # Dimensionality of points
        axis = depth % k

        if query_point[axis] <= current_point[axis]:
            nearer_subtree = node.left
            further_subtree = node.right
        else:
            nearer_subtree = node.right
            further_subtree = node.left

        # Search the nearer subtree first
        search(nearer_subtree, depth + 1)

        # If the further subtree has a closer point
        if (query_point[axis] - current_point[axis]) ** 2 < nearest_dist:
            search(further_subtree, depth + 1)

    search(root, 0)
    return nearest_point, nearest_dist, nodes_visited


================================================
FILE: data_structures/kd_tree/tests/__init__.py
================================================


================================================
FILE: data_structures/kd_tree/tests/test_kdtree.py
================================================
#  Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
#  in Pull Request: #11532
#  https://github.com/TheAlgorithms/Python/pull/11532
#
#  Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
#  addressing bugs/corrections to this file.
#  Thank you!

import numpy as np
import pytest

from data_structures.kd_tree.build_kdtree import build_kdtree
from data_structures.kd_tree.example.hypercube_points import hypercube_points
from data_structures.kd_tree.kd_node import KDNode
from data_structures.kd_tree.nearest_neighbour_search import nearest_neighbour_search


@pytest.mark.parametrize(
    ("num_points", "cube_size", "num_dimensions", "depth", "expected_result"),
    [
        (0, 10.0, 2, 0, None),  # Empty points list
        (10, 10.0, 2, 2, KDNode),  # Depth = 2, 2D points
        (10, 10.0, 3, -2, KDNode),  # Depth = -2, 3D points
    ],
)
def test_build_kdtree(num_points, cube_size, num_dimensions, depth, expected_result):
    """
    Test that KD-Tree is built correctly.

    Cases:
        - Empty points list.
        - Positive depth value.
        - Negative depth value.
    """
    points = (
        hypercube_points(num_points, cube_size, num_dimensions).tolist()
        if num_points > 0
        else []
    )

    kdtree = build_kdtree(points, depth=depth)

    if expected_result is None:
        # Empty points list case
        assert kdtree is None, f"Expected None for empty points list, got {kdtree}"
    else:
        # Check if root node is not None
        assert kdtree is not None, "Expected a KDNode, got None"

        # Check if root has correct dimensions
        assert len(kdtree.point) == num_dimensions, (
            f"Expected point dimension {num_dimensions}, got {len(kdtree.point)}"
        )

        # Check that the tree is balanced to some extent (simplistic check)
        assert isinstance(kdtree, KDNode), (
            f"Expected KDNode instance, got {type(kdtree)}"
        )


def test_nearest_neighbour_search():
    """
    Test the nearest neighbor search function.
    """
    num_points = 10
    cube_size = 10.0
    num_dimensions = 2
    points = hypercube_points(num_points, cube_size, num_dimensions)
    kdtree = build_kdtree(points.tolist())

    rng = np.random.default_rng()
    query_point = rng.random(num_dimensions).tolist()

    nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(
        kdtree, query_point
    )

    # Check that nearest point is not None
    assert nearest_point is not None

    # Check that distance is a non-negative number
    assert nearest_dist >= 0

    # Check that nodes visited is a non-negative integer
    assert nodes_visited >= 0


def test_edge_cases():
    """
    Test edge cases such as an empty KD-Tree.
    """
    empty_kdtree = build_kdtree([])
    query_point = [0.0] * 2  # Using a default 2D query point

    nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(
        empty_kdtree, query_point
    )

    # With an empty KD-Tree, nearest_point should be None
    assert nearest_point is None
    assert nearest_dist == float("inf")
    assert nodes_visited == 0


if __name__ == "__main__":
    import pytest

    pytest.main()


================================================
FILE: data_structures/linked_list/__init__.py
================================================
"""
Linked Lists consists of Nodes.
Nodes contain data and also may link to other nodes:
    - Head Node: First node, the address of the
                 head node gives us access of the complete list
    - Last node: points to null
"""

from __future__ import annotations

from typing import Any


class Node:
    def __init__(self, item: Any, next: Any) -> None:  # noqa: A002
        self.item = item
        self.next = next


class LinkedList:
    def __init__(self) -> None:
        self.head: Node | None = None
        self.size = 0

    def add(self, item: Any, position: int = 0) -> None:
        """
        Add an item to the LinkedList at the specified position.
        Default position is 0 (the head).

        Args:
            item (Any): The item to add to the LinkedList.
            position (int, optional): The position at which to add the item.
                Defaults to 0.

        Raises:
            ValueError: If the position is negative or out of bounds.

        >>> linked_list = LinkedList()
        >>> linked_list.add(1)
        >>> linked_list.add(2)
        >>> linked_list.add(3)
        >>> linked_list.add(4, 2)
        >>> print(linked_list)
        3 --> 2 --> 4 --> 1

        # Test adding to a negative position
        >>> linked_list.add(5, -3)
        Traceback (most recent call last):
            ...
        ValueError: Position must be non-negative

        # Test adding to an out-of-bounds position
        >>> linked_list.add(5,7)
        Traceback (most recent call last):
            ...
        ValueError: Out of bounds
        >>> linked_list.add(5, 4)
        >>> print(linked_list)
        3 --> 2 --> 4 --> 1 --> 5
        """
        if position < 0:
            raise ValueError("Position must be non-negative")

        if position == 0 or self.head is None:
            new_node = Node(item, self.head)
            self.head = new_node
        else:
            current = self.head
            for _ in range(position - 1):
                current = current.next
                if current is None:
                    raise ValueError("Out of bounds")
            new_node = Node(item, current.next)
            current.next = new_node
        self.size += 1

    def remove(self) -> Any:
        # Switched 'self.is_empty()' to 'self.head is None'
        # because mypy was considering the possibility that 'self.head'
        # can be None in below else part and giving error
        if self.head is None:
            return None
        else:
            item = self.head.item
            self.head = self.head.next
            self.size -= 1
            return item

    def is_empty(self) -> bool:
        return self.head is None

    def __str__(self) -> str:
        """
        >>> linked_list = LinkedList()
        >>> linked_list.add(23)
        >>> linked_list.add(14)
        >>> linked_list.add(9)
        >>> print(linked_list)
        9 --> 14 --> 23
        """
        if self.is_empty():
            return ""
        else:
            iterate = self.head
            item_str = ""
            item_list: list[str] = []
            while iterate:
                item_list.append(str(iterate.item))
                iterate = iterate.next

            item_str = " --> ".join(item_list)

            return item_str

    def __len__(self) -> int:
        """
        >>> linked_list = LinkedList()
        >>> len(linked_list)
        0
        >>> linked_list.add("a")
        >>> len(linked_list)
        1
        >>> linked_list.add("b")
        >>> len(linked_list)
        2
        >>> _ = linked_list.remove()
        >>> len(linked_list)
        1
        >>> _ = linked_list.remove()
        >>> len(linked_list)
        0
        """
        return self.size


================================================
FILE: data_structures/linked_list/circular_linked_list.py
================================================
from __future__ import annotations

from collections.abc import Iterator
from dataclasses import dataclass
from typing import Any


@dataclass
class Node:
    data: Any
    next_node: Node | None = None


@dataclass
class CircularLinkedList:
    head: Node | None = None  # Reference to the head (first node)
    tail: Node | None = None  # Reference to the tail (last node)

    def __iter__(self) -> Iterator[Any]:
        """
        Iterate through all nodes in the Circular Linked List yielding their data.
        Yields:
            The data of each node in the linked list.
        """
        node = self.head
        while node:
            yield node.data
            node = node.next_node
            if node == self.head:
                break

    def __len__(self) -> int:
        """
        Get the length (number of nodes) in the Circular Linked List.
        """
        return sum(1 for _ in self)

    def __repr__(self) -> str:
        """
        Generate a string representation of the Circular Linked List.
        Returns:
            A string of the format "1->2->....->N".
        """
        return "->".join(str(item) for item in iter(self))

    def insert_tail(self, data: Any) -> None:
        """
        Insert a node with the given data at the end of the Circular Linked List.
        """
        self.insert_nth(len(self), data)

    def insert_head(self, data: Any) -> None:
        """
        Insert a node with the given data at the beginning of the Circular Linked List.
        """
        self.insert_nth(0, data)

    def insert_nth(self, index: int, data: Any) -> None:
        """
        Insert the data of the node at the nth pos in the Circular Linked List.
        Args:
            index: The index at which the data should be inserted.
            data: The data to be inserted.

        Raises:
            IndexError: If the index is out of range.
        """
        if index < 0 or index > len(self):
            raise IndexError("list index out of range.")
        new_node: Node = Node(data)
        if self.head is None:
            new_node.next_node = new_node  # First node points to itself
            self.tail = self.head = new_node
        elif index == 0:  # Insert at the head
            new_node.next_node = self.head
            assert self.tail is not None  # List is not empty, tail exists
            self.head = self.tail.next_node = new_node
        else:
            temp: Node | None = self.head
            for _ in range(index - 1):
                assert temp is not None
                temp = temp.next_node
            assert temp is not None
            new_node.next_node = temp.next_node
            temp.next_node = new_node
            if index == len(self) - 1:  # Insert at the tail
                self.tail = new_node

    def delete_front(self) -> Any:
        """
        Delete and return the data of the node at the front of the Circular Linked List.
        Raises:
            IndexError: If the list is empty.
        """
        return self.delete_nth(0)

    def delete_tail(self) -> Any:
        """
        Delete and return the data of the node at the end of the Circular Linked List.
        Returns:
            Any: The data of the deleted node.
        Raises:
            IndexError: If the index is out of range.
        """
        return self.delete_nth(len(self) - 1)

    def delete_nth(self, index: int = 0) -> Any:
        """
        Delete and return the data of the node at the nth pos in Circular Linked List.
        Args:
            index (int): The index of the node to be deleted. Defaults to 0.
        Returns:
            Any: The data of the deleted node.
        Raises:
            IndexError: If the index is out of range.
        """
        if not 0 <= index < len(self):
            raise IndexError("list index out of range.")

        assert self.head is not None
        assert self.tail is not None
        delete_node: Node = self.head
        if self.head == self.tail:  # Just one node
            self.head = self.tail = None
        elif index == 0:  # Delete head node
            assert self.tail.next_node is not None
            self.tail.next_node = self.tail.next_node.next_node
            self.head = self.head.next_node
        else:
            temp: Node | None = self.head
            for _ in range(index - 1):
                assert temp is not None
                temp = temp.next_node
            assert temp is not None
            assert temp.next_node is not None
            delete_node = temp.next_node
            temp.next_node = temp.next_node.next_node
            if index == len(self) - 1:  # Delete at tail
                self.tail = temp
        return delete_node.data

    def is_empty(self) -> bool:
        """
        Check if the Circular Linked List is empty.
        Returns:
            bool: True if the list is empty, False otherwise.
        """
        return len(self) == 0


def test_circular_linked_list() -> None:
    """
    Test cases for the CircularLinkedList class.
    >>> test_circular_linked_list()
    """
    circular_linked_list = CircularLinkedList()
    assert len(circular_linked_list) == 0
    assert circular_linked_list.is_empty() is True
    assert str(circular_linked_list) == ""

    try:
        circular_linked_list.delete_front()
        raise AssertionError  # This should not happen
    except IndexError:
        assert True  # This should happen

    try:
        circular_linked_list.delete_tail()
        raise AssertionError  # This should not happen
    except IndexError:
        assert True  # This should happen

    try:
        circular_linked_list.delete_nth(-1)
        raise AssertionError
    except IndexError:
        assert True

    try:
        circular_linked_list.delete_nth(0)
        raise AssertionError
    except IndexError:
        assert True

    assert circular_linked_list.is_empty() is True
    for i in range(5):
        assert len(circular_linked_list) == i
        circular_linked_list.insert_nth(i, i + 1)
    assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 6))

    circular_linked_list.insert_tail(6)
    assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 7))
    circular_linked_list.insert_head(0)
    assert str(circular_linked_list) == "->".join(str(i) for i in range(7))

    assert circular_linked_list.delete_front() == 0
    assert circular_linked_list.delete_tail() == 6
    assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 6))
    assert circular_linked_list.delete_nth(2) == 3

    circular_linked_list.insert_nth(2, 3)
    assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 6))

    assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/linked_list/deque_doubly.py
================================================
"""
Implementing Deque using DoublyLinkedList ...
Operations:
    1. insertion in the front -> O(1)
    2. insertion in the end -> O(1)
    3. remove from the front -> O(1)
    4. remove from the end -> O(1)
"""


class _DoublyLinkedBase:
    """A Private class (to be inherited)"""

    class _Node:
        __slots__ = "_data", "_next", "_prev"

        def __init__(self, link_p, element, link_n):
            self._prev = link_p
            self._data = element
            self._next = link_n

        def has_next_and_prev(self):
            return (
                f" Prev -> {self._prev is not None}, Next -> {self._next is not None}"
            )

    def __init__(self):
        self._header = self._Node(None, None, None)
        self._trailer = self._Node(None, None, None)
        self._header._next = self._trailer
        self._trailer._prev = self._header
        self._size = 0

    def __len__(self):
        return self._size

    def is_empty(self):
        return self.__len__() == 0

    def _insert(self, predecessor, e, successor):
        # Create new_node by setting it's prev.link -> header
        # setting it's next.link -> trailer
        new_node = self._Node(predecessor, e, successor)
        predecessor._next = new_node
        successor._prev = new_node
        self._size += 1
        return self

    def _delete(self, node):
        predecessor = node._prev
        successor = node._next

        predecessor._next = successor
        successor._prev = predecessor
        self._size -= 1
        temp = node._data
        node._prev = node._next = node._data = None
        del node
        return temp


class LinkedDeque(_DoublyLinkedBase):
    def first(self):
        """return first element
        >>> d = LinkedDeque()
        >>> d.add_first('A').first()
        'A'
        >>> d.add_first('B').first()
        'B'
        """
        if self.is_empty():
            raise Exception("List is empty")
        return self._header._next._data

    def last(self):
        """return last element
        >>> d = LinkedDeque()
        >>> d.add_last('A').last()
        'A'
        >>> d.add_last('B').last()
        'B'
        """
        if self.is_empty():
            raise Exception("List is empty")
        return self._trailer._prev._data

    # DEque Insert Operations (At the front, At the end)

    def add_first(self, element):
        """insertion in the front
        >>> LinkedDeque().add_first('AV').first()
        'AV'
        """
        return self._insert(self._header, element, self._header._next)

    def add_last(self, element):
        """insertion in the end
        >>> LinkedDeque().add_last('B').last()
        'B'
        """
        return self._insert(self._trailer._prev, element, self._trailer)

    # DEqueu Remove Operations (At the front, At the end)

    def remove_first(self):
        """removal from the front
        >>> d = LinkedDeque()
        >>> d.is_empty()
        True
        >>> d.remove_first()
        Traceback (most recent call last):
           ...
        IndexError: remove_first from empty list
        >>> d.add_first('A') # doctest: +ELLIPSIS
        <data_structures.linked_list.deque_doubly.LinkedDeque object at ...
        >>> d.remove_first()
        'A'
        >>> d.is_empty()
        True
        """
        if self.is_empty():
            raise IndexError("remove_first from empty list")
        return self._delete(self._header._next)

    def remove_last(self):
        """removal in the end
        >>> d = LinkedDeque()
        >>> d.is_empty()
        True
        >>> d.remove_last()
        Traceback (most recent call last):
           ...
        IndexError: remove_first from empty list
        >>> d.add_first('A') # doctest: +ELLIPSIS
        <data_structures.linked_list.deque_doubly.LinkedDeque object at ...
        >>> d.remove_last()
        'A'
        >>> d.is_empty()
        True
        """
        if self.is_empty():
            raise IndexError("remove_first from empty list")
        return self._delete(self._trailer._prev)


================================================
FILE: data_structures/linked_list/doubly_linked_list.py
================================================
"""
https://en.wikipedia.org/wiki/Doubly_linked_list
"""


class Node:
    def __init__(self, data):
        self.data = data
        self.previous = None
        self.next = None

    def __str__(self):
        return f"{self.data}"


class DoublyLinkedList:
    def __init__(self):
        self.head = None
        self.tail = None

    def __iter__(self):
        """
        >>> linked_list = DoublyLinkedList()
        >>> linked_list.insert_at_head('b')
        >>> linked_list.insert_at_head('a')
        >>> linked_list.insert_at_tail('c')
        >>> tuple(linked_list)
        ('a', 'b', 'c')
        """
        node = self.head
        while node:
            yield node.data
            node = node.next

    def __str__(self):
        """
        >>> linked_list = DoublyLinkedList()
        >>> linked_list.insert_at_tail('a')
        >>> linked_list.insert_at_tail('b')
        >>> linked_list.insert_at_tail('c')
        >>> str(linked_list)
        'a->b->c'
        """
        return "->".join([str(item) for item in self])

    def __len__(self):
        """
        >>> linked_list = DoublyLinkedList()
        >>> for i in range(0, 5):
        ...     linked_list.insert_at_nth(i, i + 1)
        >>> len(linked_list) == 5
        True
        """
        return sum(1 for _ in self)

    def insert_at_head(self, data):
        self.insert_at_nth(0, data)

    def insert_at_tail(self, data):
        self.insert_at_nth(len(self), data)

    def insert_at_nth(self, index: int, data):
        """
        >>> linked_list = DoublyLinkedList()
        >>> linked_list.insert_at_nth(-1, 666)
        Traceback (most recent call last):
            ....
        IndexError: list index out of range
        >>> linked_list.insert_at_nth(1, 666)
        Traceback (most recent call last):
            ....
        IndexError: list index out of range
        >>> linked_list.insert_at_nth(0, 2)
        >>> linked_list.insert_at_nth(0, 1)
        >>> linked_list.insert_at_nth(2, 4)
        >>> linked_list.insert_at_nth(2, 3)
        >>> str(linked_list)
        '1->2->3->4'
        >>> linked_list.insert_at_nth(5, 5)
        Traceback (most recent call last):
            ....
        IndexError: list index out of range
        """
        length = len(self)

        if not 0 <= index <= length:
            raise IndexError("list index out of range")
        new_node = Node(data)
        if self.head is None:
            self.head = self.tail = new_node
        elif index == 0:
            self.head.previous = new_node
            new_node.next = self.head
            self.head = new_node
        elif index == length:
            self.tail.next = new_node
            new_node.previous = self.tail
            self.tail = new_node
        else:
            temp = self.head
            for _ in range(index):
                temp = temp.next
            temp.previous.next = new_node
            new_node.previous = temp.previous
            new_node.next = temp
            temp.previous = new_node

    def delete_head(self):
        return self.delete_at_nth(0)

    def delete_tail(self):
        return self.delete_at_nth(len(self) - 1)

    def delete_at_nth(self, index: int):
        """
        >>> linked_list = DoublyLinkedList()
        >>> linked_list.delete_at_nth(0)
        Traceback (most recent call last):
            ....
        IndexError: list index out of range
        >>> for i in range(0, 5):
        ...     linked_list.insert_at_nth(i, i + 1)
        >>> linked_list.delete_at_nth(0) == 1
        True
        >>> linked_list.delete_at_nth(3) == 5
        True
        >>> linked_list.delete_at_nth(1) == 3
        True
        >>> str(linked_list)
        '2->4'
        >>> linked_list.delete_at_nth(2)
        Traceback (most recent call last):
            ....
        IndexError: list index out of range
        """
        length = len(self)

        if not 0 <= index <= length - 1:
            raise IndexError("list index out of range")
        delete_node = self.head  # default first node
        if length == 1:
            self.head = self.tail = None
        elif index == 0:
            self.head = self.head.next
            self.head.previous = None
        elif index == length - 1:
            delete_node = self.tail
            self.tail = self.tail.previous
            self.tail.next = None
        else:
            temp = self.head
            for _ in range(index):
                temp = temp.next
            delete_node = temp
            temp.next.previous = temp.previous
            temp.previous.next = temp.next
        return delete_node.data

    def delete(self, data) -> str:
        current = self.head

        while current.data != data:  # Find the position to delete
            if current.next:
                current = current.next
            else:  # We have reached the end an no value matches
                raise ValueError("No data matching given value")

        if current == self.head:
            self.delete_head()

        elif current == self.tail:
            self.delete_tail()

        else:  # Before: 1 <--> 2(current) <--> 3
            current.previous.next = current.next  # 1 --> 3
            current.next.previous = current.previous  # 1 <--> 3
        return data

    def is_empty(self):
        """
        >>> linked_list = DoublyLinkedList()
        >>> linked_list.is_empty()
        True
        >>> linked_list.insert_at_tail(1)
        >>> linked_list.is_empty()
        False
        """
        return len(self) == 0


def test_doubly_linked_list() -> None:
    """
    >>> test_doubly_linked_list()
    """
    linked_list = DoublyLinkedList()
    assert linked_list.is_empty() is True
    assert str(linked_list) == ""

    try:
        linked_list.delete_head()
        raise AssertionError  # This should not happen.
    except IndexError:
        assert True  # This should happen.

    try:
        linked_list.delete_tail()
        raise AssertionError  # This should not happen.
    except IndexError:
        assert True  # This should happen.

    for i in range(10):
        assert len(linked_list) == i
        linked_list.insert_at_nth(i, i + 1)
    assert str(linked_list) == "->".join(str(i) for i in range(1, 11))

    linked_list.insert_at_head(0)
    linked_list.insert_at_tail(11)
    assert str(linked_list) == "->".join(str(i) for i in range(12))

    assert linked_list.delete_head() == 0
    assert linked_list.delete_at_nth(9) == 10
    assert linked_list.delete_tail() == 11
    assert len(linked_list) == 9
    assert str(linked_list) == "->".join(str(i) for i in range(1, 10))


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: data_structures/linked_list/doubly_linked_list_two.py
================================================
"""
- A linked list is similar to an array, it holds values. However, links in a linked
    list do not have indexes.
- This is an example of a double ended, doubly linked list.
- Each link references the next link and the previous one.
- A Doubly Linked List (DLL) contains an extra pointer, typically called previous
    pointer, together with next pointer and data which are there in singly linked list.
 - Advantages over SLL - It can be traversed in both forward and backward direction.
     Delete operation is more efficient
"""

from dataclasses import dataclass
from typing import Self, TypeVar

DataType = TypeVar("DataType")


@dataclass
class Node[DataType]:
    data: DataType
    previous: Self | None = None
    next: Self | None = None

    def __str__(self) -> str:
        return f"{self.data}"


class LinkedListIterator:
    def __init__(self, head):
        self.current = head

    def __iter__(self):
        return self

    def __next__(self):
        if not self.current:
            raise StopIteration
        else:
            value = self.current.data
            self.current = self.current.next
            return value


@dataclass
class LinkedList:
    head: Node | None = None  # First node in list
    tail: Node | None = None  # Last node in list

    def __str__(self):
        current = self.head
        nodes = []
        while current is not None:
            nodes.append(current.data)
            current = current.next
        return " ".join(str(node) for node in nodes)

    def __contains__(self, value: DataType):
        current = self.head
        while current:
            if current.data == value:
                return True
            current = current.next
        return False

    def __iter__(self):
        return LinkedListIterator(self.head)

    def get_head_data(self):
        if self.head:
            return self.head.data
        return None

    def get_tail_data(self):
        if self.tail:
            return self.tail.data
        return None

    def set_head(self, node: Node) -> None:
        if self.head is None:
            self.head = node
            self.tail = node
        else:
            self.insert_before_node(self.head, node)

    def set_tail(self, node: Node) -> None:
        if self.tail is None:
            self.head = node
            self.tail = node
        else:
            self.insert_after_node(self.tail, node)

    def insert(self, value: DataType) -> None:
        node = Node(value)
        if self.head is None:
            self.set_head(node)
        else:
            self.set_tail(node)

    def insert_before_node(self, node: Node, node_to_insert: Node) -> None:
        node_to_insert.next = node
        node_to_insert.previous = node.previous

        if node.previous is None:
            self.head = node_to_insert
        else:
            node.previous.next = node_to_insert

        node.previous = node_to_insert

    def insert_after_node(self, node: Node, node_to_insert: Node) -> None:
        node_to_insert.previous = node
        node_to_insert.next = node.next

        if node.next is None:
            self.tail = node_to_insert
        else:
            node.next.previous = node_to_insert

        node.next = node_to_insert

    def insert_at_position(self, position: int, value: DataType) -> None:
        current_position = 1
        new_node = Node(value)
        node = self.head
        while node:
            if current_position == position:
                self.insert_before_node(node, new_node)
                return
            current_position += 1
            node = node.next
        self.set_tail(new_node)

    def get_node(self, item: DataType) -> Node:
        node = self.head
        while node:
            if node.data == item:
                return node
            node = node.next
        raise Exception("Node not found")

    def delete_value(self, value):
        if (node := self.get_node(value)) is not None:
            if node == self.head:
                self.head = self.head.next

            if node == self.tail:
                self.tail = self.tail.previous

            self.remove_node_pointers(node)

    @staticmethod
    def remove_node_pointers(node: Node) -> None:
        if node.next:
            node.next.previous = node.previous

        if node.previous:
            node.previous.next = node.next

        node.next = None
        node.previous = None

    def is_empty(self):
        return self.head is None


def create_linked_list() -> None:
    """
    >>> new_linked_list = LinkedList()
    >>> new_linked_list.get_head_data() is None
    True
    >>> new_linked_list.get_tail_data() is None
    True
    >>> new_linked_list.is_empty()
    True
    >>> new_linked_list.insert(10)
    >>> new_linked_list.get_head_data()
    10
    >>> new_linked_list.get_tail_data()
    10
    >>> new_linked_list.insert_at_position(position=3, value=20)
    >>> new_linked_list.get_head_data()
    10
    >>> new_linked_list.get_tail_data()
    20
    >>> new_linked_list.set_head(Node(1000))
    >>> new_linked_list.get_head_data()
    1000
    >>> new_linked_list.get_tail_data()
    20
    >>> new_linked_list.set_tail(Node(2000))
    >>> new_linked_list.get_head_data()
    1000
    >>> new_linked_list.get_tail_data()
    2000
    >>> for value in new_linked_list:
    ...    print(value)
    1000
    10
    20
    2000
    >>> new_linked_list.is_empty()
    False
    >>> for value in new_linked_list:
    ...    print(value)
    1000
    10
    20
    2000
    >>> 10 in new_linked_list
    True
    >>> new_linked_list.delete_value(value=10)
    >>> 10 in new_linked_list
    False
    >>> new_linked_list.delete_value(value=2000)
    >>> new_linked_list.get_tail_data()
    20
    >>> new_linked_list.delete_value(value=1000)
    >>> new_linked_list.get_tail_data()
    20
    >>> new_linked_list.get_head_data()
    20
    >>> for value in new_linked_list:
    ...    print(value)
    20
    >>> new_linked_list.delete_value(value=20)
    >>> for value in new_linked_list:
    ...    print(value)
    >>> for value in range(1,10):
    ...    new_linked_list.insert(value=value)
    >>> for value in new_linked_list:
    ...    print(value)
    1
    2
    3
    4
    5
    6
    7
    8
    9
    >>> linked_list = LinkedList()
    >>> linked_list.insert_at_position(position=1, value=10)
    >>> str(linked_list)
    '10'
    >>> linked_list.insert_at_position(position=2, value=20)
    >>> str(linked_list)
    '10 20'
    >>> linked_list.insert_at_position(position=1, value=30)
    >>> str(linked_list)
    '30 10 20'
    >>> linked_list.insert_at_position(position=3, value=40)
    >>> str(linked_list)
    '30 10 40 20'
    >>> linked_list.insert_at_position(position=5, value=50)
    >>> str(linked_list)
    '30 10 40 20 50'
    """


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/linked_list/floyds_cycle_detection.py
================================================
"""
Floyd's cycle detection algorithm is a popular algorithm used to detect cycles
in a linked list. It uses two pointers, a slow pointer and a fast pointer,
to traverse the linked list. The slow pointer moves one node at a time while the fast
pointer moves two nodes at a time. If there is a cycle in the linked list,
the fast pointer will eventually catch up to the slow pointer and they will
meet at the same node. If there is no cycle, the fast pointer will reach the end of
the linked list and the algorithm will terminate.

For more information: https://en.wikipedia.org/wiki/Cycle_detection#Floyd's_tortoise_and_hare
"""

from collections.abc import Iterator
from dataclasses import dataclass
from typing import Any, Self


@dataclass
class Node:
    """
    A class representing a node in a singly linked list.
    """

    data: Any
    next_node: Self | None = None


@dataclass
class LinkedList:
    """
    A class representing a singly linked list.
    """

    head: Node | None = None

    def __iter__(self) -> Iterator:
        """
        Iterates through the linked list.

        Returns:
            Iterator: An iterator over the linked list.

        Examples:
        >>> linked_list = LinkedList()
        >>> list(linked_list)
        []
        >>> linked_list.add_node(1)
        >>> tuple(linked_list)
        (1,)
        """
        visited = []
        node = self.head
        while node:
            # Avoid infinite loop in there's a cycle
            if node in visited:
                return
            visited.append(node)
            yield node.data
            node = node.next_node

    def add_node(self, data: Any) -> None:
        """
        Adds a new node to the end of the linked list.

        Args:
            data (Any): The data to be stored in the new node.

        Examples:
        >>> linked_list = LinkedList()
        >>> linked_list.add_node(1)
        >>> linked_list.add_node(2)
        >>> linked_list.add_node(3)
        >>> linked_list.add_node(4)
        >>> tuple(linked_list)
        (1, 2, 3, 4)
        """
        new_node = Node(data)

        if self.head is None:
            self.head = new_node
            return

        current_node = self.head
        while current_node.next_node is not None:
            current_node = current_node.next_node

        current_node.next_node = new_node

    def detect_cycle(self) -> bool:
        """
        Detects if there is a cycle in the linked list using
        Floyd's cycle detection algorithm.

        Returns:
            bool: True if there is a cycle, False otherwise.

        Examples:
        >>> linked_list = LinkedList()
        >>> linked_list.add_node(1)
        >>> linked_list.add_node(2)
        >>> linked_list.add_node(3)
        >>> linked_list.add_node(4)

        >>> linked_list.detect_cycle()
        False

        # Create a cycle in the linked list
        >>> linked_list.head.next_node.next_node.next_node = linked_list.head.next_node

        >>> linked_list.detect_cycle()
        True
        """
        if self.head is None:
            return False

        slow_pointer: Node | None = self.head
        fast_pointer: Node | None = self.head

        while fast_pointer is not None and fast_pointer.next_node is not None:
            slow_pointer = slow_pointer.next_node if slow_pointer else None
            fast_pointer = fast_pointer.next_node.next_node
            if slow_pointer == fast_pointer:
                return True

        return False


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    linked_list = LinkedList()
    linked_list.add_node(1)
    linked_list.add_node(2)
    linked_list.add_node(3)
    linked_list.add_node(4)

    # Create a cycle in the linked list
    # It first checks if the head, next_node, and next_node.next_node attributes of the
    # linked list are not None to avoid any potential type errors.
    if (
        linked_list.head
        and linked_list.head.next_node
        and linked_list.head.next_node.next_node
    ):
        linked_list.head.next_node.next_node.next_node = linked_list.head.next_node

    has_cycle = linked_list.detect_cycle()
    print(has_cycle)  # Output: True


================================================
FILE: data_structures/linked_list/from_sequence.py
================================================
"""
Recursive Program to create a Linked List from a sequence and
print a string representation of it.
"""


class Node:
    def __init__(self, data=None):
        self.data = data
        self.next = None

    def __repr__(self):
        """Returns a visual representation of the node and all its following nodes."""
        string_rep = ""
        temp = self
        while temp:
            string_rep += f"<{temp.data}> ---> "
            temp = temp.next
        string_rep += "<END>"
        return string_rep


def make_linked_list(elements_list: list | tuple) -> Node:
    """
    Creates a Linked List from the elements of the given sequence
    (list/tuple) and returns the head of the Linked List.

    >>> make_linked_list([])
    Traceback (most recent call last):
        ...
    ValueError: The Elements List is empty
    >>> make_linked_list(())
    Traceback (most recent call last):
        ...
    ValueError: The Elements List is empty
    >>> make_linked_list([1])
    <1> ---> <END>
    >>> make_linked_list((1,))
    <1> ---> <END>
    >>> make_linked_list([1, 3, 5, 32, 44, 12, 43])
    <1> ---> <3> ---> <5> ---> <32> ---> <44> ---> <12> ---> <43> ---> <END>
    >>> make_linked_list((1, 3, 5, 32, 44, 12, 43))
    <1> ---> <3> ---> <5> ---> <32> ---> <44> ---> <12> ---> <43> ---> <END>
    """

    # if elements_list is empty
    if not elements_list:
        raise ValueError("The Elements List is empty")

    # Set first element as Head
    head = Node(elements_list[0])
    current = head
    # Loop through elements from position 1
    for data in elements_list[1:]:
        current.next = Node(data)
        current = current.next
    return head


================================================
FILE: data_structures/linked_list/has_loop.py
================================================
from __future__ import annotations

from typing import Any


class ContainsLoopError(Exception):
    pass


class Node:
    def __init__(self, data: Any) -> None:
        self.data: Any = data
        self.next_node: Node | None = None

    def __iter__(self):
        node = self
        visited = set()
        while node:
            if node in visited:
                raise ContainsLoopError
            visited.add(node)
            yield node.data
            node = node.next_node

    @property
    def has_loop(self) -> bool:
        """
        A loop is when the exact same Node appears more than once in a linked list.
        >>> root_node = Node(1)
        >>> root_node.next_node = Node(2)
        >>> root_node.next_node.next_node = Node(3)
        >>> root_node.next_node.next_node.next_node = Node(4)
        >>> root_node.has_loop
        False
        >>> root_node.next_node.next_node.next_node = root_node.next_node
        >>> root_node.has_loop
        True
        """
        try:
            list(self)
            return False
        except ContainsLoopError:
            return True


if __name__ == "__main__":
    root_node = Node(1)
    root_node.next_node = Node(2)
    root_node.next_node.next_node = Node(3)
    root_node.next_node.next_node.next_node = Node(4)
    print(root_node.has_loop)  # False
    root_node.next_node.next_node.next_node = root_node.next_node
    print(root_node.has_loop)  # True

    root_node = Node(5)
    root_node.next_node = Node(6)
    root_node.next_node.next_node = Node(5)
    root_node.next_node.next_node.next_node = Node(6)
    print(root_node.has_loop)  # False

    root_node = Node(1)
    print(root_node.has_loop)  # False


================================================
FILE: data_structures/linked_list/is_palindrome.py
================================================
from __future__ import annotations

from dataclasses import dataclass


@dataclass
class ListNode:
    val: int = 0
    next_node: ListNode | None = None


def is_palindrome(head: ListNode | None) -> bool:
    """
    Check if a linked list is a palindrome.

    Args:
        head: The head of the linked list.

    Returns:
        bool: True if the linked list is a palindrome, False otherwise.

    Examples:
        >>> is_palindrome(None)
        True

        >>> is_palindrome(ListNode(1))
        True

        >>> is_palindrome(ListNode(1, ListNode(2)))
        False

        >>> is_palindrome(ListNode(1, ListNode(2, ListNode(1))))
        True

        >>> is_palindrome(ListNode(1, ListNode(2, ListNode(2, ListNode(1)))))
        True
    """
    if not head:
        return True
    # split the list to two parts
    fast: ListNode | None = head.next_node
    slow: ListNode | None = head
    while fast and fast.next_node:
        fast = fast.next_node.next_node
        slow = slow.next_node if slow else None
    if slow:
        # slow will always be defined,
        # adding this check to resolve mypy static check
        second = slow.next_node
        slow.next_node = None  # Don't forget here! But forget still works!
    # reverse the second part
    node: ListNode | None = None
    while second:
        nxt = second.next_node
        second.next_node = node
        node = second
        second = nxt
    # compare two parts
    # second part has the same or one less node
    while node and head:
        if node.val != head.val:
            return False
        node = node.next_node
        head = head.next_node
    return True


def is_palindrome_stack(head: ListNode | None) -> bool:
    """
    Check if a linked list is a palindrome using a stack.

    Args:
        head (ListNode): The head of the linked list.

    Returns:
        bool: True if the linked list is a palindrome, False otherwise.

    Examples:
        >>> is_palindrome_stack(None)
        True

        >>> is_palindrome_stack(ListNode(1))
        True

        >>> is_palindrome_stack(ListNode(1, ListNode(2)))
        False

        >>> is_palindrome_stack(ListNode(1, ListNode(2, ListNode(1))))
        True

        >>> is_palindrome_stack(ListNode(1, ListNode(2, ListNode(2, ListNode(1)))))
        True
    """
    if not head or not head.next_node:
        return True

    # 1. Get the midpoint (slow)
    slow: ListNode | None = head
    fast: ListNode | None = head
    while fast and fast.next_node:
        fast = fast.next_node.next_node
        slow = slow.next_node if slow else None

    # slow will always be defined,
    # adding this check to resolve mypy static check
    if slow:
        stack = [slow.val]

        # 2. Push the second half into the stack
        while slow.next_node:
            slow = slow.next_node
            stack.append(slow.val)

        # 3. Comparison
        cur: ListNode | None = head
        while stack and cur:
            if stack.pop() != cur.val:
                return False
            cur = cur.next_node

    return True


def is_palindrome_dict(head: ListNode | None) -> bool:
    """
    Check if a linked list is a palindrome using a dictionary.

    Args:
        head (ListNode): The head of the linked list.

    Returns:
        bool: True if the linked list is a palindrome, False otherwise.

    Examples:
        >>> is_palindrome_dict(None)
        True

        >>> is_palindrome_dict(ListNode(1))
        True

        >>> is_palindrome_dict(ListNode(1, ListNode(2)))
        False

        >>> is_palindrome_dict(ListNode(1, ListNode(2, ListNode(1))))
        True

        >>> is_palindrome_dict(ListNode(1, ListNode(2, ListNode(2, ListNode(1)))))
        True

        >>> is_palindrome_dict(
        ...     ListNode(
        ...         1, ListNode(2, ListNode(1, ListNode(3, ListNode(2, ListNode(1)))))
        ...     )
        ... )
        False
    """
    if not head or not head.next_node:
        return True
    d: dict[int, list[int]] = {}
    pos = 0
    while head:
        if head.val in d:
            d[head.val].append(pos)
        else:
            d[head.val] = [pos]
        head = head.next_node
        pos += 1
    checksum = pos - 1
    middle = 0
    for v in d.values():
        if len(v) % 2 != 0:
            middle += 1
        else:
            for step, i in enumerate(range(len(v))):
                if v[i] + v[len(v) - 1 - step] != checksum:
                    return False
        if middle > 1:
            return False
    return True


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/linked_list/merge_two_lists.py
================================================
"""
Algorithm that merges two sorted linked lists into one sorted linked list.
"""

from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

test_data_odd = (3, 9, -11, 0, 7, 5, 1, -1)
test_data_even = (4, 6, 2, 0, 8, 10, 3, -2)


@dataclass
class Node:
    data: int
    next_node: Node | None


class SortedLinkedList:
    def __init__(self, ints: Iterable[int]) -> None:
        self.head: Node | None = None
        for i in sorted(ints, reverse=True):
            self.head = Node(i, self.head)

    def __iter__(self) -> Iterator[int]:
        """
        >>> tuple(SortedLinkedList(test_data_odd)) == tuple(sorted(test_data_odd))
        True
        >>> tuple(SortedLinkedList(test_data_even)) == tuple(sorted(test_data_even))
        True
        """
        node = self.head
        while node:
            yield node.data
            node = node.next_node

    def __len__(self) -> int:
        """
        >>> for i in range(3):
        ...     len(SortedLinkedList(range(i))) == i
        True
        True
        True
        >>> len(SortedLinkedList(test_data_odd))
        8
        """
        return sum(1 for _ in self)

    def __str__(self) -> str:
        """
        >>> str(SortedLinkedList([]))
        ''
        >>> str(SortedLinkedList(test_data_odd))
        '-11 -> -1 -> 0 -> 1 -> 3 -> 5 -> 7 -> 9'
        >>> str(SortedLinkedList(test_data_even))
        '-2 -> 0 -> 2 -> 3 -> 4 -> 6 -> 8 -> 10'
        """
        return " -> ".join([str(node) for node in self])


def merge_lists(
    sll_one: SortedLinkedList, sll_two: SortedLinkedList
) -> SortedLinkedList:
    """
    >>> SSL = SortedLinkedList
    >>> merged = merge_lists(SSL(test_data_odd), SSL(test_data_even))
    >>> len(merged)
    16
    >>> str(merged)
    '-11 -> -2 -> -1 -> 0 -> 0 -> 1 -> 2 -> 3 -> 3 -> 4 -> 5 -> 6 -> 7 -> 8 -> 9 -> 10'
    >>> list(merged) == list(sorted(test_data_odd + test_data_even))
    True
    """
    return SortedLinkedList(list(sll_one) + list(sll_two))


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    SSL = SortedLinkedList
    print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))


================================================
FILE: data_structures/linked_list/middle_element_of_linked_list.py
================================================
from __future__ import annotations


class Node:
    def __init__(self, data: int) -> None:
        self.data = data
        self.next = None


class LinkedList:
    def __init__(self):
        self.head = None

    def push(self, new_data: int) -> int:
        new_node = Node(new_data)
        new_node.next = self.head
        self.head = new_node
        return self.head.data

    def middle_element(self) -> int | None:
        """
        >>> link = LinkedList()
        >>> link.middle_element()
        No element found.
        >>> link.push(5)
        5
        >>> link.push(6)
        6
        >>> link.push(8)
        8
        >>> link.push(8)
        8
        >>> link.push(10)
        10
        >>> link.push(12)
        12
        >>> link.push(17)
        17
        >>> link.push(7)
        7
        >>> link.push(3)
        3
        >>> link.push(20)
        20
        >>> link.push(-20)
        -20
        >>> link.middle_element()
        12
        >>>
        """
        slow_pointer = self.head
        fast_pointer = self.head
        if self.head:
            while fast_pointer and fast_pointer.next:
                fast_pointer = fast_pointer.next.next
                slow_pointer = slow_pointer.next
            return slow_pointer.data
        else:
            print("No element found.")
            return None


if __name__ == "__main__":
    link = LinkedList()
    for _ in range(int(input().strip())):
        data = int(input().strip())
        link.push(data)
    print(link.middle_element())


================================================
FILE: data_structures/linked_list/print_reverse.py
================================================
from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass


@dataclass
class Node:
    data: int
    next_node: Node | None = None


class LinkedList:
    """A class to represent a Linked List.
    Use a tail pointer to speed up the append() operation.
    """

    def __init__(self) -> None:
        """Initialize a LinkedList with the head node set to None.
        >>> linked_list = LinkedList()
        >>> (linked_list.head, linked_list.tail)
        (None, None)
        """
        self.head: Node | None = None
        self.tail: Node | None = None  # Speeds up the append() operation

    def __iter__(self) -> Iterator[int]:
        """Iterate the LinkedList yielding each Node's data.
        >>> linked_list = LinkedList()
        >>> items = (1, 2, 3, 4, 5)
        >>> linked_list.extend(items)
        >>> tuple(linked_list) == items
        True
        """
        node = self.head
        while node:
            yield node.data
            node = node.next_node

    def __repr__(self) -> str:
        """Returns a string representation of the LinkedList.
        >>> linked_list = LinkedList()
        >>> str(linked_list)
        ''
        >>> linked_list.append(1)
        >>> str(linked_list)
        '1'
        >>> linked_list.extend([2, 3, 4, 5])
        >>> str(linked_list)
        '1 -> 2 -> 3 -> 4 -> 5'
        """
        return " -> ".join([str(data) for data in self])

    def append(self, data: int) -> None:
        """Appends a new node with the given data to the end of the LinkedList.
        >>> linked_list = LinkedList()
        >>> str(linked_list)
        ''
        >>> linked_list.append(1)
        >>> str(linked_list)
        '1'
        >>> linked_list.append(2)
        >>> str(linked_list)
        '1 -> 2'
        """
        if self.tail:
            self.tail.next_node = self.tail = Node(data)
        else:
            self.head = self.tail = Node(data)

    def extend(self, items: Iterable[int]) -> None:
        """Appends each item to the end of the LinkedList.
        >>> linked_list = LinkedList()
        >>> linked_list.extend([])
        >>> str(linked_list)
        ''
        >>> linked_list.extend([1, 2])
        >>> str(linked_list)
        '1 -> 2'
        >>> linked_list.extend([3,4])
        >>> str(linked_list)
        '1 -> 2 -> 3 -> 4'
        """
        for item in items:
            self.append(item)


def make_linked_list(elements_list: Iterable[int]) -> LinkedList:
    """Creates a Linked List from the elements of the given sequence
    (list/tuple) and returns the head of the Linked List.
    >>> make_linked_list([])
    Traceback (most recent call last):
        ...
    Exception: The Elements List is empty
    >>> make_linked_list([7])
    7
    >>> make_linked_list(['abc'])
    abc
    >>> make_linked_list([7, 25])
    7 -> 25
    """
    if not elements_list:
        raise Exception("The Elements List is empty")

    linked_list = LinkedList()
    linked_list.extend(elements_list)
    return linked_list


def in_reverse(linked_list: LinkedList) -> str:
    """Prints the elements of the given Linked List in reverse order
    >>> in_reverse(LinkedList())
    ''
    >>> in_reverse(make_linked_list([69, 88, 73]))
    '73 <- 88 <- 69'
    """
    return " <- ".join(str(line) for line in reversed(tuple(linked_list)))


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    linked_list = make_linked_list((14, 52, 14, 12, 43))
    print(f"Linked List:  {linked_list}")
    print(f"Reverse List: {in_reverse(linked_list)}")


================================================
FILE: data_structures/linked_list/reverse_k_group.py
================================================
from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass


@dataclass
class Node:
    data: int
    next_node: Node | None = None


class LinkedList:
    def __init__(self, ints: Iterable[int]) -> None:
        self.head: Node | None = None
        for i in ints:
            self.append(i)

    def __iter__(self) -> Iterator[int]:
        """
        >>> ints = []
        >>> list(LinkedList(ints)) == ints
        True
        >>> ints = tuple(range(5))
        >>> tuple(LinkedList(ints)) == ints
        True
        """
        node = self.head
        while node:
            yield node.data
            node = node.next_node

    def __len__(self) -> int:
        """
        >>> for i in range(3):
        ...     len(LinkedList(range(i))) == i
        True
        True
        True
        >>> len(LinkedList("abcdefgh"))
        8
        """
        return sum(1 for _ in self)

    def __str__(self) -> str:
        """
        >>> str(LinkedList([]))
        ''
        >>> str(LinkedList(range(5)))
        '0 -> 1 -> 2 -> 3 -> 4'
        """
        return " -> ".join([str(node) for node in self])

    def append(self, data: int) -> None:
        """
        >>> ll = LinkedList([1, 2])
        >>> tuple(ll)
        (1, 2)
        >>> ll.append(3)
        >>> tuple(ll)
        (1, 2, 3)
        >>> ll.append(4)
        >>> tuple(ll)
        (1, 2, 3, 4)
        >>> len(ll)
        4
        """
        if not self.head:
            self.head = Node(data)
            return
        node = self.head
        while node.next_node:
            node = node.next_node
        node.next_node = Node(data)

    def reverse_k_nodes(self, group_size: int) -> None:
        """
        reverse nodes within groups of size k
        >>> ll = LinkedList([1, 2, 3, 4, 5])
        >>> ll.reverse_k_nodes(2)
        >>> tuple(ll)
        (2, 1, 4, 3, 5)
        >>> str(ll)
        '2 -> 1 -> 4 -> 3 -> 5'
        """
        if self.head is None or self.head.next_node is None:
            return

        length = len(self)
        dummy_head = Node(0)
        dummy_head.next_node = self.head
        previous_node = dummy_head

        while length >= group_size:
            current_node = previous_node.next_node
            assert current_node
            next_node = current_node.next_node
            for _ in range(1, group_size):
                assert next_node, current_node
                current_node.next_node = next_node.next_node
                assert previous_node
                next_node.next_node = previous_node.next_node
                previous_node.next_node = next_node
                next_node = current_node.next_node
            previous_node = current_node
            length -= group_size
        self.head = dummy_head.next_node


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    ll = LinkedList([1, 2, 3, 4, 5])
    print(f"Original Linked List: {ll}")
    k = 2
    ll.reverse_k_nodes(k)
    print(f"After reversing groups of size {k}: {ll}")


================================================
FILE: data_structures/linked_list/rotate_to_the_right.py
================================================
from __future__ import annotations

from dataclasses import dataclass


@dataclass
class Node:
    data: int
    next_node: Node | None = None


def print_linked_list(head: Node | None) -> None:
    """
        Print the entire linked list iteratively.

        This function prints the elements of a linked list separated by '->'.

        Parameters:
            head (Node | None): The head of the linked list to be printed,
    or None if the linked list is empty.

        >>> head = insert_node(None, 0)
        >>> head = insert_node(head, 2)
        >>> head = insert_node(head, 1)
        >>> print_linked_list(head)
        0->2->1
        >>> head = insert_node(head, 4)
        >>> head = insert_node(head, 5)
        >>> print_linked_list(head)
        0->2->1->4->5
    """
    if head is None:
        return
    while head.next_node is not None:
        print(head.data, end="->")
        head = head.next_node
    print(head.data)


def insert_node(head: Node | None, data: int) -> Node:
    """
    Insert a new node at the end of a linked list and return the new head.

    Parameters:
        head (Node | None): The head of the linked list.
        data (int): The data to be inserted into the new node.

    Returns:
        Node: The new head of the linked list.

    >>> head = insert_node(None, 10)
    >>> head = insert_node(head, 9)
    >>> head = insert_node(head, 8)
    >>> print_linked_list(head)
    10->9->8
    """
    new_node = Node(data)
    # If the linked list is empty, the new_node becomes the head
    if head is None:
        return new_node

    temp_node = head
    while temp_node.next_node:
        temp_node = temp_node.next_node

    temp_node.next_node = new_node
    return head


def rotate_to_the_right(head: Node, places: int) -> Node:
    """
    Rotate a linked list to the right by places times.

    Parameters:
        head: The head of the linked list.
        places: The number of places to rotate.

    Returns:
        Node: The head of the rotated linked list.

    >>> rotate_to_the_right(None, places=1)
    Traceback (most recent call last):
        ...
    ValueError: The linked list is empty.
    >>> head = insert_node(None, 1)
    >>> rotate_to_the_right(head, places=1) == head
    True
    >>> head = insert_node(None, 1)
    >>> head = insert_node(head, 2)
    >>> head = insert_node(head, 3)
    >>> head = insert_node(head, 4)
    >>> head = insert_node(head, 5)
    >>> new_head = rotate_to_the_right(head, places=2)
    >>> print_linked_list(new_head)
    4->5->1->2->3
    """
    # Check if the list is empty or has only one element
    if not head:
        raise ValueError("The linked list is empty.")

    if head.next_node is None:
        return head

    # Calculate the length of the linked list
    length = 1
    temp_node = head
    while temp_node.next_node is not None:
        length += 1
        temp_node = temp_node.next_node

    # Adjust the value of places to avoid places longer than the list.
    places %= length

    if places == 0:
        return head  # As no rotation is needed.

    # Find the new head position after rotation.
    new_head_index = length - places

    # Traverse to the new head position
    temp_node = head
    for _ in range(new_head_index - 1):
        assert temp_node.next_node
        temp_node = temp_node.next_node

    # Update pointers to perform rotation
    assert temp_node.next_node
    new_head = temp_node.next_node
    temp_node.next_node = None
    temp_node = new_head
    while temp_node.next_node:
        temp_node = temp_node.next_node
    temp_node.next_node = head

    assert new_head
    return new_head


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    head = insert_node(None, 5)
    head = insert_node(head, 1)
    head = insert_node(head, 2)
    head = insert_node(head, 4)
    head = insert_node(head, 3)

    print("Original list: ", end="")
    print_linked_list(head)

    places = 3
    new_head = rotate_to_the_right(head, places)

    print(f"After {places} iterations: ", end="")
    print_linked_list(new_head)


================================================
FILE: data_structures/linked_list/singly_linked_list.py
================================================
from __future__ import annotations

from collections.abc import Iterator
from dataclasses import dataclass
from typing import Any


@dataclass
class Node:
    """
    Create and initialize Node class instance.
    >>> Node(20)
    Node(20)
    >>> Node("Hello, world!")
    Node(Hello, world!)
    >>> Node(None)
    Node(None)
    >>> Node(True)
    Node(True)
    """

    data: Any
    next_node: Node | None = None

    def __repr__(self) -> str:
        """
        Get the string representation of this node.
        >>> Node(10).__repr__()
        'Node(10)'
        >>> repr(Node(10))
        'Node(10)'
        >>> str(Node(10))
        'Node(10)'
        >>> Node(10)
        Node(10)
        """
        return f"Node({self.data})"


class LinkedList:
    def __init__(self):
        """
        Create and initialize LinkedList class instance.
        >>> linked_list = LinkedList()
        >>> linked_list.head is None
        True
        """
        self.head = None

    def __iter__(self) -> Iterator[Any]:
        """
        This function is intended for iterators to access
        and iterate through data inside linked list.
        >>> linked_list = LinkedList()
        >>> linked_list.insert_tail("tail")
        >>> linked_list.insert_tail("tail_1")
        >>> linked_list.insert_tail("tail_2")
        >>> for node in linked_list: # __iter__ used here.
        ...     node
        'tail'
        'tail_1'
        'tail_2'
        """
        node = self.head
        while node:
            yield node.data
            node = node.next_node

    def __len__(self) -> int:
        """
        Return length of linked list i.e. number of nodes
        >>> linked_list = LinkedList()
        >>> len(linked_list)
        0
        >>> linked_list.insert_tail("tail")
        >>> len(linked_list)
        1
        >>> linked_list.insert_head("head")
        >>> len(linked_list)
        2
        >>> _ = linked_list.delete_tail()
        >>> len(linked_list)
        1
        >>> _ = linked_list.delete_head()
        >>> len(linked_list)
        0
        """
        return sum(1 for _ in self)

    def __repr__(self) -> str:
        """
        String representation/visualization of a Linked Lists
        >>> linked_list = LinkedList()
        >>> linked_list.insert_tail(1)
        >>> linked_list.insert_tail(3)
        >>> linked_list.__repr__()
        '1 -> 3'
        >>> repr(linked_list)
        '1 -> 3'
        >>> str(linked_list)
        '1 -> 3'
        >>> linked_list.insert_tail(5)
        >>> f"{linked_list}"
        '1 -> 3 -> 5'
        """
        return " -> ".join([str(item) for item in self])

    def __getitem__(self, index: int) -> Any:
        """
        Indexing Support. Used to get a node at particular position
        >>> linked_list = LinkedList()
        >>> for i in range(0, 10):
        ...     linked_list.insert_nth(i, i)
        >>> all(str(linked_list[i]) == str(i) for i in range(0, 10))
        True
        >>> linked_list[-10]
        Traceback (most recent call last):
            ...
        ValueError: list index out of range.
        >>> linked_list[len(linked_list)]
        Traceback (most recent call last):
            ...
        ValueError: list index out of range.
        """
        if not 0 <= index < len(self):
            raise ValueError("list index out of range.")
        for i, node in enumerate(self):
            if i == index:
                return node
        return None

    # Used to change the data of a particular node
    def __setitem__(self, index: int, data: Any) -> None:
        """
        >>> linked_list = LinkedList()
        >>> for i in range(0, 10):
        ...     linked_list.insert_nth(i, i)
        >>> linked_list[0] = 666
        >>> linked_list[0]
        666
        >>> linked_list[5] = -666
        >>> linked_list[5]
        -666
        >>> linked_list[-10] = 666
        Traceback (most recent call last):
            ...
        ValueError: list index out of range.
        >>> linked_list[len(linked_list)] = 666
        Traceback (most recent call last):
            ...
        ValueError: list index out of range.
        """
        if not 0 <= index < len(self):
            raise ValueError("list index out of range.")
        current = self.head
        for _ in range(index):
            current = current.next_node
        current.data = data

    def insert_tail(self, data: Any) -> None:
        """
        Insert data to the end of linked list.
        >>> linked_list = LinkedList()
        >>> linked_list.insert_tail("tail")
        >>> linked_list
        tail
        >>> linked_list.insert_tail("tail_2")
        >>> linked_list
        tail -> tail_2
        >>> linked_list.insert_tail("tail_3")
        >>> linked_list
        tail -> tail_2 -> tail_3
        """
        self.insert_nth(len(self), data)

    def insert_head(self, data: Any) -> None:
        """
        Insert data to the beginning of linked list.
        >>> linked_list = LinkedList()
        >>> linked_list.insert_head("head")
        >>> linked_list
        head
        >>> linked_list.insert_head("head_2")
        >>> linked_list
        head_2 -> head
        >>> linked_list.insert_head("head_3")
        >>> linked_list
        head_3 -> head_2 -> head
        """
        self.insert_nth(0, data)

    def insert_nth(self, index: int, data: Any) -> None:
        """
        Insert data at given index.
        >>> linked_list = LinkedList()
        >>> linked_list.insert_tail("first")
        >>> linked_list.insert_tail("second")
        >>> linked_list.insert_tail("third")
        >>> linked_list
        first -> second -> third
        >>> linked_list.insert_nth(1, "fourth")
        >>> linked_list
        first -> fourth -> second -> third
        >>> linked_list.insert_nth(3, "fifth")
        >>> linked_list
        first -> fourth -> second -> fifth -> third
        """
        if not 0 <= index <= len(self):
            raise IndexError("list index out of range")
        new_node = Node(data)
        if self.head is None:
            self.head = new_node
        elif index == 0:
            new_node.next_node = self.head  # link new_node to head
            self.head = new_node
        else:
            temp = self.head
            for _ in range(index - 1):
                temp = temp.next_node
            new_node.next_node = temp.next_node
            temp.next_node = new_node

    def print_list(self) -> None:  # print every node data
        """
        This method prints every node data.
        >>> linked_list = LinkedList()
        >>> linked_list.insert_tail("first")
        >>> linked_list.insert_tail("second")
        >>> linked_list.insert_tail("third")
        >>> linked_list
        first -> second -> third
        """
        print(self)

    def delete_head(self) -> Any:
        """
        Delete the first node and return the
        node's data.
        >>> linked_list = LinkedList()
        >>> linked_list.insert_tail("first")
        >>> linked_list.insert_tail("second")
        >>> linked_list.insert_tail("third")
        >>> linked_list
        first -> second -> third
        >>> linked_list.delete_head()
        'first'
        >>> linked_list
        second -> third
        >>> linked_list.delete_head()
        'second'
        >>> linked_list
        third
        >>> linked_list.delete_head()
        'third'
        >>> linked_list.delete_head()
        Traceback (most recent call last):
            ...
        IndexError: List index out of range.
        """
        return self.delete_nth(0)

    def delete_tail(self) -> Any:  # delete from tail
        """
        Delete the tail end node and return the
        node's data.
        >>> linked_list = LinkedList()
        >>> linked_list.insert_tail("first")
        >>> linked_list.insert_tail("second")
        >>> linked_list.insert_tail("third")
        >>> linked_list
        first -> second -> third
        >>> linked_list.delete_tail()
        'third'
        >>> linked_list
        first -> second
        >>> linked_list.delete_tail()
        'second'
        >>> linked_list
        first
        >>> linked_list.delete_tail()
        'first'
        >>> linked_list.delete_tail()
        Traceback (most recent call last):
            ...
        IndexError: List index out of range.
        """
        return self.delete_nth(len(self) - 1)

    def delete_nth(self, index: int = 0) -> Any:
        """
        Delete node at given index and return the
        node's data.
        >>> linked_list = LinkedList()
        >>> linked_list.insert_tail("first")
        >>> linked_list.insert_tail("second")
        >>> linked_list.insert_tail("third")
        >>> linked_list
        first -> second -> third
        >>> linked_list.delete_nth(1) # delete middle
        'second'
        >>> linked_list
        first -> third
        >>> linked_list.delete_nth(5) # this raises error
        Traceback (most recent call last):
            ...
        IndexError: List index out of range.
        >>> linked_list.delete_nth(-1) # this also raises error
        Traceback (most recent call last):
            ...
        IndexError: List index out of range.
        """
        if not 0 <= index <= len(self) - 1:  # test if index is valid
            raise IndexError("List index out of range.")
        delete_node = self.head  # default first node
        if index == 0:
            self.head = self.head.next_node
        else:
            temp = self.head
            for _ in range(index - 1):
                temp = temp.next_node
            delete_node = temp.next_node
            temp.next_node = temp.next_node.next_node
        return delete_node.data

    def is_empty(self) -> bool:
        """
        Check if linked list is empty.
        >>> linked_list = LinkedList()
        >>> linked_list.is_empty()
        True
        >>> linked_list.insert_head("first")
        >>> linked_list.is_empty()
        False
        """
        return self.head is None

    def reverse(self) -> None:
        """
        This reverses the linked list order.
        >>> linked_list = LinkedList()
        >>> linked_list.insert_tail("first")
        >>> linked_list.insert_tail("second")
        >>> linked_list.insert_tail("third")
        >>> linked_list
        first -> second -> third
        >>> linked_list.reverse()
        >>> linked_list
        third -> second -> first
        """
        prev = None
        current = self.head

        while current:
            # Store the current node's next node.
            next_node = current.next_node
            # Make the current node's next_node point backwards
            current.next_node = prev
            # Make the previous node be the current node
            prev = current
            # Make the current node the next_node node (to progress iteration)
            current = next_node
        # Return prev in order to put the head at the end
        self.head = prev


def test_singly_linked_list() -> None:
    """
    >>> test_singly_linked_list()
    """
    linked_list = LinkedList()
    assert linked_list.is_empty() is True
    assert str(linked_list) == ""

    try:
        linked_list.delete_head()
        raise AssertionError  # This should not happen.
    except IndexError:
        assert True  # This should happen.

    try:
        linked_list.delete_tail()
        raise AssertionError  # This should not happen.
    except IndexError:
        assert True  # This should happen.

    for i in range(10):
        assert len(linked_list) == i
        linked_list.insert_nth(i, i + 1)
    assert str(linked_list) == " -> ".join(str(i) for i in range(1, 11))

    linked_list.insert_head(0)
    linked_list.insert_tail(11)
    assert str(linked_list) == " -> ".join(str(i) for i in range(12))

    assert linked_list.delete_head() == 0
    assert linked_list.delete_nth(9) == 10
    assert linked_list.delete_tail() == 11
    assert len(linked_list) == 9
    assert str(linked_list) == " -> ".join(str(i) for i in range(1, 10))

    assert all(linked_list[i] == i + 1 for i in range(9)) is True

    for i in range(9):
        linked_list[i] = -i
    assert all(linked_list[i] == -i for i in range(9)) is True

    linked_list.reverse()
    assert str(linked_list) == " -> ".join(str(i) for i in range(-8, 1))


def test_singly_linked_list_2() -> None:
    """
    This section of the test used varying data types for input.
    >>> test_singly_linked_list_2()
    """
    test_input = [
        -9,
        100,
        Node(77345112),
        "dlrow olleH",
        7,
        5555,
        0,
        -192.55555,
        "Hello, world!",
        77.9,
        Node(10),
        None,
        None,
        12.20,
    ]
    linked_list = LinkedList()

    for i in test_input:
        linked_list.insert_tail(i)

    # Check if it's empty or not
    assert linked_list.is_empty() is False
    assert (
        str(linked_list)
        == "-9 -> 100 -> Node(77345112) -> dlrow olleH -> 7 -> 5555 -> "
        "0 -> -192.55555 -> Hello, world! -> 77.9 -> Node(10) -> None -> None -> 12.2"
    )

    # Delete the head
    result = linked_list.delete_head()
    assert result == -9
    assert (
        str(linked_list) == "100 -> Node(77345112) -> dlrow olleH -> 7 -> 5555 -> 0 -> "
        "-192.55555 -> Hello, world! -> 77.9 -> Node(10) -> None -> None -> 12.2"
    )

    # Delete the tail
    result = linked_list.delete_tail()
    assert result == 12.2
    assert (
        str(linked_list) == "100 -> Node(77345112) -> dlrow olleH -> 7 -> 5555 -> 0 -> "
        "-192.55555 -> Hello, world! -> 77.9 -> Node(10) -> None -> None"
    )

    # Delete a node in specific location in linked list
    result = linked_list.delete_nth(10)
    assert result is None
    assert (
        str(linked_list) == "100 -> Node(77345112) -> dlrow olleH -> 7 -> 5555 -> 0 -> "
        "-192.55555 -> Hello, world! -> 77.9 -> Node(10) -> None"
    )

    # Add a Node instance to its head
    linked_list.insert_head(Node("Hello again, world!"))
    assert (
        str(linked_list)
        == "Node(Hello again, world!) -> 100 -> Node(77345112) -> dlrow olleH -> "
        "7 -> 5555 -> 0 -> -192.55555 -> Hello, world! -> 77.9 -> Node(10) -> None"
    )

    # Add None to its tail
    linked_list.insert_tail(None)
    assert (
        str(linked_list)
        == "Node(Hello again, world!) -> 100 -> Node(77345112) -> dlrow olleH -> 7 -> "
        "5555 -> 0 -> -192.55555 -> Hello, world! -> 77.9 -> Node(10) -> None -> None"
    )

    # Reverse the linked list
    linked_list.reverse()
    assert (
        str(linked_list)
        == "None -> None -> Node(10) -> 77.9 -> Hello, world! -> -192.55555 -> 0 -> "
        "5555 -> 7 -> dlrow olleH -> Node(77345112) -> 100 -> Node(Hello again, world!)"
    )


def main():
    from doctest import testmod

    testmod()

    linked_list = LinkedList()
    linked_list.insert_head(input("Inserting 1st at head ").strip())
    linked_list.insert_head(input("Inserting 2nd at head ").strip())
    print("\nPrint list:")
    linked_list.print_list()
    linked_list.insert_tail(input("\nInserting 1st at tail ").strip())
    linked_list.insert_tail(input("Inserting 2nd at tail ").strip())
    print("\nPrint list:")
    linked_list.print_list()
    print("\nDelete head")
    linked_list.delete_head()
    print("Delete tail")
    linked_list.delete_tail()
    print("\nPrint list:")
    linked_list.print_list()
    print("\nReverse linked list")
    linked_list.reverse()
    print("\nPrint list:")
    linked_list.print_list()
    print("\nString representation of linked list:")
    print(linked_list)
    print("\nReading/changing Node data using indexing:")
    print(f"Element at Position 1: {linked_list[1]}")
    linked_list[1] = input("Enter New Value: ").strip()
    print("New list:")
    print(linked_list)
    print(f"length of linked_list is : {len(linked_list)}")


if __name__ == "__main__":
    main()


================================================
FILE: data_structures/linked_list/skip_list.py
================================================
"""
Based on "Skip Lists: A Probabilistic Alternative to Balanced Trees" by William Pugh
https://epaperpress.com/sortsearch/download/skiplist.pdf
"""

from __future__ import annotations

from itertools import pairwise
from random import random
from typing import TypeVar

KT = TypeVar("KT")
VT = TypeVar("VT")


class Node[KT, VT]:
    def __init__(self, key: KT | str = "root", value: VT | None = None):
        self.key = key
        self.value = value
        self.forward: list[Node[KT, VT]] = []

    def __repr__(self) -> str:
        """
        :return: Visual representation of Node

        >>> node = Node("Key", 2)
        >>> repr(node)
        'Node(Key: 2)'
        """

        return f"Node({self.key}: {self.value})"

    @property
    def level(self) -> int:
        """
        :return: Number of forward references

        >>> node = Node("Key", 2)
        >>> node.level
        0
        >>> node.forward.append(Node("Key2", 4))
        >>> node.level
        1
        >>> node.forward.append(Node("Key3", 6))
        >>> node.level
        2
        """

        return len(self.forward)


class SkipList[KT, VT]:
    def __init__(self, p: float = 0.5, max_level: int = 16):
        self.head: Node[KT, VT] = Node[KT, VT]()
        self.level = 0
        self.p = p
        self.max_level = max_level

    def __str__(self) -> str:
        """
        :return: Visual representation of SkipList

        >>> skip_list = SkipList()
        >>> print(skip_list)
        SkipList(level=0)
        >>> skip_list.insert("Key1", "Value")
        >>> print(skip_list) # doctest: +ELLIPSIS
        SkipList(level=...
        [root]--...
        [Key1]--Key1...
        None    *...
        >>> skip_list.insert("Key2", "OtherValue")
        >>> print(skip_list) # doctest: +ELLIPSIS
        SkipList(level=...
        [root]--...
        [Key1]--Key1...
        [Key2]--Key2...
        None    *...
        """

        items = list(self)

        if len(items) == 0:
            return f"SkipList(level={self.level})"

        label_size = max((len(str(item)) for item in items), default=4)
        label_size = max(label_size, 4) + 4

        node = self.head
        lines = []

        forwards = node.forward.copy()
        lines.append(f"[{node.key}]".ljust(label_size, "-") + "* " * len(forwards))
        lines.append(" " * label_size + "| " * len(forwards))

        while len(node.forward) != 0:
            node = node.forward[0]

            lines.append(
                f"[{node.key}]".ljust(label_size, "-")
                + " ".join(str(n.key) if n.key == node.key else "|" for n in forwards)
            )
            lines.append(" " * label_size + "| " * len(forwards))
            forwards[: node.level] = node.forward

        lines.append("None".ljust(label_size) + "* " * len(forwards))
        return f"SkipList(level={self.level})\n" + "\n".join(lines)

    def __iter__(self):
        node = self.head

        while len(node.forward) != 0:
            yield node.forward[0].key
            node = node.forward[0]

    def random_level(self) -> int:
        """
        :return: Random level from [1, self.max_level] interval.
                 Higher values are less likely.
        """

        level = 1
        while random() < self.p and level < self.max_level:
            level += 1

        return level

    def _locate_node(self, key) -> tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:
        """
        :param key: Searched key,
        :return: Tuple with searched node (or None if given key is not present)
                 and list of nodes that refer (if key is present) of should refer to
                 given node.
        """

        # Nodes with refer or should refer to output node
        update_vector = []

        node = self.head

        for i in reversed(range(self.level)):
            # i < node.level - When node level is lesser than `i` decrement `i`.
            # node.forward[i].key < key - Jumping to node with key value higher
            #                             or equal to searched key would result
            #                             in skipping searched key.
            while i < node.level and node.forward[i].key < key:
                node = node.forward[i]
            # Each leftmost node (relative to searched node) will potentially have to
            # be updated.
            update_vector.append(node)

        update_vector.reverse()  # Note that we were inserting values in reverse order.

        # len(node.forward) != 0 - If current node doesn't contain any further
        #                          references then searched key is not present.
        # node.forward[0].key == key - Next node key should be equal to search key
        #                              if key is present.
        if len(node.forward) != 0 and node.forward[0].key == key:
            return node.forward[0], update_vector
        else:
            return None, update_vector

    def delete(self, key: KT):
        """
        :param key: Key to remove from list.

        >>> skip_list = SkipList()
        >>> skip_list.insert(2, "Two")
        >>> skip_list.insert(1, "One")
        >>> skip_list.insert(3, "Three")
        >>> list(skip_list)
        [1, 2, 3]
        >>> skip_list.delete(2)
        >>> list(skip_list)
        [1, 3]
        """

        node, update_vector = self._locate_node(key)

        if node is not None:
            for i, update_node in enumerate(update_vector):
                # Remove or replace all references to removed node.
                if update_node.level > i and update_node.forward[i].key == key:
                    if node.level > i:
                        update_node.forward[i] = node.forward[i]
                    else:
                        update_node.forward = update_node.forward[:i]

    def insert(self, key: KT, value: VT):
        """
        :param key: Key to insert.
        :param value: Value associated with given key.

        >>> skip_list = SkipList()
        >>> skip_list.insert(2, "Two")
        >>> skip_list.find(2)
        'Two'
        >>> list(skip_list)
        [2]
        """

        node, update_vector = self._locate_node(key)
        if node is not None:
            node.value = value
        else:
            level = self.random_level()

            if level > self.level:
                # After level increase we have to add additional nodes to head.
                for _ in range(self.level - 1, level):
                    update_vector.append(self.head)
                self.level = level

            new_node = Node(key, value)

            for i, update_node in enumerate(update_vector[:level]):
                # Change references to pass through new node.
                if update_node.level > i:
                    new_node.forward.append(update_node.forward[i])

                if update_node.level < i + 1:
                    update_node.forward.append(new_node)
                else:
                    update_node.forward[i] = new_node

    def find(self, key: VT) -> VT | None:
        """
        :param key: Search key.
        :return: Value associated with given key or None if given key is not present.

        >>> skip_list = SkipList()
        >>> skip_list.find(2)
        >>> skip_list.insert(2, "Two")
        >>> skip_list.find(2)
        'Two'
        >>> skip_list.insert(2, "Three")
        >>> skip_list.find(2)
        'Three'
        """

        node, _ = self._locate_node(key)

        if node is not None:
            return node.value

        return None


def test_insert():
    skip_list = SkipList()
    skip_list.insert("Key1", 3)
    skip_list.insert("Key2", 12)
    skip_list.insert("Key3", 41)
    skip_list.insert("Key4", -19)

    node = skip_list.head
    all_values = {}
    while node.level != 0:
        node = node.forward[0]
        all_values[node.key] = node.value

    assert len(all_values) == 4
    assert all_values["Key1"] == 3
    assert all_values["Key2"] == 12
    assert all_values["Key3"] == 41
    assert all_values["Key4"] == -19


def test_insert_overrides_existing_value():
    skip_list = SkipList()
    skip_list.insert("Key1", 10)
    skip_list.insert("Key1", 12)

    skip_list.insert("Key5", 7)
    skip_list.insert("Key7", 10)
    skip_list.insert("Key10", 5)

    skip_list.insert("Key7", 7)
    skip_list.insert("Key5", 5)
    skip_list.insert("Key10", 10)

    node = skip_list.head
    all_values = {}
    while node.level != 0:
        node = node.forward[0]
        all_values[node.key] = node.value

    if len(all_values) != 4:
        print()
    assert len(all_values) == 4
    assert all_values["Key1"] == 12
    assert all_values["Key7"] == 7
    assert all_values["Key5"] == 5
    assert all_values["Key10"] == 10


def test_searching_empty_list_returns_none():
    skip_list = SkipList()
    assert skip_list.find("Some key") is None


def test_search():
    skip_list = SkipList()

    skip_list.insert("Key2", 20)
    assert skip_list.find("Key2") == 20

    skip_list.insert("Some Key", 10)
    skip_list.insert("Key2", 8)
    skip_list.insert("V", 13)

    assert skip_list.find("Y") is None
    assert skip_list.find("Key2") == 8
    assert skip_list.find("Some Key") == 10
    assert skip_list.find("V") == 13


def test_deleting_item_from_empty_list_do_nothing():
    skip_list = SkipList()
    skip_list.delete("Some key")

    assert len(skip_list.head.forward) == 0


def test_deleted_items_are_not_founded_by_find_method():
    skip_list = SkipList()

    skip_list.insert("Key1", 12)
    skip_list.insert("V", 13)
    skip_list.insert("X", 14)
    skip_list.insert("Key2", 15)

    skip_list.delete("V")
    skip_list.delete("Key2")

    assert skip_list.find("V") is None
    assert skip_list.find("Key2") is None


def test_delete_removes_only_given_key():
    skip_list = SkipList()

    skip_list.insert("Key1", 12)
    skip_list.insert("V", 13)
    skip_list.insert("X", 14)
    skip_list.insert("Key2", 15)

    skip_list.delete("V")
    assert skip_list.find("V") is None
    assert skip_list.find("X") == 14
    assert skip_list.find("Key1") == 12
    assert skip_list.find("Key2") == 15

    skip_list.delete("X")
    assert skip_list.find("V") is None
    assert skip_list.find("X") is None
    assert skip_list.find("Key1") == 12
    assert skip_list.find("Key2") == 15

    skip_list.delete("Key1")
    assert skip_list.find("V") is None
    assert skip_list.find("X") is None
    assert skip_list.find("Key1") is None
    assert skip_list.find("Key2") == 15

    skip_list.delete("Key2")
    assert skip_list.find("V") is None
    assert skip_list.find("X") is None
    assert skip_list.find("Key1") is None
    assert skip_list.find("Key2") is None


def test_delete_doesnt_leave_dead_nodes():
    skip_list = SkipList()

    skip_list.insert("Key1", 12)
    skip_list.insert("V", 13)
    skip_list.insert("X", 142)
    skip_list.insert("Key2", 15)

    skip_list.delete("X")

    def traverse_keys(node):
        yield node.key
        for forward_node in node.forward:
            yield from traverse_keys(forward_node)

    assert len(set(traverse_keys(skip_list.head))) == 4


def test_iter_always_yields_sorted_values():
    def is_sorted(lst):
        return all(next_item >= item for item, next_item in pairwise(lst))

    skip_list = SkipList()
    for i in range(10):
        skip_list.insert(i, i)
    assert is_sorted(list(skip_list))
    skip_list.delete(5)
    skip_list.delete(8)
    skip_list.delete(2)
    assert is_sorted(list(skip_list))
    skip_list.insert(-12, -12)
    skip_list.insert(77, 77)
    assert is_sorted(list(skip_list))


def pytests():
    for _ in range(100):
        # Repeat test 100 times due to the probabilistic nature of skip list
        # random values == random bugs
        test_insert()
        test_insert_overrides_existing_value()

        test_searching_empty_list_returns_none()
        test_search()

        test_deleting_item_from_empty_list_do_nothing()
        test_deleted_items_are_not_founded_by_find_method()
        test_delete_removes_only_given_key()
        test_delete_doesnt_leave_dead_nodes()

        test_iter_always_yields_sorted_values()


def main():
    """
    >>> pytests()
    """

    skip_list = SkipList()
    skip_list.insert(2, "2")
    skip_list.insert(4, "4")
    skip_list.insert(6, "4")
    skip_list.insert(4, "5")
    skip_list.insert(8, "4")
    skip_list.insert(9, "4")

    skip_list.delete(4)

    print(skip_list)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: data_structures/linked_list/swap_nodes.py
================================================
from __future__ import annotations

from collections.abc import Iterator
from dataclasses import dataclass
from typing import Any


@dataclass
class Node:
    data: Any
    next_node: Node | None = None


@dataclass
class LinkedList:
    head: Node | None = None

    def __iter__(self) -> Iterator:
        """
        >>> linked_list = LinkedList()
        >>> list(linked_list)
        []
        >>> linked_list.push(0)
        >>> tuple(linked_list)
        (0,)
        """
        node = self.head
        while node:
            yield node.data
            node = node.next_node

    def __len__(self) -> int:
        """
        >>> linked_list = LinkedList()
        >>> len(linked_list)
        0
        >>> linked_list.push(0)
        >>> len(linked_list)
        1
        """
        return sum(1 for _ in self)

    def push(self, new_data: Any) -> None:
        """
        Add a new node with the given data to the beginning of the Linked List.

        Args:
            new_data (Any): The data to be added to the new node.

        Returns:
            None

        Examples:
            >>> linked_list = LinkedList()
            >>> linked_list.push(5)
            >>> linked_list.push(4)
            >>> linked_list.push(3)
            >>> linked_list.push(2)
            >>> linked_list.push(1)
            >>> list(linked_list)
            [1, 2, 3, 4, 5]
        """
        new_node = Node(new_data)
        new_node.next_node = self.head
        self.head = new_node

    def swap_nodes(self, node_data_1: Any, node_data_2: Any) -> None:
        """
        Swap the positions of two nodes in the Linked List based on their data values.

        Args:
            node_data_1: Data value of the first node to be swapped.
            node_data_2: Data value of the second node to be swapped.


        Note:
            If either of the specified data values isn't found then, no swapping occurs.

        Examples:
        When both values are present in a linked list.
            >>> linked_list = LinkedList()
            >>> linked_list.push(5)
            >>> linked_list.push(4)
            >>> linked_list.push(3)
            >>> linked_list.push(2)
            >>> linked_list.push(1)
            >>> list(linked_list)
            [1, 2, 3, 4, 5]
            >>> linked_list.swap_nodes(1, 5)
            >>> tuple(linked_list)
            (5, 2, 3, 4, 1)

        When one value is present and the other isn't in the linked list.
            >>> second_list = LinkedList()
            >>> second_list.push(6)
            >>> second_list.push(7)
            >>> second_list.push(8)
            >>> second_list.push(9)
            >>> second_list.swap_nodes(1, 6) is None
            True

        When both values are absent in the linked list.
            >>> second_list = LinkedList()
            >>> second_list.push(10)
            >>> second_list.push(9)
            >>> second_list.push(8)
            >>> second_list.push(7)
            >>> second_list.swap_nodes(1, 3) is None
            True

        When linkedlist is empty.
            >>> second_list = LinkedList()
            >>> second_list.swap_nodes(1, 3) is None
            True

        Returns:
            None
        """
        if node_data_1 == node_data_2:
            return

        node_1 = self.head
        while node_1 and node_1.data != node_data_1:
            node_1 = node_1.next_node
        node_2 = self.head
        while node_2 and node_2.data != node_data_2:
            node_2 = node_2.next_node
        if node_1 is None or node_2 is None:
            return
        # Swap the data values of the two nodes
        node_1.data, node_2.data = node_2.data, node_1.data


if __name__ == "__main__":
    """
    Python script that outputs the swap of nodes in a linked list.
    """
    from doctest import testmod

    testmod()
    linked_list = LinkedList()
    for i in range(5, 0, -1):
        linked_list.push(i)

    print(f"Original Linked List: {list(linked_list)}")
    linked_list.swap_nodes(1, 4)
    print(f"Modified Linked List: {list(linked_list)}")
    print("After swapping the nodes whose data is 1 and 4.")


================================================
FILE: data_structures/queues/__init__.py
================================================


================================================
FILE: data_structures/queues/circular_queue.py
================================================
# Implementation of Circular Queue (using Python lists)


class CircularQueue:
    """Circular FIFO queue with a fixed capacity"""

    def __init__(self, n: int):
        self.n = n
        self.array = [None] * self.n
        self.front = 0  # index of the first element
        self.rear = 0
        self.size = 0

    def __len__(self) -> int:
        """
        >>> cq = CircularQueue(5)
        >>> len(cq)
        0
        >>> cq.enqueue("A")  # doctest: +ELLIPSIS
        <data_structures.queues.circular_queue.CircularQueue object at ...>
        >>> cq.array
        ['A', None, None, None, None]
        >>> len(cq)
        1
        """
        return self.size

    def is_empty(self) -> bool:
        """
        Checks whether the queue is empty or not
        >>> cq = CircularQueue(5)
        >>> cq.is_empty()
        True
        >>> cq.enqueue("A").is_empty()
        False
        """
        return self.size == 0

    def first(self):
        """
        Returns the first element of the queue
        >>> cq = CircularQueue(5)
        >>> cq.first()
        False
        >>> cq.enqueue("A").first()
        'A'
        """
        return False if self.is_empty() else self.array[self.front]

    def enqueue(self, data):
        """
        This function inserts an element at the end of the queue using self.rear value
        as an index.

        >>> cq = CircularQueue(5)
        >>> cq.enqueue("A")  # doctest: +ELLIPSIS
        <data_structures.queues.circular_queue.CircularQueue object at ...>
        >>> (cq.size, cq.first())
        (1, 'A')
        >>> cq.enqueue("B")  # doctest: +ELLIPSIS
        <data_structures.queues.circular_queue.CircularQueue object at ...>
        >>> cq.array
        ['A', 'B', None, None, None]
        >>> (cq.size, cq.first())
        (2, 'A')
        >>> cq.enqueue("C").enqueue("D").enqueue("E")  # doctest: +ELLIPSIS
        <data_structures.queues.circular_queue.CircularQueue object at ...>
        >>> cq.enqueue("F")
        Traceback (most recent call last):
           ...
        Exception: QUEUE IS FULL
        """
        if self.size >= self.n:
            raise Exception("QUEUE IS FULL")

        self.array[self.rear] = data
        self.rear = (self.rear + 1) % self.n
        self.size += 1
        return self

    def dequeue(self):
        """
        This function removes an element from the queue using on self.front value as an
        index and returns it

        >>> cq = CircularQueue(5)
        >>> cq.dequeue()
        Traceback (most recent call last):
           ...
        Exception: UNDERFLOW
        >>> cq.enqueue("A").enqueue("B").dequeue()
        'A'
        >>> (cq.size, cq.first())
        (1, 'B')
        >>> cq.dequeue()
        'B'
        >>> cq.dequeue()
        Traceback (most recent call last):
           ...
        Exception: UNDERFLOW
        """
        if self.size == 0:
            raise Exception("UNDERFLOW")

        temp = self.array[self.front]
        self.array[self.front] = None
        self.front = (self.front + 1) % self.n
        self.size -= 1
        return temp


================================================
FILE: data_structures/queues/circular_queue_linked_list.py
================================================
# Implementation of Circular Queue using linked lists
# https://en.wikipedia.org/wiki/Circular_buffer

from __future__ import annotations

from typing import Any


class CircularQueueLinkedList:
    """
    Circular FIFO list with the given capacity (default queue length : 6)

    >>> cq = CircularQueueLinkedList(2)
    >>> cq.enqueue('a')
    >>> cq.enqueue('b')
    >>> cq.enqueue('c')
    Traceback (most recent call last):
       ...
    Exception: Full Queue
    """

    def __init__(self, initial_capacity: int = 6) -> None:
        self.front: Node | None = None
        self.rear: Node | None = None
        self.create_linked_list(initial_capacity)

    def create_linked_list(self, initial_capacity: int) -> None:
        current_node = Node()
        self.front = current_node
        self.rear = current_node
        previous_node = current_node
        for _ in range(1, initial_capacity):
            current_node = Node()
            previous_node.next = current_node
            current_node.prev = previous_node
            previous_node = current_node
        previous_node.next = self.front
        self.front.prev = previous_node

    def is_empty(self) -> bool:
        """
        Checks whether the queue is empty or not
        >>> cq = CircularQueueLinkedList()
        >>> cq.is_empty()
        True
        >>> cq.enqueue('a')
        >>> cq.is_empty()
        False
        >>> cq.dequeue()
        'a'
        >>> cq.is_empty()
        True
        """

        return (
            self.front == self.rear
            and self.front is not None
            and self.front.data is None
        )

    def first(self) -> Any | None:
        """
        Returns the first element of the queue
        >>> cq = CircularQueueLinkedList()
        >>> cq.first()
        Traceback (most recent call last):
           ...
        Exception: Empty Queue
        >>> cq.enqueue('a')
        >>> cq.first()
        'a'
        >>> cq.dequeue()
        'a'
        >>> cq.first()
        Traceback (most recent call last):
           ...
        Exception: Empty Queue
        >>> cq.enqueue('b')
        >>> cq.enqueue('c')
        >>> cq.first()
        'b'
        """
        self.check_can_perform_operation()
        return self.front.data if self.front else None

    def enqueue(self, data: Any) -> None:
        """
        Saves data at the end of the queue

        >>> cq = CircularQueueLinkedList()
        >>> cq.enqueue('a')
        >>> cq.enqueue('b')
        >>> cq.dequeue()
        'a'
        >>> cq.dequeue()
        'b'
        >>> cq.dequeue()
        Traceback (most recent call last):
           ...
        Exception: Empty Queue
        """
        if self.rear is None:
            return

        self.check_is_full()
        if not self.is_empty():
            self.rear = self.rear.next
        if self.rear:
            self.rear.data = data

    def dequeue(self) -> Any:
        """
        Removes and retrieves the first element of the queue

        >>> cq = CircularQueueLinkedList()
        >>> cq.dequeue()
        Traceback (most recent call last):
           ...
        Exception: Empty Queue
        >>> cq.enqueue('a')
        >>> cq.dequeue()
        'a'
        >>> cq.dequeue()
        Traceback (most recent call last):
           ...
        Exception: Empty Queue
        """
        self.check_can_perform_operation()
        if self.rear is None or self.front is None:
            return None
        if self.front == self.rear:
            data = self.front.data
            self.front.data = None
            return data

        old_front = self.front
        self.front = old_front.next
        data = old_front.data
        old_front.data = None
        return data

    def check_can_perform_operation(self) -> None:
        if self.is_empty():
            raise Exception("Empty Queue")

    def check_is_full(self) -> None:
        if self.rear and self.rear.next == self.front:
            raise Exception("Full Queue")


class Node:
    def __init__(self) -> None:
        self.data: Any | None = None
        self.next: Node | None = None
        self.prev: Node | None = None


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/queues/double_ended_queue.py
================================================
"""
Implementation of double ended queue.
"""

from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any


class Deque:
    """
    Deque data structure.
    Operations
    ----------
    append(val: Any) -> None
    appendleft(val: Any) -> None
    extend(iterable: Iterable) -> None
    extendleft(iterable: Iterable) -> None
    pop() -> Any
    popleft() -> Any
    Observers
    ---------
    is_empty() -> bool
    Attributes
    ----------
    _front: _Node
        front of the deque a.k.a. the first element
    _back: _Node
        back of the element a.k.a. the last element
    _len: int
        the number of nodes
    """

    __slots__ = ("_back", "_front", "_len")

    @dataclass
    class _Node:
        """
        Representation of a node.
        Contains a value and a pointer to the next node as well as to the previous one.
        """

        val: Any = None
        next_node: Deque._Node | None = None
        prev_node: Deque._Node | None = None

    class _Iterator:
        """
        Helper class for iteration. Will be used to implement iteration.
        Attributes
        ----------
        _cur: _Node
            the current node of the iteration.
        """

        __slots__ = ("_cur",)

        def __init__(self, cur: Deque._Node | None) -> None:
            self._cur = cur

        def __iter__(self) -> Deque._Iterator:
            """
            >>> our_deque = Deque([1, 2, 3])
            >>> iterator = iter(our_deque)
            """
            return self

        def __next__(self) -> Any:
            """
            >>> our_deque = Deque([1, 2, 3])
            >>> iterator = iter(our_deque)
            >>> next(iterator)
            1
            >>> next(iterator)
            2
            >>> next(iterator)
            3
            """
            if self._cur is None:
                # finished iterating
                raise StopIteration
            val = self._cur.val
            self._cur = self._cur.next_node

            return val

    def __init__(self, iterable: Iterable[Any] | None = None) -> None:
        self._front: Any = None
        self._back: Any = None
        self._len: int = 0

        if iterable is not None:
            # append every value to the deque
            for val in iterable:
                self.append(val)

    def append(self, val: Any) -> None:
        """
        Adds val to the end of the deque.
        Time complexity: O(1)
        >>> our_deque_1 = Deque([1, 2, 3])
        >>> our_deque_1.append(4)
        >>> our_deque_1
        [1, 2, 3, 4]
        >>> our_deque_2 = Deque('ab')
        >>> our_deque_2.append('c')
        >>> our_deque_2
        ['a', 'b', 'c']
        >>> from collections import deque
        >>> deque_collections_1 = deque([1, 2, 3])
        >>> deque_collections_1.append(4)
        >>> deque_collections_1
        deque([1, 2, 3, 4])
        >>> deque_collections_2 = deque('ab')
        >>> deque_collections_2.append('c')
        >>> deque_collections_2
        deque(['a', 'b', 'c'])
        >>> list(our_deque_1) == list(deque_collections_1)
        True
        >>> list(our_deque_2) == list(deque_collections_2)
        True
        """
        node = self._Node(val, None, None)
        if self.is_empty():
            # front = back
            self._front = self._back = node
            self._len = 1
        else:
            # connect nodes
            self._back.next_node = node
            node.prev_node = self._back
            self._back = node  # assign new back to the new node

            self._len += 1

            # make sure there were no errors
            assert not self.is_empty(), "Error on appending value."

    def appendleft(self, val: Any) -> None:
        """
        Adds val to the beginning of the deque.
        Time complexity: O(1)
        >>> our_deque_1 = Deque([2, 3])
        >>> our_deque_1.appendleft(1)
        >>> our_deque_1
        [1, 2, 3]
        >>> our_deque_2 = Deque('bc')
        >>> our_deque_2.appendleft('a')
        >>> our_deque_2
        ['a', 'b', 'c']
        >>> from collections import deque
        >>> deque_collections_1 = deque([2, 3])
        >>> deque_collections_1.appendleft(1)
        >>> deque_collections_1
        deque([1, 2, 3])
        >>> deque_collections_2 = deque('bc')
        >>> deque_collections_2.appendleft('a')
        >>> deque_collections_2
        deque(['a', 'b', 'c'])
        >>> list(our_deque_1) == list(deque_collections_1)
        True
        >>> list(our_deque_2) == list(deque_collections_2)
        True
        """
        node = self._Node(val, None, None)
        if self.is_empty():
            # front = back
            self._front = self._back = node
            self._len = 1
        else:
            # connect nodes
            node.next_node = self._front
            self._front.prev_node = node
            self._front = node  # assign new front to the new node

            self._len += 1

            # make sure there were no errors
            assert not self.is_empty(), "Error on appending value."

    def extend(self, iterable: Iterable[Any]) -> None:
        """
        Appends every value of iterable to the end of the deque.
        Time complexity: O(n)
        >>> our_deque_1 = Deque([1, 2, 3])
        >>> our_deque_1.extend([4, 5])
        >>> our_deque_1
        [1, 2, 3, 4, 5]
        >>> our_deque_2 = Deque('ab')
        >>> our_deque_2.extend('cd')
        >>> our_deque_2
        ['a', 'b', 'c', 'd']
        >>> from collections import deque
        >>> deque_collections_1 = deque([1, 2, 3])
        >>> deque_collections_1.extend([4, 5])
        >>> deque_collections_1
        deque([1, 2, 3, 4, 5])
        >>> deque_collections_2 = deque('ab')
        >>> deque_collections_2.extend('cd')
        >>> deque_collections_2
        deque(['a', 'b', 'c', 'd'])
        >>> list(our_deque_1) == list(deque_collections_1)
        True
        >>> list(our_deque_2) == list(deque_collections_2)
        True
        """
        for val in iterable:
            self.append(val)

    def extendleft(self, iterable: Iterable[Any]) -> None:
        """
        Appends every value of iterable to the beginning of the deque.
        Time complexity: O(n)
        >>> our_deque_1 = Deque([1, 2, 3])
        >>> our_deque_1.extendleft([0, -1])
        >>> our_deque_1
        [-1, 0, 1, 2, 3]
        >>> our_deque_2 = Deque('cd')
        >>> our_deque_2.extendleft('ba')
        >>> our_deque_2
        ['a', 'b', 'c', 'd']
        >>> from collections import deque
        >>> deque_collections_1 = deque([1, 2, 3])
        >>> deque_collections_1.extendleft([0, -1])
        >>> deque_collections_1
        deque([-1, 0, 1, 2, 3])
        >>> deque_collections_2 = deque('cd')
        >>> deque_collections_2.extendleft('ba')
        >>> deque_collections_2
        deque(['a', 'b', 'c', 'd'])
        >>> list(our_deque_1) == list(deque_collections_1)
        True
        >>> list(our_deque_2) == list(deque_collections_2)
        True
        """
        for val in iterable:
            self.appendleft(val)

    def pop(self) -> Any:
        """
        Removes the last element of the deque and returns it.
        Time complexity: O(1)
        @returns topop.val: the value of the node to pop.
        >>> our_deque1 = Deque([1])
        >>> our_popped1 = our_deque1.pop()
        >>> our_popped1
        1
        >>> our_deque1
        []

        >>> our_deque2 = Deque([1, 2, 3, 15182])
        >>> our_popped2 = our_deque2.pop()
        >>> our_popped2
        15182
        >>> our_deque2
        [1, 2, 3]

        >>> from collections import deque
        >>> deque_collections = deque([1, 2, 3, 15182])
        >>> collections_popped = deque_collections.pop()
        >>> collections_popped
        15182
        >>> deque_collections
        deque([1, 2, 3])
        >>> list(our_deque2) == list(deque_collections)
        True
        >>> our_popped2 == collections_popped
        True
        """
        # make sure the deque has elements to pop
        assert not self.is_empty(), "Deque is empty."

        topop = self._back
        # if only one element in the queue: point the front and back to None
        # else remove one element from back
        if self._front == self._back:
            self._front = None
            self._back = None
        else:
            self._back = self._back.prev_node  # set new back
            # drop the last node, python will deallocate memory automatically
            self._back.next_node = None

        self._len -= 1

        return topop.val

    def popleft(self) -> Any:
        """
        Removes the first element of the deque and returns it.
        Time complexity: O(1)
        @returns topop.val: the value of the node to pop.
        >>> our_deque1 = Deque([1])
        >>> our_popped1 = our_deque1.pop()
        >>> our_popped1
        1
        >>> our_deque1
        []
        >>> our_deque2 = Deque([15182, 1, 2, 3])
        >>> our_popped2 = our_deque2.popleft()
        >>> our_popped2
        15182
        >>> our_deque2
        [1, 2, 3]
        >>> from collections import deque
        >>> deque_collections = deque([15182, 1, 2, 3])
        >>> collections_popped = deque_collections.popleft()
        >>> collections_popped
        15182
        >>> deque_collections
        deque([1, 2, 3])
        >>> list(our_deque2) == list(deque_collections)
        True
        >>> our_popped2 == collections_popped
        True
        """
        # make sure the deque has elements to pop
        assert not self.is_empty(), "Deque is empty."

        topop = self._front
        # if only one element in the queue: point the front and back to None
        # else remove one element from front
        if self._front == self._back:
            self._front = None
            self._back = None
        else:
            self._front = self._front.next_node  # set new front and drop the first node
            self._front.prev_node = None

        self._len -= 1

        return topop.val

    def is_empty(self) -> bool:
        """
        Checks if the deque is empty.
        Time complexity: O(1)
        >>> our_deque = Deque([1, 2, 3])
        >>> our_deque.is_empty()
        False
        >>> our_empty_deque = Deque()
        >>> our_empty_deque.is_empty()
        True
        >>> from collections import deque
        >>> empty_deque_collections = deque()
        >>> list(our_empty_deque) == list(empty_deque_collections)
        True
        """
        return self._front is None

    def __len__(self) -> int:
        """
        Implements len() function. Returns the length of the deque.
        Time complexity: O(1)
        >>> our_deque = Deque([1, 2, 3])
        >>> len(our_deque)
        3
        >>> our_empty_deque = Deque()
        >>> len(our_empty_deque)
        0
        >>> from collections import deque
        >>> deque_collections = deque([1, 2, 3])
        >>> len(deque_collections)
        3
        >>> empty_deque_collections = deque()
        >>> len(empty_deque_collections)
        0
        >>> len(our_empty_deque) == len(empty_deque_collections)
        True
        """
        return self._len

    def __eq__(self, other: object) -> bool:
        """
        Implements "==" operator. Returns if *self* is equal to *other*.
        Time complexity: O(n)
        >>> our_deque_1 = Deque([1, 2, 3])
        >>> our_deque_2 = Deque([1, 2, 3])
        >>> our_deque_1 == our_deque_2
        True
        >>> our_deque_3 = Deque([1, 2])
        >>> our_deque_1 == our_deque_3
        False
        >>> from collections import deque
        >>> deque_collections_1 = deque([1, 2, 3])
        >>> deque_collections_2 = deque([1, 2, 3])
        >>> deque_collections_1 == deque_collections_2
        True
        >>> deque_collections_3 = deque([1, 2])
        >>> deque_collections_1 == deque_collections_3
        False
        >>> (our_deque_1 == our_deque_2) == (deque_collections_1 == deque_collections_2)
        True
        >>> (our_deque_1 == our_deque_3) == (deque_collections_1 == deque_collections_3)
        True
        """

        if not isinstance(other, Deque):
            return NotImplemented

        me = self._front
        oth = other._front

        # if the length of the dequeues are not the same, they are not equal
        if len(self) != len(other):
            return False

        while me is not None and oth is not None:
            # compare every value
            if me.val != oth.val:
                return False
            me = me.next_node
            oth = oth.next_node

        return True

    def __iter__(self) -> Deque._Iterator:
        """
        Implements iteration.
        Time complexity: O(1)
        >>> our_deque = Deque([1, 2, 3])
        >>> for v in our_deque:
        ...     print(v)
        1
        2
        3
        >>> from collections import deque
        >>> deque_collections = deque([1, 2, 3])
        >>> for v in deque_collections:
        ...     print(v)
        1
        2
        3
        """
        return Deque._Iterator(self._front)

    def __repr__(self) -> str:
        """
        Implements representation of the deque.
        Represents it as a list, with its values between '[' and ']'.
        Time complexity: O(n)
        >>> our_deque = Deque([1, 2, 3])
        >>> our_deque
        [1, 2, 3]
        """
        values_list = []
        aux = self._front
        while aux is not None:
            # append the values in a list to display
            values_list.append(aux.val)
            aux = aux.next_node

        return f"[{', '.join(repr(val) for val in values_list)}]"


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    dq = Deque([3])
    dq.pop()


================================================
FILE: data_structures/queues/linked_queue.py
================================================
"""A Queue using a linked list like structure"""

from __future__ import annotations

from collections.abc import Iterator
from typing import Any


class Node:
    def __init__(self, data: Any) -> None:
        self.data: Any = data
        self.next: Node | None = None

    def __str__(self) -> str:
        return f"{self.data}"


class LinkedQueue:
    """
    >>> queue = LinkedQueue()
    >>> queue.is_empty()
    True
    >>> queue.put(5)
    >>> queue.put(9)
    >>> queue.put('python')
    >>> queue.is_empty()
    False
    >>> queue.get()
    5
    >>> queue.put('algorithms')
    >>> queue.get()
    9
    >>> queue.get()
    'python'
    >>> queue.get()
    'algorithms'
    >>> queue.is_empty()
    True
    >>> queue.get()
    Traceback (most recent call last):
        ...
    IndexError: dequeue from empty queue
    """

    def __init__(self) -> None:
        self.front: Node | None = None
        self.rear: Node | None = None

    def __iter__(self) -> Iterator[Any]:
        node = self.front
        while node:
            yield node.data
            node = node.next

    def __len__(self) -> int:
        """
        >>> queue = LinkedQueue()
        >>> for i in range(1, 6):
        ...     queue.put(i)
        >>> len(queue)
        5
        >>> for i in range(1, 6):
        ...     assert len(queue) == 6 - i
        ...     _ = queue.get()
        >>> len(queue)
        0
        """
        return len(tuple(iter(self)))

    def __str__(self) -> str:
        """
        >>> queue = LinkedQueue()
        >>> for i in range(1, 4):
        ...     queue.put(i)
        >>> queue.put("Python")
        >>> queue.put(3.14)
        >>> queue.put(True)
        >>> str(queue)
        '1 <- 2 <- 3 <- Python <- 3.14 <- True'
        """
        return " <- ".join(str(item) for item in self)

    def is_empty(self) -> bool:
        """
        >>> queue = LinkedQueue()
        >>> queue.is_empty()
        True
        >>> for i in range(1, 6):
        ...     queue.put(i)
        >>> queue.is_empty()
        False
        """
        return len(self) == 0

    def put(self, item: Any) -> None:
        """
        >>> queue = LinkedQueue()
        >>> queue.get()
        Traceback (most recent call last):
            ...
        IndexError: dequeue from empty queue
        >>> for i in range(1, 6):
        ...     queue.put(i)
        >>> str(queue)
        '1 <- 2 <- 3 <- 4 <- 5'
        """
        node = Node(item)
        if self.is_empty():
            self.front = self.rear = node
        else:
            assert isinstance(self.rear, Node)
            self.rear.next = node
            self.rear = node

    def get(self) -> Any:
        """
        >>> queue = LinkedQueue()
        >>> queue.get()
        Traceback (most recent call last):
            ...
        IndexError: dequeue from empty queue
        >>> queue = LinkedQueue()
        >>> for i in range(1, 6):
        ...     queue.put(i)
        >>> for i in range(1, 6):
        ...     assert queue.get() == i
        >>> len(queue)
        0
        """
        if self.is_empty():
            raise IndexError("dequeue from empty queue")
        assert isinstance(self.front, Node)
        node = self.front
        self.front = self.front.next
        if self.front is None:
            self.rear = None
        return node.data

    def clear(self) -> None:
        """
        >>> queue = LinkedQueue()
        >>> for i in range(1, 6):
        ...     queue.put(i)
        >>> queue.clear()
        >>> len(queue)
        0
        >>> str(queue)
        ''
        """
        self.front = self.rear = None


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: data_structures/queues/priority_queue_using_list.py
================================================
"""
Pure Python implementations of a Fixed Priority Queue and an Element Priority Queue
using Python lists.
"""


class OverFlowError(Exception):
    pass


class UnderFlowError(Exception):
    pass


class FixedPriorityQueue:
    """
    Tasks can be added to a Priority Queue at any time and in any order but when Tasks
    are removed then the Task with the highest priority is removed in FIFO order.  In
    code we will use three levels of priority with priority zero Tasks being the most
    urgent (high priority) and priority 2 tasks being the least urgent.

    Examples
    >>> fpq = FixedPriorityQueue()
    >>> fpq.enqueue(0, 10)
    >>> fpq.enqueue(1, 70)
    >>> fpq.enqueue(0, 100)
    >>> fpq.enqueue(2, 1)
    >>> fpq.enqueue(2, 5)
    >>> fpq.enqueue(1, 7)
    >>> fpq.enqueue(2, 4)
    >>> fpq.enqueue(1, 64)
    >>> fpq.enqueue(0, 128)
    >>> print(fpq)
    Priority 0: [10, 100, 128]
    Priority 1: [70, 7, 64]
    Priority 2: [1, 5, 4]
    >>> fpq.dequeue()
    10
    >>> fpq.dequeue()
    100
    >>> fpq.dequeue()
    128
    >>> fpq.dequeue()
    70
    >>> fpq.dequeue()
    7
    >>> print(fpq)
    Priority 0: []
    Priority 1: [64]
    Priority 2: [1, 5, 4]
    >>> fpq.dequeue()
    64
    >>> fpq.dequeue()
    1
    >>> fpq.dequeue()
    5
    >>> fpq.dequeue()
    4
    >>> fpq.dequeue()
    Traceback (most recent call last):
        ...
    data_structures.queues.priority_queue_using_list.UnderFlowError: All queues are empty
    >>> print(fpq)
    Priority 0: []
    Priority 1: []
    Priority 2: []
    """  # noqa: E501

    def __init__(self):
        self.queues = [
            [],
            [],
            [],
        ]

    def enqueue(self, priority: int, data: int) -> None:
        """
        Add an element to a queue based on its priority.
        If the priority is invalid ValueError is raised.
        If the queue is full an OverFlowError is raised.
        """
        try:
            if len(self.queues[priority]) >= 100:
                raise OverflowError("Maximum queue size is 100")
            self.queues[priority].append(data)
        except IndexError:
            raise ValueError("Valid priorities are 0, 1, and 2")

    def dequeue(self) -> int:
        """
        Return the highest priority element in FIFO order.
        If the queue is empty then an under flow exception is raised.
        """
        for queue in self.queues:
            if queue:
                return queue.pop(0)
        raise UnderFlowError("All queues are empty")

    def __str__(self) -> str:
        return "\n".join(f"Priority {i}: {q}" for i, q in enumerate(self.queues))


class ElementPriorityQueue:
    """
    Element Priority Queue is the same as Fixed Priority Queue except that the value of
    the element itself is the priority. The rules for priorities are the same the as
    Fixed Priority Queue.

    >>> epq = ElementPriorityQueue()
    >>> epq.enqueue(10)
    >>> epq.enqueue(70)
    >>> epq.enqueue(4)
    >>> epq.enqueue(1)
    >>> epq.enqueue(5)
    >>> epq.enqueue(7)
    >>> epq.enqueue(4)
    >>> epq.enqueue(64)
    >>> epq.enqueue(128)
    >>> print(epq)
    [10, 70, 4, 1, 5, 7, 4, 64, 128]
    >>> epq.dequeue()
    1
    >>> epq.dequeue()
    4
    >>> epq.dequeue()
    4
    >>> epq.dequeue()
    5
    >>> epq.dequeue()
    7
    >>> epq.dequeue()
    10
    >>> print(epq)
    [70, 64, 128]
    >>> epq.dequeue()
    64
    >>> epq.dequeue()
    70
    >>> epq.dequeue()
    128
    >>> epq.dequeue()
    Traceback (most recent call last):
        ...
    data_structures.queues.priority_queue_using_list.UnderFlowError: The queue is empty
    >>> print(epq)
    []
    """

    def __init__(self):
        self.queue = []

    def enqueue(self, data: int) -> None:
        """
        This function enters the element into the queue
        If the queue is full an Exception is raised saying Over Flow!
        """
        if len(self.queue) == 100:
            raise OverFlowError("Maximum queue size is 100")
        self.queue.append(data)

    def dequeue(self) -> int:
        """
        Return the highest priority element in FIFO order.
        If the queue is empty then an under flow exception is raised.
        """
        if not self.queue:
            raise UnderFlowError("The queue is empty")
        else:
            data = min(self.queue)
            self.queue.remove(data)
            return data

    def __str__(self) -> str:
        """
        Prints all the elements within the Element Priority Queue
        """
        return str(self.queue)


def fixed_priority_queue():
    fpq = FixedPriorityQueue()
    fpq.enqueue(0, 10)
    fpq.enqueue(1, 70)
    fpq.enqueue(0, 100)
    fpq.enqueue(2, 1)
    fpq.enqueue(2, 5)
    fpq.enqueue(1, 7)
    fpq.enqueue(2, 4)
    fpq.enqueue(1, 64)
    fpq.enqueue(0, 128)
    print(fpq)
    print(fpq.dequeue())
    print(fpq.dequeue())
    print(fpq.dequeue())
    print(fpq.dequeue())
    print(fpq.dequeue())
    print(fpq)
    print(fpq.dequeue())
    print(fpq.dequeue())
    print(fpq.dequeue())
    print(fpq.dequeue())
    print(fpq.dequeue())


def element_priority_queue():
    epq = ElementPriorityQueue()
    epq.enqueue(10)
    epq.enqueue(70)
    epq.enqueue(100)
    epq.enqueue(1)
    epq.enqueue(5)
    epq.enqueue(7)
    epq.enqueue(4)
    epq.enqueue(64)
    epq.enqueue(128)
    print(epq)
    print(epq.dequeue())
    print(epq.dequeue())
    print(epq.dequeue())
    print(epq.dequeue())
    print(epq.dequeue())
    print(epq)
    print(epq.dequeue())
    print(epq.dequeue())
    print(epq.dequeue())
    print(epq.dequeue())
    print(epq.dequeue())


if __name__ == "__main__":
    fixed_priority_queue()
    element_priority_queue()


================================================
FILE: data_structures/queues/queue_by_list.py
================================================
"""Queue represented by a Python list"""

from collections.abc import Iterable


class QueueByList[T]:
    def __init__(self, iterable: Iterable[T] | None = None) -> None:
        """
        >>> QueueByList()
        Queue(())
        >>> QueueByList([10, 20, 30])
        Queue((10, 20, 30))
        >>> QueueByList((i**2 for i in range(1, 4)))
        Queue((1, 4, 9))
        """
        self.entries: list[T] = list(iterable or [])

    def __len__(self) -> int:
        """
        >>> len(QueueByList())
        0
        >>> from string import ascii_lowercase
        >>> len(QueueByList(ascii_lowercase))
        26
        >>> queue = QueueByList()
        >>> for i in range(1, 11):
        ...     queue.put(i)
        >>> len(queue)
        10
        >>> for i in range(2):
        ...   queue.get()
        1
        2
        >>> len(queue)
        8
        """

        return len(self.entries)

    def __repr__(self) -> str:
        """
        >>> queue = QueueByList()
        >>> queue
        Queue(())
        >>> str(queue)
        'Queue(())'
        >>> queue.put(10)
        >>> queue
        Queue((10,))
        >>> queue.put(20)
        >>> queue.put(30)
        >>> queue
        Queue((10, 20, 30))
        """

        return f"Queue({tuple(self.entries)})"

    def put(self, item: T) -> None:
        """Put `item` to the Queue

        >>> queue = QueueByList()
        >>> queue.put(10)
        >>> queue.put(20)
        >>> len(queue)
        2
        >>> queue
        Queue((10, 20))
        """

        self.entries.append(item)

    def get(self) -> T:
        """
        Get `item` from the Queue

        >>> queue = QueueByList((10, 20, 30))
        >>> queue.get()
        10
        >>> queue.put(40)
        >>> queue.get()
        20
        >>> queue.get()
        30
        >>> len(queue)
        1
        >>> queue.get()
        40
        >>> queue.get()
        Traceback (most recent call last):
            ...
        IndexError: Queue is empty
        """

        if not self.entries:
            raise IndexError("Queue is empty")
        return self.entries.pop(0)

    def rotate(self, rotation: int) -> None:
        """Rotate the items of the Queue `rotation` times

        >>> queue = QueueByList([10, 20, 30, 40])
        >>> queue
        Queue((10, 20, 30, 40))
        >>> queue.rotate(1)
        >>> queue
        Queue((20, 30, 40, 10))
        >>> queue.rotate(2)
        >>> queue
        Queue((40, 10, 20, 30))
        """

        put = self.entries.append
        get = self.entries.pop

        for _ in range(rotation):
            put(get(0))

    def get_front(self) -> T:
        """Get the front item from the Queue

        >>> queue = QueueByList((10, 20, 30))
        >>> queue.get_front()
        10
        >>> queue
        Queue((10, 20, 30))
        >>> queue.get()
        10
        >>> queue.get_front()
        20
        """

        return self.entries[0]


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: data_structures/queues/queue_by_two_stacks.py
================================================
"""Queue implementation using two stacks"""

from collections.abc import Iterable


class QueueByTwoStacks[T]:
    def __init__(self, iterable: Iterable[T] | None = None) -> None:
        """
        >>> QueueByTwoStacks()
        Queue(())
        >>> QueueByTwoStacks([10, 20, 30])
        Queue((10, 20, 30))
        >>> QueueByTwoStacks((i**2 for i in range(1, 4)))
        Queue((1, 4, 9))
        """
        self._stack1: list[T] = list(iterable or [])
        self._stack2: list[T] = []

    def __len__(self) -> int:
        """
        >>> len(QueueByTwoStacks())
        0
        >>> from string import ascii_lowercase
        >>> len(QueueByTwoStacks(ascii_lowercase))
        26
        >>> queue = QueueByTwoStacks()
        >>> for i in range(1, 11):
        ...     queue.put(i)
        ...
        >>> len(queue)
        10
        >>> for i in range(2):
        ...   queue.get()
        1
        2
        >>> len(queue)
        8
        """

        return len(self._stack1) + len(self._stack2)

    def __repr__(self) -> str:
        """
        >>> queue = QueueByTwoStacks()
        >>> queue
        Queue(())
        >>> str(queue)
        'Queue(())'
        >>> queue.put(10)
        >>> queue
        Queue((10,))
        >>> queue.put(20)
        >>> queue.put(30)
        >>> queue
        Queue((10, 20, 30))
        """
        return f"Queue({tuple(self._stack2[::-1] + self._stack1)})"

    def put(self, item: T) -> None:
        """
        Put `item` into the Queue

        >>> queue = QueueByTwoStacks()
        >>> queue.put(10)
        >>> queue.put(20)
        >>> len(queue)
        2
        >>> queue
        Queue((10, 20))
        """

        self._stack1.append(item)

    def get(self) -> T:
        """
        Get `item` from the Queue

        >>> queue = QueueByTwoStacks((10, 20, 30))
        >>> queue.get()
        10
        >>> queue.put(40)
        >>> queue.get()
        20
        >>> queue.get()
        30
        >>> len(queue)
        1
        >>> queue.get()
        40
        >>> queue.get()
        Traceback (most recent call last):
            ...
        IndexError: Queue is empty
        """

        # To reduce number of attribute look-ups in `while` loop.
        stack1_pop = self._stack1.pop
        stack2_append = self._stack2.append

        if not self._stack2:
            while self._stack1:
                stack2_append(stack1_pop())

        if not self._stack2:
            raise IndexError("Queue is empty")
        return self._stack2.pop()


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: data_structures/queues/queue_on_pseudo_stack.py
================================================
"""Queue represented by a pseudo stack (represented by a list with pop and append)"""

from typing import Any


class Queue:
    def __init__(self):
        self.stack = []
        self.length = 0

    def __str__(self):
        printed = "<" + str(self.stack)[1:-1] + ">"
        return printed

    """Enqueues {@code item}
    @param item
        item to enqueue"""

    def put(self, item: Any) -> None:
        self.stack.append(item)
        self.length = self.length + 1

    """Dequeues {@code item}
    @requirement: |self.length| > 0
    @return dequeued
        item that was dequeued"""

    def get(self) -> Any:
        self.rotate(1)
        dequeued = self.stack[self.length - 1]
        self.stack = self.stack[:-1]
        self.rotate(self.length - 1)
        self.length = self.length - 1
        return dequeued

    """Rotates the queue {@code rotation} times
    @param rotation
        number of times to rotate queue"""

    def rotate(self, rotation: int) -> None:
        for _ in range(rotation):
            temp = self.stack[0]
            self.stack = self.stack[1:]
            self.put(temp)
            self.length = self.length - 1

    """Reports item at the front of self
    @return item at front of self.stack"""

    def front(self) -> Any:
        front = self.get()
        self.put(front)
        self.rotate(self.length - 1)
        return front

    """Returns the length of this.stack"""

    def size(self) -> int:
        return self.length


================================================
FILE: data_structures/stacks/__init__.py
================================================


================================================
FILE: data_structures/stacks/balanced_parentheses.py
================================================
from .stack import Stack


def balanced_parentheses(parentheses: str) -> bool:
    """Use a stack to check if a string of parentheses is balanced.
    >>> balanced_parentheses("([]{})")
    True
    >>> balanced_parentheses("[()]{}{[()()]()}")
    True
    >>> balanced_parentheses("[(])")
    False
    >>> balanced_parentheses("1+2*3-4")
    True
    >>> balanced_parentheses("")
    True
    """
    stack: Stack[str] = Stack()
    bracket_pairs = {"(": ")", "[": "]", "{": "}"}
    for bracket in parentheses:
        if bracket in bracket_pairs:
            stack.push(bracket)
        elif bracket in (")", "]", "}") and (
            stack.is_empty() or bracket_pairs[stack.pop()] != bracket
        ):
            return False
    return stack.is_empty()


if __name__ == "__main__":
    from doctest import testmod

    testmod()

    examples = ["((()))", "((())", "(()))"]
    print("Balanced parentheses demonstration:\n")
    for example in examples:
        not_str = "" if balanced_parentheses(example) else "not "
        print(f"{example} is {not_str}balanced")


================================================
FILE: data_structures/stacks/dijkstras_two_stack_algorithm.py
================================================
"""
Author: Alexander Joslin
GitHub: github.com/echoaj

Explanation:  https://medium.com/@haleesammar/implemented-in-js-dijkstras-2-stack-
              algorithm-for-evaluating-mathematical-expressions-fc0837dae1ea

We can use Dijkstra's two stack algorithm to solve an equation
such as: (5 + ((4 * 2) * (2 + 3)))

THESE ARE THE ALGORITHM'S RULES:
RULE 1: Scan the expression from left to right. When an operand is encountered,
        push it onto the operand stack.

RULE 2: When an operator is encountered in the expression,
        push it onto the operator stack.

RULE 3: When a left parenthesis is encountered in the expression, ignore it.

RULE 4: When a right parenthesis is encountered in the expression,
        pop an operator off the operator stack.  The two operands it must
        operate on must be the last two operands pushed onto the operand stack.
        We therefore pop the operand stack twice, perform the operation,
        and push the result back onto the operand stack so it will be available
        for use as an operand of the next operator popped off the operator stack.

RULE 5: When the entire infix expression has been scanned, the value left on
        the operand stack represents the value of the expression.

NOTE:   It only works with whole numbers.
"""

__author__ = "Alexander Joslin"

import operator as op

from .stack import Stack


def dijkstras_two_stack_algorithm(equation: str) -> int:
    """
    DocTests
    >>> dijkstras_two_stack_algorithm("(5 + 3)")
    8
    >>> dijkstras_two_stack_algorithm("((9 - (2 + 9)) + (8 - 1))")
    5
    >>> dijkstras_two_stack_algorithm("((((3 - 2) - (2 + 3)) + (2 - 4)) + 3)")
    -3

    :param equation: a string
    :return: result: an integer
    """
    operators = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

    operand_stack: Stack[int] = Stack()
    operator_stack: Stack[str] = Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(i))
        elif i in operators:
            # RULE 2
            operator_stack.push(i)
        elif i == ")":
            # RULE 4
            opr = operator_stack.peek()
            operator_stack.pop()
            num1 = operand_stack.peek()
            operand_stack.pop()
            num2 = operand_stack.peek()
            operand_stack.pop()

            total = operators[opr](num2, num1)
            operand_stack.push(total)

    # RULE 5
    return operand_stack.peek()


if __name__ == "__main__":
    equation = "(5 + ((4 * 2) * (2 + 3)))"
    # answer = 45
    print(f"{equation} = {dijkstras_two_stack_algorithm(equation)}")


================================================
FILE: data_structures/stacks/infix_to_postfix_conversion.py
================================================
"""
https://en.wikipedia.org/wiki/Infix_notation
https://en.wikipedia.org/wiki/Reverse_Polish_notation
https://en.wikipedia.org/wiki/Shunting-yard_algorithm
"""

from typing import Literal

from .balanced_parentheses import balanced_parentheses
from .stack import Stack

PRECEDENCES: dict[str, int] = {
    "+": 1,
    "-": 1,
    "*": 2,
    "/": 2,
    "^": 3,
}
ASSOCIATIVITIES: dict[str, Literal["LR", "RL"]] = {
    "+": "LR",
    "-": "LR",
    "*": "LR",
    "/": "LR",
    "^": "RL",
}


def precedence(char: str) -> int:
    """
    Return integer value representing an operator's precedence, or
    order of operation.
    https://en.wikipedia.org/wiki/Order_of_operations
    """
    return PRECEDENCES.get(char, -1)


def associativity(char: str) -> Literal["LR", "RL"]:
    """
    Return the associativity of the operator `char`.
    https://en.wikipedia.org/wiki/Operator_associativity
    """
    return ASSOCIATIVITIES[char]


def infix_to_postfix(expression_str: str) -> str:
    """
    >>> infix_to_postfix("(1*(2+3)+4))")
    Traceback (most recent call last):
        ...
    ValueError: Mismatched parentheses
    >>> infix_to_postfix("")
    ''
    >>> infix_to_postfix("3+2")
    '3 2 +'
    >>> infix_to_postfix("(3+4)*5-6")
    '3 4 + 5 * 6 -'
    >>> infix_to_postfix("(1+2)*3/4-5")
    '1 2 + 3 * 4 / 5 -'
    >>> infix_to_postfix("a+b*c+(d*e+f)*g")
    'a b c * + d e * f + g * +'
    >>> infix_to_postfix("x^y/(5*z)+2")
    'x y ^ 5 z * / 2 +'
    >>> infix_to_postfix("2^3^2")
    '2 3 2 ^ ^'
    """
    if not balanced_parentheses(expression_str):
        raise ValueError("Mismatched parentheses")
    stack: Stack[str] = Stack()
    postfix = []
    for char in expression_str:
        if char.isalpha() or char.isdigit():
            postfix.append(char)
        elif char == "(":
            stack.push(char)
        elif char == ")":
            while not stack.is_empty() and stack.peek() != "(":
                postfix.append(stack.pop())
            stack.pop()
        else:
            while True:
                if stack.is_empty():
                    stack.push(char)
                    break

                char_precedence = precedence(char)
                tos_precedence = precedence(stack.peek())

                if char_precedence > tos_precedence:
                    stack.push(char)
                    break
                if char_precedence < tos_precedence:
                    postfix.append(stack.pop())
                    continue
                # Precedences are equal
                if associativity(char) == "RL":
                    stack.push(char)
                    break
                postfix.append(stack.pop())

    while not stack.is_empty():
        postfix.append(stack.pop())
    return " ".join(postfix)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    expression = "a+b*(c^d-e)^(f+g*h)-i"

    print("Infix to Postfix Notation demonstration:\n")
    print("Infix notation: " + expression)
    print("Postfix notation: " + infix_to_postfix(expression))


================================================
FILE: data_structures/stacks/infix_to_prefix_conversion.py
================================================
"""
Output:

Enter an Infix Equation = a + b ^c
 Symbol  |  Stack  | Postfix
----------------------------
   c     |         | c
   ^     | ^       | c
   b     | ^       | cb
   +     | +       | cb^
   a     | +       | cb^a
         |         | cb^a+

         a+b^c (Infix) ->  +a^bc (Prefix)
"""


def infix_2_postfix(infix: str) -> str:
    """
    >>> infix_2_postfix("a+b^c")  # doctest: +NORMALIZE_WHITESPACE
     Symbol  |  Stack  | Postfix
    ----------------------------
       a     |         | a
       +     | +       | a
       b     | +       | ab
       ^     | +^      | ab
       c     | +^      | abc
             | +       | abc^
             |         | abc^+
    'abc^+'

    >>> infix_2_postfix("1*((-a)*2+b)")   # doctest: +NORMALIZE_WHITESPACE
      Symbol  |    Stack     |   Postfix
    -------------------------------------------
       1     |              | 1
       *     | *            | 1
       (     | *(           | 1
       (     | *((          | 1
       -     | *((-         | 1
       a     | *((-         | 1a
       )     | *(           | 1a-
       *     | *(*          | 1a-
       2     | *(*          | 1a-2
       +     | *(+          | 1a-2*
       b     | *(+          | 1a-2*b
       )     | *            | 1a-2*b+
             |              | 1a-2*b+*
    '1a-2*b+*'

    >>> infix_2_postfix("")
     Symbol  |  Stack  | Postfix
    ----------------------------
    ''

    >>> infix_2_postfix("(()")
    Traceback (most recent call last):
        ...
    ValueError: invalid expression

    >>> infix_2_postfix("())")
    Traceback (most recent call last):
        ...
    IndexError: list index out of range
    """
    stack = []
    post_fix = []
    priority = {
        "^": 3,
        "*": 2,
        "/": 2,
        "%": 2,
        "+": 1,
        "-": 1,
    }  # Priority of each operator
    print_width = max(len(infix), 7)

    # Print table header for output
    print(
        "Symbol".center(8),
        "Stack".center(print_width),
        "Postfix".center(print_width),
        sep=" | ",
    )
    print("-" * (print_width * 3 + 7))

    for x in infix:
        if x.isalpha() or x.isdigit():
            post_fix.append(x)  # if x is Alphabet / Digit, add it to Postfix
        elif x == "(":
            stack.append(x)  # if x is "(" push to Stack
        elif x == ")":  # if x is ")" pop stack until "(" is encountered
            if len(stack) == 0:  # close bracket without open bracket
                raise IndexError("list index out of range")

            while stack[-1] != "(":
                post_fix.append(stack.pop())  # Pop stack & add the content to Postfix
            stack.pop()
        elif len(stack) == 0:
            stack.append(x)  # If stack is empty, push x to stack
        else:  # while priority of x is not > priority of element in the stack
            while stack and stack[-1] != "(" and priority[x] <= priority[stack[-1]]:
                post_fix.append(stack.pop())  # pop stack & add to Postfix
            stack.append(x)  # push x to stack

        print(
            x.center(8),
            ("".join(stack)).ljust(print_width),
            ("".join(post_fix)).ljust(print_width),
            sep=" | ",
        )  # Output in tabular format

    while len(stack) > 0:  # while stack is not empty
        if stack[-1] == "(":  # open bracket with no close bracket
            raise ValueError("invalid expression")

        post_fix.append(stack.pop())  # pop stack & add to Postfix
        print(
            " ".center(8),
            ("".join(stack)).ljust(print_width),
            ("".join(post_fix)).ljust(print_width),
            sep=" | ",
        )  # Output in tabular format

    return "".join(post_fix)  # return Postfix as str


def infix_2_prefix(infix: str) -> str:
    """
    >>> infix_2_prefix("a+b^c")  # doctest: +NORMALIZE_WHITESPACE
     Symbol  |  Stack  | Postfix
    ----------------------------
       c     |         | c
       ^     | ^       | c
       b     | ^       | cb
       +     | +       | cb^
       a     | +       | cb^a
             |         | cb^a+
    '+a^bc'

    >>> infix_2_prefix("1*((-a)*2+b)") # doctest: +NORMALIZE_WHITESPACE
     Symbol  |    Stack     |   Postfix
    -------------------------------------------
       (     | (            |
       b     | (            | b
       +     | (+           | b
       2     | (+           | b2
       *     | (+*          | b2
       (     | (+*(         | b2
       a     | (+*(         | b2a
       -     | (+*(-        | b2a
       )     | (+*          | b2a-
       )     |              | b2a-*+
       *     | *            | b2a-*+
       1     | *            | b2a-*+1
             |              | b2a-*+1*
    '*1+*-a2b'

    >>> infix_2_prefix('')
     Symbol  |  Stack  | Postfix
    ----------------------------
    ''

    >>> infix_2_prefix('(()')
    Traceback (most recent call last):
        ...
    IndexError: list index out of range

    >>> infix_2_prefix('())')
    Traceback (most recent call last):
        ...
    ValueError: invalid expression
    """
    reversed_infix = list(infix[::-1])  # reverse the infix equation

    for i in range(len(reversed_infix)):
        if reversed_infix[i] == "(":
            reversed_infix[i] = ")"  # change "(" to ")"
        elif reversed_infix[i] == ")":
            reversed_infix[i] = "("  # change ")" to "("

    # call infix_2_postfix on Infix, return reverse of Postfix
    return (infix_2_postfix("".join(reversed_infix)))[::-1]


if __name__ == "__main__":
    from doctest import testmod

    testmod()

    Infix = input("\nEnter an Infix Equation = ")  # Input an Infix equation
    Infix = "".join(Infix.split())  # Remove spaces from the input
    print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")


================================================
FILE: data_structures/stacks/largest_rectangle_histogram.py
================================================
def largest_rectangle_area(heights: list[int]) -> int:
    """
    Inputs an array of integers representing the heights of bars,
    and returns the area of the largest rectangle that can be formed

    >>> largest_rectangle_area([2, 1, 5, 6, 2, 3])
    10

    >>> largest_rectangle_area([2, 4])
    4

    >>> largest_rectangle_area([6, 2, 5, 4, 5, 1, 6])
    12

    >>> largest_rectangle_area([1])
    1
    """
    stack: list[int] = []
    max_area = 0
    heights = [*heights, 0]  # make a new list by appending the sentinel 0
    n = len(heights)

    for i in range(n):
        # make sure the stack remains in increasing order
        while stack and heights[i] < heights[stack[-1]]:
            h = heights[stack.pop()]  # height of the bar
            # if stack is empty, it means entire width can be taken from index 0 to i-1
            w = i if not stack else i - stack[-1] - 1  # calculate width
            max_area = max(max_area, h * w)

        stack.append(i)

    return max_area


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: data_structures/stacks/lexicographical_numbers.py
================================================
from collections.abc import Iterator


def lexical_order(max_number: int) -> Iterator[int]:
    """
    Generate numbers in lexical order from 1 to max_number.

    >>> " ".join(map(str, lexical_order(13)))
    '1 10 11 12 13 2 3 4 5 6 7 8 9'
    >>> list(lexical_order(1))
    [1]
    >>> " ".join(map(str, lexical_order(20)))
    '1 10 11 12 13 14 15 16 17 18 19 2 20 3 4 5 6 7 8 9'
    >>> " ".join(map(str, lexical_order(25)))
    '1 10 11 12 13 14 15 16 17 18 19 2 20 21 22 23 24 25 3 4 5 6 7 8 9'
    >>> list(lexical_order(12))
    [1, 10, 11, 12, 2, 3, 4, 5, 6, 7, 8, 9]
    """

    stack = [1]

    while stack:
        num = stack.pop()
        if num > max_number:
            continue

        yield num
        if (num % 10) != 9:
            stack.append(num + 1)

        stack.append(num * 10)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    print(f"Numbers from 1 to 25 in lexical order: {list(lexical_order(26))}")


================================================
FILE: data_structures/stacks/next_greater_element.py
================================================
from __future__ import annotations

arr = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
expect = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]


def next_greatest_element_slow(arr: list[float]) -> list[float]:
    """
    Get the Next Greatest Element (NGE) for each element in the array
    by checking all subsequent elements to find the next greater one.

    This is a brute-force implementation, and it has a time complexity
    of O(n^2), where n is the size of the array.

    Args:
        arr: List of numbers for which the NGE is calculated.

    Returns:
        List containing the next greatest elements. If no
        greater element is found, -1 is placed in the result.

    Example:
    >>> next_greatest_element_slow(arr) == expect
    True
    """

    result = []
    arr_size = len(arr)

    for i in range(arr_size):
        next_element: float = -1
        for j in range(i + 1, arr_size):
            if arr[i] < arr[j]:
                next_element = arr[j]
                break
        result.append(next_element)
    return result


def next_greatest_element_fast(arr: list[float]) -> list[float]:
    """
    Find the Next Greatest Element (NGE) for each element in the array
    using a more readable approach. This implementation utilizes
    enumerate() for the outer loop and slicing for the inner loop.

    While this improves readability over next_greatest_element_slow(),
    it still has a time complexity of O(n^2).

    Args:
        arr: List of numbers for which the NGE is calculated.

    Returns:
        List containing the next greatest elements. If no
        greater element is found, -1 is placed in the result.

    Example:
    >>> next_greatest_element_fast(arr) == expect
    True
    """
    result = []
    for i, outer in enumerate(arr):
        next_item: float = -1
        for inner in arr[i + 1 :]:
            if outer < inner:
                next_item = inner
                break
        result.append(next_item)
    return result


def next_greatest_element(arr: list[float]) -> list[float]:
    """
    Efficient solution to find the Next Greatest Element (NGE) for all elements
    using a stack. The time complexity is reduced to O(n), making it suitable
    for larger arrays.

    The stack keeps track of elements for which the next greater element hasn't
    been found yet. By iterating through the array in reverse (from the last
    element to the first), the stack is used to efficiently determine the next
    greatest element for each element.

    Args:
        arr: List of numbers for which the NGE is calculated.

    Returns:
        List containing the next greatest elements. If no
        greater element is found, -1 is placed in the result.

    Example:
    >>> next_greatest_element(arr) == expect
    True
    """
    arr_size = len(arr)
    stack: list[float] = []
    result: list[float] = [-1] * arr_size

    for index in reversed(range(arr_size)):
        if stack:
            while stack[-1] <= arr[index]:
                stack.pop()
                if not stack:
                    break
        if stack:
            result[index] = stack[-1]
        stack.append(arr[index])
    return result


if __name__ == "__main__":
    from doctest import testmod
    from timeit import timeit

    testmod()
    print(next_greatest_element_slow(arr))
    print(next_greatest_element_fast(arr))
    print(next_greatest_element(arr))

    setup = (
        "from __main__ import arr, next_greatest_element_slow, "
        "next_greatest_element_fast, next_greatest_element"
    )
    print(
        "next_greatest_element_slow():",
        timeit("next_greatest_element_slow(arr)", setup=setup),
    )
    print(
        "next_greatest_element_fast():",
        timeit("next_greatest_element_fast(arr)", setup=setup),
    )
    print(
        "     next_greatest_element():",
        timeit("next_greatest_element(arr)", setup=setup),
    )


================================================
FILE: data_structures/stacks/postfix_evaluation.py
================================================
"""
Reverse Polish Nation is also known as Polish postfix notation or simply postfix
notation.
https://en.wikipedia.org/wiki/Reverse_Polish_notation
Classic examples of simple stack implementations.
Valid operators are +, -, *, /.
Each operand may be an integer or another expression.

Output:

Enter a Postfix Equation (space separated) = 5 6 9 * +
 Symbol  |    Action    | Stack
-----------------------------------
       5 | push(5)      | 5
       6 | push(6)      | 5,6
       9 | push(9)      | 5,6,9
         | pop(9)       | 5,6
         | pop(6)       | 5
       * | push(6*9)    | 5,54
         | pop(54)      | 5
         | pop(5)       |
       + | push(5+54)   | 59

        Result =  59
"""

# Defining valid unary operator symbols
UNARY_OP_SYMBOLS = ("-", "+")

# operators & their respective operation
OPERATORS = {
    "^": lambda p, q: p**q,
    "*": lambda p, q: p * q,
    "/": lambda p, q: p / q,
    "+": lambda p, q: p + q,
    "-": lambda p, q: p - q,
}


def parse_token(token: str | float) -> float | str:
    """
    Converts the given data to the appropriate number if it is indeed a number, else
    returns the data as it is with a False flag. This function also serves as a check
    of whether the input is a number or not.

    Parameters
    ----------
    token: The data that needs to be converted to the appropriate operator or number.

    Returns
    -------
    float or str
        Returns a float if `token` is a number or a str if `token` is an operator
    """
    if token in OPERATORS:
        return token
    try:
        return float(token)
    except ValueError:
        msg = f"{token} is neither a number nor a valid operator"
        raise ValueError(msg)


def evaluate(post_fix: list[str], verbose: bool = False) -> float:
    """
    Evaluate postfix expression using a stack.
    >>> evaluate(["0"])
    0.0
    >>> evaluate(["-0"])
    -0.0
    >>> evaluate(["1"])
    1.0
    >>> evaluate(["-1"])
    -1.0
    >>> evaluate(["-1.1"])
    -1.1
    >>> evaluate(["2", "1", "+", "3", "*"])
    9.0
    >>> evaluate(["2", "1.9", "+", "3", "*"])
    11.7
    >>> evaluate(["2", "-1.9", "+", "3", "*"])
    0.30000000000000027
    >>> evaluate(["4", "13", "5", "/", "+"])
    6.6
    >>> evaluate(["2", "-", "3", "+"])
    1.0
    >>> evaluate(["-4", "5", "*", "6", "-"])
    -26.0
    >>> evaluate([])
    0
    >>> evaluate(["4", "-", "6", "7", "/", "9", "8"])
    Traceback (most recent call last):
    ...
    ArithmeticError: Input is not a valid postfix expression

    Parameters
    ----------
    post_fix:
        The postfix expression is tokenized into operators and operands and stored
        as a Python list

    verbose:
        Display stack contents while evaluating the expression if verbose is True

    Returns
    -------
    float
        The evaluated value
    """
    if not post_fix:
        return 0
    # Checking the list to find out whether the postfix expression is valid
    valid_expression = [parse_token(token) for token in post_fix]
    if verbose:
        # print table header
        print("Symbol".center(8), "Action".center(12), "Stack", sep=" | ")
        print("-" * (30 + len(post_fix)))
    stack = []
    for x in valid_expression:
        if x not in OPERATORS:
            stack.append(x)  # append x to stack
            if verbose:
                # output in tabular format
                print(
                    f"{x}".rjust(8),
                    f"push({x})".ljust(12),
                    stack,
                    sep=" | ",
                )
            continue
        # If x is operator
        # If only 1 value is inside the stack and + or - is encountered
        # then this is unary + or - case
        if x in UNARY_OP_SYMBOLS and len(stack) < 2:
            b = stack.pop()  # pop stack
            if x == "-":
                b *= -1  # negate b
            stack.append(b)
            if verbose:
                # output in tabular format
                print(
                    "".rjust(8),
                    f"pop({b})".ljust(12),
                    stack,
                    sep=" | ",
                )
                print(
                    str(x).rjust(8),
                    f"push({x}{b})".ljust(12),
                    stack,
                    sep=" | ",
                )
            continue
        b = stack.pop()  # pop stack
        if verbose:
            # output in tabular format
            print(
                "".rjust(8),
                f"pop({b})".ljust(12),
                stack,
                sep=" | ",
            )

        a = stack.pop()  # pop stack
        if verbose:
            # output in tabular format
            print(
                "".rjust(8),
                f"pop({a})".ljust(12),
                stack,
                sep=" | ",
            )
        # evaluate the 2 values popped from stack & push result to stack
        stack.append(OPERATORS[x](a, b))  # type: ignore[index]
        if verbose:
            # output in tabular format
            print(
                f"{x}".rjust(8),
                f"push({a}{x}{b})".ljust(12),
                stack,
                sep=" | ",
            )
    # If everything is executed correctly, the stack will contain
    # only one element which is the result
    if len(stack) != 1:
        raise ArithmeticError("Input is not a valid postfix expression")
    return float(stack[0])


if __name__ == "__main__":
    # Create a loop so that the user can evaluate postfix expressions multiple times
    while True:
        expression = input("Enter a Postfix Expression (space separated): ").split(" ")
        prompt = "Do you want to see stack contents while evaluating? [y/N]: "
        verbose = input(prompt).strip().lower() == "y"
        output = evaluate(expression, verbose)
        print("Result = ", output)
        prompt = "Do you want to enter another expression? [y/N]: "
        if input(prompt).strip().lower() != "y":
            break


================================================
FILE: data_structures/stacks/prefix_evaluation.py
================================================
"""
Program to evaluate a prefix expression.
https://en.wikipedia.org/wiki/Polish_notation
"""

operators = {
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}


def is_operand(c):
    """
    Return True if the given char c is an operand, e.g. it is a number

    >>> is_operand("1")
    True
    >>> is_operand("+")
    False
    """
    return c.isdigit()


def evaluate(expression):
    """
    Evaluate a given expression in prefix notation.
    Asserts that the given expression is valid.

    >>> evaluate("+ 9 * 2 6")
    21
    >>> evaluate("/ * 10 2 + 4 1 ")
    4.0
    >>> evaluate("2")
    2
    >>> evaluate("+ * 2 3 / 8 4")
    8.0
    """
    stack = []

    # iterate over the string in reverse order
    for c in expression.split()[::-1]:
        # push operand to stack
        if is_operand(c):
            stack.append(int(c))

        else:
            # pop values from stack can calculate the result
            # push the result onto the stack again
            o1 = stack.pop()
            o2 = stack.pop()
            stack.append(operators[c](o1, o2))

    return stack.pop()


def evaluate_recursive(expression: list[str]):
    """
    Alternative recursive implementation

    >>> evaluate_recursive(['2'])
    2
    >>> expression = ['+', '*', '2', '3', '/', '8', '4']
    >>> evaluate_recursive(expression)
    8.0
    >>> expression
    []
    >>> evaluate_recursive(['+', '9', '*', '2', '6'])
    21
    >>> evaluate_recursive(['/', '*', '10', '2', '+', '4', '1'])
    4.0
    """

    op = expression.pop(0)
    if is_operand(op):
        return int(op)

    operation = operators[op]

    a = evaluate_recursive(expression)
    b = evaluate_recursive(expression)
    return operation(a, b)


# Driver code
if __name__ == "__main__":
    test_expression = "+ 9 * 2 6"
    print(evaluate(test_expression))

    test_expression = "/ * 10 2 + 4 1 "
    print(evaluate(test_expression))


================================================
FILE: data_structures/stacks/stack.py
================================================
from __future__ import annotations

from typing import TypeVar

T = TypeVar("T")


class StackOverflowError(BaseException):
    pass


class StackUnderflowError(BaseException):
    pass


class Stack[T]:
    """A stack is an abstract data type that serves as a collection of
    elements with two principal operations: push() and pop(). push() adds an
    element to the top of the stack, and pop() removes an element from the top
    of a stack. The order in which elements come off of a stack are
    Last In, First Out (LIFO).
    https://en.wikipedia.org/wiki/Stack_(abstract_data_type)
    """

    def __init__(self, limit: int = 10):
        self.stack: list[T] = []
        self.limit = limit

    def __bool__(self) -> bool:
        return bool(self.stack)

    def __str__(self) -> str:
        return str(self.stack)

    def push(self, data: T) -> None:
        """
        Push an element to the top of the stack.

        >>> S = Stack(2) # stack size = 2
        >>> S.push(10)
        >>> S.push(20)
        >>> print(S)
        [10, 20]

        >>> S = Stack(1) # stack size = 1
        >>> S.push(10)
        >>> S.push(20)
        Traceback (most recent call last):
        ...
        data_structures.stacks.stack.StackOverflowError

        """
        if len(self.stack) >= self.limit:
            raise StackOverflowError
        self.stack.append(data)

    def pop(self) -> T:
        """
        Pop an element off of the top of the stack.

        >>> S = Stack()
        >>> S.push(-5)
        >>> S.push(10)
        >>> S.pop()
        10

        >>> Stack().pop()
        Traceback (most recent call last):
            ...
        data_structures.stacks.stack.StackUnderflowError
        """
        if not self.stack:
            raise StackUnderflowError
        return self.stack.pop()

    def peek(self) -> T:
        """
        Peek at the top-most element of the stack.

        >>> S = Stack()
        >>> S.push(-5)
        >>> S.push(10)
        >>> S.peek()
        10

        >>> Stack().peek()
        Traceback (most recent call last):
            ...
        data_structures.stacks.stack.StackUnderflowError
        """
        if not self.stack:
            raise StackUnderflowError
        return self.stack[-1]

    def is_empty(self) -> bool:
        """
        Check if a stack is empty.

        >>> S = Stack()
        >>> S.is_empty()
        True

        >>> S = Stack()
        >>> S.push(10)
        >>> S.is_empty()
        False
        """
        return not bool(self.stack)

    def is_full(self) -> bool:
        """
        >>> S = Stack()
        >>> S.is_full()
        False

        >>> S = Stack(1)
        >>> S.push(10)
        >>> S.is_full()
        True
        """
        return self.size() == self.limit

    def size(self) -> int:
        """
        Return the size of the stack.

        >>> S = Stack(3)
        >>> S.size()
        0

        >>> S = Stack(3)
        >>> S.push(10)
        >>> S.size()
        1

        >>> S = Stack(3)
        >>> S.push(10)
        >>> S.push(20)
        >>> S.size()
        2
        """
        return len(self.stack)

    def __contains__(self, item: T) -> bool:
        """
        Check if item is in stack

        >>> S = Stack(3)
        >>> S.push(10)
        >>> 10 in S
        True

        >>> S = Stack(3)
        >>> S.push(10)
        >>> 20 in S
        False
        """
        return item in self.stack


def test_stack() -> None:
    """
    >>> test_stack()
    """
    stack: Stack[int] = Stack(10)
    assert bool(stack) is False
    assert stack.is_empty() is True
    assert stack.is_full() is False
    assert str(stack) == "[]"

    try:
        _ = stack.pop()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    try:
        _ = stack.peek()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    for i in range(10):
        assert stack.size() == i
        stack.push(i)

    assert bool(stack)
    assert not stack.is_empty()
    assert stack.is_full()
    assert str(stack) == str(list(range(10)))
    assert stack.pop() == 9
    assert stack.peek() == 8

    stack.push(100)
    assert str(stack) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100])

    try:
        stack.push(200)
        raise AssertionError  # This should not happen
    except StackOverflowError:
        assert True  # This should happen

    assert not stack.is_empty()
    assert stack.size() == 10

    assert 5 in stack
    assert 55 not in stack


if __name__ == "__main__":
    test_stack()

    import doctest

    doctest.testmod()


================================================
FILE: data_structures/stacks/stack_using_two_queues.py
================================================
from __future__ import annotations

from collections import deque
from dataclasses import dataclass, field


@dataclass
class StackWithQueues:
    """
    https://www.geeksforgeeks.org/implement-stack-using-queue/

    >>> stack = StackWithQueues()
    >>> stack.push(1)
    >>> stack.push(2)
    >>> stack.push(3)
    >>> stack.peek()
    3
    >>> stack.pop()
    3
    >>> stack.peek()
    2
    >>> stack.pop()
    2
    >>> stack.pop()
    1
    >>> stack.peek() is None
    True
    >>> stack.pop()
    Traceback (most recent call last):
        ...
    IndexError: pop from an empty deque
    """

    main_queue: deque[int] = field(default_factory=deque)
    temp_queue: deque[int] = field(default_factory=deque)

    def push(self, item: int) -> None:
        self.temp_queue.append(item)
        while self.main_queue:
            self.temp_queue.append(self.main_queue.popleft())
        self.main_queue, self.temp_queue = self.temp_queue, self.main_queue

    def pop(self) -> int:
        return self.main_queue.popleft()

    def peek(self) -> int | None:
        return self.main_queue[0] if self.main_queue else None


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    stack: StackWithQueues | None = StackWithQueues()
    while stack:
        print("\nChoose operation:")
        print("1. Push")
        print("2. Pop")
        print("3. Peek")
        print("4. Quit")

        choice = input("Enter choice (1/2/3/4): ")

        if choice == "1":
            element = int(input("Enter an integer to push: ").strip())
            stack.push(element)
            print(f"{element} pushed onto the stack.")
        elif choice == "2":
            popped_element = stack.pop()
            if popped_element is not None:
                print(f"Popped element: {popped_element}")
            else:
                print("Stack is empty.")
        elif choice == "3":
            peeked_element = stack.peek()
            if peeked_element is not None:
                print(f"Top element: {peeked_element}")
            else:
                print("Stack is empty.")
        elif choice == "4":
            del stack
            stack = None
        else:
            print("Invalid choice. Please try again.")


================================================
FILE: data_structures/stacks/stack_with_doubly_linked_list.py
================================================
# A complete working Python program to demonstrate all
# stack operations using a doubly linked list

from __future__ import annotations

from typing import TypeVar

T = TypeVar("T")


class Node[T]:
    def __init__(self, data: T):
        self.data = data  # Assign data
        self.next: Node[T] | None = None  # Initialize next as null
        self.prev: Node[T] | None = None  # Initialize prev as null


class Stack[T]:
    """
    >>> stack = Stack()
    >>> stack.is_empty()
    True
    >>> stack.print_stack()
    stack elements are:
    >>> for i in range(4):
    ...     stack.push(i)
    ...
    >>> stack.is_empty()
    False
    >>> stack.print_stack()
    stack elements are:
    3->2->1->0->
    >>> stack.top()
    3
    >>> len(stack)
    4
    >>> stack.pop()
    3
    >>> stack.print_stack()
    stack elements are:
    2->1->0->
    """

    def __init__(self) -> None:
        self.head: Node[T] | None = None

    def push(self, data: T) -> None:
        """add a Node to the stack"""
        if self.head is None:
            self.head = Node(data)
        else:
            new_node = Node(data)
            self.head.prev = new_node
            new_node.next = self.head
            new_node.prev = None
            self.head = new_node

    def pop(self) -> T | None:
        """pop the top element off the stack"""
        if self.head is None:
            return None
        else:
            assert self.head is not None
            temp = self.head.data
            self.head = self.head.next
            if self.head is not None:
                self.head.prev = None
            return temp

    def top(self) -> T | None:
        """return the top element of the stack"""
        return self.head.data if self.head is not None else None

    def __len__(self) -> int:
        temp = self.head
        count = 0
        while temp is not None:
            count += 1
            temp = temp.next
        return count

    def is_empty(self) -> bool:
        return self.head is None

    def print_stack(self) -> None:
        print("stack elements are:")
        temp = self.head
        while temp is not None:
            print(temp.data, end="->")
            temp = temp.next


# Code execution starts here
if __name__ == "__main__":
    # Start with the empty stack
    stack: Stack[int] = Stack()

    # Insert 4 at the beginning. So stack becomes 4->None
    print("Stack operations using Doubly LinkedList")
    stack.push(4)

    # Insert 5 at the beginning. So stack becomes 4->5->None
    stack.push(5)

    # Insert 6 at the beginning. So stack becomes 4->5->6->None
    stack.push(6)

    # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
    stack.push(7)

    # Print the stack
    stack.print_stack()

    # Print the top element
    print("\nTop element is ", stack.top())

    # Print the stack size
    print("Size of the stack is ", len(stack))

    # pop the top element
    stack.pop()

    # pop the top element
    stack.pop()

    # two elements have now been popped off
    stack.print_stack()

    # Print True if the stack is empty else False
    print("\nstack is empty:", stack.is_empty())


================================================
FILE: data_structures/stacks/stack_with_singly_linked_list.py
================================================
"""A Stack using a linked list like structure"""

from __future__ import annotations

from collections.abc import Iterator
from typing import TypeVar

T = TypeVar("T")


class Node[T]:
    def __init__(self, data: T):
        self.data = data
        self.next: Node[T] | None = None

    def __str__(self) -> str:
        return f"{self.data}"


class LinkedStack[T]:
    """
    Linked List Stack implementing push (to top),
    pop (from top) and is_empty

    >>> stack = LinkedStack()
    >>> stack.is_empty()
    True
    >>> stack.push(5)
    >>> stack.push(9)
    >>> stack.push('python')
    >>> stack.is_empty()
    False
    >>> stack.pop()
    'python'
    >>> stack.push('algorithms')
    >>> stack.pop()
    'algorithms'
    >>> stack.pop()
    9
    >>> stack.pop()
    5
    >>> stack.is_empty()
    True
    >>> stack.pop()
    Traceback (most recent call last):
        ...
    IndexError: pop from empty stack
    """

    def __init__(self) -> None:
        self.top: Node[T] | None = None

    def __iter__(self) -> Iterator[T]:
        node = self.top
        while node:
            yield node.data
            node = node.next

    def __str__(self) -> str:
        """
        >>> stack = LinkedStack()
        >>> stack.push("c")
        >>> stack.push("b")
        >>> stack.push("a")
        >>> str(stack)
        'a->b->c'
        """
        return "->".join([str(item) for item in self])

    def __len__(self) -> int:
        """
        >>> stack = LinkedStack()
        >>> len(stack) == 0
        True
        >>> stack.push("c")
        >>> stack.push("b")
        >>> stack.push("a")
        >>> len(stack) == 3
        True
        """
        return len(tuple(iter(self)))

    def is_empty(self) -> bool:
        """
        >>> stack = LinkedStack()
        >>> stack.is_empty()
        True
        >>> stack.push(1)
        >>> stack.is_empty()
        False
        """
        return self.top is None

    def push(self, item: T) -> None:
        """
        >>> stack = LinkedStack()
        >>> stack.push("Python")
        >>> stack.push("Java")
        >>> stack.push("C")
        >>> str(stack)
        'C->Java->Python'
        """
        node = Node(item)
        if not self.is_empty():
            node.next = self.top
        self.top = node

    def pop(self) -> T:
        """
        >>> stack = LinkedStack()
        >>> stack.pop()
        Traceback (most recent call last):
            ...
        IndexError: pop from empty stack
        >>> stack.push("c")
        >>> stack.push("b")
        >>> stack.push("a")
        >>> stack.pop() == 'a'
        True
        >>> stack.pop() == 'b'
        True
        >>> stack.pop() == 'c'
        True
        """
        if self.is_empty():
            raise IndexError("pop from empty stack")
        assert isinstance(self.top, Node)
        pop_node = self.top
        self.top = self.top.next
        return pop_node.data

    def peek(self) -> T:
        """
        >>> stack = LinkedStack()
        >>> stack.push("Java")
        >>> stack.push("C")
        >>> stack.push("Python")
        >>> stack.peek()
        'Python'
        """
        if self.is_empty():
            raise IndexError("peek from empty stack")

        assert self.top is not None
        return self.top.data

    def clear(self) -> None:
        """
        >>> stack = LinkedStack()
        >>> stack.push("Java")
        >>> stack.push("C")
        >>> stack.push("Python")
        >>> str(stack)
        'Python->C->Java'
        >>> stack.clear()
        >>> len(stack) == 0
        True
        """
        self.top = None


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: data_structures/stacks/stock_span_problem.py
================================================
"""
The stock span problem is a financial problem where we have a series of n daily
price quotes for a stock and we need to calculate span of stock's price for all n days.

The span Si of the stock's price on a given day i is defined as the maximum
number of consecutive days just before the given day, for which the price of the stock
on the current day is less than or equal to its price on the given day.
"""


def calculate_span(price: list[int]) -> list[int]:
    """
    Calculate the span values for a given list of stock prices.
    Args:
        price: List of stock prices.
    Returns:
        List of span values.

    >>> calculate_span([10, 4, 5, 90, 120, 80])
    [1, 1, 2, 4, 5, 1]
    >>> calculate_span([100, 50, 60, 70, 80, 90])
    [1, 1, 2, 3, 4, 5]
    >>> calculate_span([5, 4, 3, 2, 1])
    [1, 1, 1, 1, 1]
    >>> calculate_span([1, 2, 3, 4, 5])
    [1, 2, 3, 4, 5]
    >>> calculate_span([10, 20, 30, 40, 50])
    [1, 2, 3, 4, 5]
    >>> calculate_span([100, 80, 60, 70, 60, 75, 85])
    [1, 1, 1, 2, 1, 4, 6]
    """
    n = len(price)
    s = [0] * n
    # Create a stack and push index of fist element to it
    st = []
    st.append(0)

    # Span value of first element is always 1
    s[0] = 1

    # Calculate span values for rest of the elements
    for i in range(1, n):
        # Pop elements from stack while stack is not
        # empty and top of stack is smaller than price[i]
        while len(st) > 0 and price[st[-1]] <= price[i]:
            st.pop()

        # If stack becomes empty, then price[i] is greater
        # than all elements on left of it, i.e. price[0],
        # price[1], ..price[i-1]. Else the price[i]  is
        # greater than elements after top of stack
        s[i] = i + 1 if len(st) <= 0 else (i - st[-1])

        # Push this element to stack
        st.append(i)

    return s


# A utility function to print elements of array
def print_array(arr, n):
    for i in range(n):
        print(arr[i], end=" ")


# Driver program to test above function
price = [10, 4, 5, 90, 120, 80]

# Calculate the span values
S = calculate_span(price)

# Print the calculated span values
print_array(S, len(price))


================================================
FILE: data_structures/suffix_tree/__init__.py
================================================


================================================
FILE: data_structures/suffix_tree/example/__init__.py
================================================


================================================
FILE: data_structures/suffix_tree/example/example_usage.py
================================================
#  Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
#  in Pull Request: #11554
#  https://github.com/TheAlgorithms/Python/pull/11554
#
#  Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
#  addressing bugs/corrections to this file.
#  Thank you!

from data_structures.suffix_tree.suffix_tree import SuffixTree


def main() -> None:
    """
    Demonstrate the usage of the SuffixTree class.

    - Initializes a SuffixTree with a predefined text.
    - Defines a list of patterns to search for within the suffix tree.
    - Searches for each pattern in the suffix tree.

    Patterns tested:
        - "ana" (found) --> True
        - "ban" (found) --> True
        - "na" (found) --> True
        - "xyz" (not found) --> False
        - "mon" (found) --> True
    """
    text = "monkey banana"
    suffix_tree = SuffixTree(text)

    patterns = ["ana", "ban", "na", "xyz", "mon"]
    for pattern in patterns:
        found = suffix_tree.search(pattern)
        print(f"Pattern '{pattern}' found: {found}")


if __name__ == "__main__":
    main()


================================================
FILE: data_structures/suffix_tree/suffix_tree.py
================================================
#  Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
#  in Pull Request: #11554
#  https://github.com/TheAlgorithms/Python/pull/11554
#
#  Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
#  addressing bugs/corrections to this file.
#  Thank you!

from data_structures.suffix_tree.suffix_tree_node import SuffixTreeNode


class SuffixTree:
    def __init__(self, text: str) -> None:
        """
        Initializes the suffix tree with the given text.

        Args:
            text (str): The text for which the suffix tree is to be built.
        """
        self.text: str = text
        self.root: SuffixTreeNode = SuffixTreeNode()
        self.build_suffix_tree()

    def build_suffix_tree(self) -> None:
        """
        Builds the suffix tree for the given text by adding all suffixes.
        """
        text = self.text
        n = len(text)
        for i in range(n):
            suffix = text[i:]
            self._add_suffix(suffix, i)

    def _add_suffix(self, suffix: str, index: int) -> None:
        """
        Adds a suffix to the suffix tree.

        Args:
            suffix (str): The suffix to add.
            index (int): The starting index of the suffix in the original text.
        """
        node = self.root
        for char in suffix:
            if char not in node.children:
                node.children[char] = SuffixTreeNode()
            node = node.children[char]
        node.is_end_of_string = True
        node.start = index
        node.end = index + len(suffix) - 1

    def search(self, pattern: str) -> bool:
        """
        Searches for a pattern in the suffix tree.

        Args:
            pattern (str): The pattern to search for.

        Returns:
            bool: True if the pattern is found, False otherwise.
        """
        node = self.root
        for char in pattern:
            if char not in node.children:
                return False
            node = node.children[char]
        return True


================================================
FILE: data_structures/suffix_tree/suffix_tree_node.py
================================================
#  Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
#  in Pull Request: #11554
#  https://github.com/TheAlgorithms/Python/pull/11554
#
#  Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
#  addressing bugs/corrections to this file.
#  Thank you!

from __future__ import annotations


class SuffixTreeNode:
    def __init__(
        self,
        children: dict[str, SuffixTreeNode] | None = None,
        is_end_of_string: bool = False,
        start: int | None = None,
        end: int | None = None,
        suffix_link: SuffixTreeNode | None = None,
    ) -> None:
        """
        Initializes a suffix tree node.

        Parameters:
            children (dict[str, SuffixTreeNode] | None): The children of this node.
            is_end_of_string (bool): Indicates if this node represents
                                     the end of a string.
            start (int | None): The start index of the suffix in the text.
            end (int | None): The end index of the suffix in the text.
            suffix_link (SuffixTreeNode | None): Link to another suffix tree node.
        """
        self.children = children or {}
        self.is_end_of_string = is_end_of_string
        self.start = start
        self.end = end
        self.suffix_link = suffix_link


================================================
FILE: data_structures/suffix_tree/tests/__init__.py
================================================


================================================
FILE: data_structures/suffix_tree/tests/test_suffix_tree.py
================================================
#  Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
#  in Pull Request: #11554
#  https://github.com/TheAlgorithms/Python/pull/11554
#
#  Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
#  addressing bugs/corrections to this file.
#  Thank you!

import unittest

from data_structures.suffix_tree.suffix_tree import SuffixTree


class TestSuffixTree(unittest.TestCase):
    def setUp(self) -> None:
        """Set up the initial conditions for each test."""
        self.text = "banana"
        self.suffix_tree = SuffixTree(self.text)

    def test_search_existing_patterns(self) -> None:
        """Test searching for patterns that exist in the suffix tree."""
        patterns = ["ana", "ban", "na"]
        for pattern in patterns:
            with self.subTest(pattern=pattern):
                assert self.suffix_tree.search(pattern), (
                    f"Pattern '{pattern}' should be found."
                )

    def test_search_non_existing_patterns(self) -> None:
        """Test searching for patterns that do not exist in the suffix tree."""
        patterns = ["xyz", "apple", "cat"]
        for pattern in patterns:
            with self.subTest(pattern=pattern):
                assert not self.suffix_tree.search(pattern), (
                    f"Pattern '{pattern}' should not be found."
                )

    def test_search_empty_pattern(self) -> None:
        """Test searching for an empty pattern."""
        assert self.suffix_tree.search(""), "An empty pattern should be found."

    def test_search_full_text(self) -> None:
        """Test searching for the full text."""
        assert self.suffix_tree.search(self.text), (
            "The full text should be found in the suffix tree."
        )

    def test_search_substrings(self) -> None:
        """Test searching for substrings of the full text."""
        substrings = ["ban", "ana", "a", "na"]
        for substring in substrings:
            with self.subTest(substring=substring):
                assert self.suffix_tree.search(substring), (
                    f"Substring '{substring}' should be found."
                )


if __name__ == "__main__":
    unittest.main()


================================================
FILE: data_structures/trie/__init__.py
================================================


================================================
FILE: data_structures/trie/radix_tree.py
================================================
"""
A Radix Tree is a data structure that represents a space-optimized
trie (prefix tree) in whicheach node that is the only child is merged
with its parent [https://en.wikipedia.org/wiki/Radix_tree]
"""


class RadixNode:
    def __init__(self, prefix: str = "", is_leaf: bool = False) -> None:
        # Mapping from the first character of the prefix of the node
        self.nodes: dict[str, RadixNode] = {}

        # A node will be a leaf if the tree contains its word
        self.is_leaf = is_leaf

        self.prefix = prefix

    def match(self, word: str) -> tuple[str, str, str]:
        """Compute the common substring of the prefix of the node and a word

        Args:
            word (str): word to compare

        Returns:
            (str, str, str): common substring, remaining prefix, remaining word

        >>> RadixNode("myprefix").match("mystring")
        ('my', 'prefix', 'string')
        """
        x = 0
        for q, w in zip(self.prefix, word):
            if q != w:
                break

            x += 1

        return self.prefix[:x], self.prefix[x:], word[x:]

    def insert_many(self, words: list[str]) -> None:
        """Insert many words in the tree

        Args:
            words (list[str]): list of words

        >>> RadixNode("myprefix").insert_many(["mystring", "hello"])
        """
        for word in words:
            self.insert(word)

    def insert(self, word: str) -> None:
        """Insert a word into the tree

        Args:
            word (str): word to insert

        >>> RadixNode("myprefix").insert("mystring")

        >>> root = RadixNode()
        >>> root.insert_many(['myprefix', 'myprefixA', 'myprefixAA'])
        >>> root.print_tree()
        - myprefix   (leaf)
        -- A   (leaf)
        --- A   (leaf)
        """
        # Case 1: If the word is the prefix of the node
        # Solution: We set the current node as leaf
        if self.prefix == word and not self.is_leaf:
            self.is_leaf = True

        # Case 2: The node has no edges that have a prefix to the word
        # Solution: We create an edge from the current node to a new one
        # containing the word
        elif word[0] not in self.nodes:
            self.nodes[word[0]] = RadixNode(prefix=word, is_leaf=True)

        else:
            incoming_node = self.nodes[word[0]]
            matching_string, remaining_prefix, remaining_word = incoming_node.match(
                word
            )

            # Case 3: The node prefix is equal to the matching
            # Solution: We insert remaining word on the next node
            if remaining_prefix == "":
                self.nodes[matching_string[0]].insert(remaining_word)

            # Case 4: The word is greater equal to the matching
            # Solution: Create a node in between both nodes, change
            # prefixes and add the new node for the remaining word
            else:
                incoming_node.prefix = remaining_prefix

                aux_node = self.nodes[matching_string[0]]
                self.nodes[matching_string[0]] = RadixNode(matching_string, False)
                self.nodes[matching_string[0]].nodes[remaining_prefix[0]] = aux_node

                if remaining_word == "":
                    self.nodes[matching_string[0]].is_leaf = True
                else:
                    self.nodes[matching_string[0]].insert(remaining_word)

    def find(self, word: str) -> bool:
        """Returns if the word is on the tree

        Args:
            word (str): word to check

        Returns:
            bool: True if the word appears on the tree

        >>> RadixNode("myprefix").find("mystring")
        False
        """
        incoming_node = self.nodes.get(word[0], None)
        if not incoming_node:
            return False
        else:
            _matching_string, remaining_prefix, remaining_word = incoming_node.match(
                word
            )
            # If there is remaining prefix, the word can't be on the tree
            if remaining_prefix != "":
                return False
            # This applies when the word and the prefix are equal
            elif remaining_word == "":
                return incoming_node.is_leaf
            # We have word remaining so we check the next node
            else:
                return incoming_node.find(remaining_word)

    def delete(self, word: str) -> bool:
        """Deletes a word from the tree if it exists

        Args:
            word (str): word to be deleted

        Returns:
            bool: True if the word was found and deleted. False if word is not found

        >>> RadixNode("myprefix").delete("mystring")
        False
        """
        incoming_node = self.nodes.get(word[0], None)
        if not incoming_node:
            return False
        else:
            _matching_string, remaining_prefix, remaining_word = incoming_node.match(
                word
            )
            # If there is remaining prefix, the word can't be on the tree
            if remaining_prefix != "":
                return False
            # We have word remaining so we check the next node
            elif remaining_word != "":
                return incoming_node.delete(remaining_word)
            # If it is not a leaf, we don't have to delete
            elif not incoming_node.is_leaf:
                return False
            else:
                # We delete the nodes if no edges go from it
                if len(incoming_node.nodes) == 0:
                    del self.nodes[word[0]]
                    # We merge the current node with its only child
                    if len(self.nodes) == 1 and not self.is_leaf:
                        merging_node = next(iter(self.nodes.values()))
                        self.is_leaf = merging_node.is_leaf
                        self.prefix += merging_node.prefix
                        self.nodes = merging_node.nodes
                # If there is more than 1 edge, we just mark it as non-leaf
                elif len(incoming_node.nodes) > 1:
                    incoming_node.is_leaf = False
                # If there is 1 edge, we merge it with its child
                else:
                    merging_node = next(iter(incoming_node.nodes.values()))
                    incoming_node.is_leaf = merging_node.is_leaf
                    incoming_node.prefix += merging_node.prefix
                    incoming_node.nodes = merging_node.nodes

                return True

    def print_tree(self, height: int = 0) -> None:
        """Print the tree

        Args:
            height (int, optional): Height of the printed node
        """
        if self.prefix != "":
            print("-" * height, self.prefix, "  (leaf)" if self.is_leaf else "")

        for value in self.nodes.values():
            value.print_tree(height + 1)


def test_trie() -> bool:
    words = "banana bananas bandana band apple all beast".split()
    root = RadixNode()
    root.insert_many(words)

    assert all(root.find(word) for word in words)
    assert not root.find("bandanas")
    assert not root.find("apps")
    root.delete("all")
    assert not root.find("all")
    root.delete("banana")
    assert not root.find("banana")
    assert root.find("bananas")

    return True


def pytests() -> None:
    assert test_trie()


def main() -> None:
    """
    >>> pytests()
    """
    root = RadixNode()
    words = "banana bananas bandanas bandana band apple all beast".split()
    root.insert_many(words)

    print("Words:", words)
    print("Tree:")
    root.print_tree()


if __name__ == "__main__":
    main()


================================================
FILE: data_structures/trie/trie.py
================================================
"""
A Trie/Prefix Tree is a kind of search tree used to provide quick lookup
of words/patterns in a set of words. A basic Trie however has O(n^2) space complexity
making it impractical in practice. It however provides O(max(search_string, length of
longest word)) lookup time making it an optimal approach when space is not an issue.
"""


class TrieNode:
    def __init__(self) -> None:
        self.nodes: dict[str, TrieNode] = {}  # Mapping from char to TrieNode
        self.is_leaf = False

    def insert_many(self, words: list[str]) -> None:
        """
        Inserts a list of words into the Trie
        :param words: list of string words
        :return: None
        """
        for word in words:
            self.insert(word)

    def insert(self, word: str) -> None:
        """
        Inserts a word into the Trie
        :param word: word to be inserted
        :return: None
        """
        curr = self
        for char in word:
            if char not in curr.nodes:
                curr.nodes[char] = TrieNode()
            curr = curr.nodes[char]
        curr.is_leaf = True

    def find(self, word: str) -> bool:
        """
        Tries to find word in a Trie
        :param word: word to look for
        :return: Returns True if word is found, False otherwise
        """
        curr = self
        for char in word:
            if char not in curr.nodes:
                return False
            curr = curr.nodes[char]
        return curr.is_leaf

    def delete(self, word: str) -> None:
        """
        Deletes a word in a Trie
        :param word: word to delete
        :return: None
        """

        def _delete(curr: TrieNode, word: str, index: int) -> bool:
            if index == len(word):
                # If word does not exist
                if not curr.is_leaf:
                    return False
                curr.is_leaf = False
                return len(curr.nodes) == 0
            char = word[index]
            char_node = curr.nodes.get(char)
            # If char not in current trie node
            if not char_node:
                return False
            # Flag to check if node can be deleted
            delete_curr = _delete(char_node, word, index + 1)
            if delete_curr:
                del curr.nodes[char]
                return len(curr.nodes) == 0
            return delete_curr

        _delete(self, word, 0)


def print_words(node: TrieNode, word: str) -> None:
    """
    Prints all the words in a Trie
    :param node: root node of Trie
    :param word: Word variable should be empty at start
    :return: None
    """
    if node.is_leaf:
        print(word, end=" ")

    for key, value in node.nodes.items():
        print_words(value, word + key)


def test_trie() -> bool:
    words = "banana bananas bandana band apple all beast".split()
    root = TrieNode()
    root.insert_many(words)
    # print_words(root, "")
    assert all(root.find(word) for word in words)
    assert root.find("banana")
    assert not root.find("bandanas")
    assert not root.find("apps")
    assert root.find("apple")
    assert root.find("all")
    root.delete("all")
    assert not root.find("all")
    root.delete("banana")
    assert not root.find("banana")
    assert root.find("bananas")
    return True


def print_results(msg: str, passes: bool) -> None:
    print(str(msg), "works!" if passes else "doesn't work :(")


def pytests() -> None:
    assert test_trie()


def main() -> None:
    """
    >>> pytests()
    """
    print_results("Testing trie functionality", test_trie())


if __name__ == "__main__":
    main()


================================================
FILE: digital_image_processing/__init__.py
================================================


================================================
FILE: digital_image_processing/change_brightness.py
================================================
from PIL import Image


def change_brightness(img: Image, level: float) -> Image:
    """
    Change the brightness of a PIL Image to a given level.
    """

    def brightness(c: int) -> float:
        """
        Fundamental Transformation/Operation that'll be performed on
        every bit.
        """
        return 128 + level + (c - 128)

    if not -255.0 <= level <= 255.0:
        raise ValueError("level must be between -255.0 (black) and 255.0 (white)")
    return img.point(brightness)


if __name__ == "__main__":
    # Load image
    with Image.open("image_data/lena.jpg") as img:
        # Change brightness to 100
        brigt_img = change_brightness(img, 100)
        brigt_img.save("image_data/lena_brightness.png", format="png")


================================================
FILE: digital_image_processing/change_contrast.py
================================================
"""
Changing contrast with PIL

This algorithm is used in
https://noivce.pythonanywhere.com/ Python web app.

psf/black: True
ruff : True
"""

from PIL import Image


def change_contrast(img: Image, level: int) -> Image:
    """
    Function to change contrast
    """
    factor = (259 * (level + 255)) / (255 * (259 - level))

    def contrast(c: int) -> int:
        """
        Fundamental Transformation/Operation that'll be performed on
        every bit.
        """
        return int(128 + factor * (c - 128))

    return img.point(contrast)


if __name__ == "__main__":
    # Load image
    with Image.open("image_data/lena.jpg") as img:
        # Change contrast to 170
        cont_img = change_contrast(img, 170)
        cont_img.save("image_data/lena_high_contrast.png", format="png")


================================================
FILE: digital_image_processing/convert_to_negative.py
================================================
"""
Implemented an algorithm using opencv to convert a colored image into its negative
"""

from cv2 import destroyAllWindows, imread, imshow, waitKey


def convert_to_negative(img):
    # getting number of pixels in the image
    pixel_h, pixel_v = img.shape[0], img.shape[1]

    # converting each pixel's color to its negative
    for i in range(pixel_h):
        for j in range(pixel_v):
            img[i][j] = [255, 255, 255] - img[i][j]

    return img


if __name__ == "__main__":
    # read original image
    img = imread("image_data/lena.jpg", 1)

    # convert to its negative
    neg = convert_to_negative(img)

    # show result image
    imshow("negative of original image", img)
    waitKey(0)
    destroyAllWindows()


================================================
FILE: digital_image_processing/dithering/__init__.py
================================================


================================================
FILE: digital_image_processing/dithering/burkes.py
================================================
"""
Implementation Burke's algorithm (dithering)
"""

import numpy as np
from cv2 import destroyAllWindows, imread, imshow, waitKey


class Burkes:
    """
    Burke's algorithm is using for converting grayscale image to black and white version
    Source: Source: https://en.wikipedia.org/wiki/Dither

    Note:
        * Best results are given with threshold= ~1/2 * max greyscale value.
        * This implementation get RGB image and converts it to greyscale in runtime.
    """

    def __init__(self, input_img, threshold: int):
        self.min_threshold = 0
        # max greyscale value for #FFFFFF
        self.max_threshold = int(self.get_greyscale(255, 255, 255))

        if not self.min_threshold < threshold < self.max_threshold:
            msg = f"Factor value should be from 0 to {self.max_threshold}"
            raise ValueError(msg)

        self.input_img = input_img
        self.threshold = threshold
        self.width, self.height = self.input_img.shape[1], self.input_img.shape[0]

        # error table size (+4 columns and +1 row) greater than input image because of
        # lack of if statements
        self.error_table = [
            [0 for _ in range(self.height + 4)] for __ in range(self.width + 1)
        ]
        self.output_img = np.ones((self.width, self.height, 3), np.uint8) * 255

    @classmethod
    def get_greyscale(cls, blue: int, green: int, red: int) -> float:
        """
        >>> Burkes.get_greyscale(3, 4, 5)
        4.185
        >>> Burkes.get_greyscale(0, 0, 0)
        0.0
        >>> Burkes.get_greyscale(255, 255, 255)
        255.0
        """
        """
        Formula from https://en.wikipedia.org/wiki/HSL_and_HSV
        cf Lightness section, and Fig 13c.
        We use the first of four possible.
        """
        return 0.114 * blue + 0.587 * green + 0.299 * red

    def process(self) -> None:
        for y in range(self.height):
            for x in range(self.width):
                greyscale = int(self.get_greyscale(*self.input_img[y][x]))
                if self.threshold > greyscale + self.error_table[y][x]:
                    self.output_img[y][x] = (0, 0, 0)
                    current_error = greyscale + self.error_table[y][x]
                else:
                    self.output_img[y][x] = (255, 255, 255)
                    current_error = greyscale + self.error_table[y][x] - 255
                """
                Burkes error propagation (`*` is current pixel):

                                 *          8/32        4/32
                2/32    4/32    8/32    4/32    2/32
                """
                self.error_table[y][x + 1] += int(8 / 32 * current_error)
                self.error_table[y][x + 2] += int(4 / 32 * current_error)
                self.error_table[y + 1][x] += int(8 / 32 * current_error)
                self.error_table[y + 1][x + 1] += int(4 / 32 * current_error)
                self.error_table[y + 1][x + 2] += int(2 / 32 * current_error)
                self.error_table[y + 1][x - 1] += int(4 / 32 * current_error)
                self.error_table[y + 1][x - 2] += int(2 / 32 * current_error)


if __name__ == "__main__":
    # create Burke's instances with original images in greyscale
    burkes_instances = [
        Burkes(imread("image_data/lena.jpg", 1), threshold)
        for threshold in (1, 126, 130, 140)
    ]

    for burkes in burkes_instances:
        burkes.process()

    for burkes in burkes_instances:
        imshow(
            f"Original image with dithering threshold: {burkes.threshold}",
            burkes.output_img,
        )

    waitKey(0)
    destroyAllWindows()


================================================
FILE: digital_image_processing/edge_detection/__init__.py
================================================


================================================
FILE: digital_image_processing/edge_detection/canny.py
================================================
import cv2
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

PI = 180


def gen_gaussian_kernel(k_size, sigma):
    center = k_size // 2
    x, y = np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    g = (
        1
        / (2 * np.pi * sigma)
        * np.exp(-(np.square(x) + np.square(y)) / (2 * np.square(sigma)))
    )
    return g


def suppress_non_maximum(image_shape, gradient_direction, sobel_grad):
    """
    Non-maximum suppression. If the edge strength of the current pixel is the largest
    compared to the other pixels in the mask with the same direction, the value will be
    preserved. Otherwise, the value will be suppressed.
    """
    destination = np.zeros(image_shape)

    for row in range(1, image_shape[0] - 1):
        for col in range(1, image_shape[1] - 1):
            direction = gradient_direction[row, col]

            if (
                0 <= direction < PI / 8
                or 15 * PI / 8 <= direction <= 2 * PI
                or 7 * PI / 8 <= direction <= 9 * PI / 8
            ):
                w = sobel_grad[row, col - 1]
                e = sobel_grad[row, col + 1]
                if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                    destination[row, col] = sobel_grad[row, col]

            elif (
                PI / 8 <= direction < 3 * PI / 8
                or 9 * PI / 8 <= direction < 11 * PI / 8
            ):
                sw = sobel_grad[row + 1, col - 1]
                ne = sobel_grad[row - 1, col + 1]
                if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                    destination[row, col] = sobel_grad[row, col]

            elif (
                3 * PI / 8 <= direction < 5 * PI / 8
                or 11 * PI / 8 <= direction < 13 * PI / 8
            ):
                n = sobel_grad[row - 1, col]
                s = sobel_grad[row + 1, col]
                if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                    destination[row, col] = sobel_grad[row, col]

            elif (
                5 * PI / 8 <= direction < 7 * PI / 8
                or 13 * PI / 8 <= direction < 15 * PI / 8
            ):
                nw = sobel_grad[row - 1, col - 1]
                se = sobel_grad[row + 1, col + 1]
                if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                    destination[row, col] = sobel_grad[row, col]

    return destination


def detect_high_low_threshold(
    image_shape, destination, threshold_low, threshold_high, weak, strong
):
    """
    High-Low threshold detection. If an edge pixel's gradient value is higher
    than the high threshold value, it is marked as a strong edge pixel. If an
    edge pixel's gradient value is smaller than the high threshold value and
    larger than the low threshold value, it is marked as a weak edge pixel. If
    an edge pixel's value is smaller than the low threshold value, it will be
    suppressed.
    """
    for row in range(1, image_shape[0] - 1):
        for col in range(1, image_shape[1] - 1):
            if destination[row, col] >= threshold_high:
                destination[row, col] = strong
            elif destination[row, col] <= threshold_low:
                destination[row, col] = 0
            else:
                destination[row, col] = weak


def track_edge(image_shape, destination, weak, strong):
    """
    Edge tracking. Usually a weak edge pixel caused from true edges will be connected
    to a strong edge pixel while noise responses are unconnected. As long as there is
    one strong edge pixel that is involved in its 8-connected neighborhood, that weak
    edge point can be identified as one that should be preserved.
    """
    for row in range(1, image_shape[0]):
        for col in range(1, image_shape[1]):
            if destination[row, col] == weak:
                if 255 in (
                    destination[row, col + 1],
                    destination[row, col - 1],
                    destination[row - 1, col],
                    destination[row + 1, col],
                    destination[row - 1, col - 1],
                    destination[row + 1, col - 1],
                    destination[row - 1, col + 1],
                    destination[row + 1, col + 1],
                ):
                    destination[row, col] = strong
                else:
                    destination[row, col] = 0


def canny(image, threshold_low=15, threshold_high=30, weak=128, strong=255):
    # gaussian_filter
    gaussian_out = img_convolve(image, gen_gaussian_kernel(9, sigma=1.4))
    # get the gradient and degree by sobel_filter
    sobel_grad, sobel_theta = sobel_filter(gaussian_out)
    gradient_direction = PI + np.rad2deg(sobel_theta)

    destination = suppress_non_maximum(image.shape, gradient_direction, sobel_grad)

    detect_high_low_threshold(
        image.shape, destination, threshold_low, threshold_high, weak, strong
    )

    track_edge(image.shape, destination, weak, strong)

    return destination


if __name__ == "__main__":
    # read original image in gray mode
    lena = cv2.imread(r"../image_data/lena.jpg", 0)
    # canny edge detection
    canny_destination = canny(lena)
    cv2.imshow("canny", canny_destination)
    cv2.waitKey(0)


================================================
FILE: digital_image_processing/filters/__init__.py
================================================


================================================
FILE: digital_image_processing/filters/bilateral_filter.py
================================================
"""
Implementation of Bilateral filter

Inputs:
    img: A 2d image with values in between 0 and 1
    varS: variance in space dimension.
    varI: variance in Intensity.
    N: Kernel size(Must be an odd number)
Output:
    img:A 2d zero padded image with values in between 0 and 1
"""

import math
import sys

import cv2
import numpy as np


def vec_gaussian(img: np.ndarray, variance: float) -> np.ndarray:
    # For applying gaussian function for each element in matrix.
    sigma = math.sqrt(variance)
    cons = 1 / (sigma * math.sqrt(2 * math.pi))
    return cons * np.exp(-((img / sigma) ** 2) * 0.5)


def get_slice(img: np.ndarray, x: int, y: int, kernel_size: int) -> np.ndarray:
    half = kernel_size // 2
    return img[x - half : x + half + 1, y - half : y + half + 1]


def get_gauss_kernel(kernel_size: int, spatial_variance: float) -> np.ndarray:
    # Creates a gaussian kernel of given dimension.
    arr = np.zeros((kernel_size, kernel_size))
    for i in range(kernel_size):
        for j in range(kernel_size):
            arr[i, j] = math.sqrt(
                abs(i - kernel_size // 2) ** 2 + abs(j - kernel_size // 2) ** 2
            )
    return vec_gaussian(arr, spatial_variance)


def bilateral_filter(
    img: np.ndarray,
    spatial_variance: float,
    intensity_variance: float,
    kernel_size: int,
) -> np.ndarray:
    img2 = np.zeros(img.shape)
    gauss_ker = get_gauss_kernel(kernel_size, spatial_variance)
    size_x, size_y = img.shape
    for i in range(kernel_size // 2, size_x - kernel_size // 2):
        for j in range(kernel_size // 2, size_y - kernel_size // 2):
            img_s = get_slice(img, i, j, kernel_size)
            img_i = img_s - img_s[kernel_size // 2, kernel_size // 2]
            img_ig = vec_gaussian(img_i, intensity_variance)
            weights = np.multiply(gauss_ker, img_ig)
            vals = np.multiply(img_s, weights)
            val = np.sum(vals) / np.sum(weights)
            img2[i, j] = val
    return img2


def parse_args(args: list) -> tuple:
    filename = args[1] if args[1:] else "../image_data/lena.jpg"
    spatial_variance = float(args[2]) if args[2:] else 1.0
    intensity_variance = float(args[3]) if args[3:] else 1.0
    if args[4:]:
        kernel_size = int(args[4])
        kernel_size = kernel_size + abs(kernel_size % 2 - 1)
    else:
        kernel_size = 5
    return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
    filename, spatial_variance, intensity_variance, kernel_size = parse_args(sys.argv)
    img = cv2.imread(filename, 0)
    cv2.imshow("input image", img)

    out = img / 255
    out = out.astype("float32")
    out = bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
    out = out * 255
    out = np.uint8(out)
    cv2.imshow("output image", out)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


================================================
FILE: digital_image_processing/filters/convolve.py
================================================
# @Author  : lightXu
# @File    : convolve.py
# @Time    : 2019/7/8 0008 下午 16:13
from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uint8, zeros


def im2col(image, block_size):
    rows, cols = image.shape
    dst_height = cols - block_size[1] + 1
    dst_width = rows - block_size[0] + 1
    image_array = zeros((dst_height * dst_width, block_size[1] * block_size[0]))
    row = 0
    for i in range(dst_height):
        for j in range(dst_width):
            window = ravel(image[i : i + block_size[0], j : j + block_size[1]])
            image_array[row, :] = window
            row += 1

    return image_array


def img_convolve(image, filter_kernel):
    height, width = image.shape[0], image.shape[1]
    k_size = filter_kernel.shape[0]
    pad_size = k_size // 2
    # Pads image with the edge values of array.
    image_tmp = pad(image, pad_size, mode="edge")

    # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
    image_array = im2col(image_tmp, (k_size, k_size))

    #  turn the kernel into shape(k*k, 1)
    kernel_array = ravel(filter_kernel)
    # reshape and get the dst image
    dst = dot(image_array, kernel_array).reshape(height, width)
    return dst


if __name__ == "__main__":
    # read original image
    img = imread(r"../image_data/lena.jpg")
    # turn image in gray scale value
    gray = cvtColor(img, COLOR_BGR2GRAY)
    # Laplace operator
    Laplace_kernel = array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
    out = img_convolve(gray, Laplace_kernel).astype(uint8)
    imshow("Laplacian", out)
    waitKey(0)


================================================
FILE: digital_image_processing/filters/gabor_filter.py
================================================
# Implementation of the Gaborfilter
# https://en.wikipedia.org/wiki/Gabor_filter
import numpy as np
from cv2 import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filter2D, imread, imshow, waitKey


def gabor_filter_kernel(
    ksize: int, sigma: int, theta: int, lambd: int, gamma: int, psi: int
) -> np.ndarray:
    """
    :param ksize:   The kernelsize of the convolutional filter (ksize x ksize)
    :param sigma:   standard deviation of the gaussian bell curve
    :param theta:   The orientation of the normal to the parallel stripes
                    of Gabor function.
    :param lambd:   Wavelength of the sinusoidal component.
    :param gamma:   The spatial aspect ratio and specifies the ellipticity
                    of the support of Gabor function.
    :param psi:     The phase offset of the sinusoidal function.

    >>> gabor_filter_kernel(3, 8, 0, 10, 0, 0).tolist()
    [[0.8027212023735046, 1.0, 0.8027212023735046], [0.8027212023735046, 1.0, \
0.8027212023735046], [0.8027212023735046, 1.0, 0.8027212023735046]]

    """

    # prepare kernel
    # the kernel size have to be odd
    if (ksize % 2) == 0:
        ksize = ksize + 1
    gabor = np.zeros((ksize, ksize), dtype=np.float32)

    # each value
    for y in range(ksize):
        for x in range(ksize):
            # distance from center
            px = x - ksize // 2
            py = y - ksize // 2

            # degree to radiant
            _theta = theta / 180 * np.pi
            cos_theta = np.cos(_theta)
            sin_theta = np.sin(_theta)

            # get kernel x
            _x = cos_theta * px + sin_theta * py

            # get kernel y
            _y = -sin_theta * px + cos_theta * py

            # fill kernel
            gabor[y, x] = np.exp(-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)) * np.cos(
                2 * np.pi * _x / lambd + psi
            )

    return gabor


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    # read original image
    img = imread("../image_data/lena.jpg")
    # turn image in gray scale value
    gray = cvtColor(img, COLOR_BGR2GRAY)

    # Apply multiple Kernel to detect edges
    out = np.zeros(gray.shape[:2])
    for theta in [0, 30, 60, 90, 120, 150]:
        """
        ksize = 10
        sigma = 8
        lambd = 10
        gamma = 0
        psi = 0
        """
        kernel_10 = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
        out += filter2D(gray, CV_8UC3, kernel_10)
    out = out / out.max() * 255
    out = out.astype(np.uint8)

    imshow("Original", gray)
    imshow("Gabor filter with 20x20 mask and 6 directions", out)

    waitKey(0)


================================================
FILE: digital_image_processing/filters/gaussian_filter.py
================================================
"""
Implementation of gaussian filter algorithm
"""

from itertools import product

from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uint8, zeros


def gen_gaussian_kernel(k_size, sigma):
    center = k_size // 2
    x, y = mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    g = 1 / (2 * pi * sigma) * exp(-(square(x) + square(y)) / (2 * square(sigma)))
    return g


def gaussian_filter(image, k_size, sigma):
    height, width = image.shape[0], image.shape[1]
    # dst image height and width
    dst_height = height - k_size + 1
    dst_width = width - k_size + 1

    # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
    image_array = zeros((dst_height * dst_width, k_size * k_size))
    for row, (i, j) in enumerate(product(range(dst_height), range(dst_width))):
        window = ravel(image[i : i + k_size, j : j + k_size])
        image_array[row, :] = window

    #  turn the kernel into shape(k*k, 1)
    gaussian_kernel = gen_gaussian_kernel(k_size, sigma)
    filter_array = ravel(gaussian_kernel)

    # reshape and get the dst image
    dst = dot(image_array, filter_array).reshape(dst_height, dst_width).astype(uint8)

    return dst


if __name__ == "__main__":
    # read original image
    img = imread(r"../image_data/lena.jpg")
    # turn image in gray scale value
    gray = cvtColor(img, COLOR_BGR2GRAY)

    # get values with two different mask size
    gaussian3x3 = gaussian_filter(gray, 3, sigma=1)
    gaussian5x5 = gaussian_filter(gray, 5, sigma=0.8)

    # show result images
    imshow("gaussian filter with 3x3 mask", gaussian3x3)
    imshow("gaussian filter with 5x5 mask", gaussian5x5)
    waitKey()


================================================
FILE: digital_image_processing/filters/laplacian_filter.py
================================================
# @Author  : ojas-wani
# @File    : laplacian_filter.py
# @Date    : 10/04/2023

import numpy as np
from cv2 import (
    BORDER_DEFAULT,
    COLOR_BGR2GRAY,
    CV_64F,
    cvtColor,
    filter2D,
    imread,
    imshow,
    waitKey,
)

from digital_image_processing.filters.gaussian_filter import gaussian_filter


def my_laplacian(src: np.ndarray, ksize: int) -> np.ndarray:
    """
    :param src: the source image, which should be a grayscale or color image.
    :param ksize: the size of the kernel used to compute the Laplacian filter,
                  which can be 1, 3, 5, or 7.

    >>> my_laplacian(src=np.array([]), ksize=0)
    Traceback (most recent call last):
        ...
    ValueError: ksize must be in (1, 3, 5, 7)
    """
    kernels = {
        1: np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]),
        3: np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]]),
        5: np.array(
            [
                [0, 0, -1, 0, 0],
                [0, -1, -2, -1, 0],
                [-1, -2, 16, -2, -1],
                [0, -1, -2, -1, 0],
                [0, 0, -1, 0, 0],
            ]
        ),
        7: np.array(
            [
                [0, 0, 0, -1, 0, 0, 0],
                [0, 0, -2, -3, -2, 0, 0],
                [0, -2, -7, -10, -7, -2, 0],
                [-1, -3, -10, 68, -10, -3, -1],
                [0, -2, -7, -10, -7, -2, 0],
                [0, 0, -2, -3, -2, 0, 0],
                [0, 0, 0, -1, 0, 0, 0],
            ]
        ),
    }
    if ksize not in kernels:
        msg = f"ksize must be in {tuple(kernels)}"
        raise ValueError(msg)

    # Apply the Laplacian kernel using convolution
    return filter2D(
        src, CV_64F, kernels[ksize], 0, borderType=BORDER_DEFAULT, anchor=(0, 0)
    )


if __name__ == "__main__":
    # read original image
    img = imread(r"../image_data/lena.jpg")

    # turn image in gray scale value
    gray = cvtColor(img, COLOR_BGR2GRAY)

    # Applying gaussian filter
    blur_image = gaussian_filter(gray, 3, sigma=1)

    # Apply multiple Kernel to detect edges
    laplacian_image = my_laplacian(ksize=3, src=blur_image)

    imshow("Original image", img)
    imshow("Detected edges using laplacian filter", laplacian_image)

    waitKey(0)


================================================
FILE: digital_image_processing/filters/local_binary_pattern.py
================================================
import cv2
import numpy as np


def get_neighbors_pixel(
    image: np.ndarray, x_coordinate: int, y_coordinate: int, center: int
) -> int:
    """
    Comparing local neighborhood pixel value with threshold value of centre pixel.
    Exception is required when neighborhood value of a center pixel value is null.
    i.e. values present at boundaries.

    :param image: The image we're working with
    :param x_coordinate: x-coordinate of the  pixel
    :param y_coordinate: The y coordinate of the pixel
    :param center: center pixel value
    :return: The value of the pixel is being returned.
    """

    try:
        return int(image[x_coordinate][y_coordinate] >= center)
    except (IndexError, TypeError):
        return 0


def local_binary_value(image: np.ndarray, x_coordinate: int, y_coordinate: int) -> int:
    """
    It takes an image, an x and y coordinate, and returns the
    decimal value of the local binary patternof the pixel
    at that coordinate

    :param image: the image to be processed
    :param x_coordinate: x coordinate of the pixel
    :param y_coordinate: the y coordinate of the pixel
    :return: The decimal value of the binary value of the pixels
    around the center pixel.
    """
    center = image[x_coordinate][y_coordinate]
    powers = [1, 2, 4, 8, 16, 32, 64, 128]

    # skip get_neighbors_pixel if center is null
    if center is None:
        return 0

    # Starting from the top right, assigning value to pixels clockwise
    binary_values = [
        get_neighbors_pixel(image, x_coordinate - 1, y_coordinate + 1, center),
        get_neighbors_pixel(image, x_coordinate, y_coordinate + 1, center),
        get_neighbors_pixel(image, x_coordinate - 1, y_coordinate, center),
        get_neighbors_pixel(image, x_coordinate + 1, y_coordinate + 1, center),
        get_neighbors_pixel(image, x_coordinate + 1, y_coordinate, center),
        get_neighbors_pixel(image, x_coordinate + 1, y_coordinate - 1, center),
        get_neighbors_pixel(image, x_coordinate, y_coordinate - 1, center),
        get_neighbors_pixel(image, x_coordinate - 1, y_coordinate - 1, center),
    ]

    # Converting the binary value to decimal.
    return sum(
        binary_value * power for binary_value, power in zip(binary_values, powers)
    )


if __name__ == "__main__":
    # Reading the image and converting it to grayscale.
    image = cv2.imread(
        "digital_image_processing/image_data/lena.jpg", cv2.IMREAD_GRAYSCALE
    )

    # Create a numpy array as the same height and width of read image
    lbp_image = np.zeros((image.shape[0], image.shape[1]))

    # Iterating through the image and calculating the
    # local binary pattern value for each pixel.
    for i in range(image.shape[0]):
        for j in range(image.shape[1]):
            lbp_image[i][j] = local_binary_value(image, i, j)

    cv2.imshow("local binary pattern", lbp_image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


================================================
FILE: digital_image_processing/filters/median_filter.py
================================================
"""
Implementation of median filter algorithm
"""

from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, int8, multiply, ravel, sort, zeros_like


def median_filter(gray_img, mask=3):
    """
    :param gray_img: gray image
    :param mask: mask size
    :return: image with median filter
    """
    # set image borders
    bd = int(mask / 2)
    # copy image size
    median_img = zeros_like(gray_img)
    for i in range(bd, gray_img.shape[0] - bd):
        for j in range(bd, gray_img.shape[1] - bd):
            # get mask according with mask
            kernel = ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1])
            # calculate mask median
            median = sort(kernel)[int8(divide((multiply(mask, mask)), 2) + 1)]
            median_img[i, j] = median
    return median_img


if __name__ == "__main__":
    # read original image
    img = imread("../image_data/lena.jpg")
    # turn image in gray scale value
    gray = cvtColor(img, COLOR_BGR2GRAY)

    # get values with two different mask size
    median3x3 = median_filter(gray, 3)
    median5x5 = median_filter(gray, 5)

    # show result images
    imshow("median filter with 3x3 mask", median3x3)
    imshow("median filter with 5x5 mask", median5x5)
    waitKey(0)


================================================
FILE: digital_image_processing/filters/sobel_filter.py
================================================
# @Author  : lightXu
# @File    : sobel_filter.py
# @Time    : 2019/7/8 0008 下午 16:26
import numpy as np
from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve


def sobel_filter(image):
    kernel_x = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
    kernel_y = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])

    dst_x = np.abs(img_convolve(image, kernel_x))
    dst_y = np.abs(img_convolve(image, kernel_y))
    # modify the pix within [0, 255]
    dst_x = dst_x * 255 / np.max(dst_x)
    dst_y = dst_y * 255 / np.max(dst_y)

    dst_xy = np.sqrt((np.square(dst_x)) + (np.square(dst_y)))
    dst_xy = dst_xy * 255 / np.max(dst_xy)
    dst = dst_xy.astype(np.uint8)

    theta = np.arctan2(dst_y, dst_x)
    return dst, theta


if __name__ == "__main__":
    # read original image
    img = imread("../image_data/lena.jpg")
    # turn image in gray scale value
    gray = cvtColor(img, COLOR_BGR2GRAY)

    sobel_grad, sobel_theta = sobel_filter(gray)

    # show result images
    imshow("sobel filter", sobel_grad)
    imshow("sobel theta", sobel_theta)
    waitKey(0)


================================================
FILE: digital_image_processing/histogram_equalization/__init__.py
================================================


================================================
FILE: digital_image_processing/histogram_equalization/histogram_stretch.py
================================================
"""
Created on Fri Sep 28 15:22:29 2018

@author: Binish125
"""

import copy
import os

import cv2
import numpy as np
from matplotlib import pyplot as plt


class ConstantStretch:
    def __init__(self):
        self.img = ""
        self.original_image = ""
        self.last_list = []
        self.rem = 0
        self.L = 256
        self.sk = 0
        self.k = 0
        self.number_of_rows = 0
        self.number_of_cols = 0

    def stretch(self, input_image):
        self.img = cv2.imread(input_image, 0)
        self.original_image = copy.deepcopy(self.img)
        x, _, _ = plt.hist(self.img.ravel(), 256, [0, 256], label="x")
        self.k = np.sum(x)
        for i in range(len(x)):
            prk = x[i] / self.k
            self.sk += prk
            last = (self.L - 1) * self.sk
            if self.rem != 0:
                self.rem = int(last % last)
            last = int(last + 1 if self.rem >= 0.5 else last)
            self.last_list.append(last)
            self.number_of_rows = int(np.ma.count(self.img) / self.img[1].size)
            self.number_of_cols = self.img[1].size
        for i in range(self.number_of_cols):
            for j in range(self.number_of_rows):
                num = self.img[j][i]
                if num != self.last_list[num]:
                    self.img[j][i] = self.last_list[num]
        cv2.imwrite("output_data/output.jpg", self.img)

    def plot_histogram(self):
        plt.hist(self.img.ravel(), 256, [0, 256])

    def show_image(self):
        cv2.imshow("Output-Image", self.img)
        cv2.imshow("Input-Image", self.original_image)
        cv2.waitKey(5000)
        cv2.destroyAllWindows()


if __name__ == "__main__":
    file_path = os.path.join(os.path.basename(__file__), "image_data/input.jpg")
    stretcher = ConstantStretch()
    stretcher.stretch(file_path)
    stretcher.plot_histogram()
    stretcher.show_image()


================================================
FILE: digital_image_processing/histogram_equalization/image_data/__init__.py
================================================


================================================
FILE: digital_image_processing/histogram_equalization/output_data/__init__.py
================================================


================================================
FILE: digital_image_processing/image_data/__init__.py
================================================


================================================
FILE: digital_image_processing/index_calculation.py
================================================
# Author: João Gustavo A. Amorim
# Author email: joaogustavoamorim@gmail.com
# Coding date:  jan 2019
# python/black: True

# Imports
import numpy as np


# Class implemented to calculus the index
class IndexCalculation:
    """
    # Class Summary
            This algorithm consists in calculating vegetation indices, these
        indices can be used for precision agriculture for example (or remote
        sensing). There are functions to define the data and to calculate the
        implemented indices.

    # Vegetation index
        https://en.wikipedia.org/wiki/Vegetation_Index
        A Vegetation Index (VI) is a spectral transformation of two or more bands
        designed to enhance the contribution of vegetation properties and allow
        reliable spatial and temporal inter-comparisons of terrestrial
        photosynthetic activity and canopy structural variations

    # Information about channels (Wavelength range for each)
        * nir - near-infrared
            https://www.malvernpanalytical.com/br/products/technology/near-infrared-spectroscopy
            Wavelength Range 700 nm to 2500 nm
        * Red Edge
            https://en.wikipedia.org/wiki/Red_edge
            Wavelength Range 680 nm to 730 nm
        * red
            https://en.wikipedia.org/wiki/Color
            Wavelength Range 635 nm to 700 nm
        * blue
            https://en.wikipedia.org/wiki/Color
            Wavelength Range 450 nm to 490 nm
        * green
            https://en.wikipedia.org/wiki/Color
            Wavelength Range 520 nm to 560 nm


    # Implemented index list
            #"abbreviationOfIndexName" -- list of channels used

            #"ARVI2"            --  red, nir
            #"CCCI"             --  red, redEdge, nir
            #"CVI"              --  red, green, nir
            #"GLI"              --  red, green, blue
            #"NDVI"             --  red, nir
            #"BNDVI"            --  blue, nir
            #"redEdgeNDVI"      --  red, redEdge
            #"GNDVI"            --  green, nir
            #"GBNDVI"           --  green, blue, nir
            #"GRNDVI"           --  red, green, nir
            #"RBNDVI"           --  red, blue, nir
            #"PNDVI"            --  red, green, blue, nir
            #"ATSAVI"           --  red, nir
            #"BWDRVI"           --  blue, nir
            #"CIgreen"          --  green, nir
            #"CIrededge"        --  redEdge, nir
            #"CI"               --  red, blue
            #"CTVI"             --  red, nir
            #"GDVI"             --  green, nir
            #"EVI"              --  red, blue, nir
            #"GEMI"             --  red, nir
            #"GOSAVI"           --  green, nir
            #"GSAVI"            --  green, nir
            #"Hue"              --  red, green, blue
            #"IVI"              --  red, nir
            #"IPVI"             --  red, nir
            #"I"                --  red, green, blue
            #"RVI"              --  red, nir
            #"MRVI"             --  red, nir
            #"MSAVI"            --  red, nir
            #"NormG"            --  red, green, nir
            #"NormNIR"          --  red, green, nir
            #"NormR"            --  red, green, nir
            #"NGRDI"            --  red, green
            #"RI"               --  red, green
            #"S"                --  red, green, blue
            #"IF"               --  red, green, blue
            #"DVI"              --  red, nir
            #"TVI"              --  red, nir
            #"NDRE"               --  redEdge, nir

    #list of all index implemented
        #allIndex = ["ARVI2", "CCCI", "CVI", "GLI", "NDVI", "BNDVI", "redEdgeNDVI",
                    "GNDVI", "GBNDVI", "GRNDVI", "RBNDVI", "PNDVI", "ATSAVI",
                    "BWDRVI", "CIgreen", "CIrededge", "CI", "CTVI", "GDVI", "EVI",
                    "GEMI", "GOSAVI", "GSAVI", "Hue", "IVI", "IPVI", "I", "RVI",
                    "MRVI", "MSAVI", "NormG", "NormNIR", "NormR", "NGRDI", "RI",
                    "S", "IF", "DVI", "TVI", "NDRE"]

    #list of index with not blue channel
        #notBlueIndex = ["ARVI2", "CCCI", "CVI", "NDVI", "redEdgeNDVI", "GNDVI",
                         "GRNDVI", "ATSAVI", "CIgreen", "CIrededge", "CTVI", "GDVI",
                         "GEMI", "GOSAVI", "GSAVI", "IVI", "IPVI", "RVI", "MRVI",
                         "MSAVI", "NormG", "NormNIR", "NormR", "NGRDI", "RI", "DVI",
                         "TVI", "NDRE"]

    #list of index just with RGB channels
        #RGBIndex = ["GLI", "CI", "Hue", "I", "NGRDI", "RI", "S", "IF"]
    """

    def __init__(self, red=None, green=None, blue=None, red_edge=None, nir=None):
        self.set_matricies(red=red, green=green, blue=blue, red_edge=red_edge, nir=nir)

    def set_matricies(self, red=None, green=None, blue=None, red_edge=None, nir=None):
        if red is not None:
            self.red = red
        if green is not None:
            self.green = green
        if blue is not None:
            self.blue = blue
        if red_edge is not None:
            self.redEdge = red_edge
        if nir is not None:
            self.nir = nir
        return True

    def calculation(
        self, index="", red=None, green=None, blue=None, red_edge=None, nir=None
    ):
        """
        performs the calculation of the index with the values instantiated in the class
        :str index: abbreviation of index name to perform
        """
        self.set_matricies(red=red, green=green, blue=blue, red_edge=red_edge, nir=nir)
        funcs = {
            "ARVI2": self.arv12,
            "CCCI": self.ccci,
            "CVI": self.cvi,
            "GLI": self.gli,
            "NDVI": self.ndvi,
            "BNDVI": self.bndvi,
            "redEdgeNDVI": self.red_edge_ndvi,
            "GNDVI": self.gndvi,
            "GBNDVI": self.gbndvi,
            "GRNDVI": self.grndvi,
            "RBNDVI": self.rbndvi,
            "PNDVI": self.pndvi,
            "ATSAVI": self.atsavi,
            "BWDRVI": self.bwdrvi,
            "CIgreen": self.ci_green,
            "CIrededge": self.ci_rededge,
            "CI": self.ci,
            "CTVI": self.ctvi,
            "GDVI": self.gdvi,
            "EVI": self.evi,
            "GEMI": self.gemi,
            "GOSAVI": self.gosavi,
            "GSAVI": self.gsavi,
            "Hue": self.hue,
            "IVI": self.ivi,
            "IPVI": self.ipvi,
            "I": self.i,
            "RVI": self.rvi,
            "MRVI": self.mrvi,
            "MSAVI": self.m_savi,
            "NormG": self.norm_g,
            "NormNIR": self.norm_nir,
            "NormR": self.norm_r,
            "NGRDI": self.ngrdi,
            "RI": self.ri,
            "S": self.s,
            "IF": self._if,
            "DVI": self.dvi,
            "TVI": self.tvi,
            "NDRE": self.ndre,
        }

        try:
            return funcs[index]()
        except KeyError:
            print("Index not in the list!")
            return False

    def arv12(self):
        """
        Atmospherically Resistant Vegetation Index 2
        https://www.indexdatabase.de/db/i-single.php?id=396
        :return: index
            -0.18+1.17*(self.nir-self.red)/(self.nir+self.red)
        """
        return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))

    def ccci(self):
        """
        Canopy Chlorophyll Content Index
        https://www.indexdatabase.de/db/i-single.php?id=224
        :return: index
        """
        return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
            (self.nir - self.red) / (self.nir + self.red)
        )

    def cvi(self):
        """
        Chlorophyll vegetation index
        https://www.indexdatabase.de/db/i-single.php?id=391
        :return: index
        """
        return self.nir * (self.red / (self.green**2))

    def gli(self):
        """
        self.green leaf index
        https://www.indexdatabase.de/db/i-single.php?id=375
        :return: index
        """
        return (2 * self.green - self.red - self.blue) / (
            2 * self.green + self.red + self.blue
        )

    def ndvi(self):
        """
        Normalized Difference self.nir/self.red Normalized Difference Vegetation
        Index, Calibrated NDVI - CDVI
        https://www.indexdatabase.de/db/i-single.php?id=58
        :return: index
        """
        return (self.nir - self.red) / (self.nir + self.red)

    def bndvi(self):
        """
            Normalized Difference self.nir/self.blue self.blue-normalized difference
        vegetation index
        https://www.indexdatabase.de/db/i-single.php?id=135
        :return: index
        """
        return (self.nir - self.blue) / (self.nir + self.blue)

    def red_edge_ndvi(self):
        """
        Normalized Difference self.rededge/self.red
        https://www.indexdatabase.de/db/i-single.php?id=235
        :return: index
        """
        return (self.redEdge - self.red) / (self.redEdge + self.red)

    def gndvi(self):
        """
        Normalized Difference self.nir/self.green self.green NDVI
        https://www.indexdatabase.de/db/i-single.php?id=401
        :return: index
        """
        return (self.nir - self.green) / (self.nir + self.green)

    def gbndvi(self):
        """
        self.green-self.blue NDVI
        https://www.indexdatabase.de/db/i-single.php?id=186
        :return: index
        """
        return (self.nir - (self.green + self.blue)) / (
            self.nir + (self.green + self.blue)
        )

    def grndvi(self):
        """
        self.green-self.red NDVI
        https://www.indexdatabase.de/db/i-single.php?id=185
        :return: index
        """
        return (self.nir - (self.green + self.red)) / (
            self.nir + (self.green + self.red)
        )

    def rbndvi(self):
        """
        self.red-self.blue NDVI
        https://www.indexdatabase.de/db/i-single.php?id=187
        :return: index
        """
        return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))

    def pndvi(self):
        """
        Pan NDVI
        https://www.indexdatabase.de/db/i-single.php?id=188
        :return: index
        """
        return (self.nir - (self.green + self.red + self.blue)) / (
            self.nir + (self.green + self.red + self.blue)
        )

    def atsavi(self, x=0.08, a=1.22, b=0.03):
        """
        Adjusted transformed soil-adjusted VI
        https://www.indexdatabase.de/db/i-single.php?id=209
        :return: index
        """
        return a * (
            (self.nir - a * self.red - b)
            / (a * self.nir + self.red - a * b + x * (1 + a**2))
        )

    def bwdrvi(self):
        """
        self.blue-wide dynamic range vegetation index
        https://www.indexdatabase.de/db/i-single.php?id=136
        :return: index
        """
        return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)

    def ci_green(self):
        """
        Chlorophyll Index self.green
        https://www.indexdatabase.de/db/i-single.php?id=128
        :return: index
        """
        return (self.nir / self.green) - 1

    def ci_rededge(self):
        """
        Chlorophyll Index self.redEdge
        https://www.indexdatabase.de/db/i-single.php?id=131
        :return: index
        """
        return (self.nir / self.redEdge) - 1

    def ci(self):
        """
        Coloration Index
        https://www.indexdatabase.de/db/i-single.php?id=11
        :return: index
        """
        return (self.red - self.blue) / self.red

    def ctvi(self):
        """
        Corrected Transformed Vegetation Index
        https://www.indexdatabase.de/db/i-single.php?id=244
        :return: index
        """
        ndvi = self.ndvi()
        return ((ndvi + 0.5) / (abs(ndvi + 0.5))) * (abs(ndvi + 0.5) ** (1 / 2))

    def gdvi(self):
        """
        Difference self.nir/self.green self.green Difference Vegetation Index
        https://www.indexdatabase.de/db/i-single.php?id=27
        :return: index
        """
        return self.nir - self.green

    def evi(self):
        """
        Enhanced Vegetation Index
        https://www.indexdatabase.de/db/i-single.php?id=16
        :return: index
        """
        return 2.5 * (
            (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
        )

    def gemi(self):
        """
        Global Environment Monitoring Index
        https://www.indexdatabase.de/db/i-single.php?id=25
        :return: index
        """
        n = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
            self.nir + self.red + 0.5
        )
        return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)

    def gosavi(self, y=0.16):
        """
        self.green Optimized Soil Adjusted Vegetation Index
        https://www.indexdatabase.de/db/i-single.php?id=29
        mit Y = 0,16
        :return: index
        """
        return (self.nir - self.green) / (self.nir + self.green + y)

    def gsavi(self, n=0.5):
        """
        self.green Soil Adjusted Vegetation Index
        https://www.indexdatabase.de/db/i-single.php?id=31
        mit N = 0,5
        :return: index
        """
        return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)

    def hue(self):
        """
        Hue
        https://www.indexdatabase.de/db/i-single.php?id=34
        :return: index
        """
        return np.arctan(
            ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)
        )

    def ivi(self, a=None, b=None):
        """
        Ideal vegetation index
        https://www.indexdatabase.de/db/i-single.php?id=276
        b=intercept of vegetation line
        a=soil line slope
        :return: index
        """
        return (self.nir - b) / (a * self.red)

    def ipvi(self):
        """
        Infraself.red percentage vegetation index
        https://www.indexdatabase.de/db/i-single.php?id=35
        :return: index
        """
        return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)

    def i(self):
        """
        Intensity
        https://www.indexdatabase.de/db/i-single.php?id=36
        :return: index
        """
        return (self.red + self.green + self.blue) / 30.5

    def rvi(self):
        """
        Ratio-Vegetation-Index
        http://www.seos-project.eu/modules/remotesensing/remotesensing-c03-s01-p01.html
        :return: index
        """
        return self.nir / self.red

    def mrvi(self):
        """
        Modified Normalized Difference Vegetation Index RVI
        https://www.indexdatabase.de/db/i-single.php?id=275
        :return: index
        """
        return (self.rvi() - 1) / (self.rvi() + 1)

    def m_savi(self):
        """
        Modified Soil Adjusted Vegetation Index
        https://www.indexdatabase.de/db/i-single.php?id=44
        :return: index
        """
        return (
            (2 * self.nir + 1)
            - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
        ) / 2

    def norm_g(self):
        """
        Norm G
        https://www.indexdatabase.de/db/i-single.php?id=50
        :return: index
        """
        return self.green / (self.nir + self.red + self.green)

    def norm_nir(self):
        """
        Norm self.nir
        https://www.indexdatabase.de/db/i-single.php?id=51
        :return: index
        """
        return self.nir / (self.nir + self.red + self.green)

    def norm_r(self):
        """
        Norm R
        https://www.indexdatabase.de/db/i-single.php?id=52
        :return: index
        """
        return self.red / (self.nir + self.red + self.green)

    def ngrdi(self):
        """
            Normalized Difference self.green/self.red Normalized self.green self.red
        difference index, Visible Atmospherically Resistant Indices self.green
        (VIself.green)
        https://www.indexdatabase.de/db/i-single.php?id=390
        :return: index
        """
        return (self.green - self.red) / (self.green + self.red)

    def ri(self):
        """
        Normalized Difference self.red/self.green self.redness Index
        https://www.indexdatabase.de/db/i-single.php?id=74
        :return: index
        """
        return (self.red - self.green) / (self.red + self.green)

    def s(self):
        """
        Saturation
        https://www.indexdatabase.de/db/i-single.php?id=77
        :return: index
        """
        max_value = np.max([np.max(self.red), np.max(self.green), np.max(self.blue)])
        min_value = np.min([np.min(self.red), np.min(self.green), np.min(self.blue)])
        return (max_value - min_value) / max_value

    def _if(self):
        """
        Shape Index
        https://www.indexdatabase.de/db/i-single.php?id=79
        :return: index
        """
        return (2 * self.red - self.green - self.blue) / (self.green - self.blue)

    def dvi(self):
        """
        Simple Ratio self.nir/self.red Difference Vegetation Index, Vegetation Index
        Number (VIN)
        https://www.indexdatabase.de/db/i-single.php?id=12
        :return: index
        """
        return self.nir / self.red

    def tvi(self):
        """
        Transformed Vegetation Index
        https://www.indexdatabase.de/db/i-single.php?id=98
        :return: index
        """
        return (self.ndvi() + 0.5) ** (1 / 2)

    def ndre(self):
        return (self.nir - self.redEdge) / (self.nir + self.redEdge)


"""
# genering a random matrices to test this class
red     = np.ones((1000,1000, 1),dtype="float64") * 46787
green   = np.ones((1000,1000, 1),dtype="float64") * 23487
blue    = np.ones((1000,1000, 1),dtype="float64") * 14578
redEdge = np.ones((1000,1000, 1),dtype="float64") * 51045
nir     = np.ones((1000,1000, 1),dtype="float64") * 52200

# Examples of how to use the class

# instantiating the class
cl = IndexCalculation()

# instantiating the class with the values
#cl = indexCalculation(red=red, green=green, blue=blue, redEdge=redEdge, nir=nir)

# how set the values after instantiate the class cl, (for update the data or when don't
# instantiating the class with the values)
cl.setMatrices(red=red, green=green, blue=blue, redEdge=redEdge, nir=nir)

# calculating the indices for the instantiated values in the class
    # Note: the CCCI index can be changed to any index implemented in the class.
indexValue_form1    = cl.calculation("CCCI", red=red, green=green, blue=blue,
                                     redEdge=redEdge, nir=nir).astype(np.float64)
indexValue_form2    = cl.CCCI()

# calculating the index with the values directly -- you can set just the values
# preferred  note: the *calculation* function performs the function *setMatrices*
indexValue_form3    = cl.calculation("CCCI", red=red, green=green, blue=blue,
                                     redEdge=redEdge, nir=nir).astype(np.float64)

print("Form 1: "+np.array2string(indexValue_form1, precision=20, separator=', ',
      floatmode='maxprec_equal'))
print("Form 2: "+np.array2string(indexValue_form2, precision=20, separator=', ',
      floatmode='maxprec_equal'))
print("Form 3: "+np.array2string(indexValue_form3, precision=20, separator=', ',
      floatmode='maxprec_equal'))

# A list of examples results for different type of data at NDVI
# float16 ->    0.31567383              #NDVI (red = 50, nir = 100)
# float32 ->    0.31578946              #NDVI (red = 50, nir = 100)
# float64 ->    0.3157894736842105      #NDVI (red = 50, nir = 100)
# longdouble -> 0.3157894736842105      #NDVI (red = 50, nir = 100)
"""


================================================
FILE: digital_image_processing/morphological_operations/__init__.py
================================================


================================================
FILE: digital_image_processing/morphological_operations/dilation_operation.py
================================================
from pathlib import Path

import numpy as np
from PIL import Image


def rgb_to_gray(rgb: np.ndarray) -> np.ndarray:
    """
    Return gray image from rgb image
    >>> rgb_to_gray(np.array([[[127, 255, 0]]]))
    array([[187.6453]])
    >>> rgb_to_gray(np.array([[[0, 0, 0]]]))
    array([[0.]])
    >>> rgb_to_gray(np.array([[[2, 4, 1]]]))
    array([[3.0598]])
    >>> rgb_to_gray(np.array([[[26, 255, 14], [5, 147, 20], [1, 200, 0]]]))
    array([[159.0524,  90.0635, 117.6989]])
    """
    r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.2989 * r + 0.5870 * g + 0.1140 * b


def gray_to_binary(gray: np.ndarray) -> np.ndarray:
    """
    Return binary image from gray image
    >>> gray_to_binary(np.array([[127, 255, 0]]))
    array([[False,  True, False]])
    >>> gray_to_binary(np.array([[0]]))
    array([[False]])
    >>> gray_to_binary(np.array([[26.2409, 4.9315, 1.4729]]))
    array([[False, False, False]])
    >>> gray_to_binary(np.array([[26, 255, 14], [5, 147, 20], [1, 200, 0]]))
    array([[False,  True, False],
           [False,  True, False],
           [False,  True, False]])
    """
    return (gray > 127) & (gray <= 255)


def dilation(image: np.ndarray, kernel: np.ndarray) -> np.ndarray:
    """
    Return dilated image
    >>> dilation(np.array([[True, False, True]]), np.array([[0, 1, 0]]))
    array([[False, False, False]])
    >>> dilation(np.array([[False, False, True]]), np.array([[1, 0, 1]]))
    array([[False, False, False]])
    """
    output = np.zeros_like(image)
    image_padded = np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)
    )

    # Copy image to padded image
    image_padded[kernel.shape[0] - 2 : -1 :, kernel.shape[1] - 2 : -1 :] = image

    # Iterate over image & apply kernel
    for x in range(image.shape[1]):
        for y in range(image.shape[0]):
            summation = (
                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
            ).sum()
            output[y, x] = int(summation > 0)
    return output


if __name__ == "__main__":
    # read original image
    lena_path = Path(__file__).resolve().parent / "image_data" / "lena.jpg"
    lena = np.array(Image.open(lena_path))
    # kernel to be applied
    structuring_element = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
    output = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
    # Save the output image
    pil_img = Image.fromarray(output).convert("RGB")
    pil_img.save("result_dilation.png")


================================================
FILE: digital_image_processing/morphological_operations/erosion_operation.py
================================================
from pathlib import Path

import numpy as np
from PIL import Image


def rgb_to_gray(rgb: np.ndarray) -> np.ndarray:
    """
    Return gray image from rgb image

    >>> rgb_to_gray(np.array([[[127, 255, 0]]]))
    array([[187.6453]])
    >>> rgb_to_gray(np.array([[[0, 0, 0]]]))
    array([[0.]])
    >>> rgb_to_gray(np.array([[[2, 4, 1]]]))
    array([[3.0598]])
    >>> rgb_to_gray(np.array([[[26, 255, 14], [5, 147, 20], [1, 200, 0]]]))
    array([[159.0524,  90.0635, 117.6989]])
    """
    r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.2989 * r + 0.5870 * g + 0.1140 * b


def gray_to_binary(gray: np.ndarray) -> np.ndarray:
    """
    Return binary image from gray image

    >>> gray_to_binary(np.array([[127, 255, 0]]))
    array([[False,  True, False]])
    >>> gray_to_binary(np.array([[0]]))
    array([[False]])
    >>> gray_to_binary(np.array([[26.2409, 4.9315, 1.4729]]))
    array([[False, False, False]])
    >>> gray_to_binary(np.array([[26, 255, 14], [5, 147, 20], [1, 200, 0]]))
    array([[False,  True, False],
           [False,  True, False],
           [False,  True, False]])
    """
    return (gray > 127) & (gray <= 255)


def erosion(image: np.ndarray, kernel: np.ndarray) -> np.ndarray:
    """
    Return eroded image

    >>> erosion(np.array([[True, True, False]]), np.array([[0, 1, 0]]))
    array([[False, False, False]])
    >>> erosion(np.array([[True, False, False]]), np.array([[1, 1, 0]]))
    array([[False, False, False]])
    """
    output = np.zeros_like(image)
    image_padded = np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)
    )

    # Copy image to padded image
    image_padded[kernel.shape[0] - 2 : -1 :, kernel.shape[1] - 2 : -1 :] = image

    # Iterate over image & apply kernel
    for x in range(image.shape[1]):
        for y in range(image.shape[0]):
            summation = (
                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
            ).sum()
            output[y, x] = int(summation == 5)
    return output


if __name__ == "__main__":
    # read original image
    lena_path = Path(__file__).resolve().parent / "image_data" / "lena.jpg"
    lena = np.array(Image.open(lena_path))

    # kernel to be applied
    structuring_element = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

    # Apply erosion operation to a binary image
    output = erosion(gray_to_binary(rgb_to_gray(lena)), structuring_element)

    # Save the output image
    pil_img = Image.fromarray(output).convert("RGB")
    pil_img.save("result_erosion.png")


================================================
FILE: digital_image_processing/resize/__init__.py
================================================


================================================
FILE: digital_image_processing/resize/resize.py
================================================
"""Multiple image resizing techniques"""

import numpy as np
from cv2 import destroyAllWindows, imread, imshow, waitKey


class NearestNeighbour:
    """
    Simplest and fastest version of image resizing.
    Source: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
    """

    def __init__(self, img, dst_width: int, dst_height: int):
        if dst_width < 0 or dst_height < 0:
            raise ValueError("Destination width/height should be > 0")

        self.img = img
        self.src_w = img.shape[1]
        self.src_h = img.shape[0]
        self.dst_w = dst_width
        self.dst_h = dst_height

        self.ratio_x = self.src_w / self.dst_w
        self.ratio_y = self.src_h / self.dst_h

        self.output = self.output_img = (
            np.ones((self.dst_h, self.dst_w, 3), np.uint8) * 255
        )

    def process(self):
        for i in range(self.dst_h):
            for j in range(self.dst_w):
                self.output[i][j] = self.img[self.get_y(i)][self.get_x(j)]

    def get_x(self, x: int) -> int:
        """
        Get parent X coordinate for destination X
        :param x: Destination X coordinate
        :return: Parent X coordinate based on `x ratio`
        >>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg",
        ...                              1), 100, 100)
        >>> nn.ratio_x = 0.5
        >>> nn.get_x(4)
        2
        """
        return int(self.ratio_x * x)

    def get_y(self, y: int) -> int:
        """
        Get parent Y coordinate for destination Y
        :param y: Destination X coordinate
        :return: Parent X coordinate based on `y ratio`
        >>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg",
        ...                              1), 100, 100)
        >>> nn.ratio_y = 0.5
        >>> nn.get_y(4)
        2
        """
        return int(self.ratio_y * y)


if __name__ == "__main__":
    dst_w, dst_h = 800, 600
    im = imread("image_data/lena.jpg", 1)
    n = NearestNeighbour(im, dst_w, dst_h)
    n.process()

    imshow(
        f"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
    )
    waitKey(0)
    destroyAllWindows()


================================================
FILE: digital_image_processing/rotation/__init__.py
================================================


================================================
FILE: digital_image_processing/rotation/rotation.py
================================================
from pathlib import Path

import cv2
import numpy as np
from matplotlib import pyplot as plt


def get_rotation(
    img: np.ndarray, pt1: np.ndarray, pt2: np.ndarray, rows: int, cols: int
) -> np.ndarray:
    """
    Get image rotation
    :param img: np.ndarray
    :param pt1: 3x2 list
    :param pt2: 3x2 list
    :param rows: columns image shape
    :param cols: rows image shape
    :return: np.ndarray
    """
    matrix = cv2.getAffineTransform(pt1, pt2)
    return cv2.warpAffine(img, matrix, (rows, cols))


if __name__ == "__main__":
    # read original image
    image = cv2.imread(
        str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
    )
    # turn image in gray scale value
    gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # get image shape
    img_rows, img_cols = gray_img.shape

    # set different points to rotate image
    pts1 = np.array([[50, 50], [200, 50], [50, 200]], np.float32)
    pts2 = np.array([[10, 100], [200, 50], [100, 250]], np.float32)
    pts3 = np.array([[50, 50], [150, 50], [120, 200]], np.float32)
    pts4 = np.array([[10, 100], [80, 50], [180, 250]], np.float32)

    # add all rotated images in a list
    images = [
        gray_img,
        get_rotation(gray_img, pts1, pts2, img_rows, img_cols),
        get_rotation(gray_img, pts2, pts3, img_rows, img_cols),
        get_rotation(gray_img, pts2, pts4, img_rows, img_cols),
    ]

    # plot different image rotations
    fig = plt.figure(1)
    titles = ["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
    for i, image in enumerate(images):
        plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
        plt.title(titles[i])
        plt.axis("off")
        plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
    plt.show()


================================================
FILE: digital_image_processing/sepia.py
================================================
"""
Implemented an algorithm using opencv to tone an image with sepia technique
"""

from cv2 import destroyAllWindows, imread, imshow, waitKey


def make_sepia(img, factor: int):
    """
    Function create sepia tone.
    Source: https://en.wikipedia.org/wiki/Sepia_(color)
    """
    pixel_h, pixel_v = img.shape[0], img.shape[1]

    def to_grayscale(blue, green, red):
        """
        Helper function to create pixel's greyscale representation
        Src: https://pl.wikipedia.org/wiki/YUV
        """
        return 0.2126 * red + 0.587 * green + 0.114 * blue

    def normalize(value):
        """Helper function to normalize R/G/B value -> return 255 if value > 255"""
        return min(value, 255)

    for i in range(pixel_h):
        for j in range(pixel_v):
            greyscale = int(to_grayscale(*img[i][j]))
            img[i][j] = [
                normalize(greyscale),
                normalize(greyscale + factor),
                normalize(greyscale + 2 * factor),
            ]

    return img


if __name__ == "__main__":
    # read original image
    images = {
        percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
    }

    for percentage, img in images.items():
        make_sepia(img, percentage)

    for percentage, img in images.items():
        imshow(f"Original image with sepia (factor: {percentage})", img)

    waitKey(0)
    destroyAllWindows()


================================================
FILE: digital_image_processing/test_digital_image_processing.py
================================================
"""
PyTest's for Digital Image Processing
"""

import numpy as np
from cv2 import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uint8
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

img = imread(r"digital_image_processing/image_data/lena_small.jpg")
gray = cvtColor(img, COLOR_BGR2GRAY)


# Test: convert_to_negative()
def test_convert_to_negative():
    negative_img = cn.convert_to_negative(img)
    # assert negative_img array for at least one True
    assert negative_img.any()


# Test: change_contrast()
def test_change_contrast():
    with Image.open("digital_image_processing/image_data/lena_small.jpg") as img:
        # Work around assertion for response
        assert str(cc.change_contrast(img, 110)).startswith(
            "<PIL.Image.Image image mode=RGB size=100x100 at"
        )


# canny.gen_gaussian_kernel()
def test_gen_gaussian_kernel():
    resp = canny.gen_gaussian_kernel(9, sigma=1.4)
    # Assert ambiguous array
    assert resp.all()


# canny.py
def test_canny():
    canny_img = imread("digital_image_processing/image_data/lena_small.jpg", 0)
    # assert ambiguous array for all == True
    assert canny_img.all()
    canny_array = canny.canny(canny_img)
    # assert canny array for at least one True
    assert canny_array.any()


# filters/gaussian_filter.py
def test_gen_gaussian_kernel_filter():
    assert gg.gaussian_filter(gray, 5, sigma=0.9).all()


def test_convolve_filter():
    # laplace diagonals
    laplace = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]])
    res = conv.img_convolve(gray, laplace).astype(uint8)
    assert res.any()


def test_median_filter():
    assert med.median_filter(gray, 3).any()


def test_sobel_filter():
    grad, theta = sob.sobel_filter(gray)
    assert grad.any()
    assert theta.any()


def test_sepia():
    sepia = sp.make_sepia(img, 20)
    assert sepia.all()


def test_burkes(file_path: str = "digital_image_processing/image_data/lena_small.jpg"):
    burkes = bs.Burkes(imread(file_path, 1), 120)
    burkes.process()
    assert burkes.output_img.any()


def test_nearest_neighbour(
    file_path: str = "digital_image_processing/image_data/lena_small.jpg",
):
    nn = rs.NearestNeighbour(imread(file_path, 1), 400, 200)
    nn.process()
    assert nn.output.any()


def test_local_binary_pattern():
    # pull request 10161 before:
    # "digital_image_processing/image_data/lena.jpg"
    # after: "digital_image_processing/image_data/lena_small.jpg"

    from os import getenv  # Speed up our Continuous Integration tests

    file_name = "lena_small.jpg" if getenv("CI") else "lena.jpg"
    file_path = f"digital_image_processing/image_data/{file_name}"

    # Reading the image and converting it to grayscale
    image = imread(file_path, 0)

    # Test for get_neighbors_pixel function() return not None
    x_coordinate = 0
    y_coordinate = 0
    center = image[x_coordinate][y_coordinate]

    neighbors_pixels = lbp.get_neighbors_pixel(
        image, x_coordinate, y_coordinate, center
    )

    assert neighbors_pixels is not None

    # Test for local_binary_pattern function()
    # Create a numpy array as the same height and width of read image
    lbp_image = np.zeros((image.shape[0], image.shape[1]))

    # Iterating through the image and calculating the local binary pattern value
    # for each pixel.
    for i in range(image.shape[0]):
        for j in range(image.shape[1]):
            lbp_image[i][j] = lbp.local_binary_value(image, i, j)

    assert lbp_image.any()


================================================
FILE: divide_and_conquer/__init__.py
================================================


================================================
FILE: divide_and_conquer/closest_pair_of_points.py
================================================
"""
The algorithm finds distance between closest pair of points
in the given n points.
Approach used -> Divide and conquer
The points are sorted based on Xco-ords and
then based on Yco-ords separately.
And by applying divide and conquer approach,
minimum distance is obtained recursively.

>> Closest points can lie on different sides of partition.
This case handled by forming a strip of points
whose Xco-ords distance is less than closest_pair_dis
from mid-point's Xco-ords. Points sorted based on Yco-ords
are used in this step to reduce sorting time.
Closest pair distance is found in the strip of points. (closest_in_strip)

min(closest_pair_dis, closest_in_strip) would be the final answer.

Time complexity: O(n * log n)
"""


def euclidean_distance_sqr(point1, point2):
    """
    >>> euclidean_distance_sqr([1,2],[2,4])
    5
    """
    return (point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2


def column_based_sort(array, column=0):
    """
    >>> column_based_sort([(5, 1), (4, 2), (3, 0)], 1)
    [(3, 0), (5, 1), (4, 2)]
    """
    return sorted(array, key=lambda x: x[column])


def dis_between_closest_pair(points, points_counts, min_dis=float("inf")):
    """
    brute force approach to find distance between closest pair points

    Parameters :
    points, points_count, min_dis (list(tuple(int, int)), int, int)

    Returns :
    min_dis (float):  distance between closest pair of points

    >>> dis_between_closest_pair([[1,2],[2,4],[5,7],[8,9],[11,0]],5)
    5

    """

    for i in range(points_counts - 1):
        for j in range(i + 1, points_counts):
            current_dis = euclidean_distance_sqr(points[i], points[j])
            min_dis = min(min_dis, current_dis)
    return min_dis


def dis_between_closest_in_strip(points, points_counts, min_dis=float("inf")):
    """
    closest pair of points in strip

    Parameters :
    points, points_count, min_dis (list(tuple(int, int)), int, int)

    Returns :
    min_dis (float):  distance btw closest pair of points in the strip (< min_dis)

    >>> dis_between_closest_in_strip([[1,2],[2,4],[5,7],[8,9],[11,0]],5)
    85
    """

    for i in range(min(6, points_counts - 1), points_counts):
        for j in range(max(0, i - 6), i):
            current_dis = euclidean_distance_sqr(points[i], points[j])
            min_dis = min(min_dis, current_dis)
    return min_dis


def closest_pair_of_points_sqr(points_sorted_on_x, points_sorted_on_y, points_counts):
    """divide and conquer approach

    Parameters :
    points, points_count (list(tuple(int, int)), int)

    Returns :
    (float):  distance btw closest pair of points

    >>> closest_pair_of_points_sqr([(1, 2), (3, 4)], [(5, 6), (7, 8)], 2)
    8
    """

    # base case
    if points_counts <= 3:
        return dis_between_closest_pair(points_sorted_on_x, points_counts)

    # recursion
    mid = points_counts // 2
    closest_in_left = closest_pair_of_points_sqr(
        points_sorted_on_x, points_sorted_on_y[:mid], mid
    )
    closest_in_right = closest_pair_of_points_sqr(
        points_sorted_on_y, points_sorted_on_y[mid:], points_counts - mid
    )
    closest_pair_dis = min(closest_in_left, closest_in_right)

    """
    cross_strip contains the points, whose Xcoords are at a
    distance(< closest_pair_dis) from mid's Xcoord
    """

    cross_strip = []
    for point in points_sorted_on_x:
        if abs(point[0] - points_sorted_on_x[mid][0]) < closest_pair_dis:
            cross_strip.append(point)

    closest_in_strip = dis_between_closest_in_strip(
        cross_strip, len(cross_strip), closest_pair_dis
    )
    return min(closest_pair_dis, closest_in_strip)


def closest_pair_of_points(points, points_counts):
    """
    >>> closest_pair_of_points([(2, 3), (12, 30)], len([(2, 3), (12, 30)]))
    28.792360097775937
    """
    points_sorted_on_x = column_based_sort(points, column=0)
    points_sorted_on_y = column_based_sort(points, column=1)
    return (
        closest_pair_of_points_sqr(
            points_sorted_on_x, points_sorted_on_y, points_counts
        )
    ) ** 0.5


if __name__ == "__main__":
    points = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
    print("Distance:", closest_pair_of_points(points, len(points)))


================================================
FILE: divide_and_conquer/convex_hull.py
================================================
"""
The convex hull problem is problem of finding all the vertices of convex polygon, P of
a set of points in a plane such that all the points are either on the vertices of P or
inside P. TH convex hull problem has several applications in geometrical problems,
computer graphics and game development.

Two algorithms have been implemented for the convex hull problem here.
1. A brute-force algorithm which runs in O(n^3)
2. A divide-and-conquer algorithm which runs in O(n log(n))

There are other several other algorithms for the convex hull problem
which have not been implemented here, yet.

"""

from __future__ import annotations

from collections.abc import Iterable


class Point:
    """
    Defines a 2-d point for use by all convex-hull algorithms.

    Parameters
    ----------
    x: an int or a float, the x-coordinate of the 2-d point
    y: an int or a float, the y-coordinate of the 2-d point

    Examples
    --------
    >>> Point(1, 2)
    (1.0, 2.0)
    >>> Point("1", "2")
    (1.0, 2.0)
    >>> Point(1, 2) > Point(0, 1)
    True
    >>> Point(1, 1) == Point(1, 1)
    True
    >>> Point(-0.5, 1) == Point(0.5, 1)
    False
    >>> Point("pi", "e")
    Traceback (most recent call last):
        ...
    ValueError: could not convert string to float: 'pi'
    """

    def __init__(self, x, y):
        self.x, self.y = float(x), float(y)

    def __eq__(self, other):
        return self.x == other.x and self.y == other.y

    def __ne__(self, other):
        return not self == other

    def __gt__(self, other):
        if self.x > other.x:
            return True
        elif self.x == other.x:
            return self.y > other.y
        return False

    def __lt__(self, other):
        return not self > other

    def __ge__(self, other):
        if self.x > other.x:
            return True
        elif self.x == other.x:
            return self.y >= other.y
        return False

    def __le__(self, other):
        if self.x < other.x:
            return True
        elif self.x == other.x:
            return self.y <= other.y
        return False

    def __repr__(self):
        return f"({self.x}, {self.y})"

    def __hash__(self):
        return hash(self.x)


def _construct_points(
    list_of_tuples: list[Point] | list[list[float]] | Iterable[list[float]],
) -> list[Point]:
    """
    constructs a list of points from an array-like object of numbers

    Arguments
    ---------

    list_of_tuples: array-like object of type numbers. Acceptable types so far
    are lists, tuples and sets.

    Returns
    --------
    points: a list where each item is of type Point. This contains only objects
    which can be converted into a Point.

    Examples
    -------
    >>> _construct_points([[1, 1], [2, -1], [0.3, 4]])
    [(1.0, 1.0), (2.0, -1.0), (0.3, 4.0)]
    >>> _construct_points([1, 2])
    Ignoring deformed point 1. All points must have at least 2 coordinates.
    Ignoring deformed point 2. All points must have at least 2 coordinates.
    []
    >>> _construct_points([])
    []
    >>> _construct_points(None)
    []
    """

    points: list[Point] = []
    if list_of_tuples:
        for p in list_of_tuples:
            if isinstance(p, Point):
                points.append(p)
            else:
                try:
                    points.append(Point(p[0], p[1]))
                except (IndexError, TypeError):
                    print(
                        f"Ignoring deformed point {p}. All points"
                        " must have at least 2 coordinates."
                    )
    return points


def _validate_input(points: list[Point] | list[list[float]]) -> list[Point]:
    """
    validates an input instance before a convex-hull algorithms uses it

    Parameters
    ---------
    points: array-like, the 2d points to validate before using with
    a convex-hull algorithm. The elements of points must be either lists, tuples or
    Points.

    Returns
    -------
    points: array_like, an iterable of all well-defined Points constructed passed in.


    Exception
    ---------
    ValueError: if points is empty or None, or if a wrong data structure like a scalar
                 is passed

    TypeError: if an iterable but non-indexable object (eg. dictionary) is passed.
                The exception to this a set which we'll convert to a list before using


    Examples
    -------
    >>> _validate_input([[1, 2]])
    [(1.0, 2.0)]
    >>> _validate_input([(1, 2)])
    [(1.0, 2.0)]
    >>> _validate_input([Point(2, 1), Point(-1, 2)])
    [(2.0, 1.0), (-1.0, 2.0)]
    >>> _validate_input([])
    Traceback (most recent call last):
        ...
    ValueError: Expecting a list of points but got []
    >>> _validate_input(1)
    Traceback (most recent call last):
        ...
    ValueError: Expecting an iterable object but got an non-iterable type 1
    """

    if not hasattr(points, "__iter__"):
        msg = f"Expecting an iterable object but got an non-iterable type {points}"
        raise ValueError(msg)

    if not points:
        msg = f"Expecting a list of points but got {points}"
        raise ValueError(msg)

    return _construct_points(points)


def _det(a: Point, b: Point, c: Point) -> float:
    """
    Computes the sign perpendicular distance of a 2d point c from a line segment
    ab. The sign indicates the direction of c relative to ab.
    A Positive value means c is above ab (to the left), while a negative value
    means c is below ab (to the right). 0 means all three points are on a straight line.

    As a side note, 0.5 * abs|det| is the area of triangle abc

    Parameters
    ----------
    a: point, the point on the left end of line segment ab
    b: point, the point on the right end of line segment ab
    c: point, the point for which the direction and location is desired.

    Returns
    --------
    det: float, abs(det) is the distance of c from ab. The sign
    indicates which side of line segment ab c is. det is computed as
    (a_xb_y + c_xa_y + b_xc_y) - (a_yb_x + c_ya_x + b_yc_x)

    Examples
    ----------
    >>> _det(Point(1, 1), Point(1, 2), Point(1, 5))
    0.0
    >>> _det(Point(0, 0), Point(10, 0), Point(0, 10))
    100.0
    >>> _det(Point(0, 0), Point(10, 0), Point(0, -10))
    -100.0
    """

    det = (a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
    return det


def convex_hull_bf(points: list[Point]) -> list[Point]:
    """
    Constructs the convex hull of a set of 2D points using a brute force algorithm.
    The algorithm basically considers all combinations of points (i, j) and uses the
    definition of convexity to determine whether (i, j) is part of the convex hull or
    not.  (i, j) is part of the convex hull if and only iff there are no points on both
    sides of the line segment connecting the ij, and there is no point k such that k is
    on either end of the ij.

    Runtime: O(n^3) - definitely horrible

    Parameters
    ---------
    points: array-like of object of Points, lists or tuples.
    The set of  2d points for which the convex-hull is needed

    Returns
    ------
    convex_set: list, the convex-hull of points sorted in non-decreasing order.

    See Also
    --------
    convex_hull_recursive,

     Examples
     ---------
     >>> convex_hull_bf([[0, 0], [1, 0], [10, 1]])
     [(0.0, 0.0), (1.0, 0.0), (10.0, 1.0)]
     >>> convex_hull_bf([[0, 0], [1, 0], [10, 0]])
     [(0.0, 0.0), (10.0, 0.0)]
     >>> convex_hull_bf([[-1, 1],[-1, -1], [0, 0], [0.5, 0.5], [1, -1], [1, 1],
     ...                 [-0.75, 1]])
     [(-1.0, -1.0), (-1.0, 1.0), (1.0, -1.0), (1.0, 1.0)]
     >>> convex_hull_bf([(0, 3), (2, 2), (1, 1), (2, 1), (3, 0), (0, 0), (3, 3),
     ...                 (2, -1), (2, -4), (1, -3)])
     [(0.0, 0.0), (0.0, 3.0), (1.0, -3.0), (2.0, -4.0), (3.0, 0.0), (3.0, 3.0)]
    """

    points = sorted(_validate_input(points))
    n = len(points)
    convex_set = set()

    for i in range(n - 1):
        for j in range(i + 1, n):
            points_left_of_ij = points_right_of_ij = False
            ij_part_of_convex_hull = True
            for k in range(n):
                if k not in {i, j}:
                    det_k = _det(points[i], points[j], points[k])

                    if det_k > 0:
                        points_left_of_ij = True
                    elif det_k < 0:
                        points_right_of_ij = True
                    # point[i], point[j], point[k] all lie on a straight line
                    # if point[k] is to the left of point[i] or it's to the
                    # right of point[j], then point[i], point[j] cannot be
                    # part of the convex hull of A
                    elif points[k] < points[i] or points[k] > points[j]:
                        ij_part_of_convex_hull = False
                        break

                if points_left_of_ij and points_right_of_ij:
                    ij_part_of_convex_hull = False
                    break

            if ij_part_of_convex_hull:
                convex_set.update([points[i], points[j]])

    return sorted(convex_set)


def convex_hull_recursive(points: list[Point]) -> list[Point]:
    """
    Constructs the convex hull of a set of 2D points using a divide-and-conquer strategy
    The algorithm exploits the geometric properties of the problem by repeatedly
    partitioning the set of points into smaller hulls, and finding the convex hull of
    these smaller hulls.  The union of the convex hull from smaller hulls is the
    solution to the convex hull of the larger problem.

    Parameter
    ---------
    points: array-like of object of Points, lists or tuples.
    The set of  2d points for which the convex-hull is needed

    Runtime: O(n log n)

    Returns
    -------
    convex_set: list, the convex-hull of points sorted in non-decreasing order.

    Examples
    ---------
    >>> convex_hull_recursive([[0, 0], [1, 0], [10, 1]])
    [(0.0, 0.0), (1.0, 0.0), (10.0, 1.0)]
    >>> convex_hull_recursive([[0, 0], [1, 0], [10, 0]])
    [(0.0, 0.0), (10.0, 0.0)]
    >>> convex_hull_recursive([[-1, 1],[-1, -1], [0, 0], [0.5, 0.5], [1, -1], [1, 1],
    ...                        [-0.75, 1]])
    [(-1.0, -1.0), (-1.0, 1.0), (1.0, -1.0), (1.0, 1.0)]
    >>> convex_hull_recursive([(0, 3), (2, 2), (1, 1), (2, 1), (3, 0), (0, 0), (3, 3),
    ...                        (2, -1), (2, -4), (1, -3)])
    [(0.0, 0.0), (0.0, 3.0), (1.0, -3.0), (2.0, -4.0), (3.0, 0.0), (3.0, 3.0)]

    """
    points = sorted(_validate_input(points))
    n = len(points)

    # divide all the points into an upper hull and a lower hull
    # the left most point and the right most point are definitely
    # members of the convex hull by definition.
    # use these two anchors to divide all the points into two hulls,
    # an upper hull and a lower hull.

    # all points to the left (above) the line joining the extreme points belong to the
    # upper hull
    # all points to the right (below) the line joining the extreme points below to the
    # lower hull
    # ignore all points on the line joining the extreme points since they cannot be
    # part of the convex hull

    left_most_point = points[0]
    right_most_point = points[n - 1]

    convex_set = {left_most_point, right_most_point}
    upper_hull = []
    lower_hull = []

    for i in range(1, n - 1):
        det = _det(left_most_point, right_most_point, points[i])

        if det > 0:
            upper_hull.append(points[i])
        elif det < 0:
            lower_hull.append(points[i])

    _construct_hull(upper_hull, left_most_point, right_most_point, convex_set)
    _construct_hull(lower_hull, right_most_point, left_most_point, convex_set)

    return sorted(convex_set)


def _construct_hull(
    points: list[Point], left: Point, right: Point, convex_set: set[Point]
) -> None:
    """

    Parameters
    ---------
    points: list or None, the hull of points from which to choose the next convex-hull
        point
    left: Point, the point to the left  of line segment joining left and right
    right: The point to the right of the line segment joining left and right
    convex_set: set, the current convex-hull. The state of convex-set gets updated by
        this function

    Note
    ----
    For the line segment 'ab', 'a' is on the left and 'b' on the right.
    but the reverse is true for the line segment 'ba'.

    Returns
    -------
    Nothing, only updates the state of convex-set
    """
    if points:
        extreme_point = None
        extreme_point_distance = float("-inf")
        candidate_points = []

        for p in points:
            det = _det(left, right, p)

            if det > 0:
                candidate_points.append(p)

                if det > extreme_point_distance:
                    extreme_point_distance = det
                    extreme_point = p

        if extreme_point:
            _construct_hull(candidate_points, left, extreme_point, convex_set)
            convex_set.add(extreme_point)
            _construct_hull(candidate_points, extreme_point, right, convex_set)


def convex_hull_melkman(points: list[Point]) -> list[Point]:
    """
    Constructs the convex hull of a set of 2D points using the melkman algorithm.
    The algorithm works by iteratively inserting points of a simple polygonal chain
    (meaning that no line segments between two consecutive points cross each other).
    Sorting the points yields such a polygonal chain.

    For a detailed description, see http://cgm.cs.mcgill.ca/~athens/cs601/Melkman.html

    Runtime: O(n log n) - O(n) if points are already sorted in the input

    Parameters
    ---------
    points: array-like of object of Points, lists or tuples.
    The set of 2d points for which the convex-hull is needed

    Returns
    ------
    convex_set: list, the convex-hull of points sorted in non-decreasing order.

    See Also
    --------

    Examples
    ---------
    >>> convex_hull_melkman([[0, 0], [1, 0], [10, 1]])
    [(0.0, 0.0), (1.0, 0.0), (10.0, 1.0)]
    >>> convex_hull_melkman([[0, 0], [1, 0], [10, 0]])
    [(0.0, 0.0), (10.0, 0.0)]
    >>> convex_hull_melkman([[-1, 1],[-1, -1], [0, 0], [0.5, 0.5], [1, -1], [1, 1],
    ...                 [-0.75, 1]])
    [(-1.0, -1.0), (-1.0, 1.0), (1.0, -1.0), (1.0, 1.0)]
    >>> convex_hull_melkman([(0, 3), (2, 2), (1, 1), (2, 1), (3, 0), (0, 0), (3, 3),
    ...                 (2, -1), (2, -4), (1, -3)])
    [(0.0, 0.0), (0.0, 3.0), (1.0, -3.0), (2.0, -4.0), (3.0, 0.0), (3.0, 3.0)]
    """
    points = sorted(_validate_input(points))
    n = len(points)

    convex_hull = points[:2]
    for i in range(2, n):
        det = _det(convex_hull[1], convex_hull[0], points[i])
        if det > 0:
            convex_hull.insert(0, points[i])
            break
        elif det < 0:
            convex_hull.append(points[i])
            break
        else:
            convex_hull[1] = points[i]
    i += 1

    for j in range(i, n):
        if (
            _det(convex_hull[0], convex_hull[-1], points[j]) > 0
            and _det(convex_hull[-1], convex_hull[0], points[1]) < 0
        ):
            # The point lies within the convex hull
            continue

        convex_hull.insert(0, points[j])
        convex_hull.append(points[j])
        while _det(convex_hull[0], convex_hull[1], convex_hull[2]) >= 0:
            del convex_hull[1]
        while _det(convex_hull[-1], convex_hull[-2], convex_hull[-3]) <= 0:
            del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
    return sorted(convex_hull[1:] if len(convex_hull) > 3 else convex_hull)


def main():
    points = [
        (0, 3),
        (2, 2),
        (1, 1),
        (2, 1),
        (3, 0),
        (0, 0),
        (3, 3),
        (2, -1),
        (2, -4),
        (1, -3),
    ]
    # the convex set of points is
    # [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
    results_bf = convex_hull_bf(points)

    results_recursive = convex_hull_recursive(points)
    assert results_bf == results_recursive

    results_melkman = convex_hull_melkman(points)
    assert results_bf == results_melkman

    print(results_bf)


if __name__ == "__main__":
    main()


================================================
FILE: divide_and_conquer/heaps_algorithm.py
================================================
"""
Heap's algorithm returns the list of all permutations possible from a list.
It minimizes movement by generating each permutation from the previous one
by swapping only two elements.
More information:
https://en.wikipedia.org/wiki/Heap%27s_algorithm.
"""


def heaps(arr: list) -> list:
    """
    Pure python implementation of the Heap's algorithm (recursive version),
    returning all permutations of a list.
    >>> heaps([])
    [()]
    >>> heaps([0])
    [(0,)]
    >>> heaps([-1, 1])
    [(-1, 1), (1, -1)]
    >>> heaps([1, 2, 3])
    [(1, 2, 3), (2, 1, 3), (3, 1, 2), (1, 3, 2), (2, 3, 1), (3, 2, 1)]
    >>> from itertools import permutations
    >>> sorted(heaps([1,2,3])) == sorted(permutations([1,2,3]))
    True
    >>> all(sorted(heaps(x)) == sorted(permutations(x))
    ...     for x in ([], [0], [-1, 1], [1, 2, 3]))
    True
    """

    if len(arr) <= 1:
        return [tuple(arr)]

    res = []

    def generate(k: int, arr: list):
        if k == 1:
            res.append(tuple(arr[:]))
            return

        generate(k - 1, arr)

        for i in range(k - 1):
            if k % 2 == 0:  # k is even
                arr[i], arr[k - 1] = arr[k - 1], arr[i]
            else:  # k is odd
                arr[0], arr[k - 1] = arr[k - 1], arr[0]
            generate(k - 1, arr)

    generate(len(arr), arr)
    return res


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    arr = [int(item) for item in user_input.split(",")]
    print(heaps(arr))


================================================
FILE: divide_and_conquer/heaps_algorithm_iterative.py
================================================
"""
Heap's (iterative) algorithm returns the list of all permutations possible from a list.
It minimizes movement by generating each permutation from the previous one
by swapping only two elements.
More information:
https://en.wikipedia.org/wiki/Heap%27s_algorithm.
"""


def heaps(arr: list) -> list:
    """
    Pure python implementation of the iterative Heap's algorithm,
    returning all permutations of a list.
    >>> heaps([])
    [()]
    >>> heaps([0])
    [(0,)]
    >>> heaps([-1, 1])
    [(-1, 1), (1, -1)]
    >>> heaps([1, 2, 3])
    [(1, 2, 3), (2, 1, 3), (3, 1, 2), (1, 3, 2), (2, 3, 1), (3, 2, 1)]
    >>> from itertools import permutations
    >>> sorted(heaps([1,2,3])) == sorted(permutations([1,2,3]))
    True
    >>> all(sorted(heaps(x)) == sorted(permutations(x))
    ...     for x in ([], [0], [-1, 1], [1, 2, 3]))
    True
    """

    if len(arr) <= 1:
        return [tuple(arr)]

    res = []

    def generate(n: int, arr: list):
        c = [0] * n
        res.append(tuple(arr))

        i = 0
        while i < n:
            if c[i] < i:
                if i % 2 == 0:
                    arr[0], arr[i] = arr[i], arr[0]
                else:
                    arr[c[i]], arr[i] = arr[i], arr[c[i]]
                res.append(tuple(arr))
                c[i] += 1
                i = 0
            else:
                c[i] = 0
                i += 1

    generate(len(arr), arr)
    return res


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    arr = [int(item) for item in user_input.split(",")]
    print(heaps(arr))


================================================
FILE: divide_and_conquer/inversions.py
================================================
"""
Given an array-like data structure A[1..n], how many pairs
(i, j) for all 1 <= i < j <= n such that A[i] > A[j]? These pairs are
called inversions. Counting the number of such inversions in an array-like
object is the important. Among other things, counting inversions can help
us determine how close a given array is to being sorted.
In this implementation, I provide two algorithms, a divide-and-conquer
algorithm which runs in nlogn and the brute-force n^2 algorithm.
"""


def count_inversions_bf(arr):
    """
    Counts the number of inversions using a naive brute-force algorithm
    Parameters
    ----------
    arr: arr: array-like, the list containing the items for which the number
    of inversions is desired. The elements of `arr` must be comparable.
    Returns
    -------
    num_inversions: The total number of inversions in `arr`
    Examples
    ---------
     >>> count_inversions_bf([1, 4, 2, 4, 1])
     4
     >>> count_inversions_bf([1, 1, 2, 4, 4])
     0
     >>> count_inversions_bf([])
     0
    """

    num_inversions = 0
    n = len(arr)

    for i in range(n - 1):
        for j in range(i + 1, n):
            if arr[i] > arr[j]:
                num_inversions += 1

    return num_inversions


def count_inversions_recursive(arr):
    """
    Counts the number of inversions using a divide-and-conquer algorithm
    Parameters
    -----------
    arr: array-like, the list containing the items for which the number
    of inversions is desired. The elements of `arr` must be comparable.
    Returns
    -------
    C: a sorted copy of `arr`.
    num_inversions: int, the total number of inversions in 'arr'
    Examples
    --------
    >>> count_inversions_recursive([1, 4, 2, 4, 1])
    ([1, 1, 2, 4, 4], 4)
    >>> count_inversions_recursive([1, 1, 2, 4, 4])
    ([1, 1, 2, 4, 4], 0)
    >>> count_inversions_recursive([])
    ([], 0)
    """
    if len(arr) <= 1:
        return arr, 0
    mid = len(arr) // 2
    p = arr[0:mid]
    q = arr[mid:]

    a, inversion_p = count_inversions_recursive(p)
    b, inversions_q = count_inversions_recursive(q)
    c, cross_inversions = _count_cross_inversions(a, b)

    num_inversions = inversion_p + inversions_q + cross_inversions
    return c, num_inversions


def _count_cross_inversions(p, q):
    """
    Counts the inversions across two sorted arrays.
    And combine the two arrays into one sorted array
    For all 1<= i<=len(P) and for all 1 <= j <= len(Q),
    if P[i] > Q[j], then (i, j) is a cross inversion
    Parameters
    ----------
    P: array-like, sorted in non-decreasing order
    Q: array-like, sorted in non-decreasing order
    Returns
    ------
    R: array-like, a sorted array of the elements of `P` and `Q`
    num_inversion: int, the number of inversions across `P` and `Q`
    Examples
    --------
    >>> _count_cross_inversions([1, 2, 3], [0, 2, 5])
    ([0, 1, 2, 2, 3, 5], 4)
    >>> _count_cross_inversions([1, 2, 3], [3, 4, 5])
    ([1, 2, 3, 3, 4, 5], 0)
    """

    r = []
    i = j = num_inversion = 0
    while i < len(p) and j < len(q):
        if p[i] > q[j]:
            # if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
            # These are all inversions. The claim emerges from the
            # property that P is sorted.
            num_inversion += len(p) - i
            r.append(q[j])
            j += 1
        else:
            r.append(p[i])
            i += 1

    if i < len(p):
        r.extend(p[i:])
    else:
        r.extend(q[j:])

    return r, num_inversion


def main():
    arr_1 = [10, 2, 1, 5, 5, 2, 11]

    # this arr has 8 inversions:
    # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

    num_inversions_bf = count_inversions_bf(arr_1)
    _, num_inversions_recursive = count_inversions_recursive(arr_1)

    assert num_inversions_bf == num_inversions_recursive == 8

    print("number of inversions = ", num_inversions_bf)

    # testing an array with zero inversion (a sorted arr_1)

    arr_1.sort()
    num_inversions_bf = count_inversions_bf(arr_1)
    _, num_inversions_recursive = count_inversions_recursive(arr_1)

    assert num_inversions_bf == num_inversions_recursive == 0
    print("number of inversions = ", num_inversions_bf)

    # an empty list should also have zero inversions
    arr_1 = []
    num_inversions_bf = count_inversions_bf(arr_1)
    _, num_inversions_recursive = count_inversions_recursive(arr_1)

    assert num_inversions_bf == num_inversions_recursive == 0
    print("number of inversions = ", num_inversions_bf)


if __name__ == "__main__":
    main()


================================================
FILE: divide_and_conquer/kth_order_statistic.py
================================================
"""
Find the kth smallest element in linear time using divide and conquer.
Recall we can do this trivially in O(nlogn) time. Sort the list and
access kth element in constant time.

This is a divide and conquer algorithm that can find a solution in O(n) time.

For more information of this algorithm:
https://web.stanford.edu/class/archive/cs/cs161/cs161.1138/lectures/08/Small08.pdf
"""

from __future__ import annotations

from random import choice


def random_pivot(lst):
    """
    Choose a random pivot for the list.
    We can use a more sophisticated algorithm here, such as the median-of-medians
    algorithm.
    """
    return choice(lst)


def kth_number(lst: list[int], k: int) -> int:
    """
    Return the kth smallest number in lst.
    >>> kth_number([2, 1, 3, 4, 5], 3)
    3
    >>> kth_number([2, 1, 3, 4, 5], 1)
    1
    >>> kth_number([2, 1, 3, 4, 5], 5)
    5
    >>> kth_number([3, 2, 5, 6, 7, 8], 2)
    3
    >>> kth_number([25, 21, 98, 100, 76, 22, 43, 60, 89, 87], 4)
    43
    """
    # pick a pivot and separate into list based on pivot.
    pivot = random_pivot(lst)

    # partition based on pivot
    # linear time
    small = [e for e in lst if e < pivot]
    big = [e for e in lst if e > pivot]

    # if we get lucky, pivot might be the element we want.
    # we can easily see this:
    # small (elements smaller than k)
    # + pivot (kth element)
    # + big (elements larger than k)
    if len(small) == k - 1:
        return pivot
    # pivot is in elements bigger than k
    elif len(small) < k - 1:
        return kth_number(big, k - len(small) - 1)
    # pivot is in elements smaller than k
    else:
        return kth_number(small, k)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: divide_and_conquer/max_difference_pair.py
================================================
def max_difference(a: list[int]) -> tuple[int, int]:
    """
    We are given an array A[1..n] of integers, n >= 1. We want to
    find a pair of indices (i, j) such that
    1 <= i <= j <= n and A[j] - A[i] is as large as possible.

    Explanation:
    https://www.geeksforgeeks.org/maximum-difference-between-two-elements/

    >>> max_difference([5, 11, 2, 1, 7, 9, 0, 7])
    (1, 9)
    """
    # base case
    if len(a) == 1:
        return a[0], a[0]
    else:
        # split A into half.
        first = a[: len(a) // 2]
        second = a[len(a) // 2 :]

        # 2 sub problems, 1/2 of original size.
        small1, big1 = max_difference(first)
        small2, big2 = max_difference(second)

        # get min of first and max of second
        # linear time
        min_first = min(first)
        max_second = max(second)

        # 3 cases, either (small1, big1),
        # (min_first, max_second), (small2, big2)
        # constant comparisons
        if big2 - small2 > max_second - min_first and big2 - small2 > big1 - small1:
            return small2, big2
        elif big1 - small1 > max_second - min_first:
            return small1, big1
        else:
            return min_first, max_second


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: divide_and_conquer/max_subarray.py
================================================
"""
The maximum subarray problem is the task of finding the continuous subarray that has the
maximum sum within a given array of numbers. For example, given the array
[-2, 1, -3, 4, -1, 2, 1, -5, 4], the contiguous subarray with the maximum sum is
[4, -1, 2, 1], which has a sum of 6.

This divide-and-conquer algorithm finds the maximum subarray in O(n log n) time.
"""

from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt


def max_subarray(
    arr: Sequence[float], low: int, high: int
) -> tuple[int | None, int | None, float]:
    """
    Solves the maximum subarray problem using divide and conquer.
    :param arr:     the given array of numbers
    :param low:     the start index
    :param high:    the end index
    :return:        the start index of the maximum subarray, the end index of the
                    maximum subarray, and the maximum subarray sum

    >>> nums = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
    >>> max_subarray(nums, 0, len(nums) - 1)
    (3, 6, 6)
    >>> nums = [2, 8, 9]
    >>> max_subarray(nums, 0, len(nums) - 1)
    (0, 2, 19)
    >>> nums = [0, 0]
    >>> max_subarray(nums, 0, len(nums) - 1)
    (0, 0, 0)
    >>> nums = [-1.0, 0.0, 1.0]
    >>> max_subarray(nums, 0, len(nums) - 1)
    (2, 2, 1.0)
    >>> nums = [-2, -3, -1, -4, -6]
    >>> max_subarray(nums, 0, len(nums) - 1)
    (2, 2, -1)
    >>> max_subarray([], 0, 0)
    (None, None, 0)
    """
    if not arr:
        return None, None, 0
    if low == high:
        return low, high, arr[low]

    mid = (low + high) // 2
    left_low, left_high, left_sum = max_subarray(arr, low, mid)
    right_low, right_high, right_sum = max_subarray(arr, mid + 1, high)
    cross_left, cross_right, cross_sum = max_cross_sum(arr, low, mid, high)
    if left_sum >= right_sum and left_sum >= cross_sum:
        return left_low, left_high, left_sum
    elif right_sum >= left_sum and right_sum >= cross_sum:
        return right_low, right_high, right_sum
    return cross_left, cross_right, cross_sum


def max_cross_sum(
    arr: Sequence[float], low: int, mid: int, high: int
) -> tuple[int, int, float]:
    left_sum, max_left = float("-inf"), -1
    right_sum, max_right = float("-inf"), -1

    summ: int | float = 0
    for i in range(mid, low - 1, -1):
        summ += arr[i]
        if summ > left_sum:
            left_sum = summ
            max_left = i

    summ = 0
    for i in range(mid + 1, high + 1):
        summ += arr[i]
        if summ > right_sum:
            right_sum = summ
            max_right = i

    return max_left, max_right, (left_sum + right_sum)


def time_max_subarray(input_size: int) -> float:
    arr = [randint(1, input_size) for _ in range(input_size)]
    start = time.time()
    max_subarray(arr, 0, input_size - 1)
    end = time.time()
    return end - start


def plot_runtimes() -> None:
    input_sizes = [10, 100, 1000, 10000, 50000, 100000, 200000, 300000, 400000, 500000]
    runtimes = [time_max_subarray(input_size) for input_size in input_sizes]
    print("No of Inputs\t\tTime Taken")
    for input_size, runtime in zip(input_sizes, runtimes):
        print(input_size, "\t\t", runtime)
    plt.plot(input_sizes, runtimes)
    plt.xlabel("Number of Inputs")
    plt.ylabel("Time taken in seconds")
    plt.show()


if __name__ == "__main__":
    """
    A random simulation of this algorithm.
    """
    from doctest import testmod

    testmod()


================================================
FILE: divide_and_conquer/mergesort.py
================================================
from __future__ import annotations


def merge(left_half: list, right_half: list) -> list:
    """Helper function for mergesort.

    >>> left_half = [-2]
    >>> right_half = [-1]
    >>> merge(left_half, right_half)
    [-2, -1]

    >>> left_half = [1,2,3]
    >>> right_half = [4,5,6]
    >>> merge(left_half, right_half)
    [1, 2, 3, 4, 5, 6]

    >>> left_half = [-2]
    >>> right_half = [-1]
    >>> merge(left_half, right_half)
    [-2, -1]

    >>> left_half = [12, 15]
    >>> right_half = [13, 14]
    >>> merge(left_half, right_half)
    [12, 13, 14, 15]

    >>> left_half = []
    >>> right_half = []
    >>> merge(left_half, right_half)
    []
    """
    sorted_array = [None] * (len(right_half) + len(left_half))

    pointer1 = 0  # pointer to current index for left Half
    pointer2 = 0  # pointer to current index for the right Half
    index = 0  # pointer to current index for the sorted array Half

    while pointer1 < len(left_half) and pointer2 < len(right_half):
        if left_half[pointer1] < right_half[pointer2]:
            sorted_array[index] = left_half[pointer1]
            pointer1 += 1
            index += 1
        else:
            sorted_array[index] = right_half[pointer2]
            pointer2 += 1
            index += 1
    while pointer1 < len(left_half):
        sorted_array[index] = left_half[pointer1]
        pointer1 += 1
        index += 1

    while pointer2 < len(right_half):
        sorted_array[index] = right_half[pointer2]
        pointer2 += 1
        index += 1

    return sorted_array


def merge_sort(array: list) -> list:
    """Returns a list of sorted array elements using merge sort.

    >>> from random import shuffle
    >>> array = [-2, 3, -10, 11, 99, 100000, 100, -200]
    >>> shuffle(array)
    >>> merge_sort(array)
    [-200, -10, -2, 3, 11, 99, 100, 100000]

    >>> shuffle(array)
    >>> merge_sort(array)
    [-200, -10, -2, 3, 11, 99, 100, 100000]

    >>> array = [-200]
    >>> merge_sort(array)
    [-200]

    >>> array = [-2, 3, -10, 11, 99, 100000, 100, -200]
    >>> shuffle(array)
    >>> sorted(array) == merge_sort(array)
    True

    >>> array = [-2]
    >>> merge_sort(array)
    [-2]

    >>> array = []
    >>> merge_sort(array)
    []

    >>> array = [10000000, 1, -1111111111, 101111111112, 9000002]
    >>> sorted(array) == merge_sort(array)
    True
    """
    if len(array) <= 1:
        return array
    # the actual formula to calculate the middle element = left + (right - left) // 2
    # this avoids integer overflow in case of large N
    middle = 0 + (len(array) - 0) // 2

    # Split the array into halves till the array length becomes equal to One
    # merge the arrays of single length returned by mergeSort function and
    # pass them into the merge arrays function which merges the array
    left_half = array[:middle]
    right_half = array[middle:]

    return merge(merge_sort(left_half), merge_sort(right_half))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: divide_and_conquer/peak.py
================================================
"""
Finding the peak of a unimodal list using divide and conquer.
A unimodal array is defined as follows: array is increasing up to index p,
then decreasing afterwards. (for p >= 1)
An obvious solution can be performed in O(n),
to find the maximum of the array.
(From Kleinberg and Tardos. Algorithm Design.
Addison Wesley 2006: Chapter 5 Solved Exercise 1)
"""

from __future__ import annotations


def peak(lst: list[int]) -> int:
    """
    Return the peak value of `lst`.
    >>> peak([1, 2, 3, 4, 5, 4, 3, 2, 1])
    5
    >>> peak([1, 10, 9, 8, 7, 6, 5, 4])
    10
    >>> peak([1, 9, 8, 7])
    9
    >>> peak([1, 2, 3, 4, 5, 6, 7, 0])
    7
    >>> peak([1, 2, 3, 4, 3, 2, 1, 0, -1, -2])
    4
    """
    # middle index
    m = len(lst) // 2

    # choose the middle 3 elements
    three = lst[m - 1 : m + 2]

    # if middle element is peak
    if three[1] > three[0] and three[1] > three[2]:
        return three[1]

    # if increasing, recurse on right
    elif three[0] < three[2]:
        if len(lst[:m]) == 2:
            m -= 1
        return peak(lst[m:])

    # decreasing
    else:
        if len(lst[:m]) == 2:
            m += 1
        return peak(lst[:m])


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: divide_and_conquer/power.py
================================================
def actual_power(a: int, b: int) -> int:
    """
    Function using divide and conquer to calculate a^b.
    It only works for integer a,b.

    :param a: The base of the power operation, an integer.
    :param b: The exponent of the power operation, a non-negative integer.
    :return: The result of a^b.

    Examples:
    >>> actual_power(3, 2)
    9
    >>> actual_power(5, 3)
    125
    >>> actual_power(2, 5)
    32
    >>> actual_power(7, 0)
    1
    """
    if b == 0:
        return 1
    half = actual_power(a, b // 2)

    if (b % 2) == 0:
        return half * half
    else:
        return a * half * half


def power(a: int, b: int) -> float:
    """
    :param a: The base (integer).
    :param b: The exponent (integer).
    :return: The result of a^b, as a float for negative exponents.

    >>> power(4,6)
    4096
    >>> power(2,3)
    8
    >>> power(-2,3)
    -8
    >>> power(2,-3)
    0.125
    >>> power(-2,-3)
    -0.125
    """
    if b < 0:
        return 1 / actual_power(a, -b)
    return actual_power(a, b)


if __name__ == "__main__":
    print(power(-2, -3))  # output -0.125


================================================
FILE: divide_and_conquer/strassen_matrix_multiplication.py
================================================
from __future__ import annotations

import math


def default_matrix_multiplication(a: list, b: list) -> list:
    """
    Multiplication only for 2x2 matrices
    """
    if len(a) != 2 or len(a[0]) != 2 or len(b) != 2 or len(b[0]) != 2:
        raise Exception("Matrices are not 2x2")
    new_matrix = [
        [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
        [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
    ]
    return new_matrix


def matrix_addition(matrix_a: list, matrix_b: list):
    return [
        [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]))]
        for row in range(len(matrix_a))
    ]


def matrix_subtraction(matrix_a: list, matrix_b: list):
    return [
        [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]))]
        for row in range(len(matrix_a))
    ]


def split_matrix(a: list) -> tuple[list, list, list, list]:
    """
    Given an even length matrix, returns the top_left, top_right, bot_left, bot_right
    quadrant.

    >>> split_matrix([[4,3,2,4],[2,3,1,1],[6,5,4,3],[8,4,1,6]])
    ([[4, 3], [2, 3]], [[2, 4], [1, 1]], [[6, 5], [8, 4]], [[4, 3], [1, 6]])
    >>> split_matrix([
    ...     [4,3,2,4,4,3,2,4],[2,3,1,1,2,3,1,1],[6,5,4,3,6,5,4,3],[8,4,1,6,8,4,1,6],
    ...     [4,3,2,4,4,3,2,4],[2,3,1,1,2,3,1,1],[6,5,4,3,6,5,4,3],[8,4,1,6,8,4,1,6]
    ... ])  # doctest: +NORMALIZE_WHITESPACE
    ([[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4],
      [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1],
      [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3],
      [8, 4, 1, 6]])
    """
    if len(a) % 2 != 0 or len(a[0]) % 2 != 0:
        raise Exception("Odd matrices are not supported!")

    matrix_length = len(a)
    mid = matrix_length // 2

    top_right = [[a[i][j] for j in range(mid, matrix_length)] for i in range(mid)]
    bot_right = [
        [a[i][j] for j in range(mid, matrix_length)] for i in range(mid, matrix_length)
    ]

    top_left = [[a[i][j] for j in range(mid)] for i in range(mid)]
    bot_left = [[a[i][j] for j in range(mid)] for i in range(mid, matrix_length)]

    return top_left, top_right, bot_left, bot_right


def matrix_dimensions(matrix: list) -> tuple[int, int]:
    return len(matrix), len(matrix[0])


def print_matrix(matrix: list) -> None:
    print("\n".join(str(line) for line in matrix))


def actual_strassen(matrix_a: list, matrix_b: list) -> list:
    """
    Recursive function to calculate the product of two matrices, using the Strassen
    Algorithm. It only supports square matrices of any size that is a power of 2.
    """
    if matrix_dimensions(matrix_a) == (2, 2):
        return default_matrix_multiplication(matrix_a, matrix_b)

    a, b, c, d = split_matrix(matrix_a)
    e, f, g, h = split_matrix(matrix_b)

    t1 = actual_strassen(a, matrix_subtraction(f, h))
    t2 = actual_strassen(matrix_addition(a, b), h)
    t3 = actual_strassen(matrix_addition(c, d), e)
    t4 = actual_strassen(d, matrix_subtraction(g, e))
    t5 = actual_strassen(matrix_addition(a, d), matrix_addition(e, h))
    t6 = actual_strassen(matrix_subtraction(b, d), matrix_addition(g, h))
    t7 = actual_strassen(matrix_subtraction(a, c), matrix_addition(e, f))

    top_left = matrix_addition(matrix_subtraction(matrix_addition(t5, t4), t2), t6)
    top_right = matrix_addition(t1, t2)
    bot_left = matrix_addition(t3, t4)
    bot_right = matrix_subtraction(matrix_subtraction(matrix_addition(t1, t5), t3), t7)

    # construct the new matrix from our 4 quadrants
    new_matrix = []
    for i in range(len(top_right)):
        new_matrix.append(top_left[i] + top_right[i])
    for i in range(len(bot_right)):
        new_matrix.append(bot_left[i] + bot_right[i])
    return new_matrix


def strassen(matrix1: list, matrix2: list) -> list:
    """
    >>> strassen([[2,1,3],[3,4,6],[1,4,2],[7,6,7]], [[4,2,3,4],[2,1,1,1],[8,6,4,2]])
    [[34, 23, 19, 15], [68, 46, 37, 28], [28, 18, 15, 12], [96, 62, 55, 48]]
    >>> strassen([[3,7,5,6,9],[1,5,3,7,8],[1,4,4,5,7]], [[2,4],[5,2],[1,7],[5,5],[7,8]])
    [[139, 163], [121, 134], [100, 121]]
    """
    if matrix_dimensions(matrix1)[1] != matrix_dimensions(matrix2)[0]:
        msg = (
            "Unable to multiply these matrices, please check the dimensions.\n"
            f"Matrix A: {matrix1}\n"
            f"Matrix B: {matrix2}"
        )
        raise Exception(msg)
    dimension1 = matrix_dimensions(matrix1)
    dimension2 = matrix_dimensions(matrix2)

    if dimension1[0] == dimension1[1] and dimension2[0] == dimension2[1]:
        return [matrix1, matrix2]

    maximum = max(*dimension1, *dimension2)
    maxim = int(math.pow(2, math.ceil(math.log2(maximum))))
    new_matrix1 = matrix1
    new_matrix2 = matrix2

    # Adding zeros to the matrices to convert them both into square matrices of equal
    # dimensions that are a power of 2
    for i in range(maxim):
        if i < dimension1[0]:
            for _ in range(dimension1[1], maxim):
                new_matrix1[i].append(0)
        else:
            new_matrix1.append([0] * maxim)
        if i < dimension2[0]:
            for _ in range(dimension2[1], maxim):
                new_matrix2[i].append(0)
        else:
            new_matrix2.append([0] * maxim)

    final_matrix = actual_strassen(new_matrix1, new_matrix2)

    # Removing the additional zeros
    for i in range(maxim):
        if i < dimension1[0]:
            for _ in range(dimension2[1], maxim):
                final_matrix[i].pop()
        else:
            final_matrix.pop()
    return final_matrix


if __name__ == "__main__":
    matrix1 = [
        [2, 3, 4, 5],
        [6, 4, 3, 1],
        [2, 3, 6, 7],
        [3, 1, 2, 4],
        [2, 3, 4, 5],
        [6, 4, 3, 1],
        [2, 3, 6, 7],
        [3, 1, 2, 4],
        [2, 3, 4, 5],
        [6, 2, 3, 1],
    ]
    matrix2 = [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
    print(strassen(matrix1, matrix2))


================================================
FILE: docs/__init__.py
================================================


================================================
FILE: docs/conf.py
================================================
from sphinx_pyproject import SphinxConfig

project = SphinxConfig("../pyproject.toml", globalns=globals()).name


================================================
FILE: docs/source/__init__.py
================================================


================================================
FILE: dynamic_programming/__init__.py
================================================


================================================
FILE: dynamic_programming/abbreviation.py
================================================
"""
https://www.hackerrank.com/challenges/abbr/problem
You can perform the following operation on some string, :

1. Capitalize zero or more of 's lowercase letters at some index i
   (i.e., make them uppercase).
2. Delete all of the remaining lowercase letters in .

Example:
a=daBcd and b="ABC"
daBcd -> capitalize a and c(dABCd) -> remove d (ABC)
"""


def abbr(a: str, b: str) -> bool:
    """
    >>> abbr("daBcd", "ABC")
    True
    >>> abbr("dBcd", "ABC")
    False
    """
    n = len(a)
    m = len(b)
    dp = [[False for _ in range(m + 1)] for _ in range(n + 1)]
    dp[0][0] = True
    for i in range(n):
        for j in range(m + 1):
            if dp[i][j]:
                if j < m and a[i].upper() == b[j]:
                    dp[i + 1][j + 1] = True
                if a[i].islower():
                    dp[i + 1][j] = True
    return dp[n][m]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/all_construct.py
================================================
"""
Program to list all the ways a target string can be
constructed from the given list of substrings
"""

from __future__ import annotations


def all_construct(target: str, word_bank: list[str] | None = None) -> list[list[str]]:
    """
    returns the list containing all the possible
    combinations a string(`target`) can be constructed from
    the given list of substrings(`word_bank`)

    >>> all_construct("hello", ["he", "l", "o"])
    [['he', 'l', 'l', 'o']]
    >>> all_construct("purple",["purp","p","ur","le","purpl"])
    [['purp', 'le'], ['p', 'ur', 'p', 'le']]
    """

    word_bank = word_bank or []
    # create a table
    table_size: int = len(target) + 1

    table: list[list[list[str]]] = []
    for _ in range(table_size):
        table.append([])
    # seed value
    table[0] = [[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(table_size):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(word)] == word:
                    new_combinations: list[list[str]] = [
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(word)] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(target)]:
        combination.reverse()

    return table[len(target)]


if __name__ == "__main__":
    print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
    print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
    print(
        all_construct(
            "hexagonosaurus",
            ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
        )
    )


================================================
FILE: dynamic_programming/bitmask.py
================================================
"""

This is a Python implementation for questions involving task assignments between people.
Here Bitmasking and DP are used for solving this.

Question :-
We have N tasks and M people. Each person in M can do only certain of these tasks. Also
a person can do only one task and a task is performed only by one person.
Find the total no of ways in which the tasks can be distributed.
"""

from collections import defaultdict


class AssignmentUsingBitmask:
    def __init__(self, task_performed, total):
        self.total_tasks = total  # total no of tasks (N)

        # DP table will have a dimension of (2^M)*N
        # initially all values are set to -1
        self.dp = [
            [-1 for i in range(total + 1)] for j in range(2 ** len(task_performed))
        ]

        self.task = defaultdict(list)  # stores the list of persons for each task

        # final_mask is used to check if all persons are included by setting all bits
        # to 1
        self.final_mask = (1 << len(task_performed)) - 1

    def count_ways_until(self, mask, task_no):
        # if mask == self.finalmask all persons are distributed tasks, return 1
        if mask == self.final_mask:
            return 1

        # if not everyone gets the task and no more tasks are available, return 0
        if task_no > self.total_tasks:
            return 0

        # if case already considered
        if self.dp[mask][task_no] != -1:
            return self.dp[mask][task_no]

        # Number of ways when we don't this task in the arrangement
        total_ways_until = self.count_ways_until(mask, task_no + 1)

        # now assign the tasks one by one to all possible persons and recursively
        # assign for the remaining tasks.
        if task_no in self.task:
            for p in self.task[task_no]:
                # if p is already given a task
                if mask & (1 << p):
                    continue

                # assign this task to p and change the mask value. And recursively
                # assign tasks with the new mask value.
                total_ways_until += self.count_ways_until(mask | (1 << p), task_no + 1)

        # save the value.
        self.dp[mask][task_no] = total_ways_until

        return self.dp[mask][task_no]

    def count_no_of_ways(self, task_performed):
        # Store the list of persons for each task
        for i in range(len(task_performed)):
            for j in task_performed[i]:
                self.task[j].append(i)

        # call the function to fill the DP table, final answer is stored in dp[0][1]
        return self.count_ways_until(0, 1)


if __name__ == "__main__":
    total_tasks = 5  # total no of tasks (the value of N)

    # the list of tasks that can be done by M persons.
    task_performed = [[1, 3, 4], [1, 2, 5], [3, 4]]
    print(
        AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
            task_performed
        )
    )
    """
    For the particular example the tasks can be distributed as
    (1,2,3), (1,2,4), (1,5,3), (1,5,4), (3,1,4),
    (3,2,4), (3,5,4), (4,1,3), (4,2,3), (4,5,3)
    total 10
    """


================================================
FILE: dynamic_programming/catalan_numbers.py
================================================
"""
Print all the Catalan numbers from 0 to n, n being the user input.

 * The Catalan numbers are a sequence of positive integers that
 * appear in many counting problems in combinatorics [1]. Such
 * problems include counting [2]:
 * - The number of Dyck words of length 2n
 * - The number well-formed expressions with n pairs of parentheses
 *   (e.g., `()()` is valid but `())(` is not)
 * - The number of different ways n + 1 factors can be completely
 *   parenthesized (e.g., for n = 2, C(n) = 2 and (ab)c and a(bc)
 *   are the two valid ways to parenthesize.
 * - The number of full binary trees with n + 1 leaves

 * A Catalan number satisfies the following recurrence relation
 * which we will use in this algorithm [1].
 * C(0) = C(1) = 1
 * C(n) = sum(C(i).C(n-i-1)), from i = 0 to n-1

 * In addition, the n-th Catalan number can be calculated using
 * the closed form formula below [1]:
 * C(n) = (1 / (n + 1)) * (2n choose n)

 * Sources:
 *  [1] https://brilliant.org/wiki/catalan-numbers/
 *  [2] https://en.wikipedia.org/wiki/Catalan_number
"""


def catalan_numbers(upper_limit: int) -> "list[int]":
    """
    Return a list of the Catalan number sequence from 0 through `upper_limit`.

    >>> catalan_numbers(5)
    [1, 1, 2, 5, 14, 42]
    >>> catalan_numbers(2)
    [1, 1, 2]
    >>> catalan_numbers(-1)
    Traceback (most recent call last):
    ValueError: Limit for the Catalan sequence must be ≥ 0
    """
    if upper_limit < 0:
        raise ValueError("Limit for the Catalan sequence must be ≥ 0")

    catalan_list = [0] * (upper_limit + 1)

    # Base case: C(0) = C(1) = 1
    catalan_list[0] = 1
    if upper_limit > 0:
        catalan_list[1] = 1

    # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
    for i in range(2, upper_limit + 1):
        for j in range(i):
            catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

    return catalan_list


if __name__ == "__main__":
    print("\n********* Catalan Numbers Using Dynamic Programming ************\n")
    print("\n*** Enter -1 at any time to quit ***")
    print("\nEnter the upper limit (≥ 0) for the Catalan number sequence: ", end="")
    try:
        while True:
            N = int(input().strip())
            if N < 0:
                print("\n********* Goodbye!! ************")
                break
            else:
                print(f"The Catalan numbers from 0 through {N} are:")
                print(catalan_numbers(N))
                print("Try another upper limit for the sequence: ", end="")
    except (NameError, ValueError):
        print("\n********* Invalid input, goodbye! ************\n")

    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/climbing_stairs.py
================================================
#!/usr/bin/env python3


def climb_stairs(number_of_steps: int) -> int:
    """
    LeetCdoe No.70: Climbing Stairs
    Distinct ways to climb a number_of_steps staircase where each time you can either
    climb 1 or 2 steps.

    Args:
        number_of_steps: number of steps on the staircase

    Returns:
        Distinct ways to climb a number_of_steps staircase

    Raises:
        AssertionError: number_of_steps not positive integer

    >>> climb_stairs(3)
    3
    >>> climb_stairs(1)
    1
    >>> climb_stairs(-7)  # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    AssertionError: number_of_steps needs to be positive integer, your input -7
    """
    assert isinstance(number_of_steps, int) and number_of_steps > 0, (
        f"number_of_steps needs to be positive integer, your input {number_of_steps}"
    )
    if number_of_steps == 1:
        return 1
    previous, current = 1, 1
    for _ in range(number_of_steps - 1):
        current, previous = current + previous, current
    return current


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/combination_sum_iv.py
================================================
"""
Question:
    You are given an array of distinct integers and you have to tell how many
    different ways of selecting the elements from the array are there such that
    the sum of chosen elements is equal to the target number tar.

Example

Input:
    * N = 3
    * target = 5
    * array = [1, 2, 5]

Output:
    9

Approach:
    The basic idea is to go over recursively to find the way such that the sum
    of chosen elements is `target`. For every element, we have two choices

        1. Include the element in our set of chosen elements.
        2. Don't include the element in our set of chosen elements.
"""


def combination_sum_iv(array: list[int], target: int) -> int:
    """
    Function checks the all possible combinations, and returns the count
    of possible combination in exponential Time Complexity.

    >>> combination_sum_iv([1,2,5], 5)
    9
    """

    def count_of_possible_combinations(target: int) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item) for item in array)

    return count_of_possible_combinations(target)


def combination_sum_iv_dp_array(array: list[int], target: int) -> int:
    """
    Function checks the all possible combinations, and returns the count
    of possible combination in O(N^2) Time Complexity as we are using Dynamic
    programming array here.

    >>> combination_sum_iv_dp_array([1,2,5], 5)
    9
    """

    def count_of_possible_combinations_with_dp_array(
        target: int, dp_array: list[int]
    ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        answer = sum(
            count_of_possible_combinations_with_dp_array(target - item, dp_array)
            for item in array
        )
        dp_array[target] = answer
        return answer

    dp_array = [-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(target, dp_array)


def combination_sum_iv_bottom_up(n: int, array: list[int], target: int) -> int:
    """
    Function checks the all possible combinations with using bottom up approach,
    and returns the count of possible combination in O(N^2) Time Complexity
    as we are using Dynamic programming array here.

    >>> combination_sum_iv_bottom_up(3, [1,2,5], 5)
    9
    """

    dp_array = [0] * (target + 1)
    dp_array[0] = 1

    for i in range(1, target + 1):
        for j in range(n):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    target = 5
    array = [1, 2, 5]
    print(combination_sum_iv(array, target))


================================================
FILE: dynamic_programming/edit_distance.py
================================================
"""
Author  : Turfa Auliarachman
Date    : October 12, 2016

This is a pure Python implementation of Dynamic Programming solution to the edit
distance problem.

The problem is :
Given two strings A and B. Find the minimum number of operations to string B such that
A = B. The permitted operations are removal,  insertion, and substitution.
"""


class EditDistance:
    """
    Use :
    solver              = EditDistance()
    editDistanceResult  = solver.solve(firstString, secondString)
    """

    def __init__(self):
        self.word1 = ""
        self.word2 = ""
        self.dp = []

    def __min_dist_top_down_dp(self, m: int, n: int) -> int:
        if m == -1:
            return n + 1
        elif n == -1:
            return m + 1
        elif self.dp[m][n] > -1:
            return self.dp[m][n]
        else:
            if self.word1[m] == self.word2[n]:
                self.dp[m][n] = self.__min_dist_top_down_dp(m - 1, n - 1)
            else:
                insert = self.__min_dist_top_down_dp(m, n - 1)
                delete = self.__min_dist_top_down_dp(m - 1, n)
                replace = self.__min_dist_top_down_dp(m - 1, n - 1)
                self.dp[m][n] = 1 + min(insert, delete, replace)

            return self.dp[m][n]

    def min_dist_top_down(self, word1: str, word2: str) -> int:
        """
        >>> EditDistance().min_dist_top_down("intention", "execution")
        5
        >>> EditDistance().min_dist_top_down("intention", "")
        9
        >>> EditDistance().min_dist_top_down("", "")
        0
        """
        self.word1 = word1
        self.word2 = word2
        self.dp = [[-1 for _ in range(len(word2))] for _ in range(len(word1))]

        return self.__min_dist_top_down_dp(len(word1) - 1, len(word2) - 1)

    def min_dist_bottom_up(self, word1: str, word2: str) -> int:
        """
        >>> EditDistance().min_dist_bottom_up("intention", "execution")
        5
        >>> EditDistance().min_dist_bottom_up("intention", "")
        9
        >>> EditDistance().min_dist_bottom_up("", "")
        0
        """
        self.word1 = word1
        self.word2 = word2
        m = len(word1)
        n = len(word2)
        self.dp = [[0 for _ in range(n + 1)] for _ in range(m + 1)]

        for i in range(m + 1):
            for j in range(n + 1):
                if i == 0:  # first string is empty
                    self.dp[i][j] = j
                elif j == 0:  # second string is empty
                    self.dp[i][j] = i
                elif word1[i - 1] == word2[j - 1]:  # last characters are equal
                    self.dp[i][j] = self.dp[i - 1][j - 1]
                else:
                    insert = self.dp[i][j - 1]
                    delete = self.dp[i - 1][j]
                    replace = self.dp[i - 1][j - 1]
                    self.dp[i][j] = 1 + min(insert, delete, replace)
        return self.dp[m][n]


if __name__ == "__main__":
    solver = EditDistance()

    print("****************** Testing Edit Distance DP Algorithm ******************")
    print()

    S1 = input("Enter the first string: ").strip()
    S2 = input("Enter the second string: ").strip()

    print()
    print(f"The minimum edit distance is: {solver.min_dist_top_down(S1, S2)}")
    print(f"The minimum edit distance is: {solver.min_dist_bottom_up(S1, S2)}")
    print()
    print("*************** End of Testing Edit Distance DP Algorithm ***************")


================================================
FILE: dynamic_programming/factorial.py
================================================
# Factorial of a number using memoization

from functools import lru_cache


@lru_cache
def factorial(num: int) -> int:
    """
    >>> factorial(7)
    5040
    >>> factorial(-1)
    Traceback (most recent call last):
      ...
    ValueError: Number should not be negative.
    >>> [factorial(i) for i in range(10)]
    [1, 1, 2, 6, 24, 120, 720, 5040, 40320, 362880]
    """
    if num < 0:
        raise ValueError("Number should not be negative.")

    return 1 if num in (0, 1) else num * factorial(num - 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/fast_fibonacci.py
================================================
#!/usr/bin/env python3

"""
This program calculates the nth Fibonacci number in O(log(n)).
It's possible to calculate F(1_000_000) in less than a second.
"""

from __future__ import annotations

import sys


def fibonacci(n: int) -> int:
    """
    return F(n)
    >>> [fibonacci(i) for i in range(13)]
    [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144]
    """
    if n < 0:
        raise ValueError("Negative arguments are not supported")
    return _fib(n)[0]


# returns (F(n), F(n-1))
def _fib(n: int) -> tuple[int, int]:
    if n == 0:  # (F(0), F(1))
        return (0, 1)

    # F(2n) = F(n)[2F(n+1) - F(n)]
    # F(2n+1) = F(n+1)^2+F(n)^2
    a, b = _fib(n // 2)
    c = a * (b * 2 - a)
    d = a * a + b * b
    return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
    n = int(sys.argv[1])
    print(f"fibonacci({n}) is {fibonacci(n)}")


================================================
FILE: dynamic_programming/fibonacci.py
================================================
"""
This is a pure Python implementation of Dynamic Programming solution to the fibonacci
sequence problem.
"""


class Fibonacci:
    def __init__(self) -> None:
        self.sequence = [0, 1]

    def get(self, index: int) -> list:
        """
        Get the Fibonacci number of `index`. If the number does not exist,
        calculate all missing numbers leading up to the number of `index`.

        >>> Fibonacci().get(10)
        [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
        >>> Fibonacci().get(5)
        [0, 1, 1, 2, 3]
        """
        if (difference := index - (len(self.sequence) - 2)) >= 1:
            for _ in range(difference):
                self.sequence.append(self.sequence[-1] + self.sequence[-2])
        return self.sequence[:index]


def main() -> None:
    print(
        "Fibonacci Series Using Dynamic Programming\n",
        "Enter the index of the Fibonacci number you want to calculate ",
        "in the prompt below. (To exit enter exit or Ctrl-C)\n",
        sep="",
    )
    fibonacci = Fibonacci()

    while True:
        prompt: str = input(">> ")
        if prompt in {"exit", "quit"}:
            break

        try:
            index: int = int(prompt)
        except ValueError:
            print("Enter a number or 'exit'")
            continue

        print(fibonacci.get(index))


if __name__ == "__main__":
    main()


================================================
FILE: dynamic_programming/fizz_buzz.py
================================================
# https://en.wikipedia.org/wiki/Fizz_buzz#Programming


def fizz_buzz(number: int, iterations: int) -> str:
    """
    | Plays FizzBuzz.
    | Prints Fizz if number is a multiple of ``3``.
    | Prints Buzz if its a multiple of ``5``.
    | Prints FizzBuzz if its a multiple of both ``3`` and ``5`` or ``15``.
    | Else Prints The Number Itself.

    >>> fizz_buzz(1,7)
    '1 2 Fizz 4 Buzz Fizz 7 '
    >>> fizz_buzz(1,0)
    Traceback (most recent call last):
      ...
    ValueError: Iterations must be done more than 0 times to play FizzBuzz
    >>> fizz_buzz(-5,5)
    Traceback (most recent call last):
        ...
    ValueError: starting number must be
                             and integer and be more than 0
    >>> fizz_buzz(10,-5)
    Traceback (most recent call last):
        ...
    ValueError: Iterations must be done more than 0 times to play FizzBuzz
    >>> fizz_buzz(1.5,5)
    Traceback (most recent call last):
        ...
    ValueError: starting number must be
                             and integer and be more than 0
    >>> fizz_buzz(1,5.5)
    Traceback (most recent call last):
        ...
    ValueError: iterations must be defined as integers
    """
    if not isinstance(iterations, int):
        raise ValueError("iterations must be defined as integers")
    if not isinstance(number, int) or not number >= 1:
        raise ValueError(
            """starting number must be
                         and integer and be more than 0"""
        )
    if not iterations >= 1:
        raise ValueError("Iterations must be done more than 0 times to play FizzBuzz")

    out = ""
    while number <= iterations:
        if number % 3 == 0:
            out += "Fizz"
        if number % 5 == 0:
            out += "Buzz"
        if 0 not in (number % 3, number % 5):
            out += str(number)

        # print(out)
        number += 1
        out += " "
    return out


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/floyd_warshall.py
================================================
import math


class Graph:
    def __init__(self, n=0):  # a graph with Node 0,1,...,N-1
        self.n = n
        self.w = [
            [math.inf for j in range(n)] for i in range(n)
        ]  # adjacency matrix for weight
        self.dp = [
            [math.inf for j in range(n)] for i in range(n)
        ]  # dp[i][j] stores minimum distance from i to j

    def add_edge(self, u, v, w):
        """
        Adds a directed edge from node u
        to node v with weight w.

        >>> g = Graph(3)
        >>> g.add_edge(0, 1, 5)
        >>> g.dp[0][1]
        5
        """
        self.dp[u][v] = w

    def floyd_warshall(self):
        """
        Computes the shortest paths between all pairs of
        nodes using the Floyd-Warshall algorithm.

        >>> g = Graph(3)
        >>> g.add_edge(0, 1, 1)
        >>> g.add_edge(1, 2, 2)
        >>> g.floyd_warshall()
        >>> g.show_min(0, 2)
        3
        >>> g.show_min(2, 0)
        inf
        """
        for k in range(self.n):
            for i in range(self.n):
                for j in range(self.n):
                    self.dp[i][j] = min(self.dp[i][j], self.dp[i][k] + self.dp[k][j])

    def show_min(self, u, v):
        """
        Returns the minimum distance from node u to node v.

        >>> g = Graph(3)
        >>> g.add_edge(0, 1, 3)
        >>> g.add_edge(1, 2, 4)
        >>> g.floyd_warshall()
        >>> g.show_min(0, 2)
        7
        >>> g.show_min(1, 0)
        inf
        """
        return self.dp[u][v]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Example usage
    graph = Graph(5)
    graph.add_edge(0, 2, 9)
    graph.add_edge(0, 4, 10)
    graph.add_edge(1, 3, 5)
    graph.add_edge(2, 3, 7)
    graph.add_edge(3, 0, 10)
    graph.add_edge(3, 1, 2)
    graph.add_edge(3, 2, 1)
    graph.add_edge(3, 4, 6)
    graph.add_edge(4, 1, 3)
    graph.add_edge(4, 2, 4)
    graph.add_edge(4, 3, 9)
    graph.floyd_warshall()
    print(
        graph.show_min(1, 4)
    )  # Should output the minimum distance from node 1 to node 4
    print(
        graph.show_min(0, 3)
    )  # Should output the minimum distance from node 0 to node 3


================================================
FILE: dynamic_programming/integer_partition.py
================================================
"""
The number of partitions of a number n into at least k parts equals the number of
partitions into exactly k parts plus the number of partitions into at least k-1 parts.
Subtracting 1 from each part of a partition of n into k parts gives a partition of n-k
into k parts. These two facts together are used for this algorithm.
* https://en.wikipedia.org/wiki/Partition_(number_theory)
* https://en.wikipedia.org/wiki/Partition_function_(number_theory)
"""


def partition(m: int) -> int:
    """
    >>> partition(5)
    7
    >>> partition(7)
    15
    >>> partition(100)
    190569292
    >>> partition(1_000)
    24061467864032622473692149727991
    >>> partition(-7)
    Traceback (most recent call last):
        ...
    IndexError: list index out of range
    >>> partition(0)
    Traceback (most recent call last):
        ...
    IndexError: list assignment index out of range
    >>> partition(7.8)
    Traceback (most recent call last):
        ...
    TypeError: 'float' object cannot be interpreted as an integer
    """
    memo: list[list[int]] = [[0 for _ in range(m)] for _ in range(m + 1)]
    for i in range(m + 1):
        memo[i][0] = 1

    for n in range(m + 1):
        for k in range(1, m):
            memo[n][k] += memo[n][k - 1]
            if n - k > 0:
                memo[n][k] += memo[n - k - 1][k]

    return memo[m][m - 1]


if __name__ == "__main__":
    import sys

    if len(sys.argv) == 1:
        try:
            n = int(input("Enter a number: ").strip())
            print(partition(n))
        except ValueError:
            print("Please enter a number.")
    else:
        try:
            n = int(sys.argv[1])
            print(partition(n))
        except ValueError:
            print("Please pass a number.")


================================================
FILE: dynamic_programming/iterating_through_submasks.py
================================================
"""
Author : Syed Faizan (3rd Year Student IIIT Pune)
github : faizan2700
You are given a bitmask m and you want to efficiently iterate through all of
its submasks. The mask s is submask of m if only bits that were included in
bitmask are set
"""

from __future__ import annotations


def list_of_submasks(mask: int) -> list[int]:
    """
    Args:
        mask : number which shows mask ( always integer > 0, zero does not have any
            submasks )

    Returns:
        all_submasks : the list of submasks of mask (mask s is called submask of mask
        m if only bits that were included in original mask are set

    Raises:
        AssertionError: mask not positive integer

    >>> list_of_submasks(15)
    [15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
    >>> list_of_submasks(13)
    [13, 12, 9, 8, 5, 4, 1]
    >>> list_of_submasks(-7)  # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    AssertionError: mask needs to be positive integer, your input -7
    >>> list_of_submasks(0)  # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    AssertionError: mask needs to be positive integer, your input 0

    """

    assert isinstance(mask, int) and mask > 0, (
        f"mask needs to be positive integer, your input {mask}"
    )

    """
    first submask iterated will be mask itself then operation will be performed
    to get other submasks till we reach empty submask that is zero ( zero is not
    included in final submasks list )
    """
    all_submasks = []
    submask = mask

    while submask:
        all_submasks.append(submask)
        submask = (submask - 1) & mask

    return all_submasks


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/k_means_clustering_tensorflow.py
================================================
from random import shuffle

import tensorflow as tf
from numpy import array


def tf_k_means_cluster(vectors, noofclusters):
    """
    K-Means Clustering using TensorFlow.
    'vectors' should be a n*k 2-D NumPy array, where n is the number
    of vectors of dimensionality k.
    'noofclusters' should be an integer.
    """

    noofclusters = int(noofclusters)
    assert noofclusters < len(vectors)

    # Find out the dimensionality
    dim = len(vectors[0])

    # Will help select random centroids from among the available vectors
    vector_indices = list(range(len(vectors)))
    shuffle(vector_indices)

    # GRAPH OF COMPUTATION
    # We initialize a new graph and set it as the default during each run
    # of this algorithm. This ensures that as this function is called
    # multiple times, the default graph doesn't keep getting crowded with
    # unused ops and Variables from previous function calls.

    graph = tf.Graph()

    with graph.as_default():
        # SESSION OF COMPUTATION

        sess = tf.Session()

        ##CONSTRUCTING THE ELEMENTS OF COMPUTATION

        ##First lets ensure we have a Variable vector for each centroid,
        ##initialized to one of the vectors from the available data points
        centroids = [
            tf.Variable(vectors[vector_indices[i]]) for i in range(noofclusters)
        ]
        ##These nodes will assign the centroid Variables the appropriate
        ##values
        centroid_value = tf.placeholder("float64", [dim])
        cent_assigns = []
        for centroid in centroids:
            cent_assigns.append(tf.assign(centroid, centroid_value))

        ##Variables for cluster assignments of individual vectors(initialized
        ##to 0 at first)
        assignments = [tf.Variable(0) for i in range(len(vectors))]
        ##These nodes will assign an assignment Variable the appropriate
        ##value
        assignment_value = tf.placeholder("int32")
        cluster_assigns = []
        for assignment in assignments:
            cluster_assigns.append(tf.assign(assignment, assignment_value))

        ##Now lets construct the node that will compute the mean
        # The placeholder for the input
        mean_input = tf.placeholder("float", [None, dim])
        # The Node/op takes the input and computes a mean along the 0th
        # dimension, i.e. the list of input vectors
        mean_op = tf.reduce_mean(mean_input, 0)

        ##Node for computing Euclidean distances
        # Placeholders for input
        v1 = tf.placeholder("float", [dim])
        v2 = tf.placeholder("float", [dim])
        euclid_dist = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(v1, v2), 2)))

        ##This node will figure out which cluster to assign a vector to,
        ##based on Euclidean distances of the vector from the centroids.
        # Placeholder for input
        centroid_distances = tf.placeholder("float", [noofclusters])
        cluster_assignment = tf.argmin(centroid_distances, 0)

        ##INITIALIZING STATE VARIABLES

        ##This will help initialization of all Variables defined with respect
        ##to the graph. The Variable-initializer should be defined after
        ##all the Variables have been constructed, so that each of them
        ##will be included in the initialization.
        init_op = tf.initialize_all_variables()

        # Initialize all variables
        sess.run(init_op)

        ##CLUSTERING ITERATIONS

        # Now perform the Expectation-Maximization steps of K-Means clustering
        # iterations. To keep things simple, we will only do a set number of
        # iterations, instead of using a Stopping Criterion.
        noofiterations = 100
        for _ in range(noofiterations):
            ##EXPECTATION STEP
            ##Based on the centroid locations till last iteration, compute
            ##the _expected_ centroid assignments.
            # Iterate over each vector
            for vector_n in range(len(vectors)):
                vect = vectors[vector_n]
                # Compute Euclidean distance between this vector and each
                # centroid. Remember that this list cannot be named
                #'centroid_distances', since that is the input to the
                # cluster assignment node.
                distances = [
                    sess.run(euclid_dist, feed_dict={v1: vect, v2: sess.run(centroid)})
                    for centroid in centroids
                ]
                # Now use the cluster assignment node, with the distances
                # as the input
                assignment = sess.run(
                    cluster_assignment, feed_dict={centroid_distances: distances}
                )
                # Now assign the value to the appropriate state variable
                sess.run(
                    cluster_assigns[vector_n], feed_dict={assignment_value: assignment}
                )

            ##MAXIMIZATION STEP
            # Based on the expected state computed from the Expectation Step,
            # compute the locations of the centroids so as to maximize the
            # overall objective of minimizing within-cluster Sum-of-Squares
            for cluster_n in range(noofclusters):
                # Collect all the vectors assigned to this cluster
                assigned_vects = [
                    vectors[i]
                    for i in range(len(vectors))
                    if sess.run(assignments[i]) == cluster_n
                ]
                # Compute new centroid location
                new_location = sess.run(
                    mean_op, feed_dict={mean_input: array(assigned_vects)}
                )
                # Assign value to appropriate variable
                sess.run(
                    cent_assigns[cluster_n], feed_dict={centroid_value: new_location}
                )

        # Return centroids and assignments
        centroids = sess.run(centroids)
        assignments = sess.run(assignments)
        return centroids, assignments


================================================
FILE: dynamic_programming/knapsack.py
================================================
"""
Given weights and values of n items, put these items in a knapsack of
capacity W to get the maximum total value in the knapsack.

Note that only the integer weights 0-1 knapsack problem is solvable
using dynamic programming.
"""


def mf_knapsack(i, wt, val, j):
    """
    This code involves the concept of memory functions. Here we solve the subproblems
    which are needed unlike the below example
    F is a 2D array with ``-1`` s filled up
    """
    global f  # a global dp table for knapsack
    if f[i][j] < 0:
        if j < wt[i - 1]:
            val = mf_knapsack(i - 1, wt, val, j)
        else:
            val = max(
                mf_knapsack(i - 1, wt, val, j),
                mf_knapsack(i - 1, wt, val, j - wt[i - 1]) + val[i - 1],
            )
        f[i][j] = val
    return f[i][j]


def knapsack(w, wt, val, n):
    dp = [[0] * (w + 1) for _ in range(n + 1)]

    for i in range(1, n + 1):
        for w_ in range(1, w + 1):
            if wt[i - 1] <= w_:
                dp[i][w_] = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]], dp[i - 1][w_])
            else:
                dp[i][w_] = dp[i - 1][w_]

    return dp[n][w_], dp


def knapsack_with_example_solution(w: int, wt: list, val: list):
    """
    Solves the integer weights knapsack problem returns one of
    the several possible optimal subsets.

    Parameters
    ----------

    * `w`: int, the total maximum weight for the given knapsack problem.
    * `wt`: list, the vector of weights for all items where ``wt[i]`` is the weight
       of the ``i``-th item.
    * `val`: list, the vector of values for all items where ``val[i]`` is the value
      of the ``i``-th item

    Returns
    -------

    * `optimal_val`: float, the optimal value for the given knapsack problem
    * `example_optional_set`: set, the indices of one of the optimal subsets
      which gave rise to the optimal value.

    Examples
    --------

    >>> knapsack_with_example_solution(10, [1, 3, 5, 2], [10, 20, 100, 22])
    (142, {2, 3, 4})
    >>> knapsack_with_example_solution(6, [4, 3, 2, 3], [3, 2, 4, 4])
    (8, {3, 4})
    >>> knapsack_with_example_solution(6, [4, 3, 2, 3], [3, 2, 4])
    Traceback (most recent call last):
        ...
    ValueError: The number of weights must be the same as the number of values.
    But got 4 weights and 3 values
    """
    if not (isinstance(wt, (list, tuple)) and isinstance(val, (list, tuple))):
        raise ValueError(
            "Both the weights and values vectors must be either lists or tuples"
        )

    num_items = len(wt)
    if num_items != len(val):
        msg = (
            "The number of weights must be the same as the number of values.\n"
            f"But got {num_items} weights and {len(val)} values"
        )
        raise ValueError(msg)
    for i in range(num_items):
        if not isinstance(wt[i], int):
            msg = (
                "All weights must be integers but got weight of "
                f"type {type(wt[i])} at index {i}"
            )
            raise TypeError(msg)

    optimal_val, dp_table = knapsack(w, wt, val, num_items)
    example_optional_set: set = set()
    _construct_solution(dp_table, wt, num_items, w, example_optional_set)

    return optimal_val, example_optional_set


def _construct_solution(dp: list, wt: list, i: int, j: int, optimal_set: set):
    """
    Recursively reconstructs one of the optimal subsets given
    a filled DP table and the vector of weights

    Parameters
    ----------

    * `dp`: list of list, the table of a solved integer weight dynamic programming
      problem
    * `wt`: list or tuple, the vector of weights of the items
    * `i`: int, the index of the item under consideration
    * `j`: int, the current possible maximum weight
    * `optimal_set`: set, the optimal subset so far. This gets modified by the function.

    Returns
    -------

    ``None``
    """
    # for the current item i at a maximum weight j to be part of an optimal subset,
    # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
    # where i - 1 means considering only the previous items at the given maximum weight
    if i > 0 and j > 0:
        if dp[i - 1][j] == dp[i][j]:
            _construct_solution(dp, wt, i - 1, j, optimal_set)
        else:
            optimal_set.add(i)
            _construct_solution(dp, wt, i - 1, j - wt[i - 1], optimal_set)


if __name__ == "__main__":
    """
    Adding test case for knapsack
    """
    val = [3, 2, 4, 4]
    wt = [4, 3, 2, 3]
    n = 4
    w = 6
    f = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
    optimal_solution, _ = knapsack(w, wt, val, n)
    print(optimal_solution)
    print(mf_knapsack(n, wt, val, w))  # switched the n and w

    # testing the dynamic programming problem with example
    # the optimal subset for the above example are items 3 and 4
    optimal_solution, optimal_subset = knapsack_with_example_solution(w, wt, val)
    assert optimal_solution == 8
    assert optimal_subset == {3, 4}
    print("optimal_value = ", optimal_solution)
    print("An optimal subset corresponding to the optimal value", optimal_subset)


================================================
FILE: dynamic_programming/largest_divisible_subset.py
================================================
from __future__ import annotations


def largest_divisible_subset(items: list[int]) -> list[int]:
    """
    Algorithm to find the biggest subset in the given array such that for any 2 elements
    x and y in the subset, either x divides y or y divides x.
    >>> largest_divisible_subset([1, 16, 7, 8, 4])
    [16, 8, 4, 1]
    >>> largest_divisible_subset([1, 2, 3])
    [2, 1]
    >>> largest_divisible_subset([-1, -2, -3])
    [-3]
    >>> largest_divisible_subset([1, 2, 4, 8])
    [8, 4, 2, 1]
    >>> largest_divisible_subset((1, 2, 4, 8))
    [8, 4, 2, 1]
    >>> largest_divisible_subset([1, 1, 1])
    [1, 1, 1]
    >>> largest_divisible_subset([0, 0, 0])
    [0, 0, 0]
    >>> largest_divisible_subset([-1, -1, -1])
    [-1, -1, -1]
    >>> largest_divisible_subset([])
    []
    """
    # Sort the array in ascending order as the sequence does not matter we only have to
    # pick up a subset.
    items = sorted(items)

    number_of_items = len(items)

    # Initialize memo with 1s and hash with increasing numbers
    memo = [1] * number_of_items
    hash_array = list(range(number_of_items))

    # Iterate through the array
    for i, item in enumerate(items):
        for prev_index in range(i):
            if ((items[prev_index] != 0 and item % items[prev_index]) == 0) and (
                (1 + memo[prev_index]) > memo[i]
            ):
                memo[i] = 1 + memo[prev_index]
                hash_array[i] = prev_index

    ans = -1
    last_index = -1

    # Find the maximum length and its corresponding index
    for i, memo_item in enumerate(memo):
        if memo_item > ans:
            ans = memo_item
            last_index = i

    # Reconstruct the divisible subset
    if last_index == -1:
        return []
    result = [items[last_index]]
    while hash_array[last_index] != last_index:
        last_index = hash_array[last_index]
        result.append(items[last_index])

    return result


if __name__ == "__main__":
    from doctest import testmod

    testmod()

    items = [1, 16, 7, 8, 4]
    print(
        f"The longest divisible subset of {items} is {largest_divisible_subset(items)}."
    )


================================================
FILE: dynamic_programming/longest_common_subsequence.py
================================================
"""
LCS Problem Statement: Given two sequences, find the length of longest subsequence
present in both of them.  A subsequence is a sequence that appears in the same relative
order, but not necessarily continuous.
Example:"abc", "abg" are subsequences of "abcdefgh".
"""


def longest_common_subsequence(x: str, y: str):
    """
    Finds the longest common subsequence between two strings. Also returns the
    The subsequence found

    Parameters
    ----------

    x: str, one of the strings
    y: str, the other string

    Returns
    -------
    L[m][n]: int, the length of the longest subsequence. Also equal to len(seq)
    Seq: str, the subsequence found

    >>> longest_common_subsequence("programming", "gaming")
    (6, 'gaming')
    >>> longest_common_subsequence("physics", "smartphone")
    (2, 'ph')
    >>> longest_common_subsequence("computer", "food")
    (1, 'o')
    >>> longest_common_subsequence("", "abc")  # One string is empty
    (0, '')
    >>> longest_common_subsequence("abc", "")  # Other string is empty
    (0, '')
    >>> longest_common_subsequence("", "")  # Both strings are empty
    (0, '')
    >>> longest_common_subsequence("abc", "def")  # No common subsequence
    (0, '')
    >>> longest_common_subsequence("abc", "abc")  # Identical strings
    (3, 'abc')
    >>> longest_common_subsequence("a", "a")  # Single character match
    (1, 'a')
    >>> longest_common_subsequence("a", "b")  # Single character no match
    (0, '')
    >>> longest_common_subsequence("abcdef", "ace")  # Interleaved subsequence
    (3, 'ace')
    >>> longest_common_subsequence("ABCD", "ACBD")  # No repeated characters
    (3, 'ABD')
    """
    # find the length of strings

    assert x is not None
    assert y is not None

    m = len(x)
    n = len(y)

    # declaring the array for storing the dp values
    dp = [[0] * (n + 1) for _ in range(m + 1)]

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            match = 1 if x[i - 1] == y[j - 1] else 0

            dp[i][j] = max(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1] + match)

    seq = ""
    i, j = m, n
    while i > 0 and j > 0:
        match = 1 if x[i - 1] == y[j - 1] else 0

        if dp[i][j] == dp[i - 1][j - 1] + match:
            if match == 1:
                seq = x[i - 1] + seq
            i -= 1
            j -= 1
        elif dp[i][j] == dp[i - 1][j]:
            i -= 1
        else:
            j -= 1

    return dp[m][n], seq


if __name__ == "__main__":
    a = "AGGTAB"
    b = "GXTXAYB"
    expected_ln = 4
    expected_subseq = "GTAB"

    ln, subseq = longest_common_subsequence(a, b)
    print("len =", ln, ", sub-sequence =", subseq)
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/longest_common_substring.py
================================================
"""
Longest Common Substring Problem Statement:
    Given two sequences, find the
    longest common substring present in both of them. A substring is
    necessarily continuous.

Example:
    ``abcdef`` and ``xabded`` have two longest common substrings, ``ab`` or ``de``.
    Therefore, algorithm should return any one of them.
"""


def longest_common_substring(text1: str, text2: str) -> str:
    """
    Finds the longest common substring between two strings.

    >>> longest_common_substring("", "")
    ''
    >>> longest_common_substring("a","")
    ''
    >>> longest_common_substring("", "a")
    ''
    >>> longest_common_substring("a", "a")
    'a'
    >>> longest_common_substring("abcdef", "bcd")
    'bcd'
    >>> longest_common_substring("abcdef", "xabded")
    'ab'
    >>> longest_common_substring("GeeksforGeeks", "GeeksQuiz")
    'Geeks'
    >>> longest_common_substring("abcdxyz", "xyzabcd")
    'abcd'
    >>> longest_common_substring("zxabcdezy", "yzabcdezx")
    'abcdez'
    >>> longest_common_substring("OldSite:GeeksforGeeks.org", "NewSite:GeeksQuiz.com")
    'Site:Geeks'
    >>> longest_common_substring(1, 1)
    Traceback (most recent call last):
        ...
    ValueError: longest_common_substring() takes two strings for inputs
    """

    if not (isinstance(text1, str) and isinstance(text2, str)):
        raise ValueError("longest_common_substring() takes two strings for inputs")

    if not text1 or not text2:
        return ""

    text1_length = len(text1)
    text2_length = len(text2)

    dp = [[0] * (text2_length + 1) for _ in range(text1_length + 1)]
    end_pos = 0
    max_length = 0

    for i in range(1, text1_length + 1):
        for j in range(1, text2_length + 1):
            if text1[i - 1] == text2[j - 1]:
                dp[i][j] = 1 + dp[i - 1][j - 1]
                if dp[i][j] > max_length:
                    end_pos = i
                    max_length = dp[i][j]

    return text1[end_pos - max_length : end_pos]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/longest_increasing_subsequence.py
================================================
"""
Author  : Mehdi ALAOUI

This is a pure Python implementation of Dynamic Programming solution to the longest
increasing subsequence of a given sequence.

The problem is:
    Given an array, to find the longest and increasing sub-array in that given array and
    return it.

Example:
    ``[10, 22, 9, 33, 21, 50, 41, 60, 80]`` as input will return
    ``[10, 22, 33, 41, 60, 80]`` as output
"""

from __future__ import annotations


def longest_subsequence(array: list[int]) -> list[int]:  # This function is recursive
    """
    Some examples

    >>> longest_subsequence([10, 22, 9, 33, 21, 50, 41, 60, 80])
    [10, 22, 33, 41, 60, 80]
    >>> longest_subsequence([4, 8, 7, 5, 1, 12, 2, 3, 9])
    [1, 2, 3, 9]
    >>> longest_subsequence([28, 26, 12, 23, 35, 39])
    [12, 23, 35, 39]
    >>> longest_subsequence([9, 8, 7, 6, 5, 7])
    [5, 7]
    >>> longest_subsequence([1, 1, 1])
    [1, 1, 1]
    >>> longest_subsequence([])
    []
    """
    array_length = len(array)
    # If the array contains only one element, we return it (it's the stop condition of
    # recursion)
    if array_length <= 1:
        return array
        # Else
    pivot = array[0]
    is_found = False
    i = 1
    longest_subseq: list[int] = []
    while not is_found and i < array_length:
        if array[i] < pivot:
            is_found = True
            temp_array = array[i:]
            temp_array = longest_subsequence(temp_array)
            if len(temp_array) > len(longest_subseq):
                longest_subseq = temp_array
        else:
            i += 1

    temp_array = [element for element in array[1:] if element >= pivot]
    temp_array = [pivot, *longest_subsequence(temp_array)]
    if len(temp_array) > len(longest_subseq):
        return temp_array
    else:
        return longest_subseq


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/longest_increasing_subsequence_iterative.py
================================================
"""
Author  : Sanjay Muthu <https://github.com/XenoBytesX>

This is a pure Python implementation of Dynamic Programming solution to the longest
increasing subsequence of a given sequence.

The problem is:
    Given an array, to find the longest and increasing sub-array in that given array and
    return it.

Example:
    ``[10, 22, 9, 33, 21, 50, 41, 60, 80]`` as input will return
    ``[10, 22, 33, 50, 60, 80]`` as output
"""

from __future__ import annotations

import copy


def longest_subsequence(array: list[int]) -> list[int]:
    """
    Some examples

    >>> longest_subsequence([10, 22, 9, 33, 21, 50, 41, 60, 80])
    [10, 22, 33, 50, 60, 80]
    >>> longest_subsequence([4, 8, 7, 5, 1, 12, 2, 3, 9])
    [1, 2, 3, 9]
    >>> longest_subsequence([9, 8, 7, 6, 5, 7])
    [7, 7]
    >>> longest_subsequence([28, 26, 12, 23, 35, 39])
    [12, 23, 35, 39]
    >>> longest_subsequence([1, 1, 1])
    [1, 1, 1]
    >>> longest_subsequence([])
    []
    """
    n = len(array)
    # The longest increasing subsequence ending at array[i]
    longest_increasing_subsequence = []
    for i in range(n):
        longest_increasing_subsequence.append([array[i]])

    for i in range(1, n):
        for prev in range(i):
            # If array[prev] is less than or equal to array[i], then
            # longest_increasing_subsequence[prev] + array[i]
            # is a valid increasing subsequence

            # longest_increasing_subsequence[i] is only set to
            # longest_increasing_subsequence[prev] + array[i] if the length is longer.

            if array[prev] <= array[i] and len(
                longest_increasing_subsequence[prev]
            ) + 1 > len(longest_increasing_subsequence[i]):
                longest_increasing_subsequence[i] = copy.copy(
                    longest_increasing_subsequence[prev]
                )
                longest_increasing_subsequence[i].append(array[i])

    result: list[int] = []
    for i in range(n):
        if len(longest_increasing_subsequence[i]) > len(result):
            result = longest_increasing_subsequence[i]

    return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/longest_increasing_subsequence_o_nlogn.py
================================================
#############################
# Author: Aravind Kashyap
# File: lis.py
# comments: This programme outputs the Longest Strictly Increasing Subsequence in
#           O(NLogN) Where N is the Number of elements in the list
#############################
from __future__ import annotations


def ceil_index(v, left, right, key):
    while right - left > 1:
        middle = (left + right) // 2
        if v[middle] >= key:
            right = middle
        else:
            left = middle
    return right


def longest_increasing_subsequence_length(v: list[int]) -> int:
    """
    >>> longest_increasing_subsequence_length([2, 5, 3, 7, 11, 8, 10, 13, 6])
    6
    >>> longest_increasing_subsequence_length([])
    0
    >>> longest_increasing_subsequence_length([0, 8, 4, 12, 2, 10, 6, 14, 1, 9, 5, 13,
    ...                                     3, 11, 7, 15])
    6
    >>> longest_increasing_subsequence_length([5, 4, 3, 2, 1])
    1
    """
    if len(v) == 0:
        return 0

    tail = [0] * len(v)
    length = 1

    tail[0] = v[0]

    for i in range(1, len(v)):
        if v[i] < tail[0]:
            tail[0] = v[i]
        elif v[i] > tail[length - 1]:
            tail[length] = v[i]
            length += 1
        else:
            tail[ceil_index(tail, -1, length - 1, v[i])] = v[i]

    return length


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/longest_palindromic_subsequence.py
================================================
"""
author: Sanket Kittad
Given a string s, find the longest palindromic subsequence's length in s.
Input: s = "bbbab"
Output: 4
Explanation: One possible longest palindromic subsequence is "bbbb".
Leetcode link: https://leetcode.com/problems/longest-palindromic-subsequence/description/
"""


def longest_palindromic_subsequence(input_string: str) -> int:
    """
    This function returns the longest palindromic subsequence in a string
    >>> longest_palindromic_subsequence("bbbab")
    4
    >>> longest_palindromic_subsequence("bbabcbcab")
    7
    """
    n = len(input_string)
    rev = input_string[::-1]
    m = len(rev)
    dp = [[-1] * (m + 1) for i in range(n + 1)]
    for i in range(n + 1):
        dp[i][0] = 0
    for i in range(m + 1):
        dp[0][i] = 0

    # create and initialise dp array
    for i in range(1, n + 1):
        for j in range(1, m + 1):
            # If characters at i and j are the same
            # include them in the palindromic subsequence
            if input_string[i - 1] == rev[j - 1]:
                dp[i][j] = 1 + dp[i - 1][j - 1]
            else:
                dp[i][j] = max(dp[i - 1][j], dp[i][j - 1])

    return dp[n][m]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/matrix_chain_multiplication.py
================================================
"""
| Find the minimum number of multiplications needed to multiply chain of matrices.
| Reference: https://www.geeksforgeeks.org/matrix-chain-multiplication-dp-8/

The algorithm has interesting real-world applications.

Example:
  1. Image transformations in Computer Graphics as images are composed of matrix.
  2. Solve complex polynomial equations in the field of algebra using least processing
     power.
  3. Calculate overall impact of macroeconomic decisions as economic equations involve a
     number of variables.
  4. Self-driving car navigation can be made more accurate as matrix multiplication can
     accurately determine position and orientation of obstacles in short time.

Python doctests can be run with the following command::

  python -m doctest -v matrix_chain_multiply.py

Given a sequence ``arr[]`` that represents chain of 2D matrices such that the dimension
of the ``i`` th matrix is ``arr[i-1]*arr[i]``.
So suppose ``arr = [40, 20, 30, 10, 30]`` means we have ``4`` matrices of dimensions
``40*20``, ``20*30``, ``30*10`` and ``10*30``.

``matrix_chain_multiply()`` returns an integer denoting minimum number of
multiplications to multiply the chain.

We do not need to perform actual multiplication here.
We only need to decide the order in which to perform the multiplication.

Hints:
  1. Number of multiplications (ie cost) to multiply ``2`` matrices
     of size ``m*p`` and ``p*n`` is ``m*p*n``.
  2. Cost of matrix multiplication is not associative ie ``(M1*M2)*M3 != M1*(M2*M3)``
  3. Matrix multiplication is not commutative. So, ``M1*M2`` does not mean ``M2*M1``
     can be done.
  4. To determine the required order, we can try different combinations.

So, this problem has overlapping sub-problems and can be solved using recursion.
We use Dynamic Programming for optimal time complexity.

Example input:
    ``arr = [40, 20, 30, 10, 30]``
output:
    ``26000``
"""

from collections.abc import Iterator
from contextlib import contextmanager
from functools import cache
from sys import maxsize


def matrix_chain_multiply(arr: list[int]) -> int:
    """
    Find the minimum number of multiplcations required to multiply the chain of matrices

    Args:
        `arr`: The input array of integers.

    Returns:
        Minimum number of multiplications needed to multiply the chain

    Examples:

    >>> matrix_chain_multiply([1, 2, 3, 4, 3])
    30
    >>> matrix_chain_multiply([10])
    0
    >>> matrix_chain_multiply([10, 20])
    0
    >>> matrix_chain_multiply([19, 2, 19])
    722
    >>> matrix_chain_multiply(list(range(1, 100)))
    323398
    >>> # matrix_chain_multiply(list(range(1, 251)))
    # 2626798
    """
    if len(arr) < 2:
        return 0
    # initialising 2D dp matrix
    n = len(arr)
    dp = [[maxsize for j in range(n)] for i in range(n)]
    # we want minimum cost of multiplication of matrices
    # of dimension (i*k) and (k*j). This cost is arr[i-1]*arr[k]*arr[j].
    for i in range(n - 1, 0, -1):
        for j in range(i, n):
            if i == j:
                dp[i][j] = 0
                continue
            for k in range(i, j):
                dp[i][j] = min(
                    dp[i][j], dp[i][k] + dp[k + 1][j] + arr[i - 1] * arr[k] * arr[j]
                )

    return dp[1][n - 1]


def matrix_chain_order(dims: list[int]) -> int:
    """
    Source: https://en.wikipedia.org/wiki/Matrix_chain_multiplication

    The dynamic programming solution is faster than cached the recursive solution and
    can handle larger inputs.

    >>> matrix_chain_order([1, 2, 3, 4, 3])
    30
    >>> matrix_chain_order([10])
    0
    >>> matrix_chain_order([10, 20])
    0
    >>> matrix_chain_order([19, 2, 19])
    722
    >>> matrix_chain_order(list(range(1, 100)))
    323398
    >>> # matrix_chain_order(list(range(1, 251)))  # Max before RecursionError is raised
    # 2626798
    """

    @cache
    def a(i: int, j: int) -> int:
        return min(
            (a(i, k) + dims[i] * dims[k] * dims[j] + a(k, j) for k in range(i + 1, j)),
            default=0,
        )

    return a(0, len(dims) - 1)


@contextmanager
def elapsed_time(msg: str) -> Iterator:
    # print(f"Starting: {msg}")
    from time import perf_counter_ns

    start = perf_counter_ns()
    yield
    print(f"Finished: {msg} in {(perf_counter_ns() - start) / 10**9} seconds.")


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    with elapsed_time("matrix_chain_order"):
        print(f"{matrix_chain_order(list(range(1, 251))) = }")
    with elapsed_time("matrix_chain_multiply"):
        print(f"{matrix_chain_multiply(list(range(1, 251))) = }")
    with elapsed_time("matrix_chain_order"):
        print(f"{matrix_chain_order(list(range(1, 251))) = }")
    with elapsed_time("matrix_chain_multiply"):
        print(f"{matrix_chain_multiply(list(range(1, 251))) = }")


================================================
FILE: dynamic_programming/matrix_chain_order.py
================================================
import sys

"""
Dynamic Programming
Implementation of Matrix Chain Multiplication
Time Complexity: O(n^3)
Space Complexity: O(n^2)

Reference: https://en.wikipedia.org/wiki/Matrix_chain_multiplication
"""


def matrix_chain_order(array: list[int]) -> tuple[list[list[int]], list[list[int]]]:
    """
    >>> matrix_chain_order([10, 30, 5])
    ([[0, 0, 0], [0, 0, 1500], [0, 0, 0]], [[0, 0, 0], [0, 0, 1], [0, 0, 0]])
    """
    n = len(array)
    matrix = [[0 for _ in range(n)] for _ in range(n)]
    sol = [[0 for _ in range(n)] for _ in range(n)]

    for chain_length in range(2, n):
        for a in range(1, n - chain_length + 1):
            b = a + chain_length - 1

            matrix[a][b] = sys.maxsize
            for c in range(a, b):
                cost = (
                    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
                )
                if cost < matrix[a][b]:
                    matrix[a][b] = cost
                    sol[a][b] = c
    return matrix, sol


def print_optimal_solution(optimal_solution: list[list[int]], i: int, j: int):
    """
    Print order of matrix with Ai as Matrix.
    """

    if i == j:
        print("A" + str(i), end=" ")
    else:
        print("(", end=" ")
        print_optimal_solution(optimal_solution, i, optimal_solution[i][j])
        print_optimal_solution(optimal_solution, optimal_solution[i][j] + 1, j)
        print(")", end=" ")


def main():
    """
    Size of matrix created from array [30, 35, 15, 5, 10, 20, 25] will be:
    30*35 35*15 15*5 5*10 10*20 20*25
    """

    array = [30, 35, 15, 5, 10, 20, 25]
    n = len(array)

    matrix, optimal_solution = matrix_chain_order(array)

    print("No. of Operation required: " + str(matrix[1][n - 1]))
    print_optimal_solution(optimal_solution, 1, n - 1)


if __name__ == "__main__":
    main()


================================================
FILE: dynamic_programming/max_non_adjacent_sum.py
================================================
# Video Explanation: https://www.youtube.com/watch?v=6w60Zi1NtL8&feature=emb_logo

from __future__ import annotations


def maximum_non_adjacent_sum(nums: list[int]) -> int:
    """
    Find the maximum non-adjacent sum of the integers in the nums input list

    >>> maximum_non_adjacent_sum([1, 2, 3])
    4
    >>> maximum_non_adjacent_sum([1, 5, 3, 7, 2, 2, 6])
    18
    >>> maximum_non_adjacent_sum([-1, -5, -3, -7, -2, -2, -6])
    0
    >>> maximum_non_adjacent_sum([499, 500, -3, -7, -2, -2, -6])
    500
    """
    if not nums:
        return 0
    max_including = nums[0]
    max_excluding = 0
    for num in nums[1:]:
        max_including, max_excluding = (
            max_excluding + num,
            max(max_including, max_excluding),
        )
    return max(max_excluding, max_including)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/max_product_subarray.py
================================================
def max_product_subarray(numbers: list[int]) -> int:
    """
    Returns the maximum product that can be obtained by multiplying a
    contiguous subarray of the given integer list `numbers`.

    Example:

    >>> max_product_subarray([2, 3, -2, 4])
    6
    >>> max_product_subarray((-2, 0, -1))
    0
    >>> max_product_subarray([2, 3, -2, 4, -1])
    48
    >>> max_product_subarray([-1])
    -1
    >>> max_product_subarray([0])
    0
    >>> max_product_subarray([])
    0
    >>> max_product_subarray("")
    0
    >>> max_product_subarray(None)
    0
    >>> max_product_subarray([2, 3, -2, 4.5, -1])
    Traceback (most recent call last):
        ...
    ValueError: numbers must be an iterable of integers
    >>> max_product_subarray("ABC")
    Traceback (most recent call last):
        ...
    ValueError: numbers must be an iterable of integers
    """
    if not numbers:
        return 0

    if not isinstance(numbers, (list, tuple)) or not all(
        isinstance(number, int) for number in numbers
    ):
        raise ValueError("numbers must be an iterable of integers")

    max_till_now = min_till_now = max_prod = numbers[0]

    for i in range(1, len(numbers)):
        # update the maximum and minimum subarray products
        number = numbers[i]
        if number < 0:
            max_till_now, min_till_now = min_till_now, max_till_now
        max_till_now = max(number, max_till_now * number)
        min_till_now = min(number, min_till_now * number)

        # update the maximum product found till now
        max_prod = max(max_prod, max_till_now)

    return max_prod


================================================
FILE: dynamic_programming/max_subarray_sum.py
================================================
"""
The maximum subarray sum problem is the task of finding the maximum sum that can be
obtained from a contiguous subarray within a given array of numbers. For example, given
the array [-2, 1, -3, 4, -1, 2, 1, -5, 4], the contiguous subarray with the maximum sum
is [4, -1, 2, 1], so the maximum subarray sum is 6.

Kadane's algorithm is a simple dynamic programming algorithm that solves the maximum
subarray sum problem in O(n) time and O(1) space.

Reference: https://en.wikipedia.org/wiki/Maximum_subarray_problem
"""

from collections.abc import Sequence


def max_subarray_sum(
    arr: Sequence[float], allow_empty_subarrays: bool = False
) -> float:
    """
    Solves the maximum subarray sum problem using Kadane's algorithm.
    :param arr: the given array of numbers
    :param allow_empty_subarrays: if True, then the algorithm considers empty subarrays

    >>> max_subarray_sum([2, 8, 9])
    19
    >>> max_subarray_sum([0, 0])
    0
    >>> max_subarray_sum([-1.0, 0.0, 1.0])
    1.0
    >>> max_subarray_sum([1, 2, 3, 4, -2])
    10
    >>> max_subarray_sum([-2, 1, -3, 4, -1, 2, 1, -5, 4])
    6
    >>> max_subarray_sum([2, 3, -9, 8, -2])
    8
    >>> max_subarray_sum([-2, -3, -1, -4, -6])
    -1
    >>> max_subarray_sum([-2, -3, -1, -4, -6], allow_empty_subarrays=True)
    0
    >>> max_subarray_sum([])
    0
    """
    if not arr:
        return 0

    max_sum = 0 if allow_empty_subarrays else float("-inf")
    curr_sum = 0.0
    for num in arr:
        curr_sum = max(0 if allow_empty_subarrays else num, curr_sum + num)
        max_sum = max(max_sum, curr_sum)

    return max_sum


if __name__ == "__main__":
    from doctest import testmod

    testmod()

    nums = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
    print(f"{max_subarray_sum(nums) = }")


================================================
FILE: dynamic_programming/min_distance_up_bottom.py
================================================
"""
Author  : Alexander Pantyukhin
Date    : October 14, 2022
This is an implementation of the up-bottom approach to find edit distance.
The implementation was tested on Leetcode: https://leetcode.com/problems/edit-distance/

Levinstein distance
Dynamic Programming: up -> down.
"""

import functools


def min_distance_up_bottom(word1: str, word2: str) -> int:
    """
    >>> min_distance_up_bottom("intention", "execution")
    5
    >>> min_distance_up_bottom("intention", "")
    9
    >>> min_distance_up_bottom("", "")
    0
    >>> min_distance_up_bottom("zooicoarchaeologist", "zoologist")
    10
    """
    len_word1 = len(word1)
    len_word2 = len(word2)

    @functools.cache
    def min_distance(index1: int, index2: int) -> int:
        # if first word index overflows - delete all from the second word
        if index1 >= len_word1:
            return len_word2 - index2
        # if second word index overflows - delete all from the first word
        if index2 >= len_word2:
            return len_word1 - index1
        diff = int(word1[index1] != word2[index2])  # current letters not identical
        return min(
            1 + min_distance(index1 + 1, index2),
            1 + min_distance(index1, index2 + 1),
            diff + min_distance(index1 + 1, index2 + 1),
        )

    return min_distance(0, 0)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/minimum_coin_change.py
================================================
"""
You have m types of coins available in infinite quantities
where the value of each coins is given in the array S=[S0,... Sm-1]
Can you determine number of ways of making change for n units using
the given types of coins?
https://www.hackerrank.com/challenges/coin-change/problem
"""


def dp_count(s, n):
    """
    >>> dp_count([1, 2, 3], 4)
    4
    >>> dp_count([1, 2, 3], 7)
    8
    >>> dp_count([2, 5, 3, 6], 10)
    5
    >>> dp_count([10], 99)
    0
    >>> dp_count([4, 5, 6], 0)
    1
    >>> dp_count([1, 2, 3], -5)
    0
    """
    if n < 0:
        return 0
    # table[i] represents the number of ways to get to amount i
    table = [0] * (n + 1)

    # There is exactly 1 way to get to zero(You pick no coins).
    table[0] = 1

    # Pick all coins one by one and update table[] values
    # after the index greater than or equal to the value of the
    # picked coin
    for coin_val in s:
        for j in range(coin_val, n + 1):
            table[j] += table[j - coin_val]

    return table[n]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/minimum_cost_path.py
================================================
# Youtube Explanation: https://www.youtube.com/watch?v=lBRtnuxg-gU

from __future__ import annotations


def minimum_cost_path(matrix: list[list[int]]) -> int:
    """
    Find the minimum cost traced by all possible paths from top left to bottom right in
    a given matrix

    >>> minimum_cost_path([[2, 1], [3, 1], [4, 2]])
    6

    >>> minimum_cost_path([[2, 1, 4], [2, 1, 3], [3, 2, 1]])
    7
    """

    # preprocessing the first row
    for i in range(1, len(matrix[0])):
        matrix[0][i] += matrix[0][i - 1]

    # preprocessing the first column
    for i in range(1, len(matrix)):
        matrix[i][0] += matrix[i - 1][0]

    # updating the path cost for current position
    for i in range(1, len(matrix)):
        for j in range(1, len(matrix[0])):
            matrix[i][j] += min(matrix[i - 1][j], matrix[i][j - 1])

    return matrix[-1][-1]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/minimum_partition.py
================================================
"""
Partition a set into two subsets such that the difference of subset sums is minimum
"""


def find_min(numbers: list[int]) -> int:
    """
    >>> find_min([1, 2, 3, 4, 5])
    1
    >>> find_min([5, 5, 5, 5, 5])
    5
    >>> find_min([5, 5, 5, 5])
    0
    >>> find_min([3])
    3
    >>> find_min([])
    0
    >>> find_min([1, 2, 3, 4])
    0
    >>> find_min([0, 0, 0, 0])
    0
    >>> find_min([-1, -5, 5, 1])
    0
    >>> find_min([-1, -5, 5, 1])
    0
    >>> find_min([9, 9, 9, 9, 9])
    9
    >>> find_min([1, 5, 10, 3])
    1
    >>> find_min([-1, 0, 1])
    0
    >>> find_min(range(10, 0, -1))
    1
    >>> find_min([-1])
    Traceback (most recent call last):
        --
    IndexError: list assignment index out of range
    >>> find_min([0, 0, 0, 1, 2, -4])
    Traceback (most recent call last):
        ...
    IndexError: list assignment index out of range
    >>> find_min([-1, -5, -10, -3])
    Traceback (most recent call last):
        ...
    IndexError: list assignment index out of range
    """
    n = len(numbers)
    s = sum(numbers)

    dp = [[False for x in range(s + 1)] for y in range(n + 1)]

    for i in range(n + 1):
        dp[i][0] = True

    for i in range(1, s + 1):
        dp[0][i] = False

    for i in range(1, n + 1):
        for j in range(1, s + 1):
            dp[i][j] = dp[i - 1][j]

            if numbers[i - 1] <= j:
                dp[i][j] = dp[i][j] or dp[i - 1][j - numbers[i - 1]]

    for j in range(int(s / 2), -1, -1):
        if dp[n][j] is True:
            diff = s - 2 * j
            break

    return diff


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: dynamic_programming/minimum_size_subarray_sum.py
================================================
import sys


def minimum_subarray_sum(target: int, numbers: list[int]) -> int:
    """
    Return the length of the shortest contiguous subarray in a list of numbers whose sum
    is at least target.  Reference: https://stackoverflow.com/questions/8269916

    >>> minimum_subarray_sum(7, [2, 3, 1, 2, 4, 3])
    2
    >>> minimum_subarray_sum(7, [2, 3, -1, 2, 4, -3])
    4
    >>> minimum_subarray_sum(11, [1, 1, 1, 1, 1, 1, 1, 1])
    0
    >>> minimum_subarray_sum(10, [1, 2, 3, 4, 5, 6, 7])
    2
    >>> minimum_subarray_sum(5, [1, 1, 1, 1, 1, 5])
    1
    >>> minimum_subarray_sum(0, [])
    0
    >>> minimum_subarray_sum(0, [1, 2, 3])
    1
    >>> minimum_subarray_sum(10, [10, 20, 30])
    1
    >>> minimum_subarray_sum(7, [1, 1, 1, 1, 1, 1, 10])
    1
    >>> minimum_subarray_sum(6, [])
    0
    >>> minimum_subarray_sum(2, [1, 2, 3])
    1
    >>> minimum_subarray_sum(-6, [])
    0
    >>> minimum_subarray_sum(-6, [3, 4, 5])
    1
    >>> minimum_subarray_sum(8, None)
    0
    >>> minimum_subarray_sum(2, "ABC")
    Traceback (most recent call last):
        ...
    ValueError: numbers must be an iterable of integers
    """
    if not numbers:
        return 0
    if target == 0 and target in numbers:
        return 0
    if not isinstance(numbers, (list, tuple)) or not all(
        isinstance(number, int) for number in numbers
    ):
        raise ValueError("numbers must be an iterable of integers")

    left = right = curr_sum = 0
    min_len = sys.maxsize

    while right < len(numbers):
        curr_sum += numbers[right]
        while curr_sum >= target and left <= right:
            min_len = min(min_len, right - left + 1)
            curr_sum -= numbers[left]
            left += 1
        right += 1

    return 0 if min_len == sys.maxsize else min_len


================================================
FILE: dynamic_programming/minimum_squares_to_represent_a_number.py
================================================
import math
import sys


def minimum_squares_to_represent_a_number(number: int) -> int:
    """
    Count the number of minimum squares to represent a number

    >>> minimum_squares_to_represent_a_number(25)
    1
    >>> minimum_squares_to_represent_a_number(37)
    2
    >>> minimum_squares_to_represent_a_number(21)
    3
    >>> minimum_squares_to_represent_a_number(58)
    2
    >>> minimum_squares_to_represent_a_number(-1)
    Traceback (most recent call last):
        ...
    ValueError: the value of input must not be a negative number
    >>> minimum_squares_to_represent_a_number(0)
    1
    >>> minimum_squares_to_represent_a_number(12.34)
    Traceback (most recent call last):
        ...
    ValueError: the value of input must be a natural number
    """
    if number != int(number):
        raise ValueError("the value of input must be a natural number")
    if number < 0:
        raise ValueError("the value of input must not be a negative number")
    if number == 0:
        return 1
    answers = [-1] * (number + 1)
    answers[0] = 0
    for i in range(1, number + 1):
        answer = sys.maxsize
        root = int(math.sqrt(i))
        for j in range(1, root + 1):
            current_answer = 1 + answers[i - (j**2)]
            answer = min(answer, current_answer)
        answers[i] = answer
    return answers[number]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/minimum_steps_to_one.py
================================================
"""
YouTube Explanation: https://www.youtube.com/watch?v=f2xi3c1S95M

Given an integer n, return the minimum steps from n to 1

AVAILABLE STEPS:
    * Decrement by 1
    * if n is divisible by 2, divide by 2
    * if n is divisible by 3, divide by 3


Example 1: n = 10
10 -> 9 -> 3 -> 1
Result: 3 steps

Example 2: n = 15
15 -> 5 -> 4 -> 2 -> 1
Result: 4 steps

Example 3: n = 6
6 -> 2 -> 1
Result: 2 step
"""

from __future__ import annotations

__author__ = "Alexander Joslin"


def min_steps_to_one(number: int) -> int:
    """
    Minimum steps to 1 implemented using tabulation.
    >>> min_steps_to_one(10)
    3
    >>> min_steps_to_one(15)
    4
    >>> min_steps_to_one(6)
    2

    :param number:
    :return int:
    """

    if number <= 0:
        msg = f"n must be greater than 0. Got n = {number}"
        raise ValueError(msg)

    table = [number + 1] * (number + 1)

    # starting position
    table[1] = 0
    for i in range(1, number):
        table[i + 1] = min(table[i + 1], table[i] + 1)
        # check if out of bounds
        if i * 2 <= number:
            table[i * 2] = min(table[i * 2], table[i] + 1)
        # check if out of bounds
        if i * 3 <= number:
            table[i * 3] = min(table[i * 3], table[i] + 1)
    return table[number]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/minimum_tickets_cost.py
================================================
"""
Author  : Alexander Pantyukhin
Date    : November 1, 2022

Task:
Given a list of days when you need to travel. Each day is integer from 1 to 365.
You are able to use tickets for 1 day, 7 days and 30 days.
Each ticket has a cost.

Find the minimum cost you need to travel every day in the given list of days.

Implementation notes:
implementation Dynamic Programming up bottom approach.

Runtime complexity: O(n)

The implementation was tested on the
leetcode: https://leetcode.com/problems/minimum-cost-for-tickets/


Minimum Cost For Tickets
Dynamic Programming: up -> down.
"""

import functools


def mincost_tickets(days: list[int], costs: list[int]) -> int:
    """
    >>> mincost_tickets([1, 4, 6, 7, 8, 20], [2, 7, 15])
    11

    >>> mincost_tickets([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 30, 31],  [2, 7, 15])
    17

    >>> mincost_tickets([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 30, 31], [2, 90, 150])
    24

    >>> mincost_tickets([2], [2, 90, 150])
    2

    >>> mincost_tickets([], [2, 90, 150])
    0

    >>> mincost_tickets('hello', [2, 90, 150])
    Traceback (most recent call last):
     ...
    ValueError: The parameter days should be a list of integers

    >>> mincost_tickets([], 'world')
    Traceback (most recent call last):
     ...
    ValueError: The parameter costs should be a list of three integers

    >>> mincost_tickets([0.25, 2, 3, 4, 5, 6, 7, 8, 9, 10, 30, 31], [2, 90, 150])
    Traceback (most recent call last):
     ...
    ValueError: The parameter days should be a list of integers

    >>> mincost_tickets([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 30, 31], [2, 0.9, 150])
    Traceback (most recent call last):
     ...
    ValueError: The parameter costs should be a list of three integers

    >>> mincost_tickets([-1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 30, 31], [2, 90, 150])
    Traceback (most recent call last):
     ...
    ValueError: All days elements should be greater than 0

    >>> mincost_tickets([2, 367], [2, 90, 150])
    Traceback (most recent call last):
     ...
    ValueError: All days elements should be less than 366

    >>> mincost_tickets([2, 3, 4, 5, 6, 7, 8, 9, 10, 30, 31], [])
    Traceback (most recent call last):
     ...
    ValueError: The parameter costs should be a list of three integers

    >>> mincost_tickets([], [])
    Traceback (most recent call last):
     ...
    ValueError: The parameter costs should be a list of three integers

    >>> mincost_tickets([2, 3, 4, 5, 6, 7, 8, 9, 10, 30, 31], [1, 2, 3, 4])
    Traceback (most recent call last):
     ...
    ValueError: The parameter costs should be a list of three integers
    """

    # Validation
    if not isinstance(days, list) or not all(isinstance(day, int) for day in days):
        raise ValueError("The parameter days should be a list of integers")

    if len(costs) != 3 or not all(isinstance(cost, int) for cost in costs):
        raise ValueError("The parameter costs should be a list of three integers")

    if len(days) == 0:
        return 0

    if min(days) <= 0:
        raise ValueError("All days elements should be greater than 0")

    if max(days) >= 366:
        raise ValueError("All days elements should be less than 366")

    days_set = set(days)

    @functools.cache
    def dynamic_programming(index: int) -> int:
        if index > 365:
            return 0

        if index not in days_set:
            return dynamic_programming(index + 1)

        return min(
            costs[0] + dynamic_programming(index + 1),
            costs[1] + dynamic_programming(index + 7),
            costs[2] + dynamic_programming(index + 30),
        )

    return dynamic_programming(1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/narcissistic_number.py
================================================
"""
Find all narcissistic numbers up to a given limit using dynamic programming.

A narcissistic number (also known as an Armstrong number or plus perfect number)
is a number that is the sum of its own digits each raised to the power of the
number of digits.

For example, 153 is a narcissistic number because 153 = 1^3 + 5^3 + 3^3.

This implementation uses dynamic programming with memoization to efficiently
compute digit powers and find all narcissistic numbers up to a specified limit.

The DP optimization caches digit^power calculations. When searching through many
numbers, the same digit power calculations occur repeatedly (e.g., 153, 351, 135
all need 1^3, 5^3, 3^3). Memoization avoids these redundant calculations.

Examples of narcissistic numbers:
    Single digit: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
    Three digit: 153, 370, 371, 407
    Four digit: 1634, 8208, 9474
    Five digit: 54748, 92727, 93084

Reference: https://en.wikipedia.org/wiki/Narcissistic_number
"""


def find_narcissistic_numbers(limit: int) -> list[int]:
    """
    Find all narcissistic numbers up to the given limit using dynamic programming.

    This function uses memoization to cache digit power calculations, avoiding
    redundant computations across different numbers with the same digit count.

    Args:
        limit: The upper bound for searching narcissistic numbers (exclusive)

    Returns:
        list[int]: A sorted list of all narcissistic numbers below the limit

    Examples:
        >>> find_narcissistic_numbers(10)
        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
        >>> find_narcissistic_numbers(160)
        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 153]
        >>> find_narcissistic_numbers(400)
        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 153, 370, 371]
        >>> find_narcissistic_numbers(1000)
        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 153, 370, 371, 407]
        >>> find_narcissistic_numbers(10000)
        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 153, 370, 371, 407, 1634, 8208, 9474]
        >>> find_narcissistic_numbers(1)
        [0]
        >>> find_narcissistic_numbers(0)
        []
    """
    if limit <= 0:
        return []

    narcissistic_nums = []

    # Memoization: cache[(power, digit)] = digit^power
    # This avoids recalculating the same power for different numbers
    power_cache: dict[tuple[int, int], int] = {}

    def get_digit_power(digit: int, power: int) -> int:
        """Get digit^power using memoization (DP optimization)."""
        if (power, digit) not in power_cache:
            power_cache[(power, digit)] = digit**power
        return power_cache[(power, digit)]

    # Check each number up to the limit
    for number in range(limit):
        # Count digits
        num_digits = len(str(number))

        # Calculate sum of powered digits using memoized powers
        remaining = number
        digit_sum = 0
        while remaining > 0:
            digit = remaining % 10
            digit_sum += get_digit_power(digit, num_digits)
            remaining //= 10

        # Check if narcissistic
        if digit_sum == number:
            narcissistic_nums.append(number)

    return narcissistic_nums


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Demonstrate the dynamic programming approach
    print("Finding all narcissistic numbers up to 10000:")
    print("(Using memoization to cache digit power calculations)")
    print()

    narcissistic_numbers = find_narcissistic_numbers(10000)
    print(f"Found {len(narcissistic_numbers)} narcissistic numbers:")
    print(narcissistic_numbers)


================================================
FILE: dynamic_programming/optimal_binary_search_tree.py
================================================
#!/usr/bin/env python3

# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint


class Node:
    """Binary Search Tree Node"""

    def __init__(self, key, freq):
        self.key = key
        self.freq = freq

    def __str__(self):
        """
        >>> str(Node(1, 2))
        'Node(key=1, freq=2)'
        """
        return f"Node(key={self.key}, freq={self.freq})"


def print_binary_search_tree(root, key, i, j, parent, is_left):
    """
    Recursive function to print a BST from a root table.

    >>> key = [3, 8, 9, 10, 17, 21]
    >>> root = [[0, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 3], [0, 0, 2, 3, 3, 3], \
                [0, 0, 0, 3, 3, 3], [0, 0, 0, 0, 4, 5], [0, 0, 0, 0, 0, 5]]
    >>> print_binary_search_tree(root, key, 0, 5, -1, False)
    8 is the root of the binary search tree.
    3 is the left child of key 8.
    10 is the right child of key 8.
    9 is the left child of key 10.
    21 is the right child of key 10.
    17 is the left child of key 21.
    """
    if i > j or i < 0 or j > len(root) - 1:
        return

    node = root[i][j]
    if parent == -1:  # root does not have a parent
        print(f"{key[node]} is the root of the binary search tree.")
    elif is_left:
        print(f"{key[node]} is the left child of key {parent}.")
    else:
        print(f"{key[node]} is the right child of key {parent}.")

    print_binary_search_tree(root, key, i, node - 1, key[node], True)
    print_binary_search_tree(root, key, node + 1, j, key[node], False)


def find_optimal_binary_search_tree(nodes):
    """
    This function calculates and prints the optimal binary search tree.
    The dynamic programming algorithm below runs in O(n^2) time.
    Implemented from CLRS (Introduction to Algorithms) book.
    https://en.wikipedia.org/wiki/Introduction_to_Algorithms

    >>> find_optimal_binary_search_tree([Node(12, 8), Node(10, 34), Node(20, 50), \
                                         Node(42, 3), Node(25, 40), Node(37, 30)])
    Binary search tree nodes:
    Node(key=10, freq=34)
    Node(key=12, freq=8)
    Node(key=20, freq=50)
    Node(key=25, freq=40)
    Node(key=37, freq=30)
    Node(key=42, freq=3)
    <BLANKLINE>
    The cost of optimal BST for given tree nodes is 324.
    20 is the root of the binary search tree.
    10 is the left child of key 20.
    12 is the right child of key 10.
    25 is the right child of key 20.
    37 is the right child of key 25.
    42 is the right child of key 37.
    """
    # Tree nodes must be sorted first, the code below sorts the keys in
    # increasing order and rearrange its frequencies accordingly.
    nodes.sort(key=lambda node: node.key)

    n = len(nodes)

    keys = [nodes[i].key for i in range(n)]
    freqs = [nodes[i].freq for i in range(n)]

    # This 2D array stores the overall tree cost (which's as minimized as possible);
    # for a single key, cost is equal to frequency of the key.
    dp = [[freqs[i] if i == j else 0 for j in range(n)] for i in range(n)]
    # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
    # array
    total = [[freqs[i] if i == j else 0 for j in range(n)] for i in range(n)]
    # stores tree roots that will be used later for constructing binary search tree
    root = [[i if i == j else 0 for j in range(n)] for i in range(n)]

    for interval_length in range(2, n + 1):
        for i in range(n - interval_length + 1):
            j = i + interval_length - 1

            dp[i][j] = sys.maxsize  # set the value to "infinity"
            total[i][j] = total[i][j - 1] + freqs[j]

            # Apply Knuth's optimization
            # Loop without optimization: for r in range(i, j + 1):
            for r in range(root[i][j - 1], root[i + 1][j] + 1):  # r is a temporal root
                left = dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
                right = dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
                cost = left + total[i][j] + right

                if dp[i][j] > cost:
                    dp[i][j] = cost
                    root[i][j] = r

    print("Binary search tree nodes:")
    for node in nodes:
        print(node)

    print(f"\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.")
    print_binary_search_tree(root, keys, 0, n - 1, -1, False)


def main():
    # A sample binary search tree
    nodes = [Node(i, randint(1, 50)) for i in range(10, 0, -1)]
    find_optimal_binary_search_tree(nodes)


if __name__ == "__main__":
    main()


================================================
FILE: dynamic_programming/palindrome_partitioning.py
================================================
"""
Given a string s, partition s such that every substring of the
partition is a palindrome.
Find the minimum cuts needed for a palindrome partitioning of s.

Time Complexity: O(n^2)
Space Complexity: O(n^2)
For other explanations refer to: https://www.youtube.com/watch?v=_H8V5hJUGd0
"""


def find_minimum_partitions(string: str) -> int:
    """
    Returns the minimum cuts needed for a palindrome partitioning of string

    >>> find_minimum_partitions("aab")
    1
    >>> find_minimum_partitions("aaa")
    0
    >>> find_minimum_partitions("ababbbabbababa")
    3
    """
    length = len(string)
    cut = [0] * length
    is_palindromic = [[False for i in range(length)] for j in range(length)]
    for i, c in enumerate(string):
        mincut = i
        for j in range(i + 1):
            if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
                is_palindromic[j][i] = True
                mincut = min(mincut, 0 if j == 0 else (cut[j - 1] + 1))
        cut[i] = mincut
    return cut[length - 1]


if __name__ == "__main__":
    s = input("Enter the string: ").strip()
    ans = find_minimum_partitions(s)
    print(f"Minimum number of partitions required for the '{s}' is {ans}")


================================================
FILE: dynamic_programming/range_sum_query.py
================================================
"""
Author: Sanjay Muthu <https://github.com/XenoBytesX>

This is an implementation of the Dynamic Programming solution to the Range Sum Query.

The problem statement is:
    Given an array and q queries,
    each query stating you to find the sum of elements from l to r (inclusive)

Example:
    arr = [1, 4, 6, 2, 61, 12]
    queries = 3
    l_1 = 2, r_1 = 5
    l_2 = 1, r_2 = 5
    l_3 = 3, r_3 = 4

    as input will return

    [81, 85, 63]

    as output

0-indexing:
NOTE: 0-indexing means the indexing of the array starts from 0
Example: a = [1, 2, 3, 4, 5, 6]
         Here, the 0th index of a is 1,
               the 1st index of a is 2,
               and so forth

Time Complexity: O(N + Q)
* O(N) pre-calculation time to calculate the prefix sum array
* and O(1) time per each query = O(1 * Q) = O(Q) time

Space Complexity: O(N)
* O(N) to store the prefix sum

Algorithm:
So, first we calculate the prefix sum (dp) of the array.
The prefix sum of the index i is the sum of all elements indexed
from 0 to i (inclusive).
The prefix sum of the index i is the prefix sum of index (i - 1) + the current element.
So, the state of the dp is dp[i] = dp[i - 1] + a[i].

After we calculate the prefix sum,
for each query [l, r]
the answer is dp[r] - dp[l - 1] (we need to be careful because l might be 0).
For example take this array:
    [4, 2, 1, 6, 3]
The prefix sum calculated for this array would be:
    [4, 4 + 2, 4 + 2 + 1, 4 + 2 + 1 + 6, 4 + 2 + 1 + 6 + 3]
    ==> [4, 6, 7, 13, 16]
If the query was l = 3, r = 4,
the answer would be 6 + 3 = 9 but this would require O(r - l + 1) time ≈ O(N) time

If we use prefix sums we can find it in O(1) by using the formula
prefix[r] - prefix[l - 1].
This formula works because prefix[r] is the sum of elements from [0, r]
and prefix[l - 1] is the sum of elements from [0, l - 1],
so if we do prefix[r] - prefix[l - 1] it will be
[0, r] - [0, l - 1] = [0, l - 1] + [l, r] - [0, l - 1] = [l, r]
"""


def prefix_sum(array: list[int], queries: list[tuple[int, int]]) -> list[int]:
    """
    >>> prefix_sum([1, 4, 6, 2, 61, 12], [(2, 5), (1, 5), (3, 4)])
    [81, 85, 63]
    >>> prefix_sum([4, 2, 1, 6, 3], [(3, 4), (1, 3), (0, 2)])
    [9, 9, 7]
    """
    # The prefix sum array
    dp = [0] * len(array)
    dp[0] = array[0]
    for i in range(1, len(array)):
        dp[i] = dp[i - 1] + array[i]

    # See Algorithm section (Line 44)
    result = []
    for query in queries:
        left, right = query
        res = dp[right]
        if left > 0:
            res -= dp[left - 1]
        result.append(res)

    return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/regex_match.py
================================================
"""
Regex matching check if a text matches pattern or not.
Pattern:

    1. ``.`` Matches any single character.
    2. ``*`` Matches zero or more of the preceding element.

More info:
    https://medium.com/trick-the-interviwer/regular-expression-matching-9972eb74c03
"""


def recursive_match(text: str, pattern: str) -> bool:
    r"""
    Recursive matching algorithm.

    | Time complexity: O(2^(\|text\| + \|pattern\|))
    | Space complexity: Recursion depth is O(\|text\| + \|pattern\|).

    :param text: Text to match.
    :param pattern: Pattern to match.
    :return: ``True`` if `text` matches `pattern`, ``False`` otherwise.

    >>> recursive_match('abc', 'a.c')
    True
    >>> recursive_match('abc', 'af*.c')
    True
    >>> recursive_match('abc', 'a.c*')
    True
    >>> recursive_match('abc', 'a.c*d')
    False
    >>> recursive_match('aa', '.*')
    True
    """
    if not pattern:
        return not text

    if not text:
        return pattern[-1] == "*" and recursive_match(text, pattern[:-2])

    if text[-1] == pattern[-1] or pattern[-1] == ".":
        return recursive_match(text[:-1], pattern[:-1])

    if pattern[-1] == "*":
        return recursive_match(text[:-1], pattern) or recursive_match(
            text, pattern[:-2]
        )

    return False


def dp_match(text: str, pattern: str) -> bool:
    r"""
    Dynamic programming matching algorithm.

    | Time complexity: O(\|text\| * \|pattern\|)
    | Space complexity: O(\|text\| * \|pattern\|)

    :param text: Text to match.
    :param pattern: Pattern to match.
    :return: ``True`` if `text` matches `pattern`, ``False`` otherwise.

    >>> dp_match('abc', 'a.c')
    True
    >>> dp_match('abc', 'af*.c')
    True
    >>> dp_match('abc', 'a.c*')
    True
    >>> dp_match('abc', 'a.c*d')
    False
    >>> dp_match('aa', '.*')
    True
    """
    m = len(text)
    n = len(pattern)
    dp = [[False for _ in range(n + 1)] for _ in range(m + 1)]
    dp[0][0] = True

    for j in range(1, n + 1):
        dp[0][j] = pattern[j - 1] == "*" and dp[0][j - 2]

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if pattern[j - 1] in {".", text[i - 1]}:
                dp[i][j] = dp[i - 1][j - 1]
            elif pattern[j - 1] == "*":
                dp[i][j] = dp[i][j - 2]
                if pattern[j - 2] in {".", text[i - 1]}:
                    dp[i][j] |= dp[i - 1][j]
            else:
                dp[i][j] = False

    return dp[m][n]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/rod_cutting.py
================================================
"""
This module provides two implementations for the rod-cutting problem:
  1. A naive recursive implementation which has an exponential runtime
  2. Two dynamic programming implementations which have quadratic runtime

The rod-cutting problem is the problem of finding the maximum possible revenue
obtainable from a rod of length ``n`` given a list of prices for each integral piece
of the rod. The maximum revenue can thus be obtained by cutting the rod and selling the
pieces separately or not cutting it at all if the price of it is the maximum obtainable.

"""


def naive_cut_rod_recursive(n: int, prices: list):
    """
    Solves the rod-cutting problem via naively without using the benefit of dynamic
    programming. The results is the same sub-problems are solved several times
    leading to an exponential runtime

    Runtime: O(2^n)

    Arguments
    ---------

    * `n`: int, the length of the rod
    * `prices`: list, the prices for each piece of rod. ``p[i-i]`` is the
      price for a rod of length ``i``

    Returns
    -------

    The maximum revenue obtainable for a rod of length `n` given the list of prices
    for each piece.

    Examples
    --------

    >>> naive_cut_rod_recursive(4, [1, 5, 8, 9])
    10
    >>> naive_cut_rod_recursive(10, [1, 5, 8, 9, 10, 17, 17, 20, 24, 30])
    30
    """

    _enforce_args(n, prices)
    if n == 0:
        return 0
    max_revue = float("-inf")
    for i in range(1, n + 1):
        max_revue = max(
            max_revue, prices[i - 1] + naive_cut_rod_recursive(n - i, prices)
        )

    return max_revue


def top_down_cut_rod(n: int, prices: list):
    """
    Constructs a top-down dynamic programming solution for the rod-cutting
    problem via memoization. This function serves as a wrapper for
    ``_top_down_cut_rod_recursive``

    Runtime: O(n^2)

    Arguments
    ---------

    * `n`: int, the length of the rod
    * `prices`: list, the prices for each piece of rod. ``p[i-i]`` is the
      price for a rod of length ``i``

    .. note::
      For convenience and because Python's lists using ``0``-indexing, ``length(max_rev)
      = n + 1``, to accommodate for the revenue obtainable from a rod of length ``0``.

    Returns
    -------

    The maximum revenue obtainable for a rod of length `n` given the list of prices
    for each piece.

    Examples
    --------

    >>> top_down_cut_rod(4, [1, 5, 8, 9])
    10
    >>> top_down_cut_rod(10, [1, 5, 8, 9, 10, 17, 17, 20, 24, 30])
    30
    """
    _enforce_args(n, prices)
    max_rev = [float("-inf") for _ in range(n + 1)]
    return _top_down_cut_rod_recursive(n, prices, max_rev)


def _top_down_cut_rod_recursive(n: int, prices: list, max_rev: list):
    """
    Constructs a top-down dynamic programming solution for the rod-cutting problem
    via memoization.

    Runtime: O(n^2)

    Arguments
    ---------

    * `n`: int, the length of the rod
    * `prices`: list, the prices for each piece of rod. ``p[i-i]`` is the
      price for a rod of length ``i``
    * `max_rev`: list, the computed maximum revenue for a piece of rod.
      ``max_rev[i]`` is the maximum revenue obtainable for a rod of length ``i``

    Returns
    -------

    The maximum revenue obtainable for a rod of length `n` given the list of prices
    for each piece.
    """
    if max_rev[n] >= 0:
        return max_rev[n]
    elif n == 0:
        return 0
    else:
        max_revenue = float("-inf")
        for i in range(1, n + 1):
            max_revenue = max(
                max_revenue,
                prices[i - 1] + _top_down_cut_rod_recursive(n - i, prices, max_rev),
            )

        max_rev[n] = max_revenue

    return max_rev[n]


def bottom_up_cut_rod(n: int, prices: list):
    """
    Constructs a bottom-up dynamic programming solution for the rod-cutting problem

    Runtime: O(n^2)

    Arguments
    ---------

    * `n`: int, the maximum length of the rod.
    * `prices`: list, the prices for each piece of rod. ``p[i-i]`` is the
      price for a rod of length ``i``

    Returns
    -------

    The maximum revenue obtainable from cutting a rod of length `n` given
    the prices for each piece of rod p.

    Examples
    --------

    >>> bottom_up_cut_rod(4, [1, 5, 8, 9])
    10
    >>> bottom_up_cut_rod(10, [1, 5, 8, 9, 10, 17, 17, 20, 24, 30])
    30
    """
    _enforce_args(n, prices)

    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
    # length 0.
    max_rev = [float("-inf") for _ in range(n + 1)]
    max_rev[0] = 0

    for i in range(1, n + 1):
        max_revenue_i = max_rev[i]
        for j in range(1, i + 1):
            max_revenue_i = max(max_revenue_i, prices[j - 1] + max_rev[i - j])

        max_rev[i] = max_revenue_i

    return max_rev[n]


def _enforce_args(n: int, prices: list):
    """
    Basic checks on the arguments to the rod-cutting algorithms

    * `n`: int, the length of the rod
    * `prices`: list, the price list for each piece of rod.

    Throws ``ValueError``:
        if `n` is negative or there are fewer items in the price list than the length of
        the rod
    """
    if n < 0:
        msg = f"n must be greater than or equal to 0. Got n = {n}"
        raise ValueError(msg)

    if n > len(prices):
        msg = (
            "Each integral piece of rod must have a corresponding price. "
            f"Got n = {n} but length of prices = {len(prices)}"
        )
        raise ValueError(msg)


def main():
    prices = [6, 10, 12, 15, 20, 23]
    n = len(prices)

    # the best revenue comes from cutting the rod into 6 pieces, each
    # of length 1 resulting in a revenue of 6 * 6 = 36.
    expected_max_revenue = 36

    max_rev_top_down = top_down_cut_rod(n, prices)
    max_rev_bottom_up = bottom_up_cut_rod(n, prices)
    max_rev_naive = naive_cut_rod_recursive(n, prices)

    assert expected_max_revenue == max_rev_top_down
    assert max_rev_top_down == max_rev_bottom_up
    assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
    main()


================================================
FILE: dynamic_programming/smith_waterman.py
================================================
"""
https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm
The Smith-Waterman algorithm is a dynamic programming algorithm used for sequence
alignment. It is particularly useful for finding similarities between two sequences,
such as DNA or protein sequences. In this implementation, gaps are penalized
linearly, meaning that the score is reduced by a fixed amount for each gap introduced
in the alignment. However, it's important to note that the Smith-Waterman algorithm
supports other gap penalty methods as well.
"""


def score_function(
    source_char: str,
    target_char: str,
    match: int = 1,
    mismatch: int = -1,
    gap: int = -2,
) -> int:
    """
    Calculate the score for a character pair based on whether they match or mismatch.
    Returns 1 if the characters match, -1 if they mismatch, and -2 if either of the
    characters is a gap.
    >>> score_function('A', 'A')
    1
    >>> score_function('A', 'C')
    -1
    >>> score_function('-', 'A')
    -2
    >>> score_function('A', '-')
    -2
    >>> score_function('-', '-')
    -2
    """
    if "-" in (source_char, target_char):
        return gap
    return match if source_char == target_char else mismatch


def smith_waterman(
    query: str,
    subject: str,
    match: int = 1,
    mismatch: int = -1,
    gap: int = -2,
) -> list[list[int]]:
    """
    Perform the Smith-Waterman local sequence alignment algorithm.
    Returns a 2D list representing the score matrix. Each value in the matrix
    corresponds to the score of the best local alignment ending at that point.
    >>> smith_waterman('ACAC', 'CA')
    [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 2], [0, 1, 0]]
    >>> smith_waterman('acac', 'ca')
    [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 2], [0, 1, 0]]
    >>> smith_waterman('ACAC', 'ca')
    [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 2], [0, 1, 0]]
    >>> smith_waterman('acac', 'CA')
    [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 2], [0, 1, 0]]
    >>> smith_waterman('ACAC', '')
    [[0], [0], [0], [0], [0]]
    >>> smith_waterman('', 'CA')
    [[0, 0, 0]]
    >>> smith_waterman('ACAC', 'CA')
    [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 2], [0, 1, 0]]

    >>> smith_waterman('acac', 'ca')
    [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 2], [0, 1, 0]]

    >>> smith_waterman('ACAC', 'ca')
    [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 2], [0, 1, 0]]

    >>> smith_waterman('acac', 'CA')
    [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 2], [0, 1, 0]]

    >>> smith_waterman('ACAC', '')
    [[0], [0], [0], [0], [0]]

    >>> smith_waterman('', 'CA')
    [[0, 0, 0]]

    >>> smith_waterman('AGT', 'AGT')
    [[0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 2, 0], [0, 0, 0, 3]]

    >>> smith_waterman('AGT', 'GTA')
    [[0, 0, 0, 0], [0, 0, 0, 1], [0, 1, 0, 0], [0, 0, 2, 0]]

    >>> smith_waterman('AGT', 'GTC')
    [[0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 2, 0]]

    >>> smith_waterman('AGT', 'G')
    [[0, 0], [0, 0], [0, 1], [0, 0]]

    >>> smith_waterman('G', 'AGT')
    [[0, 0, 0, 0], [0, 0, 1, 0]]

    >>> smith_waterman('AGT', 'AGTCT')
    [[0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0], [0, 0, 0, 3, 1, 1]]

    >>> smith_waterman('AGTCT', 'AGT')
    [[0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 2, 0], [0, 0, 0, 3], [0, 0, 0, 1], [0, 0, 0, 1]]

    >>> smith_waterman('AGTCT', 'GTC')
    [[0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 2, 0], [0, 0, 0, 3], [0, 0, 1, 1]]
    """
    # make both query and subject uppercase
    query = query.upper()
    subject = subject.upper()

    # Initialize score matrix
    m = len(query)
    n = len(subject)
    score = [[0] * (n + 1) for _ in range(m + 1)]
    kwargs = {"match": match, "mismatch": mismatch, "gap": gap}

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            # Calculate scores for each cell
            match = score[i - 1][j - 1] + score_function(
                query[i - 1], subject[j - 1], **kwargs
            )
            delete = score[i - 1][j] + gap
            insert = score[i][j - 1] + gap

            # Take maximum score
            score[i][j] = max(0, match, delete, insert)

    return score


def traceback(score: list[list[int]], query: str, subject: str) -> str:
    r"""
    Perform traceback to find the optimal local alignment.
    Starts from the highest scoring cell in the matrix and traces back recursively
    until a 0 score is found. Returns the alignment strings.
    >>> traceback([[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 2], [0, 1, 0]], 'ACAC', 'CA')
    'CA\nCA'
    >>> traceback([[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 2], [0, 1, 0]], 'acac', 'ca')
    'CA\nCA'
    >>> traceback([[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 2], [0, 1, 0]], 'ACAC', 'ca')
    'CA\nCA'
    >>> traceback([[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 2], [0, 1, 0]], 'acac', 'CA')
    'CA\nCA'
    >>> traceback([[0, 0, 0]], 'ACAC', '')
    ''
    """
    # make both query and subject uppercase
    query = query.upper()
    subject = subject.upper()
    # find the indices of the maximum value in the score matrix
    max_value = float("-inf")
    i_max = j_max = 0
    for i, row in enumerate(score):
        for j, value in enumerate(row):
            if value > max_value:
                max_value = value
                i_max, j_max = i, j
    # Traceback logic to find optimal alignment
    i = i_max
    j = j_max
    align1 = ""
    align2 = ""
    gap = score_function("-", "-")
    # guard against empty query or subject
    if i == 0 or j == 0:
        return ""
    while i > 0 and j > 0:
        if score[i][j] == score[i - 1][j - 1] + score_function(
            query[i - 1], subject[j - 1]
        ):
            # optimal path is a diagonal take both letters
            align1 = query[i - 1] + align1
            align2 = subject[j - 1] + align2
            i -= 1
            j -= 1
        elif score[i][j] == score[i - 1][j] + gap:
            # optimal path is a vertical
            align1 = query[i - 1] + align1
            align2 = f"-{align2}"
            i -= 1
        else:
            # optimal path is a horizontal
            align1 = f"-{align1}"
            align2 = subject[j - 1] + align2
            j -= 1

    return f"{align1}\n{align2}"


if __name__ == "__main__":
    query = "HEAGAWGHEE"
    subject = "PAWHEAE"

    score = smith_waterman(query, subject, match=1, mismatch=-1, gap=-2)
    print(traceback(score, query, subject))


================================================
FILE: dynamic_programming/subset_generation.py
================================================
def subset_combinations(elements: list[int], n: int) -> list:
    """
    Compute n-element combinations from a given list using dynamic programming.

    Args:
        * `elements`: The list of elements from which combinations will be generated.
        * `n`: The number of elements in each combination.

    Returns:
        A list of tuples, each representing a combination of `n` elements.

    >>> subset_combinations(elements=[10, 20, 30, 40], n=2)
    [(10, 20), (10, 30), (10, 40), (20, 30), (20, 40), (30, 40)]
    >>> subset_combinations(elements=[1, 2, 3], n=1)
    [(1,), (2,), (3,)]
    >>> subset_combinations(elements=[1, 2, 3], n=3)
    [(1, 2, 3)]
    >>> subset_combinations(elements=[42], n=1)
    [(42,)]
    >>> subset_combinations(elements=[6, 7, 8, 9], n=4)
    [(6, 7, 8, 9)]
    >>> subset_combinations(elements=[10, 20, 30, 40, 50], n=0)
    [()]
    >>> subset_combinations(elements=[1, 2, 3, 4], n=2)
    [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
    >>> subset_combinations(elements=[1, 'apple', 3.14], n=2)
    [(1, 'apple'), (1, 3.14), ('apple', 3.14)]
    >>> subset_combinations(elements=['single'], n=0)
    [()]
    >>> subset_combinations(elements=[], n=9)
    []
    >>> from itertools import combinations
    >>> all(subset_combinations(items, n) == list(combinations(items, n))
    ...     for items, n in (
    ...         ([10, 20, 30, 40], 2), ([1, 2, 3], 1), ([1, 2, 3], 3), ([42], 1),
    ...         ([6, 7, 8, 9], 4), ([10, 20, 30, 40, 50], 1), ([1, 2, 3, 4], 2),
    ...         ([1, 'apple', 3.14], 2), (['single'], 0), ([], 9)))
    True
    """
    r = len(elements)
    if n > r:
        return []

    dp: list[list[tuple]] = [[] for _ in range(r + 1)]

    dp[0].append(())

    for i in range(1, r + 1):
        for j in range(i, 0, -1):
            for prev_combination in dp[j - 1]:
                dp[j].append((*prev_combination, elements[i - 1]))

    try:
        return sorted(dp[n])
    except TypeError:
        return dp[n]


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    print(f"{subset_combinations(elements=[10, 20, 30, 40], n=2) = }")


================================================
FILE: dynamic_programming/sum_of_subset.py
================================================
def is_sum_subset(arr: list[int], required_sum: int) -> bool:
    """
    >>> is_sum_subset([2, 4, 6, 8], 5)
    False
    >>> is_sum_subset([2, 4, 6, 8], 14)
    True
    """
    # a subset value says 1 if that subset sum can be formed else 0
    # initially no subsets can be formed hence False/0
    arr_len = len(arr)
    subset = [[False] * (required_sum + 1) for _ in range(arr_len + 1)]

    # for each arr value, a sum of zero(0) can be formed by not taking any element
    # hence True/1
    for i in range(arr_len + 1):
        subset[i][0] = True

    # sum is not zero and set is empty then false
    for i in range(1, required_sum + 1):
        subset[0][i] = False

    for i in range(1, arr_len + 1):
        for j in range(1, required_sum + 1):
            if arr[i - 1] > j:
                subset[i][j] = subset[i - 1][j]
            if arr[i - 1] <= j:
                subset[i][j] = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

    return subset[arr_len][required_sum]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/trapped_water.py
================================================
"""
Given an array of non-negative integers representing an elevation map where the width
of each bar is 1, this program calculates how much rainwater can be trapped.

Example - height = (0, 1, 0, 2, 1, 0, 1, 3, 2, 1, 2, 1)
Output: 6
This problem can be solved using the concept of "DYNAMIC PROGRAMMING".

We calculate the maximum height of bars on the left and right of every bar in array.
Then iterate over the width of structure and at each index.
The amount of water that will be stored is equal to minimum of maximum height of bars
on both sides minus height of bar at current position.
"""


def trapped_rainwater(heights: tuple[int, ...]) -> int:
    """
    The trapped_rainwater function calculates the total amount of rainwater that can be
    trapped given an array of bar heights.
    It uses a dynamic programming approach, determining the maximum height of bars on
    both sides for each bar, and then computing the trapped water above each bar.
    The function returns the total trapped water.

    >>> trapped_rainwater((0, 1, 0, 2, 1, 0, 1, 3, 2, 1, 2, 1))
    6
    >>> trapped_rainwater((7, 1, 5, 3, 6, 4))
    9
    >>> trapped_rainwater((7, 1, 5, 3, 6, -1))
    Traceback (most recent call last):
        ...
    ValueError: No height can be negative
    """
    if not heights:
        return 0
    if any(h < 0 for h in heights):
        raise ValueError("No height can be negative")
    length = len(heights)

    left_max = [0] * length
    left_max[0] = heights[0]
    for i, height in enumerate(heights[1:], start=1):
        left_max[i] = max(height, left_max[i - 1])

    right_max = [0] * length
    right_max[-1] = heights[-1]
    for i in range(length - 2, -1, -1):
        right_max[i] = max(heights[i], right_max[i + 1])

    return sum(
        min(left, right) - height
        for left, right, height in zip(left_max, right_max, heights)
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(f"{trapped_rainwater((0, 1, 0, 2, 1, 0, 1, 3, 2, 1, 2, 1)) = }")
    print(f"{trapped_rainwater((7, 1, 5, 3, 6, 4)) = }")


================================================
FILE: dynamic_programming/tribonacci.py
================================================
# Tribonacci sequence using Dynamic Programming


def tribonacci(num: int) -> list[int]:
    """
    Given a number, return first n Tribonacci Numbers.
    >>> tribonacci(5)
    [0, 0, 1, 1, 2]
    >>> tribonacci(8)
    [0, 0, 1, 1, 2, 4, 7, 13]
    """
    dp = [0] * num
    dp[2] = 1

    for i in range(3, num):
        dp[i] = dp[i - 1] + dp[i - 2] + dp[i - 3]

    return dp


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: dynamic_programming/viterbi.py
================================================
from typing import Any


def viterbi(
    observations_space: list,
    states_space: list,
    initial_probabilities: dict,
    transition_probabilities: dict,
    emission_probabilities: dict,
) -> list:
    """
    Viterbi Algorithm, to find the most likely path of
    states from the start and the expected output.

    https://en.wikipedia.org/wiki/Viterbi_algorithm

    Wikipedia example

    >>> observations = ["normal", "cold", "dizzy"]
    >>> states = ["Healthy", "Fever"]
    >>> start_p = {"Healthy": 0.6, "Fever": 0.4}
    >>> trans_p = {
    ...     "Healthy": {"Healthy": 0.7, "Fever": 0.3},
    ...     "Fever": {"Healthy": 0.4, "Fever": 0.6},
    ... }
    >>> emit_p = {
    ...     "Healthy": {"normal": 0.5, "cold": 0.4, "dizzy": 0.1},
    ...     "Fever": {"normal": 0.1, "cold": 0.3, "dizzy": 0.6},
    ... }
    >>> viterbi(observations, states, start_p, trans_p, emit_p)
    ['Healthy', 'Healthy', 'Fever']
    >>> viterbi((), states, start_p, trans_p, emit_p)
    Traceback (most recent call last):
        ...
    ValueError: There's an empty parameter
    >>> viterbi(observations, (), start_p, trans_p, emit_p)
    Traceback (most recent call last):
        ...
    ValueError: There's an empty parameter
    >>> viterbi(observations, states, {}, trans_p, emit_p)
    Traceback (most recent call last):
        ...
    ValueError: There's an empty parameter
    >>> viterbi(observations, states, start_p, {}, emit_p)
    Traceback (most recent call last):
        ...
    ValueError: There's an empty parameter
    >>> viterbi(observations, states, start_p, trans_p, {})
    Traceback (most recent call last):
        ...
    ValueError: There's an empty parameter
    >>> viterbi("invalid", states, start_p, trans_p, emit_p)
    Traceback (most recent call last):
        ...
    ValueError: observations_space must be a list
    >>> viterbi(["valid", 123], states, start_p, trans_p, emit_p)
    Traceback (most recent call last):
        ...
    ValueError: observations_space must be a list of strings
    >>> viterbi(observations, "invalid", start_p, trans_p, emit_p)
    Traceback (most recent call last):
        ...
    ValueError: states_space must be a list
    >>> viterbi(observations, ["valid", 123], start_p, trans_p, emit_p)
    Traceback (most recent call last):
        ...
    ValueError: states_space must be a list of strings
    >>> viterbi(observations, states, "invalid", trans_p, emit_p)
    Traceback (most recent call last):
        ...
    ValueError: initial_probabilities must be a dict
    >>> viterbi(observations, states, {2:2}, trans_p, emit_p)
    Traceback (most recent call last):
        ...
    ValueError: initial_probabilities all keys must be strings
    >>> viterbi(observations, states, {"a":2}, trans_p, emit_p)
    Traceback (most recent call last):
        ...
    ValueError: initial_probabilities all values must be float
    >>> viterbi(observations, states, start_p, "invalid", emit_p)
    Traceback (most recent call last):
        ...
    ValueError: transition_probabilities must be a dict
    >>> viterbi(observations, states, start_p, {"a":2}, emit_p)
    Traceback (most recent call last):
        ...
    ValueError: transition_probabilities all values must be dict
    >>> viterbi(observations, states, start_p, {2:{2:2}}, emit_p)
    Traceback (most recent call last):
        ...
    ValueError: transition_probabilities all keys must be strings
    >>> viterbi(observations, states, start_p, {"a":{2:2}}, emit_p)
    Traceback (most recent call last):
        ...
    ValueError: transition_probabilities all keys must be strings
    >>> viterbi(observations, states, start_p, {"a":{"b":2}}, emit_p)
    Traceback (most recent call last):
        ...
    ValueError: transition_probabilities nested dictionary all values must be float
    >>> viterbi(observations, states, start_p, trans_p, "invalid")
    Traceback (most recent call last):
        ...
    ValueError: emission_probabilities must be a dict
    >>> viterbi(observations, states, start_p, trans_p, None)
    Traceback (most recent call last):
        ...
    ValueError: There's an empty parameter

    """
    _validation(
        observations_space,
        states_space,
        initial_probabilities,
        transition_probabilities,
        emission_probabilities,
    )
    # Creates data structures and fill initial step
    probabilities: dict = {}
    pointers: dict = {}
    for state in states_space:
        observation = observations_space[0]
        probabilities[(state, observation)] = (
            initial_probabilities[state] * emission_probabilities[state][observation]
        )
        pointers[(state, observation)] = None

    # Fills the data structure with the probabilities of
    # different transitions and pointers to previous states
    for o in range(1, len(observations_space)):
        observation = observations_space[o]
        prior_observation = observations_space[o - 1]
        for state in states_space:
            # Calculates the argmax for probability function
            arg_max = ""
            max_probability = -1
            for k_state in states_space:
                probability = (
                    probabilities[(k_state, prior_observation)]
                    * transition_probabilities[k_state][state]
                    * emission_probabilities[state][observation]
                )
                if probability > max_probability:
                    max_probability = probability
                    arg_max = k_state

            # Update probabilities and pointers dicts
            probabilities[(state, observation)] = (
                probabilities[(arg_max, prior_observation)]
                * transition_probabilities[arg_max][state]
                * emission_probabilities[state][observation]
            )

            pointers[(state, observation)] = arg_max

    # The final observation
    final_observation = observations_space[len(observations_space) - 1]

    # argmax for given final observation
    arg_max = ""
    max_probability = -1
    for k_state in states_space:
        probability = probabilities[(k_state, final_observation)]
        if probability > max_probability:
            max_probability = probability
            arg_max = k_state
    last_state = arg_max

    # Process pointers backwards
    previous = last_state
    result = []
    for o in range(len(observations_space) - 1, -1, -1):
        result.append(previous)
        previous = pointers[previous, observations_space[o]]
    result.reverse()

    return result


def _validation(
    observations_space: Any,
    states_space: Any,
    initial_probabilities: Any,
    transition_probabilities: Any,
    emission_probabilities: Any,
) -> None:
    """
    >>> observations = ["normal", "cold", "dizzy"]
    >>> states = ["Healthy", "Fever"]
    >>> start_p = {"Healthy": 0.6, "Fever": 0.4}
    >>> trans_p = {
    ...     "Healthy": {"Healthy": 0.7, "Fever": 0.3},
    ...     "Fever": {"Healthy": 0.4, "Fever": 0.6},
    ... }
    >>> emit_p = {
    ...     "Healthy": {"normal": 0.5, "cold": 0.4, "dizzy": 0.1},
    ...     "Fever": {"normal": 0.1, "cold": 0.3, "dizzy": 0.6},
    ... }
    >>> _validation(observations, states, start_p, trans_p, emit_p)
    >>> _validation([], states, start_p, trans_p, emit_p)
    Traceback (most recent call last):
            ...
    ValueError: There's an empty parameter
    """
    _validate_not_empty(
        observations_space,
        states_space,
        initial_probabilities,
        transition_probabilities,
        emission_probabilities,
    )
    _validate_lists(observations_space, states_space)
    _validate_dicts(
        initial_probabilities, transition_probabilities, emission_probabilities
    )


def _validate_not_empty(
    observations_space: Any,
    states_space: Any,
    initial_probabilities: Any,
    transition_probabilities: Any,
    emission_probabilities: Any,
) -> None:
    """
    >>> _validate_not_empty(["a"], ["b"], {"c":0.5},
    ... {"d": {"e": 0.6}}, {"f": {"g": 0.7}})
    >>> _validate_not_empty(["a"], ["b"], {"c":0.5}, {}, {"f": {"g": 0.7}})
    Traceback (most recent call last):
            ...
    ValueError: There's an empty parameter
    >>> _validate_not_empty(["a"], ["b"], None, {"d": {"e": 0.6}}, {"f": {"g": 0.7}})
    Traceback (most recent call last):
            ...
    ValueError: There's an empty parameter
    """
    if not all(
        [
            observations_space,
            states_space,
            initial_probabilities,
            transition_probabilities,
            emission_probabilities,
        ]
    ):
        raise ValueError("There's an empty parameter")


def _validate_lists(observations_space: Any, states_space: Any) -> None:
    """
    >>> _validate_lists(["a"], ["b"])
    >>> _validate_lists(1234, ["b"])
    Traceback (most recent call last):
            ...
    ValueError: observations_space must be a list
    >>> _validate_lists(["a"], [3])
    Traceback (most recent call last):
            ...
    ValueError: states_space must be a list of strings
    """
    _validate_list(observations_space, "observations_space")
    _validate_list(states_space, "states_space")


def _validate_list(_object: Any, var_name: str) -> None:
    """
    >>> _validate_list(["a"], "mock_name")
    >>> _validate_list("a", "mock_name")
    Traceback (most recent call last):
            ...
    ValueError: mock_name must be a list
    >>> _validate_list([0.5], "mock_name")
    Traceback (most recent call last):
            ...
    ValueError: mock_name must be a list of strings
    """
    if not isinstance(_object, list):
        msg = f"{var_name} must be a list"
        raise ValueError(msg)
    else:
        for x in _object:
            if not isinstance(x, str):
                msg = f"{var_name} must be a list of strings"
                raise ValueError(msg)


def _validate_dicts(
    initial_probabilities: Any,
    transition_probabilities: Any,
    emission_probabilities: Any,
) -> None:
    """
    >>> _validate_dicts({"c":0.5}, {"d": {"e": 0.6}}, {"f": {"g": 0.7}})
    >>> _validate_dicts("invalid", {"d": {"e": 0.6}}, {"f": {"g": 0.7}})
    Traceback (most recent call last):
            ...
    ValueError: initial_probabilities must be a dict
    >>> _validate_dicts({"c":0.5}, {2: {"e": 0.6}}, {"f": {"g": 0.7}})
    Traceback (most recent call last):
            ...
    ValueError: transition_probabilities all keys must be strings
    >>> _validate_dicts({"c":0.5}, {"d": {"e": 0.6}}, {"f": {2: 0.7}})
    Traceback (most recent call last):
            ...
    ValueError: emission_probabilities all keys must be strings
    >>> _validate_dicts({"c":0.5}, {"d": {"e": 0.6}}, {"f": {"g": "h"}})
    Traceback (most recent call last):
            ...
    ValueError: emission_probabilities nested dictionary all values must be float
    """
    _validate_dict(initial_probabilities, "initial_probabilities", float)
    _validate_nested_dict(transition_probabilities, "transition_probabilities")
    _validate_nested_dict(emission_probabilities, "emission_probabilities")


def _validate_nested_dict(_object: Any, var_name: str) -> None:
    """
    >>> _validate_nested_dict({"a":{"b": 0.5}}, "mock_name")
    >>> _validate_nested_dict("invalid", "mock_name")
    Traceback (most recent call last):
            ...
    ValueError: mock_name must be a dict
    >>> _validate_nested_dict({"a": 8}, "mock_name")
    Traceback (most recent call last):
            ...
    ValueError: mock_name all values must be dict
    >>> _validate_nested_dict({"a":{2: 0.5}}, "mock_name")
    Traceback (most recent call last):
            ...
    ValueError: mock_name all keys must be strings
    >>> _validate_nested_dict({"a":{"b": 4}}, "mock_name")
    Traceback (most recent call last):
            ...
    ValueError: mock_name nested dictionary all values must be float
    """
    _validate_dict(_object, var_name, dict)
    for x in _object.values():
        _validate_dict(x, var_name, float, True)


def _validate_dict(
    _object: Any, var_name: str, value_type: type, nested: bool = False
) -> None:
    """
    >>> _validate_dict({"b": 0.5}, "mock_name", float)
    >>> _validate_dict("invalid", "mock_name", float)
    Traceback (most recent call last):
            ...
    ValueError: mock_name must be a dict
    >>> _validate_dict({"a": 8}, "mock_name", dict)
    Traceback (most recent call last):
            ...
    ValueError: mock_name all values must be dict
    >>> _validate_dict({2: 0.5}, "mock_name",float, True)
    Traceback (most recent call last):
            ...
    ValueError: mock_name all keys must be strings
    >>> _validate_dict({"b": 4}, "mock_name", float,True)
    Traceback (most recent call last):
            ...
    ValueError: mock_name nested dictionary all values must be float
    """
    if not isinstance(_object, dict):
        msg = f"{var_name} must be a dict"
        raise ValueError(msg)
    if not all(isinstance(x, str) for x in _object):
        msg = f"{var_name} all keys must be strings"
        raise ValueError(msg)
    if not all(isinstance(x, value_type) for x in _object.values()):
        nested_text = "nested dictionary " if nested else ""
        msg = f"{var_name} {nested_text}all values must be {value_type.__name__}"
        raise ValueError(msg)


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: dynamic_programming/wildcard_matching.py
================================================
"""
Author  : ilyas dahhou
Date    : Oct 7, 2023

Task:
Given an input string and a pattern, implement wildcard pattern matching with support
for '?' and '*' where:
'?' matches any single character.
'*' matches any sequence of characters (including the empty sequence).
The matching should cover the entire input string (not partial).

Runtime complexity: O(m * n)

The implementation was tested on the
leetcode: https://leetcode.com/problems/wildcard-matching/
"""


def is_match(string: str, pattern: str) -> bool:
    """
    >>> is_match("", "")
    True
    >>> is_match("aa", "a")
    False
    >>> is_match("abc", "abc")
    True
    >>> is_match("abc", "*c")
    True
    >>> is_match("abc", "a*")
    True
    >>> is_match("abc", "*a*")
    True
    >>> is_match("abc", "?b?")
    True
    >>> is_match("abc", "*?")
    True
    >>> is_match("abc", "a*d")
    False
    >>> is_match("abc", "a*c?")
    False
    >>> is_match('baaabab','*****ba*****ba')
    False
    >>> is_match('baaabab','*****ba*****ab')
    True
    >>> is_match('aa','*')
    True
    """
    dp = [[False] * (len(pattern) + 1) for _ in string + "1"]
    dp[0][0] = True
    # Fill in the first row
    for j, char in enumerate(pattern, 1):
        if char == "*":
            dp[0][j] = dp[0][j - 1]
    # Fill in the rest of the DP table
    for i, s_char in enumerate(string, 1):
        for j, p_char in enumerate(pattern, 1):
            if p_char in (s_char, "?"):
                dp[i][j] = dp[i - 1][j - 1]
            elif pattern[j - 1] == "*":
                dp[i][j] = dp[i - 1][j] or dp[i][j - 1]
    return dp[len(string)][len(pattern)]


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(f"{is_match('baaabab','*****ba*****ab') = }")


================================================
FILE: dynamic_programming/word_break.py
================================================
"""
Author  : Alexander Pantyukhin
Date    : December 12, 2022

Task:
Given a string and a list of words, return true if the string can be
segmented into a space-separated sequence of one or more words.

Note that the same word may be reused
multiple times in the segmentation.

Implementation notes: Trie + Dynamic programming up -> down.
The Trie will be used to store the words. It will be useful for scanning
available words for the current position in the string.

Leetcode:
https://leetcode.com/problems/word-break/description/

Runtime: O(n * n)
Space: O(n)
"""

import functools
from typing import Any


def word_break(string: str, words: list[str]) -> bool:
    """
    Return True if numbers have opposite signs False otherwise.

    >>> word_break("applepenapple", ["apple","pen"])
    True
    >>> word_break("catsandog", ["cats","dog","sand","and","cat"])
    False
    >>> word_break("cars", ["car","ca","rs"])
    True
    >>> word_break('abc', [])
    False
    >>> word_break(123, ['a'])
    Traceback (most recent call last):
        ...
    ValueError: the string should be not empty string
    >>> word_break('', ['a'])
    Traceback (most recent call last):
        ...
    ValueError: the string should be not empty string
    >>> word_break('abc', [123])
    Traceback (most recent call last):
        ...
    ValueError: the words should be a list of non-empty strings
    >>> word_break('abc', [''])
    Traceback (most recent call last):
        ...
    ValueError: the words should be a list of non-empty strings
    """

    # Validation
    if not isinstance(string, str) or len(string) == 0:
        raise ValueError("the string should be not empty string")

    if not isinstance(words, list) or not all(
        isinstance(item, str) and len(item) > 0 for item in words
    ):
        raise ValueError("the words should be a list of non-empty strings")

    # Build trie
    trie: dict[str, Any] = {}
    word_keeper_key = "WORD_KEEPER"

    for word in words:
        trie_node = trie
        for c in word:
            if c not in trie_node:
                trie_node[c] = {}

            trie_node = trie_node[c]

        trie_node[word_keeper_key] = True

    len_string = len(string)

    # Dynamic programming method
    @functools.cache
    def is_breakable(index: int) -> bool:
        """
        >>> string = 'a'
        >>> is_breakable(1)
        True
        """
        if index == len_string:
            return True

        trie_node: Any = trie
        for i in range(index, len_string):
            trie_node = trie_node.get(string[i], None)

            if trie_node is None:
                return False

            if trie_node.get(word_keeper_key, False) and is_breakable(i + 1):
                return True

        return False

    return is_breakable(0)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/__init__.py
================================================


================================================
FILE: electronics/apparent_power.py
================================================
import cmath
import math


def apparent_power(
    voltage: float, current: float, voltage_angle: float, current_angle: float
) -> complex:
    """
    Calculate the apparent power in a single-phase AC circuit.

    Reference: https://en.wikipedia.org/wiki/AC_power#Apparent_power

    >>> apparent_power(100, 5, 0, 0)
    (500+0j)
    >>> apparent_power(100, 5, 90, 0)
    (3.061616997868383e-14+500j)
    >>> apparent_power(100, 5, -45, -60)
    (-129.40952255126027-482.9629131445341j)
    >>> apparent_power(200, 10, -30, -90)
    (-999.9999999999998-1732.0508075688776j)
    """
    # Convert angles from degrees to radians
    voltage_angle_rad = math.radians(voltage_angle)
    current_angle_rad = math.radians(current_angle)

    # Convert voltage and current to rectangular form
    voltage_rect = cmath.rect(voltage, voltage_angle_rad)
    current_rect = cmath.rect(current, current_angle_rad)

    # Calculate apparent power
    return voltage_rect * current_rect


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/builtin_voltage.py
================================================
from math import log

from scipy.constants import Boltzmann, physical_constants

T = 300  # TEMPERATURE (unit = K)


def builtin_voltage(
    donor_conc: float,  # donor concentration
    acceptor_conc: float,  # acceptor concentration
    intrinsic_conc: float,  # intrinsic concentration
) -> float:
    """
    This function can calculate the Builtin Voltage of a pn junction diode.
    This is calculated from the given three values.
    Examples -
    >>> builtin_voltage(donor_conc=1e17, acceptor_conc=1e17, intrinsic_conc=1e10)
    0.833370010652644
    >>> builtin_voltage(donor_conc=0, acceptor_conc=1600, intrinsic_conc=200)
    Traceback (most recent call last):
      ...
    ValueError: Donor concentration should be positive
    >>> builtin_voltage(donor_conc=1000, acceptor_conc=0, intrinsic_conc=1200)
    Traceback (most recent call last):
      ...
    ValueError: Acceptor concentration should be positive
    >>> builtin_voltage(donor_conc=1000, acceptor_conc=1000, intrinsic_conc=0)
    Traceback (most recent call last):
      ...
    ValueError: Intrinsic concentration should be positive
    >>> builtin_voltage(donor_conc=1000, acceptor_conc=3000, intrinsic_conc=2000)
    Traceback (most recent call last):
      ...
    ValueError: Donor concentration should be greater than intrinsic concentration
    >>> builtin_voltage(donor_conc=3000, acceptor_conc=1000, intrinsic_conc=2000)
    Traceback (most recent call last):
      ...
    ValueError: Acceptor concentration should be greater than intrinsic concentration
    """

    if donor_conc <= 0:
        raise ValueError("Donor concentration should be positive")
    elif acceptor_conc <= 0:
        raise ValueError("Acceptor concentration should be positive")
    elif intrinsic_conc <= 0:
        raise ValueError("Intrinsic concentration should be positive")
    elif donor_conc <= intrinsic_conc:
        raise ValueError(
            "Donor concentration should be greater than intrinsic concentration"
        )
    elif acceptor_conc <= intrinsic_conc:
        raise ValueError(
            "Acceptor concentration should be greater than intrinsic concentration"
        )
    else:
        return (
            Boltzmann
            * T
            * log((donor_conc * acceptor_conc) / intrinsic_conc**2)
            / physical_constants["electron volt"][0]
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/capacitor_equivalence.py
================================================
# https://farside.ph.utexas.edu/teaching/316/lectures/node46.html

from __future__ import annotations


def capacitor_parallel(capacitors: list[float]) -> float:
    """
    Ceq = C1 + C2 + ... + Cn
    Calculate the equivalent resistance for any number of capacitors in parallel.
    >>> capacitor_parallel([5.71389, 12, 3])
    20.71389
    >>> capacitor_parallel([5.71389, 12, -3])
    Traceback (most recent call last):
        ...
    ValueError: Capacitor at index 2 has a negative value!
    """
    sum_c = 0.0
    for index, capacitor in enumerate(capacitors):
        if capacitor < 0:
            msg = f"Capacitor at index {index} has a negative value!"
            raise ValueError(msg)
        sum_c += capacitor
    return sum_c


def capacitor_series(capacitors: list[float]) -> float:
    """
    Ceq = 1/ (1/C1 + 1/C2 + ... + 1/Cn)
    >>> capacitor_series([5.71389, 12, 3])
    1.6901062252507735
    >>> capacitor_series([5.71389, 12, -3])
    Traceback (most recent call last):
        ...
    ValueError: Capacitor at index 2 has a negative or zero value!
    >>> capacitor_series([5.71389, 12, 0.000])
    Traceback (most recent call last):
        ...
    ValueError: Capacitor at index 2 has a negative or zero value!
    """

    first_sum = 0.0
    for index, capacitor in enumerate(capacitors):
        if capacitor <= 0:
            msg = f"Capacitor at index {index} has a negative or zero value!"
            raise ValueError(msg)
        first_sum += 1 / capacitor
    return 1 / first_sum


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/carrier_concentration.py
================================================
# https://en.wikipedia.org/wiki/Charge_carrier_density
# https://www.pveducation.org/pvcdrom/pn-junctions/equilibrium-carrier-concentration
# http://www.ece.utep.edu/courses/ee3329/ee3329/Studyguide/ToC/Fundamentals/Carriers/concentrations.html

from __future__ import annotations


def carrier_concentration(
    electron_conc: float,
    hole_conc: float,
    intrinsic_conc: float,
) -> tuple:
    """
    This function can calculate any one of the three -
    1. Electron Concentration
    2, Hole Concentration
    3. Intrinsic Concentration
    given the other two.
    Examples -
    >>> carrier_concentration(electron_conc=25, hole_conc=100, intrinsic_conc=0)
    ('intrinsic_conc', 50.0)
    >>> carrier_concentration(electron_conc=0, hole_conc=1600, intrinsic_conc=200)
    ('electron_conc', 25.0)
    >>> carrier_concentration(electron_conc=1000, hole_conc=0, intrinsic_conc=1200)
    ('hole_conc', 1440.0)
    >>> carrier_concentration(electron_conc=1000, hole_conc=400, intrinsic_conc=1200)
    Traceback (most recent call last):
        ...
    ValueError: You cannot supply more or less than 2 values
    >>> carrier_concentration(electron_conc=-1000, hole_conc=0, intrinsic_conc=1200)
    Traceback (most recent call last):
        ...
    ValueError: Electron concentration cannot be negative in a semiconductor
    >>> carrier_concentration(electron_conc=0, hole_conc=-400, intrinsic_conc=1200)
    Traceback (most recent call last):
        ...
    ValueError: Hole concentration cannot be negative in a semiconductor
    >>> carrier_concentration(electron_conc=0, hole_conc=400, intrinsic_conc=-1200)
    Traceback (most recent call last):
        ...
    ValueError: Intrinsic concentration cannot be negative in a semiconductor
    """
    if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1:
        raise ValueError("You cannot supply more or less than 2 values")
    elif electron_conc < 0:
        raise ValueError("Electron concentration cannot be negative in a semiconductor")
    elif hole_conc < 0:
        raise ValueError("Hole concentration cannot be negative in a semiconductor")
    elif intrinsic_conc < 0:
        raise ValueError(
            "Intrinsic concentration cannot be negative in a semiconductor"
        )
    elif electron_conc == 0:
        return (
            "electron_conc",
            intrinsic_conc**2 / hole_conc,
        )
    elif hole_conc == 0:
        return (
            "hole_conc",
            intrinsic_conc**2 / electron_conc,
        )
    elif intrinsic_conc == 0:
        return (
            "intrinsic_conc",
            (electron_conc * hole_conc) ** 0.5,
        )
    else:
        return (-1, -1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/charging_capacitor.py
================================================
# source - The ARRL Handbook for Radio Communications
# https://en.wikipedia.org/wiki/RC_time_constant

"""
Description
-----------
When a capacitor is connected with a potential source (AC or DC). It starts to charge
at a general speed but when a resistor is connected in the  circuit with in series to
a capacitor then the capacitor charges slowly means it will take more time than usual.
while the capacitor is being charged, the voltage is in exponential function with time.

'resistance(ohms) * capacitance(farads)' is called RC-timeconstant which may also be
represented as τ (tau).  By using this RC-timeconstant we can find the voltage at any
time 't' from the initiation of charging a capacitor with the help of the exponential
function containing RC.  Both at charging and discharging of a capacitor.
"""

from math import exp  # value of exp = 2.718281828459…


def charging_capacitor(
    source_voltage: float,  # voltage in volts.
    resistance: float,  # resistance in ohms.
    capacitance: float,  # capacitance in farads.
    time_sec: float,  # time in seconds after charging initiation of capacitor.
) -> float:
    """
    Find capacitor voltage at any nth second after initiating its charging.

    Examples
    --------
    >>> charging_capacitor(source_voltage=.2,resistance=.9,capacitance=8.4,time_sec=.5)
    0.013

    >>> charging_capacitor(source_voltage=2.2,resistance=3.5,capacitance=2.4,time_sec=9)
    1.446

    >>> charging_capacitor(source_voltage=15,resistance=200,capacitance=20,time_sec=2)
    0.007

    >>> charging_capacitor(20, 2000, 30*pow(10,-5), 4)
    19.975

    >>> charging_capacitor(source_voltage=0,resistance=10.0,capacitance=.30,time_sec=3)
    Traceback (most recent call last):
        ...
    ValueError: Source voltage must be positive.

    >>> charging_capacitor(source_voltage=20,resistance=-2000,capacitance=30,time_sec=4)
    Traceback (most recent call last):
        ...
    ValueError: Resistance must be positive.

    >>> charging_capacitor(source_voltage=30,resistance=1500,capacitance=0,time_sec=4)
    Traceback (most recent call last):
        ...
    ValueError: Capacitance must be positive.
    """

    if source_voltage <= 0:
        raise ValueError("Source voltage must be positive.")
    if resistance <= 0:
        raise ValueError("Resistance must be positive.")
    if capacitance <= 0:
        raise ValueError("Capacitance must be positive.")
    return round(source_voltage * (1 - exp(-time_sec / (resistance * capacitance))), 3)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/charging_inductor.py
================================================
# source - The ARRL Handbook for Radio Communications
# https://en.wikipedia.org/wiki/RL_circuit

"""
Description
-----------
Inductor is a passive electronic device which stores energy but unlike capacitor, it
stores energy in its 'magnetic field' or 'magnetostatic field'.

When inductor is connected to 'DC' current source nothing happens it just works like a
wire because it's real effect cannot be seen while 'DC' is connected, its not even
going to store energy. Inductor stores energy only when it is working on 'AC' current.

Connecting a inductor in series with a resistor(when R = 0) to a 'AC' potential source,
from zero to a finite value causes a sudden voltage to induced in inductor which
opposes the current. which results in initially slowly current rise. However it would
cease if there is no further changes in current. With resistance zero current will never
stop rising.

'Resistance(ohms) / Inductance(henrys)' is known as RL-timeconstant. It also represents
as τ (tau). While the charging of a inductor with a resistor results in
a exponential function.

when inductor is connected across 'AC' potential source. It starts to store the energy
in its 'magnetic field'.with the help 'RL-time-constant' we can find current at any time
in inductor while it is charging.
"""

from math import exp  # value of exp = 2.718281828459…


def charging_inductor(
    source_voltage: float,  # source_voltage should be in volts.
    resistance: float,  # resistance should be in ohms.
    inductance: float,  # inductance should be in henrys.
    time: float,  # time should in seconds.
) -> float:
    """
    Find inductor current at any nth second after initiating its charging.

    Examples
    --------
    >>> charging_inductor(source_voltage=5.8,resistance=1.5,inductance=2.3,time=2)
    2.817

    >>> charging_inductor(source_voltage=8,resistance=5,inductance=3,time=2)
    1.543

    >>> charging_inductor(source_voltage=8,resistance=5*pow(10,2),inductance=3,time=2)
    0.016

    >>> charging_inductor(source_voltage=-8,resistance=100,inductance=15,time=12)
    Traceback (most recent call last):
        ...
    ValueError: Source voltage must be positive.

    >>> charging_inductor(source_voltage=80,resistance=-15,inductance=100,time=5)
    Traceback (most recent call last):
        ...
    ValueError: Resistance must be positive.

    >>> charging_inductor(source_voltage=12,resistance=200,inductance=-20,time=5)
    Traceback (most recent call last):
        ...
    ValueError: Inductance must be positive.

    >>> charging_inductor(source_voltage=0,resistance=200,inductance=20,time=5)
    Traceback (most recent call last):
        ...
    ValueError: Source voltage must be positive.

    >>> charging_inductor(source_voltage=10,resistance=0,inductance=20,time=5)
    Traceback (most recent call last):
        ...
    ValueError: Resistance must be positive.

    >>> charging_inductor(source_voltage=15, resistance=25, inductance=0, time=5)
    Traceback (most recent call last):
        ...
    ValueError: Inductance must be positive.
    """

    if source_voltage <= 0:
        raise ValueError("Source voltage must be positive.")
    if resistance <= 0:
        raise ValueError("Resistance must be positive.")
    if inductance <= 0:
        raise ValueError("Inductance must be positive.")
    return round(
        source_voltage / resistance * (1 - exp((-time * resistance) / inductance)), 3
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/circular_convolution.py
================================================
# https://en.wikipedia.org/wiki/Circular_convolution

"""
Circular convolution, also known as cyclic convolution,
is a special case of periodic convolution, which is the convolution of two
periodic functions that have the same period. Periodic convolution arises,
for example, in the context of the discrete-time Fourier transform (DTFT).
In particular, the DTFT of the product of two discrete sequences is the periodic
convolution of the DTFTs of the individual sequences. And each DTFT is a periodic
summation of a continuous Fourier transform function.

Source: https://en.wikipedia.org/wiki/Circular_convolution
"""

import doctest
from collections import deque

import numpy as np


class CircularConvolution:
    """
    This class stores the first and second signal and performs the circular convolution
    """

    def __init__(self) -> None:
        """
        First signal and second signal are stored as 1-D array
        """

        self.first_signal = [2, 1, 2, -1]
        self.second_signal = [1, 2, 3, 4]

    def circular_convolution(self) -> list[float]:
        """
        This function performs the circular convolution of the first and second signal
        using matrix method

        Usage:
        >>> convolution = CircularConvolution()
        >>> convolution.circular_convolution()
        [10.0, 10.0, 6.0, 14.0]

        >>> convolution.first_signal = [0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6]
        >>> convolution.second_signal = [0.1, 0.3, 0.5, 0.7, 0.9, 1.1, 1.3, 1.5]
        >>> convolution.circular_convolution()
        [5.2, 6.0, 6.48, 6.64, 6.48, 6.0, 5.2, 4.08]

        >>> convolution.first_signal = [-1, 1, 2, -2]
        >>> convolution.second_signal = [0.5, 1, -1, 2, 0.75]
        >>> convolution.circular_convolution()
        [6.25, -3.0, 1.5, -2.0, -2.75]

        >>> convolution.first_signal = [1, -1, 2, 3, -1]
        >>> convolution.second_signal = [1, 2, 3]
        >>> convolution.circular_convolution()
        [8.0, -2.0, 3.0, 4.0, 11.0]

        """

        length_first_signal = len(self.first_signal)
        length_second_signal = len(self.second_signal)

        max_length = max(length_first_signal, length_second_signal)

        # create a zero matrix of max_length x max_length
        matrix = [[0] * max_length for i in range(max_length)]

        # fills the smaller signal with zeros to make both signals of same length
        if length_first_signal < length_second_signal:
            self.first_signal += [0] * (max_length - length_first_signal)
        elif length_first_signal > length_second_signal:
            self.second_signal += [0] * (max_length - length_second_signal)

        """
        Fills the matrix in the following way assuming 'x' is the signal of length 4
        [
            [x[0], x[3], x[2], x[1]],
            [x[1], x[0], x[3], x[2]],
            [x[2], x[1], x[0], x[3]],
            [x[3], x[2], x[1], x[0]]
        ]
        """
        for i in range(max_length):
            rotated_signal = deque(self.second_signal)
            rotated_signal.rotate(i)
            for j, item in enumerate(rotated_signal):
                matrix[i][j] += item

        # multiply the matrix with the first signal
        final_signal = np.matmul(np.transpose(matrix), np.transpose(self.first_signal))

        # rounding-off to two decimal places
        return [float(round(i, 2)) for i in final_signal]


if __name__ == "__main__":
    doctest.testmod()


================================================
FILE: electronics/coulombs_law.py
================================================
# https://en.wikipedia.org/wiki/Coulomb%27s_law

from __future__ import annotations

COULOMBS_CONSTANT = 8.988e9  # units = N * m^s * C^-2


def couloumbs_law(
    force: float, charge1: float, charge2: float, distance: float
) -> dict[str, float]:
    """
    Apply Coulomb's Law on any three given values. These can be force, charge1,
    charge2, or distance, and then in a Python dict return name/value pair of
    the zero value.

    Coulomb's Law states that the magnitude of the electrostatic force of
    attraction or repulsion between two point charges is directly proportional
    to the product of the magnitudes of charges and inversely proportional to
    the square of the distance between them.

    Reference
    ----------
    Coulomb (1785) "Premier mémoire sur l'électricité et le magnétisme,"
    Histoire de l'Académie Royale des Sciences, pp. 569-577.

    Parameters
    ----------
    force : float with units in Newtons

    charge1 : float with units in Coulombs

    charge2 : float with units in Coulombs

    distance : float with units in meters

    Returns
    -------
    result : dict name/value pair of the zero value

    >>> couloumbs_law(force=0, charge1=3, charge2=5, distance=2000)
    {'force': 33705.0}

    >>> couloumbs_law(force=10, charge1=3, charge2=5, distance=0)
    {'distance': 116112.01488218177}

    >>> couloumbs_law(force=10, charge1=0, charge2=5, distance=2000)
    {'charge1': 0.0008900756564307966}

    >>> couloumbs_law(force=0, charge1=0, charge2=5, distance=2000)
    Traceback (most recent call last):
      ...
    ValueError: One and only one argument must be 0

    >>> couloumbs_law(force=0, charge1=3, charge2=5, distance=-2000)
    Traceback (most recent call last):
      ...
    ValueError: Distance cannot be negative

    """

    charge_product = abs(charge1 * charge2)

    if (force, charge1, charge2, distance).count(0) != 1:
        raise ValueError("One and only one argument must be 0")
    if distance < 0:
        raise ValueError("Distance cannot be negative")
    if force == 0:
        force = COULOMBS_CONSTANT * charge_product / (distance**2)
        return {"force": force}
    elif charge1 == 0:
        charge1 = abs(force) * (distance**2) / (COULOMBS_CONSTANT * charge2)
        return {"charge1": charge1}
    elif charge2 == 0:
        charge2 = abs(force) * (distance**2) / (COULOMBS_CONSTANT * charge1)
        return {"charge2": charge2}
    elif distance == 0:
        distance = (COULOMBS_CONSTANT * charge_product / abs(force)) ** 0.5
        return {"distance": distance}
    raise ValueError("Exactly one argument must be 0")


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/electric_conductivity.py
================================================
from __future__ import annotations

ELECTRON_CHARGE = 1.6021e-19  # units = C


def electric_conductivity(
    conductivity: float,
    electron_conc: float,
    mobility: float,
) -> tuple[str, float]:
    """
    This function can calculate any one of the three -
    1. Conductivity
    2. Electron Concentration
    3. Electron Mobility
    This is calculated from the other two provided values
    Examples -
    >>> electric_conductivity(conductivity=25, electron_conc=100, mobility=0)
    ('mobility', 1.5604519068722301e+18)
    >>> electric_conductivity(conductivity=0, electron_conc=1600, mobility=200)
    ('conductivity', 5.12672e-14)
    >>> electric_conductivity(conductivity=1000, electron_conc=0, mobility=1200)
    ('electron_conc', 5.201506356240767e+18)
    >>> electric_conductivity(conductivity=-10, electron_conc=100, mobility=0)
    Traceback (most recent call last):
        ...
    ValueError: Conductivity cannot be negative
    >>> electric_conductivity(conductivity=50, electron_conc=-10, mobility=0)
    Traceback (most recent call last):
        ...
    ValueError: Electron concentration cannot be negative
    >>> electric_conductivity(conductivity=50, electron_conc=0, mobility=-10)
    Traceback (most recent call last):
        ...
    ValueError: mobility cannot be negative
    >>> electric_conductivity(conductivity=50, electron_conc=0, mobility=0)
    Traceback (most recent call last):
        ...
    ValueError: You cannot supply more or less than 2 values
    >>> electric_conductivity(conductivity=50, electron_conc=200, mobility=300)
    Traceback (most recent call last):
        ...
    ValueError: You cannot supply more or less than 2 values
    """
    if (conductivity, electron_conc, mobility).count(0) != 1:
        raise ValueError("You cannot supply more or less than 2 values")
    elif conductivity < 0:
        raise ValueError("Conductivity cannot be negative")
    elif electron_conc < 0:
        raise ValueError("Electron concentration cannot be negative")
    elif mobility < 0:
        raise ValueError("mobility cannot be negative")
    elif conductivity == 0:
        return (
            "conductivity",
            mobility * electron_conc * ELECTRON_CHARGE,
        )
    elif electron_conc == 0:
        return (
            "electron_conc",
            conductivity / (mobility * ELECTRON_CHARGE),
        )
    else:
        return (
            "mobility",
            conductivity / (electron_conc * ELECTRON_CHARGE),
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/electric_power.py
================================================
# https://en.m.wikipedia.org/wiki/Electric_power
from __future__ import annotations

from typing import NamedTuple


class Result(NamedTuple):
    name: str
    value: float


def electric_power(voltage: float, current: float, power: float) -> tuple:
    """
    This function can calculate any one of the three (voltage, current, power),
    fundamental value of electrical system.
    examples are below:
    >>> electric_power(voltage=0, current=2, power=5)
    Result(name='voltage', value=2.5)
    >>> electric_power(voltage=2, current=2, power=0)
    Result(name='power', value=4.0)
    >>> electric_power(voltage=-2, current=3, power=0)
    Result(name='power', value=6.0)
    >>> electric_power(voltage=2, current=4, power=2)
    Traceback (most recent call last):
        ...
    ValueError: Exactly one argument must be 0
    >>> electric_power(voltage=0, current=0, power=2)
    Traceback (most recent call last):
        ...
    ValueError: Exactly one argument must be 0
    >>> electric_power(voltage=0, current=2, power=-4)
    Traceback (most recent call last):
        ...
    ValueError: Power cannot be negative in any electrical/electronics system
    >>> electric_power(voltage=2.2, current=2.2, power=0)
    Result(name='power', value=4.84)
    >>> electric_power(current=0, power=6, voltage=2)
    Result(name='current', value=3.0)
    """
    if (voltage, current, power).count(0) != 1:
        raise ValueError("Exactly one argument must be 0")
    elif power < 0:
        raise ValueError(
            "Power cannot be negative in any electrical/electronics system"
        )
    elif voltage == 0:
        return Result("voltage", power / current)
    elif current == 0:
        return Result("current", power / voltage)
    elif power == 0:
        return Result("power", float(round(abs(voltage * current), 2)))
    else:
        raise AssertionError


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/electrical_impedance.py
================================================
"""Electrical impedance is the measure of the opposition that a
circuit presents to a current when a voltage is applied.
Impedance extends the concept of resistance to alternating current (AC) circuits.
Source: https://en.wikipedia.org/wiki/Electrical_impedance
"""

from __future__ import annotations

from math import pow, sqrt  # noqa: A004


def electrical_impedance(
    resistance: float, reactance: float, impedance: float
) -> dict[str, float]:
    """
    Apply Electrical Impedance formula, on any two given electrical values,
    which can be resistance, reactance, and impedance, and then in a Python dict
    return name/value pair of the zero value.

    >>> electrical_impedance(3,4,0)
    {'impedance': 5.0}
    >>> electrical_impedance(0,4,5)
    {'resistance': 3.0}
    >>> electrical_impedance(3,0,5)
    {'reactance': 4.0}
    >>> electrical_impedance(3,4,5)
    Traceback (most recent call last):
      ...
    ValueError: One and only one argument must be 0
    """
    if (resistance, reactance, impedance).count(0) != 1:
        raise ValueError("One and only one argument must be 0")
    if resistance == 0:
        return {"resistance": sqrt(pow(impedance, 2) - pow(reactance, 2))}
    elif reactance == 0:
        return {"reactance": sqrt(pow(impedance, 2) - pow(resistance, 2))}
    elif impedance == 0:
        return {"impedance": sqrt(pow(resistance, 2) + pow(reactance, 2))}
    else:
        raise ValueError("Exactly one argument must be 0")


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/ic_555_timer.py
================================================
from __future__ import annotations

"""
    Calculate the frequency and/or duty cycle of an astable 555 timer.
    * https://en.wikipedia.org/wiki/555_timer_IC#Astable

    These functions take in the value of the external resistances (in ohms)
    and capacitance (in Microfarad), and calculates the following:

    -------------------------------------
    | Freq = 1.44 /[( R1+ 2 x R2) x C1] |               ... in Hz
    -------------------------------------
    where Freq is the frequency,
          R1 is the first resistance in ohms,
          R2 is the second resistance in ohms,
          C1 is the capacitance in Microfarads.

    ------------------------------------------------
    | Duty Cycle = (R1 + R2) / (R1 + 2 x R2) x 100 |    ... in %
    ------------------------------------------------
    where R1 is the first resistance in ohms,
          R2 is the second resistance in ohms.
"""


def astable_frequency(
    resistance_1: float, resistance_2: float, capacitance: float
) -> float:
    """
    Usage examples:
    >>> astable_frequency(resistance_1=45, resistance_2=45, capacitance=7)
    1523.8095238095239
    >>> astable_frequency(resistance_1=356, resistance_2=234, capacitance=976)
    1.7905459175553078
    >>> astable_frequency(resistance_1=2, resistance_2=-1, capacitance=2)
    Traceback (most recent call last):
        ...
    ValueError: All values must be positive
    >>> astable_frequency(resistance_1=45, resistance_2=45, capacitance=0)
    Traceback (most recent call last):
        ...
    ValueError: All values must be positive
    """

    if resistance_1 <= 0 or resistance_2 <= 0 or capacitance <= 0:
        raise ValueError("All values must be positive")
    return (1.44 / ((resistance_1 + 2 * resistance_2) * capacitance)) * 10**6


def astable_duty_cycle(resistance_1: float, resistance_2: float) -> float:
    """
    Usage examples:
    >>> astable_duty_cycle(resistance_1=45, resistance_2=45)
    66.66666666666666
    >>> astable_duty_cycle(resistance_1=356, resistance_2=234)
    71.60194174757282
    >>> astable_duty_cycle(resistance_1=2, resistance_2=-1)
    Traceback (most recent call last):
        ...
    ValueError: All values must be positive
    >>> astable_duty_cycle(resistance_1=0, resistance_2=0)
    Traceback (most recent call last):
        ...
    ValueError: All values must be positive
    """

    if resistance_1 <= 0 or resistance_2 <= 0:
        raise ValueError("All values must be positive")
    return (resistance_1 + resistance_2) / (resistance_1 + 2 * resistance_2) * 100


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/ind_reactance.py
================================================
# https://en.wikipedia.org/wiki/Electrical_reactance#Inductive_reactance
from __future__ import annotations

from math import pi


def ind_reactance(
    inductance: float, frequency: float, reactance: float
) -> dict[str, float]:
    """
    Calculate inductive reactance, frequency or inductance from two given electrical
    properties then return name/value pair of the zero value in a Python dict.

    Parameters
    ----------
    inductance : float with units in Henries

    frequency : float with units in Hertz

    reactance : float with units in Ohms

    >>> ind_reactance(-35e-6, 1e3, 0)
    Traceback (most recent call last):
        ...
    ValueError: Inductance cannot be negative

    >>> ind_reactance(35e-6, -1e3, 0)
    Traceback (most recent call last):
        ...
    ValueError: Frequency cannot be negative

    >>> ind_reactance(35e-6, 0, -1)
    Traceback (most recent call last):
        ...
    ValueError: Inductive reactance cannot be negative

    >>> ind_reactance(0, 10e3, 50)
    {'inductance': 0.0007957747154594767}

    >>> ind_reactance(35e-3, 0, 50)
    {'frequency': 227.36420441699332}

    >>> ind_reactance(35e-6, 1e3, 0)
    {'reactance': 0.2199114857512855}

    """

    if (inductance, frequency, reactance).count(0) != 1:
        raise ValueError("One and only one argument must be 0")
    if inductance < 0:
        raise ValueError("Inductance cannot be negative")
    if frequency < 0:
        raise ValueError("Frequency cannot be negative")
    if reactance < 0:
        raise ValueError("Inductive reactance cannot be negative")
    if inductance == 0:
        return {"inductance": reactance / (2 * pi * frequency)}
    elif frequency == 0:
        return {"frequency": reactance / (2 * pi * inductance)}
    elif reactance == 0:
        return {"reactance": 2 * pi * frequency * inductance}
    else:
        raise ValueError("Exactly one argument must be 0")


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/ohms_law.py
================================================
# https://en.wikipedia.org/wiki/Ohm%27s_law
from __future__ import annotations


def ohms_law(voltage: float, current: float, resistance: float) -> dict[str, float]:
    """
    Apply Ohm's Law, on any two given electrical values, which can be voltage, current,
    and resistance, and then in a Python dict return name/value pair of the zero value.

    >>> ohms_law(voltage=10, resistance=5, current=0)
    {'current': 2.0}
    >>> ohms_law(voltage=0, current=0, resistance=10)
    Traceback (most recent call last):
      ...
    ValueError: One and only one argument must be 0
    >>> ohms_law(voltage=0, current=1, resistance=-2)
    Traceback (most recent call last):
      ...
    ValueError: Resistance cannot be negative
    >>> ohms_law(resistance=0, voltage=-10, current=1)
    {'resistance': -10.0}
    >>> ohms_law(voltage=0, current=-1.5, resistance=2)
    {'voltage': -3.0}
    """
    if (voltage, current, resistance).count(0) != 1:
        raise ValueError("One and only one argument must be 0")
    if resistance < 0:
        raise ValueError("Resistance cannot be negative")
    if voltage == 0:
        return {"voltage": float(current * resistance)}
    elif current == 0:
        return {"current": voltage / resistance}
    elif resistance == 0:
        return {"resistance": voltage / current}
    else:
        raise ValueError("Exactly one argument must be 0")


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/real_and_reactive_power.py
================================================
import math


def real_power(apparent_power: float, power_factor: float) -> float:
    """
    Calculate real power from apparent power and power factor.

    Examples:
    >>> real_power(100, 0.9)
    90.0
    >>> real_power(0, 0.8)
    0.0
    >>> real_power(100, -0.9)
    -90.0
    """
    if (
        not isinstance(power_factor, (int, float))
        or power_factor < -1
        or power_factor > 1
    ):
        raise ValueError("power_factor must be a valid float value between -1 and 1.")
    return apparent_power * power_factor


def reactive_power(apparent_power: float, power_factor: float) -> float:
    """
    Calculate reactive power from apparent power and power factor.

    Examples:
    >>> reactive_power(100, 0.9)
    43.58898943540673
    >>> reactive_power(0, 0.8)
    0.0
    >>> reactive_power(100, -0.9)
    43.58898943540673
    """
    if (
        not isinstance(power_factor, (int, float))
        or power_factor < -1
        or power_factor > 1
    ):
        raise ValueError("power_factor must be a valid float value between -1 and 1.")
    return apparent_power * math.sqrt(1 - power_factor**2)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/resistor_color_code.py
================================================
"""
Title : Calculating the resistance of a n band resistor using the color codes

Description :
    Resistors resist the flow of electrical current.Each one has a value that tells how
    strongly it resists current flow.This value's unit is the ohm, often noted with the
    Greek letter omega: Ω.

    The colored bands on a resistor can tell you everything you need to know about its
    value and tolerance, as long as you understand how to read them. The order in which
    the colors are arranged is very important, and each value of resistor has its own
    unique combination.

    The color coding for resistors is an international standard that is defined in IEC
    60062.

    The number of bands present in a resistor varies from three to six. These represent
    significant figures, multiplier, tolerance, reliability, and temperature coefficient
    Each color used for a type of band has a value assigned to it. It is read from left
    to right.
    All resistors will have significant figures and multiplier bands. In a three band
    resistor first two bands from the left represent significant figures and the third
    represents the multiplier band.

    Significant figures - The number of significant figures band in a resistor can vary
    from two to three.
    Colors and values associated with significant figure bands -
    (Black = 0, Brown = 1, Red = 2, Orange = 3, Yellow = 4, Green = 5, Blue = 6,
    Violet = 7, Grey = 8, White = 9)

    Multiplier - There will be one multiplier band in a resistor. It is multiplied with
    the significant figures obtained from previous bands.
    Colors and values associated with multiplier band -
    (Black = 100, Brown = 10^1, Red = 10^2, Orange = 10^3, Yellow = 10^4, Green = 10^5,
    Blue = 10^6, Violet = 10^7, Grey = 10^8, White = 10^9, Gold = 10^-1, Silver = 10^-2)
    Note that multiplier bands use Gold and Silver which are not used for significant
    figure bands.

    Tolerance - The tolerance band is not always present. It can be seen in four band
    resistors and above. This is a percentage by which the resistor value can vary.
    Colors and values associated with tolerance band -
    (Brown = 1%, Red = 2%, Orange = 0.05%, Yellow = 0.02%, Green = 0.5%,Blue = 0.25%,
    Violet = 0.1%, Grey = 0.01%, Gold = 5%, Silver = 10%)
    If no color is mentioned then by default tolerance is 20%
    Note that tolerance band does not use Black and White colors.

    Temperature Coeffecient - Indicates the change in resistance of the component as
    a function of ambient temperature in terms of ppm/K.
    It is present in six band resistors.
    Colors and values associated with Temperature coeffecient -
    (Black = 250 ppm/K, Brown = 100 ppm/K, Red = 50 ppm/K, Orange = 15 ppm/K,
    Yellow = 25 ppm/K, Green = 20 ppm/K, Blue = 10 ppm/K, Violet = 5 ppm/K,
    Grey = 1 ppm/K)
    Note that temperature coeffecient band does not use White, Gold, Silver colors.

Sources :
    https://www.calculator.net/resistor-calculator.html
    https://learn.parallax.com/support/reference/resistor-color-codes
    https://byjus.com/physics/resistor-colour-codes/
"""

valid_colors: list = [
    "Black",
    "Brown",
    "Red",
    "Orange",
    "Yellow",
    "Green",
    "Blue",
    "Violet",
    "Grey",
    "White",
    "Gold",
    "Silver",
]

significant_figures_color_values: dict[str, int] = {
    "Black": 0,
    "Brown": 1,
    "Red": 2,
    "Orange": 3,
    "Yellow": 4,
    "Green": 5,
    "Blue": 6,
    "Violet": 7,
    "Grey": 8,
    "White": 9,
}

multiplier_color_values: dict[str, float] = {
    "Black": 10**0,
    "Brown": 10**1,
    "Red": 10**2,
    "Orange": 10**3,
    "Yellow": 10**4,
    "Green": 10**5,
    "Blue": 10**6,
    "Violet": 10**7,
    "Grey": 10**8,
    "White": 10**9,
    "Gold": 10**-1,
    "Silver": 10**-2,
}

tolerance_color_values: dict[str, float] = {
    "Brown": 1,
    "Red": 2,
    "Orange": 0.05,
    "Yellow": 0.02,
    "Green": 0.5,
    "Blue": 0.25,
    "Violet": 0.1,
    "Grey": 0.01,
    "Gold": 5,
    "Silver": 10,
}

temperature_coeffecient_color_values: dict[str, int] = {
    "Black": 250,
    "Brown": 100,
    "Red": 50,
    "Orange": 15,
    "Yellow": 25,
    "Green": 20,
    "Blue": 10,
    "Violet": 5,
    "Grey": 1,
}

band_types: dict[int, dict[str, int]] = {
    3: {"significant": 2, "multiplier": 1},
    4: {"significant": 2, "multiplier": 1, "tolerance": 1},
    5: {"significant": 3, "multiplier": 1, "tolerance": 1},
    6: {"significant": 3, "multiplier": 1, "tolerance": 1, "temp_coeffecient": 1},
}


def get_significant_digits(colors: list) -> str:
    """
    Function returns the digit associated with the color. Function takes a
    list containing colors as input and returns digits as string

    >>> get_significant_digits(['Black','Blue'])
    '06'

    >>> get_significant_digits(['Aqua','Blue'])
    Traceback (most recent call last):
      ...
    ValueError: Aqua is not a valid color for significant figure bands

    """
    digit = ""
    for color in colors:
        if color not in significant_figures_color_values:
            msg = f"{color} is not a valid color for significant figure bands"
            raise ValueError(msg)
        digit = digit + str(significant_figures_color_values[color])
    return str(digit)


def get_multiplier(color: str) -> float:
    """
    Function returns the multiplier value associated with the color.
    Function takes color as input and returns multiplier value

    >>> get_multiplier('Gold')
    0.1

    >>> get_multiplier('Ivory')
    Traceback (most recent call last):
      ...
    ValueError: Ivory is not a valid color for multiplier band

    """
    if color not in multiplier_color_values:
        msg = f"{color} is not a valid color for multiplier band"
        raise ValueError(msg)
    return multiplier_color_values[color]


def get_tolerance(color: str) -> float:
    """
    Function returns the tolerance value associated with the color.
    Function takes color as input and returns tolerance value.

    >>> get_tolerance('Green')
    0.5

    >>> get_tolerance('Indigo')
    Traceback (most recent call last):
      ...
    ValueError: Indigo is not a valid color for tolerance band

    """
    if color not in tolerance_color_values:
        msg = f"{color} is not a valid color for tolerance band"
        raise ValueError(msg)
    return tolerance_color_values[color]


def get_temperature_coeffecient(color: str) -> int:
    """
    Function returns the temperature coeffecient value associated with the color.
    Function takes color as input and returns temperature coeffecient value.

    >>> get_temperature_coeffecient('Yellow')
    25

    >>> get_temperature_coeffecient('Cyan')
    Traceback (most recent call last):
      ...
    ValueError: Cyan is not a valid color for temperature coeffecient band

    """
    if color not in temperature_coeffecient_color_values:
        msg = f"{color} is not a valid color for temperature coeffecient band"
        raise ValueError(msg)
    return temperature_coeffecient_color_values[color]


def get_band_type_count(total_number_of_bands: int, type_of_band: str) -> int:
    """
    Function returns the number of bands of a given type in a resistor with n bands
    Function takes total_number_of_bands and type_of_band as input and returns
    number of bands belonging to that type in the given resistor

    >>> get_band_type_count(3,'significant')
    2

    >>> get_band_type_count(2,'significant')
    Traceback (most recent call last):
      ...
    ValueError: 2 is not a valid number of bands

    >>> get_band_type_count(3,'sign')
    Traceback (most recent call last):
      ...
    ValueError: sign is not valid for a 3 band resistor

    >>> get_band_type_count(3,'tolerance')
    Traceback (most recent call last):
      ...
    ValueError: tolerance is not valid for a 3 band resistor

    >>> get_band_type_count(5,'temp_coeffecient')
    Traceback (most recent call last):
      ...
    ValueError: temp_coeffecient is not valid for a 5 band resistor

    """
    if total_number_of_bands not in band_types:
        msg = f"{total_number_of_bands} is not a valid number of bands"
        raise ValueError(msg)
    if type_of_band not in band_types[total_number_of_bands]:
        msg = f"{type_of_band} is not valid for a {total_number_of_bands} band resistor"
        raise ValueError(msg)
    return band_types[total_number_of_bands][type_of_band]


def check_validity(number_of_bands: int, colors: list) -> bool:
    """
    Function checks if the input provided is valid or not.
    Function takes number_of_bands and colors as input and returns
    True if it is valid

    >>> check_validity(3, ["Black","Blue","Orange"])
    True

    >>> check_validity(4, ["Black","Blue","Orange"])
    Traceback (most recent call last):
      ...
    ValueError: Expecting 4 colors, provided 3 colors

    >>> check_validity(3, ["Cyan","Red","Yellow"])
    Traceback (most recent call last):
      ...
    ValueError: Cyan is not a valid color

    """
    if number_of_bands >= 3 and number_of_bands <= 6:
        if number_of_bands == len(colors):
            for color in colors:
                if color not in valid_colors:
                    msg = f"{color} is not a valid color"
                    raise ValueError(msg)
            return True
        else:
            msg = f"Expecting {number_of_bands} colors, provided {len(colors)} colors"
            raise ValueError(msg)
    else:
        msg = "Invalid number of bands. Resistor bands must be 3 to 6"
        raise ValueError(msg)


def calculate_resistance(number_of_bands: int, color_code_list: list) -> dict:
    """
    Function calculates the total resistance of the resistor using the color codes.
    Function takes number_of_bands, color_code_list as input and returns
    resistance

    >>> calculate_resistance(3, ["Black","Blue","Orange"])
    {'resistance': '6000Ω ±20% '}

    >>> calculate_resistance(4, ["Orange","Green","Blue","Gold"])
    {'resistance': '35000000Ω ±5% '}

    >>> calculate_resistance(5, ["Violet","Brown","Grey","Silver","Green"])
    {'resistance': '7.18Ω ±0.5% '}

    >>> calculate_resistance(6, ["Red","Green","Blue","Yellow","Orange","Grey"])
    {'resistance': '2560000Ω ±0.05% 1 ppm/K'}

    >>> calculate_resistance(0, ["Violet","Brown","Grey","Silver","Green"])
    Traceback (most recent call last):
      ...
    ValueError: Invalid number of bands. Resistor bands must be 3 to 6

    >>> calculate_resistance(4, ["Violet","Brown","Grey","Silver","Green"])
    Traceback (most recent call last):
      ...
    ValueError: Expecting 4 colors, provided 5 colors

    >>> calculate_resistance(4, ["Violet","Silver","Brown","Grey"])
    Traceback (most recent call last):
      ...
    ValueError: Silver is not a valid color for significant figure bands

    >>> calculate_resistance(4, ["Violet","Blue","Lime","Grey"])
    Traceback (most recent call last):
      ...
    ValueError: Lime is not a valid color

    """
    is_valid = check_validity(number_of_bands, color_code_list)
    if is_valid:
        number_of_significant_bands = get_band_type_count(
            number_of_bands, "significant"
        )
        significant_colors = color_code_list[:number_of_significant_bands]
        significant_digits = int(get_significant_digits(significant_colors))
        multiplier_color = color_code_list[number_of_significant_bands]
        multiplier = get_multiplier(multiplier_color)
        if number_of_bands == 3:
            tolerance_color = None
        else:
            tolerance_color = color_code_list[number_of_significant_bands + 1]
        tolerance = (
            20 if tolerance_color is None else get_tolerance(str(tolerance_color))
        )
        if number_of_bands != 6:
            temperature_coeffecient_color = None
        else:
            temperature_coeffecient_color = color_code_list[
                number_of_significant_bands + 2
            ]
        temperature_coeffecient = (
            0
            if temperature_coeffecient_color is None
            else get_temperature_coeffecient(str(temperature_coeffecient_color))
        )
        resisitance = significant_digits * multiplier
        if temperature_coeffecient == 0:
            answer = f"{resisitance}Ω ±{tolerance}% "
        else:
            answer = f"{resisitance}Ω ±{tolerance}% {temperature_coeffecient} ppm/K"
        return {"resistance": answer}
    else:
        raise ValueError("Input is invalid")


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/resistor_equivalence.py
================================================
# https://byjus.com/equivalent-resistance-formula/

from __future__ import annotations


def resistor_parallel(resistors: list[float]) -> float:
    """
    Req = 1/ (1/R1 + 1/R2 + ... + 1/Rn)

    >>> resistor_parallel([3.21389, 2, 3])
    0.8737571620498019
    >>> resistor_parallel([3.21389, 2, -3])
    Traceback (most recent call last):
        ...
    ValueError: Resistor at index 2 has a negative or zero value!
    >>> resistor_parallel([3.21389, 2, 0.000])
    Traceback (most recent call last):
        ...
    ValueError: Resistor at index 2 has a negative or zero value!
    """

    first_sum = 0.00
    for index, resistor in enumerate(resistors):
        if resistor <= 0:
            msg = f"Resistor at index {index} has a negative or zero value!"
            raise ValueError(msg)
        first_sum += 1 / float(resistor)
    return 1 / first_sum


def resistor_series(resistors: list[float]) -> float:
    """
    Req = R1 + R2 + ... + Rn

    Calculate the equivalent resistance for any number of resistors in parallel.

    >>> resistor_series([3.21389, 2, 3])
    8.21389
    >>> resistor_series([3.21389, 2, -3])
    Traceback (most recent call last):
        ...
    ValueError: Resistor at index 2 has a negative value!
    """
    sum_r = 0.00
    for index, resistor in enumerate(resistors):
        sum_r += resistor
        if resistor < 0:
            msg = f"Resistor at index {index} has a negative value!"
            raise ValueError(msg)
    return sum_r


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/resonant_frequency.py
================================================
# https://en.wikipedia.org/wiki/LC_circuit

"""An LC circuit, also called a resonant circuit, tank circuit, or tuned circuit,
is an electric circuit consisting of an inductor, represented by the letter L,
and a capacitor, represented by the letter C, connected together.
The circuit can act as an electrical resonator, an electrical analogue of a
tuning fork, storing energy oscillating at the circuit's resonant frequency.
Source: https://en.wikipedia.org/wiki/LC_circuit
"""

from __future__ import annotations

from math import pi, sqrt


def resonant_frequency(inductance: float, capacitance: float) -> tuple:
    """
    This function can calculate the resonant frequency of LC circuit,
    for the given value of inductance and capacitnace.

    Examples are given below:
    >>> resonant_frequency(inductance=10, capacitance=5)
    ('Resonant frequency', 0.022507907903927652)
    >>> resonant_frequency(inductance=0, capacitance=5)
    Traceback (most recent call last):
      ...
    ValueError: Inductance cannot be 0 or negative
    >>> resonant_frequency(inductance=10, capacitance=0)
    Traceback (most recent call last):
      ...
    ValueError: Capacitance cannot be 0 or negative
    """

    if inductance <= 0:
        raise ValueError("Inductance cannot be 0 or negative")

    elif capacitance <= 0:
        raise ValueError("Capacitance cannot be 0 or negative")

    else:
        return (
            "Resonant frequency",
            float(1 / (2 * pi * (sqrt(inductance * capacitance)))),
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: electronics/wheatstone_bridge.py
================================================
# https://en.wikipedia.org/wiki/Wheatstone_bridge
from __future__ import annotations


def wheatstone_solver(
    resistance_1: float, resistance_2: float, resistance_3: float
) -> float:
    """
    This function can calculate the unknown resistance in an wheatstone network,
    given that the three other resistances in the network are known.
    The formula to calculate the same is:

    ---------------
    |Rx=(R2/R1)*R3|
    ---------------

    Usage examples:
    >>> wheatstone_solver(resistance_1=2, resistance_2=4, resistance_3=5)
    10.0
    >>> wheatstone_solver(resistance_1=356, resistance_2=234, resistance_3=976)
    641.5280898876405
    >>> wheatstone_solver(resistance_1=2, resistance_2=-1, resistance_3=2)
    Traceback (most recent call last):
        ...
    ValueError: All resistance values must be positive
    >>> wheatstone_solver(resistance_1=0, resistance_2=0, resistance_3=2)
    Traceback (most recent call last):
        ...
    ValueError: All resistance values must be positive
    """

    if resistance_1 <= 0 or resistance_2 <= 0 or resistance_3 <= 0:
        raise ValueError("All resistance values must be positive")
    else:
        return float((resistance_2 / resistance_1) * resistance_3)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: file_transfer/__init__.py
================================================


================================================
FILE: file_transfer/mytext.txt
================================================
Hello
This is sample data
«küßî»
“ЌύБЇ”
😀😉
😋


================================================
FILE: file_transfer/receive_file.py
================================================
import socket


def main():
    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
    host = socket.gethostname()
    port = 12312

    sock.connect((host, port))
    sock.send(b"Hello server!")

    with open("Received_file", "wb") as out_file:
        print("File opened")
        print("Receiving data...")
        while True:
            data = sock.recv(1024)
            if not data:
                break
            out_file.write(data)

    print("Successfully received the file")
    sock.close()
    print("Connection closed")


if __name__ == "__main__":
    main()


================================================
FILE: file_transfer/send_file.py
================================================
def send_file(filename: str = "mytext.txt", testing: bool = False) -> None:
    import socket

    port = 12312  # Reserve a port for your service.
    sock = socket.socket()  # Create a socket object
    host = socket.gethostname()  # Get local machine name
    sock.bind((host, port))  # Bind to the port
    sock.listen(5)  # Now wait for client connection.

    print("Server listening....")

    while True:
        conn, addr = sock.accept()  # Establish connection with client.
        print(f"Got connection from {addr}")
        data = conn.recv(1024)
        print(f"Server received: {data = }")

        with open(filename, "rb") as in_file:
            data = in_file.read(1024)
            while data:
                conn.send(data)
                print(f"Sent {data!r}")
                data = in_file.read(1024)

        print("Done sending")
        conn.close()
        if testing:  # Allow the test to complete
            break

    sock.shutdown(1)
    sock.close()


if __name__ == "__main__":
    send_file()


================================================
FILE: file_transfer/tests/__init__.py
================================================


================================================
FILE: file_transfer/tests/test_send_file.py
================================================
from unittest.mock import Mock, patch

from file_transfer.send_file import send_file


@patch("socket.socket")
@patch("builtins.open")
def test_send_file_running_as_expected(file, sock):
    # ===== initialization =====
    conn = Mock()
    sock.return_value.accept.return_value = conn, Mock()
    f = iter([1, None])
    file.return_value.__enter__.return_value.read.side_effect = lambda _: next(f)

    # ===== invoke =====
    send_file(filename="mytext.txt", testing=True)

    # ===== ensurance =====
    sock.assert_called_once()
    sock.return_value.bind.assert_called_once()
    sock.return_value.listen.assert_called_once()
    sock.return_value.accept.assert_called_once()
    conn.recv.assert_called_once()

    file.return_value.__enter__.assert_called_once()
    file.return_value.__enter__.return_value.read.assert_called()

    conn.send.assert_called_once()
    conn.close.assert_called_once()
    sock.return_value.shutdown.assert_called_once()
    sock.return_value.close.assert_called_once()


================================================
FILE: financial/README.md
================================================
# Interest

* Compound Interest: "Compound interest is calculated by multiplying the initial principal amount by one plus the annual interest rate raised to the number of compound periods minus one." [Compound Interest](https://www.investopedia.com/)
* Simple Interest: "Simple interest paid or received over a certain period is a fixed percentage of the principal amount that was borrowed or lent. " [Simple Interest](https://www.investopedia.com/)


================================================
FILE: financial/__init__.py
================================================


================================================
FILE: financial/equated_monthly_installments.py
================================================
"""
Program to calculate the amortization amount per month, given
- Principal borrowed
- Rate of interest per annum
- Years to repay the loan

Wikipedia Reference: https://en.wikipedia.org/wiki/Equated_monthly_installment
"""


def equated_monthly_installments(
    principal: float, rate_per_annum: float, years_to_repay: int
) -> float:
    """
    Formula for amortization amount per month:
    A = p * r * (1 + r)^n / ((1 + r)^n - 1)
    where p is the principal, r is the rate of interest per month
    and n is the number of payments

    >>> equated_monthly_installments(25000, 0.12, 3)
    830.3577453212793
    >>> equated_monthly_installments(25000, 0.12, 10)
    358.67737100646826
    >>> equated_monthly_installments(0, 0.12, 3)
    Traceback (most recent call last):
        ...
    Exception: Principal borrowed must be > 0
    >>> equated_monthly_installments(25000, -1, 3)
    Traceback (most recent call last):
        ...
    Exception: Rate of interest must be >= 0
    >>> equated_monthly_installments(25000, 0.12, 0)
    Traceback (most recent call last):
        ...
    Exception: Years to repay must be an integer > 0
    """
    if principal <= 0:
        raise Exception("Principal borrowed must be > 0")
    if rate_per_annum < 0:
        raise Exception("Rate of interest must be >= 0")
    if years_to_repay <= 0 or not isinstance(years_to_repay, int):
        raise Exception("Years to repay must be an integer > 0")

    # Yearly rate is divided by 12 to get monthly rate
    rate_per_month = rate_per_annum / 12

    # Years to repay is multiplied by 12 to get number of payments as payment is monthly
    number_of_payments = years_to_repay * 12

    return (
        principal
        * rate_per_month
        * (1 + rate_per_month) ** number_of_payments
        / ((1 + rate_per_month) ** number_of_payments - 1)
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: financial/exponential_moving_average.py
================================================
"""
Calculate the exponential moving average (EMA) on the series of stock prices.
Wikipedia Reference: https://en.wikipedia.org/wiki/Exponential_smoothing
https://www.investopedia.com/terms/e/ema.asp#toc-what-is-an-exponential
-moving-average-ema

Exponential moving average is used in finance to analyze changes stock prices.
EMA is used in conjunction with Simple moving average (SMA), EMA reacts to the
changes in the value quicker than SMA, which is one of the advantages of using EMA.
"""

from collections.abc import Iterator


def exponential_moving_average(
    stock_prices: Iterator[float], window_size: int
) -> Iterator[float]:
    """
    Yields exponential moving averages of the given stock prices.
    >>> tuple(exponential_moving_average(iter([2, 5, 3, 8.2, 6, 9, 10]), 3))
    (2, 3.5, 3.25, 5.725, 5.8625, 7.43125, 8.715625)

    :param stock_prices: A stream of stock prices
    :param window_size: The number of stock prices that will trigger a new calculation
                        of the exponential average (window_size > 0)
    :return: Yields a sequence of exponential moving averages

    Formula:

    st = alpha * xt + (1 - alpha) * st_prev

    Where,
    st : Exponential moving average at timestamp t
    xt : stock price in from the stock prices at timestamp t
    st_prev : Exponential moving average at timestamp t-1
    alpha : 2/(1 + window_size) - smoothing factor

    Exponential moving average (EMA) is a rule of thumb technique for
    smoothing time series data using an exponential window function.
    """

    if window_size <= 0:
        raise ValueError("window_size must be > 0")

    # Calculating smoothing factor
    alpha = 2 / (1 + window_size)

    # Exponential average at timestamp t
    moving_average = 0.0

    for i, stock_price in enumerate(stock_prices):
        if i <= window_size:
            # Assigning simple moving average till the window_size for the first time
            # is reached
            moving_average = (moving_average + stock_price) * 0.5 if i else stock_price
        else:
            # Calculating exponential moving average based on current timestamp data
            # point and previous exponential average value
            moving_average = (alpha * stock_price) + ((1 - alpha) * moving_average)
        yield moving_average


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    stock_prices = [2.0, 5, 3, 8.2, 6, 9, 10]
    window_size = 3
    result = tuple(exponential_moving_average(iter(stock_prices), window_size))
    print(f"{stock_prices = }")
    print(f"{window_size = }")
    print(f"{result = }")


================================================
FILE: financial/interest.py
================================================
# https://www.investopedia.com

from __future__ import annotations


def simple_interest(
    principal: float, daily_interest_rate: float, days_between_payments: float
) -> float:
    """
    >>> simple_interest(18000.0, 0.06, 3)
    3240.0
    >>> simple_interest(0.5, 0.06, 3)
    0.09
    >>> simple_interest(18000.0, 0.01, 10)
    1800.0
    >>> simple_interest(18000.0, 0.0, 3)
    0.0
    >>> simple_interest(5500.0, 0.01, 100)
    5500.0
    >>> simple_interest(10000.0, -0.06, 3)
    Traceback (most recent call last):
        ...
    ValueError: daily_interest_rate must be >= 0
    >>> simple_interest(-10000.0, 0.06, 3)
    Traceback (most recent call last):
        ...
    ValueError: principal must be > 0
    >>> simple_interest(5500.0, 0.01, -5)
    Traceback (most recent call last):
        ...
    ValueError: days_between_payments must be > 0
    """
    if days_between_payments <= 0:
        raise ValueError("days_between_payments must be > 0")
    if daily_interest_rate < 0:
        raise ValueError("daily_interest_rate must be >= 0")
    if principal <= 0:
        raise ValueError("principal must be > 0")
    return principal * daily_interest_rate * days_between_payments


def compound_interest(
    principal: float,
    nominal_annual_interest_rate_percentage: float,
    number_of_compounding_periods: float,
) -> float:
    """
    >>> compound_interest(10000.0, 0.05, 3)
    1576.2500000000014
    >>> compound_interest(10000.0, 0.05, 1)
    500.00000000000045
    >>> compound_interest(0.5, 0.05, 3)
    0.07881250000000006
    >>> compound_interest(10000.0, 0.06, -4)
    Traceback (most recent call last):
        ...
    ValueError: number_of_compounding_periods must be > 0
    >>> compound_interest(10000.0, -3.5, 3.0)
    Traceback (most recent call last):
        ...
    ValueError: nominal_annual_interest_rate_percentage must be >= 0
    >>> compound_interest(-5500.0, 0.01, 5)
    Traceback (most recent call last):
        ...
    ValueError: principal must be > 0
    """
    if number_of_compounding_periods <= 0:
        raise ValueError("number_of_compounding_periods must be > 0")
    if nominal_annual_interest_rate_percentage < 0:
        raise ValueError("nominal_annual_interest_rate_percentage must be >= 0")
    if principal <= 0:
        raise ValueError("principal must be > 0")

    return principal * (
        (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
        - 1
    )


def apr_interest(
    principal: float,
    nominal_annual_percentage_rate: float,
    number_of_years: float,
) -> float:
    """
    >>> apr_interest(10000.0, 0.05, 3)
    1618.223072263547
    >>> apr_interest(10000.0, 0.05, 1)
    512.6749646744732
    >>> apr_interest(0.5, 0.05, 3)
    0.08091115361317736
    >>> apr_interest(10000.0, 0.06, -4)
    Traceback (most recent call last):
        ...
    ValueError: number_of_years must be > 0
    >>> apr_interest(10000.0, -3.5, 3.0)
    Traceback (most recent call last):
        ...
    ValueError: nominal_annual_percentage_rate must be >= 0
    >>> apr_interest(-5500.0, 0.01, 5)
    Traceback (most recent call last):
        ...
    ValueError: principal must be > 0
    """
    if number_of_years <= 0:
        raise ValueError("number_of_years must be > 0")
    if nominal_annual_percentage_rate < 0:
        raise ValueError("nominal_annual_percentage_rate must be >= 0")
    if principal <= 0:
        raise ValueError("principal must be > 0")

    return compound_interest(
        principal, nominal_annual_percentage_rate / 365, number_of_years * 365
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: financial/present_value.py
================================================
"""
Reference: https://www.investopedia.com/terms/p/presentvalue.asp

An algorithm that calculates the present value of a stream of yearly cash flows given...
1. The discount rate (as a decimal, not a percent)
2. An array of cash flows, with the index of the cash flow being the associated year

Note: This algorithm assumes that cash flows are paid at the end of the specified year
"""


def present_value(discount_rate: float, cash_flows: list[float]) -> float:
    """
    >>> present_value(0.13, [10, 20.70, -293, 297])
    4.69
    >>> present_value(0.07, [-109129.39, 30923.23, 15098.93, 29734,39])
    -42739.63
    >>> present_value(0.07, [109129.39, 30923.23, 15098.93, 29734,39])
    175519.15
    >>> present_value(-1, [109129.39, 30923.23, 15098.93, 29734,39])
    Traceback (most recent call last):
        ...
    ValueError: Discount rate cannot be negative
    >>> present_value(0.03, [])
    Traceback (most recent call last):
        ...
    ValueError: Cash flows list cannot be empty
    """
    if discount_rate < 0:
        raise ValueError("Discount rate cannot be negative")
    if not cash_flows:
        raise ValueError("Cash flows list cannot be empty")
    present_value = sum(
        cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(cash_flows)
    )
    return round(present_value, ndigits=2)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: financial/price_plus_tax.py
================================================
"""
Calculate price plus tax of a good or service given its price and a tax rate.
"""


def price_plus_tax(price: float, tax_rate: float) -> float:
    """
    >>> price_plus_tax(100, 0.25)
    125.0
    >>> price_plus_tax(125.50, 0.05)
    131.775
    """
    return price * (1 + tax_rate)


if __name__ == "__main__":
    print(f"{price_plus_tax(100, 0.25) = }")
    print(f"{price_plus_tax(125.50, 0.05) = }")


================================================
FILE: financial/simple_moving_average.py
================================================
"""
The Simple Moving Average (SMA) is a statistical calculation used to analyze data points
by creating a constantly updated average price over a specific time period.
In finance, SMA is often used in time series analysis to smooth out price data
and identify trends.

Reference: https://en.wikipedia.org/wiki/Moving_average
"""

from collections.abc import Sequence


def simple_moving_average(
    data: Sequence[float], window_size: int
) -> list[float | None]:
    """
    Calculate the simple moving average (SMA) for some given time series data.

    :param data: A list of numerical data points.
    :param window_size: An integer representing the size of the SMA window.
    :return: A list of SMA values with the same length as the input data.

    Examples:
    >>> sma = simple_moving_average([10, 12, 15, 13, 14, 16, 18, 17, 19, 21], 3)
    >>> [round(value, 2) if value is not None else None for value in sma]
    [None, None, 12.33, 13.33, 14.0, 14.33, 16.0, 17.0, 18.0, 19.0]
    >>> simple_moving_average([10, 12, 15], 5)
    [None, None, None]
    >>> simple_moving_average([10, 12, 15, 13, 14, 16, 18, 17, 19, 21], 0)
    Traceback (most recent call last):
    ...
    ValueError: Window size must be a positive integer
    """
    if window_size < 1:
        raise ValueError("Window size must be a positive integer")

    sma: list[float | None] = []

    for i in range(len(data)):
        if i < window_size - 1:
            sma.append(None)  # SMA not available for early data points
        else:
            window = data[i - window_size + 1 : i + 1]
            sma_value = sum(window) / window_size
            sma.append(sma_value)
    return sma


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Example data (replace with your own time series data)
    data = [10, 12, 15, 13, 14, 16, 18, 17, 19, 21]

    # Specify the window size for the SMA
    window_size = 3

    # Calculate the Simple Moving Average
    sma_values = simple_moving_average(data, window_size)

    # Print the SMA values
    print("Simple Moving Average (SMA) Values:")
    for i, value in enumerate(sma_values):
        if value is not None:
            print(f"Day {i + 1}: {value:.2f}")
        else:
            print(f"Day {i + 1}: Not enough data for SMA")


================================================
FILE: financial/straight_line_depreciation.py
================================================
"""
In accounting, depreciation refers to the decreases in the value
of a fixed asset during the asset's useful life.
When an organization purchases a fixed asset,
the purchase expenditure is not recognized as an expense immediately.
Instead, the decreases in the asset's value are recognized as expenses
over the years during which the asset is used.

The following methods are widely used
for depreciation calculation in accounting:
- Straight-line method
- Diminishing balance method
- Units-of-production method

The straight-line method is the simplest and most widely used.
This method calculates depreciation by spreading the cost evenly
over the asset's useful life.

The following formula shows how to calculate the yearly depreciation expense:

- annual depreciation expense =
    (purchase cost of asset - residual value) / useful life of asset(years)

Further information on:
https://en.wikipedia.org/wiki/Depreciation

The function, straight_line_depreciation, returns a list of
the depreciation expenses over the given period.
"""


def straight_line_depreciation(
    useful_years: int,
    purchase_value: float,
    residual_value: float = 0.0,
) -> list[float]:
    """
    Calculate the depreciation expenses over the given period
    :param useful_years: Number of years the asset will be used
    :param purchase_value: Purchase expenditure for the asset
    :param residual_value: Residual value of the asset at the end of its useful life
    :return: A list of annual depreciation expenses over the asset's useful life
    >>> straight_line_depreciation(10, 1100.0, 100.0)
    [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0]
    >>> straight_line_depreciation(6, 1250.0, 50.0)
    [200.0, 200.0, 200.0, 200.0, 200.0, 200.0]
    >>> straight_line_depreciation(4, 1001.0)
    [250.25, 250.25, 250.25, 250.25]
    >>> straight_line_depreciation(11, 380.0, 50.0)
    [30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0]
    >>> straight_line_depreciation(1, 4985, 100)
    [4885.0]
    """

    if not isinstance(useful_years, int):
        raise TypeError("Useful years must be an integer")

    if useful_years < 1:
        raise ValueError("Useful years cannot be less than 1")

    if not isinstance(purchase_value, (float, int)):
        raise TypeError("Purchase value must be numeric")

    if not isinstance(residual_value, (float, int)):
        raise TypeError("Residual value must be numeric")

    if purchase_value < 0.0:
        raise ValueError("Purchase value cannot be less than zero")

    if purchase_value < residual_value:
        raise ValueError("Purchase value cannot be less than residual value")

    # Calculate annual depreciation expense
    depreciable_cost = purchase_value - residual_value
    annual_depreciation_expense = depreciable_cost / useful_years

    # List of annual depreciation expenses
    list_of_depreciation_expenses = []
    accumulated_depreciation_expense = 0.0
    for period in range(useful_years):
        if period != useful_years - 1:
            accumulated_depreciation_expense += annual_depreciation_expense
            list_of_depreciation_expenses.append(annual_depreciation_expense)
        else:
            depreciation_expense_in_end_year = (
                depreciable_cost - accumulated_depreciation_expense
            )
            list_of_depreciation_expenses.append(depreciation_expense_in_end_year)

    return list_of_depreciation_expenses


if __name__ == "__main__":
    user_input_useful_years = int(input("Please Enter Useful Years:\n > "))
    user_input_purchase_value = float(input("Please Enter Purchase Value:\n > "))
    user_input_residual_value = float(input("Please Enter Residual Value:\n > "))
    print(
        straight_line_depreciation(
            user_input_useful_years,
            user_input_purchase_value,
            user_input_residual_value,
        )
    )


================================================
FILE: financial/time_and_half_pay.py
================================================
"""
Calculate time and a half pay
"""


def pay(hours_worked: float, pay_rate: float, hours: float = 40) -> float:
    """
    hours_worked = The total hours worked
    pay_rate = Amount of money per hour
    hours = Number of hours that must be worked before you receive time and a half

    >>> pay(41, 1)
    41.5
    >>> pay(65, 19)
    1472.5
    >>> pay(10, 1)
    10.0
    """
    # Check that all input parameters are float or integer
    assert isinstance(hours_worked, (float, int)), (
        "Parameter 'hours_worked' must be of type 'int' or 'float'"
    )
    assert isinstance(pay_rate, (float, int)), (
        "Parameter 'pay_rate' must be of type 'int' or 'float'"
    )
    assert isinstance(hours, (float, int)), (
        "Parameter 'hours' must be of type 'int' or 'float'"
    )

    normal_pay = hours_worked * pay_rate
    over_time = max(0, hours_worked - hours)
    over_time_pay = over_time * pay_rate / 2
    return normal_pay + over_time_pay


if __name__ == "__main__":
    # Test
    import doctest

    doctest.testmod()


================================================
FILE: fractals/__init__.py
================================================


================================================
FILE: fractals/julia_sets.py
================================================
"""Author Alexandre De Zotti

Draws Julia sets of quadratic polynomials and exponential maps.
 More specifically, this iterates the function a fixed number of times
 then plots whether the absolute value of the last iterate is greater than
 a fixed threshold (named "escape radius"). For the exponential map this is not
 really an escape radius but rather a convenient way to approximate the Julia
 set with bounded orbits.

The examples presented here are:
- The Cauliflower Julia set, see e.g.
https://en.wikipedia.org/wiki/File:Julia_z2%2B0,25.png
- Other examples from https://en.wikipedia.org/wiki/Julia_set
- An exponential map Julia set, ambiantly homeomorphic to the examples in
https://www.math.univ-toulouse.fr/~cheritat/GalII/galery.html
 and
https://ddd.uab.cat/pub/pubmat/02141493v43n1/02141493v43n1p27.pdf

Remark: Some overflow runtime warnings are suppressed. This is because of the
 way the iteration loop is implemented, using numpy's efficient computations.
 Overflows and infinites are replaced after each step by a large number.
"""

import warnings
from collections.abc import Callable
from typing import Any

import matplotlib.pyplot as plt
import numpy as np

c_cauliflower = 0.25 + 0.0j
c_polynomial_1 = -0.4 + 0.6j
c_polynomial_2 = -0.1 + 0.651j
c_exponential = -2.0
nb_iterations = 56
window_size = 2.0
nb_pixels = 666


def eval_exponential(c_parameter: complex, z_values: np.ndarray) -> np.ndarray:
    """
    Evaluate $e^z + c$.
    >>> float(eval_exponential(0, 0))
    1.0
    >>> bool(abs(eval_exponential(1, np.pi*1.j)) < 1e-15)
    True
    >>> bool(abs(eval_exponential(1.j, 0)-1-1.j) < 1e-15)
    True
    """
    return np.exp(z_values) + c_parameter


def eval_quadratic_polynomial(c_parameter: complex, z_values: np.ndarray) -> np.ndarray:
    """
    >>> eval_quadratic_polynomial(0, 2)
    4
    >>> eval_quadratic_polynomial(-1, 1)
    0
    >>> round(eval_quadratic_polynomial(1.j, 0).imag)
    1
    >>> round(eval_quadratic_polynomial(1.j, 0).real)
    0
    """
    return z_values * z_values + c_parameter


def prepare_grid(window_size: float, nb_pixels: int) -> np.ndarray:
    """
    Create a grid of complex values of size nb_pixels*nb_pixels with real and
     imaginary parts ranging from -window_size to window_size (inclusive).
    Returns a numpy array.

    >>> prepare_grid(1,3)
    array([[-1.-1.j, -1.+0.j, -1.+1.j],
           [ 0.-1.j,  0.+0.j,  0.+1.j],
           [ 1.-1.j,  1.+0.j,  1.+1.j]])
    """
    x = np.linspace(-window_size, window_size, nb_pixels)
    x = x.reshape((nb_pixels, 1))
    y = np.linspace(-window_size, window_size, nb_pixels)
    y = y.reshape((1, nb_pixels))
    return x + 1.0j * y


def iterate_function(
    eval_function: Callable[[Any, np.ndarray], np.ndarray],
    function_params: Any,
    nb_iterations: int,
    z_0: np.ndarray,
    infinity: float | None = None,
) -> np.ndarray:
    """
    Iterate the function "eval_function" exactly nb_iterations times.
    The first argument of the function is a parameter which is contained in
    function_params. The variable z_0 is an array that contains the initial
    values to iterate from.
    This function returns the final iterates.

    >>> iterate_function(eval_quadratic_polynomial, 0, 3, np.array([0,1,2])).shape
    (3,)
    >>> complex(np.round(iterate_function(eval_quadratic_polynomial,
    ... 0,
    ... 3,
    ... np.array([0,1,2]))[0]))
    0j
    >>> complex(np.round(iterate_function(eval_quadratic_polynomial,
    ... 0,
    ... 3,
    ... np.array([0,1,2]))[1]))
    (1+0j)
    >>> complex(np.round(iterate_function(eval_quadratic_polynomial,
    ... 0,
    ... 3,
    ... np.array([0,1,2]))[2]))
    (256+0j)
    """

    z_n = z_0.astype("complex64")
    for _ in range(nb_iterations):
        z_n = eval_function(function_params, z_n)
        if infinity is not None:
            np.nan_to_num(z_n, copy=False, nan=infinity)
            z_n[abs(z_n) == np.inf] = infinity
    return z_n


def show_results(
    function_label: str,
    function_params: Any,
    escape_radius: float,
    z_final: np.ndarray,
) -> None:
    """
    Plots of whether the absolute value of z_final is greater than
    the value of escape_radius. Adds the function_label and function_params to
    the title.

    >>> show_results('80', 0, 1, np.array([[0,1,.5],[.4,2,1.1],[.2,1,1.3]]))
    """

    abs_z_final = (abs(z_final)).transpose()
    abs_z_final[:, :] = abs_z_final[::-1, :]
    plt.matshow(abs_z_final < escape_radius)
    plt.title(f"Julia set of ${function_label}$, $c={function_params}$")
    plt.show()


def ignore_overflow_warnings() -> None:
    """
    Ignore some overflow and invalid value warnings.

    >>> ignore_overflow_warnings()
    """
    warnings.filterwarnings(
        "ignore", category=RuntimeWarning, message="overflow encountered in multiply"
    )
    warnings.filterwarnings(
        "ignore",
        category=RuntimeWarning,
        message="invalid value encountered in multiply",
    )
    warnings.filterwarnings(
        "ignore", category=RuntimeWarning, message="overflow encountered in absolute"
    )
    warnings.filterwarnings(
        "ignore", category=RuntimeWarning, message="overflow encountered in exp"
    )


if __name__ == "__main__":
    z_0 = prepare_grid(window_size, nb_pixels)

    ignore_overflow_warnings()  # See file header for explanations

    nb_iterations = 24
    escape_radius = 2 * abs(c_cauliflower) + 1
    z_final = iterate_function(
        eval_quadratic_polynomial,
        c_cauliflower,
        nb_iterations,
        z_0,
        infinity=1.1 * escape_radius,
    )
    show_results("z^2+c", c_cauliflower, escape_radius, z_final)

    nb_iterations = 64
    escape_radius = 2 * abs(c_polynomial_1) + 1
    z_final = iterate_function(
        eval_quadratic_polynomial,
        c_polynomial_1,
        nb_iterations,
        z_0,
        infinity=1.1 * escape_radius,
    )
    show_results("z^2+c", c_polynomial_1, escape_radius, z_final)

    nb_iterations = 161
    escape_radius = 2 * abs(c_polynomial_2) + 1
    z_final = iterate_function(
        eval_quadratic_polynomial,
        c_polynomial_2,
        nb_iterations,
        z_0,
        infinity=1.1 * escape_radius,
    )
    show_results("z^2+c", c_polynomial_2, escape_radius, z_final)

    nb_iterations = 12
    escape_radius = 10000.0
    z_final = iterate_function(
        eval_exponential,
        c_exponential,
        nb_iterations,
        z_0 + 2,
        infinity=1.0e10,
    )
    show_results("e^z+c", c_exponential, escape_radius, z_final)


================================================
FILE: fractals/koch_snowflake.py
================================================
"""
Description
    The Koch snowflake is a fractal curve and one of the earliest fractals to
    have been described. The Koch snowflake can be built up iteratively, in a
    sequence of stages. The first stage is an equilateral triangle, and each
    successive stage is formed by adding outward bends to each side of the
    previous stage, making smaller equilateral triangles.
    This can be achieved through the following steps for each line:
        1. divide the line segment into three segments of equal length.
        2. draw an equilateral triangle that has the middle segment from step 1
        as its base and points outward.
        3. remove the line segment that is the base of the triangle from step 2.
    (description adapted from https://en.wikipedia.org/wiki/Koch_snowflake )
    (for a more detailed explanation and an implementation in the
    Processing language, see  https://natureofcode.com/book/chapter-8-fractals/
    #84-the-koch-curve-and-the-arraylist-technique )

Requirements (pip):
    - matplotlib
    - numpy
"""

from __future__ import annotations

import matplotlib.pyplot as plt
import numpy as np

# initial triangle of Koch snowflake
VECTOR_1 = np.array([0, 0])
VECTOR_2 = np.array([0.5, 0.8660254])
VECTOR_3 = np.array([1, 0])
INITIAL_VECTORS = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]

# uncomment for simple Koch curve instead of Koch snowflake
# INITIAL_VECTORS = [VECTOR_1, VECTOR_3]


def iterate(initial_vectors: list[np.ndarray], steps: int) -> list[np.ndarray]:
    """
    Go through the number of iterations determined by the argument "steps".
    Be careful with high values (above 5) since the time to calculate increases
    exponentially.
    >>> iterate([np.array([0, 0]), np.array([1, 0])], 1)
    [array([0, 0]), array([0.33333333, 0.        ]), array([0.5       , \
0.28867513]), array([0.66666667, 0.        ]), array([1, 0])]
    """
    vectors = initial_vectors
    for _ in range(steps):
        vectors = iteration_step(vectors)
    return vectors


def iteration_step(vectors: list[np.ndarray]) -> list[np.ndarray]:
    """
    Loops through each pair of adjacent vectors. Each line between two adjacent
    vectors is divided into 4 segments by adding 3 additional vectors in-between
    the original two vectors. The vector in the middle is constructed through a
    60 degree rotation so it is bent outwards.
    >>> iteration_step([np.array([0, 0]), np.array([1, 0])])
    [array([0, 0]), array([0.33333333, 0.        ]), array([0.5       , \
0.28867513]), array([0.66666667, 0.        ]), array([1, 0])]
    """
    new_vectors = []
    for i, start_vector in enumerate(vectors[:-1]):
        end_vector = vectors[i + 1]
        new_vectors.append(start_vector)
        difference_vector = end_vector - start_vector
        new_vectors.append(start_vector + difference_vector / 3)
        new_vectors.append(
            start_vector + difference_vector / 3 + rotate(difference_vector / 3, 60)
        )
        new_vectors.append(start_vector + difference_vector * 2 / 3)
    new_vectors.append(vectors[-1])
    return new_vectors


def rotate(vector: np.ndarray, angle_in_degrees: float) -> np.ndarray:
    """
    Standard rotation of a 2D vector with a rotation matrix
    (see https://en.wikipedia.org/wiki/Rotation_matrix )
    >>> rotate(np.array([1, 0]), 60)
    array([0.5      , 0.8660254])
    >>> rotate(np.array([1, 0]), 90)
    array([6.123234e-17, 1.000000e+00])
    """
    theta = np.radians(angle_in_degrees)
    c, s = np.cos(theta), np.sin(theta)
    rotation_matrix = np.array(((c, -s), (s, c)))
    return np.dot(rotation_matrix, vector)


def plot(vectors: list[np.ndarray]) -> None:
    """
    Utility function to plot the vectors using matplotlib.pyplot
    No doctest was implemented since this function does not have a return value
    """
    # avoid stretched display of graph
    axes = plt.gca()
    axes.set_aspect("equal")

    # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
    # y-coordinates as inputs, which are constructed from the vector-list using
    # zip()
    x_coordinates, y_coordinates = zip(*vectors)
    plt.plot(x_coordinates, y_coordinates)
    plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    processed_vectors = iterate(INITIAL_VECTORS, 5)
    plot(processed_vectors)


================================================
FILE: fractals/mandelbrot.py
================================================
"""
The Mandelbrot set is the set of complex numbers "c" for which the series
"z_(n+1) = z_n * z_n + c" does not diverge, i.e. remains bounded. Thus, a
complex number "c" is a member of the Mandelbrot set if, when starting with
"z_0 = 0" and applying the iteration repeatedly, the absolute value of
"z_n" remains bounded for all "n > 0". Complex numbers can be written as
"a + b*i": "a" is the real component, usually drawn on the x-axis, and "b*i"
is the imaginary component, usually drawn on the y-axis. Most visualizations
of the Mandelbrot set use a color-coding to indicate after how many steps in
the series the numbers outside the set diverge. Images of the Mandelbrot set
exhibit an elaborate and infinitely complicated boundary that reveals
progressively ever-finer recursive detail at increasing magnifications, making
the boundary of the Mandelbrot set a fractal curve.
(description adapted from https://en.wikipedia.org/wiki/Mandelbrot_set )
(see also https://en.wikipedia.org/wiki/Plotting_algorithms_for_the_Mandelbrot_set )
"""

import colorsys

from PIL import Image


def get_distance(x: float, y: float, max_step: int) -> float:
    """
    Return the relative distance (= step/max_step) after which the complex number
    constituted by this x-y-pair diverges. Members of the Mandelbrot set do not
    diverge so their distance is 1.

    >>> get_distance(0, 0, 50)
    1.0
    >>> get_distance(0.5, 0.5, 50)
    0.061224489795918366
    >>> get_distance(2, 0, 50)
    0.0
    """
    a = x
    b = y
    for step in range(max_step):  # noqa: B007
        a_new = a * a - b * b + x
        b = 2 * a * b + y
        a = a_new

        # divergence happens for all complex number with an absolute value
        # greater than 4
        if a * a + b * b > 4:
            break
    return step / (max_step - 1)


def get_black_and_white_rgb(distance: float) -> tuple:
    """
    Black&white color-coding that ignores the relative distance. The Mandelbrot
    set is black, everything else is white.

    >>> get_black_and_white_rgb(0)
    (255, 255, 255)
    >>> get_black_and_white_rgb(0.5)
    (255, 255, 255)
    >>> get_black_and_white_rgb(1)
    (0, 0, 0)
    """
    if distance == 1:
        return (0, 0, 0)
    else:
        return (255, 255, 255)


def get_color_coded_rgb(distance: float) -> tuple:
    """
    Color-coding taking the relative distance into account. The Mandelbrot set
    is black.

    >>> get_color_coded_rgb(0)
    (255, 0, 0)
    >>> get_color_coded_rgb(0.5)
    (0, 255, 255)
    >>> get_color_coded_rgb(1)
    (0, 0, 0)
    """
    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 255) for i in colorsys.hsv_to_rgb(distance, 1, 1))


def get_image(
    image_width: int = 800,
    image_height: int = 600,
    figure_center_x: float = -0.6,
    figure_center_y: float = 0,
    figure_width: float = 3.2,
    max_step: int = 50,
    use_distance_color_coding: bool = True,
) -> Image.Image:
    """
    Function to generate the image of the Mandelbrot set. Two types of coordinates
    are used: image-coordinates that refer to the pixels and figure-coordinates
    that refer to the complex numbers inside and outside the Mandelbrot set. The
    figure-coordinates in the arguments of this function determine which section
    of the Mandelbrot set is viewed. The main area of the Mandelbrot set is
    roughly between "-1.5 < x < 0.5" and "-1 < y < 1" in the figure-coordinates.

    Commenting out tests that slow down pytest...
    # 13.35s call     fractals/mandelbrot.py::mandelbrot.get_image
    # >>> get_image().load()[0,0]
    (255, 0, 0)
    # >>> get_image(use_distance_color_coding = False).load()[0,0]
    (255, 255, 255)
    """
    img = Image.new("RGB", (image_width, image_height))
    pixels = img.load()

    # loop through the image-coordinates
    for image_x in range(image_width):
        for image_y in range(image_height):
            # determine the figure-coordinates based on the image-coordinates
            figure_height = figure_width / image_width * image_height
            figure_x = figure_center_x + (image_x / image_width - 0.5) * figure_width
            figure_y = figure_center_y + (image_y / image_height - 0.5) * figure_height

            distance = get_distance(figure_x, figure_y, max_step)

            # color the corresponding pixel based on the selected coloring-function
            if use_distance_color_coding:
                pixels[image_x, image_y] = get_color_coded_rgb(distance)
            else:
                pixels[image_x, image_y] = get_black_and_white_rgb(distance)

    return img


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # colored version, full figure
    img = get_image()

    # uncomment for colored version, different section, zoomed in
    # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
    # figure_width = 0.8)

    # uncomment for black and white version, full figure
    # img = get_image(use_distance_color_coding = False)

    # uncomment to save the image
    # img.save("mandelbrot.png")

    img.show()


================================================
FILE: fractals/sierpinski_triangle.py
================================================
"""
Author Anurag Kumar | anuragkumarak95@gmail.com | git/anuragkumarak95

Simple example of fractal generation using recursion.

What is the Sierpiński Triangle?
    The Sierpiński triangle (sometimes spelled Sierpinski), also called the
Sierpiński gasket or Sierpiński sieve, is a fractal attractive fixed set with
the overall shape of an equilateral triangle, subdivided recursively into
smaller equilateral triangles. Originally constructed as a curve, this is one of
the basic examples of self-similar sets—that is, it is a mathematically
generated pattern that is reproducible at any magnification or reduction. It is
named after the Polish mathematician Wacław Sierpiński, but appeared as a
decorative pattern many centuries before the work of Sierpiński.


Usage: python sierpinski_triangle.py <int:depth_for_fractal>

Credits:
    The above description is taken from
    https://en.wikipedia.org/wiki/Sierpi%C5%84ski_triangle
    This code was written by editing the code from
    https://www.riannetrujillo.com/blog/python-fractal/
"""

import sys
import turtle


def get_mid(p1: tuple[float, float], p2: tuple[float, float]) -> tuple[float, float]:
    """
    Find the midpoint of two points

    >>> get_mid((0, 0), (2, 2))
    (1.0, 1.0)
    >>> get_mid((-3, -3), (3, 3))
    (0.0, 0.0)
    >>> get_mid((1, 0), (3, 2))
    (2.0, 1.0)
    >>> get_mid((0, 0), (1, 1))
    (0.5, 0.5)
    >>> get_mid((0, 0), (0, 0))
    (0.0, 0.0)
    """
    return (p1[0] + p2[0]) / 2, (p1[1] + p2[1]) / 2


def triangle(
    vertex1: tuple[float, float],
    vertex2: tuple[float, float],
    vertex3: tuple[float, float],
    depth: int,
) -> None:
    """
    Recursively draw the Sierpinski triangle given the vertices of the triangle
    and the recursion depth
    """
    my_pen.up()
    my_pen.goto(vertex1[0], vertex1[1])
    my_pen.down()
    my_pen.goto(vertex2[0], vertex2[1])
    my_pen.goto(vertex3[0], vertex3[1])
    my_pen.goto(vertex1[0], vertex1[1])

    if depth == 0:
        return

    triangle(vertex1, get_mid(vertex1, vertex2), get_mid(vertex1, vertex3), depth - 1)
    triangle(vertex2, get_mid(vertex1, vertex2), get_mid(vertex2, vertex3), depth - 1)
    triangle(vertex3, get_mid(vertex3, vertex2), get_mid(vertex1, vertex3), depth - 1)


if __name__ == "__main__":
    if len(sys.argv) != 2:
        raise ValueError(
            "Correct format for using this script: "
            "python fractals.py <int:depth_for_fractal>"
        )
    my_pen = turtle.Turtle()
    my_pen.ht()
    my_pen.speed(5)
    my_pen.pencolor("red")

    vertices = [(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
    triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))
    turtle.Screen().exitonclick()


================================================
FILE: fractals/vicsek.py
================================================
"""Authors Bastien Capiaux & Mehdi Oudghiri

The Vicsek fractal algorithm is a recursive algorithm that creates a
pattern known as the Vicsek fractal or the Vicsek square.
It is based on the concept of self-similarity, where the pattern at each
level of recursion resembles the overall pattern.
The algorithm involves dividing a square into 9 equal smaller squares,
removing the center square, and then repeating this process on the remaining 8 squares.
This results in a pattern that exhibits self-similarity and has a
square-shaped outline with smaller squares within it.

Source: https://en.wikipedia.org/wiki/Vicsek_fractal
"""

import turtle


def draw_cross(x: float, y: float, length: float):
    """
    Draw a cross at the specified position and with the specified length.
    """
    turtle.up()
    turtle.goto(x - length / 2, y - length / 6)
    turtle.down()
    turtle.seth(0)
    turtle.begin_fill()
    for _ in range(4):
        turtle.fd(length / 3)
        turtle.right(90)
        turtle.fd(length / 3)
        turtle.left(90)
        turtle.fd(length / 3)
        turtle.left(90)
    turtle.end_fill()


def draw_fractal_recursive(x: float, y: float, length: float, depth: float):
    """
    Recursively draw the Vicsek fractal at the specified position, with the
    specified length and depth.
    """
    if depth == 0:
        draw_cross(x, y, length)
        return

    draw_fractal_recursive(x, y, length / 3, depth - 1)
    draw_fractal_recursive(x + length / 3, y, length / 3, depth - 1)
    draw_fractal_recursive(x - length / 3, y, length / 3, depth - 1)
    draw_fractal_recursive(x, y + length / 3, length / 3, depth - 1)
    draw_fractal_recursive(x, y - length / 3, length / 3, depth - 1)


def set_color(rgb: str):
    turtle.color(rgb)


def draw_vicsek_fractal(x: float, y: float, length: float, depth: float, color="blue"):
    """
    Draw the Vicsek fractal at the specified position, with the specified
    length and depth.
    """
    turtle.speed(0)
    turtle.hideturtle()
    set_color(color)
    draw_fractal_recursive(x, y, length, depth)
    turtle.Screen().update()


def main():
    draw_vicsek_fractal(0, 0, 800, 4)

    turtle.done()


if __name__ == "__main__":
    main()


================================================
FILE: fuzzy_logic/__init__.py
================================================


================================================
FILE: fuzzy_logic/fuzzy_operations.py
================================================
"""
By @Shreya123714

https://en.wikipedia.org/wiki/Fuzzy_set
"""

from __future__ import annotations

from dataclasses import dataclass

import matplotlib.pyplot as plt
import numpy as np


@dataclass
class FuzzySet:
    """
    A class for representing and manipulating triangular fuzzy sets.
    Attributes:
        name: The name or label of the fuzzy set.
        left_boundary: The left boundary of the fuzzy set.
        peak: The peak (central) value of the fuzzy set.
        right_boundary: The right boundary of the fuzzy set.
    Methods:
        membership(x): Calculate the membership value of an input 'x' in the fuzzy set.
        union(other): Calculate the union of this fuzzy set with another fuzzy set.
        intersection(other): Calculate the intersection of this fuzzy set with another.
        complement(): Calculate the complement (negation) of this fuzzy set.
        plot(): Plot the membership function of the fuzzy set.

    >>> sheru = FuzzySet("Sheru", 0.4, 1, 0.6)
    >>> sheru
    FuzzySet(name='Sheru', left_boundary=0.4, peak=1, right_boundary=0.6)
    >>> str(sheru)
    'Sheru: [0.4, 1, 0.6]'

    >>> siya = FuzzySet("Siya", 0.5, 1, 0.7)
    >>> siya
    FuzzySet(name='Siya', left_boundary=0.5, peak=1, right_boundary=0.7)

    # Complement Operation
    >>> sheru.complement()
    FuzzySet(name='¬Sheru', left_boundary=0.4, peak=0.6, right_boundary=0)
    >>> siya.complement()  # doctest: +NORMALIZE_WHITESPACE
    FuzzySet(name='¬Siya', left_boundary=0.30000000000000004, peak=0.5,
     right_boundary=0)

    # Intersection Operation
    >>> siya.intersection(sheru)
    FuzzySet(name='Siya ∩ Sheru', left_boundary=0.5, peak=0.6, right_boundary=1.0)

    # Membership Operation
    >>> sheru.membership(0.5)
    0.16666666666666663
    >>> sheru.membership(0.6)
    0.0

    # Union Operations
    >>> siya.union(sheru)
    FuzzySet(name='Siya U Sheru', left_boundary=0.4, peak=0.7, right_boundary=1.0)
    """

    name: str
    left_boundary: float
    peak: float
    right_boundary: float

    def __str__(self) -> str:
        """
        >>> FuzzySet("fuzzy_set", 0.1, 0.2, 0.3)
        FuzzySet(name='fuzzy_set', left_boundary=0.1, peak=0.2, right_boundary=0.3)
        """
        return (
            f"{self.name}: [{self.left_boundary}, {self.peak}, {self.right_boundary}]"
        )

    def complement(self) -> FuzzySet:
        """
        Calculate the complement (negation) of this fuzzy set.
        Returns:
            FuzzySet: A new fuzzy set representing the complement.

        >>> FuzzySet("fuzzy_set", 0.1, 0.2, 0.3).complement()
        FuzzySet(name='¬fuzzy_set', left_boundary=0.7, peak=0.9, right_boundary=0.8)
        """
        return FuzzySet(
            f"¬{self.name}",
            1 - self.right_boundary,
            1 - self.left_boundary,
            1 - self.peak,
        )

    def intersection(self, other) -> FuzzySet:
        """
        Calculate the intersection of this fuzzy set
        with another fuzzy set.
        Args:
            other: Another fuzzy set to intersect with.
        Returns:
            A new fuzzy set representing the intersection.

        >>> FuzzySet("a", 0.1, 0.2, 0.3).intersection(FuzzySet("b", 0.4, 0.5, 0.6))
        FuzzySet(name='a ∩ b', left_boundary=0.4, peak=0.3, right_boundary=0.35)
        """
        return FuzzySet(
            f"{self.name} ∩ {other.name}",
            max(self.left_boundary, other.left_boundary),
            min(self.right_boundary, other.right_boundary),
            (self.peak + other.peak) / 2,
        )

    def membership(self, x: float) -> float:
        """
        Calculate the membership value of an input 'x' in the fuzzy set.
        Returns:
            The membership value of 'x' in the fuzzy set.

        >>> a = FuzzySet("a", 0.1, 0.2, 0.3)
        >>> a.membership(0.09)
        0.0
        >>> a.membership(0.1)
        0.0
        >>> a.membership(0.11)
        0.09999999999999995
        >>> a.membership(0.4)
        0.0
        >>> FuzzySet("A", 0, 0.5, 1).membership(0.1)
        0.2
        >>> FuzzySet("B", 0.2, 0.7, 1).membership(0.6)
        0.8
        """
        if x <= self.left_boundary or x >= self.right_boundary:
            return 0.0
        elif self.left_boundary < x <= self.peak:
            return (x - self.left_boundary) / (self.peak - self.left_boundary)
        elif self.peak < x < self.right_boundary:
            return (self.right_boundary - x) / (self.right_boundary - self.peak)
        msg = f"Invalid value {x} for fuzzy set {self}"
        raise ValueError(msg)

    def union(self, other) -> FuzzySet:
        """
        Calculate the union of this fuzzy set with another fuzzy set.
        Args:
            other (FuzzySet): Another fuzzy set to union with.
        Returns:
            FuzzySet: A new fuzzy set representing the union.

        >>> FuzzySet("a", 0.1, 0.2, 0.3).union(FuzzySet("b", 0.4, 0.5, 0.6))
        FuzzySet(name='a U b', left_boundary=0.1, peak=0.6, right_boundary=0.35)
        """
        return FuzzySet(
            f"{self.name} U {other.name}",
            min(self.left_boundary, other.left_boundary),
            max(self.right_boundary, other.right_boundary),
            (self.peak + other.peak) / 2,
        )

    def plot(self):
        """
        Plot the membership function of the fuzzy set.
        """
        x = np.linspace(0, 1, 1000)
        y = [self.membership(xi) for xi in x]

        plt.plot(x, y, label=self.name)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    a = FuzzySet("A", 0, 0.5, 1)
    b = FuzzySet("B", 0.2, 0.7, 1)

    a.plot()
    b.plot()

    plt.xlabel("x")
    plt.ylabel("Membership")
    plt.legend()
    plt.show()

    union_ab = a.union(b)
    intersection_ab = a.intersection(b)
    complement_a = a.complement()

    union_ab.plot()
    intersection_ab.plot()
    complement_a.plot()

    plt.xlabel("x")
    plt.ylabel("Membership")
    plt.legend()
    plt.show()


================================================
FILE: fuzzy_logic/fuzzy_operations.py.DISABLED.txt
================================================
"""
README, Author - Jigyasa Gandhi(mailto:jigsgandhi97@gmail.com)
Requirements:
  - scikit-fuzzy
  - numpy
  - matplotlib
Python:
  - 3.5
"""
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
    # Create universe of discourse in Python using linspace ()
    X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

    # Create two fuzzy sets by defining any membership function
    # (trapmf(), gbellmf(), gaussmf(), etc).
    abc1 = [0, 25, 50]
    abc2 = [25, 50, 75]
    young = fuzz.membership.trimf(X, abc1)
    middle_aged = fuzz.membership.trimf(X, abc2)

    # Compute the different operations using inbuilt functions.
    one = np.ones(75)
    zero = np.zeros((75,))
    # 1. Union = max(µA(x), µB(x))
    union = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
    # 2. Intersection = min(µA(x), µB(x))
    intersection = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
    # 3. Complement (A) = (1 - min(µA(x)))
    complement_a = fuzz.fuzzy_not(young)
    # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
    difference = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
    # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
    alg_sum = young + middle_aged - (young * middle_aged)
    # 6. Algebraic Product = (µA(x) * µB(x))
    alg_product = young * middle_aged
    # 7. Bounded Sum = min[1,(µA(x), µB(x))]
    bdd_sum = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
    # 8. Bounded difference = min[0,(µA(x), µB(x))]
    bdd_difference = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

    # max-min composition
    # max-product composition

    # Plot each set A, set B and each operation result using plot() and subplot().
    from matplotlib import pyplot as plt

    plt.figure()

    plt.subplot(4, 3, 1)
    plt.plot(X, young)
    plt.title("Young")
    plt.grid(True)

    plt.subplot(4, 3, 2)
    plt.plot(X, middle_aged)
    plt.title("Middle aged")
    plt.grid(True)

    plt.subplot(4, 3, 3)
    plt.plot(X, union)
    plt.title("union")
    plt.grid(True)

    plt.subplot(4, 3, 4)
    plt.plot(X, intersection)
    plt.title("intersection")
    plt.grid(True)

    plt.subplot(4, 3, 5)
    plt.plot(X, complement_a)
    plt.title("complement_a")
    plt.grid(True)

    plt.subplot(4, 3, 6)
    plt.plot(X, difference)
    plt.title("difference a/b")
    plt.grid(True)

    plt.subplot(4, 3, 7)
    plt.plot(X, alg_sum)
    plt.title("alg_sum")
    plt.grid(True)

    plt.subplot(4, 3, 8)
    plt.plot(X, alg_product)
    plt.title("alg_product")
    plt.grid(True)

    plt.subplot(4, 3, 9)
    plt.plot(X, bdd_sum)
    plt.title("bdd_sum")
    plt.grid(True)

    plt.subplot(4, 3, 10)
    plt.plot(X, bdd_difference)
    plt.title("bdd_difference")
    plt.grid(True)

    plt.subplots_adjust(hspace=0.5)
    plt.show()


================================================
FILE: genetic_algorithm/__init__.py
================================================


================================================
FILE: genetic_algorithm/basic_string.py
================================================
"""
Simple multithreaded algorithm to show how the 4 phases of a genetic algorithm works
(Evaluation, Selection, Crossover and Mutation)
https://en.wikipedia.org/wiki/Genetic_algorithm
Author: D4rkia
"""

from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
N_POPULATION = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
N_SELECTED = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
MUTATION_PROBABILITY = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))


def evaluate(item: str, main_target: str) -> tuple[str, float]:
    """
    Evaluate how similar the item is with the target by just
    counting each char in the right position
    >>> evaluate("Helxo Worlx", "Hello World")
    ('Helxo Worlx', 9.0)
    """
    score = len([g for position, g in enumerate(item) if g == main_target[position]])
    return (item, float(score))


def crossover(parent_1: str, parent_2: str) -> tuple[str, str]:
    """
    Slice and combine two strings at a random point.
    >>> random.seed(42)
    >>> crossover("123456", "abcdef")
    ('12345f', 'abcde6')
    """
    random_slice = random.randint(0, len(parent_1) - 1)
    child_1 = parent_1[:random_slice] + parent_2[random_slice:]
    child_2 = parent_2[:random_slice] + parent_1[random_slice:]
    return (child_1, child_2)


def mutate(child: str, genes: list[str]) -> str:
    """
    Mutate a random gene of a child with another one from the list.
    >>> random.seed(123)
    >>> mutate("123456", list("ABCDEF"))
    '12345A'
    """
    child_list = list(child)
    if random.uniform(0, 1) < MUTATION_PROBABILITY:
        child_list[random.randint(0, len(child)) - 1] = random.choice(genes)
    return "".join(child_list)


# Select, crossover and mutate a new population.
def select(
    parent_1: tuple[str, float],
    population_score: list[tuple[str, float]],
    genes: list[str],
) -> list[str]:
    """
    Select the second parent and generate new population

    >>> random.seed(42)
    >>> parent_1 = ("123456", 8.0)
    >>> population_score = [("abcdef", 4.0), ("ghijkl", 5.0), ("mnopqr", 7.0)]
    >>> genes = list("ABCDEF")
    >>> child_n = int(min(parent_1[1] + 1, 10))
    >>> population = []
    >>> for _ in range(child_n):
    ...     parent_2 = population_score[random.randrange(len(population_score))][0]
    ...     child_1, child_2 = crossover(parent_1[0], parent_2)
    ...     population.extend((mutate(child_1, genes), mutate(child_2, genes)))
    >>> len(population) == (int(parent_1[1]) + 1) * 2
    True
    """
    pop = []
    # Generate more children proportionally to the fitness score.
    child_n = int(parent_1[1] * 100) + 1
    child_n = 10 if child_n >= 10 else child_n
    for _ in range(child_n):
        parent_2 = population_score[random.randint(0, N_SELECTED)][0]

        child_1, child_2 = crossover(parent_1[0], parent_2)
        # Append new string to the population list.
        pop.append(mutate(child_1, genes))
        pop.append(mutate(child_2, genes))
    return pop


def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int, str]:
    """
    Verify that the target contains no genes besides the ones inside genes variable.

    >>> from string import ascii_lowercase
    >>> basic("doctest", ascii_lowercase, debug=False)[2]
    'doctest'
    >>> genes = list(ascii_lowercase)
    >>> genes.remove("e")
    >>> basic("test", genes)
    Traceback (most recent call last):
        ...
    ValueError: ['e'] is not in genes list, evolution cannot converge
    >>> genes.remove("s")
    >>> basic("test", genes)
    Traceback (most recent call last):
        ...
    ValueError: ['e', 's'] is not in genes list, evolution cannot converge
    >>> genes.remove("t")
    >>> basic("test", genes)
    Traceback (most recent call last):
        ...
    ValueError: ['e', 's', 't'] is not in genes list, evolution cannot converge
    """

    # Verify if N_POPULATION is bigger than N_SELECTED
    if N_POPULATION < N_SELECTED:
        msg = f"{N_POPULATION} must be bigger than {N_SELECTED}"
        raise ValueError(msg)
    # Verify that the target contains no genes besides the ones inside genes variable.
    not_in_genes_list = sorted({c for c in target if c not in genes})
    if not_in_genes_list:
        msg = f"{not_in_genes_list} is not in genes list, evolution cannot converge"
        raise ValueError(msg)

    # Generate random starting population.
    population = []
    for _ in range(N_POPULATION):
        population.append("".join([random.choice(genes) for i in range(len(target))]))

    # Just some logs to know what the algorithms is doing.
    generation, total_population = 0, 0

    # This loop will end when we find a perfect match for our target.
    while True:
        generation += 1
        total_population += len(population)

        # Random population created. Now it's time to evaluate.

        # (Option 1) Adding a bit of concurrency can make everything faster,
        #
        # import concurrent.futures
        # population_score: list[tuple[str, float]] = []
        # with concurrent.futures.ThreadPoolExecutor(
        #                                   max_workers=NUM_WORKERS) as executor:
        #     futures = {executor.submit(evaluate, item, target) for item in population}
        #     concurrent.futures.wait(futures)
        #     population_score = [item.result() for item in futures]
        #
        # but with a simple algorithm like this, it will probably be slower.
        # (Option 2) We just need to call evaluate for every item inside the population.
        population_score = [evaluate(item, target) for item in population]

        # Check if there is a matching evolution.
        population_score = sorted(population_score, key=lambda x: x[1], reverse=True)
        if population_score[0][0] == target:
            return (generation, total_population, population_score[0][0])

        # Print the best result every 10 generation.
        # Just to know that the algorithm is working.
        if debug and generation % 10 == 0:
            print(
                f"\nGeneration: {generation}"
                f"\nTotal Population:{total_population}"
                f"\nBest score: {population_score[0][1]}"
                f"\nBest string: {population_score[0][0]}"
            )

        # Flush the old population, keeping some of the best evolutions.
        # Keeping this avoid regression of evolution.
        population_best = population[: int(N_POPULATION / 3)]
        population.clear()
        population.extend(population_best)
        # Normalize population score to be between 0 and 1.
        population_score = [
            (item, score / len(target)) for item, score in population_score
        ]

        # This is selection
        for i in range(N_SELECTED):
            population.extend(select(population_score[int(i)], population_score, genes))
            # Check if the population has already reached the maximum value and if so,
            # break the cycle.  If this check is disabled, the algorithm will take
            # forever to compute large strings, but will also calculate small strings in
            # a far fewer generations.
            if len(population) > N_POPULATION:
                break


if __name__ == "__main__":
    target_str = (
        "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
    )
    genes_list = list(
        " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
        "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
    )
    generation, population, target = basic(target_str, genes_list)
    print(
        f"\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"
    )


================================================
FILE: geodesy/__init__.py
================================================


================================================
FILE: geodesy/haversine_distance.py
================================================
from math import asin, atan, cos, radians, sin, sqrt, tan

AXIS_A = 6378137.0
AXIS_B = 6356752.314245
RADIUS = 6378137


def haversine_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
    """
    Calculate great circle distance between two points in a sphere,
    given longitudes and latitudes https://en.wikipedia.org/wiki/Haversine_formula

    We know that the globe is "sort of" spherical, so a path between two points
    isn't exactly a straight line. We need to account for the Earth's curvature
    when calculating distance from point A to B. This effect is negligible for
    small distances but adds up as distance increases. The Haversine method treats
    the earth as a sphere which allows us to "project" the two points A and B
    onto the surface of that sphere and approximate the spherical distance between
    them. Since the Earth is not a perfect sphere, other methods which model the
    Earth's ellipsoidal nature are more accurate but a quick and modifiable
    computation like Haversine can be handy for shorter range distances.

    Args:
        * `lat1`, `lon1`: latitude and longitude of coordinate 1
        * `lat2`, `lon2`: latitude and longitude of coordinate 2
    Returns:
        geographical distance between two points in metres

    >>> from collections import namedtuple
    >>> point_2d = namedtuple("point_2d", "lat lon")
    >>> SAN_FRANCISCO = point_2d(37.774856, -122.424227)
    >>> YOSEMITE = point_2d(37.864742, -119.537521)
    >>> f"{haversine_distance(*SAN_FRANCISCO, *YOSEMITE):0,.0f} meters"
    '254,352 meters'
    """
    # CONSTANTS per WGS84 https://en.wikipedia.org/wiki/World_Geodetic_System
    # Distance in metres(m)
    # Equation parameters
    # Equation https://en.wikipedia.org/wiki/Haversine_formula#Formulation
    flattening = (AXIS_A - AXIS_B) / AXIS_A
    phi_1 = atan((1 - flattening) * tan(radians(lat1)))
    phi_2 = atan((1 - flattening) * tan(radians(lat2)))
    lambda_1 = radians(lon1)
    lambda_2 = radians(lon2)
    # Equation
    sin_sq_phi = sin((phi_2 - phi_1) / 2)
    sin_sq_lambda = sin((lambda_2 - lambda_1) / 2)
    # Square both values
    sin_sq_phi *= sin_sq_phi
    sin_sq_lambda *= sin_sq_lambda
    h_value = sqrt(sin_sq_phi + (cos(phi_1) * cos(phi_2) * sin_sq_lambda))
    return 2 * RADIUS * asin(h_value)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: geodesy/lamberts_ellipsoidal_distance.py
================================================
from math import atan, cos, radians, sin, tan

from .haversine_distance import haversine_distance

AXIS_A = 6378137.0
AXIS_B = 6356752.314245
EQUATORIAL_RADIUS = 6378137


def lamberts_ellipsoidal_distance(
    lat1: float, lon1: float, lat2: float, lon2: float
) -> float:
    """
    Calculate the shortest distance along the surface of an ellipsoid between
    two points on the surface of earth given longitudes and latitudes
    https://en.wikipedia.org/wiki/Geographical_distance#Lambert's_formula_for_long_lines

    NOTE: This algorithm uses geodesy/haversine_distance.py to compute central angle,
        sigma

    Representing the earth as an ellipsoid allows us to approximate distances between
    points on the surface much better than a sphere. Ellipsoidal formulas treat the
    Earth as an oblate ellipsoid which means accounting for the flattening that happens
    at the North and South poles. Lambert's formulae provide accuracy on the order of
    10 meteres over thousands of kilometeres. Other methods can provide
    millimeter-level accuracy but this is a simpler method to calculate long range
    distances without increasing computational intensity.

    Args:
        lat1, lon1: latitude and longitude of coordinate 1
        lat2, lon2: latitude and longitude of coordinate 2
    Returns:
        geographical distance between two points in metres

    >>> lamberts_ellipsoidal_distance(100, 0, 0, 0)
    Traceback (most recent call last):
    ...
    ValueError: Latitude must be between -90 and 90 degrees

    >>> lamberts_ellipsoidal_distance(0, 0, -100, 0)
    Traceback (most recent call last):
    ...
    ValueError: Latitude must be between -90 and 90 degrees

    >>> lamberts_ellipsoidal_distance(0, 200, 0, 0)
    Traceback (most recent call last):
    ...
    ValueError: Longitude must be between -180 and 180 degrees

    >>> lamberts_ellipsoidal_distance(0, 0, 0, -200)
    Traceback (most recent call last):
    ...
    ValueError: Longitude must be between -180 and 180 degrees

    >>> from collections import namedtuple
    >>> point_2d = namedtuple("point_2d", "lat lon")
    >>> SAN_FRANCISCO = point_2d(37.774856, -122.424227)
    >>> YOSEMITE = point_2d(37.864742, -119.537521)
    >>> NEW_YORK = point_2d(40.713019, -74.012647)
    >>> VENICE = point_2d(45.443012, 12.313071)
    >>> f"{lamberts_ellipsoidal_distance(*SAN_FRANCISCO, *YOSEMITE):0,.0f} meters"
    '254,351 meters'
    >>> f"{lamberts_ellipsoidal_distance(*SAN_FRANCISCO, *NEW_YORK):0,.0f} meters"
    '4,138,992 meters'
    >>> f"{lamberts_ellipsoidal_distance(*SAN_FRANCISCO, *VENICE):0,.0f} meters"
    '9,737,326 meters'
    """

    # Validate latitude values
    if not -90 <= lat1 <= 90 or not -90 <= lat2 <= 90:
        raise ValueError("Latitude must be between -90 and 90 degrees")

    # Validate longitude values
    if not -180 <= lon1 <= 180 or not -180 <= lon2 <= 180:
        raise ValueError("Longitude must be between -180 and 180 degrees")

    # CONSTANTS per WGS84 https://en.wikipedia.org/wiki/World_Geodetic_System
    # Distance in metres(m)
    # Equation Parameters
    # https://en.wikipedia.org/wiki/Geographical_distance#Lambert's_formula_for_long_lines
    flattening = (AXIS_A - AXIS_B) / AXIS_A
    # Parametric latitudes
    # https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
    b_lat1 = atan((1 - flattening) * tan(radians(lat1)))
    b_lat2 = atan((1 - flattening) * tan(radians(lat2)))

    # Compute central angle between two points
    # using haversine theta. sigma =  haversine_distance / equatorial radius
    sigma = haversine_distance(lat1, lon1, lat2, lon2) / EQUATORIAL_RADIUS

    # Intermediate P and Q values
    p_value = (b_lat1 + b_lat2) / 2
    q_value = (b_lat2 - b_lat1) / 2

    # Intermediate X value
    # X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
    x_numerator = (sin(p_value) ** 2) * (cos(q_value) ** 2)
    x_demonimator = cos(sigma / 2) ** 2
    x_value = (sigma - sin(sigma)) * (x_numerator / x_demonimator)

    # Intermediate Y value
    # Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
    y_numerator = (cos(p_value) ** 2) * (sin(q_value) ** 2)
    y_denominator = sin(sigma / 2) ** 2
    y_value = (sigma + sin(sigma)) * (y_numerator / y_denominator)

    return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: geometry/__init__.py
================================================


================================================
FILE: geometry/geometry.py
================================================
from __future__ import annotations

import math
from dataclasses import dataclass, field
from types import NoneType
from typing import Self

# Building block classes


@dataclass
class Angle:
    """
    An Angle in degrees (unit of measurement)

    >>> Angle()
    Angle(degrees=90)
    >>> Angle(45.5)
    Angle(degrees=45.5)
    >>> Angle(-1)
    Traceback (most recent call last):
        ...
    TypeError: degrees must be a numeric value between 0 and 360.
    >>> Angle(361)
    Traceback (most recent call last):
        ...
    TypeError: degrees must be a numeric value between 0 and 360.
    """

    degrees: float = 90

    def __post_init__(self) -> None:
        if not isinstance(self.degrees, (int, float)) or not 0 <= self.degrees <= 360:
            raise TypeError("degrees must be a numeric value between 0 and 360.")


@dataclass
class Side:
    """
    A side of a two dimensional Shape such as Polygon, etc.
    adjacent_sides: a list of sides which are adjacent to the current side
    angle: the angle in degrees between each adjacent side
    length: the length of the current side in meters

    >>> Side(5)
    Side(length=5, angle=Angle(degrees=90), next_side=None)
    >>> Side(5, Angle(45.6))
    Side(length=5, angle=Angle(degrees=45.6), next_side=None)
    >>> Side(5, Angle(45.6), Side(1, Angle(2)))  # doctest: +ELLIPSIS
    Side(length=5, angle=Angle(degrees=45.6), next_side=Side(length=1, angle=Angle(d...
    >>> Side(-1)
    Traceback (most recent call last):
        ...
    TypeError: length must be a positive numeric value.
    >>> Side(5, None)
    Traceback (most recent call last):
        ...
    TypeError: angle must be an Angle object.
    >>> Side(5, Angle(90), "Invalid next_side")
    Traceback (most recent call last):
        ...
    TypeError: next_side must be a Side or None.
    """

    length: float
    angle: Angle = field(default_factory=Angle)
    next_side: Side | None = None

    def __post_init__(self) -> None:
        if not isinstance(self.length, (int, float)) or self.length <= 0:
            raise TypeError("length must be a positive numeric value.")
        if not isinstance(self.angle, Angle):
            raise TypeError("angle must be an Angle object.")
        if not isinstance(self.next_side, (Side, NoneType)):
            raise TypeError("next_side must be a Side or None.")


@dataclass
class Ellipse:
    """
    A geometric Ellipse on a 2D surface

    >>> Ellipse(5, 10)
    Ellipse(major_radius=5, minor_radius=10)
    >>> Ellipse(5, 10) is Ellipse(5, 10)
    False
    >>> Ellipse(5, 10) == Ellipse(5, 10)
    True
    """

    major_radius: float
    minor_radius: float

    @property
    def area(self) -> float:
        """
        >>> Ellipse(5, 10).area
        157.07963267948966
        """
        return math.pi * self.major_radius * self.minor_radius

    @property
    def perimeter(self) -> float:
        """
        >>> Ellipse(5, 10).perimeter
        47.12388980384689
        """
        return math.pi * (self.major_radius + self.minor_radius)


class Circle(Ellipse):
    """
    A geometric Circle on a 2D surface

    >>> Circle(5)
    Circle(radius=5)
    >>> Circle(5) is Circle(5)
    False
    >>> Circle(5) == Circle(5)
    True
    >>> Circle(5).area
    78.53981633974483
    >>> Circle(5).perimeter
    31.41592653589793
    """

    def __init__(self, radius: float) -> None:
        super().__init__(radius, radius)
        self.radius = radius

    def __repr__(self) -> str:
        return f"Circle(radius={self.radius})"

    @property
    def diameter(self) -> float:
        """
        >>> Circle(5).diameter
        10
        """
        return self.radius * 2

    def max_parts(self, num_cuts: float) -> float:
        """
        Return the maximum number of parts that circle can be divided into if cut
        'num_cuts' times.

        >>> circle = Circle(5)
        >>> circle.max_parts(0)
        1.0
        >>> circle.max_parts(7)
        29.0
        >>> circle.max_parts(54)
        1486.0
        >>> circle.max_parts(22.5)
        265.375
        >>> circle.max_parts(-222)
        Traceback (most recent call last):
            ...
        TypeError: num_cuts must be a positive numeric value.
        >>> circle.max_parts("-222")
        Traceback (most recent call last):
            ...
        TypeError: num_cuts must be a positive numeric value.
        """
        if not isinstance(num_cuts, (int, float)) or num_cuts < 0:
            raise TypeError("num_cuts must be a positive numeric value.")
        return (num_cuts + 2 + num_cuts**2) * 0.5


@dataclass
class Polygon:
    """
    An abstract class which represents Polygon on a 2D surface.

    >>> Polygon()
    Polygon(sides=[])
    >>> polygon = Polygon()
    >>> polygon.add_side(Side(5)).get_side(0)
    Side(length=5, angle=Angle(degrees=90), next_side=None)
    >>> polygon.get_side(1)
    Traceback (most recent call last):
        ...
    IndexError: list index out of range
    >>> polygon.set_side(0, Side(10)).get_side(0)
    Side(length=10, angle=Angle(degrees=90), next_side=None)
    >>> polygon.set_side(1, Side(10))
    Traceback (most recent call last):
        ...
    IndexError: list assignment index out of range
    """

    sides: list[Side] = field(default_factory=list)

    def add_side(self, side: Side) -> Self:
        """
        >>> Polygon().add_side(Side(5))
        Polygon(sides=[Side(length=5, angle=Angle(degrees=90), next_side=None)])
        """
        self.sides.append(side)
        return self

    def get_side(self, index: int) -> Side:
        """
        >>> Polygon().get_side(0)
        Traceback (most recent call last):
            ...
        IndexError: list index out of range
        >>> Polygon().add_side(Side(5)).get_side(-1)
        Side(length=5, angle=Angle(degrees=90), next_side=None)
        """
        return self.sides[index]

    def set_side(self, index: int, side: Side) -> Self:
        """
        >>> Polygon().set_side(0, Side(5))
        Traceback (most recent call last):
            ...
        IndexError: list assignment index out of range
        >>> Polygon().add_side(Side(5)).set_side(0, Side(10))
        Polygon(sides=[Side(length=10, angle=Angle(degrees=90), next_side=None)])
        """
        self.sides[index] = side
        return self


class Rectangle(Polygon):
    """
    A geometric rectangle on a 2D surface.

    >>> rectangle_one = Rectangle(5, 10)
    >>> rectangle_one.perimeter()
    30
    >>> rectangle_one.area()
    50
    >>> Rectangle(-5, 10)
    Traceback (most recent call last):
        ...
    TypeError: length must be a positive numeric value.
    """

    def __init__(self, short_side_length: float, long_side_length: float) -> None:
        super().__init__()
        self.short_side_length = short_side_length
        self.long_side_length = long_side_length
        self.post_init()

    def post_init(self) -> None:
        """
        >>> Rectangle(5, 10)  # doctest: +NORMALIZE_WHITESPACE
        Rectangle(sides=[Side(length=5, angle=Angle(degrees=90), next_side=None),
        Side(length=10, angle=Angle(degrees=90), next_side=None)])
        """
        self.short_side = Side(self.short_side_length)
        self.long_side = Side(self.long_side_length)
        super().add_side(self.short_side)
        super().add_side(self.long_side)

    def perimeter(self) -> float:
        return (self.short_side.length + self.long_side.length) * 2

    def area(self) -> float:
        return self.short_side.length * self.long_side.length


@dataclass
class Square(Rectangle):
    """
    a structure which represents a
    geometrical square on a 2D surface
    >>> square_one = Square(5)
    >>> square_one.perimeter()
    20
    >>> square_one.area()
    25
    """

    def __init__(self, side_length: float) -> None:
        super().__init__(side_length, side_length)

    def perimeter(self) -> float:
        return super().perimeter()

    def area(self) -> float:
        return super().area()


if __name__ == "__main__":
    __import__("doctest").testmod()


================================================
FILE: geometry/graham_scan.py
================================================
"""
Graham Scan algorithm for finding the convex hull of a set of points.

The Graham scan is a method of computing the convex hull of a finite set of points
in the plane with time complexity O(n log n). It is named after Ronald Graham, who
published the original algorithm in 1972.

The algorithm finds all vertices of the convex hull ordered along its boundary.
It uses a stack to efficiently identify and remove points that would create
non-convex angles.

References:
- https://en.wikipedia.org/wiki/Graham_scan
- Graham, R.L. (1972). "An Efficient Algorithm for Determining the Convex Hull of a
  Finite Planar Set"
"""

from __future__ import annotations

from collections.abc import Sequence
from dataclasses import dataclass
from typing import TypeVar

T = TypeVar("T", bound="Point")


@dataclass
class Point:
    """
    A point in 2D space.

    >>> Point(0, 0)
    Point(x=0.0, y=0.0)
    >>> Point(1.5, 2.5)
    Point(x=1.5, y=2.5)
    """

    x: float
    y: float

    def __init__(self, x_coordinate: float, y_coordinate: float) -> None:
        """
        Initialize a 2D point.

        Args:
            x_coordinate: The x-coordinate (horizontal position) of the point
            y_coordinate: The y-coordinate (vertical position) of the point
        """
        self.x = float(x_coordinate)
        self.y = float(y_coordinate)

    def __eq__(self, other: object) -> bool:
        """
        Check if two points are equal.

        >>> Point(1, 2) == Point(1, 2)
        True
        >>> Point(1, 2) == Point(2, 1)
        False
        """
        if not isinstance(other, Point):
            return NotImplemented
        return self.x == other.x and self.y == other.y

    def __lt__(self, other: Point) -> bool:
        """
        Compare two points for sorting (bottom-most, then left-most).

        >>> Point(1, 2) < Point(1, 3)
        True
        >>> Point(1, 2) < Point(2, 2)
        True
        >>> Point(2, 2) < Point(1, 2)
        False
        """
        if self.y == other.y:
            return self.x < other.x
        return self.y < other.y

    def euclidean_distance(self, other: Point) -> float:
        """
        Calculate Euclidean distance between two points.

        >>> Point(0, 0).euclidean_distance(Point(3, 4))
        5.0
        >>> Point(1, 1).euclidean_distance(Point(4, 5))
        5.0
        """
        return ((self.x - other.x) ** 2 + (self.y - other.y) ** 2) ** 0.5

    def consecutive_orientation(self, point_a: Point, point_b: Point) -> float:
        """
        Calculate the cross product of vectors (self -> point_a) and
        (point_a -> point_b).

        Returns:
        - Positive value: counter-clockwise turn
        - Negative value: clockwise turn
        - Zero: collinear points

        >>> Point(0, 0).consecutive_orientation(Point(1, 0), Point(1, 1))
        1.0
        >>> Point(0, 0).consecutive_orientation(Point(1, 0), Point(1, -1))
        -1.0
        >>> Point(0, 0).consecutive_orientation(Point(1, 0), Point(2, 0))
        0.0
        """
        return (point_a.x - self.x) * (point_b.y - point_a.y) - (point_a.y - self.y) * (
            point_b.x - point_a.x
        )


def graham_scan(points: Sequence[Point]) -> list[Point]:
    """
    Find the convex hull of a set of points using the Graham scan algorithm.

    The algorithm works as follows:
    1. Find the bottom-most point (or left-most in case of tie)
    2. Sort all other points by polar angle with respect to the bottom-most point
    3. Process points in order, maintaining a stack of hull candidates
    4. Remove points that would create a clockwise turn

    Args:
        points: A sequence of Point objects

    Returns:
        A list of Point objects representing the convex hull in counter-clockwise order.
        Returns an empty list if there are fewer than 3 distinct points or if all
        points are collinear.

    Time Complexity: O(n log n) due to sorting
    Space Complexity: O(n) for the output hull

    >>> graham_scan([])
    []
    >>> graham_scan([Point(0, 0)])
    []
    >>> graham_scan([Point(0, 0), Point(1, 1)])
    []
    >>> hull = graham_scan([Point(0, 0), Point(1, 0), Point(0.5, 1)])
    >>> len(hull)
    3
    >>> Point(0, 0) in hull and Point(1, 0) in hull and Point(0.5, 1) in hull
    True
    """
    if len(points) <= 2:
        return []

    # Find the bottom-most point (left-most in case of tie)
    min_point = min(points)

    # Remove the min_point from the list
    points_list = [p for p in points if p != min_point]
    if not points_list:
        # Edge case where all points are the same
        return []

    def polar_angle_key(point: Point) -> tuple[float, float, float]:
        """
        Key function for sorting points by polar angle relative to min_point.

        Points are sorted counter-clockwise. When two points have the same angle,
        the farther point comes first (we'll remove duplicates later).
        """
        # We use a dummy third point (min_point itself) to calculate relative angles
        # Instead, we'll compute the angle between points
        dx = point.x - min_point.x
        dy = point.y - min_point.y

        # Use atan2 for angle, but we can also use cross product for comparison
        # For sorting, we compare orientations between consecutive points
        distance = min_point.euclidean_distance(point)
        return (dx, dy, -distance)  # Negative distance to sort farther points first

    # Sort by polar angle using a comparison based on cross product
    def compare_points(point_a: Point, point_b: Point) -> int:
        """Compare two points by polar angle relative to min_point."""
        orientation = min_point.consecutive_orientation(point_a, point_b)
        if orientation < 0.0:
            return 1  # point_a comes after point_b (clockwise)
        elif orientation > 0.0:
            return -1  # point_a comes before point_b (counter-clockwise)
        else:
            # Collinear: farther point should come first
            dist_a = min_point.euclidean_distance(point_a)
            dist_b = min_point.euclidean_distance(point_b)
            if dist_b < dist_a:
                return -1
            elif dist_b > dist_a:
                return 1
            else:
                return 0

    from functools import cmp_to_key

    points_list.sort(key=cmp_to_key(compare_points))

    # Build the convex hull
    convex_hull: list[Point] = [min_point, points_list[0]]

    for point in points_list[1:]:
        # Skip consecutive points with the same angle (collinear with min_point)
        if min_point.consecutive_orientation(point, convex_hull[-1]) == 0.0:
            continue

        # Remove points that create a clockwise turn (or are collinear)
        while len(convex_hull) >= 2:
            orientation = convex_hull[-2].consecutive_orientation(
                convex_hull[-1], point
            )
            if orientation <= 0.0:
                convex_hull.pop()
            else:
                break

        convex_hull.append(point)

    # Need at least 3 points for a valid convex hull
    if len(convex_hull) <= 2:
        return []

    return convex_hull


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Example usage
    points = [
        Point(0, 0),
        Point(1, 0),
        Point(2, 0),
        Point(2, 1),
        Point(2, 2),
        Point(1, 2),
        Point(0, 2),
        Point(0, 1),
        Point(1, 1),  # Interior point
    ]

    hull = graham_scan(points)
    print("Convex hull vertices:")
    for point in hull:
        print(f"  ({point.x}, {point.y})")


================================================
FILE: geometry/jarvis_march.py
================================================
"""
Jarvis March (Gift Wrapping) algorithm for finding the convex hull of a set of points.

The convex hull is the smallest convex polygon that contains all the points.

Time Complexity: O(n*h) where n is the number of points and h is the number of
hull points.
Space Complexity: O(h) where h is the number of hull points.

USAGE:
    -> Import this file into your project.
    -> Use the jarvis_march() function to find the convex hull of a set of points.
    -> Parameters:
        -> points: A list of Point objects representing 2D coordinates

REFERENCES:
    -> Wikipedia reference: https://en.wikipedia.org/wiki/Gift_wrapping_algorithm
    -> GeeksforGeeks:
       https://www.geeksforgeeks.org/convex-hull-set-1-jarviss-algorithm-or-wrapping/
"""

from __future__ import annotations


class Point:
    """Represents a 2D point with x and y coordinates."""

    def __init__(self, x_coordinate: float, y_coordinate: float) -> None:
        self.x = x_coordinate
        self.y = y_coordinate

    def __eq__(self, other: object) -> bool:
        if not isinstance(other, Point):
            return NotImplemented
        return self.x == other.x and self.y == other.y

    def __repr__(self) -> str:
        return f"Point({self.x}, {self.y})"

    def __hash__(self) -> int:
        return hash((self.x, self.y))


def _cross_product(origin: Point, point_a: Point, point_b: Point) -> float:
    """
    Calculate the cross product of vectors OA and OB.

    Returns:
        > 0: Counter-clockwise turn (left turn)
        = 0: Collinear
        < 0: Clockwise turn (right turn)
    """
    return (point_a.x - origin.x) * (point_b.y - origin.y) - (point_a.y - origin.y) * (
        point_b.x - origin.x
    )


def _is_point_on_segment(p1: Point, p2: Point, point: Point) -> bool:
    """Check if a point lies on the line segment between p1 and p2."""
    # Check if point is collinear with segment endpoints
    cross = (point.y - p1.y) * (p2.x - p1.x) - (point.x - p1.x) * (p2.y - p1.y)

    if abs(cross) > 1e-9:
        return False

    # Check if point is within the bounding box of the segment
    return min(p1.x, p2.x) <= point.x <= max(p1.x, p2.x) and min(
        p1.y, p2.y
    ) <= point.y <= max(p1.y, p2.y)


def _find_leftmost_point(points: list[Point]) -> int:
    """Find index of leftmost point (and bottom-most in case of tie)."""
    left_idx = 0
    for i in range(1, len(points)):
        if points[i].x < points[left_idx].x or (
            points[i].x == points[left_idx].x and points[i].y < points[left_idx].y
        ):
            left_idx = i
    return left_idx


def _find_next_hull_point(points: list[Point], current_idx: int) -> int:
    """Find the next point on the convex hull."""
    next_idx = (current_idx + 1) % len(points)
    # Ensure next_idx is not the same as current_idx
    while next_idx == current_idx:
        next_idx = (next_idx + 1) % len(points)

    for i in range(len(points)):
        if i == current_idx:
            continue
        cross = _cross_product(points[current_idx], points[i], points[next_idx])
        if cross > 0:
            next_idx = i

    return next_idx


def _is_valid_polygon(hull: list[Point]) -> bool:
    """Check if hull forms a valid polygon (has at least one non-collinear turn)."""
    for i in range(len(hull)):
        p1 = hull[i]
        p2 = hull[(i + 1) % len(hull)]
        p3 = hull[(i + 2) % len(hull)]
        if abs(_cross_product(p1, p2, p3)) > 1e-9:
            return True
    return False


def _add_point_to_hull(hull: list[Point], point: Point) -> None:
    """Add a point to hull, removing collinear intermediate points."""
    last = len(hull) - 1
    if len(hull) > 1 and _is_point_on_segment(hull[last - 1], hull[last], point):
        hull[last] = Point(point.x, point.y)
    else:
        hull.append(Point(point.x, point.y))


def jarvis_march(points: list[Point]) -> list[Point]:
    """
    Find the convex hull of a set of points using the Jarvis March algorithm.

    The algorithm starts with the leftmost point and wraps around the set of
    points, selecting the most counter-clockwise point at each step.

    Args:
        points: List of Point objects representing 2D coordinates

    Returns:
        List of Points that form the convex hull in counter-clockwise order.
        Returns empty list if there are fewer than 3 non-collinear points.
    """
    if len(points) <= 2:
        return []

    # Remove duplicate points to avoid infinite loops
    unique_points = list(set(points))

    if len(unique_points) <= 2:
        return []

    convex_hull: list[Point] = []

    # Find the leftmost point
    left_point_idx = _find_leftmost_point(unique_points)
    convex_hull.append(
        Point(unique_points[left_point_idx].x, unique_points[left_point_idx].y)
    )

    current_idx = left_point_idx
    while True:
        # Find the next counter-clockwise point
        next_idx = _find_next_hull_point(unique_points, current_idx)

        if next_idx == left_point_idx:
            break

        if next_idx == current_idx:
            break

        current_idx = next_idx
        _add_point_to_hull(convex_hull, unique_points[current_idx])

    # Check for degenerate cases
    if len(convex_hull) <= 2:
        return []

    # Check if last point is collinear with first and second-to-last
    last = len(convex_hull) - 1
    if _is_point_on_segment(convex_hull[last - 1], convex_hull[last], convex_hull[0]):
        convex_hull.pop()
        if len(convex_hull) == 2:
            return []

    # Verify the hull forms a valid polygon
    if not _is_valid_polygon(convex_hull):
        return []

    return convex_hull


if __name__ == "__main__":
    # Example usage
    points = [Point(0, 0), Point(1, 1), Point(0, 1), Point(1, 0), Point(0.5, 0.5)]
    hull = jarvis_march(points)
    print(f"Convex hull: {hull}")


================================================
FILE: geometry/tests/__init__.py
================================================


================================================
FILE: geometry/tests/test_graham_scan.py
================================================
"""
Tests for the Graham scan convex hull algorithm.
"""

from geometry.graham_scan import Point, graham_scan


def test_empty_points() -> None:
    """Test with no points."""
    assert graham_scan([]) == []


def test_single_point() -> None:
    """Test with a single point."""
    assert graham_scan([Point(0, 0)]) == []


def test_two_points() -> None:
    """Test with two points."""
    assert graham_scan([Point(0, 0), Point(1, 1)]) == []


def test_duplicate_points() -> None:
    """Test with all duplicate points."""
    p = Point(0, 0)
    points = [p, Point(0, 0), Point(0, 0), Point(0, 0), Point(0, 0)]
    assert graham_scan(points) == []


def test_collinear_points() -> None:
    """Test with all points on the same line."""
    points = [
        Point(1, 0),
        Point(2, 0),
        Point(3, 0),
        Point(4, 0),
        Point(5, 0),
    ]
    assert graham_scan(points) == []


def test_triangle() -> None:
    """Test with a triangle (3 points)."""
    p1 = Point(1, 1)
    p2 = Point(2, 1)
    p3 = Point(1.5, 2)
    points = [p1, p2, p3]
    hull = graham_scan(points)

    assert len(hull) == 3
    assert p1 in hull
    assert p2 in hull
    assert p3 in hull


def test_rectangle() -> None:
    """Test with a rectangle (4 points)."""
    p1 = Point(1, 1)
    p2 = Point(2, 1)
    p3 = Point(2, 2)
    p4 = Point(1, 2)
    points = [p1, p2, p3, p4]
    hull = graham_scan(points)

    assert len(hull) == 4
    assert all(p in hull for p in points)


def test_triangle_with_interior_points() -> None:
    """Test triangle with points inside."""
    p1 = Point(1, 1)
    p2 = Point(2, 1)
    p3 = Point(1.5, 2)
    p4 = Point(1.5, 1.5)  # Interior
    p5 = Point(1.2, 1.3)  # Interior
    p6 = Point(1.8, 1.2)  # Interior
    p7 = Point(1.5, 1.9)  # Interior

    hull_points = [p1, p2, p3]
    interior_points = [p4, p5, p6, p7]
    all_points = hull_points + interior_points

    hull = graham_scan(all_points)

    # All hull points should be in the result
    for p in hull_points:
        assert p in hull

    # No interior points should be in the result
    for p in interior_points:
        assert p not in hull


def test_rectangle_with_interior_points() -> None:
    """Test rectangle with points inside."""
    p1 = Point(1, 1)
    p2 = Point(2, 1)
    p3 = Point(2, 2)
    p4 = Point(1, 2)
    p5 = Point(1.5, 1.5)  # Interior
    p6 = Point(1.2, 1.3)  # Interior
    p7 = Point(1.8, 1.2)  # Interior
    p8 = Point(1.9, 1.7)  # Interior
    p9 = Point(1.4, 1.9)  # Interior

    hull_points = [p1, p2, p3, p4]
    interior_points = [p5, p6, p7, p8, p9]
    all_points = hull_points + interior_points

    hull = graham_scan(all_points)

    # All hull points should be in the result
    for p in hull_points:
        assert p in hull

    # No interior points should be in the result
    for p in interior_points:
        assert p not in hull


def test_star_shape() -> None:
    """Test with a star shape where only tips are on the convex hull."""
    # Tips of the star (on convex hull)
    p1 = Point(-5, 6)
    p2 = Point(-11, 0)
    p3 = Point(-9, -8)
    p4 = Point(4, 4)
    p5 = Point(6, -7)

    # Interior points (not on convex hull)
    p6 = Point(-7, -2)
    p7 = Point(-2, -4)
    p8 = Point(0, 1)
    p9 = Point(1, 0)
    p10 = Point(-6, 1)

    hull_points = [p1, p2, p3, p4, p5]
    interior_points = [p6, p7, p8, p9, p10]
    all_points = hull_points + interior_points

    hull = graham_scan(all_points)

    # All hull points should be in the result
    for p in hull_points:
        assert p in hull

    # No interior points should be in the result
    for p in interior_points:
        assert p not in hull


def test_rectangle_with_collinear_points() -> None:
    """Test rectangle with points on the edges (collinear with vertices)."""
    p1 = Point(1, 1)
    p2 = Point(2, 1)
    p3 = Point(2, 2)
    p4 = Point(1, 2)
    p5 = Point(1.5, 1)  # On edge p1-p2
    p6 = Point(1, 1.5)  # On edge p1-p4
    p7 = Point(2, 1.5)  # On edge p2-p3
    p8 = Point(1.5, 2)  # On edge p3-p4

    hull_points = [p1, p2, p3, p4]
    edge_points = [p5, p6, p7, p8]
    all_points = hull_points + edge_points

    hull = graham_scan(all_points)

    # All corner points should be in the result
    for p in hull_points:
        assert p in hull

    # Edge points should not be in the result (only corners)
    for p in edge_points:
        assert p not in hull


def test_point_equality() -> None:
    """Test Point equality."""
    p1 = Point(1, 2)
    p2 = Point(1, 2)
    p3 = Point(2, 1)

    assert p1 == p2
    assert p1 != p3


def test_point_comparison() -> None:
    """Test Point comparison for sorting."""
    p1 = Point(1, 2)
    p2 = Point(1, 3)
    p3 = Point(2, 2)

    assert p1 < p2  # Lower y value
    assert p1 < p3  # Same y, lower x
    assert not p2 < p1


def test_euclidean_distance() -> None:
    """Test Euclidean distance calculation."""
    p1 = Point(0, 0)
    p2 = Point(3, 4)

    assert p1.euclidean_distance(p2) == 5.0


def test_consecutive_orientation() -> None:
    """Test orientation calculation."""
    p1 = Point(0, 0)
    p2 = Point(1, 0)
    p3_ccw = Point(1, 1)  # Counter-clockwise
    p3_cw = Point(1, -1)  # Clockwise
    p3_collinear = Point(2, 0)  # Collinear

    assert p1.consecutive_orientation(p2, p3_ccw) > 0  # Counter-clockwise
    assert p1.consecutive_orientation(p2, p3_cw) < 0  # Clockwise
    assert p1.consecutive_orientation(p2, p3_collinear) == 0  # Collinear


def test_large_hull() -> None:
    """Test with a larger set of points."""
    # Create a circle of points
    import math

    points = []
    for i in range(20):
        angle = 2 * math.pi * i / 20
        x = math.cos(angle)
        y = math.sin(angle)
        points.append(Point(x, y))

    # Add some interior points
    points.append(Point(0, 0))
    points.append(Point(0.5, 0.5))
    points.append(Point(-0.3, 0.2))

    hull = graham_scan(points)

    # The hull should contain the circle points but not the interior points
    assert len(hull) >= 3
    assert Point(0, 0) not in hull
    assert Point(0.5, 0.5) not in hull
    assert Point(-0.3, 0.2) not in hull


def test_random_order() -> None:
    """Test that point order doesn't affect the result."""
    p1 = Point(0, 0)
    p2 = Point(4, 0)
    p3 = Point(4, 3)
    p4 = Point(0, 3)
    p5 = Point(2, 1.5)  # Interior

    # Try different orderings
    order1 = [p1, p2, p3, p4, p5]
    order2 = [p5, p4, p3, p2, p1]
    order3 = [p3, p5, p1, p4, p2]

    hull1 = graham_scan(order1)
    hull2 = graham_scan(order2)
    hull3 = graham_scan(order3)

    # All should have the same points (though possibly in different order)
    assert len(hull1) == len(hull2) == len(hull3) == 4
    assert {(p.x, p.y) for p in hull1} == {(p.x, p.y) for p in hull2}
    assert {(p.x, p.y) for p in hull2} == {(p.x, p.y) for p in hull3}


================================================
FILE: geometry/tests/test_jarvis_march.py
================================================
"""
Unit tests for Jarvis March (Gift Wrapping) algorithm.
"""

from geometry.jarvis_march import Point, jarvis_march


class TestPoint:
    """Tests for the Point class."""

    def test_point_creation(self) -> None:
        """Test Point initialization."""
        p = Point(1.0, 2.0)
        assert p.x == 1.0
        assert p.y == 2.0

    def test_point_equality(self) -> None:
        """Test Point equality comparison."""
        p1 = Point(1.0, 2.0)
        p2 = Point(1.0, 2.0)
        p3 = Point(2.0, 1.0)
        assert p1 == p2
        assert p1 != p3

    def test_point_repr(self) -> None:
        """Test Point string representation."""
        p = Point(1.5, 2.5)
        assert repr(p) == "Point(1.5, 2.5)"

    def test_point_hash(self) -> None:
        """Test Point hashing."""
        p1 = Point(1.0, 2.0)
        p2 = Point(1.0, 2.0)
        assert hash(p1) == hash(p2)


class TestJarvisMarch:
    """Tests for the jarvis_march function."""

    def test_triangle(self) -> None:
        """Test convex hull of a triangle."""
        p1, p2, p3 = Point(1, 1), Point(2, 1), Point(1.5, 2)
        hull = jarvis_march([p1, p2, p3])
        assert len(hull) == 3
        assert all(p in hull for p in [p1, p2, p3])

    def test_collinear_points(self) -> None:
        """Test that collinear points return empty hull."""
        points = [Point(i, 0) for i in range(5)]
        hull = jarvis_march(points)
        assert hull == []

    def test_rectangle_with_interior_point(self) -> None:
        """Test rectangle with interior point - interior point excluded."""
        p1, p2 = Point(1, 1), Point(2, 1)
        p3, p4 = Point(2, 2), Point(1, 2)
        p5 = Point(1.5, 1.5)
        hull = jarvis_march([p1, p2, p3, p4, p5])
        assert len(hull) == 4
        assert p5 not in hull

    def test_star_shape(self) -> None:
        """Test star shape - only tips are in hull."""
        tips = [
            Point(-5, 6),
            Point(-11, 0),
            Point(-9, -8),
            Point(4, 4),
            Point(6, -7),
        ]
        interior = [Point(-7, -2), Point(-2, -4), Point(0, 1)]
        hull = jarvis_march(tips + interior)
        assert len(hull) == 5
        assert all(p in hull for p in tips)
        assert not any(p in hull for p in interior)

    def test_empty_list(self) -> None:
        """Test empty list returns empty hull."""
        assert jarvis_march([]) == []

    def test_single_point(self) -> None:
        """Test single point returns empty hull."""
        assert jarvis_march([Point(0, 0)]) == []

    def test_two_points(self) -> None:
        """Test two points return empty hull."""
        assert jarvis_march([Point(0, 0), Point(1, 1)]) == []

    def test_square(self) -> None:
        """Test convex hull of a square."""
        p1, p2 = Point(0, 0), Point(1, 0)
        p3, p4 = Point(1, 1), Point(0, 1)
        hull = jarvis_march([p1, p2, p3, p4])
        assert len(hull) == 4
        assert all(p in hull for p in [p1, p2, p3, p4])

    def test_duplicate_points(self) -> None:
        """Test handling of duplicate points."""
        p1, p2, p3 = Point(0, 0), Point(1, 0), Point(0, 1)
        points = [p1, p2, p3, p1, p2]  # Include duplicates
        hull = jarvis_march(points)
        assert len(hull) == 3

    def test_pentagon(self) -> None:
        """Test convex hull of a pentagon."""
        points = [
            Point(0, 1),
            Point(1, 2),
            Point(2, 1),
            Point(1.5, 0),
            Point(0.5, 0),
        ]
        hull = jarvis_march(points)
        assert len(hull) == 5
        assert all(p in hull for p in points)


================================================
FILE: graphics/__init__.py
================================================


================================================
FILE: graphics/bezier_curve.py
================================================
# https://en.wikipedia.org/wiki/B%C3%A9zier_curve
# https://www.tutorialspoint.com/computer_graphics/computer_graphics_curves.htm
from __future__ import annotations

from scipy.special import comb


class BezierCurve:
    """
    Bezier curve is a weighted sum of a set of control points.
    Generate Bezier curves from a given set of control points.
    This implementation works only for 2d coordinates in the xy plane.
    """

    def __init__(self, list_of_points: list[tuple[float, float]]):
        """
        list_of_points: Control points in the xy plane on which to interpolate. These
            points control the behavior (shape) of the Bezier curve.
        """
        self.list_of_points = list_of_points
        # Degree determines the flexibility of the curve.
        # Degree = 1 will produce a straight line.
        self.degree = len(list_of_points) - 1

    def basis_function(self, t: float) -> list[float]:
        """
        The basis function determines the weight of each control point at time t.
            t: time value between 0 and 1 inclusive at which to evaluate the basis of
               the curve.
        returns the x, y values of basis function at time t

        >>> curve = BezierCurve([(1,1), (1,2)])
        >>> [float(x) for x in curve.basis_function(0)]
        [1.0, 0.0]
        >>> [float(x) for x in curve.basis_function(1)]
        [0.0, 1.0]
        """
        assert 0 <= t <= 1, "Time t must be between 0 and 1."
        output_values: list[float] = []
        for i in range(len(self.list_of_points)):
            # basis function for each i
            output_values.append(
                comb(self.degree, i) * ((1 - t) ** (self.degree - i)) * (t**i)
            )
        # the basis must sum up to 1 for it to produce a valid Bezier curve.
        assert round(sum(output_values), 5) == 1
        return output_values

    def bezier_curve_function(self, t: float) -> tuple[float, float]:
        """
        The function to produce the values of the Bezier curve at time t.
            t: the value of time t at which to evaluate the Bezier function
        Returns the x, y coordinates of the Bezier curve at time t.
            The first point in the curve is when t = 0.
            The last point in the curve is when t = 1.

        >>> curve = BezierCurve([(1,1), (1,2)])
        >>> tuple(float(x) for x in curve.bezier_curve_function(0))
        (1.0, 1.0)
        >>> tuple(float(x) for x in curve.bezier_curve_function(1))
        (1.0, 2.0)
        """

        assert 0 <= t <= 1, "Time t must be between 0 and 1."

        basis_function = self.basis_function(t)
        x = 0.0
        y = 0.0
        for i in range(len(self.list_of_points)):
            # For all points, sum up the product of i-th basis function and i-th point.
            x += basis_function[i] * self.list_of_points[i][0]
            y += basis_function[i] * self.list_of_points[i][1]
        return (x, y)

    def plot_curve(self, step_size: float = 0.01):
        """
        Plots the Bezier curve using matplotlib plotting capabilities.
            step_size: defines the step(s) at which to evaluate the Bezier curve.
            The smaller the step size, the finer the curve produced.
        """
        from matplotlib import pyplot as plt

        to_plot_x: list[float] = []  # x coordinates of points to plot
        to_plot_y: list[float] = []  # y coordinates of points to plot

        t = 0.0
        while t <= 1:
            value = self.bezier_curve_function(t)
            to_plot_x.append(value[0])
            to_plot_y.append(value[1])
            t += step_size

        x = [i[0] for i in self.list_of_points]
        y = [i[1] for i in self.list_of_points]

        plt.plot(
            to_plot_x,
            to_plot_y,
            color="blue",
            label="Curve of Degree " + str(self.degree),
        )
        plt.scatter(x, y, color="red", label="Control Points")
        plt.legend()
        plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
    BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
    BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3


================================================
FILE: graphics/butterfly_pattern.py
================================================
def butterfly_pattern(n: int) -> str:
    """
    Creates a butterfly pattern of size n and returns it as a string.

    >>> print(butterfly_pattern(3))
    *   *
    ** **
    *****
    ** **
    *   *
    >>> print(butterfly_pattern(5))
    *       *
    **     **
    ***   ***
    **** ****
    *********
    **** ****
    ***   ***
    **     **
    *       *
    """
    result = []

    # Upper part
    for i in range(1, n):
        left_stars = "*" * i
        spaces = " " * (2 * (n - i) - 1)
        right_stars = "*" * i
        result.append(left_stars + spaces + right_stars)

    # Middle part
    result.append("*" * (2 * n - 1))

    # Lower part
    for i in range(n - 1, 0, -1):
        left_stars = "*" * i
        spaces = " " * (2 * (n - i) - 1)
        right_stars = "*" * i
        result.append(left_stars + spaces + right_stars)

    return "\n".join(result)


if __name__ == "__main__":
    n = int(input("Enter the size of the butterfly pattern: "))
    print(butterfly_pattern(n))


================================================
FILE: graphics/digital_differential_analyzer_line.py
================================================
import matplotlib.pyplot as plt


def digital_differential_analyzer_line(
    p1: tuple[int, int], p2: tuple[int, int]
) -> list[tuple[int, int]]:
    """
    Draws a line between two points using the DDA algorithm.

    Args:
    - p1: Coordinates of the starting point.
    - p2: Coordinates of the ending point.
    Returns:
    - List of coordinate points that form the line.

    >>> digital_differential_analyzer_line((1, 1), (4, 4))
    [(2, 2), (3, 3), (4, 4)]
    """
    x1, y1 = p1
    x2, y2 = p2
    dx = x2 - x1
    dy = y2 - y1
    steps = max(abs(dx), abs(dy))
    x_increment = dx / float(steps)
    y_increment = dy / float(steps)
    coordinates = []
    x: float = x1
    y: float = y1
    for _ in range(steps):
        x += x_increment
        y += y_increment
        coordinates.append((round(x), round(y)))
    return coordinates


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    x1 = int(input("Enter the x-coordinate of the starting point: "))
    y1 = int(input("Enter the y-coordinate of the starting point: "))
    x2 = int(input("Enter the x-coordinate of the ending point: "))
    y2 = int(input("Enter the y-coordinate of the ending point: "))
    coordinates = digital_differential_analyzer_line((x1, y1), (x2, y2))
    x_points, y_points = zip(*coordinates)
    plt.plot(x_points, y_points, marker="o")
    plt.title("Digital Differential Analyzer Line Drawing Algorithm")
    plt.xlabel("X-axis")
    plt.ylabel("Y-axis")
    plt.grid()
    plt.show()


================================================
FILE: graphics/vector3_for_2d_rendering.py
================================================
"""
render 3d points for 2d surfaces.
"""

from __future__ import annotations

import math

__version__ = "2020.9.26"
__author__ = "xcodz-dot, cclaus, dhruvmanila"


def convert_to_2d(
    x: float, y: float, z: float, scale: float, distance: float
) -> tuple[float, float]:
    """
    Converts 3d point to a 2d drawable point

    >>> convert_to_2d(1.0, 2.0, 3.0, 10.0, 10.0)
    (7.6923076923076925, 15.384615384615385)

    >>> convert_to_2d(1, 2, 3, 10, 10)
    (7.6923076923076925, 15.384615384615385)

    >>> convert_to_2d("1", 2, 3, 10, 10)  # '1' is str
    Traceback (most recent call last):
        ...
    TypeError: Input values must either be float or int: ['1', 2, 3, 10, 10]
    """
    if not all(isinstance(val, (float, int)) for val in locals().values()):
        msg = f"Input values must either be float or int: {list(locals().values())}"
        raise TypeError(msg)
    projected_x = ((x * distance) / (z + distance)) * scale
    projected_y = ((y * distance) / (z + distance)) * scale
    return projected_x, projected_y


def rotate(
    x: float, y: float, z: float, axis: str, angle: float
) -> tuple[float, float, float]:
    """
    rotate a point around a certain axis with a certain angle
    angle can be any integer between 1, 360 and axis can be any one of
    'x', 'y', 'z'

    >>> rotate(1.0, 2.0, 3.0, 'y', 90.0)
    (3.130524675073759, 2.0, 0.4470070007889556)

    >>> rotate(1, 2, 3, "z", 180)
    (0.999736015495891, -2.0001319704760485, 3)

    >>> rotate('1', 2, 3, "z", 90.0)  # '1' is str
    Traceback (most recent call last):
        ...
    TypeError: Input values except axis must either be float or int: ['1', 2, 3, 90.0]

    >>> rotate(1, 2, 3, "n", 90)  # 'n' is not a valid axis
    Traceback (most recent call last):
        ...
    ValueError: not a valid axis, choose one of 'x', 'y', 'z'

    >>> rotate(1, 2, 3, "x", -90)
    (1, -2.5049096187183877, -2.5933429780983657)

    >>> rotate(1, 2, 3, "x", 450)  # 450 wrap around to 90
    (1, 3.5776792428178217, -0.44744970165427644)
    """
    if not isinstance(axis, str):
        raise TypeError("Axis must be a str")
    input_variables = locals()
    del input_variables["axis"]
    if not all(isinstance(val, (float, int)) for val in input_variables.values()):
        msg = (
            "Input values except axis must either be float or int: "
            f"{list(input_variables.values())}"
        )
        raise TypeError(msg)
    angle = (angle % 360) / 450 * 180 / math.pi
    if axis == "z":
        new_x = x * math.cos(angle) - y * math.sin(angle)
        new_y = y * math.cos(angle) + x * math.sin(angle)
        new_z = z
    elif axis == "x":
        new_y = y * math.cos(angle) - z * math.sin(angle)
        new_z = z * math.cos(angle) + y * math.sin(angle)
        new_x = x
    elif axis == "y":
        new_x = x * math.cos(angle) - z * math.sin(angle)
        new_z = z * math.cos(angle) + x * math.sin(angle)
        new_y = y
    else:
        raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'")

    return new_x, new_y, new_z


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(f"{convert_to_2d(1.0, 2.0, 3.0, 10.0, 10.0) = }")
    print(f"{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }")


================================================
FILE: graphs/__init__.py
================================================


================================================
FILE: graphs/a_star.py
================================================
from __future__ import annotations

DIRECTIONS = [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]


# function to search the path
def search(
    grid: list[list[int]],
    init: list[int],
    goal: list[int],
    cost: int,
    heuristic: list[list[int]],
) -> tuple[list[list[int]], list[list[int]]]:
    """
    Search for a path on a grid avoiding obstacles.
    >>> grid = [[0, 1, 0, 0, 0, 0],
    ...         [0, 1, 0, 0, 0, 0],
    ...         [0, 1, 0, 0, 0, 0],
    ...         [0, 1, 0, 0, 1, 0],
    ...         [0, 0, 0, 0, 1, 0]]
    >>> init = [0, 0]
    >>> goal = [len(grid) - 1, len(grid[0]) - 1]
    >>> cost = 1
    >>> heuristic = [[0] * len(grid[0]) for _ in range(len(grid))]
    >>> heuristic = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
    >>> for i in range(len(grid)):
    ...     for j in range(len(grid[0])):
    ...         heuristic[i][j] = abs(i - goal[0]) + abs(j - goal[1])
    ...         if grid[i][j] == 1:
    ...             heuristic[i][j] = 99
    >>> path, action = search(grid, init, goal, cost, heuristic)
    >>> path  # doctest: +NORMALIZE_WHITESPACE
    [[0, 0], [1, 0], [2, 0], [3, 0], [4, 0], [4, 1], [4, 2], [4, 3], [3, 3],
    [2, 3], [2, 4], [2, 5], [3, 5], [4, 5]]
    >>> action  # doctest: +NORMALIZE_WHITESPACE
    [[0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0], [2, 0, 0, 0, 3, 3],
    [2, 0, 0, 0, 0, 2], [2, 3, 3, 3, 0, 2]]
    """
    closed = [
        [0 for col in range(len(grid[0]))] for row in range(len(grid))
    ]  # the reference grid
    closed[init[0]][init[1]] = 1
    action = [
        [0 for col in range(len(grid[0]))] for row in range(len(grid))
    ]  # the action grid

    x = init[0]
    y = init[1]
    g = 0
    f = g + heuristic[x][y]  # cost from starting cell to destination cell
    cell = [[f, g, x, y]]

    found = False  # flag that is set when search is complete
    resign = False  # flag set if we can't find expand

    while not found and not resign:
        if len(cell) == 0:
            raise ValueError("Algorithm is unable to find solution")
        else:  # to choose the least costliest action so as to move closer to the goal
            cell.sort()
            cell.reverse()
            next_cell = cell.pop()
            x = next_cell[2]
            y = next_cell[3]
            g = next_cell[1]

            if x == goal[0] and y == goal[1]:
                found = True
            else:
                for i in range(len(DIRECTIONS)):  # to try out different valid actions
                    x2 = x + DIRECTIONS[i][0]
                    y2 = y + DIRECTIONS[i][1]
                    if (
                        x2 >= 0
                        and x2 < len(grid)
                        and y2 >= 0
                        and y2 < len(grid[0])
                        and closed[x2][y2] == 0
                        and grid[x2][y2] == 0
                    ):
                        g2 = g + cost
                        f2 = g2 + heuristic[x2][y2]
                        cell.append([f2, g2, x2, y2])
                        closed[x2][y2] = 1
                        action[x2][y2] = i
    invpath = []
    x = goal[0]
    y = goal[1]
    invpath.append([x, y])  # we get the reverse path from here
    while x != init[0] or y != init[1]:
        x2 = x - DIRECTIONS[action[x][y]][0]
        y2 = y - DIRECTIONS[action[x][y]][1]
        x = x2
        y = y2
        invpath.append([x, y])

    path = []
    for i in range(len(invpath)):
        path.append(invpath[len(invpath) - 1 - i])
    return path, action


if __name__ == "__main__":
    grid = [
        [0, 1, 0, 0, 0, 0],
        [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
        [0, 1, 0, 0, 0, 0],
        [0, 1, 0, 0, 1, 0],
        [0, 0, 0, 0, 1, 0],
    ]

    init = [0, 0]
    # all coordinates are given in format [y,x]
    goal = [len(grid) - 1, len(grid[0]) - 1]
    cost = 1

    # the cost map which pushes the path closer to the goal
    heuristic = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
    for i in range(len(grid)):
        for j in range(len(grid[0])):
            heuristic[i][j] = abs(i - goal[0]) + abs(j - goal[1])
            if grid[i][j] == 1:
                # added extra penalty in the heuristic map
                heuristic[i][j] = 99

    path, action = search(grid, init, goal, cost, heuristic)

    print("ACTION MAP")
    for i in range(len(action)):
        print(action[i])

    for i in range(len(path)):
        print(path[i])


================================================
FILE: graphs/ant_colony_optimization_algorithms.py
================================================
"""
Use an ant colony optimization algorithm to solve the travelling salesman problem (TSP)
which asks the following question:
"Given a list of cities and the distances between each pair of cities, what is the
 shortest possible route that visits each city exactly once and returns to the origin
 city?"

https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms
https://en.wikipedia.org/wiki/Travelling_salesman_problem

Author: Clark
"""

import copy
import random

cities = {
    0: [0, 0],
    1: [0, 5],
    2: [3, 8],
    3: [8, 10],
    4: [12, 8],
    5: [12, 4],
    6: [8, 0],
    7: [6, 2],
}


def main(
    cities: dict[int, list[int]],
    ants_num: int,
    iterations_num: int,
    pheromone_evaporation: float,
    alpha: float,
    beta: float,
    q: float,  # Pheromone system parameters Q, which is a constant
) -> tuple[list[int], float]:
    """
    Ant colony algorithm main function
    >>> main(cities=cities, ants_num=10, iterations_num=20,
    ...      pheromone_evaporation=0.7, alpha=1.0, beta=5.0, q=10)
    ([0, 1, 2, 3, 4, 5, 6, 7, 0], 37.909778143828696)
    >>> main(cities={0: [0, 0], 1: [2, 2]}, ants_num=5, iterations_num=5,
    ...      pheromone_evaporation=0.7, alpha=1.0, beta=5.0, q=10)
    ([0, 1, 0], 5.656854249492381)
    >>> main(cities={0: [0, 0], 1: [2, 2], 4: [4, 4]}, ants_num=5, iterations_num=5,
    ...      pheromone_evaporation=0.7, alpha=1.0, beta=5.0, q=10)
    Traceback (most recent call last):
      ...
    IndexError: list index out of range
    >>> main(cities={}, ants_num=5, iterations_num=5,
    ...      pheromone_evaporation=0.7, alpha=1.0, beta=5.0, q=10)
    Traceback (most recent call last):
      ...
    StopIteration
    >>> main(cities={0: [0, 0], 1: [2, 2]}, ants_num=0, iterations_num=5,
    ...      pheromone_evaporation=0.7, alpha=1.0, beta=5.0, q=10)
    ([], inf)
    >>> main(cities={0: [0, 0], 1: [2, 2]}, ants_num=5, iterations_num=0,
    ...      pheromone_evaporation=0.7, alpha=1.0, beta=5.0, q=10)
    ([], inf)
    >>> main(cities={0: [0, 0], 1: [2, 2]}, ants_num=5, iterations_num=5,
    ...      pheromone_evaporation=1, alpha=1.0, beta=5.0, q=10)
    ([0, 1, 0], 5.656854249492381)
    >>> main(cities={0: [0, 0], 1: [2, 2]}, ants_num=5, iterations_num=5,
    ...      pheromone_evaporation=0, alpha=1.0, beta=5.0, q=10)
    ([0, 1, 0], 5.656854249492381)
    """
    # Initialize the pheromone matrix
    cities_num = len(cities)
    pheromone = [[1.0] * cities_num] * cities_num

    best_path: list[int] = []
    best_distance = float("inf")
    for _ in range(iterations_num):
        ants_route = []
        for _ in range(ants_num):
            unvisited_cities = copy.deepcopy(cities)
            current_city = {next(iter(cities.keys())): next(iter(cities.values()))}
            del unvisited_cities[next(iter(current_city.keys()))]
            ant_route = [next(iter(current_city.keys()))]
            while unvisited_cities:
                current_city, unvisited_cities = city_select(
                    pheromone, current_city, unvisited_cities, alpha, beta
                )
                ant_route.append(next(iter(current_city.keys())))
            ant_route.append(0)
            ants_route.append(ant_route)

        pheromone, best_path, best_distance = pheromone_update(
            pheromone,
            cities,
            pheromone_evaporation,
            ants_route,
            q,
            best_path,
            best_distance,
        )
    return best_path, best_distance


def distance(city1: list[int], city2: list[int]) -> float:
    """
    Calculate the distance between two coordinate points
    >>> distance([0, 0], [3, 4] )
    5.0
    >>> distance([0, 0], [-3, 4] )
    5.0
    >>> distance([0, 0], [-3, -4] )
    5.0
    """
    return (((city1[0] - city2[0]) ** 2) + ((city1[1] - city2[1]) ** 2)) ** 0.5


def pheromone_update(
    pheromone: list[list[float]],
    cities: dict[int, list[int]],
    pheromone_evaporation: float,
    ants_route: list[list[int]],
    q: float,  # Pheromone system parameters Q, which is a constant
    best_path: list[int],
    best_distance: float,
) -> tuple[list[list[float]], list[int], float]:
    """
    Update pheromones on the route and update the best route
    >>>
    >>> pheromone_update(pheromone=[[1.0, 1.0], [1.0, 1.0]],
    ...                  cities={0: [0,0], 1: [2,2]}, pheromone_evaporation=0.7,
    ...                  ants_route=[[0, 1, 0]], q=10, best_path=[],
    ...                  best_distance=float("inf"))
    ([[0.7, 4.235533905932737], [4.235533905932737, 0.7]], [0, 1, 0], 5.656854249492381)
    >>> pheromone_update(pheromone=[],
    ...                  cities={0: [0,0], 1: [2,2]}, pheromone_evaporation=0.7,
    ...                  ants_route=[[0, 1, 0]], q=10, best_path=[],
    ...                  best_distance=float("inf"))
    Traceback (most recent call last):
      ...
    IndexError: list index out of range
    >>> pheromone_update(pheromone=[[1.0, 1.0], [1.0, 1.0]],
    ...                  cities={}, pheromone_evaporation=0.7,
    ...                  ants_route=[[0, 1, 0]], q=10, best_path=[],
    ...                  best_distance=float("inf"))
    Traceback (most recent call last):
      ...
    KeyError: 0
    """
    for a in range(len(cities)):  # Update the volatilization of pheromone on all routes
        for b in range(len(cities)):
            pheromone[a][b] *= pheromone_evaporation
    for ant_route in ants_route:
        total_distance = 0.0
        for i in range(len(ant_route) - 1):  # Calculate total distance
            total_distance += distance(cities[ant_route[i]], cities[ant_route[i + 1]])
        delta_pheromone = q / total_distance
        for i in range(len(ant_route) - 1):  # Update pheromones
            pheromone[ant_route[i]][ant_route[i + 1]] += delta_pheromone
            pheromone[ant_route[i + 1]][ant_route[i]] = pheromone[ant_route[i]][
                ant_route[i + 1]
            ]

        if total_distance < best_distance:
            best_path = ant_route
            best_distance = total_distance

    return pheromone, best_path, best_distance


def city_select(
    pheromone: list[list[float]],
    current_city: dict[int, list[int]],
    unvisited_cities: dict[int, list[int]],
    alpha: float,
    beta: float,
) -> tuple[dict[int, list[int]], dict[int, list[int]]]:
    """
    Choose the next city for ants
    >>> city_select(pheromone=[[1.0, 1.0], [1.0, 1.0]], current_city={0: [0, 0]},
    ...             unvisited_cities={1: [2, 2]}, alpha=1.0, beta=5.0)
    ({1: [2, 2]}, {})
    >>> city_select(pheromone=[], current_city={0: [0,0]},
    ...             unvisited_cities={1: [2, 2]}, alpha=1.0, beta=5.0)
    Traceback (most recent call last):
      ...
    IndexError: list index out of range
    >>> city_select(pheromone=[[1.0, 1.0], [1.0, 1.0]], current_city={},
    ...             unvisited_cities={1: [2, 2]}, alpha=1.0, beta=5.0)
    Traceback (most recent call last):
      ...
    StopIteration
    >>> city_select(pheromone=[[1.0, 1.0], [1.0, 1.0]], current_city={0: [0, 0]},
    ...             unvisited_cities={}, alpha=1.0, beta=5.0)
    Traceback (most recent call last):
      ...
    IndexError: list index out of range
    """
    probabilities = []
    for city, value in unvisited_cities.items():
        city_distance = distance(value, next(iter(current_city.values())))
        probability = (pheromone[city][next(iter(current_city.keys()))] ** alpha) * (
            (1 / city_distance) ** beta
        )
        probabilities.append(probability)

    chosen_city_i = random.choices(
        list(unvisited_cities.keys()), weights=probabilities
    )[0]
    chosen_city = {chosen_city_i: unvisited_cities[chosen_city_i]}
    del unvisited_cities[next(iter(chosen_city.keys()))]
    return chosen_city, unvisited_cities


if __name__ == "__main__":
    best_path, best_distance = main(
        cities=cities,
        ants_num=10,
        iterations_num=20,
        pheromone_evaporation=0.7,
        alpha=1.0,
        beta=5.0,
        q=10,
    )

    print(f"{best_path = }")
    print(f"{best_distance = }")


================================================
FILE: graphs/articulation_points.py
================================================
# Finding Articulation Points in Undirected Graph
def compute_ap(graph):
    n = len(graph)
    out_edge_count = 0
    low = [0] * n
    visited = [False] * n
    is_art = [False] * n

    def dfs(root, at, parent, out_edge_count):
        if parent == root:
            out_edge_count += 1
        visited[at] = True
        low[at] = at

        for to in graph[at]:
            if to == parent:
                pass
            elif not visited[to]:
                out_edge_count = dfs(root, to, at, out_edge_count)
                low[at] = min(low[at], low[to])

                # AP found via bridge
                if at < low[to]:
                    is_art[at] = True
                # AP found via cycle
                if at == low[to]:
                    is_art[at] = True
            else:
                low[at] = min(low[at], to)
        return out_edge_count

    for i in range(n):
        if not visited[i]:
            out_edge_count = 0
            out_edge_count = dfs(i, i, -1, out_edge_count)
            is_art[i] = out_edge_count > 1

    for x in range(len(is_art)):
        if is_art[x] is True:
            print(x)


# Adjacency list of graph
graph = {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}
compute_ap(graph)


================================================
FILE: graphs/basic_graphs.py
================================================
from collections import deque


def _input(message):
    return input(message).strip().split(" ")


def initialize_unweighted_directed_graph(
    node_count: int, edge_count: int
) -> dict[int, list[int]]:
    graph: dict[int, list[int]] = {}
    for i in range(node_count):
        graph[i + 1] = []

    for e in range(edge_count):
        x, y = (int(i) for i in _input(f"Edge {e + 1}: <node1> <node2> "))
        graph[x].append(y)
    return graph


def initialize_unweighted_undirected_graph(
    node_count: int, edge_count: int
) -> dict[int, list[int]]:
    graph: dict[int, list[int]] = {}
    for i in range(node_count):
        graph[i + 1] = []

    for e in range(edge_count):
        x, y = (int(i) for i in _input(f"Edge {e + 1}: <node1> <node2> "))
        graph[x].append(y)
        graph[y].append(x)
    return graph


def initialize_weighted_undirected_graph(
    node_count: int, edge_count: int
) -> dict[int, list[tuple[int, int]]]:
    graph: dict[int, list[tuple[int, int]]] = {}
    for i in range(node_count):
        graph[i + 1] = []

    for e in range(edge_count):
        x, y, w = (int(i) for i in _input(f"Edge {e + 1}: <node1> <node2> <weight> "))
        graph[x].append((y, w))
        graph[y].append((x, w))
    return graph


if __name__ == "__main__":
    n, m = (int(i) for i in _input("Number of nodes and edges: "))

    graph_choice = int(
        _input(
            "Press 1 or 2 or 3 \n"
            "1. Unweighted directed \n"
            "2. Unweighted undirected \n"
            "3. Weighted undirected \n"
        )[0]
    )

    g = {
        1: initialize_unweighted_directed_graph,
        2: initialize_unweighted_undirected_graph,
        3: initialize_weighted_undirected_graph,
    }[graph_choice](n, m)


"""
--------------------------------------------------------------------------------
    Depth First Search.
        Args :  G - Dictionary of edges
                s - Starting Node
        Vars :  vis - Set of visited nodes
                S - Traversal Stack
--------------------------------------------------------------------------------
"""


def dfs(g, s):
    """
    >>> dfs({1: [2, 3], 2: [4, 5], 3: [], 4: [], 5: []}, 1)
    1
    2
    4
    5
    3
    """
    vis, _s = {s}, [s]
    print(s)
    while _s:
        flag = 0
        for i in g[_s[-1]]:
            if i not in vis:
                _s.append(i)
                vis.add(i)
                flag = 1
                print(i)
                break
        if not flag:
            _s.pop()


"""
--------------------------------------------------------------------------------
    Breadth First Search.
        Args :  G - Dictionary of edges
                s - Starting Node
        Vars :  vis - Set of visited nodes
                Q - Traversal Stack
--------------------------------------------------------------------------------
"""


def bfs(g, s):
    """
    >>> bfs({1: [2, 3], 2: [4, 5], 3: [6, 7], 4: [], 5: [8], 6: [], 7: [], 8: []}, 1)
    1
    2
    3
    4
    5
    6
    7
    8
    """
    vis, q = {s}, deque([s])
    print(s)
    while q:
        u = q.popleft()
        for v in g[u]:
            if v not in vis:
                vis.add(v)
                q.append(v)
                print(v)


"""
--------------------------------------------------------------------------------
    Dijkstra's shortest path Algorithm
        Args :  G - Dictionary of edges
                s - Starting Node
        Vars :  dist - Dictionary storing shortest distance from s to every other node
                known - Set of knows nodes
                path - Preceding node in path
--------------------------------------------------------------------------------
"""


def dijk(g, s):
    """
    dijk({1: [(2, 7), (3, 9), (6, 14)],
        2: [(1, 7), (3, 10), (4, 15)],
        3: [(1, 9), (2, 10), (4, 11), (6, 2)],
        4: [(2, 15), (3, 11), (5, 6)],
        5: [(4, 6), (6, 9)],
        6: [(1, 14), (3, 2), (5, 9)]}, 1)
    7
    9
    11
    20
    20
    """
    dist, known, path = {s: 0}, set(), {s: 0}
    while True:
        if len(known) == len(g) - 1:
            break
        mini = 100000
        for key, value in dist:
            if key not in known and value < mini:
                mini = value
                u = key
        known.add(u)
        for v in g[u]:
            if v[0] not in known and dist[u] + v[1] < dist.get(v[0], 100000):
                dist[v[0]] = dist[u] + v[1]
                path[v[0]] = u
    for key, value in dist.items():
        if key != s:
            print(value)


"""
--------------------------------------------------------------------------------
    Topological Sort
--------------------------------------------------------------------------------
"""


def topo(g, ind=None, q=None):
    if q is None:
        q = [1]
    if ind is None:
        ind = [0] * (len(g) + 1)  # SInce oth Index is ignored
        for u in g:
            for v in g[u]:
                ind[v] += 1
        q = deque()
        for i in g:
            if ind[i] == 0:
                q.append(i)
    if len(q) == 0:
        return
    v = q.popleft()
    print(v)
    for w in g[v]:
        ind[w] -= 1
        if ind[w] == 0:
            q.append(w)
    topo(g, ind, q)


"""
--------------------------------------------------------------------------------
    Reading an Adjacency matrix
--------------------------------------------------------------------------------
"""


def adjm():
    r"""
    Reading an Adjacency matrix

    Parameters:
        None

    Returns:
        tuple: A tuple containing a list of edges and number of edges

    Example:
    >>> # Simulate user input for 3 nodes
    >>> input_data = "4\n0 1 0 1\n1 0 1 0\n0 1 0 1\n1 0 1 0\n"
    >>> import sys,io
    >>> original_input = sys.stdin
    >>> sys.stdin = io.StringIO(input_data)  # Redirect stdin for testing
    >>> adjm()
    ([(0, 1, 0, 1), (1, 0, 1, 0), (0, 1, 0, 1), (1, 0, 1, 0)], 4)
    >>> sys.stdin = original_input  # Restore original stdin
    """
    n = int(input().strip())
    a = []
    for _ in range(n):
        a.append(tuple(map(int, input().strip().split())))
    return a, n


"""
--------------------------------------------------------------------------------
    Floyd Warshall's algorithm
        Args :  G - Dictionary of edges
                s - Starting Node
        Vars :  dist - Dictionary storing shortest distance from s to every other node
                known - Set of knows nodes
                path - Preceding node in path

--------------------------------------------------------------------------------
"""


def floy(a_and_n):
    (a, n) = a_and_n
    dist = list(a)
    path = [[0] * n for i in range(n)]
    for k in range(n):
        for i in range(n):
            for j in range(n):
                if dist[i][j] > dist[i][k] + dist[k][j]:
                    dist[i][j] = dist[i][k] + dist[k][j]
                    path[i][k] = k
    print(dist)


"""
--------------------------------------------------------------------------------
    Prim's MST Algorithm
        Args :  G - Dictionary of edges
                s - Starting Node
        Vars :  dist - Dictionary storing shortest distance from s to nearest node
                known - Set of knows nodes
                path - Preceding node in path
--------------------------------------------------------------------------------
"""


def prim(g, s):
    dist, known, path = {s: 0}, set(), {s: 0}
    while True:
        if len(known) == len(g) - 1:
            break
        mini = 100000
        for key, value in dist.items():
            if key not in known and value < mini:
                mini = value
                u = key
        known.add(u)
        for v in g[u]:
            if v[0] not in known and v[1] < dist.get(v[0], 100000):
                dist[v[0]] = v[1]
                path[v[0]] = u
    return dist


"""
--------------------------------------------------------------------------------
    Accepting Edge list
        Vars :  n - Number of nodes
                m - Number of edges
        Returns : l - Edge list
                n - Number of Nodes
--------------------------------------------------------------------------------
"""


def edglist():
    r"""
    Get the edges and number of edges from the user

    Parameters:
        None

    Returns:
        tuple: A tuple containing a list of edges and number of edges

    Example:
    >>> # Simulate user input for 3 edges and 4 vertices: (1, 2), (2, 3), (3, 4)
    >>> input_data = "4 3\n1 2\n2 3\n3 4\n"
    >>> import sys,io
    >>> original_input = sys.stdin
    >>> sys.stdin = io.StringIO(input_data)  # Redirect stdin for testing
    >>> edglist()
    ([(1, 2), (2, 3), (3, 4)], 4)
    >>> sys.stdin = original_input  # Restore original stdin
    """
    n, m = tuple(map(int, input().split(" ")))
    edges = []
    for _ in range(m):
        edges.append(tuple(map(int, input().split(" "))))
    return edges, n


"""
--------------------------------------------------------------------------------
    Kruskal's MST Algorithm
        Args :  E - Edge list
                n - Number of Nodes
        Vars :  s - Set of all nodes as unique disjoint sets (initially)
--------------------------------------------------------------------------------
"""


def krusk(e_and_n):
    """
    Sort edges on the basis of distance
    """
    (e, n) = e_and_n
    e.sort(reverse=True, key=lambda x: x[2])
    s = [{i} for i in range(1, n + 1)]
    while True:
        if len(s) == 1:
            break
        print(s)
        x = e.pop()
        for i in range(len(s)):
            if x[0] in s[i]:
                break
        for j in range(len(s)):
            if x[1] in s[j]:
                if i == j:
                    break
                s[j].update(s[i])
                s.pop(i)
                break


def find_isolated_nodes(graph):
    """
    Find the isolated node in the graph

    Parameters:
    graph (dict): A dictionary representing a graph.

    Returns:
    list: A list of isolated nodes.

    Examples:
    >>> graph1 = {1: [2, 3], 2: [1, 3], 3: [1, 2], 4: []}
    >>> find_isolated_nodes(graph1)
    [4]

    >>> graph2 = {'A': ['B', 'C'], 'B': ['A'], 'C': ['A'], 'D': []}
    >>> find_isolated_nodes(graph2)
    ['D']

    >>> graph3 = {'X': [], 'Y': [], 'Z': []}
    >>> find_isolated_nodes(graph3)
    ['X', 'Y', 'Z']

    >>> graph4 = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
    >>> find_isolated_nodes(graph4)
    []

    >>> graph5 = {}
    >>> find_isolated_nodes(graph5)
    []
    """
    isolated = []
    for node in graph:
        if not graph[node]:
            isolated.append(node)
    return isolated


================================================
FILE: graphs/bellman_ford.py
================================================
from __future__ import annotations


def print_distance(distance: list[float], src):
    print(f"Vertex\tShortest Distance from vertex {src}")
    for i, d in enumerate(distance):
        print(f"{i}\t\t{d}")


def check_negative_cycle(
    graph: list[dict[str, int]], distance: list[float], edge_count: int
):
    for j in range(edge_count):
        u, v, w = (graph[j][k] for k in ["src", "dst", "weight"])
        if distance[u] != float("inf") and distance[u] + w < distance[v]:
            return True
    return False


def bellman_ford(
    graph: list[dict[str, int]], vertex_count: int, edge_count: int, src: int
) -> list[float]:
    """
    Returns shortest paths from a vertex src to all
    other vertices.
    >>> edges = [(2, 1, -10), (3, 2, 3), (0, 3, 5), (0, 1, 4)]
    >>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges]
    >>> bellman_ford(g, 4, 4, 0)
    [0.0, -2.0, 8.0, 5.0]
    >>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges + [(1, 3, 5)]]
    >>> bellman_ford(g, 4, 5, 0)
    Traceback (most recent call last):
     ...
    Exception: Negative cycle found
    """
    distance = [float("inf")] * vertex_count
    distance[src] = 0.0

    for _ in range(vertex_count - 1):
        for j in range(edge_count):
            u, v, w = (graph[j][k] for k in ["src", "dst", "weight"])

            if distance[u] != float("inf") and distance[u] + w < distance[v]:
                distance[v] = distance[u] + w

    negative_cycle_exists = check_negative_cycle(graph, distance, edge_count)
    if negative_cycle_exists:
        raise Exception("Negative cycle found")

    return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    V = int(input("Enter number of vertices: ").strip())
    E = int(input("Enter number of edges: ").strip())

    graph: list[dict[str, int]] = [{} for _ in range(E)]

    for i in range(E):
        print("Edge ", i + 1)
        src, dest, weight = (
            int(x)
            for x in input("Enter source, destination, weight: ").strip().split(" ")
        )
        graph[i] = {"src": src, "dst": dest, "weight": weight}

    source = int(input("\nEnter shortest path source:").strip())
    shortest_distance = bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)


================================================
FILE: graphs/bi_directional_dijkstra.py
================================================
"""
Bi-directional Dijkstra's algorithm.

A bi-directional approach is an efficient and
less time consuming optimization for Dijkstra's
searching algorithm

Reference: shorturl.at/exHM7
"""

# Author: Swayam Singh (https://github.com/practice404)

from queue import PriorityQueue
from typing import Any

import numpy as np


def pass_and_relaxation(
    graph: dict,
    v: str,
    visited_forward: set,
    visited_backward: set,
    cst_fwd: dict,
    cst_bwd: dict,
    queue: PriorityQueue,
    parent: dict,
    shortest_distance: float,
) -> float:
    for nxt, d in graph[v]:
        if nxt in visited_forward:
            continue
        old_cost_f = cst_fwd.get(nxt, np.inf)
        new_cost_f = cst_fwd[v] + d
        if new_cost_f < old_cost_f:
            queue.put((new_cost_f, nxt))
            cst_fwd[nxt] = new_cost_f
            parent[nxt] = v
        if (
            nxt in visited_backward
            and cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance
        ):
            shortest_distance = cst_fwd[v] + d + cst_bwd[nxt]
    return shortest_distance


def bidirectional_dij(
    source: str, destination: str, graph_forward: dict, graph_backward: dict
) -> int:
    """
    Bi-directional Dijkstra's algorithm.

    Returns:
        shortest_path_distance (int): length of the shortest path.

    Warnings:
        If the destination is not reachable, function returns -1

    >>> bidirectional_dij("E", "F", graph_fwd, graph_bwd)
    3
    """
    shortest_path_distance = -1

    visited_forward = set()
    visited_backward = set()
    cst_fwd = {source: 0}
    cst_bwd = {destination: 0}
    parent_forward = {source: None}
    parent_backward = {destination: None}
    queue_forward: PriorityQueue[Any] = PriorityQueue()
    queue_backward: PriorityQueue[Any] = PriorityQueue()

    shortest_distance = np.inf

    queue_forward.put((0, source))
    queue_backward.put((0, destination))

    if source == destination:
        return 0

    while not queue_forward.empty() and not queue_backward.empty():
        _, v_fwd = queue_forward.get()
        visited_forward.add(v_fwd)

        _, v_bwd = queue_backward.get()
        visited_backward.add(v_bwd)

        shortest_distance = pass_and_relaxation(
            graph_forward,
            v_fwd,
            visited_forward,
            visited_backward,
            cst_fwd,
            cst_bwd,
            queue_forward,
            parent_forward,
            shortest_distance,
        )

        shortest_distance = pass_and_relaxation(
            graph_backward,
            v_bwd,
            visited_backward,
            visited_forward,
            cst_bwd,
            cst_fwd,
            queue_backward,
            parent_backward,
            shortest_distance,
        )

        if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
            break

    if shortest_distance != np.inf:
        shortest_path_distance = shortest_distance
    return shortest_path_distance


graph_fwd = {
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}
graph_bwd = {
    "B": [["E", 1]],
    "C": [["B", 1]],
    "D": [["C", 1]],
    "F": [["D", 1], ["G", 1]],
    "E": [[None, np.inf]],
    "G": [["E", 2]],
}

if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: graphs/bidirectional_a_star.py
================================================
"""
https://en.wikipedia.org/wiki/Bidirectional_search
"""

from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
HEURISTIC = 0

grid = [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

delta = [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

TPosition = tuple[int, int]


class Node:
    """
    >>> k = Node(0, 0, 4, 3, 0, None)
    >>> k.calculate_heuristic()
    5.0
    >>> n = Node(1, 4, 3, 4, 2, None)
    >>> n.calculate_heuristic()
    2.0
    >>> l = [k, n]
    >>> n == l[0]
    False
    >>> l.sort()
    >>> n == l[0]
    True
    """

    def __init__(
        self,
        pos_x: int,
        pos_y: int,
        goal_x: int,
        goal_y: int,
        g_cost: int,
        parent: Node | None,
    ) -> None:
        self.pos_x = pos_x
        self.pos_y = pos_y
        self.pos = (pos_y, pos_x)
        self.goal_x = goal_x
        self.goal_y = goal_y
        self.g_cost = g_cost
        self.parent = parent
        self.h_cost = self.calculate_heuristic()
        self.f_cost = self.g_cost + self.h_cost

    def calculate_heuristic(self) -> float:
        """
        Heuristic for the A*
        """
        dy = self.pos_x - self.goal_x
        dx = self.pos_y - self.goal_y
        if HEURISTIC == 1:
            return abs(dx) + abs(dy)
        else:
            return sqrt(dy**2 + dx**2)

    def __lt__(self, other: Node) -> bool:
        return self.f_cost < other.f_cost


class AStar:
    """
    >>> astar = AStar((0, 0), (len(grid) - 1, len(grid[0]) - 1))
    >>> (astar.start.pos_y + delta[3][0], astar.start.pos_x + delta[3][1])
    (0, 1)
    >>> [x.pos for x in astar.get_successors(astar.start)]
    [(1, 0), (0, 1)]
    >>> (astar.start.pos_y + delta[2][0], astar.start.pos_x + delta[2][1])
    (1, 0)
    >>> astar.retrace_path(astar.start)
    [(0, 0)]
    >>> astar.search()  # doctest: +NORMALIZE_WHITESPACE
    [(0, 0), (1, 0), (2, 0), (2, 1), (2, 2), (2, 3), (3, 3),
     (4, 3), (4, 4), (5, 4), (5, 5), (6, 5), (6, 6)]
    """

    def __init__(self, start: TPosition, goal: TPosition):
        self.start = Node(start[1], start[0], goal[1], goal[0], 0, None)
        self.target = Node(goal[1], goal[0], goal[1], goal[0], 99999, None)

        self.open_nodes = [self.start]
        self.closed_nodes: list[Node] = []

        self.reached = False

    def search(self) -> list[TPosition]:
        while self.open_nodes:
            # Open Nodes are sorted using __lt__
            self.open_nodes.sort()
            current_node = self.open_nodes.pop(0)

            if current_node.pos == self.target.pos:
                return self.retrace_path(current_node)

            self.closed_nodes.append(current_node)
            successors = self.get_successors(current_node)

            for child_node in successors:
                if child_node in self.closed_nodes:
                    continue

                if child_node not in self.open_nodes:
                    self.open_nodes.append(child_node)
                else:
                    # retrieve the best current path
                    better_node = self.open_nodes.pop(self.open_nodes.index(child_node))

                    if child_node.g_cost < better_node.g_cost:
                        self.open_nodes.append(child_node)
                    else:
                        self.open_nodes.append(better_node)

        return [self.start.pos]

    def get_successors(self, parent: Node) -> list[Node]:
        """
        Returns a list of successors (both in the grid and free spaces)
        """
        successors = []
        for action in delta:
            pos_x = parent.pos_x + action[1]
            pos_y = parent.pos_y + action[0]
            if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(grid) - 1):
                continue

            if grid[pos_y][pos_x] != 0:
                continue

            successors.append(
                Node(
                    pos_x,
                    pos_y,
                    self.target.pos_y,
                    self.target.pos_x,
                    parent.g_cost + 1,
                    parent,
                )
            )
        return successors

    def retrace_path(self, node: Node | None) -> list[TPosition]:
        """
        Retrace the path from parents to parents until start node
        """
        current_node = node
        path = []
        while current_node is not None:
            path.append((current_node.pos_y, current_node.pos_x))
            current_node = current_node.parent
        path.reverse()
        return path


class BidirectionalAStar:
    """
    >>> bd_astar = BidirectionalAStar((0, 0), (len(grid) - 1, len(grid[0]) - 1))
    >>> bd_astar.fwd_astar.start.pos == bd_astar.bwd_astar.target.pos
    True
    >>> bd_astar.retrace_bidirectional_path(bd_astar.fwd_astar.start,
    ...                                     bd_astar.bwd_astar.start)
    [(0, 0)]
    >>> bd_astar.search()  # doctest: +NORMALIZE_WHITESPACE
    [(0, 0), (0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (2, 4),
     (2, 5), (3, 5), (4, 5), (5, 5), (5, 6), (6, 6)]
    """

    def __init__(self, start: TPosition, goal: TPosition) -> None:
        self.fwd_astar = AStar(start, goal)
        self.bwd_astar = AStar(goal, start)
        self.reached = False

    def search(self) -> list[TPosition]:
        while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
            self.fwd_astar.open_nodes.sort()
            self.bwd_astar.open_nodes.sort()
            current_fwd_node = self.fwd_astar.open_nodes.pop(0)
            current_bwd_node = self.bwd_astar.open_nodes.pop(0)

            if current_bwd_node.pos == current_fwd_node.pos:
                return self.retrace_bidirectional_path(
                    current_fwd_node, current_bwd_node
                )

            self.fwd_astar.closed_nodes.append(current_fwd_node)
            self.bwd_astar.closed_nodes.append(current_bwd_node)

            self.fwd_astar.target = current_bwd_node
            self.bwd_astar.target = current_fwd_node

            successors = {
                self.fwd_astar: self.fwd_astar.get_successors(current_fwd_node),
                self.bwd_astar: self.bwd_astar.get_successors(current_bwd_node),
            }

            for astar in [self.fwd_astar, self.bwd_astar]:
                for child_node in successors[astar]:
                    if child_node in astar.closed_nodes:
                        continue

                    if child_node not in astar.open_nodes:
                        astar.open_nodes.append(child_node)
                    else:
                        # retrieve the best current path
                        better_node = astar.open_nodes.pop(
                            astar.open_nodes.index(child_node)
                        )

                        if child_node.g_cost < better_node.g_cost:
                            astar.open_nodes.append(child_node)
                        else:
                            astar.open_nodes.append(better_node)

        return [self.fwd_astar.start.pos]

    def retrace_bidirectional_path(
        self, fwd_node: Node, bwd_node: Node
    ) -> list[TPosition]:
        fwd_path = self.fwd_astar.retrace_path(fwd_node)
        bwd_path = self.bwd_astar.retrace_path(bwd_node)
        bwd_path.pop()
        bwd_path.reverse()
        path = fwd_path + bwd_path
        return path


if __name__ == "__main__":
    # all coordinates are given in format [y,x]
    init = (0, 0)
    goal = (len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    start_time = time.time()
    a_star = AStar(init, goal)
    path = a_star.search()
    end_time = time.time() - start_time
    print(f"AStar execution time = {end_time:f} seconds")

    bd_start_time = time.time()
    bidir_astar = BidirectionalAStar(init, goal)
    bd_end_time = time.time() - bd_start_time
    print(f"BidirectionalAStar execution time = {bd_end_time:f} seconds")


================================================
FILE: graphs/bidirectional_breadth_first_search.py
================================================
"""
https://en.wikipedia.org/wiki/Bidirectional_search
"""

from __future__ import annotations

import time

Path = list[tuple[int, int]]

grid = [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

delta = [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right


class Node:
    def __init__(
        self, pos_x: int, pos_y: int, goal_x: int, goal_y: int, parent: Node | None
    ):
        self.pos_x = pos_x
        self.pos_y = pos_y
        self.pos = (pos_y, pos_x)
        self.goal_x = goal_x
        self.goal_y = goal_y
        self.parent = parent


class BreadthFirstSearch:
    """
    # Comment out slow pytests...
    # 9.15s call     graphs/bidirectional_breadth_first_search.py:: \
    #                graphs.bidirectional_breadth_first_search.BreadthFirstSearch
    # >>> bfs = BreadthFirstSearch((0, 0), (len(grid) - 1, len(grid[0]) - 1))
    # >>> (bfs.start.pos_y + delta[3][0], bfs.start.pos_x + delta[3][1])
    (0, 1)
    # >>> [x.pos for x in bfs.get_successors(bfs.start)]
    [(1, 0), (0, 1)]
    # >>> (bfs.start.pos_y + delta[2][0], bfs.start.pos_x + delta[2][1])
    (1, 0)
    # >>> bfs.retrace_path(bfs.start)
    [(0, 0)]
    # >>> bfs.search()  # doctest: +NORMALIZE_WHITESPACE
    [(0, 0), (1, 0), (2, 0), (3, 0), (3, 1), (4, 1),
     (5, 1), (5, 2), (5, 3), (5, 4), (5, 5), (6, 5), (6, 6)]
    """

    def __init__(self, start: tuple[int, int], goal: tuple[int, int]):
        self.start = Node(start[1], start[0], goal[1], goal[0], None)
        self.target = Node(goal[1], goal[0], goal[1], goal[0], None)

        self.node_queue = [self.start]
        self.reached = False

    def search(self) -> Path | None:
        while self.node_queue:
            current_node = self.node_queue.pop(0)

            if current_node.pos == self.target.pos:
                self.reached = True
                return self.retrace_path(current_node)

            successors = self.get_successors(current_node)

            for node in successors:
                self.node_queue.append(node)

        if not self.reached:
            return [self.start.pos]
        return None

    def get_successors(self, parent: Node) -> list[Node]:
        """
        Returns a list of successors (both in the grid and free spaces)
        """
        successors = []
        for action in delta:
            pos_x = parent.pos_x + action[1]
            pos_y = parent.pos_y + action[0]
            if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(grid) - 1):
                continue

            if grid[pos_y][pos_x] != 0:
                continue

            successors.append(
                Node(pos_x, pos_y, self.target.pos_y, self.target.pos_x, parent)
            )
        return successors

    def retrace_path(self, node: Node | None) -> Path:
        """
        Retrace the path from parents to parents until start node
        """
        current_node = node
        path = []
        while current_node is not None:
            path.append((current_node.pos_y, current_node.pos_x))
            current_node = current_node.parent
        path.reverse()
        return path


class BidirectionalBreadthFirstSearch:
    """
    >>> bd_bfs = BidirectionalBreadthFirstSearch((0, 0), (len(grid) - 1,
    ...                                                   len(grid[0]) - 1))
    >>> bd_bfs.fwd_bfs.start.pos == bd_bfs.bwd_bfs.target.pos
    True
    >>> bd_bfs.retrace_bidirectional_path(bd_bfs.fwd_bfs.start,
    ...                                     bd_bfs.bwd_bfs.start)
    [(0, 0)]
    >>> bd_bfs.search()  # doctest: +NORMALIZE_WHITESPACE
    [(0, 0), (0, 1), (0, 2), (1, 2), (2, 2), (2, 3),
     (2, 4), (3, 4), (3, 5), (3, 6), (4, 6), (5, 6), (6, 6)]
    """

    def __init__(self, start, goal):
        self.fwd_bfs = BreadthFirstSearch(start, goal)
        self.bwd_bfs = BreadthFirstSearch(goal, start)
        self.reached = False

    def search(self) -> Path | None:
        while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
            current_fwd_node = self.fwd_bfs.node_queue.pop(0)
            current_bwd_node = self.bwd_bfs.node_queue.pop(0)

            if current_bwd_node.pos == current_fwd_node.pos:
                self.reached = True
                return self.retrace_bidirectional_path(
                    current_fwd_node, current_bwd_node
                )

            self.fwd_bfs.target = current_bwd_node
            self.bwd_bfs.target = current_fwd_node

            successors = {
                self.fwd_bfs: self.fwd_bfs.get_successors(current_fwd_node),
                self.bwd_bfs: self.bwd_bfs.get_successors(current_bwd_node),
            }

            for bfs in [self.fwd_bfs, self.bwd_bfs]:
                for node in successors[bfs]:
                    bfs.node_queue.append(node)

        if not self.reached:
            return [self.fwd_bfs.start.pos]
        return None

    def retrace_bidirectional_path(self, fwd_node: Node, bwd_node: Node) -> Path:
        fwd_path = self.fwd_bfs.retrace_path(fwd_node)
        bwd_path = self.bwd_bfs.retrace_path(bwd_node)
        bwd_path.pop()
        bwd_path.reverse()
        path = fwd_path + bwd_path
        return path


if __name__ == "__main__":
    # all coordinates are given in format [y,x]
    import doctest

    doctest.testmod()
    init = (0, 0)
    goal = (len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    start_bfs_time = time.time()
    bfs = BreadthFirstSearch(init, goal)
    path = bfs.search()
    bfs_time = time.time() - start_bfs_time

    print("Unidirectional BFS computation time : ", bfs_time)

    start_bd_bfs_time = time.time()
    bd_bfs = BidirectionalBreadthFirstSearch(init, goal)
    bd_path = bd_bfs.search()
    bd_bfs_time = time.time() - start_bd_bfs_time

    print("Bidirectional BFS computation time : ", bd_bfs_time)


================================================
FILE: graphs/bidirectional_search.py
================================================
"""
Bidirectional Search Algorithm.

This algorithm searches from both the source and target nodes simultaneously,
meeting somewhere in the middle. This approach can significantly reduce the
search space compared to a traditional one-directional search.

Time Complexity: O(b^(d/2)) where b is the branching factor and d is the depth
Space Complexity: O(b^(d/2))

https://en.wikipedia.org/wiki/Bidirectional_search
"""

from collections import deque


def expand_search(
    graph: dict[int, list[int]],
    queue: deque[int],
    parents: dict[int, int | None],
    opposite_direction_parents: dict[int, int | None],
) -> int | None:
    if not queue:
        return None

    current = queue.popleft()
    for neighbor in graph[current]:
        if neighbor in parents:
            continue

        parents[neighbor] = current
        queue.append(neighbor)

        # Check if this creates an intersection
        if neighbor in opposite_direction_parents:
            return neighbor

    return None


def construct_path(current: int | None, parents: dict[int, int | None]) -> list[int]:
    path: list[int] = []
    while current is not None:
        path.append(current)
        current = parents[current]
    return path


def bidirectional_search(
    graph: dict[int, list[int]], start: int, goal: int
) -> list[int] | None:
    """
    Perform bidirectional search on a graph to find the shortest path.

    Args:
        graph: A dictionary where keys are nodes and values are lists of adjacent nodes
        start: The starting node
        goal: The target node

    Returns:
        A list representing the path from start to goal, or None if no path exists

    Examples:
        >>> graph = {
        ...     0: [1, 2],
        ...     1: [0, 3, 4],
        ...     2: [0, 5, 6],
        ...     3: [1, 7],
        ...     4: [1, 8],
        ...     5: [2, 9],
        ...     6: [2, 10],
        ...     7: [3, 11],
        ...     8: [4, 11],
        ...     9: [5, 11],
        ...     10: [6, 11],
        ...     11: [7, 8, 9, 10],
        ... }
        >>> bidirectional_search(graph=graph, start=0, goal=11)
        [0, 1, 3, 7, 11]
        >>> bidirectional_search(graph=graph, start=5, goal=5)
        [5]
        >>> disconnected_graph = {
        ...     0: [1, 2],
        ...     1: [0],
        ...     2: [0],
        ...     3: [4],
        ...     4: [3],
        ... }
        >>> bidirectional_search(graph=disconnected_graph, start=0, goal=3) is None
        True
    """
    if start == goal:
        return [start]

    # Check if start and goal are in the graph
    if start not in graph or goal not in graph:
        return None

    # Initialize forward and backward search dictionaries
    # Each maps a node to its parent in the search
    forward_parents: dict[int, int | None] = {start: None}
    backward_parents: dict[int, int | None] = {goal: None}

    # Initialize forward and backward search queues
    forward_queue = deque([start])
    backward_queue = deque([goal])

    # Intersection node (where the two searches meet)
    intersection = None

    # Continue until both queues are empty or an intersection is found
    while forward_queue and backward_queue and intersection is None:
        # Expand forward search
        intersection = expand_search(
            graph=graph,
            queue=forward_queue,
            parents=forward_parents,
            opposite_direction_parents=backward_parents,
        )

        # If no intersection found, expand backward search
        if intersection is not None:
            break

        intersection = expand_search(
            graph=graph,
            queue=backward_queue,
            parents=backward_parents,
            opposite_direction_parents=forward_parents,
        )

    # If no intersection found, there's no path
    if intersection is None:
        return None

    # Construct path from start to intersection
    forward_path: list[int] = construct_path(
        current=intersection, parents=forward_parents
    )
    forward_path.reverse()

    # Construct path from intersection to goal
    backward_path: list[int] = construct_path(
        current=backward_parents[intersection], parents=backward_parents
    )

    # Return the complete path
    return forward_path + backward_path


def main() -> None:
    """
    Run example of bidirectional search algorithm.

    Examples:
        >>> main()  # doctest: +NORMALIZE_WHITESPACE
        Path from 0 to 11: [0, 1, 3, 7, 11]
        Path from 5 to 5: [5]
        Path from 0 to 3: None
    """
    # Example graph represented as an adjacency list
    example_graph = {
        0: [1, 2],
        1: [0, 3, 4],
        2: [0, 5, 6],
        3: [1, 7],
        4: [1, 8],
        5: [2, 9],
        6: [2, 10],
        7: [3, 11],
        8: [4, 11],
        9: [5, 11],
        10: [6, 11],
        11: [7, 8, 9, 10],
    }

    # Test case 1: Path exists
    start, goal = 0, 11
    path = bidirectional_search(graph=example_graph, start=start, goal=goal)
    print(f"Path from {start} to {goal}: {path}")

    # Test case 2: Start and goal are the same
    start, goal = 5, 5
    path = bidirectional_search(graph=example_graph, start=start, goal=goal)
    print(f"Path from {start} to {goal}: {path}")

    # Test case 3: No path exists (disconnected graph)
    disconnected_graph = {
        0: [1, 2],
        1: [0],
        2: [0],
        3: [4],
        4: [3],
    }
    start, goal = 0, 3
    path = bidirectional_search(graph=disconnected_graph, start=start, goal=goal)
    print(f"Path from {start} to {goal}: {path}")


if __name__ == "__main__":
    main()


================================================
FILE: graphs/boruvka.py
================================================
"""Borůvka's algorithm.

Determines the minimum spanning tree (MST) of a graph using the Borůvka's algorithm.
Borůvka's algorithm is a greedy algorithm for finding a minimum spanning tree in a
connected graph, or a minimum spanning forest if a graph that is not connected.

The time complexity of this algorithm is O(ELogV), where E represents the number
of edges, while V represents the number of nodes.
O(number_of_edges Log number_of_nodes)

The space complexity of this algorithm is O(V + E), since we have to keep a couple
of lists whose sizes are equal to the number of nodes, as well as keep all the
edges of a graph inside of the data structure itself.

Borůvka's algorithm gives us pretty much the same result as other MST Algorithms -
they all find the minimum spanning tree, and the time complexity is approximately
the same.

One advantage that Borůvka's algorithm has compared to the alternatives is that it
doesn't need to presort the edges or maintain a priority queue in order to find the
minimum spanning tree.
Even though that doesn't help its complexity, since it still passes the edges logE
times, it is a bit simpler to code.

Details: https://en.wikipedia.org/wiki/Bor%C5%AFvka%27s_algorithm
"""

from __future__ import annotations

from typing import Any


class Graph:
    def __init__(self, num_of_nodes: int) -> None:
        """
        Arguments:
            num_of_nodes - the number of nodes in the graph
        Attributes:
            m_num_of_nodes - the number of nodes in the graph.
            m_edges - the list of edges.
            m_component - the dictionary which stores the index of the component which
            a node belongs to.
        """

        self.m_num_of_nodes = num_of_nodes
        self.m_edges: list[list[int]] = []
        self.m_component: dict[int, int] = {}

    def add_edge(self, u_node: int, v_node: int, weight: int) -> None:
        """Adds an edge in the format [first, second, edge weight] to graph."""

        self.m_edges.append([u_node, v_node, weight])

    def find_component(self, u_node: int) -> int:
        """Propagates a new component throughout a given component."""

        if self.m_component[u_node] == u_node:
            return u_node
        return self.find_component(self.m_component[u_node])

    def set_component(self, u_node: int) -> None:
        """Finds the component index of a given node"""

        if self.m_component[u_node] != u_node:
            for k in self.m_component:
                self.m_component[k] = self.find_component(k)

    def union(self, component_size: list[int], u_node: int, v_node: int) -> None:
        """Union finds the roots of components for two nodes, compares the components
        in terms of size, and attaches the smaller one to the larger one to form
        single component"""

        if component_size[u_node] <= component_size[v_node]:
            self.m_component[u_node] = v_node
            component_size[v_node] += component_size[u_node]
            self.set_component(u_node)

        elif component_size[u_node] >= component_size[v_node]:
            self.m_component[v_node] = self.find_component(u_node)
            component_size[u_node] += component_size[v_node]
            self.set_component(v_node)

    def boruvka(self) -> None:
        """Performs Borůvka's algorithm to find MST."""

        # Initialize additional lists required to algorithm.
        component_size = []
        mst_weight = 0

        minimum_weight_edge: list[Any] = [-1] * self.m_num_of_nodes

        # A list of components (initialized to all of the nodes)
        for node in range(self.m_num_of_nodes):
            self.m_component.update({node: node})
            component_size.append(1)

        num_of_components = self.m_num_of_nodes

        while num_of_components > 1:
            for edge in self.m_edges:
                u, v, w = edge

                u_component = self.m_component[u]
                v_component = self.m_component[v]

                if u_component != v_component:
                    """If the current minimum weight edge of component u doesn't
                    exist (is -1), or if it's greater than the edge we're
                    observing right now, we will assign the value of the edge
                    we're observing to it.

                    If the current minimum weight edge of component v doesn't
                    exist (is -1), or if it's greater than the edge we're
                    observing right now, we will assign the value of the edge
                    we're observing to it"""

                    for component in (u_component, v_component):
                        if (
                            minimum_weight_edge[component] == -1
                            or minimum_weight_edge[component][2] > w
                        ):
                            minimum_weight_edge[component] = [u, v, w]

            for edge in minimum_weight_edge:
                if isinstance(edge, list):
                    u, v, w = edge

                    u_component = self.m_component[u]
                    v_component = self.m_component[v]

                    if u_component != v_component:
                        mst_weight += w
                        self.union(component_size, u_component, v_component)
                        print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n")
                        num_of_components -= 1

            minimum_weight_edge = [-1] * self.m_num_of_nodes
        print(f"The total weight of the minimal spanning tree is: {mst_weight}")


def test_vector() -> None:
    """
    >>> g = Graph(8)
    >>> for u_v_w in ((0, 1, 10), (0, 2, 6), (0, 3, 5), (1, 3, 15), (2, 3, 4),
    ...    (3, 4, 8), (4, 5, 10), (4, 6, 6), (4, 7, 5), (5, 7, 15), (6, 7, 4)):
    ...        g.add_edge(*u_v_w)
    >>> g.boruvka()
    Added edge [0 - 3]
    Added weight: 5
    <BLANKLINE>
    Added edge [0 - 1]
    Added weight: 10
    <BLANKLINE>
    Added edge [2 - 3]
    Added weight: 4
    <BLANKLINE>
    Added edge [4 - 7]
    Added weight: 5
    <BLANKLINE>
    Added edge [4 - 5]
    Added weight: 10
    <BLANKLINE>
    Added edge [6 - 7]
    Added weight: 4
    <BLANKLINE>
    Added edge [3 - 4]
    Added weight: 8
    <BLANKLINE>
    The total weight of the minimal spanning tree is: 46
    """


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: graphs/breadth_first_search.py
================================================
#!/usr/bin/python

"""Author: OMKAR PATHAK"""

from __future__ import annotations

from queue import Queue


class Graph:
    def __init__(self) -> None:
        self.vertices: dict[int, list[int]] = {}

    def print_graph(self) -> None:
        """
        prints adjacency list representation of graaph
        >>> g = Graph()
        >>> g.print_graph()
        >>> g.add_edge(0, 1)
        >>> g.print_graph()
        0  :  1
        """
        for i in self.vertices:
            print(i, " : ", " -> ".join([str(j) for j in self.vertices[i]]))

    def add_edge(self, from_vertex: int, to_vertex: int) -> None:
        """
        adding the edge between two vertices
        >>> g = Graph()
        >>> g.print_graph()
        >>> g.add_edge(0, 1)
        >>> g.print_graph()
        0  :  1
        """
        if from_vertex in self.vertices:
            self.vertices[from_vertex].append(to_vertex)
        else:
            self.vertices[from_vertex] = [to_vertex]

    def bfs(self, start_vertex: int) -> set[int]:
        """
        >>> g = Graph()
        >>> g.add_edge(0, 1)
        >>> g.add_edge(0, 1)
        >>> g.add_edge(0, 2)
        >>> g.add_edge(1, 2)
        >>> g.add_edge(2, 0)
        >>> g.add_edge(2, 3)
        >>> g.add_edge(3, 3)
        >>> sorted(g.bfs(2))
        [0, 1, 2, 3]
        """
        # initialize set for storing already visited vertices
        visited = set()

        # create a first in first out queue to store all the vertices for BFS
        queue: Queue = Queue()

        # mark the source node as visited and enqueue it
        visited.add(start_vertex)
        queue.put(start_vertex)

        while not queue.empty():
            vertex = queue.get()

            # loop through all adjacent vertex and enqueue it if not yet visited
            for adjacent_vertex in self.vertices[vertex]:
                if adjacent_vertex not in visited:
                    queue.put(adjacent_vertex)
                    visited.add(adjacent_vertex)
        return visited


if __name__ == "__main__":
    from doctest import testmod

    testmod(verbose=True)

    g = Graph()
    g.add_edge(0, 1)
    g.add_edge(0, 2)
    g.add_edge(1, 2)
    g.add_edge(2, 0)
    g.add_edge(2, 3)
    g.add_edge(3, 3)

    g.print_graph()
    # 0  :  1 -> 2
    # 1  :  2
    # 2  :  0 -> 3
    # 3  :  3

    assert sorted(g.bfs(2)) == [0, 1, 2, 3]


================================================
FILE: graphs/breadth_first_search_2.py
================================================
"""
https://en.wikipedia.org/wiki/Breadth-first_search
pseudo-code:
breadth_first_search(graph G, start vertex s):
// all nodes initially unexplored
mark s as explored
let Q = queue data structure, initialized with s
while Q is non-empty:
    remove the first node of Q, call it v
    for each edge(v, w):  // for w in graph[v]
        if w unexplored:
            mark w as explored
            add w to Q (at the end)
"""

from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

G = {
    "A": ["B", "C"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B"],
    "E": ["B", "F"],
    "F": ["C", "E"],
}


def breadth_first_search(graph: dict, start: str) -> list[str]:
    """
    Implementation of breadth first search using queue.Queue.

    >>> ''.join(breadth_first_search(G, 'A'))
    'ABCDEF'
    """
    explored = {start}
    result = [start]
    queue: Queue = Queue()
    queue.put(start)
    while not queue.empty():
        v = queue.get()
        for w in graph[v]:
            if w not in explored:
                explored.add(w)
                result.append(w)
                queue.put(w)
    return result


def breadth_first_search_with_deque(graph: dict, start: str) -> list[str]:
    """
    Implementation of breadth first search using collection.queue.

    >>> ''.join(breadth_first_search_with_deque(G, 'A'))
    'ABCDEF'
    """
    visited = {start}
    result = [start]
    queue = deque([start])
    while queue:
        v = queue.popleft()
        for child in graph[v]:
            if child not in visited:
                visited.add(child)
                result.append(child)
                queue.append(child)
    return result


def benchmark_function(name: str) -> None:
    setup = f"from __main__ import G, {name}"
    number = 10000
    res = timeit(f"{name}(G, 'A')", setup=setup, number=number)
    print(f"{name:<35} finished {number} runs in {res:.5f} seconds")


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    benchmark_function("breadth_first_search")
    benchmark_function("breadth_first_search_with_deque")
    # breadth_first_search                finished 10000 runs in 0.20999 seconds
    # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds


================================================
FILE: graphs/breadth_first_search_shortest_path.py
================================================
"""Breath First Search (BFS) can be used when finding the shortest path
from a given source node to a target node in an unweighted graph.
"""

from __future__ import annotations

graph = {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}


class Graph:
    def __init__(self, graph: dict[str, list[str]], source_vertex: str) -> None:
        """
        Graph is implemented as dictionary of adjacency lists. Also,
        Source vertex have to be defined upon initialization.
        """
        self.graph = graph
        # mapping node to its parent in resulting breadth first tree
        self.parent: dict[str, str | None] = {}
        self.source_vertex = source_vertex

    def breath_first_search(self) -> None:
        """
        This function is a helper for running breath first search on this graph.
        >>> g = Graph(graph, "G")
        >>> g.breath_first_search()
        >>> g.parent
        {'G': None, 'C': 'G', 'A': 'C', 'F': 'C', 'B': 'A', 'E': 'A', 'D': 'B'}
        """
        visited = {self.source_vertex}
        self.parent[self.source_vertex] = None
        queue = [self.source_vertex]  # first in first out queue

        while queue:
            vertex = queue.pop(0)
            for adjacent_vertex in self.graph[vertex]:
                if adjacent_vertex not in visited:
                    visited.add(adjacent_vertex)
                    self.parent[adjacent_vertex] = vertex
                    queue.append(adjacent_vertex)

    def shortest_path(self, target_vertex: str) -> str:
        """
        This shortest path function returns a string, describing the result:
        1.) No path is found. The string is a human readable message to indicate this.
        2.) The shortest path is found. The string is in the form
            `v1(->v2->v3->...->vn)`, where v1 is the source vertex and vn is the target
            vertex, if it exists separately.

        >>> g = Graph(graph, "G")
        >>> g.breath_first_search()

        Case 1 - No path is found.
        >>> g.shortest_path("Foo")
        Traceback (most recent call last):
            ...
        ValueError: No path from vertex: G to vertex: Foo

        Case 2 - The path is found.
        >>> g.shortest_path("D")
        'G->C->A->B->D'
        >>> g.shortest_path("G")
        'G'
        """
        if target_vertex == self.source_vertex:
            return self.source_vertex

        target_vertex_parent = self.parent.get(target_vertex)
        if target_vertex_parent is None:
            msg = (
                f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
            )
            raise ValueError(msg)

        return self.shortest_path(target_vertex_parent) + f"->{target_vertex}"


if __name__ == "__main__":
    g = Graph(graph, "G")
    g.breath_first_search()
    print(g.shortest_path("D"))
    print(g.shortest_path("G"))
    print(g.shortest_path("Foo"))


================================================
FILE: graphs/breadth_first_search_shortest_path_2.py
================================================
"""Breadth-first search the shortest path implementations.
doctest:
python -m doctest -v breadth_first_search_shortest_path_2.py
Manual test:
python breadth_first_search_shortest_path_2.py
"""

from collections import deque

demo_graph = {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}


def bfs_shortest_path(graph: dict, start, goal) -> list[str]:
    """Find the shortest path between `start` and `goal` nodes.
    Args:
        graph (dict): node/list of neighboring nodes key/value pairs.
        start: start node.
        goal: target node.
    Returns:
        Shortest path between `start` and `goal` nodes as a string of nodes.
        'Not found' string if no path found.
    Example:
        >>> bfs_shortest_path(demo_graph, "G", "D")
        ['G', 'C', 'A', 'B', 'D']
        >>> bfs_shortest_path(demo_graph, "G", "G")
        ['G']
        >>> bfs_shortest_path(demo_graph, "G", "Unknown")
        []
    """
    # keep track of explored nodes
    explored = set()
    # keep track of all the paths to be checked
    queue = deque([[start]])

    # return path if start is goal
    if start == goal:
        return [start]

    # keeps looping until all possible paths have been checked
    while queue:
        # pop the first path from the queue
        path = queue.popleft()
        # get the last node from the path
        node = path[-1]
        if node not in explored:
            neighbours = graph[node]
            # go through all neighbour nodes, construct a new path and
            # push it into the queue
            for neighbour in neighbours:
                new_path = list(path)
                new_path.append(neighbour)
                queue.append(new_path)
                # return path if neighbour is goal
                if neighbour == goal:
                    return new_path

            # mark node as explored
            explored.add(node)

    # in case there's no path between the 2 nodes
    return []


def bfs_shortest_path_distance(graph: dict, start, target) -> int:
    """Find the shortest path distance between `start` and `target` nodes.
    Args:
        graph: node/list of neighboring nodes key/value pairs.
        start: node to start search from.
        target: node to search for.
    Returns:
        Number of edges in the shortest path between `start` and `target` nodes.
        -1 if no path exists.
    Example:
        >>> bfs_shortest_path_distance(demo_graph, "G", "D")
        4
        >>> bfs_shortest_path_distance(demo_graph, "A", "A")
        0
        >>> bfs_shortest_path_distance(demo_graph, "A", "Unknown")
        -1
    """
    if not graph or start not in graph or target not in graph:
        return -1
    if start == target:
        return 0
    queue = deque([start])
    visited = set(start)
    # Keep tab on distances from `start` node.
    dist = {start: 0, target: -1}
    while queue:
        node = queue.popleft()
        if node == target:
            dist[target] = (
                dist[node] if dist[target] == -1 else min(dist[target], dist[node])
            )
        for adjacent in graph[node]:
            if adjacent not in visited:
                visited.add(adjacent)
                queue.append(adjacent)
                dist[adjacent] = dist[node] + 1
    return dist[target]


if __name__ == "__main__":
    print(bfs_shortest_path(demo_graph, "G", "D"))  # returns ['G', 'C', 'A', 'B', 'D']
    print(bfs_shortest_path_distance(demo_graph, "G", "D"))  # returns 4


================================================
FILE: graphs/breadth_first_search_zero_one_shortest_path.py
================================================
"""
Finding the shortest path in 0-1-graph in O(E + V) which is faster than dijkstra.
0-1-graph is the weighted graph with the weights equal to 0 or 1.
Link: https://codeforces.com/blog/entry/22276
"""

from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass


@dataclass
class Edge:
    """Weighted directed graph edge."""

    destination_vertex: int
    weight: int


class AdjacencyList:
    """Graph adjacency list."""

    def __init__(self, size: int):
        self._graph: list[list[Edge]] = [[] for _ in range(size)]
        self._size = size

    def __getitem__(self, vertex: int) -> Iterator[Edge]:
        """Get all the vertices adjacent to the given one."""
        return iter(self._graph[vertex])

    @property
    def size(self):
        return self._size

    def add_edge(self, from_vertex: int, to_vertex: int, weight: int):
        """
        >>> g = AdjacencyList(2)
        >>> g.add_edge(0, 1, 0)
        >>> g.add_edge(1, 0, 1)
        >>> list(g[0])
        [Edge(destination_vertex=1, weight=0)]
        >>> list(g[1])
        [Edge(destination_vertex=0, weight=1)]
        >>> g.add_edge(0, 1, 2)
        Traceback (most recent call last):
            ...
        ValueError: Edge weight must be either 0 or 1.
        >>> g.add_edge(0, 2, 1)
        Traceback (most recent call last):
            ...
        ValueError: Vertex indexes must be in [0; size).
        """
        if weight not in (0, 1):
            raise ValueError("Edge weight must be either 0 or 1.")

        if to_vertex < 0 or to_vertex >= self.size:
            raise ValueError("Vertex indexes must be in [0; size).")

        self._graph[from_vertex].append(Edge(to_vertex, weight))

    def get_shortest_path(self, start_vertex: int, finish_vertex: int) -> int | None:
        """
        Return the shortest distance from start_vertex to finish_vertex in 0-1-graph.
              1                  1         1
         0--------->3        6--------7>------->8
         |          ^        ^        ^         |1
         |          |        |        |0        v
        0|          |0      1|        9-------->10
         |          |        |        ^    1
         v          |        |        |0
         1--------->2<-------4------->5
              0         1        1
        >>> g = AdjacencyList(11)
        >>> g.add_edge(0, 1, 0)
        >>> g.add_edge(0, 3, 1)
        >>> g.add_edge(1, 2, 0)
        >>> g.add_edge(2, 3, 0)
        >>> g.add_edge(4, 2, 1)
        >>> g.add_edge(4, 5, 1)
        >>> g.add_edge(4, 6, 1)
        >>> g.add_edge(5, 9, 0)
        >>> g.add_edge(6, 7, 1)
        >>> g.add_edge(7, 8, 1)
        >>> g.add_edge(8, 10, 1)
        >>> g.add_edge(9, 7, 0)
        >>> g.add_edge(9, 10, 1)
        >>> g.add_edge(1, 2, 2)
        Traceback (most recent call last):
            ...
        ValueError: Edge weight must be either 0 or 1.
        >>> g.get_shortest_path(0, 3)
        0
        >>> g.get_shortest_path(0, 4)
        Traceback (most recent call last):
            ...
        ValueError: No path from start_vertex to finish_vertex.
        >>> g.get_shortest_path(4, 10)
        2
        >>> g.get_shortest_path(4, 8)
        2
        >>> g.get_shortest_path(0, 1)
        0
        >>> g.get_shortest_path(1, 0)
        Traceback (most recent call last):
            ...
        ValueError: No path from start_vertex to finish_vertex.
        """
        queue = deque([start_vertex])
        distances: list[int | None] = [None] * self.size
        distances[start_vertex] = 0

        while queue:
            current_vertex = queue.popleft()
            current_distance = distances[current_vertex]
            if current_distance is None:
                continue

            for edge in self[current_vertex]:
                new_distance = current_distance + edge.weight
                dest_vertex_distance = distances[edge.destination_vertex]
                if (
                    isinstance(dest_vertex_distance, int)
                    and new_distance >= dest_vertex_distance
                ):
                    continue
                distances[edge.destination_vertex] = new_distance
                if edge.weight == 0:
                    queue.appendleft(edge.destination_vertex)
                else:
                    queue.append(edge.destination_vertex)

        if distances[finish_vertex] is None:
            raise ValueError("No path from start_vertex to finish_vertex.")

        return distances[finish_vertex]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: graphs/check_bipatrite.py
================================================
from collections import defaultdict, deque


def is_bipartite_dfs(graph: dict[int, list[int]]) -> bool:
    """
    Check if a graph is bipartite using depth-first search (DFS).

    Args:
        `graph`: Adjacency list representing the graph.

    Returns:
        ``True`` if bipartite, ``False`` otherwise.

    Checks if the graph can be divided into two sets of vertices, such that no two
    vertices within the same set are connected by an edge.

    Examples:

    >>> is_bipartite_dfs({0: [1, 2], 1: [0, 3], 2: [0, 4]})
    True
    >>> is_bipartite_dfs({0: [1, 2], 1: [0, 3], 2: [0, 1]})
    False
    >>> is_bipartite_dfs({})
    True
    >>> is_bipartite_dfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]})
    True
    >>> is_bipartite_dfs({0: [1, 2, 3], 1: [0, 2], 2: [0, 1, 3], 3: [0, 2]})
    False
    >>> is_bipartite_dfs({0: [4], 1: [], 2: [4], 3: [4], 4: [0, 2, 3]})
    True
    >>> is_bipartite_dfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: [0]})
    False
    >>> is_bipartite_dfs({7: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: [0]})
    False

    >>> # FIXME: This test should fails with KeyError: 4.
    >>> is_bipartite_dfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 9: [0]})
    False
    >>> is_bipartite_dfs({0: [-1, 3], 1: [0, -2]})
    False
    >>> is_bipartite_dfs({-1: [0, 2], 0: [-1, 1], 1: [0, 2], 2: [-1, 1]})
    True
    >>> is_bipartite_dfs({0.9: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]})
    True

    >>> # FIXME: This test should fails with
    >>> # TypeError: list indices must be integers or...
    >>> is_bipartite_dfs({0: [1.0, 3.0], 1.0: [0, 2.0], 2.0: [1.0, 3.0], 3.0: [0, 2.0]})
    True
    >>> is_bipartite_dfs({"a": [1, 3], "b": [0, 2], "c": [1, 3], "d": [0, 2]})
    True
    >>> is_bipartite_dfs({0: ["b", "d"], 1: ["a", "c"], 2: ["b", "d"], 3: ["a", "c"]})
    True
    """

    def depth_first_search(node: int, color: int) -> bool:
        """
        Perform Depth-First Search (DFS) on the graph starting from a node.

        Args:
            node: The current node being visited.
            color: The color assigned to the current node.

        Returns:
            True if the graph is bipartite starting from the current node,
            False otherwise.
        """
        if visited[node] == -1:
            visited[node] = color
            if node not in graph:
                return True
            for neighbor in graph[node]:
                if not depth_first_search(neighbor, 1 - color):
                    return False
        return visited[node] == color

    visited: defaultdict[int, int] = defaultdict(lambda: -1)
    for node in graph:
        if visited[node] == -1 and not depth_first_search(node, 0):
            return False
    return True


def is_bipartite_bfs(graph: dict[int, list[int]]) -> bool:
    """
    Check if a graph is bipartite using a breadth-first search (BFS).

    Args:
        `graph`: Adjacency list representing the graph.

    Returns:
        ``True`` if bipartite, ``False`` otherwise.

    Check if the graph can be divided into two sets of vertices, such that no two
    vertices within the same set are connected by an edge.

    Examples:

    >>> is_bipartite_bfs({0: [1, 2], 1: [0, 3], 2: [0, 4]})
    True
    >>> is_bipartite_bfs({0: [1, 2], 1: [0, 2], 2: [0, 1]})
    False
    >>> is_bipartite_bfs({})
    True
    >>> is_bipartite_bfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]})
    True
    >>> is_bipartite_bfs({0: [1, 2, 3], 1: [0, 2], 2: [0, 1, 3], 3: [0, 2]})
    False
    >>> is_bipartite_bfs({0: [4], 1: [], 2: [4], 3: [4], 4: [0, 2, 3]})
    True
    >>> is_bipartite_bfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: [0]})
    False
    >>> is_bipartite_bfs({7: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: [0]})
    False

    >>> # FIXME: This test should fails with KeyError: 4.
    >>> is_bipartite_bfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 9: [0]})
    False
    >>> is_bipartite_bfs({0: [-1, 3], 1: [0, -2]})
    False
    >>> is_bipartite_bfs({-1: [0, 2], 0: [-1, 1], 1: [0, 2], 2: [-1, 1]})
    True
    >>> is_bipartite_bfs({0.9: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]})
    True

    >>> # FIXME: This test should fails with
    >>> # TypeError: list indices must be integers or...
    >>> is_bipartite_bfs({0: [1.0, 3.0], 1.0: [0, 2.0], 2.0: [1.0, 3.0], 3.0: [0, 2.0]})
    True
    >>> is_bipartite_bfs({"a": [1, 3], "b": [0, 2], "c": [1, 3], "d": [0, 2]})
    True
    >>> is_bipartite_bfs({0: ["b", "d"], 1: ["a", "c"], 2: ["b", "d"], 3: ["a", "c"]})
    True
    """
    visited: defaultdict[int, int] = defaultdict(lambda: -1)
    for node in graph:
        if visited[node] == -1:
            queue: deque[int] = deque()
            queue.append(node)
            visited[node] = 0
            while queue:
                curr_node = queue.popleft()
                if curr_node not in graph:
                    continue
                for neighbor in graph[curr_node]:
                    if visited[neighbor] == -1:
                        visited[neighbor] = 1 - visited[curr_node]
                        queue.append(neighbor)
                    elif visited[neighbor] == visited[curr_node]:
                        return False
    return True


if __name__ == "__main__":
    import doctest

    result = doctest.testmod()
    if result.failed:
        print(f"{result.failed} test(s) failed.")
    else:
        print("All tests passed!")


================================================
FILE: graphs/check_cycle.py
================================================
"""
Program to check if a cycle is present in a given graph
"""


def check_cycle(graph: dict) -> bool:
    """
    Returns True if graph is cyclic else False
    >>> check_cycle(graph={0:[], 1:[0, 3], 2:[0, 4], 3:[5], 4:[5], 5:[]})
    False
    >>> check_cycle(graph={0:[1, 2], 1:[2], 2:[0, 3], 3:[3]})
    True
    """
    # Keep track of visited nodes
    visited: set[int] = set()
    # To detect a back edge, keep track of vertices currently in the recursion stack
    rec_stk: set[int] = set()
    return any(
        node not in visited and depth_first_search(graph, node, visited, rec_stk)
        for node in graph
    )


def depth_first_search(graph: dict, vertex: int, visited: set, rec_stk: set) -> bool:
    """
    Recur for all neighbours.
    If any neighbour is visited and in rec_stk then graph is cyclic.
    >>> graph = {0:[], 1:[0, 3], 2:[0, 4], 3:[5], 4:[5], 5:[]}
    >>> vertex, visited, rec_stk = 0, set(), set()
    >>> depth_first_search(graph, vertex, visited, rec_stk)
    False
    """
    # Mark current node as visited and add to recursion stack
    visited.add(vertex)
    rec_stk.add(vertex)

    for node in graph[vertex]:
        if node not in visited:
            if depth_first_search(graph, node, visited, rec_stk):
                return True
        elif node in rec_stk:
            return True

    # The node needs to be removed from recursion stack before function ends
    rec_stk.remove(vertex)
    return False


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: graphs/connected_components.py
================================================
"""
https://en.wikipedia.org/wiki/Component_(graph_theory)

Finding connected components in graph

"""

test_graph_1 = {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

test_graph_2 = {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}


def dfs(graph: dict, vert: int, visited: list) -> list:
    """
    Use depth first search to find all vertices
    being in the same component as initial vertex
    >>> dfs(test_graph_1, 0, 5 * [False])
    [0, 1, 3, 2]
    >>> dfs(test_graph_2, 0, 6 * [False])
    [0, 1, 3, 2]
    """

    visited[vert] = True
    connected_verts = []

    for neighbour in graph[vert]:
        if not visited[neighbour]:
            connected_verts += dfs(graph, neighbour, visited)

    return [vert, *connected_verts]


def connected_components(graph: dict) -> list:
    """
    This function takes graph as a parameter
    and then returns the list of connected components
    >>> connected_components(test_graph_1)
    [[0, 1, 3, 2], [4, 5, 6]]
    >>> connected_components(test_graph_2)
    [[0, 1, 3, 2], [4], [5]]
    """

    graph_size = len(graph)
    visited = graph_size * [False]
    components_list = []

    for i in range(graph_size):
        if not visited[i]:
            i_connected = dfs(graph, i, visited)
            components_list.append(i_connected)

    return components_list


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: graphs/deep_clone_graph.py
================================================
"""
LeetCode 133. Clone Graph
https://leetcode.com/problems/clone-graph/

Given a reference of a node in a connected undirected graph.

Return a deep copy (clone) of the graph.

Each node in the graph contains a value (int) and a list (List[Node]) of its
neighbors.
"""

from dataclasses import dataclass


@dataclass
class Node:
    value: int = 0
    neighbors: list["Node"] | None = None

    def __post_init__(self) -> None:
        """
        >>> Node(3).neighbors
        []
        """
        self.neighbors = self.neighbors or []

    def __hash__(self) -> int:
        """
        >>> hash(Node(3)) != 0
        True
        """
        return id(self)


def clone_graph(node: Node | None) -> Node | None:
    """
    This function returns a clone of a connected undirected graph.
    >>> clone_graph(Node(1))
    Node(value=1, neighbors=[])
    >>> clone_graph(Node(1, [Node(2)]))
    Node(value=1, neighbors=[Node(value=2, neighbors=[])])
    >>> clone_graph(None) is None
    True
    """
    if not node:
        return None

    originals_to_clones = {}  # map nodes to clones

    stack = [node]

    while stack:
        original = stack.pop()

        if original in originals_to_clones:
            continue

        originals_to_clones[original] = Node(original.value)

        stack.extend(original.neighbors or [])

    for original, clone in originals_to_clones.items():
        for neighbor in original.neighbors or []:
            cloned_neighbor = originals_to_clones[neighbor]

            if not clone.neighbors:
                clone.neighbors = []

            clone.neighbors.append(cloned_neighbor)

    return originals_to_clones[node]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: graphs/depth_first_search.py
================================================
"""Non recursive implementation of a DFS algorithm."""

from __future__ import annotations


def depth_first_search(graph: dict, start: str) -> set[str]:
    """Depth First Search on Graph
    :param graph: directed graph in dictionary format
    :param start: starting vertex as a string
    :returns: the trace of the search
    >>> input_G = { "A": ["B", "C", "D"], "B": ["A", "D", "E"],
    ... "C": ["A", "F"], "D": ["B", "D"], "E": ["B", "F"],
    ... "F": ["C", "E", "G"], "G": ["F"] }
    >>> output_G = list({'A', 'B', 'C', 'D', 'E', 'F', 'G'})
    >>> all(x in output_G for x in list(depth_first_search(input_G, "A")))
    True
    >>> all(x in output_G for x in list(depth_first_search(input_G, "G")))
    True
    """
    explored, stack = set(start), [start]

    while stack:
        v = stack.pop()
        explored.add(v)
        # Differences from BFS:
        # 1) pop last element instead of first one
        # 2) add adjacent elements to stack without exploring them
        for adj in reversed(graph[v]):
            if adj not in explored:
                stack.append(adj)
    return explored


G = {
    "A": ["B", "C", "D"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B", "D"],
    "E": ["B", "F"],
    "F": ["C", "E", "G"],
    "G": ["F"],
}

if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(depth_first_search(G, "A"))


================================================
FILE: graphs/depth_first_search_2.py
================================================
#!/usr/bin/python

"""Author: OMKAR PATHAK"""


class Graph:
    def __init__(self):
        self.vertex = {}

    # for printing the Graph vertices
    def print_graph(self) -> None:
        """
        Print the graph vertices.

        Example:
        >>> g = Graph()
        >>> g.add_edge(0, 1)
        >>> g.add_edge(0, 2)
        >>> g.add_edge(1, 2)
        >>> g.add_edge(2, 0)
        >>> g.add_edge(2, 3)
        >>> g.add_edge(3, 3)
        >>> g.print_graph()
        {0: [1, 2], 1: [2], 2: [0, 3], 3: [3]}
        0  ->  1 -> 2
        1  ->  2
        2  ->  0 -> 3
        3  ->  3
        """
        print(self.vertex)
        for i in self.vertex:
            print(i, " -> ", " -> ".join([str(j) for j in self.vertex[i]]))

    # for adding the edge between two vertices
    def add_edge(self, from_vertex: int, to_vertex: int) -> None:
        """
        Add an edge between two vertices.

        :param from_vertex: The source vertex.
        :param to_vertex: The destination vertex.

        Example:
        >>> g = Graph()
        >>> g.add_edge(0, 1)
        >>> g.add_edge(0, 2)
        >>> g.print_graph()
        {0: [1, 2]}
        0  ->  1 -> 2
        """
        # check if vertex is already present,
        if from_vertex in self.vertex:
            self.vertex[from_vertex].append(to_vertex)
        else:
            # else make a new vertex
            self.vertex[from_vertex] = [to_vertex]

    def dfs(self) -> None:
        """
        Perform depth-first search (DFS) traversal on the graph
        and print the visited vertices.

        Example:
        >>> g = Graph()
        >>> g.add_edge(0, 1)
        >>> g.add_edge(0, 2)
        >>> g.add_edge(1, 2)
        >>> g.add_edge(2, 0)
        >>> g.add_edge(2, 3)
        >>> g.add_edge(3, 3)
        >>> g.dfs()
        0 1 2 3
        """
        # visited array for storing already visited nodes
        visited = [False] * len(self.vertex)

        # call the recursive helper function
        for i in range(len(self.vertex)):
            if not visited[i]:
                self.dfs_recursive(i, visited)

    def dfs_recursive(self, start_vertex: int, visited: list) -> None:
        """
        Perform a recursive depth-first search (DFS) traversal on the graph.

        :param start_vertex: The starting vertex for the traversal.
        :param visited: A list to track visited vertices.

        Example:
        >>> g = Graph()
        >>> g.add_edge(0, 1)
        >>> g.add_edge(0, 2)
        >>> g.add_edge(1, 2)
        >>> g.add_edge(2, 0)
        >>> g.add_edge(2, 3)
        >>> g.add_edge(3, 3)
        >>> visited = [False] * len(g.vertex)
        >>> g.dfs_recursive(0, visited)
        0 1 2 3
        """
        # mark start vertex as visited
        visited[start_vertex] = True

        print(start_vertex, end="")

        # Recur for all the vertices that are adjacent to this node
        for i in self.vertex:
            if not visited[i]:
                print(" ", end="")
                self.dfs_recursive(i, visited)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    g = Graph()
    g.add_edge(0, 1)
    g.add_edge(0, 2)
    g.add_edge(1, 2)
    g.add_edge(2, 0)
    g.add_edge(2, 3)
    g.add_edge(3, 3)

    g.print_graph()
    print("DFS:")
    g.dfs()


================================================
FILE: graphs/dijkstra.py
================================================
"""
pseudo-code

DIJKSTRA(graph G, start vertex s, destination vertex d):

//all nodes initially unexplored

1 -  let H = min heap data structure, initialized with 0 and s [here 0 indicates
     the distance from start vertex s]
2 -  while H is non-empty:
3 -    remove the first node and cost of H, call it U and cost
4 -    if U has been previously explored:
5 -      go to the while loop, line 2 //Once a node is explored there is no need
         to make it again
6 -    mark U as explored
7 -    if U is d:
8 -      return cost // total cost from start to destination vertex
9 -    for each edge(U, V): c=cost of edge(U,V) // for V in graph[U]
10 -     if V explored:
11 -       go to next V in line 9
12 -     total_cost = cost + c
13 -     add (total_cost,V) to H

You can think at cost as a distance where Dijkstra finds the shortest distance
between vertices s and v in a graph G. The use of a min heap as H guarantees
that if a vertex has already been explored there will be no other path with
shortest distance, that happens because heapq.heappop will always return the
next vertex with the shortest distance, considering that the heap stores not
only the distance between previous vertex and current vertex but the entire
distance between each vertex that makes up the path from start vertex to target
vertex.
"""

import heapq


def dijkstra(graph, start, end):
    """Return the cost of the shortest path between vertices start and end.

    >>> dijkstra(G, "E", "C")
    6
    >>> dijkstra(G2, "E", "F")
    3
    >>> dijkstra(G3, "E", "F")
    3
    """

    heap = [(0, start)]  # cost from start node,end node
    visited = set()
    while heap:
        (cost, u) = heapq.heappop(heap)
        if u in visited:
            continue
        visited.add(u)
        if u == end:
            return cost
        for v, c in graph[u]:
            if v in visited:
                continue
            next_item = cost + c
            heapq.heappush(heap, (next_item, v))
    return -1


G = {
    "A": [["B", 2], ["C", 5]],
    "B": [["A", 2], ["D", 3], ["E", 1], ["F", 1]],
    "C": [["A", 5], ["F", 3]],
    "D": [["B", 3]],
    "E": [["B", 4], ["F", 3]],
    "F": [["C", 3], ["E", 3]],
}

r"""
Layout of G2:

E -- 1 --> B -- 1 --> C -- 1 --> D -- 1 --> F
 \                                         /\
  \                                        ||
    ----------------- 3 --------------------
"""
G2 = {
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["F", 3]],
    "F": [],
}

r"""
Layout of G3:

E -- 1 --> B -- 1 --> C -- 1 --> D -- 1 --> F
 \                                         /\
  \                                        ||
    -------- 2 ---------> G ------- 1 ------
"""
G3 = {
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}

short_distance = dijkstra(G, "E", "C")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

short_distance = dijkstra(G2, "E", "F")
print(short_distance)  # E -- 3 --> F == 3

short_distance = dijkstra(G3, "E", "F")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: graphs/dijkstra_2.py
================================================
def print_dist(dist, v):
    print("\nVertex Distance")
    for i in range(v):
        if dist[i] != float("inf"):
            print(i, "\t", int(dist[i]), end="\t")
        else:
            print(i, "\t", "INF", end="\t")
        print()


def min_dist(mdist, vset, v):
    min_val = float("inf")
    min_ind = -1
    for i in range(v):
        if (not vset[i]) and mdist[i] < min_val:
            min_ind = i
            min_val = mdist[i]
    return min_ind


def dijkstra(graph, v, src):
    mdist = [float("inf") for _ in range(v)]
    vset = [False for _ in range(v)]
    mdist[src] = 0.0

    for _ in range(v - 1):
        u = min_dist(mdist, vset, v)
        vset[u] = True

        for i in range(v):
            if (
                (not vset[i])
                and graph[u][i] != float("inf")
                and mdist[u] + graph[u][i] < mdist[i]
            ):
                mdist[i] = mdist[u] + graph[u][i]

    print_dist(mdist, i)


if __name__ == "__main__":
    V = int(input("Enter number of vertices: ").strip())
    E = int(input("Enter number of edges: ").strip())

    graph = [[float("inf") for i in range(V)] for j in range(V)]

    for i in range(V):
        graph[i][i] = 0.0

    for i in range(E):
        print("\nEdge ", i + 1)
        src = int(input("Enter source:").strip())
        dst = int(input("Enter destination:").strip())
        weight = float(input("Enter weight:").strip())
        graph[src][dst] = weight

    gsrc = int(input("\nEnter shortest path source:").strip())
    dijkstra(graph, V, gsrc)


================================================
FILE: graphs/dijkstra_algorithm.py
================================================
# Title: Dijkstra's Algorithm for finding single source shortest path from scratch
# Author: Shubham Malik
# References: https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm

import math
import sys

# For storing the vertex set to retrieve node with the lowest distance


class PriorityQueue:
    # Based on Min Heap
    def __init__(self):
        """
        Priority queue class constructor method.

        Examples:
        >>> priority_queue_test = PriorityQueue()
        >>> priority_queue_test.cur_size
        0
        >>> priority_queue_test.array
        []
        >>> priority_queue_test.pos
        {}
        """
        self.cur_size = 0
        self.array = []
        self.pos = {}  # To store the pos of node in array

    def is_empty(self):
        """
        Conditional boolean method to determine if the priority queue is empty or not.

        Examples:
        >>> priority_queue_test = PriorityQueue()
        >>> priority_queue_test.is_empty()
        True
        >>> priority_queue_test.insert((2, 'A'))
        >>> priority_queue_test.is_empty()
        False
        """
        return self.cur_size == 0

    def min_heapify(self, idx):
        """
        Sorts the queue array so that the minimum element is root.

        Examples:
        >>> priority_queue_test = PriorityQueue()
        >>> priority_queue_test.cur_size = 3
        >>> priority_queue_test.pos = {'A': 0, 'B': 1, 'C': 2}

        >>> priority_queue_test.array = [(5, 'A'), (10, 'B'), (15, 'C')]
        >>> priority_queue_test.min_heapify(0)
        >>> priority_queue_test.array
        [(5, 'A'), (10, 'B'), (15, 'C')]

        >>> priority_queue_test.array = [(10, 'A'), (5, 'B'), (15, 'C')]
        >>> priority_queue_test.min_heapify(0)
        >>> priority_queue_test.array
        [(5, 'B'), (10, 'A'), (15, 'C')]

        >>> priority_queue_test.array = [(10, 'A'), (15, 'B'), (5, 'C')]
        >>> priority_queue_test.min_heapify(0)
        >>> priority_queue_test.array
        [(5, 'C'), (15, 'B'), (10, 'A')]

        >>> priority_queue_test.array = [(10, 'A'), (5, 'B')]
        >>> priority_queue_test.cur_size = len(priority_queue_test.array)
        >>> priority_queue_test.pos = {'A': 0, 'B': 1}
        >>> priority_queue_test.min_heapify(0)
        >>> priority_queue_test.array
        [(5, 'B'), (10, 'A')]
        """
        lc = self.left(idx)
        rc = self.right(idx)
        if lc < self.cur_size and self.array[lc][0] < self.array[idx][0]:
            smallest = lc
        else:
            smallest = idx
        if rc < self.cur_size and self.array[rc][0] < self.array[smallest][0]:
            smallest = rc
        if smallest != idx:
            self.swap(idx, smallest)
            self.min_heapify(smallest)

    def insert(self, tup):
        """
        Inserts a node into the Priority Queue.

        Examples:
        >>> priority_queue_test = PriorityQueue()
        >>> priority_queue_test.insert((10, 'A'))
        >>> priority_queue_test.array
        [(10, 'A')]
        >>> priority_queue_test.insert((15, 'B'))
        >>> priority_queue_test.array
        [(10, 'A'), (15, 'B')]
        >>> priority_queue_test.insert((5, 'C'))
        >>> priority_queue_test.array
        [(5, 'C'), (10, 'A'), (15, 'B')]
        """
        self.pos[tup[1]] = self.cur_size
        self.cur_size += 1
        self.array.append((sys.maxsize, tup[1]))
        self.decrease_key((sys.maxsize, tup[1]), tup[0])

    def extract_min(self):
        """
        Removes and returns the min element at top of priority queue.

        Examples:
        >>> priority_queue_test = PriorityQueue()
        >>> priority_queue_test.array = [(10, 'A'), (15, 'B')]
        >>> priority_queue_test.cur_size = len(priority_queue_test.array)
        >>> priority_queue_test.pos = {'A': 0, 'B': 1}
        >>> priority_queue_test.insert((5, 'C'))
        >>> priority_queue_test.extract_min()
        'C'
        >>> priority_queue_test.array[0]
        (10, 'A')
        """
        min_node = self.array[0][1]
        self.array[0] = self.array[self.cur_size - 1]
        self.cur_size -= 1
        self.min_heapify(0)
        del self.pos[min_node]
        return min_node

    def left(self, i):
        """
        Returns the index of left child

        Examples:
        >>> priority_queue_test = PriorityQueue()
        >>> priority_queue_test.left(0)
        1
        >>> priority_queue_test.left(1)
        3
        """
        return 2 * i + 1

    def right(self, i):
        """
        Returns the index of right child

        Examples:
        >>> priority_queue_test = PriorityQueue()
        >>> priority_queue_test.right(0)
        2
        >>> priority_queue_test.right(1)
        4
        """
        return 2 * i + 2

    def par(self, i):
        """
        Returns the index of parent

        Examples:
        >>> priority_queue_test = PriorityQueue()
        >>> priority_queue_test.par(1)
        0
        >>> priority_queue_test.par(2)
        1
        >>> priority_queue_test.par(4)
        2
        """
        return math.floor(i / 2)

    def swap(self, i, j):
        """
        Swaps array elements at indices i and j, update the pos{}

        Examples:
        >>> priority_queue_test = PriorityQueue()
        >>> priority_queue_test.array = [(10, 'A'), (15, 'B')]
        >>> priority_queue_test.cur_size = len(priority_queue_test.array)
        >>> priority_queue_test.pos = {'A': 0, 'B': 1}
        >>> priority_queue_test.swap(0, 1)
        >>> priority_queue_test.array
        [(15, 'B'), (10, 'A')]
        >>> priority_queue_test.pos
        {'A': 1, 'B': 0}
        """
        self.pos[self.array[i][1]] = j
        self.pos[self.array[j][1]] = i
        temp = self.array[i]
        self.array[i] = self.array[j]
        self.array[j] = temp

    def decrease_key(self, tup, new_d):
        """
        Decrease the key value for a given tuple, assuming the new_d is at most old_d.

        Examples:
        >>> priority_queue_test = PriorityQueue()
        >>> priority_queue_test.array = [(10, 'A'), (15, 'B')]
        >>> priority_queue_test.cur_size = len(priority_queue_test.array)
        >>> priority_queue_test.pos = {'A': 0, 'B': 1}
        >>> priority_queue_test.decrease_key((10, 'A'), 5)
        >>> priority_queue_test.array
        [(5, 'A'), (15, 'B')]
        """
        idx = self.pos[tup[1]]
        # assuming the new_d is at most old_d
        self.array[idx] = (new_d, tup[1])
        while idx > 0 and self.array[self.par(idx)][0] > self.array[idx][0]:
            self.swap(idx, self.par(idx))
            idx = self.par(idx)


class Graph:
    def __init__(self, num):
        """
        Graph class constructor

        Examples:
        >>> graph_test = Graph(1)
        >>> graph_test.num_nodes
        1
        >>> graph_test.dist
        [0]
        >>> graph_test.par
        [-1]
        >>> graph_test.adjList
        {}
        """
        self.adjList = {}  # To store graph: u -> (v,w)
        self.num_nodes = num  # Number of nodes in graph
        # To store the distance from source vertex
        self.dist = [0] * self.num_nodes
        self.par = [-1] * self.num_nodes  # To store the path

    def add_edge(self, u, v, w):
        """
        Add edge going from node u to v and v to u with weight w: u (w)-> v, v (w) -> u

        Examples:
        >>> graph_test = Graph(1)
        >>> graph_test.add_edge(1, 2, 1)
        >>> graph_test.add_edge(2, 3, 2)
        >>> graph_test.adjList
        {1: [(2, 1)], 2: [(1, 1), (3, 2)], 3: [(2, 2)]}
        """
        # Check if u already in graph
        if u in self.adjList:
            self.adjList[u].append((v, w))
        else:
            self.adjList[u] = [(v, w)]

        # Assuming undirected graph
        if v in self.adjList:
            self.adjList[v].append((u, w))
        else:
            self.adjList[v] = [(u, w)]

    def show_graph(self):
        """
        Show the graph: u -> v(w)

        Examples:
        >>> graph_test = Graph(1)
        >>> graph_test.add_edge(1, 2, 1)
        >>> graph_test.show_graph()
        1 -> 2(1)
        2 -> 1(1)
        >>> graph_test.add_edge(2, 3, 2)
        >>> graph_test.show_graph()
        1 -> 2(1)
        2 -> 1(1) -> 3(2)
        3 -> 2(2)
        """
        for u in self.adjList:
            print(u, "->", " -> ".join(str(f"{v}({w})") for v, w in self.adjList[u]))

    def dijkstra(self, src):
        """
        Dijkstra algorithm

        Examples:
        >>> graph_test = Graph(3)
        >>> graph_test.add_edge(0, 1, 2)
        >>> graph_test.add_edge(1, 2, 2)
        >>> graph_test.dijkstra(0)
        Distance from node: 0
        Node 0 has distance: 0
        Node 1 has distance: 2
        Node 2 has distance: 4
        >>> graph_test.dist
        [0, 2, 4]

        >>> graph_test = Graph(2)
        >>> graph_test.add_edge(0, 1, 2)
        >>> graph_test.dijkstra(0)
        Distance from node: 0
        Node 0 has distance: 0
        Node 1 has distance: 2
        >>> graph_test.dist
        [0, 2]

        >>> graph_test = Graph(3)
        >>> graph_test.add_edge(0, 1, 2)
        >>> graph_test.dijkstra(0)
        Distance from node: 0
        Node 0 has distance: 0
        Node 1 has distance: 2
        Node 2 has distance: 0
        >>> graph_test.dist
        [0, 2, 0]

        >>> graph_test = Graph(3)
        >>> graph_test.add_edge(0, 1, 2)
        >>> graph_test.add_edge(1, 2, 2)
        >>> graph_test.add_edge(0, 2, 1)
        >>> graph_test.dijkstra(0)
        Distance from node: 0
        Node 0 has distance: 0
        Node 1 has distance: 2
        Node 2 has distance: 1
        >>> graph_test.dist
        [0, 2, 1]

        >>> graph_test = Graph(4)
        >>> graph_test.add_edge(0, 1, 4)
        >>> graph_test.add_edge(1, 2, 2)
        >>> graph_test.add_edge(2, 3, 1)
        >>> graph_test.add_edge(0, 2, 3)
        >>> graph_test.dijkstra(0)
        Distance from node: 0
        Node 0 has distance: 0
        Node 1 has distance: 4
        Node 2 has distance: 3
        Node 3 has distance: 4
        >>> graph_test.dist
        [0, 4, 3, 4]

        >>> graph_test = Graph(4)
        >>> graph_test.add_edge(0, 1, 4)
        >>> graph_test.add_edge(1, 2, 2)
        >>> graph_test.add_edge(2, 3, 1)
        >>> graph_test.add_edge(0, 2, 7)
        >>> graph_test.dijkstra(0)
        Distance from node: 0
        Node 0 has distance: 0
        Node 1 has distance: 4
        Node 2 has distance: 6
        Node 3 has distance: 7
        >>> graph_test.dist
        [0, 4, 6, 7]
        """
        # Flush old junk values in par[]
        self.par = [-1] * self.num_nodes
        # src is the source node
        self.dist[src] = 0
        q = PriorityQueue()
        q.insert((0, src))  # (dist from src, node)
        for u in self.adjList:
            if u != src:
                self.dist[u] = sys.maxsize  # Infinity
                self.par[u] = -1

        while not q.is_empty():
            u = q.extract_min()  # Returns node with the min dist from source
            # Update the distance of all the neighbours of u and
            # if their prev dist was INFINITY then push them in Q
            for v, w in self.adjList[u]:
                new_dist = self.dist[u] + w
                if self.dist[v] > new_dist:
                    if self.dist[v] == sys.maxsize:
                        q.insert((new_dist, v))
                    else:
                        q.decrease_key((self.dist[v], v), new_dist)
                    self.dist[v] = new_dist
                    self.par[v] = u

        # Show the shortest distances from src
        self.show_distances(src)

    def show_distances(self, src):
        """
        Show the distances from src to all other nodes in a graph

        Examples:
        >>> graph_test = Graph(1)
        >>> graph_test.show_distances(0)
        Distance from node: 0
        Node 0 has distance: 0
        """
        print(f"Distance from node: {src}")
        for u in range(self.num_nodes):
            print(f"Node {u} has distance: {self.dist[u]}")

    def show_path(self, src, dest):
        """
        Shows the shortest path from src to dest.
        WARNING: Use it *after* calling dijkstra.

        Examples:
        >>> graph_test = Graph(4)
        >>> graph_test.add_edge(0, 1, 1)
        >>> graph_test.add_edge(1, 2, 2)
        >>> graph_test.add_edge(2, 3, 3)
        >>> graph_test.dijkstra(0)
        Distance from node: 0
        Node 0 has distance: 0
        Node 1 has distance: 1
        Node 2 has distance: 3
        Node 3 has distance: 6
        >>> graph_test.show_path(0, 3)  # doctest: +NORMALIZE_WHITESPACE
        ----Path to reach 3 from 0----
        0 -> 1 -> 2 -> 3
        Total cost of path:  6
        """
        path = []
        cost = 0
        temp = dest
        # Backtracking from dest to src
        while self.par[temp] != -1:
            path.append(temp)
            if temp != src:
                for v, w in self.adjList[temp]:
                    if v == self.par[temp]:
                        cost += w
                        break
            temp = self.par[temp]
        path.append(src)
        path.reverse()

        print(f"----Path to reach {dest} from {src}----")
        for u in path:
            print(f"{u}", end=" ")
            if u != dest:
                print("-> ", end="")

        print("\nTotal cost of path: ", cost)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    graph = Graph(9)
    graph.add_edge(0, 1, 4)
    graph.add_edge(0, 7, 8)
    graph.add_edge(1, 2, 8)
    graph.add_edge(1, 7, 11)
    graph.add_edge(2, 3, 7)
    graph.add_edge(2, 8, 2)
    graph.add_edge(2, 5, 4)
    graph.add_edge(3, 4, 9)
    graph.add_edge(3, 5, 14)
    graph.add_edge(4, 5, 10)
    graph.add_edge(5, 6, 2)
    graph.add_edge(6, 7, 1)
    graph.add_edge(6, 8, 6)
    graph.add_edge(7, 8, 7)
    graph.show_graph()
    graph.dijkstra(0)
    graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21


================================================
FILE: graphs/dijkstra_alternate.py
================================================
from __future__ import annotations


class Graph:
    def __init__(self, vertices: int) -> None:
        """
        >>> graph = Graph(2)
        >>> graph.vertices
        2
        >>> len(graph.graph)
        2
        >>> len(graph.graph[0])
        2
        """
        self.vertices = vertices
        self.graph = [[0] * vertices for _ in range(vertices)]

    def print_solution(self, distances_from_source: list[int]) -> None:
        """
        >>> Graph(0).print_solution([])  # doctest: +NORMALIZE_WHITESPACE
        Vertex 	 Distance from Source
        """
        print("Vertex \t Distance from Source")
        for vertex in range(self.vertices):
            print(vertex, "\t\t", distances_from_source[vertex])

    def minimum_distance(
        self, distances_from_source: list[int], visited: list[bool]
    ) -> int:
        """
        A utility function to find the vertex with minimum distance value, from the set
        of vertices not yet included in shortest path tree.

        >>> Graph(3).minimum_distance([1, 2, 3], [False, False, True])
        0
        """

        # Initialize minimum distance for next node
        minimum = 1e7
        min_index = 0

        # Search not nearest vertex not in the shortest path tree
        for vertex in range(self.vertices):
            if distances_from_source[vertex] < minimum and visited[vertex] is False:
                minimum = distances_from_source[vertex]
                min_index = vertex
        return min_index

    def dijkstra(self, source: int) -> None:
        """
        Function that implements Dijkstra's single source shortest path algorithm for a
        graph represented using adjacency matrix representation.

        >>> Graph(4).dijkstra(1)  # doctest: +NORMALIZE_WHITESPACE
        Vertex  Distance from Source
        0 		 10000000
        1 		 0
        2 		 10000000
        3 		 10000000
        """

        distances = [int(1e7)] * self.vertices  # distances from the source
        distances[source] = 0
        visited = [False] * self.vertices

        for _ in range(self.vertices):
            u = self.minimum_distance(distances, visited)
            visited[u] = True

            # Update dist value of the adjacent vertices
            # of the picked vertex only if the current
            # distance is greater than new distance and
            # the vertex in not in the shortest path tree
            for v in range(self.vertices):
                if (
                    self.graph[u][v] > 0
                    and visited[v] is False
                    and distances[v] > distances[u] + self.graph[u][v]
                ):
                    distances[v] = distances[u] + self.graph[u][v]

        self.print_solution(distances)


if __name__ == "__main__":
    graph = Graph(9)
    graph.graph = [
        [0, 4, 0, 0, 0, 0, 0, 8, 0],
        [4, 0, 8, 0, 0, 0, 0, 11, 0],
        [0, 8, 0, 7, 0, 4, 0, 0, 2],
        [0, 0, 7, 0, 9, 14, 0, 0, 0],
        [0, 0, 0, 9, 0, 10, 0, 0, 0],
        [0, 0, 4, 14, 10, 0, 2, 0, 0],
        [0, 0, 0, 0, 0, 2, 0, 1, 6],
        [8, 11, 0, 0, 0, 0, 1, 0, 7],
        [0, 0, 2, 0, 0, 0, 6, 7, 0],
    ]
    graph.dijkstra(0)


================================================
FILE: graphs/dijkstra_binary_grid.py
================================================
"""
This script implements the Dijkstra algorithm on a binary grid.
The grid consists of 0s and 1s, where 1 represents
a walkable node and 0 represents an obstacle.
The algorithm finds the shortest path from a start node to a destination node.
Diagonal movement can be allowed or disallowed.
"""

from heapq import heappop, heappush

import numpy as np


def dijkstra(
    grid: np.ndarray,
    source: tuple[int, int],
    destination: tuple[int, int],
    allow_diagonal: bool,
) -> tuple[float | int, list[tuple[int, int]]]:
    """
    Implements Dijkstra's algorithm on a binary grid.

    Args:
        grid (np.ndarray): A 2D numpy array representing the grid.
        1 represents a walkable node and 0 represents an obstacle.
        source (Tuple[int, int]): A tuple representing the start node.
        destination (Tuple[int, int]): A tuple representing the
        destination node.
        allow_diagonal (bool): A boolean determining whether
        diagonal movements are allowed.

    Returns:
        Tuple[Union[float, int], List[Tuple[int, int]]]:
        The shortest distance from the start node to the destination node
        and the shortest path as a list of nodes.

    >>> dijkstra(np.array([[1, 1, 1], [0, 1, 0], [0, 1, 1]]), (0, 0), (2, 2), False)
    (4.0, [(0, 0), (0, 1), (1, 1), (2, 1), (2, 2)])

    >>> dijkstra(np.array([[1, 1, 1], [0, 1, 0], [0, 1, 1]]), (0, 0), (2, 2), True)
    (2.0, [(0, 0), (1, 1), (2, 2)])

    >>> dijkstra(np.array([[1, 1, 1], [0, 0, 1], [0, 1, 1]]), (0, 0), (2, 2), False)
    (4.0, [(0, 0), (0, 1), (0, 2), (1, 2), (2, 2)])
    """
    rows, cols = grid.shape
    dx = [-1, 1, 0, 0]
    dy = [0, 0, -1, 1]
    if allow_diagonal:
        dx += [-1, -1, 1, 1]
        dy += [-1, 1, -1, 1]

    queue, visited = [(0, source)], set()
    matrix = np.full((rows, cols), np.inf)
    matrix[source] = 0
    predecessors = np.empty((rows, cols), dtype=object)
    predecessors[source] = None

    while queue:
        (dist, (x, y)) = heappop(queue)
        if (x, y) in visited:
            continue
        visited.add((x, y))

        if (x, y) == destination:
            path = []
            while (x, y) != source:
                path.append((x, y))
                x, y = predecessors[x, y]
            path.append(source)  # add the source manually
            path.reverse()
            return float(matrix[destination]), path

        for i in range(len(dx)):
            nx, ny = x + dx[i], y + dy[i]
            if 0 <= nx < rows and 0 <= ny < cols:
                next_node = grid[nx][ny]
                if next_node == 1 and matrix[nx, ny] > dist + 1:
                    heappush(queue, (dist + 1, (nx, ny)))
                    matrix[nx, ny] = dist + 1
                    predecessors[nx, ny] = (x, y)

    return np.inf, []


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: graphs/dinic.py
================================================
INF = float("inf")


class Dinic:
    def __init__(self, n):
        self.lvl = [0] * n
        self.ptr = [0] * n
        self.q = [0] * n
        self.adj = [[] for _ in range(n)]

    """
    Here we will add our edges containing with the following parameters:
    vertex closest to source, vertex closest to sink and flow capacity
    through that edge ...
    """

    def add_edge(self, a, b, c, rcap=0):
        self.adj[a].append([b, len(self.adj[b]), c, 0])
        self.adj[b].append([a, len(self.adj[a]) - 1, rcap, 0])

    # This is a sample depth first search to be used at max_flow
    def depth_first_search(self, vertex, sink, flow):
        if vertex == sink or not flow:
            return flow

        for i in range(self.ptr[vertex], len(self.adj[vertex])):
            e = self.adj[vertex][i]
            if self.lvl[e[0]] == self.lvl[vertex] + 1:
                p = self.depth_first_search(e[0], sink, min(flow, e[2] - e[3]))
                if p:
                    self.adj[vertex][i][3] += p
                    self.adj[e[0]][e[1]][3] -= p
                    return p
            self.ptr[vertex] = self.ptr[vertex] + 1
        return 0

    # Here we calculate the flow that reaches the sink
    def max_flow(self, source, sink):
        flow, self.q[0] = 0, source
        for l in range(31):  # l = 30 maybe faster for random data  # noqa: E741
            while True:
                self.lvl, self.ptr = [0] * len(self.q), [0] * len(self.q)
                qi, qe, self.lvl[source] = 0, 1, 1
                while qi < qe and not self.lvl[sink]:
                    v = self.q[qi]
                    qi += 1
                    for e in self.adj[v]:
                        if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
                            self.q[qe] = e[0]
                            qe += 1
                            self.lvl[e[0]] = self.lvl[v] + 1

                p = self.depth_first_search(source, sink, INF)
                while p:
                    flow += p
                    p = self.depth_first_search(source, sink, INF)

                if not self.lvl[sink]:
                    break

        return flow


# Example to use

"""
Will be a bipartite graph, than it has the vertices near the source(4)
and the vertices near the sink(4)
"""
# Here we make a graphs with 10 vertex(source and sink includes)
graph = Dinic(10)
source = 0
sink = 9
"""
Now we add the vertices next to the font in the font with 1 capacity in this edge
(source -> source vertices)
"""
for vertex in range(1, 5):
    graph.add_edge(source, vertex, 1)
"""
We will do the same thing for the vertices near the sink, but from vertex to sink
(sink vertices -> sink)
"""
for vertex in range(5, 9):
    graph.add_edge(vertex, sink, 1)
"""
Finally we add the verices near the sink to the vertices near the source.
(source vertices -> sink vertices)
"""
for vertex in range(1, 5):
    graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))


================================================
FILE: graphs/directed_and_undirected_weighted_graph.py
================================================
from collections import deque
from math import floor
from random import random
from time import time

# the default weight is 1 if not assigned but all the implementation is weighted


class DirectedGraph:
    def __init__(self):
        self.graph = {}

    # adding vertices and edges
    # adding the weight is optional
    # handles repetition
    def add_pair(self, u, v, w=1):
        if self.graph.get(u):
            if self.graph[u].count([w, v]) == 0:
                self.graph[u].append([w, v])
        else:
            self.graph[u] = [[w, v]]
        if not self.graph.get(v):
            self.graph[v] = []

    def all_nodes(self):
        return list(self.graph)

    # handles if the input does not exist
    def remove_pair(self, u, v):
        if self.graph.get(u):
            for _ in self.graph[u]:
                if _[1] == v:
                    self.graph[u].remove(_)

    # if no destination is meant the default value is -1
    def dfs(self, s=-2, d=-1):
        if s == d:
            return []
        stack = []
        visited = []
        if s == -2:
            s = next(iter(self.graph))
        stack.append(s)
        visited.append(s)
        ss = s

        while True:
            # check if there is any non isolated nodes
            if len(self.graph[s]) != 0:
                ss = s
                for node in self.graph[s]:
                    if visited.count(node[1]) < 1:
                        if node[1] == d:
                            visited.append(d)
                            return visited
                        else:
                            stack.append(node[1])
                            visited.append(node[1])
                            ss = node[1]
                            break

            # check if all the children are visited
            if s == ss:
                stack.pop()
                if len(stack) != 0:
                    s = stack[len(stack) - 1]
            else:
                s = ss

            # check if se have reached the starting point
            if len(stack) == 0:
                return visited

    # c is the count of nodes you want and if you leave it or pass -1 to the function
    # the count will be random from 10 to 10000
    def fill_graph_randomly(self, c=-1):
        if c == -1:
            c = floor(random() * 10000) + 10
        for i in range(c):
            # every vertex has max 100 edges
            for _ in range(floor(random() * 102) + 1):
                n = floor(random() * c) + 1
                if n != i:
                    self.add_pair(i, n, 1)

    def bfs(self, s=-2):
        d = deque()
        visited = []
        if s == -2:
            s = next(iter(self.graph))
        d.append(s)
        visited.append(s)
        while d:
            s = d.popleft()
            if len(self.graph[s]) != 0:
                for node in self.graph[s]:
                    if visited.count(node[1]) < 1:
                        d.append(node[1])
                        visited.append(node[1])
        return visited

    def in_degree(self, u):
        count = 0
        for x in self.graph:
            for y in self.graph[x]:
                if y[1] == u:
                    count += 1
        return count

    def out_degree(self, u):
        return len(self.graph[u])

    def topological_sort(self, s=-2):
        stack = []
        visited = []
        if s == -2:
            s = next(iter(self.graph))
        stack.append(s)
        visited.append(s)
        ss = s
        sorted_nodes = []

        while True:
            # check if there is any non isolated nodes
            if len(self.graph[s]) != 0:
                ss = s
                for node in self.graph[s]:
                    if visited.count(node[1]) < 1:
                        stack.append(node[1])
                        visited.append(node[1])
                        ss = node[1]
                        break

            # check if all the children are visited
            if s == ss:
                sorted_nodes.append(stack.pop())
                if len(stack) != 0:
                    s = stack[len(stack) - 1]
            else:
                s = ss

            # check if se have reached the starting point
            if len(stack) == 0:
                return sorted_nodes

    def cycle_nodes(self):
        stack = []
        visited = []
        s = next(iter(self.graph))
        stack.append(s)
        visited.append(s)
        parent = -2
        indirect_parents = []
        ss = s
        on_the_way_back = False
        anticipating_nodes = set()

        while True:
            # check if there is any non isolated nodes
            if len(self.graph[s]) != 0:
                ss = s
                for node in self.graph[s]:
                    if (
                        visited.count(node[1]) > 0
                        and node[1] != parent
                        and indirect_parents.count(node[1]) > 0
                        and not on_the_way_back
                    ):
                        len_stack = len(stack) - 1
                        while len_stack >= 0:
                            if stack[len_stack] == node[1]:
                                anticipating_nodes.add(node[1])
                                break
                            else:
                                anticipating_nodes.add(stack[len_stack])
                                len_stack -= 1
                    if visited.count(node[1]) < 1:
                        stack.append(node[1])
                        visited.append(node[1])
                        ss = node[1]
                        break

            # check if all the children are visited
            if s == ss:
                stack.pop()
                on_the_way_back = True
                if len(stack) != 0:
                    s = stack[len(stack) - 1]
            else:
                on_the_way_back = False
                indirect_parents.append(parent)
                parent = s
                s = ss

            # check if se have reached the starting point
            if len(stack) == 0:
                return list(anticipating_nodes)

    def has_cycle(self):
        stack = []
        visited = []
        s = next(iter(self.graph))
        stack.append(s)
        visited.append(s)
        parent = -2
        indirect_parents = []
        ss = s
        on_the_way_back = False
        anticipating_nodes = set()

        while True:
            # check if there is any non isolated nodes
            if len(self.graph[s]) != 0:
                ss = s
                for node in self.graph[s]:
                    if (
                        visited.count(node[1]) > 0
                        and node[1] != parent
                        and indirect_parents.count(node[1]) > 0
                        and not on_the_way_back
                    ):
                        len_stack_minus_one = len(stack) - 1
                        while len_stack_minus_one >= 0:
                            if stack[len_stack_minus_one] == node[1]:
                                anticipating_nodes.add(node[1])
                                break
                            else:
                                return True
                    if visited.count(node[1]) < 1:
                        stack.append(node[1])
                        visited.append(node[1])
                        ss = node[1]
                        break

            # check if all the children are visited
            if s == ss:
                stack.pop()
                on_the_way_back = True
                if len(stack) != 0:
                    s = stack[len(stack) - 1]
            else:
                on_the_way_back = False
                indirect_parents.append(parent)
                parent = s
                s = ss

            # check if se have reached the starting point
            if len(stack) == 0:
                return False

    def dfs_time(self, s=-2, e=-1):
        begin = time()
        self.dfs(s, e)
        end = time()
        return end - begin

    def bfs_time(self, s=-2):
        begin = time()
        self.bfs(s)
        end = time()
        return end - begin


class Graph:
    def __init__(self):
        self.graph = {}

    # adding vertices and edges
    # adding the weight is optional
    # handles repetition
    def add_pair(self, u, v, w=1):
        # check if the u exists
        if self.graph.get(u):
            # if there already is a edge
            if self.graph[u].count([w, v]) == 0:
                self.graph[u].append([w, v])
        else:
            # if u does not exist
            self.graph[u] = [[w, v]]
        # add the other way
        if self.graph.get(v):
            # if there already is a edge
            if self.graph[v].count([w, u]) == 0:
                self.graph[v].append([w, u])
        else:
            # if u does not exist
            self.graph[v] = [[w, u]]

    # handles if the input does not exist
    def remove_pair(self, u, v):
        if self.graph.get(u):
            for _ in self.graph[u]:
                if _[1] == v:
                    self.graph[u].remove(_)
        # the other way round
        if self.graph.get(v):
            for _ in self.graph[v]:
                if _[1] == u:
                    self.graph[v].remove(_)

    # if no destination is meant the default value is -1
    def dfs(self, s=-2, d=-1):
        if s == d:
            return []
        stack = []
        visited = []
        if s == -2:
            s = next(iter(self.graph))
        stack.append(s)
        visited.append(s)
        ss = s

        while True:
            # check if there is any non isolated nodes
            if len(self.graph[s]) != 0:
                ss = s
                for node in self.graph[s]:
                    if visited.count(node[1]) < 1:
                        if node[1] == d:
                            visited.append(d)
                            return visited
                        else:
                            stack.append(node[1])
                            visited.append(node[1])
                            ss = node[1]
                            break

            # check if all the children are visited
            if s == ss:
                stack.pop()
                if len(stack) != 0:
                    s = stack[len(stack) - 1]
            else:
                s = ss

            # check if se have reached the starting point
            if len(stack) == 0:
                return visited

    # c is the count of nodes you want and if you leave it or pass -1 to the function
    # the count will be random from 10 to 10000
    def fill_graph_randomly(self, c=-1):
        if c == -1:
            c = floor(random() * 10000) + 10
        for i in range(c):
            # every vertex has max 100 edges
            for _ in range(floor(random() * 102) + 1):
                n = floor(random() * c) + 1
                if n != i:
                    self.add_pair(i, n, 1)

    def bfs(self, s=-2):
        d = deque()
        visited = []
        if s == -2:
            s = next(iter(self.graph))
        d.append(s)
        visited.append(s)
        while d:
            s = d.popleft()
            if len(self.graph[s]) != 0:
                for node in self.graph[s]:
                    if visited.count(node[1]) < 1:
                        d.append(node[1])
                        visited.append(node[1])
        return visited

    def degree(self, u):
        return len(self.graph[u])

    def cycle_nodes(self):
        stack = []
        visited = []
        s = next(iter(self.graph))
        stack.append(s)
        visited.append(s)
        parent = -2
        indirect_parents = []
        ss = s
        on_the_way_back = False
        anticipating_nodes = set()

        while True:
            # check if there is any non isolated nodes
            if len(self.graph[s]) != 0:
                ss = s
                for node in self.graph[s]:
                    if (
                        visited.count(node[1]) > 0
                        and node[1] != parent
                        and indirect_parents.count(node[1]) > 0
                        and not on_the_way_back
                    ):
                        len_stack = len(stack) - 1
                        while len_stack >= 0:
                            if stack[len_stack] == node[1]:
                                anticipating_nodes.add(node[1])
                                break
                            else:
                                anticipating_nodes.add(stack[len_stack])
                                len_stack -= 1
                    if visited.count(node[1]) < 1:
                        stack.append(node[1])
                        visited.append(node[1])
                        ss = node[1]
                        break

            # check if all the children are visited
            if s == ss:
                stack.pop()
                on_the_way_back = True
                if len(stack) != 0:
                    s = stack[len(stack) - 1]
            else:
                on_the_way_back = False
                indirect_parents.append(parent)
                parent = s
                s = ss

            # check if se have reached the starting point
            if len(stack) == 0:
                return list(anticipating_nodes)

    def has_cycle(self):
        stack = []
        visited = []
        s = next(iter(self.graph))
        stack.append(s)
        visited.append(s)
        parent = -2
        indirect_parents = []
        ss = s
        on_the_way_back = False
        anticipating_nodes = set()

        while True:
            # check if there is any non isolated nodes
            if len(self.graph[s]) != 0:
                ss = s
                for node in self.graph[s]:
                    if (
                        visited.count(node[1]) > 0
                        and node[1] != parent
                        and indirect_parents.count(node[1]) > 0
                        and not on_the_way_back
                    ):
                        len_stack_minus_one = len(stack) - 1
                        while len_stack_minus_one >= 0:
                            if stack[len_stack_minus_one] == node[1]:
                                anticipating_nodes.add(node[1])
                                break
                            else:
                                return True
                    if visited.count(node[1]) < 1:
                        stack.append(node[1])
                        visited.append(node[1])
                        ss = node[1]
                        break

            # check if all the children are visited
            if s == ss:
                stack.pop()
                on_the_way_back = True
                if len(stack) != 0:
                    s = stack[len(stack) - 1]
            else:
                on_the_way_back = False
                indirect_parents.append(parent)
                parent = s
                s = ss

            # check if se have reached the starting point
            if len(stack) == 0:
                return False

    def all_nodes(self):
        return list(self.graph)

    def dfs_time(self, s=-2, e=-1):
        begin = time()
        self.dfs(s, e)
        end = time()
        return end - begin

    def bfs_time(self, s=-2):
        begin = time()
        self.bfs(s)
        end = time()
        return end - begin


================================================
FILE: graphs/edmonds_karp_multiple_source_and_sink.py
================================================
class FlowNetwork:
    def __init__(self, graph, sources, sinks):
        self.source_index = None
        self.sink_index = None
        self.graph = graph

        self._normalize_graph(sources, sinks)
        self.vertices_count = len(graph)
        self.maximum_flow_algorithm = None

    # make only one source and one sink
    def _normalize_graph(self, sources, sinks):
        if sources is int:
            sources = [sources]
        if sinks is int:
            sinks = [sinks]

        if len(sources) == 0 or len(sinks) == 0:
            return

        self.source_index = sources[0]
        self.sink_index = sinks[0]

        # make fake vertex if there are more
        # than one source or sink
        if len(sources) > 1 or len(sinks) > 1:
            max_input_flow = 0
            for i in sources:
                max_input_flow += sum(self.graph[i])

            size = len(self.graph) + 1
            for room in self.graph:
                room.insert(0, 0)
            self.graph.insert(0, [0] * size)
            for i in sources:
                self.graph[0][i + 1] = max_input_flow
            self.source_index = 0

            size = len(self.graph) + 1
            for room in self.graph:
                room.append(0)
            self.graph.append([0] * size)
            for i in sinks:
                self.graph[i + 1][size - 1] = max_input_flow
            self.sink_index = size - 1

    def find_maximum_flow(self):
        if self.maximum_flow_algorithm is None:
            raise Exception("You need to set maximum flow algorithm before.")
        if self.source_index is None or self.sink_index is None:
            return 0

        self.maximum_flow_algorithm.execute()
        return self.maximum_flow_algorithm.getMaximumFlow()

    def set_maximum_flow_algorithm(self, algorithm):
        self.maximum_flow_algorithm = algorithm(self)


class FlowNetworkAlgorithmExecutor:
    def __init__(self, flow_network):
        self.flow_network = flow_network
        self.verticies_count = flow_network.verticesCount
        self.source_index = flow_network.sourceIndex
        self.sink_index = flow_network.sinkIndex
        # it's just a reference, so you shouldn't change
        # it in your algorithms, use deep copy before doing that
        self.graph = flow_network.graph
        self.executed = False

    def execute(self):
        if not self.executed:
            self._algorithm()
            self.executed = True

    # You should override it
    def _algorithm(self):
        pass


class MaximumFlowAlgorithmExecutor(FlowNetworkAlgorithmExecutor):
    def __init__(self, flow_network):
        super().__init__(flow_network)
        # use this to save your result
        self.maximum_flow = -1

    def get_maximum_flow(self):
        if not self.executed:
            raise Exception("You should execute algorithm before using its result!")

        return self.maximum_flow


class PushRelabelExecutor(MaximumFlowAlgorithmExecutor):
    def __init__(self, flow_network):
        super().__init__(flow_network)

        self.preflow = [[0] * self.verticies_count for i in range(self.verticies_count)]

        self.heights = [0] * self.verticies_count
        self.excesses = [0] * self.verticies_count

    def _algorithm(self):
        self.heights[self.source_index] = self.verticies_count

        # push some substance to graph
        for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]):
            self.preflow[self.source_index][nextvertex_index] += bandwidth
            self.preflow[nextvertex_index][self.source_index] -= bandwidth
            self.excesses[nextvertex_index] += bandwidth

        # Relabel-to-front selection rule
        vertices_list = [
            i
            for i in range(self.verticies_count)
            if i not in {self.source_index, self.sink_index}
        ]

        # move through list
        i = 0
        while i < len(vertices_list):
            vertex_index = vertices_list[i]
            previous_height = self.heights[vertex_index]
            self.process_vertex(vertex_index)
            if self.heights[vertex_index] > previous_height:
                # if it was relabeled, swap elements
                # and start from 0 index
                vertices_list.insert(0, vertices_list.pop(i))
                i = 0
            else:
                i += 1

        self.maximum_flow = sum(self.preflow[self.source_index])

    def process_vertex(self, vertex_index):
        while self.excesses[vertex_index] > 0:
            for neighbour_index in range(self.verticies_count):
                # if it's neighbour and current vertex is higher
                if (
                    self.graph[vertex_index][neighbour_index]
                    - self.preflow[vertex_index][neighbour_index]
                    > 0
                    and self.heights[vertex_index] > self.heights[neighbour_index]
                ):
                    self.push(vertex_index, neighbour_index)

            self.relabel(vertex_index)

    def push(self, from_index, to_index):
        preflow_delta = min(
            self.excesses[from_index],
            self.graph[from_index][to_index] - self.preflow[from_index][to_index],
        )
        self.preflow[from_index][to_index] += preflow_delta
        self.preflow[to_index][from_index] -= preflow_delta
        self.excesses[from_index] -= preflow_delta
        self.excesses[to_index] += preflow_delta

    def relabel(self, vertex_index):
        min_height = None
        for to_index in range(self.verticies_count):
            if (
                self.graph[vertex_index][to_index]
                - self.preflow[vertex_index][to_index]
                > 0
            ) and (min_height is None or self.heights[to_index] < min_height):
                min_height = self.heights[to_index]

        if min_height is not None:
            self.heights[vertex_index] = min_height + 1


if __name__ == "__main__":
    entrances = [0]
    exits = [3]
    # graph = [
    #     [0, 0, 4, 6, 0, 0],
    #     [0, 0, 5, 2, 0, 0],
    #     [0, 0, 0, 0, 4, 4],
    #     [0, 0, 0, 0, 6, 6],
    #     [0, 0, 0, 0, 0, 0],
    #     [0, 0, 0, 0, 0, 0],
    # ]
    graph = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

    # prepare our network
    flow_network = FlowNetwork(graph, entrances, exits)
    # set algorithm
    flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
    # and calculate
    maximum_flow = flow_network.find_maximum_flow()

    print(f"maximum flow is {maximum_flow}")


================================================
FILE: graphs/eulerian_path_and_circuit_for_undirected_graph.py
================================================
# Eulerian Path is a path in graph that visits every edge exactly once.
# Eulerian Circuit is an Eulerian Path which starts and ends on the same
# vertex.
# time complexity is O(V+E)
# space complexity is O(VE)


# using dfs for finding eulerian path traversal
def dfs(u, graph, visited_edge, path=None):
    path = (path or []) + [u]
    for v in graph[u]:
        if visited_edge[u][v] is False:
            visited_edge[u][v], visited_edge[v][u] = True, True
            path = dfs(v, graph, visited_edge, path)
    return path


# for checking in graph has euler path or circuit
def check_circuit_or_path(graph, max_node):
    odd_degree_nodes = 0
    odd_node = -1
    for i in range(max_node):
        if i not in graph:
            continue
        if len(graph[i]) % 2 == 1:
            odd_degree_nodes += 1
            odd_node = i
    if odd_degree_nodes == 0:
        return 1, odd_node
    if odd_degree_nodes == 2:
        return 2, odd_node
    return 3, odd_node


def check_euler(graph, max_node):
    visited_edge = [[False for _ in range(max_node + 1)] for _ in range(max_node + 1)]
    check, odd_node = check_circuit_or_path(graph, max_node)
    if check == 3:
        print("graph is not Eulerian")
        print("no path")
        return
    start_node = 1
    if check == 2:
        start_node = odd_node
        print("graph has a Euler path")
    if check == 1:
        print("graph has a Euler cycle")
    path = dfs(start_node, graph, visited_edge)
    print(path)


def main():
    g1 = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
    g2 = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
    g3 = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
    g4 = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
    g5 = {
        1: [],
        2: [],
        # all degree is zero
    }
    max_node = 10
    check_euler(g1, max_node)
    check_euler(g2, max_node)
    check_euler(g3, max_node)
    check_euler(g4, max_node)
    check_euler(g5, max_node)


if __name__ == "__main__":
    main()


================================================
FILE: graphs/even_tree.py
================================================
"""
You are given a tree(a simple connected graph with no cycles). The tree has N
nodes numbered from 1 to N and is rooted at node 1.

Find the maximum number of edges you can remove from the tree to get a forest
such that each connected component of the forest contains an even number of
nodes.

Constraints
2 <= 2 <= 100

Note: The tree input will be such that it can always be decomposed into
components containing an even number of nodes.
"""

# pylint: disable=invalid-name
from collections import defaultdict


def dfs(start: int) -> int:
    """DFS traversal"""
    # pylint: disable=redefined-outer-name
    ret = 1
    visited[start] = True
    for v in tree[start]:
        if v not in visited:
            ret += dfs(v)
    if ret % 2 == 0:
        cuts.append(start)
    return ret


def even_tree():
    """
    2 1
    3 1
    4 3
    5 2
    6 1
    7 2
    8 6
    9 8
    10 8
    On removing edges (1,3) and (1,6), we can get the desired result 2.
    """
    dfs(1)


if __name__ == "__main__":
    n, m = 10, 9
    tree = defaultdict(list)
    visited: dict[int, bool] = {}
    cuts: list[int] = []
    count = 0
    edges = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
    for u, v in edges:
        tree[u].append(v)
        tree[v].append(u)
    even_tree()
    print(len(cuts) - 1)


================================================
FILE: graphs/finding_bridges.py
================================================
"""
An edge is a bridge if, after removing it count of connected components in graph will
be increased by one. Bridges represent vulnerabilities in a connected network and are
useful for designing reliable networks. For example, in a wired computer network, an
articulation point indicates the critical computers and a bridge indicates the critical
wires or connections.

For more details, refer this article:
https://www.geeksforgeeks.org/bridge-in-a-graph/
"""


def __get_demo_graph(index):
    return [
        {
            0: [1, 2],
            1: [0, 2],
            2: [0, 1, 3, 5],
            3: [2, 4],
            4: [3],
            5: [2, 6, 8],
            6: [5, 7],
            7: [6, 8],
            8: [5, 7],
        },
        {
            0: [6],
            1: [9],
            2: [4, 5],
            3: [4],
            4: [2, 3],
            5: [2],
            6: [0, 7],
            7: [6],
            8: [],
            9: [1],
        },
        {
            0: [4],
            1: [6],
            2: [],
            3: [5, 6, 7],
            4: [0, 6],
            5: [3, 8, 9],
            6: [1, 3, 4, 7],
            7: [3, 6, 8, 9],
            8: [5, 7],
            9: [5, 7],
        },
        {
            0: [1, 3],
            1: [0, 2, 4],
            2: [1, 3, 4],
            3: [0, 2, 4],
            4: [1, 2, 3],
        },
    ][index]


def compute_bridges(graph: dict[int, list[int]]) -> list[tuple[int, int]]:
    """
    Return the list of undirected graph bridges [(a1, b1), ..., (ak, bk)]; ai <= bi
    >>> compute_bridges(__get_demo_graph(0))
    [(3, 4), (2, 3), (2, 5)]
    >>> compute_bridges(__get_demo_graph(1))
    [(6, 7), (0, 6), (1, 9), (3, 4), (2, 4), (2, 5)]
    >>> compute_bridges(__get_demo_graph(2))
    [(1, 6), (4, 6), (0, 4)]
    >>> compute_bridges(__get_demo_graph(3))
    []
    >>> compute_bridges({})
    []
    """

    id_ = 0
    n = len(graph)  # No of vertices in graph
    low = [0] * n
    visited = [False] * n

    def dfs(at, parent, bridges, id_):
        visited[at] = True
        low[at] = id_
        id_ += 1
        for to in graph[at]:
            if to == parent:
                pass
            elif not visited[to]:
                dfs(to, at, bridges, id_)
                low[at] = min(low[at], low[to])
                if id_ <= low[to]:
                    bridges.append((at, to) if at < to else (to, at))
            else:
                # This edge is a back edge and cannot be a bridge
                low[at] = min(low[at], low[to])

    bridges: list[tuple[int, int]] = []
    for i in range(n):
        if not visited[i]:
            dfs(i, -1, bridges, id_)
    return bridges


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: graphs/frequent_pattern_graph_miner.py
================================================
"""
FP-GraphMiner - A Fast Frequent Pattern Mining Algorithm for Network Graphs

A novel Frequent Pattern Graph Mining algorithm, FP-GraphMiner, that compactly
represents a set of network graphs as a Frequent Pattern Graph (or FP-Graph).
This graph can be used to efficiently mine frequent subgraphs including maximal
frequent subgraphs and maximum common subgraphs.

URL: https://www.researchgate.net/publication/235255851
"""

# fmt: off
edge_array = [
    ['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'bh-e12', 'cd-e2', 'ce-e4',
     'de-e1', 'df-e8', 'dg-e5', 'dh-e10', 'ef-e3', 'eg-e2', 'fg-e6', 'gh-e6', 'hi-e3'],
    ['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'cd-e2', 'de-e1', 'df-e8',
     'ef-e3', 'eg-e2', 'fg-e6'],
    ['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'de-e1', 'df-e8', 'dg-e5', 'ef-e3', 'eg-e2',
     'eh-e12', 'fg-e6', 'fh-e10', 'gh-e6'],
    ['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'bh-e12', 'cd-e2', 'df-e8', 'dh-e10'],
    ['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'cd-e2', 'ce-e4', 'de-e1', 'df-e8',
     'dg-e5', 'ef-e3', 'eg-e2', 'fg-e6']
]
# fmt: on


def get_distinct_edge(edge_array):
    """
    Return Distinct edges from edge array of multiple graphs
    >>> sorted(get_distinct_edge(edge_array))
    ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']
    """
    distinct_edge = set()
    for row in edge_array:
        for item in row:
            distinct_edge.add(item[0])
    return list(distinct_edge)


def get_bitcode(edge_array, distinct_edge):
    """
    Return bitcode of distinct_edge
    """
    bitcode = ["0"] * len(edge_array)
    for i, row in enumerate(edge_array):
        for item in row:
            if distinct_edge in item[0]:
                bitcode[i] = "1"
                break
    return "".join(bitcode)


def get_frequency_table(edge_array):
    """
    Returns Frequency Table
    """
    distinct_edge = get_distinct_edge(edge_array)
    frequency_table = {}

    for item in distinct_edge:
        bit = get_bitcode(edge_array, item)
        # print('bit',bit)
        # bt=''.join(bit)
        s = bit.count("1")
        frequency_table[item] = [s, bit]
    # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
    sorted_frequency_table = [
        [k, v[0], v[1]]
        for k, v in sorted(frequency_table.items(), key=lambda v: v[1][0], reverse=True)
    ]
    return sorted_frequency_table


def get_nodes(frequency_table):
    """
    Returns nodes
    format nodes={bitcode:edges that represent the bitcode}
    >>> get_nodes([['ab', 5, '11111'], ['ac', 5, '11111'], ['df', 5, '11111'],
    ...            ['bd', 5, '11111'], ['bc', 5, '11111']])
    {'11111': ['ab', 'ac', 'df', 'bd', 'bc']}
    """
    nodes = {}
    for _, item in enumerate(frequency_table):
        nodes.setdefault(item[2], []).append(item[0])
    return nodes


def get_cluster(nodes):
    """
    Returns cluster
    format cluster:{WT(bitcode):nodes with same WT}
    """
    cluster = {}
    for key, value in nodes.items():
        cluster.setdefault(key.count("1"), {})[key] = value
    return cluster


def get_support(cluster):
    """
    Returns support
    >>> get_support({5: {'11111': ['ab', 'ac', 'df', 'bd', 'bc']},
    ...              4: {'11101': ['ef', 'eg', 'de', 'fg'], '11011': ['cd']},
    ...              3: {'11001': ['ad'], '10101': ['dg']},
    ...              2: {'10010': ['dh', 'bh'], '11000': ['be'], '10100': ['gh'],
    ...                  '10001': ['ce']},
    ...              1: {'00100': ['fh', 'eh'], '10000': ['hi']}})
    [100.0, 80.0, 60.0, 40.0, 20.0]
    """
    return [i * 100 / len(cluster) for i in cluster]


def print_all() -> None:
    print("\nNodes\n")
    for key, value in nodes.items():
        print(key, value)
    print("\nSupport\n")
    print(support)
    print("\n Cluster \n")
    for key, value in sorted(cluster.items(), reverse=True):
        print(key, value)
    print("\n Graph\n")
    for key, value in graph.items():
        print(key, value)
    print("\n Edge List of Frequent subgraphs \n")
    for edge_list in freq_subgraph_edge_list:
        print(edge_list)


def create_edge(nodes, graph, cluster, c1):
    """
    create edge between the nodes
    """
    for i in cluster[c1]:
        count = 0
        c2 = c1 + 1
        while c2 < max(cluster.keys()):
            for j in cluster[c2]:
                """
                creates edge only if the condition satisfies
                """
                if int(i, 2) & int(j, 2) == int(i, 2):
                    if tuple(nodes[i]) in graph:
                        graph[tuple(nodes[i])].append(nodes[j])
                    else:
                        graph[tuple(nodes[i])] = [nodes[j]]
                    count += 1
            if count == 0:
                c2 = c2 + 1
            else:
                break


def construct_graph(cluster, nodes):
    x = cluster[max(cluster.keys())]
    cluster[max(cluster.keys()) + 1] = "Header"
    graph = {}
    for i in x:
        if (["Header"],) in graph:
            graph[(["Header"],)].append(x[i])
        else:
            graph[(["Header"],)] = [x[i]]
    for i in x:
        graph[(x[i],)] = [["Header"]]
    i = 1
    while i < max(cluster) - 1:
        create_edge(nodes, graph, cluster, i)
        i = i + 1
    return graph


def my_dfs(graph, start, end, path=None):
    """
    find different DFS walk from given node to Header node
    """
    path = (path or []) + [start]
    if start == end:
        paths.append(path)
    for node in graph[start]:
        if tuple(node) not in path:
            my_dfs(graph, tuple(node), end, path)


def find_freq_subgraph_given_support(s, cluster, graph):
    """
    find edges of multiple frequent subgraphs
    """
    k = int(s / 100 * (len(cluster) - 1))
    for i in cluster[k]:
        my_dfs(graph, tuple(cluster[k][i]), (["Header"],))


def freq_subgraphs_edge_list(paths):
    """
    returns Edge list for frequent subgraphs
    """
    freq_sub_el = []
    for edges in paths:
        el = []
        for j in range(len(edges) - 1):
            temp = list(edges[j])
            for e in temp:
                edge = (e[0], e[1])
                el.append(edge)
        freq_sub_el.append(el)
    return freq_sub_el


def preprocess(edge_array):
    """
    Preprocess the edge array
    >>> preprocess([['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'bh-e12',
    ...              'cd-e2', 'ce-e4', 'de-e1', 'df-e8', 'dg-e5', 'dh-e10', 'ef-e3',
    ...              'eg-e2', 'fg-e6', 'gh-e6', 'hi-e3']])

    """
    for i in range(len(edge_array)):
        for j in range(len(edge_array[i])):
            t = edge_array[i][j].split("-")
            edge_array[i][j] = t


if __name__ == "__main__":
    preprocess(edge_array)
    frequency_table = get_frequency_table(edge_array)
    nodes = get_nodes(frequency_table)
    cluster = get_cluster(nodes)
    support = get_support(cluster)
    graph = construct_graph(cluster, nodes)
    find_freq_subgraph_given_support(60, cluster, graph)
    paths: list = []
    freq_subgraph_edge_list = freq_subgraphs_edge_list(paths)
    print_all()


================================================
FILE: graphs/g_topological_sort.py
================================================
# Author: Phyllipe Bezerra (https://github.com/pmba)

clothes = {
    0: "underwear",
    1: "pants",
    2: "belt",
    3: "suit",
    4: "shoe",
    5: "socks",
    6: "shirt",
    7: "tie",
    8: "watch",
}

graph = [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

visited = [0 for x in range(len(graph))]
stack = []


def print_stack(stack, clothes):
    order = 1
    while stack:
        current_clothing = stack.pop()
        print(order, clothes[current_clothing])
        order += 1


def depth_first_search(u, visited, graph):
    visited[u] = 1
    for v in graph[u]:
        if not visited[v]:
            depth_first_search(v, visited, graph)

    stack.append(u)


def topological_sort(graph, visited):
    for v in range(len(graph)):
        if not visited[v]:
            depth_first_search(v, visited, graph)


if __name__ == "__main__":
    topological_sort(graph, visited)
    print(stack)
    print_stack(stack, clothes)


================================================
FILE: graphs/gale_shapley_bigraph.py
================================================
from __future__ import annotations


def stable_matching(
    donor_pref: list[list[int]], recipient_pref: list[list[int]]
) -> list[int]:
    """
    Finds the stable match in any bipartite graph, i.e a pairing where no 2 objects
    prefer each other over their partner.  The function accepts the preferences of
    oegan donors and recipients (where both are assigned numbers from 0 to n-1) and
    returns a list where the index position corresponds to the donor and value at the
    index is the organ recipient.

    To better understand the algorithm, see also:
    https://github.com/akashvshroff/Gale_Shapley_Stable_Matching (README).
    https://www.youtube.com/watch?v=Qcv1IqHWAzg&t=13s (Numberphile YouTube).

    >>> donor_pref = [[0, 1, 3, 2], [0, 2, 3, 1], [1, 0, 2, 3], [0, 3, 1, 2]]
    >>> recipient_pref = [[3, 1, 2, 0], [3, 1, 0, 2], [0, 3, 1, 2], [1, 0, 3, 2]]
    >>> stable_matching(donor_pref, recipient_pref)
    [1, 2, 3, 0]
    """
    assert len(donor_pref) == len(recipient_pref)

    n = len(donor_pref)
    unmatched_donors = list(range(n))
    donor_record = [-1] * n  # who the donor has donated to
    rec_record = [-1] * n  # who the recipient has received from
    num_donations = [0] * n

    while unmatched_donors:
        donor = unmatched_donors[0]
        donor_preference = donor_pref[donor]
        recipient = donor_preference[num_donations[donor]]
        num_donations[donor] += 1
        rec_preference = recipient_pref[recipient]
        prev_donor = rec_record[recipient]

        if prev_donor != -1:
            if rec_preference.index(prev_donor) > rec_preference.index(donor):
                rec_record[recipient] = donor
                donor_record[donor] = recipient
                unmatched_donors.append(prev_donor)
                unmatched_donors.remove(donor)
        else:
            rec_record[recipient] = donor
            donor_record[donor] = recipient
            unmatched_donors.remove(donor)
    return donor_record


================================================
FILE: graphs/graph_adjacency_list.py
================================================
#!/usr/bin/env python3
"""
Author: Vikram Nithyanandam

Description:
The following implementation is a robust unweighted Graph data structure
implemented using an adjacency list. This vertices and edges of this graph can be
effectively initialized and modified while storing your chosen generic
value in each vertex.

Adjacency List: https://en.wikipedia.org/wiki/Adjacency_list

Potential Future Ideas:
- Add a flag to set edge weights on and set edge weights
- Make edge weights and vertex values customizable to store whatever the client wants
- Support multigraph functionality if the client wants it
"""

from __future__ import annotations

import random
import unittest
from pprint import pformat
from typing import TypeVar

import pytest

T = TypeVar("T")


class GraphAdjacencyList[T]:
    def __init__(
        self, vertices: list[T], edges: list[list[T]], directed: bool = True
    ) -> None:
        """
        Parameters:
         - vertices: (list[T]) The list of vertex names the client wants to
        pass in. Default is empty.
        - edges: (list[list[T]]) The list of edges the client wants to
        pass in. Each edge is a 2-element list. Default is empty.
        - directed: (bool) Indicates if graph is directed or undirected.
        Default is True.
        """
        self.adj_list: dict[T, list[T]] = {}  # dictionary of lists of T
        self.directed = directed

        # Falsey checks
        edges = edges or []
        vertices = vertices or []

        for vertex in vertices:
            self.add_vertex(vertex)

        for edge in edges:
            if len(edge) != 2:
                msg = f"Invalid input: {edge} is the wrong length."
                raise ValueError(msg)
            self.add_edge(edge[0], edge[1])

    def add_vertex(self, vertex: T) -> None:
        """
        Adds a vertex to the graph. If the given vertex already exists,
        a ValueError will be thrown.

        >>> g = GraphAdjacencyList(vertices=[], edges=[], directed=False)
        >>> g.add_vertex("A")
        >>> g.adj_list
        {'A': []}
        >>> g.add_vertex("A")
        Traceback (most recent call last):
        ...
        ValueError: Incorrect input: A is already in the graph.
        """
        if self.contains_vertex(vertex):
            msg = f"Incorrect input: {vertex} is already in the graph."
            raise ValueError(msg)
        self.adj_list[vertex] = []

    def add_edge(self, source_vertex: T, destination_vertex: T) -> None:
        """
        Creates an edge from source vertex to destination vertex. If any
        given vertex doesn't exist or the edge already exists, a ValueError
        will be thrown.
        """
        if not (
            self.contains_vertex(source_vertex)
            and self.contains_vertex(destination_vertex)
        ):
            msg = (
                f"Incorrect input: Either {source_vertex} or "
                f"{destination_vertex} does not exist"
            )
            raise ValueError(msg)
        if self.contains_edge(source_vertex, destination_vertex):
            msg = (
                "Incorrect input: The edge already exists between "
                f"{source_vertex} and {destination_vertex}"
            )
            raise ValueError(msg)

        # add the destination vertex to the list associated with the source vertex
        # and vice versa if not directed
        self.adj_list[source_vertex].append(destination_vertex)
        if not self.directed:
            self.adj_list[destination_vertex].append(source_vertex)

    def remove_vertex(self, vertex: T) -> None:
        """
        Removes the given vertex from the graph and deletes all incoming and
        outgoing edges from the given vertex as well. If the given vertex
        does not exist, a ValueError will be thrown.
        """
        if not self.contains_vertex(vertex):
            msg = f"Incorrect input: {vertex} does not exist in this graph."
            raise ValueError(msg)

        if not self.directed:
            # If not directed, find all neighboring vertices and delete all references
            # of edges connecting to the given vertex
            for neighbor in self.adj_list[vertex]:
                self.adj_list[neighbor].remove(vertex)
        else:
            # If directed, search all neighbors of all vertices and delete all
            # references of edges connecting to the given vertex
            for edge_list in self.adj_list.values():
                if vertex in edge_list:
                    edge_list.remove(vertex)

        # Finally, delete the given vertex and all of its outgoing edge references
        self.adj_list.pop(vertex)

    def remove_edge(self, source_vertex: T, destination_vertex: T) -> None:
        """
        Removes the edge between the two vertices. If any given vertex
        doesn't exist or the edge does not exist, a ValueError will be thrown.
        """
        if not (
            self.contains_vertex(source_vertex)
            and self.contains_vertex(destination_vertex)
        ):
            msg = (
                f"Incorrect input: Either {source_vertex} or "
                f"{destination_vertex} does not exist"
            )
            raise ValueError(msg)
        if not self.contains_edge(source_vertex, destination_vertex):
            msg = (
                "Incorrect input: The edge does NOT exist between "
                f"{source_vertex} and {destination_vertex}"
            )
            raise ValueError(msg)

        # remove the destination vertex from the list associated with the source
        # vertex and vice versa if not directed
        self.adj_list[source_vertex].remove(destination_vertex)
        if not self.directed:
            self.adj_list[destination_vertex].remove(source_vertex)

    def contains_vertex(self, vertex: T) -> bool:
        """
        Returns True if the graph contains the vertex, False otherwise.
        """
        return vertex in self.adj_list

    def contains_edge(self, source_vertex: T, destination_vertex: T) -> bool:
        """
        Returns True if the graph contains the edge from the source_vertex to the
        destination_vertex, False otherwise. If any given vertex doesn't exist, a
        ValueError will be thrown.
        """
        if not (
            self.contains_vertex(source_vertex)
            and self.contains_vertex(destination_vertex)
        ):
            msg = (
                f"Incorrect input: Either {source_vertex} "
                f"or {destination_vertex} does not exist."
            )
            raise ValueError(msg)

        return destination_vertex in self.adj_list[source_vertex]

    def clear_graph(self) -> None:
        """
        Clears all vertices and edges.
        """
        self.adj_list = {}

    def __repr__(self) -> str:
        return pformat(self.adj_list)


class TestGraphAdjacencyList(unittest.TestCase):
    def __assert_graph_edge_exists_check(
        self,
        undirected_graph: GraphAdjacencyList,
        directed_graph: GraphAdjacencyList,
        edge: list[int],
    ) -> None:
        assert undirected_graph.contains_edge(edge[0], edge[1])
        assert undirected_graph.contains_edge(edge[1], edge[0])
        assert directed_graph.contains_edge(edge[0], edge[1])

    def __assert_graph_edge_does_not_exist_check(
        self,
        undirected_graph: GraphAdjacencyList,
        directed_graph: GraphAdjacencyList,
        edge: list[int],
    ) -> None:
        assert not undirected_graph.contains_edge(edge[0], edge[1])
        assert not undirected_graph.contains_edge(edge[1], edge[0])
        assert not directed_graph.contains_edge(edge[0], edge[1])

    def __assert_graph_vertex_exists_check(
        self,
        undirected_graph: GraphAdjacencyList,
        directed_graph: GraphAdjacencyList,
        vertex: int,
    ) -> None:
        assert undirected_graph.contains_vertex(vertex)
        assert directed_graph.contains_vertex(vertex)

    def __assert_graph_vertex_does_not_exist_check(
        self,
        undirected_graph: GraphAdjacencyList,
        directed_graph: GraphAdjacencyList,
        vertex: int,
    ) -> None:
        assert not undirected_graph.contains_vertex(vertex)
        assert not directed_graph.contains_vertex(vertex)

    def __generate_random_edges(
        self, vertices: list[int], edge_pick_count: int
    ) -> list[list[int]]:
        assert edge_pick_count <= len(vertices)

        random_source_vertices: list[int] = random.sample(
            vertices[0 : int(len(vertices) / 2)], edge_pick_count
        )
        random_destination_vertices: list[int] = random.sample(
            vertices[int(len(vertices) / 2) :], edge_pick_count
        )
        random_edges: list[list[int]] = []

        for source in random_source_vertices:
            for dest in random_destination_vertices:
                random_edges.append([source, dest])

        return random_edges

    def __generate_graphs(
        self, vertex_count: int, min_val: int, max_val: int, edge_pick_count: int
    ) -> tuple[GraphAdjacencyList, GraphAdjacencyList, list[int], list[list[int]]]:
        if max_val - min_val + 1 < vertex_count:
            raise ValueError(
                "Will result in duplicate vertices. Either increase range "
                "between min_val and max_val or decrease vertex count."
            )

        # generate graph input
        random_vertices: list[int] = random.sample(
            range(min_val, max_val + 1), vertex_count
        )
        random_edges: list[list[int]] = self.__generate_random_edges(
            random_vertices, edge_pick_count
        )

        # build graphs
        undirected_graph = GraphAdjacencyList(
            vertices=random_vertices, edges=random_edges, directed=False
        )
        directed_graph = GraphAdjacencyList(
            vertices=random_vertices, edges=random_edges, directed=True
        )

        return undirected_graph, directed_graph, random_vertices, random_edges

    def test_init_check(self) -> None:
        (
            undirected_graph,
            directed_graph,
            random_vertices,
            random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        # test graph initialization with vertices and edges
        for num in random_vertices:
            self.__assert_graph_vertex_exists_check(
                undirected_graph, directed_graph, num
            )

        for edge in random_edges:
            self.__assert_graph_edge_exists_check(
                undirected_graph, directed_graph, edge
            )
        assert not undirected_graph.directed
        assert directed_graph.directed

    def test_contains_vertex(self) -> None:
        random_vertices: list[int] = random.sample(range(101), 20)

        # Build graphs WITHOUT edges
        undirected_graph = GraphAdjacencyList(
            vertices=random_vertices, edges=[], directed=False
        )
        directed_graph = GraphAdjacencyList(
            vertices=random_vertices, edges=[], directed=True
        )

        # Test contains_vertex
        for num in range(101):
            assert (num in random_vertices) == undirected_graph.contains_vertex(num)
            assert (num in random_vertices) == directed_graph.contains_vertex(num)

    def test_add_vertices(self) -> None:
        random_vertices: list[int] = random.sample(range(101), 20)

        # build empty graphs
        undirected_graph: GraphAdjacencyList = GraphAdjacencyList(
            vertices=[], edges=[], directed=False
        )
        directed_graph: GraphAdjacencyList = GraphAdjacencyList(
            vertices=[], edges=[], directed=True
        )

        # run add_vertex
        for num in random_vertices:
            undirected_graph.add_vertex(num)

        for num in random_vertices:
            directed_graph.add_vertex(num)

        # test add_vertex worked
        for num in random_vertices:
            self.__assert_graph_vertex_exists_check(
                undirected_graph, directed_graph, num
            )

    def test_remove_vertices(self) -> None:
        random_vertices: list[int] = random.sample(range(101), 20)

        # build graphs WITHOUT edges
        undirected_graph = GraphAdjacencyList(
            vertices=random_vertices, edges=[], directed=False
        )
        directed_graph = GraphAdjacencyList(
            vertices=random_vertices, edges=[], directed=True
        )

        # test remove_vertex worked
        for num in random_vertices:
            self.__assert_graph_vertex_exists_check(
                undirected_graph, directed_graph, num
            )

            undirected_graph.remove_vertex(num)
            directed_graph.remove_vertex(num)

            self.__assert_graph_vertex_does_not_exist_check(
                undirected_graph, directed_graph, num
            )

    def test_add_and_remove_vertices_repeatedly(self) -> None:
        random_vertices1: list[int] = random.sample(range(51), 20)
        random_vertices2: list[int] = random.sample(range(51, 101), 20)

        # build graphs WITHOUT edges
        undirected_graph = GraphAdjacencyList(
            vertices=random_vertices1, edges=[], directed=False
        )
        directed_graph = GraphAdjacencyList(
            vertices=random_vertices1, edges=[], directed=True
        )

        # test adding and removing vertices
        for i, _ in enumerate(random_vertices1):
            undirected_graph.add_vertex(random_vertices2[i])
            directed_graph.add_vertex(random_vertices2[i])

            self.__assert_graph_vertex_exists_check(
                undirected_graph, directed_graph, random_vertices2[i]
            )

            undirected_graph.remove_vertex(random_vertices1[i])
            directed_graph.remove_vertex(random_vertices1[i])

            self.__assert_graph_vertex_does_not_exist_check(
                undirected_graph, directed_graph, random_vertices1[i]
            )

        # remove all vertices
        for i, _ in enumerate(random_vertices1):
            undirected_graph.remove_vertex(random_vertices2[i])
            directed_graph.remove_vertex(random_vertices2[i])

            self.__assert_graph_vertex_does_not_exist_check(
                undirected_graph, directed_graph, random_vertices2[i]
            )

    def test_contains_edge(self) -> None:
        # generate graphs and graph input
        vertex_count = 20
        (
            undirected_graph,
            directed_graph,
            random_vertices,
            random_edges,
        ) = self.__generate_graphs(vertex_count, 0, 100, 4)

        # generate all possible edges for testing
        all_possible_edges: list[list[int]] = []
        for i in range(vertex_count - 1):
            for j in range(i + 1, vertex_count):
                all_possible_edges.append([random_vertices[i], random_vertices[j]])
                all_possible_edges.append([random_vertices[j], random_vertices[i]])

        # test contains_edge function
        for edge in all_possible_edges:
            if edge in random_edges:
                self.__assert_graph_edge_exists_check(
                    undirected_graph, directed_graph, edge
                )
            elif [edge[1], edge[0]] in random_edges:
                # since this edge exists for undirected but the reverse
                # may not exist for directed
                self.__assert_graph_edge_exists_check(
                    undirected_graph, directed_graph, [edge[1], edge[0]]
                )
            else:
                self.__assert_graph_edge_does_not_exist_check(
                    undirected_graph, directed_graph, edge
                )

    def test_add_edge(self) -> None:
        # generate graph input
        random_vertices: list[int] = random.sample(range(101), 15)
        random_edges: list[list[int]] = self.__generate_random_edges(random_vertices, 4)

        # build graphs WITHOUT edges
        undirected_graph = GraphAdjacencyList(
            vertices=random_vertices, edges=[], directed=False
        )
        directed_graph = GraphAdjacencyList(
            vertices=random_vertices, edges=[], directed=True
        )

        # run and test add_edge
        for edge in random_edges:
            undirected_graph.add_edge(edge[0], edge[1])
            directed_graph.add_edge(edge[0], edge[1])
            self.__assert_graph_edge_exists_check(
                undirected_graph, directed_graph, edge
            )

    def test_remove_edge(self) -> None:
        # generate graph input and graphs
        (
            undirected_graph,
            directed_graph,
            _random_vertices,
            random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        # run and test remove_edge
        for edge in random_edges:
            self.__assert_graph_edge_exists_check(
                undirected_graph, directed_graph, edge
            )
            undirected_graph.remove_edge(edge[0], edge[1])
            directed_graph.remove_edge(edge[0], edge[1])
            self.__assert_graph_edge_does_not_exist_check(
                undirected_graph, directed_graph, edge
            )

    def test_add_and_remove_edges_repeatedly(self) -> None:
        (
            undirected_graph,
            directed_graph,
            random_vertices,
            random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        # make some more edge options!
        more_random_edges: list[list[int]] = []

        while len(more_random_edges) != len(random_edges):
            edges: list[list[int]] = self.__generate_random_edges(random_vertices, 4)
            for edge in edges:
                if len(more_random_edges) == len(random_edges):
                    break
                elif edge not in more_random_edges and edge not in random_edges:
                    more_random_edges.append(edge)

        for i, _ in enumerate(random_edges):
            undirected_graph.add_edge(more_random_edges[i][0], more_random_edges[i][1])
            directed_graph.add_edge(more_random_edges[i][0], more_random_edges[i][1])

            self.__assert_graph_edge_exists_check(
                undirected_graph, directed_graph, more_random_edges[i]
            )

            undirected_graph.remove_edge(random_edges[i][0], random_edges[i][1])
            directed_graph.remove_edge(random_edges[i][0], random_edges[i][1])

            self.__assert_graph_edge_does_not_exist_check(
                undirected_graph, directed_graph, random_edges[i]
            )

    def test_add_vertex_exception_check(self) -> None:
        (
            undirected_graph,
            directed_graph,
            random_vertices,
            _random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        for vertex in random_vertices:
            with pytest.raises(ValueError):
                undirected_graph.add_vertex(vertex)
            with pytest.raises(ValueError):
                directed_graph.add_vertex(vertex)

    def test_remove_vertex_exception_check(self) -> None:
        (
            undirected_graph,
            directed_graph,
            random_vertices,
            _random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        for i in range(101):
            if i not in random_vertices:
                with pytest.raises(ValueError):
                    undirected_graph.remove_vertex(i)
                with pytest.raises(ValueError):
                    directed_graph.remove_vertex(i)

    def test_add_edge_exception_check(self) -> None:
        (
            undirected_graph,
            directed_graph,
            _random_vertices,
            random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        for edge in random_edges:
            with pytest.raises(ValueError):
                undirected_graph.add_edge(edge[0], edge[1])
            with pytest.raises(ValueError):
                directed_graph.add_edge(edge[0], edge[1])

    def test_remove_edge_exception_check(self) -> None:
        (
            undirected_graph,
            directed_graph,
            random_vertices,
            random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        more_random_edges: list[list[int]] = []

        while len(more_random_edges) != len(random_edges):
            edges: list[list[int]] = self.__generate_random_edges(random_vertices, 4)
            for edge in edges:
                if len(more_random_edges) == len(random_edges):
                    break
                elif edge not in more_random_edges and edge not in random_edges:
                    more_random_edges.append(edge)

        for edge in more_random_edges:
            with pytest.raises(ValueError):
                undirected_graph.remove_edge(edge[0], edge[1])
            with pytest.raises(ValueError):
                directed_graph.remove_edge(edge[0], edge[1])

    def test_contains_edge_exception_check(self) -> None:
        (
            undirected_graph,
            directed_graph,
            random_vertices,
            _random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        for vertex in random_vertices:
            with pytest.raises(ValueError):
                undirected_graph.contains_edge(vertex, 102)
            with pytest.raises(ValueError):
                directed_graph.contains_edge(vertex, 102)

        with pytest.raises(ValueError):
            undirected_graph.contains_edge(103, 102)
        with pytest.raises(ValueError):
            directed_graph.contains_edge(103, 102)


if __name__ == "__main__":
    unittest.main()


================================================
FILE: graphs/graph_adjacency_matrix.py
================================================
#!/usr/bin/env python3
"""
Author: Vikram Nithyanandam

Description:
The following implementation is a robust unweighted Graph data structure
implemented using an adjacency matrix. This vertices and edges of this graph can be
effectively initialized and modified while storing your chosen generic
value in each vertex.

Adjacency Matrix: https://mathworld.wolfram.com/AdjacencyMatrix.html

Potential Future Ideas:
- Add a flag to set edge weights on and set edge weights
- Make edge weights and vertex values customizable to store whatever the client wants
- Support multigraph functionality if the client wants it
"""

from __future__ import annotations

import random
import unittest
from pprint import pformat
from typing import TypeVar

import pytest

T = TypeVar("T")


class GraphAdjacencyMatrix[T]:
    def __init__(
        self, vertices: list[T], edges: list[list[T]], directed: bool = True
    ) -> None:
        """
        Parameters:
        - vertices: (list[T]) The list of vertex names the client wants to
        pass in. Default is empty.
        - edges: (list[list[T]]) The list of edges the client wants to
        pass in. Each edge is a 2-element list. Default is empty.
        - directed: (bool) Indicates if graph is directed or undirected.
        Default is True.
        """
        self.directed = directed
        self.vertex_to_index: dict[T, int] = {}
        self.adj_matrix: list[list[int]] = []

        # Falsey checks
        edges = edges or []
        vertices = vertices or []

        for vertex in vertices:
            self.add_vertex(vertex)

        for edge in edges:
            if len(edge) != 2:
                msg = f"Invalid input: {edge} must have length 2."
                raise ValueError(msg)
            self.add_edge(edge[0], edge[1])

    def add_edge(self, source_vertex: T, destination_vertex: T) -> None:
        """
        Creates an edge from source vertex to destination vertex. If any
        given vertex doesn't exist or the edge already exists, a ValueError
        will be thrown.
        """
        if not (
            self.contains_vertex(source_vertex)
            and self.contains_vertex(destination_vertex)
        ):
            msg = (
                f"Incorrect input: Either {source_vertex} or "
                f"{destination_vertex} does not exist"
            )
            raise ValueError(msg)
        if self.contains_edge(source_vertex, destination_vertex):
            msg = (
                "Incorrect input: The edge already exists between "
                f"{source_vertex} and {destination_vertex}"
            )
            raise ValueError(msg)

        # Get the indices of the corresponding vertices and set their edge value to 1.
        u: int = self.vertex_to_index[source_vertex]
        v: int = self.vertex_to_index[destination_vertex]
        self.adj_matrix[u][v] = 1
        if not self.directed:
            self.adj_matrix[v][u] = 1

    def remove_edge(self, source_vertex: T, destination_vertex: T) -> None:
        """
        Removes the edge between the two vertices. If any given vertex
        doesn't exist or the edge does not exist, a ValueError will be thrown.
        """
        if not (
            self.contains_vertex(source_vertex)
            and self.contains_vertex(destination_vertex)
        ):
            msg = (
                f"Incorrect input: Either {source_vertex} or "
                f"{destination_vertex} does not exist"
            )
            raise ValueError(msg)
        if not self.contains_edge(source_vertex, destination_vertex):
            msg = (
                "Incorrect input: The edge does NOT exist between "
                f"{source_vertex} and {destination_vertex}"
            )
            raise ValueError(msg)

        # Get the indices of the corresponding vertices and set their edge value to 0.
        u: int = self.vertex_to_index[source_vertex]
        v: int = self.vertex_to_index[destination_vertex]
        self.adj_matrix[u][v] = 0
        if not self.directed:
            self.adj_matrix[v][u] = 0

    def add_vertex(self, vertex: T) -> None:
        """
        Adds a vertex to the graph. If the given vertex already exists,
        a ValueError will be thrown.
        """
        if self.contains_vertex(vertex):
            msg = f"Incorrect input: {vertex} already exists in this graph."
            raise ValueError(msg)

        # build column for vertex
        for row in self.adj_matrix:
            row.append(0)

        # build row for vertex and update other data structures
        self.adj_matrix.append([0] * (len(self.adj_matrix) + 1))
        self.vertex_to_index[vertex] = len(self.adj_matrix) - 1

    def remove_vertex(self, vertex: T) -> None:
        """
        Removes the given vertex from the graph and deletes all incoming and
        outgoing edges from the given vertex as well. If the given vertex
        does not exist, a ValueError will be thrown.
        """
        if not self.contains_vertex(vertex):
            msg = f"Incorrect input: {vertex} does not exist in this graph."
            raise ValueError(msg)

        # first slide up the rows by deleting the row corresponding to
        # the vertex being deleted.
        start_index = self.vertex_to_index[vertex]
        self.adj_matrix.pop(start_index)

        # next, slide the columns to the left by deleting the values in
        # the column corresponding to the vertex being deleted
        for lst in self.adj_matrix:
            lst.pop(start_index)

        # final clean up
        self.vertex_to_index.pop(vertex)

        # decrement indices for vertices shifted by the deleted vertex in the adj matrix
        for inner_vertex in self.vertex_to_index:
            if self.vertex_to_index[inner_vertex] >= start_index:
                self.vertex_to_index[inner_vertex] = (
                    self.vertex_to_index[inner_vertex] - 1
                )

    def contains_vertex(self, vertex: T) -> bool:
        """
        Returns True if the graph contains the vertex, False otherwise.
        """
        return vertex in self.vertex_to_index

    def contains_edge(self, source_vertex: T, destination_vertex: T) -> bool:
        """
        Returns True if the graph contains the edge from the source_vertex to the
        destination_vertex, False otherwise. If any given vertex doesn't exist, a
        ValueError will be thrown.
        """
        if not (
            self.contains_vertex(source_vertex)
            and self.contains_vertex(destination_vertex)
        ):
            msg = (
                f"Incorrect input: Either {source_vertex} "
                f"or {destination_vertex} does not exist."
            )
            raise ValueError(msg)

        u = self.vertex_to_index[source_vertex]
        v = self.vertex_to_index[destination_vertex]
        return self.adj_matrix[u][v] == 1

    def clear_graph(self) -> None:
        """
        Clears all vertices and edges.
        """
        self.vertex_to_index = {}
        self.adj_matrix = []

    def __repr__(self) -> str:
        first = "Adj Matrix:\n" + pformat(self.adj_matrix)
        second = "\nVertex to index mapping:\n" + pformat(self.vertex_to_index)
        return first + second


class TestGraphMatrix(unittest.TestCase):
    def __assert_graph_edge_exists_check(
        self,
        undirected_graph: GraphAdjacencyMatrix,
        directed_graph: GraphAdjacencyMatrix,
        edge: list[int],
    ) -> None:
        assert undirected_graph.contains_edge(edge[0], edge[1])
        assert undirected_graph.contains_edge(edge[1], edge[0])
        assert directed_graph.contains_edge(edge[0], edge[1])

    def __assert_graph_edge_does_not_exist_check(
        self,
        undirected_graph: GraphAdjacencyMatrix,
        directed_graph: GraphAdjacencyMatrix,
        edge: list[int],
    ) -> None:
        assert not undirected_graph.contains_edge(edge[0], edge[1])
        assert not undirected_graph.contains_edge(edge[1], edge[0])
        assert not directed_graph.contains_edge(edge[0], edge[1])

    def __assert_graph_vertex_exists_check(
        self,
        undirected_graph: GraphAdjacencyMatrix,
        directed_graph: GraphAdjacencyMatrix,
        vertex: int,
    ) -> None:
        assert undirected_graph.contains_vertex(vertex)
        assert directed_graph.contains_vertex(vertex)

    def __assert_graph_vertex_does_not_exist_check(
        self,
        undirected_graph: GraphAdjacencyMatrix,
        directed_graph: GraphAdjacencyMatrix,
        vertex: int,
    ) -> None:
        assert not undirected_graph.contains_vertex(vertex)
        assert not directed_graph.contains_vertex(vertex)

    def __generate_random_edges(
        self, vertices: list[int], edge_pick_count: int
    ) -> list[list[int]]:
        assert edge_pick_count <= len(vertices)

        random_source_vertices: list[int] = random.sample(
            vertices[0 : int(len(vertices) / 2)], edge_pick_count
        )
        random_destination_vertices: list[int] = random.sample(
            vertices[int(len(vertices) / 2) :], edge_pick_count
        )
        random_edges: list[list[int]] = []

        for source in random_source_vertices:
            for dest in random_destination_vertices:
                random_edges.append([source, dest])

        return random_edges

    def __generate_graphs(
        self, vertex_count: int, min_val: int, max_val: int, edge_pick_count: int
    ) -> tuple[GraphAdjacencyMatrix, GraphAdjacencyMatrix, list[int], list[list[int]]]:
        if max_val - min_val + 1 < vertex_count:
            raise ValueError(
                "Will result in duplicate vertices. Either increase "
                "range between min_val and max_val or decrease vertex count"
            )

        # generate graph input
        random_vertices: list[int] = random.sample(
            range(min_val, max_val + 1), vertex_count
        )
        random_edges: list[list[int]] = self.__generate_random_edges(
            random_vertices, edge_pick_count
        )

        # build graphs
        undirected_graph = GraphAdjacencyMatrix(
            vertices=random_vertices, edges=random_edges, directed=False
        )
        directed_graph = GraphAdjacencyMatrix(
            vertices=random_vertices, edges=random_edges, directed=True
        )

        return undirected_graph, directed_graph, random_vertices, random_edges

    def test_init_check(self) -> None:
        (
            undirected_graph,
            directed_graph,
            random_vertices,
            random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        # test graph initialization with vertices and edges
        for num in random_vertices:
            self.__assert_graph_vertex_exists_check(
                undirected_graph, directed_graph, num
            )

        for edge in random_edges:
            self.__assert_graph_edge_exists_check(
                undirected_graph, directed_graph, edge
            )

        assert not undirected_graph.directed
        assert directed_graph.directed

    def test_contains_vertex(self) -> None:
        random_vertices: list[int] = random.sample(range(101), 20)

        # Build graphs WITHOUT edges
        undirected_graph = GraphAdjacencyMatrix(
            vertices=random_vertices, edges=[], directed=False
        )
        directed_graph = GraphAdjacencyMatrix(
            vertices=random_vertices, edges=[], directed=True
        )

        # Test contains_vertex
        for num in range(101):
            assert (num in random_vertices) == undirected_graph.contains_vertex(num)
            assert (num in random_vertices) == directed_graph.contains_vertex(num)

    def test_add_vertices(self) -> None:
        random_vertices: list[int] = random.sample(range(101), 20)

        # build empty graphs
        undirected_graph: GraphAdjacencyMatrix = GraphAdjacencyMatrix(
            vertices=[], edges=[], directed=False
        )
        directed_graph: GraphAdjacencyMatrix = GraphAdjacencyMatrix(
            vertices=[], edges=[], directed=True
        )

        # run add_vertex
        for num in random_vertices:
            undirected_graph.add_vertex(num)

        for num in random_vertices:
            directed_graph.add_vertex(num)

        # test add_vertex worked
        for num in random_vertices:
            self.__assert_graph_vertex_exists_check(
                undirected_graph, directed_graph, num
            )

    def test_remove_vertices(self) -> None:
        random_vertices: list[int] = random.sample(range(101), 20)

        # build graphs WITHOUT edges
        undirected_graph = GraphAdjacencyMatrix(
            vertices=random_vertices, edges=[], directed=False
        )
        directed_graph = GraphAdjacencyMatrix(
            vertices=random_vertices, edges=[], directed=True
        )

        # test remove_vertex worked
        for num in random_vertices:
            self.__assert_graph_vertex_exists_check(
                undirected_graph, directed_graph, num
            )

            undirected_graph.remove_vertex(num)
            directed_graph.remove_vertex(num)

            self.__assert_graph_vertex_does_not_exist_check(
                undirected_graph, directed_graph, num
            )

    def test_add_and_remove_vertices_repeatedly(self) -> None:
        random_vertices1: list[int] = random.sample(range(51), 20)
        random_vertices2: list[int] = random.sample(range(51, 101), 20)

        # build graphs WITHOUT edges
        undirected_graph = GraphAdjacencyMatrix(
            vertices=random_vertices1, edges=[], directed=False
        )
        directed_graph = GraphAdjacencyMatrix(
            vertices=random_vertices1, edges=[], directed=True
        )

        # test adding and removing vertices
        for i, _ in enumerate(random_vertices1):
            undirected_graph.add_vertex(random_vertices2[i])
            directed_graph.add_vertex(random_vertices2[i])

            self.__assert_graph_vertex_exists_check(
                undirected_graph, directed_graph, random_vertices2[i]
            )

            undirected_graph.remove_vertex(random_vertices1[i])
            directed_graph.remove_vertex(random_vertices1[i])

            self.__assert_graph_vertex_does_not_exist_check(
                undirected_graph, directed_graph, random_vertices1[i]
            )

        # remove all vertices
        for i, _ in enumerate(random_vertices1):
            undirected_graph.remove_vertex(random_vertices2[i])
            directed_graph.remove_vertex(random_vertices2[i])

            self.__assert_graph_vertex_does_not_exist_check(
                undirected_graph, directed_graph, random_vertices2[i]
            )

    def test_contains_edge(self) -> None:
        # generate graphs and graph input
        vertex_count = 20
        (
            undirected_graph,
            directed_graph,
            random_vertices,
            random_edges,
        ) = self.__generate_graphs(vertex_count, 0, 100, 4)

        # generate all possible edges for testing
        all_possible_edges: list[list[int]] = []
        for i in range(vertex_count - 1):
            for j in range(i + 1, vertex_count):
                all_possible_edges.append([random_vertices[i], random_vertices[j]])
                all_possible_edges.append([random_vertices[j], random_vertices[i]])

        # test contains_edge function
        for edge in all_possible_edges:
            if edge in random_edges:
                self.__assert_graph_edge_exists_check(
                    undirected_graph, directed_graph, edge
                )
            elif [edge[1], edge[0]] in random_edges:
                # since this edge exists for undirected but the reverse may
                # not exist for directed
                self.__assert_graph_edge_exists_check(
                    undirected_graph, directed_graph, [edge[1], edge[0]]
                )
            else:
                self.__assert_graph_edge_does_not_exist_check(
                    undirected_graph, directed_graph, edge
                )

    def test_add_edge(self) -> None:
        # generate graph input
        random_vertices: list[int] = random.sample(range(101), 15)
        random_edges: list[list[int]] = self.__generate_random_edges(random_vertices, 4)

        # build graphs WITHOUT edges
        undirected_graph = GraphAdjacencyMatrix(
            vertices=random_vertices, edges=[], directed=False
        )
        directed_graph = GraphAdjacencyMatrix(
            vertices=random_vertices, edges=[], directed=True
        )

        # run and test add_edge
        for edge in random_edges:
            undirected_graph.add_edge(edge[0], edge[1])
            directed_graph.add_edge(edge[0], edge[1])
            self.__assert_graph_edge_exists_check(
                undirected_graph, directed_graph, edge
            )

    def test_remove_edge(self) -> None:
        # generate graph input and graphs
        (
            undirected_graph,
            directed_graph,
            _random_vertices,
            random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        # run and test remove_edge
        for edge in random_edges:
            self.__assert_graph_edge_exists_check(
                undirected_graph, directed_graph, edge
            )
            undirected_graph.remove_edge(edge[0], edge[1])
            directed_graph.remove_edge(edge[0], edge[1])
            self.__assert_graph_edge_does_not_exist_check(
                undirected_graph, directed_graph, edge
            )

    def test_add_and_remove_edges_repeatedly(self) -> None:
        (
            undirected_graph,
            directed_graph,
            random_vertices,
            random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        # make some more edge options!
        more_random_edges: list[list[int]] = []

        while len(more_random_edges) != len(random_edges):
            edges: list[list[int]] = self.__generate_random_edges(random_vertices, 4)
            for edge in edges:
                if len(more_random_edges) == len(random_edges):
                    break
                elif edge not in more_random_edges and edge not in random_edges:
                    more_random_edges.append(edge)

        for i, _ in enumerate(random_edges):
            undirected_graph.add_edge(more_random_edges[i][0], more_random_edges[i][1])
            directed_graph.add_edge(more_random_edges[i][0], more_random_edges[i][1])

            self.__assert_graph_edge_exists_check(
                undirected_graph, directed_graph, more_random_edges[i]
            )

            undirected_graph.remove_edge(random_edges[i][0], random_edges[i][1])
            directed_graph.remove_edge(random_edges[i][0], random_edges[i][1])

            self.__assert_graph_edge_does_not_exist_check(
                undirected_graph, directed_graph, random_edges[i]
            )

    def test_add_vertex_exception_check(self) -> None:
        (
            undirected_graph,
            directed_graph,
            random_vertices,
            _random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        for vertex in random_vertices:
            with pytest.raises(ValueError):
                undirected_graph.add_vertex(vertex)
            with pytest.raises(ValueError):
                directed_graph.add_vertex(vertex)

    def test_remove_vertex_exception_check(self) -> None:
        (
            undirected_graph,
            directed_graph,
            random_vertices,
            _random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        for i in range(101):
            if i not in random_vertices:
                with pytest.raises(ValueError):
                    undirected_graph.remove_vertex(i)
                with pytest.raises(ValueError):
                    directed_graph.remove_vertex(i)

    def test_add_edge_exception_check(self) -> None:
        (
            undirected_graph,
            directed_graph,
            _random_vertices,
            random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        for edge in random_edges:
            with pytest.raises(ValueError):
                undirected_graph.add_edge(edge[0], edge[1])
            with pytest.raises(ValueError):
                directed_graph.add_edge(edge[0], edge[1])

    def test_remove_edge_exception_check(self) -> None:
        (
            undirected_graph,
            directed_graph,
            random_vertices,
            random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        more_random_edges: list[list[int]] = []

        while len(more_random_edges) != len(random_edges):
            edges: list[list[int]] = self.__generate_random_edges(random_vertices, 4)
            for edge in edges:
                if len(more_random_edges) == len(random_edges):
                    break
                elif edge not in more_random_edges and edge not in random_edges:
                    more_random_edges.append(edge)

        for edge in more_random_edges:
            with pytest.raises(ValueError):
                undirected_graph.remove_edge(edge[0], edge[1])
            with pytest.raises(ValueError):
                directed_graph.remove_edge(edge[0], edge[1])

    def test_contains_edge_exception_check(self) -> None:
        (
            undirected_graph,
            directed_graph,
            random_vertices,
            _random_edges,
        ) = self.__generate_graphs(20, 0, 100, 4)

        for vertex in random_vertices:
            with pytest.raises(ValueError):
                undirected_graph.contains_edge(vertex, 102)
            with pytest.raises(ValueError):
                directed_graph.contains_edge(vertex, 102)

        with pytest.raises(ValueError):
            undirected_graph.contains_edge(103, 102)
        with pytest.raises(ValueError):
            directed_graph.contains_edge(103, 102)


if __name__ == "__main__":
    unittest.main()


================================================
FILE: graphs/graph_list.py
================================================
#!/usr/bin/env python3

# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import TypeVar

T = TypeVar("T")


class GraphAdjacencyList[T]:
    """
    Adjacency List type Graph Data Structure that accounts for directed and undirected
    Graphs.  Initialize graph object indicating whether it's directed or undirected.

    Directed graph example:
    >>> d_graph = GraphAdjacencyList()
    >>> print(d_graph)
    {}
    >>> d_graph.add_edge(0, 1)
    {0: [1], 1: []}
    >>> d_graph.add_edge(1, 2).add_edge(1, 4).add_edge(1, 5)
    {0: [1], 1: [2, 4, 5], 2: [], 4: [], 5: []}
    >>> d_graph.add_edge(2, 0).add_edge(2, 6).add_edge(2, 7)
    {0: [1], 1: [2, 4, 5], 2: [0, 6, 7], 4: [], 5: [], 6: [], 7: []}
    >>> d_graph
    {0: [1], 1: [2, 4, 5], 2: [0, 6, 7], 4: [], 5: [], 6: [], 7: []}
    >>> print(repr(d_graph))
    {0: [1], 1: [2, 4, 5], 2: [0, 6, 7], 4: [], 5: [], 6: [], 7: []}

    Undirected graph example:
    >>> u_graph = GraphAdjacencyList(directed=False)
    >>> u_graph.add_edge(0, 1)
    {0: [1], 1: [0]}
    >>> u_graph.add_edge(1, 2).add_edge(1, 4).add_edge(1, 5)
    {0: [1], 1: [0, 2, 4, 5], 2: [1], 4: [1], 5: [1]}
    >>> u_graph.add_edge(2, 0).add_edge(2, 6).add_edge(2, 7)
    {0: [1, 2], 1: [0, 2, 4, 5], 2: [1, 0, 6, 7], 4: [1], 5: [1], 6: [2], 7: [2]}
    >>> u_graph.add_edge(4, 5)
    {0: [1, 2],
     1: [0, 2, 4, 5],
     2: [1, 0, 6, 7],
     4: [1, 5],
     5: [1, 4],
     6: [2],
     7: [2]}
    >>> print(u_graph)
    {0: [1, 2],
     1: [0, 2, 4, 5],
     2: [1, 0, 6, 7],
     4: [1, 5],
     5: [1, 4],
     6: [2],
     7: [2]}
    >>> print(repr(u_graph))
    {0: [1, 2],
     1: [0, 2, 4, 5],
     2: [1, 0, 6, 7],
     4: [1, 5],
     5: [1, 4],
     6: [2],
     7: [2]}
     >>> char_graph = GraphAdjacencyList(directed=False)
     >>> char_graph.add_edge('a', 'b')
     {'a': ['b'], 'b': ['a']}
     >>> char_graph.add_edge('b', 'c').add_edge('b', 'e').add_edge('b', 'f')
     {'a': ['b'], 'b': ['a', 'c', 'e', 'f'], 'c': ['b'], 'e': ['b'], 'f': ['b']}
     >>> char_graph
     {'a': ['b'], 'b': ['a', 'c', 'e', 'f'], 'c': ['b'], 'e': ['b'], 'f': ['b']}
    """

    def __init__(self, directed: bool = True) -> None:
        """
        Parameters:
        directed: (bool) Indicates if graph is directed or undirected. Default is True.
        """

        self.adj_list: dict[T, list[T]] = {}  # dictionary of lists
        self.directed = directed

    def add_edge(
        self, source_vertex: T, destination_vertex: T
    ) -> GraphAdjacencyList[T]:
        """
        Connects vertices together. Creates and Edge from source vertex to destination
        vertex.
        Vertices will be created if not found in graph
        """

        if not self.directed:  # For undirected graphs
            # if both source vertex and destination vertex are both present in the
            # adjacency list, add destination vertex to source vertex list of adjacent
            # vertices and add source vertex to destination vertex list of adjacent
            # vertices.
            if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(destination_vertex)
                self.adj_list[destination_vertex].append(source_vertex)
            # if only source vertex is present in adjacency list, add destination vertex
            # to source vertex list of adjacent vertices, then create a new vertex with
            # destination vertex as key and assign a list containing the source vertex
            # as it's first adjacent vertex.
            elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(destination_vertex)
                self.adj_list[destination_vertex] = [source_vertex]
            # if only destination vertex is present in adjacency list, add source vertex
            # to destination vertex list of adjacent vertices, then create a new vertex
            # with source vertex as key and assign a list containing the source vertex
            # as it's first adjacent vertex.
            elif destination_vertex in self.adj_list:
                self.adj_list[destination_vertex].append(source_vertex)
                self.adj_list[source_vertex] = [destination_vertex]
            # if both source vertex and destination vertex are not present in adjacency
            # list, create a new vertex with source vertex as key and assign a list
            # containing the destination vertex as it's first adjacent vertex also
            # create a new vertex with destination vertex as key and assign a list
            # containing the source vertex as it's first adjacent vertex.
            else:
                self.adj_list[source_vertex] = [destination_vertex]
                self.adj_list[destination_vertex] = [source_vertex]
        # For directed graphs
        # if both source vertex and destination vertex are present in adjacency
        # list, add destination vertex to source vertex list of adjacent vertices.
        elif source_vertex in self.adj_list and destination_vertex in self.adj_list:
            self.adj_list[source_vertex].append(destination_vertex)
        # if only source vertex is present in adjacency list, add destination
        # vertex to source vertex list of adjacent vertices and create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        elif source_vertex in self.adj_list:
            self.adj_list[source_vertex].append(destination_vertex)
            self.adj_list[destination_vertex] = []
        # if only destination vertex is present in adjacency list, create a new
        # vertex with source vertex as key and assign a list containing destination
        # vertex as first adjacent vertex
        elif destination_vertex in self.adj_list:
            self.adj_list[source_vertex] = [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and a list containing
        # destination vertex as it's first adjacent vertex. Then create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        else:
            self.adj_list[source_vertex] = [destination_vertex]
            self.adj_list[destination_vertex] = []

        return self

    def __repr__(self) -> str:
        return pformat(self.adj_list)


================================================
FILE: graphs/graphs_floyd_warshall.py
================================================
# floyd_warshall.py
"""
The problem is to find the shortest distance between all pairs of vertices in a
weighted directed graph that can have negative edge weights.
"""


def _print_dist(dist, v):
    print("\nThe shortest path matrix using Floyd Warshall algorithm\n")
    for i in range(v):
        for j in range(v):
            if dist[i][j] != float("inf"):
                print(int(dist[i][j]), end="\t")
            else:
                print("INF", end="\t")
        print()


def floyd_warshall(graph, v):
    """
    :param graph: 2D array calculated from weight[edge[i, j]]
    :type graph: List[List[float]]
    :param v: number of vertices
    :type v: int
    :return: shortest distance between all vertex pairs
    distance[u][v] will contain the shortest distance from vertex u to v.

    1. For all edges from v to n, distance[i][j] = weight(edge(i, j)).
    3. The algorithm then performs distance[i][j] = min(distance[i][j], distance[i][k] +
        distance[k][j]) for each possible pair i, j of vertices.
    4. The above is repeated for each vertex k in the graph.
    5. Whenever distance[i][j] is given a new minimum value, next vertex[i][j] is
        updated to the next vertex[i][k].
    """

    dist = [[float("inf") for _ in range(v)] for _ in range(v)]

    for i in range(v):
        for j in range(v):
            dist[i][j] = graph[i][j]

            # check vertex k against all other vertices (i, j)
    for k in range(v):
        # looping through rows of graph array
        for i in range(v):
            # looping through columns of graph array
            for j in range(v):
                if (
                    dist[i][k] != float("inf")
                    and dist[k][j] != float("inf")
                    and dist[i][k] + dist[k][j] < dist[i][j]
                ):
                    dist[i][j] = dist[i][k] + dist[k][j]

    _print_dist(dist, v)
    return dist, v


if __name__ == "__main__":
    v = int(input("Enter number of vertices: "))
    e = int(input("Enter number of edges: "))

    graph = [[float("inf") for i in range(v)] for j in range(v)]

    for i in range(v):
        graph[i][i] = 0.0

        # src and dst are indices that must be within the array size graph[e][v]
        # failure to follow this will result in an error
    for i in range(e):
        print("\nEdge ", i + 1)
        src = int(input("Enter source:"))
        dst = int(input("Enter destination:"))
        weight = float(input("Enter weight:"))
        graph[src][dst] = weight

    floyd_warshall(graph, v)

    # Example Input
    # Enter number of vertices: 3
    # Enter number of edges: 2

    # # generated graph from vertex and edge inputs
    # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
    # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

    # specify source, destination and weight for edge #1
    # Edge  1
    # Enter source:1
    # Enter destination:2
    # Enter weight:2

    # specify source, destination and weight for edge #2
    # Edge  2
    # Enter source:2
    # Enter destination:1
    # Enter weight:1

    # # Expected Output from the vertice, edge and src, dst, weight inputs!!
    # 0		INF	INF
    # INF	0	2
    # INF	1	0


================================================
FILE: graphs/greedy_best_first.py
================================================
"""
https://en.wikipedia.org/wiki/Best-first_search#Greedy_BFS
"""

from __future__ import annotations

Path = list[tuple[int, int]]

# 0's are free path whereas 1's are obstacles
TEST_GRIDS = [
    [
        [0, 0, 0, 0, 0, 0, 0],
        [0, 1, 0, 0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0],
        [0, 0, 1, 0, 0, 0, 0],
        [1, 0, 1, 0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0],
        [0, 0, 0, 0, 1, 0, 0],
    ],
    [
        [0, 0, 0, 1, 1, 0, 0],
        [0, 0, 0, 0, 1, 0, 1],
        [0, 0, 0, 1, 1, 0, 0],
        [0, 1, 0, 0, 1, 0, 0],
        [1, 0, 0, 1, 1, 0, 1],
        [0, 0, 0, 0, 0, 0, 0],
    ],
    [
        [0, 0, 1, 0, 0],
        [0, 1, 0, 0, 0],
        [0, 0, 1, 0, 1],
        [1, 0, 0, 1, 1],
        [0, 0, 0, 0, 0],
    ],
]

delta = ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right


class Node:
    """
    >>> k = Node(0, 0, 4, 5, 0, None)
    >>> k.calculate_heuristic()
    9
    >>> n = Node(1, 4, 3, 4, 2, None)
    >>> n.calculate_heuristic()
    2
    >>> l = [k, n]
    >>> n == l[0]
    False
    >>> l.sort()
    >>> n == l[0]
    True
    """

    def __init__(
        self,
        pos_x: int,
        pos_y: int,
        goal_x: int,
        goal_y: int,
        g_cost: float,
        parent: Node | None,
    ):
        self.pos_x = pos_x
        self.pos_y = pos_y
        self.pos = (pos_y, pos_x)
        self.goal_x = goal_x
        self.goal_y = goal_y
        self.g_cost = g_cost
        self.parent = parent
        self.f_cost = self.calculate_heuristic()

    def calculate_heuristic(self) -> float:
        """
        The heuristic here is the Manhattan Distance
        Could elaborate to offer more than one choice
        """
        dx = abs(self.pos_x - self.goal_x)
        dy = abs(self.pos_y - self.goal_y)
        return dx + dy

    def __lt__(self, other) -> bool:
        return self.f_cost < other.f_cost

    def __eq__(self, other) -> bool:
        return self.pos == other.pos


class GreedyBestFirst:
    """
    >>> grid = TEST_GRIDS[2]
    >>> gbf = GreedyBestFirst(grid, (0, 0), (len(grid) - 1, len(grid[0]) - 1))
    >>> [x.pos for x in gbf.get_successors(gbf.start)]
    [(1, 0), (0, 1)]
    >>> (gbf.start.pos_y + delta[3][0], gbf.start.pos_x + delta[3][1])
    (0, 1)
    >>> (gbf.start.pos_y + delta[2][0], gbf.start.pos_x + delta[2][1])
    (1, 0)
    >>> gbf.retrace_path(gbf.start)
    [(0, 0)]
    >>> gbf.search()  # doctest: +NORMALIZE_WHITESPACE
    [(0, 0), (1, 0), (2, 0), (2, 1), (3, 1), (4, 1), (4, 2), (4, 3),
     (4, 4)]
    """

    def __init__(
        self, grid: list[list[int]], start: tuple[int, int], goal: tuple[int, int]
    ):
        self.grid = grid
        self.start = Node(start[1], start[0], goal[1], goal[0], 0, None)
        self.target = Node(goal[1], goal[0], goal[1], goal[0], 99999, None)

        self.open_nodes = [self.start]
        self.closed_nodes: list[Node] = []

        self.reached = False

    def search(self) -> Path | None:
        """
        Search for the path,
        if a path is not found, only the starting position is returned
        """
        while self.open_nodes:
            # Open Nodes are sorted using __lt__
            self.open_nodes.sort()
            current_node = self.open_nodes.pop(0)

            if current_node.pos == self.target.pos:
                self.reached = True
                return self.retrace_path(current_node)

            self.closed_nodes.append(current_node)
            successors = self.get_successors(current_node)

            for child_node in successors:
                if child_node in self.closed_nodes:
                    continue

                if child_node not in self.open_nodes:
                    self.open_nodes.append(child_node)

        if not self.reached:
            return [self.start.pos]
        return None

    def get_successors(self, parent: Node) -> list[Node]:
        """
        Returns a list of successors (both in the grid and free spaces)
        """
        return [
            Node(
                pos_x,
                pos_y,
                self.target.pos_x,
                self.target.pos_y,
                parent.g_cost + 1,
                parent,
            )
            for action in delta
            if (
                0 <= (pos_x := parent.pos_x + action[1]) < len(self.grid[0])
                and 0 <= (pos_y := parent.pos_y + action[0]) < len(self.grid)
                and self.grid[pos_y][pos_x] == 0
            )
        ]

    def retrace_path(self, node: Node | None) -> Path:
        """
        Retrace the path from parents to parents until start node
        """
        current_node = node
        path = []
        while current_node is not None:
            path.append((current_node.pos_y, current_node.pos_x))
            current_node = current_node.parent
        path.reverse()
        return path


if __name__ == "__main__":
    for idx, grid in enumerate(TEST_GRIDS):
        print(f"==grid-{idx + 1}==")

        init = (0, 0)
        goal = (len(grid) - 1, len(grid[0]) - 1)
        for elem in grid:
            print(elem)

        print("------")

        greedy_bf = GreedyBestFirst(grid, init, goal)
        path = greedy_bf.search()
        if path:
            for pos_x, pos_y in path:
                grid[pos_x][pos_y] = 2

            for elem in grid:
                print(elem)


================================================
FILE: graphs/greedy_min_vertex_cover.py
================================================
"""
* Author: Manuel Di Lullo (https://github.com/manueldilullo)
* Description: Approximization algorithm for minimum vertex cover problem.
               Greedy Approach. Uses graphs represented with an adjacency list
URL: https://mathworld.wolfram.com/MinimumVertexCover.html
URL: https://cs.stackexchange.com/questions/129017/greedy-algorithm-for-vertex-cover
"""

import heapq


def greedy_min_vertex_cover(graph: dict) -> set[int]:
    """
    Greedy APX Algorithm for min Vertex Cover
    @input: graph (graph stored in an adjacency list where each vertex
            is represented with an integer)
    @example:
    >>> graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    >>> greedy_min_vertex_cover(graph)
    {0, 1, 2, 4}
    """
    # queue used to store nodes and their rank
    queue: list[list] = []

    # for each node and his adjacency list add them and the rank of the node to queue
    # using heapq module the queue will be filled like a Priority Queue
    # heapq works with a min priority queue, so I used -1*len(v) to build it
    for key, value in graph.items():
        # O(log(n))
        heapq.heappush(queue, [-1 * len(value), (key, value)])

    # chosen_vertices = set of chosen vertices
    chosen_vertices = set()

    # while queue isn't empty and there are still edges
    #   (queue[0][0] is the rank of the node with max rank)
    while queue and queue[0][0] != 0:
        # extract vertex with max rank from queue and add it to chosen_vertices
        argmax = heapq.heappop(queue)[1][0]
        chosen_vertices.add(argmax)

        # Remove all arcs adjacent to argmax
        for elem in queue:
            # if v haven't adjacent node, skip
            if elem[0] == 0:
                continue
            # if argmax is reachable from elem
            # remove argmax from elem's adjacent list and update his rank
            if argmax in elem[1][1]:
                index = elem[1][1].index(argmax)
                del elem[1][1][index]
                elem[0] += 1
        # re-order the queue
        heapq.heapify(queue)
    return chosen_vertices


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    print(f"Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}")


================================================
FILE: graphs/kahns_algorithm_long.py
================================================
# Finding longest distance in Directed Acyclic Graph using KahnsAlgorithm
def longest_distance(graph):
    indegree = [0] * len(graph)
    queue = []
    long_dist = [1] * len(graph)

    for values in graph.values():
        for i in values:
            indegree[i] += 1

    for i in range(len(indegree)):
        if indegree[i] == 0:
            queue.append(i)

    while queue:
        vertex = queue.pop(0)
        for x in graph[vertex]:
            indegree[x] -= 1

            long_dist[x] = max(long_dist[x], long_dist[vertex] + 1)

            if indegree[x] == 0:
                queue.append(x)

    print(max(long_dist))


# Adjacency list of Graph
graph = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)


================================================
FILE: graphs/kahns_algorithm_topo.py
================================================
def topological_sort(graph: dict[int, list[int]]) -> list[int] | None:
    """
    Perform topological sorting of a Directed Acyclic Graph (DAG)
    using Kahn's Algorithm via Breadth-First Search (BFS).

    Topological sorting is a linear ordering of vertices in a graph such that for
    every directed edge u → v, vertex u comes before vertex v in the ordering.

    Parameters:
    graph: Adjacency list representing the directed graph where keys are
           vertices, and values are lists of adjacent vertices.

    Returns:
    The topologically sorted order of vertices if the graph is a DAG.
    Returns None if the graph contains a cycle.

    Example:
    >>> graph = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
    >>> topological_sort(graph)
    [0, 1, 2, 3, 4, 5]

    >>> graph_with_cycle = {0: [1], 1: [2], 2: [0]}
    >>> topological_sort(graph_with_cycle)
    """

    indegree = [0] * len(graph)
    queue = []
    topo_order = []
    processed_vertices_count = 0

    # Calculate the indegree of each vertex
    for values in graph.values():
        for i in values:
            indegree[i] += 1

    # Add all vertices with 0 indegree to the queue
    for i in range(len(indegree)):
        if indegree[i] == 0:
            queue.append(i)

    # Perform BFS
    while queue:
        vertex = queue.pop(0)
        processed_vertices_count += 1
        topo_order.append(vertex)

        # Traverse neighbors
        for neighbor in graph[vertex]:
            indegree[neighbor] -= 1
            if indegree[neighbor] == 0:
                queue.append(neighbor)

    if processed_vertices_count != len(graph):
        return None  # no topological ordering exists due to cycle
    return topo_order  # valid topological ordering


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: graphs/karger.py
================================================
"""
An implementation of Karger's Algorithm for partitioning a graph.
"""

from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
TEST_GRAPH = {
    "1": ["2", "3", "4", "5"],
    "2": ["1", "3", "4", "5"],
    "3": ["1", "2", "4", "5", "10"],
    "4": ["1", "2", "3", "5", "6"],
    "5": ["1", "2", "3", "4", "7"],
    "6": ["7", "8", "9", "10", "4"],
    "7": ["6", "8", "9", "10", "5"],
    "8": ["6", "7", "9", "10"],
    "9": ["6", "7", "8", "10"],
    "10": ["6", "7", "8", "9", "3"],
}


def partition_graph(graph: dict[str, list[str]]) -> set[tuple[str, str]]:
    """
    Partitions a graph using Karger's Algorithm. Implemented from
    pseudocode found here:
    https://en.wikipedia.org/wiki/Karger%27s_algorithm.
    This function involves random choices, meaning it will not give
    consistent outputs.

    Args:
        graph: A dictionary containing adacency lists for the graph.
            Nodes must be strings.

    Returns:
        The cutset of the cut found by Karger's Algorithm.

    >>> graph = {'0':['1'], '1':['0']}
    >>> partition_graph(graph)
    {('0', '1')}
    """
    # Dict that maps contracted nodes to a list of all the nodes it "contains."
    contracted_nodes = {node: {node} for node in graph}

    graph_copy = {node: graph[node][:] for node in graph}

    while len(graph_copy) > 2:
        # Choose a random edge.
        u = random.choice(list(graph_copy.keys()))
        v = random.choice(graph_copy[u])

        # Contract edge (u, v) to new node uv
        uv = u + v
        uv_neighbors = list(set(graph_copy[u] + graph_copy[v]))
        uv_neighbors.remove(u)
        uv_neighbors.remove(v)
        graph_copy[uv] = uv_neighbors
        for neighbor in uv_neighbors:
            graph_copy[neighbor].append(uv)

        contracted_nodes[uv] = set(contracted_nodes[u].union(contracted_nodes[v]))

        # Remove nodes u and v.
        del graph_copy[u]
        del graph_copy[v]
        for neighbor in uv_neighbors:
            if u in graph_copy[neighbor]:
                graph_copy[neighbor].remove(u)
            if v in graph_copy[neighbor]:
                graph_copy[neighbor].remove(v)

    # Find cutset.
    groups = [contracted_nodes[node] for node in graph_copy]
    return {
        (node, neighbor)
        for node in groups[0]
        for neighbor in graph[node]
        if neighbor in groups[1]
    }


if __name__ == "__main__":
    print(partition_graph(TEST_GRAPH))


================================================
FILE: graphs/lanczos_eigenvectors.py
================================================
"""
Lanczos Method for Finding Eigenvalues and Eigenvectors of a Graph.

This module demonstrates the Lanczos method to approximate the largest eigenvalues
and corresponding eigenvectors of a symmetric matrix represented as a graph's
adjacency list. The method efficiently handles large, sparse matrices by converting
the graph to a tridiagonal matrix, whose eigenvalues and eigenvectors are then
computed.

Key Functions:
- `find_lanczos_eigenvectors`: Computes the k largest eigenvalues and vectors.
- `lanczos_iteration`: Constructs the tridiagonal matrix and orthonormal basis vectors.
- `multiply_matrix_vector`: Multiplies an adjacency list graph with a vector.

Complexity:
- Time: O(k * n), where k is the number of eigenvalues and n is the matrix size.
- Space: O(n), due to sparse representation and tridiagonal matrix structure.

Further Reading:
- Lanczos Algorithm: https://en.wikipedia.org/wiki/Lanczos_algorithm
- Eigenvector Centrality: https://en.wikipedia.org/wiki/Eigenvector_centrality

Example Usage:
Given a graph represented by an adjacency list, the `find_lanczos_eigenvectors`
function returns the largest eigenvalues and eigenvectors. This can be used to
analyze graph centrality.
"""

import numpy as np


def validate_adjacency_list(graph: list[list[int | None]]) -> None:
    """Validates the adjacency list format for the graph.

    Args:
        graph: A list of lists where each sublist contains the neighbors of a node.

    Raises:
        ValueError: If the graph is not a list of lists, or if any node has
                    invalid neighbors (e.g., out-of-range or non-integer values).

    >>> validate_adjacency_list([[1, 2], [0], [0, 1]])
    >>> validate_adjacency_list([[]])  # No neighbors, valid case
    >>> validate_adjacency_list([[1], [2], [-1]])  # Invalid neighbor
    Traceback (most recent call last):
        ...
    ValueError: Invalid neighbor -1 in node 2 adjacency list.
    """
    if not isinstance(graph, list):
        raise ValueError("Graph should be a list of lists.")

    for node_index, neighbors in enumerate(graph):
        if not isinstance(neighbors, list):
            no_neighbors_message: str = (
                f"Node {node_index} should have a list of neighbors."
            )
            raise ValueError(no_neighbors_message)
        for neighbor_index in neighbors:
            if (
                not isinstance(neighbor_index, int)
                or neighbor_index < 0
                or neighbor_index >= len(graph)
            ):
                invalid_neighbor_message: str = (
                    f"Invalid neighbor {neighbor_index} in node {node_index} "
                    f"adjacency list."
                )
                raise ValueError(invalid_neighbor_message)


def lanczos_iteration(
    graph: list[list[int | None]], num_eigenvectors: int
) -> tuple[np.ndarray, np.ndarray]:
    """Constructs the tridiagonal matrix and orthonormal basis vectors using the
    Lanczos method.

    Args:
        graph: The graph represented as a list of adjacency lists.
        num_eigenvectors: The number of largest eigenvalues and eigenvectors
                          to approximate.

    Returns:
        A tuple containing:
            - tridiagonal_matrix: A (num_eigenvectors x num_eigenvectors) symmetric
                                  matrix.
            - orthonormal_basis: A (num_nodes x num_eigenvectors) matrix of orthonormal
                                 basis vectors.

    Raises:
        ValueError: If num_eigenvectors is less than 1 or greater than the number of
                    nodes.

    >>> graph = [[1, 2], [0, 2], [0, 1]]
    >>> T, Q = lanczos_iteration(graph, 2)
    >>> T.shape == (2, 2) and Q.shape == (3, 2)
    True
    """
    num_nodes: int = len(graph)
    if not (1 <= num_eigenvectors <= num_nodes):
        raise ValueError(
            "Number of eigenvectors must be between 1 and the number of "
            "nodes in the graph."
        )

    orthonormal_basis: np.ndarray = np.zeros((num_nodes, num_eigenvectors))
    tridiagonal_matrix: np.ndarray = np.zeros((num_eigenvectors, num_eigenvectors))

    rng = np.random.default_rng()
    initial_vector: np.ndarray = rng.random(num_nodes)
    initial_vector /= np.sqrt(np.dot(initial_vector, initial_vector))
    orthonormal_basis[:, 0] = initial_vector

    prev_beta: float = 0.0
    for iter_index in range(num_eigenvectors):
        result_vector: np.ndarray = multiply_matrix_vector(
            graph, orthonormal_basis[:, iter_index]
        )
        if iter_index > 0:
            result_vector -= prev_beta * orthonormal_basis[:, iter_index - 1]
        alpha_value: float = np.dot(orthonormal_basis[:, iter_index], result_vector)
        result_vector -= alpha_value * orthonormal_basis[:, iter_index]

        prev_beta = np.sqrt(np.dot(result_vector, result_vector))
        if iter_index < num_eigenvectors - 1 and prev_beta > 1e-10:
            orthonormal_basis[:, iter_index + 1] = result_vector / prev_beta
        tridiagonal_matrix[iter_index, iter_index] = alpha_value
        if iter_index < num_eigenvectors - 1:
            tridiagonal_matrix[iter_index, iter_index + 1] = prev_beta
            tridiagonal_matrix[iter_index + 1, iter_index] = prev_beta
    return tridiagonal_matrix, orthonormal_basis


def multiply_matrix_vector(
    graph: list[list[int | None]], vector: np.ndarray
) -> np.ndarray:
    """Performs multiplication of a graph's adjacency list representation with a vector.

    Args:
        graph: The adjacency list of the graph.
        vector: A 1D numpy array representing the vector to multiply.

    Returns:
        A numpy array representing the product of the adjacency list and the vector.

    Raises:
        ValueError: If the vector's length does not match the number of nodes in the
                    graph.

    >>> multiply_matrix_vector([[1, 2], [0, 2], [0, 1]], np.array([1, 1, 1]))
    array([2., 2., 2.])
    >>> multiply_matrix_vector([[1, 2], [0, 2], [0, 1]], np.array([0, 1, 0]))
    array([1., 0., 1.])
    """
    num_nodes: int = len(graph)
    if vector.shape[0] != num_nodes:
        raise ValueError("Vector length must match the number of nodes in the graph.")

    result: np.ndarray = np.zeros(num_nodes)
    for node_index, neighbors in enumerate(graph):
        for neighbor_index in neighbors:
            result[node_index] += vector[neighbor_index]
    return result


def find_lanczos_eigenvectors(
    graph: list[list[int | None]], num_eigenvectors: int
) -> tuple[np.ndarray, np.ndarray]:
    """Computes the largest eigenvalues and their corresponding eigenvectors using the
    Lanczos method.

    Args:
        graph: The graph as a list of adjacency lists.
        num_eigenvectors: Number of largest eigenvalues and eigenvectors to compute.

    Returns:
        A tuple containing:
            - eigenvalues: 1D array of the largest eigenvalues in descending order.
            - eigenvectors: 2D array where each column is an eigenvector corresponding
                            to an eigenvalue.

    Raises:
        ValueError: If the graph format is invalid or num_eigenvectors is out of bounds.

    >>> eigenvalues, eigenvectors = find_lanczos_eigenvectors(
    ...     [[1, 2], [0, 2], [0, 1]], 2
    ... )
    >>> len(eigenvalues) == 2 and eigenvectors.shape[1] == 2
    True
    """
    validate_adjacency_list(graph)
    tridiagonal_matrix, orthonormal_basis = lanczos_iteration(graph, num_eigenvectors)
    eigenvalues, eigenvectors = np.linalg.eigh(tridiagonal_matrix)
    return eigenvalues[::-1], np.dot(orthonormal_basis, eigenvectors[:, ::-1])


def main() -> None:
    """
    Main driver function for testing the implementation with doctests.
    """
    import doctest

    doctest.testmod()


if __name__ == "__main__":
    main()


================================================
FILE: graphs/markov_chain.py
================================================
from __future__ import annotations

from collections import Counter
from random import random


class MarkovChainGraphUndirectedUnweighted:
    """
    Undirected Unweighted Graph for running Markov Chain Algorithm
    """

    def __init__(self):
        self.connections = {}

    def add_node(self, node: str) -> None:
        self.connections[node] = {}

    def add_transition_probability(
        self, node1: str, node2: str, probability: float
    ) -> None:
        if node1 not in self.connections:
            self.add_node(node1)
        if node2 not in self.connections:
            self.add_node(node2)
        self.connections[node1][node2] = probability

    def get_nodes(self) -> list[str]:
        return list(self.connections)

    def transition(self, node: str) -> str:
        current_probability = 0
        random_value = random()

        for dest in self.connections[node]:
            current_probability += self.connections[node][dest]
            if current_probability > random_value:
                return dest
        return ""


def get_transitions(
    start: str, transitions: list[tuple[str, str, float]], steps: int
) -> dict[str, int]:
    """
    Running Markov Chain algorithm and calculating the number of times each node is
    visited

    >>> transitions = [
    ... ('a', 'a', 0.9),
    ... ('a', 'b', 0.075),
    ... ('a', 'c', 0.025),
    ... ('b', 'a', 0.15),
    ... ('b', 'b', 0.8),
    ... ('b', 'c', 0.05),
    ... ('c', 'a', 0.25),
    ... ('c', 'b', 0.25),
    ... ('c', 'c', 0.5)
    ... ]

    >>> result = get_transitions('a', transitions, 5000)

    >>> result['a'] > result['b'] > result['c']
    True
    """

    graph = MarkovChainGraphUndirectedUnweighted()

    for node1, node2, probability in transitions:
        graph.add_transition_probability(node1, node2, probability)

    visited = Counter(graph.get_nodes())
    node = start

    for _ in range(steps):
        node = graph.transition(node)
        visited[node] += 1

    return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: graphs/matching_min_vertex_cover.py
================================================
"""
* Author: Manuel Di Lullo (https://github.com/manueldilullo)
* Description: Approximization algorithm for minimum vertex cover problem.
               Matching Approach. Uses graphs represented with an adjacency list

URL: https://mathworld.wolfram.com/MinimumVertexCover.html
URL: https://www.princeton.edu/~aaa/Public/Teaching/ORF523/ORF523_Lec6.pdf
"""


def matching_min_vertex_cover(graph: dict) -> set:
    """
    APX Algorithm for min Vertex Cover using Matching Approach
    @input: graph (graph stored in an adjacency list where each vertex
            is represented as an integer)
    @example:
    >>> graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    >>> matching_min_vertex_cover(graph)
    {0, 1, 2, 4}
    """
    # chosen_vertices = set of chosen vertices
    chosen_vertices = set()
    # edges = list of graph's edges
    edges = get_edges(graph)

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        from_node, to_node = edges.pop()
        chosen_vertices.add(from_node)
        chosen_vertices.add(to_node)
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(edge)
    return chosen_vertices


def get_edges(graph: dict) -> set:
    """
    Return a set of couples that represents all of the edges.
    @input: graph (graph stored in an adjacency list where each vertex is
            represented as an integer)
    @example:
    >>> graph = {0: [1, 3], 1: [0, 3], 2: [0, 3], 3: [0, 1, 2]}
    >>> get_edges(graph)
    {(0, 1), (3, 1), (0, 3), (2, 0), (3, 0), (2, 3), (1, 0), (3, 2), (1, 3)}
    """
    edges = set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node))
    return edges


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")


================================================
FILE: graphs/minimum_path_sum.py
================================================
def min_path_sum(grid: list) -> int:
    """
    Find the path from top left to bottom right of array of numbers
    with the lowest possible sum and return the sum along this path.
    >>> min_path_sum([
    ...     [1, 3, 1],
    ...     [1, 5, 1],
    ...     [4, 2, 1],
    ... ])
    7

    >>> min_path_sum([
    ...     [1, 0, 5, 6, 7],
    ...     [8, 9, 0, 4, 2],
    ...     [4, 4, 4, 5, 1],
    ...     [9, 6, 3, 1, 0],
    ...     [8, 4, 3, 2, 7],
    ... ])
    20

    >>> min_path_sum(None)
    Traceback (most recent call last):
        ...
    TypeError: The grid does not contain the appropriate information

    >>> min_path_sum([[]])
    Traceback (most recent call last):
        ...
    TypeError: The grid does not contain the appropriate information
    """

    if not grid or not grid[0]:
        raise TypeError("The grid does not contain the appropriate information")

    for cell_n in range(1, len(grid[0])):
        grid[0][cell_n] += grid[0][cell_n - 1]
    row_above = grid[0]

    for row_n in range(1, len(grid)):
        current_row = grid[row_n]
        grid[row_n] = fill_row(current_row, row_above)
        row_above = grid[row_n]

    return grid[-1][-1]


def fill_row(current_row: list, row_above: list) -> list:
    """
    >>> fill_row([2, 2, 2], [1, 2, 3])
    [3, 4, 5]
    """

    current_row[0] += row_above[0]
    for cell_n in range(1, len(current_row)):
        current_row[cell_n] += min(current_row[cell_n - 1], row_above[cell_n])

    return current_row


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: graphs/minimum_spanning_tree_boruvka.py
================================================
class Graph:
    """
    Data structure to store graphs (based on adjacency lists)
    """

    def __init__(self):
        self.num_vertices = 0
        self.num_edges = 0
        self.adjacency = {}

    def add_vertex(self, vertex):
        """
        Adds a vertex to the graph

        """
        if vertex not in self.adjacency:
            self.adjacency[vertex] = {}
            self.num_vertices += 1

    def add_edge(self, head, tail, weight):
        """
        Adds an edge to the graph

        """

        self.add_vertex(head)
        self.add_vertex(tail)

        if head == tail:
            return

        self.adjacency[head][tail] = weight
        self.adjacency[tail][head] = weight

    def distinct_weight(self):
        """
        For Boruvks's algorithm the weights should be distinct
        Converts the weights to be distinct

        """
        edges = self.get_edges()
        for edge in edges:
            head, tail, weight = edge
            edges.remove((tail, head, weight))
        for i in range(len(edges)):
            edges[i] = list(edges[i])

        edges.sort(key=lambda e: e[2])
        for i in range(len(edges) - 1):
            if edges[i][2] >= edges[i + 1][2]:
                edges[i + 1][2] = edges[i][2] + 1
        for edge in edges:
            head, tail, weight = edge
            self.adjacency[head][tail] = weight
            self.adjacency[tail][head] = weight

    def __str__(self):
        """
        Returns string representation of the graph
        """
        string = ""
        for tail in self.adjacency:
            for head in self.adjacency[tail]:
                weight = self.adjacency[head][tail]
                string += f"{head} -> {tail} == {weight}\n"
        return string.rstrip("\n")

    def get_edges(self):
        """
        Returna all edges in the graph
        """
        output = []
        for tail in self.adjacency:
            for head in self.adjacency[tail]:
                output.append((tail, head, self.adjacency[head][tail]))
        return output

    def get_vertices(self):
        """
        Returns all vertices in the graph
        """
        return self.adjacency.keys()

    @staticmethod
    def build(vertices=None, edges=None):
        """
        Builds a graph from the given set of vertices and edges

        """
        g = Graph()
        if vertices is None:
            vertices = []
        if edges is None:
            edge = []
        for vertex in vertices:
            g.add_vertex(vertex)
        for edge in edges:
            g.add_edge(*edge)
        return g

    class UnionFind:
        """
        Disjoint set Union and Find for Boruvka's algorithm
        """

        def __init__(self):
            self.parent = {}
            self.rank = {}

        def __len__(self):
            return len(self.parent)

        def make_set(self, item):
            if item in self.parent:
                return self.find(item)

            self.parent[item] = item
            self.rank[item] = 0
            return item

        def find(self, item):
            if item not in self.parent:
                return self.make_set(item)
            if item != self.parent[item]:
                self.parent[item] = self.find(self.parent[item])
            return self.parent[item]

        def union(self, item1, item2):
            root1 = self.find(item1)
            root2 = self.find(item2)

            if root1 == root2:
                return root1

            if self.rank[root1] > self.rank[root2]:
                self.parent[root2] = root1
                return root1

            if self.rank[root1] < self.rank[root2]:
                self.parent[root1] = root2
                return root2

            if self.rank[root1] == self.rank[root2]:
                self.rank[root1] += 1
                self.parent[root2] = root1
                return root1
            return None

    @staticmethod
    def boruvka_mst(graph):
        """
        Implementation of Boruvka's algorithm
        >>> g = Graph()
        >>> g = Graph.build([0, 1, 2, 3], [[0, 1, 1], [0, 2, 1],[2, 3, 1]])
        >>> g.distinct_weight()
        >>> bg = Graph.boruvka_mst(g)
        >>> print(bg)
        1 -> 0 == 1
        2 -> 0 == 2
        0 -> 1 == 1
        0 -> 2 == 2
        3 -> 2 == 3
        2 -> 3 == 3
        """
        num_components = graph.num_vertices

        union_find = Graph.UnionFind()
        mst_edges = []
        while num_components > 1:
            cheap_edge = {}
            for vertex in graph.get_vertices():
                cheap_edge[vertex] = -1

            edges = graph.get_edges()
            for edge in edges:
                head, tail, weight = edge
                edges.remove((tail, head, weight))
            for edge in edges:
                head, tail, weight = edge
                set1 = union_find.find(head)
                set2 = union_find.find(tail)
                if set1 != set2:
                    if cheap_edge[set1] == -1 or cheap_edge[set1][2] > weight:
                        cheap_edge[set1] = [head, tail, weight]

                    if cheap_edge[set2] == -1 or cheap_edge[set2][2] > weight:
                        cheap_edge[set2] = [head, tail, weight]
            for head_tail_weight in cheap_edge.values():
                if head_tail_weight != -1:
                    head, tail, weight = head_tail_weight
                    if union_find.find(head) != union_find.find(tail):
                        union_find.union(head, tail)
                        mst_edges.append(head_tail_weight)
                        num_components = num_components - 1
        mst = Graph.build(edges=mst_edges)
        return mst


================================================
FILE: graphs/minimum_spanning_tree_kruskal.py
================================================
def kruskal(
    num_nodes: int, edges: list[tuple[int, int, int]]
) -> list[tuple[int, int, int]]:
    """
    >>> kruskal(4, [(0, 1, 3), (1, 2, 5), (2, 3, 1)])
    [(2, 3, 1), (0, 1, 3), (1, 2, 5)]

    >>> kruskal(4, [(0, 1, 3), (1, 2, 5), (2, 3, 1), (0, 2, 1), (0, 3, 2)])
    [(2, 3, 1), (0, 2, 1), (0, 1, 3)]

    >>> kruskal(4, [(0, 1, 3), (1, 2, 5), (2, 3, 1), (0, 2, 1), (0, 3, 2),
    ... (2, 1, 1)])
    [(2, 3, 1), (0, 2, 1), (2, 1, 1)]
    """
    edges = sorted(edges, key=lambda edge: edge[2])

    parent = list(range(num_nodes))

    def find_parent(i):
        if i != parent[i]:
            parent[i] = find_parent(parent[i])
        return parent[i]

    minimum_spanning_tree_cost = 0
    minimum_spanning_tree = []

    for edge in edges:
        parent_a = find_parent(edge[0])
        parent_b = find_parent(edge[1])
        if parent_a != parent_b:
            minimum_spanning_tree_cost += edge[2]
            minimum_spanning_tree.append(edge)
            parent[parent_a] = parent_b

    return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
    num_nodes, num_edges = list(map(int, input().strip().split()))
    edges = []

    for _ in range(num_edges):
        node1, node2, cost = (int(x) for x in input().strip().split())
        edges.append((node1, node2, cost))

    kruskal(num_nodes, edges)


================================================
FILE: graphs/minimum_spanning_tree_kruskal2.py
================================================
from __future__ import annotations

from typing import TypeVar

T = TypeVar("T")


class DisjointSetTreeNode[T]:
    # Disjoint Set Node to store the parent and rank
    def __init__(self, data: T) -> None:
        self.data = data
        self.parent = self
        self.rank = 0


class DisjointSetTree[T]:
    # Disjoint Set DataStructure
    def __init__(self) -> None:
        # map from node name to the node object
        self.map: dict[T, DisjointSetTreeNode[T]] = {}

    def make_set(self, data: T) -> None:
        # create a new set with x as its member
        self.map[data] = DisjointSetTreeNode(data)

    def find_set(self, data: T) -> DisjointSetTreeNode[T]:
        # find the set x belongs to (with path-compression)
        elem_ref = self.map[data]
        if elem_ref != elem_ref.parent:
            elem_ref.parent = self.find_set(elem_ref.parent.data)
        return elem_ref.parent

    def link(
        self, node1: DisjointSetTreeNode[T], node2: DisjointSetTreeNode[T]
    ) -> None:
        # helper function for union operation
        if node1.rank > node2.rank:
            node2.parent = node1
        else:
            node1.parent = node2
            if node1.rank == node2.rank:
                node2.rank += 1

    def union(self, data1: T, data2: T) -> None:
        # merge 2 disjoint sets
        self.link(self.find_set(data1), self.find_set(data2))


class GraphUndirectedWeighted[T]:
    def __init__(self) -> None:
        # connections: map from the node to the neighbouring nodes (with weights)
        self.connections: dict[T, dict[T, int]] = {}

    def add_node(self, node: T) -> None:
        # add a node ONLY if its not present in the graph
        if node not in self.connections:
            self.connections[node] = {}

    def add_edge(self, node1: T, node2: T, weight: int) -> None:
        # add an edge with the given weight
        self.add_node(node1)
        self.add_node(node2)
        self.connections[node1][node2] = weight
        self.connections[node2][node1] = weight

    def kruskal(self) -> GraphUndirectedWeighted[T]:
        # Kruskal's Algorithm to generate a Minimum Spanning Tree (MST) of a graph
        """
        Details: https://en.wikipedia.org/wiki/Kruskal%27s_algorithm

        Example:
        >>> g1 = GraphUndirectedWeighted[int]()
        >>> g1.add_edge(1, 2, 1)
        >>> g1.add_edge(2, 3, 2)
        >>> g1.add_edge(3, 4, 1)
        >>> g1.add_edge(3, 5, 100) # Removed in MST
        >>> g1.add_edge(4, 5, 5)
        >>> assert 5 in g1.connections[3]
        >>> mst = g1.kruskal()
        >>> assert 5 not in mst.connections[3]

        >>> g2 = GraphUndirectedWeighted[str]()
        >>> g2.add_edge('A', 'B', 1)
        >>> g2.add_edge('B', 'C', 2)
        >>> g2.add_edge('C', 'D', 1)
        >>> g2.add_edge('C', 'E', 100) # Removed in MST
        >>> g2.add_edge('D', 'E', 5)
        >>> assert 'E' in g2.connections["C"]
        >>> mst = g2.kruskal()
        >>> assert 'E' not in mst.connections['C']
        """

        # getting the edges in ascending order of weights
        edges = []
        seen = set()
        for start in self.connections:
            for end in self.connections[start]:
                if (start, end) not in seen:
                    seen.add((end, start))
                    edges.append((start, end, self.connections[start][end]))
        edges.sort(key=lambda x: x[2])

        # creating the disjoint set
        disjoint_set = DisjointSetTree[T]()
        for node in self.connections:
            disjoint_set.make_set(node)

        # MST generation
        num_edges = 0
        index = 0
        graph = GraphUndirectedWeighted[T]()
        while num_edges < len(self.connections) - 1:
            u, v, w = edges[index]
            index += 1
            parent_u = disjoint_set.find_set(u)
            parent_v = disjoint_set.find_set(v)
            if parent_u != parent_v:
                num_edges += 1
                graph.add_edge(u, v, w)
                disjoint_set.union(u, v)
        return graph


================================================
FILE: graphs/minimum_spanning_tree_prims.py
================================================
import sys
from collections import defaultdict


class Heap:
    def __init__(self):
        self.node_position = []

    def get_position(self, vertex):
        return self.node_position[vertex]

    def set_position(self, vertex, pos):
        self.node_position[vertex] = pos

    def top_to_bottom(self, heap, start, size, positions):
        if start > size // 2 - 1:
            return
        else:
            if 2 * start + 2 >= size:  # noqa: SIM114
                smallest_child = 2 * start + 1
            elif heap[2 * start + 1] < heap[2 * start + 2]:
                smallest_child = 2 * start + 1
            else:
                smallest_child = 2 * start + 2
            if heap[smallest_child] < heap[start]:
                temp, temp1 = heap[smallest_child], positions[smallest_child]
                heap[smallest_child], positions[smallest_child] = (
                    heap[start],
                    positions[start],
                )
                heap[start], positions[start] = temp, temp1

                temp = self.get_position(positions[smallest_child])
                self.set_position(
                    positions[smallest_child], self.get_position(positions[start])
                )
                self.set_position(positions[start], temp)

                self.top_to_bottom(heap, smallest_child, size, positions)

    # Update function if value of any node in min-heap decreases
    def bottom_to_top(self, val, index, heap, position):
        temp = position[index]

        while index != 0:
            parent = int((index - 2) / 2) if index % 2 == 0 else int((index - 1) / 2)

            if val < heap[parent]:
                heap[index] = heap[parent]
                position[index] = position[parent]
                self.set_position(position[parent], index)
            else:
                heap[index] = val
                position[index] = temp
                self.set_position(temp, index)
                break
            index = parent
        else:
            heap[0] = val
            position[0] = temp
            self.set_position(temp, 0)

    def heapify(self, heap, positions):
        start = len(heap) // 2 - 1
        for i in range(start, -1, -1):
            self.top_to_bottom(heap, i, len(heap), positions)

    def delete_minimum(self, heap, positions):
        temp = positions[0]
        heap[0] = sys.maxsize
        self.top_to_bottom(heap, 0, len(heap), positions)
        return temp


def prisms_algorithm(adjacency_list):
    """
    >>> adjacency_list = {0: [[1, 1], [3, 3]],
    ...                   1: [[0, 1], [2, 6], [3, 5], [4, 1]],
    ...                   2: [[1, 6], [4, 5], [5, 2]],
    ...                   3: [[0, 3], [1, 5], [4, 1]],
    ...                   4: [[1, 1], [2, 5], [3, 1], [5, 4]],
    ...                   5: [[2, 2], [4, 4]]}
    >>> prisms_algorithm(adjacency_list)
    [(0, 1), (1, 4), (4, 3), (4, 5), (5, 2)]
    """

    heap = Heap()

    visited = [0] * len(adjacency_list)
    nbr_tv = [-1] * len(adjacency_list)  # Neighboring Tree Vertex of selected vertex
    # Minimum Distance of explored vertex with neighboring vertex of partial tree
    # formed in graph
    distance_tv = []  # Heap of Distance of vertices from their neighboring vertex
    positions = []

    for vertex in range(len(adjacency_list)):
        distance_tv.append(sys.maxsize)
        positions.append(vertex)
        heap.node_position.append(vertex)

    tree_edges = []
    visited[0] = 1
    distance_tv[0] = sys.maxsize
    for neighbor, distance in adjacency_list[0]:
        nbr_tv[neighbor] = 0
        distance_tv[neighbor] = distance
    heap.heapify(distance_tv, positions)

    for _ in range(1, len(adjacency_list)):
        vertex = heap.delete_minimum(distance_tv, positions)
        if visited[vertex] == 0:
            tree_edges.append((nbr_tv[vertex], vertex))
            visited[vertex] = 1
            for neighbor, distance in adjacency_list[vertex]:
                if (
                    visited[neighbor] == 0
                    and distance < distance_tv[heap.get_position(neighbor)]
                ):
                    distance_tv[heap.get_position(neighbor)] = distance
                    heap.bottom_to_top(
                        distance, heap.get_position(neighbor), distance_tv, positions
                    )
                    nbr_tv[neighbor] = vertex
    return tree_edges


if __name__ == "__main__":  # pragma: no cover
    # < --------- Prims Algorithm --------- >
    edges_number = int(input("Enter number of edges: ").strip())
    adjacency_list = defaultdict(list)
    for _ in range(edges_number):
        edge = [int(x) for x in input().strip().split()]
        adjacency_list[edge[0]].append([edge[1], edge[2]])
        adjacency_list[edge[1]].append([edge[0], edge[2]])
    print(prisms_algorithm(adjacency_list))


================================================
FILE: graphs/minimum_spanning_tree_prims2.py
================================================
"""
Prim's (also known as Jarník's) algorithm is a greedy algorithm that finds a minimum
spanning tree for a weighted undirected graph. This means it finds a subset of the
edges that forms a tree that includes every vertex, where the total weight of all the
edges in the tree is minimized. The algorithm operates by building this tree one vertex
at a time, from an arbitrary starting vertex, at each step adding the cheapest possible
connection from the tree to another vertex.
"""

from __future__ import annotations

from sys import maxsize
from typing import TypeVar

T = TypeVar("T")


def get_parent_position(position: int) -> int:
    """
    heap helper function get the position of the parent of the current node

    >>> get_parent_position(1)
    0
    >>> get_parent_position(2)
    0
    """
    return (position - 1) // 2


def get_child_left_position(position: int) -> int:
    """
    heap helper function get the position of the left child of the current node

    >>> get_child_left_position(0)
    1
    """
    return (2 * position) + 1


def get_child_right_position(position: int) -> int:
    """
    heap helper function get the position of the right child of the current node

    >>> get_child_right_position(0)
    2
    """
    return (2 * position) + 2


class MinPriorityQueue[T]:
    """
    Minimum Priority Queue Class

    Functions:
    is_empty: function to check if the priority queue is empty
    push: function to add an element with given priority to the queue
    extract_min: function to remove and return the element with lowest weight (highest
                 priority)
    update_key: function to update the weight of the given key
    _bubble_up: helper function to place a node at the proper position (upward
                movement)
    _bubble_down: helper function to place a node at the proper position (downward
                movement)
    _swap_nodes: helper function to swap the nodes at the given positions

    >>> queue = MinPriorityQueue()

    >>> queue.push(1, 1000)
    >>> queue.push(2, 100)
    >>> queue.push(3, 4000)
    >>> queue.push(4, 3000)

    >>> queue.extract_min()
    2

    >>> queue.update_key(4, 50)

    >>> queue.extract_min()
    4
    >>> queue.extract_min()
    1
    >>> queue.extract_min()
    3
    """

    def __init__(self) -> None:
        self.heap: list[tuple[T, int]] = []
        self.position_map: dict[T, int] = {}
        self.elements: int = 0

    def __len__(self) -> int:
        return self.elements

    def __repr__(self) -> str:
        return str(self.heap)

    def is_empty(self) -> bool:
        # Check if the priority queue is empty
        return self.elements == 0

    def push(self, elem: T, weight: int) -> None:
        # Add an element with given priority to the queue
        self.heap.append((elem, weight))
        self.position_map[elem] = self.elements
        self.elements += 1
        self._bubble_up(elem)

    def extract_min(self) -> T:
        # Remove and return the element with lowest weight (highest priority)
        if self.elements > 1:
            self._swap_nodes(0, self.elements - 1)
        elem, _ = self.heap.pop()
        del self.position_map[elem]
        self.elements -= 1
        if self.elements > 0:
            bubble_down_elem, _ = self.heap[0]
            self._bubble_down(bubble_down_elem)
        return elem

    def update_key(self, elem: T, weight: int) -> None:
        # Update the weight of the given key
        position = self.position_map[elem]
        self.heap[position] = (elem, weight)
        if position > 0:
            parent_position = get_parent_position(position)
            _, parent_weight = self.heap[parent_position]
            if parent_weight > weight:
                self._bubble_up(elem)
            else:
                self._bubble_down(elem)
        else:
            self._bubble_down(elem)

    def _bubble_up(self, elem: T) -> None:
        # Place a node at the proper position (upward movement) [to be used internally
        # only]
        curr_pos = self.position_map[elem]
        if curr_pos == 0:
            return None
        parent_position = get_parent_position(curr_pos)
        _, weight = self.heap[curr_pos]
        _, parent_weight = self.heap[parent_position]
        if parent_weight > weight:
            self._swap_nodes(parent_position, curr_pos)
            return self._bubble_up(elem)
        return None

    def _bubble_down(self, elem: T) -> None:
        # Place a node at the proper position (downward movement) [to be used
        # internally only]
        curr_pos = self.position_map[elem]
        _, weight = self.heap[curr_pos]
        child_left_position = get_child_left_position(curr_pos)
        child_right_position = get_child_right_position(curr_pos)
        if child_left_position < self.elements and child_right_position < self.elements:
            _, child_left_weight = self.heap[child_left_position]
            _, child_right_weight = self.heap[child_right_position]
            if child_right_weight < child_left_weight and child_right_weight < weight:
                self._swap_nodes(child_right_position, curr_pos)
                return self._bubble_down(elem)
        if child_left_position < self.elements:
            _, child_left_weight = self.heap[child_left_position]
            if child_left_weight < weight:
                self._swap_nodes(child_left_position, curr_pos)
                return self._bubble_down(elem)
        else:
            return None
        if child_right_position < self.elements:
            _, child_right_weight = self.heap[child_right_position]
            if child_right_weight < weight:
                self._swap_nodes(child_right_position, curr_pos)
                return self._bubble_down(elem)
        return None

    def _swap_nodes(self, node1_pos: int, node2_pos: int) -> None:
        # Swap the nodes at the given positions
        node1_elem = self.heap[node1_pos][0]
        node2_elem = self.heap[node2_pos][0]
        self.heap[node1_pos], self.heap[node2_pos] = (
            self.heap[node2_pos],
            self.heap[node1_pos],
        )
        self.position_map[node1_elem] = node2_pos
        self.position_map[node2_elem] = node1_pos


class GraphUndirectedWeighted[T]:
    """
    Graph Undirected Weighted Class

    Functions:
    add_node: function to add a node in the graph
    add_edge: function to add an edge between 2 nodes in the graph
    """

    def __init__(self) -> None:
        self.connections: dict[T, dict[T, int]] = {}
        self.nodes: int = 0

    def __repr__(self) -> str:
        return str(self.connections)

    def __len__(self) -> int:
        return self.nodes

    def add_node(self, node: T) -> None:
        # Add a node in the graph if it is not in the graph
        if node not in self.connections:
            self.connections[node] = {}
            self.nodes += 1

    def add_edge(self, node1: T, node2: T, weight: int) -> None:
        # Add an edge between 2 nodes in the graph
        self.add_node(node1)
        self.add_node(node2)
        self.connections[node1][node2] = weight
        self.connections[node2][node1] = weight


def prims_algo[T](
    graph: GraphUndirectedWeighted[T],
) -> tuple[dict[T, int], dict[T, T | None]]:
    """
    >>> graph = GraphUndirectedWeighted()

    >>> graph.add_edge("a", "b", 3)
    >>> graph.add_edge("b", "c", 10)
    >>> graph.add_edge("c", "d", 5)
    >>> graph.add_edge("a", "c", 15)
    >>> graph.add_edge("b", "d", 100)

    >>> dist, parent = prims_algo(graph)

    >>> abs(dist["a"] - dist["b"])
    3
    >>> abs(dist["d"] - dist["b"])
    15
    >>> abs(dist["a"] - dist["c"])
    13
    """
    # prim's algorithm for minimum spanning tree
    dist: dict[T, int] = dict.fromkeys(graph.connections, maxsize)
    parent: dict[T, T | None] = dict.fromkeys(graph.connections)

    priority_queue: MinPriorityQueue[T] = MinPriorityQueue()
    for node, weight in dist.items():
        priority_queue.push(node, weight)

    if priority_queue.is_empty():
        return dist, parent

    # initialization
    node = priority_queue.extract_min()
    dist[node] = 0
    for neighbour in graph.connections[node]:
        if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            dist[neighbour] = dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(neighbour, dist[neighbour])
            parent[neighbour] = node

    # running prim's algorithm
    while not priority_queue.is_empty():
        node = priority_queue.extract_min()
        for neighbour in graph.connections[node]:
            if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                dist[neighbour] = dist[node] + graph.connections[node][neighbour]
                priority_queue.update_key(neighbour, dist[neighbour])
                parent[neighbour] = node
    return dist, parent


================================================
FILE: graphs/multi_heuristic_astar.py
================================================
import heapq
import sys

import numpy as np

TPos = tuple[int, int]


class PriorityQueue:
    def __init__(self):
        self.elements = []
        self.set = set()

    def minkey(self):
        if not self.empty():
            return self.elements[0][0]
        else:
            return float("inf")

    def empty(self):
        return len(self.elements) == 0

    def put(self, item, priority):
        if item not in self.set:
            heapq.heappush(self.elements, (priority, item))
            self.set.add(item)
        else:
            # update
            # print("update", item)
            temp = []
            (pri, x) = heapq.heappop(self.elements)
            while x != item:
                temp.append((pri, x))
                (pri, x) = heapq.heappop(self.elements)
            temp.append((priority, item))
            for pro, xxx in temp:
                heapq.heappush(self.elements, (pro, xxx))

    def remove_element(self, item):
        if item in self.set:
            self.set.remove(item)
            temp = []
            (pro, x) = heapq.heappop(self.elements)
            while x != item:
                temp.append((pro, x))
                (pro, x) = heapq.heappop(self.elements)
            for prito, yyy in temp:
                heapq.heappush(self.elements, (prito, yyy))

    def top_show(self):
        return self.elements[0][1]

    def get(self):
        (priority, item) = heapq.heappop(self.elements)
        self.set.remove(item)
        return (priority, item)


def consistent_heuristic(p: TPos, goal: TPos):
    # euclidean distance
    a = np.array(p)
    b = np.array(goal)
    return np.linalg.norm(a - b)


def heuristic_2(p: TPos, goal: TPos):
    # integer division by time variable
    return consistent_heuristic(p, goal) // t


def heuristic_1(p: TPos, goal: TPos):
    # manhattan distance
    return abs(p[0] - goal[0]) + abs(p[1] - goal[1])


def key(start: TPos, i: int, goal: TPos, g_function: dict[TPos, float]):
    ans = g_function[start] + W1 * heuristics[i](start, goal)
    return ans


def do_something(back_pointer, goal, start):
    grid = np.char.chararray((n, n))
    for i in range(n):
        for j in range(n):
            grid[i][j] = "*"

    for i in range(n):
        for j in range(n):
            if (j, (n - 1) - i) in blocks:
                grid[i][j] = "#"

    grid[0][(n - 1)] = "-"
    x = back_pointer[goal]
    while x != start:
        (x_c, y_c) = x
        # print(x)
        grid[(n - 1) - y_c][x_c] = "-"
        x = back_pointer[x]
    grid[(n - 1)][0] = "-"

    for i in range(n):
        for j in range(n):
            if (i, j) == (0, n - 1):
                print(grid[i][j], end=" ")
                print("<-- End position", end=" ")
            else:
                print(grid[i][j], end=" ")
        print()
    print("^")
    print("Start position")
    print()
    print("# is an obstacle")
    print("- is the path taken by algorithm")
    print("PATH TAKEN BY THE ALGORITHM IS:-")
    x = back_pointer[goal]
    while x != start:
        print(x, end=" ")
        x = back_pointer[x]
    print(x)
    sys.exit()


def valid(p: TPos):
    if p[0] < 0 or p[0] > n - 1:
        return False
    return not (p[1] < 0 or p[1] > n - 1)


def expand_state(
    s,
    j,
    visited,
    g_function,
    close_list_anchor,
    close_list_inad,
    open_list,
    back_pointer,
):
    for itera in range(n_heuristic):
        open_list[itera].remove_element(s)
    # print("s", s)
    # print("j", j)
    (x, y) = s
    left = (x - 1, y)
    right = (x + 1, y)
    up = (x, y + 1)
    down = (x, y - 1)

    for neighbours in [left, right, up, down]:
        if neighbours not in blocks:
            if valid(neighbours) and neighbours not in visited:
                # print("neighbour", neighbours)
                visited.add(neighbours)
                back_pointer[neighbours] = -1
                g_function[neighbours] = float("inf")

            if valid(neighbours) and g_function[neighbours] > g_function[s] + 1:
                g_function[neighbours] = g_function[s] + 1
                back_pointer[neighbours] = s
                if neighbours not in close_list_anchor:
                    open_list[0].put(neighbours, key(neighbours, 0, goal, g_function))
                    if neighbours not in close_list_inad:
                        for var in range(1, n_heuristic):
                            if key(neighbours, var, goal, g_function) <= W2 * key(
                                neighbours, 0, goal, g_function
                            ):
                                open_list[j].put(
                                    neighbours, key(neighbours, var, goal, g_function)
                                )


def make_common_ground():
    some_list = []
    for x in range(1, 5):
        for y in range(1, 6):
            some_list.append((x, y))

    for x in range(15, 20):
        some_list.append((x, 17))

    for x in range(10, 19):
        for y in range(1, 15):
            some_list.append((x, y))

    # L block
    for x in range(1, 4):
        for y in range(12, 19):
            some_list.append((x, y))
    for x in range(3, 13):
        for y in range(16, 19):
            some_list.append((x, y))
    return some_list


heuristics = {0: consistent_heuristic, 1: heuristic_1, 2: heuristic_2}

blocks_blk = [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
blocks_all = make_common_ground()


blocks = blocks_blk
# hyper parameters
W1 = 1
W2 = 1
n = 20
n_heuristic = 3  # one consistent and two other inconsistent

# start and end destination
start = (0, 0)
goal = (n - 1, n - 1)

t = 1


def multi_a_star(start: TPos, goal: TPos, n_heuristic: int):
    g_function = {start: 0, goal: float("inf")}
    back_pointer = {start: -1, goal: -1}
    open_list = []
    visited = set()

    for i in range(n_heuristic):
        open_list.append(PriorityQueue())
        open_list[i].put(start, key(start, i, goal, g_function))

    close_list_anchor: list[int] = []
    close_list_inad: list[int] = []
    while open_list[0].minkey() < float("inf"):
        for i in range(1, n_heuristic):
            # print(open_list[0].minkey(), open_list[i].minkey())
            if open_list[i].minkey() <= W2 * open_list[0].minkey():
                global t
                t += 1
                if g_function[goal] <= open_list[i].minkey():
                    if g_function[goal] < float("inf"):
                        do_something(back_pointer, goal, start)
                else:
                    _, get_s = open_list[i].top_show()
                    visited.add(get_s)
                    expand_state(
                        get_s,
                        i,
                        visited,
                        g_function,
                        close_list_anchor,
                        close_list_inad,
                        open_list,
                        back_pointer,
                    )
                    close_list_inad.append(get_s)
            elif g_function[goal] <= open_list[0].minkey():
                if g_function[goal] < float("inf"):
                    do_something(back_pointer, goal, start)
            else:
                get_s = open_list[0].top_show()
                visited.add(get_s)
                expand_state(
                    get_s,
                    0,
                    visited,
                    g_function,
                    close_list_anchor,
                    close_list_inad,
                    open_list,
                    back_pointer,
                )
                close_list_anchor.append(get_s)
    print("No path found to goal")
    print()
    for i in range(n - 1, -1, -1):
        for j in range(n):
            if (j, i) in blocks:
                print("#", end=" ")
            elif (j, i) in back_pointer:
                if (j, i) == (n - 1, n - 1):
                    print("*", end=" ")
                else:
                    print("-", end=" ")
            else:
                print("*", end=" ")
            if (j, i) == (n - 1, n - 1):
                print("<-- End position", end=" ")
        print()
    print("^")
    print("Start position")
    print()
    print("# is an obstacle")
    print("- is the path taken by algorithm")


if __name__ == "__main__":
    multi_a_star(start, goal, n_heuristic)


================================================
FILE: graphs/page_rank.py
================================================
"""
Author: https://github.com/bhushan-borole
"""

"""
The input graph for the algorithm is:

  A B C
A 0 1 1
B 0 0 1
C 1 0 0

"""

graph = [[0, 1, 1], [0, 0, 1], [1, 0, 0]]


class Node:
    def __init__(self, name):
        self.name = name
        self.inbound = []
        self.outbound = []

    def add_inbound(self, node):
        self.inbound.append(node)

    def add_outbound(self, node):
        self.outbound.append(node)

    def __repr__(self):
        return f"<node={self.name} inbound={self.inbound} outbound={self.outbound}>"


def page_rank(nodes, limit=3, d=0.85):
    ranks = {}
    for node in nodes:
        ranks[node.name] = 1

    outbounds = {}
    for node in nodes:
        outbounds[node.name] = len(node.outbound)

    for i in range(limit):
        print(f"======= Iteration {i + 1} =======")
        for _, node in enumerate(nodes):
            ranks[node.name] = (1 - d) + d * sum(
                ranks[ib] / outbounds[ib] for ib in node.inbound
            )
        print(ranks)


def main():
    names = list(input("Enter Names of the Nodes: ").split())

    nodes = [Node(name) for name in names]

    for ri, row in enumerate(graph):
        for ci, col in enumerate(row):
            if col == 1:
                nodes[ci].add_inbound(names[ri])
                nodes[ri].add_outbound(names[ci])

    print("======= Nodes =======")
    for node in nodes:
        print(node)

    page_rank(nodes)


if __name__ == "__main__":
    main()


================================================
FILE: graphs/prim.py
================================================
"""Prim's Algorithm.

Determines the minimum spanning tree(MST) of a graph using the Prim's Algorithm.

Details: https://en.wikipedia.org/wiki/Prim%27s_algorithm
"""

import heapq as hq
import math
from collections.abc import Iterator


class Vertex:
    """Class Vertex."""

    def __init__(self, id_):
        """
        Arguments:
            id - input an id to identify the vertex
        Attributes:
            neighbors - a list of the vertices it is linked to
            edges     - a dict to store the edges's weight
        """
        self.id = str(id_)
        self.key = None
        self.pi = None
        self.neighbors = []
        self.edges = {}  # {vertex:distance}

    def __lt__(self, other):
        """Comparison rule to < operator."""
        return self.key < other.key

    def __repr__(self):
        """Return the vertex id."""
        return self.id

    def add_neighbor(self, vertex):
        """Add a pointer to a vertex at neighbor's list."""
        self.neighbors.append(vertex)

    def add_edge(self, vertex, weight):
        """Destination vertex and weight."""
        self.edges[vertex.id] = weight


def connect(graph, a, b, edge):
    # add the neighbors:
    graph[a - 1].add_neighbor(graph[b - 1])
    graph[b - 1].add_neighbor(graph[a - 1])
    # add the edges:
    graph[a - 1].add_edge(graph[b - 1], edge)
    graph[b - 1].add_edge(graph[a - 1], edge)


def prim(graph: list, root: Vertex) -> list:
    """Prim's Algorithm.

    Runtime:
        O(mn) with `m` edges and `n` vertices

    Return:
        List with the edges of a Minimum Spanning Tree

    Usage:
        prim(graph, graph[0])
    """
    a = []
    for u in graph:
        u.key = math.inf
        u.pi = None
    root.key = 0
    q = graph[:]
    while q:
        u = min(q)
        q.remove(u)
        for v in u.neighbors:
            if (v in q) and (u.edges[v.id] < v.key):
                v.pi = u
                v.key = u.edges[v.id]
    for i in range(1, len(graph)):
        a.append((int(graph[i].id) + 1, int(graph[i].pi.id) + 1))
    return a


def prim_heap(graph: list, root: Vertex) -> Iterator[tuple]:
    """Prim's Algorithm with min heap.

    Runtime:
        O((m + n)log n) with `m` edges and `n` vertices

    Yield:
        Edges of a Minimum Spanning Tree

    Usage:
        prim(graph, graph[0])
    """
    for u in graph:
        u.key = math.inf
        u.pi = None
    root.key = 0

    h = list(graph)
    hq.heapify(h)

    while h:
        u = hq.heappop(h)
        for v in u.neighbors:
            if (v in h) and (u.edges[v.id] < v.key):
                v.pi = u
                v.key = u.edges[v.id]
                hq.heapify(h)

    for i in range(1, len(graph)):
        yield (int(graph[i].id) + 1, int(graph[i].pi.id) + 1)


def test_vector() -> None:
    """
    # Creates a list to store x vertices.
    >>> x = 5
    >>> G = [Vertex(n) for n in range(x)]

    >>> connect(G, 1, 2, 15)
    >>> connect(G, 1, 3, 12)
    >>> connect(G, 2, 4, 13)
    >>> connect(G, 2, 5, 5)
    >>> connect(G, 3, 2, 6)
    >>> connect(G, 3, 4, 6)
    >>> connect(G, 0, 0, 0)  # Generate the minimum spanning tree:
    >>> G_heap = G[:]
    >>> MST = prim(G, G[0])
    >>> MST_heap = prim_heap(G, G[0])
    >>> for i in MST:
    ...     print(i)
    (2, 3)
    (3, 1)
    (4, 3)
    (5, 2)
    >>> for i in MST_heap:
    ...     print(i)
    (2, 3)
    (3, 1)
    (4, 3)
    (5, 2)
    """


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: graphs/random_graph_generator.py
================================================
"""
* Author: Manuel Di Lullo (https://github.com/manueldilullo)
* Description: Random graphs generator.
               Uses graphs represented with an adjacency list.

URL: https://en.wikipedia.org/wiki/Random_graph
"""

import random


def random_graph(
    vertices_number: int, probability: float, directed: bool = False
) -> dict:
    """
    Generate a random graph
    @input: vertices_number (number of vertices),
            probability (probability that a generic edge (u,v) exists),
            directed (if True: graph will be a directed graph,
                      otherwise it will be an undirected graph)
    @examples:
    >>> random.seed(1)
    >>> random_graph(4, 0.5)
    {0: [1], 1: [0, 2, 3], 2: [1, 3], 3: [1, 2]}
    >>> random.seed(1)
    >>> random_graph(4, 0.5, True)
    {0: [1], 1: [2, 3], 2: [3], 3: []}
    """
    graph: dict = {i: [] for i in range(vertices_number)}

    # if probability is greater or equal than 1, then generate a complete graph
    if probability >= 1:
        return complete_graph(vertices_number)
    # if probability is lower or equal than 0, then return a graph without edges
    if probability <= 0:
        return graph

    # for each couple of nodes, add an edge from u to v
    # if the number randomly generated is greater than probability probability
    for i in range(vertices_number):
        for j in range(i + 1, vertices_number):
            if random.random() < probability:
                graph[i].append(j)
                if not directed:
                    # if the graph is undirected, add an edge in from j to i, either
                    graph[j].append(i)
    return graph


def complete_graph(vertices_number: int) -> dict:
    """
    Generate a complete graph with vertices_number vertices.
    @input: vertices_number (number of vertices),
            directed (False if the graph is undirected, True otherwise)
    @example:
    >>> complete_graph(3)
    {0: [1, 2], 1: [0, 2], 2: [0, 1]}
    """
    return {
        i: [j for j in range(vertices_number) if i != j] for i in range(vertices_number)
    }


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: graphs/scc_kosaraju.py
================================================
from __future__ import annotations


def dfs(u):
    global graph, reversed_graph, scc, component, visit, stack
    if visit[u]:
        return
    visit[u] = True
    for v in graph[u]:
        dfs(v)
    stack.append(u)


def dfs2(u):
    global graph, reversed_graph, scc, component, visit, stack
    if visit[u]:
        return
    visit[u] = True
    component.append(u)
    for v in reversed_graph[u]:
        dfs2(v)


def kosaraju():
    global graph, reversed_graph, scc, component, visit, stack
    for i in range(n):
        dfs(i)
    visit = [False] * n
    for i in stack[::-1]:
        if visit[i]:
            continue
        component = []
        dfs2(i)
        scc.append(component)
    return scc


if __name__ == "__main__":
    # n - no of nodes, m - no of edges
    n, m = list(map(int, input().strip().split()))

    graph: list[list[int]] = [[] for _ in range(n)]  # graph
    reversed_graph: list[list[int]] = [[] for i in range(n)]  # reversed graph
    # input graph data (edges)
    for _ in range(m):
        u, v = list(map(int, input().strip().split()))
        graph[u].append(v)
        reversed_graph[v].append(u)

    stack: list[int] = []
    visit: list[bool] = [False] * n
    scc: list[int] = []
    component: list[int] = []
    print(kosaraju())


================================================
FILE: graphs/strongly_connected_components.py
================================================
"""
https://en.wikipedia.org/wiki/Strongly_connected_component

Finding strongly connected components in directed graph

"""

test_graph_1 = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

test_graph_2 = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}


def topology_sort(
    graph: dict[int, list[int]], vert: int, visited: list[bool]
) -> list[int]:
    """
    Use depth first search to sort graph
    At this time graph is the same as input
    >>> topology_sort(test_graph_1, 0, 5 * [False])
    [1, 2, 4, 3, 0]
    >>> topology_sort(test_graph_2, 0, 6 * [False])
    [2, 1, 5, 4, 3, 0]
    """

    visited[vert] = True
    order = []

    for neighbour in graph[vert]:
        if not visited[neighbour]:
            order += topology_sort(graph, neighbour, visited)

    order.append(vert)

    return order


def find_components(
    reversed_graph: dict[int, list[int]], vert: int, visited: list[bool]
) -> list[int]:
    """
    Use depth first search to find strongly connected
    vertices. Now graph is reversed
    >>> find_components({0: [1], 1: [2], 2: [0]}, 0, 5 * [False])
    [0, 1, 2]
    >>> find_components({0: [2], 1: [0], 2: [0, 1]}, 0, 6 * [False])
    [0, 2, 1]
    """

    visited[vert] = True
    component = [vert]

    for neighbour in reversed_graph[vert]:
        if not visited[neighbour]:
            component += find_components(reversed_graph, neighbour, visited)

    return component


def strongly_connected_components(graph: dict[int, list[int]]) -> list[list[int]]:
    """
    This function takes graph as a parameter
    and then returns the list of strongly connected components
    >>> strongly_connected_components(test_graph_1)
    [[0, 1, 2], [3], [4]]
    >>> strongly_connected_components(test_graph_2)
    [[0, 2, 1], [3, 5, 4]]
    """

    visited = len(graph) * [False]
    reversed_graph: dict[int, list[int]] = {vert: [] for vert in range(len(graph))}

    for vert, neighbours in graph.items():
        for neighbour in neighbours:
            reversed_graph[neighbour].append(vert)

    order = []
    for i, was_visited in enumerate(visited):
        if not was_visited:
            order += topology_sort(graph, i, visited)

    components_list = []
    visited = len(graph) * [False]

    for i in range(len(graph)):
        vert = order[len(graph) - i - 1]
        if not visited[vert]:
            component = find_components(reversed_graph, vert, visited)
            components_list.append(component)

    return components_list


================================================
FILE: graphs/tarjans_scc.py
================================================
from collections import deque


def tarjan(g: list[list[int]]) -> list[list[int]]:
    """
    Tarjan's algo for finding strongly connected components in a directed graph

    Uses two main attributes of each node to track reachability, the index of that node
    within a component(index), and the lowest index reachable from that node(lowlink).

    We then perform a dfs of the each component making sure to update these parameters
    for each node and saving the nodes we visit on the way.

    If ever we find that the lowest reachable node from a current node is equal to the
    index of the current node then it must be the root of a strongly connected
    component and so we save it and it's equireachable vertices as a strongly
    connected component.

    Complexity: strong_connect() is called at most once for each node and has a
    complexity of O(|E|) as it is DFS.
    Therefore this has complexity O(|V| + |E|) for a graph G = (V, E)

    >>> tarjan([[2, 3, 4], [2, 3, 4], [0, 1, 3], [0, 1, 2], [1]])
    [[4, 3, 1, 2, 0]]
    >>> tarjan([[], [], [], []])
    [[0], [1], [2], [3]]
    >>> a = [0, 1, 2, 3, 4, 5, 4]
    >>> b = [1, 0, 3, 2, 5, 4, 0]
    >>> n = 7
    >>> sorted(tarjan(create_graph(n, list(zip(a, b))))) == sorted(
    ...     tarjan(create_graph(n, list(zip(a[::-1], b[::-1])))))
    True
    >>> a = [0, 1, 2, 3, 4, 5, 6]
    >>> b = [0, 1, 2, 3, 4, 5, 6]
    >>> sorted(tarjan(create_graph(n, list(zip(a, b)))))
    [[0], [1], [2], [3], [4], [5], [6]]
    """

    n = len(g)
    stack: deque[int] = deque()
    on_stack = [False for _ in range(n)]
    index_of = [-1 for _ in range(n)]
    lowlink_of = index_of[:]

    def strong_connect(v: int, index: int, components: list[list[int]]) -> int:
        index_of[v] = index  # the number when this node is seen
        lowlink_of[v] = index  # lowest rank node reachable from here
        index += 1
        stack.append(v)
        on_stack[v] = True

        for w in g[v]:
            if index_of[w] == -1:
                index = strong_connect(w, index, components)
                lowlink_of[v] = (
                    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                )
            elif on_stack[w]:
                lowlink_of[v] = (
                    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                )

        if lowlink_of[v] == index_of[v]:
            component = []
            w = stack.pop()
            on_stack[w] = False
            component.append(w)
            while w != v:
                w = stack.pop()
                on_stack[w] = False
                component.append(w)
            components.append(component)
        return index

    components: list[list[int]] = []
    for v in range(n):
        if index_of[v] == -1:
            strong_connect(v, 0, components)

    return components


def create_graph(n: int, edges: list[tuple[int, int]]) -> list[list[int]]:
    """
    >>> n = 7
    >>> source = [0, 0, 1, 2, 3, 3, 4, 4, 6]
    >>> target = [1, 3, 2, 0, 1, 4, 5, 6, 5]
    >>> edges = list(zip(source, target))
    >>> create_graph(n, edges)
    [[1, 3], [2], [0], [1, 4], [5, 6], [], [5]]
    """
    g: list[list[int]] = [[] for _ in range(n)]
    for u, v in edges:
        g[u].append(v)
    return g


if __name__ == "__main__":
    # Test
    n_vertices = 7
    source = [0, 0, 1, 2, 3, 3, 4, 4, 6]
    target = [1, 3, 2, 0, 1, 4, 5, 6, 5]
    edges = list(zip(source, target))
    g = create_graph(n_vertices, edges)

    assert tarjan(g) == [[5], [6], [4], [3, 2, 1, 0]]


================================================
FILE: graphs/tests/__init__.py
================================================


================================================
FILE: graphs/tests/test_min_spanning_tree_kruskal.py
================================================
from graphs.minimum_spanning_tree_kruskal import kruskal


def test_kruskal_successful_result():
    num_nodes = 9
    edges = [
        [0, 1, 4],
        [0, 7, 8],
        [1, 2, 8],
        [7, 8, 7],
        [7, 6, 1],
        [2, 8, 2],
        [8, 6, 6],
        [2, 3, 7],
        [2, 5, 4],
        [6, 5, 2],
        [3, 5, 14],
        [3, 4, 9],
        [5, 4, 10],
        [1, 7, 11],
    ]

    result = kruskal(num_nodes, edges)

    expected = [
        [7, 6, 1],
        [2, 8, 2],
        [6, 5, 2],
        [0, 1, 4],
        [2, 5, 4],
        [2, 3, 7],
        [0, 7, 8],
        [3, 4, 9],
    ]

    assert sorted(expected) == sorted(result)


================================================
FILE: graphs/tests/test_min_spanning_tree_prim.py
================================================
from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst


def test_prim_successful_result():
    num_nodes, num_edges = 9, 14  # noqa: F841
    edges = [
        [0, 1, 4],
        [0, 7, 8],
        [1, 2, 8],
        [7, 8, 7],
        [7, 6, 1],
        [2, 8, 2],
        [8, 6, 6],
        [2, 3, 7],
        [2, 5, 4],
        [6, 5, 2],
        [3, 5, 14],
        [3, 4, 9],
        [5, 4, 10],
        [1, 7, 11],
    ]

    adjacency = defaultdict(list)
    for node1, node2, cost in edges:
        adjacency[node1].append([node2, cost])
        adjacency[node2].append([node1, cost])

    result = mst(adjacency)

    expected = [
        [7, 6, 1],
        [2, 8, 2],
        [6, 5, 2],
        [0, 1, 4],
        [2, 5, 4],
        [2, 3, 7],
        [0, 7, 8],
        [3, 4, 9],
    ]

    for answer in expected:
        edge = tuple(answer[:2])
        reverse = tuple(edge[::-1])
        assert edge in result or reverse in result


================================================
FILE: greedy_methods/__init__.py
================================================


================================================
FILE: greedy_methods/best_time_to_buy_and_sell_stock.py
================================================
"""
Given a list of stock prices calculate the maximum profit that can be made from a
single buy and sell of one share of stock.  We only allowed to complete one buy
transaction and one sell transaction but must buy before we sell.

Example : prices = [7, 1, 5, 3, 6, 4]
max_profit will return 5 - which is by buying at price 1 and selling at price 6.

This problem can be solved using the concept of "GREEDY ALGORITHM".

We iterate over the price array once, keeping track of the lowest price point
(buy) and the maximum profit we can get at each point.  The greedy choice at each point
is to either buy at the current price if it's less than our current buying price, or
sell at the current price if the profit is more than our current maximum profit.
"""


def max_profit(prices: list[int]) -> int:
    """
    >>> max_profit([7, 1, 5, 3, 6, 4])
    5
    >>> max_profit([7, 6, 4, 3, 1])
    0
    """
    if not prices:
        return 0

    min_price = prices[0]
    max_profit: int = 0

    for price in prices:
        min_price = min(price, min_price)
        max_profit = max(price - min_price, max_profit)

    return max_profit


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(max_profit([7, 1, 5, 3, 6, 4]))


================================================
FILE: greedy_methods/fractional_cover_problem.py
================================================
# https://en.wikipedia.org/wiki/Set_cover_problem

from dataclasses import dataclass
from operator import attrgetter


@dataclass
class Item:
    weight: int
    value: int

    @property
    def ratio(self) -> float:
        """
        Return the value-to-weight ratio for the item.

        Returns:
            float: The value-to-weight ratio for the item.

        Examples:
        >>> Item(10, 65).ratio
        6.5

        >>> Item(20, 100).ratio
        5.0

        >>> Item(30, 120).ratio
        4.0
        """
        return self.value / self.weight


def fractional_cover(items: list[Item], capacity: int) -> float:
    """
    Solve the Fractional Cover Problem.

    Args:
        items: A list of items, where each item has weight and value attributes.
        capacity: The maximum weight capacity of the knapsack.

    Returns:
        The maximum value that can be obtained by selecting fractions of items to cover
        the knapsack's capacity.

    Raises:
        ValueError: If capacity is negative.

    Examples:
    >>> fractional_cover((Item(10, 60), Item(20, 100), Item(30, 120)), capacity=50)
    240.0

    >>> fractional_cover([Item(20, 100), Item(30, 120), Item(10, 60)], capacity=25)
    135.0

    >>> fractional_cover([Item(10, 60), Item(20, 100), Item(30, 120)], capacity=60)
    280.0

    >>> fractional_cover(items=[Item(5, 30), Item(10, 60), Item(15, 90)], capacity=30)
    180.0

    >>> fractional_cover(items=[], capacity=50)
    0.0

    >>> fractional_cover(items=[Item(10, 60)], capacity=5)
    30.0

    >>> fractional_cover(items=[Item(10, 60)], capacity=1)
    6.0

    >>> fractional_cover(items=[Item(10, 60)], capacity=0)
    0.0

    >>> fractional_cover(items=[Item(10, 60)], capacity=-1)
    Traceback (most recent call last):
        ...
    ValueError: Capacity cannot be negative
    """
    if capacity < 0:
        raise ValueError("Capacity cannot be negative")

    total_value = 0.0
    remaining_capacity = capacity

    # Sort the items by their value-to-weight ratio in descending order
    for item in sorted(items, key=attrgetter("ratio"), reverse=True):
        if remaining_capacity == 0:
            break

        weight_taken = min(item.weight, remaining_capacity)
        total_value += weight_taken * item.ratio
        remaining_capacity -= weight_taken

    return total_value


if __name__ == "__main__":
    import doctest

    if result := doctest.testmod().failed:
        print(f"{result} test(s) failed")
    else:
        print("All tests passed")


================================================
FILE: greedy_methods/fractional_knapsack.py
================================================
from bisect import bisect
from itertools import accumulate


def frac_knapsack(vl, wt, w, n):
    """
    >>> frac_knapsack([60, 100, 120], [10, 20, 30], 50, 3)
    240.0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], 10, 4)
    105.0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], 8, 4)
    95.0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6], 8, 4)
    60.0
    >>> frac_knapsack([10, 40, 30], [5, 4, 6, 3], 8, 4)
    60.0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], 0, 4)
    0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], 8, 0)
    95.0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], -8, 4)
    0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], 8, -4)
    95.0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], 800, 4)
    130
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], 8, 400)
    95.0
    >>> frac_knapsack("ABCD", [5, 4, 6, 3], 8, 400)
    Traceback (most recent call last):
        ...
    TypeError: unsupported operand type(s) for /: 'str' and 'int'
    """

    r = sorted(zip(vl, wt), key=lambda x: x[0] / x[1], reverse=True)
    vl, wt = [i[0] for i in r], [i[1] for i in r]
    acc = list(accumulate(wt))
    k = bisect(acc, w)
    return (
        0
        if k == 0
        else sum(vl[:k]) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
        if k != n
        else sum(vl[:k])
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: greedy_methods/fractional_knapsack_2.py
================================================
# https://en.wikipedia.org/wiki/Continuous_knapsack_problem
# https://www.guru99.com/fractional-knapsack-problem-greedy.html
# https://medium.com/walkinthecode/greedy-algorithm-fractional-knapsack-problem-9aba1daecc93

from __future__ import annotations


def fractional_knapsack(
    value: list[int], weight: list[int], capacity: int
) -> tuple[float, list[float]]:
    """
    >>> value = [1, 3, 5, 7, 9]
    >>> weight = [0.9, 0.7, 0.5, 0.3, 0.1]
    >>> fractional_knapsack(value, weight, 5)
    (25, [1, 1, 1, 1, 1])
    >>> fractional_knapsack(value, weight, 15)
    (25, [1, 1, 1, 1, 1])
    >>> fractional_knapsack(value, weight, 25)
    (25, [1, 1, 1, 1, 1])
    >>> fractional_knapsack(value, weight, 26)
    (25, [1, 1, 1, 1, 1])
    >>> fractional_knapsack(value, weight, -1)
    (-90.0, [0, 0, 0, 0, -10.0])
    >>> fractional_knapsack([1, 3, 5, 7], weight, 30)
    (16, [1, 1, 1, 1])
    >>> fractional_knapsack(value, [0.9, 0.7, 0.5, 0.3, 0.1], 30)
    (25, [1, 1, 1, 1, 1])
    >>> fractional_knapsack([], [], 30)
    (0, [])
    """
    index = list(range(len(value)))
    ratio = [v / w for v, w in zip(value, weight)]
    index.sort(key=lambda i: ratio[i], reverse=True)

    max_value: float = 0
    fractions: list[float] = [0] * len(value)
    for i in index:
        if weight[i] <= capacity:
            fractions[i] = 1
            max_value += value[i]
            capacity -= weight[i]
        else:
            fractions[i] = capacity / weight[i]
            max_value += value[i] * capacity / weight[i]
            break

    return max_value, fractions


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: greedy_methods/gas_station.py
================================================
"""
Task:
There are n gas stations along a circular route, where the amount of gas
at the ith station is gas_quantities[i].

You have a car with an unlimited gas tank and it costs costs[i] of gas
to travel from the ith station to its next (i + 1)th station.
You begin the journey with an empty tank at one of the gas stations.

Given two integer arrays gas_quantities and costs, return the starting
gas station's index if you can travel around the circuit once
in the clockwise direction otherwise, return -1.
If there exists a solution, it is guaranteed to be unique

Reference: https://leetcode.com/problems/gas-station/description

Implementation notes:
First, check whether the total gas is enough to complete the journey. If not, return -1.
However, if there is enough gas, it is guaranteed that there is a valid
starting index to reach the end of the journey.
Greedily calculate the net gain (gas_quantity - cost) at each station.
If the net gain ever goes below 0 while iterating through the stations,
start checking from the next station.

"""

from dataclasses import dataclass


@dataclass
class GasStation:
    gas_quantity: int
    cost: int


def get_gas_stations(
    gas_quantities: list[int], costs: list[int]
) -> tuple[GasStation, ...]:
    """
    This function returns a tuple of gas stations.

    Args:
        gas_quantities: Amount of gas available at each station
        costs: The cost of gas required to move from one station to the next

    Returns:
        A tuple of gas stations

    >>> gas_stations = get_gas_stations([1, 2, 3, 4, 5], [3, 4, 5, 1, 2])
    >>> len(gas_stations)
    5
    >>> gas_stations[0]
    GasStation(gas_quantity=1, cost=3)
    >>> gas_stations[-1]
    GasStation(gas_quantity=5, cost=2)
    """
    return tuple(
        GasStation(quantity, cost) for quantity, cost in zip(gas_quantities, costs)
    )


def can_complete_journey(gas_stations: tuple[GasStation, ...]) -> int:
    """
    This function returns the index from which to start the journey
    in order to reach the end.

    Args:
        gas_quantities [list]: Amount of gas available at each station
        cost [list]: The cost of gas required to move from one station to the next

    Returns:
        start [int]: start index needed to complete the journey

    Examples:
    >>> can_complete_journey(get_gas_stations([1, 2, 3, 4, 5], [3, 4, 5, 1, 2]))
    3
    >>> can_complete_journey(get_gas_stations([2, 3, 4], [3, 4, 3]))
    -1
    """
    total_gas = sum(gas_station.gas_quantity for gas_station in gas_stations)
    total_cost = sum(gas_station.cost for gas_station in gas_stations)
    if total_gas < total_cost:
        return -1

    start = 0
    net = 0
    for i, gas_station in enumerate(gas_stations):
        net += gas_station.gas_quantity - gas_station.cost
        if net < 0:
            start = i + 1
            net = 0
    return start


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: greedy_methods/minimum_coin_change.py
================================================
"""
Test cases:
Do you want to enter your denominations ? (Y/N) :N
Enter the change you want to make in Indian Currency: 987
Following is minimal  change for 987 :
500 100 100 100 100 50 20 10 5 2

Do you want to enter your denominations ? (Y/N) :Y
Enter number of denomination:10
1
5
10
20
50
100
200
500
1000
2000
Enter the change you want to make: 18745
Following is minimal  change for 18745 :
2000 2000 2000 2000 2000 2000 2000 2000 2000 500 200 20 20 5

Do you want to enter your denominations ? (Y/N) :N
Enter the change you want to make: 0
The total value cannot be zero or negative.
Do you want to enter your denominations ? (Y/N) :N
Enter the change you want to make: -98
The total value cannot be zero or negative.

Do you want to enter your denominations ? (Y/N) :Y
Enter number of denomination:5
1
5
100
500
1000
Enter the change you want to make: 456
Following is minimal   change for 456 :
100 100 100 100 5 5 5 5 5 5 5 5 5 5 5 1
"""


def find_minimum_change(denominations: list[int], value: str) -> list[int]:
    """
    Find the minimum change from the given denominations and value
    >>> find_minimum_change([1, 5, 10, 20, 50, 100, 200, 500, 1000,2000], 18745)
    [2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 500, 200, 20, 20, 5]
    >>> find_minimum_change([1, 2, 5, 10, 20, 50, 100, 500, 2000], 987)
    [500, 100, 100, 100, 100, 50, 20, 10, 5, 2]
    >>> find_minimum_change([1, 2, 5, 10, 20, 50, 100, 500, 2000], 0)
    []
    >>> find_minimum_change([1, 2, 5, 10, 20, 50, 100, 500, 2000], -98)
    []
    >>> find_minimum_change([1, 5, 100, 500, 1000], 456)
    [100, 100, 100, 100, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1]
    """
    total_value = int(value)

    # Initialize Result
    answer = []

    # Traverse through all denomination
    for denomination in reversed(denominations):
        # Find denominations
        while int(total_value) >= int(denomination):
            total_value -= int(denomination)
            answer.append(denomination)  # Append the "answers" array

    return answer


# Driver Code
if __name__ == "__main__":
    denominations = []
    value = "0"

    if (
        input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
        == "y"
    ):
        n = int(input("Enter the number of denominations you want to add: ").strip())

        for i in range(n):
            denominations.append(int(input(f"Denomination {i}: ").strip()))
        value = input("Enter the change you want to make in Indian Currency: ").strip()
    else:
        # All denominations of Indian Currency if user does not enter
        denominations = [1, 2, 5, 10, 20, 50, 100, 500, 2000]
        value = input("Enter the change you want to make: ").strip()

    if int(value) == 0 or int(value) < 0:
        print("The total value cannot be zero or negative.")

    else:
        print(f"Following is minimal change for {value}: ")
        answer = find_minimum_change(denominations, value)
        # Print result
        for i in range(len(answer)):
            print(answer[i], end=" ")


================================================
FILE: greedy_methods/minimum_waiting_time.py
================================================
"""
Calculate the minimum waiting time using a greedy algorithm.
reference: https://www.youtube.com/watch?v=Sf3eiO12eJs

For doctests run following command:
python -m doctest -v minimum_waiting_time.py

The minimum_waiting_time function uses a greedy algorithm to calculate the minimum
time for queries to complete. It sorts the list in non-decreasing order, calculates
the waiting time for each query by multiplying its position in the list with the
sum of all remaining query times, and returns the total waiting time. A doctest
ensures that the function produces the correct output.
"""


def minimum_waiting_time(queries: list[int]) -> int:
    """
    This function takes a list of query times and returns the minimum waiting time
    for all queries to be completed.

    Args:
        queries: A list of queries measured in picoseconds

    Returns:
        total_waiting_time: Minimum waiting time measured in picoseconds

    Examples:
    >>> minimum_waiting_time([3, 2, 1, 2, 6])
    17
    >>> minimum_waiting_time([3, 2, 1])
    4
    >>> minimum_waiting_time([1, 2, 3, 4])
    10
    >>> minimum_waiting_time([5, 5, 5, 5])
    30
    >>> minimum_waiting_time([])
    0
    """
    n = len(queries)
    if n in (0, 1):
        return 0
    return sum(query * (n - i - 1) for i, query in enumerate(sorted(queries)))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: greedy_methods/optimal_merge_pattern.py
================================================
"""
This is a pure Python implementation of the greedy-merge-sort algorithm
reference: https://www.geeksforgeeks.org/optimal-file-merge-patterns/

For doctests run following command:
python3 -m doctest -v greedy_merge_sort.py

Objective
Merge a set of sorted files of different length into a single sorted file.
We need to find an optimal solution, where the resultant file
will be generated in minimum time.

Approach
If the number of sorted files are given, there are many ways
to merge them into a single sorted file.
This merge can be performed pair wise.
To merge a m-record file and a n-record file requires possibly m+n record moves
the optimal choice being,
merge the two smallest files together at each step (greedy approach).
"""


def optimal_merge_pattern(files: list) -> float:
    """Function to merge all the files with optimum cost

    Args:
        files [list]: A list of sizes of different files to be merged

    Returns:
        optimal_merge_cost [int]: Optimal cost to merge all those files

    Examples:
    >>> optimal_merge_pattern([2, 3, 4])
    14
    >>> optimal_merge_pattern([5, 10, 20, 30, 30])
    205
    >>> optimal_merge_pattern([8, 8, 8, 8, 8])
    96
    """
    optimal_merge_cost = 0
    while len(files) > 1:
        temp = 0
        # Consider two files with minimum cost to be merged
        for _ in range(2):
            min_index = files.index(min(files))
            temp += files[min_index]
            files.pop(min_index)
        files.append(temp)
        optimal_merge_cost += temp
    return optimal_merge_cost


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: greedy_methods/smallest_range.py
================================================
"""
smallest_range function takes a list of sorted integer lists and finds the smallest
range that includes at least one number from each list, using a min heap for efficiency.
"""

from heapq import heappop, heappush
from sys import maxsize


def smallest_range(nums: list[list[int]]) -> list[int]:
    """
    Find the smallest range from each list in nums.

    Uses min heap for efficiency. The range includes at least one number from each list.

    Args:
        `nums`: List of k sorted integer lists.

    Returns:
        list: Smallest range as a two-element list.

    Examples:

    >>> smallest_range([[4, 10, 15, 24, 26], [0, 9, 12, 20], [5, 18, 22, 30]])
    [20, 24]
    >>> smallest_range([[1, 2, 3], [1, 2, 3], [1, 2, 3]])
    [1, 1]
    >>> smallest_range(((1, 2, 3), (1, 2, 3), (1, 2, 3)))
    [1, 1]
    >>> smallest_range(((-3, -2, -1), (0, 0, 0), (1, 2, 3)))
    [-1, 1]
    >>> smallest_range([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    [3, 7]
    >>> smallest_range([[0, 0, 0], [0, 0, 0], [0, 0, 0]])
    [0, 0]
    >>> smallest_range([[], [], []])
    Traceback (most recent call last):
        ...
    IndexError: list index out of range
    """

    min_heap: list[tuple[int, int, int]] = []
    current_max = -maxsize - 1

    for i, items in enumerate(nums):
        heappush(min_heap, (items[0], i, 0))
        current_max = max(current_max, items[0])

    # Initialize smallest_range with large integer values
    smallest_range = [-maxsize - 1, maxsize]

    while min_heap:
        current_min, list_index, element_index = heappop(min_heap)

        if current_max - current_min < smallest_range[1] - smallest_range[0]:
            smallest_range = [current_min, current_max]

        if element_index == len(nums[list_index]) - 1:
            break

        next_element = nums[list_index][element_index + 1]
        heappush(min_heap, (next_element, list_index, element_index + 1))
        current_max = max(current_max, next_element)

    return smallest_range


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    print(f"{smallest_range([[1, 2, 3], [1, 2, 3], [1, 2, 3]])}")  # Output: [1, 1]


================================================
FILE: hashes/README.md
================================================
# Hashes
Hashing is the process of mapping any amount of data to a specified size using an algorithm. This is known as a hash value (or, if you're feeling fancy, a hash code, hash sums, or even a hash digest). Hashing is a one-way function, whereas encryption is a two-way function. While it is functionally conceivable to reverse-hash stuff, the required computing power makes it impractical. Hashing is a one-way street.
Unlike encryption, which is intended to protect data in transit, hashing is intended to authenticate that a file or piece of data has not been altered—that it is authentic. In other words, it functions as a checksum.

## Common hashing algorithms
### MD5
This is one of the first algorithms that has gained widespread acceptance. MD5 is hashing algorithm made by Ray Rivest that is known to suffer vulnerabilities. It was created in 1992 as the successor to MD4. Currently MD6 is in the works, but as of 2009 Rivest had removed it from NIST consideration for SHA-3.

### SHA
SHA stands for Security Hashing Algorithm and it’s probably best known as the hashing algorithm used in most SSL/TLS cipher suites. A cipher suite is a collection of ciphers and algorithms that are used for SSL/TLS connections. SHA handles the hashing aspects. SHA-1, as we mentioned earlier, is now deprecated. SHA-2 is now mandatory. SHA-2 is sometimes known as SHA-256, though variants with longer bit lengths are also available.

### SHA256
SHA 256 is a member of the SHA 2 algorithm family, under which SHA stands for Secure Hash Algorithm. It was a collaborative effort between both the NSA and NIST to implement a successor to the SHA 1 family, which was beginning to lose potency against brute force attacks. It was published in 2001.
The importance of the 256 in the name refers to the final hash digest value, i.e. the hash value will remain 256 bits regardless of the size of the plaintext/cleartext. Other algorithms in the SHA family are similar to SHA 256 in some ways.

### Luhn
The Luhn algorithm, also renowned as the modulus 10 or mod 10 algorithm, is a straightforward checksum formula used to validate a wide range of identification numbers, including credit card numbers, IMEI numbers, and Canadian Social Insurance Numbers. A community of mathematicians developed the LUHN formula in the late 1960s. Companies offering credit cards quickly followed suit. Since the algorithm is in the public interest, anyone can use it. The algorithm is used by most credit cards and many government identification numbers as a simple method of differentiating valid figures from mistyped or otherwise incorrect numbers. It was created to guard against unintentional errors, not malicious attacks.


================================================
FILE: hashes/__init__.py
================================================


================================================
FILE: hashes/adler32.py
================================================
"""
Adler-32 is a checksum algorithm which was invented by Mark Adler in 1995.
Compared to a cyclic redundancy check of the same length, it trades reliability for
speed (preferring the latter).
Adler-32 is more reliable than Fletcher-16, and slightly less reliable than
Fletcher-32.[2]

source: https://en.wikipedia.org/wiki/Adler-32
"""

MOD_ADLER = 65521


def adler32(plain_text: str) -> int:
    """
    Function implements adler-32 hash.
    Iterates and evaluates a new value for each character

    >>> adler32('Algorithms')
    363791387

    >>> adler32('go adler em all')
    708642122
    """
    a = 1
    b = 0
    for plain_chr in plain_text:
        a = (a + ord(plain_chr)) % MOD_ADLER
        b = (b + a) % MOD_ADLER
    return (b << 16) | a


================================================
FILE: hashes/chaos_machine.py
================================================
"""example of simple chaos machine"""

# Chaos Machine (K, t, m)
K = [0.33, 0.44, 0.55, 0.44, 0.33]
t = 3
m = 5

# Buffer Space (with Parameters Space)
buffer_space: list[float] = []
params_space: list[float] = []

# Machine Time
machine_time = 0


def push(seed):
    global buffer_space, params_space, machine_time, K, m, t

    # Choosing Dynamical Systems (All)
    for key, value in enumerate(buffer_space):
        # Evolution Parameter
        e = float(seed / value)

        # Control Theory: Orbit Change
        value = (buffer_space[(key + 1) % m] + e) % 1

        # Control Theory: Trajectory Change
        r = (params_space[key] + e) % 1 + 3

        # Modification (Transition Function) - Jumps
        buffer_space[key] = round(float(r * value * (1 - value)), 10)
        params_space[key] = r  # Saving to Parameters Space

    # Logistic Map
    assert max(buffer_space) < 1
    assert max(params_space) < 4

    # Machine Time
    machine_time += 1


def pull():
    global buffer_space, params_space, machine_time, K, m, t

    # PRNG (Xorshift by George Marsaglia)
    def xorshift(x, y):
        x ^= y >> 13
        y ^= x << 17
        x ^= y >> 5
        return x

    # Choosing Dynamical Systems (Increment)
    key = machine_time % m

    # Evolution (Time Length)
    for _ in range(t):
        # Variables (Position + Parameters)
        r = params_space[key]
        value = buffer_space[key]

        # Modification (Transition Function) - Flow
        buffer_space[key] = round(float(r * value * (1 - value)), 10)
        params_space[key] = (machine_time * 0.01 + r * 1.01) % 1 + 3

    # Choosing Chaotic Data
    x = int(buffer_space[(key + 2) % m] * (10**10))
    y = int(buffer_space[(key - 2) % m] * (10**10))

    # Machine Time
    machine_time += 1

    return xorshift(x, y) % 0xFFFFFFFF


def reset():
    global buffer_space, params_space, machine_time, K, m, t

    buffer_space = K
    params_space = [0] * m
    machine_time = 0


if __name__ == "__main__":
    # Initialization
    reset()

    # Pushing Data (Input)
    import random

    message = random.sample(range(0xFFFFFFFF), 100)
    for chunk in message:
        push(chunk)

    # for controlling
    inp = ""

    # Pulling Data (Output)
    while inp in ("e", "E"):
        print(f"{format(pull(), '#04x')}")
        print(buffer_space)
        print(params_space)
        inp = input("(e)exit? ").strip()


================================================
FILE: hashes/djb2.py
================================================
"""
This algorithm (k=33) was first reported by Dan Bernstein many years ago in comp.lang.c
Another version of this algorithm (now favored by Bernstein) uses xor:
    hash(i) = hash(i - 1) * 33 ^ str[i];

    First Magic constant 33:
    It has never been adequately explained.
    It's magic because it works better than many other constants, prime or not.

    Second Magic Constant 5381:

    1. odd number
    2. prime number
    3. deficient number
    4. 001/010/100/000/101 b

    source: http://www.cse.yorku.ca/~oz/hash.html
"""


def djb2(s: str) -> int:
    """
    Implementation of djb2 hash algorithm that
    is popular because of it's magic constants.

    >>> djb2('Algorithms')
    3782405311

    >>> djb2('scramble bits')
    1609059040
    """
    hash_value = 5381
    for x in s:
        hash_value = ((hash_value << 5) + hash_value) + ord(x)
    return hash_value & 0xFFFFFFFF


================================================
FILE: hashes/elf.py
================================================
def elf_hash(data: str) -> int:
    """
    Implementation of ElfHash Algorithm, a variant of PJW hash function.

    >>> elf_hash('lorem ipsum')
    253956621
    """
    hash_ = x = 0
    for letter in data:
        hash_ = (hash_ << 4) + ord(letter)
        x = hash_ & 0xF0000000
        if x != 0:
            hash_ ^= x >> 24
        hash_ &= ~x
    return hash_


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: hashes/enigma_machine.py
================================================
alphabets = [chr(i) for i in range(32, 126)]
gear_one = list(range(len(alphabets)))
gear_two = list(range(len(alphabets)))
gear_three = list(range(len(alphabets)))
reflector = list(reversed(range(len(alphabets))))
code = []
gear_one_pos = gear_two_pos = gear_three_pos = 0


def rotator():
    global gear_one_pos
    global gear_two_pos
    global gear_three_pos
    i = gear_one[0]
    gear_one.append(i)
    del gear_one[0]
    gear_one_pos += 1
    if gear_one_pos % len(alphabets) == 0:
        i = gear_two[0]
        gear_two.append(i)
        del gear_two[0]
        gear_two_pos += 1
        if gear_two_pos % len(alphabets) == 0:
            i = gear_three[0]
            gear_three.append(i)
            del gear_three[0]
            gear_three_pos += 1


def engine(input_character):
    target = alphabets.index(input_character)
    target = gear_one[target]
    target = gear_two[target]
    target = gear_three[target]
    target = reflector[target]
    target = gear_three.index(target)
    target = gear_two.index(target)
    target = gear_one.index(target)
    code.append(alphabets[target])
    rotator()


if __name__ == "__main__":
    decode = list(input("Type your message:\n"))
    while True:
        try:
            token = int(input("Please set token:(must be only digits)\n"))
            break
        except Exception as error:
            print(error)
    for _ in range(token):
        rotator()
    for j in decode:
        engine(j)
    print("\n" + "".join(code))
    print(
        f"\nYour Token is {token} please write it down.\nIf you want to decode "
        "this message again you should input same digits as token!"
    )


================================================
FILE: hashes/fletcher16.py
================================================
"""
The Fletcher checksum is an algorithm for computing a position-dependent
checksum devised by John G. Fletcher (1934-2012) at Lawrence Livermore Labs
in the late 1970s.[1] The objective of the Fletcher checksum was to
provide error-detection properties approaching those of a cyclic
redundancy check but with the lower computational effort associated
with summation techniques.

Source: https://en.wikipedia.org/wiki/Fletcher%27s_checksum
"""


def fletcher16(text: str) -> int:
    """
    Loop through every character in the data and add to two sums.

    >>> fletcher16('hello world')
    6752
    >>> fletcher16('onethousandfourhundredthirtyfour')
    28347
    >>> fletcher16('The quick brown fox jumps over the lazy dog.')
    5655
    """
    data = bytes(text, "ascii")
    sum1 = 0
    sum2 = 0
    for character in data:
        sum1 = (sum1 + character) % 255
        sum2 = (sum1 + sum2) % 255
    return (sum2 << 8) | sum1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: hashes/hamming_code.py
================================================
# Author: João Gustavo A. Amorim & Gabriel Kunz
# Author email: joaogustavoamorim@gmail.com and gabriel-kunz@uergs.edu.br
# Coding date:  apr 2019
# Black: True

"""
* This code implement the Hamming code:
    https://en.wikipedia.org/wiki/Hamming_code - In telecommunication,
Hamming codes are a family of linear error-correcting codes. Hamming
codes can detect up to two-bit errors or correct one-bit errors
without detection of uncorrected errors. By contrast, the simple
parity code cannot correct errors, and can detect only an odd number
of bits in error. Hamming codes are perfect codes, that is, they
achieve the highest possible rate for codes with their block length
and minimum distance of three.

* the implemented code consists of:
    * a function responsible for encoding the message (emitterConverter)
        * return the encoded message
    * a function responsible for decoding the message (receptorConverter)
        * return the decoded message and a ack of data integrity

* how to use:
        to be used you must declare how many parity bits (sizePari)
    you want to include in the message.
        it is desired (for test purposes) to select a bit to be set
    as an error. This serves to check whether the code is working correctly.
        Lastly, the variable of the message/word that must be desired to be
    encoded (text).

* how this work:
        declaration of variables (sizePari, be, text)

        converts the message/word (text) to binary using the
    text_to_bits function
        encodes the message using the rules of hamming encoding
        decodes the message using the rules of hamming encoding
        print the original message, the encoded message and the
    decoded message

        forces an error in the coded text variable
        decodes the message that was forced the error
        print the original message, the encoded message, the bit changed
    message and the decoded message
"""

# Imports
import numpy as np


# Functions of binary conversion--------------------------------------
def text_to_bits(text, encoding="utf-8", errors="surrogatepass"):
    """
    >>> text_to_bits("msg")
    '011011010111001101100111'
    """
    bits = bin(int.from_bytes(text.encode(encoding, errors), "big"))[2:]
    return bits.zfill(8 * ((len(bits) + 7) // 8))


def text_from_bits(bits, encoding="utf-8", errors="surrogatepass"):
    """
    >>> text_from_bits('011011010111001101100111')
    'msg'
    """
    n = int(bits, 2)
    return n.to_bytes((n.bit_length() + 7) // 8, "big").decode(encoding, errors) or "\0"


# Functions of hamming code-------------------------------------------
def emitter_converter(size_par, data):
    """
    :param size_par: how many parity bits the message must have
    :param data:  information bits
    :return: message to be transmitted by unreliable medium
            - bits of information merged with parity bits

    >>> emitter_converter(4, "101010111111")
    ['1', '1', '1', '1', '0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1']
    >>> emitter_converter(5, "101010111111")
    Traceback (most recent call last):
        ...
    ValueError: size of parity don't match with size of data
    """
    if size_par + len(data) <= 2**size_par - (len(data) - 1):
        raise ValueError("size of parity don't match with size of data")

    data_out = []
    parity = []
    bin_pos = [bin(x)[2:] for x in range(1, size_par + len(data) + 1)]

    # sorted information data for the size of the output data
    data_ord = []
    # data position template + parity
    data_out_gab = []
    # parity bit counter
    qtd_bp = 0
    # counter position of data bits
    cont_data = 0

    for x in range(1, size_par + len(data) + 1):
        # Performs a template of bit positions - who should be given,
        # and who should be parity
        if qtd_bp < size_par:
            if (np.log(x) / np.log(2)).is_integer():
                data_out_gab.append("P")
                qtd_bp = qtd_bp + 1
            else:
                data_out_gab.append("D")
        else:
            data_out_gab.append("D")

        # Sorts the data to the new output size
        if data_out_gab[-1] == "D":
            data_ord.append(data[cont_data])
            cont_data += 1
        else:
            data_ord.append(None)

    # Calculates parity
    qtd_bp = 0  # parity bit counter
    for bp in range(1, size_par + 1):
        # Bit counter one for a given parity
        cont_bo = 0
        # counter to control the loop reading
        for cont_loop, x in enumerate(data_ord):
            if x is not None:
                try:
                    aux = (bin_pos[cont_loop])[-1 * (bp)]
                except IndexError:
                    aux = "0"
                if aux == "1" and x == "1":
                    cont_bo += 1
        parity.append(cont_bo % 2)

        qtd_bp += 1

    # Mount the message
    cont_bp = 0  # parity bit counter
    for x in range(size_par + len(data)):
        if data_ord[x] is None:
            data_out.append(str(parity[cont_bp]))
            cont_bp += 1
        else:
            data_out.append(data_ord[x])

    return data_out


def receptor_converter(size_par, data):
    """
    >>> receptor_converter(4, "1111010010111111")
    (['1', '0', '1', '0', '1', '0', '1', '1', '1', '1', '1', '1'], True)
    """
    # data position template + parity
    data_out_gab = []
    # Parity bit counter
    qtd_bp = 0
    # Counter p data bit reading
    cont_data = 0
    # list of parity received
    parity_received = []
    data_output = []

    for i, item in enumerate(data, 1):
        # Performs a template of bit positions - who should be given,
        #  and who should be parity
        if qtd_bp < size_par and (np.log(i) / np.log(2)).is_integer():
            data_out_gab.append("P")
            qtd_bp = qtd_bp + 1
        else:
            data_out_gab.append("D")

        # Sorts the data to the new output size
        if data_out_gab[-1] == "D":
            data_output.append(item)
        else:
            parity_received.append(item)

    # -----------calculates the parity with the data
    data_out = []
    parity = []
    bin_pos = [bin(x)[2:] for x in range(1, size_par + len(data_output) + 1)]

    #  sorted information data for the size of the output data
    data_ord = []
    # Data position feedback + parity
    data_out_gab = []
    # Parity bit counter
    qtd_bp = 0
    # Counter p data bit reading
    cont_data = 0

    for x in range(1, size_par + len(data_output) + 1):
        # Performs a template position of bits - who should be given,
        # and who should be parity
        if qtd_bp < size_par and (np.log(x) / np.log(2)).is_integer():
            data_out_gab.append("P")
            qtd_bp = qtd_bp + 1
        else:
            data_out_gab.append("D")

        # Sorts the data to the new output size
        if data_out_gab[-1] == "D":
            data_ord.append(data_output[cont_data])
            cont_data += 1
        else:
            data_ord.append(None)

    # Calculates parity
    qtd_bp = 0  # parity bit counter
    for bp in range(1, size_par + 1):
        # Bit counter one for a certain parity
        cont_bo = 0
        for cont_loop, x in enumerate(data_ord):
            if x is not None:
                try:
                    aux = (bin_pos[cont_loop])[-1 * (bp)]
                except IndexError:
                    aux = "0"
                if aux == "1" and x == "1":
                    cont_bo += 1
        parity.append(str(cont_bo % 2))

        qtd_bp += 1

    # Mount the message
    cont_bp = 0  # Parity bit counter
    for x in range(size_par + len(data_output)):
        if data_ord[x] is None:
            data_out.append(str(parity[cont_bp]))
            cont_bp += 1
        else:
            data_out.append(data_ord[x])

    ack = parity_received == parity
    return data_output, ack


# ---------------------------------------------------------------------
"""
# Example how to use

# number of parity bits
sizePari = 4

# location of the bit that will be forced an error
be = 2

# Message/word to be encoded and decoded with hamming
# text = input("Enter the word to be read: ")
text = "Message01"

# Convert the message to binary
binaryText = text_to_bits(text)

# Prints the binary of the string
print("Text input in binary is '" + binaryText + "'")

# total transmitted bits
totalBits = len(binaryText) + sizePari
print("Size of data is " + str(totalBits))

print("\n --Message exchange--")
print("Data to send ------------> " + binaryText)
dataOut = emitterConverter(sizePari, binaryText)
print("Data converted ----------> " + "".join(dataOut))
dataReceiv, ack = receptorConverter(sizePari, dataOut)
print(
    "Data receive ------------> "
    + "".join(dataReceiv)
    + "\t\t -- Data integrity: "
    + str(ack)
)


print("\n --Force error--")
print("Data to send ------------> " + binaryText)
dataOut = emitterConverter(sizePari, binaryText)
print("Data converted ----------> " + "".join(dataOut))

# forces error
dataOut[-be] = "1" * (dataOut[-be] == "0") + "0" * (dataOut[-be] == "1")
print("Data after transmission -> " + "".join(dataOut))
dataReceiv, ack = receptorConverter(sizePari, dataOut)
print(
    "Data receive ------------> "
    + "".join(dataReceiv)
    + "\t\t -- Data integrity: "
    + str(ack)
)
"""


================================================
FILE: hashes/luhn.py
================================================
"""Luhn Algorithm"""

from __future__ import annotations


def is_luhn(string: str) -> bool:
    """
    Perform Luhn validation on an input string
    Algorithm:
    * Double every other digit starting from 2nd last digit.
    * Subtract 9 if number is greater than 9.
    * Sum the numbers
    *
    >>> test_cases = (79927398710, 79927398711, 79927398712, 79927398713,
    ...     79927398714, 79927398715, 79927398716, 79927398717, 79927398718,
    ...     79927398719)
    >>> [is_luhn(str(test_case)) for test_case in test_cases]
    [False, False, False, True, False, False, False, False, False, False]
    """
    check_digit: int
    _vector: list[str] = list(string)
    __vector, check_digit = _vector[:-1], int(_vector[-1])
    vector: list[int] = [int(digit) for digit in __vector]

    vector.reverse()
    for i, digit in enumerate(vector):
        if i & 1 == 0:
            doubled: int = digit * 2
            if doubled > 9:
                doubled -= 9
            check_digit += doubled
        else:
            check_digit += digit

    return check_digit % 10 == 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    assert is_luhn("79927398713")
    assert not is_luhn("79927398714")


================================================
FILE: hashes/md5.py
================================================
"""
The MD5 algorithm is a hash function that's commonly used as a checksum to
detect data corruption. The algorithm works by processing a given message in
blocks of 512 bits, padding the message as needed. It uses the blocks to operate
a 128-bit state and performs a total of 64 such operations. Note that all values
are little-endian, so inputs are converted as needed.

Although MD5 was used as a cryptographic hash function in the past, it's since
been cracked, so it shouldn't be used for security purposes.

For more info, see https://en.wikipedia.org/wiki/MD5
"""

from collections.abc import Generator
from math import sin


def to_little_endian(string_32: bytes) -> bytes:
    """
    Converts the given string to little-endian in groups of 8 chars.

    Arguments:
        string_32 {[string]} -- [32-char string]

    Raises:
        ValueError -- [input is not 32 char]

    Returns:
        32-char little-endian string
    >>> to_little_endian(b'1234567890abcdfghijklmnopqrstuvw')
    b'pqrstuvwhijklmno90abcdfg12345678'
    >>> to_little_endian(b'1234567890')
    Traceback (most recent call last):
    ...
    ValueError: Input must be of length 32
    """
    if len(string_32) != 32:
        raise ValueError("Input must be of length 32")

    little_endian = b""
    for i in [3, 2, 1, 0]:
        little_endian += string_32[8 * i : 8 * i + 8]
    return little_endian


def reformat_hex(i: int) -> bytes:
    """
    Converts the given non-negative integer to hex string.

    Example: Suppose the input is the following:
        i = 1234

        The input is 0x000004d2 in hex, so the little-endian hex string is
        "d2040000".

    Arguments:
        i {[int]} -- [integer]

    Raises:
        ValueError -- [input is negative]

    Returns:
        8-char little-endian hex string

    >>> reformat_hex(1234)
    b'd2040000'
    >>> reformat_hex(666)
    b'9a020000'
    >>> reformat_hex(0)
    b'00000000'
    >>> reformat_hex(1234567890)
    b'd2029649'
    >>> reformat_hex(1234567890987654321)
    b'b11c6cb1'
    >>> reformat_hex(-1)
    Traceback (most recent call last):
    ...
    ValueError: Input must be non-negative
    """
    if i < 0:
        raise ValueError("Input must be non-negative")

    hex_rep = format(i, "08x")[-8:]
    little_endian_hex = b""
    for j in [3, 2, 1, 0]:
        little_endian_hex += hex_rep[2 * j : 2 * j + 2].encode("utf-8")
    return little_endian_hex


def preprocess(message: bytes) -> bytes:
    """
    Preprocesses the message string:
    - Convert message to bit string
    - Pad bit string to a multiple of 512 chars:
        - Append a 1
        - Append 0's until length = 448 (mod 512)
        - Append length of original message (64 chars)

    Example: Suppose the input is the following:
        message = "a"

        The message bit string is "01100001", which is 8 bits long. Thus, the
        bit string needs 439 bits of padding so that
        (bit_string + "1" + padding) = 448 (mod 512).
        The message length is "000010000...0" in 64-bit little-endian binary.
        The combined bit string is then 512 bits long.

    Arguments:
        message {[string]} -- [message string]

    Returns:
        processed bit string padded to a multiple of 512 chars

    >>> preprocess(b"a") == (b"01100001" + b"1" +
    ...                     (b"0" * 439) + b"00001000" + (b"0" * 56))
    True
    >>> preprocess(b"") == b"1" + (b"0" * 447) + (b"0" * 64)
    True
    """
    bit_string = b""
    for char in message:
        bit_string += format(char, "08b").encode("utf-8")
    start_len = format(len(bit_string), "064b").encode("utf-8")

    # Pad bit_string to a multiple of 512 chars
    bit_string += b"1"
    while len(bit_string) % 512 != 448:
        bit_string += b"0"
    bit_string += to_little_endian(start_len[32:]) + to_little_endian(start_len[:32])

    return bit_string


def get_block_words(bit_string: bytes) -> Generator[list[int]]:
    """
    Splits bit string into blocks of 512 chars and yields each block as a list
    of 32-bit words

    Example: Suppose the input is the following:
        bit_string =
            "000000000...0" +  # 0x00 (32 bits, padded to the right)
            "000000010...0" +  # 0x01 (32 bits, padded to the right)
            "000000100...0" +  # 0x02 (32 bits, padded to the right)
            "000000110...0" +  # 0x03 (32 bits, padded to the right)
            ...
            "000011110...0"    # 0x0a (32 bits, padded to the right)

        Then len(bit_string) == 512, so there'll be 1 block. The block is split
        into 32-bit words, and each word is converted to little endian. The
        first word is interpreted as 0 in decimal, the second word is
        interpreted as 1 in decimal, etc.

        Thus, block_words == [[0, 1, 2, 3, ..., 15]].

    Arguments:
        bit_string {[string]} -- [bit string with multiple of 512 as length]

    Raises:
        ValueError -- [length of bit string isn't multiple of 512]

    Yields:
        a list of 16 32-bit words

    >>> test_string = ("".join(format(n << 24, "032b") for n in range(16))
    ...                  .encode("utf-8"))
    >>> list(get_block_words(test_string))
    [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]]
    >>> list(get_block_words(test_string * 4)) == [list(range(16))] * 4
    True
    >>> list(get_block_words(b"1" * 512)) == [[4294967295] * 16]
    True
    >>> list(get_block_words(b""))
    []
    >>> list(get_block_words(b"1111"))
    Traceback (most recent call last):
    ...
    ValueError: Input must have length that's a multiple of 512
    """
    if len(bit_string) % 512 != 0:
        raise ValueError("Input must have length that's a multiple of 512")

    for pos in range(0, len(bit_string), 512):
        block = bit_string[pos : pos + 512]
        block_words = []
        for i in range(0, 512, 32):
            block_words.append(int(to_little_endian(block[i : i + 32]), 2))
        yield block_words


def not_32(i: int) -> int:
    """
    Perform bitwise NOT on given int.

    Arguments:
        i {[int]} -- [given int]

    Raises:
        ValueError -- [input is negative]

    Returns:
        Result of bitwise NOT on i

    >>> not_32(34)
    4294967261
    >>> not_32(1234)
    4294966061
    >>> not_32(4294966061)
    1234
    >>> not_32(0)
    4294967295
    >>> not_32(1)
    4294967294
    >>> not_32(-1)
    Traceback (most recent call last):
    ...
    ValueError: Input must be non-negative
    """
    if i < 0:
        raise ValueError("Input must be non-negative")

    i_str = format(i, "032b")
    new_str = ""
    for c in i_str:
        new_str += "1" if c == "0" else "0"
    return int(new_str, 2)


def sum_32(a: int, b: int) -> int:
    """
    Add two numbers as 32-bit ints.

    Arguments:
        a {[int]} -- [first given int]
        b {[int]} -- [second given int]

    Returns:
        (a + b) as an unsigned 32-bit int

    >>> sum_32(1, 1)
    2
    >>> sum_32(2, 3)
    5
    >>> sum_32(0, 0)
    0
    >>> sum_32(-1, -1)
    4294967294
    >>> sum_32(4294967295, 1)
    0
    """
    return (a + b) % 2**32


def left_rotate_32(i: int, shift: int) -> int:
    """
    Rotate the bits of a given int left by a given amount.

    Arguments:
        i {[int]} -- [given int]
        shift {[int]} -- [shift amount]

    Raises:
        ValueError -- [either given int or shift is negative]

    Returns:
        `i` rotated to the left by `shift` bits

    >>> left_rotate_32(1234, 1)
    2468
    >>> left_rotate_32(1111, 4)
    17776
    >>> left_rotate_32(2147483648, 1)
    1
    >>> left_rotate_32(2147483648, 3)
    4
    >>> left_rotate_32(4294967295, 4)
    4294967295
    >>> left_rotate_32(1234, 0)
    1234
    >>> left_rotate_32(0, 0)
    0
    >>> left_rotate_32(-1, 0)
    Traceback (most recent call last):
    ...
    ValueError: Input must be non-negative
    >>> left_rotate_32(0, -1)
    Traceback (most recent call last):
    ...
    ValueError: Shift must be non-negative
    """
    if i < 0:
        raise ValueError("Input must be non-negative")
    if shift < 0:
        raise ValueError("Shift must be non-negative")
    return ((i << shift) ^ (i >> (32 - shift))) % 2**32


def md5_me(message: bytes) -> bytes:
    """
    Returns the 32-char MD5 hash of a given message.

    Reference: https://en.wikipedia.org/wiki/MD5#Algorithm

    Arguments:
        message {[string]} -- [message]

    Returns:
        32-char MD5 hash string

    >>> md5_me(b"")
    b'd41d8cd98f00b204e9800998ecf8427e'
    >>> md5_me(b"The quick brown fox jumps over the lazy dog")
    b'9e107d9d372bb6826bd81d3542a419d6'
    >>> md5_me(b"The quick brown fox jumps over the lazy dog.")
    b'e4d909c290d0fb1ca068ffaddf22cbd0'

    >>> import hashlib
    >>> from string import ascii_letters
    >>> msgs = [b"", ascii_letters.encode("utf-8"), "Üñîçø∂é".encode("utf-8"),
    ...         b"The quick brown fox jumps over the lazy dog."]
    >>> all(md5_me(msg) == hashlib.md5(msg).hexdigest().encode("utf-8") for msg in msgs)
    True
    """

    # Convert to bit string, add padding and append message length
    bit_string = preprocess(message)

    added_consts = [int(2**32 * abs(sin(i + 1))) for i in range(64)]

    # Starting states
    a0 = 0x67452301
    b0 = 0xEFCDAB89
    c0 = 0x98BADCFE
    d0 = 0x10325476

    shift_amounts = [
        7,
        12,
        17,
        22,
        7,
        12,
        17,
        22,
        7,
        12,
        17,
        22,
        7,
        12,
        17,
        22,
        5,
        9,
        14,
        20,
        5,
        9,
        14,
        20,
        5,
        9,
        14,
        20,
        5,
        9,
        14,
        20,
        4,
        11,
        16,
        23,
        4,
        11,
        16,
        23,
        4,
        11,
        16,
        23,
        4,
        11,
        16,
        23,
        6,
        10,
        15,
        21,
        6,
        10,
        15,
        21,
        6,
        10,
        15,
        21,
        6,
        10,
        15,
        21,
    ]

    # Process bit string in chunks, each with 16 32-char words
    for block_words in get_block_words(bit_string):
        a = a0
        b = b0
        c = c0
        d = d0

        # Hash current chunk
        for i in range(64):
            if i <= 15:
                # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                f = d ^ (b & (c ^ d))
                g = i
            elif i <= 31:
                # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                f = c ^ (d & (b ^ c))
                g = (5 * i + 1) % 16
            elif i <= 47:
                f = b ^ c ^ d
                g = (3 * i + 5) % 16
            else:
                f = c ^ (b | not_32(d))
                g = (7 * i) % 16
            f = (f + a + added_consts[i] + block_words[g]) % 2**32
            a = d
            d = c
            c = b
            b = sum_32(b, left_rotate_32(f, shift_amounts[i]))

        # Add hashed chunk to running total
        a0 = sum_32(a0, a)
        b0 = sum_32(b0, b)
        c0 = sum_32(c0, c)
        d0 = sum_32(d0, d)

    digest = reformat_hex(a0) + reformat_hex(b0) + reformat_hex(c0) + reformat_hex(d0)
    return digest


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: hashes/sdbm.py
================================================
"""
This algorithm was created for sdbm (a public-domain reimplementation of ndbm)
database library.
It was found to do well in scrambling bits, causing better distribution of the keys
and fewer splits.
It also happens to be a good general hashing function with good distribution.
The actual function (pseudo code) is:
    for i in i..len(str):
        hash(i) = hash(i - 1) * 65599 + str[i];

What is included below is the faster version used in gawk. [there is even a faster,
duff-device version]
The magic constant 65599 was picked out of thin air while experimenting with
different constants.
It turns out to be a prime.
This is one of the algorithms used in berkeley db (see sleepycat) and elsewhere.

source: http://www.cse.yorku.ca/~oz/hash.html
"""


def sdbm(plain_text: str) -> int:
    """
    Function implements sdbm hash, easy to use, great for bits scrambling.
    iterates over each character in the given string and applies function to each of
    them.

    >>> sdbm('Algorithms')
    1462174910723540325254304520539387479031000036

    >>> sdbm('scramble bits')
    730247649148944819640658295400555317318720608290373040936089
    """
    hash_value = 0
    for plain_chr in plain_text:
        hash_value = (
            ord(plain_chr) + (hash_value << 6) + (hash_value << 16) - hash_value
        )
    return hash_value


================================================
FILE: hashes/sha1.py
================================================
"""
Implementation of the SHA1 hash function and gives utilities to find hash of string or
hash of text from a file. Also contains a Test class to verify that the generated hash
matches what is returned by the hashlib library

Usage: python sha1.py --string "Hello World!!"
       python sha1.py --file "hello_world.txt"
       When run without any arguments, it prints the hash of the string "Hello World!!
       Welcome to Cryptography"

SHA1 hash or SHA1 sum of a string is a cryptographic function, which means it is easy
to calculate forwards but extremely difficult to calculate backwards. What this means
is you can easily calculate the hash of a string, but it is extremely difficult to know
the original string if you have its hash. This property is useful for communicating
securely, send encrypted messages and is very useful in payment systems, blockchain and
cryptocurrency etc.

The algorithm as described in the reference:
First we start with a message. The message is padded and the length of the message
is added to the end. It is then split into blocks of 512 bits or 64 bytes. The blocks
are then processed one at a time. Each block must be expanded and compressed.
The value after each compression is added to a 160-bit buffer called the current hash
state. After the last block is processed, the current hash state is returned as
the final hash.

Reference: https://deadhacker.com/2006/02/21/sha-1-illustrated/
"""

import argparse
import hashlib  # hashlib is only used inside the Test class
import struct


class SHA1Hash:
    """
    Class to contain the entire pipeline for SHA1 hashing algorithm
    >>> SHA1Hash(bytes('Allan', 'utf-8')).final_hash()
    '872af2d8ac3d8695387e7c804bf0e02c18df9e6e'
    """

    def __init__(self, data):
        """
        Initiates the variables data and h. h is a list of 5 8-digit hexadecimal
        numbers corresponding to
        (1732584193, 4023233417, 2562383102, 271733878, 3285377520)
        respectively. We will start with this as a message digest. 0x is how you write
        hexadecimal numbers in Python
        """
        self.data = data
        self.h = [0x67452301, 0xEFCDAB89, 0x98BADCFE, 0x10325476, 0xC3D2E1F0]

    @staticmethod
    def rotate(n, b):
        """
        Static method to be used inside other methods. Left rotates n by b.
        >>> SHA1Hash('').rotate(12,2)
        48
        """
        return ((n << b) | (n >> (32 - b))) & 0xFFFFFFFF

    def padding(self):
        """
        Pads the input message with zeros so that padded_data has 64 bytes or 512 bits
        """
        padding = b"\x80" + b"\x00" * (63 - (len(self.data) + 8) % 64)
        padded_data = self.data + padding + struct.pack(">Q", 8 * len(self.data))
        return padded_data

    def split_blocks(self):
        """
        Returns a list of bytestrings each of length 64
        """
        return [
            self.padded_data[i : i + 64] for i in range(0, len(self.padded_data), 64)
        ]

    # @staticmethod
    def expand_block(self, block):
        """
        Takes a bytestring-block of length 64, unpacks it to a list of integers and
        returns a list of 80 integers after some bit operations
        """
        w = list(struct.unpack(">16L", block)) + [0] * 64
        for i in range(16, 80):
            w[i] = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]), 1)
        return w

    def final_hash(self):
        """
        Calls all the other methods to process the input. Pads the data, then splits
        into blocks and then does a series of operations for each block (including
        expansion).
        For each block, the variable h that was initialized is copied to a,b,c,d,e
        and these 5 variables a,b,c,d,e undergo several changes. After all the blocks
        are processed, these 5 variables are pairwise added to h ie a to h[0], b to h[1]
        and so on. This h becomes our final hash which is returned.
        """
        self.padded_data = self.padding()
        self.blocks = self.split_blocks()
        for block in self.blocks:
            expanded_block = self.expand_block(block)
            a, b, c, d, e = self.h
            for i in range(80):
                if 0 <= i < 20:
                    f = (b & c) | ((~b) & d)
                    k = 0x5A827999
                elif 20 <= i < 40:
                    f = b ^ c ^ d
                    k = 0x6ED9EBA1
                elif 40 <= i < 60:
                    f = (b & c) | (b & d) | (c & d)
                    k = 0x8F1BBCDC
                elif 60 <= i < 80:
                    f = b ^ c ^ d
                    k = 0xCA62C1D6
                a, b, c, d, e = (
                    self.rotate(a, 5) + f + e + k + expanded_block[i] & 0xFFFFFFFF,
                    a,
                    self.rotate(b, 30),
                    c,
                    d,
                )
            self.h = (
                self.h[0] + a & 0xFFFFFFFF,
                self.h[1] + b & 0xFFFFFFFF,
                self.h[2] + c & 0xFFFFFFFF,
                self.h[3] + d & 0xFFFFFFFF,
                self.h[4] + e & 0xFFFFFFFF,
            )
        return ("{:08x}" * 5).format(*self.h)


def test_sha1_hash():
    msg = b"Test String"
    assert SHA1Hash(msg).final_hash() == hashlib.sha1(msg).hexdigest()  # noqa: S324


def main():
    """
    Provides option 'string' or 'file' to take input and prints the calculated SHA1
    hash. unittest.main() has been commented out because we probably don't want to run
    the test each time.
    """
    # unittest.main()
    parser = argparse.ArgumentParser(description="Process some strings or files")
    parser.add_argument(
        "--string",
        dest="input_string",
        default="Hello World!! Welcome to Cryptography",
        help="Hash the string",
    )
    parser.add_argument("--file", dest="input_file", help="Hash contents of a file")
    args = parser.parse_args()
    input_string = args.input_string
    # In any case hash input should be a bytestring
    if args.input_file:
        with open(args.input_file, "rb") as f:
            hash_input = f.read()
    else:
        hash_input = bytes(input_string, "utf-8")
    print(SHA1Hash(hash_input).final_hash())


if __name__ == "__main__":
    main()
    import doctest

    doctest.testmod()


================================================
FILE: hashes/sha256.py
================================================
# Author: M. Yathurshan
# Black Formatter: True

"""
Implementation of SHA256 Hash function in a Python class and provides utilities
to find hash of string or hash of text from a file.

Usage: python sha256.py --string "Hello World!!"
       python sha256.py --file "hello_world.txt"
       When run without any arguments,
       it prints the hash of the string "Hello World!! Welcome to Cryptography"

References:
https://qvault.io/cryptography/how-sha-2-works-step-by-step-sha-256/
https://en.wikipedia.org/wiki/SHA-2
"""

import argparse
import struct
import unittest


class SHA256:
    """
    Class to contain the entire pipeline for SHA1 Hashing Algorithm

    >>> SHA256(b'Python').hash
    '18885f27b5af9012df19e496460f9294d5ab76128824c6f993787004f6d9a7db'

    >>> SHA256(b'hello world').hash
    'b94d27b9934d3e08a52e52d7da7dabfac484efe37a5380ee9088f7ace2efcde9'
    """

    def __init__(self, data: bytes) -> None:
        self.data = data

        # Initialize hash values
        self.hashes = [
            0x6A09E667,
            0xBB67AE85,
            0x3C6EF372,
            0xA54FF53A,
            0x510E527F,
            0x9B05688C,
            0x1F83D9AB,
            0x5BE0CD19,
        ]

        # Initialize round constants
        self.round_constants = [
            0x428A2F98,
            0x71374491,
            0xB5C0FBCF,
            0xE9B5DBA5,
            0x3956C25B,
            0x59F111F1,
            0x923F82A4,
            0xAB1C5ED5,
            0xD807AA98,
            0x12835B01,
            0x243185BE,
            0x550C7DC3,
            0x72BE5D74,
            0x80DEB1FE,
            0x9BDC06A7,
            0xC19BF174,
            0xE49B69C1,
            0xEFBE4786,
            0x0FC19DC6,
            0x240CA1CC,
            0x2DE92C6F,
            0x4A7484AA,
            0x5CB0A9DC,
            0x76F988DA,
            0x983E5152,
            0xA831C66D,
            0xB00327C8,
            0xBF597FC7,
            0xC6E00BF3,
            0xD5A79147,
            0x06CA6351,
            0x14292967,
            0x27B70A85,
            0x2E1B2138,
            0x4D2C6DFC,
            0x53380D13,
            0x650A7354,
            0x766A0ABB,
            0x81C2C92E,
            0x92722C85,
            0xA2BFE8A1,
            0xA81A664B,
            0xC24B8B70,
            0xC76C51A3,
            0xD192E819,
            0xD6990624,
            0xF40E3585,
            0x106AA070,
            0x19A4C116,
            0x1E376C08,
            0x2748774C,
            0x34B0BCB5,
            0x391C0CB3,
            0x4ED8AA4A,
            0x5B9CCA4F,
            0x682E6FF3,
            0x748F82EE,
            0x78A5636F,
            0x84C87814,
            0x8CC70208,
            0x90BEFFFA,
            0xA4506CEB,
            0xBEF9A3F7,
            0xC67178F2,
        ]

        self.preprocessed_data = self.preprocessing(self.data)
        self.final_hash()

    @staticmethod
    def preprocessing(data: bytes) -> bytes:
        padding = b"\x80" + (b"\x00" * (63 - (len(data) + 8) % 64))
        big_endian_integer = struct.pack(">Q", (len(data) * 8))
        return data + padding + big_endian_integer

    def final_hash(self) -> None:
        # Convert into blocks of 64 bytes
        self.blocks = [
            self.preprocessed_data[x : x + 64]
            for x in range(0, len(self.preprocessed_data), 64)
        ]

        for block in self.blocks:
            # Convert the given block into a list of 4 byte integers
            words = list(struct.unpack(">16L", block))
            # add 48 0-ed integers
            words += [0] * 48

            a, b, c, d, e, f, g, h = self.hashes

            for index in range(64):
                if index > 15:
                    # modify the zero-ed indexes at the end of the array
                    s0 = (
                        self.ror(words[index - 15], 7)
                        ^ self.ror(words[index - 15], 18)
                        ^ (words[index - 15] >> 3)
                    )
                    s1 = (
                        self.ror(words[index - 2], 17)
                        ^ self.ror(words[index - 2], 19)
                        ^ (words[index - 2] >> 10)
                    )

                    words[index] = (
                        words[index - 16] + s0 + words[index - 7] + s1
                    ) % 0x100000000

                # Compression
                s1 = self.ror(e, 6) ^ self.ror(e, 11) ^ self.ror(e, 25)
                ch = (e & f) ^ ((~e & (0xFFFFFFFF)) & g)
                temp1 = (
                    h + s1 + ch + self.round_constants[index] + words[index]
                ) % 0x100000000
                s0 = self.ror(a, 2) ^ self.ror(a, 13) ^ self.ror(a, 22)
                maj = (a & b) ^ (a & c) ^ (b & c)
                temp2 = (s0 + maj) % 0x100000000

                h, g, f, e, d, c, b, a = (
                    g,
                    f,
                    e,
                    ((d + temp1) % 0x100000000),
                    c,
                    b,
                    a,
                    ((temp1 + temp2) % 0x100000000),
                )

            mutated_hash_values = [a, b, c, d, e, f, g, h]

            # Modify final values
            self.hashes = [
                ((element + mutated_hash_values[index]) % 0x100000000)
                for index, element in enumerate(self.hashes)
            ]

        self.hash = "".join([hex(value)[2:].zfill(8) for value in self.hashes])

    def ror(self, value: int, rotations: int) -> int:
        """
        Right rotate a given unsigned number by a certain amount of rotations
        """
        return 0xFFFFFFFF & (value << (32 - rotations)) | (value >> rotations)


class SHA256HashTest(unittest.TestCase):
    """
    Test class for the SHA256 class. Inherits the TestCase class from unittest
    """

    def test_match_hashes(self) -> None:
        import hashlib

        msg = bytes("Test String", "utf-8")
        assert SHA256(msg).hash == hashlib.sha256(msg).hexdigest()


def main() -> None:
    """
    Provides option 'string' or 'file' to take input
    and prints the calculated SHA-256 hash
    """

    # unittest.main()

    import doctest

    doctest.testmod()

    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-s",
        "--string",
        dest="input_string",
        default="Hello World!! Welcome to Cryptography",
        help="Hash the string",
    )
    parser.add_argument(
        "-f", "--file", dest="input_file", help="Hash contents of a file"
    )

    args = parser.parse_args()

    input_string = args.input_string

    # hash input should be a bytestring
    if args.input_file:
        with open(args.input_file, "rb") as f:
            hash_input = f.read()
    else:
        hash_input = bytes(input_string, "utf-8")

    print(SHA256(hash_input).hash)


if __name__ == "__main__":
    main()


================================================
FILE: index.md
================================================
# TheAlgorithms/Python
```{toctree}
:maxdepth: 2
:caption: index.md

<!-- CONTRIBUTING.md must be the FIRST doc and README.md can come after. -->
CONTRIBUTING.md
README.md
LICENSE.md
```


================================================
FILE: knapsack/README.md
================================================
# A recursive implementation of 0-N Knapsack Problem

This overview is taken from:

    https://en.wikipedia.org/wiki/Knapsack_problem

---

## Overview

The knapsack problem is a problem in combinatorial optimization: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible. It derives its name from the problem faced by someone who is constrained by a fixed-size knapsack and must fill it with the most valuable items. The problem often arises in resource allocation where the decision makers have to choose from a set of non-divisible projects or tasks under a fixed budget or time constraint, respectively.

The knapsack problem has been studied for more than a century, with early works dating as far back as 1897 The name "knapsack problem" dates back to the early works of mathematician Tobias Dantzig (1884–1956), and refers to the commonplace problem of packing the most valuable or useful items without overloading the luggage.

---

## Documentation

This module uses docstrings to enable the use of Python's in-built `help(...)` function.
For instance, try `help(Vector)`, `help(unit_basis_vector)`, and `help(CLASSNAME.METHODNAME)`.

---

## Usage

Import the module `knapsack.py` from the **.** directory into your project.

---

## Tests

`.` contains Python unit tests which can be run with `python3 -m unittest -v`.


================================================
FILE: knapsack/__init__.py
================================================


================================================
FILE: knapsack/greedy_knapsack.py
================================================
# To get an insight into Greedy Algorithm through the Knapsack problem


"""
A shopkeeper has bags of wheat that each have different weights and different profits.
eg.
profit 5 8 7 1 12 3 4
weight 2 7 1 6  4 2 5
max_weight 100

Constraints:
max_weight > 0
profit[i] >= 0
weight[i] >= 0
Calculate the maximum profit that the shopkeeper can make given maxmum weight that can
be carried.
"""


def calc_profit(profit: list, weight: list, max_weight: int) -> int:
    """
    Function description is as follows-
    :param profit: Take a list of profits
    :param weight: Take a list of weight if bags corresponding to the profits
    :param max_weight: Maximum weight that could be carried
    :return: Maximum expected gain

    >>> calc_profit([1, 2, 3], [3, 4, 5], 15)
    6
    >>> calc_profit([10, 9 , 8], [3 ,4 , 5], 25)
    27
    """
    if len(profit) != len(weight):
        raise ValueError("The length of profit and weight must be same.")
    if max_weight <= 0:
        raise ValueError("max_weight must greater than zero.")
    if any(p < 0 for p in profit):
        raise ValueError("Profit can not be negative.")
    if any(w < 0 for w in weight):
        raise ValueError("Weight can not be negative.")

    # List created to store profit gained for the 1kg in case of each weight
    # respectively.  Calculate and append profit/weight for each element.
    profit_by_weight = [p / w for p, w in zip(profit, weight)]

    # Creating a copy of the list and sorting profit/weight in ascending order
    sorted_profit_by_weight = sorted(profit_by_weight)

    # declaring useful variables
    length = len(sorted_profit_by_weight)
    limit = 0
    gain = 0
    i = 0

    # loop till the total weight do not reach max limit e.g. 15 kg and till i<length
    while limit <= max_weight and i < length:
        # flag value for encountered greatest element in sorted_profit_by_weight
        biggest_profit_by_weight = sorted_profit_by_weight[length - i - 1]
        """
        Calculate the index of the biggest_profit_by_weight in profit_by_weight list.
        This will give the index of the first encountered element which is same as of
        biggest_profit_by_weight.  There may be one or more values same as that of
        biggest_profit_by_weight but index always encounter the very first element
        only.  To curb this alter the values in profit_by_weight once they are used
        here it is done to -1 because neither profit nor weight can be in negative.
        """
        index = profit_by_weight.index(biggest_profit_by_weight)
        profit_by_weight[index] = -1

        # check if the weight encountered is less than the total weight
        # encountered before.
        if max_weight - limit >= weight[index]:
            limit += weight[index]
            # Adding profit gained for the given weight 1 ===
            # weight[index]/weight[index]
            gain += 1 * profit[index]
        else:
            # Since the weight encountered is greater than limit, therefore take the
            # required number of remaining kgs and calculate profit for it.
            # weight remaining / weight[index]
            gain += (max_weight - limit) / weight[index] * profit[index]
            break
        i += 1
    return gain


if __name__ == "__main__":
    print(
        "Input profits, weights, and then max_weight (all positive ints) separated by "
        "spaces."
    )

    profit = [int(x) for x in input("Input profits separated by spaces: ").split()]
    weight = [int(x) for x in input("Input weights separated by spaces: ").split()]
    max_weight = int(input("Max weight allowed: "))

    # Function Call
    calc_profit(profit, weight, max_weight)


================================================
FILE: knapsack/knapsack.py
================================================
"""A recursive implementation of 0-N Knapsack Problem
https://en.wikipedia.org/wiki/Knapsack_problem
"""

from __future__ import annotations

from functools import lru_cache


def knapsack(
    capacity: int,
    weights: list[int],
    values: list[int],
    counter: int,
    allow_repetition=False,
) -> int:
    """
    Returns the maximum value that can be put in a knapsack of a capacity cap,
    whereby each weight w has a specific value val
    with option to allow repetitive selection of items

    >>> cap = 50
    >>> val = [60, 100, 120]
    >>> w = [10, 20, 30]
    >>> c = len(val)
    >>> knapsack(cap, w, val, c)
    220

    Given the repetition is NOT allowed,
    the result is 220 cause the values of 100 and 120 got the weight of 50
    which is the limit of the capacity.
    >>> knapsack(cap, w, val, c, True)
    300

    Given the repetition is allowed,
    the result is 300 cause the values of 60*5 (pick 5 times)
    got the weight of 10*5 which is the limit of the capacity.
    """

    @lru_cache
    def knapsack_recur(capacity: int, counter: int) -> int:
        # Base Case
        if counter == 0 or capacity == 0:
            return 0

        # If weight of the nth item is more than Knapsack of capacity,
        #   then this item cannot be included in the optimal solution,
        # else return the maximum of two cases:
        #   (1) nth item included only once (0-1), if allow_repetition is False
        #       nth item included one or more times (0-N), if allow_repetition is True
        #   (2) not included
        if weights[counter - 1] > capacity:
            return knapsack_recur(capacity, counter - 1)
        else:
            left_capacity = capacity - weights[counter - 1]
            new_value_included = values[counter - 1] + knapsack_recur(
                left_capacity, counter - 1 if not allow_repetition else counter
            )
            without_new_value = knapsack_recur(capacity, counter - 1)
            return max(new_value_included, without_new_value)

    return knapsack_recur(capacity, counter)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: knapsack/recursive_approach_knapsack.py
================================================
# To get an insight into naive recursive way to solve the Knapsack problem


"""
A shopkeeper has bags of wheat that each have different weights and different profits.
eg.
no_of_items 4
profit 5 4 8 6
weight 1 2 4 5
max_weight 5
Constraints:
max_weight > 0
profit[i] >= 0
weight[i] >= 0
Calculate the maximum profit that the shopkeeper can make given maxmum weight that can
be carried.
"""


def knapsack(
    weights: list, values: list, number_of_items: int, max_weight: int, index: int
) -> int:
    """
    Function description is as follows-
    :param weights: Take a list of weights
    :param values: Take a list of profits corresponding to the weights
    :param number_of_items: number of items available to pick from
    :param max_weight: Maximum weight that could be carried
    :param index: the element we are looking at
    :return: Maximum expected gain
    >>> knapsack([1, 2, 4, 5], [5, 4, 8, 6], 4, 5, 0)
    13
    >>> knapsack([3 ,4 , 5], [10, 9 , 8], 3, 25, 0)
    27
    """
    if index == number_of_items:
        return 0
    ans1 = 0
    ans2 = 0
    ans1 = knapsack(weights, values, number_of_items, max_weight, index + 1)
    if weights[index] <= max_weight:
        ans2 = values[index] + knapsack(
            weights, values, number_of_items, max_weight - weights[index], index + 1
        )
    return max(ans1, ans2)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: knapsack/tests/__init__.py
================================================


================================================
FILE: knapsack/tests/test_greedy_knapsack.py
================================================
import unittest

import pytest

from knapsack import greedy_knapsack as kp


class TestClass(unittest.TestCase):
    """
    Test cases for knapsack
    """

    def test_sorted(self):
        """
        kp.calc_profit takes the required argument (profit, weight, max_weight)
        and returns whether the answer matches to the expected ones
        """
        profit = [10, 20, 30, 40, 50, 60]
        weight = [2, 4, 6, 8, 10, 12]
        max_weight = 100
        assert kp.calc_profit(profit, weight, max_weight) == 210

    def test_negative_max_weight(self):
        """
        Returns ValueError for any negative max_weight value
        :return: ValueError
        """
        # profit = [10, 20, 30, 40, 50, 60]
        # weight = [2, 4, 6, 8, 10, 12]
        # max_weight = -15
        pytest.raises(ValueError, match=r"max_weight must greater than zero.")

    def test_negative_profit_value(self):
        """
        Returns ValueError for any negative profit value in the list
        :return: ValueError
        """
        # profit = [10, -20, 30, 40, 50, 60]
        # weight = [2, 4, 6, 8, 10, 12]
        # max_weight = 15
        pytest.raises(ValueError, match=r"Weight can not be negative.")

    def test_negative_weight_value(self):
        """
        Returns ValueError for any negative weight value in the list
        :return: ValueError
        """
        # profit = [10, 20, 30, 40, 50, 60]
        # weight = [2, -4, 6, -8, 10, 12]
        # max_weight = 15
        pytest.raises(ValueError, match=r"Profit can not be negative.")

    def test_null_max_weight(self):
        """
        Returns ValueError for any zero max_weight value
        :return: ValueError
        """
        # profit = [10, 20, 30, 40, 50, 60]
        # weight = [2, 4, 6, 8, 10, 12]
        # max_weight = null
        pytest.raises(ValueError, match=r"max_weight must greater than zero.")

    def test_unequal_list_length(self):
        """
        Returns IndexError if length of lists (profit and weight) are unequal.
        :return: IndexError
        """
        # profit = [10, 20, 30, 40, 50]
        # weight = [2, 4, 6, 8, 10, 12]
        # max_weight = 100
        pytest.raises(
            IndexError, match=r"The length of profit and weight must be same."
        )


if __name__ == "__main__":
    unittest.main()


================================================
FILE: knapsack/tests/test_knapsack.py
================================================
"""
Created on Fri Oct 16 09:31:07 2020

@author: Dr. Tobias Schröder
@license: MIT-license

This file contains the test-suite for the knapsack problem.
"""

import unittest

from knapsack import knapsack as k


class Test(unittest.TestCase):
    def test_base_case(self):
        """
        test for the base case
        """
        cap = 0
        val = [0]
        w = [0]
        c = len(val)
        assert k.knapsack(cap, w, val, c) == 0

        val = [60]
        w = [10]
        c = len(val)
        assert k.knapsack(cap, w, val, c) == 0

    def test_easy_case(self):
        """
        test for the easy case
        """
        cap = 3
        val = [1, 2, 3]
        w = [3, 2, 1]
        c = len(val)
        assert k.knapsack(cap, w, val, c) == 5

    def test_knapsack(self):
        """
        test for the knapsack
        """
        cap = 50
        val = [60, 100, 120]
        w = [10, 20, 30]
        c = len(val)
        assert k.knapsack(cap, w, val, c) == 220

    def test_knapsack_repetition(self):
        """
        test for the knapsack repetition
        """
        cap = 50
        val = [60, 100, 120]
        w = [10, 20, 30]
        c = len(val)
        assert k.knapsack(cap, w, val, c, True) == 300


if __name__ == "__main__":
    unittest.main()


================================================
FILE: linear_algebra/README.md
================================================
# Linear algebra library for Python

This module contains classes and functions for doing linear algebra.

---

## Overview

### class Vector
-
    - This class represents a vector of arbitrary size and related operations.

    **Overview of the methods:**

    - constructor(components) : init the vector
    - set(components) : changes the vector components.
    - \_\_str\_\_() : toString method
    - component(i): gets the i-th component (0-indexed)
    - \_\_len\_\_() : gets the size / length of the vector (number of components)
    - euclidean_length() : returns the eulidean length of the vector
    - operator + : vector addition
    - operator - : vector subtraction
    - operator * : scalar multiplication and dot product
    - copy() : copies this vector and returns it
    - change_component(pos,value) : changes the specified component

- function zero_vector(dimension)
    - returns a zero vector of 'dimension'
- function unit_basis_vector(dimension, pos)
    - returns a unit basis vector with a one at index 'pos' (0-indexed)
- function axpy(scalar, vector1, vector2)
    - computes the axpy operation
- function random_vector(N, a, b)
    - returns a random vector of size N, with random integer components between 'a' and 'b' inclusive

### class Matrix
-
    - This class represents a matrix of arbitrary size and operations on it.

    **Overview of the methods:**

    -  \_\_str\_\_() : returns a string representation
    - operator * : implements the matrix vector multiplication
                   implements the matrix-scalar multiplication.
    - change_component(x, y, value) : changes the specified component.
    - component(x, y) : returns the specified component.
    - width() : returns the width of the matrix
    - height() : returns the height of the matrix
    - determinant() : returns the determinant of the matrix if it is square
    - operator + : implements the matrix-addition.
    - operator - : implements the matrix-subtraction

- function square_zero_matrix(N)
    - returns a square zero-matrix of dimension NxN
- function random_matrix(W, H, a, b)
    - returns a random matrix WxH with integer components between 'a' and 'b' inclusive
---

## Documentation

This module uses docstrings to enable the use of Python's in-built `help(...)` function.
For instance, try `help(Vector)`, `help(unit_basis_vector)`, and `help(CLASSNAME.METHODNAME)`.

---

## Usage

Import the module `lib.py` from the **src** directory into your project.
Alternatively, you can directly use the Python bytecode file `lib.pyc`.

---

## Tests

`src/tests.py` contains Python unit tests which can be run with `python3 -m unittest -v`.


================================================
FILE: linear_algebra/__init__.py
================================================


================================================
FILE: linear_algebra/gaussian_elimination.py
================================================
"""
| Gaussian elimination method for solving a system of linear equations.
| Gaussian elimination - https://en.wikipedia.org/wiki/Gaussian_elimination
"""

import numpy as np
from numpy import float64
from numpy.typing import NDArray


def retroactive_resolution(
    coefficients: NDArray[float64], vector: NDArray[float64]
) -> NDArray[float64]:
    """
    This function performs a retroactive linear system resolution
    for triangular matrix

    Examples:
        1.
            * 2x1 + 2x2 - 1x3 = 5
            * 0x1 - 2x2 - 1x3 = -7
            * 0x1 + 0x2 + 5x3 = 15
        2.
            * 2x1 + 2x2 = -1
            * 0x1 - 2x2 = -1

    >>> gaussian_elimination([[2, 2, -1], [0, -2, -1], [0, 0, 5]], [[5], [-7], [15]])
    array([[2.],
           [2.],
           [3.]])
    >>> gaussian_elimination([[2, 2], [0, -2]], [[-1], [-1]])
    array([[-1. ],
           [ 0.5]])
    """

    rows, _columns = np.shape(coefficients)

    x: NDArray[float64] = np.zeros((rows, 1), dtype=float)
    for row in reversed(range(rows)):
        total = np.dot(coefficients[row, row + 1 :], x[row + 1 :])
        x[row, 0] = (vector[row][0] - total[0]) / coefficients[row, row]

    return x


def gaussian_elimination(
    coefficients: NDArray[float64], vector: NDArray[float64]
) -> NDArray[float64]:
    """
    This function performs Gaussian elimination method

    Examples:
        1.
            * 1x1 - 4x2 - 2x3 = -2
            * 5x1 + 2x2 - 2x3 = -3
            * 1x1 - 1x2 + 0x3 = 4
        2.
            * 1x1 + 2x2 = 5
            * 5x1 + 2x2 = 5

    >>> gaussian_elimination([[1, -4, -2], [5, 2, -2], [1, -1, 0]], [[-2], [-3], [4]])
    array([[ 2.3 ],
           [-1.7 ],
           [ 5.55]])
    >>> gaussian_elimination([[1, 2], [5, 2]], [[5], [5]])
    array([[0. ],
           [2.5]])
    """
    # coefficients must to be a square matrix so we need to check first
    rows, columns = np.shape(coefficients)
    if rows != columns:
        return np.array((), dtype=float)

    # augmented matrix
    augmented_mat: NDArray[float64] = np.concatenate((coefficients, vector), axis=1)
    augmented_mat = augmented_mat.astype("float64")

    # scale the matrix leaving it triangular
    for row in range(rows - 1):
        pivot = augmented_mat[row, row]
        for col in range(row + 1, columns):
            factor = augmented_mat[col, row] / pivot
            augmented_mat[col, :] -= factor * augmented_mat[row, :]

    x = retroactive_resolution(
        augmented_mat[:, 0:columns], augmented_mat[:, columns : columns + 1]
    )

    return x


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: linear_algebra/jacobi_iteration_method.py
================================================
"""
Jacobi Iteration Method - https://en.wikipedia.org/wiki/Jacobi_method
"""

from __future__ import annotations

import numpy as np
from numpy import float64
from numpy.typing import NDArray


# Method to find solution of system of linear equations
def jacobi_iteration_method(
    coefficient_matrix: NDArray[float64],
    constant_matrix: NDArray[float64],
    init_val: list[float],
    iterations: int,
) -> list[float]:
    """
    Jacobi Iteration Method:
    An iterative algorithm to determine the solutions of strictly diagonally dominant
    system of linear equations

    4x1 +  x2 +  x3 =  2
     x1 + 5x2 + 2x3 = -6
     x1 + 2x2 + 4x3 = -4

    x_init = [0.5, -0.5 , -0.5]

    Examples:

    >>> coefficient = np.array([[4, 1, 1], [1, 5, 2], [1, 2, 4]])
    >>> constant = np.array([[2], [-6], [-4]])
    >>> init_val = [0.5, -0.5, -0.5]
    >>> iterations = 3
    >>> jacobi_iteration_method(coefficient, constant, init_val, iterations)
    [0.909375, -1.14375, -0.7484375]


    >>> coefficient = np.array([[4, 1, 1], [1, 5, 2]])
    >>> constant = np.array([[2], [-6], [-4]])
    >>> init_val = [0.5, -0.5, -0.5]
    >>> iterations = 3
    >>> jacobi_iteration_method(coefficient, constant, init_val, iterations)
    Traceback (most recent call last):
        ...
    ValueError: Coefficient matrix dimensions must be nxn but received 2x3

    >>> coefficient = np.array([[4, 1, 1], [1, 5, 2], [1, 2, 4]])
    >>> constant = np.array([[2], [-6]])
    >>> init_val = [0.5, -0.5, -0.5]
    >>> iterations = 3
    >>> jacobi_iteration_method(
    ...     coefficient, constant, init_val, iterations
    ... )  # doctest: +NORMALIZE_WHITESPACE
    Traceback (most recent call last):
        ...
    ValueError: Coefficient and constant matrices dimensions must be nxn and nx1 but
                received 3x3 and 2x1

    >>> coefficient = np.array([[4, 1, 1], [1, 5, 2], [1, 2, 4]])
    >>> constant = np.array([[2], [-6], [-4]])
    >>> init_val = [0.5, -0.5]
    >>> iterations = 3
    >>> jacobi_iteration_method(
    ...     coefficient, constant, init_val, iterations
    ... )  # doctest: +NORMALIZE_WHITESPACE
    Traceback (most recent call last):
        ...
    ValueError: Number of initial values must be equal to number of rows in coefficient
                matrix but received 2 and 3

    >>> coefficient = np.array([[4, 1, 1], [1, 5, 2], [1, 2, 4]])
    >>> constant = np.array([[2], [-6], [-4]])
    >>> init_val = [0.5, -0.5, -0.5]
    >>> iterations = 0
    >>> jacobi_iteration_method(coefficient, constant, init_val, iterations)
    Traceback (most recent call last):
        ...
    ValueError: Iterations must be at least 1
    """

    rows1, cols1 = coefficient_matrix.shape
    rows2, cols2 = constant_matrix.shape

    if rows1 != cols1:
        msg = f"Coefficient matrix dimensions must be nxn but received {rows1}x{cols1}"
        raise ValueError(msg)

    if cols2 != 1:
        msg = f"Constant matrix must be nx1 but received {rows2}x{cols2}"
        raise ValueError(msg)

    if rows1 != rows2:
        msg = (
            "Coefficient and constant matrices dimensions must be nxn and nx1 but "
            f"received {rows1}x{cols1} and {rows2}x{cols2}"
        )
        raise ValueError(msg)

    if len(init_val) != rows1:
        msg = (
            "Number of initial values must be equal to number of rows in coefficient "
            f"matrix but received {len(init_val)} and {rows1}"
        )
        raise ValueError(msg)

    if iterations <= 0:
        raise ValueError("Iterations must be at least 1")

    table: NDArray[float64] = np.concatenate(
        (coefficient_matrix, constant_matrix), axis=1
    )

    rows, _cols = table.shape

    strictly_diagonally_dominant(table)

    """
    # Iterates the whole matrix for given number of times
    for _ in range(iterations):
        new_val = []
        for row in range(rows):
            temp = 0
            for col in range(cols):
                if col == row:
                    denom = table[row][col]
                elif col == cols - 1:
                    val = table[row][col]
                else:
                    temp += (-1) * table[row][col] * init_val[col]
            temp = (temp + val) / denom
            new_val.append(temp)
        init_val = new_val
    """

    # denominator - a list of values along the diagonal
    denominator = np.diag(coefficient_matrix)

    # val_last - values of the last column of the table array
    val_last = table[:, -1]

    # masks - boolean mask of all strings without diagonal
    # elements array coefficient_matrix
    masks = ~np.eye(coefficient_matrix.shape[0], dtype=bool)

    # no_diagonals - coefficient_matrix array values without diagonal elements
    no_diagonals = coefficient_matrix[masks].reshape(-1, rows - 1)

    # Here we get 'i_col' - these are the column numbers, for each row
    # without diagonal elements, except for the last column.
    _i_row, i_col = np.where(masks)
    ind = i_col.reshape(-1, rows - 1)

    #'i_col' is converted to a two-dimensional list 'ind', which will be
    # used to make selections from 'init_val' ('arr' array see below).

    # Iterates the whole matrix for given number of times
    for _ in range(iterations):
        arr = np.take(init_val, ind)
        sum_product_rows = np.sum((-1) * no_diagonals * arr, axis=1)
        new_val = (sum_product_rows + val_last) / denominator
        init_val = new_val

    return new_val.tolist()


# Checks if the given matrix is strictly diagonally dominant
def strictly_diagonally_dominant(table: NDArray[float64]) -> bool:
    """
    >>> table = np.array([[4, 1, 1, 2], [1, 5, 2, -6], [1, 2, 4, -4]])
    >>> strictly_diagonally_dominant(table)
    True

    >>> table = np.array([[4, 1, 1, 2], [1, 5, 2, -6], [1, 2, 3, -4]])
    >>> strictly_diagonally_dominant(table)
    Traceback (most recent call last):
        ...
    ValueError: Coefficient matrix is not strictly diagonally dominant
    """

    rows, cols = table.shape

    is_diagonally_dominant = True

    for i in range(rows):
        total = 0
        for j in range(cols - 1):
            if i == j:
                continue
            else:
                total += table[i][j]

        if table[i][i] <= total:
            raise ValueError("Coefficient matrix is not strictly diagonally dominant")

    return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: linear_algebra/lu_decomposition.py
================================================
"""
Lower-upper (LU) decomposition factors a matrix as a product of a lower
triangular matrix and an upper triangular matrix. A square matrix has an LU
decomposition under the following conditions:

    - If the matrix is invertible, then it has an LU decomposition if and only
      if all of its leading principal minors are non-zero (see
      https://en.wikipedia.org/wiki/Minor_(linear_algebra) for an explanation of
      leading principal minors of a matrix).
    - If the matrix is singular (i.e., not invertible) and it has a rank of k
      (i.e., it has k linearly independent columns), then it has an LU
      decomposition if its first k leading principal minors are non-zero.

This algorithm will simply attempt to perform LU decomposition on any square
matrix and raise an error if no such decomposition exists.

Reference: https://en.wikipedia.org/wiki/LU_decomposition
"""

from __future__ import annotations

import numpy as np


def lower_upper_decomposition(table: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
    """
    Perform LU decomposition on a given matrix and raises an error if the matrix
    isn't square or if no such decomposition exists

    >>> matrix = np.array([[2, -2, 1], [0, 1, 2], [5, 3, 1]])
    >>> lower_mat, upper_mat = lower_upper_decomposition(matrix)
    >>> lower_mat
    array([[1. , 0. , 0. ],
           [0. , 1. , 0. ],
           [2.5, 8. , 1. ]])
    >>> upper_mat
    array([[  2. ,  -2. ,   1. ],
           [  0. ,   1. ,   2. ],
           [  0. ,   0. , -17.5]])

    >>> matrix = np.array([[4, 3], [6, 3]])
    >>> lower_mat, upper_mat = lower_upper_decomposition(matrix)
    >>> lower_mat
    array([[1. , 0. ],
           [1.5, 1. ]])
    >>> upper_mat
    array([[ 4. ,  3. ],
           [ 0. , -1.5]])

    >>> # Matrix is not square
    >>> matrix = np.array([[2, -2, 1], [0, 1, 2]])
    >>> lower_mat, upper_mat = lower_upper_decomposition(matrix)
    Traceback (most recent call last):
        ...
    ValueError: 'table' has to be of square shaped array but got a 2x3 array:
    [[ 2 -2  1]
     [ 0  1  2]]

    >>> # Matrix is invertible, but its first leading principal minor is 0
    >>> matrix = np.array([[0, 1], [1, 0]])
    >>> lower_mat, upper_mat = lower_upper_decomposition(matrix)
    Traceback (most recent call last):
    ...
    ArithmeticError: No LU decomposition exists

    >>> # Matrix is singular, but its first leading principal minor is 1
    >>> matrix = np.array([[1, 0], [1, 0]])
    >>> lower_mat, upper_mat = lower_upper_decomposition(matrix)
    >>> lower_mat
    array([[1., 0.],
           [1., 1.]])
    >>> upper_mat
    array([[1., 0.],
           [0., 0.]])

    >>> # Matrix is singular, but its first leading principal minor is 0
    >>> matrix = np.array([[0, 1], [0, 1]])
    >>> lower_mat, upper_mat = lower_upper_decomposition(matrix)
    Traceback (most recent call last):
    ...
    ArithmeticError: No LU decomposition exists
    """
    # Ensure that table is a square array
    rows, columns = np.shape(table)
    if rows != columns:
        msg = (
            "'table' has to be of square shaped array but got a "
            f"{rows}x{columns} array:\n{table}"
        )
        raise ValueError(msg)

    lower = np.zeros((rows, columns))
    upper = np.zeros((rows, columns))

    # in 'total', the necessary data is extracted through slices
    # and the sum of the products is obtained.

    for i in range(columns):
        for j in range(i):
            total = np.sum(lower[i, :i] * upper[:i, j])
            if upper[j][j] == 0:
                raise ArithmeticError("No LU decomposition exists")
            lower[i][j] = (table[i][j] - total) / upper[j][j]
        lower[i][i] = 1
        for j in range(i, columns):
            total = np.sum(lower[i, :i] * upper[:i, j])
            upper[i][j] = table[i][j] - total
    return lower, upper


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: linear_algebra/matrix_inversion.py
================================================
import numpy as np


def invert_matrix(matrix: list[list[float]]) -> list[list[float]]:
    """
    Returns the inverse of a square matrix using NumPy.

    Parameters:
    matrix (list[list[float]]): A square matrix.

    Returns:
    list[list[float]]: Inverted matrix if invertible, else raises error.

    >>> invert_matrix([[4.0, 7.0], [2.0, 6.0]])
    [[0.6000000000000001, -0.7000000000000001], [-0.2, 0.4]]
    >>> invert_matrix([[1.0, 2.0], [0.0, 0.0]])
    Traceback (most recent call last):
        ...
    ValueError: Matrix is not invertible
    """
    np_matrix = np.array(matrix)

    try:
        inv_matrix = np.linalg.inv(np_matrix)
    except np.linalg.LinAlgError:
        raise ValueError("Matrix is not invertible")

    return inv_matrix.tolist()


if __name__ == "__main__":
    mat = [[4.0, 7.0], [2.0, 6.0]]
    print("Original Matrix:")
    print(mat)
    print("Inverted Matrix:")
    print(invert_matrix(mat))


================================================
FILE: linear_algebra/src/__init__.py
================================================


================================================
FILE: linear_algebra/src/conjugate_gradient.py
================================================
"""
Resources:
- https://en.wikipedia.org/wiki/Conjugate_gradient_method
- https://en.wikipedia.org/wiki/Definite_symmetric_matrix
"""

from typing import Any

import numpy as np


def _is_matrix_spd(matrix: np.ndarray) -> bool:
    """
    Returns True if input matrix is symmetric positive definite.
    Returns False otherwise.

    For a matrix to be SPD, all eigenvalues must be positive.

    >>> import numpy as np
    >>> matrix = np.array([
    ... [4.12401784, -5.01453636, -0.63865857],
    ... [-5.01453636, 12.33347422, -3.40493586],
    ... [-0.63865857, -3.40493586,  5.78591885]])
    >>> _is_matrix_spd(matrix)
    True
    >>> matrix = np.array([
    ... [0.34634879,  1.96165514,  2.18277744],
    ... [0.74074469, -1.19648894, -1.34223498],
    ... [-0.7687067 ,  0.06018373, -1.16315631]])
    >>> _is_matrix_spd(matrix)
    False
    """
    # Ensure matrix is square.
    assert np.shape(matrix)[0] == np.shape(matrix)[1]

    # If matrix not symmetric, exit right away.
    if np.allclose(matrix, matrix.T) is False:
        return False

    # Get eigenvalues and eignevectors for a symmetric matrix.
    eigen_values, _ = np.linalg.eigh(matrix)

    # Check sign of all eigenvalues.
    # np.all returns a value of type np.bool_
    return bool(np.all(eigen_values > 0))


def _create_spd_matrix(dimension: int) -> Any:
    """
    Returns a symmetric positive definite matrix given a dimension.

    Input:
    dimension gives the square matrix dimension.

    Output:
    spd_matrix is an diminesion x dimensions symmetric positive definite (SPD) matrix.

    >>> import numpy as np
    >>> dimension = 3
    >>> spd_matrix = _create_spd_matrix(dimension)
    >>> _is_matrix_spd(spd_matrix)
    True
    """
    rng = np.random.default_rng()
    random_matrix = rng.normal(size=(dimension, dimension))
    spd_matrix = np.dot(random_matrix, random_matrix.T)
    assert _is_matrix_spd(spd_matrix)
    return spd_matrix


def conjugate_gradient(
    spd_matrix: np.ndarray,
    load_vector: np.ndarray,
    max_iterations: int = 1000,
    tol: float = 1e-8,
) -> Any:
    """
    Returns solution to the linear system np.dot(spd_matrix, x) = b.

    Input:
    spd_matrix is an NxN Symmetric Positive Definite (SPD) matrix.
    load_vector is an Nx1 vector.

    Output:
    x is an Nx1 vector that is the solution vector.

    >>> import numpy as np
    >>> spd_matrix = np.array([
    ... [8.73256573, -5.02034289, -2.68709226],
    ... [-5.02034289,  3.78188322,  0.91980451],
    ... [-2.68709226,  0.91980451,  1.94746467]])
    >>> b = np.array([
    ... [-5.80872761],
    ... [ 3.23807431],
    ... [ 1.95381422]])
    >>> conjugate_gradient(spd_matrix, b)
    array([[-0.63114139],
           [-0.01561498],
           [ 0.13979294]])
    """
    # Ensure proper dimensionality.
    assert np.shape(spd_matrix)[0] == np.shape(spd_matrix)[1]
    assert np.shape(load_vector)[0] == np.shape(spd_matrix)[0]
    assert _is_matrix_spd(spd_matrix)

    # Initialize solution guess, residual, search direction.
    x0 = np.zeros((np.shape(load_vector)[0], 1))
    r0 = np.copy(load_vector)
    p0 = np.copy(r0)

    # Set initial errors in solution guess and residual.
    error_residual = 1e9
    error_x_solution = 1e9
    error = 1e9

    # Set iteration counter to threshold number of iterations.
    iterations = 0

    while error > tol:
        # Save this value so we only calculate the matrix-vector product once.
        w = np.dot(spd_matrix, p0)

        # The main algorithm.

        # Update search direction magnitude.
        alpha = np.dot(r0.T, r0) / np.dot(p0.T, w)
        # Update solution guess.
        x = x0 + alpha * p0
        # Calculate new residual.
        r = r0 - alpha * w
        # Calculate new Krylov subspace scale.
        beta = np.dot(r.T, r) / np.dot(r0.T, r0)
        # Calculate new A conjuage search direction.
        p = r + beta * p0

        # Calculate errors.
        error_residual = np.linalg.norm(r - r0)
        error_x_solution = np.linalg.norm(x - x0)
        error = np.maximum(error_residual, error_x_solution)

        # Update variables.
        x0 = np.copy(x)
        r0 = np.copy(r)
        p0 = np.copy(p)

        # Update number of iterations.
        iterations += 1
        if iterations > max_iterations:
            break

    return x


def test_conjugate_gradient() -> None:
    """
    >>> test_conjugate_gradient()  # self running tests
    """
    # Create linear system with SPD matrix and known solution x_true.
    dimension = 3
    spd_matrix = _create_spd_matrix(dimension)
    rng = np.random.default_rng()
    x_true = rng.normal(size=(dimension, 1))
    b = np.dot(spd_matrix, x_true)

    # Numpy solution.
    x_numpy = np.linalg.solve(spd_matrix, b)

    # Our implementation.
    x_conjugate_gradient = conjugate_gradient(spd_matrix, b)

    # Ensure both solutions are close to x_true (and therefore one another).
    assert np.linalg.norm(x_numpy - x_true) <= 1e-6
    assert np.linalg.norm(x_conjugate_gradient - x_true) <= 1e-6


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    test_conjugate_gradient()


================================================
FILE: linear_algebra/src/gaussian_elimination_pivoting.py
================================================
import numpy as np


def solve_linear_system(matrix: np.ndarray) -> np.ndarray:
    """
    Solve a linear system of equations using Gaussian elimination with partial pivoting

    Args:
      - `matrix`: Coefficient matrix with the last column representing the constants.

    Returns:
      - Solution vector.

    Raises:
      - ``ValueError``: If the matrix is not correct (i.e., singular).

    https://courses.engr.illinois.edu/cs357/su2013/lect.htm Lecture 7

    Example:

    >>> A = np.array([[2, 1, -1], [-3, -1, 2], [-2, 1, 2]], dtype=float)
    >>> B = np.array([8, -11, -3], dtype=float)
    >>> solution = solve_linear_system(np.column_stack((A, B)))
    >>> np.allclose(solution, np.array([2., 3., -1.]))
    True
    >>> solve_linear_system(np.array([[0, 0, 0]], dtype=float))
    Traceback (most recent call last):
        ...
    ValueError: Matrix is not square
    >>> solve_linear_system(np.array([[0, 0, 0], [0, 0, 0]], dtype=float))
    Traceback (most recent call last):
        ...
    ValueError: Matrix is singular
    """
    ab = np.copy(matrix)
    num_of_rows = ab.shape[0]
    num_of_columns = ab.shape[1] - 1
    x_lst: list[float] = []

    if num_of_rows != num_of_columns:
        raise ValueError("Matrix is not square")

    for column_num in range(num_of_rows):
        # Lead element search
        for i in range(column_num, num_of_columns):
            if abs(ab[i][column_num]) > abs(ab[column_num][column_num]):
                ab[[column_num, i]] = ab[[i, column_num]]

        # Upper triangular matrix
        if abs(ab[column_num, column_num]) < 1e-8:
            raise ValueError("Matrix is singular")

        if column_num != 0:
            for i in range(column_num, num_of_rows):
                ab[i, :] -= (
                    ab[i, column_num - 1]
                    / ab[column_num - 1, column_num - 1]
                    * ab[column_num - 1, :]
                )

    # Find x vector (Back Substitution)
    for column_num in range(num_of_rows - 1, -1, -1):
        x = ab[column_num, -1] / ab[column_num, column_num]
        x_lst.insert(0, x)
        for i in range(column_num - 1, -1, -1):
            ab[i, -1] -= ab[i, column_num] * x

    # Return the solution vector
    return np.asarray(x_lst)


if __name__ == "__main__":
    from doctest import testmod

    testmod()

    example_matrix = np.array(
        [
            [5.0, -5.0, -3.0, 4.0, -11.0],
            [1.0, -4.0, 6.0, -4.0, -10.0],
            [-2.0, -5.0, 4.0, -5.0, -12.0],
            [-3.0, -3.0, 5.0, -5.0, 8.0],
        ],
        dtype=float,
    )

    print(f"Matrix:\n{example_matrix}")
    print(f"{solve_linear_system(example_matrix) = }")


================================================
FILE: linear_algebra/src/lib.py
================================================
"""
Created on Mon Feb 26 14:29:11 2018

@author: Christian Bender
@license: MIT-license

This module contains some useful classes and functions for dealing
with linear algebra in python.

Overview:

- class Vector
- function zero_vector(dimension)
- function unit_basis_vector(dimension, pos)
- function axpy(scalar, vector1, vector2)
- function random_vector(N, a, b)
- class Matrix
- function square_zero_matrix(N)
- function random_matrix(W, H, a, b)
"""

from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload


class Vector:
    """
    This class represents a vector of arbitrary size.
    You need to give the vector components.

    Overview of the methods:

        __init__(components: Collection[float] | None): init the vector
        __len__(): gets the size of the vector (number of components)
        __str__(): returns a string representation
        __add__(other: Vector): vector addition
        __sub__(other: Vector): vector subtraction
        __mul__(other: float): scalar multiplication
        __mul__(other: Vector): dot product
        copy(): copies this vector and returns it
        component(i): gets the i-th component (0-indexed)
        change_component(pos: int, value: float): changes specified component
        euclidean_length(): returns the euclidean length of the vector
        angle(other: Vector, deg: bool): returns the angle between two vectors
    """

    def __init__(self, components: Collection[float] | None = None) -> None:
        """
        input: components or nothing
        simple constructor for init the vector
        """
        if components is None:
            components = []
        self.__components = list(components)

    def __len__(self) -> int:
        """
        returns the size of the vector
        """
        return len(self.__components)

    def __str__(self) -> str:
        """
        returns a string representation of the vector
        """
        return "(" + ",".join(map(str, self.__components)) + ")"

    def __add__(self, other: Vector) -> Vector:
        """
        input: other vector
        assumes: other vector has the same size
        returns a new vector that represents the sum.
        """
        size = len(self)
        if size == len(other):
            result = [self.__components[i] + other.component(i) for i in range(size)]
            return Vector(result)
        else:
            raise Exception("must have the same size")

    def __sub__(self, other: Vector) -> Vector:
        """
        input: other vector
        assumes: other vector has the same size
        returns a new vector that represents the difference.
        """
        size = len(self)
        if size == len(other):
            result = [self.__components[i] - other.component(i) for i in range(size)]
            return Vector(result)
        else:  # error case
            raise Exception("must have the same size")

    def __eq__(self, other: object) -> bool:
        """
        performs the comparison between two vectors
        """
        if not isinstance(other, Vector):
            return NotImplemented
        if len(self) != len(other):
            return False
        return all(self.component(i) == other.component(i) for i in range(len(self)))

    @overload
    def __mul__(self, other: float) -> Vector: ...

    @overload
    def __mul__(self, other: Vector) -> float: ...

    def __mul__(self, other: float | Vector) -> float | Vector:
        """
        mul implements the scalar multiplication
        and the dot-product
        """
        if isinstance(other, (float, int)):
            ans = [c * other for c in self.__components]
            return Vector(ans)
        elif isinstance(other, Vector) and len(self) == len(other):
            size = len(self)
            prods = [self.__components[i] * other.component(i) for i in range(size)]
            return sum(prods)
        else:  # error case
            raise Exception("invalid operand!")

    def copy(self) -> Vector:
        """
        copies this vector and returns it.
        """
        return Vector(self.__components)

    def component(self, i: int) -> float:
        """
        input: index (0-indexed)
        output: the i-th component of the vector.
        """
        if isinstance(i, int) and -len(self.__components) <= i < len(self.__components):
            return self.__components[i]
        else:
            raise Exception("index out of range")

    def change_component(self, pos: int, value: float) -> None:
        """
        input: an index (pos) and a value
        changes the specified component (pos) with the
        'value'
        """
        # precondition
        assert -len(self.__components) <= pos < len(self.__components)
        self.__components[pos] = value

    def euclidean_length(self) -> float:
        """
        returns the euclidean length of the vector

        >>> Vector([2, 3, 4]).euclidean_length()
        5.385164807134504
        >>> Vector([1]).euclidean_length()
        1.0
        >>> Vector([0, -1, -2, -3, 4, 5, 6]).euclidean_length()
        9.539392014169456
        >>> Vector([]).euclidean_length()
        Traceback (most recent call last):
            ...
        Exception: Vector is empty
        """
        if len(self.__components) == 0:
            raise Exception("Vector is empty")
        squares = [c**2 for c in self.__components]
        return math.sqrt(sum(squares))

    def angle(self, other: Vector, deg: bool = False) -> float:
        """
        find angle between two Vector (self, Vector)

        >>> Vector([3, 4, -1]).angle(Vector([2, -1, 1]))
        1.4906464636572374
        >>> Vector([3, 4, -1]).angle(Vector([2, -1, 1]), deg = True)
        85.40775111366095
        >>> Vector([3, 4, -1]).angle(Vector([2, -1]))
        Traceback (most recent call last):
            ...
        Exception: invalid operand!
        """
        num = self * other
        den = self.euclidean_length() * other.euclidean_length()
        if deg:
            return math.degrees(math.acos(num / den))
        else:
            return math.acos(num / den)


def zero_vector(dimension: int) -> Vector:
    """
    returns a zero-vector of size 'dimension'
    """
    # precondition
    assert isinstance(dimension, int)
    return Vector([0] * dimension)


def unit_basis_vector(dimension: int, pos: int) -> Vector:
    """
    returns a unit basis vector with a One
    at index 'pos' (indexing at 0)
    """
    # precondition
    assert isinstance(dimension, int)
    assert isinstance(pos, int)
    ans = [0] * dimension
    ans[pos] = 1
    return Vector(ans)


def axpy(scalar: float, x: Vector, y: Vector) -> Vector:
    """
    input: a 'scalar' and two vectors 'x' and 'y'
    output: a vector
    computes the axpy operation
    """
    # precondition
    assert isinstance(x, Vector)
    assert isinstance(y, Vector)
    assert isinstance(scalar, (int, float))
    return x * scalar + y


def random_vector(n: int, a: int, b: int) -> Vector:
    """
    input: size (N) of the vector.
           random range (a,b)
    output: returns a random vector of size N, with
            random integer components between 'a' and 'b'.
    """
    random.seed(None)
    ans = [random.randint(a, b) for _ in range(n)]
    return Vector(ans)


class Matrix:
    """
    class: Matrix
    This class represents an arbitrary matrix.

    Overview of the methods:

        __init__():
        __str__(): returns a string representation
        __add__(other: Matrix): matrix addition
        __sub__(other: Matrix): matrix subtraction
        __mul__(other: float): scalar multiplication
        __mul__(other: Vector): vector multiplication
        height() : returns height
        width() : returns width
        component(x: int, y: int): returns specified component
        change_component(x: int, y: int, value: float): changes specified component
        minor(x: int, y: int): returns minor along (x, y)
        cofactor(x: int, y: int): returns cofactor along (x, y)
        determinant() : returns determinant
    """

    def __init__(self, matrix: list[list[float]], w: int, h: int) -> None:
        """
        simple constructor for initializing the matrix with components.
        """
        self.__matrix = matrix
        self.__width = w
        self.__height = h

    def __str__(self) -> str:
        """
        returns a string representation of this matrix.
        """
        ans = ""
        for i in range(self.__height):
            ans += "|"
            for j in range(self.__width):
                if j < self.__width - 1:
                    ans += str(self.__matrix[i][j]) + ","
                else:
                    ans += str(self.__matrix[i][j]) + "|\n"
        return ans

    def __add__(self, other: Matrix) -> Matrix:
        """
        implements matrix addition.
        """
        if self.__width == other.width() and self.__height == other.height():
            matrix = []
            for i in range(self.__height):
                row = [
                    self.__matrix[i][j] + other.component(i, j)
                    for j in range(self.__width)
                ]
                matrix.append(row)
            return Matrix(matrix, self.__width, self.__height)
        else:
            raise Exception("matrix must have the same dimension!")

    def __sub__(self, other: Matrix) -> Matrix:
        """
        implements matrix subtraction.
        """
        if self.__width == other.width() and self.__height == other.height():
            matrix = []
            for i in range(self.__height):
                row = [
                    self.__matrix[i][j] - other.component(i, j)
                    for j in range(self.__width)
                ]
                matrix.append(row)
            return Matrix(matrix, self.__width, self.__height)
        else:
            raise Exception("matrices must have the same dimension!")

    @overload
    def __mul__(self, other: float) -> Matrix: ...

    @overload
    def __mul__(self, other: Vector) -> Vector: ...

    def __mul__(self, other: float | Vector) -> Vector | Matrix:
        """
        implements the matrix-vector multiplication.
        implements the matrix-scalar multiplication
        """
        if isinstance(other, Vector):  # matrix-vector
            if len(other) == self.__width:
                ans = zero_vector(self.__height)
                for i in range(self.__height):
                    prods = [
                        self.__matrix[i][j] * other.component(j)
                        for j in range(self.__width)
                    ]
                    ans.change_component(i, sum(prods))
                return ans
            else:
                raise Exception(
                    "vector must have the same size as the "
                    "number of columns of the matrix!"
                )
        elif isinstance(other, (int, float)):  # matrix-scalar
            matrix = [
                [self.__matrix[i][j] * other for j in range(self.__width)]
                for i in range(self.__height)
            ]
            return Matrix(matrix, self.__width, self.__height)
        return None

    def height(self) -> int:
        """
        getter for the height
        """
        return self.__height

    def width(self) -> int:
        """
        getter for the width
        """
        return self.__width

    def component(self, x: int, y: int) -> float:
        """
        returns the specified (x,y) component
        """
        if 0 <= x < self.__height and 0 <= y < self.__width:
            return self.__matrix[x][y]
        else:
            raise Exception("change_component: indices out of bounds")

    def change_component(self, x: int, y: int, value: float) -> None:
        """
        changes the x-y component of this matrix
        """
        if 0 <= x < self.__height and 0 <= y < self.__width:
            self.__matrix[x][y] = value
        else:
            raise Exception("change_component: indices out of bounds")

    def minor(self, x: int, y: int) -> float:
        """
        returns the minor along (x, y)
        """
        if self.__height != self.__width:
            raise Exception("Matrix is not square")
        minor = self.__matrix[:x] + self.__matrix[x + 1 :]
        for i in range(len(minor)):
            minor[i] = minor[i][:y] + minor[i][y + 1 :]
        return Matrix(minor, self.__width - 1, self.__height - 1).determinant()

    def cofactor(self, x: int, y: int) -> float:
        """
        returns the cofactor (signed minor) along (x, y)
        """
        if self.__height != self.__width:
            raise Exception("Matrix is not square")
        if 0 <= x < self.__height and 0 <= y < self.__width:
            return (-1) ** (x + y) * self.minor(x, y)
        else:
            raise Exception("Indices out of bounds")

    def determinant(self) -> float:
        """
        returns the determinant of an nxn matrix using Laplace expansion
        """
        if self.__height != self.__width:
            raise Exception("Matrix is not square")
        if self.__height < 1:
            raise Exception("Matrix has no element")
        elif self.__height == 1:
            return self.__matrix[0][0]
        elif self.__height == 2:
            return (
                self.__matrix[0][0] * self.__matrix[1][1]
                - self.__matrix[0][1] * self.__matrix[1][0]
            )
        else:
            cofactor_prods = [
                self.__matrix[0][y] * self.cofactor(0, y) for y in range(self.__width)
            ]
            return sum(cofactor_prods)


def square_zero_matrix(n: int) -> Matrix:
    """
    returns a square zero-matrix of dimension NxN
    """
    ans: list[list[float]] = [[0] * n for _ in range(n)]
    return Matrix(ans, n, n)


def random_matrix(width: int, height: int, a: int, b: int) -> Matrix:
    """
    returns a random matrix WxH with integer components
    between 'a' and 'b'
    """
    random.seed(None)
    matrix: list[list[float]] = [
        [random.randint(a, b) for _ in range(width)] for _ in range(height)
    ]
    return Matrix(matrix, width, height)


================================================
FILE: linear_algebra/src/polynom_for_points.py
================================================
def points_to_polynomial(coordinates: list[list[int]]) -> str:
    """
    coordinates is a two dimensional matrix: [[x, y], [x, y], ...]
    number of points you want to use

    >>> points_to_polynomial([])
    Traceback (most recent call last):
        ...
    ValueError: The program cannot work out a fitting polynomial.
    >>> points_to_polynomial([[]])
    Traceback (most recent call last):
        ...
    ValueError: The program cannot work out a fitting polynomial.
    >>> points_to_polynomial([[1, 0], [2, 0], [3, 0]])
    'f(x)=x^2*0.0+x^1*-0.0+x^0*0.0'
    >>> points_to_polynomial([[1, 1], [2, 1], [3, 1]])
    'f(x)=x^2*0.0+x^1*-0.0+x^0*1.0'
    >>> points_to_polynomial([[1, 3], [2, 3], [3, 3]])
    'f(x)=x^2*0.0+x^1*-0.0+x^0*3.0'
    >>> points_to_polynomial([[1, 1], [2, 2], [3, 3]])
    'f(x)=x^2*0.0+x^1*1.0+x^0*0.0'
    >>> points_to_polynomial([[1, 1], [2, 4], [3, 9]])
    'f(x)=x^2*1.0+x^1*-0.0+x^0*0.0'
    >>> points_to_polynomial([[1, 3], [2, 6], [3, 11]])
    'f(x)=x^2*1.0+x^1*-0.0+x^0*2.0'
    >>> points_to_polynomial([[1, -3], [2, -6], [3, -11]])
    'f(x)=x^2*-1.0+x^1*-0.0+x^0*-2.0'
    >>> points_to_polynomial([[1, 5], [2, 2], [3, 9]])
    'f(x)=x^2*5.0+x^1*-18.0+x^0*18.0'
    >>> points_to_polynomial([[1, 1], [1, 2], [1, 3]])
    'x=1'
    >>> points_to_polynomial([[1, 1], [2, 2], [2, 2]])
    Traceback (most recent call last):
        ...
    ValueError: The program cannot work out a fitting polynomial.
    """
    if len(coordinates) == 0 or not all(len(pair) == 2 for pair in coordinates):
        raise ValueError("The program cannot work out a fitting polynomial.")

    if len({tuple(pair) for pair in coordinates}) != len(coordinates):
        raise ValueError("The program cannot work out a fitting polynomial.")

    set_x = {x for x, _ in coordinates}
    if len(set_x) == 1:
        return f"x={coordinates[0][0]}"

    if len(set_x) != len(coordinates):
        raise ValueError("The program cannot work out a fitting polynomial.")

    x = len(coordinates)

    # put the x and x to the power values in a matrix
    matrix: list[list[float]] = [
        [
            coordinates[count_of_line][0] ** (x - (count_in_line + 1))
            for count_in_line in range(x)
        ]
        for count_of_line in range(x)
    ]

    # put the y values into a vector
    vector: list[float] = [coordinates[count_of_line][1] for count_of_line in range(x)]

    for count in range(x):
        for number in range(x):
            if count == number:
                continue
            fraction = matrix[number][count] / matrix[count][count]
            for counting_columns, item in enumerate(matrix[count]):
                # manipulating all the values in the matrix
                matrix[number][counting_columns] -= item * fraction
            # manipulating the values in the vector
            vector[number] -= vector[count] * fraction

    # make solutions
    solution: list[str] = [
        str(vector[count] / matrix[count][count]) for count in range(x)
    ]

    solved = "f(x)="

    for count in range(x):
        remove_e: list[str] = solution[count].split("E")
        if len(remove_e) > 1:
            solution[count] = f"{remove_e[0]}*10^{remove_e[1]}"
        solved += f"x^{x - (count + 1)}*{solution[count]}"
        if count + 1 != x:
            solved += "+"

    return solved


if __name__ == "__main__":
    print(points_to_polynomial([]))
    print(points_to_polynomial([[]]))
    print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
    print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
    print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
    print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
    print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
    print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))


================================================
FILE: linear_algebra/src/power_iteration.py
================================================
import numpy as np


def power_iteration(
    input_matrix: np.ndarray,
    vector: np.ndarray,
    error_tol: float = 1e-12,
    max_iterations: int = 100,
) -> tuple[float, np.ndarray]:
    """
    Power Iteration.
    Find the largest eigenvalue and corresponding eigenvector
    of matrix input_matrix given a random vector in the same space.
    Will work so long as vector has component of largest eigenvector.
    input_matrix must be either real or Hermitian.

    Input
    input_matrix: input matrix whose largest eigenvalue we will find.
    Numpy array. np.shape(input_matrix) == (N,N).
    vector: random initial vector in same space as matrix.
    Numpy array. np.shape(vector) == (N,) or (N,1)

    Output
    largest_eigenvalue: largest eigenvalue of the matrix input_matrix.
    Float. Scalar.
    largest_eigenvector: eigenvector corresponding to largest_eigenvalue.
    Numpy array. np.shape(largest_eigenvector) == (N,) or (N,1).

    >>> import numpy as np
    >>> input_matrix = np.array([
    ... [41,  4, 20],
    ... [ 4, 26, 30],
    ... [20, 30, 50]
    ... ])
    >>> vector = np.array([41,4,20])
    >>> power_iteration(input_matrix,vector)
    (79.66086378788381, array([0.44472726, 0.46209842, 0.76725662]))
    """

    # Ensure matrix is square.
    assert np.shape(input_matrix)[0] == np.shape(input_matrix)[1]
    # Ensure proper dimensionality.
    assert np.shape(input_matrix)[0] == np.shape(vector)[0]
    # Ensure inputs are either both complex or both real
    assert np.iscomplexobj(input_matrix) == np.iscomplexobj(vector)
    is_complex = np.iscomplexobj(input_matrix)
    if is_complex:
        # Ensure complex input_matrix is Hermitian
        assert np.array_equal(input_matrix, input_matrix.conj().T)

    # Set convergence to False. Will define convergence when we exceed max_iterations
    # or when we have small changes from one iteration to next.

    convergence = False
    lambda_previous = 0
    iterations = 0
    error = 1e12

    while not convergence:
        # Multiple matrix by the vector.
        w = np.dot(input_matrix, vector)
        # Normalize the resulting output vector.
        vector = w / np.linalg.norm(w)
        # Find rayleigh quotient
        # (faster than usual b/c we know vector is normalized already)
        vector_h = vector.conj().T if is_complex else vector.T
        lambda_ = np.dot(vector_h, np.dot(input_matrix, vector))

        # Check convergence.
        error = np.abs(lambda_ - lambda_previous) / lambda_
        iterations += 1

        if error <= error_tol or iterations >= max_iterations:
            convergence = True

        lambda_previous = lambda_

    if is_complex:
        lambda_ = np.real(lambda_)

    return float(lambda_), vector


def test_power_iteration() -> None:
    """
    >>> test_power_iteration()  # self running tests
    """
    real_input_matrix = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]])
    real_vector = np.array([41, 4, 20])
    complex_input_matrix = real_input_matrix.astype(np.complex128)
    imag_matrix = np.triu(1j * complex_input_matrix, 1)
    complex_input_matrix += imag_matrix
    complex_input_matrix += -1 * imag_matrix.T
    complex_vector = np.array([41, 4, 20]).astype(np.complex128)

    for problem_type in ["real", "complex"]:
        if problem_type == "real":
            input_matrix = real_input_matrix
            vector = real_vector
        elif problem_type == "complex":
            input_matrix = complex_input_matrix
            vector = complex_vector

        # Our implementation.
        eigen_value, eigen_vector = power_iteration(input_matrix, vector)

        # Numpy implementation.

        # Get eigenvalues and eigenvectors using built-in numpy
        # eigh (eigh used for symmetric or hermetian matrices).
        eigen_values, eigen_vectors = np.linalg.eigh(input_matrix)
        # Last eigenvalue is the maximum one.
        eigen_value_max = eigen_values[-1]
        # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
        eigen_vector_max = eigen_vectors[:, -1]

        # Check our implementation and numpy gives close answers.
        assert np.abs(eigen_value - eigen_value_max) <= 1e-6
        # Take absolute values element wise of each eigenvector.
        # as they are only unique to a minus sign.
        assert np.linalg.norm(np.abs(eigen_vector) - np.abs(eigen_vector_max)) <= 1e-6


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    test_power_iteration()


================================================
FILE: linear_algebra/src/rank_of_matrix.py
================================================
"""
Calculate the rank of a matrix.

See: https://en.wikipedia.org/wiki/Rank_(linear_algebra)
"""


def rank_of_matrix(matrix: list[list[int | float]]) -> int:
    """
    Finds the rank of a matrix.

    Args:
        `matrix`: The matrix as a list of lists.

    Returns:
        The rank of the matrix.

    Example:

    >>> matrix1 = [[1, 2, 3],
    ...            [4, 5, 6],
    ...            [7, 8, 9]]
    >>> rank_of_matrix(matrix1)
    2
    >>> matrix2 = [[1, 0, 0],
    ...            [0, 1, 0],
    ...            [0, 0, 0]]
    >>> rank_of_matrix(matrix2)
    2
    >>> matrix3 = [[1, 2, 3, 4],
    ...            [5, 6, 7, 8],
    ...            [9, 10, 11, 12]]
    >>> rank_of_matrix(matrix3)
    2
    >>> rank_of_matrix([[2,3,-1,-1],
    ...                [1,-1,-2,4],
    ...                [3,1,3,-2],
    ...                [6,3,0,-7]])
    4
    >>> rank_of_matrix([[2,1,-3,-6],
    ...                [3,-3,1,2],
    ...                [1,1,1,2]])
    3
    >>> rank_of_matrix([[2,-1,0],
    ...                [1,3,4],
    ...                [4,1,-3]])
    3
    >>> rank_of_matrix([[3,2,1],
    ...                [-6,-4,-2]])
    1
    >>> rank_of_matrix([[],[]])
    0
    >>> rank_of_matrix([[1]])
    1
    >>> rank_of_matrix([[]])
    0
    """

    rows = len(matrix)
    columns = len(matrix[0])
    rank = min(rows, columns)

    for row in range(rank):
        # Check if diagonal element is not zero
        if matrix[row][row] != 0:
            # Eliminate all the elements below the diagonal
            for col in range(row + 1, rows):
                multiplier = matrix[col][row] / matrix[row][row]
                for i in range(row, columns):
                    matrix[col][i] -= multiplier * matrix[row][i]
        else:
            # Find a non-zero diagonal element to swap rows
            reduce = True
            for i in range(row + 1, rows):
                if matrix[i][row] != 0:
                    matrix[row], matrix[i] = matrix[i], matrix[row]
                    reduce = False
                    break
            if reduce:
                rank -= 1
                for i in range(rows):
                    matrix[i][row] = matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
            row -= 1

    return rank


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: linear_algebra/src/rayleigh_quotient.py
================================================
"""
https://en.wikipedia.org/wiki/Rayleigh_quotient
"""

from typing import Any

import numpy as np


def is_hermitian(matrix: np.ndarray) -> bool:
    """
    Checks if a matrix is Hermitian.
    >>> import numpy as np
    >>> A = np.array([
    ... [2,    2+1j, 4],
    ... [2-1j,  3,  1j],
    ... [4,    -1j,  1]])
    >>> is_hermitian(A)
    True
    >>> A = np.array([
    ... [2,    2+1j, 4+1j],
    ... [2-1j,  3,  1j],
    ... [4,    -1j,  1]])
    >>> is_hermitian(A)
    False
    """
    return np.array_equal(matrix, matrix.conjugate().T)


def rayleigh_quotient(a: np.ndarray, v: np.ndarray) -> Any:
    """
    Returns the Rayleigh quotient of a Hermitian matrix A and
    vector v.
    >>> import numpy as np
    >>> A = np.array([
    ... [1,  2, 4],
    ... [2,  3,  -1],
    ... [4, -1,  1]
    ... ])
    >>> v = np.array([
    ... [1],
    ... [2],
    ... [3]
    ... ])
    >>> rayleigh_quotient(A, v)
    array([[3.]])
    """
    v_star = v.conjugate().T
    v_star_dot = v_star.dot(a)
    assert isinstance(v_star_dot, np.ndarray)
    return (v_star_dot.dot(v)) / (v_star.dot(v))


def tests() -> None:
    a = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]])
    v = np.array([[1], [2], [3]])
    assert is_hermitian(a), f"{a} is not hermitian."
    print(rayleigh_quotient(a, v))

    a = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]])
    assert is_hermitian(a), f"{a} is not hermitian."
    assert rayleigh_quotient(a, v) == float(3)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    tests()


================================================
FILE: linear_algebra/src/schur_complement.py
================================================
import unittest

import numpy as np
import pytest


def schur_complement(
    mat_a: np.ndarray,
    mat_b: np.ndarray,
    mat_c: np.ndarray,
    pseudo_inv: np.ndarray | None = None,
) -> np.ndarray:
    """
    Schur complement of a symmetric matrix X given as a 2x2 block matrix
    consisting of matrices `A`, `B` and `C`.
    Matrix `A` must be quadratic and non-singular.
    In case `A` is singular, a pseudo-inverse may be provided using
    the `pseudo_inv` argument.

    | Link to Wiki: https://en.wikipedia.org/wiki/Schur_complement
    | See also Convex Optimization - Boyd and Vandenberghe, A.5.5

    >>> import numpy as np
    >>> a = np.array([[1, 2], [2, 1]])
    >>> b = np.array([[0, 3], [3, 0]])
    >>> c = np.array([[2, 1], [6, 3]])
    >>> schur_complement(a, b, c)
    array([[ 5., -5.],
           [ 0.,  6.]])
    """
    shape_a = np.shape(mat_a)
    shape_b = np.shape(mat_b)
    shape_c = np.shape(mat_c)

    if shape_a[0] != shape_b[0]:
        msg = (
            "Expected the same number of rows for A and B. "
            f"Instead found A of size {shape_a} and B of size {shape_b}"
        )
        raise ValueError(msg)

    if shape_b[1] != shape_c[1]:
        msg = (
            "Expected the same number of columns for B and C. "
            f"Instead found B of size {shape_b} and C of size {shape_c}"
        )
        raise ValueError(msg)

    a_inv = pseudo_inv
    if a_inv is None:
        try:
            a_inv = np.linalg.inv(mat_a)
        except np.linalg.LinAlgError:
            raise ValueError(
                "Input matrix A is not invertible. Cannot compute Schur complement."
            )

    return mat_c - mat_b.T @ a_inv @ mat_b


class TestSchurComplement(unittest.TestCase):
    def test_schur_complement(self) -> None:
        a = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]])
        b = np.array([[0, 3], [3, 0], [2, 3]])
        c = np.array([[2, 1], [6, 3]])

        s = schur_complement(a, b, c)

        input_matrix = np.block([[a, b], [b.T, c]])

        det_x = np.linalg.det(input_matrix)
        det_a = np.linalg.det(a)
        det_s = np.linalg.det(s)

        assert np.is_close(det_x, det_a * det_s)

    def test_improper_a_b_dimensions(self) -> None:
        a = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]])
        b = np.array([[0, 3], [3, 0], [2, 3]])
        c = np.array([[2, 1], [6, 3]])

        with pytest.raises(ValueError):
            schur_complement(a, b, c)

    def test_improper_b_c_dimensions(self) -> None:
        a = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]])
        b = np.array([[0, 3], [3, 0], [2, 3]])
        c = np.array([[2, 1, 3], [6, 3, 5]])

        with pytest.raises(ValueError):
            schur_complement(a, b, c)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    unittest.main()


================================================
FILE: linear_algebra/src/test_linear_algebra.py
================================================
"""
Created on Mon Feb 26 15:40:07 2018

@author: Christian Bender
@license: MIT-license

This file contains the test-suite for the linear algebra library.
"""

import unittest

import pytest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)


class Test(unittest.TestCase):
    def test_component(self) -> None:
        """
        test for method component()
        """
        x = Vector([1, 2, 3])
        assert x.component(0) == 1
        assert x.component(2) == 3
        _ = Vector()

    def test_str(self) -> None:
        """
        test for method toString()
        """
        x = Vector([0, 0, 0, 0, 0, 1])
        assert str(x) == "(0,0,0,0,0,1)"

    def test_size(self) -> None:
        """
        test for method size()
        """
        x = Vector([1, 2, 3, 4])
        assert len(x) == 4

    def test_euclidean_length(self) -> None:
        """
        test for method euclidean_length()
        """
        x = Vector([1, 2])
        y = Vector([1, 2, 3, 4, 5])
        z = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
        w = Vector([1, -1, 1, -1, 2, -3, 4, -5])
        assert x.euclidean_length() == pytest.approx(2.236, abs=1e-3)
        assert y.euclidean_length() == pytest.approx(7.416, abs=1e-3)
        assert z.euclidean_length() == 0
        assert w.euclidean_length() == pytest.approx(7.616, abs=1e-3)

    def test_add(self) -> None:
        """
        test for + operator
        """
        x = Vector([1, 2, 3])
        y = Vector([1, 1, 1])
        assert (x + y).component(0) == 2
        assert (x + y).component(1) == 3
        assert (x + y).component(2) == 4

    def test_sub(self) -> None:
        """
        test for - operator
        """
        x = Vector([1, 2, 3])
        y = Vector([1, 1, 1])
        assert (x - y).component(0) == 0
        assert (x - y).component(1) == 1
        assert (x - y).component(2) == 2

    def test_mul(self) -> None:
        """
        test for * operator
        """
        x = Vector([1, 2, 3])
        a = Vector([2, -1, 4])  # for test of dot product
        b = Vector([1, -2, -1])
        assert str(x * 3.0) == "(3.0,6.0,9.0)"
        assert a * b == 0

    def test_zero_vector(self) -> None:
        """
        test for global function zero_vector()
        """
        assert str(zero_vector(10)).count("0") == 10

    def test_unit_basis_vector(self) -> None:
        """
        test for global function unit_basis_vector()
        """
        assert str(unit_basis_vector(3, 1)) == "(0,1,0)"

    def test_axpy(self) -> None:
        """
        test for global function axpy() (operation)
        """
        x = Vector([1, 2, 3])
        y = Vector([1, 0, 1])
        assert str(axpy(2, x, y)) == "(3,4,7)"

    def test_copy(self) -> None:
        """
        test for method copy()
        """
        x = Vector([1, 0, 0, 0, 0, 0])
        y = x.copy()
        assert str(x) == str(y)

    def test_change_component(self) -> None:
        """
        test for method change_component()
        """
        x = Vector([1, 0, 0])
        x.change_component(0, 0)
        x.change_component(1, 1)
        assert str(x) == "(0,1,0)"

    def test_str_matrix(self) -> None:
        """
        test for Matrix method str()
        """
        a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
        assert str(a) == "|1,2,3|\n|2,4,5|\n|6,7,8|\n"

    def test_minor(self) -> None:
        """
        test for Matrix method minor()
        """
        a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
        minors = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
        for x in range(a.height()):
            for y in range(a.width()):
                assert minors[x][y] == a.minor(x, y)

    def test_cofactor(self) -> None:
        """
        test for Matrix method cofactor()
        """
        a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
        cofactors = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
        for x in range(a.height()):
            for y in range(a.width()):
                assert cofactors[x][y] == a.cofactor(x, y)

    def test_determinant(self) -> None:
        """
        test for Matrix method determinant()
        """
        a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
        assert a.determinant() == -5

    def test__mul__matrix(self) -> None:
        """
        test for Matrix * operator
        """
        a = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]], 3, 3)
        x = Vector([1, 2, 3])
        assert str(a * x) == "(14,32,50)"
        assert str(a * 2) == "|2,4,6|\n|8,10,12|\n|14,16,18|\n"

    def test_change_component_matrix(self) -> None:
        """
        test for Matrix method change_component()
        """
        a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
        a.change_component(0, 2, 5)
        assert str(a) == "|1,2,5|\n|2,4,5|\n|6,7,8|\n"

    def test_component_matrix(self) -> None:
        """
        test for Matrix method component()
        """
        a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
        assert a.component(2, 1) == 7, "0.01"

    def test__add__matrix(self) -> None:
        """
        test for Matrix + operator
        """
        a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
        b = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]], 3, 3)
        assert str(a + b) == "|2,4,10|\n|4,8,10|\n|12,14,18|\n"

    def test__sub__matrix(self) -> None:
        """
        test for Matrix - operator
        """
        a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
        b = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]], 3, 3)
        assert str(a - b) == "|0,0,-4|\n|0,0,0|\n|0,0,-2|\n"

    def test_square_zero_matrix(self) -> None:
        """
        test for global function square_zero_matrix()
        """
        assert str(square_zero_matrix(5)) == (
            "|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n"
        )


if __name__ == "__main__":
    unittest.main()


================================================
FILE: linear_algebra/src/transformations_2d.py
================================================
"""
2D Transformations are regularly used in Linear Algebra.

I have added the codes for reflection, projection, scaling and rotation 2D matrices.

.. code-block:: python

    scaling(5) = [[5.0, 0.0], [0.0, 5.0]]
    rotation(45) = [[0.5253219888177297, -0.8509035245341184],
                    [0.8509035245341184, 0.5253219888177297]]
    projection(45) = [[0.27596319193541496, 0.446998331800279],
                      [0.446998331800279, 0.7240368080645851]]
    reflection(45) = [[0.05064397763545947, 0.893996663600558],
                      [0.893996663600558, 0.7018070490682369]]
"""

from math import cos, sin


def scaling(scaling_factor: float) -> list[list[float]]:
    """
    >>> scaling(5)
    [[5.0, 0.0], [0.0, 5.0]]
    """
    scaling_factor = float(scaling_factor)
    return [[scaling_factor * int(x == y) for x in range(2)] for y in range(2)]


def rotation(angle: float) -> list[list[float]]:
    """
    >>> rotation(45)  # doctest: +NORMALIZE_WHITESPACE
    [[0.5253219888177297, -0.8509035245341184],
     [0.8509035245341184, 0.5253219888177297]]
    """
    c, s = cos(angle), sin(angle)
    return [[c, -s], [s, c]]


def projection(angle: float) -> list[list[float]]:
    """
    >>> projection(45)  # doctest: +NORMALIZE_WHITESPACE
    [[0.27596319193541496, 0.446998331800279],
     [0.446998331800279, 0.7240368080645851]]
    """
    c, s = cos(angle), sin(angle)
    cs = c * s
    return [[c * c, cs], [cs, s * s]]


def reflection(angle: float) -> list[list[float]]:
    """
    >>> reflection(45)  # doctest: +NORMALIZE_WHITESPACE
    [[0.05064397763545947, 0.893996663600558],
     [0.893996663600558, 0.7018070490682369]]
    """
    c, s = cos(angle), sin(angle)
    cs = c * s
    return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(f"    {scaling(5) = }")
print(f"  {rotation(45) = }")
print(f"{projection(45) = }")
print(f"{reflection(45) = }")


================================================
FILE: linear_programming/__init__.py
================================================


================================================
FILE: linear_programming/simplex.py
================================================
"""
Python implementation of the simplex algorithm for solving linear programs in
tabular form with
- `>=`, `<=`, and `=` constraints and
- each variable `x1, x2, ...>= 0`.

See https://gist.github.com/imengus/f9619a568f7da5bc74eaf20169a24d98 for how to
convert linear programs to simplex tableaus, and the steps taken in the simplex
algorithm.

Resources:
https://en.wikipedia.org/wiki/Simplex_algorithm
https://tinyurl.com/simplex4beginners
"""

from typing import Any

import numpy as np


class Tableau:
    """Operate on simplex tableaus

    >>> Tableau(np.array([[-1,-1,0,0,1],[1,3,1,0,4],[3,1,0,1,4]]), 2, 2)
    Traceback (most recent call last):
    ...
    TypeError: Tableau must have type float64

    >>> Tableau(np.array([[-1,-1,0,0,-1],[1,3,1,0,4],[3,1,0,1,4.]]), 2, 2)
    Traceback (most recent call last):
    ...
    ValueError: RHS must be > 0

    >>> Tableau(np.array([[-1,-1,0,0,1],[1,3,1,0,4],[3,1,0,1,4.]]), -2, 2)
    Traceback (most recent call last):
    ...
    ValueError: number of (artificial) variables must be a natural number
    """

    # Max iteration number to prevent cycling
    maxiter = 100

    def __init__(
        self, tableau: np.ndarray, n_vars: int, n_artificial_vars: int
    ) -> None:
        if tableau.dtype != "float64":
            raise TypeError("Tableau must have type float64")

        # Check if RHS is negative
        if not (tableau[:, -1] >= 0).all():
            raise ValueError("RHS must be > 0")

        if n_vars < 2 or n_artificial_vars < 0:
            raise ValueError(
                "number of (artificial) variables must be a natural number"
            )

        self.tableau = tableau
        self.n_rows, n_cols = tableau.shape

        # Number of decision variables x1, x2, x3...
        self.n_vars, self.n_artificial_vars = n_vars, n_artificial_vars

        # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
        self.n_stages = (self.n_artificial_vars > 0) + 1

        # Number of slack variables added to make inequalities into equalities
        self.n_slack = n_cols - self.n_vars - self.n_artificial_vars - 1

        # Objectives for each stage
        self.objectives = ["max"]

        # In two stage simplex, first minimise then maximise
        if self.n_artificial_vars:
            self.objectives.append("min")

        self.col_titles = self.generate_col_titles()

        # Index of current pivot row and column
        self.row_idx = None
        self.col_idx = None

        # Does objective row only contain (non)-negative values?
        self.stop_iter = False

    def generate_col_titles(self) -> list[str]:
        """Generate column titles for tableau of specific dimensions

        >>> Tableau(np.array([[-1,-1,0,0,1],[1,3,1,0,4],[3,1,0,1,4.]]),
        ... 2, 0).generate_col_titles()
        ['x1', 'x2', 's1', 's2', 'RHS']

        >>> Tableau(np.array([[-1,-1,0,0,1],[1,3,1,0,4],[3,1,0,1,4.]]),
        ... 2, 2).generate_col_titles()
        ['x1', 'x2', 'RHS']
        """
        args = (self.n_vars, self.n_slack)

        # decision | slack
        string_starts = ["x", "s"]
        titles = []
        for i in range(2):
            for j in range(args[i]):
                titles.append(string_starts[i] + str(j + 1))
        titles.append("RHS")
        return titles

    def find_pivot(self) -> tuple[Any, Any]:
        """Finds the pivot row and column.
        >>> tuple(int(x) for x in Tableau(np.array([[-2,1,0,0,0], [3,1,1,0,6],
        ... [1,2,0,1,7.]]), 2, 0).find_pivot())
        (1, 0)
        """
        objective = self.objectives[-1]

        # Find entries of highest magnitude in objective rows
        sign = (objective == "min") - (objective == "max")
        col_idx = np.argmax(sign * self.tableau[0, :-1])

        # Choice is only valid if below 0 for maximise, and above for minimise
        if sign * self.tableau[0, col_idx] <= 0:
            self.stop_iter = True
            return 0, 0

        # Pivot row is chosen as having the lowest quotient when elements of
        # the pivot column divide the right-hand side

        # Slice excluding the objective rows
        s = slice(self.n_stages, self.n_rows)

        # RHS
        dividend = self.tableau[s, -1]

        # Elements of pivot column within slice
        divisor = self.tableau[s, col_idx]

        # Array filled with nans
        nans = np.full(self.n_rows - self.n_stages, np.nan)

        # If element in pivot column is greater than zero, return
        # quotient or nan otherwise
        quotients = np.divide(dividend, divisor, out=nans, where=divisor > 0)

        # Arg of minimum quotient excluding the nan values. n_stages is added
        # to compensate for earlier exclusion of objective columns
        row_idx = np.nanargmin(quotients) + self.n_stages
        return row_idx, col_idx

    def pivot(self, row_idx: int, col_idx: int) -> np.ndarray:
        """Pivots on value on the intersection of pivot row and column.

        >>> Tableau(np.array([[-2,-3,0,0,0],[1,3,1,0,4],[3,1,0,1,4.]]),
        ... 2, 2).pivot(1, 0).tolist()
        ... # doctest: +NORMALIZE_WHITESPACE
        [[0.0, 3.0, 2.0, 0.0, 8.0],
        [1.0, 3.0, 1.0, 0.0, 4.0],
        [0.0, -8.0, -3.0, 1.0, -8.0]]
        """
        # Avoid changes to original tableau
        piv_row = self.tableau[row_idx].copy()

        piv_val = piv_row[col_idx]

        # Entry becomes 1
        piv_row *= 1 / piv_val

        # Variable in pivot column becomes basic, ie the only non-zero entry
        for idx, coeff in enumerate(self.tableau[:, col_idx]):
            self.tableau[idx] += -coeff * piv_row
        self.tableau[row_idx] = piv_row
        return self.tableau

    def change_stage(self) -> np.ndarray:
        """Exits first phase of the two-stage method by deleting artificial
        rows and columns, or completes the algorithm if exiting the standard
        case.

        >>> Tableau(np.array([
        ... [3, 3, -1, -1, 0, 0, 4],
        ... [2, 1, 0, 0, 0, 0, 0.],
        ... [1, 2, -1, 0, 1, 0, 2],
        ... [2, 1, 0, -1, 0, 1, 2]
        ... ]), 2, 2).change_stage().tolist()
        ... # doctest: +NORMALIZE_WHITESPACE
        [[2.0, 1.0, 0.0, 0.0, 0.0],
        [1.0, 2.0, -1.0, 0.0, 2.0],
        [2.0, 1.0, 0.0, -1.0, 2.0]]
        """
        # Objective of original objective row remains
        self.objectives.pop()

        if not self.objectives:
            return self.tableau

        # Slice containing ids for artificial columns
        s = slice(-self.n_artificial_vars - 1, -1)

        # Delete the artificial variable columns
        self.tableau = np.delete(self.tableau, s, axis=1)

        # Delete the objective row of the first stage
        self.tableau = np.delete(self.tableau, 0, axis=0)

        self.n_stages = 1
        self.n_rows -= 1
        self.n_artificial_vars = 0
        self.stop_iter = False
        return self.tableau

    def run_simplex(self) -> dict[Any, Any]:
        """Operate on tableau until objective function cannot be
        improved further.

        # Standard linear program:
        Max:  x1 +  x2
        ST:   x1 + 3x2 <= 4
             3x1 +  x2 <= 4
        >>> {key: float(value) for key, value in Tableau(np.array([[-1,-1,0,0,0],
        ... [1,3,1,0,4],[3,1,0,1,4.]]), 2, 0).run_simplex().items()}
        {'P': 2.0, 'x1': 1.0, 'x2': 1.0}

        # Standard linear program with 3 variables:
        Max: 3x1 +  x2 + 3x3
        ST:  2x1 +  x2 +  x3 ≤ 2
              x1 + 2x2 + 3x3 ≤ 5
             2x1 + 2x2 +  x3 ≤ 6
        >>> {key: float(value) for key, value in Tableau(np.array([
        ... [-3,-1,-3,0,0,0,0],
        ... [2,1,1,1,0,0,2],
        ... [1,2,3,0,1,0,5],
        ... [2,2,1,0,0,1,6.]
        ... ]),3,0).run_simplex().items()} # doctest: +ELLIPSIS
        {'P': 5.4, 'x1': 0.199..., 'x3': 1.6}


        # Optimal tableau input:
        >>> {key: float(value) for key, value in Tableau(np.array([
        ... [0, 0, 0.25, 0.25, 2],
        ... [0, 1, 0.375, -0.125, 1],
        ... [1, 0, -0.125, 0.375, 1]
        ... ]), 2, 0).run_simplex().items()}
        {'P': 2.0, 'x1': 1.0, 'x2': 1.0}

        # Non-standard: >= constraints
        Max: 2x1 + 3x2 +  x3
        ST:   x1 +  x2 +  x3 <= 40
             2x1 +  x2 -  x3 >= 10
                 -  x2 +  x3 >= 10
        >>> {key: float(value) for key, value in Tableau(np.array([
        ... [2, 0, 0, 0, -1, -1, 0, 0, 20],
        ... [-2, -3, -1, 0, 0, 0, 0, 0, 0],
        ... [1, 1, 1, 1, 0, 0, 0, 0, 40],
        ... [2, 1, -1, 0, -1, 0, 1, 0, 10],
        ... [0, -1, 1, 0, 0, -1, 0, 1, 10.]
        ... ]), 3, 2).run_simplex().items()}
        {'P': 70.0, 'x1': 10.0, 'x2': 10.0, 'x3': 20.0}

        # Non standard: minimisation and equalities
        Min: x1 +  x2
        ST: 2x1 +  x2 = 12
            6x1 + 5x2 = 40
        >>> {key: float(value) for key, value in Tableau(np.array([
        ... [8, 6, 0, 0, 52],
        ... [1, 1, 0, 0, 0],
        ... [2, 1, 1, 0, 12],
        ... [6, 5, 0, 1, 40.],
        ... ]), 2, 2).run_simplex().items()}
        {'P': 7.0, 'x1': 5.0, 'x2': 2.0}


        # Pivot on slack variables
        Max: 8x1 + 6x2
        ST:   x1 + 3x2 <= 33
             4x1 + 2x2 <= 48
             2x1 + 4x2 <= 48
              x1 +  x2 >= 10
             x1        >= 2
        >>> {key: float(value) for key, value in Tableau(np.array([
        ... [2, 1, 0, 0, 0, -1, -1, 0, 0, 12.0],
        ... [-8, -6, 0, 0, 0, 0, 0, 0, 0, 0.0],
        ... [1, 3, 1, 0, 0, 0, 0, 0, 0, 33.0],
        ... [4, 2, 0, 1, 0, 0, 0, 0, 0, 60.0],
        ... [2, 4, 0, 0, 1, 0, 0, 0, 0, 48.0],
        ... [1, 1, 0, 0, 0, -1, 0, 1, 0, 10.0],
        ... [1, 0, 0, 0, 0, 0, -1, 0, 1, 2.0]
        ... ]), 2, 2).run_simplex().items()} # doctest: +ELLIPSIS
        {'P': 132.0, 'x1': 12.000... 'x2': 5.999...}
        """
        # Stop simplex algorithm from cycling.
        for _ in range(Tableau.maxiter):
            # Completion of each stage removes an objective. If both stages
            # are complete, then no objectives are left
            if not self.objectives:
                # Find the values of each variable at optimal solution
                return self.interpret_tableau()

            row_idx, col_idx = self.find_pivot()

            # If there are no more negative values in objective row
            if self.stop_iter:
                # Delete artificial variable columns and rows. Update attributes
                self.tableau = self.change_stage()
            else:
                self.tableau = self.pivot(row_idx, col_idx)
        return {}

    def interpret_tableau(self) -> dict[str, float]:
        """Given the final tableau, add the corresponding values of the basic
        decision variables to the `output_dict`
        >>> {key: float(value) for key, value in Tableau(np.array([
        ... [0,0,0.875,0.375,5],
        ... [0,1,0.375,-0.125,1],
        ... [1,0,-0.125,0.375,1]
        ... ]),2, 0).interpret_tableau().items()}
        {'P': 5.0, 'x1': 1.0, 'x2': 1.0}
        """
        # P = RHS of final tableau
        output_dict = {"P": abs(self.tableau[0, -1])}

        for i in range(self.n_vars):
            # Gives indices of nonzero entries in the ith column
            nonzero = np.nonzero(self.tableau[:, i])
            n_nonzero = len(nonzero[0])

            # First entry in the nonzero indices
            nonzero_rowidx = nonzero[0][0]
            nonzero_val = self.tableau[nonzero_rowidx, i]

            # If there is only one nonzero value in column, which is one
            if n_nonzero == 1 and nonzero_val == 1:
                rhs_val = self.tableau[nonzero_rowidx, -1]
                output_dict[self.col_titles[i]] = rhs_val
        return output_dict


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: machine_learning/__init__.py
================================================


================================================
FILE: machine_learning/apriori_algorithm.py
================================================
"""
Apriori Algorithm is a Association rule mining technique, also known as market basket
analysis, aims to discover interesting relationships or associations among a set of
items in a transactional or relational database.

For example, Apriori Algorithm states: "If a customer buys item A and item B, then they
are likely to buy item C."  This rule suggests a relationship between items A, B, and C,
indicating that customers who purchased A and B are more likely to also purchase item C.

WIKI: https://en.wikipedia.org/wiki/Apriori_algorithm
Examples: https://www.kaggle.com/code/earthian/apriori-association-rules-mining
"""

from collections import Counter
from itertools import combinations


def load_data() -> list[list[str]]:
    """
    Returns a sample transaction dataset.

    >>> load_data()
    [['milk'], ['milk', 'butter'], ['milk', 'bread'], ['milk', 'bread', 'chips']]
    """
    return [["milk"], ["milk", "butter"], ["milk", "bread"], ["milk", "bread", "chips"]]


def prune(itemset: list, candidates: list, length: int) -> list:
    """
    Prune candidate itemsets that are not frequent.
    The goal of pruning is to filter out candidate itemsets that are not frequent.  This
    is done by checking if all the (k-1) subsets of a candidate itemset are present in
    the frequent itemsets of the previous iteration (valid subsequences of the frequent
    itemsets from the previous iteration).

    Prunes candidate itemsets that are not frequent.

    >>> itemset = ['X', 'Y', 'Z']
    >>> candidates = [['X', 'Y'], ['X', 'Z'], ['Y', 'Z']]
    >>> prune(itemset, candidates, 2)
    [['X', 'Y'], ['X', 'Z'], ['Y', 'Z']]

    >>> itemset = ['1', '2', '3', '4']
    >>> candidates = ['1', '2', '4']
    >>> prune(itemset, candidates, 3)
    []
    """
    itemset_counter = Counter(tuple(item) for item in itemset)
    pruned = []
    for candidate in candidates:
        is_subsequence = True
        for item in candidate:
            item_tuple = tuple(item)
            if (
                item_tuple not in itemset_counter
                or itemset_counter[item_tuple] < length - 1
            ):
                is_subsequence = False
                break
        if is_subsequence:
            pruned.append(candidate)
    return pruned


def apriori(data: list[list[str]], min_support: int) -> list[tuple[list[str], int]]:
    """
    Returns a list of frequent itemsets and their support counts.

    >>> data = [['A', 'B', 'C'], ['A', 'B'], ['A', 'C'], ['A', 'D'], ['B', 'C']]
    >>> apriori(data, 2)
    [(['A', 'B'], 1), (['A', 'C'], 2), (['B', 'C'], 2)]

    >>> data = [['1', '2', '3'], ['1', '2'], ['1', '3'], ['1', '4'], ['2', '3']]
    >>> apriori(data, 3)
    []
    """
    itemset = [list(transaction) for transaction in data]
    frequent_itemsets = []
    length = 1

    while itemset:
        # Count itemset support
        counts = [0] * len(itemset)
        for transaction in data:
            for j, candidate in enumerate(itemset):
                if all(item in transaction for item in candidate):
                    counts[j] += 1

        # Prune infrequent itemsets
        itemset = [item for i, item in enumerate(itemset) if counts[i] >= min_support]

        # Append frequent itemsets (as a list to maintain order)
        for i, item in enumerate(itemset):
            frequent_itemsets.append((sorted(item), counts[i]))

        length += 1
        itemset = prune(itemset, list(combinations(itemset, length)), length)

    return frequent_itemsets


if __name__ == "__main__":
    """
    Apriori algorithm for finding frequent itemsets.

    Args:
        data: A list of transactions, where each transaction is a list of items.
        min_support: The minimum support threshold for frequent itemsets.

    Returns:
        A list of frequent itemsets along with their support counts.
    """
    import doctest

    doctest.testmod()

    # user-defined threshold or minimum support level
    frequent_itemsets = apriori(data=load_data(), min_support=2)
    print("\n".join(f"{itemset}: {support}" for itemset, support in frequent_itemsets))


================================================
FILE: machine_learning/astar.py
================================================
"""
The A* algorithm combines features of uniform-cost search and pure heuristic search to
efficiently compute optimal solutions.

The A* algorithm is a best-first search algorithm in which the cost associated with a
node is f(n) = g(n) + h(n), where g(n) is the cost of the path from the initial state to
node n and h(n) is the heuristic estimate or the cost or a path from node n to a goal.

The A* algorithm introduces a heuristic into a regular graph-searching algorithm,
essentially planning ahead at each step so a more optimal decision is made. For this
reason, A* is known as an algorithm with brains.

https://en.wikipedia.org/wiki/A*_search_algorithm
"""

import numpy as np


class Cell:
    """
    Class cell represents a cell in the world which have the properties:
    position: represented by tuple of x and y coordinates initially set to (0,0).
    parent: Contains the parent cell object visited before we arrived at this cell.
    g, h, f: Parameters used when calling our heuristic function.
    """

    def __init__(self):
        self.position = (0, 0)
        self.parent = None
        self.g = 0
        self.h = 0
        self.f = 0

    """
    Overrides equals method because otherwise cell assign will give
    wrong results.
    """

    def __eq__(self, cell):
        return self.position == cell.position

    def showcell(self):
        print(self.position)


class Gridworld:
    """
    Gridworld class represents the  external world here a grid M*M
    matrix.
    world_size: create a numpy array with the given world_size default is 5.
    """

    def __init__(self, world_size=(5, 5)):
        self.w = np.zeros(world_size)
        self.world_x_limit = world_size[0]
        self.world_y_limit = world_size[1]

    def show(self):
        print(self.w)

    def get_neighbours(self, cell):
        """
        Return the neighbours of cell
        """
        neughbour_cord = [
            (-1, -1),
            (-1, 0),
            (-1, 1),
            (0, -1),
            (0, 1),
            (1, -1),
            (1, 0),
            (1, 1),
        ]
        current_x = cell.position[0]
        current_y = cell.position[1]
        neighbours = []
        for n in neughbour_cord:
            x = current_x + n[0]
            y = current_y + n[1]
            if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                c = Cell()
                c.position = (x, y)
                c.parent = cell
                neighbours.append(c)
        return neighbours


def astar(world, start, goal):
    """
    Implementation of a start algorithm.
    world : Object of the world object.
    start : Object of the cell as  start position.
    stop  : Object of the cell as goal position.

    >>> p = Gridworld()
    >>> start = Cell()
    >>> start.position = (0,0)
    >>> goal = Cell()
    >>> goal.position = (4,4)
    >>> astar(p, start, goal)
    [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]
    """
    _open = []
    _closed = []
    _open.append(start)

    while _open:
        min_f = np.argmin([n.f for n in _open])
        current = _open[min_f]
        _closed.append(_open.pop(min_f))
        if current == goal:
            break
        for n in world.get_neighbours(current):
            for c in _closed:
                if c == n:
                    continue
            n.g = current.g + 1
            x1, y1 = n.position
            x2, y2 = goal.position
            n.h = (y2 - y1) ** 2 + (x2 - x1) ** 2
            n.f = n.h + n.g

            for c in _open:
                if c == n and c.f < n.f:
                    continue
            _open.append(n)
    path = []
    while current.parent is not None:
        path.append(current.position)
        current = current.parent
    path.append(current.position)
    return path[::-1]


if __name__ == "__main__":
    world = Gridworld()
    # Start position and goal
    start = Cell()
    start.position = (0, 0)
    goal = Cell()
    goal.position = (4, 4)
    print(f"path from {start.position} to {goal.position}")
    s = astar(world, start, goal)
    #   Just for visual reasons.
    for i in s:
        world.w[i] = 1
    print(world.w)


================================================
FILE: machine_learning/automatic_differentiation.py
================================================
"""
Demonstration of the Automatic Differentiation (Reverse mode).

Reference: https://en.wikipedia.org/wiki/Automatic_differentiation

Author: Poojan Smart
Email: smrtpoojan@gmail.com
"""

from __future__ import annotations

from collections import defaultdict
from enum import Enum
from types import TracebackType
from typing import Any

import numpy as np
from typing_extensions import Self  # noqa: UP035


class OpType(Enum):
    """
    Class represents list of supported operations on Variable for gradient calculation.
    """

    ADD = 0
    SUB = 1
    MUL = 2
    DIV = 3
    MATMUL = 4
    POWER = 5
    NOOP = 6


class Variable:
    """
    Class represents n-dimensional object which is used to wrap numpy array on which
    operations will be performed and the gradient will be calculated.

    Examples:
    >>> Variable(5.0)
    Variable(5.0)
    >>> Variable([5.0, 2.9])
    Variable([5.  2.9])
    >>> Variable([5.0, 2.9]) + Variable([1.0, 5.5])
    Variable([6.  8.4])
    >>> Variable([[8.0, 10.0]])
    Variable([[ 8. 10.]])
    """

    def __init__(self, value: Any) -> None:
        self.value = np.array(value)

        # pointers to the operations to which the Variable is input
        self.param_to: list[Operation] = []
        # pointer to the operation of which the Variable is output of
        self.result_of: Operation = Operation(OpType.NOOP)

    def __repr__(self) -> str:
        return f"Variable({self.value})"

    def to_ndarray(self) -> np.ndarray:
        return self.value

    def __add__(self, other: Variable) -> Variable:
        result = Variable(self.value + other.value)

        with GradientTracker() as tracker:
            # if tracker is enabled, computation graph will be updated
            if tracker.enabled:
                tracker.append(OpType.ADD, params=[self, other], output=result)
        return result

    def __sub__(self, other: Variable) -> Variable:
        result = Variable(self.value - other.value)

        with GradientTracker() as tracker:
            # if tracker is enabled, computation graph will be updated
            if tracker.enabled:
                tracker.append(OpType.SUB, params=[self, other], output=result)
        return result

    def __mul__(self, other: Variable) -> Variable:
        result = Variable(self.value * other.value)

        with GradientTracker() as tracker:
            # if tracker is enabled, computation graph will be updated
            if tracker.enabled:
                tracker.append(OpType.MUL, params=[self, other], output=result)
        return result

    def __truediv__(self, other: Variable) -> Variable:
        result = Variable(self.value / other.value)

        with GradientTracker() as tracker:
            # if tracker is enabled, computation graph will be updated
            if tracker.enabled:
                tracker.append(OpType.DIV, params=[self, other], output=result)
        return result

    def __matmul__(self, other: Variable) -> Variable:
        result = Variable(self.value @ other.value)

        with GradientTracker() as tracker:
            # if tracker is enabled, computation graph will be updated
            if tracker.enabled:
                tracker.append(OpType.MATMUL, params=[self, other], output=result)
        return result

    def __pow__(self, power: int) -> Variable:
        result = Variable(self.value**power)

        with GradientTracker() as tracker:
            # if tracker is enabled, computation graph will be updated
            if tracker.enabled:
                tracker.append(
                    OpType.POWER,
                    params=[self],
                    output=result,
                    other_params={"power": power},
                )
        return result

    def add_param_to(self, param_to: Operation) -> None:
        self.param_to.append(param_to)

    def add_result_of(self, result_of: Operation) -> None:
        self.result_of = result_of


class Operation:
    """
    Class represents operation between single or two Variable objects.
    Operation objects contains type of operation, pointers to input Variable
    objects and pointer to resulting Variable from the operation.
    """

    def __init__(
        self,
        op_type: OpType,
        other_params: dict | None = None,
    ) -> None:
        self.op_type = op_type
        self.other_params = {} if other_params is None else other_params

    def add_params(self, params: list[Variable]) -> None:
        self.params = params

    def add_output(self, output: Variable) -> None:
        self.output = output

    def __eq__(self, value) -> bool:
        return self.op_type == value if isinstance(value, OpType) else False


class GradientTracker:
    """
    Class contains methods to compute partial derivatives of Variable
    based on the computation graph.

    Examples:

    >>> with GradientTracker() as tracker:
    ...     a = Variable([2.0, 5.0])
    ...     b = Variable([1.0, 2.0])
    ...     m = Variable([1.0, 2.0])
    ...     c = a + b
    ...     d = a * b
    ...     e = c / d
    >>> tracker.gradient(e, a)
    array([-0.25, -0.04])
    >>> tracker.gradient(e, b)
    array([-1.  , -0.25])
    >>> tracker.gradient(e, m) is None
    True

    >>> with GradientTracker() as tracker:
    ...     a = Variable([[2.0, 5.0]])
    ...     b = Variable([[1.0], [2.0]])
    ...     c = a @ b
    >>> tracker.gradient(c, a)
    array([[1., 2.]])
    >>> tracker.gradient(c, b)
    array([[2.],
           [5.]])

    >>> with GradientTracker() as tracker:
    ...     a = Variable([[2.0, 5.0]])
    ...     b = a ** 3
    >>> tracker.gradient(b, a)
    array([[12., 75.]])
    """

    instance = None

    def __new__(cls) -> Self:
        """
        Executes at the creation of class object and returns if
        object is already created. This class follows singleton
        design pattern.
        """
        if cls.instance is None:
            cls.instance = super().__new__(cls)
        return cls.instance

    def __init__(self) -> None:
        self.enabled = False

    def __enter__(self) -> Self:
        self.enabled = True
        return self

    def __exit__(
        self,
        exc_type: type[BaseException] | None,
        exc: BaseException | None,
        traceback: TracebackType | None,
    ) -> None:
        self.enabled = False

    def append(
        self,
        op_type: OpType,
        params: list[Variable],
        output: Variable,
        other_params: dict | None = None,
    ) -> None:
        """
        Adds Operation object to the related Variable objects for
        creating computational graph for calculating gradients.

        Args:
            op_type: Operation type
            params: Input parameters to the operation
            output: Output variable of the operation
        """
        operation = Operation(op_type, other_params=other_params)
        param_nodes = []
        for param in params:
            param.add_param_to(operation)
            param_nodes.append(param)
        output.add_result_of(operation)

        operation.add_params(param_nodes)
        operation.add_output(output)

    def gradient(self, target: Variable, source: Variable) -> np.ndarray | None:
        """
        Reverse accumulation of partial derivatives to calculate gradients
        of target variable with respect to source variable.

        Args:
            target: target variable for which gradients are calculated.
            source: source variable with respect to which the gradients are
            calculated.

        Returns:
            Gradient of the source variable with respect to the target variable
        """

        # partial derivatives with respect to target
        partial_deriv = defaultdict(lambda: 0)
        partial_deriv[target] = np.ones_like(target.to_ndarray())

        # iterating through each operations in the computation graph
        operation_queue = [target.result_of]
        while len(operation_queue) > 0:
            operation = operation_queue.pop()
            for param in operation.params:
                # as per the chain rule, multiplying partial derivatives
                # of variables with respect to the target
                dparam_doutput = self.derivative(param, operation)
                dparam_dtarget = dparam_doutput * partial_deriv[operation.output]
                partial_deriv[param] += dparam_dtarget

                if param.result_of and param.result_of != OpType.NOOP:
                    operation_queue.append(param.result_of)

        return partial_deriv.get(source)

    def derivative(self, param: Variable, operation: Operation) -> np.ndarray:
        """
        Compute the derivative of given operation/function

        Args:
            param: variable to be differentiated
            operation: function performed on the input variable

        Returns:
            Derivative of input variable with respect to the output of
            the operation
        """
        params = operation.params

        if operation == OpType.ADD:
            return np.ones_like(params[0].to_ndarray(), dtype=np.float64)
        if operation == OpType.SUB:
            if params[0] == param:
                return np.ones_like(params[0].to_ndarray(), dtype=np.float64)
            return -np.ones_like(params[1].to_ndarray(), dtype=np.float64)
        if operation == OpType.MUL:
            return (
                params[1].to_ndarray().T
                if params[0] == param
                else params[0].to_ndarray().T
            )
        if operation == OpType.DIV:
            if params[0] == param:
                return 1 / params[1].to_ndarray()
            return -params[0].to_ndarray() / (params[1].to_ndarray() ** 2)
        if operation == OpType.MATMUL:
            return (
                params[1].to_ndarray().T
                if params[0] == param
                else params[0].to_ndarray().T
            )
        if operation == OpType.POWER:
            power = operation.other_params["power"]
            return power * (params[0].to_ndarray() ** (power - 1))

        err_msg = f"invalid operation type: {operation.op_type}"
        raise ValueError(err_msg)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: machine_learning/data_transformations.py
================================================
"""
Normalization.

Wikipedia: https://en.wikipedia.org/wiki/Normalization
Normalization is the process of converting numerical data to a standard range of values.
This range is typically between [0, 1] or [-1, 1]. The equation for normalization is
x_norm = (x - x_min)/(x_max - x_min) where x_norm is the normalized value, x is the
value, x_min is the minimum value within the column or list of data, and x_max is the
maximum value within the column or list of data. Normalization is used to speed up the
training of data and put all of the data on a similar scale. This is useful because
variance in the range of values of a dataset can heavily impact optimization
(particularly Gradient Descent).

Standardization Wikipedia: https://en.wikipedia.org/wiki/Standardization
Standardization is the process of converting numerical data to a normally distributed
range of values. This range will have a mean of 0 and standard deviation of 1. This is
also known as z-score normalization. The equation for standardization is
x_std = (x - mu)/(sigma) where mu is the mean of the column or list of values and sigma
is the standard deviation of the column or list of values.

Choosing between Normalization & Standardization is more of an art of a science, but it
is often recommended to run experiments with both to see which performs better.
Additionally, a few rules of thumb are:
    1. gaussian (normal) distributions work better with standardization
    2. non-gaussian (non-normal) distributions work better with normalization
    3. If a column or list of values has extreme values / outliers, use standardization
"""

from statistics import mean, stdev


def normalization(data: list, ndigits: int = 3) -> list:
    """
    Return a normalized list of values.

    @params: data, a list of values to normalize
    @returns: a list of normalized values (rounded to ndigits decimal places)
    @examples:
    >>> normalization([2, 7, 10, 20, 30, 50])
    [0.0, 0.104, 0.167, 0.375, 0.583, 1.0]
    >>> normalization([5, 10, 15, 20, 25])
    [0.0, 0.25, 0.5, 0.75, 1.0]
    """
    # variables for calculation
    x_min = min(data)
    x_max = max(data)
    # normalize data
    return [round((x - x_min) / (x_max - x_min), ndigits) for x in data]


def standardization(data: list, ndigits: int = 3) -> list:
    """
    Return a standardized list of values.

    @params: data, a list of values to standardize
    @returns: a list of standardized values (rounded to ndigits decimal places)
    @examples:
    >>> standardization([2, 7, 10, 20, 30, 50])
    [-0.999, -0.719, -0.551, 0.009, 0.57, 1.69]
    >>> standardization([5, 10, 15, 20, 25])
    [-1.265, -0.632, 0.0, 0.632, 1.265]
    """
    # variables for calculation
    mu = mean(data)
    sigma = stdev(data)
    # standardize data
    return [round((x - mu) / (sigma), ndigits) for x in data]


================================================
FILE: machine_learning/decision_tree.py
================================================
"""
Implementation of a basic regression decision tree.
Input data set: The input data set must be 1-dimensional with continuous labels.
Output: The decision tree maps a real number input to a real number output.
"""

import numpy as np


class DecisionTree:
    def __init__(self, depth=5, min_leaf_size=5):
        self.depth = depth
        self.decision_boundary = 0
        self.left = None
        self.right = None
        self.min_leaf_size = min_leaf_size
        self.prediction = None

    def mean_squared_error(self, labels, prediction):
        """
        mean_squared_error:
        @param labels: a one-dimensional numpy array
        @param prediction: a floating point value
        return value: mean_squared_error calculates the error if prediction is used to
            estimate the labels
        >>> tester = DecisionTree()
        >>> test_labels = np.array([1,2,3,4,5,6,7,8,9,10])
        >>> test_prediction = float(6)
        >>> bool(tester.mean_squared_error(test_labels, test_prediction) == (
        ...     TestDecisionTree.helper_mean_squared_error_test(test_labels,
        ...         test_prediction)))
        True
        >>> test_labels = np.array([1,2,3])
        >>> test_prediction = float(2)
        >>> bool(tester.mean_squared_error(test_labels, test_prediction) == (
        ...     TestDecisionTree.helper_mean_squared_error_test(test_labels,
        ...         test_prediction)))
        True
        """
        if labels.ndim != 1:
            print("Error: Input labels must be one dimensional")

        return np.mean((labels - prediction) ** 2)

    def train(self, x, y):
        """
        train:
        @param x: a one-dimensional numpy array
        @param y: a one-dimensional numpy array.
        The contents of y are the labels for the corresponding X values

        train() does not have a return value

        Examples:
        1. Try to train when x & y are of same length & 1 dimensions (No errors)
        >>> dt = DecisionTree()
        >>> dt.train(np.array([10,20,30,40,50]),np.array([0,0,0,1,1]))

        2. Try to train when x is 2 dimensions
        >>> dt = DecisionTree()
        >>> dt.train(np.array([[1,2,3,4,5],[1,2,3,4,5]]),np.array([0,0,0,1,1]))
        Traceback (most recent call last):
            ...
        ValueError: Input data set must be one-dimensional

        3. Try to train when x and y are not of the same length
        >>> dt = DecisionTree()
        >>> dt.train(np.array([1,2,3,4,5]),np.array([[0,0,0,1,1],[0,0,0,1,1]]))
        Traceback (most recent call last):
            ...
        ValueError: x and y have different lengths

        4. Try to train when x & y are of the same length but different dimensions
        >>> dt = DecisionTree()
        >>> dt.train(np.array([1,2,3,4,5]),np.array([[1],[2],[3],[4],[5]]))
        Traceback (most recent call last):
            ...
        ValueError: Data set labels must be one-dimensional

        This section is to check that the inputs conform to our dimensionality
        constraints
        """
        if x.ndim != 1:
            raise ValueError("Input data set must be one-dimensional")
        if len(x) != len(y):
            raise ValueError("x and y have different lengths")
        if y.ndim != 1:
            raise ValueError("Data set labels must be one-dimensional")

        if len(x) < 2 * self.min_leaf_size:
            self.prediction = np.mean(y)
            return

        if self.depth == 1:
            self.prediction = np.mean(y)
            return

        best_split = 0
        min_error = self.mean_squared_error(x, np.mean(y)) * 2

        """
        loop over all possible splits for the decision tree. find the best split.
        if no split exists that is less than 2 * error for the entire array
        then the data set is not split and the average for the entire array is used as
        the predictor
        """
        for i in range(len(x)):
            if len(x[:i]) < self.min_leaf_size:  # noqa: SIM114
                continue
            elif len(x[i:]) < self.min_leaf_size:
                continue
            else:
                error_left = self.mean_squared_error(x[:i], np.mean(y[:i]))
                error_right = self.mean_squared_error(x[i:], np.mean(y[i:]))
                error = error_left + error_right
                if error < min_error:
                    best_split = i
                    min_error = error

        if best_split != 0:
            left_x = x[:best_split]
            left_y = y[:best_split]
            right_x = x[best_split:]
            right_y = y[best_split:]

            self.decision_boundary = x[best_split]
            self.left = DecisionTree(
                depth=self.depth - 1, min_leaf_size=self.min_leaf_size
            )
            self.right = DecisionTree(
                depth=self.depth - 1, min_leaf_size=self.min_leaf_size
            )
            self.left.train(left_x, left_y)
            self.right.train(right_x, right_y)
        else:
            self.prediction = np.mean(y)

        return

    def predict(self, x):
        """
        predict:
        @param x: a floating point value to predict the label of
        the prediction function works by recursively calling the predict function
        of the appropriate subtrees based on the tree's decision boundary
        """
        if self.prediction is not None:
            return self.prediction
        elif self.left is not None and self.right is not None:
            if x >= self.decision_boundary:
                return self.right.predict(x)
            else:
                return self.left.predict(x)
        else:
            raise ValueError("Decision tree not yet trained")


class TestDecisionTree:
    """Decision Tres test class"""

    @staticmethod
    def helper_mean_squared_error_test(labels, prediction):
        """
        helper_mean_squared_error_test:
        @param labels: a one dimensional numpy array
        @param prediction: a floating point value
        return value: helper_mean_squared_error_test calculates the mean squared error
        """
        squared_error_sum = float(0)
        for label in labels:
            squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size)


def main():
    """
    In this demonstration we're generating a sample data set from the sin function in
    numpy.  We then train a decision tree on the data set and use the decision tree to
    predict the label of 10 different test values. Then the mean squared error over
    this test is displayed.
    """
    x = np.arange(-1.0, 1.0, 0.005)
    y = np.sin(x)

    tree = DecisionTree(depth=10, min_leaf_size=10)
    tree.train(x, y)

    rng = np.random.default_rng()
    test_cases = (rng.random(10) * 2) - 1
    predictions = np.array([tree.predict(x) for x in test_cases])
    avg_error = np.mean((predictions - test_cases) ** 2)

    print("Test values: " + str(test_cases))
    print("Predictions: " + str(predictions))
    print("Average error: " + str(avg_error))


if __name__ == "__main__":
    main()
    import doctest

    doctest.testmod(name="mean_squared_error", verbose=True)


================================================
FILE: machine_learning/dimensionality_reduction.py
================================================
#  Copyright (c) 2023 Diego Gasco (diego.gasco99@gmail.com), Diegomangasco on GitHub

"""
Requirements:
  - numpy version 1.21
  - scipy version 1.3.3
Notes:
  - Each column of the features matrix corresponds to a class item
"""

import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="%(message)s")


def column_reshape(input_array: np.ndarray) -> np.ndarray:
    """Function to reshape a row Numpy array into a column Numpy array
    >>> input_array = np.array([1, 2, 3])
    >>> column_reshape(input_array)
    array([[1],
           [2],
           [3]])
    """

    return input_array.reshape((input_array.size, 1))


def covariance_within_classes(
    features: np.ndarray, labels: np.ndarray, classes: int
) -> np.ndarray:
    """Function to compute the covariance matrix inside each class.
    >>> features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    >>> labels = np.array([0, 1, 0])
    >>> covariance_within_classes(features, labels, 2)
    array([[0.66666667, 0.66666667, 0.66666667],
           [0.66666667, 0.66666667, 0.66666667],
           [0.66666667, 0.66666667, 0.66666667]])
    """

    covariance_sum = np.nan
    for i in range(classes):
        data = features[:, labels == i]
        data_mean = data.mean(1)
        # Centralize the data of class i
        centered_data = data - column_reshape(data_mean)
        if i > 0:
            # If covariance_sum is not None
            covariance_sum += np.dot(centered_data, centered_data.T)
        else:
            # If covariance_sum is np.nan (i.e. first loop)
            covariance_sum = np.dot(centered_data, centered_data.T)

    return covariance_sum / features.shape[1]


def covariance_between_classes(
    features: np.ndarray, labels: np.ndarray, classes: int
) -> np.ndarray:
    """Function to compute the covariance matrix between multiple classes
    >>> features = np.array([[9, 2, 3], [4, 3, 6], [1, 8, 9]])
    >>> labels = np.array([0, 1, 0])
    >>> covariance_between_classes(features, labels, 2)
    array([[ 3.55555556,  1.77777778, -2.66666667],
           [ 1.77777778,  0.88888889, -1.33333333],
           [-2.66666667, -1.33333333,  2.        ]])
    """

    general_data_mean = features.mean(1)
    covariance_sum = np.nan
    for i in range(classes):
        data = features[:, labels == i]
        device_data = data.shape[1]
        data_mean = data.mean(1)
        if i > 0:
            # If covariance_sum is not None
            covariance_sum += device_data * np.dot(
                column_reshape(data_mean) - column_reshape(general_data_mean),
                (column_reshape(data_mean) - column_reshape(general_data_mean)).T,
            )
        else:
            # If covariance_sum is np.nan (i.e. first loop)
            covariance_sum = device_data * np.dot(
                column_reshape(data_mean) - column_reshape(general_data_mean),
                (column_reshape(data_mean) - column_reshape(general_data_mean)).T,
            )

    return covariance_sum / features.shape[1]


def principal_component_analysis(features: np.ndarray, dimensions: int) -> np.ndarray:
    """
    Principal Component Analysis.

    For more details, see: https://en.wikipedia.org/wiki/Principal_component_analysis.
    Parameters:
        * features: the features extracted from the dataset
        * dimensions: to filter the projected data for the desired dimension

    >>> test_principal_component_analysis()
    """

    # Check if the features have been loaded
    if features.any():
        data_mean = features.mean(1)
        # Center the dataset
        centered_data = features - np.reshape(data_mean, (data_mean.size, 1))
        covariance_matrix = np.dot(centered_data, centered_data.T) / features.shape[1]
        _, eigenvectors = np.linalg.eigh(covariance_matrix)
        # Take all the columns in the reverse order (-1), and then takes only the first
        filtered_eigenvectors = eigenvectors[:, ::-1][:, 0:dimensions]
        # Project the database on the new space
        projected_data = np.dot(filtered_eigenvectors.T, features)
        logging.info("Principal Component Analysis computed")

        return projected_data
    else:
        logging.basicConfig(level=logging.ERROR, format="%(message)s", force=True)
        logging.error("Dataset empty")
        raise AssertionError


def linear_discriminant_analysis(
    features: np.ndarray, labels: np.ndarray, classes: int, dimensions: int
) -> np.ndarray:
    """
    Linear Discriminant Analysis.

    For more details, see: https://en.wikipedia.org/wiki/Linear_discriminant_analysis.
    Parameters:
        * features: the features extracted from the dataset
        * labels: the class labels of the features
        * classes: the number of classes present in the dataset
        * dimensions: to filter the projected data for the desired dimension

    >>> test_linear_discriminant_analysis()
    """

    # Check if the dimension desired is less than the number of classes
    assert classes > dimensions

    # Check if features have been already loaded
    if features.any:
        _, eigenvectors = eigh(
            covariance_between_classes(features, labels, classes),
            covariance_within_classes(features, labels, classes),
        )
        filtered_eigenvectors = eigenvectors[:, ::-1][:, :dimensions]
        svd_matrix, _, _ = np.linalg.svd(filtered_eigenvectors)
        filtered_svd_matrix = svd_matrix[:, 0:dimensions]
        projected_data = np.dot(filtered_svd_matrix.T, features)
        logging.info("Linear Discriminant Analysis computed")

        return projected_data
    else:
        logging.basicConfig(level=logging.ERROR, format="%(message)s", force=True)
        logging.error("Dataset empty")
        raise AssertionError


def test_linear_discriminant_analysis() -> None:
    # Create dummy dataset with 2 classes and 3 features
    features = np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]])
    labels = np.array([0, 0, 0, 1, 1])
    classes = 2
    dimensions = 2

    # Assert that the function raises an AssertionError if dimensions > classes
    with pytest.raises(AssertionError) as error_info:  # noqa: PT012
        projected_data = linear_discriminant_analysis(
            features, labels, classes, dimensions
        )
        if isinstance(projected_data, np.ndarray):
            raise AssertionError(
                "Did not raise AssertionError for dimensions > classes"
            )
        assert error_info.type is AssertionError


def test_principal_component_analysis() -> None:
    features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    dimensions = 2
    expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])

    with pytest.raises(AssertionError) as error_info:  # noqa: PT012
        output = principal_component_analysis(features, dimensions)
        if not np.allclose(expected_output, output):
            raise AssertionError
        assert error_info.type is AssertionError


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: machine_learning/forecasting/__init__.py
================================================


================================================
FILE: machine_learning/forecasting/ex_data.csv
================================================
total_users,total_events,days
18231,0.0,1
22621,1.0,2
15675,0.0,3
23583,1.0,4
68351,5.0,5
34338,3.0,6
19238,0.0,0
24192,0.0,1
70349,0.0,2
103510,0.0,3
128355,1.0,4
148484,6.0,5
153489,3.0,6
162667,1.0,0
311430,3.0,1
435663,7.0,2
273526,0.0,3
628588,2.0,4
454989,13.0,5
539040,3.0,6
52974,1.0,0
103451,2.0,1
810020,5.0,2
580982,3.0,3
216515,0.0,4
134694,10.0,5
93563,1.0,6
55432,1.0,0
169634,1.0,1
254908,4.0,2
315285,3.0,3
191764,0.0,4
514284,7.0,5
181214,4.0,6
78459,2.0,0
161620,3.0,1
245610,4.0,2
326722,5.0,3
214578,0.0,4
312365,5.0,5
232454,4.0,6
178368,1.0,0
97152,1.0,1
222813,4.0,2
285852,4.0,3
192149,1.0,4
142241,1.0,5
173011,2.0,6
56488,3.0,0
89572,2.0,1
356082,2.0,2
172799,0.0,3
142300,1.0,4
78432,2.0,5
539023,9.0,6
62389,1.0,0
70247,1.0,1
89229,0.0,2
94583,1.0,3
102455,0.0,4
129270,0.0,5
311409,1.0,6
1837026,0.0,0
361824,0.0,1
111379,2.0,2
76337,2.0,3
96747,0.0,4
92058,0.0,5
81929,2.0,6
143423,0.0,0
82939,0.0,1
74403,1.0,2
68234,0.0,3
94556,1.0,4
80311,0.0,5
75283,3.0,6
77724,0.0,0
49229,2.0,1
65708,2.0,2
273864,1.0,3
1711281,0.0,4
1900253,5.0,5
343071,1.0,6
1551326,0.0,0
56636,1.0,1
272782,2.0,2
1785678,0.0,3
241866,0.0,4
461904,0.0,5
2191901,2.0,6
102925,0.0,0
242778,1.0,1
298608,0.0,2
322458,10.0,3
216027,9.0,4
916052,12.0,5
193278,12.0,6
263207,8.0,0
672948,10.0,1
281909,1.0,2
384562,1.0,3
1027375,2.0,4
828905,9.0,5
624188,22.0,6
392218,8.0,0
292581,10.0,1
299869,12.0,2
769455,20.0,3
316443,8.0,4
1212864,24.0,5
1397338,28.0,6
223249,8.0,0
191264,14.0,1


================================================
FILE: machine_learning/forecasting/run.py
================================================
"""
this is code for forecasting
but I modified it and used it for safety checker of data
for ex: you have an online shop and for some reason some data are
missing (the amount of data that u expected are not supposed to be)
        then we can use it
*ps : 1. ofc we can use normal statistic method but in this case
         the data is quite absurd and only a little^^
      2. ofc u can use this and modified it for forecasting purpose
         for the next 3 months sales or something,
         u can just adjust it for ur own purpose
"""

from warnings import simplefilter

import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX


def linear_regression_prediction(
    train_dt: list, train_usr: list, train_mtch: list, test_dt: list, test_mtch: list
) -> float:
    """
    First method: linear regression
    input : training data (date, total_user, total_event) in list of float
    output : list of total user prediction in float
    >>> n = linear_regression_prediction([2,3,4,5], [5,3,4,6], [3,1,2,4], [2,1], [2,2])
    >>> bool(abs(n - 5.0) < 1e-6)  # Checking precision because of floating point errors
    True
    """
    x = np.array([[1, item, train_mtch[i]] for i, item in enumerate(train_dt)])
    y = np.array(train_usr)
    beta = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose(), x)), x.transpose()), y)
    return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2])


def sarimax_predictor(train_user: list, train_match: list, test_match: list) -> float:
    """
    second method: Sarimax
    sarimax is a statistic method which using previous input
    and learn its pattern to predict future data
    input : training data (total_user, with exog data = total_event) in list of float
    output : list of total user prediction in float
    >>> sarimax_predictor([4,2,6,8], [3,1,2,4], [2])
    6.6666671111109626
    """
    # Suppress the User Warning raised by SARIMAX due to insufficient observations
    simplefilter("ignore", UserWarning)
    order = (1, 2, 1)
    seasonal_order = (1, 1, 1, 7)
    model = SARIMAX(
        train_user, exog=train_match, order=order, seasonal_order=seasonal_order
    )
    model_fit = model.fit(disp=False, maxiter=600, method="nm")
    result = model_fit.predict(1, len(test_match), exog=[test_match])
    return float(result[0])


def support_vector_regressor(x_train: list, x_test: list, train_user: list) -> float:
    """
    Third method: Support vector regressor
    svr is quite the same with svm(support vector machine)
    it uses the same principles as the SVM for classification,
    with only a few minor differences and the only different is that
    it suits better for regression purpose
    input : training data (date, total_user, total_event) in list of float
    where x = list of set (date and total event)
    output : list of total user prediction in float
    >>> support_vector_regressor([[5,2],[1,5],[6,2]], [[3,2]], [2,1,4])
    1.634932078116079
    """
    regressor = SVR(kernel="rbf", C=1, gamma=0.1, epsilon=0.1)
    regressor.fit(x_train, train_user)
    y_pred = regressor.predict(x_test)
    return float(y_pred[0])


def interquartile_range_checker(train_user: list) -> float:
    """
    Optional method: interquatile range
    input : list of total user in float
    output : low limit of input in float
    this method can be used to check whether some data is outlier or not
    >>> interquartile_range_checker([1,2,3,4,5,6,7,8,9,10])
    2.8
    """
    train_user.sort()
    q1 = np.percentile(train_user, 25)
    q3 = np.percentile(train_user, 75)
    iqr = q3 - q1
    low_lim = q1 - (iqr * 0.1)
    return float(low_lim)


def data_safety_checker(list_vote: list, actual_result: float) -> bool:
    """
    Used to review all the votes (list result prediction)
    and compare it to the actual result.
    input : list of predictions
    output : print whether it's safe or not
    >>> data_safety_checker([2, 3, 4], 5.0)
    False
    """
    safe = 0
    not_safe = 0

    if not isinstance(actual_result, float):
        raise TypeError("Actual result should be float. Value passed is a list")

    for i in list_vote:
        if i > actual_result:
            safe = not_safe + 1
        elif abs(abs(i) - abs(actual_result)) <= 0.1:
            safe += 1
        else:
            not_safe += 1
    return safe > not_safe


if __name__ == "__main__":
    """
    data column = total user in a day, how much online event held in one day,
    what day is that(sunday-saturday)
    """
    data_input_df = pd.read_csv("ex_data.csv")

    # start normalization
    normalize_df = Normalizer().fit_transform(data_input_df.values)
    # split data
    total_date = normalize_df[:, 2].tolist()
    total_user = normalize_df[:, 0].tolist()
    total_match = normalize_df[:, 1].tolist()

    # for svr (input variable = total date and total match)
    x = normalize_df[:, [1, 2]].tolist()
    x_train = x[: len(x) - 1]
    x_test = x[len(x) - 1 :]

    # for linear regression & sarimax
    train_date = total_date[: len(total_date) - 1]
    train_user = total_user[: len(total_user) - 1]
    train_match = total_match[: len(total_match) - 1]

    test_date = total_date[len(total_date) - 1 :]
    test_user = total_user[len(total_user) - 1 :]
    test_match = total_match[len(total_match) - 1 :]

    # voting system with forecasting
    res_vote = [
        linear_regression_prediction(
            train_date, train_user, train_match, test_date, test_match
        ),
        sarimax_predictor(train_user, train_match, test_match),
        support_vector_regressor(x_train, x_test, train_user),
    ]

    # check the safety of today's data
    not_str = "" if data_safety_checker(res_vote, test_user[0]) else "not "
    print(f"Today's data is {not_str}safe.")


================================================
FILE: machine_learning/frequent_pattern_growth.py
================================================
"""
The Frequent Pattern Growth algorithm (FP-Growth) is a widely used data mining
technique for discovering frequent itemsets in large transaction databases.

It overcomes some of the limitations of traditional methods such as Apriori by
efficiently constructing the FP-Tree

WIKI: https://athena.ecs.csus.edu/~mei/associationcw/FpGrowth.html

Examples: https://www.javatpoint.com/fp-growth-algorithm-in-data-mining
"""

from __future__ import annotations

from dataclasses import dataclass, field


@dataclass
class TreeNode:
    """
    A node in a Frequent Pattern tree.

    Args:
        name: The name of this node.
        num_occur: The number of occurrences of the node.
        parent_node: The parent node.

    Example:
    >>> parent = TreeNode("Parent", 1, None)
    >>> child = TreeNode("Child", 2, parent)
    >>> child.name
    'Child'
    >>> child.count
    2
    """

    name: str
    count: int
    parent: TreeNode | None = None
    children: dict[str, TreeNode] = field(default_factory=dict)
    node_link: TreeNode | None = None

    def __repr__(self) -> str:
        return f"TreeNode({self.name!r}, {self.count!r}, {self.parent!r})"

    def inc(self, num_occur: int) -> None:
        self.count += num_occur

    def disp(self, ind: int = 1) -> None:
        print(f"{'  ' * ind} {self.name}  {self.count}")
        for child in self.children.values():
            child.disp(ind + 1)


def create_tree(data_set: list, min_sup: int = 1) -> tuple[TreeNode, dict]:
    """
    Create Frequent Pattern tree

    Args:
        data_set: A list of transactions, where each transaction is a list of items.
        min_sup: The minimum support threshold.
        Items with support less than this will be pruned. Default is 1.

    Returns:
        The root of the FP-Tree.
        header_table: The header table dictionary with item information.

    Example:
    >>> data_set = [
    ...    ['A', 'B', 'C'],
    ...    ['A', 'C'],
    ...    ['A', 'B', 'E'],
    ...    ['A', 'B', 'C', 'E'],
    ...    ['B', 'E']
    ... ]
    >>> min_sup = 2
    >>> fp_tree, header_table = create_tree(data_set, min_sup)
    >>> fp_tree
    TreeNode('Null Set', 1, None)
    >>> len(header_table)
    4
    >>> header_table["A"]
    [[4, None], TreeNode('A', 4, TreeNode('Null Set', 1, None))]
    >>> header_table["E"][1]  # doctest: +NORMALIZE_WHITESPACE
    TreeNode('E', 1, TreeNode('B', 3, TreeNode('A', 4, TreeNode('Null Set', 1, None))))
    >>> sorted(header_table)
    ['A', 'B', 'C', 'E']
    >>> fp_tree.name
    'Null Set'
    >>> sorted(fp_tree.children)
    ['A', 'B']
    >>> fp_tree.children['A'].name
    'A'
    >>> sorted(fp_tree.children['A'].children)
    ['B', 'C']
    """
    header_table: dict = {}
    for trans in data_set:
        for item in trans:
            header_table[item] = header_table.get(item, [0, None])
            header_table[item][0] += 1

    for k in list(header_table):
        if header_table[k][0] < min_sup:
            del header_table[k]

    if not (freq_item_set := set(header_table)):
        return TreeNode("Null Set", 1, None), {}

    for key, value in header_table.items():
        header_table[key] = [value, None]

    fp_tree = TreeNode("Null Set", 1, None)  # Parent is None for the root node
    for tran_set in data_set:
        local_d = {
            item: header_table[item][0] for item in tran_set if item in freq_item_set
        }
        if local_d:
            sorted_items = sorted(
                local_d.items(), key=lambda item_info: item_info[1], reverse=True
            )
            ordered_items = [item[0] for item in sorted_items]
            update_tree(ordered_items, fp_tree, header_table, 1)

    return fp_tree, header_table


def update_tree(items: list, in_tree: TreeNode, header_table: dict, count: int) -> None:
    """
    Update the FP-Tree with a transaction.

    Args:
        items: List of items in the transaction.
        in_tree: The current node in the FP-Tree.
        header_table: The header table dictionary with item information.
        count: The count of the transaction.

    Example:
    >>> data_set = [
    ...    ['A', 'B', 'C'],
    ...    ['A', 'C'],
    ...    ['A', 'B', 'E'],
    ...    ['A', 'B', 'C', 'E'],
    ...    ['B', 'E']
    ... ]
    >>> min_sup = 2
    >>> fp_tree, header_table = create_tree(data_set, min_sup)
    >>> fp_tree
    TreeNode('Null Set', 1, None)
    >>> transaction = ['A', 'B', 'E']
    >>> update_tree(transaction, fp_tree, header_table, 1)
    >>> fp_tree
    TreeNode('Null Set', 1, None)
    >>> fp_tree.children['A'].children['B'].children['E'].children
    {}
    >>> fp_tree.children['A'].children['B'].children['E'].count
    2
    >>> header_table['E'][1].name
    'E'
    """
    if items[0] in in_tree.children:
        in_tree.children[items[0]].inc(count)
    else:
        in_tree.children[items[0]] = TreeNode(items[0], count, in_tree)
        if header_table[items[0]][1] is None:
            header_table[items[0]][1] = in_tree.children[items[0]]
        else:
            update_header(header_table[items[0]][1], in_tree.children[items[0]])
    if len(items) > 1:
        update_tree(items[1:], in_tree.children[items[0]], header_table, count)


def update_header(node_to_test: TreeNode, target_node: TreeNode) -> TreeNode:
    """
    Update the header table with a node link.

    Args:
        node_to_test: The node to be updated in the header table.
        target_node: The node to link to.

    Example:
    >>> data_set = [
    ...    ['A', 'B', 'C'],
    ...    ['A', 'C'],
    ...    ['A', 'B', 'E'],
    ...    ['A', 'B', 'C', 'E'],
    ...    ['B', 'E']
    ... ]
    >>> min_sup = 2
    >>> fp_tree, header_table = create_tree(data_set, min_sup)
    >>> fp_tree
    TreeNode('Null Set', 1, None)
    >>> node1 = TreeNode("A", 3, None)
    >>> node2 = TreeNode("B", 4, None)
    >>> node1
    TreeNode('A', 3, None)
    >>> node1 = update_header(node1, node2)
    >>> node1
    TreeNode('A', 3, None)
    >>> node1.node_link
    TreeNode('B', 4, None)
    >>> node2.node_link is None
    True
    """
    while node_to_test.node_link is not None:
        node_to_test = node_to_test.node_link
    if node_to_test.node_link is None:
        node_to_test.node_link = target_node
    # Return the updated node
    return node_to_test


def ascend_tree(leaf_node: TreeNode, prefix_path: list[str]) -> None:
    """
    Ascend the FP-Tree from a leaf node to its root, adding item names to the prefix
    path.

    Args:
        leaf_node: The leaf node to start ascending from.
        prefix_path: A list to store the item as they are ascended.

    Example:
    >>> data_set = [
    ...    ['A', 'B', 'C'],
    ...    ['A', 'C'],
    ...    ['A', 'B', 'E'],
    ...    ['A', 'B', 'C', 'E'],
    ...    ['B', 'E']
    ... ]
    >>> min_sup = 2
    >>> fp_tree, header_table = create_tree(data_set, min_sup)

    >>> path = []
    >>> ascend_tree(fp_tree.children['A'], path)
    >>> path # ascending from a leaf node 'A'
    ['A']
    """
    if leaf_node.parent is not None:
        prefix_path.append(leaf_node.name)
        ascend_tree(leaf_node.parent, prefix_path)


def find_prefix_path(base_pat: frozenset, tree_node: TreeNode | None) -> dict:  # noqa: ARG001
    """
    Find the conditional pattern base for a given base pattern.

    Args:
        base_pat: The base pattern for which to find the conditional pattern base.
        tree_node: The node in the FP-Tree.

    Example:
    >>> data_set = [
    ...    ['A', 'B', 'C'],
    ...    ['A', 'C'],
    ...    ['A', 'B', 'E'],
    ...    ['A', 'B', 'C', 'E'],
    ...    ['B', 'E']
    ... ]
    >>> min_sup = 2
    >>> fp_tree, header_table = create_tree(data_set, min_sup)
    >>> fp_tree
    TreeNode('Null Set', 1, None)
    >>> len(header_table)
    4
    >>> base_pattern = frozenset(['A'])
    >>> sorted(find_prefix_path(base_pattern, fp_tree.children['A']))
    []
    """
    cond_pats: dict = {}
    while tree_node is not None:
        prefix_path: list = []
        ascend_tree(tree_node, prefix_path)
        if len(prefix_path) > 1:
            cond_pats[frozenset(prefix_path[1:])] = tree_node.count
        tree_node = tree_node.node_link
    return cond_pats


def mine_tree(
    in_tree: TreeNode,  # noqa: ARG001
    header_table: dict,
    min_sup: int,
    pre_fix: set,
    freq_item_list: list,
) -> None:
    """
    Mine the FP-Tree recursively to discover frequent itemsets.

    Args:
        in_tree: The FP-Tree to mine.
        header_table: The header table dictionary with item information.
        min_sup: The minimum support threshold.
        pre_fix: A set of items as a prefix for the itemsets being mined.
        freq_item_list: A list to store the frequent itemsets.

    Example:
    >>> data_set = [
    ...    ['A', 'B', 'C'],
    ...    ['A', 'C'],
    ...    ['A', 'B', 'E'],
    ...    ['A', 'B', 'C', 'E'],
    ...    ['B', 'E']
    ... ]
    >>> min_sup = 2
    >>> fp_tree, header_table = create_tree(data_set, min_sup)
    >>> fp_tree
    TreeNode('Null Set', 1, None)
    >>> frequent_itemsets = []
    >>> mine_tree(fp_tree, header_table, min_sup, set([]), frequent_itemsets)
    >>> expe_itm = [{'C'}, {'C', 'A'}, {'E'}, {'A', 'E'}, {'E', 'B'}, {'A'}, {'B'}]
    >>> all(expected in frequent_itemsets for expected in expe_itm)
    True
    """
    sorted_items = sorted(header_table.items(), key=lambda item_info: item_info[1][0])
    big_l = [item[0] for item in sorted_items]
    for base_pat in big_l:
        new_freq_set = pre_fix.copy()
        new_freq_set.add(base_pat)
        freq_item_list.append(new_freq_set)
        cond_patt_bases = find_prefix_path(base_pat, header_table[base_pat][1])
        my_cond_tree, my_head = create_tree(list(cond_patt_bases), min_sup)
        if my_head is not None:
            # Pass header_table[base_pat][1] as node_to_test to update_header
            header_table[base_pat][1] = update_header(
                header_table[base_pat][1], my_cond_tree
            )
            mine_tree(my_cond_tree, my_head, min_sup, new_freq_set, freq_item_list)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    data_set: list[frozenset] = [
        frozenset(["bread", "milk", "cheese"]),
        frozenset(["bread", "milk"]),
        frozenset(["bread", "diapers"]),
        frozenset(["bread", "milk", "diapers"]),
        frozenset(["milk", "diapers"]),
        frozenset(["milk", "cheese"]),
        frozenset(["diapers", "cheese"]),
        frozenset(["bread", "milk", "cheese", "diapers"]),
    ]
    print(f"{len(data_set) = }")
    fp_tree, header_table = create_tree(data_set, min_sup=3)
    print(f"{fp_tree = }")
    print(f"{len(header_table) = }")
    freq_items: list = []
    mine_tree(fp_tree, header_table, 3, set(), freq_items)
    print(f"{freq_items = }")


================================================
FILE: machine_learning/gaussian_naive_bayes.py.broken.txt
================================================
# Gaussian Naive Bayes Example
import time

from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score, plot_confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB


def main():

    """
    Gaussian Naive Bayes Example using sklearn function.
    Iris type dataset is used to demonstrate algorithm.
    """

    # Load Iris dataset
    iris = load_iris()

    # Split dataset into train and test data
    x = iris["data"]  # features
    y = iris["target"]
    x_train, x_test, y_train, y_test = train_test_split(
        x, y, test_size=0.3, random_state=1
    )

    # Gaussian Naive Bayes
    nb_model = GaussianNB()
    time.sleep(2.9)
    model_fit = nb_model.fit(x_train, y_train)
    y_pred = model_fit.predict(x_test)  # Predictions on the test set

    # Display Confusion Matrix
    plot_confusion_matrix(
        nb_model,
        x_test,
        y_test,
        display_labels=iris["target_names"],
        cmap="Blues",  # although, Greys_r has a better contrast...
        normalize="true",
    )
    plt.title("Normalized Confusion Matrix - IRIS Dataset")
    plt.show()

    time.sleep(1.8)
    final_accuracy = 100 * accuracy_score(y_true=y_test, y_pred=y_pred)
    print(f"The overall accuracy of the model is: {round(final_accuracy, 2)}%")


if __name__ == "__main__":
    main()


================================================
FILE: machine_learning/gradient_boosting_classifier.py
================================================
import numpy as np
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor


class GradientBoostingClassifier:
    def __init__(self, n_estimators: int = 100, learning_rate: float = 0.1) -> None:
        """
        Initialize a GradientBoostingClassifier.

        Parameters:
        - n_estimators (int): The number of weak learners to train.
        - learning_rate (float): The learning rate for updating the model.

        Attributes:
        - n_estimators (int): The number of weak learners.
        - learning_rate (float): The learning rate.
        - models (list): A list to store the trained weak learners.
        """
        self.n_estimators = n_estimators
        self.learning_rate = learning_rate
        self.models: list[tuple[DecisionTreeRegressor, float]] = []

    def fit(self, features: np.ndarray, target: np.ndarray) -> None:
        """
        Fit the GradientBoostingClassifier to the training data.

        Parameters:
        - features (np.ndarray): The training features.
        - target (np.ndarray): The target values.

        Returns:
        None

        >>> import numpy as np
        >>> from sklearn.datasets import load_iris
        >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
        >>> iris = load_iris()
        >>> X, y = iris.data, iris.target
        >>> clf.fit(X, y)
        >>> # Check if the model is trained
        >>> len(clf.models) == 100
        True
        """
        for _ in range(self.n_estimators):
            # Calculate the pseudo-residuals
            residuals = -self.gradient(target, self.predict(features))
            # Fit a weak learner (e.g., decision tree) to the residuals
            model = DecisionTreeRegressor(max_depth=1)
            model.fit(features, residuals)
            # Update the model by adding the weak learner with a learning rate
            self.models.append((model, self.learning_rate))

    def predict(self, features: np.ndarray) -> np.ndarray:
        """
        Make predictions on input data.

        Parameters:
        - features (np.ndarray): The input data for making predictions.

        Returns:
        - np.ndarray: An array of binary predictions (-1 or 1).

        >>> import numpy as np
        >>> from sklearn.datasets import load_iris
        >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
        >>> iris = load_iris()
        >>> X, y = iris.data, iris.target
        >>> clf.fit(X, y)
        >>> y_pred = clf.predict(X)
        >>> # Check if the predictions have the correct shape
        >>> y_pred.shape == y.shape
        True
        """
        # Initialize predictions with zeros
        predictions = np.zeros(features.shape[0])
        for model, learning_rate in self.models:
            predictions += learning_rate * model.predict(features)
        return np.sign(predictions)  # Convert to binary predictions (-1 or 1)

    def gradient(self, target: np.ndarray, y_pred: np.ndarray) -> np.ndarray:
        """
        Calculate the negative gradient (pseudo-residuals) for logistic loss.

        Parameters:
        - target (np.ndarray): The target values.
        - y_pred (np.ndarray): The predicted values.

        Returns:
        - np.ndarray: An array of pseudo-residuals.

        >>> import numpy as np
        >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
        >>> target = np.array([0, 1, 0, 1])
        >>> y_pred = np.array([0.2, 0.8, 0.3, 0.7])
        >>> residuals = clf.gradient(target, y_pred)
        >>> # Check if residuals have the correct shape
        >>> residuals.shape == target.shape
        True
        """
        return -target / (1 + np.exp(target * y_pred))


if __name__ == "__main__":
    iris = load_iris()
    X, y = iris.data, iris.target
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42
    )

    clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
    clf.fit(X_train, y_train)

    y_pred = clf.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    print(f"Accuracy: {accuracy:.2f}")


================================================
FILE: machine_learning/gradient_boosting_regressor.py.broken.txt
================================================
"""Implementation of GradientBoostingRegressor in sklearn using the
   boston dataset which is very popular for regression problem to
   predict house price.
"""

import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split


def main():

    # loading the dataset from the sklearn
    df = load_boston()
    print(df.keys())
    # now let construct a data frame
    df_boston = pd.DataFrame(df.data, columns=df.feature_names)
    # let add the target to the dataframe
    df_boston["Price"] = df.target
    # print the first five rows using the head function
    print(df_boston.head())
    # Summary statistics
    print(df_boston.describe().T)
    # Feature selection

    x = df_boston.iloc[:, :-1]
    y = df_boston.iloc[:, -1]  # target variable
    # split the data with 75% train and 25% test sets.
    x_train, x_test, y_train, y_test = train_test_split(
        x, y, random_state=0, test_size=0.25
    )

    model = GradientBoostingRegressor(
        n_estimators=500, max_depth=5, min_samples_split=4, learning_rate=0.01
    )
    # training the model
    model.fit(x_train, y_train)
    # to see how good the model fit the data
    training_score = model.score(x_train, y_train).round(3)
    test_score = model.score(x_test, y_test).round(3)
    print("Training score of GradientBoosting is :", training_score)
    print("The test score of GradientBoosting is :", test_score)
    # Let us evaluation the model by finding the errors
    y_pred = model.predict(x_test)

    # The mean squared error
    print(f"Mean squared error: {mean_squared_error(y_test, y_pred):.2f}")
    # Explained variance score: 1 is perfect prediction
    print(f"Test Variance score: {r2_score(y_test, y_pred):.2f}")

    # So let's run the model against the test data
    fig, ax = plt.subplots()
    ax.scatter(y_test, y_pred, edgecolors=(0, 0, 0))
    ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "k--", lw=4)
    ax.set_xlabel("Actual")
    ax.set_ylabel("Predicted")
    ax.set_title("Truth vs Predicted")
    # this show function will display the plotting
    plt.show()


if __name__ == "__main__":
    main()


================================================
FILE: machine_learning/gradient_descent.py
================================================
"""
Implementation of gradient descent algorithm for minimizing cost of a linear hypothesis
function.
"""

import numpy as np

# List of input, output pairs
train_data = (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
test_data = (((515, 22, 13), 555), ((61, 35, 49), 150))
parameter_vector = [2, 4, 1, 5]
m = len(train_data)
LEARNING_RATE = 0.009


def _error(example_no, data_set="train"):
    """
    :param data_set: train data or test data
    :param example_no: example number whose error has to be checked
    :return: error in example pointed by example number.
    """
    return calculate_hypothesis_value(example_no, data_set) - output(
        example_no, data_set
    )


def _hypothesis_value(data_input_tuple):
    """
    Calculates hypothesis function value for a given input
    :param data_input_tuple: Input tuple of a particular example
    :return: Value of hypothesis function at that point.
    Note that there is an 'biased input' whose value is fixed as 1.
    It is not explicitly mentioned in input data.. But, ML hypothesis functions use it.
    So, we have to take care of it separately. Line 36 takes care of it.
    """
    hyp_val = 0
    for i in range(len(parameter_vector) - 1):
        hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
    hyp_val += parameter_vector[0]
    return hyp_val


def output(example_no, data_set):
    """
    :param data_set: test data or train data
    :param example_no: example whose output is to be fetched
    :return: output for that example
    """
    if data_set == "train":
        return train_data[example_no][1]
    elif data_set == "test":
        return test_data[example_no][1]
    return None


def calculate_hypothesis_value(example_no, data_set):
    """
    Calculates hypothesis value for a given example
    :param data_set: test data or train_data
    :param example_no: example whose hypothesis value is to be calculated
    :return: hypothesis value for that example
    """
    if data_set == "train":
        return _hypothesis_value(train_data[example_no][0])
    elif data_set == "test":
        return _hypothesis_value(test_data[example_no][0])
    return None


def summation_of_cost_derivative(index, end=m):
    """
    Calculates the sum of cost function derivative
    :param index: index wrt derivative is being calculated
    :param end: value where summation ends, default is m, number of examples
    :return: Returns the summation of cost derivative
    Note: If index is -1, this means we are calculating summation wrt to biased
        parameter.
    """
    summation_value = 0
    for i in range(end):
        if index == -1:
            summation_value += _error(i)
        else:
            summation_value += _error(i) * train_data[i][0][index]
    return summation_value


def get_cost_derivative(index):
    """
    :param index: index of the parameter vector wrt to derivative is to be calculated
    :return: derivative wrt to that index
    Note: If index is -1, this means we are calculating summation wrt to biased
        parameter.
    """
    cost_derivative_value = summation_of_cost_derivative(index, m) / m
    return cost_derivative_value


def run_gradient_descent():
    global parameter_vector
    # Tune these values to set a tolerance value for predicted output
    absolute_error_limit = 0.000002
    relative_error_limit = 0
    j = 0
    while True:
        j += 1
        temp_parameter_vector = [0, 0, 0, 0]
        for i in range(len(parameter_vector)):
            cost_derivative = get_cost_derivative(i - 1)
            temp_parameter_vector[i] = (
                parameter_vector[i] - LEARNING_RATE * cost_derivative
            )
        if np.allclose(
            parameter_vector,
            temp_parameter_vector,
            atol=absolute_error_limit,
            rtol=relative_error_limit,
        ):
            break
        parameter_vector = temp_parameter_vector
    print(("Number of iterations:", j))


def test_gradient_descent():
    for i in range(len(test_data)):
        print(("Actual output value:", output(i, "test")))
        print(("Hypothesis output:", calculate_hypothesis_value(i, "test")))


if __name__ == "__main__":
    run_gradient_descent()
    print("\nTesting gradient descent for a linear hypothesis function.\n")
    test_gradient_descent()


================================================
FILE: machine_learning/k_means_clust.py
================================================
"""README, Author - Anurag Kumar(mailto:anuragkumarak95@gmail.com)
Requirements:
  - sklearn
  - numpy
  - matplotlib
Python:
  - 3.5
Inputs:
  - X , a 2D numpy array of features.
  - k , number of clusters to create.
  - initial_centroids , initial centroid values generated by utility function(mentioned
    in usage).
  - maxiter , maximum number of iterations to process.
  - heterogeneity , empty list that will be filled with heterogeneity values if passed
    to kmeans func.
Usage:
  1. define 'k' value, 'X' features array and 'heterogeneity' empty list
  2. create initial_centroids,
        initial_centroids = get_initial_centroids(
            X,
            k,
            seed=0 # seed value for initial centroid generation,
                   # None for randomness(default=None)
            )
  3. find centroids and clusters using kmeans function.
        centroids, cluster_assignment = kmeans(
            X,
            k,
            initial_centroids,
            maxiter=400,
            record_heterogeneity=heterogeneity,
            verbose=True # whether to print logs in console or not.(default=False)
            )
  4. Plot the loss function and heterogeneity values for every iteration saved in
     heterogeneity list.
        plot_heterogeneity(
            heterogeneity,
            k
        )
  5. Plot the labeled 3D data points with centroids.
        plot_kmeans(
            X,
            centroids,
            cluster_assignment
        )
  6. Transfers Dataframe into excel format it must have feature called
      'Clust' with k means clustering numbers in it.
"""

import warnings

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import pairwise_distances

warnings.filterwarnings("ignore")

TAG = "K-MEANS-CLUST/ "


def get_initial_centroids(data, k, seed=None):
    """Randomly choose k data points as initial centroids"""
    # useful for obtaining consistent results
    rng = np.random.default_rng(seed)
    n = data.shape[0]  # number of data points

    # Pick K indices from range [0, N).
    rand_indices = rng.integers(0, n, k)

    # Keep centroids as dense format, as many entries will be nonzero due to averaging.
    # As long as at least one document in a cluster contains a word,
    # it will carry a nonzero weight in the TF-IDF vector of the centroid.
    centroids = data[rand_indices, :]

    return centroids


def centroid_pairwise_dist(x, centroids):
    return pairwise_distances(x, centroids, metric="euclidean")


def assign_clusters(data, centroids):
    # Compute distances between each data point and the set of centroids:
    # Fill in the blank (RHS only)
    distances_from_centroids = centroid_pairwise_dist(data, centroids)

    # Compute cluster assignments for each data point:
    # Fill in the blank (RHS only)
    cluster_assignment = np.argmin(distances_from_centroids, axis=1)

    return cluster_assignment


def revise_centroids(data, k, cluster_assignment):
    new_centroids = []
    for i in range(k):
        # Select all data points that belong to cluster i. Fill in the blank (RHS only)
        member_data_points = data[cluster_assignment == i]
        # Compute the mean of the data points. Fill in the blank (RHS only)
        centroid = member_data_points.mean(axis=0)
        new_centroids.append(centroid)
    new_centroids = np.array(new_centroids)

    return new_centroids


def compute_heterogeneity(data, k, centroids, cluster_assignment):
    heterogeneity = 0.0
    for i in range(k):
        # Select all data points that belong to cluster i. Fill in the blank (RHS only)
        member_data_points = data[cluster_assignment == i, :]

        if member_data_points.shape[0] > 0:  # check if i-th cluster is non-empty
            # Compute distances from centroid to data points (RHS only)
            distances = pairwise_distances(
                member_data_points, [centroids[i]], metric="euclidean"
            )
            squared_distances = distances**2
            heterogeneity += np.sum(squared_distances)

    return heterogeneity


def plot_heterogeneity(heterogeneity, k):
    plt.figure(figsize=(7, 4))
    plt.plot(heterogeneity, linewidth=4)
    plt.xlabel("# Iterations")
    plt.ylabel("Heterogeneity")
    plt.title(f"Heterogeneity of clustering over time, K={k:d}")
    plt.rcParams.update({"font.size": 16})
    plt.show()


def plot_kmeans(data, centroids, cluster_assignment):
    ax = plt.axes(projection="3d")
    ax.scatter(data[:, 0], data[:, 1], data[:, 2], c=cluster_assignment, cmap="viridis")
    ax.scatter(
        centroids[:, 0], centroids[:, 1], centroids[:, 2], c="red", s=100, marker="x"
    )
    ax.set_xlabel("X")
    ax.set_ylabel("Y")
    ax.set_zlabel("Z")
    ax.set_title("3D K-Means Clustering Visualization")
    plt.show()


def kmeans(
    data, k, initial_centroids, maxiter=500, record_heterogeneity=None, verbose=False
):
    """Runs k-means on given data and initial set of centroids.
    maxiter: maximum number of iterations to run.(default=500)
    record_heterogeneity: (optional) a list, to store the history of heterogeneity
                          as function of iterations
                          if None, do not store the history.
    verbose: if True, print how many data points changed their cluster labels in
                          each iteration"""
    centroids = initial_centroids[:]
    prev_cluster_assignment = None

    for itr in range(maxiter):
        if verbose:
            print(itr, end="")

        # 1. Make cluster assignments using nearest centroids
        cluster_assignment = assign_clusters(data, centroids)

        # 2. Compute a new centroid for each of the k clusters, averaging all data
        #    points assigned to that cluster.
        centroids = revise_centroids(data, k, cluster_assignment)

        # Check for convergence: if none of the assignments changed, stop
        if (
            prev_cluster_assignment is not None
            and (prev_cluster_assignment == cluster_assignment).all()
        ):
            break

        # Print number of new assignments
        if prev_cluster_assignment is not None:
            num_changed = np.sum(prev_cluster_assignment != cluster_assignment)
            if verbose:
                print(
                    f"    {num_changed:5d} elements changed their cluster assignment."
                )

        # Record heterogeneity convergence metric
        if record_heterogeneity is not None:
            # YOUR CODE HERE
            score = compute_heterogeneity(data, k, centroids, cluster_assignment)
            record_heterogeneity.append(score)

        prev_cluster_assignment = cluster_assignment[:]

    return centroids, cluster_assignment


# Mock test below
if False:  # change to true to run this test case.
    from sklearn import datasets as ds

    dataset = ds.load_iris()
    k = 3
    heterogeneity = []
    initial_centroids = get_initial_centroids(dataset["data"], k, seed=0)
    centroids, cluster_assignment = kmeans(
        dataset["data"],
        k,
        initial_centroids,
        maxiter=400,
        record_heterogeneity=heterogeneity,
        verbose=True,
    )
    plot_heterogeneity(heterogeneity, k)
    plot_kmeans(dataset["data"], centroids, cluster_assignment)


def report_generator(
    predicted: pd.DataFrame, clustering_variables: np.ndarray, fill_missing_report=None
) -> pd.DataFrame:
    """
    Generate a clustering report given these two arguments:
        predicted - dataframe with predicted cluster column
        fill_missing_report - dictionary of rules on how we are going to fill in missing
        values for final generated report (not included in modelling);
    >>> predicted = pd.DataFrame()
    >>> predicted['numbers'] = [1, 2, 3]
    >>> predicted['col1'] = [0.5, 2.5, 4.5]
    >>> predicted['col2'] = [100, 200, 300]
    >>> predicted['col3'] = [10, 20, 30]
    >>> predicted['Cluster'] = [1, 1, 2]
    >>> report_generator(predicted, ['col1', 'col2'], 0)
               Features               Type   Mark           1           2
    0    # of Customers        ClusterSize  False    2.000000    1.000000
    1    % of Customers  ClusterProportion  False    0.666667    0.333333
    2              col1    mean_with_zeros   True    1.500000    4.500000
    3              col2    mean_with_zeros   True  150.000000  300.000000
    4           numbers    mean_with_zeros  False    1.500000    3.000000
    ..              ...                ...    ...         ...         ...
    99            dummy                 5%  False    1.000000    1.000000
    100           dummy                95%  False    1.000000    1.000000
    101           dummy              stdev  False    0.000000         NaN
    102           dummy               mode  False    1.000000    1.000000
    103           dummy             median  False    1.000000    1.000000
    <BLANKLINE>
    [104 rows x 5 columns]
    """
    # Fill missing values with given rules
    if fill_missing_report:
        predicted = predicted.fillna(value=fill_missing_report)
    predicted["dummy"] = 1
    numeric_cols = predicted.select_dtypes(np.number).columns
    report = (
        predicted.groupby(["Cluster"])[  # construct report dataframe
            numeric_cols
        ]  # group by cluster number
        .agg(
            [
                ("sum", "sum"),
                ("mean_with_zeros", lambda x: np.mean(np.nan_to_num(x))),
                ("mean_without_zeros", lambda x: x.replace(0, np.nan).mean()),
                (
                    "mean_25-75",
                    lambda x: np.mean(
                        np.nan_to_num(
                            sorted(x)[
                                round(len(x) * 25 / 100) : round(len(x) * 75 / 100)
                            ]
                        )
                    ),
                ),
                ("mean_with_na", "mean"),
                ("min", lambda x: x.min()),
                ("5%", lambda x: x.quantile(0.05)),
                ("25%", lambda x: x.quantile(0.25)),
                ("50%", lambda x: x.quantile(0.50)),
                ("75%", lambda x: x.quantile(0.75)),
                ("95%", lambda x: x.quantile(0.95)),
                ("max", lambda x: x.max()),
                ("count", lambda x: x.count()),
                ("stdev", lambda x: x.std()),
                ("mode", lambda x: x.mode()[0]),
                ("median", lambda x: x.median()),
                ("# > 0", lambda x: (x > 0).sum()),
            ]
        )
        .T.reset_index()
        .rename(index=str, columns={"level_0": "Features", "level_1": "Type"})
    )  # rename columns
    # calculate the size of cluster(count of clientID's)
    # avoid SettingWithCopyWarning
    clustersize = report[
        (report["Features"] == "dummy") & (report["Type"] == "count")
    ].copy()
    # rename created predicted cluster to match report column names
    clustersize.Type = "ClusterSize"
    clustersize.Features = "# of Customers"
    # calculating the proportion of cluster
    clusterproportion = pd.DataFrame(
        clustersize.iloc[:, 2:].to_numpy() / clustersize.iloc[:, 2:].to_numpy().sum()
    )
    # rename created predicted cluster to match report column names
    clusterproportion["Type"] = "% of Customers"
    clusterproportion["Features"] = "ClusterProportion"
    cols = clusterproportion.columns.tolist()
    cols = cols[-2:] + cols[:-2]
    clusterproportion = clusterproportion[cols]  # rearrange columns to match report
    clusterproportion.columns = report.columns
    # generating dataframe with count of nan values
    a = pd.DataFrame(
        abs(
            report[report["Type"] == "count"].iloc[:, 2:].to_numpy()
            - clustersize.iloc[:, 2:].to_numpy()
        )
    )
    a["Features"] = 0
    a["Type"] = "# of nan"
    # filling values in order to match report
    a.Features = report[report["Type"] == "count"].Features.tolist()
    cols = a.columns.tolist()
    cols = cols[-2:] + cols[:-2]
    a = a[cols]  # rearrange columns to match report
    a.columns = report.columns  # rename columns to match report
    # drop count values except for cluster size
    report = report.drop(report[report.Type == "count"].index)
    # concat report with cluster size and nan values
    report = pd.concat([report, a, clustersize, clusterproportion], axis=0)
    report["Mark"] = report["Features"].isin(clustering_variables)
    cols = report.columns.tolist()
    cols = cols[0:2] + cols[-1:] + cols[2:-1]
    report = report[cols]
    sorter1 = {
        "ClusterSize": 9,
        "ClusterProportion": 8,
        "mean_with_zeros": 7,
        "mean_with_na": 6,
        "max": 5,
        "50%": 4,
        "min": 3,
        "25%": 2,
        "75%": 1,
        "# of nan": 0,
        "# > 0": -1,
        "sum_with_na": -2,
    }
    report = (
        report.assign(
            Sorter1=lambda x: x.Type.map(sorter1),
            Sorter2=lambda x: list(reversed(range(len(x)))),
        )
        .sort_values(["Sorter1", "Mark", "Sorter2"], ascending=False)
        .drop(["Sorter1", "Sorter2"], axis=1)
    )
    report.columns.name = ""
    report = report.reset_index()
    report = report.drop(columns=["index"])
    return report


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: machine_learning/k_nearest_neighbours.py
================================================
"""
k-Nearest Neighbours (kNN) is a simple non-parametric supervised learning
algorithm used for classification. Given some labelled training data, a given
point is classified using its k nearest neighbours according to some distance
metric. The most commonly occurring label among the neighbours becomes the label
of the given point. In effect, the label of the given point is decided by a
majority vote.

This implementation uses the commonly used Euclidean distance metric, but other
distance metrics can also be used.

Reference: https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
"""

from collections import Counter
from heapq import nsmallest

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split


class KNN:
    def __init__(
        self,
        train_data: np.ndarray[float],
        train_target: np.ndarray[int],
        class_labels: list[str],
    ) -> None:
        """
        Create a kNN classifier using the given training data and class labels
        """
        self.data = zip(train_data, train_target)
        self.labels = class_labels

    @staticmethod
    def _euclidean_distance(a: np.ndarray[float], b: np.ndarray[float]) -> float:
        """
        Calculate the Euclidean distance between two points
        >>> KNN._euclidean_distance(np.array([0, 0]), np.array([3, 4]))
        5.0
        >>> KNN._euclidean_distance(np.array([1, 2, 3]), np.array([1, 8, 11]))
        10.0
        """
        return float(np.linalg.norm(a - b))

    def classify(self, pred_point: np.ndarray[float], k: int = 5) -> str:
        """
        Classify a given point using the kNN algorithm
        >>> train_X = np.array(
        ...     [[0, 0], [1, 0], [0, 1], [0.5, 0.5], [3, 3], [2, 3], [3, 2]]
        ... )
        >>> train_y = np.array([0, 0, 0, 0, 1, 1, 1])
        >>> classes = ['A', 'B']
        >>> knn = KNN(train_X, train_y, classes)
        >>> point = np.array([1.2, 1.2])
        >>> knn.classify(point)
        'A'
        """
        # Distances of all points from the point to be classified
        distances = (
            (self._euclidean_distance(data_point[0], pred_point), data_point[1])
            for data_point in self.data
        )

        # Choosing k points with the shortest distances
        votes = (i[1] for i in nsmallest(k, distances))

        # Most commonly occurring class is the one into which the point is classified
        result = Counter(votes).most_common(1)[0][0]
        return self.labels[result]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    iris = datasets.load_iris()

    X = np.array(iris["data"])
    y = np.array(iris["target"])
    iris_classes = iris["target_names"]

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    iris_point = np.array([4.4, 3.1, 1.3, 1.4])
    classifier = KNN(X_train, y_train, iris_classes)
    print(classifier.classify(iris_point, k=3))


================================================
FILE: machine_learning/linear_discriminant_analysis.py
================================================
"""
Linear Discriminant Analysis



Assumptions About Data :
    1. The input variables has a gaussian distribution.
    2. The variance calculated for each input variables by class grouping is the
       same.
    3. The mix of classes in your training set is representative of the problem.


Learning The Model :
    The LDA model requires the estimation of statistics from the training data :
        1. Mean of each input value for each class.
        2. Probability of an instance belong to each class.
        3. Covariance for the input data for each class

    Calculate the class means :
        mean(x) = 1/n ( for i = 1 to i = n --> sum(xi))

    Calculate the class probabilities :
        P(y = 0) = count(y = 0) / (count(y = 0) + count(y = 1))
        P(y = 1) = count(y = 1) / (count(y = 0) + count(y = 1))

    Calculate the variance :
        We can calculate the variance for dataset in two steps :
            1. Calculate the squared difference for each input variable from the
               group mean.
            2. Calculate the mean of the squared difference.
            ------------------------------------------------
            Squared_Difference = (x - mean(k)) ** 2
            Variance = (1 / (count(x) - count(classes))) *
                (for i = 1 to i = n --> sum(Squared_Difference(xi)))

Making Predictions :
    discriminant(x) = x * (mean / variance) -
        ((mean ** 2) / (2 * variance)) + Ln(probability)
    ---------------------------------------------------------------------------
    After calculating the discriminant value for each class, the class with the
    largest discriminant value is taken as the prediction.

Author: @EverLookNeverSee
"""

from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed


# Make a training dataset drawn from a gaussian distribution
def gaussian_distribution(mean: float, std_dev: float, instance_count: int) -> list:
    """
    Generate gaussian distribution instances based-on given mean and standard deviation
    :param mean: mean value of class
    :param std_dev: value of standard deviation entered by usr or default value of it
    :param instance_count: instance number of class
    :return: a list containing generated values based-on given mean, std_dev and
        instance_count

    >>> gaussian_distribution(5.0, 1.0, 20) # doctest: +NORMALIZE_WHITESPACE
    [6.288184753155463, 6.4494456086997705, 5.066335808938262, 4.235456349028368,
     3.9078267848958586, 5.031334516831717, 3.977896829989127, 3.56317055489747,
      5.199311976483754, 5.133374604658605, 5.546468300338232, 4.086029056264687,
       5.005005283626573, 4.935258239627312, 3.494170998739258, 5.537997178661033,
        5.320711100998849, 7.3891120432406865, 5.202969177309964, 4.855297691835079]
    """
    seed(1)
    return [gauss(mean, std_dev) for _ in range(instance_count)]


# Make corresponding Y flags to detecting classes
def y_generator(class_count: int, instance_count: list) -> list:
    """
    Generate y values for corresponding classes
    :param class_count: Number of classes(data groupings) in dataset
    :param instance_count: number of instances in class
    :return: corresponding values for data groupings in dataset

    >>> y_generator(1, [10])
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    >>> y_generator(2, [5, 10])
    [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
    >>> y_generator(4, [10, 5, 15, 20]) # doctest: +NORMALIZE_WHITESPACE
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
     2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
    """

    return [k for k in range(class_count) for _ in range(instance_count[k])]


# Calculate the class means
def calculate_mean(instance_count: int, items: list) -> float:
    """
    Calculate given class mean
    :param instance_count: Number of instances in class
    :param items: items that related to specific class(data grouping)
    :return: calculated actual mean of considered class

    >>> items = gaussian_distribution(5.0, 1.0, 20)
    >>> calculate_mean(len(items), items)
    5.011267842911003
    """
    # the sum of all items divided by number of instances
    return sum(items) / instance_count


# Calculate the class probabilities
def calculate_probabilities(instance_count: int, total_count: int) -> float:
    """
    Calculate the probability that a given instance will belong to which class
    :param instance_count: number of instances in class
    :param total_count: the number of all instances
    :return: value of probability for considered class

    >>> calculate_probabilities(20, 60)
    0.3333333333333333
    >>> calculate_probabilities(30, 100)
    0.3
    """
    # number of instances in specific class divided by number of all instances
    return instance_count / total_count


# Calculate the variance
def calculate_variance(items: list, means: list, total_count: int) -> float:
    """
    Calculate the variance
    :param items: a list containing all items(gaussian distribution of all classes)
    :param means: a list containing real mean values of each class
    :param total_count: the number of all instances
    :return: calculated variance for considered dataset

    >>> items = gaussian_distribution(5.0, 1.0, 20)
    >>> means = [5.011267842911003]
    >>> total_count = 20
    >>> calculate_variance([items], means, total_count)
    0.9618530973487491
    """
    squared_diff = []  # An empty list to store all squared differences
    # iterate over number of elements in items
    for i in range(len(items)):
        # for loop iterates over number of elements in inner layer of items
        for j in range(len(items[i])):
            # appending squared differences to 'squared_diff' list
            squared_diff.append((items[i][j] - means[i]) ** 2)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
    n_classes = len(means)  # Number of classes in dataset
    return 1 / (total_count - n_classes) * sum(squared_diff)


# Making predictions
def predict_y_values(
    x_items: list, means: list, variance: float, probabilities: list
) -> list:
    """This function predicts new indexes(groups for our data)
    :param x_items: a list containing all items(gaussian distribution of all classes)
    :param means: a list containing real mean values of each class
    :param variance: calculated value of variance by calculate_variance function
    :param probabilities: a list containing all probabilities of classes
    :return: a list containing predicted Y values

    >>> x_items = [[6.288184753155463, 6.4494456086997705, 5.066335808938262,
    ...                4.235456349028368, 3.9078267848958586, 5.031334516831717,
    ...                3.977896829989127, 3.56317055489747, 5.199311976483754,
    ...                5.133374604658605, 5.546468300338232, 4.086029056264687,
    ...                5.005005283626573, 4.935258239627312, 3.494170998739258,
    ...                5.537997178661033, 5.320711100998849, 7.3891120432406865,
    ...                5.202969177309964, 4.855297691835079], [11.288184753155463,
    ...                11.44944560869977, 10.066335808938263, 9.235456349028368,
    ...                8.907826784895859, 10.031334516831716, 8.977896829989128,
    ...                8.56317055489747, 10.199311976483754, 10.133374604658606,
    ...                10.546468300338232, 9.086029056264687, 10.005005283626572,
    ...                9.935258239627313, 8.494170998739259, 10.537997178661033,
    ...                10.320711100998848, 12.389112043240686, 10.202969177309964,
    ...                9.85529769183508], [16.288184753155463, 16.449445608699772,
    ...                15.066335808938263, 14.235456349028368, 13.907826784895859,
    ...                15.031334516831716, 13.977896829989128, 13.56317055489747,
    ...                15.199311976483754, 15.133374604658606, 15.546468300338232,
    ...                14.086029056264687, 15.005005283626572, 14.935258239627313,
    ...                13.494170998739259, 15.537997178661033, 15.320711100998848,
    ...                17.389112043240686, 15.202969177309964, 14.85529769183508]]

    >>> means = [5.011267842911003, 10.011267842911003, 15.011267842911002]
    >>> variance = 0.9618530973487494
    >>> probabilities = [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]
    >>> predict_y_values(x_items, means, variance,
    ...                  probabilities)  # doctest: +NORMALIZE_WHITESPACE
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
    2, 2, 2, 2, 2, 2, 2, 2, 2]

    """
    # An empty list to store generated discriminant values of all items in dataset for
    # each class
    results = []
    # for loop iterates over number of elements in list
    for i in range(len(x_items)):
        # for loop iterates over number of inner items of each element
        for j in range(len(x_items[i])):
            temp = []  # to store all discriminant values of each item as a list
            # for loop iterates over number of classes we have in our dataset
            for k in range(len(x_items)):
                # appending values of discriminants for each class to 'temp' list
                temp.append(
                    x_items[i][j] * (means[k] / variance)
                    - (means[k] ** 2 / (2 * variance))
                    + log(probabilities[k])
                )
            # appending discriminant values of each item to 'results' list
            results.append(temp)

    return [result.index(max(result)) for result in results]


# Calculating Accuracy
def accuracy(actual_y: list, predicted_y: list) -> float:
    """
    Calculate the value of accuracy based-on predictions
    :param actual_y:a list containing initial Y values generated by 'y_generator'
        function
    :param predicted_y: a list containing predicted Y values generated by
        'predict_y_values' function
    :return: percentage of accuracy

    >>> actual_y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
    ... 1, 1 ,1 ,1 ,1 ,1 ,1]
    >>> predicted_y = [0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0,
    ... 0, 0, 1, 1, 1, 0, 1, 1, 1]
    >>> accuracy(actual_y, predicted_y)
    50.0

    >>> actual_y = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
    ... 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
    >>> predicted_y = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
    ... 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
    >>> accuracy(actual_y, predicted_y)
    100.0
    """
    # iterate over one element of each list at a time (zip mode)
    # prediction is correct if actual Y value equals to predicted Y value
    correct = sum(1 for i, j in zip(actual_y, predicted_y) if i == j)
    # percentage of accuracy equals to number of correct predictions divided by number
    # of all data and multiplied by 100
    return (correct / len(actual_y)) * 100


def valid_input[num](
    input_type: Callable[[object], num],  # Usually float or int
    input_msg: str,
    err_msg: str,
    condition: Callable[[num], bool] = lambda _: True,
    default: str | None = None,
) -> num:
    """
    Ask for user value and validate that it fulfill a condition.

    :input_type: user input expected type of value
    :input_msg: message to show user in the screen
    :err_msg: message to show in the screen in case of error
    :condition: function that represents the condition that user input is valid.
    :default: Default value in case the user does not type anything
    :return: user's input
    """
    while True:
        try:
            user_input = input_type(input(input_msg).strip() or default)
            if condition(user_input):
                return user_input
            else:
                print(f"{user_input}: {err_msg}")
                continue
        except ValueError:
            print(
                f"{user_input}: Incorrect input type, expected {input_type.__name__!r}"
            )


# Main Function
def main():
    """This function starts execution phase"""
    while True:
        print(" Linear Discriminant Analysis ".center(50, "*"))
        print("*" * 50, "\n")
        print("First of all we should specify the number of classes that")
        print("we want to generate as training dataset")
        # Trying to get number of classes
        n_classes = valid_input(
            input_type=int,
            condition=lambda x: x > 0,
            input_msg="Enter the number of classes (Data Groupings): ",
            err_msg="Number of classes should be positive!",
        )

        print("-" * 100)

        # Trying to get the value of standard deviation
        std_dev = valid_input(
            input_type=float,
            condition=lambda x: x >= 0,
            input_msg=(
                "Enter the value of standard deviation"
                "(Default value is 1.0 for all classes): "
            ),
            err_msg="Standard deviation should not be negative!",
            default="1.0",
        )

        print("-" * 100)

        # Trying to get number of instances in classes and theirs means to generate
        # dataset
        counts = []  # An empty list to store instance counts of classes in dataset
        for i in range(n_classes):
            user_count = valid_input(
                input_type=int,
                condition=lambda x: x > 0,
                input_msg=(f"Enter The number of instances for class_{i + 1}: "),
                err_msg="Number of instances should be positive!",
            )
            counts.append(user_count)
        print("-" * 100)

        # An empty list to store values of user-entered means of classes
        user_means = []
        for a in range(n_classes):
            user_mean = valid_input(
                input_type=float,
                input_msg=(f"Enter the value of mean for class_{a + 1}: "),
                err_msg="This is an invalid value.",
            )
            user_means.append(user_mean)
        print("-" * 100)

        print("Standard deviation: ", std_dev)
        # print out the number of instances in classes in separated line
        for i, count in enumerate(counts, 1):
            print(f"Number of instances in class_{i} is: {count}")
        print("-" * 100)

        # print out mean values of classes separated line
        for i, user_mean in enumerate(user_means, 1):
            print(f"Mean of class_{i} is: {user_mean}")
        print("-" * 100)

        # Generating training dataset drawn from gaussian distribution
        x = [
            gaussian_distribution(user_means[j], std_dev, counts[j])
            for j in range(n_classes)
        ]
        print("Generated Normal Distribution: \n", x)
        print("-" * 100)

        # Generating Ys to detecting corresponding classes
        y = y_generator(n_classes, counts)
        print("Generated Corresponding Ys: \n", y)
        print("-" * 100)

        # Calculating the value of actual mean for each class
        actual_means = [calculate_mean(counts[k], x[k]) for k in range(n_classes)]
        # for loop iterates over number of elements in 'actual_means' list and print
        # out them in separated line
        for i, actual_mean in enumerate(actual_means, 1):
            print(f"Actual(Real) mean of class_{i} is: {actual_mean}")
        print("-" * 100)

        # Calculating the value of probabilities for each class
        probabilities = [
            calculate_probabilities(counts[i], sum(counts)) for i in range(n_classes)
        ]

        # for loop iterates over number of elements in 'probabilities' list and print
        # out them in separated line
        for i, probability in enumerate(probabilities, 1):
            print(f"Probability of class_{i} is: {probability}")
        print("-" * 100)

        # Calculating the values of variance for each class
        variance = calculate_variance(x, actual_means, sum(counts))
        print("Variance: ", variance)
        print("-" * 100)

        # Predicting Y values
        # storing predicted Y values in 'pre_indexes' variable
        pre_indexes = predict_y_values(x, actual_means, variance, probabilities)
        print("-" * 100)

        # Calculating Accuracy of the model
        print(f"Accuracy: {accuracy(y, pre_indexes)}")
        print("-" * 100)
        print(" DONE ".center(100, "+"))

        if input("Press any key to restart or 'q' for quit: ").strip().lower() == "q":
            print("\n" + "GoodBye!".center(100, "-") + "\n")
            break
        system("cls" if name == "nt" else "clear")  # noqa: S605


if __name__ == "__main__":
    main()


================================================
FILE: machine_learning/linear_regression.py
================================================
"""
Linear regression is the most basic type of regression commonly used for
predictive analysis. The idea is pretty simple: we have a dataset and we have
features associated with it. Features should be chosen very cautiously
as they determine how much our model will be able to make future predictions.
We try to set the weight of these features, over many iterations, so that they best
fit our dataset. In this particular code, I had used a CSGO dataset (ADR vs
Rating). We try to best fit a line through dataset and estimate the parameters.
"""

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
#     "numpy",
# ]
# ///

import httpx
import numpy as np


def collect_dataset():
    """Collect dataset of CSGO
    The dataset contains ADR vs Rating of a Player
    :return : dataset obtained from the link, as matrix
    """
    response = httpx.get(
        "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
        "master/Week1/ADRvsRating.csv",
        timeout=10,
    )
    lines = response.text.splitlines()
    data = []
    for item in lines:
        item = item.split(",")
        data.append(item)
    data.pop(0)  # This is for removing the labels from the list
    dataset = np.matrix(data)
    return dataset


def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):
    """Run steep gradient descent and updates the Feature vector accordingly_
    :param data_x   : contains the dataset
    :param data_y   : contains the output associated with each data-entry
    :param len_data : length of the data_
    :param alpha    : Learning rate of the model
    :param theta    : Feature vector (weight's for our model)
    ;param return    : Updated Feature's, using
                       curr_features - alpha_ * gradient(w.r.t. feature)
    >>> import numpy as np
    >>> data_x = np.array([[1, 2], [3, 4]])
    >>> data_y = np.array([5, 6])
    >>> len_data = len(data_x)
    >>> alpha = 0.01
    >>> theta = np.array([0.1, 0.2])
    >>> run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta)
    array([0.196, 0.343])
    """
    n = len_data

    prod = np.dot(theta, data_x.transpose())
    prod -= data_y.transpose()
    sum_grad = np.dot(prod, data_x)
    theta = theta - (alpha / n) * sum_grad
    return theta


def sum_of_square_error(data_x, data_y, len_data, theta):
    """Return sum of square error for error calculation
    :param data_x    : contains our dataset
    :param data_y    : contains the output (result vector)
    :param len_data  : len of the dataset
    :param theta     : contains the feature vector
    :return          : sum of square error computed from given feature's

    Example:
    >>> vc_x = np.array([[1.1], [2.1], [3.1]])
    >>> vc_y = np.array([1.2, 2.2, 3.2])
    >>> round(sum_of_square_error(vc_x, vc_y, 3, np.array([1])),3)
    np.float64(0.005)
    """
    prod = np.dot(theta, data_x.transpose())
    prod -= data_y.transpose()
    sum_elem = np.sum(np.square(prod))
    error = sum_elem / (2 * len_data)
    return error


def run_linear_regression(data_x, data_y):
    """Implement Linear regression over the dataset
    :param data_x  : contains our dataset
    :param data_y  : contains the output (result vector)
    :return        : feature for line of best fit (Feature vector)
    """
    iterations = 100000
    alpha = 0.0001550

    no_features = data_x.shape[1]
    len_data = data_x.shape[0] - 1

    theta = np.zeros((1, no_features))

    for i in range(iterations):
        theta = run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta)
        error = sum_of_square_error(data_x, data_y, len_data, theta)
        print(f"At Iteration {i + 1} - Error is {error:.5f}")

    return theta


def mean_absolute_error(predicted_y, original_y):
    """Return sum of square error for error calculation
    :param predicted_y   : contains the output of prediction (result vector)
    :param original_y    : contains values of expected outcome
    :return          : mean absolute error computed from given feature's

    >>> predicted_y = [3, -0.5, 2, 7]
    >>> original_y = [2.5, 0.0, 2, 8]
    >>> mean_absolute_error(predicted_y, original_y)
    0.5
    """
    total = sum(abs(y - predicted_y[i]) for i, y in enumerate(original_y))
    return total / len(original_y)


def main():
    """Driver function"""
    data = collect_dataset()

    len_data = data.shape[0]
    data_x = np.c_[np.ones(len_data), data[:, :-1]].astype(float)
    data_y = data[:, -1].astype(float)

    theta = run_linear_regression(data_x, data_y)
    len_result = theta.shape[1]
    print("Resultant Feature vector : ")
    for i in range(len_result):
        print(f"{theta[0, i]:.5f}")


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: machine_learning/local_weighted_learning/README.md
================================================
# Locally Weighted Linear Regression
It is a non-parametric ML algorithm that does not learn on a fixed set of parameters such as **linear regression**. \
So, here comes a question of what is *linear regression*? \
**Linear regression** is a supervised learning algorithm used for computing linear relationships between input (X) and output (Y). \

### Terminology Involved

number_of_features(i) = Number of features involved. \
number_of_training_examples(m) = Number of training examples. \
output_sequence(y) = Output Sequence. \
$\theta$ $^T$ x = predicted point. \
J($\theta$) = COst function of point.

The steps involved in ordinary linear regression are:

Training phase: Compute \theta to minimize the cost. \
J($\theta$) = $\sum_{i=1}^m$ (($\theta$)$^T$ $x^i$ - $y^i$)$^2$

Predict output: for given query point x, \
 return:  ($\theta$)$^T$ x

<img src="https://miro.medium.com/max/700/1*FZsLp8yTULf77qrp0Qd91g.png" alt="Linear Regression">

This training phase is possible when data points are linear, but there again comes a question can we predict non-linear relationship between x and y ? as shown below

<img src="https://miro.medium.com/max/700/1*DHYvJg55uN-Kj8jHaxDKvQ.png" alt="Non-linear Data">
<br />
<br />
So, here comes the role of non-parametric algorithm which doesn't compute predictions based on fixed set of params. Rather parameters $\theta$ are computed individually for each query point/data point x.
<br />
<br />
While Computing $\theta$ , a higher preference is given to points in the vicinity of x than points farther from x.

Cost Function J($\theta$) = $\sum_{i=1}^m$ $w^i$ (($\theta$)$^T$ $x^i$ - $y^i$)$^2$

$w^i$ is non-negative weight associated to training point $x^i$. \
$w^i$ is large fr $x^i$'s lying closer to query point $x_i$. \
$w^i$ is small for $x^i$'s lying farther to query point $x_i$.

A Typical weight can be computed using \

$w^i$ = $\exp$(-$\frac{(x^i-x)(x^i-x)^T}{2\tau^2}$)

Where $\tau$ is the bandwidth parameter that controls $w^i$ distance from x.

Let's look at a example :

Suppose, we had a query point x=5.0 and training points $x^1$=4.9 and $x^2$=5.0 than we can calculate weights as :

$w^i$ = $\exp$(-$\frac{(x^i-x)(x^i-x)^T}{2\tau^2}$) with $\tau$=0.5

$w^1$ = $\exp$(-$\frac{(4.9-5)^2}{2(0.5)^2}$) = 0.9802

$w^2$ = $\exp$(-$\frac{(3-5)^2}{2(0.5)^2}$) = 0.000335

So, J($\theta$) = 0.9802*($\theta$ $^T$ $x^1$ - $y^1$) + 0.000335*($\theta$ $^T$ $x^2$ - $y^2$)

So, here by we can conclude that the weight fall exponentially as the distance between x & $x^i$ increases and So, does the contribution of error in prediction for $x^i$ to the cost.

Steps involved in LWL are : \
Compute \theta to minimize the cost.
J($\theta$) = $\sum_{i=1}^m$ $w^i$ (($\theta$)$^T$ $x^i$ - $y^i$)$^2$ \
Predict Output: for given query point x, \
return : $\theta$ $^T$ x

<img src="https://miro.medium.com/max/700/1*H3QS05Q1GJtY-tiBL00iug.png" alt="LWL">


================================================
FILE: machine_learning/local_weighted_learning/__init__.py
================================================


================================================
FILE: machine_learning/local_weighted_learning/local_weighted_learning.py
================================================
"""
Locally weighted linear regression, also called local regression, is a type of
non-parametric linear regression that prioritizes data closest to a given
prediction point. The algorithm estimates the vector of model coefficients β
using weighted least squares regression:

β = (XᵀWX)⁻¹(XᵀWy),

where X is the design matrix, y is the response vector, and W is the diagonal
weight matrix.

This implementation calculates wᵢ, the weight of the ith training sample, using
the Gaussian weight:

wᵢ = exp(-‖xᵢ - x‖²/(2τ²)),

where xᵢ is the ith training sample, x is the prediction point, τ is the
"bandwidth", and ‖x‖ is the Euclidean norm (also called the 2-norm or the L²
norm). The bandwidth τ controls how quickly the weight of a training sample
decreases as its distance from the prediction point increases. One can think of
the Gaussian weight as a bell curve centered around the prediction point: a
training sample is weighted lower if it's farther from the center, and τ
controls the spread of the bell curve.

Other types of locally weighted regression such as locally estimated scatterplot
smoothing (LOESS) typically use different weight functions.

References:
    - https://en.wikipedia.org/wiki/Local_regression
    - https://en.wikipedia.org/wiki/Weighted_least_squares
    - https://cs229.stanford.edu/notes2022fall/main_notes.pdf
"""

import matplotlib.pyplot as plt
import numpy as np


def weight_matrix(point: np.ndarray, x_train: np.ndarray, tau: float) -> np.ndarray:
    """
    Calculate the weight of every point in the training data around a given
    prediction point

    Args:
        point: x-value at which the prediction is being made
        x_train: ndarray of x-values for training
        tau: bandwidth value, controls how quickly the weight of training values
            decreases as the distance from the prediction point increases

    Returns:
        m x m weight matrix around the prediction point, where m is the size of
        the training set
    >>> weight_matrix(
    ...     np.array([1., 1.]),
    ...     np.array([[16.99, 10.34], [21.01,23.68], [24.59,25.69]]),
    ...     0.6
    ... )
    array([[1.43807972e-207, 0.00000000e+000, 0.00000000e+000],
           [0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
           [0.00000000e+000, 0.00000000e+000, 0.00000000e+000]])
    """
    m = len(x_train)  # Number of training samples
    weights = np.eye(m)  # Initialize weights as identity matrix
    for j in range(m):
        diff = point - x_train[j]
        weights[j, j] = np.exp(diff @ diff.T / (-2.0 * tau**2))

    return weights


def local_weight(
    point: np.ndarray, x_train: np.ndarray, y_train: np.ndarray, tau: float
) -> np.ndarray:
    """
    Calculate the local weights at a given prediction point using the weight
    matrix for that point

    Args:
        point: x-value at which the prediction is being made
        x_train: ndarray of x-values for training
        y_train: ndarray of y-values for training
        tau: bandwidth value, controls how quickly the weight of training values
            decreases as the distance from the prediction point increases
    Returns:
        ndarray of local weights
    >>> local_weight(
    ...     np.array([1., 1.]),
    ...     np.array([[16.99, 10.34], [21.01,23.68], [24.59,25.69]]),
    ...     np.array([[1.01, 1.66, 3.5]]),
    ...     0.6
    ... )
    array([[0.00873174],
           [0.08272556]])
    """
    weight_mat = weight_matrix(point, x_train, tau)
    weight = np.linalg.inv(x_train.T @ weight_mat @ x_train) @ (
        x_train.T @ weight_mat @ y_train.T
    )

    return weight


def local_weight_regression(
    x_train: np.ndarray, y_train: np.ndarray, tau: float
) -> np.ndarray:
    """
    Calculate predictions for each point in the training data

    Args:
        x_train: ndarray of x-values for training
        y_train: ndarray of y-values for training
        tau: bandwidth value, controls how quickly the weight of training values
            decreases as the distance from the prediction point increases

    Returns:
        ndarray of predictions
    >>> local_weight_regression(
    ...     np.array([[16.99, 10.34], [21.01, 23.68], [24.59, 25.69]]),
    ...     np.array([[1.01, 1.66, 3.5]]),
    ...     0.6
    ... )
    array([1.07173261, 1.65970737, 3.50160179])
    """
    y_pred = np.zeros(len(x_train))  # Initialize array of predictions
    for i, item in enumerate(x_train):
        y_pred[i] = np.dot(item, local_weight(item, x_train, y_train, tau)).item()

    return y_pred


def load_data(
    dataset_name: str, x_name: str, y_name: str
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
    """
    Load data from seaborn and split it into x and y points
    >>> pass    # No doctests, function is for demo purposes only
    """
    import seaborn as sns

    data = sns.load_dataset(dataset_name)
    x_data = np.array(data[x_name])
    y_data = np.array(data[y_name])

    one = np.ones(len(y_data))

    # pairing elements of one and x_data
    x_train = np.column_stack((one, x_data))

    return x_train, x_data, y_data


def plot_preds(
    x_train: np.ndarray,
    preds: np.ndarray,
    x_data: np.ndarray,
    y_data: np.ndarray,
    x_name: str,
    y_name: str,
) -> None:
    """
    Plot predictions and display the graph
    >>> pass    # No doctests, function is for demo purposes only
    """
    x_train_sorted = np.sort(x_train, axis=0)
    plt.scatter(x_data, y_data, color="blue")
    plt.plot(
        x_train_sorted[:, 1],
        preds[x_train[:, 1].argsort(0)],
        color="yellow",
        linewidth=5,
    )
    plt.title("Local Weighted Regression")
    plt.xlabel(x_name)
    plt.ylabel(y_name)
    plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Demo with a dataset from the seaborn module
    training_data_x, total_bill, tip = load_data("tips", "total_bill", "tip")
    predictions = local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")


================================================
FILE: machine_learning/logistic_regression.py
================================================
#!/usr/bin/python

# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

"""
Implementing logistic regression for classification problem
Helpful resources:
Coursera ML course
https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac
"""

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets

# get_ipython().run_line_magic('matplotlib', 'inline')


# In[67]:

# sigmoid function or logistic function is used as a hypothesis function in
# classification problems


def sigmoid_function(z: float | np.ndarray) -> float | np.ndarray:
    """
    Also known as Logistic Function.

                1
    f(x) =   -------
              1 + e⁻ˣ

    The sigmoid function approaches a value of 1 as its input 'x' becomes
    increasing positive. Opposite for negative values.

    Reference: https://en.wikipedia.org/wiki/Sigmoid_function

    @param z:  input to the function
    @returns: returns value in the range 0 to 1

    Examples:
    >>> float(sigmoid_function(4))
    0.9820137900379085
    >>> sigmoid_function(np.array([-3, 3]))
    array([0.04742587, 0.95257413])
    >>> sigmoid_function(np.array([-3, 3, 1]))
    array([0.04742587, 0.95257413, 0.73105858])
    >>> sigmoid_function(np.array([-0.01, -2, -1.9]))
    array([0.49750002, 0.11920292, 0.13010847])
    >>> sigmoid_function(np.array([-1.3, 5.3, 12]))
    array([0.21416502, 0.9950332 , 0.99999386])
    >>> sigmoid_function(np.array([0.01, 0.02, 4.1]))
    array([0.50249998, 0.50499983, 0.9836975 ])
    >>> sigmoid_function(np.array([0.8]))
    array([0.68997448])
    """
    return 1 / (1 + np.exp(-z))


def cost_function(h: np.ndarray, y: np.ndarray) -> float:
    """
    Cost function quantifies the error between predicted and expected values.
    The cost function used in Logistic Regression is called Log Loss
    or Cross Entropy Function.

    J(θ) = (1/m) * Σ [ -y * log(hθ(x)) - (1 - y) * log(1 - hθ(x)) ]

    Where:
       - J(θ) is the cost that we want to minimize during training
       - m is the number of training examples
       - Σ represents the summation over all training examples
       - y is the actual binary label (0 or 1) for a given example
       - hθ(x) is the predicted probability that x belongs to the positive class

    @param h: the output of sigmoid function. It is the estimated probability
    that the input example 'x' belongs to the positive class

    @param y: the actual binary label associated with input example 'x'

    Examples:
    >>> estimations = sigmoid_function(np.array([0.3, -4.3, 8.1]))
    >>> cost_function(h=estimations,y=np.array([1, 0, 1]))
    0.18937868932131605
    >>> estimations = sigmoid_function(np.array([4, 3, 1]))
    >>> cost_function(h=estimations,y=np.array([1, 0, 0]))
    1.459999655669926
    >>> estimations = sigmoid_function(np.array([4, -3, -1]))
    >>> cost_function(h=estimations,y=np.array([1,0,0]))
    0.1266663223365915
    >>> estimations = sigmoid_function(0)
    >>> cost_function(h=estimations,y=np.array([1]))
    0.6931471805599453

    References:
       - https://en.wikipedia.org/wiki/Logistic_regression
    """
    return float((-y * np.log(h) - (1 - y) * np.log(1 - h)).mean())


def log_likelihood(x, y, weights):
    scores = np.dot(x, weights)
    return np.sum(y * scores - np.log(1 + np.exp(scores)))


# here alpha is the learning rate, X is the feature matrix,y is the target matrix
def logistic_reg(alpha, x, y, max_iterations=70000):
    theta = np.zeros(x.shape[1])

    for iterations in range(max_iterations):
        z = np.dot(x, theta)
        h = sigmoid_function(z)
        gradient = np.dot(x.T, h - y) / y.size
        theta = theta - alpha * gradient  # updating the weights
        z = np.dot(x, theta)
        h = sigmoid_function(z)
        j = cost_function(h, y)
        if iterations % 100 == 0:
            print(f"loss: {j} \t")  # printing the loss after every 100 iterations
    return theta


# In[68]:

if __name__ == "__main__":
    import doctest

    doctest.testmod()

    iris = datasets.load_iris()
    x = iris.data[:, :2]
    y = (iris.target != 0) * 1

    alpha = 0.1
    theta = logistic_reg(alpha, x, y, max_iterations=70000)
    print("theta: ", theta)  # printing the theta i.e our weights vector

    def predict_prob(x):
        return sigmoid_function(
            np.dot(x, theta)
        )  # predicting the value of probability from the logistic regression algorithm

    plt.figure(figsize=(10, 6))
    plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="b", label="0")
    plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="r", label="1")
    (x1_min, x1_max) = (x[:, 0].min(), x[:, 0].max())
    (x2_min, x2_max) = (x[:, 1].min(), x[:, 1].max())
    (xx1, xx2) = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max))
    grid = np.c_[xx1.ravel(), xx2.ravel()]
    probs = predict_prob(grid).reshape(xx1.shape)
    plt.contour(xx1, xx2, probs, [0.5], linewidths=1, colors="black")

    plt.legend()
    plt.show()


================================================
FILE: machine_learning/loss_functions.py
================================================
import numpy as np


def binary_cross_entropy(
    y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-15
) -> float:
    """
    Calculate the mean binary cross-entropy (BCE) loss between true labels and predicted
    probabilities.

    BCE loss quantifies dissimilarity between true labels (0 or 1) and predicted
    probabilities. It's widely used in binary classification tasks.

    BCE = -Σ(y_true * ln(y_pred) + (1 - y_true) * ln(1 - y_pred))

    Reference: https://en.wikipedia.org/wiki/Cross_entropy

    Parameters:
    - y_true: True binary labels (0 or 1)
    - y_pred: Predicted probabilities for class 1
    - epsilon: Small constant to avoid numerical instability

    >>> true_labels = np.array([0, 1, 1, 0, 1])
    >>> predicted_probs = np.array([0.2, 0.7, 0.9, 0.3, 0.8])
    >>> float(binary_cross_entropy(true_labels, predicted_probs))
    0.2529995012327421
    >>> true_labels = np.array([0, 1, 1, 0, 1])
    >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
    >>> binary_cross_entropy(true_labels, predicted_probs)
    Traceback (most recent call last):
        ...
    ValueError: Input arrays must have the same length.
    """
    if len(y_true) != len(y_pred):
        raise ValueError("Input arrays must have the same length.")

    y_pred = np.clip(y_pred, epsilon, 1 - epsilon)  # Clip predictions to avoid log(0)
    bce_loss = -(y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred))
    return np.mean(bce_loss)


def binary_focal_cross_entropy(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    gamma: float = 2.0,
    alpha: float = 0.25,
    epsilon: float = 1e-15,
) -> float:
    """
    Calculate the mean binary focal cross-entropy (BFCE) loss between true labels
    and predicted probabilities.

    BFCE loss quantifies dissimilarity between true labels (0 or 1) and predicted
    probabilities. It's a variation of binary cross-entropy that addresses class
    imbalance by focusing on hard examples.

    BCFE = -Σ(alpha * (1 - y_pred)**gamma * y_true * log(y_pred)
                + (1 - alpha) * y_pred**gamma * (1 - y_true) * log(1 - y_pred))

    Reference: [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf)

    Parameters:
    - y_true: True binary labels (0 or 1).
    - y_pred: Predicted probabilities for class 1.
    - gamma: Focusing parameter for modulating the loss (default: 2.0).
    - alpha: Weighting factor for class 1 (default: 0.25).
    - epsilon: Small constant to avoid numerical instability.

    >>> true_labels = np.array([0, 1, 1, 0, 1])
    >>> predicted_probs = np.array([0.2, 0.7, 0.9, 0.3, 0.8])
    >>> float(binary_focal_cross_entropy(true_labels, predicted_probs))
    0.008257977659239775
    >>> true_labels = np.array([0, 1, 1, 0, 1])
    >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
    >>> binary_focal_cross_entropy(true_labels, predicted_probs)
    Traceback (most recent call last):
        ...
    ValueError: Input arrays must have the same length.
    """
    if len(y_true) != len(y_pred):
        raise ValueError("Input arrays must have the same length.")
    # Clip predicted probabilities to avoid log(0)
    y_pred = np.clip(y_pred, epsilon, 1 - epsilon)

    bcfe_loss = -(
        alpha * (1 - y_pred) ** gamma * y_true * np.log(y_pred)
        + (1 - alpha) * y_pred**gamma * (1 - y_true) * np.log(1 - y_pred)
    )

    return np.mean(bcfe_loss)


def categorical_cross_entropy(
    y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-15
) -> float:
    """
    Calculate categorical cross-entropy (CCE) loss between true class labels and
    predicted class probabilities.

    CCE = -Σ(y_true * ln(y_pred))

    Reference: https://en.wikipedia.org/wiki/Cross_entropy

    Parameters:
    - y_true: True class labels (one-hot encoded)
    - y_pred: Predicted class probabilities
    - epsilon: Small constant to avoid numerical instability

    >>> true_labels = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
    >>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1], [0.0, 0.1, 0.9]])
    >>> float(categorical_cross_entropy(true_labels, pred_probs))
    0.567395975254385
    >>> true_labels = np.array([[1, 0], [0, 1]])
    >>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
    >>> categorical_cross_entropy(true_labels, pred_probs)
    Traceback (most recent call last):
        ...
    ValueError: Input arrays must have the same shape.
    >>> true_labels = np.array([[2, 0, 1], [1, 0, 0]])
    >>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
    >>> categorical_cross_entropy(true_labels, pred_probs)
    Traceback (most recent call last):
        ...
    ValueError: y_true must be one-hot encoded.
    >>> true_labels = np.array([[1, 0, 1], [1, 0, 0]])
    >>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
    >>> categorical_cross_entropy(true_labels, pred_probs)
    Traceback (most recent call last):
        ...
    ValueError: y_true must be one-hot encoded.
    >>> true_labels = np.array([[1, 0, 0], [0, 1, 0]])
    >>> pred_probs = np.array([[0.9, 0.1, 0.1], [0.2, 0.7, 0.1]])
    >>> categorical_cross_entropy(true_labels, pred_probs)
    Traceback (most recent call last):
        ...
    ValueError: Predicted probabilities must sum to approximately 1.
    """
    if y_true.shape != y_pred.shape:
        raise ValueError("Input arrays must have the same shape.")

    if np.any((y_true != 0) & (y_true != 1)) or np.any(y_true.sum(axis=1) != 1):
        raise ValueError("y_true must be one-hot encoded.")

    if not np.all(np.isclose(np.sum(y_pred, axis=1), 1, rtol=epsilon, atol=epsilon)):
        raise ValueError("Predicted probabilities must sum to approximately 1.")

    y_pred = np.clip(y_pred, epsilon, 1)  # Clip predictions to avoid log(0)
    return -np.sum(y_true * np.log(y_pred))


def categorical_focal_cross_entropy(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    alpha: np.ndarray = None,
    gamma: float = 2.0,
    epsilon: float = 1e-15,
) -> float:
    """
    Calculate the mean categorical focal cross-entropy (CFCE) loss between true
    labels and predicted probabilities for multi-class classification.

    CFCE loss is a generalization of binary focal cross-entropy for multi-class
    classification. It addresses class imbalance by focusing on hard examples.

    CFCE = -Σ alpha * (1 - y_pred)**gamma * y_true * log(y_pred)

    Reference: [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf)

    Parameters:
    - y_true: True labels in one-hot encoded form.
    - y_pred: Predicted probabilities for each class.
    - alpha: Array of weighting factors for each class.
    - gamma: Focusing parameter for modulating the loss (default: 2.0).
    - epsilon: Small constant to avoid numerical instability.

    Returns:
    - The mean categorical focal cross-entropy loss.

    >>> true_labels = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
    >>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1], [0.0, 0.1, 0.9]])
    >>> alpha = np.array([0.6, 0.2, 0.7])
    >>> float(categorical_focal_cross_entropy(true_labels, pred_probs, alpha))
    0.0025966118981496423

    >>> true_labels = np.array([[0, 1, 0], [0, 0, 1]])
    >>> pred_probs = np.array([[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
    >>> alpha = np.array([0.25, 0.25, 0.25])
    >>> float(categorical_focal_cross_entropy(true_labels, pred_probs, alpha))
    0.23315276982014324

    >>> true_labels = np.array([[1, 0], [0, 1]])
    >>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
    >>> categorical_cross_entropy(true_labels, pred_probs)
    Traceback (most recent call last):
        ...
    ValueError: Input arrays must have the same shape.

    >>> true_labels = np.array([[2, 0, 1], [1, 0, 0]])
    >>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
    >>> categorical_focal_cross_entropy(true_labels, pred_probs)
    Traceback (most recent call last):
        ...
    ValueError: y_true must be one-hot encoded.

    >>> true_labels = np.array([[1, 0, 1], [1, 0, 0]])
    >>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
    >>> categorical_focal_cross_entropy(true_labels, pred_probs)
    Traceback (most recent call last):
        ...
    ValueError: y_true must be one-hot encoded.

    >>> true_labels = np.array([[1, 0, 0], [0, 1, 0]])
    >>> pred_probs = np.array([[0.9, 0.1, 0.1], [0.2, 0.7, 0.1]])
    >>> categorical_focal_cross_entropy(true_labels, pred_probs)
    Traceback (most recent call last):
        ...
    ValueError: Predicted probabilities must sum to approximately 1.

    >>> true_labels = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
    >>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1], [0.0, 0.1, 0.9]])
    >>> alpha = np.array([0.6, 0.2])
    >>> categorical_focal_cross_entropy(true_labels, pred_probs, alpha)
    Traceback (most recent call last):
        ...
    ValueError: Length of alpha must match the number of classes.
    """
    if y_true.shape != y_pred.shape:
        raise ValueError("Shape of y_true and y_pred must be the same.")

    if alpha is None:
        alpha = np.ones(y_true.shape[1])

    if np.any((y_true != 0) & (y_true != 1)) or np.any(y_true.sum(axis=1) != 1):
        raise ValueError("y_true must be one-hot encoded.")

    if len(alpha) != y_true.shape[1]:
        raise ValueError("Length of alpha must match the number of classes.")

    if not np.all(np.isclose(np.sum(y_pred, axis=1), 1, rtol=epsilon, atol=epsilon)):
        raise ValueError("Predicted probabilities must sum to approximately 1.")

    # Clip predicted probabilities to avoid log(0)
    y_pred = np.clip(y_pred, epsilon, 1 - epsilon)

    # Calculate loss for each class and sum across classes
    cfce_loss = -np.sum(
        alpha * np.power(1 - y_pred, gamma) * y_true * np.log(y_pred), axis=1
    )

    return np.mean(cfce_loss)


def hinge_loss(y_true: np.ndarray, y_pred: np.ndarray) -> float:
    """
    Calculate the mean hinge loss for between true labels and predicted probabilities
    for training support vector machines (SVMs).

    Hinge loss = max(0, 1 - true * pred)

    Reference: https://en.wikipedia.org/wiki/Hinge_loss

    Args:
    - y_true: actual values (ground truth) encoded as -1 or 1
    - y_pred: predicted values

    >>> true_labels = np.array([-1, 1, 1, -1, 1])
    >>> pred = np.array([-4, -0.3, 0.7, 5, 10])
    >>> float(hinge_loss(true_labels, pred))
    1.52
    >>> true_labels = np.array([-1, 1, 1, -1, 1, 1])
    >>> pred = np.array([-4, -0.3, 0.7, 5, 10])
    >>> hinge_loss(true_labels, pred)
    Traceback (most recent call last):
    ...
    ValueError: Length of predicted and actual array must be same.
    >>> true_labels = np.array([-1, 1, 10, -1, 1])
    >>> pred = np.array([-4, -0.3, 0.7, 5, 10])
    >>> hinge_loss(true_labels, pred)
    Traceback (most recent call last):
    ...
    ValueError: y_true can have values -1 or 1 only.
    """
    if len(y_true) != len(y_pred):
        raise ValueError("Length of predicted and actual array must be same.")

    if np.any((y_true != -1) & (y_true != 1)):
        raise ValueError("y_true can have values -1 or 1 only.")

    hinge_losses = np.maximum(0, 1.0 - (y_true * y_pred))
    return np.mean(hinge_losses)


def huber_loss(y_true: np.ndarray, y_pred: np.ndarray, delta: float) -> float:
    """
    Calculate the mean Huber loss between the given ground truth and predicted values.

    The Huber loss describes the penalty incurred by an estimation procedure, and it
    serves as a measure of accuracy for regression models.

    Huber loss =
        0.5 * (y_true - y_pred)^2                   if |y_true - y_pred| <= delta
        delta * |y_true - y_pred| - 0.5 * delta^2   otherwise

    Reference: https://en.wikipedia.org/wiki/Huber_loss

    Parameters:
    - y_true: The true values (ground truth)
    - y_pred: The predicted values

    >>> true_values = np.array([0.9, 10.0, 2.0, 1.0, 5.2])
    >>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
    >>> bool(np.isclose(huber_loss(true_values, predicted_values, 1.0), 2.102))
    True
    >>> true_labels = np.array([11.0, 21.0, 3.32, 4.0, 5.0])
    >>> predicted_probs = np.array([8.3, 20.8, 2.9, 11.2, 5.0])
    >>> bool(np.isclose(huber_loss(true_labels, predicted_probs, 1.0), 1.80164))
    True
    >>> true_labels = np.array([11.0, 21.0, 3.32, 4.0])
    >>> predicted_probs = np.array([8.3, 20.8, 2.9, 11.2, 5.0])
    >>> huber_loss(true_labels, predicted_probs, 1.0)
    Traceback (most recent call last):
    ...
    ValueError: Input arrays must have the same length.
    """
    if len(y_true) != len(y_pred):
        raise ValueError("Input arrays must have the same length.")

    huber_mse = 0.5 * (y_true - y_pred) ** 2
    huber_mae = delta * (np.abs(y_true - y_pred) - 0.5 * delta)
    return np.where(np.abs(y_true - y_pred) <= delta, huber_mse, huber_mae).mean()


def mean_squared_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
    """
    Calculate the mean squared error (MSE) between ground truth and predicted values.

    MSE measures the squared difference between true values and predicted values, and it
    serves as a measure of accuracy for regression models.

    MSE = (1/n) * Σ(y_true - y_pred)^2

    Reference: https://en.wikipedia.org/wiki/Mean_squared_error

    Parameters:
    - y_true: The true values (ground truth)
    - y_pred: The predicted values

    >>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
    >>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
    >>> bool(np.isclose(mean_squared_error(true_values, predicted_values), 0.028))
    True
    >>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
    >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
    >>> mean_squared_error(true_labels, predicted_probs)
    Traceback (most recent call last):
    ...
    ValueError: Input arrays must have the same length.
    """
    if len(y_true) != len(y_pred):
        raise ValueError("Input arrays must have the same length.")

    squared_errors = (y_true - y_pred) ** 2
    return np.mean(squared_errors)


def mean_absolute_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
    """
    Calculates the Mean Absolute Error (MAE) between ground truth (observed)
        and predicted values.

    MAE measures the absolute difference between true values and predicted values.

    Equation:
    MAE = (1/n) * Σ(abs(y_true - y_pred))

    Reference: https://en.wikipedia.org/wiki/Mean_absolute_error

    Parameters:
    - y_true: The true values (ground truth)
    - y_pred: The predicted values

    >>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
    >>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
    >>> bool(np.isclose(mean_absolute_error(true_values, predicted_values), 0.16))
    True
    >>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
    >>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
    >>> bool(np.isclose(mean_absolute_error(true_values, predicted_values), 2.16))
    False
    >>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
    >>> predicted_probs = np.array([0.3, 0.8, 0.9, 5.2])
    >>> mean_absolute_error(true_labels, predicted_probs)
    Traceback (most recent call last):
    ...
    ValueError: Input arrays must have the same length.
    """
    if len(y_true) != len(y_pred):
        raise ValueError("Input arrays must have the same length.")

    return np.mean(abs(y_true - y_pred))


def mean_squared_logarithmic_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
    """
    Calculate the mean squared logarithmic error (MSLE) between ground truth and
    predicted values.

    MSLE measures the squared logarithmic difference between true values and predicted
    values for regression models. It's particularly useful for dealing with skewed or
    large-value data, and it's often used when the relative differences between
    predicted and true values are more important than absolute differences.

    MSLE = (1/n) * Σ(log(1 + y_true) - log(1 + y_pred))^2

    Reference: https://insideaiml.com/blog/MeanSquared-Logarithmic-Error-Loss-1035

    Parameters:
    - y_true: The true values (ground truth)
    - y_pred: The predicted values

    >>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
    >>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
    >>> float(mean_squared_logarithmic_error(true_values, predicted_values))
    0.0030860877925181344
    >>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
    >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
    >>> mean_squared_logarithmic_error(true_labels, predicted_probs)
    Traceback (most recent call last):
    ...
    ValueError: Input arrays must have the same length.
    """
    if len(y_true) != len(y_pred):
        raise ValueError("Input arrays must have the same length.")

    squared_logarithmic_errors = (np.log1p(y_true) - np.log1p(y_pred)) ** 2
    return np.mean(squared_logarithmic_errors)


def mean_absolute_percentage_error(
    y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-15
) -> float:
    """
    Calculate the Mean Absolute Percentage Error between y_true and y_pred.

    Mean Absolute Percentage Error calculates the average of the absolute
    percentage differences between the predicted and true values.

    Formula = (Σ|y_true[i]-Y_pred[i]/y_true[i]|)/n

    Source: https://stephenallwright.com/good-mape-score/

    Parameters:
    y_true (np.ndarray): Numpy array containing true/target values.
    y_pred (np.ndarray): Numpy array containing predicted values.

    Returns:
    float: The Mean Absolute Percentage error between y_true and y_pred.

    Examples:
    >>> y_true = np.array([10, 20, 30, 40])
    >>> y_pred = np.array([12, 18, 33, 45])
    >>> float(mean_absolute_percentage_error(y_true, y_pred))
    0.13125

    >>> y_true = np.array([1, 2, 3, 4])
    >>> y_pred = np.array([2, 3, 4, 5])
    >>> float(mean_absolute_percentage_error(y_true, y_pred))
    0.5208333333333333

    >>> y_true = np.array([34, 37, 44, 47, 48, 48, 46, 43, 32, 27, 26, 24])
    >>> y_pred = np.array([37, 40, 46, 44, 46, 50, 45, 44, 34, 30, 22, 23])
    >>> float(mean_absolute_percentage_error(y_true, y_pred))
    0.064671076436071
    """
    if len(y_true) != len(y_pred):
        raise ValueError("The length of the two arrays should be the same.")

    y_true = np.where(y_true == 0, epsilon, y_true)
    absolute_percentage_diff = np.abs((y_true - y_pred) / y_true)

    return np.mean(absolute_percentage_diff)


def perplexity_loss(
    y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-7
) -> float:
    """
    Calculate the perplexity for the y_true and y_pred.

    Compute the Perplexity which useful in predicting language model
    accuracy in Natural Language Processing (NLP.)
    Perplexity is measure of how certain the model in its predictions.

    Perplexity Loss = exp(-1/N (Σ ln(p(x)))

    Reference:
    https://en.wikipedia.org/wiki/Perplexity

    Args:
        y_true: Actual label encoded sentences of shape (batch_size, sentence_length)
        y_pred: Predicted sentences of shape (batch_size, sentence_length, vocab_size)
        epsilon: Small floating point number to avoid getting inf for log(0)

    Returns:
        Perplexity loss between y_true and y_pred.

    >>> y_true = np.array([[1, 4], [2, 3]])
    >>> y_pred = np.array(
    ...    [[[0.28, 0.19, 0.21 , 0.15, 0.15],
    ...      [0.24, 0.19, 0.09, 0.18, 0.27]],
    ...      [[0.03, 0.26, 0.21, 0.18, 0.30],
    ...       [0.28, 0.10, 0.33, 0.15, 0.12]]]
    ... )
    >>> float(perplexity_loss(y_true, y_pred))
    5.0247347775367945
    >>> y_true = np.array([[1, 4], [2, 3]])
    >>> y_pred = np.array(
    ...    [[[0.28, 0.19, 0.21 , 0.15, 0.15],
    ...      [0.24, 0.19, 0.09, 0.18, 0.27],
    ...      [0.30, 0.10, 0.20, 0.15, 0.25]],
    ...      [[0.03, 0.26, 0.21, 0.18, 0.30],
    ...       [0.28, 0.10, 0.33, 0.15, 0.12],
    ...       [0.30, 0.10, 0.20, 0.15, 0.25]],]
    ... )
    >>> perplexity_loss(y_true, y_pred)
    Traceback (most recent call last):
    ...
    ValueError: Sentence length of y_true and y_pred must be equal.
    >>> y_true = np.array([[1, 4], [2, 11]])
    >>> y_pred = np.array(
    ...    [[[0.28, 0.19, 0.21 , 0.15, 0.15],
    ...      [0.24, 0.19, 0.09, 0.18, 0.27]],
    ...      [[0.03, 0.26, 0.21, 0.18, 0.30],
    ...       [0.28, 0.10, 0.33, 0.15, 0.12]]]
    ... )
    >>> perplexity_loss(y_true, y_pred)
    Traceback (most recent call last):
    ...
    ValueError: Label value must not be greater than vocabulary size.
    >>> y_true = np.array([[1, 4]])
    >>> y_pred = np.array(
    ...    [[[0.28, 0.19, 0.21 , 0.15, 0.15],
    ...      [0.24, 0.19, 0.09, 0.18, 0.27]],
    ...      [[0.03, 0.26, 0.21, 0.18, 0.30],
    ...       [0.28, 0.10, 0.33, 0.15, 0.12]]]
    ... )
    >>> perplexity_loss(y_true, y_pred)
    Traceback (most recent call last):
    ...
    ValueError: Batch size of y_true and y_pred must be equal.
    """

    vocab_size = y_pred.shape[2]

    if y_true.shape[0] != y_pred.shape[0]:
        raise ValueError("Batch size of y_true and y_pred must be equal.")
    if y_true.shape[1] != y_pred.shape[1]:
        raise ValueError("Sentence length of y_true and y_pred must be equal.")
    if np.max(y_true) > vocab_size:
        raise ValueError("Label value must not be greater than vocabulary size.")

    # Matrix to select prediction value only for true class
    filter_matrix = np.array(
        [[list(np.eye(vocab_size)[word]) for word in sentence] for sentence in y_true]
    )

    # Getting the matrix containing prediction for only true class
    true_class_pred = np.sum(y_pred * filter_matrix, axis=2).clip(epsilon, 1)

    # Calculating perplexity for each sentence
    perp_losses = np.exp(np.negative(np.mean(np.log(true_class_pred), axis=1)))

    return np.mean(perp_losses)


def smooth_l1_loss(y_true: np.ndarray, y_pred: np.ndarray, beta: float = 1.0) -> float:
    """
    Calculate the Smooth L1 Loss between y_true and y_pred.

    The Smooth L1 Loss is less sensitive to outliers than the L2 Loss and is often used
    in regression problems, such as object detection.

    Smooth L1 Loss =
        0.5 * (x - y)^2 / beta, if |x - y| < beta
        |x - y| - 0.5 * beta, otherwise

    Reference:
    https://pytorch.org/docs/stable/generated/torch.nn.SmoothL1Loss.html

    Args:
        y_true: Array of true values.
        y_pred: Array of predicted values.
        beta: Specifies the threshold at which to change between L1 and L2 loss.

    Returns:
        The calculated Smooth L1 Loss between y_true and y_pred.

    Raises:
        ValueError: If the length of the two arrays is not the same.

    >>> y_true = np.array([3, 5, 2, 7])
    >>> y_pred = np.array([2.9, 4.8, 2.1, 7.2])
    >>> float(smooth_l1_loss(y_true, y_pred, 1.0))
    0.012500000000000022

    >>> y_true = np.array([2, 4, 6])
    >>> y_pred = np.array([1, 5, 7])
    >>> float(smooth_l1_loss(y_true, y_pred, 1.0))
    0.5

    >>> y_true = np.array([1, 3, 5, 7])
    >>> y_pred = np.array([1, 3, 5, 7])
    >>> float(smooth_l1_loss(y_true, y_pred, 1.0))
    0.0

    >>> y_true = np.array([1, 3, 5])
    >>> y_pred = np.array([1, 3, 5, 7])
    >>> smooth_l1_loss(y_true, y_pred, 1.0)
    Traceback (most recent call last):
    ...
    ValueError: The length of the two arrays should be the same.
    """

    if len(y_true) != len(y_pred):
        raise ValueError("The length of the two arrays should be the same.")

    diff = np.abs(y_true - y_pred)
    loss = np.where(diff < beta, 0.5 * diff**2 / beta, diff - 0.5 * beta)
    return np.mean(loss)


def kullback_leibler_divergence(y_true: np.ndarray, y_pred: np.ndarray) -> float:
    """
    Calculate the Kullback-Leibler divergence (KL divergence) loss between true labels
    and predicted probabilities.

    KL divergence loss quantifies dissimilarity between true labels and predicted
    probabilities. It's often used in training generative models.

    KL = Σ(y_true * ln(y_true / y_pred))

    Reference: https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence

    Parameters:
    - y_true: True class probabilities
    - y_pred: Predicted class probabilities

    >>> true_labels = np.array([0.2, 0.3, 0.5])
    >>> predicted_probs = np.array([0.3, 0.3, 0.4])
    >>> float(kullback_leibler_divergence(true_labels, predicted_probs))
    0.030478754035472025
    >>> true_labels = np.array([0.2, 0.3, 0.5])
    >>> predicted_probs = np.array([0.3, 0.3, 0.4, 0.5])
    >>> kullback_leibler_divergence(true_labels, predicted_probs)
    Traceback (most recent call last):
        ...
    ValueError: Input arrays must have the same length.
    """
    if len(y_true) != len(y_pred):
        raise ValueError("Input arrays must have the same length.")

    kl_loss = y_true * np.log(y_true / y_pred)
    return np.sum(kl_loss)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: machine_learning/lstm/__init__.py
================================================


================================================
FILE: machine_learning/lstm/lstm_prediction.py
================================================
"""
Create a Long Short Term Memory (LSTM) network model
An LSTM is a type of Recurrent Neural Network (RNN) as discussed at:
* https://colah.github.io/posts/2015-08-Understanding-LSTMs
* https://en.wikipedia.org/wiki/Long_short-term_memory
"""

import numpy as np
import pandas as pd
from keras.layers import LSTM, Dense
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler

if __name__ == "__main__":
    """
    First part of building a model is to get the data and prepare
    it for our model. You can use any dataset for stock prediction
    make sure you set the price column on line number 21.  Here we
    use a dataset which have the price on 3rd column.
    """
    sample_data = pd.read_csv("sample_data.csv", header=None)
    len_data = sample_data.shape[:1][0]
    # If you're using some other dataset input the target column
    actual_data = sample_data.iloc[:, 1:2]
    actual_data = actual_data.to_numpy().reshape(len_data, 1)
    actual_data = MinMaxScaler().fit_transform(actual_data)
    look_back = 10
    forward_days = 5
    periods = 20
    division = len_data - periods * look_back
    train_data = actual_data[:division]
    test_data = actual_data[division - look_back :]
    train_x, train_y = [], []
    test_x, test_y = [], []

    for i in range(len(train_data) - forward_days - look_back + 1):
        train_x.append(train_data[i : i + look_back])
        train_y.append(train_data[i + look_back : i + look_back + forward_days])
    for i in range(len(test_data) - forward_days - look_back + 1):
        test_x.append(test_data[i : i + look_back])
        test_y.append(test_data[i + look_back : i + look_back + forward_days])
    x_train = np.array(train_x)
    x_test = np.array(test_x)
    y_train = np.array([list(i.ravel()) for i in train_y])
    y_test = np.array([list(i.ravel()) for i in test_y])

    model = Sequential()
    model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
    model.add(LSTM(64, input_shape=(128, 1)))
    model.add(Dense(forward_days))
    model.compile(loss="mean_squared_error", optimizer="adam")
    history = model.fit(
        x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
    )
    pred = model.predict(x_test)


================================================
FILE: machine_learning/lstm/sample_data.csv
================================================
04/24/2020,  1279.31, 1640394,  1261.17,  1280.4,  1249.45
04/23/2020,  1276.31, 1566203,  1271.55,  1293.31,  1265.67
04/22/2020,  1263.21, 2093140,  1245.54,  1285.6133,  1242
04/21/2020,  1216.34, 2153003,  1247,  1254.27,  1209.71
04/20/2020,  1266.61, 1695488,  1271,  1281.6,  1261.37
04/17/2020,  1283.25, 1949042,  1284.85,  1294.43,  1271.23
04/16/2020,  1263.47, 2518099,  1274.1,  1279,  1242.62
04/15/2020,  1262.47, 1671703,  1245.61,  1280.46,  1240.4
04/14/2020,  1269.23, 2470353,  1245.09,  1282.07,  1236.93
04/13/2020,  1217.56, 1739828,  1209.18,  1220.51,  1187.5984
04/09/2020,  1211.45, 2175421,  1224.08,  1225.57,  1196.7351
04/08/2020,  1210.28, 1975135,  1206.5,  1219.07,  1188.16
04/07/2020,  1186.51, 2387329,  1221,  1225,  1182.23
04/06/2020,  1186.92, 2664723,  1138,  1194.66,  1130.94
04/03/2020,  1097.88, 2313400,  1119.015,  1123.54,  1079.81
04/02/2020,  1120.84, 1964881,  1098.26,  1126.86,  1096.4
04/01/2020,  1105.62, 2344173,  1122,  1129.69,  1097.45
03/31/2020,  1162.81, 2487983,  1147.3,  1175.31,  1138.14
03/30/2020,  1146.82, 2574061,  1125.04,  1151.63,  1096.48
03/27/2020,  1110.71, 3208495,  1125.67,  1150.6702,  1105.91
03/26/2020,  1161.75, 3573755,  1111.8,  1169.97,  1093.53
03/25/2020,  1102.49, 4081528,  1126.47,  1148.9,  1086.01
03/24/2020,  1134.46, 3344450,  1103.77,  1135,  1090.62
03/23/2020,  1056.62, 4044137,  1061.32,  1071.32,  1013.5361
03/20/2020,  1072.32, 3601750,  1135.72,  1143.99,  1065.49
03/19/2020,  1115.29, 3651106,  1093.05,  1157.9699,  1060.1075
03/18/2020,  1096.8, 4233435,  1056.51,  1106.5,  1037.28
03/17/2020,  1119.8, 3861489,  1093.11,  1130.86,  1056.01
03/16/2020,  1084.33, 4252365,  1096,  1152.2665,  1074.44
03/13/2020,  1219.73, 3700125,  1179,  1219.76,  1117.1432
03/12/2020,  1114.91, 4226748,  1126,  1193.87,  1113.3
03/11/2020,  1215.41, 2611229,  1249.7,  1260.96,  1196.07
03/10/2020,  1280.39, 2611373,  1260,  1281.15,  1218.77
03/09/2020,  1215.56, 3365365,  1205.3,  1254.7599,  1200
03/06/2020,  1298.41, 2660628,  1277.06,  1306.22,  1261.05
03/05/2020,  1319.04, 2561288,  1350.2,  1358.91,  1305.1
03/04/2020,  1386.52, 1913315,  1359.23,  1388.09,  1343.11
03/03/2020,  1341.39, 2402326,  1399.42,  1410.15,  1332
03/02/2020,  1389.11, 2431468,  1351.61,  1390.87,  1326.815
02/28/2020,  1339.33, 3790618,  1277.5,  1341.14,  1271
02/27/2020,  1318.09, 2978300,  1362.06,  1371.7037,  1317.17
02/26/2020,  1393.18, 2204037,  1396.14,  1415.7,  1379
02/25/2020,  1388.45, 2478278,  1433,  1438.14,  1382.4
02/24/2020,  1421.59, 2867053,  1426.11,  1436.97,  1411.39
02/21/2020,  1485.11, 1732273,  1508.03,  1512.215,  1480.44
02/20/2020,  1518.15, 1096552,  1522,  1529.64,  1506.82
02/19/2020,  1526.69, 949268,  1525.07,  1532.1063,  1521.4
02/18/2020,  1519.67, 1121140,  1515,  1531.63,  1512.59
02/14/2020,  1520.74, 1197836,  1515.6,  1520.74,  1507.34
02/13/2020,  1514.66, 929730,  1512.69,  1527.18,  1504.6
02/12/2020,  1518.27, 1167565,  1514.48,  1520.695,  1508.11
02/11/2020,  1508.79, 1344633,  1511.81,  1529.63,  1505.6378
02/10/2020,  1508.68, 1419876,  1474.32,  1509.5,  1474.32
02/07/2020,  1479.23, 1172270,  1467.3,  1485.84,  1466.35
02/06/2020,  1476.23, 1679384,  1450.33,  1481.9997,  1449.57
02/05/2020,  1448.23, 1986157,  1462.42,  1463.84,  1430.56
02/04/2020,  1447.07, 3932954,  1457.07,  1469.5,  1426.3
02/03/2020,  1485.94, 3055216,  1462,  1490,  1458.99
01/31/2020,  1434.23, 2417214,  1468.9,  1470.13,  1428.53
01/30/2020,  1455.84, 1339421,  1439.96,  1457.28,  1436.4
01/29/2020,  1458.63, 1078667,  1458.8,  1465.43,  1446.74
01/28/2020,  1452.56, 1577422,  1443,  1456,  1432.47
01/27/2020,  1433.9, 1755201,  1431,  1438.07,  1421.2
01/24/2020,  1466.71, 1784644,  1493.59,  1495.495,  1465.25
01/23/2020,  1486.65, 1351354,  1487.64,  1495.52,  1482.1
01/22/2020,  1485.95, 1610846,  1491,  1503.2143,  1484.93
01/21/2020,  1484.4, 2036780,  1479.12,  1491.85,  1471.2
01/17/2020,  1480.39, 2396215,  1462.91,  1481.2954,  1458.22
01/16/2020,  1451.7, 1173688,  1447.44,  1451.99,  1440.92
01/15/2020,  1439.2, 1282685,  1430.21,  1441.395,  1430.21
01/14/2020,  1430.88, 1560453,  1439.01,  1441.8,  1428.37
01/13/2020,  1439.23, 1653482,  1436.13,  1440.52,  1426.02
01/10/2020,  1429.73, 1821566,  1427.56,  1434.9292,  1418.35
01/09/2020,  1419.83, 1502664,  1420.57,  1427.33,  1410.27
01/08/2020,  1404.32, 1529177,  1392.08,  1411.58,  1390.84
01/07/2020,  1393.34, 1511693,  1397.94,  1402.99,  1390.38
01/06/2020,  1394.21, 1733149,  1350,  1396.5,  1350
01/03/2020,  1360.66, 1187006,  1347.86,  1372.5,  1345.5436
01/02/2020,  1367.37, 1406731,  1341.55,  1368.14,  1341.55
12/31/2019,  1337.02, 962468,  1330.11,  1338,  1329.085
12/30/2019,  1336.14, 1051323,  1350,  1353,  1334.02
12/27/2019,  1351.89, 1038718,  1362.99,  1364.53,  1349.31
12/26/2019,  1360.4, 667754,  1346.17,  1361.3269,  1344.47
12/24/2019,  1343.56, 347518,  1348.5,  1350.26,  1342.78
12/23/2019,  1348.84, 883200,  1355.87,  1359.7999,  1346.51
12/20/2019,  1349.59, 3316905,  1363.35,  1363.64,  1349
12/19/2019,  1356.04, 1470112,  1351.82,  1358.1,  1348.985
12/18/2019,  1352.62, 1657069,  1356.6,  1360.47,  1351
12/17/2019,  1355.12, 1855259,  1362.89,  1365,  1351.3231
12/16/2019,  1361.17, 1397451,  1356.5,  1364.68,  1352.67
12/13/2019,  1347.83, 1550028,  1347.95,  1353.0931,  1343.87
12/12/2019,  1350.27, 1281722,  1345.94,  1355.775,  1340.5
12/11/2019,  1345.02, 850796,  1350.84,  1351.2,  1342.67
12/10/2019,  1344.66, 1094653,  1341.5,  1349.975,  1336.04
12/09/2019,  1343.56, 1355795,  1338.04,  1359.45,  1337.84
12/06/2019,  1340.62, 1315510,  1333.44,  1344,  1333.44
12/05/2019,  1328.13, 1212818,  1328,  1329.3579,  1316.44
12/04/2019,  1320.54, 1538110,  1307.01,  1325.8,  1304.87
12/03/2019,  1295.28, 1268647,  1279.57,  1298.461,  1279
12/02/2019,  1289.92, 1511851,  1301,  1305.83,  1281
11/29/2019,  1304.96, 586981,  1307.12,  1310.205,  1303.97
11/27/2019,  1312.99, 996329,  1315,  1318.36,  1309.63
11/26/2019,  1313.55, 1069795,  1309.86,  1314.8,  1305.09
11/25/2019,  1306.69, 1036487,  1299.18,  1311.31,  1298.13
11/22/2019,  1295.34, 1386506,  1305.62,  1308.73,  1291.41
11/21/2019,  1301.35, 995499,  1301.48,  1312.59,  1293
11/20/2019,  1303.05, 1309835,  1311.74,  1315,  1291.15
11/19/2019,  1315.46, 1269372,  1327.7,  1327.7,  1312.8
11/18/2019,  1320.7, 1488083,  1332.22,  1335.5288,  1317.5
11/15/2019,  1334.87, 1782955,  1318.94,  1334.88,  1314.2796
11/14/2019,  1311.46, 1194305,  1297.5,  1317,  1295.65
11/13/2019,  1298, 853861,  1294.07,  1304.3,  1293.51
11/12/2019,  1298.8, 1085859,  1300,  1310,  1295.77
11/11/2019,  1299.19, 1012429,  1303.18,  1306.425,  1297.41
11/08/2019,  1311.37, 1251916,  1305.28,  1318,  1304.365
11/07/2019,  1308.86, 2029970,  1294.28,  1323.74,  1294.245
11/06/2019,  1291.8, 1152977,  1289.46,  1293.73,  1282.5
11/05/2019,  1292.03, 1282711,  1292.89,  1298.93,  1291.2289
11/04/2019,  1291.37, 1500964,  1276.45,  1294.13,  1276.355
11/01/2019,  1273.74, 1670072,  1265,  1274.62,  1260.5
10/31/2019,  1260.11, 1455651,  1261.28,  1267.67,  1250.8428
10/30/2019,  1261.29, 1408851,  1252.97,  1269.36,  1252
10/29/2019,  1262.62, 1886380,  1276.23,  1281.59,  1257.2119
10/28/2019,  1290, 2613237,  1275.45,  1299.31,  1272.54
10/25/2019,  1265.13, 1213051,  1251.03,  1269.6,  1250.01
10/24/2019,  1260.99, 1039868,  1260.9,  1264,  1253.715
10/23/2019,  1259.13, 928595,  1242.36,  1259.89,  1242.36
10/22/2019,  1242.8, 1047851,  1247.85,  1250.6,  1241.38
10/21/2019,  1246.15, 1038042,  1252.26,  1254.6287,  1240.6
10/18/2019,  1245.49, 1352839,  1253.46,  1258.89,  1241.08
10/17/2019,  1253.07, 980510,  1250.93,  1263.325,  1249.94
10/16/2019,  1243.64, 1168174,  1241.17,  1254.74,  1238.45
10/15/2019,  1243.01, 1395259,  1220.4,  1247.33,  1220.4
10/14/2019,  1217.14, 882039,  1212.34,  1226.33,  1211.76
10/11/2019,  1215.45, 1277144,  1222.21,  1228.39,  1213.74
10/10/2019,  1208.67, 932531,  1198.58,  1215,  1197.34
10/09/2019,  1202.31, 876632,  1199.35,  1208.35,  1197.63
10/08/2019,  1189.13, 1141784,  1197.59,  1206.08,  1189.01
10/07/2019,  1207.68, 867149,  1204.4,  1218.2036,  1203.75
10/04/2019,  1209, 1183264,  1191.89,  1211.44,  1189.17
10/03/2019,  1187.83, 1663656,  1180,  1189.06,  1162.43
10/02/2019,  1176.63, 1639237,  1196.98,  1196.99,  1171.29
10/01/2019,  1205.1, 1358279,  1219,  1231.23,  1203.58
09/30/2019,  1219, 1419676,  1220.97,  1226,  1212.3
09/27/2019,  1225.09, 1354432,  1243.01,  1244.02,  1214.45
09/26/2019,  1241.39, 1561882,  1241.96,  1245,  1232.268
09/25/2019,  1246.52, 1593875,  1215.82,  1248.3,  1210.09
09/24/2019,  1218.76, 1591786,  1240,  1246.74,  1210.68
09/23/2019,  1234.03, 1075253,  1226,  1239.09,  1224.17
09/20/2019,  1229.93, 2337269,  1233.12,  1243.32,  1223.08
09/19/2019,  1238.71, 1000155,  1232.06,  1244.44,  1232.02
09/18/2019,  1232.41, 1144333,  1227.51,  1235.61,  1216.53
09/17/2019,  1229.15, 958112,  1230.4,  1235,  1223.69
09/16/2019,  1231.3, 1053299,  1229.52,  1239.56,  1225.61
09/13/2019,  1239.56, 1301350,  1231.35,  1240.88,  1227.01
09/12/2019,  1234.25, 1725908,  1224.3,  1241.86,  1223.02
09/11/2019,  1220.17, 1307033,  1203.41,  1222.6,  1202.2
09/10/2019,  1206, 1260115,  1195.15,  1210,  1194.58
09/09/2019,  1204.41, 1471880,  1204,  1220,  1192.62
09/06/2019,  1204.93, 1072143,  1208.13,  1212.015,  1202.5222
09/05/2019,  1211.38, 1408601,  1191.53,  1213.04,  1191.53
09/04/2019,  1181.41, 1068968,  1176.71,  1183.48,  1171
09/03/2019,  1168.39, 1480420,  1177.03,  1186.89,  1163.2
08/30/2019,  1188.1, 1129959,  1198.5,  1198.5,  1183.8026
08/29/2019,  1192.85, 1088858,  1181.12,  1196.06,  1181.12
08/28/2019,  1171.02, 802243,  1161.71,  1176.4199,  1157.3
08/27/2019,  1167.84, 1077452,  1180.53,  1182.4,  1161.45
08/26/2019,  1168.89, 1226441,  1157.26,  1169.47,  1152.96
08/23/2019,  1151.29, 1688271,  1181.99,  1194.08,  1147.75
08/22/2019,  1189.53, 947906,  1194.07,  1198.0115,  1178.58
08/21/2019,  1191.25, 741053,  1193.15,  1199,  1187.43
08/20/2019,  1182.69, 915605,  1195.25,  1196.06,  1182.11
08/19/2019,  1198.45, 1232517,  1190.09,  1206.99,  1190.09
08/16/2019,  1177.6, 1349436,  1179.55,  1182.72,  1171.81
08/15/2019,  1167.26, 1224739,  1163.5,  1175.84,  1162.11
08/14/2019,  1164.29, 1578668,  1176.31,  1182.3,  1160.54
08/13/2019,  1197.27, 1318009,  1171.46,  1204.78,  1171.46
08/12/2019,  1174.71, 1003187,  1179.21,  1184.96,  1167.6723
08/09/2019,  1188.01, 1065658,  1197.99,  1203.88,  1183.603
08/08/2019,  1204.8, 1467997,  1182.83,  1205.01,  1173.02
08/07/2019,  1173.99, 1444324,  1156,  1178.4451,  1149.6239
08/06/2019,  1169.95, 1709374,  1163.31,  1179.96,  1160
08/05/2019,  1152.32, 2597455,  1170.04,  1175.24,  1140.14
08/02/2019,  1193.99, 1645067,  1200.74,  1206.9,  1188.94
08/01/2019,  1209.01, 1698510,  1214.03,  1234.11,  1205.72
07/31/2019,  1216.68, 1725454,  1223,  1234,  1207.7635
07/30/2019,  1225.14, 1453263,  1225.41,  1234.87,  1223.3
07/29/2019,  1239.41, 2223731,  1241.05,  1247.37,  1228.23
07/26/2019,  1250.41, 4805752,  1224.04,  1265.5499,  1224
07/25/2019,  1132.12, 2209823,  1137.82,  1141.7,  1120.92
07/24/2019,  1137.81, 1590101,  1131.9,  1144,  1126.99
07/23/2019,  1146.21, 1093688,  1144,  1146.9,  1131.8
07/22/2019,  1138.07, 1301846,  1133.45,  1139.25,  1124.24
07/19/2019,  1130.1, 1647245,  1148.19,  1151.14,  1129.62
07/18/2019,  1146.33, 1291281,  1141.74,  1147.605,  1132.73
07/17/2019,  1146.35, 1170047,  1150.97,  1158.36,  1145.77
07/16/2019,  1153.58, 1238807,  1146,  1158.58,  1145
07/15/2019,  1150.34, 903780,  1146.86,  1150.82,  1139.4
07/12/2019,  1144.9, 863973,  1143.99,  1147.34,  1138.78
07/11/2019,  1144.21, 1195569,  1143.25,  1153.07,  1139.58
07/10/2019,  1140.48, 1209466,  1131.22,  1142.05,  1130.97
07/09/2019,  1124.83, 1330370,  1111.8,  1128.025,  1107.17
07/08/2019,  1116.35, 1236419,  1125.17,  1125.98,  1111.21
07/05/2019,  1131.59, 1264540,  1117.8,  1132.88,  1116.14
07/03/2019,  1121.58, 767011,  1117.41,  1126.76,  1113.86
07/02/2019,  1111.25, 991755,  1102.24,  1111.77,  1098.17
07/01/2019,  1097.95, 1438504,  1098,  1107.58,  1093.703
06/28/2019,  1080.91, 1693450,  1076.39,  1081,  1073.37
06/27/2019,  1076.01, 1004477,  1084,  1087.1,  1075.29
06/26/2019,  1079.8, 1810869,  1086.5,  1092.97,  1072.24
06/25/2019,  1086.35, 1546913,  1112.66,  1114.35,  1083.8
06/24/2019,  1115.52, 1395696,  1119.61,  1122,  1111.01
06/21/2019,  1121.88, 1947591,  1109.24,  1124.11,  1108.08
06/20/2019,  1111.42, 1262011,  1119.99,  1120.12,  1104.74
06/19/2019,  1102.33, 1339218,  1105.6,  1107,  1093.48
06/18/2019,  1103.6, 1386684,  1109.69,  1116.39,  1098.99
06/17/2019,  1092.5, 941602,  1086.28,  1099.18,  1086.28
06/14/2019,  1085.35, 1111643,  1086.42,  1092.69,  1080.1721
06/13/2019,  1088.77, 1058000,  1083.64,  1094.17,  1080.15
06/12/2019,  1077.03, 1061255,  1078,  1080.93,  1067.54
06/11/2019,  1078.72, 1437063,  1093.98,  1101.99,  1077.6025
06/10/2019,  1080.38, 1464248,  1072.98,  1092.66,  1072.3216
06/07/2019,  1066.04, 1802370,  1050.63,  1070.92,  1048.4
06/06/2019,  1044.34, 1703244,  1044.99,  1047.49,  1033.7
06/05/2019,  1042.22, 2168439,  1051.54,  1053.55,  1030.49
06/04/2019,  1053.05, 2833483,  1042.9,  1056.05,  1033.69
06/03/2019,  1036.23, 5130576,  1065.5,  1065.5,  1025
05/31/2019,  1103.63, 1508203,  1101.29,  1109.6,  1100.18
05/30/2019,  1117.95, 951873,  1115.54,  1123.13,  1112.12
05/29/2019,  1116.46, 1538212,  1127.52,  1129.1,  1108.2201
05/28/2019,  1134.15, 1365166,  1134,  1151.5871,  1133.12
05/24/2019,  1133.47, 1112341,  1147.36,  1149.765,  1131.66
05/23/2019,  1140.77, 1199300,  1140.5,  1145.9725,  1129.224
05/22/2019,  1151.42, 914839,  1146.75,  1158.52,  1145.89
05/21/2019,  1149.63, 1160158,  1148.49,  1152.7077,  1137.94
05/20/2019,  1138.85, 1353292,  1144.5,  1146.7967,  1131.4425
05/17/2019,  1162.3, 1208623,  1168.47,  1180.15,  1160.01
05/16/2019,  1178.98, 1531404,  1164.51,  1188.16,  1162.84
05/15/2019,  1164.21, 2289302,  1117.87,  1171.33,  1116.6657
05/14/2019,  1120.44, 1836604,  1137.21,  1140.42,  1119.55
05/13/2019,  1132.03, 1860648,  1141.96,  1147.94,  1122.11
05/10/2019,  1164.27, 1314546,  1163.59,  1172.6,  1142.5
05/09/2019,  1162.38, 1185973,  1159.03,  1169.66,  1150.85
05/08/2019,  1166.27, 1309514,  1172.01,  1180.4243,  1165.74
05/07/2019,  1174.1, 1551368,  1180.47,  1190.44,  1161.04
05/06/2019,  1189.39, 1563943,  1166.26,  1190.85,  1166.26
05/03/2019,  1185.4, 1980653,  1173.65,  1186.8,  1169
05/02/2019,  1162.61, 1944817,  1167.76,  1174.1895,  1155.0018
05/01/2019,  1168.08, 2642983,  1188.05,  1188.05,  1167.18
04/30/2019,  1188.48, 6194691,  1185,  1192.81,  1175
04/29/2019,  1287.58, 2412788,  1274,  1289.27,  1266.2949
04/26/2019,  1272.18, 1228276,  1269,  1273.07,  1260.32
04/25/2019,  1263.45, 1099614,  1264.77,  1267.4083,  1252.03
04/24/2019,  1256, 1015006,  1264.12,  1268.01,  1255
04/23/2019,  1264.55, 1271195,  1250.69,  1269,  1246.38
04/22/2019,  1248.84, 806577,  1235.99,  1249.09,  1228.31
04/18/2019,  1236.37, 1315676,  1239.18,  1242,  1234.61
04/17/2019,  1236.34, 1211866,  1233,  1240.56,  1227.82
04/16/2019,  1227.13, 855258,  1225,  1230.82,  1220.12
04/15/2019,  1221.1, 1187353,  1218,  1224.2,  1209.1101
04/12/2019,  1217.87, 926799,  1210,  1218.35,  1208.11
04/11/2019,  1204.62, 709417,  1203.96,  1207.96,  1200.13
04/10/2019,  1202.16, 724524,  1200.68,  1203.785,  1196.435
04/09/2019,  1197.25, 865416,  1196,  1202.29,  1193.08
04/08/2019,  1203.84, 859969,  1207.89,  1208.69,  1199.86
04/05/2019,  1207.15, 900950,  1214.99,  1216.22,  1205.03
04/04/2019,  1215, 949962,  1205.94,  1215.67,  1204.13
04/03/2019,  1205.92, 1014195,  1207.48,  1216.3,  1200.5
04/02/2019,  1200.49, 800820,  1195.32,  1201.35,  1185.71
04/01/2019,  1194.43, 1188235,  1184.1,  1196.66,  1182
03/29/2019,  1173.31, 1269573,  1174.9,  1178.99,  1162.88
03/28/2019,  1168.49, 966843,  1171.54,  1171.565,  1159.4312
03/27/2019,  1173.02, 1362217,  1185.5,  1187.559,  1159.37
03/26/2019,  1184.62, 1894639,  1198.53,  1202.83,  1176.72
03/25/2019,  1193, 1493841,  1196.93,  1206.3975,  1187.04
03/22/2019,  1205.5, 1668910,  1226.32,  1230,  1202.825
03/21/2019,  1231.54, 1195899,  1216,  1231.79,  1213.15
03/20/2019,  1223.97, 2089367,  1197.35,  1227.14,  1196.17
03/19/2019,  1198.85, 1404863,  1188.81,  1200,  1185.87
03/18/2019,  1184.26, 1212506,  1183.3,  1190,  1177.4211
03/15/2019,  1184.46, 2457597,  1193.38,  1196.57,  1182.61
03/14/2019,  1185.55, 1150950,  1194.51,  1197.88,  1184.48
03/13/2019,  1193.32, 1434816,  1200.645,  1200.93,  1191.94
03/12/2019,  1193.2, 2012306,  1178.26,  1200,  1178.26
03/11/2019,  1175.76, 1569332,  1144.45,  1176.19,  1144.45
03/08/2019,  1142.32, 1212271,  1126.73,  1147.08,  1123.3
03/07/2019,  1143.3, 1166076,  1155.72,  1156.755,  1134.91
03/06/2019,  1157.86, 1094100,  1162.49,  1167.5658,  1155.49
03/05/2019,  1162.03, 1422357,  1150.06,  1169.61,  1146.195
03/04/2019,  1147.8, 1444774,  1146.99,  1158.2804,  1130.69
03/01/2019,  1140.99, 1447454,  1124.9,  1142.97,  1124.75
02/28/2019,  1119.92, 1541068,  1111.3,  1127.65,  1111.01
02/27/2019,  1116.05, 968362,  1106.95,  1117.98,  1101
02/26/2019,  1115.13, 1469761,  1105.75,  1119.51,  1099.92
02/25/2019,  1109.4, 1395281,  1116,  1118.54,  1107.27
02/22/2019,  1110.37, 1048361,  1100.9,  1111.24,  1095.6
02/21/2019,  1096.97, 1414744,  1110.84,  1111.94,  1092.52
02/20/2019,  1113.8, 1080144,  1119.99,  1123.41,  1105.28
02/19/2019,  1118.56, 1046315,  1110,  1121.89,  1110
02/15/2019,  1113.65, 1442461,  1130.08,  1131.67,  1110.65
02/14/2019,  1121.67, 941678,  1118.05,  1128.23,  1110.445
02/13/2019,  1120.16, 1048630,  1124.99,  1134.73,  1118.5
02/12/2019,  1121.37, 1608658,  1106.8,  1125.295,  1105.85
02/11/2019,  1095.01, 1063825,  1096.95,  1105.945,  1092.86
02/08/2019,  1095.06, 1072031,  1087,  1098.91,  1086.55
02/07/2019,  1098.71, 2040615,  1104.16,  1104.84,  1086
02/06/2019,  1115.23, 2101674,  1139.57,  1147,  1112.77
02/05/2019,  1145.99, 3529974,  1124.84,  1146.85,  1117.248
02/04/2019,  1132.8, 2518184,  1112.66,  1132.8,  1109.02
02/01/2019,  1110.75, 1455609,  1112.4,  1125,  1104.89
01/31/2019,  1116.37, 1531463,  1103,  1117.33,  1095.41
01/30/2019,  1089.06, 1241760,  1068.43,  1091,  1066.85
01/29/2019,  1060.62, 1006731,  1072.68,  1075.15,  1055.8647
01/28/2019,  1070.08, 1277745,  1080.11,  1083,  1063.8
01/25/2019,  1090.99, 1114785,  1085,  1094,  1081.82
01/24/2019,  1073.9, 1317718,  1076.48,  1079.475,  1060.7
01/23/2019,  1075.57, 956526,  1077.35,  1084.93,  1059.75
01/22/2019,  1070.52, 1607398,  1088,  1091.51,  1063.47
01/18/2019,  1098.26, 1933754,  1100,  1108.352,  1090.9
01/17/2019,  1089.9, 1223674,  1079.47,  1091.8,  1073.5
01/16/2019,  1080.97, 1320530,  1080,  1092.375,  1079.34
01/15/2019,  1077.15, 1452238,  1050.17,  1080.05,  1047.34
01/14/2019,  1044.69, 1127417,  1046.92,  1051.53,  1041.255
01/11/2019,  1057.19, 1512651,  1063.18,  1063.775,  1048.48
01/10/2019,  1070.33, 1444976,  1067.66,  1071.15,  1057.71
01/09/2019,  1074.66, 1198369,  1081.65,  1082.63,  1066.4
01/08/2019,  1076.28, 1748371,  1076.11,  1084.56,  1060.53
01/07/2019,  1068.39, 1978077,  1071.5,  1073.9999,  1054.76
01/04/2019,  1070.71, 2080144,  1032.59,  1070.84,  1027.4179
01/03/2019,  1016.06, 1829379,  1041,  1056.98,  1014.07
01/02/2019,  1045.85, 1516681,  1016.57,  1052.32,  1015.71
12/31/2018,  1035.61, 1492541,  1050.96,  1052.7,  1023.59
12/28/2018,  1037.08, 1399218,  1049.62,  1055.56,  1033.1
12/27/2018,  1043.88, 2102069,  1017.15,  1043.89,  997
12/26/2018,  1039.46, 2337212,  989.01,  1040,  983
12/24/2018,  976.22, 1590328,  973.9,  1003.54,  970.11
12/21/2018,  979.54, 4560424,  1015.3,  1024.02,  973.69
12/20/2018,  1009.41, 2659047,  1018.13,  1034.22,  996.36
12/19/2018,  1023.01, 2419322,  1033.99,  1062,  1008.05
12/18/2018,  1028.71, 2101854,  1026.09,  1049.48,  1021.44
12/17/2018,  1016.53, 2337631,  1037.51,  1053.15,  1007.9
12/14/2018,  1042.1, 1685802,  1049.98,  1062.6,  1040.79
12/13/2018,  1061.9, 1329198,  1068.07,  1079.7597,  1053.93
12/12/2018,  1063.68, 1523276,  1068,  1081.65,  1062.79
12/11/2018,  1051.75, 1354751,  1056.49,  1060.6,  1039.84
12/10/2018,  1039.55, 1793465,  1035.05,  1048.45,  1023.29
12/07/2018,  1036.58, 2098526,  1060.01,  1075.26,  1028.5
12/06/2018,  1068.73, 2758098,  1034.26,  1071.2,  1030.7701
12/04/2018,  1050.82, 2278200,  1103.12,  1104.42,  1049.98
12/03/2018,  1106.43, 1900355,  1123.14,  1124.65,  1103.6645
11/30/2018,  1094.43, 2554416,  1089.07,  1095.57,  1077.88
11/29/2018,  1088.3, 1403540,  1076.08,  1094.245,  1076
11/28/2018,  1086.23, 2399374,  1048.76,  1086.84,  1035.76
11/27/2018,  1044.41, 1801334,  1041,  1057.58,  1038.49
11/26/2018,  1048.62, 1846430,  1038.35,  1049.31,  1033.91
11/23/2018,  1023.88, 691462,  1030,  1037.59,  1022.3992
11/21/2018,  1037.61, 1531676,  1036.76,  1048.56,  1033.47
11/20/2018,  1025.76, 2447254,  1000,  1031.74,  996.02
11/19/2018,  1020, 1837207,  1057.2,  1060.79,  1016.2601
11/16/2018,  1061.49, 1641232,  1059.41,  1067,  1048.98
11/15/2018,  1064.71, 1819132,  1044.71,  1071.85,  1031.78
11/14/2018,  1043.66, 1561656,  1050,  1054.5643,  1031
11/13/2018,  1036.05, 1496534,  1043.29,  1056.605,  1031.15
11/12/2018,  1038.63, 1429319,  1061.39,  1062.12,  1031
11/09/2018,  1066.15, 1343154,  1073.99,  1075.56,  1053.11
11/08/2018,  1082.4, 1463022,  1091.38,  1093.27,  1072.2048
11/07/2018,  1093.39, 2057155,  1069,  1095.46,  1065.9
11/06/2018,  1055.81, 1225197,  1039.48,  1064.345,  1038.07
11/05/2018,  1040.09, 2436742,  1055,  1058.47,  1021.24
11/02/2018,  1057.79, 1829295,  1073.73,  1082.975,  1054.61
11/01/2018,  1070, 1456222,  1075.8,  1083.975,  1062.46
10/31/2018,  1076.77, 2528584,  1059.81,  1091.94,  1057
10/30/2018,  1036.21, 3209126,  1008.46,  1037.49,  1000.75
10/29/2018,  1020.08, 3873644,  1082.47,  1097.04,  995.83
10/26/2018,  1071.47, 4185201,  1037.03,  1106.53,  1034.09
10/25/2018,  1095.57, 2511884,  1071.79,  1110.98,  1069.55
10/24/2018,  1050.71, 1910060,  1104.25,  1106.12,  1048.74
10/23/2018,  1103.69, 1847798,  1080.89,  1107.89,  1070
10/22/2018,  1101.16, 1494285,  1103.06,  1112.23,  1091
10/19/2018,  1096.46, 1264605,  1093.37,  1110.36,  1087.75
10/18/2018,  1087.97, 2056606,  1121.84,  1121.84,  1077.09
10/17/2018,  1115.69, 1397613,  1126.46,  1128.99,  1102.19
10/16/2018,  1121.28, 1845491,  1104.59,  1124.22,  1102.5
10/15/2018,  1092.25, 1343231,  1108.91,  1113.4464,  1089
10/12/2018,  1110.08, 2029872,  1108,  1115,  1086.402
10/11/2018,  1079.32, 2939514,  1072.94,  1106.4,  1068.27
10/10/2018,  1081.22, 2574985,  1131.08,  1132.17,  1081.13
10/09/2018,  1138.82, 1308706,  1146.15,  1154.35,  1137.572
10/08/2018,  1148.97, 1877142,  1150.11,  1168,  1127.3636
10/05/2018,  1157.35, 1184245,  1167.5,  1173.4999,  1145.12
10/04/2018,  1168.19, 2151762,  1195.33,  1197.51,  1155.576
10/03/2018,  1202.95, 1207280,  1205,  1206.41,  1193.83
10/02/2018,  1200.11, 1655602,  1190.96,  1209.96,  1186.63
10/01/2018,  1195.31, 1345250,  1199.89,  1209.9,  1190.3
09/28/2018,  1193.47, 1306822,  1191.87,  1195.41,  1184.5
09/27/2018,  1194.64, 1244278,  1186.73,  1202.1,  1183.63
09/26/2018,  1180.49, 1346434,  1185.15,  1194.23,  1174.765
09/25/2018,  1184.65, 937577,  1176.15,  1186.88,  1168
09/24/2018,  1173.37, 1218532,  1157.17,  1178,  1146.91
09/21/2018,  1166.09, 4363929,  1192,  1192.21,  1166.04
09/20/2018,  1186.87, 1209855,  1179.99,  1189.89,  1173.36
09/19/2018,  1171.09, 1185321,  1164.98,  1173.21,  1154.58
09/18/2018,  1161.22, 1184407,  1157.09,  1176.08,  1157.09
09/17/2018,  1156.05, 1279147,  1170.14,  1177.24,  1154.03
09/14/2018,  1172.53, 934300,  1179.1,  1180.425,  1168.3295
09/13/2018,  1175.33, 1402005,  1170.74,  1178.61,  1162.85
09/12/2018,  1162.82, 1291304,  1172.72,  1178.61,  1158.36
09/11/2018,  1177.36, 1209171,  1161.63,  1178.68,  1156.24
09/10/2018,  1164.64, 1115259,  1172.19,  1174.54,  1160.11
09/07/2018,  1164.83, 1401034,  1158.67,  1175.26,  1157.215
09/06/2018,  1171.44, 1886690,  1186.3,  1186.3,  1152
09/05/2018,  1186.48, 2043732,  1193.8,  1199.0096,  1162
09/04/2018,  1197, 1800509,  1204.27,  1212.99,  1192.5
08/31/2018,  1218.19, 1812366,  1234.98,  1238.66,  1211.2854
08/30/2018,  1239.12, 1320261,  1244.23,  1253.635,  1232.59
08/29/2018,  1249.3, 1295939,  1237.45,  1250.66,  1236.3588
08/28/2018,  1231.15, 1296532,  1241.29,  1242.545,  1228.69
08/27/2018,  1241.82, 1154962,  1227.6,  1243.09,  1225.716
08/24/2018,  1220.65, 946529,  1208.82,  1221.65,  1206.3588
08/23/2018,  1205.38, 988509,  1207.14,  1221.28,  1204.24
08/22/2018,  1207.33, 881463,  1200,  1211.84,  1199
08/21/2018,  1201.62, 1187884,  1208,  1217.26,  1200.3537
08/20/2018,  1207.77, 864462,  1205.02,  1211,  1194.6264
08/17/2018,  1200.96, 1381724,  1202.03,  1209.02,  1188.24
08/16/2018,  1206.49, 1319985,  1224.73,  1225.9999,  1202.55
08/15/2018,  1214.38, 1815642,  1229.26,  1235.24,  1209.51
08/14/2018,  1242.1, 1342534,  1235.19,  1245.8695,  1225.11
08/13/2018,  1235.01, 957153,  1236.98,  1249.2728,  1233.6405
08/10/2018,  1237.61, 1107323,  1243,  1245.695,  1232
08/09/2018,  1249.1, 805227,  1249.9,  1255.542,  1246.01
08/08/2018,  1245.61, 1369650,  1240.47,  1256.5,  1238.0083
08/07/2018,  1242.22, 1493073,  1237,  1251.17,  1236.17
08/06/2018,  1224.77, 1080923,  1225,  1226.0876,  1215.7965
08/03/2018,  1223.71, 1072524,  1229.62,  1230,  1215.06
08/02/2018,  1226.15, 1520488,  1205.9,  1229.88,  1204.79
08/01/2018,  1220.01, 1567142,  1228,  1233.47,  1210.21
07/31/2018,  1217.26, 1632823,  1220.01,  1227.5877,  1205.6
07/30/2018,  1219.74, 1822782,  1228.01,  1234.916,  1211.47
07/27/2018,  1238.5, 2115802,  1271,  1273.89,  1231
07/26/2018,  1268.33, 2334881,  1251,  1269.7707,  1249.02
07/25/2018,  1263.7, 2115890,  1239.13,  1265.86,  1239.13
07/24/2018,  1248.08, 3303268,  1262.59,  1266,  1235.56
07/23/2018,  1205.5, 2584034,  1181.01,  1206.49,  1181
07/20/2018,  1184.91, 1246898,  1186.96,  1196.86,  1184.22
07/19/2018,  1186.96, 1256113,  1191,  1200,  1183.32
07/18/2018,  1195.88, 1391232,  1196.56,  1204.5,  1190.34
07/17/2018,  1198.8, 1585091,  1172.22,  1203.04,  1170.6
07/16/2018,  1183.86, 1049560,  1189.39,  1191,  1179.28
07/13/2018,  1188.82, 1221687,  1185,  1195.4173,  1180
07/12/2018,  1183.48, 1251083,  1159.89,  1184.41,  1155.935
07/11/2018,  1153.9, 1094301,  1144.59,  1164.29,  1141.0003
07/10/2018,  1152.84, 789249,  1156.98,  1159.59,  1149.59
07/09/2018,  1154.05, 906073,  1148.48,  1154.67,  1143.42
07/06/2018,  1140.17, 966155,  1123.58,  1140.93,  1120.7371
07/05/2018,  1124.27, 1060752,  1110.53,  1127.5,  1108.48
07/03/2018,  1102.89, 679034,  1135.82,  1135.82,  1100.02
07/02/2018,  1127.46, 1188616,  1099,  1128,  1093.8
06/29/2018,  1115.65, 1275979,  1120,  1128.2265,  1115
06/28/2018,  1114.22, 1072438,  1102.09,  1122.31,  1096.01
06/27/2018,  1103.98, 1287698,  1121.34,  1131.8362,  1103.62
06/26/2018,  1118.46, 1559791,  1128,  1133.21,  1116.6589
06/25/2018,  1124.81, 2155276,  1143.6,  1143.91,  1112.78
06/22/2018,  1155.48, 1310164,  1159.14,  1162.4965,  1147.26
06/21/2018,  1157.66, 1232352,  1174.85,  1177.295,  1152.232
06/20/2018,  1169.84, 1648248,  1175.31,  1186.2856,  1169.16
06/19/2018,  1168.06, 1616125,  1158.5,  1171.27,  1154.01
06/18/2018,  1173.46, 1400641,  1143.65,  1174.31,  1143.59
06/15/2018,  1152.26, 2119134,  1148.86,  1153.42,  1143.485
06/14/2018,  1152.12, 1350085,  1143.85,  1155.47,  1140.64
06/13/2018,  1134.79, 1490017,  1141.12,  1146.5,  1133.38
06/12/2018,  1139.32, 899231,  1131.07,  1139.79,  1130.735
06/11/2018,  1129.99, 1071114,  1118.6,  1137.26,  1118.6
06/08/2018,  1120.87, 1289859,  1118.18,  1126.67,  1112.15
06/07/2018,  1123.86, 1519860,  1131.32,  1135.82,  1116.52
06/06/2018,  1136.88, 1697489,  1142.17,  1143,  1125.7429
06/05/2018,  1139.66, 1538169,  1140.99,  1145.738,  1133.19
06/04/2018,  1139.29, 1881046,  1122.33,  1141.89,  1122.005
06/01/2018,  1119.5, 2416755,  1099.35,  1120,  1098.5
05/31/2018,  1084.99, 3085325,  1067.56,  1097.19,  1067.56
05/30/2018,  1067.8, 1129958,  1063.03,  1069.21,  1056.83
05/29/2018,  1060.32, 1858676,  1064.89,  1073.37,  1055.22
05/25/2018,  1075.66, 878903,  1079.02,  1082.56,  1073.775
05/24/2018,  1079.24, 757752,  1079,  1080.47,  1066.15
05/23/2018,  1079.69, 1057712,  1065.13,  1080.78,  1061.71
05/22/2018,  1069.73, 1088700,  1083.56,  1086.59,  1066.69
05/21/2018,  1079.58, 1012258,  1074.06,  1088,  1073.65
05/18/2018,  1066.36, 1496448,  1061.86,  1069.94,  1060.68
05/17/2018,  1078.59, 1031190,  1079.89,  1086.87,  1073.5
05/16/2018,  1081.77, 989819,  1077.31,  1089.27,  1076.26
05/15/2018,  1079.23, 1494306,  1090,  1090.05,  1073.47
05/14/2018,  1100.2, 1450140,  1100,  1110.75,  1099.11
05/11/2018,  1098.26, 1253205,  1093.6,  1101.3295,  1090.91
05/10/2018,  1097.57, 1441456,  1086.03,  1100.44,  1085.64
05/09/2018,  1082.76, 2032319,  1058.1,  1085.44,  1056.365
05/08/2018,  1053.91, 1217260,  1058.54,  1060.55,  1047.145
05/07/2018,  1054.79, 1464008,  1049.23,  1061.68,  1047.1
05/04/2018,  1048.21, 1936797,  1016.9,  1048.51,  1016.9
05/03/2018,  1023.72, 1813623,  1019,  1029.675,  1006.29
05/02/2018,  1024.38, 1534094,  1028.1,  1040.389,  1022.87
05/01/2018,  1037.31, 1427171,  1013.66,  1038.47,  1008.21
04/30/2018,  1017.33, 1664084,  1030.01,  1037,  1016.85
04/27/2018,  1030.05, 1617452,  1046,  1049.5,  1025.59
04/26/2018,  1040.04, 1984448,  1029.51,  1047.98,  1018.19
04/25/2018,  1021.18, 2225495,  1025.52,  1032.49,  1015.31
04/24/2018,  1019.98, 4750851,  1052,  1057,  1010.59
04/23/2018,  1067.45, 2278846,  1077.86,  1082.72,  1060.7
04/20/2018,  1072.96, 1887698,  1082,  1092.35,  1069.57
04/19/2018,  1087.7, 1741907,  1069.4,  1094.165,  1068.18
04/18/2018,  1072.08, 1336678,  1077.43,  1077.43,  1066.225
04/17/2018,  1074.16, 2311903,  1051.37,  1077.88,  1048.26
04/16/2018,  1037.98, 1194144,  1037,  1043.24,  1026.74
04/13/2018,  1029.27, 1175754,  1040.88,  1046.42,  1022.98
04/12/2018,  1032.51, 1357599,  1025.04,  1040.69,  1021.4347
04/11/2018,  1019.97, 1476133,  1027.99,  1031.3641,  1015.87
04/10/2018,  1031.64, 1983510,  1026.44,  1036.28,  1011.34
04/09/2018,  1015.45, 1738682,  1016.8,  1039.6,  1014.08
04/06/2018,  1007.04, 1740896,  1020,  1031.42,  1003.03
04/05/2018,  1027.81, 1345681,  1041.33,  1042.79,  1020.1311
04/04/2018,  1025.14, 2464418,  993.41,  1028.7175,  993
04/03/2018,  1013.41, 2271858,  1013.91,  1020.99,  994.07
04/02/2018,  1006.47, 2679214,  1022.82,  1034.8,  990.37
03/29/2018,  1031.79, 2714402,  1011.63,  1043,  1002.9
03/28/2018,  1004.56, 3345046,  998,  1024.23,  980.64
03/27/2018,  1005.1, 3081612,  1063,  1064.8393,  996.92
03/26/2018,  1053.21, 2593808,  1046,  1055.63,  1008.4
03/23/2018,  1021.57, 2147097,  1047.03,  1063.36,  1021.22
03/22/2018,  1049.08, 2584639,  1081.88,  1082.9,  1045.91
03/21/2018,  1090.88, 1878294,  1092.74,  1106.2999,  1085.15
03/20/2018,  1097.71, 1802209,  1099,  1105.2,  1083.46
03/19/2018,  1099.82, 2355186,  1120.01,  1121.99,  1089.01
03/16/2018,  1135.73, 2614871,  1154.14,  1155.88,  1131.96
03/15/2018,  1149.58, 1397767,  1149.96,  1161.08,  1134.54
03/14/2018,  1149.49, 1290638,  1145.21,  1158.59,  1141.44
03/13/2018,  1138.17, 1874176,  1170,  1176.76,  1133.33
03/12/2018,  1164.5, 2106548,  1163.85,  1177.05,  1157.42
03/09/2018,  1160.04, 2121425,  1136,  1160.8,  1132.4606
03/08/2018,  1126, 1393529,  1115.32,  1127.6,  1112.8
03/07/2018,  1109.64, 1277439,  1089.19,  1112.22,  1085.4823
03/06/2018,  1095.06, 1497087,  1099.22,  1101.85,  1089.775
03/05/2018,  1090.93, 1141932,  1075.14,  1097.1,  1069.0001
03/02/2018,  1078.92, 2271394,  1053.08,  1081.9986,  1048.115
03/01/2018,  1069.52, 2511872,  1107.87,  1110.12,  1067.001
02/28/2018,  1104.73, 1873737,  1123.03,  1127.53,  1103.24
02/27/2018,  1118.29, 1772866,  1141.24,  1144.04,  1118
02/26/2018,  1143.75, 1514920,  1127.8,  1143.96,  1126.695
02/23/2018,  1126.79, 1190432,  1112.64,  1127.28,  1104.7135
02/22/2018,  1106.63, 1309536,  1116.19,  1122.82,  1102.59
02/21/2018,  1111.34, 1507152,  1106.47,  1133.97,  1106.33
02/20/2018,  1102.46, 1389491,  1090.57,  1113.95,  1088.52
02/16/2018,  1094.8, 1680283,  1088.41,  1104.67,  1088.3134
02/15/2018,  1089.52, 1785552,  1079.07,  1091.4794,  1064.34
02/14/2018,  1069.7, 1547665,  1048.95,  1071.72,  1046.75
02/13/2018,  1052.1, 1213800,  1045,  1058.37,  1044.0872
02/12/2018,  1051.94, 2054002,  1048,  1061.5,  1040.928
02/09/2018,  1037.78, 3503970,  1017.25,  1043.97,  992.56
02/08/2018,  1001.52, 2809890,  1055.41,  1058.62,  1000.66
02/07/2018,  1048.58, 2353003,  1081.54,  1081.78,  1048.26
02/06/2018,  1080.6, 3432313,  1027.18,  1081.71,  1023.1367
02/05/2018,  1055.8, 3769453,  1090.6,  1110,  1052.03
02/02/2018,  1111.9, 4837979,  1122,  1123.07,  1107.2779
02/01/2018,  1167.7, 2380221,  1162.61,  1174,  1157.52
01/31/2018,  1169.94, 1523820,  1170.57,  1173,  1159.13
01/30/2018,  1163.69, 1541771,  1167.83,  1176.52,  1163.52
01/29/2018,  1175.58, 1337324,  1176.48,  1186.89,  1171.98
01/26/2018,  1175.84, 1981173,  1175.08,  1175.84,  1158.11
01/25/2018,  1170.37, 1461518,  1172.53,  1175.94,  1162.76
01/24/2018,  1164.24, 1382904,  1177.33,  1179.86,  1161.05
01/23/2018,  1169.97, 1309862,  1159.85,  1171.6266,  1158.75
01/22/2018,  1155.81, 1616120,  1137.49,  1159.88,  1135.1101
01/19/2018,  1137.51, 1390118,  1131.83,  1137.86,  1128.3
01/18/2018,  1129.79, 1194943,  1131.41,  1132.51,  1117.5
01/17/2018,  1131.98, 1200476,  1126.22,  1132.6,  1117.01
01/16/2018,  1121.76, 1566662,  1132.51,  1139.91,  1117.8316
01/12/2018,  1122.26, 1718491,  1102.41,  1124.29,  1101.15
01/11/2018,  1105.52, 977727,  1106.3,  1106.525,  1099.59
01/10/2018,  1102.61, 1042273,  1097.1,  1104.6,  1096.11
01/09/2018,  1106.26, 900089,  1109.4,  1110.57,  1101.2307
01/08/2018,  1106.94, 1046767,  1102.23,  1111.27,  1101.62
01/05/2018,  1102.23, 1279990,  1094,  1104.25,  1092
01/04/2018,  1086.4, 1002945,  1088,  1093.5699,  1084.0017
01/03/2018,  1082.48, 1429757,  1064.31,  1086.29,  1063.21
01/02/2018,  1065, 1236401,  1048.34,  1066.94,  1045.23
12/29/2017,  1046.4, 886845,  1046.72,  1049.7,  1044.9
12/28/2017,  1048.14, 833011,  1051.6,  1054.75,  1044.77
12/27/2017,  1049.37, 1271780,  1057.39,  1058.37,  1048.05
12/26/2017,  1056.74, 761097,  1058.07,  1060.12,  1050.2
12/22/2017,  1060.12, 755089,  1061.11,  1064.2,  1059.44
12/21/2017,  1063.63, 986548,  1064.95,  1069.33,  1061.7938
12/20/2017,  1064.95, 1268285,  1071.78,  1073.38,  1061.52
12/19/2017,  1070.68, 1307894,  1075.2,  1076.84,  1063.55
12/18/2017,  1077.14, 1552016,  1066.08,  1078.49,  1062
12/15/2017,  1064.19, 3275091,  1054.61,  1067.62,  1049.5
12/14/2017,  1049.15, 1558684,  1045,  1058.5,  1043.11
12/13/2017,  1040.61, 1220364,  1046.12,  1046.665,  1038.38
12/12/2017,  1040.48, 1279511,  1039.63,  1050.31,  1033.6897
12/11/2017,  1041.1, 1190527,  1035.5,  1043.8,  1032.0504
12/08/2017,  1037.05, 1288419,  1037.49,  1042.05,  1032.5222
12/07/2017,  1030.93, 1458145,  1020.43,  1034.24,  1018.071
12/06/2017,  1018.38, 1258496,  1001.5,  1024.97,  1001.14
12/05/2017,  1005.15, 2066247,  995.94,  1020.61,  988.28
12/04/2017,  998.68, 1906058,  1012.66,  1016.1,  995.57
12/01/2017,  1010.17, 1908962,  1015.8,  1022.4897,  1002.02
11/30/2017,  1021.41, 1723003,  1022.37,  1028.4899,  1015
11/29/2017,  1021.66, 2442974,  1042.68,  1044.08,  1015.65
11/28/2017,  1047.41, 1421027,  1055.09,  1062.375,  1040
11/27/2017,  1054.21, 1307471,  1040,  1055.46,  1038.44
11/24/2017,  1040.61, 536996,  1035.87,  1043.178,  1035
11/22/2017,  1035.96, 746351,  1035,  1039.706,  1031.43
11/21/2017,  1034.49, 1096161,  1023.31,  1035.11,  1022.655
11/20/2017,  1018.38, 898389,  1020.26,  1022.61,  1017.5
11/17/2017,  1019.09, 1366936,  1034.01,  1034.42,  1017.75
11/16/2017,  1032.5, 1129424,  1022.52,  1035.92,  1022.52
11/15/2017,  1020.91, 847932,  1019.21,  1024.09,  1015.42
11/14/2017,  1026, 958708,  1022.59,  1026.81,  1014.15
11/13/2017,  1025.75, 885565,  1023.42,  1031.58,  1022.57
11/10/2017,  1028.07, 720674,  1026.46,  1030.76,  1025.28
11/09/2017,  1031.26, 1244701,  1033.99,  1033.99,  1019.6656
11/08/2017,  1039.85, 1088395,  1030.52,  1043.522,  1028.45
11/07/2017,  1033.33, 1112123,  1027.27,  1033.97,  1025.13
11/06/2017,  1025.9, 1124757,  1028.99,  1034.87,  1025
11/03/2017,  1032.48, 1075134,  1022.11,  1032.65,  1020.31
11/02/2017,  1025.58, 1048584,  1021.76,  1028.09,  1013.01
11/01/2017,  1025.5, 1371619,  1017.21,  1029.67,  1016.95
10/31/2017,  1016.64, 1331265,  1015.22,  1024,  1010.42
10/30/2017,  1017.11, 2083490,  1014,  1024.97,  1007.5
10/27/2017,  1019.27, 5165922,  1009.19,  1048.39,  1008.2
10/26/2017,  972.56, 2027218,  980,  987.6,  972.2
10/25/2017,  973.33, 1210368,  968.37,  976.09,  960.5201
10/24/2017,  970.54, 1206074,  970,  972.23,  961
10/23/2017,  968.45, 1471544,  989.52,  989.52,  966.12
10/20/2017,  988.2, 1176177,  989.44,  991,  984.58
10/19/2017,  984.45, 1312706,  986,  988.88,  978.39
10/18/2017,  992.81, 1057285,  991.77,  996.72,  986.9747
10/17/2017,  992.18, 1290152,  990.29,  996.44,  988.59
10/16/2017,  992, 910246,  992.1,  993.9065,  984
10/13/2017,  989.68, 1169584,  992,  997.21,  989
10/12/2017,  987.83, 1278357,  987.45,  994.12,  985
10/11/2017,  989.25, 1692843,  973.72,  990.71,  972.25
10/10/2017,  972.6, 968113,  980,  981.57,  966.0801
10/09/2017,  977, 890620,  980,  985.425,  976.11
10/06/2017,  978.89, 1146207,  966.7,  979.46,  963.36
10/05/2017,  969.96, 1210427,  955.49,  970.91,  955.18
10/04/2017,  951.68, 951766,  957,  960.39,  950.69
10/03/2017,  957.79, 888303,  954,  958,  949.14
10/02/2017,  953.27, 1282850,  959.98,  962.54,  947.84
09/29/2017,  959.11, 1576365,  952,  959.7864,  951.51
09/28/2017,  949.5, 997036,  941.36,  950.69,  940.55
09/27/2017,  944.49, 2237538,  927.74,  949.9,  927.74
09/26/2017,  924.86, 1666749,  923.72,  930.82,  921.14
09/25/2017,  920.97, 1855742,  925.45,  926.4,  909.7
09/22/2017,  928.53, 1052170,  927.75,  934.73,  926.48
09/21/2017,  932.45, 1227059,  933,  936.53,  923.83
09/20/2017,  931.58, 1535626,  922.98,  933.88,  922
09/19/2017,  921.81, 912967,  917.42,  922.4199,  912.55
09/18/2017,  915, 1300759,  920.01,  922.08,  910.6
09/15/2017,  920.29, 2499466,  924.66,  926.49,  916.36
09/14/2017,  925.11, 1395497,  931.25,  932.77,  924
09/13/2017,  935.09, 1101145,  930.66,  937.25,  929.86
09/12/2017,  932.07, 1133638,  932.59,  933.48,  923.861
09/11/2017,  929.08, 1266020,  934.25,  938.38,  926.92
09/08/2017,  926.5, 997699,  936.49,  936.99,  924.88
09/07/2017,  935.95, 1211472,  931.73,  936.41,  923.62
09/06/2017,  927.81, 1526209,  930.15,  930.915,  919.27
09/05/2017,  928.45, 1346791,  933.08,  937,  921.96
09/01/2017,  937.34, 943657,  941.13,  942.48,  935.15
08/31/2017,  939.33, 1566888,  931.76,  941.98,  931.76
08/30/2017,  929.57, 1300616,  920.05,  930.819,  919.65
08/29/2017,  921.29, 1181391,  905.1,  923.33,  905
08/28/2017,  913.81, 1085014,  916,  919.245,  911.87
08/25/2017,  915.89, 1052764,  923.49,  925.555,  915.5
08/24/2017,  921.28, 1266191,  928.66,  930.84,  915.5
08/23/2017,  927, 1088575,  921.93,  929.93,  919.36
08/22/2017,  924.69, 1166320,  912.72,  925.86,  911.4751
08/21/2017,  906.66, 942328,  910,  913,  903.4
08/18/2017,  910.67, 1341990,  910.31,  915.275,  907.1543
08/17/2017,  910.98, 1241782,  925.78,  926.86,  910.98
08/16/2017,  926.96, 1005261,  925.29,  932.7,  923.445
08/15/2017,  922.22, 882479,  924.23,  926.5499,  919.82
08/14/2017,  922.67, 1063404,  922.53,  924.668,  918.19
08/11/2017,  914.39, 1205652,  907.97,  917.78,  905.58
08/10/2017,  907.24, 1755521,  917.55,  919.26,  906.13
08/09/2017,  922.9, 1191332,  920.61,  925.98,  917.2501
08/08/2017,  926.79, 1057351,  927.09,  935.814,  925.6095
08/07/2017,  929.36, 1031710,  929.06,  931.7,  926.5
08/04/2017,  927.96, 1081814,  926.75,  930.3068,  923.03
08/03/2017,  923.65, 1201519,  930.34,  932.24,  922.24
08/02/2017,  930.39, 1822272,  928.61,  932.6,  916.68
08/01/2017,  930.83, 1234612,  932.38,  937.447,  929.26
07/31/2017,  930.5, 1964748,  941.89,  943.59,  926.04
07/28/2017,  941.53, 1802343,  929.4,  943.83,  927.5
07/27/2017,  934.09, 3128819,  951.78,  951.78,  920
07/26/2017,  947.8, 2069349,  954.68,  955,  942.2788
07/25/2017,  950.7, 4656609,  953.81,  959.7,  945.4
07/24/2017,  980.34, 3205374,  972.22,  986.2,  970.77
07/21/2017,  972.92, 1697190,  962.25,  973.23,  960.15
07/20/2017,  968.15, 1620636,  975,  975.9,  961.51
07/19/2017,  970.89, 1221155,  967.84,  973.04,  964.03
07/18/2017,  965.4, 1152741,  953,  968.04,  950.6
07/17/2017,  953.42, 1164141,  957,  960.74,  949.2407
07/14/2017,  955.99, 1052855,  952,  956.91,  948.005
07/13/2017,  947.16, 1294674,  946.29,  954.45,  943.01
07/12/2017,  943.83, 1517168,  938.68,  946.3,  934.47
07/11/2017,  930.09, 1112417,  929.54,  931.43,  922
07/10/2017,  928.8, 1190237,  921.77,  930.38,  919.59
07/07/2017,  918.59, 1590456,  908.85,  921.54,  908.85
07/06/2017,  906.69, 1424290,  904.12,  914.9444,  899.7
07/05/2017,  911.71, 1813309,  901.76,  914.51,  898.5
07/03/2017,  898.7, 1710373,  912.18,  913.94,  894.79
06/30/2017,  908.73, 2086340,  926.05,  926.05,  908.31
06/29/2017,  917.79, 3287991,  929.92,  931.26,  910.62
06/28/2017,  940.49, 2719213,  929,  942.75,  916
06/27/2017,  927.33, 2566047,  942.46,  948.29,  926.85
06/26/2017,  952.27, 1596664,  969.9,  973.31,  950.79
06/23/2017,  965.59, 1527513,  956.83,  966,  954.2
06/22/2017,  957.09, 941639,  958.7,  960.72,  954.55
06/21/2017,  959.45, 1201971,  953.64,  960.1,  950.76
06/20/2017,  950.63, 1125520,  957.52,  961.62,  950.01
06/19/2017,  957.37, 1520715,  949.96,  959.99,  949.05
06/16/2017,  939.78, 3061794,  940,  942.04,  931.595
06/15/2017,  942.31, 2065271,  933.97,  943.339,  924.44
06/14/2017,  950.76, 1487378,  959.92,  961.15,  942.25
06/13/2017,  953.4, 2012980,  951.91,  959.98,  944.09
06/12/2017,  942.9, 3762434,  939.56,  949.355,  915.2328
06/09/2017,  949.83, 3305545,  984.5,  984.5,  935.63
06/08/2017,  983.41, 1477151,  982.35,  984.57,  977.2
06/07/2017,  981.08, 1447172,  979.65,  984.15,  975.77
06/06/2017,  976.57, 1814323,  983.16,  988.25,  975.14
06/05/2017,  983.68, 1251903,  976.55,  986.91,  975.1
06/02/2017,  975.6, 1750723,  969.46,  975.88,  966
06/01/2017,  966.95, 1408958,  968.95,  971.5,  960.01
05/31/2017,  964.86, 2447176,  975.02,  979.27,  960.18
05/30/2017,  975.88, 1466288,  970.31,  976.2,  969.49
05/26/2017,  971.47, 1251425,  969.7,  974.98,  965.03
05/25/2017,  969.54, 1659422,  957.33,  972.629,  955.47
05/24/2017,  954.96, 1031408,  952.98,  955.09,  949.5
05/23/2017,  948.82, 1269438,  947.92,  951.4666,  942.575
05/22/2017,  941.86, 1118456,  935,  941.8828,  935
05/19/2017,  934.01, 1389848,  931.47,  937.755,  931
05/18/2017,  930.24, 1596058,  921,  933.17,  918.75
05/17/2017,  919.62, 2357922,  935.67,  939.3325,  918.14
05/16/2017,  943, 968288,  940,  943.11,  937.58
05/15/2017,  937.08, 1104595,  932.95,  938.25,  929.34
05/12/2017,  932.22, 1050377,  931.53,  933.44,  927.85
05/11/2017,  930.6, 834997,  925.32,  932.53,  923.0301
05/10/2017,  928.78, 1173887,  931.98,  932,  925.16
05/09/2017,  932.17, 1581236,  936.95,  937.5,  929.53
05/08/2017,  934.3, 1328885,  926.12,  936.925,  925.26
05/05/2017,  927.13, 1910317,  933.54,  934.9,  925.2
05/04/2017,  931.66, 1421938,  926.07,  935.93,  924.59
05/03/2017,  927.04, 1497565,  914.86,  928.1,  912.5426
05/02/2017,  916.44, 1543696,  909.62,  920.77,  909.4526
05/01/2017,  912.57, 2114629,  901.94,  915.68,  901.45
04/28/2017,  905.96, 3223850,  910.66,  916.85,  905.77
04/27/2017,  874.25, 2009509,  873.6,  875.4,  870.38
04/26/2017,  871.73, 1233724,  874.23,  876.05,  867.7481
04/25/2017,  872.3, 1670095,  865,  875,  862.81
04/24/2017,  862.76, 1371722,  851.2,  863.45,  849.86
04/21/2017,  843.19, 1323364,  842.88,  843.88,  840.6
04/20/2017,  841.65, 957994,  841.44,  845.2,  839.32
04/19/2017,  838.21, 954324,  839.79,  842.22,  836.29
04/18/2017,  836.82, 835433,  834.22,  838.93,  832.71
04/17/2017,  837.17, 894540,  825.01,  837.75,  824.47
04/13/2017,  823.56, 1118221,  822.14,  826.38,  821.44
04/12/2017,  824.32, 900059,  821.93,  826.66,  821.02
04/11/2017,  823.35, 1078951,  824.71,  827.4267,  817.0201
04/10/2017,  824.73, 978825,  825.39,  829.35,  823.77
04/07/2017,  824.67, 1056692,  827.96,  828.485,  820.5127
04/06/2017,  827.88, 1254235,  832.4,  836.39,  826.46
04/05/2017,  831.41, 1553163,  835.51,  842.45,  830.72
04/04/2017,  834.57, 1044455,  831.36,  835.18,  829.0363
04/03/2017,  838.55, 1670349,  829.22,  840.85,  829.22
03/31/2017,  829.56, 1401756,  828.97,  831.64,  827.39
03/30/2017,  831.5, 1055263,  833.5,  833.68,  829
03/29/2017,  831.41, 1785006,  825,  832.765,  822.3801
03/28/2017,  820.92, 1620532,  820.41,  825.99,  814.027
03/27/2017,  819.51, 1894735,  806.95,  821.63,  803.37
03/24/2017,  814.43, 1980415,  820.08,  821.93,  808.89
03/23/2017,  817.58, 3485390,  821,  822.57,  812.257
03/22/2017,  829.59, 1399409,  831.91,  835.55,  827.1801
03/21/2017,  830.46, 2461375,  851.4,  853.5,  829.02
03/20/2017,  848.4, 1217560,  850.01,  850.22,  845.15
03/17/2017,  852.12, 1712397,  851.61,  853.4,  847.11
03/16/2017,  848.78, 977384,  849.03,  850.85,  846.13
03/15/2017,  847.2, 1381328,  847.59,  848.63,  840.77
03/14/2017,  845.62, 779920,  843.64,  847.24,  840.8
03/13/2017,  845.54, 1149928,  844,  848.685,  843.25
03/10/2017,  843.25, 1702731,  843.28,  844.91,  839.5
03/09/2017,  838.68, 1261393,  836,  842,  834.21
03/08/2017,  835.37, 988900,  833.51,  838.15,  831.79
03/07/2017,  831.91, 1037573,  827.4,  833.41,  826.52
03/06/2017,  827.78, 1108799,  826.95,  828.88,  822.4
03/03/2017,  829.08, 890640,  830.56,  831.36,  825.751
03/02/2017,  830.63, 937824,  833.85,  834.51,  829.64
03/01/2017,  835.24, 1495934,  828.85,  836.255,  827.26
02/28/2017,  823.21, 2258695,  825.61,  828.54,  820.2
02/27/2017,  829.28, 1101120,  824.55,  830.5,  824
02/24/2017,  828.64, 1392039,  827.73,  829,  824.2
02/23/2017,  831.33, 1471342,  830.12,  832.46,  822.88
02/22/2017,  830.76, 983058,  828.66,  833.25,  828.64
02/21/2017,  831.66, 1259841,  828.66,  833.45,  828.35
02/17/2017,  828.07, 1602549,  823.02,  828.07,  821.655
02/16/2017,  824.16, 1285919,  819.93,  824.4,  818.98
02/15/2017,  818.98, 1311316,  819.36,  823,  818.47
02/14/2017,  820.45, 1054472,  819,  823,  816
02/13/2017,  819.24, 1205835,  816,  820.959,  815.49
02/10/2017,  813.67, 1134701,  811.7,  815.25,  809.78
02/09/2017,  809.56, 990260,  809.51,  810.66,  804.54
02/08/2017,  808.38, 1155892,  807,  811.84,  803.1903
02/07/2017,  806.97, 1240257,  803.99,  810.5,  801.78
02/06/2017,  801.34, 1182882,  799.7,  801.67,  795.2501
02/03/2017,  801.49, 1461217,  802.99,  806,  800.37
02/02/2017,  798.53, 1530827,  793.8,  802.7,  792
02/01/2017,  795.695, 2027708,  799.68,  801.19,  791.19
01/31/2017,  796.79, 2153957,  796.86,  801.25,  790.52
01/30/2017,  802.32, 3243568,  814.66,  815.84,  799.8
01/27/2017,  823.31, 2964989,  834.71,  841.95,  820.44
01/26/2017,  832.15, 2944642,  837.81,  838,  827.01
01/25/2017,  835.67, 1612854,  829.62,  835.77,  825.06
01/24/2017,  823.87, 1472228,  822.3,  825.9,  817.821
01/23/2017,  819.31, 1962506,  807.25,  820.87,  803.74
01/20/2017,  805.02, 1668638,  806.91,  806.91,  801.69
01/19/2017,  802.175, 917085,  805.12,  809.48,  801.8
01/18/2017,  806.07, 1293893,  805.81,  806.205,  800.99
01/17/2017,  804.61, 1361935,  807.08,  807.14,  800.37
01/13/2017,  807.88, 1098154,  807.48,  811.2244,  806.69
01/12/2017,  806.36, 1352872,  807.14,  807.39,  799.17
01/11/2017,  807.91, 1065360,  805,  808.15,  801.37
01/10/2017,  804.79, 1176637,  807.86,  809.1299,  803.51
01/09/2017,  806.65, 1274318,  806.4,  809.9664,  802.83
01/06/2017,  806.15, 1639246,  795.26,  807.9,  792.2041
01/05/2017,  794.02, 1334028,  786.08,  794.48,  785.02
01/04/2017,  786.9, 1071198,  788.36,  791.34,  783.16
01/03/2017,  786.14, 1657291,  778.81,  789.63,  775.8
12/30/2016,  771.82, 1769809,  782.75,  782.78,  770.41
12/29/2016,  782.79, 743808,  783.33,  785.93,  778.92
12/28/2016,  785.05, 1142148,  793.7,  794.23,  783.2
12/27/2016,  791.55, 789151,  790.68,  797.86,  787.657
12/23/2016,  789.91, 623682,  790.9,  792.74,  787.28
12/22/2016,  791.26, 972147,  792.36,  793.32,  788.58
12/21/2016,  794.56, 1208770,  795.84,  796.6757,  787.1
12/20/2016,  796.42, 950345,  796.76,  798.65,  793.27
12/19/2016,  794.2, 1231966,  790.22,  797.66,  786.27
12/16/2016,  790.8, 2435100,  800.4,  800.8558,  790.29
12/15/2016,  797.85, 1623709,  797.34,  803,  792.92
12/14/2016,  797.07, 1700875,  797.4,  804,  794.01
12/13/2016,  796.1, 2122735,  793.9,  804.3799,  793.34
12/12/2016,  789.27, 2102288,  785.04,  791.25,  784.3554
12/09/2016,  789.29, 1821146,  780,  789.43,  779.021
12/08/2016,  776.42, 1487517,  772.48,  778.18,  767.23
12/07/2016,  771.19, 1757710,  761,  771.36,  755.8
12/06/2016,  759.11, 1690365,  764.73,  768.83,  757.34
12/05/2016,  762.52, 1393566,  757.71,  763.9,  752.9
12/02/2016,  750.5, 1452181,  744.59,  754,  743.1
12/01/2016,  747.92, 3017001,  757.44,  759.85,  737.0245
11/30/2016,  758.04, 2386628,  770.07,  772.99,  754.83
11/29/2016,  770.84, 1616427,  771.53,  778.5,  768.24
11/28/2016,  768.24, 2177039,  760,  779.53,  759.8
11/25/2016,  761.68, 587421,  764.26,  765,  760.52
11/23/2016,  760.99, 1477501,  767.73,  768.2825,  755.25
11/22/2016,  768.27, 1592372,  772.63,  776.96,  767
11/21/2016,  769.2, 1324431,  762.61,  769.7,  760.6
11/18/2016,  760.54, 1528555,  771.37,  775,  760
11/17/2016,  771.23, 1298484,  766.92,  772.7,  764.23
11/16/2016,  764.48, 1468196,  755.2,  766.36,  750.51
11/15/2016,  758.49, 2375056,  746.97,  764.4162,  746.97
11/14/2016,  736.08, 3644965,  755.6,  757.85,  727.54
11/11/2016,  754.02, 2421889,  756.54,  760.78,  750.38
11/10/2016,  762.56, 4733916,  791.17,  791.17,  752.18
11/09/2016,  785.31, 2603860,  779.94,  791.2265,  771.67
11/08/2016,  790.51, 1361472,  783.4,  795.633,  780.19
11/07/2016,  782.52, 1574426,  774.5,  785.19,  772.55
11/04/2016,  762.02, 2131948,  750.66,  770.36,  750.5611
11/03/2016,  762.13, 1933937,  767.25,  769.95,  759.03
11/02/2016,  768.7, 1905814,  778.2,  781.65,  763.4496
11/01/2016,  783.61, 2404898,  782.89,  789.49,  775.54
10/31/2016,  784.54, 2420892,  795.47,  796.86,  784
10/28/2016,  795.37, 4261912,  808.35,  815.49,  793.59
10/27/2016,  795.35, 2723097,  801,  803.49,  791.5
10/26/2016,  799.07, 1645403,  806.34,  806.98,  796.32
10/25/2016,  807.67, 1575020,  816.68,  816.68,  805.14
10/24/2016,  813.11, 1693162,  804.9,  815.18,  804.82
10/21/2016,  799.37, 1262042,  795,  799.5,  794
10/20/2016,  796.97, 1755546,  803.3,  803.97,  796.03
10/19/2016,  801.56, 1762990,  798.86,  804.63,  797.635
10/18/2016,  795.26, 2046338,  787.85,  801.61,  785.565
10/17/2016,  779.96, 1091524,  779.8,  785.85,  777.5
10/14/2016,  778.53, 851512,  781.65,  783.95,  776
10/13/2016,  778.19, 1360619,  781.22,  781.22,  773
10/12/2016,  786.14, 935138,  783.76,  788.13,  782.06
10/11/2016,  783.07, 1371461,  786.66,  792.28,  780.58
10/10/2016,  785.94, 1161410,  777.71,  789.38,  775.87
10/07/2016,  775.08, 932444,  779.66,  779.66,  770.75
10/06/2016,  776.86, 1066910,  779,  780.48,  775.54
10/05/2016,  776.47, 1457661,  779.31,  782.07,  775.65
10/04/2016,  776.43, 1198361,  776.03,  778.71,  772.89
10/03/2016,  772.56, 1276614,  774.25,  776.065,  769.5
09/30/2016,  777.29, 1583293,  776.33,  780.94,  774.09
09/29/2016,  775.01, 1310252,  781.44,  785.8,  774.232
09/28/2016,  781.56, 1108249,  777.85,  781.81,  774.97
09/27/2016,  783.01, 1152760,  775.5,  785.9899,  774.308
09/26/2016,  774.21, 1531788,  782.74,  782.74,  773.07
09/23/2016,  786.9, 1411439,  786.59,  788.93,  784.15
09/22/2016,  787.21, 1483899,  780,  789.85,  778.44
09/21/2016,  776.22, 1166290,  772.66,  777.16,  768.301
09/20/2016,  771.41, 975434,  769,  773.33,  768.53
09/19/2016,  765.7, 1171969,  772.42,  774,  764.4406
09/16/2016,  768.88, 2047036,  769.75,  769.75,  764.66
09/15/2016,  771.76, 1344945,  762.89,  773.8,  759.96
09/14/2016,  762.49, 1093723,  759.61,  767.68,  759.11
09/13/2016,  759.69, 1394158,  764.48,  766.2195,  755.8
09/12/2016,  769.02, 1310493,  755.13,  770.29,  754.0001
09/09/2016,  759.66, 1879903,  770.1,  773.245,  759.66
09/08/2016,  775.32, 1268663,  778.59,  780.35,  773.58
09/07/2016,  780.35, 893874,  780,  782.73,  776.2
09/06/2016,  780.08, 1441864,  773.45,  782,  771
09/02/2016,  771.46, 1070725,  773.01,  773.9199,  768.41
09/01/2016,  768.78, 925019,  769.25,  771.02,  764.3
08/31/2016,  767.05, 1247937,  767.01,  769.09,  765.38
08/30/2016,  769.09, 1129932,  769.33,  774.466,  766.84
08/29/2016,  772.15, 847537,  768.74,  774.99,  766.615
08/26/2016,  769.54, 1164713,  769,  776.0799,  765.85
08/25/2016,  769.41, 926856,  767,  771.89,  763.1846
08/24/2016,  769.64, 1071569,  770.58,  774.5,  767.07
08/23/2016,  772.08, 925356,  775.48,  776.44,  771.785
08/22/2016,  772.15, 950417,  773.27,  774.54,  770.0502
08/19/2016,  775.42, 860899,  775,  777.1,  773.13
08/18/2016,  777.5, 718882,  780.01,  782.86,  777
08/17/2016,  779.91, 921666,  777.32,  780.81,  773.53
08/16/2016,  777.14, 1027836,  780.3,  780.98,  773.444
08/15/2016,  782.44, 938183,  783.75,  787.49,  780.11
08/12/2016,  783.22, 739761,  781.5,  783.395,  780.4
08/11/2016,  784.85, 971742,  785,  789.75,  782.97
08/10/2016,  784.68, 784559,  783.75,  786.8123,  782.778
08/09/2016,  784.26, 1318457,  781.1,  788.94,  780.57
08/08/2016,  781.76, 1106693,  782,  782.63,  778.091
08/05/2016,  782.22, 1799478,  773.78,  783.04,  772.34
08/04/2016,  771.61, 1139972,  772.22,  774.07,  768.795
08/03/2016,  773.18, 1283186,  767.18,  773.21,  766.82
08/02/2016,  771.07, 1782822,  768.69,  775.84,  767.85
08/01/2016,  772.88, 2697699,  761.09,  780.43,  761.09
07/29/2016,  768.79, 3830103,  772.71,  778.55,  766.77
07/28/2016,  745.91, 3473040,  747.04,  748.65,  739.3
07/27/2016,  741.77, 1509133,  738.28,  744.46,  737
07/26/2016,  738.42, 1182993,  739.04,  741.69,  734.27
07/25/2016,  739.77, 1031643,  740.67,  742.61,  737.5
07/22/2016,  742.74, 1256741,  741.86,  743.24,  736.56
07/21/2016,  738.63, 1022229,  740.36,  741.69,  735.831
07/20/2016,  741.19, 1283931,  737.33,  742.13,  737.1
07/19/2016,  736.96, 1225467,  729.89,  736.99,  729
07/18/2016,  733.78, 1284740,  722.71,  736.13,  721.19
07/15/2016,  719.85, 1277514,  725.73,  725.74,  719.055
07/14/2016,  720.95, 949456,  721.58,  722.21,  718.03
07/13/2016,  716.98, 933352,  723.62,  724,  716.85
07/12/2016,  720.64, 1336112,  719.12,  722.94,  715.91
07/11/2016,  715.09, 1107039,  708.05,  716.51,  707.24
07/08/2016,  705.63, 1573909,  699.5,  705.71,  696.435
07/07/2016,  695.36, 1303661,  698.08,  698.2,  688.215
07/06/2016,  697.77, 1411080,  689.98,  701.68,  689.09
07/05/2016,  694.49, 1462879,  696.06,  696.94,  688.88
07/01/2016,  699.21, 1344387,  692.2,  700.65,  692.1301
06/30/2016,  692.1, 1597298,  685.47,  692.32,  683.65
06/29/2016,  684.11, 1931436,  683,  687.4292,  681.41
06/28/2016,  680.04, 2169704,  678.97,  680.33,  673
06/27/2016,  668.26, 2632011,  671,  672.3,  663.284
06/24/2016,  675.22, 4442943,  675.17,  689.4,  673.45
06/23/2016,  701.87, 2166183,  697.45,  701.95,  687
06/22/2016,  697.46, 1182161,  699.06,  700.86,  693.0819
06/21/2016,  695.94, 1464836,  698.4,  702.77,  692.01
06/20/2016,  693.71, 2080645,  698.77,  702.48,  693.41
06/17/2016,  691.72, 3397720,  708.65,  708.82,  688.4515
06/16/2016,  710.36, 1981657,  714.91,  716.65,  703.26
06/15/2016,  718.92, 1213386,  719,  722.98,  717.31
06/14/2016,  718.27, 1303808,  716.48,  722.47,  713.12
06/13/2016,  718.36, 1255199,  716.51,  725.44,  716.51
06/10/2016,  719.41, 1213989,  719.47,  725.89,  716.43
06/09/2016,  728.58, 987635,  722.87,  729.54,  722.3361
06/08/2016,  728.28, 1583325,  723.96,  728.57,  720.58
06/07/2016,  716.65, 1336348,  719.84,  721.98,  716.55
06/06/2016,  716.55, 1565955,  724.91,  724.91,  714.61
06/03/2016,  722.34, 1225924,  729.27,  729.49,  720.56
06/02/2016,  730.4, 1340664,  732.5,  733.02,  724.17
06/01/2016,  734.15, 1251468,  734.53,  737.21,  730.66
05/31/2016,  735.72, 2128358,  731.74,  739.73,  731.26
05/27/2016,  732.66, 1974425,  724.01,  733.936,  724
05/26/2016,  724.12, 1573635,  722.87,  728.33,  720.28
05/25/2016,  725.27, 1629790,  720.76,  727.51,  719.7047
05/24/2016,  720.09, 1926828,  706.86,  720.97,  706.86
05/23/2016,  704.24, 1326386,  706.53,  711.4781,  704.18
05/20/2016,  709.74, 1825830,  701.62,  714.58,  700.52
05/19/2016,  700.32, 1668887,  702.36,  706,  696.8
05/18/2016,  706.63, 1765632,  703.67,  711.6,  700.63
05/17/2016,  706.23, 1999883,  715.99,  721.52,  704.11
05/16/2016,  716.49, 1316719,  709.13,  718.48,  705.65
05/13/2016,  710.83, 1307559,  711.93,  716.6619,  709.26
05/12/2016,  713.31, 1361170,  717.06,  719.25,  709
05/11/2016,  715.29, 1690862,  723.41,  724.48,  712.8
05/10/2016,  723.18, 1568621,  716.75,  723.5,  715.72
05/09/2016,  712.9, 1509892,  712,  718.71,  710
05/06/2016,  711.12, 1828508,  698.38,  711.86,  698.1067
05/05/2016,  701.43, 1680220,  697.7,  702.3199,  695.72
05/04/2016,  695.7, 1692757,  690.49,  699.75,  689.01
05/03/2016,  692.36, 1541297,  696.87,  697.84,  692
05/02/2016,  698.21, 1645013,  697.63,  700.64,  691
04/29/2016,  693.01, 2486584,  690.7,  697.62,  689
04/28/2016,  691.02, 2859790,  708.26,  714.17,  689.55
04/27/2016,  705.84, 3094905,  707.29,  708.98,  692.3651
04/26/2016,  708.14, 2739133,  725.42,  725.766,  703.0264
04/25/2016,  723.15, 1956956,  716.1,  723.93,  715.59
04/22/2016,  718.77, 5949699,  726.3,  736.12,  713.61
04/21/2016,  759.14, 2995094,  755.38,  760.45,  749.55
04/20/2016,  752.67, 1526776,  758,  758.1315,  750.01
04/19/2016,  753.93, 2027962,  769.51,  769.9,  749.33
04/18/2016,  766.61, 1557199,  760.46,  768.05,  757.3
04/15/2016,  759, 1807062,  753.98,  761,  752.6938
04/14/2016,  753.2, 1134056,  754.01,  757.31,  752.705
04/13/2016,  751.72, 1707397,  749.16,  754.38,  744.261
04/12/2016,  743.09, 1349780,  738,  743.83,  731.01
04/11/2016,  736.1, 1218789,  743.02,  745,  736.05
04/08/2016,  739.15, 1289869,  743.97,  745.45,  735.55
04/07/2016,  740.28, 1452369,  745.37,  746.9999,  736.28
04/06/2016,  745.69, 1052171,  735.77,  746.24,  735.56
04/05/2016,  737.8, 1130817,  738,  742.8,  735.37
04/04/2016,  745.29, 1134214,  750.06,  752.8,  742.43
04/01/2016,  749.91, 1576240,  738.6,  750.34,  737
03/31/2016,  744.95, 1718638,  749.25,  750.85,  740.94
03/30/2016,  750.53, 1782278,  750.1,  757.88,  748.74
03/29/2016,  744.77, 1902254,  734.59,  747.25,  728.76
03/28/2016,  733.53, 1300817,  736.79,  738.99,  732.5
03/24/2016,  735.3, 1570474,  732.01,  737.747,  731
03/23/2016,  738.06, 1431130,  742.36,  745.7199,  736.15
03/22/2016,  740.75, 1269263,  737.46,  745,  737.46
03/21/2016,  742.09, 1835963,  736.5,  742.5,  733.5157
03/18/2016,  737.6, 2982194,  741.86,  742,  731.83
03/17/2016,  737.78, 1859562,  736.45,  743.07,  736
03/16/2016,  736.09, 1621412,  726.37,  737.47,  724.51
03/15/2016,  728.33, 1720790,  726.92,  732.29,  724.77
03/14/2016,  730.49, 1717002,  726.81,  735.5,  725.15
03/11/2016,  726.82, 1968164,  720,  726.92,  717.125
03/10/2016,  712.82, 2830630,  708.12,  716.44,  703.36
03/09/2016,  705.24, 1419661,  698.47,  705.68,  694
03/08/2016,  693.97, 2075305,  688.59,  703.79,  685.34
03/07/2016,  695.16, 2986064,  706.9,  708.0912,  686.9
03/04/2016,  710.89, 1971379,  714.99,  716.49,  706.02
03/03/2016,  712.42, 1956958,  718.68,  719.45,  706.02
03/02/2016,  718.85, 1629501,  719,  720,  712
03/01/2016,  718.81, 2148608,  703.62,  718.81,  699.77
02/29/2016,  697.77, 2478214,  700.32,  710.89,  697.68
02/26/2016,  705.07, 2241785,  708.58,  713.43,  700.86
02/25/2016,  705.75, 1640430,  700.01,  705.98,  690.585
02/24/2016,  699.56, 1961258,  688.92,  700,  680.78
02/23/2016,  695.85, 2006572,  701.45,  708.4,  693.58
02/22/2016,  706.46, 1949046,  707.45,  713.24,  702.51
02/19/2016,  700.91, 1585152,  695.03,  703.0805,  694.05
02/18/2016,  697.35, 1880306,  710,  712.35,  696.03
02/17/2016,  708.4, 2490021,  699,  709.75,  691.38
02/16/2016,  691, 2517324,  692.98,  698,  685.05
02/12/2016,  682.4, 2138937,  690.26,  693.75,  678.6
02/11/2016,  683.11, 3021587,  675,  689.35,  668.8675
02/10/2016,  684.12, 2629130,  686.86,  701.31,  682.13
02/09/2016,  678.11, 3605792,  672.32,  699.9,  668.77
02/08/2016,  682.74, 4241416,  667.85,  684.03,  663.06
02/05/2016,  683.57, 5098357,  703.87,  703.99,  680.15
02/04/2016,  708.01, 5157988,  722.81,  727,  701.86
02/03/2016,  726.95, 6166731,  770.22,  774.5,  720.5
02/02/2016,  764.65, 6340548,  784.5,  789.8699,  764.65
02/01/2016,  752, 5065235,  750.46,  757.86,  743.27
01/29/2016,  742.95, 3464432,  731.53,  744.9899,  726.8
01/28/2016,  730.96, 2664956,  722.22,  733.69,  712.35
01/27/2016,  699.99, 2175913,  713.67,  718.235,  694.39
01/26/2016,  713.04, 1329141,  713.85,  718.28,  706.48
01/25/2016,  711.67, 1709777,  723.58,  729.68,  710.01
01/22/2016,  725.25, 2009951,  723.6,  728.13,  720.121
01/21/2016,  706.59, 2411079,  702.18,  719.19,  694.46
01/20/2016,  698.45, 3441642,  688.61,  706.85,  673.26
01/19/2016,  701.79, 2264747,  703.3,  709.98,  693.4101
01/15/2016,  694.45, 3604137,  692.29,  706.74,  685.37
01/14/2016,  714.72, 2225495,  705.38,  721.925,  689.1
01/13/2016,  700.56, 2497086,  730.85,  734.74,  698.61
01/12/2016,  726.07, 2010026,  721.68,  728.75,  717.3165
01/11/2016,  716.03, 2089495,  716.61,  718.855,  703.54
01/08/2016,  714.47, 2449420,  731.45,  733.23,  713
01/07/2016,  726.39, 2960578,  730.31,  738.5,  719.06
01/06/2016,  743.62, 1943685,  730,  747.18,  728.92
01/05/2016,  742.58, 1949386,  746.45,  752,  738.64
01/04/2016,  741.84, 3271348,  743,  744.06,  731.2577
12/31/2015,  758.88, 1500129,  769.5,  769.5,  758.34
12/30/2015,  771, 1293514,  776.6,  777.6,  766.9
12/29/2015,  776.6, 1764044,  766.69,  779.98,  766.43
12/28/2015,  762.51, 1515574,  752.92,  762.99,  749.52
12/24/2015,  748.4, 527223,  749.55,  751.35,  746.62
12/23/2015,  750.31, 1566723,  753.47,  754.21,  744
12/22/2015,  750, 1365420,  751.65,  754.85,  745.53
12/21/2015,  747.77, 1524535,  746.13,  750,  740
12/18/2015,  739.31, 3140906,  746.51,  754.13,  738.15
12/17/2015,  749.43, 1551087,  762.42,  762.68,  749
12/16/2015,  758.09, 1986319,  750,  760.59,  739.435
12/15/2015,  743.4, 2661199,  753,  758.08,  743.01
12/14/2015,  747.77, 2417778,  741.79,  748.73,  724.17
12/11/2015,  738.87, 2223284,  741.16,  745.71,  736.75
12/10/2015,  749.46, 1988035,  752.85,  755.85,  743.83
12/09/2015,  751.61, 2697978,  759.17,  764.23,  737.001
12/08/2015,  762.37, 1829004,  757.89,  764.8,  754.2
12/07/2015,  763.25, 1811336,  767.77,  768.73,  755.09
12/04/2015,  766.81, 2756194,  753.1,  768.49,  750
12/03/2015,  752.54, 2589641,  766.01,  768.995,  745.63
12/02/2015,  762.38, 2196721,  768.9,  775.955,  758.96
12/01/2015,  767.04, 2131827,  747.11,  768.95,  746.7
11/30/2015,  742.6, 2045584,  748.81,  754.93,  741.27
11/27/2015,  750.26, 838528,  748.46,  753.41,  747.49
11/25/2015,  748.15, 1122224,  748.14,  752,  746.06
11/24/2015,  748.28, 2333700,  752,  755.279,  737.63
11/23/2015,  755.98, 1414640,  757.45,  762.7075,  751.82
11/20/2015,  756.6, 2212934,  746.53,  757.92,  743
11/19/2015,  738.41, 1327265,  738.74,  742,  737.43
11/18/2015,  740, 1683978,  727.58,  741.41,  727
11/17/2015,  725.3, 1507449,  729.29,  731.845,  723.027
11/16/2015,  728.96, 1904395,  715.6,  729.49,  711.33
11/13/2015,  717, 2072392,  729.17,  731.15,  716.73
11/12/2015,  731.23, 1836567,  731,  737.8,  728.645
11/11/2015,  735.4, 1366611,  732.46,  741,  730.23
11/10/2015,  728.32, 1606499,  724.4,  730.59,  718.5001
11/09/2015,  724.89, 2068920,  730.2,  734.71,  719.43
11/06/2015,  733.76, 1510586,  731.5,  735.41,  727.01
11/05/2015,  731.25, 1861100,  729.47,  739.48,  729.47
11/04/2015,  728.11, 1705745,  722,  733.1,  721.9
11/03/2015,  722.16, 1565355,  718.86,  724.65,  714.72
11/02/2015,  721.11, 1885155,  711.06,  721.62,  705.85
10/30/2015,  710.81, 1907732,  715.73,  718,  710.05
10/29/2015,  716.92, 1455508,  710.5,  718.26,  710.01
10/28/2015,  712.95, 2178841,  707.33,  712.98,  703.08
10/27/2015,  708.49, 2232183,  707.38,  713.62,  704.55
10/26/2015,  712.78, 2709292,  701.55,  719.15,  701.26
10/23/2015,  702, 6651909,  727.5,  730,  701.5
10/22/2015,  651.79, 3994360,  646.7,  657.8,  644.01
10/21/2015,  642.61, 1792869,  654.15,  655.87,  641.73
10/20/2015,  650.28, 2498077,  664.04,  664.7197,  644.195
10/19/2015,  666.1, 1465691,  661.18,  666.82,  659.58
10/16/2015,  662.2, 1610712,  664.11,  664.97,  657.2
10/15/2015,  661.74, 1832832,  654.66,  663.13,  654.46
10/14/2015,  651.16, 1413798,  653.21,  659.39,  648.85
10/13/2015,  652.3, 1806003,  643.15,  657.8125,  643.15
10/12/2015,  646.67, 1275565,  642.09,  648.5,  639.01
10/09/2015,  643.61, 1648656,  640,  645.99,  635.318
10/08/2015,  639.16, 2181990,  641.36,  644.45,  625.56
10/07/2015,  642.36, 2092536,  649.24,  650.609,  632.15
10/06/2015,  645.44, 2235078,  638.84,  649.25,  636.5295
10/05/2015,  641.47, 1802263,  632,  643.01,  627
10/02/2015,  626.91, 2681241,  607.2,  627.34,  603.13
10/01/2015,  611.29, 1866223,  608.37,  612.09,  599.85
09/30/2015,  608.42, 2412754,  603.28,  608.76,  600.73
09/29/2015,  594.97, 2310065,  597.28,  605,  590.22
09/28/2015,  594.89, 3118693,  610.34,  614.605,  589.38
09/25/2015,  611.97, 2173134,  629.77,  629.77,  611
09/24/2015,  625.8, 2238097,  616.64,  627.32,  612.4
09/23/2015,  622.36, 1470633,  622.05,  628.93,  620
09/22/2015,  622.69, 2561551,  627,  627.55,  615.43
09/21/2015,  635.44, 1786543,  634.4,  636.49,  625.94
09/18/2015,  629.25, 5123314,  636.79,  640,  627.02
09/17/2015,  642.9, 2259404,  637.79,  650.9,  635.02
09/16/2015,  635.98, 1276250,  635.47,  637.95,  632.32
09/15/2015,  635.14, 2082426,  626.7,  638.7,  623.78
09/14/2015,  623.24, 1701618,  625.7,  625.86,  619.43
09/11/2015,  625.77, 1372803,  619.75,  625.78,  617.42
09/10/2015,  621.35, 1903334,  613.1,  624.16,  611.43
09/09/2015,  612.72, 1699686,  621.22,  626.52,  609.6
09/08/2015,  614.66, 2277487,  612.49,  616.31,  604.12
09/04/2015,  600.7, 2087028,  600,  603.47,  595.25
09/03/2015,  606.25, 1757851,  617,  619.71,  602.8213
09/02/2015,  614.34, 2573982,  605.59,  614.34,  599.71
09/01/2015,  597.79, 3699844,  602.36,  612.86,  594.1
08/31/2015,  618.25, 2172168,  627.54,  635.8,  617.68
08/28/2015,  630.38, 1975818,  632.82,  636.88,  624.56
08/27/2015,  637.61, 3485906,  639.4,  643.59,  622
08/26/2015,  628.62, 4187276,  610.35,  631.71,  599.05
08/25/2015,  582.06, 3521916,  614.91,  617.45,  581.11
08/24/2015,  589.61, 5727282,  573,  614,  565.05
08/21/2015,  612.48, 4261666,  639.78,  640.05,  612.33
08/20/2015,  646.83, 2854028,  655.46,  662.99,  642.9
08/19/2015,  660.9, 2132265,  656.6,  667,  654.19
08/18/2015,  656.13, 1455664,  661.9,  664,  653.46
08/17/2015,  660.87, 1050553,  656.8,  661.38,  651.24
08/14/2015,  657.12, 1071333,  655.01,  659.855,  652.66
08/13/2015,  656.45, 1807182,  659.323,  664.5,  651.661
08/12/2015,  659.56, 2938651,  663.08,  665,  652.29
08/11/2015,  660.78, 5016425,  669.2,  674.9,  654.27
08/10/2015,  633.73, 1653836,  639.48,  643.44,  631.249
08/07/2015,  635.3, 1403441,  640.23,  642.68,  629.71
08/06/2015,  642.68, 1572150,  645,  645.379,  632.25
08/05/2015,  643.78, 2331720,  634.33,  647.86,  633.16
08/04/2015,  629.25, 1486858,  628.42,  634.81,  627.16
08/03/2015,  631.21, 1301439,  625.34,  633.0556,  625.34
07/31/2015,  625.61, 1705286,  631.38,  632.91,  625.5
07/30/2015,  632.59, 1472286,  630,  635.22,  622.05
07/29/2015,  631.93, 1573146,  628.8,  633.36,  622.65
07/28/2015,  628, 1713684,  632.83,  632.83,  623.31
07/27/2015,  627.26, 2673801,  621,  634.3,  620.5
07/24/2015,  623.56, 3622089,  647,  648.17,  622.52
07/23/2015,  644.28, 3014035,  661.27,  663.63,  641
07/22/2015,  662.1, 3707818,  660.89,  678.64,  659
07/21/2015,  662.3, 3363342,  655.21,  673,  654.3
07/20/2015,  663.02, 5857092,  659.24,  668.88,  653.01
07/17/2015,  672.93, 11153500,  649,  674.468,  645
07/16/2015,  579.85, 4559712,  565.12,  580.68,  565
07/15/2015,  560.22, 1782264,  560.13,  566.5029,  556.79
07/14/2015,  561.1, 3231284,  546.76,  565.8487,  546.71
07/13/2015,  546.55, 2204610,  532.88,  547.11,  532.4001
07/10/2015,  530.13, 1954951,  526.29,  532.56,  525.55
07/09/2015,  520.68, 1840155,  523.12,  523.77,  520.35
07/08/2015,  516.83, 1293372,  521.05,  522.734,  516.11
07/07/2015,  525.02, 1595672,  523.13,  526.18,  515.18
07/06/2015,  522.86, 1278587,  519.5,  525.25,  519
07/02/2015,  523.4, 1235773,  521.08,  524.65,  521.08
07/01/2015,  521.84, 1961197,  524.73,  525.69,  518.2305
06/30/2015,  520.51, 2234284,  526.02,  526.25,  520.5
06/29/2015,  521.52, 1935361,  525.01,  528.61,  520.54
06/26/2015,  531.69, 2108629,  537.26,  537.76,  531.35
06/25/2015,  535.23, 1332412,  538.87,  540.9,  535.23
06/24/2015,  537.84, 1286576,  540,  540,  535.66
06/23/2015,  540.48, 1196115,  539.64,  541.499,  535.25
06/22/2015,  538.19, 1243535,  539.59,  543.74,  537.53
06/19/2015,  536.69, 1890916,  537.21,  538.25,  533.01
06/18/2015,  536.73, 1832450,  531,  538.15,  530.79
06/17/2015,  529.26, 1269113,  529.37,  530.98,  525.1
06/16/2015,  528.15, 1071728,  528.4,  529.6399,  525.56
06/15/2015,  527.2, 1632675,  528,  528.3,  524
06/12/2015,  532.33, 955489,  531.6,  533.12,  530.16
06/11/2015,  534.61, 1208632,  538.425,  538.98,  533.02
06/10/2015,  536.69, 1813775,  529.36,  538.36,  529.35
06/09/2015,  526.69, 1454172,  527.56,  529.2,  523.01
06/08/2015,  526.83, 1523960,  533.31,  534.12,  526.24
06/05/2015,  533.33, 1375008,  536.35,  537.2,  532.52
06/04/2015,  536.7, 1346044,  537.76,  540.59,  534.32
06/03/2015,  540.31, 1716836,  539.91,  543.5,  537.11
06/02/2015,  539.18, 1936721,  532.93,  543,  531.33
06/01/2015,  533.99, 1900257,  536.79,  536.79,  529.76
05/29/2015,  532.11, 2590445,  537.37,  538.63,  531.45
05/28/2015,  539.78, 1029764,  538.01,  540.61,  536.25
05/27/2015,  539.79, 1524783,  532.8,  540.55,  531.71
05/26/2015,  532.32, 2404462,  538.12,  539,  529.88
05/22/2015,  540.11, 1175065,  540.15,  544.19,  539.51
05/21/2015,  542.51, 1461431,  537.95,  543.8399,  535.98
05/20/2015,  539.27, 1430565,  538.49,  542.92,  532.972
05/19/2015,  537.36, 1964037,  533.98,  540.66,  533.04
05/18/2015,  532.3, 2001117,  532.01,  534.82,  528.85
05/15/2015,  533.85, 1965088,  539.18,  539.2743,  530.38
05/14/2015,  538.4, 1401005,  533.77,  539,  532.41
05/13/2015,  529.62, 1253005,  530.56,  534.3215,  528.655
05/12/2015,  529.04, 1633180,  531.6,  533.2089,  525.26
05/11/2015,  535.7, 904465,  538.37,  541.98,  535.4
05/08/2015,  538.22, 1527181,  536.65,  541.15,  536
05/07/2015,  530.7, 1543986,  523.99,  533.46,  521.75
05/06/2015,  524.22, 1566865,  531.24,  532.38,  521.085
05/05/2015,  530.8, 1380519,  538.21,  539.74,  530.3906
05/04/2015,  540.78, 1303830,  538.53,  544.07,  535.06
05/01/2015,  537.9, 1758085,  538.43,  539.54,  532.1
04/30/2015,  537.34, 2080834,  547.87,  548.59,  535.05
04/29/2015,  549.08, 1696886,  550.47,  553.68,  546.905
04/28/2015,  553.68, 1490735,  554.64,  556.02,  550.366
04/27/2015,  555.37, 2390696,  563.39,  565.95,  553.2001


================================================
FILE: machine_learning/mfcc.py
================================================
"""
Mel Frequency Cepstral Coefficients (MFCC) Calculation

MFCC is an algorithm widely used in audio and speech processing to represent the
short-term power spectrum of a sound signal in a more compact and
discriminative way. It is particularly popular in speech and audio processing
tasks such as speech recognition and speaker identification.

How Mel Frequency Cepstral Coefficients are Calculated:
1. Preprocessing:
   - Load an audio signal and normalize it to ensure that the values fall
     within a specific range (e.g., between -1 and 1).
   - Frame the audio signal into overlapping, fixed-length segments, typically
     using a technique like windowing to reduce spectral leakage.

2. Fourier Transform:
   - Apply a Fast Fourier Transform (FFT) to each audio frame to convert it
     from the time domain to the frequency domain. This results in a
     representation of the audio frame as a sequence of frequency components.

3. Power Spectrum:
   - Calculate the power spectrum by taking the squared magnitude of each
     frequency component obtained from the FFT. This step measures the energy
     distribution across different frequency bands.

4. Mel Filterbank:
   - Apply a set of triangular filterbanks spaced in the Mel frequency scale
     to the power spectrum. These filters mimic the human auditory system's
     frequency response. Each filterbank sums the power spectrum values within
     its band.

5. Logarithmic Compression:
   - Take the logarithm (typically base 10) of the filterbank values to
     compress the dynamic range. This step mimics the logarithmic response of
     the human ear to sound intensity.

6. Discrete Cosine Transform (DCT):
   - Apply the Discrete Cosine Transform to the log filterbank energies to
     obtain the MFCC coefficients. This transformation helps decorrelate the
     filterbank energies and captures the most important features of the audio
     signal.

7. Feature Extraction:
   - Select a subset of the DCT coefficients to form the feature vector.
     Often, the first few coefficients (e.g., 12-13) are used for most
     applications.

References:
- Mel-Frequency Cepstral Coefficients (MFCCs):
  https://en.wikipedia.org/wiki/Mel-frequency_cepstrum
- Speech and Language Processing by Daniel Jurafsky & James H. Martin:
  https://web.stanford.edu/~jurafsky/slp3/
- Mel Frequency Cepstral Coefficient (MFCC) tutorial
  http://practicalcryptography.com/miscellaneous/machine-learning
  /guide-mel-frequency-cepstral-coefficients-mfccs/

Author: Amir Lavasani
"""

import logging

import numpy as np
import scipy.fftpack as fft
from scipy.signal import get_window

logging.basicConfig(filename=f"{__file__}.log", level=logging.INFO)


def mfcc(
    audio: np.ndarray,
    sample_rate: int,
    ftt_size: int = 1024,
    hop_length: int = 20,
    mel_filter_num: int = 10,
    dct_filter_num: int = 40,
) -> np.ndarray:
    """
    Calculate Mel Frequency Cepstral Coefficients (MFCCs) from an audio signal.

    Args:
        audio: The input audio signal.
        sample_rate: The sample rate of the audio signal (in Hz).
        ftt_size: The size of the FFT window (default is 1024).
        hop_length: The hop length for frame creation (default is 20ms).
        mel_filter_num: The number of Mel filters (default is 10).
        dct_filter_num: The number of DCT filters (default is 40).

    Returns:
        A matrix of MFCCs for the input audio.

    Raises:
        ValueError: If the input audio is empty.

    Example:
    >>> sample_rate = 44100  # Sample rate of 44.1 kHz
    >>> duration = 2.0  # Duration of 1 second
    >>> t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
    >>> audio = 0.5 * np.sin(2 * np.pi * 440.0 * t)  # Generate a 440 Hz sine wave
    >>> mfccs = mfcc(audio, sample_rate)
    >>> mfccs.shape
    (40, 101)
    """
    logging.info(f"Sample rate: {sample_rate}Hz")
    logging.info(f"Audio duration: {len(audio) / sample_rate}s")
    logging.info(f"Audio min: {np.min(audio)}")
    logging.info(f"Audio max: {np.max(audio)}")

    # normalize audio
    audio_normalized = normalize(audio)

    logging.info(f"Normalized audio min: {np.min(audio_normalized)}")
    logging.info(f"Normalized audio max: {np.max(audio_normalized)}")

    # frame audio into
    audio_framed = audio_frames(
        audio_normalized, sample_rate, ftt_size=ftt_size, hop_length=hop_length
    )

    logging.info(f"Framed audio shape: {audio_framed.shape}")
    logging.info(f"First frame: {audio_framed[0]}")

    # convert to frequency domain
    # For simplicity we will choose the Hanning window.
    window = get_window("hann", ftt_size, fftbins=True)
    audio_windowed = audio_framed * window

    logging.info(f"Windowed audio shape: {audio_windowed.shape}")
    logging.info(f"First frame: {audio_windowed[0]}")

    audio_fft = calculate_fft(audio_windowed, ftt_size)
    logging.info(f"fft audio shape: {audio_fft.shape}")
    logging.info(f"First frame: {audio_fft[0]}")

    audio_power = calculate_signal_power(audio_fft)
    logging.info(f"power audio shape: {audio_power.shape}")
    logging.info(f"First frame: {audio_power[0]}")

    filters = mel_spaced_filterbank(sample_rate, mel_filter_num, ftt_size)
    logging.info(f"filters shape: {filters.shape}")

    audio_filtered = np.dot(filters, np.transpose(audio_power))
    audio_log = 10.0 * np.log10(audio_filtered)
    logging.info(f"audio_log shape: {audio_log.shape}")

    dct_filters = discrete_cosine_transform(dct_filter_num, mel_filter_num)
    cepstral_coefficents = np.dot(dct_filters, audio_log)

    logging.info(f"cepstral_coefficents shape: {cepstral_coefficents.shape}")
    return cepstral_coefficents


def normalize(audio: np.ndarray) -> np.ndarray:
    """
    Normalize an audio signal by scaling it to have values between -1 and 1.

    Args:
        audio: The input audio signal.

    Returns:
        The normalized audio signal.

    Examples:
    >>> audio = np.array([1, 2, 3, 4, 5])
    >>> normalized_audio = normalize(audio)
    >>> float(np.max(normalized_audio))
    1.0
    >>> float(np.min(normalized_audio))
    0.2
    """
    # Divide the entire audio signal by the maximum absolute value
    return audio / np.max(np.abs(audio))


def audio_frames(
    audio: np.ndarray,
    sample_rate: int,
    hop_length: int = 20,
    ftt_size: int = 1024,
) -> np.ndarray:
    """
    Split an audio signal into overlapping frames.

    Args:
        audio: The input audio signal.
        sample_rate: The sample rate of the audio signal.
        hop_length: The length of the hopping (default is 20ms).
        ftt_size: The size of the FFT window (default is 1024).

    Returns:
        An array of overlapping frames.

    Examples:
    >>> audio = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]*1000)
    >>> sample_rate = 8000
    >>> frames = audio_frames(audio, sample_rate, hop_length=10, ftt_size=512)
    >>> frames.shape
    (126, 512)
    """

    hop_size = np.round(sample_rate * hop_length / 1000).astype(int)

    # Pad the audio signal to handle edge cases
    audio = np.pad(audio, int(ftt_size / 2), mode="reflect")

    # Calculate the number of frames
    frame_count = int((len(audio) - ftt_size) / hop_size) + 1

    # Initialize an array to store the frames
    frames = np.zeros((frame_count, ftt_size))

    # Split the audio signal into frames
    for n in range(frame_count):
        frames[n] = audio[n * hop_size : n * hop_size + ftt_size]

    return frames


def calculate_fft(audio_windowed: np.ndarray, ftt_size: int = 1024) -> np.ndarray:
    """
    Calculate the Fast Fourier Transform (FFT) of windowed audio data.

    Args:
        audio_windowed: The windowed audio signal.
        ftt_size: The size of the FFT (default is 1024).

    Returns:
        The FFT of the audio data.

    Examples:
    >>> audio_windowed = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
    >>> audio_fft = calculate_fft(audio_windowed, ftt_size=4)
    >>> bool(np.allclose(audio_fft[0], np.array([6.0+0.j, -1.5+0.8660254j,
    ...     -1.5-0.8660254j])))
    True
    """
    # Transpose the audio data to have time in rows and channels in columns
    audio_transposed = np.transpose(audio_windowed)

    # Initialize an array to store the FFT results
    audio_fft = np.empty(
        (int(1 + ftt_size // 2), audio_transposed.shape[1]),
        dtype=np.complex64,
        order="F",
    )

    # Compute FFT for each channel
    for n in range(audio_fft.shape[1]):
        audio_fft[:, n] = fft.fft(audio_transposed[:, n], axis=0)[: audio_fft.shape[0]]

    # Transpose the FFT results back to the original shape
    return np.transpose(audio_fft)


def calculate_signal_power(audio_fft: np.ndarray) -> np.ndarray:
    """
    Calculate the power of the audio signal from its FFT.

    Args:
        audio_fft: The FFT of the audio signal.

    Returns:
        The power of the audio signal.

    Examples:
    >>> audio_fft = np.array([1+2j, 2+3j, 3+4j, 4+5j])
    >>> power = calculate_signal_power(audio_fft)
    >>> np.allclose(power, np.array([5, 13, 25, 41]))
    True
    """
    # Calculate the power by squaring the absolute values of the FFT coefficients
    return np.square(np.abs(audio_fft))


def freq_to_mel(freq: float) -> float:
    """
    Convert a frequency in Hertz to the mel scale.

    Args:
        freq: The frequency in Hertz.

    Returns:
        The frequency in mel scale.

    Examples:
    >>> float(round(freq_to_mel(1000), 2))
    999.99
    """
    # Use the formula to convert frequency to the mel scale
    return 2595.0 * np.log10(1.0 + freq / 700.0)


def mel_to_freq(mels: float) -> float:
    """
    Convert a frequency in the mel scale to Hertz.

    Args:
        mels: The frequency in mel scale.

    Returns:
        The frequency in Hertz.

    Examples:
    >>> round(mel_to_freq(999.99), 2)
    1000.01
    """
    # Use the formula to convert mel scale to frequency
    return 700.0 * (10.0 ** (mels / 2595.0) - 1.0)


def mel_spaced_filterbank(
    sample_rate: int, mel_filter_num: int = 10, ftt_size: int = 1024
) -> np.ndarray:
    """
    Create a Mel-spaced filter bank for audio processing.

    Args:
        sample_rate: The sample rate of the audio.
        mel_filter_num: The number of mel filters (default is 10).
        ftt_size: The size of the FFT (default is 1024).

    Returns:
        Mel-spaced filter bank.

    Examples:
    >>> float(round(mel_spaced_filterbank(8000, 10, 1024)[0][1], 10))
    0.0004603981
    """
    freq_min = 0
    freq_high = sample_rate // 2

    logging.info(f"Minimum frequency: {freq_min}")
    logging.info(f"Maximum frequency: {freq_high}")

    # Calculate filter points and mel frequencies
    filter_points, mel_freqs = get_filter_points(
        sample_rate,
        freq_min,
        freq_high,
        mel_filter_num,
        ftt_size,
    )

    filters = get_filters(filter_points, ftt_size)

    # normalize filters
    # taken from the librosa library
    enorm = 2.0 / (mel_freqs[2 : mel_filter_num + 2] - mel_freqs[:mel_filter_num])
    return filters * enorm[:, np.newaxis]


def get_filters(filter_points: np.ndarray, ftt_size: int) -> np.ndarray:
    """
    Generate filters for audio processing.

    Args:
        filter_points: A list of filter points.
        ftt_size: The size of the FFT.

    Returns:
        A matrix of filters.

    Examples:
    >>> get_filters(np.array([0, 20, 51, 95, 161, 256], dtype=int), 512).shape
    (4, 257)
    """
    num_filters = len(filter_points) - 2
    filters = np.zeros((num_filters, int(ftt_size / 2) + 1))

    for n in range(num_filters):
        start = filter_points[n]
        mid = filter_points[n + 1]
        end = filter_points[n + 2]

        # Linearly increase values from 0 to 1
        filters[n, start:mid] = np.linspace(0, 1, mid - start)

        # Linearly decrease values from 1 to 0
        filters[n, mid:end] = np.linspace(1, 0, end - mid)

    return filters


def get_filter_points(
    sample_rate: int,
    freq_min: int,
    freq_high: int,
    mel_filter_num: int = 10,
    ftt_size: int = 1024,
) -> tuple[np.ndarray, np.ndarray]:
    """
    Calculate the filter points and frequencies for mel frequency filters.

    Args:
        sample_rate: The sample rate of the audio.
        freq_min: The minimum frequency in Hertz.
        freq_high: The maximum frequency in Hertz.
        mel_filter_num: The number of mel filters (default is 10).
        ftt_size: The size of the FFT (default is 1024).

    Returns:
        Filter points and corresponding frequencies.

    Examples:
    >>> filter_points = get_filter_points(8000, 0, 4000, mel_filter_num=4, ftt_size=512)
    >>> filter_points[0]
    array([  0,  20,  51,  95, 161, 256])
    >>> filter_points[1]
    array([   0.        ,  324.46707094,  799.33254207, 1494.30973963,
           2511.42581671, 4000.        ])
    """
    # Convert minimum and maximum frequencies to mel scale
    fmin_mel = freq_to_mel(freq_min)
    fmax_mel = freq_to_mel(freq_high)

    logging.info(f"MEL min: {fmin_mel}")
    logging.info(f"MEL max: {fmax_mel}")

    # Generate equally spaced mel frequencies
    mels = np.linspace(fmin_mel, fmax_mel, num=mel_filter_num + 2)

    # Convert mel frequencies back to Hertz
    freqs = mel_to_freq(mels)

    # Calculate filter points as integer values
    filter_points = np.floor((ftt_size + 1) / sample_rate * freqs).astype(int)

    return filter_points, freqs


def discrete_cosine_transform(dct_filter_num: int, filter_num: int) -> np.ndarray:
    """
    Compute the Discrete Cosine Transform (DCT) basis matrix.

    Args:
        dct_filter_num: The number of DCT filters to generate.
        filter_num: The number of the fbank filters.

    Returns:
        The DCT basis matrix.

    Examples:
    >>> float(round(discrete_cosine_transform(3, 5)[0][0], 5))
    0.44721
    """
    basis = np.empty((dct_filter_num, filter_num))
    basis[0, :] = 1.0 / np.sqrt(filter_num)

    samples = np.arange(1, 2 * filter_num, 2) * np.pi / (2.0 * filter_num)

    for i in range(1, dct_filter_num):
        basis[i, :] = np.cos(i * samples) * np.sqrt(2.0 / filter_num)

    return basis


def example(wav_file_path: str = "./path-to-file/sample.wav") -> np.ndarray:
    """
    Example function to calculate Mel Frequency Cepstral Coefficients
    (MFCCs) from an audio file.

    Args:
        wav_file_path: The path to the WAV audio file.

    Returns:
        np.ndarray: The computed MFCCs for the audio.
    """
    from scipy.io import wavfile

    # Load the audio from the WAV file
    sample_rate, audio = wavfile.read(wav_file_path)

    # Calculate MFCCs
    return mfcc(audio, sample_rate)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: machine_learning/multilayer_perceptron_classifier.py
================================================
from sklearn.neural_network import MLPClassifier

X = [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
y = [0, 1, 0, 0]


clf = MLPClassifier(
    solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


test = [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
Y = clf.predict(test)


def wrapper(y):
    """
    >>> [int(x) for x in wrapper(Y)]
    [0, 0, 1]
    """
    return list(y)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: machine_learning/polynomial_regression.py
================================================
"""
Polynomial regression is a type of regression analysis that models the relationship
between a predictor x and the response y as an mth-degree polynomial:

y = β₀ + β₁x + β₂x² + ... + βₘxᵐ + ε

By treating x, x², ..., xᵐ as distinct variables, we see that polynomial regression is a
special case of multiple linear regression. Therefore, we can use ordinary least squares
(OLS) estimation to estimate the vector of model parameters β = (β₀, β₁, β₂, ..., βₘ)
for polynomial regression:

β = (XᵀX)⁻¹Xᵀy = X⁺y

where X is the design matrix, y is the response vector, and X⁺ denotes the Moore-Penrose
pseudoinverse of X. In the case of polynomial regression, the design matrix is

    |1  x₁  x₁² ⋯ x₁ᵐ|
X = |1  x₂  x₂² ⋯ x₂ᵐ|
    |⋮  ⋮   ⋮   ⋱ ⋮  |
    |1  xₙ  xₙ² ⋯  xₙᵐ|

In OLS estimation, inverting XᵀX to compute X⁺ can be very numerically unstable. This
implementation sidesteps this need to invert XᵀX by computing X⁺ using singular value
decomposition (SVD):

β = VΣ⁺Uᵀy

where UΣVᵀ is an SVD of X.

References:
    - https://en.wikipedia.org/wiki/Polynomial_regression
    - https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse
    - https://en.wikipedia.org/wiki/Numerical_methods_for_linear_least_squares
    - https://en.wikipedia.org/wiki/Singular_value_decomposition
"""

import matplotlib.pyplot as plt
import numpy as np


class PolynomialRegression:
    __slots__ = "degree", "params"

    def __init__(self, degree: int) -> None:
        """
        @raises ValueError: if the polynomial degree is negative
        """
        if degree < 0:
            raise ValueError("Polynomial degree must be non-negative")

        self.degree = degree
        self.params = None

    @staticmethod
    def _design_matrix(data: np.ndarray, degree: int) -> np.ndarray:
        """
        Constructs a polynomial regression design matrix for the given input data. For
        input data x = (x₁, x₂, ..., xₙ) and polynomial degree m, the design matrix is
        the Vandermonde matrix

            |1  x₁  x₁² ⋯ x₁ᵐ|
        X = |1  x₂  x₂² ⋯ x₂ᵐ|
            |⋮  ⋮   ⋮   ⋱ ⋮  |
            |1  xₙ  xₙ² ⋯  xₙᵐ|

        Reference: https://en.wikipedia.org/wiki/Vandermonde_matrix

        @param data:    the input predictor values x, either for model fitting or for
                        prediction
        @param degree:  the polynomial degree m
        @returns:       the Vandermonde matrix X (see above)
        @raises ValueError: if input data is not N x 1

        >>> x = np.array([0, 1, 2])
        >>> PolynomialRegression._design_matrix(x, degree=0)
        array([[1],
               [1],
               [1]])
        >>> PolynomialRegression._design_matrix(x, degree=1)
        array([[1, 0],
               [1, 1],
               [1, 2]])
        >>> PolynomialRegression._design_matrix(x, degree=2)
        array([[1, 0, 0],
               [1, 1, 1],
               [1, 2, 4]])
        >>> PolynomialRegression._design_matrix(x, degree=3)
        array([[1, 0, 0, 0],
               [1, 1, 1, 1],
               [1, 2, 4, 8]])
        >>> PolynomialRegression._design_matrix(np.array([[0, 0], [0 , 0]]), degree=3)
        Traceback (most recent call last):
        ...
        ValueError: Data must have dimensions N x 1
        """
        _rows, *remaining = data.shape
        if remaining:
            raise ValueError("Data must have dimensions N x 1")

        return np.vander(data, N=degree + 1, increasing=True)

    def fit(self, x_train: np.ndarray, y_train: np.ndarray) -> None:
        """
        Computes the polynomial regression model parameters using ordinary least squares
        (OLS) estimation:

        β = (XᵀX)⁻¹Xᵀy = X⁺y

        where X⁺ denotes the Moore-Penrose pseudoinverse of the design matrix X. This
        function computes X⁺ using singular value decomposition (SVD).

        References:
            - https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse
            - https://en.wikipedia.org/wiki/Singular_value_decomposition
            - https://en.wikipedia.org/wiki/Multicollinearity

        @param x_train: the predictor values x for model fitting
        @param y_train: the response values y for model fitting
        @raises ArithmeticError:    if X isn't full rank, then XᵀX is singular and β
                                    doesn't exist

        >>> x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
        >>> y = x**3 - 2 * x**2 + 3 * x - 5
        >>> poly_reg = PolynomialRegression(degree=3)
        >>> poly_reg.fit(x, y)
        >>> poly_reg.params
        array([-5.,  3., -2.,  1.])
        >>> poly_reg = PolynomialRegression(degree=20)
        >>> poly_reg.fit(x, y)
        Traceback (most recent call last):
        ...
        ArithmeticError: Design matrix is not full rank, can't compute coefficients

        Make sure errors don't grow too large:
        >>> coefs = np.array([-250, 50, -2, 36, 20, -12, 10, 2, -1, -15, 1])
        >>> y = PolynomialRegression._design_matrix(x, len(coefs) - 1) @ coefs
        >>> poly_reg = PolynomialRegression(degree=len(coefs) - 1)
        >>> poly_reg.fit(x, y)
        >>> np.allclose(poly_reg.params, coefs, atol=10e-3)
        True
        """
        X = PolynomialRegression._design_matrix(x_train, self.degree)  # noqa: N806
        _, cols = X.shape
        if np.linalg.matrix_rank(X) < cols:
            raise ArithmeticError(
                "Design matrix is not full rank, can't compute coefficients"
            )

        # np.linalg.pinv() computes the Moore-Penrose pseudoinverse using SVD
        self.params = np.linalg.pinv(X) @ y_train

    def predict(self, data: np.ndarray) -> np.ndarray:
        """
        Computes the predicted response values y for the given input data by
        constructing the design matrix X and evaluating y = Xβ.

        @param data:    the predictor values x for prediction
        @returns:       the predicted response values y = Xβ
        @raises ArithmeticError:    if this function is called before the model
                                    parameters are fit

        >>> x = np.array([0, 1, 2, 3, 4])
        >>> y = x**3 - 2 * x**2 + 3 * x - 5
        >>> poly_reg = PolynomialRegression(degree=3)
        >>> poly_reg.fit(x, y)
        >>> poly_reg.predict(np.array([-1]))
        array([-11.])
        >>> poly_reg.predict(np.array([-2]))
        array([-27.])
        >>> poly_reg.predict(np.array([6]))
        array([157.])
        >>> PolynomialRegression(degree=3).predict(x)
        Traceback (most recent call last):
        ...
        ArithmeticError: Predictor hasn't been fit yet
        """
        if self.params is None:
            raise ArithmeticError("Predictor hasn't been fit yet")

        return PolynomialRegression._design_matrix(data, self.degree) @ self.params


def main() -> None:
    """
    Fit a polynomial regression model to predict fuel efficiency using seaborn's mpg
    dataset

    >>> pass    # Placeholder, function is only for demo purposes
    """
    import seaborn as sns

    mpg_data = sns.load_dataset("mpg")

    poly_reg = PolynomialRegression(degree=2)
    poly_reg.fit(mpg_data.weight, mpg_data.mpg)

    weight_sorted = np.sort(mpg_data.weight)
    predictions = poly_reg.predict(weight_sorted)

    plt.scatter(mpg_data.weight, mpg_data.mpg, color="gray", alpha=0.5)
    plt.plot(weight_sorted, predictions, color="red", linewidth=3)
    plt.title("Predicting Fuel Efficiency Using Polynomial Regression")
    plt.xlabel("Weight (lbs)")
    plt.ylabel("Fuel Efficiency (mpg)")
    plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    main()


================================================
FILE: machine_learning/principle_component_analysis.py
================================================
"""
Principal Component Analysis (PCA) is a dimensionality reduction technique
used in machine learning. It transforms high-dimensional data into a lower-dimensional
representation while retaining as much variance as possible.

This implementation follows best practices, including:
- Standardizing the dataset.
- Computing principal components using Singular Value Decomposition (SVD).
- Returning transformed data and explained variance ratio.
"""

import doctest

import numpy as np
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler


def collect_dataset() -> tuple[np.ndarray, np.ndarray]:
    """
    Collects the dataset (Iris dataset) and returns feature matrix and target values.

    :return: Tuple containing feature matrix (X) and target labels (y)

    Example:
    >>> X, y = collect_dataset()
    >>> X.shape
    (150, 4)
    >>> y.shape
    (150,)
    """
    data = load_iris()
    return np.array(data.data), np.array(data.target)


def apply_pca(data_x: np.ndarray, n_components: int) -> tuple[np.ndarray, np.ndarray]:
    """
    Applies Principal Component Analysis (PCA) to reduce dimensionality.

    :param data_x: Original dataset (features)
    :param n_components: Number of principal components to retain
    :return: Tuple containing transformed dataset and explained variance ratio

    Example:
    >>> X, _ = collect_dataset()
    >>> transformed_X, variance = apply_pca(X, 2)
    >>> transformed_X.shape
    (150, 2)
    >>> len(variance) == 2
    True
    """
    # Standardizing the dataset
    scaler = StandardScaler()
    data_x_scaled = scaler.fit_transform(data_x)

    # Applying PCA
    pca = PCA(n_components=n_components)
    principal_components = pca.fit_transform(data_x_scaled)

    return principal_components, pca.explained_variance_ratio_


def main() -> None:
    """
    Driver function to execute PCA and display results.
    """
    data_x, _data_y = collect_dataset()

    # Number of principal components to retain
    n_components = 2

    # Apply PCA
    transformed_data, variance_ratio = apply_pca(data_x, n_components)

    print("Transformed Dataset (First 5 rows):")
    print(transformed_data[:5])

    print("\nExplained Variance Ratio:")
    print(variance_ratio)


if __name__ == "__main__":
    doctest.testmod()
    main()


================================================
FILE: machine_learning/random_forest_classifier.py.broken.txt
================================================
# Random Forest Classifier Example
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import plot_confusion_matrix
from sklearn.model_selection import train_test_split


def main():

    """
    Random Forest Classifier Example using sklearn function.
    Iris type dataset is used to demonstrate algorithm.
    """

    # Load Iris dataset
    iris = load_iris()

    # Split dataset into train and test data
    x = iris["data"]  # features
    y = iris["target"]
    x_train, x_test, y_train, y_test = train_test_split(
        x, y, test_size=0.3, random_state=1
    )

    # Random Forest Classifier
    rand_for = RandomForestClassifier(random_state=42, n_estimators=100)
    rand_for.fit(x_train, y_train)

    # Display Confusion Matrix of Classifier
    plot_confusion_matrix(
        rand_for,
        x_test,
        y_test,
        display_labels=iris["target_names"],
        cmap="Blues",
        normalize="true",
    )
    plt.title("Normalized Confusion Matrix - IRIS Dataset")
    plt.show()


if __name__ == "__main__":
    main()


================================================
FILE: machine_learning/random_forest_regressor.py.broken.txt
================================================
# Random Forest Regressor Example
from sklearn.datasets import load_boston
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split


def main():

    """
    Random Forest Regressor Example using sklearn function.
    Boston house price dataset is used to demonstrate the algorithm.
    """

    # Load Boston house price dataset
    boston = load_boston()
    print(boston.keys())

    # Split dataset into train and test data
    x = boston["data"]  # features
    y = boston["target"]
    x_train, x_test, y_train, y_test = train_test_split(
        x, y, test_size=0.3, random_state=1
    )

    # Random Forest Regressor
    rand_for = RandomForestRegressor(random_state=42, n_estimators=300)
    rand_for.fit(x_train, y_train)

    # Predict target for test data
    predictions = rand_for.predict(x_test)
    predictions = predictions.reshape(len(predictions), 1)

    # Error printing
    print(f"Mean Absolute Error:\t {mean_absolute_error(y_test, predictions)}")
    print(f"Mean Square Error  :\t {mean_squared_error(y_test, predictions)}")


if __name__ == "__main__":
    main()


================================================
FILE: machine_learning/scoring_functions.py
================================================
import numpy as np

""" Here I implemented the scoring functions.
    MAE, MSE, RMSE, RMSLE are included.

    Those are used for calculating differences between
    predicted values and actual values.

    Metrics are slightly differentiated. Sometimes squared, rooted,
    even log is used.

    Using log and roots can be perceived as tools for penalizing big
    errors. However, using appropriate metrics depends on the situations,
    and types of data
"""


# Mean Absolute Error
def mae(predict, actual):
    """
    Examples(rounded for precision):
    >>> actual = [1,2,3];predict = [1,4,3]
    >>> float(np.around(mae(predict,actual),decimals = 2))
    0.67

    >>> actual = [1,1,1];predict = [1,1,1]
    >>> float(mae(predict,actual))
    0.0
    """
    predict = np.array(predict)
    actual = np.array(actual)

    difference = abs(predict - actual)
    score = difference.mean()

    return score


# Mean Squared Error
def mse(predict, actual):
    """
    Examples(rounded for precision):
    >>> actual = [1,2,3];predict = [1,4,3]
    >>> float(np.around(mse(predict,actual),decimals = 2))
    1.33

    >>> actual = [1,1,1];predict = [1,1,1]
    >>> float(mse(predict,actual))
    0.0
    """
    predict = np.array(predict)
    actual = np.array(actual)

    difference = predict - actual
    square_diff = np.square(difference)

    score = square_diff.mean()
    return score


# Root Mean Squared Error
def rmse(predict, actual):
    """
    Examples(rounded for precision):
    >>> actual = [1,2,3];predict = [1,4,3]
    >>> float(np.around(rmse(predict,actual),decimals = 2))
    1.15

    >>> actual = [1,1,1];predict = [1,1,1]
    >>> float(rmse(predict,actual))
    0.0
    """
    predict = np.array(predict)
    actual = np.array(actual)

    difference = predict - actual
    square_diff = np.square(difference)
    mean_square_diff = square_diff.mean()
    score = np.sqrt(mean_square_diff)
    return score


# Root Mean Square Logarithmic Error
def rmsle(predict, actual):
    """
    Examples(rounded for precision):
    >>> float(np.around(rmsle(predict=[10, 2, 30], actual=[10, 10, 30]), decimals=2))
    0.75

    >>> float(rmsle(predict=[1, 1, 1], actual=[1, 1, 1]))
    0.0
    """
    predict = np.array(predict)
    actual = np.array(actual)

    log_predict = np.log(predict + 1)
    log_actual = np.log(actual + 1)

    difference = log_predict - log_actual
    square_diff = np.square(difference)
    mean_square_diff = square_diff.mean()

    score = np.sqrt(mean_square_diff)

    return score


# Mean Bias Deviation
def mbd(predict, actual):
    """
    This value is Negative, if the model underpredicts,
    positive, if it overpredicts.

    Example(rounded for precision):

    Here the model overpredicts
    >>> actual = [1,2,3];predict = [2,3,4]
    >>> float(np.around(mbd(predict,actual),decimals = 2))
    50.0

    Here the model underpredicts
    >>> actual = [1,2,3];predict = [0,1,1]
    >>> float(np.around(mbd(predict,actual),decimals = 2))
    -66.67
    """
    predict = np.array(predict)
    actual = np.array(actual)

    difference = predict - actual
    numerator = np.sum(difference) / len(predict)
    denumerator = np.sum(actual) / len(predict)
    # print(numerator, denumerator)
    score = float(numerator) / denumerator * 100

    return score


def manual_accuracy(predict, actual):
    return np.mean(np.array(actual) == np.array(predict))


================================================
FILE: machine_learning/self_organizing_map.py
================================================
"""
https://en.wikipedia.org/wiki/Self-organizing_map
"""

import math


class SelfOrganizingMap:
    def get_winner(self, weights: list[list[float]], sample: list[int]) -> int:
        """
        Compute the winning vector by Euclidean distance

        >>> SelfOrganizingMap().get_winner([[1, 2, 3], [4, 5, 6]], [1, 2, 3])
        1
        """
        d0 = 0.0
        d1 = 0.0
        for i in range(len(sample)):
            d0 += math.pow((sample[i] - weights[0][i]), 2)
            d1 += math.pow((sample[i] - weights[1][i]), 2)
            return 0 if d0 > d1 else 1
        return 0

    def update(
        self, weights: list[list[int | float]], sample: list[int], j: int, alpha: float
    ) -> list[list[int | float]]:
        """
        Update the winning vector.

        >>> SelfOrganizingMap().update([[1, 2, 3], [4, 5, 6]], [1, 2, 3], 1, 0.1)
        [[1, 2, 3], [3.7, 4.7, 6]]
        """
        for i in range(len(weights)):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
        return weights


# Driver code
def main() -> None:
    # Training Examples ( m, n )
    training_samples = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

    # weight initialization ( n, C )
    weights = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

    # training
    self_organizing_map = SelfOrganizingMap()
    epochs = 3
    alpha = 0.5

    for _ in range(epochs):
        for j in range(len(training_samples)):
            # training sample
            sample = training_samples[j]

            # Compute the winning vector
            winner = self_organizing_map.get_winner(weights, sample)

            # Update the winning vector
            weights = self_organizing_map.update(weights, sample, winner, alpha)

    # classify test sample
    sample = [0, 0, 0, 1]
    winner = self_organizing_map.get_winner(weights, sample)

    # results
    print(f"Clusters that the test sample belongs to : {winner}")
    print(f"Weights that have been trained : {weights}")


# running the main() function
if __name__ == "__main__":
    main()


================================================
FILE: machine_learning/sequential_minimum_optimization.py
================================================
"""
Sequential minimal optimization (SMO) for support vector machines (SVM)

Sequential minimal optimization (SMO) is an algorithm for solving the quadratic
programming (QP) problem that arises during the training of SVMs. It was invented by
John Platt in 1998.

Input:
    0: type: numpy.ndarray.
    1: first column of ndarray must be tags of samples, must be 1 or -1.
    2: rows of ndarray represent samples.

Usage:
    Command:
        python3 sequential_minimum_optimization.py
    Code:
        from sequential_minimum_optimization import SmoSVM, Kernel

        kernel = Kernel(kernel='poly', degree=3., coef0=1., gamma=0.5)
        init_alphas = np.zeros(train.shape[0])
        SVM = SmoSVM(train=train, alpha_list=init_alphas, kernel_func=kernel, cost=0.4,
                     b=0.0, tolerance=0.001)
        SVM.fit()
        predict = SVM.predict(test_samples)

Reference:
    https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/smo-book.pdf
    https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.pdf
"""

import os
import sys
import urllib.request

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.datasets import make_blobs, make_circles
from sklearn.preprocessing import StandardScaler

CANCER_DATASET_URL = (
    "https://archive.ics.uci.edu/ml/machine-learning-databases/"
    "breast-cancer-wisconsin/wdbc.data"
)


class SmoSVM:
    def __init__(
        self,
        train,
        kernel_func,
        alpha_list=None,
        cost=0.4,
        b=0.0,
        tolerance=0.001,
        auto_norm=True,
    ):
        self._init = True
        self._auto_norm = auto_norm
        self._c = np.float64(cost)
        self._b = np.float64(b)
        self._tol = np.float64(tolerance) if tolerance > 0.0001 else np.float64(0.001)

        self.tags = train[:, 0]
        self.samples = self._norm(train[:, 1:]) if self._auto_norm else train[:, 1:]
        self.alphas = alpha_list if alpha_list is not None else np.zeros(train.shape[0])
        self.Kernel = kernel_func

        self._eps = 0.001
        self._all_samples = list(range(self.length))
        self._K_matrix = self._calculate_k_matrix()
        self._error = np.zeros(self.length)
        self._unbound = []

        self.choose_alpha = self._choose_alphas()

    # Calculate alphas using SMO algorithm
    def fit(self):
        k = self._k
        state = None
        while True:
            # 1: Find alpha1, alpha2
            try:
                i1, i2 = self.choose_alpha.send(state)
                state = None
            except StopIteration:
                print("Optimization done!\nEvery sample satisfy the KKT condition!")
                break

            # 2: calculate new alpha2 and new alpha1
            y1, y2 = self.tags[i1], self.tags[i2]
            a1, a2 = self.alphas[i1].copy(), self.alphas[i2].copy()
            e1, e2 = self._e(i1), self._e(i2)
            args = (i1, i2, a1, a2, e1, e2, y1, y2)
            a1_new, a2_new = self._get_new_alpha(*args)
            if not a1_new and not a2_new:
                state = False
                continue
            self.alphas[i1], self.alphas[i2] = a1_new, a2_new

            # 3: update threshold(b)
            b1_new = np.float64(
                -e1
                - y1 * k(i1, i1) * (a1_new - a1)
                - y2 * k(i2, i1) * (a2_new - a2)
                + self._b
            )
            b2_new = np.float64(
                -e2
                - y2 * k(i2, i2) * (a2_new - a2)
                - y1 * k(i1, i2) * (a1_new - a1)
                + self._b
            )
            if 0.0 < a1_new < self._c:
                b = b1_new
            if 0.0 < a2_new < self._c:
                b = b2_new
            if not (np.float64(0) < a2_new < self._c) and not (
                np.float64(0) < a1_new < self._c
            ):
                b = (b1_new + b2_new) / 2.0
            b_old = self._b
            self._b = b

            # 4: update error, here we only calculate the error for non-bound samples
            self._unbound = [i for i in self._all_samples if self._is_unbound(i)]
            for s in self.unbound:
                if s in (i1, i2):
                    continue
                self._error[s] += (
                    y1 * (a1_new - a1) * k(i1, s)
                    + y2 * (a2_new - a2) * k(i2, s)
                    + (self._b - b_old)
                )

            # if i1 or i2 is non-bound, update their error value to zero
            if self._is_unbound(i1):
                self._error[i1] = 0
            if self._is_unbound(i2):
                self._error[i2] = 0

    # Predict test samples
    def predict(self, test_samples, classify=True):
        if test_samples.shape[1] > self.samples.shape[1]:
            raise ValueError(
                "Test samples' feature length does not equal to that of train samples"
            )

        if self._auto_norm:
            test_samples = self._norm(test_samples)

        results = []
        for test_sample in test_samples:
            result = self._predict(test_sample)
            if classify:
                results.append(1 if result > 0 else -1)
            else:
                results.append(result)
        return np.array(results)

    # Check if alpha violates the KKT condition
    def _check_obey_kkt(self, index):
        alphas = self.alphas
        tol = self._tol
        r = self._e(index) * self.tags[index]
        c = self._c

        return (r < -tol and alphas[index] < c) or (r > tol and alphas[index] > 0.0)

    # Get value calculated from kernel function
    def _k(self, i1, i2):
        # for test samples, use kernel function
        if isinstance(i2, np.ndarray):
            return self.Kernel(self.samples[i1], i2)
        # for training samples, kernel values have been saved in matrix
        else:
            return self._K_matrix[i1, i2]

    # Get error for sample
    def _e(self, index):
        """
        Two cases:
            1: Sample[index] is non-bound, fetch error from list: _error
            2: sample[index] is bound, use predicted value minus true value: g(xi) - yi
        """
        # get from error data
        if self._is_unbound(index):
            return self._error[index]
        # get by g(xi) - yi
        else:
            gx = np.dot(self.alphas * self.tags, self._K_matrix[:, index]) + self._b
            yi = self.tags[index]
            return gx - yi

    # Calculate kernel matrix of all possible i1, i2, saving time
    def _calculate_k_matrix(self):
        k_matrix = np.zeros([self.length, self.length])
        for i in self._all_samples:
            for j in self._all_samples:
                k_matrix[i, j] = np.float64(
                    self.Kernel(self.samples[i, :], self.samples[j, :])
                )
        return k_matrix

    # Predict tag for test sample
    def _predict(self, sample):
        k = self._k
        predicted_value = (
            np.sum(
                [
                    self.alphas[i1] * self.tags[i1] * k(i1, sample)
                    for i1 in self._all_samples
                ]
            )
            + self._b
        )
        return predicted_value

    # Choose alpha1 and alpha2
    def _choose_alphas(self):
        loci = yield from self._choose_a1()
        if not loci:
            return None
        return loci

    def _choose_a1(self):
        """
        Choose first alpha
        Steps:
            1: First loop over all samples
            2: Second loop over all non-bound samples until no non-bound samples violate
               the KKT condition.
            3: Repeat these two processes until no samples violate the KKT condition
               after the first loop.
        """
        while True:
            all_not_obey = True
            # all sample
            print("Scanning all samples!")
            for i1 in [i for i in self._all_samples if self._check_obey_kkt(i)]:
                all_not_obey = False
                yield from self._choose_a2(i1)

            # non-bound sample
            print("Scanning non-bound samples!")
            while True:
                not_obey = True
                for i1 in [
                    i
                    for i in self._all_samples
                    if self._check_obey_kkt(i) and self._is_unbound(i)
                ]:
                    not_obey = False
                    yield from self._choose_a2(i1)
                if not_obey:
                    print("All non-bound samples satisfy the KKT condition!")
                    break
            if all_not_obey:
                print("All samples satisfy the KKT condition!")
                break
        return False

    def _choose_a2(self, i1):
        """
        Choose the second alpha using a heuristic algorithm
        Steps:
            1: Choose alpha2 that maximizes the step size (|E1 - E2|).
            2: Start in a random point, loop over all non-bound samples till alpha1 and
               alpha2 are optimized.
            3: Start in a random point, loop over all samples till alpha1 and alpha2 are
               optimized.
        """
        self._unbound = [i for i in self._all_samples if self._is_unbound(i)]

        if len(self.unbound) > 0:
            tmp_error = self._error.copy().tolist()
            tmp_error_dict = {
                index: value
                for index, value in enumerate(tmp_error)
                if self._is_unbound(index)
            }
            if self._e(i1) >= 0:
                i2 = min(tmp_error_dict, key=lambda index: tmp_error_dict[index])
            else:
                i2 = max(tmp_error_dict, key=lambda index: tmp_error_dict[index])
            cmd = yield i1, i2
            if cmd is None:
                return

        rng = np.random.default_rng()
        for i2 in np.roll(self.unbound, rng.choice(self.length)):
            cmd = yield i1, i2
            if cmd is None:
                return

        for i2 in np.roll(self._all_samples, rng.choice(self.length)):
            cmd = yield i1, i2
            if cmd is None:
                return

    # Get the new alpha2 and new alpha1
    def _get_new_alpha(self, i1, i2, a1, a2, e1, e2, y1, y2):
        k = self._k
        if i1 == i2:
            return None, None

        # calculate L and H which bound the new alpha2
        s = y1 * y2
        if s == -1:
            l, h = max(0.0, a2 - a1), min(self._c, self._c + a2 - a1)  # noqa: E741
        else:
            l, h = max(0.0, a2 + a1 - self._c), min(self._c, a2 + a1)  # noqa: E741
        if l == h:
            return None, None

        # calculate eta
        k11 = k(i1, i1)
        k22 = k(i2, i2)
        k12 = k(i1, i2)

        # select the new alpha2 which could achieve the minimal objectives
        if (eta := k11 + k22 - 2.0 * k12) > 0.0:
            a2_new_unc = a2 + (y2 * (e1 - e2)) / eta
            # a2_new has a boundary
            if a2_new_unc >= h:
                a2_new = h
            elif a2_new_unc <= l:
                a2_new = l
            else:
                a2_new = a2_new_unc
        else:
            b = self._b
            l1 = a1 + s * (a2 - l)
            h1 = a1 + s * (a2 - h)

            # Method 1
            f1 = y1 * (e1 + b) - a1 * k(i1, i1) - s * a2 * k(i1, i2)
            f2 = y2 * (e2 + b) - a2 * k(i2, i2) - s * a1 * k(i1, i2)
            ol = (
                l1 * f1
                + l * f2
                + 1 / 2 * l1**2 * k(i1, i1)
                + 1 / 2 * l**2 * k(i2, i2)
                + s * l * l1 * k(i1, i2)
            )
            oh = (
                h1 * f1
                + h * f2
                + 1 / 2 * h1**2 * k(i1, i1)
                + 1 / 2 * h**2 * k(i2, i2)
                + s * h * h1 * k(i1, i2)
            )
            """
            Method 2: Use objective function to check which alpha2_new could achieve the
            minimal objectives
            """
            if ol < (oh - self._eps):
                a2_new = l
            elif ol > oh + self._eps:
                a2_new = h
            else:
                a2_new = a2

        # a1_new has a boundary too
        a1_new = a1 + s * (a2 - a2_new)
        if a1_new < 0:
            a2_new += s * a1_new
            a1_new = 0
        if a1_new > self._c:
            a2_new += s * (a1_new - self._c)
            a1_new = self._c

        return a1_new, a2_new

    # Normalize data using min-max method
    def _norm(self, data):
        if self._init:
            self._min = np.min(data, axis=0)
            self._max = np.max(data, axis=0)
            self._init = False
            return (data - self._min) / (self._max - self._min)
        else:
            return (data - self._min) / (self._max - self._min)

    def _is_unbound(self, index):
        return bool(0.0 < self.alphas[index] < self._c)

    def _is_support(self, index):
        return bool(self.alphas[index] > 0)

    @property
    def unbound(self):
        return self._unbound

    @property
    def support(self):
        return [i for i in range(self.length) if self._is_support(i)]

    @property
    def length(self):
        return self.samples.shape[0]


class Kernel:
    def __init__(self, kernel, degree=1.0, coef0=0.0, gamma=1.0):
        self.degree = np.float64(degree)
        self.coef0 = np.float64(coef0)
        self.gamma = np.float64(gamma)
        self._kernel_name = kernel
        self._kernel = self._get_kernel(kernel_name=kernel)
        self._check()

    def _polynomial(self, v1, v2):
        return (self.gamma * np.inner(v1, v2) + self.coef0) ** self.degree

    def _linear(self, v1, v2):
        return np.inner(v1, v2) + self.coef0

    def _rbf(self, v1, v2):
        return np.exp(-1 * (self.gamma * np.linalg.norm(v1 - v2) ** 2))

    def _check(self):
        if self._kernel == self._rbf and self.gamma < 0:
            raise ValueError("gamma value must be non-negative")

    def _get_kernel(self, kernel_name):
        maps = {"linear": self._linear, "poly": self._polynomial, "rbf": self._rbf}
        return maps[kernel_name]

    def __call__(self, v1, v2):
        return self._kernel(v1, v2)

    def __repr__(self):
        return self._kernel_name


def count_time(func):
    def call_func(*args, **kwargs):
        import time

        start_time = time.time()
        func(*args, **kwargs)
        end_time = time.time()
        print(f"SMO algorithm cost {end_time - start_time} seconds")

    return call_func


@count_time
def test_cancer_data():
    print("Hello!\nStart test SVM using the SMO algorithm!")
    # 0: download dataset and load into pandas' dataframe
    if not os.path.exists(r"cancer_data.csv"):
        request = urllib.request.Request(  # noqa: S310
            CANCER_DATASET_URL,
            headers={"User-Agent": "Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)"},
        )
        response = urllib.request.urlopen(request)  # noqa: S310
        content = response.read().decode("utf-8")
        with open(r"cancer_data.csv", "w") as f:
            f.write(content)

    data = pd.read_csv(
        "cancer_data.csv",
        header=None,
        dtype={0: str},  # Assuming the first column contains string data
    )

    # 1: pre-processing data
    del data[data.columns.tolist()[0]]
    data = data.dropna(axis=0)
    data = data.replace({"M": np.float64(1), "B": np.float64(-1)})
    samples = np.array(data)[:, :]

    # 2: dividing data into train_data data and test_data data
    train_data, test_data = samples[:328, :], samples[328:, :]
    test_tags, test_samples = test_data[:, 0], test_data[:, 1:]

    # 3: choose kernel function, and set initial alphas to zero (optional)
    my_kernel = Kernel(kernel="rbf", degree=5, coef0=1, gamma=0.5)
    al = np.zeros(train_data.shape[0])

    # 4: calculating best alphas using SMO algorithm and predict test_data samples
    mysvm = SmoSVM(
        train=train_data,
        kernel_func=my_kernel,
        alpha_list=al,
        cost=0.4,
        b=0.0,
        tolerance=0.001,
    )
    mysvm.fit()
    predict = mysvm.predict(test_samples)

    # 5: check accuracy
    score = 0
    test_num = test_tags.shape[0]
    for i in range(test_tags.shape[0]):
        if test_tags[i] == predict[i]:
            score += 1
    print(f"\nAll: {test_num}\nCorrect: {score}\nIncorrect: {test_num - score}")
    print(f"Rough Accuracy: {score / test_tags.shape[0]}")


def test_demonstration():
    # change stdout
    print("\nStarting plot, please wait!")
    sys.stdout = open(os.devnull, "w")

    ax1 = plt.subplot2grid((2, 2), (0, 0))
    ax2 = plt.subplot2grid((2, 2), (0, 1))
    ax3 = plt.subplot2grid((2, 2), (1, 0))
    ax4 = plt.subplot2grid((2, 2), (1, 1))
    ax1.set_title("Linear SVM, cost = 0.1")
    test_linear_kernel(ax1, cost=0.1)
    ax2.set_title("Linear SVM, cost = 500")
    test_linear_kernel(ax2, cost=500)
    ax3.set_title("RBF kernel SVM, cost = 0.1")
    test_rbf_kernel(ax3, cost=0.1)
    ax4.set_title("RBF kernel SVM, cost = 500")
    test_rbf_kernel(ax4, cost=500)

    sys.stdout = sys.__stdout__
    print("Plot done!")


def test_linear_kernel(ax, cost):
    train_x, train_y = make_blobs(
        n_samples=500, centers=2, n_features=2, random_state=1
    )
    train_y[train_y == 0] = -1
    scaler = StandardScaler()
    train_x_scaled = scaler.fit_transform(train_x, train_y)
    train_data = np.hstack((train_y.reshape(500, 1), train_x_scaled))
    my_kernel = Kernel(kernel="linear", degree=5, coef0=1, gamma=0.5)
    mysvm = SmoSVM(
        train=train_data,
        kernel_func=my_kernel,
        cost=cost,
        tolerance=0.001,
        auto_norm=False,
    )
    mysvm.fit()
    plot_partition_boundary(mysvm, train_data, ax=ax)


def test_rbf_kernel(ax, cost):
    train_x, train_y = make_circles(
        n_samples=500, noise=0.1, factor=0.1, random_state=1
    )
    train_y[train_y == 0] = -1
    scaler = StandardScaler()
    train_x_scaled = scaler.fit_transform(train_x, train_y)
    train_data = np.hstack((train_y.reshape(500, 1), train_x_scaled))
    my_kernel = Kernel(kernel="rbf", degree=5, coef0=1, gamma=0.5)
    mysvm = SmoSVM(
        train=train_data,
        kernel_func=my_kernel,
        cost=cost,
        tolerance=0.001,
        auto_norm=False,
    )
    mysvm.fit()
    plot_partition_boundary(mysvm, train_data, ax=ax)


def plot_partition_boundary(
    model, train_data, ax, resolution=100, colors=("b", "k", "r")
):
    """
    We cannot get the optimal w of our kernel SVM model, which is different from a
    linear SVM.  For this reason, we generate randomly distributed points with high
    density, and predicted values of these points are calculated using our trained
    model. Then we could use this predicted values to draw contour map, and this contour
    map represents the SVM's partition boundary.
    """
    train_data_x = train_data[:, 1]
    train_data_y = train_data[:, 2]
    train_data_tags = train_data[:, 0]
    xrange = np.linspace(train_data_x.min(), train_data_x.max(), resolution)
    yrange = np.linspace(train_data_y.min(), train_data_y.max(), resolution)
    test_samples = np.array([(x, y) for x in xrange for y in yrange]).reshape(
        resolution * resolution, 2
    )

    test_tags = model.predict(test_samples, classify=False)
    grid = test_tags.reshape((len(xrange), len(yrange)))

    # Plot contour map which represents the partition boundary
    ax.contour(
        xrange,
        yrange,
        np.asmatrix(grid).T,
        levels=(-1, 0, 1),
        linestyles=("--", "-", "--"),
        linewidths=(1, 1, 1),
        colors=colors,
    )
    # Plot all train samples
    ax.scatter(
        train_data_x,
        train_data_y,
        c=train_data_tags,
        cmap=plt.cm.Dark2,
        lw=0,
        alpha=0.5,
    )

    # Plot support vectors
    support = model.support
    ax.scatter(
        train_data_x[support],
        train_data_y[support],
        c=train_data_tags[support],
        cmap=plt.cm.Dark2,
    )


if __name__ == "__main__":
    test_cancer_data()
    test_demonstration()
    plt.show()


================================================
FILE: machine_learning/similarity_search.py
================================================
"""
Similarity Search : https://en.wikipedia.org/wiki/Similarity_search
Similarity search is a search algorithm for finding the nearest vector from
vectors, used in natural language processing.
In this algorithm, it calculates distance with euclidean distance and
returns a list containing two data for each vector:
    1. the nearest vector
    2. distance between the vector and the nearest vector (float)
"""

from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def euclidean(input_a: np.ndarray, input_b: np.ndarray) -> float:
    """
    Calculates euclidean distance between two data.
    :param input_a: ndarray of first vector.
    :param input_b: ndarray of second vector.
    :return: Euclidean distance of input_a and input_b. By using math.sqrt(),
             result will be float.

    >>> euclidean(np.array([0]), np.array([1]))
    1.0
    >>> euclidean(np.array([0, 1]), np.array([1, 1]))
    1.0
    >>> euclidean(np.array([0, 0, 0]), np.array([0, 0, 1]))
    1.0
    """
    return math.sqrt(sum(pow(a - b, 2) for a, b in zip(input_a, input_b)))


def similarity_search(
    dataset: np.ndarray, value_array: np.ndarray
) -> list[list[list[float] | float]]:
    """
    :param dataset: Set containing the vectors. Should be ndarray.
    :param value_array: vector/vectors we want to know the nearest vector from dataset.
    :return: Result will be a list containing
            1. the nearest vector
            2. distance from the vector

    >>> dataset = np.array([[0], [1], [2]])
    >>> value_array = np.array([[0]])
    >>> similarity_search(dataset, value_array)
    [[[0], 0.0]]

    >>> dataset = np.array([[0, 0], [1, 1], [2, 2]])
    >>> value_array = np.array([[0, 1]])
    >>> similarity_search(dataset, value_array)
    [[[0, 0], 1.0]]

    >>> dataset = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]])
    >>> value_array = np.array([[0, 0, 1]])
    >>> similarity_search(dataset, value_array)
    [[[0, 0, 0], 1.0]]

    >>> dataset = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]])
    >>> value_array = np.array([[0, 0, 0], [0, 0, 1]])
    >>> similarity_search(dataset, value_array)
    [[[0, 0, 0], 0.0], [[0, 0, 0], 1.0]]

    These are the errors that might occur:

    1. If dimensions are different.
    For example, dataset has 2d array and value_array has 1d array:
    >>> dataset = np.array([[1]])
    >>> value_array = np.array([1])
    >>> similarity_search(dataset, value_array)
    Traceback (most recent call last):
        ...
    ValueError: Wrong input data's dimensions... dataset : 2, value_array : 1

    2. If data's shapes are different.
    For example, dataset has shape of (3, 2) and value_array has (2, 3).
    We are expecting same shapes of two arrays, so it is wrong.
    >>> dataset = np.array([[0, 0], [1, 1], [2, 2]])
    >>> value_array = np.array([[0, 0, 0], [0, 0, 1]])
    >>> similarity_search(dataset, value_array)
    Traceback (most recent call last):
        ...
    ValueError: Wrong input data's shape... dataset : 2, value_array : 3

    3. If data types are different.
    When trying to compare, we are expecting same types so they should be same.
    If not, it'll come up with errors.
    >>> dataset = np.array([[0, 0], [1, 1], [2, 2]], dtype=np.float32)
    >>> value_array = np.array([[0, 0], [0, 1]], dtype=np.int32)
    >>> similarity_search(dataset, value_array)  # doctest: +NORMALIZE_WHITESPACE
    Traceback (most recent call last):
        ...
    TypeError: Input data have different datatype...
    dataset : float32, value_array : int32
    """

    if dataset.ndim != value_array.ndim:
        msg = (
            "Wrong input data's dimensions... "
            f"dataset : {dataset.ndim}, value_array : {value_array.ndim}"
        )
        raise ValueError(msg)

    try:
        if dataset.shape[1] != value_array.shape[1]:
            msg = (
                "Wrong input data's shape... "
                f"dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"
            )
            raise ValueError(msg)
    except IndexError:
        if dataset.ndim != value_array.ndim:
            raise TypeError("Wrong shape")

    if dataset.dtype != value_array.dtype:
        msg = (
            "Input data have different datatype... "
            f"dataset : {dataset.dtype}, value_array : {value_array.dtype}"
        )
        raise TypeError(msg)

    answer = []

    for value in value_array:
        dist = euclidean(value, dataset[0])
        vector = dataset[0].tolist()

        for dataset_value in dataset[1:]:
            temp_dist = euclidean(value, dataset_value)

            if dist > temp_dist:
                dist = temp_dist
                vector = dataset_value.tolist()

        answer.append([vector, dist])

    return answer


def cosine_similarity(input_a: np.ndarray, input_b: np.ndarray) -> float:
    """
    Calculates cosine similarity between two data.
    :param input_a: ndarray of first vector.
    :param input_b: ndarray of second vector.
    :return: Cosine similarity of input_a and input_b. By using math.sqrt(),
             result will be float.

    >>> cosine_similarity(np.array([1]), np.array([1]))
    1.0
    >>> cosine_similarity(np.array([1, 2]), np.array([6, 32]))
    0.9615239476408232
    """
    return float(np.dot(input_a, input_b) / (norm(input_a) * norm(input_b)))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: machine_learning/support_vector_machines.py
================================================
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize


def norm_squared(vector: ndarray) -> float:
    """
    Return the squared second norm of vector
    norm_squared(v) = sum(x * x for x in v)

    Args:
        vector (ndarray): input vector

    Returns:
        float: squared second norm of vector

    >>> int(norm_squared([1, 2]))
    5
    >>> int(norm_squared(np.asarray([1, 2])))
    5
    >>> int(norm_squared([0, 0]))
    0
    """
    return np.dot(vector, vector)


class SVC:
    """
    Support Vector Classifier

    Args:
        kernel (str): kernel to use. Default: linear
            Possible choices:
                - linear
        regularization: constraint for soft margin (data not linearly separable)
            Default: unbound

    >>> SVC(kernel="asdf")
    Traceback (most recent call last):
        ...
    ValueError: Unknown kernel: asdf

    >>> SVC(kernel="rbf")
    Traceback (most recent call last):
        ...
    ValueError: rbf kernel requires gamma

    >>> SVC(kernel="rbf", gamma=-1)
    Traceback (most recent call last):
        ...
    ValueError: gamma must be > 0
    """

    def __init__(
        self,
        *,
        regularization: float = np.inf,
        kernel: str = "linear",
        gamma: float = 0.0,
    ) -> None:
        self.regularization = regularization
        self.gamma = gamma
        if kernel == "linear":
            self.kernel = self.__linear
        elif kernel == "rbf":
            if self.gamma == 0:
                raise ValueError("rbf kernel requires gamma")
            if not isinstance(self.gamma, (float, int)):
                raise ValueError("gamma must be float or int")
            if not self.gamma > 0:
                raise ValueError("gamma must be > 0")
            self.kernel = self.__rbf
            # in the future, there could be a default value like in sklearn
            # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
            # previously it was 1/(n_features)
        else:
            msg = f"Unknown kernel: {kernel}"
            raise ValueError(msg)

    # kernels
    def __linear(self, vector1: ndarray, vector2: ndarray) -> float:
        """Linear kernel (as if no kernel used at all)"""
        return np.dot(vector1, vector2)

    def __rbf(self, vector1: ndarray, vector2: ndarray) -> float:
        """
        RBF: Radial Basis Function Kernel

        Note: for more information see:
            https://en.wikipedia.org/wiki/Radial_basis_function_kernel

        Args:
            vector1 (ndarray): first vector
            vector2 (ndarray): second vector)

        Returns:
            float: exp(-(gamma * norm_squared(vector1 - vector2)))
        """
        return np.exp(-(self.gamma * norm_squared(vector1 - vector2)))

    def fit(self, observations: list[ndarray], classes: ndarray) -> None:
        """
        Fits the SVC with a set of observations.

        Args:
            observations (list[ndarray]): list of observations
            classes (ndarray): classification of each observation (in {1, -1})
        """

        self.observations = observations
        self.classes = classes

        # using Wolfe's Dual to calculate w.
        # Primal problem: minimize 1/2*norm_squared(w)
        #   constraint: yn(w . xn + b) >= 1
        #
        # With l a vector
        # Dual problem: maximize sum_n(ln) -
        #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
        #   constraint: self.C >= ln >= 0
        #           and sum_n(ln*yn) = 0
        # Then we get w using w = sum_n(ln*yn*xn)
        # At the end we can get b ~= mean(yn - w . xn)
        #
        # Since we use kernels, we only need l_star to calculate b
        # and to classify observations

        (n,) = np.shape(classes)

        def to_minimize(candidate: ndarray) -> float:
            """
            Opposite of the function to maximize

            Args:
                candidate (ndarray): candidate array to test

            Return:
                float: Wolfe's Dual result to minimize
            """
            s = 0
            (n,) = np.shape(candidate)
            for i in range(n):
                for j in range(n):
                    s += (
                        candidate[i]
                        * candidate[j]
                        * classes[i]
                        * classes[j]
                        * self.kernel(observations[i], observations[j])
                    )
            return 1 / 2 * s - sum(candidate)

        ly_contraint = LinearConstraint(classes, 0, 0)
        l_bounds = Bounds(0, self.regularization)

        l_star = minimize(
            to_minimize, np.ones(n), bounds=l_bounds, constraints=[ly_contraint]
        ).x
        self.optimum = l_star

        # calculating mean offset of separation plane to points
        s = 0
        for i in range(n):
            for j in range(n):
                s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                    observations[i], observations[j]
                )
        self.offset = s / n

    def predict(self, observation: ndarray) -> int:
        """
        Get the expected class of an observation

        Args:
            observation (Vector): observation

        Returns:
            int {1, -1}: expected class

        >>> xs = [
        ...     np.asarray([0, 1]), np.asarray([0, 2]),
        ...     np.asarray([1, 1]), np.asarray([1, 2])
        ... ]
        >>> y = np.asarray([1, 1, -1, -1])
        >>> s = SVC()
        >>> s.fit(xs, y)
        >>> s.predict(np.asarray([0, 1]))
        1
        >>> s.predict(np.asarray([1, 1]))
        -1
        >>> s.predict(np.asarray([2, 2]))
        -1
        """
        s = sum(
            self.optimum[n]
            * self.classes[n]
            * self.kernel(self.observations[n], observation)
            for n in range(len(self.classes))
        )
        return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: machine_learning/t_stochastic_neighbour_embedding.py
================================================
"""
t-distributed stochastic neighbor embedding (t-SNE)

For more details, see:
https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding
"""

import doctest

import numpy as np
from numpy import ndarray
from sklearn.datasets import load_iris


def collect_dataset() -> tuple[ndarray, ndarray]:
    """
    Load the Iris dataset and return features and labels.

    Returns:
        tuple[ndarray, ndarray]: Feature matrix and target labels.

    >>> features, targets = collect_dataset()
    >>> features.shape
    (150, 4)
    >>> targets.shape
    (150,)
    """
    iris_dataset = load_iris()
    return np.array(iris_dataset.data), np.array(iris_dataset.target)


def compute_pairwise_affinities(data_matrix: ndarray, sigma: float = 1.0) -> ndarray:
    """
    Compute high-dimensional affinities (P matrix) using a Gaussian kernel.

    Args:
        data_matrix: Input data of shape (n_samples, n_features).
        sigma: Gaussian kernel bandwidth.

    Returns:
        ndarray: Symmetrized probability matrix.

    >>> x = np.array([[0.0, 0.0], [1.0, 0.0]])
    >>> probabilities = compute_pairwise_affinities(x)
    >>> float(round(probabilities[0, 1], 3))
    0.25
    """
    n_samples = data_matrix.shape[0]
    squared_sum = np.sum(np.square(data_matrix), axis=1)
    squared_distance = np.add(
        np.add(-2 * np.dot(data_matrix, data_matrix.T), squared_sum).T, squared_sum
    )

    affinity_matrix = np.exp(-squared_distance / (2 * sigma**2))
    np.fill_diagonal(affinity_matrix, 0)

    affinity_matrix /= np.sum(affinity_matrix)
    return (affinity_matrix + affinity_matrix.T) / (2 * n_samples)


def compute_low_dim_affinities(embedding_matrix: ndarray) -> tuple[ndarray, ndarray]:
    """
    Compute low-dimensional affinities (Q matrix) using a Student-t distribution.

    Args:
        embedding_matrix: Low-dimensional embedding of shape (n_samples, n_components).

    Returns:
        tuple[ndarray, ndarray]: (Q probability matrix, numerator matrix).

    >>> y = np.array([[0.0, 0.0], [1.0, 0.0]])
    >>> q_matrix, numerators = compute_low_dim_affinities(y)
    >>> q_matrix.shape
    (2, 2)
    """
    squared_sum = np.sum(np.square(embedding_matrix), axis=1)
    numerator_matrix = 1 / (
        1
        + np.add(
            np.add(-2 * np.dot(embedding_matrix, embedding_matrix.T), squared_sum).T,
            squared_sum,
        )
    )
    np.fill_diagonal(numerator_matrix, 0)

    q_matrix = numerator_matrix / np.sum(numerator_matrix)
    return q_matrix, numerator_matrix


def apply_tsne(
    data_matrix: ndarray,
    n_components: int = 2,
    learning_rate: float = 200.0,
    n_iter: int = 500,
) -> ndarray:
    """
    Apply t-SNE for dimensionality reduction.

    Args:
        data_matrix: Original dataset (features).
        n_components: Target dimension (2D or 3D).
        learning_rate: Step size for gradient descent.
        n_iter: Number of iterations.

    Returns:
        ndarray: Low-dimensional embedding of the data.

    >>> features, _ = collect_dataset()
    >>> embedding = apply_tsne(features, n_components=2, n_iter=50)
    >>> embedding.shape
    (150, 2)
    """
    if n_components < 1 or n_iter < 1:
        raise ValueError("n_components and n_iter must be >= 1")

    n_samples = data_matrix.shape[0]
    rng = np.random.default_rng()
    embedding = rng.standard_normal((n_samples, n_components)) * 1e-4

    high_dim_affinities = compute_pairwise_affinities(data_matrix)
    high_dim_affinities = np.maximum(high_dim_affinities, 1e-12)

    embedding_increment = np.zeros_like(embedding)
    momentum = 0.5

    for iteration in range(n_iter):
        low_dim_affinities, numerator_matrix = compute_low_dim_affinities(embedding)
        low_dim_affinities = np.maximum(low_dim_affinities, 1e-12)

        affinity_diff = high_dim_affinities - low_dim_affinities

        gradient = 4 * (
            np.dot((affinity_diff * numerator_matrix), embedding)
            - np.multiply(
                np.sum(affinity_diff * numerator_matrix, axis=1)[:, np.newaxis],
                embedding,
            )
        )

        embedding_increment = momentum * embedding_increment - learning_rate * gradient
        embedding += embedding_increment

        if iteration == int(n_iter / 4):
            momentum = 0.8

    return embedding


def main() -> None:
    """
    Run t-SNE on the Iris dataset and display the first 5 embeddings.

    >>> main()  # doctest: +ELLIPSIS
    t-SNE embedding (first 5 points):
    [[...
    """
    features, _labels = collect_dataset()
    embedding = apply_tsne(features, n_components=2, n_iter=300)

    if not isinstance(embedding, np.ndarray):
        raise TypeError("t-SNE embedding must be an ndarray")

    print("t-SNE embedding (first 5 points):")
    print(embedding[:5])

    # Optional visualization (Ruff/mypy compliant)

    # import matplotlib.pyplot as plt
    # plt.scatter(embedding[:, 0], embedding[:, 1], c=labels, cmap="viridis")
    # plt.title("t-SNE Visualization of the Iris Dataset")
    # plt.xlabel("Dimension 1")
    # plt.ylabel("Dimension 2")
    # plt.show()


if __name__ == "__main__":
    doctest.testmod()
    main()


================================================
FILE: machine_learning/word_frequency_functions.py
================================================
import string
from math import log10

"""
    tf-idf Wikipedia: https://en.wikipedia.org/wiki/Tf%E2%80%93idf
    tf-idf and other word frequency algorithms are often used
    as a weighting factor in information retrieval and text
    mining. 83% of text-based recommender systems use
    tf-idf for term weighting. In Layman's terms, tf-idf
    is a statistic intended to reflect how important a word
    is to a document in a corpus (a collection of documents)


    Here I've implemented several word frequency algorithms
    that are commonly used in information retrieval: Term Frequency,
    Document Frequency, and TF-IDF (Term-Frequency*Inverse-Document-Frequency)
    are included.

    Term Frequency is a statistical function that
    returns a number representing how frequently
    an expression occurs in a document. This
    indicates how significant a particular term is in
    a given document.

    Document Frequency is a statistical function that returns
    an integer representing the number of documents in a
    corpus that a term occurs in (where the max number returned
    would be the number of documents in the corpus).

    Inverse Document Frequency is mathematically written as
    log10(N/df), where N is the number of documents in your
    corpus and df is the Document Frequency. If df is 0, a
    ZeroDivisionError will be thrown.

    Term-Frequency*Inverse-Document-Frequency is a measure
    of the originality of a term. It is mathematically written
    as tf*log10(N/df). It compares the number of times
    a term appears in a document with the number of documents
    the term appears in. If df is 0, a ZeroDivisionError will be thrown.
"""


def term_frequency(term: str, document: str) -> int:
    """
    Return the number of times a term occurs within
    a given document.
    @params: term, the term to search a document for, and document,
            the document to search within
    @returns: an integer representing the number of times a term is
            found within the document

    @examples:
    >>> term_frequency("to", "To be, or not to be")
    2
    """
    # strip all punctuation and newlines and replace it with ''
    document_without_punctuation = document.translate(
        str.maketrans("", "", string.punctuation)
    ).replace("\n", "")
    tokenize_document = document_without_punctuation.split(" ")  # word tokenization
    return len([word for word in tokenize_document if word.lower() == term.lower()])


def document_frequency(term: str, corpus: str) -> tuple[int, int]:
    """
    Calculate the number of documents in a corpus that contain a
    given term
    @params : term, the term to search each document for, and corpus, a collection of
             documents. Each document should be separated by a newline.
    @returns : the number of documents in the corpus that contain the term you are
               searching for and the number of documents in the corpus
    @examples :
    >>> document_frequency("first", "This is the first document in the corpus.\\nThIs\
is the second document in the corpus.\\nTHIS is \
the third document in the corpus.")
    (1, 3)
    """
    corpus_without_punctuation = corpus.lower().translate(
        str.maketrans("", "", string.punctuation)
    )  # strip all punctuation and replace it with ''
    docs = corpus_without_punctuation.split("\n")
    term = term.lower()
    return (len([doc for doc in docs if term in doc]), len(docs))


def inverse_document_frequency(df: int, n: int, smoothing=False) -> float:
    """
    Return an integer denoting the importance
    of a word. This measure of importance is
    calculated by log10(N/df), where N is the
    number of documents and df is
    the Document Frequency.
    @params : df, the Document Frequency, N,
    the number of documents in the corpus and
    smoothing, if True return the idf-smooth
    @returns : log10(N/df) or 1+log10(N/1+df)
    @examples :
    >>> inverse_document_frequency(3, 0)
    Traceback (most recent call last):
     ...
    ValueError: log10(0) is undefined.
    >>> inverse_document_frequency(1, 3)
    0.477
    >>> inverse_document_frequency(0, 3)
    Traceback (most recent call last):
     ...
    ZeroDivisionError: df must be > 0
    >>> inverse_document_frequency(0, 3,True)
    1.477
    """
    if smoothing:
        if n == 0:
            raise ValueError("log10(0) is undefined.")
        return round(1 + log10(n / (1 + df)), 3)

    if df == 0:
        raise ZeroDivisionError("df must be > 0")
    elif n == 0:
        raise ValueError("log10(0) is undefined.")
    return round(log10(n / df), 3)


def tf_idf(tf: int, idf: int) -> float:
    """
    Combine the term frequency
    and inverse document frequency functions to
    calculate the originality of a term. This
    'originality' is calculated by multiplying
    the term frequency and the inverse document
    frequency : tf-idf = TF * IDF
    @params : tf, the term frequency, and idf, the inverse document
    frequency
    @examples :
    >>> tf_idf(2, 0.477)
    0.954
    """
    return round(tf * idf, 3)


================================================
FILE: machine_learning/xgboost_classifier.py
================================================
# XGBoost Classifier Example
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier


def data_handling(data: dict) -> tuple:
    # Split dataset into features and target
    # data is features
    """
    >>> data_handling(({'data':'[5.1, 3.5, 1.4, 0.2]','target':([0])}))
    ('[5.1, 3.5, 1.4, 0.2]', [0])
    >>> data_handling(
    ...     {'data': '[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', 'target': ([0, 0])}
    ... )
    ('[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', [0, 0])
    """
    return (data["data"], data["target"])


def xgboost(features: np.ndarray, target: np.ndarray) -> XGBClassifier:
    """
    # THIS TEST IS BROKEN!! >>> xgboost(np.array([[5.1, 3.6, 1.4, 0.2]]), np.array([0]))
    XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
                  colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
                  early_stopping_rounds=None, enable_categorical=False,
                  eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
                  importance_type=None, interaction_constraints='',
                  learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
                  max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
                  missing=nan, monotone_constraints='()', n_estimators=100,
                  n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,
                  reg_alpha=0, reg_lambda=1, ...)
    """
    classifier = XGBClassifier()
    classifier.fit(features, target)
    return classifier


def main() -> None:
    """
    Url for the algorithm:
    https://xgboost.readthedocs.io/en/stable/
    Iris type dataset is used to demonstrate algorithm.
    """

    # Load Iris dataset
    iris = load_iris()
    features, targets = data_handling(iris)
    x_train, x_test, y_train, y_test = train_test_split(
        features, targets, test_size=0.25
    )

    names = iris["target_names"]

    # Create an XGBoost Classifier from the training data
    xgboost_classifier = xgboost(x_train, y_train)

    # Display the confusion matrix of the classifier with both training and test sets
    ConfusionMatrixDisplay.from_estimator(
        xgboost_classifier,
        x_test,
        y_test,
        display_labels=names,
        cmap="Blues",
        normalize="true",
    )
    plt.title("Normalized Confusion Matrix - IRIS Dataset")
    plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    main()


================================================
FILE: machine_learning/xgboost_regressor.py
================================================
# XGBoost Regressor Example
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor


def data_handling(data: dict) -> tuple:
    # Split dataset into features and target.  Data is features.
    """
    >>> data_handling((
    ...  {'data':'[ 8.3252 41. 6.9841269 1.02380952  322. 2.55555556   37.88 -122.23 ]'
    ...  ,'target':([4.526])}))
    ('[ 8.3252 41. 6.9841269 1.02380952  322. 2.55555556   37.88 -122.23 ]', [4.526])
    """
    return (data["data"], data["target"])


def xgboost(
    features: np.ndarray, target: np.ndarray, test_features: np.ndarray
) -> np.ndarray:
    """
    >>> xgboost(np.array([[ 2.3571 ,   52. , 6.00813008, 1.06775068,
    ...    907. , 2.45799458,   40.58 , -124.26]]),np.array([1.114]),
    ... np.array([[1.97840000e+00,  3.70000000e+01,  4.98858447e+00,  1.03881279e+00,
    ...    1.14300000e+03,  2.60958904e+00,  3.67800000e+01, -1.19780000e+02]]))
    array([[1.1139996]], dtype=float32)
    """
    xgb = XGBRegressor(
        verbosity=0, random_state=42, tree_method="exact", base_score=0.5
    )
    xgb.fit(features, target)
    # Predict target for test data
    predictions = xgb.predict(test_features)
    predictions = predictions.reshape(len(predictions), 1)
    return predictions


def main() -> None:
    """
    The URL for this algorithm
    https://xgboost.readthedocs.io/en/stable/
    California house price dataset is used to demonstrate the algorithm.

    Expected error values:
    Mean Absolute Error: 0.30957163379906033
    Mean Square Error: 0.22611560196662744
    """
    # Load California house price dataset
    california = fetch_california_housing()
    data, target = data_handling(california)
    x_train, x_test, y_train, y_test = train_test_split(
        data, target, test_size=0.25, random_state=1
    )
    predictions = xgboost(x_train, y_train, x_test)
    # Error printing
    print(f"Mean Absolute Error: {mean_absolute_error(y_test, predictions)}")
    print(f"Mean Square Error: {mean_squared_error(y_test, predictions)}")


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    main()


================================================
FILE: maths/__init__.py
================================================


================================================
FILE: maths/abs.py
================================================
"""Absolute Value."""


def abs_val(num: float) -> float:
    """
    Find the absolute value of a number.

    >>> abs_val(-5.1)
    5.1
    >>> abs_val(-5) == abs_val(5)
    True
    >>> abs_val(0)
    0
    """
    return -num if num < 0 else num


def abs_min(x: list[int]) -> int:
    """
    >>> abs_min([0,5,1,11])
    0
    >>> abs_min([3,-10,-2])
    -2
    >>> abs_min([])
    Traceback (most recent call last):
        ...
    ValueError: abs_min() arg is an empty sequence
    """
    if len(x) == 0:
        raise ValueError("abs_min() arg is an empty sequence")
    j = x[0]
    for i in x:
        if abs_val(i) < abs_val(j):
            j = i
    return j


def abs_max(x: list[int]) -> int:
    """
    >>> abs_max([0,5,1,11])
    11
    >>> abs_max([3,-10,-2])
    -10
    >>> abs_max([])
    Traceback (most recent call last):
        ...
    ValueError: abs_max() arg is an empty sequence
    """
    if len(x) == 0:
        raise ValueError("abs_max() arg is an empty sequence")
    j = x[0]
    for i in x:
        if abs(i) > abs(j):
            j = i
    return j


def abs_max_sort(x: list[int]) -> int:
    """
    >>> abs_max_sort([0,5,1,11])
    11
    >>> abs_max_sort([3,-10,-2])
    -10
    >>> abs_max_sort([])
    Traceback (most recent call last):
        ...
    ValueError: abs_max_sort() arg is an empty sequence
    """
    if len(x) == 0:
        raise ValueError("abs_max_sort() arg is an empty sequence")
    return sorted(x, key=abs)[-1]


def test_abs_val():
    """
    >>> test_abs_val()
    """
    assert abs_val(0) == 0
    assert abs_val(34) == 34
    assert abs_val(-100000000000) == 100000000000

    a = [-3, -1, 2, -11]
    assert abs_max(a) == -11
    assert abs_max_sort(a) == -11
    assert abs_min(a) == -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    test_abs_val()
    print(abs_val(-34))  # --> 34


================================================
FILE: maths/addition_without_arithmetic.py
================================================
"""
Illustrate how to add the integer without arithmetic operation
Author: suraj Kumar
Time Complexity: 1
https://en.wikipedia.org/wiki/Bitwise_operation
"""


def add(first: int, second: int) -> int:
    """
    Implementation of addition of integer

    Examples:
    >>> add(3, 5)
    8
    >>> add(13, 5)
    18
    >>> add(-7, 2)
    -5
    >>> add(0, -7)
    -7
    >>> add(-321, 0)
    -321
    """
    while second != 0:
        c = first & second
        first ^= second
        second = c << 1
    return first


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    first = int(input("Enter the first number: ").strip())
    second = int(input("Enter the second number: ").strip())
    print(f"{add(first, second) = }")


================================================
FILE: maths/aliquot_sum.py
================================================
def aliquot_sum(input_num: int) -> int:
    """
    Finds the aliquot sum of an input integer, where the
    aliquot sum of a number n is defined as the sum of all
    natural numbers less than n that divide n evenly. For
    example, the aliquot sum of 15 is 1 + 3 + 5 = 9. This is
    a simple O(n) implementation.
    @param input_num: a positive integer whose aliquot sum is to be found
    @return: the aliquot sum of input_num, if input_num is positive.
    Otherwise, raise a ValueError
    Wikipedia Explanation: https://en.wikipedia.org/wiki/Aliquot_sum

    >>> aliquot_sum(15)
    9
    >>> aliquot_sum(6)
    6
    >>> aliquot_sum(-1)
    Traceback (most recent call last):
      ...
    ValueError: Input must be positive
    >>> aliquot_sum(0)
    Traceback (most recent call last):
      ...
    ValueError: Input must be positive
    >>> aliquot_sum(1.6)
    Traceback (most recent call last):
      ...
    ValueError: Input must be an integer
    >>> aliquot_sum(12)
    16
    >>> aliquot_sum(1)
    0
    >>> aliquot_sum(19)
    1
    """
    if not isinstance(input_num, int):
        raise ValueError("Input must be an integer")
    if input_num <= 0:
        raise ValueError("Input must be positive")
    return sum(
        divisor for divisor in range(1, input_num // 2 + 1) if input_num % divisor == 0
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/allocation_number.py
================================================
"""
In a multi-threaded download, this algorithm could be used to provide
each worker thread with a block of non-overlapping bytes to download.
For example:
    for i in allocation_list:
        requests.get(url,headers={'Range':f'bytes={i}'})
"""

from __future__ import annotations


def allocation_num(number_of_bytes: int, partitions: int) -> list[str]:
    """
    Divide a number of bytes into x partitions.
    :param number_of_bytes: the total of bytes.
    :param partitions: the number of partition need to be allocated.
    :return: list of bytes to be assigned to each worker thread

    >>> allocation_num(16647, 4)
    ['1-4161', '4162-8322', '8323-12483', '12484-16647']
    >>> allocation_num(50000, 5)
    ['1-10000', '10001-20000', '20001-30000', '30001-40000', '40001-50000']
    >>> allocation_num(888, 999)
    Traceback (most recent call last):
        ...
    ValueError: partitions can not > number_of_bytes!
    >>> allocation_num(888, -4)
    Traceback (most recent call last):
        ...
    ValueError: partitions must be a positive number!
    """
    if partitions <= 0:
        raise ValueError("partitions must be a positive number!")
    if partitions > number_of_bytes:
        raise ValueError("partitions can not > number_of_bytes!")
    bytes_per_partition = number_of_bytes // partitions
    allocation_list = []
    for i in range(partitions):
        start_bytes = i * bytes_per_partition + 1
        end_bytes = (
            number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
        )
        allocation_list.append(f"{start_bytes}-{end_bytes}")
    return allocation_list


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/arc_length.py
================================================
from math import pi


def arc_length(angle: int, radius: int) -> float:
    """
    >>> arc_length(45, 5)
    3.9269908169872414
    >>> arc_length(120, 15)
    31.415926535897928
    >>> arc_length(90, 10)
    15.707963267948966
    """
    return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
    print(arc_length(90, 10))


================================================
FILE: maths/area.py
================================================
"""
Find the area of various geometric shapes
Wikipedia reference: https://en.wikipedia.org/wiki/Area
"""

from math import pi, sqrt, tan


def surface_area_cube(side_length: float) -> float:
    """
    Calculate the Surface Area of a Cube.

    >>> surface_area_cube(1)
    6
    >>> surface_area_cube(1.6)
    15.360000000000003
    >>> surface_area_cube(0)
    0
    >>> surface_area_cube(3)
    54
    >>> surface_area_cube(-1)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_cube() only accepts non-negative values
    """
    if side_length < 0:
        raise ValueError("surface_area_cube() only accepts non-negative values")
    return 6 * side_length**2


def surface_area_cuboid(length: float, breadth: float, height: float) -> float:
    """
    Calculate the Surface Area of a Cuboid.

    >>> surface_area_cuboid(1, 2, 3)
    22
    >>> surface_area_cuboid(0, 0, 0)
    0
    >>> surface_area_cuboid(1.6, 2.6, 3.6)
    38.56
    >>> surface_area_cuboid(-1, 2, 3)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_cuboid() only accepts non-negative values
    >>> surface_area_cuboid(1, -2, 3)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_cuboid() only accepts non-negative values
    >>> surface_area_cuboid(1, 2, -3)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_cuboid() only accepts non-negative values
    """
    if length < 0 or breadth < 0 or height < 0:
        raise ValueError("surface_area_cuboid() only accepts non-negative values")
    return 2 * ((length * breadth) + (breadth * height) + (length * height))


def surface_area_sphere(radius: float) -> float:
    """
    Calculate the Surface Area of a Sphere.
    Wikipedia reference: https://en.wikipedia.org/wiki/Sphere
    Formula: 4 * pi * r^2

    >>> surface_area_sphere(5)
    314.1592653589793
    >>> surface_area_sphere(1)
    12.566370614359172
    >>> surface_area_sphere(1.6)
    32.169908772759484
    >>> surface_area_sphere(0)
    0.0
    >>> surface_area_sphere(-1)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_sphere() only accepts non-negative values
    """
    if radius < 0:
        raise ValueError("surface_area_sphere() only accepts non-negative values")
    return 4 * pi * radius**2


def surface_area_hemisphere(radius: float) -> float:
    """
    Calculate the Surface Area of a Hemisphere.
    Formula: 3 * pi * r^2

    >>> surface_area_hemisphere(5)
    235.61944901923448
    >>> surface_area_hemisphere(1)
    9.42477796076938
    >>> surface_area_hemisphere(0)
    0.0
    >>> surface_area_hemisphere(1.1)
    11.40398133253095
    >>> surface_area_hemisphere(-1)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_hemisphere() only accepts non-negative values
    """
    if radius < 0:
        raise ValueError("surface_area_hemisphere() only accepts non-negative values")
    return 3 * pi * radius**2


def surface_area_cone(radius: float, height: float) -> float:
    """
    Calculate the Surface Area of a Cone.
    Wikipedia reference: https://en.wikipedia.org/wiki/Cone
    Formula: pi * r * (r + (h ** 2 + r ** 2) ** 0.5)

    >>> surface_area_cone(10, 24)
    1130.9733552923256
    >>> surface_area_cone(6, 8)
    301.59289474462014
    >>> surface_area_cone(1.6, 2.6)
    23.387862992395807
    >>> surface_area_cone(0, 0)
    0.0
    >>> surface_area_cone(-1, -2)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_cone() only accepts non-negative values
    >>> surface_area_cone(1, -2)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_cone() only accepts non-negative values
    >>> surface_area_cone(-1, 2)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_cone() only accepts non-negative values
    """
    if radius < 0 or height < 0:
        raise ValueError("surface_area_cone() only accepts non-negative values")
    return pi * radius * (radius + (height**2 + radius**2) ** 0.5)


def surface_area_conical_frustum(
    radius_1: float, radius_2: float, height: float
) -> float:
    """
    Calculate the Surface Area of a Conical Frustum.

    >>> surface_area_conical_frustum(1, 2, 3)
    45.511728065337266
    >>> surface_area_conical_frustum(4, 5, 6)
    300.7913575056268
    >>> surface_area_conical_frustum(0, 0, 0)
    0.0
    >>> surface_area_conical_frustum(1.6, 2.6, 3.6)
    78.57907060751548
    >>> surface_area_conical_frustum(-1, 2, 3)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_conical_frustum() only accepts non-negative values
    >>> surface_area_conical_frustum(1, -2, 3)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_conical_frustum() only accepts non-negative values
    >>> surface_area_conical_frustum(1, 2, -3)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_conical_frustum() only accepts non-negative values
    """
    if radius_1 < 0 or radius_2 < 0 or height < 0:
        raise ValueError(
            "surface_area_conical_frustum() only accepts non-negative values"
        )
    slant_height = (height**2 + (radius_1 - radius_2) ** 2) ** 0.5
    return pi * ((slant_height * (radius_1 + radius_2)) + radius_1**2 + radius_2**2)


def surface_area_cylinder(radius: float, height: float) -> float:
    """
    Calculate the Surface Area of a Cylinder.
    Wikipedia reference: https://en.wikipedia.org/wiki/Cylinder
    Formula: 2 * pi * r * (h + r)

    >>> surface_area_cylinder(7, 10)
    747.6990515543707
    >>> surface_area_cylinder(1.6, 2.6)
    42.22300526424682
    >>> surface_area_cylinder(0, 0)
    0.0
    >>> surface_area_cylinder(6, 8)
    527.7875658030853
    >>> surface_area_cylinder(-1, -2)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_cylinder() only accepts non-negative values
    >>> surface_area_cylinder(1, -2)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_cylinder() only accepts non-negative values
    >>> surface_area_cylinder(-1, 2)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_cylinder() only accepts non-negative values
    """
    if radius < 0 or height < 0:
        raise ValueError("surface_area_cylinder() only accepts non-negative values")
    return 2 * pi * radius * (height + radius)


def surface_area_torus(torus_radius: float, tube_radius: float) -> float:
    """Calculate the Area of a Torus.
    Wikipedia reference: https://en.wikipedia.org/wiki/Torus
    :return 4pi^2 * torus_radius * tube_radius
    >>> surface_area_torus(1, 1)
    39.47841760435743
    >>> surface_area_torus(4, 3)
    473.7410112522892
    >>> surface_area_torus(3, 4)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_torus() does not support spindle or self intersecting tori
    >>> surface_area_torus(1.6, 1.6)
    101.06474906715503
    >>> surface_area_torus(0, 0)
    0.0
    >>> surface_area_torus(-1, 1)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_torus() only accepts non-negative values
    >>> surface_area_torus(1, -1)
    Traceback (most recent call last):
        ...
    ValueError: surface_area_torus() only accepts non-negative values
    """
    if torus_radius < 0 or tube_radius < 0:
        raise ValueError("surface_area_torus() only accepts non-negative values")
    if torus_radius < tube_radius:
        raise ValueError(
            "surface_area_torus() does not support spindle or self intersecting tori"
        )
    return 4 * pow(pi, 2) * torus_radius * tube_radius


def area_rectangle(length: float, width: float) -> float:
    """
    Calculate the area of a rectangle.

    >>> area_rectangle(10, 20)
    200
    >>> area_rectangle(1.6, 2.6)
    4.16
    >>> area_rectangle(0, 0)
    0
    >>> area_rectangle(-1, -2)
    Traceback (most recent call last):
        ...
    ValueError: area_rectangle() only accepts non-negative values
    >>> area_rectangle(1, -2)
    Traceback (most recent call last):
        ...
    ValueError: area_rectangle() only accepts non-negative values
    >>> area_rectangle(-1, 2)
    Traceback (most recent call last):
        ...
    ValueError: area_rectangle() only accepts non-negative values
    """
    if length < 0 or width < 0:
        raise ValueError("area_rectangle() only accepts non-negative values")
    return length * width


def area_square(side_length: float) -> float:
    """
    Calculate the area of a square.

    >>> area_square(10)
    100
    >>> area_square(0)
    0
    >>> area_square(1.6)
    2.5600000000000005
    >>> area_square(-1)
    Traceback (most recent call last):
        ...
    ValueError: area_square() only accepts non-negative values
    """
    if side_length < 0:
        raise ValueError("area_square() only accepts non-negative values")
    return side_length**2


def area_triangle(base: float, height: float) -> float:
    """
    Calculate the area of a triangle given the base and height.

    >>> area_triangle(10, 10)
    50.0
    >>> area_triangle(1.6, 2.6)
    2.08
    >>> area_triangle(0, 0)
    0.0
    >>> area_triangle(-1, -2)
    Traceback (most recent call last):
        ...
    ValueError: area_triangle() only accepts non-negative values
    >>> area_triangle(1, -2)
    Traceback (most recent call last):
        ...
    ValueError: area_triangle() only accepts non-negative values
    >>> area_triangle(-1, 2)
    Traceback (most recent call last):
        ...
    ValueError: area_triangle() only accepts non-negative values
    """
    if base < 0 or height < 0:
        raise ValueError("area_triangle() only accepts non-negative values")
    return (base * height) / 2


def area_triangle_three_sides(side1: float, side2: float, side3: float) -> float:
    """
    Calculate area of triangle when the length of 3 sides are known.
    This function uses Heron's formula: https://en.wikipedia.org/wiki/Heron%27s_formula

    >>> area_triangle_three_sides(5, 12, 13)
    30.0
    >>> area_triangle_three_sides(10, 11, 12)
    51.521233486786784
    >>> area_triangle_three_sides(0, 0, 0)
    0.0
    >>> area_triangle_three_sides(1.6, 2.6, 3.6)
    1.8703742940919619
    >>> area_triangle_three_sides(-1, -2, -1)
    Traceback (most recent call last):
        ...
    ValueError: area_triangle_three_sides() only accepts non-negative values
    >>> area_triangle_three_sides(1, -2, 1)
    Traceback (most recent call last):
        ...
    ValueError: area_triangle_three_sides() only accepts non-negative values
    >>> area_triangle_three_sides(2, 4, 7)
    Traceback (most recent call last):
        ...
    ValueError: Given three sides do not form a triangle
    >>> area_triangle_three_sides(2, 7, 4)
    Traceback (most recent call last):
        ...
    ValueError: Given three sides do not form a triangle
    >>> area_triangle_three_sides(7, 2, 4)
    Traceback (most recent call last):
        ...
    ValueError: Given three sides do not form a triangle
    """
    if side1 < 0 or side2 < 0 or side3 < 0:
        raise ValueError("area_triangle_three_sides() only accepts non-negative values")
    elif side1 + side2 < side3 or side1 + side3 < side2 or side2 + side3 < side1:
        raise ValueError("Given three sides do not form a triangle")
    semi_perimeter = (side1 + side2 + side3) / 2
    area = sqrt(
        semi_perimeter
        * (semi_perimeter - side1)
        * (semi_perimeter - side2)
        * (semi_perimeter - side3)
    )
    return area


def area_parallelogram(base: float, height: float) -> float:
    """
    Calculate the area of a parallelogram.

    >>> area_parallelogram(10, 20)
    200
    >>> area_parallelogram(1.6, 2.6)
    4.16
    >>> area_parallelogram(0, 0)
    0
    >>> area_parallelogram(-1, -2)
    Traceback (most recent call last):
        ...
    ValueError: area_parallelogram() only accepts non-negative values
    >>> area_parallelogram(1, -2)
    Traceback (most recent call last):
        ...
    ValueError: area_parallelogram() only accepts non-negative values
    >>> area_parallelogram(-1, 2)
    Traceback (most recent call last):
        ...
    ValueError: area_parallelogram() only accepts non-negative values
    """
    if base < 0 or height < 0:
        raise ValueError("area_parallelogram() only accepts non-negative values")
    return base * height


def area_trapezium(base1: float, base2: float, height: float) -> float:
    """
    Calculate the area of a trapezium.

    >>> area_trapezium(10, 20, 30)
    450.0
    >>> area_trapezium(1.6, 2.6, 3.6)
    7.5600000000000005
    >>> area_trapezium(0, 0, 0)
    0.0
    >>> area_trapezium(-1, -2, -3)
    Traceback (most recent call last):
        ...
    ValueError: area_trapezium() only accepts non-negative values
    >>> area_trapezium(-1, 2, 3)
    Traceback (most recent call last):
        ...
    ValueError: area_trapezium() only accepts non-negative values
    >>> area_trapezium(1, -2, 3)
    Traceback (most recent call last):
        ...
    ValueError: area_trapezium() only accepts non-negative values
    >>> area_trapezium(1, 2, -3)
    Traceback (most recent call last):
        ...
    ValueError: area_trapezium() only accepts non-negative values
    >>> area_trapezium(-1, -2, 3)
    Traceback (most recent call last):
        ...
    ValueError: area_trapezium() only accepts non-negative values
    >>> area_trapezium(1, -2, -3)
    Traceback (most recent call last):
        ...
    ValueError: area_trapezium() only accepts non-negative values
    >>> area_trapezium(-1, 2, -3)
    Traceback (most recent call last):
        ...
    ValueError: area_trapezium() only accepts non-negative values
    """
    if base1 < 0 or base2 < 0 or height < 0:
        raise ValueError("area_trapezium() only accepts non-negative values")
    return 1 / 2 * (base1 + base2) * height


def area_circle(radius: float) -> float:
    """
    Calculate the area of a circle.

    >>> area_circle(20)
    1256.6370614359173
    >>> area_circle(1.6)
    8.042477193189871
    >>> area_circle(0)
    0.0
    >>> area_circle(-1)
    Traceback (most recent call last):
        ...
    ValueError: area_circle() only accepts non-negative values
    """
    if radius < 0:
        raise ValueError("area_circle() only accepts non-negative values")
    return pi * radius**2


def area_ellipse(radius_x: float, radius_y: float) -> float:
    """
    Calculate the area of a ellipse.

    >>> area_ellipse(10, 10)
    314.1592653589793
    >>> area_ellipse(10, 20)
    628.3185307179587
    >>> area_ellipse(0, 0)
    0.0
    >>> area_ellipse(1.6, 2.6)
    13.06902543893354
    >>> area_ellipse(-10, 20)
    Traceback (most recent call last):
        ...
    ValueError: area_ellipse() only accepts non-negative values
    >>> area_ellipse(10, -20)
    Traceback (most recent call last):
        ...
    ValueError: area_ellipse() only accepts non-negative values
    >>> area_ellipse(-10, -20)
    Traceback (most recent call last):
        ...
    ValueError: area_ellipse() only accepts non-negative values
    """
    if radius_x < 0 or radius_y < 0:
        raise ValueError("area_ellipse() only accepts non-negative values")
    return pi * radius_x * radius_y


def area_rhombus(diagonal_1: float, diagonal_2: float) -> float:
    """
    Calculate the area of a rhombus.

    >>> area_rhombus(10, 20)
    100.0
    >>> area_rhombus(1.6, 2.6)
    2.08
    >>> area_rhombus(0, 0)
    0.0
    >>> area_rhombus(-1, -2)
    Traceback (most recent call last):
        ...
    ValueError: area_rhombus() only accepts non-negative values
    >>> area_rhombus(1, -2)
    Traceback (most recent call last):
        ...
    ValueError: area_rhombus() only accepts non-negative values
    >>> area_rhombus(-1, 2)
    Traceback (most recent call last):
        ...
    ValueError: area_rhombus() only accepts non-negative values
    """
    if diagonal_1 < 0 or diagonal_2 < 0:
        raise ValueError("area_rhombus() only accepts non-negative values")
    return 1 / 2 * diagonal_1 * diagonal_2


def area_reg_polygon(sides: int, length: float) -> float:
    """
    Calculate the area of a regular polygon.
    Wikipedia reference: https://en.wikipedia.org/wiki/Polygon#Regular_polygons
    Formula: (n*s^2*cot(pi/n))/4

    >>> area_reg_polygon(3, 10)
    43.301270189221945
    >>> area_reg_polygon(4, 10)
    100.00000000000001
    >>> area_reg_polygon(0, 0)
    Traceback (most recent call last):
        ...
    ValueError: area_reg_polygon() only accepts integers greater than or equal to \
three as number of sides
    >>> area_reg_polygon(-1, -2)
    Traceback (most recent call last):
        ...
    ValueError: area_reg_polygon() only accepts integers greater than or equal to \
three as number of sides
    >>> area_reg_polygon(5, -2)
    Traceback (most recent call last):
        ...
    ValueError: area_reg_polygon() only accepts non-negative values as \
length of a side
    >>> area_reg_polygon(-1, 2)
    Traceback (most recent call last):
        ...
    ValueError: area_reg_polygon() only accepts integers greater than or equal to \
three as number of sides
    """
    if not isinstance(sides, int) or sides < 3:
        raise ValueError(
            "area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"
        )
    elif length < 0:
        raise ValueError(
            "area_reg_polygon() only accepts non-negative values as \
length of a side"
        )
    return (sides * length**2) / (4 * tan(pi / sides))


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

    print("[DEMO] Areas of various geometric shapes: \n")
    print(f"Rectangle: {area_rectangle(10, 20) = }")
    print(f"Square: {area_square(10) = }")
    print(f"Triangle: {area_triangle(10, 10) = }")
    print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
    print(f"Parallelogram: {area_parallelogram(10, 20) = }")
    print(f"Rhombus: {area_rhombus(10, 20) = }")
    print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
    print(f"Circle: {area_circle(20) = }")
    print(f"Ellipse: {area_ellipse(10, 20) = }")
    print("\nSurface Areas of various geometric shapes: \n")
    print(f"Cube: {surface_area_cube(20) = }")
    print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
    print(f"Sphere: {surface_area_sphere(20) = }")
    print(f"Hemisphere: {surface_area_hemisphere(20) = }")
    print(f"Cone: {surface_area_cone(10, 20) = }")
    print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
    print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
    print(f"Torus: {surface_area_torus(20, 10) = }")
    print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
    print(f"Square: {area_reg_polygon(4, 10) = }")
    print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")


================================================
FILE: maths/area_under_curve.py
================================================
"""
Approximates the area under the curve using the trapezoidal rule
"""

from __future__ import annotations

from collections.abc import Callable


def trapezoidal_area(
    fnc: Callable[[float], float],
    x_start: float,
    x_end: float,
    steps: int = 100,
) -> float:
    """
    Treats curve as a collection of linear lines and sums the area of the
    trapezium shape they form
    :param fnc: a function which defines a curve
    :param x_start: left end point to indicate the start of line segment
    :param x_end: right end point to indicate end of line segment
    :param steps: an accuracy gauge; more steps increases the accuracy
    :return: a float representing the length of the curve

    >>> def f(x):
    ...    return 5
    >>> f"{trapezoidal_area(f, 12.0, 14.0, 1000):.3f}"
    '10.000'
    >>> def f(x):
    ...    return 9*x**2
    >>> f"{trapezoidal_area(f, -4.0, 0, 10000):.4f}"
    '192.0000'
    >>> f"{trapezoidal_area(f, -4.0, 4.0, 10000):.4f}"
    '384.0000'
    """
    x1 = x_start
    fx1 = fnc(x_start)
    area = 0.0
    for _ in range(steps):
        # Approximates small segments of curve as linear and solve
        # for trapezoidal area
        x2 = (x_end - x_start) / steps + x1
        fx2 = fnc(x2)
        area += abs(fx2 + fx1) * (x2 - x1) / 2
        # Increment step
        x1 = x2
        fx1 = fx2
    return area


if __name__ == "__main__":

    def f(x):
        return x**3 + x**2

    print("f(x) = x^3 + x^2")
    print("The area between the curve, x = -5, x = 5 and the x axis is:")
    i = 10
    while i <= 100000:
        print(f"with {i} steps: {trapezoidal_area(f, -5, 5, i)}")
        i *= 10


================================================
FILE: maths/average_absolute_deviation.py
================================================
def average_absolute_deviation(nums: list[int]) -> float:
    """
    Return the average absolute deviation of a list of numbers.
    Wiki: https://en.wikipedia.org/wiki/Average_absolute_deviation

    >>> average_absolute_deviation([0])
    0.0
    >>> average_absolute_deviation([4, 1, 3, 2])
    1.0
    >>> average_absolute_deviation([2, 70, 6, 50, 20, 8, 4, 0])
    20.0
    >>> average_absolute_deviation([-20, 0, 30, 15])
    16.25
    >>> average_absolute_deviation([])
    Traceback (most recent call last):
        ...
    ValueError: List is empty
    """
    if not nums:  # Makes sure that the list is not empty
        raise ValueError("List is empty")

    average = sum(nums) / len(nums)  # Calculate the average
    return sum(abs(x - average) for x in nums) / len(nums)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/average_mean.py
================================================
from __future__ import annotations


def mean(nums: list) -> float:
    """
    Find mean of a list of numbers.
    Wiki: https://en.wikipedia.org/wiki/Mean

    >>> mean([3, 6, 9, 12, 15, 18, 21])
    12.0
    >>> mean([5, 10, 15, 20, 25, 30, 35])
    20.0
    >>> mean([1, 2, 3, 4, 5, 6, 7, 8])
    4.5
    >>> mean([])
    Traceback (most recent call last):
        ...
    ValueError: List is empty
    """
    if not nums:
        raise ValueError("List is empty")
    return sum(nums) / len(nums)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/average_median.py
================================================
from __future__ import annotations


def median(nums: list) -> int | float:
    """
    Find median of a list of numbers.
    Wiki: https://en.wikipedia.org/wiki/Median

    >>> median([0])
    0
    >>> median([4, 1, 3, 2])
    2.5
    >>> median([2, 70, 6, 50, 20, 8, 4])
    8

    Args:
        nums: List of nums

    Returns:
        Median.
    """
    # The sorted function returns list[SupportsRichComparisonT@sorted]
    # which does not support `+`
    sorted_list: list[int] = sorted(nums)
    length = len(sorted_list)
    mid_index = length >> 1
    return (
        (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
        if length % 2 == 0
        else sorted_list[mid_index]
    )


def main():
    import doctest

    doctest.testmod()


if __name__ == "__main__":
    main()


================================================
FILE: maths/average_mode.py
================================================
from typing import Any


def mode(input_list: list) -> list[Any]:
    """This function returns the mode(Mode as in the measures of
    central tendency) of the input data.

    The input list may contain any Datastructure or any Datatype.

    >>> mode([2, 3, 4, 5, 3, 4, 2, 5, 2, 2, 4, 2, 2, 2])
    [2]
    >>> mode([3, 4, 5, 3, 4, 2, 5, 2, 2, 4, 4, 2, 2, 2])
    [2]
    >>> mode([3, 4, 5, 3, 4, 2, 5, 2, 2, 4, 4, 4, 2, 2, 4, 2])
    [2, 4]
    >>> mode(["x", "y", "y", "z"])
    ['y']
    >>> mode(["x", "x" , "y", "y", "z"])
    ['x', 'y']
    """
    if not input_list:
        return []
    result = [input_list.count(value) for value in input_list]
    y = max(result)  # Gets the maximum count in the input list.
    # Gets values of modes
    return sorted({input_list[i] for i, value in enumerate(result) if value == y})


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/bailey_borwein_plouffe.py
================================================
def bailey_borwein_plouffe(digit_position: int, precision: int = 1000) -> str:
    """
    Implement a popular pi-digit-extraction algorithm known as the
    Bailey-Borwein-Plouffe (BBP) formula to calculate the nth hex digit of pi.
    Wikipedia page:
    https://en.wikipedia.org/wiki/Bailey%E2%80%93Borwein%E2%80%93Plouffe_formula
    @param digit_position: a positive integer representing the position of the digit to
    extract.
    The digit immediately after the decimal point is located at position 1.
    @param precision: number of terms in the second summation to calculate.
    A higher number reduces the chance of an error but increases the runtime.
    @return: a hexadecimal digit representing the digit at the nth position
    in pi's decimal expansion.

    >>> "".join(bailey_borwein_plouffe(i) for i in range(1, 11))
    '243f6a8885'
    >>> bailey_borwein_plouffe(5, 10000)
    '6'
    >>> bailey_borwein_plouffe(-10)
    Traceback (most recent call last):
      ...
    ValueError: Digit position must be a positive integer
    >>> bailey_borwein_plouffe(0)
    Traceback (most recent call last):
      ...
    ValueError: Digit position must be a positive integer
    >>> bailey_borwein_plouffe(1.7)
    Traceback (most recent call last):
      ...
    ValueError: Digit position must be a positive integer
    >>> bailey_borwein_plouffe(2, -10)
    Traceback (most recent call last):
      ...
    ValueError: Precision must be a nonnegative integer
    >>> bailey_borwein_plouffe(2, 1.6)
    Traceback (most recent call last):
      ...
    ValueError: Precision must be a nonnegative integer
    """
    if (not isinstance(digit_position, int)) or (digit_position <= 0):
        raise ValueError("Digit position must be a positive integer")
    elif (not isinstance(precision, int)) or (precision < 0):
        raise ValueError("Precision must be a nonnegative integer")

    # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
    # accurate
    sum_result = (
        4 * _subsum(digit_position, 1, precision)
        - 2 * _subsum(digit_position, 4, precision)
        - _subsum(digit_position, 5, precision)
        - _subsum(digit_position, 6, precision)
    )

    # return the first hex digit of the fractional part of the result
    return hex(int((sum_result % 1) * 16))[2:]


def _subsum(
    digit_pos_to_extract: int, denominator_addend: int, precision: int
) -> float:
    # only care about first digit of fractional part; don't need decimal
    """
    Private helper function to implement the summation
    functionality.
    @param digit_pos_to_extract: digit position to extract
    @param denominator_addend: added to denominator of fractions in the formula
    @param precision: same as precision in main function
    @return: floating-point number whose integer part is not important
    """
    total = 0.0
    for sum_index in range(digit_pos_to_extract + precision):
        denominator = 8 * sum_index + denominator_addend
        if sum_index < digit_pos_to_extract:
            # if the exponential term is an integer and we mod it by the denominator
            # before dividing, only the integer part of the sum will change;
            # the fractional part will not
            exponential_term = pow(
                16, digit_pos_to_extract - 1 - sum_index, denominator
            )
        else:
            exponential_term = pow(16, digit_pos_to_extract - 1 - sum_index)
        total += exponential_term / denominator
    return total


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/base_neg2_conversion.py
================================================
def decimal_to_negative_base_2(num: int) -> int:
    """
    This function returns the number negative base 2
        of the decimal number of the input data.

    Args:
        int: The decimal number to convert.

    Returns:
        int: The negative base 2 number.

    Examples:
        >>> decimal_to_negative_base_2(0)
        0
        >>> decimal_to_negative_base_2(-19)
        111101
        >>> decimal_to_negative_base_2(4)
        100
        >>> decimal_to_negative_base_2(7)
        11011
    """
    if num == 0:
        return 0
    ans = ""
    while num != 0:
        num, rem = divmod(num, -2)
        if rem < 0:
            rem += 2
            num += 1
        ans = str(rem) + ans
    return int(ans)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/basic_maths.py
================================================
"""Implementation of Basic Math in Python."""

import math


def prime_factors(n: int) -> list:
    """Find Prime Factors.
    >>> prime_factors(100)
    [2, 2, 5, 5]
    >>> prime_factors(0)
    Traceback (most recent call last):
        ...
    ValueError: Only positive integers have prime factors
    >>> prime_factors(-10)
    Traceback (most recent call last):
        ...
    ValueError: Only positive integers have prime factors
    """
    if n <= 0:
        raise ValueError("Only positive integers have prime factors")
    pf = []
    while n % 2 == 0:
        pf.append(2)
        n = int(n / 2)
    for i in range(3, int(math.sqrt(n)) + 1, 2):
        while n % i == 0:
            pf.append(i)
            n = int(n / i)
    if n > 2:
        pf.append(n)
    return pf


def number_of_divisors(n: int) -> int:
    """Calculate Number of Divisors of an Integer.
    >>> number_of_divisors(100)
    9
    >>> number_of_divisors(0)
    Traceback (most recent call last):
        ...
    ValueError: Only positive numbers are accepted
    >>> number_of_divisors(-10)
    Traceback (most recent call last):
        ...
    ValueError: Only positive numbers are accepted
    """
    if n <= 0:
        raise ValueError("Only positive numbers are accepted")
    div = 1
    temp = 1
    while n % 2 == 0:
        temp += 1
        n = int(n / 2)
    div *= temp
    for i in range(3, int(math.sqrt(n)) + 1, 2):
        temp = 1
        while n % i == 0:
            temp += 1
            n = int(n / i)
        div *= temp
    if n > 1:
        div *= 2
    return div


def sum_of_divisors(n: int) -> int:
    """Calculate Sum of Divisors.
    >>> sum_of_divisors(100)
    217
    >>> sum_of_divisors(0)
    Traceback (most recent call last):
        ...
    ValueError: Only positive numbers are accepted
    >>> sum_of_divisors(-10)
    Traceback (most recent call last):
        ...
    ValueError: Only positive numbers are accepted
    """
    if n <= 0:
        raise ValueError("Only positive numbers are accepted")
    s = 1
    temp = 1
    while n % 2 == 0:
        temp += 1
        n = int(n / 2)
    if temp > 1:
        s *= (2**temp - 1) / (2 - 1)
    for i in range(3, int(math.sqrt(n)) + 1, 2):
        temp = 1
        while n % i == 0:
            temp += 1
            n = int(n / i)
        if temp > 1:
            s *= (i**temp - 1) / (i - 1)
    return int(s)


def euler_phi(n: int) -> int:
    """Calculate Euler's Phi Function.
    >>> euler_phi(100)
    40
    >>> euler_phi(0)
    Traceback (most recent call last):
        ...
    ValueError: Only positive numbers are accepted
    >>> euler_phi(-10)
    Traceback (most recent call last):
        ...
    ValueError: Only positive numbers are accepted
    """
    if n <= 0:
        raise ValueError("Only positive numbers are accepted")
    s = n
    for x in set(prime_factors(n)):
        s *= (x - 1) / x
    return int(s)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/binary_exponentiation.py
================================================
"""
Binary Exponentiation

This is a method to find a^b in O(log b) time complexity and is one of the most commonly
used methods of exponentiation. The method is also useful for modular exponentiation,
when the solution to (a^b) % c is required.

To calculate a^b:
- If b is even, then a^b = (a * a)^(b / 2)
- If b is odd, then a^b = a * a^(b - 1)
Repeat until b = 1 or b = 0

For modular exponentiation, we use the fact that (a * b) % c = ((a % c) * (b % c)) % c
"""


def binary_exp_recursive(base: float, exponent: int) -> float:
    """
    Computes a^b recursively, where a is the base and b is the exponent

    >>> binary_exp_recursive(3, 5)
    243
    >>> binary_exp_recursive(11, 13)
    34522712143931
    >>> binary_exp_recursive(-1, 3)
    -1
    >>> binary_exp_recursive(0, 5)
    0
    >>> binary_exp_recursive(3, 1)
    3
    >>> binary_exp_recursive(3, 0)
    1
    >>> binary_exp_recursive(1.5, 4)
    5.0625
    >>> binary_exp_recursive(3, -1)
    Traceback (most recent call last):
        ...
    ValueError: Exponent must be a non-negative integer
    """
    if exponent < 0:
        raise ValueError("Exponent must be a non-negative integer")

    if exponent == 0:
        return 1

    if exponent % 2 == 1:
        return binary_exp_recursive(base, exponent - 1) * base

    b = binary_exp_recursive(base, exponent // 2)
    return b * b


def binary_exp_iterative(base: float, exponent: int) -> float:
    """
    Computes a^b iteratively, where a is the base and b is the exponent

    >>> binary_exp_iterative(3, 5)
    243
    >>> binary_exp_iterative(11, 13)
    34522712143931
    >>> binary_exp_iterative(-1, 3)
    -1
    >>> binary_exp_iterative(0, 5)
    0
    >>> binary_exp_iterative(3, 1)
    3
    >>> binary_exp_iterative(3, 0)
    1
    >>> binary_exp_iterative(1.5, 4)
    5.0625
    >>> binary_exp_iterative(3, -1)
    Traceback (most recent call last):
        ...
    ValueError: Exponent must be a non-negative integer
    """
    if exponent < 0:
        raise ValueError("Exponent must be a non-negative integer")

    res: int | float = 1
    while exponent > 0:
        if exponent & 1:
            res *= base

        base *= base
        exponent >>= 1

    return res


def binary_exp_mod_recursive(base: float, exponent: int, modulus: int) -> float:
    """
    Computes a^b % c recursively, where a is the base, b is the exponent, and c is the
    modulus

    >>> binary_exp_mod_recursive(3, 4, 5)
    1
    >>> binary_exp_mod_recursive(11, 13, 7)
    4
    >>> binary_exp_mod_recursive(1.5, 4, 3)
    2.0625
    >>> binary_exp_mod_recursive(7, -1, 10)
    Traceback (most recent call last):
        ...
    ValueError: Exponent must be a non-negative integer
    >>> binary_exp_mod_recursive(7, 13, 0)
    Traceback (most recent call last):
        ...
    ValueError: Modulus must be a positive integer
    """
    if exponent < 0:
        raise ValueError("Exponent must be a non-negative integer")
    if modulus <= 0:
        raise ValueError("Modulus must be a positive integer")

    if exponent == 0:
        return 1

    if exponent % 2 == 1:
        return (binary_exp_mod_recursive(base, exponent - 1, modulus) * base) % modulus

    r = binary_exp_mod_recursive(base, exponent // 2, modulus)
    return (r * r) % modulus


def binary_exp_mod_iterative(base: float, exponent: int, modulus: int) -> float:
    """
    Computes a^b % c iteratively, where a is the base, b is the exponent, and c is the
    modulus

    >>> binary_exp_mod_iterative(3, 4, 5)
    1
    >>> binary_exp_mod_iterative(11, 13, 7)
    4
    >>> binary_exp_mod_iterative(1.5, 4, 3)
    2.0625
    >>> binary_exp_mod_iterative(7, -1, 10)
    Traceback (most recent call last):
        ...
    ValueError: Exponent must be a non-negative integer
    >>> binary_exp_mod_iterative(7, 13, 0)
    Traceback (most recent call last):
        ...
    ValueError: Modulus must be a positive integer
    """
    if exponent < 0:
        raise ValueError("Exponent must be a non-negative integer")
    if modulus <= 0:
        raise ValueError("Modulus must be a positive integer")

    res: int | float = 1
    while exponent > 0:
        if exponent & 1:
            res = ((res % modulus) * (base % modulus)) % modulus

        base *= base
        exponent >>= 1

    return res


if __name__ == "__main__":
    from timeit import timeit

    a = 1269380576
    b = 374
    c = 34

    runs = 100_000
    print(
        timeit(
            f"binary_exp_recursive({a}, {b})",
            setup="from __main__ import binary_exp_recursive",
            number=runs,
        )
    )
    print(
        timeit(
            f"binary_exp_iterative({a}, {b})",
            setup="from __main__ import binary_exp_iterative",
            number=runs,
        )
    )
    print(
        timeit(
            f"binary_exp_mod_recursive({a}, {b}, {c})",
            setup="from __main__ import binary_exp_mod_recursive",
            number=runs,
        )
    )
    print(
        timeit(
            f"binary_exp_mod_iterative({a}, {b}, {c})",
            setup="from __main__ import binary_exp_mod_iterative",
            number=runs,
        )
    )


================================================
FILE: maths/binary_multiplication.py
================================================
"""
Binary Multiplication
This is a method to find a*b in a time complexity of O(log b)
This is one of the most commonly used methods of finding result of multiplication.
Also useful in cases where solution to (a*b)%c is required,
where a,b,c can be numbers over the computers calculation limits.
Done using iteration, can also be done using recursion

Let's say you need to calculate a * b
RULE 1 : a * b = (a+a) * (b/2) ---- example : 4 * 4 = (4+4) * (4/2) = 8 * 2
RULE 2 : IF b is odd, then ---- a * b = a + (a * (b - 1)), where (b - 1) is even.
Once b is even, repeat the process to get a * b
Repeat the process until b = 1 or b = 0, because a*1 = a and a*0 = 0

As far as the modulo is concerned,
the fact : (a+b) % c = ((a%c) + (b%c)) % c
Now apply RULE 1 or 2, whichever is required.

@author chinmoy159
"""


def binary_multiply(a: int, b: int) -> int:
    """
    Multiply 'a' and 'b' using bitwise multiplication.

    Parameters:
    a (int): The first number.
    b (int): The second number.

    Returns:
    int: a * b

    Examples:
    >>> binary_multiply(2, 3)
    6
    >>> binary_multiply(5, 0)
    0
    >>> binary_multiply(3, 4)
    12
    >>> binary_multiply(10, 5)
    50
    >>> binary_multiply(0, 5)
    0
    >>> binary_multiply(2, 1)
    2
    >>> binary_multiply(1, 10)
    10
    """
    res = 0
    while b > 0:
        if b & 1:
            res += a

        a += a
        b >>= 1

    return res


def binary_mod_multiply(a: int, b: int, modulus: int) -> int:
    """
    Calculate (a * b) % c using binary multiplication and modular arithmetic.

    Parameters:
    a (int): The first number.
    b (int): The second number.
    modulus (int): The modulus.

    Returns:
    int: (a * b) % modulus.

    Examples:
    >>> binary_mod_multiply(2, 3, 5)
    1
    >>> binary_mod_multiply(5, 0, 7)
    0
    >>> binary_mod_multiply(3, 4, 6)
    0
    >>> binary_mod_multiply(10, 5, 13)
    11
    >>> binary_mod_multiply(2, 1, 5)
    2
    >>> binary_mod_multiply(1, 10, 3)
    1
    """
    res = 0
    while b > 0:
        if b & 1:
            res = ((res % modulus) + (a % modulus)) % modulus

        a += a
        b >>= 1

    return res


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/binomial_coefficient.py
================================================
def binomial_coefficient(n: int, r: int) -> int:
    """
    Find binomial coefficient using Pascal's triangle.

    Calculate C(n, r) using Pascal's triangle.

    :param n: The total number of items.
    :param r: The number of items to choose.
    :return: The binomial coefficient C(n, r).

    >>> binomial_coefficient(10, 5)
    252
    >>> binomial_coefficient(10, 0)
    1
    >>> binomial_coefficient(0, 10)
    1
    >>> binomial_coefficient(10, 10)
    1
    >>> binomial_coefficient(5, 2)
    10
    >>> binomial_coefficient(5, 6)
    0
    >>> binomial_coefficient(3, 5)
    0
    >>> binomial_coefficient(-2, 3)
    Traceback (most recent call last):
        ...
    ValueError: n and r must be non-negative integers
    >>> binomial_coefficient(5, -1)
    Traceback (most recent call last):
        ...
    ValueError: n and r must be non-negative integers
    >>> binomial_coefficient(10.1, 5)
    Traceback (most recent call last):
        ...
    TypeError: 'float' object cannot be interpreted as an integer
    >>> binomial_coefficient(10, 5.1)
    Traceback (most recent call last):
        ...
    TypeError: 'float' object cannot be interpreted as an integer
    """
    if n < 0 or r < 0:
        raise ValueError("n and r must be non-negative integers")
    if 0 in (n, r):
        return 1
    c = [0 for i in range(r + 1)]
    # nc0 = 1
    c[0] = 1
    for i in range(1, n + 1):
        # to compute current row from previous row.
        j = min(i, r)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    print(binomial_coefficient(n=10, r=5))


================================================
FILE: maths/binomial_distribution.py
================================================
"""For more information about the Binomial Distribution -
https://en.wikipedia.org/wiki/Binomial_distribution"""

from math import factorial


def binomial_distribution(successes: int, trials: int, prob: float) -> float:
    """
    Return probability of k successes out of n tries, with p probability for one
    success

    The function uses the factorial function in order to calculate the binomial
    coefficient

    >>> binomial_distribution(3, 5, 0.7)
    0.30870000000000003
    >>> binomial_distribution (2, 4, 0.5)
    0.375
    """
    if successes > trials:
        raise ValueError("""successes must be lower or equal to trials""")
    if trials < 0 or successes < 0:
        raise ValueError("the function is defined for non-negative integers")
    if not isinstance(successes, int) or not isinstance(trials, int):
        raise ValueError("the function is defined for non-negative integers")
    if not 0 < prob < 1:
        raise ValueError("prob has to be in range of 1 - 0")
    probability = (prob**successes) * ((1 - prob) ** (trials - successes))
    # Calculate the binomial coefficient: n! / k!(n-k)!
    coefficient = float(factorial(trials))
    coefficient /= factorial(successes) * factorial(trials - successes)
    return probability * coefficient


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    print("Probability of 2 successes out of 4 trails")
    print("with probability of 0.75 is:", end=" ")
    print(binomial_distribution(2, 4, 0.75))


================================================
FILE: maths/ceil.py
================================================
"""
https://en.wikipedia.org/wiki/Floor_and_ceiling_functions
"""


def ceil(x: float) -> int:
    """
    Return the ceiling of x as an Integral.

    :param x: the number
    :return: the smallest integer >= x.

    >>> import math
    >>> all(ceil(n) == math.ceil(n) for n
    ...     in (1, -1, 0, -0, 1.1, -1.1, 1.0, -1.0, 1_000_000_000))
    True
    """
    return int(x) if x - int(x) <= 0 else int(x) + 1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/chebyshev_distance.py
================================================
def chebyshev_distance(point_a: list[float], point_b: list[float]) -> float:
    """
    This function calculates the Chebyshev distance (also known as the
    Chessboard distance) between two n-dimensional points represented as lists.

    https://en.wikipedia.org/wiki/Chebyshev_distance

    >>> chebyshev_distance([1.0, 1.0], [2.0, 2.0])
    1.0
    >>> chebyshev_distance([1.0, 1.0, 9.0], [2.0, 2.0, -5.2])
    14.2
    >>> chebyshev_distance([1.0], [2.0, 2.0])
    Traceback (most recent call last):
        ...
    ValueError: Both points must have the same dimension.
    """
    if len(point_a) != len(point_b):
        raise ValueError("Both points must have the same dimension.")

    return max(abs(a - b) for a, b in zip(point_a, point_b))


================================================
FILE: maths/check_polygon.py
================================================
from __future__ import annotations


def check_polygon(nums: list[float]) -> bool:
    """
    Takes list of possible side lengths and determines whether a
    two-dimensional polygon with such side lengths can exist.

    Returns a boolean value for the < comparison
    of the largest side length with sum of the rest.
    Wiki: https://en.wikipedia.org/wiki/Triangle_inequality

    >>> check_polygon([6, 10, 5])
    True
    >>> check_polygon([3, 7, 13, 2])
    False
    >>> check_polygon([1, 4.3, 5.2, 12.2])
    False
    >>> nums = [3, 7, 13, 2]
    >>> _ = check_polygon(nums) #   Run function, do not show answer in output
    >>> nums #  Check numbers are not reordered
    [3, 7, 13, 2]
    >>> check_polygon([])
    Traceback (most recent call last):
        ...
    ValueError: Monogons and Digons are not polygons in the Euclidean space
    >>> check_polygon([-2, 5, 6])
    Traceback (most recent call last):
        ...
    ValueError: All values must be greater than 0
    """
    if len(nums) < 2:
        raise ValueError("Monogons and Digons are not polygons in the Euclidean space")
    if any(i <= 0 for i in nums):
        raise ValueError("All values must be greater than 0")
    copy_nums = nums.copy()
    copy_nums.sort()
    return copy_nums[-1] < sum(copy_nums[:-1])


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/chinese_remainder_theorem.py
================================================
"""
Chinese Remainder Theorem:
GCD ( Greatest Common Divisor ) or HCF ( Highest Common Factor )

If GCD(a,b) = 1, then for any remainder ra modulo a and any remainder rb modulo b
there exists integer n, such that n = ra (mod a) and n = ra(mod b).  If n1 and n2 are
two such integers, then n1=n2(mod ab)

Algorithm :

1. Use extended euclid algorithm to find x,y such that a*x + b*y = 1
2. Take n = ra*by + rb*ax
"""

from __future__ import annotations


# Extended Euclid
def extended_euclid(a: int, b: int) -> tuple[int, int]:
    """
    >>> extended_euclid(10, 6)
    (-1, 2)

    >>> extended_euclid(7, 5)
    (-2, 3)

    """
    if b == 0:
        return (1, 0)
    (x, y) = extended_euclid(b, a % b)
    k = a // b
    return (y, x - k * y)


# Uses ExtendedEuclid to find inverses
def chinese_remainder_theorem(n1: int, r1: int, n2: int, r2: int) -> int:
    """
    >>> chinese_remainder_theorem(5,1,7,3)
    31

    Explanation : 31 is the smallest number such that
                (i)  When we divide it by 5, we get remainder 1
                (ii) When we divide it by 7, we get remainder 3

    >>> chinese_remainder_theorem(6,1,4,3)
    14

    """
    (x, y) = extended_euclid(n1, n2)
    m = n1 * n2
    n = r2 * x * n1 + r1 * y * n2
    return (n % m + m) % m


# ----------SAME SOLUTION USING InvertModulo instead ExtendedEuclid----------------


# This function find the inverses of a i.e., a^(-1)
def invert_modulo(a: int, n: int) -> int:
    """
    >>> invert_modulo(2, 5)
    3

    >>> invert_modulo(8,7)
    1

    """
    (b, _x) = extended_euclid(a, n)
    if b < 0:
        b = (b % n + n) % n
    return b


# Same a above using InvertingModulo
def chinese_remainder_theorem2(n1: int, r1: int, n2: int, r2: int) -> int:
    """
    >>> chinese_remainder_theorem2(5,1,7,3)
    31

    >>> chinese_remainder_theorem2(6,1,4,3)
    14

    """
    x, y = invert_modulo(n1, n2), invert_modulo(n2, n1)
    m = n1 * n2
    n = r2 * x * n1 + r1 * y * n2
    return (n % m + m) % m


if __name__ == "__main__":
    from doctest import testmod

    testmod(name="chinese_remainder_theorem", verbose=True)
    testmod(name="chinese_remainder_theorem2", verbose=True)
    testmod(name="invert_modulo", verbose=True)
    testmod(name="extended_euclid", verbose=True)


================================================
FILE: maths/chudnovsky_algorithm.py
================================================
from decimal import Decimal, getcontext
from math import ceil, factorial


def pi(precision: int) -> str:
    """
    The Chudnovsky algorithm is a fast method for calculating the digits of PI,
    based on Ramanujan's PI formulae.

    https://en.wikipedia.org/wiki/Chudnovsky_algorithm

    PI = constant_term / ((multinomial_term * linear_term) / exponential_term)
        where constant_term = 426880 * sqrt(10005)

    The linear_term and the exponential_term can be defined iteratively as follows:
        L_k+1 = L_k + 545140134            where L_0 = 13591409
        X_k+1 = X_k * -262537412640768000  where X_0 = 1

    The multinomial_term is defined as follows:
        6k! / ((3k)! * (k!) ^ 3)
            where k is the k_th iteration.

    This algorithm correctly calculates around 14 digits of PI per iteration

    >>> pi(10)
    '3.14159265'
    >>> pi(100)
    '3.14159265358979323846264338327950288419716939937510582097494459230781640628620899862803482534211706'
    >>> pi('hello')
    Traceback (most recent call last):
        ...
    TypeError: Undefined for non-integers
    >>> pi(-1)
    Traceback (most recent call last):
        ...
    ValueError: Undefined for non-natural numbers
    """

    if not isinstance(precision, int):
        raise TypeError("Undefined for non-integers")
    elif precision < 1:
        raise ValueError("Undefined for non-natural numbers")

    getcontext().prec = precision
    num_iterations = ceil(precision / 14)
    constant_term = 426880 * Decimal(10005).sqrt()
    exponential_term = 1
    linear_term = 13591409
    partial_sum = Decimal(linear_term)
    for k in range(1, num_iterations):
        multinomial_term = factorial(6 * k) // (factorial(3 * k) * factorial(k) ** 3)
        linear_term += 545140134
        exponential_term *= -262537412640768000
        partial_sum += Decimal(multinomial_term * linear_term) / exponential_term
    return str(constant_term / partial_sum)[:-1]


if __name__ == "__main__":
    n = 50
    print(f"The first {n} digits of pi is: {pi(n)}")


================================================
FILE: maths/collatz_sequence.py
================================================
"""
The Collatz conjecture is a famous unsolved problem in mathematics. Given a starting
positive integer, define the following sequence:
- If the current term n is even, then the next term is n/2.
- If the current term n is odd, then the next term is 3n + 1.
The conjecture claims that this sequence will always reach 1 for any starting number.

Other names for this problem include the 3n + 1 problem, the Ulam conjecture, Kakutani's
problem, the Thwaites conjecture, Hasse's algorithm, the Syracuse problem, and the
hailstone sequence.

Reference: https://en.wikipedia.org/wiki/Collatz_conjecture
"""

from __future__ import annotations

from collections.abc import Generator


def collatz_sequence(n: int) -> Generator[int]:
    """
    Generate the Collatz sequence starting at n.
    >>> tuple(collatz_sequence(2.1))
    Traceback (most recent call last):
        ...
    Exception: Sequence only defined for positive integers
    >>> tuple(collatz_sequence(0))
    Traceback (most recent call last):
        ...
    Exception: Sequence only defined for positive integers
    >>> tuple(collatz_sequence(4))
    (4, 2, 1)
    >>> tuple(collatz_sequence(11))
    (11, 34, 17, 52, 26, 13, 40, 20, 10, 5, 16, 8, 4, 2, 1)
    >>> tuple(collatz_sequence(31))     # doctest: +NORMALIZE_WHITESPACE
    (31, 94, 47, 142, 71, 214, 107, 322, 161, 484, 242, 121, 364, 182, 91, 274, 137,
    412, 206, 103, 310, 155, 466, 233, 700, 350, 175, 526, 263, 790, 395, 1186, 593,
    1780, 890, 445, 1336, 668, 334, 167, 502, 251, 754, 377, 1132, 566, 283, 850, 425,
    1276, 638, 319, 958, 479, 1438, 719, 2158, 1079, 3238, 1619, 4858, 2429, 7288, 3644,
    1822, 911, 2734, 1367, 4102, 2051, 6154, 3077, 9232, 4616, 2308, 1154, 577, 1732,
    866, 433, 1300, 650, 325, 976, 488, 244, 122, 61, 184, 92, 46, 23, 70, 35, 106, 53,
    160, 80, 40, 20, 10, 5, 16, 8, 4, 2, 1)
    >>> tuple(collatz_sequence(43))     # doctest: +NORMALIZE_WHITESPACE
    (43, 130, 65, 196, 98, 49, 148, 74, 37, 112, 56, 28, 14, 7, 22, 11, 34, 17, 52, 26,
    13, 40, 20, 10, 5, 16, 8, 4, 2, 1)
    """
    if not isinstance(n, int) or n < 1:
        raise Exception("Sequence only defined for positive integers")

    yield n
    while n != 1:
        if n % 2 == 0:
            n //= 2
        else:
            n = 3 * n + 1
        yield n


def main():
    n = int(input("Your number: "))
    sequence = tuple(collatz_sequence(n))
    print(sequence)
    print(f"Collatz sequence from {n} took {len(sequence)} steps.")


if __name__ == "__main__":
    main()


================================================
FILE: maths/combinations.py
================================================
"""
https://en.wikipedia.org/wiki/Combination
"""


def combinations(n: int, k: int) -> int:
    """
    Returns the number of different combinations of k length which can
    be made from n values, where n >= k.

    Examples:
    >>> combinations(10,5)
    252

    >>> combinations(6,3)
    20

    >>> combinations(20,5)
    15504

    >>> combinations(52, 5)
    2598960

    >>> combinations(0, 0)
    1

    >>> combinations(-4, -5)
    ...
    Traceback (most recent call last):
    ValueError: Please enter positive integers for n and k where n >= k
    """

    # If either of the conditions are true, the function is being asked
    # to calculate a factorial of a negative number, which is not possible
    if n < k or k < 0:
        raise ValueError("Please enter positive integers for n and k where n >= k")
    res = 1
    for i in range(k):
        res *= n - i
        res //= i + 1
    return res


if __name__ == "__main__":
    print(
        "The number of five-card hands possible from a standard",
        f"fifty-two card deck is: {combinations(52, 5)}\n",
    )

    print(
        "If a class of 40 students must be arranged into groups of",
        f"4 for group projects, there are {combinations(40, 4)} ways",
        "to arrange them.\n",
    )

    print(
        "If 10 teams are competing in a Formula One race, there",
        f"are {combinations(10, 3)} ways that first, second and",
        "third place can be awarded.",
    )


================================================
FILE: maths/continued_fraction.py
================================================
"""
Finding the continuous fraction for a rational number using python

https://en.wikipedia.org/wiki/Continued_fraction
"""

from fractions import Fraction
from math import floor


def continued_fraction(num: Fraction) -> list[int]:
    """
    :param num:
    Fraction of the number whose continued fractions to be found.
    Use Fraction(str(number)) for more accurate results due to
    float inaccuracies.

    :return:
    The continued fraction of rational number.
    It is the all commas in the (n + 1)-tuple notation.

    >>> continued_fraction(Fraction(2))
    [2]
    >>> continued_fraction(Fraction("3.245"))
    [3, 4, 12, 4]
    >>> continued_fraction(Fraction("2.25"))
    [2, 4]
    >>> continued_fraction(1/Fraction("2.25"))
    [0, 2, 4]
    >>> continued_fraction(Fraction("415/93"))
    [4, 2, 6, 7]
    >>> continued_fraction(Fraction(0))
    [0]
    >>> continued_fraction(Fraction(0.75))
    [0, 1, 3]
    >>> continued_fraction(Fraction("-2.25"))    # -2.25 = -3 + 0.75
    [-3, 1, 3]
    """
    numerator, denominator = num.as_integer_ratio()
    continued_fraction_list: list[int] = []
    while True:
        integer_part = floor(numerator / denominator)
        continued_fraction_list.append(integer_part)
        numerator -= integer_part * denominator
        if numerator == 0:
            break
        numerator, denominator = denominator, numerator

    return continued_fraction_list


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print("Continued Fraction of 0.84375 is: ", continued_fraction(Fraction("0.84375")))


================================================
FILE: maths/decimal_isolate.py
================================================
"""
Isolate the Decimal part of a Number
https://stackoverflow.com/questions/3886402/how-to-get-numbers-after-decimal-point
"""


def decimal_isolate(number: float, digit_amount: int) -> float:
    """
    Isolates the decimal part of a number.
    If digitAmount > 0 round to that decimal place, else print the entire decimal.
    >>> decimal_isolate(1.53, 0)
    0.53
    >>> decimal_isolate(35.345, 1)
    0.3
    >>> decimal_isolate(35.345, 2)
    0.34
    >>> decimal_isolate(35.345, 3)
    0.345
    >>> decimal_isolate(-14.789, 3)
    -0.789
    >>> decimal_isolate(0, 2)
    0
    >>> decimal_isolate(-14.123, 1)
    -0.1
    >>> decimal_isolate(-14.123, 2)
    -0.12
    >>> decimal_isolate(-14.123, 3)
    -0.123
    """
    if digit_amount > 0:
        return round(number - int(number), digit_amount)
    return number - int(number)


if __name__ == "__main__":
    print(decimal_isolate(1.53, 0))
    print(decimal_isolate(35.345, 1))
    print(decimal_isolate(35.345, 2))
    print(decimal_isolate(35.345, 3))
    print(decimal_isolate(-14.789, 3))
    print(decimal_isolate(0, 2))
    print(decimal_isolate(-14.123, 1))
    print(decimal_isolate(-14.123, 2))
    print(decimal_isolate(-14.123, 3))


================================================
FILE: maths/decimal_to_fraction.py
================================================
def decimal_to_fraction(decimal: float | str) -> tuple[int, int]:
    """
    Return a decimal number in its simplest fraction form
    >>> decimal_to_fraction(2)
    (2, 1)
    >>> decimal_to_fraction(89.)
    (89, 1)
    >>> decimal_to_fraction("67")
    (67, 1)
    >>> decimal_to_fraction("45.0")
    (45, 1)
    >>> decimal_to_fraction(1.5)
    (3, 2)
    >>> decimal_to_fraction("6.25")
    (25, 4)
    >>> decimal_to_fraction("78td")
    Traceback (most recent call last):
    ValueError: Please enter a valid number
    >>> decimal_to_fraction(0)
    (0, 1)
    >>> decimal_to_fraction(-2.5)
    (-5, 2)
    >>> decimal_to_fraction(0.125)
    (1, 8)
    >>> decimal_to_fraction(1000000.25)
    (4000001, 4)
    >>> decimal_to_fraction(1.3333)
    (13333, 10000)
    >>> decimal_to_fraction("1.23e2")
    (123, 1)
    >>> decimal_to_fraction("0.500")
    (1, 2)
    """
    try:
        decimal = float(decimal)
    except ValueError:
        raise ValueError("Please enter a valid number")
    fractional_part = decimal - int(decimal)
    if fractional_part == 0:
        return int(decimal), 1
    else:
        number_of_frac_digits = len(str(decimal).split(".")[1])
        numerator = int(decimal * (10**number_of_frac_digits))
        denominator = 10**number_of_frac_digits
        divisor, dividend = denominator, numerator
        while True:
            remainder = dividend % divisor
            if remainder == 0:
                break
            dividend, divisor = divisor, remainder
        numerator, denominator = numerator // divisor, denominator // divisor
        return numerator, denominator


if __name__ == "__main__":
    print(f"{decimal_to_fraction(2) = }")
    print(f"{decimal_to_fraction(89.0) = }")
    print(f"{decimal_to_fraction('67') = }")
    print(f"{decimal_to_fraction('45.0') = }")
    print(f"{decimal_to_fraction(1.5) = }")
    print(f"{decimal_to_fraction('6.25') = }")
    print(f"{decimal_to_fraction('78td') = }")


================================================
FILE: maths/dodecahedron.py
================================================
# dodecahedron.py

"""
A regular dodecahedron is a three-dimensional figure made up of
12 pentagon faces having the same equal size.
"""


def dodecahedron_surface_area(edge: float) -> float:
    """
    Calculates the surface area of a regular dodecahedron
    a = 3 * ((25 + 10 * (5** (1 / 2))) ** (1 / 2 )) * (e**2)
    where:
    a --> is the area of the dodecahedron
    e --> is the length of the edge
    reference-->"Dodecahedron" Study.com
    <https://study.com/academy/lesson/dodecahedron-volume-surface-area-formulas.html>

    :param edge: length of the edge of the dodecahedron
    :type edge: float
    :return: the surface area of the dodecahedron as a float


    Tests:
    >>> dodecahedron_surface_area(5)
    516.1432201766901
    >>> dodecahedron_surface_area(10)
    2064.5728807067603
    >>> dodecahedron_surface_area(-1)
    Traceback (most recent call last):
      ...
    ValueError: Length must be a positive.
    """

    if edge <= 0 or not isinstance(edge, int):
        raise ValueError("Length must be a positive.")
    return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)


def dodecahedron_volume(edge: float) -> float:
    """
    Calculates the volume of a regular dodecahedron
    v = ((15 + (7 * (5** (1 / 2)))) / 4) * (e**3)
    where:
    v --> is the volume of the dodecahedron
    e --> is the length of the edge
    reference-->"Dodecahedron" Study.com
    <https://study.com/academy/lesson/dodecahedron-volume-surface-area-formulas.html>

    :param edge: length of the edge of the dodecahedron
    :type edge: float
    :return: the volume of the dodecahedron as a float

    Tests:
    >>> dodecahedron_volume(5)
    957.8898700780791
    >>> dodecahedron_volume(10)
    7663.118960624633
    >>> dodecahedron_volume(-1)
    Traceback (most recent call last):
      ...
    ValueError: Length must be a positive.
    """

    if edge <= 0 or not isinstance(edge, int):
        raise ValueError("Length must be a positive.")
    return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/double_factorial.py
================================================
def double_factorial_recursive(n: int) -> int:
    """
    Compute double factorial using recursive method.
    Recursion can be costly for large numbers.

    To learn about the theory behind this algorithm:
    https://en.wikipedia.org/wiki/Double_factorial

    >>> from math import prod
    >>> all(double_factorial_recursive(i) == prod(range(i, 0, -2)) for i in range(20))
    True
    >>> double_factorial_recursive(0.1)
    Traceback (most recent call last):
        ...
    ValueError: double_factorial_recursive() only accepts integral values
    >>> double_factorial_recursive(-1)
    Traceback (most recent call last):
        ...
    ValueError: double_factorial_recursive() not defined for negative values
    """
    if not isinstance(n, int):
        raise ValueError("double_factorial_recursive() only accepts integral values")
    if n < 0:
        raise ValueError("double_factorial_recursive() not defined for negative values")
    return 1 if n <= 1 else n * double_factorial_recursive(n - 2)


def double_factorial_iterative(num: int) -> int:
    """
    Compute double factorial using iterative method.

    To learn about the theory behind this algorithm:
    https://en.wikipedia.org/wiki/Double_factorial

    >>> from math import prod
    >>> all(double_factorial_iterative(i) == prod(range(i, 0, -2)) for i in range(20))
    True
    >>> double_factorial_iterative(0.1)
    Traceback (most recent call last):
        ...
    ValueError: double_factorial_iterative() only accepts integral values
    >>> double_factorial_iterative(-1)
    Traceback (most recent call last):
        ...
    ValueError: double_factorial_iterative() not defined for negative values
    """
    if not isinstance(num, int):
        raise ValueError("double_factorial_iterative() only accepts integral values")
    if num < 0:
        raise ValueError("double_factorial_iterative() not defined for negative values")
    value = 1
    for i in range(num, 0, -2):
        value *= i
    return value


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/dual_number_automatic_differentiation.py
================================================
from math import factorial

"""
https://en.wikipedia.org/wiki/Automatic_differentiation#Automatic_differentiation_using_dual_numbers
https://blog.jliszka.org/2013/10/24/exact-numeric-nth-derivatives.html

Note this only works for basic functions, f(x) where the power of x is positive.
"""


class Dual:
    def __init__(self, real, rank):
        self.real = real
        if isinstance(rank, int):
            self.duals = [1] * rank
        else:
            self.duals = rank

    def __repr__(self):
        s = "+".join(f"{dual}E{n}" for n, dual in enumerate(self.duals, 1))
        return f"{self.real}+{s}"

    def reduce(self):
        cur = self.duals.copy()
        while cur[-1] == 0:
            cur.pop(-1)
        return Dual(self.real, cur)

    def __add__(self, other):
        if not isinstance(other, Dual):
            return Dual(self.real + other, self.duals)
        s_dual = self.duals.copy()
        o_dual = other.duals.copy()
        if len(s_dual) > len(o_dual):
            o_dual.extend([1] * (len(s_dual) - len(o_dual)))
        elif len(s_dual) < len(o_dual):
            s_dual.extend([1] * (len(o_dual) - len(s_dual)))
        new_duals = []
        for i in range(len(s_dual)):
            new_duals.append(s_dual[i] + o_dual[i])
        return Dual(self.real + other.real, new_duals)

    __radd__ = __add__

    def __sub__(self, other):
        return self + other * -1

    def __mul__(self, other):
        if not isinstance(other, Dual):
            new_duals = []
            for i in self.duals:
                new_duals.append(i * other)
            return Dual(self.real * other, new_duals)
        new_duals = [0] * (len(self.duals) + len(other.duals) + 1)
        for i, item in enumerate(self.duals):
            for j, jtem in enumerate(other.duals):
                new_duals[i + j + 1] += item * jtem
        for k in range(len(self.duals)):
            new_duals[k] += self.duals[k] * other.real
        for index in range(len(other.duals)):
            new_duals[index] += other.duals[index] * self.real
        return Dual(self.real * other.real, new_duals)

    __rmul__ = __mul__

    def __truediv__(self, other):
        if not isinstance(other, Dual):
            new_duals = []
            for i in self.duals:
                new_duals.append(i / other)
            return Dual(self.real / other, new_duals)
        raise ValueError

    def __floordiv__(self, other):
        if not isinstance(other, Dual):
            new_duals = []
            for i in self.duals:
                new_duals.append(i // other)
            return Dual(self.real // other, new_duals)
        raise ValueError

    def __pow__(self, n):
        if n < 0 or isinstance(n, float):
            raise ValueError("power must be a positive integer")
        if n == 0:
            return 1
        if n == 1:
            return self
        x = self
        for _ in range(n - 1):
            x *= self
        return x


def differentiate(func, position, order):
    """
    >>> differentiate(lambda x: x**2, 2, 2)
    2
    >>> differentiate(lambda x: x**2 * x**4, 9, 2)
    196830
    >>> differentiate(lambda y: 0.5 * (y + 3) ** 6, 3.5, 4)
    7605.0
    >>> differentiate(lambda y: y ** 2, 4, 3)
    0
    >>> differentiate(8, 8, 8)
    Traceback (most recent call last):
        ...
    ValueError: differentiate() requires a function as input for func
    >>> differentiate(lambda x: x **2, "", 1)
    Traceback (most recent call last):
        ...
    ValueError: differentiate() requires a float as input for position
    >>> differentiate(lambda x: x**2, 3, "")
    Traceback (most recent call last):
        ...
    ValueError: differentiate() requires an int as input for order
    """
    if not callable(func):
        raise ValueError("differentiate() requires a function as input for func")
    if not isinstance(position, (float, int)):
        raise ValueError("differentiate() requires a float as input for position")
    if not isinstance(order, int):
        raise ValueError("differentiate() requires an int as input for order")
    d = Dual(position, 1)
    result = func(d)
    if order == 0:
        return result.real
    return result.duals[order - 1] * factorial(order)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    def f(y):
        return y**2 * y**4

    print(differentiate(f, 9, 2))


================================================
FILE: maths/entropy.py
================================================
#!/usr/bin/env python3

"""
Implementation of entropy of information
https://en.wikipedia.org/wiki/Entropy_(information_theory)
"""

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def calculate_prob(text: str) -> None:
    """
    This method takes path and two dict as argument
    and than calculates entropy of them.
    :param dict:
    :param dict:
    :return: Prints
    1) Entropy of information based on 1 alphabet
    2) Entropy of information based on couples of 2 alphabet
    3) print Entropy of H(X n|Xn-1)

    Text from random books. Also, random quotes.
    >>> text = ("Behind Winston's back the voice "
    ...         "from the telescreen was still "
    ...         "babbling and the overfulfilment")
    >>> calculate_prob(text)
    4.0
    6.0
    2.0

    >>> text = ("The Ministry of Truth—Minitrue, in Newspeak [Newspeak was the official"
    ...         "face in elegant lettering, the three")
    >>> calculate_prob(text)
    4.0
    5.0
    1.0
    >>> text = ("Had repulsive dashwoods suspicion sincerity but advantage now him. "
    ...         "Remark easily garret nor nay.  Civil those mrs enjoy shy fat merry. "
    ...         "You greatest jointure saw horrible. He private he on be imagine "
    ...         "suppose. Fertile beloved evident through no service elderly is. Blind "
    ...         "there if every no so at. Own neglected you preferred way sincerity "
    ...         "delivered his attempted. To of message cottage windows do besides "
    ...         "against uncivil.  Delightful unreserved impossible few estimating "
    ...         "men favourable see entreaties. She propriety immediate was improving. "
    ...         "He or entrance humoured likewise moderate. Much nor game son say "
    ...         "feel. Fat make met can must form into gate. Me we offending prevailed "
    ...         "discovery.")
    >>> calculate_prob(text)
    4.0
    7.0
    3.0
    """
    single_char_strings, two_char_strings = analyze_text(text)
    my_alphas = list(" " + ascii_lowercase)
    # what is our total sum of probabilities.
    all_sum = sum(single_char_strings.values())

    # one length string
    my_fir_sum = 0
    # for each alpha we go in our dict and if it is in it we calculate entropy
    for ch in my_alphas:
        if ch in single_char_strings:
            my_str = single_char_strings[ch]
            prob = my_str / all_sum
            my_fir_sum += prob * math.log2(prob)  # entropy formula.

    # print entropy
    print(f"{round(-1 * my_fir_sum):.1f}")

    # two len string
    all_sum = sum(two_char_strings.values())
    my_sec_sum = 0
    # for each alpha (two in size) calculate entropy.
    for ch0 in my_alphas:
        for ch1 in my_alphas:
            sequence = ch0 + ch1
            if sequence in two_char_strings:
                my_str = two_char_strings[sequence]
                prob = int(my_str) / all_sum
                my_sec_sum += prob * math.log2(prob)

    # print second entropy
    print(f"{round(-1 * my_sec_sum):.1f}")

    # print the difference between them
    print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum)):.1f}")


def analyze_text(text: str) -> tuple[dict, dict]:
    """
    Convert text input into two dicts of counts.
    The first dictionary stores the frequency of single character strings.
    The second dictionary stores the frequency of two character strings.
    """
    single_char_strings = Counter()  # type: ignore[var-annotated]
    two_char_strings = Counter()  # type: ignore[var-annotated]
    single_char_strings[text[-1]] += 1

    # first case when we have space at start.
    two_char_strings[" " + text[0]] += 1
    for i in range(len(text) - 1):
        single_char_strings[text[i]] += 1
        two_char_strings[text[i : i + 2]] += 1
    return single_char_strings, two_char_strings


def main():
    import doctest

    doctest.testmod()
    # text = (
    #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
    #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
    #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
    #     "beloved evident through no service elderly is. Blind there if every no so "
    #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
    #     "of message cottage windows do besides against uncivil.  Delightful "
    #     "unreserved impossible few estimating men favourable see entreaties. She "
    #     "propriety immediate was improving. He or entrance humoured likewise "
    #     "moderate. Much nor game son say feel. Fat make met can must form into "
    #     "gate. Me we offending prevailed discovery. "
    # )

    # calculate_prob(text)


if __name__ == "__main__":
    main()


================================================
FILE: maths/euclidean_distance.py
================================================
from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

Vector = typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
VectorOut = typing.Union[np.float64, int, float]  # noqa: UP007


def euclidean_distance(vector_1: Vector, vector_2: Vector) -> VectorOut:
    """
    Calculate the distance between the two endpoints of two vectors.
    A vector is defined as a list, tuple, or numpy 1D array.
    >>> float(euclidean_distance((0, 0), (2, 2)))
    2.8284271247461903
    >>> float(euclidean_distance(np.array([0, 0, 0]), np.array([2, 2, 2])))
    3.4641016151377544
    >>> float(euclidean_distance(np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8])))
    8.0
    >>> float(euclidean_distance([1, 2, 3, 4], [5, 6, 7, 8]))
    8.0
    """
    return np.sqrt(np.sum((np.asarray(vector_1) - np.asarray(vector_2)) ** 2))


def euclidean_distance_no_np(vector_1: Vector, vector_2: Vector) -> VectorOut:
    """
    Calculate the distance between the two endpoints of two vectors without numpy.
    A vector is defined as a list, tuple, or numpy 1D array.
    >>> euclidean_distance_no_np((0, 0), (2, 2))
    2.8284271247461903
    >>> euclidean_distance_no_np([1, 2, 3, 4], [5, 6, 7, 8])
    8.0
    """
    return sum((v1 - v2) ** 2 for v1, v2 in zip(vector_1, vector_2)) ** (1 / 2)


if __name__ == "__main__":

    def benchmark() -> None:
        """
        Benchmarks
        """
        from timeit import timeit

        print("Without Numpy")
        print(
            timeit(
                "euclidean_distance_no_np([1, 2, 3], [4, 5, 6])",
                number=10000,
                globals=globals(),
            )
        )
        print("With Numpy")
        print(
            timeit(
                "euclidean_distance([1, 2, 3], [4, 5, 6])",
                number=10000,
                globals=globals(),
            )
        )

    benchmark()


================================================
FILE: maths/euler_method.py
================================================
from collections.abc import Callable

import numpy as np


def explicit_euler(
    ode_func: Callable, y0: float, x0: float, step_size: float, x_end: float
) -> np.ndarray:
    """Calculate numeric solution at each step to an ODE using Euler's Method

    For reference to Euler's method refer to https://en.wikipedia.org/wiki/Euler_method.

    Args:
        ode_func (Callable):  The ordinary differential equation
            as a function of x and y.
        y0 (float): The initial value for y.
        x0 (float): The initial value for x.
        step_size (float): The increment value for x.
        x_end (float): The final value of x to be calculated.

    Returns:
        np.ndarray: Solution of y for every step in x.

    >>> # the exact solution is math.exp(x)
    >>> def f(x, y):
    ...     return y
    >>> y0 = 1
    >>> y = explicit_euler(f, y0, 0.0, 0.01, 5)
    >>> float(y[-1])
    144.77277243257308
    """
    n = int(np.ceil((x_end - x0) / step_size))
    y = np.zeros((n + 1,))
    y[0] = y0
    x = x0

    for k in range(n):
        y[k + 1] = y[k] + step_size * ode_func(x, y[k])
        x += step_size

    return y


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/euler_modified.py
================================================
from collections.abc import Callable

import numpy as np


def euler_modified(
    ode_func: Callable, y0: float, x0: float, step_size: float, x_end: float
) -> np.ndarray:
    """
    Calculate solution at each step to an ODE using Euler's Modified Method
    The Euler Method is straightforward to implement, but can't give accurate solutions.
    So, some changes were proposed to improve accuracy.

    https://en.wikipedia.org/wiki/Euler_method

    Arguments:
    ode_func -- The ode as a function of x and y
    y0 -- the initial value for y
    x0 -- the initial value for x
    stepsize -- the increment value for x
    x_end -- the end value for x

    >>> # the exact solution is math.exp(x)
    >>> def f1(x, y):
    ...     return -2*x*(y**2)
    >>> y = euler_modified(f1, 1.0, 0.0, 0.2, 1.0)
    >>> float(y[-1])
    0.503338255442106
    >>> import math
    >>> def f2(x, y):
    ...     return -2*y + (x**3)*math.exp(-2*x)
    >>> y = euler_modified(f2, 1.0, 0.0, 0.1, 0.3)
    >>> float(y[-1])
    0.5525976431951775
    """
    n = int(np.ceil((x_end - x0) / step_size))
    y = np.zeros((n + 1,))
    y[0] = y0
    x = x0

    for k in range(n):
        y_get = y[k] + step_size * ode_func(x, y[k])
        y[k + 1] = y[k] + (
            (step_size / 2) * (ode_func(x, y[k]) + ode_func(x + step_size, y_get))
        )
        x += step_size

    return y


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/eulers_totient.py
================================================
# Eulers Totient function finds the number of relative primes of a number n from 1 to n
def totient(n: int) -> list:
    """
    >>> n = 10
    >>> totient_calculation = totient(n)
    >>> for i in range(1, n):
    ...     print(f"{i} has {totient_calculation[i]} relative primes.")
    1 has 0 relative primes.
    2 has 1 relative primes.
    3 has 2 relative primes.
    4 has 2 relative primes.
    5 has 4 relative primes.
    6 has 2 relative primes.
    7 has 6 relative primes.
    8 has 4 relative primes.
    9 has 6 relative primes.
    """
    is_prime = [True for i in range(n + 1)]
    totients = [i - 1 for i in range(n + 1)]
    primes = []
    for i in range(2, n + 1):
        if is_prime[i]:
            primes.append(i)
        for j in range(len(primes)):
            if i * primes[j] >= n:
                break
            is_prime[i * primes[j]] = False

            if i % primes[j] == 0:
                totients[i * primes[j]] = totients[i] * primes[j]
                break

            totients[i * primes[j]] = totients[i] * (primes[j] - 1)

    return totients


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/extended_euclidean_algorithm.py
================================================
"""
Extended Euclidean Algorithm.

Finds 2 numbers a and b such that it satisfies
the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity)

https://en.wikipedia.org/wiki/Extended_Euclidean_algorithm
"""

# @Author: S. Sharma <silentcat>
# @Date:   2019-02-25T12:08:53-06:00
# @Email:  silentcat@protonmail.com
# @Last modified by:   pikulet
# @Last modified time: 2020-10-02
from __future__ import annotations

import sys


def extended_euclidean_algorithm(a: int, b: int) -> tuple[int, int]:
    """
    Extended Euclidean Algorithm.

    Finds 2 numbers a and b such that it satisfies
    the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity)

    >>> extended_euclidean_algorithm(1, 24)
    (1, 0)

    >>> extended_euclidean_algorithm(8, 14)
    (2, -1)

    >>> extended_euclidean_algorithm(240, 46)
    (-9, 47)

    >>> extended_euclidean_algorithm(1, -4)
    (1, 0)

    >>> extended_euclidean_algorithm(-2, -4)
    (-1, 0)

    >>> extended_euclidean_algorithm(0, -4)
    (0, -1)

    >>> extended_euclidean_algorithm(2, 0)
    (1, 0)

    """
    # base cases
    if abs(a) == 1:
        return a, 0
    elif abs(b) == 1:
        return 0, b

    old_remainder, remainder = a, b
    old_coeff_a, coeff_a = 1, 0
    old_coeff_b, coeff_b = 0, 1

    while remainder != 0:
        quotient = old_remainder // remainder
        old_remainder, remainder = remainder, old_remainder - quotient * remainder
        old_coeff_a, coeff_a = coeff_a, old_coeff_a - quotient * coeff_a
        old_coeff_b, coeff_b = coeff_b, old_coeff_b - quotient * coeff_b

    # sign correction for negative numbers
    if a < 0:
        old_coeff_a = -old_coeff_a
    if b < 0:
        old_coeff_b = -old_coeff_b

    return old_coeff_a, old_coeff_b


def main():
    """Call Extended Euclidean Algorithm."""
    if len(sys.argv) < 3:
        print("2 integer arguments required")
        return 1
    a = int(sys.argv[1])
    b = int(sys.argv[2])
    print(extended_euclidean_algorithm(a, b))
    return 0


if __name__ == "__main__":
    raise SystemExit(main())


================================================
FILE: maths/factorial.py
================================================
"""
Factorial of a positive integer -- https://en.wikipedia.org/wiki/Factorial
"""


def factorial(number: int) -> int:
    """
    Calculate the factorial of specified number (n!).

    >>> import math
    >>> all(factorial(i) == math.factorial(i) for i in range(20))
    True
    >>> factorial(0.1)
    Traceback (most recent call last):
        ...
    ValueError: factorial() only accepts integral values
    >>> factorial(-1)
    Traceback (most recent call last):
        ...
    ValueError: factorial() not defined for negative values
    >>> factorial(1)
    1
    >>> factorial(6)
    720
    >>> factorial(0)
    1
    """
    if number != int(number):
        raise ValueError("factorial() only accepts integral values")
    if number < 0:
        raise ValueError("factorial() not defined for negative values")
    value = 1
    for i in range(1, number + 1):
        value *= i
    return value


def factorial_recursive(n: int) -> int:
    """
    Calculate the factorial of a positive integer
    https://en.wikipedia.org/wiki/Factorial

    >>> import math
    >>> all(factorial_recursive(i) == math.factorial(i) for i in range(20))
    True
    >>> factorial_recursive(0.1)
    Traceback (most recent call last):
        ...
    ValueError: factorial_recursive() only accepts integral values
    >>> factorial_recursive(-1)
    Traceback (most recent call last):
        ...
    ValueError: factorial_recursive() not defined for negative values
    """
    if not isinstance(n, int):
        raise ValueError("factorial_recursive() only accepts integral values")
    if n < 0:
        raise ValueError("factorial_recursive() not defined for negative values")
    return 1 if n in {0, 1} else n * factorial_recursive(n - 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    n = int(input("Enter a positive integer: ").strip() or 0)
    print(f"factorial{n} is {factorial(n)}")


================================================
FILE: maths/factors.py
================================================
from doctest import testmod
from math import sqrt


def factors_of_a_number(num: int) -> list:
    """
    >>> factors_of_a_number(1)
    [1]
    >>> factors_of_a_number(5)
    [1, 5]
    >>> factors_of_a_number(24)
    [1, 2, 3, 4, 6, 8, 12, 24]
    >>> factors_of_a_number(-24)
    []
    """
    facs: list[int] = []
    if num < 1:
        return facs
    facs.append(1)
    if num == 1:
        return facs
    facs.append(num)
    for i in range(2, int(sqrt(num)) + 1):
        if num % i == 0:  # If i is a factor of num
            facs.append(i)
            d = num // i  # num//i is the other factor of num
            if d != i:  # If d and i are distinct
                facs.append(d)  # we have found another factor
    facs.sort()
    return facs


if __name__ == "__main__":
    testmod(name="factors_of_a_number", verbose=True)


================================================
FILE: maths/fast_inverse_sqrt.py
================================================
"""
Fast inverse square root (1/sqrt(x)) using the Quake III algorithm.
Reference: https://en.wikipedia.org/wiki/Fast_inverse_square_root
Accuracy: https://en.wikipedia.org/wiki/Fast_inverse_square_root#Accuracy
"""

import struct


def fast_inverse_sqrt(number: float) -> float:
    """
    Compute the fast inverse square root of a floating-point number using the famous
    Quake III algorithm.

    :param float number: Input number for which to calculate the inverse square root.
    :return float: The fast inverse square root of the input number.

    Example:
    >>> fast_inverse_sqrt(10)
    0.3156857923527257
    >>> fast_inverse_sqrt(4)
    0.49915357479239103
    >>> fast_inverse_sqrt(4.1)
    0.4932849504615651
    >>> fast_inverse_sqrt(0)
    Traceback (most recent call last):
        ...
    ValueError: Input must be a positive number.
    >>> fast_inverse_sqrt(-1)
    Traceback (most recent call last):
        ...
    ValueError: Input must be a positive number.
    >>> from math import isclose, sqrt
    >>> all(isclose(fast_inverse_sqrt(i), 1 / sqrt(i), rel_tol=0.00132)
    ...     for i in range(50, 60))
    True
    """
    if number <= 0:
        raise ValueError("Input must be a positive number.")
    i = struct.unpack(">i", struct.pack(">f", number))[0]
    i = 0x5F3759DF - (i >> 1)
    y = struct.unpack(">f", struct.pack(">i", i))[0]
    return y * (1.5 - 0.5 * number * y * y)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    # https://en.wikipedia.org/wiki/Fast_inverse_square_root#Accuracy
    from math import sqrt

    for i in range(5, 101, 5):
        print(f"{i:>3}: {(1 / sqrt(i)) - fast_inverse_sqrt(i):.5f}")


================================================
FILE: maths/fermat_little_theorem.py
================================================
# Python program to show the usage of Fermat's little theorem in a division
# According to Fermat's little theorem, (a / b) mod p always equals
# a * (b ^ (p - 2)) mod p
# Here we assume that p is a prime number, b divides a, and p doesn't divide b
# Wikipedia reference: https://en.wikipedia.org/wiki/Fermat%27s_little_theorem


def binary_exponentiation(a: int, n: float, mod: int) -> int:
    if n == 0:
        return 1

    elif n % 2 == 1:
        return (binary_exponentiation(a, n - 1, mod) * a) % mod

    else:
        b = binary_exponentiation(a, n / 2, mod)
        return (b * b) % mod


# a prime number
p = 701

a = 1000000000
b = 10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)

# using Python operators:
print((a / b) % p == (a * b ** (p - 2)) % p)


================================================
FILE: maths/fibonacci.py
================================================
"""
Calculates the Fibonacci sequence using iteration, recursion, memoization,
and a simplified form of Binet's formula

NOTE 1: the iterative, recursive, memoization functions are more accurate than
the Binet's formula function because the Binet formula function  uses floats

NOTE 2: the Binet's formula function is much more limited in the size of inputs
that it can handle due to the size limitations of Python floats
NOTE 3: the matrix function is the fastest and most memory efficient for large n


See benchmark numbers in __main__ for performance comparisons/
https://en.wikipedia.org/wiki/Fibonacci_number for more information
"""

import functools
from collections.abc import Iterator
from math import sqrt
from time import time

import numpy as np
from numpy import ndarray


def time_func(func, *args, **kwargs):
    """
    Times the execution of a function with parameters
    """
    start = time()
    output = func(*args, **kwargs)
    end = time()
    if int(end - start) > 0:
        print(f"{func.__name__} runtime: {(end - start):0.4f} s")
    else:
        print(f"{func.__name__} runtime: {(end - start) * 1000:0.4f} ms")
    return output


def fib_iterative_yield(n: int) -> Iterator[int]:
    """
    Calculates the first n (1-indexed) Fibonacci numbers using iteration with yield
    >>> list(fib_iterative_yield(0))
    [0]
    >>> tuple(fib_iterative_yield(1))
    (0, 1)
    >>> tuple(fib_iterative_yield(5))
    (0, 1, 1, 2, 3, 5)
    >>> tuple(fib_iterative_yield(10))
    (0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55)
    >>> tuple(fib_iterative_yield(-1))
    Traceback (most recent call last):
        ...
    ValueError: n is negative
    """
    if n < 0:
        raise ValueError("n is negative")
    a, b = 0, 1
    yield a
    for _ in range(n):
        yield b
        a, b = b, a + b


def fib_iterative(n: int) -> list[int]:
    """
    Calculates the first n (0-indexed) Fibonacci numbers using iteration
    >>> fib_iterative(0)
    [0]
    >>> fib_iterative(1)
    [0, 1]
    >>> fib_iterative(5)
    [0, 1, 1, 2, 3, 5]
    >>> fib_iterative(10)
    [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
    >>> fib_iterative(-1)
    Traceback (most recent call last):
        ...
    ValueError: n is negative
    """
    if n < 0:
        raise ValueError("n is negative")
    if n == 0:
        return [0]
    fib = [0, 1]
    for _ in range(n - 1):
        fib.append(fib[-1] + fib[-2])
    return fib


def fib_recursive(n: int) -> list[int]:
    """
    Calculates the first n (0-indexed) Fibonacci numbers using recursion
    >>> fib_recursive(0)
    [0]
    >>> fib_recursive(1)
    [0, 1]
    >>> fib_recursive(5)
    [0, 1, 1, 2, 3, 5]
    >>> fib_recursive(10)
    [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
    >>> fib_recursive(-1)
    Traceback (most recent call last):
        ...
    ValueError: n is negative
    """

    def fib_recursive_term(i: int) -> int:
        """
        Calculates the i-th (0-indexed) Fibonacci number using recursion
        >>> fib_recursive_term(0)
        0
        >>> fib_recursive_term(1)
        1
        >>> fib_recursive_term(5)
        5
        >>> fib_recursive_term(10)
        55
        >>> fib_recursive_term(-1)
        Traceback (most recent call last):
            ...
        ValueError: n is negative
        """
        if i < 0:
            raise ValueError("n is negative")
        if i < 2:
            return i
        return fib_recursive_term(i - 1) + fib_recursive_term(i - 2)

    if n < 0:
        raise ValueError("n is negative")
    return [fib_recursive_term(i) for i in range(n + 1)]


def fib_recursive_cached(n: int) -> list[int]:
    """
    Calculates the first n (0-indexed) Fibonacci numbers using recursion
    >>> fib_recursive_cached(0)
    [0]
    >>> fib_recursive_cached(1)
    [0, 1]
    >>> fib_recursive_cached(5)
    [0, 1, 1, 2, 3, 5]
    >>> fib_recursive_cached(10)
    [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
    >>> fib_recursive_cached(-1)
    Traceback (most recent call last):
        ...
    ValueError: n is negative
    """

    @functools.cache
    def fib_recursive_term(i: int) -> int:
        """
        Calculates the i-th (0-indexed) Fibonacci number using recursion
        """
        if i < 0:
            raise ValueError("n is negative")
        if i < 2:
            return i
        return fib_recursive_term(i - 1) + fib_recursive_term(i - 2)

    if n < 0:
        raise ValueError("n is negative")
    return [fib_recursive_term(i) for i in range(n + 1)]


def fib_memoization(n: int) -> list[int]:
    """
    Calculates the first n (0-indexed) Fibonacci numbers using memoization
    >>> fib_memoization(0)
    [0]
    >>> fib_memoization(1)
    [0, 1]
    >>> fib_memoization(5)
    [0, 1, 1, 2, 3, 5]
    >>> fib_memoization(10)
    [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
    >>> fib_memoization(-1)
    Traceback (most recent call last):
        ...
    ValueError: n is negative
    """
    if n < 0:
        raise ValueError("n is negative")
    # Cache must be outside recursive function
    # other it will reset every time it calls itself.
    cache: dict[int, int] = {0: 0, 1: 1, 2: 1}  # Prefilled cache

    def rec_fn_memoized(num: int) -> int:
        if num in cache:
            return cache[num]

        value = rec_fn_memoized(num - 1) + rec_fn_memoized(num - 2)
        cache[num] = value
        return value

    return [rec_fn_memoized(i) for i in range(n + 1)]


def fib_binet(n: int) -> list[int]:
    """
    Calculates the first n (0-indexed) Fibonacci numbers using a simplified form
    of Binet's formula:
    https://en.m.wikipedia.org/wiki/Fibonacci_number#Computation_by_rounding

    NOTE 1: this function diverges from fib_iterative at around n = 71, likely
    due to compounding floating-point arithmetic errors

    NOTE 2: this function doesn't accept n >= 1475 because it overflows
    thereafter due to the size limitations of Python floats
    >>> fib_binet(0)
    [0]
    >>> fib_binet(1)
    [0, 1]
    >>> fib_binet(5)
    [0, 1, 1, 2, 3, 5]
    >>> fib_binet(10)
    [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
    >>> fib_binet(-1)
    Traceback (most recent call last):
        ...
    ValueError: n is negative
    >>> fib_binet(1475)
    Traceback (most recent call last):
        ...
    ValueError: n is too large
    """
    if n < 0:
        raise ValueError("n is negative")
    if n >= 1475:
        raise ValueError("n is too large")
    sqrt_5 = sqrt(5)
    phi = (1 + sqrt_5) / 2
    return [round(phi**i / sqrt_5) for i in range(n + 1)]


def matrix_pow_np(m: ndarray, power: int) -> ndarray:
    """
    Raises a matrix to the power of 'power' using binary exponentiation.

    Args:
        m: Matrix as a numpy array.
        power: The power to which the matrix is to be raised.

    Returns:
        The matrix raised to the power.

    Raises:
        ValueError: If power is negative.

    >>> m = np.array([[1, 1], [1, 0]], dtype=int)
    >>> matrix_pow_np(m, 0)  # Identity matrix when raised to the power of 0
    array([[1, 0],
           [0, 1]])

    >>> matrix_pow_np(m, 1)  # Same matrix when raised to the power of 1
    array([[1, 1],
           [1, 0]])

    >>> matrix_pow_np(m, 5)
    array([[8, 5],
           [5, 3]])

    >>> matrix_pow_np(m, -1)
    Traceback (most recent call last):
        ...
    ValueError: power is negative
    """
    result = np.array([[1, 0], [0, 1]], dtype=int)  # Identity Matrix
    base = m
    if power < 0:  # Negative power is not allowed
        raise ValueError("power is negative")
    while power:
        if power % 2 == 1:
            result = np.dot(result, base)
        base = np.dot(base, base)
        power //= 2
    return result


def fib_matrix_np(n: int) -> int:
    """
    Calculates the n-th Fibonacci number using matrix exponentiation.
    https://www.nayuki.io/page/fast-fibonacci-algorithms#:~:text=
    Summary:%20The%20two%20fast%20Fibonacci%20algorithms%20are%20matrix

    Args:
        n: Fibonacci sequence index

    Returns:
        The n-th Fibonacci number.

    Raises:
        ValueError: If n is negative.

    >>> fib_matrix_np(0)
    0
    >>> fib_matrix_np(1)
    1
    >>> fib_matrix_np(5)
    5
    >>> fib_matrix_np(10)
    55
    >>> fib_matrix_np(-1)
    Traceback (most recent call last):
        ...
    ValueError: n is negative
    """
    if n < 0:
        raise ValueError("n is negative")
    if n == 0:
        return 0

    m = np.array([[1, 1], [1, 0]], dtype=int)
    result = matrix_pow_np(m, n - 1)
    return int(result[0, 0])


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    # Time on an M1 MacBook Pro -- Fastest to slowest
    num = 30
    time_func(fib_iterative_yield, num)  # 0.0012 ms
    time_func(fib_iterative, num)  # 0.0031 ms
    time_func(fib_binet, num)  # 0.0062 ms
    time_func(fib_memoization, num)  # 0.0100 ms
    time_func(fib_recursive_cached, num)  # 0.0153 ms
    time_func(fib_recursive, num)  # 257.0910 ms
    time_func(fib_matrix_np, num)  # 0.0000 ms


================================================
FILE: maths/find_max.py
================================================
from __future__ import annotations


def find_max_iterative(nums: list[int | float]) -> int | float:
    """
    >>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
    ...     find_max_iterative(nums) == max(nums)
    True
    True
    True
    True
    >>> find_max_iterative([2, 4, 9, 7, 19, 94, 5])
    94
    >>> find_max_iterative([])
    Traceback (most recent call last):
        ...
    ValueError: find_max_iterative() arg is an empty sequence
    """
    if len(nums) == 0:
        raise ValueError("find_max_iterative() arg is an empty sequence")
    max_num = nums[0]
    for x in nums:
        if x > max_num:  # noqa: PLR1730
            max_num = x
    return max_num


# Divide and Conquer algorithm
def find_max_recursive(nums: list[int | float], left: int, right: int) -> int | float:
    """
    find max value in list
    :param nums: contains elements
    :param left: index of first element
    :param right: index of last element
    :return: max in nums

    >>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
    ...     find_max_recursive(nums, 0, len(nums) - 1) == max(nums)
    True
    True
    True
    True
    >>> nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10]
    >>> find_max_recursive(nums, 0, len(nums) - 1) == max(nums)
    True
    >>> find_max_recursive([], 0, 0)
    Traceback (most recent call last):
        ...
    ValueError: find_max_recursive() arg is an empty sequence
    >>> find_max_recursive(nums, 0, len(nums)) == max(nums)
    Traceback (most recent call last):
        ...
    IndexError: list index out of range
    >>> find_max_recursive(nums, -len(nums), -1) == max(nums)
    True
    >>> find_max_recursive(nums, -len(nums) - 1, -1) == max(nums)
    Traceback (most recent call last):
        ...
    IndexError: list index out of range
    """
    if len(nums) == 0:
        raise ValueError("find_max_recursive() arg is an empty sequence")
    if (
        left >= len(nums)
        or left < -len(nums)
        or right >= len(nums)
        or right < -len(nums)
    ):
        raise IndexError("list index out of range")
    if left == right:
        return nums[left]
    mid = (left + right) >> 1  # the middle
    left_max = find_max_recursive(nums, left, mid)  # find max in range[left, mid]
    right_max = find_max_recursive(
        nums, mid + 1, right
    )  # find max in range[mid + 1, right]

    return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)


================================================
FILE: maths/find_min.py
================================================
from __future__ import annotations


def find_min_iterative(nums: list[int | float]) -> int | float:
    """
    Find Minimum Number in a List
    :param nums: contains elements
    :return: min number in list

    >>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
    ...     find_min_iterative(nums) == min(nums)
    True
    True
    True
    True
    >>> find_min_iterative([0, 1, 2, 3, 4, 5, -3, 24, -56])
    -56
    >>> find_min_iterative([])
    Traceback (most recent call last):
        ...
    ValueError: find_min_iterative() arg is an empty sequence
    """
    if len(nums) == 0:
        raise ValueError("find_min_iterative() arg is an empty sequence")
    min_num = nums[0]
    for num in nums:
        min_num = min(min_num, num)
    return min_num


# Divide and Conquer algorithm
def find_min_recursive(nums: list[int | float], left: int, right: int) -> int | float:
    """
    find min value in list
    :param nums: contains elements
    :param left: index of first element
    :param right: index of last element
    :return: min in nums

    >>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
    ...     find_min_recursive(nums, 0, len(nums) - 1) == min(nums)
    True
    True
    True
    True
    >>> nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10]
    >>> find_min_recursive(nums, 0, len(nums) - 1) == min(nums)
    True
    >>> find_min_recursive([], 0, 0)
    Traceback (most recent call last):
        ...
    ValueError: find_min_recursive() arg is an empty sequence
    >>> find_min_recursive(nums, 0, len(nums)) == min(nums)
    Traceback (most recent call last):
        ...
    IndexError: list index out of range
    >>> find_min_recursive(nums, -len(nums), -1) == min(nums)
    True
    >>> find_min_recursive(nums, -len(nums) - 1, -1) == min(nums)
    Traceback (most recent call last):
        ...
    IndexError: list index out of range
    """
    if len(nums) == 0:
        raise ValueError("find_min_recursive() arg is an empty sequence")
    if (
        left >= len(nums)
        or left < -len(nums)
        or right >= len(nums)
        or right < -len(nums)
    ):
        raise IndexError("list index out of range")
    if left == right:
        return nums[left]
    mid = (left + right) >> 1  # the middle
    left_min = find_min_recursive(nums, left, mid)  # find min in range[left, mid]
    right_min = find_min_recursive(
        nums, mid + 1, right
    )  # find min in range[mid + 1, right]

    return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)


================================================
FILE: maths/floor.py
================================================
"""
https://en.wikipedia.org/wiki/Floor_and_ceiling_functions
"""


def floor(x: float) -> int:
    """
    Return the floor of x as an Integral.
    :param x: the number
    :return: the largest integer <= x.
    >>> import math
    >>> all(floor(n) == math.floor(n) for n
    ...     in (1, -1, 0, -0, 1.1, -1.1, 1.0, -1.0, 1_000_000_000))
    True
    """
    return int(x) if x - int(x) >= 0 else int(x) - 1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/gamma.py
================================================
"""
Gamma function is a very useful tool in math and physics.
It helps calculating complex integral in a convenient way.
for more info: https://en.wikipedia.org/wiki/Gamma_function
In mathematics, the gamma function is one commonly
used extension of the factorial function to complex numbers.
The gamma function is defined for all complex numbers except
the non-positive integers
Python's Standard Library math.gamma() function overflows around gamma(171.624).
"""

import math

from numpy import inf
from scipy.integrate import quad


def gamma_iterative(num: float) -> float:
    """
    Calculates the value of Gamma function of num
    where num is either an integer (1, 2, 3..) or a half-integer (0.5, 1.5, 2.5 ...).

    >>> gamma_iterative(-1)
    Traceback (most recent call last):
        ...
    ValueError: math domain error
    >>> gamma_iterative(0)
    Traceback (most recent call last):
        ...
    ValueError: math domain error
    >>> gamma_iterative(9)
    40320.0
    >>> from math import gamma as math_gamma
    >>> all(.99999999 < gamma_iterative(i) / math_gamma(i) <= 1.000000001
    ...     for i in range(1, 50))
    True
    >>> gamma_iterative(-1)/math_gamma(-1) <= 1.000000001
    Traceback (most recent call last):
        ...
    ValueError: math domain error
    >>> gamma_iterative(3.3) - math_gamma(3.3) <= 0.00000001
    True
    """
    if num <= 0:
        raise ValueError("math domain error")

    return quad(integrand, 0, inf, args=(num))[0]


def integrand(x: float, z: float) -> float:
    return math.pow(x, z - 1) * math.exp(-x)


def gamma_recursive(num: float) -> float:
    """
    Calculates the value of Gamma function of num
    where num is either an integer (1, 2, 3..) or a half-integer (0.5, 1.5, 2.5 ...).
    Implemented using recursion
    Examples:
    >>> from math import isclose, gamma as math_gamma
    >>> gamma_recursive(0.5)
    1.7724538509055159
    >>> gamma_recursive(1)
    1.0
    >>> gamma_recursive(2)
    1.0
    >>> gamma_recursive(3.5)
    3.3233509704478426
    >>> gamma_recursive(171.5)
    9.483367566824795e+307
    >>> all(isclose(gamma_recursive(num), math_gamma(num))
    ...     for num in (0.5, 2, 3.5, 171.5))
    True
    >>> gamma_recursive(0)
    Traceback (most recent call last):
        ...
    ValueError: math domain error
    >>> gamma_recursive(-1.1)
    Traceback (most recent call last):
        ...
    ValueError: math domain error
    >>> gamma_recursive(-4)
    Traceback (most recent call last):
        ...
    ValueError: math domain error
    >>> gamma_recursive(172)
    Traceback (most recent call last):
        ...
    OverflowError: math range error
    >>> gamma_recursive(1.1)
    Traceback (most recent call last):
        ...
    NotImplementedError: num must be an integer or a half-integer
    """
    if num <= 0:
        raise ValueError("math domain error")
    if num > 171.5:
        raise OverflowError("math range error")
    elif num - int(num) not in (0, 0.5):
        raise NotImplementedError("num must be an integer or a half-integer")
    elif num == 0.5:
        return math.sqrt(math.pi)
    else:
        return 1.0 if num == 1 else (num - 1) * gamma_recursive(num - 1)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    num = 1.0
    while num:
        num = float(input("Gamma of: "))
        print(f"gamma_iterative({num}) = {gamma_iterative(num)}")
        print(f"gamma_recursive({num}) = {gamma_recursive(num)}")
        print("\nEnter 0 to exit...")


================================================
FILE: maths/gaussian.py
================================================
"""
Reference: https://en.wikipedia.org/wiki/Gaussian_function
"""

from numpy import exp, pi, sqrt


def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> float:
    """
    >>> float(gaussian(1))
    0.24197072451914337

    >>> float(gaussian(24))
    3.342714441794458e-126

    >>> float(gaussian(1, 4, 2))
    0.06475879783294587

    >>> float(gaussian(1, 5, 3))
    0.05467002489199788

    Supports NumPy Arrays
    Use numpy.meshgrid with this to generate gaussian blur on images.
    >>> import numpy as np
    >>> x = np.arange(15)
    >>> gaussian(x)
    array([3.98942280e-01, 2.41970725e-01, 5.39909665e-02, 4.43184841e-03,
           1.33830226e-04, 1.48671951e-06, 6.07588285e-09, 9.13472041e-12,
           5.05227108e-15, 1.02797736e-18, 7.69459863e-23, 2.11881925e-27,
           2.14638374e-32, 7.99882776e-38, 1.09660656e-43])

    >>> float(gaussian(15))
    5.530709549844416e-50

    >>> gaussian([1,2, 'string'])
    Traceback (most recent call last):
        ...
    TypeError: unsupported operand type(s) for -: 'list' and 'float'

    >>> gaussian('hello world')
    Traceback (most recent call last):
        ...
    TypeError: unsupported operand type(s) for -: 'str' and 'float'

    >>> gaussian(10**234) # doctest: +IGNORE_EXCEPTION_DETAIL
    Traceback (most recent call last):
        ...
    OverflowError: (34, 'Result too large')

    >>> float(gaussian(10**-326))
    0.3989422804014327

    >>> float(gaussian(2523, mu=234234, sigma=3425))
    0.0
    """
    return 1 / sqrt(2 * pi * sigma**2) * exp(-((x - mu) ** 2) / (2 * sigma**2))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/gcd_of_n_numbers.py
================================================
"""
Gcd of N Numbers
Reference: https://en.wikipedia.org/wiki/Greatest_common_divisor
"""

from collections import Counter


def get_factors(
    number: int, factors: Counter | None = None, factor: int = 2
) -> Counter:
    """
    this is a recursive function for get all factors of number
    >>> get_factors(45)
    Counter({3: 2, 5: 1})
    >>> get_factors(2520)
    Counter({2: 3, 3: 2, 5: 1, 7: 1})
    >>> get_factors(23)
    Counter({23: 1})
    >>> get_factors(0)
    Traceback (most recent call last):
        ...
    TypeError: number must be integer and greater than zero
    >>> get_factors(-1)
    Traceback (most recent call last):
        ...
    TypeError: number must be integer and greater than zero
    >>> get_factors(1.5)
    Traceback (most recent call last):
        ...
    TypeError: number must be integer and greater than zero

    factor can be all numbers from 2 to number that we check if number % factor == 0
    if it is equal to zero, we check again with number // factor
    else we increase factor by one
    """

    match number:
        case int(number) if number == 1:
            return Counter({1: 1})
        case int(num) if number > 0:
            number = num
        case _:
            raise TypeError("number must be integer and greater than zero")

    factors = factors or Counter()

    if number == factor:  # break condition
        # all numbers are factors of itself
        factors[factor] += 1
        return factors

    if number % factor > 0:
        # if it is greater than zero
        # so it is not a factor of number and we check next number
        return get_factors(number, factors, factor + 1)

    factors[factor] += 1
    # else we update factors (that is Counter(dict-like) type) and check again
    return get_factors(number // factor, factors, factor)


def get_greatest_common_divisor(*numbers: int) -> int:
    """
    get gcd of n numbers:
    >>> get_greatest_common_divisor(18, 45)
    9
    >>> get_greatest_common_divisor(23, 37)
    1
    >>> get_greatest_common_divisor(2520, 8350)
    10
    >>> get_greatest_common_divisor(-10, 20)
    Traceback (most recent call last):
        ...
    Exception: numbers must be integer and greater than zero
    >>> get_greatest_common_divisor(1.5, 2)
    Traceback (most recent call last):
        ...
    Exception: numbers must be integer and greater than zero
    >>> get_greatest_common_divisor(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
    1
    >>> get_greatest_common_divisor("1", 2, 3, 4, 5, 6, 7, 8, 9, 10)
    Traceback (most recent call last):
        ...
    Exception: numbers must be integer and greater than zero
    """

    # we just need factors, not numbers itself
    try:
        same_factors, *factors = map(get_factors, numbers)
    except TypeError as e:
        raise Exception("numbers must be integer and greater than zero") from e

    for factor in factors:
        same_factors &= factor
        # get common factor between all
        # `&` return common elements with smaller value (for Counter type)

    # now, same_factors is something like {2: 2, 3: 4} that means 2 * 2 * 3 * 3 * 3 * 3
    mult = 1
    # power each factor and multiply
    # for {2: 2, 3: 4}, it is [4, 81] and then 324
    for m in [factor**power for factor, power in same_factors.items()]:
        mult *= m
    return mult


if __name__ == "__main__":
    print(get_greatest_common_divisor(18, 45))  # 9


================================================
FILE: maths/geometric_mean.py
================================================
"""
The Geometric Mean of n numbers is defined as the n-th root of the product
of those numbers. It is used to measure the central tendency of the numbers.
https://en.wikipedia.org/wiki/Geometric_mean
"""


def compute_geometric_mean(*args: int) -> float:
    """
    Return the geometric mean of the argument numbers.
    >>> compute_geometric_mean(2,8)
    4.0
    >>> compute_geometric_mean('a', 4)
    Traceback (most recent call last):
        ...
    TypeError: Not a Number
    >>> compute_geometric_mean(5, 125)
    25.0
    >>> compute_geometric_mean(1, 0)
    0.0
    >>> compute_geometric_mean(1, 5, 25, 5)
    5.0
    >>> compute_geometric_mean(2, -2)
    Traceback (most recent call last):
        ...
    ArithmeticError: Cannot Compute Geometric Mean for these numbers.
    >>> compute_geometric_mean(-5, 25, 1)
    -5.0
    """
    product = 1
    for number in args:
        if not isinstance(number, int) and not isinstance(number, float):
            raise TypeError("Not a Number")
        product *= number
    # Cannot calculate the even root for negative product.
    # Frequently they are restricted to being positive.
    if product < 0 and len(args) % 2 == 0:
        raise ArithmeticError("Cannot Compute Geometric Mean for these numbers.")
    mean = abs(product) ** (1 / len(args))
    # Since python calculates complex roots for negative products with odd roots.
    if product < 0:
        mean = -mean
    # Since it does floating point arithmetic, it gives 64**(1/3) as 3.99999996
    possible_mean = float(round(mean))
    # To check if the rounded number is actually the mean.
    if possible_mean ** len(args) == product:
        mean = possible_mean
    return mean


if __name__ == "__main__":
    from doctest import testmod

    testmod(name="compute_geometric_mean")
    print(compute_geometric_mean(-3, -27))


================================================
FILE: maths/germain_primes.py
================================================
"""
A Sophie Germain prime is any prime p, where 2p + 1 is also prime.
The second number, 2p + 1 is called a safe prime.

Examples of Germain primes include: 2, 3, 5, 11, 23

Their corresponding safe primes: 5, 7, 11, 23, 47
https://en.wikipedia.org/wiki/Safe_and_Sophie_Germain_primes
"""

from maths.prime_check import is_prime


def is_germain_prime(number: int) -> bool:
    """Checks if input number and 2*number + 1 are prime.

    >>> is_germain_prime(3)
    True
    >>> is_germain_prime(11)
    True
    >>> is_germain_prime(4)
    False
    >>> is_germain_prime(23)
    True
    >>> is_germain_prime(13)
    False
    >>> is_germain_prime(20)
    False
    >>> is_germain_prime('abc')
    Traceback (most recent call last):
        ...
    TypeError: Input value must be a positive integer. Input value: abc
    """
    if not isinstance(number, int) or number < 1:
        msg = f"Input value must be a positive integer. Input value: {number}"
        raise TypeError(msg)

    return is_prime(number) and is_prime(2 * number + 1)


def is_safe_prime(number: int) -> bool:
    """Checks if input number and (number - 1)/2 are prime.
    The smallest safe prime is 5, with the Germain prime is 2.

    >>> is_safe_prime(5)
    True
    >>> is_safe_prime(11)
    True
    >>> is_safe_prime(1)
    False
    >>> is_safe_prime(2)
    False
    >>> is_safe_prime(3)
    False
    >>> is_safe_prime(47)
    True
    >>> is_safe_prime('abc')
    Traceback (most recent call last):
        ...
    TypeError: Input value must be a positive integer. Input value: abc
    """
    if not isinstance(number, int) or number < 1:
        msg = f"Input value must be a positive integer. Input value: {number}"
        raise TypeError(msg)

    return (number - 1) % 2 == 0 and is_prime(number) and is_prime((number - 1) // 2)


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: maths/greatest_common_divisor.py
================================================
"""
Greatest Common Divisor.

Wikipedia reference: https://en.wikipedia.org/wiki/Greatest_common_divisor

gcd(a, b) = gcd(a, -b) = gcd(-a, b) = gcd(-a, -b) by definition of divisibility
"""


def greatest_common_divisor(a: int, b: int) -> int:
    """
    Calculate Greatest Common Divisor (GCD).
    >>> greatest_common_divisor(24, 40)
    8
    >>> greatest_common_divisor(1, 1)
    1
    >>> greatest_common_divisor(1, 800)
    1
    >>> greatest_common_divisor(11, 37)
    1
    >>> greatest_common_divisor(3, 5)
    1
    >>> greatest_common_divisor(16, 4)
    4
    >>> greatest_common_divisor(-3, 9)
    3
    >>> greatest_common_divisor(9, -3)
    3
    >>> greatest_common_divisor(3, -9)
    3
    >>> greatest_common_divisor(-3, -9)
    3
    >>> greatest_common_divisor(0, 0)
    0
    """
    return abs(b) if a == 0 else greatest_common_divisor(b % a, a)


def gcd_by_iterative(x: int, y: int) -> int:
    """
    Below method is more memory efficient because it does not create additional
    stack frames for recursive functions calls (as done in the above method).
    >>> gcd_by_iterative(24, 40)
    8
    >>> greatest_common_divisor(24, 40) == gcd_by_iterative(24, 40)
    True
    >>> gcd_by_iterative(-3, -9)
    3
    >>> gcd_by_iterative(3, -9)
    3
    >>> gcd_by_iterative(1, -800)
    1
    >>> gcd_by_iterative(11, 37)
    1
    >>> gcd_by_iterative(0, 0)
    0
    """
    while y:  # --> when y=0 then loop will terminate and return x as final GCD.
        x, y = y, x % y
    return abs(x)


def main():
    """
    Call Greatest Common Divisor function.
    """
    try:
        nums = input("Enter two integers separated by comma (,): ").split(",")
        num_1 = int(nums[0])
        num_2 = int(nums[1])
        print(
            f"greatest_common_divisor({num_1}, {num_2}) = "
            f"{greatest_common_divisor(num_1, num_2)}"
        )
        print(f"By iterative gcd({num_1}, {num_2}) = {gcd_by_iterative(num_1, num_2)}")
    except (IndexError, UnboundLocalError, ValueError):
        print("Wrong input")


if __name__ == "__main__":
    main()


================================================
FILE: maths/hardy_ramanujanalgo.py
================================================
# This theorem states that the number of prime factors of n
# will be approximately log(log(n)) for most natural numbers n

import math


def exact_prime_factor_count(n: int) -> int:
    """
    >>> exact_prime_factor_count(51242183)
    3
    """
    count = 0
    if n % 2 == 0:
        count += 1
        while n % 2 == 0:
            n = int(n / 2)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

    i = 3

    while i <= int(math.sqrt(n)):
        if n % i == 0:
            count += 1
            while n % i == 0:
                n = int(n / i)
        i = i + 2

    # this condition checks the prime
    # number n is greater than 2

    if n > 2:
        count += 1
    return count


if __name__ == "__main__":
    n = 51242183
    print(f"The number of distinct prime factors is/are {exact_prime_factor_count(n)}")
    print(f"The value of log(log(n)) is {math.log(math.log(n)):.4f}")

    """
    The number of distinct prime factors is/are 3
    The value of log(log(n)) is 2.8765
    """


================================================
FILE: maths/images/__init__.py
================================================


================================================
FILE: maths/integer_square_root.py
================================================
"""
Integer Square Root Algorithm -- An efficient method to calculate the square root of a
non-negative integer 'num' rounded down to the nearest integer. It uses a binary search
approach to find the integer square root without using any built-in exponent functions
or operators.
* https://en.wikipedia.org/wiki/Integer_square_root
* https://docs.python.org/3/library/math.html#math.isqrt
Note:
    - This algorithm is designed for non-negative integers only.
    - The result is rounded down to the nearest integer.
    - The algorithm has a time complexity of O(log(x)).
    - Original algorithm idea based on binary search.
"""


def integer_square_root(num: int) -> int:
    """
    Returns the integer square root of a non-negative integer num.
    Args:
        num: A non-negative integer.
    Returns:
        The integer square root of num.
    Raises:
        ValueError: If num is not an integer or is negative.
    >>> [integer_square_root(i) for i in range(18)]
    [0, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4]
    >>> integer_square_root(625)
    25
    >>> integer_square_root(2_147_483_647)
    46340
    >>> from math import isqrt
    >>> all(integer_square_root(i) == isqrt(i) for i in range(20))
    True
    >>> integer_square_root(-1)
    Traceback (most recent call last):
        ...
    ValueError: num must be non-negative integer
    >>> integer_square_root(1.5)
    Traceback (most recent call last):
        ...
    ValueError: num must be non-negative integer
    >>> integer_square_root("0")
    Traceback (most recent call last):
        ...
    ValueError: num must be non-negative integer
    """
    if not isinstance(num, int) or num < 0:
        raise ValueError("num must be non-negative integer")

    if num < 2:
        return num

    left_bound = 0
    right_bound = num // 2

    while left_bound <= right_bound:
        mid = left_bound + (right_bound - left_bound) // 2
        mid_squared = mid * mid
        if mid_squared == num:
            return mid

        if mid_squared < num:
            left_bound = mid + 1
        else:
            right_bound = mid - 1

    return right_bound


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/interquartile_range.py
================================================
"""
An implementation of interquartile range (IQR) which is a measure of statistical
dispersion, which is the spread of the data.

The function takes the list of numeric values as input and returns the IQR.

Script inspired by this Wikipedia article:
https://en.wikipedia.org/wiki/Interquartile_range
"""

from __future__ import annotations


def find_median(nums: list[int | float]) -> float:
    """
    This is the implementation of the median.
    :param nums: The list of numeric nums
    :return: Median of the list
    >>> find_median(nums=([1, 2, 2, 3, 4]))
    2
    >>> find_median(nums=([1, 2, 2, 3, 4, 4]))
    2.5
    >>> find_median(nums=([-1, 2, 0, 3, 4, -4]))
    1.5
    >>> find_median(nums=([1.1, 2.2, 2, 3.3, 4.4, 4]))
    2.65
    """
    div, mod = divmod(len(nums), 2)
    if mod:
        return nums[div]
    return (nums[div] + nums[(div) - 1]) / 2


def interquartile_range(nums: list[int | float]) -> float:
    """
    Return the interquartile range for a list of numeric values.
    :param nums: The list of numeric values.
    :return: interquartile range

    >>> interquartile_range(nums=[4, 1, 2, 3, 2])
    2.0
    >>> interquartile_range(nums = [-2, -7, -10, 9, 8, 4, -67, 45])
    17.0
    >>> interquartile_range(nums = [-2.1, -7.1, -10.1, 9.1, 8.1, 4.1, -67.1, 45.1])
    17.2
    >>> interquartile_range(nums = [0, 0, 0, 0, 0])
    0.0
    >>> interquartile_range(nums=[])
    Traceback (most recent call last):
    ...
    ValueError: The list is empty. Provide a non-empty list.
    """
    if not nums:
        raise ValueError("The list is empty. Provide a non-empty list.")
    nums.sort()
    length = len(nums)
    div, mod = divmod(length, 2)
    q1 = find_median(nums[:div])
    half_length = sum((div, mod))
    q3 = find_median(nums[half_length:length])
    return q3 - q1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/is_int_palindrome.py
================================================
def is_int_palindrome(num: int) -> bool:
    """
    Returns whether `num` is a palindrome or not
    (see for reference https://en.wikipedia.org/wiki/Palindromic_number).

    >>> is_int_palindrome(-121)
    False
    >>> is_int_palindrome(0)
    True
    >>> is_int_palindrome(10)
    False
    >>> is_int_palindrome(11)
    True
    >>> is_int_palindrome(101)
    True
    >>> is_int_palindrome(120)
    False
    """
    if num < 0:
        return False

    num_copy: int = num
    rev_num: int = 0
    while num > 0:
        rev_num = rev_num * 10 + (num % 10)
        num //= 10

    return num_copy == rev_num


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/is_ip_v4_address_valid.py
================================================
"""
wiki: https://en.wikipedia.org/wiki/IPv4

Is IP v4 address valid?
A valid IP address must be four octets in the form of A.B.C.D,
where A, B, C and D are numbers from 0-255
for example: 192.168.23.1, 172.255.255.255 are valid IP address
             192.168.256.0, 256.192.3.121 are invalid IP address
"""


def is_ip_v4_address_valid(ip: str) -> bool:
    """
    print "Valid IP address" If IP is valid.
    or
    print "Invalid IP address" If IP is invalid.

    >>> is_ip_v4_address_valid("192.168.0.23")
    True

    >>> is_ip_v4_address_valid("192.256.15.8")
    False

    >>> is_ip_v4_address_valid("172.100.0.8")
    True

    >>> is_ip_v4_address_valid("255.256.0.256")
    False

    >>> is_ip_v4_address_valid("1.2.33333333.4")
    False

    >>> is_ip_v4_address_valid("1.2.-3.4")
    False

    >>> is_ip_v4_address_valid("1.2.3")
    False

    >>> is_ip_v4_address_valid("1.2.3.4.5")
    False

    >>> is_ip_v4_address_valid("1.2.A.4")
    False

    >>> is_ip_v4_address_valid("0.0.0.0")
    True

    >>> is_ip_v4_address_valid("1.2.3.")
    False

    >>> is_ip_v4_address_valid("1.2.3.05")
    False
    """
    octets = ip.split(".")
    if len(octets) != 4:
        return False

    for octet in octets:
        if not octet.isdigit():
            return False

        number = int(octet)
        if len(str(number)) != len(octet):
            return False

        if not 0 <= number <= 255:
            return False

    return True


if __name__ == "__main__":
    ip = input().strip()
    valid_or_invalid = "valid" if is_ip_v4_address_valid(ip) else "invalid"
    print(f"{ip} is a {valid_or_invalid} IPv4 address.")


================================================
FILE: maths/is_square_free.py
================================================
"""
References: wikipedia:square free number
psf/black : True
ruff : True
"""

from __future__ import annotations


def is_square_free(factors: list[int]) -> bool:
    """
    # doctest: +NORMALIZE_WHITESPACE
    This functions takes a list of prime factors as input.
    returns True if the factors are square free.
    >>> is_square_free([1, 1, 2, 3, 4])
    False

    These are wrong but should return some value
    it simply checks for repetition in the numbers.
    >>> is_square_free([1, 3, 4, 'sd', 0.0])
    True

    >>> is_square_free([1, 0.5, 2, 0.0])
    True
    >>> is_square_free([1, 2, 2, 5])
    False
    >>> is_square_free('asd')
    True
    >>> is_square_free(24)
    Traceback (most recent call last):
        ...
    TypeError: 'int' object is not iterable
    """
    return len(set(factors)) == len(factors)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/jaccard_similarity.py
================================================
"""
The Jaccard similarity coefficient is a commonly used indicator of the
similarity between two sets. Let U be a set and A and B be subsets of U,
then the Jaccard index/similarity is defined to be the ratio of the number
of elements of their intersection and the number of elements of their union.

Inspired from Wikipedia and
the book Mining of Massive Datasets [MMDS 2nd Edition, Chapter 3]

https://en.wikipedia.org/wiki/Jaccard_index
https://mmds.org

Jaccard similarity is widely used with MinHashing.
"""


def jaccard_similarity(
    set_a: set[str] | list[str] | tuple[str],
    set_b: set[str] | list[str] | tuple[str],
    alternative_union=False,
):
    """
    Finds the jaccard similarity between two sets.
    Essentially, its intersection over union.

    The alternative way to calculate this is to take union as sum of the
    number of items in the two sets. This will lead to jaccard similarity
    of a set with itself be 1/2 instead of 1. [MMDS 2nd Edition, Page 77]

    Parameters:
        :set_a (set,list,tuple): A non-empty set/list
        :set_b (set,list,tuple): A non-empty set/list
        :alternativeUnion (boolean): If True, use sum of number of
        items as union

    Output:
        (float) The jaccard similarity between the two sets.

    Examples:
    >>> set_a = {'a', 'b', 'c', 'd', 'e'}
    >>> set_b = {'c', 'd', 'e', 'f', 'h', 'i'}
    >>> jaccard_similarity(set_a, set_b)
    0.375
    >>> jaccard_similarity(set_a, set_a)
    1.0
    >>> jaccard_similarity(set_a, set_a, True)
    0.5
    >>> set_a = ['a', 'b', 'c', 'd', 'e']
    >>> set_b = ('c', 'd', 'e', 'f', 'h', 'i')
    >>> jaccard_similarity(set_a, set_b)
    0.375
    >>> set_a = ('c', 'd', 'e', 'f', 'h', 'i')
    >>> set_b = ['a', 'b', 'c', 'd', 'e']
    >>> jaccard_similarity(set_a, set_b)
    0.375
    >>> set_a = ('c', 'd', 'e', 'f', 'h', 'i')
    >>> set_b = ['a', 'b', 'c', 'd']
    >>> jaccard_similarity(set_a, set_b, True)
    0.2
    >>> set_a = {'a', 'b'}
    >>> set_b = ['c', 'd']
    >>> jaccard_similarity(set_a, set_b)
    Traceback (most recent call last):
        ...
    ValueError: Set a and b must either both be sets or be either a list or a tuple.
    """

    if isinstance(set_a, set) and isinstance(set_b, set):
        intersection_length = len(set_a.intersection(set_b))

        if alternative_union:
            union_length = len(set_a) + len(set_b)
        else:
            union_length = len(set_a.union(set_b))

        return intersection_length / union_length

    elif isinstance(set_a, (list, tuple)) and isinstance(set_b, (list, tuple)):
        intersection = [element for element in set_a if element in set_b]

        if alternative_union:
            return len(intersection) / (len(set_a) + len(set_b))
        else:
            # Cast set_a to list because tuples cannot be mutated
            union = list(set_a) + [element for element in set_b if element not in set_a]
            return len(intersection) / len(union)
    raise ValueError(
        "Set a and b must either both be sets or be either a list or a tuple."
    )


if __name__ == "__main__":
    set_a = {"a", "b", "c", "d", "e"}
    set_b = {"c", "d", "e", "f", "h", "i"}
    print(jaccard_similarity(set_a, set_b))


================================================
FILE: maths/joint_probability_distribution.py
================================================
"""
Calculate joint probability distribution
https://en.wikipedia.org/wiki/Joint_probability_distribution
"""


def joint_probability_distribution(
    x_values: list[int],
    y_values: list[int],
    x_probabilities: list[float],
    y_probabilities: list[float],
) -> dict:
    """
    >>> joint_distribution =  joint_probability_distribution(
    ...     [1, 2], [-2, 5, 8], [0.7, 0.3], [0.3, 0.5, 0.2]
    ... )
    >>> from math import isclose
    >>> isclose(joint_distribution.pop((1, 8)), 0.14)
    True
    >>> joint_distribution
    {(1, -2): 0.21, (1, 5): 0.35, (2, -2): 0.09, (2, 5): 0.15, (2, 8): 0.06}
    """
    return {
        (x, y): x_prob * y_prob
        for x, x_prob in zip(x_values, x_probabilities)
        for y, y_prob in zip(y_values, y_probabilities)
    }


# Function to calculate the expectation (mean)
def expectation(values: list, probabilities: list) -> float:
    """
    >>> from math import isclose
    >>> isclose(expectation([1, 2], [0.7, 0.3]), 1.3)
    True
    """
    return sum(x * p for x, p in zip(values, probabilities))


# Function to calculate the variance
def variance(values: list[int], probabilities: list[float]) -> float:
    """
    >>> from math import isclose
    >>> isclose(variance([1,2],[0.7,0.3]), 0.21)
    True
    """
    mean = expectation(values, probabilities)
    return sum((x - mean) ** 2 * p for x, p in zip(values, probabilities))


# Function to calculate the covariance
def covariance(
    x_values: list[int],
    y_values: list[int],
    x_probabilities: list[float],
    y_probabilities: list[float],
) -> float:
    """
    >>> covariance([1, 2], [-2, 5, 8], [0.7, 0.3], [0.3, 0.5, 0.2])
    -2.7755575615628914e-17
    """
    mean_x = expectation(x_values, x_probabilities)
    mean_y = expectation(y_values, y_probabilities)
    return sum(
        (x - mean_x) * (y - mean_y) * px * py
        for x, px in zip(x_values, x_probabilities)
        for y, py in zip(y_values, y_probabilities)
    )


# Function to calculate the standard deviation
def standard_deviation(variance: float) -> float:
    """
    >>> standard_deviation(0.21)
    0.458257569495584
    """
    return variance**0.5


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    # Input values for X and Y
    x_vals = input("Enter values of X separated by spaces: ").split()
    y_vals = input("Enter values of Y separated by spaces: ").split()

    # Convert input values to integers
    x_values = [int(x) for x in x_vals]
    y_values = [int(y) for y in y_vals]

    # Input probabilities for X and Y
    x_probs = input("Enter probabilities for X separated by spaces: ").split()
    y_probs = input("Enter probabilities for Y separated by spaces: ").split()
    assert len(x_values) == len(x_probs)
    assert len(y_values) == len(y_probs)

    # Convert input probabilities to floats
    x_probabilities = [float(p) for p in x_probs]
    y_probabilities = [float(p) for p in y_probs]

    # Calculate the joint probability distribution
    jpd = joint_probability_distribution(
        x_values, y_values, x_probabilities, y_probabilities
    )

    # Print the joint probability distribution
    print(
        "\n".join(
            f"P(X={x}, Y={y}) = {probability}" for (x, y), probability in jpd.items()
        )
    )
    mean_xy = expectation(
        [x * y for x in x_values for y in y_values],
        [px * py for px in x_probabilities for py in y_probabilities],
    )
    print(f"x mean: {expectation(x_values, x_probabilities) = }")
    print(f"y mean: {expectation(y_values, y_probabilities) = }")
    print(f"xy mean: {mean_xy}")
    print(f"x: {variance(x_values, x_probabilities) = }")
    print(f"y: {variance(y_values, y_probabilities) = }")
    print(f"{covariance(x_values, y_values, x_probabilities, y_probabilities) = }")
    print(f"x: {standard_deviation(variance(x_values, x_probabilities)) = }")
    print(f"y: {standard_deviation(variance(y_values, y_probabilities)) = }")


================================================
FILE: maths/josephus_problem.py
================================================
"""
The Josephus problem is a famous theoretical problem related to a certain
counting-out game. This module provides functions to solve the Josephus problem
for num_people and a step_size.

The Josephus problem is defined as follows:
- num_people are standing in a circle.
- Starting with a specified person, you count around the circle,
  skipping a fixed number of people (step_size).
- The person at which you stop counting is eliminated from the circle.
- The counting continues until only one person remains.

For more information about the Josephus problem, refer to:
https://en.wikipedia.org/wiki/Josephus_problem
"""


def josephus_recursive(num_people: int, step_size: int) -> int:
    """
    Solve the Josephus problem for num_people and a step_size recursively.

    Args:
        num_people: A positive integer representing the number of people.
        step_size: A positive integer representing the step size for elimination.

    Returns:
        The position of the last person remaining.

    Raises:
        ValueError: If num_people or step_size is not a positive integer.

    Examples:
        >>> josephus_recursive(7, 3)
        3
        >>> josephus_recursive(10, 2)
        4
        >>> josephus_recursive(0, 2)
        Traceback (most recent call last):
            ...
        ValueError: num_people or step_size is not a positive integer.
        >>> josephus_recursive(1.9, 2)
        Traceback (most recent call last):
            ...
        ValueError: num_people or step_size is not a positive integer.
        >>> josephus_recursive(-2, 2)
        Traceback (most recent call last):
            ...
        ValueError: num_people or step_size is not a positive integer.
        >>> josephus_recursive(7, 0)
        Traceback (most recent call last):
            ...
        ValueError: num_people or step_size is not a positive integer.
        >>> josephus_recursive(7, -2)
        Traceback (most recent call last):
            ...
        ValueError: num_people or step_size is not a positive integer.
        >>> josephus_recursive(1_000, 0.01)
        Traceback (most recent call last):
            ...
        ValueError: num_people or step_size is not a positive integer.
        >>> josephus_recursive("cat", "dog")
        Traceback (most recent call last):
            ...
        ValueError: num_people or step_size is not a positive integer.
    """
    if (
        not isinstance(num_people, int)
        or not isinstance(step_size, int)
        or num_people <= 0
        or step_size <= 0
    ):
        raise ValueError("num_people or step_size is not a positive integer.")

    if num_people == 1:
        return 0

    return (josephus_recursive(num_people - 1, step_size) + step_size) % num_people


def find_winner(num_people: int, step_size: int) -> int:
    """
    Find the winner of the Josephus problem for num_people and a step_size.

    Args:
        num_people (int): Number of people.
        step_size (int): Step size for elimination.

    Returns:
        int: The position of the last person remaining (1-based index).

    Examples:
        >>> find_winner(7, 3)
        4
        >>> find_winner(10, 2)
        5
    """
    return josephus_recursive(num_people, step_size) + 1


def josephus_iterative(num_people: int, step_size: int) -> int:
    """
    Solve the Josephus problem for num_people and a step_size iteratively.

    Args:
        num_people (int): The number of people in the circle.
        step_size (int): The number of steps to take before eliminating someone.

    Returns:
        int: The position of the last person standing.

    Examples:
        >>> josephus_iterative(5, 2)
        3
        >>> josephus_iterative(7, 3)
        4
    """
    circle = list(range(1, num_people + 1))
    current = 0

    while len(circle) > 1:
        current = (current + step_size - 1) % len(circle)
        circle.pop(current)

    return circle[0]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/juggler_sequence.py
================================================
"""
== Juggler Sequence ==
Juggler sequence start with any positive integer n. The next term is
obtained as follows:
    If n term is even, the next term is floor value of square root of n .
    If n is odd, the next term is floor value of 3 time the square root of n.

https://en.wikipedia.org/wiki/Juggler_sequence
"""

# Author : Akshay Dubey (https://github.com/itsAkshayDubey)
import math


def juggler_sequence(number: int) -> list[int]:
    """
    >>> juggler_sequence(0)
    Traceback (most recent call last):
        ...
    ValueError: Input value of [number=0] must be a positive integer
    >>> juggler_sequence(1)
    [1]
    >>> juggler_sequence(2)
    [2, 1]
    >>> juggler_sequence(3)
    [3, 5, 11, 36, 6, 2, 1]
    >>> juggler_sequence(5)
    [5, 11, 36, 6, 2, 1]
    >>> juggler_sequence(10)
    [10, 3, 5, 11, 36, 6, 2, 1]
    >>> juggler_sequence(25)
    [25, 125, 1397, 52214, 228, 15, 58, 7, 18, 4, 2, 1]
    >>> juggler_sequence(6.0)
    Traceback (most recent call last):
        ...
    TypeError: Input value of [number=6.0] must be an integer
    >>> juggler_sequence(-1)
    Traceback (most recent call last):
        ...
    ValueError: Input value of [number=-1] must be a positive integer
    """
    if not isinstance(number, int):
        msg = f"Input value of [number={number}] must be an integer"
        raise TypeError(msg)
    if number < 1:
        msg = f"Input value of [number={number}] must be a positive integer"
        raise ValueError(msg)
    sequence = [number]
    while number != 1:
        if number % 2 == 0:
            number = math.floor(math.sqrt(number))
        else:
            number = math.floor(
                math.sqrt(number) * math.sqrt(number) * math.sqrt(number)
            )
        sequence.append(number)
    return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/karatsuba.py
================================================
"""Multiply two numbers using Karatsuba algorithm"""


def karatsuba(a: int, b: int) -> int:
    """
    >>> karatsuba(15463, 23489) == 15463 * 23489
    True
    >>> karatsuba(3, 9) == 3 * 9
    True
    """
    if len(str(a)) == 1 or len(str(b)) == 1:
        return a * b

    m1 = max(len(str(a)), len(str(b)))
    m2 = m1 // 2

    a1, a2 = divmod(a, 10**m2)
    b1, b2 = divmod(b, 10**m2)

    x = karatsuba(a2, b2)
    y = karatsuba((a1 + a2), (b1 + b2))
    z = karatsuba(a1, b1)

    return (z * 10 ** (2 * m2)) + ((y - z - x) * 10 ** (m2)) + (x)


def main():
    print(karatsuba(15463, 23489))


if __name__ == "__main__":
    main()


================================================
FILE: maths/kth_lexicographic_permutation.py
================================================
def kth_permutation(k, n):
    """
    Finds k'th lexicographic permutation (in increasing order) of
    0,1,2,...n-1 in O(n^2) time.

    Examples:
    First permutation is always 0,1,2,...n
    >>> kth_permutation(0,5)
    [0, 1, 2, 3, 4]

    The order of permutation of 0,1,2,3 is [0,1,2,3], [0,1,3,2], [0,2,1,3],
    [0,2,3,1], [0,3,1,2], [0,3,2,1], [1,0,2,3], [1,0,3,2], [1,2,0,3],
    [1,2,3,0], [1,3,0,2]
    >>> kth_permutation(10,4)
    [1, 3, 0, 2]
    """
    # Factorails from 1! to (n-1)!
    factorials = [1]
    for i in range(2, n):
        factorials.append(factorials[-1] * i)
    assert 0 <= k < factorials[-1] * n, "k out of bounds"

    permutation = []
    elements = list(range(n))

    # Find permutation
    while factorials:
        factorial = factorials.pop()
        number, k = divmod(k, factorial)
        permutation.append(elements[number])
        elements.remove(elements[number])
    permutation.append(elements[0])

    return permutation


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/largest_of_very_large_numbers.py
================================================
# Author: Abhijeeth S

import math


def res(x, y):
    """
    Reduces large number to a more manageable number
    >>> res(5, 7)
    4.892790030352132
    >>> res(0, 5)
    0
    >>> res(3, 0)
    1
    >>> res(-1, 5)
    Traceback (most recent call last):
    ...
    ValueError: expected a positive input
    """
    if 0 not in (x, y):
        # We use the relation x^y = y*log10(x), where 10 is the base.
        return y * math.log10(x)
    elif x == 0:  # 0 raised to any number is 0
        return 0
    elif y == 0:
        return 1  # any number raised to 0 is 1
    raise AssertionError("This should never happen")


if __name__ == "__main__":  # Main function
    # Read two numbers from input and typecast them to int using map function.
    # Here x is the base and y is the power.
    prompt = "Enter the base and the power separated by a comma: "
    x1, y1 = map(int, input(prompt).split(","))
    x2, y2 = map(int, input(prompt).split(","))

    # We find the log of each number, using the function res(), which takes two
    # arguments.
    res1 = res(x1, y1)
    res2 = res(x2, y2)

    # We check for the largest number
    if res1 > res2:
        print("Largest number is", x1, "^", y1)
    elif res2 > res1:
        print("Largest number is", x2, "^", y2)
    else:
        print("Both are equal")


================================================
FILE: maths/least_common_multiple.py
================================================
import unittest
from timeit import timeit

from maths.greatest_common_divisor import greatest_common_divisor


def least_common_multiple_slow(first_num: int, second_num: int) -> int:
    """
    Find the least common multiple of two numbers.

    Learn more: https://en.wikipedia.org/wiki/Least_common_multiple

    >>> least_common_multiple_slow(5, 2)
    10
    >>> least_common_multiple_slow(12, 76)
    228
    """
    max_num = first_num if first_num >= second_num else second_num
    common_mult = max_num
    while (common_mult % first_num > 0) or (common_mult % second_num > 0):
        common_mult += max_num
    return common_mult


def least_common_multiple_fast(first_num: int, second_num: int) -> int:
    """
    Find the least common multiple of two numbers.
    https://en.wikipedia.org/wiki/Least_common_multiple#Using_the_greatest_common_divisor
    >>> least_common_multiple_fast(5,2)
    10
    >>> least_common_multiple_fast(12,76)
    228
    """
    return first_num // greatest_common_divisor(first_num, second_num) * second_num


def benchmark():
    setup = (
        "from __main__ import least_common_multiple_slow, least_common_multiple_fast"
    )
    print(
        "least_common_multiple_slow():",
        timeit("least_common_multiple_slow(1000, 999)", setup=setup),
    )
    print(
        "least_common_multiple_fast():",
        timeit("least_common_multiple_fast(1000, 999)", setup=setup),
    )


class TestLeastCommonMultiple(unittest.TestCase):
    test_inputs = (
        (10, 20),
        (13, 15),
        (4, 31),
        (10, 42),
        (43, 34),
        (5, 12),
        (12, 25),
        (10, 25),
        (6, 9),
    )
    expected_results = (20, 195, 124, 210, 1462, 60, 300, 50, 18)

    def test_lcm_function(self):
        for i, (first_num, second_num) in enumerate(self.test_inputs):
            slow_result = least_common_multiple_slow(first_num, second_num)
            fast_result = least_common_multiple_fast(first_num, second_num)
            with self.subTest(i=i):
                assert slow_result == self.expected_results[i]
                assert fast_result == self.expected_results[i]


if __name__ == "__main__":
    benchmark()
    unittest.main()


================================================
FILE: maths/line_length.py
================================================
from __future__ import annotations

import math
from collections.abc import Callable


def line_length(
    fnc: Callable[[float], float],
    x_start: float,
    x_end: float,
    steps: int = 100,
) -> float:
    """
    Approximates the arc length of a line segment by treating the curve as a
    sequence of linear lines and summing their lengths
    :param fnc: a function which defines a curve
    :param x_start: left end point to indicate the start of line segment
    :param x_end: right end point to indicate end of line segment
    :param steps: an accuracy gauge; more steps increases accuracy
    :return: a float representing the length of the curve

    >>> def f(x):
    ...    return x
    >>> f"{line_length(f, 0, 1, 10):.6f}"
    '1.414214'

    >>> def f(x):
    ...    return 1
    >>> f"{line_length(f, -5.5, 4.5):.6f}"
    '10.000000'

    >>> def f(x):
    ...    return math.sin(5 * x) + math.cos(10 * x) + x * x/10
    >>> f"{line_length(f, 0.0, 10.0, 10000):.6f}"
    '69.534930'
    """

    x1 = x_start
    fx1 = fnc(x_start)
    length = 0.0

    for _ in range(steps):
        # Approximates curve as a sequence of linear lines and sums their length
        x2 = (x_end - x_start) / steps + x1
        fx2 = fnc(x2)
        length += math.hypot(x2 - x1, fx2 - fx1)

        # Increment step
        x1 = x2
        fx1 = fx2

    return length


if __name__ == "__main__":

    def f(x):
        return math.sin(10 * x)

    print("f(x) = sin(10 * x)")
    print("The length of the curve from x = -10 to x = 10 is:")
    i = 10
    while i <= 100000:
        print(f"With {i} steps: {line_length(f, -10, 10, i)}")
        i *= 10


================================================
FILE: maths/liouville_lambda.py
================================================
"""
== Liouville Lambda Function ==
The Liouville Lambda function, denoted by λ(n)
and λ(n) is 1 if n is the product of an even number of prime numbers,
and -1 if it is the product of an odd number of primes.

https://en.wikipedia.org/wiki/Liouville_function
"""

# Author : Akshay Dubey (https://github.com/itsAkshayDubey)
from maths.prime_factors import prime_factors


def liouville_lambda(number: int) -> int:
    """
    This functions takes an integer number as input.
    returns 1 if n has even number of prime factors and -1 otherwise.
    >>> liouville_lambda(10)
    1
    >>> liouville_lambda(11)
    -1
    >>> liouville_lambda(0)
    Traceback (most recent call last):
        ...
    ValueError: Input must be a positive integer
    >>> liouville_lambda(-1)
    Traceback (most recent call last):
        ...
    ValueError: Input must be a positive integer
    >>> liouville_lambda(11.0)
    Traceback (most recent call last):
        ...
    TypeError: Input value of [number=11.0] must be an integer
    """
    if not isinstance(number, int):
        msg = f"Input value of [number={number}] must be an integer"
        raise TypeError(msg)
    if number < 1:
        raise ValueError("Input must be a positive integer")
    return -1 if len(prime_factors(number)) % 2 else 1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/lucas_lehmer_primality_test.py
================================================
"""
In mathematics, the Lucas-Lehmer test (LLT) is a primality test for Mersenne
numbers.  https://en.wikipedia.org/wiki/Lucas%E2%80%93Lehmer_primality_test

A Mersenne number is a number that is one less than a power of two.
That is M_p = 2^p - 1
https://en.wikipedia.org/wiki/Mersenne_prime

The Lucas-Lehmer test is the primality test used by the
Great Internet Mersenne Prime Search (GIMPS) to locate large primes.
"""


# Primality test 2^p - 1
# Return true if 2^p - 1 is prime
def lucas_lehmer_test(p: int) -> bool:
    """
    >>> lucas_lehmer_test(p=7)
    True

    >>> lucas_lehmer_test(p=11)
    False

    # M_11 = 2^11 - 1 = 2047 = 23 * 89
    """

    if p < 2:
        raise ValueError("p should not be less than 2!")
    elif p == 2:
        return True

    s = 4
    m = (1 << p) - 1
    for _ in range(p - 2):
        s = ((s * s) - 2) % m
    return s == 0


if __name__ == "__main__":
    print(lucas_lehmer_test(7))
    print(lucas_lehmer_test(11))


================================================
FILE: maths/lucas_series.py
================================================
"""
https://en.wikipedia.org/wiki/Lucas_number
"""


def recursive_lucas_number(n_th_number: int) -> int:
    """
    Returns the nth lucas number
    >>> recursive_lucas_number(1)
    1
    >>> recursive_lucas_number(20)
    15127
    >>> recursive_lucas_number(0)
    2
    >>> recursive_lucas_number(25)
    167761
    >>> recursive_lucas_number(-1.5)
    Traceback (most recent call last):
        ...
    TypeError: recursive_lucas_number accepts only integer arguments.
    """
    if not isinstance(n_th_number, int):
        raise TypeError("recursive_lucas_number accepts only integer arguments.")
    if n_th_number == 0:
        return 2
    if n_th_number == 1:
        return 1

    return recursive_lucas_number(n_th_number - 1) + recursive_lucas_number(
        n_th_number - 2
    )


def dynamic_lucas_number(n_th_number: int) -> int:
    """
    Returns the nth lucas number
    >>> dynamic_lucas_number(1)
    1
    >>> dynamic_lucas_number(20)
    15127
    >>> dynamic_lucas_number(0)
    2
    >>> dynamic_lucas_number(25)
    167761
    >>> dynamic_lucas_number(-1.5)
    Traceback (most recent call last):
        ...
    TypeError: dynamic_lucas_number accepts only integer arguments.
    """
    if not isinstance(n_th_number, int):
        raise TypeError("dynamic_lucas_number accepts only integer arguments.")
    a, b = 2, 1
    for _ in range(n_th_number):
        a, b = b, a + b
    return a


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    n = int(input("Enter the number of terms in lucas series:\n").strip())
    print("Using recursive function to calculate lucas series:")
    print(" ".join(str(recursive_lucas_number(i)) for i in range(n)))
    print("\nUsing dynamic function to calculate lucas series:")
    print(" ".join(str(dynamic_lucas_number(i)) for i in range(n)))


================================================
FILE: maths/maclaurin_series.py
================================================
"""
https://en.wikipedia.org/wiki/Taylor_series#Trigonometric_functions
"""

from math import factorial, pi


def maclaurin_sin(theta: float, accuracy: int = 30) -> float:
    """
    Finds the maclaurin approximation of sin

    :param theta: the angle to which sin is found
    :param accuracy: the degree of accuracy wanted minimum
    :return: the value of sine in radians


    >>> from math import isclose, sin
    >>> all(isclose(maclaurin_sin(x, 50), sin(x)) for x in range(-25, 25))
    True
    >>> maclaurin_sin(10)
    -0.5440211108893691
    >>> maclaurin_sin(-10)
    0.5440211108893704
    >>> maclaurin_sin(10, 15)
    -0.544021110889369
    >>> maclaurin_sin(-10, 15)
    0.5440211108893704
    >>> maclaurin_sin("10")
    Traceback (most recent call last):
        ...
    ValueError: maclaurin_sin() requires either an int or float for theta
    >>> maclaurin_sin(10, -30)
    Traceback (most recent call last):
        ...
    ValueError: maclaurin_sin() requires a positive int for accuracy
    >>> maclaurin_sin(10, 30.5)
    Traceback (most recent call last):
        ...
    ValueError: maclaurin_sin() requires a positive int for accuracy
    >>> maclaurin_sin(10, "30")
    Traceback (most recent call last):
        ...
    ValueError: maclaurin_sin() requires a positive int for accuracy
    """

    if not isinstance(theta, (int, float)):
        raise ValueError("maclaurin_sin() requires either an int or float for theta")

    if not isinstance(accuracy, int) or accuracy <= 0:
        raise ValueError("maclaurin_sin() requires a positive int for accuracy")

    theta = float(theta)
    div = theta // (2 * pi)
    theta -= 2 * div * pi
    return sum(
        (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(accuracy)
    )


def maclaurin_cos(theta: float, accuracy: int = 30) -> float:
    """
    Finds the maclaurin approximation of cos

    :param theta: the angle to which cos is found
    :param accuracy: the degree of accuracy wanted
    :return: the value of cosine in radians


    >>> from math import isclose, cos
    >>> all(isclose(maclaurin_cos(x, 50), cos(x)) for x in range(-25, 25))
    True
    >>> maclaurin_cos(5)
    0.2836621854632268
    >>> maclaurin_cos(-5)
    0.2836621854632265
    >>> maclaurin_cos(10, 15)
    -0.8390715290764524
    >>> maclaurin_cos(-10, 15)
    -0.8390715290764521
    >>> maclaurin_cos("10")
    Traceback (most recent call last):
        ...
    ValueError: maclaurin_cos() requires either an int or float for theta
    >>> maclaurin_cos(10, -30)
    Traceback (most recent call last):
        ...
    ValueError: maclaurin_cos() requires a positive int for accuracy
    >>> maclaurin_cos(10, 30.5)
    Traceback (most recent call last):
        ...
    ValueError: maclaurin_cos() requires a positive int for accuracy
    >>> maclaurin_cos(10, "30")
    Traceback (most recent call last):
        ...
    ValueError: maclaurin_cos() requires a positive int for accuracy
    """

    if not isinstance(theta, (int, float)):
        raise ValueError("maclaurin_cos() requires either an int or float for theta")

    if not isinstance(accuracy, int) or accuracy <= 0:
        raise ValueError("maclaurin_cos() requires a positive int for accuracy")

    theta = float(theta)
    div = theta // (2 * pi)
    theta -= 2 * div * pi
    return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(accuracy))


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print(maclaurin_sin(10))
    print(maclaurin_sin(-10))
    print(maclaurin_sin(10, 15))
    print(maclaurin_sin(-10, 15))

    print(maclaurin_cos(5))
    print(maclaurin_cos(-5))
    print(maclaurin_cos(10, 15))
    print(maclaurin_cos(-10, 15))


================================================
FILE: maths/manhattan_distance.py
================================================
def manhattan_distance(point_a: list, point_b: list) -> float:
    """
    Expectts two list of numbers representing two points in the same
    n-dimensional space

    https://en.wikipedia.org/wiki/Taxicab_geometry

    >>> manhattan_distance([1,1], [2,2])
    2.0
    >>> manhattan_distance([1.5,1.5], [2,2])
    1.0
    >>> manhattan_distance([1.5,1.5], [2.5,2])
    1.5
    >>> manhattan_distance([-3, -3, -3], [0, 0, 0])
    9.0
    >>> manhattan_distance([1,1], None)
    Traceback (most recent call last):
        ...
    ValueError: Missing an input
    >>> manhattan_distance([1,1], [2, 2, 2])
    Traceback (most recent call last):
        ...
    ValueError: Both points must be in the same n-dimensional space
    >>> manhattan_distance([1,"one"], [2, 2, 2])
    Traceback (most recent call last):
        ...
    TypeError: Expected a list of numbers as input, found str
    >>> manhattan_distance(1, [2, 2, 2])
    Traceback (most recent call last):
         ...
    TypeError: Expected a list of numbers as input, found int
    >>> manhattan_distance([1,1], "not_a_list")
    Traceback (most recent call last):
         ...
    TypeError: Expected a list of numbers as input, found str
    """

    _validate_point(point_a)
    _validate_point(point_b)
    if len(point_a) != len(point_b):
        raise ValueError("Both points must be in the same n-dimensional space")

    return float(sum(abs(a - b) for a, b in zip(point_a, point_b)))


def _validate_point(point: list[float]) -> None:
    """
    >>> _validate_point(None)
    Traceback (most recent call last):
         ...
    ValueError: Missing an input
    >>> _validate_point([1,"one"])
    Traceback (most recent call last):
         ...
    TypeError: Expected a list of numbers as input, found str
    >>> _validate_point(1)
    Traceback (most recent call last):
         ...
    TypeError: Expected a list of numbers as input, found int
    >>> _validate_point("not_a_list")
    Traceback (most recent call last):
         ...
    TypeError: Expected a list of numbers as input, found str
    """
    if point:
        if isinstance(point, list):
            for item in point:
                if not isinstance(item, (int, float)):
                    msg = (
                        "Expected a list of numbers as input, found "
                        f"{type(item).__name__}"
                    )
                    raise TypeError(msg)
        else:
            msg = f"Expected a list of numbers as input, found {type(point).__name__}"
            raise TypeError(msg)
    else:
        raise ValueError("Missing an input")


def manhattan_distance_one_liner(point_a: list, point_b: list) -> float:
    """
    Version with one liner

    >>> manhattan_distance_one_liner([1,1], [2,2])
    2.0
    >>> manhattan_distance_one_liner([1.5,1.5], [2,2])
    1.0
    >>> manhattan_distance_one_liner([1.5,1.5], [2.5,2])
    1.5
    >>> manhattan_distance_one_liner([-3, -3, -3], [0, 0, 0])
    9.0
    >>> manhattan_distance_one_liner([1,1], None)
    Traceback (most recent call last):
         ...
    ValueError: Missing an input
    >>> manhattan_distance_one_liner([1,1], [2, 2, 2])
    Traceback (most recent call last):
         ...
    ValueError: Both points must be in the same n-dimensional space
    >>> manhattan_distance_one_liner([1,"one"], [2, 2, 2])
    Traceback (most recent call last):
         ...
    TypeError: Expected a list of numbers as input, found str
    >>> manhattan_distance_one_liner(1, [2, 2, 2])
    Traceback (most recent call last):
         ...
    TypeError: Expected a list of numbers as input, found int
    >>> manhattan_distance_one_liner([1,1], "not_a_list")
    Traceback (most recent call last):
         ...
    TypeError: Expected a list of numbers as input, found str
    """

    _validate_point(point_a)
    _validate_point(point_b)
    if len(point_a) != len(point_b):
        raise ValueError("Both points must be in the same n-dimensional space")

    return float(sum(abs(x - y) for x, y in zip(point_a, point_b)))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/matrix_exponentiation.py
================================================
"""Matrix Exponentiation"""

import timeit

"""
Matrix Exponentiation is a technique to solve linear recurrences in logarithmic time.
You read more about it here:
https://zobayer.blogspot.com/2010/11/matrix-exponentiation.html
https://www.hackerearth.com/practice/notes/matrix-exponentiation-1/
"""


class Matrix:
    def __init__(self, arg: list[list] | int) -> None:
        if isinstance(arg, list):  # Initializes a matrix identical to the one provided.
            self.t = arg
            self.n = len(arg)
        else:  # Initializes a square matrix of the given size and set values to zero.
            self.n = arg
            self.t = [[0 for _ in range(self.n)] for _ in range(self.n)]

    def __mul__(self, b: Matrix) -> Matrix:
        matrix = Matrix(self.n)
        for i in range(self.n):
            for j in range(self.n):
                for k in range(self.n):
                    matrix.t[i][j] += self.t[i][k] * b.t[k][j]
        return matrix


def modular_exponentiation(a: Matrix, b: int) -> Matrix:
    matrix = Matrix([[1, 0], [0, 1]])
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix


def fibonacci_with_matrix_exponentiation(n: int, f1: int, f2: int) -> int:
    """
    Returns the nth number of the Fibonacci sequence that
    starts with f1 and f2
    Uses the matrix exponentiation
    >>> fibonacci_with_matrix_exponentiation(1, 5, 6)
    5
    >>> fibonacci_with_matrix_exponentiation(2, 10, 11)
    11
    >>> fibonacci_with_matrix_exponentiation(13, 0, 1)
    144
    >>> fibonacci_with_matrix_exponentiation(10, 5, 9)
    411
    >>> fibonacci_with_matrix_exponentiation(9, 2, 3)
    89
    """
    # Trivial Cases
    if n == 1:
        return f1
    elif n == 2:
        return f2
    matrix = Matrix([[1, 1], [1, 0]])
    matrix = modular_exponentiation(matrix, n - 2)
    return f2 * matrix.t[0][0] + f1 * matrix.t[0][1]


def simple_fibonacci(n: int, f1: int, f2: int) -> int:
    """
    Returns the nth number of the Fibonacci sequence that
    starts with f1 and f2
    Uses the definition
    >>> simple_fibonacci(1, 5, 6)
    5
    >>> simple_fibonacci(2, 10, 11)
    11
    >>> simple_fibonacci(13, 0, 1)
    144
    >>> simple_fibonacci(10, 5, 9)
    411
    >>> simple_fibonacci(9, 2, 3)
    89
    """
    # Trivial Cases
    if n == 1:
        return f1
    elif n == 2:
        return f2

    n -= 2

    while n > 0:
        f2, f1 = f1 + f2, f2
        n -= 1

    return f2


def matrix_exponentiation_time() -> float:
    setup = """
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
"""
    code = "fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"
    exec_time = timeit.timeit(setup=setup, stmt=code, number=100)
    print("With matrix exponentiation the average execution time is ", exec_time / 100)
    return exec_time


def simple_fibonacci_time() -> float:
    setup = """
from random import randint
from __main__ import simple_fibonacci
"""
    code = "simple_fibonacci(randint(1,70000), 1, 1)"
    exec_time = timeit.timeit(setup=setup, stmt=code, number=100)
    print(
        "Without matrix exponentiation the average execution time is ", exec_time / 100
    )
    return exec_time


def main() -> None:
    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
    main()


================================================
FILE: maths/max_sum_sliding_window.py
================================================
"""
Given an array of integer elements and an integer 'k', we are required to find the
maximum sum of 'k' consecutive elements in the array.

Instead of using a nested for loop, in a Brute force approach we will use a technique
called 'Window sliding technique' where the nested loops can be converted to a single
loop to reduce time complexity.
"""

from __future__ import annotations


def max_sum_in_array(array: list[int], k: int) -> int:
    """
    Returns the maximum sum of k consecutive elements
    >>> arr = [1, 4, 2, 10, 2, 3, 1, 0, 20]
    >>> k = 4
    >>> max_sum_in_array(arr, k)
    24
    >>> k = 10
    >>> max_sum_in_array(arr,k)
    Traceback (most recent call last):
        ...
    ValueError: Invalid Input
    >>> arr = [1, 4, 2, 10, 2, 13, 1, 0, 2]
    >>> k = 4
    >>> max_sum_in_array(arr, k)
    27
    """
    if len(array) < k or k < 0:
        raise ValueError("Invalid Input")
    max_sum = current_sum = sum(array[:k])
    for i in range(len(array) - k):
        current_sum = current_sum - array[i] + array[i + k]
        max_sum = max(max_sum, current_sum)
    return max_sum


if __name__ == "__main__":
    from doctest import testmod
    from random import randint

    testmod()
    array = [randint(-1000, 1000) for i in range(100)]
    k = randint(0, 110)
    print(
        f"The maximum sum of {k} consecutive elements is {max_sum_in_array(array, k)}"
    )


================================================
FILE: maths/minkowski_distance.py
================================================
def minkowski_distance(
    point_a: list[float],
    point_b: list[float],
    order: int,
) -> float:
    """
    This function calculates the Minkowski distance for a given order between
    two n-dimensional points represented as lists. For the case of order = 1,
    the Minkowski distance degenerates to the Manhattan distance. For
    order = 2, the usual Euclidean distance is obtained.

    https://en.wikipedia.org/wiki/Minkowski_distance

    Note: due to floating point calculation errors the output of this
    function may be inaccurate.

    >>> minkowski_distance([1.0, 1.0], [2.0, 2.0], 1)
    2.0
    >>> minkowski_distance([1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], 2)
    8.0
    >>> import numpy as np
    >>> bool(np.isclose(5.0, minkowski_distance([5.0], [0.0], 3)))
    True
    >>> minkowski_distance([1.0], [2.0], -1)
    Traceback (most recent call last):
        ...
    ValueError: The order must be greater than or equal to 1.
    >>> minkowski_distance([1.0], [1.0, 2.0], 1)
    Traceback (most recent call last):
        ...
    ValueError: Both points must have the same dimension.
    """
    if order < 1:
        raise ValueError("The order must be greater than or equal to 1.")

    if len(point_a) != len(point_b):
        raise ValueError("Both points must have the same dimension.")

    return sum(abs(a - b) ** order for a, b in zip(point_a, point_b)) ** (1 / order)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/mobius_function.py
================================================
"""
References: https://en.wikipedia.org/wiki/M%C3%B6bius_function
References: wikipedia:square free number
psf/black : True
ruff : True
"""

from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors


def mobius(n: int) -> int:
    """
    Mobius function
    >>> mobius(24)
    0
    >>> mobius(-1)
    1
    >>> mobius('asd')
    Traceback (most recent call last):
        ...
    TypeError: '<=' not supported between instances of 'int' and 'str'
    >>> mobius(10**400)
    0
    >>> mobius(10**-400)
    1
    >>> mobius(-1424)
    1
    >>> mobius([1, '2', 2.0])
    Traceback (most recent call last):
        ...
    TypeError: '<=' not supported between instances of 'int' and 'list'
    """
    factors = prime_factors(n)
    if is_square_free(factors):
        return -1 if len(factors) % 2 else 1
    return 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/modular_division.py
================================================
from __future__ import annotations


def modular_division(a: int, b: int, n: int) -> int:
    """
    Modular Division :
    An efficient algorithm for dividing b by a modulo n.

    GCD ( Greatest Common Divisor ) or HCF ( Highest Common Factor )

    Given three integers a, b, and n, such that gcd(a,n)=1 and n>1, the algorithm should
    return an integer x such that 0≤x≤n-1, and  b/a=x(modn) (that is, b=ax(modn)).

    Theorem:
    a has a multiplicative inverse modulo n iff gcd(a,n) = 1


    This find x = b*a^(-1) mod n
    Uses ExtendedEuclid to find the inverse of a

    >>> modular_division(4,8,5)
    2

    >>> modular_division(3,8,5)
    1

    >>> modular_division(4, 11, 5)
    4

    """
    if n <= 1:
        raise ValueError("Modulus n must be greater than 1")
    if a <= 0:
        raise ValueError("Divisor a must be a positive integer")
    if greatest_common_divisor(a, n) != 1:
        raise ValueError("a and n must be coprime (gcd(a, n) = 1)")

    (_d, _t, s) = extended_gcd(n, a)  # Implemented below
    x = (b * s) % n
    return x


def invert_modulo(a: int, n: int) -> int:
    """
    This function find the inverses of a i.e., a^(-1)

    >>> invert_modulo(2, 5)
    3

    >>> invert_modulo(8,7)
    1

    """
    (b, _x) = extended_euclid(a, n)  # Implemented below
    if b < 0:
        b = (b % n + n) % n
    return b


# ------------------ Finding Modular division using invert_modulo -------------------


def modular_division2(a: int, b: int, n: int) -> int:
    """
    This function used the above inversion of a to find x = (b*a^(-1))mod n

    >>> modular_division2(4,8,5)
    2

    >>> modular_division2(3,8,5)
    1

    >>> modular_division2(4, 11, 5)
    4

    """
    s = invert_modulo(a, n)
    x = (b * s) % n
    return x


def extended_gcd(a: int, b: int) -> tuple[int, int, int]:
    """
    Extended Euclid's Algorithm : If d divides a and b and d = a*x + b*y for integers x
    and y, then d = gcd(a,b)
    >>> extended_gcd(10, 6)
    (2, -1, 2)

    >>> extended_gcd(7, 5)
    (1, -2, 3)

    ** extended_gcd function is used when d = gcd(a,b) is required in output

    """
    assert a >= 0
    assert b >= 0

    if b == 0:
        d, x, y = a, 1, 0
    else:
        (d, p, q) = extended_gcd(b, a % b)
        x = q
        y = p - q * (a // b)

    assert a % d == 0
    assert b % d == 0
    assert d == a * x + b * y

    return (d, x, y)


def extended_euclid(a: int, b: int) -> tuple[int, int]:
    """
    Extended Euclid
    >>> extended_euclid(10, 6)
    (-1, 2)

    >>> extended_euclid(7, 5)
    (-2, 3)

    """
    if b == 0:
        return (1, 0)
    (x, y) = extended_euclid(b, a % b)
    k = a // b
    return (y, x - k * y)


def greatest_common_divisor(a: int, b: int) -> int:
    """
    Euclid's Lemma :  d divides a and b, if and only if d divides a-b and b
    Euclid's Algorithm

    >>> greatest_common_divisor(7,5)
    1

    Note : In number theory, two integers a and b are said to be relatively prime,
        mutually prime, or co-prime if the only positive integer (factor) that divides
        both of them is 1  i.e., gcd(a,b) = 1.

    >>> greatest_common_divisor(121, 11)
    11

    """
    if a < b:
        a, b = b, a

    while a % b != 0:
        a, b = b, a % b

    return b


if __name__ == "__main__":
    from doctest import testmod

    testmod(name="modular_division", verbose=True)
    testmod(name="modular_division2", verbose=True)
    testmod(name="invert_modulo", verbose=True)
    testmod(name="extended_gcd", verbose=True)
    testmod(name="extended_euclid", verbose=True)
    testmod(name="greatest_common_divisor", verbose=True)


================================================
FILE: maths/modular_exponential.py
================================================
"""
Modular Exponential.
Modular exponentiation is a type of exponentiation performed over a modulus.
For more explanation, please check
https://en.wikipedia.org/wiki/Modular_exponentiation
"""

"""Calculate Modular Exponential."""


def modular_exponential(base: int, power: int, mod: int):
    """
    >>> modular_exponential(5, 0, 10)
    1
    >>> modular_exponential(2, 8, 7)
    4
    >>> modular_exponential(3, -2, 9)
    -1
    """

    if power < 0:
        return -1
    base %= mod
    result = 1

    while power > 0:
        if power & 1:
            result = (result * base) % mod
        power = power >> 1
        base = (base * base) % mod

    return result


def main():
    """Call Modular Exponential Function."""
    print(modular_exponential(3, 200, 13))


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    main()


================================================
FILE: maths/monte_carlo.py
================================================
"""
@author: MatteoRaso
"""

from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean


def pi_estimator(iterations: int) -> None:
    """
    An implementation of the Monte Carlo method used to find pi.
    1. Draw a 2x2 square centred at (0,0).
    2. Inscribe a circle within the square.
    3. For each iteration, place a dot anywhere in the square.
       a. Record the number of dots within the circle.
    4. After all the dots are placed, divide the dots in the circle by the total.
    5. Multiply this value by 4 to get your estimate of pi.
    6. Print the estimated and numpy value of pi
    """

    # A local function to see if a dot lands in the circle.
    def is_in_circle(x: float, y: float) -> bool:
        distance_from_centre = sqrt((x**2) + (y**2))
        # Our circle has a radius of 1, so a distance
        # greater than 1 would land outside the circle.
        return distance_from_centre <= 1

    # The proportion of guesses that landed in the circle
    proportion = mean(
        int(is_in_circle(uniform(-1.0, 1.0), uniform(-1.0, 1.0)))
        for _ in range(iterations)
    )
    # The ratio of the area for circle to square is pi/4.
    pi_estimate = proportion * 4
    print(f"The estimated value of pi is {pi_estimate}")
    print(f"The numpy value of pi is {pi}")
    print(f"The total error is {abs(pi - pi_estimate)}")


def area_under_curve_estimator(
    iterations: int,
    function_to_integrate: Callable[[float], float],
    min_value: float = 0.0,
    max_value: float = 1.0,
) -> float:
    """
    An implementation of the Monte Carlo method to find area under
       a single variable non-negative real-valued continuous function,
       say f(x), where x lies within a continuous bounded interval,
       say [min_value, max_value], where min_value and max_value are
       finite numbers
    1. Let x be a uniformly distributed random variable between min_value to
       max_value
    2. Expected value of f(x) =
       (integrate f(x) from min_value to max_value)/(max_value - min_value)
    3. Finding expected value of f(x):
        a. Repeatedly draw x from uniform distribution
        b. Evaluate f(x) at each of the drawn x values
        c. Expected value = average of the function evaluations
    4. Estimated value of integral = Expected value * (max_value - min_value)
    5. Returns estimated value
    """

    return mean(
        function_to_integrate(uniform(min_value, max_value)) for _ in range(iterations)
    ) * (max_value - min_value)


def area_under_line_estimator_check(
    iterations: int, min_value: float = 0.0, max_value: float = 1.0
) -> None:
    """
    Checks estimation error for area_under_curve_estimator function
    for f(x) = x where x lies within min_value to max_value
    1. Calls "area_under_curve_estimator" function
    2. Compares with the expected value
    3. Prints estimated, expected and error value
    """

    def identity_function(x: float) -> float:
        """
        Represents identity function
        >>> [function_to_integrate(x) for x in [-2.0, -1.0, 0.0, 1.0, 2.0]]
        [-2.0, -1.0, 0.0, 1.0, 2.0]
        """
        return x

    estimated_value = area_under_curve_estimator(
        iterations, identity_function, min_value, max_value
    )
    expected_value = (max_value * max_value - min_value * min_value) / 2

    print("******************")
    print(f"Estimating area under y=x where x varies from {min_value} to {max_value}")
    print(f"Estimated value is {estimated_value}")
    print(f"Expected value is {expected_value}")
    print(f"Total error is {abs(estimated_value - expected_value)}")
    print("******************")


def pi_estimator_using_area_under_curve(iterations: int) -> None:
    """
    Area under curve y = sqrt(4 - x^2) where x lies in 0 to 2 is equal to pi
    """

    def function_to_integrate(x: float) -> float:
        """
        Represents semi-circle with radius 2
        >>> [function_to_integrate(x) for x in [-2.0, 0.0, 2.0]]
        [0.0, 2.0, 0.0]
        """
        return sqrt(4.0 - x * x)

    estimated_value = area_under_curve_estimator(
        iterations, function_to_integrate, 0.0, 2.0
    )

    print("******************")
    print("Estimating pi using area_under_curve_estimator")
    print(f"Estimated value is {estimated_value}")
    print(f"Expected value is {pi}")
    print(f"Total error is {abs(estimated_value - pi)}")
    print("******************")


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/monte_carlo_dice.py
================================================
from __future__ import annotations

import random


class Dice:
    NUM_SIDES = 6

    def __init__(self):
        """Initialize a six sided dice"""
        self.sides = list(range(1, Dice.NUM_SIDES + 1))

    def roll(self):
        return random.choice(self.sides)


def throw_dice(num_throws: int, num_dice: int = 2) -> list[float]:
    """
    Return probability list of all possible sums when throwing dice.

    >>> random.seed(0)
    >>> throw_dice(10, 1)
    [10.0, 0.0, 30.0, 50.0, 10.0, 0.0]
    >>> throw_dice(100, 1)
    [19.0, 17.0, 17.0, 11.0, 23.0, 13.0]
    >>> throw_dice(1000, 1)
    [18.8, 15.5, 16.3, 17.6, 14.2, 17.6]
    >>> throw_dice(10000, 1)
    [16.35, 16.89, 16.93, 16.6, 16.52, 16.71]
    >>> throw_dice(10000, 2)
    [2.74, 5.6, 7.99, 11.26, 13.92, 16.7, 14.44, 10.63, 8.05, 5.92, 2.75]
    """
    dices = [Dice() for i in range(num_dice)]
    count_of_sum = [0] * (len(dices) * Dice.NUM_SIDES + 1)
    for _ in range(num_throws):
        count_of_sum[sum(dice.roll() for dice in dices)] += 1
    probability = [round((count * 100) / num_throws, 2) for count in count_of_sum]
    return probability[num_dice:]  # remove probability of sums that never appear


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/number_of_digits.py
================================================
import math
from timeit import timeit


def num_digits(n: int) -> int:
    """
    Find the number of digits in a number.

    >>> num_digits(12345)
    5
    >>> num_digits(123)
    3
    >>> num_digits(0)
    1
    >>> num_digits(-1)
    1
    >>> num_digits(-123456)
    6
    >>> num_digits('123')  # Raises a TypeError for non-integer input
    Traceback (most recent call last):
        ...
    TypeError: Input must be an integer
    """

    if not isinstance(n, int):
        raise TypeError("Input must be an integer")

    digits = 0
    n = abs(n)
    while True:
        n = n // 10
        digits += 1
        if n == 0:
            break
    return digits


def num_digits_fast(n: int) -> int:
    """
    Find the number of digits in a number.
    abs() is used as logarithm for negative numbers is not defined.

    >>> num_digits_fast(12345)
    5
    >>> num_digits_fast(123)
    3
    >>> num_digits_fast(0)
    1
    >>> num_digits_fast(-1)
    1
    >>> num_digits_fast(-123456)
    6
    >>> num_digits('123')  # Raises a TypeError for non-integer input
    Traceback (most recent call last):
        ...
    TypeError: Input must be an integer
    """

    if not isinstance(n, int):
        raise TypeError("Input must be an integer")

    return 1 if n == 0 else math.floor(math.log(abs(n), 10) + 1)


def num_digits_faster(n: int) -> int:
    """
    Find the number of digits in a number.
    abs() is used for negative numbers

    >>> num_digits_faster(12345)
    5
    >>> num_digits_faster(123)
    3
    >>> num_digits_faster(0)
    1
    >>> num_digits_faster(-1)
    1
    >>> num_digits_faster(-123456)
    6
    >>> num_digits('123')  # Raises a TypeError for non-integer input
    Traceback (most recent call last):
        ...
    TypeError: Input must be an integer
    """

    if not isinstance(n, int):
        raise TypeError("Input must be an integer")

    return len(str(abs(n)))


def benchmark() -> None:
    """
    Benchmark multiple functions, with three different length int values.
    """
    from collections.abc import Callable

    def benchmark_a_function(func: Callable, value: int) -> None:
        call = f"{func.__name__}({value})"
        timing = timeit(f"__main__.{call}", setup="import __main__")
        print(f"{call}: {func(value)} -- {timing} seconds")

    for value in (262144, 1125899906842624, 1267650600228229401496703205376):
        for func in (num_digits, num_digits_fast, num_digits_faster):
            benchmark_a_function(func, value)
        print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()


================================================
FILE: maths/numerical_analysis/__init__.py
================================================


================================================
FILE: maths/numerical_analysis/adams_bashforth.py
================================================
"""
Use the Adams-Bashforth methods to solve Ordinary Differential Equations.

https://en.wikipedia.org/wiki/Linear_multistep_method
Author : Ravi Kumar
"""

from collections.abc import Callable
from dataclasses import dataclass

import numpy as np


@dataclass
class AdamsBashforth:
    """
    args:
    func: An ordinary differential equation (ODE) as function of x and y.
    x_initials: List containing initial required values of x.
    y_initials: List containing initial required values of y.
    step_size: The increment value of x.
    x_final: The final value of x.

    Returns: Solution of y at each nodal point

    >>> def f(x, y):
    ...     return x + y
    >>> AdamsBashforth(f, [0, 0.2, 0.4], [0, 0.2, 1], 0.2, 1)  # doctest: +ELLIPSIS
    AdamsBashforth(func=..., x_initials=[0, 0.2, 0.4], y_initials=[0, 0.2, 1], step...)
    >>> AdamsBashforth(f, [0, 0.2, 1], [0, 0, 0.04], 0.2, 1).step_2()
    Traceback (most recent call last):
        ...
    ValueError: The final value of x must be greater than the initial values of x.

    >>> AdamsBashforth(f, [0, 0.2, 0.3], [0, 0, 0.04], 0.2, 1).step_3()
    Traceback (most recent call last):
        ...
    ValueError: x-values must be equally spaced according to step size.

    >>> AdamsBashforth(f,[0,0.2,0.4,0.6,0.8],[0,0,0.04,0.128,0.307],-0.2,1).step_5()
    Traceback (most recent call last):
        ...
    ValueError: Step size must be positive.
    """

    func: Callable[[float, float], float]
    x_initials: list[float]
    y_initials: list[float]
    step_size: float
    x_final: float

    def __post_init__(self) -> None:
        if self.x_initials[-1] >= self.x_final:
            raise ValueError(
                "The final value of x must be greater than the initial values of x."
            )

        if self.step_size <= 0:
            raise ValueError("Step size must be positive.")

        if not all(
            round(x1 - x0, 10) == self.step_size
            for x0, x1 in zip(self.x_initials, self.x_initials[1:])
        ):
            raise ValueError("x-values must be equally spaced according to step size.")

    def step_2(self) -> np.ndarray:
        """
        >>> def f(x, y):
        ...     return x
        >>> AdamsBashforth(f, [0, 0.2], [0, 0], 0.2, 1).step_2()
        array([0.  , 0.  , 0.06, 0.16, 0.3 , 0.48])

        >>> AdamsBashforth(f, [0, 0.2, 0.4], [0, 0, 0.04], 0.2, 1).step_2()
        Traceback (most recent call last):
            ...
        ValueError: Insufficient initial points information.
        """

        if len(self.x_initials) != 2 or len(self.y_initials) != 2:
            raise ValueError("Insufficient initial points information.")

        x_0, x_1 = self.x_initials[:2]
        y_0, y_1 = self.y_initials[:2]

        n = int((self.x_final - x_1) / self.step_size)
        y = np.zeros(n + 2)
        y[0] = y_0
        y[1] = y_1

        for i in range(n):
            y[i + 2] = y[i + 1] + (self.step_size / 2) * (
                3 * self.func(x_1, y[i + 1]) - self.func(x_0, y[i])
            )
            x_0 = x_1
            x_1 += self.step_size

        return y

    def step_3(self) -> np.ndarray:
        """
        >>> def f(x, y):
        ...     return x + y
        >>> y = AdamsBashforth(f, [0, 0.2, 0.4], [0, 0, 0.04], 0.2, 1).step_3()
        >>> float(y[3])
        0.15533333333333332

        >>> AdamsBashforth(f, [0, 0.2], [0, 0], 0.2, 1).step_3()
        Traceback (most recent call last):
            ...
        ValueError: Insufficient initial points information.
        """
        if len(self.x_initials) != 3 or len(self.y_initials) != 3:
            raise ValueError("Insufficient initial points information.")

        x_0, x_1, x_2 = self.x_initials[:3]
        y_0, y_1, y_2 = self.y_initials[:3]

        n = int((self.x_final - x_2) / self.step_size)
        y = np.zeros(n + 4)
        y[0] = y_0
        y[1] = y_1
        y[2] = y_2

        for i in range(n + 1):
            y[i + 3] = y[i + 2] + (self.step_size / 12) * (
                23 * self.func(x_2, y[i + 2])
                - 16 * self.func(x_1, y[i + 1])
                + 5 * self.func(x_0, y[i])
            )
            x_0 = x_1
            x_1 = x_2
            x_2 += self.step_size

        return y

    def step_4(self) -> np.ndarray:
        """
        >>> def f(x,y):
        ...     return x + y
        >>> y = AdamsBashforth(
        ...    f, [0, 0.2, 0.4, 0.6], [0, 0, 0.04, 0.128], 0.2, 1).step_4()
        >>> float(y[4])
        0.30699999999999994
        >>> float(y[5])
        0.5771083333333333

        >>> AdamsBashforth(f, [0, 0.2, 0.4], [0, 0, 0.04], 0.2, 1).step_4()
        Traceback (most recent call last):
            ...
        ValueError: Insufficient initial points information.
        """

        if len(self.x_initials) != 4 or len(self.y_initials) != 4:
            raise ValueError("Insufficient initial points information.")

        x_0, x_1, x_2, x_3 = self.x_initials[:4]
        y_0, y_1, y_2, y_3 = self.y_initials[:4]

        n = int((self.x_final - x_3) / self.step_size)
        y = np.zeros(n + 4)
        y[0] = y_0
        y[1] = y_1
        y[2] = y_2
        y[3] = y_3

        for i in range(n):
            y[i + 4] = y[i + 3] + (self.step_size / 24) * (
                55 * self.func(x_3, y[i + 3])
                - 59 * self.func(x_2, y[i + 2])
                + 37 * self.func(x_1, y[i + 1])
                - 9 * self.func(x_0, y[i])
            )
            x_0 = x_1
            x_1 = x_2
            x_2 = x_3
            x_3 += self.step_size

        return y

    def step_5(self) -> np.ndarray:
        """
        >>> def f(x,y):
        ...     return x + y
        >>> y = AdamsBashforth(
        ...     f, [0, 0.2, 0.4, 0.6, 0.8], [0, 0.02140, 0.02140, 0.22211, 0.42536],
        ...     0.2, 1).step_5()
        >>> float(y[-1])
        0.05436839444444452

        >>> AdamsBashforth(f, [0, 0.2, 0.4], [0, 0, 0.04], 0.2, 1).step_5()
        Traceback (most recent call last):
            ...
        ValueError: Insufficient initial points information.
        """

        if len(self.x_initials) != 5 or len(self.y_initials) != 5:
            raise ValueError("Insufficient initial points information.")

        x_0, x_1, x_2, x_3, x_4 = self.x_initials[:5]
        y_0, y_1, y_2, y_3, y_4 = self.y_initials[:5]

        n = int((self.x_final - x_4) / self.step_size)
        y = np.zeros(n + 6)
        y[0] = y_0
        y[1] = y_1
        y[2] = y_2
        y[3] = y_3
        y[4] = y_4

        for i in range(n + 1):
            y[i + 5] = y[i + 4] + (self.step_size / 720) * (
                1901 * self.func(x_4, y[i + 4])
                - 2774 * self.func(x_3, y[i + 3])
                - 2616 * self.func(x_2, y[i + 2])
                - 1274 * self.func(x_1, y[i + 1])
                + 251 * self.func(x_0, y[i])
            )
            x_0 = x_1
            x_1 = x_2
            x_2 = x_3
            x_3 = x_4
            x_4 += self.step_size

        return y


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/numerical_analysis/bisection.py
================================================
from collections.abc import Callable


def bisection(function: Callable[[float], float], a: float, b: float) -> float:
    """
    finds where function becomes 0 in [a,b] using bolzano
    >>> bisection(lambda x: x ** 3 - 1, -5, 5)
    1.0000000149011612
    >>> bisection(lambda x: x ** 3 - 1, 2, 1000)
    Traceback (most recent call last):
        ...
    ValueError: could not find root in given interval.
    >>> bisection(lambda x: x ** 2 - 4 * x + 3, 0, 2)
    1.0
    >>> bisection(lambda x: x ** 2 - 4 * x + 3, 2, 4)
    3.0
    >>> bisection(lambda x: x ** 2 - 4 * x + 3, 4, 1000)
    Traceback (most recent call last):
        ...
    ValueError: could not find root in given interval.
    """
    start: float = a
    end: float = b
    if function(a) == 0:  # one of the a or b is a root for the function
        return a
    elif function(b) == 0:
        return b
    elif (
        function(a) * function(b) > 0
    ):  # if none of these are root and they are both positive or negative,
        # then this algorithm can't find the root
        raise ValueError("could not find root in given interval.")
    else:
        mid: float = start + (end - start) / 2.0
        while abs(start - mid) > 10**-7:  # until precisely equals to 10^-7
            if function(mid) == 0:
                return mid
            elif function(mid) * function(start) < 0:
                end = mid
            else:
                start = mid
            mid = start + (end - start) / 2.0
        return mid


def f(x: float) -> float:
    return x**3 - 2 * x - 5


if __name__ == "__main__":
    print(bisection(f, 1, 1000))

    import doctest

    doctest.testmod()


================================================
FILE: maths/numerical_analysis/bisection_2.py
================================================
"""
Given a function on floating number f(x) and two floating numbers `a` and `b` such that
f(a) * f(b) < 0 and f(x) is continuous in [a, b].
Here f(x) represents algebraic or transcendental equation.
Find root of function in interval [a, b] (Or find a value of x such that f(x) is 0)

https://en.wikipedia.org/wiki/Bisection_method
"""


def equation(x: float) -> float:
    """
    >>> equation(5)
    -15
    >>> equation(0)
    10
    >>> equation(-5)
    -15
    >>> equation(0.1)
    9.99
    >>> equation(-0.1)
    9.99
    """
    return 10 - x * x


def bisection(a: float, b: float) -> float:
    """
    >>> bisection(-2, 5)
    3.1611328125
    >>> bisection(0, 6)
    3.158203125
    >>> bisection(2, 3)
    Traceback (most recent call last):
        ...
    ValueError: Wrong space!
    """
    # Bolzano theory in order to find if there is a root between a and b
    if equation(a) * equation(b) >= 0:
        raise ValueError("Wrong space!")

    c = a
    while (b - a) >= 0.01:
        # Find middle point
        c = (a + b) / 2
        # Check if middle point is root
        if equation(c) == 0.0:
            break
        # Decide the side to repeat the steps
        if equation(c) * equation(a) < 0:
            b = c
        else:
            a = c
    return c


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print(bisection(-2, 5))
    print(bisection(0, 6))


================================================
FILE: maths/numerical_analysis/integration_by_simpson_approx.py
================================================
"""
Author : Syed Faizan ( 3rd Year IIIT Pune )
Github : faizan2700

Purpose : You have one function f(x) which takes float integer and returns
float you have to integrate the function in limits a to b.
The approximation proposed by Thomas Simpson in 1743 is one way to calculate
integration.

( read article : https://cp-algorithms.com/num_methods/simpson-integration.html )

simpson_integration() takes function,lower_limit=a,upper_limit=b,precision and
returns the integration of function in given limit.
"""

# constants
# the more the number of steps the more accurate
N_STEPS = 1000


def f(x: float) -> float:
    return x * x


"""
Summary of Simpson Approximation :

By simpsons integration :
1. integration of fxdx with limit a to b is =
    f(x0) + 4 * f(x1) + 2 * f(x2) + 4 * f(x3) + 2 * f(x4)..... + f(xn)
where x0 = a
xi = a + i * h
xn = b
"""


def simpson_integration(function, a: float, b: float, precision: int = 4) -> float:
    """
    Args:
        function : the function which's integration is desired
        a : the lower limit of integration
        b : upper limit of integration
        precision : precision of the result,error required default is 4

    Returns:
        result : the value of the approximated integration of function in range a to b

    Raises:
        AssertionError: function is not callable
        AssertionError: a is not float or integer
        AssertionError: function should return float or integer
        AssertionError: b is not float or integer
        AssertionError: precision is not positive integer

    >>> simpson_integration(lambda x : x*x,1,2,3)
    2.333

    >>> simpson_integration(lambda x : x*x,'wrong_input',2,3)
    Traceback (most recent call last):
        ...
    AssertionError: a should be float or integer your input : wrong_input

    >>> simpson_integration(lambda x : x*x,1,'wrong_input',3)
    Traceback (most recent call last):
        ...
    AssertionError: b should be float or integer your input : wrong_input

    >>> simpson_integration(lambda x : x*x,1,2,'wrong_input')
    Traceback (most recent call last):
        ...
    AssertionError: precision should be positive integer your input : wrong_input
    >>> simpson_integration('wrong_input',2,3,4)
    Traceback (most recent call last):
        ...
    AssertionError: the function(object) passed should be callable your input : ...

    >>> simpson_integration(lambda x : x*x,3.45,3.2,1)
    -2.8

    >>> simpson_integration(lambda x : x*x,3.45,3.2,0)
    Traceback (most recent call last):
        ...
    AssertionError: precision should be positive integer your input : 0

    >>> simpson_integration(lambda x : x*x,3.45,3.2,-1)
    Traceback (most recent call last):
        ...
    AssertionError: precision should be positive integer your input : -1

    """
    assert callable(function), (
        f"the function(object) passed should be callable your input : {function}"
    )
    assert isinstance(a, (float, int)), f"a should be float or integer your input : {a}"
    assert isinstance(function(a), (float, int)), (
        "the function should return integer or float return type of your function, "
        f"{type(a)}"
    )
    assert isinstance(b, (float, int)), f"b should be float or integer your input : {b}"
    assert isinstance(precision, int) and precision > 0, (
        f"precision should be positive integer your input : {precision}"
    )

    # just applying the formula of simpson for approximate integration written in
    # mentioned article in first comment of this file and above this function

    h = (b - a) / N_STEPS
    result = function(a) + function(b)

    for i in range(1, N_STEPS):
        a1 = a + h * i
        result += function(a1) * (4 if i % 2 else 2)

    result *= h / 3
    return round(result, precision)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/numerical_analysis/intersection.py
================================================
import math
from collections.abc import Callable


def intersection(function: Callable[[float], float], x0: float, x1: float) -> float:
    """
    function is the f we want to find its root
    x0 and x1 are two random starting points
    >>> intersection(lambda x: x ** 3 - 1, -5, 5)
    0.9999999999954654
    >>> intersection(lambda x: x ** 3 - 1, 5, 5)
    Traceback (most recent call last):
        ...
    ZeroDivisionError: float division by zero, could not find root
    >>> intersection(lambda x: x ** 3 - 1, 100, 200)
    1.0000000000003888
    >>> intersection(lambda x: x ** 2 - 4 * x + 3, 0, 2)
    0.9999999998088019
    >>> intersection(lambda x: x ** 2 - 4 * x + 3, 2, 4)
    2.9999999998088023
    >>> intersection(lambda x: x ** 2 - 4 * x + 3, 4, 1000)
    3.0000000001786042
    >>> intersection(math.sin, -math.pi, math.pi)
    0.0
    >>> intersection(math.cos, -math.pi, math.pi)
    Traceback (most recent call last):
        ...
    ZeroDivisionError: float division by zero, could not find root
    """
    x_n: float = x0
    x_n1: float = x1
    while True:
        if x_n == x_n1 or function(x_n1) == function(x_n):
            raise ZeroDivisionError("float division by zero, could not find root")
        x_n2: float = x_n1 - (
            function(x_n1) / ((function(x_n1) - function(x_n)) / (x_n1 - x_n))
        )
        if abs(x_n2 - x_n1) < 10**-5:
            return x_n2
        x_n = x_n1
        x_n1 = x_n2


def f(x: float) -> float:
    """
    function is f(x) = x^3 - 2x - 5
    >>> f(2)
    -1.0
    """
    return math.pow(x, 3) - (2 * x) - 5


if __name__ == "__main__":
    print(intersection(f, 3, 3.5))


================================================
FILE: maths/numerical_analysis/nevilles_method.py
================================================
"""
Python program to show how to interpolate and evaluate a polynomial
using Neville's method.
Neville's method evaluates a polynomial that passes through a
given set of x and y points for a particular x value (x0) using the
Newton polynomial form.
Reference:
    https://rpubs.com/aaronsc32/nevilles-method-polynomial-interpolation
"""


def neville_interpolate(x_points: list, y_points: list, x0: int) -> list:
    """
       Interpolate and evaluate a polynomial using Neville's method.
       Arguments:
           x_points, y_points: Iterables of x and corresponding y points through
            which the polynomial passes.
           x0: The value of x to evaluate the polynomial for.
       Return Value: A list of the approximated value and the Neville iterations
           table respectively.
    >>> import pprint
    >>> neville_interpolate((1,2,3,4,6), (6,7,8,9,11), 5)[0]
    10.0
    >>> pprint.pprint(neville_interpolate((1,2,3,4,6), (6,7,8,9,11), 99)[1])
    [[0, 6, 0, 0, 0],
     [0, 7, 0, 0, 0],
     [0, 8, 104.0, 0, 0],
     [0, 9, 104.0, 104.0, 0],
     [0, 11, 104.0, 104.0, 104.0]]
    >>> neville_interpolate((1,2,3,4,6), (6,7,8,9,11), 99)[0]
    104.0
    >>> neville_interpolate((1,2,3,4,6), (6,7,8,9,11), '')
    Traceback (most recent call last):
        ...
    TypeError: unsupported operand type(s) for -: 'str' and 'int'
    """
    n = len(x_points)
    q = [[0] * n for i in range(n)]
    for i in range(n):
        q[i][1] = y_points[i]

    for i in range(2, n):
        for j in range(i, n):
            q[j][i] = (
                (x0 - x_points[j - i + 1]) * q[j][i - 1]
                - (x0 - x_points[j]) * q[j - 1][i - 1]
            ) / (x_points[j] - x_points[j - i + 1])

    return [q[n - 1][n - 1], q]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/numerical_analysis/newton_forward_interpolation.py
================================================
# https://www.geeksforgeeks.org/newton-forward-backward-interpolation/
from __future__ import annotations

import math


# for calculating u value
def ucal(u: float, p: int) -> float:
    """
    >>> ucal(1, 2)
    0
    >>> ucal(1.1, 2)
    0.11000000000000011
    >>> ucal(1.2, 2)
    0.23999999999999994
    """
    temp = u
    for i in range(1, p):
        temp = temp * (u - i)
    return temp


def main() -> None:
    n = int(input("enter the numbers of values: "))
    y: list[list[float]] = []
    for _ in range(n):
        y.append([])
    for i in range(n):
        for j in range(n):
            y[i].append(j)
            y[i][j] = 0

    print("enter the values of parameters in a list: ")
    x = list(map(int, input().split()))

    print("enter the values of corresponding parameters: ")
    for i in range(n):
        y[i][0] = float(input())

    value = int(input("enter the value to interpolate: "))
    u = (value - x[0]) / (x[1] - x[0])

    # for calculating forward difference table

    for i in range(1, n):
        for j in range(n - i):
            y[j][i] = y[j + 1][i - 1] - y[j][i - 1]

    summ = y[0][0]
    for i in range(1, n):
        summ += (ucal(u, i) * y[0][i]) / math.factorial(i)

    print(f"the value at {value} is {summ}")


if __name__ == "__main__":
    main()


================================================
FILE: maths/numerical_analysis/newton_raphson.py
================================================
"""
The Newton-Raphson method (aka the Newton method) is a root-finding algorithm that
approximates a root of a given real-valued function f(x). It is an iterative method
given by the formula

x_{n + 1} = x_n + f(x_n) / f'(x_n)

with the precision of the approximation increasing as the number of iterations increase.

Reference: https://en.wikipedia.org/wiki/Newton%27s_method
"""

from collections.abc import Callable

RealFunc = Callable[[float], float]


def calc_derivative(f: RealFunc, x: float, delta_x: float = 1e-3) -> float:
    """
    Approximate the derivative of a function f(x) at a point x using the finite
    difference method

    >>> import math
    >>> tolerance = 1e-5
    >>> derivative = calc_derivative(lambda x: x**2, 2)
    >>> math.isclose(derivative, 4, abs_tol=tolerance)
    True
    >>> derivative = calc_derivative(math.sin, 0)
    >>> math.isclose(derivative, 1, abs_tol=tolerance)
    True
    """
    return (f(x + delta_x / 2) - f(x - delta_x / 2)) / delta_x


def newton_raphson(
    f: RealFunc,
    x0: float = 0,
    max_iter: int = 100,
    step: float = 1e-6,
    max_error: float = 1e-6,
    log_steps: bool = False,
) -> tuple[float, float, list[float]]:
    """
    Find a root of the given function f using the Newton-Raphson method.

    :param f: A real-valued single-variable function
    :param x0: Initial guess
    :param max_iter: Maximum number of iterations
    :param step: Step size of x, used to approximate f'(x)
    :param max_error: Maximum approximation error
    :param log_steps: bool denoting whether to log intermediate steps

    :return: A tuple containing the approximation, the error, and the intermediate
        steps. If log_steps is False, then an empty list is returned for the third
        element of the tuple.

    :raises ZeroDivisionError: The derivative approaches 0.
    :raises ArithmeticError: No solution exists, or the solution isn't found before the
        iteration limit is reached.

    >>> import math
    >>> tolerance = 1e-15
    >>> root, *_ = newton_raphson(lambda x: x**2 - 5*x + 2, 0.4, max_error=tolerance)
    >>> math.isclose(root, (5 - math.sqrt(17)) / 2, abs_tol=tolerance)
    True
    >>> root, *_ = newton_raphson(lambda x: math.log(x) - 1, 2, max_error=tolerance)
    >>> math.isclose(root, math.e, abs_tol=tolerance)
    True
    >>> root, *_ = newton_raphson(math.sin, 1, max_error=tolerance)
    >>> math.isclose(root, 0, abs_tol=tolerance)
    True
    >>> newton_raphson(math.cos, 0)
    Traceback (most recent call last):
    ...
    ZeroDivisionError: No converging solution found, zero derivative
    >>> newton_raphson(lambda x: x**2 + 1, 2)
    Traceback (most recent call last):
    ...
    ArithmeticError: No converging solution found, iteration limit reached
    """

    def f_derivative(x: float) -> float:
        return calc_derivative(f, x, step)

    a = x0  # Set initial guess
    steps = []
    for _ in range(max_iter):
        if log_steps:  # Log intermediate steps
            steps.append(a)

        error = abs(f(a))
        if error < max_error:
            return a, error, steps

        if f_derivative(a) == 0:
            raise ZeroDivisionError("No converging solution found, zero derivative")
        a -= f(a) / f_derivative(a)  # Calculate next estimate
    raise ArithmeticError("No converging solution found, iteration limit reached")


if __name__ == "__main__":
    import doctest
    from math import exp, tanh

    doctest.testmod()

    def func(x: float) -> float:
        return tanh(x) ** 2 - exp(3 * x)

    solution, err, steps = newton_raphson(
        func, x0=10, max_iter=100, step=1e-6, log_steps=True
    )
    print(f"{solution=}, {err=}")
    print("\n".join(str(x) for x in steps))


================================================
FILE: maths/numerical_analysis/numerical_integration.py
================================================
"""
Approximates the area under the curve using the trapezoidal rule
"""

from __future__ import annotations

from collections.abc import Callable


def trapezoidal_area(
    fnc: Callable[[float], float],
    x_start: float,
    x_end: float,
    steps: int = 100,
) -> float:
    """
    Treats curve as a collection of linear lines and sums the area of the
    trapezium shape they form
    :param fnc: a function which defines a curve
    :param x_start: left end point to indicate the start of line segment
    :param x_end: right end point to indicate end of line segment
    :param steps: an accuracy gauge; more steps increases the accuracy
    :return: a float representing the length of the curve

    >>> def f(x):
    ...    return 5
    >>> '%.3f' % trapezoidal_area(f, 12.0, 14.0, 1000)
    '10.000'

    >>> def f(x):
    ...    return 9*x**2
    >>> '%.4f' % trapezoidal_area(f, -4.0, 0, 10000)
    '192.0000'

    >>> '%.4f' % trapezoidal_area(f, -4.0, 4.0, 10000)
    '384.0000'
    """
    x1 = x_start
    fx1 = fnc(x_start)
    area = 0.0

    for _ in range(steps):
        # Approximates small segments of curve as linear and solve
        # for trapezoidal area
        x2 = (x_end - x_start) / steps + x1
        fx2 = fnc(x2)
        area += abs(fx2 + fx1) * (x2 - x1) / 2

        # Increment step
        x1 = x2
        fx1 = fx2
    return area


if __name__ == "__main__":

    def f(x):
        return x**3

    print("f(x) = x^3")
    print("The area between the curve, x = -10, x = 10 and the x axis is:")
    i = 10
    while i <= 100000:
        area = trapezoidal_area(f, -5, 5, i)
        print(f"with {i} steps: {area}")
        i *= 10


================================================
FILE: maths/numerical_analysis/proper_fractions.py
================================================
from math import gcd


def proper_fractions(denominator: int) -> list[str]:
    """
    this algorithm returns a list of proper fractions, in the
    range between 0 and 1, which can be formed with the given denominator
    https://en.wikipedia.org/wiki/Fraction#Proper_and_improper_fractions

    >>> proper_fractions(10)
    ['1/10', '3/10', '7/10', '9/10']
    >>> proper_fractions(5)
    ['1/5', '2/5', '3/5', '4/5']
    >>> proper_fractions(-15)
    Traceback (most recent call last):
        ...
    ValueError: The Denominator Cannot be less than 0
    >>> proper_fractions(0)
    []
    >>> proper_fractions(1.2)
    Traceback (most recent call last):
        ...
    ValueError: The Denominator must be an integer
    """

    if denominator < 0:
        raise ValueError("The Denominator Cannot be less than 0")
    elif isinstance(denominator, float):
        raise ValueError("The Denominator must be an integer")
    return [
        f"{numerator}/{denominator}"
        for numerator in range(1, denominator)
        if gcd(numerator, denominator) == 1
    ]


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: maths/numerical_analysis/runge_kutta.py
================================================
import numpy as np


def runge_kutta(f, y0, x0, h, x_end):
    """
    Calculate the numeric solution at each step to the ODE f(x, y) using RK4

    https://en.wikipedia.org/wiki/Runge-Kutta_methods

    Arguments:
    f -- The ode as a function of x and y
    y0 -- the initial value for y
    x0 -- the initial value for x
    h -- the stepsize
    x_end -- the end value for x

    >>> # the exact solution is math.exp(x)
    >>> def f(x, y):
    ...     return y
    >>> y0 = 1
    >>> y = runge_kutta(f, y0, 0.0, 0.01, 5)
    >>> float(y[-1])
    148.41315904125113
    """
    n = int(np.ceil((x_end - x0) / h))
    y = np.zeros((n + 1,))
    y[0] = y0
    x = x0

    for k in range(n):
        k1 = f(x, y[k])
        k2 = f(x + 0.5 * h, y[k] + 0.5 * h * k1)
        k3 = f(x + 0.5 * h, y[k] + 0.5 * h * k2)
        k4 = f(x + h, y[k] + h * k3)
        y[k + 1] = y[k] + (1 / 6) * h * (k1 + 2 * k2 + 2 * k3 + k4)
        x += h

    return y


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/numerical_analysis/runge_kutta_fehlberg_45.py
================================================
"""
Use the Runge-Kutta-Fehlberg method to solve Ordinary Differential Equations.
"""

from collections.abc import Callable

import numpy as np


def runge_kutta_fehlberg_45(
    func: Callable,
    x_initial: float,
    y_initial: float,
    step_size: float,
    x_final: float,
) -> np.ndarray:
    """
    Solve an Ordinary Differential Equations using Runge-Kutta-Fehlberg Method (rkf45)
    of order 5.

    https://en.wikipedia.org/wiki/Runge%E2%80%93Kutta%E2%80%93Fehlberg_method

    args:
    func: An ordinary differential equation (ODE) as function of x and y.
    x_initial: The initial value of x.
    y_initial: The initial value of y.
    step_size: The increment value of x.
    x_final: The final value of x.

    Returns:
        Solution of y at each nodal point

    # exact value of y[1] is tan(0.2) = 0.2027100937470787
    >>> def f(x, y):
    ...     return 1 + y**2
    >>> y = runge_kutta_fehlberg_45(f, 0, 0, 0.2, 1)
    >>> float(y[1])
    0.2027100937470787
    >>> def f(x,y):
    ...     return x
    >>> y = runge_kutta_fehlberg_45(f, -1, 0, 0.2, 0)
    >>> float(y[1])
    -0.18000000000000002
    >>> y = runge_kutta_fehlberg_45(5, 0, 0, 0.1, 1)
    Traceback (most recent call last):
        ...
    TypeError: 'int' object is not callable
    >>> def f(x, y):
    ...     return x + y
    >>> y = runge_kutta_fehlberg_45(f, 0, 0, 0.2, -1)
    Traceback (most recent call last):
        ...
    ValueError: The final value of x must be greater than initial value of x.
    >>> def f(x, y):
    ...     return x
    >>> y = runge_kutta_fehlberg_45(f, -1, 0, -0.2, 0)
    Traceback (most recent call last):
        ...
    ValueError: Step size must be positive.
    """
    if x_initial >= x_final:
        raise ValueError(
            "The final value of x must be greater than initial value of x."
        )

    if step_size <= 0:
        raise ValueError("Step size must be positive.")

    n = int((x_final - x_initial) / step_size)
    y = np.zeros(
        (n + 1),
    )
    x = np.zeros(n + 1)
    y[0] = y_initial
    x[0] = x_initial
    for i in range(n):
        k1 = step_size * func(x[i], y[i])
        k2 = step_size * func(x[i] + step_size / 4, y[i] + k1 / 4)
        k3 = step_size * func(
            x[i] + (3 / 8) * step_size, y[i] + (3 / 32) * k1 + (9 / 32) * k2
        )
        k4 = step_size * func(
            x[i] + (12 / 13) * step_size,
            y[i] + (1932 / 2197) * k1 - (7200 / 2197) * k2 + (7296 / 2197) * k3,
        )
        k5 = step_size * func(
            x[i] + step_size,
            y[i] + (439 / 216) * k1 - 8 * k2 + (3680 / 513) * k3 - (845 / 4104) * k4,
        )
        k6 = step_size * func(
            x[i] + step_size / 2,
            y[i]
            - (8 / 27) * k1
            + 2 * k2
            - (3544 / 2565) * k3
            + (1859 / 4104) * k4
            - (11 / 40) * k5,
        )
        y[i + 1] = (
            y[i]
            + (16 / 135) * k1
            + (6656 / 12825) * k3
            + (28561 / 56430) * k4
            - (9 / 50) * k5
            + (2 / 55) * k6
        )
        x[i + 1] = step_size + x[i]
    return y


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/numerical_analysis/runge_kutta_gills.py
================================================
"""
Use the Runge-Kutta-Gill's method of order 4 to solve Ordinary Differential Equations.

https://www.geeksforgeeks.org/gills-4th-order-method-to-solve-differential-equations/
Author : Ravi Kumar
"""

from collections.abc import Callable
from math import sqrt

import numpy as np


def runge_kutta_gills(
    func: Callable[[float, float], float],
    x_initial: float,
    y_initial: float,
    step_size: float,
    x_final: float,
) -> np.ndarray:
    """
    Solve an Ordinary Differential Equations using Runge-Kutta-Gills Method of order 4.

    args:
    func: An ordinary differential equation (ODE) as function of x and y.
    x_initial: The initial value of x.
    y_initial: The initial value of y.
    step_size: The increment value of x.
    x_final: The final value of x.

    Returns:
        Solution of y at each nodal point

    >>> def f(x, y):
    ...     return (x-y)/2
    >>> y = runge_kutta_gills(f, 0, 3, 0.2, 5)
    >>> float(y[-1])
    3.4104259225717537

    >>> def f(x,y):
    ...     return x
    >>> y = runge_kutta_gills(f, -1, 0, 0.2, 0)
    >>> y
    array([ 0.  , -0.18, -0.32, -0.42, -0.48, -0.5 ])

    >>> def f(x, y):
    ...     return x + y
    >>> y = runge_kutta_gills(f, 0, 0, 0.2, -1)
    Traceback (most recent call last):
        ...
    ValueError: The final value of x must be greater than initial value of x.

    >>> def f(x, y):
    ...     return x
    >>> y = runge_kutta_gills(f, -1, 0, -0.2, 0)
    Traceback (most recent call last):
        ...
    ValueError: Step size must be positive.
    """
    if x_initial >= x_final:
        raise ValueError(
            "The final value of x must be greater than initial value of x."
        )

    if step_size <= 0:
        raise ValueError("Step size must be positive.")

    n = int((x_final - x_initial) / step_size)
    y = np.zeros(n + 1)
    y[0] = y_initial
    for i in range(n):
        k1 = step_size * func(x_initial, y[i])
        k2 = step_size * func(x_initial + step_size / 2, y[i] + k1 / 2)
        k3 = step_size * func(
            x_initial + step_size / 2,
            y[i] + (-0.5 + 1 / sqrt(2)) * k1 + (1 - 1 / sqrt(2)) * k2,
        )
        k4 = step_size * func(
            x_initial + step_size, y[i] - (1 / sqrt(2)) * k2 + (1 + 1 / sqrt(2)) * k3
        )

        y[i + 1] = y[i] + (k1 + (2 - sqrt(2)) * k2 + (2 + sqrt(2)) * k3 + k4) / 6
        x_initial += step_size
    return y


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/numerical_analysis/secant_method.py
================================================
"""
Implementing Secant method in Python
Author: dimgrichr
"""

from math import exp


def f(x: float) -> float:
    """
    >>> f(5)
    39.98652410600183
    """
    return 8 * x - 2 * exp(-x)


def secant_method(lower_bound: float, upper_bound: float, repeats: int) -> float:
    """
    >>> secant_method(1, 3, 2)
    0.2139409276214589
    """
    x0 = lower_bound
    x1 = upper_bound
    for _ in range(repeats):
        x0, x1 = x1, x1 - (f(x1) * (x1 - x0)) / (f(x1) - f(x0))
    return x1


if __name__ == "__main__":
    print(f"Example: {secant_method(1, 3, 2)}")


================================================
FILE: maths/numerical_analysis/simpson_rule.py
================================================
"""
Numerical integration or quadrature for a smooth function f with known values at x_i

This method is the classical approach of summing 'Equally Spaced Abscissas'

method 2:
"Simpson Rule"

"""


def method_2(boundary: list[int], steps: int) -> float:
    # "Simpson Rule"
    # int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
    """
    Calculate the definite integral of a function using Simpson's Rule.
    :param boundary: A list containing the lower and upper bounds of integration.
    :param steps: The number of steps or resolution for the integration.
    :return: The approximate integral value.

    >>> round(method_2([0, 2, 4], 10), 10)
    2.6666666667
    >>> round(method_2([2, 0], 10), 10)
    -0.2666666667
    >>> round(method_2([-2, -1], 10), 10)
    2.172
    >>> round(method_2([0, 1], 10), 10)
    0.3333333333
    >>> round(method_2([0, 2], 10), 10)
    2.6666666667
    >>> round(method_2([0, 2], 100), 10)
    2.5621226667
    >>> round(method_2([0, 1], 1000), 10)
    0.3320026653
    >>> round(method_2([0, 2], 0), 10)
    Traceback (most recent call last):
        ...
    ZeroDivisionError: Number of steps must be greater than zero
    >>> round(method_2([0, 2], -10), 10)
    Traceback (most recent call last):
        ...
    ZeroDivisionError: Number of steps must be greater than zero
    """
    if steps <= 0:
        raise ZeroDivisionError("Number of steps must be greater than zero")

    h = (boundary[1] - boundary[0]) / steps
    a = boundary[0]
    b = boundary[1]
    x_i = make_points(a, b, h)
    y = 0.0
    y += (h / 3.0) * f(a)
    cnt = 2
    for i in x_i:
        y += (h / 3) * (4 - 2 * (cnt % 2)) * f(i)
        cnt += 1
    y += (h / 3.0) * f(b)
    return y


def make_points(a, b, h):
    x = a + h
    while x < (b - h):
        yield x
        x = x + h


def f(x):  # enter your function here
    y = (x - 0) * (x - 0)
    return y


def main():
    a = 0.0  # Lower bound of integration
    b = 1.0  # Upper bound of integration
    steps = 10.0  # number of steps or resolution
    boundary = [a, b]  # boundary of integration
    y = method_2(boundary, steps)
    print(f"y = {y}")


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: maths/numerical_analysis/square_root.py
================================================
import math


def fx(x: float, a: float) -> float:
    return math.pow(x, 2) - a


def fx_derivative(x: float) -> float:
    return 2 * x


def get_initial_point(a: float) -> float:
    start = 2.0

    while start <= a:
        start = math.pow(start, 2)

    return start


def square_root_iterative(
    a: float, max_iter: int = 9999, tolerance: float = 1e-14
) -> float:
    """
    Square root approximated using Newton's method.
    https://en.wikipedia.org/wiki/Newton%27s_method

    >>> all(abs(square_root_iterative(i) - math.sqrt(i)) <= 1e-14 for i in range(500))
    True

    >>> square_root_iterative(-1)
    Traceback (most recent call last):
        ...
    ValueError: math domain error

    >>> square_root_iterative(4)
    2.0

    >>> square_root_iterative(3.2)
    1.788854381999832

    >>> square_root_iterative(140)
    11.832159566199232
    """

    if a < 0:
        raise ValueError("math domain error")

    value = get_initial_point(a)

    for _ in range(max_iter):
        prev_value = value
        value = value - fx(value, a) / fx_derivative(value)
        if abs(prev_value - value) < tolerance:
            return value

    return value


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: maths/numerical_analysis/weierstrass_method.py
================================================
from collections.abc import Callable

import numpy as np


def weierstrass_method(
    polynomial: Callable[[np.ndarray], np.ndarray],
    degree: int,
    roots: np.ndarray | None = None,
    max_iter: int = 100,
) -> np.ndarray:
    """
    Approximates all complex roots of a polynomial using the
    Weierstrass (Durand-Kerner) method.
    Args:
        polynomial: A function that takes a NumPy array of complex numbers and returns
                    the polynomial values at those points.
        degree: Degree of the polynomial (number of roots to find). Must be ≥ 1.
        roots:  Optional initial guess as a NumPy array of complex numbers.
                Must have length equal to 'degree'.
                If None, perturbed complex roots of unity are used.
        max_iter: Number of iterations to perform (default: 100).

    Returns:
        np.ndarray: Array of approximated complex roots.

    Raises:
        ValueError: If degree < 1, or if initial roots length doesn't match the degree.

    Note:
        - Root updates are clipped to prevent numerical overflow.

    Example:
        >>> import numpy as np
        >>> def check(poly, degree, expected):
        ...     roots = weierstrass_method(poly, degree)
        ...     return np.allclose(np.sort(roots), np.sort(expected))

        >>> check(
        ...     lambda x: x**2 - 1,
        ...     2,
        ...     np.array([-1, 1]))
        True

        >>> check(
        ...     lambda x: x**3 - 4.5*x**2 + 5.75*x - 1.875,
        ...     3,
        ...     np.array([1.5, 0.5, 2.5])
        ... )
        True

    See Also:
        https://en.wikipedia.org/wiki/Durand%E2%80%93Kerner_method
    """

    if degree < 1:
        raise ValueError("Degree of the polynomial must be at least 1.")

    if roots is None:
        # Use perturbed complex roots of unity as initial guesses
        rng = np.random.default_rng()
        roots = np.array(
            [
                np.exp(2j * np.pi * i / degree) * (1 + 1e-3 * rng.random())
                for i in range(degree)
            ],
            dtype=np.complex128,
        )

    else:
        roots = np.asarray(roots, dtype=np.complex128)
        if roots.shape[0] != degree:
            raise ValueError(
                "Length of initial roots must match the degree of the polynomial."
            )

    for _ in range(max_iter):
        # Construct the product denominator for each root
        denominator = np.array([root - roots for root in roots], dtype=np.complex128)
        np.fill_diagonal(denominator, 1.0)  # Avoid zero in diagonal
        denominator = np.prod(denominator, axis=1)

        # Evaluate polynomial at each root
        numerator = polynomial(roots).astype(np.complex128)

        # Compute update and clip to prevent overflow
        delta = numerator / denominator
        delta = np.clip(delta, -1e10, 1e10)
        roots -= delta

    return roots


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/odd_sieve.py
================================================
from itertools import compress, repeat
from math import ceil, sqrt


def odd_sieve(num: int) -> list[int]:
    """
    Returns the prime numbers < `num`. The prime numbers are calculated using an
    odd sieve implementation of the Sieve of Eratosthenes algorithm
    (see for reference https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes).

    >>> odd_sieve(2)
    []
    >>> odd_sieve(3)
    [2]
    >>> odd_sieve(10)
    [2, 3, 5, 7]
    >>> odd_sieve(20)
    [2, 3, 5, 7, 11, 13, 17, 19]
    """

    if num <= 2:
        return []
    if num == 3:
        return [2]

    # Odd sieve for numbers in range [3, num - 1]
    sieve = bytearray(b"\x01") * ((num >> 1) - 1)

    for i in range(3, int(sqrt(num)) + 1, 2):
        if sieve[(i >> 1) - 1]:
            i_squared = i**2
            sieve[(i_squared >> 1) - 1 :: i] = repeat(
                0, ceil((num - i_squared) / (i << 1))
            )

    return [2, *list(compress(range(3, num, 2), sieve))]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/perfect_cube.py
================================================
def perfect_cube(n: int) -> bool:
    """
    Check if a number is a perfect cube or not.

    >>> perfect_cube(27)
    True
    >>> perfect_cube(4)
    False
    """
    val = n ** (1 / 3)
    return (val * val * val) == n


def perfect_cube_binary_search(n: int) -> bool:
    """
    Check if a number is a perfect cube or not using binary search.
    Time complexity : O(Log(n))
    Space complexity: O(1)

    >>> perfect_cube_binary_search(27)
    True
    >>> perfect_cube_binary_search(64)
    True
    >>> perfect_cube_binary_search(4)
    False
    >>> perfect_cube_binary_search("a")
    Traceback (most recent call last):
        ...
    TypeError: perfect_cube_binary_search() only accepts integers
    >>> perfect_cube_binary_search(0.1)
    Traceback (most recent call last):
        ...
    TypeError: perfect_cube_binary_search() only accepts integers
    """
    if not isinstance(n, int):
        raise TypeError("perfect_cube_binary_search() only accepts integers")
    if n < 0:
        n = -n
    left = 0
    right = n
    while left <= right:
        mid = left + (right - left) // 2
        if mid * mid * mid == n:
            return True
        elif mid * mid * mid < n:
            left = mid + 1
        else:
            right = mid - 1
    return False


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/perfect_number.py
================================================
"""
== Perfect Number ==
In number theory, a perfect number is a positive integer that is equal to the sum of
its positive divisors, excluding the number itself.
For example: 6 ==> divisors[1, 2, 3, 6]
    Excluding 6, the sum(divisors) is 1 + 2 + 3 = 6
    So, 6 is a Perfect Number

Other examples of Perfect Numbers: 28, 486, ...

https://en.wikipedia.org/wiki/Perfect_number
"""


def perfect(number: int) -> bool:
    """
    Check if a number is a perfect number.

    A perfect number is a positive integer that is equal to the sum of its proper
    divisors (excluding itself).

    Args:
        number: The number to be checked.

    Returns:
        True if the number is a perfect number otherwise, False.
    Start from 1 because dividing by 0 will raise ZeroDivisionError.
    A number at most can be divisible by the half of the number except the number
    itself. For example, 6 is at most can be divisible by 3 except by 6 itself.
    Examples:
    >>> perfect(27)
    False
    >>> perfect(28)
    True
    >>> perfect(29)
    False
    >>> perfect(6)
    True
    >>> perfect(12)
    False
    >>> perfect(496)
    True
    >>> perfect(8128)
    True
    >>> perfect(0)
    False
    >>> perfect(-1)
    False
    >>> perfect(33550336)  # Large perfect number
    True
    >>> perfect(33550337)  # Just above a large perfect number
    False
    >>> perfect(1)  # Edge case: 1 is not a perfect number
    False
    >>> perfect("123")  # String representation of a number
    Traceback (most recent call last):
    ...
    ValueError: number must be an integer
    >>> perfect(12.34)
    Traceback (most recent call last):
      ...
    ValueError: number must be an integer
    >>> perfect("Hello")
    Traceback (most recent call last):
      ...
    ValueError: number must be an integer
    """
    if not isinstance(number, int):
        raise ValueError("number must be an integer")
    if number <= 0:
        return False
    return sum(i for i in range(1, number // 2 + 1) if number % i == 0) == number


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    print("Program to check whether a number is a Perfect number or not...")
    try:
        number = int(input("Enter a positive integer: ").strip())
    except ValueError:
        msg = "number must be an integer"
        raise ValueError(msg)

    print(f"{number} is {'' if perfect(number) else 'not '}a Perfect Number.")


================================================
FILE: maths/perfect_square.py
================================================
import math


def perfect_square(num: int) -> bool:
    """
    Check if a number is perfect square number or not
    :param num: the number to be checked
    :return: True if number is square number, otherwise False

    >>> perfect_square(9)
    True
    >>> perfect_square(16)
    True
    >>> perfect_square(1)
    True
    >>> perfect_square(0)
    True
    >>> perfect_square(10)
    False
    """
    return math.sqrt(num) * math.sqrt(num) == num


def perfect_square_binary_search(n: int) -> bool:
    """
    Check if a number is perfect square using binary search.
    Time complexity : O(Log(n))
    Space complexity: O(1)

    >>> perfect_square_binary_search(9)
    True
    >>> perfect_square_binary_search(16)
    True
    >>> perfect_square_binary_search(1)
    True
    >>> perfect_square_binary_search(0)
    True
    >>> perfect_square_binary_search(10)
    False
    >>> perfect_square_binary_search(-1)
    False
    >>> perfect_square_binary_search(1.1)
    False
    >>> perfect_square_binary_search("a")
    Traceback (most recent call last):
        ...
    TypeError: '<=' not supported between instances of 'int' and 'str'
    >>> perfect_square_binary_search(None)
    Traceback (most recent call last):
        ...
    TypeError: '<=' not supported between instances of 'int' and 'NoneType'
    >>> perfect_square_binary_search([])
    Traceback (most recent call last):
        ...
    TypeError: '<=' not supported between instances of 'int' and 'list'
    """
    left = 0
    right = n
    while left <= right:
        mid = (left + right) // 2
        if mid**2 == n:
            return True
        elif mid**2 > n:
            right = mid - 1
        else:
            left = mid + 1
    return False


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/persistence.py
================================================
def multiplicative_persistence(num: int) -> int:
    """
    Return the persistence of a given number.

    https://en.wikipedia.org/wiki/Persistence_of_a_number

    >>> multiplicative_persistence(217)
    2
    >>> multiplicative_persistence(-1)
    Traceback (most recent call last):
        ...
    ValueError: multiplicative_persistence() does not accept negative values
    >>> multiplicative_persistence("long number")
    Traceback (most recent call last):
        ...
    ValueError: multiplicative_persistence() only accepts integral values
    """

    if not isinstance(num, int):
        raise ValueError("multiplicative_persistence() only accepts integral values")
    if num < 0:
        raise ValueError("multiplicative_persistence() does not accept negative values")

    steps = 0
    num_string = str(num)

    while len(num_string) != 1:
        numbers = [int(i) for i in num_string]

        total = 1
        for i in range(len(numbers)):
            total *= numbers[i]

        num_string = str(total)

        steps += 1
    return steps


def additive_persistence(num: int) -> int:
    """
    Return the persistence of a given number.

    https://en.wikipedia.org/wiki/Persistence_of_a_number

    >>> additive_persistence(199)
    3
    >>> additive_persistence(-1)
    Traceback (most recent call last):
        ...
    ValueError: additive_persistence() does not accept negative values
    >>> additive_persistence("long number")
    Traceback (most recent call last):
        ...
    ValueError: additive_persistence() only accepts integral values
    """

    if not isinstance(num, int):
        raise ValueError("additive_persistence() only accepts integral values")
    if num < 0:
        raise ValueError("additive_persistence() does not accept negative values")

    steps = 0
    num_string = str(num)

    while len(num_string) != 1:
        numbers = [int(i) for i in num_string]

        total = 0
        for i in range(len(numbers)):
            total += numbers[i]

        num_string = str(total)

        steps += 1
    return steps


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/pi_generator.py
================================================
def calculate_pi(limit: int) -> str:
    """
    https://en.wikipedia.org/wiki/Leibniz_formula_for_%CF%80
    Leibniz Formula for Pi

    The Leibniz formula is the special case arctan(1) = pi / 4.
    Leibniz's formula converges extremely slowly: it exhibits sublinear convergence.

    Convergence (https://en.wikipedia.org/wiki/Leibniz_formula_for_%CF%80#Convergence)

    We cannot try to prove against an interrupted, uncompleted generation.
    https://en.wikipedia.org/wiki/Leibniz_formula_for_%CF%80#Unusual_behaviour
    The errors can in fact be predicted, but those calculations also approach infinity
    for accuracy.

    Our output will be a string so that we can definitely store all digits.

    >>> import math
    >>> float(calculate_pi(15)) == math.pi
    True

    Since we cannot predict errors or interrupt any infinite alternating series
    generation since they approach infinity, or interrupt any alternating series, we'll
    need math.isclose()

    >>> math.isclose(float(calculate_pi(50)), math.pi)
    True
    >>> math.isclose(float(calculate_pi(100)), math.pi)
    True

    Since math.pi contains only 16 digits, here are some tests with known values:

    >>> calculate_pi(50)
    '3.14159265358979323846264338327950288419716939937510'
    >>> calculate_pi(80)
    '3.14159265358979323846264338327950288419716939937510582097494459230781640628620899'
    """
    # Variables used for the iteration process
    q = 1
    r = 0
    t = 1
    k = 1
    n = 3
    m = 3

    decimal = limit
    counter = 0

    result = ""

    # We can't compare against anything if we make a generator,
    # so we'll stick with plain return logic
    while counter != decimal + 1:
        if 4 * q + r - t < n * t:
            result += str(n)
            if counter == 0:
                result += "."

            if decimal == counter:
                break

            counter += 1
            nr = 10 * (r - n * t)
            n = ((10 * (3 * q + r)) // t) - 10 * n
            q *= 10
            r = nr
        else:
            nr = (2 * q + r) * m
            nn = (q * (7 * k) + 2 + (r * m)) // (t * m)
            q *= k
            t *= m
            m += 2
            k += 1
            n = nn
            r = nr
    return result


def main() -> None:
    print(f"{calculate_pi(50) = }")
    import doctest

    doctest.testmod()


if __name__ == "__main__":
    main()


================================================
FILE: maths/pi_monte_carlo_estimation.py
================================================
import random


class Point:
    def __init__(self, x: float, y: float) -> None:
        self.x = x
        self.y = y

    def is_in_unit_circle(self) -> bool:
        """
        True, if the point lies in the unit circle
        False, otherwise
        """
        return (self.x**2 + self.y**2) <= 1

    @classmethod
    def random_unit_square(cls):
        """
        Generates a point randomly drawn from the unit square [0, 1) x [0, 1).
        """
        return cls(x=random.random(), y=random.random())


def estimate_pi(number_of_simulations: int) -> float:
    """
    Generates an estimate of the mathematical constant PI.
    See https://en.wikipedia.org/wiki/Monte_Carlo_method#Overview

    The estimate is generated by Monte Carlo simulations. Let U be uniformly drawn from
    the unit square [0, 1) x [0, 1). The probability that U lies in the unit circle is:

        P[U in unit circle] = 1/4 PI

    and therefore

        PI = 4 * P[U in unit circle]

    We can get an estimate of the probability P[U in unit circle].
    See https://en.wikipedia.org/wiki/Empirical_probability by:

        1. Draw a point uniformly from the unit square.
        2. Repeat the first step n times and count the number of points in the unit
            circle, which is called m.
        3. An estimate of P[U in unit circle] is m/n
    """
    if number_of_simulations < 1:
        raise ValueError("At least one simulation is necessary to estimate PI.")

    number_in_unit_circle = 0
    for _ in range(number_of_simulations):
        random_point = Point.random_unit_square()

        if random_point.is_in_unit_circle():
            number_in_unit_circle += 1

    return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
    # import doctest

    # doctest.testmod()
    from math import pi

    prompt = "Please enter the desired number of Monte Carlo simulations: "
    my_pi = estimate_pi(int(input(prompt).strip()))
    print(f"An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}")


================================================
FILE: maths/points_are_collinear_3d.py
================================================
"""
Check if three points are collinear in 3D.

In short, the idea is that we are able to create a triangle using three points,
and the area of that triangle can determine if the three points are collinear or not.


First, we create two vectors with the same initial point from the three points,
then we will calculate the cross-product of them.

The length of the cross vector is numerically equal to the area of a parallelogram.

Finally, the area of the triangle is equal to half of the area of the parallelogram.

Since we are only differentiating between zero and anything else,
we can get rid of the square root when calculating the length of the vector,
and also the division by two at the end.

From a second perspective, if the two vectors are parallel and overlapping,
we can't get a nonzero perpendicular vector,
since there will be an infinite number of orthogonal vectors.

To simplify the solution we will not calculate the length,
but we will decide directly from the vector whether it is equal to (0, 0, 0) or not.


Read More:
    https://math.stackexchange.com/a/1951650
"""

Vector3d = tuple[float, float, float]
Point3d = tuple[float, float, float]


def create_vector(end_point1: Point3d, end_point2: Point3d) -> Vector3d:
    """
    Pass two points to get the vector from them in the form (x, y, z).

    >>> create_vector((0, 0, 0), (1, 1, 1))
    (1, 1, 1)
    >>> create_vector((45, 70, 24), (47, 32, 1))
    (2, -38, -23)
    >>> create_vector((-14, -1, -8), (-7, 6, 4))
    (7, 7, 12)
    """
    x = end_point2[0] - end_point1[0]
    y = end_point2[1] - end_point1[1]
    z = end_point2[2] - end_point1[2]
    return (x, y, z)


def get_3d_vectors_cross(ab: Vector3d, ac: Vector3d) -> Vector3d:
    """
    Get the cross of the two vectors AB and AC.

    I used determinant of 2x2 to get the determinant of the 3x3 matrix in the process.

    Read More:
        https://en.wikipedia.org/wiki/Cross_product
        https://en.wikipedia.org/wiki/Determinant

    >>> get_3d_vectors_cross((3, 4, 7), (4, 9, 2))
    (-55, 22, 11)
    >>> get_3d_vectors_cross((1, 1, 1), (1, 1, 1))
    (0, 0, 0)
    >>> get_3d_vectors_cross((-4, 3, 0), (3, -9, -12))
    (-36, -48, 27)
    >>> get_3d_vectors_cross((17.67, 4.7, 6.78), (-9.5, 4.78, -19.33))
    (-123.2594, 277.15110000000004, 129.11260000000001)
    """
    x = ab[1] * ac[2] - ab[2] * ac[1]  # *i
    y = (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
    z = ab[0] * ac[1] - ab[1] * ac[0]  # *k
    return (x, y, z)


def is_zero_vector(vector: Vector3d, accuracy: int) -> bool:
    """
    Check if vector is equal to (0, 0, 0) or not.

    Since the algorithm is very accurate, we will never get a zero vector,
    so we need to round the vector axis,
    because we want a result that is either True or False.
    In other applications, we can return a float that represents the collinearity ratio.

    >>> is_zero_vector((0, 0, 0), accuracy=10)
    True
    >>> is_zero_vector((15, 74, 32), accuracy=10)
    False
    >>> is_zero_vector((-15, -74, -32), accuracy=10)
    False
    """
    return tuple(round(x, accuracy) for x in vector) == (0, 0, 0)


def are_collinear(a: Point3d, b: Point3d, c: Point3d, accuracy: int = 10) -> bool:
    """
    Check if three points are collinear or not.

    1- Create two vectors AB and AC.
    2- Get the cross vector of the two vectors.
    3- Calculate the length of the cross vector.
    4- If the length is zero then the points are collinear, else they are not.

    The use of the accuracy parameter is explained in is_zero_vector docstring.

    >>> are_collinear((4.802293498137402, 3.536233125455244, 0),
    ...               (-2.186788107953106, -9.24561398001649, 7.141509524846482),
    ...               (1.530169574640268, -2.447927606600034, 3.343487096469054))
    True
    >>> are_collinear((-6, -2, 6),
    ...               (6.200213806439997, -4.930157614926678, -4.482371908289856),
    ...               (-4.085171149525941, -2.459889509029438, 4.354787180795383))
    True
    >>> are_collinear((2.399001826862445, -2.452009976680793, 4.464656666157666),
    ...               (-3.682816335934376, 5.753788986533145, 9.490993909044244),
    ...               (1.962903518985307, 3.741415730125627, 7))
    False
    >>> are_collinear((1.875375340689544, -7.268426006071538, 7.358196269835993),
    ...               (-3.546599383667157, -4.630005261513976, 3.208784032924246),
    ...               (-2.564606140206386, 3.937845170672183, 7))
    False
    """
    ab = create_vector(a, b)
    ac = create_vector(a, c)
    return is_zero_vector(get_3d_vectors_cross(ab, ac), accuracy)


================================================
FILE: maths/pollard_rho.py
================================================
from __future__ import annotations

from math import gcd


def pollard_rho(
    num: int,
    seed: int = 2,
    step: int = 1,
    attempts: int = 3,
) -> int | None:
    """
    Use Pollard's Rho algorithm to return a nontrivial factor of ``num``.
    The returned factor may be composite and require further factorization.
    If the algorithm will return None if it fails to find a factor within
    the specified number of attempts or within the specified number of steps.
    If ``num`` is prime, this algorithm is guaranteed to return None.
    https://en.wikipedia.org/wiki/Pollard%27s_rho_algorithm

    >>> pollard_rho(18446744073709551617)
    274177
    >>> pollard_rho(97546105601219326301)
    9876543191
    >>> pollard_rho(100)
    2
    >>> pollard_rho(17)
    >>> pollard_rho(17**3)
    17
    >>> pollard_rho(17**3, attempts=1)
    >>> pollard_rho(3*5*7)
    21
    >>> pollard_rho(1)
    Traceback (most recent call last):
        ...
    ValueError: The input value cannot be less than 2
    """
    # A value less than 2 can cause an infinite loop in the algorithm.
    if num < 2:
        raise ValueError("The input value cannot be less than 2")

    # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
    # algorithm struggles to find factors that are divisible by two.
    # As a workaround, we specifically check for two and even inputs.
    #   See: https://math.stackexchange.com/a/2856214/165820
    if num > 2 and num % 2 == 0:
        return 2

    # Pollard's Rho algorithm requires a function that returns pseudorandom
    # values between 0 <= X < ``num``.  It doesn't need to be random in the
    # sense that the output value is cryptographically secure or difficult
    # to calculate, it only needs to be random in the sense that all output
    # values should be equally likely to appear.
    # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
    # However, the success of Pollard's algorithm isn't guaranteed and is
    # determined in part by the initial seed and the chosen random function.
    # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
    # where ``C`` is a value that we can modify between each attempt.
    def rand_fn(value: int, step: int, modulus: int) -> int:
        """
        Returns a pseudorandom value modulo ``modulus`` based on the
        input ``value`` and attempt-specific ``step`` size.

        >>> rand_fn(0, 0, 0)
        Traceback (most recent call last):
            ...
        ZeroDivisionError: integer division or modulo by zero
        >>> rand_fn(1, 2, 3)
        0
        >>> rand_fn(0, 10, 7)
        3
        >>> rand_fn(1234, 1, 17)
        16
        """
        return (pow(value, 2) + step) % modulus

    for _ in range(attempts):
        # These track the position within the cycle detection logic.
        tortoise = seed
        hare = seed

        while True:
            # At each iteration, the tortoise moves one step and the hare moves two.
            tortoise = rand_fn(tortoise, step, num)
            hare = rand_fn(hare, step, num)
            hare = rand_fn(hare, step, num)

            # At some point both the tortoise and the hare will enter a cycle whose
            # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
            # the tortoise and hare will end up on the same value modulo ``p``.
            # We can detect when this happens because the position difference between
            # the tortoise and the hare will share a common divisor with ``num``.
            divisor = gcd(hare - tortoise, num)

            if divisor == 1:
                # No common divisor yet, just keep searching.
                continue
            # We found a common divisor!
            elif divisor == num:
                # Unfortunately, the divisor is ``num`` itself and is useless.
                break
            else:
                # The divisor is a nontrivial factor of ``num``!
                return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
        seed = hare

        # The new step value for the random function can just be incremented.
        # At first the results will be similar to what the old function would
        # have produced, but the value will quickly diverge after a bit.
        step += 1

    # We haven't found a divisor within the requested number of attempts.
    # We were unlucky or ``num`` itself is actually prime.
    return None


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument(
        "num",
        type=int,
        help="The value to find a divisor of",
    )
    parser.add_argument(
        "--attempts",
        type=int,
        default=3,
        help="The number of attempts before giving up",
    )
    args = parser.parse_args()

    divisor = pollard_rho(args.num, attempts=args.attempts)
    if divisor is None:
        print(f"{args.num} is probably prime")
    else:
        quotient = args.num // divisor
        print(f"{args.num} = {divisor} * {quotient}")


================================================
FILE: maths/polynomial_evaluation.py
================================================
from collections.abc import Sequence


def evaluate_poly(poly: Sequence[float], x: float) -> float:
    """Evaluate a polynomial f(x) at specified point x and return the value.

    Arguments:
    poly -- the coefficients of a polynomial as an iterable in order of
            ascending degree
    x -- the point at which to evaluate the polynomial

    >>> evaluate_poly((0.0, 0.0, 5.0, 9.3, 7.0), 10.0)
    79800.0
    """
    return sum(c * (x**i) for i, c in enumerate(poly))


def horner(poly: Sequence[float], x: float) -> float:
    """Evaluate a polynomial at specified point using Horner's method.

    In terms of computational complexity, Horner's method is an efficient method
    of evaluating a polynomial. It avoids the use of expensive exponentiation,
    and instead uses only multiplication and addition to evaluate the polynomial
    in O(n), where n is the degree of the polynomial.

    https://en.wikipedia.org/wiki/Horner's_method

    Arguments:
    poly -- the coefficients of a polynomial as an iterable in order of
            ascending degree
    x -- the point at which to evaluate the polynomial

    >>> horner((0.0, 0.0, 5.0, 9.3, 7.0), 10.0)
    79800.0
    """
    result = 0.0
    for coeff in reversed(poly):
        result = result * x + coeff
    return result


if __name__ == "__main__":
    """
    Example:
    >>> poly = (0.0, 0.0, 5.0, 9.3, 7.0)  # f(x) = 7.0x^4 + 9.3x^3 + 5.0x^2
    >>> x = -13.0
    >>> # f(-13) = 7.0(-13)^4 + 9.3(-13)^3 + 5.0(-13)^2 = 180339.9
    >>> evaluate_poly(poly, x)
    180339.9
    """
    poly = (0.0, 0.0, 5.0, 9.3, 7.0)
    x = 10.0
    print(evaluate_poly(poly, x))
    print(horner(poly, x))


================================================
FILE: maths/polynomials/__init__.py
================================================


================================================
FILE: maths/polynomials/single_indeterminate_operations.py
================================================
"""

This module implements a single indeterminate polynomials class
with some basic operations

Reference: https://en.wikipedia.org/wiki/Polynomial

"""

from __future__ import annotations

from collections.abc import MutableSequence


class Polynomial:
    def __init__(self, degree: int, coefficients: MutableSequence[float]) -> None:
        """
        The coefficients should be in order of degree, from smallest to largest.
        >>> p = Polynomial(2, [1, 2, 3])
        >>> p = Polynomial(2, [1, 2, 3, 4])
        Traceback (most recent call last):
        ...
        ValueError: The number of coefficients should be equal to the degree + 1.

        """
        if len(coefficients) != degree + 1:
            raise ValueError(
                "The number of coefficients should be equal to the degree + 1."
            )

        self.coefficients: list[float] = list(coefficients)
        self.degree = degree

    def __add__(self, polynomial_2: Polynomial) -> Polynomial:
        """
        Polynomial addition
        >>> p = Polynomial(2, [1, 2, 3])
        >>> q = Polynomial(2, [1, 2, 3])
        >>> p + q
        6x^2 + 4x + 2
        """

        if self.degree > polynomial_2.degree:
            coefficients = self.coefficients[:]
            for i in range(polynomial_2.degree + 1):
                coefficients[i] += polynomial_2.coefficients[i]
            return Polynomial(self.degree, coefficients)
        else:
            coefficients = polynomial_2.coefficients[:]
            for i in range(self.degree + 1):
                coefficients[i] += self.coefficients[i]
            return Polynomial(polynomial_2.degree, coefficients)

    def __sub__(self, polynomial_2: Polynomial) -> Polynomial:
        """
        Polynomial subtraction
        >>> p = Polynomial(2, [1, 2, 4])
        >>> q = Polynomial(2, [1, 2, 3])
        >>> p - q
        1x^2
        """
        return self + polynomial_2 * Polynomial(0, [-1])

    def __neg__(self) -> Polynomial:
        """
        Polynomial negation
        >>> p = Polynomial(2, [1, 2, 3])
        >>> -p
         - 3x^2 - 2x - 1
        """
        return Polynomial(self.degree, [-c for c in self.coefficients])

    def __mul__(self, polynomial_2: Polynomial) -> Polynomial:
        """
        Polynomial multiplication
        >>> p = Polynomial(2, [1, 2, 3])
        >>> q = Polynomial(2, [1, 2, 3])
        >>> p * q
        9x^4 + 12x^3 + 10x^2 + 4x + 1
        """
        coefficients: list[float] = [0] * (self.degree + polynomial_2.degree + 1)
        for i in range(self.degree + 1):
            for j in range(polynomial_2.degree + 1):
                coefficients[i + j] += (
                    self.coefficients[i] * polynomial_2.coefficients[j]
                )

        return Polynomial(self.degree + polynomial_2.degree, coefficients)

    def evaluate(self, substitution: float) -> float:
        """
        Evaluates the polynomial at x.
        >>> p = Polynomial(2, [1, 2, 3])
        >>> p.evaluate(2)
        17
        """
        result: int | float = 0
        for i in range(self.degree + 1):
            result += self.coefficients[i] * (substitution**i)
        return result

    def __str__(self) -> str:
        """
        >>> p = Polynomial(2, [1, 2, 3])
        >>> print(p)
        3x^2 + 2x + 1
        """
        polynomial = ""
        for i in range(self.degree, -1, -1):
            if self.coefficients[i] == 0:
                continue
            elif self.coefficients[i] > 0:
                if polynomial:
                    polynomial += " + "
            else:
                polynomial += " - "

            if i == 0:
                polynomial += str(abs(self.coefficients[i]))
            elif i == 1:
                polynomial += str(abs(self.coefficients[i])) + "x"
            else:
                polynomial += str(abs(self.coefficients[i])) + "x^" + str(i)

        return polynomial

    def __repr__(self) -> str:
        """
        >>> p = Polynomial(2, [1, 2, 3])
        >>> p
        3x^2 + 2x + 1
        """
        return self.__str__()

    def derivative(self) -> Polynomial:
        """
        Returns the derivative of the polynomial.
        >>> p = Polynomial(2, [1, 2, 3])
        >>> p.derivative()
        6x + 2
        """
        coefficients: list[float] = [0] * self.degree
        for i in range(self.degree):
            coefficients[i] = self.coefficients[i + 1] * (i + 1)
        return Polynomial(self.degree - 1, coefficients)

    def integral(self, constant: float = 0) -> Polynomial:
        """
        Returns the integral of the polynomial.
        >>> p = Polynomial(2, [1, 2, 3])
        >>> p.integral()
        1.0x^3 + 1.0x^2 + 1.0x
        """
        coefficients: list[float] = [0] * (self.degree + 2)
        coefficients[0] = constant
        for i in range(self.degree + 1):
            coefficients[i + 1] = self.coefficients[i] / (i + 1)
        return Polynomial(self.degree + 1, coefficients)

    def __eq__(self, polynomial_2: object) -> bool:
        """
        Checks if two polynomials are equal.
        >>> p = Polynomial(2, [1, 2, 3])
        >>> q = Polynomial(2, [1, 2, 3])
        >>> p == q
        True
        """
        if not isinstance(polynomial_2, Polynomial):
            return False

        if self.degree != polynomial_2.degree:
            return False

        for i in range(self.degree + 1):
            if self.coefficients[i] != polynomial_2.coefficients[i]:
                return False

        return True

    def __ne__(self, polynomial_2: object) -> bool:
        """
        Checks if two polynomials are not equal.
        >>> p = Polynomial(2, [1, 2, 3])
        >>> q = Polynomial(2, [1, 2, 3])
        >>> p != q
        False
        """
        return not self.__eq__(polynomial_2)


================================================
FILE: maths/power_using_recursion.py
================================================
"""
== Raise base to the power of exponent using recursion ==
    Input -->
        Enter the base: 3
        Enter the exponent: 4
    Output  -->
        3 to the power of 4 is 81
    Input -->
        Enter the base: 2
        Enter the exponent: 0
    Output -->
        2 to the power of 0 is 1
"""


def power(base: int, exponent: int) -> float:
    """
    Calculate the power of a base raised to an exponent.

    >>> power(3, 4)
    81
    >>> power(2, 0)
    1
    >>> all(power(base, exponent) == pow(base, exponent)
    ...     for base in range(-10, 10) for exponent in range(10))
    True
    >>> power('a', 1)
    'a'
    >>> power('a', 2)
    Traceback (most recent call last):
        ...
    TypeError: can't multiply sequence by non-int of type 'str'
    >>> power('a', 'b')
    Traceback (most recent call last):
        ...
    TypeError: unsupported operand type(s) for -: 'str' and 'int'
    >>> power(2, -1)
    Traceback (most recent call last):
        ...
    RecursionError: maximum recursion depth exceeded
    >>> power(0, 0)
    1
    >>> power(0, 1)
    0
    >>> power(5,6)
    15625
    >>> power(23, 12)
    21914624432020321
    """
    return base * power(base, (exponent - 1)) if exponent else 1


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    print("Raise base to the power of exponent using recursion...")
    base = int(input("Enter the base: ").strip())
    exponent = int(input("Enter the exponent: ").strip())
    result = power(base, abs(exponent))
    if exponent < 0:  # power() does not properly deal w/ negative exponents
        result = 1 / result
    print(f"{base} to the power of {exponent} is {result}")


================================================
FILE: maths/prime_check.py
================================================
"""Prime Check."""

import math
import unittest

import pytest


def is_prime(number: int) -> bool:
    """Checks to see if a number is a prime in O(sqrt(n)).

    A number is prime if it has exactly two factors: 1 and itself.

    >>> is_prime(0)
    False
    >>> is_prime(1)
    False
    >>> is_prime(2)
    True
    >>> is_prime(3)
    True
    >>> is_prime(27)
    False
    >>> is_prime(87)
    False
    >>> is_prime(563)
    True
    >>> is_prime(2999)
    True
    >>> is_prime(67483)
    False
    >>> is_prime(16.1)
    Traceback (most recent call last):
        ...
    ValueError: is_prime() only accepts positive integers
    >>> is_prime(-4)
    Traceback (most recent call last):
        ...
    ValueError: is_prime() only accepts positive integers
    """

    # precondition
    if not isinstance(number, int) or not number >= 0:
        raise ValueError("is_prime() only accepts positive integers")

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5, int(math.sqrt(number) + 1), 6):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


class Test(unittest.TestCase):
    def test_primes(self):
        assert is_prime(2)
        assert is_prime(3)
        assert is_prime(5)
        assert is_prime(7)
        assert is_prime(11)
        assert is_prime(13)
        assert is_prime(17)
        assert is_prime(19)
        assert is_prime(23)
        assert is_prime(29)

    def test_not_primes(self):
        with pytest.raises(ValueError):
            is_prime(-19)
        assert not is_prime(0), (
            "Zero doesn't have any positive factors, primes must have exactly two."
        )
        assert not is_prime(1), (
            "One only has 1 positive factor, primes must have exactly two."
        )
        assert not is_prime(2 * 2)
        assert not is_prime(2 * 3)
        assert not is_prime(3 * 3)
        assert not is_prime(3 * 5)
        assert not is_prime(3 * 5 * 7)


if __name__ == "__main__":
    unittest.main()


================================================
FILE: maths/prime_factors.py
================================================
"""
python/black : True
"""

from __future__ import annotations


def prime_factors(n: int) -> list[int]:
    """
    Returns prime factors of n as a list.

    >>> prime_factors(0)
    []
    >>> prime_factors(100)
    [2, 2, 5, 5]
    >>> prime_factors(2560)
    [2, 2, 2, 2, 2, 2, 2, 2, 2, 5]
    >>> prime_factors(10**-2)
    []
    >>> prime_factors(0.02)
    []
    >>> x = prime_factors(10**241) # doctest: +NORMALIZE_WHITESPACE
    >>> x == [2]*241 + [5]*241
    True
    >>> prime_factors(10**-354)
    []
    >>> prime_factors('hello')
    Traceback (most recent call last):
        ...
    TypeError: '<=' not supported between instances of 'int' and 'str'
    >>> prime_factors([1,2,'hello'])
    Traceback (most recent call last):
        ...
    TypeError: '<=' not supported between instances of 'int' and 'list'

    """
    i = 2
    factors = []
    while i * i <= n:
        if n % i:
            i += 1
        else:
            n //= i
            factors.append(i)
    if n > 1:
        factors.append(n)
    return factors


def unique_prime_factors(n: int) -> list[int]:
    """
    Returns unique prime factors of n as a list.

    >>> unique_prime_factors(0)
    []
    >>> unique_prime_factors(100)
    [2, 5]
    >>> unique_prime_factors(2560)
    [2, 5]
    >>> unique_prime_factors(10**-2)
    []
    >>> unique_prime_factors(0.02)
    []
    >>> unique_prime_factors(10**241)
    [2, 5]
    >>> unique_prime_factors(10**-354)
    []
    >>> unique_prime_factors('hello')
    Traceback (most recent call last):
        ...
    TypeError: '<=' not supported between instances of 'int' and 'str'
    >>> unique_prime_factors([1,2,'hello'])
    Traceback (most recent call last):
        ...
    TypeError: '<=' not supported between instances of 'int' and 'list'
    """
    i = 2
    factors = []
    while i * i <= n:
        if not n % i:
            while not n % i:
                n //= i
            factors.append(i)
        i += 1
    if n > 1:
        factors.append(n)
    return factors


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/prime_numbers.py
================================================
import math
from collections.abc import Generator


def slow_primes(max_n: int) -> Generator[int]:
    """
    Return a list of all primes numbers up to max.
    >>> list(slow_primes(0))
    []
    >>> list(slow_primes(-1))
    []
    >>> list(slow_primes(-10))
    []
    >>> list(slow_primes(25))
    [2, 3, 5, 7, 11, 13, 17, 19, 23]
    >>> list(slow_primes(11))
    [2, 3, 5, 7, 11]
    >>> list(slow_primes(33))
    [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31]
    >>> list(slow_primes(1000))[-1]
    997
    """
    numbers: Generator = (i for i in range(1, (max_n + 1)))
    for i in (n for n in numbers if n > 1):
        for j in range(2, i):
            if (i % j) == 0:
                break
        else:
            yield i


def primes(max_n: int) -> Generator[int]:
    """
    Return a list of all primes numbers up to max.
    >>> list(primes(0))
    []
    >>> list(primes(-1))
    []
    >>> list(primes(-10))
    []
    >>> list(primes(25))
    [2, 3, 5, 7, 11, 13, 17, 19, 23]
    >>> list(primes(11))
    [2, 3, 5, 7, 11]
    >>> list(primes(33))
    [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31]
    >>> list(primes(1000))[-1]
    997
    """
    numbers: Generator = (i for i in range(1, (max_n + 1)))
    for i in (n for n in numbers if n > 1):
        # only need to check for factors up to sqrt(i)
        bound = int(math.sqrt(i)) + 1
        for j in range(2, bound):
            if (i % j) == 0:
                break
        else:
            yield i


def fast_primes(max_n: int) -> Generator[int]:
    """
    Return a list of all primes numbers up to max.
    >>> list(fast_primes(0))
    []
    >>> list(fast_primes(-1))
    []
    >>> list(fast_primes(-10))
    []
    >>> list(fast_primes(25))
    [2, 3, 5, 7, 11, 13, 17, 19, 23]
    >>> list(fast_primes(11))
    [2, 3, 5, 7, 11]
    >>> list(fast_primes(33))
    [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31]
    >>> list(fast_primes(1000))[-1]
    997
    """
    numbers: Generator = (i for i in range(1, (max_n + 1), 2))
    # It's useless to test even numbers as they will not be prime
    if max_n > 2:
        yield 2  # Because 2 will not be tested, it's necessary to yield it now
    for i in (n for n in numbers if n > 1):
        bound = int(math.sqrt(i)) + 1
        for j in range(3, bound, 2):
            # As we removed the even numbers, we don't need them now
            if (i % j) == 0:
                break
        else:
            yield i


def benchmark():
    """
    Let's benchmark our functions side-by-side...
    """
    from timeit import timeit

    setup = "from __main__ import slow_primes, primes, fast_primes"
    print(timeit("slow_primes(1_000_000_000_000)", setup=setup, number=1_000_000))
    print(timeit("primes(1_000_000_000_000)", setup=setup, number=1_000_000))
    print(timeit("fast_primes(1_000_000_000_000)", setup=setup, number=1_000_000))


if __name__ == "__main__":
    number = int(input("Calculate primes up to:\n>> ").strip())
    for ret in primes(number):
        print(ret)
    benchmark()


================================================
FILE: maths/prime_sieve_eratosthenes.py
================================================
"""
Sieve of Eratosthenes

Input: n = 10
Output: 2 3 5 7

Input: n = 20
Output: 2 3 5 7 11 13 17 19

you can read in detail about this at
https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes
"""


def prime_sieve_eratosthenes(num: int) -> list[int]:
    """
    Print the prime numbers up to n

    >>> prime_sieve_eratosthenes(10)
    [2, 3, 5, 7]
    >>> prime_sieve_eratosthenes(20)
    [2, 3, 5, 7, 11, 13, 17, 19]
    >>> prime_sieve_eratosthenes(2)
    [2]
    >>> prime_sieve_eratosthenes(1)
    []
    >>> prime_sieve_eratosthenes(-1)
    Traceback (most recent call last):
    ...
    ValueError: Input must be a positive integer
    """

    if num <= 0:
        raise ValueError("Input must be a positive integer")

    primes = [True] * (num + 1)

    p = 2
    while p * p <= num:
        if primes[p]:
            for i in range(p * p, num + 1, p):
                primes[i] = False
        p += 1

    return [prime for prime in range(2, num + 1) if primes[prime]]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    user_num = int(input("Enter a positive integer: ").strip())
    print(prime_sieve_eratosthenes(user_num))


================================================
FILE: maths/primelib.py
================================================
"""
Created on Thu Oct  5 16:44:23 2017

@author: Christian Bender

This Python library contains some useful functions to deal with
prime numbers and whole numbers.

Overview:

is_prime(number)
sieve_er(N)
get_prime_numbers(N)
prime_factorization(number)
greatest_prime_factor(number)
smallest_prime_factor(number)
get_prime(n)
get_primes_between(pNumber1, pNumber2)

----

is_even(number)
is_odd(number)
kg_v(number1, number2)  // least common multiple
get_divisors(number)    // all divisors of 'number' inclusive 1, number
is_perfect_number(number)

NEW-FUNCTIONS

simplify_fraction(numerator, denominator)
factorial (n) // n!
fib (n) // calculate the n-th fibonacci term.

-----

goldbach(number)  // Goldbach's assumption

"""

from math import sqrt

from maths.greatest_common_divisor import gcd_by_iterative


def is_prime(number: int) -> bool:
    """
    input: positive integer 'number'
    returns true if 'number' is prime otherwise false.

    >>> is_prime(3)
    True
    >>> is_prime(10)
    False
    >>> is_prime(97)
    True
    >>> is_prime(9991)
    False
    >>> is_prime(-1)
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int and positive
    >>> is_prime("test")
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int and positive
    """

    # precondition
    assert isinstance(number, int) and (number >= 0), (
        "'number' must been an int and positive"
    )

    status = True

    # 0 and 1 are none primes.
    if number <= 1:
        status = False

    for divisor in range(2, round(sqrt(number)) + 1):
        # if 'number' divisible by 'divisor' then sets 'status'
        # of false and break up the loop.
        if number % divisor == 0:
            status = False
            break

    # precondition
    assert isinstance(status, bool), "'status' must been from type bool"

    return status


# ------------------------------------------


def sieve_er(n):
    """
    input: positive integer 'N' > 2
    returns a list of prime numbers from 2 up to N.

    This function implements the algorithm called
    sieve of erathostenes.

    >>> sieve_er(8)
    [2, 3, 5, 7]
    >>> sieve_er(-1)
    Traceback (most recent call last):
        ...
    AssertionError: 'N' must been an int and > 2
    >>> sieve_er("test")
    Traceback (most recent call last):
        ...
    AssertionError: 'N' must been an int and > 2
    """

    # precondition
    assert isinstance(n, int) and (n > 2), "'N' must been an int and > 2"

    # beginList: contains all natural numbers from 2 up to N
    begin_list = list(range(2, n + 1))

    ans = []  # this list will be returns.

    # actual sieve of erathostenes
    for i in range(len(begin_list)):
        for j in range(i + 1, len(begin_list)):
            if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
                begin_list[j] = 0

    # filters actual prime numbers.
    ans = [x for x in begin_list if x != 0]

    # precondition
    assert isinstance(ans, list), "'ans' must been from type list"

    return ans


# --------------------------------


def get_prime_numbers(n):
    """
    input: positive integer 'N' > 2
    returns a list of prime numbers from 2 up to N (inclusive)
    This function is more efficient as function 'sieveEr(...)'

    >>> get_prime_numbers(8)
    [2, 3, 5, 7]
    >>> get_prime_numbers(-1)
    Traceback (most recent call last):
        ...
    AssertionError: 'N' must been an int and > 2
    >>> get_prime_numbers("test")
    Traceback (most recent call last):
        ...
    AssertionError: 'N' must been an int and > 2
    """

    # precondition
    assert isinstance(n, int) and (n > 2), "'N' must been an int and > 2"

    ans = []

    # iterates over all numbers between 2 up to N+1
    # if a number is prime then appends to list 'ans'
    for number in range(2, n + 1):
        if is_prime(number):
            ans.append(number)

    # precondition
    assert isinstance(ans, list), "'ans' must been from type list"

    return ans


# -----------------------------------------


def prime_factorization(number):
    """
    input: positive integer 'number'
    returns a list of the prime number factors of 'number'

    >>> prime_factorization(0)
    [0]
    >>> prime_factorization(8)
    [2, 2, 2]
    >>> prime_factorization(287)
    [7, 41]
    >>> prime_factorization(-1)
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int and >= 0
    >>> prime_factorization("test")
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int and >= 0
    """

    # precondition
    assert isinstance(number, int) and number >= 0, "'number' must been an int and >= 0"

    ans = []  # this list will be returns of the function.

    # potential prime number factors.

    factor = 2

    quotient = number

    if number in {0, 1}:
        ans.append(number)

    # if 'number' not prime then builds the prime factorization of 'number'
    elif not is_prime(number):
        while quotient != 1:
            if is_prime(factor) and (quotient % factor == 0):
                ans.append(factor)
                quotient /= factor
            else:
                factor += 1

    else:
        ans.append(number)

    # precondition
    assert isinstance(ans, list), "'ans' must been from type list"

    return ans


# -----------------------------------------


def greatest_prime_factor(number):
    """
    input: positive integer 'number' >= 0
    returns the greatest prime number factor of 'number'

    >>> greatest_prime_factor(0)
    0
    >>> greatest_prime_factor(8)
    2
    >>> greatest_prime_factor(287)
    41
    >>> greatest_prime_factor(-1)
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int and >= 0
    >>> greatest_prime_factor("test")
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int and >= 0
    """

    # precondition
    assert isinstance(number, int) and (number >= 0), (
        "'number' must been an int and >= 0"
    )

    ans = 0

    # prime factorization of 'number'
    prime_factors = prime_factorization(number)

    ans = max(prime_factors)

    # precondition
    assert isinstance(ans, int), "'ans' must been from type int"

    return ans


# ----------------------------------------------


def smallest_prime_factor(number):
    """
    input: integer 'number' >= 0
    returns the smallest prime number factor of 'number'

    >>> smallest_prime_factor(0)
    0
    >>> smallest_prime_factor(8)
    2
    >>> smallest_prime_factor(287)
    7
    >>> smallest_prime_factor(-1)
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int and >= 0
    >>> smallest_prime_factor("test")
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int and >= 0
    """

    # precondition
    assert isinstance(number, int) and (number >= 0), (
        "'number' must been an int and >= 0"
    )

    ans = 0

    # prime factorization of 'number'
    prime_factors = prime_factorization(number)

    ans = min(prime_factors)

    # precondition
    assert isinstance(ans, int), "'ans' must been from type int"

    return ans


# ----------------------


def is_even(number):
    """
    input: integer 'number'
    returns true if 'number' is even, otherwise false.

    >>> is_even(0)
    True
    >>> is_even(8)
    True
    >>> is_even(287)
    False
    >>> is_even(-1)
    False
    >>> is_even("test")
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int
    """

    # precondition
    assert isinstance(number, int), "'number' must been an int"
    assert isinstance(number % 2 == 0, bool), "compare must been from type bool"

    return number % 2 == 0


# ------------------------


def is_odd(number):
    """
    input: integer 'number'
    returns true if 'number' is odd, otherwise false.

    >>> is_odd(0)
    False
    >>> is_odd(8)
    False
    >>> is_odd(287)
    True
    >>> is_odd(-1)
    True
    >>> is_odd("test")
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int
    """

    # precondition
    assert isinstance(number, int), "'number' must been an int"
    assert isinstance(number % 2 != 0, bool), "compare must been from type bool"

    return number % 2 != 0


# ------------------------


def goldbach(number):
    """
    Goldbach's assumption
    input: a even positive integer 'number' > 2
    returns a list of two prime numbers whose sum is equal to 'number'

    >>> goldbach(8)
    [3, 5]
    >>> goldbach(824)
    [3, 821]
    >>> goldbach(0)
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int, even and > 2
    >>> goldbach(-1)
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int, even and > 2
    >>> goldbach("test")
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int, even and > 2
    """

    # precondition
    assert isinstance(number, int) and (number > 2) and is_even(number), (
        "'number' must been an int, even and > 2"
    )

    ans = []  # this list will returned

    # creates a list of prime numbers between 2 up to 'number'
    prime_numbers = get_prime_numbers(number)
    len_pn = len(prime_numbers)

    # run variable for while-loops.
    i = 0
    j = None

    # exit variable. for break up the loops
    loop = True

    while i < len_pn and loop:
        j = i + 1

        while j < len_pn and loop:
            if prime_numbers[i] + prime_numbers[j] == number:
                loop = False
                ans.append(prime_numbers[i])
                ans.append(prime_numbers[j])

            j += 1

        i += 1

    # precondition
    assert (
        isinstance(ans, list)
        and (len(ans) == 2)
        and (ans[0] + ans[1] == number)
        and is_prime(ans[0])
        and is_prime(ans[1])
    ), "'ans' must contains two primes. And sum of elements must been eq 'number'"

    return ans


# ----------------------------------------------


def kg_v(number1, number2):
    """
    Least common multiple
    input: two positive integer 'number1' and 'number2'
    returns the least common multiple of 'number1' and 'number2'

    >>> kg_v(8,10)
    40
    >>> kg_v(824,67)
    55208
    >>> kg_v(1, 10)
    10
    >>> kg_v(0)
    Traceback (most recent call last):
        ...
    TypeError: kg_v() missing 1 required positional argument: 'number2'
    >>> kg_v(10,-1)
    Traceback (most recent call last):
        ...
    AssertionError: 'number1' and 'number2' must been positive integer.
    >>> kg_v("test","test2")
    Traceback (most recent call last):
        ...
    AssertionError: 'number1' and 'number2' must been positive integer.
    """

    # precondition
    assert (
        isinstance(number1, int)
        and isinstance(number2, int)
        and (number1 >= 1)
        and (number2 >= 1)
    ), "'number1' and 'number2' must been positive integer."

    ans = 1  # actual answer that will be return.

    # for kgV (x,1)
    if number1 > 1 and number2 > 1:
        # builds the prime factorization of 'number1' and 'number2'
        prime_fac_1 = prime_factorization(number1)
        prime_fac_2 = prime_factorization(number2)

    elif number1 == 1 or number2 == 1:
        prime_fac_1 = []
        prime_fac_2 = []
        ans = max(number1, number2)

    count1 = 0
    count2 = 0

    done = []  # captured numbers int both 'primeFac1' and 'primeFac2'

    # iterates through primeFac1
    for n in prime_fac_1:
        if n not in done:
            if n in prime_fac_2:
                count1 = prime_fac_1.count(n)
                count2 = prime_fac_2.count(n)

                for _ in range(max(count1, count2)):
                    ans *= n

            else:
                count1 = prime_fac_1.count(n)

                for _ in range(count1):
                    ans *= n

            done.append(n)

    # iterates through primeFac2
    for n in prime_fac_2:
        if n not in done:
            count2 = prime_fac_2.count(n)

            for _ in range(count2):
                ans *= n

            done.append(n)

    # precondition
    assert isinstance(ans, int) and (ans >= 0), (
        "'ans' must been from type int and positive"
    )

    return ans


# ----------------------------------


def get_prime(n):
    """
    Gets the n-th prime number.
    input: positive integer 'n' >= 0
    returns the n-th prime number, beginning at index 0

    >>> get_prime(0)
    2
    >>> get_prime(8)
    23
    >>> get_prime(824)
    6337
    >>> get_prime(-1)
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been a positive int
    >>> get_prime("test")
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been a positive int
    """

    # precondition
    assert isinstance(n, int) and (n >= 0), "'number' must been a positive int"

    index = 0
    ans = 2  # this variable holds the answer

    while index < n:
        index += 1

        ans += 1  # counts to the next number

        # if ans not prime then
        # runs to the next prime number.
        while not is_prime(ans):
            ans += 1

    # precondition
    assert isinstance(ans, int) and is_prime(ans), (
        "'ans' must been a prime number and from type int"
    )

    return ans


# ---------------------------------------------------


def get_primes_between(p_number_1, p_number_2):
    """
    input: prime numbers 'pNumber1' and 'pNumber2'
            pNumber1 < pNumber2
    returns a list of all prime numbers between 'pNumber1' (exclusive)
            and 'pNumber2' (exclusive)

    >>> get_primes_between(3, 67)
    [5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61]
    >>> get_primes_between(0)
    Traceback (most recent call last):
        ...
    TypeError: get_primes_between() missing 1 required positional argument: 'p_number_2'
    >>> get_primes_between(0, 1)
    Traceback (most recent call last):
        ...
    AssertionError: The arguments must been prime numbers and 'pNumber1' < 'pNumber2'
    >>> get_primes_between(-1, 3)
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int and positive
    >>> get_primes_between("test","test")
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int and positive
    """

    # precondition
    assert (
        is_prime(p_number_1) and is_prime(p_number_2) and (p_number_1 < p_number_2)
    ), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"

    number = p_number_1 + 1  # jump to the next number

    ans = []  # this list will be returns.

    # if number is not prime then
    # fetch the next prime number.
    while not is_prime(number):
        number += 1

    while number < p_number_2:
        ans.append(number)

        number += 1

        # fetch the next prime number.
        while not is_prime(number):
            number += 1

    # precondition
    assert (
        isinstance(ans, list)
        and ans[0] != p_number_1
        and ans[len(ans) - 1] != p_number_2
    ), "'ans' must been a list without the arguments"

    # 'ans' contains not 'pNumber1' and 'pNumber2' !
    return ans


# ----------------------------------------------------


def get_divisors(n):
    """
    input: positive integer 'n' >= 1
    returns all divisors of n (inclusive 1 and 'n')

    >>> get_divisors(8)
    [1, 2, 4, 8]
    >>> get_divisors(824)
    [1, 2, 4, 8, 103, 206, 412, 824]
    >>> get_divisors(-1)
    Traceback (most recent call last):
        ...
    AssertionError: 'n' must been int and >= 1
    >>> get_divisors("test")
    Traceback (most recent call last):
        ...
    AssertionError: 'n' must been int and >= 1
    """

    # precondition
    assert isinstance(n, int) and (n >= 1), "'n' must been int and >= 1"

    ans = []  # will be returned.

    for divisor in range(1, n + 1):
        if n % divisor == 0:
            ans.append(divisor)

    # precondition
    assert ans[0] == 1 and ans[len(ans) - 1] == n, "Error in function getDivisiors(...)"

    return ans


# ----------------------------------------------------


def is_perfect_number(number):
    """
    input: positive integer 'number' > 1
    returns true if 'number' is a perfect number otherwise false.

    >>> is_perfect_number(28)
    True
    >>> is_perfect_number(824)
    False
    >>> is_perfect_number(-1)
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int and >= 1
    >>> is_perfect_number("test")
    Traceback (most recent call last):
        ...
    AssertionError: 'number' must been an int and >= 1
    """

    # precondition
    assert isinstance(number, int) and (number > 1), (
        "'number' must been an int and >= 1"
    )

    divisors = get_divisors(number)

    # precondition
    assert (
        isinstance(divisors, list)
        and (divisors[0] == 1)
        and (divisors[len(divisors) - 1] == number)
    ), "Error in help-function getDivisiors(...)"

    # summed all divisors up to 'number' (exclusive), hence [:-1]
    return sum(divisors[:-1]) == number


# ------------------------------------------------------------


def simplify_fraction(numerator, denominator):
    """
    input: two integer 'numerator' and 'denominator'
    assumes: 'denominator' != 0
    returns: a tuple with simplify numerator and denominator.

    >>> simplify_fraction(10, 20)
    (1, 2)
    >>> simplify_fraction(10, -1)
    (10, -1)
    >>> simplify_fraction("test","test")
    Traceback (most recent call last):
        ...
    AssertionError: The arguments must been from type int and 'denominator' != 0
    """

    # precondition
    assert (
        isinstance(numerator, int)
        and isinstance(denominator, int)
        and (denominator != 0)
    ), "The arguments must been from type int and 'denominator' != 0"

    # build the greatest common divisor of numerator and denominator.
    gcd_of_fraction = gcd_by_iterative(abs(numerator), abs(denominator))

    # precondition
    assert (
        isinstance(gcd_of_fraction, int)
        and (numerator % gcd_of_fraction == 0)
        and (denominator % gcd_of_fraction == 0)
    ), "Error in function gcd_by_iterative(...,...)"

    return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)


# -----------------------------------------------------------------


def factorial(n):
    """
    input: positive integer 'n'
    returns the factorial of 'n' (n!)

    >>> factorial(0)
    1
    >>> factorial(20)
    2432902008176640000
    >>> factorial(-1)
    Traceback (most recent call last):
        ...
    AssertionError: 'n' must been a int and >= 0
    >>> factorial("test")
    Traceback (most recent call last):
        ...
    AssertionError: 'n' must been a int and >= 0
    """

    # precondition
    assert isinstance(n, int) and (n >= 0), "'n' must been a int and >= 0"

    ans = 1  # this will be return.

    for factor in range(1, n + 1):
        ans *= factor

    return ans


# -------------------------------------------------------------------


def fib(n: int) -> int:
    """
    input: positive integer 'n'
    returns the n-th fibonacci term , indexing by 0

    >>> fib(0)
    1
    >>> fib(5)
    8
    >>> fib(20)
    10946
    >>> fib(99)
    354224848179261915075
    >>> fib(-1)
    Traceback (most recent call last):
    ...
    AssertionError: 'n' must been an int and >= 0
    >>> fib("test")
    Traceback (most recent call last):
    ...
    AssertionError: 'n' must been an int and >= 0
    """

    # precondition
    assert isinstance(n, int) and (n >= 0), "'n' must been an int and >= 0"

    tmp = 0
    fib1 = 1
    ans = 1  # this will be return

    for _ in range(n - 1):
        tmp = ans
        ans += fib1
        fib1 = tmp

    return ans


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/print_multiplication_table.py
================================================
def multiplication_table(number: int, number_of_terms: int) -> str:
    """
    Prints the multiplication table of a given number till the given number of terms

    >>> print(multiplication_table(3, 5))
    3 * 1 = 3
    3 * 2 = 6
    3 * 3 = 9
    3 * 4 = 12
    3 * 5 = 15

    >>> print(multiplication_table(-4, 6))
    -4 * 1 = -4
    -4 * 2 = -8
    -4 * 3 = -12
    -4 * 4 = -16
    -4 * 5 = -20
    -4 * 6 = -24
    """
    return "\n".join(
        f"{number} * {i} = {number * i}" for i in range(1, number_of_terms + 1)
    )


if __name__ == "__main__":
    print(multiplication_table(number=5, number_of_terms=10))


================================================
FILE: maths/pythagoras.py
================================================
"""Uses Pythagoras theorem to calculate the distance between two points in space."""

import math


class Point:
    def __init__(self, x, y, z):
        self.x = x
        self.y = y
        self.z = z

    def __repr__(self) -> str:
        return f"Point({self.x}, {self.y}, {self.z})"


def distance(a: Point, b: Point) -> float:
    """
    >>> point1 = Point(2, -1, 7)
    >>> point2 = Point(1, -3, 5)
    >>> print(f"Distance from {point1} to {point2} is {distance(point1, point2)}")
    Distance from Point(2, -1, 7) to Point(1, -3, 5) is 3.0
    """
    return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/qr_decomposition.py
================================================
import numpy as np


def qr_householder(a: np.ndarray):
    """Return a QR-decomposition of the matrix A using Householder reflection.

    The QR-decomposition decomposes the matrix A of shape (m, n) into an
    orthogonal matrix Q of shape (m, m) and an upper triangular matrix R of
    shape (m, n).  Note that the matrix A does not have to be square.  This
    method of decomposing A uses the Householder reflection, which is
    numerically stable and of complexity O(n^3).

    https://en.wikipedia.org/wiki/QR_decomposition#Using_Householder_reflections

    Arguments:
    A -- a numpy.ndarray of shape (m, n)

    Note: several optimizations can be made for numeric efficiency, but this is
    intended to demonstrate how it would be represented in a mathematics
    textbook.  In cases where efficiency is particularly important, an optimized
    version from BLAS should be used.

    >>> A = np.array([[12, -51, 4], [6, 167, -68], [-4, 24, -41]], dtype=float)
    >>> Q, R = qr_householder(A)

    >>> # check that the decomposition is correct
    >>> np.allclose(Q@R, A)
    True

    >>> # check that Q is orthogonal
    >>> np.allclose(Q@Q.T, np.eye(A.shape[0]))
    True
    >>> np.allclose(Q.T@Q, np.eye(A.shape[0]))
    True

    >>> # check that R is upper triangular
    >>> np.allclose(np.triu(R), R)
    True
    """
    m, n = a.shape
    t = min(m, n)
    q = np.eye(m)
    r = a.copy()

    for k in range(t - 1):
        # select a column of modified matrix A':
        x = r[k:, [k]]
        # construct first basis vector
        e1 = np.zeros_like(x)
        e1[0] = 1.0
        # determine scaling factor
        alpha = np.linalg.norm(x)
        # construct vector v for Householder reflection
        v = x + np.sign(x[0]) * alpha * e1
        v /= np.linalg.norm(v)

        # construct the Householder matrix
        q_k = np.eye(m - k) - 2.0 * v @ v.T
        # pad with ones and zeros as necessary
        q_k = np.block([[np.eye(k), np.zeros((k, m - k))], [np.zeros((m - k, k)), q_k]])

        q = q @ q_k.T
        r = q_k @ r

    return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/quadratic_equations_complex_numbers.py
================================================
from __future__ import annotations

from cmath import sqrt


def quadratic_roots(a: int, b: int, c: int) -> tuple[complex, complex]:
    """
    Given the numerical coefficients a, b and c,
    calculates the roots for any quadratic equation of the form ax^2 + bx + c

    >>> quadratic_roots(a=1, b=3, c=-4)
    (1.0, -4.0)
    >>> quadratic_roots(5, 6, 1)
    (-0.2, -1.0)
    >>> quadratic_roots(1, -6, 25)
    ((3+4j), (3-4j))
    """

    if a == 0:
        raise ValueError("Coefficient 'a' must not be zero.")
    delta = b * b - 4 * a * c

    root_1 = (-b + sqrt(delta)) / (2 * a)
    root_2 = (-b - sqrt(delta)) / (2 * a)

    return (
        root_1.real if not root_1.imag else root_1,
        root_2.real if not root_2.imag else root_2,
    )


def main():
    solution1, solution2 = quadratic_roots(a=5, b=6, c=1)
    print(f"The solutions are: {solution1} and {solution2}")


if __name__ == "__main__":
    main()


================================================
FILE: maths/radians.py
================================================
from math import pi


def radians(degree: float) -> float:
    """
    Converts the given angle from degrees to radians
    https://en.wikipedia.org/wiki/Radian

    >>> radians(180)
    3.141592653589793
    >>> radians(92)
    1.6057029118347832
    >>> radians(274)
    4.782202150464463
    >>> radians(109.82)
    1.9167205845401725

    >>> from math import radians as math_radians
    >>> all(abs(radians(i) - math_radians(i)) <= 1e-8 for i in range(-2, 361))
    True
    """

    return degree / (180 / pi)


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: maths/radix2_fft.py
================================================
"""
Fast Polynomial Multiplication using radix-2 fast Fourier Transform.
"""

import mpmath  # for roots of unity
import numpy as np


class FFT:
    """
    Fast Polynomial Multiplication using radix-2 fast Fourier Transform.

    Reference:
    https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm#The_radix-2_DIT_case

    For polynomials of degree m and n the algorithms has complexity
    O(n*logn + m*logm)

    The main part of the algorithm is split in two parts:
        1) __DFT: We compute the discrete fourier transform (DFT) of A and B using a
        bottom-up dynamic approach -
        2) __multiply: Once we obtain the DFT of A*B, we can similarly
        invert it to obtain A*B

    The class FFT takes two polynomials A and B with complex coefficients as arguments;
    The two polynomials should be represented as a sequence of coefficients starting
    from the free term. Thus, for instance x + 2*x^3 could be represented as
    [0,1,0,2] or (0,1,0,2). The constructor adds some zeros at the end so that the
    polynomials have the same length which is a power of 2 at least the length of
    their product.

    Example:

    Create two polynomials as sequences
    >>> A = [0, 1, 0, 2]  # x+2x^3
    >>> B = (2, 3, 4, 0)  # 2+3x+4x^2

    Create an FFT object with them
    >>> x = FFT(A, B)

    Print product
    >>> x.product  # 2x + 3x^2 + 8x^3 + 6x^4 + 8x^5
    [(-0-0j), (2+0j), (3-0j), (8-0j), (6+0j), (8+0j)]

    __str__ test
    >>> print(x)
    A = 0*x^0 + 1*x^1 + 0*x^2 + 2*x^3
    B = 2*x^0 + 3*x^1 + 4*x^2
    A*B = (-0-0j)*x^0 + (2+0j)*x^1 + (3-0j)*x^2 + (8-0j)*x^3 + (6+0j)*x^4 + (8+0j)*x^5
    """

    def __init__(self, poly_a=None, poly_b=None):
        # Input as list
        self.polyA = list(poly_a or [0])[:]
        self.polyB = list(poly_b or [0])[:]

        # Remove leading zero coefficients
        while self.polyA[-1] == 0:
            self.polyA.pop()
        self.len_A = len(self.polyA)

        while self.polyB[-1] == 0:
            self.polyB.pop()
        self.len_B = len(self.polyB)

        # Add 0 to make lengths equal a power of 2
        self.c_max_length = int(
            2 ** np.ceil(np.log2(len(self.polyA) + len(self.polyB) - 1))
        )

        while len(self.polyA) < self.c_max_length:
            self.polyA.append(0)
        while len(self.polyB) < self.c_max_length:
            self.polyB.append(0)
        # A complex root used for the fourier transform
        self.root = complex(mpmath.root(x=1, n=self.c_max_length, k=1))

        # The product
        self.product = self.__multiply()

    # Discrete fourier transform of A and B
    def __dft(self, which):
        dft = [[x] for x in self.polyA] if which == "A" else [[x] for x in self.polyB]
        # Corner case
        if len(dft) <= 1:
            return dft[0]
        next_ncol = self.c_max_length // 2
        while next_ncol > 0:
            new_dft = [[] for i in range(next_ncol)]
            root = self.root**next_ncol

            # First half of next step
            current_root = 1
            for j in range(self.c_max_length // (next_ncol * 2)):
                for i in range(next_ncol):
                    new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
                current_root *= root
            # Second half of next step
            current_root = 1
            for j in range(self.c_max_length // (next_ncol * 2)):
                for i in range(next_ncol):
                    new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
                current_root *= root
            # Update
            dft = new_dft
            next_ncol = next_ncol // 2
        return dft[0]

    # multiply the DFTs of  A and B and find A*B
    def __multiply(self):
        dft_a = self.__dft("A")
        dft_b = self.__dft("B")
        inverce_c = [[dft_a[i] * dft_b[i] for i in range(self.c_max_length)]]
        del dft_a
        del dft_b

        # Corner Case
        if len(inverce_c[0]) <= 1:
            return inverce_c[0]
        # Inverse DFT
        next_ncol = 2
        while next_ncol <= self.c_max_length:
            new_inverse_c = [[] for i in range(next_ncol)]
            root = self.root ** (next_ncol // 2)
            current_root = 1
            # First half of next step
            for j in range(self.c_max_length // next_ncol):
                for i in range(next_ncol // 2):
                    # Even positions
                    new_inverse_c[i].append(
                        (
                            inverce_c[i][j]
                            + inverce_c[i][j + self.c_max_length // next_ncol]
                        )
                        / 2
                    )
                    # Odd positions
                    new_inverse_c[i + next_ncol // 2].append(
                        (
                            inverce_c[i][j]
                            - inverce_c[i][j + self.c_max_length // next_ncol]
                        )
                        / (2 * current_root)
                    )
                current_root *= root
            # Update
            inverce_c = new_inverse_c
            next_ncol *= 2
        # Unpack
        inverce_c = [
            complex(round(x[0].real, 8), round(x[0].imag, 8)) for x in inverce_c
        ]

        # Remove leading 0's
        while inverce_c[-1] == 0:
            inverce_c.pop()
        return inverce_c

    # Overwrite __str__ for print(); Shows A, B and A*B
    def __str__(self):
        a = "A = " + " + ".join(
            f"{coef}*x^{i}" for i, coef in enumerate(self.polyA[: self.len_A])
        )
        b = "B = " + " + ".join(
            f"{coef}*x^{i}" for i, coef in enumerate(self.polyB[: self.len_B])
        )
        c = "A*B = " + " + ".join(
            f"{coef}*x^{i}" for i, coef in enumerate(self.product)
        )

        return f"{a}\n{b}\n{c}"


# Unit tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/remove_digit.py
================================================
def remove_digit(num: int) -> int:
    """

    returns the biggest possible result
    that can be achieved by removing
    one digit from the given number

    >>> remove_digit(152)
    52
    >>> remove_digit(6385)
    685
    >>> remove_digit(-11)
    1
    >>> remove_digit(2222222)
    222222
    >>> remove_digit("2222222")
    Traceback (most recent call last):
    TypeError: only integers accepted as input
    >>> remove_digit("string input")
    Traceback (most recent call last):
    TypeError: only integers accepted as input
    """

    if not isinstance(num, int):
        raise TypeError("only integers accepted as input")
    else:
        num_str = str(abs(num))
        num_transpositions = [list(num_str) for char in range(len(num_str))]
        for index in range(len(num_str)):
            num_transpositions[index].pop(index)
        return max(
            int("".join(list(transposition))) for transposition in num_transpositions
        )


if __name__ == "__main__":
    __import__("doctest").testmod()


================================================
FILE: maths/segmented_sieve.py
================================================
"""Segmented Sieve."""

import math


def sieve(n: int) -> list[int]:
    """
    Segmented Sieve.

    Examples:
    >>> sieve(8)
    [2, 3, 5, 7]

    >>> sieve(27)
    [2, 3, 5, 7, 11, 13, 17, 19, 23]

    >>> sieve(0)
    Traceback (most recent call last):
        ...
    ValueError: Number 0 must instead be a positive integer

    >>> sieve(-1)
    Traceback (most recent call last):
        ...
    ValueError: Number -1 must instead be a positive integer

    >>> sieve(22.2)
    Traceback (most recent call last):
        ...
    ValueError: Number 22.2 must instead be a positive integer
    """

    if n <= 0 or isinstance(n, float):
        msg = f"Number {n} must instead be a positive integer"
        raise ValueError(msg)

    in_prime = []
    start = 2
    end = int(math.sqrt(n))  # Size of every segment
    temp = [True] * (end + 1)
    prime = []

    while start <= end:
        if temp[start] is True:
            in_prime.append(start)
            for i in range(start * start, end + 1, start):
                temp[i] = False
        start += 1
    prime += in_prime

    low = end + 1
    high = min(2 * end, n)

    while low <= n:
        temp = [True] * (high - low + 1)
        for each in in_prime:
            t = math.floor(low / each) * each
            if t < low:
                t += each

            for j in range(t, high + 1, each):
                temp[j - low] = False

        for j in range(len(temp)):
            if temp[j] is True:
                prime.append(j + low)

        low = high + 1
        high = min(high + end, n)

    return prime


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print(f"{sieve(10**6) = }")


================================================
FILE: maths/series/__init__.py
================================================


================================================
FILE: maths/series/arithmetic.py
================================================
"""
Arithmetic mean
Reference: https://en.wikipedia.org/wiki/Arithmetic_mean

Arithmetic series
Reference: https://en.wikipedia.org/wiki/Arithmetic_series
(The URL above will redirect you to arithmetic progression)
"""


def is_arithmetic_series(series: list) -> bool:
    """
    checking whether the input series is arithmetic series or not
    >>> is_arithmetic_series([2, 4, 6])
    True
    >>> is_arithmetic_series([3, 6, 12, 24])
    False
    >>> is_arithmetic_series([1, 2, 3])
    True
    >>> is_arithmetic_series(4)
    Traceback (most recent call last):
        ...
    ValueError: Input series is not valid, valid series - [2, 4, 6]
    >>> is_arithmetic_series([])
    Traceback (most recent call last):
        ...
    ValueError: Input list must be a non empty list
    """
    if not isinstance(series, list):
        raise ValueError("Input series is not valid, valid series - [2, 4, 6]")
    if len(series) == 0:
        raise ValueError("Input list must be a non empty list")
    if len(series) == 1:
        return True
    common_diff = series[1] - series[0]
    for index in range(len(series) - 1):
        if series[index + 1] - series[index] != common_diff:
            return False
    return True


def arithmetic_mean(series: list) -> float:
    """
    return the arithmetic mean of series

    >>> arithmetic_mean([2, 4, 6])
    4.0
    >>> arithmetic_mean([3, 6, 9, 12])
    7.5
    >>> arithmetic_mean(4)
    Traceback (most recent call last):
        ...
    ValueError: Input series is not valid, valid series - [2, 4, 6]
    >>> arithmetic_mean([4, 8, 1])
    4.333333333333333
    >>> arithmetic_mean([1, 2, 3])
    2.0
    >>> arithmetic_mean([])
    Traceback (most recent call last):
        ...
    ValueError: Input list must be a non empty list

    """
    if not isinstance(series, list):
        raise ValueError("Input series is not valid, valid series - [2, 4, 6]")
    if len(series) == 0:
        raise ValueError("Input list must be a non empty list")
    answer = 0
    for val in series:
        answer += val
    return answer / len(series)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/series/geometric.py
================================================
"""
Geometric Mean
Reference :  https://en.wikipedia.org/wiki/Geometric_mean

Geometric series
Reference: https://en.wikipedia.org/wiki/Geometric_series
"""


def is_geometric_series(series: list) -> bool:
    """
    checking whether the input series is geometric series or not
    >>> is_geometric_series([2, 4, 8])
    True
    >>> is_geometric_series([3, 6, 12, 24])
    True
    >>> is_geometric_series([1, 2, 3])
    False
    >>> is_geometric_series([0, 0, 3])
    False
    >>> is_geometric_series([])
    Traceback (most recent call last):
        ...
    ValueError: Input list must be a non empty list
    >>> is_geometric_series(4)
    Traceback (most recent call last):
        ...
    ValueError: Input series is not valid, valid series - [2, 4, 8]
    """
    if not isinstance(series, list):
        raise ValueError("Input series is not valid, valid series - [2, 4, 8]")
    if len(series) == 0:
        raise ValueError("Input list must be a non empty list")
    if len(series) == 1:
        return True
    try:
        common_ratio = series[1] / series[0]
        for index in range(len(series) - 1):
            if series[index + 1] / series[index] != common_ratio:
                return False
    except ZeroDivisionError:
        return False
    return True


def geometric_mean(series: list) -> float:
    """
    return the geometric mean of series

    >>> geometric_mean([2, 4, 8])
    3.9999999999999996
    >>> geometric_mean([3, 6, 12, 24])
    8.48528137423857
    >>> geometric_mean([4, 8, 16])
    7.999999999999999
    >>> geometric_mean(4)
    Traceback (most recent call last):
        ...
    ValueError: Input series is not valid, valid series - [2, 4, 8]
    >>> geometric_mean([1, 2, 3])
    1.8171205928321397
    >>> geometric_mean([0, 2, 3])
    0.0
    >>> geometric_mean([])
    Traceback (most recent call last):
        ...
    ValueError: Input list must be a non empty list

    """
    if not isinstance(series, list):
        raise ValueError("Input series is not valid, valid series - [2, 4, 8]")
    if len(series) == 0:
        raise ValueError("Input list must be a non empty list")
    answer = 1
    for value in series:
        answer *= value
    return pow(answer, 1 / len(series))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/series/geometric_series.py
================================================
"""
This is a pure Python implementation of the Geometric Series algorithm
https://en.wikipedia.org/wiki/Geometric_series
Run the doctests with the following command:
python3 -m doctest -v geometric_series.py
or
python -m doctest -v geometric_series.py
For manual testing run:
python3 geometric_series.py
"""

from __future__ import annotations


def geometric_series(
    nth_term: float,
    start_term_a: float,
    common_ratio_r: float,
) -> list[float]:
    """
    Pure Python implementation of Geometric Series algorithm

    :param nth_term: The last term (nth term of Geometric Series)
    :param start_term_a : The first term of Geometric Series
    :param common_ratio_r : The common ratio between all the terms
    :return: The Geometric Series starting from first term a and multiple of common
        ration with first term with increase in power till last term (nth term)
    Examples:
    >>> geometric_series(4, 2, 2)
    [2, 4.0, 8.0, 16.0]
    >>> geometric_series(4.0, 2.0, 2.0)
    [2.0, 4.0, 8.0, 16.0]
    >>> geometric_series(4.1, 2.1, 2.1)
    [2.1, 4.41, 9.261000000000001, 19.448100000000004]
    >>> geometric_series(4, 2, -2)
    [2, -4.0, 8.0, -16.0]
    >>> geometric_series(4, -2, 2)
    [-2, -4.0, -8.0, -16.0]
    >>> geometric_series(-4, 2, 2)
    []
    >>> geometric_series(0, 100, 500)
    []
    >>> geometric_series(1, 1, 1)
    [1]
    >>> geometric_series(0, 0, 0)
    []
    """
    if not all((nth_term, start_term_a, common_ratio_r)):
        return []
    series: list[float] = []
    power = 1
    multiple = common_ratio_r
    for _ in range(int(nth_term)):
        if not series:
            series.append(start_term_a)
        else:
            power += 1
            series.append(float(start_term_a * multiple))
            multiple = pow(float(common_ratio_r), power)
    return series


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    nth_term = float(input("Enter the last number (n term) of the Geometric Series"))
    start_term_a = float(input("Enter the starting term (a) of the Geometric Series"))
    common_ratio_r = float(
        input("Enter the common ratio between two terms (r) of the Geometric Series")
    )
    print("Formula of Geometric Series => a + ar + ar^2 ... +ar^n")
    print(geometric_series(nth_term, start_term_a, common_ratio_r))


================================================
FILE: maths/series/harmonic.py
================================================
"""
Harmonic mean
Reference: https://en.wikipedia.org/wiki/Harmonic_mean

Harmonic series
Reference: https://en.wikipedia.org/wiki/Harmonic_series(mathematics)
"""


def is_harmonic_series(series: list) -> bool:
    """
    checking whether the input series is arithmetic series or not
    >>> is_harmonic_series([ 1, 2/3, 1/2, 2/5, 1/3])
    True
    >>> is_harmonic_series([ 1, 2/3, 2/5, 1/3])
    False
    >>> is_harmonic_series([1, 2, 3])
    False
    >>> is_harmonic_series([1/2, 1/3, 1/4])
    True
    >>> is_harmonic_series([2/5, 2/10, 2/15, 2/20, 2/25])
    True
    >>> is_harmonic_series(4)
    Traceback (most recent call last):
        ...
    ValueError: Input series is not valid, valid series - [1, 2/3, 2]
    >>> is_harmonic_series([])
    Traceback (most recent call last):
        ...
    ValueError: Input list must be a non empty list
    >>> is_harmonic_series([0])
    Traceback (most recent call last):
        ...
    ValueError: Input series cannot have 0 as an element
    >>> is_harmonic_series([1,2,0,6])
    Traceback (most recent call last):
        ...
    ValueError: Input series cannot have 0 as an element
    """
    if not isinstance(series, list):
        raise ValueError("Input series is not valid, valid series - [1, 2/3, 2]")
    if len(series) == 0:
        raise ValueError("Input list must be a non empty list")
    if len(series) == 1 and series[0] != 0:
        return True
    rec_series = []
    series_len = len(series)
    for i in range(series_len):
        if series[i] == 0:
            raise ValueError("Input series cannot have 0 as an element")
        rec_series.append(1 / series[i])
    common_diff = rec_series[1] - rec_series[0]
    for index in range(2, series_len):
        if rec_series[index] - rec_series[index - 1] != common_diff:
            return False
    return True


def harmonic_mean(series: list) -> float:
    """
    return the harmonic mean of series

    >>> harmonic_mean([1, 4, 4])
    2.0
    >>> harmonic_mean([3, 6, 9, 12])
    5.759999999999999
    >>> harmonic_mean(4)
    Traceback (most recent call last):
        ...
    ValueError: Input series is not valid, valid series - [2, 4, 6]
    >>> harmonic_mean([1, 2, 3])
    1.6363636363636365
    >>> harmonic_mean([])
    Traceback (most recent call last):
        ...
    ValueError: Input list must be a non empty list

    """
    if not isinstance(series, list):
        raise ValueError("Input series is not valid, valid series - [2, 4, 6]")
    if len(series) == 0:
        raise ValueError("Input list must be a non empty list")
    answer = 0
    for val in series:
        answer += 1 / val
    return len(series) / answer


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/series/harmonic_series.py
================================================
"""
This is a pure Python implementation of the Harmonic Series algorithm
https://en.wikipedia.org/wiki/Harmonic_series_(mathematics)

For doctests run following command:
python -m doctest -v harmonic_series.py
or
python3 -m doctest -v harmonic_series.py

For manual testing run:
python3 harmonic_series.py
"""


def harmonic_series(n_term: str) -> list:
    """Pure Python implementation of Harmonic Series algorithm

    :param n_term: The last (nth) term of Harmonic Series
    :return: The Harmonic Series starting from 1 to last (nth) term

    Examples:
    >>> harmonic_series(5)
    ['1', '1/2', '1/3', '1/4', '1/5']
    >>> harmonic_series(5.0)
    ['1', '1/2', '1/3', '1/4', '1/5']
    >>> harmonic_series(5.1)
    ['1', '1/2', '1/3', '1/4', '1/5']
    >>> harmonic_series(-5)
    []
    >>> harmonic_series(0)
    []
    >>> harmonic_series(1)
    ['1']
    """
    if n_term == "":
        return []
    series: list = []
    for temp in range(int(n_term)):
        series.append(f"1/{temp + 1}" if series else "1")
    return series


if __name__ == "__main__":
    nth_term = input("Enter the last number (nth term) of the Harmonic Series")
    print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
    print(harmonic_series(nth_term))


================================================
FILE: maths/series/hexagonal_numbers.py
================================================
"""
A hexagonal number sequence is a sequence of figurate numbers
where the nth hexagonal number hₙ is the number of distinct dots
in a pattern of dots consisting of the outlines of regular
hexagons with sides up to n dots, when the hexagons are overlaid
so that they share one vertex.

    Calculates the hexagonal numbers sequence with a formula
        hₙ = n(2n-1)
        where:
        hₙ --> is nth element of the sequence
        n --> is the number of element in the sequence
        reference-->"Hexagonal number" Wikipedia
        <https://en.wikipedia.org/wiki/Hexagonal_number>
"""


def hexagonal_numbers(length: int) -> list[int]:
    """
    :param len: max number of elements
    :type len: int
    :return: Hexagonal numbers as a list

    Tests:
    >>> hexagonal_numbers(10)
    [0, 1, 6, 15, 28, 45, 66, 91, 120, 153]
    >>> hexagonal_numbers(5)
    [0, 1, 6, 15, 28]
    >>> hexagonal_numbers(0)
    Traceback (most recent call last):
      ...
    ValueError: Length must be a positive integer.
    """

    if length <= 0 or not isinstance(length, int):
        raise ValueError("Length must be a positive integer.")
    return [n * (2 * n - 1) for n in range(length)]


if __name__ == "__main__":
    print(hexagonal_numbers(length=5))
    print(hexagonal_numbers(length=10))


================================================
FILE: maths/series/p_series.py
================================================
"""
This is a pure Python implementation of the P-Series algorithm
https://en.wikipedia.org/wiki/Harmonic_series_(mathematics)#P-series
For doctests run following command:
python -m doctest -v p_series.py
or
python3 -m doctest -v p_series.py
For manual testing run:
python3 p_series.py
"""

from __future__ import annotations


def p_series(nth_term: float | str, power: float | str) -> list[str]:
    """
    Pure Python implementation of P-Series algorithm
    :return: The P-Series starting from 1 to last (nth) term
    Examples:
    >>> p_series(5, 2)
    ['1', '1 / 4', '1 / 9', '1 / 16', '1 / 25']
    >>> p_series(-5, 2)
    []
    >>> p_series(5, -2)
    ['1', '1 / 0.25', '1 / 0.1111111111111111', '1 / 0.0625', '1 / 0.04']
    >>> p_series("", 1000)
    ['']
    >>> p_series(0, 0)
    []
    >>> p_series(1, 1)
    ['1']
    """
    if nth_term == "":
        return [""]
    nth_term = int(nth_term)
    power = int(power)
    series: list[str] = []
    for temp in range(int(nth_term)):
        series.append(f"1 / {pow(temp + 1, int(power))}" if series else "1")
    return series


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    nth_term = int(input("Enter the last number (nth term) of the P-Series"))
    power = int(input("Enter the power for  P-Series"))
    print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
    print(p_series(nth_term, power))


================================================
FILE: maths/sieve_of_eratosthenes.py
================================================
"""
Sieve of Eratosthones

The sieve of Eratosthenes is an algorithm used to find prime numbers, less than or
equal to a given value.
Illustration:
https://upload.wikimedia.org/wikipedia/commons/b/b9/Sieve_of_Eratosthenes_animation.gif
Reference: https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes

doctest provider: Bruno Simas Hadlich (https://github.com/brunohadlich)
Also thanks to Dmitry (https://github.com/LizardWizzard) for finding the problem
"""

from __future__ import annotations

import math


def prime_sieve(num: int) -> list[int]:
    """
    Returns a list with all prime numbers up to n.

    >>> prime_sieve(50)
    [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
    >>> prime_sieve(25)
    [2, 3, 5, 7, 11, 13, 17, 19, 23]
    >>> prime_sieve(10)
    [2, 3, 5, 7]
    >>> prime_sieve(9)
    [2, 3, 5, 7]
    >>> prime_sieve(2)
    [2]
    >>> prime_sieve(1)
    []
    """

    if num <= 0:
        msg = f"{num}: Invalid input, please enter a positive integer."
        raise ValueError(msg)

    sieve = [True] * (num + 1)
    prime = []
    start = 2
    end = int(math.sqrt(num))

    while start <= end:
        # If start is a prime
        if sieve[start] is True:
            prime.append(start)

            # Set multiples of start be False
            for i in range(start * start, num + 1, start):
                if sieve[i] is True:
                    sieve[i] = False

        start += 1

    for j in range(end + 1, num + 1):
        if sieve[j] is True:
            prime.append(j)

    return prime


if __name__ == "__main__":
    print(prime_sieve(int(input("Enter a positive integer: ").strip())))


================================================
FILE: maths/sigmoid.py
================================================
"""
This script demonstrates the implementation of the Sigmoid function.

The function takes a vector of K real numbers as input and then 1 / (1 + exp(-x)).
After through Sigmoid, the element of the vector mostly 0 between 1. or 1 between -1.

Script inspired from its corresponding Wikipedia article
https://en.wikipedia.org/wiki/Sigmoid_function
"""

import numpy as np


def sigmoid(vector: np.ndarray) -> np.ndarray:
    """
    Implements the sigmoid function

    Parameters:
        vector (np.array): A  numpy array of shape (1,n)
        consisting of real values

    Returns:
        sigmoid_vec (np.array): The input numpy array, after applying
        sigmoid.

    Examples:
    >>> sigmoid(np.array([-1.0, 1.0, 2.0]))
    array([0.26894142, 0.73105858, 0.88079708])

    >>> sigmoid(np.array([0.0]))
    array([0.5])
    """
    return 1 / (1 + np.exp(-vector))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/signum.py
================================================
"""
Signum function -- https://en.wikipedia.org/wiki/Sign_function
"""


def signum(num: float) -> int:
    """
    Applies signum function on the number

    Custom test cases:
    >>> signum(-10)
    -1
    >>> signum(10)
    1
    >>> signum(0)
    0
    >>> signum(-20.5)
    -1
    >>> signum(20.5)
    1
    >>> signum(-1e-6)
    -1
    >>> signum(1e-6)
    1
    >>> signum("Hello")
    Traceback (most recent call last):
        ...
    TypeError: '<' not supported between instances of 'str' and 'int'
    >>> signum([])
    Traceback (most recent call last):
        ...
    TypeError: '<' not supported between instances of 'list' and 'int'
    """
    if num < 0:
        return -1
    return 1 if num else 0


def test_signum() -> None:
    """
    Tests the signum function
    >>> test_signum()
    """
    assert signum(5) == 1
    assert signum(-5) == -1
    assert signum(0) == 0
    assert signum(10.5) == 1
    assert signum(-10.5) == -1
    assert signum(1e-6) == 1
    assert signum(-1e-6) == -1
    assert signum(123456789) == 1
    assert signum(-123456789) == -1


if __name__ == "__main__":
    print(signum(12))
    print(signum(-12))
    print(signum(0))


================================================
FILE: maths/simultaneous_linear_equation_solver.py
================================================
"""
https://en.wikipedia.org/wiki/Augmented_matrix

This algorithm solves simultaneous linear equations of the form
λa + λb + λc + λd + ... = y as [λ, λ, λ, λ, ..., y]
Where λ & y are individual coefficients, the no. of equations = no. of coefficients - 1

Note in order to work there must exist 1 equation where all instances of λ and y != 0
"""


def simplify(current_set: list[list]) -> list[list]:
    """
    >>> simplify([[1, 2, 3], [4, 5, 6]])
    [[1.0, 2.0, 3.0], [0.0, 0.75, 1.5]]
    >>> simplify([[5, 2, 5], [5, 1, 10]])
    [[1.0, 0.4, 1.0], [0.0, 0.2, -1.0]]
    """
    # Divide each row by magnitude of first term --> creates 'unit' matrix
    duplicate_set = current_set.copy()
    for row_index, row in enumerate(duplicate_set):
        magnitude = row[0]
        for column_index, column in enumerate(row):
            if magnitude == 0:
                current_set[row_index][column_index] = column
                continue
            current_set[row_index][column_index] = column / magnitude
    # Subtract to cancel term
    first_row = current_set[0]
    final_set = [first_row]
    current_set = current_set[1::]
    for row in current_set:
        temp_row = []
        # If first term is 0, it is already in form we want, so we preserve it
        if row[0] == 0:
            final_set.append(row)
            continue
        for column_index in range(len(row)):
            temp_row.append(first_row[column_index] - row[column_index])
        final_set.append(temp_row)
    # Create next recursion iteration set
    if len(final_set[0]) != 3:
        current_first_row = final_set[0]
        current_first_column = []
        next_iteration = []
        for row in final_set[1::]:
            current_first_column.append(row[0])
            next_iteration.append(row[1::])
        resultant = simplify(next_iteration)
        for i in range(len(resultant)):
            resultant[i].insert(0, current_first_column[i])
        resultant.insert(0, current_first_row)
        final_set = resultant
    return final_set


def solve_simultaneous(equations: list[list]) -> list:
    """
    >>> solve_simultaneous([[1, 2, 3],[4, 5, 6]])
    [-1.0, 2.0]
    >>> solve_simultaneous([[0, -3, 1, 7],[3, 2, -1, 11],[5, 1, -2, 12]])
    [6.4, 1.2, 10.6]
    >>> solve_simultaneous([])
    Traceback (most recent call last):
        ...
    IndexError: solve_simultaneous() requires n lists of length n+1
    >>> solve_simultaneous([[1, 2, 3],[1, 2]])
    Traceback (most recent call last):
        ...
    IndexError: solve_simultaneous() requires n lists of length n+1
    >>> solve_simultaneous([[1, 2, 3],["a", 7, 8]])
    Traceback (most recent call last):
        ...
    ValueError: solve_simultaneous() requires lists of integers
    >>> solve_simultaneous([[0, 2, 3],[4, 0, 6]])
    Traceback (most recent call last):
        ...
    ValueError: solve_simultaneous() requires at least 1 full equation
    """
    if len(equations) == 0:
        raise IndexError("solve_simultaneous() requires n lists of length n+1")
    _length = len(equations) + 1
    if any(len(item) != _length for item in equations):
        raise IndexError("solve_simultaneous() requires n lists of length n+1")
    for row in equations:
        if any(not isinstance(column, (int, float)) for column in row):
            raise ValueError("solve_simultaneous() requires lists of integers")
    if len(equations) == 1:
        return [equations[0][-1] / equations[0][0]]
    data_set = equations.copy()
    if any(0 in row for row in data_set):
        temp_data = data_set.copy()
        full_row = []
        for row_index, row in enumerate(temp_data):
            if 0 not in row:
                full_row = data_set.pop(row_index)
                break
        if not full_row:
            raise ValueError("solve_simultaneous() requires at least 1 full equation")
        data_set.insert(0, full_row)
    useable_form = data_set.copy()
    simplified = simplify(useable_form)
    simplified = simplified[::-1]
    solutions: list = []
    for row in simplified:
        current_solution = row[-1]
        if not solutions:
            if row[-2] == 0:
                solutions.append(0)
                continue
            solutions.append(current_solution / row[-2])
            continue
        temp_row = row.copy()[: len(row) - 1 :]
        while temp_row[0] == 0:
            temp_row.pop(0)
        if len(temp_row) == 0:
            solutions.append(0)
            continue
        temp_row = temp_row[1::]
        temp_row = temp_row[::-1]
        for column_index, column in enumerate(temp_row):
            current_solution -= column * solutions[column_index]
        solutions.append(current_solution)
    final = []
    for item in solutions:
        final.append(float(round(item, 5)))
    return final[::-1]


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    eq = [
        [2, 1, 1, 1, 1, 4],
        [1, 2, 1, 1, 1, 5],
        [1, 1, 2, 1, 1, 6],
        [1, 1, 1, 2, 1, 7],
        [1, 1, 1, 1, 2, 8],
    ]
    print(solve_simultaneous(eq))
    print(solve_simultaneous([[4, 2]]))


================================================
FILE: maths/sin.py
================================================
"""
Calculate sin function.

It's not a perfect function so I am rounding the result to 10 decimal places by default.

Formula: sin(x) = x - x^3/3! + x^5/5! - x^7/7! + ...
Where: x = angle in randians.

Source:
    https://www.homeschoolmath.net/teaching/sine_calculator.php

"""

from math import factorial, radians


def sin(
    angle_in_degrees: float, accuracy: int = 18, rounded_values_count: int = 10
) -> float:
    """
    Implement sin function.

    >>> sin(0.0)
    0.0
    >>> sin(90.0)
    1.0
    >>> sin(180.0)
    0.0
    >>> sin(270.0)
    -1.0
    >>> sin(0.68)
    0.0118679603
    >>> sin(1.97)
    0.0343762121
    >>> sin(64.0)
    0.8987940463
    >>> sin(9999.0)
    -0.9876883406
    >>> sin(-689.0)
    0.5150380749
    >>> sin(89.7)
    0.9999862922
    """
    # Simplify the angle to be between 360 and -360 degrees.
    angle_in_degrees = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

    # Converting from degrees to radians
    angle_in_radians = radians(angle_in_degrees)

    result = angle_in_radians
    a = 3
    b = -1

    for _ in range(accuracy):
        result += (b * (angle_in_radians**a)) / factorial(a)

        b = -b  # One positive term and the next will be negative and so on...
        a += 2  # Increased by 2 for every term.

    return round(result, rounded_values_count)


if __name__ == "__main__":
    __import__("doctest").testmod()


================================================
FILE: maths/sock_merchant.py
================================================
from collections import Counter


def sock_merchant(colors: list[int]) -> int:
    """
    >>> sock_merchant([10, 20, 20, 10, 10, 30, 50, 10, 20])
    3
    >>> sock_merchant([1, 1, 3, 3])
    2
    """
    return sum(socks_by_color // 2 for socks_by_color in Counter(colors).values())


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    colors = [int(x) for x in input("Enter socks by color :").rstrip().split()]
    print(f"sock_merchant({colors}) = {sock_merchant(colors)}")


================================================
FILE: maths/softmax.py
================================================
"""
This script demonstrates the implementation of the Softmax function.

Its a function that takes as input a vector of K real numbers, and normalizes
it into a probability distribution consisting of K probabilities proportional
to the exponentials of the input numbers. After softmax, the elements of the
vector always sum up to 1.

Script inspired from its corresponding Wikipedia article
https://en.wikipedia.org/wiki/Softmax_function
"""

import numpy as np


def softmax(vector):
    """
    Implements the softmax function

    Parameters:
        vector (np.array,list,tuple): A  numpy array of shape (1,n)
        consisting of real values or a similar list,tuple


    Returns:
        softmax_vec (np.array): The input numpy array  after applying
        softmax.

    The softmax vector adds up to one. We need to ceil to mitigate for
    precision
    >>> float(np.ceil(np.sum(softmax([1,2,3,4]))))
    1.0

    >>> vec = np.array([5,5])
    >>> softmax(vec)
    array([0.5, 0.5])

    >>> softmax([0])
    array([1.])
    """

    # Calculate e^x for each x in your vector where e is Euler's
    # number (approximately 2.718)
    exponent_vector = np.exp(vector)

    # Add up the all the exponentials
    sum_of_exponents = np.sum(exponent_vector)

    # Divide every exponent by the sum of all exponents
    softmax_vector = exponent_vector / sum_of_exponents

    return softmax_vector


if __name__ == "__main__":
    print(softmax((0,)))


================================================
FILE: maths/solovay_strassen_primality_test.py
================================================
"""
This script implements the Solovay-Strassen Primality test.

This probabilistic primality test is based on Euler's criterion. It is similar
to the Fermat test but uses quadratic residues. It can quickly identify
composite numbers but may occasionally classify composite numbers as prime.

More details and concepts about this can be found on:
https://en.wikipedia.org/wiki/Solovay%E2%80%93Strassen_primality_test
"""

import random


def jacobi_symbol(random_a: int, number: int) -> int:
    """
    Calculate the Jacobi symbol. The Jacobi symbol is a generalization
    of the Legendre symbol, which can be used to simplify computations involving
    quadratic residues. The Jacobi symbol is used in primality tests, like the
    Solovay-Strassen test, because it helps determine if an integer is a
    quadratic residue modulo a given modulus, providing valuable information
    about the number's potential primality or compositeness.

    Parameters:
        random_a: A randomly chosen integer from 2 to n-2 (inclusive)
        number: The number that is tested for primality

    Returns:
        jacobi_symbol: The Jacobi symbol is a mathematical function
        used to determine whether an integer is a quadratic residue modulo
        another integer (usually prime) or not.

    >>> jacobi_symbol(2, 13)
    -1
    >>> jacobi_symbol(5, 19)
    1
    >>> jacobi_symbol(7, 14)
    0
    """

    if random_a in (0, 1):
        return random_a

    random_a %= number
    t = 1

    while random_a != 0:
        while random_a % 2 == 0:
            random_a //= 2
            r = number % 8
            if r in (3, 5):
                t = -t

        random_a, number = number, random_a

        if random_a % 4 == number % 4 == 3:
            t = -t

        random_a %= number

    return t if number == 1 else 0


def solovay_strassen(number: int, iterations: int) -> bool:
    """
    Check whether the input number is prime or not using
    the Solovay-Strassen Primality test

    Parameters:
        number: The number that is tested for primality
        iterations: The number of times that the test is run
        which effects the accuracy

    Returns:
        result: True if number is probably prime and false
        if not

    >>> random.seed(10)
    >>> solovay_strassen(13, 5)
    True
    >>> solovay_strassen(9, 10)
    False
    >>> solovay_strassen(17, 15)
    True
    """

    if number <= 1:
        return False
    if number <= 3:
        return True

    for _ in range(iterations):
        a = random.randint(2, number - 2)
        x = jacobi_symbol(a, number)
        y = pow(a, (number - 1) // 2, number)

        if x == 0 or y != x % number:
            return False

    return True


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/spearman_rank_correlation_coefficient.py
================================================
from collections.abc import Sequence


def assign_ranks(data: Sequence[float]) -> list[int]:
    """
    Assigns ranks to elements in the array.

    :param data: List of floats.
    :return: List of ints representing the ranks.

    Example:
    >>> assign_ranks([3.2, 1.5, 4.0, 2.7, 5.1])
    [3, 1, 4, 2, 5]

    >>> assign_ranks([10.5, 8.1, 12.4, 9.3, 11.0])
    [3, 1, 5, 2, 4]
    """
    ranked_data = sorted((value, index) for index, value in enumerate(data))
    ranks = [0] * len(data)

    for position, (_, index) in enumerate(ranked_data):
        ranks[index] = position + 1

    return ranks


def calculate_spearman_rank_correlation(
    variable_1: Sequence[float], variable_2: Sequence[float]
) -> float:
    """
    Calculates Spearman's rank correlation coefficient.

    :param variable_1: List of floats representing the first variable.
    :param variable_2: List of floats representing the second variable.
    :return: Spearman's rank correlation coefficient.

    Example Usage:

    >>> x = [1, 2, 3, 4, 5]
    >>> y = [5, 4, 3, 2, 1]
    >>> calculate_spearman_rank_correlation(x, y)
    -1.0

    >>> x = [1, 2, 3, 4, 5]
    >>> y = [2, 4, 6, 8, 10]
    >>> calculate_spearman_rank_correlation(x, y)
    1.0

    >>> x = [1, 2, 3, 4, 5]
    >>> y = [5, 1, 2, 9, 5]
    >>> calculate_spearman_rank_correlation(x, y)
    0.6
    """
    n = len(variable_1)
    rank_var1 = assign_ranks(variable_1)
    rank_var2 = assign_ranks(variable_2)

    # Calculate differences of ranks
    d = [rx - ry for rx, ry in zip(rank_var1, rank_var2)]

    # Calculate the sum of squared differences
    d_squared = sum(di**2 for di in d)

    # Calculate the Spearman's rank correlation coefficient
    rho = 1 - (6 * d_squared) / (n * (n**2 - 1))

    return rho


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Example usage:
    print(
        f"{calculate_spearman_rank_correlation([1, 2, 3, 4, 5], [2, 4, 6, 8, 10]) = }"
    )

    print(f"{calculate_spearman_rank_correlation([1, 2, 3, 4, 5], [5, 4, 3, 2, 1]) = }")

    print(f"{calculate_spearman_rank_correlation([1, 2, 3, 4, 5], [5, 1, 2, 9, 5]) = }")


================================================
FILE: maths/special_numbers/__init__.py
================================================


================================================
FILE: maths/special_numbers/armstrong_numbers.py
================================================
"""
An Armstrong number is equal to the sum of its own digits each raised to the
power of the number of digits.

For example, 370 is an Armstrong number because 3*3*3 + 7*7*7 + 0*0*0 = 370.

Armstrong numbers are also called Narcissistic numbers and Pluperfect numbers.

On-Line Encyclopedia of Integer Sequences entry: https://oeis.org/A005188
"""

PASSING = (1, 153, 370, 371, 1634, 24678051, 115132219018763992565095597973971522401)
FAILING: tuple = (-153, -1, 0, 1.2, 200, "A", [], {}, None)


def armstrong_number(n: int) -> bool:
    """
    Return True if n is an Armstrong number or False if it is not.

    >>> all(armstrong_number(n) for n in PASSING)
    True
    >>> any(armstrong_number(n) for n in FAILING)
    False
    """
    if not isinstance(n, int) or n < 1:
        return False

    # Initialization of sum and number of digits.
    total = 0
    number_of_digits = 0
    temp = n
    # Calculation of digits of the number
    number_of_digits = len(str(n))
    # Dividing number into separate digits and find Armstrong number
    temp = n
    while temp > 0:
        rem = temp % 10
        total += rem**number_of_digits
        temp //= 10
    return n == total


def pluperfect_number(n: int) -> bool:
    """Return True if n is a pluperfect number or False if it is not

    >>> all(pluperfect_number(n) for n in PASSING)
    True
    >>> any(pluperfect_number(n) for n in FAILING)
    False
    """
    if not isinstance(n, int) or n < 1:
        return False

    # Init a "histogram" of the digits
    digit_histogram = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    digit_total = 0
    total = 0
    temp = n
    while temp > 0:
        temp, rem = divmod(temp, 10)
        digit_histogram[rem] += 1
        digit_total += 1

    for cnt, i in zip(digit_histogram, range(len(digit_histogram))):
        total += cnt * i**digit_total

    return n == total


def narcissistic_number(n: int) -> bool:
    """Return True if n is a narcissistic number or False if it is not.

    >>> all(narcissistic_number(n) for n in PASSING)
    True
    >>> any(narcissistic_number(n) for n in FAILING)
    False
    """
    if not isinstance(n, int) or n < 1:
        return False
    expo = len(str(n))  # the power that all digits will be raised to
    # check if sum of each digit multiplied expo times is equal to number
    return n == sum(int(i) ** expo for i in str(n))


def main():
    """
    Request that user input an integer and tell them if it is Armstrong number.
    """
    num = int(input("Enter an integer to see if it is an Armstrong number: ").strip())
    print(f"{num} is {'' if armstrong_number(num) else 'not '}an Armstrong number.")
    print(f"{num} is {'' if narcissistic_number(num) else 'not '}an Armstrong number.")
    print(f"{num} is {'' if pluperfect_number(num) else 'not '}an Armstrong number.")


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: maths/special_numbers/automorphic_number.py
================================================
"""
== Automorphic Numbers ==
A number n is said to be a Automorphic number if
the square of n "ends" in the same digits as n itself.

Examples of Automorphic Numbers: 0, 1, 5, 6, 25, 76, 376, 625, 9376, 90625, ...
https://en.wikipedia.org/wiki/Automorphic_number
"""

# Author : Akshay Dubey (https://github.com/itsAkshayDubey)
# Time Complexity : O(log10n)


def is_automorphic_number(number: int) -> bool:
    """
    # doctest: +NORMALIZE_WHITESPACE
    This functions takes an integer number as input.
    returns True if the number is automorphic.
    >>> is_automorphic_number(-1)
    False
    >>> is_automorphic_number(0)
    True
    >>> is_automorphic_number(5)
    True
    >>> is_automorphic_number(6)
    True
    >>> is_automorphic_number(7)
    False
    >>> is_automorphic_number(25)
    True
    >>> is_automorphic_number(259918212890625)
    True
    >>> is_automorphic_number(259918212890636)
    False
    >>> is_automorphic_number(740081787109376)
    True
    >>> is_automorphic_number(5.0)
    Traceback (most recent call last):
        ...
    TypeError: Input value of [number=5.0] must be an integer
    """
    if not isinstance(number, int):
        msg = f"Input value of [number={number}] must be an integer"
        raise TypeError(msg)
    if number < 0:
        return False
    number_square = number * number
    while number > 0:
        if number % 10 != number_square % 10:
            return False
        number //= 10
        number_square //= 10
    return True


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/special_numbers/bell_numbers.py
================================================
"""
Bell numbers represent the number of ways to partition a set into non-empty
subsets. This module provides functions to calculate Bell numbers for sets of
integers. In other words, the first (n + 1) Bell numbers.

For more information about Bell numbers, refer to:
https://en.wikipedia.org/wiki/Bell_number
"""


def bell_numbers(max_set_length: int) -> list[int]:
    """
    Calculate Bell numbers for the sets of lengths from 0 to max_set_length.
    In other words, calculate first (max_set_length + 1) Bell numbers.

    Args:
        max_set_length (int): The maximum length of the sets for which
        Bell numbers are calculated.

    Returns:
        list: A list of Bell numbers for sets of lengths from 0 to max_set_length.

    Examples:
    >>> bell_numbers(-2)
    Traceback (most recent call last):
        ...
    ValueError: max_set_length must be non-negative
    >>> bell_numbers(0)
    [1]
    >>> bell_numbers(1)
    [1, 1]
    >>> bell_numbers(5)
    [1, 1, 2, 5, 15, 52]
    """
    if max_set_length < 0:
        raise ValueError("max_set_length must be non-negative")

    bell = [0] * (max_set_length + 1)
    bell[0] = 1

    for i in range(1, max_set_length + 1):
        for j in range(i):
            bell[i] += _binomial_coefficient(i - 1, j) * bell[j]

    return bell


def _binomial_coefficient(total_elements: int, elements_to_choose: int) -> int:
    """
    Calculate the binomial coefficient C(total_elements, elements_to_choose)

    Args:
        total_elements (int): The total number of elements.
        elements_to_choose (int): The number of elements to choose.

    Returns:
        int: The binomial coefficient C(total_elements, elements_to_choose).

    Examples:
    >>> _binomial_coefficient(5, 2)
    10
    >>> _binomial_coefficient(6, 3)
    20
    """
    if elements_to_choose in {0, total_elements}:
        return 1

    elements_to_choose = min(elements_to_choose, total_elements - elements_to_choose)

    coefficient = 1
    for i in range(elements_to_choose):
        coefficient *= total_elements - i
        coefficient //= i + 1

    return coefficient


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/special_numbers/carmichael_number.py
================================================
"""
== Carmichael Numbers ==
A number n is said to be a Carmichael number if it
satisfies the following modular arithmetic condition:

    power(b, n-1) MOD n = 1,
    for all b ranging from 1 to n such that b and
    n are relatively prime, i.e, gcd(b, n) = 1

Examples of Carmichael Numbers: 561, 1105, ...
https://en.wikipedia.org/wiki/Carmichael_number
"""

from maths.greatest_common_divisor import greatest_common_divisor


def power(x: int, y: int, mod: int) -> int:
    """
    Examples:
    >>> power(2, 15, 3)
    2
    >>> power(5, 1, 30)
    5
    """

    if y == 0:
        return 1
    temp = power(x, y // 2, mod) % mod
    temp = (temp * temp) % mod
    if y % 2 == 1:
        temp = (temp * x) % mod
    return temp


def is_carmichael_number(n: int) -> bool:
    """
    Examples:
    >>> is_carmichael_number(4)
    False
    >>> is_carmichael_number(561)
    True
    >>> is_carmichael_number(562)
    False
    >>> is_carmichael_number(900)
    False
    >>> is_carmichael_number(1105)
    True
    >>> is_carmichael_number(8911)
    True
    >>> is_carmichael_number(5.1)
    Traceback (most recent call last):
         ...
    ValueError: Number 5.1 must instead be a positive integer

    >>> is_carmichael_number(-7)
    Traceback (most recent call last):
         ...
    ValueError: Number -7 must instead be a positive integer

    >>> is_carmichael_number(0)
    Traceback (most recent call last):
         ...
    ValueError: Number 0 must instead be a positive integer
    """

    if n <= 0 or not isinstance(n, int):
        msg = f"Number {n} must instead be a positive integer"
        raise ValueError(msg)

    return all(
        power(b, n - 1, n) == 1
        for b in range(2, n)
        if greatest_common_divisor(b, n) == 1
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    number = int(input("Enter number: ").strip())
    if is_carmichael_number(number):
        print(f"{number} is a Carmichael Number.")
    else:
        print(f"{number} is not a Carmichael Number.")


================================================
FILE: maths/special_numbers/catalan_number.py
================================================
"""

Calculate the nth Catalan number

Source:
    https://en.wikipedia.org/wiki/Catalan_number

"""


def catalan(number: int) -> int:
    """
    :param number: nth catalan number to calculate
    :return: the nth catalan number
    Note: A catalan number is only defined for positive integers

    >>> catalan(5)
    14
    >>> catalan(0)
    Traceback (most recent call last):
        ...
    ValueError: Input value of [number=0] must be > 0
    >>> catalan(-1)
    Traceback (most recent call last):
        ...
    ValueError: Input value of [number=-1] must be > 0
    >>> catalan(5.0)
    Traceback (most recent call last):
        ...
    TypeError: Input value of [number=5.0] must be an integer
    """

    if not isinstance(number, int):
        msg = f"Input value of [number={number}] must be an integer"
        raise TypeError(msg)

    if number < 1:
        msg = f"Input value of [number={number}] must be > 0"
        raise ValueError(msg)

    current_number = 1

    for i in range(1, number):
        current_number *= 4 * i - 2
        current_number //= i + 1

    return current_number


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/special_numbers/hamming_numbers.py
================================================
"""
A Hamming number is a positive integer of the form 2^i*3^j*5^k, for some
non-negative integers i, j, and k. They are often referred to as regular numbers.
More info at: https://en.wikipedia.org/wiki/Regular_number.
"""


def hamming(n_element: int) -> list:
    """
    This function creates an ordered list of n length as requested, and afterwards
    returns the last value of the list. It must be given a positive integer.

    :param n_element: The number of elements on the list
    :return: The nth element of the list

    >>> hamming(-5)
    Traceback (most recent call last):
        ...
    ValueError: n_element should be a positive number
    >>> hamming(5)
    [1, 2, 3, 4, 5]
    >>> hamming(10)
    [1, 2, 3, 4, 5, 6, 8, 9, 10, 12]
    >>> hamming(15)
    [1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 18, 20, 24]
    """
    n_element = int(n_element)
    if n_element < 1:
        my_error = ValueError("n_element should be a positive number")
        raise my_error

    hamming_list = [1]
    i, j, k = (0, 0, 0)
    index = 1
    while index < n_element:
        while hamming_list[i] * 2 <= hamming_list[-1]:
            i += 1
        while hamming_list[j] * 3 <= hamming_list[-1]:
            j += 1
        while hamming_list[k] * 5 <= hamming_list[-1]:
            k += 1
        hamming_list.append(
            min(hamming_list[i] * 2, hamming_list[j] * 3, hamming_list[k] * 5)
        )
        index += 1
    return hamming_list


if __name__ == "__main__":
    n = input("Enter the last number (nth term) of the Hamming Number Series: ")
    print("Formula of Hamming Number Series => 2^i * 3^j * 5^k")
    hamming_numbers = hamming(int(n))
    print("-----------------------------------------------------")
    print(f"The list with nth numbers is: {hamming_numbers}")
    print("-----------------------------------------------------")


================================================
FILE: maths/special_numbers/happy_number.py
================================================
def is_happy_number(number: int) -> bool:
    """
    A happy number is a number which eventually reaches 1 when replaced by the sum of
    the square of each digit.

    :param number: The number to check for happiness.
    :return: True if the number is a happy number, False otherwise.

    >>> is_happy_number(19)
    True
    >>> is_happy_number(2)
    False
    >>> is_happy_number(23)
    True
    >>> is_happy_number(1)
    True
    >>> is_happy_number(0)
    Traceback (most recent call last):
        ...
    ValueError: number=0 must be a positive integer
    >>> is_happy_number(-19)
    Traceback (most recent call last):
        ...
    ValueError: number=-19 must be a positive integer
    >>> is_happy_number(19.1)
    Traceback (most recent call last):
        ...
    ValueError: number=19.1 must be a positive integer
    >>> is_happy_number("happy")
    Traceback (most recent call last):
        ...
    ValueError: number='happy' must be a positive integer
    """
    if not isinstance(number, int) or number <= 0:
        msg = f"{number=} must be a positive integer"
        raise ValueError(msg)

    seen = set()
    while number != 1 and number not in seen:
        seen.add(number)
        number = sum(int(digit) ** 2 for digit in str(number))
    return number == 1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/special_numbers/harshad_numbers.py
================================================
"""
A harshad number (or more specifically an n-harshad number) is a number that's
divisible by the sum of its digits in some given base n.
Reference: https://en.wikipedia.org/wiki/Harshad_number
"""


def int_to_base(number: int, base: int) -> str:
    """
    Convert a given positive decimal integer to base 'base'.
    Where 'base' ranges from 2 to 36.

    Examples:
    >>> int_to_base(0, 21)
    '0'
    >>> int_to_base(23, 2)
    '10111'
    >>> int_to_base(58, 5)
    '213'
    >>> int_to_base(167, 16)
    'A7'
    >>> # bases below 2 and beyond 36 will error
    >>> int_to_base(98, 1)
    Traceback (most recent call last):
        ...
    ValueError: 'base' must be between 2 and 36 inclusive
    >>> int_to_base(98, 37)
    Traceback (most recent call last):
        ...
    ValueError: 'base' must be between 2 and 36 inclusive
    >>> int_to_base(-99, 16)
    Traceback (most recent call last):
        ...
    ValueError: number must be a positive integer
    """

    if base < 2 or base > 36:
        raise ValueError("'base' must be between 2 and 36 inclusive")

    digits = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ"
    result = ""

    if number < 0:
        raise ValueError("number must be a positive integer")

    while number > 0:
        number, remainder = divmod(number, base)
        result = digits[remainder] + result

    if result == "":
        result = "0"

    return result


def sum_of_digits(num: int, base: int) -> str:
    """
    Calculate the sum of digit values in a positive integer
    converted to the given 'base'.
    Where 'base' ranges from 2 to 36.

    Examples:
    >>> sum_of_digits(103, 12)
    '13'
    >>> sum_of_digits(1275, 4)
    '30'
    >>> sum_of_digits(6645, 2)
    '1001'
    >>> # bases below 2 and beyond 36 will error
    >>> sum_of_digits(543, 1)
    Traceback (most recent call last):
        ...
    ValueError: 'base' must be between 2 and 36 inclusive
    >>> sum_of_digits(543, 37)
    Traceback (most recent call last):
        ...
    ValueError: 'base' must be between 2 and 36 inclusive
    """

    if base < 2 or base > 36:
        raise ValueError("'base' must be between 2 and 36 inclusive")

    num_str = int_to_base(num, base)
    res = sum(int(char, base) for char in num_str)
    res_str = int_to_base(res, base)
    return res_str


def harshad_numbers_in_base(limit: int, base: int) -> list[str]:
    """
    Finds all Harshad numbers smaller than num in base 'base'.
    Where 'base' ranges from 2 to 36.

    Examples:
    >>> harshad_numbers_in_base(15, 2)
    ['1', '10', '100', '110', '1000', '1010', '1100']
    >>> harshad_numbers_in_base(12, 34)
    ['1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B']
    >>> harshad_numbers_in_base(12, 4)
    ['1', '2', '3', '10', '12', '20', '21']
    >>> # bases below 2 and beyond 36 will error
    >>> harshad_numbers_in_base(234, 37)
    Traceback (most recent call last):
        ...
    ValueError: 'base' must be between 2 and 36 inclusive
    >>> harshad_numbers_in_base(234, 1)
    Traceback (most recent call last):
        ...
    ValueError: 'base' must be between 2 and 36 inclusive
    >>> harshad_numbers_in_base(-12, 6)
    []
    """

    if base < 2 or base > 36:
        raise ValueError("'base' must be between 2 and 36 inclusive")

    if limit < 0:
        return []

    numbers = [
        int_to_base(i, base)
        for i in range(1, limit)
        if i % int(sum_of_digits(i, base), base) == 0
    ]

    return numbers


def is_harshad_number_in_base(num: int, base: int) -> bool:
    """
    Determines whether n in base 'base' is a harshad number.
    Where 'base' ranges from 2 to 36.

    Examples:
    >>> is_harshad_number_in_base(18, 10)
    True
    >>> is_harshad_number_in_base(21, 10)
    True
    >>> is_harshad_number_in_base(-21, 5)
    False
    >>> # bases below 2 and beyond 36 will error
    >>> is_harshad_number_in_base(45, 37)
    Traceback (most recent call last):
        ...
    ValueError: 'base' must be between 2 and 36 inclusive
    >>> is_harshad_number_in_base(45, 1)
    Traceback (most recent call last):
        ...
    ValueError: 'base' must be between 2 and 36 inclusive
    """

    if base < 2 or base > 36:
        raise ValueError("'base' must be between 2 and 36 inclusive")

    if num < 0:
        return False

    n = int_to_base(num, base)
    d = sum_of_digits(num, base)
    return int(n, base) % int(d, base) == 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/special_numbers/hexagonal_number.py
================================================
"""
== Hexagonal Number ==
The nth hexagonal number hn is the number of distinct dots
in a pattern of dots consisting of the outlines of regular
hexagons with sides up to n dots, when the hexagons are
overlaid so that they share one vertex.

https://en.wikipedia.org/wiki/Hexagonal_number
"""

# Author : Akshay Dubey (https://github.com/itsAkshayDubey)


def hexagonal(number: int) -> int:
    """
    :param number: nth hexagonal number to calculate
    :return: the nth hexagonal number
    Note: A hexagonal number is only defined for positive integers
    >>> hexagonal(4)
    28
    >>> hexagonal(11)
    231
    >>> hexagonal(22)
    946
    >>> hexagonal(0)
    Traceback (most recent call last):
        ...
    ValueError: Input must be a positive integer
    >>> hexagonal(-1)
    Traceback (most recent call last):
        ...
    ValueError: Input must be a positive integer
    >>> hexagonal(11.0)
    Traceback (most recent call last):
        ...
    TypeError: Input value of [number=11.0] must be an integer
    """
    if not isinstance(number, int):
        msg = f"Input value of [number={number}] must be an integer"
        raise TypeError(msg)
    if number < 1:
        raise ValueError("Input must be a positive integer")
    return number * (2 * number - 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/special_numbers/krishnamurthy_number.py
================================================
"""
 == Krishnamurthy Number ==
It is also known as Peterson Number
A Krishnamurthy Number is a number whose sum of the
factorial of the digits equals to the original
number itself.

For example: 145 = 1! + 4! + 5!
    So, 145 is a Krishnamurthy Number
"""


def factorial(digit: int) -> int:
    """
    >>> factorial(3)
    6
    >>> factorial(0)
    1
    >>> factorial(5)
    120
    """

    return 1 if digit in (0, 1) else (digit * factorial(digit - 1))


def krishnamurthy(number: int) -> bool:
    """
    >>> krishnamurthy(145)
    True
    >>> krishnamurthy(240)
    False
    >>> krishnamurthy(1)
    True
    """

    fact_sum = 0
    duplicate = number
    while duplicate > 0:
        duplicate, digit = divmod(duplicate, 10)
        fact_sum += factorial(digit)
    return fact_sum == number


if __name__ == "__main__":
    print("Program to check whether a number is a Krisnamurthy Number or not.")
    number = int(input("Enter number: ").strip())
    print(
        f"{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."
    )


================================================
FILE: maths/special_numbers/perfect_number.py
================================================
"""
== Perfect Number ==
In number theory, a perfect number is a positive integer that is equal to the sum of
its positive divisors, excluding the number itself.
For example: 6 ==> divisors[1, 2, 3, 6]
    Excluding 6, the sum(divisors) is 1 + 2 + 3 = 6
    So, 6 is a Perfect Number

Other examples of Perfect Numbers: 28, 486, ...

https://en.wikipedia.org/wiki/Perfect_number
"""


def perfect(number: int) -> bool:
    """
    Check if a number is a perfect number.

    A perfect number is a positive integer that is equal to the sum of its proper
    divisors (excluding itself).

    Args:
        number: The number to be checked.

    Returns:
        True if the number is a perfect number, False otherwise.

    Start from 1 because dividing by 0 will raise ZeroDivisionError.
    A number at most can be divisible by the half of the number except the number
    itself. For example, 6 is at most can be divisible by 3 except by 6 itself.

    Examples:
    >>> perfect(27)
    False
    >>> perfect(28)
    True
    >>> perfect(29)
    False
    >>> perfect(6)
    True
    >>> perfect(12)
    False
    >>> perfect(496)
    True
    >>> perfect(8128)
    True
    >>> perfect(0)
    False
    >>> perfect(-1)
    False
    >>> perfect(12.34)
    Traceback (most recent call last):
      ...
    ValueError: number must be an integer
    >>> perfect("Hello")
    Traceback (most recent call last):
      ...
    ValueError: number must be an integer
    """
    if not isinstance(number, int):
        raise ValueError("number must be an integer")
    if number <= 0:
        return False
    return sum(i for i in range(1, number // 2 + 1) if number % i == 0) == number


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    print("Program to check whether a number is a Perfect number or not...")
    try:
        number = int(input("Enter a positive integer: ").strip())
    except ValueError:
        msg = "number must be an integer"
        print(msg)
        raise ValueError(msg)

    print(f"{number} is {'' if perfect(number) else 'not '}a Perfect Number.")


================================================
FILE: maths/special_numbers/polygonal_numbers.py
================================================
def polygonal_num(num: int, sides: int) -> int:
    """
    Returns the `num`th `sides`-gonal number. It is assumed that `num` >= 0 and
    `sides` >= 3 (see for reference https://en.wikipedia.org/wiki/Polygonal_number).

    >>> polygonal_num(0, 3)
    0
    >>> polygonal_num(3, 3)
    6
    >>> polygonal_num(5, 4)
    25
    >>> polygonal_num(2, 5)
    5
    >>> polygonal_num(-1, 0)
    Traceback (most recent call last):
        ...
    ValueError: Invalid input: num must be >= 0 and sides must be >= 3.
    >>> polygonal_num(0, 2)
    Traceback (most recent call last):
        ...
    ValueError: Invalid input: num must be >= 0 and sides must be >= 3.
    """
    if num < 0 or sides < 3:
        raise ValueError("Invalid input: num must be >= 0 and sides must be >= 3.")

    return ((sides - 2) * num**2 - (sides - 4) * num) // 2


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/special_numbers/pronic_number.py
================================================
"""
== Pronic Number ==
A number n is said to be a Proic number if
there exists an integer m such that n = m * (m + 1)

Examples of Proic Numbers: 0, 2, 6, 12, 20, 30, 42, 56, 72, 90, 110 ...
https://en.wikipedia.org/wiki/Pronic_number
"""

# Author : Akshay Dubey (https://github.com/itsAkshayDubey)


def is_pronic(number: int) -> bool:
    """
    # doctest: +NORMALIZE_WHITESPACE
    This functions takes an integer number as input.
    returns True if the number is pronic.
    >>> is_pronic(-1)
    False
    >>> is_pronic(0)
    True
    >>> is_pronic(2)
    True
    >>> is_pronic(5)
    False
    >>> is_pronic(6)
    True
    >>> is_pronic(8)
    False
    >>> is_pronic(30)
    True
    >>> is_pronic(32)
    False
    >>> is_pronic(2147441940)
    True
    >>> is_pronic(9223372033963249500)
    True
    >>> is_pronic(6.0)
    Traceback (most recent call last):
        ...
    TypeError: Input value of [number=6.0] must be an integer
    """
    if not isinstance(number, int):
        msg = f"Input value of [number={number}] must be an integer"
        raise TypeError(msg)
    if number < 0 or number % 2 == 1:
        return False
    number_sqrt = int(number**0.5)
    return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/special_numbers/proth_number.py
================================================
"""
Calculate the nth Proth number
Source:
    https://handwiki.org/wiki/Proth_number
"""

import math


def proth(number: int) -> int:
    """
    :param number: nth number to calculate in the sequence
    :return: the nth number in Proth number
    Note: indexing starts at 1 i.e. proth(1) gives the first Proth number of 3
    >>> proth(6)
    25
    >>> proth(0)
    Traceback (most recent call last):
        ...
    ValueError: Input value of [number=0] must be > 0
    >>> proth(-1)
    Traceback (most recent call last):
        ...
    ValueError: Input value of [number=-1] must be > 0
    >>> proth(6.0)
    Traceback (most recent call last):
        ...
    TypeError: Input value of [number=6.0] must be an integer
    """

    if not isinstance(number, int):
        msg = f"Input value of [number={number}] must be an integer"
        raise TypeError(msg)

    if number < 1:
        msg = f"Input value of [number={number}] must be > 0"
        raise ValueError(msg)
    elif number == 1:
        return 3
    elif number == 2:
        return 5
    else:
        """
        +1 for binary starting at 0 i.e. 2^0, 2^1, etc.
        +1 to start the sequence at the 3rd Proth number
        Hence, we have a +2 in the below statement
        """
        block_index = int(math.log(number // 3, 2)) + 2

        proth_list = [3, 5]
        proth_index = 2
        increment = 3
        for block in range(1, block_index):
            for _ in range(increment):
                proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1])
                proth_index += 1
            increment *= 2

    return proth_list[number - 1]


def is_proth_number(number: int) -> bool:
    """
    :param number: positive integer number
    :return: true if number is a Proth number, false otherwise
    >>> is_proth_number(1)
    False
    >>> is_proth_number(2)
    False
    >>> is_proth_number(3)
    True
    >>> is_proth_number(4)
    False
    >>> is_proth_number(5)
    True
    >>> is_proth_number(34)
    False
    >>> is_proth_number(-1)
    Traceback (most recent call last):
        ...
    ValueError: Input value of [number=-1] must be > 0
    >>> is_proth_number(6.0)
    Traceback (most recent call last):
        ...
    TypeError: Input value of [number=6.0] must be an integer
    """
    if not isinstance(number, int):
        message = f"Input value of [{number=}] must be an integer"
        raise TypeError(message)

    if number <= 0:
        message = f"Input value of [{number=}] must be > 0"
        raise ValueError(message)

    if number == 1:
        return False

    number -= 1
    n = 0
    while number % 2 == 0:
        n += 1
        number //= 2
    return number < 2**n


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    for number in range(11):
        value = 0
        try:
            value = proth(number)
        except ValueError:
            print(f"ValueError: there is no {number}th Proth number")
            continue

        print(f"The {number}th Proth number: {value}")

    for number in [1, 2, 3, 4, 5, 9, 13, 49, 57, 193, 241, 163, 201]:
        if is_proth_number(number):
            print(f"{number} is a Proth number")
        else:
            print(f"{number} is not a Proth number")


================================================
FILE: maths/special_numbers/triangular_numbers.py
================================================
"""
A triangular number or triangle number counts objects arranged in an
equilateral triangle. This module provides a function to generate n'th
triangular number.

For more information about triangular numbers, refer to:
https://en.wikipedia.org/wiki/Triangular_number
"""


def triangular_number(position: int) -> int:
    """
    Generate the triangular number at the specified position.

    Args:
        position (int): The position of the triangular number to generate.

    Returns:
        int: The triangular number at the specified position.

    Raises:
        ValueError: If `position` is negative.

    Examples:
    >>> triangular_number(1)
    1
    >>> triangular_number(3)
    6
    >>> triangular_number(-1)
    Traceback (most recent call last):
        ...
    ValueError: param `position` must be non-negative
    """
    if position < 0:
        raise ValueError("param `position` must be non-negative")

    return position * (position + 1) // 2


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/special_numbers/ugly_numbers.py
================================================
"""
Ugly numbers are numbers whose only prime factors are 2, 3 or 5. The sequence
1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, … shows the first 11 ugly numbers. By convention,
1 is included.
Given an integer n, we have to find the nth ugly number.

For more details, refer this article
https://www.geeksforgeeks.org/ugly-numbers/
"""


def ugly_numbers(n: int) -> int:
    """
    Returns the nth ugly number.
    >>> ugly_numbers(100)
    1536
    >>> ugly_numbers(0)
    1
    >>> ugly_numbers(20)
    36
    >>> ugly_numbers(-5)
    1
    >>> ugly_numbers(-5.5)
    Traceback (most recent call last):
        ...
    TypeError: 'float' object cannot be interpreted as an integer
    """
    ugly_nums = [1]

    i2, i3, i5 = 0, 0, 0
    next_2 = ugly_nums[i2] * 2
    next_3 = ugly_nums[i3] * 3
    next_5 = ugly_nums[i5] * 5

    for _ in range(1, n):
        next_num = min(next_2, next_3, next_5)
        ugly_nums.append(next_num)
        if next_num == next_2:
            i2 += 1
            next_2 = ugly_nums[i2] * 2
        if next_num == next_3:
            i3 += 1
            next_3 = ugly_nums[i3] * 3
        if next_num == next_5:
            i5 += 1
            next_5 = ugly_nums[i5] * 5
    return ugly_nums[-1]


if __name__ == "__main__":
    from doctest import testmod

    testmod(verbose=True)
    print(f"{ugly_numbers(200) = }")


================================================
FILE: maths/special_numbers/weird_number.py
================================================
"""
https://en.wikipedia.org/wiki/Weird_number

Fun fact: The set of weird numbers has positive asymptotic density.
"""

from math import sqrt


def factors(number: int) -> list[int]:
    """
    >>> factors(12)
    [1, 2, 3, 4, 6]
    >>> factors(1)
    [1]
    >>> factors(100)
    [1, 2, 4, 5, 10, 20, 25, 50]

    # >>> factors(-12)
    # [1, 2, 3, 4, 6]
    """

    values = [1]
    for i in range(2, int(sqrt(number)) + 1, 1):
        if number % i == 0:
            values.append(i)
            if int(number // i) != i:
                values.append(int(number // i))
    return sorted(values)


def abundant(n: int) -> bool:
    """
    >>> abundant(0)
    True
    >>> abundant(1)
    False
    >>> abundant(12)
    True
    >>> abundant(13)
    False
    >>> abundant(20)
    True

    # >>> abundant(-12)
    # True
    """
    return sum(factors(n)) > n


def semi_perfect(number: int) -> bool:
    """
    >>> semi_perfect(0)
    True
    >>> semi_perfect(1)
    True
    >>> semi_perfect(12)
    True
    >>> semi_perfect(13)
    False

    # >>> semi_perfect(-12)
    # True
    """
    values = factors(number)
    r = len(values)
    subset = [[0 for i in range(number + 1)] for j in range(r + 1)]
    for i in range(r + 1):
        subset[i][0] = True

    for i in range(1, number + 1):
        subset[0][i] = False

    for i in range(1, r + 1):
        for j in range(1, number + 1):
            if j < values[i - 1]:
                subset[i][j] = subset[i - 1][j]
            else:
                subset[i][j] = subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

    return subset[r][number] != 0


def weird(number: int) -> bool:
    """
    >>> weird(0)
    False
    >>> weird(70)
    True
    >>> weird(77)
    False
    """
    return abundant(number) and not semi_perfect(number)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    for number in (69, 70, 71):
        print(f"{number} is {'' if weird(number) else 'not '}weird.")


================================================
FILE: maths/sum_of_arithmetic_series.py
================================================
# DarkCoder
def sum_of_series(first_term: int, common_diff: int, num_of_terms: int) -> float:
    """
    Find the sum of n terms in an arithmetic progression.

    >>> sum_of_series(1, 1, 10)
    55.0
    >>> sum_of_series(1, 10, 100)
    49600.0
    """
    total = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
    # formula for sum of series
    return total


def main():
    print(sum_of_series(1, 1, 10))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/sum_of_digits.py
================================================
def sum_of_digits(n: int) -> int:
    """
    Find the sum of digits of a number.
    >>> sum_of_digits(12345)
    15
    >>> sum_of_digits(123)
    6
    >>> sum_of_digits(-123)
    6
    >>> sum_of_digits(0)
    0
    """
    n = abs(n)
    res = 0
    while n > 0:
        res += n % 10
        n //= 10
    return res


def sum_of_digits_recursion(n: int) -> int:
    """
    Find the sum of digits of a number using recursion
    >>> sum_of_digits_recursion(12345)
    15
    >>> sum_of_digits_recursion(123)
    6
    >>> sum_of_digits_recursion(-123)
    6
    >>> sum_of_digits_recursion(0)
    0
    """
    n = abs(n)
    return n if n < 10 else n % 10 + sum_of_digits(n // 10)


def sum_of_digits_compact(n: int) -> int:
    """
    Find the sum of digits of a number
    >>> sum_of_digits_compact(12345)
    15
    >>> sum_of_digits_compact(123)
    6
    >>> sum_of_digits_compact(-123)
    6
    >>> sum_of_digits_compact(0)
    0
    """
    return sum(int(c) for c in str(abs(n)))


def benchmark() -> None:
    """
    Benchmark multiple functions, with three different length int values.
    """
    from collections.abc import Callable
    from timeit import timeit

    def benchmark_a_function(func: Callable, value: int) -> None:
        call = f"{func.__name__}({value})"
        timing = timeit(f"__main__.{call}", setup="import __main__")
        print(f"{call:56} = {func(value)} -- {timing:.4f} seconds")

    for value in (262144, 1125899906842624, 1267650600228229401496703205376):
        for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
            benchmark_a_function(func, value)
        print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()


================================================
FILE: maths/sum_of_geometric_progression.py
================================================
def sum_of_geometric_progression(
    first_term: int, common_ratio: int, num_of_terms: int
) -> float:
    """ "
    Return the sum of n terms in a geometric progression.
    >>> sum_of_geometric_progression(1, 2, 10)
    1023.0
    >>> sum_of_geometric_progression(1, 10, 5)
    11111.0
    >>> sum_of_geometric_progression(0, 2, 10)
    0.0
    >>> sum_of_geometric_progression(1, 0, 10)
    1.0
    >>> sum_of_geometric_progression(1, 2, 0)
    -0.0
    >>> sum_of_geometric_progression(-1, 2, 10)
    -1023.0
    >>> sum_of_geometric_progression(1, -2, 10)
    -341.0
    >>> sum_of_geometric_progression(1, 2, -10)
    -0.9990234375
    """
    if common_ratio == 1:
        # Formula for sum if common ratio is 1
        return num_of_terms * first_term

    # Formula for finding sum of n terms of a GeometricProgression
    return (first_term / (1 - common_ratio)) * (1 - common_ratio**num_of_terms)


================================================
FILE: maths/sum_of_harmonic_series.py
================================================
def sum_of_harmonic_progression(
    first_term: float, common_difference: float, number_of_terms: int
) -> float:
    """
    https://en.wikipedia.org/wiki/Harmonic_progression_(mathematics)

    Find the sum of n terms in an harmonic progression.  The calculation starts with the
    first_term and loops adding the common difference of Arithmetic Progression by which
    the given Harmonic Progression is linked.

    >>> sum_of_harmonic_progression(1 / 2, 2, 2)
    0.75
    >>> sum_of_harmonic_progression(1 / 5, 5, 5)
    0.45666666666666667
    """
    arithmetic_progression = [1 / first_term]
    first_term = 1 / first_term
    for _ in range(number_of_terms - 1):
        first_term += common_difference
        arithmetic_progression.append(first_term)
    harmonic_series = [1 / step for step in arithmetic_progression]
    return sum(harmonic_series)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(sum_of_harmonic_progression(1 / 2, 2, 2))


================================================
FILE: maths/sumset.py
================================================
"""

Calculates the SumSet of two sets of numbers (A and B)

Source:
    https://en.wikipedia.org/wiki/Sumset

"""


def sumset(set_a: set, set_b: set) -> set:
    """
    :param first set: a set of numbers
    :param second set: a set of numbers
    :return: the nth number in Sylvester's sequence

    >>> sumset({1, 2, 3}, {4, 5, 6})
    {5, 6, 7, 8, 9}

    >>> sumset({1, 2, 3}, {4, 5, 6, 7})
    {5, 6, 7, 8, 9, 10}

    >>> sumset({1, 2, 3, 4}, 3)
    Traceback (most recent call last):
    ...
    AssertionError: The input value of [set_b=3] is not a set
    """
    assert isinstance(set_a, set), f"The input value of [set_a={set_a}] is not a set"
    assert isinstance(set_b, set), f"The input value of [set_b={set_b}] is not a set"

    return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: maths/sylvester_sequence.py
================================================
"""

Calculates the nth number in Sylvester's sequence

Source:
    https://en.wikipedia.org/wiki/Sylvester%27s_sequence

"""


def sylvester(number: int) -> int:
    """
    :param number: nth number to calculate in the sequence
    :return: the nth number in Sylvester's sequence

    >>> sylvester(8)
    113423713055421844361000443

    >>> sylvester(-1)
    Traceback (most recent call last):
        ...
    ValueError: The input value of [n=-1] has to be > 0

    >>> sylvester(8.0)
    Traceback (most recent call last):
        ...
    AssertionError: The input value of [n=8.0] is not an integer
    """
    assert isinstance(number, int), f"The input value of [n={number}] is not an integer"

    if number == 1:
        return 2
    elif number < 1:
        msg = f"The input value of [n={number}] has to be > 0"
        raise ValueError(msg)
    else:
        num = sylvester(number - 1)
        lower = num - 1
        upper = num
        return lower * upper + 1


if __name__ == "__main__":
    print(f"The 8th number in Sylvester's sequence: {sylvester(8)}")


================================================
FILE: maths/tanh.py
================================================
"""
This script demonstrates the implementation of the tangent hyperbolic
or tanh function.

The function takes a vector of K real numbers as input and
then (e^x - e^(-x))/(e^x + e^(-x)). After through tanh, the
element of the vector mostly -1 between 1.

Script inspired from its corresponding Wikipedia article
https://en.wikipedia.org/wiki/Activation_function
"""

import numpy as np


def tangent_hyperbolic(vector: np.ndarray) -> np.ndarray:
    """
        Implements the tanh function

        Parameters:
            vector: np.ndarray

        Returns:
            tanh (np.array): The input numpy array after applying tanh.

        mathematically (e^x - e^(-x))/(e^x + e^(-x)) can be written as (2/(1+e^(-2x))-1

    Examples:
        >>> tangent_hyperbolic(np.array([1,5,6,-0.67]))
        array([ 0.76159416,  0.9999092 ,  0.99998771, -0.58497988])

        >>> tangent_hyperbolic(np.array([8,10,2,-0.98,13]))
        array([ 0.99999977,  1.        ,  0.96402758, -0.7530659 ,  1.        ])

    """

    return (2 / (1 + np.exp(-2 * vector))) - 1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/test_factorial.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "pytest",
# ]
# ///

import pytest

from maths.factorial import factorial, factorial_recursive


@pytest.mark.parametrize("function", [factorial, factorial_recursive])
def test_zero(function):
    assert function(0) == 1


@pytest.mark.parametrize("function", [factorial, factorial_recursive])
def test_positive_integers(function):
    assert function(1) == 1
    assert function(5) == 120
    assert function(7) == 5040


@pytest.mark.parametrize("function", [factorial, factorial_recursive])
def test_large_number(function):
    assert function(10) == 3628800


@pytest.mark.parametrize("function", [factorial, factorial_recursive])
def test_negative_number(function):
    with pytest.raises(ValueError):
        function(-3)


@pytest.mark.parametrize("function", [factorial, factorial_recursive])
def test_float_number(function):
    with pytest.raises(ValueError):
        function(1.5)


if __name__ == "__main__":
    pytest.main(["-v", __file__])


================================================
FILE: maths/test_prime_check.py
================================================
"""
Minimalist file that allows pytest to find and run the Test unittest.  For details, see:
https://doc.pytest.org/en/latest/goodpractices.html#conventions-for-python-test-discovery
"""

from .prime_check import Test

Test()


================================================
FILE: maths/three_sum.py
================================================
"""
https://en.wikipedia.org/wiki/3SUM
"""


def three_sum(nums: list[int]) -> list[list[int]]:
    """
    Find all unique triplets in a sorted array of integers that sum up to zero.

    Args:
        nums: A sorted list of integers.

    Returns:
        A list of lists containing unique triplets that sum up to zero.

    >>> three_sum([-1, 0, 1, 2, -1, -4])
    [[-1, -1, 2], [-1, 0, 1]]
    >>> three_sum([1, 2, 3, 4])
    []
    """
    nums.sort()
    ans = []
    for i in range(len(nums) - 2):
        if i == 0 or (nums[i] != nums[i - 1]):
            low, high, c = i + 1, len(nums) - 1, 0 - nums[i]
            while low < high:
                if nums[low] + nums[high] == c:
                    ans.append([nums[i], nums[low], nums[high]])

                    while low < high and nums[low] == nums[low + 1]:
                        low += 1
                    while low < high and nums[high] == nums[high - 1]:
                        high -= 1

                    low += 1
                    high -= 1
                elif nums[low] + nums[high] < c:
                    low += 1
                else:
                    high -= 1
    return ans


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/trapezoidal_rule.py
================================================
"""
Numerical integration or quadrature for a smooth function f with known values at x_i
"""


def trapezoidal_rule(boundary, steps):
    """
    Implements the extended trapezoidal rule for numerical integration.
    The function f(x) is provided below.

    :param boundary: List containing the lower and upper bounds of integration [a, b]
    :param steps: The number of steps (intervals) used in the approximation
    :return: The numerical approximation of the integral

    >>> abs(trapezoidal_rule([0, 1], 10) - 0.33333) < 0.01
    True
    >>> abs(trapezoidal_rule([0, 1], 100) - 0.33333) < 0.01
    True
    >>> abs(trapezoidal_rule([0, 2], 1000) - 2.66667) < 0.01
    True
    >>> abs(trapezoidal_rule([1, 2], 1000) - 2.33333) < 0.01
    True
    """
    h = (boundary[1] - boundary[0]) / steps
    a = boundary[0]
    b = boundary[1]
    x_i = make_points(a, b, h)
    y = 0.0
    y += (h / 2.0) * f(a)
    for i in x_i:
        y += h * f(i)
    y += (h / 2.0) * f(b)
    return y


def make_points(a, b, h):
    """
    Generates points between a and b with step size h for trapezoidal integration.

    :param a: The lower bound of integration
    :param b: The upper bound of integration
    :param h: The step size
    :yield: The next x-value in the range (a, b)

    >>> list(make_points(0, 1, 0.1))    # doctest: +NORMALIZE_WHITESPACE
    [0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6, 0.7, 0.7999999999999999, \
    0.8999999999999999]
    >>> list(make_points(0, 10, 2.5))
    [2.5, 5.0, 7.5]
    >>> list(make_points(0, 10, 2))
    [2, 4, 6, 8]
    >>> list(make_points(1, 21, 5))
    [6, 11, 16]
    >>> list(make_points(1, 5, 2))
    [3]
    >>> list(make_points(1, 4, 3))
    []
    """
    x = a + h
    while x <= (b - h):
        yield x
        x += h


def f(x):
    """
    This is the function to integrate, f(x) = (x - 0)^2 = x^2.

    :param x: The input value
    :return: The value of f(x)

    >>> f(0)
    0
    >>> f(1)
    1
    >>> f(0.5)
    0.25
    """
    return x**2


def main():
    """
    Main function to test the trapezoidal rule.
    :a: Lower bound of integration
    :b: Upper bound of integration
    :steps: define number of steps or resolution
    :boundary: define boundary of integration

    >>> main()
    y = 0.3349999999999999
    """
    a = 0.0
    b = 1.0
    steps = 10.0
    boundary = [a, b]
    y = trapezoidal_rule(boundary, steps)
    print(f"y = {y}")


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: maths/triplet_sum.py
================================================
"""
Given an array of integers and another integer target,
we are required to find a triplet from the array such that it's sum is equal to
the target.
"""

from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat


def make_dataset() -> tuple[list[int], int]:
    arr = [randint(-1000, 1000) for i in range(10)]
    r = randint(-5000, 5000)
    return (arr, r)


dataset = make_dataset()


def triplet_sum1(arr: list[int], target: int) -> tuple[int, ...]:
    """
    Returns a triplet in the array with sum equal to target,
    else (0, 0, 0).
    >>> triplet_sum1([13, 29, 7, 23, 5], 35)
    (5, 7, 23)
    >>> triplet_sum1([37, 9, 19, 50, 44], 65)
    (9, 19, 37)
    >>> arr = [6, 47, 27, 1, 15]
    >>> target = 11
    >>> triplet_sum1(arr, target)
    (0, 0, 0)
    """
    for triplet in permutations(arr, 3):
        if sum(triplet) == target:
            return tuple(sorted(triplet))
    return (0, 0, 0)


def triplet_sum2(arr: list[int], target: int) -> tuple[int, int, int]:
    """
    Returns a triplet in the array with sum equal to target,
    else (0, 0, 0).
    >>> triplet_sum2([13, 29, 7, 23, 5], 35)
    (5, 7, 23)
    >>> triplet_sum2([37, 9, 19, 50, 44], 65)
    (9, 19, 37)
    >>> arr = [6, 47, 27, 1, 15]
    >>> target = 11
    >>> triplet_sum2(arr, target)
    (0, 0, 0)
    """
    arr.sort()
    n = len(arr)
    for i in range(n - 1):
        left, right = i + 1, n - 1
        while left < right:
            if arr[i] + arr[left] + arr[right] == target:
                return (arr[i], arr[left], arr[right])
            elif arr[i] + arr[left] + arr[right] < target:
                left += 1
            elif arr[i] + arr[left] + arr[right] > target:
                right -= 1
    return (0, 0, 0)


def solution_times() -> tuple[float, float]:
    setup_code = """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
    test_code1 = """
triplet_sum1(*dataset)
"""
    test_code2 = """
triplet_sum2(*dataset)
"""
    times1 = repeat(setup=setup_code, stmt=test_code1, repeat=5, number=10000)
    times2 = repeat(setup=setup_code, stmt=test_code2, repeat=5, number=10000)
    return (min(times1), min(times2))


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    times = solution_times()
    print(f"The time for naive implementation is {times[0]}.")
    print(f"The time for optimized implementation is {times[1]}.")


================================================
FILE: maths/twin_prime.py
================================================
"""
== Twin Prime ==
A number n+2 is said to be a Twin prime of number n if
both n and n+2 are prime.

Examples of Twin pairs: (3, 5), (5, 7), (11, 13), (17, 19), (29, 31), (41, 43), ...
https://en.wikipedia.org/wiki/Twin_prime
"""

# Author : Akshay Dubey (https://github.com/itsAkshayDubey)
from maths.prime_check import is_prime


def twin_prime(number: int) -> int:
    """
    # doctest: +NORMALIZE_WHITESPACE
    This functions takes an integer number as input.
    returns n+2 if n and n+2 are prime numbers and -1 otherwise.
    >>> twin_prime(3)
    5
    >>> twin_prime(4)
    -1
    >>> twin_prime(5)
    7
    >>> twin_prime(17)
    19
    >>> twin_prime(0)
    -1
    >>> twin_prime(6.0)
    Traceback (most recent call last):
        ...
    TypeError: Input value of [number=6.0] must be an integer
    """
    if not isinstance(number, int):
        msg = f"Input value of [number={number}] must be an integer"
        raise TypeError(msg)
    if is_prime(number) and is_prime(number + 2):
        return number + 2
    else:
        return -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: maths/two_pointer.py
================================================
"""
Given a sorted array of integers, return indices of the two numbers such
that they add up to a specific target using the two pointers technique.

You may assume that each input would have exactly one solution, and you
may not use the same element twice.

This is an alternative solution of the two-sum problem, which uses a
map to solve the problem. Hence can not solve the issue if there is a
constraint not use the same index twice. [1]

Example:
Given nums = [2, 7, 11, 15], target = 9,

Because nums[0] + nums[1] = 2 + 7 = 9,
return [0, 1].

[1]: https://github.com/TheAlgorithms/Python/blob/master/other/two_sum.py
"""

from __future__ import annotations


def two_pointer(nums: list[int], target: int) -> list[int]:
    """
    >>> two_pointer([2, 7, 11, 15], 9)
    [0, 1]
    >>> two_pointer([2, 7, 11, 15], 17)
    [0, 3]
    >>> two_pointer([2, 7, 11, 15], 18)
    [1, 2]
    >>> two_pointer([2, 7, 11, 15], 26)
    [2, 3]
    >>> two_pointer([1, 3, 3], 6)
    [1, 2]
    >>> two_pointer([2, 7, 11, 15], 8)
    []
    >>> two_pointer([3 * i for i in range(10)], 19)
    []
    >>> two_pointer([1, 2, 3], 6)
    []
    """
    i = 0
    j = len(nums) - 1

    while i < j:
        if nums[i] + nums[j] == target:
            return [i, j]
        elif nums[i] + nums[j] < target:
            i = i + 1
        else:
            j = j - 1

    return []


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(f"{two_pointer([2, 7, 11, 15], 9) = }")


================================================
FILE: maths/two_sum.py
================================================
"""
Given an array of integers, return indices of the two numbers such that they add up to
a specific target.

You may assume that each input would have exactly one solution, and you may not use the
same element twice.

Example:
Given nums = [2, 7, 11, 15], target = 9,

Because nums[0] + nums[1] = 2 + 7 = 9,
return [0, 1].
"""

from __future__ import annotations


def two_sum(nums: list[int], target: int) -> list[int]:
    """
    >>> two_sum([2, 7, 11, 15], 9)
    [0, 1]
    >>> two_sum([15, 2, 11, 7], 13)
    [1, 2]
    >>> two_sum([2, 7, 11, 15], 17)
    [0, 3]
    >>> two_sum([7, 15, 11, 2], 18)
    [0, 2]
    >>> two_sum([2, 7, 11, 15], 26)
    [2, 3]
    >>> two_sum([2, 7, 11, 15], 8)
    []
    >>> two_sum([3 * i for i in range(10)], 19)
    []
    """
    chk_map: dict[int, int] = {}
    for index, val in enumerate(nums):
        compl = target - val
        if compl in chk_map:
            return [chk_map[compl], index]
        chk_map[val] = index
    return []


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(f"{two_sum([2, 7, 11, 15], 9) = }")


================================================
FILE: maths/volume.py
================================================
"""
Find the volume of various shapes.

* https://en.wikipedia.org/wiki/Volume
* https://en.wikipedia.org/wiki/Spherical_cap
"""

from __future__ import annotations

from math import pi, pow  # noqa: A004


def vol_cube(side_length: float) -> float:
    """
    Calculate the Volume of a Cube.

    >>> vol_cube(1)
    1.0
    >>> vol_cube(3)
    27.0
    >>> vol_cube(0)
    0.0
    >>> vol_cube(1.6)
    4.096000000000001
    >>> vol_cube(-1)
    Traceback (most recent call last):
        ...
    ValueError: vol_cube() only accepts non-negative values
    """
    if side_length < 0:
        raise ValueError("vol_cube() only accepts non-negative values")
    return pow(side_length, 3)


def vol_spherical_cap(height: float, radius: float) -> float:
    """
    Calculate the volume of the spherical cap.

    >>> vol_spherical_cap(1, 2)
    5.235987755982988
    >>> vol_spherical_cap(1.6, 2.6)
    16.621119532592402
    >>> vol_spherical_cap(0, 0)
    0.0
    >>> vol_spherical_cap(-1, 2)
    Traceback (most recent call last):
        ...
    ValueError: vol_spherical_cap() only accepts non-negative values
    >>> vol_spherical_cap(1, -2)
    Traceback (most recent call last):
        ...
    ValueError: vol_spherical_cap() only accepts non-negative values
    """
    if height < 0 or radius < 0:
        raise ValueError("vol_spherical_cap() only accepts non-negative values")
    # Volume is 1/3 pi * height squared * (3 * radius - height)
    return 1 / 3 * pi * pow(height, 2) * (3 * radius - height)


def vol_spheres_intersect(
    radius_1: float, radius_2: float, centers_distance: float
) -> float:
    r"""
    Calculate the volume of the intersection of two spheres.

    The intersection is composed by two spherical caps and therefore its volume is the
    sum of the volumes of the spherical caps.
    First, it calculates the heights :math:`(h_1, h_2)` of the spherical caps,
    then the two volumes and it returns the sum.
    The height formulas are

    .. math::
        h_1 = \frac{(radius_1 - radius_2 + centers\_distance)
                    \cdot (radius_1 + radius_2 - centers\_distance)}
                   {2 \cdot centers\_distance}

        h_2 = \frac{(radius_2 - radius_1 + centers\_distance)
                    \cdot (radius_2 + radius_1 - centers\_distance)}
                   {2 \cdot centers\_distance}

    if `centers_distance` is 0 then it returns the volume of the smallers sphere

    :return: ``vol_spherical_cap`` (:math:`h_1`, :math:`radius_2`)
             + ``vol_spherical_cap`` (:math:`h_2`, :math:`radius_1`)

    >>> vol_spheres_intersect(2, 2, 1)
    21.205750411731103
    >>> vol_spheres_intersect(2.6, 2.6, 1.6)
    40.71504079052372
    >>> vol_spheres_intersect(0, 0, 0)
    0.0
    >>> vol_spheres_intersect(-2, 2, 1)
    Traceback (most recent call last):
        ...
    ValueError: vol_spheres_intersect() only accepts non-negative values
    >>> vol_spheres_intersect(2, -2, 1)
    Traceback (most recent call last):
        ...
    ValueError: vol_spheres_intersect() only accepts non-negative values
    >>> vol_spheres_intersect(2, 2, -1)
    Traceback (most recent call last):
        ...
    ValueError: vol_spheres_intersect() only accepts non-negative values
    """
    if radius_1 < 0 or radius_2 < 0 or centers_distance < 0:
        raise ValueError("vol_spheres_intersect() only accepts non-negative values")
    if centers_distance == 0:
        return vol_sphere(min(radius_1, radius_2))

    h1 = (
        (radius_1 - radius_2 + centers_distance)
        * (radius_1 + radius_2 - centers_distance)
        / (2 * centers_distance)
    )
    h2 = (
        (radius_2 - radius_1 + centers_distance)
        * (radius_2 + radius_1 - centers_distance)
        / (2 * centers_distance)
    )

    return vol_spherical_cap(h1, radius_2) + vol_spherical_cap(h2, radius_1)


def vol_spheres_union(
    radius_1: float, radius_2: float, centers_distance: float
) -> float:
    r"""
    Calculate the volume of the union of two spheres that possibly intersect.

    It is the sum of sphere :math:`A` and sphere :math:`B` minus their intersection.
    First, it calculates the volumes :math:`(v_1, v_2)` of the spheres,
    then the volume of the intersection :math:`i` and
    it returns the sum :math:`v_1 + v_2 - i`.
    If `centers_distance` is 0 then it returns the volume of the larger sphere

    :return: ``vol_sphere`` (:math:`radius_1`) + ``vol_sphere`` (:math:`radius_2`)
             - ``vol_spheres_intersect``
             (:math:`radius_1`, :math:`radius_2`, :math:`centers\_distance`)

    >>> vol_spheres_union(2, 2, 1)
    45.814892864851146
    >>> vol_spheres_union(1.56, 2.2, 1.4)
    48.77802773671288
    >>> vol_spheres_union(0, 2, 1)
    Traceback (most recent call last):
        ...
    ValueError: vol_spheres_union() only accepts non-negative values, non-zero radius
    >>> vol_spheres_union('1.56', '2.2', '1.4')
    Traceback (most recent call last):
        ...
    TypeError: '<=' not supported between instances of 'str' and 'int'
    >>> vol_spheres_union(1, None, 1)
    Traceback (most recent call last):
        ...
    TypeError: '<=' not supported between instances of 'NoneType' and 'int'
    """

    if radius_1 <= 0 or radius_2 <= 0 or centers_distance < 0:
        raise ValueError(
            "vol_spheres_union() only accepts non-negative values, non-zero radius"
        )

    if centers_distance == 0:
        return vol_sphere(max(radius_1, radius_2))

    return (
        vol_sphere(radius_1)
        + vol_sphere(radius_2)
        - vol_spheres_intersect(radius_1, radius_2, centers_distance)
    )


def vol_cuboid(width: float, height: float, length: float) -> float:
    """
    Calculate the Volume of a Cuboid.

    :return: multiple of `width`, `length` and `height`

    >>> vol_cuboid(1, 1, 1)
    1.0
    >>> vol_cuboid(1, 2, 3)
    6.0
    >>> vol_cuboid(1.6, 2.6, 3.6)
    14.976
    >>> vol_cuboid(0, 0, 0)
    0.0
    >>> vol_cuboid(-1, 2, 3)
    Traceback (most recent call last):
        ...
    ValueError: vol_cuboid() only accepts non-negative values
    >>> vol_cuboid(1, -2, 3)
    Traceback (most recent call last):
        ...
    ValueError: vol_cuboid() only accepts non-negative values
    >>> vol_cuboid(1, 2, -3)
    Traceback (most recent call last):
        ...
    ValueError: vol_cuboid() only accepts non-negative values
    """
    if width < 0 or height < 0 or length < 0:
        raise ValueError("vol_cuboid() only accepts non-negative values")
    return float(width * height * length)


def vol_cone(area_of_base: float, height: float) -> float:
    r"""
    | Calculate the Volume of a Cone.
    | Wikipedia reference: https://en.wikipedia.org/wiki/Cone

    :return: :math:`\frac{1}{3} \cdot area\_of\_base \cdot height`

    >>> vol_cone(10, 3)
    10.0
    >>> vol_cone(1, 1)
    0.3333333333333333
    >>> vol_cone(1.6, 1.6)
    0.8533333333333335
    >>> vol_cone(0, 0)
    0.0
    >>> vol_cone(-1, 1)
    Traceback (most recent call last):
        ...
    ValueError: vol_cone() only accepts non-negative values
    >>> vol_cone(1, -1)
    Traceback (most recent call last):
        ...
    ValueError: vol_cone() only accepts non-negative values
    """
    if height < 0 or area_of_base < 0:
        raise ValueError("vol_cone() only accepts non-negative values")
    return area_of_base * height / 3.0


def vol_right_circ_cone(radius: float, height: float) -> float:
    r"""
    | Calculate the Volume of a Right Circular Cone.
    | Wikipedia reference: https://en.wikipedia.org/wiki/Cone

    :return: :math:`\frac{1}{3} \cdot \pi \cdot radius^2 \cdot height`

    >>> vol_right_circ_cone(2, 3)
    12.566370614359172
    >>> vol_right_circ_cone(0, 0)
    0.0
    >>> vol_right_circ_cone(1.6, 1.6)
    4.289321169701265
    >>> vol_right_circ_cone(-1, 1)
    Traceback (most recent call last):
        ...
    ValueError: vol_right_circ_cone() only accepts non-negative values
    >>> vol_right_circ_cone(1, -1)
    Traceback (most recent call last):
        ...
    ValueError: vol_right_circ_cone() only accepts non-negative values
    """
    if height < 0 or radius < 0:
        raise ValueError("vol_right_circ_cone() only accepts non-negative values")
    return pi * pow(radius, 2) * height / 3.0


def vol_prism(area_of_base: float, height: float) -> float:
    r"""
    | Calculate the Volume of a Prism.
    | Wikipedia reference: https://en.wikipedia.org/wiki/Prism_(geometry)

    :return: :math:`V = B \cdot h`

    >>> vol_prism(10, 2)
    20.0
    >>> vol_prism(11, 1)
    11.0
    >>> vol_prism(1.6, 1.6)
    2.5600000000000005
    >>> vol_prism(0, 0)
    0.0
    >>> vol_prism(-1, 1)
    Traceback (most recent call last):
        ...
    ValueError: vol_prism() only accepts non-negative values
    >>> vol_prism(1, -1)
    Traceback (most recent call last):
        ...
    ValueError: vol_prism() only accepts non-negative values
    """
    if height < 0 or area_of_base < 0:
        raise ValueError("vol_prism() only accepts non-negative values")
    return float(area_of_base * height)


def vol_pyramid(area_of_base: float, height: float) -> float:
    r"""
    | Calculate the Volume of a Pyramid.
    | Wikipedia reference: https://en.wikipedia.org/wiki/Pyramid_(geometry)

    :return: :math:`\frac{1}{3} \cdot B \cdot h`

    >>> vol_pyramid(10, 3)
    10.0
    >>> vol_pyramid(1.5, 3)
    1.5
    >>> vol_pyramid(1.6, 1.6)
    0.8533333333333335
    >>> vol_pyramid(0, 0)
    0.0
    >>> vol_pyramid(-1, 1)
    Traceback (most recent call last):
        ...
    ValueError: vol_pyramid() only accepts non-negative values
    >>> vol_pyramid(1, -1)
    Traceback (most recent call last):
        ...
    ValueError: vol_pyramid() only accepts non-negative values
    """
    if height < 0 or area_of_base < 0:
        raise ValueError("vol_pyramid() only accepts non-negative values")
    return area_of_base * height / 3.0


def vol_sphere(radius: float) -> float:
    r"""
    | Calculate the Volume of a Sphere.
    | Wikipedia reference: https://en.wikipedia.org/wiki/Sphere

    :return: :math:`\frac{4}{3} \cdot \pi \cdot r^3`

    >>> vol_sphere(5)
    523.5987755982989
    >>> vol_sphere(1)
    4.1887902047863905
    >>> vol_sphere(1.6)
    17.15728467880506
    >>> vol_sphere(0)
    0.0
    >>> vol_sphere(-1)
    Traceback (most recent call last):
        ...
    ValueError: vol_sphere() only accepts non-negative values
    """
    if radius < 0:
        raise ValueError("vol_sphere() only accepts non-negative values")
    # Volume is 4/3 * pi * radius cubed
    return 4 / 3 * pi * pow(radius, 3)


def vol_hemisphere(radius: float) -> float:
    r"""
    | Calculate the volume of a hemisphere
    | Wikipedia reference: https://en.wikipedia.org/wiki/Hemisphere
    | Other references: https://www.cuemath.com/geometry/hemisphere

    :return: :math:`\frac{2}{3} \cdot \pi \cdot radius^3`

    >>> vol_hemisphere(1)
    2.0943951023931953
    >>> vol_hemisphere(7)
    718.377520120866
    >>> vol_hemisphere(1.6)
    8.57864233940253
    >>> vol_hemisphere(0)
    0.0
    >>> vol_hemisphere(-1)
    Traceback (most recent call last):
        ...
    ValueError: vol_hemisphere() only accepts non-negative values
    """
    if radius < 0:
        raise ValueError("vol_hemisphere() only accepts non-negative values")
    # Volume is radius cubed * pi * 2/3
    return pow(radius, 3) * pi * 2 / 3


def vol_circular_cylinder(radius: float, height: float) -> float:
    r"""
    | Calculate the Volume of a Circular Cylinder.
    | Wikipedia reference: https://en.wikipedia.org/wiki/Cylinder

    :return: :math:`\pi \cdot radius^2 \cdot height`

    >>> vol_circular_cylinder(1, 1)
    3.141592653589793
    >>> vol_circular_cylinder(4, 3)
    150.79644737231007
    >>> vol_circular_cylinder(1.6, 1.6)
    12.867963509103795
    >>> vol_circular_cylinder(0, 0)
    0.0
    >>> vol_circular_cylinder(-1, 1)
    Traceback (most recent call last):
        ...
    ValueError: vol_circular_cylinder() only accepts non-negative values
    >>> vol_circular_cylinder(1, -1)
    Traceback (most recent call last):
        ...
    ValueError: vol_circular_cylinder() only accepts non-negative values
    """
    if height < 0 or radius < 0:
        raise ValueError("vol_circular_cylinder() only accepts non-negative values")
    # Volume is radius squared * height * pi
    return pow(radius, 2) * height * pi


def vol_hollow_circular_cylinder(
    inner_radius: float, outer_radius: float, height: float
) -> float:
    """
    Calculate the Volume of a Hollow Circular Cylinder.

    >>> vol_hollow_circular_cylinder(1, 2, 3)
    28.274333882308138
    >>> vol_hollow_circular_cylinder(1.6, 2.6, 3.6)
    47.50088092227767
    >>> vol_hollow_circular_cylinder(-1, 2, 3)
    Traceback (most recent call last):
        ...
    ValueError: vol_hollow_circular_cylinder() only accepts non-negative values
    >>> vol_hollow_circular_cylinder(1, -2, 3)
    Traceback (most recent call last):
        ...
    ValueError: vol_hollow_circular_cylinder() only accepts non-negative values
    >>> vol_hollow_circular_cylinder(1, 2, -3)
    Traceback (most recent call last):
        ...
    ValueError: vol_hollow_circular_cylinder() only accepts non-negative values
    >>> vol_hollow_circular_cylinder(2, 1, 3)
    Traceback (most recent call last):
        ...
    ValueError: outer_radius must be greater than inner_radius
    >>> vol_hollow_circular_cylinder(0, 0, 0)
    Traceback (most recent call last):
        ...
    ValueError: outer_radius must be greater than inner_radius
    """
    # Volume - (outer_radius squared - inner_radius squared) * pi * height
    if inner_radius < 0 or outer_radius < 0 or height < 0:
        raise ValueError(
            "vol_hollow_circular_cylinder() only accepts non-negative values"
        )
    if outer_radius <= inner_radius:
        raise ValueError("outer_radius must be greater than inner_radius")
    return pi * (pow(outer_radius, 2) - pow(inner_radius, 2)) * height


def vol_conical_frustum(height: float, radius_1: float, radius_2: float) -> float:
    """
    | Calculate the Volume of a Conical Frustum.
    | Wikipedia reference: https://en.wikipedia.org/wiki/Frustum

    >>> vol_conical_frustum(45, 7, 28)
    48490.482608158454
    >>> vol_conical_frustum(1, 1, 2)
    7.330382858376184
    >>> vol_conical_frustum(1.6, 2.6, 3.6)
    48.7240076620753
    >>> vol_conical_frustum(0, 0, 0)
    0.0
    >>> vol_conical_frustum(-2, 2, 1)
    Traceback (most recent call last):
        ...
    ValueError: vol_conical_frustum() only accepts non-negative values
    >>> vol_conical_frustum(2, -2, 1)
    Traceback (most recent call last):
        ...
    ValueError: vol_conical_frustum() only accepts non-negative values
    >>> vol_conical_frustum(2, 2, -1)
    Traceback (most recent call last):
        ...
    ValueError: vol_conical_frustum() only accepts non-negative values
    """
    # Volume is 1/3 * pi * height *
    #           (radius_1 squared + radius_2 squared + radius_1 * radius_2)
    if radius_1 < 0 or radius_2 < 0 or height < 0:
        raise ValueError("vol_conical_frustum() only accepts non-negative values")
    return (
        1
        / 3
        * pi
        * height
        * (pow(radius_1, 2) + pow(radius_2, 2) + radius_1 * radius_2)
    )


def vol_torus(torus_radius: float, tube_radius: float) -> float:
    r"""
    | Calculate the Volume of a Torus.
    | Wikipedia reference: https://en.wikipedia.org/wiki/Torus

    :return: :math:`2 \pi^2 \cdot torus\_radius \cdot tube\_radius^2`

    >>> vol_torus(1, 1)
    19.739208802178716
    >>> vol_torus(4, 3)
    710.6115168784338
    >>> vol_torus(3, 4)
    947.4820225045784
    >>> vol_torus(1.6, 1.6)
    80.85179925372404
    >>> vol_torus(0, 0)
    0.0
    >>> vol_torus(-1, 1)
    Traceback (most recent call last):
        ...
    ValueError: vol_torus() only accepts non-negative values
    >>> vol_torus(1, -1)
    Traceback (most recent call last):
        ...
    ValueError: vol_torus() only accepts non-negative values
    """
    if torus_radius < 0 or tube_radius < 0:
        raise ValueError("vol_torus() only accepts non-negative values")
    return 2 * pow(pi, 2) * torus_radius * pow(tube_radius, 2)


def vol_icosahedron(tri_side: float) -> float:
    """
    | Calculate the Volume of an Icosahedron.
    | Wikipedia reference: https://en.wikipedia.org/wiki/Regular_icosahedron

    >>> from math import isclose
    >>> isclose(vol_icosahedron(2.5), 34.088984228514256)
    True
    >>> isclose(vol_icosahedron(10), 2181.694990624912374)
    True
    >>> isclose(vol_icosahedron(5), 272.711873828114047)
    True
    >>> isclose(vol_icosahedron(3.49), 92.740688412033628)
    True
    >>> vol_icosahedron(0)
    0.0
    >>> vol_icosahedron(-1)
    Traceback (most recent call last):
        ...
    ValueError: vol_icosahedron() only accepts non-negative values
    >>> vol_icosahedron(-0.2)
    Traceback (most recent call last):
        ...
    ValueError: vol_icosahedron() only accepts non-negative values
    """
    if tri_side < 0:
        raise ValueError("vol_icosahedron() only accepts non-negative values")
    return tri_side**3 * (3 + 5**0.5) * 5 / 12


def main():
    """Print the Results of Various Volume Calculations."""
    print("Volumes:")
    print(f"Cube: {vol_cube(2) = }")  # = 8
    print(f"Cuboid: {vol_cuboid(2, 2, 2) = }")  # = 8
    print(f"Cone: {vol_cone(2, 2) = }")  # ~= 1.33
    print(f"Right Circular Cone: {vol_right_circ_cone(2, 2) = }")  # ~= 8.38
    print(f"Prism: {vol_prism(2, 2) = }")  # = 4
    print(f"Pyramid: {vol_pyramid(2, 2) = }")  # ~= 1.33
    print(f"Sphere: {vol_sphere(2) = }")  # ~= 33.5
    print(f"Hemisphere: {vol_hemisphere(2) = }")  # ~= 16.75
    print(f"Circular Cylinder: {vol_circular_cylinder(2, 2) = }")  # ~= 25.1
    print(f"Torus: {vol_torus(2, 2) = }")  # ~= 157.9
    print(f"Conical Frustum: {vol_conical_frustum(2, 2, 4) = }")  # ~= 58.6
    print(f"Spherical cap: {vol_spherical_cap(1, 2) = }")  # ~= 5.24
    print(f"Spheres intersection: {vol_spheres_intersect(2, 2, 1) = }")  # ~= 21.21
    print(f"Spheres union: {vol_spheres_union(2, 2, 1) = }")  # ~= 45.81
    print(
        f"Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1, 2, 3) = }"
    )  # ~= 28.3
    print(f"Icosahedron: {vol_icosahedron(2.5) = }")  # ~=34.09


if __name__ == "__main__":
    main()


================================================
FILE: maths/zellers_congruence.py
================================================
import argparse
import datetime


def zeller(date_input: str) -> str:
    """
    | Zellers Congruence Algorithm
    | Find the day of the week for nearly any Gregorian or Julian calendar date

    >>> zeller('01-31-2010')
    'Your date 01-31-2010, is a Sunday!'

    Validate out of range month:

    >>> zeller('13-31-2010')
    Traceback (most recent call last):
        ...
    ValueError: Month must be between 1 - 12
    >>> zeller('.2-31-2010')
    Traceback (most recent call last):
        ...
    ValueError: invalid literal for int() with base 10: '.2'

    Validate out of range date:

    >>> zeller('01-33-2010')
    Traceback (most recent call last):
        ...
    ValueError: Date must be between 1 - 31
    >>> zeller('01-.4-2010')
    Traceback (most recent call last):
        ...
    ValueError: invalid literal for int() with base 10: '.4'

    Validate second separator:

    >>> zeller('01-31*2010')
    Traceback (most recent call last):
        ...
    ValueError: Date separator must be '-' or '/'

    Validate first separator:

    >>> zeller('01^31-2010')
    Traceback (most recent call last):
        ...
    ValueError: Date separator must be '-' or '/'

    Validate out of range year:

    >>> zeller('01-31-8999')
    Traceback (most recent call last):
        ...
    ValueError: Year out of range. There has to be some sort of limit...right?

    Test null input:

    >>> zeller()
    Traceback (most recent call last):
        ...
    TypeError: zeller() missing 1 required positional argument: 'date_input'

    Test length of `date_input`:

    >>> zeller('')
    Traceback (most recent call last):
        ...
    ValueError: Must be 10 characters long
    >>> zeller('01-31-19082939')
    Traceback (most recent call last):
        ...
    ValueError: Must be 10 characters long"""

    # Days of the week for response
    days = {
        "0": "Sunday",
        "1": "Monday",
        "2": "Tuesday",
        "3": "Wednesday",
        "4": "Thursday",
        "5": "Friday",
        "6": "Saturday",
    }

    convert_datetime_days = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}

    # Validate
    if not 0 < len(date_input) < 11:
        raise ValueError("Must be 10 characters long")

    # Get month
    m: int = int(date_input[0] + date_input[1])
    # Validate
    if not 0 < m < 13:
        raise ValueError("Month must be between 1 - 12")

    sep_1: str = date_input[2]
    # Validate
    if sep_1 not in ["-", "/"]:
        raise ValueError("Date separator must be '-' or '/'")

    # Get day
    d: int = int(date_input[3] + date_input[4])
    # Validate
    if not 0 < d < 32:
        raise ValueError("Date must be between 1 - 31")

    # Get second separator
    sep_2: str = date_input[5]
    # Validate
    if sep_2 not in ["-", "/"]:
        raise ValueError("Date separator must be '-' or '/'")

    # Get year
    y: int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9])
    # Arbitrary year range
    if not 45 < y < 8500:
        raise ValueError(
            "Year out of range. There has to be some sort of limit...right?"
        )

    # Get datetime obj for validation
    dt_ck = datetime.date(int(y), int(m), int(d))

    # Start math
    if m <= 2:
        y = y - 1
        m = m + 12
    # maths var
    c: int = int(str(y)[:2])
    k: int = int(str(y)[2:])
    t: int = int(2.6 * m - 5.39)
    u: int = int(c / 4)
    v: int = int(k / 4)
    x: int = int(d + k)
    z: int = int(t + u + v + x)
    w: int = int(z - (2 * c))
    f: int = round(w % 7)
    # End math

    # Validate math
    if f != convert_datetime_days[dt_ck.weekday()]:
        raise AssertionError("The date was evaluated incorrectly. Contact developer.")

    # Response
    response: str = f"Your date {date_input}, is a {days[str(f)]}!"
    return response


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    parser = argparse.ArgumentParser(
        description=(
            "Find out what day of the week nearly any date is or was. Enter "
            "date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
        )
    )
    parser.add_argument(
        "date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)"
    )
    args = parser.parse_args()
    zeller(args.date_input)


================================================
FILE: matrix/__init__.py
================================================


================================================
FILE: matrix/binary_search_matrix.py
================================================
def binary_search(array: list, lower_bound: int, upper_bound: int, value: int) -> int:
    """
    This function carries out Binary search on a 1d array and
    return -1 if it do not exist
    array: A 1d sorted array
    value : the value meant to be searched
    >>> matrix = [1, 4, 7, 11, 15]
    >>> binary_search(matrix, 0, len(matrix) - 1, 1)
    0
    >>> binary_search(matrix, 0, len(matrix) - 1, 23)
    -1
    """

    r = int((lower_bound + upper_bound) // 2)
    if array[r] == value:
        return r
    if lower_bound >= upper_bound:
        return -1
    if array[r] < value:
        return binary_search(array, r + 1, upper_bound, value)
    else:
        return binary_search(array, lower_bound, r - 1, value)


def mat_bin_search(value: int, matrix: list) -> list:
    """
    This function loops over a 2d matrix and calls binarySearch on
    the selected 1d array and returns [-1, -1] is it do not exist
    value : value meant to be searched
    matrix = a sorted 2d matrix
    >>> matrix = [[1, 4, 7, 11, 15],
    ...           [2, 5, 8, 12, 19],
    ...           [3, 6, 9, 16, 22],
    ...           [10, 13, 14, 17, 24],
    ...           [18, 21, 23, 26, 30]]
    >>> target = 1
    >>> mat_bin_search(target, matrix)
    [0, 0]
    >>> target = 34
    >>> mat_bin_search(target, matrix)
    [-1, -1]
    """
    index = 0
    if matrix[index][0] == value:
        return [index, 0]
    while index < len(matrix) and matrix[index][0] < value:
        r = binary_search(matrix[index], 0, len(matrix[index]) - 1, value)
        if r != -1:
            return [index, r]
        index += 1
    return [-1, -1]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: matrix/count_islands_in_matrix.py
================================================
# An island in matrix is a group of linked areas, all having the same value.
# This code counts number of islands in a given matrix, with including diagonal
# connections.


class Matrix:  # Public class to implement a graph
    def __init__(self, row: int, col: int, graph: list[list[bool]]) -> None:
        self.ROW = row
        self.COL = col
        self.graph = graph

    def is_safe(self, i: int, j: int, visited: list[list[bool]]) -> bool:
        return (
            0 <= i < self.ROW
            and 0 <= j < self.COL
            and not visited[i][j]
            and self.graph[i][j]
        )

    def diffs(self, i: int, j: int, visited: list[list[bool]]) -> None:
        # Checking all 8 elements surrounding nth element
        row_nbr = [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
        col_nbr = [-1, 0, 1, -1, 1, -1, 0, 1]
        visited[i][j] = True  # Make those cells visited
        for k in range(8):
            if self.is_safe(i + row_nbr[k], j + col_nbr[k], visited):
                self.diffs(i + row_nbr[k], j + col_nbr[k], visited)

    def count_islands(self) -> int:  # And finally, count all islands.
        visited = [[False for j in range(self.COL)] for i in range(self.ROW)]
        count = 0
        for i in range(self.ROW):
            for j in range(self.COL):
                if visited[i][j] is False and self.graph[i][j] == 1:
                    self.diffs(i, j, visited)
                    count += 1
        return count


================================================
FILE: matrix/count_negative_numbers_in_sorted_matrix.py
================================================
"""
Given an matrix of numbers in which all rows and all columns are sorted in decreasing
order, return the number of negative numbers in grid.

Reference: https://leetcode.com/problems/count-negative-numbers-in-a-sorted-matrix
"""


def generate_large_matrix() -> list[list[int]]:
    """
    >>> generate_large_matrix() # doctest: +ELLIPSIS
    [[1000, ..., -999], [999, ..., -1001], ..., [2, ..., -1998]]
    """
    return [list(range(1000 - i, -1000 - i, -1)) for i in range(1000)]


grid = generate_large_matrix()
test_grids = (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)


def validate_grid(grid: list[list[int]]) -> None:
    """
    Validate that the rows and columns of the grid is sorted in decreasing order.
    >>> for grid in test_grids:
    ...     validate_grid(grid)
    """
    assert all(row == sorted(row, reverse=True) for row in grid)
    assert all(list(col) == sorted(col, reverse=True) for col in zip(*grid))


def find_negative_index(array: list[int]) -> int:
    """
    Find the smallest negative index

    >>> find_negative_index([0,0,0,0])
    4
    >>> find_negative_index([4,3,2,-1])
    3
    >>> find_negative_index([1,0,-1,-10])
    2
    >>> find_negative_index([0,0,0,-1])
    3
    >>> find_negative_index([11,8,7,-3,-5,-9])
    3
    >>> find_negative_index([-1,-1,-2,-3])
    0
    >>> find_negative_index([5,1,0])
    3
    >>> find_negative_index([-5,-5,-5])
    0
    >>> find_negative_index([0])
    1
    >>> find_negative_index([])
    0
    """
    left = 0
    right = len(array) - 1

    # Edge cases such as no values or all numbers are negative.
    if not array or array[0] < 0:
        return 0

    while right + 1 > left:
        mid = (left + right) // 2
        num = array[mid]

        # Num must be negative and the index must be greater than or equal to 0.
        if num < 0 and array[mid - 1] >= 0:
            return mid

        if num >= 0:
            left = mid + 1
        else:
            right = mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
    return len(array)


def count_negatives_binary_search(grid: list[list[int]]) -> int:
    """
    An O(m logn) solution that uses binary search in order to find the boundary between
    positive and negative numbers

    >>> [count_negatives_binary_search(grid) for grid in test_grids]
    [8, 0, 0, 3, 1498500]
    """
    total = 0
    bound = len(grid[0])

    for i in range(len(grid)):
        bound = find_negative_index(grid[i][:bound])
        total += bound
    return (len(grid) * len(grid[0])) - total


def count_negatives_brute_force(grid: list[list[int]]) -> int:
    """
    This solution is O(n^2) because it iterates through every column and row.

    >>> [count_negatives_brute_force(grid) for grid in test_grids]
    [8, 0, 0, 3, 1498500]
    """
    return len([number for row in grid for number in row if number < 0])


def count_negatives_brute_force_with_break(grid: list[list[int]]) -> int:
    """
    Similar to the brute force solution above but uses break in order to reduce the
    number of iterations.

    >>> [count_negatives_brute_force_with_break(grid) for grid in test_grids]
    [8, 0, 0, 3, 1498500]
    """
    total = 0
    for row in grid:
        for i, number in enumerate(row):
            if number < 0:
                total += len(row) - i
                break
    return total


def benchmark() -> None:
    """Benchmark our functions next to each other"""
    from timeit import timeit

    print("Running benchmarks")
    setup = (
        "from __main__ import count_negatives_binary_search, "
        "count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
    )
    for func in (
        "count_negatives_binary_search",  # took 0.7727 seconds
        "count_negatives_brute_force_with_break",  # took 4.6505 seconds
        "count_negatives_brute_force",  # took 12.8160 seconds
    ):
        time = timeit(f"{func}(grid=grid)", setup=setup, number=500)
        print(f"{func}() took {time:0.4f} seconds")


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()


================================================
FILE: matrix/count_paths.py
================================================
"""
Given a grid, where you start from the top left position [0, 0],
you want to find how many paths you can take to get to the bottom right position.

start here  ->   0  0  0  0
                 1  1  0  0
                 0  0  0  1
                 0  1  0  0  <- finish here
how many 'distinct' paths can you take to get to the finish?
Using a recursive depth-first search algorithm below, you are able to
find the number of distinct unique paths (count).

'*' will demonstrate a path
In the example above, there are two distinct paths:
1.                2.
    *  *  *  0      *  *  *  *
    1  1  *  0      1  1  *  *
    0  0  *  1      0  0  *  1
    0  1  *  *      0  1  *  *
"""


def depth_first_search(grid: list[list[int]], row: int, col: int, visit: set) -> int:
    """
    Recursive Backtracking Depth First Search Algorithm

    Starting from top left of a matrix, count the number of
    paths that can reach the bottom right of a matrix.
    1 represents a block (inaccessible)
    0 represents a valid space (accessible)

    0  0  0  0
    1  1  0  0
    0  0  0  1
    0  1  0  0
    >>> grid = [[0, 0, 0, 0], [1, 1, 0, 0], [0, 0, 0, 1], [0, 1, 0, 0]]
    >>> depth_first_search(grid, 0, 0, set())
    2

    0  0  0  0  0
    0  1  1  1  0
    0  1  1  1  0
    0  0  0  0  0
    >>> grid = [[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]]
    >>> depth_first_search(grid, 0, 0, set())
    2
    """
    row_length, col_length = len(grid), len(grid[0])
    if (
        min(row, col) < 0
        or row == row_length
        or col == col_length
        or (row, col) in visit
        or grid[row][col] == 1
    ):
        return 0
    if row == row_length - 1 and col == col_length - 1:
        return 1

    visit.add((row, col))

    count = 0
    count += depth_first_search(grid, row + 1, col, visit)
    count += depth_first_search(grid, row - 1, col, visit)
    count += depth_first_search(grid, row, col + 1, visit)
    count += depth_first_search(grid, row, col - 1, visit)

    visit.remove((row, col))
    return count


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: matrix/cramers_rule_2x2.py
================================================
# https://www.chilimath.com/lessons/advanced-algebra/cramers-rule-with-two-variables
# https://en.wikipedia.org/wiki/Cramer%27s_rule


def cramers_rule_2x2(equation1: list[int], equation2: list[int]) -> tuple[float, float]:
    """
    Solves the system of linear equation in 2 variables.
    :param: equation1: list of 3 numbers
    :param: equation2: list of 3 numbers
    :return: String of result
    input format : [a1, b1, d1], [a2, b2, d2]
    determinant = [[a1, b1], [a2, b2]]
    determinant_x = [[d1, b1], [d2, b2]]
    determinant_y = [[a1, d1], [a2, d2]]

    >>> cramers_rule_2x2([2, 3, 0], [5, 1, 0])
    (0.0, 0.0)
    >>> cramers_rule_2x2([0, 4, 50], [2, 0, 26])
    (13.0, 12.5)
    >>> cramers_rule_2x2([11, 2, 30], [1, 0, 4])
    (4.0, -7.0)
    >>> cramers_rule_2x2([4, 7, 1], [1, 2, 0])
    (2.0, -1.0)

    >>> cramers_rule_2x2([1, 2, 3], [2, 4, 6])
    Traceback (most recent call last):
        ...
    ValueError: Infinite solutions. (Consistent system)
    >>> cramers_rule_2x2([1, 2, 3], [2, 4, 7])
    Traceback (most recent call last):
        ...
    ValueError: No solution. (Inconsistent system)
    >>> cramers_rule_2x2([1, 2, 3], [11, 22])
    Traceback (most recent call last):
        ...
    ValueError: Please enter a valid equation.
    >>> cramers_rule_2x2([0, 1, 6], [0, 0, 3])
    Traceback (most recent call last):
        ...
    ValueError: No solution. (Inconsistent system)
    >>> cramers_rule_2x2([0, 0, 6], [0, 0, 3])
    Traceback (most recent call last):
        ...
    ValueError: Both a & b of two equations can't be zero.
    >>> cramers_rule_2x2([1, 2, 3], [1, 2, 3])
    Traceback (most recent call last):
        ...
    ValueError: Infinite solutions. (Consistent system)
    >>> cramers_rule_2x2([0, 4, 50], [0, 3, 99])
    Traceback (most recent call last):
        ...
    ValueError: No solution. (Inconsistent system)
    """

    # Check if the input is valid
    if not len(equation1) == len(equation2) == 3:
        raise ValueError("Please enter a valid equation.")
    if equation1[0] == equation1[1] == equation2[0] == equation2[1] == 0:
        raise ValueError("Both a & b of two equations can't be zero.")

    # Extract the coefficients
    a1, b1, c1 = equation1
    a2, b2, c2 = equation2

    # Calculate the determinants of the matrices
    determinant = a1 * b2 - a2 * b1
    determinant_x = c1 * b2 - c2 * b1
    determinant_y = a1 * c2 - a2 * c1

    # Check if the system of linear equations has a solution (using Cramer's rule)
    if determinant == 0:
        if determinant_x == determinant_y == 0:
            raise ValueError("Infinite solutions. (Consistent system)")
        else:
            raise ValueError("No solution. (Inconsistent system)")
    elif determinant_x == determinant_y == 0:
        # Trivial solution (Inconsistent system)
        return (0.0, 0.0)
    else:
        x = determinant_x / determinant
        y = determinant_y / determinant
        # Non-Trivial Solution (Consistent system)
        return (x, y)


================================================
FILE: matrix/inverse_of_matrix.py
================================================
from __future__ import annotations

from decimal import Decimal

from numpy import array


def inverse_of_matrix(matrix: list[list[float]]) -> list[list[float]]:
    """
    A matrix multiplied with its inverse gives the identity matrix.
    This function finds the inverse of a 2x2 and 3x3 matrix.
    If the determinant of a matrix is 0, its inverse does not exist.

    Sources for fixing inaccurate float arithmetic:
    https://stackoverflow.com/questions/6563058/how-do-i-use-accurate-float-arithmetic-in-python
    https://docs.python.org/3/library/decimal.html

    Doctests for 2x2
    >>> inverse_of_matrix([[2, 5], [2, 0]])
    [[0.0, 0.5], [0.2, -0.2]]
    >>> inverse_of_matrix([[2.5, 5], [1, 2]])
    Traceback (most recent call last):
        ...
    ValueError: This matrix has no inverse.
    >>> inverse_of_matrix([[12, -16], [-9, 0]])
    [[0.0, -0.1111111111111111], [-0.0625, -0.08333333333333333]]
    >>> inverse_of_matrix([[12, 3], [16, 8]])
    [[0.16666666666666666, -0.0625], [-0.3333333333333333, 0.25]]
    >>> inverse_of_matrix([[10, 5], [3, 2.5]])
    [[0.25, -0.5], [-0.3, 1.0]]

    Doctests for 3x3
    >>> inverse_of_matrix([[2, 5, 7], [2, 0, 1], [1, 2, 3]])
    [[2.0, 5.0, -4.0], [1.0, 1.0, -1.0], [-5.0, -12.0, 10.0]]
    >>> inverse_of_matrix([[1, 2, 2], [1, 2, 2], [3, 2, -1]])
    Traceback (most recent call last):
        ...
    ValueError: This matrix has no inverse.

    >>> inverse_of_matrix([[],[]])
    Traceback (most recent call last):
        ...
    ValueError: Please provide a matrix of size 2x2 or 3x3.

    >>> inverse_of_matrix([[1, 2], [3, 4], [5, 6]])
    Traceback (most recent call last):
        ...
    ValueError: Please provide a matrix of size 2x2 or 3x3.

    >>> inverse_of_matrix([[1, 2, 1], [0,3, 4]])
    Traceback (most recent call last):
        ...
    ValueError: Please provide a matrix of size 2x2 or 3x3.

    >>> inverse_of_matrix([[1, 2, 3], [7, 8, 9], [7, 8, 9]])
    Traceback (most recent call last):
        ...
    ValueError: This matrix has no inverse.

    >>> inverse_of_matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
    [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]
    """

    d = Decimal

    # Check if the provided matrix has 2 rows and 2 columns
    # since this implementation only works for 2x2 matrices
    if len(matrix) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
        # Calculate the determinant of the matrix
        determinant = float(
            d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1])
        )
        if determinant == 0:
            raise ValueError("This matrix has no inverse.")

        # Creates a copy of the matrix with swapped positions of the elements
        swapped_matrix = [[0.0, 0.0], [0.0, 0.0]]
        swapped_matrix[0][0], swapped_matrix[1][1] = matrix[1][1], matrix[0][0]
        swapped_matrix[1][0], swapped_matrix[0][1] = -matrix[1][0], -matrix[0][1]

        # Calculate the inverse of the matrix
        return [
            [(float(d(n)) / determinant) or 0.0 for n in row] for row in swapped_matrix
        ]
    elif (
        len(matrix) == 3
        and len(matrix[0]) == 3
        and len(matrix[1]) == 3
        and len(matrix[2]) == 3
    ):
        # Calculate the determinant of the matrix using Sarrus rule
        determinant = float(
            (
                (d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
                + (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
                + (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
            )
            - (
                (d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
                + (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
                + (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
            )
        )
        if determinant == 0:
            raise ValueError("This matrix has no inverse.")

        # Creating cofactor matrix
        cofactor_matrix = [
            [d(0.0), d(0.0), d(0.0)],
            [d(0.0), d(0.0), d(0.0)],
            [d(0.0), d(0.0), d(0.0)],
        ]
        cofactor_matrix[0][0] = (d(matrix[1][1]) * d(matrix[2][2])) - (
            d(matrix[1][2]) * d(matrix[2][1])
        )
        cofactor_matrix[0][1] = -(
            (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
        )
        cofactor_matrix[0][2] = (d(matrix[1][0]) * d(matrix[2][1])) - (
            d(matrix[1][1]) * d(matrix[2][0])
        )
        cofactor_matrix[1][0] = -(
            (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
        )
        cofactor_matrix[1][1] = (d(matrix[0][0]) * d(matrix[2][2])) - (
            d(matrix[0][2]) * d(matrix[2][0])
        )
        cofactor_matrix[1][2] = -(
            (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
        )
        cofactor_matrix[2][0] = (d(matrix[0][1]) * d(matrix[1][2])) - (
            d(matrix[0][2]) * d(matrix[1][1])
        )
        cofactor_matrix[2][1] = -(
            (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
        )
        cofactor_matrix[2][2] = (d(matrix[0][0]) * d(matrix[1][1])) - (
            d(matrix[0][1]) * d(matrix[1][0])
        )

        # Transpose the cofactor matrix (Adjoint matrix)
        adjoint_matrix = array(cofactor_matrix)
        for i in range(3):
            for j in range(3):
                adjoint_matrix[i][j] = cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
        inverse_matrix = array(cofactor_matrix)
        for i in range(3):
            for j in range(3):
                inverse_matrix[i][j] /= d(determinant)

        # Calculate the inverse of the matrix
        return [[float(d(n)) or 0.0 for n in row] for row in inverse_matrix]
    raise ValueError("Please provide a matrix of size 2x2 or 3x3.")


================================================
FILE: matrix/largest_square_area_in_matrix.py
================================================
"""
Question:
Given a binary matrix mat of size n * m, find out the maximum size square
sub-matrix with all 1s.

---
Example 1:

Input:
n = 2, m = 2
mat = [[1, 1],
       [1, 1]]

Output:
2

Explanation: The maximum size of the square
sub-matrix is 2. The matrix itself is the
maximum sized sub-matrix in this case.
---
Example 2

Input:
n = 2, m = 2
mat = [[0, 0],
       [0, 0]]
Output: 0

Explanation: There is no 1 in the matrix.


Approach:
We initialize another matrix (dp) with the same dimensions
as the original one initialized with all 0's.

dp_array(i,j) represents the side length of the maximum square whose
bottom right corner is the cell with index (i,j) in the original matrix.

Starting from index (0,0), for every 1 found in the original matrix,
we update the value of the current element as

dp_array(i,j)=dp_array(dp(i-1,j),dp_array(i-1,j-1),dp_array(i,j-1)) + 1.
"""


def largest_square_area_in_matrix_top_down_approch(
    rows: int, cols: int, mat: list[list[int]]
) -> int:
    """
    Function updates the largest_square_area[0], if recursive call found
    square with maximum area.

    We aren't using dp_array here, so the time complexity would be exponential.

    >>> largest_square_area_in_matrix_top_down_approch(2, 2, [[1,1], [1,1]])
    2
    >>> largest_square_area_in_matrix_top_down_approch(2, 2, [[0,0], [0,0]])
    0
    """

    def update_area_of_max_square(row: int, col: int) -> int:
        # BASE CASE
        if row >= rows or col >= cols:
            return 0

        right = update_area_of_max_square(row, col + 1)
        diagonal = update_area_of_max_square(row + 1, col + 1)
        down = update_area_of_max_square(row + 1, col)

        if mat[row][col]:
            sub_problem_sol = 1 + min([right, diagonal, down])
            largest_square_area[0] = max(largest_square_area[0], sub_problem_sol)
            return sub_problem_sol
        else:
            return 0

    largest_square_area = [0]
    update_area_of_max_square(0, 0)
    return largest_square_area[0]


def largest_square_area_in_matrix_top_down_approch_with_dp(
    rows: int, cols: int, mat: list[list[int]]
) -> int:
    """
    Function updates the largest_square_area[0], if recursive call found
    square with maximum area.

    We are using dp_array here, so the time complexity would be O(N^2).

    >>> largest_square_area_in_matrix_top_down_approch_with_dp(2, 2, [[1,1], [1,1]])
    2
    >>> largest_square_area_in_matrix_top_down_approch_with_dp(2, 2, [[0,0], [0,0]])
    0
    """

    def update_area_of_max_square_using_dp_array(
        row: int, col: int, dp_array: list[list[int]]
    ) -> int:
        if row >= rows or col >= cols:
            return 0
        if dp_array[row][col] != -1:
            return dp_array[row][col]

        right = update_area_of_max_square_using_dp_array(row, col + 1, dp_array)
        diagonal = update_area_of_max_square_using_dp_array(row + 1, col + 1, dp_array)
        down = update_area_of_max_square_using_dp_array(row + 1, col, dp_array)

        if mat[row][col]:
            sub_problem_sol = 1 + min([right, diagonal, down])
            largest_square_area[0] = max(largest_square_area[0], sub_problem_sol)
            dp_array[row][col] = sub_problem_sol
            return sub_problem_sol
        else:
            return 0

    largest_square_area = [0]
    dp_array = [[-1] * cols for _ in range(rows)]
    update_area_of_max_square_using_dp_array(0, 0, dp_array)

    return largest_square_area[0]


def largest_square_area_in_matrix_bottom_up(
    rows: int, cols: int, mat: list[list[int]]
) -> int:
    """
    Function updates the largest_square_area, using bottom up approach.

    >>> largest_square_area_in_matrix_bottom_up(2, 2, [[1,1], [1,1]])
    2
    >>> largest_square_area_in_matrix_bottom_up(2, 2, [[0,0], [0,0]])
    0

    """
    dp_array = [[0] * (cols + 1) for _ in range(rows + 1)]
    largest_square_area = 0
    for row in range(rows - 1, -1, -1):
        for col in range(cols - 1, -1, -1):
            right = dp_array[row][col + 1]
            diagonal = dp_array[row + 1][col + 1]
            bottom = dp_array[row + 1][col]

            if mat[row][col] == 1:
                dp_array[row][col] = 1 + min(right, diagonal, bottom)
                largest_square_area = max(dp_array[row][col], largest_square_area)
            else:
                dp_array[row][col] = 0

    return largest_square_area


def largest_square_area_in_matrix_bottom_up_space_optimization(
    rows: int, cols: int, mat: list[list[int]]
) -> int:
    """
    Function updates the largest_square_area, using bottom up
    approach. with space optimization.

    >>> largest_square_area_in_matrix_bottom_up_space_optimization(2, 2, [[1,1], [1,1]])
    2
    >>> largest_square_area_in_matrix_bottom_up_space_optimization(2, 2, [[0,0], [0,0]])
    0
    """
    current_row = [0] * (cols + 1)
    next_row = [0] * (cols + 1)
    largest_square_area = 0
    for row in range(rows - 1, -1, -1):
        for col in range(cols - 1, -1, -1):
            right = current_row[col + 1]
            diagonal = next_row[col + 1]
            bottom = next_row[col]

            if mat[row][col] == 1:
                current_row[col] = 1 + min(right, diagonal, bottom)
                largest_square_area = max(current_row[col], largest_square_area)
            else:
                current_row[col] = 0
        next_row = current_row

    return largest_square_area


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))


================================================
FILE: matrix/matrix_based_game.py
================================================
"""
Matrix-Based Game Script
=========================
This script implements a matrix-based game where players interact with a grid of
elements. The primary goals are to:
- Identify connected elements of the same type from a selected position.
- Remove those elements, adjust the matrix by simulating gravity, and reorganize empty
  columns.
- Calculate and display the score based on the number of elements removed in each move.

Functions:
-----------
1. `find_repeat`: Finds all connected elements of the same type.
2. `increment_score`: Calculates the score for a given move.
3. `move_x`: Simulates gravity in a column.
4. `move_y`: Reorganizes the matrix by shifting columns leftward when a column becomes
    empty.
5. `play`: Executes a single move, updating the matrix and returning the score.

Input Format:
--------------
1. Matrix size (`lines`): Integer specifying the size of the matrix (N x N).
2. Matrix content (`matrix`): Rows of the matrix, each consisting of characters.
3. Number of moves (`movs`): Integer indicating the number of moves.
4. List of moves (`movements`): A comma-separated string of coordinates for each move.

(0,0) position starts from first left column to last right, and below row to up row


Example Input:
---------------
4
RRBG
RBBG
YYGG
XYGG
2
0 1,1 1

Example (0,0) = X

Output:
--------
The script outputs the total score after processing all moves.

Usage:
-------
Run the script and provide the required inputs as prompted.

"""


def validate_matrix_size(size: int) -> None:
    """
    >>> validate_matrix_size(-1)
    Traceback (most recent call last):
        ...
    ValueError: Matrix size must be a positive integer.
    """
    if not isinstance(size, int) or size <= 0:
        raise ValueError("Matrix size must be a positive integer.")


def validate_matrix_content(matrix: list[str], size: int) -> None:
    """
    Validates that the number of elements in the matrix matches the given size.

    >>> validate_matrix_content(['aaaa', 'aaaa', 'aaaa', 'aaaa'], 3)
    Traceback (most recent call last):
        ...
    ValueError: The matrix dont match with size.
    >>> validate_matrix_content(['aa%', 'aaa', 'aaa'], 3)
    Traceback (most recent call last):
        ...
    ValueError: Matrix rows can only contain letters and numbers.
    >>> validate_matrix_content(['aaa', 'aaa', 'aaaa'], 3)
    Traceback (most recent call last):
        ...
    ValueError: Each row in the matrix must have exactly 3 characters.
    """
    print(matrix)
    if len(matrix) != size:
        raise ValueError("The matrix dont match with size.")
    for row in matrix:
        if len(row) != size:
            msg = f"Each row in the matrix must have exactly {size} characters."
            raise ValueError(msg)
        if not all(char.isalnum() for char in row):
            raise ValueError("Matrix rows can only contain letters and numbers.")


def validate_moves(moves: list[tuple[int, int]], size: int) -> None:
    """
    >>> validate_moves([(1, 2), (-1, 0)], 3)
    Traceback (most recent call last):
        ...
    ValueError: Move is out of bounds for a matrix.
    """
    for move in moves:
        x, y = move
        if not (0 <= x < size and 0 <= y < size):
            raise ValueError("Move is out of bounds for a matrix.")


def parse_moves(input_str: str) -> list[tuple[int, int]]:
    """
    >>> parse_moves("0 1, 1 1")
    [(0, 1), (1, 1)]
    >>> parse_moves("0 1, 1 1, 2")
    Traceback (most recent call last):
        ...
    ValueError: Each move must have exactly two numbers.
    >>> parse_moves("0 1, 1 1, 2 4 5 6")
    Traceback (most recent call last):
        ...
    ValueError: Each move must have exactly two numbers.
    """
    moves = []
    for pair in input_str.split(","):
        parts = pair.strip().split()
        if len(parts) != 2:
            raise ValueError("Each move must have exactly two numbers.")
        x, y = map(int, parts)
        moves.append((x, y))
    return moves


def find_repeat(
    matrix_g: list[list[str]], row: int, column: int, size: int
) -> set[tuple[int, int]]:
    """
    Finds all connected elements of the same type from a given position.

    >>> find_repeat([['A', 'B', 'A'], ['A', 'B', 'A'], ['A', 'A', 'A']], 0, 0, 3)
    {(1, 2), (2, 1), (0, 0), (2, 0), (0, 2), (2, 2), (1, 0)}
    >>> find_repeat([['-', '-', '-'], ['-', '-', '-'], ['-', '-', '-']], 1, 1, 3)
    set()
    """

    column = size - 1 - column
    visited = set()
    repeated = set()

    if (color := matrix_g[column][row]) != "-":

        def dfs(row_n: int, column_n: int) -> None:
            if row_n < 0 or row_n >= size or column_n < 0 or column_n >= size:
                return
            if (row_n, column_n) in visited:
                return
            visited.add((row_n, column_n))
            if matrix_g[row_n][column_n] == color:
                repeated.add((row_n, column_n))
                dfs(row_n - 1, column_n)
                dfs(row_n + 1, column_n)
                dfs(row_n, column_n - 1)
                dfs(row_n, column_n + 1)

        dfs(column, row)

    return repeated


def increment_score(count: int) -> int:
    """
    Calculates the score for a move based on the number of elements removed.

    >>> increment_score(3)
    6
    >>> increment_score(0)
    0
    """
    return int(count * (count + 1) / 2)


def move_x(matrix_g: list[list[str]], column: int, size: int) -> list[list[str]]:
    """
    Simulates gravity in a specific column.

    >>> move_x([['-', 'A'], ['-', '-'], ['-', 'C']], 1, 2)
    [['-', '-'], ['-', 'A'], ['-', 'C']]
    """

    new_list = []

    for row in range(size):
        if matrix_g[row][column] != "-":
            new_list.append(matrix_g[row][column])
        else:
            new_list.insert(0, matrix_g[row][column])
    for row in range(size):
        matrix_g[row][column] = new_list[row]
    return matrix_g


def move_y(matrix_g: list[list[str]], size: int) -> list[list[str]]:
    """
    Shifts all columns leftward when an entire column becomes empty.

    >>> move_y([['-', 'A'], ['-', '-'], ['-', 'C']], 2)
    [['A', '-'], ['-', '-'], ['-', 'C']]
    """

    empty_columns = []

    for column in range(size - 1, -1, -1):
        if all(matrix_g[row][column] == "-" for row in range(size)):
            empty_columns.append(column)

    for column in empty_columns:
        for col in range(column + 1, size):
            for row in range(size):
                matrix_g[row][col - 1] = matrix_g[row][col]
        for row in range(size):
            matrix_g[row][-1] = "-"

    return matrix_g


def play(
    matrix_g: list[list[str]], pos_x: int, pos_y: int, size: int
) -> tuple[list[list[str]], int]:
    """
    Processes a single move, updating the matrix and calculating the score.

    >>> play([['R', 'G'], ['R', 'G']], 0, 0, 2)
    ([['G', '-'], ['G', '-']], 3)
    """

    same_colors = find_repeat(matrix_g, pos_x, pos_y, size)

    if len(same_colors) != 0:
        for pos in same_colors:
            matrix_g[pos[0]][pos[1]] = "-"
        for column in range(size):
            matrix_g = move_x(matrix_g, column, size)

        matrix_g = move_y(matrix_g, size)

    return (matrix_g, increment_score(len(same_colors)))


def process_game(size: int, matrix: list[str], moves: list[tuple[int, int]]) -> int:
    """Processes the game logic for the given matrix and moves.

    Args:
        size (int): Size of the game board.
        matrix (List[str]): Initial game matrix.
        moves (List[Tuple[int, int]]): List of moves as (x, y) coordinates.

    Returns:
        int: The total score obtained.
    >>> process_game(3, ['aaa', 'bbb', 'ccc'], [(0, 0)])
    6
    """

    game_matrix = [list(row) for row in matrix]
    total_score = 0

    for move in moves:
        pos_x, pos_y = move
        game_matrix, score = play(game_matrix, pos_x, pos_y, size)
        total_score += score

    return total_score


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    try:
        size = int(input("Enter the size of the matrix: "))
        validate_matrix_size(size)
        print(f"Enter the {size} rows of the matrix:")
        matrix = [input(f"Row {i + 1}: ") for i in range(size)]
        validate_matrix_content(matrix, size)
        moves_input = input("Enter the moves (e.g., '0 0, 1 1'): ")
        moves = parse_moves(moves_input)
        validate_moves(moves, size)
        score = process_game(size, matrix, moves)
        print(f"Total score: {score}")
    except ValueError as e:
        print(f"{e}")


================================================
FILE: matrix/matrix_class.py
================================================
# An OOP approach to representing and manipulating matrices

from __future__ import annotations


class Matrix:
    """
    Matrix object generated from a 2D array where each element is an array representing
    a row.
    Rows can contain type int or float.
    Common operations and information available.
    >>> rows = [
    ...     [1, 2, 3],
    ...     [4, 5, 6],
    ...     [7, 8, 9]
    ... ]
    >>> matrix = Matrix(rows)
    >>> print(matrix)
    [[1. 2. 3.]
     [4. 5. 6.]
     [7. 8. 9.]]

    Matrix rows and columns are available as 2D arrays
    >>> matrix.rows
    [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
    >>> matrix.columns()
    [[1, 4, 7], [2, 5, 8], [3, 6, 9]]

    Order is returned as a tuple
    >>> matrix.order
    (3, 3)

    Squareness and invertability are represented as bool
    >>> matrix.is_square
    True
    >>> matrix.is_invertable()
    False

    Identity, Minors, Cofactors and Adjugate are returned as Matrices.  Inverse can be
    a Matrix or Nonetype
    >>> print(matrix.identity())
    [[1. 0. 0.]
     [0. 1. 0.]
     [0. 0. 1.]]
    >>> print(matrix.minors())
    [[-3. -6. -3.]
     [-6. -12. -6.]
     [-3. -6. -3.]]
    >>> print(matrix.cofactors())
    [[-3. 6. -3.]
     [6. -12. 6.]
     [-3. 6. -3.]]
    >>>  # won't be apparent due to the nature of the cofactor matrix
    >>> print(matrix.adjugate())
    [[-3. 6. -3.]
     [6. -12. 6.]
     [-3. 6. -3.]]
    >>> matrix.inverse()
    Traceback (most recent call last):
        ...
    TypeError: Only matrices with a non-zero determinant have an inverse

    Determinant is an int, float, or Nonetype
    >>> matrix.determinant()
    0

    Negation, scalar multiplication, addition, subtraction, multiplication and
    exponentiation are available and all return a Matrix
    >>> print(-matrix)
    [[-1. -2. -3.]
     [-4. -5. -6.]
     [-7. -8. -9.]]
    >>> matrix2 = matrix * 3
    >>> print(matrix2)
    [[3. 6. 9.]
     [12. 15. 18.]
     [21. 24. 27.]]
    >>> print(matrix + matrix2)
    [[4. 8. 12.]
     [16. 20. 24.]
     [28. 32. 36.]]
    >>> print(matrix - matrix2)
    [[-2. -4. -6.]
     [-8. -10. -12.]
     [-14. -16. -18.]]
    >>> print(matrix ** 3)
    [[468. 576. 684.]
     [1062. 1305. 1548.]
     [1656. 2034. 2412.]]

    Matrices can also be modified
    >>> matrix.add_row([10, 11, 12])
    >>> print(matrix)
    [[1. 2. 3.]
     [4. 5. 6.]
     [7. 8. 9.]
     [10. 11. 12.]]
    >>> matrix2.add_column([8, 16, 32])
    >>> print(matrix2)
    [[3. 6. 9. 8.]
     [12. 15. 18. 16.]
     [21. 24. 27. 32.]]
    >>> print(matrix *  matrix2)
    [[90. 108. 126. 136.]
     [198. 243. 288. 304.]
     [306. 378. 450. 472.]
     [414. 513. 612. 640.]]
    """

    def __init__(self, rows: list[list[int]]):
        error = TypeError(
            "Matrices must be formed from a list of zero or more lists containing at "
            "least one and the same number of values, each of which must be of type "
            "int or float."
        )
        if len(rows) != 0:
            cols = len(rows[0])
            if cols == 0:
                raise error
            for row in rows:
                if len(row) != cols:
                    raise error
                for value in row:
                    if not isinstance(value, (int, float)):
                        raise error
            self.rows = rows
        else:
            self.rows = []

    # MATRIX INFORMATION
    def columns(self) -> list[list[int]]:
        return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))]

    @property
    def num_rows(self) -> int:
        return len(self.rows)

    @property
    def num_columns(self) -> int:
        return len(self.rows[0])

    @property
    def order(self) -> tuple[int, int]:
        return self.num_rows, self.num_columns

    @property
    def is_square(self) -> bool:
        return self.order[0] == self.order[1]

    def identity(self) -> Matrix:
        values = [
            [0 if column_num != row_num else 1 for column_num in range(self.num_rows)]
            for row_num in range(self.num_rows)
        ]
        return Matrix(values)

    def determinant(self) -> int:
        if not self.is_square:
            return 0
        if self.order == (0, 0):
            return 1
        if self.order == (1, 1):
            return int(self.rows[0][0])
        if self.order == (2, 2):
            return int(
                (self.rows[0][0] * self.rows[1][1])
                - (self.rows[0][1] * self.rows[1][0])
            )
        else:
            return sum(
                self.rows[0][column] * self.cofactors().rows[0][column]
                for column in range(self.num_columns)
            )

    def is_invertable(self) -> bool:
        return bool(self.determinant())

    def get_minor(self, row: int, column: int) -> int:
        values = [
            [
                self.rows[other_row][other_column]
                for other_column in range(self.num_columns)
                if other_column != column
            ]
            for other_row in range(self.num_rows)
            if other_row != row
        ]
        return Matrix(values).determinant()

    def get_cofactor(self, row: int, column: int) -> int:
        if (row + column) % 2 == 0:
            return self.get_minor(row, column)
        return -1 * self.get_minor(row, column)

    def minors(self) -> Matrix:
        return Matrix(
            [
                [self.get_minor(row, column) for column in range(self.num_columns)]
                for row in range(self.num_rows)
            ]
        )

    def cofactors(self) -> Matrix:
        return Matrix(
            [
                [
                    self.minors().rows[row][column]
                    if (row + column) % 2 == 0
                    else self.minors().rows[row][column] * -1
                    for column in range(self.minors().num_columns)
                ]
                for row in range(self.minors().num_rows)
            ]
        )

    def adjugate(self) -> Matrix:
        values = [
            [self.cofactors().rows[column][row] for column in range(self.num_columns)]
            for row in range(self.num_rows)
        ]
        return Matrix(values)

    def inverse(self) -> Matrix:
        determinant = self.determinant()
        if not determinant:
            raise TypeError("Only matrices with a non-zero determinant have an inverse")
        return self.adjugate() * (1 / determinant)

    def __repr__(self) -> str:
        return str(self.rows)

    def __str__(self) -> str:
        if self.num_rows == 0:
            return "[]"
        if self.num_rows == 1:
            return "[[" + ". ".join(str(self.rows[0])) + "]]"
        return (
            "["
            + "\n ".join(
                [
                    "[" + ". ".join([str(value) for value in row]) + ".]"
                    for row in self.rows
                ]
            )
            + "]"
        )

    # MATRIX MANIPULATION
    def add_row(self, row: list[int], position: int | None = None) -> None:
        type_error = TypeError("Row must be a list containing all ints and/or floats")
        if not isinstance(row, list):
            raise type_error
        for value in row:
            if not isinstance(value, (int, float)):
                raise type_error
        if len(row) != self.num_columns:
            raise ValueError(
                "Row must be equal in length to the other rows in the matrix"
            )
        if position is None:
            self.rows.append(row)
        else:
            self.rows = [*self.rows[0:position], row, *self.rows[position:]]

    def add_column(self, column: list[int], position: int | None = None) -> None:
        type_error = TypeError(
            "Column must be a list containing all ints and/or floats"
        )
        if not isinstance(column, list):
            raise type_error
        for value in column:
            if not isinstance(value, (int, float)):
                raise type_error
        if len(column) != self.num_rows:
            raise ValueError(
                "Column must be equal in length to the other columns in the matrix"
            )
        if position is None:
            self.rows = [self.rows[i] + [column[i]] for i in range(self.num_rows)]
        else:
            self.rows = [
                [*self.rows[i][0:position], column[i], *self.rows[i][position:]]
                for i in range(self.num_rows)
            ]

    # MATRIX OPERATIONS
    def __eq__(self, other: object) -> bool:
        if not isinstance(other, Matrix):
            return NotImplemented
        return self.rows == other.rows

    def __ne__(self, other: object) -> bool:
        return not self == other

    def __neg__(self) -> Matrix:
        return self * -1

    def __add__(self, other: Matrix) -> Matrix:
        if self.order != other.order:
            raise ValueError("Addition requires matrices of the same order")
        return Matrix(
            [
                [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)]
                for i in range(self.num_rows)
            ]
        )

    def __sub__(self, other: Matrix) -> Matrix:
        if self.order != other.order:
            raise ValueError("Subtraction requires matrices of the same order")
        return Matrix(
            [
                [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)]
                for i in range(self.num_rows)
            ]
        )

    def __mul__(self, other: Matrix | float) -> Matrix:
        if isinstance(other, (int, float)):
            return Matrix(
                [[int(element * other) for element in row] for row in self.rows]
            )
        elif isinstance(other, Matrix):
            if self.num_columns != other.num_rows:
                raise ValueError(
                    "The number of columns in the first matrix must "
                    "be equal to the number of rows in the second"
                )
            return Matrix(
                [
                    [Matrix.dot_product(row, column) for column in other.columns()]
                    for row in self.rows
                ]
            )
        else:
            raise TypeError(
                "A Matrix can only be multiplied by an int, float, or another matrix"
            )

    def __pow__(self, other: int) -> Matrix:
        if not isinstance(other, int):
            raise TypeError("A Matrix can only be raised to the power of an int")
        if not self.is_square:
            raise ValueError("Only square matrices can be raised to a power")
        if other == 0:
            return self.identity()
        if other < 0:
            if self.is_invertable():
                return self.inverse() ** (-other)
            raise ValueError(
                "Only invertable matrices can be raised to a negative power"
            )
        result = self
        for _ in range(other - 1):
            result *= self
        return result

    @classmethod
    def dot_product(cls, row: list[int], column: list[int]) -> int:
        return sum(row[i] * column[i] for i in range(len(row)))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: matrix/matrix_equalization.py
================================================
from sys import maxsize


def array_equalization(vector: list[int], step_size: int) -> int:
    """
    This algorithm equalizes all elements of the input vector
    to a common value, by making the minimal number of
    "updates" under the constraint of a step size (step_size).

    >>> array_equalization([1, 1, 6, 2, 4, 6, 5, 1, 7, 2, 2, 1, 7, 2, 2], 4)
    4
    >>> array_equalization([22, 81, 88, 71, 22, 81, 632, 81, 81, 22, 92], 2)
    5
    >>> array_equalization([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 5)
    0
    >>> array_equalization([22, 22, 22, 33, 33, 33], 2)
    2
    >>> array_equalization([1, 2, 3], 0)
    Traceback (most recent call last):
    ValueError: Step size must be positive and non-zero.
    >>> array_equalization([1, 2, 3], -1)
    Traceback (most recent call last):
    ValueError: Step size must be positive and non-zero.
    >>> array_equalization([1, 2, 3], 0.5)
    Traceback (most recent call last):
    ValueError: Step size must be an integer.
    >>> array_equalization([1, 2, 3], maxsize)
    1
    """
    if step_size <= 0:
        raise ValueError("Step size must be positive and non-zero.")
    if not isinstance(step_size, int):
        raise ValueError("Step size must be an integer.")

    unique_elements = set(vector)
    min_updates = maxsize

    for element in unique_elements:
        elem_index = 0
        updates = 0
        while elem_index < len(vector):
            if vector[elem_index] != element:
                updates += 1
                elem_index += step_size
            else:
                elem_index += 1
        min_updates = min(min_updates, updates)

    return min_updates


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: matrix/matrix_multiplication_recursion.py
================================================
# @Author  : ojas-wani
# @File    : matrix_multiplication_recursion.py
# @Date    : 10/06/2023


"""
Perform matrix multiplication using a recursive algorithm.
https://en.wikipedia.org/wiki/Matrix_multiplication
"""

# type Matrix = list[list[int]]  # psf/black currenttly fails on this line
Matrix = list[list[int]]

matrix_1_to_4 = [
    [1, 2],
    [3, 4],
]

matrix_5_to_8 = [
    [5, 6],
    [7, 8],
]

matrix_5_to_9_high = [
    [5, 6],
    [7, 8],
    [9],
]

matrix_5_to_9_wide = [
    [5, 6],
    [7, 8, 9],
]

matrix_count_up = [
    [1, 2, 3, 4],
    [5, 6, 7, 8],
    [9, 10, 11, 12],
    [13, 14, 15, 16],
]

matrix_unordered = [
    [5, 8, 1, 2],
    [6, 7, 3, 0],
    [4, 5, 9, 1],
    [2, 6, 10, 14],
]
matrices = (
    matrix_1_to_4,
    matrix_5_to_8,
    matrix_5_to_9_high,
    matrix_5_to_9_wide,
    matrix_count_up,
    matrix_unordered,
)


def is_square(matrix: Matrix) -> bool:
    """
    >>> is_square([])
    True
    >>> is_square(matrix_1_to_4)
    True
    >>> is_square(matrix_5_to_9_high)
    False
    """
    len_matrix = len(matrix)
    return all(len(row) == len_matrix for row in matrix)


def matrix_multiply(matrix_a: Matrix, matrix_b: Matrix) -> Matrix:
    """
    >>> matrix_multiply(matrix_1_to_4, matrix_5_to_8)
    [[19, 22], [43, 50]]
    """
    return [
        [sum(a * b for a, b in zip(row, col)) for col in zip(*matrix_b)]
        for row in matrix_a
    ]


def matrix_multiply_recursive(matrix_a: Matrix, matrix_b: Matrix) -> Matrix:
    """
    :param matrix_a: A square Matrix.
    :param matrix_b: Another square Matrix with the same dimensions as matrix_a.
    :return: Result of matrix_a * matrix_b.
    :raises ValueError: If the matrices cannot be multiplied.

    >>> matrix_multiply_recursive([], [])
    []
    >>> matrix_multiply_recursive(matrix_1_to_4, matrix_5_to_8)
    [[19, 22], [43, 50]]
    >>> matrix_multiply_recursive(matrix_count_up, matrix_unordered)
    [[37, 61, 74, 61], [105, 165, 166, 129], [173, 269, 258, 197], [241, 373, 350, 265]]
    >>> matrix_multiply_recursive(matrix_1_to_4, matrix_5_to_9_wide)
    Traceback (most recent call last):
        ...
    ValueError: Invalid matrix dimensions
    >>> matrix_multiply_recursive(matrix_1_to_4, matrix_5_to_9_high)
    Traceback (most recent call last):
        ...
    ValueError: Invalid matrix dimensions
    >>> matrix_multiply_recursive(matrix_1_to_4, matrix_count_up)
    Traceback (most recent call last):
        ...
    ValueError: Invalid matrix dimensions
    """
    if not matrix_a or not matrix_b:
        return []
    if not all(
        (len(matrix_a) == len(matrix_b), is_square(matrix_a), is_square(matrix_b))
    ):
        raise ValueError("Invalid matrix dimensions")

    # Initialize the result matrix with zeros
    result = [[0] * len(matrix_b[0]) for _ in range(len(matrix_a))]

    # Recursive multiplication of matrices
    def multiply(
        i_loop: int,
        j_loop: int,
        k_loop: int,
        matrix_a: Matrix,
        matrix_b: Matrix,
        result: Matrix,
    ) -> None:
        """
        :param matrix_a: A square Matrix.
        :param matrix_b: Another square Matrix with the same dimensions as matrix_a.
        :param result: Result matrix
        :param i: Index used for iteration during multiplication.
        :param j: Index used for iteration during multiplication.
        :param k: Index used for iteration during multiplication.
        >>> 0 > 1  # Doctests in inner functions are never run
        True
        """
        if i_loop >= len(matrix_a):
            return
        if j_loop >= len(matrix_b[0]):
            return multiply(i_loop + 1, 0, 0, matrix_a, matrix_b, result)
        if k_loop >= len(matrix_b):
            return multiply(i_loop, j_loop + 1, 0, matrix_a, matrix_b, result)
        result[i_loop][j_loop] += matrix_a[i_loop][k_loop] * matrix_b[k_loop][j_loop]
        return multiply(i_loop, j_loop, k_loop + 1, matrix_a, matrix_b, result)

    # Perform the recursive matrix multiplication
    multiply(0, 0, 0, matrix_a, matrix_b, result)
    return result


if __name__ == "__main__":
    from doctest import testmod

    failure_count, test_count = testmod()
    if not failure_count:
        matrix_a = matrices[0]
        for matrix_b in matrices[1:]:
            print("Multiplying:")
            for row in matrix_a:
                print(row)
            print("By:")
            for row in matrix_b:
                print(row)
            print("Result:")
            try:
                result = matrix_multiply_recursive(matrix_a, matrix_b)
                for row in result:
                    print(row)
                assert result == matrix_multiply(matrix_a, matrix_b)
            except ValueError as e:
                print(f"{e!r}")
            print()
            matrix_a = matrix_b

    print("Benchmark:")
    from functools import partial
    from timeit import timeit

    mytimeit = partial(timeit, globals=globals(), number=100_000)
    for func in ("matrix_multiply", "matrix_multiply_recursive"):
        print(f"{func:>25}(): {mytimeit(f'{func}(matrix_count_up, matrix_unordered)')}")


================================================
FILE: matrix/matrix_operation.py
================================================
"""
Functions for 2D matrix operations
"""

from __future__ import annotations

from typing import Any


def add(*matrix_s: list[list[int]]) -> list[list[int]]:
    """
    >>> add([[1,2],[3,4]],[[2,3],[4,5]])
    [[3, 5], [7, 9]]
    >>> add([[1.2,2.4],[3,4]],[[2,3],[4,5]])
    [[3.2, 5.4], [7, 9]]
    >>> add([[1, 2], [4, 5]], [[3, 7], [3, 4]], [[3, 5], [5, 7]])
    [[7, 14], [12, 16]]
    >>> add([3], [4, 5])
    Traceback (most recent call last):
      ...
    TypeError: Expected a matrix, got int/list instead
    """
    if all(_check_not_integer(m) for m in matrix_s):
        for i in matrix_s[1:]:
            _verify_matrix_sizes(matrix_s[0], i)
        return [[sum(t) for t in zip(*m)] for m in zip(*matrix_s)]
    raise TypeError("Expected a matrix, got int/list instead")


def subtract(matrix_a: list[list[int]], matrix_b: list[list[int]]) -> list[list[int]]:
    """
    >>> subtract([[1,2],[3,4]],[[2,3],[4,5]])
    [[-1, -1], [-1, -1]]
    >>> subtract([[1,2.5],[3,4]],[[2,3],[4,5.5]])
    [[-1, -0.5], [-1, -1.5]]
    >>> subtract([3], [4, 5])
    Traceback (most recent call last):
      ...
    TypeError: Expected a matrix, got int/list instead
    """
    if (
        _check_not_integer(matrix_a)
        and _check_not_integer(matrix_b)
        and _verify_matrix_sizes(matrix_a, matrix_b)
    ):
        return [[i - j for i, j in zip(*m)] for m in zip(matrix_a, matrix_b)]
    raise TypeError("Expected a matrix, got int/list instead")


def scalar_multiply(matrix: list[list[int]], n: float) -> list[list[float]]:
    """
    >>> scalar_multiply([[1,2],[3,4]],5)
    [[5, 10], [15, 20]]
    >>> scalar_multiply([[1.4,2.3],[3,4]],5)
    [[7.0, 11.5], [15, 20]]
    """
    return [[x * n for x in row] for row in matrix]


def multiply(matrix_a: list[list[int]], matrix_b: list[list[int]]) -> list[list[int]]:
    """
    >>> multiply([[1,2],[3,4]],[[5,5],[7,5]])
    [[19, 15], [43, 35]]
    >>> multiply([[1,2.5],[3,4.5]],[[5,5],[7,5]])
    [[22.5, 17.5], [46.5, 37.5]]
    >>> multiply([[1, 2, 3]], [[2], [3], [4]])
    [[20]]
    """
    if _check_not_integer(matrix_a) and _check_not_integer(matrix_b):
        rows, cols = _verify_matrix_sizes(matrix_a, matrix_b)

    if cols[0] != rows[1]:
        msg = (
            "Cannot multiply matrix of dimensions "
            f"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
        )
        raise ValueError(msg)
    return [
        [sum(m * n for m, n in zip(i, j)) for j in zip(*matrix_b)] for i in matrix_a
    ]


def identity(n: int) -> list[list[int]]:
    """
    :param n: dimension for nxn matrix
    :type n: int
    :return: Identity matrix of shape [n, n]
    >>> identity(3)
    [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
    """
    n = int(n)
    return [[int(row == column) for column in range(n)] for row in range(n)]


def transpose(
    matrix: list[list[int]], return_map: bool = True
) -> list[list[int]] | map[list[int]]:
    """
    >>> transpose([[1,2],[3,4]]) # doctest: +ELLIPSIS
    <map object at ...
    >>> transpose([[1,2],[3,4]], return_map=False)
    [[1, 3], [2, 4]]
    >>> transpose([1, [2, 3]])
    Traceback (most recent call last):
      ...
    TypeError: Expected a matrix, got int/list instead
    """
    if _check_not_integer(matrix):
        if return_map:
            return map(list, zip(*matrix))
        else:
            return list(map(list, zip(*matrix)))
    raise TypeError("Expected a matrix, got int/list instead")


def minor(matrix: list[list[int]], row: int, column: int) -> list[list[int]]:
    """
    >>> minor([[1, 2], [3, 4]], 1, 1)
    [[1]]
    """
    minor = matrix[:row] + matrix[row + 1 :]
    return [row[:column] + row[column + 1 :] for row in minor]


def determinant(matrix: list[list[int]]) -> Any:
    """
    >>> determinant([[1, 2], [3, 4]])
    -2
    >>> determinant([[1.5, 2.5], [3, 4]])
    -1.5
    """
    if len(matrix) == 1:
        return matrix[0][0]

    return sum(
        x * determinant(minor(matrix, 0, i)) * (-1) ** i
        for i, x in enumerate(matrix[0])
    )


def inverse(matrix: list[list[int]]) -> list[list[float]] | None:
    """
    >>> inverse([[1, 2], [3, 4]])
    [[-2.0, 1.0], [1.5, -0.5]]
    >>> inverse([[1, 1], [1, 1]])
    """
    # https://stackoverflow.com/questions/20047519/python-doctests-test-for-none
    det = determinant(matrix)
    if det == 0:
        return None

    matrix_minor = [
        [determinant(minor(matrix, i, j)) for j in range(len(matrix))]
        for i in range(len(matrix))
    ]

    cofactors = [
        [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row])]
        for row in range(len(matrix))
    ]
    adjugate = list(transpose(cofactors))
    return scalar_multiply(adjugate, 1 / det)


def _check_not_integer(matrix: list[list[int]]) -> bool:
    return not isinstance(matrix, int) and not isinstance(matrix[0], int)


def _shape(matrix: list[list[int]]) -> tuple[int, int]:
    return len(matrix), len(matrix[0])


def _verify_matrix_sizes(
    matrix_a: list[list[int]], matrix_b: list[list[int]]
) -> tuple[tuple[int, int], tuple[int, int]]:
    shape = _shape(matrix_a) + _shape(matrix_b)
    if shape[0] != shape[3] or shape[1] != shape[2]:
        msg = (
            "operands could not be broadcast together with shape "
            f"({shape[0], shape[1]}), ({shape[2], shape[3]})"
        )
        raise ValueError(msg)
    return (shape[0], shape[2]), (shape[1], shape[3])


def main() -> None:
    matrix_a = [[12, 10], [3, 9]]
    matrix_b = [[3, 4], [7, 4]]
    matrix_c = [[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
    matrix_d = [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
    print(f"Add Operation, {add(matrix_a, matrix_b) = } \n")
    print(f"Multiply Operation, {multiply(matrix_a, matrix_b) = } \n")
    print(f"Identity: {identity(5)}\n")
    print(f"Minor of {matrix_c} = {minor(matrix_c, 1, 2)} \n")
    print(f"Determinant of {matrix_b} = {determinant(matrix_b)} \n")
    print(f"Inverse of {matrix_d} = {inverse(matrix_d)}\n")


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: matrix/max_area_of_island.py
================================================
"""
Given an two dimensional binary matrix grid. An island is a group of 1's (representing
land) connected 4-directionally (horizontal or vertical.) You may assume all four edges
of the grid are surrounded by water.  The area of an island is the number of cells with
a value 1 in the island. Return the maximum area of an island in a grid. If there is no
island, return 0.
"""

matrix = [
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]


def is_safe(row: int, col: int, rows: int, cols: int) -> bool:
    """
    Checking whether coordinate (row, col) is valid or not.

    >>> is_safe(0, 0, 5, 5)
    True
    >>> is_safe(-1,-1, 5, 5)
    False
    """
    return 0 <= row < rows and 0 <= col < cols


def depth_first_search(row: int, col: int, seen: set, mat: list[list[int]]) -> int:
    """
    Returns the current area of the island

    >>> depth_first_search(0, 0, set(), matrix)
    0
    """
    rows = len(mat)
    cols = len(mat[0])
    if is_safe(row, col, rows, cols) and (row, col) not in seen and mat[row][col] == 1:
        seen.add((row, col))
        return (
            1
            + depth_first_search(row + 1, col, seen, mat)
            + depth_first_search(row - 1, col, seen, mat)
            + depth_first_search(row, col + 1, seen, mat)
            + depth_first_search(row, col - 1, seen, mat)
        )
    else:
        return 0


def find_max_area(mat: list[list[int]]) -> int:
    """
    Finds the area of all islands and returns the maximum area.

    >>> find_max_area(matrix)
    6
    """
    seen: set = set()

    max_area = 0
    for row, line in enumerate(mat):
        for col, item in enumerate(line):
            if item == 1 and (row, col) not in seen:
                # Maximizing the area
                max_area = max(max_area, depth_first_search(row, col, seen, mat))
    return max_area


if __name__ == "__main__":
    import doctest

    print(find_max_area(matrix))  # Output -> 6

    """
    Explanation:
    We are allowed to move in four directions (horizontal or vertical) so the possible
    in a matrix if we are at x and y position the possible moving are

    Directions are [(x, y+1), (x, y-1), (x+1, y), (x-1, y)] but we need to take care of
    boundary cases as well which are x and y can not be smaller than 0 and greater than
    the number of rows and columns respectively.

    Visualization
    mat = [
        [0,0,A,0,0,0,0,B,0,0,0,0,0],
        [0,0,0,0,0,0,0,B,B,B,0,0,0],
        [0,C,C,0,D,0,0,0,0,0,0,0,0],
        [0,C,0,0,D,D,0,0,E,0,E,0,0],
        [0,C,0,0,D,D,0,0,E,E,E,0,0],
        [0,0,0,0,0,0,0,0,0,0,E,0,0],
        [0,0,0,0,0,0,0,F,F,F,0,0,0],
        [0,0,0,0,0,0,0,F,F,0,0,0,0]
    ]

    For visualization, I have defined the connected island with letters
    by observation, we can see that
        A island is of area 1
        B island is of area 4
        C island is of area 4
        D island is of area 5
        E island is of area 6 and
        F island is of area 5

    it has 6 unique islands of mentioned areas
    and the maximum of all of them is 6 so we return 6.
    """

    doctest.testmod()


================================================
FILE: matrix/median_matrix.py
================================================
"""
https://en.wikipedia.org/wiki/Median
"""


def median(matrix: list[list[int]]) -> int:
    """
    Calculate the median of a sorted matrix.

    Args:
        matrix: A 2D matrix of integers.

    Returns:
        The median value of the matrix.

    Examples:
        >>> matrix = [[1, 3, 5], [2, 6, 9], [3, 6, 9]]
        >>> median(matrix)
        5

        >>> matrix = [[1, 2, 3], [4, 5, 6]]
        >>> median(matrix)
        3
    """
    # Flatten the matrix into a sorted 1D list
    linear = sorted(num for row in matrix for num in row)

    # Calculate the middle index
    mid = (len(linear) - 1) // 2

    # Return the median
    return linear[mid]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: matrix/nth_fibonacci_using_matrix_exponentiation.py
================================================
"""
Implementation of finding nth fibonacci number using matrix exponentiation.
Time Complexity is about O(log(n)*8), where 8 is the complexity of matrix
multiplication of size 2 by 2.
And on the other hand complexity of bruteforce solution is O(n).
As we know
    f[n] = f[n-1] + f[n-1]
Converting to matrix,
    [f(n),f(n-1)] = [[1,1],[1,0]] * [f(n-1),f(n-2)]
->  [f(n),f(n-1)] = [[1,1],[1,0]]^2 * [f(n-2),f(n-3)]
    ...
    ...
->  [f(n),f(n-1)] = [[1,1],[1,0]]^(n-1) * [f(1),f(0)]
So we just need the n times multiplication of the matrix [1,1],[1,0]].
We can decrease the n times multiplication by following the divide and conquer approach.
"""


def multiply(matrix_a: list[list[int]], matrix_b: list[list[int]]) -> list[list[int]]:
    matrix_c = []
    n = len(matrix_a)
    for i in range(n):
        list_1 = []
        for j in range(n):
            val = 0
            for k in range(n):
                val = val + matrix_a[i][k] * matrix_b[k][j]
            list_1.append(val)
        matrix_c.append(list_1)
    return matrix_c


def identity(n: int) -> list[list[int]]:
    return [[int(row == column) for column in range(n)] for row in range(n)]


def nth_fibonacci_matrix(n: int) -> int:
    """
    >>> nth_fibonacci_matrix(100)
    354224848179261915075
    >>> nth_fibonacci_matrix(-100)
    -100
    """
    if n <= 1:
        return n
    res_matrix = identity(2)
    fibonacci_matrix = [[1, 1], [1, 0]]
    n = n - 1
    while n > 0:
        if n % 2 == 1:
            res_matrix = multiply(res_matrix, fibonacci_matrix)
        fibonacci_matrix = multiply(fibonacci_matrix, fibonacci_matrix)
        n = int(n / 2)
    return res_matrix[0][0]


def nth_fibonacci_bruteforce(n: int) -> int:
    """
    >>> nth_fibonacci_bruteforce(100)
    354224848179261915075
    >>> nth_fibonacci_bruteforce(-100)
    -100
    """
    if n <= 1:
        return n
    fib0 = 0
    fib1 = 1
    for _ in range(2, n + 1):
        fib0, fib1 = fib1, fib0 + fib1
    return fib1


def main() -> None:
    for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
        n = int("".join(c for c in ordinal if c in "0123456789"))  # 1000th --> 1000
        print(
            f"{ordinal} fibonacci number using matrix exponentiation is "
            f"{nth_fibonacci_matrix(n)} and using bruteforce is "
            f"{nth_fibonacci_bruteforce(n)}\n"
        )
    # from timeit import timeit
    # print(timeit("nth_fibonacci_matrix(1000000)",
    #              "from main import nth_fibonacci_matrix", number=5))
    # print(timeit("nth_fibonacci_bruteforce(1000000)",
    #              "from main import nth_fibonacci_bruteforce", number=5))
    # 2.3342058970001744
    # 57.256506615000035


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: matrix/pascal_triangle.py
================================================
"""
This implementation demonstrates how to generate the elements of a Pascal's triangle.
The element havingva row index of r and column index of c can be derivedvas follows:
triangle[r][c] = triangle[r-1][c-1]+triangle[r-1][c]

A Pascal's triangle is a triangular array containing binomial coefficients.
https://en.wikipedia.org/wiki/Pascal%27s_triangle
"""


def print_pascal_triangle(num_rows: int) -> None:
    """
    Print Pascal's triangle for different number of rows
    >>> print_pascal_triangle(5)
        1
       1 1
      1 2 1
     1 3 3 1
    1 4 6 4 1
    """
    triangle = generate_pascal_triangle(num_rows)
    for row_idx in range(num_rows):
        # Print left spaces
        for _ in range(num_rows - row_idx - 1):
            print(end=" ")
        # Print row values
        for col_idx in range(row_idx + 1):
            if col_idx != row_idx:
                print(triangle[row_idx][col_idx], end=" ")
            else:
                print(triangle[row_idx][col_idx], end="")
        print()


def generate_pascal_triangle(num_rows: int) -> list[list[int]]:
    """
    Create Pascal's triangle for different number of rows
    >>> generate_pascal_triangle(0)
    []
    >>> generate_pascal_triangle(1)
    [[1]]
    >>> generate_pascal_triangle(2)
    [[1], [1, 1]]
    >>> generate_pascal_triangle(3)
    [[1], [1, 1], [1, 2, 1]]
    >>> generate_pascal_triangle(4)
    [[1], [1, 1], [1, 2, 1], [1, 3, 3, 1]]
    >>> generate_pascal_triangle(5)
    [[1], [1, 1], [1, 2, 1], [1, 3, 3, 1], [1, 4, 6, 4, 1]]
    >>> generate_pascal_triangle(-5)
    Traceback (most recent call last):
        ...
    ValueError: The input value of 'num_rows' should be greater than or equal to 0
    >>> generate_pascal_triangle(7.89)
    Traceback (most recent call last):
        ...
    TypeError: The input value of 'num_rows' should be 'int'
    """

    if not isinstance(num_rows, int):
        raise TypeError("The input value of 'num_rows' should be 'int'")

    if num_rows == 0:
        return []
    elif num_rows < 0:
        raise ValueError(
            "The input value of 'num_rows' should be greater than or equal to 0"
        )

    triangle: list[list[int]] = []
    for current_row_idx in range(num_rows):
        current_row = populate_current_row(triangle, current_row_idx)
        triangle.append(current_row)
    return triangle


def populate_current_row(triangle: list[list[int]], current_row_idx: int) -> list[int]:
    """
    >>> triangle = [[1]]
    >>> populate_current_row(triangle, 1)
    [1, 1]
    """
    current_row = [-1] * (current_row_idx + 1)
    # first and last elements of current row are equal to 1
    current_row[0], current_row[-1] = 1, 1
    for current_col_idx in range(1, current_row_idx):
        calculate_current_element(
            triangle, current_row, current_row_idx, current_col_idx
        )
    return current_row


def calculate_current_element(
    triangle: list[list[int]],
    current_row: list[int],
    current_row_idx: int,
    current_col_idx: int,
) -> None:
    """
    >>> triangle = [[1], [1, 1]]
    >>> current_row = [1, -1, 1]
    >>> calculate_current_element(triangle, current_row, 2, 1)
    >>> current_row
    [1, 2, 1]
    """
    above_to_left_elt = triangle[current_row_idx - 1][current_col_idx - 1]
    above_to_right_elt = triangle[current_row_idx - 1][current_col_idx]
    current_row[current_col_idx] = above_to_left_elt + above_to_right_elt


def generate_pascal_triangle_optimized(num_rows: int) -> list[list[int]]:
    """
    This function returns a matrix representing the corresponding pascal's triangle
    according to the given input of number of rows of Pascal's triangle to be generated.
    It reduces the operations done to generate a row by half
    by eliminating redundant calculations.

    :param num_rows: Integer specifying the number of rows in the Pascal's triangle
    :return: 2-D List (matrix) representing the Pascal's triangle

    Return the Pascal's triangle of given rows
    >>> generate_pascal_triangle_optimized(3)
    [[1], [1, 1], [1, 2, 1]]
    >>> generate_pascal_triangle_optimized(1)
    [[1]]
    >>> generate_pascal_triangle_optimized(0)
    []
    >>> generate_pascal_triangle_optimized(-5)
    Traceback (most recent call last):
        ...
    ValueError: The input value of 'num_rows' should be greater than or equal to 0
    >>> generate_pascal_triangle_optimized(7.89)
    Traceback (most recent call last):
        ...
    TypeError: The input value of 'num_rows' should be 'int'
    """

    if not isinstance(num_rows, int):
        raise TypeError("The input value of 'num_rows' should be 'int'")

    if num_rows == 0:
        return []
    elif num_rows < 0:
        raise ValueError(
            "The input value of 'num_rows' should be greater than or equal to 0"
        )

    result: list[list[int]] = [[1]]

    for row_index in range(1, num_rows):
        temp_row = [0] + result[-1] + [0]
        row_length = row_index + 1
        # Calculate the number of distinct elements in a row
        distinct_elements = sum(divmod(row_length, 2))
        row_first_half = [
            temp_row[i - 1] + temp_row[i] for i in range(1, distinct_elements + 1)
        ]
        row_second_half = row_first_half[: (row_index + 1) // 2]
        row_second_half.reverse()
        row = row_first_half + row_second_half
        result.append(row)

    return result


def benchmark() -> None:
    """
    Benchmark multiple functions, with three different length int values.
    """
    from collections.abc import Callable
    from timeit import timeit

    def benchmark_a_function(func: Callable, value: int) -> None:
        call = f"{func.__name__}({value})"
        timing = timeit(f"__main__.{call}", setup="import __main__")
        # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
        print(f"{call:38} -- {timing:.4f} seconds")

    for value in range(15):  # (1, 7, 14):
        for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
            benchmark_a_function(func, value)
        print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()


================================================
FILE: matrix/rotate_matrix.py
================================================
"""
In this problem, we want to rotate the matrix elements by 90, 180, 270
(counterclockwise)
Discussion in stackoverflow:
https://stackoverflow.com/questions/42519/how-do-you-rotate-a-two-dimensional-array
"""

from __future__ import annotations


def make_matrix(row_size: int = 4) -> list[list[int]]:
    """
    >>> make_matrix()
    [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]
    >>> make_matrix(1)
    [[1]]
    >>> make_matrix(-2)
    [[1, 2], [3, 4]]
    >>> make_matrix(3)
    [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
    >>> make_matrix() == make_matrix(4)
    True
    """
    row_size = abs(row_size) or 4
    return [[1 + x + y * row_size for x in range(row_size)] for y in range(row_size)]


def rotate_90(matrix: list[list[int]]) -> list[list[int]]:
    """
    >>> rotate_90(make_matrix())
    [[4, 8, 12, 16], [3, 7, 11, 15], [2, 6, 10, 14], [1, 5, 9, 13]]
    >>> rotate_90(make_matrix()) == transpose(reverse_column(make_matrix()))
    True
    """

    return reverse_row(transpose(matrix))
    # OR.. transpose(reverse_column(matrix))


def rotate_180(matrix: list[list[int]]) -> list[list[int]]:
    """
    >>> rotate_180(make_matrix())
    [[16, 15, 14, 13], [12, 11, 10, 9], [8, 7, 6, 5], [4, 3, 2, 1]]
    >>> rotate_180(make_matrix()) == reverse_column(reverse_row(make_matrix()))
    True
    """

    return reverse_row(reverse_column(matrix))
    # OR.. reverse_column(reverse_row(matrix))


def rotate_270(matrix: list[list[int]]) -> list[list[int]]:
    """
    >>> rotate_270(make_matrix())
    [[13, 9, 5, 1], [14, 10, 6, 2], [15, 11, 7, 3], [16, 12, 8, 4]]
    >>> rotate_270(make_matrix()) == transpose(reverse_row(make_matrix()))
    True
    """

    return reverse_column(transpose(matrix))
    # OR.. transpose(reverse_row(matrix))


def transpose(matrix: list[list[int]]) -> list[list[int]]:
    matrix[:] = [list(x) for x in zip(*matrix)]
    return matrix


def reverse_row(matrix: list[list[int]]) -> list[list[int]]:
    matrix[:] = matrix[::-1]
    return matrix


def reverse_column(matrix: list[list[int]]) -> list[list[int]]:
    matrix[:] = [x[::-1] for x in matrix]
    return matrix


def print_matrix(matrix: list[list[int]]) -> None:
    for i in matrix:
        print(*i)


if __name__ == "__main__":
    matrix = make_matrix()
    print("\norigin:\n")
    print_matrix(matrix)
    print("\nrotate 90 counterclockwise:\n")
    print_matrix(rotate_90(matrix))

    matrix = make_matrix()
    print("\norigin:\n")
    print_matrix(matrix)
    print("\nrotate 180:\n")
    print_matrix(rotate_180(matrix))

    matrix = make_matrix()
    print("\norigin:\n")
    print_matrix(matrix)
    print("\nrotate 270 counterclockwise:\n")
    print_matrix(rotate_270(matrix))


================================================
FILE: matrix/searching_in_sorted_matrix.py
================================================
from __future__ import annotations


def search_in_a_sorted_matrix(mat: list[list[int]], m: int, n: int, key: float) -> None:
    """
    >>> search_in_a_sorted_matrix(
    ...     [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]], 3, 3, 5)
    Key 5 found at row- 1 column- 2
    >>> search_in_a_sorted_matrix(
    ...     [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]], 3, 3, 21)
    Key 21 not found
    >>> search_in_a_sorted_matrix(
    ...     [[2.1, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]], 3, 3, 2.1)
    Key 2.1 found at row- 1 column- 1
    >>> search_in_a_sorted_matrix(
    ...     [[2.1, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]], 3, 3, 2.2)
    Key 2.2 not found
    """
    i, j = m - 1, 0
    while i >= 0 and j < n:
        if key == mat[i][j]:
            print(f"Key {key} found at row- {i + 1} column- {j + 1}")
            return
        if key < mat[i][j]:
            i -= 1
        else:
            j += 1
    print(f"Key {key} not found")


def main() -> None:
    mat = [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
    x = int(input("Enter the element to be searched:"))
    print(mat)
    search_in_a_sorted_matrix(mat, len(mat), len(mat[0]), x)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: matrix/sherman_morrison.py
================================================
from __future__ import annotations

from typing import Any


class Matrix:
    """
    <class Matrix>
    Matrix structure.
    """

    def __init__(self, row: int, column: int, default_value: float = 0) -> None:
        """
        <method Matrix.__init__>
        Initialize matrix with given size and default value.
        Example:
        >>> a = Matrix(2, 3, 1)
        >>> a
        Matrix consist of 2 rows and 3 columns
        [1, 1, 1]
        [1, 1, 1]
        """

        self.row, self.column = row, column
        self.array = [[default_value for _ in range(column)] for _ in range(row)]

    def __str__(self) -> str:
        """
        <method Matrix.__str__>
        Return string representation of this matrix.
        """

        # Prefix
        s = f"Matrix consist of {self.row} rows and {self.column} columns\n"

        # Make string identifier
        max_element_length = 0
        for row_vector in self.array:
            for obj in row_vector:
                max_element_length = max(max_element_length, len(str(obj)))
        string_format_identifier = f"%{max_element_length}s"

        # Make string and return
        def single_line(row_vector: list[float]) -> str:
            nonlocal string_format_identifier
            line = "["
            line += ", ".join(string_format_identifier % (obj,) for obj in row_vector)
            line += "]"
            return line

        s += "\n".join(single_line(row_vector) for row_vector in self.array)
        return s

    def __repr__(self) -> str:
        return str(self)

    def validate_indices(self, loc: tuple[int, int]) -> bool:
        """
        <method Matrix.validate_indicies>
        Check if given indices are valid to pick element from matrix.
        Example:
        >>> a = Matrix(2, 6, 0)
        >>> a.validate_indices((2, 7))
        False
        >>> a.validate_indices((0, 0))
        True
        """
        if not (isinstance(loc, (list, tuple)) and len(loc) == 2):  # noqa: SIM114
            return False
        elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
            return False
        else:
            return True

    def __getitem__(self, loc: tuple[int, int]) -> Any:
        """
        <method Matrix.__getitem__>
        Return array[row][column] where loc = (row, column).
        Example:
        >>> a = Matrix(3, 2, 7)
        >>> a[1, 0]
        7
        """
        assert self.validate_indices(loc)
        return self.array[loc[0]][loc[1]]

    def __setitem__(self, loc: tuple[int, int], value: float) -> None:
        """
        <method Matrix.__setitem__>
        Set array[row][column] = value where loc = (row, column).
        Example:
        >>> a = Matrix(2, 3, 1)
        >>> a[1, 2] = 51
        >>> a
        Matrix consist of 2 rows and 3 columns
        [ 1,  1,  1]
        [ 1,  1, 51]
        """
        assert self.validate_indices(loc)
        self.array[loc[0]][loc[1]] = value

    def __add__(self, another: Matrix) -> Matrix:
        """
        <method Matrix.__add__>
        Return self + another.
        Example:
        >>> a = Matrix(2, 1, -4)
        >>> b = Matrix(2, 1, 3)
        >>> a+b
        Matrix consist of 2 rows and 1 columns
        [-1]
        [-1]
        """

        # Validation
        assert isinstance(another, Matrix)
        assert self.row == another.row
        assert self.column == another.column

        # Add
        result = Matrix(self.row, self.column)
        for r in range(self.row):
            for c in range(self.column):
                result[r, c] = self[r, c] + another[r, c]
        return result

    def __neg__(self) -> Matrix:
        """
        <method Matrix.__neg__>
        Return -self.
        Example:
        >>> a = Matrix(2, 2, 3)
        >>> a[0, 1] = a[1, 0] = -2
        >>> -a
        Matrix consist of 2 rows and 2 columns
        [-3,  2]
        [ 2, -3]
        """

        result = Matrix(self.row, self.column)
        for r in range(self.row):
            for c in range(self.column):
                result[r, c] = -self[r, c]
        return result

    def __sub__(self, another: Matrix) -> Matrix:
        return self + (-another)

    def __mul__(self, another: float | Matrix) -> Matrix:
        """
        <method Matrix.__mul__>
        Return self * another.
        Example:
        >>> a = Matrix(2, 3, 1)
        >>> a[0,2] = a[1,2] = 3
        >>> a * -2
        Matrix consist of 2 rows and 3 columns
        [-2, -2, -6]
        [-2, -2, -6]
        """

        if isinstance(another, (int, float)):  # Scalar multiplication
            result = Matrix(self.row, self.column)
            for r in range(self.row):
                for c in range(self.column):
                    result[r, c] = self[r, c] * another
            return result
        elif isinstance(another, Matrix):  # Matrix multiplication
            assert self.column == another.row
            result = Matrix(self.row, another.column)
            for r in range(self.row):
                for c in range(another.column):
                    for i in range(self.column):
                        result[r, c] += self[r, i] * another[i, c]
            return result
        else:
            msg = f"Unsupported type given for another ({type(another)})"
            raise TypeError(msg)

    def transpose(self) -> Matrix:
        """
        <method Matrix.transpose>
        Return self^T.
        Example:
        >>> a = Matrix(2, 3)
        >>> for r in range(2):
        ...     for c in range(3):
        ...             a[r,c] = r*c
        ...
        >>> a.transpose()
        Matrix consist of 3 rows and 2 columns
        [0, 0]
        [0, 1]
        [0, 2]
        """

        result = Matrix(self.column, self.row)
        for r in range(self.row):
            for c in range(self.column):
                result[c, r] = self[r, c]
        return result

    def sherman_morrison(self, u: Matrix, v: Matrix) -> Any:
        """
        <method Matrix.sherman_morrison>
        Apply Sherman-Morrison formula in O(n^2).
        To learn this formula, please look this:
        https://en.wikipedia.org/wiki/Sherman%E2%80%93Morrison_formula
        This method returns (A + uv^T)^(-1) where A^(-1) is self. Returns None if it's
        impossible to calculate.
        Warning: This method doesn't check if self is invertible.
            Make sure self is invertible before execute this method.
        Example:
        >>> ainv = Matrix(3, 3, 0)
        >>> for i in range(3): ainv[i,i] = 1
        ...
        >>> u = Matrix(3, 1, 0)
        >>> u[0,0], u[1,0], u[2,0] = 1, 2, -3
        >>> v = Matrix(3, 1, 0)
        >>> v[0,0], v[1,0], v[2,0] = 4, -2, 5
        >>> ainv.sherman_morrison(u, v)
        Matrix consist of 3 rows and 3 columns
        [  1.2857142857142856, -0.14285714285714285,   0.3571428571428571]
        [  0.5714285714285714,   0.7142857142857143,   0.7142857142857142]
        [ -0.8571428571428571,  0.42857142857142855,  -0.0714285714285714]
        """

        # Size validation
        assert isinstance(u, Matrix)
        assert isinstance(v, Matrix)
        assert self.row == self.column == u.row == v.row  # u, v should be column vector
        assert u.column == v.column == 1  # u, v should be column vector

        # Calculate
        v_t = v.transpose()
        numerator_factor = (v_t * self * u)[0, 0] + 1
        if numerator_factor == 0:
            return None  # It's not invertible
        return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":

    def test1() -> None:
        # a^(-1)
        ainv = Matrix(3, 3, 0)
        for i in range(3):
            ainv[i, i] = 1
        print(f"a^(-1) is {ainv}")
        # u, v
        u = Matrix(3, 1, 0)
        u[0, 0], u[1, 0], u[2, 0] = 1, 2, -3
        v = Matrix(3, 1, 0)
        v[0, 0], v[1, 0], v[2, 0] = 4, -2, 5
        print(f"u is {u}")
        print(f"v is {v}")
        print(f"uv^T is {u * v.transpose()}")
        # Sherman Morrison
        print(f"(a + uv^T)^(-1) is {ainv.sherman_morrison(u, v)}")

    def test2() -> None:
        import doctest

        doctest.testmod()

    test2()


================================================
FILE: matrix/spiral_print.py
================================================
"""
This program print the matrix in spiral form.
This problem has been solved through recursive way.
      Matrix must satisfy below conditions
        i) matrix should be only one or two dimensional
        ii) number of column of all rows should be equal
"""


def check_matrix(matrix: list[list[int]]) -> bool:
    # must be
    matrix = [list(row) for row in matrix]
    if matrix and isinstance(matrix, list):
        if isinstance(matrix[0], list):
            prev_len = 0
            for row in matrix:
                if prev_len == 0:
                    prev_len = len(row)
                    result = True
                else:
                    result = prev_len == len(row)
        else:
            result = True
    else:
        result = False

    return result


def spiral_print_clockwise(a: list[list[int]]) -> None:
    """
    >>> spiral_print_clockwise([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
    1
    2
    3
    4
    8
    12
    11
    10
    9
    5
    6
    7
    """
    if check_matrix(a) and len(a) > 0:
        a = [list(row) for row in a]
        mat_row = len(a)
        if isinstance(a[0], list):
            mat_col = len(a[0])
        else:
            for dat in a:
                print(dat)
            return

        # horizotal printing increasing
        for i in range(mat_col):
            print(a[0][i])
        # vertical printing down
        for i in range(1, mat_row):
            print(a[i][mat_col - 1])
        # horizotal printing decreasing
        if mat_row > 1:
            for i in range(mat_col - 2, -1, -1):
                print(a[mat_row - 1][i])
        # vertical printing up
        for i in range(mat_row - 2, 0, -1):
            print(a[i][0])
        remain_mat = [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
        if len(remain_mat) > 0:
            spiral_print_clockwise(remain_mat)
        else:
            return
    else:
        print("Not a valid matrix")
        return


# Other Easy to understand Approach


def spiral_traversal(matrix: list[list]) -> list[int]:
    """
    >>> spiral_traversal([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
    [1, 2, 3, 4, 8, 12, 11, 10, 9, 5, 6, 7]

    Example:
    matrix = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
    Algorithm:
        Step 1. first pop the 0 index list. (which is [1,2,3,4] and concatenate the
                output of [step 2])
        Step 2. Now perform matrix's Transpose operation (Change rows to column
                and vice versa) and reverse the resultant matrix.
        Step 3. Pass the output of [2nd step], to same recursive function till
                base case hits.
    Dry Run:
    Stage 1.
    [1, 2, 3, 4] +   spiral_traversal([
        [8, 12], [7, 11], [6, 10], [5, 9]]
     ])
    Stage 2.
    [1, 2, 3, 4, 8, 12] + spiral_traversal([
        [11, 10, 9], [7, 6, 5]
    ])
    Stage 3.
    [1, 2, 3, 4, 8, 12, 11, 10, 9] + spiral_traversal([
        [5], [6], [7]
    ])
    Stage 4.
    [1, 2, 3, 4, 8, 12, 11, 10, 9, 5] + spiral_traversal([
        [5], [6], [7]
    ])
    Stage 5.
    [1, 2, 3, 4, 8, 12, 11, 10, 9, 5] + spiral_traversal([[6, 7]])
    Stage 6.
    [1, 2, 3, 4, 8, 12, 11, 10, 9, 5, 6, 7] + spiral_traversal([])
    """
    if matrix:
        return list(matrix.pop(0)) + spiral_traversal(
            [list(row) for row in zip(*matrix)][::-1]
        )
    else:
        return []


# driver code
if __name__ == "__main__":
    import doctest

    doctest.testmod()

    a = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
    spiral_print_clockwise(a)


================================================
FILE: matrix/tests/__init__.py
================================================


================================================
FILE: matrix/tests/pytest.ini
================================================
[pytest]
markers =
    mat_ops: tests for matrix operations


================================================
FILE: matrix/tests/test_matrix_operation.py
================================================
"""
Testing here assumes that numpy and linalg is ALWAYS correct!!!!

If running from PyCharm you can place the following line in "Additional Arguments" for
the pytest run configuration
-vv -m mat_ops -p no:cacheprovider
"""

import logging

# standard libraries
import sys

import numpy as np
import pytest

# Custom/local libraries
from matrix import matrix_operation as matop

mat_a = [[12, 10], [3, 9]]
mat_b = [[3, 4], [7, 4]]
mat_c = [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
mat_d = [[3, 0, -2], [2, 0, 2], [0, 1, 1]]
mat_e = [[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
mat_f = [1]
mat_h = [2]

logger = logging.getLogger()
logger.level = logging.DEBUG
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2"), [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]
)
def test_addition(mat1, mat2):
    if (np.array(mat1)).shape < (2, 2) or (np.array(mat2)).shape < (2, 2):
        logger.info(f"\n\t{test_addition.__name__} returned integer")
        with pytest.raises(TypeError):
            matop.add(mat1, mat2)
    elif (np.array(mat1)).shape == (np.array(mat2)).shape:
        logger.info(f"\n\t{test_addition.__name__} with same matrix dims")
        act = (np.array(mat1) + np.array(mat2)).tolist()
        theo = matop.add(mat1, mat2)
        assert theo == act
    else:
        logger.info(f"\n\t{test_addition.__name__} with different matrix dims")
        with pytest.raises(ValueError):
            matop.add(mat1, mat2)


@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2"), [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]
)
def test_subtraction(mat1, mat2):
    if (np.array(mat1)).shape < (2, 2) or (np.array(mat2)).shape < (2, 2):
        logger.info(f"\n\t{test_subtraction.__name__} returned integer")
        with pytest.raises(TypeError):
            matop.subtract(mat1, mat2)
    elif (np.array(mat1)).shape == (np.array(mat2)).shape:
        logger.info(f"\n\t{test_subtraction.__name__} with same matrix dims")
        act = (np.array(mat1) - np.array(mat2)).tolist()
        theo = matop.subtract(mat1, mat2)
        assert theo == act
    else:
        logger.info(f"\n\t{test_subtraction.__name__} with different matrix dims")
        with pytest.raises(ValueError):
            assert matop.subtract(mat1, mat2)


@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2"), [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]
)
def test_multiplication(mat1, mat2):
    if (np.array(mat1)).shape < (2, 2) or (np.array(mat2)).shape < (2, 2):
        logger.info(f"\n\t{test_multiplication.__name__} returned integer")
        with pytest.raises(TypeError):
            matop.add(mat1, mat2)
    elif (np.array(mat1)).shape == (np.array(mat2)).shape:
        logger.info(f"\n\t{test_multiplication.__name__} meets dim requirements")
        act = (np.matmul(mat1, mat2)).tolist()
        theo = matop.multiply(mat1, mat2)
        assert theo == act
    else:
        logger.info(
            f"\n\t{test_multiplication.__name__} does not meet dim requirements"
        )
        with pytest.raises(ValueError):
            assert matop.subtract(mat1, mat2)


@pytest.mark.mat_ops
def test_scalar_multiply():
    act = (3.5 * np.array(mat_a)).tolist()
    theo = matop.scalar_multiply(mat_a, 3.5)
    assert theo == act


@pytest.mark.mat_ops
def test_identity():
    act = (np.identity(5)).tolist()
    theo = matop.identity(5)
    assert theo == act


@pytest.mark.mat_ops
@pytest.mark.parametrize("mat", [mat_a, mat_b, mat_c, mat_d, mat_e, mat_f])
def test_transpose(mat):
    if (np.array(mat)).shape < (2, 2):
        logger.info(f"\n\t{test_transpose.__name__} returned integer")
        with pytest.raises(TypeError):
            matop.transpose(mat)
    else:
        act = (np.transpose(mat)).tolist()
        theo = matop.transpose(mat, return_map=False)
        assert theo == act


================================================
FILE: matrix/validate_sudoku_board.py
================================================
"""
LeetCode 36. Valid Sudoku
https://leetcode.com/problems/valid-sudoku/
https://en.wikipedia.org/wiki/Sudoku

Determine if a 9 x 9 Sudoku board is valid. Only the filled cells need to be
validated according to the following rules:

- Each row must contain the digits 1-9 without repetition.
- Each column must contain the digits 1-9 without repetition.
- Each of the nine 3 x 3 sub-boxes of the grid must contain the digits 1-9
  without repetition.

Note:

A Sudoku board (partially filled) could be valid but is not necessarily
solvable.

Only the filled cells need to be validated according to the mentioned rules.
"""

from collections import defaultdict

NUM_SQUARES = 9
EMPTY_CELL = "."


def is_valid_sudoku_board(sudoku_board: list[list[str]]) -> bool:
    """
    This function validates (but does not solve) a sudoku board.
    The board may be valid but unsolvable.

    >>> is_valid_sudoku_board([
    ...  ["5","3",".",".","7",".",".",".","."]
    ... ,["6",".",".","1","9","5",".",".","."]
    ... ,[".","9","8",".",".",".",".","6","."]
    ... ,["8",".",".",".","6",".",".",".","3"]
    ... ,["4",".",".","8",".","3",".",".","1"]
    ... ,["7",".",".",".","2",".",".",".","6"]
    ... ,[".","6",".",".",".",".","2","8","."]
    ... ,[".",".",".","4","1","9",".",".","5"]
    ... ,[".",".",".",".","8",".",".","7","9"]
    ... ])
    True
    >>> is_valid_sudoku_board([
    ...  ["8","3",".",".","7",".",".",".","."]
    ... ,["6",".",".","1","9","5",".",".","."]
    ... ,[".","9","8",".",".",".",".","6","."]
    ... ,["8",".",".",".","6",".",".",".","3"]
    ... ,["4",".",".","8",".","3",".",".","1"]
    ... ,["7",".",".",".","2",".",".",".","6"]
    ... ,[".","6",".",".",".",".","2","8","."]
    ... ,[".",".",".","4","1","9",".",".","5"]
    ... ,[".",".",".",".","8",".",".","7","9"]
    ... ])
    False
    >>> is_valid_sudoku_board([
    ...  ["1","2","3","4","5","6","7","8","9"]
    ... ,["4","5","6","7","8","9","1","2","3"]
    ... ,["7","8","9","1","2","3","4","5","6"]
    ... ,[".",".",".",".",".",".",".",".","."]
    ... ,[".",".",".",".",".",".",".",".","."]
    ... ,[".",".",".",".",".",".",".",".","."]
    ... ,[".",".",".",".",".",".",".",".","."]
    ... ,[".",".",".",".",".",".",".",".","."]
    ... ,[".",".",".",".",".",".",".",".","."]
    ... ])
    True
    >>> is_valid_sudoku_board([
    ...  ["1","2","3",".",".",".",".",".","."]
    ... ,["4","5","6",".",".",".",".",".","."]
    ... ,["7","8","9",".",".",".",".",".","."]
    ... ,[".",".",".","4","5","6",".",".","."]
    ... ,[".",".",".","7","8","9",".",".","."]
    ... ,[".",".",".","1","2","3",".",".","."]
    ... ,[".",".",".",".",".",".","7","8","9"]
    ... ,[".",".",".",".",".",".","1","2","3"]
    ... ,[".",".",".",".",".",".","4","5","6"]
    ... ])
    True
    >>> is_valid_sudoku_board([
    ...  ["1","2","3",".",".",".","5","6","4"]
    ... ,["4","5","6",".",".",".","8","9","7"]
    ... ,["7","8","9",".",".",".","2","3","1"]
    ... ,[".",".",".","4","5","6",".",".","."]
    ... ,[".",".",".","7","8","9",".",".","."]
    ... ,[".",".",".","1","2","3",".",".","."]
    ... ,["3","1","2",".",".",".","7","8","9"]
    ... ,["6","4","5",".",".",".","1","2","3"]
    ... ,["9","7","8",".",".",".","4","5","6"]
    ... ])
    True
    >>> is_valid_sudoku_board([
    ...  ["1","2","3","4","5","6","7","8","9"]
    ... ,["2",".",".",".",".",".",".",".","8"]
    ... ,["3",".",".",".",".",".",".",".","7"]
    ... ,["4",".",".",".",".",".",".",".","6"]
    ... ,["5",".",".",".",".",".",".",".","5"]
    ... ,["6",".",".",".",".",".",".",".","4"]
    ... ,["7",".",".",".",".",".",".",".","3"]
    ... ,["8",".",".",".",".",".",".",".","2"]
    ... ,["9","8","7","6","5","4","3","2","1"]
    ... ])
    False
    >>> is_valid_sudoku_board([
    ...  ["1","2","3","8","9","7","5","6","4"]
    ... ,["4","5","6","2","3","1","8","9","7"]
    ... ,["7","8","9","5","6","4","2","3","1"]
    ... ,["2","3","1","4","5","6","9","7","8"]
    ... ,["5","6","4","7","8","9","3","1","2"]
    ... ,["8","9","7","1","2","3","6","4","5"]
    ... ,["3","1","2","6","4","5","7","8","9"]
    ... ,["6","4","5","9","7","8","1","2","3"]
    ... ,["9","7","8","3","1","2","4","5","6"]
    ... ])
    True
    >>> is_valid_sudoku_board([["1", "2", "3", "4", "5", "6", "7", "8", "9"]])
    Traceback (most recent call last):
        ...
    ValueError: Sudoku boards must be 9x9 squares.
    >>> is_valid_sudoku_board(
    ...        [["1"], ["2"], ["3"], ["4"], ["5"], ["6"], ["7"], ["8"], ["9"]]
    ...  )
    Traceback (most recent call last):
        ...
    ValueError: Sudoku boards must be 9x9 squares.
    """
    if len(sudoku_board) != NUM_SQUARES or (
        any(len(row) != NUM_SQUARES for row in sudoku_board)
    ):
        error_message = f"Sudoku boards must be {NUM_SQUARES}x{NUM_SQUARES} squares."
        raise ValueError(error_message)

    row_values: defaultdict[int, set[str]] = defaultdict(set)
    col_values: defaultdict[int, set[str]] = defaultdict(set)
    box_values: defaultdict[tuple[int, int], set[str]] = defaultdict(set)

    for row in range(NUM_SQUARES):
        for col in range(NUM_SQUARES):
            value = sudoku_board[row][col]

            if value == EMPTY_CELL:
                continue

            box = (row // 3, col // 3)

            if (
                value in row_values[row]
                or value in col_values[col]
                or value in box_values[box]
            ):
                return False

            row_values[row].add(value)
            col_values[col].add(value)
            box_values[box].add(value)

    return True


if __name__ == "__main__":
    from doctest import testmod
    from timeit import timeit

    testmod()
    print(timeit("is_valid_sudoku_board(valid_board)", globals=globals()))
    print(timeit("is_valid_sudoku_board(invalid_board)", globals=globals()))


================================================
FILE: networking_flow/__init__.py
================================================


================================================
FILE: networking_flow/ford_fulkerson.py
================================================
"""
Ford-Fulkerson Algorithm for Maximum Flow Problem
* https://en.wikipedia.org/wiki/Ford%E2%80%93Fulkerson_algorithm

Description:
    (1) Start with initial flow as 0
    (2) Choose the augmenting path from source to sink and add the path to flow
"""

graph = [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]


def breadth_first_search(graph: list, source: int, sink: int, parents: list) -> bool:
    """
    This function returns True if there is a node that has not iterated.

    Args:
        graph: Adjacency matrix of graph
        source: Source
        sink: Sink
        parents: Parent list

    Returns:
        True if there is a node that has not iterated.

    >>> breadth_first_search(graph, 0, 5, [-1, -1, -1, -1, -1, -1])
    True
    >>> breadth_first_search(graph, 0, 6, [-1, -1, -1, -1, -1, -1])
    Traceback (most recent call last):
        ...
    IndexError: list index out of range
    """
    visited = [False] * len(graph)  # Mark all nodes as not visited
    queue = []  # breadth-first search queue

    # Source node
    queue.append(source)
    visited[source] = True

    while queue:
        u = queue.pop(0)  # Pop the front node
        # Traverse all adjacent nodes of u
        for ind, node in enumerate(graph[u]):
            if visited[ind] is False and node > 0:
                queue.append(ind)
                visited[ind] = True
                parents[ind] = u
    return visited[sink]


def ford_fulkerson(graph: list, source: int, sink: int) -> int:
    """
    This function returns the maximum flow from source to sink in the given graph.

    CAUTION: This function changes the given graph.

    Args:
        graph: Adjacency matrix of graph
        source: Source
        sink: Sink

    Returns:
        Maximum flow

    >>> test_graph = [
    ...     [0, 16, 13, 0, 0, 0],
    ...     [0, 0, 10, 12, 0, 0],
    ...     [0, 4, 0, 0, 14, 0],
    ...     [0, 0, 9, 0, 0, 20],
    ...     [0, 0, 0, 7, 0, 4],
    ...     [0, 0, 0, 0, 0, 0],
    ... ]
    >>> ford_fulkerson(test_graph, 0, 5)
    23
    """
    # This array is filled by breadth-first search and to store path
    parent = [-1] * (len(graph))
    max_flow = 0

    # While there is a path from source to sink
    while breadth_first_search(graph, source, sink, parent):
        path_flow = int(1e9)  # Infinite value
        s = sink

        while s != source:
            # Find the minimum value in the selected path
            path_flow = min(path_flow, graph[parent[s]][s])
            s = parent[s]

        max_flow += path_flow
        v = sink

        while v != source:
            u = parent[v]
            graph[u][v] -= path_flow
            graph[v][u] += path_flow
            v = parent[v]

    return max_flow


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    print(f"{ford_fulkerson(graph, source=0, sink=5) = }")


================================================
FILE: networking_flow/minimum_cut.py
================================================
# Minimum cut on Ford_Fulkerson algorithm.

test_graph = [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]


def bfs(graph, s, t, parent):
    # Return True if there is node that has not iterated.
    visited = [False] * len(graph)
    queue = [s]
    visited[s] = True

    while queue:
        u = queue.pop(0)
        for ind in range(len(graph[u])):
            if visited[ind] is False and graph[u][ind] > 0:
                queue.append(ind)
                visited[ind] = True
                parent[ind] = u

    return visited[t]


def mincut(graph, source, sink):
    """This array is filled by BFS and to store path
    >>> mincut(test_graph, source=0, sink=5)
    [(1, 3), (4, 3), (4, 5)]
    """
    parent = [-1] * (len(graph))
    max_flow = 0
    res = []
    temp = [i[:] for i in graph]  # Record original cut, copy.
    while bfs(graph, source, sink, parent):
        path_flow = float("Inf")
        s = sink

        while s != source:
            # Find the minimum value in select path
            path_flow = min(path_flow, graph[parent[s]][s])
            s = parent[s]

        max_flow += path_flow
        v = sink

        while v != source:
            u = parent[v]
            graph[u][v] -= path_flow
            graph[v][u] += path_flow
            v = parent[v]

    for i in range(len(graph)):
        for j in range(len(graph[0])):
            if graph[i][j] == 0 and temp[i][j] > 0:
                res.append((i, j))

    return res


if __name__ == "__main__":
    print(mincut(test_graph, source=0, sink=5))


================================================
FILE: neural_network/__init__.py
================================================


================================================
FILE: neural_network/activation_functions/__init__.py
================================================


================================================
FILE: neural_network/activation_functions/binary_step.py
================================================
"""
This script demonstrates the implementation of the Binary Step function.

It's an activation function in which the neuron is activated if the input is positive
or 0, else it is deactivated

It's a simple activation function which is mentioned in this wikipedia article:
https://en.wikipedia.org/wiki/Activation_function
"""

import numpy as np


def binary_step(vector: np.ndarray) -> np.ndarray:
    """
    Implements the binary step function

    Parameters:
        vector (ndarray): A vector that consists of numeric values

    Returns:
        vector (ndarray): Input vector after applying binary step function

    >>> vector = np.array([-1.2, 0, 2, 1.45, -3.7, 0.3])
    >>> binary_step(vector)
    array([0, 1, 1, 1, 0, 1])
    """

    return np.where(vector >= 0, 1, 0)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: neural_network/activation_functions/exponential_linear_unit.py
================================================
"""
Implements the Exponential Linear Unit or ELU function.

The function takes a vector of K real numbers and a real number alpha as
input and then applies the ELU function to each element of the vector.

Script inspired from its corresponding Wikipedia article
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
"""

import numpy as np


def exponential_linear_unit(vector: np.ndarray, alpha: float) -> np.ndarray:
    """
         Implements the ELU activation function.
         Parameters:
             vector: the array containing input of elu activation
             alpha: hyper-parameter
         return:
         elu (np.array): The input numpy array after applying elu.

         Mathematically, f(x) = x, x>0 else (alpha * (e^x -1)), x<=0, alpha >=0

    Examples:
    >>> exponential_linear_unit(vector=np.array([2.3,0.6,-2,-3.8]), alpha=0.3)
    array([ 2.3       ,  0.6       , -0.25939942, -0.29328877])

    >>> exponential_linear_unit(vector=np.array([-9.2,-0.3,0.45,-4.56]), alpha=0.067)
    array([-0.06699323, -0.01736518,  0.45      , -0.06629904])


    """
    return np.where(vector > 0, vector, (alpha * (np.exp(vector) - 1)))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: neural_network/activation_functions/gaussian_error_linear_unit.py
================================================
"""
This script demonstrates an implementation of the Gaussian Error Linear Unit function.
* https://en.wikipedia.org/wiki/Activation_function#Comparison_of_activation_functions

The function takes a vector of K real numbers as input and returns x * sigmoid(1.702*x).
Gaussian Error Linear Unit (GELU) is a high-performing neural network activation
function.

This script is inspired by a corresponding research paper.
* https://arxiv.org/abs/1606.08415
"""

import numpy as np


def sigmoid(vector: np.ndarray) -> np.ndarray:
    """
    Mathematical function sigmoid takes a vector x of K real numbers as input and
    returns 1/ (1 + e^-x).
    https://en.wikipedia.org/wiki/Sigmoid_function

    >>> sigmoid(np.array([-1.0, 1.0, 2.0]))
    array([0.26894142, 0.73105858, 0.88079708])
    """
    return 1 / (1 + np.exp(-vector))


def gaussian_error_linear_unit(vector: np.ndarray) -> np.ndarray:
    """
    Implements the Gaussian Error Linear Unit (GELU) function

    Parameters:
        vector (np.ndarray): A  numpy array of shape (1, n) consisting of real values

    Returns:
        gelu_vec (np.ndarray): The input numpy array, after applying gelu

    Examples:
    >>> gaussian_error_linear_unit(np.array([-1.0, 1.0, 2.0]))
    array([-0.15420423,  0.84579577,  1.93565862])

    >>> gaussian_error_linear_unit(np.array([-3]))
    array([-0.01807131])
    """
    return vector * sigmoid(1.702 * vector)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: neural_network/activation_functions/leaky_rectified_linear_unit.py
================================================
"""
Leaky Rectified Linear Unit (Leaky ReLU)

Use Case: Leaky ReLU addresses the problem of the vanishing gradient.
For more detailed information, you can refer to the following link:
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)#Leaky_ReLU
"""

import numpy as np


def leaky_rectified_linear_unit(vector: np.ndarray, alpha: float) -> np.ndarray:
    """
        Implements the LeakyReLU activation function.

        Parameters:
            vector (np.ndarray): The input array for LeakyReLU activation.
            alpha (float): The slope for negative values.

        Returns:
            np.ndarray: The input array after applying the LeakyReLU activation.

        Formula: f(x) = x if x > 0 else f(x) = alpha * x

    Examples:
    >>> leaky_rectified_linear_unit(vector=np.array([2.3,0.6,-2,-3.8]), alpha=0.3)
    array([ 2.3 ,  0.6 , -0.6 , -1.14])

    >>> leaky_rectified_linear_unit(np.array([-9.2, -0.3, 0.45, -4.56]), alpha=0.067)
    array([-0.6164 , -0.0201 ,  0.45   , -0.30552])

    """
    return np.where(vector > 0, vector, alpha * vector)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: neural_network/activation_functions/mish.py
================================================
"""
Mish Activation Function

Use Case: Improved version of the ReLU activation function used in Computer Vision.
For more detailed information, you can refer to the following link:
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)#Mish
"""

import numpy as np

from .softplus import softplus


def mish(vector: np.ndarray) -> np.ndarray:
    """
        Implements the Mish activation function.

        Parameters:
            vector (np.ndarray): The input array for Mish activation.

        Returns:
            np.ndarray: The input array after applying the Mish activation.

        Formula:
            f(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + e^x))

    Examples:
    >>> mish(vector=np.array([2.3,0.6,-2,-3.8]))
    array([ 2.26211893,  0.46613649, -0.25250148, -0.08405831])

    >>> mish(np.array([-9.2, -0.3, 0.45, -4.56]))
    array([-0.00092952, -0.15113318,  0.33152014, -0.04745745])

    """
    return vector * np.tanh(softplus(vector))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: neural_network/activation_functions/rectified_linear_unit.py
================================================
"""
This script demonstrates the implementation of the ReLU function.

It's a kind of activation function defined as the positive part of its argument in the
context of neural network.
The function takes a vector of K real numbers as input and then argmax(x, 0).
After through ReLU, the element of the vector always 0 or real number.

Script inspired from its corresponding Wikipedia article
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
"""

from __future__ import annotations

import numpy as np


def relu(vector: list[float]):
    """
    Implements the relu function

    Parameters:
        vector (np.array,list,tuple): A  numpy array of shape (1,n)
        consisting of real values or a similar list,tuple


    Returns:
        relu_vec (np.array): The input numpy array, after applying
        relu.

    >>> vec = np.array([-1, 0, 5])
    >>> relu(vec)
    array([0, 0, 5])
    """

    # compare two arrays and then return element-wise maxima.
    return np.maximum(0, vector)


if __name__ == "__main__":
    print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


================================================
FILE: neural_network/activation_functions/scaled_exponential_linear_unit.py
================================================
"""
Implements the Scaled Exponential Linear Unit or SELU function.
The function takes a vector of K real numbers and two real numbers
alpha(default = 1.6732) & lambda (default = 1.0507) as input and
then applies the SELU function to each element of the vector.
SELU is a self-normalizing activation function. It is a variant
of the ELU. The main advantage of SELU is that we can be sure
that the output will always be standardized due to its
self-normalizing behavior. That means there is no need to
include Batch-Normalization layers.
References :
https://iq.opengenus.org/scaled-exponential-linear-unit/
"""

import numpy as np


def scaled_exponential_linear_unit(
    vector: np.ndarray, alpha: float = 1.6732, lambda_: float = 1.0507
) -> np.ndarray:
    """
    Applies the Scaled Exponential Linear Unit function to each element of the vector.
    Parameters :
        vector : np.ndarray
        alpha : float (default = 1.6732)
        lambda_ : float (default = 1.0507)

    Returns : np.ndarray
    Formula : f(x) = lambda_ * x if x > 0
                     lambda_ * alpha * (e**x - 1) if x <= 0
    Examples :
    >>> scaled_exponential_linear_unit(vector=np.array([1.3, 3.7, 2.4]))
    array([1.36591, 3.88759, 2.52168])

    >>> scaled_exponential_linear_unit(vector=np.array([1.3, 4.7, 8.2]))
    array([1.36591, 4.93829, 8.61574])
    """
    return lambda_ * np.where(vector > 0, vector, alpha * (np.exp(vector) - 1))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: neural_network/activation_functions/soboleva_modified_hyperbolic_tangent.py
================================================
"""
This script implements the Soboleva Modified Hyperbolic Tangent function.

The function applies the Soboleva Modified Hyperbolic Tangent function
to each element of the vector.

More details about the activation function can be found on:
https://en.wikipedia.org/wiki/Soboleva_modified_hyperbolic_tangent
"""

import numpy as np


def soboleva_modified_hyperbolic_tangent(
    vector: np.ndarray, a_value: float, b_value: float, c_value: float, d_value: float
) -> np.ndarray:
    """
    Implements the Soboleva Modified Hyperbolic Tangent function

    Parameters:
        vector (ndarray): A vector that consists of numeric values
        a_value (float): parameter a of the equation
        b_value (float): parameter b of the equation
        c_value (float): parameter c of the equation
        d_value (float): parameter d of the equation

    Returns:
        vector (ndarray): Input array after applying SMHT function

    >>> vector = np.array([5.4, -2.4, 6.3, -5.23, 3.27, 0.56])
    >>> soboleva_modified_hyperbolic_tangent(vector, 0.2, 0.4, 0.6, 0.8)
    array([ 0.11075085, -0.28236685,  0.07861169, -0.1180085 ,  0.22999056,
            0.1566043 ])
    """

    # Separate the numerator and denominator for simplicity
    # Calculate the numerator and denominator element-wise
    numerator = np.exp(a_value * vector) - np.exp(-b_value * vector)
    denominator = np.exp(c_value * vector) + np.exp(-d_value * vector)

    # Calculate and return the final result element-wise
    return numerator / denominator


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: neural_network/activation_functions/softplus.py
================================================
"""
Softplus Activation Function

Use Case: The Softplus function is a smooth approximation of the ReLU function.
For more detailed information, you can refer to the following link:
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)#Softplus
"""

import numpy as np


def softplus(vector: np.ndarray) -> np.ndarray:
    """
    Implements the Softplus activation function.

    Parameters:
        vector (np.ndarray): The input array for the Softplus activation.

    Returns:
        np.ndarray: The input array after applying the Softplus activation.

    Formula: f(x) = ln(1 + e^x)

    Examples:
    >>> softplus(np.array([2.3, 0.6, -2, -3.8]))
    array([2.39554546, 1.03748795, 0.12692801, 0.02212422])

    >>> softplus(np.array([-9.2, -0.3, 0.45, -4.56]))
    array([1.01034298e-04, 5.54355244e-01, 9.43248946e-01, 1.04077103e-02])
    """
    return np.log(1 + np.exp(vector))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: neural_network/activation_functions/squareplus.py
================================================
"""
Squareplus Activation Function

Use Case: Squareplus designed to enhance positive values and suppress negative values.
For more detailed information, you can refer to the following link:
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)#Squareplus
"""

import numpy as np


def squareplus(vector: np.ndarray, beta: float) -> np.ndarray:
    """
    Implements the SquarePlus activation function.

    Parameters:
        vector (np.ndarray): The input array for the SquarePlus activation.
        beta (float): size of the curved region

    Returns:
        np.ndarray: The input array after applying the SquarePlus activation.

    Formula: f(x) = ( x + sqrt(x^2 + b) ) / 2

    Examples:
    >>> squareplus(np.array([2.3, 0.6, -2, -3.8]), beta=2)
    array([2.5       , 1.06811457, 0.22474487, 0.12731349])

    >>> squareplus(np.array([-9.2, -0.3, 0.45, -4.56]), beta=3)
    array([0.0808119 , 0.72891979, 1.11977651, 0.15893419])
    """
    return (vector + np.sqrt(vector**2 + beta)) / 2


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: neural_network/activation_functions/swish.py
================================================
"""
This script demonstrates the implementation of the Sigmoid Linear Unit (SiLU)
or swish function.
* https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
* https://en.wikipedia.org/wiki/Swish_function

The function takes a vector x of K real numbers as input and returns x * sigmoid(x).
Swish is a smooth, non-monotonic function defined as f(x) = x * sigmoid(x).
Extensive experiments shows that Swish consistently matches or outperforms ReLU
on deep networks applied to a variety of challenging domains such as
image classification and machine translation.

This script is inspired by a corresponding research paper.
* https://arxiv.org/abs/1710.05941
* https://blog.paperspace.com/swish-activation-function/
"""

import numpy as np


def sigmoid(vector: np.ndarray) -> np.ndarray:
    """
    Mathematical function sigmoid takes a vector x of K real numbers as input and
    returns 1/ (1 + e^-x).
    https://en.wikipedia.org/wiki/Sigmoid_function

    >>> sigmoid(np.array([-1.0, 1.0, 2.0]))
    array([0.26894142, 0.73105858, 0.88079708])
    """
    return 1 / (1 + np.exp(-vector))


def sigmoid_linear_unit(vector: np.ndarray) -> np.ndarray:
    """
    Implements the Sigmoid Linear Unit (SiLU) or swish function

    Parameters:
        vector (np.ndarray): A  numpy array consisting of real values

    Returns:
        swish_vec (np.ndarray): The input numpy array, after applying swish

    Examples:
    >>> sigmoid_linear_unit(np.array([-1.0, 1.0, 2.0]))
    array([-0.26894142,  0.73105858,  1.76159416])

    >>> sigmoid_linear_unit(np.array([-2]))
    array([-0.23840584])
    """
    return vector * sigmoid(vector)


def swish(vector: np.ndarray, trainable_parameter: int) -> np.ndarray:
    """
    Parameters:
        vector (np.ndarray): A  numpy array consisting of real values
        trainable_parameter: Use to implement various Swish Activation Functions

    Returns:
        swish_vec (np.ndarray): The input numpy array, after applying swish

    Examples:
    >>> swish(np.array([-1.0, 1.0, 2.0]), 2)
    array([-0.11920292,  0.88079708,  1.96402758])

    >>> swish(np.array([-2]), 1)
    array([-0.23840584])
    """
    return vector * sigmoid(trainable_parameter * vector)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: neural_network/back_propagation_neural_network.py
================================================
#!/usr/bin/python

"""

A Framework of Back Propagation Neural Network (BP) model

Easy to use:
    * add many layers as you want ! ! !
    * clearly see how the loss decreasing
Easy to expand:
    * more activation functions
    * more loss functions
    * more optimization method

Author: Stephen Lee
Github : https://github.com/RiptideBo
Date: 2017.11.23

"""

import numpy as np
from matplotlib import pyplot as plt


def sigmoid(x: np.ndarray) -> np.ndarray:
    return 1 / (1 + np.exp(-x))


class DenseLayer:
    """
    Layers of BP neural network
    """

    def __init__(
        self, units, activation=None, learning_rate=None, is_input_layer=False
    ):
        """
        common connected layer of bp network
        :param units: numbers of neural units
        :param activation: activation function
        :param learning_rate: learning rate for paras
        :param is_input_layer: whether it is input layer or not
        """
        self.units = units
        self.weight = None
        self.bias = None
        self.activation = activation
        if learning_rate is None:
            learning_rate = 0.3
        self.learn_rate = learning_rate
        self.is_input_layer = is_input_layer

    def initializer(self, back_units):
        rng = np.random.default_rng()
        self.weight = np.asmatrix(rng.normal(0, 0.5, (self.units, back_units)))
        self.bias = np.asmatrix(rng.normal(0, 0.5, self.units)).T
        if self.activation is None:
            self.activation = sigmoid

    def cal_gradient(self):
        # activation function may be sigmoid or linear
        if self.activation == sigmoid:
            gradient_mat = np.dot(self.output, (1 - self.output).T)
            gradient_activation = np.diag(np.diag(gradient_mat))
        else:
            gradient_activation = 1
        return gradient_activation

    def forward_propagation(self, xdata):
        self.xdata = xdata
        if self.is_input_layer:
            # input layer
            self.wx_plus_b = xdata
            self.output = xdata
            return xdata
        else:
            self.wx_plus_b = np.dot(self.weight, self.xdata) - self.bias
            self.output = self.activation(self.wx_plus_b)
            return self.output

    def back_propagation(self, gradient):
        gradient_activation = self.cal_gradient()  # i * i 维
        gradient = np.asmatrix(np.dot(gradient.T, gradient_activation))

        self._gradient_weight = np.asmatrix(self.xdata)
        self._gradient_bias = -1
        self._gradient_x = self.weight

        self.gradient_weight = np.dot(gradient.T, self._gradient_weight.T)
        self.gradient_bias = gradient * self._gradient_bias
        self.gradient = np.dot(gradient, self._gradient_x).T
        # upgrade: the Negative gradient direction
        self.weight = self.weight - self.learn_rate * self.gradient_weight
        self.bias = self.bias - self.learn_rate * self.gradient_bias.T
        # updates the weights and bias according to learning rate (0.3 if undefined)
        return self.gradient


class BPNN:
    """
    Back Propagation Neural Network model
    """

    def __init__(self):
        self.layers = []
        self.train_mse = []
        self.fig_loss = plt.figure()
        self.ax_loss = self.fig_loss.add_subplot(1, 1, 1)

    def add_layer(self, layer):
        self.layers.append(layer)

    def build(self):
        for i, layer in enumerate(self.layers[:]):
            if i < 1:
                layer.is_input_layer = True
            else:
                layer.initializer(self.layers[i - 1].units)

    def summary(self):
        for i, layer in enumerate(self.layers[:]):
            print(f"------- layer {i} -------")
            print("weight.shape ", np.shape(layer.weight))
            print("bias.shape ", np.shape(layer.bias))

    def train(self, xdata, ydata, train_round, accuracy):
        self.train_round = train_round
        self.accuracy = accuracy

        self.ax_loss.hlines(self.accuracy, 0, self.train_round * 1.1)

        x_shape = np.shape(xdata)
        for _ in range(train_round):
            all_loss = 0
            for row in range(x_shape[0]):
                _xdata = np.asmatrix(xdata[row, :]).T
                _ydata = np.asmatrix(ydata[row, :]).T

                # forward propagation
                for layer in self.layers:
                    _xdata = layer.forward_propagation(_xdata)

                loss, gradient = self.cal_loss(_ydata, _xdata)
                all_loss = all_loss + loss

                # back propagation: the input_layer does not upgrade
                for layer in self.layers[:0:-1]:
                    gradient = layer.back_propagation(gradient)

            mse = all_loss / x_shape[0]
            self.train_mse.append(mse)

            self.plot_loss()

            if mse < self.accuracy:
                print("----达到精度----")
                return mse
        return None

    def cal_loss(self, ydata, ydata_):
        self.loss = np.sum(np.power((ydata - ydata_), 2))
        self.loss_gradient = 2 * (ydata_ - ydata)
        # vector (shape is the same as _ydata.shape)
        return self.loss, self.loss_gradient

    def plot_loss(self):
        if self.ax_loss.lines:
            self.ax_loss.lines.remove(self.ax_loss.lines[0])
        self.ax_loss.plot(self.train_mse, "r-")
        plt.ion()
        plt.xlabel("step")
        plt.ylabel("loss")
        plt.show()
        plt.pause(0.1)


def example():
    rng = np.random.default_rng()
    x = rng.normal(size=(10, 10))
    y = np.asarray(
        [
            [0.8, 0.4],
            [0.4, 0.3],
            [0.34, 0.45],
            [0.67, 0.32],
            [0.88, 0.67],
            [0.78, 0.77],
            [0.55, 0.66],
            [0.55, 0.43],
            [0.54, 0.1],
            [0.1, 0.5],
        ]
    )
    model = BPNN()
    for i in (10, 20, 30, 2):
        model.add_layer(DenseLayer(i))
    model.build()
    model.summary()
    model.train(xdata=x, ydata=y, train_round=100, accuracy=0.01)


if __name__ == "__main__":
    example()


================================================
FILE: neural_network/convolution_neural_network.py
================================================
"""
 - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
Name - - CNN - Convolution Neural Network For Photo Recognizing
Goal - - Recognize Handwriting Word Photo
Detail: Total 5 layers neural network
        * Convolution layer
        * Pooling layer
        * Input layer layer of BP
        * Hidden layer of BP
        * Output layer of BP
Author: Stephen Lee
Github: 245885195@qq.com
Date: 2017.9.20
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
"""

import pickle

import numpy as np
from matplotlib import pyplot as plt


class CNN:
    def __init__(
        self, conv1_get, size_p1, bp_num1, bp_num2, bp_num3, rate_w=0.2, rate_t=0.2
    ):
        """
        :param conv1_get: [a,c,d], size, number, step of convolution kernel
        :param size_p1: pooling size
        :param bp_num1: units number of flatten layer
        :param bp_num2: units number of hidden layer
        :param bp_num3: units number of output layer
        :param rate_w: rate of weight learning
        :param rate_t: rate of threshold learning
        """
        self.num_bp1 = bp_num1
        self.num_bp2 = bp_num2
        self.num_bp3 = bp_num3
        self.conv1 = conv1_get[:2]
        self.step_conv1 = conv1_get[2]
        self.size_pooling1 = size_p1
        self.rate_weight = rate_w
        self.rate_thre = rate_t
        rng = np.random.default_rng()
        self.w_conv1 = [
            np.asmatrix(-1 * rng.random((self.conv1[0], self.conv1[0])) + 0.5)
            for i in range(self.conv1[1])
        ]
        self.wkj = np.asmatrix(-1 * rng.random((self.num_bp3, self.num_bp2)) + 0.5)
        self.vji = np.asmatrix(-1 * rng.random((self.num_bp2, self.num_bp1)) + 0.5)
        self.thre_conv1 = -2 * rng.random(self.conv1[1]) + 1
        self.thre_bp2 = -2 * rng.random(self.num_bp2) + 1
        self.thre_bp3 = -2 * rng.random(self.num_bp3) + 1

    def save_model(self, save_path):
        # save model dict with pickle
        model_dic = {
            "num_bp1": self.num_bp1,
            "num_bp2": self.num_bp2,
            "num_bp3": self.num_bp3,
            "conv1": self.conv1,
            "step_conv1": self.step_conv1,
            "size_pooling1": self.size_pooling1,
            "rate_weight": self.rate_weight,
            "rate_thre": self.rate_thre,
            "w_conv1": self.w_conv1,
            "wkj": self.wkj,
            "vji": self.vji,
            "thre_conv1": self.thre_conv1,
            "thre_bp2": self.thre_bp2,
            "thre_bp3": self.thre_bp3,
        }
        with open(save_path, "wb") as f:
            pickle.dump(model_dic, f)

        print(f"Model saved: {save_path}")

    @classmethod
    def read_model(cls, model_path):
        # read saved model
        with open(model_path, "rb") as f:
            model_dic = pickle.load(f)  # noqa: S301

        conv_get = model_dic.get("conv1")
        conv_get.append(model_dic.get("step_conv1"))
        size_p1 = model_dic.get("size_pooling1")
        bp1 = model_dic.get("num_bp1")
        bp2 = model_dic.get("num_bp2")
        bp3 = model_dic.get("num_bp3")
        r_w = model_dic.get("rate_weight")
        r_t = model_dic.get("rate_thre")
        # create model instance
        conv_ins = CNN(conv_get, size_p1, bp1, bp2, bp3, r_w, r_t)
        # modify model parameter
        conv_ins.w_conv1 = model_dic.get("w_conv1")
        conv_ins.wkj = model_dic.get("wkj")
        conv_ins.vji = model_dic.get("vji")
        conv_ins.thre_conv1 = model_dic.get("thre_conv1")
        conv_ins.thre_bp2 = model_dic.get("thre_bp2")
        conv_ins.thre_bp3 = model_dic.get("thre_bp3")
        return conv_ins

    def sig(self, x):
        return 1 / (1 + np.exp(-1 * x))

    def do_round(self, x):
        return round(x, 3)

    def convolute(self, data, convs, w_convs, thre_convs, conv_step):
        # convolution process
        size_conv = convs[0]
        num_conv = convs[1]
        size_data = np.shape(data)[0]
        # get the data slice of original image data, data_focus
        data_focus = []
        for i_focus in range(0, size_data - size_conv + 1, conv_step):
            for j_focus in range(0, size_data - size_conv + 1, conv_step):
                focus = data[
                    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
                ]
                data_focus.append(focus)
        # calculate the feature map of every single kernel, and saved as list of matrix
        data_featuremap = []
        size_feature_map = int((size_data - size_conv) / conv_step + 1)
        for i_map in range(num_conv):
            featuremap = []
            for i_focus in range(len(data_focus)):
                net_focus = (
                    np.sum(np.multiply(data_focus[i_focus], w_convs[i_map]))
                    - thre_convs[i_map]
                )
                featuremap.append(self.sig(net_focus))
            featuremap = np.asmatrix(featuremap).reshape(
                size_feature_map, size_feature_map
            )
            data_featuremap.append(featuremap)

        # expanding the data slice to one dimension
        focus1_list = []
        for each_focus in data_focus:
            focus1_list.extend(self.Expand_Mat(each_focus))
        focus_list = np.asarray(focus1_list)
        return focus_list, data_featuremap

    def pooling(self, featuremaps, size_pooling, pooling_type="average_pool"):
        # pooling process
        size_map = len(featuremaps[0])
        size_pooled = int(size_map / size_pooling)
        featuremap_pooled = []
        for i_map in range(len(featuremaps)):
            feature_map = featuremaps[i_map]
            map_pooled = []
            for i_focus in range(0, size_map, size_pooling):
                for j_focus in range(0, size_map, size_pooling):
                    focus = feature_map[
                        i_focus : i_focus + size_pooling,
                        j_focus : j_focus + size_pooling,
                    ]
                    if pooling_type == "average_pool":
                        # average pooling
                        map_pooled.append(np.average(focus))
                    elif pooling_type == "max_pooling":
                        # max pooling
                        map_pooled.append(np.max(focus))
            map_pooled = np.asmatrix(map_pooled).reshape(size_pooled, size_pooled)
            featuremap_pooled.append(map_pooled)
        return featuremap_pooled

    def _expand(self, data):
        # expanding three dimension data to one dimension list
        data_expanded = []
        for i in range(len(data)):
            shapes = np.shape(data[i])
            data_listed = data[i].reshape(1, shapes[0] * shapes[1])
            data_listed = data_listed.getA().tolist()[0]
            data_expanded.extend(data_listed)
        data_expanded = np.asarray(data_expanded)
        return data_expanded

    def _expand_mat(self, data_mat):
        # expanding matrix to one dimension list
        data_mat = np.asarray(data_mat)
        shapes = np.shape(data_mat)
        data_expanded = data_mat.reshape(1, shapes[0] * shapes[1])
        return data_expanded

    def _calculate_gradient_from_pool(
        self, out_map, pd_pool, num_map, size_map, size_pooling
    ):
        """
        calculate the gradient from the data slice of pool layer
        pd_pool: list of matrix
        out_map: the shape of data slice(size_map*size_map)
        return: pd_all: list of matrix, [num, size_map, size_map]
        """
        pd_all = []
        i_pool = 0
        for i_map in range(num_map):
            pd_conv1 = np.ones((size_map, size_map))
            for i in range(0, size_map, size_pooling):
                for j in range(0, size_map, size_pooling):
                    pd_conv1[i : i + size_pooling, j : j + size_pooling] = pd_pool[
                        i_pool
                    ]
                    i_pool = i_pool + 1
            pd_conv2 = np.multiply(
                pd_conv1, np.multiply(out_map[i_map], (1 - out_map[i_map]))
            )
            pd_all.append(pd_conv2)
        return pd_all

    def train(
        self, patterns, datas_train, datas_teach, n_repeat, error_accuracy, draw_e=bool
    ):
        # model training
        print("----------------------Start Training-------------------------")
        print((" - - Shape: Train_Data  ", np.shape(datas_train)))
        print((" - - Shape: Teach_Data  ", np.shape(datas_teach)))
        rp = 0
        all_mse = []
        mse = 10000
        while rp < n_repeat and mse >= error_accuracy:
            error_count = 0
            print(f"-------------Learning Time {rp}--------------")
            for p in range(len(datas_train)):
                # print('------------Learning Image: %d--------------'%p)
                data_train = np.asmatrix(datas_train[p])
                data_teach = np.asarray(datas_teach[p])
                data_focus1, data_conved1 = self.convolute(
                    data_train,
                    self.conv1,
                    self.w_conv1,
                    self.thre_conv1,
                    conv_step=self.step_conv1,
                )
                data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
                shape_featuremap1 = np.shape(data_conved1)
                """
                print('  -----original shape   ', np.shape(data_train))
                print('  ---- after convolution  ',np.shape(data_conv1))
                print('  -----after pooling  ',np.shape(data_pooled1))
               """
                data_bp_input = self._expand(data_pooled1)
                bp_out1 = data_bp_input

                bp_net_j = np.dot(bp_out1, self.vji.T) - self.thre_bp2
                bp_out2 = self.sig(bp_net_j)
                bp_net_k = np.dot(bp_out2, self.wkj.T) - self.thre_bp3
                bp_out3 = self.sig(bp_net_k)

                # --------------Model Leaning ------------------------
                # calculate error and gradient---------------
                pd_k_all = np.multiply(
                    (data_teach - bp_out3), np.multiply(bp_out3, (1 - bp_out3))
                )
                pd_j_all = np.multiply(
                    np.dot(pd_k_all, self.wkj), np.multiply(bp_out2, (1 - bp_out2))
                )
                pd_i_all = np.dot(pd_j_all, self.vji)

                pd_conv1_pooled = pd_i_all / (self.size_pooling1 * self.size_pooling1)
                pd_conv1_pooled = pd_conv1_pooled.T.getA().tolist()
                pd_conv1_all = self._calculate_gradient_from_pool(
                    data_conved1,
                    pd_conv1_pooled,
                    shape_featuremap1[0],
                    shape_featuremap1[1],
                    self.size_pooling1,
                )
                # weight and threshold learning process---------
                # convolution layer
                for k_conv in range(self.conv1[1]):
                    pd_conv_list = self._expand_mat(pd_conv1_all[k_conv])
                    delta_w = self.rate_weight * np.dot(pd_conv_list, data_focus1)

                    self.w_conv1[k_conv] = self.w_conv1[k_conv] + delta_w.reshape(
                        (self.conv1[0], self.conv1[0])
                    )

                    self.thre_conv1[k_conv] = (
                        self.thre_conv1[k_conv]
                        - np.sum(pd_conv1_all[k_conv]) * self.rate_thre
                    )
                # all connected layer
                self.wkj = self.wkj + pd_k_all.T * bp_out2 * self.rate_weight
                self.vji = self.vji + pd_j_all.T * bp_out1 * self.rate_weight
                self.thre_bp3 = self.thre_bp3 - pd_k_all * self.rate_thre
                self.thre_bp2 = self.thre_bp2 - pd_j_all * self.rate_thre
                # calculate the sum error of all single image
                errors = np.sum(abs(data_teach - bp_out3))
                error_count += errors
                # print('   ----Teach      ',data_teach)
                # print('   ----BP_output  ',bp_out3)
            rp = rp + 1
            mse = error_count / patterns
            all_mse.append(mse)

        def draw_error():
            yplot = [error_accuracy for i in range(int(n_repeat * 1.2))]
            plt.plot(all_mse, "+-")
            plt.plot(yplot, "r--")
            plt.xlabel("Learning Times")
            plt.ylabel("All_mse")
            plt.grid(True, alpha=0.5)
            plt.show()

        print("------------------Training Complete---------------------")
        print((" - - Training epoch: ", rp, f"     - - Mse: {mse:.6f}"))
        if draw_e:
            draw_error()
        return mse

    def predict(self, datas_test):
        # model predict
        produce_out = []
        print("-------------------Start Testing-------------------------")
        print((" - - Shape: Test_Data  ", np.shape(datas_test)))
        for p in range(len(datas_test)):
            data_test = np.asmatrix(datas_test[p])
            _data_focus1, data_conved1 = self.convolute(
                data_test,
                self.conv1,
                self.w_conv1,
                self.thre_conv1,
                conv_step=self.step_conv1,
            )
            data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
            data_bp_input = self._expand(data_pooled1)

            bp_out1 = data_bp_input
            bp_net_j = bp_out1 * self.vji.T - self.thre_bp2
            bp_out2 = self.sig(bp_net_j)
            bp_net_k = bp_out2 * self.wkj.T - self.thre_bp3
            bp_out3 = self.sig(bp_net_k)
            produce_out.extend(bp_out3.getA().tolist())
        res = [list(map(self.do_round, each)) for each in produce_out]
        return np.asarray(res)

    def convolution(self, data):
        # return the data of image after convoluting process so we can check it out
        data_test = np.asmatrix(data)
        _data_focus1, data_conved1 = self.convolute(
            data_test,
            self.conv1,
            self.w_conv1,
            self.thre_conv1,
            conv_step=self.step_conv1,
        )
        data_pooled1 = self.pooling(data_conved1, self.size_pooling1)

        return data_conved1, data_pooled1


if __name__ == "__main__":
    """
    I will put the example in another file
    """


================================================
FILE: neural_network/gan.py_tf
================================================
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
from sklearn.utils import shuffle
import input_data

random_numer = 42

np.random.seed(random_numer)


def ReLu(x):
    mask = (x > 0) * 1.0
    return mask * x


def d_ReLu(x):
    mask = (x > 0) * 1.0
    return mask


def arctan(x):
    return np.arctan(x)


def d_arctan(x):
    return 1 / (1 + x ** 2)


def log(x):
    return 1 / (1 + np.exp(-1 * x))


def d_log(x):
    return log(x) * (1 - log(x))


def tanh(x):
    return np.tanh(x)


def d_tanh(x):
    return 1 - np.tanh(x) ** 2


def plot(samples):
    fig = plt.figure(figsize=(4, 4))
    gs = gridspec.GridSpec(4, 4)
    gs.update(wspace=0.05, hspace=0.05)

    for i, sample in enumerate(samples):
        ax = plt.subplot(gs[i])
        plt.axis("off")
        ax.set_xticklabels([])
        ax.set_yticklabels([])
        ax.set_aspect("equal")
        plt.imshow(sample.reshape(28, 28), cmap="Greys_r")

    return fig


if __name__ == "__main__":
    # 1. Load Data and declare hyper
    print("--------- Load Data ----------")
    mnist = input_data.read_data_sets("MNIST_data", one_hot=False)
    temp = mnist.test
    images, labels = temp.images, temp.labels
    images, labels = shuffle(np.asarray(images), np.asarray(labels))
    num_epoch = 10
    learing_rate = 0.00009
    G_input = 100
    hidden_input, hidden_input2, hidden_input3 = 128, 256, 346
    hidden_input4, hidden_input5, hidden_input6 = 480, 560, 686

    print("--------- Declare Hyper Parameters ----------")
    # 2. Declare Weights
    D_W1 = (
        np.random.normal(size=(784, hidden_input), scale=(1.0 / np.sqrt(784 / 2.0)))
        * 0.002
    )
    # D_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))       *0.002
    D_b1 = np.zeros(hidden_input)

    D_W2 = (
        np.random.normal(
            size=(hidden_input, 1), scale=(1.0 / np.sqrt(hidden_input / 2.0))
        )
        * 0.002
    )
    # D_b2 = np.random.normal(size=(1),scale=(1. / np.sqrt(1 / 2.)))           *0.002
    D_b2 = np.zeros(1)

    G_W1 = (
        np.random.normal(
            size=(G_input, hidden_input), scale=(1.0 / np.sqrt(G_input / 2.0))
        )
        * 0.002
    )
    # G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
    G_b1 = np.zeros(hidden_input)

    G_W2 = (
        np.random.normal(
            size=(hidden_input, hidden_input2),
            scale=(1.0 / np.sqrt(hidden_input / 2.0)),
        )
        * 0.002
    )
    # G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
    G_b2 = np.zeros(hidden_input2)

    G_W3 = (
        np.random.normal(
            size=(hidden_input2, hidden_input3),
            scale=(1.0 / np.sqrt(hidden_input2 / 2.0)),
        )
        * 0.002
    )
    # G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
    G_b3 = np.zeros(hidden_input3)

    G_W4 = (
        np.random.normal(
            size=(hidden_input3, hidden_input4),
            scale=(1.0 / np.sqrt(hidden_input3 / 2.0)),
        )
        * 0.002
    )
    # G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
    G_b4 = np.zeros(hidden_input4)

    G_W5 = (
        np.random.normal(
            size=(hidden_input4, hidden_input5),
            scale=(1.0 / np.sqrt(hidden_input4 / 2.0)),
        )
        * 0.002
    )
    # G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
    G_b5 = np.zeros(hidden_input5)

    G_W6 = (
        np.random.normal(
            size=(hidden_input5, hidden_input6),
            scale=(1.0 / np.sqrt(hidden_input5 / 2.0)),
        )
        * 0.002
    )
    # G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
    G_b6 = np.zeros(hidden_input6)

    G_W7 = (
        np.random.normal(
            size=(hidden_input6, 784), scale=(1.0 / np.sqrt(hidden_input6 / 2.0))
        )
        * 0.002
    )
    # G_b2 = np.random.normal(size=(784),scale=(1. / np.sqrt(784 / 2.)))      *0.002
    G_b7 = np.zeros(784)

    # 3. For Adam Optimizer
    v1, m1 = 0, 0
    v2, m2 = 0, 0
    v3, m3 = 0, 0
    v4, m4 = 0, 0

    v5, m5 = 0, 0
    v6, m6 = 0, 0
    v7, m7 = 0, 0
    v8, m8 = 0, 0
    v9, m9 = 0, 0
    v10, m10 = 0, 0
    v11, m11 = 0, 0
    v12, m12 = 0, 0

    v13, m13 = 0, 0
    v14, m14 = 0, 0

    v15, m15 = 0, 0
    v16, m16 = 0, 0

    v17, m17 = 0, 0
    v18, m18 = 0, 0

    beta_1, beta_2, eps = 0.9, 0.999, 0.00000001

    print("--------- Started Training ----------")
    for iter in range(num_epoch):

        random_int = np.random.randint(len(images) - 5)
        current_image = np.expand_dims(images[random_int], axis=0)

        # Func: Generate The first Fake Data
        Z = np.random.uniform(-1.0, 1.0, size=[1, G_input])
        Gl1 = Z.dot(G_W1) + G_b1
        Gl1A = arctan(Gl1)
        Gl2 = Gl1A.dot(G_W2) + G_b2
        Gl2A = ReLu(Gl2)
        Gl3 = Gl2A.dot(G_W3) + G_b3
        Gl3A = arctan(Gl3)

        Gl4 = Gl3A.dot(G_W4) + G_b4
        Gl4A = ReLu(Gl4)
        Gl5 = Gl4A.dot(G_W5) + G_b5
        Gl5A = tanh(Gl5)
        Gl6 = Gl5A.dot(G_W6) + G_b6
        Gl6A = ReLu(Gl6)
        Gl7 = Gl6A.dot(G_W7) + G_b7

        current_fake_data = log(Gl7)

        # Func: Forward Feed for Real data
        Dl1_r = current_image.dot(D_W1) + D_b1
        Dl1_rA = ReLu(Dl1_r)
        Dl2_r = Dl1_rA.dot(D_W2) + D_b2
        Dl2_rA = log(Dl2_r)

        # Func: Forward Feed for Fake Data
        Dl1_f = current_fake_data.dot(D_W1) + D_b1
        Dl1_fA = ReLu(Dl1_f)
        Dl2_f = Dl1_fA.dot(D_W2) + D_b2
        Dl2_fA = log(Dl2_f)

        # Func: Cost D
        D_cost = -np.log(Dl2_rA) + np.log(1.0 - Dl2_fA)

        # Func: Gradient
        grad_f_w2_part_1 = 1 / (1.0 - Dl2_fA)
        grad_f_w2_part_2 = d_log(Dl2_f)
        grad_f_w2_part_3 = Dl1_fA
        grad_f_w2 = grad_f_w2_part_3.T.dot(grad_f_w2_part_1 * grad_f_w2_part_2)
        grad_f_b2 = grad_f_w2_part_1 * grad_f_w2_part_2

        grad_f_w1_part_1 = (grad_f_w2_part_1 * grad_f_w2_part_2).dot(D_W2.T)
        grad_f_w1_part_2 = d_ReLu(Dl1_f)
        grad_f_w1_part_3 = current_fake_data
        grad_f_w1 = grad_f_w1_part_3.T.dot(grad_f_w1_part_1 * grad_f_w1_part_2)
        grad_f_b1 = grad_f_w1_part_1 * grad_f_w1_part_2

        grad_r_w2_part_1 = -1 / Dl2_rA
        grad_r_w2_part_2 = d_log(Dl2_r)
        grad_r_w2_part_3 = Dl1_rA
        grad_r_w2 = grad_r_w2_part_3.T.dot(grad_r_w2_part_1 * grad_r_w2_part_2)
        grad_r_b2 = grad_r_w2_part_1 * grad_r_w2_part_2

        grad_r_w1_part_1 = (grad_r_w2_part_1 * grad_r_w2_part_2).dot(D_W2.T)
        grad_r_w1_part_2 = d_ReLu(Dl1_r)
        grad_r_w1_part_3 = current_image
        grad_r_w1 = grad_r_w1_part_3.T.dot(grad_r_w1_part_1 * grad_r_w1_part_2)
        grad_r_b1 = grad_r_w1_part_1 * grad_r_w1_part_2

        grad_w1 = grad_f_w1 + grad_r_w1
        grad_b1 = grad_f_b1 + grad_r_b1

        grad_w2 = grad_f_w2 + grad_r_w2
        grad_b2 = grad_f_b2 + grad_r_b2

        # ---- Update Gradient ----
        m1 = beta_1 * m1 + (1 - beta_1) * grad_w1
        v1 = beta_2 * v1 + (1 - beta_2) * grad_w1 ** 2

        m2 = beta_1 * m2 + (1 - beta_1) * grad_b1
        v2 = beta_2 * v2 + (1 - beta_2) * grad_b1 ** 2

        m3 = beta_1 * m3 + (1 - beta_1) * grad_w2
        v3 = beta_2 * v3 + (1 - beta_2) * grad_w2 ** 2

        m4 = beta_1 * m4 + (1 - beta_1) * grad_b2
        v4 = beta_2 * v4 + (1 - beta_2) * grad_b2 ** 2

        D_W1 = D_W1 - (learing_rate / (np.sqrt(v1 / (1 - beta_2)) + eps)) * (
            m1 / (1 - beta_1)
        )
        D_b1 = D_b1 - (learing_rate / (np.sqrt(v2 / (1 - beta_2)) + eps)) * (
            m2 / (1 - beta_1)
        )

        D_W2 = D_W2 - (learing_rate / (np.sqrt(v3 / (1 - beta_2)) + eps)) * (
            m3 / (1 - beta_1)
        )
        D_b2 = D_b2 - (learing_rate / (np.sqrt(v4 / (1 - beta_2)) + eps)) * (
            m4 / (1 - beta_1)
        )

        # Func: Forward Feed for G
        Z = np.random.uniform(-1.0, 1.0, size=[1, G_input])
        Gl1 = Z.dot(G_W1) + G_b1
        Gl1A = arctan(Gl1)
        Gl2 = Gl1A.dot(G_W2) + G_b2
        Gl2A = ReLu(Gl2)
        Gl3 = Gl2A.dot(G_W3) + G_b3
        Gl3A = arctan(Gl3)

        Gl4 = Gl3A.dot(G_W4) + G_b4
        Gl4A = ReLu(Gl4)
        Gl5 = Gl4A.dot(G_W5) + G_b5
        Gl5A = tanh(Gl5)
        Gl6 = Gl5A.dot(G_W6) + G_b6
        Gl6A = ReLu(Gl6)
        Gl7 = Gl6A.dot(G_W7) + G_b7

        current_fake_data = log(Gl7)

        Dl1 = current_fake_data.dot(D_W1) + D_b1
        Dl1_A = ReLu(Dl1)
        Dl2 = Dl1_A.dot(D_W2) + D_b2
        Dl2_A = log(Dl2)

        # Func: Cost G
        G_cost = -np.log(Dl2_A)

        # Func: Gradient
        grad_G_w7_part_1 = ((-1 / Dl2_A) * d_log(Dl2).dot(D_W2.T) * (d_ReLu(Dl1))).dot(
            D_W1.T
        )
        grad_G_w7_part_2 = d_log(Gl7)
        grad_G_w7_part_3 = Gl6A
        grad_G_w7 = grad_G_w7_part_3.T.dot(grad_G_w7_part_1 * grad_G_w7_part_1)
        grad_G_b7 = grad_G_w7_part_1 * grad_G_w7_part_2

        grad_G_w6_part_1 = (grad_G_w7_part_1 * grad_G_w7_part_2).dot(G_W7.T)
        grad_G_w6_part_2 = d_ReLu(Gl6)
        grad_G_w6_part_3 = Gl5A
        grad_G_w6 = grad_G_w6_part_3.T.dot(grad_G_w6_part_1 * grad_G_w6_part_2)
        grad_G_b6 = grad_G_w6_part_1 * grad_G_w6_part_2

        grad_G_w5_part_1 = (grad_G_w6_part_1 * grad_G_w6_part_2).dot(G_W6.T)
        grad_G_w5_part_2 = d_tanh(Gl5)
        grad_G_w5_part_3 = Gl4A
        grad_G_w5 = grad_G_w5_part_3.T.dot(grad_G_w5_part_1 * grad_G_w5_part_2)
        grad_G_b5 = grad_G_w5_part_1 * grad_G_w5_part_2

        grad_G_w4_part_1 = (grad_G_w5_part_1 * grad_G_w5_part_2).dot(G_W5.T)
        grad_G_w4_part_2 = d_ReLu(Gl4)
        grad_G_w4_part_3 = Gl3A
        grad_G_w4 = grad_G_w4_part_3.T.dot(grad_G_w4_part_1 * grad_G_w4_part_2)
        grad_G_b4 = grad_G_w4_part_1 * grad_G_w4_part_2

        grad_G_w3_part_1 = (grad_G_w4_part_1 * grad_G_w4_part_2).dot(G_W4.T)
        grad_G_w3_part_2 = d_arctan(Gl3)
        grad_G_w3_part_3 = Gl2A
        grad_G_w3 = grad_G_w3_part_3.T.dot(grad_G_w3_part_1 * grad_G_w3_part_2)
        grad_G_b3 = grad_G_w3_part_1 * grad_G_w3_part_2

        grad_G_w2_part_1 = (grad_G_w3_part_1 * grad_G_w3_part_2).dot(G_W3.T)
        grad_G_w2_part_2 = d_ReLu(Gl2)
        grad_G_w2_part_3 = Gl1A
        grad_G_w2 = grad_G_w2_part_3.T.dot(grad_G_w2_part_1 * grad_G_w2_part_2)
        grad_G_b2 = grad_G_w2_part_1 * grad_G_w2_part_2

        grad_G_w1_part_1 = (grad_G_w2_part_1 * grad_G_w2_part_2).dot(G_W2.T)
        grad_G_w1_part_2 = d_arctan(Gl1)
        grad_G_w1_part_3 = Z
        grad_G_w1 = grad_G_w1_part_3.T.dot(grad_G_w1_part_1 * grad_G_w1_part_2)
        grad_G_b1 = grad_G_w1_part_1 * grad_G_w1_part_2

        # ---- Update Gradient ----
        m5 = beta_1 * m5 + (1 - beta_1) * grad_G_w1
        v5 = beta_2 * v5 + (1 - beta_2) * grad_G_w1 ** 2

        m6 = beta_1 * m6 + (1 - beta_1) * grad_G_b1
        v6 = beta_2 * v6 + (1 - beta_2) * grad_G_b1 ** 2

        m7 = beta_1 * m7 + (1 - beta_1) * grad_G_w2
        v7 = beta_2 * v7 + (1 - beta_2) * grad_G_w2 ** 2

        m8 = beta_1 * m8 + (1 - beta_1) * grad_G_b2
        v8 = beta_2 * v8 + (1 - beta_2) * grad_G_b2 ** 2

        m9 = beta_1 * m9 + (1 - beta_1) * grad_G_w3
        v9 = beta_2 * v9 + (1 - beta_2) * grad_G_w3 ** 2

        m10 = beta_1 * m10 + (1 - beta_1) * grad_G_b3
        v10 = beta_2 * v10 + (1 - beta_2) * grad_G_b3 ** 2

        m11 = beta_1 * m11 + (1 - beta_1) * grad_G_w4
        v11 = beta_2 * v11 + (1 - beta_2) * grad_G_w4 ** 2

        m12 = beta_1 * m12 + (1 - beta_1) * grad_G_b4
        v12 = beta_2 * v12 + (1 - beta_2) * grad_G_b4 ** 2

        m13 = beta_1 * m13 + (1 - beta_1) * grad_G_w5
        v13 = beta_2 * v13 + (1 - beta_2) * grad_G_w5 ** 2

        m14 = beta_1 * m14 + (1 - beta_1) * grad_G_b5
        v14 = beta_2 * v14 + (1 - beta_2) * grad_G_b5 ** 2

        m15 = beta_1 * m15 + (1 - beta_1) * grad_G_w6
        v15 = beta_2 * v15 + (1 - beta_2) * grad_G_w6 ** 2

        m16 = beta_1 * m16 + (1 - beta_1) * grad_G_b6
        v16 = beta_2 * v16 + (1 - beta_2) * grad_G_b6 ** 2

        m17 = beta_1 * m17 + (1 - beta_1) * grad_G_w7
        v17 = beta_2 * v17 + (1 - beta_2) * grad_G_w7 ** 2

        m18 = beta_1 * m18 + (1 - beta_1) * grad_G_b7
        v18 = beta_2 * v18 + (1 - beta_2) * grad_G_b7 ** 2

        G_W1 = G_W1 - (learing_rate / (np.sqrt(v5 / (1 - beta_2)) + eps)) * (
            m5 / (1 - beta_1)
        )
        G_b1 = G_b1 - (learing_rate / (np.sqrt(v6 / (1 - beta_2)) + eps)) * (
            m6 / (1 - beta_1)
        )

        G_W2 = G_W2 - (learing_rate / (np.sqrt(v7 / (1 - beta_2)) + eps)) * (
            m7 / (1 - beta_1)
        )
        G_b2 = G_b2 - (learing_rate / (np.sqrt(v8 / (1 - beta_2)) + eps)) * (
            m8 / (1 - beta_1)
        )

        G_W3 = G_W3 - (learing_rate / (np.sqrt(v9 / (1 - beta_2)) + eps)) * (
            m9 / (1 - beta_1)
        )
        G_b3 = G_b3 - (learing_rate / (np.sqrt(v10 / (1 - beta_2)) + eps)) * (
            m10 / (1 - beta_1)
        )

        G_W4 = G_W4 - (learing_rate / (np.sqrt(v11 / (1 - beta_2)) + eps)) * (
            m11 / (1 - beta_1)
        )
        G_b4 = G_b4 - (learing_rate / (np.sqrt(v12 / (1 - beta_2)) + eps)) * (
            m12 / (1 - beta_1)
        )

        G_W5 = G_W5 - (learing_rate / (np.sqrt(v13 / (1 - beta_2)) + eps)) * (
            m13 / (1 - beta_1)
        )
        G_b5 = G_b5 - (learing_rate / (np.sqrt(v14 / (1 - beta_2)) + eps)) * (
            m14 / (1 - beta_1)
        )

        G_W6 = G_W6 - (learing_rate / (np.sqrt(v15 / (1 - beta_2)) + eps)) * (
            m15 / (1 - beta_1)
        )
        G_b6 = G_b6 - (learing_rate / (np.sqrt(v16 / (1 - beta_2)) + eps)) * (
            m16 / (1 - beta_1)
        )

        G_W7 = G_W7 - (learing_rate / (np.sqrt(v17 / (1 - beta_2)) + eps)) * (
            m17 / (1 - beta_1)
        )
        G_b7 = G_b7 - (learing_rate / (np.sqrt(v18 / (1 - beta_2)) + eps)) * (
            m18 / (1 - beta_1)
        )

        # --- Print Error ----
        # print("Current Iter: ",iter, " Current D cost:",D_cost, " Current G cost: ", G_cost,end='\r')

        if iter == 0:
            learing_rate = learing_rate * 0.01
        if iter == 40:
            learing_rate = learing_rate * 0.01

        # ---- Print to Out put ----
        if iter % 10 == 0:

            print(
                "Current Iter: ",
                iter,
                " Current D cost:",
                D_cost,
                " Current G cost: ",
                G_cost,
                end="\r",
            )
            print("--------- Show Example Result See Tab Above ----------")
            print("--------- Wait for the image to load ---------")
            Z = np.random.uniform(-1.0, 1.0, size=[16, G_input])

            Gl1 = Z.dot(G_W1) + G_b1
            Gl1A = arctan(Gl1)
            Gl2 = Gl1A.dot(G_W2) + G_b2
            Gl2A = ReLu(Gl2)
            Gl3 = Gl2A.dot(G_W3) + G_b3
            Gl3A = arctan(Gl3)

            Gl4 = Gl3A.dot(G_W4) + G_b4
            Gl4A = ReLu(Gl4)
            Gl5 = Gl4A.dot(G_W5) + G_b5
            Gl5A = tanh(Gl5)
            Gl6 = Gl5A.dot(G_W6) + G_b6
            Gl6A = ReLu(Gl6)
            Gl7 = Gl6A.dot(G_W7) + G_b7

            current_fake_data = log(Gl7)

            fig = plot(current_fake_data)
            fig.savefig(
                "Click_Me_{}.png".format(
                    str(iter).zfill(3)
                    + "_Ginput_"
                    + str(G_input)
                    + "_hiddenone"
                    + str(hidden_input)
                    + "_hiddentwo"
                    + str(hidden_input2)
                    + "_LR_"
                    + str(learing_rate)
                ),
                bbox_inches="tight",
            )
    # for complete explanation visit https://towardsdatascience.com/only-numpy-implementing-gan-general-adversarial-networks-and-adam-optimizer-using-numpy-with-2a7e4e032021
    # -- end code --


================================================
FILE: neural_network/input_data.py
================================================
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions for downloading and reading MNIST data (deprecated).

This module and all its submodules are deprecated.
"""

import gzip
import os
import typing
import urllib

import numpy as np
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated


class _Datasets(typing.NamedTuple):
    train: "_DataSet"
    validation: "_DataSet"
    test: "_DataSet"


# CVDF mirror of http://yann.lecun.com/exdb/mnist/
DEFAULT_SOURCE_URL = "https://storage.googleapis.com/cvdf-datasets/mnist/"


def _read32(bytestream):
    dt = np.dtype(np.uint32).newbyteorder(">")
    return np.frombuffer(bytestream.read(4), dtype=dt)[0]


@deprecated(None, "Please use tf.data to implement this functionality.")
def _extract_images(f):
    """Extract the images into a 4D uint8 numpy array [index, y, x, depth].

    Args:
      f: A file object that can be passed into a gzip reader.

    Returns:
      data: A 4D uint8 numpy array [index, y, x, depth].

    Raises:
      ValueError: If the bytestream does not start with 2051.

    """
    print("Extracting", f.name)
    with gzip.GzipFile(fileobj=f) as bytestream:
        magic = _read32(bytestream)
        if magic != 2051:
            msg = f"Invalid magic number {magic} in MNIST image file: {f.name}"
            raise ValueError(msg)
        num_images = _read32(bytestream)
        rows = _read32(bytestream)
        cols = _read32(bytestream)
        buf = bytestream.read(rows * cols * num_images)
        data = np.frombuffer(buf, dtype=np.uint8)
        data = data.reshape(num_images, rows, cols, 1)
        return data


@deprecated(None, "Please use tf.one_hot on tensors.")
def _dense_to_one_hot(labels_dense, num_classes):
    """Convert class labels from scalars to one-hot vectors."""
    num_labels = labels_dense.shape[0]
    index_offset = np.arange(num_labels) * num_classes
    labels_one_hot = np.zeros((num_labels, num_classes))
    labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
    return labels_one_hot


@deprecated(None, "Please use tf.data to implement this functionality.")
def _extract_labels(f, one_hot=False, num_classes=10):
    """Extract the labels into a 1D uint8 numpy array [index].

    Args:
      f: A file object that can be passed into a gzip reader.
      one_hot: Does one hot encoding for the result.
      num_classes: Number of classes for the one hot encoding.

    Returns:
      labels: a 1D uint8 numpy array.

    Raises:
      ValueError: If the bystream doesn't start with 2049.
    """
    print("Extracting", f.name)
    with gzip.GzipFile(fileobj=f) as bytestream:
        magic = _read32(bytestream)
        if magic != 2049:
            msg = f"Invalid magic number {magic} in MNIST label file: {f.name}"
            raise ValueError(msg)
        num_items = _read32(bytestream)
        buf = bytestream.read(num_items)
        labels = np.frombuffer(buf, dtype=np.uint8)
        if one_hot:
            return _dense_to_one_hot(labels, num_classes)
        return labels


class _DataSet:
    """Container class for a _DataSet (deprecated).

    THIS CLASS IS DEPRECATED.
    """

    @deprecated(
        None,
        "Please use alternatives such as official/mnist/_DataSet.py"
        " from tensorflow/models.",
    )
    def __init__(
        self,
        images,
        labels,
        fake_data=False,
        one_hot=False,
        dtype=dtypes.float32,
        reshape=True,
        seed=None,
    ):
        """Construct a _DataSet.

        one_hot arg is used only if fake_data is true.  `dtype` can be either
        `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
        `[0, 1]`.  Seed arg provides for convenient deterministic testing.

        Args:
          images: The images
          labels: The labels
          fake_data: Ignore inages and labels, use fake data.
          one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
            False).
          dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
            range [0,255]. float32 output has range [0,1].
          reshape: Bool. If True returned images are returned flattened to vectors.
          seed: The random seed to use.
        """
        seed1, seed2 = random_seed.get_seed(seed)
        # If op level seed is not set, use whatever graph level seed is returned
        self._rng = np.random.default_rng(seed1 if seed is None else seed2)
        dtype = dtypes.as_dtype(dtype).base_dtype
        if dtype not in (dtypes.uint8, dtypes.float32):
            msg = f"Invalid image dtype {dtype!r}, expected uint8 or float32"
            raise TypeError(msg)
        if fake_data:
            self._num_examples = 10000
            self.one_hot = one_hot
        else:
            assert images.shape[0] == labels.shape[0], (
                f"images.shape: {images.shape} labels.shape: {labels.shape}"
            )
            self._num_examples = images.shape[0]

            # Convert shape from [num examples, rows, columns, depth]
            # to [num examples, rows*columns] (assuming depth == 1)
            if reshape:
                assert images.shape[3] == 1
                images = images.reshape(
                    images.shape[0], images.shape[1] * images.shape[2]
                )
            if dtype == dtypes.float32:
                # Convert from [0, 255] -> [0.0, 1.0].
                images = images.astype(np.float32)
                images = np.multiply(images, 1.0 / 255.0)
        self._images = images
        self._labels = labels
        self._epochs_completed = 0
        self._index_in_epoch = 0

    @property
    def images(self):
        return self._images

    @property
    def labels(self):
        return self._labels

    @property
    def num_examples(self):
        return self._num_examples

    @property
    def epochs_completed(self):
        return self._epochs_completed

    def next_batch(self, batch_size, fake_data=False, shuffle=True):
        """Return the next `batch_size` examples from this data set."""
        if fake_data:
            fake_image = [1] * 784
            fake_label = [1] + [0] * 9 if self.one_hot else 0
            return (
                [fake_image for _ in range(batch_size)],
                [fake_label for _ in range(batch_size)],
            )
        start = self._index_in_epoch
        # Shuffle for the first epoch
        if self._epochs_completed == 0 and start == 0 and shuffle:
            perm0 = np.arange(self._num_examples)
            self._rng.shuffle(perm0)
            self._images = self.images[perm0]
            self._labels = self.labels[perm0]
        # Go to the next epoch
        if start + batch_size > self._num_examples:
            # Finished epoch
            self._epochs_completed += 1
            # Get the rest examples in this epoch
            rest_num_examples = self._num_examples - start
            images_rest_part = self._images[start : self._num_examples]
            labels_rest_part = self._labels[start : self._num_examples]
            # Shuffle the data
            if shuffle:
                perm = np.arange(self._num_examples)
                self._rng.shuffle(perm)
                self._images = self.images[perm]
                self._labels = self.labels[perm]
            # Start next epoch
            start = 0
            self._index_in_epoch = batch_size - rest_num_examples
            end = self._index_in_epoch
            images_new_part = self._images[start:end]
            labels_new_part = self._labels[start:end]
            return (
                np.concatenate((images_rest_part, images_new_part), axis=0),
                np.concatenate((labels_rest_part, labels_new_part), axis=0),
            )
        else:
            self._index_in_epoch += batch_size
            end = self._index_in_epoch
            return self._images[start:end], self._labels[start:end]


@deprecated(None, "Please write your own downloading logic.")
def _maybe_download(filename, work_directory, source_url):
    """Download the data from source url, unless it's already here.

    Args:
        filename: string, name of the file in the directory.
        work_directory: string, path to working directory.
        source_url: url to download from if file doesn't exist.

    Returns:
        Path to resulting file.
    """
    if not gfile.Exists(work_directory):
        gfile.MakeDirs(work_directory)
    filepath = os.path.join(work_directory, filename)
    if not gfile.Exists(filepath):
        urllib.request.urlretrieve(source_url, filepath)  # noqa: S310
        with gfile.GFile(filepath) as f:
            size = f.size()
        print("Successfully downloaded", filename, size, "bytes.")
    return filepath


@deprecated(None, "Please use alternatives such as: tensorflow_datasets.load('mnist')")
def read_data_sets(
    train_dir,
    fake_data=False,
    one_hot=False,
    dtype=dtypes.float32,
    reshape=True,
    validation_size=5000,
    seed=None,
    source_url=DEFAULT_SOURCE_URL,
):
    if fake_data:

        def fake():
            return _DataSet(
                [], [], fake_data=True, one_hot=one_hot, dtype=dtype, seed=seed
            )

        train = fake()
        validation = fake()
        test = fake()
        return _Datasets(train=train, validation=validation, test=test)

    if not source_url:  # empty string check
        source_url = DEFAULT_SOURCE_URL

    train_images_file = "train-images-idx3-ubyte.gz"
    train_labels_file = "train-labels-idx1-ubyte.gz"
    test_images_file = "t10k-images-idx3-ubyte.gz"
    test_labels_file = "t10k-labels-idx1-ubyte.gz"

    local_file = _maybe_download(
        train_images_file, train_dir, source_url + train_images_file
    )
    with gfile.Open(local_file, "rb") as f:
        train_images = _extract_images(f)

    local_file = _maybe_download(
        train_labels_file, train_dir, source_url + train_labels_file
    )
    with gfile.Open(local_file, "rb") as f:
        train_labels = _extract_labels(f, one_hot=one_hot)

    local_file = _maybe_download(
        test_images_file, train_dir, source_url + test_images_file
    )
    with gfile.Open(local_file, "rb") as f:
        test_images = _extract_images(f)

    local_file = _maybe_download(
        test_labels_file, train_dir, source_url + test_labels_file
    )
    with gfile.Open(local_file, "rb") as f:
        test_labels = _extract_labels(f, one_hot=one_hot)

    if not 0 <= validation_size <= len(train_images):
        msg = (
            "Validation size should be between 0 and "
            f"{len(train_images)}. Received: {validation_size}."
        )
        raise ValueError(msg)

    validation_images = train_images[:validation_size]
    validation_labels = train_labels[:validation_size]
    train_images = train_images[validation_size:]
    train_labels = train_labels[validation_size:]

    options = {"dtype": dtype, "reshape": reshape, "seed": seed}

    train = _DataSet(train_images, train_labels, **options)
    validation = _DataSet(validation_images, validation_labels, **options)
    test = _DataSet(test_images, test_labels, **options)

    return _Datasets(train=train, validation=validation, test=test)


================================================
FILE: neural_network/perceptron.py.DISABLED
================================================
"""
    Perceptron
    w = w + N * (d(k) - y) * x(k)

    Using perceptron network for oil analysis, with Measuring of 3 parameters
    that represent chemical characteristics we can classify the oil, in p1 or p2
    p1 = -1
    p2 = 1
"""
import random


class Perceptron:
    def __init__(
        self,
        sample: list[list[float]],
        target: list[int],
        learning_rate: float = 0.01,
        epoch_number: int = 1000,
        bias: float = -1,
    ) -> None:
        """
        Initializes a Perceptron network for oil analysis
        :param sample: sample dataset of 3 parameters with shape [30,3]
        :param target: variable for classification with two possible states -1 or 1
        :param learning_rate: learning rate used in optimizing.
        :param epoch_number: number of epochs to train network on.
        :param bias: bias value for the network.

        >>> p = Perceptron([], (0, 1, 2))
        Traceback (most recent call last):
            ...
        ValueError: Sample data can not be empty
        >>> p = Perceptron(([0], 1, 2), [])
        Traceback (most recent call last):
            ...
        ValueError: Target data can not be empty
        >>> p = Perceptron(([0], 1, 2), (0, 1))
        Traceback (most recent call last):
            ...
        ValueError: Sample data and Target data do not have matching lengths
        """
        self.sample = sample
        if len(self.sample) == 0:
            raise ValueError("Sample data can not be empty")
        self.target = target
        if len(self.target) == 0:
            raise ValueError("Target data can not be empty")
        if len(self.sample) != len(self.target):
            raise ValueError("Sample data and Target data do not have matching lengths")
        self.learning_rate = learning_rate
        self.epoch_number = epoch_number
        self.bias = bias
        self.number_sample = len(sample)
        self.col_sample = len(sample[0])  # number of columns in dataset
        self.weight: list = []

    def training(self) -> None:
        """
        Trains perceptron for epochs <= given number of epochs
        :return: None
        >>> data = [[2.0149, 0.6192, 10.9263]]
        >>> targets = [-1]
        >>> perceptron = Perceptron(data,targets)
        >>> perceptron.training() # doctest: +ELLIPSIS
        ('\\nEpoch:\\n', ...)
        ...
        """
        for sample in self.sample:
            sample.insert(0, self.bias)

        for _ in range(self.col_sample):
            self.weight.append(random.random())

        self.weight.insert(0, self.bias)

        epoch_count = 0

        while True:
            has_misclassified = False
            for i in range(self.number_sample):
                u = 0
                for j in range(self.col_sample + 1):
                    u = u + self.weight[j] * self.sample[i][j]
                y = self.sign(u)
                if y != self.target[i]:
                    for j in range(self.col_sample + 1):
                        self.weight[j] = (
                            self.weight[j]
                            + self.learning_rate
                            * (self.target[i] - y)
                            * self.sample[i][j]
                        )
                    has_misclassified = True
            # print('Epoch: \n',epoch_count)
            epoch_count = epoch_count + 1
            # if you want control the epoch or just by error
            if not has_misclassified:
                print(("\nEpoch:\n", epoch_count))
                print("------------------------\n")
                # if epoch_count > self.epoch_number or not error:
                break

    def sort(self, sample: list[float]) -> None:
        """
        :param sample: example row to classify as P1 or P2
        :return: None
        >>> data = [[2.0149, 0.6192, 10.9263]]
        >>> targets = [-1]
        >>> perceptron = Perceptron(data,targets)
        >>> perceptron.training() # doctest: +ELLIPSIS
        ('\\nEpoch:\\n', ...)
        ...
        >>> perceptron.sort([-0.6508, 0.1097, 4.0009]) # doctest: +ELLIPSIS
        ('Sample: ', ...)
        classification: P...
        """
        if len(self.sample) == 0:
            raise ValueError("Sample data can not be empty")
        sample.insert(0, self.bias)
        u = 0
        for i in range(self.col_sample + 1):
            u = u + self.weight[i] * sample[i]

        y = self.sign(u)

        if y == -1:
            print(("Sample: ", sample))
            print("classification: P1")
        else:
            print(("Sample: ", sample))
            print("classification: P2")

    def sign(self, u: float) -> int:
        """
        threshold function for classification
        :param u: input number
        :return: 1 if the input is greater than 0, otherwise -1
        >>> data = [[0],[-0.5],[0.5]]
        >>> targets = [1,-1,1]
        >>> perceptron = Perceptron(data,targets)
        >>> perceptron.sign(0)
        1
        >>> perceptron.sign(-0.5)
        -1
        >>> perceptron.sign(0.5)
        1
        """
        return 1 if u >= 0 else -1


samples = [
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

target = [
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    network = Perceptron(
        sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
    )
    network.training()
    print("Finished training perceptron")
    print("Enter values to predict or q to exit")
    while True:
        sample: list = []
        for i in range(len(samples[0])):
            user_input = input("value: ").strip()
            if user_input == "q":
                break
            observation = float(user_input)
            sample.insert(i, observation)
        network.sort(sample)


================================================
FILE: neural_network/simple_neural_network.py
================================================
"""
Forward propagation explanation:
https://towardsdatascience.com/forward-propagation-in-neural-networks-simplified-math-and-code-version-bbcfef6f9250
"""

import math
import random


# Sigmoid
def sigmoid_function(value: float, deriv: bool = False) -> float:
    """Return the sigmoid function of a float.

    >>> sigmoid_function(3.5)
    0.9706877692486436
    >>> sigmoid_function(3.5, True)
    -8.75
    """
    if deriv:
        return value * (1 - value)
    return 1 / (1 + math.exp(-value))


# Initial Value
INITIAL_VALUE = 0.02


def forward_propagation(expected: int, number_propagations: int) -> float:
    """Return the value found after the forward propagation training.

    >>> res = forward_propagation(32, 450_000)  # Was 10_000_000
    >>> res > 31 and res < 33
    True

    >>> res = forward_propagation(32, 1000)
    >>> res > 31 and res < 33
    False
    """

    # Random weight
    weight = float(2 * (random.randint(1, 100)) - 1)

    for _ in range(number_propagations):
        # Forward propagation
        layer_1 = sigmoid_function(INITIAL_VALUE * weight)
        # How much did we miss?
        layer_1_error = (expected / 100) - layer_1
        # Error delta
        layer_1_delta = layer_1_error * sigmoid_function(layer_1, True)
        # Update weight
        weight += INITIAL_VALUE * layer_1_delta

    return layer_1 * 100


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    expected = int(input("Expected value: "))
    number_propagations = int(input("Number of propagations: "))
    print(forward_propagation(expected, number_propagations))


================================================
FILE: neural_network/two_hidden_layers_neural_network.py
================================================
"""
References:
    - http://neuralnetworksanddeeplearning.com/chap2.html (Backpropagation)
    - https://en.wikipedia.org/wiki/Sigmoid_function (Sigmoid activation function)
    - https://en.wikipedia.org/wiki/Feedforward_neural_network (Feedforward)
"""

import numpy as np


class TwoHiddenLayerNeuralNetwork:
    def __init__(self, input_array: np.ndarray, output_array: np.ndarray) -> None:
        """
        This function initializes the TwoHiddenLayerNeuralNetwork class with random
        weights for every layer and initializes predicted output with zeroes.

        input_array : input values for training the neural network (i.e training data) .
        output_array : expected output values of the given inputs.
        """

        # Input values provided for training the model.
        self.input_array = input_array

        # Random initial weights are assigned where first argument is the
        # number of nodes in previous layer and second argument is the
        # number of nodes in the next layer.

        # Random initial weights are assigned.
        # self.input_array.shape[1] is used to represent number of nodes in input layer.
        # First hidden layer consists of 4 nodes.
        rng = np.random.default_rng()
        self.input_layer_and_first_hidden_layer_weights = rng.random(
            (self.input_array.shape[1], 4)
        )

        # Random initial values for the first hidden layer.
        # First hidden layer has 4 nodes.
        # Second hidden layer has 3 nodes.
        self.first_hidden_layer_and_second_hidden_layer_weights = rng.random((4, 3))

        # Random initial values for the second hidden layer.
        # Second hidden layer has 3 nodes.
        # Output layer has 1 node.
        self.second_hidden_layer_and_output_layer_weights = rng.random((3, 1))

        # Real output values provided.
        self.output_array = output_array

        # Predicted output values by the neural network.
        # Predicted_output array initially consists of zeroes.
        self.predicted_output = np.zeros(output_array.shape)

    def feedforward(self) -> np.ndarray:
        """
        The information moves in only one direction i.e. forward from the input nodes,
        through the two hidden nodes and to the output nodes.
        There are no cycles or loops in the network.

        Return layer_between_second_hidden_layer_and_output
            (i.e the last layer of the neural network).

        >>> input_val = np.array(([0, 0, 0], [0, 0, 0], [0, 0, 0]), dtype=float)
        >>> output_val = np.array(([0], [0], [0]), dtype=float)
        >>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
        >>> res = nn.feedforward()
        >>> array_sum = np.sum(res)
        >>> bool(np.isnan(array_sum))
        False
        """
        # Layer_between_input_and_first_hidden_layer is the layer connecting the
        # input nodes with the first hidden layer nodes.
        self.layer_between_input_and_first_hidden_layer = sigmoid(
            np.dot(self.input_array, self.input_layer_and_first_hidden_layer_weights)
        )

        # layer_between_first_hidden_layer_and_second_hidden_layer is the layer
        # connecting the first hidden set of nodes with the second hidden set of nodes.
        self.layer_between_first_hidden_layer_and_second_hidden_layer = sigmoid(
            np.dot(
                self.layer_between_input_and_first_hidden_layer,
                self.first_hidden_layer_and_second_hidden_layer_weights,
            )
        )

        # layer_between_second_hidden_layer_and_output is the layer connecting
        # second hidden layer with the output node.
        self.layer_between_second_hidden_layer_and_output = sigmoid(
            np.dot(
                self.layer_between_first_hidden_layer_and_second_hidden_layer,
                self.second_hidden_layer_and_output_layer_weights,
            )
        )

        return self.layer_between_second_hidden_layer_and_output

    def back_propagation(self) -> None:
        """
        Function for fine-tuning the weights of the neural net based on the
        error rate obtained in the previous epoch (i.e., iteration).
        Updation is done using derivative of sogmoid activation function.

        >>> input_val = np.array(([0, 0, 0], [0, 0, 0], [0, 0, 0]), dtype=float)
        >>> output_val = np.array(([0], [0], [0]), dtype=float)
        >>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
        >>> res = nn.feedforward()
        >>> nn.back_propagation()
        >>> updated_weights = nn.second_hidden_layer_and_output_layer_weights
        >>> bool((res == updated_weights).all())
        False
        """

        updated_second_hidden_layer_and_output_layer_weights = np.dot(
            self.layer_between_first_hidden_layer_and_second_hidden_layer.T,
            2
            * (self.output_array - self.predicted_output)
            * sigmoid_derivative(self.predicted_output),
        )
        updated_first_hidden_layer_and_second_hidden_layer_weights = np.dot(
            self.layer_between_input_and_first_hidden_layer.T,
            np.dot(
                2
                * (self.output_array - self.predicted_output)
                * sigmoid_derivative(self.predicted_output),
                self.second_hidden_layer_and_output_layer_weights.T,
            )
            * sigmoid_derivative(
                self.layer_between_first_hidden_layer_and_second_hidden_layer
            ),
        )
        updated_input_layer_and_first_hidden_layer_weights = np.dot(
            self.input_array.T,
            np.dot(
                np.dot(
                    2
                    * (self.output_array - self.predicted_output)
                    * sigmoid_derivative(self.predicted_output),
                    self.second_hidden_layer_and_output_layer_weights.T,
                )
                * sigmoid_derivative(
                    self.layer_between_first_hidden_layer_and_second_hidden_layer
                ),
                self.first_hidden_layer_and_second_hidden_layer_weights.T,
            )
            * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer),
        )

        self.input_layer_and_first_hidden_layer_weights += (
            updated_input_layer_and_first_hidden_layer_weights
        )
        self.first_hidden_layer_and_second_hidden_layer_weights += (
            updated_first_hidden_layer_and_second_hidden_layer_weights
        )
        self.second_hidden_layer_and_output_layer_weights += (
            updated_second_hidden_layer_and_output_layer_weights
        )

    def train(self, output: np.ndarray, iterations: int, give_loss: bool) -> None:
        """
        Performs the feedforwarding and back propagation process for the
        given number of iterations.
        Every iteration will update the weights of neural network.

        output : real output values,required for calculating loss.
        iterations : number of times the weights are to be updated.
        give_loss : boolean value, If True then prints loss for each iteration,
                    If False then nothing is printed

        >>> input_val = np.array(([0, 0, 0], [0, 1, 0], [0, 0, 1]), dtype=float)
        >>> output_val = np.array(([0], [1], [1]), dtype=float)
        >>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
        >>> first_iteration_weights = nn.feedforward()
        >>> nn.back_propagation()
        >>> updated_weights = nn.second_hidden_layer_and_output_layer_weights
        >>> bool((first_iteration_weights == updated_weights).all())
        False
        """
        for iteration in range(1, iterations + 1):
            self.output = self.feedforward()
            self.back_propagation()
            if give_loss:
                loss = np.mean(np.square(output - self.feedforward()))
                print(f"Iteration {iteration} Loss: {loss}")

    def predict(self, input_arr: np.ndarray) -> int:
        """
        Predict's the output for the given input values using
        the trained neural network.

        The output value given by the model ranges in-between 0 and 1.
        The predict function returns 1 if the model value is greater
        than the threshold value else returns 0,
        as the real output values are in binary.

        >>> input_val = np.array(([0, 0, 0], [0, 1, 0], [0, 0, 1]), dtype=float)
        >>> output_val = np.array(([0], [1], [1]), dtype=float)
        >>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
        >>> nn.train(output_val, 1000, False)
        >>> nn.predict([0, 1, 0]) in (0, 1)
        True
        """

        # Input values for which the predictions are to be made.
        self.array = input_arr

        self.layer_between_input_and_first_hidden_layer = sigmoid(
            np.dot(self.array, self.input_layer_and_first_hidden_layer_weights)
        )

        self.layer_between_first_hidden_layer_and_second_hidden_layer = sigmoid(
            np.dot(
                self.layer_between_input_and_first_hidden_layer,
                self.first_hidden_layer_and_second_hidden_layer_weights,
            )
        )

        self.layer_between_second_hidden_layer_and_output = sigmoid(
            np.dot(
                self.layer_between_first_hidden_layer_and_second_hidden_layer,
                self.second_hidden_layer_and_output_layer_weights,
            )
        )

        return int((self.layer_between_second_hidden_layer_and_output > 0.6)[0])


def sigmoid(value: np.ndarray) -> np.ndarray:
    """
    Applies sigmoid activation function.

    return normalized values

    >>> sigmoid(np.array(([1, 0, 2], [1, 0, 0]), dtype=np.float64))
    array([[0.73105858, 0.5       , 0.88079708],
           [0.73105858, 0.5       , 0.5       ]])
    """
    return 1 / (1 + np.exp(-value))


def sigmoid_derivative(value: np.ndarray) -> np.ndarray:
    """
    Provides the derivative value of the sigmoid function.

    returns derivative of the sigmoid value

    >>> sigmoid_derivative(np.array(([1, 0, 2], [1, 0, 0]), dtype=np.float64))
    array([[ 0.,  0., -2.],
           [ 0.,  0.,  0.]])
    """
    return (value) * (1 - (value))


def example() -> int:
    """
    Example for "how to use the neural network class and use the
    respected methods for the desired output".
    Calls the TwoHiddenLayerNeuralNetwork class and
    provides the fixed input output values to the model.
    Model is trained for a fixed amount of iterations then the predict method is called.
    In this example the output is divided into 2 classes i.e. binary classification,
    the two classes are represented by '0' and '1'.

    >>> example() in (0, 1)
    True
    """
    # Input values.
    test_input = np.array(
        (
            [0, 0, 0],
            [0, 0, 1],
            [0, 1, 0],
            [0, 1, 1],
            [1, 0, 0],
            [1, 0, 1],
            [1, 1, 0],
            [1, 1, 1],
        ),
        dtype=np.float64,
    )

    # True output values for the given input values.
    output = np.array(([0], [1], [1], [0], [1], [0], [0], [1]), dtype=np.float64)

    # Calling neural network class.
    neural_network = TwoHiddenLayerNeuralNetwork(
        input_array=test_input, output_array=output
    )

    # Calling training function.
    # Set give_loss to True if you want to see loss in every iteration.
    neural_network.train(output=output, iterations=10, give_loss=False)

    return neural_network.predict(np.array(([1, 1, 1]), dtype=np.float64))


if __name__ == "__main__":
    example()


================================================
FILE: other/__init__.py
================================================


================================================
FILE: other/activity_selection.py
================================================
"""The following implementation assumes that the activities
are already sorted according to their finish time"""

"""Prints a maximum set of activities that can be done by a
single person, one at a time"""
# n --> Total number of activities
# start[]--> An array that contains start time of all activities
# finish[] --> An array that contains finish time of all activities


def print_max_activities(start: list[int], finish: list[int]) -> None:
    """
    >>> start = [1, 3, 0, 5, 8, 5]
    >>> finish = [2, 4, 6, 7, 9, 9]
    >>> print_max_activities(start, finish)
    The following activities are selected:
    0,1,3,4,
    """
    n = len(finish)
    print("The following activities are selected:")

    # The first activity is always selected
    i = 0
    print(i, end=",")

    # Consider rest of the activities
    for j in range(n):
        # If this activity has start time greater than
        # or equal to the finish time of previously
        # selected activity, then select it
        if start[j] >= finish[i]:
            print(j, end=",")
            i = j


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    start = [1, 3, 0, 5, 8, 5]
    finish = [2, 4, 6, 7, 9, 9]
    print_max_activities(start, finish)


================================================
FILE: other/alternative_list_arrange.py
================================================
def alternative_list_arrange(first_input_list: list, second_input_list: list) -> list:
    """
    The method arranges two lists as one list in alternative forms of the list elements.
    :param first_input_list:
    :param second_input_list:
    :return: List
    >>> alternative_list_arrange([1, 2, 3, 4, 5], ["A", "B", "C"])
    [1, 'A', 2, 'B', 3, 'C', 4, 5]
    >>> alternative_list_arrange(["A", "B", "C"], [1, 2, 3, 4, 5])
    ['A', 1, 'B', 2, 'C', 3, 4, 5]
    >>> alternative_list_arrange(["X", "Y", "Z"], [9, 8, 7, 6])
    ['X', 9, 'Y', 8, 'Z', 7, 6]
    >>> alternative_list_arrange([1, 2, 3, 4, 5], [])
    [1, 2, 3, 4, 5]
    """
    first_input_list_length: int = len(first_input_list)
    second_input_list_length: int = len(second_input_list)
    abs_length: int = (
        first_input_list_length
        if first_input_list_length > second_input_list_length
        else second_input_list_length
    )
    output_result_list: list = []
    for char_count in range(abs_length):
        if char_count < first_input_list_length:
            output_result_list.append(first_input_list[char_count])
        if char_count < second_input_list_length:
            output_result_list.append(second_input_list[char_count])

    return output_result_list


if __name__ == "__main__":
    print(alternative_list_arrange(["A", "B", "C"], [1, 2, 3, 4, 5]), end=" ")


================================================
FILE: other/bankers_algorithm.py
================================================
# A Python implementation of the Banker's Algorithm in Operating Systems using
# Processes and Resources
# {
# "Author: "Biney Kingsley (bluedistro@github.io), bineykingsley36@gmail.com",
# "Date": 28-10-2018
# }
"""
The Banker's algorithm is a resource allocation and deadlock avoidance algorithm
developed by Edsger Dijkstra that tests for safety by simulating the allocation of
predetermined maximum possible amounts of all resources, and then makes a "s-state"
check to test for possible deadlock conditions for all other pending activities,
before deciding whether allocation should be allowed to continue.

| [Source] Wikipedia
| [Credit] Rosetta Code C implementation helped very much.
| (https://rosettacode.org/wiki/Banker%27s_algorithm)
"""

from __future__ import annotations

import numpy as np

test_claim_vector = [8, 5, 9, 7]
test_allocated_res_table = [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
test_maximum_claim_table = [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]


class BankersAlgorithm:
    def __init__(
        self,
        claim_vector: list[int],
        allocated_resources_table: list[list[int]],
        maximum_claim_table: list[list[int]],
    ) -> None:
        """
        :param claim_vector: A nxn/nxm list depicting the amount of each resources
         (eg. memory, interface, semaphores, etc.) available.
        :param allocated_resources_table: A nxn/nxm list depicting the amount of each
         resource each process is currently holding
        :param maximum_claim_table: A nxn/nxm list depicting how much of each resource
         the system currently has available
        """
        self.__claim_vector = claim_vector
        self.__allocated_resources_table = allocated_resources_table
        self.__maximum_claim_table = maximum_claim_table

    def __processes_resource_summation(self) -> list[int]:
        """
        Check for allocated resources in line with each resource in the claim vector
        """
        return [
            sum(p_item[i] for p_item in self.__allocated_resources_table)
            for i in range(len(self.__allocated_resources_table[0]))
        ]

    def __available_resources(self) -> list[int]:
        """
        Check for available resources in line with each resource in the claim vector
        """
        return np.array(self.__claim_vector) - np.array(
            self.__processes_resource_summation()
        )

    def __need(self) -> list[list[int]]:
        """
        Implement safety checker that calculates the needs by ensuring that
        ``max_claim[i][j] - alloc_table[i][j] <= avail[j]``
        """
        return [
            list(np.array(self.__maximum_claim_table[i]) - np.array(allocated_resource))
            for i, allocated_resource in enumerate(self.__allocated_resources_table)
        ]

    def __need_index_manager(self) -> dict[int, list[int]]:
        """
        This function builds an index control dictionary to track original ids/indices
        of processes when altered during execution of method "main"

            :Return: {0: [a: int, b: int], 1: [c: int, d: int]}

        >>> index_control = BankersAlgorithm(
        ...     test_claim_vector, test_allocated_res_table, test_maximum_claim_table
        ... )._BankersAlgorithm__need_index_manager()
        >>> {key: [int(x) for x in value] for key, value
        ...     in index_control.items()}  # doctest: +NORMALIZE_WHITESPACE
        {0: [1, 2, 0, 3], 1: [0, 1, 3, 1], 2: [1, 1, 0, 2], 3: [1, 3, 2, 0],
         4: [2, 0, 0, 3]}
        """
        return {self.__need().index(i): i for i in self.__need()}

    def main(self, **kwargs) -> None:
        """
        Utilize various methods in this class to simulate the Banker's algorithm
            :Return: None

        >>> BankersAlgorithm(test_claim_vector, test_allocated_res_table,
        ...    test_maximum_claim_table).main(describe=True)
                 Allocated Resource Table
        P1       2        0        1        1
        <BLANKLINE>
        P2       0        1        2        1
        <BLANKLINE>
        P3       4        0        0        3
        <BLANKLINE>
        P4       0        2        1        0
        <BLANKLINE>
        P5       1        0        3        0
        <BLANKLINE>
                 System Resource Table
        P1       3        2        1        4
        <BLANKLINE>
        P2       0        2        5        2
        <BLANKLINE>
        P3       5        1        0        5
        <BLANKLINE>
        P4       1        5        3        0
        <BLANKLINE>
        P5       3        0        3        3
        <BLANKLINE>
        Current Usage by Active Processes: 8 5 9 7
        Initial Available Resources:       1 2 2 2
        __________________________________________________
        <BLANKLINE>
        Process 3 is executing.
        Updated available resource stack for processes: 5 2 2 5
        The process is in a safe state.
        <BLANKLINE>
        Process 1 is executing.
        Updated available resource stack for processes: 7 2 3 6
        The process is in a safe state.
        <BLANKLINE>
        Process 2 is executing.
        Updated available resource stack for processes: 7 3 5 7
        The process is in a safe state.
        <BLANKLINE>
        Process 4 is executing.
        Updated available resource stack for processes: 7 5 6 7
        The process is in a safe state.
        <BLANKLINE>
        Process 5 is executing.
        Updated available resource stack for processes: 8 5 9 7
        The process is in a safe state.
        <BLANKLINE>
        """
        need_list = self.__need()
        alloc_resources_table = self.__allocated_resources_table
        available_resources = self.__available_resources()
        need_index_manager = self.__need_index_manager()
        for kw, val in kwargs.items():
            if kw and val is True:
                self.__pretty_data()
        print("_" * 50 + "\n")
        while need_list:
            safe = False
            for each_need in need_list:
                execution = True
                for index, need in enumerate(each_need):
                    if need > available_resources[index]:
                        execution = False
                        break
                if execution:
                    safe = True
                    # get the original index of the process from ind_ctrl db
                    for original_need_index, need_clone in need_index_manager.items():
                        if each_need == need_clone:
                            process_number = original_need_index
                    print(f"Process {process_number + 1} is executing.")
                    # remove the process run from stack
                    need_list.remove(each_need)
                    # update available/freed resources stack
                    available_resources = np.array(available_resources) + np.array(
                        alloc_resources_table[process_number]
                    )
                    print(
                        "Updated available resource stack for processes: "
                        + " ".join([str(x) for x in available_resources])
                    )
                    break
            if safe:
                print("The process is in a safe state.\n")
            else:
                print("System in unsafe state. Aborting...\n")
                break

    def __pretty_data(self):
        """
        Properly align display of the algorithm's solution
        """
        print(" " * 9 + "Allocated Resource Table")
        for item in self.__allocated_resources_table:
            print(
                f"P{self.__allocated_resources_table.index(item) + 1}"
                + " ".join(f"{it:>8}" for it in item)
                + "\n"
            )
        print(" " * 9 + "System Resource Table")
        for item in self.__maximum_claim_table:
            print(
                f"P{self.__maximum_claim_table.index(item) + 1}"
                + " ".join(f"{it:>8}" for it in item)
                + "\n"
            )
        print(
            "Current Usage by Active Processes: "
            + " ".join(str(x) for x in self.__claim_vector)
        )
        print(
            "Initial Available Resources:       "
            + " ".join(str(x) for x in self.__available_resources())
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: other/davis_putnam_logemann_loveland.py
================================================
#!/usr/bin/env python3

"""
Davis-Putnam-Logemann-Loveland (DPLL) algorithm is a complete, backtracking-based
search algorithm for deciding the satisfiability of propositional logic formulae in
conjunctive normal form, i.e, for solving the Conjunctive Normal Form SATisfiability
(CNF-SAT) problem.

For more information about the algorithm: https://en.wikipedia.org/wiki/DPLL_algorithm
"""

from __future__ import annotations

import random
from collections.abc import Iterable


class Clause:
    """
    | A clause represented in Conjunctive Normal Form.
    | A clause is a set of literals, either complemented or otherwise.

    For example:
        * {A1, A2, A3'} is the clause (A1 v A2 v A3')
        * {A5', A2', A1} is the clause (A5' v A2' v A1)

    Create model

    >>> clause = Clause(["A1", "A2'", "A3"])
    >>> clause.evaluate({"A1": True})
    True
    """

    def __init__(self, literals: list[str]) -> None:
        """
        Represent the literals and an assignment in a clause."
        """
        # Assign all literals to None initially
        self.literals: dict[str, bool | None] = dict.fromkeys(literals)

    def __str__(self) -> str:
        """
        To print a clause as in Conjunctive Normal Form.

        >>> str(Clause(["A1", "A2'", "A3"]))
        "{A1 , A2' , A3}"
        """
        return "{" + " , ".join(self.literals) + "}"

    def __len__(self) -> int:
        """
        To print a clause as in Conjunctive Normal Form.

        >>> len(Clause([]))
        0
        >>> len(Clause(["A1", "A2'", "A3"]))
        3
        """
        return len(self.literals)

    def assign(self, model: dict[str, bool | None]) -> None:
        """
        Assign values to literals of the clause as given by model.
        """
        for literal in self.literals:
            symbol = literal[:2]
            if symbol in model:
                value = model[symbol]
            else:
                continue
            # Complement assignment if literal is in complemented form
            if value is not None and literal.endswith("'"):
                value = not value
            self.literals[literal] = value

    def evaluate(self, model: dict[str, bool | None]) -> bool | None:
        """
        Evaluates the clause with the assignments in model.

        This has the following steps:
          1. Return ``True`` if both a literal and its complement exist in the clause.
          2. Return ``True`` if a single literal has the assignment ``True``.
          3. Return ``None`` (unable to complete evaluation)
             if a literal has no assignment.
          4. Compute disjunction of all values assigned in clause.
        """
        for literal in self.literals:
            symbol = literal.rstrip("'") if literal.endswith("'") else literal + "'"
            if symbol in self.literals:
                return True

        self.assign(model)
        for value in self.literals.values():
            if value in (True, None):
                return value
        return any(self.literals.values())


class Formula:
    """
    | A formula represented in Conjunctive Normal Form.
    | A formula is a set of clauses.
    | For example,
    |   {{A1, A2, A3'}, {A5', A2', A1}} is ((A1 v A2 v A3') and (A5' v A2' v A1))
    """

    def __init__(self, clauses: Iterable[Clause]) -> None:
        """
        Represent the number of clauses and the clauses themselves.
        """
        self.clauses = list(clauses)

    def __str__(self) -> str:
        """
        To print a formula as in Conjunctive Normal Form.

        >>> str(Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])]))
        "{{A1 , A2' , A3} , {A5' , A2' , A1}}"
        """
        return "{" + " , ".join(str(clause) for clause in self.clauses) + "}"


def generate_clause() -> Clause:
    """
    | Randomly generate a clause.
    | All literals have the name Ax, where x is an integer from ``1`` to ``5``.
    """
    literals = []
    no_of_literals = random.randint(1, 5)
    base_var = "A"
    i = 0
    while i < no_of_literals:
        var_no = random.randint(1, 5)
        var_name = base_var + str(var_no)
        var_complement = random.randint(0, 1)
        if var_complement == 1:
            var_name += "'"
        if var_name in literals:
            i -= 1
        else:
            literals.append(var_name)
        i += 1
    return Clause(literals)


def generate_formula() -> Formula:
    """
    Randomly generate a formula.
    """
    clauses: set[Clause] = set()
    no_of_clauses = random.randint(1, 10)
    while len(clauses) < no_of_clauses:
        clauses.add(generate_clause())
    return Formula(clauses)


def generate_parameters(formula: Formula) -> tuple[list[Clause], list[str]]:
    """
    | Return the clauses and symbols from a formula.
    | A symbol is the uncomplemented form of a literal.

    For example,
      * Symbol of A3 is A3.
      * Symbol of A5' is A5.

    >>> formula = Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])])
    >>> clauses, symbols = generate_parameters(formula)
    >>> clauses_list = [str(i) for i in clauses]
    >>> clauses_list
    ["{A1 , A2' , A3}", "{A5' , A2' , A1}"]
    >>> symbols
    ['A1', 'A2', 'A3', 'A5']
    """
    clauses = formula.clauses
    symbols_set = []
    for clause in formula.clauses:
        for literal in clause.literals:
            symbol = literal[:2]
            if symbol not in symbols_set:
                symbols_set.append(symbol)
    return clauses, symbols_set


def find_pure_symbols(
    clauses: list[Clause], symbols: list[str], model: dict[str, bool | None]
) -> tuple[list[str], dict[str, bool | None]]:
    """
    | Return pure symbols and their values to satisfy clause.
    | Pure symbols are symbols in a formula that exist only in one form,
    | either complemented or otherwise.
    | For example,
    |   {{A4 , A3 , A5' , A1 , A3'} , {A4} , {A3}} has pure symbols A4, A5' and A1.

    This has the following steps:
      1. Ignore clauses that have already evaluated to be ``True``.
      2. Find symbols that occur only in one form in the rest of the clauses.
      3. Assign value ``True`` or ``False`` depending on whether the symbols occurs
         in normal or complemented form respectively.

    >>> formula = Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])])
    >>> clauses, symbols = generate_parameters(formula)
    >>> pure_symbols, values = find_pure_symbols(clauses, symbols, {})
    >>> pure_symbols
    ['A1', 'A2', 'A3', 'A5']
    >>> values
    {'A1': True, 'A2': False, 'A3': True, 'A5': False}
    """
    pure_symbols = []
    assignment: dict[str, bool | None] = {}
    literals = []

    for clause in clauses:
        if clause.evaluate(model):
            continue
        for literal in clause.literals:
            literals.append(literal)

    for s in symbols:
        sym = s + "'"
        if (s in literals and sym not in literals) or (
            s not in literals and sym in literals
        ):
            pure_symbols.append(s)
    for p in pure_symbols:
        assignment[p] = None
    for s in pure_symbols:
        sym = s + "'"
        if s in literals:
            assignment[s] = True
        elif sym in literals:
            assignment[s] = False
    return pure_symbols, assignment


def find_unit_clauses(
    clauses: list[Clause],
    model: dict[str, bool | None],  # noqa: ARG001
) -> tuple[list[str], dict[str, bool | None]]:
    """
    Returns the unit symbols and their values to satisfy clause.

    Unit symbols are symbols in a formula that are:
      - Either the only symbol in a clause
      - Or all other literals in that clause have been assigned ``False``

    This has the following steps:
      1. Find symbols that are the only occurrences in a clause.
      2. Find symbols in a clause where all other literals are assigned ``False``.
      3. Assign ``True`` or ``False`` depending on whether the symbols occurs in
         normal or complemented form respectively.

    >>> clause1 = Clause(["A4", "A3", "A5'", "A1", "A3'"])
    >>> clause2 = Clause(["A4"])
    >>> clause3 = Clause(["A3"])
    >>> clauses, symbols = generate_parameters(Formula([clause1, clause2, clause3]))
    >>> unit_clauses, values = find_unit_clauses(clauses, {})
    >>> unit_clauses
    ['A4', 'A3']
    >>> values
    {'A4': True, 'A3': True}
    """
    unit_symbols = []
    for clause in clauses:
        if len(clause) == 1:
            unit_symbols.append(next(iter(clause.literals.keys())))
        else:
            f_count, n_count = 0, 0
            for literal, value in clause.literals.items():
                if value is False:
                    f_count += 1
                elif value is None:
                    sym = literal
                    n_count += 1
            if f_count == len(clause) - 1 and n_count == 1:
                unit_symbols.append(sym)
    assignment: dict[str, bool | None] = {}
    for i in unit_symbols:
        symbol = i[:2]
        assignment[symbol] = len(i) == 2
    unit_symbols = [i[:2] for i in unit_symbols]

    return unit_symbols, assignment


def dpll_algorithm(
    clauses: list[Clause], symbols: list[str], model: dict[str, bool | None]
) -> tuple[bool | None, dict[str, bool | None] | None]:
    """
    Returns the model if the formula is satisfiable, else ``None``

    This has the following steps:
      1. If every clause in clauses is ``True``, return ``True``.
      2. If some clause in clauses is ``False``, return ``False``.
      3. Find pure symbols.
      4. Find unit symbols.

    >>> formula = Formula([Clause(["A4", "A3", "A5'", "A1", "A3'"]), Clause(["A4"])])
    >>> clauses, symbols = generate_parameters(formula)
    >>> soln, model = dpll_algorithm(clauses, symbols, {})
    >>> soln
    True
    >>> model
    {'A4': True}
    """
    check_clause_all_true = True
    for clause in clauses:
        clause_check = clause.evaluate(model)
        if clause_check is False:
            return False, None
        elif clause_check is None:
            check_clause_all_true = False
            continue

    if check_clause_all_true:
        return True, model

    try:
        pure_symbols, assignment = find_pure_symbols(clauses, symbols, model)
    except RecursionError:
        print("raises a RecursionError and is")
        return None, {}
    p = None
    if len(pure_symbols) > 0:
        p, value = pure_symbols[0], assignment[pure_symbols[0]]

    if p:
        tmp_model = model
        tmp_model[p] = value
        tmp_symbols = list(symbols)
        if p in tmp_symbols:
            tmp_symbols.remove(p)
        return dpll_algorithm(clauses, tmp_symbols, tmp_model)

    unit_symbols, assignment = find_unit_clauses(clauses, model)
    p = None
    if len(unit_symbols) > 0:
        p, value = unit_symbols[0], assignment[unit_symbols[0]]
    if p:
        tmp_model = model
        tmp_model[p] = value
        tmp_symbols = list(symbols)
        if p in tmp_symbols:
            tmp_symbols.remove(p)
        return dpll_algorithm(clauses, tmp_symbols, tmp_model)
    p = symbols[0]
    rest = symbols[1:]
    tmp1, tmp2 = model, model
    tmp1[p], tmp2[p] = True, False

    return dpll_algorithm(clauses, rest, tmp1) or dpll_algorithm(clauses, rest, tmp2)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    formula = generate_formula()
    print(f"The formula {formula} is", end=" ")

    clauses, symbols = generate_parameters(formula)
    solution, model = dpll_algorithm(clauses, symbols, {})

    if solution:
        print(f"satisfiable with the assignment {model}.")
    else:
        print("not satisfiable.")


================================================
FILE: other/doomsday.py
================================================
#!/bin/python3
# Doomsday algorithm info: https://en.wikipedia.org/wiki/Doomsday_rule

DOOMSDAY_LEAP = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
DOOMSDAY_NOT_LEAP = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
WEEK_DAY_NAMES = {
    0: "Sunday",
    1: "Monday",
    2: "Tuesday",
    3: "Wednesday",
    4: "Thursday",
    5: "Friday",
    6: "Saturday",
}


def get_week_day(year: int, month: int, day: int) -> str:
    """Returns the week-day name out of a given date.

    >>> get_week_day(2020, 10, 24)
    'Saturday'
    >>> get_week_day(2017, 10, 24)
    'Tuesday'
    >>> get_week_day(2019, 5, 3)
    'Friday'
    >>> get_week_day(1970, 9, 16)
    'Wednesday'
    >>> get_week_day(1870, 8, 13)
    'Saturday'
    >>> get_week_day(2040, 3, 14)
    'Wednesday'

    """
    # minimal input check:
    assert len(str(year)) > 2, "year should be in YYYY format"
    assert 1 <= month <= 12, "month should be between 1 to 12"
    assert 1 <= day <= 31, "day should be between 1 to 31"

    # Doomsday algorithm:
    century = year // 100
    century_anchor = (5 * (century % 4) + 2) % 7
    centurian = year % 100
    centurian_m = centurian % 12
    dooms_day = (
        (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
    ) % 7
    day_anchor = (
        DOOMSDAY_NOT_LEAP[month - 1]
        if year % 4 != 0 or (centurian == 0 and year % 400 != 0)
        else DOOMSDAY_LEAP[month - 1]
    )
    week_day = (dooms_day + day - day_anchor) % 7
    return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: other/fischer_yates_shuffle.py
================================================
#!/usr/bin/python
"""
The Fisher-Yates shuffle is an algorithm for generating a random permutation of a
finite sequence.
For more details visit
wikipedia/Fischer-Yates-Shuffle.
"""

import random
from typing import Any


def fisher_yates_shuffle(data: list) -> list[Any]:
    for _ in range(len(data)):
        a = random.randint(0, len(data) - 1)
        b = random.randint(0, len(data) - 1)
        data[a], data[b] = data[b], data[a]
    return data


if __name__ == "__main__":
    integers = [0, 1, 2, 3, 4, 5, 6, 7]
    strings = ["python", "says", "hello", "!"]
    print("Fisher-Yates Shuffle:")
    print("List", integers, strings)
    print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


================================================
FILE: other/gauss_easter.py
================================================
"""
https://en.wikipedia.org/wiki/Computus#Gauss'_Easter_algorithm
"""

import math
from datetime import UTC, datetime, timedelta


def gauss_easter(year: int) -> datetime:
    """
    Calculation Gregorian easter date for given year

    >>> gauss_easter(2007)
    datetime.datetime(2007, 4, 8, 0, 0, tzinfo=datetime.timezone.utc)

    >>> gauss_easter(2008)
    datetime.datetime(2008, 3, 23, 0, 0, tzinfo=datetime.timezone.utc)

    >>> gauss_easter(2020)
    datetime.datetime(2020, 4, 12, 0, 0, tzinfo=datetime.timezone.utc)

    >>> gauss_easter(2021)
    datetime.datetime(2021, 4, 4, 0, 0, tzinfo=datetime.timezone.utc)
    """
    metonic_cycle = year % 19
    julian_leap_year = year % 4
    non_leap_year = year % 7
    leap_day_inhibits = math.floor(year / 100)
    lunar_orbit_correction = math.floor((13 + 8 * leap_day_inhibits) / 25)
    leap_day_reinstall_number = leap_day_inhibits / 4
    secular_moon_shift = (
        15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
    ) % 30
    century_starting_point = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7

    # days to be added to March 21
    days_to_add = (19 * metonic_cycle + secular_moon_shift) % 30

    # PHM -> Paschal Full Moon
    days_from_phm_to_sunday = (
        2 * julian_leap_year
        + 4 * non_leap_year
        + 6 * days_to_add
        + century_starting_point
    ) % 7

    if days_to_add == 29 and days_from_phm_to_sunday == 6:
        return datetime(year, 4, 19, tzinfo=UTC)
    elif days_to_add == 28 and days_from_phm_to_sunday == 6:
        return datetime(year, 4, 18, tzinfo=UTC)
    else:
        return datetime(year, 3, 22, tzinfo=UTC) + timedelta(
            days=int(days_to_add + days_from_phm_to_sunday)
        )


if __name__ == "__main__":
    for year in (1994, 2000, 2010, 2021, 2023, 2032, 2100):
        tense = "will be" if year > datetime.now(tz=UTC).year else "was"
        print(f"Easter in {year} {tense} {gauss_easter(year)}")


================================================
FILE: other/graham_scan.py
================================================
"""
This is a pure Python implementation of the Graham scan algorithm
Source: https://en.wikipedia.org/wiki/Graham_scan

For doctests run following command:
python3 -m doctest -v graham_scan.py
"""

from __future__ import annotations

from collections import deque
from enum import Enum
from math import atan2, degrees
from sys import maxsize


# traversal from the lowest and the most left point in anti-clockwise direction
# if direction gets right, the previous point is not the convex hull.
class Direction(Enum):
    left = 1
    straight = 2
    right = 3

    def __repr__(self):
        return f"{self.__class__.__name__}.{self.name}"


def angle_comparer(point: tuple[int, int], minx: int, miny: int) -> float:
    """Return the angle toward to point from (minx, miny)

    :param point: The target point
           minx: The starting point's x
           miny: The starting point's y
    :return: the angle

    Examples:
    >>> angle_comparer((1,1), 0, 0)
    45.0

    >>> angle_comparer((100,1), 10, 10)
    -5.710593137499642

    >>> angle_comparer((5,5), 2, 3)
    33.690067525979785
    """
    # sort the points accorgind to the angle from the lowest and the most left point
    x, y = point
    return degrees(atan2(y - miny, x - minx))


def check_direction(
    starting: tuple[int, int], via: tuple[int, int], target: tuple[int, int]
) -> Direction:
    """Return the direction toward to the line from via to target from starting

    :param starting: The starting point
           via: The via point
           target: The target point
    :return: the Direction

    Examples:
    >>> check_direction((1,1), (2,2), (3,3))
    Direction.straight

    >>> check_direction((60,1), (-50,199), (30,2))
    Direction.left

    >>> check_direction((0,0), (5,5), (10,0))
    Direction.right
    """
    x0, y0 = starting
    x1, y1 = via
    x2, y2 = target
    via_angle = degrees(atan2(y1 - y0, x1 - x0))
    via_angle %= 360
    target_angle = degrees(atan2(y2 - y0, x2 - x0))
    target_angle %= 360
    # t-
    #  \ \
    #   \ v
    #    \|
    #     s
    # via_angle is always lower than target_angle, if direction is left.
    # If they are same, it means they are on a same line of convex hull.
    if target_angle > via_angle:
        return Direction.left
    elif target_angle == via_angle:
        return Direction.straight
    else:
        return Direction.right


def graham_scan(points: list[tuple[int, int]]) -> list[tuple[int, int]]:
    """Pure implementation of graham scan algorithm in Python

    :param points: The unique points on coordinates.
    :return: The points on convex hell.

    Examples:
    >>> graham_scan([(9, 6), (3, 1), (0, 0), (5, 5), (5, 2), (7, 0), (3, 3), (1, 4)])
    [(0, 0), (7, 0), (9, 6), (5, 5), (1, 4)]

    >>> graham_scan([(0, 0), (1, 0), (1, 1), (0, 1)])
    [(0, 0), (1, 0), (1, 1), (0, 1)]

    >>> graham_scan([(0, 0), (1, 1), (2, 2), (3, 3), (-1, 2)])
    [(0, 0), (1, 1), (2, 2), (3, 3), (-1, 2)]

    >>> graham_scan([(-100, 20), (99, 3), (1, 10000001), (5133186, -25), (-66, -4)])
    [(5133186, -25), (1, 10000001), (-100, 20), (-66, -4)]
    """

    if len(points) <= 2:
        # There is no convex hull
        raise ValueError("graham_scan: argument must contain more than 3 points.")
    if len(points) == 3:
        return points
    # find the lowest and the most left point
    minidx = 0
    miny, minx = maxsize, maxsize
    for i, point in enumerate(points):
        x = point[0]
        y = point[1]
        if y < miny:
            miny = y
            minx = x
            minidx = i
        if y == miny and x < minx:
            minx = x
            minidx = i

    # remove the lowest and the most left point from points for preparing for sort
    points.pop(minidx)

    sorted_points = sorted(points, key=lambda point: angle_comparer(point, minx, miny))
    # This insert actually costs complexity,
    # and you should instead add (minx, miny) into stack later.
    # I'm using insert just for easy understanding.
    sorted_points.insert(0, (minx, miny))

    stack: deque[tuple[int, int]] = deque()
    stack.append(sorted_points[0])
    stack.append(sorted_points[1])
    stack.append(sorted_points[2])
    # The first 3 points lines are towards the left because we sort them by their angle
    # from minx, miny.
    current_direction = Direction.left

    for i in range(3, len(sorted_points)):
        while True:
            starting = stack[-2]
            via = stack[-1]
            target = sorted_points[i]
            next_direction = check_direction(starting, via, target)

            if next_direction == Direction.left:
                current_direction = Direction.left
                break
            if next_direction == Direction.straight:
                if current_direction == Direction.left:
                    # We keep current_direction as left.
                    # Because if the straight line keeps as straight,
                    # we want to know if this straight line is towards left.
                    break
                elif current_direction == Direction.right:
                    # If the straight line is towards right,
                    # every previous points on that straight line is not convex hull.
                    stack.pop()
            if next_direction == Direction.right:
                stack.pop()
        stack.append(sorted_points[i])
    return list(stack)


================================================
FILE: other/greedy.py
================================================
class Things:
    def __init__(self, name, value, weight):
        self.name = name
        self.value = value
        self.weight = weight

    def __repr__(self):
        return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"

    def get_value(self):
        return self.value

    def get_name(self):
        return self.name

    def get_weight(self):
        return self.weight

    def value_weight(self):
        return self.value / self.weight


def build_menu(name, value, weight):
    menu = []
    for i in range(len(value)):
        menu.append(Things(name[i], value[i], weight[i]))
    return menu


def greedy(item, max_cost, key_func):
    items_copy = sorted(item, key=key_func, reverse=True)
    result = []
    total_value, total_cost = 0.0, 0.0
    for i in range(len(items_copy)):
        if (total_cost + items_copy[i].get_weight()) <= max_cost:
            result.append(items_copy[i])
            total_cost += items_copy[i].get_weight()
            total_value += items_copy[i].get_value()
    return (result, total_value)


def test_greedy():
    """
    >>> food = ["Burger", "Pizza", "Coca Cola", "Rice",
    ...         "Sambhar", "Chicken", "Fries", "Milk"]
    >>> value = [80, 100, 60, 70, 50, 110, 90, 60]
    >>> weight = [40, 60, 40, 70, 100, 85, 55, 70]
    >>> foods = build_menu(food, value, weight)
    >>> foods  # doctest: +NORMALIZE_WHITESPACE
    [Things(Burger, 80, 40), Things(Pizza, 100, 60), Things(Coca Cola, 60, 40),
     Things(Rice, 70, 70), Things(Sambhar, 50, 100), Things(Chicken, 110, 85),
     Things(Fries, 90, 55), Things(Milk, 60, 70)]
    >>> greedy(foods, 500, Things.get_value)  # doctest: +NORMALIZE_WHITESPACE
    ([Things(Chicken, 110, 85), Things(Pizza, 100, 60), Things(Fries, 90, 55),
      Things(Burger, 80, 40), Things(Rice, 70, 70), Things(Coca Cola, 60, 40),
      Things(Milk, 60, 70)], 570.0)
    """


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: other/guess_the_number_search.py
================================================
"""
guess the number using lower,higher and the value to find or guess

solution works by dividing lower and higher of number guessed

suppose lower is 0, higher is 1000 and the number to guess is 355

>>> guess_the_number(10, 1000, 17)
started...
guess the number : 17
details : [505, 257, 133, 71, 40, 25, 17]

"""


def temp_input_value(
    min_val: int = 10, max_val: int = 1000, option: bool = True
) -> int:
    """
    Temporary input values for tests

    >>> temp_input_value(option=True)
    10

    >>> temp_input_value(option=False)
    1000

    >>> temp_input_value(min_val=100, option=True)
    100

    >>> temp_input_value(min_val=100, max_val=50)
    Traceback (most recent call last):
        ...
    ValueError: Invalid value for min_val or max_val (min_value < max_value)

    >>> temp_input_value("ten","fifty",1)
    Traceback (most recent call last):
        ...
    AssertionError: Invalid type of value(s) specified to function!

    >>> temp_input_value(min_val=-100, max_val=500)
    -100

    >>> temp_input_value(min_val=-5100, max_val=-100)
    -5100
    """
    assert (
        isinstance(min_val, int)
        and isinstance(max_val, int)
        and isinstance(option, bool)
    ), "Invalid type of value(s) specified to function!"

    if min_val > max_val:
        raise ValueError("Invalid value for min_val or max_val (min_value < max_value)")
    return min_val if option else max_val


def get_avg(number_1: int, number_2: int) -> int:
    """
    Return the mid-number(whole) of two integers a and b

    >>> get_avg(10, 15)
    12

    >>> get_avg(20, 300)
    160

    >>> get_avg("abcd", 300)
    Traceback (most recent call last):
        ...
    TypeError: can only concatenate str (not "int") to str

    >>> get_avg(10.5,50.25)
    30
    """
    return int((number_1 + number_2) / 2)


def guess_the_number(lower: int, higher: int, to_guess: int) -> None:
    """
    The `guess_the_number` function that guess the number by some operations
    and using inner functions

    >>> guess_the_number(10, 1000, 17)
    started...
    guess the number : 17
    details : [505, 257, 133, 71, 40, 25, 17]

    >>> guess_the_number(-10000, 10000, 7)
    started...
    guess the number : 7
    details : [0, 5000, 2500, 1250, 625, 312, 156, 78, 39, 19, 9, 4, 6, 7]

    >>> guess_the_number(10, 1000, "a")
    Traceback (most recent call last):
        ...
    AssertionError: argument values must be type of "int"

    >>> guess_the_number(10, 1000, 5)
    Traceback (most recent call last):
        ...
    ValueError: guess value must be within the range of lower and higher value

    >>> guess_the_number(10000, 100, 5)
    Traceback (most recent call last):
        ...
    ValueError: argument value for lower and higher must be(lower > higher)
    """
    assert (
        isinstance(lower, int) and isinstance(higher, int) and isinstance(to_guess, int)
    ), 'argument values must be type of "int"'

    if lower > higher:
        raise ValueError("argument value for lower and higher must be(lower > higher)")

    if not lower < to_guess < higher:
        raise ValueError(
            "guess value must be within the range of lower and higher value"
        )

    def answer(number: int) -> str:
        """
        Returns value by comparing with entered `to_guess` number
        """
        if number > to_guess:
            return "high"
        elif number < to_guess:
            return "low"
        else:
            return "same"

    print("started...")

    last_lowest = lower
    last_highest = higher

    last_numbers = []

    while True:
        number = get_avg(last_lowest, last_highest)
        last_numbers.append(number)

        if answer(number) == "low":
            last_lowest = number
        elif answer(number) == "high":
            last_highest = number
        else:
            break

    print(f"guess the number : {last_numbers[-1]}")
    print(f"details : {last_numbers!s}")


def main() -> None:
    """
    starting point or function of script
    """
    lower = int(input("Enter lower value : ").strip())
    higher = int(input("Enter high value : ").strip())
    guess = int(input("Enter value to guess : ").strip())
    guess_the_number(lower, higher, guess)


if __name__ == "__main__":
    main()


================================================
FILE: other/h_index.py
================================================
"""
Task:
Given an array of integers citations where citations[i] is the number of
citations a researcher received for their ith paper, return compute the
researcher's h-index.

According to the definition of h-index on Wikipedia: A scientist has an
index h if h of their n papers have at least h citations each, and the other
n - h papers have no more than h citations each.

If there are several possible values for h, the maximum one is taken as the
h-index.

H-Index link: https://en.wikipedia.org/wiki/H-index

Implementation notes:
Use sorting of array

Leetcode link: https://leetcode.com/problems/h-index/description/

n = len(citations)
Runtime Complexity: O(n * log(n))
Space  Complexity: O(1)

"""


def h_index(citations: list[int]) -> int:
    """
    Return H-index of citations

    >>> h_index([3, 0, 6, 1, 5])
    3
    >>> h_index([1, 3, 1])
    1
    >>> h_index([1, 2, 3])
    2
    >>> h_index('test')
    Traceback (most recent call last):
        ...
    ValueError: The citations should be a list of non negative integers.
    >>> h_index([1,2,'3'])
    Traceback (most recent call last):
        ...
    ValueError: The citations should be a list of non negative integers.
    >>> h_index([1,2,-3])
    Traceback (most recent call last):
        ...
    ValueError: The citations should be a list of non negative integers.
    """

    # validate:
    if not isinstance(citations, list) or not all(
        isinstance(item, int) and item >= 0 for item in citations
    ):
        raise ValueError("The citations should be a list of non negative integers.")

    citations.sort()
    len_citations = len(citations)

    for i in range(len_citations):
        if citations[len_citations - 1 - i] <= i:
            return i

    return len_citations


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: other/least_recently_used.py
================================================
from __future__ import annotations

import sys
from collections import deque
from typing import TypeVar

T = TypeVar("T")


class LRUCache[T]:
    """
    Page Replacement Algorithm, Least Recently Used (LRU) Caching.

    >>> lru_cache: LRUCache[str | int] = LRUCache(4)
    >>> lru_cache.refer("A")
    >>> lru_cache.refer(2)
    >>> lru_cache.refer(3)

    >>> lru_cache
    LRUCache(4) => [3, 2, 'A']

    >>> lru_cache.refer("A")
    >>> lru_cache
    LRUCache(4) => ['A', 3, 2]

    >>> lru_cache.refer(4)
    >>> lru_cache.refer(5)
    >>> lru_cache
    LRUCache(4) => [5, 4, 'A', 3]

    """

    dq_store: deque[T]  # Cache store of keys
    key_reference: set[T]  # References of the keys in cache
    _MAX_CAPACITY: int = 10  # Maximum capacity of cache

    def __init__(self, n: int) -> None:
        """Creates an empty store and map for the keys.
        The LRUCache is set the size n.
        """
        self.dq_store = deque()
        self.key_reference = set()
        if not n:
            LRUCache._MAX_CAPACITY = sys.maxsize
        elif n < 0:
            raise ValueError("n should be an integer greater than 0.")
        else:
            LRUCache._MAX_CAPACITY = n

    def refer(self, x: T) -> None:
        """
        Looks for a page in the cache store and adds reference to the set.
        Remove the least recently used key if the store is full.
        Update store to reflect recent access.
        """
        if x not in self.key_reference:
            if len(self.dq_store) == LRUCache._MAX_CAPACITY:
                last_element = self.dq_store.pop()
                self.key_reference.remove(last_element)
        else:
            self.dq_store.remove(x)

        self.dq_store.appendleft(x)
        self.key_reference.add(x)

    def display(self) -> None:
        """
        Prints all the elements in the store.
        """
        for k in self.dq_store:
            print(k)

    def __repr__(self) -> str:
        return f"LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store)}"


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lru_cache: LRUCache[str | int] = LRUCache(4)
    lru_cache.refer("A")
    lru_cache.refer(2)
    lru_cache.refer(3)
    lru_cache.refer("A")
    lru_cache.refer(4)
    lru_cache.refer(5)
    lru_cache.display()

    print(lru_cache)
    assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"


================================================
FILE: other/lfu_cache.py
================================================
from __future__ import annotations

from collections.abc import Callable
from typing import TypeVar

T = TypeVar("T")
U = TypeVar("U")


class DoubleLinkedListNode[T, U]:
    """
    Double Linked List Node built specifically for LFU Cache

    >>> node = DoubleLinkedListNode(1,1)
    >>> node
    Node: key: 1, val: 1, freq: 0, has next: False, has prev: False
    """

    def __init__(self, key: T | None, val: U | None):
        self.key = key
        self.val = val
        self.freq: int = 0
        self.next: DoubleLinkedListNode[T, U] | None = None
        self.prev: DoubleLinkedListNode[T, U] | None = None

    def __repr__(self) -> str:
        return (
            f"Node: key: {self.key}, val: {self.val}, freq: {self.freq}, "
            f"has next: {self.next is not None}, has prev: {self.prev is not None}"
        )


class DoubleLinkedList[T, U]:
    """
    Double Linked List built specifically for LFU Cache

    >>> dll: DoubleLinkedList = DoubleLinkedList()
    >>> dll
    DoubleLinkedList,
        Node: key: None, val: None, freq: 0, has next: True, has prev: False,
        Node: key: None, val: None, freq: 0, has next: False, has prev: True

    >>> first_node = DoubleLinkedListNode(1,10)
    >>> first_node
    Node: key: 1, val: 10, freq: 0, has next: False, has prev: False


    >>> dll.add(first_node)
    >>> dll
    DoubleLinkedList,
        Node: key: None, val: None, freq: 0, has next: True, has prev: False,
        Node: key: 1, val: 10, freq: 1, has next: True, has prev: True,
        Node: key: None, val: None, freq: 0, has next: False, has prev: True

    >>> # node is mutated
    >>> first_node
    Node: key: 1, val: 10, freq: 1, has next: True, has prev: True

    >>> second_node = DoubleLinkedListNode(2,20)
    >>> second_node
    Node: key: 2, val: 20, freq: 0, has next: False, has prev: False

    >>> dll.add(second_node)
    >>> dll
    DoubleLinkedList,
        Node: key: None, val: None, freq: 0, has next: True, has prev: False,
        Node: key: 1, val: 10, freq: 1, has next: True, has prev: True,
        Node: key: 2, val: 20, freq: 1, has next: True, has prev: True,
        Node: key: None, val: None, freq: 0, has next: False, has prev: True

    >>> removed_node = dll.remove(first_node)
    >>> assert removed_node == first_node
    >>> dll
    DoubleLinkedList,
        Node: key: None, val: None, freq: 0, has next: True, has prev: False,
        Node: key: 2, val: 20, freq: 1, has next: True, has prev: True,
        Node: key: None, val: None, freq: 0, has next: False, has prev: True


    >>> # Attempt to remove node not on list
    >>> removed_node = dll.remove(first_node)
    >>> removed_node is None
    True

    >>> # Attempt to remove head or rear
    >>> dll.head
    Node: key: None, val: None, freq: 0, has next: True, has prev: False
    >>> dll.remove(dll.head) is None
    True

    >>> # Attempt to remove head or rear
    >>> dll.rear
    Node: key: None, val: None, freq: 0, has next: False, has prev: True
    >>> dll.remove(dll.rear) is None
    True


    """

    def __init__(self) -> None:
        self.head: DoubleLinkedListNode[T, U] = DoubleLinkedListNode(None, None)
        self.rear: DoubleLinkedListNode[T, U] = DoubleLinkedListNode(None, None)
        self.head.next, self.rear.prev = self.rear, self.head

    def __repr__(self) -> str:
        rep = ["DoubleLinkedList"]
        node = self.head
        while node.next is not None:
            rep.append(str(node))
            node = node.next
        rep.append(str(self.rear))
        return ",\n    ".join(rep)

    def add(self, node: DoubleLinkedListNode[T, U]) -> None:
        """
        Adds the given node at the tail of the list and shifting it to proper position
        """

        previous = self.rear.prev

        # All nodes other than self.head are guaranteed to have non-None previous
        assert previous is not None

        previous.next = node
        node.prev = previous
        self.rear.prev = node
        node.next = self.rear
        node.freq += 1
        self._position_node(node)

    def _position_node(self, node: DoubleLinkedListNode[T, U]) -> None:
        """
        Moves node forward to maintain invariant of sort by freq value
        """

        while node.prev is not None and node.prev.freq > node.freq:
            # swap node with previous node
            previous_node = node.prev

            node.prev = previous_node.prev
            previous_node.next = node.prev
            node.next = previous_node
            previous_node.prev = node

    def remove(
        self, node: DoubleLinkedListNode[T, U]
    ) -> DoubleLinkedListNode[T, U] | None:
        """
        Removes and returns the given node from the list

        Returns None if node.prev or node.next is None
        """

        if node.prev is None or node.next is None:
            return None

        node.prev.next = node.next
        node.next.prev = node.prev
        node.prev = None
        node.next = None
        return node


class LFUCache[T, U]:
    """
    LFU Cache to store a given capacity of data. Can be used as a stand-alone object
    or as a function decorator.

    >>> cache = LFUCache(2)
    >>> cache.put(1, 1)
    >>> cache.put(2, 2)
    >>> cache.get(1)
    1
    >>> cache.put(3, 3)
    >>> cache.get(2) is None
    True
    >>> cache.put(4, 4)
    >>> cache.get(1) is None
    True
    >>> cache.get(3)
    3
    >>> cache.get(4)
    4
    >>> cache
    CacheInfo(hits=3, misses=2, capacity=2, current_size=2)
    >>> @LFUCache.decorator(100)
    ... def fib(num):
    ...     if num in (1, 2):
    ...         return 1
    ...     return fib(num - 1) + fib(num - 2)

    >>> for i in range(1, 101):
    ...     res = fib(i)

    >>> fib.cache_info()
    CacheInfo(hits=196, misses=100, capacity=100, current_size=100)
    """

    def __init__(self, capacity: int):
        self.list: DoubleLinkedList[T, U] = DoubleLinkedList()
        self.capacity = capacity
        self.num_keys = 0
        self.hits = 0
        self.miss = 0
        self.cache: dict[T, DoubleLinkedListNode[T, U]] = {}

    def __repr__(self) -> str:
        """
        Return the details for the cache instance
        [hits, misses, capacity, current_size]
        """

        return (
            f"CacheInfo(hits={self.hits}, misses={self.miss}, "
            f"capacity={self.capacity}, current_size={self.num_keys})"
        )

    def __contains__(self, key: T) -> bool:
        """
        >>> cache = LFUCache(1)

        >>> 1 in cache
        False

        >>> cache.put(1, 1)
        >>> 1 in cache
        True
        """

        return key in self.cache

    def get(self, key: T) -> U | None:
        """
        Returns the value for the input key and updates the Double Linked List. Returns
        Returns None if key is not present in cache
        """

        if key in self.cache:
            self.hits += 1
            value_node: DoubleLinkedListNode[T, U] = self.cache[key]
            node = self.list.remove(self.cache[key])
            assert node == value_node

            # node is guaranteed not None because it is in self.cache
            assert node is not None
            self.list.add(node)
            return node.val
        self.miss += 1
        return None

    def put(self, key: T, value: U) -> None:
        """
        Sets the value for the input key and updates the Double Linked List
        """

        if key not in self.cache:
            if self.num_keys >= self.capacity:
                # delete first node when over capacity
                first_node = self.list.head.next

                # guaranteed to have a non-None first node when num_keys > 0
                # explain to type checker via assertions
                assert first_node is not None
                assert first_node.key is not None
                assert self.list.remove(first_node) is not None
                # first_node guaranteed to be in list

                del self.cache[first_node.key]
                self.num_keys -= 1
            self.cache[key] = DoubleLinkedListNode(key, value)
            self.list.add(self.cache[key])
            self.num_keys += 1

        else:
            node = self.list.remove(self.cache[key])
            assert node is not None  # node guaranteed to be in list
            node.val = value
            self.list.add(node)

    @classmethod
    def decorator(
        cls: type[LFUCache[T, U]], size: int = 128
    ) -> Callable[[Callable[[T], U]], Callable[..., U]]:
        """
        Decorator version of LFU Cache

        Decorated function must be function of T -> U
        """

        def cache_decorator_inner(func: Callable[[T], U]) -> Callable[..., U]:
            # variable to map the decorator functions to their respective instance
            decorator_function_to_instance_map: dict[
                Callable[[T], U], LFUCache[T, U]
            ] = {}

            def cache_decorator_wrapper(*args: T) -> U:
                if func not in decorator_function_to_instance_map:
                    decorator_function_to_instance_map[func] = LFUCache(size)

                result = decorator_function_to_instance_map[func].get(args[0])
                if result is None:
                    result = func(*args)
                    decorator_function_to_instance_map[func].put(args[0], result)
                return result

            def cache_info() -> LFUCache[T, U]:
                return decorator_function_to_instance_map[func]

            setattr(cache_decorator_wrapper, "cache_info", cache_info)  # noqa: B010

            return cache_decorator_wrapper

        return cache_decorator_inner


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: other/linear_congruential_generator.py
================================================
__author__ = "Tobias Carryer"

from time import time


class LinearCongruentialGenerator:
    """
    A pseudorandom number generator.
    """

    # The default value for **seed** is the result of a function call, which is not
    # normally recommended and causes ruff to raise a B008 error. However, in this case,
    # it is acceptable because `LinearCongruentialGenerator.__init__()` will only be
    # called once per instance and it ensures that each instance will generate a unique
    # sequence of numbers.

    def __init__(self, multiplier, increment, modulo, seed=int(time())):  # noqa: B008
        """
        These parameters are saved and used when nextNumber() is called.

        modulo is the largest number that can be generated (exclusive). The most
        efficient values are powers of 2. 2^32 is a common value.
        """
        self.multiplier = multiplier
        self.increment = increment
        self.modulo = modulo
        self.seed = seed

    def next_number(self):
        """
        The smallest number that can be generated is zero.
        The largest number that can be generated is modulo-1. modulo is set in the
        constructor.
        """
        self.seed = (self.multiplier * self.seed + self.increment) % self.modulo
        return self.seed


if __name__ == "__main__":
    # Show the LCG in action.
    lcg = LinearCongruentialGenerator(1664525, 1013904223, 2 << 31)
    while True:
        print(lcg.next_number())


================================================
FILE: other/lru_cache.py
================================================
from __future__ import annotations

from collections.abc import Callable
from typing import TypeVar

T = TypeVar("T")
U = TypeVar("U")


class DoubleLinkedListNode[T, U]:
    """
    Double Linked List Node built specifically for LRU Cache

    >>> DoubleLinkedListNode(1,1)
    Node: key: 1, val: 1, has next: False, has prev: False
    """

    def __init__(self, key: T | None, val: U | None):
        self.key = key
        self.val = val
        self.next: DoubleLinkedListNode[T, U] | None = None
        self.prev: DoubleLinkedListNode[T, U] | None = None

    def __repr__(self) -> str:
        return (
            f"Node: key: {self.key}, val: {self.val}, "
            f"has next: {bool(self.next)}, has prev: {bool(self.prev)}"
        )


class DoubleLinkedList[T, U]:
    """
    Double Linked List built specifically for LRU Cache

    >>> dll: DoubleLinkedList = DoubleLinkedList()
    >>> dll
    DoubleLinkedList,
        Node: key: None, val: None, has next: True, has prev: False,
        Node: key: None, val: None, has next: False, has prev: True

    >>> first_node = DoubleLinkedListNode(1,10)
    >>> first_node
    Node: key: 1, val: 10, has next: False, has prev: False


    >>> dll.add(first_node)
    >>> dll
    DoubleLinkedList,
        Node: key: None, val: None, has next: True, has prev: False,
        Node: key: 1, val: 10, has next: True, has prev: True,
        Node: key: None, val: None, has next: False, has prev: True

    >>> # node is mutated
    >>> first_node
    Node: key: 1, val: 10, has next: True, has prev: True

    >>> second_node = DoubleLinkedListNode(2,20)
    >>> second_node
    Node: key: 2, val: 20, has next: False, has prev: False

    >>> dll.add(second_node)
    >>> dll
    DoubleLinkedList,
        Node: key: None, val: None, has next: True, has prev: False,
        Node: key: 1, val: 10, has next: True, has prev: True,
        Node: key: 2, val: 20, has next: True, has prev: True,
        Node: key: None, val: None, has next: False, has prev: True

    >>> removed_node = dll.remove(first_node)
    >>> assert removed_node == first_node
    >>> dll
    DoubleLinkedList,
        Node: key: None, val: None, has next: True, has prev: False,
        Node: key: 2, val: 20, has next: True, has prev: True,
        Node: key: None, val: None, has next: False, has prev: True


    >>> # Attempt to remove node not on list
    >>> removed_node = dll.remove(first_node)
    >>> removed_node is None
    True

    >>> # Attempt to remove head or rear
    >>> dll.head
    Node: key: None, val: None, has next: True, has prev: False
    >>> dll.remove(dll.head) is None
    True

    >>> # Attempt to remove head or rear
    >>> dll.rear
    Node: key: None, val: None, has next: False, has prev: True
    >>> dll.remove(dll.rear) is None
    True


    """

    def __init__(self) -> None:
        self.head: DoubleLinkedListNode[T, U] = DoubleLinkedListNode(None, None)
        self.rear: DoubleLinkedListNode[T, U] = DoubleLinkedListNode(None, None)
        self.head.next, self.rear.prev = self.rear, self.head

    def __repr__(self) -> str:
        rep = ["DoubleLinkedList"]
        node = self.head
        while node.next is not None:
            rep.append(str(node))
            node = node.next
        rep.append(str(self.rear))
        return ",\n    ".join(rep)

    def add(self, node: DoubleLinkedListNode[T, U]) -> None:
        """
        Adds the given node to the end of the list (before rear)
        """

        previous = self.rear.prev

        # All nodes other than self.head are guaranteed to have non-None previous
        assert previous is not None

        previous.next = node
        node.prev = previous
        self.rear.prev = node
        node.next = self.rear

    def remove(
        self, node: DoubleLinkedListNode[T, U]
    ) -> DoubleLinkedListNode[T, U] | None:
        """
        Removes and returns the given node from the list

        Returns None if node.prev or node.next is None
        """

        if node.prev is None or node.next is None:
            return None

        node.prev.next = node.next
        node.next.prev = node.prev
        node.prev = None
        node.next = None
        return node


class LRUCache[T, U]:
    """
    LRU Cache to store a given capacity of data. Can be used as a stand-alone object
    or as a function decorator.

    >>> cache = LRUCache(2)

    >>> cache.put(1, 1)
    >>> cache.put(2, 2)
    >>> cache.get(1)
    1

    >>> cache.list
    DoubleLinkedList,
        Node: key: None, val: None, has next: True, has prev: False,
        Node: key: 2, val: 2, has next: True, has prev: True,
        Node: key: 1, val: 1, has next: True, has prev: True,
        Node: key: None, val: None, has next: False, has prev: True

    >>> cache.cache  # doctest: +NORMALIZE_WHITESPACE
    {1: Node: key: 1, val: 1, has next: True, has prev: True, \
     2: Node: key: 2, val: 2, has next: True, has prev: True}

    >>> cache.put(3, 3)

    >>> cache.list
    DoubleLinkedList,
        Node: key: None, val: None, has next: True, has prev: False,
        Node: key: 1, val: 1, has next: True, has prev: True,
        Node: key: 3, val: 3, has next: True, has prev: True,
        Node: key: None, val: None, has next: False, has prev: True

    >>> cache.cache  # doctest: +NORMALIZE_WHITESPACE
    {1: Node: key: 1, val: 1, has next: True, has prev: True, \
     3: Node: key: 3, val: 3, has next: True, has prev: True}

    >>> cache.get(2) is None
    True

    >>> cache.put(4, 4)

    >>> cache.get(1) is None
    True

    >>> cache.get(3)
    3

    >>> cache.get(4)
    4

    >>> cache
    CacheInfo(hits=3, misses=2, capacity=2, current size=2)

    >>> @LRUCache.decorator(100)
    ... def fib(num):
    ...     if num in (1, 2):
    ...         return 1
    ...     return fib(num - 1) + fib(num - 2)

    >>> for i in range(1, 100):
    ...     res = fib(i)

    >>> fib.cache_info()
    CacheInfo(hits=194, misses=99, capacity=100, current size=99)
    """

    def __init__(self, capacity: int):
        self.list: DoubleLinkedList[T, U] = DoubleLinkedList()
        self.capacity = capacity
        self.num_keys = 0
        self.hits = 0
        self.miss = 0
        self.cache: dict[T, DoubleLinkedListNode[T, U]] = {}

    def __repr__(self) -> str:
        """
        Return the details for the cache instance
        [hits, misses, capacity, current_size]
        """

        return (
            f"CacheInfo(hits={self.hits}, misses={self.miss}, "
            f"capacity={self.capacity}, current size={self.num_keys})"
        )

    def __contains__(self, key: T) -> bool:
        """
        >>> cache = LRUCache(1)

        >>> 1 in cache
        False

        >>> cache.put(1, 1)

        >>> 1 in cache
        True
        """

        return key in self.cache

    def get(self, key: T) -> U | None:
        """
        Returns the value for the input key and updates the Double Linked List.
        Returns None if key is not present in cache
        """
        # Note: pythonic interface would throw KeyError rather than return None

        if key in self.cache:
            self.hits += 1
            value_node: DoubleLinkedListNode[T, U] = self.cache[key]
            node = self.list.remove(self.cache[key])
            assert node == value_node

            # node is guaranteed not None because it is in self.cache
            assert node is not None
            self.list.add(node)
            return node.val
        self.miss += 1
        return None

    def put(self, key: T, value: U) -> None:
        """
        Sets the value for the input key and updates the Double Linked List
        """

        if key not in self.cache:
            if self.num_keys >= self.capacity:
                # delete first node (oldest) when over capacity
                first_node = self.list.head.next

                # guaranteed to have a non-None first node when num_keys > 0
                # explain to type checker via assertions
                assert first_node is not None
                assert first_node.key is not None
                assert (
                    self.list.remove(first_node) is not None
                )  # node guaranteed to be in list assert node.key is not None

                del self.cache[first_node.key]
                self.num_keys -= 1
            self.cache[key] = DoubleLinkedListNode(key, value)
            self.list.add(self.cache[key])
            self.num_keys += 1

        else:
            # bump node to the end of the list, update value
            node = self.list.remove(self.cache[key])
            assert node is not None  # node guaranteed to be in list
            node.val = value
            self.list.add(node)

    @classmethod
    def decorator(
        cls, size: int = 128
    ) -> Callable[[Callable[[T], U]], Callable[..., U]]:
        """
        Decorator version of LRU Cache

        Decorated function must be function of T -> U
        """

        def cache_decorator_inner(func: Callable[[T], U]) -> Callable[..., U]:
            # variable to map the decorator functions to their respective instance
            decorator_function_to_instance_map: dict[
                Callable[[T], U], LRUCache[T, U]
            ] = {}

            def cache_decorator_wrapper(*args: T) -> U:
                if func not in decorator_function_to_instance_map:
                    decorator_function_to_instance_map[func] = LRUCache(size)

                result = decorator_function_to_instance_map[func].get(args[0])
                if result is None:
                    result = func(*args)
                    decorator_function_to_instance_map[func].put(args[0], result)
                return result

            def cache_info() -> LRUCache[T, U]:
                return decorator_function_to_instance_map[func]

            setattr(cache_decorator_wrapper, "cache_info", cache_info)  # noqa: B010

            return cache_decorator_wrapper

        return cache_decorator_inner


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: other/magicdiamondpattern.py
================================================
# Python program for generating diamond pattern in Python 3.7+


# Function to print upper half of diamond (pyramid)
def floyd(n):
    """
    Print the upper half of a diamond pattern with '*' characters.

    Args:
        n (int): Size of the pattern.

    Examples:
        >>> floyd(3)
        '  * \\n * * \\n* * * \\n'

        >>> floyd(5)
        '    * \\n   * * \\n  * * * \\n * * * * \\n* * * * * \\n'
    """
    result = ""
    for i in range(n):
        for _ in range(n - i - 1):  # printing spaces
            result += " "
        for _ in range(i + 1):  # printing stars
            result += "* "
        result += "\n"
    return result


# Function to print lower half of diamond (pyramid)
def reverse_floyd(n):
    """
    Print the lower half of a diamond pattern with '*' characters.

    Args:
        n (int): Size of the pattern.

    Examples:
        >>> reverse_floyd(3)
        '* * * \\n * * \\n  * \\n   '

        >>> reverse_floyd(5)
        '* * * * * \\n * * * * \\n  * * * \\n   * * \\n    * \\n     '
    """
    result = ""
    for i in range(n, 0, -1):
        for _ in range(i, 0, -1):  # printing stars
            result += "* "
        result += "\n"
        for _ in range(n - i + 1, 0, -1):  # printing spaces
            result += " "
    return result


# Function to print complete diamond pattern of "*"
def pretty_print(n):
    """
    Print a complete diamond pattern with '*' characters.

    Args:
        n (int): Size of the pattern.

    Examples:
        >>> pretty_print(0)
        '       ...       ....        nothing printing :('

        >>> pretty_print(3)
        '  * \\n * * \\n* * * \\n* * * \\n * * \\n  * \\n   '
    """
    if n <= 0:
        return "       ...       ....        nothing printing :("
    upper_half = floyd(n)  # upper half
    lower_half = reverse_floyd(n)  # lower half
    return upper_half + lower_half


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: other/majority_vote_algorithm.py
================================================
"""
This is Booyer-Moore Majority Vote Algorithm. The problem statement goes like this:
Given an integer array of size n, find all elements that appear more than ⌊ n/k ⌋ times.
We have to solve in O(n) time and O(1) Space.
URL : https://en.wikipedia.org/wiki/Boyer%E2%80%93Moore_majority_vote_algorithm
"""

from collections import Counter


def majority_vote(votes: list[int], votes_needed_to_win: int) -> list[int]:
    """
    >>> majority_vote([1, 2, 2, 3, 1, 3, 2], 3)
    [2]
    >>> majority_vote([1, 2, 2, 3, 1, 3, 2], 2)
    []
    >>> majority_vote([1, 2, 2, 3, 1, 3, 2], 4)
    [1, 2, 3]
    """
    majority_candidate_counter: Counter[int] = Counter()
    for vote in votes:
        majority_candidate_counter[vote] += 1
        if len(majority_candidate_counter) == votes_needed_to_win:
            majority_candidate_counter -= Counter(set(majority_candidate_counter))
    majority_candidate_counter = Counter(
        vote for vote in votes if vote in majority_candidate_counter
    )
    return [
        vote
        for vote in majority_candidate_counter
        if majority_candidate_counter[vote] > len(votes) / votes_needed_to_win
    ]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: other/maximum_subsequence.py
================================================
from collections.abc import Sequence


def max_subsequence_sum(nums: Sequence[int] | None = None) -> int:
    """Return the maximum possible sum amongst all non - empty subsequences.

    Raises:
      ValueError: when nums is empty.

    >>> max_subsequence_sum([1,2,3,4,-2])
    10
    >>> max_subsequence_sum([-2, -3, -1, -4, -6])
    -1
    >>> max_subsequence_sum([])
    Traceback (most recent call last):
        . . .
    ValueError: Input sequence should not be empty
    >>> max_subsequence_sum()
    Traceback (most recent call last):
        . . .
    ValueError: Input sequence should not be empty
    """
    if nums is None or not nums:
        raise ValueError("Input sequence should not be empty")

    ans = nums[0]
    for i in range(1, len(nums)):
        num = nums[i]
        ans = max(ans, ans + num, num)

    return ans


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Try on a sample input from the user
    n = int(input("Enter number of elements : ").strip())
    array = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
    print(max_subsequence_sum(array))


================================================
FILE: other/nested_brackets.py
================================================
"""
The nested brackets problem is a problem that determines if a sequence of
brackets are properly nested.  A sequence of brackets s is considered properly nested
if any of the following conditions are true:

    - s is empty
    - s has the form (U) or [U] or {U} where U is a properly nested string
    - s has the form VW where V and W are properly nested strings

For example, the string "()()[()]" is properly nested but "[(()]" is not.

The function called is_balanced takes as input a string S which is a sequence of
brackets and returns true if S is nested and false otherwise.
"""


def is_balanced(s: str) -> bool:
    """
    >>> is_balanced("")
    True
    >>> is_balanced("()")
    True
    >>> is_balanced("[]")
    True
    >>> is_balanced("{}")
    True
    >>> is_balanced("()[]{}")
    True
    >>> is_balanced("(())")
    True
    >>> is_balanced("[[")
    False
    >>> is_balanced("([{}])")
    True
    >>> is_balanced("(()[)]")
    False
    >>> is_balanced("([)]")
    False
    >>> is_balanced("[[()]]")
    True
    >>> is_balanced("(()(()))")
    True
    >>> is_balanced("]")
    False
    >>> is_balanced("Life is a bowl of cherries.")
    True
    >>> is_balanced("Life is a bowl of che{}ies.")
    True
    >>> is_balanced("Life is a bowl of che}{ies.")
    False
    """
    open_to_closed = {"{": "}", "[": "]", "(": ")"}
    stack = []
    for symbol in s:
        if symbol in open_to_closed:
            stack.append(symbol)
        elif symbol in open_to_closed.values() and (
            not stack or open_to_closed[stack.pop()] != symbol
        ):
            return False
    return not stack  # stack should be empty


def main():
    s = input("Enter sequence of brackets: ")
    print(f"'{s}' is {'' if is_balanced(s) else 'not '}balanced.")


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    main()


================================================
FILE: other/number_container_system.py
================================================
"""
A number container system that uses binary search to delete and insert values into
arrays with O(log n) write times and O(1) read times.

This container system holds integers at indexes.

Further explained in this leetcode problem
> https://leetcode.com/problems/minimum-cost-tree-from-leaf-values
"""


class NumberContainer:
    def __init__(self) -> None:
        # numbermap keys are the number and its values are lists of indexes sorted
        # in ascending order
        self.numbermap: dict[int, list[int]] = {}
        # indexmap keys are an index and it's values are the number at that index
        self.indexmap: dict[int, int] = {}

    def binary_search_delete(self, array: list | str | range, item: int) -> list[int]:
        """
        Removes the item from the sorted array and returns
        the new array.

        >>> NumberContainer().binary_search_delete([1,2,3], 2)
        [1, 3]
        >>> NumberContainer().binary_search_delete([0, 0, 0], 0)
        [0, 0]
        >>> NumberContainer().binary_search_delete([-1, -1, -1], -1)
        [-1, -1]
        >>> NumberContainer().binary_search_delete([-1, 0], 0)
        [-1]
        >>> NumberContainer().binary_search_delete([-1, 0], -1)
        [0]
        >>> NumberContainer().binary_search_delete(range(7), 3)
        [0, 1, 2, 4, 5, 6]
        >>> NumberContainer().binary_search_delete([1.1, 2.2, 3.3], 2.2)
        [1.1, 3.3]
        >>> NumberContainer().binary_search_delete("abcde", "c")
        ['a', 'b', 'd', 'e']
        >>> NumberContainer().binary_search_delete([0, -1, 2, 4], 0)
        Traceback (most recent call last):
            ...
        ValueError: Either the item is not in the array or the array was unsorted
        >>> NumberContainer().binary_search_delete([2, 0, 4, -1, 11], -1)
        Traceback (most recent call last):
            ...
        ValueError: Either the item is not in the array or the array was unsorted
        >>> NumberContainer().binary_search_delete(125, 1)
        Traceback (most recent call last):
            ...
        TypeError: binary_search_delete() only accepts either a list, range or str
        """
        if isinstance(array, (range, str)):
            array = list(array)
        elif not isinstance(array, list):
            raise TypeError(
                "binary_search_delete() only accepts either a list, range or str"
            )

        low = 0
        high = len(array) - 1

        while low <= high:
            mid = (low + high) // 2
            if array[mid] == item:
                array.pop(mid)
                return array
            elif array[mid] < item:
                low = mid + 1
            else:
                high = mid - 1
        raise ValueError(
            "Either the item is not in the array or the array was unsorted"
        )

    def binary_search_insert(self, array: list | str | range, index: int) -> list[int]:
        """
        Inserts the index into the sorted array
        at the correct position.

        >>> NumberContainer().binary_search_insert([1,2,3], 2)
        [1, 2, 2, 3]
        >>> NumberContainer().binary_search_insert([0,1,3], 2)
        [0, 1, 2, 3]
        >>> NumberContainer().binary_search_insert([-5, -3, 0, 0, 11, 103], 51)
        [-5, -3, 0, 0, 11, 51, 103]
        >>> NumberContainer().binary_search_insert([-5, -3, 0, 0, 11, 100, 103], 101)
        [-5, -3, 0, 0, 11, 100, 101, 103]
        >>> NumberContainer().binary_search_insert(range(10), 4)
        [0, 1, 2, 3, 4, 4, 5, 6, 7, 8, 9]
        >>> NumberContainer().binary_search_insert("abd", "c")
        ['a', 'b', 'c', 'd']
        >>> NumberContainer().binary_search_insert(131, 23)
        Traceback (most recent call last):
            ...
        TypeError: binary_search_insert() only accepts either a list, range or str
        """
        if isinstance(array, (range, str)):
            array = list(array)
        elif not isinstance(array, list):
            raise TypeError(
                "binary_search_insert() only accepts either a list, range or str"
            )

        low = 0
        high = len(array) - 1

        while low <= high:
            mid = (low + high) // 2
            if array[mid] == index:
                # If the item already exists in the array,
                # insert it after the existing item
                array.insert(mid + 1, index)
                return array
            elif array[mid] < index:
                low = mid + 1
            else:
                high = mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
        array.insert(low, index)
        return array

    def change(self, index: int, number: int) -> None:
        """
        Changes (sets) the index as number

        >>> cont = NumberContainer()
        >>> cont.change(0, 10)
        >>> cont.change(0, 20)
        >>> cont.change(-13, 20)
        >>> cont.change(-100030, 20032903290)
        """
        # Remove previous index
        if index in self.indexmap:
            n = self.indexmap[index]
            if len(self.numbermap[n]) == 1:
                del self.numbermap[n]
            else:
                self.numbermap[n] = self.binary_search_delete(self.numbermap[n], index)

        # Set new index
        self.indexmap[index] = number

        # Number not seen before or empty so insert number value
        if number not in self.numbermap:
            self.numbermap[number] = [index]

        # Here we need to perform a binary search insertion in order to insert
        # The item in the correct place
        else:
            self.numbermap[number] = self.binary_search_insert(
                self.numbermap[number], index
            )

    def find(self, number: int) -> int:
        """
        Returns the smallest index where the number is.

        >>> cont = NumberContainer()
        >>> cont.find(10)
        -1
        >>> cont.change(0, 10)
        >>> cont.find(10)
        0
        >>> cont.change(0, 20)
        >>> cont.find(10)
        -1
        >>> cont.find(20)
        0
        """
        # Simply return the 0th index (smallest) of the indexes found (or -1)
        return self.numbermap.get(number, [-1])[0]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: other/password.py
================================================
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation


def password_generator(length: int = 8) -> str:
    """
    Password Generator allows you to generate a random password of length N.

    >>> len(password_generator())
    8
    >>> len(password_generator(length=16))
    16
    >>> len(password_generator(257))
    257
    >>> len(password_generator(length=0))
    0
    >>> len(password_generator(-1))
    0
    """
    chars = ascii_letters + digits + punctuation
    return "".join(secrets.choice(chars) for _ in range(length))


# ALTERNATIVE METHODS
# chars_incl= characters that must be in password
# i= how many letters or characters the password length will be
def alternative_password_generator(chars_incl: str, i: int) -> str:
    # Password Generator = full boot with random_number, random_letters, and
    # random_character FUNCTIONS
    # Put your code here...
    i -= len(chars_incl)
    quotient = i // 3
    remainder = i % 3
    # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
    #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
    chars = (
        chars_incl
        + random(ascii_letters, quotient + remainder)
        + random(digits, quotient)
        + random(punctuation, quotient)
    )
    list_of_chars = list(chars)
    shuffle(list_of_chars)
    return "".join(list_of_chars)

    # random is a generalised function for letters, characters and numbers


def random(chars_incl: str, i: int) -> str:
    return "".join(secrets.choice(chars_incl) for _ in range(i))


def is_strong_password(password: str, min_length: int = 8) -> bool:
    """
    This will check whether a given password is strong or not. The password must be at
    least as long as the provided minimum length, and it must contain at least 1
    lowercase letter, 1 uppercase letter, 1 number and 1 special character.

    >>> is_strong_password('Hwea7$2!')
    True
    >>> is_strong_password('Sh0r1')
    False
    >>> is_strong_password('Hello123')
    False
    >>> is_strong_password('Hello1238udfhiaf038fajdvjjf!jaiuFhkqi1')
    True
    >>> is_strong_password('0')
    False
    """

    if len(password) < min_length:
        return False

    upper = any(char in ascii_uppercase for char in password)
    lower = any(char in ascii_lowercase for char in password)
    num = any(char in digits for char in password)
    spec_char = any(char in punctuation for char in password)

    return upper and lower and num and spec_char


def main():
    length = int(input("Please indicate the max length of your password: ").strip())
    chars_incl = input(
        "Please indicate the characters that must be in your password: "
    ).strip()
    print("Password generated:", password_generator(length))
    print(
        "Alternative Password generated:",
        alternative_password_generator(chars_incl, length),
    )
    print("[If you are thinking of using this password, You better save it.]")


if __name__ == "__main__":
    main()


================================================
FILE: other/quine.py
================================================
#!/bin/python3
# ruff: noqa: PLC3002
"""
Quine:

A quine is a computer program which takes no input and produces a copy of its
own source code as its only output (disregarding this docstring and the shebang).

More info on: https://en.wikipedia.org/wiki/Quine_(computing)
"""

print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))


================================================
FILE: other/scoring_algorithm.py
================================================
"""
| developed by: markmelnic
| original repo: https://github.com/markmelnic/Scoring-Algorithm

Analyse data using a range based percentual proximity algorithm
and calculate the linear maximum likelihood estimation.
The basic principle is that all values supplied will be broken
down to a range from ``0`` to ``1`` and each column's score will be added
up to get the total score.

Example for data of vehicles
::

    price|mileage|registration_year
    20k  |60k    |2012
    22k  |50k    |2011
    23k  |90k    |2015
    16k  |210k   |2010

We want the vehicle with the lowest price,
lowest mileage but newest registration year.
Thus the weights for each column are as follows:
``[0, 0, 1]``
"""


def get_data(source_data: list[list[float]]) -> list[list[float]]:
    """
    >>> get_data([[20, 60, 2012],[23, 90, 2015],[22, 50, 2011]])
    [[20.0, 23.0, 22.0], [60.0, 90.0, 50.0], [2012.0, 2015.0, 2011.0]]
    """
    data_lists: list[list[float]] = []
    for data in source_data:
        for i, el in enumerate(data):
            if len(data_lists) < i + 1:
                data_lists.append([])
            data_lists[i].append(float(el))
    return data_lists


def calculate_each_score(
    data_lists: list[list[float]], weights: list[int]
) -> list[list[float]]:
    """
    >>> calculate_each_score([[20, 23, 22], [60, 90, 50], [2012, 2015, 2011]],
    ...                      [0, 0, 1])
    [[1.0, 0.0, 0.33333333333333337], [0.75, 0.0, 1.0], [0.25, 1.0, 0.0]]
    """
    score_lists: list[list[float]] = []
    for dlist, weight in zip(data_lists, weights):
        mind = min(dlist)
        maxd = max(dlist)

        score: list[float] = []
        # for weight 0 score is 1 - actual score
        if weight == 0:
            for item in dlist:
                try:
                    score.append(1 - ((item - mind) / (maxd - mind)))
                except ZeroDivisionError:
                    score.append(1)

        elif weight == 1:
            for item in dlist:
                try:
                    score.append((item - mind) / (maxd - mind))
                except ZeroDivisionError:
                    score.append(0)

        # weight not 0 or 1
        else:
            msg = f"Invalid weight of {weight:f} provided"
            raise ValueError(msg)

        score_lists.append(score)

    return score_lists


def generate_final_scores(score_lists: list[list[float]]) -> list[float]:
    """
    >>> generate_final_scores([[1.0, 0.0, 0.33333333333333337],
    ...                        [0.75, 0.0, 1.0],
    ...                        [0.25, 1.0, 0.0]])
    [2.0, 1.0, 1.3333333333333335]
    """
    # initialize final scores
    final_scores: list[float] = [0 for i in range(len(score_lists[0]))]

    for slist in score_lists:
        for j, ele in enumerate(slist):
            final_scores[j] = final_scores[j] + ele

    return final_scores


def procentual_proximity(
    source_data: list[list[float]], weights: list[int]
) -> list[list[float]]:
    """
    | `weights` - ``int`` list
    | possible values - ``0`` / ``1``

        * ``0`` if lower values have higher weight in the data set
        * ``1`` if higher values have higher weight in the data set

    >>> procentual_proximity([[20, 60, 2012],[23, 90, 2015],[22, 50, 2011]], [0, 0, 1])
    [[20, 60, 2012, 2.0], [23, 90, 2015, 1.0], [22, 50, 2011, 1.3333333333333335]]
    """

    data_lists = get_data(source_data)
    score_lists = calculate_each_score(data_lists, weights)
    final_scores = generate_final_scores(score_lists)

    # append scores to source data
    for i, ele in enumerate(final_scores):
        source_data[i].append(ele)

    return source_data


================================================
FILE: other/sdes.py
================================================
def apply_table(inp, table):
    """
    >>> apply_table("0123456789", list(range(10)))
    '9012345678'
    >>> apply_table("0123456789", list(range(9, -1, -1)))
    '8765432109'
    """
    res = ""
    for i in table:
        res += inp[i - 1]
    return res


def left_shift(data):
    """
    >>> left_shift("0123456789")
    '1234567890'
    """
    return data[1:] + data[0]


def xor(a, b):
    """
    >>> xor("01010101", "00001111")
    '01011010'
    """
    res = ""
    for i in range(len(a)):
        if a[i] == b[i]:
            res += "0"
        else:
            res += "1"
    return res


def apply_sbox(s, data):
    row = int("0b" + data[0] + data[-1], 2)
    col = int("0b" + data[1:3], 2)
    return bin(s[row][col])[2:]


def function(expansion, s0, s1, key, message):
    left = message[:4]
    right = message[4:]
    temp = apply_table(right, expansion)
    temp = xor(temp, key)
    left_bin_str = apply_sbox(s0, temp[:4])
    right_bin_str = apply_sbox(s1, temp[4:])
    left_bin_str = "0" * (2 - len(left_bin_str)) + left_bin_str
    right_bin_str = "0" * (2 - len(right_bin_str)) + right_bin_str
    temp = apply_table(left_bin_str + right_bin_str, p4_table)
    temp = xor(left, temp)
    return temp + right


if __name__ == "__main__":
    key = input("Enter 10 bit key: ")
    message = input("Enter 8 bit message: ")

    p8_table = [6, 3, 7, 4, 8, 5, 10, 9]
    p10_table = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
    p4_table = [2, 4, 3, 1]
    IP = [2, 6, 3, 1, 4, 8, 5, 7]
    IP_inv = [4, 1, 3, 5, 7, 2, 8, 6]
    expansion = [4, 1, 2, 3, 2, 3, 4, 1]
    s0 = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
    s1 = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

    # key generation
    temp = apply_table(key, p10_table)
    left = temp[:5]
    right = temp[5:]
    left = left_shift(left)
    right = left_shift(right)
    key1 = apply_table(left + right, p8_table)
    left = left_shift(left)
    right = left_shift(right)
    left = left_shift(left)
    right = left_shift(right)
    key2 = apply_table(left + right, p8_table)

    # encryption
    temp = apply_table(message, IP)
    temp = function(expansion, s0, s1, key1, temp)
    temp = temp[4:] + temp[:4]
    temp = function(expansion, s0, s1, key2, temp)
    CT = apply_table(temp, IP_inv)
    print("Cipher text is:", CT)

    # decryption
    temp = apply_table(CT, IP)
    temp = function(expansion, s0, s1, key2, temp)
    temp = temp[4:] + temp[:4]
    temp = function(expansion, s0, s1, key1, temp)
    PT = apply_table(temp, IP_inv)
    print("Plain text after decypting is:", PT)


================================================
FILE: other/sliding_window_maximum.py
================================================
from collections import deque


def sliding_window_maximum(numbers: list[int], window_size: int) -> list[int]:
    """
    Return a list containing the maximum of each sliding window of size window_size.

    This implementation uses a monotonic deque to achieve O(n) time complexity.

    Args:
        numbers: List of integers representing the input array.
        window_size: Size of the sliding window (must be positive).

    Returns:
        List of maximum values for each valid window.

    Raises:
        ValueError: If window_size is not a positive integer.

    Time Complexity: O(n) - each element is added and removed at most once
    Space Complexity: O(k) - deque stores at most window_size indices

    Examples:
    >>> sliding_window_maximum([1, 3, -1, -3, 5, 3, 6, 7], 3)
    [3, 3, 5, 5, 6, 7]
    >>> sliding_window_maximum([9, 11], 2)
    [11]
    >>> sliding_window_maximum([], 3)
    []
    >>> sliding_window_maximum([4, 2, 12, 3], 1)
    [4, 2, 12, 3]
    >>> sliding_window_maximum([1], 1)
    [1]
    """
    if window_size <= 0:
        raise ValueError("Window size must be a positive integer")
    if not numbers:
        return []

    result: list[int] = []
    index_deque: deque[int] = deque()

    for current_index, current_value in enumerate(numbers):
        # Remove the element which is out of this window
        if index_deque and index_deque[0] == current_index - window_size:
            index_deque.popleft()

        # Remove useless elements (smaller than current) from back
        while index_deque and numbers[index_deque[-1]] < current_value:
            index_deque.pop()

        index_deque.append(current_index)

        # Start adding to result once we have a full window
        if current_index >= window_size - 1:
            result.append(numbers[index_deque[0]])

    return result


================================================
FILE: other/tower_of_hanoi.py
================================================
def move_tower(height, from_pole, to_pole, with_pole):
    """
    >>> move_tower(3, 'A', 'B', 'C')
    moving disk from A to B
    moving disk from A to C
    moving disk from B to C
    moving disk from A to B
    moving disk from C to A
    moving disk from C to B
    moving disk from A to B
    """
    if height >= 1:
        move_tower(height - 1, from_pole, with_pole, to_pole)
        move_disk(from_pole, to_pole)
        move_tower(height - 1, with_pole, to_pole, from_pole)


def move_disk(fp, tp):
    print("moving disk from", fp, "to", tp)


def main():
    height = int(input("Height of hanoi: ").strip())
    move_tower(height, "A", "B", "C")


if __name__ == "__main__":
    main()


================================================
FILE: other/word_search.py
================================================
"""
Creates a random wordsearch with eight different directions
that are best described as compass locations.

@ https://en.wikipedia.org/wiki/Word_search
"""

from random import choice, randint, shuffle

# The words to display on the word search -
# can be made dynamic by randonly selecting a certain number of
# words from a predefined word file, while ensuring the character
# count fits within the matrix size (n x m)
WORDS = ["cat", "dog", "snake", "fish"]

WIDTH = 10
HEIGHT = 10


class WordSearch:
    """
    >>> ws = WordSearch(WORDS, WIDTH, HEIGHT)
    >>> ws.board  # doctest: +ELLIPSIS
    [[None, ..., None], ..., [None, ..., None]]
    >>> ws.generate_board()
    """

    def __init__(self, words: list[str], width: int, height: int) -> None:
        self.words = words
        self.width = width
        self.height = height

        # Board matrix holding each letter
        self.board: list[list[str | None]] = [[None] * width for _ in range(height)]

    def insert_north(self, word: str, rows: list[int], cols: list[int]) -> None:
        """
        >>> ws = WordSearch(WORDS, 3, 3)
        >>> ws.insert_north("cat", [2], [2])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [[None, None, 't'],
        [None, None, 'a'],
        [None, None, 'c']]
        >>> ws.insert_north("at", [0, 1, 2], [2, 1])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [[None, 't', 't'],
        [None, 'a', 'a'],
        [None, None, 'c']]
        """
        word_length = len(word)
        # Attempt to insert the word into each row and when successful, exit
        for row in rows:
            # Check if there is space above the row to fit in the word
            if word_length > row + 1:
                continue

            # Attempt to insert the word into each column
            for col in cols:
                # Only check to be made here is if there are existing letters
                # above the column that will be overwritten
                letters_above = [self.board[row - i][col] for i in range(word_length)]
                if all(letter is None for letter in letters_above):
                    # Successful, insert the word north
                    for i in range(word_length):
                        self.board[row - i][col] = word[i]
                    return

    def insert_northeast(self, word: str, rows: list[int], cols: list[int]) -> None:
        """
        >>> ws = WordSearch(WORDS, 3, 3)
        >>> ws.insert_northeast("cat", [2], [0])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [[None, None, 't'],
        [None, 'a', None],
        ['c', None, None]]
        >>> ws.insert_northeast("at", [0, 1], [2, 1, 0])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [[None, 't', 't'],
        ['a', 'a', None],
        ['c', None, None]]
        """
        word_length = len(word)
        # Attempt to insert the word into each row and when successful, exit
        for row in rows:
            # Check if there is space for the word above the row
            if word_length > row + 1:
                continue

            # Attempt to insert the word into each column
            for col in cols:
                # Check if there is space to the right of the word as well as above
                if word_length + col > self.width:
                    continue

                # Check if there are existing letters
                # to the right of the column that will be overwritten
                letters_diagonal_left = [
                    self.board[row - i][col + i] for i in range(word_length)
                ]
                if all(letter is None for letter in letters_diagonal_left):
                    # Successful, insert the word northeast
                    for i in range(word_length):
                        self.board[row - i][col + i] = word[i]
                    return

    def insert_east(self, word: str, rows: list[int], cols: list[int]) -> None:
        """
        >>> ws = WordSearch(WORDS, 3, 3)
        >>> ws.insert_east("cat", [1], [0])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [[None, None, None],
        ['c', 'a', 't'],
        [None, None, None]]
        >>> ws.insert_east("at", [1, 0], [2, 1, 0])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [[None, 'a', 't'],
        ['c', 'a', 't'],
        [None, None, None]]
        """
        word_length = len(word)
        # Attempt to insert the word into each row and when successful, exit
        for row in rows:
            # Attempt to insert the word into each column
            for col in cols:
                # Check if there is space to the right of the word
                if word_length + col > self.width:
                    continue

                # Check if there are existing letters
                # to the right of the column that will be overwritten
                letters_left = [self.board[row][col + i] for i in range(word_length)]
                if all(letter is None for letter in letters_left):
                    # Successful, insert the word east
                    for i in range(word_length):
                        self.board[row][col + i] = word[i]
                    return

    def insert_southeast(self, word: str, rows: list[int], cols: list[int]) -> None:
        """
        >>> ws = WordSearch(WORDS, 3, 3)
        >>> ws.insert_southeast("cat", [0], [0])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [['c', None, None],
        [None, 'a', None],
        [None, None, 't']]
        >>> ws.insert_southeast("at", [1, 0], [2, 1, 0])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [['c', None, None],
        ['a', 'a', None],
        [None, 't', 't']]
        """
        word_length = len(word)
        # Attempt to insert the word into each row and when successful, exit
        for row in rows:
            # Check if there is space for the word below the row
            if word_length + row > self.height:
                continue

            # Attempt to insert the word into each column
            for col in cols:
                # Check if there is space to the right of the word as well as below
                if word_length + col > self.width:
                    continue

                # Check if there are existing letters
                # to the right of the column that will be overwritten
                letters_diagonal_left = [
                    self.board[row + i][col + i] for i in range(word_length)
                ]
                if all(letter is None for letter in letters_diagonal_left):
                    # Successful, insert the word southeast
                    for i in range(word_length):
                        self.board[row + i][col + i] = word[i]
                    return

    def insert_south(self, word: str, rows: list[int], cols: list[int]) -> None:
        """
        >>> ws = WordSearch(WORDS, 3, 3)
        >>> ws.insert_south("cat", [0], [0])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [['c', None, None],
        ['a', None, None],
        ['t', None, None]]
        >>> ws.insert_south("at", [2, 1, 0], [0, 1, 2])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [['c', None, None],
        ['a', 'a', None],
        ['t', 't', None]]
        """
        word_length = len(word)
        # Attempt to insert the word into each row and when successful, exit
        for row in rows:
            # Check if there is space below the row to fit in the word
            if word_length + row > self.height:
                continue

            # Attempt to insert the word into each column
            for col in cols:
                # Only check to be made here is if there are existing letters
                # below the column that will be overwritten
                letters_below = [self.board[row + i][col] for i in range(word_length)]
                if all(letter is None for letter in letters_below):
                    # Successful, insert the word south
                    for i in range(word_length):
                        self.board[row + i][col] = word[i]
                    return

    def insert_southwest(self, word: str, rows: list[int], cols: list[int]) -> None:
        """
        >>> ws = WordSearch(WORDS, 3, 3)
        >>> ws.insert_southwest("cat", [0], [2])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [[None, None, 'c'],
        [None, 'a', None],
        ['t', None, None]]
        >>> ws.insert_southwest("at", [1, 2], [2, 1, 0])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [[None, None, 'c'],
        [None, 'a', 'a'],
        ['t', 't', None]]
        """
        word_length = len(word)
        # Attempt to insert the word into each row and when successful, exit
        for row in rows:
            # Check if there is space for the word below the row
            if word_length + row > self.height:
                continue

            # Attempt to insert the word into each column
            for col in cols:
                # Check if there is space to the left of the word as well as below
                if word_length > col + 1:
                    continue

                # Check if there are existing letters
                # to the right of the column that will be overwritten
                letters_diagonal_left = [
                    self.board[row + i][col - i] for i in range(word_length)
                ]
                if all(letter is None for letter in letters_diagonal_left):
                    # Successful, insert the word southwest
                    for i in range(word_length):
                        self.board[row + i][col - i] = word[i]
                    return

    def insert_west(self, word: str, rows: list[int], cols: list[int]) -> None:
        """
        >>> ws = WordSearch(WORDS, 3, 3)
        >>> ws.insert_west("cat", [1], [2])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [[None, None, None],
        ['t', 'a', 'c'],
        [None, None, None]]
        >>> ws.insert_west("at", [1, 0], [1, 2, 0])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [['t', 'a', None],
        ['t', 'a', 'c'],
        [None, None, None]]
        """
        word_length = len(word)
        # Attempt to insert the word into each row and when successful, exit
        for row in rows:
            # Attempt to insert the word into each column
            for col in cols:
                # Check if there is space to the left of the word
                if word_length > col + 1:
                    continue

                # Check if there are existing letters
                # to the left of the column that will be overwritten
                letters_left = [self.board[row][col - i] for i in range(word_length)]
                if all(letter is None for letter in letters_left):
                    # Successful, insert the word west
                    for i in range(word_length):
                        self.board[row][col - i] = word[i]
                    return

    def insert_northwest(self, word: str, rows: list[int], cols: list[int]) -> None:
        """
        >>> ws = WordSearch(WORDS, 3, 3)
        >>> ws.insert_northwest("cat", [2], [2])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [['t', None, None],
        [None, 'a', None],
        [None, None, 'c']]
        >>> ws.insert_northwest("at", [1, 2], [0, 1])
        >>> ws.board  # doctest: +NORMALIZE_WHITESPACE
        [['t', None, None],
        ['t', 'a', None],
        [None, 'a', 'c']]
        """
        word_length = len(word)
        # Attempt to insert the word into each row and when successful, exit
        for row in rows:
            # Check if there is space for the word above the row
            if word_length > row + 1:
                continue

            # Attempt to insert the word into each column
            for col in cols:
                # Check if there is space to the left of the word as well as above
                if word_length > col + 1:
                    continue

                # Check if there are existing letters
                # to the right of the column that will be overwritten
                letters_diagonal_left = [
                    self.board[row - i][col - i] for i in range(word_length)
                ]
                if all(letter is None for letter in letters_diagonal_left):
                    # Successful, insert the word northwest
                    for i in range(word_length):
                        self.board[row - i][col - i] = word[i]
                    return

    def generate_board(self) -> None:
        """
        Generates a board with a random direction for each word.

        >>> wt = WordSearch(WORDS, WIDTH, HEIGHT)
        >>> wt.generate_board()
        >>> len(list(filter(lambda word: word is not None, sum(wt.board, start=[])))
        ... ) == sum(map(lambda word: len(word), WORDS))
        True
        """
        directions = (
            self.insert_north,
            self.insert_northeast,
            self.insert_east,
            self.insert_southeast,
            self.insert_south,
            self.insert_southwest,
            self.insert_west,
            self.insert_northwest,
        )
        for word in self.words:
            # Shuffle the row order and column order that is used when brute forcing
            # the insertion of the word
            rows, cols = list(range(self.height)), list(range(self.width))
            shuffle(rows)
            shuffle(cols)

            # Insert the word via the direction
            choice(directions)(word, rows, cols)


def visualise_word_search(
    board: list[list[str | None]] | None = None, *, add_fake_chars: bool = True
) -> None:
    """
    Graphically displays the word search in the terminal.

    >>> ws = WordSearch(WORDS, 5, 5)
    >>> ws.insert_north("cat", [4], [4])
    >>> visualise_word_search(
    ...     ws.board, add_fake_chars=False)  # doctest: +NORMALIZE_WHITESPACE
    # # # # #
    # # # # #
    # # # # t
    # # # # a
    # # # # c
    >>> ws.insert_northeast("snake", [4], [4, 3, 2, 1, 0])
    >>> visualise_word_search(
    ...     ws.board, add_fake_chars=False)  # doctest: +NORMALIZE_WHITESPACE
    # # # # e
    # # # k #
    # # a # t
    # n # # a
    s # # # c
    """
    if board is None:
        word_search = WordSearch(WORDS, WIDTH, HEIGHT)
        word_search.generate_board()
        board = word_search.board

    result = ""
    for row in range(len(board)):
        for col in range(len(board[0])):
            character = "#"
            if (letter := board[row][col]) is not None:
                character = letter
            # Empty char, so add a fake char
            elif add_fake_chars:
                character = chr(randint(97, 122))
            result += f"{character} "
        result += "\n"
    print(result, end="")


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    visualise_word_search()


================================================
FILE: physics/__init__.py
================================================


================================================
FILE: physics/altitude_pressure.py
================================================
"""
Title : Calculate altitude using Pressure

Description :
    The below algorithm approximates the altitude using Barometric formula


"""


def get_altitude_at_pressure(pressure: float) -> float:
    """
    This method calculates the altitude from Pressure wrt to
    Sea level pressure as reference .Pressure is in Pascals
    https://en.wikipedia.org/wiki/Pressure_altitude
    https://community.bosch-sensortec.com/t5/Question-and-answers/How-to-calculate-the-altitude-from-the-pressure-sensor-data/qaq-p/5702

    H = 44330 * [1 - (P/p0)^(1/5.255) ]

    Where :
    H = altitude (m)
    P = measured pressure
    p0 = reference pressure at sea level 101325 Pa

    Examples:
    >>> get_altitude_at_pressure(pressure=100_000)
    105.47836610778828
    >>> get_altitude_at_pressure(pressure=101_325)
    0.0
    >>> get_altitude_at_pressure(pressure=80_000)
    1855.873388064995
    >>> get_altitude_at_pressure(pressure=201_325)
    Traceback (most recent call last):
      ...
    ValueError: Value Higher than Pressure at Sea Level !
    >>> get_altitude_at_pressure(pressure=-80_000)
    Traceback (most recent call last):
      ...
    ValueError: Atmospheric Pressure can not be negative !
    """

    if pressure > 101325:
        raise ValueError("Value Higher than Pressure at Sea Level !")
    if pressure < 0:
        raise ValueError("Atmospheric Pressure can not be negative !")
    return 44_330 * (1 - (pressure / 101_325) ** (1 / 5.5255))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: physics/archimedes_principle_of_buoyant_force.py
================================================
"""
Calculate the buoyant force of any body completely or partially submerged in a static
fluid.  This principle was discovered by the Greek mathematician Archimedes.

Equation for calculating buoyant force:
Fb = p * V * g

https://en.wikipedia.org/wiki/Archimedes%27_principle
"""

# Acceleration Constant on Earth (unit m/s^2)
g = 9.80665  # Also available in scipy.constants.g


def archimedes_principle(
    fluid_density: float, volume: float, gravity: float = g
) -> float:
    """
    Args:
        fluid_density: density of fluid (kg/m^3)
        volume: volume of object/liquid being displaced by the object (m^3)
        gravity: Acceleration from gravity. Gravitational force on the system,
            The default is Earth Gravity
    returns:
        the buoyant force on an object in Newtons

    >>> archimedes_principle(fluid_density=500, volume=4, gravity=9.8)
    19600.0
    >>> archimedes_principle(fluid_density=997, volume=0.5, gravity=9.8)
    4885.3
    >>> archimedes_principle(fluid_density=997, volume=0.7)
    6844.061035
    >>> archimedes_principle(fluid_density=997, volume=-0.7)
    Traceback (most recent call last):
        ...
    ValueError: Impossible object volume
    >>> archimedes_principle(fluid_density=0, volume=0.7)
    Traceback (most recent call last):
        ...
    ValueError: Impossible fluid density
    >>> archimedes_principle(fluid_density=997, volume=0.7, gravity=0)
    0.0
    >>> archimedes_principle(fluid_density=997, volume=0.7, gravity=-9.8)
    Traceback (most recent call last):
        ...
    ValueError: Impossible gravity
    """

    if fluid_density <= 0:
        raise ValueError("Impossible fluid density")
    if volume <= 0:
        raise ValueError("Impossible object volume")
    if gravity < 0:
        raise ValueError("Impossible gravity")

    return fluid_density * gravity * volume


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: physics/basic_orbital_capture.py
================================================
"""
These two functions will return the radii of impact for a target object
of mass M and radius R as well as it's effective cross sectional area sigma.
That is to say any projectile with velocity v passing within sigma, will impact the
target object with mass M. The derivation of which is given at the bottom
of this file.

The derivation shows that a projectile does not need to aim directly at the target
body in order to hit it, as  R_capture>R_target. Astronomers refer to the effective
cross section for capture as sigma=π*R_capture**2.

This algorithm does not account for an N-body problem.
"""

from math import pow, sqrt  # noqa: A004

from scipy.constants import G, c, pi


def capture_radii(
    target_body_radius: float, target_body_mass: float, projectile_velocity: float
) -> float:
    """
    Input Params:
    -------------
    target_body_radius: Radius of the central body SI units: meters | m
    target_body_mass: Mass of the central body SI units: kilograms | kg
    projectile_velocity: Velocity of object moving toward central body
        SI units: meters/second | m/s
    Returns:
    --------
    >>> capture_radii(6.957e8, 1.99e30, 25000.0)
    17209590691.0
    >>> capture_radii(-6.957e8, 1.99e30, 25000.0)
    Traceback (most recent call last):
        ...
    ValueError: Radius cannot be less than 0
    >>> capture_radii(6.957e8, -1.99e30, 25000.0)
    Traceback (most recent call last):
        ...
    ValueError: Mass cannot be less than 0
    >>> capture_radii(6.957e8, 1.99e30, c+1)
    Traceback (most recent call last):
        ...
    ValueError: Cannot go beyond speed of light

    Returned SI units:
    ------------------
    meters | m
    """

    if target_body_mass < 0:
        raise ValueError("Mass cannot be less than 0")
    if target_body_radius < 0:
        raise ValueError("Radius cannot be less than 0")
    if projectile_velocity > c:
        raise ValueError("Cannot go beyond speed of light")

    escape_velocity_squared = (2 * G * target_body_mass) / target_body_radius
    capture_radius = target_body_radius * sqrt(
        1 + escape_velocity_squared / pow(projectile_velocity, 2)
    )
    return round(capture_radius, 0)


def capture_area(capture_radius: float) -> float:
    """
    Input Param:
    ------------
    capture_radius: The radius of orbital capture and impact for a central body of
    mass M and a projectile moving towards it with velocity v
        SI units: meters | m
    Returns:
    --------
    >>> capture_area(17209590691)
    9.304455331329126e+20
    >>> capture_area(-1)
    Traceback (most recent call last):
        ...
    ValueError: Cannot have a capture radius less than 0

    Returned SI units:
    ------------------
    meters*meters | m**2
    """

    if capture_radius < 0:
        raise ValueError("Cannot have a capture radius less than 0")
    sigma = pi * pow(capture_radius, 2)
    return round(sigma, 0)


if __name__ == "__main__":
    from doctest import testmod

    testmod()

"""
Derivation:

Let: Mt=target mass, Rt=target radius, v=projectile_velocity,
     r_0=radius of projectile at instant 0 to CM of target
     v_p=v at closest approach,
     r_p=radius from projectile to target CM at closest approach,
     R_capture= radius of impact for projectile with velocity v

(1)At time=0  the projectile's energy falling from infinity| E=K+U=0.5*m*(v**2)+0

    E_initial=0.5*m*(v**2)

(2)at time=0 the angular momentum of the projectile relative to CM target|
    L_initial=m*r_0*v*sin(Θ)->m*r_0*v*(R_capture/r_0)->m*v*R_capture

    L_i=m*v*R_capture

(3)The energy of the projectile at closest approach will be its kinetic energy
   at closest approach plus gravitational potential energy(-(GMm)/R)|
    E_p=K_p+U_p->E_p=0.5*m*(v_p**2)-(G*Mt*m)/r_p

    E_p=0.0.5*m*(v_p**2)-(G*Mt*m)/r_p

(4)The angular momentum of the projectile relative to the target at closest
   approach will be L_p=m*r_p*v_p*sin(Θ), however relative to the target Θ=90°
   sin(90°)=1|

    L_p=m*r_p*v_p
(5)Using conservation of angular momentum and energy, we can write a quadratic
   equation that solves for r_p|

   (a)
    Ei=Ep-> 0.5*m*(v**2)=0.5*m*(v_p**2)-(G*Mt*m)/r_p-> v**2=v_p**2-(2*G*Mt)/r_p

   (b)
    Li=Lp-> m*v*R_capture=m*r_p*v_p-> v*R_capture=r_p*v_p-> v_p=(v*R_capture)/r_p

   (c) b plugs int a|
    v**2=((v*R_capture)/r_p)**2-(2*G*Mt)/r_p->

    v**2-(v**2)*(R_c**2)/(r_p**2)+(2*G*Mt)/r_p=0->

    (v**2)*(r_p**2)+2*G*Mt*r_p-(v**2)*(R_c**2)=0

   (d) Using the quadratic formula, we'll solve for r_p then rearrange to solve to
       R_capture

    r_p=(-2*G*Mt ± sqrt(4*G^2*Mt^2+ 4(v^4*R_c^2)))/(2*v^2)->

    r_p=(-G*Mt ± sqrt(G^2*Mt+v^4*R_c^2))/v^2->

    r_p<0 is something we can ignore, as it has no physical meaning for our purposes.->

    r_p=(-G*Mt)/v^2 + sqrt(G^2*Mt^2/v^4 + R_c^2)

   (e)We are trying to solve for R_c. We are looking for impact, so we want r_p=Rt

    Rt + G*Mt/v^2 = sqrt(G^2*Mt^2/v^4 + R_c^2)->

    (Rt + G*Mt/v^2)^2 = G^2*Mt^2/v^4 + R_c^2->

    Rt^2 + 2*G*Mt*Rt/v^2 + G^2*Mt^2/v^4 = G^2*Mt^2/v^4 + R_c^2->

    Rt**2 + 2*G*Mt*Rt/v**2 = R_c**2->

    Rt**2 * (1 + 2*G*Mt/Rt *1/v**2) = R_c**2->

    escape velocity = sqrt(2GM/R)= v_escape**2=2GM/R->

    Rt**2 * (1 + v_esc**2/v**2) = R_c**2->

(6)
    R_capture = Rt * sqrt(1 + v_esc**2/v**2)

Source: Problem Set 3 #8 c.Fall_2017|Honors Astronomy|Professor Rachel Bezanson

Source #2: http://www.nssc.ac.cn/wxzygx/weixin/201607/P020160718380095698873.pdf
           8.8 Planetary Rendezvous: Pg.368
"""


================================================
FILE: physics/casimir_effect.py
================================================
"""
Title : Finding the value of magnitude of either the Casimir force, the surface area
of one of the plates or distance between the plates provided that the other
two parameters are given.

Description : In quantum field theory, the Casimir effect is a physical force
acting on the macroscopic boundaries of a confined space which arises from the
quantum fluctuations of the field. It is a physical force exerted between separate
objects, which is due to neither charge, gravity, nor the exchange of particles,
but instead is due to resonance of all-pervasive energy fields in the intervening
space between the objects. Since the strength of the force falls off rapidly with
distance it is only measurable when the distance between the objects is extremely
small. On a submicron scale, this force becomes so strong that it becomes the
dominant force between uncharged conductors.

Dutch physicist Hendrik B. G. Casimir first proposed the existence of the force,
and he formulated an experiment to detect it in 1948 while participating in research
at Philips Research Labs. The classic form of his experiment used a pair of uncharged
parallel metal plates in a vacuum, and successfully demonstrated the force to within
15% of the value he had predicted according to his theory.

The Casimir force F for idealized, perfectly conducting plates of surface area
A square meter and placed at a distance of a meter apart with vacuum between
them is expressed as -

F = - ((Reduced Planck Constant ℏ) * c * Pi^2 * A) / (240 * a^4)

Here, the negative sign indicates the force is attractive in nature. For the ease
of calculation, only the magnitude of the force is considered.

Source :
- https://en.wikipedia.org/wiki/Casimir_effect
- https://www.cs.mcgill.ca/~rwest/wikispeedia/wpcd/wp/c/Casimir_effect.htm
- Casimir, H. B. ; Polder, D. (1948) "The Influence of Retardation on the
  London-van der Waals Forces", Physical Review, vol. 73, Issue 4, pp. 360-372
"""

from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
REDUCED_PLANCK_CONSTANT = 1.054571817e-34  # unit of ℏ : J * s

SPEED_OF_LIGHT = 3e8  # unit of c : m * s^-1


def casimir_force(force: float, area: float, distance: float) -> dict[str, float]:
    """
    Input Parameters
    ----------------
    force -> Casimir Force : magnitude in Newtons

    area -> Surface area of each plate : magnitude in square meters

    distance -> Distance between two plates : distance in Meters

    Returns
    -------
    result : dict name, value pair of the parameter having Zero as it's value

    Returns the value of one of the parameters specified as 0, provided the values of
    other parameters are given.
    >>> casimir_force(force = 0, area = 4, distance = 0.03)
    {'force': 6.4248189174864216e-21}

    >>> casimir_force(force = 2635e-13, area = 0.0023, distance = 0)
    {'distance': 1.0323056015031114e-05}

    >>> casimir_force(force = 2737e-21, area = 0, distance = 0.0023746)
    {'area': 0.06688838837354052}

    >>> casimir_force(force = 3457e-12, area = 0, distance = 0)
    Traceback (most recent call last):
        ...
    ValueError: One and only one argument must be 0

    >>> casimir_force(force = 3457e-12, area = 0, distance = -0.00344)
    Traceback (most recent call last):
        ...
    ValueError: Distance can not be negative

    >>> casimir_force(force = -912e-12, area = 0, distance = 0.09374)
    Traceback (most recent call last):
        ...
    ValueError: Magnitude of force can not be negative
    """

    if (force, area, distance).count(0) != 1:
        raise ValueError("One and only one argument must be 0")
    if force < 0:
        raise ValueError("Magnitude of force can not be negative")
    if distance < 0:
        raise ValueError("Distance can not be negative")
    if area < 0:
        raise ValueError("Area can not be negative")
    if force == 0:
        force = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
            240 * (distance) ** 4
        )
        return {"force": force}
    elif area == 0:
        area = (240 * force * (distance) ** 4) / (
            REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
        )
        return {"area": area}
    elif distance == 0:
        distance = (
            (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
        ) ** (1 / 4)
        return {"distance": distance}
    raise ValueError("One and only one argument must be 0")


# Run doctest
if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: physics/center_of_mass.py
================================================
"""
Calculating the center of mass for a discrete system of particles, given their
positions and masses.

Description:

In physics, the center of mass of a distribution of mass in space (sometimes referred
to as the barycenter or balance point) is the unique point at any given time where the
weighted relative position of the distributed mass sums to zero. This is the point to
which a force may be applied to cause a linear acceleration without an angular
acceleration.

Calculations in mechanics are often simplified when formulated with respect to the
center of mass. It is a hypothetical point where the entire mass of an object may be
assumed to be concentrated to visualize its motion. In other words, the center of mass
is the particle equivalent of a given object for the application of Newton's laws of
motion.

In the case of a system of particles P_i, i = 1, ..., n , each with mass m_i that are
located in space with coordinates r_i, i = 1, ..., n , the coordinates R of the center
of mass corresponds to:

R = (Σ(mi * ri) / Σ(mi))

Reference: https://en.wikipedia.org/wiki/Center_of_mass
"""

from collections import namedtuple

Particle = namedtuple("Particle", "x y z mass")  # noqa: PYI024
Coord3D = namedtuple("Coord3D", "x y z")  # noqa: PYI024


def center_of_mass(particles: list[Particle]) -> Coord3D:
    """
    Input Parameters
    ----------------
    particles: list(Particle):
    A list of particles where each particle is a tuple with it's (x, y, z) position and
    it's mass.

    Returns
    -------
    Coord3D:
    A tuple with the coordinates of the center of mass (Xcm, Ycm, Zcm) rounded to two
    decimal places.

    Examples
    --------
    >>> center_of_mass([
    ...     Particle(1.5, 4, 3.4, 4),
    ...     Particle(5, 6.8, 7, 8.1),
    ...     Particle(9.4, 10.1, 11.6, 12)
    ... ])
    Coord3D(x=6.61, y=7.98, z=8.69)

    >>> center_of_mass([
    ...     Particle(1, 2, 3, 4),
    ...     Particle(5, 6, 7, 8),
    ...     Particle(9, 10, 11, 12)
    ... ])
    Coord3D(x=6.33, y=7.33, z=8.33)

    >>> center_of_mass([
    ...     Particle(1, 2, 3, -4),
    ...     Particle(5, 6, 7, 8),
    ...     Particle(9, 10, 11, 12)
    ... ])
    Traceback (most recent call last):
        ...
    ValueError: Mass of all particles must be greater than 0

    >>> center_of_mass([
    ...     Particle(1, 2, 3, 0),
    ...     Particle(5, 6, 7, 8),
    ...     Particle(9, 10, 11, 12)
    ... ])
    Traceback (most recent call last):
        ...
    ValueError: Mass of all particles must be greater than 0

    >>> center_of_mass([])
    Traceback (most recent call last):
        ...
    ValueError: No particles provided
    """
    if not particles:
        raise ValueError("No particles provided")

    if any(particle.mass <= 0 for particle in particles):
        raise ValueError("Mass of all particles must be greater than 0")

    total_mass = sum(particle.mass for particle in particles)

    center_of_mass_x = round(
        sum(particle.x * particle.mass for particle in particles) / total_mass, 2
    )
    center_of_mass_y = round(
        sum(particle.y * particle.mass for particle in particles) / total_mass, 2
    )
    center_of_mass_z = round(
        sum(particle.z * particle.mass for particle in particles) / total_mass, 2
    )
    return Coord3D(center_of_mass_x, center_of_mass_y, center_of_mass_z)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: physics/centripetal_force.py
================================================
"""
Description : Centripetal force is the force acting on an object in
curvilinear motion directed towards the axis of rotation
or centre of curvature.

The unit of centripetal force is newton.

The centripetal force is always directed perpendicular to the
direction of the object's displacement. Using Newton's second
law of motion, it is found that the centripetal force of an object
moving in a circular path always acts towards the centre of the circle.
The Centripetal Force Formula is given as the product of mass (in kg)
and tangential velocity (in meters per second) squared, divided by the
radius (in meters) that implies that on doubling the tangential velocity,
the centripetal force will be quadrupled. Mathematically it is written as:
F = mv²/r
Where, F is the Centripetal force, m is the mass of the object, v is the
speed or velocity of the object and r is the radius.

Reference: https://byjus.com/physics/centripetal-and-centrifugal-force/
"""


def centripetal(mass: float, velocity: float, radius: float) -> float:
    """
    The Centripetal Force formula is given as: (m*v*v)/r

    >>> round(centripetal(15.5,-30,10),2)
    1395.0
    >>> round(centripetal(10,15,5),2)
    450.0
    >>> round(centripetal(20,-50,15),2)
    3333.33
    >>> round(centripetal(12.25,40,25),2)
    784.0
    >>> round(centripetal(50,100,50),2)
    10000.0
    """
    if mass < 0:
        raise ValueError("The mass of the body cannot be negative")
    if radius <= 0:
        raise ValueError("The radius is always a positive non zero integer")
    return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)


================================================
FILE: physics/coulombs_law.py
================================================
"""
Coulomb's law states that the magnitude of the electrostatic force of attraction
or repulsion between two point charges is directly proportional to the product
of the magnitudes of charges and inversely proportional to the square of the
distance between them.

F = k * q1 * q2 / r^2

k is Coulomb's constant and equals 1/(4π*ε0)
q1 is charge of first body (C)
q2 is charge of second body (C)
r is distance between two charged bodies (m)

Reference: https://en.wikipedia.org/wiki/Coulomb%27s_law
"""


def coulombs_law(q1: float, q2: float, radius: float) -> float:
    """
    Calculate the electrostatic force of attraction or repulsion
    between two point charges

    >>> coulombs_law(15.5, 20, 15)
    12382849136.06
    >>> coulombs_law(1, 15, 5)
    5392531075.38
    >>> coulombs_law(20, -50, 15)
    -39944674632.44
    >>> coulombs_law(-5, -8, 10)
    3595020716.92
    >>> coulombs_law(50, 100, 50)
    17975103584.6
    """
    if radius <= 0:
        raise ValueError("The radius is always a positive number")
    return round(((8.9875517923 * 10**9) * q1 * q2) / (radius**2), 2)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: physics/doppler_frequency.py
================================================
"""
Doppler's effect

The Doppler effect (also Doppler shift) is the change in the frequency of a wave in
relation to an observer who is moving relative to the source of the wave.  The Doppler
effect is named after the physicist Christian Doppler.  A common example of Doppler
shift is the change of pitch heard when a vehicle sounding a horn approaches and
recedes from an observer.

The reason for the Doppler effect is that when the source of the waves is moving
towards the observer, each successive wave crest is emitted from a position closer to
the observer than the crest of the previous wave.  Therefore, each wave takes slightly
less time to reach the observer than the previous wave. Hence, the time between the
arrivals of successive wave crests at the observer is reduced, causing an increase in
the frequency.  Similarly, if the source of waves is moving away from the observer,
each wave is emitted from a position farther from the observer than the previous wave,
so the arrival time between successive waves is increased, reducing the frequency.

If the source of waves is stationary but the observer is moving with respect to the
source, the transmission velocity of the waves changes (ie the rate at which the
observer receives waves) even if the wavelength and frequency emitted from the source
remain constant.

These results are all summarized by the Doppler formula:

    f = (f0 * (v + v0)) / (v - vs)

where:
    f: frequency of the wave
    f0: frequency of the wave when the source is stationary
    v: velocity of the wave in the medium
    v0: velocity of the observer, positive if the observer is moving towards the source
    vs: velocity of the source, positive if the source is moving towards the observer

Doppler's effect has many applications in physics and engineering, such as radar,
astronomy, medical imaging, and seismology.

References:
https://en.wikipedia.org/wiki/Doppler_effect

Now, we will implement a function that calculates the frequency of a wave as a function
of the frequency of the wave when the source is stationary, the velocity of the wave
in the medium, the velocity of the observer and the velocity of the source.
"""


def doppler_effect(
    org_freq: float, wave_vel: float, obs_vel: float, src_vel: float
) -> float:
    """
    Input Parameters:
    -----------------
    org_freq: frequency of the wave when the source is stationary
    wave_vel: velocity of the wave in the medium
    obs_vel: velocity of the observer, +ve if the observer is moving towards the source
    src_vel: velocity of the source, +ve if the source is moving towards the observer

    Returns:
    --------
    f: frequency of the wave as perceived by the observer

    Docstring Tests:
    >>> doppler_effect(100, 330, 10, 0)  # observer moving towards the source
    103.03030303030303
    >>> doppler_effect(100, 330, -10, 0)  # observer moving away from the source
    96.96969696969697
    >>> doppler_effect(100, 330, 0, 10)  # source moving towards the observer
    103.125
    >>> doppler_effect(100, 330, 0, -10)  # source moving away from the observer
    97.05882352941177
    >>> doppler_effect(100, 330, 10, 10)  # source & observer moving towards each other
    106.25
    >>> doppler_effect(100, 330, -10, -10)  # source and observer moving away
    94.11764705882354
    >>> doppler_effect(100, 330, 10, 330)  # source moving at same speed as the wave
    Traceback (most recent call last):
        ...
    ZeroDivisionError: Division by zero implies vs=v and observer in front of the source
    >>> doppler_effect(100, 330, 10, 340)  # source moving faster than the wave
    Traceback (most recent call last):
        ...
    ValueError: Non-positive frequency implies vs>v or v0>v (in the opposite direction)
    >>> doppler_effect(100, 330, -340, 10)  # observer moving faster than the wave
    Traceback (most recent call last):
        ...
    ValueError: Non-positive frequency implies vs>v or v0>v (in the opposite direction)
    """

    if wave_vel == src_vel:
        raise ZeroDivisionError(
            "Division by zero implies vs=v and observer in front of the source"
        )
    doppler_freq = (org_freq * (wave_vel + obs_vel)) / (wave_vel - src_vel)
    if doppler_freq <= 0:
        raise ValueError(
            "Non-positive frequency implies vs>v or v0>v (in the opposite direction)"
        )
    return doppler_freq


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: physics/escape_velocity.py
================================================
import math


def escape_velocity(mass: float, radius: float) -> float:
    """
    Calculates the escape velocity needed to break free from a celestial body's
    gravitational field.

    The formula used is:
        v = sqrt(2 * G * M / R)

    where:
        v = escape velocity (m/s)
        G = gravitational constant (6.67430 * 10^-11 m^3 kg^-1 s^-2)
        M = mass of the celestial body (kg)
        R = radius from the center of mass (m)

    Source:
        https://en.wikipedia.org/wiki/Escape_velocity

    Args:
        mass (float): Mass of the celestial body in kilograms.
        radius (float): Radius from the center of mass in meters.

    Returns:
        float: Escape velocity in meters per second, rounded to 3 decimal places.

    Examples:
        >>> escape_velocity(mass=5.972e24, radius=6.371e6)  # Earth
        11185.978
        >>> escape_velocity(mass=7.348e22, radius=1.737e6)  # Moon
        2376.307
        >>> escape_velocity(mass=1.898e27, radius=6.9911e7)  # Jupiter
        60199.545
        >>> escape_velocity(mass=0, radius=1.0)
        0.0
        >>> escape_velocity(mass=1.0, radius=0)
        Traceback (most recent call last):
            ...
        ZeroDivisionError: Radius cannot be zero.
    """
    gravitational_constant = 6.67430e-11  # m^3 kg^-1 s^-2

    if radius == 0:
        raise ZeroDivisionError("Radius cannot be zero.")

    velocity = math.sqrt(2 * gravitational_constant * mass / radius)
    return round(velocity, 3)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print("Calculate escape velocity of a celestial body...\n")

    try:
        mass = float(input("Enter mass of the celestial body (in kgs): ").strip())
        radius = float(input("Enter radius from the center of mass (in ms): ").strip())

        velocity = escape_velocity(mass=mass, radius=radius)
        print(f"Escape velocity is {velocity} m/s")

    except ValueError:
        print("Invalid input. Please enter valid numeric values.")
    except ZeroDivisionError as e:
        print(e)


================================================
FILE: physics/grahams_law.py
================================================
"""
Title: Graham's Law of Effusion

Description: Graham's law of effusion states that the rate of effusion of a gas is
inversely proportional to the square root of the molar mass of its particles:

r1/r2 = sqrt(m2/m1)

r1 = Rate of effusion for the first gas.
r2 = Rate of effusion for the second gas.
m1 = Molar mass of the first gas.
m2 = Molar mass of the second gas.

(Description adapted from https://en.wikipedia.org/wiki/Graham%27s_law)
"""

from math import pow, sqrt  # noqa: A004


def validate(*values: float) -> bool:
    """
    Input Parameters:
    -----------------
    effusion_rate_1: Effustion rate of first gas (m^2/s, mm^2/s, etc.)
    effusion_rate_2: Effustion rate of second gas (m^2/s, mm^2/s, etc.)
    molar_mass_1: Molar mass of the first gas (g/mol, kg/kmol, etc.)
    molar_mass_2: Molar mass of the second gas (g/mol, kg/kmol, etc.)

    Returns:
    --------
    >>> validate(2.016, 4.002)
    True
    >>> validate(-2.016, 4.002)
    False
    >>> validate()
    False
    """
    result = len(values) > 0 and all(value > 0.0 for value in values)
    return result


def effusion_ratio(molar_mass_1: float, molar_mass_2: float) -> float | ValueError:
    """
    Input Parameters:
    -----------------
    molar_mass_1: Molar mass of the first gas (g/mol, kg/kmol, etc.)
    molar_mass_2: Molar mass of the second gas (g/mol, kg/kmol, etc.)

    Returns:
    --------
    >>> effusion_ratio(2.016, 4.002)
    1.408943
    >>> effusion_ratio(-2.016, 4.002)
    ValueError('Input Error: Molar mass values must greater than 0.')
    >>> effusion_ratio(2.016)
    Traceback (most recent call last):
      ...
    TypeError: effusion_ratio() missing 1 required positional argument: 'molar_mass_2'
    """
    return (
        round(sqrt(molar_mass_2 / molar_mass_1), 6)
        if validate(molar_mass_1, molar_mass_2)
        else ValueError("Input Error: Molar mass values must greater than 0.")
    )


def first_effusion_rate(
    effusion_rate: float, molar_mass_1: float, molar_mass_2: float
) -> float | ValueError:
    """
    Input Parameters:
    -----------------
    effusion_rate: Effustion rate of second gas (m^2/s, mm^2/s, etc.)
    molar_mass_1: Molar mass of the first gas (g/mol, kg/kmol, etc.)
    molar_mass_2: Molar mass of the second gas (g/mol, kg/kmol, etc.)

    Returns:
    --------
    >>> first_effusion_rate(1, 2.016, 4.002)
    1.408943
    >>> first_effusion_rate(-1, 2.016, 4.002)
    ValueError('Input Error: Molar mass and effusion rate values must greater than 0.')
    >>> first_effusion_rate(1)
    Traceback (most recent call last):
      ...
    TypeError: first_effusion_rate() missing 2 required positional arguments: \
'molar_mass_1' and 'molar_mass_2'
    >>> first_effusion_rate(1, 2.016)
    Traceback (most recent call last):
      ...
    TypeError: first_effusion_rate() missing 1 required positional argument: \
'molar_mass_2'
    """
    return (
        round(effusion_rate * sqrt(molar_mass_2 / molar_mass_1), 6)
        if validate(effusion_rate, molar_mass_1, molar_mass_2)
        else ValueError(
            "Input Error: Molar mass and effusion rate values must greater than 0."
        )
    )


def second_effusion_rate(
    effusion_rate: float, molar_mass_1: float, molar_mass_2: float
) -> float | ValueError:
    """
    Input Parameters:
    -----------------
    effusion_rate: Effustion rate of second gas (m^2/s, mm^2/s, etc.)
    molar_mass_1: Molar mass of the first gas (g/mol, kg/kmol, etc.)
    molar_mass_2: Molar mass of the second gas (g/mol, kg/kmol, etc.)

    Returns:
    --------
    >>> second_effusion_rate(1, 2.016, 4.002)
    0.709752
    >>> second_effusion_rate(-1, 2.016, 4.002)
    ValueError('Input Error: Molar mass and effusion rate values must greater than 0.')
    >>> second_effusion_rate(1)
    Traceback (most recent call last):
      ...
    TypeError: second_effusion_rate() missing 2 required positional arguments: \
'molar_mass_1' and 'molar_mass_2'
    >>> second_effusion_rate(1, 2.016)
    Traceback (most recent call last):
      ...
    TypeError: second_effusion_rate() missing 1 required positional argument: \
'molar_mass_2'
    """
    return (
        round(effusion_rate / sqrt(molar_mass_2 / molar_mass_1), 6)
        if validate(effusion_rate, molar_mass_1, molar_mass_2)
        else ValueError(
            "Input Error: Molar mass and effusion rate values must greater than 0."
        )
    )


def first_molar_mass(
    molar_mass: float, effusion_rate_1: float, effusion_rate_2: float
) -> float | ValueError:
    """
    Input Parameters:
    -----------------
    molar_mass: Molar mass of the first gas (g/mol, kg/kmol, etc.)
    effusion_rate_1: Effustion rate of first gas (m^2/s, mm^2/s, etc.)
    effusion_rate_2: Effustion rate of second gas (m^2/s, mm^2/s, etc.)

    Returns:
    --------
    >>> first_molar_mass(2, 1.408943, 0.709752)
    0.507524
    >>> first_molar_mass(-1, 2.016, 4.002)
    ValueError('Input Error: Molar mass and effusion rate values must greater than 0.')
    >>> first_molar_mass(1)
    Traceback (most recent call last):
      ...
    TypeError: first_molar_mass() missing 2 required positional arguments: \
'effusion_rate_1' and 'effusion_rate_2'
    >>> first_molar_mass(1, 2.016)
    Traceback (most recent call last):
      ...
    TypeError: first_molar_mass() missing 1 required positional argument: \
'effusion_rate_2'
    """
    return (
        round(molar_mass / pow(effusion_rate_1 / effusion_rate_2, 2), 6)
        if validate(molar_mass, effusion_rate_1, effusion_rate_2)
        else ValueError(
            "Input Error: Molar mass and effusion rate values must greater than 0."
        )
    )


def second_molar_mass(
    molar_mass: float, effusion_rate_1: float, effusion_rate_2: float
) -> float | ValueError:
    """
    Input Parameters:
    -----------------
    molar_mass: Molar mass of the first gas (g/mol, kg/kmol, etc.)
    effusion_rate_1: Effustion rate of first gas (m^2/s, mm^2/s, etc.)
    effusion_rate_2: Effustion rate of second gas (m^2/s, mm^2/s, etc.)

    Returns:
    --------
    >>> second_molar_mass(2, 1.408943, 0.709752)
    1.970351
    >>> second_molar_mass(-2, 1.408943, 0.709752)
    ValueError('Input Error: Molar mass and effusion rate values must greater than 0.')
    >>> second_molar_mass(1)
    Traceback (most recent call last):
      ...
    TypeError: second_molar_mass() missing 2 required positional arguments: \
'effusion_rate_1' and 'effusion_rate_2'
    >>> second_molar_mass(1, 2.016)
    Traceback (most recent call last):
      ...
    TypeError: second_molar_mass() missing 1 required positional argument: \
'effusion_rate_2'
    """
    return (
        round(pow(effusion_rate_1 / effusion_rate_2, 2) / molar_mass, 6)
        if validate(molar_mass, effusion_rate_1, effusion_rate_2)
        else ValueError(
            "Input Error: Molar mass and effusion rate values must greater than 0."
        )
    )


================================================
FILE: physics/horizontal_projectile_motion.py
================================================
"""
Horizontal Projectile Motion problem in physics.

This algorithm solves a specific problem in which
the motion starts from the ground as can be seen below::

          (v = 0)
                   *  *
               *          *
            *                *
          *                    *
        *                        *
       *                          *
    GROUND                      GROUND

For more info: https://en.wikipedia.org/wiki/Projectile_motion
"""

# Importing packages
from math import radians as deg_to_rad
from math import sin

# Acceleration Constant on Earth (unit m/s^2)
g = 9.80665


def check_args(init_velocity: float, angle: float) -> None:
    """
    Check that the arguments are valid
    """

    # Ensure valid instance
    if not isinstance(init_velocity, (int, float)):
        raise TypeError("Invalid velocity. Should be an integer or float.")

    if not isinstance(angle, (int, float)):
        raise TypeError("Invalid angle. Should be an integer or float.")

    # Ensure valid angle
    if angle > 90 or angle < 1:
        raise ValueError("Invalid angle. Range is 1-90 degrees.")

    # Ensure valid velocity
    if init_velocity < 0:
        raise ValueError("Invalid velocity. Should be a positive number.")


def horizontal_distance(init_velocity: float, angle: float) -> float:
    r"""
    Returns the horizontal distance that the object cover

    Formula:
        .. math::
            \frac{v_0^2 \cdot \sin(2 \alpha)}{g}

            v_0 - \text{initial velocity}

            \alpha - \text{angle}

    >>> horizontal_distance(30, 45)
    91.77
    >>> horizontal_distance(100, 78)
    414.76
    >>> horizontal_distance(-1, 20)
    Traceback (most recent call last):
        ...
    ValueError: Invalid velocity. Should be a positive number.
    >>> horizontal_distance(30, -20)
    Traceback (most recent call last):
        ...
    ValueError: Invalid angle. Range is 1-90 degrees.
    """
    check_args(init_velocity, angle)
    radians = deg_to_rad(2 * angle)
    return round(init_velocity**2 * sin(radians) / g, 2)


def max_height(init_velocity: float, angle: float) -> float:
    r"""
    Returns the maximum height that the object reach

    Formula:
        .. math::
            \frac{v_0^2 \cdot \sin^2 (\alpha)}{2 g}

            v_0 - \text{initial velocity}

            \alpha - \text{angle}

    >>> max_height(30, 45)
    22.94
    >>> max_height(100, 78)
    487.82
    >>> max_height("a", 20)
    Traceback (most recent call last):
        ...
    TypeError: Invalid velocity. Should be an integer or float.
    >>> horizontal_distance(30, "b")
    Traceback (most recent call last):
        ...
    TypeError: Invalid angle. Should be an integer or float.
    """
    check_args(init_velocity, angle)
    radians = deg_to_rad(angle)
    return round(init_velocity**2 * sin(radians) ** 2 / (2 * g), 2)


def total_time(init_velocity: float, angle: float) -> float:
    r"""
    Returns total time of the motion

    Formula:
        .. math::
            \frac{2 v_0 \cdot \sin (\alpha)}{g}

            v_0 - \text{initial velocity}

            \alpha - \text{angle}

    >>> total_time(30, 45)
    4.33
    >>> total_time(100, 78)
    19.95
    >>> total_time(-10, 40)
    Traceback (most recent call last):
        ...
    ValueError: Invalid velocity. Should be a positive number.
    >>> total_time(30, "b")
    Traceback (most recent call last):
        ...
    TypeError: Invalid angle. Should be an integer or float.
    """
    check_args(init_velocity, angle)
    radians = deg_to_rad(angle)
    return round(2 * init_velocity * sin(radians) / g, 2)


def test_motion() -> None:
    """
    Test motion

    >>> test_motion()
    """
    v0, angle = 25, 20
    assert horizontal_distance(v0, angle) == 40.97
    assert max_height(v0, angle) == 3.73
    assert total_time(v0, angle) == 1.74


if __name__ == "__main__":
    from doctest import testmod

    testmod()

    # Get input from user
    init_vel = float(input("Initial Velocity: ").strip())

    # Get input from user
    angle = float(input("angle: ").strip())

    # Print results
    print()
    print("Results: ")
    print(f"Horizontal Distance: {horizontal_distance(init_vel, angle)!s} [m]")
    print(f"Maximum Height: {max_height(init_vel, angle)!s} [m]")
    print(f"Total Time: {total_time(init_vel, angle)!s} [s]")


================================================
FILE: physics/hubble_parameter.py
================================================
"""
Title : Calculating the Hubble Parameter

Description : The Hubble parameter H is the Universe expansion rate
in any time. In cosmology is customary to use the redshift redshift
in place of time, becausethe redshift is directily mensure
in the light of galaxies moving away from us.

So, the general relation that we obtain is

H = hubble_constant*(radiation_density*(redshift+1)**4
                     + matter_density*(redshift+1)**3
                     + curvature*(redshift+1)**2 + dark_energy)**(1/2)

where radiation_density, matter_density, dark_energy are the relativity
(the percentage) energy densities that exist
in the Universe today. Here, matter_density is the
sum of the barion density and the
dark matter. Curvature is the curvature parameter and can be written in term
of the densities by the completeness


curvature = 1 - (matter_density + radiation_density + dark_energy)

Source :
https://www.sciencedirect.com/topics/mathematics/hubble-parameter
"""


def hubble_parameter(
    hubble_constant: float,
    radiation_density: float,
    matter_density: float,
    dark_energy: float,
    redshift: float,
) -> float:
    """
    Input Parameters
    ----------------
    hubble_constant: Hubble constante is the expansion rate today usually
    given in km/(s*Mpc)

    radiation_density: relative radiation density today

    matter_density: relative mass density today

    dark_energy: relative dark energy density today

    redshift: the light redshift

    Returns
    -------
    result : Hubble parameter in and the unit km/s/Mpc (the unit can be
    changed if you want, just need to change the unit of the Hubble constant)

    >>> hubble_parameter(hubble_constant=68.3, radiation_density=1e-4,
    ... matter_density=-0.3, dark_energy=0.7, redshift=1)
    Traceback (most recent call last):
    ...
    ValueError: All input parameters must be positive

    >>> hubble_parameter(hubble_constant=68.3, radiation_density=1e-4,
    ... matter_density= 1.2, dark_energy=0.7, redshift=1)
    Traceback (most recent call last):
    ...
    ValueError: Relative densities cannot be greater than one

    >>> hubble_parameter(hubble_constant=68.3, radiation_density=1e-4,
    ... matter_density= 0.3, dark_energy=0.7, redshift=0)
    68.3
    """
    parameters = [redshift, radiation_density, matter_density, dark_energy]
    if any(p < 0 for p in parameters):
        raise ValueError("All input parameters must be positive")

    if any(p > 1 for p in parameters[1:4]):
        raise ValueError("Relative densities cannot be greater than one")
    else:
        curvature = 1 - (matter_density + radiation_density + dark_energy)

        e_2 = (
            radiation_density * (redshift + 1) ** 4
            + matter_density * (redshift + 1) ** 3
            + curvature * (redshift + 1) ** 2
            + dark_energy
        )

        hubble = hubble_constant * e_2 ** (1 / 2)
        return hubble


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()

    # demo LCDM approximation
    matter_density = 0.3

    print(
        hubble_parameter(
            hubble_constant=68.3,
            radiation_density=1e-4,
            matter_density=matter_density,
            dark_energy=1 - matter_density,
            redshift=0,
        )
    )


================================================
FILE: physics/ideal_gas_law.py
================================================
"""
The ideal gas law, also called the general gas equation, is the
equation of state of a hypothetical ideal gas. It is a good approximation
of the behavior of many gases under many conditions, although it has
several limitations. It was first stated by Benoît Paul Émile Clapeyron
in 1834 as a combination of the empirical Boyle's law, Charles's law,
Avogadro's law, and Gay-Lussac's law.[1] The ideal gas law is often written
in an empirical form:
 ------------
 | PV = nRT |
 ------------
P	=	Pressure (Pa)
V	=	Volume (m^3)
n	=	Amount of substance (mol)
R	=	Universal gas constant
T	=	Absolute temperature (Kelvin)

(Description adapted from https://en.wikipedia.org/wiki/Ideal_gas_law )
"""

UNIVERSAL_GAS_CONSTANT = 8.314462  # Unit - J mol-1 K-1


def pressure_of_gas_system(moles: float, kelvin: float, volume: float) -> float:
    """
    >>> pressure_of_gas_system(2, 100, 5)
    332.57848
    >>> pressure_of_gas_system(0.5, 273, 0.004)
    283731.01575
    >>> pressure_of_gas_system(3, -0.46, 23.5)
    Traceback (most recent call last):
        ...
    ValueError: Invalid inputs. Enter positive value.
    """
    if moles < 0 or kelvin < 0 or volume < 0:
        raise ValueError("Invalid inputs. Enter positive value.")
    return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume


def volume_of_gas_system(moles: float, kelvin: float, pressure: float) -> float:
    """
    >>> volume_of_gas_system(2, 100, 5)
    332.57848
    >>> volume_of_gas_system(0.5, 273, 0.004)
    283731.01575
    >>> volume_of_gas_system(3, -0.46, 23.5)
    Traceback (most recent call last):
        ...
    ValueError: Invalid inputs. Enter positive value.
    """
    if moles < 0 or kelvin < 0 or pressure < 0:
        raise ValueError("Invalid inputs. Enter positive value.")
    return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


def temperature_of_gas_system(moles: float, volume: float, pressure: float) -> float:
    """
    >>> temperature_of_gas_system(2, 100, 5)
    30.068090996146232
    >>> temperature_of_gas_system(11, 5009, 1000)
    54767.66101807144
    >>> temperature_of_gas_system(3, -0.46, 23.5)
    Traceback (most recent call last):
        ...
    ValueError: Invalid inputs. Enter positive value.
    """
    if moles < 0 or volume < 0 or pressure < 0:
        raise ValueError("Invalid inputs. Enter positive value.")

    return pressure * volume / (moles * UNIVERSAL_GAS_CONSTANT)


def moles_of_gas_system(kelvin: float, volume: float, pressure: float) -> float:
    """
    >>> moles_of_gas_system(100, 5, 10)
    0.06013618199229246
    >>> moles_of_gas_system(110, 5009, 1000)
    5476.766101807144
    >>> moles_of_gas_system(3, -0.46, 23.5)
    Traceback (most recent call last):
        ...
    ValueError: Invalid inputs. Enter positive value.
    """
    if kelvin < 0 or volume < 0 or pressure < 0:
        raise ValueError("Invalid inputs. Enter positive value.")

    return pressure * volume / (kelvin * UNIVERSAL_GAS_CONSTANT)


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: physics/image_data/__init__.py
================================================


================================================
FILE: physics/in_static_equilibrium.py
================================================
"""
Checks if a system of forces is in static equilibrium.
"""

from __future__ import annotations

from numpy import array, cos, cross, float64, radians, sin
from numpy.typing import NDArray


def polar_force(
    magnitude: float, angle: float, radian_mode: bool = False
) -> list[float]:
    """
    Resolves force along rectangular components.
    (force, angle) => (force_x, force_y)
    >>> import math
    >>> force = polar_force(10, 45)
    >>> math.isclose(force[0], 7.071067811865477)
    True
    >>> math.isclose(force[1], 7.0710678118654755)
    True
    >>> force = polar_force(10, 3.14, radian_mode=True)
    >>> math.isclose(force[0], -9.999987317275396)
    True
    >>> math.isclose(force[1], 0.01592652916486828)
    True
    """
    if radian_mode:
        return [magnitude * cos(angle), magnitude * sin(angle)]
    return [magnitude * cos(radians(angle)), magnitude * sin(radians(angle))]


def in_static_equilibrium(
    forces: NDArray[float64], location: NDArray[float64], eps: float = 10**-1
) -> bool:
    """
    Check if a system is in equilibrium.
    It takes two numpy.array objects.
    forces ==>  [
                        [force1_x, force1_y],
                        [force2_x, force2_y],
                        ....]
    location ==>  [
                        [x1, y1],
                        [x2, y2],
                        ....]
    >>> force = array([[1, 1], [-1, 2]])
    >>> location = array([[1, 0], [10, 0]])
    >>> in_static_equilibrium(force, location)
    False
    """
    # summation of moments is zero
    moments: NDArray[float64] = cross(location, forces)
    sum_moments: float = sum(moments)
    return bool(abs(sum_moments) < eps)


if __name__ == "__main__":
    # Test to check if it works
    forces = array(
        [
            polar_force(718.4, 180 - 30),
            polar_force(879.54, 45),
            polar_force(100, -90),
        ]
    )

    location: NDArray[float64] = array([[0, 0], [0, 0], [0, 0]])

    assert in_static_equilibrium(forces, location)

    # Problem 1 in image_data/2D_problems.jpg
    forces = array(
        [
            polar_force(30 * 9.81, 15),
            polar_force(215, 180 - 45),
            polar_force(264, 90 - 30),
        ]
    )

    location = array([[0, 0], [0, 0], [0, 0]])

    assert in_static_equilibrium(forces, location)

    # Problem in image_data/2D_problems_1.jpg
    forces = array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])

    location = array([[0, 0], [6, 0], [10, 0], [12, 0]])

    assert in_static_equilibrium(forces, location)

    import doctest

    doctest.testmod()


================================================
FILE: physics/kinetic_energy.py
================================================
"""
Find the kinetic energy of an object, given its mass and velocity.

Description : In physics, the kinetic energy of an object is the energy that it
possesses due to its motion.It is defined as the work needed to accelerate a body of a
given mass from rest to its stated velocity.Having gained this energy during its
acceleration, the body maintains this kinetic energy unless its speed changes.The same
amount of work is done by the body when decelerating from its current speed to a state
of rest.Formally, a kinetic energy is any term in a system's Lagrangian which includes
a derivative with respect to time.

In classical mechanics, the kinetic energy of a non-rotating object of mass m traveling
at a speed v is ½mv².In relativistic mechanics, this is a good approximation only when
v is much less than the speed of light.The standard unit of kinetic energy is the
joule, while the English unit of kinetic energy is the foot-pound.

Reference : https://en.m.wikipedia.org/wiki/Kinetic_energy
"""


def kinetic_energy(mass: float, velocity: float) -> float:
    """
    Calculate kinetic energy.

    The kinetic energy of a non-rotating object of mass m traveling at a speed v is ½mv²

    >>> kinetic_energy(10,10)
    500.0
    >>> kinetic_energy(0,10)
    0.0
    >>> kinetic_energy(10,0)
    0.0
    >>> kinetic_energy(20,-20)
    4000.0
    >>> kinetic_energy(0,0)
    0.0
    >>> kinetic_energy(2,2)
    4.0
    >>> kinetic_energy(100,100)
    500000.0
    """
    if mass < 0:
        raise ValueError("The mass of a body cannot be negative")
    return 0.5 * mass * abs(velocity) * abs(velocity)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)


================================================
FILE: physics/lens_formulae.py
================================================
"""
This module has functions which calculate focal length of lens, distance of
image from the lens and distance of object from the lens.
The above is calculated using the lens formula.

In optics, the relationship between the distance of the image (v),
the distance of the object (u), and
the focal length (f) of the lens is given by the formula known as the Lens formula.
The Lens formula is applicable for convex as well as concave lenses. The formula
is given as follows:

-------------------
| 1/f = 1/v + 1/u |
-------------------

Where
    f = focal length of the lens in meters.
    v = distance of the image from the lens in meters.
    u = distance of the object from the lens in meters.

To make our calculations easy few assumptions are made while deriving the formula
which are important to keep in mind before solving this equation.
The assumptions are as follows:
    1. The object O is a point object lying somewhere on the principle axis.
    2. The lens is thin.
    3. The aperture of the lens taken must be small.
    4. The angles of incidence and angle of refraction should be small.

Sign convention is a set of rules to set signs for image distance, object distance,
focal length, etc
for mathematical analysis of image formation. According to it:
    1. Object is always placed to the left of lens.
    2. All distances are measured from the optical centre of the mirror.
    3. Distances measured in the direction of the incident ray are positive and
    the distances measured in the direction opposite
    to that of the incident rays are negative.
    4. Distances measured along y-axis above the principal axis are positive and
    that measured along y-axis below the principal
    axis are negative.

Note: Sign convention can be reversed and will still give the correct results.

Reference for Sign convention:
https://www.toppr.com/ask/content/concept/sign-convention-for-lenses-210246/

Reference for assumptions:
https://testbook.com/physics/derivation-of-lens-maker-formula
"""


def focal_length_of_lens(
    object_distance_from_lens: float, image_distance_from_lens: float
) -> float:
    """
    Doctests:
    >>> from math import isclose
    >>> isclose(focal_length_of_lens(10,4), 6.666666666666667)
    True
    >>> from math import isclose
    >>> isclose(focal_length_of_lens(2.7,5.8), -5.0516129032258075)
    True
    >>> focal_length_of_lens(0, 20)  # doctest: +NORMALIZE_WHITESPACE
    Traceback (most recent call last):
        ...
    ValueError: Invalid inputs. Enter non zero values with respect
    to the sign convention.
    """

    if object_distance_from_lens == 0 or image_distance_from_lens == 0:
        raise ValueError(
            "Invalid inputs. Enter non zero values with respect to the sign convention."
        )
    focal_length = 1 / (
        (1 / image_distance_from_lens) - (1 / object_distance_from_lens)
    )
    return focal_length


def object_distance(
    focal_length_of_lens: float, image_distance_from_lens: float
) -> float:
    """
    Doctests:
    >>> from math import isclose
    >>> isclose(object_distance(10,40), -13.333333333333332)
    True

    >>> from math import isclose
    >>> isclose(object_distance(6.2,1.5), 1.9787234042553192)
    True

    >>> object_distance(0, 20)  # doctest: +NORMALIZE_WHITESPACE
    Traceback (most recent call last):
        ...
    ValueError: Invalid inputs. Enter non zero values with respect
    to the sign convention.
    """

    if image_distance_from_lens == 0 or focal_length_of_lens == 0:
        raise ValueError(
            "Invalid inputs. Enter non zero values with respect to the sign convention."
        )

    object_distance = 1 / ((1 / image_distance_from_lens) - (1 / focal_length_of_lens))
    return object_distance


def image_distance(
    focal_length_of_lens: float, object_distance_from_lens: float
) -> float:
    """
    Doctests:
    >>> from math import isclose
    >>> isclose(image_distance(50,40), 22.22222222222222)
    True
    >>> from math import isclose
    >>> isclose(image_distance(5.3,7.9), 3.1719696969696973)
    True

    >>> object_distance(0, 20)  # doctest: +NORMALIZE_WHITESPACE
    Traceback (most recent call last):
        ...
    ValueError: Invalid inputs. Enter non zero values with respect
    to the sign convention.
    """
    if object_distance_from_lens == 0 or focal_length_of_lens == 0:
        raise ValueError(
            "Invalid inputs. Enter non zero values with respect to the sign convention."
        )
    image_distance = 1 / ((1 / object_distance_from_lens) + (1 / focal_length_of_lens))
    return image_distance


================================================
FILE: physics/lorentz_transformation_four_vector.py
================================================
"""
Lorentz transformations describe the transition between two inertial reference
frames F and F', each of which is moving in some direction with respect to the
other. This code only calculates Lorentz transformations for movement in the x
direction with no spatial rotation (i.e., a Lorentz boost in the x direction).
The Lorentz transformations are calculated here as linear transformations of
four-vectors [ct, x, y, z] described by Minkowski space. Note that t (time) is
multiplied by c (the speed of light) in the first entry of each four-vector.

Thus, if X = [ct; x; y; z] and X' = [ct'; x'; y'; z'] are the four-vectors for
two inertial reference frames and X' moves in the x direction with velocity v
with respect to X, then the Lorentz transformation from X to X' is X' = BX,
where

    | y  -γβ  0  0|
B = |-γβ  y   0  0|
    | 0   0   1  0|
    | 0   0   0  1|

is the matrix describing the Lorentz boost between X and X',
y = 1 / √(1 - v²/c²) is the Lorentz factor, and β = v/c is the velocity as
a fraction of c.

Reference: https://en.wikipedia.org/wiki/Lorentz_transformation
"""

from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
c = 299792458

# Symbols
ct, x, y, z = symbols("ct x y z")


# Vehicle's speed divided by speed of light (no units)
def beta(velocity: float) -> float:
    """
    Calculates β = v/c, the given velocity as a fraction of c
    >>> beta(c)
    1.0
    >>> beta(199792458)
    0.666435904801848
    >>> beta(1e5)
    0.00033356409519815205
    >>> beta(0.2)
    Traceback (most recent call last):
      ...
    ValueError: Speed must be greater than or equal to 1!
    """
    if velocity > c:
        raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!")
    elif velocity < 1:
        # Usually the speed should be much higher than 1 (c order of magnitude)
        raise ValueError("Speed must be greater than or equal to 1!")

    return velocity / c


def gamma(velocity: float) -> float:
    """
    Calculate the Lorentz factor y = 1 / √(1 - v²/c²) for a given velocity
    >>> gamma(4)
    1.0000000000000002
    >>> gamma(1e5)
    1.0000000556325075
    >>> gamma(3e7)
    1.005044845777813
    >>> gamma(2.8e8)
    2.7985595722318277
    >>> gamma(299792451)
    4627.49902669495
    >>> gamma(0.3)
    Traceback (most recent call last):
      ...
    ValueError: Speed must be greater than or equal to 1!
    >>> gamma(2 * c)
    Traceback (most recent call last):
      ...
    ValueError: Speed must not exceed light speed 299,792,458 [m/s]!
    """
    return 1 / sqrt(1 - beta(velocity) ** 2)


def transformation_matrix(velocity: float) -> np.ndarray:
    """
    Calculate the Lorentz transformation matrix for movement in the x direction:

    | y  -γβ  0  0|
    |-γβ  y   0  0|
    | 0   0   1  0|
    | 0   0   0  1|

    where y is the Lorentz factor and β is the velocity as a fraction of c
    >>> transformation_matrix(29979245)
    array([[ 1.00503781, -0.10050378,  0.        ,  0.        ],
           [-0.10050378,  1.00503781,  0.        ,  0.        ],
           [ 0.        ,  0.        ,  1.        ,  0.        ],
           [ 0.        ,  0.        ,  0.        ,  1.        ]])
    >>> transformation_matrix(19979245.2)
    array([[ 1.00222811, -0.06679208,  0.        ,  0.        ],
           [-0.06679208,  1.00222811,  0.        ,  0.        ],
           [ 0.        ,  0.        ,  1.        ,  0.        ],
           [ 0.        ,  0.        ,  0.        ,  1.        ]])
    >>> transformation_matrix(1)
    array([[ 1.00000000e+00, -3.33564095e-09,  0.00000000e+00,
             0.00000000e+00],
           [-3.33564095e-09,  1.00000000e+00,  0.00000000e+00,
             0.00000000e+00],
           [ 0.00000000e+00,  0.00000000e+00,  1.00000000e+00,
             0.00000000e+00],
           [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
             1.00000000e+00]])
    >>> transformation_matrix(0)
    Traceback (most recent call last):
      ...
    ValueError: Speed must be greater than or equal to 1!
    >>> transformation_matrix(c * 1.5)
    Traceback (most recent call last):
      ...
    ValueError: Speed must not exceed light speed 299,792,458 [m/s]!
    """
    return np.array(
        [
            [gamma(velocity), -gamma(velocity) * beta(velocity), 0, 0],
            [-gamma(velocity) * beta(velocity), gamma(velocity), 0, 0],
            [0, 0, 1, 0],
            [0, 0, 0, 1],
        ]
    )


def transform(velocity: float, event: np.ndarray | None = None) -> np.ndarray:
    """
    Calculate a Lorentz transformation for movement in the x direction given a
    velocity and a four-vector for an inertial reference frame

    If no four-vector is given, then calculate the transformation symbolically
    with variables
    >>> transform(29979245, np.array([1, 2, 3, 4]))
    array([ 3.01302757e+08, -3.01302729e+07,  3.00000000e+00,  4.00000000e+00])
    >>> transform(29979245)
    array([1.00503781498831*ct - 0.100503778816875*x,
           -0.100503778816875*ct + 1.00503781498831*x, 1.0*y, 1.0*z],
          dtype=object)
    >>> transform(19879210.2)
    array([1.0022057787097*ct - 0.066456172618675*x,
           -0.066456172618675*ct + 1.0022057787097*x, 1.0*y, 1.0*z],
          dtype=object)
    >>> transform(299792459, np.array([1, 1, 1, 1]))
    Traceback (most recent call last):
      ...
    ValueError: Speed must not exceed light speed 299,792,458 [m/s]!
    >>> transform(-1, np.array([1, 1, 1, 1]))
    Traceback (most recent call last):
      ...
    ValueError: Speed must be greater than or equal to 1!
    """
    # Ensure event is not empty
    if event is None:
        event = np.array([ct, x, y, z])  # Symbolic four vector
    else:
        event[0] *= c  # x0 is ct (speed of light * time)

    return transformation_matrix(velocity) @ event


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Example of symbolic vector:
    four_vector = transform(29979245)
    print("Example of four vector: ")
    print(f"ct' = {four_vector[0]}")
    print(f"x' = {four_vector[1]}")
    print(f"y' = {four_vector[2]}")
    print(f"z' = {four_vector[3]}")

    # Substitute symbols with numerical values
    sub_dict = {ct: c, x: 1, y: 1, z: 1}
    numerical_vector = [four_vector[i].subs(sub_dict) for i in range(4)]

    print(f"\n{numerical_vector}")


================================================
FILE: physics/malus_law.py
================================================
import math

"""
Finding the intensity of light transmitted through a polariser using Malus Law
and by taking initial intensity and angle between polariser and axis as input

Description : Malus's law, which is named after Étienne-Louis Malus,
says that when a perfect polarizer is placed in a polarized
beam of light, the irradiance, I, of the light that passes
through is given by
 I=I'cos²θ
where I' is the initial intensity and θ is the angle between the light's
initial polarization direction and the axis of the polarizer.
A beam of unpolarized light can be thought of as containing a
uniform mixture of linear polarizations at all possible angles.
Since the average value of cos²θ is 1/2, the transmission coefficient becomes
I/I' = 1/2
In practice, some light is lost in the polarizer and the actual transmission
will be somewhat lower than this, around 38% for Polaroid-type polarizers but
considerably higher (>49.9%) for some birefringent prism types.
If two polarizers are placed one after another (the second polarizer is
generally called an analyzer), the mutual angle between their polarizing axes
gives the value of θ in Malus's law. If the two axes are orthogonal, the
polarizers are crossed and in theory no light is transmitted, though again
practically speaking no polarizer is perfect and the transmission is not exactly
zero (for example, crossed Polaroid sheets appear slightly blue in colour because
their extinction ratio is better in the red). If a transparent object is placed
between the crossed polarizers, any polarization effects present in the sample
(such as birefringence) will be shown as an increase in transmission.
This effect is used in polarimetry to measure the optical activity of a sample.
Real polarizers are also not perfect blockers of the polarization orthogonal to
their polarization axis; the ratio of the transmission of the unwanted component
to the wanted component is called the extinction ratio, and varies from around
1:500 for Polaroid to about 1:106 for Glan-Taylor prism polarizers.

Reference : "https://en.wikipedia.org/wiki/Polarizer#Malus's_law_and_other_properties"
"""


def malus_law(initial_intensity: float, angle: float) -> float:
    """
    >>> round(malus_law(10,45),2)
    5.0
    >>> round(malus_law(100,60),2)
    25.0
    >>> round(malus_law(50,150),2)
    37.5
    >>> round(malus_law(75,270),2)
    0.0
    >>> round(malus_law(10,-900),2)
    Traceback (most recent call last):
        ...
    ValueError: In Malus Law, the angle is in the range 0-360 degrees
    >>> round(malus_law(10,900),2)
    Traceback (most recent call last):
        ...
    ValueError: In Malus Law, the angle is in the range 0-360 degrees
    >>> round(malus_law(-100,900),2)
    Traceback (most recent call last):
        ...
    ValueError: The value of intensity cannot be negative
    >>> round(malus_law(100,180),2)
    100.0
    >>> round(malus_law(100,360),2)
    100.0
    """

    if initial_intensity < 0:
        raise ValueError("The value of intensity cannot be negative")
        # handling of negative values of initial intensity
    if angle < 0 or angle > 360:
        raise ValueError("In Malus Law, the angle is in the range 0-360 degrees")
        # handling of values out of allowed range
    return initial_intensity * (math.cos(math.radians(angle)) ** 2)


if __name__ == "__main__":
    import doctest

    doctest.testmod(name="malus_law")


================================================
FILE: physics/mass_energy_equivalence.py
================================================
"""
Title:
Finding the energy equivalence of mass and mass equivalence of energy
by Einstein's equation.

Description:
Einstein's mass-energy equivalence is a pivotal concept in theoretical physics.
It asserts that energy (E) and mass (m) are directly related by the speed of
light in vacuum (c) squared, as described in the equation E = mc². This means that
mass and energy are interchangeable; a mass increase corresponds to an energy increase,
and vice versa. This principle has profound implications in nuclear reactions,
explaining the release of immense energy from minuscule changes in atomic nuclei.

Equations:
E = mc² and m = E/c², where m is mass, E is Energy, c is speed of light in vacuum.

Reference:
https://en.wikipedia.org/wiki/Mass%E2%80%93energy_equivalence
"""

from scipy.constants import c  # speed of light in vacuum (299792458 m/s)


def energy_from_mass(mass: float) -> float:
    """
    Calculates the Energy equivalence of the Mass using E = mc²
    in SI units J from Mass in kg.

    mass (float): Mass of body.

    Usage example:
    >>> energy_from_mass(124.56)
    1.11948945063458e+19
    >>> energy_from_mass(320)
    2.8760165719578165e+19
    >>> energy_from_mass(0)
    0.0
    >>> energy_from_mass(-967.9)
    Traceback (most recent call last):
        ...
    ValueError: Mass can't be negative.

    """
    if mass < 0:
        raise ValueError("Mass can't be negative.")
    return mass * c**2


def mass_from_energy(energy: float) -> float:
    """
    Calculates the Mass equivalence of the Energy using m = E/c²
    in SI units kg from Energy in J.

    energy (float): Mass of body.

    Usage example:
    >>> mass_from_energy(124.56)
    1.3859169098203872e-15
    >>> mass_from_energy(320)
    3.560480179371579e-15
    >>> mass_from_energy(0)
    0.0
    >>> mass_from_energy(-967.9)
    Traceback (most recent call last):
        ...
    ValueError: Energy can't be negative.

    """
    if energy < 0:
        raise ValueError("Energy can't be negative.")
    return energy / c**2


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: physics/mirror_formulae.py
================================================
"""
This module contains the functions to calculate the focal length, object distance
and image distance of a mirror.

The mirror formula is an equation that relates the object distance (u),
image distance (v), and focal length (f) of a spherical mirror.
It is commonly used in optics to determine the position and characteristics
of an image formed by a mirror. It is expressed using the formulae :

-------------------
| 1/f = 1/v + 1/u |
-------------------

Where,
f = Focal length of the spherical mirror (metre)
v = Image distance from the mirror (metre)
u = Object distance from the mirror (metre)


The signs of the distances are taken with respect to the sign convention.
The sign convention is as follows:
    1) Object is always placed to the left of mirror
    2) Distances measured in the direction of the incident ray are positive
    and the distances measured in the direction opposite to that of the incident
    rays are negative.
    3) All distances are measured from the pole of the mirror.


There are a few assumptions that are made while using the mirror formulae.
They are as follows:
    1) Thin Mirror: The mirror is assumed to be thin, meaning its thickness is
    negligible compared to its radius of curvature. This assumption allows
    us to treat the mirror as a two-dimensional surface.
    2) Spherical Mirror: The mirror is assumed to have a spherical shape. While this
    assumption may not hold exactly for all mirrors, it is a reasonable approximation
    for most practical purposes.
    3) Small Angles: The angles involved in the derivation are assumed to be small.
    This assumption allows us to use the small-angle approximation, where the tangent
    of a small angle is approximately equal to the angle itself. It simplifies the
    calculations and makes the derivation more manageable.
    4) Paraxial Rays: The mirror formula is derived using paraxial rays, which are
    rays that are close to the principal axis and make small angles with it. This
    assumption ensures that the rays are close enough to the principal axis, making the
    calculations more accurate.
    5) Reflection and Refraction Laws: The derivation assumes that the laws of
    reflection and refraction hold.
    These laws state that the angle of incidence is equal to the angle of reflection
    for reflection, and the incident and refracted rays lie in the same plane and
    obey Snell's law for refraction.

(Description and Assumptions adapted from
https://www.collegesearch.in/articles/mirror-formula-derivation)

(Sign Convention adapted from
https://www.toppr.com/ask/content/concept/sign-convention-for-mirrors-210189/)


"""


def focal_length(distance_of_object: float, distance_of_image: float) -> float:
    """
    >>> from math import isclose
    >>> isclose(focal_length(10, 20), 6.66666666666666)
    True
    >>> from math import isclose
    >>> isclose(focal_length(9.5, 6.7), 3.929012346)
    True
    >>> focal_length(0, 20)  # doctest: +NORMALIZE_WHITESPACE
    Traceback (most recent call last):
        ...
    ValueError: Invalid inputs. Enter non zero values with respect
    to the sign convention.
    """

    if distance_of_object == 0 or distance_of_image == 0:
        raise ValueError(
            "Invalid inputs. Enter non zero values with respect to the sign convention."
        )
    focal_length = 1 / ((1 / distance_of_object) + (1 / distance_of_image))
    return focal_length


def object_distance(focal_length: float, distance_of_image: float) -> float:
    """
    >>> from math import isclose
    >>> isclose(object_distance(30, 20), -60.0)
    True
    >>> from math import isclose
    >>> isclose(object_distance(10.5, 11.7), 102.375)
    True
    >>> object_distance(90, 0)  # doctest: +NORMALIZE_WHITESPACE
    Traceback (most recent call last):
        ...
    ValueError: Invalid inputs. Enter non zero values with respect
    to the sign convention.
    """

    if distance_of_image == 0 or focal_length == 0:
        raise ValueError(
            "Invalid inputs. Enter non zero values with respect to the sign convention."
        )
    object_distance = 1 / ((1 / focal_length) - (1 / distance_of_image))
    return object_distance


def image_distance(focal_length: float, distance_of_object: float) -> float:
    """
    >>> from math import isclose
    >>> isclose(image_distance(10, 40), 13.33333333)
    True
    >>> from math import isclose
    >>> isclose(image_distance(1.5, 6.7), 1.932692308)
    True
    >>> image_distance(0, 0)  # doctest: +NORMALIZE_WHITESPACE
    Traceback (most recent call last):
        ...
    ValueError: Invalid inputs. Enter non zero values with respect
    to the sign convention.
    """

    if distance_of_object == 0 or focal_length == 0:
        raise ValueError(
            "Invalid inputs. Enter non zero values with respect to the sign convention."
        )
    image_distance = 1 / ((1 / focal_length) - (1 / distance_of_object))
    return image_distance


================================================
FILE: physics/n_body_simulation.py
================================================
"""
In physics and astronomy, a gravitational N-body simulation is a simulation of a
dynamical system of particles under the influence of gravity. The system
consists of a number of bodies, each of which exerts a gravitational force on all
other bodies. These forces are calculated using Newton's law of universal
gravitation. The Euler method is used at each time-step to calculate the change in
velocity and position brought about by these forces. Softening is used to prevent
numerical divergences when a particle comes too close to another (and the force
goes to infinity).
(Description adapted from https://en.wikipedia.org/wiki/N-body_simulation )
(See also http://www.shodor.org/refdesk/Resources/Algorithms/EulersMethod/ )
"""

from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
INTERVAL = 20

# Time between time steps in seconds
DELTA_TIME = INTERVAL / 1000


class Body:
    def __init__(
        self,
        position_x: float,
        position_y: float,
        velocity_x: float,
        velocity_y: float,
        mass: float = 1.0,
        size: float = 1.0,
        color: str = "blue",
    ) -> None:
        """
        The parameters "size" & "color" are not relevant for the simulation itself,
        they are only used for plotting.
        """
        self.position_x = position_x
        self.position_y = position_y
        self.velocity_x = velocity_x
        self.velocity_y = velocity_y
        self.mass = mass
        self.size = size
        self.color = color

    @property
    def position(self) -> tuple[float, float]:
        return self.position_x, self.position_y

    @property
    def velocity(self) -> tuple[float, float]:
        return self.velocity_x, self.velocity_y

    def update_velocity(
        self, force_x: float, force_y: float, delta_time: float
    ) -> None:
        """
        Euler algorithm for velocity

        >>> body_1 = Body(0.,0.,0.,0.)
        >>> body_1.update_velocity(1.,0.,1.)
        >>> body_1.velocity
        (1.0, 0.0)

        >>> body_1.update_velocity(1.,0.,1.)
        >>> body_1.velocity
        (2.0, 0.0)

        >>> body_2 = Body(0.,0.,5.,0.)
        >>> body_2.update_velocity(0.,-10.,10.)
        >>> body_2.velocity
        (5.0, -100.0)

        >>> body_2.update_velocity(0.,-10.,10.)
        >>> body_2.velocity
        (5.0, -200.0)
        """
        self.velocity_x += force_x * delta_time
        self.velocity_y += force_y * delta_time

    def update_position(self, delta_time: float) -> None:
        """
        Euler algorithm for position

        >>> body_1 = Body(0.,0.,1.,0.)
        >>> body_1.update_position(1.)
        >>> body_1.position
        (1.0, 0.0)

        >>> body_1.update_position(1.)
        >>> body_1.position
        (2.0, 0.0)

        >>> body_2 = Body(10.,10.,0.,-2.)
        >>> body_2.update_position(1.)
        >>> body_2.position
        (10.0, 8.0)

        >>> body_2.update_position(1.)
        >>> body_2.position
        (10.0, 6.0)
        """
        self.position_x += self.velocity_x * delta_time
        self.position_y += self.velocity_y * delta_time


class BodySystem:
    """
    This class is used to hold the bodies, the gravitation constant, the time
    factor and the softening factor. The time factor is used to control the speed
    of the simulation. The softening factor is used for softening, a numerical
    trick for N-body simulations to prevent numerical divergences when two bodies
    get too close to each other.
    """

    def __init__(
        self,
        bodies: list[Body],
        gravitation_constant: float = 1.0,
        time_factor: float = 1.0,
        softening_factor: float = 0.0,
    ) -> None:
        self.bodies = bodies
        self.gravitation_constant = gravitation_constant
        self.time_factor = time_factor
        self.softening_factor = softening_factor

    def __len__(self) -> int:
        return len(self.bodies)

    def update_system(self, delta_time: float) -> None:
        """
        For each body, loop through all other bodies to calculate the total
        force they exert on it. Use that force to update the body's velocity.

        >>> body_system_1 = BodySystem([Body(0,0,0,0), Body(10,0,0,0)])
        >>> len(body_system_1)
        2
        >>> body_system_1.update_system(1)
        >>> body_system_1.bodies[0].position
        (0.01, 0.0)
        >>> body_system_1.bodies[0].velocity
        (0.01, 0.0)

        >>> body_system_2 = BodySystem([Body(-10,0,0,0), Body(10,0,0,0, mass=4)], 1, 10)
        >>> body_system_2.update_system(1)
        >>> body_system_2.bodies[0].position
        (-9.0, 0.0)
        >>> body_system_2.bodies[0].velocity
        (0.1, 0.0)
        """
        for body1 in self.bodies:
            force_x = 0.0
            force_y = 0.0
            for body2 in self.bodies:
                if body1 != body2:
                    dif_x = body2.position_x - body1.position_x
                    dif_y = body2.position_y - body1.position_y

                    # Calculation of the distance using Pythagoras's theorem
                    # Extra factor due to the softening technique
                    distance = (dif_x**2 + dif_y**2 + self.softening_factor) ** (1 / 2)

                    # Newton's law of universal gravitation.
                    force_x += (
                        self.gravitation_constant * body2.mass * dif_x / distance**3
                    )
                    force_y += (
                        self.gravitation_constant * body2.mass * dif_y / distance**3
                    )

            # Update the body's velocity once all the force components have been added
            body1.update_velocity(force_x, force_y, delta_time * self.time_factor)

        # Update the positions only after all the velocities have been updated
        for body in self.bodies:
            body.update_position(delta_time * self.time_factor)


def update_step(
    body_system: BodySystem, delta_time: float, patches: list[plt.Circle]
) -> None:
    """
    Updates the body-system and applies the change to the patch-list used for plotting

    >>> body_system_1 = BodySystem([Body(0,0,0,0), Body(10,0,0,0)])
    >>> patches_1 = [plt.Circle((body.position_x, body.position_y), body.size,
    ... fc=body.color)for body in body_system_1.bodies] #doctest: +ELLIPSIS
    >>> update_step(body_system_1, 1, patches_1)
    >>> patches_1[0].center
    (0.01, 0.0)

    >>> body_system_2 = BodySystem([Body(-10,0,0,0), Body(10,0,0,0, mass=4)], 1, 10)
    >>> patches_2 = [plt.Circle((body.position_x, body.position_y), body.size,
    ... fc=body.color)for body in body_system_2.bodies] #doctest: +ELLIPSIS
    >>> update_step(body_system_2, 1, patches_2)
    >>> patches_2[0].center
    (-9.0, 0.0)
    """
    # Update the positions of the bodies
    body_system.update_system(delta_time)

    # Update the positions of the patches
    for patch, body in zip(patches, body_system.bodies):
        patch.center = (body.position_x, body.position_y)


def plot(
    title: str,
    body_system: BodySystem,
    x_start: float = -1,
    x_end: float = 1,
    y_start: float = -1,
    y_end: float = 1,
) -> None:
    """
    Utility function to plot how the given body-system evolves over time.
    No doctest provided since this function does not have a return value.
    """
    fig = plt.figure()
    fig.canvas.manager.set_window_title(title)
    ax = plt.axes(
        xlim=(x_start, x_end), ylim=(y_start, y_end)
    )  # Set section to be plotted
    plt.gca().set_aspect("equal")  # Fix aspect ratio

    # Each body is drawn as a patch by the plt-function
    patches = [
        plt.Circle((body.position_x, body.position_y), body.size, fc=body.color)
        for body in body_system.bodies
    ]

    for patch in patches:
        ax.add_patch(patch)

    # Function called at each step of the animation
    def update(frame: int) -> list[plt.Circle]:  # noqa: ARG001
        update_step(body_system, DELTA_TIME, patches)
        return patches

    anim = animation.FuncAnimation(  # noqa: F841
        fig, update, interval=INTERVAL, blit=True
    )

    plt.show()


def example_1() -> BodySystem:
    """
    Example 1: figure-8 solution to the 3-body-problem
    This example can be seen as a test of the implementation: given the right
    initial conditions, the bodies should move in a figure-8.
    (initial conditions taken from http://www.artcompsci.org/vol_1/v1_web/node56.html)
    >>> body_system = example_1()
    >>> len(body_system)
    3
    """

    position_x = 0.9700436
    position_y = -0.24308753
    velocity_x = 0.466203685
    velocity_y = 0.43236573

    bodies1 = [
        Body(position_x, position_y, velocity_x, velocity_y, size=0.2, color="red"),
        Body(-position_x, -position_y, velocity_x, velocity_y, size=0.2, color="green"),
        Body(0, 0, -2 * velocity_x, -2 * velocity_y, size=0.2, color="blue"),
    ]
    return BodySystem(bodies1, time_factor=3)


def example_2() -> BodySystem:
    """
    Example 2: Moon's orbit around the earth
    This example can be seen as a test of the implementation: given the right
    initial conditions, the moon should orbit around the earth as it actually does.
    (mass, velocity and distance taken from https://en.wikipedia.org/wiki/Earth
    and https://en.wikipedia.org/wiki/Moon)
    No doctest provided since this function does not have a return value.
    """

    moon_mass = 7.3476e22
    earth_mass = 5.972e24
    velocity_dif = 1022
    earth_moon_distance = 384399000
    gravitation_constant = 6.674e-11

    # Calculation of the respective velocities so that total impulse is zero,
    # i.e. the two bodies together don't move
    moon_velocity = earth_mass * velocity_dif / (earth_mass + moon_mass)
    earth_velocity = moon_velocity - velocity_dif

    moon = Body(-earth_moon_distance, 0, 0, moon_velocity, moon_mass, 10000000, "grey")
    earth = Body(0, 0, 0, earth_velocity, earth_mass, 50000000, "blue")
    return BodySystem([earth, moon], gravitation_constant, time_factor=1000000)


def example_3() -> BodySystem:
    """
    Example 3: Random system with many bodies.
    No doctest provided since this function does not have a return value.
    """

    bodies = []
    for _ in range(10):
        velocity_x = random.uniform(-0.5, 0.5)
        velocity_y = random.uniform(-0.5, 0.5)

        # Bodies are created pairwise with opposite velocities so that the
        # total impulse remains zero
        bodies.append(
            Body(
                random.uniform(-0.5, 0.5),
                random.uniform(-0.5, 0.5),
                velocity_x,
                velocity_y,
                size=0.05,
            )
        )
        bodies.append(
            Body(
                random.uniform(-0.5, 0.5),
                random.uniform(-0.5, 0.5),
                -velocity_x,
                -velocity_y,
                size=0.05,
            )
        )
    return BodySystem(bodies, 0.01, 10, 0.1)


if __name__ == "__main__":
    plot("Figure-8 solution to the 3-body-problem", example_1(), -2, 2, -2, 2)
    plot(
        "Moon's orbit around the earth",
        example_2(),
        -430000000,
        430000000,
        -430000000,
        430000000,
    )
    plot("Random system with many bodies", example_3(), -1.5, 1.5, -1.5, 1.5)


================================================
FILE: physics/newtons_law_of_gravitation.py
================================================
"""
Title : Finding the value of either Gravitational Force, one of the masses or distance
provided that the other three parameters are given.

Description : Newton's Law of Universal Gravitation explains the presence of force of
attraction between bodies having a definite mass situated at a distance. It is usually
stated as that, every particle attracts every other particle in the universe with a
force that is directly proportional to the product of their masses and inversely
proportional to the square of the distance between their centers. The publication of the
theory has become known as the "first great unification", as it marked the unification
of the previously described phenomena of gravity on Earth with known astronomical
behaviors.

The equation for the universal gravitation is as follows:
F = (G * mass_1 * mass_2) / (distance)^2

Source :
- https://en.wikipedia.org/wiki/Newton%27s_law_of_universal_gravitation
- Newton (1687) "Philosophiæ Naturalis Principia Mathematica"
"""

from __future__ import annotations

# Define the Gravitational Constant G and the function
GRAVITATIONAL_CONSTANT = 6.6743e-11  # unit of G : m^3 * kg^-1 * s^-2


def gravitational_law(
    force: float, mass_1: float, mass_2: float, distance: float
) -> dict[str, float]:
    """
    Input Parameters
    ----------------
    force : magnitude in Newtons

    mass_1 : mass in Kilograms

    mass_2 : mass in Kilograms

    distance : distance in Meters

    Returns
    -------
    result : dict name, value pair of the parameter having Zero as it's value

    Returns the value of one of the parameters specified as 0, provided the values of
    other parameters are given.
    >>> gravitational_law(force=0, mass_1=5, mass_2=10, distance=20)
    {'force': 8.342875e-12}

    >>> gravitational_law(force=7367.382, mass_1=0, mass_2=74, distance=3048)
    {'mass_1': 1.385816317292268e+19}

    >>> gravitational_law(force=36337.283, mass_1=0, mass_2=0, distance=35584)
    Traceback (most recent call last):
        ...
    ValueError: One and only one argument must be 0

    >>> gravitational_law(force=36337.283, mass_1=-674, mass_2=0, distance=35584)
    Traceback (most recent call last):
        ...
    ValueError: Mass can not be negative

    >>> gravitational_law(force=-847938e12, mass_1=674, mass_2=0, distance=9374)
    Traceback (most recent call last):
        ...
    ValueError: Gravitational force can not be negative
    """

    product_of_mass = mass_1 * mass_2

    if (force, mass_1, mass_2, distance).count(0) != 1:
        raise ValueError("One and only one argument must be 0")
    if force < 0:
        raise ValueError("Gravitational force can not be negative")
    if distance < 0:
        raise ValueError("Distance can not be negative")
    if mass_1 < 0 or mass_2 < 0:
        raise ValueError("Mass can not be negative")
    if force == 0:
        force = GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
        return {"force": force}
    elif mass_1 == 0:
        mass_1 = (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_2)
        return {"mass_1": mass_1}
    elif mass_2 == 0:
        mass_2 = (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_1)
        return {"mass_2": mass_2}
    elif distance == 0:
        distance = (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
        return {"distance": distance}
    raise ValueError("One and only one argument must be 0")


# Run doctest
if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: physics/newtons_second_law_of_motion.py
================================================
r"""
Description:
    Newton's second law of motion pertains to the behavior of objects for which
    all existing forces are not balanced.
    The second law states that the acceleration of an object is dependent upon
    two variables - the net force acting upon the object and the mass of the object.
    The acceleration of an object depends directly
    upon the net force acting upon the object,
    and inversely upon the mass of the object.
    As the force acting upon an object is increased,
    the acceleration of the object is increased.
    As the mass of an object is increased, the acceleration of the object is decreased.

Source: https://www.physicsclassroom.com/class/newtlaws/Lesson-3/Newton-s-Second-Law

Formulation: F_net = m • a

Diagrammatic Explanation::

              Forces are unbalanced
                        |
                        |
                        |
                        V
               There is acceleration
                        /\
                       /  \
                      /    \
                     /      \
                    /        \
                   /          \
                  /            \
    __________________      ____________________
   | The acceleration |    | The acceleration   |
   | depends directly |    | depends inversely  |
   | on the net force |    | upon the object's  |
   |                  |    | mass               |
   |__________________|    |____________________|

Units: 1 Newton = 1 kg • meters/seconds^2

How to use?

Inputs::

    ______________ _____________________ ___________
   | Name         | Units               | Type      |
   |--------------|---------------------|-----------|
   | mass         | in kgs              | float     |
   |--------------|---------------------|-----------|
   | acceleration | in meters/seconds^2 | float     |
   |______________|_____________________|___________|

Output::

    ______________ _______________________ ___________
   | Name         | Units                 | Type      |
   |--------------|-----------------------|-----------|
   | force        | in Newtons            | float     |
   |______________|_______________________|___________|

"""


def newtons_second_law_of_motion(mass: float, acceleration: float) -> float:
    """
    Calculates force from `mass` and `acceleration`

    >>> newtons_second_law_of_motion(10, 10)
    100
    >>> newtons_second_law_of_motion(2.0, 1)
    2.0
    """
    force = 0.0
    try:
        force = mass * acceleration
    except Exception:
        return -0.0
    return force


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()

    # demo
    mass = 12.5
    acceleration = 10
    force = newtons_second_law_of_motion(mass, acceleration)
    print("The force is ", force, "N")


================================================
FILE: physics/orbital_transfer_work.py
================================================
def orbital_transfer_work(
    mass_central: float, mass_object: float, r_initial: float, r_final: float
) -> str:
    """
    Calculates the work required to move an object from one orbit to another in a
    gravitational field based on the change in total mechanical energy.

    The formula used is:
        W = (G * M * m / 2) * (1/r_initial - 1/r_final)

    where:
        W = work done (Joules)
        G = gravitational constant (6.67430 * 10^-11 m^3 kg^-1 s^-2)
        M = mass of the central body (kg)
        m = mass of the orbiting object (kg)
        r_initial = initial orbit radius (m)
        r_final = final orbit radius (m)

    Args:
        mass_central (float): Mass of the central body (kg)
        mass_object (float): Mass of the object being moved (kg)
        r_initial (float): Initial orbital radius (m)
        r_final (float): Final orbital radius (m)

    Returns:
        str: Work done in Joules as a string in scientific notation (3 decimals)

    Examples:
        >>> orbital_transfer_work(5.972e24, 1000, 6.371e6, 7e6)
        '2.811e+09'
        >>> orbital_transfer_work(5.972e24, 500, 7e6, 6.371e6)
        '-1.405e+09'
        >>> orbital_transfer_work(1.989e30, 1000, 1.5e11, 2.28e11)
        '1.514e+11'
    """
    gravitational_constant = 6.67430e-11

    if r_initial <= 0 or r_final <= 0:
        raise ValueError("Orbital radii must be greater than zero.")

    work = (gravitational_constant * mass_central * mass_object / 2) * (
        1 / r_initial - 1 / r_final
    )
    return f"{work:.3e}"


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print("Orbital transfer work calculator\n")

    try:
        M = float(input("Enter mass of central body (kg): ").strip())
        if M <= 0:
            r1 = float(input("Enter initial orbit radius (m): ").strip())
        if r1 <= 0:
            raise ValueError("Initial orbit radius must be greater than zero.")

        r2 = float(input("Enter final orbit radius (m): ").strip())
        if r2 <= 0:
            raise ValueError("Final orbit radius must be greater than zero.")
        m = float(input("Enter mass of orbiting object (kg): ").strip())
        if m <= 0:
            raise ValueError("Mass of the orbiting object must be greater than zero.")
        r1 = float(input("Enter initial orbit radius (m): ").strip())
        r2 = float(input("Enter final orbit radius (m): ").strip())

        result = orbital_transfer_work(M, m, r1, r2)
        print(f"Work done in orbital transfer: {result} Joules")

    except ValueError as e:
        print(f"Input error: {e}")


================================================
FILE: physics/period_of_pendulum.py
================================================
"""
Title : Computing the time period of a simple pendulum

The simple pendulum is a mechanical system that sways or moves in an
oscillatory motion. The simple pendulum comprises of a small bob of
mass m suspended by a thin string of length L and secured to a platform
at its upper end. Its motion occurs in a vertical plane and is mainly
driven by gravitational force. The period of the pendulum depends on the
length of the string and the amplitude (the maximum angle) of oscillation.
However, the effect of the amplitude can be ignored if the amplitude is
small. It should be noted that the period does not depend on the mass of
the bob.

For small amplitudes, the period of a simple pendulum is given by the
following approximation:
T ≈ 2π * √(L / g)

where:
L = length of string from which the bob is hanging (in m)
g = acceleration due to gravity (approx 9.8 m/s²)

Reference : https://byjus.com/jee/simple-pendulum/
"""

from math import pi

from scipy.constants import g


def period_of_pendulum(length: float) -> float:
    """
    >>> period_of_pendulum(1.23)
    2.2252155506257845
    >>> period_of_pendulum(2.37)
    3.0888278441908574
    >>> period_of_pendulum(5.63)
    4.76073193364765
    >>> period_of_pendulum(-12)
    Traceback (most recent call last):
        ...
    ValueError: The length should be non-negative
    >>> period_of_pendulum(0)
    0.0
    """
    if length < 0:
        raise ValueError("The length should be non-negative")
    return 2 * pi * (length / g) ** 0.5


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: physics/photoelectric_effect.py
================================================
"""
The photoelectric effect is the emission of electrons when electromagnetic radiation ,
such as light, hits a material. Electrons emitted in this manner are called
photoelectrons.

In 1905, Einstein proposed a theory of the photoelectric effect using a concept that
light consists of tiny packets of energy known as photons or light quanta. Each packet
carries energy hv that is proportional to the frequency v of the corresponding
electromagnetic wave. The proportionality constant h has become known as the
Planck constant. In the range of kinetic energies of the electrons that are removed from
their varying atomic bindings by the absorption of a photon of energy hv, the highest
kinetic energy K_max is :

K_max = hv-W

Here, W is the minimum energy required to remove an electron from the surface of the
material. It is called the work function of the surface

Reference: https://en.wikipedia.org/wiki/Photoelectric_effect

"""

PLANCK_CONSTANT_JS = 6.6261 * pow(10, -34)  # in SI (Js)
PLANCK_CONSTANT_EVS = 4.1357 * pow(10, -15)  # in eVs


def maximum_kinetic_energy(
    frequency: float, work_function: float, in_ev: bool = False
) -> float:
    """
    Calculates the maximum kinetic energy of emitted electron from the surface.
    if the maximum kinetic energy is zero then no electron will be emitted
    or given electromagnetic wave frequency is small.

    frequency (float): Frequency of electromagnetic wave.
    work_function (float): Work function of the surface.
    in_ev (optional)(bool): Pass True if values are in eV.

    Usage example:
    >>> maximum_kinetic_energy(1000000,2)
    0
    >>> maximum_kinetic_energy(1000000,2,True)
    0
    >>> maximum_kinetic_energy(10000000000000000,2,True)
    39.357000000000006
    >>> maximum_kinetic_energy(-9,20)
    Traceback (most recent call last):
        ...
    ValueError: Frequency can't be negative.

    >>> maximum_kinetic_energy(1000,"a")
    Traceback (most recent call last):
        ...
    TypeError: unsupported operand type(s) for -: 'float' and 'str'

    """
    if frequency < 0:
        raise ValueError("Frequency can't be negative.")
    if in_ev:
        return max(PLANCK_CONSTANT_EVS * frequency - work_function, 0)
    return max(PLANCK_CONSTANT_JS * frequency - work_function, 0)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: physics/potential_energy.py
================================================
from scipy.constants import g

"""
Finding the gravitational potential energy of an object with reference
to the earth,by taking its mass and height above the ground as input


Description : Gravitational energy or gravitational potential energy
is the potential energy a massive object has in relation to another
massive object due to gravity. It is the potential energy associated
with the gravitational field, which is released (converted into
kinetic energy) when the objects fall towards each other.
Gravitational potential energy increases when two objects
are brought further apart.

For two pairwise interacting point particles, the gravitational
potential energy U is given by
U=-GMm/R
where M and m are the masses of the two particles, R is the distance
between them, and G is the gravitational constant.
Close to the Earth's surface, the gravitational field is approximately
constant, and the gravitational potential energy of an object reduces to
U=mgh
where m is the object's mass, g=GM/R² is the gravity of Earth, and h is
the height of the object's center of mass above a chosen reference level.

Reference : "https://en.m.wikipedia.org/wiki/Gravitational_energy"
"""


def potential_energy(mass: float, height: float) -> float:
    # function will accept mass and height as parameters and return potential energy
    """
    >>> potential_energy(10,10)
    980.665
    >>> potential_energy(0,5)
    0.0
    >>> potential_energy(8,0)
    0.0
    >>> potential_energy(10,5)
    490.3325
    >>> potential_energy(0,0)
    0.0
    >>> potential_energy(2,8)
    156.9064
    >>> potential_energy(20,100)
    19613.3
    """
    if mass < 0:
        # handling of negative values of mass
        raise ValueError("The mass of a body cannot be negative")
    if height < 0:
        # handling of negative values of height
        raise ValueError("The height above the ground cannot be negative")
    return mass * g * height


if __name__ == "__main__":
    from doctest import testmod

    testmod(name="potential_energy")


================================================
FILE: physics/rainfall_intensity.py
================================================
"""
Rainfall Intensity
==================
This module contains functions to calculate the intensity of
a rainfall event for a given duration and return period.

This function uses the Sherman intensity-duration-frequency curve.

References
----------
- Aparicio, F. (1997): Fundamentos de Hidrología de Superficie.
    Balderas, México, Limusa. 303 p.
- https://en.wikipedia.org/wiki/Intensity-duration-frequency_curve
"""


def rainfall_intensity(
    coefficient_k: float,
    coefficient_a: float,
    coefficient_b: float,
    coefficient_c: float,
    return_period: float,
    duration: float,
) -> float:
    """
    Calculate the intensity of a rainfall event for a given duration and return period.
    It's based on the Sherman intensity-duration-frequency curve:

    I = k * T^a / (D + b)^c

    where:
        I = Intensity of the rainfall event [mm/h]
        k, a, b, c = Coefficients obtained through statistical distribution adjust
        T = Return period in years
        D = Rainfall event duration in minutes

    Parameters
    ----------
    coefficient_k : float
        Coefficient obtained through statistical distribution adjust.
    coefficient_a : float
        Coefficient obtained through statistical distribution adjust.
    coefficient_b : float
        Coefficient obtained through statistical distribution adjust.
    coefficient_c : float
        Coefficient obtained through statistical distribution adjust.
    return_period : float
        Return period in years.
    duration : float
        Rainfall event duration in minutes.

    Returns
    -------
    intensity : float
        Intensity of the rainfall event in mm/h.

    Raises
    ------
    ValueError
        If any of the parameters are not positive.

    Examples
    --------

    >>> rainfall_intensity(1000, 0.2, 11.6, 0.81, 10, 60)
    49.83339231138578

    >>> rainfall_intensity(1000, 0.2, 11.6, 0.81, 10, 30)
    77.36319588106228

    >>> rainfall_intensity(1000, 0.2, 11.6, 0.81, 5, 60)
    43.382487747633625

    >>> rainfall_intensity(0, 0.2, 11.6, 0.81, 10, 60)
    Traceback (most recent call last):
    ...
    ValueError: All parameters must be positive.

    >>> rainfall_intensity(1000, -0.2, 11.6, 0.81, 10, 60)
    Traceback (most recent call last):
    ...
    ValueError: All parameters must be positive.

    >>> rainfall_intensity(1000, 0.2, -11.6, 0.81, 10, 60)
    Traceback (most recent call last):
    ...
    ValueError: All parameters must be positive.

    >>> rainfall_intensity(1000, 0.2, 11.6, -0.81, 10, 60)
    Traceback (most recent call last):
    ...
    ValueError: All parameters must be positive.

    >>> rainfall_intensity(1000, 0, 11.6, 0.81, 10, 60)
    Traceback (most recent call last):
    ...
    ValueError: All parameters must be positive.

    >>> rainfall_intensity(1000, 0.2, 0, 0.81, 10, 60)
    Traceback (most recent call last):
    ...
    ValueError: All parameters must be positive.

    >>> rainfall_intensity(1000, 0.2, 11.6, 0, 10, 60)
    Traceback (most recent call last):
    ...
    ValueError: All parameters must be positive.

    >>> rainfall_intensity(0, 0.2, 11.6, 0.81, 10, 60)
    Traceback (most recent call last):
    ...
    ValueError: All parameters must be positive.

    >>> rainfall_intensity(1000, 0.2, 11.6, 0.81, 0, 60)
    Traceback (most recent call last):
    ...
    ValueError: All parameters must be positive.

    >>> rainfall_intensity(1000, 0.2, 11.6, 0.81, 10, 0)
    Traceback (most recent call last):
    ...
    ValueError: All parameters must be positive.

    """
    if (
        coefficient_k <= 0
        or coefficient_a <= 0
        or coefficient_b <= 0
        or coefficient_c <= 0
        or return_period <= 0
        or duration <= 0
    ):
        raise ValueError("All parameters must be positive.")
    intensity = (coefficient_k * (return_period**coefficient_a)) / (
        (duration + coefficient_b) ** coefficient_c
    )
    return intensity


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: physics/reynolds_number.py
================================================
"""
Title : computing the Reynolds number to find
        out the type of flow (laminar or turbulent)

Reynolds number is a dimensionless quantity that is used to determine
the type of flow pattern as laminar or turbulent while flowing through a
pipe. Reynolds number is defined by the ratio of inertial forces to that of
viscous forces.

R = Inertial Forces / Viscous Forces
R = (p * V * D)/μ

where :
p = Density of fluid (in Kg/m^3)
D = Diameter of pipe through which fluid flows (in m)
V = Velocity of flow of the fluid (in m/s)
μ = Viscosity of the fluid (in Ns/m^2)

If the Reynolds number calculated is high (greater than 2000), then the
flow through the pipe is said to be turbulent. If Reynolds number is low
(less than 2000), the flow is said to be laminar. Numerically, these are
acceptable values, although in general the laminar and turbulent flows
are classified according to a range. Laminar flow falls below Reynolds
number of 1100 and turbulent falls in a range greater than 2200.
Laminar flow is the type of flow in which the fluid travels smoothly in
regular paths. Conversely, turbulent flow isn't smooth and follows an
irregular path with lots of mixing.

Reference : https://byjus.com/physics/reynolds-number/
"""


def reynolds_number(
    density: float, velocity: float, diameter: float, viscosity: float
) -> float:
    """
    >>> reynolds_number(900, 2.5, 0.05, 0.4)
    281.25
    >>> reynolds_number(450, 3.86, 0.078, 0.23)
    589.0695652173912
    >>> reynolds_number(234, -4.5, 0.3, 0.44)
    717.9545454545454
    >>> reynolds_number(-90, 2, 0.045, 1)
    Traceback (most recent call last):
        ...
    ValueError: please ensure that density, diameter and viscosity are positive
    >>> reynolds_number(0, 2, -0.4, -2)
    Traceback (most recent call last):
        ...
    ValueError: please ensure that density, diameter and viscosity are positive
    """

    if density <= 0 or diameter <= 0 or viscosity <= 0:
        raise ValueError(
            "please ensure that density, diameter and viscosity are positive"
        )
    return (density * abs(velocity) * diameter) / viscosity


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: physics/rms_speed_of_molecule.py
================================================
"""
The root-mean-square speed is essential in measuring the average speed of particles
contained in a gas, defined as,
 -----------------
 | Vrms = √3RT/M |
 -----------------

In Kinetic Molecular Theory, gasified particles are in a condition of constant random
motion; each particle moves at a completely different pace, perpetually clashing and
changing directions consistently velocity is used to describe the movement of gas
particles, thereby taking into account both speed and direction. Although the velocity
of gaseous particles is constantly changing, the distribution of velocities does not
change.
We cannot gauge the velocity of every individual particle, thus we frequently reason
in terms of the particles average behavior. Particles moving in opposite directions
have velocities of opposite signs. Since gas particles are in random motion, it's
plausible that there'll be about as several moving in one direction as within the other
way, which means that the average velocity for a collection of gas particles equals
zero; as this value is unhelpful, the average of velocities can be determined using an
alternative method.
"""

UNIVERSAL_GAS_CONSTANT = 8.3144598


def rms_speed_of_molecule(temperature: float, molar_mass: float) -> float:
    """
    >>> rms_speed_of_molecule(100, 2)
    35.315279554323226
    >>> rms_speed_of_molecule(273, 12)
    23.821458421977443
    """
    if temperature < 0:
        raise Exception("Temperature cannot be less than 0 K")
    if molar_mass <= 0:
        raise Exception("Molar mass cannot be less than or equal to 0 kg/mol")
    else:
        return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()

    # example
    temperature = 300
    molar_mass = 28
    vrms = rms_speed_of_molecule(temperature, molar_mass)
    print(f"Vrms of Nitrogen gas at 300 K is {vrms} m/s")


================================================
FILE: physics/shear_stress.py
================================================
from __future__ import annotations

"""
Shear stress is a component of stress that is coplanar to the material cross-section.
It arises due to a shear force, the component of the force vector parallel to the
material cross-section.

https://en.wikipedia.org/wiki/Shear_stress
"""


def shear_stress(
    stress: float,
    tangential_force: float,
    area: float,
) -> tuple[str, float]:
    """
    This function can calculate any one of the three -
    1. Shear Stress
    2. Tangential Force
    3. Cross-sectional Area
    This is calculated from the other two provided values
    Examples -
    >>> shear_stress(stress=25, tangential_force=100, area=0)
    ('area', 4.0)
    >>> shear_stress(stress=0, tangential_force=1600, area=200)
    ('stress', 8.0)
    >>> shear_stress(stress=1000, tangential_force=0, area=1200)
    ('tangential_force', 1200000)
    """
    if (stress, tangential_force, area).count(0) != 1:
        raise ValueError("You cannot supply more or less than 2 values")
    elif stress < 0:
        raise ValueError("Stress cannot be negative")
    elif tangential_force < 0:
        raise ValueError("Tangential Force cannot be negative")
    elif area < 0:
        raise ValueError("Area cannot be negative")
    elif stress == 0:
        return (
            "stress",
            tangential_force / area,
        )
    elif tangential_force == 0:
        return (
            "tangential_force",
            stress * area,
        )
    else:
        return (
            "area",
            tangential_force / stress,
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: physics/speed_of_sound.py
================================================
"""
Title : Calculating the speed of sound

Description :
    The speed of sound (c) is the speed that a sound wave travels per unit time (m/s).
    During propagation, the sound wave propagates through an elastic medium.

    Sound propagates as longitudinal waves in liquids and gases and as transverse waves
    in solids. This file calculates the speed of sound in a fluid based on its bulk
    module and density.

    Equation for the speed of sound in a fluid:
    c_fluid = sqrt(K_s / p)

    c_fluid: speed of sound in fluid
    K_s: isentropic bulk modulus
    p: density of fluid

Source : https://en.wikipedia.org/wiki/Speed_of_sound
"""


def speed_of_sound_in_a_fluid(density: float, bulk_modulus: float) -> float:
    """
    Calculates the speed of sound in a fluid from its density and bulk modulus

    Examples:
    Example 1 --> Water 20°C: bulk_modulus= 2.15MPa, density=998kg/m³
    Example 2 --> Mercury 20°C: bulk_modulus= 28.5MPa, density=13600kg/m³

    >>> speed_of_sound_in_a_fluid(bulk_modulus=2.15e9, density=998)
    1467.7563207952705
    >>> speed_of_sound_in_a_fluid(bulk_modulus=28.5e9, density=13600)
    1447.614670861731
    """

    if density <= 0:
        raise ValueError("Impossible fluid density")
    if bulk_modulus <= 0:
        raise ValueError("Impossible bulk modulus")

    return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: physics/speeds_of_gas_molecules.py
================================================
"""
The root-mean-square, average and most probable speeds of gas molecules are
derived from the Maxwell-Boltzmann distribution. The Maxwell-Boltzmann
distribution is a probability distribution that describes the distribution of
speeds of particles in an ideal gas.

The distribution is given by the following equation::

        -------------------------------------------------
        | f(v) = (M/2πRT)^(3/2) * 4πv^2 * e^(-Mv^2/2RT) |
        -------------------------------------------------

where:
    * ``f(v)`` is the fraction of molecules with a speed ``v``
    * ``M`` is the molar mass of the gas in kg/mol
    * ``R`` is the gas constant
    * ``T`` is the absolute temperature

More information about the Maxwell-Boltzmann distribution can be found here:
https://en.wikipedia.org/wiki/Maxwell%E2%80%93Boltzmann_distribution

The average speed can be calculated by integrating the Maxwell-Boltzmann distribution
from 0 to infinity and dividing by the total number of molecules. The result is::

        ----------------------
        | v_avg = √(8RT/πM)  |
        ----------------------

The most probable speed is the speed at which the Maxwell-Boltzmann distribution
is at its maximum. This can be found by differentiating the Maxwell-Boltzmann
distribution with respect to ``v`` and setting the result equal to zero. The result is::

        ----------------------
        | v_mp = √(2RT/M)    |
        ----------------------

The root-mean-square speed is another measure of the average speed
of the molecules in a gas. It is calculated by taking the square root
of the average of the squares of the speeds of the molecules. The result is::

        ----------------------
        | v_rms = √(3RT/M)   |
        ----------------------

Here we have defined functions to calculate the average and
most probable speeds of molecules in a gas given the
temperature and molar mass of the gas.
"""

# import the constants R and pi from the scipy.constants library
from scipy.constants import R, pi


def avg_speed_of_molecule(temperature: float, molar_mass: float) -> float:
    """
    Takes the temperature (in K) and molar mass (in kg/mol) of a gas
    and returns the average speed of a molecule in the gas (in m/s).

    Examples:

    >>> avg_speed_of_molecule(273, 0.028) # nitrogen at 273 K
    454.3488755062257
    >>> avg_speed_of_molecule(300, 0.032) # oxygen at 300 K
    445.5257273433045
    >>> avg_speed_of_molecule(-273, 0.028) # invalid temperature
    Traceback (most recent call last):
        ...
    Exception: Absolute temperature cannot be less than 0 K
    >>> avg_speed_of_molecule(273, 0) # invalid molar mass
    Traceback (most recent call last):
        ...
    Exception: Molar mass should be greater than 0 kg/mol
    """

    if temperature < 0:
        raise Exception("Absolute temperature cannot be less than 0 K")
    if molar_mass <= 0:
        raise Exception("Molar mass should be greater than 0 kg/mol")
    return (8 * R * temperature / (pi * molar_mass)) ** 0.5


def mps_speed_of_molecule(temperature: float, molar_mass: float) -> float:
    """
    Takes the temperature (in K) and molar mass (in kg/mol) of a gas
    and returns the most probable speed of a molecule in the gas (in m/s).

    Examples:

    >>> mps_speed_of_molecule(273, 0.028) # nitrogen at 273 K
    402.65620702280023
    >>> mps_speed_of_molecule(300, 0.032) # oxygen at 300 K
    394.8368955535605
    >>> mps_speed_of_molecule(-273, 0.028) # invalid temperature
    Traceback (most recent call last):
        ...
    Exception: Absolute temperature cannot be less than 0 K
    >>> mps_speed_of_molecule(273, 0) # invalid molar mass
    Traceback (most recent call last):
        ...
    Exception: Molar mass should be greater than 0 kg/mol
    """

    if temperature < 0:
        raise Exception("Absolute temperature cannot be less than 0 K")
    if molar_mass <= 0:
        raise Exception("Molar mass should be greater than 0 kg/mol")
    return (2 * R * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: physics/terminal_velocity.py
================================================
"""
Title : Computing the terminal velocity of an object falling
        through a fluid.

Terminal velocity is defined as the highest velocity attained by an
object falling through a fluid. It is observed when the sum of drag force
and buoyancy is equal to the downward gravity force acting on the
object. The acceleration of the object is zero as the net force acting on
the object is zero.

Vt = ((2 * m * g)/(p * A * Cd))^0.5

where :
Vt = Terminal velocity (in m/s)
m = Mass of the falling object (in Kg)
g = Acceleration due to gravity (value taken : imported from scipy)
p = Density of the fluid through which the object is falling (in Kg/m^3)
A = Projected area of the object (in m^2)
Cd = Drag coefficient (dimensionless)

Reference : https://byjus.com/physics/derivation-of-terminal-velocity/
"""

from scipy.constants import g


def terminal_velocity(
    mass: float, density: float, area: float, drag_coefficient: float
) -> float:
    """
    >>> terminal_velocity(1, 25, 0.6, 0.77)
    1.3031197996044768
    >>> terminal_velocity(2, 100, 0.45, 0.23)
    1.9467947148674276
    >>> terminal_velocity(5, 50, 0.2, 0.5)
    4.428690551393267
    >>> terminal_velocity(-5, 50, -0.2, -2)
    Traceback (most recent call last):
        ...
    ValueError: mass, density, area and the drag coefficient all need to be positive
    >>> terminal_velocity(3, -20, -1, 2)
    Traceback (most recent call last):
        ...
    ValueError: mass, density, area and the drag coefficient all need to be positive
    >>> terminal_velocity(-2, -1, -0.44, -1)
    Traceback (most recent call last):
        ...
    ValueError: mass, density, area and the drag coefficient all need to be positive
    """
    if mass <= 0 or density <= 0 or area <= 0 or drag_coefficient <= 0:
        raise ValueError(
            "mass, density, area and the drag coefficient all need to be positive"
        )
    return ((2 * mass * g) / (density * area * drag_coefficient)) ** 0.5


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: project_euler/README.md
================================================
# Project Euler

Problems are taken from https://projecteuler.net/, the Project Euler. [Problems are licensed under CC BY-NC-SA 4.0](https://projecteuler.net/copyright).

Project Euler is a series of challenging mathematical/computer programming problems that require more than just mathematical
insights to solve. Project Euler is ideal for mathematicians who are learning to code.

The solutions will be checked by our [automated testing on GitHub Actions](https://github.com/TheAlgorithms/Python/actions) with the help of [this script](https://github.com/TheAlgorithms/Python/blob/master/scripts/validate_solutions.py). The efficiency of your code is also checked. You can view the top 10 slowest solutions on GitHub Actions logs (under `slowest 10 durations`) and open a pull request to improve those solutions.


## Solution Guidelines

Welcome to [TheAlgorithms/Python](https://github.com/TheAlgorithms/Python)! Before reading the solution guidelines, make sure you read the whole [Contributing Guidelines](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md) as it won't be repeated in here. If you have any doubt on the guidelines, please feel free to [state it clearly in an issue](https://github.com/TheAlgorithms/Python/issues/new) or ask the community in [Gitter](https://gitter.im/TheAlgorithms/community). You can use the [template](https://github.com/TheAlgorithms/Python/blob/master/project_euler/README.md#solution-template) we have provided below as your starting point but be sure to read the [Coding Style](https://github.com/TheAlgorithms/Python/blob/master/project_euler/README.md#coding-style) part first.

### Coding Style

* Please maintain consistency in project directory and solution file names. Keep the following points in mind:
  * Create a new directory only for the problems which do not exist yet.
  * If you create a new directory, please create an empty `__init__.py` file inside it as well.
  * Please name the project **directory** as `problem_<problem_number>` where `problem_number` should be filled with 0s so as to occupy 3 digits. Example: `problem_001`, `problem_002`, `problem_067`, `problem_145`, and so on.

* Please provide a link to the problem and other references, if used, in the **module-level docstring**.

* All imports should come ***after*** the module-level docstring.

* You can have as many helper functions as you want but there should be one main function called `solution` which should satisfy the conditions as stated below:
  * It should contain positional argument(s) whose default value is the question input. Example: Please take a look at [Problem 1](https://projecteuler.net/problem=1) where the question is to *Find the sum of all the multiples of 3 or 5 below 1000.* In this case the main solution function will be `solution(limit: int = 1000)`.
  * When the `solution` function is called without any arguments like so: `solution()`, it should return the answer to the problem.

* Every function, which includes all the helper functions, if any, and the main solution function, should have `doctest` in the function docstring along with a brief statement mentioning what the function is about.
  * There should not be a `doctest` for testing the answer as that is done by our GitHub Actions build using this [script](https://github.com/TheAlgorithms/Python/blob/master/scripts/validate_solutions.py). Keeping in mind the above example of [Problem 1](https://projecteuler.net/problem=1):

  ```python
  def solution(limit: int = 1000):
      """
      A brief statement mentioning what the function is about.

      You can have a detailed explanation about the solution method in the
      module-level docstring.

      >>> solution(1)
      ...
      >>> solution(16)
      ...
      >>> solution(100)
      ...
      """
    ```

### Solution Template

You can use the below template as your starting point but please read the [Coding Style](https://github.com/TheAlgorithms/Python/blob/master/project_euler/README.md#coding-style) first to understand how the template works.

Please change the name of the helper functions accordingly, change the parameter names with a descriptive one, replace the content within `[square brackets]` (including the brackets) with the appropriate content.

```python
"""
Project Euler Problem [problem number]: [link to the original problem]

... [Entire problem statement] ...

... [Solution explanation - Optional] ...

References [Optional]:
- [Wikipedia link to the topic]
- [Stackoverflow link]
...

"""
import module1
import module2
...

def helper1(arg1: [type hint], arg2: [type hint], ...) -> [Return type hint]:
    """
    A brief statement explaining what the function is about.

    ... A more elaborate description ... [Optional]

    ...
    [Doctest]
    ...

    """
    ...
    # calculations
    ...

    return


# You can have multiple helper functions but the solution function should be
# after all the helper functions ...


def solution(arg1: [type hint], arg2: [type hint], ...) -> [Return type hint]:
    """
    A brief statement mentioning what the function is about.

    You can have a detailed explanation about the solution in the
    module-level docstring.

    ...
    [Doctest as mentioned above]
    ...

    """

    ...
    # calculations
    ...

    return answer


if __name__ == "__main__":
    print(f"{solution() = }")
```


================================================
FILE: project_euler/__init__.py
================================================


================================================
FILE: project_euler/problem_001/__init__.py
================================================


================================================
FILE: project_euler/problem_001/sol1.py
================================================
"""
Project Euler Problem 1: https://projecteuler.net/problem=1

Multiples of 3 and 5

If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.

Find the sum of all the multiples of 3 or 5 below 1000.
"""


def solution(n: int = 1000) -> int:
    """
    Returns the sum of all the multiples of 3 or 5 below n.

    >>> solution(3)
    0
    >>> solution(4)
    3
    >>> solution(10)
    23
    >>> solution(600)
    83700
    >>> solution(-7)
    0
    """

    return sum(e for e in range(3, n) if e % 3 == 0 or e % 5 == 0)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_001/sol2.py
================================================
"""
Project Euler Problem 1: https://projecteuler.net/problem=1

Multiples of 3 and 5

If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.

Find the sum of all the multiples of 3 or 5 below 1000.
"""


def solution(n: int = 1000) -> int:
    """
    Returns the sum of all the multiples of 3 or 5 below n.

    >>> solution(3)
    0
    >>> solution(4)
    3
    >>> solution(10)
    23
    >>> solution(600)
    83700
    """

    total = 0
    terms = (n - 1) // 3
    total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
    terms = (n - 1) // 5
    total += ((terms) * (10 + (terms - 1) * 5)) // 2
    terms = (n - 1) // 15
    total -= ((terms) * (30 + (terms - 1) * 15)) // 2
    return total


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_001/sol3.py
================================================
"""
Project Euler Problem 1: https://projecteuler.net/problem=1

Multiples of 3 and 5

If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.

Find the sum of all the multiples of 3 or 5 below 1000.
"""


def solution(n: int = 1000) -> int:
    """
    This solution is based on the pattern that the successive numbers in the
    series follow: 0+3,+2,+1,+3,+1,+2,+3.
    Returns the sum of all the multiples of 3 or 5 below n.

    >>> solution(3)
    0
    >>> solution(4)
    3
    >>> solution(10)
    23
    >>> solution(600)
    83700
    """

    total = 0
    num = 0
    while 1:
        num += 3
        if num >= n:
            break
        total += num
        num += 2
        if num >= n:
            break
        total += num
        num += 1
        if num >= n:
            break
        total += num
        num += 3
        if num >= n:
            break
        total += num
        num += 1
        if num >= n:
            break
        total += num
        num += 2
        if num >= n:
            break
        total += num
        num += 3
        if num >= n:
            break
        total += num
    return total


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_001/sol4.py
================================================
"""
Project Euler Problem 1: https://projecteuler.net/problem=1

Multiples of 3 and 5

If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.

Find the sum of all the multiples of 3 or 5 below 1000.
"""


def solution(n: int = 1000) -> int:
    """
    Returns the sum of all the multiples of 3 or 5 below n.

    >>> solution(3)
    0
    >>> solution(4)
    3
    >>> solution(10)
    23
    >>> solution(600)
    83700
    """

    xmulti = []
    zmulti = []
    z = 3
    x = 5
    temp = 1
    while True:
        result = z * temp
        if result < n:
            zmulti.append(result)
            temp += 1
        else:
            temp = 1
            break
    while True:
        result = x * temp
        if result < n:
            xmulti.append(result)
            temp += 1
        else:
            break
    collection = list(set(xmulti + zmulti))
    return sum(collection)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_001/sol5.py
================================================
"""
Project Euler Problem 1: https://projecteuler.net/problem=1

Multiples of 3 and 5

If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.

Find the sum of all the multiples of 3 or 5 below 1000.
"""


def solution(n: int = 1000) -> int:
    """
    Returns the sum of all the multiples of 3 or 5 below n.
    A straightforward pythonic solution using list comprehension.

    >>> solution(3)
    0
    >>> solution(4)
    3
    >>> solution(10)
    23
    >>> solution(600)
    83700
    """

    return sum(i for i in range(n) if i % 3 == 0 or i % 5 == 0)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_001/sol6.py
================================================
"""
Project Euler Problem 1: https://projecteuler.net/problem=1

Multiples of 3 and 5

If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.

Find the sum of all the multiples of 3 or 5 below 1000.
"""


def solution(n: int = 1000) -> int:
    """
    Returns the sum of all the multiples of 3 or 5 below n.

    >>> solution(3)
    0
    >>> solution(4)
    3
    >>> solution(10)
    23
    >>> solution(600)
    83700
    """

    a = 3
    result = 0
    while a < n:
        if a % 3 == 0 or a % 5 == 0:
            result += a
        elif a % 15 == 0:
            result -= a
        a += 1
    return result


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_001/sol7.py
================================================
"""
Project Euler Problem 1: https://projecteuler.net/problem=1

Multiples of 3 and 5

If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.

Find the sum of all the multiples of 3 or 5 below 1000.
"""


def solution(n: int = 1000) -> int:
    """
    Returns the sum of all the multiples of 3 or 5 below n.

    >>> solution(3)
    0
    >>> solution(4)
    3
    >>> solution(10)
    23
    >>> solution(600)
    83700
    """

    result = 0
    for i in range(n):
        if i % 3 == 0 or i % 5 == 0:
            result += i
    return result


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_002/__init__.py
================================================


================================================
FILE: project_euler/problem_002/sol1.py
================================================
"""
Project Euler Problem 2: https://projecteuler.net/problem=2

Even Fibonacci Numbers

Each new term in the Fibonacci sequence is generated by adding the previous
two terms. By starting with 1 and 2, the first 10 terms will be:

1, 2, 3, 5, 8, 13, 21, 34, 55, 89, ...

By considering the terms in the Fibonacci sequence whose values do not exceed
four million, find the sum of the even-valued terms.

References:
    - https://en.wikipedia.org/wiki/Fibonacci_number
"""


def solution(n: int = 4000000) -> int:
    """
    Returns the sum of all even fibonacci sequence elements that are lower
    or equal to n.

    >>> solution(10)
    10
    >>> solution(15)
    10
    >>> solution(2)
    2
    >>> solution(1)
    0
    >>> solution(34)
    44
    """

    i = 1
    j = 2
    total = 0
    while j <= n:
        if j % 2 == 0:
            total += j
        i, j = j, i + j

    return total


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_002/sol2.py
================================================
"""
Project Euler Problem 2: https://projecteuler.net/problem=2

Even Fibonacci Numbers

Each new term in the Fibonacci sequence is generated by adding the previous
two terms. By starting with 1 and 2, the first 10 terms will be:

1, 2, 3, 5, 8, 13, 21, 34, 55, 89, ...

By considering the terms in the Fibonacci sequence whose values do not exceed
four million, find the sum of the even-valued terms.

References:
    - https://en.wikipedia.org/wiki/Fibonacci_number
"""


def solution(n: int = 4000000) -> int:
    """
    Returns the sum of all even fibonacci sequence elements that are lower
    or equal to n.

    >>> solution(10)
    10
    >>> solution(15)
    10
    >>> solution(2)
    2
    >>> solution(1)
    0
    >>> solution(34)
    44
    """

    even_fibs = []
    a, b = 0, 1
    while b <= n:
        if b % 2 == 0:
            even_fibs.append(b)
        a, b = b, a + b
    return sum(even_fibs)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_002/sol3.py
================================================
"""
Project Euler Problem 2: https://projecteuler.net/problem=2

Even Fibonacci Numbers

Each new term in the Fibonacci sequence is generated by adding the previous
two terms. By starting with 1 and 2, the first 10 terms will be:

1, 2, 3, 5, 8, 13, 21, 34, 55, 89, ...

By considering the terms in the Fibonacci sequence whose values do not exceed
four million, find the sum of the even-valued terms.

References:
    - https://en.wikipedia.org/wiki/Fibonacci_number
"""


def solution(n: int = 4000000) -> int:
    """
    Returns the sum of all even fibonacci sequence elements that are lower
    or equal to n.

    >>> solution(10)
    10
    >>> solution(15)
    10
    >>> solution(2)
    2
    >>> solution(1)
    0
    >>> solution(34)
    44
    """

    if n <= 1:
        return 0
    a = 0
    b = 2
    count = 0
    while 4 * b + a <= n:
        a, b = b, 4 * b + a
        count += a
    return count + b


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_002/sol4.py
================================================
"""
Project Euler Problem 2: https://projecteuler.net/problem=2

Even Fibonacci Numbers

Each new term in the Fibonacci sequence is generated by adding the previous
two terms. By starting with 1 and 2, the first 10 terms will be:

1, 2, 3, 5, 8, 13, 21, 34, 55, 89, ...

By considering the terms in the Fibonacci sequence whose values do not exceed
four million, find the sum of the even-valued terms.

References:
    - https://en.wikipedia.org/wiki/Fibonacci_number
"""

import math
from decimal import Decimal, getcontext


def solution(n: int = 4000000) -> int:
    """
    Returns the sum of all even fibonacci sequence elements that are lower
    or equal to n.

    >>> solution(10)
    10
    >>> solution(15)
    10
    >>> solution(2)
    2
    >>> solution(1)
    0
    >>> solution(34)
    44
    >>> solution(3.4)
    2
    >>> solution(0)
    Traceback (most recent call last):
        ...
    ValueError: Parameter n must be greater than or equal to one.
    >>> solution(-17)
    Traceback (most recent call last):
        ...
    ValueError: Parameter n must be greater than or equal to one.
    >>> solution([])
    Traceback (most recent call last):
        ...
    TypeError: Parameter n must be int or castable to int.
    >>> solution("asd")
    Traceback (most recent call last):
        ...
    TypeError: Parameter n must be int or castable to int.
    """

    try:
        n = int(n)
    except (TypeError, ValueError):
        raise TypeError("Parameter n must be int or castable to int.")
    if n <= 0:
        raise ValueError("Parameter n must be greater than or equal to one.")
    getcontext().prec = 100
    phi = (Decimal(5) ** Decimal("0.5") + 1) / Decimal(2)

    index = (math.floor(math.log(n * (phi + 2), phi) - 1) // 3) * 3 + 2
    num = Decimal(round(phi ** Decimal(index + 1))) / (phi + 2)
    total = num // 2
    return int(total)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_002/sol5.py
================================================
"""
Project Euler Problem 2: https://projecteuler.net/problem=2

Even Fibonacci Numbers

Each new term in the Fibonacci sequence is generated by adding the previous
two terms. By starting with 1 and 2, the first 10 terms will be:

1, 2, 3, 5, 8, 13, 21, 34, 55, 89, ...

By considering the terms in the Fibonacci sequence whose values do not exceed
four million, find the sum of the even-valued terms.

References:
    - https://en.wikipedia.org/wiki/Fibonacci_number
"""


def solution(n: int = 4000000) -> int:
    """
    Returns the sum of all even fibonacci sequence elements that are lower
    or equal to n.

    >>> solution(10)
    10
    >>> solution(15)
    10
    >>> solution(2)
    2
    >>> solution(1)
    0
    >>> solution(34)
    44
    """

    fib = [0, 1]
    i = 0
    while fib[i] <= n:
        fib.append(fib[i] + fib[i + 1])
        if fib[i + 2] > n:
            break
        i += 1
    total = 0
    for j in range(len(fib) - 1):
        if fib[j] % 2 == 0:
            total += fib[j]

    return total


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_003/__init__.py
================================================


================================================
FILE: project_euler/problem_003/sol1.py
================================================
"""
Project Euler Problem 3: https://projecteuler.net/problem=3

Largest prime factor

The prime factors of 13195 are 5, 7, 13 and 29.

What is the largest prime factor of the number 600851475143?

References:
    - https://en.wikipedia.org/wiki/Prime_number#Unique_factorization
"""

import math


def is_prime(number: int) -> bool:
    """Checks to see if a number is a prime in O(sqrt(n)).
    A number is prime if it has exactly two factors: 1 and itself.
    Returns boolean representing primality of given number (i.e., if the
    result is true, then the number is indeed prime else it is not).

    >>> is_prime(2)
    True
    >>> is_prime(3)
    True
    >>> is_prime(27)
    False
    >>> is_prime(2999)
    True
    >>> is_prime(0)
    False
    >>> is_prime(1)
    False
    """

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5, int(math.sqrt(number) + 1), 6):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


def solution(n: int = 600851475143) -> int:
    """
    Returns the largest prime factor of a given number n.

    >>> solution(13195)
    29
    >>> solution(10)
    5
    >>> solution(17)
    17
    >>> solution(3.4)
    3
    >>> solution(0)
    Traceback (most recent call last):
        ...
    ValueError: Parameter n must be greater than or equal to one.
    >>> solution(-17)
    Traceback (most recent call last):
        ...
    ValueError: Parameter n must be greater than or equal to one.
    >>> solution([])
    Traceback (most recent call last):
        ...
    TypeError: Parameter n must be int or castable to int.
    >>> solution("asd")
    Traceback (most recent call last):
        ...
    TypeError: Parameter n must be int or castable to int.
    """

    try:
        n = int(n)
    except (TypeError, ValueError):
        raise TypeError("Parameter n must be int or castable to int.")
    if n <= 0:
        raise ValueError("Parameter n must be greater than or equal to one.")
    max_number = 0
    if is_prime(n):
        return n
    while n % 2 == 0:
        n //= 2
    if is_prime(n):
        return n
    for i in range(3, int(math.sqrt(n)) + 1, 2):
        if n % i == 0:
            if is_prime(n // i):
                max_number = n // i
                break
            elif is_prime(i):
                max_number = i
    return max_number


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_003/sol2.py
================================================
"""
Project Euler Problem 3: https://projecteuler.net/problem=3

Largest prime factor

The prime factors of 13195 are 5, 7, 13 and 29.

What is the largest prime factor of the number 600851475143?

References:
    - https://en.wikipedia.org/wiki/Prime_number#Unique_factorization
"""


def solution(n: int = 600851475143) -> int:
    """
    Returns the largest prime factor of a given number n.

    >>> solution(13195)
    29
    >>> solution(10)
    5
    >>> solution(17)
    17
    >>> solution(3.4)
    3
    >>> solution(0)
    Traceback (most recent call last):
        ...
    ValueError: Parameter n must be greater than or equal to one.
    >>> solution(-17)
    Traceback (most recent call last):
        ...
    ValueError: Parameter n must be greater than or equal to one.
    >>> solution([])
    Traceback (most recent call last):
        ...
    TypeError: Parameter n must be int or castable to int.
    >>> solution("asd")
    Traceback (most recent call last):
        ...
    TypeError: Parameter n must be int or castable to int.
    """

    try:
        n = int(n)
    except (TypeError, ValueError):
        raise TypeError("Parameter n must be int or castable to int.")
    if n <= 0:
        raise ValueError("Parameter n must be greater than or equal to one.")
    prime = 1
    i = 2
    while i * i <= n:
        while n % i == 0:
            prime = i
            n //= i
        i += 1
    if n > 1:
        prime = n
    return int(prime)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_003/sol3.py
================================================
"""
Project Euler Problem 3: https://projecteuler.net/problem=3

Largest prime factor

The prime factors of 13195 are 5, 7, 13 and 29.

What is the largest prime factor of the number 600851475143?

References:
    - https://en.wikipedia.org/wiki/Prime_number#Unique_factorization
"""


def solution(n: int = 600851475143) -> int:
    """
    Returns the largest prime factor of a given number n.

    >>> solution(13195)
    29
    >>> solution(10)
    5
    >>> solution(17)
    17
    >>> solution(3.4)
    3
    >>> solution(0)
    Traceback (most recent call last):
        ...
    ValueError: Parameter n must be greater than or equal to one.
    >>> solution(-17)
    Traceback (most recent call last):
        ...
    ValueError: Parameter n must be greater than or equal to one.
    >>> solution([])
    Traceback (most recent call last):
        ...
    TypeError: Parameter n must be int or castable to int.
    >>> solution("asd")
    Traceback (most recent call last):
        ...
    TypeError: Parameter n must be int or castable to int.
    """

    try:
        n = int(n)
    except (TypeError, ValueError):
        raise TypeError("Parameter n must be int or castable to int.")
    if n <= 0:
        raise ValueError("Parameter n must be greater than or equal to one.")
    i = 2
    ans = 0
    if n == 2:
        return 2
    while n > 2:
        while n % i != 0:
            i += 1
        ans = i
        while n % i == 0:
            n = n // i
        i += 1
    return int(ans)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_004/__init__.py
================================================


================================================
FILE: project_euler/problem_004/sol1.py
================================================
"""
Project Euler Problem 4: https://projecteuler.net/problem=4

Largest palindrome product

A palindromic number reads the same both ways. The largest palindrome made
from the product of two 2-digit numbers is 9009 = 91 x 99.

Find the largest palindrome made from the product of two 3-digit numbers.

References:
    - https://en.wikipedia.org/wiki/Palindromic_number
"""


def solution(n: int = 998001) -> int:
    """
    Returns the largest palindrome made from the product of two 3-digit
    numbers which is less than n.

    >>> solution(20000)
    19591
    >>> solution(30000)
    29992
    >>> solution(40000)
    39893
    >>> solution(10000)
    Traceback (most recent call last):
        ...
    ValueError: That number is larger than our acceptable range.
    """

    # fetches the next number
    for number in range(n - 1, 9999, -1):
        str_number = str(number)

        # checks whether 'str_number' is a palindrome.
        if str_number == str_number[::-1]:
            divisor = 999

            # if 'number' is a product of two 3-digit numbers
            # then number is the answer otherwise fetch next number.
            while divisor != 99:
                if (number % divisor == 0) and (len(str(number // divisor)) == 3.0):
                    return number
                divisor -= 1
    raise ValueError("That number is larger than our acceptable range.")


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_004/sol2.py
================================================
"""
Project Euler Problem 4: https://projecteuler.net/problem=4

Largest palindrome product

A palindromic number reads the same both ways. The largest palindrome made
from the product of two 2-digit numbers is 9009 = 91 x 99.

Find the largest palindrome made from the product of two 3-digit numbers.

References:
    - https://en.wikipedia.org/wiki/Palindromic_number
"""


def solution(n: int = 998001) -> int:
    """
    Returns the largest palindrome made from the product of two 3-digit
    numbers which is less than n.

    >>> solution(20000)
    19591
    >>> solution(30000)
    29992
    >>> solution(40000)
    39893
    """

    answer = 0
    for i in range(999, 99, -1):  # 3 digit numbers range from 999 down to 100
        for j in range(999, 99, -1):
            product_string = str(i * j)
            if product_string == product_string[::-1] and i * j < n:
                answer = max(answer, i * j)
    return answer


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_005/__init__.py
================================================


================================================
FILE: project_euler/problem_005/sol1.py
================================================
"""
Project Euler Problem 5: https://projecteuler.net/problem=5

Smallest multiple

2520 is the smallest number that can be divided by each of the numbers
from 1 to 10 without any remainder.

What is the smallest positive number that is _evenly divisible_ by all
of the numbers from 1 to 20?

References:
    - https://en.wiktionary.org/wiki/evenly_divisible
"""


def solution(n: int = 20) -> int:
    """
    Returns the smallest positive number that is evenly divisible (divisible
    with no remainder) by all of the numbers from 1 to n.

    >>> solution(10)
    2520
    >>> solution(15)
    360360
    >>> solution(22)
    232792560
    >>> solution(3.4)
    6
    >>> solution(0)
    Traceback (most recent call last):
        ...
    ValueError: Parameter n must be greater than or equal to one.
    >>> solution(-17)
    Traceback (most recent call last):
        ...
    ValueError: Parameter n must be greater than or equal to one.
    >>> solution([])
    Traceback (most recent call last):
        ...
    TypeError: Parameter n must be int or castable to int.
    >>> solution("asd")
    Traceback (most recent call last):
        ...
    TypeError: Parameter n must be int or castable to int.
    """

    try:
        n = int(n)
    except (TypeError, ValueError):
        raise TypeError("Parameter n must be int or castable to int.")
    if n <= 0:
        raise ValueError("Parameter n must be greater than or equal to one.")
    i = 0
    while 1:
        i += n * (n - 1)
        nfound = 0
        for j in range(2, n):
            if i % j != 0:
                nfound = 1
                break
        if nfound == 0:
            if i == 0:
                i = 1
            return i
    return None


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_005/sol2.py
================================================
from maths.greatest_common_divisor import greatest_common_divisor

"""
Project Euler Problem 5: https://projecteuler.net/problem=5

Smallest multiple

2520 is the smallest number that can be divided by each of the numbers
from 1 to 10 without any remainder.

What is the smallest positive number that is _evenly divisible_ by all
of the numbers from 1 to 20?

References:
    - https://en.wiktionary.org/wiki/evenly_divisible
    - https://en.wikipedia.org/wiki/Euclidean_algorithm
    - https://en.wikipedia.org/wiki/Least_common_multiple
"""


def lcm(x: int, y: int) -> int:
    """
    Least Common Multiple.

    Using the property that lcm(a, b) * greatest_common_divisor(a, b) = a*b

    >>> lcm(3, 15)
    15
    >>> lcm(1, 27)
    27
    >>> lcm(13, 27)
    351
    >>> lcm(64, 48)
    192
    """

    return (x * y) // greatest_common_divisor(x, y)


def solution(n: int = 20) -> int:
    """
    Returns the smallest positive number that is evenly divisible (divisible
    with no remainder) by all of the numbers from 1 to n.

    >>> solution(10)
    2520
    >>> solution(15)
    360360
    >>> solution(22)
    232792560
    """

    g = 1
    for i in range(1, n + 1):
        g = lcm(g, i)
    return g


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_006/__init__.py
================================================


================================================
FILE: project_euler/problem_006/sol1.py
================================================
"""
Project Euler Problem 6: https://projecteuler.net/problem=6

Sum square difference

The sum of the squares of the first ten natural numbers is,
    1^2 + 2^2 + ... + 10^2 = 385

The square of the sum of the first ten natural numbers is,
    (1 + 2 + ... + 10)^2 = 55^2 = 3025

Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.

Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""


def solution(n: int = 100) -> int:
    """
    Returns the difference between the sum of the squares of the first n
    natural numbers and the square of the sum.

    >>> solution(10)
    2640
    >>> solution(15)
    13160
    >>> solution(20)
    41230
    >>> solution(50)
    1582700
    """

    sum_of_squares = 0
    sum_of_ints = 0
    for i in range(1, n + 1):
        sum_of_squares += i**2
        sum_of_ints += i
    return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_006/sol2.py
================================================
"""
Project Euler Problem 6: https://projecteuler.net/problem=6

Sum square difference

The sum of the squares of the first ten natural numbers is,
    1^2 + 2^2 + ... + 10^2 = 385

The square of the sum of the first ten natural numbers is,
    (1 + 2 + ... + 10)^2 = 55^2 = 3025

Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.

Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""


def solution(n: int = 100) -> int:
    """
    Returns the difference between the sum of the squares of the first n
    natural numbers and the square of the sum.

    >>> solution(10)
    2640
    >>> solution(15)
    13160
    >>> solution(20)
    41230
    >>> solution(50)
    1582700
    """

    sum_cubes = (n * (n + 1) // 2) ** 2
    sum_squares = n * (n + 1) * (2 * n + 1) // 6
    return sum_cubes - sum_squares


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_006/sol3.py
================================================
"""
Project Euler Problem 6: https://projecteuler.net/problem=6

Sum square difference

The sum of the squares of the first ten natural numbers is,
    1^2 + 2^2 + ... + 10^2 = 385

The square of the sum of the first ten natural numbers is,
    (1 + 2 + ... + 10)^2 = 55^2 = 3025

Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.

Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""

import math


def solution(n: int = 100) -> int:
    """
    Returns the difference between the sum of the squares of the first n
    natural numbers and the square of the sum.

    >>> solution(10)
    2640
    >>> solution(15)
    13160
    >>> solution(20)
    41230
    >>> solution(50)
    1582700
    """

    sum_of_squares = sum(i * i for i in range(1, n + 1))
    square_of_sum = int(math.pow(sum(range(1, n + 1)), 2))
    return square_of_sum - sum_of_squares


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_006/sol4.py
================================================
"""
Project Euler Problem 6: https://projecteuler.net/problem=6

Sum square difference

The sum of the squares of the first ten natural numbers is,
    1^2 + 2^2 + ... + 10^2 = 385

The square of the sum of the first ten natural numbers is,
    (1 + 2 + ... + 10)^2 = 55^2 = 3025

Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.

Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""


def solution(n: int = 100) -> int:
    """
    Returns the difference between the sum of the squares of the first n
    natural numbers and the square of the sum.

    >>> solution(10)
    2640
    >>> solution(15)
    13160
    >>> solution(20)
    41230
    >>> solution(50)
    1582700
    """

    sum_of_squares = n * (n + 1) * (2 * n + 1) / 6
    square_of_sum = (n * (n + 1) / 2) ** 2
    return int(square_of_sum - sum_of_squares)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_007/__init__.py
================================================


================================================
FILE: project_euler/problem_007/sol1.py
================================================
"""
Project Euler Problem 7: https://projecteuler.net/problem=7

10001st prime

By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we
can see that the 6th prime is 13.

What is the 10001st prime number?

References:
    - https://en.wikipedia.org/wiki/Prime_number
"""

from math import sqrt


def is_prime(number: int) -> bool:
    """Checks to see if a number is a prime in O(sqrt(n)).
    A number is prime if it has exactly two factors: 1 and itself.
    Returns boolean representing primality of given number (i.e., if the
    result is true, then the number is indeed prime else it is not).

    >>> is_prime(2)
    True
    >>> is_prime(3)
    True
    >>> is_prime(27)
    False
    >>> is_prime(2999)
    True
    >>> is_prime(0)
    False
    >>> is_prime(1)
    False
    """

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5, int(sqrt(number) + 1), 6):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


def solution(nth: int = 10001) -> int:
    """
    Returns the n-th prime number.

    >>> solution(6)
    13
    >>> solution(1)
    2
    >>> solution(3)
    5
    >>> solution(20)
    71
    >>> solution(50)
    229
    >>> solution(100)
    541
    """

    count = 0
    number = 1
    while count != nth and number < 3:
        number += 1
        if is_prime(number):
            count += 1
    while count != nth:
        number += 2
        if is_prime(number):
            count += 1
    return number


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_007/sol2.py
================================================
"""
Project Euler Problem 7: https://projecteuler.net/problem=7

10001st prime

By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we
can see that the 6th prime is 13.

What is the 10001st prime number?

References:
    - https://en.wikipedia.org/wiki/Prime_number
"""

import math


def is_prime(number: int) -> bool:
    """Checks to see if a number is a prime in O(sqrt(n)).
    A number is prime if it has exactly two factors: 1 and itself.
    Returns boolean representing primality of given number (i.e., if the
    result is true, then the number is indeed prime else it is not).

    >>> is_prime(2)
    True
    >>> is_prime(3)
    True
    >>> is_prime(27)
    False
    >>> is_prime(2999)
    True
    >>> is_prime(0)
    False
    >>> is_prime(1)
    False
    """

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5, int(math.sqrt(number) + 1), 6):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


def solution(nth: int = 10001) -> int:
    """
    Returns the n-th prime number.

    >>> solution(6)
    13
    >>> solution(1)
    2
    >>> solution(3)
    5
    >>> solution(20)
    71
    >>> solution(50)
    229
    >>> solution(100)
    541
    >>> solution(3.4)
    5
    >>> solution(0)
    Traceback (most recent call last):
        ...
    ValueError: Parameter nth must be greater than or equal to one.
    >>> solution(-17)
    Traceback (most recent call last):
        ...
    ValueError: Parameter nth must be greater than or equal to one.
    >>> solution([])
    Traceback (most recent call last):
        ...
    TypeError: Parameter nth must be int or castable to int.
    >>> solution("asd")
    Traceback (most recent call last):
        ...
    TypeError: Parameter nth must be int or castable to int.
    """

    try:
        nth = int(nth)
    except (TypeError, ValueError):
        raise TypeError("Parameter nth must be int or castable to int.") from None
    if nth <= 0:
        raise ValueError("Parameter nth must be greater than or equal to one.")
    primes: list[int] = []
    num = 2
    while len(primes) < nth:
        if is_prime(num):
            primes.append(num)
            num += 1
        else:
            num += 1
    return primes[len(primes) - 1]


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_007/sol3.py
================================================
"""
Project Euler Problem 7: https://projecteuler.net/problem=7

10001st prime

By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we
can see that the 6th prime is 13.

What is the 10001st prime number?

References:
    - https://en.wikipedia.org/wiki/Prime_number
"""

import itertools
import math


def is_prime(number: int) -> bool:
    """Checks to see if a number is a prime in O(sqrt(n)).
    A number is prime if it has exactly two factors: 1 and itself.
    Returns boolean representing primality of given number (i.e., if the
    result is true, then the number is indeed prime else it is not).

    >>> is_prime(2)
    True
    >>> is_prime(3)
    True
    >>> is_prime(27)
    False
    >>> is_prime(2999)
    True
    >>> is_prime(0)
    False
    >>> is_prime(1)
    False
    """

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5, int(math.sqrt(number) + 1), 6):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


def prime_generator():
    """
    Generate a sequence of prime numbers
    """

    num = 2
    while True:
        if is_prime(num):
            yield num
        num += 1


def solution(nth: int = 10001) -> int:
    """
    Returns the n-th prime number.

    >>> solution(6)
    13
    >>> solution(1)
    2
    >>> solution(3)
    5
    >>> solution(20)
    71
    >>> solution(50)
    229
    >>> solution(100)
    541
    """
    return next(itertools.islice(prime_generator(), nth - 1, nth))


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_008/__init__.py
================================================


================================================
FILE: project_euler/problem_008/sol1.py
================================================
"""
Project Euler Problem 8: https://projecteuler.net/problem=8

Largest product in a series

The four adjacent digits in the 1000-digit number that have the greatest
product are 9 x 9 x 8 x 9 = 5832.

    73167176531330624919225119674426574742355349194934
    96983520312774506326239578318016984801869478851843
    85861560789112949495459501737958331952853208805511
    12540698747158523863050715693290963295227443043557
    66896648950445244523161731856403098711121722383113
    62229893423380308135336276614282806444486645238749
    30358907296290491560440772390713810515859307960866
    70172427121883998797908792274921901699720888093776
    65727333001053367881220235421809751254540594752243
    52584907711670556013604839586446706324415722155397
    53697817977846174064955149290862569321978468622482
    83972241375657056057490261407972968652414535100474
    82166370484403199890008895243450658541227588666881
    16427171479924442928230863465674813919123162824586
    17866458359124566529476545682848912883142607690042
    24219022671055626321111109370544217506941658960408
    07198403850962455444362981230987879927244284909188
    84580156166097919133875499200524063689912560717606
    05886116467109405077541002256983155200055935729725
    71636269561882670428252483600823257530420752963450

Find the thirteen adjacent digits in the 1000-digit number that have the
greatest product. What is the value of this product?
"""

import sys

N = (
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)


def solution(n: str = N) -> int:
    """
    Find the thirteen adjacent digits in the 1000-digit number n that have
    the greatest product and returns it.

    >>> solution("13978431290823798458352374")
    609638400
    >>> solution("13978431295823798458352374")
    2612736000
    >>> solution("1397843129582379841238352374")
    209018880
    """

    largest_product = -sys.maxsize - 1
    for i in range(len(n) - 12):
        product = 1
        for j in range(13):
            product *= int(n[i + j])
        largest_product = max(largest_product, product)
    return largest_product


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_008/sol2.py
================================================
"""
Project Euler Problem 8: https://projecteuler.net/problem=8

Largest product in a series

The four adjacent digits in the 1000-digit number that have the greatest
product are 9 x 9 x 8 x 9 = 5832.

    73167176531330624919225119674426574742355349194934
    96983520312774506326239578318016984801869478851843
    85861560789112949495459501737958331952853208805511
    12540698747158523863050715693290963295227443043557
    66896648950445244523161731856403098711121722383113
    62229893423380308135336276614282806444486645238749
    30358907296290491560440772390713810515859307960866
    70172427121883998797908792274921901699720888093776
    65727333001053367881220235421809751254540594752243
    52584907711670556013604839586446706324415722155397
    53697817977846174064955149290862569321978468622482
    83972241375657056057490261407972968652414535100474
    82166370484403199890008895243450658541227588666881
    16427171479924442928230863465674813919123162824586
    17866458359124566529476545682848912883142607690042
    24219022671055626321111109370544217506941658960408
    07198403850962455444362981230987879927244284909188
    84580156166097919133875499200524063689912560717606
    05886116467109405077541002256983155200055935729725
    71636269561882670428252483600823257530420752963450

Find the thirteen adjacent digits in the 1000-digit number that have the
greatest product. What is the value of this product?
"""

from functools import reduce

N = (
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)


def solution(n: str = N) -> int:
    """
    Find the thirteen adjacent digits in the 1000-digit number n that have
    the greatest product and returns it.

    >>> solution("13978431290823798458352374")
    609638400
    >>> solution("13978431295823798458352374")
    2612736000
    >>> solution("1397843129582379841238352374")
    209018880
    """

    return max(
        # mypy cannot properly interpret reduce
        int(reduce(lambda x, y: str(int(x) * int(y)), n[i : i + 13]))
        for i in range(len(n) - 12)
    )


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_008/sol3.py
================================================
"""
Project Euler Problem 8: https://projecteuler.net/problem=8

Largest product in a series

The four adjacent digits in the 1000-digit number that have the greatest
product are 9 x 9 x 8 x 9 = 5832.

    73167176531330624919225119674426574742355349194934
    96983520312774506326239578318016984801869478851843
    85861560789112949495459501737958331952853208805511
    12540698747158523863050715693290963295227443043557
    66896648950445244523161731856403098711121722383113
    62229893423380308135336276614282806444486645238749
    30358907296290491560440772390713810515859307960866
    70172427121883998797908792274921901699720888093776
    65727333001053367881220235421809751254540594752243
    52584907711670556013604839586446706324415722155397
    53697817977846174064955149290862569321978468622482
    83972241375657056057490261407972968652414535100474
    82166370484403199890008895243450658541227588666881
    16427171479924442928230863465674813919123162824586
    17866458359124566529476545682848912883142607690042
    24219022671055626321111109370544217506941658960408
    07198403850962455444362981230987879927244284909188
    84580156166097919133875499200524063689912560717606
    05886116467109405077541002256983155200055935729725
    71636269561882670428252483600823257530420752963450

Find the thirteen adjacent digits in the 1000-digit number that have the
greatest product. What is the value of this product?
"""

import sys

N = (
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)


def str_eval(s: str) -> int:
    """
    Returns product of digits in given string n

    >>> str_eval("987654321")
    362880
    >>> str_eval("22222222")
    256
    """

    product = 1
    for digit in s:
        product *= int(digit)
    return product


def solution(n: str = N) -> int:
    """
    Find the thirteen adjacent digits in the 1000-digit number n that have
    the greatest product and returns it.
    """

    largest_product = -sys.maxsize - 1
    substr = n[:13]
    cur_index = 13
    while cur_index < len(n) - 13:
        if int(n[cur_index]) >= int(substr[0]):
            substr = substr[1:] + n[cur_index]
            cur_index += 1
        else:
            largest_product = max(largest_product, str_eval(substr))
            substr = n[cur_index : cur_index + 13]
            cur_index += 13
    return largest_product


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_009/__init__.py
================================================


================================================
FILE: project_euler/problem_009/sol1.py
================================================
"""
Project Euler Problem 9: https://projecteuler.net/problem=9

Special Pythagorean triplet

A Pythagorean triplet is a set of three natural numbers, a < b < c, for which,

    a^2 + b^2 = c^2

For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2.

There exists exactly one Pythagorean triplet for which a + b + c = 1000.
Find the product a*b*c.

References:
    - https://en.wikipedia.org/wiki/Pythagorean_triple
"""


def solution() -> int:
    """
    Returns the product of a,b,c which are Pythagorean Triplet that satisfies
    the following:
      1. a < b < c
      2. a**2 + b**2 = c**2
      3. a + b + c = 1000

    >>> solution()
    31875000
    """

    for a in range(300):
        for b in range(a + 1, 400):
            for c in range(b + 1, 500):
                if (a + b + c) == 1000 and (a**2) + (b**2) == (c**2):
                    return a * b * c

    return -1


def solution_fast() -> int:
    """
    Returns the product of a,b,c which are Pythagorean Triplet that satisfies
    the following:
      1. a < b < c
      2. a**2 + b**2 = c**2
      3. a + b + c = 1000

    >>> solution_fast()
    31875000
    """

    for a in range(300):
        for b in range(400):
            c = 1000 - a - b
            if a < b < c and (a**2) + (b**2) == (c**2):
                return a * b * c

    return -1


def benchmark() -> None:
    """
    Benchmark code comparing two different version function.
    """
    import timeit

    print(
        timeit.timeit("solution()", setup="from __main__ import solution", number=1000)
    )
    print(
        timeit.timeit(
            "solution_fast()", setup="from __main__ import solution_fast", number=1000
        )
    )


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_009/sol2.py
================================================
"""
Project Euler Problem 9: https://projecteuler.net/problem=9

Special Pythagorean triplet

A Pythagorean triplet is a set of three natural numbers, a < b < c, for which,

    a^2 + b^2 = c^2

For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2.

There exists exactly one Pythagorean triplet for which a + b + c = 1000.
Find the product a*b*c.

References:
    - https://en.wikipedia.org/wiki/Pythagorean_triple
"""


def solution(n: int = 1000) -> int:
    """
    Return the product of a,b,c which are Pythagorean Triplet that satisfies
    the following:
      1. a < b < c
      2. a**2 + b**2 = c**2
      3. a + b + c = n

    >>> solution(36)
    1620
    >>> solution(126)
    66780
    """

    product = -1
    candidate = 0
    for a in range(1, n // 3):
        # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
        b = (n * n - 2 * a * n) // (2 * n - 2 * a)
        c = n - a - b
        if c * c == (a * a + b * b):
            candidate = a * b * c
            product = max(product, candidate)
    return product


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_009/sol3.py
================================================
"""
Project Euler Problem 9: https://projecteuler.net/problem=9

Special Pythagorean triplet

A Pythagorean triplet is a set of three natural numbers, a < b < c, for which,

    a^2 + b^2 = c^2

For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2.

There exists exactly one Pythagorean triplet for which a + b + c = 1000.
Find the product a*b*c.

References:
    - https://en.wikipedia.org/wiki/Pythagorean_triple
"""


def solution() -> int:
    """
    Returns the product of a,b,c which are Pythagorean Triplet that satisfies
    the following:
      1. a**2 + b**2 = c**2
      2. a + b + c = 1000

    >>> solution()
    31875000
    """

    return next(
        iter(
            [
                a * b * (1000 - a - b)
                for a in range(1, 999)
                for b in range(a, 999)
                if (a * a + b * b == (1000 - a - b) ** 2)
            ]
        )
    )


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_009/sol4.py
================================================
"""
Project Euler Problem 9: https://projecteuler.net/problem=9

Special Pythagorean triplet

A Pythagorean triplet is a set of three natural numbers, a < b < c, for which,

    a^2 + b^2 = c^2.

For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2.

There exists exactly one Pythagorean triplet for which a + b + c = 1000.
Find the product abc.

References:
    - https://en.wikipedia.org/wiki/Pythagorean_triple
"""


def get_squares(n: int) -> list[int]:
    """
    >>> get_squares(0)
    []
    >>> get_squares(1)
    [0]
    >>> get_squares(2)
    [0, 1]
    >>> get_squares(3)
    [0, 1, 4]
    >>> get_squares(4)
    [0, 1, 4, 9]
    """
    return [number * number for number in range(n)]


def solution(n: int = 1000) -> int:
    """
    Precomputing squares and checking if a^2 + b^2 is the square by set look-up.

    >>> solution(12)
    60
    >>> solution(36)
    1620
    """

    squares = get_squares(n)
    squares_set = set(squares)
    for a in range(1, n // 3):
        for b in range(a + 1, (n - a) // 2 + 1):
            if (
                squares[a] + squares[b] in squares_set
                and squares[n - a - b] == squares[a] + squares[b]
            ):
                return a * b * (n - a - b)

    return -1


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_010/__init__.py
================================================


================================================
FILE: project_euler/problem_010/sol1.py
================================================
"""
Project Euler Problem 10: https://projecteuler.net/problem=10

Summation of primes

The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.

Find the sum of all the primes below two million.

References:
    - https://en.wikipedia.org/wiki/Prime_number
"""

import math


def is_prime(number: int) -> bool:
    """Checks to see if a number is a prime in O(sqrt(n)).
    A number is prime if it has exactly two factors: 1 and itself.
    Returns boolean representing primality of given number num (i.e., if the
    result is true, then the number is indeed prime else it is not).

    >>> is_prime(2)
    True
    >>> is_prime(3)
    True
    >>> is_prime(27)
    False
    >>> is_prime(2999)
    True
    >>> is_prime(0)
    False
    >>> is_prime(1)
    False
    """

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5, int(math.sqrt(number) + 1), 6):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


def solution(n: int = 2000000) -> int:
    """
    Returns the sum of all the primes below n.

    >>> solution(1000)
    76127
    >>> solution(5000)
    1548136
    >>> solution(10000)
    5736396
    >>> solution(7)
    10
    """

    return sum(num for num in range(3, n, 2) if is_prime(num)) + 2 if n > 2 else 0


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_010/sol2.py
================================================
"""
Project Euler Problem 10: https://projecteuler.net/problem=10

Summation of primes

The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.

Find the sum of all the primes below two million.

References:
    - https://en.wikipedia.org/wiki/Prime_number
"""

import math
from collections.abc import Iterator
from itertools import takewhile


def is_prime(number: int) -> bool:
    """Checks to see if a number is a prime in O(sqrt(n)).
    A number is prime if it has exactly two factors: 1 and itself.
    Returns boolean representing primality of given number num (i.e., if the
    result is true, then the number is indeed prime else it is not).

    >>> is_prime(2)
    True
    >>> is_prime(3)
    True
    >>> is_prime(27)
    False
    >>> is_prime(2999)
    True
    >>> is_prime(0)
    False
    >>> is_prime(1)
    False
    """

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5, int(math.sqrt(number) + 1), 6):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


def prime_generator() -> Iterator[int]:
    """
    Generate a list sequence of prime numbers
    """

    num = 2
    while True:
        if is_prime(num):
            yield num
        num += 1


def solution(n: int = 2000000) -> int:
    """
    Returns the sum of all the primes below n.

    >>> solution(1000)
    76127
    >>> solution(5000)
    1548136
    >>> solution(10000)
    5736396
    >>> solution(7)
    10
    """

    return sum(takewhile(lambda x: x < n, prime_generator()))


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_010/sol3.py
================================================
"""
Project Euler Problem 10: https://projecteuler.net/problem=10

Summation of primes

The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.

Find the sum of all the primes below two million.

References:
    - https://en.wikipedia.org/wiki/Prime_number
    - https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes
"""


def solution(n: int = 2000000) -> int:
    """
    Returns the sum of all the primes below n using Sieve of Eratosthenes:

    The sieve of Eratosthenes is one of the most efficient ways to find all primes
    smaller than n when n is smaller than 10 million.  Only for positive numbers.

    >>> solution(1000)
    76127
    >>> solution(5000)
    1548136
    >>> solution(10000)
    5736396
    >>> solution(7)
    10
    >>> solution(7.1)  # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    TypeError: 'float' object cannot be interpreted as an integer
    >>> solution(-7)  # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    IndexError: list assignment index out of range
    >>> solution("seven")  # doctest: +ELLIPSIS
    Traceback (most recent call last):
        ...
    TypeError: can only concatenate str (not "int") to str
    """

    primality_list = [0 for i in range(n + 1)]
    primality_list[0] = 1
    primality_list[1] = 1

    for i in range(2, int(n**0.5) + 1):
        if primality_list[i] == 0:
            for j in range(i * i, n + 1, i):
                primality_list[j] = 1
    sum_of_primes = 0
    for i in range(n):
        if primality_list[i] == 0:
            sum_of_primes += i
    return sum_of_primes


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_011/__init__.py
================================================


================================================
FILE: project_euler/problem_011/grid.txt
================================================
08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08
49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00
81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65
52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91
22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80
24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50
32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70
67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21
24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72
21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95
78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92
16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57
86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58
19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40
04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66
88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69
04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36
20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16
20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54
01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48


================================================
FILE: project_euler/problem_011/sol1.py
================================================
"""
What is the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally) in this 20x20 array?

08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08
49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00
81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65
52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91
22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80
24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50
32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70
67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21
24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72
21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95
78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92
16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57
86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58
19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40
04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66
88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69
04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36
20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16
20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54
01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48
"""

import os


def largest_product(grid):
    n_columns = len(grid[0])
    n_rows = len(grid)

    largest = 0
    lr_diag_product = 0
    rl_diag_product = 0

    # Check vertically, horizontally, diagonally at the same time (only works
    # for nxn grid)
    for i in range(n_columns):
        for j in range(n_rows - 3):
            vert_product = grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
            horz_product = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]

            # Left-to-right diagonal (\) product
            if i < n_columns - 3:
                lr_diag_product = (
                    grid[i][j]
                    * grid[i + 1][j + 1]
                    * grid[i + 2][j + 2]
                    * grid[i + 3][j + 3]
                )

            # Right-to-left diagonal(/) product
            if i > 2:
                rl_diag_product = (
                    grid[i][j]
                    * grid[i - 1][j + 1]
                    * grid[i - 2][j + 2]
                    * grid[i - 3][j + 3]
                )

            max_product = max(
                vert_product, horz_product, lr_diag_product, rl_diag_product
            )
            largest = max(largest, max_product)

    return largest


def solution():
    """Returns the greatest product of four adjacent numbers (horizontally,
    vertically, or diagonally).

    >>> solution()
    70600674
    """
    grid = []
    with open(os.path.dirname(__file__) + "/grid.txt") as file:
        for line in file:
            grid.append(line.strip("\n").split(" "))

    grid = [[int(i) for i in grid[j]] for j in range(len(grid))]

    return largest_product(grid)


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_011/sol2.py
================================================
"""
What is the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally) in this 20x20 array?

08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08
49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00
81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65
52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91
22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80
24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50
32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70
67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21
24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72
21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95
78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92
16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57
86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58
19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40
04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66
88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69
04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36
20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16
20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54
01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48
"""

import os


def solution():
    """Returns the greatest product of four adjacent numbers (horizontally,
    vertically, or diagonally).

    >>> solution()
    70600674
    """
    with open(os.path.dirname(__file__) + "/grid.txt") as f:
        grid = []
        for _ in range(20):
            grid.append([int(x) for x in f.readline().split()])

        maximum = 0

        # right
        for i in range(20):
            for j in range(17):
                temp = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
                maximum = max(maximum, temp)

        # down
        for i in range(17):
            for j in range(20):
                temp = grid[i][j] * grid[i + 1][j] * grid[i + 2][j] * grid[i + 3][j]
                maximum = max(maximum, temp)

        # diagonal 1
        for i in range(17):
            for j in range(17):
                temp = (
                    grid[i][j]
                    * grid[i + 1][j + 1]
                    * grid[i + 2][j + 2]
                    * grid[i + 3][j + 3]
                )
                maximum = max(maximum, temp)

        # diagonal 2
        for i in range(17):
            for j in range(3, 20):
                temp = (
                    grid[i][j]
                    * grid[i + 1][j - 1]
                    * grid[i + 2][j - 2]
                    * grid[i + 3][j - 3]
                )
                maximum = max(maximum, temp)
        return maximum


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_012/__init__.py
================================================


================================================
FILE: project_euler/problem_012/sol1.py
================================================
"""
Highly divisible triangular numbers
Problem 12
The sequence of triangle numbers is generated by adding the natural numbers. So
the 7th triangle number would be 1 + 2 + 3 + 4 + 5 + 6 + 7 = 28. The first ten
terms would be:

1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ...

Let us list the factors of the first seven triangle numbers:

 1: 1
 3: 1,3
 6: 1,2,3,6
10: 1,2,5,10
15: 1,3,5,15
21: 1,3,7,21
28: 1,2,4,7,14,28
We can see that 28 is the first triangle number to have over five divisors.

What is the value of the first triangle number to have over five hundred
divisors?
"""


def count_divisors(n):
    n_divisors = 1
    i = 2
    while i * i <= n:
        multiplicity = 0
        while n % i == 0:
            n //= i
            multiplicity += 1
        n_divisors *= multiplicity + 1
        i += 1
    if n > 1:
        n_divisors *= 2
    return n_divisors


def solution():
    """Returns the value of the first triangle number to have over five hundred
    divisors.

    >>> solution()
    76576500
    """
    t_num = 1
    i = 1

    while True:
        i += 1
        t_num += i

        if count_divisors(t_num) > 500:
            break

    return t_num


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_012/sol2.py
================================================
"""
Highly divisible triangular numbers
Problem 12
The sequence of triangle numbers is generated by adding the natural numbers. So
the 7th triangle number would be 1 + 2 + 3 + 4 + 5 + 6 + 7 = 28. The first ten
terms would be:

1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ...

Let us list the factors of the first seven triangle numbers:

 1: 1
 3: 1,3
 6: 1,2,3,6
10: 1,2,5,10
15: 1,3,5,15
21: 1,3,7,21
28: 1,2,4,7,14,28
We can see that 28 is the first triangle number to have over five divisors.

What is the value of the first triangle number to have over five hundred
divisors?
"""


def triangle_number_generator():
    for n in range(1, 1000000):
        yield n * (n + 1) // 2


def count_divisors(n):
    divisors_count = 1
    i = 2
    while i * i <= n:
        multiplicity = 0
        while n % i == 0:
            n //= i
            multiplicity += 1
        divisors_count *= multiplicity + 1
        i += 1
    if n > 1:
        divisors_count *= 2
    return divisors_count


def solution():
    """Returns the value of the first triangle number to have over five hundred
    divisors.

    >>> solution()
    76576500
    """
    return next(i for i in triangle_number_generator() if count_divisors(i) > 500)


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_013/__init__.py
================================================


================================================
FILE: project_euler/problem_013/num.txt
================================================
37107287533902102798797998220837590246510135740250
46376937677490009712648124896970078050417018260538
74324986199524741059474233309513058123726617309629
91942213363574161572522430563301811072406154908250
23067588207539346171171980310421047513778063246676
89261670696623633820136378418383684178734361726757
28112879812849979408065481931592621691275889832738
44274228917432520321923589422876796487670272189318
47451445736001306439091167216856844588711603153276
70386486105843025439939619828917593665686757934951
62176457141856560629502157223196586755079324193331
64906352462741904929101432445813822663347944758178
92575867718337217661963751590579239728245598838407
58203565325359399008402633568948830189458628227828
80181199384826282014278194139940567587151170094390
35398664372827112653829987240784473053190104293586
86515506006295864861532075273371959191420517255829
71693888707715466499115593487603532921714970056938
54370070576826684624621495650076471787294438377604
53282654108756828443191190634694037855217779295145
36123272525000296071075082563815656710885258350721
45876576172410976447339110607218265236877223636045
17423706905851860660448207621209813287860733969412
81142660418086830619328460811191061556940512689692
51934325451728388641918047049293215058642563049483
62467221648435076201727918039944693004732956340691
15732444386908125794514089057706229429197107928209
55037687525678773091862540744969844508330393682126
18336384825330154686196124348767681297534375946515
80386287592878490201521685554828717201219257766954
78182833757993103614740356856449095527097864797581
16726320100436897842553539920931837441497806860984
48403098129077791799088218795327364475675590848030
87086987551392711854517078544161852424320693150332
59959406895756536782107074926966537676326235447210
69793950679652694742597709739166693763042633987085
41052684708299085211399427365734116182760315001271
65378607361501080857009149939512557028198746004375
35829035317434717326932123578154982629742552737307
94953759765105305946966067683156574377167401875275
88902802571733229619176668713819931811048770190271
25267680276078003013678680992525463401061632866526
36270218540497705585629946580636237993140746255962
24074486908231174977792365466257246923322810917141
91430288197103288597806669760892938638285025333403
34413065578016127815921815005561868836468420090470
23053081172816430487623791969842487255036638784583
11487696932154902810424020138335124462181441773470
63783299490636259666498587618221225225512486764533
67720186971698544312419572409913959008952310058822
95548255300263520781532296796249481641953868218774
76085327132285723110424803456124867697064507995236
37774242535411291684276865538926205024910326572967
23701913275725675285653248258265463092207058596522
29798860272258331913126375147341994889534765745501
18495701454879288984856827726077713721403798879715
38298203783031473527721580348144513491373226651381
34829543829199918180278916522431027392251122869539
40957953066405232632538044100059654939159879593635
29746152185502371307642255121183693803580388584903
41698116222072977186158236678424689157993532961922
62467957194401269043877107275048102390895523597457
23189706772547915061505504953922979530901129967519
86188088225875314529584099251203829009407770775672
11306739708304724483816533873502340845647058077308
82959174767140363198008187129011875491310547126581
97623331044818386269515456334926366572897563400500
42846280183517070527831839425882145521227251250327
55121603546981200581762165212827652751691296897789
32238195734329339946437501907836945765883352399886
75506164965184775180738168837861091527357929701337
62177842752192623401942399639168044983993173312731
32924185707147349566916674687634660915035914677504
99518671430235219628894890102423325116913619626622
73267460800591547471830798392868535206946944540724
76841822524674417161514036427982273348055556214818
97142617910342598647204516893989422179826088076852
87783646182799346313767754307809363333018982642090
10848802521674670883215120185883543223812876952786
71329612474782464538636993009049310363619763878039
62184073572399794223406235393808339651327408011116
66627891981488087797941876876144230030984490851411
60661826293682836764744779239180335110989069790714
85786944089552990653640447425576083659976645795096
66024396409905389607120198219976047599490197230297
64913982680032973156037120041377903785566085089252
16730939319872750275468906903707539413042652315011
94809377245048795150954100921645863754710598436791
78639167021187492431995700641917969777599028300699
15368713711936614952811305876380278410754449733078
40789923115535562561142322423255033685442488917353
44889911501440648020369068063960672322193204149535
41503128880339536053299340368006977710650566631954
81234880673210146739058568557934581403627822703280
82616570773948327592232845941706525094512325230608
22918802058777319719839450180888072429661980811197
77158542502016545090413245809786882778948721859617
72107838435069186155435662884062257473692284509516
20849603980134001723930671666823555245252804609722
53503534226472524250874054075591789781264330331690


================================================
FILE: project_euler/problem_013/sol1.py
================================================
"""
Problem 13: https://projecteuler.net/problem=13

Problem Statement:
Work out the first ten digits of the sum of the following one-hundred 50-digit
numbers.
"""

import os


def solution():
    """
    Returns the first ten digits of the sum of the array elements
    from the file num.txt

    >>> solution()
    '5537376230'
    """
    file_path = os.path.join(os.path.dirname(__file__), "num.txt")
    with open(file_path) as file_hand:
        return str(sum(int(line) for line in file_hand))[:10]


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_014/__init__.py
================================================


================================================
FILE: project_euler/problem_014/sol1.py
================================================
"""
Problem 14: https://projecteuler.net/problem=14

Problem Statement:
The following iterative sequence is defined for the set of positive integers:

    n → n/2 (n is even)
    n → 3n + 1 (n is odd)

Using the rule above and starting with 13, we generate the following sequence:

    13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1

It can be seen that this sequence (starting at 13 and finishing at 1) contains
10 terms. Although it has not been proved yet (Collatz Problem), it is thought
that all starting numbers finish at 1.

Which starting number, under one million, produces the longest chain?
"""


def solution(n: int = 1000000) -> int:
    """Returns the number under n that generates the longest sequence using the
    formula:
    n → n/2 (n is even)
    n → 3n + 1 (n is odd)

    >>> solution(1000000)
    837799
    >>> solution(200)
    171
    >>> solution(5000)
    3711
    >>> solution(15000)
    13255
    """
    largest_number = 1
    pre_counter = 1
    counters = {1: 1}

    for input1 in range(2, n):
        counter = 0
        number = input1

        while True:
            if number in counters:
                counter += counters[number]
                break
            if number % 2 == 0:
                number //= 2
                counter += 1
            else:
                number = (3 * number) + 1
                counter += 1

        if input1 not in counters:
            counters[input1] = counter

        if counter > pre_counter:
            largest_number = input1
            pre_counter = counter
    return largest_number


if __name__ == "__main__":
    print(solution(int(input().strip())))


================================================
FILE: project_euler/problem_014/sol2.py
================================================
"""
Problem 14: https://projecteuler.net/problem=14

Collatz conjecture: start with any positive integer n. Next term obtained from
the previous term as follows:

If the previous term is even, the next term is one half the previous term.
If the previous term is odd, the next term is 3 times the previous term plus 1.
The conjecture states the sequence will always reach 1 regardless of starting
n.

Problem Statement:
The following iterative sequence is defined for the set of positive integers:

    n → n/2 (n is even)
    n → 3n + 1 (n is odd)

Using the rule above and starting with 13, we generate the following sequence:

    13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1

It can be seen that this sequence (starting at 13 and finishing at 1) contains
10 terms. Although it has not been proved yet (Collatz Problem), it is thought
that all starting numbers finish at 1.

Which starting number, under one million, produces the longest chain?
"""

from __future__ import annotations

COLLATZ_SEQUENCE_LENGTHS = {1: 1}


def collatz_sequence_length(n: int) -> int:
    """Returns the Collatz sequence length for n."""
    if n in COLLATZ_SEQUENCE_LENGTHS:
        return COLLATZ_SEQUENCE_LENGTHS[n]
    next_n = n // 2 if n % 2 == 0 else 3 * n + 1
    sequence_length = collatz_sequence_length(next_n) + 1
    COLLATZ_SEQUENCE_LENGTHS[n] = sequence_length
    return sequence_length


def solution(n: int = 1000000) -> int:
    """Returns the number under n that generates the longest Collatz sequence.

    >>> solution(1000000)
    837799
    >>> solution(200)
    171
    >>> solution(5000)
    3711
    >>> solution(15000)
    13255
    """

    result = max((collatz_sequence_length(i), i) for i in range(1, n))
    return result[1]


if __name__ == "__main__":
    print(solution(int(input().strip())))


================================================
FILE: project_euler/problem_015/__init__.py
================================================


================================================
FILE: project_euler/problem_015/sol1.py
================================================
"""
Problem 15: https://projecteuler.net/problem=15

Starting in the top left corner of a 2x2 grid, and only being able to move to
the right and down, there are exactly 6 routes to the bottom right corner.
How many such routes are there through a 20x20 grid?
"""

from math import factorial


def solution(n: int = 20) -> int:
    """
    Returns the number of paths possible in a n x n grid starting at top left
    corner going to bottom right corner and being able to move right and down
    only.
    >>> solution(25)
    126410606437752
    >>> solution(23)
    8233430727600
    >>> solution(20)
    137846528820
    >>> solution(15)
    155117520
    >>> solution(1)
    2
    """
    n = 2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
    # 2, 3,...
    k = n // 2

    return int(factorial(n) / (factorial(k) * factorial(n - k)))


if __name__ == "__main__":
    import sys

    if len(sys.argv) == 1:
        print(solution(20))
    else:
        try:
            n = int(sys.argv[1])
            print(solution(n))
        except ValueError:
            print("Invalid entry - please enter a number.")


================================================
FILE: project_euler/problem_015/sol2.py
================================================
"""
Problem 15: https://projecteuler.net/problem=15

Starting in the top left corner of a 2x2 grid, and only being able to move to
the right and down, there are exactly 6 routes to the bottom right corner.
How many such routes are there through a 20x20 grid?
"""


def solution(n: int = 20) -> int:
    """
    Solve by explicitly counting the paths with dynamic programming.

    >>> solution(6)
    924
    >>> solution(2)
    6
    >>> solution(1)
    2
    """

    counts = [[1 for _ in range(n + 1)] for _ in range(n + 1)]

    for i in range(1, n + 1):
        for j in range(1, n + 1):
            counts[i][j] = counts[i - 1][j] + counts[i][j - 1]

    return counts[n][n]


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_016/__init__.py
================================================


================================================
FILE: project_euler/problem_016/sol1.py
================================================
"""
Problem 16: https://projecteuler.net/problem=16

2^15 = 32768 and the sum of its digits is 3 + 2 + 7 + 6 + 8 = 26.

What is the sum of the digits of the number 2^1000?
"""


def solution(power: int = 1000) -> int:
    """Returns the sum of the digits of the number 2^power.
    >>> solution(1000)
    1366
    >>> solution(50)
    76
    >>> solution(20)
    31
    >>> solution(15)
    26
    """
    num = 2**power
    string_num = str(num)
    list_num = list(string_num)
    sum_of_num = 0

    for i in list_num:
        sum_of_num += int(i)

    return sum_of_num


if __name__ == "__main__":
    power = int(input("Enter the power of 2: ").strip())
    print("2 ^ ", power, " = ", 2**power)
    result = solution(power)
    print("Sum of the digits is: ", result)


================================================
FILE: project_euler/problem_016/sol2.py
================================================
"""
Problem 16: https://projecteuler.net/problem=16

2^15 = 32768 and the sum of its digits is 3 + 2 + 7 + 6 + 8 = 26.

What is the sum of the digits of the number 2^1000?
"""


def solution(power: int = 1000) -> int:
    """Returns the sum of the digits of the number 2^power.

    >>> solution(1000)
    1366
    >>> solution(50)
    76
    >>> solution(20)
    31
    >>> solution(15)
    26
    """
    n = 2**power
    r = 0
    while n:
        r, n = r + n % 10, n // 10
    return r


if __name__ == "__main__":
    print(solution(int(str(input()).strip())))


================================================
FILE: project_euler/problem_017/__init__.py
================================================


================================================
FILE: project_euler/problem_017/sol1.py
================================================
"""
Number letter counts
Problem 17: https://projecteuler.net/problem=17

If the numbers 1 to 5 are written out in words: one, two, three, four, five,
then there are 3 + 3 + 5 + 4 + 4 = 19 letters used in total.

If all the numbers from 1 to 1000 (one thousand) inclusive were written out in
words, how many letters would be used?


NOTE: Do not count spaces or hyphens. For example, 342 (three hundred and
forty-two) contains 23 letters and 115 (one hundred and fifteen) contains 20
letters. The use of "and" when writing out numbers is in compliance withBritish
usage.
"""


def solution(n: int = 1000) -> int:
    """Returns the number of letters used to write all numbers from 1 to n.
    where n is lower or equals to 1000.
    >>> solution(1000)
    21124
    >>> solution(5)
    19
    """
    # number of letters in zero, one, two, ..., nineteen (0 for zero since it's
    # never said aloud)
    ones_counts = [0, 3, 3, 5, 4, 4, 3, 5, 5, 4, 3, 6, 6, 8, 8, 7, 7, 9, 8, 8]
    # number of letters in twenty, thirty, ..., ninety (0 for numbers less than
    # 20 due to inconsistency in teens)
    tens_counts = [0, 0, 6, 6, 5, 5, 5, 7, 6, 6]

    count = 0

    for i in range(1, n + 1):
        if i < 1000:
            if i >= 100:
                # add number of letters for "n hundred"
                count += ones_counts[i // 100] + 7

                if i % 100 != 0:
                    # add number of letters for "and" if number is not multiple
                    # of 100
                    count += 3

            if 0 < i % 100 < 20:
                # add number of letters for one, two, three, ..., nineteen
                # (could be combined with below if not for inconsistency in
                # teens)
                count += ones_counts[i % 100]
            else:
                # add number of letters for twenty, twenty one, ..., ninety
                # nine
                count += ones_counts[i % 10]
                count += tens_counts[(i % 100 - i % 10) // 10]
        else:
            count += ones_counts[i // 1000] + 8
    return count


if __name__ == "__main__":
    print(solution(int(input().strip())))


================================================
FILE: project_euler/problem_018/__init__.py
================================================


================================================
FILE: project_euler/problem_018/solution.py
================================================
"""
By starting at the top of the triangle below and moving to adjacent numbers on
the row below, the maximum total from top to bottom is 23.

3
7 4
2 4 6
8 5 9 3

That is, 3 + 7 + 4 + 9 = 23.

Find the maximum total from top to bottom of the triangle below:

75
95 64
17 47 82
18 35 87 10
20 04 82 47 65
19 01 23 75 03 34
88 02 77 73 07 63 67
99 65 04 28 06 16 70 92
41 41 26 56 83 40 80 70 33
41 48 72 33 47 32 37 16 94 29
53 71 44 65 25 43 91 52 97 51 14
70 11 33 28 77 73 17 78 39 68 17 57
91 71 52 38 17 14 91 43 58 50 27 29 48
63 66 04 68 89 53 67 30 73 16 69 87 40 31
04 62 98 27 23 09 70 98 73 93 38 53 60 04 23
"""

import os


def solution():
    """
    Finds the maximum total in a triangle as described by the problem statement
    above.

    >>> solution()
    1074
    """
    script_dir = os.path.dirname(os.path.realpath(__file__))
    triangle = os.path.join(script_dir, "triangle.txt")

    with open(triangle) as f:
        triangle = f.readlines()

    a = [[int(y) for y in x.rstrip("\r\n").split(" ")] for x in triangle]

    for i in range(1, len(a)):
        for j in range(len(a[i])):
            number1 = a[i - 1][j] if j != len(a[i - 1]) else 0
            number2 = a[i - 1][j - 1] if j > 0 else 0
            a[i][j] += max(number1, number2)
    return max(a[-1])


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_018/triangle.txt
================================================
75
95 64
17 47 82
18 35 87 10
20 04 82 47 65
19 01 23 75 03 34
88 02 77 73 07 63 67
99 65 04 28 06 16 70 92
41 41 26 56 83 40 80 70 33
41 48 72 33 47 32 37 16 94 29
53 71 44 65 25 43 91 52 97 51 14
70 11 33 28 77 73 17 78 39 68 17 57
91 71 52 38 17 14 91 43 58 50 27 29 48
63 66 04 68 89 53 67 30 73 16 69 87 40 31
04 62 98 27 23 09 70 98 73 93 38 53 60 04 23


================================================
FILE: project_euler/problem_019/__init__.py
================================================


================================================
FILE: project_euler/problem_019/sol1.py
================================================
"""
Counting Sundays
Problem 19

You are given the following information, but you may prefer to do some research
for yourself.

1 Jan 1900 was a Monday.
Thirty days has September,
April, June and November.
All the rest have thirty-one,
Saving February alone,
Which has twenty-eight, rain or shine.
And on leap years, twenty-nine.

A leap year occurs on any year evenly divisible by 4, but not on a century
unless it is divisible by 400.

How many Sundays fell on the first of the month during the twentieth century
(1 Jan 1901 to 31 Dec 2000)?
"""


def solution():
    """Returns the number of mondays that fall on the first of the month during
    the twentieth century (1 Jan 1901 to 31 Dec 2000)?

    >>> solution()
    171
    """
    days_per_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]

    day = 6
    month = 1
    year = 1901

    sundays = 0

    while year < 2001:
        day += 7

        if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
            if day > days_per_month[month - 1] and month != 2:
                month += 1
                day = day - days_per_month[month - 2]
            elif day > 29 and month == 2:
                month += 1
                day = day - 29
        elif day > days_per_month[month - 1]:
            month += 1
            day = day - days_per_month[month - 2]

        if month > 12:
            year += 1
            month = 1

        if year < 2001 and day == 1:
            sundays += 1
    return sundays


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_020/__init__.py
================================================


================================================
FILE: project_euler/problem_020/sol1.py
================================================
"""
Problem 20: https://projecteuler.net/problem=20

n! means n x (n - 1) x ... x 3 x 2 x 1

For example, 10! = 10 x 9 x ... x 3 x 2 x 1 = 3628800,
and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27.

Find the sum of the digits in the number 100!
"""


def factorial(num: int) -> int:
    """Find the factorial of a given number n"""
    fact = 1
    for i in range(1, num + 1):
        fact *= i
    return fact


def split_and_add(number: int) -> int:
    """Split number digits and add them."""
    sum_of_digits = 0
    while number > 0:
        last_digit = number % 10
        sum_of_digits += last_digit
        number = number // 10  # Removing the last_digit from the given number
    return sum_of_digits


def solution(num: int = 100) -> int:
    """Returns the sum of the digits in the factorial of num
    >>> solution(100)
    648
    >>> solution(50)
    216
    >>> solution(10)
    27
    >>> solution(5)
    3
    >>> solution(3)
    6
    >>> solution(2)
    2
    >>> solution(1)
    1
    """
    nfact = factorial(num)
    result = split_and_add(nfact)
    return result


if __name__ == "__main__":
    print(solution(int(input("Enter the Number: ").strip())))


================================================
FILE: project_euler/problem_020/sol2.py
================================================
"""
Problem 20: https://projecteuler.net/problem=20

n! means n x (n - 1) x ... x 3 x 2 x 1

For example, 10! = 10 x 9 x ... x 3 x 2 x 1 = 3628800,
and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27.

Find the sum of the digits in the number 100!
"""

from math import factorial


def solution(num: int = 100) -> int:
    """Returns the sum of the digits in the factorial of num
    >>> solution(100)
    648
    >>> solution(50)
    216
    >>> solution(10)
    27
    >>> solution(5)
    3
    >>> solution(3)
    6
    >>> solution(2)
    2
    >>> solution(1)
    1
    """
    return sum(int(x) for x in str(factorial(num)))


if __name__ == "__main__":
    print(solution(int(input("Enter the Number: ").strip())))


================================================
FILE: project_euler/problem_020/sol3.py
================================================
"""
Problem 20: https://projecteuler.net/problem=20

n! means n x (n - 1) x ... x 3 x 2 x 1

For example, 10! = 10 x 9 x ... x 3 x 2 x 1 = 3628800,
and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27.

Find the sum of the digits in the number 100!
"""

from math import factorial


def solution(num: int = 100) -> int:
    """Returns the sum of the digits in the factorial of num
    >>> solution(1000)
    10539
    >>> solution(200)
    1404
    >>> solution(100)
    648
    >>> solution(50)
    216
    >>> solution(10)
    27
    >>> solution(5)
    3
    >>> solution(3)
    6
    >>> solution(2)
    2
    >>> solution(1)
    1
    >>> solution(0)
    1
    """
    return sum(map(int, str(factorial(num))))


if __name__ == "__main__":
    print(solution(int(input("Enter the Number: ").strip())))


================================================
FILE: project_euler/problem_020/sol4.py
================================================
"""
Problem 20: https://projecteuler.net/problem=20

n! means n x (n - 1) x ... x 3 x 2 x 1

For example, 10! = 10 x 9 x ... x 3 x 2 x 1 = 3628800,
and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27.

Find the sum of the digits in the number 100!
"""


def solution(num: int = 100) -> int:
    """Returns the sum of the digits in the factorial of num
    >>> solution(100)
    648
    >>> solution(50)
    216
    >>> solution(10)
    27
    >>> solution(5)
    3
    >>> solution(3)
    6
    >>> solution(2)
    2
    >>> solution(1)
    1
    """
    fact = 1
    result = 0
    for i in range(1, num + 1):
        fact *= i

    for j in str(fact):
        result += int(j)

    return result


if __name__ == "__main__":
    print(solution(int(input("Enter the Number: ").strip())))


================================================
FILE: project_euler/problem_021/__init__.py
================================================


================================================
FILE: project_euler/problem_021/sol1.py
================================================
"""
Amicable Numbers
Problem 21

Let d(n) be defined as the sum of proper divisors of n (numbers less than n
which divide evenly into n).
If d(a) = b and d(b) = a, where a ≠ b, then a and b are an amicable pair and
each of a and b are called amicable numbers.

For example, the proper divisors of 220 are 1, 2, 4, 5, 10, 11, 20, 22, 44, 55
and 110; therefore d(220) = 284. The proper divisors of 284 are 1, 2, 4, 71 and
142; so d(284) = 220.

Evaluate the sum of all the amicable numbers under 10000.
"""

from math import sqrt


def sum_of_divisors(n: int) -> int:
    total = 0
    for i in range(1, int(sqrt(n) + 1)):
        if n % i == 0 and i != sqrt(n):
            total += i + n // i
        elif i == sqrt(n):
            total += i
    return total - n


def solution(n: int = 10000) -> int:
    """Returns the sum of all the amicable numbers under n.

    >>> solution(10000)
    31626
    >>> solution(5000)
    8442
    >>> solution(1000)
    504
    >>> solution(100)
    0
    >>> solution(50)
    0
    """
    total = sum(
        i
        for i in range(1, n)
        if sum_of_divisors(sum_of_divisors(i)) == i and sum_of_divisors(i) != i
    )
    return total


if __name__ == "__main__":
    print(solution(int(str(input()).strip())))


================================================
FILE: project_euler/problem_022/__init__.py
================================================


================================================
FILE: project_euler/problem_022/p022_names.txt
================================================
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================================================
FILE: project_euler/problem_022/sol1.py
================================================
"""
Name scores
Problem 22

Using names.txt (right click and 'Save Link/Target As...'), a 46K text file
containing over five-thousand first names, begin by sorting it into
alphabetical order. Then working out the alphabetical value for each name,
multiply this value by its alphabetical position in the list to obtain a name
score.

For example, when the list is sorted into alphabetical order, COLIN, which is
worth 3 + 15 + 12 + 9 + 14 = 53, is the 938th name in the list. So, COLIN would
obtain a score of 938 x 53 = 49714.

What is the total of all the name scores in the file?
"""

import os


def solution():
    """Returns the total of all the name scores in the file.

    >>> solution()
    871198282
    """
    with open(os.path.dirname(__file__) + "/p022_names.txt") as file:
        names = str(file.readlines()[0])
        names = names.replace('"', "").split(",")

    names.sort()

    name_score = 0
    total_score = 0

    for i, name in enumerate(names):
        for letter in name:
            name_score += ord(letter) - 64

        total_score += (i + 1) * name_score
        name_score = 0
    return total_score


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_022/sol2.py
================================================
"""
Name scores
Problem 22

Using names.txt (right click and 'Save Link/Target As...'), a 46K text file
containing over five-thousand first names, begin by sorting it into
alphabetical order. Then working out the alphabetical value for each name,
multiply this value by its alphabetical position in the list to obtain a name
score.

For example, when the list is sorted into alphabetical order, COLIN, which is
worth 3 + 15 + 12 + 9 + 14 = 53, is the 938th name in the list. So, COLIN would
obtain a score of 938 x 53 = 49714.

What is the total of all the name scores in the file?
"""

import os


def solution():
    """Returns the total of all the name scores in the file.

    >>> solution()
    871198282
    """
    total_sum = 0
    temp_sum = 0
    with open(os.path.dirname(__file__) + "/p022_names.txt") as file:
        name = str(file.readlines()[0])
        name = name.replace('"', "").split(",")

    name.sort()
    for i in range(len(name)):
        for j in name[i]:
            temp_sum += ord(j) - ord("A") + 1
        total_sum += (i + 1) * temp_sum
        temp_sum = 0
    return total_sum


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_023/__init__.py
================================================


================================================
FILE: project_euler/problem_023/sol1.py
================================================
"""
A perfect number is a number for which the sum of its proper divisors is exactly
equal to the number. For example, the sum of the proper divisors of 28 would be
1 + 2 + 4 + 7 + 14 = 28, which means that 28 is a perfect number.

A number n is called deficient if the sum of its proper divisors is less than n
and it is called abundant if this sum exceeds n.

As 12 is the smallest abundant number, 1 + 2 + 3 + 4 + 6 = 16, the smallest
number that can be written as the sum of two abundant numbers is 24. By
mathematical analysis, it can be shown that all integers greater than 28123
can be written as the sum of two abundant numbers. However, this upper limit
cannot be reduced any further by analysis even though it is known that the
greatest number that cannot be expressed as the sum of two abundant numbers
is less than this limit.

Find the sum of all the positive integers which cannot be written as the sum
of two abundant numbers.
"""


def solution(limit=28123):
    """
    Finds the sum of all the positive integers which cannot be written as
    the sum of two abundant numbers
    as described by the statement above.

    >>> solution()
    4179871
    """
    sum_divs = [1] * (limit + 1)

    for i in range(2, int(limit**0.5) + 1):
        sum_divs[i * i] += i
        for k in range(i + 1, limit // i + 1):
            sum_divs[k * i] += k + i

    abundants = set()
    res = 0

    for n in range(1, limit + 1):
        if sum_divs[n] > n:
            abundants.add(n)

        if not any((n - a in abundants) for a in abundants):
            res += n

    return res


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_024/__init__.py
================================================


================================================
FILE: project_euler/problem_024/sol1.py
================================================
"""
A permutation is an ordered arrangement of objects. For example, 3124 is one
possible permutation of the digits 1, 2, 3 and 4. If all of the permutations
are listed numerically or alphabetically, we call it lexicographic order. The
lexicographic permutations of 0, 1 and 2 are:

    012   021   102   120   201   210

What is the millionth lexicographic permutation of the digits 0, 1, 2, 3, 4, 5,
6, 7, 8 and 9?
"""

from itertools import permutations


def solution():
    """Returns the millionth lexicographic permutation of the digits 0, 1, 2,
    3, 4, 5, 6, 7, 8 and 9.

    >>> solution()
    '2783915460'
    """
    result = list(map("".join, permutations("0123456789")))
    return result[999999]


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_025/__init__.py
================================================


================================================
FILE: project_euler/problem_025/sol1.py
================================================
"""
The Fibonacci sequence is defined by the recurrence relation:

    Fn = Fn-1 + Fn-2, where F1 = 1 and F2 = 1.

Hence the first 12 terms will be:

    F1 = 1
    F2 = 1
    F3 = 2
    F4 = 3
    F5 = 5
    F6 = 8
    F7 = 13
    F8 = 21
    F9 = 34
    F10 = 55
    F11 = 89
    F12 = 144

The 12th term, F12, is the first term to contain three digits.

What is the index of the first term in the Fibonacci sequence to contain 1000
digits?
"""


def fibonacci(n: int) -> int:
    """
    Computes the Fibonacci number for input n by iterating through n numbers
    and creating an array of ints using the Fibonacci formula.
    Returns the nth element of the array.

    >>> fibonacci(2)
    1
    >>> fibonacci(3)
    2
    >>> fibonacci(5)
    5
    >>> fibonacci(10)
    55
    >>> fibonacci(12)
    144

    """
    if n == 1 or not isinstance(n, int):
        return 0
    elif n == 2:
        return 1
    else:
        sequence = [0, 1]
        for i in range(2, n + 1):
            sequence.append(sequence[i - 1] + sequence[i - 2])

        return sequence[n]


def fibonacci_digits_index(n: int) -> int:
    """
    Computes incrementing Fibonacci numbers starting from 3 until the length
    of the resulting Fibonacci result is the input value n. Returns the term
    of the Fibonacci sequence where this occurs.

    >>> fibonacci_digits_index(1000)
    4782
    >>> fibonacci_digits_index(100)
    476
    >>> fibonacci_digits_index(50)
    237
    >>> fibonacci_digits_index(3)
    12
    """
    digits = 0
    index = 2

    while digits < n:
        index += 1
        digits = len(str(fibonacci(index)))

    return index


def solution(n: int = 1000) -> int:
    """
    Returns the index of the first term in the Fibonacci sequence to contain
    n digits.

    >>> solution(1000)
    4782
    >>> solution(100)
    476
    >>> solution(50)
    237
    >>> solution(3)
    12
    """
    return fibonacci_digits_index(n)


if __name__ == "__main__":
    print(solution(int(str(input()).strip())))


================================================
FILE: project_euler/problem_025/sol2.py
================================================
"""
The Fibonacci sequence is defined by the recurrence relation:

    Fn = Fn-1 + Fn-2, where F1 = 1 and F2 = 1.

Hence the first 12 terms will be:

    F1 = 1
    F2 = 1
    F3 = 2
    F4 = 3
    F5 = 5
    F6 = 8
    F7 = 13
    F8 = 21
    F9 = 34
    F10 = 55
    F11 = 89
    F12 = 144

The 12th term, F12, is the first term to contain three digits.

What is the index of the first term in the Fibonacci sequence to contain 1000
digits?
"""

from collections.abc import Generator


def fibonacci_generator() -> Generator[int]:
    """
    A generator that produces numbers in the Fibonacci sequence

    >>> generator = fibonacci_generator()
    >>> next(generator)
    1
    >>> next(generator)
    2
    >>> next(generator)
    3
    >>> next(generator)
    5
    >>> next(generator)
    8
    """
    a, b = 0, 1
    while True:
        a, b = b, a + b
        yield b


def solution(n: int = 1000) -> int:
    """Returns the index of the first term in the Fibonacci sequence to contain
    n digits.

    >>> solution(1000)
    4782
    >>> solution(100)
    476
    >>> solution(50)
    237
    >>> solution(3)
    12
    """
    answer = 1
    gen = fibonacci_generator()
    while len(str(next(gen))) < n:
        answer += 1
    return answer + 1


if __name__ == "__main__":
    print(solution(int(str(input()).strip())))


================================================
FILE: project_euler/problem_025/sol3.py
================================================
"""
The Fibonacci sequence is defined by the recurrence relation:

    Fn = Fn-1 + Fn-2, where F1 = 1 and F2 = 1.

Hence the first 12 terms will be:

    F1 = 1
    F2 = 1
    F3 = 2
    F4 = 3
    F5 = 5
    F6 = 8
    F7 = 13
    F8 = 21
    F9 = 34
    F10 = 55
    F11 = 89
    F12 = 144

The 12th term, F12, is the first term to contain three digits.

What is the index of the first term in the Fibonacci sequence to contain 1000
digits?
"""


def solution(n: int = 1000) -> int:
    """Returns the index of the first term in the Fibonacci sequence to contain
    n digits.

    >>> solution(1000)
    4782
    >>> solution(100)
    476
    >>> solution(50)
    237
    >>> solution(3)
    12
    """
    f1, f2 = 1, 1
    index = 2
    while True:
        i = 0
        f = f1 + f2
        f1, f2 = f2, f
        index += 1
        for _ in str(f):
            i += 1
        if i == n:
            break
    return index


if __name__ == "__main__":
    print(solution(int(str(input()).strip())))


================================================
FILE: project_euler/problem_026/__init__.py
================================================


================================================
FILE: project_euler/problem_026/sol1.py
================================================
"""
Euler Problem 26
https://projecteuler.net/problem=26

Problem Statement:

A unit fraction contains 1 in the numerator. The decimal representation of the
unit fractions with denominators 2 to 10 are given:

1/2	= 	0.5
1/3	= 	0.(3)
1/4	= 	0.25
1/5	= 	0.2
1/6	= 	0.1(6)
1/7	= 	0.(142857)
1/8	= 	0.125
1/9	= 	0.(1)
1/10	= 	0.1
Where 0.1(6) means 0.166666..., and has a 1-digit recurring cycle. It can be
seen that 1/7 has a 6-digit recurring cycle.

Find the value of d < 1000 for which 1/d contains the longest recurring cycle
in its decimal fraction part.
"""


def solution(numerator: int = 1, digit: int = 1000) -> int:
    """
    Considering any range can be provided,
    because as per the problem, the digit d < 1000
    >>> solution(1, 10)
    7
    >>> solution(10, 100)
    97
    >>> solution(10, 1000)
    983
    """
    the_digit = 1
    longest_list_length = 0

    for divide_by_number in range(numerator, digit + 1):
        has_been_divided: list[int] = []
        now_divide = numerator
        for _ in range(1, digit + 1):
            if now_divide in has_been_divided:
                if longest_list_length < len(has_been_divided):
                    longest_list_length = len(has_been_divided)
                    the_digit = divide_by_number
            else:
                has_been_divided.append(now_divide)
                now_divide = now_divide * 10 % divide_by_number

    return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: project_euler/problem_027/__init__.py
================================================


================================================
FILE: project_euler/problem_027/sol1.py
================================================
"""
Project Euler Problem 27
https://projecteuler.net/problem=27

Problem Statement:

Euler discovered the remarkable quadratic formula:
n2 + n + 41
It turns out that the formula will produce 40 primes for the consecutive values
n = 0 to 39. However, when n = 40, 402 + 40 + 41 = 40(40 + 1) + 41 is divisible
by 41, and certainly when n = 41, 412 + 41 + 41 is clearly divisible by 41.
The incredible formula  n2 - 79n + 1601 was discovered, which produces 80 primes
for the consecutive values n = 0 to 79. The product of the coefficients, -79 and
1601, is -126479.
Considering quadratics of the form:
n² + an + b, where |a| &lt; 1000 and |b| &lt; 1000
where |n| is the modulus/absolute value of ne.g. |11| = 11 and |-4| = 4
Find the product of the coefficients, a and b, for the quadratic expression that
produces the maximum number of primes for consecutive values of n, starting with
n = 0.
"""

import math


def is_prime(number: int) -> bool:
    """Checks to see if a number is a prime in O(sqrt(n)).
    A number is prime if it has exactly two factors: 1 and itself.
    Returns boolean representing primality of given number num (i.e., if the
    result is true, then the number is indeed prime else it is not).

    >>> is_prime(2)
    True
    >>> is_prime(3)
    True
    >>> is_prime(27)
    False
    >>> is_prime(2999)
    True
    >>> is_prime(0)
    False
    >>> is_prime(1)
    False
    >>> is_prime(-10)
    False
    """

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5, int(math.sqrt(number) + 1), 6):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


def solution(a_limit: int = 1000, b_limit: int = 1000) -> int:
    """
    >>> solution(1000, 1000)
    -59231
    >>> solution(200, 1000)
    -59231
    >>> solution(200, 200)
    -4925
    >>> solution(-1000, 1000)
    0
    >>> solution(-1000, -1000)
    0
    """
    longest = [0, 0, 0]  # length, a, b
    for a in range((a_limit * -1) + 1, a_limit):
        for b in range(2, b_limit):
            if is_prime(b):
                count = 0
                n = 0
                while is_prime((n**2) + (a * n) + b):
                    count += 1
                    n += 1
                if count > longest[0]:
                    longest = [count, a, b]
    ans = longest[1] * longest[2]
    return ans


if __name__ == "__main__":
    print(solution(1000, 1000))


================================================
FILE: project_euler/problem_028/__init__.py
================================================


================================================
FILE: project_euler/problem_028/sol1.py
================================================
"""
Problem 28
Url: https://projecteuler.net/problem=28
Statement:
Starting with the number 1 and moving to the right in a clockwise direction a 5
by 5 spiral is formed as follows:

    21 22 23 24 25
    20  7  8  9 10
    19  6  1  2 11
    18  5  4  3 12
    17 16 15 14 13

It can be verified that the sum of the numbers on the diagonals is 101.

What is the sum of the numbers on the diagonals in a 1001 by 1001 spiral formed
in the same way?
"""

from math import ceil


def solution(n: int = 1001) -> int:
    """Returns the sum of the numbers on the diagonals in a n by n spiral
    formed in the same way.

    >>> solution(1001)
    669171001
    >>> solution(500)
    82959497
    >>> solution(100)
    651897
    >>> solution(50)
    79697
    >>> solution(10)
    537
    """
    total = 1

    for i in range(1, ceil(n / 2.0)):
        odd = 2 * i + 1
        even = 2 * i
        total = total + 4 * odd**2 - 6 * even

    return total


if __name__ == "__main__":
    import sys

    if len(sys.argv) == 1:
        print(solution())
    else:
        try:
            n = int(sys.argv[1])
            print(solution(n))
        except ValueError:
            print("Invalid entry - please enter a number")


================================================
FILE: project_euler/problem_029/__init__.py
================================================


================================================
FILE: project_euler/problem_029/sol1.py
================================================
"""
Consider all integer combinations of ab for 2 <= a <= 5 and 2 <= b <= 5:

2^2=4,  2^3=8,   2^4=16,  2^5=32
3^2=9,  3^3=27,  3^4=81,  3^5=243
4^2=16, 4^3=64,  4^4=256, 4^5=1024
5^2=25, 5^3=125, 5^4=625, 5^5=3125

If they are then placed in numerical order, with any repeats removed, we get
the following sequence of 15 distinct terms:

4, 8, 9, 16, 25, 27, 32, 64, 81, 125, 243, 256, 625, 1024, 3125

How many distinct terms are in the sequence generated by ab
for 2 <= a <= 100 and 2 <= b <= 100?
"""


def solution(n: int = 100) -> int:
    """Returns the number of distinct terms in the sequence generated by a^b
    for 2 <= a <= 100 and 2 <= b <= 100.

    >>> solution(100)
    9183
    >>> solution(50)
    2184
    >>> solution(20)
    324
    >>> solution(5)
    15
    >>> solution(2)
    1
    >>> solution(1)
    0
    """
    collect_powers = set()

    current_pow = 0

    n = n + 1  # maximum limit

    for a in range(2, n):
        for b in range(2, n):
            current_pow = a**b  # calculates the current power
            collect_powers.add(current_pow)  # adds the result to the set
    return len(collect_powers)


if __name__ == "__main__":
    print("Number of terms ", solution(int(str(input()).strip())))


================================================
FILE: project_euler/problem_030/__init__.py
================================================


================================================
FILE: project_euler/problem_030/sol1.py
================================================
"""Problem Statement (Digit Fifth Powers): https://projecteuler.net/problem=30

Surprisingly there are only three numbers that can be written as the sum of fourth
powers of their digits:

1634 = 1^4 + 6^4 + 3^4 + 4^4
8208 = 8^4 + 2^4 + 0^4 + 8^4
9474 = 9^4 + 4^4 + 7^4 + 4^4
As 1 = 1^4 is not a sum it is not included.

The sum of these numbers is 1634 + 8208 + 9474 = 19316.

Find the sum of all the numbers that can be written as the sum of fifth powers of their
digits.

9^5 = 59049
59049 * 7 = 413343 (which is only 6 digit number)
So, numbers greater than 999999 are rejected
and also 59049 * 3 = 177147 (which exceeds the criteria of number being 3 digit)
So, number > 999
and hence a number between 1000 and 1000000
"""

DIGITS_FIFTH_POWER = {str(digit): digit**5 for digit in range(10)}


def digits_fifth_powers_sum(number: int) -> int:
    """
    >>> digits_fifth_powers_sum(1234)
    1300
    """
    return sum(DIGITS_FIFTH_POWER[digit] for digit in str(number))


def solution() -> int:
    return sum(
        number
        for number in range(1000, 1000000)
        if number == digits_fifth_powers_sum(number)
    )


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_031/__init__.py
================================================


================================================
FILE: project_euler/problem_031/sol1.py
================================================
"""
Coin sums
Problem 31: https://projecteuler.net/problem=31

In England the currency is made up of pound, f, and pence, p, and there are
eight coins in general circulation:

1p, 2p, 5p, 10p, 20p, 50p, f1 (100p) and f2 (200p).
It is possible to make f2 in the following way:

1xf1 + 1x50p + 2x20p + 1x5p + 1x2p + 3x1p
How many different ways can f2 be made using any number of coins?
"""


def one_pence() -> int:
    return 1


def two_pence(x: int) -> int:
    return 0 if x < 0 else two_pence(x - 2) + one_pence()


def five_pence(x: int) -> int:
    return 0 if x < 0 else five_pence(x - 5) + two_pence(x)


def ten_pence(x: int) -> int:
    return 0 if x < 0 else ten_pence(x - 10) + five_pence(x)


def twenty_pence(x: int) -> int:
    return 0 if x < 0 else twenty_pence(x - 20) + ten_pence(x)


def fifty_pence(x: int) -> int:
    return 0 if x < 0 else fifty_pence(x - 50) + twenty_pence(x)


def one_pound(x: int) -> int:
    return 0 if x < 0 else one_pound(x - 100) + fifty_pence(x)


def two_pound(x: int) -> int:
    return 0 if x < 0 else two_pound(x - 200) + one_pound(x)


def solution(n: int = 200) -> int:
    """Returns the number of different ways can n pence be made using any number of
    coins?

    >>> solution(500)
    6295434
    >>> solution(200)
    73682
    >>> solution(50)
    451
    >>> solution(10)
    11
    """
    return two_pound(n)


if __name__ == "__main__":
    print(solution(int(input().strip())))


================================================
FILE: project_euler/problem_031/sol2.py
================================================
"""
Problem 31: https://projecteuler.net/problem=31

Coin sums

In England the currency is made up of pound, f, and pence, p, and there are
eight coins in general circulation:

1p, 2p, 5p, 10p, 20p, 50p, f1 (100p) and f2 (200p).
It is possible to make f2 in the following way:

1xf1 + 1x50p + 2x20p + 1x5p + 1x2p + 3x1p
How many different ways can f2 be made using any number of coins?

Hint:
    > There are 100 pence in a pound (f1 = 100p)
    > There are coins(in pence) are available: 1, 2, 5, 10, 20, 50, 100 and 200.
    > how many different ways you can combine these values to create 200 pence.

Example:
    to make 6p there are 5 ways
      1,1,1,1,1,1
      1,1,1,1,2
      1,1,2,2
      2,2,2
      1,5
    to make 5p there are 4 ways
      1,1,1,1,1
      1,1,1,2
      1,2,2
      5
"""


def solution(pence: int = 200) -> int:
    """Returns the number of different ways to make X pence using any number of coins.
    The solution is based on dynamic programming paradigm in a bottom-up fashion.

    >>> solution(500)
    6295434
    >>> solution(200)
    73682
    >>> solution(50)
    451
    >>> solution(10)
    11
    """
    coins = [1, 2, 5, 10, 20, 50, 100, 200]
    number_of_ways = [0] * (pence + 1)
    number_of_ways[0] = 1  # base case: 1 way to make 0 pence

    for coin in coins:
        for i in range(coin, pence + 1, 1):
            number_of_ways[i] += number_of_ways[i - coin]
    return number_of_ways[pence]


if __name__ == "__main__":
    assert solution(200) == 73682


================================================
FILE: project_euler/problem_032/__init__.py
================================================


================================================
FILE: project_euler/problem_032/sol32.py
================================================
"""
We shall say that an n-digit number is pandigital if it makes use of all the
digits 1 to n exactly once; for example, the 5-digit number, 15234, is 1 through
5 pandigital.

The product 7254 is unusual, as the identity, 39 x 186 = 7254, containing
multiplicand, multiplier, and product is 1 through 9 pandigital.

Find the sum of all products whose multiplicand/multiplier/product identity can
be written as a 1 through 9 pandigital.

HINT: Some products can be obtained in more than one way so be sure to only
include it once in your sum.
"""

import itertools


def is_combination_valid(combination):
    """
    Checks if a combination (a tuple of 9 digits)
    is a valid product equation.

    >>> is_combination_valid(('3', '9', '1', '8', '6', '7', '2', '5', '4'))
    True

    >>> is_combination_valid(('1', '2', '3', '4', '5', '6', '7', '8', '9'))
    False

    """
    return (
        int("".join(combination[0:2])) * int("".join(combination[2:5]))
        == int("".join(combination[5:9]))
    ) or (
        int("".join(combination[0])) * int("".join(combination[1:5]))
        == int("".join(combination[5:9]))
    )


def solution():
    """
    Finds the sum of all products whose multiplicand/multiplier/product identity
    can be written as a 1 through 9 pandigital

    >>> solution()
    45228
    """

    return sum(
        {
            int("".join(pandigital[5:9]))
            for pandigital in itertools.permutations("123456789")
            if is_combination_valid(pandigital)
        }
    )


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_033/__init__.py
================================================


================================================
FILE: project_euler/problem_033/sol1.py
================================================
"""
Problem 33: https://projecteuler.net/problem=33

The fraction 49/98 is a curious fraction, as an inexperienced
mathematician in attempting to simplify it may incorrectly believe
that 49/98 = 4/8, which is correct, is obtained by cancelling the 9s.

We shall consider fractions like, 30/50 = 3/5, to be trivial examples.

There are exactly four non-trivial examples of this type of fraction,
less than one in value, and containing two digits in the numerator
and denominator.

If the product of these four fractions is given in its lowest common
terms, find the value of the denominator.
"""

from __future__ import annotations

from fractions import Fraction


def is_digit_cancelling(num: int, den: int) -> bool:
    return (
        num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
    )


def fraction_list(digit_len: int) -> list[str]:
    """
    >>> fraction_list(2)
    ['16/64', '19/95', '26/65', '49/98']
    >>> fraction_list(3)
    ['16/64', '19/95', '26/65', '49/98']
    >>> fraction_list(4)
    ['16/64', '19/95', '26/65', '49/98']
    >>> fraction_list(0)
    []
    >>> fraction_list(5)
    ['16/64', '19/95', '26/65', '49/98']
    """
    solutions = []
    den = 11
    last_digit = int("1" + "0" * digit_len)
    for num in range(den, last_digit):
        while den <= 99:
            if (
                (num != den)
                and (num % 10 == den // 10)
                and (den % 10 != 0)
                and is_digit_cancelling(num, den)
            ):
                solutions.append(f"{num}/{den}")
            den += 1
        num += 1
        den = 10
    return solutions


def solution(n: int = 2) -> int:
    """
    Return the solution to the problem
    """
    result = 1.0
    for fraction in fraction_list(n):
        frac = Fraction(fraction)
        result *= frac.denominator / frac.numerator
    return int(result)


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_034/__init__.py
================================================


================================================
FILE: project_euler/problem_034/sol1.py
================================================
"""
Problem 34: https://projecteuler.net/problem=34

145 is a curious number, as 1! + 4! + 5! = 1 + 24 + 120 = 145.
Find the sum of all numbers which are equal to the sum of the factorial of their digits.
Note: As 1! = 1 and 2! = 2 are not sums they are not included.
"""

from math import factorial

DIGIT_FACTORIAL = {str(d): factorial(d) for d in range(10)}


def sum_of_digit_factorial(n: int) -> int:
    """
    Returns the sum of the factorial of digits in n
    >>> sum_of_digit_factorial(15)
    121
    >>> sum_of_digit_factorial(0)
    1
    """
    return sum(DIGIT_FACTORIAL[d] for d in str(n))


def solution() -> int:
    """
    Returns the sum of all numbers whose
    sum of the factorials of all digits
    add up to the number itself.
    >>> solution()
    40730
    """
    limit = 7 * factorial(9) + 1
    return sum(i for i in range(3, limit) if sum_of_digit_factorial(i) == i)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_035/__init__.py
================================================


================================================
FILE: project_euler/problem_035/sol1.py
================================================
"""
Project Euler Problem 35
https://projecteuler.net/problem=35

Problem Statement:

The number 197 is called a circular prime because all rotations of the digits:
197, 971, and 719, are themselves prime.
There are thirteen such primes below 100: 2, 3, 5, 7, 11, 13, 17, 31, 37, 71, 73,
79, and 97.
How many circular primes are there below one million?

To solve this problem in an efficient manner, we will first mark all the primes
below 1 million using the Sieve of Eratosthenes. Then, out of all these primes,
we will rule out the numbers which contain an even digit. After this we will
generate each circular combination of the number and check if all are prime.
"""

from __future__ import annotations

sieve = [True] * 1000001
i = 2
while i * i <= 1000000:
    if sieve[i]:
        for j in range(i * i, 1000001, i):
            sieve[j] = False
    i += 1


def is_prime(n: int) -> bool:
    """
    For 2 <= n <= 1000000, return True if n is prime.
    >>> is_prime(87)
    False
    >>> is_prime(23)
    True
    >>> is_prime(25363)
    False
    """
    return sieve[n]


def contains_an_even_digit(n: int) -> bool:
    """
    Return True if n contains an even digit.
    >>> contains_an_even_digit(0)
    True
    >>> contains_an_even_digit(975317933)
    False
    >>> contains_an_even_digit(-245679)
    True
    """
    return any(digit in "02468" for digit in str(n))


def find_circular_primes(limit: int = 1000000) -> list[int]:
    """
    Return circular primes below limit.
    >>> len(find_circular_primes(100))
    13
    >>> len(find_circular_primes(1000000))
    55
    """
    result = [2]  # result already includes the number 2.
    for num in range(3, limit + 1, 2):
        if is_prime(num) and not contains_an_even_digit(num):
            str_num = str(num)
            list_nums = [int(str_num[j:] + str_num[:j]) for j in range(len(str_num))]
            if all(is_prime(i) for i in list_nums):
                result.append(num)
    return result


def solution() -> int:
    """
    >>> solution()
    55
    """
    return len(find_circular_primes())


if __name__ == "__main__":
    print(f"{len(find_circular_primes()) = }")


================================================
FILE: project_euler/problem_036/__init__.py
================================================


================================================
FILE: project_euler/problem_036/sol1.py
================================================
"""
Project Euler Problem 36
https://projecteuler.net/problem=36

Problem Statement:

Double-base palindromes
Problem 36
The decimal number, 585 = 10010010012 (binary), is palindromic in both bases.

Find the sum of all numbers, less than one million, which are palindromic in
base 10 and base 2.

(Please note that the palindromic number, in either base, may not include
leading zeros.)
"""

from __future__ import annotations


def is_palindrome(n: int | str) -> bool:
    """
    Return true if the input n is a palindrome.
    Otherwise return false. n can be an integer or a string.

    >>> is_palindrome(909)
    True
    >>> is_palindrome(908)
    False
    >>> is_palindrome('10101')
    True
    >>> is_palindrome('10111')
    False
    """
    n = str(n)
    return n == n[::-1]


def solution(n: int = 1000000):
    """Return the sum of all numbers, less than n , which are palindromic in
    base 10 and base 2.

    >>> solution(1000000)
    872187
    >>> solution(500000)
    286602
    >>> solution(100000)
    286602
    >>> solution(1000)
    1772
    >>> solution(100)
    157
    >>> solution(10)
    25
    >>> solution(2)
    1
    >>> solution(1)
    0
    """
    total = 0

    for i in range(1, n):
        if is_palindrome(i) and is_palindrome(bin(i).split("b")[1]):
            total += i
    return total


if __name__ == "__main__":
    print(solution(int(str(input().strip()))))


================================================
FILE: project_euler/problem_037/__init__.py
================================================


================================================
FILE: project_euler/problem_037/sol1.py
================================================
"""
Truncatable primes
Problem 37: https://projecteuler.net/problem=37

The number 3797 has an interesting property. Being prime itself, it is possible
to continuously remove digits from left to right, and remain prime at each stage:
3797, 797, 97, and 7. Similarly we can work from right to left: 3797, 379, 37, and 3.

Find the sum of the only eleven primes that are both truncatable from left to right
and right to left.

NOTE: 2, 3, 5, and 7 are not considered to be truncatable primes.
"""

from __future__ import annotations

import math


def is_prime(number: int) -> bool:
    """Checks to see if a number is a prime in O(sqrt(n)).

    A number is prime if it has exactly two factors: 1 and itself.

    >>> is_prime(0)
    False
    >>> is_prime(1)
    False
    >>> is_prime(2)
    True
    >>> is_prime(3)
    True
    >>> is_prime(27)
    False
    >>> is_prime(87)
    False
    >>> is_prime(563)
    True
    >>> is_prime(2999)
    True
    >>> is_prime(67483)
    False
    """

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5, int(math.sqrt(number) + 1), 6):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


def list_truncated_nums(n: int) -> list[int]:
    """
    Returns a list of all left and right truncated numbers of n
    >>> list_truncated_nums(927628)
    [927628, 27628, 92762, 7628, 9276, 628, 927, 28, 92, 8, 9]
    >>> list_truncated_nums(467)
    [467, 67, 46, 7, 4]
    >>> list_truncated_nums(58)
    [58, 8, 5]
    """
    str_num = str(n)
    list_nums = [n]
    for i in range(1, len(str_num)):
        list_nums.append(int(str_num[i:]))
        list_nums.append(int(str_num[:-i]))
    return list_nums


def validate(n: int) -> bool:
    """
    To optimize the approach, we will rule out the numbers above 1000,
    whose first or last three digits are not prime
    >>> validate(74679)
    False
    >>> validate(235693)
    False
    >>> validate(3797)
    True
    """
    return not (
        len(str(n)) > 3
        and (not is_prime(int(str(n)[-3:])) or not is_prime(int(str(n)[:3])))
    )


def compute_truncated_primes(count: int = 11) -> list[int]:
    """
    Returns the list of truncated primes
    >>> compute_truncated_primes(11)
    [23, 37, 53, 73, 313, 317, 373, 797, 3137, 3797, 739397]
    """
    list_truncated_primes: list[int] = []
    num = 13
    while len(list_truncated_primes) != count:
        if validate(num):
            list_nums = list_truncated_nums(num)
            if all(is_prime(i) for i in list_nums):
                list_truncated_primes.append(num)
        num += 2
    return list_truncated_primes


def solution() -> int:
    """
    Returns the sum of truncated primes
    """
    return sum(compute_truncated_primes(11))


if __name__ == "__main__":
    print(f"{sum(compute_truncated_primes(11)) = }")


================================================
FILE: project_euler/problem_038/__init__.py
================================================


================================================
FILE: project_euler/problem_038/sol1.py
================================================
"""
Project Euler Problem 38: https://projecteuler.net/problem=38

Take the number 192 and multiply it by each of 1, 2, and 3:

192 x 1 = 192
192 x 2 = 384
192 x 3 = 576

By concatenating each product we get the 1 to 9 pandigital, 192384576. We will call
192384576 the concatenated product of 192 and (1,2,3)

The same can be achieved by starting with 9 and multiplying by 1, 2, 3, 4, and 5,
giving the pandigital, 918273645, which is the concatenated product of 9 and
(1,2,3,4,5).

What is the largest 1 to 9 pandigital 9-digit number that can be formed as the
concatenated product of an integer with (1,2, ... , n) where n > 1?

Solution:
Since n>1, the largest candidate for the solution will be a concactenation of
a 4-digit number and its double, a 5-digit number.
Let a be the 4-digit number.
a  has 4 digits  =>  1000 <=  a  < 10000
2a has 5 digits  => 10000 <= 2a  < 100000
=>  5000 <= a < 10000

The concatenation of a with 2a = a * 10^5 + 2a
so our candidate for a given a is 100002 * a.
We iterate through the search space 5000 <= a < 10000 in reverse order,
calculating the candidates for each a and checking if they are 1-9 pandigital.

In case there are no 4-digit numbers that satisfy this property, we check
the 3-digit numbers with a similar formula (the example a=192 gives a lower
bound on the length of a):
a has 3 digits, etc...
=>  100 <= a < 334, candidate = a * 10^6 + 2a * 10^3 + 3a
                              = 1002003 * a
"""

from __future__ import annotations


def is_9_pandigital(n: int) -> bool:
    """
    Checks whether n is a 9-digit 1 to 9 pandigital number.
    >>> is_9_pandigital(12345)
    False
    >>> is_9_pandigital(156284973)
    True
    >>> is_9_pandigital(1562849733)
    False
    """
    s = str(n)
    return len(s) == 9 and set(s) == set("123456789")


def solution() -> int | None:
    """
    Return the largest 1 to 9 pandigital 9-digital number that can be formed as the
    concatenated product of an integer with (1,2,...,n) where n > 1.
    """
    for base_num in range(9999, 4999, -1):
        candidate = 100002 * base_num
        if is_9_pandigital(candidate):
            return candidate

    for base_num in range(333, 99, -1):
        candidate = 1002003 * base_num
        if is_9_pandigital(candidate):
            return candidate

    return None


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_039/__init__.py
================================================


================================================
FILE: project_euler/problem_039/sol1.py
================================================
"""
Problem 39: https://projecteuler.net/problem=39

If p is the perimeter of a right angle triangle with integral length sides,
{a,b,c}, there are exactly three solutions for p = 120.
{20,48,52}, {24,45,51}, {30,40,50}

For which value of p ≤ 1000, is the number of solutions maximised?
"""

from __future__ import annotations

import typing
from collections import Counter


def pythagorean_triple(max_perimeter: int) -> typing.Counter[int]:
    """
    Returns a dictionary with keys as the perimeter of a right angled triangle
    and value as the number of corresponding triplets.
    >>> pythagorean_triple(15)
    Counter({12: 1})
    >>> pythagorean_triple(40)
    Counter({12: 1, 30: 1, 24: 1, 40: 1, 36: 1})
    >>> pythagorean_triple(50)
    Counter({12: 1, 30: 1, 24: 1, 40: 1, 36: 1, 48: 1})
    """
    triplets: typing.Counter[int] = Counter()
    for base in range(1, max_perimeter + 1):
        for perpendicular in range(base, max_perimeter + 1):
            hypotenuse = (base * base + perpendicular * perpendicular) ** 0.5
            if hypotenuse == int(hypotenuse):
                perimeter = int(base + perpendicular + hypotenuse)
                if perimeter > max_perimeter:
                    continue
                triplets[perimeter] += 1
    return triplets


def solution(n: int = 1000) -> int:
    """
    Returns perimeter with maximum solutions.
    >>> solution(100)
    90
    >>> solution(200)
    180
    >>> solution(1000)
    840
    """
    triplets = pythagorean_triple(n)
    return triplets.most_common(1)[0][0]


if __name__ == "__main__":
    print(f"Perimeter {solution()} has maximum solutions")


================================================
FILE: project_euler/problem_040/__init__.py
================================================


================================================
FILE: project_euler/problem_040/sol1.py
================================================
"""
Champernowne's constant
Problem 40
An irrational decimal fraction is created by concatenating the positive
integers:

0.123456789101112131415161718192021...

It can be seen that the 12th digit of the fractional part is 1.

If dn represents the nth digit of the fractional part, find the value of the
following expression.

d1 x d10 x d100 x d1000 x d10000 x d100000 x d1000000
"""


def solution():
    """Returns

    >>> solution()
    210
    """
    constant = []
    i = 1

    while len(constant) < 1e6:
        constant.append(str(i))
        i += 1

    constant = "".join(constant)

    return (
        int(constant[0])
        * int(constant[9])
        * int(constant[99])
        * int(constant[999])
        * int(constant[9999])
        * int(constant[99999])
        * int(constant[999999])
    )


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_041/__init__.py
================================================


================================================
FILE: project_euler/problem_041/sol1.py
================================================
"""
Pandigital prime
Problem 41: https://projecteuler.net/problem=41

We shall say that an n-digit number is pandigital if it makes use of all the digits
1 to n exactly once. For example, 2143 is a 4-digit pandigital and is also prime.
What is the largest n-digit pandigital prime that exists?

All pandigital numbers except for 1, 4 ,7 pandigital numbers are divisible by 3.
So we will check only 7 digit pandigital numbers to obtain the largest possible
pandigital prime.
"""

from __future__ import annotations

import math
from itertools import permutations


def is_prime(number: int) -> bool:
    """Checks to see if a number is a prime in O(sqrt(n)).

    A number is prime if it has exactly two factors: 1 and itself.

    >>> is_prime(0)
    False
    >>> is_prime(1)
    False
    >>> is_prime(2)
    True
    >>> is_prime(3)
    True
    >>> is_prime(27)
    False
    >>> is_prime(87)
    False
    >>> is_prime(563)
    True
    >>> is_prime(2999)
    True
    >>> is_prime(67483)
    False
    """

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5, int(math.sqrt(number) + 1), 6):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


def solution(n: int = 7) -> int:
    """
    Returns the maximum pandigital prime number of length n.
    If there are none, then it will return 0.
    >>> solution(2)
    0
    >>> solution(4)
    4231
    >>> solution(7)
    7652413
    """
    pandigital_str = "".join(str(i) for i in range(1, n + 1))
    perm_list = [int("".join(i)) for i in permutations(pandigital_str, n)]
    pandigitals = [num for num in perm_list if is_prime(num)]
    return max(pandigitals) if pandigitals else 0


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_042/__init__.py
================================================


================================================
FILE: project_euler/problem_042/solution42.py
================================================
"""
The nth term of the sequence of triangle numbers is given by, tn = ½n(n+1); so
the first ten triangle numbers are:

1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ...

By converting each letter in a word to a number corresponding to its
alphabetical position and adding these values we form a word value. For example,
the word value for SKY is 19 + 11 + 25 = 55 = t10. If the word value is a
triangle number then we shall call the word a triangle word.

Using words.txt (right click and 'Save Link/Target As...'), a 16K text file
containing nearly two-thousand common English words, how many are triangle
words?
"""

import os

# Precomputes a list of the 100 first triangular numbers
TRIANGULAR_NUMBERS = [int(0.5 * n * (n + 1)) for n in range(1, 101)]


def solution():
    """
    Finds the amount of triangular words in the words file.

    >>> solution()
    162
    """
    script_dir = os.path.dirname(os.path.realpath(__file__))
    words_file_path = os.path.join(script_dir, "words.txt")

    words = ""
    with open(words_file_path) as f:
        words = f.readline()

    words = [word.strip('"') for word in words.strip("\r\n").split(",")]
    words = [
        word
        for word in [sum(ord(x) - 64 for x in word) for word in words]
        if word in TRIANGULAR_NUMBERS
    ]
    return len(words)


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_042/words.txt
================================================
"A","ABILITY","ABLE","ABOUT","ABOVE","ABSENCE","ABSOLUTELY","ACADEMIC","ACCEPT","ACCESS","ACCIDENT","ACCOMPANY","ACCORDING","ACCOUNT","ACHIEVE","ACHIEVEMENT","ACID","ACQUIRE","ACROSS","ACT","ACTION","ACTIVE","ACTIVITY","ACTUAL","ACTUALLY","ADD","ADDITION","ADDITIONAL","ADDRESS","ADMINISTRATION","ADMIT","ADOPT","ADULT","ADVANCE","ADVANTAGE","ADVICE","ADVISE","AFFAIR","AFFECT","AFFORD","AFRAID","AFTER","AFTERNOON","AFTERWARDS","AGAIN","AGAINST","AGE","AGENCY","AGENT","AGO","AGREE","AGREEMENT","AHEAD","AID","AIM","AIR","AIRCRAFT","ALL","ALLOW","ALMOST","ALONE","ALONG","ALREADY","ALRIGHT","ALSO","ALTERNATIVE","ALTHOUGH","ALWAYS","AMONG","AMONGST","AMOUNT","AN","ANALYSIS","ANCIENT","AND","ANIMAL","ANNOUNCE","ANNUAL","ANOTHER","ANSWER","ANY","ANYBODY","ANYONE","ANYTHING","ANYWAY","APART","APPARENT","APPARENTLY","APPEAL","APPEAR","APPEARANCE","APPLICATION","APPLY","APPOINT","APPOINTMENT","APPROACH","APPROPRIATE","APPROVE","AREA","ARGUE","ARGUMENT","ARISE","ARM","ARMY","AROUND","ARRANGE","ARRANGEMENT","ARRIVE","ART","ARTICLE","ARTIST","AS","ASK","ASPECT","ASSEMBLY","ASSESS","ASSESSMENT","ASSET","ASSOCIATE","ASSOCIATION","ASSUME","ASSUMPTION","AT","ATMOSPHERE","ATTACH","ATTACK","ATTEMPT","ATTEND","ATTENTION","ATTITUDE","ATTRACT","ATTRACTIVE","AUDIENCE","AUTHOR","AUTHORITY","AVAILABLE","AVERAGE","AVOID","AWARD","AWARE","AWAY","AYE","BABY","BACK","BACKGROUND","BAD","BAG","BALANCE","BALL","BAND","BANK","BAR","BASE","BASIC","BASIS","BATTLE","BE","BEAR","BEAT","BEAUTIFUL","BECAUSE","BECOME","BED","BEDROOM","BEFORE","BEGIN","BEGINNING","BEHAVIOUR","BEHIND","BELIEF","BELIEVE","BELONG","BELOW","BENEATH","BENEFIT","BESIDE","BEST","BETTER","BETWEEN","BEYOND","BIG","BILL","BIND","BIRD","BIRTH","BIT","BLACK","BLOCK","BLOOD","BLOODY","BLOW","BLUE","BOARD","BOAT","BODY","BONE","BOOK","BORDER","BOTH","BOTTLE","BOTTOM","BOX","BOY","BRAIN","BRANCH","BREAK","BREATH","BRIDGE","BRIEF","BRIGHT","BRING","BROAD","BROTHER","BUDGET","BUILD","BUILDING","BURN","BUS","BUSINESS","BUSY","BUT","BUY","BY","CABINET","CALL","CAMPAIGN","CAN","CANDIDATE","CAPABLE","CAPACITY","CAPITAL","CAR","CARD","CARE","CAREER","CAREFUL","CAREFULLY","CARRY","CASE","CASH","CAT","CATCH","CATEGORY","CAUSE","CELL","CENTRAL","CENTRE","CENTURY","CERTAIN","CERTAINLY","CHAIN","CHAIR","CHAIRMAN","CHALLENGE","CHANCE","CHANGE","CHANNEL","CHAPTER","CHARACTER","CHARACTERISTIC","CHARGE","CHEAP","CHECK","CHEMICAL","CHIEF","CHILD","CHOICE","CHOOSE","CHURCH","CIRCLE","CIRCUMSTANCE","CITIZEN","CITY","CIVIL","CLAIM","CLASS","CLEAN","CLEAR","CLEARLY","CLIENT","CLIMB","CLOSE","CLOSELY","CLOTHES","CLUB","COAL","CODE","COFFEE","COLD","COLLEAGUE","COLLECT","COLLECTION","COLLEGE","COLOUR","COMBINATION","COMBINE","COME","COMMENT","COMMERCIAL","COMMISSION","COMMIT","COMMITMENT","COMMITTEE","COMMON","COMMUNICATION","COMMUNITY","COMPANY","COMPARE","COMPARISON","COMPETITION","COMPLETE","COMPLETELY","COMPLEX","COMPONENT","COMPUTER","CONCENTRATE","CONCENTRATION","CONCEPT","CONCERN","CONCERNED","CONCLUDE","CONCLUSION","CONDITION","CONDUCT","CONFERENCE","CONFIDENCE","CONFIRM","CONFLICT","CONGRESS","CONNECT","CONNECTION","CONSEQUENCE","CONSERVATIVE","CONSIDER","CONSIDERABLE","CONSIDERATION","CONSIST","CONSTANT","CONSTRUCTION","CONSUMER","CONTACT","CONTAIN","CONTENT","CONTEXT","CONTINUE","CONTRACT","CONTRAST","CONTRIBUTE","CONTRIBUTION","CONTROL","CONVENTION","CONVERSATION","COPY","CORNER","CORPORATE","CORRECT","COS","COST","COULD","COUNCIL","COUNT","COUNTRY","COUNTY","COUPLE","COURSE","COURT","COVER","CREATE","CREATION","CREDIT","CRIME","CRIMINAL","CRISIS","CRITERION","CRITICAL","CRITICISM","CROSS","CROWD","CRY","CULTURAL","CULTURE","CUP","CURRENT","CURRENTLY","CURRICULUM","CUSTOMER","CUT","DAMAGE","DANGER","DANGEROUS","DARK","DATA","DATE","DAUGHTER","DAY","DEAD","DEAL","DEATH","DEBATE","DEBT","DECADE","DECIDE","DECISION","DECLARE","DEEP","DEFENCE","DEFENDANT","DEFINE","DEFINITION","DEGREE","DELIVER","DEMAND","DEMOCRATIC","DEMONSTRATE","DENY","DEPARTMENT","DEPEND","DEPUTY","DERIVE","DESCRIBE","DESCRIPTION","DESIGN","DESIRE","DESK","DESPITE","DESTROY","DETAIL","DETAILED","DETERMINE","DEVELOP","DEVELOPMENT","DEVICE","DIE","DIFFERENCE","DIFFERENT","DIFFICULT","DIFFICULTY","DINNER","DIRECT","DIRECTION","DIRECTLY","DIRECTOR","DISAPPEAR","DISCIPLINE","DISCOVER","DISCUSS","DISCUSSION","DISEASE","DISPLAY","DISTANCE","DISTINCTION","DISTRIBUTION","DISTRICT","DIVIDE","DIVISION","DO","DOCTOR","DOCUMENT","DOG","DOMESTIC","DOOR","DOUBLE","DOUBT","DOWN","DRAW","DRAWING","DREAM","DRESS","DRINK","DRIVE","DRIVER","DROP","DRUG","DRY","DUE","DURING","DUTY","EACH","EAR","EARLY","EARN","EARTH","EASILY","EAST","EASY","EAT","ECONOMIC","ECONOMY","EDGE","EDITOR","EDUCATION","EDUCATIONAL","EFFECT","EFFECTIVE","EFFECTIVELY","EFFORT","EGG","EITHER","ELDERLY","ELECTION","ELEMENT","ELSE","ELSEWHERE","EMERGE","EMPHASIS","EMPLOY","EMPLOYEE","EMPLOYER","EMPLOYMENT","EMPTY","ENABLE","ENCOURAGE","END","ENEMY","ENERGY","ENGINE","ENGINEERING","ENJOY","ENOUGH","ENSURE","ENTER","ENTERPRISE","ENTIRE","ENTIRELY","ENTITLE","ENTRY","ENVIRONMEN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","TRADITION","TRADITIONAL","TRAFFIC","TRAIN","TRAINING","TRANSFER","TRANSPORT","TRAVEL","TREAT","TREATMENT","TREATY","TREE","TREND","TRIAL","TRIP","TROOP","TROUBLE","TRUE","TRUST","TRUTH","TRY","TURN","TWICE","TYPE","TYPICAL","UNABLE","UNDER","UNDERSTAND","UNDERSTANDING","UNDERTAKE","UNEMPLOYMENT","UNFORTUNATELY","UNION","UNIT","UNITED","UNIVERSITY","UNLESS","UNLIKELY","UNTIL","UP","UPON","UPPER","URBAN","US","USE","USED","USEFUL","USER","USUAL","USUALLY","VALUE","VARIATION","VARIETY","VARIOUS","VARY","VAST","VEHICLE","VERSION","VERY","VIA","VICTIM","VICTORY","VIDEO","VIEW","VILLAGE","VIOLENCE","VISION","VISIT","VISITOR","VITAL","VOICE","VOLUME","VOTE","WAGE","WAIT","WALK","WALL","WANT","WAR","WARM","WARN","WASH","WATCH","WATER","WAVE","WAY","WE","WEAK","WEAPON","WEAR","WEATHER","WEEK","WEEKEND","WEIGHT","WELCOME","WELFARE","WELL","WEST","WESTERN","WHAT","WHATEVER","WHEN","WHERE","WHEREAS","WHETHER","WHICH","WHILE","WHILST","WHITE","WHO","WHOLE","WHOM","WHOSE","WHY","WIDE","WIDELY","WIFE","WILD","WILL","WIN","WIND","WINDOW","WINE","WING","WINNER","WINTER","WISH","WITH","WITHDRAW","WITHIN","WITHOUT","WOMAN","WONDER","WONDERFUL","WOOD","WORD","WORK","WORKER","WORKING","WORKS","WORLD","WORRY","WORTH","WOULD","WRITE","WRITER","WRITING","WRONG","YARD","YEAH","YEAR","YES","YESTERDAY","YET","YOU","YOUNG","YOUR","YOURSELF","YOUTH"


================================================
FILE: project_euler/problem_043/__init__.py
================================================


================================================
FILE: project_euler/problem_043/sol1.py
================================================
"""
Problem 43: https://projecteuler.net/problem=43

The number, 1406357289, is a 0 to 9 pandigital number because it is made up of
each of the digits 0 to 9 in some order, but it also has a rather interesting
sub-string divisibility property.

Let d1 be the 1st digit, d2 be the 2nd digit, and so on. In this way, we note
the following:

d2d3d4=406 is divisible by 2
d3d4d5=063 is divisible by 3
d4d5d6=635 is divisible by 5
d5d6d7=357 is divisible by 7
d6d7d8=572 is divisible by 11
d7d8d9=728 is divisible by 13
d8d9d10=289 is divisible by 17
Find the sum of all 0 to 9 pandigital numbers with this property.
"""

from itertools import permutations


def is_substring_divisible(num: tuple) -> bool:
    """
    Returns True if the pandigital number passes
    all the divisibility tests.
    >>> is_substring_divisible((0, 1, 2, 4, 6, 5, 7, 3, 8, 9))
    False
    >>> is_substring_divisible((5, 1, 2, 4, 6, 0, 7, 8, 3, 9))
    False
    >>> is_substring_divisible((1, 4, 0, 6, 3, 5, 7, 2, 8, 9))
    True
    """
    if num[3] % 2 != 0:
        return False

    if (num[2] + num[3] + num[4]) % 3 != 0:
        return False

    if num[5] % 5 != 0:
        return False

    tests = [7, 11, 13, 17]
    for i, test in enumerate(tests):
        if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
            return False
    return True


def solution(n: int = 10) -> int:
    """
    Returns the sum of all pandigital numbers which pass the
    divisibility tests.
    >>> solution(10)
    16695334890
    """
    return sum(
        int("".join(map(str, num)))
        for num in permutations(range(n))
        if is_substring_divisible(num)
    )


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_044/__init__.py
================================================


================================================
FILE: project_euler/problem_044/sol1.py
================================================
"""
Problem 44: https://projecteuler.net/problem=44

Pentagonal numbers are generated by the formula, Pn=n(3n-1)/2. The first ten
pentagonal numbers are:
1, 5, 12, 22, 35, 51, 70, 92, 117, 145, ...
It can be seen that P4 + P7 = 22 + 70 = 92 = P8. However, their difference,
70 - 22 = 48, is not pentagonal.

Find the pair of pentagonal numbers, Pj and Pk, for which their sum and difference
are pentagonal and D = |Pk - Pj| is minimised; what is the value of D?
"""


def is_pentagonal(n: int) -> bool:
    """
    Returns True if n is pentagonal, False otherwise.
    >>> is_pentagonal(330)
    True
    >>> is_pentagonal(7683)
    False
    >>> is_pentagonal(2380)
    True
    """
    root = (1 + 24 * n) ** 0.5
    return ((1 + root) / 6) % 1 == 0


def solution(limit: int = 5000) -> int:
    """
    Returns the minimum difference of two pentagonal numbers P1 and P2 such that
    P1 + P2 is pentagonal and P2 - P1 is pentagonal.
    >>> solution(5000)
    5482660
    """
    pentagonal_nums = [(i * (3 * i - 1)) // 2 for i in range(1, limit)]
    for i, pentagonal_i in enumerate(pentagonal_nums):
        for j in range(i, len(pentagonal_nums)):
            pentagonal_j = pentagonal_nums[j]
            a = pentagonal_i + pentagonal_j
            b = pentagonal_j - pentagonal_i
            if is_pentagonal(a) and is_pentagonal(b):
                return b

    return -1


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_045/__init__.py
================================================


================================================
FILE: project_euler/problem_045/sol1.py
================================================
"""
Problem 45: https://projecteuler.net/problem=45

Triangle, pentagonal, and hexagonal numbers are generated by the following formulae:
Triangle	 	T(n) = (n * (n + 1)) / 2	 	1, 3, 6, 10, 15, ...
Pentagonal	 	P(n) = (n * (3 * n - 1)) / 2	 	1, 5, 12, 22, 35, ...
Hexagonal	 	H(n) = n * (2 * n - 1)	 	1, 6, 15, 28, 45, ...
It can be verified that T(285) = P(165) = H(143) = 40755.

Find the next triangle number that is also pentagonal and hexagonal.
All triangle numbers are hexagonal numbers.
T(2n-1) = n * (2 * n - 1) = H(n)
So we shall check only for hexagonal numbers which are also pentagonal.
"""


def hexagonal_num(n: int) -> int:
    """
    Returns nth hexagonal number
    >>> hexagonal_num(143)
    40755
    >>> hexagonal_num(21)
    861
    >>> hexagonal_num(10)
    190
    """
    return n * (2 * n - 1)


def is_pentagonal(n: int) -> bool:
    """
    Returns True if n is pentagonal, False otherwise.
    >>> is_pentagonal(330)
    True
    >>> is_pentagonal(7683)
    False
    >>> is_pentagonal(2380)
    True
    """
    root = (1 + 24 * n) ** 0.5
    return ((1 + root) / 6) % 1 == 0


def solution(start: int = 144) -> int:
    """
    Returns the next number which is triangular, pentagonal and hexagonal.
    >>> solution(144)
    1533776805
    """
    n = start
    num = hexagonal_num(n)
    while not is_pentagonal(num):
        n += 1
        num = hexagonal_num(n)
    return num


if __name__ == "__main__":
    print(f"{solution()} = ")


================================================
FILE: project_euler/problem_046/__init__.py
================================================


================================================
FILE: project_euler/problem_046/sol1.py
================================================
"""
Problem 46: https://projecteuler.net/problem=46

It was proposed by Christian Goldbach that every odd composite number can be
written as the sum of a prime and twice a square.

9 = 7 + 2 x 12
15 = 7 + 2 x 22
21 = 3 + 2 x 32
25 = 7 + 2 x 32
27 = 19 + 2 x 22
33 = 31 + 2 x 12

It turns out that the conjecture was false.

What is the smallest odd composite that cannot be written as the sum of a
prime and twice a square?
"""

from __future__ import annotations

import math


def is_prime(number: int) -> bool:
    """Checks to see if a number is a prime in O(sqrt(n)).

    A number is prime if it has exactly two factors: 1 and itself.

    >>> is_prime(0)
    False
    >>> is_prime(1)
    False
    >>> is_prime(2)
    True
    >>> is_prime(3)
    True
    >>> is_prime(27)
    False
    >>> is_prime(87)
    False
    >>> is_prime(563)
    True
    >>> is_prime(2999)
    True
    >>> is_prime(67483)
    False
    """

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5, int(math.sqrt(number) + 1), 6):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


odd_composites = [num for num in range(3, 100001, 2) if not is_prime(num)]


def compute_nums(n: int) -> list[int]:
    """
    Returns a list of first n odd composite numbers which do
    not follow the conjecture.
    >>> compute_nums(1)
    [5777]
    >>> compute_nums(2)
    [5777, 5993]
    >>> compute_nums(0)
    Traceback (most recent call last):
        ...
    ValueError: n must be >= 0
    >>> compute_nums("a")
    Traceback (most recent call last):
        ...
    ValueError: n must be an integer
    >>> compute_nums(1.1)
    Traceback (most recent call last):
        ...
    ValueError: n must be an integer

    """
    if not isinstance(n, int):
        raise ValueError("n must be an integer")
    if n <= 0:
        raise ValueError("n must be >= 0")

    list_nums = []
    for num in range(len(odd_composites)):
        i = 0
        while 2 * i * i <= odd_composites[num]:
            rem = odd_composites[num] - 2 * i * i
            if is_prime(rem):
                break
            i += 1
        else:
            list_nums.append(odd_composites[num])
            if len(list_nums) == n:
                return list_nums

    return []


def solution() -> int:
    """Return the solution to the problem"""
    return compute_nums(1)[0]


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_047/__init__.py
================================================


================================================
FILE: project_euler/problem_047/sol1.py
================================================
"""
Combinatoric selections

Problem 47

The first two consecutive numbers to have two distinct prime factors are:

14 = 2 x 7
15 = 3 x 5

The first three consecutive numbers to have three distinct prime factors are:

644 = 2² x 7 x 23
645 = 3 x 5 x 43
646 = 2 x 17 x 19.

Find the first four consecutive integers to have four distinct prime factors each.
What is the first of these numbers?
"""

from functools import lru_cache


def unique_prime_factors(n: int) -> set:
    """
    Find unique prime factors of an integer.
    Tests include sorting because only the set matters,
    not the order in which it is produced.
    >>> sorted(set(unique_prime_factors(14)))
    [2, 7]
    >>> sorted(set(unique_prime_factors(644)))
    [2, 7, 23]
    >>> sorted(set(unique_prime_factors(646)))
    [2, 17, 19]
    """
    i = 2
    factors = set()
    while i * i <= n:
        if n % i:
            i += 1
        else:
            n //= i
            factors.add(i)
    if n > 1:
        factors.add(n)
    return factors


@lru_cache
def upf_len(num: int) -> int:
    """
    Memoize upf() length results for a given value.
    >>> upf_len(14)
    2
    """
    return len(unique_prime_factors(num))


def equality(iterable: list) -> bool:
    """
    Check the equality of ALL elements in an iterable
    >>> equality([1, 2, 3, 4])
    False
    >>> equality([2, 2, 2, 2])
    True
    >>> equality([1, 2, 3, 2, 1])
    False
    """
    return len(set(iterable)) in (0, 1)


def run(n: int) -> list[int]:
    """
    Runs core process to find problem solution.
    >>> run(3)
    [644, 645, 646]
    """

    # Incrementor variable for our group list comprehension.
    # This is the first number in each list of values
    # to test.
    base = 2

    while True:
        # Increment each value of a generated range
        group = [base + i for i in range(n)]

        # Run elements through the unique_prime_factors function
        # Append our target number to the end.
        checker = [upf_len(x) for x in group]
        checker.append(n)

        # If all numbers in the list are equal, return the group variable.
        if equality(checker):
            return group

        # Increment our base variable by 1
        base += 1


def solution(n: int = 4) -> int | None:
    """Return the first value of the first four consecutive integers to have four
    distinct prime factors each.
    >>> solution()
    134043
    """
    results = run(n)
    return results[0] if len(results) else None


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_048/__init__.py
================================================


================================================
FILE: project_euler/problem_048/sol1.py
================================================
"""
Self Powers
Problem 48

The series, 1^1 + 2^2 + 3^3 + ... + 10^10 = 10405071317.

Find the last ten digits of the series, 1^1 + 2^2 + 3^3 + ... + 1000^1000.
"""


def solution():
    """
    Returns the last 10 digits of the series, 1^1 + 2^2 + 3^3 + ... + 1000^1000.

    >>> solution()
    '9110846700'
    """
    total = 0
    for i in range(1, 1001):
        total += i**i
    return str(total)[-10:]


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_049/__init__.py
================================================


================================================
FILE: project_euler/problem_049/sol1.py
================================================
"""
Prime permutations

Problem 49

The arithmetic sequence, 1487, 4817, 8147, in which each of
the terms increases by 3330, is unusual in two ways:
(i) each of the three terms are prime,
(ii) each of the 4-digit numbers are permutations of one another.

There are no arithmetic sequences made up of three 1-, 2-, or 3-digit primes,
exhibiting this property, but there is one other 4-digit increasing sequence.

What 12-digit number do you form by concatenating the three terms in this sequence?

Solution:

First, we need to generate all 4 digits prime numbers. Then greedy
all of them and use permutation to form new numbers. Use binary search
to check if the permutated numbers is in our prime list and include
them in a candidate list.

After that, bruteforce all passed candidates sequences using
3 nested loops since we know the answer will be 12 digits.
The bruteforce of this solution will be about 1 sec.
"""

import math
from itertools import permutations


def is_prime(number: int) -> bool:
    """Checks to see if a number is a prime in O(sqrt(n)).

    A number is prime if it has exactly two factors: 1 and itself.

    >>> is_prime(0)
    False
    >>> is_prime(1)
    False
    >>> is_prime(2)
    True
    >>> is_prime(3)
    True
    >>> is_prime(27)
    False
    >>> is_prime(87)
    False
    >>> is_prime(563)
    True
    >>> is_prime(2999)
    True
    >>> is_prime(67483)
    False
    """

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5, int(math.sqrt(number) + 1), 6):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


def search(target: int, prime_list: list) -> bool:
    """
    function to search a number in a list using Binary Search.
    >>> search(3, [1, 2, 3])
    True
    >>> search(4, [1, 2, 3])
    False
    >>> search(101, list(range(-100, 100)))
    False
    """

    left, right = 0, len(prime_list) - 1
    while left <= right:
        middle = (left + right) // 2
        if prime_list[middle] == target:
            return True
        elif prime_list[middle] < target:
            left = middle + 1
        else:
            right = middle - 1

    return False


def solution():
    """
    Return the solution of the problem.
    >>> solution()
    296962999629
    """
    prime_list = [n for n in range(1001, 10000, 2) if is_prime(n)]
    candidates = []

    for number in prime_list:
        tmp_numbers = []

        for prime_member in permutations(list(str(number))):
            prime = int("".join(prime_member))

            if prime % 2 == 0:
                continue

            if search(prime, prime_list):
                tmp_numbers.append(prime)

        tmp_numbers.sort()
        if len(tmp_numbers) >= 3:
            candidates.append(tmp_numbers)

    passed = []
    for candidate in candidates:
        length = len(candidate)
        found = False

        for i in range(length):
            for j in range(i + 1, length):
                for k in range(j + 1, length):
                    if (
                        abs(candidate[i] - candidate[j])
                        == abs(candidate[j] - candidate[k])
                        and len({candidate[i], candidate[j], candidate[k]}) == 3
                    ):
                        passed.append(
                            sorted([candidate[i], candidate[j], candidate[k]])
                        )
                        found = True

                    if found:
                        break
                if found:
                    break
            if found:
                break

    answer = set()
    for seq in passed:
        answer.add("".join([str(i) for i in seq]))

    return max(int(x) for x in answer)


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_050/__init__.py
================================================


================================================
FILE: project_euler/problem_050/sol1.py
================================================
"""
Project Euler Problem 50: https://projecteuler.net/problem=50

Consecutive prime sum

The prime 41, can be written as the sum of six consecutive primes:
41 = 2 + 3 + 5 + 7 + 11 + 13

This is the longest sum of consecutive primes that adds to a prime below
one-hundred.

The longest sum of consecutive primes below one-thousand that adds to a prime,
contains 21 terms, and is equal to 953.

Which prime, below one-million, can be written as the sum of the most
consecutive primes?
"""

from __future__ import annotations


def prime_sieve(limit: int) -> list[int]:
    """
    Sieve of Erotosthenes
    Function to return all the prime numbers up to a number 'limit'
    https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes

    >>> prime_sieve(3)
    [2]

    >>> prime_sieve(50)
    [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
    """
    is_prime = [True] * limit
    is_prime[0] = False
    is_prime[1] = False
    is_prime[2] = True

    for i in range(3, int(limit**0.5 + 1), 2):
        index = i * 2
        while index < limit:
            is_prime[index] = False
            index = index + i

    primes = [2]

    for i in range(3, limit, 2):
        if is_prime[i]:
            primes.append(i)

    return primes


def solution(ceiling: int = 1_000_000) -> int:
    """
    Returns the biggest prime, below the celing, that can be written as the sum
    of consecutive the most consecutive primes.

    >>> solution(500)
    499

    >>> solution(1_000)
    953

    >>> solution(10_000)
    9521
    """
    primes = prime_sieve(ceiling)
    length = 0
    largest = 0

    for i in range(len(primes)):
        for j in range(i + length, len(primes)):
            sol = sum(primes[i:j])
            if sol >= ceiling:
                break

            if sol in primes:
                length = j - i
                largest = sol

    return largest


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_051/__init__.py
================================================


================================================
FILE: project_euler/problem_051/sol1.py
================================================
"""
https://projecteuler.net/problem=51
Prime digit replacements
Problem 51

By replacing the 1st digit of the 2-digit number *3, it turns out that six of
the nine possible values: 13, 23, 43, 53, 73, and 83, are all prime.

By replacing the 3rd and 4th digits of 56**3 with the same digit, this 5-digit
number is the first example having seven primes among the ten generated numbers,
yielding the family: 56003, 56113, 56333, 56443, 56663, 56773, and 56993.
Consequently 56003, being the first member of this family, is the smallest prime
with this property.

Find the smallest prime which, by replacing part of the number (not necessarily
adjacent digits) with the same digit, is part of an eight prime value family.
"""

from __future__ import annotations

from collections import Counter


def prime_sieve(n: int) -> list[int]:
    """
    Sieve of Erotosthenes
    Function to return all the prime numbers up to a certain number
    https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes

    >>> prime_sieve(3)
    [2]

    >>> prime_sieve(50)
    [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
    """
    is_prime = [True] * n
    is_prime[0] = False
    is_prime[1] = False
    is_prime[2] = True

    for i in range(3, int(n**0.5 + 1), 2):
        index = i * 2
        while index < n:
            is_prime[index] = False
            index = index + i

    primes = [2]

    for i in range(3, n, 2):
        if is_prime[i]:
            primes.append(i)

    return primes


def digit_replacements(number: int) -> list[list[int]]:
    """
    Returns all the possible families of digit replacements in a number which
    contains at least one repeating digit

    >>> digit_replacements(544)
    [[500, 511, 522, 533, 544, 555, 566, 577, 588, 599]]

    >>> digit_replacements(3112)
    [[3002, 3112, 3222, 3332, 3442, 3552, 3662, 3772, 3882, 3992]]
    """
    number_str = str(number)
    replacements = []
    digits = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]

    for duplicate in Counter(number_str) - Counter(set(number_str)):
        family = [int(number_str.replace(duplicate, digit)) for digit in digits]
        replacements.append(family)

    return replacements


def solution(family_length: int = 8) -> int:
    """
    Returns the solution of the problem

    >>> solution(2)
    229399

    >>> solution(3)
    221311
    """
    numbers_checked = set()

    # Filter primes with less than 3 replaceable digits
    primes = {
        x for x in set(prime_sieve(1_000_000)) if len(str(x)) - len(set(str(x))) >= 3
    }

    for prime in primes:
        if prime in numbers_checked:
            continue

        replacements = digit_replacements(prime)

        for family in replacements:
            numbers_checked.update(family)
            primes_in_family = primes.intersection(family)

            if len(primes_in_family) != family_length:
                continue

            return min(primes_in_family)

    return -1


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_052/__init__.py
================================================


================================================
FILE: project_euler/problem_052/sol1.py
================================================
"""
Permuted multiples
Problem 52

It can be seen that the number, 125874, and its double, 251748, contain exactly
the same digits, but in a different order.

Find the smallest positive integer, x, such that 2x, 3x, 4x, 5x, and 6x,
contain the same digits.
"""


def solution():
    """Returns the smallest positive integer, x, such that 2x, 3x, 4x, 5x, and
    6x, contain the same digits.

    >>> solution()
    142857
    """
    i = 1

    while True:
        if (
            sorted(str(i))
            == sorted(str(2 * i))
            == sorted(str(3 * i))
            == sorted(str(4 * i))
            == sorted(str(5 * i))
            == sorted(str(6 * i))
        ):
            return i

        i += 1


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_053/__init__.py
================================================


================================================
FILE: project_euler/problem_053/sol1.py
================================================
"""
Combinatoric selections
Problem 53

There are exactly ten ways of selecting three from five, 12345:

    123, 124, 125, 134, 135, 145, 234, 235, 245, and 345

In combinatorics, we use the notation, 5C3 = 10.

In general,

nCr = n!/(r!(n-r)!),where r ≤ n, n! = nx(n-1)x...x3x2x1, and 0! = 1.
It is not until n = 23, that a value exceeds one-million: 23C10 = 1144066.

How many, not necessarily distinct, values of nCr, for 1 ≤ n ≤ 100, are greater
than one-million?
"""

from math import factorial


def combinations(n, r):
    return factorial(n) / (factorial(r) * factorial(n - r))


def solution():
    """Returns the number of values of nCr, for 1 ≤ n ≤ 100, are greater than
    one-million

    >>> solution()
    4075
    """
    total = 0

    for i in range(1, 101):
        for j in range(1, i + 1):
            if combinations(i, j) > 1e6:
                total += 1
    return total


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_054/__init__.py
================================================


================================================
FILE: project_euler/problem_054/poker_hands.txt
================================================
8C TS KC 9H 4S 7D 2S 5D 3S AC
5C AD 5D AC 9C 7C 5H 8D TD KS
3H 7H 6S KC JS QH TD JC 2D 8S
TH 8H 5C QS TC 9H 4D JC KS JS
7C 5H KC QH JD AS KH 4C AD 4S
5H KS 9C 7D 9H 8D 3S 5D 5C AH
6H 4H 5C 3H 2H 3S QH 5S 6S AS
TD 8C 4H 7C TC KC 4C 3H 7S KS
7C 9C 6D KD 3H 4C QS QC AC KH
JC 6S 5H 2H 2D KD 9D 7C AS JS
AD QH TH 9D 8H TS 6D 3S AS AC
2H 4S 5C 5S TC KC JD 6C TS 3C
QD AS 6H JS 2C 3D 9H KC 4H 8S
KD 8S 9S 7C 2S 3S 6D 6S 4H KC
3C 8C 2D 7D 4D 9S 4S QH 4H JD
8C KC 7S TC 2D TS 8H QD AC 5C
3D KH QD 6C 6S AD AS 8H 2H QS
6S 8D 4C 8S 6C QH TC 6D 7D 9D
2S 8D 8C 4C TS 9S 9D 9C AC 3D
3C QS 2S 4H JH 3D 2D TD 8S 9H
5H QS 8S 6D 3C 8C JD AS 7H 7D
6H TD 9D AS JH 6C QC 9S KD JC
AH 8S QS 4D TH AC TS 3C 3D 5C
5S 4D JS 3D 8H 6C TS 3S AD 8C
6D 7C 5D 5H 3S 5C JC 2H 5S 3D
5H 6H 2S KS 3D 5D JD 7H JS 8H
KH 4H AS JS QS QC TC 6D 7C KS
3D QS TS 2H JS 4D AS 9S JC KD
QD 5H 4D 5D KH 7H 3D JS KD 4H
2C 9H 6H 5C 9D 6C JC 2D TH 9S
7D 6D AS QD JH 4D JS 7C QS 5C
3H KH QD AD 8C 8H 3S TH 9D 5S
AH 9S 4D 9D 8S 4H JS 3C TC 8D
2C KS 5H QD 3S TS 9H AH AD 8S
5C 7H 5D KD 9H 4D 3D 2D KS AD
KS KC 9S 6D 2C QH 9D 9H TS TC
9C 6H 5D QH 4D AD 6D QC JS KH
9S 3H 9D JD 5C 4D 9H AS TC QH
2C 6D JC 9C 3C AD 9S KH 9D 7D
KC 9C 7C JC JS KD 3H AS 3C 7D
QD KH QS 2C 3S 8S 8H 9H 9C JC
QH 8D 3C KC 4C 4H 6D AD 9H 9D
3S KS QS 7H KH 7D 5H 5D JD AD
2H 2C 6H TH TC 7D 8D 4H 8C AS
4S 2H AC QC 3S 6D TH 4D 4C KH
4D TC KS AS 7C 3C 6D 2D 9H 6C
8C TD 5D QS 2C 7H 4C 9C 3H 9H
5H JH TS 7S TD 6H AD QD 8H 8S
5S AD 9C 8C 7C 8D 5H 9D 8S 2S
4H KH KS 9S 2S KC 5S AD 4S 7D
QS 9C QD 6H JS 5D AC 8D 2S AS
KH AC JC 3S 9D 9S 3C 9C 5S JS
AD 3C 3D KS 3S 5C 9C 8C TS 4S
JH 8D 5D 6H KD QS QD 3D 6C KC
8S JD 6C 3S 8C TC QC 3C QH JS
KC JC 8H 2S 9H 9C JH 8S 8C 9S
8S 2H QH 4D QC 9D KC AS TH 3C
8S 6H TH 7C 2H 6S 3C 3H AS 7S
QH 5S JS 4H 5H TS 8H AH AC JC
9D 8H 2S 4S TC JC 3C 7H 3H 5C
3D AD 3C 3S 4C QC AS 5D TH 8C
6S 9D 4C JS KH AH TS JD 8H AD
4C 6S 9D 7S AC 4D 3D 3S TC JD
AD 7H 6H 4H JH KC TD TS 7D 6S
8H JH TC 3S 8D 8C 9S 2C 5C 4D
2C 9D KC QH TH QS JC 9C 4H TS
QS 3C QD 8H KH 4H 8D TD 8S AC
7C 3C TH 5S 8H 8C 9C JD TC KD
QC TC JD TS 8C 3H 6H KD 7C TD
JH QS KS 9C 6D 6S AS 9H KH 6H
2H 4D AH 2D JH 6H TD 5D 4H JD
KD 8C 9S JH QD JS 2C QS 5C 7C
4S TC 7H 8D 2S 6H 7S 9C 7C KC
8C 5D 7H 4S TD QC 8S JS 4H KS
AD 8S JH 6D TD KD 7C 6C 2D 7D
JC 6H 6S JS 4H QH 9H AH 4C 3C
6H 5H AS 7C 7S 3D KH KC 5D 5C
JC 3D TD AS 4D 6D 6S QH JD KS
8C 7S 8S QH 2S JD 5C 7H AH QD
8S 3C 6H 6C 2C 8D TD 7D 4C 4D
5D QH KH 7C 2S 7H JS 6D QC QD
AD 6C 6S 7D TH 6H 2H 8H KH 4H
KS JS KD 5D 2D KH 7D 9C 8C 3D
9C 6D QD 3C KS 3S 7S AH JD 2D
AH QH AS JC 8S 8H 4C KC TH 7D
JC 5H TD 7C 5D KD 4C AD 8H JS
KC 2H AC AH 7D JH KH 5D 7S 6D
9S 5S 9C 6H 8S TD JD 9H 6C AC
7D 8S 6D TS KD 7H AC 5S 7C 5D
AH QC JC 4C TC 8C 2H TS 2C 7D
KD KC 6S 3D 7D 2S 8S 3H 5S 5C
8S 5D 8H 4C 6H KC 3H 7C 5S KD
JH 8C 3D 3C 6C KC TD 7H 7C 4C
JC KC 6H TS QS TD KS 8H 8C 9S
6C 5S 9C QH 7D AH KS KC 9S 2C
4D 4S 8H TD 9C 3S 7D 9D AS TH
6S 7D 3C 6H 5D KD 2C 5C 9D 9C
2H KC 3D AD 3H QD QS 8D JC 4S
8C 3H 9C 7C AD 5D JC 9D JS AS
5D 9H 5C 7H 6S 6C QC JC QD 9S
JC QS JH 2C 6S 9C QC 3D 4S TC
4H 5S 8D 3D 4D 2S KC 2H JS 2C
TD 3S TH KD 4D 7H JH JS KS AC
7S 8C 9S 2D 8S 7D 5C AD 9D AS
8C 7H 2S 6C TH 3H 4C 3S 8H AC
KD 5H JC 8H JD 2D 4H TD JH 5C
3D AS QH KS 7H JD 8S 5S 6D 5H
9S 6S TC QS JC 5C 5D 9C TH 8C
5H 3S JH 9H 2S 2C 6S 7S AS KS
8C QD JC QS TC QC 4H AC KH 6C
TC 5H 7D JH 4H 2H 8D JC KS 4D
5S 9C KH KD 9H 5C TS 3D 7D 2D
5H AS TC 4D 8C 2C TS 9D 3H 8D
6H 8D 2D 9H JD 6C 4S 5H 5S 6D
AD 9C JC 7D 6H 9S 6D JS 9H 3C
AD JH TC QS 4C 5D 9S 7C 9C AH
KD 6H 2H TH 8S QD KS 9D 9H AS
4H 8H 8D 5H 6C AH 5S AS AD 8S
QS 5D 4S 2H TD KS 5H AC 3H JC
9C 7D QD KD AC 6D 5H QH 6H 5S
KC AH QH 2H 7D QS 3H KS 7S JD
6C 8S 3H 6D KS QD 5D 5C 8H TC
9H 4D 4S 6S 9D KH QC 4H 6C JD
TD 2D QH 4S 6H JH KD 3C QD 8C
4S 6H 7C QD 9D AS AH 6S AD 3C
2C KC TH 6H 8D AH 5C 6D 8S 5D
TD TS 7C AD JC QD 9H 3C KC 7H
5D 4D 5S 8H 4H 7D 3H JD KD 2D
JH TD 6H QS 4S KD 5C 8S 7D 8H
AC 3D AS 8C TD 7H KH 5D 6C JD
9D KS 7C 6D QH TC JD KD AS KC
JH 8S 5S 7S 7D AS 2D 3D AD 2H
2H 5D AS 3C QD KC 6H 9H 9S 2C
9D 5D TH 4C JH 3H 8D TC 8H 9H
6H KD 2C TD 2H 6C 9D 2D JS 8C
KD 7S 3C 7C AS QH TS AD 8C 2S
QS 8H 6C JS 4C 9S QC AD TD TS
2H 7C TS TC 8C 3C 9H 2D 6D JC
TC 2H 8D JH KS 6D 3H TD TH 8H
9D TD 9H QC 5D 6C 8H 8C KC TS
2H 8C 3D AH 4D TH TC 7D 8H KC
TS 5C 2D 8C 6S KH AH 5H 6H KC
5S 5D AH TC 4C JD 8D 6H 8C 6C
KC QD 3D 8H 2D JC 9H 4H AD 2S
TD 6S 7D JS KD 4H QS 2S 3S 8C
4C 9H JH TS 3S 4H QC 5S 9S 9C
2C KD 9H JS 9S 3H JC TS 5D AC
AS 2H 5D AD 5H JC 7S TD JS 4C
2D 4S 8H 3D 7D 2C AD KD 9C TS
7H QD JH 5H JS AC 3D TH 4C 8H
6D KH KC QD 5C AD 7C 2D 4H AC
3D 9D TC 8S QD 2C JC 4H JD AH
6C TD 5S TC 8S AH 2C 5D AS AC
TH 7S 3D AS 6C 4C 7H 7D 4H AH
5C 2H KS 6H 7S 4H 5H 3D 3C 7H
3C 9S AC 7S QH 2H 3D 6S 3S 3H
2D 3H AS 2C 6H TC JS 6S 9C 6C
QH KD QD 6D AC 6H KH 2C TS 8C
8H 7D 3S 9H 5D 3H 4S QC 9S 5H
2D 9D 7H 6H 3C 8S 5H 4D 3S 4S
KD 9S 4S TC 7S QC 3S 8S 2H 7H
TC 3D 8C 3H 6C 2H 6H KS KD 4D
KC 3D 9S 3H JS 4S 8H 2D 6C 8S
6H QS 6C TC QD 9H 7D 7C 5H 4D
TD 9D 8D 6S 6C TC 5D TS JS 8H
4H KC JD 9H TC 2C 6S 5H 8H AS
JS 9C 5C 6S 9D JD 8H KC 4C 6D
4D 8D 8S 6C 7C 6H 7H 8H 5C KC
TC 3D JC 6D KS 9S 6H 7S 9C 2C
6C 3S KD 5H TS 7D 9H 9S 6H KH
3D QD 4C 6H TS AC 3S 5C 2H KD
4C AS JS 9S 7C TS 7H 9H JC KS
4H 8C JD 3H 6H AD 9S 4S 5S KS
4C 2C 7D 3D AS 9C 2S QS KC 6C
8S 5H 3D 2S AC 9D 6S 3S 4D TD
QD TH 7S TS 3D AC 7H 6C 5D QC
TC QD AD 9C QS 5C 8D KD 3D 3C
9D 8H AS 3S 7C 8S JD 2D 8D KC
4C TH AC QH JS 8D 7D 7S 9C KH
9D 8D 4C JH 2C 2S QD KD TS 4H
4D 6D 5D 2D JH 3S 8S 3H TC KH
AD 4D 2C QS 8C KD JH JD AH 5C
5C 6C 5H 2H JH 4H KS 7C TC 3H
3C 4C QC 5D JH 9C QD KH 8D TC
3H 9C JS 7H QH AS 7C 9H 5H JC
2D 5S QD 4S 3C KC 6S 6C 5C 4C
5D KH 2D TS 8S 9C AS 9S 7C 4C
7C AH 8C 8D 5S KD QH QS JH 2C
8C 9D AH 2H AC QC 5S 8H 7H 2C
QD 9H 5S QS QC 9C 5H JC TH 4H
6C 6S 3H 5H 3S 6H KS 8D AC 7S
AC QH 7H 8C 4S KC 6C 3D 3S TC
9D 3D JS TH AC 5H 3H 8S 3S TC
QD KH JS KS 9S QC 8D AH 3C AC
5H 6C KH 3S 9S JH 2D QD AS 8C
6C 4D 7S 7H 5S JC 6S 9H 4H JH
AH 5S 6H 9S AD 3S TH 2H 9D 8C
4C 8D 9H 7C QC AD 4S 9C KC 5S
9D 6H 4D TC 4C JH 2S 5D 3S AS
2H 6C 7C KH 5C AD QS TH JD 8S
3S 4S 7S AH AS KC JS 2S AD TH
JS KC 2S 7D 8C 5C 9C TS 5H 9D
7S 9S 4D TD JH JS KH 6H 5D 2C
JD JS JC TH 2D 3D QD 8C AC 5H
7S KH 5S 9D 5D TD 4S 6H 3C 2D
4S 5D AC 8D 4D 7C AD AS AH 9C
6S TH TS KS 2C QC AH AS 3C 4S
2H 8C 3S JC 5C 7C 3H 3C KH JH
7S 3H JC 5S 6H 4C 2S 4D KC 7H
4D 7C 4H 9S 8S 6S AD TC 6C JC
KH QS 3S TC 4C 8H 8S AC 3C TS
QD QS TH 3C TS 7H 7D AH TD JC
TD JD QC 4D 9S 7S TS AD 7D AC
AH 7H 4S 6D 7C 2H 9D KS JC TD
7C AH JD 4H 6D QS TS 2H 2C 5C
TC KC 8C 9S 4C JS 3C JC 6S AH
AS 7D QC 3D 5S JC JD 9D TD KH
TH 3C 2S 6H AH AC 5H 5C 7S 8H
QC 2D AC QD 2S 3S JD QS 6S 8H
KC 4H 3C 9D JS 6H 3S 8S AS 8C
7H KC 7D JD 2H JC QH 5S 3H QS
9H TD 3S 8H 7S AC 5C 6C AH 7C
8D 9H AH JD TD QS 7D 3S 9C 8S
AH QH 3C JD KC 4S 5S 5D TD KS
9H 7H 6S JH TH 4C 7C AD 5C 2D
7C KD 5S TC 9D 6S 6C 5D 2S TH
KC 9H 8D 5H 7H 4H QC 3D 7C AS
6S 8S QC TD 4S 5C TH QS QD 2S
8S 5H TH QC 9H 6S KC 7D 7C 5C
7H KD AH 4D KH 5C 4S 2D KC QH
6S 2C TD JC AS 4D 6C 8C 4H 5S
JC TC JD 5S 6S 8D AS 9D AD 3S
6D 6H 5D 5S TC 3D 7D QS 9D QD
4S 6C 8S 3S 7S AD KS 2D 7D 7C
KC QH JC AC QD 5D 8D QS 7H 7D
JS AH 8S 5H 3D TD 3H 4S 6C JH
4S QS 7D AS 9H JS KS 6D TC 5C
2D 5C 6H TC 4D QH 3D 9H 8S 6C
6D 7H TC TH 5S JD 5C 9C KS KD
8D TD QH 6S 4S 6C 8S KC 5C TC
5S 3D KS AC 4S 7D QD 4C TH 2S
TS 8H 9S 6S 7S QH 3C AH 7H 8C
4C 8C TS JS QC 3D 7D 5D 7S JH
8S 7S 9D QC AC 7C 6D 2H JH KC
JS KD 3C 6S 4S 7C AH QC KS 5H
KS 6S 4H JD QS TC 8H KC 6H AS
KH 7C TC 6S TD JC 5C 7D AH 3S
3H 4C 4H TC TH 6S 7H 6D 9C QH
7D 5H 4S 8C JS 4D 3D 8S QH KC
3H 6S AD 7H 3S QC 8S 4S 7S JS
3S JD KH TH 6H QS 9C 6C 2D QD
4S QH 4D 5H KC 7D 6D 8D TH 5S
TD AD 6S 7H KD KH 9H 5S KC JC
3H QC AS TS 4S QD KS 9C 7S KC
TS 6S QC 6C TH TC 9D 5C 5D KD
JS 3S 4H KD 4C QD 6D 9S JC 9D
8S JS 6D 4H JH 6H 6S 6C KS KH
AC 7D 5D TC 9S KH 6S QD 6H AS
AS 7H 6D QH 8D TH 2S KH 5C 5H
4C 7C 3D QC TC 4S KH 8C 2D JS
6H 5D 7S 5H 9C 9H JH 8S TH 7H
AS JS 2S QD KH 8H 4S AC 8D 8S
3H 4C TD KD 8C JC 5C QS 2D JD
TS 7D 5D 6C 2C QS 2H 3C AH KS
4S 7C 9C 7D JH 6C 5C 8H 9D QD
2S TD 7S 6D 9C 9S QS KH QH 5C
JC 6S 9C QH JH 8D 7S JS KH 2H
8D 5H TH KC 4D 4S 3S 6S 3D QS
2D JD 4C TD 7C 6D TH 7S JC AH
QS 7S 4C TH 9D TS AD 4D 3H 6H
2D 3H 7D JD 3D AS 2S 9C QC 8S
4H 9H 9C 2C 7S JH KD 5C 5D 6H
TC 9H 8H JC 3C 9S 8D KS AD KC
TS 5H JD QS QH QC 8D 5D KH AH
5D AS 8S 6S 4C AH QC QD TH 7H
3H 4H 7D 6S 4S 9H AS 8H JS 9D
JD 8C 2C 9D 7D 5H 5S 9S JC KD
KD 9C 4S QD AH 7C AD 9D AC TD
6S 4H 4S 9C 8D KS TC 9D JH 7C
5S JC 5H 4S QH AC 2C JS 2S 9S
8C 5H AS QD AD 5C 7D 8S QC TD
JC 4C 8D 5C KH QS 4D 6H 2H 2C
TH 4S 2D KC 3H QD AC 7H AD 9D
KH QD AS 8H TH KC 8D 7S QH 8C
JC 6C 7D 8C KH AD QS 2H 6S 2D
JC KH 2D 7D JS QC 5H 4C 5D AD
TS 3S AD 4S TD 2D TH 6S 9H JH
9H 2D QS 2C 4S 3D KH AS AC 9D
KH 6S 8H 4S KD 7D 9D TS QD QC
JH 5H AH KS AS AD JC QC 5S KH
5D 7D 6D KS KD 3D 7C 4D JD 3S
AC JS 8D 5H 9C 3H 4H 4D TS 2C
6H KS KH 9D 7C 2S 6S 8S 2H 3D
6H AC JS 7S 3S TD 8H 3H 4H TH
9H TC QC KC 5C KS 6H 4H AC 8S
TC 7D QH 4S JC TS 6D 6C AC KH
QH 7D 7C JH QS QD TH 3H 5D KS
3D 5S 8D JS 4C 2C KS 7H 9C 4H
5H 8S 4H TD 2C 3S QD QC 3H KC
QC JS KD 9C AD 5S 9D 7D 7H TS
8C JC KH 7C 7S 6C TS 2C QD TH
5S 9D TH 3C 7S QH 8S 9C 2H 5H
5D 9H 6H 2S JS KH 3H 7C 2H 5S
JD 5D 5S 2C TC 2S 6S 6C 3C 8S
4D KH 8H 4H 2D KS 3H 5C 2S 9H
3S 2D TD 7H 8S 6H JD KC 9C 8D
6S QD JH 7C 9H 5H 8S 8H TH TD
QS 7S TD 7D TS JC KD 7C 3C 2C
3C JD 8S 4H 2D 2S TD AS 4D AC
AH KS 6C 4C 4S 7D 8C 9H 6H AS
5S 3C 9S 2C QS KD 4D 4S AC 5D
2D TS 2C JS KH QH 5D 8C AS KC
KD 3H 6C TH 8S 7S KH 6H 9S AC
6H 7S 6C QS AH 2S 2H 4H 5D 5H
5H JC QD 2C 2S JD AS QC 6S 7D
6C TC AS KD 8H 9D 2C 7D JH 9S
2H 4C 6C AH 8S TD 3H TH 7C TS
KD 4S TS 6C QH 8D 9D 9C AH 7D
6D JS 5C QD QC 9C 5D 8C 2H KD
3C QH JH AD 6S AH KC 8S 6D 6H
3D 7C 4C 7S 5S 3S 6S 5H JC 3C
QH 7C 5H 3C 3S 8C TS 4C KD 9C
QD 3S 7S 5H 7H QH JC 7C 8C KD
3C KD KH 2S 4C TS AC 6S 2C 7C
2C KH 3C 4C 6H 4D 5H 5S 7S QD
4D 7C 8S QD TS 9D KS 6H KD 3C
QS 4D TS 7S 4C 3H QD 8D 9S TC
TS QH AC 6S 3C 9H 9D QS 8S 6H
3S 7S 5D 4S JS 2D 6C QH 6S TH
4C 4H AS JS 5D 3D TS 9C AC 8S
6S 9C 7C 3S 5C QS AD AS 6H 3C
9S 8C 7H 3H 6S 7C AS 9H JD KH
3D 3H 7S 4D 6C 7C AC 2H 9C TH
4H 5S 3H AC TC TH 9C 9H 9S 8D
8D 9H 5H 4D 6C 2H QD 6S 5D 3S
4C 5C JD QS 4D 3H TH AC QH 8C
QC 5S 3C 7H AD 4C KS 4H JD 6D
QS AH 3H KS 9H 2S JS JH 5H 2H
2H 5S TH 6S TS 3S KS 3C 5H JS
2D 9S 7H 3D KC JH 6D 7D JS TD
AC JS 8H 2C 8C JH JC 2D TH 7S
5D 9S 8H 2H 3D TC AH JC KD 9C
9D QD JC 2H 6D KH TS 9S QH TH
2C 8D 4S JD 5H 3H TH TC 9C KC
AS 3D 9H 7D 4D TH KH 2H 7S 3H
4H 7S KS 2S JS TS 8S 2H QD 8D
5S 6H JH KS 8H 2S QC AC 6S 3S
JC AS AD QS 8H 6C KH 4C 4D QD
2S 3D TS TD 9S KS 6S QS 5C 8D
3C 6D 4S QC KC JH QD TH KH AD
9H AH 4D KS 2S 8D JH JC 7C QS
2D 6C TH 3C 8H QD QH 2S 3S KS
6H 5D 9S 4C TS TD JS QD 9D JD
5H 8H KH 8S KS 7C TD AD 4S KD
2C 7C JC 5S AS 6C 7D 8S 5H 9C
6S QD 9S TS KH QS 5S QH 3C KC
7D 3H 3C KD 5C AS JH 7H 6H JD
9D 5C 9H KC 8H KS 4S AD 4D 2S
3S JD QD 8D 2S 7C 5S 6S 5H TS
6D 9S KC TD 3S 6H QD JD 5C 8D
5H 9D TS KD 8D 6H TD QC 4C 7D
6D 4S JD 9D AH 9S AS TD 9H QD
2D 5S 2H 9C 6H 9S TD QC 7D TC
3S 2H KS TS 2C 9C 8S JS 9D 7D
3C KC 6D 5D 6C 6H 8S AS 7S QS
JH 9S 2H 8D 4C 8H 9H AD TH KH
QC AS 2S JS 5C 6H KD 3H 7H 2C
QD 8H 2S 8D 3S 6D AH 2C TC 5C
JD JS TS 8S 3H 5D TD KC JC 6H
6S QS TC 3H 5D AH JC 7C 7D 4H
7C 5D 8H 9C 2H 9H JH KH 5S 2C
9C 7H 6S TH 3S QC QD 4C AC JD
2H 5D 9S 7D KC 3S QS 2D AS KH
2S 4S 2H 7D 5C TD TH QH 9S 4D
6D 3S TS 6H 4H KS 9D 8H 5S 2D
9H KS 4H 3S 5C 5D KH 6H 6S JS
KC AS 8C 4C JC KH QC TH QD AH
6S KH 9S 2C 5H TC 3C 7H JC 4D
JD 4S 6S 5S 8D 7H 7S 4D 4C 2H
7H 9H 5D KH 9C 7C TS TC 7S 5H
4C 8D QC TS 4S 9H 3D AD JS 7C
8C QS 5C 5D 3H JS AH KC 4S 9D
TS JD 8S QS TH JH KH 2D QD JS
JD QC 5D 6S 9H 3S 2C 8H 9S TS
2S 4C AD 7H JC 5C 2D 6D 4H 3D
7S JS 2C 4H 8C AD QD 9C 3S TD
JD TS 4C 6H 9H 7D QD 6D 3C AS
AS 7C 4C 6S 5D 5S 5C JS QC 4S
KD 6S 9S 7C 3C 5S 7D JH QD JS
4S 7S JH 2C 8S 5D 7H 3D QH AD
TD 6H 2H 8D 4H 2D 7C AD KH 5D
TS 3S 5H 2C QD AH 2S 5C KH TD
KC 4D 8C 5D AS 6C 2H 2S 9H 7C
KD JS QC TS QS KH JH 2C 5D AD
3S 5H KC 6C 9H 3H 2H AD 7D 7S
7S JS JH KD 8S 7D 2S 9H 7C 2H
9H 2D 8D QC 6S AD AS 8H 5H 6C
2S 7H 6C 6D 7D 8C 5D 9D JC 3C
7C 9C 7H JD 2H KD 3S KH AD 4S
QH AS 9H 4D JD KS KD TS KH 5H
4C 8H 5S 3S 3D 7D TD AD 7S KC
JS 8S 5S JC 8H TH 9C 4D 5D KC
7C 5S 9C QD 2C QH JS 5H 8D KH
TD 2S KS 3D AD KC 7S TC 3C 5D
4C 2S AD QS 6C 9S QD TH QH 5C
8C AD QS 2D 2S KC JD KS 6C JC
8D 4D JS 2H 5D QD 7S 7D QH TS
6S 7H 3S 8C 8S 9D QS 8H 6C 9S
4S TC 2S 5C QD 4D QS 6D TH 6S
3S 5C 9D 6H 8D 4C 7D TC 7C TD
AH 6S AS 7H 5S KD 3H 5H AC 4C
8D 8S AH KS QS 2C AD 6H 7D 5D
6H 9H 9S 2H QS 8S 9C 5D 2D KD
TS QC 5S JH 7D 7S TH 9S 9H AC
7H 3H 6S KC 4D 6D 5C 4S QD TS
TD 2S 7C QD 3H JH 9D 4H 7S 7H
KS 3D 4H 5H TC 2S AS 2D 6D 7D
8H 3C 7H TD 3H AD KC TH 9C KH
TC 4C 2C 9S 9D 9C 5C 2H JD 3C
3H AC TS 5D AD 8D 6H QC 6S 8C
2S TS 3S JD 7H 8S QH 4C 5S 8D
AC 4S 6C 3C KH 3D 7C 2D 8S 2H
4H 6C 8S TH 2H 4S 8H 9S 3H 7S
7C 4C 9C 2C 5C AS 5D KD 4D QH
9H 4H TS AS 7D 8D 5D 9S 8C 2H
QC KD AC AD 2H 7S AS 3S 2D 9S
2H QC 8H TC 6D QD QS 5D KH 3C
TH JD QS 4C 2S 5S AD 7H 3S AS
7H JS 3D 6C 3S 6D AS 9S AC QS
9C TS AS 8C TC 8S 6H 9D 8D 6C
4D JD 9C KC 7C 6D KS 3S 8C AS
3H 6S TC 8D TS 3S KC 9S 7C AS
8C QC 4H 4S 8S 6C 3S TC AH AC
4D 7D 5C AS 2H 6S TS QC AD TC
QD QC 8S 4S TH 3D AH TS JH 4H
5C 2D 9S 2C 3H 3C 9D QD QH 7D
KC 9H 6C KD 7S 3C 4D AS TC 2D
3D JS 4D 9D KS 7D TH QC 3H 3C
8D 5S 2H 9D 3H 8C 4C 4H 3C TH
JC TH 4S 6S JD 2D 4D 6C 3D 4C
TS 3S 2D 4H AC 2C 6S 2H JH 6H
TD 8S AD TC AH AC JH 9S 6S 7S
6C KC 4S JD 8D 9H 5S 7H QH AH
KD 8D TS JH 5C 5H 3H AD AS JS
2D 4H 3D 6C 8C 7S AD 5D 5C 8S
TD 5D 7S 9C 4S 5H 6C 8C 4C 8S
JS QH 9C AS 5C QS JC 3D QC 7C
JC 9C KH JH QS QC 2C TS 3D AD
5D JH AC 5C 9S TS 4C JD 8C KS
KC AS 2D KH 9H 2C 5S 4D 3D 6H
TH AH 2D 8S JC 3D 8C QH 7S 3S
8H QD 4H JC AS KH KS 3C 9S 6D
9S QH 7D 9C 4S AC 7H KH 4D KD
AH AD TH 6D 9C 9S KD KS QH 4H
QD 6H 9C 7C QS 6D 6S 9D 5S JH
AH 8D 5H QD 2H JC KS 4H KH 5S
5C 2S JS 8D 9C 8C 3D AS KC AH
JD 9S 2H QS 8H 5S 8C TH 5C 4C
QC QS 8C 2S 2C 3S 9C 4C KS KH
2D 5D 8S AH AD TD 2C JS KS 8C
TC 5S 5H 8H QC 9H 6H JD 4H 9S
3C JH 4H 9H AH 4S 2H 4C 8D AC
8S TH 4D 7D 6D QD QS 7S TC 7C
KH 6D 2D JD 5H JS QD JH 4H 4S
9C 7S JH 4S 3S TS QC 8C TC 4H
QH 9D 4D JH QS 3S 2C 7C 6C 2D
4H 9S JD 5C 5H AH 9D TS 2D 4C
KS JH TS 5D 2D AH JS 7H AS 8D
JS AH 8C AD KS 5S 8H 2C 6C TH
2H 5D AD AC KS 3D 8H TS 6H QC
6D 4H TS 9C 5H JS JH 6S JD 4C
JH QH 4H 2C 6D 3C 5D 4C QS KC
6H 4H 6C 7H 6S 2S 8S KH QC 8C
3H 3D 5D KS 4H TD AD 3S 4D TS
5S 7C 8S 7D 2C KS 7S 6C 8C JS
5D 2H 3S 7C 5C QD 5H 6D 9C 9H
JS 2S KD 9S 8D TD TS AC 8C 9D
5H QD 2S AC 8C 9H KS 7C 4S 3C
KH AS 3H 8S 9C JS QS 4S AD 4D
AS 2S TD AD 4D 9H JC 4C 5H QS
5D 7C 4H TC 2D 6C JS 4S KC 3S
4C 2C 5D AC 9H 3D JD 8S QS QH
2C 8S 6H 3C QH 6D TC KD AC AH
QC 6C 3S QS 4S AC 8D 5C AD KH
5S 4C AC KH AS QC 2C 5C 8D 9C
8H JD 3C KH 8D 5C 9C QD QH 9D
7H TS 2C 8C 4S TD JC 9C 5H QH
JS 4S 2C 7C TH 6C AS KS 7S JD
JH 7C 9H 7H TC 5H 3D 6D 5D 4D
2C QD JH 2H 9D 5S 3D TD AD KS
JD QH 3S 4D TH 7D 6S QS KS 4H
TC KS 5S 8D 8H AD 2S 2D 4C JH
5S JH TC 3S 2D QS 9D 4C KD 9S
AC KH 3H AS 9D KC 9H QD 6C 6S
9H 7S 3D 5C 7D KC TD 8H 4H 6S
3C 7H 8H TC QD 4D 7S 6S QH 6C
6D AD 4C QD 6C 5D 7D 9D KS TS
JH 2H JD 9S 7S TS KH 8D 5D 8H
2D 9S 4C 7D 9D 5H QD 6D AC 6S
7S 6D JC QD JH 4C 6S QS 2H 7D
8C TD JH KD 2H 5C QS 2C JS 7S
TC 5H 4H JH QD 3S 5S 5D 8S KH
KS KH 7C 2C 5D JH 6S 9C 6D JC
5H AH JD 9C JS KC 2H 6H 4D 5S
AS 3C TH QC 6H 9C 8S 8C TD 7C
KC 2C QD 9C KH 4D 7S 3C TS 9H
9C QC 2S TS 8C TD 9S QD 3S 3C
4D 9D TH JH AH 6S 2S JD QH JS
QD 9H 6C KD 7D 7H 5D 6S 8H AH
8H 3C 4S 2H 5H QS QH 7S 4H AC
QS 3C 7S 9S 4H 3S AH KS 9D 7C
AD 5S 6S 2H 2D 5H TC 4S 3C 8C
QH TS 6S 4D JS KS JH AS 8S 6D
2C 8S 2S TD 5H AS TC TS 6C KC
KC TS 8H 2H 3H 7C 4C 5S TH TD
KD AD KH 7H 7S 5D 5H 5S 2D 9C
AD 9S 3D 7S 8C QC 7C 9C KD KS
3C QC 9S 8C 4D 5C AS QD 6C 2C
2H KC 8S JD 7S AC 8D 5C 2S 4D
9D QH 3D 2S TC 3S KS 3C 9H TD
KD 6S AC 2C 7H 5H 3S 6C 6H 8C
QH TC 8S 6S KH TH 4H 5D TS 4D
8C JS 4H 6H 2C 2H 7D AC QD 3D
QS KC 6S 2D 5S 4H TD 3H JH 4C
7S 5H 7H 8H KH 6H QS TH KD 7D
5H AD KD 7C KH 5S TD 6D 3C 6C
8C 9C 5H JD 7C KC KH 7H 2H 3S
7S 4H AD 4D 8S QS TH 3D 7H 5S
8D TC KS KD 9S 6D AD JD 5C 2S
7H 8H 6C QD 2H 6H 9D TC 9S 7C
8D 6D 4C 7C 6C 3C TH KH JS JH
5S 3S 8S JS 9H AS AD 8H 7S KD
JH 7C 2C KC 5H AS AD 9C 9S JS
AD AC 2C 6S QD 7C 3H TH KS KD
9D JD 4H 8H 4C KH 7S TS 8C KC
3S 5S 2H 7S 6H 7D KS 5C 6D AD
5S 8C 9H QS 7H 7S 2H 6C 7D TD
QS 5S TD AC 9D KC 3D TC 2D 4D
TD 2H 7D JD QD 4C 7H 5D KC 3D
4C 3H 8S KD QH 5S QC 9H TC 5H
9C QD TH 5H TS 5C 9H AH QH 2C
4D 6S 3C AC 6C 3D 2C 2H TD TH
AC 9C 5D QC 4D AD 8D 6D 8C KC
AD 3C 4H AC 8D 8H 7S 9S TD JC
4H 9H QH JS 2D TH TD TC KD KS
5S 6S 9S 8D TH AS KH 5H 5C 8S
JD 2S 9S 6S 5S 8S 5D 7S 7H 9D
5D 8C 4C 9D AD TS 2C 7D KD TC
8S QS 4D KC 5C 8D 4S KH JD KD
AS 5C AD QH 7D 2H 9S 7H 7C TC
2S 8S JD KH 7S 6C 6D AD 5D QC
9H 6H 3S 8C 8H AH TC 4H JS TD
2C TS 4D 7H 2D QC 9C 5D TH 7C
6C 8H QC 5D TS JH 5C 5H 9H 4S
2D QC 7H AS JS 8S 2H 4C 4H 8D
JS 6S AC KD 3D 3C 4S 7H TH KC
QH KH 6S QS 5S 4H 3C QD 3S 3H
7H AS KH 8C 4H 9C 5S 3D 6S TS
9C 7C 3H 5S QD 2C 3D AD AC 5H
JH TD 2D 4C TS 3H KH AD 3S 7S
AS 4C 5H 4D 6S KD JC 3C 6H 2D
3H 6S 8C 2D TH 4S AH QH AD 5H
7C 2S 9H 7H KC 5C 6D 5S 3H JC
3C TC 9C 4H QD TD JH 6D 9H 5S
7C 6S 5C 5D 6C 4S 7H 9H 6H AH
AD 2H 7D KC 2C 4C 2S 9S 7H 3S
TH 4C 8S 6S 3S AD KS AS JH TD
5C TD 4S 4D AD 6S 5D TC 9C 7D
8H 3S 4D 4S 5S 6H 5C AC 3H 3D
9H 3C AC 4S QS 8S 9D QH 5H 4D
JC 6C 5H TS AC 9C JD 8C 7C QD
8S 8H 9C JD 2D QC QH 6H 3C 8D
KS JS 2H 6H 5H QH QS 3H 7C 6D
TC 3H 4S 7H QC 2H 3S 8C JS KH
AH 8H 5S 4C 9H JD 3H 7S JC AC
3C 2D 4C 5S 6C 4S QS 3S JD 3D
5H 2D TC AH KS 6D 7H AD 8C 6H
6C 7S 3C JD 7C 8H KS KH AH 6D
AH 7D 3H 8H 8S 7H QS 5H 9D 2D
JD AC 4H 7S 8S 9S KS AS 9D QH
7S 2C 8S 5S JH QS JC AH KD 4C
AH 2S 9H 4H 8D TS TD 6H QH JD
4H JC 3H QS 6D 7S 9C 8S 9D 8D
5H TD 4S 9S 4C 8C 8D 7H 3H 3D
QS KH 3S 2C 2S 3C 7S TD 4S QD
7C TD 4D 5S KH AC AS 7H 4C 6C
2S 5H 6D JD 9H QS 8S 2C 2H TD
2S TS 6H 9H 7S 4H JC 4C 5D 5S
2C 5H 7D 4H 3S QH JC JS 6D 8H
4C QH 7C QD 3S AD TH 8S 5S TS
9H TC 2S TD JC 7D 3S 3D TH QH
7D 4C 8S 5C JH 8H 6S 3S KC 3H
JC 3H KH TC QH TH 6H 2C AC 5H
QS 2H 9D 2C AS 6S 6C 2S 8C 8S
9H 7D QC TH 4H KD QS AC 7S 3C
4D JH 6S 5S 8H KS 9S QC 3S AS
JD 2D 6S 7S TC 9H KC 3H 7D KD
2H KH 7C 4D 4S 3H JS QD 7D KC
4C JC AS 9D 3C JS 6C 8H QD 4D
AH JS 3S 6C 4C 3D JH 6D 9C 9H
9H 2D 8C 7H 5S KS 6H 9C 2S TC
6C 8C AD 7H 6H 3D KH AS 5D TH
KS 8C 3S TS 8S 4D 5S 9S 6C 4H
9H 4S 4H 5C 7D KC 2D 2H 9D JH
5C JS TC 9D 9H 5H 7S KH JC 6S
7C 9H 8H 4D JC KH JD 2H TD TC
8H 6C 2H 2C KH 6H 9D QS QH 5H
AC 7D 2S 3D QD JC 2D 8D JD JH
2H JC 2D 7H 2C 3C 8D KD TD 4H
3S 4H 6D 8D TS 3H TD 3D 6H TH
JH JC 3S AC QH 9H 7H 8S QC 2C
7H TD QS 4S 8S 9C 2S 5D 4D 2H
3D TS 3H 2S QC 8H 6H KC JC KS
5D JD 7D TC 8C 6C 9S 3D 8D AC
8H 6H JH 6C 5D 8D 8S 4H AD 2C
9D 4H 2D 2C 3S TS AS TC 3C 5D
4D TH 5H KS QS 6C 4S 2H 3D AD
5C KC 6H 2C 5S 3C 4D 2D 9H 9S
JD 4C 3H TH QH 9H 5S AH 8S AC
7D 9S 6S 2H TD 9C 4H 8H QS 4C
3C 6H 5D 4H 8C 9C KC 6S QD QS
3S 9H KD TC 2D JS 8C 6S 4H 4S
2S 4C 8S QS 6H KH 3H TH 8C 5D
2C KH 5S 3S 7S 7H 6C 9D QD 8D
8H KS AC 2D KH TS 6C JS KC 7H
9C KS 5C TD QC AH 6C 5H 9S 7C
5D 4D 3H 4H 6S 7C 7S AH QD TD
2H 7D QC 6S TC TS AH 7S 9D 3H
TH 5H QD 9S KS 7S 7C 6H 8C TD
TH 2D 4D QC 5C 7D JD AH 9C 4H
4H 3H AH 8D 6H QC QH 9H 2H 2C
2D AD 4C TS 6H 7S TH 4H QS TD
3C KD 2H 3H QS JD TC QC 5D 8H
KS JC QD TH 9S KD 8D 8C 2D 9C
3C QD KD 6D 4D 8D AH AD QC 8S
8H 3S 9D 2S 3H KS 6H 4C 7C KC
TH 9S 5C 3D 7D 6H AC 7S 4D 2C
5C 3D JD 4D 2D 6D 5H 9H 4C KH
AS 7H TD 6C 2H 3D QD KS 4C 4S
JC 3C AC 7C JD JS 8H 9S QC 5D
JD 6S 5S 2H AS 8C 7D 5H JH 3D
8D TC 5S 9S 8S 3H JC 5H 7S AS
5C TD 3D 7D 4H 8D 7H 4D 5D JS
QS 9C KS TD 2S 8S 5C 2H 4H AS
TH 7S 4H 7D 3H JD KD 5D 2S KC
JD 7H 4S 8H 4C JS 6H QH 5S 4H
2C QS 8C 5S 3H QC 2S 6C QD AD
8C 3D JD TC 4H 2H AD 5S AC 2S
5D 2C JS 2D AD 9D 3D 4C 4S JH
8D 5H 5D 6H 7S 4D KS 9D TD JD
3D 6D 9C 2S AS 7D 5S 5C 8H JD
7C 8S 3S 6S 5H JD TC AD 7H 7S
2S 9D TS 4D AC 8D 6C QD JD 3H
9S KH 2C 3C AC 3D 5H 6H 8D 5D
KS 3D 2D 6S AS 4C 2S 7C 7H KH
AC 2H 3S JC 5C QH 4D 2D 5H 7S
TS AS JD 8C 6H JC 8S 5S 2C 5D
7S QH 7H 6C QC 8H 2D 7C JD 2S
2C QD 2S 2H JC 9C 5D 2D JD JH
7C 5C 9C 8S 7D 6D 8D 6C 9S JH
2C AD 6S 5H 3S KS 7S 9D KH 4C
7H 6C 2C 5C TH 9D 8D 3S QC AH
5S KC 6H TC 5H 8S TH 6D 3C AH
9C KD 4H AD TD 9S 4S 7D 6H 5D
7H 5C 5H 6D AS 4C KD KH 4H 9D
3C 2S 5C 6C JD QS 2H 9D 7D 3H
AC 2S 6S 7S JS QD 5C QS 6H AD
5H TH QC 7H TC 3S 7C 6D KC 3D
4H 3D QC 9S 8H 2C 3S JC KS 5C
4S 6S 2C 6H 8S 3S 3D 9H 3H JS
4S 8C 4D 2D 8H 9H 7D 9D AH TS
9S 2C 9H 4C 8D AS 7D 3D 6D 5S
6S 4C 7H 8C 3H 5H JC AH 9D 9C
2S 7C 5S JD 8C 3S 3D 4D 7D 6S
3C KC 4S 5D 7D 3D JD 7H 3H 4H
9C 9H 4H 4D TH 6D QD 8S 9S 7S
2H AC 8S 4S AD 8C 2C AH 7D TC
TS 9H 3C AD KS TC 3D 8C 8H JD
QC 8D 2C 3C 7D 7C JD 9H 9C 6C
AH 6S JS JH 5D AS QC 2C JD TD
9H KD 2H 5D 2D 3S 7D TC AH TS
TD 8H AS 5D AH QC AC 6S TC 5H
KS 4S 7H 4D 8D 9C TC 2H 6H 3H
3H KD 4S QD QH 3D 8H 8C TD 7S
8S JD TC AH JS QS 2D KH KS 4D
3C AD JC KD JS KH 4S TH 9H 2C
QC 5S JS 9S KS AS 7C QD 2S JD
KC 5S QS 3S 2D AC 5D 9H 8H KS
6H 9C TC AD 2C 6D 5S JD 6C 7C
QS KH TD QD 2C 3H 8S 2S QC AH
9D 9H JH TC QH 3C 2S JS 5C 7H
6C 3S 3D 2S 4S QD 2D TH 5D 2C
2D 6H 6D 2S JC QH AS 7H 4H KH
5H 6S KS AD TC TS 7C AC 4S 4H
AD 3C 4H QS 8C 9D KS 2H 2D 4D
4S 9D 6C 6D 9C AC 8D 3H 7H KD
JC AH 6C TS JD 6D AD 3S 5D QD
JC JH JD 3S 7S 8S JS QC 3H 4S
JD TH 5C 2C AD JS 7H 9S 2H 7S
8D 3S JH 4D QC AS JD 2C KC 6H
2C AC 5H KD 5S 7H QD JH AH 2D
JC QH 8D 8S TC 5H 5C AH 8C 6C
3H JS 8S QD JH 3C 4H 6D 5C 3S
6D 4S 4C AH 5H 5S 3H JD 7C 8D
8H AH 2H 3H JS 3C 7D QC 4H KD
6S 2H KD 5H 8H 2D 3C 8S 7S QD
2S 7S KC QC AH TC QS 6D 4C 8D
5S 9H 2C 3S QD 7S 6C 2H 7C 9D
3C 6C 5C 5S JD JC KS 3S 5D TS
7C KS 6S 5S 2S 2D TC 2H 5H QS
AS 7H 6S TS 5H 9S 9D 3C KD 2H
4S JS QS 3S 4H 7C 2S AC 6S 9D
8C JH 2H 5H 7C 5D QH QS KH QC
3S TD 3H 7C KC 8D 5H 8S KH 8C
4H KH JD TS 3C 7H AS QC JS 5S
AH 9D 2C 8D 4D 2D 6H 6C KC 6S
2S 6H 9D 3S 7H 4D KH 8H KD 3D
9C TC AC JH KH 4D JD 5H TD 3S
7S 4H 9D AS 4C 7D QS 9S 2S KH
3S 8D 8S KS 8C JC 5C KH 2H 5D
8S QH 2C 4D KC JS QC 9D AC 6H
8S 8C 7C JS JD 6S 4C 9C AC 4S
QH 5D 2C 7D JC 8S 2D JS JH 4C
JS 4C 7S TS JH KC KH 5H QD 4S
QD 8C 8D 2D 6S TD 9D AC QH 5S
QH QC JS 3D 3C 5C 4H KH 8S 7H
7C 2C 5S JC 8S 3H QC 5D 2H KC
5S 8D KD 6H 4H QD QH 6D AH 3D
7S KS 6C 2S 4D AC QS 5H TS JD
7C 2D TC 5D QS AC JS QC 6C KC
2C KS 4D 3H TS 8S AD 4H 7S 9S
QD 9H QH 5H 4H 4D KH 3S JC AD
4D AC KC 8D 6D 4C 2D KH 2C JD
2C 9H 2D AH 3H 6D 9C 7D TC KS
8C 3H KD 7C 5C 2S 4S 5H AS AH
TH JD 4H KD 3H TC 5C 3S AC KH
6D 7H AH 7S QC 6H 2D TD JD AS
JH 5D 7H TC 9S 7D JC AS 5S KH
2H 8C AD TH 6H QD KD 9H 6S 6C
QH KC 9D 4D 3S JS JH 4H 2C 9H
TC 7H KH 4H JC 7D 9S 3H QS 7S
AD 7D JH 6C 7H 4H 3S 3H 4D QH
JD 2H 5C AS 6C QC 4D 3C TC JH
AC JD 3H 6H 4C JC AD 7D 7H 9H
4H TC TS 2C 8C 6S KS 2H JD 9S
4C 3H QS QC 9S 9H 6D KC 9D 9C
5C AD 8C 2C QH TH QD JC 8D 8H
QC 2C 2S QD 9C 4D 3S 8D JH QS
9D 3S 2C 7S 7C JC TD 3C TC 9H
3C TS 8H 5C 4C 2C 6S 8D 7C 4H
KS 7H 2H TC 4H 2C 3S AS AH QS
8C 2D 2H 2C 4S 4C 6S 7D 5S 3S
TH QC 5D TD 3C QS KD KC KS AS
4D AH KD 9H KS 5C 4C 6H JC 7S
KC 4H 5C QS TC 2H JC 9S AH QH
4S 9H 3H 5H 3C QD 2H QC JH 8H
5D AS 7H 2C 3D JH 6H 4C 6S 7D
9C JD 9H AH JS 8S QH 3H KS 8H
3S AC QC TS 4D AD 3D AH 8S 9H
7H 3H QS 9C 9S 5H JH JS AH AC
8D 3C JD 2H AC 9C 7H 5S 4D 8H
7C JH 9H 6C JS 9S 7H 8C 9D 4H
2D AS 9S 6H 4D JS JH 9H AD QD
6H 7S JH KH AH 7H TD 5S 6S 2C
8H JH 6S 5H 5S 9D TC 4C QC 9S
7D 2C KD 3H 5H AS QD 7H JS 4D
TS QH 6C 8H TH 5H 3C 3H 9C 9D
AD KH JS 5D 3H AS AC 9S 5C KC
2C KH 8C JC QS 6D AH 2D KC TC
9D 3H 2S 7C 4D 6D KH KS 8D 7D
9H 2S TC JH AC QC 3H 5S 3S 8H
3S AS KD 8H 4C 3H 7C JH QH TS
7S 6D 7H 9D JH 4C 3D 3S 6C AS
4S 2H 2C 4C 8S 5H KC 8C QC QD
3H 3S 6C QS QC 2D 6S 5D 2C 9D
2H 8D JH 2S 3H 2D 6C 5C 7S AD
9H JS 5D QH 8S TS 2H 7S 6S AD
6D QC 9S 7H 5H 5C 7D KC JD 4H
QC 5S 9H 9C 4D 6S KS 2S 4C 7C
9H 7C 4H 8D 3S 6H 5C 8H JS 7S
2D 6H JS TD 4H 4D JC TH 5H KC
AC 7C 8D TH 3H 9S 2D 4C KC 4D
KD QS 9C 7S 3D KS AD TS 4C 4H
QH 9C 8H 2S 7D KS 7H 5D KD 4C
9C 2S 2H JC 6S 6C TC QC JH 5C
7S AC 8H KC 8S 6H QS JC 3D 6S
JS 2D JH 8C 4S 6H 8H 6D 5D AD
6H 7D 2S 4H 9H 7C AS AC 8H 5S
3C JS 4S 6D 5H 2S QH 6S 9C 2C
3D 5S 6S 9S 4C QS 8D QD 8S TC
9C 3D AH 9H 5S 2C 7D AD JC 3S
7H TC AS 3C 6S 6D 7S KH KC 9H
3S TC 8H 6S 5H JH 8C 7D AC 2S
QD 9D 9C 3S JC 8C KS 8H 5D 4D
JS AH JD 6D 9D 8C 9H 9S 8H 3H
2D 6S 4C 4D 8S AD 4S TC AH 9H
TS AC QC TH KC 6D 4H 7S 8C 2H
3C QD JS 9D 5S JC AH 2H TS 9H
3H 4D QH 5D 9C 5H 7D 4S JC 3S
8S TH 3H 7C 2H JD JS TS AC 8D
9C 2H TD KC JD 2S 8C 5S AD 2C
3D KD 7C 5H 4D QH QD TC 6H 7D
7H 2C KC 5S KD 6H AH QC 7S QH
6H 5C AC 5H 2C 9C 2D 7C TD 2S
4D 9D AH 3D 7C JD 4H 8C 4C KS
TH 3C JS QH 8H 4C AS 3D QS QC
4D 7S 5H JH 6D 7D 6H JS KH 3C
QD 8S 7D 2H 2C 7C JC 2S 5H 8C
QH 8S 9D TC 2H AD 7C 8D QD 6S
3S 7C AD 9H 2H 9S JD TS 4C 2D
3S AS 4H QC 2C 8H 8S 7S TD TC
JH TH TD 3S 4D 4H 5S 5D QS 2C
8C QD QH TC 6D 4S 9S 9D 4H QC
8C JS 9D 6H JD 3H AD 6S TD QC
KC 8S 3D 7C TD 7D 8D 9H 4S 3S
6C 4S 3D 9D KD TC KC KS AC 5S
7C 6S QH 3D JS KD 6H 6D 2D 8C
JD 2S 5S 4H 8S AC 2D 6S TS 5C
5H 8C 5S 3C 4S 3D 7C 8D AS 3H
AS TS 7C 3H AD 7D JC QS 6C 6H
3S 9S 4C AC QH 5H 5D 9H TS 4H
6C 5C 7H 7S TD AD JD 5S 2H 2S
7D 6C KC 3S JD 8D 8S TS QS KH
8S QS 8D 6C TH AC AH 2C 8H 9S
7H TD KH QH 8S 3D 4D AH JD AS
TS 3D 2H JC 2S JH KH 6C QC JS
KC TH 2D 6H 7S 2S TC 8C 9D QS
3C 9D 6S KH 8H 6D 5D TH 2C 2H
6H TC 7D AD 4D 8S TS 9H TD 7S
JS 6D JD JC 2H AC 6C 3D KH 8D
KH JD 9S 5D 4H 4C 3H 7S QS 5C
4H JD 5D 3S 3C 4D KH QH QS 7S
JD TS 8S QD AH 4C 6H 3S 5S 2C
QS 3D JD AS 8D TH 7C 6S QC KS
7S 2H 8C QC 7H AC 6D 2D TH KH
5S 6C 7H KH 7D AH 8C 5C 7S 3D
3C KD AD 7D 6C 4D KS 2D 8C 4S
7C 8D 5S 2D 2S AH AD 2C 9D TD
3C AD 4S KS JH 7C 5C 8C 9C TH
AS TD 4D 7C JD 8C QH 3C 5H 9S
3H 9C 8S 9S 6S QD KS AH 5H JH
QC 9C 5S 4H 2H TD 7D AS 8C 9D
8C 2C 9D KD TC 7S 3D KH QC 3C
4D AS 4C QS 5S 9D 6S JD QH KS
6D AH 6C 4C 5H TS 9H 7D 3D 5S
QS JD 7C 8D 9C AC 3S 6S 6C KH
8H JH 5D 9S 6D AS 6S 3S QC 7H
QD AD 5C JH 2H AH 4H AS KC 2C
JH 9C 2C 6H 2D JS 5D 9H KC 6D
7D 9D KD TH 3H AS 6S QC 6H AD
JD 4H 7D KC 3H JS 3C TH 3D QS
4C 3H 8C QD 5H 6H AS 8H AD JD
TH 8S KD 5D QC 7D JS 5S 5H TS
7D KC 9D QS 3H 3C 6D TS 7S AH
7C 4H 7H AH QC AC 4D 5D 6D TH
3C 4H 2S KD 8H 5H JH TC 6C JD
4S 8C 3D 4H JS TD 7S JH QS KD
7C QC KD 4D 7H 6S AD TD TC KH
5H 9H KC 3H 4D 3D AD 6S QD 6H
TH 7C 6H TS QH 5S 2C KC TD 6S
7C 4D 5S JD JH 7D AC KD KH 4H
7D 6C 8D 8H 5C JH 8S QD TH JD
8D 7D 6C 7C 9D KD AS 5C QH JH
9S 2C 8C 3C 4C KS JH 2D 8D 4H
7S 6C JH KH 8H 3H 9D 2D AH 6D
4D TC 9C 8D 7H TD KS TH KD 3C
JD 9H 8D QD AS KD 9D 2C 2S 9C
8D 3H 5C 7H KS 5H QH 2D 8C 9H
2D TH 6D QD 6C KC 3H 3S AD 4C
4H 3H JS 9D 3C TC 5H QH QC JC
3D 5C 6H 3S 3C JC 5S 7S 2S QH
AC 5C 8C 4D 5D 4H 2S QD 3C 3H
2C TD AH 9C KD JS 6S QD 4C QC
QS 8C 3S 4H TC JS 3H 7C JC AD
5H 4D 9C KS JC TD 9S TS 8S 9H
QD TS 7D AS AC 2C TD 6H 8H AH
6S AD 8C 4S 9H 8D 9D KH 8S 3C
QS 4D 2D 7S KH JS JC AD 4C 3C
QS 9S 7H KC TD TH 5H JS AC JH
6D AC 2S QS 7C AS KS 6S KH 5S
6D 8H KH 3C QS 2H 5C 9C 9D 6C
JS 2C 4C 6H 7D JC AC QD TD 3H
4H QC 8H JD 4C KD KS 5C KC 7S
6D 2D 3H 2S QD 5S 7H AS TH 6S
AS 6D 8D 2C 8S TD 8H QD JC AH
9C 9H 2D TD QH 2H 5C TC 3D 8H
KC 8S 3D KH 2S TS TC 6S 4D JH
9H 9D QS AC KC 6H 5D 4D 8D AH
9S 5C QS 4H 7C 7D 2H 8S AD JS
3D AC 9S AS 2C 2D 2H 3H JC KH
7H QH KH JD TC KS 5S 8H 4C 8D
2H 7H 3S 2S 5H QS 3C AS 9H KD
AD 3D JD 6H 5S 9C 6D AC 9S 3S
3D 5D 9C 2D AC 4S 2S AD 6C 6S
QC 4C 2D 3H 6S KC QH QD 2H JH
QC 3C 8S 4D 9S 2H 5C 8H QS QD
6D KD 6S 7H 3S KH 2H 5C JC 6C
3S 9S TC 6S 8H 2D AD 7S 8S TS
3C 6H 9C 3H 5C JC 8H QH TD QD
3C JS QD 5D TD 2C KH 9H TH AS
9S TC JD 3D 5C 5H AD QH 9H KC
TC 7H 4H 8H 3H TD 6S AC 7C 2S
QS 9D 5D 3C JC KS 4D 6C JH 2S
9S 6S 3C 7H TS 4C KD 6D 3D 9C
2D 9H AH AC 7H 2S JH 3S 7C QC
QD 9H 3C 2H AC AS 8S KD 8C KH
2D 7S TD TH 6D JD 8D 4D 2H 5S
8S QH KD JD QS JH 4D KC 5H 3S
3C KH QC 6D 8H 3S AH 7D TD 2D
5S 9H QH 4S 6S 6C 6D TS TH 7S
6C 4C 6D QS JS 9C TS 3H 8D 8S
JS 5C 7S AS 2C AH 2H AD 5S TC
KD 6C 9C 9D TS 2S JC 4H 2C QD
QS 9H TC 3H KC KS 4H 3C AD TH
KH 9C 2H KD 9D TC 7S KC JH 2D
7C 3S KC AS 8C 5D 9C 9S QH 3H
2D 8C TD 4C 2H QC 5D TC 2C 7D
KS 4D 6C QH TD KH 5D 7C AD 8D
2S 9S 8S 4C 8C 3D 6H QD 7C 7H
6C 8S QH 5H TS 5C 3C 4S 2S 2H
8S 6S 2H JC 3S 3H 9D 8C 2S 7H
QC 2C 8H 9C AC JD 4C 4H 6S 3S
3H 3S 7D 4C 9S 5H 8H JC 3D TC
QH 2S 2D 9S KD QD 9H AD 6D 9C
8D 2D KS 9S JC 4C JD KC 4S TH
KH TS 6D 4D 5C KD 5H AS 9H AD
QD JS 7C 6D 5D 5C TH 5H QH QS
9D QH KH 5H JH 4C 4D TC TH 6C
KH AS TS 9D KD 9C 7S 4D 8H 5S
KH AS 2S 7D 9D 4C TS TH AH 7C
KS 4D AC 8S 9S 8D TH QH 9D 5C
5D 5C 8C QS TC 4C 3D 3S 2C 8D
9D KS 2D 3C KC 4S 8C KH 6C JC
8H AH 6H 7D 7S QD 3C 4C 6C KC
3H 2C QH 8H AS 7D 4C 8C 4H KC
QD 5S 4H 2C TD AH JH QH 4C 8S
3H QS 5S JS 8H 2S 9H 9C 3S 2C
6H TS 7S JC QD AC TD KC 5S 3H
QH AS QS 7D JC KC 2C 4C 5C 5S
QH 3D AS JS 4H 8D 7H JC 2S 9C
5D 4D 2S 4S 9D 9C 2D QS 8H 7H
6D 7H 3H JS TS AC 2D JH 7C 8S
JH 5H KC 3C TC 5S 9H 4C 8H 9D
8S KC 5H 9H AD KS 9D KH 8D AH
JC 2H 9H KS 6S 3H QC 5H AH 9C
5C KH 5S AD 6C JC 9H QC 9C TD
5S 5D JC QH 2D KS 8H QS 2H TS
JH 5H 5S AH 7H 3C 8S AS TD KH
6H 3D JD 2C 4C KC 7S AH 6C JH
4C KS 9D AD 7S KC 7D 8H 3S 9C
7H 5C 5H 3C 8H QC 3D KH 6D JC
2D 4H 5D 7D QC AD AH 9H QH 8H
KD 8C JS 9D 3S 3C 2H 5D 6D 2S
8S 6S TS 3C 6H 8D 5S 3H TD 6C
KS 3D JH 9C 7C 9S QS 5S 4H 6H
7S 6S TH 4S KC KD 3S JC JH KS
7C 3C 2S 6D QH 2C 7S 5H 8H AH
KC 8D QD 6D KH 5C 7H 9D 3D 9C
6H 2D 8S JS 9S 2S 6D KC 7C TC
KD 9C JH 7H KC 8S 2S 7S 3D 6H
4H 9H 2D 4C 8H 7H 5S 8S 2H 8D
AD 7C 3C 7S 5S 4D 9H 3D JC KH
5D AS 7D 6D 9C JC 4C QH QS KH
KD JD 7D 3D QS QC 8S 6D JS QD
6S 8C 5S QH TH 9H AS AC 2C JD
QC KS QH 7S 3C 4C 5C KC 5D AH
6C 4H 9D AH 2C 3H KD 3D TS 5C
TD 8S QS AS JS 3H KD AC 4H KS
7D 5D TS 9H 4H 4C 9C 2H 8C QC
2C 7D 9H 4D KS 4C QH AD KD JS
QD AD AH KH 9D JS 9H JC KD JD
8S 3C 4S TS 7S 4D 5C 2S 6H 7C
JS 7S 5C KD 6D QH 8S TD 2H 6S
QH 6C TC 6H TD 4C 9D 2H QC 8H
3D TS 4D 2H 6H 6S 2C 7H 8S 6C
9H 9D JD JH 3S AH 2C 6S 3H 8S
2C QS 8C 5S 3H 2S 7D 3C AD 4S
5C QC QH AS TS 4S 6S 4C 5H JS
JH 5C TD 4C 6H JS KD KH QS 4H
TC KH JC 4D 9H 9D 8D KC 3C 8H
2H TC 8S AD 9S 4H TS 7H 2C 5C
4H 2S 6C 5S KS AH 9C 7C 8H KD
TS QH TD QS 3C JH AH 2C 8D 7D
5D KC 3H 5S AC 4S 7H QS 4C 2H
3D 7D QC KH JH 6D 6C TD TH KD
5S 8D TH 6C 9D 7D KH 8C 9S 6D
JD QS 7S QC 2S QH JC 4S KS 8D
7S 5S 9S JD KD 9C JC AD 2D 7C
4S 5H AH JH 9C 5D TD 7C 2D 6S
KC 6C 7H 6S 9C QD 5S 4H KS TD
6S 8D KS 2D TH TD 9H JD TS 3S
KH JS 4H 5D 9D TC TD QC JD TS
QS QD AC AD 4C 6S 2D AS 3H KC
4C 7C 3C TD QS 9C KC AS 8D AD
KC 7H QC 6D 8H 6S 5S AH 7S 8C
3S AD 9H JC 6D JD AS KH 6S JH
AD 3D TS KS 7H JH 2D JS QD AC
9C JD 7C 6D TC 6H 6C JC 3D 3S
QC KC 3S JC KD 2C 8D AH QS TS
AS KD 3D JD 8H 7C 8C 5C QD 6C


================================================
FILE: project_euler/problem_054/sol1.py
================================================
"""
Problem: https://projecteuler.net/problem=54

In the card game poker, a hand consists of five cards and are ranked,
from lowest to highest, in the following way:

High Card: Highest value card.
One Pair: Two cards of the same value.
Two Pairs: Two different pairs.
Three of a Kind: Three cards of the same value.
Straight: All cards are consecutive values.
Flush: All cards of the same suit.
Full House: Three of a kind and a pair.
Four of a Kind: Four cards of the same value.
Straight Flush: All cards are consecutive values of same suit.
Royal Flush: Ten, Jack, Queen, King, Ace, in same suit.

The cards are valued in the order:
2, 3, 4, 5, 6, 7, 8, 9, 10, Jack, Queen, King, Ace.

If two players have the same ranked hands then the rank made up of the highest
value wins; for example, a pair of eights beats a pair of fives.
But if two ranks tie, for example, both players have a pair of queens, then highest
cards in each hand are compared; if the highest cards tie then the next highest
cards are compared, and so on.

The file, poker.txt, contains one-thousand random hands dealt to two players.
Each line of the file contains ten cards (separated by a single space): the
first five are Player 1's cards and the last five are Player 2's cards.
You can assume that all hands are valid (no invalid characters or repeated cards),
each player's hand is in no specific order, and in each hand there is a clear winner.

How many hands does Player 1 win?

Resources used:
https://en.wikipedia.org/wiki/Texas_hold_%27em
https://en.wikipedia.org/wiki/List_of_poker_hands

Similar problem on codewars:
https://www.codewars.com/kata/ranking-poker-hands
https://www.codewars.com/kata/sortable-poker-hands
"""

from __future__ import annotations

import os


class PokerHand:
    """Create an object representing a Poker Hand based on an input of a
    string which represents the best 5-card combination from the player's hand
    and board cards.

    Attributes: (read-only)
        hand: a string representing the hand consisting of five cards

    Methods:
        compare_with(opponent): takes in player's hand (self) and
            opponent's hand (opponent) and compares both hands according to
            the rules of Texas Hold'em.
            Returns one of 3 strings (Win, Loss, Tie) based on whether
            player's hand is better than the opponent's hand.

        hand_name(): Returns a string made up of two parts: hand name
            and high card.

    Supported operators:
        Rich comparison operators: <, >, <=, >=, ==, !=

    Supported built-in methods and functions:
        list.sort(), sorted()
    """

    _HAND_NAME = (
        "High card",
        "One pair",
        "Two pairs",
        "Three of a kind",
        "Straight",
        "Flush",
        "Full house",
        "Four of a kind",
        "Straight flush",
        "Royal flush",
    )

    _CARD_NAME = (
        "",  # placeholder as tuples are zero-indexed
        "One",
        "Two",
        "Three",
        "Four",
        "Five",
        "Six",
        "Seven",
        "Eight",
        "Nine",
        "Ten",
        "Jack",
        "Queen",
        "King",
        "Ace",
    )

    def __init__(self, hand: str) -> None:
        """
        Initialize hand.
        Hand should of type str and should contain only five cards each
        separated by a space.

        The cards should be of the following format:
        [card value][card suit]

        The first character is the value of the card:
        2, 3, 4, 5, 6, 7, 8, 9, T(en), J(ack), Q(ueen), K(ing), A(ce)

        The second character represents the suit:
        S(pades), H(earts), D(iamonds), C(lubs)

        For example: "6S 4C KC AS TH"
        """
        if not isinstance(hand, str):
            msg = f"Hand should be of type 'str': {hand!r}"
            raise TypeError(msg)
        # split removes duplicate whitespaces so no need of strip
        if len(hand.split(" ")) != 5:
            msg = f"Hand should contain only 5 cards: {hand!r}"
            raise ValueError(msg)
        self._hand = hand
        self._first_pair = 0
        self._second_pair = 0
        self._card_values, self._card_suit = self._internal_state()
        self._hand_type = self._get_hand_type()
        self._high_card = self._card_values[0]

    @property
    def hand(self):
        """Returns the self hand"""
        return self._hand

    def compare_with(self, other: PokerHand) -> str:
        """
        Determines the outcome of comparing self hand with other hand.
        Returns the output as 'Win', 'Loss', 'Tie' according to the rules of
        Texas Hold'em.

        Here are some examples:
        >>> player = PokerHand("2H 3H 4H 5H 6H")  # Stright flush
        >>> opponent = PokerHand("KS AS TS QS JS")  # Royal flush
        >>> player.compare_with(opponent)
        'Loss'

        >>> player = PokerHand("2S AH 2H AS AC")  # Full house
        >>> opponent = PokerHand("2H 3H 5H 6H 7H")  # Flush
        >>> player.compare_with(opponent)
        'Win'

        >>> player = PokerHand("2S AH 4H 5S 6C")  # High card
        >>> opponent = PokerHand("AD 4C 5H 6H 2C")  # High card
        >>> player.compare_with(opponent)
        'Tie'
        """
        # Breaking the tie works on the following order of precedence:
        # 1. First pair (default 0)
        # 2. Second pair (default 0)
        # 3. Compare all cards in reverse order because they are sorted.

        # First pair and second pair will only be a non-zero value if the card
        # type is either from the following:
        # 21: Four of a kind
        # 20: Full house
        # 17: Three of a kind
        # 16: Two pairs
        # 15: One pair
        if self._hand_type > other._hand_type:
            return "Win"
        elif self._hand_type < other._hand_type:
            return "Loss"
        elif self._first_pair == other._first_pair:
            if self._second_pair == other._second_pair:
                return self._compare_cards(other)
            else:
                return "Win" if self._second_pair > other._second_pair else "Loss"
        return "Win" if self._first_pair > other._first_pair else "Loss"

    # This function is not part of the problem, I did it just for fun
    def hand_name(self) -> str:
        """
        Return the name of the hand in the following format:
        'hand name, high card'

        Here are some examples:
        >>> PokerHand("KS AS TS QS JS").hand_name()
        'Royal flush'

        >>> PokerHand("2D 6D 3D 4D 5D").hand_name()
        'Straight flush, Six-high'

        >>> PokerHand("JC 6H JS JD JH").hand_name()
        'Four of a kind, Jacks'

        >>> PokerHand("3D 2H 3H 2C 2D").hand_name()
        'Full house, Twos over Threes'

        >>> PokerHand("2H 4D 3C AS 5S").hand_name()  # Low ace
        'Straight, Five-high'

        Source: https://en.wikipedia.org/wiki/List_of_poker_hands
        """
        name = PokerHand._HAND_NAME[self._hand_type - 14]
        high = PokerHand._CARD_NAME[self._high_card]
        pair1 = PokerHand._CARD_NAME[self._first_pair]
        pair2 = PokerHand._CARD_NAME[self._second_pair]
        if self._hand_type in [22, 19, 18]:
            return name + f", {high}-high"
        elif self._hand_type in [21, 17, 15]:
            return name + f", {pair1}s"
        elif self._hand_type in [20, 16]:
            join = "over" if self._hand_type == 20 else "and"
            return name + f", {pair1}s {join} {pair2}s"
        elif self._hand_type == 23:
            return name
        else:
            return name + f", {high}"

    def _compare_cards(self, other: PokerHand) -> str:
        # Enumerate gives us the index as well as the element of a list
        for index, card_value in enumerate(self._card_values):
            if card_value != other._card_values[index]:
                return "Win" if card_value > other._card_values[index] else "Loss"
        return "Tie"

    def _get_hand_type(self) -> int:
        # Number representing the type of hand internally:
        # 23: Royal flush
        # 22: Straight flush
        # 21: Four of a kind
        # 20: Full house
        # 19: Flush
        # 18: Straight
        # 17: Three of a kind
        # 16: Two pairs
        # 15: One pair
        # 14: High card
        if self._is_flush():
            if self._is_five_high_straight() or self._is_straight():
                return 23 if sum(self._card_values) == 60 else 22
            return 19
        elif self._is_five_high_straight() or self._is_straight():
            return 18
        return 14 + self._is_same_kind()

    def _is_flush(self) -> bool:
        return len(self._card_suit) == 1

    def _is_five_high_straight(self) -> bool:
        # If a card is a five high straight (low ace) change the location of
        # ace from the start of the list to the end. Check whether the first
        # element is ace or not. (Don't want to change again)
        # Five high straight (low ace): AH 2H 3S 4C 5D
        # Why use sorted here? One call to this function will mutate the list to
        # [5, 4, 3, 2, 14] and so for subsequent calls (which will be rare) we
        # need to compare the sorted version.
        # Refer test_multiple_calls_five_high_straight in test_poker_hand.py
        if sorted(self._card_values) == [2, 3, 4, 5, 14]:
            if self._card_values[0] == 14:
                # Remember, our list is sorted in reverse order
                ace_card = self._card_values.pop(0)
                self._card_values.append(ace_card)
            return True
        return False

    def _is_straight(self) -> bool:
        for i in range(4):
            if self._card_values[i] - self._card_values[i + 1] != 1:
                return False
        return True

    def _is_same_kind(self) -> int:
        # Kind Values for internal use:
        # 7: Four of a kind
        # 6: Full house
        # 3: Three of a kind
        # 2: Two pairs
        # 1: One pair
        # 0: False
        kind = val1 = val2 = 0
        for i in range(4):
            # Compare two cards at a time, if they are same increase 'kind',
            # add the value of the card to val1, if it is repeating again we
            # will add 2 to 'kind' as there are now 3 cards with same value.
            # If we get card of different value than val1, we will do the same
            # thing with val2
            if self._card_values[i] == self._card_values[i + 1]:
                if not val1:
                    val1 = self._card_values[i]
                    kind += 1
                elif val1 == self._card_values[i]:
                    kind += 2
                elif not val2:
                    val2 = self._card_values[i]
                    kind += 1
                elif val2 == self._card_values[i]:
                    kind += 2
        # For consistency in hand type (look at note in _get_hand_type function)
        kind = kind + 2 if kind in [4, 5] else kind
        # first meaning first pair to compare in 'compare_with'
        first = max(val1, val2)
        second = min(val1, val2)
        # If it's full house (three count pair + two count pair), make sure
        # first pair is three count and if not then switch them both.
        if kind == 6 and self._card_values.count(first) != 3:
            first, second = second, first
        self._first_pair = first
        self._second_pair = second
        return kind

    def _internal_state(self) -> tuple[list[int], set[str]]:
        # Internal representation of hand as a list of card values and
        # a set of card suit
        trans: dict = {"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
        new_hand = self._hand.translate(str.maketrans(trans)).split()
        card_values = [int(card[:-1]) for card in new_hand]
        card_suit = {card[-1] for card in new_hand}
        return sorted(card_values, reverse=True), card_suit

    def __repr__(self):
        return f'{self.__class__}("{self._hand}")'

    def __str__(self):
        return self._hand

    # Rich comparison operators (used in list.sort() and sorted() builtin functions)
    # Note that this is not part of the problem but another extra feature where
    # if you have a list of PokerHand objects, you can sort them just through
    # the builtin functions.
    def __eq__(self, other):
        if isinstance(other, PokerHand):
            return self.compare_with(other) == "Tie"
        return NotImplemented

    def __lt__(self, other):
        if isinstance(other, PokerHand):
            return self.compare_with(other) == "Loss"
        return NotImplemented

    def __le__(self, other):
        if isinstance(other, PokerHand):
            return self < other or self == other
        return NotImplemented

    def __gt__(self, other):
        if isinstance(other, PokerHand):
            return not self < other and self != other
        return NotImplemented

    def __ge__(self, other):
        if isinstance(other, PokerHand):
            return not self < other
        return NotImplemented

    def __hash__(self):
        return object.__hash__(self)


def solution() -> int:
    # Solution for problem number 54 from Project Euler
    # Input from poker_hands.txt file
    answer = 0
    script_dir = os.path.abspath(os.path.dirname(__file__))
    poker_hands = os.path.join(script_dir, "poker_hands.txt")
    with open(poker_hands) as file_hand:
        for line in file_hand:
            player_hand = line[:14].strip()
            opponent_hand = line[15:].strip()
            player, opponent = PokerHand(player_hand), PokerHand(opponent_hand)
            output = player.compare_with(opponent)
            if output == "Win":
                answer += 1
    return answer


if __name__ == "__main__":
    solution()


================================================
FILE: project_euler/problem_054/test_poker_hand.py
================================================
import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sol1 import PokerHand

SORTED_HANDS = (
    "4S 3H 2C 7S 5H",
    "9D 8H 2C 6S 7H",
    "2D 6D 9D TH 7D",
    "TC 8C 2S JH 6C",
    "JH 8S TH AH QH",
    "TS KS 5S 9S AC",
    "KD 6S 9D TH AD",
    "KS 8D 4D 9S 4S",  # pair
    "8C 4S KH JS 4D",  # pair
    "QH 8H KD JH 8S",  # pair
    "KC 4H KS 2H 8D",  # pair
    "KD 4S KC 3H 8S",  # pair
    "AH 8S AS KC JH",  # pair
    "3H 4C 4H 3S 2H",  # 2 pairs
    "5S 5D 2C KH KH",  # 2 pairs
    "3C KH 5D 5S KH",  # 2 pairs
    "AS 3C KH AD KH",  # 2 pairs
    "7C 7S 3S 7H 5S",  # 3 of a kind
    "7C 7S KH 2H 7H",  # 3 of a kind
    "AC KH QH AH AS",  # 3 of a kind
    "2H 4D 3C AS 5S",  # straight (low ace)
    "3C 5C 4C 2C 6H",  # straight
    "6S 8S 7S 5H 9H",  # straight
    "JS QS 9H TS KH",  # straight
    "QC KH TS JS AH",  # straight (high ace)
    "8C 9C 5C 3C TC",  # flush
    "3S 8S 9S 5S KS",  # flush
    "4C 5C 9C 8C KC",  # flush
    "JH 8H AH KH QH",  # flush
    "3D 2H 3H 2C 2D",  # full house
    "2H 2C 3S 3H 3D",  # full house
    "KH KC 3S 3H 3D",  # full house
    "JC 6H JS JD JH",  # 4 of a kind
    "JC 7H JS JD JH",  # 4 of a kind
    "JC KH JS JD JH",  # 4 of a kind
    "2S AS 4S 5S 3S",  # straight flush (low ace)
    "2D 6D 3D 4D 5D",  # straight flush
    "5C 6C 3C 7C 4C",  # straight flush
    "JH 9H TH KH QH",  # straight flush
    "JH AH TH KH QH",  # royal flush (high ace straight flush)
)

TEST_COMPARE = (
    ("2H 3H 4H 5H 6H", "KS AS TS QS JS", "Loss"),
    ("2H 3H 4H 5H 6H", "AS AD AC AH JD", "Win"),
    ("AS AH 2H AD AC", "JS JD JC JH 3D", "Win"),
    ("2S AH 2H AS AC", "JS JD JC JH AD", "Loss"),
    ("2S AH 2H AS AC", "2H 3H 5H 6H 7H", "Win"),
    ("AS 3S 4S 8S 2S", "2H 3H 5H 6H 7H", "Win"),
    ("2H 3H 5H 6H 7H", "2S 3H 4H 5S 6C", "Win"),
    ("2S 3H 4H 5S 6C", "3D 4C 5H 6H 2S", "Tie"),
    ("2S 3H 4H 5S 6C", "AH AC 5H 6H AS", "Win"),
    ("2S 2H 4H 5S 4C", "AH AC 5H 6H AS", "Loss"),
    ("2S 2H 4H 5S 4C", "AH AC 5H 6H 7S", "Win"),
    ("6S AD 7H 4S AS", "AH AC 5H 6H 7S", "Loss"),
    ("2S AH 4H 5S KC", "AH AC 5H 6H 7S", "Loss"),
    ("2S 3H 6H 7S 9C", "7H 3C TH 6H 9S", "Loss"),
    ("4S 5H 6H TS AC", "3S 5H 6H TS AC", "Win"),
    ("2S AH 4H 5S 6C", "AD 4C 5H 6H 2C", "Tie"),
    ("AS AH 3H AD AC", "AS AH 2H AD AC", "Win"),
    ("AH AC 5H 5C QS", "AH AC 5H 5C KS", "Loss"),
    ("AH AC 5H 5C QS", "KH KC 5H 5C QS", "Win"),
    ("7C 7S KH 2H 7H", "3C 3S AH 2H 3H", "Win"),
    ("3C 3S AH 2H 3H", "7C 7S KH 2H 7H", "Loss"),
    ("6H 5H 4H 3H 2H", "5H 4H 3H 2H AH", "Win"),
    ("5H 4H 3H 2H AH", "5H 4H 3H 2H AH", "Tie"),
    ("5H 4H 3H 2H AH", "6H 5H 4H 3H 2H", "Loss"),
    ("AH AD KS KC AC", "AH KD KH AC KC", "Win"),
    ("2H 4D 3C AS 5S", "2H 4D 3C 6S 5S", "Loss"),
    ("2H 3S 3C 3H 2S", "3S 3C 2S 2H 2D", "Win"),
    ("4D 6D 5D 2D JH", "3S 8S 3H TC KH", "Loss"),
    ("4S 6C 8S 3S 7S", "AD KS 2D 7D 7C", "Loss"),
    ("6S 4C 7H 8C 3H", "5H JC AH 9D 9C", "Loss"),
    ("9D 9H JH TC QH", "3C 2S JS 5C 7H", "Win"),
    ("2H TC 8S AD 9S", "4H TS 7H 2C 5C", "Win"),
    ("9D 3S 2C 7S 7C", "JC TD 3C TC 9H", "Loss"),
)

TEST_FLUSH = (
    ("2H 3H 4H 5H 6H", True),
    ("AS AH 2H AD AC", False),
    ("2H 3H 5H 6H 7H", True),
    ("KS AS TS QS JS", True),
    ("8H 9H QS JS TH", False),
    ("AS 3S 4S 8S 2S", True),
)

TEST_STRAIGHT = (
    ("2H 3H 4H 5H 6H", True),
    ("AS AH 2H AD AC", False),
    ("2H 3H 5H 6H 7H", False),
    ("KS AS TS QS JS", True),
    ("8H 9H QS JS TH", True),
)

TEST_FIVE_HIGH_STRAIGHT = (
    ("2H 4D 3C AS 5S", True, [5, 4, 3, 2, 14]),
    ("2H 5D 3C AS 5S", False, [14, 5, 5, 3, 2]),
    ("JH QD KC AS TS", False, [14, 13, 12, 11, 10]),
    ("9D 3S 2C 7S 7C", False, [9, 7, 7, 3, 2]),
)

TEST_KIND = (
    ("JH AH TH KH QH", 0),
    ("JH 9H TH KH QH", 0),
    ("JC KH JS JD JH", 7),
    ("KH KC 3S 3H 3D", 6),
    ("8C 9C 5C 3C TC", 0),
    ("JS QS 9H TS KH", 0),
    ("7C 7S KH 2H 7H", 3),
    ("3C KH 5D 5S KH", 2),
    ("QH 8H KD JH 8S", 1),
    ("2D 6D 9D TH 7D", 0),
)

TEST_TYPES = (
    ("JH AH TH KH QH", 23),
    ("JH 9H TH KH QH", 22),
    ("JC KH JS JD JH", 21),
    ("KH KC 3S 3H 3D", 20),
    ("8C 9C 5C 3C TC", 19),
    ("JS QS 9H TS KH", 18),
    ("7C 7S KH 2H 7H", 17),
    ("3C KH 5D 5S KH", 16),
    ("QH 8H KD JH 8S", 15),
    ("2D 6D 9D TH 7D", 14),
)


def generate_random_hand():
    play, oppo = randrange(len(SORTED_HANDS)), randrange(len(SORTED_HANDS))
    expected = ["Loss", "Tie", "Win"][(play >= oppo) + (play > oppo)]
    hand, other = SORTED_HANDS[play], SORTED_HANDS[oppo]
    return hand, other, expected


def generate_random_hands(number_of_hands: int = 100):
    return (generate_random_hand() for _ in range(number_of_hands))


@pytest.mark.parametrize(("hand", "expected"), TEST_FLUSH)
def test_hand_is_flush(hand, expected):
    assert PokerHand(hand)._is_flush() == expected


@pytest.mark.parametrize(("hand", "expected"), TEST_STRAIGHT)
def test_hand_is_straight(hand, expected):
    assert PokerHand(hand)._is_straight() == expected


@pytest.mark.parametrize(("hand", "expected", "card_values"), TEST_FIVE_HIGH_STRAIGHT)
def test_hand_is_five_high_straight(hand, expected, card_values):
    player = PokerHand(hand)
    assert player._is_five_high_straight() == expected
    assert player._card_values == card_values


@pytest.mark.parametrize(("hand", "expected"), TEST_KIND)
def test_hand_is_same_kind(hand, expected):
    assert PokerHand(hand)._is_same_kind() == expected


@pytest.mark.parametrize(("hand", "expected"), TEST_TYPES)
def test_hand_values(hand, expected):
    assert PokerHand(hand)._hand_type == expected


@pytest.mark.parametrize(("hand", "other", "expected"), TEST_COMPARE)
def test_compare_simple(hand, other, expected):
    assert PokerHand(hand).compare_with(PokerHand(other)) == expected


@pytest.mark.parametrize(("hand", "other", "expected"), generate_random_hands())
def test_compare_random(hand, other, expected):
    assert PokerHand(hand).compare_with(PokerHand(other)) == expected


def test_hand_sorted():
    poker_hands = [PokerHand(hand) for hand in SORTED_HANDS]
    list_copy = poker_hands.copy()
    shuffle(list_copy)
    user_sorted = chain(sorted(list_copy))
    for index, hand in enumerate(user_sorted):
        assert hand == poker_hands[index]


def test_custom_sort_five_high_straight():
    # Test that five high straights are compared correctly.
    pokerhands = [PokerHand("2D AC 3H 4H 5S"), PokerHand("2S 3H 4H 5S 6C")]
    pokerhands.sort(reverse=True)
    assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"


def test_multiple_calls_five_high_straight():
    # Multiple calls to five_high_straight function should still return True
    # and shouldn't mutate the list in every call other than the first.
    pokerhand = PokerHand("2C 4S AS 3D 5C")
    expected = True
    expected_card_values = [5, 4, 3, 2, 14]
    for _ in range(10):
        assert pokerhand._is_five_high_straight() == expected
        assert pokerhand._card_values == expected_card_values


def test_euler_project():
    # Problem number 54 from Project Euler
    # Testing from poker_hands.txt file
    answer = 0
    script_dir = os.path.abspath(os.path.dirname(__file__))
    poker_hands = os.path.join(script_dir, "poker_hands.txt")
    with open(poker_hands) as file_hand:
        for line in file_hand:
            player_hand = line[:14].strip()
            opponent_hand = line[15:].strip()
            player, opponent = PokerHand(player_hand), PokerHand(opponent_hand)
            output = player.compare_with(opponent)
            if output == "Win":
                answer += 1
    assert answer == 376


================================================
FILE: project_euler/problem_055/__init__.py
================================================


================================================
FILE: project_euler/problem_055/sol1.py
================================================
"""
Lychrel numbers
Problem 55: https://projecteuler.net/problem=55

If we take 47, reverse and add, 47 + 74 = 121, which is palindromic.

Not all numbers produce palindromes so quickly. For example,
349 + 943 = 1292,
1292 + 2921 = 4213
4213 + 3124 = 7337
That is, 349 took three iterations to arrive at a palindrome.

Although no one has proved it yet, it is thought that some numbers, like 196,
never produce a palindrome. A number that never forms a palindrome through the
reverse and add process is called a Lychrel number. Due to the theoretical nature
of these numbers, and for the purpose of this problem, we shall assume that a number
is Lychrel until proven otherwise. In addition you are given that for every number
below ten-thousand, it will either (i) become a palindrome in less than fifty
iterations, or, (ii) no one, with all the computing power that exists, has managed
so far to map it to a palindrome. In fact, 10677 is the first number to be shown
to require over fifty iterations before producing a palindrome:
4668731596684224866951378664 (53 iterations, 28-digits).

Surprisingly, there are palindromic numbers that are themselves Lychrel numbers;
the first example is 4994.
How many Lychrel numbers are there below ten-thousand?
"""


def is_palindrome(n: int) -> bool:
    """
    Returns True if a number is palindrome.
    >>> is_palindrome(12567321)
    False
    >>> is_palindrome(1221)
    True
    >>> is_palindrome(9876789)
    True
    """
    return str(n) == str(n)[::-1]


def sum_reverse(n: int) -> int:
    """
    Returns the sum of n and reverse of n.
    >>> sum_reverse(123)
    444
    >>> sum_reverse(3478)
    12221
    >>> sum_reverse(12)
    33
    """
    return int(n) + int(str(n)[::-1])


def solution(limit: int = 10000) -> int:
    """
    Returns the count of all lychrel numbers below limit.
    >>> solution(10000)
    249
    >>> solution(5000)
    76
    >>> solution(1000)
    13
    """
    lychrel_nums = []
    for num in range(1, limit):
        iterations = 0
        a = num
        while iterations < 50:
            num = sum_reverse(num)
            iterations += 1
            if is_palindrome(num):
                break
        else:
            lychrel_nums.append(a)
    return len(lychrel_nums)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_056/__init__.py
================================================


================================================
FILE: project_euler/problem_056/sol1.py
================================================
"""
Project Euler Problem 56: https://projecteuler.net/problem=56

A googol (10^100) is a massive number: one followed by one-hundred zeros;
100^100 is almost unimaginably large: one followed by two-hundred zeros.
Despite their size, the sum of the digits in each number is only 1.

Considering natural numbers of the form, ab, where a, b < 100,
what is the maximum digital sum?
"""


def solution(a: int = 100, b: int = 100) -> int:
    """
    Considering natural numbers of the form, a**b, where a, b < 100,
    what is the maximum digital sum?
    :param a:
    :param b:
    :return:
    >>> solution(10,10)
    45

    >>> solution(100,100)
    972

    >>> solution(100,200)
    1872
    """

    # RETURN the MAXIMUM from the list of SUMs of the list of INT converted from STR of
    # BASE raised to the POWER
    return max(
        sum(int(x) for x in str(base**power)) for base in range(a) for power in range(b)
    )


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: project_euler/problem_057/__init__.py
================================================


================================================
FILE: project_euler/problem_057/sol1.py
================================================
"""
Project Euler Problem 57: https://projecteuler.net/problem=57
It is possible to show that the square root of two can be expressed as an infinite
continued fraction.

sqrt(2) = 1 + 1 / (2 + 1 / (2 + 1 / (2 + ...)))

By expanding this for the first four iterations, we get:
1 + 1 / 2 = 3 / 2 = 1.5
1 + 1 / (2 + 1 / 2} = 7 / 5 = 1.4
1 + 1 / (2 + 1 / (2 + 1 / 2)) = 17 / 12 = 1.41666...
1 + 1 / (2 + 1 / (2 + 1 / (2 + 1 / 2))) = 41/ 29 = 1.41379...

The next three expansions are 99/70, 239/169, and 577/408, but the eighth expansion,
1393/985, is the first example where the number of digits in the numerator exceeds
the number of digits in the denominator.

In the first one-thousand expansions, how many fractions contain a numerator with
more digits than the denominator?
"""


def solution(n: int = 1000) -> int:
    """
    returns number of fractions containing a numerator with more digits than
    the denominator in the first n expansions.
    >>> solution(14)
    2
    >>> solution(100)
    15
    >>> solution(10000)
    1508
    """
    prev_numerator, prev_denominator = 1, 1
    result = []
    for i in range(1, n + 1):
        numerator = prev_numerator + 2 * prev_denominator
        denominator = prev_numerator + prev_denominator
        if len(str(numerator)) > len(str(denominator)):
            result.append(i)
        prev_numerator = numerator
        prev_denominator = denominator

    return len(result)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_058/__init__.py
================================================


================================================
FILE: project_euler/problem_058/sol1.py
================================================
"""
Project Euler Problem 58:https://projecteuler.net/problem=58


Starting with 1 and spiralling anticlockwise in the following way,
a square spiral with side length 7 is formed.

37 36 35 34 33 32 31
38 17 16 15 14 13 30
39 18  5  4  3 12 29
40 19  6  1  2 11 28
41 20  7  8  9 10 27
42 21 22 23 24 25 26
43 44 45 46 47 48 49

It is interesting to note that the odd squares lie along the bottom right
diagonal ,but what is more interesting is that 8 out of the 13 numbers
lying along both diagonals are prime; that is, a ratio of 8/13 ≈ 62%.

If one complete new layer is wrapped around the spiral above,
a square spiral with side length 9 will be formed.
If this process is continued,
what is the side length of the square spiral for which
the ratio of primes along both diagonals first falls below 10%?

Solution: We have to find an odd length side for which square falls below
10%. With every layer we add 4 elements are being added to the diagonals
,lets say we have a square spiral of odd length with side length j,
then if we move from j to j+2, we are adding j*j+j+1,j*j+2*(j+1),j*j+3*(j+1)
j*j+4*(j+1). Out of these 4 only the first three can become prime
because last one reduces to (j+2)*(j+2).
So we check individually each one of these before incrementing our
count of current primes.

"""

import math


def is_prime(number: int) -> bool:
    """Checks to see if a number is a prime in O(sqrt(n)).

    A number is prime if it has exactly two factors: 1 and itself.

    >>> is_prime(0)
    False
    >>> is_prime(1)
    False
    >>> is_prime(2)
    True
    >>> is_prime(3)
    True
    >>> is_prime(27)
    False
    >>> is_prime(87)
    False
    >>> is_prime(563)
    True
    >>> is_prime(2999)
    True
    >>> is_prime(67483)
    False
    """

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5, int(math.sqrt(number) + 1), 6):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


def solution(ratio: float = 0.1) -> int:
    """
    Returns the side length of the square spiral of odd length greater
    than 1 for which the ratio of primes along both diagonals
    first falls below the given ratio.
    >>> solution(.5)
    11
    >>> solution(.2)
    309
    >>> solution(.111)
    11317
    """

    j = 3
    primes = 3

    while primes / (2 * j - 1) >= ratio:
        for i in range(j * j + j + 1, (j + 2) * (j + 2), j + 1):
            primes += is_prime(i)
        j += 2
    return j


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: project_euler/problem_059/__init__.py
================================================


================================================
FILE: project_euler/problem_059/p059_cipher.txt
================================================
36,22,80,0,0,4,23,25,19,17,88,4,4,19,21,11,88,22,23,23,29,69,12,24,0,88,25,11,12,2,10,28,5,6,12,25,10,22,80,10,30,80,10,22,21,69,23,22,69,61,5,9,29,2,66,11,80,8,23,3,17,88,19,0,20,21,7,10,17,17,29,20,69,8,17,21,29,2,22,84,80,71,60,21,69,11,5,8,21,25,22,88,3,0,10,25,0,10,5,8,88,2,0,27,25,21,10,31,6,25,2,16,21,82,69,35,63,11,88,4,13,29,80,22,13,29,22,88,31,3,88,3,0,10,25,0,11,80,10,30,80,23,29,19,12,8,2,10,27,17,9,11,45,95,88,57,69,16,17,19,29,80,23,29,19,0,22,4,9,1,80,3,23,5,11,28,92,69,9,5,12,12,21,69,13,30,0,0,0,0,27,4,0,28,28,28,84,80,4,22,80,0,20,21,2,25,30,17,88,21,29,8,2,0,11,3,12,23,30,69,30,31,23,88,4,13,29,80,0,22,4,12,10,21,69,11,5,8,88,31,3,88,4,13,17,3,69,11,21,23,17,21,22,88,65,69,83,80,84,87,68,69,83,80,84,87,73,69,83,80,84,87,65,83,88,91,69,29,4,6,86,92,69,15,24,12,27,24,69,28,21,21,29,30,1,11,80,10,22,80,17,16,21,69,9,5,4,28,2,4,12,5,23,29,80,10,30,80,17,16,21,69,27,25,23,27,28,0,84,80,22,23,80,17,16,17,17,88,25,3,88,4,13,29,80,17,10,5,0,88,3,16,21,80,10,30,80,17,16,25,22,88,3,0,10,25,0,11,80,12,11,80,10,26,4,4,17,30,0,28,92,69,30,2,10,21,80,12,12,80,4,12,80,10,22,19,0,88,4,13,29,80,20,13,17,1,10,17,17,13,2,0,88,31,3,88,4,13,29,80,6,17,2,6,20,21,69,30,31,9,20,31,18,11,94,69,54,17,8,29,28,28,84,80,44,88,24,4,14,21,69,30,31,16,22,20,69,12,24,4,12,80,17,16,21,69,11,5,8,88,31,3,88,4,13,17,3,69,11,21,23,17,21,22,88,25,22,88,17,69,11,25,29,12,24,69,8,17,23,12,80,10,30,80,17,16,21,69,11,1,16,25,2,0,88,31,3,88,4,13,29,80,21,29,2,12,21,21,17,29,2,69,23,22,69,12,24,0,88,19,12,10,19,9,29,80,18,16,31,22,29,80,1,17,17,8,29,4,0,10,80,12,11,80,84,67,80,10,10,80,7,1,80,21,13,4,17,17,30,2,88,4,13,29,80,22,13,29,69,23,22,69,12,24,12,11,80,22,29,2,12,29,3,69,29,1,16,25,28,69,12,31,69,11,92,69,17,4,69,16,17,22,88,4,13,29,80,23,25,4,12,23,80,22,9,2,17,80,70,76,88,29,16,20,4,12,8,28,12,29,20,69,26,9,69,11,80,17,23,80,84,88,31,3,88,4,13,29,80,21,29,2,12,21,21,17,29,2,69,12,31,69,12,24,0,88,20,12,25,29,0,12,21,23,86,80,44,88,7,12,20,28,69,11,31,10,22,80,22,16,31,18,88,4,13,25,4,69,12,24,0,88,3,16,21,80,10,30,80,17,16,25,22,88,3,0,10,25,0,11,80,17,23,80,7,29,80,4,8,0,23,23,8,12,21,17,17,29,28,28,88,65,75,78,68,81,65,67,81,72,70,83,64,68,87,74,70,81,75,70,81,67,80,4,22,20,69,30,2,10,21,80,8,13,28,17,17,0,9,1,25,11,31,80,17,16,25,22,88,30,16,21,18,0,10,80,7,1,80,22,17,8,73,88,17,11,28,80,17,16,21,11,88,4,4,19,25,11,31,80,17,16,21,69,11,1,16,25,2,0,88,2,10,23,4,73,88,4,13,29,80,11,13,29,7,29,2,69,75,94,84,76,65,80,65,66,83,77,67,80,64,73,82,65,67,87,75,72,69,17,3,69,17,30,1,29,21,1,88,0,23,23,20,16,27,21,1,84,80,18,16,25,6,16,80,0,0,0,23,29,3,22,29,3,69,12,24,0,88,0,0,10,25,8,29,4,0,10,80,10,30,80,4,88,19,12,10,19,9,29,80,18,16,31,22,29,80,1,17,17,8,29,4,0,10,80,12,11,80,84,86,80,35,23,28,9,23,7,12,22,23,69,25,23,4,17,30,69,12,24,0,88,3,4,21,21,69,11,4,0,8,3,69,26,9,69,15,24,12,27,24,69,49,80,13,25,20,69,25,2,23,17,6,0,28,80,4,12,80,17,16,25,22,88,3,16,21,92,69,49,80,13,25,6,0,88,20,12,11,19,10,14,21,23,29,20,69,12,24,4,12,80,17,16,21,69,11,5,8,88,31,3,88,4,13,29,80,22,29,2,12,29,3,69,73,80,78,88,65,74,73,70,69,83,80,84,87,72,84,88,91,69,73,95,87,77,70,69,83,80,84,87,70,87,77,80,78,88,21,17,27,94,69,25,28,22,23,80,1,29,0,0,22,20,22,88,31,11,88,4,13,29,80,20,13,17,1,10,17,17,13,2,0,88,31,3,88,4,13,29,80,6,17,2,6,20,21,75,88,62,4,21,21,9,1,92,69,12,24,0,88,3,16,21,80,10,30,80,17,16,25,22,88,29,16,20,4,12,8,28,12,29,20,69,26,9,69,65,64,69,31,25,19,29,3,69,12,24,0,88,18,12,9,5,4,28,2,4,12,21,69,80,22,10,13,2,17,16,80,21,23,7,0,10,89,69,23,22,69,12,24,0,88,19,12,10,19,16,21,22,0,10,21,11,27,21,69,23,22,69,12,24,0,88,0,0,10,25,8,29,4,0,10,80,10,30,80,4,88,19,12,10,19,9,29,80,18,16,31,22,29,80,1,17,17,8,29,4,0,10,80,12,11,80,84,86,80,36,22,20,69,26,9,69,11,25,8,17,28,4,10,80,23,29,17,22,23,30,12,22,23,69,49,80,13,25,6,0,88,28,12,19,21,18,17,3,0,88,18,0,29,30,69,25,18,9,29,80,17,23,80,1,29,4,0,10,29,12,22,21,69,12,24,0,88,3,16,21,3,69,23,22,69,12,24,0,88,3,16,26,3,0,9,5,0,22,4,69,11,21,23,17,21,22,88,25,11,88,7,13,17,19,13,88,4,13,29,80,0,0,0,10,22,21,11,12,3,69,25,2,0,88,21,19,29,30,69,22,5,8,26,21,23,11,94

================================================
FILE: project_euler/problem_059/sol1.py
================================================
"""
Each character on a computer is assigned a unique code and the preferred standard is
ASCII (American Standard Code for Information Interchange).
For example, uppercase A = 65, asterisk (*) = 42, and lowercase k = 107.

A modern encryption method is to take a text file, convert the bytes to ASCII, then
XOR each byte with a given value, taken from a secret key. The advantage with the
XOR function is that using the same encryption key on the cipher text, restores
the plain text; for example, 65 XOR 42 = 107, then 107 XOR 42 = 65.

For unbreakable encryption, the key is the same length as the plain text message, and
the key is made up of random bytes. The user would keep the encrypted message and the
encryption key in different locations, and without both "halves", it is impossible to
decrypt the message.

Unfortunately, this method is impractical for most users, so the modified method is
to use a password as a key. If the password is shorter than the message, which is
likely, the key is repeated cyclically throughout the message. The balance for this
method is using a sufficiently long password key for security, but short enough to
be memorable.

Your task has been made easy, as the encryption key consists of three lower case
characters. Using p059_cipher.txt (right click and 'Save Link/Target As...'), a
file containing the encrypted ASCII codes, and the knowledge that the plain text
must contain common English words, decrypt the message and find the sum of the ASCII
values in the original text.
"""

from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

VALID_CHARS: str = (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
LOWERCASE_INTS: list[int] = [ord(letter) for letter in string.ascii_lowercase]
VALID_INTS: set[int] = {ord(char) for char in VALID_CHARS}

COMMON_WORDS: list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]


def try_key(ciphertext: list[int], key: tuple[int, ...]) -> str | None:
    """
    Given an encrypted message and a possible 3-character key, decrypt the message.
    If the decrypted message contains a invalid character, i.e. not an ASCII letter,
    a digit, punctuation or whitespace, then we know the key is incorrect, so return
    None.
    >>> try_key([0, 17, 20, 4, 27], (104, 116, 120))
    'hello'
    >>> try_key([68, 10, 300, 4, 27], (104, 116, 120)) is None
    True
    """
    decoded: str = ""
    keychar: int
    cipherchar: int
    decodedchar: int

    for keychar, cipherchar in zip(cycle(key), ciphertext):
        decodedchar = cipherchar ^ keychar
        if decodedchar not in VALID_INTS:
            return None
        decoded += chr(decodedchar)

    return decoded


def filter_valid_chars(ciphertext: list[int]) -> list[str]:
    """
    Given an encrypted message, test all 3-character strings to try and find the
    key. Return a list of the possible decrypted messages.
    >>> from itertools import cycle
    >>> text = "The enemy's gate is down"
    >>> key = "end"
    >>> encoded = [ord(k) ^ ord(c) for k,c in zip(cycle(key), text)]
    >>> text in filter_valid_chars(encoded)
    True
    """
    possibles: list[str] = []
    for key in product(LOWERCASE_INTS, repeat=3):
        encoded = try_key(ciphertext, key)
        if encoded is not None:
            possibles.append(encoded)
    return possibles


def filter_common_word(possibles: list[str], common_word: str) -> list[str]:
    """
    Given a list of possible decoded messages, narrow down the possibilities
    for checking for the presence of a specified common word. Only decoded messages
    containing common_word will be returned.
    >>> filter_common_word(['asfla adf', 'I am here', '   !?! #a'], 'am')
    ['I am here']
    >>> filter_common_word(['athla amf', 'I am here', '   !?! #a'], 'am')
    ['athla amf', 'I am here']
    """
    return [possible for possible in possibles if common_word in possible.lower()]


def solution(filename: str = "p059_cipher.txt") -> int:
    """
    Test the ciphertext against all possible 3-character keys, then narrow down the
    possibilities by filtering using common words until there's only one possible
    decoded message.
    >>> solution("test_cipher.txt")
    3000
    """
    ciphertext: list[int]
    possibles: list[str]
    common_word: str
    decoded_text: str
    data: str = Path(__file__).parent.joinpath(filename).read_text(encoding="utf-8")

    ciphertext = [int(number) for number in data.strip().split(",")]

    possibles = filter_valid_chars(ciphertext)
    for common_word in COMMON_WORDS:
        possibles = filter_common_word(possibles, common_word)
        if len(possibles) == 1:
            break

    decoded_text = possibles[0]
    return sum(ord(char) for char in decoded_text)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_059/test_cipher.txt
================================================
63,13,28,75,0,23,14,8,0,76,22,89,12,4,13,14,69,16,24,69,29,4,18,23,69,69,59,14,69,11,14,4,29,18


================================================
FILE: project_euler/problem_062/__init__.py
================================================


================================================
FILE: project_euler/problem_062/sol1.py
================================================
"""
Project Euler 62
https://projecteuler.net/problem=62

The cube, 41063625 (345^3), can be permuted to produce two other cubes:
56623104 (384^3) and 66430125 (405^3). In fact, 41063625 is the smallest cube
which has exactly three permutations of its digits which are also cube.

Find the smallest cube for which exactly five permutations of its digits are
cube.
"""

from collections import defaultdict


def solution(max_base: int = 5) -> int:
    """
    Iterate through every possible cube and sort the cube's digits in
    ascending order. Sorting maintains an ordering of the digits that allows
    you to compare permutations. Store each sorted sequence of digits in a
    dictionary, whose key is the sequence of digits and value is a list of
    numbers that are the base of the cube.

    Once you find 5 numbers that produce the same sequence of digits, return
    the smallest one, which is at index 0 since we insert each base number in
    ascending order.

    >>> solution(2)
    125
    >>> solution(3)
    41063625
    """
    freqs = defaultdict(list)
    num = 0

    while True:
        digits = get_digits(num)
        freqs[digits].append(num)

        if len(freqs[digits]) == max_base:
            base = freqs[digits][0] ** 3
            return base

        num += 1


def get_digits(num: int) -> str:
    """
    Computes the sorted sequence of digits of the cube of num.

    >>> get_digits(3)
    '27'
    >>> get_digits(99)
    '027999'
    >>> get_digits(123)
    '0166788'
    """
    return "".join(sorted(str(num**3)))


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_063/__init__.py
================================================


================================================
FILE: project_euler/problem_063/sol1.py
================================================
"""
The 5-digit number, 16807=75, is also a fifth power. Similarly, the 9-digit number,
134217728=89, is a ninth power.
How many n-digit positive integers exist which are also an nth power?
"""

"""
The maximum base can be 9 because all n-digit numbers < 10^n.
Now 9**23 has 22 digits so the maximum power can be 22.
Using these conclusions, we will calculate the result.
"""


def solution(max_base: int = 10, max_power: int = 22) -> int:
    """
    Returns the count of all n-digit numbers which are nth power
    >>> solution(10, 22)
    49
    >>> solution(0, 0)
    0
    >>> solution(1, 1)
    0
    >>> solution(-1, -1)
    0
    """
    bases = range(1, max_base)
    powers = range(1, max_power)
    return sum(
        1 for power in powers for base in bases if len(str(base**power)) == power
    )


if __name__ == "__main__":
    print(f"{solution(10, 22) = }")


================================================
FILE: project_euler/problem_064/__init__.py
================================================


================================================
FILE: project_euler/problem_064/sol1.py
================================================
"""
Project Euler Problem 64: https://projecteuler.net/problem=64

All square roots are periodic when written as continued fractions.
For example, let us consider sqrt(23).
It can be seen that the sequence is repeating.
For conciseness, we use the notation sqrt(23)=[4;(1,3,1,8)],
to indicate that the block (1,3,1,8) repeats indefinitely.
Exactly four continued fractions, for N<=13, have an odd period.
How many continued fractions for N<=10000 have an odd period?

References:
- https://en.wikipedia.org/wiki/Continued_fraction
"""

from math import floor, sqrt


def continuous_fraction_period(n: int) -> int:
    """
    Returns the continued fraction period of a number n.

    >>> continuous_fraction_period(2)
    1
    >>> continuous_fraction_period(5)
    1
    >>> continuous_fraction_period(7)
    4
    >>> continuous_fraction_period(11)
    2
    >>> continuous_fraction_period(13)
    5
    """
    numerator = 0.0
    denominator = 1.0
    root = int(sqrt(n))
    integer_part = root
    period = 0
    while integer_part != 2 * root:
        numerator = denominator * integer_part - numerator
        denominator = (n - numerator**2) / denominator
        integer_part = int((root + numerator) / denominator)
        period += 1
    return period


def solution(n: int = 10000) -> int:
    """
    Returns the count of numbers <= 10000 with odd periods.
    This function calls continuous_fraction_period for numbers which are
    not perfect squares.
    This is checked in if sr - floor(sr) != 0 statement.
    If an odd period is returned by continuous_fraction_period,
    count_odd_periods is increased by 1.

    >>> solution(2)
    1
    >>> solution(5)
    2
    >>> solution(7)
    2
    >>> solution(11)
    3
    >>> solution(13)
    4
    """
    count_odd_periods = 0
    for i in range(2, n + 1):
        sr = sqrt(i)
        if sr - floor(sr) != 0 and continuous_fraction_period(i) % 2 == 1:
            count_odd_periods += 1
    return count_odd_periods


if __name__ == "__main__":
    print(f"{solution(int(input().strip()))}")


================================================
FILE: project_euler/problem_065/__init__.py
================================================


================================================
FILE: project_euler/problem_065/sol1.py
================================================
"""
Project Euler Problem 65: https://projecteuler.net/problem=65

The square root of 2 can be written as an infinite continued fraction.

sqrt(2) = 1 + 1 / (2 + 1 / (2 + 1 / (2 + 1 / (2 + ...))))

The infinite continued fraction can be written, sqrt(2) = [1;(2)], (2)
indicates that 2 repeats ad infinitum. In a similar way, sqrt(23) =
[4;(1,3,1,8)].

It turns out that the sequence of partial values of continued
fractions for square roots provide the best rational approximations.
Let us consider the convergents for sqrt(2).

1 + 1 / 2 = 3/2
1 + 1 / (2 + 1 / 2) = 7/5
1 + 1 / (2 + 1 / (2 + 1 / 2)) = 17/12
1 + 1 / (2 + 1 / (2 + 1 / (2 + 1 / 2))) = 41/29

Hence the sequence of the first ten convergents for sqrt(2) are:
1, 3/2, 7/5, 17/12, 41/29, 99/70, 239/169, 577/408, 1393/985, 3363/2378, ...

What is most surprising is that the important mathematical constant,
e = [2;1,2,1,1,4,1,1,6,1,...,1,2k,1,...].

The first ten terms in the sequence of convergents for e are:
2, 3, 8/3, 11/4, 19/7, 87/32, 106/39, 193/71, 1264/465, 1457/536, ...

The sum of digits in the numerator of the 10th convergent is
1 + 4 + 5 + 7 = 17.

Find the sum of the digits in the numerator of the 100th convergent
of the continued fraction for e.

-----

The solution mostly comes down to finding an equation that will generate
the numerator of the continued fraction. For the i-th numerator, the
pattern is:

n_i = m_i * n_(i-1) + n_(i-2)

for m_i = the i-th index of the continued fraction representation of e,
n_0 = 1, and n_1 = 2 as the first 2 numbers of the representation.

For example:
n_9 = 6 * 193 + 106 = 1264
1 + 2 + 6 + 4 = 13

n_10 = 1 * 193 + 1264 = 1457
1 + 4 + 5 + 7 = 17
"""


def sum_digits(num: int) -> int:
    """
    Returns the sum of every digit in num.

    >>> sum_digits(1)
    1
    >>> sum_digits(12345)
    15
    >>> sum_digits(999001)
    28
    """
    digit_sum = 0
    while num > 0:
        digit_sum += num % 10
        num //= 10
    return digit_sum


def solution(max_n: int = 100) -> int:
    """
    Returns the sum of the digits in the numerator of the max-th convergent of
    the continued fraction for e.

    >>> solution(9)
    13
    >>> solution(10)
    17
    >>> solution(50)
    91
    """
    pre_numerator = 1
    cur_numerator = 2

    for i in range(2, max_n + 1):
        temp = pre_numerator
        e_cont = 2 * i // 3 if i % 3 == 0 else 1
        pre_numerator = cur_numerator
        cur_numerator = e_cont * pre_numerator + temp

    return sum_digits(cur_numerator)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_067/__init__.py
================================================


================================================
FILE: project_euler/problem_067/sol1.py
================================================
"""
Problem Statement:
By starting at the top of the triangle below and moving to adjacent numbers on
the row below, the maximum total from top to bottom is 23.
3
7 4
2 4 6
8 5 9 3
That is, 3 + 7 + 4 + 9 = 23.
Find the maximum total from top to bottom in triangle.txt (right click and
'Save Link/Target As...'), a 15K text file containing a triangle with
one-hundred rows.
"""

import os


def solution():
    """
    Finds the maximum total in a triangle as described by the problem statement
    above.

    >>> solution()
    7273
    """
    script_dir = os.path.dirname(os.path.realpath(__file__))
    triangle = os.path.join(script_dir, "triangle.txt")

    with open(triangle) as f:
        triangle = f.readlines()

    a = []
    for line in triangle:
        numbers_from_line = []
        for number in line.strip().split(" "):
            numbers_from_line.append(int(number))
        a.append(numbers_from_line)

    for i in range(1, len(a)):
        for j in range(len(a[i])):
            number1 = a[i - 1][j] if j != len(a[i - 1]) else 0
            number2 = a[i - 1][j - 1] if j > 0 else 0
            a[i][j] += max(number1, number2)
    return max(a[-1])


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_067/sol2.py
================================================
"""
Problem Statement:
By starting at the top of the triangle below and moving to adjacent numbers on
the row below, the maximum total from top to bottom is 23.
3
7 4
2 4 6
8 5 9 3
That is, 3 + 7 + 4 + 9 = 23.
Find the maximum total from top to bottom in triangle.txt (right click and
'Save Link/Target As...'), a 15K text file containing a triangle with
one-hundred rows.
"""

import os


def solution() -> int:
    """
    Finds the maximum total in a triangle as described by the problem statement
    above.
    >>> solution()
    7273
    """
    script_dir = os.path.dirname(os.path.realpath(__file__))
    triangle_path = os.path.join(script_dir, "triangle.txt")

    with open(triangle_path) as in_file:
        triangle = [[int(i) for i in line.split()] for line in in_file]

    while len(triangle) != 1:
        last_row = triangle.pop()
        curr_row = triangle[-1]
        for j in range(len(last_row) - 1):
            curr_row[j] += max(last_row[j], last_row[j + 1])
    return triangle[0][0]


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_067/triangle.txt
================================================
59
73 41
52 40 09
26 53 06 34
10 51 87 86 81
61 95 66 57 25 68
90 81 80 38 92 67 73
30 28 51 76 81 18 75 44
84 14 95 87 62 81 17 78 58
21 46 71 58 02 79 62 39 31 09
56 34 35 53 78 31 81 18 90 93 15
78 53 04 21 84 93 32 13 97 11 37 51
45 03 81 79 05 18 78 86 13 30 63 99 95
39 87 96 28 03 38 42 17 82 87 58 07 22 57
06 17 51 17 07 93 09 07 75 97 95 78 87 08 53
67 66 59 60 88 99 94 65 55 77 55 34 27 53 78 28
76 40 41 04 87 16 09 42 75 69 23 97 30 60 10 79 87
12 10 44 26 21 36 32 84 98 60 13 12 36 16 63 31 91 35
70 39 06 05 55 27 38 48 28 22 34 35 62 62 15 14 94 89 86
66 56 68 84 96 21 34 34 34 81 62 40 65 54 62 05 98 03 02 60
38 89 46 37 99 54 34 53 36 14 70 26 02 90 45 13 31 61 83 73 47
36 10 63 96 60 49 41 05 37 42 14 58 84 93 96 17 09 43 05 43 06 59
66 57 87 57 61 28 37 51 84 73 79 15 39 95 88 87 43 39 11 86 77 74 18
54 42 05 79 30 49 99 73 46 37 50 02 45 09 54 52 27 95 27 65 19 45 26 45
71 39 17 78 76 29 52 90 18 99 78 19 35 62 71 19 23 65 93 85 49 33 75 09 02
33 24 47 61 60 55 32 88 57 55 91 54 46 57 07 77 98 52 80 99 24 25 46 78 79 05
92 09 13 55 10 67 26 78 76 82 63 49 51 31 24 68 05 57 07 54 69 21 67 43 17 63 12
24 59 06 08 98 74 66 26 61 60 13 03 09 09 24 30 71 08 88 70 72 70 29 90 11 82 41 34
66 82 67 04 36 60 92 77 91 85 62 49 59 61 30 90 29 94 26 41 89 04 53 22 83 41 09 74 90
48 28 26 37 28 52 77 26 51 32 18 98 79 36 62 13 17 08 19 54 89 29 73 68 42 14 08 16 70 37
37 60 69 70 72 71 09 59 13 60 38 13 57 36 09 30 43 89 30 39 15 02 44 73 05 73 26 63 56 86 12
55 55 85 50 62 99 84 77 28 85 03 21 27 22 19 26 82 69 54 04 13 07 85 14 01 15 70 59 89 95 10 19
04 09 31 92 91 38 92 86 98 75 21 05 64 42 62 84 36 20 73 42 21 23 22 51 51 79 25 45 85 53 03 43 22
75 63 02 49 14 12 89 14 60 78 92 16 44 82 38 30 72 11 46 52 90 27 08 65 78 03 85 41 57 79 39 52 33 48
78 27 56 56 39 13 19 43 86 72 58 95 39 07 04 34 21 98 39 15 39 84 89 69 84 46 37 57 59 35 59 50 26 15 93
42 89 36 27 78 91 24 11 17 41 05 94 07 69 51 96 03 96 47 90 90 45 91 20 50 56 10 32 36 49 04 53 85 92 25 65
52 09 61 30 61 97 66 21 96 92 98 90 06 34 96 60 32 69 68 33 75 84 18 31 71 50 84 63 03 03 19 11 28 42 75 45 45
61 31 61 68 96 34 49 39 05 71 76 59 62 67 06 47 96 99 34 21 32 47 52 07 71 60 42 72 94 56 82 83 84 40 94 87 82 46
01 20 60 14 17 38 26 78 66 81 45 95 18 51 98 81 48 16 53 88 37 52 69 95 72 93 22 34 98 20 54 27 73 61 56 63 60 34 63
93 42 94 83 47 61 27 51 79 79 45 01 44 73 31 70 83 42 88 25 53 51 30 15 65 94 80 44 61 84 12 77 02 62 02 65 94 42 14 94
32 73 09 67 68 29 74 98 10 19 85 48 38 31 85 67 53 93 93 77 47 67 39 72 94 53 18 43 77 40 78 32 29 59 24 06 02 83 50 60 66
32 01 44 30 16 51 15 81 98 15 10 62 86 79 50 62 45 60 70 38 31 85 65 61 64 06 69 84 14 22 56 43 09 48 66 69 83 91 60 40 36 61
92 48 22 99 15 95 64 43 01 16 94 02 99 19 17 69 11 58 97 56 89 31 77 45 67 96 12 73 08 20 36 47 81 44 50 64 68 85 40 81 85 52 09
91 35 92 45 32 84 62 15 19 64 21 66 06 01 52 80 62 59 12 25 88 28 91 50 40 16 22 99 92 79 87 51 21 77 74 77 07 42 38 42 74 83 02 05
46 19 77 66 24 18 05 32 02 84 31 99 92 58 96 72 91 36 62 99 55 29 53 42 12 37 26 58 89 50 66 19 82 75 12 48 24 87 91 85 02 07 03 76 86
99 98 84 93 07 17 33 61 92 20 66 60 24 66 40 30 67 05 37 29 24 96 03 27 70 62 13 04 45 47 59 88 43 20 66 15 46 92 30 04 71 66 78 70 53 99
67 60 38 06 88 04 17 72 10 99 71 07 42 25 54 05 26 64 91 50 45 71 06 30 67 48 69 82 08 56 80 67 18 46 66 63 01 20 08 80 47 07 91 16 03 79 87
18 54 78 49 80 48 77 40 68 23 60 88 58 80 33 57 11 69 55 53 64 02 94 49 60 92 16 35 81 21 82 96 25 24 96 18 02 05 49 03 50 77 06 32 84 27 18 38
68 01 50 04 03 21 42 94 53 24 89 05 92 26 52 36 68 11 85 01 04 42 02 45 15 06 50 04 53 73 25 74 81 88 98 21 67 84 79 97 99 20 95 04 40 46 02 58 87
94 10 02 78 88 52 21 03 88 60 06 53 49 71 20 91 12 65 07 49 21 22 11 41 58 99 36 16 09 48 17 24 52 36 23 15 72 16 84 56 02 99 43 76 81 71 29 39 49 17
64 39 59 84 86 16 17 66 03 09 43 06 64 18 63 29 68 06 23 07 87 14 26 35 17 12 98 41 53 64 78 18 98 27 28 84 80 67 75 62 10 11 76 90 54 10 05 54 41 39 66
43 83 18 37 32 31 52 29 95 47 08 76 35 11 04 53 35 43 34 10 52 57 12 36 20 39 40 55 78 44 07 31 38 26 08 15 56 88 86 01 52 62 10 24 32 05 60 65 53 28 57 99
03 50 03 52 07 73 49 92 66 80 01 46 08 67 25 36 73 93 07 42 25 53 13 96 76 83 87 90 54 89 78 22 78 91 73 51 69 09 79 94 83 53 09 40 69 62 10 79 49 47 03 81 30
71 54 73 33 51 76 59 54 79 37 56 45 84 17 62 21 98 69 41 95 65 24 39 37 62 03 24 48 54 64 46 82 71 78 33 67 09 16 96 68 52 74 79 68 32 21 13 78 96 60 09 69 20 36
73 26 21 44 46 38 17 83 65 98 07 23 52 46 61 97 33 13 60 31 70 15 36 77 31 58 56 93 75 68 21 36 69 53 90 75 25 82 39 50 65 94 29 30 11 33 11 13 96 02 56 47 07 49 02
76 46 73 30 10 20 60 70 14 56 34 26 37 39 48 24 55 76 84 91 39 86 95 61 50 14 53 93 64 67 37 31 10 84 42 70 48 20 10 72 60 61 84 79 69 65 99 73 89 25 85 48 92 56 97 16
03 14 80 27 22 30 44 27 67 75 79 32 51 54 81 29 65 14 19 04 13 82 04 91 43 40 12 52 29 99 07 76 60 25 01 07 61 71 37 92 40 47 99 66 57 01 43 44 22 40 53 53 09 69 26 81 07
49 80 56 90 93 87 47 13 75 28 87 23 72 79 32 18 27 20 28 10 37 59 21 18 70 04 79 96 03 31 45 71 81 06 14 18 17 05 31 50 92 79 23 47 09 39 47 91 43 54 69 47 42 95 62 46 32 85
37 18 62 85 87 28 64 05 77 51 47 26 30 65 05 70 65 75 59 80 42 52 25 20 44 10 92 17 71 95 52 14 77 13 24 55 11 65 26 91 01 30 63 15 49 48 41 17 67 47 03 68 20 90 98 32 04 40 68
90 51 58 60 06 55 23 68 05 19 76 94 82 36 96 43 38 90 87 28 33 83 05 17 70 83 96 93 06 04 78 47 80 06 23 84 75 23 87 72 99 14 50 98 92 38 90 64 61 58 76 94 36 66 87 80 51 35 61 38
57 95 64 06 53 36 82 51 40 33 47 14 07 98 78 65 39 58 53 06 50 53 04 69 40 68 36 69 75 78 75 60 03 32 39 24 74 47 26 90 13 40 44 71 90 76 51 24 36 50 25 45 70 80 61 80 61 43 90 64 11
18 29 86 56 68 42 79 10 42 44 30 12 96 18 23 18 52 59 02 99 67 46 60 86 43 38 55 17 44 93 42 21 55 14 47 34 55 16 49 24 23 29 96 51 55 10 46 53 27 92 27 46 63 57 30 65 43 27 21 20 24 83
81 72 93 19 69 52 48 01 13 83 92 69 20 48 69 59 20 62 05 42 28 89 90 99 32 72 84 17 08 87 36 03 60 31 36 36 81 26 97 36 48 54 56 56 27 16 91 08 23 11 87 99 33 47 02 14 44 73 70 99 43 35 33
90 56 61 86 56 12 70 59 63 32 01 15 81 47 71 76 95 32 65 80 54 70 34 51 40 45 33 04 64 55 78 68 88 47 31 47 68 87 03 84 23 44 89 72 35 08 31 76 63 26 90 85 96 67 65 91 19 14 17 86 04 71 32 95
37 13 04 22 64 37 37 28 56 62 86 33 07 37 10 44 52 82 52 06 19 52 57 75 90 26 91 24 06 21 14 67 76 30 46 14 35 89 89 41 03 64 56 97 87 63 22 34 03 79 17 45 11 53 25 56 96 61 23 18 63 31 37 37 47
77 23 26 70 72 76 77 04 28 64 71 69 14 85 96 54 95 48 06 62 99 83 86 77 97 75 71 66 30 19 57 90 33 01 60 61 14 12 90 99 32 77 56 41 18 14 87 49 10 14 90 64 18 50 21 74 14 16 88 05 45 73 82 47 74 44
22 97 41 13 34 31 54 61 56 94 03 24 59 27 98 77 04 09 37 40 12 26 87 09 71 70 07 18 64 57 80 21 12 71 83 94 60 39 73 79 73 19 97 32 64 29 41 07 48 84 85 67 12 74 95 20 24 52 41 67 56 61 29 93 35 72 69
72 23 63 66 01 11 07 30 52 56 95 16 65 26 83 90 50 74 60 18 16 48 43 77 37 11 99 98 30 94 91 26 62 73 45 12 87 73 47 27 01 88 66 99 21 41 95 80 02 53 23 32 61 48 32 43 43 83 14 66 95 91 19 81 80 67 25 88
08 62 32 18 92 14 83 71 37 96 11 83 39 99 05 16 23 27 10 67 02 25 44 11 55 31 46 64 41 56 44 74 26 81 51 31 45 85 87 09 81 95 22 28 76 69 46 48 64 87 67 76 27 89 31 11 74 16 62 03 60 94 42 47 09 34 94 93 72
56 18 90 18 42 17 42 32 14 86 06 53 33 95 99 35 29 15 44 20 49 59 25 54 34 59 84 21 23 54 35 90 78 16 93 13 37 88 54 19 86 67 68 55 66 84 65 42 98 37 87 56 33 28 58 38 28 38 66 27 52 21 81 15 08 22 97 32 85 27
91 53 40 28 13 34 91 25 01 63 50 37 22 49 71 58 32 28 30 18 68 94 23 83 63 62 94 76 80 41 90 22 82 52 29 12 18 56 10 08 35 14 37 57 23 65 67 40 72 39 93 39 70 89 40 34 07 46 94 22 20 05 53 64 56 30 05 56 61 88 27
23 95 11 12 37 69 68 24 66 10 87 70 43 50 75 07 62 41 83 58 95 93 89 79 45 39 02 22 05 22 95 43 62 11 68 29 17 40 26 44 25 71 87 16 70 85 19 25 59 94 90 41 41 80 61 70 55 60 84 33 95 76 42 63 15 09 03 40 38 12 03 32
09 84 56 80 61 55 85 97 16 94 82 94 98 57 84 30 84 48 93 90 71 05 95 90 73 17 30 98 40 64 65 89 07 79 09 19 56 36 42 30 23 69 73 72 07 05 27 61 24 31 43 48 71 84 21 28 26 65 65 59 65 74 77 20 10 81 61 84 95 08 52 23 70
47 81 28 09 98 51 67 64 35 51 59 36 92 82 77 65 80 24 72 53 22 07 27 10 21 28 30 22 48 82 80 48 56 20 14 43 18 25 50 95 90 31 77 08 09 48 44 80 90 22 93 45 82 17 13 96 25 26 08 73 34 99 06 49 24 06 83 51 40 14 15 10 25 01
54 25 10 81 30 64 24 74 75 80 36 75 82 60 22 69 72 91 45 67 03 62 79 54 89 74 44 83 64 96 66 73 44 30 74 50 37 05 09 97 70 01 60 46 37 91 39 75 75 18 58 52 72 78 51 81 86 52 08 97 01 46 43 66 98 62 81 18 70 93 73 08 32 46 34
96 80 82 07 59 71 92 53 19 20 88 66 03 26 26 10 24 27 50 82 94 73 63 08 51 33 22 45 19 13 58 33 90 15 22 50 36 13 55 06 35 47 82 52 33 61 36 27 28 46 98 14 73 20 73 32 16 26 80 53 47 66 76 38 94 45 02 01 22 52 47 96 64 58 52 39
88 46 23 39 74 63 81 64 20 90 33 33 76 55 58 26 10 46 42 26 74 74 12 83 32 43 09 02 73 55 86 54 85 34 28 23 29 79 91 62 47 41 82 87 99 22 48 90 20 05 96 75 95 04 43 28 81 39 81 01 28 42 78 25 39 77 90 57 58 98 17 36 73 22 63 74 51
29 39 74 94 95 78 64 24 38 86 63 87 93 06 70 92 22 16 80 64 29 52 20 27 23 50 14 13 87 15 72 96 81 22 08 49 72 30 70 24 79 31 16 64 59 21 89 34 96 91 48 76 43 53 88 01 57 80 23 81 90 79 58 01 80 87 17 99 86 90 72 63 32 69 14 28 88 69
37 17 71 95 56 93 71 35 43 45 04 98 92 94 84 96 11 30 31 27 31 60 92 03 48 05 98 91 86 94 35 90 90 08 48 19 33 28 68 37 59 26 65 96 50 68 22 07 09 49 34 31 77 49 43 06 75 17 81 87 61 79 52 26 27 72 29 50 07 98 86 01 17 10 46 64 24 18 56
51 30 25 94 88 85 79 91 40 33 63 84 49 67 98 92 15 26 75 19 82 05 18 78 65 93 61 48 91 43 59 41 70 51 22 15 92 81 67 91 46 98 11 11 65 31 66 10 98 65 83 21 05 56 05 98 73 67 46 74 69 34 08 30 05 52 07 98 32 95 30 94 65 50 24 63 28 81 99 57
19 23 61 36 09 89 71 98 65 17 30 29 89 26 79 74 94 11 44 48 97 54 81 55 39 66 69 45 28 47 13 86 15 76 74 70 84 32 36 33 79 20 78 14 41 47 89 28 81 05 99 66 81 86 38 26 06 25 13 60 54 55 23 53 27 05 89 25 23 11 13 54 59 54 56 34 16 24 53 44 06
13 40 57 72 21 15 60 08 04 19 11 98 34 45 09 97 86 71 03 15 56 19 15 44 97 31 90 04 87 87 76 08 12 30 24 62 84 28 12 85 82 53 99 52 13 94 06 65 97 86 09 50 94 68 69 74 30 67 87 94 63 07 78 27 80 36 69 41 06 92 32 78 37 82 30 05 18 87 99 72 19 99
44 20 55 77 69 91 27 31 28 81 80 27 02 07 97 23 95 98 12 25 75 29 47 71 07 47 78 39 41 59 27 76 13 15 66 61 68 35 69 86 16 53 67 63 99 85 41 56 08 28 33 40 94 76 90 85 31 70 24 65 84 65 99 82 19 25 54 37 21 46 33 02 52 99 51 33 26 04 87 02 08 18 96
54 42 61 45 91 06 64 79 80 82 32 16 83 63 42 49 19 78 65 97 40 42 14 61 49 34 04 18 25 98 59 30 82 72 26 88 54 36 21 75 03 88 99 53 46 51 55 78 22 94 34 40 68 87 84 25 30 76 25 08 92 84 42 61 40 38 09 99 40 23 29 39 46 55 10 90 35 84 56 70 63 23 91 39
52 92 03 71 89 07 09 37 68 66 58 20 44 92 51 56 13 71 79 99 26 37 02 06 16 67 36 52 58 16 79 73 56 60 59 27 44 77 94 82 20 50 98 33 09 87 94 37 40 83 64 83 58 85 17 76 53 02 83 52 22 27 39 20 48 92 45 21 09 42 24 23 12 37 52 28 50 78 79 20 86 62 73 20 59
54 96 80 15 91 90 99 70 10 09 58 90 93 50 81 99 54 38 36 10 30 11 35 84 16 45 82 18 11 97 36 43 96 79 97 65 40 48 23 19 17 31 64 52 65 65 37 32 65 76 99 79 34 65 79 27 55 33 03 01 33 27 61 28 66 08 04 70 49 46 48 83 01 45 19 96 13 81 14 21 31 79 93 85 50 05
92 92 48 84 59 98 31 53 23 27 15 22 79 95 24 76 05 79 16 93 97 89 38 89 42 83 02 88 94 95 82 21 01 97 48 39 31 78 09 65 50 56 97 61 01 07 65 27 21 23 14 15 80 97 44 78 49 35 33 45 81 74 34 05 31 57 09 38 94 07 69 54 69 32 65 68 46 68 78 90 24 28 49 51 45 86 35
41 63 89 76 87 31 86 09 46 14 87 82 22 29 47 16 13 10 70 72 82 95 48 64 58 43 13 75 42 69 21 12 67 13 64 85 58 23 98 09 37 76 05 22 31 12 66 50 29 99 86 72 45 25 10 28 19 06 90 43 29 31 67 79 46 25 74 14 97 35 76 37 65 46 23 82 06 22 30 76 93 66 94 17 96 13 20 72
63 40 78 08 52 09 90 41 70 28 36 14 46 44 85 96 24 52 58 15 87 37 05 98 99 39 13 61 76 38 44 99 83 74 90 22 53 80 56 98 30 51 63 39 44 30 91 91 04 22 27 73 17 35 53 18 35 45 54 56 27 78 48 13 69 36 44 38 71 25 30 56 15 22 73 43 32 69 59 25 93 83 45 11 34 94 44 39 92
12 36 56 88 13 96 16 12 55 54 11 47 19 78 17 17 68 81 77 51 42 55 99 85 66 27 81 79 93 42 65 61 69 74 14 01 18 56 12 01 58 37 91 22 42 66 83 25 19 04 96 41 25 45 18 69 96 88 36 93 10 12 98 32 44 83 83 04 72 91 04 27 73 07 34 37 71 60 59 31 01 54 54 44 96 93 83 36 04 45
30 18 22 20 42 96 65 79 17 41 55 69 94 81 29 80 91 31 85 25 47 26 43 49 02 99 34 67 99 76 16 14 15 93 08 32 99 44 61 77 67 50 43 55 87 55 53 72 17 46 62 25 50 99 73 05 93 48 17 31 70 80 59 09 44 59 45 13 74 66 58 94 87 73 16 14 85 38 74 99 64 23 79 28 71 42 20 37 82 31 23
51 96 39 65 46 71 56 13 29 68 53 86 45 33 51 49 12 91 21 21 76 85 02 17 98 15 46 12 60 21 88 30 92 83 44 59 42 50 27 88 46 86 94 73 45 54 23 24 14 10 94 21 20 34 23 51 04 83 99 75 90 63 60 16 22 33 83 70 11 32 10 50 29 30 83 46 11 05 31 17 86 42 49 01 44 63 28 60 07 78 95 40
44 61 89 59 04 49 51 27 69 71 46 76 44 04 09 34 56 39 15 06 94 91 75 90 65 27 56 23 74 06 23 33 36 69 14 39 05 34 35 57 33 22 76 46 56 10 61 65 98 09 16 69 04 62 65 18 99 76 49 18 72 66 73 83 82 40 76 31 89 91 27 88 17 35 41 35 32 51 32 67 52 68 74 85 80 57 07 11 62 66 47 22 67
65 37 19 97 26 17 16 24 24 17 50 37 64 82 24 36 32 11 68 34 69 31 32 89 79 93 96 68 49 90 14 23 04 04 67 99 81 74 70 74 36 96 68 09 64 39 88 35 54 89 96 58 66 27 88 97 32 14 06 35 78 20 71 06 85 66 57 02 58 91 72 05 29 56 73 48 86 52 09 93 22 57 79 42 12 01 31 68 17 59 63 76 07 77
73 81 14 13 17 20 11 09 01 83 08 85 91 70 84 63 62 77 37 07 47 01 59 95 39 69 39 21 99 09 87 02 97 16 92 36 74 71 90 66 33 73 73 75 52 91 11 12 26 53 05 26 26 48 61 50 90 65 01 87 42 47 74 35 22 73 24 26 56 70 52 05 48 41 31 18 83 27 21 39 80 85 26 08 44 02 71 07 63 22 05 52 19 08 20
17 25 21 11 72 93 33 49 64 23 53 82 03 13 91 65 85 02 40 05 42 31 77 42 05 36 06 54 04 58 07 76 87 83 25 57 66 12 74 33 85 37 74 32 20 69 03 97 91 68 82 44 19 14 89 28 85 85 80 53 34 87 58 98 88 78 48 65 98 40 11 57 10 67 70 81 60 79 74 72 97 59 79 47 30 20 54 80 89 91 14 05 33 36 79 39
60 85 59 39 60 07 57 76 77 92 06 35 15 72 23 41 45 52 95 18 64 79 86 53 56 31 69 11 91 31 84 50 44 82 22 81 41 40 30 42 30 91 48 94 74 76 64 58 74 25 96 57 14 19 03 99 28 83 15 75 99 01 89 85 79 50 03 95 32 67 44 08 07 41 62 64 29 20 14 76 26 55 48 71 69 66 19 72 44 25 14 01 48 74 12 98 07
64 66 84 24 18 16 27 48 20 14 47 69 30 86 48 40 23 16 61 21 51 50 26 47 35 33 91 28 78 64 43 68 04 79 51 08 19 60 52 95 06 68 46 86 35 97 27 58 04 65 30 58 99 12 12 75 91 39 50 31 42 64 70 04 46 07 98 73 98 93 37 89 77 91 64 71 64 65 66 21 78 62 81 74 42 20 83 70 73 95 78 45 92 27 34 53 71 15
30 11 85 31 34 71 13 48 05 14 44 03 19 67 23 73 19 57 06 90 94 72 57 69 81 62 59 68 88 57 55 69 49 13 07 87 97 80 89 05 71 05 05 26 38 40 16 62 45 99 18 38 98 24 21 26 62 74 69 04 85 57 77 35 58 67 91 79 79 57 86 28 66 34 72 51 76 78 36 95 63 90 08 78 47 63 45 31 22 70 52 48 79 94 15 77 61 67 68
23 33 44 81 80 92 93 75 94 88 23 61 39 76 22 03 28 94 32 06 49 65 41 34 18 23 08 47 62 60 03 63 33 13 80 52 31 54 73 43 70 26 16 69 57 87 83 31 03 93 70 81 47 95 77 44 29 68 39 51 56 59 63 07 25 70 07 77 43 53 64 03 94 42 95 39 18 01 66 21 16 97 20 50 90 16 70 10 95 69 29 06 25 61 41 26 15 59 63 35


================================================
FILE: project_euler/problem_068/__init__.py
================================================


================================================
FILE: project_euler/problem_068/sol1.py
================================================
"""
Project Euler Problem 68: https://projecteuler.net/problem=68

Magic 5-gon ring

Problem Statement:
Consider the following "magic" 3-gon ring,
filled with the numbers 1 to 6, and each line adding to nine.

   4
    \
     3
    / \
   1 - 2 - 6
  /
 5

Working clockwise, and starting from the group of three
with the numerically lowest external node (4,3,2 in this example),
each solution can be described uniquely.
For example, the above solution can be described by the set: 4,3,2; 6,2,1; 5,1,3.

It is possible to complete the ring with four different totals: 9, 10, 11, and 12.
There are eight solutions in total.
Total   Solution Set
9       4,2,3; 5,3,1; 6,1,2
9       4,3,2; 6,2,1; 5,1,3
10      2,3,5; 4,5,1; 6,1,3
10      2,5,3; 6,3,1; 4,1,5
11      1,4,6; 3,6,2; 5,2,4
11      1,6,4; 5,4,2; 3,2,6
12      1,5,6; 2,6,4; 3,4,5
12      1,6,5; 3,5,4; 2,4,6

By concatenating each group it is possible to form 9-digit strings;
the maximum string for a 3-gon ring is 432621513.

Using the numbers 1 to 10, and depending on arrangements,
it is possible to form 16- and 17-digit strings.
What is the maximum 16-digit string for a "magic" 5-gon ring?
"""

from itertools import permutations


def solution(gon_side: int = 5) -> int:
    """
    Find the maximum number for a "magic" gon_side-gon ring

    The gon_side parameter should be in the range [3, 5],
    other side numbers aren't tested

    >>> solution(3)
    432621513
    >>> solution(4)
    426561813732
    >>> solution()
    6531031914842725
    >>> solution(6)
    Traceback (most recent call last):
    ValueError: gon_side must be in the range [3, 5]
    """
    if gon_side < 3 or gon_side > 5:
        raise ValueError("gon_side must be in the range [3, 5]")

    # Since it's 16, we know 10 is on the outer ring
    # Put the big numbers at the end so that they are never the first number
    small_numbers = list(range(gon_side + 1, 0, -1))
    big_numbers = list(range(gon_side + 2, gon_side * 2 + 1))

    for perm in permutations(small_numbers + big_numbers):
        numbers = generate_gon_ring(gon_side, list(perm))
        if is_magic_gon(numbers):
            return int("".join(str(n) for n in numbers))

    msg = f"Magic {gon_side}-gon ring is impossible"
    raise ValueError(msg)


def generate_gon_ring(gon_side: int, perm: list[int]) -> list[int]:
    """
    Generate a gon_side-gon ring from a permutation state
    The permutation state is the ring, but every duplicate is removed

    >>> generate_gon_ring(3, [4, 2, 3, 5, 1, 6])
    [4, 2, 3, 5, 3, 1, 6, 1, 2]
    >>> generate_gon_ring(5, [6, 5, 4, 3, 2, 1, 7, 8, 9, 10])
    [6, 5, 4, 3, 4, 2, 1, 2, 7, 8, 7, 9, 10, 9, 5]
    """
    result = [0] * (gon_side * 3)
    result[0:3] = perm[0:3]
    perm.append(perm[1])

    magic_number = 1 if gon_side < 5 else 2

    for i in range(1, len(perm) // 3 + magic_number):
        result[3 * i] = perm[2 * i + 1]
        result[3 * i + 1] = result[3 * i - 1]
        result[3 * i + 2] = perm[2 * i + 2]

    return result


def is_magic_gon(numbers: list[int]) -> bool:
    """
    Check if the solution set is a magic n-gon ring
    Check that the first number is the smallest number on the outer ring
    Take a list, and check if the sum of each 3 numbers chunk is equal to the same total

    >>> is_magic_gon([4, 2, 3, 5, 3, 1, 6, 1, 2])
    True
    >>> is_magic_gon([4, 3, 2, 6, 2, 1, 5, 1, 3])
    True
    >>> is_magic_gon([2, 3, 5, 4, 5, 1, 6, 1, 3])
    True
    >>> is_magic_gon([1, 2, 3, 4, 5, 6, 7, 8, 9])
    False
    >>> is_magic_gon([1])
    Traceback (most recent call last):
    ValueError: a gon ring should have a length that is a multiple of 3
    """
    if len(numbers) % 3 != 0:
        raise ValueError("a gon ring should have a length that is a multiple of 3")

    if min(numbers[::3]) != numbers[0]:
        return False

    total = sum(numbers[:3])

    return all(sum(numbers[i : i + 3]) == total for i in range(3, len(numbers), 3))


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_069/__init__.py
================================================


================================================
FILE: project_euler/problem_069/sol1.py
================================================
"""
Totient maximum
Problem 69: https://projecteuler.net/problem=69

Euler's Totient function, φ(n) [sometimes called the phi function],
is used to determine the number of numbers less than n which are relatively prime to n.
For example, as 1, 2, 4, 5, 7, and 8,
are all less than nine and relatively prime to nine, φ(9)=6.

n	Relatively Prime	φ(n)	n/φ(n)
2	1	                1	    2
3	1,2	                2	    1.5
4	1,3	                2	    2
5	1,2,3,4	            4	    1.25
6	1,5		            2	    3
7	1,2,3,4,5,6	        6	    1.1666...
8	1,3,5,7		        4	    2
9	1,2,4,5,7,8	        6	    1.5
10	1,3,7,9	            4	    2.5

It can be seen that n=6 produces a maximum n/φ(n) for n ≤ 10.

Find the value of n ≤ 1,000,000 for which n/φ(n) is a maximum.
"""


def solution(n: int = 10**6) -> int:
    """
    Returns solution to problem.
    Algorithm:
    1. Precompute φ(k) for all natural k, k <= n using product formula (wikilink below)
    https://en.wikipedia.org/wiki/Euler%27s_totient_function#Euler's_product_formula

    2. Find k/φ(k) for all k ≤ n and return the k that attains maximum

    >>> solution(10)
    6

    >>> solution(100)
    30

    >>> solution(9973)
    2310

    """

    if n <= 0:
        raise ValueError("Please enter an integer greater than 0")

    phi = list(range(n + 1))
    for number in range(2, n + 1):
        if phi[number] == number:
            phi[number] -= 1
            for multiple in range(number * 2, n + 1, number):
                phi[multiple] = (phi[multiple] // number) * (number - 1)

    answer = 1
    for number in range(1, n + 1):
        if (answer / phi[answer]) < (number / phi[number]):
            answer = number

    return answer


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_070/__init__.py
================================================


================================================
FILE: project_euler/problem_070/sol1.py
================================================
"""
Project Euler Problem 70: https://projecteuler.net/problem=70

Euler's Totient function, φ(n) [sometimes called the phi function], is used to
determine the number of positive numbers less than or equal to n which are
relatively prime to n. For example, as 1, 2, 4, 5, 7, and 8, are all less than
nine and relatively prime to nine, φ(9)=6.

The number 1 is considered to be relatively prime to every positive number, so
φ(1)=1.

Interestingly, φ(87109)=79180, and it can be seen that 87109 is a permutation
of 79180.

Find the value of n, 1 < n < 10^7, for which φ(n) is a permutation of n and
the ratio n/φ(n) produces a minimum.

-----

This is essentially brute force. Calculate all totients up to 10^7 and
find the minimum ratio of n/φ(n) that way. To minimize the ratio, we want
to minimize n and maximize φ(n) as much as possible, so we can store the
minimum fraction's numerator and denominator and calculate new fractions
with each totient to compare against. To avoid dividing by zero, I opt to
use cross multiplication.

References:
Finding totients
https://en.wikipedia.org/wiki/Euler's_totient_function#Euler's_product_formula
"""

from __future__ import annotations

import numpy as np


def get_totients(max_one: int) -> list[int]:
    """
    Calculates a list of totients from 0 to max_one exclusive, using the
    definition of Euler's product formula.

    >>> get_totients(5)
    [0, 1, 1, 2, 2]

    >>> get_totients(10)
    [0, 1, 1, 2, 2, 4, 2, 6, 4, 6]
    """
    totients = np.arange(max_one)

    for i in range(2, max_one):
        if totients[i] == i:
            x = np.arange(i, max_one, i)  # array of indexes to select
            totients[x] -= totients[x] // i

    return totients.tolist()


def has_same_digits(num1: int, num2: int) -> bool:
    """
    Return True if num1 and num2 have the same frequency of every digit, False
    otherwise.

    >>> has_same_digits(123456789, 987654321)
    True

    >>> has_same_digits(123, 23)
    False

    >>> has_same_digits(1234566, 123456)
    False
    """
    return sorted(str(num1)) == sorted(str(num2))


def solution(max_n: int = 10000000) -> int:
    """
    Finds the value of n from 1 to max such that n/φ(n) produces a minimum.

    >>> solution(100)
    21

    >>> solution(10000)
    4435
    """

    min_numerator = 1  # i
    min_denominator = 0  # φ(i)
    totients = get_totients(max_n + 1)

    for i in range(2, max_n + 1):
        t = totients[i]

        if i * min_denominator < min_numerator * t and has_same_digits(i, t):
            min_numerator = i
            min_denominator = t

    return min_numerator


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_071/__init__.py
================================================


================================================
FILE: project_euler/problem_071/sol1.py
================================================
"""
Ordered fractions
Problem 71
https://projecteuler.net/problem=71

Consider the fraction n/d, where n and d are positive
integers. If n<d and HCF(n,d)=1, it is called a reduced proper fraction.

If we list the set of reduced proper fractions for d ≤ 8
in ascending order of size, we get:
    1/8, 1/7, 1/6, 1/5, 1/4, 2/7, 1/3, 3/8, 2/5, 3/7,
    1/2, 4/7, 3/5, 5/8, 2/3, 5/7, 3/4, 4/5, 5/6, 6/7, 7/8

It can be seen that 2/5 is the fraction immediately to the left of 3/7.

By listing the set of reduced proper fractions for d ≤ 1,000,000
in ascending order of size, find the numerator of the fraction
immediately to the left of 3/7.
"""


def solution(numerator: int = 3, denominator: int = 7, limit: int = 1000000) -> int:
    """
    Returns the closest numerator of the fraction immediately to the
    left of given fraction (numerator/denominator) from a list of reduced
    proper fractions.
    >>> solution()
    428570
    >>> solution(3, 7, 8)
    2
    >>> solution(6, 7, 60)
    47
    """
    max_numerator = 0
    max_denominator = 1

    for current_denominator in range(1, limit + 1):
        current_numerator = current_denominator * numerator // denominator
        if current_denominator % denominator == 0:
            current_numerator -= 1
        if current_numerator * max_denominator > current_denominator * max_numerator:
            max_numerator = current_numerator
            max_denominator = current_denominator
    return max_numerator


if __name__ == "__main__":
    print(solution(numerator=3, denominator=7, limit=1000000))


================================================
FILE: project_euler/problem_072/__init__.py
================================================


================================================
FILE: project_euler/problem_072/sol1.py
================================================
"""
Problem 72 Counting fractions: https://projecteuler.net/problem=72

Description:

Consider the fraction, n/d, where n and d are positive integers. If n<d and HCF(n,d)=1,
it is called a reduced proper fraction.
If we list the set of reduced proper fractions for d ≤ 8 in ascending order of size, we
get: 1/8, 1/7, 1/6, 1/5, 1/4, 2/7, 1/3, 3/8, 2/5, 3/7, 1/2, 4/7, 3/5, 5/8, 2/3, 5/7,
3/4, 4/5, 5/6, 6/7, 7/8
It can be seen that there are 21 elements in this set.
How many elements would be contained in the set of reduced proper fractions for
d ≤ 1,000,000?

Solution:

Number of numbers between 1 and n that are coprime to n is given by the Euler's Totient
function, phi(n). So, the answer is simply the sum of phi(n) for 2 <= n <= 1,000,000
Sum of phi(d), for all d|n = n. This result can be used to find phi(n) using a sieve.

Time: 1 sec
"""

import numpy as np


def solution(limit: int = 1_000_000) -> int:
    """
    Returns an integer, the solution to the problem
    >>> solution(10)
    31
    >>> solution(100)
    3043
    >>> solution(1_000)
    304191
    """

    # generating an array from -1 to limit
    phi = np.arange(-1, limit)

    for i in range(2, limit + 1):
        if phi[i] == i - 1:
            ind = np.arange(2 * i, limit + 1, i)  # indexes for selection
            phi[ind] -= phi[ind] // i

    return int(np.sum(phi[2 : limit + 1]))


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_072/sol2.py
================================================
"""
Project Euler Problem 72: https://projecteuler.net/problem=72

Consider the fraction, n/d, where n and d are positive integers. If n<d and HCF(n,d)=1,
it is called a reduced proper fraction.

If we list the set of reduced proper fractions for d ≤ 8 in ascending order of size,
we get:

1/8, 1/7, 1/6, 1/5, 1/4, 2/7, 1/3, 3/8, 2/5, 3/7, 1/2,
4/7, 3/5, 5/8, 2/3, 5/7, 3/4, 4/5, 5/6, 6/7, 7/8

It can be seen that there are 21 elements in this set.

How many elements would be contained in the set of reduced proper fractions
for d ≤ 1,000,000?
"""


def solution(limit: int = 1000000) -> int:
    """
    Return the number of reduced proper fractions with denominator less than limit.
    >>> solution(8)
    21
    >>> solution(1000)
    304191
    """
    primes = set(range(3, limit, 2))
    primes.add(2)
    for p in range(3, limit, 2):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p, limit, p)))

    phi = [float(n) for n in range(limit + 1)]

    for p in primes:
        for n in range(p, limit + 1, p):
            phi[n] *= 1 - 1 / p

    return int(sum(phi[2:]))


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_073/__init__.py
================================================


================================================
FILE: project_euler/problem_073/sol1.py
================================================
"""
Project Euler Problem 73: https://projecteuler.net/problem=73

Consider the fraction, n/d, where n and d are positive integers.
If n<d and HCF(n,d)=1, it is called a reduced proper fraction.

If we list the set of reduced proper fractions for d ≤ 8 in ascending order of size,
we get:

1/8, 1/7, 1/6, 1/5, 1/4, 2/7, 1/3, 3/8, 2/5, 3/7, 1/2, 4/7, 3/5, 5/8, 2/3,
5/7, 3/4, 4/5, 5/6, 6/7, 7/8

It can be seen that there are 3 fractions between 1/3 and 1/2.

How many fractions lie between 1/3 and 1/2 in the sorted set
of reduced proper fractions for d ≤ 12,000?
"""

from math import gcd


def solution(max_d: int = 12_000) -> int:
    """
    Returns number of fractions lie between 1/3 and 1/2 in the sorted set
    of reduced proper fractions for d ≤ max_d

    >>> solution(4)
    0

    >>> solution(5)
    1

    >>> solution(8)
    3
    """

    fractions_number = 0
    for d in range(max_d + 1):
        n_start = d // 3 + 1
        n_step = 1
        if d % 2 == 0:
            n_start += 1 - n_start % 2
            n_step = 2
        for n in range(n_start, (d + 1) // 2, n_step):
            if gcd(n, d) == 1:
                fractions_number += 1
    return fractions_number


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_074/__init__.py
================================================


================================================
FILE: project_euler/problem_074/sol1.py
================================================
"""
Project Euler Problem 74: https://projecteuler.net/problem=74

The number 145 is well known for the property that the sum of the factorial of its
digits is equal to 145:

1! + 4! + 5! = 1 + 24 + 120 = 145

Perhaps less well known is 169, in that it produces the longest chain of numbers that
link back to 169; it turns out that there are only three such loops that exist:

169 → 363601 → 1454 → 169
871 → 45361 → 871
872 → 45362 → 872

It is not difficult to prove that EVERY starting number will eventually get stuck in
a loop. For example,

69 → 363600 → 1454 → 169 → 363601 (→ 1454)
78 → 45360 → 871 → 45361 (→ 871)
540 → 145 (→ 145)

Starting with 69 produces a chain of five non-repeating terms, but the longest
non-repeating chain with a starting number below one million is sixty terms.

How many chains, with a starting number below one million, contain exactly sixty
non-repeating terms?
"""

DIGIT_FACTORIALS = {
    "0": 1,
    "1": 1,
    "2": 2,
    "3": 6,
    "4": 24,
    "5": 120,
    "6": 720,
    "7": 5040,
    "8": 40320,
    "9": 362880,
}

CACHE_SUM_DIGIT_FACTORIALS = {145: 145}

CHAIN_LENGTH_CACHE = {
    145: 0,
    169: 3,
    36301: 3,
    1454: 3,
    871: 2,
    45361: 2,
    872: 2,
}


def sum_digit_factorials(n: int) -> int:
    """
    Return the sum of the factorial of the digits of n.
    >>> sum_digit_factorials(145)
    145
    >>> sum_digit_factorials(45361)
    871
    >>> sum_digit_factorials(540)
    145
    """
    if n in CACHE_SUM_DIGIT_FACTORIALS:
        return CACHE_SUM_DIGIT_FACTORIALS[n]
    ret = sum(DIGIT_FACTORIALS[let] for let in str(n))
    CACHE_SUM_DIGIT_FACTORIALS[n] = ret
    return ret


def chain_length(n: int, previous: set | None = None) -> int:
    """
    Calculate the length of the chain of non-repeating terms starting with n.
    Previous is a set containing the previous member of the chain.
    >>> chain_length(10101)
    11
    >>> chain_length(555)
    20
    >>> chain_length(178924)
    39
    """
    previous = previous or set()
    if n in CHAIN_LENGTH_CACHE:
        return CHAIN_LENGTH_CACHE[n]
    next_number = sum_digit_factorials(n)
    if next_number in previous:
        CHAIN_LENGTH_CACHE[n] = 0
        return 0
    else:
        previous.add(n)
        ret = 1 + chain_length(next_number, previous)
        CHAIN_LENGTH_CACHE[n] = ret
        return ret


def solution(num_terms: int = 60, max_start: int = 1000000) -> int:
    """
    Return the number of chains with a starting number below one million which
    contain exactly n non-repeating terms.
    >>> solution(10,1000)
    28
    """
    return sum(1 for i in range(1, max_start) if chain_length(i) == num_terms)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_074/sol2.py
================================================
"""
Project Euler Problem 074: https://projecteuler.net/problem=74

The number 145 is well known for the property that the sum of the factorial of its
digits is equal to 145:

1! + 4! + 5! = 1 + 24 + 120 = 145

Perhaps less well known is 169, in that it produces the longest chain of numbers that
link back to 169; it turns out that there are only three such loops that exist:

169 → 363601 → 1454 → 169
871 → 45361 → 871
872 → 45362 → 872

It is not difficult to prove that EVERY starting number will eventually get stuck in a
loop. For example,

69 → 363600 → 1454 → 169 → 363601 (→ 1454)
78 → 45360 → 871 → 45361 (→ 871)
540 → 145 (→ 145)

Starting with 69 produces a chain of five non-repeating terms, but the longest
non-repeating chain with a starting number below one million is sixty terms.

How many chains, with a starting number below one million, contain exactly sixty
non-repeating terms?

Solution approach:
This solution simply consists in a loop that generates the chains of non repeating
items using the cached sizes of the previous chains.
The generation of the chain stops before a repeating item or if the size of the chain
is greater then the desired one.
After generating each chain, the length is checked and the counter increases.
"""

from math import factorial

DIGIT_FACTORIAL: dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}


def digit_factorial_sum(number: int) -> int:
    """
    Function to perform the sum of the factorial of all the digits in number

    >>> digit_factorial_sum(69.0)
    Traceback (most recent call last):
        ...
    TypeError: Parameter number must be int

    >>> digit_factorial_sum(-1)
    Traceback (most recent call last):
        ...
    ValueError: Parameter number must be greater than or equal to 0

    >>> digit_factorial_sum(0)
    1

    >>> digit_factorial_sum(69)
    363600
    """
    if not isinstance(number, int):
        raise TypeError("Parameter number must be int")

    if number < 0:
        raise ValueError("Parameter number must be greater than or equal to 0")

    # Converts number in string to iterate on its digits and adds its factorial.
    return sum(DIGIT_FACTORIAL[digit] for digit in str(number))


def solution(chain_length: int = 60, number_limit: int = 1000000) -> int:
    """
    Returns the number of numbers below number_limit that produce chains with exactly
    chain_length non repeating elements.

    >>> solution(10.0, 1000)
    Traceback (most recent call last):
        ...
    TypeError: Parameters chain_length and number_limit must be int

    >>> solution(10, 1000.0)
    Traceback (most recent call last):
        ...
    TypeError: Parameters chain_length and number_limit must be int

    >>> solution(0, 1000)
    Traceback (most recent call last):
        ...
    ValueError: Parameters chain_length and number_limit must be greater than 0

    >>> solution(10, 0)
    Traceback (most recent call last):
        ...
    ValueError: Parameters chain_length and number_limit must be greater than 0

    >>> solution(10, 1000)
    26
    """

    if not isinstance(chain_length, int) or not isinstance(number_limit, int):
        raise TypeError("Parameters chain_length and number_limit must be int")

    if chain_length <= 0 or number_limit <= 0:
        raise ValueError(
            "Parameters chain_length and number_limit must be greater than 0"
        )

    # the counter for the chains with the exact desired length
    chains_counter = 0
    # the cached sizes of the previous chains
    chain_sets_lengths: dict[int, int] = {}

    for start_chain_element in range(1, number_limit):
        # The temporary set will contain the elements of the chain
        chain_set = set()
        chain_set_length = 0

        # Stop computing the chain when you find a cached size, a repeating item or the
        # length is greater then the desired one.
        chain_element = start_chain_element
        while (
            chain_element not in chain_sets_lengths
            and chain_element not in chain_set
            and chain_set_length <= chain_length
        ):
            chain_set.add(chain_element)
            chain_set_length += 1
            chain_element = digit_factorial_sum(chain_element)

        if chain_element in chain_sets_lengths:
            chain_set_length += chain_sets_lengths[chain_element]

        chain_sets_lengths[start_chain_element] = chain_set_length

        # If chain contains the exact amount of elements increase the counter
        if chain_set_length == chain_length:
            chains_counter += 1

    return chains_counter


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(f"{solution()}")


================================================
FILE: project_euler/problem_075/__init__.py
================================================


================================================
FILE: project_euler/problem_075/sol1.py
================================================
"""
Project Euler Problem 75: https://projecteuler.net/problem=75

It turns out that 12 cm is the smallest length of wire that can be bent to form an
integer sided right angle triangle in exactly one way, but there are many more examples.

12 cm: (3,4,5)
24 cm: (6,8,10)
30 cm: (5,12,13)
36 cm: (9,12,15)
40 cm: (8,15,17)
48 cm: (12,16,20)

In contrast, some lengths of wire, like 20 cm, cannot be bent to form an integer sided
right angle triangle, and other lengths allow more than one solution to be found; for
example, using 120 cm it is possible to form exactly three different integer sided
right angle triangles.

120 cm: (30,40,50), (20,48,52), (24,45,51)

Given that L is the length of the wire, for how many values of L ≤ 1,500,000 can
exactly one integer sided right angle triangle be formed?

Solution: we generate all pythagorean triples using Euclid's formula and
keep track of the frequencies of the perimeters.

Reference: https://en.wikipedia.org/wiki/Pythagorean_triple#Generating_a_triple
"""

from collections import defaultdict
from math import gcd


def solution(limit: int = 1500000) -> int:
    """
    Return the number of values of L <= limit such that a wire of length L can be
    formmed into an integer sided right angle triangle in exactly one way.
    >>> solution(50)
    6
    >>> solution(1000)
    112
    >>> solution(50000)
    5502
    """
    frequencies: defaultdict = defaultdict(int)
    euclid_m = 2
    while 2 * euclid_m * (euclid_m + 1) <= limit:
        for euclid_n in range((euclid_m % 2) + 1, euclid_m, 2):
            if gcd(euclid_m, euclid_n) > 1:
                continue
            primitive_perimeter = 2 * euclid_m * (euclid_m + euclid_n)
            for perimeter in range(primitive_perimeter, limit + 1, primitive_perimeter):
                frequencies[perimeter] += 1
        euclid_m += 1
    return sum(1 for frequency in frequencies.values() if frequency == 1)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_076/__init__.py
================================================


================================================
FILE: project_euler/problem_076/sol1.py
================================================
"""
Counting Summations
Problem 76: https://projecteuler.net/problem=76

It is possible to write five as a sum in exactly six different ways:

4 + 1
3 + 2
3 + 1 + 1
2 + 2 + 1
2 + 1 + 1 + 1
1 + 1 + 1 + 1 + 1

How many different ways can one hundred be written as a sum of at least two
positive integers?
"""


def solution(m: int = 100) -> int:
    """
    Returns the number of different ways the number m can be written as a
    sum of at least two positive integers.

    >>> solution(100)
    190569291
    >>> solution(50)
    204225
    >>> solution(30)
    5603
    >>> solution(10)
    41
    >>> solution(5)
    6
    >>> solution(3)
    2
    >>> solution(2)
    1
    >>> solution(1)
    0
    """
    memo = [[0 for _ in range(m)] for _ in range(m + 1)]
    for i in range(m + 1):
        memo[i][0] = 1

    for n in range(m + 1):
        for k in range(1, m):
            memo[n][k] += memo[n][k - 1]
            if n > k:
                memo[n][k] += memo[n - k - 1][k]

    return memo[m][m - 1] - 1


if __name__ == "__main__":
    print(solution(int(input("Enter a number: ").strip())))


================================================
FILE: project_euler/problem_077/__init__.py
================================================


================================================
FILE: project_euler/problem_077/sol1.py
================================================
"""
Project Euler Problem 77: https://projecteuler.net/problem=77

It is possible to write ten as the sum of primes in exactly five different ways:

7 + 3
5 + 5
5 + 3 + 2
3 + 3 + 2 + 2
2 + 2 + 2 + 2 + 2

What is the first value which can be written as the sum of primes in over
five thousand different ways?
"""

from __future__ import annotations

from functools import lru_cache
from math import ceil

NUM_PRIMES = 100

primes = set(range(3, NUM_PRIMES, 2))
primes.add(2)
prime: int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
    if prime not in primes:
        continue
    primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))


@lru_cache(maxsize=100)
def partition(number_to_partition: int) -> set[int]:
    """
    Return a set of integers corresponding to unique prime partitions of n.
    The unique prime partitions can be represented as unique prime decompositions,
    e.g. (7+3) <-> 7*3 = 12, (3+3+2+2) = 3*3*2*2 = 36
    >>> partition(10)
    {32, 36, 21, 25, 30}
    >>> partition(15)
    {192, 160, 105, 44, 112, 243, 180, 150, 216, 26, 125, 126}
    >>> len(partition(20))
    26
    """
    if number_to_partition < 0:
        return set()
    elif number_to_partition == 0:
        return {1}

    ret: set[int] = set()
    prime: int
    sub: int

    for prime in primes:
        if prime > number_to_partition:
            continue
        for sub in partition(number_to_partition - prime):
            ret.add(sub * prime)

    return ret


def solution(number_unique_partitions: int = 5000) -> int | None:
    """
    Return the smallest integer that can be written as the sum of primes in over
    m unique ways.
    >>> solution(4)
    10
    >>> solution(500)
    45
    >>> solution(1000)
    53
    """
    for number_to_partition in range(1, NUM_PRIMES):
        if len(partition(number_to_partition)) > number_unique_partitions:
            return number_to_partition
    return None


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_078/__init__.py
================================================


================================================
FILE: project_euler/problem_078/sol1.py
================================================
"""
Problem 78
Url: https://projecteuler.net/problem=78
Statement:
Let p(n) represent the number of different ways in which n coins
can be separated into piles. For example, five coins can be separated
into piles in exactly seven different ways, so p(5)=7.

            OOOOO
            OOOO   O
            OOO   OO
            OOO   O   O
            OO   OO   O
            OO   O   O   O
            O   O   O   O   O
Find the least value of n for which p(n) is divisible by one million.
"""

import itertools


def solution(number: int = 1000000) -> int:
    """
    >>> solution(1)
    1

    >>> solution(9)
    14

    >>> solution()
    55374
    """
    partitions = [1]

    for i in itertools.count(len(partitions)):
        item = 0
        for j in itertools.count(1):
            sign = -1 if j % 2 == 0 else +1
            index = (j * j * 3 - j) // 2
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number
            index += j
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number

        if item == 0:
            return i
        partitions.append(item)

    return 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print(f"{solution() = }")


================================================
FILE: project_euler/problem_079/__init__.py
================================================


================================================
FILE: project_euler/problem_079/keylog.txt
================================================
319
680
180
690
129
620
762
689
762
318
368
710
720
710
629
168
160
689
716
731
736
729
316
729
729
710
769
290
719
680
318
389
162
289
162
718
729
319
790
680
890
362
319
760
316
729
380
319
728
716


================================================
FILE: project_euler/problem_079/keylog_test.txt
================================================
319
680
180
690
129
620
698
318
328
310
320
610
629
198
190
631


================================================
FILE: project_euler/problem_079/sol1.py
================================================
"""
Project Euler Problem 79: https://projecteuler.net/problem=79

Passcode derivation

A common security method used for online banking is to ask the user for three
random characters from a passcode. For example, if the passcode was 531278,
they may ask for the 2nd, 3rd, and 5th characters; the expected reply would
be: 317.

The text file, keylog.txt, contains fifty successful login attempts.

Given that the three characters are always asked for in order, analyse the file
so as to determine the shortest possible secret passcode of unknown length.
"""

import itertools
from pathlib import Path


def find_secret_passcode(logins: list[str]) -> int:
    """
    Returns the shortest possible secret passcode of unknown length.

    >>> find_secret_passcode(["135", "259", "235", "189", "690", "168", "120",
    ...     "136", "289", "589", "160", "165", "580", "369", "250", "280"])
    12365890

    >>> find_secret_passcode(["426", "281", "061", "819" "268", "406", "420",
    ...     "428", "209", "689", "019", "421", "469", "261", "681", "201"])
    4206819
    """

    # Split each login by character e.g. '319' -> ('3', '1', '9')
    split_logins = [tuple(login) for login in logins]

    unique_chars = {char for login in split_logins for char in login}

    for permutation in itertools.permutations(unique_chars):
        satisfied = True
        for login in logins:
            if not (
                permutation.index(login[0])
                < permutation.index(login[1])
                < permutation.index(login[2])
            ):
                satisfied = False
                break

        if satisfied:
            return int("".join(permutation))

    raise Exception("Unable to find the secret passcode")


def solution(input_file: str = "keylog.txt") -> int:
    """
    Returns the shortest possible secret passcode of unknown length
    for successful login attempts given by `input_file` text file.

    >>> solution("keylog_test.txt")
    6312980
    """
    logins = Path(__file__).parent.joinpath(input_file).read_text().splitlines()

    return find_secret_passcode(logins)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_080/__init__.py
================================================


================================================
FILE: project_euler/problem_080/sol1.py
================================================
"""
Project Euler Problem 80: https://projecteuler.net/problem=80
Author: Sandeep Gupta
Problem statement: For the first one hundred natural numbers, find the total of
the digital sums of the first one hundred decimal digits for all the irrational
square roots.
Time: 5 October 2020, 18:30
"""

import decimal


def solution() -> int:
    """
    To evaluate the sum, Used decimal python module to calculate the decimal
    places up to 100, the most important thing would be take calculate
    a few extra places for decimal otherwise there will be rounding
    error.

    >>> solution()
    40886
    """
    answer = 0
    decimal_context = decimal.Context(prec=105)
    for i in range(2, 100):
        number = decimal.Decimal(i)
        sqrt_number = number.sqrt(decimal_context)
        if len(str(sqrt_number)) > 1:
            answer += int(str(sqrt_number)[0])
            sqrt_number_str = str(sqrt_number)[2:101]
            answer += sum(int(x) for x in sqrt_number_str)
    return answer


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_081/__init__.py
================================================


================================================
FILE: project_euler/problem_081/matrix.txt
================================================
4445,2697,5115,718,2209,2212,654,4348,3079,6821,7668,3276,8874,4190,3785,2752,9473,7817,9137,496,7338,3434,7152,4355,4552,7917,7827,2460,2350,691,3514,5880,3145,7633,7199,3783,5066,7487,3285,1084,8985,760,872,8609,8051,1134,9536,5750,9716,9371,7619,5617,275,9721,2997,2698,1887,8825,6372,3014,2113,7122,7050,6775,5948,2758,1219,3539,348,7989,2735,9862,1263,8089,6401,9462,3168,2758,3748,5870
1096,20,1318,7586,5167,2642,1443,5741,7621,7030,5526,4244,2348,4641,9827,2448,6918,5883,3737,300,7116,6531,567,5997,3971,6623,820,6148,3287,1874,7981,8424,7672,7575,6797,6717,1078,5008,4051,8795,5820,346,1851,6463,2117,6058,3407,8211,117,4822,1317,4377,4434,5925,8341,4800,1175,4173,690,8978,7470,1295,3799,8724,3509,9849,618,3320,7068,9633,2384,7175,544,6583,1908,9983,481,4187,9353,9377
9607,7385,521,6084,1364,8983,7623,1585,6935,8551,2574,8267,4781,3834,2764,2084,2669,4656,9343,7709,2203,9328,8004,6192,5856,3555,2260,5118,6504,1839,9227,1259,9451,1388,7909,5733,6968,8519,9973,1663,5315,7571,3035,4325,4283,2304,6438,3815,9213,9806,9536,196,5542,6907,2475,1159,5820,9075,9470,2179,9248,1828,4592,9167,3713,4640,47,3637,309,7344,6955,346,378,9044,8635,7466,5036,9515,6385,9230
7206,3114,7760,1094,6150,5182,7358,7387,4497,955,101,1478,7777,6966,7010,8417,6453,4955,3496,107,449,8271,131,2948,6185,784,5937,8001,6104,8282,4165,3642,710,2390,575,715,3089,6964,4217,192,5949,7006,715,3328,1152,66,8044,4319,1735,146,4818,5456,6451,4113,1063,4781,6799,602,1504,6245,6550,1417,1343,2363,3785,5448,4545,9371,5420,5068,4613,4882,4241,5043,7873,8042,8434,3939,9256,2187
3620,8024,577,9997,7377,7682,1314,1158,6282,6310,1896,2509,5436,1732,9480,706,496,101,6232,7375,2207,2306,110,6772,3433,2878,8140,5933,8688,1399,2210,7332,6172,6403,7333,4044,2291,1790,2446,7390,8698,5723,3678,7104,1825,2040,140,3982,4905,4160,2200,5041,2512,1488,2268,1175,7588,8321,8078,7312,977,5257,8465,5068,3453,3096,1651,7906,253,9250,6021,8791,8109,6651,3412,345,4778,5152,4883,7505
1074,5438,9008,2679,5397,5429,2652,3403,770,9188,4248,2493,4361,8327,9587,707,9525,5913,93,1899,328,2876,3604,673,8576,6908,7659,2544,3359,3883,5273,6587,3065,1749,3223,604,9925,6941,2823,8767,7039,3290,3214,1787,7904,3421,7137,9560,8451,2669,9219,6332,1576,5477,6755,8348,4164,4307,2984,4012,6629,1044,2874,6541,4942,903,1404,9125,5160,8836,4345,2581,460,8438,1538,5507,668,3352,2678,6942
4295,1176,5596,1521,3061,9868,7037,7129,8933,6659,5947,5063,3653,9447,9245,2679,767,714,116,8558,163,3927,8779,158,5093,2447,5782,3967,1716,931,7772,8164,1117,9244,5783,7776,3846,8862,6014,2330,6947,1777,3112,6008,3491,1906,5952,314,4602,8994,5919,9214,3995,5026,7688,6809,5003,3128,2509,7477,110,8971,3982,8539,2980,4689,6343,5411,2992,5270,5247,9260,2269,7474,1042,7162,5206,1232,4556,4757
510,3556,5377,1406,5721,4946,2635,7847,4251,8293,8281,6351,4912,287,2870,3380,3948,5322,3840,4738,9563,1906,6298,3234,8959,1562,6297,8835,7861,239,6618,1322,2553,2213,5053,5446,4402,6500,5182,8585,6900,5756,9661,903,5186,7687,5998,7997,8081,8955,4835,6069,2621,1581,732,9564,1082,1853,5442,1342,520,1737,3703,5321,4793,2776,1508,1647,9101,2499,6891,4336,7012,3329,3212,1442,9993,3988,4930,7706
9444,3401,5891,9716,1228,7107,109,3563,2700,6161,5039,4992,2242,8541,7372,2067,1294,3058,1306,320,8881,5756,9326,411,8650,8824,5495,8282,8397,2000,1228,7817,2099,6473,3571,5994,4447,1299,5991,543,7874,2297,1651,101,2093,3463,9189,6872,6118,872,1008,1779,2805,9084,4048,2123,5877,55,3075,1737,9459,4535,6453,3644,108,5982,4437,5213,1340,6967,9943,5815,669,8074,1838,6979,9132,9315,715,5048
3327,4030,7177,6336,9933,5296,2621,4785,2755,4832,2512,2118,2244,4407,2170,499,7532,9742,5051,7687,970,6924,3527,4694,5145,1306,2165,5940,2425,8910,3513,1909,6983,346,6377,4304,9330,7203,6605,3709,3346,970,369,9737,5811,4427,9939,3693,8436,5566,1977,3728,2399,3985,8303,2492,5366,9802,9193,7296,1033,5060,9144,2766,1151,7629,5169,5995,58,7619,7565,4208,1713,6279,3209,4908,9224,7409,1325,8540
6882,1265,1775,3648,4690,959,5837,4520,5394,1378,9485,1360,4018,578,9174,2932,9890,3696,116,1723,1178,9355,7063,1594,1918,8574,7594,7942,1547,6166,7888,354,6932,4651,1010,7759,6905,661,7689,6092,9292,3845,9605,8443,443,8275,5163,7720,7265,6356,7779,1798,1754,5225,6661,1180,8024,5666,88,9153,1840,3508,1193,4445,2648,3538,6243,6375,8107,5902,5423,2520,1122,5015,6113,8859,9370,966,8673,2442
7338,3423,4723,6533,848,8041,7921,8277,4094,5368,7252,8852,9166,2250,2801,6125,8093,5738,4038,9808,7359,9494,601,9116,4946,2702,5573,2921,9862,1462,1269,2410,4171,2709,7508,6241,7522,615,2407,8200,4189,5492,5649,7353,2590,5203,4274,710,7329,9063,956,8371,3722,4253,4785,1194,4828,4717,4548,940,983,2575,4511,2938,1827,2027,2700,1236,841,5760,1680,6260,2373,3851,1841,4968,1172,5179,7175,3509
4420,1327,3560,2376,6260,2988,9537,4064,4829,8872,9598,3228,1792,7118,9962,9336,4368,9189,6857,1829,9863,6287,7303,7769,2707,8257,2391,2009,3975,4993,3068,9835,3427,341,8412,2134,4034,8511,6421,3041,9012,2983,7289,100,1355,7904,9186,6920,5856,2008,6545,8331,3655,5011,839,8041,9255,6524,3862,8788,62,7455,3513,5003,8413,3918,2076,7960,6108,3638,6999,3436,1441,4858,4181,1866,8731,7745,3744,1000
356,8296,8325,1058,1277,4743,3850,2388,6079,6462,2815,5620,8495,5378,75,4324,3441,9870,1113,165,1544,1179,2834,562,6176,2313,6836,8839,2986,9454,5199,6888,1927,5866,8760,320,1792,8296,7898,6121,7241,5886,5814,2815,8336,1576,4314,3109,2572,6011,2086,9061,9403,3947,5487,9731,7281,3159,1819,1334,3181,5844,5114,9898,4634,2531,4412,6430,4262,8482,4546,4555,6804,2607,9421,686,8649,8860,7794,6672
9870,152,1558,4963,8750,4754,6521,6256,8818,5208,5691,9659,8377,9725,5050,5343,2539,6101,1844,9700,7750,8114,5357,3001,8830,4438,199,9545,8496,43,2078,327,9397,106,6090,8181,8646,6414,7499,5450,4850,6273,5014,4131,7639,3913,6571,8534,9703,4391,7618,445,1320,5,1894,6771,7383,9191,4708,9706,6939,7937,8726,9382,5216,3685,2247,9029,8154,1738,9984,2626,9438,4167,6351,5060,29,1218,1239,4785
192,5213,8297,8974,4032,6966,5717,1179,6523,4679,9513,1481,3041,5355,9303,9154,1389,8702,6589,7818,6336,3539,5538,3094,6646,6702,6266,2759,4608,4452,617,9406,8064,6379,444,5602,4950,1810,8391,1536,316,8714,1178,5182,5863,5110,5372,4954,1978,2971,5680,4863,2255,4630,5723,2168,538,1692,1319,7540,440,6430,6266,7712,7385,5702,620,641,3136,7350,1478,3155,2820,9109,6261,1122,4470,14,8493,2095
1046,4301,6082,474,4974,7822,2102,5161,5172,6946,8074,9716,6586,9962,9749,5015,2217,995,5388,4402,7652,6399,6539,1349,8101,3677,1328,9612,7922,2879,231,5887,2655,508,4357,4964,3554,5930,6236,7384,4614,280,3093,9600,2110,7863,2631,6626,6620,68,1311,7198,7561,1768,5139,1431,221,230,2940,968,5283,6517,2146,1646,869,9402,7068,8645,7058,1765,9690,4152,2926,9504,2939,7504,6074,2944,6470,7859
4659,736,4951,9344,1927,6271,8837,8711,3241,6579,7660,5499,5616,3743,5801,4682,9748,8796,779,1833,4549,8138,4026,775,4170,2432,4174,3741,7540,8017,2833,4027,396,811,2871,1150,9809,2719,9199,8504,1224,540,2051,3519,7982,7367,2761,308,3358,6505,2050,4836,5090,7864,805,2566,2409,6876,3361,8622,5572,5895,3280,441,7893,8105,1634,2929,274,3926,7786,6123,8233,9921,2674,5340,1445,203,4585,3837
5759,338,7444,7968,7742,3755,1591,4839,1705,650,7061,2461,9230,9391,9373,2413,1213,431,7801,4994,2380,2703,6161,6878,8331,2538,6093,1275,5065,5062,2839,582,1014,8109,3525,1544,1569,8622,7944,2905,6120,1564,1839,5570,7579,1318,2677,5257,4418,5601,7935,7656,5192,1864,5886,6083,5580,6202,8869,1636,7907,4759,9082,5854,3185,7631,6854,5872,5632,5280,1431,2077,9717,7431,4256,8261,9680,4487,4752,4286
1571,1428,8599,1230,7772,4221,8523,9049,4042,8726,7567,6736,9033,2104,4879,4967,6334,6716,3994,1269,8995,6539,3610,7667,6560,6065,874,848,4597,1711,7161,4811,6734,5723,6356,6026,9183,2586,5636,1092,7779,7923,8747,6887,7505,9909,1792,3233,4526,3176,1508,8043,720,5212,6046,4988,709,5277,8256,3642,1391,5803,1468,2145,3970,6301,7767,2359,8487,9771,8785,7520,856,1605,8972,2402,2386,991,1383,5963
1822,4824,5957,6511,9868,4113,301,9353,6228,2881,2966,6956,9124,9574,9233,1601,7340,973,9396,540,4747,8590,9535,3650,7333,7583,4806,3593,2738,8157,5215,8472,2284,9473,3906,6982,5505,6053,7936,6074,7179,6688,1564,1103,6860,5839,2022,8490,910,7551,7805,881,7024,1855,9448,4790,1274,3672,2810,774,7623,4223,4850,6071,9975,4935,1915,9771,6690,3846,517,463,7624,4511,614,6394,3661,7409,1395,8127
8738,3850,9555,3695,4383,2378,87,6256,6740,7682,9546,4255,6105,2000,1851,4073,8957,9022,6547,5189,2487,303,9602,7833,1628,4163,6678,3144,8589,7096,8913,5823,4890,7679,1212,9294,5884,2972,3012,3359,7794,7428,1579,4350,7246,4301,7779,7790,3294,9547,4367,3549,1958,8237,6758,3497,3250,3456,6318,1663,708,7714,6143,6890,3428,6853,9334,7992,591,6449,9786,1412,8500,722,5468,1371,108,3939,4199,2535
7047,4323,1934,5163,4166,461,3544,2767,6554,203,6098,2265,9078,2075,4644,6641,8412,9183,487,101,7566,5622,1975,5726,2920,5374,7779,5631,3753,3725,2672,3621,4280,1162,5812,345,8173,9785,1525,955,5603,2215,2580,5261,2765,2990,5979,389,3907,2484,1232,5933,5871,3304,1138,1616,5114,9199,5072,7442,7245,6472,4760,6359,9053,7876,2564,9404,3043,9026,2261,3374,4460,7306,2326,966,828,3274,1712,3446
3975,4565,8131,5800,4570,2306,8838,4392,9147,11,3911,7118,9645,4994,2028,6062,5431,2279,8752,2658,7836,994,7316,5336,7185,3289,1898,9689,2331,5737,3403,1124,2679,3241,7748,16,2724,5441,6640,9368,9081,5618,858,4969,17,2103,6035,8043,7475,2181,939,415,1617,8500,8253,2155,7843,7974,7859,1746,6336,3193,2617,8736,4079,6324,6645,8891,9396,5522,6103,1857,8979,3835,2475,1310,7422,610,8345,7615
9248,5397,5686,2988,3446,4359,6634,9141,497,9176,6773,7448,1907,8454,916,1596,2241,1626,1384,2741,3649,5362,8791,7170,2903,2475,5325,6451,924,3328,522,90,4813,9737,9557,691,2388,1383,4021,1609,9206,4707,5200,7107,8104,4333,9860,5013,1224,6959,8527,1877,4545,7772,6268,621,4915,9349,5970,706,9583,3071,4127,780,8231,3017,9114,3836,7503,2383,1977,4870,8035,2379,9704,1037,3992,3642,1016,4303
5093,138,4639,6609,1146,5565,95,7521,9077,2272,974,4388,2465,2650,722,4998,3567,3047,921,2736,7855,173,2065,4238,1048,5,6847,9548,8632,9194,5942,4777,7910,8971,6279,7253,2516,1555,1833,3184,9453,9053,6897,7808,8629,4877,1871,8055,4881,7639,1537,7701,2508,7564,5845,5023,2304,5396,3193,2955,1088,3801,6203,1748,3737,1276,13,4120,7715,8552,3047,2921,106,7508,304,1280,7140,2567,9135,5266
6237,4607,7527,9047,522,7371,4883,2540,5867,6366,5301,1570,421,276,3361,527,6637,4861,2401,7522,5808,9371,5298,2045,5096,5447,7755,5115,7060,8529,4078,1943,1697,1764,5453,7085,960,2405,739,2100,5800,728,9737,5704,5693,1431,8979,6428,673,7540,6,7773,5857,6823,150,5869,8486,684,5816,9626,7451,5579,8260,3397,5322,6920,1879,2127,2884,5478,4977,9016,6165,6292,3062,5671,5968,78,4619,4763
9905,7127,9390,5185,6923,3721,9164,9705,4341,1031,1046,5127,7376,6528,3248,4941,1178,7889,3364,4486,5358,9402,9158,8600,1025,874,1839,1783,309,9030,1843,845,8398,1433,7118,70,8071,2877,3904,8866,6722,4299,10,1929,5897,4188,600,1889,3325,2485,6473,4474,7444,6992,4846,6166,4441,2283,2629,4352,7775,1101,2214,9985,215,8270,9750,2740,8361,7103,5930,8664,9690,8302,9267,344,2077,1372,1880,9550
5825,8517,7769,2405,8204,1060,3603,7025,478,8334,1997,3692,7433,9101,7294,7498,9415,5452,3850,3508,6857,9213,6807,4412,7310,854,5384,686,4978,892,8651,3241,2743,3801,3813,8588,6701,4416,6990,6490,3197,6838,6503,114,8343,5844,8646,8694,65,791,5979,2687,2621,2019,8097,1423,3644,9764,4921,3266,3662,5561,2476,8271,8138,6147,1168,3340,1998,9874,6572,9873,6659,5609,2711,3931,9567,4143,7833,8887
6223,2099,2700,589,4716,8333,1362,5007,2753,2848,4441,8397,7192,8191,4916,9955,6076,3370,6396,6971,3156,248,3911,2488,4930,2458,7183,5455,170,6809,6417,3390,1956,7188,577,7526,2203,968,8164,479,8699,7915,507,6393,4632,1597,7534,3604,618,3280,6061,9793,9238,8347,568,9645,2070,5198,6482,5000,9212,6655,5961,7513,1323,3872,6170,3812,4146,2736,67,3151,5548,2781,9679,7564,5043,8587,1893,4531
5826,3690,6724,2121,9308,6986,8106,6659,2142,1642,7170,2877,5757,6494,8026,6571,8387,9961,6043,9758,9607,6450,8631,8334,7359,5256,8523,2225,7487,1977,9555,8048,5763,2414,4948,4265,2427,8978,8088,8841,9208,9601,5810,9398,8866,9138,4176,5875,7212,3272,6759,5678,7649,4922,5422,1343,8197,3154,3600,687,1028,4579,2084,9467,4492,7262,7296,6538,7657,7134,2077,1505,7332,6890,8964,4879,7603,7400,5973,739
1861,1613,4879,1884,7334,966,2000,7489,2123,4287,1472,3263,4726,9203,1040,4103,6075,6049,330,9253,4062,4268,1635,9960,577,1320,3195,9628,1030,4092,4979,6474,6393,2799,6967,8687,7724,7392,9927,2085,3200,6466,8702,265,7646,8665,7986,7266,4574,6587,612,2724,704,3191,8323,9523,3002,704,5064,3960,8209,2027,2758,8393,4875,4641,9584,6401,7883,7014,768,443,5490,7506,1852,2005,8850,5776,4487,4269
4052,6687,4705,7260,6645,6715,3706,5504,8672,2853,1136,8187,8203,4016,871,1809,1366,4952,9294,5339,6872,2645,6083,7874,3056,5218,7485,8796,7401,3348,2103,426,8572,4163,9171,3176,948,7654,9344,3217,1650,5580,7971,2622,76,2874,880,2034,9929,1546,2659,5811,3754,7096,7436,9694,9960,7415,2164,953,2360,4194,2397,1047,2196,6827,575,784,2675,8821,6802,7972,5996,6699,2134,7577,2887,1412,4349,4380
4629,2234,6240,8132,7592,3181,6389,1214,266,1910,2451,8784,2790,1127,6932,1447,8986,2492,5476,397,889,3027,7641,5083,5776,4022,185,3364,5701,2442,2840,4160,9525,4828,6602,2614,7447,3711,4505,7745,8034,6514,4907,2605,7753,6958,7270,6936,3006,8968,439,2326,4652,3085,3425,9863,5049,5361,8688,297,7580,8777,7916,6687,8683,7141,306,9569,2384,1500,3346,4601,7329,9040,6097,2727,6314,4501,4974,2829
8316,4072,2025,6884,3027,1808,5714,7624,7880,8528,4205,8686,7587,3230,1139,7273,6163,6986,3914,9309,1464,9359,4474,7095,2212,7302,2583,9462,7532,6567,1606,4436,8981,5612,6796,4385,5076,2007,6072,3678,8331,1338,3299,8845,4783,8613,4071,1232,6028,2176,3990,2148,3748,103,9453,538,6745,9110,926,3125,473,5970,8728,7072,9062,1404,1317,5139,9862,6496,6062,3338,464,1600,2532,1088,8232,7739,8274,3873
2341,523,7096,8397,8301,6541,9844,244,4993,2280,7689,4025,4196,5522,7904,6048,2623,9258,2149,9461,6448,8087,7245,1917,8340,7127,8466,5725,6996,3421,5313,512,9164,9837,9794,8369,4185,1488,7210,1524,1016,4620,9435,2478,7765,8035,697,6677,3724,6988,5853,7662,3895,9593,1185,4727,6025,5734,7665,3070,138,8469,6748,6459,561,7935,8646,2378,462,7755,3115,9690,8877,3946,2728,8793,244,6323,8666,4271
6430,2406,8994,56,1267,3826,9443,7079,7579,5232,6691,3435,6718,5698,4144,7028,592,2627,217,734,6194,8156,9118,58,2640,8069,4127,3285,694,3197,3377,4143,4802,3324,8134,6953,7625,3598,3584,4289,7065,3434,2106,7132,5802,7920,9060,7531,3321,1725,1067,3751,444,5503,6785,7937,6365,4803,198,6266,8177,1470,6390,1606,2904,7555,9834,8667,2033,1723,5167,1666,8546,8152,473,4475,6451,7947,3062,3281
2810,3042,7759,1741,2275,2609,7676,8640,4117,1958,7500,8048,1757,3954,9270,1971,4796,2912,660,5511,3553,1012,5757,4525,6084,7198,8352,5775,7726,8591,7710,9589,3122,4392,6856,5016,749,2285,3356,7482,9956,7348,2599,8944,495,3462,3578,551,4543,7207,7169,7796,1247,4278,6916,8176,3742,8385,2310,1345,8692,2667,4568,1770,8319,3585,4920,3890,4928,7343,5385,9772,7947,8786,2056,9266,3454,2807,877,2660
6206,8252,5928,5837,4177,4333,207,7934,5581,9526,8906,1498,8411,2984,5198,5134,2464,8435,8514,8674,3876,599,5327,826,2152,4084,2433,9327,9697,4800,2728,3608,3849,3861,3498,9943,1407,3991,7191,9110,5666,8434,4704,6545,5944,2357,1163,4995,9619,6754,4200,9682,6654,4862,4744,5953,6632,1054,293,9439,8286,2255,696,8709,1533,1844,6441,430,1999,6063,9431,7018,8057,2920,6266,6799,356,3597,4024,6665
3847,6356,8541,7225,2325,2946,5199,469,5450,7508,2197,9915,8284,7983,6341,3276,3321,16,1321,7608,5015,3362,8491,6968,6818,797,156,2575,706,9516,5344,5457,9210,5051,8099,1617,9951,7663,8253,9683,2670,1261,4710,1068,8753,4799,1228,2621,3275,6188,4699,1791,9518,8701,5932,4275,6011,9877,2933,4182,6059,2930,6687,6682,9771,654,9437,3169,8596,1827,5471,8909,2352,123,4394,3208,8756,5513,6917,2056
5458,8173,3138,3290,4570,4892,3317,4251,9699,7973,1163,1935,5477,6648,9614,5655,9592,975,9118,2194,7322,8248,8413,3462,8560,1907,7810,6650,7355,2939,4973,6894,3933,3784,3200,2419,9234,4747,2208,2207,1945,2899,1407,6145,8023,3484,5688,7686,2737,3828,3704,9004,5190,9740,8643,8650,5358,4426,1522,1707,3613,9887,6956,2447,2762,833,1449,9489,2573,1080,4167,3456,6809,2466,227,7125,2759,6250,6472,8089
3266,7025,9756,3914,1265,9116,7723,9788,6805,5493,2092,8688,6592,9173,4431,4028,6007,7131,4446,4815,3648,6701,759,3312,8355,4485,4187,5188,8746,7759,3528,2177,5243,8379,3838,7233,4607,9187,7216,2190,6967,2920,6082,7910,5354,3609,8958,6949,7731,494,8753,8707,1523,4426,3543,7085,647,6771,9847,646,5049,824,8417,5260,2730,5702,2513,9275,4279,2767,8684,1165,9903,4518,55,9682,8963,6005,2102,6523
1998,8731,936,1479,5259,7064,4085,91,7745,7136,3773,3810,730,8255,2705,2653,9790,6807,2342,355,9344,2668,3690,2028,9679,8102,574,4318,6481,9175,5423,8062,2867,9657,7553,3442,3920,7430,3945,7639,3714,3392,2525,4995,4850,2867,7951,9667,486,9506,9888,781,8866,1702,3795,90,356,1483,4200,2131,6969,5931,486,6880,4404,1084,5169,4910,6567,8335,4686,5043,2614,3352,2667,4513,6472,7471,5720,1616
8878,1613,1716,868,1906,2681,564,665,5995,2474,7496,3432,9491,9087,8850,8287,669,823,347,6194,2264,2592,7871,7616,8508,4827,760,2676,4660,4881,7572,3811,9032,939,4384,929,7525,8419,5556,9063,662,8887,7026,8534,3111,1454,2082,7598,5726,6687,9647,7608,73,3014,5063,670,5461,5631,3367,9796,8475,7908,5073,1565,5008,5295,4457,1274,4788,1728,338,600,8415,8535,9351,7750,6887,5845,1741,125
3637,6489,9634,9464,9055,2413,7824,9517,7532,3577,7050,6186,6980,9365,9782,191,870,2497,8498,2218,2757,5420,6468,586,3320,9230,1034,1393,9886,5072,9391,1178,8464,8042,6869,2075,8275,3601,7715,9470,8786,6475,8373,2159,9237,2066,3264,5000,679,355,3069,4073,494,2308,5512,4334,9438,8786,8637,9774,1169,1949,6594,6072,4270,9158,7916,5752,6794,9391,6301,5842,3285,2141,3898,8027,4310,8821,7079,1307
8497,6681,4732,7151,7060,5204,9030,7157,833,5014,8723,3207,9796,9286,4913,119,5118,7650,9335,809,3675,2597,5144,3945,5090,8384,187,4102,1260,2445,2792,4422,8389,9290,50,1765,1521,6921,8586,4368,1565,5727,7855,2003,4834,9897,5911,8630,5070,1330,7692,7557,7980,6028,5805,9090,8265,3019,3802,698,9149,5748,1965,9658,4417,5994,5584,8226,2937,272,5743,1278,5698,8736,2595,6475,5342,6596,1149,6920
8188,8009,9546,6310,8772,2500,9846,6592,6872,3857,1307,8125,7042,1544,6159,2330,643,4604,7899,6848,371,8067,2062,3200,7295,1857,9505,6936,384,2193,2190,301,8535,5503,1462,7380,5114,4824,8833,1763,4974,8711,9262,6698,3999,2645,6937,7747,1128,2933,3556,7943,2885,3122,9105,5447,418,2899,5148,3699,9021,9501,597,4084,175,1621,1,1079,6067,5812,4326,9914,6633,5394,4233,6728,9084,1864,5863,1225
9935,8793,9117,1825,9542,8246,8437,3331,9128,9675,6086,7075,319,1334,7932,3583,7167,4178,1726,7720,695,8277,7887,6359,5912,1719,2780,8529,1359,2013,4498,8072,1129,9998,1147,8804,9405,6255,1619,2165,7491,1,8882,7378,3337,503,5758,4109,3577,985,3200,7615,8058,5032,1080,6410,6873,5496,1466,2412,9885,5904,4406,3605,8770,4361,6205,9193,1537,9959,214,7260,9566,1685,100,4920,7138,9819,5637,976
3466,9854,985,1078,7222,8888,5466,5379,3578,4540,6853,8690,3728,6351,7147,3134,6921,9692,857,3307,4998,2172,5783,3931,9417,2541,6299,13,787,2099,9131,9494,896,8600,1643,8419,7248,2660,2609,8579,91,6663,5506,7675,1947,6165,4286,1972,9645,3805,1663,1456,8853,5705,9889,7489,1107,383,4044,2969,3343,152,7805,4980,9929,5033,1737,9953,7197,9158,4071,1324,473,9676,3984,9680,3606,8160,7384,5432
1005,4512,5186,3953,2164,3372,4097,3247,8697,3022,9896,4101,3871,6791,3219,2742,4630,6967,7829,5991,6134,1197,1414,8923,8787,1394,8852,5019,7768,5147,8004,8825,5062,9625,7988,1110,3992,7984,9966,6516,6251,8270,421,3723,1432,4830,6935,8095,9059,2214,6483,6846,3120,1587,6201,6691,9096,9627,6671,4002,3495,9939,7708,7465,5879,6959,6634,3241,3401,2355,9061,2611,7830,3941,2177,2146,5089,7079,519,6351
7280,8586,4261,2831,7217,3141,9994,9940,5462,2189,4005,6942,9848,5350,8060,6665,7519,4324,7684,657,9453,9296,2944,6843,7499,7847,1728,9681,3906,6353,5529,2822,3355,3897,7724,4257,7489,8672,4356,3983,1948,6892,7415,4153,5893,4190,621,1736,4045,9532,7701,3671,1211,1622,3176,4524,9317,7800,5638,6644,6943,5463,3531,2821,1347,5958,3436,1438,2999,994,850,4131,2616,1549,3465,5946,690,9273,6954,7991
9517,399,3249,2596,7736,2142,1322,968,7350,1614,468,3346,3265,7222,6086,1661,5317,2582,7959,4685,2807,2917,1037,5698,1529,3972,8716,2634,3301,3412,8621,743,8001,4734,888,7744,8092,3671,8941,1487,5658,7099,2781,99,1932,4443,4756,4652,9328,1581,7855,4312,5976,7255,6480,3996,2748,1973,9731,4530,2790,9417,7186,5303,3557,351,7182,9428,1342,9020,7599,1392,8304,2070,9138,7215,2008,9937,1106,7110
7444,769,9688,632,1571,6820,8743,4338,337,3366,3073,1946,8219,104,4210,6986,249,5061,8693,7960,6546,1004,8857,5997,9352,4338,6105,5008,2556,6518,6694,4345,3727,7956,20,3954,8652,4424,9387,2035,8358,5962,5304,5194,8650,8282,1256,1103,2138,6679,1985,3653,2770,2433,4278,615,2863,1715,242,3790,2636,6998,3088,1671,2239,957,5411,4595,6282,2881,9974,2401,875,7574,2987,4587,3147,6766,9885,2965
3287,3016,3619,6818,9073,6120,5423,557,2900,2015,8111,3873,1314,4189,1846,4399,7041,7583,2427,2864,3525,5002,2069,748,1948,6015,2684,438,770,8367,1663,7887,7759,1885,157,7770,4520,4878,3857,1137,3525,3050,6276,5569,7649,904,4533,7843,2199,5648,7628,9075,9441,3600,7231,2388,5640,9096,958,3058,584,5899,8150,1181,9616,1098,8162,6819,8171,1519,1140,7665,8801,2632,1299,9192,707,9955,2710,7314
1772,2963,7578,3541,3095,1488,7026,2634,6015,4633,4370,2762,1650,2174,909,8158,2922,8467,4198,4280,9092,8856,8835,5457,2790,8574,9742,5054,9547,4156,7940,8126,9824,7340,8840,6574,3547,1477,3014,6798,7134,435,9484,9859,3031,4,1502,4133,1738,1807,4825,463,6343,9701,8506,9822,9555,8688,8168,3467,3234,6318,1787,5591,419,6593,7974,8486,9861,6381,6758,194,3061,4315,2863,4665,3789,2201,1492,4416
126,8927,6608,5682,8986,6867,1715,6076,3159,788,3140,4744,830,9253,5812,5021,7616,8534,1546,9590,1101,9012,9821,8132,7857,4086,1069,7491,2988,1579,2442,4321,2149,7642,6108,250,6086,3167,24,9528,7663,2685,1220,9196,1397,5776,1577,1730,5481,977,6115,199,6326,2183,3767,5928,5586,7561,663,8649,9688,949,5913,9160,1870,5764,9887,4477,6703,1413,4995,5494,7131,2192,8969,7138,3997,8697,646,1028
8074,1731,8245,624,4601,8706,155,8891,309,2552,8208,8452,2954,3124,3469,4246,3352,1105,4509,8677,9901,4416,8191,9283,5625,7120,2952,8881,7693,830,4580,8228,9459,8611,4499,1179,4988,1394,550,2336,6089,6872,269,7213,1848,917,6672,4890,656,1478,6536,3165,4743,4990,1176,6211,7207,5284,9730,4738,1549,4986,4942,8645,3698,9429,1439,2175,6549,3058,6513,1574,6988,8333,3406,5245,5431,7140,7085,6407
7845,4694,2530,8249,290,5948,5509,1588,5940,4495,5866,5021,4626,3979,3296,7589,4854,1998,5627,3926,8346,6512,9608,1918,7070,4747,4182,2858,2766,4606,6269,4107,8982,8568,9053,4244,5604,102,2756,727,5887,2566,7922,44,5986,621,1202,374,6988,4130,3627,6744,9443,4568,1398,8679,397,3928,9159,367,2917,6127,5788,3304,8129,911,2669,1463,9749,264,4478,8940,1109,7309,2462,117,4692,7724,225,2312
4164,3637,2000,941,8903,39,3443,7172,1031,3687,4901,8082,4945,4515,7204,9310,9349,9535,9940,218,1788,9245,2237,1541,5670,6538,6047,5553,9807,8101,1925,8714,445,8332,7309,6830,5786,5736,7306,2710,3034,1838,7969,6318,7912,2584,2080,7437,6705,2254,7428,820,782,9861,7596,3842,3631,8063,5240,6666,394,4565,7865,4895,9890,6028,6117,4724,9156,4473,4552,602,470,6191,4927,5387,884,3146,1978,3000
4258,6880,1696,3582,5793,4923,2119,1155,9056,9698,6603,3768,5514,9927,9609,6166,6566,4536,4985,4934,8076,9062,6741,6163,7399,4562,2337,5600,2919,9012,8459,1308,6072,1225,9306,8818,5886,7243,7365,8792,6007,9256,6699,7171,4230,7002,8720,7839,4533,1671,478,7774,1607,2317,5437,4705,7886,4760,6760,7271,3081,2997,3088,7675,6208,3101,6821,6840,122,9633,4900,2067,8546,4549,2091,7188,5605,8599,6758,5229
7854,5243,9155,3556,8812,7047,2202,1541,5993,4600,4760,713,434,7911,7426,7414,8729,322,803,7960,7563,4908,6285,6291,736,3389,9339,4132,8701,7534,5287,3646,592,3065,7582,2592,8755,6068,8597,1982,5782,1894,2900,6236,4039,6569,3037,5837,7698,700,7815,2491,7272,5878,3083,6778,6639,3589,5010,8313,2581,6617,5869,8402,6808,2951,2321,5195,497,2190,6187,1342,1316,4453,7740,4154,2959,1781,1482,8256
7178,2046,4419,744,8312,5356,6855,8839,319,2962,5662,47,6307,8662,68,4813,567,2712,9931,1678,3101,8227,6533,4933,6656,92,5846,4780,6256,6361,4323,9985,1231,2175,7178,3034,9744,6155,9165,7787,5836,9318,7860,9644,8941,6480,9443,8188,5928,161,6979,2352,5628,6991,1198,8067,5867,6620,3778,8426,2994,3122,3124,6335,3918,8897,2655,9670,634,1088,1576,8935,7255,474,8166,7417,9547,2886,5560,3842
6957,3111,26,7530,7143,1295,1744,6057,3009,1854,8098,5405,2234,4874,9447,2620,9303,27,7410,969,40,2966,5648,7596,8637,4238,3143,3679,7187,690,9980,7085,7714,9373,5632,7526,6707,3951,9734,4216,2146,3602,5371,6029,3039,4433,4855,4151,1449,3376,8009,7240,7027,4602,2947,9081,4045,8424,9352,8742,923,2705,4266,3232,2264,6761,363,2651,3383,7770,6730,7856,7340,9679,2158,610,4471,4608,910,6241
4417,6756,1013,8797,658,8809,5032,8703,7541,846,3357,2920,9817,1745,9980,7593,4667,3087,779,3218,6233,5568,4296,2289,2654,7898,5021,9461,5593,8214,9173,4203,2271,7980,2983,5952,9992,8399,3468,1776,3188,9314,1720,6523,2933,621,8685,5483,8986,6163,3444,9539,4320,155,3992,2828,2150,6071,524,2895,5468,8063,1210,3348,9071,4862,483,9017,4097,6186,9815,3610,5048,1644,1003,9865,9332,2145,1944,2213
9284,3803,4920,1927,6706,4344,7383,4786,9890,2010,5228,1224,3158,6967,8580,8990,8883,5213,76,8306,2031,4980,5639,9519,7184,5645,7769,3259,8077,9130,1317,3096,9624,3818,1770,695,2454,947,6029,3474,9938,3527,5696,4760,7724,7738,2848,6442,5767,6845,8323,4131,2859,7595,2500,4815,3660,9130,8580,7016,8231,4391,8369,3444,4069,4021,556,6154,627,2778,1496,4206,6356,8434,8491,3816,8231,3190,5575,1015
3787,7572,1788,6803,5641,6844,1961,4811,8535,9914,9999,1450,8857,738,4662,8569,6679,2225,7839,8618,286,2648,5342,2294,3205,4546,176,8705,3741,6134,8324,8021,7004,5205,7032,6637,9442,5539,5584,4819,5874,5807,8589,6871,9016,983,1758,3786,1519,6241,185,8398,495,3370,9133,3051,4549,9674,7311,9738,3316,9383,2658,2776,9481,7558,619,3943,3324,6491,4933,153,9738,4623,912,3595,7771,7939,1219,4405
2650,3883,4154,5809,315,7756,4430,1788,4451,1631,6461,7230,6017,5751,138,588,5282,2442,9110,9035,6349,2515,1570,6122,4192,4174,3530,1933,4186,4420,4609,5739,4135,2963,6308,1161,8809,8619,2796,3819,6971,8228,4188,1492,909,8048,2328,6772,8467,7671,9068,2226,7579,6422,7056,8042,3296,2272,3006,2196,7320,3238,3490,3102,37,1293,3212,4767,5041,8773,5794,4456,6174,7279,7054,2835,7053,9088,790,6640
3101,1057,7057,3826,6077,1025,2955,1224,1114,6729,5902,4698,6239,7203,9423,1804,4417,6686,1426,6941,8071,1029,4985,9010,6122,6597,1622,1574,3513,1684,7086,5505,3244,411,9638,4150,907,9135,829,981,1707,5359,8781,9751,5,9131,3973,7159,1340,6955,7514,7993,6964,8198,1933,2797,877,3993,4453,8020,9349,8646,2779,8679,2961,3547,3374,3510,1129,3568,2241,2625,9138,5974,8206,7669,7678,1833,8700,4480
4865,9912,8038,8238,782,3095,8199,1127,4501,7280,2112,2487,3626,2790,9432,1475,6312,8277,4827,2218,5806,7132,8752,1468,7471,6386,739,8762,8323,8120,5169,9078,9058,3370,9560,7987,8585,8531,5347,9312,1058,4271,1159,5286,5404,6925,8606,9204,7361,2415,560,586,4002,2644,1927,2824,768,4409,2942,3345,1002,808,4941,6267,7979,5140,8643,7553,9438,7320,4938,2666,4609,2778,8158,6730,3748,3867,1866,7181
171,3771,7134,8927,4778,2913,3326,2004,3089,7853,1378,1729,4777,2706,9578,1360,5693,3036,1851,7248,2403,2273,8536,6501,9216,613,9671,7131,7719,6425,773,717,8803,160,1114,7554,7197,753,4513,4322,8499,4533,2609,4226,8710,6627,644,9666,6260,4870,5744,7385,6542,6203,7703,6130,8944,5589,2262,6803,6381,7414,6888,5123,7320,9392,9061,6780,322,8975,7050,5089,1061,2260,3199,1150,1865,5386,9699,6501
3744,8454,6885,8277,919,1923,4001,6864,7854,5519,2491,6057,8794,9645,1776,5714,9786,9281,7538,6916,3215,395,2501,9618,4835,8846,9708,2813,3303,1794,8309,7176,2206,1602,1838,236,4593,2245,8993,4017,10,8215,6921,5206,4023,5932,6997,7801,262,7640,3107,8275,4938,7822,2425,3223,3886,2105,8700,9526,2088,8662,8034,7004,5710,2124,7164,3574,6630,9980,4242,2901,9471,1491,2117,4562,1130,9086,4117,6698
2810,2280,2331,1170,4554,4071,8387,1215,2274,9848,6738,1604,7281,8805,439,1298,8318,7834,9426,8603,6092,7944,1309,8828,303,3157,4638,4439,9175,1921,4695,7716,1494,1015,1772,5913,1127,1952,1950,8905,4064,9890,385,9357,7945,5035,7082,5369,4093,6546,5187,5637,2041,8946,1758,7111,6566,1027,1049,5148,7224,7248,296,6169,375,1656,7993,2816,3717,4279,4675,1609,3317,42,6201,3100,3144,163,9530,4531
7096,6070,1009,4988,3538,5801,7149,3063,2324,2912,7911,7002,4338,7880,2481,7368,3516,2016,7556,2193,1388,3865,8125,4637,4096,8114,750,3144,1938,7002,9343,4095,1392,4220,3455,6969,9647,1321,9048,1996,1640,6626,1788,314,9578,6630,2813,6626,4981,9908,7024,4355,3201,3521,3864,3303,464,1923,595,9801,3391,8366,8084,9374,1041,8807,9085,1892,9431,8317,9016,9221,8574,9981,9240,5395,2009,6310,2854,9255
8830,3145,2960,9615,8220,6061,3452,2918,6481,9278,2297,3385,6565,7066,7316,5682,107,7646,4466,68,1952,9603,8615,54,7191,791,6833,2560,693,9733,4168,570,9127,9537,1925,8287,5508,4297,8452,8795,6213,7994,2420,4208,524,5915,8602,8330,2651,8547,6156,1812,6271,7991,9407,9804,1553,6866,1128,2119,4691,9711,8315,5879,9935,6900,482,682,4126,1041,428,6247,3720,5882,7526,2582,4327,7725,3503,2631
2738,9323,721,7434,1453,6294,2957,3786,5722,6019,8685,4386,3066,9057,6860,499,5315,3045,5194,7111,3137,9104,941,586,3066,755,4177,8819,7040,5309,3583,3897,4428,7788,4721,7249,6559,7324,825,7311,3760,6064,6070,9672,4882,584,1365,9739,9331,5783,2624,7889,1604,1303,1555,7125,8312,425,8936,3233,7724,1480,403,7440,1784,1754,4721,1569,652,3893,4574,5692,9730,4813,9844,8291,9199,7101,3391,8914
6044,2928,9332,3328,8588,447,3830,1176,3523,2705,8365,6136,5442,9049,5526,8575,8869,9031,7280,706,2794,8814,5767,4241,7696,78,6570,556,5083,1426,4502,3336,9518,2292,1885,3740,3153,9348,9331,8051,2759,5407,9028,7840,9255,831,515,2612,9747,7435,8964,4971,2048,4900,5967,8271,1719,9670,2810,6777,1594,6367,6259,8316,3815,1689,6840,9437,4361,822,9619,3065,83,6344,7486,8657,8228,9635,6932,4864
8478,4777,6334,4678,7476,4963,6735,3096,5860,1405,5127,7269,7793,4738,227,9168,2996,8928,765,733,1276,7677,6258,1528,9558,3329,302,8901,1422,8277,6340,645,9125,8869,5952,141,8141,1816,9635,4025,4184,3093,83,2344,2747,9352,7966,1206,1126,1826,218,7939,2957,2729,810,8752,5247,4174,4038,8884,7899,9567,301,5265,5752,7524,4381,1669,3106,8270,6228,6373,754,2547,4240,2313,5514,3022,1040,9738
2265,8192,1763,1369,8469,8789,4836,52,1212,6690,5257,8918,6723,6319,378,4039,2421,8555,8184,9577,1432,7139,8078,5452,9628,7579,4161,7490,5159,8559,1011,81,478,5840,1964,1334,6875,8670,9900,739,1514,8692,522,9316,6955,1345,8132,2277,3193,9773,3923,4177,2183,1236,6747,6575,4874,6003,6409,8187,745,8776,9440,7543,9825,2582,7381,8147,7236,5185,7564,6125,218,7991,6394,391,7659,7456,5128,5294
2132,8992,8160,5782,4420,3371,3798,5054,552,5631,7546,4716,1332,6486,7892,7441,4370,6231,4579,2121,8615,1145,9391,1524,1385,2400,9437,2454,7896,7467,2928,8400,3299,4025,7458,4703,7206,6358,792,6200,725,4275,4136,7390,5984,4502,7929,5085,8176,4600,119,3568,76,9363,6943,2248,9077,9731,6213,5817,6729,4190,3092,6910,759,2682,8380,1254,9604,3011,9291,5329,9453,9746,2739,6522,3765,5634,1113,5789
5304,5499,564,2801,679,2653,1783,3608,7359,7797,3284,796,3222,437,7185,6135,8571,2778,7488,5746,678,6140,861,7750,803,9859,9918,2425,3734,2698,9005,4864,9818,6743,2475,132,9486,3825,5472,919,292,4411,7213,7699,6435,9019,6769,1388,802,2124,1345,8493,9487,8558,7061,8777,8833,2427,2238,5409,4957,8503,3171,7622,5779,6145,2417,5873,5563,5693,9574,9491,1937,7384,4563,6842,5432,2751,3406,7981


================================================
FILE: project_euler/problem_081/sol1.py
================================================
"""
Problem 81: https://projecteuler.net/problem=81
In the 5 by 5 matrix below, the minimal path sum from the top left to the bottom right,
by only moving to the right and down, is indicated in bold red and is equal to 2427.

    [131]   673   234    103    18
    [201]  [96]  [342]   965   150
     630   803   [746]  [422]  111
     537   699   497    [121]  956
     805   732   524    [37]  [331]

Find the minimal path sum from the top left to the bottom right by only moving right
and down in matrix.txt (https://projecteuler.net/project/resources/p081_matrix.txt),
a 31K text file containing an 80 by 80 matrix.
"""

import os


def solution(filename: str = "matrix.txt") -> int:
    """
    Returns the minimal path sum from the top left to the bottom right of the matrix.
    >>> solution()
    427337
    """
    with open(os.path.join(os.path.dirname(__file__), filename)) as in_file:
        data = in_file.read()

    grid = [[int(cell) for cell in row.split(",")] for row in data.strip().splitlines()]
    dp = [[0 for cell in row] for row in grid]
    n = len(grid[0])

    dp = [[0 for i in range(n)] for j in range(n)]
    dp[0][0] = grid[0][0]
    for i in range(1, n):
        dp[0][i] = grid[0][i] + dp[0][i - 1]
    for i in range(1, n):
        dp[i][0] = grid[i][0] + dp[i - 1][0]

    for i in range(1, n):
        for j in range(1, n):
            dp[i][j] = grid[i][j] + min(dp[i - 1][j], dp[i][j - 1])

    return dp[-1][-1]


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_082/__init__.py
================================================


================================================
FILE: project_euler/problem_082/input.txt
================================================
4445,2697,5115,718,2209,2212,654,4348,3079,6821,7668,3276,8874,4190,3785,2752,9473,7817,9137,496,7338,3434,7152,4355,4552,7917,7827,2460,2350,691,3514,5880,3145,7633,7199,3783,5066,7487,3285,1084,8985,760,872,8609,8051,1134,9536,5750,9716,9371,7619,5617,275,9721,2997,2698,1887,8825,6372,3014,2113,7122,7050,6775,5948,2758,1219,3539,348,7989,2735,9862,1263,8089,6401,9462,3168,2758,3748,5870
1096,20,1318,7586,5167,2642,1443,5741,7621,7030,5526,4244,2348,4641,9827,2448,6918,5883,3737,300,7116,6531,567,5997,3971,6623,820,6148,3287,1874,7981,8424,7672,7575,6797,6717,1078,5008,4051,8795,5820,346,1851,6463,2117,6058,3407,8211,117,4822,1317,4377,4434,5925,8341,4800,1175,4173,690,8978,7470,1295,3799,8724,3509,9849,618,3320,7068,9633,2384,7175,544,6583,1908,9983,481,4187,9353,9377
9607,7385,521,6084,1364,8983,7623,1585,6935,8551,2574,8267,4781,3834,2764,2084,2669,4656,9343,7709,2203,9328,8004,6192,5856,3555,2260,5118,6504,1839,9227,1259,9451,1388,7909,5733,6968,8519,9973,1663,5315,7571,3035,4325,4283,2304,6438,3815,9213,9806,9536,196,5542,6907,2475,1159,5820,9075,9470,2179,9248,1828,4592,9167,3713,4640,47,3637,309,7344,6955,346,378,9044,8635,7466,5036,9515,6385,9230
7206,3114,7760,1094,6150,5182,7358,7387,4497,955,101,1478,7777,6966,7010,8417,6453,4955,3496,107,449,8271,131,2948,6185,784,5937,8001,6104,8282,4165,3642,710,2390,575,715,3089,6964,4217,192,5949,7006,715,3328,1152,66,8044,4319,1735,146,4818,5456,6451,4113,1063,4781,6799,602,1504,6245,6550,1417,1343,2363,3785,5448,4545,9371,5420,5068,4613,4882,4241,5043,7873,8042,8434,3939,9256,2187
3620,8024,577,9997,7377,7682,1314,1158,6282,6310,1896,2509,5436,1732,9480,706,496,101,6232,7375,2207,2306,110,6772,3433,2878,8140,5933,8688,1399,2210,7332,6172,6403,7333,4044,2291,1790,2446,7390,8698,5723,3678,7104,1825,2040,140,3982,4905,4160,2200,5041,2512,1488,2268,1175,7588,8321,8078,7312,977,5257,8465,5068,3453,3096,1651,7906,253,9250,6021,8791,8109,6651,3412,345,4778,5152,4883,7505
1074,5438,9008,2679,5397,5429,2652,3403,770,9188,4248,2493,4361,8327,9587,707,9525,5913,93,1899,328,2876,3604,673,8576,6908,7659,2544,3359,3883,5273,6587,3065,1749,3223,604,9925,6941,2823,8767,7039,3290,3214,1787,7904,3421,7137,9560,8451,2669,9219,6332,1576,5477,6755,8348,4164,4307,2984,4012,6629,1044,2874,6541,4942,903,1404,9125,5160,8836,4345,2581,460,8438,1538,5507,668,3352,2678,6942
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510,3556,5377,1406,5721,4946,2635,7847,4251,8293,8281,6351,4912,287,2870,3380,3948,5322,3840,4738,9563,1906,6298,3234,8959,1562,6297,8835,7861,239,6618,1322,2553,2213,5053,5446,4402,6500,5182,8585,6900,5756,9661,903,5186,7687,5998,7997,8081,8955,4835,6069,2621,1581,732,9564,1082,1853,5442,1342,520,1737,3703,5321,4793,2776,1508,1647,9101,2499,6891,4336,7012,3329,3212,1442,9993,3988,4930,7706
9444,3401,5891,9716,1228,7107,109,3563,2700,6161,5039,4992,2242,8541,7372,2067,1294,3058,1306,320,8881,5756,9326,411,8650,8824,5495,8282,8397,2000,1228,7817,2099,6473,3571,5994,4447,1299,5991,543,7874,2297,1651,101,2093,3463,9189,6872,6118,872,1008,1779,2805,9084,4048,2123,5877,55,3075,1737,9459,4535,6453,3644,108,5982,4437,5213,1340,6967,9943,5815,669,8074,1838,6979,9132,9315,715,5048
3327,4030,7177,6336,9933,5296,2621,4785,2755,4832,2512,2118,2244,4407,2170,499,7532,9742,5051,7687,970,6924,3527,4694,5145,1306,2165,5940,2425,8910,3513,1909,6983,346,6377,4304,9330,7203,6605,3709,3346,970,369,9737,5811,4427,9939,3693,8436,5566,1977,3728,2399,3985,8303,2492,5366,9802,9193,7296,1033,5060,9144,2766,1151,7629,5169,5995,58,7619,7565,4208,1713,6279,3209,4908,9224,7409,1325,8540
6882,1265,1775,3648,4690,959,5837,4520,5394,1378,9485,1360,4018,578,9174,2932,9890,3696,116,1723,1178,9355,7063,1594,1918,8574,7594,7942,1547,6166,7888,354,6932,4651,1010,7759,6905,661,7689,6092,9292,3845,9605,8443,443,8275,5163,7720,7265,6356,7779,1798,1754,5225,6661,1180,8024,5666,88,9153,1840,3508,1193,4445,2648,3538,6243,6375,8107,5902,5423,2520,1122,5015,6113,8859,9370,966,8673,2442
7338,3423,4723,6533,848,8041,7921,8277,4094,5368,7252,8852,9166,2250,2801,6125,8093,5738,4038,9808,7359,9494,601,9116,4946,2702,5573,2921,9862,1462,1269,2410,4171,2709,7508,6241,7522,615,2407,8200,4189,5492,5649,7353,2590,5203,4274,710,7329,9063,956,8371,3722,4253,4785,1194,4828,4717,4548,940,983,2575,4511,2938,1827,2027,2700,1236,841,5760,1680,6260,2373,3851,1841,4968,1172,5179,7175,3509
4420,1327,3560,2376,6260,2988,9537,4064,4829,8872,9598,3228,1792,7118,9962,9336,4368,9189,6857,1829,9863,6287,7303,7769,2707,8257,2391,2009,3975,4993,3068,9835,3427,341,8412,2134,4034,8511,6421,3041,9012,2983,7289,100,1355,7904,9186,6920,5856,2008,6545,8331,3655,5011,839,8041,9255,6524,3862,8788,62,7455,3513,5003,8413,3918,2076,7960,6108,3638,6999,3436,1441,4858,4181,1866,8731,7745,3744,1000
356,8296,8325,1058,1277,4743,3850,2388,6079,6462,2815,5620,8495,5378,75,4324,3441,9870,1113,165,1544,1179,2834,562,6176,2313,6836,8839,2986,9454,5199,6888,1927,5866,8760,320,1792,8296,7898,6121,7241,5886,5814,2815,8336,1576,4314,3109,2572,6011,2086,9061,9403,3947,5487,9731,7281,3159,1819,1334,3181,5844,5114,9898,4634,2531,4412,6430,4262,8482,4546,4555,6804,2607,9421,686,8649,8860,7794,6672
9870,152,1558,4963,8750,4754,6521,6256,8818,5208,5691,9659,8377,9725,5050,5343,2539,6101,1844,9700,7750,8114,5357,3001,8830,4438,199,9545,8496,43,2078,327,9397,106,6090,8181,8646,6414,7499,5450,4850,6273,5014,4131,7639,3913,6571,8534,9703,4391,7618,445,1320,5,1894,6771,7383,9191,4708,9706,6939,7937,8726,9382,5216,3685,2247,9029,8154,1738,9984,2626,9438,4167,6351,5060,29,1218,1239,4785
192,5213,8297,8974,4032,6966,5717,1179,6523,4679,9513,1481,3041,5355,9303,9154,1389,8702,6589,7818,6336,3539,5538,3094,6646,6702,6266,2759,4608,4452,617,9406,8064,6379,444,5602,4950,1810,8391,1536,316,8714,1178,5182,5863,5110,5372,4954,1978,2971,5680,4863,2255,4630,5723,2168,538,1692,1319,7540,440,6430,6266,7712,7385,5702,620,641,3136,7350,1478,3155,2820,9109,6261,1122,4470,14,8493,2095
1046,4301,6082,474,4974,7822,2102,5161,5172,6946,8074,9716,6586,9962,9749,5015,2217,995,5388,4402,7652,6399,6539,1349,8101,3677,1328,9612,7922,2879,231,5887,2655,508,4357,4964,3554,5930,6236,7384,4614,280,3093,9600,2110,7863,2631,6626,6620,68,1311,7198,7561,1768,5139,1431,221,230,2940,968,5283,6517,2146,1646,869,9402,7068,8645,7058,1765,9690,4152,2926,9504,2939,7504,6074,2944,6470,7859
4659,736,4951,9344,1927,6271,8837,8711,3241,6579,7660,5499,5616,3743,5801,4682,9748,8796,779,1833,4549,8138,4026,775,4170,2432,4174,3741,7540,8017,2833,4027,396,811,2871,1150,9809,2719,9199,8504,1224,540,2051,3519,7982,7367,2761,308,3358,6505,2050,4836,5090,7864,805,2566,2409,6876,3361,8622,5572,5895,3280,441,7893,8105,1634,2929,274,3926,7786,6123,8233,9921,2674,5340,1445,203,4585,3837
5759,338,7444,7968,7742,3755,1591,4839,1705,650,7061,2461,9230,9391,9373,2413,1213,431,7801,4994,2380,2703,6161,6878,8331,2538,6093,1275,5065,5062,2839,582,1014,8109,3525,1544,1569,8622,7944,2905,6120,1564,1839,5570,7579,1318,2677,5257,4418,5601,7935,7656,5192,1864,5886,6083,5580,6202,8869,1636,7907,4759,9082,5854,3185,7631,6854,5872,5632,5280,1431,2077,9717,7431,4256,8261,9680,4487,4752,4286
1571,1428,8599,1230,7772,4221,8523,9049,4042,8726,7567,6736,9033,2104,4879,4967,6334,6716,3994,1269,8995,6539,3610,7667,6560,6065,874,848,4597,1711,7161,4811,6734,5723,6356,6026,9183,2586,5636,1092,7779,7923,8747,6887,7505,9909,1792,3233,4526,3176,1508,8043,720,5212,6046,4988,709,5277,8256,3642,1391,5803,1468,2145,3970,6301,7767,2359,8487,9771,8785,7520,856,1605,8972,2402,2386,991,1383,5963
1822,4824,5957,6511,9868,4113,301,9353,6228,2881,2966,6956,9124,9574,9233,1601,7340,973,9396,540,4747,8590,9535,3650,7333,7583,4806,3593,2738,8157,5215,8472,2284,9473,3906,6982,5505,6053,7936,6074,7179,6688,1564,1103,6860,5839,2022,8490,910,7551,7805,881,7024,1855,9448,4790,1274,3672,2810,774,7623,4223,4850,6071,9975,4935,1915,9771,6690,3846,517,463,7624,4511,614,6394,3661,7409,1395,8127
8738,3850,9555,3695,4383,2378,87,6256,6740,7682,9546,4255,6105,2000,1851,4073,8957,9022,6547,5189,2487,303,9602,7833,1628,4163,6678,3144,8589,7096,8913,5823,4890,7679,1212,9294,5884,2972,3012,3359,7794,7428,1579,4350,7246,4301,7779,7790,3294,9547,4367,3549,1958,8237,6758,3497,3250,3456,6318,1663,708,7714,6143,6890,3428,6853,9334,7992,591,6449,9786,1412,8500,722,5468,1371,108,3939,4199,2535
7047,4323,1934,5163,4166,461,3544,2767,6554,203,6098,2265,9078,2075,4644,6641,8412,9183,487,101,7566,5622,1975,5726,2920,5374,7779,5631,3753,3725,2672,3621,4280,1162,5812,345,8173,9785,1525,955,5603,2215,2580,5261,2765,2990,5979,389,3907,2484,1232,5933,5871,3304,1138,1616,5114,9199,5072,7442,7245,6472,4760,6359,9053,7876,2564,9404,3043,9026,2261,3374,4460,7306,2326,966,828,3274,1712,3446
3975,4565,8131,5800,4570,2306,8838,4392,9147,11,3911,7118,9645,4994,2028,6062,5431,2279,8752,2658,7836,994,7316,5336,7185,3289,1898,9689,2331,5737,3403,1124,2679,3241,7748,16,2724,5441,6640,9368,9081,5618,858,4969,17,2103,6035,8043,7475,2181,939,415,1617,8500,8253,2155,7843,7974,7859,1746,6336,3193,2617,8736,4079,6324,6645,8891,9396,5522,6103,1857,8979,3835,2475,1310,7422,610,8345,7615
9248,5397,5686,2988,3446,4359,6634,9141,497,9176,6773,7448,1907,8454,916,1596,2241,1626,1384,2741,3649,5362,8791,7170,2903,2475,5325,6451,924,3328,522,90,4813,9737,9557,691,2388,1383,4021,1609,9206,4707,5200,7107,8104,4333,9860,5013,1224,6959,8527,1877,4545,7772,6268,621,4915,9349,5970,706,9583,3071,4127,780,8231,3017,9114,3836,7503,2383,1977,4870,8035,2379,9704,1037,3992,3642,1016,4303
5093,138,4639,6609,1146,5565,95,7521,9077,2272,974,4388,2465,2650,722,4998,3567,3047,921,2736,7855,173,2065,4238,1048,5,6847,9548,8632,9194,5942,4777,7910,8971,6279,7253,2516,1555,1833,3184,9453,9053,6897,7808,8629,4877,1871,8055,4881,7639,1537,7701,2508,7564,5845,5023,2304,5396,3193,2955,1088,3801,6203,1748,3737,1276,13,4120,7715,8552,3047,2921,106,7508,304,1280,7140,2567,9135,5266
6237,4607,7527,9047,522,7371,4883,2540,5867,6366,5301,1570,421,276,3361,527,6637,4861,2401,7522,5808,9371,5298,2045,5096,5447,7755,5115,7060,8529,4078,1943,1697,1764,5453,7085,960,2405,739,2100,5800,728,9737,5704,5693,1431,8979,6428,673,7540,6,7773,5857,6823,150,5869,8486,684,5816,9626,7451,5579,8260,3397,5322,6920,1879,2127,2884,5478,4977,9016,6165,6292,3062,5671,5968,78,4619,4763
9905,7127,9390,5185,6923,3721,9164,9705,4341,1031,1046,5127,7376,6528,3248,4941,1178,7889,3364,4486,5358,9402,9158,8600,1025,874,1839,1783,309,9030,1843,845,8398,1433,7118,70,8071,2877,3904,8866,6722,4299,10,1929,5897,4188,600,1889,3325,2485,6473,4474,7444,6992,4846,6166,4441,2283,2629,4352,7775,1101,2214,9985,215,8270,9750,2740,8361,7103,5930,8664,9690,8302,9267,344,2077,1372,1880,9550
5825,8517,7769,2405,8204,1060,3603,7025,478,8334,1997,3692,7433,9101,7294,7498,9415,5452,3850,3508,6857,9213,6807,4412,7310,854,5384,686,4978,892,8651,3241,2743,3801,3813,8588,6701,4416,6990,6490,3197,6838,6503,114,8343,5844,8646,8694,65,791,5979,2687,2621,2019,8097,1423,3644,9764,4921,3266,3662,5561,2476,8271,8138,6147,1168,3340,1998,9874,6572,9873,6659,5609,2711,3931,9567,4143,7833,8887
6223,2099,2700,589,4716,8333,1362,5007,2753,2848,4441,8397,7192,8191,4916,9955,6076,3370,6396,6971,3156,248,3911,2488,4930,2458,7183,5455,170,6809,6417,3390,1956,7188,577,7526,2203,968,8164,479,8699,7915,507,6393,4632,1597,7534,3604,618,3280,6061,9793,9238,8347,568,9645,2070,5198,6482,5000,9212,6655,5961,7513,1323,3872,6170,3812,4146,2736,67,3151,5548,2781,9679,7564,5043,8587,1893,4531
5826,3690,6724,2121,9308,6986,8106,6659,2142,1642,7170,2877,5757,6494,8026,6571,8387,9961,6043,9758,9607,6450,8631,8334,7359,5256,8523,2225,7487,1977,9555,8048,5763,2414,4948,4265,2427,8978,8088,8841,9208,9601,5810,9398,8866,9138,4176,5875,7212,3272,6759,5678,7649,4922,5422,1343,8197,3154,3600,687,1028,4579,2084,9467,4492,7262,7296,6538,7657,7134,2077,1505,7332,6890,8964,4879,7603,7400,5973,739
1861,1613,4879,1884,7334,966,2000,7489,2123,4287,1472,3263,4726,9203,1040,4103,6075,6049,330,9253,4062,4268,1635,9960,577,1320,3195,9628,1030,4092,4979,6474,6393,2799,6967,8687,7724,7392,9927,2085,3200,6466,8702,265,7646,8665,7986,7266,4574,6587,612,2724,704,3191,8323,9523,3002,704,5064,3960,8209,2027,2758,8393,4875,4641,9584,6401,7883,7014,768,443,5490,7506,1852,2005,8850,5776,4487,4269
4052,6687,4705,7260,6645,6715,3706,5504,8672,2853,1136,8187,8203,4016,871,1809,1366,4952,9294,5339,6872,2645,6083,7874,3056,5218,7485,8796,7401,3348,2103,426,8572,4163,9171,3176,948,7654,9344,3217,1650,5580,7971,2622,76,2874,880,2034,9929,1546,2659,5811,3754,7096,7436,9694,9960,7415,2164,953,2360,4194,2397,1047,2196,6827,575,784,2675,8821,6802,7972,5996,6699,2134,7577,2887,1412,4349,4380
4629,2234,6240,8132,7592,3181,6389,1214,266,1910,2451,8784,2790,1127,6932,1447,8986,2492,5476,397,889,3027,7641,5083,5776,4022,185,3364,5701,2442,2840,4160,9525,4828,6602,2614,7447,3711,4505,7745,8034,6514,4907,2605,7753,6958,7270,6936,3006,8968,439,2326,4652,3085,3425,9863,5049,5361,8688,297,7580,8777,7916,6687,8683,7141,306,9569,2384,1500,3346,4601,7329,9040,6097,2727,6314,4501,4974,2829
8316,4072,2025,6884,3027,1808,5714,7624,7880,8528,4205,8686,7587,3230,1139,7273,6163,6986,3914,9309,1464,9359,4474,7095,2212,7302,2583,9462,7532,6567,1606,4436,8981,5612,6796,4385,5076,2007,6072,3678,8331,1338,3299,8845,4783,8613,4071,1232,6028,2176,3990,2148,3748,103,9453,538,6745,9110,926,3125,473,5970,8728,7072,9062,1404,1317,5139,9862,6496,6062,3338,464,1600,2532,1088,8232,7739,8274,3873
2341,523,7096,8397,8301,6541,9844,244,4993,2280,7689,4025,4196,5522,7904,6048,2623,9258,2149,9461,6448,8087,7245,1917,8340,7127,8466,5725,6996,3421,5313,512,9164,9837,9794,8369,4185,1488,7210,1524,1016,4620,9435,2478,7765,8035,697,6677,3724,6988,5853,7662,3895,9593,1185,4727,6025,5734,7665,3070,138,8469,6748,6459,561,7935,8646,2378,462,7755,3115,9690,8877,3946,2728,8793,244,6323,8666,4271
6430,2406,8994,56,1267,3826,9443,7079,7579,5232,6691,3435,6718,5698,4144,7028,592,2627,217,734,6194,8156,9118,58,2640,8069,4127,3285,694,3197,3377,4143,4802,3324,8134,6953,7625,3598,3584,4289,7065,3434,2106,7132,5802,7920,9060,7531,3321,1725,1067,3751,444,5503,6785,7937,6365,4803,198,6266,8177,1470,6390,1606,2904,7555,9834,8667,2033,1723,5167,1666,8546,8152,473,4475,6451,7947,3062,3281
2810,3042,7759,1741,2275,2609,7676,8640,4117,1958,7500,8048,1757,3954,9270,1971,4796,2912,660,5511,3553,1012,5757,4525,6084,7198,8352,5775,7726,8591,7710,9589,3122,4392,6856,5016,749,2285,3356,7482,9956,7348,2599,8944,495,3462,3578,551,4543,7207,7169,7796,1247,4278,6916,8176,3742,8385,2310,1345,8692,2667,4568,1770,8319,3585,4920,3890,4928,7343,5385,9772,7947,8786,2056,9266,3454,2807,877,2660
6206,8252,5928,5837,4177,4333,207,7934,5581,9526,8906,1498,8411,2984,5198,5134,2464,8435,8514,8674,3876,599,5327,826,2152,4084,2433,9327,9697,4800,2728,3608,3849,3861,3498,9943,1407,3991,7191,9110,5666,8434,4704,6545,5944,2357,1163,4995,9619,6754,4200,9682,6654,4862,4744,5953,6632,1054,293,9439,8286,2255,696,8709,1533,1844,6441,430,1999,6063,9431,7018,8057,2920,6266,6799,356,3597,4024,6665
3847,6356,8541,7225,2325,2946,5199,469,5450,7508,2197,9915,8284,7983,6341,3276,3321,16,1321,7608,5015,3362,8491,6968,6818,797,156,2575,706,9516,5344,5457,9210,5051,8099,1617,9951,7663,8253,9683,2670,1261,4710,1068,8753,4799,1228,2621,3275,6188,4699,1791,9518,8701,5932,4275,6011,9877,2933,4182,6059,2930,6687,6682,9771,654,9437,3169,8596,1827,5471,8909,2352,123,4394,3208,8756,5513,6917,2056
5458,8173,3138,3290,4570,4892,3317,4251,9699,7973,1163,1935,5477,6648,9614,5655,9592,975,9118,2194,7322,8248,8413,3462,8560,1907,7810,6650,7355,2939,4973,6894,3933,3784,3200,2419,9234,4747,2208,2207,1945,2899,1407,6145,8023,3484,5688,7686,2737,3828,3704,9004,5190,9740,8643,8650,5358,4426,1522,1707,3613,9887,6956,2447,2762,833,1449,9489,2573,1080,4167,3456,6809,2466,227,7125,2759,6250,6472,8089
3266,7025,9756,3914,1265,9116,7723,9788,6805,5493,2092,8688,6592,9173,4431,4028,6007,7131,4446,4815,3648,6701,759,3312,8355,4485,4187,5188,8746,7759,3528,2177,5243,8379,3838,7233,4607,9187,7216,2190,6967,2920,6082,7910,5354,3609,8958,6949,7731,494,8753,8707,1523,4426,3543,7085,647,6771,9847,646,5049,824,8417,5260,2730,5702,2513,9275,4279,2767,8684,1165,9903,4518,55,9682,8963,6005,2102,6523
1998,8731,936,1479,5259,7064,4085,91,7745,7136,3773,3810,730,8255,2705,2653,9790,6807,2342,355,9344,2668,3690,2028,9679,8102,574,4318,6481,9175,5423,8062,2867,9657,7553,3442,3920,7430,3945,7639,3714,3392,2525,4995,4850,2867,7951,9667,486,9506,9888,781,8866,1702,3795,90,356,1483,4200,2131,6969,5931,486,6880,4404,1084,5169,4910,6567,8335,4686,5043,2614,3352,2667,4513,6472,7471,5720,1616
8878,1613,1716,868,1906,2681,564,665,5995,2474,7496,3432,9491,9087,8850,8287,669,823,347,6194,2264,2592,7871,7616,8508,4827,760,2676,4660,4881,7572,3811,9032,939,4384,929,7525,8419,5556,9063,662,8887,7026,8534,3111,1454,2082,7598,5726,6687,9647,7608,73,3014,5063,670,5461,5631,3367,9796,8475,7908,5073,1565,5008,5295,4457,1274,4788,1728,338,600,8415,8535,9351,7750,6887,5845,1741,125
3637,6489,9634,9464,9055,2413,7824,9517,7532,3577,7050,6186,6980,9365,9782,191,870,2497,8498,2218,2757,5420,6468,586,3320,9230,1034,1393,9886,5072,9391,1178,8464,8042,6869,2075,8275,3601,7715,9470,8786,6475,8373,2159,9237,2066,3264,5000,679,355,3069,4073,494,2308,5512,4334,9438,8786,8637,9774,1169,1949,6594,6072,4270,9158,7916,5752,6794,9391,6301,5842,3285,2141,3898,8027,4310,8821,7079,1307
8497,6681,4732,7151,7060,5204,9030,7157,833,5014,8723,3207,9796,9286,4913,119,5118,7650,9335,809,3675,2597,5144,3945,5090,8384,187,4102,1260,2445,2792,4422,8389,9290,50,1765,1521,6921,8586,4368,1565,5727,7855,2003,4834,9897,5911,8630,5070,1330,7692,7557,7980,6028,5805,9090,8265,3019,3802,698,9149,5748,1965,9658,4417,5994,5584,8226,2937,272,5743,1278,5698,8736,2595,6475,5342,6596,1149,6920
8188,8009,9546,6310,8772,2500,9846,6592,6872,3857,1307,8125,7042,1544,6159,2330,643,4604,7899,6848,371,8067,2062,3200,7295,1857,9505,6936,384,2193,2190,301,8535,5503,1462,7380,5114,4824,8833,1763,4974,8711,9262,6698,3999,2645,6937,7747,1128,2933,3556,7943,2885,3122,9105,5447,418,2899,5148,3699,9021,9501,597,4084,175,1621,1,1079,6067,5812,4326,9914,6633,5394,4233,6728,9084,1864,5863,1225
9935,8793,9117,1825,9542,8246,8437,3331,9128,9675,6086,7075,319,1334,7932,3583,7167,4178,1726,7720,695,8277,7887,6359,5912,1719,2780,8529,1359,2013,4498,8072,1129,9998,1147,8804,9405,6255,1619,2165,7491,1,8882,7378,3337,503,5758,4109,3577,985,3200,7615,8058,5032,1080,6410,6873,5496,1466,2412,9885,5904,4406,3605,8770,4361,6205,9193,1537,9959,214,7260,9566,1685,100,4920,7138,9819,5637,976
3466,9854,985,1078,7222,8888,5466,5379,3578,4540,6853,8690,3728,6351,7147,3134,6921,9692,857,3307,4998,2172,5783,3931,9417,2541,6299,13,787,2099,9131,9494,896,8600,1643,8419,7248,2660,2609,8579,91,6663,5506,7675,1947,6165,4286,1972,9645,3805,1663,1456,8853,5705,9889,7489,1107,383,4044,2969,3343,152,7805,4980,9929,5033,1737,9953,7197,9158,4071,1324,473,9676,3984,9680,3606,8160,7384,5432
1005,4512,5186,3953,2164,3372,4097,3247,8697,3022,9896,4101,3871,6791,3219,2742,4630,6967,7829,5991,6134,1197,1414,8923,8787,1394,8852,5019,7768,5147,8004,8825,5062,9625,7988,1110,3992,7984,9966,6516,6251,8270,421,3723,1432,4830,6935,8095,9059,2214,6483,6846,3120,1587,6201,6691,9096,9627,6671,4002,3495,9939,7708,7465,5879,6959,6634,3241,3401,2355,9061,2611,7830,3941,2177,2146,5089,7079,519,6351
7280,8586,4261,2831,7217,3141,9994,9940,5462,2189,4005,6942,9848,5350,8060,6665,7519,4324,7684,657,9453,9296,2944,6843,7499,7847,1728,9681,3906,6353,5529,2822,3355,3897,7724,4257,7489,8672,4356,3983,1948,6892,7415,4153,5893,4190,621,1736,4045,9532,7701,3671,1211,1622,3176,4524,9317,7800,5638,6644,6943,5463,3531,2821,1347,5958,3436,1438,2999,994,850,4131,2616,1549,3465,5946,690,9273,6954,7991
9517,399,3249,2596,7736,2142,1322,968,7350,1614,468,3346,3265,7222,6086,1661,5317,2582,7959,4685,2807,2917,1037,5698,1529,3972,8716,2634,3301,3412,8621,743,8001,4734,888,7744,8092,3671,8941,1487,5658,7099,2781,99,1932,4443,4756,4652,9328,1581,7855,4312,5976,7255,6480,3996,2748,1973,9731,4530,2790,9417,7186,5303,3557,351,7182,9428,1342,9020,7599,1392,8304,2070,9138,7215,2008,9937,1106,7110
7444,769,9688,632,1571,6820,8743,4338,337,3366,3073,1946,8219,104,4210,6986,249,5061,8693,7960,6546,1004,8857,5997,9352,4338,6105,5008,2556,6518,6694,4345,3727,7956,20,3954,8652,4424,9387,2035,8358,5962,5304,5194,8650,8282,1256,1103,2138,6679,1985,3653,2770,2433,4278,615,2863,1715,242,3790,2636,6998,3088,1671,2239,957,5411,4595,6282,2881,9974,2401,875,7574,2987,4587,3147,6766,9885,2965
3287,3016,3619,6818,9073,6120,5423,557,2900,2015,8111,3873,1314,4189,1846,4399,7041,7583,2427,2864,3525,5002,2069,748,1948,6015,2684,438,770,8367,1663,7887,7759,1885,157,7770,4520,4878,3857,1137,3525,3050,6276,5569,7649,904,4533,7843,2199,5648,7628,9075,9441,3600,7231,2388,5640,9096,958,3058,584,5899,8150,1181,9616,1098,8162,6819,8171,1519,1140,7665,8801,2632,1299,9192,707,9955,2710,7314
1772,2963,7578,3541,3095,1488,7026,2634,6015,4633,4370,2762,1650,2174,909,8158,2922,8467,4198,4280,9092,8856,8835,5457,2790,8574,9742,5054,9547,4156,7940,8126,9824,7340,8840,6574,3547,1477,3014,6798,7134,435,9484,9859,3031,4,1502,4133,1738,1807,4825,463,6343,9701,8506,9822,9555,8688,8168,3467,3234,6318,1787,5591,419,6593,7974,8486,9861,6381,6758,194,3061,4315,2863,4665,3789,2201,1492,4416
126,8927,6608,5682,8986,6867,1715,6076,3159,788,3140,4744,830,9253,5812,5021,7616,8534,1546,9590,1101,9012,9821,8132,7857,4086,1069,7491,2988,1579,2442,4321,2149,7642,6108,250,6086,3167,24,9528,7663,2685,1220,9196,1397,5776,1577,1730,5481,977,6115,199,6326,2183,3767,5928,5586,7561,663,8649,9688,949,5913,9160,1870,5764,9887,4477,6703,1413,4995,5494,7131,2192,8969,7138,3997,8697,646,1028
8074,1731,8245,624,4601,8706,155,8891,309,2552,8208,8452,2954,3124,3469,4246,3352,1105,4509,8677,9901,4416,8191,9283,5625,7120,2952,8881,7693,830,4580,8228,9459,8611,4499,1179,4988,1394,550,2336,6089,6872,269,7213,1848,917,6672,4890,656,1478,6536,3165,4743,4990,1176,6211,7207,5284,9730,4738,1549,4986,4942,8645,3698,9429,1439,2175,6549,3058,6513,1574,6988,8333,3406,5245,5431,7140,7085,6407
7845,4694,2530,8249,290,5948,5509,1588,5940,4495,5866,5021,4626,3979,3296,7589,4854,1998,5627,3926,8346,6512,9608,1918,7070,4747,4182,2858,2766,4606,6269,4107,8982,8568,9053,4244,5604,102,2756,727,5887,2566,7922,44,5986,621,1202,374,6988,4130,3627,6744,9443,4568,1398,8679,397,3928,9159,367,2917,6127,5788,3304,8129,911,2669,1463,9749,264,4478,8940,1109,7309,2462,117,4692,7724,225,2312
4164,3637,2000,941,8903,39,3443,7172,1031,3687,4901,8082,4945,4515,7204,9310,9349,9535,9940,218,1788,9245,2237,1541,5670,6538,6047,5553,9807,8101,1925,8714,445,8332,7309,6830,5786,5736,7306,2710,3034,1838,7969,6318,7912,2584,2080,7437,6705,2254,7428,820,782,9861,7596,3842,3631,8063,5240,6666,394,4565,7865,4895,9890,6028,6117,4724,9156,4473,4552,602,470,6191,4927,5387,884,3146,1978,3000
4258,6880,1696,3582,5793,4923,2119,1155,9056,9698,6603,3768,5514,9927,9609,6166,6566,4536,4985,4934,8076,9062,6741,6163,7399,4562,2337,5600,2919,9012,8459,1308,6072,1225,9306,8818,5886,7243,7365,8792,6007,9256,6699,7171,4230,7002,8720,7839,4533,1671,478,7774,1607,2317,5437,4705,7886,4760,6760,7271,3081,2997,3088,7675,6208,3101,6821,6840,122,9633,4900,2067,8546,4549,2091,7188,5605,8599,6758,5229
7854,5243,9155,3556,8812,7047,2202,1541,5993,4600,4760,713,434,7911,7426,7414,8729,322,803,7960,7563,4908,6285,6291,736,3389,9339,4132,8701,7534,5287,3646,592,3065,7582,2592,8755,6068,8597,1982,5782,1894,2900,6236,4039,6569,3037,5837,7698,700,7815,2491,7272,5878,3083,6778,6639,3589,5010,8313,2581,6617,5869,8402,6808,2951,2321,5195,497,2190,6187,1342,1316,4453,7740,4154,2959,1781,1482,8256
7178,2046,4419,744,8312,5356,6855,8839,319,2962,5662,47,6307,8662,68,4813,567,2712,9931,1678,3101,8227,6533,4933,6656,92,5846,4780,6256,6361,4323,9985,1231,2175,7178,3034,9744,6155,9165,7787,5836,9318,7860,9644,8941,6480,9443,8188,5928,161,6979,2352,5628,6991,1198,8067,5867,6620,3778,8426,2994,3122,3124,6335,3918,8897,2655,9670,634,1088,1576,8935,7255,474,8166,7417,9547,2886,5560,3842
6957,3111,26,7530,7143,1295,1744,6057,3009,1854,8098,5405,2234,4874,9447,2620,9303,27,7410,969,40,2966,5648,7596,8637,4238,3143,3679,7187,690,9980,7085,7714,9373,5632,7526,6707,3951,9734,4216,2146,3602,5371,6029,3039,4433,4855,4151,1449,3376,8009,7240,7027,4602,2947,9081,4045,8424,9352,8742,923,2705,4266,3232,2264,6761,363,2651,3383,7770,6730,7856,7340,9679,2158,610,4471,4608,910,6241
4417,6756,1013,8797,658,8809,5032,8703,7541,846,3357,2920,9817,1745,9980,7593,4667,3087,779,3218,6233,5568,4296,2289,2654,7898,5021,9461,5593,8214,9173,4203,2271,7980,2983,5952,9992,8399,3468,1776,3188,9314,1720,6523,2933,621,8685,5483,8986,6163,3444,9539,4320,155,3992,2828,2150,6071,524,2895,5468,8063,1210,3348,9071,4862,483,9017,4097,6186,9815,3610,5048,1644,1003,9865,9332,2145,1944,2213
9284,3803,4920,1927,6706,4344,7383,4786,9890,2010,5228,1224,3158,6967,8580,8990,8883,5213,76,8306,2031,4980,5639,9519,7184,5645,7769,3259,8077,9130,1317,3096,9624,3818,1770,695,2454,947,6029,3474,9938,3527,5696,4760,7724,7738,2848,6442,5767,6845,8323,4131,2859,7595,2500,4815,3660,9130,8580,7016,8231,4391,8369,3444,4069,4021,556,6154,627,2778,1496,4206,6356,8434,8491,3816,8231,3190,5575,1015
3787,7572,1788,6803,5641,6844,1961,4811,8535,9914,9999,1450,8857,738,4662,8569,6679,2225,7839,8618,286,2648,5342,2294,3205,4546,176,8705,3741,6134,8324,8021,7004,5205,7032,6637,9442,5539,5584,4819,5874,5807,8589,6871,9016,983,1758,3786,1519,6241,185,8398,495,3370,9133,3051,4549,9674,7311,9738,3316,9383,2658,2776,9481,7558,619,3943,3324,6491,4933,153,9738,4623,912,3595,7771,7939,1219,4405
2650,3883,4154,5809,315,7756,4430,1788,4451,1631,6461,7230,6017,5751,138,588,5282,2442,9110,9035,6349,2515,1570,6122,4192,4174,3530,1933,4186,4420,4609,5739,4135,2963,6308,1161,8809,8619,2796,3819,6971,8228,4188,1492,909,8048,2328,6772,8467,7671,9068,2226,7579,6422,7056,8042,3296,2272,3006,2196,7320,3238,3490,3102,37,1293,3212,4767,5041,8773,5794,4456,6174,7279,7054,2835,7053,9088,790,6640
3101,1057,7057,3826,6077,1025,2955,1224,1114,6729,5902,4698,6239,7203,9423,1804,4417,6686,1426,6941,8071,1029,4985,9010,6122,6597,1622,1574,3513,1684,7086,5505,3244,411,9638,4150,907,9135,829,981,1707,5359,8781,9751,5,9131,3973,7159,1340,6955,7514,7993,6964,8198,1933,2797,877,3993,4453,8020,9349,8646,2779,8679,2961,3547,3374,3510,1129,3568,2241,2625,9138,5974,8206,7669,7678,1833,8700,4480
4865,9912,8038,8238,782,3095,8199,1127,4501,7280,2112,2487,3626,2790,9432,1475,6312,8277,4827,2218,5806,7132,8752,1468,7471,6386,739,8762,8323,8120,5169,9078,9058,3370,9560,7987,8585,8531,5347,9312,1058,4271,1159,5286,5404,6925,8606,9204,7361,2415,560,586,4002,2644,1927,2824,768,4409,2942,3345,1002,808,4941,6267,7979,5140,8643,7553,9438,7320,4938,2666,4609,2778,8158,6730,3748,3867,1866,7181
171,3771,7134,8927,4778,2913,3326,2004,3089,7853,1378,1729,4777,2706,9578,1360,5693,3036,1851,7248,2403,2273,8536,6501,9216,613,9671,7131,7719,6425,773,717,8803,160,1114,7554,7197,753,4513,4322,8499,4533,2609,4226,8710,6627,644,9666,6260,4870,5744,7385,6542,6203,7703,6130,8944,5589,2262,6803,6381,7414,6888,5123,7320,9392,9061,6780,322,8975,7050,5089,1061,2260,3199,1150,1865,5386,9699,6501
3744,8454,6885,8277,919,1923,4001,6864,7854,5519,2491,6057,8794,9645,1776,5714,9786,9281,7538,6916,3215,395,2501,9618,4835,8846,9708,2813,3303,1794,8309,7176,2206,1602,1838,236,4593,2245,8993,4017,10,8215,6921,5206,4023,5932,6997,7801,262,7640,3107,8275,4938,7822,2425,3223,3886,2105,8700,9526,2088,8662,8034,7004,5710,2124,7164,3574,6630,9980,4242,2901,9471,1491,2117,4562,1130,9086,4117,6698
2810,2280,2331,1170,4554,4071,8387,1215,2274,9848,6738,1604,7281,8805,439,1298,8318,7834,9426,8603,6092,7944,1309,8828,303,3157,4638,4439,9175,1921,4695,7716,1494,1015,1772,5913,1127,1952,1950,8905,4064,9890,385,9357,7945,5035,7082,5369,4093,6546,5187,5637,2041,8946,1758,7111,6566,1027,1049,5148,7224,7248,296,6169,375,1656,7993,2816,3717,4279,4675,1609,3317,42,6201,3100,3144,163,9530,4531
7096,6070,1009,4988,3538,5801,7149,3063,2324,2912,7911,7002,4338,7880,2481,7368,3516,2016,7556,2193,1388,3865,8125,4637,4096,8114,750,3144,1938,7002,9343,4095,1392,4220,3455,6969,9647,1321,9048,1996,1640,6626,1788,314,9578,6630,2813,6626,4981,9908,7024,4355,3201,3521,3864,3303,464,1923,595,9801,3391,8366,8084,9374,1041,8807,9085,1892,9431,8317,9016,9221,8574,9981,9240,5395,2009,6310,2854,9255
8830,3145,2960,9615,8220,6061,3452,2918,6481,9278,2297,3385,6565,7066,7316,5682,107,7646,4466,68,1952,9603,8615,54,7191,791,6833,2560,693,9733,4168,570,9127,9537,1925,8287,5508,4297,8452,8795,6213,7994,2420,4208,524,5915,8602,8330,2651,8547,6156,1812,6271,7991,9407,9804,1553,6866,1128,2119,4691,9711,8315,5879,9935,6900,482,682,4126,1041,428,6247,3720,5882,7526,2582,4327,7725,3503,2631
2738,9323,721,7434,1453,6294,2957,3786,5722,6019,8685,4386,3066,9057,6860,499,5315,3045,5194,7111,3137,9104,941,586,3066,755,4177,8819,7040,5309,3583,3897,4428,7788,4721,7249,6559,7324,825,7311,3760,6064,6070,9672,4882,584,1365,9739,9331,5783,2624,7889,1604,1303,1555,7125,8312,425,8936,3233,7724,1480,403,7440,1784,1754,4721,1569,652,3893,4574,5692,9730,4813,9844,8291,9199,7101,3391,8914
6044,2928,9332,3328,8588,447,3830,1176,3523,2705,8365,6136,5442,9049,5526,8575,8869,9031,7280,706,2794,8814,5767,4241,7696,78,6570,556,5083,1426,4502,3336,9518,2292,1885,3740,3153,9348,9331,8051,2759,5407,9028,7840,9255,831,515,2612,9747,7435,8964,4971,2048,4900,5967,8271,1719,9670,2810,6777,1594,6367,6259,8316,3815,1689,6840,9437,4361,822,9619,3065,83,6344,7486,8657,8228,9635,6932,4864
8478,4777,6334,4678,7476,4963,6735,3096,5860,1405,5127,7269,7793,4738,227,9168,2996,8928,765,733,1276,7677,6258,1528,9558,3329,302,8901,1422,8277,6340,645,9125,8869,5952,141,8141,1816,9635,4025,4184,3093,83,2344,2747,9352,7966,1206,1126,1826,218,7939,2957,2729,810,8752,5247,4174,4038,8884,7899,9567,301,5265,5752,7524,4381,1669,3106,8270,6228,6373,754,2547,4240,2313,5514,3022,1040,9738
2265,8192,1763,1369,8469,8789,4836,52,1212,6690,5257,8918,6723,6319,378,4039,2421,8555,8184,9577,1432,7139,8078,5452,9628,7579,4161,7490,5159,8559,1011,81,478,5840,1964,1334,6875,8670,9900,739,1514,8692,522,9316,6955,1345,8132,2277,3193,9773,3923,4177,2183,1236,6747,6575,4874,6003,6409,8187,745,8776,9440,7543,9825,2582,7381,8147,7236,5185,7564,6125,218,7991,6394,391,7659,7456,5128,5294
2132,8992,8160,5782,4420,3371,3798,5054,552,5631,7546,4716,1332,6486,7892,7441,4370,6231,4579,2121,8615,1145,9391,1524,1385,2400,9437,2454,7896,7467,2928,8400,3299,4025,7458,4703,7206,6358,792,6200,725,4275,4136,7390,5984,4502,7929,5085,8176,4600,119,3568,76,9363,6943,2248,9077,9731,6213,5817,6729,4190,3092,6910,759,2682,8380,1254,9604,3011,9291,5329,9453,9746,2739,6522,3765,5634,1113,5789
5304,5499,564,2801,679,2653,1783,3608,7359,7797,3284,796,3222,437,7185,6135,8571,2778,7488,5746,678,6140,861,7750,803,9859,9918,2425,3734,2698,9005,4864,9818,6743,2475,132,9486,3825,5472,919,292,4411,7213,7699,6435,9019,6769,1388,802,2124,1345,8493,9487,8558,7061,8777,8833,2427,2238,5409,4957,8503,3171,7622,5779,6145,2417,5873,5563,5693,9574,9491,1937,7384,4563,6842,5432,2751,3406,7981


================================================
FILE: project_euler/problem_082/sol1.py
================================================
"""
Project Euler Problem 82: https://projecteuler.net/problem=82

The minimal path sum in the 5 by 5 matrix below, by starting in any cell
in the left column and finishing in any cell in the right column,
and only moving up, down, and right, is indicated in red and bold;
the sum is equal to 994.

     131    673   [234]  [103]  [18]
    [201]  [96]   [342]   965    150
     630    803    746    422    111
     537    699    497    121    956
     805    732    524    37     331

Find the minimal path sum from the left column to the right column in matrix.txt
(https://projecteuler.net/project/resources/p082_matrix.txt)
(right click and "Save Link/Target As..."),
a 31K text file containing an 80 by 80 matrix.
"""

import os


def solution(filename: str = "input.txt") -> int:
    """
    Returns the minimal path sum in the matrix from the file, by starting in any cell
    in the left column and finishing in any cell in the right column,
    and only moving up, down, and right

    >>> solution("test_matrix.txt")
    994
    """

    with open(os.path.join(os.path.dirname(__file__), filename)) as input_file:
        matrix = [
            [int(element) for element in line.split(",")]
            for line in input_file.readlines()
        ]

    rows = len(matrix)
    cols = len(matrix[0])

    minimal_path_sums = [[-1 for _ in range(cols)] for _ in range(rows)]
    for i in range(rows):
        minimal_path_sums[i][0] = matrix[i][0]

    for j in range(1, cols):
        for i in range(rows):
            minimal_path_sums[i][j] = minimal_path_sums[i][j - 1] + matrix[i][j]

        for i in range(1, rows):
            minimal_path_sums[i][j] = min(
                minimal_path_sums[i][j], minimal_path_sums[i - 1][j] + matrix[i][j]
            )

        for i in range(rows - 2, -1, -1):
            minimal_path_sums[i][j] = min(
                minimal_path_sums[i][j], minimal_path_sums[i + 1][j] + matrix[i][j]
            )

    return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_082/test_matrix.txt
================================================
131,673,234,103,18
201,96,342,965,150
630,803,746,422,111
537,699,497,121,956
805,732,524,37,331


================================================
FILE: project_euler/problem_085/__init__.py
================================================


================================================
FILE: project_euler/problem_085/sol1.py
================================================
"""
Project Euler Problem 85: https://projecteuler.net/problem=85

By counting carefully it can be seen that a rectangular grid measuring 3 by 2
contains eighteen rectangles.

Although there exists no rectangular grid that contains exactly two million
rectangles, find the area of the grid with the nearest solution.

Solution:

    For a grid with side-lengths a and b, the number of rectangles contained in the grid
    is [a*(a+1)/2] * [b*(b+1)/2)], which happens to be the product of the a-th and b-th
    triangle numbers. So to find the solution grid (a,b), we need to find the two
    triangle numbers whose product is closest to two million.

    Denote these two triangle numbers Ta and Tb. We want their product Ta*Tb to be
    as close as possible to 2m. Assuming that the best solution is fairly close to 2m,
    We can assume that both Ta and Tb are roughly bounded by 2m. Since Ta = a(a+1)/2,
    we can assume that a (and similarly b) are roughly bounded by sqrt(2 * 2m) = 2000.
    Since this is a rough bound, to be on the safe side we add 10%. Therefore we start
    by generating all the triangle numbers Ta for 1 <= a <= 2200. This can be done
    iteratively since the ith triangle number is the sum of 1,2, ... ,i, and so
    T(i) = T(i-1) + i.

    We then search this list of triangle numbers for the two that give a product
    closest to our target of two million. Rather than testing every combination of 2
    elements of the list, which would find the result in quadratic time, we can find
    the best pair in linear time.

    We iterate through the list of triangle numbers using enumerate() so we have a
    and Ta. Since we want Ta * Tb to be as close as possible to 2m, we know that Tb
    needs to be roughly 2m / Ta. Using the formula Tb = b*(b+1)/2 as well as the
    quadratic formula, we can solve for b:
    b is roughly (-1 + sqrt(1 + 8 * 2m / Ta)) / 2.

    Since the closest integers to this estimate will give product closest to 2m,
    we only need to consider the integers above and below. It's then a simple matter
    to get the triangle numbers corresponding to those integers, calculate the product
    Ta * Tb, compare that product to our target 2m, and keep track of the (a,b) pair
    that comes the closest.


Reference: https://en.wikipedia.org/wiki/Triangular_number
           https://en.wikipedia.org/wiki/Quadratic_formula
"""

from __future__ import annotations

from math import ceil, floor, sqrt


def solution(target: int = 2000000) -> int:
    """
    Find the area of the grid which contains as close to two million rectangles
    as possible.
    >>> solution(20)
    6
    >>> solution(2000)
    72
    >>> solution(2000000000)
    86595
    """
    triangle_numbers: list[int] = [0]
    idx: int

    for idx in range(1, ceil(sqrt(target * 2) * 1.1)):
        triangle_numbers.append(triangle_numbers[-1] + idx)

    # we want this to be as close as possible to target
    best_product: int = 0
    # the area corresponding to the grid that gives the product closest to target
    area: int = 0
    # an estimate of b, using the quadratic formula
    b_estimate: float
    # the largest integer less than b_estimate
    b_floor: int
    # the largest integer less than b_estimate
    b_ceil: int
    # the triangle number corresponding to b_floor
    triangle_b_first_guess: int
    # the triangle number corresponding to b_ceil
    triangle_b_second_guess: int

    for idx_a, triangle_a in enumerate(triangle_numbers[1:], 1):
        b_estimate = (-1 + sqrt(1 + 8 * target / triangle_a)) / 2
        b_floor = floor(b_estimate)
        b_ceil = ceil(b_estimate)
        triangle_b_first_guess = triangle_numbers[b_floor]
        triangle_b_second_guess = triangle_numbers[b_ceil]

        if abs(target - triangle_b_first_guess * triangle_a) < abs(
            target - best_product
        ):
            best_product = triangle_b_first_guess * triangle_a
            area = idx_a * b_floor

        if abs(target - triangle_b_second_guess * triangle_a) < abs(
            target - best_product
        ):
            best_product = triangle_b_second_guess * triangle_a
            area = idx_a * b_ceil

    return area


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_086/__init__.py
================================================


================================================
FILE: project_euler/problem_086/sol1.py
================================================
"""
Project Euler Problem 86: https://projecteuler.net/problem=86

A spider, S, sits in one corner of a cuboid room, measuring 6 by 5 by 3, and a fly, F,
sits in the opposite corner. By travelling on the surfaces of the room the shortest
"straight line" distance from S to F is 10 and the path is shown on the diagram.

However, there are up to three "shortest" path candidates for any given cuboid and the
shortest route doesn't always have integer length.

It can be shown that there are exactly 2060 distinct cuboids, ignoring rotations, with
integer dimensions, up to a maximum size of M by M by M, for which the shortest route
has integer length when M = 100. This is the least value of M for which the number of
solutions first exceeds two thousand; the number of solutions when M = 99 is 1975.

Find the least value of M such that the number of solutions first exceeds one million.

Solution:
    Label the 3 side-lengths of the cuboid a,b,c such that 1 <= a <= b <= c <= M.
    By conceptually "opening up" the cuboid and laying out its faces on a plane,
    it can be seen that the shortest distance between 2 opposite corners is
    sqrt((a+b)^2 + c^2). This distance is an integer if and only if (a+b),c make up
    the first 2 sides of a pythagorean triplet.

    The second useful insight is rather than calculate the number of cuboids
    with integral shortest distance for each maximum cuboid side-length M,
    we can calculate this number iteratively each time we increase M, as follows.
    The set of cuboids satisfying this property with maximum side-length M-1 is a
    subset of the cuboids satisfying the property with maximum side-length M
    (since any cuboids with side lengths <= M-1 are also <= M). To calculate the
    number of cuboids in the larger set (corresponding to M) we need only consider
    the cuboids which have at least one side of length M. Since we have ordered the
    side lengths a <= b <= c, we can assume that c = M. Then we just need to count
    the number of pairs a,b satisfying the conditions:
        sqrt((a+b)^2 + M^2) is integer
        1 <= a <= b <= M

    To count the number of pairs (a,b) satisfying these conditions, write d = a+b.
    Now we have:
        1 <= a <= b <= M  =>  2 <= d <= 2*M
                                   we can actually make the second equality strict,
                                   since d = 2*M => d^2 + M^2 = 5M^2
                                              => shortest distance = M * sqrt(5)
                                              => not integral.
        a + b = d => b = d - a
                 and a <= b
                  => a <= d/2
                also a <= M
                  => a <= min(M, d//2)

        a + b = d => a = d - b
                 and b <= M
                  => a >= d - M
                also a >= 1
                  => a >= max(1, d - M)

        So a is in range(max(1, d - M), min(M, d // 2) + 1)

    For a given d, the number of cuboids satisfying the required property with c = M
    and a + b = d is the length of this range, which is
        min(M, d // 2) + 1 - max(1, d - M).

    In the code below, d is sum_shortest_sides
                   and M is max_cuboid_size.


"""

from math import sqrt


def solution(limit: int = 1000000) -> int:
    """
    Return the least value of M such that there are more than one million cuboids
    of side lengths 1 <= a,b,c <= M such that the shortest distance between two
    opposite vertices of the cuboid is integral.
    >>> solution(100)
    24
    >>> solution(1000)
    72
    >>> solution(2000)
    100
    >>> solution(20000)
    288
    """
    num_cuboids: int = 0
    max_cuboid_size: int = 0
    sum_shortest_sides: int

    while num_cuboids <= limit:
        max_cuboid_size += 1
        for sum_shortest_sides in range(2, 2 * max_cuboid_size + 1):
            if sqrt(sum_shortest_sides**2 + max_cuboid_size**2).is_integer():
                num_cuboids += (
                    min(max_cuboid_size, sum_shortest_sides // 2)
                    - max(1, sum_shortest_sides - max_cuboid_size)
                    + 1
                )

    return max_cuboid_size


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_087/__init__.py
================================================


================================================
FILE: project_euler/problem_087/sol1.py
================================================
"""
Project Euler Problem 87: https://projecteuler.net/problem=87

The smallest number expressible as the sum of a prime square, prime cube, and prime
fourth power is 28. In fact, there are exactly four numbers below fifty that can be
expressed in such a way:

28 = 22 + 23 + 24
33 = 32 + 23 + 24
49 = 52 + 23 + 24
47 = 22 + 33 + 24

How many numbers below fifty million can be expressed as the sum of a prime square,
prime cube, and prime fourth power?
"""


def solution(limit: int = 50000000) -> int:
    """
    Return the number of integers less than limit which can be expressed as the sum
    of a prime square, prime cube, and prime fourth power.
    >>> solution(50)
    4
    """
    ret = set()
    prime_square_limit = int((limit - 24) ** (1 / 2))

    primes = set(range(3, prime_square_limit + 1, 2))
    primes.add(2)
    for p in range(3, prime_square_limit + 1, 2):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p, prime_square_limit + 1, p)))

    for prime1 in primes:
        square = prime1 * prime1
        for prime2 in primes:
            cube = prime2 * prime2 * prime2
            if square + cube >= limit - 16:
                break
            for prime3 in primes:
                tetr = prime3 * prime3 * prime3 * prime3
                total = square + cube + tetr
                if total >= limit:
                    break
                ret.add(total)

    return len(ret)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_089/__init__.py
================================================


================================================
FILE: project_euler/problem_089/numeralcleanup_test.txt
================================================
IIII
IV
IIIIIIIIII
X
VIIIII


================================================
FILE: project_euler/problem_089/p089_roman.txt
================================================
MMMMDCLXXII
MMDCCCLXXXIII
MMMDLXVIIII
MMMMDXCV
DCCCLXXII
MMCCCVI
MMMCDLXXXVII
MMMMCCXXI
MMMCCXX
MMMMDCCCLXXIII
MMMCCXXXVII
MMCCCLXXXXIX
MDCCCXXIIII
MMCXCVI
CCXCVIII
MMMCCCXXXII
MDCCXXX
MMMDCCCL
MMMMCCLXXXVI
MMDCCCXCVI
MMMDCII
MMMCCXII
MMMMDCCCCI
MMDCCCXCII
MDCXX
CMLXXXVII
MMMXXI
MMMMCCCXIV
MLXXII
MCCLXXVIIII
MMMMCCXXXXI
MMDCCCLXXII
MMMMXXXI
MMMDCCLXXX
MMDCCCLXXIX
MMMMLXXXV
MCXXI
MDCCCXXXVII
MMCCCLXVII
MCDXXXV
CCXXXIII
CMXX
MMMCLXIV
MCCCLXXXVI
DCCCXCVIII
MMMDCCCCXXXIV
CDXVIIII
MMCCXXXV
MDCCCXXXII
MMMMD
MMDCCLXIX
MMMMCCCLXXXXVI
MMDCCXLII
MMMDCCCVIIII
DCCLXXXIIII
MDCCCCXXXII
MMCXXVII
DCCCXXX
CCLXIX
MMMXI
MMMMCMLXXXXVIII
MMMMDLXXXVII
MMMMDCCCLX
MMCCLIV
CMIX
MMDCCCLXXXIIII
CLXXXII
MMCCCCXXXXV
MMMMDLXXXVIIII
MMMDCCCXXI
MMDCCCCLXXVI
MCCCCLXX
MMCDLVIIII
MMMDCCCLIX
MMMMCCCCXIX
MMMDCCCLXXV
XXXI
CDLXXXIII
MMMCXV
MMDCCLXIII
MMDXXX
MMMMCCCLVII
MMMDCI
MMMMCDLXXXIIII
MMMMCCCXVI
CCCLXXXVIII
MMMMCML
MMMMXXIV
MMMCCCCXXX
DCCX
MMMCCLX
MMDXXXIII
CCCLXIII
MMDCCXIII
MMMCCCXLIV
CLXXXXI
CXVI
MMMMCXXXIII
CLXX
DCCCXVIII
MLXVII
DLXXXX
MMDXXI
MMMMDLXXXXVIII
MXXII
LXI
DCCCCXLIII
MMMMDV
MMMMXXXIV
MDCCCLVIII
MMMCCLXXII
MMMMDCCXXXVI
MMMMLXXXIX
MDCCCLXXXI
MMMMDCCCXV
MMMMCCCCXI
MMMMCCCLIII
MDCCCLXXI
MMCCCCXI
MLXV
MMCDLXII
MMMMDXXXXII
MMMMDCCCXL
MMMMCMLVI
CCLXXXIV
MMMDCCLXXXVI
MMCLII
MMMCCCCXV
MMLXXXIII
MMMV
MMMV
DCCLXII
MMDCCCCXVI
MMDCXLVIII
CCLIIII
CCCXXV
MMDCCLXXXVIIII
MMMMDCLXXVIII
MMMMDCCCXCI
MMMMCCCXX
MMCCXLV
MMMDCCCLXIX
MMCCLXIIII
MMMDCCCXLIX
MMMMCCCLXIX
CMLXXXXI
MCMLXXXIX
MMCDLXI
MMDCLXXVIII
MMMMDCCLXI
MCDXXV
DL
CCCLXXII
MXVIIII
MCCCCLXVIII
CIII
MMMDCCLXXIIII
MMMDVIII
MMMMCCCLXXXXVII
MMDXXVII
MMDCCLXXXXV
MMMMCXLVI
MMMDCCLXXXII
MMMDXXXVI
MCXXII
CLI
DCLXXXIX
MMMCLI
MDCLXIII
MMMMDCCXCVII
MMCCCLXXXV
MMMDCXXVIII
MMMCDLX
MMMCMLII
MMMIV
MMMMDCCCLVIII
MMMDLXXXVIII
MCXXIV
MMMMLXXVI
CLXXIX
MMMCCCCXXVIIII
DCCLXXXV
MMMDCCCVI
LI
CLXXXVI
MMMMCCCLXXVI
MCCCLXVI
CCXXXIX
MMDXXXXI
MMDCCCXLI
DCCCLXXXVIII
MMMMDCCCIV
MDCCCCXV
MMCMVI
MMMMCMLXXXXV
MMDCCLVI
MMMMCCXLVIII
DCCCCIIII
MMCCCCIII
MMMDCCLXXXVIIII
MDCCCLXXXXV
DVII
MMMV
DCXXV
MMDCCCXCV
DCVIII
MMCDLXVI
MCXXVIII
MDCCXCVIII
MMDCLX
MMMDCCLXIV
MMCDLXXVII
MMDLXXXIIII
MMMMCCCXXII
MMMDCCCXLIIII
DCCCCLXVII
MMMCLXXXXIII
MCCXV
MMMMDCXI
MMMMDCLXXXXV
MMMCCCLII
MMCMIX
MMDCCXXV
MMDLXXXVI
MMMMDCXXVIIII
DCCCCXXXVIIII
MMCCXXXIIII
MMDCCLXXVIII
MDCCLXVIIII
MMCCLXXXV
MMMMDCCCLXXXVIII
MMCMXCI
MDXLII
MMMMDCCXIV
MMMMLI
DXXXXIII
MMDCCXI
MMMMCCLXXXIII
MMMDCCCLXXIII
MDCLVII
MMCD
MCCCXXVII
MMMMDCCIIII
MMMDCCXLVI
MMMCLXXXVII
MMMCCVIIII
MCCCCLXXIX
DL
DCCCLXXVI
MMDXCI
MMMMDCCCCXXXVI
MMCII
MMMDCCCXXXXV
MMMCDXLV
MMDCXXXXIV
MMD
MDCCCLXXXX
MMDCXLIII
MMCCXXXII
MMDCXXXXVIIII
DCCCLXXI
MDXCVIIII
MMMMCCLXXVIII
MDCLVIIII
MMMCCCLXXXIX
MDCLXXXV
MDLVIII
MMMMCCVII
MMMMDCXIV
MMMCCCLXIIII
MMIIII
MMMMCCCLXXIII
CCIII
MMMCCLV
MMMDXIII
MMMCCCXC
MMMDCCCXXI
MMMMCCCCXXXII
CCCLVI
MMMCCCLXXXVI
MXVIIII
MMMCCCCXIIII
CLXVII
MMMCCLXX
CCCCLXIV
MMXXXXII
MMMMCCLXXXX
MXL
CCXVI
CCCCLVIIII
MMCCCII
MCCCLVIII
MMMMCCCX
MCDLXXXXIV
MDCCCXIII
MMDCCCXL
MMMMCCCXXIII
DXXXIV
CVI
MMMMDCLXXX
DCCCVII
MMCMLXIIII
MMMDCCCXXXIII
DCCC
MDIII
MMCCCLXVI
MMMCCCCLXXI
MMDCCCCXVIII
CCXXXVII
CCCXXV
MDCCCXII
MMMCMV
MMMMCMXV
MMMMDCXCI
DXXI
MMCCXLVIIII
MMMMCMLII
MDLXXX
MMDCLXVI
CXXI
MMMDCCCLIIII
MMMCXXI
MCCIII
MMDCXXXXI
CCXCII
MMMMDXXXV
MMMCCCLXV
MMMMDLXV
MMMCCCCXXXII
MMMCCCVIII
DCCCCLXXXXII
MMCLXIV
MMMMCXI
MLXXXXVII
MMMCDXXXVIII
MDXXII
MLV
MMMMDLXVI
MMMCXII
XXXIII
MMMMDCCCXXVI
MMMLXVIIII
MMMLX
MMMCDLXVII
MDCCCLVII
MMCXXXVII
MDCCCCXXX
MMDCCCLXIII
MMMMDCXLIX
MMMMCMXLVIII
DCCCLXXVIIII
MDCCCLIII
MMMCMLXI
MMMMCCLXI
MMDCCCLIII
MMMDCCCVI
MMDXXXXIX
MMCLXXXXV
MMDXXX
MMMXIII
DCLXXIX
DCCLXII
MMMMDCCLXVIII
MDCCXXXXIII
CCXXXII
MMMMDCXXV
MMMCCCXXVIII
MDCVIII
MMMCLXXXXIIII
CLXXXI
MDCCCCXXXIII
MMMMDCXXX
MMMDCXXIV
MMMCCXXXVII
MCCCXXXXIIII
CXVIII
MMDCCCCIV
MMMMCDLXXV
MMMDLXIV
MDXCIII
MCCLXXXI
MMMDCCCXXIV
MCXLIII
MMMDCCCI
MCCLXXX
CCXV
MMDCCLXXI
MMDLXXXIII
MMMMDCXVII
MMMCMLXV
MCLXVIII
MMMMCCLXXVI
MMMDCCLXVIIII
MMMMDCCCIX
DLXXXXIX
DCCCXXII
MMMMIII
MMMMCCCLXXVI
DCCCXCIII
DXXXI
MXXXIIII
CCXII
MMMDCCLXXXIIII
MMMCXX
MMMCMXXVII
DCCCXXXX
MMCDXXXVIIII
MMMMDCCXVIII
LV
MMMDCCCCVI
MCCCII
MMCMLXVIIII
MDCCXI
MMMMDLXVII
MMCCCCLXI
MMDCCV
MMMCCCXXXIIII
MMMMDI
MMMDCCCXCV
MMDCCLXXXXI
MMMDXXVI
MMMDCCCLVI
MMDCXXX
MCCCVII
MMMMCCCLXII
MMMMXXV
MMCMXXV
MMLVI
MMDXXX
MMMMCVII
MDC
MCCIII
MMMMDCC
MMCCLXXV
MMDCCCXXXXVI
MMMMCCCLXV
CDXIIII
MLXIIII
CCV
MMMCMXXXI
CCCCLXVI
MDXXXII
MMMMCCCLVIII
MMV
MMMCLII
MCMLI
MMDCCXX
MMMMCCCCXXXVI
MCCLXXXI
MMMCMVI
DCCXXX
MMMMCCCLXV
DCCCXI
MMMMDCCCXIV
CCCXXI
MMDLXXV
CCCCLXXXX
MCCCLXXXXII
MMDCIX
DCCXLIIII
DXIV
MMMMCLII
CDLXI
MMMCXXVII
MMMMDCCCCLXIII
MMMDCLIIII
MCCCCXXXXII
MMCCCLX
CCCCLIII
MDCCLXXVI
MCMXXIII
MMMMDLXXVIII
MMDCCCCLX
MMMCCCLXXXX
MMMCDXXVI
MMMDLVIII
CCCLXI
MMMMDCXXII
MMDCCCXXI
MMDCCXIII
MMMMCLXXXVI
MDCCCCXXVI
MDV
MMDCCCCLXXVI
MMMMCCXXXVII
MMMDCCLXXVIIII
MMMCCCCLXVII
DCCXLI
MMCLXXXVIII
MCCXXXVI
MMDCXLVIII
MMMMCXXXII
MMMMDCCLXVI
MMMMCMLI
MMMMCLXV
MMMMDCCCXCIV
MCCLXXVII
LXXVIIII
DCCLII
MMMCCCXCVI
MMMCLV
MMDCCCXXXXVIII
DCCCXV
MXC
MMDCCLXXXXVII
MMMMCML
MMDCCCLXXVIII
DXXI
MCCCXLI
DCLXXXXI
MMCCCLXXXXVIII
MDCCCCLXXVIII
MMMMDXXV
MMMDCXXXVI
MMMCMXCVII
MMXVIIII
MMMDCCLXXIV
MMMCXXV
DXXXVIII
MMMMCLXVI
MDXII
MMCCCLXX
CCLXXI
DXIV
MMMCLIII
DLII
MMMCCCXLIX
MMCCCCXXVI
MMDCXLIII
MXXXXII
CCCLXXXV
MDCLXXVI
MDCXII
MMMCCCLXXXIII
MMDCCCCLXXXII
MMMMCCCLXXXV
MMDCXXI
DCCCXXX
MMMDCCCCLII
MMMDCCXXII
MMMMCDXCVIII
MMMCCLXVIIII
MMXXV
MMMMCDXIX
MMMMCCCX
MMMCCCCLXVI
MMMMDCLXXVIIII
MMMMDCXXXXIV
MMMCMXII
MMMMXXXIII
MMMMDLXXXII
DCCCLIV
MDXVIIII
MMMCLXXXXV
CCCCXX
MMDIX
MMCMLXXXVIII
DCCXLIII
DCCLX
D
MCCCVII
MMMMCCCLXXXIII
MDCCCLXXIIII
MMMDCCCCLXXXVII
MMMMCCCVII
MMMDCCLXXXXVI
CDXXXIV
MCCLXVIII
MMMMDLX
MMMMDXII
MMMMCCCCLIIII
MCMLXXXXIII
MMMMDCCCIII
MMDCLXXXIII
MDCCCXXXXIV
XXXXVII
MMMDCCCXXXII
MMMDCCCXLII
MCXXXV
MDCXXVIIII
MMMCXXXXIIII
MMMMCDXVII
MMMDXXIII
MMMMCCCCLXI
DCLXXXXVIIII
LXXXXI
CXXXIII
MCDX
MCCLVII
MDCXXXXII
MMMCXXIV
MMMMLXXXX
MMDCCCCXLV
MLXXX
MMDCCCCLX
MCDLIII
MMMCCCLXVII
MMMMCCCLXXIV
MMMDCVIII
DCCCCXXIII
MMXCI
MMDCCIV
MMMMDCCCXXXIV
CCCLXXI
MCCLXXXII
MCMIII
CCXXXI
DCCXXXVIII
MMMMDCCXLVIIII
MMMMCMXXXV
DCCCLXXV
DCCXCI
MMMMDVII
MMMMDCCCLXVIIII
CCCXCV
MMMMDCCXX
MCCCCII
MMMCCCXC
MMMCCCII
MMDCCLXXVII
MMDCLIIII
CCXLIII
MMMDCXVIII
MMMCCCIX
MCXV
MMCCXXV
MLXXIIII
MDCCXXVI
MMMCCCXX
MMDLXX
MMCCCCVI
MMDCCXX
MMMMDCCCCXCV
MDCCCXXXII
MMMMDCCCCXXXX
XCIV
MMCCCCLX
MMXVII
MLXXI
MMMDXXVIII
MDCCCCII
MMMCMLVII
MMCLXXXXVIII
MDCCCCLV
MCCCCLXXIIII
MCCCLII
MCDXLVI
MMMMDXVIII
DCCLXXXIX
MMMDCCLXIV
MDCCCCXLIII
CLXXXXV
MMMMCCXXXVI
MMMDCCCXXI
MMMMCDLXXVII
MCDLIII
MMCCXLVI
DCCCLV
MCDLXX
DCLXXVIII
MMDCXXXIX
MMMMDCLX
MMDCCLI
MMCXXXV
MMMCCXII
MMMMCMLXII
MMMMCCV
MCCCCLXIX
MMMMCCIII
CLXVII
MCCCLXXXXIIII
MMMMDCVIII
MMDCCCLXI
MMLXXIX
CMLXIX
MMDCCCXLVIIII
DCLXII
MMMCCCXLVII
MDCCCXXXV
MMMMDCCXCVI
DCXXX
XXVI
MMLXIX
MMCXI
DCXXXVII
MMMMCCCXXXXVIII
MMMMDCLXI
MMMMDCLXXIIII
MMMMVIII
MMMMDCCCLXII
MDCXCI
MMCCCXXIIII
CCCCXXXXV
MMDCCCXXI
MCVI
MMDCCLXVIII
MMMMCXL
MLXVIII
CMXXVII
CCCLV
MDCCLXXXIX
MMMCCCCLXV
MMDCCLXII
MDLXVI
MMMCCCXVIII
MMMMCCLXXXI
MMCXXVII
MMDCCCLXVIII
MMMCXCII
MMMMDCLVIII
MMMMDCCCXXXXII
MMDCCCCLXXXXVI
MDCCXL
MDCCLVII
MMMMDCCCLXXXVI
DCCXXXIII
MMMMDCCCCLXXXV
MMCCXXXXVIII
MMMCCLXXVIII
MMMDCLXXVIII
DCCCI
MMMMLXXXXVIIII
MMMCCCCLXXII
MMCLXXXVII
CCLXVI
MCDXLIII
MMCXXVIII
MDXIV
CCCXCVIII
CLXXVIII
MMCXXXXVIIII
MMMDCLXXXIV
CMLVIII
MCDLIX
MMMMDCCCXXXII
MMMMDCXXXIIII
MDCXXI
MMMDCXLV
MCLXXVIII
MCDXXII
IV
MCDLXXXXIII
MMMMDCCLXV
CCLI
MMMMDCCCXXXVIII
DCLXII
MCCCLXVII
MMMMDCCCXXXVI
MMDCCXLI
MLXI
MMMCDLXVIII
MCCCCXCIII
XXXIII
MMMDCLXIII
MMMMDCL
DCCCXXXXIIII
MMDLVII
DXXXVII
MCCCCXXIIII
MCVII
MMMMDCCXL
MMMMCXXXXIIII
MCCCCXXIV
MMCLXVIII
MMXCIII
MDCCLXXX
MCCCLIIII
MMDCLXXI
MXI
MCMLIV
MMMCCIIII
DCCLXXXVIIII
MDCLIV
MMMDCXIX
CMLXXXI
DCCLXXXVII
XXV
MMMXXXVI
MDVIIII
CLXIII
MMMCDLVIIII
MMCCCCVII
MMMLXX
MXXXXII
MMMMCCCLXVIII
MMDCCCXXVIII
MMMMDCXXXXI
MMMMDCCCXXXXV
MMMXV
MMMMCCXVIIII
MMDCCXIIII
MMMXXVII
MDCCLVIIII
MMCXXIIII
MCCCLXXIV
DCLVIII
MMMLVII
MMMCXLV
MMXCVII
MMMCCCLXXXVII
MMMMCCXXII
DXII
MMMDLV
MCCCLXXVIII
MMMCLIIII
MMMMCLXXXX
MMMCLXXXIIII
MDCXXIII
MMMMCCXVI
MMMMDLXXXIII
MMMDXXXXIII
MMMMCCCCLV
MMMDLXXXI
MMMCCLXXVI
MMMMXX
MMMMDLVI
MCCCCLXXX
MMMXXII
MMXXII
MMDCCCCXXXI
MMMDXXV
MMMDCLXXXVIIII
MMMDLXXXXVII
MDLXIIII
CMXC
MMMXXXVIII
MDLXXXVIII
MCCCLXXVI
MMCDLIX
MMDCCCXVIII
MDCCCXXXXVI
MMMMCMIV
MMMMDCIIII
MMCCXXXV
XXXXVI
MMMMCCXVII
MMCCXXIV
MCMLVIIII
MLXXXIX
MMMMLXXXIX
CLXXXXIX
MMMDCCCCLVIII
MMMMCCLXXIII
MCCCC
DCCCLIX
MMMCCCLXXXII
MMMCCLXVIIII
MCLXXXV
CDLXXXVII
DCVI
MMX
MMCCXIII
MMMMDCXX
MMMMXXVIII
DCCCLXII
MMMMCCCXLIII
MMMMCLXV
DXCI
MMMMCLXXX
MMMDCCXXXXI
MMMMXXXXVI
DCLX
MMMCCCXI
MCCLXXX
MMCDLXXII
DCCLXXI
MMMCCCXXXVI
MCCCCLXXXVIIII
CDLVIII
DCCLVI
MMMMDCXXXVIII
MMCCCLXXXIII
MMMMDCCLXXV
MMMXXXVI
CCCLXXXXIX
CV
CCCCXIII
CCCCXVI
MDCCCLXXXIIII
MMDCCLXXXII
MMMMCCCCLXXXI
MXXV
MMCCCLXXVIIII
MMMCCXII
MMMMCCXXXIII
MMCCCLXXXVI
MMMDCCCLVIIII
MCCXXXVII
MDCLXXV
XXXV
MMDLI
MMMCCXXX
MMMMCXXXXV
CCCCLIX
MMMMDCCCLXXIII
MMCCCXVII
DCCCXVI
MMMCCCXXXXV
MDCCCCXCV
CLXXXI
MMMMDCCLXX
MMMDCCCIII
MMCLXXVII
MMMDCCXXIX
MMDCCCXCIIII
MMMCDXXIIII
MMMMXXVIII
MMMMDCCCCLXVIII
MDCCCXX
MMMMCDXXI
MMMMDLXXXIX
CCXVI
MDVIII
MMCCLXXI
MMMDCCCLXXI
MMMCCCLXXVI
MMCCLXI
MMMMDCCCXXXIV
DLXXXVI
MMMMDXXXII
MMMXXIIII
MMMMCDIV
MMMMCCCXLVIII
MMMMCXXXVIII
MMMCCCLXVI
MDCCXVIII
MMCXX
CCCLIX
MMMMDCCLXXII
MDCCCLXXV
MMMMDCCCXXIV
DCCCXXXXVIII
MMMDCCCCXXXVIIII
MMMMCCXXXV
MDCLXXXIII
MMCCLXXXIV
MCLXXXXIIII
DXXXXIII
MCCCXXXXVIII
MMCLXXIX
MMMMCCLXIV
MXXII
MMMCXIX
MDCXXXVII
MMDCCVI
MCLXXXXVIII
MMMCXVI
MCCCLX
MMMCDX
CCLXVIIII
MMMCCLX
MCXXVIII
LXXXII
MCCCCLXXXI
MMMI
MMMCCCLXIV
MMMCCCXXVIIII
CXXXVIII
MMCCCXX
MMMCCXXVIIII
MCCLXVI
MMMCCCCXXXXVI
MMDCCXCIX
MCMLXXI
MMCCLXVIII
CDLXXXXIII
MMMMDCCXXII
MMMMDCCLXXXVII
MMMDCCLIV
MMCCLXIII
MDXXXVII
DCCXXXIIII
MCII
MMMDCCCLXXI
MMMLXXIII
MDCCCLIII
MMXXXVIII
MDCCXVIIII
MDCCCCXXXVII
MMCCCXVI
MCMXXII
MMMCCCLVIII
MMMMDCCCXX
MCXXIII
MMMDLXI
MMMMDXXII
MDCCCX
MMDXCVIIII
MMMDCCCCVIII
MMMMDCCCCXXXXVI
MMDCCCXXXV
MMCXCIV
MCMLXXXXIII
MMMCCCLXXVI
MMMMDCLXXXV
CMLXIX
DCXCII
MMXXVIII
MMMMCCCXXX
XXXXVIIII

================================================
FILE: project_euler/problem_089/sol1.py
================================================
"""
Project Euler Problem 89: https://projecteuler.net/problem=89

For a number written in Roman numerals to be considered valid there are basic rules
which must be followed. Even though the rules allow some numbers to be expressed in
more than one way there is always a "best" way of writing a particular number.

For example, it would appear that there are at least six ways of writing the number
sixteen:

IIIIIIIIIIIIIIII
VIIIIIIIIIII
VVIIIIII
XIIIIII
VVVI
XVI

However, according to the rules only XIIIIII and XVI are valid, and the last example
is considered to be the most efficient, as it uses the least number of numerals.

The 11K text file, roman.txt (right click and 'Save Link/Target As...'), contains one
thousand numbers written in valid, but not necessarily minimal, Roman numerals; see
About... Roman Numerals for the definitive rules for this problem.

Find the number of characters saved by writing each of these in their minimal form.

Note: You can assume that all the Roman numerals in the file contain no more than four
consecutive identical units.
"""

import os

SYMBOLS = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}


def parse_roman_numerals(numerals: str) -> int:
    """
    Converts a string of roman numerals to an integer.
    e.g.
    >>> parse_roman_numerals("LXXXIX")
    89
    >>> parse_roman_numerals("IIII")
    4
    """

    total_value = 0

    index = 0
    while index < len(numerals) - 1:
        current_value = SYMBOLS[numerals[index]]
        next_value = SYMBOLS[numerals[index + 1]]
        if current_value < next_value:
            total_value -= current_value
        else:
            total_value += current_value
        index += 1
    total_value += SYMBOLS[numerals[index]]

    return total_value


def generate_roman_numerals(num: int) -> str:
    """
    Generates a string of roman numerals for a given integer.
    e.g.
    >>> generate_roman_numerals(89)
    'LXXXIX'
    >>> generate_roman_numerals(4)
    'IV'
    """

    numerals = ""

    m_count = num // 1000
    numerals += m_count * "M"
    num %= 1000

    c_count = num // 100
    if c_count == 9:
        numerals += "CM"
        c_count -= 9
    elif c_count == 4:
        numerals += "CD"
        c_count -= 4
    if c_count >= 5:
        numerals += "D"
        c_count -= 5
    numerals += c_count * "C"
    num %= 100

    x_count = num // 10
    if x_count == 9:
        numerals += "XC"
        x_count -= 9
    elif x_count == 4:
        numerals += "XL"
        x_count -= 4
    if x_count >= 5:
        numerals += "L"
        x_count -= 5
    numerals += x_count * "X"
    num %= 10

    if num == 9:
        numerals += "IX"
        num -= 9
    elif num == 4:
        numerals += "IV"
        num -= 4
    if num >= 5:
        numerals += "V"
        num -= 5
    numerals += num * "I"

    return numerals


def solution(roman_numerals_filename: str = "/p089_roman.txt") -> int:
    """
    Calculates and returns the answer to project euler problem 89.

    >>> solution("/numeralcleanup_test.txt")
    16
    """

    savings = 0

    with open(os.path.dirname(__file__) + roman_numerals_filename) as file1:
        lines = file1.readlines()

    for line in lines:
        original = line.strip()
        num = parse_roman_numerals(original)
        shortened = generate_roman_numerals(num)
        savings += len(original) - len(shortened)

    return savings


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_091/__init__.py
================================================


================================================
FILE: project_euler/problem_091/sol1.py
================================================
"""
Project Euler Problem 91: https://projecteuler.net/problem=91

The points P (x1, y1) and Q (x2, y2) are plotted at integer coordinates and
are joined to the origin, O(0,0), to form ΔOPQ.

There are exactly fourteen triangles containing a right angle that can be formed
when each coordinate lies between 0 and 2 inclusive; that is,
0 ≤ x1, y1, x2, y2 ≤ 2.

Given that 0 ≤ x1, y1, x2, y2 ≤ 50, how many right triangles can be formed?
"""

from itertools import combinations, product


def is_right(x1: int, y1: int, x2: int, y2: int) -> bool:
    """
    Check if the triangle described by P(x1,y1), Q(x2,y2) and O(0,0) is right-angled.
    Note: this doesn't check if P and Q are equal, but that's handled by the use of
    itertools.combinations in the solution function.

    >>> is_right(0, 1, 2, 0)
    True
    >>> is_right(1, 0, 2, 2)
    False
    """
    if x1 == y1 == 0 or x2 == y2 == 0:
        return False
    a_square = x1 * x1 + y1 * y1
    b_square = x2 * x2 + y2 * y2
    c_square = (x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2)
    return (
        a_square + b_square == c_square
        or a_square + c_square == b_square
        or b_square + c_square == a_square
    )


def solution(limit: int = 50) -> int:
    """
    Return the number of right triangles OPQ that can be formed by two points P, Q
    which have both x- and y- coordinates between 0 and limit inclusive.

    >>> solution(2)
    14
    >>> solution(10)
    448
    """
    return sum(
        1
        for pt1, pt2 in combinations(product(range(limit + 1), repeat=2), 2)
        if is_right(*pt1, *pt2)
    )


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_092/__init__.py
================================================


================================================
FILE: project_euler/problem_092/sol1.py
================================================
"""
Project Euler Problem 092: https://projecteuler.net/problem=92
Square digit chains
A number chain is created by continuously adding the square of the digits in
a number to form a new number until it has been seen before.
For example,
44 → 32 → 13 → 10 → 1 → 1
85 → 89 → 145 → 42 → 20 → 4 → 16 → 37 → 58 → 89
Therefore any chain that arrives at 1 or 89 will become stuck in an endless loop.
What is most amazing is that EVERY starting number will eventually arrive at 1 or 89.
How many starting numbers below ten million will arrive at 89?
"""

DIGITS_SQUARED = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]


def next_number(number: int) -> int:
    """
    Returns the next number of the chain by adding the square of each digit
    to form a new number.
    For example, if number = 12, next_number() will return 1^2 + 2^2 = 5.
    Therefore, 5 is the next number of the chain.
    >>> next_number(44)
    32
    >>> next_number(10)
    1
    >>> next_number(32)
    13
    """

    sum_of_digits_squared = 0
    while number:
        # Increased Speed Slightly by checking every 5 digits together.
        sum_of_digits_squared += DIGITS_SQUARED[number % 100000]
        number //= 100000

    return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
CHAINS: list[bool | None] = [None] * 10000000
CHAINS[0] = True
CHAINS[57] = False


def chain(number: int) -> bool:
    """
    The function generates the chain of numbers until the next number is 1 or 89.
    For example, if starting number is 44, then the function generates the
    following chain of numbers:
    44 → 32 → 13 → 10 → 1 → 1.
    Once the next number generated is 1 or 89, the function returns whether
    or not the next number generated by next_number() is 1.
    >>> chain(10)
    True
    >>> chain(58)
    False
    >>> chain(1)
    True
    """

    if CHAINS[number - 1] is not None:
        return CHAINS[number - 1]  # type: ignore[return-value]

    number_chain = chain(next_number(number))
    CHAINS[number - 1] = number_chain

    while number < 10000000:
        CHAINS[number - 1] = number_chain
        number *= 10

    return number_chain


def solution(number: int = 10000000) -> int:
    """
    The function returns the number of integers that end up being 89 in each chain.
    The function accepts a range number and the function checks all the values
    under value number.

    >>> solution(100)
    80
    >>> solution(10000000)
    8581146
    """
    for i in range(1, number):
        if CHAINS[i] is None:
            chain(i + 1)

    return CHAINS[:number].count(False)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_094/__init__.py
================================================


================================================
FILE: project_euler/problem_094/sol1.py
================================================
"""
Project Euler Problem 94: https://projecteuler.net/problem=94

It is easily proved that no equilateral triangle exists with integral length sides and
integral area. However, the almost equilateral triangle 5-5-6 has an area of 12 square
units.

We shall define an almost equilateral triangle to be a triangle for which two sides are
equal and the third differs by no more than one unit.

Find the sum of the perimeters of all almost equilateral triangles with integral side
lengths and area and whose perimeters do not exceed one billion (1,000,000,000).
"""


def solution(max_perimeter: int = 10**9) -> int:
    """
    Returns the sum of the perimeters of all almost equilateral triangles with integral
    side lengths and area and whose perimeters do not exceed max_perimeter

    >>> solution(20)
    16
    """

    prev_value = 1
    value = 2

    perimeters_sum = 0
    i = 0
    perimeter = 0
    while perimeter <= max_perimeter:
        perimeters_sum += perimeter

        prev_value += 2 * value
        value += prev_value

        perimeter = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
        i += 1

    return perimeters_sum


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_095/__init__.py
================================================


================================================
FILE: project_euler/problem_095/sol1.py
================================================
"""
Project Euler Problem 95: https://projecteuler.net/problem=95

Amicable Chains

The proper divisors of a number are all the divisors excluding the number itself.
For example, the proper divisors of 28 are 1, 2, 4, 7, and 14.
As the sum of these divisors is equal to 28, we call it a perfect number.

Interestingly the sum of the proper divisors of 220 is 284 and
the sum of the proper divisors of 284 is 220, forming a chain of two numbers.
For this reason, 220 and 284 are called an amicable pair.

Perhaps less well known are longer chains.
For example, starting with 12496, we form a chain of five numbers:
    12496 -> 14288 -> 15472 -> 14536 -> 14264 (-> 12496 -> ...)

Since this chain returns to its starting point, it is called an amicable chain.

Find the smallest member of the longest amicable chain with
no element exceeding one million.

Solution is doing the following:
- Get relevant prime numbers
- Iterate over product combination of prime numbers to generate all non-prime
  numbers up to max number, by keeping track of prime factors
- Calculate the sum of factors for each number
- Iterate over found some factors to find longest chain
"""

from math import isqrt


def generate_primes(max_num: int) -> list[int]:
    """
    Calculates the list of primes up to and including `max_num`.

    >>> generate_primes(6)
    [2, 3, 5]
    """
    are_primes = [True] * (max_num + 1)
    are_primes[0] = are_primes[1] = False
    for i in range(2, isqrt(max_num) + 1):
        if are_primes[i]:
            for j in range(i * i, max_num + 1, i):
                are_primes[j] = False

    return [prime for prime, is_prime in enumerate(are_primes) if is_prime]


def multiply(
    chain: list[int],
    primes: list[int],
    min_prime_idx: int,
    prev_num: int,
    max_num: int,
    prev_sum: int,
    primes_degrees: dict[int, int],
) -> None:
    """
    Run over all prime combinations to generate non-prime numbers.

    >>> chain = [0] * 3
    >>> primes_degrees = {}
    >>> multiply(
    ...     chain=chain,
    ...     primes=[2],
    ...     min_prime_idx=0,
    ...     prev_num=1,
    ...     max_num=2,
    ...     prev_sum=0,
    ...     primes_degrees=primes_degrees,
    ... )
    >>> chain
    [0, 0, 1]
    >>> primes_degrees
    {2: 1}
    """

    min_prime = primes[min_prime_idx]
    num = prev_num * min_prime

    min_prime_degree = primes_degrees.get(min_prime, 0)
    min_prime_degree += 1
    primes_degrees[min_prime] = min_prime_degree

    new_sum = prev_sum * min_prime + (prev_sum + prev_num) * (min_prime - 1) // (
        min_prime**min_prime_degree - 1
    )
    chain[num] = new_sum

    for prime_idx in range(min_prime_idx, len(primes)):
        if primes[prime_idx] * num > max_num:
            break

        multiply(
            chain=chain,
            primes=primes,
            min_prime_idx=prime_idx,
            prev_num=num,
            max_num=max_num,
            prev_sum=new_sum,
            primes_degrees=primes_degrees.copy(),
        )


def find_longest_chain(chain: list[int], max_num: int) -> int:
    """
    Finds the smallest element of longest chain

    >>> find_longest_chain(chain=[0, 0, 0, 0, 0, 0, 6], max_num=6)
    6
    """

    max_len = 0
    min_elem = 0
    for start in range(2, len(chain)):
        visited = {start}
        elem = chain[start]
        length = 1

        while elem > 1 and elem <= max_num and elem not in visited:
            visited.add(elem)
            elem = chain[elem]
            length += 1

        if elem == start and length > max_len:
            max_len = length
            min_elem = start

    return min_elem


def solution(max_num: int = 1000000) -> int:
    """
    Runs the calculation for numbers <= `max_num`.

    >>> solution(10)
    6
    >>> solution(200000)
    12496
    """

    primes = generate_primes(max_num)
    chain = [0] * (max_num + 1)
    for prime_idx, prime in enumerate(primes):
        if prime**2 > max_num:
            break

        multiply(
            chain=chain,
            primes=primes,
            min_prime_idx=prime_idx,
            prev_num=1,
            max_num=max_num,
            prev_sum=0,
            primes_degrees={},
        )

    return find_longest_chain(chain=chain, max_num=max_num)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_097/__init__.py
================================================


================================================
FILE: project_euler/problem_097/sol1.py
================================================
"""
The first known prime found to exceed one million digits was discovered in 1999,
and is a Mersenne prime of the form 2**6972593 - 1; it contains exactly 2,098,960
digits. Subsequently other Mersenne primes, of the form 2**p - 1, have been found
which contain more digits.
However, in 2004 there was found a massive non-Mersenne prime which contains
2,357,207 digits: (28433 * (2 ** 7830457 + 1)).

Find the last ten digits of this prime number.
"""


def solution(n: int = 10) -> str:
    """
    Returns the last n digits of NUMBER.
    >>> solution()
    '8739992577'
    >>> solution(8)
    '39992577'
    >>> solution(1)
    '7'
    >>> solution(-1)
    Traceback (most recent call last):
        ...
    ValueError: Invalid input
    >>> solution(8.3)
    Traceback (most recent call last):
        ...
    ValueError: Invalid input
    >>> solution("a")
    Traceback (most recent call last):
        ...
    ValueError: Invalid input
    """
    if not isinstance(n, int) or n < 0:
        raise ValueError("Invalid input")
    modulus = 10**n
    number = 28433 * (pow(2, 7830457, modulus)) + 1
    return str(number % modulus)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    print(f"{solution(10) = }")


================================================
FILE: project_euler/problem_099/__init__.py
================================================


================================================
FILE: project_euler/problem_099/base_exp.txt
================================================
519432,525806
632382,518061
78864,613712
466580,530130
780495,510032
525895,525320
15991,714883
960290,502358
760018,511029
166800,575487
210884,564478
555151,523163
681146,515199
563395,522587
738250,512126
923525,503780
595148,520429
177108,572629
750923,511482
440902,532446
881418,505504
422489,534197
979858,501616
685893,514935
747477,511661
167214,575367
234140,559696
940238,503122
728969,512609
232083,560102
900971,504694
688801,514772
189664,569402
891022,505104
445689,531996
119570,591871
821453,508118
371084,539600
911745,504251
623655,518600
144361,582486
352442,541775
420726,534367
295298,549387
6530,787777
468397,529976
672336,515696
431861,533289
84228,610150
805376,508857
444409,532117
33833,663511
381850,538396
402931,536157
92901,604930
304825,548004
731917,512452
753734,511344
51894,637373
151578,580103
295075,549421
303590,548183
333594,544123
683952,515042
60090,628880
951420,502692
28335,674991
714940,513349
343858,542826
549279,523586
804571,508887
260653,554881
291399,549966
402342,536213
408889,535550
40328,652524
375856,539061
768907,510590
165993,575715
976327,501755
898500,504795
360404,540830
478714,529095
694144,514472
488726,528258
841380,507226
328012,544839
22389,690868
604053,519852
329514,544641
772965,510390
492798,527927
30125,670983
895603,504906
450785,531539
840237,507276
380711,538522
63577,625673
76801,615157
502694,527123
597706,520257
310484,547206
944468,502959
121283,591152
451131,531507
566499,522367
425373,533918
40240,652665
39130,654392
714926,513355
469219,529903
806929,508783
287970,550487
92189,605332
103841,599094
671839,515725
452048,531421
987837,501323
935192,503321
88585,607450
613883,519216
144551,582413
647359,517155
213902,563816
184120,570789
258126,555322
502546,527130
407655,535678
401528,536306
477490,529193
841085,507237
732831,512408
833000,507595
904694,504542
581435,521348
455545,531110
873558,505829
94916,603796
720176,513068
545034,523891
246348,557409
556452,523079
832015,507634
173663,573564
502634,527125
250732,556611
569786,522139
216919,563178
521815,525623
92304,605270
164446,576167
753413,511364
11410,740712
448845,531712
925072,503725
564888,522477
7062,780812
641155,517535
738878,512100
636204,517828
372540,539436
443162,532237
571192,522042
655350,516680
299741,548735
581914,521307
965471,502156
513441,526277
808682,508700
237589,559034
543300,524025
804712,508889
247511,557192
543486,524008
504383,526992
326529,545039
792493,509458
86033,609017
126554,589005
579379,521481
948026,502823
404777,535969
265767,554022
266876,553840
46631,643714
492397,527958
856106,506581
795757,509305
748946,511584
294694,549480
409781,535463
775887,510253
543747,523991
210592,564536
517119,525990
520253,525751
247926,557124
592141,520626
346580,542492
544969,523902
506501,526817
244520,557738
144745,582349
69274,620858
292620,549784
926027,503687
736320,512225
515528,526113
407549,535688
848089,506927
24141,685711
9224,757964
980684,501586
175259,573121
489160,528216
878970,505604
969546,502002
525207,525365
690461,514675
156510,578551
659778,516426
468739,529945
765252,510770
76703,615230
165151,575959
29779,671736
928865,503569
577538,521605
927555,503618
185377,570477
974756,501809
800130,509093
217016,563153
365709,540216
774508,510320
588716,520851
631673,518104
954076,502590
777828,510161
990659,501222
597799,520254
786905,509727
512547,526348
756449,511212
869787,505988
653747,516779
84623,609900
839698,507295
30159,670909
797275,509234
678136,515373
897144,504851
989554,501263
413292,535106
55297,633667
788650,509637
486748,528417
150724,580377
56434,632490
77207,614869
588631,520859
611619,519367
100006,601055
528924,525093
190225,569257
851155,506789
682593,515114
613043,519275
514673,526183
877634,505655
878905,505602
1926,914951
613245,519259
152481,579816
841774,507203
71060,619442
865335,506175
90244,606469
302156,548388
399059,536557
478465,529113
558601,522925
69132,620966
267663,553700
988276,501310
378354,538787
529909,525014
161733,576968
758541,511109
823425,508024
149821,580667
269258,553438
481152,528891
120871,591322
972322,501901
981350,501567
676129,515483
950860,502717
119000,592114
392252,537272
191618,568919
946699,502874
289555,550247
799322,509139
703886,513942
194812,568143
261823,554685
203052,566221
217330,563093
734748,512313
391759,537328
807052,508777
564467,522510
59186,629748
113447,594545
518063,525916
905944,504492
613922,519213
439093,532607
445946,531981
230530,560399
297887,549007
459029,530797
403692,536075
855118,506616
963127,502245
841711,507208
407411,535699
924729,503735
914823,504132
333725,544101
176345,572832
912507,504225
411273,535308
259774,555036
632853,518038
119723,591801
163902,576321
22691,689944
402427,536212
175769,572988
837260,507402
603432,519893
313679,546767
538165,524394
549026,523608
61083,627945
898345,504798
992556,501153
369999,539727
32847,665404
891292,505088
152715,579732
824104,507997
234057,559711
730507,512532
960529,502340
388395,537687
958170,502437
57105,631806
186025,570311
993043,501133
576770,521664
215319,563513
927342,503628
521353,525666
39563,653705
752516,511408
110755,595770
309749,547305
374379,539224
919184,503952
990652,501226
647780,517135
187177,570017
168938,574877
649558,517023
278126,552016
162039,576868
658512,516499
498115,527486
896583,504868
561170,522740
747772,511647
775093,510294
652081,516882
724905,512824
499707,527365
47388,642755
646668,517204
571700,522007
180430,571747
710015,513617
435522,532941
98137,602041
759176,511070
486124,528467
526942,525236
878921,505604
408313,535602
926980,503640
882353,505459
566887,522345
3326,853312
911981,504248
416309,534800
392991,537199
622829,518651
148647,581055
496483,527624
666314,516044
48562,641293
672618,515684
443676,532187
274065,552661
265386,554079
347668,542358
31816,667448
181575,571446
961289,502320
365689,540214
987950,501317
932299,503440
27388,677243
746701,511701
492258,527969
147823,581323
57918,630985
838849,507333
678038,515375
27852,676130
850241,506828
818403,508253
131717,587014
850216,506834
904848,504529
189758,569380
392845,537217
470876,529761
925353,503711
285431,550877
454098,531234
823910,508003
318493,546112
766067,510730
261277,554775
421530,534289
694130,514478
120439,591498
213308,563949
854063,506662
365255,540263
165437,575872
662240,516281
289970,550181
847977,506933
546083,523816
413252,535113
975829,501767
361540,540701
235522,559435
224643,561577
736350,512229
328303,544808
35022,661330
307838,547578
474366,529458
873755,505819
73978,617220
827387,507845
670830,515791
326511,545034
309909,547285
400970,536363
884827,505352
718307,513175
28462,674699
599384,520150
253565,556111
284009,551093
343403,542876
446557,531921
992372,501160
961601,502308
696629,514342
919537,503945
894709,504944
892201,505051
358160,541097
448503,531745
832156,507636
920045,503924
926137,503675
416754,534757
254422,555966
92498,605151
826833,507873
660716,516371
689335,514746
160045,577467
814642,508425
969939,501993
242856,558047
76302,615517
472083,529653
587101,520964
99066,601543
498005,527503
709800,513624
708000,513716
20171,698134
285020,550936
266564,553891
981563,501557
846502,506991
334,1190800
209268,564829
9844,752610
996519,501007
410059,535426
432931,533188
848012,506929
966803,502110
983434,501486
160700,577267
504374,526989
832061,507640
392825,537214
443842,532165
440352,532492
745125,511776
13718,726392
661753,516312
70500,619875
436952,532814
424724,533973
21954,692224
262490,554567
716622,513264
907584,504425
60086,628882
837123,507412
971345,501940
947162,502855
139920,584021
68330,621624
666452,516038
731446,512481
953350,502619
183157,571042
845400,507045
651548,516910
20399,697344
861779,506331
629771,518229
801706,509026
189207,569512
737501,512168
719272,513115
479285,529045
136046,585401
896746,504860
891735,505067
684771,514999
865309,506184
379066,538702
503117,527090
621780,518717
209518,564775
677135,515423
987500,501340
197049,567613
329315,544673
236756,559196
357092,541226
520440,525733
213471,563911
956852,502490
702223,514032
404943,535955
178880,572152
689477,514734
691351,514630
866669,506128
370561,539656
739805,512051
71060,619441
624861,518534
261660,554714
366137,540160
166054,575698
601878,519990
153445,579501
279899,551729
379166,538691
423209,534125
675310,515526
145641,582050
691353,514627
917468,504026
284778,550976
81040,612235
161699,576978
616394,519057
767490,510661
156896,578431
427408,533714
254849,555884
737217,512182
897133,504851
203815,566051
270822,553189
135854,585475
778805,510111
784373,509847
305426,547921
733418,512375
732087,512448
540668,524215
702898,513996
628057,518328
640280,517587
422405,534204
10604,746569
746038,511733
839808,507293
457417,530938
479030,529064
341758,543090
620223,518824
251661,556451
561790,522696
497733,527521
724201,512863
489217,528217
415623,534867
624610,518548
847541,506953
432295,533249
400391,536421
961158,502319
139173,584284
421225,534315
579083,521501
74274,617000
701142,514087
374465,539219
217814,562985
358972,540995
88629,607424
288597,550389
285819,550812
538400,524385
809930,508645
738326,512126
955461,502535
163829,576343
826475,507891
376488,538987
102234,599905
114650,594002
52815,636341
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804744,508880
98385,601905
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506835,526794
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63515,625730
666326,516041
619226,518883
443613,532186
597717,520257
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86940,608450
40725,651929
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268875,553508
270671,553214
363254,540500
384248,538137
762889,510892
377941,538833
278878,551890
176615,572755
860008,506412
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608395,519571
225283,561450
45095,645728
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13846,725685


================================================
FILE: project_euler/problem_099/sol1.py
================================================
"""
Problem:

Comparing two numbers written in index form like 2'11 and 3'7 is not difficult, as any
calculator would confirm that 2^11 = 2048 < 3^7 = 2187.

However, confirming that 632382^518061 > 519432^525806 would be much more difficult, as
both numbers contain over three million digits.

Using base_exp.txt, a 22K text file containing one thousand lines with a base/exponent
pair on each line, determine which line number has the greatest numerical value.

NOTE: The first two lines in the file represent the numbers in the example given above.
"""

import os
from math import log10


def solution(data_file: str = "base_exp.txt") -> int:
    """
    >>> solution()
    709
    """
    largest: float = 0
    result = 0
    for i, line in enumerate(open(os.path.join(os.path.dirname(__file__), data_file))):
        a, x = list(map(int, line.split(",")))
        if x * log10(a) > largest:
            largest = x * log10(a)
            result = i + 1
    return result


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_100/__init__.py
================================================


================================================
FILE: project_euler/problem_100/sol1.py
================================================
"""
Project Euler Problem 100: https://projecteuler.net/problem=100

If a box contains twenty-one coloured discs, composed of fifteen blue discs and
six red discs, and two discs were taken at random, it can be seen that
the probability of taking two blue discs, P(BB) = (15/21) x (14/20) = 1/2.

The next such arrangement, for which there is exactly 50% chance of taking two blue
discs at random, is a box containing eighty-five blue discs and thirty-five red discs.

By finding the first arrangement to contain over 10^12 = 1,000,000,000,000 discs
in total, determine the number of blue discs that the box would contain.
"""


def solution(min_total: int = 10**12) -> int:
    """
    Returns the number of blue discs for the first arrangement to contain
    over min_total discs in total

    >>> solution(2)
    3

    >>> solution(4)
    15

    >>> solution(21)
    85
    """

    prev_numerator = 1
    prev_denominator = 0

    numerator = 1
    denominator = 1

    while numerator <= 2 * min_total - 1:
        prev_numerator += 2 * numerator
        numerator += 2 * prev_numerator

        prev_denominator += 2 * denominator
        denominator += 2 * prev_denominator

    return (denominator + 1) // 2


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_101/__init__.py
================================================


================================================
FILE: project_euler/problem_101/sol1.py
================================================
"""
If we are presented with the first k terms of a sequence it is impossible to say with
certainty the value of the next term, as there are infinitely many polynomial functions
that can model the sequence.

As an example, let us consider the sequence of cube
numbers. This is defined by the generating function,
u(n) = n3: 1, 8, 27, 64, 125, 216, ...

Suppose we were only given the first two terms of this sequence. Working on the
principle that "simple is best" we should assume a linear relationship and predict the
next term to be 15 (common difference 7). Even if we were presented with the first three
terms, by the same principle of simplicity, a quadratic relationship should be
assumed.

We shall define OP(k, n) to be the nth term of the optimum polynomial
generating function for the first k terms of a sequence. It should be clear that
OP(k, n) will accurately generate the terms of the sequence for n ≤ k, and potentially
the first incorrect term (FIT) will be OP(k, k+1); in which case we shall call it a
bad OP (BOP).

As a basis, if we were only given the first term of sequence, it would be most
sensible to assume constancy; that is, for n ≥ 2, OP(1, n) = u(1).

Hence we obtain the
following OPs for the cubic sequence:

OP(1, n) = 1            1, 1, 1, 1, ...
OP(2, n) = 7n-6         1, 8, 15, ...
OP(3, n) = 6n^2-11n+6   1, 8, 27, 58, ...
OP(4, n) = n^3          1, 8, 27, 64, 125, ...

Clearly no BOPs exist for k ≥ 4.

By considering the sum of FITs generated by the BOPs (indicated in red above), we
obtain 1 + 15 + 58 = 74.

Consider the following tenth degree polynomial generating function:

1 - n + n^2 - n^3 + n^4 - n^5 + n^6 - n^7 + n^8 - n^9 + n^10

Find the sum of FITs for the BOPs.
"""

from __future__ import annotations

from collections.abc import Callable

Matrix = list[list[float | int]]


def solve(matrix: Matrix, vector: Matrix) -> Matrix:
    """
    Solve the linear system of equations Ax = b (A = "matrix", b = "vector")
    for x using Gaussian elimination and back substitution. We assume that A
    is an invertible square matrix and that b is a column vector of the
    same height.
    >>> solve([[1, 0], [0, 1]], [[1],[2]])
    [[1.0], [2.0]]
    >>> solve([[2, 1, -1],[-3, -1, 2],[-2, 1, 2]],[[8], [-11],[-3]])
    [[2.0], [3.0], [-1.0]]
    """
    size: int = len(matrix)
    augmented: Matrix = [[0 for _ in range(size + 1)] for _ in range(size)]
    row: int
    row2: int
    col: int
    col2: int
    pivot_row: int
    ratio: float

    for row in range(size):
        for col in range(size):
            augmented[row][col] = matrix[row][col]

        augmented[row][size] = vector[row][0]

    row = 0
    col = 0
    while row < size and col < size:
        # pivoting
        pivot_row = max((abs(augmented[row2][col]), row2) for row2 in range(col, size))[
            1
        ]
        if augmented[pivot_row][col] == 0:
            col += 1
            continue
        else:
            augmented[row], augmented[pivot_row] = augmented[pivot_row], augmented[row]

        for row2 in range(row + 1, size):
            ratio = augmented[row2][col] / augmented[row][col]
            augmented[row2][col] = 0
            for col2 in range(col + 1, size + 1):
                augmented[row2][col2] -= augmented[row][col2] * ratio

        row += 1
        col += 1

    # back substitution
    for col in range(1, size):
        for row in range(col):
            ratio = augmented[row][col] / augmented[col][col]
            for col2 in range(col, size + 1):
                augmented[row][col2] -= augmented[col][col2] * ratio

    # round to get rid of numbers like 2.000000000000004
    return [
        [round(augmented[row][size] / augmented[row][row], 10)] for row in range(size)
    ]


def interpolate(y_list: list[int]) -> Callable[[int], int]:
    """
    Given a list of data points (1,y0),(2,y1), ..., return a function that
    interpolates the data points. We find the coefficients of the interpolating
    polynomial by solving a system of linear equations corresponding to
    x = 1, 2, 3...

    >>> interpolate([1])(3)
    1
    >>> interpolate([1, 8])(3)
    15
    >>> interpolate([1, 8, 27])(4)
    58
    >>> interpolate([1, 8, 27, 64])(6)
    216
    """

    size: int = len(y_list)
    matrix: Matrix = [[0 for _ in range(size)] for _ in range(size)]
    vector: Matrix = [[0] for _ in range(size)]
    coeffs: Matrix
    x_val: int
    y_val: int
    col: int

    for x_val, y_val in enumerate(y_list):
        for col in range(size):
            matrix[x_val][col] = (x_val + 1) ** (size - col - 1)
        vector[x_val][0] = y_val

    coeffs = solve(matrix, vector)

    def interpolated_func(var: int) -> int:
        """
        >>> interpolate([1])(3)
        1
        >>> interpolate([1, 8])(3)
        15
        >>> interpolate([1, 8, 27])(4)
        58
        >>> interpolate([1, 8, 27, 64])(6)
        216
        """
        return sum(
            round(coeffs[x_val][0]) * (var ** (size - x_val - 1))
            for x_val in range(size)
        )

    return interpolated_func


def question_function(variable: int) -> int:
    """
    The generating function u as specified in the question.
    >>> question_function(0)
    1
    >>> question_function(1)
    1
    >>> question_function(5)
    8138021
    >>> question_function(10)
    9090909091
    """
    return (
        1
        - variable
        + variable**2
        - variable**3
        + variable**4
        - variable**5
        + variable**6
        - variable**7
        + variable**8
        - variable**9
        + variable**10
    )


def solution(func: Callable[[int], int] = question_function, order: int = 10) -> int:
    """
    Find the sum of the FITs of the BOPS. For each interpolating polynomial of order
    1, 2, ... , 10, find the first x such that the value of the polynomial at x does
    not equal u(x).
    >>> solution(lambda n: n ** 3, 3)
    74
    """
    data_points: list[int] = [func(x_val) for x_val in range(1, order + 1)]

    polynomials: list[Callable[[int], int]] = [
        interpolate(data_points[:max_coeff]) for max_coeff in range(1, order + 1)
    ]

    ret: int = 0
    poly: Callable[[int], int]
    x_val: int

    for poly in polynomials:
        x_val = 1
        while func(x_val) == poly(x_val):
            x_val += 1

        ret += poly(x_val)

    return ret


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_102/__init__.py
================================================


================================================
FILE: project_euler/problem_102/p102_triangles.txt
================================================
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-175,41,-421,-714,574,-645
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419,-864,-83,650,-399,171
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162,570,628,485,-807,-896
641,91,-65,700,887,759
215,-496,46,-931,422,-30
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440,929,968,214,760,-857
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323,5,561,-36,919,-560
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411,998,813,520,316,-949
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620,27,263,54,-439,-726
804,87,998,859,871,-78
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633,837,-971,917,-764,207
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833,-713,-213,578,-296,699
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================================================
FILE: project_euler/problem_102/sol1.py
================================================
"""
Three distinct points are plotted at random on a Cartesian plane,
for which -1000 ≤ x, y ≤ 1000, such that a triangle is formed.

Consider the following two triangles:

A(-340,495), B(-153,-910), C(835,-947)

X(-175,41), Y(-421,-714), Z(574,-645)

It can be verified that triangle ABC contains the origin, whereas
triangle XYZ does not.

Using triangles.txt (right click and 'Save Link/Target As...'), a 27K text
file containing the coordinates of one thousand "random" triangles, find
the number of triangles for which the interior contains the origin.

NOTE: The first two examples in the file represent the triangles in the
example given above.
"""

from __future__ import annotations

from pathlib import Path


def vector_product(point1: tuple[int, int], point2: tuple[int, int]) -> int:
    """
    Return the 2-d vector product of two vectors.
    >>> vector_product((1, 2), (-5, 0))
    10
    >>> vector_product((3, 1), (6, 10))
    24
    """
    return point1[0] * point2[1] - point1[1] * point2[0]


def contains_origin(x1: int, y1: int, x2: int, y2: int, x3: int, y3: int) -> bool:
    """
    Check if the triangle given by the points A(x1, y1), B(x2, y2), C(x3, y3)
    contains the origin.
    >>> contains_origin(-340, 495, -153, -910, 835, -947)
    True
    >>> contains_origin(-175, 41, -421, -714, 574, -645)
    False
    """
    point_a: tuple[int, int] = (x1, y1)
    point_a_to_b: tuple[int, int] = (x2 - x1, y2 - y1)
    point_a_to_c: tuple[int, int] = (x3 - x1, y3 - y1)
    a: float = -vector_product(point_a, point_a_to_b) / vector_product(
        point_a_to_c, point_a_to_b
    )
    b: float = +vector_product(point_a, point_a_to_c) / vector_product(
        point_a_to_c, point_a_to_b
    )

    return a > 0 and b > 0 and a + b < 1


def solution(filename: str = "p102_triangles.txt") -> int:
    """
    Find the number of triangles whose interior contains the origin.
    >>> solution("test_triangles.txt")
    1
    """
    data: str = Path(__file__).parent.joinpath(filename).read_text(encoding="utf-8")

    triangles: list[list[int]] = []
    for line in data.strip().split("\n"):
        triangles.append([int(number) for number in line.split(",")])

    ret: int = 0
    triangle: list[int]

    for triangle in triangles:
        ret += contains_origin(*triangle)

    return ret


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_102/test_triangles.txt
================================================
-340,495,-153,-910,835,-947
-175,41,-421,-714,574,-645


================================================
FILE: project_euler/problem_104/__init__.py
================================================


================================================
FILE: project_euler/problem_104/sol1.py
================================================
"""
Project Euler Problem 104 : https://projecteuler.net/problem=104

The Fibonacci sequence is defined by the recurrence relation:

Fn = Fn-1 + Fn-2, where F1 = 1 and F2 = 1.
It turns out that F541, which contains 113 digits, is the first Fibonacci number
for which the last nine digits are 1-9 pandigital (contain all the digits 1 to 9,
but not necessarily in order). And F2749, which contains 575 digits, is the first
Fibonacci number for which the first nine digits are 1-9 pandigital.

Given that Fk is the first Fibonacci number for which the first nine digits AND
the last nine digits are 1-9 pandigital, find k.
"""

import sys

sys.set_int_max_str_digits(0)


def check(number: int) -> bool:
    """
    Takes a number and checks if it is pandigital both from start and end


    >>> check(123456789987654321)
    True

    >>> check(120000987654321)
    False

    >>> check(1234567895765677987654321)
    True

    """

    check_last = [0] * 11
    check_front = [0] * 11

    # mark last 9 numbers
    for _ in range(9):
        check_last[int(number % 10)] = 1
        number = number // 10
    # flag
    f = True

    # check last 9 numbers for pandigitality

    for x in range(9):
        if not check_last[x + 1]:
            f = False
    if not f:
        return f

    # mark first 9 numbers
    number = int(str(number)[:9])

    for _ in range(9):
        check_front[int(number % 10)] = 1
        number = number // 10

    # check first 9 numbers for pandigitality

    for x in range(9):
        if not check_front[x + 1]:
            f = False
    return f


def check1(number: int) -> bool:
    """
    Takes a number and checks if it is pandigital from END

    >>> check1(123456789987654321)
    True

    >>> check1(120000987654321)
    True

    >>> check1(12345678957656779870004321)
    False

    """

    check_last = [0] * 11

    # mark last 9 numbers
    for _ in range(9):
        check_last[int(number % 10)] = 1
        number = number // 10
    # flag
    f = True

    # check last 9 numbers for pandigitality

    for x in range(9):
        if not check_last[x + 1]:
            f = False
    return f


def solution() -> int:
    """
    Outputs the answer is the least Fibonacci number pandigital from both sides.
    >>> solution()
    329468
    """

    a = 1
    b = 1
    c = 2
    # temporary Fibonacci numbers

    a1 = 1
    b1 = 1
    c1 = 2
    # temporary Fibonacci numbers mod 1e9

    # mod m=1e9, done for fast optimisation
    tocheck = [0] * 1000000
    m = 1000000000

    for x in range(1000000):
        c1 = (a1 + b1) % m
        a1 = b1 % m
        b1 = c1 % m
        if check1(b1):
            tocheck[x + 3] = 1

    for x in range(1000000):
        c = a + b
        a = b
        b = c
        # perform check only if in tocheck
        if tocheck[x + 3] and check(b):
            return x + 3  # first 2 already done
    return -1


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_107/__init__.py
================================================


================================================
FILE: project_euler/problem_107/p107_network.txt
================================================
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762,920,225,875,589,-,797,-,330,25,406,165,68,364,258,-,-,-,222,-,651,140,463,639,-,-,156,486,212,-,-,349,723,-,-,186,-,36,240,752
182,-,578,-,-,786,358,978,936,385,989,-,359,966,112,950,765,807,-,991,80,403,361,936,889,-,339,943,684,-,-,965,302,676,725,-,327,134,-,147
56,-,699,615,873,-,232,900,883,-,772,675,-,227,-,68,196,-,649,182,927,962,-,-,349,736,-,-,505,349,965,-,474,178,833,-,-,555,853,-
-,315,-,-,477,554,550,-,286,342,932,250,814,-,900,193,504,-,-,351,-,785,-,-,-,576,102,779,341,723,302,474,-,689,-,-,-,451,-,-
884,649,898,409,-,817,-,901,-,470,7,686,218,-,-,-,757,-,-,477,974,-,512,490,963,-,790,-,384,-,676,178,689,-,245,596,445,-,-,343
412,937,294,758,-,33,305,-,174,-,-,995,186,807,-,697,-,461,-,867,977,511,931,-,150,697,359,6,9,-,725,833,-,245,-,949,-,270,-,112
273,-,-,221,19,-,997,-,-,-,823,366,-,993,818,-,542,501,-,-,-,-,-,695,447,946,-,880,992,186,-,-,-,596,949,-,91,-,768,273
636,185,575,-,450,54,662,225,-,-,391,191,-,-,639,390,-,-,-,-,-,1,224,-,-,443,439,116,507,-,327,-,-,445,-,91,-,248,-,344
-,102,168,-,-,506,744,533,-,730,-,-,929,-,268,588,395,-,654,889,457,-,690,505,292,-,938,775,48,36,134,555,451,-,270,-,248,-,371,680
-,636,432,76,-,386,686,770,828,582,-,433,203,526,600,848,227,616,-,217,117,707,369,109,586,205,809,-,-,240,-,853,-,-,-,768,-,371,-,540
774,289,833,257,-,381,239,722,711,468,933,-,-,17,-,-,148,-,-,853,-,-,-,-,264,194,260,947,-,752,147,-,-,343,112,273,344,680,540,-


================================================
FILE: project_euler/problem_107/sol1.py
================================================
"""
The following undirected network consists of seven vertices and twelve edges
with a total weight of 243.

The same network can be represented by the matrix below.

    A   B   C   D   E   F   G
A   -   16  12  21  -   -   -
B   16  -   -   17  20  -   -
C   12  -   -   28  -   31  -
D   21  17  28  -   18  19  23
E   -   20  -   18  -   -   11
F   -   -   31  19  -   -   27
G   -   -   -   23  11  27  -

However, it is possible to optimise the network by removing some edges and still
ensure that all points on the network remain connected. The network which achieves
the maximum saving is shown below. It has a weight of 93, representing a saving of
243 - 93 = 150 from the original network.

Using network.txt (right click and 'Save Link/Target As...'), a 6K text file
containing a network with forty vertices, and given in matrix form, find the maximum
saving which can be achieved by removing redundant edges whilst ensuring that the
network remains connected.

Solution:
    We use Prim's algorithm to find a Minimum Spanning Tree.
    Reference: https://en.wikipedia.org/wiki/Prim%27s_algorithm
"""

from __future__ import annotations

import os
from collections.abc import Mapping

EdgeT = tuple[int, int]


class Graph:
    """
    A class representing an undirected weighted graph.
    """

    def __init__(self, vertices: set[int], edges: Mapping[EdgeT, int]) -> None:
        self.vertices: set[int] = vertices
        self.edges: dict[EdgeT, int] = {
            (min(edge), max(edge)): weight for edge, weight in edges.items()
        }

    def add_edge(self, edge: EdgeT, weight: int) -> None:
        """
        Add a new edge to the graph.
        >>> graph = Graph({1, 2}, {(2, 1): 4})
        >>> graph.add_edge((3, 1), 5)
        >>> sorted(graph.vertices)
        [1, 2, 3]
        >>> sorted([(v,k) for k,v in graph.edges.items()])
        [(4, (1, 2)), (5, (1, 3))]
        """
        self.vertices.add(edge[0])
        self.vertices.add(edge[1])
        self.edges[(min(edge), max(edge))] = weight

    def prims_algorithm(self) -> Graph:
        """
        Run Prim's algorithm to find the minimum spanning tree.
        Reference: https://en.wikipedia.org/wiki/Prim%27s_algorithm
        >>> graph = Graph({1,2,3,4},{(1,2):5, (1,3):10, (1,4):20, (2,4):30, (3,4):1})
        >>> mst = graph.prims_algorithm()
        >>> sorted(mst.vertices)
        [1, 2, 3, 4]
        >>> sorted(mst.edges)
        [(1, 2), (1, 3), (3, 4)]
        """
        subgraph: Graph = Graph({min(self.vertices)}, {})
        min_edge: EdgeT
        min_weight: int
        edge: EdgeT
        weight: int

        while len(subgraph.vertices) < len(self.vertices):
            min_weight = max(self.edges.values()) + 1
            for edge, weight in self.edges.items():
                if (edge[0] in subgraph.vertices) ^ (
                    edge[1] in subgraph.vertices
                ) and weight < min_weight:
                    min_edge = edge
                    min_weight = weight

            subgraph.add_edge(min_edge, min_weight)

        return subgraph


def solution(filename: str = "p107_network.txt") -> int:
    """
    Find the maximum saving which can be achieved by removing redundant edges
    whilst ensuring that the network remains connected.
    >>> solution("test_network.txt")
    150
    """
    script_dir: str = os.path.abspath(os.path.dirname(__file__))
    network_file: str = os.path.join(script_dir, filename)
    edges: dict[EdgeT, int] = {}
    data: list[str]
    edge1: int
    edge2: int

    with open(network_file) as f:
        data = f.read().strip().split("\n")

    adjaceny_matrix = [line.split(",") for line in data]

    for edge1 in range(1, len(adjaceny_matrix)):
        for edge2 in range(edge1):
            if adjaceny_matrix[edge1][edge2] != "-":
                edges[(edge2, edge1)] = int(adjaceny_matrix[edge1][edge2])

    graph: Graph = Graph(set(range(len(adjaceny_matrix))), edges)

    subgraph: Graph = graph.prims_algorithm()

    initial_total: int = sum(graph.edges.values())
    optimal_total: int = sum(subgraph.edges.values())

    return initial_total - optimal_total


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_107/test_network.txt
================================================
-,16,12,21,-,-,-
16,-,-,17,20,-,-
12,-,-,28,-,31,-
21,17,28,-,18,19,23
-,20,-,18,-,-,11
-,-,31,19,-,-,27
-,-,-,23,11,27,-


================================================
FILE: project_euler/problem_109/__init__.py
================================================


================================================
FILE: project_euler/problem_109/sol1.py
================================================
"""
In the game of darts a player throws three darts at a target board which is
split into twenty equal sized sections numbered one to twenty.

The score of a dart is determined by the number of the region that the dart
lands in. A dart landing outside the red/green outer ring scores zero. The black
and cream regions inside this ring represent single scores. However, the red/green
outer ring and middle ring score double and treble scores respectively.

At the centre of the board are two concentric circles called the bull region, or
bulls-eye. The outer bull is worth 25 points and the inner bull is a double,
worth 50 points.

There are many variations of rules but in the most popular game the players will
begin with a score 301 or 501 and the first player to reduce their running total
to zero is a winner. However, it is normal to play a "doubles out" system, which
means that the player must land a double (including the double bulls-eye at the
centre of the board) on their final dart to win; any other dart that would reduce
their running total to one or lower means the score for that set of three darts
is "bust".

When a player is able to finish on their current score it is called a "checkout"
and the highest checkout is 170: T20 T20 D25 (two treble 20s and double bull).

There are exactly eleven distinct ways to checkout on a score of 6:

D3
D1  D2
S2  D2
D2  D1
S4  D1
S1  S1  D2
S1  T1  D1
S1  S3  D1
D1  D1  D1
D1  S2  D1
S2  S2  D1

Note that D1 D2 is considered different to D2 D1 as they finish on different
doubles. However, the combination S1 T1 D1 is considered the same as T1 S1 D1.

In addition we shall not include misses in considering combinations; for example,
D3 is the same as 0 D3 and 0 0 D3.

Incredibly there are 42336 distinct ways of checking out in total.

How many distinct ways can a player checkout with a score less than 100?

Solution:
    We first construct a list of the possible dart values, separated by type.
    We then iterate through the doubles, followed by the possible 2 following throws.
    If the total of these three darts is less than the given limit, we increment
    the counter.
"""

from itertools import combinations_with_replacement


def solution(limit: int = 100) -> int:
    """
    Count the number of distinct ways a player can checkout with a score
    less than limit.
    >>> solution(171)
    42336
    >>> solution(50)
    12577
    """
    singles: list[int] = [*list(range(1, 21)), 25]
    doubles: list[int] = [2 * x for x in range(1, 21)] + [50]
    triples: list[int] = [3 * x for x in range(1, 21)]
    all_values: list[int] = singles + doubles + triples + [0]

    num_checkouts: int = 0
    double: int
    throw1: int
    throw2: int
    checkout_total: int

    for double in doubles:
        for throw1, throw2 in combinations_with_replacement(all_values, 2):
            checkout_total = double + throw1 + throw2
            if checkout_total < limit:
                num_checkouts += 1

    return num_checkouts


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_112/__init__.py
================================================


================================================
FILE: project_euler/problem_112/sol1.py
================================================
"""
Problem 112: https://projecteuler.net/problem=112

Working from left-to-right if no digit is exceeded by the digit to its left it is
called an increasing number; for example, 134468.
Similarly if no digit is exceeded by the digit to its right it is called a decreasing
number; for example, 66420.
We shall call a positive integer that is neither increasing nor decreasing a "bouncy"
number, for example, 155349.
Clearly there cannot be any bouncy numbers below one-hundred, but just over half of
the numbers below one-thousand (525) are bouncy. In fact, the least number for which
the proportion of bouncy numbers first reaches 50% is 538.
Surprisingly, bouncy numbers become more and more common and by the time we reach
21780 the proportion of bouncy numbers is equal to 90%.

Find the least number for which the proportion of bouncy numbers is exactly 99%.
"""


def check_bouncy(n: int) -> bool:
    """
    Returns True if number is bouncy, False otherwise
    >>> check_bouncy(6789)
    False
    >>> check_bouncy(-12345)
    False
    >>> check_bouncy(0)
    False
    >>> check_bouncy(6.74)
    Traceback (most recent call last):
        ...
    ValueError: check_bouncy() accepts only integer arguments
    >>> check_bouncy(132475)
    True
    >>> check_bouncy(34)
    False
    >>> check_bouncy(341)
    True
    >>> check_bouncy(47)
    False
    >>> check_bouncy(-12.54)
    Traceback (most recent call last):
        ...
    ValueError: check_bouncy() accepts only integer arguments
    >>> check_bouncy(-6548)
    True
    """
    if not isinstance(n, int):
        raise ValueError("check_bouncy() accepts only integer arguments")
    str_n = str(n)
    sorted_str_n = "".join(sorted(str_n))
    return str_n not in {sorted_str_n, sorted_str_n[::-1]}


def solution(percent: float = 99) -> int:
    """
    Returns the least number for which the proportion of bouncy numbers is
    exactly 'percent'
    >>> solution(50)
    538
    >>> solution(90)
    21780
    >>> solution(80)
    4770
    >>> solution(105)
    Traceback (most recent call last):
        ...
    ValueError: solution() only accepts values from 0 to 100
    >>> solution(100.011)
    Traceback (most recent call last):
        ...
    ValueError: solution() only accepts values from 0 to 100
    """
    if not 0 < percent < 100:
        raise ValueError("solution() only accepts values from 0 to 100")
    bouncy_num = 0
    num = 1

    while True:
        if check_bouncy(num):
            bouncy_num += 1
        if (bouncy_num / num) * 100 >= percent:
            return num
        num += 1


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    print(f"{solution(99)}")


================================================
FILE: project_euler/problem_113/__init__.py
================================================


================================================
FILE: project_euler/problem_113/sol1.py
================================================
"""
Project Euler Problem 113: https://projecteuler.net/problem=113

Working from left-to-right if no digit is exceeded by the digit to its left it is
called an increasing number; for example, 134468.

Similarly if no digit is exceeded by the digit to its right it is called a decreasing
number; for example, 66420.

We shall call a positive integer that is neither increasing nor decreasing a
"bouncy" number; for example, 155349.

As n increases, the proportion of bouncy numbers below n increases such that there
are only 12951 numbers below one-million that are not bouncy and only 277032
non-bouncy numbers below 10^10.

How many numbers below a googol (10^100) are not bouncy?
"""


def choose(n: int, r: int) -> int:
    """
    Calculate the binomial coefficient c(n,r) using the multiplicative formula.
    >>> choose(4,2)
    6
    >>> choose(5,3)
    10
    >>> choose(20,6)
    38760
    """
    ret = 1.0
    for i in range(1, r + 1):
        ret *= (n + 1 - i) / i
    return round(ret)


def non_bouncy_exact(n: int) -> int:
    """
    Calculate the number of non-bouncy numbers with at most n digits.
    >>> non_bouncy_exact(1)
    9
    >>> non_bouncy_exact(6)
    7998
    >>> non_bouncy_exact(10)
    136126
    """
    return choose(8 + n, n) + choose(9 + n, n) - 10


def non_bouncy_upto(n: int) -> int:
    """
    Calculate the number of non-bouncy numbers with at most n digits.
    >>> non_bouncy_upto(1)
    9
    >>> non_bouncy_upto(6)
    12951
    >>> non_bouncy_upto(10)
    277032
    """
    return sum(non_bouncy_exact(i) for i in range(1, n + 1))


def solution(num_digits: int = 100) -> int:
    """
    Calculate the number of non-bouncy numbers less than a googol.
    >>> solution(6)
    12951
    >>> solution(10)
    277032
    """
    return non_bouncy_upto(num_digits)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_114/__init__.py
================================================


================================================
FILE: project_euler/problem_114/sol1.py
================================================
"""
Project Euler Problem 114: https://projecteuler.net/problem=114

A row measuring seven units in length has red blocks with a minimum length
of three units placed on it, such that any two red blocks
(which are allowed to be different lengths) are separated by at least one grey square.
There are exactly seventeen ways of doing this.

    |g|g|g|g|g|g|g|    |r,r,r|g|g|g|g|

    |g|r,r,r|g|g|g|    |g|g|r,r,r|g|g|

    |g|g|g|r,r,r|g|    |g|g|g|g|r,r,r|

    |r,r,r|g|r,r,r|    |r,r,r,r|g|g|g|

    |g|r,r,r,r|g|g|    |g|g|r,r,r,r|g|

    |g|g|g|r,r,r,r|    |r,r,r,r,r|g|g|

    |g|r,r,r,r,r|g|    |g|g|r,r,r,r,r|

    |r,r,r,r,r,r|g|    |g|r,r,r,r,r,r|

    |r,r,r,r,r,r,r|

How many ways can a row measuring fifty units in length be filled?

NOTE: Although the example above does not lend itself to the possibility,
in general it is permitted to mix block sizes. For example,
on a row measuring eight units in length you could use red (3), grey (1), and red (4).
"""


def solution(length: int = 50) -> int:
    """
    Returns the number of ways a row of the given length can be filled

    >>> solution(7)
    17
    """

    ways_number = [1] * (length + 1)

    for row_length in range(3, length + 1):
        for block_length in range(3, row_length + 1):
            for block_start in range(row_length - block_length):
                ways_number[row_length] += ways_number[
                    row_length - block_start - block_length - 1
                ]

            ways_number[row_length] += 1

    return ways_number[length]


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_115/__init__.py
================================================


================================================
FILE: project_euler/problem_115/sol1.py
================================================
"""
Project Euler Problem 115: https://projecteuler.net/problem=115

NOTE: This is a more difficult version of Problem 114
(https://projecteuler.net/problem=114).

A row measuring n units in length has red blocks
with a minimum length of m units placed on it, such that any two red blocks
(which are allowed to be different lengths) are separated by at least one black square.

Let the fill-count function, F(m, n),
represent the number of ways that a row can be filled.

For example, F(3, 29) = 673135 and F(3, 30) = 1089155.

That is, for m = 3, it can be seen that n = 30 is the smallest value
for which the fill-count function first exceeds one million.

In the same way, for m = 10, it can be verified that
F(10, 56) = 880711 and F(10, 57) = 1148904, so n = 57 is the least value
for which the fill-count function first exceeds one million.

For m = 50, find the least value of n
for which the fill-count function first exceeds one million.
"""

from itertools import count


def solution(min_block_length: int = 50) -> int:
    """
    Returns for given minimum block length the least value of n
    for which the fill-count function first exceeds one million

    >>> solution(3)
    30

    >>> solution(10)
    57
    """

    fill_count_functions = [1] * min_block_length

    for n in count(min_block_length):
        fill_count_functions.append(1)

        for block_length in range(min_block_length, n + 1):
            for block_start in range(n - block_length):
                fill_count_functions[n] += fill_count_functions[
                    n - block_start - block_length - 1
                ]

            fill_count_functions[n] += 1

        if fill_count_functions[n] > 1_000_000:
            break

    return n


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_116/__init__.py
================================================


================================================
FILE: project_euler/problem_116/sol1.py
================================================
"""
Project Euler Problem 116: https://projecteuler.net/problem=116

A row of five grey square tiles is to have a number of its tiles
replaced with coloured oblong tiles chosen
from red (length two), green (length three), or blue (length four).

If red tiles are chosen there are exactly seven ways this can be done.

    |red,red|grey|grey|grey|    |grey|red,red|grey|grey|

    |grey|grey|red,red|grey|    |grey|grey|grey|red,red|

    |red,red|red,red|grey|      |red,red|grey|red,red|

    |grey|red,red|red,red|

If green tiles are chosen there are three ways.

    |green,green,green|grey|grey|    |grey|green,green,green|grey|

    |grey|grey|green,green,green|

And if blue tiles are chosen there are two ways.

    |blue,blue,blue,blue|grey|    |grey|blue,blue,blue,blue|

Assuming that colours cannot be mixed there are 7 + 3 + 2 = 12 ways
of replacing the grey tiles in a row measuring five units in length.

How many different ways can the grey tiles in a row measuring fifty units in length
be replaced if colours cannot be mixed and at least one coloured tile must be used?

NOTE: This is related to Problem 117 (https://projecteuler.net/problem=117).
"""


def solution(length: int = 50) -> int:
    """
    Returns the number of different ways can the grey tiles in a row
    of the given length be replaced if colours cannot be mixed
    and at least one coloured tile must be used

    >>> solution(5)
    12
    """

    different_colour_ways_number = [[0] * 3 for _ in range(length + 1)]

    for row_length in range(length + 1):
        for tile_length in range(2, 5):
            for tile_start in range(row_length - tile_length + 1):
                different_colour_ways_number[row_length][tile_length - 2] += (
                    different_colour_ways_number[row_length - tile_start - tile_length][
                        tile_length - 2
                    ]
                    + 1
                )

    return sum(different_colour_ways_number[length])


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_117/__init__.py
================================================


================================================
FILE: project_euler/problem_117/sol1.py
================================================
"""
Project Euler Problem 117: https://projecteuler.net/problem=117

Using a combination of grey square tiles and oblong tiles chosen from:
red tiles (measuring two units), green tiles (measuring three units),
and blue tiles (measuring four units),
it is possible to tile a row measuring five units in length
in exactly fifteen different ways.

    |grey|grey|grey|grey|grey|       |red,red|grey|grey|grey|

    |grey|red,red|grey|grey|         |grey|grey|red,red|grey|

    |grey|grey|grey|red,red|         |red,red|red,red|grey|

    |red,red|grey|red,red|           |grey|red,red|red,red|

    |green,green,green|grey|grey|    |grey|green,green,green|grey|

    |grey|grey|green,green,green|    |red,red|green,green,green|

    |green,green,green|red,red|      |blue,blue,blue,blue|grey|

    |grey|blue,blue,blue,blue|

How many ways can a row measuring fifty units in length be tiled?

NOTE: This is related to Problem 116 (https://projecteuler.net/problem=116).
"""


def solution(length: int = 50) -> int:
    """
    Returns the number of ways can a row of the given length be tiled

    >>> solution(5)
    15
    """

    ways_number = [1] * (length + 1)

    for row_length in range(length + 1):
        for tile_length in range(2, 5):
            for tile_start in range(row_length - tile_length + 1):
                ways_number[row_length] += ways_number[
                    row_length - tile_start - tile_length
                ]

    return ways_number[length]


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_119/__init__.py
================================================


================================================
FILE: project_euler/problem_119/sol1.py
================================================
"""
Problem 119: https://projecteuler.net/problem=119

Name: Digit power sum

The number 512 is interesting because it is equal to the sum of its digits
raised to some power: 5 + 1 + 2 = 8, and 8^3 = 512. Another example of a number
with this property is 614656 = 28^4. We shall define an to be the nth term of
this sequence and insist that a number must contain at least two digits to have a sum.
You are given that a2 = 512 and a10 = 614656. Find a30
"""

import math


def digit_sum(n: int) -> int:
    """
    Returns the sum of the digits of the number.
    >>> digit_sum(123)
    6
    >>> digit_sum(456)
    15
    >>> digit_sum(78910)
    25
    """
    return sum(int(digit) for digit in str(n))


def solution(n: int = 30) -> int:
    """
    Returns the value of 30th digit power sum.
    >>> solution(2)
    512
    >>> solution(5)
    5832
    >>> solution(10)
    614656
    """
    digit_to_powers = []
    for digit in range(2, 100):
        for power in range(2, 100):
            number = int(math.pow(digit, power))
            if digit == digit_sum(number):
                digit_to_powers.append(number)

    digit_to_powers.sort()
    return digit_to_powers[n - 1]


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_120/__init__.py
================================================


================================================
FILE: project_euler/problem_120/sol1.py
================================================
"""
Problem 120 Square remainders: https://projecteuler.net/problem=120

Description:

Let r be the remainder when (a-1)^n + (a+1)^n is divided by a^2.
For example, if a = 7 and n = 3, then r = 42: 6^3 + 8^3 = 728 ≡ 42 mod 49.
And as n varies, so too will r, but for a = 7 it turns out that r_max = 42.
For 3 ≤ a ≤ 1000, find ∑ r_max.

Solution:

On expanding the terms, we get 2 if n is even and 2an if n is odd.
For maximizing the value, 2an < a*a => n <= (a - 1)/2 (integer division)
"""


def solution(n: int = 1000) -> int:
    """
    Returns ∑ r_max for 3 <= a <= n as explained above
    >>> solution(10)
    300
    >>> solution(100)
    330750
    >>> solution(1000)
    333082500
    """
    return sum(2 * a * ((a - 1) // 2) for a in range(3, n + 1))


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_121/__init__.py
================================================


================================================
FILE: project_euler/problem_121/sol1.py
================================================
"""
A bag contains one red disc and one blue disc. In a game of chance a player takes a
disc at random and its colour is noted. After each turn the disc is returned to the
bag, an extra red disc is added, and another disc is taken at random.

The player pays £1 to play and wins if they have taken more blue discs than red
discs at the end of the game.

If the game is played for four turns, the probability of a player winning is exactly
11/120, and so the maximum prize fund the banker should allocate for winning in this
game would be £10 before they would expect to incur a loss. Note that any payout will
be a whole number of pounds and also includes the original £1 paid to play the game,
so in the example given the player actually wins £9.

Find the maximum prize fund that should be allocated to a single game in which
fifteen turns are played.


Solution:
    For each 15-disc sequence of red and blue for which there are more red than blue,
    we calculate the probability of that sequence and add it to the total probability
    of the player winning. The inverse of this probability gives an upper bound for
    the prize if the banker wants to avoid an expected loss.
"""

from itertools import product


def solution(num_turns: int = 15) -> int:
    """
    Find the maximum prize fund that should be allocated to a single game in which
    fifteen turns are played.
    >>> solution(4)
    10
    >>> solution(10)
    225
    """
    total_prob: float = 0.0
    prob: float
    num_blue: int
    num_red: int
    ind: int
    col: int
    series: tuple[int, ...]

    for series in product(range(2), repeat=num_turns):
        num_blue = series.count(1)
        num_red = num_turns - num_blue
        if num_red >= num_blue:
            continue
        prob = 1.0
        for ind, col in enumerate(series, 2):
            if col == 0:
                prob *= (ind - 1) / ind
            else:
                prob *= 1 / ind

        total_prob += prob

    return int(1 / total_prob)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_122/__init__.py
================================================


================================================
FILE: project_euler/problem_122/sol1.py
================================================
"""
Project Euler Problem 122: https://projecteuler.net/problem=122

Efficient Exponentiation

The most naive way of computing n^15 requires fourteen multiplications:

                                               n x n x ... x n = n^15.

But using a "binary" method you can compute it in six multiplications:

                                                         n x n = n^2
                                                     n^2 x n^2 = n^4
                                                     n^4 x n^4 = n^8
                                                     n^8 x n^4 = n^12
                                                    n^12 x n^2 = n^14
                                                      n^14 x n = n^15

However it is yet possible to compute it in only five multiplications:

                                                                n x n = n^2
                                                              n^2 x n = n^3
                                                            n^3 x n^3 = n^6
                                                            n^6 x n^6 = n^12
                                                           n^12 x n^3 = n^15

We shall define m(k) to be the minimum number of multiplications to compute n^k;
for example m(15) = 5.

Find sum_{k = 1}^200 m(k).

It uses the fact that for rather small n, applicable for this problem, the solution
for each number can be formed by increasing the largest element.

References:
- https://en.wikipedia.org/wiki/Addition_chain
"""


def solve(nums: list[int], goal: int, depth: int) -> bool:
    """
    Checks if nums can have a sum equal to goal, given that length of nums does
    not exceed depth.

    >>> solve([1], 2, 2)
    True
    >>> solve([1], 2, 0)
    False
    """
    if len(nums) > depth:
        return False
    for el in nums:
        if el + nums[-1] == goal:
            return True
        nums.append(el + nums[-1])
        if solve(nums=nums, goal=goal, depth=depth):
            return True
        del nums[-1]
    return False


def solution(n: int = 200) -> int:
    """
    Calculates sum of smallest number of multiplactions for each number up to
    and including n.

    >>> solution(1)
    0
    >>> solution(2)
    1
    >>> solution(14)
    45
    >>> solution(15)
    50
    """
    total = 0
    for i in range(2, n + 1):
        max_length = 0
        while True:
            nums = [1]
            max_length += 1
            if solve(nums=nums, goal=i, depth=max_length):
                break
        total += max_length
    return total


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_123/__init__.py
================================================


================================================
FILE: project_euler/problem_123/sol1.py
================================================
"""
Problem 123: https://projecteuler.net/problem=123

Name: Prime square remainders

Let pn be the nth prime: 2, 3, 5, 7, 11, ..., and
let r be the remainder when (pn-1)^n + (pn+1)^n is divided by pn^2.

For example, when n = 3, p3 = 5, and 43 + 63 = 280 ≡ 5 mod 25.
The least value of n for which the remainder first exceeds 10^9 is 7037.

Find the least value of n for which the remainder first exceeds 10^10.


Solution:

n=1: (p-1) + (p+1) = 2p
n=2: (p-1)^2 + (p+1)^2
     = p^2 + 1 - 2p + p^2 + 1 + 2p  (Using (p+b)^2 = (p^2 + b^2 + 2pb),
                                           (p-b)^2 = (p^2 + b^2 - 2pb) and b = 1)
     = 2p^2 + 2
n=3: (p-1)^3 + (p+1)^3  (Similarly using (p+b)^3 & (p-b)^3 formula and so on)
     = 2p^3 + 6p
n=4: 2p^4 + 12p^2 + 2
n=5: 2p^5 + 20p^3 + 10p

As you could see, when the expression is divided by p^2.
Except for the last term, the rest will result in the remainder 0.

n=1: 2p
n=2: 2
n=3: 6p
n=4: 2
n=5: 10p

So it could be simplified as,
    r = 2pn when n is odd
    r = 2   when n is even.
"""

from __future__ import annotations

from collections.abc import Generator


def sieve() -> Generator[int]:
    """
    Returns a prime number generator using sieve method.
    >>> type(sieve())
    <class 'generator'>
    >>> primes = sieve()
    >>> next(primes)
    2
    >>> next(primes)
    3
    >>> next(primes)
    5
    >>> next(primes)
    7
    >>> next(primes)
    11
    >>> next(primes)
    13
    """
    factor_map: dict[int, int] = {}
    prime = 2
    while True:
        factor = factor_map.pop(prime, None)
        if factor:
            x = factor + prime
            while x in factor_map:
                x += factor
            factor_map[x] = factor
        else:
            factor_map[prime * prime] = prime
            yield prime
        prime += 1


def solution(limit: float = 1e10) -> int:
    """
    Returns the least value of n for which the remainder first exceeds 10^10.
    >>> solution(1e8)
    2371
    >>> solution(1e9)
    7037
    """
    primes = sieve()

    n = 1
    while True:
        prime = next(primes)
        if (2 * prime * n) > limit:
            return n
        # Ignore the next prime as the reminder will be 2.
        next(primes)
        n += 2


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_125/__init__.py
================================================


================================================
FILE: project_euler/problem_125/sol1.py
================================================
"""
Problem 125: https://projecteuler.net/problem=125

The palindromic number 595 is interesting because it can be written as the sum
of consecutive squares: 6^2 + 7^2 + 8^2 + 9^2 + 10^2 + 11^2 + 12^2.

There are exactly eleven palindromes below one-thousand that can be written as
consecutive square sums, and the sum of these palindromes is 4164. Note that
1 = 0^2 + 1^2 has not been included as this problem is concerned with the
squares of positive integers.

Find the sum of all the numbers less than 10^8 that are both palindromic and can
be written as the sum of consecutive squares.
"""

LIMIT = 10**8


def is_palindrome(n: int) -> bool:
    """
    Check if an integer is palindromic.
    >>> is_palindrome(12521)
    True
    >>> is_palindrome(12522)
    False
    >>> is_palindrome(12210)
    False
    """
    if n % 10 == 0:
        return False
    s = str(n)
    return s == s[::-1]


def solution() -> int:
    """
    Returns the sum of all numbers less than 1e8 that are both palindromic and
    can be written as the sum of consecutive squares.
    """
    answer = set()
    first_square = 1
    sum_squares = 5
    while sum_squares < LIMIT:
        last_square = first_square + 1
        while sum_squares < LIMIT:
            if is_palindrome(sum_squares):
                answer.add(sum_squares)
            last_square += 1
            sum_squares += last_square**2
        first_square += 1
        sum_squares = first_square**2 + (first_square + 1) ** 2

    return sum(answer)


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_129/__init__.py
================================================


================================================
FILE: project_euler/problem_129/sol1.py
================================================
"""
Project Euler Problem 129: https://projecteuler.net/problem=129

A number consisting entirely of ones is called a repunit. We shall define R(k) to be
a repunit of length k; for example, R(6) = 111111.

Given that n is a positive integer and GCD(n, 10) = 1, it can be shown that there
always exists a value, k, for which R(k) is divisible by n, and let A(n) be the least
such value of k; for example, A(7) = 6 and A(41) = 5.

The least value of n for which A(n) first exceeds ten is 17.

Find the least value of n for which A(n) first exceeds one-million.
"""


def least_divisible_repunit(divisor: int) -> int:
    """
    Return the least value k such that the Repunit of length k is divisible by divisor.
    >>> least_divisible_repunit(7)
    6
    >>> least_divisible_repunit(41)
    5
    >>> least_divisible_repunit(1234567)
    34020
    """
    if divisor % 5 == 0 or divisor % 2 == 0:
        return 0
    repunit = 1
    repunit_index = 1
    while repunit:
        repunit = (10 * repunit + 1) % divisor
        repunit_index += 1
    return repunit_index


def solution(limit: int = 1000000) -> int:
    """
    Return the least value of n for which least_divisible_repunit(n)
    first exceeds limit.
    >>> solution(10)
    17
    >>> solution(100)
    109
    >>> solution(1000)
    1017
    """
    divisor = limit - 1
    if divisor % 2 == 0:
        divisor += 1
    while least_divisible_repunit(divisor) <= limit:
        divisor += 2
    return divisor


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_131/__init__.py
================================================


================================================
FILE: project_euler/problem_131/sol1.py
================================================
"""
Project Euler Problem 131: https://projecteuler.net/problem=131

There are some prime values, p, for which there exists a positive integer, n,
such that the expression n^3 + n^2p is a perfect cube.

For example, when p = 19, 8^3 + 8^2 x 19 = 12^3.

What is perhaps most surprising is that for each prime with this property
the value of n is unique, and there are only four such primes below one-hundred.

How many primes below one million have this remarkable property?
"""

from math import isqrt


def is_prime(number: int) -> bool:
    """
    Determines whether number is prime

    >>> is_prime(3)
    True

    >>> is_prime(4)
    False
    """

    return all(number % divisor != 0 for divisor in range(2, isqrt(number) + 1))


def solution(max_prime: int = 10**6) -> int:
    """
    Returns number of primes below max_prime with the property

    >>> solution(100)
    4
    """

    primes_count = 0
    cube_index = 1
    prime_candidate = 7
    while prime_candidate < max_prime:
        primes_count += is_prime(prime_candidate)

        cube_index += 1
        prime_candidate += 6 * cube_index

    return primes_count


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_135/__init__.py
================================================


================================================
FILE: project_euler/problem_135/sol1.py
================================================
"""
Project Euler Problem 135: https://projecteuler.net/problem=135

Given the positive integers, x, y, and z, are consecutive terms of an arithmetic
progression, the least value of the positive integer, n, for which the equation,
x2 - y2 - z2 = n, has exactly two solutions is n = 27:

342 - 272 - 202 = 122 - 92 - 62 = 27

It turns out that n = 1155 is the least value which has exactly ten solutions.

How many values of n less than one million have exactly ten distinct solutions?


Taking x, y, z of the form a + d, a, a - d respectively, the given equation reduces to
a * (4d - a) = n.
Calculating no of solutions for every n till 1 million by fixing a, and n must be a
multiple of a. Total no of steps = n * (1/1 + 1/2 + 1/3 + 1/4 + ... + 1/n), so roughly
O(nlogn) time complexity.
"""


def solution(limit: int = 1000000) -> int:
    """
    returns the values of n less than or equal to the limit
    have exactly ten distinct solutions.
    >>> solution(100)
    0
    >>> solution(10000)
    45
    >>> solution(50050)
    292
    """
    limit = limit + 1
    frequency = [0] * limit
    for first_term in range(1, limit):
        for n in range(first_term, limit, first_term):
            common_difference = first_term + n / first_term
            if common_difference % 4:  # d must be divisible by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x, y, z are positive integers
                    frequency[n] += 1  # so z > 0, a > d and 4d < a

    count = sum(1 for x in frequency[1:limit] if x == 10)

    return count


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_136/__init__.py
================================================


================================================
FILE: project_euler/problem_136/sol1.py
================================================
"""
Project Euler Problem 136: https://projecteuler.net/problem=136

Singleton Difference

The positive integers, x, y, and z, are consecutive terms of an arithmetic progression.
Given that n is a positive integer, the equation, x^2 - y^2 - z^2 = n,
has exactly one solution when n = 20:
                              13^2 - 10^2 - 7^2 = 20.

In fact there are twenty-five values of n below one hundred for which
the equation has a unique solution.

How many values of n less than fifty million have exactly one solution?

By change of variables

x = y + delta
z = y - delta

The expression can be rewritten:

x^2 - y^2 - z^2 = y * (4 * delta - y) = n

The algorithm loops over delta and y, which is restricted in upper and lower limits,
to count how many solutions each n has.
In the end it is counted how many n's have one solution.
"""


def solution(n_limit: int = 50 * 10**6) -> int:
    """
    Define n count list and loop over delta, y to get the counts, then check
    which n has count == 1.

    >>> solution(3)
    0
    >>> solution(10)
    3
    >>> solution(100)
    25
    >>> solution(110)
    27
    """
    n_sol = [0] * n_limit

    for delta in range(1, (n_limit + 1) // 4 + 1):
        for y in range(4 * delta - 1, delta, -1):
            n = y * (4 * delta - y)
            if n >= n_limit:
                break
            n_sol[n] += 1

    ans = 0
    for i in range(n_limit):
        if n_sol[i] == 1:
            ans += 1

    return ans


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_144/__init__.py
================================================


================================================
FILE: project_euler/problem_144/sol1.py
================================================
"""
In laser physics, a "white cell" is a mirror system that acts as a delay line for the
laser beam. The beam enters the cell, bounces around on the mirrors, and eventually
works its way back out.

The specific white cell we will be considering is an ellipse with the equation
4x^2 + y^2 = 100

The section corresponding to -0.01 ≤ x ≤ +0.01 at the top is missing, allowing the
light to enter and exit through the hole.

The light beam in this problem starts at the point (0.0,10.1) just outside the white
cell, and the beam first impacts the mirror at (1.4,-9.6).

Each time the laser beam hits the surface of the ellipse, it follows the usual law of
reflection "angle of incidence equals angle of reflection." That is, both the incident
and reflected beams make the same angle with the normal line at the point of incidence.

In the figure on the left, the red line shows the first two points of contact between
the laser beam and the wall of the white cell; the blue line shows the line tangent to
the ellipse at the point of incidence of the first bounce.

The slope m of the tangent line at any point (x,y) of the given ellipse is: m = -4x/y

The normal line is perpendicular to this tangent line at the point of incidence.

The animation on the right shows the first 10 reflections of the beam.

How many times does the beam hit the internal surface of the white cell before exiting?
"""

from math import isclose, sqrt


def next_point(
    point_x: float, point_y: float, incoming_gradient: float
) -> tuple[float, float, float]:
    """
    Given that a laser beam hits the interior of the white cell at point
    (point_x, point_y) with gradient incoming_gradient, return a tuple (x,y,m1)
    where the next point of contact with the interior is (x,y) with gradient m1.
    >>> next_point(5.0, 0.0, 0.0)
    (-5.0, 0.0, 0.0)
    >>> next_point(5.0, 0.0, -2.0)
    (0.0, -10.0, 2.0)
    """
    # normal_gradient = gradient of line through which the beam is reflected
    # outgoing_gradient = gradient of reflected line
    normal_gradient = point_y / 4 / point_x
    s2 = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
    c2 = (1 - normal_gradient * normal_gradient) / (
        1 + normal_gradient * normal_gradient
    )
    outgoing_gradient = (s2 - c2 * incoming_gradient) / (c2 + s2 * incoming_gradient)

    # to find the next point, solve the simultaeneous equations:
    # y^2 + 4x^2 = 100
    # y - b = m * (x - a)
    # ==> A x^2 + B x + C = 0
    quadratic_term = outgoing_gradient**2 + 4
    linear_term = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
    constant_term = (point_y - outgoing_gradient * point_x) ** 2 - 100

    x_minus = (
        -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term)
    ) / (2 * quadratic_term)
    x_plus = (
        -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term)
    ) / (2 * quadratic_term)

    # two solutions, one of which is our input point
    next_x = x_minus if isclose(x_plus, point_x) else x_plus
    next_y = point_y + outgoing_gradient * (next_x - point_x)

    return next_x, next_y, outgoing_gradient


def solution(first_x_coord: float = 1.4, first_y_coord: float = -9.6) -> int:
    """
    Return the number of times that the beam hits the interior wall of the
    cell before exiting.
    >>> solution(0.00001,-10)
    1
    >>> solution(5, 0)
    287
    """
    num_reflections: int = 0
    point_x: float = first_x_coord
    point_y: float = first_y_coord
    gradient: float = (10.1 - point_y) / (0.0 - point_x)

    while not (-0.01 <= point_x <= 0.01 and point_y > 0):
        point_x, point_y, gradient = next_point(point_x, point_y, gradient)
        num_reflections += 1

    return num_reflections


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_145/__init__.py
================================================


================================================
FILE: project_euler/problem_145/sol1.py
================================================
"""
Project Euler problem 145: https://projecteuler.net/problem=145
Author: Vineet Rao, Maxim Smolskiy
Problem statement:

Some positive integers n have the property that the sum [ n + reverse(n) ]
consists entirely of odd (decimal) digits.
For instance, 36 + 63 = 99 and 409 + 904 = 1313.
We will call such numbers reversible; so 36, 63, 409, and 904 are reversible.
Leading zeroes are not allowed in either n or reverse(n).

There are 120 reversible numbers below one-thousand.

How many reversible numbers are there below one-billion (10^9)?
"""

EVEN_DIGITS = [0, 2, 4, 6, 8]
ODD_DIGITS = [1, 3, 5, 7, 9]


def slow_reversible_numbers(
    remaining_length: int, remainder: int, digits: list[int], length: int
) -> int:
    """
    Count the number of reversible numbers of given length.
    Iterate over possible digits considering parity of current sum remainder.
    >>> slow_reversible_numbers(1, 0, [0], 1)
    0
    >>> slow_reversible_numbers(2, 0, [0] * 2, 2)
    20
    >>> slow_reversible_numbers(3, 0, [0] * 3, 3)
    100
    """
    if remaining_length == 0:
        if digits[0] == 0 or digits[-1] == 0:
            return 0

        for i in range(length // 2 - 1, -1, -1):
            remainder += digits[i] + digits[length - i - 1]

            if remainder % 2 == 0:
                return 0

            remainder //= 10

        return 1

    if remaining_length == 1:
        if remainder % 2 == 0:
            return 0

        result = 0
        for digit in range(10):
            digits[length // 2] = digit
            result += slow_reversible_numbers(
                0, (remainder + 2 * digit) // 10, digits, length
            )
        return result

    result = 0
    for digit1 in range(10):
        digits[(length + remaining_length) // 2 - 1] = digit1

        if (remainder + digit1) % 2 == 0:
            other_parity_digits = ODD_DIGITS
        else:
            other_parity_digits = EVEN_DIGITS

        for digit2 in other_parity_digits:
            digits[(length - remaining_length) // 2] = digit2
            result += slow_reversible_numbers(
                remaining_length - 2,
                (remainder + digit1 + digit2) // 10,
                digits,
                length,
            )
    return result


def slow_solution(max_power: int = 9) -> int:
    """
    To evaluate the solution, use solution()
    >>> slow_solution(3)
    120
    >>> slow_solution(6)
    18720
    >>> slow_solution(7)
    68720
    """
    result = 0
    for length in range(1, max_power + 1):
        result += slow_reversible_numbers(length, 0, [0] * length, length)
    return result


def reversible_numbers(
    remaining_length: int, remainder: int, digits: list[int], length: int
) -> int:
    """
    Count the number of reversible numbers of given length.
    Iterate over possible digits considering parity of current sum remainder.
    >>> reversible_numbers(1, 0, [0], 1)
    0
    >>> reversible_numbers(2, 0, [0] * 2, 2)
    20
    >>> reversible_numbers(3, 0, [0] * 3, 3)
    100
    """
    # There exist no reversible 1, 5, 9, 13 (ie. 4k+1) digit numbers
    if (length - 1) % 4 == 0:
        return 0

    return slow_reversible_numbers(remaining_length, remainder, digits, length)


def solution(max_power: int = 9) -> int:
    """
    To evaluate the solution, use solution()
    >>> solution(3)
    120
    >>> solution(6)
    18720
    >>> solution(7)
    68720
    """
    result = 0
    for length in range(1, max_power + 1):
        result += reversible_numbers(length, 0, [0] * length, length)
    return result


def benchmark() -> None:
    """
    Benchmarks
    """
    # Running performance benchmarks...
    # slow_solution : 292.9300301000003
    # solution      : 54.90970860000016

    from timeit import timeit

    print("Running performance benchmarks...")

    print(f"slow_solution : {timeit('slow_solution()', globals=globals(), number=10)}")
    print(f"solution      : {timeit('solution()', globals=globals(), number=10)}")


if __name__ == "__main__":
    print(f"Solution : {solution()}")
    benchmark()

    # for i in range(1, 15):
    #     print(f"{i}. {reversible_numbers(i, 0, [0]*i, i)}")


================================================
FILE: project_euler/problem_164/__init__.py
================================================


================================================
FILE: project_euler/problem_164/sol1.py
================================================
"""
Project Euler Problem 164: https://projecteuler.net/problem=164

Three Consecutive Digital Sum Limit

How many 20 digit numbers n (without any leading zero) exist such that no three
consecutive digits of n have a sum greater than 9?

Brute-force recursive solution with caching of intermediate results.
"""


def solve(
    digit: int, prev1: int, prev2: int, sum_max: int, first: bool, cache: dict[str, int]
) -> int:
    """
    Solve for remaining 'digit' digits, with previous 'prev1' digit, and
    previous-previous 'prev2' digit, total sum of 'sum_max'.
    Pass around 'cache' to store/reuse intermediate results.

    >>> solve(digit=1, prev1=0, prev2=0, sum_max=9, first=True, cache={})
    9
    >>> solve(digit=1, prev1=0, prev2=0, sum_max=9, first=False, cache={})
    10
    """
    if digit == 0:
        return 1

    cache_str = f"{digit},{prev1},{prev2}"
    if cache_str in cache:
        return cache[cache_str]

    comb = 0
    for curr in range(sum_max - prev1 - prev2 + 1):
        if first and curr == 0:
            continue

        comb += solve(
            digit=digit - 1,
            prev1=curr,
            prev2=prev1,
            sum_max=sum_max,
            first=False,
            cache=cache,
        )

    cache[cache_str] = comb
    return comb


def solution(n_digits: int = 20) -> int:
    """
    Solves the problem for n_digits number of digits.

    >>> solution(2)
    45
    >>> solution(10)
    21838806
    """
    cache: dict[str, int] = {}
    return solve(digit=n_digits, prev1=0, prev2=0, sum_max=9, first=True, cache=cache)


if __name__ == "__main__":
    print(f"{solution(10) = }")


================================================
FILE: project_euler/problem_173/__init__.py
================================================


================================================
FILE: project_euler/problem_173/sol1.py
================================================
"""
Project Euler Problem 173: https://projecteuler.net/problem=173

We shall define a square lamina to be a square outline with a square "hole" so that
the shape possesses vertical and horizontal symmetry. For example, using exactly
thirty-two square tiles we can form two different square laminae:

With one-hundred tiles, and not necessarily using all of the tiles at one time, it is
possible to form forty-one different square laminae.

Using up to one million tiles how many different square laminae can be formed?
"""

from math import ceil, sqrt


def solution(limit: int = 1000000) -> int:
    """
    Return the number of different square laminae that can be formed using up to
    one million tiles.
    >>> solution(100)
    41
    """
    answer = 0

    for outer_width in range(3, (limit // 4) + 2):
        if outer_width**2 > limit:
            hole_width_lower_bound = max(ceil(sqrt(outer_width**2 - limit)), 1)
        else:
            hole_width_lower_bound = 1
        if (outer_width - hole_width_lower_bound) % 2:
            hole_width_lower_bound += 1

        answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

    return answer


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_174/__init__.py
================================================


================================================
FILE: project_euler/problem_174/sol1.py
================================================
"""
Project Euler Problem 174: https://projecteuler.net/problem=174

We shall define a square lamina to be a square outline with a square "hole" so that
the shape possesses vertical and horizontal symmetry.

Given eight tiles it is possible to form a lamina in only one way: 3x3 square with a
1x1 hole in the middle. However, using thirty-two tiles it is possible to form two
distinct laminae.

If t represents the number of tiles used, we shall say that t = 8 is type L(1) and
t = 32 is type L(2).

Let N(n) be the number of t ≤ 1000000 such that t is type L(n); for example,
N(15) = 832.

What is sum N(n) for 1 ≤ n ≤ 10?
"""

from collections import defaultdict
from math import ceil, sqrt


def solution(t_limit: int = 1000000, n_limit: int = 10) -> int:
    """
    Return the sum of N(n) for 1 <= n <= n_limit.

    >>> solution(1000,5)
    222
    >>> solution(1000,10)
    249
    >>> solution(10000,10)
    2383
    """
    count: defaultdict = defaultdict(int)

    for outer_width in range(3, (t_limit // 4) + 2):
        if outer_width * outer_width > t_limit:
            hole_width_lower_bound = max(
                ceil(sqrt(outer_width * outer_width - t_limit)), 1
            )
        else:
            hole_width_lower_bound = 1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(hole_width_lower_bound, outer_width - 1, 2):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= n_limit)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_180/__init__.py
================================================


================================================
FILE: project_euler/problem_180/sol1.py
================================================
"""
Project Euler Problem 234: https://projecteuler.net/problem=234

For any integer n, consider the three functions

f1,n(x,y,z) = x^(n+1) + y^(n+1) - z^(n+1)
f2,n(x,y,z) = (xy + yz + zx)*(x^(n-1) + y^(n-1) - z^(n-1))
f3,n(x,y,z) = xyz*(xn-2 + yn-2 - zn-2)

and their combination

fn(x,y,z) = f1,n(x,y,z) + f2,n(x,y,z) - f3,n(x,y,z)

We call (x,y,z) a golden triple of order k if x, y, and z are all rational numbers
of the form a / b with 0 < a < b ≤ k and there is (at least) one integer n,
so that fn(x,y,z) = 0.

Let s(x,y,z) = x + y + z.
Let t = u / v be the sum of all distinct s(x,y,z) for all golden triples
(x,y,z) of order 35.
All the s(x,y,z) and t must be in reduced form.

Find u + v.


Solution:

By expanding the brackets it is easy to show that
fn(x, y, z) = (x + y + z) * (x^n + y^n - z^n).

Since x,y,z are positive, the requirement fn(x, y, z) = 0 is fulfilled if and
only if x^n + y^n = z^n.

By Fermat's Last Theorem, this means that the absolute value of n can not
exceed 2, i.e. n is in {-2, -1, 0, 1, 2}. We can eliminate n = 0 since then the
equation would reduce to 1 + 1 = 1, for which there are no solutions.

So all we have to do is iterate through the possible numerators and denominators
of x and y, calculate the corresponding z, and check if the corresponding numerator and
denominator are integer and satisfy 0 < z_num < z_den <= 0. We use a set "uniquq_s"
to make sure there are no duplicates, and the fractions.Fraction class to make sure
we get the right numerator and denominator.

Reference:
https://en.wikipedia.org/wiki/Fermat%27s_Last_Theorem
"""

from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt


def is_sq(number: int) -> bool:
    """
    Check if number is a perfect square.

    >>> is_sq(1)
    True
    >>> is_sq(1000001)
    False
    >>> is_sq(1000000)
    True
    """
    sq: int = int(number**0.5)
    return number == sq * sq


def add_three(
    x_num: int, x_den: int, y_num: int, y_den: int, z_num: int, z_den: int
) -> tuple[int, int]:
    """
    Given the numerators and denominators of three fractions, return the
    numerator and denominator of their sum in lowest form.
    >>> add_three(1, 3, 1, 3, 1, 3)
    (1, 1)
    >>> add_three(2, 5, 4, 11, 12, 3)
    (262, 55)
    """
    top: int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
    bottom: int = x_den * y_den * z_den
    hcf: int = gcd(top, bottom)
    top //= hcf
    bottom //= hcf
    return top, bottom


def solution(order: int = 35) -> int:
    """
    Find the sum of the numerator and denominator of the sum of all s(x,y,z) for
    golden triples (x,y,z) of the given order.

    >>> solution(5)
    296
    >>> solution(10)
    12519
    >>> solution(20)
    19408891927
    """
    unique_s: set = set()
    hcf: int
    total: Fraction = Fraction(0)
    fraction_sum: tuple[int, int]

    for x_num in range(1, order + 1):
        for x_den in range(x_num + 1, order + 1):
            for y_num in range(1, order + 1):
                for y_den in range(y_num + 1, order + 1):
                    # n=1
                    z_num = x_num * y_den + x_den * y_num
                    z_den = x_den * y_den
                    hcf = gcd(z_num, z_den)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        fraction_sum = add_three(
                            x_num, x_den, y_num, y_den, z_num, z_den
                        )
                        unique_s.add(fraction_sum)

                    # n=2
                    z_num = (
                        x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                    )
                    z_den = x_den * x_den * y_den * y_den
                    if is_sq(z_num) and is_sq(z_den):
                        z_num = int(sqrt(z_num))
                        z_den = int(sqrt(z_den))
                        hcf = gcd(z_num, z_den)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            fraction_sum = add_three(
                                x_num, x_den, y_num, y_den, z_num, z_den
                            )
                            unique_s.add(fraction_sum)

                    # n=-1
                    z_num = x_num * y_num
                    z_den = x_den * y_num + x_num * y_den
                    hcf = gcd(z_num, z_den)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        fraction_sum = add_three(
                            x_num, x_den, y_num, y_den, z_num, z_den
                        )
                        unique_s.add(fraction_sum)

                    # n=2
                    z_num = x_num * x_num * y_num * y_num
                    z_den = (
                        x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                    )
                    if is_sq(z_num) and is_sq(z_den):
                        z_num = int(sqrt(z_num))
                        z_den = int(sqrt(z_den))
                        hcf = gcd(z_num, z_den)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            fraction_sum = add_three(
                                x_num, x_den, y_num, y_den, z_num, z_den
                            )
                            unique_s.add(fraction_sum)

    for num, den in unique_s:
        total += Fraction(num, den)

    return total.denominator + total.numerator


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_187/__init__.py
================================================


================================================
FILE: project_euler/problem_187/sol1.py
================================================
"""
Project Euler Problem 187: https://projecteuler.net/problem=187

A composite is a number containing at least two prime factors.
For example, 15 = 3 x 5; 9 = 3 x 3; 12 = 2 x 2 x 3.

There are ten composites below thirty containing precisely two,
not necessarily distinct, prime factors: 4, 6, 9, 10, 14, 15, 21, 22, 25, 26.

How many composite integers, n < 10^8, have precisely two,
not necessarily distinct, prime factors?
"""

from math import isqrt


def slow_calculate_prime_numbers(max_number: int) -> list[int]:
    """
    Returns prime numbers below max_number.
    See: https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes

    >>> slow_calculate_prime_numbers(10)
    [2, 3, 5, 7]

    >>> slow_calculate_prime_numbers(2)
    []
    """

    # List containing a bool value for every number below max_number/2
    is_prime = [True] * max_number

    for i in range(2, isqrt(max_number - 1) + 1):
        if is_prime[i]:
            # Mark all multiple of i as not prime
            for j in range(i**2, max_number, i):
                is_prime[j] = False

    return [i for i in range(2, max_number) if is_prime[i]]


def calculate_prime_numbers(max_number: int) -> list[int]:
    """
    Returns prime numbers below max_number.
    See: https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes

    >>> calculate_prime_numbers(10)
    [2, 3, 5, 7]

    >>> calculate_prime_numbers(2)
    []
    """

    if max_number <= 2:
        return []

    # List containing a bool value for every odd number below max_number/2
    is_prime = [True] * (max_number // 2)

    for i in range(3, isqrt(max_number - 1) + 1, 2):
        if is_prime[i // 2]:
            # Mark all multiple of i as not prime using list slicing
            is_prime[i**2 // 2 :: i] = [False] * (
                # Same as: (max_number - (i**2)) // (2 * i) + 1
                # but faster than len(is_prime[i**2 // 2 :: i])
                len(range(i**2 // 2, max_number // 2, i))
            )

    return [2] + [2 * i + 1 for i in range(1, max_number // 2) if is_prime[i]]


def slow_solution(max_number: int = 10**8) -> int:
    """
    Returns the number of composite integers below max_number have precisely two,
    not necessarily distinct, prime factors.

    >>> slow_solution(30)
    10
    """

    prime_numbers = slow_calculate_prime_numbers(max_number // 2)

    semiprimes_count = 0
    left = 0
    right = len(prime_numbers) - 1
    while left <= right:
        while prime_numbers[left] * prime_numbers[right] >= max_number:
            right -= 1
        semiprimes_count += right - left + 1
        left += 1

    return semiprimes_count


def while_solution(max_number: int = 10**8) -> int:
    """
    Returns the number of composite integers below max_number have precisely two,
    not necessarily distinct, prime factors.

    >>> while_solution(30)
    10
    """

    prime_numbers = calculate_prime_numbers(max_number // 2)

    semiprimes_count = 0
    left = 0
    right = len(prime_numbers) - 1
    while left <= right:
        while prime_numbers[left] * prime_numbers[right] >= max_number:
            right -= 1
        semiprimes_count += right - left + 1
        left += 1

    return semiprimes_count


def solution(max_number: int = 10**8) -> int:
    """
    Returns the number of composite integers below max_number have precisely two,
    not necessarily distinct, prime factors.

    >>> solution(30)
    10
    """

    prime_numbers = calculate_prime_numbers(max_number // 2)

    semiprimes_count = 0
    right = len(prime_numbers) - 1
    for left in range(len(prime_numbers)):
        if left > right:
            break
        for r in range(right, left - 2, -1):
            if prime_numbers[left] * prime_numbers[r] < max_number:
                break
        right = r
        semiprimes_count += right - left + 1

    return semiprimes_count


def benchmark() -> None:
    """
    Benchmarks
    """
    # Running performance benchmarks...
    # slow_solution : 108.50874730000032
    # while_sol     : 28.09581200000048
    # solution      : 25.063097400000515

    from timeit import timeit

    print("Running performance benchmarks...")

    print(f"slow_solution : {timeit('slow_solution()', globals=globals(), number=10)}")
    print(f"while_sol     : {timeit('while_solution()', globals=globals(), number=10)}")
    print(f"solution      : {timeit('solution()', globals=globals(), number=10)}")


if __name__ == "__main__":
    print(f"Solution: {solution()}")
    benchmark()


================================================
FILE: project_euler/problem_188/__init__.py
================================================


================================================
FILE: project_euler/problem_188/sol1.py
================================================
"""
Project Euler Problem 188: https://projecteuler.net/problem=188

The hyperexponentiation of a number

The hyperexponentiation or tetration of a number a by a positive integer b,
denoted by a↑↑b or b^a, is recursively defined by:

a↑↑1 = a,
a↑↑(k+1) = a(a↑↑k).

Thus we have e.g. 3↑↑2 = 3^3 = 27, hence 3↑↑3 = 3^27 = 7625597484987 and
3↑↑4 is roughly 103.6383346400240996*10^12.

Find the last 8 digits of 1777↑↑1855.

References:
    - https://en.wikipedia.org/wiki/Tetration
"""


# small helper function for modular exponentiation (fast exponentiation algorithm)
def _modexpt(base: int, exponent: int, modulo_value: int) -> int:
    """
    Returns the modular exponentiation, that is the value
    of `base ** exponent % modulo_value`, without calculating
    the actual number.
    >>> _modexpt(2, 4, 10)
    6
    >>> _modexpt(2, 1024, 100)
    16
    >>> _modexpt(13, 65535, 7)
    6
    """

    if exponent == 1:
        return base
    if exponent % 2 == 0:
        x = _modexpt(base, exponent // 2, modulo_value) % modulo_value
        return (x * x) % modulo_value
    else:
        return (base * _modexpt(base, exponent - 1, modulo_value)) % modulo_value


def solution(base: int = 1777, height: int = 1855, digits: int = 8) -> int:
    """
    Returns the last 8 digits of the hyperexponentiation of base by
    height, i.e. the number base↑↑height:

    >>> solution(base=3, height=2)
    27
    >>> solution(base=3, height=3)
    97484987
    >>> solution(base=123, height=456, digits=4)
    2547
    """

    # calculate base↑↑height by right-assiciative repeated modular
    # exponentiation
    result = base
    for _ in range(1, height):
        result = _modexpt(base, result, 10**digits)

    return result


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_190/__init__.py
================================================


================================================
FILE: project_euler/problem_190/sol1.py
================================================
"""
Project Euler Problem 190: https://projecteuler.net/problem=190

Maximising a Weighted Product

Let S_m = (x_1, x_2, ..., x_m) be the m-tuple of positive real numbers with
x_1 + x_2 + ... + x_m = m for which P_m = x_1 * x_2^2 * ... * x_m^m is maximised.

For example, it can be verified that |_ P_10 _| = 4112
(|_ _| is the integer part function).

Find Sum_{m=2}^15 = |_ P_m _|.

Solution:
- Fix x_1 = m - x_2 - ... - x_m.
- Calculate partial derivatives of P_m wrt the x_2, ..., x_m. This gives that
  x_2 = 2 * x_1, x_3 = 3 * x_1, ..., x_m = m * x_1.
- Calculate partial second order derivatives of P_m wrt the x_2, ..., x_m.
  By plugging in the values from the previous step, can verify that solution is maximum.
"""


def solution(n: int = 15) -> int:
    """
    Calculate sum of |_ P_m _| for m from 2 to n.

    >>> solution(2)
    1
    >>> solution(3)
    2
    >>> solution(4)
    4
    >>> solution(5)
    10
    """
    total = 0
    for m in range(2, n + 1):
        x1 = 2 / (m + 1)
        p = 1.0
        for i in range(1, m + 1):
            xi = i * x1
            p *= xi**i
        total += int(p)
    return total


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_191/__init__.py
================================================


================================================
FILE: project_euler/problem_191/sol1.py
================================================
"""
Prize Strings
Problem 191

A particular school offers cash rewards to children with good attendance and
punctuality. If they are absent for three consecutive days or late on more
than one occasion then they forfeit their prize.

During an n-day period a trinary string is formed for each child consisting
of L's (late), O's (on time), and A's (absent).

Although there are eighty-one trinary strings for a 4-day period that can be
formed, exactly forty-three strings would lead to a prize:

OOOO OOOA OOOL OOAO OOAA OOAL OOLO OOLA OAOO OAOA
OAOL OAAO OAAL OALO OALA OLOO OLOA OLAO OLAA AOOO
AOOA AOOL AOAO AOAA AOAL AOLO AOLA AAOO AAOA AAOL
AALO AALA ALOO ALOA ALAO ALAA LOOO LOOA LOAO LOAA
LAOO LAOA LAAO

How many "prize" strings exist over a 30-day period?

References:
    - The original Project Euler project page:
      https://projecteuler.net/problem=191
"""

cache: dict[tuple[int, int, int], int] = {}


def _calculate(days: int, absent: int, late: int) -> int:
    """
    A small helper function for the recursion, mainly to have
    a clean interface for the solution() function below.

    It should get called with the number of days (corresponding
    to the desired length of the 'prize strings'), and the
    initial values for the number of consecutive absent days and
    number of total late days.

    >>> _calculate(days=4, absent=0, late=0)
    43
    >>> _calculate(days=30, absent=2, late=0)
    0
    >>> _calculate(days=30, absent=1, late=0)
    98950096
    """

    # if we are absent twice, or late 3 consecutive days,
    # no further prize strings are possible
    if late == 3 or absent == 2:
        return 0

    # if we have no days left, and have not failed any other rules,
    # we have a prize string
    if days == 0:
        return 1

    # No easy solution, so now we need to do the recursive calculation

    # First, check if the combination is already in the cache, and
    # if yes, return the stored value from there since we already
    # know the number of possible prize strings from this point on
    key = (days, absent, late)
    if key in cache:
        return cache[key]

    # now we calculate the three possible ways that can unfold from
    # this point on, depending on our attendance today

    # 1) if we are late (but not absent), the "absent" counter stays as
    # it is, but the "late" counter increases by one
    state_late = _calculate(days - 1, absent, late + 1)

    # 2) if we are absent, the "absent" counter increases by 1, and the
    # "late" counter resets to 0
    state_absent = _calculate(days - 1, absent + 1, 0)

    # 3) if we are on time, this resets the "late" counter and keeps the
    # absent counter
    state_ontime = _calculate(days - 1, absent, 0)

    prizestrings = state_late + state_absent + state_ontime

    cache[key] = prizestrings
    return prizestrings


def solution(days: int = 30) -> int:
    """
    Returns the number of possible prize strings for a particular number
    of days, using a simple recursive function with caching to speed it up.

    >>> solution()
    1918080160
    >>> solution(4)
    43
    """

    return _calculate(days, absent=0, late=0)


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_203/__init__.py
================================================


================================================
FILE: project_euler/problem_203/sol1.py
================================================
"""
Project Euler Problem 203: https://projecteuler.net/problem=203

The binomial coefficients (n k) can be arranged in triangular form, Pascal's
triangle, like this:
                            1
                        1       1
                    1		2       1
                1		3		3       1
            1		4		6		4		1
        1		5		10		10		5		1
    1		6		15		20		15		6		1
1		7		21		35		35		21		7		1
                        .........

It can be seen that the first eight rows of Pascal's triangle contain twelve
distinct numbers: 1, 2, 3, 4, 5, 6, 7, 10, 15, 20, 21 and 35.

A positive integer n is called squarefree if no square of a prime divides n.
Of the twelve distinct numbers in the first eight rows of Pascal's triangle,
all except 4 and 20 are squarefree. The sum of the distinct squarefree numbers
in the first eight rows is 105.

Find the sum of the distinct squarefree numbers in the first 51 rows of
Pascal's triangle.

References:
- https://en.wikipedia.org/wiki/Pascal%27s_triangle
"""

from __future__ import annotations


def get_pascal_triangle_unique_coefficients(depth: int) -> set[int]:
    """
    Returns the unique coefficients of a Pascal's triangle of depth "depth".

    The coefficients of this triangle are symmetric. A further improvement to this
    method could be to calculate the coefficients once per level. Nonetheless,
    the current implementation is fast enough for the original problem.

    >>> get_pascal_triangle_unique_coefficients(1)
    {1}
    >>> get_pascal_triangle_unique_coefficients(2)
    {1}
    >>> get_pascal_triangle_unique_coefficients(3)
    {1, 2}
    >>> get_pascal_triangle_unique_coefficients(8)
    {1, 2, 3, 4, 5, 6, 7, 35, 10, 15, 20, 21}
    """
    coefficients = {1}
    previous_coefficients = [1]
    for _ in range(2, depth + 1):
        coefficients_begins_one = [*previous_coefficients, 0]
        coefficients_ends_one = [0, *previous_coefficients]
        previous_coefficients = []
        for x, y in zip(coefficients_begins_one, coefficients_ends_one):
            coefficients.add(x + y)
            previous_coefficients.append(x + y)
    return coefficients


def get_squarefrees(unique_coefficients: set[int]) -> set[int]:
    """
    Calculates the squarefree numbers inside unique_coefficients.

    Based on the definition of a non-squarefree number, then any non-squarefree
    n can be decomposed as n = p*p*r, where p is positive prime number and r
    is a positive integer.

    Under the previous formula, any coefficient that is lower than p*p is
    squarefree as r cannot be negative. On the contrary, if any r exists such
    that n = p*p*r, then the number is non-squarefree.

    >>> get_squarefrees({1})
    {1}
    >>> get_squarefrees({1, 2})
    {1, 2}
    >>> get_squarefrees({1, 2, 3, 4, 5, 6, 7, 35, 10, 15, 20, 21})
    {1, 2, 3, 5, 6, 7, 35, 10, 15, 21}
    """

    non_squarefrees = set()
    for number in unique_coefficients:
        divisor = 2
        copy_number = number
        while divisor**2 <= copy_number:
            multiplicity = 0
            while copy_number % divisor == 0:
                copy_number //= divisor
                multiplicity += 1
            if multiplicity >= 2:
                non_squarefrees.add(number)
                break
            divisor += 1

    return unique_coefficients.difference(non_squarefrees)


def solution(n: int = 51) -> int:
    """
    Returns the sum of squarefrees for a given Pascal's Triangle of depth n.

    >>> solution(1)
    1
    >>> solution(8)
    105
    >>> solution(9)
    175
    """
    unique_coefficients = get_pascal_triangle_unique_coefficients(n)
    squarefrees = get_squarefrees(unique_coefficients)
    return sum(squarefrees)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_205/__init__.py
================================================


================================================
FILE: project_euler/problem_205/sol1.py
================================================
"""
Project Euler Problem 205: https://projecteuler.net/problem=205

Peter has nine four-sided (pyramidal) dice, each with faces numbered 1, 2, 3, 4.
Colin has six six-sided (cubic) dice, each with faces numbered 1, 2, 3, 4, 5, 6.

Peter and Colin roll their dice and compare totals: the highest total wins.
The result is a draw if the totals are equal.

What is the probability that Pyramidal Peter beats Cubic Colin?
Give your answer rounded to seven decimal places in the form 0.abcdefg
"""

from itertools import product


def total_frequency_distribution(sides_number: int, dice_number: int) -> list[int]:
    """
    Returns frequency distribution of total

    >>> total_frequency_distribution(sides_number=6, dice_number=1)
    [0, 1, 1, 1, 1, 1, 1]

    >>> total_frequency_distribution(sides_number=4, dice_number=2)
    [0, 0, 1, 2, 3, 4, 3, 2, 1]
    """

    max_face_number = sides_number
    max_total = max_face_number * dice_number
    totals_frequencies = [0] * (max_total + 1)

    min_face_number = 1
    faces_numbers = range(min_face_number, max_face_number + 1)
    for dice_numbers in product(faces_numbers, repeat=dice_number):
        total = sum(dice_numbers)
        totals_frequencies[total] += 1

    return totals_frequencies


def solution() -> float:
    """
    Returns probability that Pyramidal Peter beats Cubic Colin
    rounded to seven decimal places in the form 0.abcdefg

    >>> solution()
    0.5731441
    """

    peter_totals_frequencies = total_frequency_distribution(
        sides_number=4, dice_number=9
    )
    colin_totals_frequencies = total_frequency_distribution(
        sides_number=6, dice_number=6
    )

    peter_wins_count = 0
    min_peter_total = 9
    max_peter_total = 4 * 9
    min_colin_total = 6
    for peter_total in range(min_peter_total, max_peter_total + 1):
        peter_wins_count += peter_totals_frequencies[peter_total] * sum(
            colin_totals_frequencies[min_colin_total:peter_total]
        )

    total_games_number = (4**9) * (6**6)
    peter_win_probability = peter_wins_count / total_games_number

    rounded_peter_win_probability = round(peter_win_probability, ndigits=7)

    return rounded_peter_win_probability


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_206/__init__.py
================================================


================================================
FILE: project_euler/problem_206/sol1.py
================================================
"""
Project Euler Problem 206: https://projecteuler.net/problem=206

Find the unique positive integer whose square has the form 1_2_3_4_5_6_7_8_9_0,
where each “_” is a single digit.

-----

Instead of computing every single permutation of that number and going
through a 10^9 search space, we can narrow it down considerably.

If the square ends in a 0, then the square root must also end in a 0. Thus,
the last missing digit must be 0 and the square root is a multiple of 10.
We can narrow the search space down to the first 8 digits and multiply the
result of that by 10 at the end.

Now the last digit is a 9, which can only happen if the square root ends
in a 3 or 7. From this point, we can try one of two different methods to find
the answer:

1. Start at the lowest possible base number whose square would be in the
format, and count up. The base we would start at is 101010103, whose square is
the closest number to 10203040506070809. Alternate counting up by 4 and 6 so
the last digit of the base is always a 3 or 7.

2. Start at the highest possible base number whose square would be in the
format, and count down. That base would be 138902663, whose square is the
closest number to 1929394959697989. Alternate counting down by 6 and 4 so the
last digit of the base is always a 3 or 7.

The solution does option 2 because the answer happens to be much closer to the
starting point.
"""


def is_square_form(num: int) -> bool:
    """
    Determines if num is in the form 1_2_3_4_5_6_7_8_9

    >>> is_square_form(1)
    False
    >>> is_square_form(112233445566778899)
    True
    >>> is_square_form(123456789012345678)
    False
    """
    digit = 9

    while num > 0:
        if num % 10 != digit:
            return False
        num //= 100
        digit -= 1

    return True


def solution() -> int:
    """
    Returns the first integer whose square is of the form 1_2_3_4_5_6_7_8_9_0
    """
    num = 138902663

    while not is_square_form(num * num):
        if num % 10 == 3:
            num -= 6  # (3 - 6) % 10 = 7
        else:
            num -= 4  # (7 - 4) % 10 = 3

    return num * 10


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_207/__init__.py
================================================


================================================
FILE: project_euler/problem_207/sol1.py
================================================
"""

Project Euler Problem 207: https://projecteuler.net/problem=207

Problem Statement:
For some positive integers k, there exists an integer partition of the form
4**t = 2**t + k, where 4**t, 2**t, and k are all positive integers and t is a real
number. The first two such partitions are 4**1 = 2**1 + 2 and
4**1.5849625... = 2**1.5849625... + 6.
Partitions where t is also an integer are called perfect.
For any m ≥ 1 let P(m) be the proportion of such partitions that are perfect with
k ≤ m.
Thus P(6) = 1/2.
In the following table are listed some values of P(m)

   P(5) = 1/1
   P(10) = 1/2
   P(15) = 2/3
   P(20) = 1/2
   P(25) = 1/2
   P(30) = 2/5
   ...
   P(180) = 1/4
   P(185) = 3/13

Find the smallest m for which P(m) < 1/12345

Solution:
Equation 4**t = 2**t + k solved for t gives:
    t = log2(sqrt(4*k+1)/2 + 1/2)
For t to be real valued, sqrt(4*k+1) must be an integer which is implemented in
function check_t_real(k). For a perfect partition t must be an integer.
To speed up significantly the search for partitions, instead of incrementing k by one
per iteration, the next valid k is found by k = (i**2 - 1) / 4 with an integer i and
k has to be a positive integer. If this is the case a partition is found. The partition
is perfect if t os an integer. The integer i is increased with increment 1 until the
proportion perfect partitions / total partitions drops under the given value.

"""

import math


def check_partition_perfect(positive_integer: int) -> bool:
    """

    Check if t = f(positive_integer) = log2(sqrt(4*positive_integer+1)/2 + 1/2) is a
    real number.

    >>> check_partition_perfect(2)
    True

    >>> check_partition_perfect(6)
    False

    """

    exponent = math.log2(math.sqrt(4 * positive_integer + 1) / 2 + 1 / 2)

    return exponent == int(exponent)


def solution(max_proportion: float = 1 / 12345) -> int:
    """
    Find m for which the proportion of perfect partitions to total partitions is lower
    than max_proportion

    >>> solution(1) > 5
    True

    >>> solution(1/2) > 10
    True

    >>> solution(3 / 13) > 185
    True

    """

    total_partitions = 0
    perfect_partitions = 0

    integer = 3
    while True:
        partition_candidate = (integer**2 - 1) / 4
        # if candidate is an integer, then there is a partition for k
        if partition_candidate == int(partition_candidate):
            partition_candidate = int(partition_candidate)
            total_partitions += 1
            if check_partition_perfect(partition_candidate):
                perfect_partitions += 1
        if (
            perfect_partitions > 0
            and perfect_partitions / total_partitions < max_proportion
        ):
            return int(partition_candidate)
        integer += 1


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_234/__init__.py
================================================


================================================
FILE: project_euler/problem_234/sol1.py
================================================
"""
https://projecteuler.net/problem=234

For an integer n ≥ 4, we define the lower prime square root of n, denoted by
lps(n), as the largest prime ≤ √n and the upper prime square root of n, ups(n),
as the smallest prime ≥ √n.

So, for example, lps(4) = 2 = ups(4), lps(1000) = 31, ups(1000) = 37. Let us
call an integer n ≥ 4 semidivisible, if one of lps(n) and ups(n) divides n,
but not both.

The sum of the semidivisible numbers not exceeding 15 is 30, the numbers are 8,
10 and 12. 15 is not semidivisible because it is a multiple of both lps(15) = 3
and ups(15) = 5. As a further example, the sum of the 92 semidivisible numbers
up to 1000 is 34825.

What is the sum of all semidivisible numbers not exceeding 999966663333 ?
"""

import math


def prime_sieve(n: int) -> list:
    """
    Sieve of Erotosthenes
    Function to return all the prime numbers up to a certain number
    https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes
    >>> prime_sieve(3)
    [2]
    >>> prime_sieve(50)
    [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
    """
    is_prime = [True] * n
    is_prime[0] = False
    is_prime[1] = False
    is_prime[2] = True

    for i in range(3, int(n**0.5 + 1), 2):
        index = i * 2
        while index < n:
            is_prime[index] = False
            index = index + i

    primes = [2]

    for i in range(3, n, 2):
        if is_prime[i]:
            primes.append(i)

    return primes


def solution(limit: int = 999_966_663_333) -> int:
    """
    Computes the solution to the problem up to the specified limit
    >>> solution(1000)
    34825

    >>> solution(10_000)
    1134942

    >>> solution(100_000)
    36393008
    """
    primes_upper_bound = math.floor(math.sqrt(limit)) + 100
    primes = prime_sieve(primes_upper_bound)

    matches_sum = 0
    prime_index = 0
    last_prime = primes[prime_index]

    while (last_prime**2) <= limit:
        next_prime = primes[prime_index + 1]

        lower_bound = last_prime**2
        upper_bound = next_prime**2

        # Get numbers divisible by lps(current)
        current = lower_bound + last_prime
        while upper_bound > current <= limit:
            matches_sum += current
            current += last_prime

        # Reset the upper_bound
        while (upper_bound - next_prime) > limit:
            upper_bound -= next_prime

        # Add the numbers divisible by ups(current)
        current = upper_bound - next_prime
        while current > lower_bound:
            matches_sum += current
            current -= next_prime

        # Remove the numbers divisible by both ups and lps
        current = 0
        while upper_bound > current <= limit:
            if current <= lower_bound:
                # Increment the current number
                current += last_prime * next_prime
                continue

            if current > limit:
                break

            # Remove twice since it was added by both ups and lps
            matches_sum -= current * 2

            # Increment the current number
            current += last_prime * next_prime

        # Setup for next pair
        last_prime = next_prime
        prime_index += 1

    return matches_sum


if __name__ == "__main__":
    print(solution())


================================================
FILE: project_euler/problem_301/__init__.py
================================================


================================================
FILE: project_euler/problem_301/sol1.py
================================================
"""
Project Euler Problem 301: https://projecteuler.net/problem=301

Problem Statement:
Nim is a game played with heaps of stones, where two players take
it in turn to remove any number of stones from any heap until no stones remain.

We'll consider the three-heap normal-play version of
Nim, which works as follows:
- At the start of the game there are three heaps of stones.
- On each player's turn, the player may remove any positive
  number of stones from any single heap.
- The first player unable to move (because no stones remain) loses.

If (n1, n2, n3) indicates a Nim position consisting of heaps of size
n1, n2, and n3, then there is a simple function, which you may look up
or attempt to deduce for yourself, X(n1, n2, n3) that returns:
- zero if, with perfect strategy, the player about to
  move will eventually lose; or
- non-zero if, with perfect strategy, the player about
  to move will eventually win.

For example X(1,2,3) = 0 because, no matter what the current player does,
the opponent can respond with a move that leaves two heaps of equal size,
at which point every move by the current player can be mirrored by the
opponent until no stones remain; so the current player loses. To illustrate:
- current player moves to (1,2,1)
- opponent moves to (1,0,1)
- current player moves to (0,0,1)
- opponent moves to (0,0,0), and so wins.

For how many positive integers n <= 2^30 does X(n,2n,3n) = 0?
"""


def solution(exponent: int = 30) -> int:
    """
    For any given exponent x >= 0, 1 <= n <= 2^x.
    This function returns how many Nim games are lost given that
    each Nim game has three heaps of the form (n, 2*n, 3*n).
    >>> solution(0)
    1
    >>> solution(2)
    3
    >>> solution(10)
    144
    """
    # To find how many total games were lost for a given exponent x,
    # we need to find the Fibonacci number F(x+2).
    fibonacci_index = exponent + 2
    phi = (1 + 5**0.5) / 2
    fibonacci = (phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

    return int(fibonacci)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_345/__init__.py
================================================


================================================
FILE: project_euler/problem_345/sol1.py
================================================
"""
Project Euler Problem 345: https://projecteuler.net/problem=345

Matrix Sum

We define the Matrix Sum of a matrix as the maximum possible sum of
matrix elements such that none of the selected elements share the same row or column.

For example, the Matrix Sum of the matrix below equals
3315 ( = 863 + 383 + 343 + 959 + 767):
      7  53 183 439 863
    497 383 563  79 973
    287  63 343 169 583
    627 343 773 959 943
    767 473 103 699 303

Find the Matrix Sum of:
      7  53 183 439 863 497 383 563  79 973 287  63 343 169 583
    627 343 773 959 943 767 473 103 699 303 957 703 583 639 913
    447 283 463  29  23 487 463 993 119 883 327 493 423 159 743
    217 623   3 399 853 407 103 983  89 463 290 516 212 462 350
    960 376 682 962 300 780 486 502 912 800 250 346 172 812 350
    870 456 192 162 593 473 915  45 989 873 823 965 425 329 803
    973 965 905 919 133 673 665 235 509 613 673 815 165 992 326
    322 148 972 962 286 255 941 541 265 323 925 281 601  95 973
    445 721  11 525 473  65 511 164 138 672  18 428 154 448 848
    414 456 310 312 798 104 566 520 302 248 694 976 430 392 198
    184 829 373 181 631 101 969 613 840 740 778 458 284 760 390
    821 461 843 513  17 901 711 993 293 157 274  94 192 156 574
     34 124   4 878 450 476 712 914 838 669 875 299 823 329 699
    815 559 813 459 522 788 168 586 966 232 308 833 251 631 107
    813 883 451 509 615  77 281 613 459 205 380 274 302  35 805

Brute force solution, with caching intermediate steps to speed up the calculation.
"""

import numpy as np
from numpy.typing import NDArray

MATRIX_1 = [
    "7 53 183 439 863",
    "497 383 563 79 973",
    "287 63 343 169 583",
    "627 343 773 959 943",
    "767 473 103 699 303",
]

MATRIX_2 = [
    "7 53 183 439 863 497 383 563 79 973 287 63 343 169 583",
    "627 343 773 959 943 767 473 103 699 303 957 703 583 639 913",
    "447 283 463 29 23 487 463 993 119 883 327 493 423 159 743",
    "217 623 3 399 853 407 103 983 89 463 290 516 212 462 350",
    "960 376 682 962 300 780 486 502 912 800 250 346 172 812 350",
    "870 456 192 162 593 473 915 45 989 873 823 965 425 329 803",
    "973 965 905 919 133 673 665 235 509 613 673 815 165 992 326",
    "322 148 972 962 286 255 941 541 265 323 925 281 601 95 973",
    "445 721 11 525 473 65 511 164 138 672 18 428 154 448 848",
    "414 456 310 312 798 104 566 520 302 248 694 976 430 392 198",
    "184 829 373 181 631 101 969 613 840 740 778 458 284 760 390",
    "821 461 843 513 17 901 711 993 293 157 274 94 192 156 574",
    "34 124 4 878 450 476 712 914 838 669 875 299 823 329 699",
    "815 559 813 459 522 788 168 586 966 232 308 833 251 631 107",
    "813 883 451 509 615 77 281 613 459 205 380 274 302 35 805",
]


def solve(arr: NDArray, row: int, cols: set[int], cache: dict[str, int]) -> int:
    """
    Finds the max sum for array `arr` starting with row index `row`, and with columns
    included in `cols`. `cache` is used for caching intermediate results.

    >>> solve(arr=np.array([[1, 2], [3, 4]]), row=0, cols={0, 1}, cache={})
    5
    """

    cache_id = f"{row}, {sorted(cols)}"
    if cache_id in cache:
        return cache[cache_id]

    if row == len(arr):
        return 0

    max_sum = 0
    for col in cols:
        new_cols = cols - {col}
        max_sum = max(
            max_sum,
            int(arr[row, col])
            + solve(arr=arr, row=row + 1, cols=new_cols, cache=cache),
        )
    cache[cache_id] = max_sum
    return max_sum


def solution(matrix_str: list[str] = MATRIX_2) -> int:
    """
    Takes list of strings `matrix_str` to parse the matrix and calculates the max sum.

    >>> solution(["1 2", "3 4"])
    5
    >>> solution(MATRIX_1)
    3315
    """

    n = len(matrix_str)
    arr = np.empty(shape=(n, n), dtype=int)
    for row, matrix_row_str in enumerate(matrix_str):
        matrix_row_list_str = matrix_row_str.split()
        for col, elem_str in enumerate(matrix_row_list_str):
            arr[row, col] = int(elem_str)

    cache: dict[str, int] = {}
    return solve(arr=arr, row=0, cols=set(range(n)), cache=cache)


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_493/__init__.py
================================================


================================================
FILE: project_euler/problem_493/sol1.py
================================================
"""
Project Euler Problem 493: https://projecteuler.net/problem=493

70 coloured balls are placed in an urn, 10 for each of the seven rainbow colours.
What is the expected number of distinct colours in 20 randomly picked balls?
Give your answer with nine digits after the decimal point (a.bcdefghij).

-----

This combinatorial problem can be solved by decomposing the problem into the
following steps:
1. Calculate the total number of possible picking combinations
[combinations := binom_coeff(70, 20)]
2. Calculate the number of combinations with one colour missing
[missing := binom_coeff(60, 20)]
3. Calculate the probability of one colour missing
[missing_prob := missing / combinations]
4. Calculate the probability of no colour missing
[no_missing_prob := 1 - missing_prob]
5. Calculate the expected number of distinct colours
[expected = 7 * no_missing_prob]

References:
- https://en.wikipedia.org/wiki/Binomial_coefficient
"""

import math

BALLS_PER_COLOUR = 10
NUM_COLOURS = 7
NUM_BALLS = BALLS_PER_COLOUR * NUM_COLOURS


def solution(num_picks: int = 20) -> str:
    """
    Calculates the expected number of distinct colours

    >>> solution(10)
    '5.669644129'

    >>> solution(30)
    '6.985042712'
    """
    total = math.comb(NUM_BALLS, num_picks)
    missing_colour = math.comb(NUM_BALLS - BALLS_PER_COLOUR, num_picks)

    result = NUM_COLOURS * (1 - missing_colour / total)

    return f"{result:.9f}"


if __name__ == "__main__":
    print(solution(20))


================================================
FILE: project_euler/problem_551/__init__.py
================================================


================================================
FILE: project_euler/problem_551/sol1.py
================================================
"""
Sum of digits sequence
Problem 551

Let a(0), a(1),... be an integer sequence defined by:
     a(0) = 1
     for n >= 1, a(n) is the sum of the digits of all preceding terms

The sequence starts with 1, 1, 2, 4, 8, ...
You are given a(10^6) = 31054319.

Find a(10^15)
"""

ks = range(2, 20 + 1)
base = [10**k for k in range(ks[-1] + 1)]
memo: dict[int, dict[int, list[list[int]]]] = {}


def next_term(a_i, k, i, n):
    """
    Calculates and updates a_i in-place to either the n-th term or the
    smallest term for which c > 10^k when the terms are written in the form:
            a(i) = b * 10^k + c

    For any a(i), if digitsum(b) and c have the same value, the difference
    between subsequent terms will be the same until c >= 10^k.  This difference
    is cached to greatly speed up the computation.

    Arguments:
    a_i -- array of digits starting from the one's place that represent
           the i-th term in the sequence
    k --  k when terms are written in the from a(i) = b*10^k + c.
          Term are calulcated until c > 10^k or the n-th term is reached.
    i -- position along the sequence
    n -- term to calculate up to if k is large enough

    Return: a tuple of difference between ending term and starting term, and
    the number of terms calculated. ex. if starting term is a_0=1, and
    ending term is a_10=62, then (61, 9) is returned.
    """
    # ds_b - digitsum(b)
    ds_b = sum(a_i[j] for j in range(k, len(a_i)))
    c = sum(a_i[j] * base[j] for j in range(min(len(a_i), k)))

    diff, dn = 0, 0
    max_dn = n - i

    sub_memo = memo.get(ds_b)

    if sub_memo is not None:
        jumps = sub_memo.get(c)

        if jumps is not None and len(jumps) > 0:
            # find and make the largest jump without going over
            max_jump = -1
            for _k in range(len(jumps) - 1, -1, -1):
                if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
                    max_jump = _k
                    break

            if max_jump >= 0:
                diff, dn, _kk = jumps[max_jump]
                # since the difference between jumps is cached, add c
                new_c = diff + c
                for j in range(min(k, len(a_i))):
                    new_c, a_i[j] = divmod(new_c, 10)
                if new_c > 0:
                    add(a_i, k, new_c)

        else:
            sub_memo[c] = []
    else:
        sub_memo = {c: []}
        memo[ds_b] = sub_memo

    if dn >= max_dn or c + diff >= base[k]:
        return diff, dn

    if k > ks[0]:
        while True:
            # keep doing smaller jumps
            _diff, terms_jumped = next_term(a_i, k - 1, i + dn, n)
            diff += _diff
            dn += terms_jumped

            if dn >= max_dn or c + diff >= base[k]:
                break
    else:
        # would be too small a jump, just compute sequential terms instead
        _diff, terms_jumped = compute(a_i, k, i + dn, n)
        diff += _diff
        dn += terms_jumped

    jumps = sub_memo[c]

    # keep jumps sorted by # of terms skipped
    j = 0
    while j < len(jumps):
        if jumps[j][1] > dn:
            break
        j += 1

    # cache the jump for this value digitsum(b) and c
    sub_memo[c].insert(j, (diff, dn, k))
    return (diff, dn)


def compute(a_i, k, i, n):
    """
    same as next_term(a_i, k, i, n) but computes terms without memoizing results.
    """
    if i >= n:
        return 0, i
    if k > len(a_i):
        a_i.extend([0 for _ in range(k - len(a_i))])

    # note: a_i -> b * 10^k + c
    # ds_b -> digitsum(b)
    # ds_c -> digitsum(c)
    start_i = i
    ds_b, ds_c, diff = 0, 0, 0
    for j in range(len(a_i)):
        if j >= k:
            ds_b += a_i[j]
        else:
            ds_c += a_i[j]

    while i < n:
        i += 1
        addend = ds_c + ds_b
        diff += addend
        ds_c = 0
        for j in range(k):
            s = a_i[j] + addend
            addend, a_i[j] = divmod(s, 10)

            ds_c += a_i[j]

        if addend > 0:
            break

    if addend > 0:
        add(a_i, k, addend)
    return diff, i - start_i


def add(digits, k, addend):
    """
    adds addend to digit array given in digits
    starting at index k
    """
    for j in range(k, len(digits)):
        s = digits[j] + addend
        if s >= 10:
            quotient, digits[j] = divmod(s, 10)
            addend = addend // 10 + quotient
        else:
            digits[j] = s
            addend = addend // 10

        if addend == 0:
            break

    while addend > 0:
        addend, digit = divmod(addend, 10)
        digits.append(digit)


def solution(n: int = 10**15) -> int:
    """
    returns n-th term of sequence

    >>> solution(10)
    62

    >>> solution(10**6)
    31054319

    >>> solution(10**15)
    73597483551591773
    """

    digits = [1]
    i = 1
    dn = 0
    while True:
        _diff, terms_jumped = next_term(digits, 20, i + dn, n)
        dn += terms_jumped
        if dn == n - i:
            break

    a_n = 0
    for j in range(len(digits)):
        a_n += digits[j] * 10**j
    return a_n


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_587/__init__.py
================================================


================================================
FILE: project_euler/problem_587/sol1.py
================================================
"""
Project Euler Problem 587: https://projecteuler.net/problem=587

A square is drawn around a circle as shown in the diagram below on the left.
We shall call the blue shaded region the L-section.
A line is drawn from the bottom left of the square to the top right
as shown in the diagram on the right.
We shall call the orange shaded region a concave triangle.

It should be clear that the concave triangle occupies exactly half of the L-section.

Two circles are placed next to each other horizontally,
a rectangle is drawn around both circles, and
a line is drawn from the bottom left to the top right as shown in the diagram below.

This time the concave triangle occupies approximately 36.46% of the L-section.

If n circles are placed next to each other horizontally,
a rectangle is drawn around the n circles, and
a line is drawn from the bottom left to the top right,
then it can be shown that the least value of n
for which the concave triangle occupies less than 10% of the L-section is n = 15.

What is the least value of n
for which the concave triangle occupies less than 0.1% of the L-section?
"""

from itertools import count
from math import asin, pi, sqrt


def circle_bottom_arc_integral(point: float) -> float:
    """
    Returns integral of circle bottom arc y = 1 / 2 - sqrt(1 / 4 - (x - 1 / 2) ^ 2)

    >>> circle_bottom_arc_integral(0)
    0.39269908169872414

    >>> circle_bottom_arc_integral(1 / 2)
    0.44634954084936207

    >>> circle_bottom_arc_integral(1)
    0.5
    """

    return (
        (1 - 2 * point) * sqrt(point - point**2) + 2 * point + asin(sqrt(1 - point))
    ) / 4


def concave_triangle_area(circles_number: int) -> float:
    """
    Returns area of concave triangle

    >>> concave_triangle_area(1)
    0.026825229575318944

    >>> concave_triangle_area(2)
    0.01956236140083944
    """

    intersection_y = (circles_number + 1 - sqrt(2 * circles_number)) / (
        2 * (circles_number**2 + 1)
    )
    intersection_x = circles_number * intersection_y

    triangle_area = intersection_x * intersection_y / 2
    concave_region_area = circle_bottom_arc_integral(
        1 / 2
    ) - circle_bottom_arc_integral(intersection_x)

    return triangle_area + concave_region_area


def solution(fraction: float = 1 / 1000) -> int:
    """
    Returns least value of n
    for which the concave triangle occupies less than fraction of the L-section

    >>> solution(1 / 10)
    15
    """

    l_section_area = (1 - pi / 4) / 4

    for n in count(1):
        if concave_triangle_area(n) / l_section_area < fraction:
            return n

    return -1


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: project_euler/problem_686/__init__.py
================================================


================================================
FILE: project_euler/problem_686/sol1.py
================================================
"""
Project Euler Problem 686: https://projecteuler.net/problem=686

2^7 = 128 is the first power of two whose leading digits are "12".
The next power of two whose leading digits are "12" is 2^80.

Define p(L,n) to be the nth-smallest value of j such that
the base 10 representation of 2^j begins with the digits of L.

So p(12, 1) = 7 and p(12, 2) = 80.

You are given that p(123, 45) = 12710.

Find p(123, 678910).
"""

import math


def log_difference(number: int) -> float:
    """
    This function returns the decimal value of a number multiplied with log(2)
    Since the problem is on powers of two, finding the powers of two with
    large exponents is time consuming. Hence we use log to reduce compute time.

    We can find out that the first power of 2 with starting digits 123 is 90.
    Computing 2^90 is time consuming.
    Hence we find log(2^90) = 90*log(2) = 27.092699609758302
    But we require only the decimal part to determine whether the power starts with 123.
    So we just return the decimal part of the log product.
    Therefore we return 0.092699609758302

    >>> log_difference(90)
    0.092699609758302
    >>> log_difference(379)
    0.090368356648852

    """

    log_number = math.log(2, 10) * number
    difference = round((log_number - int(log_number)), 15)

    return difference


def solution(number: int = 678910) -> int:
    """
    This function calculates the power of two which is nth (n = number)
    smallest value of power of 2
    such that the starting digits of the 2^power is 123.

    For example the powers of 2 for which starting digits is 123 are:
    90, 379, 575, 864, 1060, 1545, 1741, 2030, 2226, 2515 and so on.
    90 is the first power of 2 whose starting digits are 123,
    379 is second power of 2 whose starting digits are 123,
    and so on.

    So if number = 10, then solution returns 2515 as we observe from above series.

    We will define a lowerbound and upperbound.
    lowerbound = log(1.23), upperbound = log(1.24)
    because we need to find the powers that yield 123 as starting digits.

    log(1.23) = 0.08990511143939792, log(1,24) = 0.09342168516223506.
    We use 1.23 and not 12.3 or 123, because log(1.23) yields only decimal value
    which is less than 1.
    log(12.3) will be same decimal value but 1 added to it
    which is log(12.3) = 1.093421685162235.
    We observe that decimal value remains same no matter 1.23 or 12.3
    Since we use the function log_difference(),
    which returns the value that is only decimal part, using 1.23 is logical.

    If we see, 90*log(2) = 27.092699609758302,
    decimal part = 0.092699609758302, which is inside the range of lowerbound
    and upperbound.

    If we compute the difference between all the powers which lead to 123
    starting digits is as follows:

    379 - 90 = 289
    575 - 379 = 196
    864 - 575 = 289
    1060 - 864 = 196

    We see a pattern here. The difference is either 196 or 289 = 196 + 93.

    Hence to optimize the algorithm we will increment by 196 or 93 depending upon the
    log_difference() value.

    Let's take for example 90.
    Since 90 is the first power leading to staring digits as 123,
    we will increment iterator by 196.
    Because the difference between any two powers leading to 123
    as staring digits is greater than or equal to 196.
    After incrementing by 196 we get 286.

    log_difference(286) = 0.09457875989861 which is greater than upperbound.
    The next power is 379, and we need to add 93 to get there.
    The iterator will now become 379,
    which is the next power leading to 123 as starting digits.

    Let's take 1060. We increment by 196, we get 1256.
    log_difference(1256) = 0.09367455396034,
    Which is greater than upperbound hence we increment by 93. Now iterator is 1349.
    log_difference(1349) = 0.08946415071057 which is less than lowerbound.
    The next power is 1545 and we need to add 196 to get 1545.

    Conditions are as follows:

    1) If we find a power whose log_difference() is in the range of
    lower and upperbound, we will increment by 196.
    which implies that the power is a number which will lead to 123 as starting digits.
    2) If we find a power, whose log_difference() is greater than or equal upperbound,
    we will increment by 93.
    3) if log_difference() < lowerbound, we increment by 196.

    Reference to the above logic:
    https://math.stackexchange.com/questions/4093970/powers-of-2-starting-with-123-does-a-pattern-exist

    >>> solution(1000)
    284168

    >>> solution(56000)
    15924915

    >>> solution(678910)
    193060223

    """

    power_iterator = 90
    position = 0

    lower_limit = math.log(1.23, 10)
    upper_limit = math.log(1.24, 10)
    previous_power = 0

    while position < number:
        difference = log_difference(power_iterator)

        if difference >= upper_limit:
            power_iterator += 93

        elif difference < lower_limit:
            power_iterator += 196

        else:
            previous_power = power_iterator
            power_iterator += 196
            position += 1

    return previous_power


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print(f"{solution() = }")


================================================
FILE: project_euler/problem_800/__init__.py
================================================


================================================
FILE: project_euler/problem_800/sol1.py
================================================
"""
Project Euler Problem 800: https://projecteuler.net/problem=800

An integer of the form p^q q^p with prime numbers p != q is called a hybrid-integer.
For example, 800 = 2^5 5^2 is a hybrid-integer.

We define C(n) to be the number of hybrid-integers less than or equal to n.
You are given C(800) = 2 and C(800^800) = 10790

Find C(800800^800800)
"""

from math import isqrt, log2


def calculate_prime_numbers(max_number: int) -> list[int]:
    """
    Returns prime numbers below max_number

    >>> calculate_prime_numbers(10)
    [2, 3, 5, 7]
    """

    is_prime = [True] * max_number
    for i in range(2, isqrt(max_number - 1) + 1):
        if is_prime[i]:
            for j in range(i**2, max_number, i):
                is_prime[j] = False

    return [i for i in range(2, max_number) if is_prime[i]]


def solution(base: int = 800800, degree: int = 800800) -> int:
    """
    Returns the number of hybrid-integers less than or equal to base^degree

    >>> solution(800, 1)
    2

    >>> solution(800, 800)
    10790
    """

    upper_bound = degree * log2(base)
    max_prime = int(upper_bound)
    prime_numbers = calculate_prime_numbers(max_prime)

    hybrid_integers_count = 0
    left = 0
    right = len(prime_numbers) - 1
    while left < right:
        while (
            prime_numbers[right] * log2(prime_numbers[left])
            + prime_numbers[left] * log2(prime_numbers[right])
            > upper_bound
        ):
            right -= 1
        hybrid_integers_count += right - left
        left += 1

    return hybrid_integers_count


if __name__ == "__main__":
    print(f"{solution() = }")


================================================
FILE: pyproject.toml
================================================
[project]
name = "thealgorithms-python"
version = "0.0.1"
description = "TheAlgorithms in Python"
authors = [ { name = "TheAlgorithms Contributors" } ]
requires-python = ">=3.14"
classifiers = [
  "Programming Language :: Python :: 3 :: Only",
  "Programming Language :: Python :: 3.14",
]
dependencies = [
  "beautifulsoup4>=4.12.3",
  "cython>=3.1.2",
  "fake-useragent>=1.5.1",
  "httpx>=0.28.1",
  "imageio>=2.36.1",
  "keras>=3.7",
  "lxml>=6",
  "matplotlib>=3.9.3",
  "numpy>=2.1.3",
  "opencv-python>=4.10.0.84",
  "pandas>=2.2.3",
  "pillow>=11.3",
  "rich>=13.9.4",
  "scikit-learn>=1.5.2",
  "scipy>=1.16.2",
  "sphinx-pyproject>=0.3",
  "statsmodels>=0.14.4",
  "sympy>=1.13.3",
  "tweepy>=4.14",
  "typing-extensions>=4.12.2",
  "xgboost>=2.1.3",
]

[dependency-groups]
test = [
  "pytest>=8.4.1",
  "pytest-cov>=6",
]

docs = [
  "myst-parser>=4",
  "sphinx-autoapi>=3.4",
  "sphinx-pyproject>=0.3",
]
euler-validate = [
  "httpx>=0.28.1",
  "numpy>=2.1.3",
]

[tool.ruff]
target-version = "py314"

output-format = "full"
lint.select = [
  # https://beta.ruff.rs/docs/rules
  "A",     # flake8-builtins
  "ARG",   # flake8-unused-arguments
  "ASYNC", # flake8-async
  "B",     # flake8-bugbear
  "BLE",   # flake8-blind-except
  "C4",    # flake8-comprehensions
  "C90",   # McCabe cyclomatic complexity
  "DJ",    # flake8-django
  "DTZ",   # flake8-datetimez
  "E",     # pycodestyle
  "EM",    # flake8-errmsg
  "EXE",   # flake8-executable
  "F",     # Pyflakes
  "FA",    # flake8-future-annotations
  "FLY",   # flynt
  "G",     # flake8-logging-format
  "I",     # isort
  "ICN",   # flake8-import-conventions
  "INP",   # flake8-no-pep420
  "INT",   # flake8-gettext
  "ISC",   # flake8-implicit-str-concat
  "N",     # pep8-naming
  "NPY",   # NumPy-specific rules
  "PD",    # pandas-vet
  "PGH",   # pygrep-hooks
  "PIE",   # flake8-pie
  "PL",    # Pylint
  "PT",    # flake8-pytest-style
  "PYI",   # flake8-pyi
  "RSE",   # flake8-raise
  "RUF",   # Ruff-specific rules
  "S",     # flake8-bandit
  "SIM",   # flake8-simplify
  "SLF",   # flake8-self
  "T10",   # flake8-debugger
  "TD",    # flake8-todos
  "TID",   # flake8-tidy-imports
  "UP",    # pyupgrade
  "W",     # pycodestyle
  "YTT",   # flake8-2020
  # "ANN",  # flake8-annotations -- FIX ME?
  # "COM",  # flake8-commas -- DO NOT FIX
  # "D",    # pydocstyle -- FIX ME?
  # "ERA",  # eradicate -- DO NOT FIX
  # "FBT",  # flake8-boolean-trap  # FIX ME
  # "PTH",  # flake8-use-pathlib  # FIX ME
  # "Q",    # flake8-quotes
  # "RET",  # flake8-return  # FIX ME?
  # "T20",  # flake8-print
  # "TCH",  # flake8-type-checking
  # "TRY",  # tryceratops
]
lint.ignore = [
  # `ruff rule S101` for a description of that rule
  "B904",    # Within an `except` clause, raise exceptions with `raise ... from err` -- FIX ME
  "B905",    # `zip()` without an explicit `strict=` parameter -- FIX ME
  "EM101",   # Exception must not use a string literal, assign to a variable first
  "EXE001",  # Shebang is present but file is not executable -- DO NOT FIX
  "G004",    # Logging statement uses f-string
  "ISC001",  # Conflicts with ruff format -- DO NOT FIX
  "PLC0415", # import-outside-top-level -- DO NOT FIX
  "PLC1901", # `{}` can be simplified to `{}` as an empty string is falsey
  "PLW060",  # Using global for `{name}` but no assignment is done -- DO NOT FIX
  "PLW1641", # eq-without-hash
  "PLW2901", # PLW2901: Redefined loop variable -- FIX ME
  "PT011",   # `pytest.raises(Exception)` is too broad, set the `match` parameter or use a more specific exception
  "PT018",   # Assertion should be broken down into multiple parts
  "PT028",   # pytest-parameter-with-default-argument
  "S101",    # Use of `assert` detected -- DO NOT FIX
  "S311",    # Standard pseudo-random generators are not suitable for cryptographic purposes -- FIX ME
  "SIM905",  # Consider using a list literal instead of `str.split` -- DO NOT FIX
  "SLF001",  # Private member accessed: `_Iterator` -- FIX ME
  "UP037",   # FIX ME
]

lint.per-file-ignores."data_structures/hashing/tests/test_hash_map.py" = [
  "BLE001",
]
lint.per-file-ignores."hashes/enigma_machine.py" = [
  "BLE001",
]
lint.per-file-ignores."machine_learning/sequential_minimum_optimization.py" = [
  "SIM115",
]
lint.per-file-ignores."matrix/sherman_morrison.py" = [
  "SIM103",
]
lint.per-file-ignores."physics/newtons_second_law_of_motion.py" = [
  "BLE001",
]
lint.per-file-ignores."project_euler/problem_099/sol1.py" = [
  "SIM115",
]
lint.per-file-ignores."sorts/external_sort.py" = [
  "SIM115",
]
lint.mccabe.max-complexity = 17 # default: 10
lint.pylint.allow-magic-value-types = [
  "float",
  "int",
  "str",
]
lint.pylint.max-args = 10 # default: 5
lint.pylint.max-branches = 20 # default: 12
lint.pylint.max-returns = 8 # default: 6
lint.pylint.max-statements = 88 # default: 50

[tool.codespell]
ignore-words-list = "3rt,abd,aer,ans,bitap,crate,damon,fo,followings,hist,iff,kwanza,manuel,mater,secant,som,sur,tim,toi,zar"
skip = "./.*,*.json,*.lock,ciphers/prehistoric_men.txt,project_euler/problem_022/p022_names.txt,pyproject.toml,strings/dictionary.txt,strings/words.txt"

[tool.pytest.ini_options]
markers = [
  "mat_ops: mark a test as utilizing matrix operations.",
]
addopts = [
  "--durations=10",
  "--doctest-modules",
  "--showlocals",
]

[tool.coverage.report]
omit = [
  ".env/*",
  "project_euler/*",
]
sort = "Cover"

[tool.sphinx-pyproject]
copyright = "2014, TheAlgorithms"
autoapi_dirs = [
  "audio_filters",
  "backtracking",
  "bit_manipulation",
  "blockchain",
  "boolean_algebra",
  "cellular_automata",
  "ciphers",
  "computer_vision",
  "conversions",
  "data_compression",
  "data_structures",
  "digital_image_processing",
  "divide_and_conquer",
  "dynamic_programming",
  "electronics",
  "file_transfer",
  "financial",
  "fractals",
  "fuzzy_logic",
  "genetic_algorithm",
  "geodesy",
  "geometry",
  "graphics",
  "graphs",
  "greedy_methods",
  "hashes",
  "knapsack",
  "linear_algebra",
  "linear_programming",
  "machine_learning",
  "maths",
  "matrix",
  "networking_flow",
  "neural_network",
  "other",
  "physics",
  "project_euler",
  "quantum",
  "scheduling",
  "searches",
  "sorts",
  "strings",
  "web_programming",
]
autoapi_member_order = "groupwise"
# autoapi_python_use_implicit_namespaces = true
exclude_patterns = [
  ".*/*",
  "docs/",
]
extensions = [
  "autoapi.extension",
  "myst_parser",
]
html_static_path = [ "_static" ]
html_theme = "alabaster"
myst_enable_extensions = [
  "amsmath",
  "attrs_inline",
  "colon_fence",
  "deflist",
  "dollarmath",
  "fieldlist",
  "html_admonition",
  "html_image",
  # "linkify",
  "replacements",
  "smartquotes",
  "strikethrough",
  "substitution",
  "tasklist",
]
myst_fence_as_directive = [
  "include",
]
templates_path = [ "_templates" ]
[tool.sphinx-pyproject.source_suffix]
".rst" = "restructuredtext"
# ".txt" = "markdown"
".md" = "markdown"


================================================
FILE: quantum/README.md
================================================
# Welcome to Quantum Algorithms

Started at https://github.com/TheAlgorithms/Python/issues/1831

* D-Wave: https://www.dwavesys.com and https://github.com/dwavesystems
* Google: https://research.google/teams/applied-science/quantum
* IBM: https://qiskit.org and https://github.com/Qiskit
* Rigetti: https://rigetti.com and https://github.com/rigetti
* Zapata: https://www.zapatacomputing.com and https://github.com/zapatacomputing

## IBM Qiskit
- Start using by installing `pip install qiskit`, refer the [docs](https://qiskit.org/documentation/install.html) for more info.
- Tutorials & References
  - https://github.com/Qiskit/qiskit-tutorials
  - https://quantum-computing.ibm.com/docs/iql/first-circuit
  - https://medium.com/qiskit/how-to-program-a-quantum-computer-982a9329ed02

## Google Cirq
- Start using by installing `python -m pip install cirq`, refer the [docs](https://quantumai.google/cirq/start/install) for more info.
- Tutorials & references
  - https://github.com/quantumlib/cirq
  - https://quantumai.google/cirq/experiments
  - https://tanishabassan.medium.com/quantum-programming-with-google-cirq-3209805279bc


================================================
FILE: quantum/__init__.py
================================================


================================================
FILE: quantum/bb84.py.DISABLED.txt
================================================
#!/usr/bin/env python3
"""
Simulation of the Quantum Key Distribution (QKD) protocol called BB84,
created by Charles Bennett and Gilles Brassard in 1984.

BB84 is a key-distribution protocol that ensures secure key distribution
using qubits instead of classical bits. The generated key is the result
of simulating a quantum circuit. Our algorithm to construct the circuit
is as follows:

Alice generates two binary strings. One encodes the basis for each qubit:

 - 0 -> {0,1} basis.
 - 1 -> {+,-} basis.

The other encodes the state:

 - 0 -> |0> or |+>.
 - 1 -> |1> or |->.

Bob also generates a binary string and uses the same convention to choose
a basis for measurement. Based on the following results, we follow the
algorithm below:

X|0> = |1>

H|0> = |+>

HX|0> = |->

1. Whenever Alice wants to encode 1 in a qubit, she applies an
X (NOT) gate to the qubit. To encode 0, no action is needed.

2. Wherever she wants to encode it in the {+,-} basis, she applies
an H (Hadamard) gate. No action is necessary to encode a qubit in
the {0,1} basis.

3. She then sends the qubits to Bob (symbolically represented in
this circuit using wires).

4. Bob measures the qubits according to his binary string for
measurement. To measure a qubit in the {+,-} basis, he applies
an H gate to the corresponding qubit and then performs a measurement.

References:
https://en.wikipedia.org/wiki/BB84
https://qiskit.org/textbook/ch-algorithms/quantum-key-distribution.html
"""
import numpy as np
import qiskit


def bb84(key_len: int = 8, seed: int | None = None) -> str:
    """
    Performs the BB84 protocol using a key made of `key_len` bits.
    The two parties in the key distribution are called Alice and Bob.
    Args:
        key_len: The length of the generated key in bits. The default is 8.

        seed: Seed for the random number generator.
        Mostly used for testing. Default is None.

    Returns:
        key: The key generated using BB84 protocol.

    >>> bb84(16, seed=0)
    '0111110111010010'

    >>> bb84(8, seed=0)
    '10110001'
    """
    # Set up the random number generator.
    rng = np.random.default_rng(seed=seed)

    # Roughly 25% of the qubits will contribute to the key.
    # So we take more than we need.
    num_qubits = 6 * key_len
    # Measurement basis for Alice's qubits.
    alice_basis = rng.integers(2, size=num_qubits)
    # The set of states Alice will prepare.
    alice_state = rng.integers(2, size=num_qubits)
    # Measurement basis for Bob's qubits.
    bob_basis = rng.integers(2, size=num_qubits)

    # Quantum Circuit to simulate BB84
    bb84_circ = qiskit.QuantumCircuit(num_qubits, name="BB84")

    # Alice prepares her qubits according to rules above.
    for index, _ in enumerate(alice_basis):
        if alice_state[index] == 1:
            bb84_circ.x(index)
        if alice_basis[index] == 1:
            bb84_circ.h(index)
    bb84_circ.barrier()

    # Bob measures the received qubits according to rules above.
    for index, _ in enumerate(bob_basis):
        if bob_basis[index] == 1:
            bb84_circ.h(index)

    bb84_circ.barrier()
    bb84_circ.measure_all()

    # Simulate the quantum circuit.
    sim = qiskit.Aer.get_backend("aer_simulator")
    # We only need to run one shot because the key is unique.
    # Multiple shots will produce the same key.
    job = qiskit.execute(bb84_circ, sim, shots=1, seed_simulator=seed)
    # Returns the result of measurement.
    result = job.result().get_counts(bb84_circ).most_frequent()

    # Extracting the generated key from the simulation results.
    # Only keep measurement results where Alice and Bob chose the same basis.
    gen_key = "".join(
        [
            result_bit
            for alice_basis_bit, bob_basis_bit, result_bit in zip(
                alice_basis, bob_basis, result
            )
            if alice_basis_bit == bob_basis_bit
        ]
    )

    # Get final key. Pad with 0 if too short, otherwise truncate.
    key = gen_key[:key_len] if len(gen_key) >= key_len else gen_key.ljust(key_len, "0")
    return key


if __name__ == "__main__":
    print(f"The generated key is : {bb84(8, seed=0)}")
    from doctest import testmod

    testmod()


================================================
FILE: quantum/deutsch_jozsa.py.DISABLED.txt
================================================
# DISABLED!!
#!/usr/bin/env python3
"""
Deutsch-Jozsa Algorithm is one of the first examples of a quantum
algorithm that is exponentially faster than any possible deterministic
classical algorithm

Premise:
We are given a hidden Boolean function f,
which takes as input a string of bits, and returns either 0 or 1:

f({x0,x1,x2,...}) -> 0 or 1, where xn is 0 or 1

The property of the given Boolean function is that it is guaranteed to
either be balanced or constant. A constant function returns all 0's
or all 1's for any input, while a balanced function returns  0's for
exactly half of all inputs and 1's for the other half. Our task is to
determine whether the given function is balanced or constant.

References:
- https://en.wikipedia.org/wiki/Deutsch-Jozsa_algorithm
- https://qiskit.org/textbook/ch-algorithms/deutsch-jozsa.html
"""

import numpy as np
import qiskit


def dj_oracle(case: str, num_qubits: int) -> qiskit.QuantumCircuit:
    """
    Returns a Quantum Circuit for the Oracle function.
    The circuit returned can represent balanced or constant function,
    according to the arguments passed
    """
    # This circuit has num_qubits+1 qubits: the size of the input,
    # plus one output qubit
    oracle_qc = qiskit.QuantumCircuit(num_qubits + 1)

    # First, let's deal with the case in which oracle is balanced
    if case == "balanced":
        # First generate a random number that tells us which CNOTs to
        # wrap in X-gates:
        b = np.random.randint(1, 2**num_qubits)
        # Next, format 'b' as a binary string of length 'n', padded with zeros:
        b_str = format(b, f"0{num_qubits}b")
        # Next, we place the first X-gates. Each digit in our binary string
        # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
        # we apply an X-gate to that qubit:
        for index, bit in enumerate(b_str):
            if bit == "1":
                oracle_qc.x(index)
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
        for index in range(num_qubits):
            oracle_qc.cx(index, num_qubits)
        # Next, place the final X-gates
        for index, bit in enumerate(b_str):
            if bit == "1":
                oracle_qc.x(index)

    # Case in which oracle is constant
    if case == "constant":
        # First decide what the fixed output of the oracle will be
        # (either always 0 or always 1)
        output = np.random.randint(2)
        if output == 1:
            oracle_qc.x(num_qubits)

    oracle_gate = oracle_qc.to_gate()
    oracle_gate.name = "Oracle"  # To show when we display the circuit
    return oracle_gate


def dj_algorithm(
    oracle: qiskit.QuantumCircuit, num_qubits: int
) -> qiskit.QuantumCircuit:
    """
    Returns the complete Deutsch-Jozsa Quantum Circuit,
    adding Input & Output registers and Hadamard & Measurement Gates,
    to the Oracle Circuit passed in arguments
    """
    dj_circuit = qiskit.QuantumCircuit(num_qubits + 1, num_qubits)
    # Set up the output qubit:
    dj_circuit.x(num_qubits)
    dj_circuit.h(num_qubits)
    # And set up the input register:
    for qubit in range(num_qubits):
        dj_circuit.h(qubit)
    # Let's append the oracle gate to our circuit:
    dj_circuit.append(oracle, range(num_qubits + 1))
    # Finally, perform the H-gates again and measure:
    for qubit in range(num_qubits):
        dj_circuit.h(qubit)

    for i in range(num_qubits):
        dj_circuit.measure(i, i)

    return dj_circuit


def deutsch_jozsa(case: str, num_qubits: int) -> qiskit.result.counts.Counts:
    """
    Main function that builds the circuit using other helper functions,
    runs the experiment 1000 times & returns the resultant qubit counts
    >>> deutsch_jozsa("constant", 3)
    {'000': 1000}
    >>> deutsch_jozsa("balanced", 3)
    {'111': 1000}
    """
    # Use Aer's simulator
    simulator = qiskit.Aer.get_backend("aer_simulator")

    oracle_gate = dj_oracle(case, num_qubits)
    dj_circuit = dj_algorithm(oracle_gate, num_qubits)

    # Execute the circuit on the simulator
    job = qiskit.execute(dj_circuit, simulator, shots=1000)

    # Return the histogram data of the results of the experiment.
    return job.result().get_counts(dj_circuit)


if __name__ == "__main__":
    print(f"Deutsch Jozsa - Constant Oracle: {deutsch_jozsa('constant', 3)}")
    print(f"Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa('balanced', 3)}")


================================================
FILE: quantum/half_adder.py.DISABLED.txt
================================================
# DISABLED!!
#!/usr/bin/env python3
"""
Build a half-adder quantum circuit that takes two bits as input,
encodes them into qubits, then runs the half-adder circuit calculating
the sum and carry qubits, observed over 1000 runs of the experiment
.

References:
https://en.wikipedia.org/wiki/Adder_(electronics)
https://qiskit.org/textbook/ch-states/atoms-computation.html#4.2-Remembering-how-to-add-
"""

import qiskit


def half_adder(bit0: int, bit1: int) -> qiskit.result.counts.Counts:
    """
    >>> half_adder(0, 0)
    {'00': 1000}
    >>> half_adder(0, 1)
    {'01': 1000}
    >>> half_adder(1, 0)
    {'01': 1000}
    >>> half_adder(1, 1)
    {'10': 1000}
    """
    # Use Aer's simulator
    simulator = qiskit.Aer.get_backend("aer_simulator")

    qc_ha = qiskit.QuantumCircuit(4, 2)
    # encode inputs in qubits 0 and 1
    if bit0 == 1:
        qc_ha.x(0)
    if bit1 == 1:
        qc_ha.x(1)
    qc_ha.barrier()

    # use cnots to write XOR of the inputs on qubit2
    qc_ha.cx(0, 2)
    qc_ha.cx(1, 2)

    # use ccx / toffoli gate to write AND of the inputs on qubit3
    qc_ha.ccx(0, 1, 3)
    qc_ha.barrier()

    # extract outputs
    qc_ha.measure(2, 0)  # extract XOR value
    qc_ha.measure(3, 1)  # extract AND value

    # Execute the circuit on the qasm simulator
    job = qiskit.execute(qc_ha, simulator, shots=1000)

    # Return the histogram data of the results of the experiment
    return job.result().get_counts(qc_ha)


if __name__ == "__main__":
    counts = half_adder(1, 1)
    print(f"Half Adder Output Qubit Counts: {counts}")


================================================
FILE: quantum/not_gate.py.DISABLED.txt
================================================
#!/usr/bin/env python3
"""
Build a simple bare-minimum quantum circuit that starts with a single
qubit (by default, in state 0) and inverts it. Run the experiment 1000
times and print the total count of the states finally observed.
Qiskit Docs: https://qiskit.org/documentation/getting_started.html
"""

import qiskit


def single_qubit_measure(
    qubits: int, classical_bits: int
) -> qiskit.result.counts.Counts:
    """
    >>> single_qubit_measure(2, 2)
    {'11': 1000}
    >>> single_qubit_measure(4, 4)
    {'0011': 1000}
    """
    # Use Aer's simulator
    simulator = qiskit.Aer.get_backend("aer_simulator")

    # Create a Quantum Circuit acting on the q register
    circuit = qiskit.QuantumCircuit(qubits, classical_bits)

    # Apply X (NOT) Gate to Qubits 0 & 1
    circuit.x(0)
    circuit.x(1)

    # Map the quantum measurement to the classical bits
    circuit.measure([0, 1], [0, 1])

    # Execute the circuit on the qasm simulator
    job = qiskit.execute(circuit, simulator, shots=1000)

    # Return the histogram data of the results of the experiment.
    return job.result().get_counts(circuit)


if __name__ == "__main__":
    counts = single_qubit_measure(2, 2)
    print(f"Total count for various states are: {counts}")


================================================
FILE: quantum/q_fourier_transform.py
================================================
"""
Build the quantum fourier transform (qft) for a desire
number of quantum bits using Qiskit framework. This
experiment run in IBM Q simulator with 10000 shots.
This circuit can be use as a building block to design
the Shor's algorithm in quantum computing. As well as,
quantum phase estimation among others.
.
References:
https://en.wikipedia.org/wiki/Quantum_Fourier_transform
https://qiskit.org/textbook/ch-algorithms/quantum-fourier-transform.html
"""

import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute


def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts.Counts:
    """
    # >>> quantum_fourier_transform(2)
    # {'00': 2500, '01': 2500, '11': 2500, '10': 2500}
    # quantum circuit for number_of_qubits = 3:
                                               ┌───┐
    qr_0: ──────■──────────────────────■───────┤ H ├─X─
                │                ┌───┐ │P(π/2) └───┘ │
    qr_1: ──────┼────────■───────┤ H ├─■─────────────┼─
          ┌───┐ │P(π/4)  │P(π/2) └───┘               │
    qr_2: ┤ H ├─■────────■───────────────────────────X─
          └───┘
    cr: 3/═════════════════════════════════════════════
    Args:
        n : number of qubits
    Returns:
        qiskit.result.counts.Counts: distribute counts.

    >>> quantum_fourier_transform(2)
    {'00': 2500, '01': 2500, '10': 2500, '11': 2500}
    >>> quantum_fourier_transform(-1)
    Traceback (most recent call last):
        ...
    ValueError: number of qubits must be > 0.
    >>> quantum_fourier_transform('a')
    Traceback (most recent call last):
        ...
    TypeError: number of qubits must be a integer.
    >>> quantum_fourier_transform(100)
    Traceback (most recent call last):
        ...
    ValueError: number of qubits too large to simulate(>10).
    >>> quantum_fourier_transform(0.5)
    Traceback (most recent call last):
        ...
    ValueError: number of qubits must be exact integer.
    """
    if isinstance(number_of_qubits, str):
        raise TypeError("number of qubits must be a integer.")
    if number_of_qubits <= 0:
        raise ValueError("number of qubits must be > 0.")
    if math.floor(number_of_qubits) != number_of_qubits:
        raise ValueError("number of qubits must be exact integer.")
    if number_of_qubits > 10:
        raise ValueError("number of qubits too large to simulate(>10).")

    qr = QuantumRegister(number_of_qubits, "qr")
    cr = ClassicalRegister(number_of_qubits, "cr")

    quantum_circuit = QuantumCircuit(qr, cr)

    counter = number_of_qubits

    for i in range(counter):
        quantum_circuit.h(number_of_qubits - i - 1)
        counter -= 1
        for j in range(counter):
            quantum_circuit.cp(np.pi / 2 ** (counter - j), j, counter)

    for k in range(number_of_qubits // 2):
        quantum_circuit.swap(k, number_of_qubits - k - 1)

    # measure all the qubits
    quantum_circuit.measure(qr, cr)
    # simulate with 10000 shots
    backend = Aer.get_backend("qasm_simulator")
    job = execute(quantum_circuit, backend, shots=10000)

    return job.result().get_counts(quantum_circuit)


if __name__ == "__main__":
    print(
        f"Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"
    )


================================================
FILE: quantum/q_full_adder.py.DISABLED.txt
================================================
"""
Build the quantum full adder (QFA) for any sum of
two quantum registers and one carry in. This circuit
is designed using the Qiskit framework. This
experiment run in IBM Q simulator with 1000 shots.
.
References:
https://www.quantum-inspire.com/kbase/full-adder/
"""

import math

import qiskit


def quantum_full_adder(
    input_1: int = 1, input_2: int = 1, carry_in: int = 1
) -> qiskit.result.counts.Counts:
    """
    # >>> q_full_adder(inp_1, inp_2, cin)
    # the inputs can be 0/1 for qubits in define
    # values, or can be in a superposition of both
    # states with hadamard gate using the input value 2.
    # result for default values: {11: 1000}
    qr_0: ──■────■──────────────■──
            │  ┌─┴─┐          ┌─┴─┐
    qr_1: ──■──┤ X ├──■────■──┤ X ├
            │  └───┘  │  ┌─┴─┐└───┘
    qr_2: ──┼─────────■──┤ X ├─────
          ┌─┴─┐     ┌─┴─┐└───┘
    qr_3: ┤ X ├─────┤ X ├──────────
          └───┘     └───┘
    cr: 2/═════════════════════════
    Args:
        input_1: input 1 for the circuit.
        input_2: input 2 for the circuit.
        carry_in: carry in for the circuit.
    Returns:
        qiskit.result.counts.Counts: sum result counts.
    >>> quantum_full_adder(1, 1, 1)
    {'11': 1000}
    >>> quantum_full_adder(0, 0, 1)
    {'01': 1000}
    >>> quantum_full_adder(1, 0, 1)
    {'10': 1000}
    >>> quantum_full_adder(1, -4, 1)
    Traceback (most recent call last):
        ...
    ValueError: inputs must be positive.
    >>> quantum_full_adder('q', 0, 1)
    Traceback (most recent call last):
        ...
    TypeError: inputs must be integers.
    >>> quantum_full_adder(0.5, 0, 1)
    Traceback (most recent call last):
        ...
    ValueError: inputs must be exact integers.
    >>> quantum_full_adder(0, 1, 3)
    Traceback (most recent call last):
        ...
    ValueError: inputs must be less or equal to 2.
    """
    if (
        isinstance(input_1, str)
        or isinstance(input_2, str)
        or isinstance(carry_in, str)
    ):
        raise TypeError("inputs must be integers.")

    if (input_1 < 0) or (input_2 < 0) or (carry_in < 0):
        raise ValueError("inputs must be positive.")

    if (
        (math.floor(input_1) != input_1)
        or (math.floor(input_2) != input_2)
        or (math.floor(carry_in) != carry_in)
    ):
        raise ValueError("inputs must be exact integers.")

    if (input_1 > 2) or (input_2 > 2) or (carry_in > 2):
        raise ValueError("inputs must be less or equal to 2.")

    # build registers
    qr = qiskit.QuantumRegister(4, "qr")
    cr = qiskit.ClassicalRegister(2, "cr")
    # list the entries
    entry = [input_1, input_2, carry_in]

    quantum_circuit = qiskit.QuantumCircuit(qr, cr)

    for i in range(3):
        if entry[i] == 2:
            quantum_circuit.h(i)  # for hadamard entries
        elif entry[i] == 1:
            quantum_circuit.x(i)  # for 1 entries
        elif entry[i] == 0:
            quantum_circuit.i(i)  # for 0 entries

    # build the circuit
    quantum_circuit.ccx(0, 1, 3)  # ccx = toffoli gate
    quantum_circuit.cx(0, 1)
    quantum_circuit.ccx(1, 2, 3)
    quantum_circuit.cx(1, 2)
    quantum_circuit.cx(0, 1)

    quantum_circuit.measure([2, 3], cr)  # measure the last two qbits

    backend = qiskit.Aer.get_backend("aer_simulator")
    job = qiskit.execute(quantum_circuit, backend, shots=1000)

    return job.result().get_counts(quantum_circuit)


if __name__ == "__main__":
    print(f"Total sum count for state is: {quantum_full_adder(1, 1, 1)}")


================================================
FILE: quantum/quantum_entanglement.py.DISABLED.txt
================================================
#!/usr/bin/env python3
"""
Build a quantum circuit with pair or group of qubits to perform
quantum entanglement.
Quantum entanglement is a phenomenon observed at the quantum scale
where entangled particles stay connected (in some sense) so that
the actions performed on one of the particles affects the other,
no matter the distance between two particles.
"""

import qiskit


def quantum_entanglement(qubits: int = 2) -> qiskit.result.counts.Counts:
    """
    # >>> quantum_entanglement(2)
    # {'00': 500, '11': 500}
    #      ┌───┐     ┌─┐
    # q_0: ┤ H ├──■──┤M├───
    #      └───┘┌─┴─┐└╥┘┌─┐
    # q_1: ─────┤ X ├─╫─┤M├
    #           └───┘ ║ └╥┘
    # c: 2/═══════════╩══╩═
    #                 0  1
    Args:
        qubits (int): number of quibits to use. Defaults to 2
    Returns:
        qiskit.result.counts.Counts: mapping of states to its counts
    """
    classical_bits = qubits

    # Using Aer's simulator
    simulator = qiskit.Aer.get_backend("aer_simulator")

    # Creating a Quantum Circuit acting on the q register
    circuit = qiskit.QuantumCircuit(qubits, classical_bits)

    # Adding a H gate on qubit 0 (now q0 in superposition)
    circuit.h(0)

    for i in range(1, qubits):
        # Adding CX (CNOT) gate
        circuit.cx(i - 1, i)

    # Mapping the quantum measurement to the classical bits
    circuit.measure(list(range(qubits)), list(range(classical_bits)))

    # Now measuring any one qubit would affect other qubits to collapse
    # their super position and have same state as the measured one.

    # Executing the circuit on the simulator
    job = qiskit.execute(circuit, simulator, shots=1000)

    return job.result().get_counts(circuit)


if __name__ == "__main__":
    print(f"Total count for various states are: {quantum_entanglement(3)}")


================================================
FILE: quantum/quantum_random.py.DISABLED.txt
================================================
import doctest

import projectq
from projectq.ops import H, Measure


def get_random_number(quantum_engine: projectq.cengines._main.MainEngine) -> int:
    """
    >>> isinstance(get_random_number(projectq.MainEngine()), int)
    True
    """
    qubit = quantum_engine.allocate_qubit()
    H | qubit
    Measure | qubit
    return int(qubit)


if __name__ == "__main__":
    doctest.testmod()

    # initialises a new quantum backend
    quantum_engine = projectq.MainEngine()

    # Generate a list of 10 random numbers
    random_numbers_list = [get_random_number(quantum_engine) for _ in range(10)]

    # Flushes the quantum engine from memory
    quantum_engine.flush()

    print("Random numbers", random_numbers_list)


================================================
FILE: quantum/quantum_teleportation.py.DISABLED.txt
================================================
#!/usr/bin/env python3
"""
Build quantum teleportation circuit using three quantum bits
and 1 classical bit. The main idea is to send one qubit from
Alice to Bob using the entanglement properties. This experiment
run in IBM Q simulator with 1000 shots.
.
References:
https://en.wikipedia.org/wiki/Quantum_teleportation
https://qiskit.org/textbook/ch-algorithms/teleportation.html
"""

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute


def quantum_teleportation(
    theta: float = np.pi / 2, phi: float = np.pi / 2, lam: float = np.pi / 2
) -> qiskit.result.counts.Counts:
    """
    # >>> quantum_teleportation()
    #{'00': 500, '11': 500} # ideally
    #      ┌─────────────────┐          ┌───┐
    #qr_0: ┤  U(π/2,π/2,π/2) ├───────■──┤ H ├─■─────────
    #      └──────┬───┬──────┘     ┌─┴─┐└───┘ │
    #qr_1: ───────┤ H ├─────────■──┤ X ├──────┼───■─────
    #             └───┘       ┌─┴─┐└───┘      │ ┌─┴─┐┌─┐
    #qr_2: ───────────────────┤ X ├───────────■─┤ X ├┤M├
    #                         └───┘             └───┘└╥┘
    #cr: 1/═══════════════════════════════════════════╩═
    Args:
        theta (float): Single qubit rotation U Gate theta parameter. Default to np.pi/2
        phi (float): Single qubit rotation U Gate phi parameter. Default to np.pi/2
        lam (float): Single qubit rotation U Gate lam parameter. Default to np.pi/2
    Returns:
        qiskit.result.counts.Counts: Teleported qubit counts.
    """

    qr = QuantumRegister(3, "qr")  # Define the number of quantum bits
    cr = ClassicalRegister(1, "cr")  # Define the number of classical bits

    quantum_circuit = QuantumCircuit(qr, cr)  # Define the quantum circuit.

    # Build the circuit
    quantum_circuit.u(theta, phi, lam, 0)  # Quantum State to teleport
    quantum_circuit.h(1)  # add hadamard gate
    quantum_circuit.cx(
        1, 2
    )  # add control gate with qubit 1 as control and 2 as target.
    quantum_circuit.cx(0, 1)
    quantum_circuit.h(0)
    quantum_circuit.cz(0, 2)  # add control z gate.
    quantum_circuit.cx(1, 2)

    quantum_circuit.measure([2], [0])  # measure the qubit.

    # Simulate the circuit using qasm simulator
    backend = Aer.get_backend("aer_simulator")
    job = execute(quantum_circuit, backend, shots=1000)

    return job.result().get_counts(quantum_circuit)


if __name__ == "__main__":
    print(
        "Total count for teleported state is: "
        f"{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}"
    )


================================================
FILE: quantum/ripple_adder_classic.py.DISABLED.txt
================================================
# https://github.com/rupansh/QuantumComputing/blob/master/rippleadd.py
# https://en.wikipedia.org/wiki/Adder_(electronics)#Full_adder
# https://en.wikipedia.org/wiki/Controlled_NOT_gate

import qiskit
from qiskit.providers import Backend


def store_two_classics(val1: int, val2: int) -> tuple[qiskit.QuantumCircuit, str, str]:
    """
    Generates a Quantum Circuit which stores two classical integers
    Returns the circuit and binary representation of the integers
    """
    x, y = bin(val1)[2:], bin(val2)[2:]  # Remove leading '0b'

    # Ensure that both strings are of the same length
    if len(x) > len(y):
        y = y.zfill(len(x))
    else:
        x = x.zfill(len(y))

    # We need (3 * number of bits in the larger number)+1 qBits
    # The second parameter is the number of classical registers, to measure the result
    circuit = qiskit.QuantumCircuit((len(x) * 3) + 1, len(x) + 1)

    # We are essentially "not-ing" the bits that are 1
    # Reversed because it's easier to perform ops on more significant bits
    for i in range(len(x)):
        if x[::-1][i] == "1":
            circuit.x(i)
    for j in range(len(y)):
        if y[::-1][j] == "1":
            circuit.x(len(x) + j)

    return circuit, x, y


def full_adder(
    circuit: qiskit.QuantumCircuit,
    input1_loc: int,
    input2_loc: int,
    carry_in: int,
    carry_out: int,
):
    """
    Quantum Equivalent of a Full Adder Circuit
    CX/CCX is like 2-way/3-way XOR
    """
    circuit.ccx(input1_loc, input2_loc, carry_out)
    circuit.cx(input1_loc, input2_loc)
    circuit.ccx(input2_loc, carry_in, carry_out)
    circuit.cx(input2_loc, carry_in)
    circuit.cx(input1_loc, input2_loc)


# The default value for **backend** is the result of a function call which is not
# normally recommended and causes ruff to raise a B008 error. However, in this case,
# this is acceptable because `Aer.get_backend()` is called when the function is defined
# and that same backend is then reused for all function calls.


def ripple_adder(
    val1: int,
    val2: int,
    backend: Backend = qiskit.Aer.get_backend("aer_simulator"),  # noqa: B008
) -> int:
    """
    Quantum Equivalent of a Ripple Adder Circuit
    Uses qasm_simulator backend by default

    Currently only adds 'emulated' Classical Bits
    but nothing prevents us from doing this with hadamard'd bits :)

    Only supports adding positive integers

    >>> ripple_adder(3, 4)
    7
    >>> ripple_adder(10, 4)
    14
    >>> ripple_adder(-1, 10)
    Traceback (most recent call last):
        ...
    ValueError: Both Integers must be positive!
    """

    if val1 < 0 or val2 < 0:
        raise ValueError("Both Integers must be positive!")

    # Store the Integers
    circuit, x, y = store_two_classics(val1, val2)

    """
    We are essentially using each bit of x & y respectively as full_adder's input
    the carry_input is used from the previous circuit (for circuit num > 1)

    the carry_out is just below carry_input because
    it will be essentially the carry_input for the next full_adder
    """
    for i in range(len(x)):
        full_adder(circuit, i, len(x) + i, len(x) + len(y) + i, len(x) + len(y) + i + 1)
        circuit.barrier()  # Optional, just for aesthetics

    # Measure the resultant qBits
    for i in range(len(x) + 1):
        circuit.measure([(len(x) * 2) + i], [i])

    res = qiskit.execute(circuit, backend, shots=1).result()

    # The result is in binary. Convert it back to int
    return int(next(iter(res.get_counts())), 2)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: quantum/single_qubit_measure.py.DISABLED.txt
================================================
#!/usr/bin/env python3
"""
Build a simple bare-minimum quantum circuit that starts with a single
qubit (by default, in state 0), runs the experiment 1000 times, and
finally prints the total count of the states finally observed.
Qiskit Docs: https://qiskit.org/documentation/getting_started.html
"""

import qiskit


def single_qubit_measure(
    qubits: int, classical_bits: int
) -> qiskit.result.counts.Counts:
    """
    >>> single_qubit_measure(1, 1)
    {'0': 1000}
    """
    # Use Aer's simulator
    simulator = qiskit.Aer.get_backend("aer_simulator")

    # Create a Quantum Circuit acting on the q register
    circuit = qiskit.QuantumCircuit(qubits, classical_bits)

    # Map the quantum measurement to the classical bits
    circuit.measure([0], [0])

    # Execute the circuit on the simulator
    job = qiskit.execute(circuit, simulator, shots=1000)

    # Return the histogram data of the results of the experiment.
    return job.result().get_counts(circuit)


if __name__ == "__main__":
    print(f"Total count for various states are: {single_qubit_measure(1, 1)}")


================================================
FILE: quantum/superdense_coding.py.DISABLED.txt
================================================
"""
Build the superdense coding protocol. This quantum
circuit can send two classical bits using one quantum
bit. This circuit is designed using the Qiskit
framework. This experiment run in IBM Q simulator
with 1000 shots.
.
References:
https://qiskit.org/textbook/ch-algorithms/superdense-coding.html
https://en.wikipedia.org/wiki/Superdense_coding
"""

import math

import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute


def superdense_coding(bit_1: int = 1, bit_2: int = 1) -> qiskit.result.counts.Counts:
    """
    The input refer to the classical message
    that you wants to send. {'00','01','10','11'}
    result for default values: {11: 1000}
               ┌───┐          ┌───┐
    qr_0: ─────┤ X ├──────────┤ X ├─────
          ┌───┐└─┬─┘┌───┐┌───┐└─┬─┘┌───┐
    qr_1: ┤ H ├──■──┤ X ├┤ Z ├──■──┤ H ├
          └───┘     └───┘└───┘     └───┘
    cr: 2/══════════════════════════════
    Args:
        bit_1: bit 1 of classical information to send.
        bit_2: bit 2 of classical information to send.
    Returns:
        qiskit.result.counts.Counts: counts of send state.
    >>> superdense_coding(0,0)
    {'00': 1000}
    >>> superdense_coding(0,1)
    {'01': 1000}
    >>> superdense_coding(-1,0)
    Traceback (most recent call last):
        ...
    ValueError: inputs must be positive.
    >>> superdense_coding(1,'j')
    Traceback (most recent call last):
        ...
    TypeError: inputs must be integers.
    >>> superdense_coding(1,0.5)
    Traceback (most recent call last):
        ...
    ValueError: inputs must be exact integers.
    >>> superdense_coding(2,1)
    Traceback (most recent call last):
        ...
    ValueError: inputs must be less or equal to 1.
    """
    if isinstance(bit_1, str) or isinstance(bit_2, str):
        raise TypeError("inputs must be integers.")
    if (bit_1 < 0) or (bit_2 < 0):
        raise ValueError("inputs must be positive.")
    if (math.floor(bit_1) != bit_1) or (math.floor(bit_2) != bit_2):
        raise ValueError("inputs must be exact integers.")
    if (bit_1 > 1) or (bit_2 > 1):
        raise ValueError("inputs must be less or equal to 1.")

    # build registers
    qr = QuantumRegister(2, "qr")
    cr = ClassicalRegister(2, "cr")

    quantum_circuit = QuantumCircuit(qr, cr)

    # entanglement the qubits
    quantum_circuit.h(1)
    quantum_circuit.cx(1, 0)

    # send the information
    c_information = str(bit_1) + str(bit_2)

    if c_information == "11":
        quantum_circuit.x(1)
        quantum_circuit.z(1)
    elif c_information == "10":
        quantum_circuit.z(1)
    elif c_information == "01":
        quantum_circuit.x(1)
    else:
        quantum_circuit.i(1)

    # unentangled the circuit
    quantum_circuit.cx(1, 0)
    quantum_circuit.h(1)

    # measure the circuit
    quantum_circuit.measure(qr, cr)

    backend = Aer.get_backend("aer_simulator")
    job = execute(quantum_circuit, backend, shots=1000)

    return job.result().get_counts(quantum_circuit)


if __name__ == "__main__":
    print(f"Counts for classical state send: {superdense_coding(1,1)}")


================================================
FILE: scheduling/__init__.py
================================================


================================================
FILE: scheduling/first_come_first_served.py
================================================
# Implementation of First Come First Served scheduling algorithm
# In this Algorithm we just care about the order that the processes arrived
# without carring about their duration time
# https://en.wikipedia.org/wiki/Scheduling_(computing)#First_come,_first_served
from __future__ import annotations


def calculate_waiting_times(duration_times: list[int]) -> list[int]:
    """
    This function calculates the waiting time of some processes that have a
    specified duration time.
        Return: The waiting time for each process.
    >>> calculate_waiting_times([5, 10, 15])
    [0, 5, 15]
    >>> calculate_waiting_times([1, 2, 3, 4, 5])
    [0, 1, 3, 6, 10]
    >>> calculate_waiting_times([10, 3])
    [0, 10]
    """
    waiting_times = [0] * len(duration_times)
    for i in range(1, len(duration_times)):
        waiting_times[i] = duration_times[i - 1] + waiting_times[i - 1]
    return waiting_times


def calculate_turnaround_times(
    duration_times: list[int], waiting_times: list[int]
) -> list[int]:
    """
    This function calculates the turnaround time of some processes.
        Return: The time difference between the completion time and the
                arrival time.
                Practically waiting_time + duration_time
    >>> calculate_turnaround_times([5, 10, 15], [0, 5, 15])
    [5, 15, 30]
    >>> calculate_turnaround_times([1, 2, 3, 4, 5], [0, 1, 3, 6, 10])
    [1, 3, 6, 10, 15]
    >>> calculate_turnaround_times([10, 3], [0, 10])
    [10, 13]
    """
    return [
        duration_time + waiting_times[i]
        for i, duration_time in enumerate(duration_times)
    ]


def calculate_average_turnaround_time(turnaround_times: list[int]) -> float:
    """
    This function calculates the average of the turnaround times
        Return: The average of the turnaround times.
    >>> calculate_average_turnaround_time([0, 5, 16])
    7.0
    >>> calculate_average_turnaround_time([1, 5, 8, 12])
    6.5
    >>> calculate_average_turnaround_time([10, 24])
    17.0
    """
    return sum(turnaround_times) / len(turnaround_times)


def calculate_average_waiting_time(waiting_times: list[int]) -> float:
    """
    This function calculates the average of the waiting times
        Return: The average of the waiting times.
    >>> calculate_average_waiting_time([0, 5, 16])
    7.0
    >>> calculate_average_waiting_time([1, 5, 8, 12])
    6.5
    >>> calculate_average_waiting_time([10, 24])
    17.0
    """
    return sum(waiting_times) / len(waiting_times)


if __name__ == "__main__":
    # process id's
    processes = [1, 2, 3]

    # ensure that we actually have processes
    if len(processes) == 0:
        print("Zero amount of processes")
        raise SystemExit(0)

    # duration time of all processes
    duration_times = [19, 8, 9]

    # ensure we can match each id to a duration time
    if len(duration_times) != len(processes):
        print("Unable to match all id's with their duration time")
        raise SystemExit(0)

    # get the waiting times and the turnaround times
    waiting_times = calculate_waiting_times(duration_times)
    turnaround_times = calculate_turnaround_times(duration_times, waiting_times)

    # get the average times
    average_waiting_time = calculate_average_waiting_time(waiting_times)
    average_turnaround_time = calculate_average_turnaround_time(turnaround_times)

    # print all the results
    print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
    for i, process in enumerate(processes):
        print(
            f"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
            f"{turnaround_times[i]}"
        )
    print(f"Average waiting time = {average_waiting_time}")
    print(f"Average turn around time = {average_turnaround_time}")


================================================
FILE: scheduling/highest_response_ratio_next.py
================================================
"""
Highest response ratio next (HRRN) scheduling is a non-preemptive discipline.
It was developed as modification of shortest job next or shortest job first (SJN or SJF)
to mitigate the problem of process starvation.
https://en.wikipedia.org/wiki/Highest_response_ratio_next
"""

from statistics import mean

import numpy as np


def calculate_turn_around_time(
    process_name: list, arrival_time: list, burst_time: list, no_of_process: int
) -> list:
    """
    Calculate the turn around time of each processes

    Return: The turn around time time for each process.
    >>> calculate_turn_around_time(["A", "B", "C"], [3, 5, 8], [2, 4, 6], 3)
    [2, 4, 7]
    >>> calculate_turn_around_time(["A", "B", "C"], [0, 2, 4], [3, 5, 7], 3)
    [3, 6, 11]
    """

    current_time = 0
    # Number of processes finished
    finished_process_count = 0
    # Displays the finished process.
    # If it is 0, the performance is completed if it is 1, before the performance.
    finished_process = [0] * no_of_process
    # List to include calculation results
    turn_around_time = [0] * no_of_process

    # Sort by arrival time.
    burst_time = [burst_time[i] for i in np.argsort(arrival_time)]
    process_name = [process_name[i] for i in np.argsort(arrival_time)]
    arrival_time.sort()

    while no_of_process > finished_process_count:
        """
        If the current time is less than the arrival time of
        the process that arrives first among the processes that have not been performed,
        change the current time.
        """
        i = 0
        while finished_process[i] == 1:
            i += 1
        current_time = max(current_time, arrival_time[i])

        response_ratio = 0
        # Index showing the location of the process being performed
        loc = 0
        # Saves the current response ratio.
        temp = 0
        for i in range(no_of_process):
            if finished_process[i] == 0 and arrival_time[i] <= current_time:
                temp = (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                    i
                ]
            if response_ratio < temp:
                response_ratio = temp
                loc = i

        # Calculate the turn around time
        turn_around_time[loc] = current_time + burst_time[loc] - arrival_time[loc]
        current_time += burst_time[loc]
        # Indicates that the process has been performed.
        finished_process[loc] = 1
        # Increase finished_process_count by 1
        finished_process_count += 1

    return turn_around_time


def calculate_waiting_time(
    process_name: list,  # noqa: ARG001
    turn_around_time: list,
    burst_time: list,
    no_of_process: int,
) -> list:
    """
    Calculate the waiting time of each processes.

    Return: The waiting time for each process.
    >>> calculate_waiting_time(["A", "B", "C"], [2, 4, 7], [2, 4, 6], 3)
    [0, 0, 1]
    >>> calculate_waiting_time(["A", "B", "C"], [3, 6, 11], [3, 5, 7], 3)
    [0, 1, 4]
    """

    waiting_time = [0] * no_of_process
    for i in range(no_of_process):
        waiting_time[i] = turn_around_time[i] - burst_time[i]
    return waiting_time


if __name__ == "__main__":
    no_of_process = 5
    process_name = ["A", "B", "C", "D", "E"]
    arrival_time = [1, 2, 3, 4, 5]
    burst_time = [1, 2, 3, 4, 5]

    turn_around_time = calculate_turn_around_time(
        process_name, arrival_time, burst_time, no_of_process
    )
    waiting_time = calculate_waiting_time(
        process_name, turn_around_time, burst_time, no_of_process
    )

    print("Process name \tArrival time \tBurst time \tTurn around time \tWaiting time")
    for i in range(no_of_process):
        print(
            f"{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t"
            f"{turn_around_time[i]}\t\t\t{waiting_time[i]}"
        )

    print(f"average waiting time : {mean(waiting_time):.5f}")
    print(f"average turn around time : {mean(turn_around_time):.5f}")


================================================
FILE: scheduling/job_sequence_with_deadline.py
================================================
"""
Given a list of tasks, each with a deadline and reward, calculate which tasks can be
completed to yield the maximum reward.  Each task takes one unit of time to complete,
and we can only work on one task at a time.  Once a task has passed its deadline, it
can no longer be scheduled.

Example :
tasks_info = [(4, 20), (1, 10), (1, 40), (1, 30)]
max_tasks will return (2, [2, 0]) -
Scheduling these tasks would result in a reward of 40 + 20

This problem can be solved using the concept of "GREEDY ALGORITHM".
Time Complexity - O(n log n)
https://medium.com/@nihardudhat2000/job-sequencing-with-deadline-17ddbb5890b5
"""

from dataclasses import dataclass
from operator import attrgetter


@dataclass
class Task:
    task_id: int
    deadline: int
    reward: int


def max_tasks(tasks_info: list[tuple[int, int]]) -> list[int]:
    """
    Create a list of Task objects that are sorted so the highest rewards come first.
    Return a list of those task ids that can be completed before i becomes too high.
    >>> max_tasks([(4, 20), (1, 10), (1, 40), (1, 30)])
    [2, 0]
    >>> max_tasks([(1, 10), (2, 20), (3, 30), (2, 40)])
    [3, 2]
    >>> max_tasks([(9, 10)])
    [0]
    >>> max_tasks([(-9, 10)])
    []
    >>> max_tasks([])
    []
    >>> max_tasks([(0, 10), (0, 20), (0, 30), (0, 40)])
    []
    >>> max_tasks([(-1, 10), (-2, 20), (-3, 30), (-4, 40)])
    []
    """
    tasks = sorted(
        (
            Task(task_id, deadline, reward)
            for task_id, (deadline, reward) in enumerate(tasks_info)
        ),
        key=attrgetter("reward"),
        reverse=True,
    )
    return [task.task_id for i, task in enumerate(tasks, start=1) if task.deadline >= i]


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(f"{max_tasks([(4, 20), (1, 10), (1, 40), (1, 30)]) = }")
    print(f"{max_tasks([(1, 10), (2, 20), (3, 30), (2, 40)]) = }")


================================================
FILE: scheduling/job_sequencing_with_deadline.py
================================================
def job_sequencing_with_deadlines(jobs: list) -> list:
    """
    Function to find the maximum profit by doing jobs in a given time frame

    Args:
        jobs [list]: A list of tuples of (job_id, deadline, profit)

    Returns:
        max_profit [int]: Maximum profit that can be earned by doing jobs
        in a given time frame

    Examples:
    >>> job_sequencing_with_deadlines(
    ... [(1, 4, 20), (2, 1, 10), (3, 1, 40), (4, 1, 30)])
    [2, 60]
    >>> job_sequencing_with_deadlines(
    ... [(1, 2, 100), (2, 1, 19), (3, 2, 27), (4, 1, 25), (5, 1, 15)])
    [2, 127]
    """

    # Sort the jobs in descending order of profit
    jobs = sorted(jobs, key=lambda value: value[2], reverse=True)

    # Create a list of size equal to the maximum deadline
    # and initialize it with -1
    max_deadline = max(jobs, key=lambda value: value[1])[1]
    time_slots = [-1] * max_deadline

    # Finding the maximum profit and the count of jobs
    count = 0
    max_profit = 0
    for job in jobs:
        # Find a free time slot for this job
        # (Note that we start from the last possible slot)
        for i in range(job[1] - 1, -1, -1):
            if time_slots[i] == -1:
                time_slots[i] = job[0]
                count += 1
                max_profit += job[2]
                break
    return [count, max_profit]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: scheduling/multi_level_feedback_queue.py
================================================
from collections import deque


class Process:
    def __init__(self, process_name: str, arrival_time: int, burst_time: int) -> None:
        self.process_name = process_name  # process name
        self.arrival_time = arrival_time  # arrival time of the process
        # completion time of finished process or last interrupted time
        self.stop_time = arrival_time
        self.burst_time = burst_time  # remaining burst time
        self.waiting_time = 0  # total time of the process wait in ready queue
        self.turnaround_time = 0  # time from arrival time to completion time


class MLFQ:
    """
    MLFQ(Multi Level Feedback Queue)
    https://en.wikipedia.org/wiki/Multilevel_feedback_queue
    MLFQ has a lot of queues that have different priority
    In this MLFQ,
    The first Queue(0) to last second Queue(N-2) of MLFQ have Round Robin Algorithm
    The last Queue(N-1) has First Come, First Served Algorithm
    """

    def __init__(
        self,
        number_of_queues: int,
        time_slices: list[int],
        queue: deque[Process],
        current_time: int,
    ) -> None:
        # total number of mlfq's queues
        self.number_of_queues = number_of_queues
        # time slice of queues that round robin algorithm applied
        self.time_slices = time_slices
        # unfinished process is in this ready_queue
        self.ready_queue = queue
        # current time
        self.current_time = current_time
        # finished process is in this sequence queue
        self.finish_queue: deque[Process] = deque()

    def calculate_sequence_of_finish_queue(self) -> list[str]:
        """
        This method returns the sequence of finished processes
        >>> P1 = Process("P1", 0, 53)
        >>> P2 = Process("P2", 0, 17)
        >>> P3 = Process("P3", 0, 68)
        >>> P4 = Process("P4", 0, 24)
        >>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
        >>> _ = mlfq.multi_level_feedback_queue()
        >>> mlfq.calculate_sequence_of_finish_queue()
        ['P2', 'P4', 'P1', 'P3']
        """
        sequence = []
        for i in range(len(self.finish_queue)):
            sequence.append(self.finish_queue[i].process_name)
        return sequence

    def calculate_waiting_time(self, queue: list[Process]) -> list[int]:
        """
        This method calculates waiting time of processes
        >>> P1 = Process("P1", 0, 53)
        >>> P2 = Process("P2", 0, 17)
        >>> P3 = Process("P3", 0, 68)
        >>> P4 = Process("P4", 0, 24)
        >>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
        >>> _ = mlfq.multi_level_feedback_queue()
        >>> mlfq.calculate_waiting_time([P1, P2, P3, P4])
        [83, 17, 94, 101]
        """
        waiting_times = []
        for i in range(len(queue)):
            waiting_times.append(queue[i].waiting_time)
        return waiting_times

    def calculate_turnaround_time(self, queue: list[Process]) -> list[int]:
        """
        This method calculates turnaround time of processes
        >>> P1 = Process("P1", 0, 53)
        >>> P2 = Process("P2", 0, 17)
        >>> P3 = Process("P3", 0, 68)
        >>> P4 = Process("P4", 0, 24)
        >>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
        >>> _ = mlfq.multi_level_feedback_queue()
        >>> mlfq.calculate_turnaround_time([P1, P2, P3, P4])
        [136, 34, 162, 125]
        """
        turnaround_times = []
        for i in range(len(queue)):
            turnaround_times.append(queue[i].turnaround_time)
        return turnaround_times

    def calculate_completion_time(self, queue: list[Process]) -> list[int]:
        """
        This method calculates completion time of processes
        >>> P1 = Process("P1", 0, 53)
        >>> P2 = Process("P2", 0, 17)
        >>> P3 = Process("P3", 0, 68)
        >>> P4 = Process("P4", 0, 24)
        >>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
        >>> _ = mlfq.multi_level_feedback_queue()
        >>> mlfq.calculate_turnaround_time([P1, P2, P3, P4])
        [136, 34, 162, 125]
        """
        completion_times = []
        for i in range(len(queue)):
            completion_times.append(queue[i].stop_time)
        return completion_times

    def calculate_remaining_burst_time_of_processes(
        self, queue: deque[Process]
    ) -> list[int]:
        """
        This method calculate remaining burst time of processes
        >>> P1 = Process("P1", 0, 53)
        >>> P2 = Process("P2", 0, 17)
        >>> P3 = Process("P3", 0, 68)
        >>> P4 = Process("P4", 0, 24)
        >>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
        >>> finish_queue, ready_queue = mlfq.round_robin(deque([P1, P2, P3, P4]), 17)
        >>> mlfq.calculate_remaining_burst_time_of_processes(mlfq.finish_queue)
        [0]
        >>> mlfq.calculate_remaining_burst_time_of_processes(ready_queue)
        [36, 51, 7]
        >>> finish_queue, ready_queue = mlfq.round_robin(ready_queue, 25)
        >>> mlfq.calculate_remaining_burst_time_of_processes(mlfq.finish_queue)
        [0, 0]
        >>> mlfq.calculate_remaining_burst_time_of_processes(ready_queue)
        [11, 26]
        """
        return [q.burst_time for q in queue]

    def update_waiting_time(self, process: Process) -> int:
        """
        This method updates waiting times of unfinished processes
        >>> P1 = Process("P1", 0, 53)
        >>> P2 = Process("P2", 0, 17)
        >>> P3 = Process("P3", 0, 68)
        >>> P4 = Process("P4", 0, 24)
        >>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
        >>> mlfq.current_time = 10
        >>> P1.stop_time = 5
        >>> mlfq.update_waiting_time(P1)
        5
        """
        process.waiting_time += self.current_time - process.stop_time
        return process.waiting_time

    def first_come_first_served(self, ready_queue: deque[Process]) -> deque[Process]:
        """
        FCFS(First Come, First Served)
        FCFS will be applied to MLFQ's last queue
        A first came process will be finished at first
        >>> P1 = Process("P1", 0, 53)
        >>> P2 = Process("P2", 0, 17)
        >>> P3 = Process("P3", 0, 68)
        >>> P4 = Process("P4", 0, 24)
        >>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
        >>> _ = mlfq.first_come_first_served(mlfq.ready_queue)
        >>> mlfq.calculate_sequence_of_finish_queue()
        ['P1', 'P2', 'P3', 'P4']
        """
        finished: deque[Process] = deque()  # sequence deque of finished process
        while len(ready_queue) != 0:
            cp = ready_queue.popleft()  # current process

            # if process's arrival time is later than current time, update current time
            if self.current_time < cp.arrival_time:
                self.current_time += cp.arrival_time

            # update waiting time of current process
            self.update_waiting_time(cp)
            # update current time
            self.current_time += cp.burst_time
            # finish the process and set the process's burst-time 0
            cp.burst_time = 0
            # set the process's turnaround time because it is finished
            cp.turnaround_time = self.current_time - cp.arrival_time
            # set the completion time
            cp.stop_time = self.current_time
            # add the process to queue that has finished queue
            finished.append(cp)

        self.finish_queue.extend(finished)  # add finished process to finish queue
        # FCFS will finish all remaining processes
        return finished

    def round_robin(
        self, ready_queue: deque[Process], time_slice: int
    ) -> tuple[deque[Process], deque[Process]]:
        """
        RR(Round Robin)
        RR will be applied to MLFQ's all queues except last queue
        All processes can't use CPU for time more than time_slice
        If the process consume CPU up to time_slice, it will go back to ready queue
        >>> P1 = Process("P1", 0, 53)
        >>> P2 = Process("P2", 0, 17)
        >>> P3 = Process("P3", 0, 68)
        >>> P4 = Process("P4", 0, 24)
        >>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
        >>> finish_queue, ready_queue = mlfq.round_robin(mlfq.ready_queue, 17)
        >>> mlfq.calculate_sequence_of_finish_queue()
        ['P2']
        """
        finished: deque[Process] = deque()  # sequence deque of terminated process
        # just for 1 cycle and unfinished processes will go back to queue
        for _ in range(len(ready_queue)):
            cp = ready_queue.popleft()  # current process

            # if process's arrival time is later than current time, update current time
            if self.current_time < cp.arrival_time:
                self.current_time += cp.arrival_time

            # update waiting time of unfinished processes
            self.update_waiting_time(cp)
            # if the burst time of process is bigger than time-slice
            if cp.burst_time > time_slice:
                # use CPU for only time-slice
                self.current_time += time_slice
                # update remaining burst time
                cp.burst_time -= time_slice
                # update end point time
                cp.stop_time = self.current_time
                # locate the process behind the queue because it is not finished
                ready_queue.append(cp)
            else:
                # use CPU for remaining burst time
                self.current_time += cp.burst_time
                # set burst time 0 because the process is finished
                cp.burst_time = 0
                # set the finish time
                cp.stop_time = self.current_time
                # update the process' turnaround time because it is finished
                cp.turnaround_time = self.current_time - cp.arrival_time
                # add the process to queue that has finished queue
                finished.append(cp)

        self.finish_queue.extend(finished)  # add finished process to finish queue
        # return finished processes queue and remaining processes queue
        return finished, ready_queue

    def multi_level_feedback_queue(self) -> deque[Process]:
        """
        MLFQ(Multi Level Feedback Queue)
        >>> P1 = Process("P1", 0, 53)
        >>> P2 = Process("P2", 0, 17)
        >>> P3 = Process("P3", 0, 68)
        >>> P4 = Process("P4", 0, 24)
        >>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
        >>> finish_queue = mlfq.multi_level_feedback_queue()
        >>> mlfq.calculate_sequence_of_finish_queue()
        ['P2', 'P4', 'P1', 'P3']
        """

        #  all queues except last one have round_robin algorithm
        for i in range(self.number_of_queues - 1):
            _finished, self.ready_queue = self.round_robin(
                self.ready_queue, self.time_slices[i]
            )
        #  the last queue has first_come_first_served algorithm
        self.first_come_first_served(self.ready_queue)

        return self.finish_queue


if __name__ == "__main__":
    import doctest

    P1 = Process("P1", 0, 53)
    P2 = Process("P2", 0, 17)
    P3 = Process("P3", 0, 68)
    P4 = Process("P4", 0, 24)
    number_of_queues = 3
    time_slices = [17, 25]
    queue = deque([P1, P2, P3, P4])

    if len(time_slices) != number_of_queues - 1:
        raise SystemExit(0)

    doctest.testmod(extraglobs={"queue": deque([P1, P2, P3, P4])})

    P1 = Process("P1", 0, 53)
    P2 = Process("P2", 0, 17)
    P3 = Process("P3", 0, 68)
    P4 = Process("P4", 0, 24)
    number_of_queues = 3
    time_slices = [17, 25]
    queue = deque([P1, P2, P3, P4])
    mlfq = MLFQ(number_of_queues, time_slices, queue, 0)
    finish_queue = mlfq.multi_level_feedback_queue()

    # print total waiting times of processes(P1, P2, P3, P4)
    print(
        f"waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [P1, P2, P3, P4])}"
    )
    # print completion times of processes(P1, P2, P3, P4)
    print(
        f"completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [P1, P2, P3, P4])}"
    )
    # print total turnaround times of processes(P1, P2, P3, P4)
    print(
        f"turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [P1, P2, P3, P4])}"
    )
    # print sequence of finished processes
    print(
        f"sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}"
    )


================================================
FILE: scheduling/non_preemptive_shortest_job_first.py
================================================
"""
Non-preemptive Shortest Job First
Shortest execution time process is chosen for the next execution.
https://www.guru99.com/shortest-job-first-sjf-scheduling.html
https://en.wikipedia.org/wiki/Shortest_job_next
"""

from __future__ import annotations

from statistics import mean


def calculate_waitingtime(
    arrival_time: list[int], burst_time: list[int], no_of_processes: int
) -> list[int]:
    """
    Calculate the waiting time of each processes

    Return: The waiting time for each process.
    >>> calculate_waitingtime([0,1,2], [10, 5, 8], 3)
    [0, 9, 13]
    >>> calculate_waitingtime([1,2,2,4], [4, 6, 3, 1], 4)
    [0, 7, 4, 1]
    >>> calculate_waitingtime([0,0,0], [12, 2, 10],3)
    [12, 0, 2]
    """

    waiting_time = [0] * no_of_processes
    remaining_time = [0] * no_of_processes

    # Initialize remaining_time to waiting_time.

    for i in range(no_of_processes):
        remaining_time[i] = burst_time[i]
    ready_process: list[int] = []

    completed = 0
    total_time = 0

    # When processes are not completed,
    # A process whose arrival time has passed \
    # and has remaining execution time is put into the ready_process.
    # The shortest process in the ready_process, target_process is executed.

    while completed != no_of_processes:
        ready_process = []
        target_process = -1

        for i in range(no_of_processes):
            if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
                ready_process.append(i)

        if len(ready_process) > 0:
            target_process = ready_process[0]
            for i in ready_process:
                if remaining_time[i] < remaining_time[target_process]:
                    target_process = i
            total_time += burst_time[target_process]
            completed += 1
            remaining_time[target_process] = 0
            waiting_time[target_process] = (
                total_time - arrival_time[target_process] - burst_time[target_process]
            )
        else:
            total_time += 1

    return waiting_time


def calculate_turnaroundtime(
    burst_time: list[int], no_of_processes: int, waiting_time: list[int]
) -> list[int]:
    """
    Calculate the turnaround time of each process.

    Return: The turnaround time for each process.
    >>> calculate_turnaroundtime([0,1,2], 3, [0, 10, 15])
    [0, 11, 17]
    >>> calculate_turnaroundtime([1,2,2,4], 4, [1, 8, 5, 4])
    [2, 10, 7, 8]
    >>> calculate_turnaroundtime([0,0,0], 3, [12, 0, 2])
    [12, 0, 2]
    """

    turn_around_time = [0] * no_of_processes
    for i in range(no_of_processes):
        turn_around_time[i] = burst_time[i] + waiting_time[i]
    return turn_around_time


if __name__ == "__main__":
    print("[TEST CASE 01]")

    no_of_processes = 4
    burst_time = [2, 5, 3, 7]
    arrival_time = [0, 0, 0, 0]
    waiting_time = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
    turn_around_time = calculate_turnaroundtime(
        burst_time, no_of_processes, waiting_time
    )

    # Printing the Result
    print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
    for i, process_id in enumerate(list(range(1, 5))):
        print(
            f"{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"
            f"{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"
        )
    print(f"\nAverage waiting time = {mean(waiting_time):.5f}")
    print(f"Average turnaround time = {mean(turn_around_time):.5f}")


================================================
FILE: scheduling/round_robin.py
================================================
"""
Round Robin is a scheduling algorithm.
In Round Robin each process is assigned a fixed time slot in a cyclic way.
https://en.wikipedia.org/wiki/Round-robin_scheduling
"""

from __future__ import annotations

from statistics import mean


def calculate_waiting_times(burst_times: list[int]) -> list[int]:
    """
    Calculate the waiting times of a list of processes that have a specified duration.

    Return: The waiting time for each process.
    >>> calculate_waiting_times([10, 5, 8])
    [13, 10, 13]
    >>> calculate_waiting_times([4, 6, 3, 1])
    [5, 8, 9, 6]
    >>> calculate_waiting_times([12, 2, 10])
    [12, 2, 12]
    """
    quantum = 2
    rem_burst_times = list(burst_times)
    waiting_times = [0] * len(burst_times)
    t = 0
    while True:
        done = True
        for i, burst_time in enumerate(burst_times):
            if rem_burst_times[i] > 0:
                done = False
                if rem_burst_times[i] > quantum:
                    t += quantum
                    rem_burst_times[i] -= quantum
                else:
                    t += rem_burst_times[i]
                    waiting_times[i] = t - burst_time
                    rem_burst_times[i] = 0
        if done is True:
            return waiting_times


def calculate_turn_around_times(
    burst_times: list[int], waiting_times: list[int]
) -> list[int]:
    """
    >>> calculate_turn_around_times([1, 2, 3, 4], [0, 1, 3])
    [1, 3, 6]
    >>> calculate_turn_around_times([10, 3, 7], [10, 6, 11])
    [20, 9, 18]
    """
    return [burst + waiting for burst, waiting in zip(burst_times, waiting_times)]


if __name__ == "__main__":
    burst_times = [3, 5, 7]
    waiting_times = calculate_waiting_times(burst_times)
    turn_around_times = calculate_turn_around_times(burst_times, waiting_times)
    print("Process ID \tBurst Time \tWaiting Time \tTurnaround Time")
    for i, burst_time in enumerate(burst_times):
        print(
            f"  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  "
            f"{turn_around_times[i]}"
        )
    print(f"\nAverage waiting time = {mean(waiting_times):.5f}")
    print(f"Average turn around time = {mean(turn_around_times):.5f}")


================================================
FILE: scheduling/shortest_job_first.py
================================================
"""
Shortest job remaining first
Please note arrival time and burst
Please use spaces to separate times entered.
"""

from __future__ import annotations

import pandas as pd


def calculate_waitingtime(
    arrival_time: list[int], burst_time: list[int], no_of_processes: int
) -> list[int]:
    """
    Calculate the waiting time of each processes
    Return: List of waiting times.
    >>> calculate_waitingtime([1,2,3,4],[3,3,5,1],4)
    [0, 3, 5, 0]
    >>> calculate_waitingtime([1,2,3],[2,5,1],3)
    [0, 2, 0]
    >>> calculate_waitingtime([2,3],[5,1],2)
    [1, 0]
    """
    remaining_time = [0] * no_of_processes
    waiting_time = [0] * no_of_processes
    # Copy the burst time into remaining_time[]
    for i in range(no_of_processes):
        remaining_time[i] = burst_time[i]

    complete = 0
    increment_time = 0
    minm = 999999999
    short = 0
    check = False

    # Process until all processes are completed
    while complete != no_of_processes:
        for j in range(no_of_processes):
            if (
                arrival_time[j] <= increment_time
                and remaining_time[j] > 0
                and remaining_time[j] < minm
            ):
                minm = remaining_time[j]
                short = j
                check = True

        if not check:
            increment_time += 1
            continue
        remaining_time[short] -= 1

        minm = remaining_time[short]
        if minm == 0:
            minm = 999999999

        if remaining_time[short] == 0:
            complete += 1
            check = False

            # Find finish time of current process
            finish_time = increment_time + 1

            # Calculate waiting time
            finar = finish_time - arrival_time[short]
            waiting_time[short] = finar - burst_time[short]

            waiting_time[short] = max(waiting_time[short], 0)

        # Increment time
        increment_time += 1
    return waiting_time


def calculate_turnaroundtime(
    burst_time: list[int], no_of_processes: int, waiting_time: list[int]
) -> list[int]:
    """
    Calculate the turn around time of each Processes
    Return: list of turn around times.
    >>> calculate_turnaroundtime([3,3,5,1], 4, [0,3,5,0])
    [3, 6, 10, 1]
    >>> calculate_turnaroundtime([3,3], 2, [0,3])
    [3, 6]
    >>> calculate_turnaroundtime([8,10,1], 3, [1,0,3])
    [9, 10, 4]
    """
    turn_around_time = [0] * no_of_processes
    for i in range(no_of_processes):
        turn_around_time[i] = burst_time[i] + waiting_time[i]
    return turn_around_time


def calculate_average_times(
    waiting_time: list[int], turn_around_time: list[int], no_of_processes: int
) -> None:
    """
    This function calculates the average of the waiting & turnaround times
    Prints: Average Waiting time & Average Turn Around Time
    >>> calculate_average_times([0,3,5,0],[3,6,10,1],4)
    Average waiting time = 2.00000
    Average turn around time = 5.0
    >>> calculate_average_times([2,3],[3,6],2)
    Average waiting time = 2.50000
    Average turn around time = 4.5
    >>> calculate_average_times([10,4,3],[2,7,6],3)
    Average waiting time = 5.66667
    Average turn around time = 5.0
    """
    total_waiting_time = 0
    total_turn_around_time = 0
    for i in range(no_of_processes):
        total_waiting_time = total_waiting_time + waiting_time[i]
        total_turn_around_time = total_turn_around_time + turn_around_time[i]
    print(f"Average waiting time = {total_waiting_time / no_of_processes:.5f}")
    print("Average turn around time =", total_turn_around_time / no_of_processes)


if __name__ == "__main__":
    print("Enter how many process you want to analyze")
    no_of_processes = int(input())
    burst_time = [0] * no_of_processes
    arrival_time = [0] * no_of_processes
    processes = list(range(1, no_of_processes + 1))

    for i in range(no_of_processes):
        print("Enter the arrival time and burst time for process:--" + str(i + 1))
        arrival_time[i], burst_time[i] = map(int, input().split())

    waiting_time = calculate_waitingtime(arrival_time, burst_time, no_of_processes)

    bt = burst_time
    n = no_of_processes
    wt = waiting_time
    turn_around_time = calculate_turnaroundtime(bt, n, wt)

    calculate_average_times(waiting_time, turn_around_time, no_of_processes)

    fcfs = pd.DataFrame(
        list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
        columns=[
            "Process",
            "BurstTime",
            "ArrivalTime",
            "WaitingTime",
            "TurnAroundTime",
        ],
    )

    # Printing the dataFrame
    pd.set_option("display.max_rows", fcfs.shape[0] + 1)
    print(fcfs)


================================================
FILE: scripts/README.md
================================================
Dealing with the onslaught of Hacktoberfest
* https://hacktoberfest.com

Each year, October brings a swarm of new contributors participating in Hacktoberfest.  This event has its pros and cons, but it presents a monumental workload for the few active maintainers of this repo.  The maintainer workload is further impacted by a new version of CPython being released in the first week of each October.

To help make our algorithms more valuable to visitors, our CONTRIBUTING.md file outlines several strict requirements, such as tests, type hints, descriptive names, functions, and/or classes. Maintainers reviewing pull requests should try to encourage improvements to meet these goals, but when the workload becomes overwhelming (esp. in October), pull requests that do not meet these goals should be closed.

Below are a few [`gh`](https://cli.github.com) scripts that should close pull requests that do not match the definition of an acceptable algorithm as defined in CONTRIBUTING.md.  I tend to run these scripts in the following order.

* close_pull_requests_with_require_descriptive_names.sh
* close_pull_requests_with_require_tests.sh
* close_pull_requests_with_require_type_hints.sh
* close_pull_requests_with_failing_tests.sh
* close_pull_requests_with_awaiting_changes.sh
* find_git_conflicts.sh

### Run on 14 Oct 2025: 107 of 541 (19.77%) pull requests closed.

Script run | Open pull requests | Pull requests closed
--- | --- | ---
None | 541 | 0
require_descriptive_names | 515 | 26
require_tests | 498 | 17
require_type_hints | 496 | 2
failing_tests | 438 | ___58___
awaiting_changes | 434 | 4
git_conflicts | [ broken ] | 0


================================================
FILE: scripts/__init__.py
================================================


================================================
FILE: scripts/build_directory_md.py
================================================
#!/usr/bin/env python3

import os
from collections.abc import Iterator


def good_file_paths(top_dir: str = ".") -> Iterator[str]:
    for dir_path, dir_names, filenames in os.walk(top_dir):
        dir_names[:] = [
            d
            for d in dir_names
            if d != "scripts" and d[0] not in "._" and "venv" not in d
        ]
        for filename in filenames:
            if filename == "__init__.py":
                continue
            if os.path.splitext(filename)[1] in (".py", ".ipynb"):
                yield os.path.join(dir_path, filename).lstrip("./")


def md_prefix(indent: int) -> str:
    """
    Markdown prefix based on indent for bullet points

    >>> md_prefix(0)
    '\\n##'
    >>> md_prefix(1)
    '  *'
    >>> md_prefix(2)
    '    *'
    >>> md_prefix(3)
    '      *'
    """
    return f"{indent * '  '}*" if indent else "\n##"


def print_path(old_path: str, new_path: str) -> str:
    old_parts = old_path.split(os.sep)
    for i, new_part in enumerate(new_path.split(os.sep)):
        if (i + 1 > len(old_parts) or old_parts[i] != new_part) and new_part:
            print(f"{md_prefix(i)} {new_part.replace('_', ' ').title()}")
    return new_path


def print_directory_md(top_dir: str = ".") -> None:
    old_path = ""
    for filepath in sorted(good_file_paths(top_dir)):
        filepath, filename = os.path.split(filepath)
        if filepath != old_path:
            old_path = print_path(old_path, filepath)
        indent = (filepath.count(os.sep) + 1) if filepath else 0
        url = f"{filepath}/{filename}".replace(" ", "%20")
        filename = os.path.splitext(filename.replace("_", " ").title())[0]
        print(f"{md_prefix(indent)} [{filename}]({url})")


if __name__ == "__main__":
    print_directory_md(".")


================================================
FILE: scripts/close_pull_requests_with_awaiting_changes.sh
================================================
#!/bin/bash

# List all open pull requests
prs=$(gh pr list --state open --json number,title,labels --limit 500)

# Loop through each pull request
echo "$prs" | jq -c '.[]' | while read -r pr; do
  pr_number=$(echo "$pr" | jq -r '.number')
  pr_title=$(echo "$pr" | jq -r '.title')
  pr_labels=$(echo "$pr" | jq -r '.labels')

  # Check if the "awaiting changes" label is present
  awaiting_changes=$(echo "$pr_labels" | jq -r '.[] | select(.name == "awaiting changes")')
  echo "Checking PR #$pr_number $pr_title ($awaiting_changes) ($pr_labels)"

  # If awaiting_changes, close the pull request
  if [[ -n "$awaiting_changes" ]]; then
    echo "Closing PR #$pr_number $pr_title due to awaiting_changes label"
    gh pr close "$pr_number" --comment "Closing awaiting_changes PRs to prepare for Hacktoberfest"
    sleep 2
  fi
done


================================================
FILE: scripts/close_pull_requests_with_failing_tests.sh
================================================
#!/bin/bash

# List all open pull requests
prs=$(gh pr list --state open --json number,title,labels --limit 500)

# Loop through each pull request
echo "$prs" | jq -c '.[]' | while read -r pr; do
  pr_number=$(echo "$pr" | jq -r '.number')
  pr_title=$(echo "$pr" | jq -r '.title')
  pr_labels=$(echo "$pr" | jq -r '.labels')

  # Check if the "tests are failing" label is present
  tests_are_failing=$(echo "$pr_labels" | jq -r '.[] | select(.name == "tests are failing")')
  echo "Checking PR #$pr_number $pr_title ($tests_are_failing) ($pr_labels)"

  # If there are failing tests, close the pull request
  if [[ -n "$tests_are_failing" ]]; then
    echo "Closing PR #$pr_number $pr_title due to tests_are_failing label"
    gh pr close "$pr_number" --comment "Closing tests_are_failing PRs to prepare for Hacktoberfest"
    sleep 2
  fi
done


================================================
FILE: scripts/close_pull_requests_with_require_descriptive_names.sh
================================================
#!/bin/bash

# List all open pull requests
prs=$(gh pr list --state open --json number,title,labels --limit 500)

# Loop through each pull request
echo "$prs" | jq -c '.[]' | while read -r pr; do
  pr_number=$(echo "$pr" | jq -r '.number')
  pr_title=$(echo "$pr" | jq -r '.title')
  pr_labels=$(echo "$pr" | jq -r '.labels')

  # Check if the "require descriptive names" label is present
  require_descriptive_names=$(echo "$pr_labels" | jq -r '.[] | select(.name == "require descriptive names")')
  echo "Checking PR #$pr_number $pr_title ($require_descriptive_names) ($pr_labels)"

  # If there are require_descriptive_names, close the pull request
  if [[ -n "$require_descriptive_names" ]]; then
    echo "Closing PR #$pr_number $pr_title due to require_descriptive_names label"
    gh pr close "$pr_number" --comment "Closing require_descriptive_names PRs to prepare for Hacktoberfest"
  fi
done


================================================
FILE: scripts/close_pull_requests_with_require_tests.sh
================================================
#!/bin/bash

# List all open pull requests
prs=$(gh pr list --state open --json number,title,labels --limit 500)

# Loop through each pull request
echo "$prs" | jq -c '.[]' | while read -r pr; do
  pr_number=$(echo "$pr" | jq -r '.number')
  pr_title=$(echo "$pr" | jq -r '.title')
  pr_labels=$(echo "$pr" | jq -r '.labels')

  # Check if the "require_tests" label is present
  require_tests=$(echo "$pr_labels" | jq -r '.[] | select(.name == "require tests")')
  echo "Checking PR #$pr_number $pr_title ($require_tests) ($pr_labels)"

  # If there require tests, close the pull request
  if [[ -n "$require_tests" ]]; then
    echo "Closing PR #$pr_number $pr_title due to require_tests label"
    gh pr close "$pr_number" --comment "Closing require_tests PRs to prepare for Hacktoberfest"
    # sleep 2
  fi
done


================================================
FILE: scripts/close_pull_requests_with_require_type_hints.sh
================================================
#!/bin/bash

# List all open pull requests
prs=$(gh pr list --state open --json number,title,labels --limit 500)

# Loop through each pull request
echo "$prs" | jq -c '.[]' | while read -r pr; do
  pr_number=$(echo "$pr" | jq -r '.number')
  pr_title=$(echo "$pr" | jq -r '.title')
  pr_labels=$(echo "$pr" | jq -r '.labels')

  # Check if the "require type hints" label is present
  require_type_hints=$(echo "$pr_labels" | jq -r '.[] | select(.name == "require type hints")')
  echo "Checking PR #$pr_number $pr_title ($require_type_hints) ($pr_labels)"

  # If require_type_hints, close the pull request
  if [[ -n "$require_type_hints" ]]; then
    echo "Closing PR #$pr_number $pr_title due to require_type_hints label"
    gh pr close "$pr_number" --comment "Closing require_type_hints PRs to prepare for Hacktoberfest"
  fi
done


================================================
FILE: scripts/find_git_conflicts.sh
================================================
#!/bin/bash

# Replace with your repository (format: owner/repo)
REPO="TheAlgorithms/Python"

# Fetch open pull requests with conflicts into a variable
echo "Checking for pull requests with conflicts in $REPO..."

prs=$(gh pr list --repo "$REPO" --state open --json number,title,mergeable --jq '.[] | select(.mergeable == "CONFLICTING") | {number, title}' --limit 500)

# Process each conflicting PR
echo "$prs" | jq -c '.[]' | while read -r pr; do
    PR_NUMBER=$(echo "$pr" | jq -r '.number')
    PR_TITLE=$(echo "$pr" | jq -r '.title')
    echo "PR #$PR_NUMBER - $PR_TITLE has conflicts."
done


================================================
FILE: scripts/project_euler_answers.json
================================================
{
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}


================================================
FILE: scripts/validate_filenames.py
================================================
#!/usr/bin/env python3

import os

try:
    from .build_directory_md import good_file_paths
except ImportError:
    from build_directory_md import good_file_paths  # type: ignore[no-redef]

filepaths = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

if upper_files := [file for file in filepaths if file != file.lower()]:
    print(f"{len(upper_files)} files contain uppercase characters:")
    print("\n".join(upper_files) + "\n")

if space_files := [file for file in filepaths if " " in file]:
    print(f"{len(space_files)} files contain space characters:")
    print("\n".join(space_files) + "\n")

if hyphen_files := [
    file for file in filepaths if "-" in file and "/site-packages/" not in file
]:
    print(f"{len(hyphen_files)} files contain hyphen characters:")
    print("\n".join(hyphen_files) + "\n")

if nodir_files := [file for file in filepaths if os.sep not in file]:
    print(f"{len(nodir_files)} files are not in a directory:")
    print("\n".join(nodir_files) + "\n")

if bad_files := len(upper_files + space_files + hyphen_files + nodir_files):
    import sys

    sys.exit(bad_files)


================================================
FILE: scripts/validate_solutions.py
================================================
#!/usr/bin/env python3

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
#     "pytest",
# ]
# ///

import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import httpx
import pytest

PROJECT_EULER_DIR_PATH = pathlib.Path.cwd().joinpath("project_euler")
PROJECT_EULER_ANSWERS_PATH = pathlib.Path.cwd().joinpath(
    "scripts", "project_euler_answers.json"
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
    PROBLEM_ANSWERS: dict[str, str] = json.load(file_handle)


def convert_path_to_module(file_path: pathlib.Path) -> ModuleType:
    """Converts a file path to a Python module"""
    spec = importlib.util.spec_from_file_location(file_path.name, str(file_path))
    module = importlib.util.module_from_spec(spec)  # type: ignore[arg-type]
    spec.loader.exec_module(module)  # type: ignore[union-attr]
    return module


def all_solution_file_paths() -> list[pathlib.Path]:
    """Collects all the solution file path in the Project Euler directory"""
    solution_file_paths = []
    for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
        if problem_dir_path.is_file() or problem_dir_path.name.startswith("_"):
            continue
        for file_path in problem_dir_path.iterdir():
            if file_path.suffix != ".py" or file_path.name.startswith(("_", "test")):
                continue
            solution_file_paths.append(file_path)
    return solution_file_paths


def get_files_url() -> str:
    """Return the pull request number which triggered this action."""
    with open(os.environ["GITHUB_EVENT_PATH"]) as file:
        event = json.load(file)
    return event["pull_request"]["url"] + "/files"


def added_solution_file_path() -> list[pathlib.Path]:
    """Collects only the solution file path which got added in the current
    pull request.

    This will only be triggered if the script is ran from GitHub Actions.
    """
    solution_file_paths = []
    headers = {
        "Accept": "application/vnd.github.v3+json",
        "Authorization": "token " + os.environ["GITHUB_TOKEN"],
    }
    files = httpx.get(get_files_url(), headers=headers, timeout=10).json()
    for file in files:
        filepath = pathlib.Path.cwd().joinpath(file["filename"])
        if (
            filepath.suffix != ".py"
            or filepath.name.startswith(("_", "test"))
            or not filepath.name.startswith("sol")
        ):
            continue
        solution_file_paths.append(filepath)
    return solution_file_paths


def collect_solution_file_paths() -> list[pathlib.Path]:
    # Return only if there are any, otherwise default to all solutions
    if (
        os.environ.get("CI")
        and os.environ.get("GITHUB_EVENT_NAME") == "pull_request"
        and (filepaths := added_solution_file_path())
    ):
        return filepaths
    return all_solution_file_paths()


@pytest.mark.parametrize(
    "solution_path",
    collect_solution_file_paths(),
    ids=lambda path: f"{path.parent.name}/{path.name}",
)
def test_project_euler(solution_path: pathlib.Path) -> None:
    """Testing for all Project Euler solutions"""
    # problem_[extract this part] and pad it with zeroes for width 3
    problem_number: str = solution_path.parent.name[8:].zfill(3)
    expected: str = PROBLEM_ANSWERS[problem_number]
    solution_module = convert_path_to_module(solution_path)
    answer = str(solution_module.solution())
    answer = hashlib.sha256(answer.encode()).hexdigest()
    assert answer == expected, (
        f"Expected solution to {problem_number} to have hash {expected}, got {answer}"
    )


================================================
FILE: searches/__init__.py
================================================


================================================
FILE: searches/binary_search.py
================================================
#!/usr/bin/env python3

"""
Pure Python implementations of binary search algorithms

For doctests run the following command:
python3 -m doctest -v binary_search.py

For manual testing run:
python3 binary_search.py
"""

import bisect
from itertools import pairwise


def bisect_left(
    sorted_collection: list[int], item: int, lo: int = 0, hi: int = -1
) -> int:
    """
    Locates the first element in a sorted array that is larger or equal to a given
    value.

    It has the same interface as
    https://docs.python.org/3/library/bisect.html#bisect.bisect_left .

    :param sorted_collection: some ascending sorted collection with comparable items
    :param item: item to bisect
    :param lo: lowest index to consider (as in sorted_collection[lo:hi])
    :param hi: past the highest index to consider (as in sorted_collection[lo:hi])
    :return: index i such that all values in sorted_collection[lo:i] are < item and all
        values in sorted_collection[i:hi] are >= item.

    Examples:
    >>> bisect_left([0, 5, 7, 10, 15], 0)
    0
    >>> bisect_left([0, 5, 7, 10, 15], 6)
    2
    >>> bisect_left([0, 5, 7, 10, 15], 20)
    5
    >>> bisect_left([0, 5, 7, 10, 15], 15, 1, 3)
    3
    >>> bisect_left([0, 5, 7, 10, 15], 6, 2)
    2
    """
    if hi < 0:
        hi = len(sorted_collection)

    while lo < hi:
        mid = lo + (hi - lo) // 2
        if sorted_collection[mid] < item:
            lo = mid + 1
        else:
            hi = mid

    return lo


def bisect_right(
    sorted_collection: list[int], item: int, lo: int = 0, hi: int = -1
) -> int:
    """
    Locates the first element in a sorted array that is larger than a given value.

    It has the same interface as
    https://docs.python.org/3/library/bisect.html#bisect.bisect_right .

    :param sorted_collection: some ascending sorted collection with comparable items
    :param item: item to bisect
    :param lo: lowest index to consider (as in sorted_collection[lo:hi])
    :param hi: past the highest index to consider (as in sorted_collection[lo:hi])
    :return: index i such that all values in sorted_collection[lo:i] are <= item and
        all values in sorted_collection[i:hi] are > item.

    Examples:
    >>> bisect_right([0, 5, 7, 10, 15], 0)
    1
    >>> bisect_right([0, 5, 7, 10, 15], 15)
    5
    >>> bisect_right([0, 5, 7, 10, 15], 6)
    2
    >>> bisect_right([0, 5, 7, 10, 15], 15, 1, 3)
    3
    >>> bisect_right([0, 5, 7, 10, 15], 6, 2)
    2
    """
    if hi < 0:
        hi = len(sorted_collection)

    while lo < hi:
        mid = lo + (hi - lo) // 2
        if sorted_collection[mid] <= item:
            lo = mid + 1
        else:
            hi = mid

    return lo


def insort_left(
    sorted_collection: list[int], item: int, lo: int = 0, hi: int = -1
) -> None:
    """
    Inserts a given value into a sorted array before other values with the same value.

    It has the same interface as
    https://docs.python.org/3/library/bisect.html#bisect.insort_left .

    :param sorted_collection: some ascending sorted collection with comparable items
    :param item: item to insert
    :param lo: lowest index to consider (as in sorted_collection[lo:hi])
    :param hi: past the highest index to consider (as in sorted_collection[lo:hi])

    Examples:
    >>> sorted_collection = [0, 5, 7, 10, 15]
    >>> insort_left(sorted_collection, 6)
    >>> sorted_collection
    [0, 5, 6, 7, 10, 15]
    >>> sorted_collection = [(0, 0), (5, 5), (7, 7), (10, 10), (15, 15)]
    >>> item = (5, 5)
    >>> insort_left(sorted_collection, item)
    >>> sorted_collection
    [(0, 0), (5, 5), (5, 5), (7, 7), (10, 10), (15, 15)]
    >>> item is sorted_collection[1]
    True
    >>> item is sorted_collection[2]
    False
    >>> sorted_collection = [0, 5, 7, 10, 15]
    >>> insort_left(sorted_collection, 20)
    >>> sorted_collection
    [0, 5, 7, 10, 15, 20]
    >>> sorted_collection = [0, 5, 7, 10, 15]
    >>> insort_left(sorted_collection, 15, 1, 3)
    >>> sorted_collection
    [0, 5, 7, 15, 10, 15]
    """
    sorted_collection.insert(bisect_left(sorted_collection, item, lo, hi), item)


def insort_right(
    sorted_collection: list[int], item: int, lo: int = 0, hi: int = -1
) -> None:
    """
    Inserts a given value into a sorted array after other values with the same value.

    It has the same interface as
    https://docs.python.org/3/library/bisect.html#bisect.insort_right .

    :param sorted_collection: some ascending sorted collection with comparable items
    :param item: item to insert
    :param lo: lowest index to consider (as in sorted_collection[lo:hi])
    :param hi: past the highest index to consider (as in sorted_collection[lo:hi])

    Examples:
    >>> sorted_collection = [0, 5, 7, 10, 15]
    >>> insort_right(sorted_collection, 6)
    >>> sorted_collection
    [0, 5, 6, 7, 10, 15]
    >>> sorted_collection = [(0, 0), (5, 5), (7, 7), (10, 10), (15, 15)]
    >>> item = (5, 5)
    >>> insort_right(sorted_collection, item)
    >>> sorted_collection
    [(0, 0), (5, 5), (5, 5), (7, 7), (10, 10), (15, 15)]
    >>> item is sorted_collection[1]
    False
    >>> item is sorted_collection[2]
    True
    >>> sorted_collection = [0, 5, 7, 10, 15]
    >>> insort_right(sorted_collection, 20)
    >>> sorted_collection
    [0, 5, 7, 10, 15, 20]
    >>> sorted_collection = [0, 5, 7, 10, 15]
    >>> insort_right(sorted_collection, 15, 1, 3)
    >>> sorted_collection
    [0, 5, 7, 15, 10, 15]
    """
    sorted_collection.insert(bisect_right(sorted_collection, item, lo, hi), item)


def binary_search(sorted_collection: list[int], item: int) -> int:
    """Pure implementation of a binary search algorithm in Python

    Be careful collection must be ascending sorted otherwise, the result will be
    unpredictable

    :param sorted_collection: some ascending sorted collection with comparable items
    :param item: item value to search
    :return: index of the found item or -1 if the item is not found

    Examples:
    >>> binary_search([0, 5, 7, 10, 15], 0)
    0
    >>> binary_search([0, 5, 7, 10, 15], 15)
    4
    >>> binary_search([0, 5, 7, 10, 15], 5)
    1
    >>> binary_search([0, 5, 7, 10, 15], 6)
    -1
    """
    if any(a > b for a, b in pairwise(sorted_collection)):
        raise ValueError("sorted_collection must be sorted in ascending order")
    left = 0
    right = len(sorted_collection) - 1

    while left <= right:
        midpoint = left + (right - left) // 2
        current_item = sorted_collection[midpoint]
        if current_item == item:
            return midpoint
        elif item < current_item:
            right = midpoint - 1
        else:
            left = midpoint + 1
    return -1


def binary_search_std_lib(sorted_collection: list[int], item: int) -> int:
    """Pure implementation of a binary search algorithm in Python using stdlib

    Be careful collection must be ascending sorted otherwise, the result will be
    unpredictable

    :param sorted_collection: some ascending sorted collection with comparable items
    :param item: item value to search
    :return: index of the found item or -1 if the item is not found

    Examples:
    >>> binary_search_std_lib([0, 5, 7, 10, 15], 0)
    0
    >>> binary_search_std_lib([0, 5, 7, 10, 15], 15)
    4
    >>> binary_search_std_lib([0, 5, 7, 10, 15], 5)
    1
    >>> binary_search_std_lib([0, 5, 7, 10, 15], 6)
    -1
    """
    if list(sorted_collection) != sorted(sorted_collection):
        raise ValueError("sorted_collection must be sorted in ascending order")
    index = bisect.bisect_left(sorted_collection, item)
    if index != len(sorted_collection) and sorted_collection[index] == item:
        return index
    return -1


def binary_search_with_duplicates(sorted_collection: list[int], item: int) -> list[int]:
    """Pure implementation of a binary search algorithm in Python that supports
    duplicates.

    Resources used:
    https://stackoverflow.com/questions/13197552/using-binary-search-with-sorted-array-with-duplicates

    The collection must be sorted in ascending order; otherwise the result will be
    unpredictable. If the target appears multiple times, this function returns a
    list of all indexes where the target occurs. If the target is not found,
    this function returns an empty list.

    :param sorted_collection: some ascending sorted collection with comparable items
    :param item: item value to search for
    :return: a list of indexes where the item is found (empty list if not found)

    Examples:
    >>> binary_search_with_duplicates([0, 5, 7, 10, 15], 0)
    [0]
    >>> binary_search_with_duplicates([0, 5, 7, 10, 15], 15)
    [4]
    >>> binary_search_with_duplicates([1, 2, 2, 2, 3], 2)
    [1, 2, 3]
    >>> binary_search_with_duplicates([1, 2, 2, 2, 3], 4)
    []
    """
    if list(sorted_collection) != sorted(sorted_collection):
        raise ValueError("sorted_collection must be sorted in ascending order")

    def lower_bound(sorted_collection: list[int], item: int) -> int:
        """
        Returns the index of the first element greater than or equal to the item.

        :param sorted_collection: The sorted list to search.
        :param item: The item to find the lower bound for.
        :return: The index where the item can be inserted while maintaining order.
        """
        left = 0
        right = len(sorted_collection)
        while left < right:
            midpoint = left + (right - left) // 2
            current_item = sorted_collection[midpoint]
            if current_item < item:
                left = midpoint + 1
            else:
                right = midpoint
        return left

    def upper_bound(sorted_collection: list[int], item: int) -> int:
        """
        Returns the index of the first element strictly greater than the item.

        :param sorted_collection: The sorted list to search.
        :param item: The item to find the upper bound for.
        :return: The index where the item can be inserted after all existing instances.
        """
        left = 0
        right = len(sorted_collection)
        while left < right:
            midpoint = left + (right - left) // 2
            current_item = sorted_collection[midpoint]
            if current_item <= item:
                left = midpoint + 1
            else:
                right = midpoint
        return left

    left = lower_bound(sorted_collection, item)
    right = upper_bound(sorted_collection, item)

    if left == len(sorted_collection) or sorted_collection[left] != item:
        return []
    return list(range(left, right))


def binary_search_by_recursion(
    sorted_collection: list[int], item: int, left: int = 0, right: int = -1
) -> int:
    """Pure implementation of a binary search algorithm in Python by recursion

    Be careful collection must be ascending sorted otherwise, the result will be
    unpredictable
    First recursion should be started with left=0 and right=(len(sorted_collection)-1)

    :param sorted_collection: some ascending sorted collection with comparable items
    :param item: item value to search
    :return: index of the found item or -1 if the item is not found

    Examples:
    >>> binary_search_by_recursion([0, 5, 7, 10, 15], 0, 0, 4)
    0
    >>> binary_search_by_recursion([0, 5, 7, 10, 15], 15, 0, 4)
    4
    >>> binary_search_by_recursion([0, 5, 7, 10, 15], 5, 0, 4)
    1
    >>> binary_search_by_recursion([0, 5, 7, 10, 15], 6, 0, 4)
    -1
    """
    if right < 0:
        right = len(sorted_collection) - 1
    if list(sorted_collection) != sorted(sorted_collection):
        raise ValueError("sorted_collection must be sorted in ascending order")
    if right < left:
        return -1

    midpoint = left + (right - left) // 2

    if sorted_collection[midpoint] == item:
        return midpoint
    elif sorted_collection[midpoint] > item:
        return binary_search_by_recursion(sorted_collection, item, left, midpoint - 1)
    else:
        return binary_search_by_recursion(sorted_collection, item, midpoint + 1, right)


def exponential_search(sorted_collection: list[int], item: int) -> int:
    """Pure implementation of an exponential search algorithm in Python
    Resources used:
    https://en.wikipedia.org/wiki/Exponential_search

    Be careful collection must be ascending sorted otherwise, result will be
    unpredictable

    :param sorted_collection: some ascending sorted collection with comparable items
    :param item: item value to search
    :return: index of the found item or -1 if the item is not found

    the order of this algorithm is O(lg I) where I is index position of item if exist

    Examples:
    >>> exponential_search([0, 5, 7, 10, 15], 0)
    0
    >>> exponential_search([0, 5, 7, 10, 15], 15)
    4
    >>> exponential_search([0, 5, 7, 10, 15], 5)
    1
    >>> exponential_search([0, 5, 7, 10, 15], 6)
    -1
    """
    if list(sorted_collection) != sorted(sorted_collection):
        raise ValueError("sorted_collection must be sorted in ascending order")
    bound = 1
    while bound < len(sorted_collection) and sorted_collection[bound] < item:
        bound *= 2
    left = bound // 2
    right = min(bound, len(sorted_collection) - 1)
    last_result = binary_search_by_recursion(
        sorted_collection=sorted_collection, item=item, left=left, right=right
    )
    if last_result is None:
        return -1
    return last_result


searches = (  # Fastest to slowest...
    binary_search_std_lib,
    binary_search,
    exponential_search,
    binary_search_by_recursion,
)


if __name__ == "__main__":
    import doctest
    import timeit

    doctest.testmod()
    for search in searches:
        name = f"{search.__name__:>26}"
        print(f"{name}: {search([0, 5, 7, 10, 15], 10) = }")  # type: ignore[operator]

    print("\nBenchmarks...")
    setup = "collection = range(1000)"
    for search in searches:
        name = search.__name__
        print(
            f"{name:>26}:",
            timeit.timeit(
                f"{name}(collection, 500)", setup=setup, number=5_000, globals=globals()
            ),
        )

    user_input = input("\nEnter numbers separated by comma: ").strip()
    collection = sorted(int(item) for item in user_input.split(","))
    target = int(input("Enter a single number to be found in the list: "))
    result = binary_search(sorted_collection=collection, item=target)
    if result == -1:
        print(f"{target} was not found in {collection}.")
    else:
        print(f"{target} was found at position {result} of {collection}.")


================================================
FILE: searches/binary_tree_traversal.py
================================================
"""
This is pure Python implementation of tree traversal algorithms
"""

from __future__ import annotations

import queue


class TreeNode:
    def __init__(self, data):
        self.data = data
        self.right = None
        self.left = None


def build_tree() -> TreeNode:
    print("\n********Press N to stop entering at any point of time********\n")
    check = input("Enter the value of the root node: ").strip().lower()
    q: queue.Queue = queue.Queue()
    tree_node = TreeNode(int(check))
    q.put(tree_node)
    while not q.empty():
        node_found = q.get()
        msg = f"Enter the left node of {node_found.data}: "
        check = input(msg).strip().lower() or "n"
        if check == "n":
            return tree_node
        left_node = TreeNode(int(check))
        node_found.left = left_node
        q.put(left_node)
        msg = f"Enter the right node of {node_found.data}: "
        check = input(msg).strip().lower() or "n"
        if check == "n":
            return tree_node
        right_node = TreeNode(int(check))
        node_found.right = right_node
        q.put(right_node)
    raise ValueError("Something went wrong")


def pre_order(node: TreeNode) -> None:
    """
    >>> root = TreeNode(1)
    >>> tree_node2 = TreeNode(2)
    >>> tree_node3 = TreeNode(3)
    >>> tree_node4 = TreeNode(4)
    >>> tree_node5 = TreeNode(5)
    >>> tree_node6 = TreeNode(6)
    >>> tree_node7 = TreeNode(7)
    >>> root.left, root.right = tree_node2, tree_node3
    >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
    >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
    >>> pre_order(root)
    1,2,4,5,3,6,7,
    """
    if not isinstance(node, TreeNode) or not node:
        return
    print(node.data, end=",")
    pre_order(node.left)
    pre_order(node.right)


def in_order(node: TreeNode) -> None:
    """
    >>> root = TreeNode(1)
    >>> tree_node2 = TreeNode(2)
    >>> tree_node3 = TreeNode(3)
    >>> tree_node4 = TreeNode(4)
    >>> tree_node5 = TreeNode(5)
    >>> tree_node6 = TreeNode(6)
    >>> tree_node7 = TreeNode(7)
    >>> root.left, root.right = tree_node2, tree_node3
    >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
    >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
    >>> in_order(root)
    4,2,5,1,6,3,7,
    """
    if not isinstance(node, TreeNode) or not node:
        return
    in_order(node.left)
    print(node.data, end=",")
    in_order(node.right)


def post_order(node: TreeNode) -> None:
    """
    >>> root = TreeNode(1)
    >>> tree_node2 = TreeNode(2)
    >>> tree_node3 = TreeNode(3)
    >>> tree_node4 = TreeNode(4)
    >>> tree_node5 = TreeNode(5)
    >>> tree_node6 = TreeNode(6)
    >>> tree_node7 = TreeNode(7)
    >>> root.left, root.right = tree_node2, tree_node3
    >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
    >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
    >>> post_order(root)
    4,5,2,6,7,3,1,
    """
    if not isinstance(node, TreeNode) or not node:
        return
    post_order(node.left)
    post_order(node.right)
    print(node.data, end=",")


def level_order(node: TreeNode) -> None:
    """
    >>> root = TreeNode(1)
    >>> tree_node2 = TreeNode(2)
    >>> tree_node3 = TreeNode(3)
    >>> tree_node4 = TreeNode(4)
    >>> tree_node5 = TreeNode(5)
    >>> tree_node6 = TreeNode(6)
    >>> tree_node7 = TreeNode(7)
    >>> root.left, root.right = tree_node2, tree_node3
    >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
    >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
    >>> level_order(root)
    1,2,3,4,5,6,7,
    """
    if not isinstance(node, TreeNode) or not node:
        return
    q: queue.Queue = queue.Queue()
    q.put(node)
    while not q.empty():
        node_dequeued = q.get()
        print(node_dequeued.data, end=",")
        if node_dequeued.left:
            q.put(node_dequeued.left)
        if node_dequeued.right:
            q.put(node_dequeued.right)


def level_order_actual(node: TreeNode) -> None:
    """
    >>> root = TreeNode(1)
    >>> tree_node2 = TreeNode(2)
    >>> tree_node3 = TreeNode(3)
    >>> tree_node4 = TreeNode(4)
    >>> tree_node5 = TreeNode(5)
    >>> tree_node6 = TreeNode(6)
    >>> tree_node7 = TreeNode(7)
    >>> root.left, root.right = tree_node2, tree_node3
    >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
    >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
    >>> level_order_actual(root)
    1,
    2,3,
    4,5,6,7,
    """
    if not isinstance(node, TreeNode) or not node:
        return
    q: queue.Queue = queue.Queue()
    q.put(node)
    while not q.empty():
        list_ = []
        while not q.empty():
            node_dequeued = q.get()
            print(node_dequeued.data, end=",")
            if node_dequeued.left:
                list_.append(node_dequeued.left)
            if node_dequeued.right:
                list_.append(node_dequeued.right)
        print()
        for inner_node in list_:
            q.put(inner_node)


# iteration version
def pre_order_iter(node: TreeNode) -> None:
    """
    >>> root = TreeNode(1)
    >>> tree_node2 = TreeNode(2)
    >>> tree_node3 = TreeNode(3)
    >>> tree_node4 = TreeNode(4)
    >>> tree_node5 = TreeNode(5)
    >>> tree_node6 = TreeNode(6)
    >>> tree_node7 = TreeNode(7)
    >>> root.left, root.right = tree_node2, tree_node3
    >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
    >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
    >>> pre_order_iter(root)
    1,2,4,5,3,6,7,
    """
    if not isinstance(node, TreeNode) or not node:
        return
    stack: list[TreeNode] = []
    n = node
    while n or stack:
        while n:  # start from root node, find its left child
            print(n.data, end=",")
            stack.append(n)
            n = n.left
        # end of while means current node doesn't have left child
        n = stack.pop()
        # start to traverse its right child
        n = n.right


def in_order_iter(node: TreeNode) -> None:
    """
    >>> root = TreeNode(1)
    >>> tree_node2 = TreeNode(2)
    >>> tree_node3 = TreeNode(3)
    >>> tree_node4 = TreeNode(4)
    >>> tree_node5 = TreeNode(5)
    >>> tree_node6 = TreeNode(6)
    >>> tree_node7 = TreeNode(7)
    >>> root.left, root.right = tree_node2, tree_node3
    >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
    >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
    >>> in_order_iter(root)
    4,2,5,1,6,3,7,
    """
    if not isinstance(node, TreeNode) or not node:
        return
    stack: list[TreeNode] = []
    n = node
    while n or stack:
        while n:
            stack.append(n)
            n = n.left
        n = stack.pop()
        print(n.data, end=",")
        n = n.right


def post_order_iter(node: TreeNode) -> None:
    """
    >>> root = TreeNode(1)
    >>> tree_node2 = TreeNode(2)
    >>> tree_node3 = TreeNode(3)
    >>> tree_node4 = TreeNode(4)
    >>> tree_node5 = TreeNode(5)
    >>> tree_node6 = TreeNode(6)
    >>> tree_node7 = TreeNode(7)
    >>> root.left, root.right = tree_node2, tree_node3
    >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
    >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
    >>> post_order_iter(root)
    4,5,2,6,7,3,1,
    """
    if not isinstance(node, TreeNode) or not node:
        return
    stack1, stack2 = [], []
    n = node
    stack1.append(n)
    while stack1:  # to find the reversed order of post order, store it in stack2
        n = stack1.pop()
        if n.left:
            stack1.append(n.left)
        if n.right:
            stack1.append(n.right)
        stack2.append(n)
    while stack2:  # pop up from stack2 will be the post order
        print(stack2.pop().data, end=",")


def prompt(s: str = "", width=50, char="*") -> str:
    if not s:
        return "\n" + width * char
    left, extra = divmod(width - len(s) - 2, 2)
    return f"{left * char} {s} {(left + extra) * char}"


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(prompt("Binary Tree Traversals"))

    node: TreeNode = build_tree()
    print(prompt("Pre Order Traversal"))
    pre_order(node)
    print(prompt() + "\n")

    print(prompt("In Order Traversal"))
    in_order(node)
    print(prompt() + "\n")

    print(prompt("Post Order Traversal"))
    post_order(node)
    print(prompt() + "\n")

    print(prompt("Level Order Traversal"))
    level_order(node)
    print(prompt() + "\n")

    print(prompt("Actual Level Order Traversal"))
    level_order_actual(node)
    print("*" * 50 + "\n")

    print(prompt("Pre Order Traversal - Iteration Version"))
    pre_order_iter(node)
    print(prompt() + "\n")

    print(prompt("In Order Traversal - Iteration Version"))
    in_order_iter(node)
    print(prompt() + "\n")

    print(prompt("Post Order Traversal - Iteration Version"))
    post_order_iter(node)
    print(prompt())


================================================
FILE: searches/double_linear_search.py
================================================
from __future__ import annotations


def double_linear_search(array: list[int], search_item: int) -> int:
    """
    Iterate through the array from both sides to find the index of search_item.

    :param array: the array to be searched
    :param search_item: the item to be searched
    :return the index of search_item, if search_item is in array, else -1

    Examples:
    >>> double_linear_search([1, 5, 5, 10], 1)
    0
    >>> double_linear_search([1, 5, 5, 10], 5)
    1
    >>> double_linear_search([1, 5, 5, 10], 100)
    -1
    >>> double_linear_search([1, 5, 5, 10], 10)
    3
    """
    # define the start and end index of the given array
    start_ind, end_ind = 0, len(array) - 1
    while start_ind <= end_ind:
        if array[start_ind] == search_item:
            return start_ind
        elif array[end_ind] == search_item:
            return end_ind
        else:
            start_ind += 1
            end_ind -= 1
    # returns -1 if search_item is not found in array
    return -1


if __name__ == "__main__":
    print(double_linear_search(list(range(100)), 40))


================================================
FILE: searches/double_linear_search_recursion.py
================================================
def search(list_data: list, key: int, left: int = 0, right: int = 0) -> int:
    """
    Iterate through the array to find the index of key using recursion.
    :param list_data: the list to be searched
    :param key: the key to be searched
    :param left: the index of first element
    :param right: the index of last element
    :return: the index of key value if found, -1 otherwise.

    >>> search(list(range(0, 11)), 5)
    5
    >>> search([1, 2, 4, 5, 3], 4)
    2
    >>> search([1, 2, 4, 5, 3], 6)
    -1
    >>> search([5], 5)
    0
    >>> search([], 1)
    -1
    """
    right = right or len(list_data) - 1
    if left > right:
        return -1
    elif list_data[left] == key:
        return left
    elif list_data[right] == key:
        return right
    else:
        return search(list_data, key, left + 1, right - 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: searches/exponential_search.py
================================================
#!/usr/bin/env python3

"""
Pure Python implementation of exponential search algorithm

For more information, see the Wikipedia page:
https://en.wikipedia.org/wiki/Exponential_search

For doctests run the following command:
python3 -m doctest -v exponential_search.py

For manual testing run:
python3 exponential_search.py
"""

from __future__ import annotations


def binary_search_by_recursion(
    sorted_collection: list[int], item: int, left: int = 0, right: int = -1
) -> int:
    """Pure implementation of binary search algorithm in Python using recursion

    Be careful: the collection must be ascending sorted otherwise, the result will be
    unpredictable.

    :param sorted_collection: some ascending sorted collection with comparable items
    :param item: item value to search
    :param left: starting index for the search
    :param right: ending index for the search
    :return: index of the found item or -1 if the item is not found

    Examples:
    >>> binary_search_by_recursion([0, 5, 7, 10, 15], 0, 0, 4)
    0
    >>> binary_search_by_recursion([0, 5, 7, 10, 15], 15, 0, 4)
    4
    >>> binary_search_by_recursion([0, 5, 7, 10, 15], 5, 0, 4)
    1
    >>> binary_search_by_recursion([0, 5, 7, 10, 15], 6, 0, 4)
    -1
    """
    if right < 0:
        right = len(sorted_collection) - 1
    if list(sorted_collection) != sorted(sorted_collection):
        raise ValueError("sorted_collection must be sorted in ascending order")
    if right < left:
        return -1

    midpoint = left + (right - left) // 2

    if sorted_collection[midpoint] == item:
        return midpoint
    elif sorted_collection[midpoint] > item:
        return binary_search_by_recursion(sorted_collection, item, left, midpoint - 1)
    else:
        return binary_search_by_recursion(sorted_collection, item, midpoint + 1, right)


def exponential_search(sorted_collection: list[int], item: int) -> int:
    """
    Pure implementation of an exponential search algorithm in Python.
    For more information, refer to:
    https://en.wikipedia.org/wiki/Exponential_search

    Be careful: the collection must be ascending sorted, otherwise the result will be
    unpredictable.

    :param sorted_collection: some ascending sorted collection with comparable items
    :param item: item value to search
    :return: index of the found item or -1 if the item is not found

    The time complexity of this algorithm is O(log i) where i is the index of the item.

    Examples:
    >>> exponential_search([0, 5, 7, 10, 15], 0)
    0
    >>> exponential_search([0, 5, 7, 10, 15], 15)
    4
    >>> exponential_search([0, 5, 7, 10, 15], 5)
    1
    >>> exponential_search([0, 5, 7, 10, 15], 6)
    -1
    """
    if list(sorted_collection) != sorted(sorted_collection):
        raise ValueError("sorted_collection must be sorted in ascending order")

    if sorted_collection[0] == item:
        return 0

    bound = 1
    while bound < len(sorted_collection) and sorted_collection[bound] < item:
        bound *= 2

    left = bound // 2
    right = min(bound, len(sorted_collection) - 1)
    return binary_search_by_recursion(sorted_collection, item, left, right)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Manual testing
    user_input = input("Enter numbers separated by commas: ").strip()
    collection = sorted(int(item) for item in user_input.split(","))
    target = int(input("Enter a number to search for: "))
    result = exponential_search(sorted_collection=collection, item=target)
    if result == -1:
        print(f"{target} was not found in {collection}.")
    else:
        print(f"{target} was found at index {result} in {collection}.")


================================================
FILE: searches/fibonacci_search.py
================================================
"""
This is pure Python implementation of fibonacci search.

Resources used:
https://en.wikipedia.org/wiki/Fibonacci_search_technique

For doctests run following command:
python3 -m doctest -v fibonacci_search.py

For manual testing run:
python3 fibonacci_search.py
"""

from functools import lru_cache


@lru_cache
def fibonacci(k: int) -> int:
    """Finds fibonacci number in index k.

    Parameters
    ----------
    k :
        Index of fibonacci.

    Returns
    -------
    int
        Fibonacci number in position k.

    >>> fibonacci(0)
    0
    >>> fibonacci(2)
    1
    >>> fibonacci(5)
    5
    >>> fibonacci(15)
    610
    >>> fibonacci('a')
    Traceback (most recent call last):
    TypeError: k must be an integer.
    >>> fibonacci(-5)
    Traceback (most recent call last):
    ValueError: k integer must be greater or equal to zero.
    """
    if not isinstance(k, int):
        raise TypeError("k must be an integer.")
    if k < 0:
        raise ValueError("k integer must be greater or equal to zero.")
    if k == 0:
        return 0
    elif k == 1:
        return 1
    else:
        return fibonacci(k - 1) + fibonacci(k - 2)


def fibonacci_search(arr: list, val: int) -> int:
    """A pure Python implementation of a fibonacci search algorithm.

    Parameters
    ----------
    arr
        List of sorted elements.
    val
        Element to search in list.

    Returns
    -------
    int
        The index of the element in the array.
        -1 if the element is not found.

    >>> fibonacci_search([4, 5, 6, 7], 4)
    0
    >>> fibonacci_search([4, 5, 6, 7], -10)
    -1
    >>> fibonacci_search([-18, 2], -18)
    0
    >>> fibonacci_search([5], 5)
    0
    >>> fibonacci_search(['a', 'c', 'd'], 'c')
    1
    >>> fibonacci_search(['a', 'c', 'd'], 'f')
    -1
    >>> fibonacci_search([], 1)
    -1
    >>> fibonacci_search([.1, .4 , 7], .4)
    1
    >>> fibonacci_search([], 9)
    -1
    >>> fibonacci_search(list(range(100)), 63)
    63
    >>> fibonacci_search(list(range(100)), 99)
    99
    >>> fibonacci_search(list(range(-100, 100, 3)), -97)
    1
    >>> fibonacci_search(list(range(-100, 100, 3)), 0)
    -1
    >>> fibonacci_search(list(range(-100, 100, 5)), 0)
    20
    >>> fibonacci_search(list(range(-100, 100, 5)), 95)
    39
    """
    len_list = len(arr)
    # Find m such that F_m >= n where F_i is the i_th fibonacci number.
    i = 0
    while True:
        if fibonacci(i) >= len_list:
            fibb_k = i
            break
        i += 1
    offset = 0
    while fibb_k > 0:
        index_k = min(
            offset + fibonacci(fibb_k - 1), len_list - 1
        )  # Prevent out of range
        item_k_1 = arr[index_k]
        if item_k_1 == val:
            return index_k
        elif val < item_k_1:
            fibb_k -= 1
        elif val > item_k_1:
            offset += fibonacci(fibb_k - 1)
            fibb_k -= 2
    return -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: searches/hill_climbing.py
================================================
# https://en.wikipedia.org/wiki/Hill_climbing
import math


class SearchProblem:
    """
    An interface to define search problems.
    The interface will be illustrated using the example of mathematical function.
    """

    def __init__(self, x: int, y: int, step_size: int, function_to_optimize):
        """
        The constructor of the search problem.

        x: the x coordinate of the current search state.
        y: the y coordinate of the current search state.
        step_size: size of the step to take when looking for neighbors.
        function_to_optimize: a function to optimize having the signature f(x, y).
        """
        self.x = x
        self.y = y
        self.step_size = step_size
        self.function = function_to_optimize

    def score(self) -> int:
        """
        Returns the output of the function called with current x and y coordinates.
        >>> def test_function(x, y):
        ...     return x + y
        >>> SearchProblem(0, 0, 1, test_function).score()  # 0 + 0 = 0
        0
        >>> SearchProblem(5, 7, 1, test_function).score()  # 5 + 7 = 12
        12
        """
        return self.function(self.x, self.y)

    def get_neighbors(self):
        """
        Returns a list of coordinates of neighbors adjacent to the current coordinates.

        Neighbors:
        | 0 | 1 | 2 |
        | 3 | _ | 4 |
        | 5 | 6 | 7 |
        """
        step_size = self.step_size
        return [
            SearchProblem(x, y, step_size, self.function)
            for x, y in (
                (self.x - step_size, self.y - step_size),
                (self.x - step_size, self.y),
                (self.x - step_size, self.y + step_size),
                (self.x, self.y - step_size),
                (self.x, self.y + step_size),
                (self.x + step_size, self.y - step_size),
                (self.x + step_size, self.y),
                (self.x + step_size, self.y + step_size),
            )
        ]

    def __hash__(self):
        """
        hash the string representation of the current search state.
        """
        return hash(str(self))

    def __eq__(self, obj):
        """
        Check if the 2 objects are equal.
        """
        if isinstance(obj, SearchProblem):
            return hash(str(self)) == hash(str(obj))
        return False

    def __str__(self):
        """
        string representation of the current search state.
        >>> str(SearchProblem(0, 0, 1, None))
        'x: 0 y: 0'
        >>> str(SearchProblem(2, 5, 1, None))
        'x: 2 y: 5'
        """
        return f"x: {self.x} y: {self.y}"


def hill_climbing(
    search_prob,
    find_max: bool = True,
    max_x: float = math.inf,
    min_x: float = -math.inf,
    max_y: float = math.inf,
    min_y: float = -math.inf,
    visualization: bool = False,
    max_iter: int = 10000,
) -> SearchProblem:
    """
    Implementation of the hill climbling algorithm.
    We start with a given state, find all its neighbors,
    move towards the neighbor which provides the maximum (or minimum) change.
    We keep doing this until we are at a state where we do not have any
    neighbors which can improve the solution.
        Args:
            search_prob: The search state at the start.
            find_max: If True, the algorithm should find the maximum else the minimum.
            max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
            visualization: If True, a matplotlib graph is displayed.
            max_iter: number of times to run the iteration.
        Returns a search state having the maximum (or minimum) score.
    """
    current_state = search_prob
    scores = []  # list to store the current score at each iteration
    iterations = 0
    solution_found = False
    visited = set()
    while not solution_found and iterations < max_iter:
        visited.add(current_state)
        iterations += 1
        current_score = current_state.score()
        scores.append(current_score)
        neighbors = current_state.get_neighbors()
        max_change = -math.inf
        min_change = math.inf
        next_state = None  # to hold the next best neighbor
        for neighbor in neighbors:
            if neighbor in visited:
                continue  # do not want to visit the same state again
            if (
                neighbor.x > max_x
                or neighbor.x < min_x
                or neighbor.y > max_y
                or neighbor.y < min_y
            ):
                continue  # neighbor outside our bounds
            change = neighbor.score() - current_score
            if find_max:  # finding max
                # going to direction with greatest ascent
                if change > max_change and change > 0:
                    max_change = change
                    next_state = neighbor
            elif change < min_change and change < 0:  # finding min
                # to direction with greatest descent
                min_change = change
                next_state = neighbor
        if next_state is not None:
            # we found at least one neighbor which improved the current state
            current_state = next_state
        else:
            # since we have no neighbor that improves the solution we stop the search
            solution_found = True

    if visualization:
        from matplotlib import pyplot as plt

        plt.plot(range(iterations), scores)
        plt.xlabel("Iterations")
        plt.ylabel("Function values")
        plt.show()

    return current_state


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    def test_f1(x, y):
        return (x**2) + (y**2)

    # starting the problem with initial coordinates (3, 4)
    prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
    local_min = hill_climbing(prob, find_max=False)
    print(
        "The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
        f"{local_min.score()}"
    )

    # starting the problem with initial coordinates (12, 47)
    prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
    local_min = hill_climbing(
        prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
    )
    print(
        "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
        f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
    )

    def test_f2(x, y):
        return (3 * x**2) - (6 * y)

    prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
    local_min = hill_climbing(prob, find_max=True)
    print(
        "The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
        f"{local_min.score()}"
    )


================================================
FILE: searches/interpolation_search.py
================================================
"""
This is pure Python implementation of interpolation search algorithm
"""


def interpolation_search(sorted_collection: list[int], item: int) -> int | None:
    """
    Searches for an item in a sorted collection by interpolation search algorithm.

    Args:
        sorted_collection: sorted list of integers
        item: item value to search

    Returns:
        int: The index of the found item, or None if the item is not found.
    Examples:
    >>> interpolation_search([1, 2, 3, 4, 5], 2)
    1
    >>> interpolation_search([1, 2, 3, 4, 5], 4)
    3
    >>> interpolation_search([1, 2, 3, 4, 5], 6) is None
    True
    >>> interpolation_search([], 1) is None
    True
    >>> interpolation_search([100], 100)
    0
    >>> interpolation_search([1, 2, 3, 4, 5], 0) is None
    True
    >>> interpolation_search([1, 2, 3, 4, 5], 7) is None
    True
    >>> interpolation_search([1, 2, 3, 4, 5], 2)
    1
    >>> interpolation_search([1, 2, 3, 4, 5], 0) is None
    True
    >>> interpolation_search([1, 2, 3, 4, 5], 7) is None
    True
    >>> interpolation_search([1, 2, 3, 4, 5], 2)
    1
    >>> interpolation_search([5, 5, 5, 5, 5], 3) is None
    True
    """
    left = 0
    right = len(sorted_collection) - 1

    while left <= right:
        # avoid divided by 0 during interpolation
        if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                return left
            return None

        point = left + ((item - sorted_collection[left]) * (right - left)) // (
            sorted_collection[right] - sorted_collection[left]
        )

        # out of range check
        if point < 0 or point >= len(sorted_collection):
            return None

        current_item = sorted_collection[point]
        if current_item == item:
            return point
        if point < left:
            right = left
            left = point
        elif point > right:
            left = right
            right = point
        elif item < current_item:
            right = point - 1
        else:
            left = point + 1
    return None


def interpolation_search_by_recursion(
    sorted_collection: list[int], item: int, left: int = 0, right: int | None = None
) -> int | None:
    """Pure implementation of interpolation search algorithm in Python by recursion
    Be careful collection must be ascending sorted, otherwise result will be
    unpredictable
    First recursion should be started with left=0 and right=(len(sorted_collection)-1)

    Args:
        sorted_collection: some sorted collection with comparable items
        item: item value to search
        left: left index in collection
        right: right index in collection

    Returns:
        index of item in collection or None if item is not present

    Examples:
    >>> interpolation_search_by_recursion([0, 5, 7, 10, 15], 0)
    0
    >>> interpolation_search_by_recursion([0, 5, 7, 10, 15], 15)
    4
    >>> interpolation_search_by_recursion([0, 5, 7, 10, 15], 5)
    1
    >>> interpolation_search_by_recursion([0, 5, 7, 10, 15], 100) is None
    True
    >>> interpolation_search_by_recursion([5, 5, 5, 5, 5], 3) is None
    True
    """
    if right is None:
        right = len(sorted_collection) - 1
    # avoid divided by 0 during interpolation
    if sorted_collection[left] == sorted_collection[right]:
        if sorted_collection[left] == item:
            return left
        return None

    point = left + ((item - sorted_collection[left]) * (right - left)) // (
        sorted_collection[right] - sorted_collection[left]
    )

    # out of range check
    if point < 0 or point >= len(sorted_collection):
        return None

    if sorted_collection[point] == item:
        return point
    if point < left:
        return interpolation_search_by_recursion(sorted_collection, item, point, left)
    if point > right:
        return interpolation_search_by_recursion(sorted_collection, item, right, left)
    if sorted_collection[point] > item:
        return interpolation_search_by_recursion(
            sorted_collection, item, left, point - 1
        )
    return interpolation_search_by_recursion(sorted_collection, item, point + 1, right)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: searches/jump_search.py
================================================
"""
Pure Python implementation of the jump search algorithm.
This algorithm iterates through a sorted collection with a step of n^(1/2),
until the element compared is bigger than the one searched.
It will then perform a linear search until it matches the wanted number.
If not found, it returns -1.

https://en.wikipedia.org/wiki/Jump_search
"""

import math
from collections.abc import Sequence
from typing import Any, Protocol


class Comparable(Protocol):
    def __lt__(self, other: Any, /) -> bool: ...


def jump_search[T: Comparable](arr: Sequence[T], item: T) -> int:
    """
    Python implementation of the jump search algorithm.
    Return the index if the `item` is found, otherwise return -1.

    Examples:
    >>> jump_search([0, 1, 2, 3, 4, 5], 3)
    3
    >>> jump_search([-5, -2, -1], -1)
    2
    >>> jump_search([0, 5, 10, 20], 8)
    -1
    >>> jump_search([0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610], 55)
    10
    >>> jump_search(["aa", "bb", "cc", "dd", "ee", "ff"], "ee")
    4
    """

    arr_size = len(arr)
    block_size = int(math.sqrt(arr_size))

    prev = 0
    step = block_size
    while arr[min(step, arr_size) - 1] < item:
        prev = step
        step += block_size
        if prev >= arr_size:
            return -1

    while arr[prev] < item:
        prev += 1
        if prev == min(step, arr_size):
            return -1
    if arr[prev] == item:
        return prev
    return -1


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    array = [int(item) for item in user_input.split(",")]
    x = int(input("Enter the number to be searched:\n"))

    res = jump_search(array, x)
    if res == -1:
        print("Number not found!")
    else:
        print(f"Number {x} is at index {res}")


================================================
FILE: searches/linear_search.py
================================================
"""
This is a pure Python implementation of the linear search algorithm.

For doctests run following command:
python3 -m doctest -v linear_search.py

For manual testing run:
python3 linear_search.py
"""


def linear_search(sequence: list, target: int) -> int:
    """A pure Python implementation of a linear search algorithm

    :param sequence: a collection with comparable items (sorting is not required for
        linear search)
    :param target: item value to search
    :return: index of found item or -1 if item is not found

    Examples:
    >>> linear_search([0, 5, 7, 10, 15], 0)
    0
    >>> linear_search([0, 5, 7, 10, 15], 15)
    4
    >>> linear_search([0, 5, 7, 10, 15], 5)
    1
    >>> linear_search([0, 5, 7, 10, 15], 6)
    -1
    """
    for index, item in enumerate(sequence):
        if item == target:
            return index
    return -1


def rec_linear_search(sequence: list, low: int, high: int, target: int) -> int:
    """
    A pure Python implementation of a recursive linear search algorithm

    :param sequence: a collection with comparable items (as sorted items not required
        in Linear Search)
    :param low: Lower bound of the array
    :param high: Higher bound of the array
    :param target: The element to be found
    :return: Index of the key or -1 if key not found

    Examples:
    >>> rec_linear_search([0, 30, 500, 100, 700], 0, 4, 0)
    0
    >>> rec_linear_search([0, 30, 500, 100, 700], 0, 4, 700)
    4
    >>> rec_linear_search([0, 30, 500, 100, 700], 0, 4, 30)
    1
    >>> rec_linear_search([0, 30, 500, 100, 700], 0, 4, -6)
    -1
    """
    if not (0 <= high < len(sequence) and 0 <= low < len(sequence)):
        raise Exception("Invalid upper or lower bound!")
    if high < low:
        return -1
    if sequence[low] == target:
        return low
    if sequence[high] == target:
        return high
    return rec_linear_search(sequence, low + 1, high - 1, target)


if __name__ == "__main__":
    user_input = input("Enter numbers separated by comma:\n").strip()
    sequence = [int(item.strip()) for item in user_input.split(",")]

    target = int(input("Enter a single number to be found in the list:\n").strip())
    result = linear_search(sequence, target)
    if result != -1:
        print(f"linear_search({sequence}, {target}) = {result}")
    else:
        print(f"{target} was not found in {sequence}")


================================================
FILE: searches/median_of_medians.py
================================================
"""
A Python implementation of the Median of Medians algorithm
to select pivots for quick_select, which is efficient for
calculating the value that would appear in the index of a
list if it would be sorted, even if it is not already
sorted. Search in time complexity O(n) at any rank
deterministically
https://en.wikipedia.org/wiki/Median_of_medians
"""


def median_of_five(arr: list) -> int:
    """
    Return the median of the input list
    :param arr: Array to find median of
    :return: median of arr

    >>> median_of_five([2, 4, 5, 7, 899])
    5
    >>> median_of_five([5, 7, 899, 54, 32])
    32
    >>> median_of_five([5, 4, 3, 2])
    4
    >>> median_of_five([3, 5, 7, 10, 2])
    5
    """
    arr = sorted(arr)
    return arr[len(arr) // 2]


def median_of_medians(arr: list) -> int:
    """
    Return a pivot to partition data on by calculating
    Median of medians of input data
    :param arr: The data to be checked (a list)
    :return: median of medians of input array

    >>> median_of_medians([2, 4, 5, 7, 899, 54, 32])
    54
    >>> median_of_medians([5, 7, 899, 54, 32])
    32
    >>> median_of_medians([5, 4, 3, 2])
    4
    >>> median_of_medians([3, 5, 7, 10, 2, 12])
    12
    """

    if len(arr) <= 5:
        return median_of_five(arr)
    medians = []
    i = 0
    while i < len(arr):
        if (i + 4) <= len(arr):
            medians.append(median_of_five(arr[i:].copy()))
        else:
            medians.append(median_of_five(arr[i : i + 5].copy()))
        i += 5
    return median_of_medians(medians)


def quick_select(arr: list, target: int) -> int:
    """
    Two way partition the data into smaller and greater lists,
    in relationship to the pivot
    :param arr: The data to be searched (a list)
    :param target: The rank to be searched
    :return: element at rank target

    >>> quick_select([2, 4, 5, 7, 899, 54, 32], 5)
    32
    >>> quick_select([2, 4, 5, 7, 899, 54, 32], 1)
    2
    >>> quick_select([5, 4, 3, 2], 2)
    3
    >>> quick_select([3, 5, 7, 10, 2, 12], 3)
    5
    """

    # Invalid Input
    if target > len(arr):
        return -1

    # x is the estimated pivot by median of medians algorithm
    x = median_of_medians(arr)
    left = []
    right = []
    check = False
    for i in range(len(arr)):
        if arr[i] < x:
            left.append(arr[i])
        elif arr[i] > x:
            right.append(arr[i])
        elif arr[i] == x and not check:
            check = True
        else:
            right.append(arr[i])
    rank_x = len(left) + 1
    if rank_x == target:
        answer = x
    elif rank_x > target:
        answer = quick_select(left, target)
    elif rank_x < target:
        answer = quick_select(right, target - rank_x)
    return answer


print(median_of_five([5, 4, 3, 2]))


================================================
FILE: searches/quick_select.py
================================================
"""
A Python implementation of the quick select algorithm, which is efficient for
calculating the value that would appear in the index of a list if it would be
sorted, even if it is not already sorted
https://en.wikipedia.org/wiki/Quickselect
"""

import random


def _partition(data: list, pivot) -> tuple:
    """
    Three way partition the data into smaller, equal and greater lists,
    in relationship to the pivot
    :param data: The data to be sorted (a list)
    :param pivot: The value to partition the data on
    :return: Three list: smaller, equal and greater
    """
    less, equal, greater = [], [], []
    for element in data:
        if element < pivot:
            less.append(element)
        elif element > pivot:
            greater.append(element)
        else:
            equal.append(element)
    return less, equal, greater


def quick_select(items: list, index: int):
    """
    >>> quick_select([2, 4, 5, 7, 899, 54, 32], 5)
    54
    >>> quick_select([2, 4, 5, 7, 899, 54, 32], 1)
    4
    >>> quick_select([5, 4, 3, 2], 2)
    4
    >>> quick_select([3, 5, 7, 10, 2, 12], 3)
    7
    """
    # index = len(items) // 2 when trying to find the median
    #   (value of index when items is sorted)

    # invalid input
    if index >= len(items) or index < 0:
        return None

    pivot = items[random.randint(0, len(items) - 1)]
    count = 0
    smaller, equal, larger = _partition(items, pivot)
    count = len(equal)
    m = len(smaller)

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(smaller, index)
    # must be in larger
    else:
        return quick_select(larger, index - (m + count))


def median(items: list):
    """
    One common application of Quickselect is finding the median, which is
    the middle element (or average of the two middle elements) in a sorted dataset.
    It works efficiently on unsorted lists by partially sorting the data without
    fully sorting the entire list.

    >>> median([3, 2, 2, 9, 9])
    3

    >>> median([2, 2, 9, 9, 9, 3])
    6.0
    """
    mid, rem = divmod(len(items), 2)
    if rem != 0:
        return quick_select(items=items, index=mid)
    else:
        low_mid = quick_select(items=items, index=mid - 1)
        high_mid = quick_select(items=items, index=mid)
        return (low_mid + high_mid) / 2


================================================
FILE: searches/sentinel_linear_search.py
================================================
"""
This is pure Python implementation of sentinel linear search algorithm

For doctests run following command:
python -m doctest -v sentinel_linear_search.py
or
python3 -m doctest -v sentinel_linear_search.py

For manual testing run:
python sentinel_linear_search.py
"""


def sentinel_linear_search(sequence, target):
    """Pure implementation of sentinel linear search algorithm in Python

    :param sequence: some sequence with comparable items
    :param target: item value to search
    :return: index of found item or None if item is not found

    Examples:
    >>> sentinel_linear_search([0, 5, 7, 10, 15], 0)
    0

    >>> sentinel_linear_search([0, 5, 7, 10, 15], 15)
    4

    >>> sentinel_linear_search([0, 5, 7, 10, 15], 5)
    1

    >>> sentinel_linear_search([0, 5, 7, 10, 15], 6)

    """
    sequence.append(target)

    index = 0
    while sequence[index] != target:
        index += 1

    sequence.pop()

    if index == len(sequence):
        return None

    return index


if __name__ == "__main__":
    user_input = input("Enter numbers separated by comma:\n").strip()
    sequence = [int(item) for item in user_input.split(",")]

    target_input = input("Enter a single number to be found in the list:\n")
    target = int(target_input)
    result = sentinel_linear_search(sequence, target)
    if result is not None:
        print(f"{target} found at positions: {result}")
    else:
        print("Not found")


================================================
FILE: searches/simple_binary_search.py
================================================
"""
Pure Python implementation of a binary search algorithm.

For doctests run following command:
python3 -m doctest -v simple_binary_search.py

For manual testing run:
python3 simple_binary_search.py
"""

from __future__ import annotations


def binary_search(a_list: list[int], item: int) -> bool:
    """
    >>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
    >>> binary_search(test_list, 3)
    False
    >>> binary_search(test_list, 13)
    True
    >>> binary_search([4, 4, 5, 6, 7], 4)
    True
    >>> binary_search([4, 4, 5, 6, 7], -10)
    False
    >>> binary_search([-18, 2], -18)
    True
    >>> binary_search([5], 5)
    True
    >>> binary_search(['a', 'c', 'd'], 'c')
    True
    >>> binary_search(['a', 'c', 'd'], 'f')
    False
    >>> binary_search([], 1)
    False
    >>> binary_search([-.1, .1 , .8], .1)
    True
    >>> binary_search(range(-5000, 5000, 10), 80)
    True
    >>> binary_search(range(-5000, 5000, 10), 1255)
    False
    >>> binary_search(range(0, 10000, 5), 2)
    False
    """
    if len(a_list) == 0:
        return False
    midpoint = len(a_list) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint], item)
    else:
        return binary_search(a_list[midpoint + 1 :], item)


if __name__ == "__main__":
    user_input = input("Enter numbers separated by comma:\n").strip()
    sequence = [int(item.strip()) for item in user_input.split(",")]
    target = int(input("Enter the number to be found in the list:\n").strip())
    not_str = "" if binary_search(sequence, target) else "not "
    print(f"{target} was {not_str}found in {sequence}")


================================================
FILE: searches/simulated_annealing.py
================================================
# https://en.wikipedia.org/wiki/Simulated_annealing
import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def simulated_annealing(
    search_prob,
    find_max: bool = True,
    max_x: float = math.inf,
    min_x: float = -math.inf,
    max_y: float = math.inf,
    min_y: float = -math.inf,
    visualization: bool = False,
    start_temperate: float = 100,
    rate_of_decrease: float = 0.01,
    threshold_temp: float = 1,
) -> Any:
    """
    Implementation of the simulated annealing algorithm. We start with a given state,
    find all its neighbors. Pick a random neighbor, if that neighbor improves the
    solution, we move in that direction, if that neighbor does not improve the solution,
    we generate a random real number between 0 and 1, if the number is within a certain
    range (calculated using temperature) we move in that direction, else we pick
    another neighbor randomly and repeat the process.

    Args:
        search_prob: The search state at the start.
        find_max: If True, the algorithm should find the minimum else the minimum.
        max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
        visualization: If True, a matplotlib graph is displayed.
        start_temperate: the initial temperate of the system when the program starts.
        rate_of_decrease: the rate at which the temperate decreases in each iteration.
        threshold_temp: the threshold temperature below which we end the search
    Returns a search state having the maximum (or minimum) score.
    """
    search_end = False
    current_state = search_prob
    current_temp = start_temperate
    scores = []
    iterations = 0
    best_state = None

    while not search_end:
        current_score = current_state.score()
        if best_state is None or current_score > best_state.score():
            best_state = current_state
        scores.append(current_score)
        iterations += 1
        next_state = None
        neighbors = current_state.get_neighbors()
        while (
            next_state is None and neighbors
        ):  # till we do not find a neighbor that we can move to
            index = random.randint(0, len(neighbors) - 1)  # picking a random neighbor
            picked_neighbor = neighbors.pop(index)
            change = picked_neighbor.score() - current_score

            if (
                picked_neighbor.x > max_x
                or picked_neighbor.x < min_x
                or picked_neighbor.y > max_y
                or picked_neighbor.y < min_y
            ):
                continue  # neighbor outside our bounds

            if not find_max:
                change = change * -1  # in case we are finding minimum
            if change > 0:  # improves the solution
                next_state = picked_neighbor
            else:
                probability = (math.e) ** (
                    change / current_temp
                )  # probability generation function
                if random.random() < probability:  # random number within probability
                    next_state = picked_neighbor
        current_temp = current_temp - (current_temp * rate_of_decrease)

        if current_temp < threshold_temp or next_state is None:
            # temperature below threshold, or could not find a suitable neighbor
            search_end = True
        else:
            current_state = next_state

    if visualization:
        from matplotlib import pyplot as plt

        plt.plot(range(iterations), scores)
        plt.xlabel("Iterations")
        plt.ylabel("Function values")
        plt.show()
    return best_state


if __name__ == "__main__":

    def test_f1(x, y):
        return (x**2) + (y**2)

    # starting the problem with initial coordinates (12, 47)
    prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
    local_min = simulated_annealing(
        prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
    )
    print(
        "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
        f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
    )

    # starting the problem with initial coordinates (12, 47)
    prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
    local_min = simulated_annealing(
        prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
    )
    print(
        "The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
        f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
    )

    def test_f2(x, y):
        return (3 * x**2) - (6 * y)

    prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
    local_min = simulated_annealing(prob, find_max=False, visualization=True)
    print(
        "The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
        f"{local_min.score()}"
    )

    prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
    local_min = simulated_annealing(prob, find_max=True, visualization=True)
    print(
        "The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
        f"{local_min.score()}"
    )


================================================
FILE: searches/tabu_search.py
================================================
"""
This is pure Python implementation of Tabu search algorithm for a Travelling Salesman
Problem, that the distances between the cities are symmetric (the distance between city
'a' and city 'b' is the same between city 'b' and city 'a').
The TSP can be represented into a graph. The cities are represented by nodes and the
distance between them is represented by the weight of the ark between the nodes.

The .txt file with the graph has the form:

node1 node2 distance_between_node1_and_node2
node1 node3 distance_between_node1_and_node3
...

Be careful node1, node2 and the distance between them, must exist only once. This means
in the .txt file should not exist:
node1 node2 distance_between_node1_and_node2
node2 node1 distance_between_node2_and_node1

For pytests run following command:
pytest

For manual testing run:
python tabu_search.py -f your_file_name.txt -number_of_iterations_of_tabu_search \
    -s size_of_tabu_search
e.g. python tabu_search.py -f tabudata2.txt -i 4 -s 3
"""

import argparse
import copy


def generate_neighbours(path):
    """
    Pure implementation of generating a dictionary of neighbors and the cost with each
    neighbor, given a path file that includes a graph.

    :param path: The path to the .txt file that includes the graph (e.g.tabudata2.txt)
    :return dict_of_neighbours: Dictionary with key each node and value a list of lists
        with the neighbors of the node and the cost (distance) for each neighbor.

    Example of dict_of_neighbours:
    >>) dict_of_neighbours[a]
    [[b,20],[c,18],[d,22],[e,26]]

    This indicates the neighbors of node (city) 'a', which has neighbor the node 'b'
    with distance 20, the node 'c' with distance 18, the node 'd' with distance 22 and
    the node 'e' with distance 26.
    """

    dict_of_neighbours = {}

    with open(path) as f:
        for line in f:
            if line.split()[0] not in dict_of_neighbours:
                _list = []
                _list.append([line.split()[1], line.split()[2]])
                dict_of_neighbours[line.split()[0]] = _list
            else:
                dict_of_neighbours[line.split()[0]].append(
                    [line.split()[1], line.split()[2]]
                )
            if line.split()[1] not in dict_of_neighbours:
                _list = []
                _list.append([line.split()[0], line.split()[2]])
                dict_of_neighbours[line.split()[1]] = _list
            else:
                dict_of_neighbours[line.split()[1]].append(
                    [line.split()[0], line.split()[2]]
                )

    return dict_of_neighbours


def generate_first_solution(path, dict_of_neighbours):
    """
    Pure implementation of generating the first solution for the Tabu search to start,
    with the redundant resolution strategy. That means that we start from the starting
    node (e.g. node 'a'), then we go to the city nearest (lowest distance) to this node
    (let's assume is node 'c'), then we go to the nearest city of the node 'c', etc.
    till we have visited all cities and return to the starting node.

    :param path: The path to the .txt file that includes the graph (e.g.tabudata2.txt)
    :param dict_of_neighbours: Dictionary with key each node and value a list of lists
        with the neighbors of the node and the cost (distance) for each neighbor.
    :return first_solution: The solution for the first iteration of Tabu search using
        the redundant resolution strategy in a list.
    :return distance_of_first_solution: The total distance that Travelling Salesman
        will travel, if he follows the path in first_solution.
    """

    with open(path) as f:
        start_node = f.read(1)
    end_node = start_node

    first_solution = []

    visiting = start_node

    distance_of_first_solution = 0
    while visiting not in first_solution:
        minim = 10000
        for k in dict_of_neighbours[visiting]:
            if int(k[1]) < int(minim) and k[0] not in first_solution:
                minim = k[1]
                best_node = k[0]

        first_solution.append(visiting)
        distance_of_first_solution = distance_of_first_solution + int(minim)
        visiting = best_node

    first_solution.append(end_node)

    position = 0
    for k in dict_of_neighbours[first_solution[-2]]:
        if k[0] == start_node:
            break
        position += 1

    distance_of_first_solution = (
        distance_of_first_solution
        + int(dict_of_neighbours[first_solution[-2]][position][1])
        - 10000
    )
    return first_solution, distance_of_first_solution


def find_neighborhood(solution, dict_of_neighbours):
    """
    Pure implementation of generating the neighborhood (sorted by total distance of
    each solution from lowest to highest) of a solution with 1-1 exchange method, that
    means we exchange each node in a solution with each other node and generating a
    number of solution named neighborhood.

    :param solution: The solution in which we want to find the neighborhood.
    :param dict_of_neighbours: Dictionary with key each node and value a list of lists
        with the neighbors of the node and the cost (distance) for each neighbor.
    :return neighborhood_of_solution: A list that includes the solutions and the total
        distance of each solution (in form of list) that are produced with 1-1 exchange
        from the solution that the method took as an input

    Example:
    >>> find_neighborhood(['a', 'c', 'b', 'd', 'e', 'a'],
    ...                   {'a': [['b', '20'], ['c', '18'], ['d', '22'], ['e', '26']],
    ...                    'c': [['a', '18'], ['b', '10'], ['d', '23'], ['e', '24']],
    ...                    'b': [['a', '20'], ['c', '10'], ['d', '11'], ['e', '12']],
    ...                    'e': [['a', '26'], ['b', '12'], ['c', '24'], ['d', '40']],
    ...                    'd': [['a', '22'], ['b', '11'], ['c', '23'], ['e', '40']]}
    ...                   )  # doctest: +NORMALIZE_WHITESPACE
    [['a', 'e', 'b', 'd', 'c', 'a', 90],
     ['a', 'c', 'd', 'b', 'e', 'a', 90],
     ['a', 'd', 'b', 'c', 'e', 'a', 93],
     ['a', 'c', 'b', 'e', 'd', 'a', 102],
     ['a', 'c', 'e', 'd', 'b', 'a', 113],
     ['a', 'b', 'c', 'd', 'e', 'a', 119]]
    """

    neighborhood_of_solution = []

    for n in solution[1:-1]:
        idx1 = solution.index(n)
        for kn in solution[1:-1]:
            idx2 = solution.index(kn)
            if n == kn:
                continue

            _tmp = copy.deepcopy(solution)
            _tmp[idx1] = kn
            _tmp[idx2] = n

            distance = 0

            for k in _tmp[:-1]:
                next_node = _tmp[_tmp.index(k) + 1]
                for i in dict_of_neighbours[k]:
                    if i[0] == next_node:
                        distance = distance + int(i[1])
            _tmp.append(distance)

            if _tmp not in neighborhood_of_solution:
                neighborhood_of_solution.append(_tmp)

    index_of_last_item_in_the_list = len(neighborhood_of_solution[0]) - 1

    neighborhood_of_solution.sort(key=lambda x: x[index_of_last_item_in_the_list])
    return neighborhood_of_solution


def tabu_search(
    first_solution, distance_of_first_solution, dict_of_neighbours, iters, size
):
    """
    Pure implementation of Tabu search algorithm for a Travelling Salesman Problem in
    Python.

    :param first_solution: The solution for the first iteration of Tabu search using
        the redundant resolution strategy in a list.
    :param distance_of_first_solution: The total distance that Travelling Salesman will
        travel, if he follows the path in first_solution.
    :param dict_of_neighbours: Dictionary with key each node and value a list of lists
        with the neighbors of the node and the cost (distance) for each neighbor.
    :param iters: The number of iterations that Tabu search will execute.
    :param size: The size of Tabu List.
    :return best_solution_ever: The solution with the lowest distance that occurred
        during the execution of Tabu search.
    :return best_cost: The total distance that Travelling Salesman will travel, if he
        follows the path in best_solution ever.
    """
    count = 1
    solution = first_solution
    tabu_list = []
    best_cost = distance_of_first_solution
    best_solution_ever = solution

    while count <= iters:
        neighborhood = find_neighborhood(solution, dict_of_neighbours)
        index_of_best_solution = 0
        best_solution = neighborhood[index_of_best_solution]
        best_cost_index = len(best_solution) - 1

        found = False
        while not found:
            i = 0
            while i < len(best_solution):
                if best_solution[i] != solution[i]:
                    first_exchange_node = best_solution[i]
                    second_exchange_node = solution[i]
                    break
                i = i + 1

            if [first_exchange_node, second_exchange_node] not in tabu_list and [
                second_exchange_node,
                first_exchange_node,
            ] not in tabu_list:
                tabu_list.append([first_exchange_node, second_exchange_node])
                found = True
                solution = best_solution[:-1]
                cost = neighborhood[index_of_best_solution][best_cost_index]
                if cost < best_cost:
                    best_cost = cost
                    best_solution_ever = solution
            else:
                index_of_best_solution = index_of_best_solution + 1
                best_solution = neighborhood[index_of_best_solution]

        if len(tabu_list) >= size:
            tabu_list.pop(0)

        count = count + 1

    return best_solution_ever, best_cost


def main(args=None):
    dict_of_neighbours = generate_neighbours(args.File)

    first_solution, distance_of_first_solution = generate_first_solution(
        args.File, dict_of_neighbours
    )

    best_sol, best_cost = tabu_search(
        first_solution,
        distance_of_first_solution,
        dict_of_neighbours,
        args.Iterations,
        args.Size,
    )

    print(f"Best solution: {best_sol}, with total distance: {best_cost}.")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Tabu Search")
    parser.add_argument(
        "-f",
        "--File",
        type=str,
        help="Path to the file containing the data",
        required=True,
    )
    parser.add_argument(
        "-i",
        "--Iterations",
        type=int,
        help="How many iterations the algorithm should perform",
        required=True,
    )
    parser.add_argument(
        "-s", "--Size", type=int, help="Size of the tabu list", required=True
    )

    # Pass the arguments to main method
    main(parser.parse_args())


================================================
FILE: searches/tabu_test_data.txt
================================================
a b 20
a c 18
a d 22
a e 26
b c 10
b d 11
b e 12
c d 23
c e 24
d e 40


================================================
FILE: searches/ternary_search.py
================================================
"""
This is a type of divide and conquer algorithm which divides the search space into
3 parts and finds the target value based on the property of the array or list
(usually monotonic property).

Time Complexity  : O(log3 N)
Space Complexity : O(1)
"""

from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
precision = 10


# This is the linear search that will occur after the search space has become smaller.


def lin_search(left: int, right: int, array: list[int], target: int) -> int:
    """Perform linear search in list. Returns -1 if element is not found.

    Parameters
    ----------
    left : int
        left index bound.
    right : int
        right index bound.
    array : List[int]
        List of elements to be searched on
    target : int
        Element that is searched

    Returns
    -------
    int
        index of element that is looked for.

    Examples
    --------
    >>> lin_search(0, 4, [4, 5, 6, 7], 7)
    3
    >>> lin_search(0, 3, [4, 5, 6, 7], 7)
    -1
    >>> lin_search(0, 2, [-18, 2], -18)
    0
    >>> lin_search(0, 1, [5], 5)
    0
    >>> lin_search(0, 3, ['a', 'c', 'd'], 'c')
    1
    >>> lin_search(0, 3, [.1, .4 , -.1], .1)
    0
    >>> lin_search(0, 3, [.1, .4 , -.1], -.1)
    2
    """
    for i in range(left, right):
        if array[i] == target:
            return i
    return -1


def ite_ternary_search(array: list[int], target: int) -> int:
    """Iterative method of the ternary search algorithm.
    >>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
    >>> ite_ternary_search(test_list, 3)
    -1
    >>> ite_ternary_search(test_list, 13)
    4
    >>> ite_ternary_search([4, 5, 6, 7], 4)
    0
    >>> ite_ternary_search([4, 5, 6, 7], -10)
    -1
    >>> ite_ternary_search([-18, 2], -18)
    0
    >>> ite_ternary_search([5], 5)
    0
    >>> ite_ternary_search(['a', 'c', 'd'], 'c')
    1
    >>> ite_ternary_search(['a', 'c', 'd'], 'f')
    -1
    >>> ite_ternary_search([], 1)
    -1
    >>> ite_ternary_search([.1, .4 , -.1], .1)
    0
    """

    left = 0
    right = len(array)
    while left <= right:
        if right - left < precision:
            return lin_search(left, right, array, target)

        one_third = (left + right) // 3 + 1
        two_third = 2 * (left + right) // 3 + 1

        if array[one_third] == target:
            return one_third
        elif array[two_third] == target:
            return two_third

        elif target < array[one_third]:
            right = one_third - 1
        elif array[two_third] < target:
            left = two_third + 1

        else:
            left = one_third + 1
            right = two_third - 1
    return -1


def rec_ternary_search(left: int, right: int, array: list[int], target: int) -> int:
    """Recursive method of the ternary search algorithm.

    >>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
    >>> rec_ternary_search(0, len(test_list), test_list, 3)
    -1
    >>> rec_ternary_search(4, len(test_list), test_list, 42)
    8
    >>> rec_ternary_search(0, 2, [4, 5, 6, 7], 4)
    0
    >>> rec_ternary_search(0, 3, [4, 5, 6, 7], -10)
    -1
    >>> rec_ternary_search(0, 1, [-18, 2], -18)
    0
    >>> rec_ternary_search(0, 1, [5], 5)
    0
    >>> rec_ternary_search(0, 2, ['a', 'c', 'd'], 'c')
    1
    >>> rec_ternary_search(0, 2, ['a', 'c', 'd'], 'f')
    -1
    >>> rec_ternary_search(0, 0, [], 1)
    -1
    >>> rec_ternary_search(0, 3, [.1, .4 , -.1], .1)
    0
    """
    if left < right:
        if right - left < precision:
            return lin_search(left, right, array, target)
        one_third = (left + right) // 3 + 1
        two_third = 2 * (left + right) // 3 + 1

        if array[one_third] == target:
            return one_third
        elif array[two_third] == target:
            return two_third

        elif target < array[one_third]:
            return rec_ternary_search(left, one_third - 1, array, target)
        elif array[two_third] < target:
            return rec_ternary_search(two_third + 1, right, array, target)
        else:
            return rec_ternary_search(one_third + 1, two_third - 1, array, target)
    else:
        return -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    user_input = input("Enter numbers separated by comma:\n").strip()
    collection = [int(item.strip()) for item in user_input.split(",")]
    assert collection == sorted(collection), f"List must be ordered.\n{collection}."
    target = int(input("Enter the number to be found in the list:\n").strip())
    result1 = ite_ternary_search(collection, target)
    result2 = rec_ternary_search(0, len(collection) - 1, collection, target)
    if result2 != -1:
        print(f"Iterative search: {target} found at positions: {result1}")
        print(f"Recursive search: {target} found at positions: {result2}")
    else:
        print("Not found")


================================================
FILE: sorts/README.md
================================================
# Sorting Algorithms
Sorting is the process of putting data in a specific order. The way to arrange data in a specific order
is specified by the sorting algorithm. The most typical orders are lexical or numerical. The significance
of sorting lies in the fact that, if data is stored in a sorted manner, data searching can be highly optimised.
Another use for sorting is to represent data in a more readable manner.

This section contains a lot of important algorithms that help us to use sorting algorithms in various scenarios.
## References
* <https://www.tutorialspoint.com/python_data_structure/python_sorting_algorithms.htm>
* <https://www.geeksforgeeks.org/sorting-algorithms-in-python>
* <https://realpython.com/sorting-algorithms-python>


================================================
FILE: sorts/__init__.py
================================================


================================================
FILE: sorts/bead_sort.py
================================================
"""
Bead sort only works for sequences of non-negative integers.
https://en.wikipedia.org/wiki/Bead_sort
"""


def bead_sort(sequence: list) -> list:
    """
    >>> bead_sort([6, 11, 12, 4, 1, 5])
    [1, 4, 5, 6, 11, 12]

    >>> bead_sort([9, 8, 7, 6, 5, 4 ,3, 2, 1])
    [1, 2, 3, 4, 5, 6, 7, 8, 9]

    >>> bead_sort([5, 0, 4, 3])
    [0, 3, 4, 5]

    >>> bead_sort([8, 2, 1])
    [1, 2, 8]

    >>> bead_sort([1, .9, 0.0, 0, -1, -.9])
    Traceback (most recent call last):
        ...
    TypeError: Sequence must be list of non-negative integers

    >>> bead_sort("Hello world")
    Traceback (most recent call last):
        ...
    TypeError: Sequence must be list of non-negative integers
    """
    if any(not isinstance(x, int) or x < 0 for x in sequence):
        raise TypeError("Sequence must be list of non-negative integers")
    for _ in range(len(sequence)):
        for i, (rod_upper, rod_lower) in enumerate(zip(sequence, sequence[1:])):  # noqa: RUF007
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
    assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
    assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]


================================================
FILE: sorts/binary_insertion_sort.py
================================================
"""
This is a pure Python implementation of the binary insertion sort algorithm

For doctests run following command:
python -m doctest -v binary_insertion_sort.py
or
python3 -m doctest -v binary_insertion_sort.py

For manual testing run:
python binary_insertion_sort.py
"""


def binary_insertion_sort(collection: list) -> list:
    """
    Sorts a list using the binary insertion sort algorithm.

    :param collection: A mutable ordered collection with comparable items.
    :return: The same collection ordered in ascending order.

    Examples:
    >>> binary_insertion_sort([0, 4, 1234, 4, 1])
    [0, 1, 4, 4, 1234]
    >>> binary_insertion_sort([]) == sorted([])
    True
    >>> binary_insertion_sort([-1, -2, -3]) == sorted([-1, -2, -3])
    True
    >>> lst = ['d', 'a', 'b', 'e', 'c']
    >>> binary_insertion_sort(lst) == sorted(lst)
    True
    >>> import random
    >>> collection = random.sample(range(-50, 50), 100)
    >>> binary_insertion_sort(collection) == sorted(collection)
    True
    >>> import string
    >>> collection = random.choices(string.ascii_letters + string.digits, k=100)
    >>> binary_insertion_sort(collection) == sorted(collection)
    True
    """

    n = len(collection)
    for i in range(1, n):
        value_to_insert = collection[i]
        low = 0
        high = i - 1

        while low <= high:
            mid = (low + high) // 2
            if value_to_insert < collection[mid]:
                high = mid - 1
            else:
                low = mid + 1
        for j in range(i, low, -1):
            collection[j] = collection[j - 1]
        collection[low] = value_to_insert
    return collection


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    try:
        unsorted = [int(item) for item in user_input.split(",")]
    except ValueError:
        print("Invalid input. Please enter valid integers separated by commas.")
        raise
    print(f"{binary_insertion_sort(unsorted) = }")


================================================
FILE: sorts/bitonic_sort.py
================================================
"""
Python program for Bitonic Sort.

Note that this program works only when size of input is a power of 2.
"""

from __future__ import annotations


def comp_and_swap(array: list[int], index1: int, index2: int, direction: int) -> None:
    """Compare the value at given index1 and index2 of the array and swap them as per
    the given direction.

    The parameter direction indicates the sorting direction, ASCENDING(1) or
    DESCENDING(0); if (a[i] > a[j]) agrees with the direction, then a[i] and a[j] are
    interchanged.

    >>> arr = [12, 42, -21, 1]
    >>> comp_and_swap(arr, 1, 2, 1)
    >>> arr
    [12, -21, 42, 1]

    >>> comp_and_swap(arr, 1, 2, 0)
    >>> arr
    [12, 42, -21, 1]

    >>> comp_and_swap(arr, 0, 3, 1)
    >>> arr
    [1, 42, -21, 12]

    >>> comp_and_swap(arr, 0, 3, 0)
    >>> arr
    [12, 42, -21, 1]
    """
    if (direction == 1 and array[index1] > array[index2]) or (
        direction == 0 and array[index1] < array[index2]
    ):
        array[index1], array[index2] = array[index2], array[index1]


def bitonic_merge(array: list[int], low: int, length: int, direction: int) -> None:
    """
    It recursively sorts a bitonic sequence in ascending order, if direction = 1, and in
    descending if direction = 0.
    The sequence to be sorted starts at index position low, the parameter length is the
    number of elements to be sorted.

    >>> arr = [12, 42, -21, 1]
    >>> bitonic_merge(arr, 0, 4, 1)
    >>> arr
    [-21, 1, 12, 42]

    >>> bitonic_merge(arr, 0, 4, 0)
    >>> arr
    [42, 12, 1, -21]
    """
    if length > 1:
        middle = int(length / 2)
        for i in range(low, low + middle):
            comp_and_swap(array, i, i + middle, direction)
        bitonic_merge(array, low, middle, direction)
        bitonic_merge(array, low + middle, middle, direction)


def bitonic_sort(array: list[int], low: int, length: int, direction: int) -> None:
    """
    This function first produces a bitonic sequence by recursively sorting its two
    halves in opposite sorting orders, and then calls bitonic_merge to make them in the
    same order.

    >>> arr = [12, 34, 92, -23, 0, -121, -167, 145]
    >>> bitonic_sort(arr, 0, 8, 1)
    >>> arr
    [-167, -121, -23, 0, 12, 34, 92, 145]

    >>> bitonic_sort(arr, 0, 8, 0)
    >>> arr
    [145, 92, 34, 12, 0, -23, -121, -167]
    """
    if length > 1:
        middle = int(length / 2)
        bitonic_sort(array, low, middle, 1)
        bitonic_sort(array, low + middle, middle, 0)
        bitonic_merge(array, low, length, direction)


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item.strip()) for item in user_input.split(",")]

    bitonic_sort(unsorted, 0, len(unsorted), 1)
    print("\nSorted array in ascending order is: ", end="")
    print(*unsorted, sep=", ")

    bitonic_merge(unsorted, 0, len(unsorted), 0)
    print("Sorted array in descending order is: ", end="")
    print(*unsorted, sep=", ")


================================================
FILE: sorts/bogo_sort.py
================================================
"""
This is a pure Python implementation of the bogosort algorithm,
also known as permutation sort, stupid sort, slowsort, shotgun sort, or monkey sort.
Bogosort generates random permutations until it guesses the correct one.

More info on: https://en.wikipedia.org/wiki/Bogosort

For doctests run following command:
python -m doctest -v bogo_sort.py
or
python3 -m doctest -v bogo_sort.py
For manual testing run:
python bogo_sort.py
"""

import random


def bogo_sort(collection: list) -> list:
    """Pure implementation of the bogosort algorithm in Python
    :param collection: some mutable ordered collection with heterogeneous
    comparable items inside
    :return: the same collection ordered by ascending
    Examples:
    >>> bogo_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]
    >>> bogo_sort([])
    []
    >>> bogo_sort([-2, -5, -45])
    [-45, -5, -2]
    """

    def is_sorted(collection: list) -> bool:
        for i in range(len(collection) - 1):
            if collection[i] > collection[i + 1]:
                return False
        return True

    while not is_sorted(collection):
        random.shuffle(collection)
    return collection


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    print(bogo_sort(unsorted))


================================================
FILE: sorts/bubble_sort.py
================================================
from typing import Any


def bubble_sort_iterative(collection: list[Any]) -> list[Any]:
    """Pure implementation of bubble sort algorithm in Python

    :param collection: some mutable ordered collection with heterogeneous
    comparable items inside
    :return: the same collection ordered in ascending order

    Examples:
    >>> bubble_sort_iterative([0, 5, 2, 3, 2])
    [0, 2, 2, 3, 5]
    >>> bubble_sort_iterative([])
    []
    >>> bubble_sort_iterative([-2, -45, -5])
    [-45, -5, -2]
    >>> bubble_sort_iterative([-23, 0, 6, -4, 34])
    [-23, -4, 0, 6, 34]
    >>> bubble_sort_iterative([1, 2, 3, 4])
    [1, 2, 3, 4]
    >>> bubble_sort_iterative([3, 3, 3, 3])
    [3, 3, 3, 3]
    >>> bubble_sort_iterative([56])
    [56]
    >>> bubble_sort_iterative([0, 5, 2, 3, 2]) == sorted([0, 5, 2, 3, 2])
    True
    >>> bubble_sort_iterative([]) == sorted([])
    True
    >>> bubble_sort_iterative([-2, -45, -5]) == sorted([-2, -45, -5])
    True
    >>> bubble_sort_iterative([-23, 0, 6, -4, 34]) == sorted([-23, 0, 6, -4, 34])
    True
    >>> bubble_sort_iterative(['d', 'a', 'b', 'e']) == sorted(['d', 'a', 'b', 'e'])
    True
    >>> bubble_sort_iterative(['z', 'a', 'y', 'b', 'x', 'c'])
    ['a', 'b', 'c', 'x', 'y', 'z']
    >>> bubble_sort_iterative([1.1, 3.3, 5.5, 7.7, 2.2, 4.4, 6.6])
    [1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7]
    >>> bubble_sort_iterative([1, 3.3, 5, 7.7, 2, 4.4, 6])
    [1, 2, 3.3, 4.4, 5, 6, 7.7]
    >>> import random
    >>> collection_arg = random.sample(range(-50, 50), 100)
    >>> bubble_sort_iterative(collection_arg) == sorted(collection_arg)
    True
    >>> import string
    >>> collection_arg = random.choices(string.ascii_letters + string.digits, k=100)
    >>> bubble_sort_iterative(collection_arg) == sorted(collection_arg)
    True
    """
    length = len(collection)
    for i in reversed(range(length)):
        swapped = False
        for j in range(i):
            if collection[j] > collection[j + 1]:
                swapped = True
                collection[j], collection[j + 1] = collection[j + 1], collection[j]
        if not swapped:
            break  # Stop iteration if the collection is sorted.
    return collection


def bubble_sort_recursive(collection: list[Any]) -> list[Any]:
    """It is similar iterative bubble sort but recursive.

    :param collection: mutable ordered sequence of elements
    :return: the same list in ascending order

    Examples:
    >>> bubble_sort_recursive([0, 5, 2, 3, 2])
    [0, 2, 2, 3, 5]
    >>> bubble_sort_recursive([])
    []
    >>> bubble_sort_recursive([-2, -45, -5])
    [-45, -5, -2]
    >>> bubble_sort_recursive([-23, 0, 6, -4, 34])
    [-23, -4, 0, 6, 34]
    >>> bubble_sort_recursive([0, 5, 2, 3, 2]) == sorted([0, 5, 2, 3, 2])
    True
    >>> bubble_sort_recursive([]) == sorted([])
    True
    >>> bubble_sort_recursive([-2, -45, -5]) == sorted([-2, -45, -5])
    True
    >>> bubble_sort_recursive([-23, 0, 6, -4, 34]) == sorted([-23, 0, 6, -4, 34])
    True
    >>> bubble_sort_recursive(['d', 'a', 'b', 'e']) == sorted(['d', 'a', 'b', 'e'])
    True
    >>> bubble_sort_recursive(['z', 'a', 'y', 'b', 'x', 'c'])
    ['a', 'b', 'c', 'x', 'y', 'z']
    >>> bubble_sort_recursive([1.1, 3.3, 5.5, 7.7, 2.2, 4.4, 6.6])
    [1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7]
    >>> bubble_sort_recursive([1, 3.3, 5, 7.7, 2, 4.4, 6])
    [1, 2, 3.3, 4.4, 5, 6, 7.7]
    >>> bubble_sort_recursive(['a', 'Z', 'B', 'C', 'A', 'c'])
    ['A', 'B', 'C', 'Z', 'a', 'c']
    >>> import random
    >>> collection_arg = random.sample(range(-50, 50), 100)
    >>> bubble_sort_recursive(collection_arg) == sorted(collection_arg)
    True
    >>> import string
    >>> collection_arg = random.choices(string.ascii_letters + string.digits, k=100)
    >>> bubble_sort_recursive(collection_arg) == sorted(collection_arg)
    True
    """
    length = len(collection)
    swapped = False
    for i in range(length - 1):
        if collection[i] > collection[i + 1]:
            collection[i], collection[i + 1] = collection[i + 1], collection[i]
            swapped = True

    return collection if not swapped else bubble_sort_recursive(collection)


if __name__ == "__main__":
    import doctest
    from random import sample
    from timeit import timeit

    doctest.testmod()

    # Benchmark: Iterative seems slightly faster than recursive.
    num_runs = 10_000
    unsorted = sample(range(-50, 50), 100)
    timer_iterative = timeit(
        "bubble_sort_iterative(unsorted[:])", globals=globals(), number=num_runs
    )
    print("\nIterative bubble sort:")
    print(*bubble_sort_iterative(unsorted), sep=",")
    print(f"Processing time (iterative): {timer_iterative:.5f}s for {num_runs:,} runs")

    unsorted = sample(range(-50, 50), 100)
    timer_recursive = timeit(
        "bubble_sort_recursive(unsorted[:])", globals=globals(), number=num_runs
    )
    print("\nRecursive bubble sort:")
    print(*bubble_sort_recursive(unsorted), sep=",")
    print(f"Processing time (recursive): {timer_recursive:.5f}s for {num_runs:,} runs")


================================================
FILE: sorts/bucket_sort.py
================================================
#!/usr/bin/env python3
"""
Illustrate how to implement bucket sort algorithm.

Author: OMKAR PATHAK
This program will illustrate how to implement bucket sort algorithm

Wikipedia says: Bucket sort, or bin sort, is a sorting algorithm that works
by distributing the elements of an array into a number of buckets.
Each bucket is then sorted individually, either using a different sorting
algorithm, or by recursively applying the bucket sorting algorithm. It is a
distribution sort, and is a cousin of radix sort in the most to least
significant digit flavour.
Bucket sort is a generalization of pigeonhole sort. Bucket sort can be
implemented with comparisons and therefore can also be considered a
comparison sort algorithm. The computational complexity estimates involve the
number of buckets.

Time Complexity of Solution:
Worst case scenario occurs when all the elements are placed in a single bucket.
The overall performance would then be dominated by the algorithm used to sort each
bucket. In this case, O(n log n), because of TimSort

Average Case O(n + (n^2)/k + k), where k is the number of buckets

If k = O(n), time complexity is O(n)

Source: https://en.wikipedia.org/wiki/Bucket_sort
"""

from __future__ import annotations


def bucket_sort(my_list: list, bucket_count: int = 10) -> list:
    """
    >>> data = [-1, 2, -5, 0]
    >>> bucket_sort(data) == sorted(data)
    True
    >>> data = [9, 8, 7, 6, -12]
    >>> bucket_sort(data) == sorted(data)
    True
    >>> data = [.4, 1.2, .1, .2, -.9]
    >>> bucket_sort(data) == sorted(data)
    True
    >>> bucket_sort([]) == sorted([])
    True
    >>> data = [-1e10, 1e10]
    >>> bucket_sort(data) == sorted(data)
    True
    >>> import random
    >>> collection = random.sample(range(-50, 50), 50)
    >>> bucket_sort(collection) == sorted(collection)
    True
    >>> data = [1, 2, 2, 1, 1, 3]
    >>> bucket_sort(data) == sorted(data)
    True
    >>> data = [5, 5, 5, 5, 5]
    >>> bucket_sort(data) == sorted(data)
    True
    >>> data = [1000, -1000, 500, -500, 0]
    >>> bucket_sort(data) == sorted(data)
    True
    >>> data = [5.5, 2.2, -1.1, 3.3, 0.0]
    >>> bucket_sort(data) == sorted(data)
    True
    >>> bucket_sort([1]) == [1]
    True
    >>> data = [-1.1, -1.5, -3.4, 2.5, 3.6, -3.3]
    >>> bucket_sort(data) == sorted(data)
    True
    >>> data = [9, 2, 7, 1, 5]
    >>> bucket_sort(data) == sorted(data)
    True
    """

    if len(my_list) == 0 or bucket_count <= 0:
        return []

    min_value, max_value = min(my_list), max(my_list)
    if min_value == max_value:
        return my_list

    bucket_size = (max_value - min_value) / bucket_count
    buckets: list[list] = [[] for _ in range(bucket_count)]

    for val in my_list:
        index = min(int((val - min_value) / bucket_size), bucket_count - 1)
        buckets[index].append(val)

    return [val for bucket in buckets for val in sorted(bucket)]


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
    assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
    assert bucket_sort([1.1, 1.2, -1.2, 0, 2.4]) == [-1.2, 0, 1.1, 1.2, 2.4]
    assert bucket_sort([5, 5, 5, 5, 5]) == [5, 5, 5, 5, 5]
    assert bucket_sort([-5, -1, -6, -2]) == [-6, -5, -2, -1]


================================================
FILE: sorts/circle_sort.py
================================================
"""
This is a Python implementation of the circle sort algorithm

For doctests run following command:
python3 -m doctest -v circle_sort.py

For manual testing run:
python3 circle_sort.py
"""


def circle_sort(collection: list) -> list:
    """A pure Python implementation of circle sort algorithm

    :param collection: a mutable collection of comparable items in any order
    :return: the same collection in ascending order

    Examples:
    >>> circle_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]
    >>> circle_sort([])
    []
    >>> circle_sort([-2, 5, 0, -45])
    [-45, -2, 0, 5]
    >>> collections = ([], [0, 5, 3, 2, 2], [-2, 5, 0, -45])
    >>> all(sorted(collection) == circle_sort(collection) for collection in collections)
    True
    """

    if len(collection) < 2:
        return collection

    def circle_sort_util(collection: list, low: int, high: int) -> bool:
        """
        >>> arr = [5,4,3,2,1]
        >>> circle_sort_util(lst, 0, 2)
        True
        >>> arr
        [3, 4, 5, 2, 1]
        """

        swapped = False

        if low == high:
            return swapped

        left = low
        right = high

        while left < right:
            if collection[left] > collection[right]:
                collection[left], collection[right] = (
                    collection[right],
                    collection[left],
                )
                swapped = True

            left += 1
            right -= 1

        if left == right and collection[left] > collection[right + 1]:
            collection[left], collection[right + 1] = (
                collection[right + 1],
                collection[left],
            )

            swapped = True

        mid = low + int((high - low) / 2)
        left_swap = circle_sort_util(collection, low, mid)
        right_swap = circle_sort_util(collection, mid + 1, high)

        return swapped or left_swap or right_swap

    is_not_sorted = True

    while is_not_sorted is True:
        is_not_sorted = circle_sort_util(collection, 0, len(collection) - 1)

    return collection


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    print(circle_sort(unsorted))


================================================
FILE: sorts/cocktail_shaker_sort.py
================================================
"""
An implementation of the cocktail shaker sort algorithm in pure Python.

https://en.wikipedia.org/wiki/Cocktail_shaker_sort
"""


def cocktail_shaker_sort(arr: list[int]) -> list[int]:
    """
    Sorts a list using the Cocktail Shaker Sort algorithm.

    :param arr: List of elements to be sorted.
    :return: Sorted list.

    >>> cocktail_shaker_sort([4, 5, 2, 1, 2])
    [1, 2, 2, 4, 5]
    >>> cocktail_shaker_sort([-4, 5, 0, 1, 2, 11])
    [-4, 0, 1, 2, 5, 11]
    >>> cocktail_shaker_sort([0.1, -2.4, 4.4, 2.2])
    [-2.4, 0.1, 2.2, 4.4]
    >>> cocktail_shaker_sort([1, 2, 3, 4, 5])
    [1, 2, 3, 4, 5]
    >>> cocktail_shaker_sort([-4, -5, -24, -7, -11])
    [-24, -11, -7, -5, -4]
    >>> cocktail_shaker_sort(["elderberry", "banana", "date", "apple", "cherry"])
    ['apple', 'banana', 'cherry', 'date', 'elderberry']
    >>> cocktail_shaker_sort((-4, -5, -24, -7, -11))
    Traceback (most recent call last):
        ...
    TypeError: 'tuple' object does not support item assignment
    """
    start, end = 0, len(arr) - 1

    while start < end:
        swapped = False

        # Pass from left to right
        for i in range(start, end):
            if arr[i] > arr[i + 1]:
                arr[i], arr[i + 1] = arr[i + 1], arr[i]
                swapped = True

        if not swapped:
            break

        end -= 1  # Decrease the end pointer after each pass

        # Pass from right to left
        for i in range(end, start, -1):
            if arr[i] < arr[i - 1]:
                arr[i], arr[i - 1] = arr[i - 1], arr[i]
                swapped = True

        if not swapped:
            break

        start += 1  # Increase the start pointer after each pass

    return arr


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    print(f"{cocktail_shaker_sort(unsorted) = }")


================================================
FILE: sorts/comb_sort.py
================================================
"""
This is pure Python implementation of comb sort algorithm.
Comb sort is a relatively simple sorting algorithm originally designed by Wlodzimierz
Dobosiewicz in 1980.  It was rediscovered by Stephen Lacey and Richard Box in 1991.
Comb sort improves on bubble sort algorithm.
In bubble sort, distance (or gap) between two compared elements is always one.
Comb sort improvement is that gap can be much more than 1, in order to prevent slowing
down by small values at the end of a list.

More info on: https://en.wikipedia.org/wiki/Comb_sort

For doctests run following command:
python -m doctest -v comb_sort.py
or
python3 -m doctest -v comb_sort.py

For manual testing run:
python comb_sort.py
"""


def comb_sort(data: list) -> list:
    """Pure implementation of comb sort algorithm in Python
    :param data: mutable collection with comparable items
    :return: the same collection in ascending order
    Examples:
    >>> comb_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]
    >>> comb_sort([])
    []
    >>> comb_sort([99, 45, -7, 8, 2, 0, -15, 3])
    [-15, -7, 0, 2, 3, 8, 45, 99]
    """
    shrink_factor = 1.3
    gap = len(data)
    completed = False

    while not completed:
        # Update the gap value for a next comb
        gap = int(gap / shrink_factor)
        if gap <= 1:
            completed = True

        index = 0
        while index + gap < len(data):
            if data[index] > data[index + gap]:
                # Swap values
                data[index], data[index + gap] = data[index + gap], data[index]
                completed = False
            index += 1

    return data


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    print(comb_sort(unsorted))


================================================
FILE: sorts/counting_sort.py
================================================
"""
This is pure Python implementation of counting sort algorithm
For doctests run following command:
python -m doctest -v counting_sort.py
or
python3 -m doctest -v counting_sort.py
For manual testing run:
python counting_sort.py
"""


def counting_sort(collection):
    """Pure implementation of counting sort algorithm in Python
    :param collection: some mutable ordered collection with heterogeneous
    comparable items inside
    :return: the same collection ordered by ascending
    Examples:
    >>> counting_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]
    >>> counting_sort([])
    []
    >>> counting_sort([-2, -5, -45])
    [-45, -5, -2]
    """
    # if the collection is empty, returns empty
    if collection == []:
        return []

    # get some information about the collection
    coll_len = len(collection)
    coll_max = max(collection)
    coll_min = min(collection)

    # create the counting array
    counting_arr_length = coll_max + 1 - coll_min
    counting_arr = [0] * counting_arr_length

    # count how much a number appears in the collection
    for number in collection:
        counting_arr[number - coll_min] += 1

    # sum each position with it's predecessors. now, counting_arr[i] tells
    # us how many elements <= i has in the collection
    for i in range(1, counting_arr_length):
        counting_arr[i] = counting_arr[i] + counting_arr[i - 1]

    # create the output collection
    ordered = [0] * coll_len

    # place the elements in the output, respecting the original order (stable
    # sort) from end to begin, updating counting_arr
    for i in reversed(range(coll_len)):
        ordered[counting_arr[collection[i] - coll_min] - 1] = collection[i]
        counting_arr[collection[i] - coll_min] -= 1

    return ordered


def counting_sort_string(string):
    """
    >>> counting_sort_string("thisisthestring")
    'eghhiiinrsssttt'
    """
    return "".join([chr(i) for i in counting_sort([ord(c) for c in string])])


if __name__ == "__main__":
    # Test string sort
    assert counting_sort_string("thisisthestring") == "eghhiiinrsssttt"

    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    print(counting_sort(unsorted))


================================================
FILE: sorts/cycle_sort.py
================================================
"""
Code contributed by Honey Sharma
Source: https://en.wikipedia.org/wiki/Cycle_sort
"""


def cycle_sort(array: list) -> list:
    """
    >>> cycle_sort([4, 3, 2, 1])
    [1, 2, 3, 4]

    >>> cycle_sort([-4, 20, 0, -50, 100, -1])
    [-50, -4, -1, 0, 20, 100]

    >>> cycle_sort([-.1, -.2, 1.3, -.8])
    [-0.8, -0.2, -0.1, 1.3]

    >>> cycle_sort([])
    []
    """
    array_len = len(array)
    for cycle_start in range(array_len - 1):
        item = array[cycle_start]

        pos = cycle_start
        for i in range(cycle_start + 1, array_len):
            if array[i] < item:
                pos += 1

        if pos == cycle_start:
            continue

        while item == array[pos]:
            pos += 1

        array[pos], item = item, array[pos]
        while pos != cycle_start:
            pos = cycle_start
            for i in range(cycle_start + 1, array_len):
                if array[i] < item:
                    pos += 1

            while item == array[pos]:
                pos += 1

            array[pos], item = item, array[pos]

    return array


if __name__ == "__main__":
    assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
    assert cycle_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]


================================================
FILE: sorts/cyclic_sort.py
================================================
"""
This is a pure Python implementation of the Cyclic Sort algorithm.

For doctests run following command:
python -m doctest -v cyclic_sort.py
or
python3 -m doctest -v cyclic_sort.py
For manual testing run:
python cyclic_sort.py
or
python3 cyclic_sort.py
"""


def cyclic_sort(nums: list[int]) -> list[int]:
    """
    Sorts the input list of n integers from 1 to n in-place
    using the Cyclic Sort algorithm.

    :param nums: List of n integers from 1 to n to be sorted.
    :return: The same list sorted in ascending order.

    Time complexity: O(n), where n is the number of integers in the list.

    Examples:
    >>> cyclic_sort([])
    []
    >>> cyclic_sort([3, 5, 2, 1, 4])
    [1, 2, 3, 4, 5]
    """

    # Perform cyclic sort
    index = 0
    while index < len(nums):
        # Calculate the correct index for the current element
        correct_index = nums[index] - 1
        # If the current element is not at its correct position,
        # swap it with the element at its correct index
        if index != correct_index:
            nums[index], nums[correct_index] = nums[correct_index], nums[index]
        else:
            # If the current element is already in its correct position,
            # move to the next element
            index += 1

    return nums


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    print(*cyclic_sort(unsorted), sep=",")


================================================
FILE: sorts/double_sort.py
================================================
from typing import Any


def double_sort(collection: list[Any]) -> list[Any]:
    """This sorting algorithm sorts an array using the principle of bubble sort,
    but does it both from left to right and right to left.
    Hence, it's called "Double sort"
    :param collection: mutable ordered sequence of elements
    :return: the same collection in ascending order
    Examples:
    >>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6 ,-7])
    [-7, -6, -5, -4, -3, -2, -1]
    >>> double_sort([])
    []
    >>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6])
    [-6, -5, -4, -3, -2, -1]
    >>> double_sort([-3, 10, 16, -42, 29]) == sorted([-3, 10, 16, -42, 29])
    True
    """
    no_of_elements = len(collection)
    for _ in range(
        int(((no_of_elements - 1) / 2) + 1)
    ):  # we don't need to traverse to end of list as
        for j in range(no_of_elements - 1):
            # apply the bubble sort algorithm from left to right (or forwards)
            if collection[j + 1] < collection[j]:
                collection[j], collection[j + 1] = collection[j + 1], collection[j]
            # apply the bubble sort algorithm from right to left (or backwards)
            if collection[no_of_elements - 1 - j] < collection[no_of_elements - 2 - j]:
                (
                    collection[no_of_elements - 1 - j],
                    collection[no_of_elements - 2 - j],
                ) = (
                    collection[no_of_elements - 2 - j],
                    collection[no_of_elements - 1 - j],
                )
    return collection


if __name__ == "__main__":
    # allow the user to input the elements of the list on one line
    unsorted = [int(x) for x in input("Enter the list to be sorted: ").split() if x]
    print("the sorted list is")
    print(f"{double_sort(unsorted) = }")


================================================
FILE: sorts/dutch_national_flag_sort.py
================================================
"""
A pure implementation of Dutch national flag (DNF) sort algorithm in Python.
Dutch National Flag algorithm is an algorithm originally designed by Edsger Dijkstra.
It is the most optimal sort for 3 unique values (eg. 0, 1, 2) in a sequence.  DNF can
sort a sequence of n size with [0 <= a[i] <= 2] at guaranteed O(n) complexity in a
single pass.

The flag of the Netherlands consists of three colors: white, red, and blue.
The task is to randomly arrange balls of white, red, and blue in such a way that balls
of the same color are placed together.  DNF sorts a sequence of 0, 1, and 2's in linear
time that does not consume any extra space.  This algorithm can be implemented only on
a sequence that contains three unique elements.

1) Time complexity is O(n).
2) Space complexity is O(1).

More info on: https://en.wikipedia.org/wiki/Dutch_national_flag_problem

For doctests run following command:
python3 -m doctest -v dutch_national_flag_sort.py

For manual testing run:
python dnf_sort.py
"""

# Python program to sort a sequence containing only 0, 1 and 2 in a single pass.
red = 0  # The first color of the flag.
white = 1  # The second color of the flag.
blue = 2  # The third color of the flag.
colors = (red, white, blue)


def dutch_national_flag_sort(sequence: list) -> list:
    """
    A pure Python implementation of Dutch National Flag sort algorithm.
    :param data: 3 unique integer values (e.g., 0, 1, 2) in an sequence
    :return: The same collection in ascending order

    >>> dutch_national_flag_sort([])
    []
    >>> dutch_national_flag_sort([0])
    [0]
    >>> dutch_national_flag_sort([2, 1, 0, 0, 1, 2])
    [0, 0, 1, 1, 2, 2]
    >>> dutch_national_flag_sort([0, 1, 1, 0, 1, 2, 1, 2, 0, 0, 0, 1])
    [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2]
    >>> dutch_national_flag_sort("abacab")
    Traceback (most recent call last):
      ...
    ValueError: The elements inside the sequence must contains only (0, 1, 2) values
    >>> dutch_national_flag_sort("Abacab")
    Traceback (most recent call last):
      ...
    ValueError: The elements inside the sequence must contains only (0, 1, 2) values
    >>> dutch_national_flag_sort([3, 2, 3, 1, 3, 0, 3])
    Traceback (most recent call last):
      ...
    ValueError: The elements inside the sequence must contains only (0, 1, 2) values
    >>> dutch_national_flag_sort([-1, 2, -1, 1, -1, 0, -1])
    Traceback (most recent call last):
      ...
    ValueError: The elements inside the sequence must contains only (0, 1, 2) values
    >>> dutch_national_flag_sort([1.1, 2, 1.1, 1, 1.1, 0, 1.1])
    Traceback (most recent call last):
      ...
    ValueError: The elements inside the sequence must contains only (0, 1, 2) values
    """
    if not sequence:
        return []
    if len(sequence) == 1:
        return list(sequence)
    low = 0
    high = len(sequence) - 1
    mid = 0
    while mid <= high:
        if sequence[mid] == colors[0]:
            sequence[low], sequence[mid] = sequence[mid], sequence[low]
            low += 1
            mid += 1
        elif sequence[mid] == colors[1]:
            mid += 1
        elif sequence[mid] == colors[2]:
            sequence[mid], sequence[high] = sequence[high], sequence[mid]
            high -= 1
        else:
            msg = f"The elements inside the sequence must contains only {colors} values"
            raise ValueError(msg)
    return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    user_input = input("Enter numbers separated by commas:\n").strip()
    unsorted = [int(item.strip()) for item in user_input.split(",")]
    print(f"{dutch_national_flag_sort(unsorted)}")


================================================
FILE: sorts/exchange_sort.py
================================================
def exchange_sort(numbers: list[int]) -> list[int]:
    """
    Uses exchange sort to sort a list of numbers.
    Source: https://en.wikipedia.org/wiki/Sorting_algorithm#Exchange_sort
    >>> exchange_sort([5, 4, 3, 2, 1])
    [1, 2, 3, 4, 5]
    >>> exchange_sort([-1, -2, -3])
    [-3, -2, -1]
    >>> exchange_sort([1, 2, 3, 4, 5])
    [1, 2, 3, 4, 5]
    >>> exchange_sort([0, 10, -2, 5, 3])
    [-2, 0, 3, 5, 10]
    >>> exchange_sort([])
    []
    """
    numbers_length = len(numbers)
    for i in range(numbers_length):
        for j in range(i + 1, numbers_length):
            if numbers[j] < numbers[i]:
                numbers[i], numbers[j] = numbers[j], numbers[i]
    return numbers


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    print(exchange_sort(unsorted))


================================================
FILE: sorts/external_sort.py
================================================
#!/usr/bin/env python

#
# Sort large text files in a minimum amount of memory
#
import argparse
import os


class FileSplitter:
    BLOCK_FILENAME_FORMAT = "block_{0}.dat"

    def __init__(self, filename):
        self.filename = filename
        self.block_filenames = []

    def write_block(self, data, block_number):
        filename = self.BLOCK_FILENAME_FORMAT.format(block_number)
        with open(filename, "w") as file:
            file.write(data)
        self.block_filenames.append(filename)

    def get_block_filenames(self):
        return self.block_filenames

    def split(self, block_size, sort_key=None):
        i = 0
        with open(self.filename) as file:
            while True:
                lines = file.readlines(block_size)

                if lines == []:
                    break

                if sort_key is None:
                    lines.sort()
                else:
                    lines.sort(key=sort_key)

                self.write_block("".join(lines), i)
                i += 1

    def cleanup(self):
        map(os.remove, self.block_filenames)


class NWayMerge:
    def select(self, choices):
        min_index = -1
        min_str = None

        for i in range(len(choices)):
            if min_str is None or choices[i] < min_str:
                min_index = i

        return min_index


class FilesArray:
    def __init__(self, files):
        self.files = files
        self.empty = set()
        self.num_buffers = len(files)
        self.buffers = dict.fromkeys(range(self.num_buffers))

    def get_dict(self):
        return {
            i: self.buffers[i] for i in range(self.num_buffers) if i not in self.empty
        }

    def refresh(self):
        for i in range(self.num_buffers):
            if self.buffers[i] is None and i not in self.empty:
                self.buffers[i] = self.files[i].readline()

                if self.buffers[i] == "":
                    self.empty.add(i)
                    self.files[i].close()

        return len(self.empty) != self.num_buffers

    def unshift(self, index):
        value = self.buffers[index]
        self.buffers[index] = None

        return value


class FileMerger:
    def __init__(self, merge_strategy):
        self.merge_strategy = merge_strategy

    def merge(self, filenames, outfilename, buffer_size):
        buffers = FilesArray(self.get_file_handles(filenames, buffer_size))
        with open(outfilename, "w", buffer_size) as outfile:
            while buffers.refresh():
                min_index = self.merge_strategy.select(buffers.get_dict())
                outfile.write(buffers.unshift(min_index))

    def get_file_handles(self, filenames, buffer_size):
        files = {}

        for i in range(len(filenames)):
            files[i] = open(filenames[i], "r", buffer_size)  # noqa: UP015

        return files


class ExternalSort:
    def __init__(self, block_size):
        self.block_size = block_size

    def sort(self, filename, sort_key=None):
        num_blocks = self.get_number_blocks(filename, self.block_size)
        splitter = FileSplitter(filename)
        splitter.split(self.block_size, sort_key)

        merger = FileMerger(NWayMerge())
        buffer_size = self.block_size / (num_blocks + 1)
        merger.merge(splitter.get_block_filenames(), filename + ".out", buffer_size)

        splitter.cleanup()

    def get_number_blocks(self, filename, block_size):
        return (os.stat(filename).st_size / block_size) + 1


def parse_memory(string):
    if string[-1].lower() == "k":
        return int(string[:-1]) * 1024
    elif string[-1].lower() == "m":
        return int(string[:-1]) * 1024 * 1024
    elif string[-1].lower() == "g":
        return int(string[:-1]) * 1024 * 1024 * 1024
    else:
        return int(string)


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-m", "--mem", help="amount of memory to use for sorting", default="100M"
    )
    parser.add_argument(
        "filename", metavar="<filename>", nargs=1, help="name of file to sort"
    )
    args = parser.parse_args()

    sorter = ExternalSort(parse_memory(args.mem))
    sorter.sort(args.filename[0])


if __name__ == "__main__":
    main()


================================================
FILE: sorts/gnome_sort.py
================================================
"""
Gnome Sort Algorithm (A.K.A. Stupid Sort)

This algorithm iterates over a list comparing an element with the previous one.
If order is not respected, it swaps element backward until order is respected with
previous element.  It resumes the initial iteration from element new position.

For doctests run following command:
python3 -m doctest -v gnome_sort.py

For manual testing run:
python3 gnome_sort.py
"""


def gnome_sort(lst: list) -> list:
    """
    Pure implementation of the gnome sort algorithm in Python

    Take some mutable ordered collection with heterogeneous comparable items inside as
    arguments, return the same collection ordered by ascending.

    Examples:
    >>> gnome_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]

    >>> gnome_sort([])
    []

    >>> gnome_sort([-2, -5, -45])
    [-45, -5, -2]

    >>> "".join(gnome_sort(list(set("Gnomes are stupid!"))))
    ' !Gadeimnoprstu'
    """
    if len(lst) <= 1:
        return lst

    i = 1

    while i < len(lst):
        if lst[i - 1] <= lst[i]:
            i += 1
        else:
            lst[i - 1], lst[i] = lst[i], lst[i - 1]
            i -= 1
            if i == 0:
                i = 1

    return lst


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    print(gnome_sort(unsorted))


================================================
FILE: sorts/heap_sort.py
================================================
"""
A pure Python implementation of the heap sort algorithm.
"""


def heapify(unsorted: list[int], index: int, heap_size: int) -> None:
    """
    :param unsorted: unsorted list containing integers numbers
    :param index: index
    :param heap_size: size of the heap
    :return: None
    >>> unsorted = [1, 4, 3, 5, 2]
    >>> heapify(unsorted, 0, len(unsorted))
    >>> unsorted
    [4, 5, 3, 1, 2]
    >>> heapify(unsorted, 0, len(unsorted))
    >>> unsorted
    [5, 4, 3, 1, 2]
    """
    largest = index
    left_index = 2 * index + 1
    right_index = 2 * index + 2
    if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
        largest = left_index

    if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
        largest = right_index

    if largest != index:
        unsorted[largest], unsorted[index] = (unsorted[index], unsorted[largest])
        heapify(unsorted, largest, heap_size)


def heap_sort(unsorted: list[int]) -> list[int]:
    """
    A pure Python implementation of the heap sort algorithm

    :param collection: a mutable ordered collection of heterogeneous comparable items
    :return: the same collection ordered by ascending

    Examples:
    >>> heap_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]
    >>> heap_sort([])
    []
    >>> heap_sort([-2, -5, -45])
    [-45, -5, -2]
    >>> heap_sort([3, 7, 9, 28, 123, -5, 8, -30, -200, 0, 4])
    [-200, -30, -5, 0, 3, 4, 7, 8, 9, 28, 123]
    """
    n = len(unsorted)
    for i in range(n // 2 - 1, -1, -1):
        heapify(unsorted, i, n)
    for i in range(n - 1, 0, -1):
        unsorted[0], unsorted[i] = unsorted[i], unsorted[0]
        heapify(unsorted, 0, i)
    return unsorted


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    user_input = input("Enter numbers separated by a comma:\n").strip()
    if user_input:
        unsorted = [int(item) for item in user_input.split(",")]
        print(f"{heap_sort(unsorted) = }")


================================================
FILE: sorts/insertion_sort.py
================================================
"""
A pure Python implementation of the insertion sort algorithm

This algorithm sorts a collection by comparing adjacent elements.
When it finds that order is not respected, it moves the element compared
backward until the order is correct.  It then goes back directly to the
element's initial position resuming forward comparison.

For doctests run following command:
python3 -m doctest -v insertion_sort.py

For manual testing run:
python3 insertion_sort.py
"""

from collections.abc import MutableSequence
from typing import Any, Protocol, TypeVar


class Comparable(Protocol):
    def __lt__(self, other: Any, /) -> bool: ...


T = TypeVar("T", bound=Comparable)


def insertion_sort[T: Comparable](collection: MutableSequence[T]) -> MutableSequence[T]:
    """A pure Python implementation of the insertion sort algorithm

    :param collection: some mutable ordered collection with heterogeneous
    comparable items inside
    :return: the same collection ordered by ascending

    Examples:
    >>> insertion_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]
    >>> insertion_sort([]) == sorted([])
    True
    >>> insertion_sort([-2, -5, -45]) == sorted([-2, -5, -45])
    True
    >>> insertion_sort(['d', 'a', 'b', 'e', 'c']) == sorted(['d', 'a', 'b', 'e', 'c'])
    True
    >>> import random
    >>> collection = random.sample(range(-50, 50), 100)
    >>> insertion_sort(collection) == sorted(collection)
    True
    >>> import string
    >>> collection = random.choices(string.ascii_letters + string.digits, k=100)
    >>> insertion_sort(collection) == sorted(collection)
    True
    """

    for insert_index in range(1, len(collection)):
        insert_value = collection[insert_index]
        while insert_index > 0 and insert_value < collection[insert_index - 1]:
            collection[insert_index] = collection[insert_index - 1]
            insert_index -= 1
        collection[insert_index] = insert_value
    return collection


if __name__ == "__main__":
    from doctest import testmod

    testmod()

    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    print(f"{insertion_sort(unsorted) = }")


================================================
FILE: sorts/intro_sort.py
================================================
"""
Introspective Sort is a hybrid sort (Quick Sort + Heap Sort + Insertion Sort)
if the size of the list is under 16, use insertion sort
https://en.wikipedia.org/wiki/Introsort
"""

import math


def insertion_sort(array: list, start: int = 0, end: int = 0) -> list:
    """
    >>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
    >>> insertion_sort(array, 0, len(array))
    [1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
    >>> array = [21, 15, 11, 45, -2, -11, 46]
    >>> insertion_sort(array, 0, len(array))
    [-11, -2, 11, 15, 21, 45, 46]
    >>> array = [-2, 0, 89, 11, 48, 79, 12]
    >>> insertion_sort(array, 0, len(array))
    [-2, 0, 11, 12, 48, 79, 89]
    >>> array = ['a', 'z', 'd', 'p', 'v', 'l', 'o', 'o']
    >>> insertion_sort(array, 0, len(array))
    ['a', 'd', 'l', 'o', 'o', 'p', 'v', 'z']
    >>> array = [73.568, 73.56, -45.03, 1.7, 0, 89.45]
    >>> insertion_sort(array, 0, len(array))
    [-45.03, 0, 1.7, 73.56, 73.568, 89.45]
    """
    end = end or len(array)
    for i in range(start, end):
        temp_index = i
        temp_index_value = array[i]
        while temp_index != start and temp_index_value < array[temp_index - 1]:
            array[temp_index] = array[temp_index - 1]
            temp_index -= 1
        array[temp_index] = temp_index_value
    return array


def heapify(array: list, index: int, heap_size: int) -> None:  # Max Heap
    """
    >>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
    >>> heapify(array, len(array) // 2, len(array))
    """
    largest = index
    left_index = 2 * index + 1  # Left Node
    right_index = 2 * index + 2  # Right Node

    if left_index < heap_size and array[largest] < array[left_index]:
        largest = left_index

    if right_index < heap_size and array[largest] < array[right_index]:
        largest = right_index

    if largest != index:
        array[index], array[largest] = array[largest], array[index]
        heapify(array, largest, heap_size)


def heap_sort(array: list) -> list:
    """
    >>> heap_sort([4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12])
    [1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
    >>> heap_sort([-2, -11, 0, 0, 0, 87, 45, -69, 78, 12, 10, 103, 89, 52])
    [-69, -11, -2, 0, 0, 0, 10, 12, 45, 52, 78, 87, 89, 103]
    >>> heap_sort(['b', 'd', 'e', 'f', 'g', 'p', 'x', 'z', 'b', 's', 'e', 'u', 'v'])
    ['b', 'b', 'd', 'e', 'e', 'f', 'g', 'p', 's', 'u', 'v', 'x', 'z']
    >>> heap_sort([6.2, -45.54, 8465.20, 758.56, -457.0, 0, 1, 2.879, 1.7, 11.7])
    [-457.0, -45.54, 0, 1, 1.7, 2.879, 6.2, 11.7, 758.56, 8465.2]
    """
    n = len(array)

    for i in range(n // 2, -1, -1):
        heapify(array, i, n)

    for i in range(n - 1, 0, -1):
        array[i], array[0] = array[0], array[i]
        heapify(array, 0, i)

    return array


def median_of_3(
    array: list, first_index: int, middle_index: int, last_index: int
) -> int:
    """
    >>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
    >>> median_of_3(array, 0, ((len(array) - 0) // 2) + 1, len(array) - 1)
    12
    >>> array = [13, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
    >>> median_of_3(array, 0, ((len(array) - 0) // 2) + 1, len(array) - 1)
    13
    >>> array = [4, 2, 6, 8, 1, 7, 8, 22, 15, 14, 27, 79, 23, 45, 14, 16]
    >>> median_of_3(array, 0, ((len(array) - 0) // 2) + 1, len(array) - 1)
    14
    """
    if (array[first_index] > array[middle_index]) != (
        array[first_index] > array[last_index]
    ):
        return array[first_index]
    elif (array[middle_index] > array[first_index]) != (
        array[middle_index] > array[last_index]
    ):
        return array[middle_index]
    else:
        return array[last_index]


def partition(array: list, low: int, high: int, pivot: int) -> int:
    """
    >>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
    >>> partition(array, 0, len(array), 12)
    8
    >>> array = [21, 15, 11, 45, -2, -11, 46]
    >>> partition(array, 0, len(array), 15)
    3
    >>> array = ['a', 'z', 'd', 'p', 'v', 'l', 'o', 'o']
    >>> partition(array, 0, len(array), 'p')
    5
    >>> array = [6.2, -45.54, 8465.20, 758.56, -457.0, 0, 1, 2.879, 1.7, 11.7]
    >>> partition(array, 0, len(array), 2.879)
    6
    """
    i = low
    j = high
    while True:
        while array[i] < pivot:
            i += 1
        j -= 1
        while pivot < array[j]:
            j -= 1
        if i >= j:
            return i
        array[i], array[j] = array[j], array[i]
        i += 1


def sort(array: list) -> list:
    """
    :param collection: some mutable ordered collection with heterogeneous
    comparable items inside
    :return: the same collection ordered by ascending

    Examples:
    >>> sort([4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12])
    [1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
    >>> sort([-1, -5, -3, -13, -44])
    [-44, -13, -5, -3, -1]
    >>> sort([])
    []
    >>> sort([5])
    [5]
    >>> sort([-3, 0, -7, 6, 23, -34])
    [-34, -7, -3, 0, 6, 23]
    >>> sort([1.7, 1.0, 3.3, 2.1, 0.3 ])
    [0.3, 1.0, 1.7, 2.1, 3.3]
    >>> sort(['d', 'a', 'b', 'e', 'c'])
    ['a', 'b', 'c', 'd', 'e']
    """
    if len(array) == 0:
        return array
    max_depth = 2 * math.ceil(math.log2(len(array)))
    size_threshold = 16
    return intro_sort(array, 0, len(array), size_threshold, max_depth)


def intro_sort(
    array: list, start: int, end: int, size_threshold: int, max_depth: int
) -> list:
    """
    >>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
    >>> max_depth = 2 * math.ceil(math.log2(len(array)))
    >>> intro_sort(array, 0, len(array), 16, max_depth)
    [1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
    """
    while end - start > size_threshold:
        if max_depth == 0:
            return heap_sort(array)
        max_depth -= 1
        pivot = median_of_3(array, start, start + ((end - start) // 2) + 1, end - 1)
        p = partition(array, start, end, pivot)
        intro_sort(array, p, end, size_threshold, max_depth)
        end = p
    return insertion_sort(array, start, end)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    user_input = input("Enter numbers separated by a comma : ").strip()
    unsorted = [float(item) for item in user_input.split(",")]
    print(f"{sort(unsorted) = }")


================================================
FILE: sorts/iterative_merge_sort.py
================================================
"""
Implementation of iterative merge sort in Python
Author: Aman Gupta

For doctests run following command:
python3 -m doctest -v iterative_merge_sort.py

For manual testing run:
python3 iterative_merge_sort.py
"""

from __future__ import annotations


def merge(input_list: list, low: int, mid: int, high: int) -> list:
    """
    sorting left-half and right-half individually
    then merging them into result
    """
    result = []
    left, right = input_list[low:mid], input_list[mid : high + 1]
    while left and right:
        result.append((left if left[0] <= right[0] else right).pop(0))
    input_list[low : high + 1] = result + left + right
    return input_list


# iteration over the unsorted list
def iter_merge_sort(input_list: list) -> list:
    """
    Return a sorted copy of the input list

    >>> iter_merge_sort([5, 9, 8, 7, 1, 2, 7])
    [1, 2, 5, 7, 7, 8, 9]
    >>> iter_merge_sort([1])
    [1]
    >>> iter_merge_sort([2, 1])
    [1, 2]
    >>> iter_merge_sort([2, 1, 3])
    [1, 2, 3]
    >>> iter_merge_sort([4, 3, 2, 1])
    [1, 2, 3, 4]
    >>> iter_merge_sort([5, 4, 3, 2, 1])
    [1, 2, 3, 4, 5]
    >>> iter_merge_sort(['c', 'b', 'a'])
    ['a', 'b', 'c']
    >>> iter_merge_sort([0.3, 0.2, 0.1])
    [0.1, 0.2, 0.3]
    >>> iter_merge_sort(['dep', 'dang', 'trai'])
    ['dang', 'dep', 'trai']
    >>> iter_merge_sort([6])
    [6]
    >>> iter_merge_sort([])
    []
    >>> iter_merge_sort([-2, -9, -1, -4])
    [-9, -4, -2, -1]
    >>> iter_merge_sort([1.1, 1, 0.0, -1, -1.1])
    [-1.1, -1, 0.0, 1, 1.1]
    >>> iter_merge_sort(['c', 'b', 'a'])
    ['a', 'b', 'c']
    >>> iter_merge_sort('cba')
    ['a', 'b', 'c']
    """
    if len(input_list) <= 1:
        return input_list
    input_list = list(input_list)

    # iteration for two-way merging
    p = 2
    while p <= len(input_list):
        # getting low, high and middle value for merge-sort of single list
        for i in range(0, len(input_list), p):
            low = i
            high = i + p - 1
            mid = (low + high + 1) // 2
            input_list = merge(input_list, low, mid, high)
        # final merge of last two parts
        if p * 2 >= len(input_list):
            mid = i
            input_list = merge(input_list, 0, mid, len(input_list) - 1)
            break
        p *= 2

    return input_list


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    if user_input == "":
        unsorted = []
    else:
        unsorted = [int(item.strip()) for item in user_input.split(",")]
    print(iter_merge_sort(unsorted))


================================================
FILE: sorts/merge_insertion_sort.py
================================================
"""
This is a pure Python implementation of the merge-insertion sort algorithm
Source: https://en.wikipedia.org/wiki/Merge-insertion_sort

For doctests run following command:
python3 -m doctest -v merge_insertion_sort.py
or
python -m doctest -v merge_insertion_sort.py

For manual testing run:
python3 merge_insertion_sort.py
"""

from __future__ import annotations


def binary_search_insertion(sorted_list, item):
    """
    >>> binary_search_insertion([1, 2, 7, 9, 10], 4)
    [1, 2, 4, 7, 9, 10]
    """
    left = 0
    right = len(sorted_list) - 1
    while left <= right:
        middle = (left + right) // 2
        if left == right:
            if sorted_list[middle] < item:
                left = middle + 1
            break
        elif sorted_list[middle] < item:
            left = middle + 1
        else:
            right = middle - 1
    sorted_list.insert(left, item)
    return sorted_list


def merge(left, right):
    """
    >>> merge([[1, 6], [9, 10]], [[2, 3], [4, 5], [7, 8]])
    [[1, 6], [2, 3], [4, 5], [7, 8], [9, 10]]
    """
    result = []
    while left and right:
        if left[0][0] < right[0][0]:
            result.append(left.pop(0))
        else:
            result.append(right.pop(0))
    return result + left + right


def sortlist_2d(list_2d):
    """
    >>> sortlist_2d([[9, 10], [1, 6], [7, 8], [2, 3], [4, 5]])
    [[1, 6], [2, 3], [4, 5], [7, 8], [9, 10]]
    """
    length = len(list_2d)
    if length <= 1:
        return list_2d
    middle = length // 2
    return merge(sortlist_2d(list_2d[:middle]), sortlist_2d(list_2d[middle:]))


def merge_insertion_sort(collection: list[int]) -> list[int]:
    """Pure implementation of merge-insertion sort algorithm in Python

    :param collection: some mutable ordered collection with heterogeneous
    comparable items inside
    :return: the same collection ordered by ascending

    Examples:
    >>> merge_insertion_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]

    >>> merge_insertion_sort([99])
    [99]

    >>> merge_insertion_sort([-2, -5, -45])
    [-45, -5, -2]

    Testing with all permutations on range(0,5):
    >>> import itertools
    >>> permutations = list(itertools.permutations([0, 1, 2, 3, 4]))
    >>> all(merge_insertion_sort(p) == [0, 1, 2, 3, 4] for p in permutations)
    True
    """

    if len(collection) <= 1:
        return collection

    """
    Group the items into two pairs, and leave one element if there is a last odd item.

    Example: [999, 100, 75, 40, 10000]
                -> [999, 100], [75, 40]. Leave 10000.
    """
    two_paired_list = []
    has_last_odd_item = False
    for i in range(0, len(collection), 2):
        if i == len(collection) - 1:
            has_last_odd_item = True
        else:
            """
            Sort two-pairs in each groups.

            Example: [999, 100], [75, 40]
                        -> [100, 999], [40, 75]
            """
            if collection[i] < collection[i + 1]:
                two_paired_list.append([collection[i], collection[i + 1]])
            else:
                two_paired_list.append([collection[i + 1], collection[i]])

    """
    Sort two_paired_list.

    Example: [100, 999], [40, 75]
                -> [40, 75], [100, 999]
    """
    sorted_list_2d = sortlist_2d(two_paired_list)

    """
    40 < 100 is sure because it has already been sorted.
    Generate the sorted_list of them so that you can avoid unnecessary comparison.

    Example:
           group0 group1
           40     100
           75     999
        ->
           group0 group1
           [40,   100]
           75     999
    """
    result = [i[0] for i in sorted_list_2d]

    """
    100 < 999 is sure because it has already been sorted.
    Put 999 in last of the sorted_list so that you can avoid unnecessary comparison.

    Example:
           group0 group1
           [40,   100]
           75     999
        ->
           group0 group1
           [40,   100,   999]
           75
    """
    result.append(sorted_list_2d[-1][1])

    """
    Insert the last odd item left if there is.

    Example:
           group0 group1
           [40,   100,   999]
           75
        ->
           group0 group1
           [40,   100,   999,   10000]
           75
    """
    if has_last_odd_item:
        pivot = collection[-1]
        result = binary_search_insertion(result, pivot)

    """
    Insert the remaining items.
    In this case, 40 < 75 is sure because it has already been sorted.
    Therefore, you only need to insert 75 into [100, 999, 10000],
    so that you can avoid unnecessary comparison.

    Example:
           group0 group1
           [40,   100,   999,   10000]
            ^ You don't need to compare with this as 40 < 75 is already sure.
           75
        ->
           [40,   75,    100,   999,   10000]
    """
    is_last_odd_item_inserted_before_this_index = False
    for i in range(len(sorted_list_2d) - 1):
        if result[i] == collection[-1] and has_last_odd_item:
            is_last_odd_item_inserted_before_this_index = True
        pivot = sorted_list_2d[i][1]
        # If last_odd_item is inserted before the item's index,
        # you should forward index one more.
        if is_last_odd_item_inserted_before_this_index:
            result = result[: i + 2] + binary_search_insertion(result[i + 2 :], pivot)
        else:
            result = result[: i + 1] + binary_search_insertion(result[i + 1 :], pivot)

    return result


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    print(merge_insertion_sort(unsorted))


================================================
FILE: sorts/merge_sort.py
================================================
"""
This is a pure Python implementation of the merge sort algorithm.

For doctests run following command:
python -m doctest -v merge_sort.py
or
python3 -m doctest -v merge_sort.py
For manual testing run:
python merge_sort.py
"""


def merge_sort(collection: list) -> list:
    """
    Sorts a list using the merge sort algorithm.

    :param collection: A mutable ordered collection with comparable items.
    :return: The same collection ordered in ascending order.

    Time Complexity: O(n log n)
    Space Complexity: O(n)

    Examples:
    >>> merge_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]
    >>> merge_sort([])
    []
    >>> merge_sort([-2, -5, -45])
    [-45, -5, -2]
    """

    def merge(left: list, right: list) -> list:
        """
        Merge two sorted lists into a single sorted list.

        :param left: Left collection
        :param right: Right collection
        :return: Merged result
        """
        result = []
        while left and right:
            result.append(left.pop(0) if left[0] <= right[0] else right.pop(0))
        result.extend(left)
        result.extend(right)
        return result

    if len(collection) <= 1:
        return collection
    mid_index = len(collection) // 2
    return merge(merge_sort(collection[:mid_index]), merge_sort(collection[mid_index:]))


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    try:
        user_input = input("Enter numbers separated by a comma:\n").strip()
        unsorted = [int(item) for item in user_input.split(",")]
        sorted_list = merge_sort(unsorted)
        print(*sorted_list, sep=",")
    except ValueError:
        print("Invalid input. Please enter valid integers separated by commas.")


================================================
FILE: sorts/msd_radix_sort.py
================================================
"""
Python implementation of the MSD radix sort algorithm.
It used the binary representation of the integers to sort
them.
https://en.wikipedia.org/wiki/Radix_sort
"""

from __future__ import annotations


def msd_radix_sort(list_of_ints: list[int]) -> list[int]:
    """
    Implementation of the MSD radix sort algorithm. Only works
    with positive integers
    :param list_of_ints: A list of integers
    :return: Returns the sorted list
    >>> msd_radix_sort([40, 12, 1, 100, 4])
    [1, 4, 12, 40, 100]
    >>> msd_radix_sort([])
    []
    >>> msd_radix_sort([123, 345, 123, 80])
    [80, 123, 123, 345]
    >>> msd_radix_sort([1209, 834598, 1, 540402, 45])
    [1, 45, 1209, 540402, 834598]
    >>> msd_radix_sort([-1, 34, 45])
    Traceback (most recent call last):
        ...
    ValueError: All numbers must be positive
    """
    if not list_of_ints:
        return []

    if min(list_of_ints) < 0:
        raise ValueError("All numbers must be positive")

    most_bits = max(len(bin(x)[2:]) for x in list_of_ints)
    return _msd_radix_sort(list_of_ints, most_bits)


def _msd_radix_sort(list_of_ints: list[int], bit_position: int) -> list[int]:
    """
    Sort the given list based on the bit at bit_position. Numbers with a
    0 at that position will be at the start of the list, numbers with a
    1 at the end.
    :param list_of_ints: A list of integers
    :param bit_position: the position of the bit that gets compared
    :return: Returns a partially sorted list
    >>> _msd_radix_sort([45, 2, 32], 1)
    [2, 32, 45]
    >>> _msd_radix_sort([10, 4, 12], 2)
    [4, 12, 10]
    """
    if bit_position == 0 or len(list_of_ints) in [0, 1]:
        return list_of_ints

    zeros = []
    ones = []
    # Split numbers based on bit at bit_position from the right
    for number in list_of_ints:
        if (number >> (bit_position - 1)) & 1:
            # number has a one at bit bit_position
            ones.append(number)
        else:
            # number has a zero at bit bit_position
            zeros.append(number)

    # recursively split both lists further
    zeros = _msd_radix_sort(zeros, bit_position - 1)
    ones = _msd_radix_sort(ones, bit_position - 1)

    # recombine lists
    res = zeros
    res.extend(ones)

    return res


def msd_radix_sort_inplace(list_of_ints: list[int]):
    """
    Inplace implementation of the MSD radix sort algorithm.
    Sorts based on the binary representation of the integers.
    >>> lst = [1, 345, 23, 89, 0, 3]
    >>> msd_radix_sort_inplace(lst)
    >>> lst == sorted(lst)
    True
    >>> lst = [1, 43, 0, 0, 0, 24, 3, 3]
    >>> msd_radix_sort_inplace(lst)
    >>> lst == sorted(lst)
    True
    >>> lst = []
    >>> msd_radix_sort_inplace(lst)
    >>> lst == []
    True
    >>> lst = [-1, 34, 23, 4, -42]
    >>> msd_radix_sort_inplace(lst)
    Traceback (most recent call last):
        ...
    ValueError: All numbers must be positive
    """

    length = len(list_of_ints)
    if not list_of_ints or length == 1:
        return

    if min(list_of_ints) < 0:
        raise ValueError("All numbers must be positive")

    most_bits = max(len(bin(x)[2:]) for x in list_of_ints)
    _msd_radix_sort_inplace(list_of_ints, most_bits, 0, length)


def _msd_radix_sort_inplace(
    list_of_ints: list[int], bit_position: int, begin_index: int, end_index: int
):
    """
    Sort the given list based on the bit at bit_position. Numbers with a
    0 at that position will be at the start of the list, numbers with a
    1 at the end.
    >>> lst = [45, 2, 32, 24, 534, 2932]
    >>> _msd_radix_sort_inplace(lst, 1, 0, 3)
    >>> lst == [32, 2, 45, 24, 534, 2932]
    True
    >>> lst = [0, 2, 1, 3, 12, 10, 4, 90, 54, 2323, 756]
    >>> _msd_radix_sort_inplace(lst, 2, 4, 7)
    >>> lst == [0, 2, 1, 3, 12, 4, 10, 90, 54, 2323, 756]
    True
    """
    if bit_position == 0 or end_index - begin_index <= 1:
        return

    bit_position -= 1

    i = begin_index
    j = end_index - 1
    while i <= j:
        changed = False
        if not (list_of_ints[i] >> bit_position) & 1:
            # found zero at the beginning
            i += 1
            changed = True
        if (list_of_ints[j] >> bit_position) & 1:
            # found one at the end
            j -= 1
            changed = True

        if changed:
            continue

        list_of_ints[i], list_of_ints[j] = list_of_ints[j], list_of_ints[i]
        j -= 1
        if j != i:
            i += 1

    _msd_radix_sort_inplace(list_of_ints, bit_position, begin_index, i)
    _msd_radix_sort_inplace(list_of_ints, bit_position, i, end_index)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: sorts/natural_sort.py
================================================
from __future__ import annotations

import re


def natural_sort(input_list: list[str]) -> list[str]:
    """
    Sort the given list of strings in the way that humans expect.

    The normal Python sort algorithm sorts lexicographically,
    so you might not get the results that you expect...

    >>> example1 = ['2 ft 7 in', '1 ft 5 in', '10 ft 2 in', '2 ft 11 in', '7 ft 6 in']
    >>> sorted(example1)
    ['1 ft 5 in', '10 ft 2 in', '2 ft 11 in', '2 ft 7 in', '7 ft 6 in']
    >>> # The natural sort algorithm sort based on meaning and not computer code point.
    >>> natural_sort(example1)
    ['1 ft 5 in', '2 ft 7 in', '2 ft 11 in', '7 ft 6 in', '10 ft 2 in']

    >>> example2 = ['Elm11', 'Elm12', 'Elm2', 'elm0', 'elm1', 'elm10', 'elm13', 'elm9']
    >>> sorted(example2)
    ['Elm11', 'Elm12', 'Elm2', 'elm0', 'elm1', 'elm10', 'elm13', 'elm9']
    >>> natural_sort(example2)
    ['elm0', 'elm1', 'Elm2', 'elm9', 'elm10', 'Elm11', 'Elm12', 'elm13']
    """

    def alphanum_key(key):
        return [int(s) if s.isdigit() else s.lower() for s in re.split("([0-9]+)", key)]

    return sorted(input_list, key=alphanum_key)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: sorts/normal_distribution_quick_sort.md
================================================
# Normal Distribution QuickSort

QuickSort Algorithm where the pivot element is chosen randomly between first and last elements of the array, and the array elements are taken from Standard Normal Distribution.

## Array elements

The array elements are taken from a Standard Normal Distribution, having mean = 0 and standard deviation = 1.

### The code

```python

>>> import numpy as np
>>> from tempfile import TemporaryFile
>>> outfile = TemporaryFile()
>>> p = 100 # 100 elements are to be sorted
>>> mu, sigma = 0, 1 # mean and standard deviation
>>> X = np.random.normal(mu, sigma, p)
>>> np.save(outfile, X)
>>> 'The array is'
>>> X

```

------

#### The distribution of the array elements

```python
>>> mu, sigma = 0, 1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, p)
>>> count, bins, ignored = plt.hist(s, 30, normed=True)
>>> plt.plot(bins , 1/(sigma * np.sqrt(2 * np.pi)) *np.exp( - (bins - mu)**2 / (2 * sigma**2) ),linewidth=2, color='r')
>>> plt.show()
```

------
![normal distribution large](https://upload.wikimedia.org/wikipedia/commons/thumb/2/25/The_Normal_Distribution.svg/1280px-The_Normal_Distribution.svg.png)

------

## Comparing the numbers of comparisons

We can plot the function for Checking 'The Number of Comparisons' taking place between Normal Distribution QuickSort and Ordinary QuickSort:

```python
>>> import matplotlib.pyplot as plt

    # Normal Distribution QuickSort is red
>>> plt.plot([1,2,4,16,32,64,128,256,512,1024,2048],[1,1,6,15,43,136,340,800,2156,6821,16325],linewidth=2, color='r')

    # Ordinary QuickSort is green
>>> plt.plot([1,2,4,16,32,64,128,256,512,1024,2048],[1,1,4,16,67,122,362,949,2131,5086,12866],linewidth=2, color='g')

>>> plt.show()
```


================================================
FILE: sorts/odd_even_sort.py
================================================
"""
Odd even sort implementation.

https://en.wikipedia.org/wiki/Odd%E2%80%93even_sort
"""


def odd_even_sort(input_list: list) -> list:
    """
    Sort input with odd even sort.

    This algorithm uses the same idea of bubblesort,
    but by first dividing in two phase (odd and even).
    Originally developed for use on parallel processors
    with local interconnections.
    :param collection: mutable ordered sequence of elements
    :return: same collection in ascending order
    Examples:
    >>> odd_even_sort([5 , 4 ,3 ,2 ,1])
    [1, 2, 3, 4, 5]
    >>> odd_even_sort([])
    []
    >>> odd_even_sort([-10 ,-1 ,10 ,2])
    [-10, -1, 2, 10]
    >>> odd_even_sort([1 ,2 ,3 ,4])
    [1, 2, 3, 4]
    """
    is_sorted = False
    while is_sorted is False:  # Until all the indices are traversed keep looping
        is_sorted = True
        for i in range(0, len(input_list) - 1, 2):  # iterating over all even indices
            if input_list[i] > input_list[i + 1]:
                input_list[i], input_list[i + 1] = input_list[i + 1], input_list[i]
                # swapping if elements not in order
                is_sorted = False

        for i in range(1, len(input_list) - 1, 2):  # iterating over all odd indices
            if input_list[i] > input_list[i + 1]:
                input_list[i], input_list[i + 1] = input_list[i + 1], input_list[i]
                # swapping if elements not in order
                is_sorted = False
    return input_list


if __name__ == "__main__":
    print("Enter list to be sorted")
    input_list = [int(x) for x in input().split()]
    # inputing elements of the list in one line
    sorted_list = odd_even_sort(input_list)
    print("The sorted list is")
    print(sorted_list)


================================================
FILE: sorts/odd_even_transposition_parallel.py
================================================
"""
This is an implementation of odd-even transposition sort.

It works by performing a series of parallel swaps between odd and even pairs of
variables in the list.

This implementation represents each variable in the list with a process and
each process communicates with its neighboring processes in the list to perform
comparisons.
They are synchronized with locks and message passing but other forms of
synchronization could be used.
"""

import multiprocessing as mp

# lock used to ensure that two processes do not access a pipe at the same time
# NOTE This breaks testing on build runner. May work better locally
# process_lock = mp.Lock()

"""
The function run by the processes that sorts the list

position = the position in the list the process represents, used to know which
            neighbor we pass our value to
value = the initial value at list[position]
LSend, RSend = the pipes we use to send to our left and right neighbors
LRcv, RRcv = the pipes we use to receive from our left and right neighbors
resultPipe = the pipe used to send results back to main
"""


def oe_process(
    position,
    value,
    l_send,
    r_send,
    lr_cv,
    rr_cv,
    result_pipe,
    multiprocessing_context,
):
    process_lock = multiprocessing_context.Lock()

    # we perform n swaps since after n swaps we know we are sorted
    # we *could* stop early if we are sorted already, but it takes as long to
    # find out we are sorted as it does to sort the list with this algorithm
    for i in range(10):
        if (i + position) % 2 == 0 and r_send is not None:
            # send your value to your right neighbor
            with process_lock:
                r_send[1].send(value)

            # receive your right neighbor's value
            with process_lock:
                temp = rr_cv[0].recv()

            # take the lower value since you are on the left
            value = min(value, temp)
        elif (i + position) % 2 != 0 and l_send is not None:
            # send your value to your left neighbor
            with process_lock:
                l_send[1].send(value)

            # receive your left neighbor's value
            with process_lock:
                temp = lr_cv[0].recv()

            # take the higher value since you are on the right
            value = max(value, temp)
    # after all swaps are performed, send the values back to main
    result_pipe[1].send(value)


"""
the function which creates the processes that perform the parallel swaps

arr = the list to be sorted
"""


def odd_even_transposition(arr):
    """
    >>> odd_even_transposition(list(range(10)[::-1])) == sorted(list(range(10)[::-1]))
    True
    >>> odd_even_transposition(["a", "x", "c"]) == sorted(["x", "a", "c"])
    True
    >>> odd_even_transposition([1.9, 42.0, 2.8]) == sorted([1.9, 42.0, 2.8])
    True
    >>> odd_even_transposition([False, True, False]) == sorted([False, False, True])
    True
    >>> odd_even_transposition([1, 32.0, 9]) == sorted([False, False, True])
    False
    >>> odd_even_transposition([1, 32.0, 9]) == sorted([1.0, 32, 9.0])
    True
    >>> unsorted_list = [-442, -98, -554, 266, -491, 985, -53, -529, 82, -429]
    >>> odd_even_transposition(unsorted_list) == sorted(unsorted_list)
    True
    >>> unsorted_list = [-442, -98, -554, 266, -491, 985, -53, -529, 82, -429]
    >>> odd_even_transposition(unsorted_list) == sorted(unsorted_list + [1])
    False
    """
    # spawn method is considered safer than fork
    multiprocessing_context = mp.get_context("spawn")

    process_array_ = []
    result_pipe = []
    # initialize the list of pipes where the values will be retrieved
    for _ in arr:
        result_pipe.append(multiprocessing_context.Pipe())
    # creates the processes
    # the first and last process only have one neighbor so they are made outside
    # of the loop
    temp_rs = multiprocessing_context.Pipe()
    temp_rr = multiprocessing_context.Pipe()
    process_array_.append(
        multiprocessing_context.Process(
            target=oe_process,
            args=(
                0,
                arr[0],
                None,
                temp_rs,
                None,
                temp_rr,
                result_pipe[0],
                multiprocessing_context,
            ),
        )
    )
    temp_lr = temp_rs
    temp_ls = temp_rr

    for i in range(1, len(arr) - 1):
        temp_rs = multiprocessing_context.Pipe()
        temp_rr = multiprocessing_context.Pipe()
        process_array_.append(
            multiprocessing_context.Process(
                target=oe_process,
                args=(
                    i,
                    arr[i],
                    temp_ls,
                    temp_rs,
                    temp_lr,
                    temp_rr,
                    result_pipe[i],
                    multiprocessing_context,
                ),
            )
        )
        temp_lr = temp_rs
        temp_ls = temp_rr

    process_array_.append(
        multiprocessing_context.Process(
            target=oe_process,
            args=(
                len(arr) - 1,
                arr[len(arr) - 1],
                temp_ls,
                None,
                temp_lr,
                None,
                result_pipe[len(arr) - 1],
                multiprocessing_context,
            ),
        )
    )

    # start the processes
    for p in process_array_:
        p.start()

    # wait for the processes to end and write their values to the list
    for p in range(len(result_pipe)):
        arr[p] = result_pipe[p][0].recv()
        process_array_[p].join()
    return arr


# creates a reverse sorted list and sorts it
def main():
    arr = list(range(10, 0, -1))
    print("Initial List")
    print(*arr)
    arr = odd_even_transposition(arr)
    print("Sorted List\n")
    print(*arr)


if __name__ == "__main__":
    main()


================================================
FILE: sorts/odd_even_transposition_single_threaded.py
================================================
"""
Source: https://en.wikipedia.org/wiki/Odd%E2%80%93even_sort

This is a non-parallelized implementation of odd-even transposition sort.

Normally the swaps in each set happen simultaneously, without that the algorithm
is no better than bubble sort.
"""


def odd_even_transposition(arr: list) -> list:
    """
    >>> odd_even_transposition([5, 4, 3, 2, 1])
    [1, 2, 3, 4, 5]

    >>> odd_even_transposition([13, 11, 18, 0, -1])
    [-1, 0, 11, 13, 18]

    >>> odd_even_transposition([-.1, 1.1, .1, -2.9])
    [-2.9, -0.1, 0.1, 1.1]
    """
    arr_size = len(arr)
    for _ in range(arr_size):
        for i in range(_ % 2, arr_size - 1, 2):
            if arr[i + 1] < arr[i]:
                arr[i], arr[i + 1] = arr[i + 1], arr[i]

    return arr


if __name__ == "__main__":
    arr = list(range(10, 0, -1))
    print(f"Original: {arr}. Sorted: {odd_even_transposition(arr)}")


================================================
FILE: sorts/pancake_sort.py
================================================
"""
This is a pure Python implementation of the pancake sort algorithm
For doctests run following command:
python3 -m doctest -v pancake_sort.py
or
python -m doctest -v pancake_sort.py
For manual testing run:
python pancake_sort.py
"""


def pancake_sort(arr):
    """Sort Array with Pancake Sort.
    :param arr: Collection containing comparable items
    :return: Collection ordered in ascending order of items
    Examples:
    >>> pancake_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]
    >>> pancake_sort([])
    []
    >>> pancake_sort([-2, -5, -45])
    [-45, -5, -2]
    """
    cur = len(arr)
    while cur > 1:
        # Find the maximum number in arr
        mi = arr.index(max(arr[0:cur]))
        # Reverse from 0 to mi
        arr = arr[mi::-1] + arr[mi + 1 : len(arr)]
        # Reverse whole list
        arr = arr[cur - 1 :: -1] + arr[cur : len(arr)]
        cur -= 1
    return arr


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    print(pancake_sort(unsorted))


================================================
FILE: sorts/patience_sort.py
================================================
from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

"""
A pure Python implementation of the patience sort algorithm

For more information: https://en.wikipedia.org/wiki/Patience_sorting

This algorithm is based on the card game patience

For doctests run following command:
python3 -m doctest -v patience_sort.py

For manual testing run:
python3 patience_sort.py
"""


@total_ordering
class Stack(list):
    def __lt__(self, other):
        return self[-1] < other[-1]

    def __eq__(self, other):
        return self[-1] == other[-1]


def patience_sort(collection: list) -> list:
    """A pure implementation of patience sort algorithm in Python

    :param collection: some mutable ordered collection with heterogeneous
    comparable items inside
    :return: the same collection ordered by ascending

    Examples:
    >>> patience_sort([1, 9, 5, 21, 17, 6])
    [1, 5, 6, 9, 17, 21]

    >>> patience_sort([])
    []

    >>> patience_sort([-3, -17, -48])
    [-48, -17, -3]
    """
    stacks: list[Stack] = []
    # sort into stacks
    for element in collection:
        new_stacks = Stack([element])
        i = bisect_left(stacks, new_stacks)
        if i != len(stacks):
            stacks[i].append(element)
        else:
            stacks.append(new_stacks)

    # use a heap-based merge to merge stack efficiently
    collection[:] = merge(*(reversed(stack) for stack in stacks))
    return collection


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    print(patience_sort(unsorted))


================================================
FILE: sorts/pigeon_sort.py
================================================
"""
This is an implementation of Pigeon Hole Sort.
For doctests run following command:

python3 -m doctest -v pigeon_sort.py
or
python -m doctest -v pigeon_sort.py

For manual testing run:
python pigeon_sort.py
"""

from __future__ import annotations


def pigeon_sort(array: list[int]) -> list[int]:
    """
    Implementation of pigeon hole sort algorithm
    :param array: Collection of comparable items
    :return: Collection sorted in ascending order
    >>> pigeon_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]
    >>> pigeon_sort([])
    []
    >>> pigeon_sort([-2, -5, -45])
    [-45, -5, -2]
    """
    if len(array) == 0:
        return array

    _min, _max = min(array), max(array)

    # Compute the variables
    holes_range = _max - _min + 1
    holes, holes_repeat = [0] * holes_range, [0] * holes_range

    # Make the sorting.
    for i in array:
        index = i - _min
        holes[index] = i
        holes_repeat[index] += 1

    # Makes the array back by replacing the numbers.
    index = 0
    for i in range(holes_range):
        while holes_repeat[i] > 0:
            array[index] = holes[i]
            index += 1
            holes_repeat[i] -= 1

    # Returns the sorted array.
    return array


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    user_input = input("Enter numbers separated by comma:\n")
    unsorted = [int(x) for x in user_input.split(",")]
    print(pigeon_sort(unsorted))


================================================
FILE: sorts/pigeonhole_sort.py
================================================
# Python program to implement Pigeonhole Sorting in python

# Algorithm for the pigeonhole sorting


def pigeonhole_sort(a):
    """
    >>> a = [8, 3, 2, 7, 4, 6, 8]
    >>> b = sorted(a)  # a nondestructive sort
    >>> pigeonhole_sort(a)  # a destructive sort
    >>> a == b
    True
    """
    # size of range of values in the list (ie, number of pigeonholes we need)

    min_val = min(a)  # min() finds the minimum value
    max_val = max(a)  # max() finds the maximum value

    size = max_val - min_val + 1  # size is difference of max and min values plus one

    # list of pigeonholes of size equal to the variable size
    holes = [0] * size

    # Populate the pigeonholes.
    for x in a:
        assert isinstance(x, int), "integers only please"
        holes[x - min_val] += 1

    # Putting the elements back into the array in an order.
    i = 0
    for count in range(size):
        while holes[count] > 0:
            holes[count] -= 1
            a[i] = count + min_val
            i += 1


def main():
    a = [8, 3, 2, 7, 4, 6, 8]
    pigeonhole_sort(a)
    print("Sorted order is:", " ".join(a))


if __name__ == "__main__":
    main()


================================================
FILE: sorts/quick_sort.py
================================================
"""
A pure Python implementation of the quick sort algorithm

For doctests run following command:
python3 -m doctest -v quick_sort.py

For manual testing run:
python3 quick_sort.py
"""

from __future__ import annotations

from random import randrange


def quick_sort(collection: list) -> list:
    """A pure Python implementation of quicksort algorithm.

    :param collection: a mutable collection of comparable items
    :return: the same collection ordered in ascending order

    Examples:
    >>> quick_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]
    >>> quick_sort([])
    []
    >>> quick_sort([-2, 5, 0, -45])
    [-45, -2, 0, 5]
    """
    # Base case: if the collection has 0 or 1 elements, it is already sorted
    if len(collection) < 2:
        return collection

    # Randomly select a pivot index and remove the pivot element from the collection
    pivot_index = randrange(len(collection))
    pivot = collection.pop(pivot_index)

    # Partition the remaining elements into two groups: lesser or equal, and greater
    lesser = [item for item in collection if item <= pivot]
    greater = [item for item in collection if item > pivot]

    # Recursively sort the lesser and greater groups, and combine with the pivot
    return [*quick_sort(lesser), pivot, *quick_sort(greater)]


if __name__ == "__main__":
    # Get user input and convert it into a list of integers
    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]

    # Print the result of sorting the user-provided list
    print(quick_sort(unsorted))


================================================
FILE: sorts/quick_sort_3_partition.py
================================================
def quick_sort_3partition(sorting: list, left: int, right: int) -> None:
    """ "
    Python implementation of quick sort algorithm with 3-way partition.
    The idea of 3-way quick sort is based on "Dutch National Flag algorithm".

    :param sorting: sort list
    :param left: left endpoint of sorting
    :param right: right endpoint of sorting
    :return: None

    Examples:
    >>> array1 = [5, -1, -1, 5, 5, 24, 0]
    >>> quick_sort_3partition(array1, 0, 6)
    >>> array1
    [-1, -1, 0, 5, 5, 5, 24]
    >>> array2 = [9, 0, 2, 6]
    >>> quick_sort_3partition(array2, 0, 3)
    >>> array2
    [0, 2, 6, 9]
    >>> array3 = []
    >>> quick_sort_3partition(array3, 0, 0)
    >>> array3
    []
    """
    if right <= left:
        return
    a = i = left
    b = right
    pivot = sorting[left]
    while i <= b:
        if sorting[i] < pivot:
            sorting[a], sorting[i] = sorting[i], sorting[a]
            a += 1
            i += 1
        elif sorting[i] > pivot:
            sorting[b], sorting[i] = sorting[i], sorting[b]
            b -= 1
        else:
            i += 1
    quick_sort_3partition(sorting, left, a - 1)
    quick_sort_3partition(sorting, b + 1, right)


def quick_sort_lomuto_partition(sorting: list, left: int, right: int) -> None:
    """
    A pure Python implementation of quick sort algorithm(in-place)
    with Lomuto partition scheme:
    https://en.wikipedia.org/wiki/Quicksort#Lomuto_partition_scheme

    :param sorting: sort list
    :param left: left endpoint of sorting
    :param right: right endpoint of sorting
    :return: None

    Examples:
    >>> nums1 = [0, 5, 3, 1, 2]
    >>> quick_sort_lomuto_partition(nums1, 0, 4)
    >>> nums1
    [0, 1, 2, 3, 5]
    >>> nums2 = []
    >>> quick_sort_lomuto_partition(nums2, 0, 0)
    >>> nums2
    []
    >>> nums3 = [-2, 5, 0, -4]
    >>> quick_sort_lomuto_partition(nums3, 0, 3)
    >>> nums3
    [-4, -2, 0, 5]
    """
    if left < right:
        pivot_index = lomuto_partition(sorting, left, right)
        quick_sort_lomuto_partition(sorting, left, pivot_index - 1)
        quick_sort_lomuto_partition(sorting, pivot_index + 1, right)


def lomuto_partition(sorting: list, left: int, right: int) -> int:
    """
    Example:
    >>> lomuto_partition([1,5,7,6], 0, 3)
    2
    """
    pivot = sorting[right]
    store_index = left
    for i in range(left, right):
        if sorting[i] < pivot:
            sorting[store_index], sorting[i] = sorting[i], sorting[store_index]
            store_index += 1
    sorting[right], sorting[store_index] = sorting[store_index], sorting[right]
    return store_index


def three_way_radix_quicksort(sorting: list) -> list:
    """
    Three-way radix quicksort:
    https://en.wikipedia.org/wiki/Quicksort#Three-way_radix_quicksort
    First divide the list into three parts.
    Then recursively sort the "less than" and "greater than" partitions.

    >>> three_way_radix_quicksort([])
    []
    >>> three_way_radix_quicksort([1])
    [1]
    >>> three_way_radix_quicksort([-5, -2, 1, -2, 0, 1])
    [-5, -2, -2, 0, 1, 1]
    >>> three_way_radix_quicksort([1, 2, 5, 1, 2, 0, 0, 5, 2, -1])
    [-1, 0, 0, 1, 1, 2, 2, 2, 5, 5]
    """
    if len(sorting) <= 1:
        return sorting
    return (
        three_way_radix_quicksort([i for i in sorting if i < sorting[0]])
        + [i for i in sorting if i == sorting[0]]
        + three_way_radix_quicksort([i for i in sorting if i > sorting[0]])
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)

    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    quick_sort_3partition(unsorted, 0, len(unsorted) - 1)
    print(unsorted)


================================================
FILE: sorts/radix_sort.py
================================================
"""
This is a pure Python implementation of the radix sort algorithm

Source: https://en.wikipedia.org/wiki/Radix_sort
"""

from __future__ import annotations

RADIX = 10


def radix_sort(list_of_ints: list[int]) -> list[int]:
    """
    Examples:
    >>> radix_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]

    >>> radix_sort(list(range(15))) == sorted(range(15))
    True
    >>> radix_sort(list(range(14,-1,-1))) == sorted(range(15))
    True
    >>> radix_sort([1,100,10,1000]) == sorted([1,100,10,1000])
    True
    """
    placement = 1
    max_digit = max(list_of_ints)
    while placement <= max_digit:
        # declare and initialize empty buckets
        buckets: list[list] = [[] for _ in range(RADIX)]
        # split list_of_ints between the buckets
        for i in list_of_ints:
            tmp = int((i / placement) % RADIX)
            buckets[tmp].append(i)
        # put each buckets' contents into list_of_ints
        a = 0
        for b in range(RADIX):
            for i in buckets[b]:
                list_of_ints[a] = i
                a += 1
        # move to next
        placement *= RADIX
    return list_of_ints


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: sorts/recursive_insertion_sort.py
================================================
"""
A recursive implementation of the insertion sort algorithm
"""

from __future__ import annotations


def rec_insertion_sort(collection: list, n: int):
    """
    Given a collection of numbers and its length, sorts the collections
    in ascending order

    :param collection: A mutable collection of comparable elements
    :param n: The length of collections

    >>> col = [1, 2, 1]
    >>> rec_insertion_sort(col, len(col))
    >>> col
    [1, 1, 2]

    >>> col = [2, 1, 0, -1, -2]
    >>> rec_insertion_sort(col, len(col))
    >>> col
    [-2, -1, 0, 1, 2]

    >>> col = [1]
    >>> rec_insertion_sort(col, len(col))
    >>> col
    [1]
    """
    # Checks if the entire collection has been sorted
    if len(collection) <= 1 or n <= 1:
        return

    insert_next(collection, n - 1)
    rec_insertion_sort(collection, n - 1)


def insert_next(collection: list, index: int):
    """
    Inserts the '(index-1)th' element into place

    >>> col = [3, 2, 4, 2]
    >>> insert_next(col, 1)
    >>> col
    [2, 3, 4, 2]

    >>> col = [3, 2, 3]
    >>> insert_next(col, 2)
    >>> col
    [3, 2, 3]

    >>> col = []
    >>> insert_next(col, 1)
    >>> col
    []
    """
    # Checks order between adjacent elements
    if index >= len(collection) or collection[index - 1] <= collection[index]:
        return

    # Swaps adjacent elements since they are not in ascending order
    collection[index - 1], collection[index] = (
        collection[index],
        collection[index - 1],
    )

    insert_next(collection, index + 1)


if __name__ == "__main__":
    numbers = input("Enter integers separated by spaces: ")
    number_list: list[int] = [int(num) for num in numbers.split()]
    rec_insertion_sort(number_list, len(number_list))
    print(number_list)


================================================
FILE: sorts/recursive_mergesort_array.py
================================================
"""A merge sort which accepts an array as input and recursively
splits an array in half and sorts and combines them.
"""

"""https://en.wikipedia.org/wiki/Merge_sort """


def merge(arr: list[int]) -> list[int]:
    """Return a sorted array.
    >>> merge([10,9,8,7,6,5,4,3,2,1])
    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    >>> merge([1,2,3,4,5,6,7,8,9,10])
    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    >>> merge([10,22,1,2,3,9,15,23])
    [1, 2, 3, 9, 10, 15, 22, 23]
    >>> merge([100])
    [100]
    >>> merge([])
    []
    """
    if len(arr) > 1:
        middle_length = len(arr) // 2  # Finds the middle of the array
        left_array = arr[
            :middle_length
        ]  # Creates an array of the elements in the first half.
        right_array = arr[
            middle_length:
        ]  # Creates an array of the elements in the second half.
        left_size = len(left_array)
        right_size = len(right_array)
        merge(left_array)  # Starts sorting the left.
        merge(right_array)  # Starts sorting the right
        left_index = 0  # Left Counter
        right_index = 0  # Right Counter
        index = 0  # Position Counter
        while (
            left_index < left_size and right_index < right_size
        ):  # Runs until the lowers size of the left and right are sorted.
            if left_array[left_index] < right_array[right_index]:
                arr[index] = left_array[left_index]
                left_index += 1
            else:
                arr[index] = right_array[right_index]
                right_index += 1
            index += 1
        while (
            left_index < left_size
        ):  # Adds the left over elements in the left half of the array
            arr[index] = left_array[left_index]
            left_index += 1
            index += 1
        while (
            right_index < right_size
        ):  # Adds the left over elements in the right half of the array
            arr[index] = right_array[right_index]
            right_index += 1
            index += 1
    return arr


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: sorts/recursive_quick_sort.py
================================================
def quick_sort(data: list) -> list:
    """
    >>> for data in ([2, 1, 0], [2.2, 1.1, 0], "quick_sort"):
    ...     quick_sort(data) == sorted(data)
    True
    True
    True
    """
    if len(data) <= 1:
        return data
    else:
        return [
            *quick_sort([e for e in data[1:] if e <= data[0]]),
            data[0],
            *quick_sort([e for e in data[1:] if e > data[0]]),
        ]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: sorts/selection_sort.py
================================================
def selection_sort(collection: list[int]) -> list[int]:
    """
    Sorts a list in ascending order using the selection sort algorithm.

    :param collection: A list of integers to be sorted.
    :return: The sorted list.

    Examples:
    >>> selection_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]

    >>> selection_sort([])
    []

    >>> selection_sort([-2, -5, -45])
    [-45, -5, -2]
    """

    length = len(collection)
    for i in range(length - 1):
        min_index = i
        for k in range(i + 1, length):
            if collection[k] < collection[min_index]:
                min_index = k
        if min_index != i:
            collection[i], collection[min_index] = collection[min_index], collection[i]
    return collection


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    sorted_list = selection_sort(unsorted)
    print("Sorted List:", sorted_list)


================================================
FILE: sorts/shell_sort.py
================================================
"""
https://en.wikipedia.org/wiki/Shellsort#Pseudocode
"""


def shell_sort(collection: list[int]) -> list[int]:
    """Pure implementation of shell sort algorithm in Python
    :param collection:  Some mutable ordered collection with heterogeneous
    comparable items inside
    :return:  the same collection ordered by ascending

    >>> shell_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]
    >>> shell_sort([])
    []
    >>> shell_sort([-2, -5, -45])
    [-45, -5, -2]
    """
    # Marcin Ciura's gap sequence

    gaps = [701, 301, 132, 57, 23, 10, 4, 1]
    for gap in gaps:
        for i in range(gap, len(collection)):
            insert_value = collection[i]
            j = i
            while j >= gap and collection[j - gap] > insert_value:
                collection[j] = collection[j - gap]
                j -= gap
            if j != i:
                collection[j] = insert_value
    return collection


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    print(shell_sort(unsorted))


================================================
FILE: sorts/shrink_shell_sort.py
================================================
"""
This function implements the shell sort algorithm
which is slightly faster than its pure implementation.

This shell sort is implemented using a gap, which
shrinks by a certain factor each iteration. In this
implementation, the gap is initially set to the
length of the collection. The gap is then reduced by
a certain factor (1.3) each iteration.

For each iteration, the algorithm compares elements
that are a certain number of positions apart
(determined by the gap). If the element at the higher
position is greater than the element at the lower
position, the two elements are swapped. The process
is repeated until the gap is equal to 1.

The reason this is more efficient is that it reduces
the number of comparisons that need to be made. By
using a smaller gap, the list is sorted more quickly.
"""


def shell_sort(collection: list) -> list:
    """Implementation of shell sort algorithm in Python
    :param collection:  Some mutable ordered collection with heterogeneous
    comparable items inside
    :return:  the same collection ordered by ascending

    >>> shell_sort([3, 2, 1])
    [1, 2, 3]
    >>> shell_sort([])
    []
    >>> shell_sort([1])
    [1]
    """

    # Choose an initial gap value
    gap = len(collection)

    # Set the gap value to be decreased by a factor of 1.3
    # after each iteration
    shrink = 1.3

    # Continue sorting until the gap is 1
    while gap > 1:
        # Decrease the gap value
        gap = int(gap / shrink)

        # Sort the elements using insertion sort
        for i in range(gap, len(collection)):
            temp = collection[i]
            j = i
            while j >= gap and collection[j - gap] > temp:
                collection[j] = collection[j - gap]
                j -= gap
            collection[j] = temp

    return collection


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: sorts/slowsort.py
================================================
"""
Slowsort is a sorting algorithm. It is of humorous nature and not useful.
It's based on the principle of multiply and surrender,
a tongue-in-cheek joke of divide and conquer.
It was published in 1986 by Andrei Broder and Jorge Stolfi
in their paper Pessimal Algorithms and Simplexity Analysis
(a parody of optimal algorithms and complexity analysis).

Source: https://en.wikipedia.org/wiki/Slowsort
"""

from __future__ import annotations


def slowsort(sequence: list, start: int | None = None, end: int | None = None) -> None:
    """
    Sorts sequence[start..end] (both inclusive) in-place.
    start defaults to 0 if not given.
    end defaults to len(sequence) - 1 if not given.
    It returns None.
    >>> seq = [1, 6, 2, 5, 3, 4, 4, 5]; slowsort(seq); seq
    [1, 2, 3, 4, 4, 5, 5, 6]
    >>> seq = []; slowsort(seq); seq
    []
    >>> seq = [2]; slowsort(seq); seq
    [2]
    >>> seq = [1, 2, 3, 4]; slowsort(seq); seq
    [1, 2, 3, 4]
    >>> seq = [4, 3, 2, 1]; slowsort(seq); seq
    [1, 2, 3, 4]
    >>> seq = [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]; slowsort(seq, 2, 7); seq
    [9, 8, 2, 3, 4, 5, 6, 7, 1, 0]
    >>> seq = [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]; slowsort(seq, end = 4); seq
    [5, 6, 7, 8, 9, 4, 3, 2, 1, 0]
    >>> seq = [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]; slowsort(seq, start = 5); seq
    [9, 8, 7, 6, 5, 0, 1, 2, 3, 4]
    """
    if start is None:
        start = 0

    if end is None:
        end = len(sequence) - 1

    if start >= end:
        return

    mid = (start + end) // 2

    slowsort(sequence, start, mid)
    slowsort(sequence, mid + 1, end)

    if sequence[end] < sequence[mid]:
        sequence[end], sequence[mid] = sequence[mid], sequence[end]

    slowsort(sequence, start, end - 1)


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: sorts/stalin_sort.py
================================================
"""
Stalin Sort algorithm: Removes elements that are out of order.
Elements that are not greater than or equal to the previous element are discarded.
Reference: https://medium.com/@kaweendra/the-ultimate-sorting-algorithm-6513d6968420
"""


def stalin_sort(sequence: list[int]) -> list[int]:
    """
    Sorts a list using the Stalin sort algorithm.

    >>> stalin_sort([4, 3, 5, 2, 1, 7])
    [4, 5, 7]

    >>> stalin_sort([1, 2, 3, 4])
    [1, 2, 3, 4]

    >>> stalin_sort([4, 5, 5, 2, 3])
    [4, 5, 5]

    >>> stalin_sort([6, 11, 12, 4, 1, 5])
    [6, 11, 12]

    >>> stalin_sort([5, 0, 4, 3])
    [5]

    >>> stalin_sort([5, 4, 3, 2, 1])
    [5]

    >>> stalin_sort([1, 2, 3, 4, 5])
    [1, 2, 3, 4, 5]

    >>> stalin_sort([1, 2, 8, 7, 6])
    [1, 2, 8]
    """
    result = [sequence[0]]
    for element in sequence[1:]:
        if element >= result[-1]:
            result.append(element)

    return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: sorts/stooge_sort.py
================================================
def stooge_sort(arr: list[int]) -> list[int]:
    """
    Examples:
    >>> stooge_sort([18.1, 0, -7.1, -1, 2, 2])
    [-7.1, -1, 0, 2, 2, 18.1]

    >>> stooge_sort([])
    []
    """
    stooge(arr, 0, len(arr) - 1)
    return arr


def stooge(arr: list[int], i: int, h: int) -> None:
    if i >= h:
        return

    # If first element is smaller than the last then swap them
    if arr[i] > arr[h]:
        arr[i], arr[h] = arr[h], arr[i]

    # If there are more than 2 elements in the array
    if h - i + 1 > 2:
        t = (int)((h - i + 1) / 3)

        # Recursively sort first 2/3 elements
        stooge(arr, i, (h - t))

        # Recursively sort last 2/3 elements
        stooge(arr, i + t, (h))

        # Recursively sort first 2/3 elements
        stooge(arr, i, (h - t))


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    print(stooge_sort(unsorted))


================================================
FILE: sorts/strand_sort.py
================================================
import operator


def strand_sort(arr: list, reverse: bool = False, solution: list | None = None) -> list:
    """
    Strand sort implementation
    source: https://en.wikipedia.org/wiki/Strand_sort

    :param arr: Unordered input list
    :param reverse: Descent ordering flag
    :param solution: Ordered items container

    Examples:
    >>> strand_sort([4, 2, 5, 3, 0, 1])
    [0, 1, 2, 3, 4, 5]

    >>> strand_sort([4, 2, 5, 3, 0, 1], reverse=True)
    [5, 4, 3, 2, 1, 0]
    """
    _operator = operator.lt if reverse else operator.gt
    solution = solution or []

    if not arr:
        return solution

    sublist = [arr.pop(0)]
    for i, item in enumerate(arr):
        if _operator(item, sublist[-1]):
            sublist.append(item)
            arr.pop(i)

    #  merging sublist into solution list
    if not solution:
        solution.extend(sublist)
    else:
        while sublist:
            item = sublist.pop(0)
            for i, xx in enumerate(solution):
                if not _operator(item, xx):
                    solution.insert(i, item)
                    break
            else:
                solution.append(item)

    strand_sort(arr, reverse, solution)
    return solution


if __name__ == "__main__":
    assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
    assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]


================================================
FILE: sorts/tim_sort.py
================================================
def binary_search(lst, item, start, end):
    if start == end:
        return start if lst[start] > item else start + 1
    if start > end:
        return start

    mid = (start + end) // 2
    if lst[mid] < item:
        return binary_search(lst, item, mid + 1, end)
    elif lst[mid] > item:
        return binary_search(lst, item, start, mid - 1)
    else:
        return mid


def insertion_sort(lst):
    length = len(lst)

    for index in range(1, length):
        value = lst[index]
        pos = binary_search(lst, value, 0, index - 1)
        lst = [*lst[:pos], value, *lst[pos:index], *lst[index + 1 :]]

    return lst


def merge(left, right):
    if not left:
        return right

    if not right:
        return left

    if left[0] < right[0]:
        return [left[0], *merge(left[1:], right)]

    return [right[0], *merge(left, right[1:])]


def tim_sort(lst):
    """
    >>> tim_sort("Python")
    ['P', 'h', 'n', 'o', 't', 'y']
    >>> tim_sort((1.1, 1, 0, -1, -1.1))
    [-1.1, -1, 0, 1, 1.1]
    >>> tim_sort(list(reversed(list(range(7)))))
    [0, 1, 2, 3, 4, 5, 6]
    >>> tim_sort([3, 2, 1]) == insertion_sort([3, 2, 1])
    True
    >>> tim_sort([3, 2, 1]) == sorted([3, 2, 1])
    True
    """
    length = len(lst)
    runs, sorted_runs = [], []
    new_run = [lst[0]]
    sorted_array = []
    i = 1
    while i < length:
        if lst[i] < lst[i - 1]:
            runs.append(new_run)
            new_run = [lst[i]]
        else:
            new_run.append(lst[i])
        i += 1
    runs.append(new_run)

    for run in runs:
        sorted_runs.append(insertion_sort(run))
    for run in sorted_runs:
        sorted_array = merge(sorted_array, run)

    return sorted_array


def main():
    lst = [5, 9, 10, 3, -4, 5, 178, 92, 46, -18, 0, 7]
    sorted_lst = tim_sort(lst)
    print(sorted_lst)


if __name__ == "__main__":
    main()


================================================
FILE: sorts/topological_sort.py
================================================
"""Topological Sort."""

#     a
#    / \
#   b  c
#  / \
# d  e
edges: dict[str, list[str]] = {
    "a": ["c", "b"],
    "b": ["d", "e"],
    "c": [],
    "d": [],
    "e": [],
}
vertices: list[str] = ["a", "b", "c", "d", "e"]


def topological_sort(start: str, visited: list[str], sort: list[str]) -> list[str]:
    """Perform topological sort on a directed acyclic graph."""
    current = start
    # add current to visited
    visited.append(current)
    neighbors = edges[current]
    for neighbor in neighbors:
        # if neighbor not in visited, visit
        if neighbor not in visited:
            sort = topological_sort(neighbor, visited, sort)
    # if all neighbors visited add current to sort
    sort.append(current)
    # if all vertices haven't been visited select a new one to visit
    if len(visited) != len(vertices):
        for vertice in vertices:
            if vertice not in visited:
                sort = topological_sort(vertice, visited, sort)
    # return sort
    return sort


if __name__ == "__main__":
    sort = topological_sort("a", [], [])
    print(sort)


================================================
FILE: sorts/tree_sort.py
================================================
"""
Tree_sort algorithm.

Build a Binary Search Tree and then iterate thru it to get a sorted list.
"""

from __future__ import annotations

from collections.abc import Iterator
from dataclasses import dataclass


@dataclass
class Node:
    val: int
    left: Node | None = None
    right: Node | None = None

    def __iter__(self) -> Iterator[int]:
        if self.left:
            yield from self.left
        yield self.val
        if self.right:
            yield from self.right

    def __len__(self) -> int:
        return sum(1 for _ in self)

    def insert(self, val: int) -> None:
        if val < self.val:
            if self.left is None:
                self.left = Node(val)
            else:
                self.left.insert(val)
        elif val > self.val:
            if self.right is None:
                self.right = Node(val)
            else:
                self.right.insert(val)


def tree_sort(arr: list[int]) -> tuple[int, ...]:
    """
    >>> tree_sort([])
    ()
    >>> tree_sort((1,))
    (1,)
    >>> tree_sort((1, 2))
    (1, 2)
    >>> tree_sort([5, 2, 7])
    (2, 5, 7)
    >>> tree_sort((5, -4, 9, 2, 7))
    (-4, 2, 5, 7, 9)
    >>> tree_sort([5, 6, 1, -1, 4, 37, 2, 7])
    (-1, 1, 2, 4, 5, 6, 7, 37)

    # >>> tree_sort(range(10, -10, -1)) == tuple(sorted(range(10, -10, -1)))
    # True
    """
    if len(arr) == 0:
        return tuple(arr)
    root = Node(arr[0])
    for item in arr[1:]:
        root.insert(item)
    return tuple(root)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(f"{tree_sort([5, 6, 1, -1, 4, 37, -3, 7]) = }")


================================================
FILE: sorts/unknown_sort.py
================================================
"""
Python implementation of a sort algorithm.
Best Case Scenario : O(n)
Worst Case Scenario : O(n^2) because native Python functions:min, max and remove are
already O(n)
"""


def merge_sort(collection):
    """Pure implementation of the fastest merge sort algorithm in Python

    :param collection: some mutable ordered collection with heterogeneous
    comparable items inside
    :return: a collection ordered by ascending

    Examples:
    >>> merge_sort([0, 5, 3, 2, 2])
    [0, 2, 2, 3, 5]

    >>> merge_sort([])
    []

    >>> merge_sort([-2, -5, -45])
    [-45, -5, -2]
    """
    start, end = [], []
    while len(collection) > 1:
        min_one, max_one = min(collection), max(collection)
        start.append(min_one)
        end.append(max_one)
        collection.remove(min_one)
        collection.remove(max_one)
    end.reverse()
    return start + collection + end


if __name__ == "__main__":
    user_input = input("Enter numbers separated by a comma:\n").strip()
    unsorted = [int(item) for item in user_input.split(",")]
    print(*merge_sort(unsorted), sep=",")


================================================
FILE: sorts/wiggle_sort.py
================================================
"""
Wiggle Sort.

Given an unsorted array nums, reorder it such
that nums[0] < nums[1] > nums[2] < nums[3]....
For example:
if input numbers = [3, 5, 2, 1, 6, 4]
one possible Wiggle Sorted answer is [3, 5, 1, 6, 2, 4].
"""


def wiggle_sort(nums: list) -> list:
    """
    Python implementation of wiggle.
    Example:
    >>> wiggle_sort([0, 5, 3, 2, 2])
    [0, 5, 2, 3, 2]
    >>> wiggle_sort([])
    []
    >>> wiggle_sort([-2, -5, -45])
    [-45, -2, -5]
    >>> wiggle_sort([-2.1, -5.68, -45.11])
    [-45.11, -2.1, -5.68]
    """
    for i, _ in enumerate(nums):
        if (i % 2 == 1) == (nums[i - 1] > nums[i]):
            nums[i - 1], nums[i] = nums[i], nums[i - 1]

    return nums


if __name__ == "__main__":
    print("Enter the array elements:")
    array = list(map(int, input().split()))
    print("The unsorted array is:")
    print(array)
    print("Array after Wiggle sort:")
    print(wiggle_sort(array))


================================================
FILE: strings/__init__.py
================================================


================================================
FILE: strings/aho_corasick.py
================================================
from __future__ import annotations

from collections import deque


class Automaton:
    def __init__(self, keywords: list[str]):
        self.adlist: list[dict] = []
        self.adlist.append(
            {"value": "", "next_states": [], "fail_state": 0, "output": []}
        )

        for keyword in keywords:
            self.add_keyword(keyword)
        self.set_fail_transitions()

    def find_next_state(self, current_state: int, char: str) -> int | None:
        for state in self.adlist[current_state]["next_states"]:
            if char == self.adlist[state]["value"]:
                return state
        return None

    def add_keyword(self, keyword: str) -> None:
        current_state = 0
        for character in keyword:
            next_state = self.find_next_state(current_state, character)
            if next_state is None:
                self.adlist.append(
                    {
                        "value": character,
                        "next_states": [],
                        "fail_state": 0,
                        "output": [],
                    }
                )
                self.adlist[current_state]["next_states"].append(len(self.adlist) - 1)
                current_state = len(self.adlist) - 1
            else:
                current_state = next_state
        self.adlist[current_state]["output"].append(keyword)

    def set_fail_transitions(self) -> None:
        q: deque = deque()
        for node in self.adlist[0]["next_states"]:
            q.append(node)
            self.adlist[node]["fail_state"] = 0
        while q:
            r = q.popleft()
            for child in self.adlist[r]["next_states"]:
                q.append(child)
                state = self.adlist[r]["fail_state"]
                while (
                    self.find_next_state(state, self.adlist[child]["value"]) is None
                    and state != 0
                ):
                    state = self.adlist[state]["fail_state"]
                self.adlist[child]["fail_state"] = self.find_next_state(
                    state, self.adlist[child]["value"]
                )
                if self.adlist[child]["fail_state"] is None:
                    self.adlist[child]["fail_state"] = 0
                self.adlist[child]["output"] = (
                    self.adlist[child]["output"]
                    + self.adlist[self.adlist[child]["fail_state"]]["output"]
                )

    def search_in(self, string: str) -> dict[str, list[int]]:
        """
        >>> A = Automaton(["what", "hat", "ver", "er"])
        >>> A.search_in("whatever, err ... , wherever")
        {'what': [0], 'hat': [1], 'ver': [5, 25], 'er': [6, 10, 22, 26]}
        """
        result: dict = {}  # returns a dict with keywords and list of its occurrences
        current_state = 0
        for i in range(len(string)):
            while (
                self.find_next_state(current_state, string[i]) is None
                and current_state != 0
            ):
                current_state = self.adlist[current_state]["fail_state"]
            next_state = self.find_next_state(current_state, string[i])
            if next_state is None:
                current_state = 0
            else:
                current_state = next_state
                for key in self.adlist[current_state]["output"]:
                    if key not in result:
                        result[key] = []
                    result[key].append(i - len(key) + 1)
        return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: strings/alternative_string_arrange.py
================================================
def alternative_string_arrange(first_str: str, second_str: str) -> str:
    """
    Return the alternative arrangements of the two strings.
    :param first_str:
    :param second_str:
    :return: String
    >>> alternative_string_arrange("ABCD", "XY")
    'AXBYCD'
    >>> alternative_string_arrange("XY", "ABCD")
    'XAYBCD'
    >>> alternative_string_arrange("AB", "XYZ")
    'AXBYZ'
    >>> alternative_string_arrange("ABC", "")
    'ABC'
    """
    first_str_length: int = len(first_str)
    second_str_length: int = len(second_str)
    abs_length: int = (
        first_str_length if first_str_length > second_str_length else second_str_length
    )
    output_list: list = []
    for char_count in range(abs_length):
        if char_count < first_str_length:
            output_list.append(first_str[char_count])
        if char_count < second_str_length:
            output_list.append(second_str[char_count])
    return "".join(output_list)


if __name__ == "__main__":
    print(alternative_string_arrange("AB", "XYZ"), end=" ")


================================================
FILE: strings/anagrams.py
================================================
from __future__ import annotations

import collections
import pprint
from pathlib import Path


def signature(word: str) -> str:
    """
    Return a word's frequency-based signature.

    >>> signature("test")
    'e1s1t2'
    >>> signature("this is a test")
    ' 3a1e1h1i2s3t3'
    >>> signature("finaltest")
    'a1e1f1i1l1n1s1t2'
    """
    frequencies = collections.Counter(word)
    return "".join(
        f"{char}{frequency}" for char, frequency in sorted(frequencies.items())
    )


def anagram(my_word: str) -> list[str]:
    """
    Return every anagram of the given word from the dictionary.

    >>> anagram('test')
    ['sett', 'stet', 'test']
    >>> anagram('this is a test')
    []
    >>> anagram('final')
    ['final']
    """
    return word_by_signature[signature(my_word)]


data: str = Path(__file__).parent.joinpath("words.txt").read_text(encoding="utf-8")
word_list = sorted({word.strip().lower() for word in data.splitlines()})

word_by_signature = collections.defaultdict(list)
for word in word_list:
    word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
    all_anagrams = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

    with open("anagrams.txt", "w") as file:
        file.write("all_anagrams = \n")
        file.write(pprint.pformat(all_anagrams))


================================================
FILE: strings/anagrams.txt
================================================
all_anagrams =
 {'aal': ['aal', 'ala'],
 'aam': ['aam', 'ama'],
 'aaronic': ['aaronic', 'nicarao', 'ocarina'],
 'aaronite': ['aaronite', 'aeration'],
 'aaru': ['aaru', 'aura'],
 'ab': ['ab', 'ba'],
 'aba': ['aba', 'baa'],
 'abac': ['abac', 'caba'],
 'abactor': ['abactor', 'acrobat'],
 'abaft': ['abaft', 'bafta'],
 'abalone': ['abalone', 'balonea'],
 'abandoner': ['abandoner', 'reabandon'],
 'abanic': ['abanic', 'bianca'],
 'abaris': ['abaris', 'arabis'],
 'abas': ['abas', 'saba'],
 'abaser': ['abaser', 'abrase'],
 'abate': ['abate', 'ateba', 'batea', 'beata'],
 'abater': ['abater', 'artabe', 'eartab', 'trabea'],
 'abb': ['abb', 'bab'],
 'abba': ['abba', 'baba'],
 'abbey': ['abbey', 'bebay'],
 'abby': ['abby', 'baby'],
 'abdat': ['abdat', 'batad'],
 'abdiel': ['abdiel', 'baldie'],
 'abdominovaginal': ['abdominovaginal', 'vaginoabdominal'],
 'abdominovesical': ['abdominovesical', 'vesicoabdominal'],
 'abe': ['abe', 'bae', 'bea'],
 'abed': ['abed', 'bade', 'bead'],
 'abel': ['abel', 'able', 'albe', 'bale', 'beal', 'bela', 'blae'],
 'abele': ['abele', 'albee'],
 'abelian': ['abelian', 'nebalia'],
 'abenteric': ['abenteric', 'bicrenate'],
 'aberia': ['aberia', 'baeria', 'baiera'],
 'abet': ['abet', 'bate', 'beat', 'beta'],
 'abetment': ['abetment', 'batement'],
 'abettor': ['abettor', 'taboret'],
 'abhorrent': ['abhorrent', 'earthborn'],
 'abhorrer': ['abhorrer', 'harborer'],
 'abider': ['abider', 'bardie'],
 'abies': ['abies', 'beisa'],
 'abilla': ['abilla', 'labial'],
 'abilo': ['abilo', 'aboil'],
 'abir': ['abir', 'bari', 'rabi'],
 'abiston': ['abiston', 'bastion'],
 'abiuret': ['abiuret', 'aubrite', 'biurate', 'rubiate'],
 'abkar': ['abkar', 'arkab'],
 'abkhas': ['abkhas', 'kasbah'],
 'ablactate': ['ablactate', 'cabaletta'],
 'ablare': ['ablare', 'arable', 'arbela'],
 'ablastemic': ['ablastemic', 'masticable'],
 'ablation': ['ablation', 'obtainal'],
 'ablaut': ['ablaut', 'tabula'],
 'able': ['abel', 'able', 'albe', 'bale', 'beal', 'bela', 'blae'],
 'ableness': ['ableness', 'blaeness', 'sensable'],
 'ablepsia': ['ablepsia', 'epibasal'],
 'abler': ['abler', 'baler', 'belar', 'blare', 'blear'],
 'ablest': ['ablest', 'stable', 'tables'],
 'abloom': ['abloom', 'mabolo'],
 'ablow': ['ablow', 'balow', 'bowla'],
 'ablude': ['ablude', 'belaud'],
 'abluent': ['abluent', 'tunable'],
 'ablution': ['ablution', 'abutilon'],
 'ably': ['ably', 'blay', 'yalb'],
 'abmho': ['abmho', 'abohm'],
 'abner': ['abner', 'arneb', 'reban'],
 'abnet': ['abnet', 'beant'],
 'abo': ['abo', 'boa'],
 'aboard': ['aboard', 'aborad', 'abroad'],
 'abode': ['abode', 'adobe'],
 'abohm': ['abmho', 'abohm'],
 'aboil': ['abilo', 'aboil'],
 'abolisher': ['abolisher', 'reabolish'],
 'abongo': ['abongo', 'gaboon'],
 'aborad': ['aboard', 'aborad', 'abroad'],
 'aboral': ['aboral', 'arbalo'],
 'abord': ['abord', 'bardo', 'board', 'broad', 'dobra', 'dorab'],
 'abort': ['abort', 'tabor'],
 'aborticide': ['aborticide', 'bacterioid'],
 'abortient': ['abortient', 'torbanite'],
 'abortin': ['abortin', 'taborin'],
 'abortion': ['abortion', 'robotian'],
 'abortive': ['abortive', 'bravoite'],
 'abouts': ['abouts', 'basuto'],
 'abram': ['abram', 'ambar'],
 'abramis': ['abramis', 'arabism'],
 'abrasax': ['abrasax', 'abraxas'],
 'abrase': ['abaser', 'abrase'],
 'abrasion': ['abrasion', 'sorabian'],
 'abrastol': ['abrastol', 'albatros'],
 'abraxas': ['abrasax', 'abraxas'],
 'abreact': ['abreact', 'bractea', 'cabaret'],
 'abret': ['abret', 'bater', 'berat'],
 'abridge': ['abridge', 'brigade'],
 'abrim': ['abrim', 'birma'],
 'abrin': ['abrin', 'bairn', 'brain', 'brian', 'rabin'],
 'abristle': ['abristle', 'libertas'],
 'abroad': ['aboard', 'aborad', 'abroad'],
 'abrotine': ['abrotine', 'baritone', 'obtainer', 'reobtain'],
 'abrus': ['abrus', 'bursa', 'subra'],
 'absalom': ['absalom', 'balsamo'],
 'abscise': ['abscise', 'scabies'],
 'absent': ['absent', 'basten'],
 'absenter': ['absenter', 'reabsent'],
 'absi': ['absi', 'bais', 'bias', 'isba'],
 'absit': ['absit', 'batis'],
 'absmho': ['absmho', 'absohm'],
 'absohm': ['absmho', 'absohm'],
 'absorber': ['absorber', 'reabsorb'],
 'absorpt': ['absorpt', 'barpost'],
 'abthain': ['abthain', 'habitan'],
 'abulic': ['abulic', 'baculi'],
 'abut': ['abut', 'tabu', 'tuba'],
 'abuta': ['abuta', 'bauta'],
 'abutilon': ['ablution', 'abutilon'],
 'aby': ['aby', 'bay'],
 'abysmal': ['abysmal', 'balsamy'],
 'academite': ['academite', 'acetamide'],
 'acadie': ['acadie', 'acedia', 'adicea'],
 'acaleph': ['acaleph', 'acephal'],
 'acalepha': ['acalepha', 'acephala'],
 'acalephae': ['acalephae', 'apalachee'],
 'acalephan': ['acalephan', 'acephalan'],
 'acalyptrate': ['acalyptrate', 'calyptratae'],
 'acamar': ['acamar', 'camara', 'maraca'],
 'acanth': ['acanth', 'anchat', 'tanach'],
 'acanthia': ['acanthia', 'achatina'],
 'acanthial': ['acanthial', 'calathian'],
 'acanthin': ['acanthin', 'chinanta'],
 'acara': ['acara', 'araca'],
 'acardia': ['acardia', 'acarida', 'arcadia'],
 'acarian': ['acarian', 'acarina', 'acrania'],
 'acarid': ['acarid', 'cardia', 'carida'],
 'acarida': ['acardia', 'acarida', 'arcadia'],
 'acarina': ['acarian', 'acarina', 'acrania'],
 'acarine': ['acarine', 'acraein', 'arecain'],
 'acastus': ['acastus', 'astacus'],
 'acatholic': ['acatholic', 'chaotical'],
 'acaudate': ['acaudate', 'ecaudata'],
 'acca': ['acca', 'caca'],
 'accelerator': ['accelerator', 'retrocaecal'],
 'acception': ['acception', 'peccation'],
 'accessioner': ['accessioner', 'reaccession'],
 'accipitres': ['accipitres', 'preascitic'],
 'accite': ['accite', 'acetic'],
 'acclinate': ['acclinate', 'analectic'],
 'accoil': ['accoil', 'calico'],
 'accomplisher': ['accomplisher', 'reaccomplish'],
 'accompt': ['accompt', 'compact'],
 'accorder': ['accorder', 'reaccord'],
 'accoy': ['accoy', 'ccoya'],
 'accretion': ['accretion', 'anorectic', 'neoarctic'],
 'accrual': ['accrual', 'carucal'],
 'accurate': ['accurate', 'carucate'],
 'accurse': ['accurse', 'accuser'],
 'accusable': ['accusable', 'subcaecal'],
 'accused': ['accused', 'succade'],
 'accuser': ['accurse', 'accuser'],
 'acedia': ['acadie', 'acedia', 'adicea'],
 'acedy': ['acedy', 'decay'],
 'acentric': ['acentric', 'encratic', 'nearctic'],
 'acentrous': ['acentrous', 'courtesan', 'nectarous'],
 'acephal': ['acaleph', 'acephal'],
 'acephala': ['acalepha', 'acephala'],
 'acephalan': ['acalephan', 'acephalan'],
 'acephali': ['acephali', 'phacelia'],
 'acephalina': ['acephalina', 'phalaecian'],
 'acer': ['acer', 'acre', 'care', 'crea', 'race'],
 'aceraceae': ['aceraceae', 'arecaceae'],
 'aceraceous': ['aceraceous', 'arecaceous'],
 'acerb': ['acerb', 'brace', 'caber'],
 'acerbic': ['acerbic', 'breccia'],
 'acerdol': ['acerdol', 'coraled'],
 'acerin': ['acerin', 'cearin'],
 'acerous': ['acerous', 'carouse', 'euscaro'],
 'acervate': ['acervate', 'revacate'],
 'acervation': ['acervation', 'vacationer'],
 'acervuline': ['acervuline', 'avirulence'],
 'acetamide': ['academite', 'acetamide'],
 'acetamido': ['acetamido', 'coadamite'],
 'acetanilid': ['acetanilid', 'laciniated', 'teniacidal'],
 'acetanion': ['acetanion', 'antoecian'],
 'acetation': ['acetation', 'itaconate'],
 'acetic': ['accite', 'acetic'],
 'acetin': ['acetin', 'actine', 'enatic'],
 'acetmethylanilide': ['acetmethylanilide', 'methylacetanilide'],
 'acetoin': ['acetoin', 'aconite', 'anoetic', 'antoeci', 'cetonia'],
 'acetol': ['acetol', 'colate', 'locate'],
 'acetone': ['acetone', 'oceanet'],
 'acetonuria': ['acetonuria', 'aeronautic'],
 'acetopyrin': ['acetopyrin', 'capernoity'],
 'acetous': ['acetous', 'outcase'],
 'acetum': ['acetum', 'tecuma'],
 'aceturic': ['aceturic', 'cruciate'],
 'ach': ['ach', 'cha'],
 'achar': ['achar', 'chara'],
 'achate': ['achate', 'chaeta'],
 'achatina': ['acanthia', 'achatina'],
 'ache': ['ache', 'each', 'haec'],
 'acheirus': ['acheirus', 'eucharis'],
 'achen': ['achen', 'chane', 'chena', 'hance'],
 'acher': ['acher', 'arche', 'chare', 'chera', 'rache', 'reach'],
 'acherontic': ['acherontic', 'anchoretic'],
 'acherontical': ['acherontical', 'anchoretical'],
 'achete': ['achete', 'hecate', 'teache', 'thecae'],
 'acheulean': ['acheulean', 'euchlaena'],
 'achill': ['achill', 'cahill', 'chilla'],
 'achillea': ['achillea', 'heliacal'],
 'acholia': ['acholia', 'alochia'],
 'achondrite': ['achondrite', 'ditrochean', 'ordanchite'],
 'achor': ['achor', 'chora', 'corah', 'orach', 'roach'],
 'achras': ['achras', 'charas'],
 'achromat': ['achromat', 'trachoma'],
 'achromatin': ['achromatin', 'chariotman', 'machinator'],
 'achromatinic': ['achromatinic', 'chromatician'],
 'achtel': ['achtel', 'chalet', 'thecal', 'thecla'],
 'achy': ['achy', 'chay'],
 'aciculated': ['aciculated', 'claudicate'],
 'acid': ['acid', 'cadi', 'caid'],
 'acidanthera': ['acidanthera', 'cantharidae'],
 'acider': ['acider', 'ericad'],
 'acidimeter': ['acidimeter', 'mediatrice'],
 'acidity': ['acidity', 'adicity'],
 'acidly': ['acidly', 'acidyl'],
 'acidometry': ['acidometry', 'medicatory', 'radiectomy'],
 'acidophilous': ['acidophilous', 'aphidicolous'],
 'acidyl': ['acidly', 'acidyl'],
 'acier': ['acier', 'aeric', 'ceria', 'erica'],
 'acieral': ['acieral', 'aerical'],
 'aciform': ['aciform', 'formica'],
 'acilius': ['acilius', 'iliacus'],
 'acinar': ['acinar',
            'arnica',
            'canari',
            'carian',
            'carina',
            'crania',
            'narica'],
 'acinic': ['acinic', 'incaic'],
 'aciniform': ['aciniform', 'formicina'],
 'acipenserid': ['acipenserid', 'presidencia'],
 'acis': ['acis', 'asci', 'saic'],
 'acker': ['acker', 'caker', 'crake', 'creak'],
 'ackey': ['ackey', 'cakey'],
 'acle': ['acle', 'alec', 'lace'],
 'acleistous': ['acleistous', 'ossiculate'],
 'aclemon': ['aclemon', 'cloamen'],
 'aclinal': ['aclinal', 'ancilla'],
 'aclys': ['aclys', 'scaly'],
 'acme': ['acme', 'came', 'mace'],
 'acmite': ['acmite', 'micate'],
 'acne': ['acne', 'cane', 'nace'],
 'acnemia': ['acnemia', 'anaemic'],
 'acnida': ['acnida', 'anacid', 'dacian'],
 'acnodal': ['acnodal', 'canadol', 'locanda'],
 'acnode': ['acnode', 'deacon'],
 'acoin': ['acoin', 'oncia'],
 'acoma': ['acoma', 'macao'],
 'acone': ['acone', 'canoe', 'ocean'],
 'aconital': ['aconital', 'actional', 'anatolic'],
 'aconite': ['acetoin', 'aconite', 'anoetic', 'antoeci', 'cetonia'],
 'aconitic': ['aconitic', 'cationic', 'itaconic'],
 'aconitin': ['aconitin', 'inaction', 'nicotian'],
 'aconitum': ['aconitum', 'acontium'],
 'acontias': ['acontias', 'tacsonia'],
 'acontium': ['aconitum', 'acontium'],
 'acontius': ['acontius', 'anticous'],
 'acopon': ['acopon', 'poonac'],
 'acor': ['acor', 'caro', 'cora', 'orca'],
 'acorn': ['acorn', 'acron', 'racon'],
 'acorus': ['acorus', 'soucar'],
 'acosmist': ['acosmist', 'massicot', 'somatics'],
 'acquest': ['acquest', 'casquet'],
 'acrab': ['acrab', 'braca'],
 'acraein': ['acarine', 'acraein', 'arecain'],
 'acrania': ['acarian', 'acarina', 'acrania'],
 'acraniate': ['acraniate', 'carinatae'],
 'acratia': ['acratia', 'cataria'],
 'acre': ['acer', 'acre', 'care', 'crea', 'race'],
 'acream': ['acream', 'camera', 'mareca'],
 'acred': ['acred', 'cader', 'cadre', 'cedar'],
 'acrid': ['acrid', 'caird', 'carid', 'darci', 'daric', 'dirca'],
 'acridan': ['acridan', 'craniad'],
 'acridian': ['acridian', 'cnidaria'],
 'acrididae': ['acrididae', 'cardiidae', 'cidaridae'],
 'acridly': ['acridly', 'acridyl'],
 'acridonium': ['acridonium', 'dicoumarin'],
 'acridyl': ['acridly', 'acridyl'],
 'acrimonious': ['acrimonious', 'isocoumarin'],
 'acrisius': ['acrisius', 'sicarius'],
 'acrita': ['acrita', 'arctia'],
 'acritan': ['acritan', 'arctian'],
 'acrite': ['acrite', 'arcite', 'tercia', 'triace', 'tricae'],
 'acroa': ['acroa', 'caroa'],
 'acrobat': ['abactor', 'acrobat'],
 'acrocera': ['acrocera', 'caracore'],
 'acroclinium': ['acroclinium', 'alcicornium'],
 'acrodus': ['acrodus', 'crusado'],
 'acrogen': ['acrogen', 'cornage'],
 'acrolein': ['acrolein',
              'arecolin',
              'caroline',
              'colinear',
              'cornelia',
              'creolian',
              'lonicera'],
 'acrolith': ['acrolith', 'trochila'],
 'acron': ['acorn', 'acron', 'racon'],
 'acronical': ['acronical', 'alcoranic'],
 'acronym': ['acronym', 'romancy'],
 'acropetal': ['acropetal', 'cleopatra'],
 'acrose': ['acrose', 'coarse'],
 'acrostic': ['acrostic', 'sarcotic', 'socratic'],
 'acrostical': ['acrostical', 'socratical'],
 'acrostically': ['acrostically', 'socratically'],
 'acrosticism': ['acrosticism', 'socraticism'],
 'acrotic': ['acrotic', 'carotic'],
 'acrotism': ['acrotism', 'rotacism'],
 'acrotrophic': ['acrotrophic', 'prothoracic'],
 'acryl': ['acryl', 'caryl', 'clary'],
 'act': ['act', 'cat'],
 'actaeonidae': ['actaeonidae', 'donatiaceae'],
 'actian': ['actian', 'natica', 'tanica'],
 'actifier': ['actifier', 'artifice'],
 'actin': ['actin', 'antic'],
 'actinal': ['actinal', 'alantic', 'alicant', 'antical'],
 'actine': ['acetin', 'actine', 'enatic'],
 'actiniform': ['actiniform', 'naticiform'],
 'actinine': ['actinine', 'naticine'],
 'actinism': ['actinism', 'manistic'],
 'actinogram': ['actinogram', 'morganatic'],
 'actinoid': ['actinoid', 'diatonic', 'naticoid'],
 'actinon': ['actinon', 'cantion', 'contain'],
 'actinopteran': ['actinopteran', 'precantation'],
 'actinopteri': ['actinopteri', 'crepitation', 'precitation'],
 'actinost': ['actinost', 'oscitant'],
 'actinula': ['actinula', 'nautical'],
 'action': ['action', 'atonic', 'cation'],
 'actional': ['aconital', 'actional', 'anatolic'],
 'actioner': ['actioner', 'anerotic', 'ceration', 'creation', 'reaction'],
 'activable': ['activable', 'biclavate'],
 'activate': ['activate', 'cavitate'],
 'activation': ['activation', 'cavitation'],
 'activin': ['activin', 'civitan'],
 'actomyosin': ['actomyosin', 'inocystoma'],
 'acton': ['acton', 'canto', 'octan'],
 'actor': ['actor', 'corta', 'croat', 'rocta', 'taroc', 'troca'],
 'actorship': ['actorship', 'strophaic'],
 'acts': ['acts', 'cast', 'scat'],
 'actuator': ['actuator', 'autocrat'],
 'acture': ['acture', 'cauter', 'curate'],
 'acuan': ['acuan', 'aucan'],
 'acubens': ['acubens', 'benacus'],
 'acumen': ['acumen', 'cueman'],
 'acuminose': ['acuminose', 'mniaceous'],
 'acutenaculum': ['acutenaculum', 'unaccumulate'],
 'acuteness': ['acuteness', 'encaustes'],
 'acutorsion': ['acutorsion', 'octonarius'],
 'acyl': ['acyl', 'clay', 'lacy'],
 'acylation': ['acylation', 'claytonia'],
 'acylogen': ['acylogen', 'cynogale'],
 'ad': ['ad', 'da'],
 'adad': ['adad', 'adda', 'dada'],
 'adage': ['adage', 'agade'],
 'adam': ['adam', 'dama'],
 'adamic': ['adamic', 'cadmia'],
 'adamine': ['adamine', 'manidae'],
 'adamite': ['adamite', 'amidate'],
 'adamsite': ['adamsite', 'diastema'],
 'adance': ['adance', 'ecanda'],
 'adapter': ['adapter', 'predata', 'readapt'],
 'adaption': ['adaption', 'adoptian'],
 'adaptionism': ['adaptionism', 'adoptianism'],
 'adar': ['adar', 'arad', 'raad', 'rada'],
 'adarme': ['adarme', 'adream'],
 'adat': ['adat', 'data'],
 'adawn': ['adawn', 'wadna'],
 'adays': ['adays', 'dasya'],
 'add': ['add', 'dad'],
 'adda': ['adad', 'adda', 'dada'],
 'addendum': ['addendum', 'unmadded'],
 'adder': ['adder', 'dread', 'readd'],
 'addicent': ['addicent', 'dedicant'],
 'addlings': ['addlings', 'saddling'],
 'addresser': ['addresser', 'readdress'],
 'addu': ['addu', 'dadu', 'daud', 'duad'],
 'addy': ['addy', 'dyad'],
 'ade': ['ade', 'dae'],
 'adeem': ['adeem', 'ameed', 'edema'],
 'adela': ['adela', 'dalea'],
 'adeline': ['adeline', 'daniele', 'delaine'],
 'adeling': ['adeling', 'dealing', 'leading'],
 'adelops': ['adelops', 'deposal'],
 'ademonist': ['ademonist', 'demoniast', 'staminode'],
 'ademption': ['ademption', 'tampioned'],
 'adendric': ['adendric', 'riddance'],
 'adenectopic': ['adenectopic', 'pentadecoic'],
 'adenia': ['adenia', 'idaean'],
 'adenochondroma': ['adenochondroma', 'chondroadenoma'],
 'adenocystoma': ['adenocystoma', 'cystoadenoma'],
 'adenofibroma': ['adenofibroma', 'fibroadenoma'],
 'adenolipoma': ['adenolipoma', 'palaemonoid'],
 'adenosarcoma': ['adenosarcoma', 'sarcoadenoma'],
 'adenylic': ['adenylic', 'lycaenid'],
 'adeptness': ['adeptness', 'pedantess'],
 'adequation': ['adequation', 'deaquation'],
 'adermia': ['adermia', 'madeira'],
 'adermin': ['adermin', 'amerind', 'dimeran'],
 'adet': ['adet', 'date', 'tade', 'tead', 'teda'],
 'adevism': ['adevism', 'vedaism'],
 'adhere': ['adhere', 'header', 'hedera', 'rehead'],
 'adherent': ['adherent', 'headrent', 'neatherd', 'threaden'],
 'adiaphon': ['adiaphon', 'aphodian'],
 'adib': ['adib', 'ibad'],
 'adicea': ['acadie', 'acedia', 'adicea'],
 'adicity': ['acidity', 'adicity'],
 'adiel': ['adiel', 'delia', 'ideal'],
 'adieux': ['adieux', 'exaudi'],
 'adighe': ['adighe', 'hidage'],
 'adin': ['adin', 'andi', 'dain', 'dani', 'dian', 'naid'],
 'adinole': ['adinole', 'idoneal'],
 'adion': ['adion', 'danio', 'doina', 'donia'],
 'adipocele': ['adipocele', 'cepolidae', 'ploceidae'],
 'adipocere': ['adipocere', 'percoidea'],
 'adipyl': ['adipyl', 'plaidy'],
 'adit': ['adit', 'dita'],
 'adital': ['adital', 'altaid'],
 'aditus': ['aditus', 'studia'],
 'adjuster': ['adjuster', 'readjust'],
 'adlai': ['adlai', 'alida'],
 'adlay': ['adlay', 'dayal'],
 'adlet': ['adlet', 'dealt', 'delta', 'lated', 'taled'],
 'adlumine': ['adlumine', 'unmailed'],
 'adman': ['adman', 'daman', 'namda'],
 'admi': ['admi', 'amid', 'madi', 'maid'],
 'adminicle': ['adminicle', 'medicinal'],
 'admire': ['admire', 'armied', 'damier', 'dimera', 'merida'],
 'admired': ['admired', 'diaderm'],
 'admirer': ['admirer', 'madrier', 'married'],
 'admissive': ['admissive', 'misadvise'],
 'admit': ['admit', 'atmid'],
 'admittee': ['admittee', 'meditate'],
 'admonisher': ['admonisher', 'rhamnoside'],
 'admonition': ['admonition', 'domination'],
 'admonitive': ['admonitive', 'dominative'],
 'admonitor': ['admonitor', 'dominator'],
 'adnascence': ['adnascence', 'ascendance'],
 'adnascent': ['adnascent', 'ascendant'],
 'adnate': ['adnate', 'entada'],
 'ado': ['ado', 'dao', 'oda'],
 'adobe': ['abode', 'adobe'],
 'adolph': ['adolph', 'pholad'],
 'adonai': ['adonai', 'adonia'],
 'adonia': ['adonai', 'adonia'],
 'adonic': ['adonic', 'anodic'],
 'adonin': ['adonin', 'nanoid', 'nonaid'],
 'adoniram': ['adoniram', 'radioman'],
 'adonize': ['adonize', 'anodize'],
 'adopter': ['adopter', 'protead', 'readopt'],
 'adoptian': ['adaption', 'adoptian'],
 'adoptianism': ['adaptionism', 'adoptianism'],
 'adoptional': ['adoptional', 'aplodontia'],
 'adorability': ['adorability', 'roadability'],
 'adorable': ['adorable', 'roadable'],
 'adorant': ['adorant', 'ondatra'],
 'adore': ['adore', 'oared', 'oread'],
 'adorer': ['adorer', 'roader'],
 'adorn': ['adorn', 'donar', 'drona', 'radon'],
 'adorner': ['adorner', 'readorn'],
 'adpao': ['adpao', 'apoda'],
 'adpromission': ['adpromission', 'proadmission'],
 'adream': ['adarme', 'adream'],
 'adrenin': ['adrenin', 'nardine'],
 'adrenine': ['adrenine', 'adrienne'],
 'adrenolytic': ['adrenolytic', 'declinatory'],
 'adrenotropic': ['adrenotropic', 'incorporated'],
 'adrian': ['adrian', 'andira', 'andria', 'radian', 'randia'],
 'adrienne': ['adrenine', 'adrienne'],
 'adrip': ['adrip', 'rapid'],
 'adroitly': ['adroitly', 'dilatory', 'idolatry'],
 'adrop': ['adrop', 'pardo'],
 'adry': ['adry', 'dray', 'yard'],
 'adscendent': ['adscendent', 'descendant'],
 'adsmith': ['adsmith', 'mahdist'],
 'adular': ['adular', 'aludra', 'radula'],
 'adulation': ['adulation', 'laudation'],
 'adulator': ['adulator', 'laudator'],
 'adulatory': ['adulatory', 'laudatory'],
 'adult': ['adult', 'dulat'],
 'adulterine': ['adulterine', 'laurentide'],
 'adultness': ['adultness', 'dauntless'],
 'adustion': ['adustion', 'sudation'],
 'advene': ['advene', 'evadne'],
 'adventism': ['adventism', 'vedantism'],
 'adventist': ['adventist', 'vedantist'],
 'adventure': ['adventure', 'unaverted'],
 'advice': ['advice', 'vedaic'],
 'ady': ['ady', 'day', 'yad'],
 'adz': ['adz', 'zad'],
 'adze': ['adze', 'daze'],
 'adzer': ['adzer', 'zerda'],
 'ae': ['ae', 'ea'],
 'aecidioform': ['aecidioform', 'formicoidea'],
 'aedilian': ['aedilian', 'laniidae'],
 'aedilic': ['aedilic', 'elaidic'],
 'aedility': ['aedility', 'ideality'],
 'aegipan': ['aegipan', 'apinage'],
 'aegirine': ['aegirine', 'erigenia'],
 'aegirite': ['aegirite', 'ariegite'],
 'aegle': ['aegle', 'eagle', 'galee'],
 'aenean': ['aenean', 'enaena'],
 'aeolharmonica': ['aeolharmonica', 'chloroanaemia'],
 'aeolian': ['aeolian', 'aeolina', 'aeonial'],
 'aeolic': ['aeolic', 'coelia'],
 'aeolina': ['aeolian', 'aeolina', 'aeonial'],
 'aeolis': ['aeolis', 'laiose'],
 'aeolist': ['aeolist', 'isolate'],
 'aeolistic': ['aeolistic', 'socialite'],
 'aeon': ['aeon', 'eoan'],
 'aeonial': ['aeolian', 'aeolina', 'aeonial'],
 'aeonist': ['aeonist', 'asiento', 'satieno'],
 'aer': ['aer', 'are', 'ear', 'era', 'rea'],
 'aerage': ['aerage', 'graeae'],
 'aerarian': ['aerarian', 'arenaria'],
 'aeration': ['aaronite', 'aeration'],
 'aerial': ['aerial', 'aralie'],
 'aeric': ['acier', 'aeric', 'ceria', 'erica'],
 'aerical': ['acieral', 'aerical'],
 'aeried': ['aeried', 'dearie'],
 'aerogenic': ['aerogenic', 'recoinage'],
 'aerographer': ['aerographer', 'areographer'],
 'aerographic': ['aerographic', 'areographic'],
 'aerographical': ['aerographical', 'areographical'],
 'aerography': ['aerography', 'areography'],
 'aerologic': ['aerologic', 'areologic'],
 'aerological': ['aerological', 'areological'],
 'aerologist': ['aerologist', 'areologist'],
 'aerology': ['aerology', 'areology'],
 'aeromantic': ['aeromantic', 'cameration', 'maceration', 'racemation'],
 'aerometer': ['aerometer', 'areometer'],
 'aerometric': ['aerometric', 'areometric'],
 'aerometry': ['aerometry', 'areometry'],
 'aeronautic': ['acetonuria', 'aeronautic'],
 'aeronautism': ['aeronautism', 'measuration'],
 'aerope': ['aerope', 'operae'],
 'aerophilic': ['aerophilic', 'epichorial'],
 'aerosol': ['aerosol', 'roseola'],
 'aerostatics': ['aerostatics', 'aortectasis'],
 'aery': ['aery', 'eyra', 'yare', 'year'],
 'aes': ['aes', 'ase', 'sea'],
 'aesthetic': ['aesthetic', 'chaetites'],
 'aethalioid': ['aethalioid', 'haliotidae'],
 'aetian': ['aetian', 'antiae', 'taenia'],
 'aetobatus': ['aetobatus', 'eastabout'],
 'afebrile': ['afebrile', 'balefire', 'fireable'],
 'afenil': ['afenil', 'finale'],
 'affair': ['affair', 'raffia'],
 'affecter': ['affecter', 'reaffect'],
 'affeer': ['affeer', 'raffee'],
 'affiance': ['affiance', 'caffeina'],
 'affirmer': ['affirmer', 'reaffirm'],
 'afflicter': ['afflicter', 'reafflict'],
 'affy': ['affy', 'yaff'],
 'afghan': ['afghan', 'hafgan'],
 'afield': ['afield', 'defial'],
 'afire': ['afire', 'feria'],
 'aflare': ['aflare', 'rafael'],
 'aflat': ['aflat', 'fatal'],
 'afresh': ['afresh', 'fasher', 'ferash'],
 'afret': ['afret', 'after'],
 'afric': ['afric', 'firca'],
 'afshar': ['afshar', 'ashraf'],
 'aft': ['aft', 'fat'],
 'after': ['afret', 'after'],
 'afteract': ['afteract', 'artefact', 'farcetta', 'farctate'],
 'afterage': ['afterage', 'fregatae'],
 'afterblow': ['afterblow', 'batfowler'],
 'aftercome': ['aftercome', 'forcemeat'],
 'aftercrop': ['aftercrop', 'prefactor'],
 'aftergo': ['aftergo', 'fagoter'],
 'afterguns': ['afterguns', 'transfuge'],
 'aftermath': ['aftermath', 'hamfatter'],
 'afterstate': ['afterstate', 'aftertaste'],
 'aftertaste': ['afterstate', 'aftertaste'],
 'afunctional': ['afunctional', 'unfactional'],
 'agade': ['adage', 'agade'],
 'agal': ['agal', 'agla', 'alga', 'gala'],
 'agalite': ['agalite', 'tailage', 'taliage'],
 'agalma': ['agalma', 'malaga'],
 'agama': ['agama', 'amaga'],
 'agamid': ['agamid', 'madiga'],
 'agapeti': ['agapeti', 'agpaite'],
 'agapornis': ['agapornis', 'sporangia'],
 'agar': ['agar', 'agra', 'gara', 'raga'],
 'agaricin': ['agaricin', 'garcinia'],
 'agau': ['agau', 'agua'],
 'aged': ['aged', 'egad', 'gade'],
 'ageless': ['ageless', 'eagless'],
 'agen': ['agen', 'gaen', 'gane', 'gean', 'gena'],
 'agenesic': ['agenesic', 'genesiac'],
 'agenesis': ['agenesis', 'assignee'],
 'agential': ['agential', 'alginate'],
 'agentive': ['agentive', 'negative'],
 'ager': ['ager', 'agre', 'gare', 'gear', 'rage'],
 'agger': ['agger', 'gager', 'regga'],
 'aggeration': ['aggeration', 'agregation'],
 'aggry': ['aggry', 'raggy'],
 'agialid': ['agialid', 'galidia'],
 'agib': ['agib', 'biga', 'gabi'],
 'agiel': ['agiel', 'agile', 'galei'],
 'agile': ['agiel', 'agile', 'galei'],
 'agileness': ['agileness', 'signalese'],
 'agistment': ['agistment', 'magnetist'],
 'agistor': ['agistor', 'agrotis', 'orgiast'],
 'agla': ['agal', 'agla', 'alga', 'gala'],
 'aglaos': ['aglaos', 'salago'],
 'aglare': ['aglare', 'alegar', 'galera', 'laager'],
 'aglet': ['aglet', 'galet'],
 'agley': ['agley', 'galey'],
 'agmatine': ['agmatine', 'agminate'],
 'agminate': ['agmatine', 'agminate'],
 'agminated': ['agminated', 'diamagnet'],
 'agnail': ['agnail', 'linaga'],
 'agname': ['agname', 'manage'],
 'agnel': ['agnel', 'angel', 'angle', 'galen', 'genal', 'glean', 'lagen'],
 'agnes': ['agnes', 'gesan'],
 'agnize': ['agnize', 'ganzie'],
 'agnosis': ['agnosis', 'ganosis'],
 'agnostic': ['agnostic', 'coasting'],
 'agnus': ['agnus', 'angus', 'sugan'],
 'ago': ['ago', 'goa'],
 'agon': ['agon', 'ango', 'gaon', 'goan', 'gona'],
 'agonal': ['agonal', 'angola'],
 'agone': ['agone', 'genoa'],
 'agoniadin': ['agoniadin', 'anangioid', 'ganoidian'],
 'agonic': ['agonic', 'angico', 'gaonic', 'goniac'],
 'agonista': ['agonista', 'santiago'],
 'agonizer': ['agonizer', 'orangize', 'organize'],
 'agpaite': ['agapeti', 'agpaite'],
 'agra': ['agar', 'agra', 'gara', 'raga'],
 'agral': ['agral', 'argal'],
 'agrania': ['agrania', 'angaria', 'niagara'],
 'agre': ['ager', 'agre', 'gare', 'gear', 'rage'],
 'agree': ['agree', 'eager', 'eagre'],
 'agreed': ['agreed', 'geared'],
 'agregation': ['aggeration', 'agregation'],
 'agrege': ['agrege', 'raggee'],
 'agrestian': ['agrestian', 'gerastian', 'stangeria'],
 'agrestic': ['agrestic', 'ergastic'],
 'agria': ['agria', 'igara'],
 'agricolist': ['agricolist', 'algoristic'],
 'agrilus': ['agrilus', 'gularis'],
 'agrin': ['agrin', 'grain'],
 'agrito': ['agrito', 'ortiga'],
 'agroan': ['agroan', 'angora', 'anogra', 'arango', 'argoan', 'onagra'],
 'agrom': ['agrom', 'morga'],
 'agrotis': ['agistor', 'agrotis', 'orgiast'],
 'aground': ['aground', 'durango'],
 'agrufe': ['agrufe', 'gaufer', 'gaufre'],
 'agrypnia': ['agrypnia', 'paginary'],
 'agsam': ['agsam', 'magas'],
 'agua': ['agau', 'agua'],
 'ague': ['ague', 'auge'],
 'agush': ['agush', 'saugh'],
 'agust': ['agust', 'tsuga'],
 'agy': ['agy', 'gay'],
 'ah': ['ah', 'ha'],
 'ahem': ['ahem', 'haem', 'hame'],
 'ahet': ['ahet', 'haet', 'hate', 'heat', 'thea'],
 'ahey': ['ahey', 'eyah', 'yeah'],
 'ahind': ['ahind', 'dinah'],
 'ahint': ['ahint', 'hiant', 'tahin'],
 'ahir': ['ahir', 'hair'],
 'ahmed': ['ahmed', 'hemad'],
 'ahmet': ['ahmet', 'thema'],
 'aho': ['aho', 'hao'],
 'ahom': ['ahom', 'moha'],
 'ahong': ['ahong', 'hogan'],
 'ahorse': ['ahorse', 'ashore', 'hoarse', 'shorea'],
 'ahoy': ['ahoy', 'hoya'],
 'ahriman': ['ahriman', 'miranha'],
 'ahsan': ['ahsan', 'hansa', 'hasan'],
 'aht': ['aht', 'hat', 'tha'],
 'ahtena': ['ahtena', 'aneath', 'athena'],
 'ahu': ['ahu', 'auh', 'hau'],
 'ahum': ['ahum', 'huma'],
 'ahunt': ['ahunt', 'haunt', 'thuan', 'unhat'],
 'aid': ['aid', 'ida'],
 'aidance': ['aidance', 'canidae'],
 'aide': ['aide', 'idea'],
 'aidenn': ['aidenn', 'andine', 'dannie', 'indane'],
 'aider': ['aider', 'deair', 'irade', 'redia'],
 'aides': ['aides', 'aside', 'sadie'],
 'aiel': ['aiel', 'aile', 'elia'],
 'aiglet': ['aiglet', 'ligate', 'taigle', 'tailge'],
 'ail': ['ail', 'ila', 'lai'],
 'ailantine': ['ailantine', 'antialien'],
 'ailanto': ['ailanto', 'alation', 'laotian', 'notalia'],
 'aile': ['aiel', 'aile', 'elia'],
 'aileen': ['aileen', 'elaine'],
 'aileron': ['aileron', 'alienor'],
 'ailing': ['ailing', 'angili', 'nilgai'],
 'ailment': ['ailment', 'aliment'],
 'aim': ['aim', 'ami', 'ima'],
 'aimer': ['aimer', 'maire', 'marie', 'ramie'],
 'aimless': ['aimless', 'melissa', 'seismal'],
 'ainaleh': ['ainaleh', 'halenia'],
 'aint': ['aint', 'anti', 'tain', 'tina'],
 'aion': ['aion', 'naio'],
 'air': ['air', 'ira', 'ria'],
 'aira': ['aira', 'aria', 'raia'],
 'airan': ['airan', 'arain', 'arian'],
 'airdrome': ['airdrome', 'armoried'],
 'aire': ['aire', 'eria'],
 'airer': ['airer', 'arrie'],
 'airlike': ['airlike', 'kiliare'],
 'airman': ['airman', 'amarin', 'marian', 'marina', 'mirana'],
 'airplane': ['airplane', 'perianal'],
 'airplanist': ['airplanist', 'triplasian'],
 'airt': ['airt', 'rita', 'tari', 'tiar'],
 'airy': ['airy', 'yair'],
 'aisle': ['aisle', 'elias'],
 'aisled': ['aisled', 'deasil', 'ladies', 'sailed'],
 'aisling': ['aisling', 'sailing'],
 'aissor': ['aissor', 'rissoa'],
 'ait': ['ait', 'ati', 'ita', 'tai'],
 'aitch': ['aitch', 'chait', 'chati', 'chita', 'taich', 'tchai'],
 'aition': ['aition', 'itonia'],
 'aizle': ['aizle', 'eliza'],
 'ajar': ['ajar', 'jara', 'raja'],
 'ajhar': ['ajhar', 'rajah'],
 'ajuga': ['ajuga', 'jagua'],
 'ak': ['ak', 'ka'],
 'akal': ['akal', 'kala'],
 'akali': ['akali', 'alaki'],
 'akan': ['akan', 'kana'],
 'ake': ['ake', 'kea'],
 'akebi': ['akebi', 'bakie'],
 'akha': ['akha', 'kaha'],
 'akim': ['akim', 'maki'],
 'akin': ['akin', 'kina', 'naik'],
 'akka': ['akka', 'kaka'],
 'aknee': ['aknee', 'ankee', 'keena'],
 'ako': ['ako', 'koa', 'oak', 'oka'],
 'akoasma': ['akoasma', 'amakosa'],
 'aku': ['aku', 'auk', 'kua'],
 'al': ['al', 'la'],
 'ala': ['aal', 'ala'],
 'alacritous': ['alacritous', 'lactarious', 'lactosuria'],
 'alain': ['alain', 'alani', 'liana'],
 'alaki': ['akali', 'alaki'],
 'alalite': ['alalite', 'tillaea'],
 'alamo': ['alamo', 'aloma'],
 'alan': ['alan', 'anal', 'lana'],
 'alangin': ['alangin', 'anginal', 'anglian', 'nagnail'],
 'alangine': ['alangine', 'angelina', 'galenian'],
 'alani': ['alain', 'alani', 'liana'],
 'alanine': ['alanine', 'linnaea'],
 'alans': ['alans', 'lanas', 'nasal'],
 'alantic': ['actinal', 'alantic', 'alicant', 'antical'],
 'alantolic': ['alantolic', 'allantoic'],
 'alanyl': ['alanyl', 'anally'],
 'alares': ['alares', 'arales'],
 'alaria': ['alaria', 'aralia'],
 'alaric': ['alaric', 'racial'],
 'alarm': ['alarm', 'malar', 'maral', 'marla', 'ramal'],
 'alarmable': ['alarmable', 'ambarella'],
 'alarmedly': ['alarmedly', 'medallary'],
 'alarming': ['alarming', 'marginal'],
 'alarmingly': ['alarmingly', 'marginally'],
 'alarmist': ['alarmist', 'alastrim'],
 'alas': ['alas', 'lasa'],
 'alastair': ['alastair', 'salariat'],
 'alaster': ['alaster', 'tarsale'],
 'alastrim': ['alarmist', 'alastrim'],
 'alatern': ['alatern', 'lateran'],
 'alaternus': ['alaternus', 'saturnale'],
 'alation': ['ailanto', 'alation', 'laotian', 'notalia'],
 'alb': ['alb', 'bal', 'lab'],
 'alba': ['alba', 'baal', 'bala'],
 'alban': ['alban', 'balan', 'banal', 'laban', 'nabal', 'nabla'],
 'albanite': ['albanite', 'balanite', 'nabalite'],
 'albardine': ['albardine', 'drainable'],
 'albarium': ['albarium', 'brumalia'],
 'albata': ['albata', 'atabal', 'balata'],
 'albatros': ['abrastol', 'albatros'],
 'albe': ['abel', 'able', 'albe', 'bale', 'beal', 'bela', 'blae'],
 'albedo': ['albedo', 'doable'],
 'albee': ['abele', 'albee'],
 'albeit': ['albeit',
            'albite',
            'baltei',
            'belait',
            'betail',
            'bletia',
            'libate'],
 'albert': ['albert', 'balter', 'labret', 'tabler'],
 'alberta': ['alberta', 'latebra', 'ratable'],
 'albertina': ['albertina', 'trainable'],
 'alberto': ['alberto', 'bloater', 'latrobe'],
 'albetad': ['albetad', 'datable'],
 'albi': ['albi', 'bail', 'bali'],
 'albian': ['albian', 'bilaan'],
 'albin': ['albin', 'binal', 'blain'],
 'albino': ['albino', 'albion', 'alboin', 'oliban'],
 'albion': ['albino', 'albion', 'alboin', 'oliban'],
 'albite': ['albeit',
            'albite',
            'baltei',
            'belait',
            'betail',
            'bletia',
            'libate'],
 'alboin': ['albino', 'albion', 'alboin', 'oliban'],
 'alboranite': ['alboranite', 'rationable'],
 'albronze': ['albronze', 'blazoner'],
 'albuca': ['albuca', 'bacula'],
 'albuminate': ['albuminate', 'antelabium'],
 'alburnum': ['alburnum', 'laburnum'],
 'alcaic': ['alcaic', 'cicala'],
 'alcaide': ['alcaide', 'alcidae'],
 'alcamine': ['alcamine', 'analcime', 'calamine', 'camelina'],
 'alcantarines': ['alcantarines', 'lancasterian'],
 'alcedo': ['alcedo', 'dacelo'],
 'alces': ['alces', 'casel', 'scale'],
 'alchemic': ['alchemic', 'chemical'],
 'alchemistic': ['alchemistic', 'hemiclastic'],
 'alchera': ['alchera', 'archeal'],
 'alchitran': ['alchitran', 'clathrina'],
 'alcicornium': ['acroclinium', 'alcicornium'],
 'alcidae': ['alcaide', 'alcidae'],
 'alcidine': ['alcidine', 'danielic', 'lecaniid'],
 'alcine': ['alcine', 'ancile'],
 'alco': ['alco', 'coal', 'cola', 'loca'],
 'alcoate': ['alcoate', 'coelata'],
 'alcogel': ['alcogel', 'collage'],
 'alcor': ['alcor', 'calor', 'carlo', 'carol', 'claro', 'coral'],
 'alcoran': ['alcoran', 'ancoral', 'carolan'],
 'alcoranic': ['acronical', 'alcoranic'],
 'alcove': ['alcove', 'coeval', 'volcae'],
 'alcyon': ['alcyon', 'cyanol'],
 'alcyone': ['alcyone', 'cyanole'],
 'aldamine': ['aldamine', 'lamnidae'],
 'aldeament': ['aldeament', 'mandelate'],
 'alder': ['alder', 'daler', 'lader'],
 'aldermanry': ['aldermanry', 'marylander'],
 'aldern': ['aldern',
            'darnel',
            'enlard',
            'lander',
            'lenard',
            'randle',
            'reland'],
 'aldime': ['aldime', 'mailed', 'medial'],
 'aldine': ['aldine', 'daniel', 'delian', 'denial', 'enalid', 'leadin'],
 'aldus': ['aldus', 'sauld'],
 'ale': ['ale', 'lea'],
 'alec': ['acle', 'alec', 'lace'],
 'aleconner': ['aleconner', 'noncereal'],
 'alecost': ['alecost', 'lactose', 'scotale', 'talcose'],
 'alectoris': ['alectoris', 'sarcolite', 'sclerotia', 'sectorial'],
 'alectrion': ['alectrion', 'clarionet', 'crotaline', 'locarnite'],
 'alectryon': ['alectryon', 'tolerancy'],
 'alecup': ['alecup', 'clupea'],
 'alef': ['alef', 'feal', 'flea', 'leaf'],
 'aleft': ['aleft', 'alfet', 'fetal', 'fleta'],
 'alegar': ['aglare', 'alegar', 'galera', 'laager'],
 'alem': ['alem', 'alme', 'lame', 'leam', 'male', 'meal', 'mela'],
 'alemanni': ['alemanni', 'melanian'],
 'alemite': ['alemite', 'elamite'],
 'alen': ['alen', 'lane', 'lean', 'lena', 'nael', 'neal'],
 'aleph': ['aleph', 'pheal'],
 'alepot': ['alepot', 'pelota'],
 'alerce': ['alerce', 'cereal', 'relace'],
 'alerse': ['alerse', 'leaser', 'reales', 'resale', 'reseal', 'sealer'],
 'alert': ['alert', 'alter', 'artel', 'later', 'ratel', 'taler', 'telar'],
 'alertly': ['alertly', 'elytral'],
 'alestake': ['alestake', 'eastlake'],
 'aletap': ['aletap', 'palate', 'platea'],
 'aletris': ['aletris', 'alister', 'listera', 'realist', 'saltier'],
 'aleuronic': ['aleuronic', 'urceolina'],
 'aleut': ['aleut', 'atule'],
 'aleutic': ['aleutic', 'auletic', 'caulite', 'lutecia'],
 'alevin': ['alevin', 'alvine', 'valine', 'veinal', 'venial', 'vineal'],
 'alex': ['alex', 'axel', 'axle'],
 'alexandrian': ['alexandrian', 'alexandrina'],
 'alexandrina': ['alexandrian', 'alexandrina'],
 'alexin': ['alexin', 'xenial'],
 'aleyard': ['aleyard', 'already'],
 'alfenide': ['alfenide', 'enfilade'],
 'alfet': ['aleft', 'alfet', 'fetal', 'fleta'],
 'alfred': ['alfred', 'fardel'],
 'alfur': ['alfur', 'fural'],
 'alga': ['agal', 'agla', 'alga', 'gala'],
 'algae': ['algae', 'galea'],
 'algal': ['algal', 'galla'],
 'algebar': ['algebar', 'algebra'],
 'algebra': ['algebar', 'algebra'],
 'algedi': ['algedi', 'galeid'],
 'algedo': ['algedo', 'geodal'],
 'algedonic': ['algedonic', 'genocidal'],
 'algenib': ['algenib', 'bealing', 'belgian', 'bengali'],
 'algerian': ['algerian', 'geranial', 'regalian'],
 'algernon': ['algernon', 'nonglare'],
 'algesia': ['algesia', 'sailage'],
 'algesis': ['algesis', 'glassie'],
 'algieba': ['algieba', 'bailage'],
 'algin': ['algin', 'align', 'langi', 'liang', 'linga'],
 'alginate': ['agential', 'alginate'],
 'algine': ['algine', 'genial', 'linage'],
 'algist': ['algist', 'gaslit'],
 'algometer': ['algometer', 'glomerate'],
 'algometric': ['algometric', 'melotragic'],
 'algomian': ['algomian', 'magnolia'],
 'algor': ['algor', 'argol', 'goral', 'largo'],
 'algoristic': ['agricolist', 'algoristic'],
 'algorithm': ['algorithm', 'logarithm'],
 'algorithmic': ['algorithmic', 'logarithmic'],
 'algraphic': ['algraphic', 'graphical'],
 'algum': ['algum', 'almug', 'glaum', 'gluma', 'mulga'],
 'alibility': ['alibility', 'liability'],
 'alible': ['alible', 'belial', 'labile', 'liable'],
 'alicant': ['actinal', 'alantic', 'alicant', 'antical'],
 'alice': ['alice', 'celia', 'ileac'],
 'alichel': ['alichel', 'challie', 'helical'],
 'alida': ['adlai', 'alida'],
 'alien': ['alien', 'aline', 'anile', 'elain', 'elian', 'laine', 'linea'],
 'alienation': ['alienation', 'alineation'],
 'alienator': ['alienator', 'rationale'],
 'alienism': ['alienism', 'milesian'],
 'alienor': ['aileron', 'alienor'],
 'alif': ['alif', 'fail'],
 'aligerous': ['aligerous', 'glaireous'],
 'align': ['algin', 'align', 'langi', 'liang', 'linga'],
 'aligner': ['aligner', 'engrail', 'realign', 'reginal'],
 'alignment': ['alignment', 'lamenting'],
 'alike': ['alike', 'lakie'],
 'alikeness': ['alikeness', 'leakiness'],
 'alima': ['alima', 'lamia'],
 'aliment': ['ailment', 'aliment'],
 'alimenter': ['alimenter', 'marteline'],
 'alimentic': ['alimentic', 'antilemic', 'melanitic', 'metanilic'],
 'alimonied': ['alimonied', 'maleinoid'],
 'alin': ['alin', 'anil', 'lain', 'lina', 'nail'],
 'aline': ['alien', 'aline', 'anile', 'elain', 'elian', 'laine', 'linea'],
 'alineation': ['alienation', 'alineation'],
 'aliped': ['aliped', 'elapid'],
 'aliptes': ['aliptes', 'pastile', 'talipes'],
 'aliptic': ['aliptic', 'aplitic'],
 'aliseptal': ['aliseptal', 'pallasite'],
 'alish': ['alish', 'hilsa'],
 'alisier': ['alisier', 'israeli'],
 'aliso': ['aliso', 'alois'],
 'alison': ['alison', 'anolis'],
 'alisp': ['alisp', 'lapsi'],
 'alist': ['alist', 'litas', 'slait', 'talis'],
 'alister': ['aletris', 'alister', 'listera', 'realist', 'saltier'],
 'alit': ['alit', 'tail', 'tali'],
 'alite': ['alite', 'laeti'],
 'aliunde': ['aliunde', 'unideal'],
 'aliveness': ['aliveness', 'vealiness'],
 'alix': ['alix', 'axil'],
 'alk': ['alk', 'lak'],
 'alkalizer': ['alkalizer', 'lazarlike'],
 'alkamin': ['alkamin', 'malakin'],
 'alkene': ['alkene', 'lekane'],
 'alkes': ['alkes', 'sakel', 'slake'],
 'alkine': ['alkine', 'ilkane', 'inlake', 'inleak'],
 'alky': ['alky', 'laky'],
 'alkylic': ['alkylic', 'lilacky'],
 'allagite': ['allagite', 'alligate', 'talliage'],
 'allah': ['allah', 'halal'],
 'allantoic': ['alantolic', 'allantoic'],
 'allay': ['allay', 'yalla'],
 'allayer': ['allayer', 'yallaer'],
 'allbone': ['allbone', 'bellona'],
 'alle': ['alle', 'ella', 'leal'],
 'allecret': ['allecret', 'cellaret'],
 'allegate': ['allegate', 'ellagate'],
 'allegorist': ['allegorist', 'legislator'],
 'allergen': ['allergen', 'generall'],
 'allergia': ['allergia', 'galleria'],
 'allergin': ['allergin', 'gralline'],
 'allergy': ['allergy', 'gallery', 'largely', 'regally'],
 'alliable': ['alliable', 'labiella'],
 'alliably': ['alliably', 'labially'],
 'alliance': ['alliance', 'canaille'],
 'alliancer': ['alliancer', 'ralliance'],
 'allie': ['allie', 'leila', 'lelia'],
 'allies': ['allies', 'aselli'],
 'alligate': ['allagite', 'alligate', 'talliage'],
 'allium': ['allium', 'alulim', 'muilla'],
 'allocation': ['allocation', 'locational'],
 'allocute': ['allocute', 'loculate'],
 'allocution': ['allocution', 'loculation'],
 'allogenic': ['allogenic', 'collegian'],
 'allonym': ['allonym', 'malonyl'],
 'allopathy': ['allopathy', 'lalopathy'],
 'allopatric': ['allopatric', 'patrilocal'],
 'allot': ['allot', 'atoll'],
 'allothogenic': ['allothogenic', 'ethnological'],
 'allover': ['allover', 'overall'],
 'allower': ['allower', 'reallow'],
 'alloy': ['alloy', 'loyal'],
 'allude': ['allude', 'aludel'],
 'allure': ['allure', 'laurel'],
 'alma': ['alma', 'amla', 'lama', 'mala'],
 'almach': ['almach', 'chamal'],
 'almaciga': ['almaciga', 'macaglia'],
 'almain': ['almain', 'animal', 'lamina', 'manila'],
 'alman': ['alman', 'lamna', 'manal'],
 'almandite': ['almandite', 'laminated'],
 'alme': ['alem', 'alme', 'lame', 'leam', 'male', 'meal', 'mela'],
 'almerian': ['almerian', 'manerial'],
 'almoign': ['almoign', 'loaming'],
 'almon': ['almon', 'monal'],
 'almond': ['almond', 'dolman'],
 'almoner': ['almoner', 'moneral', 'nemoral'],
 'almonry': ['almonry', 'romanly'],
 'alms': ['alms', 'salm', 'slam'],
 'almuce': ['almuce', 'caelum', 'macule'],
 'almude': ['almude', 'maudle'],
 'almug': ['algum', 'almug', 'glaum', 'gluma', 'mulga'],
 'almuten': ['almuten', 'emulant'],
 'aln': ['aln', 'lan'],
 'alnage': ['alnage', 'angela', 'galena', 'lagena'],
 'alnico': ['alnico', 'cliona', 'oilcan'],
 'alnilam': ['alnilam', 'manilla'],
 'alnoite': ['alnoite', 'elation', 'toenail'],
 'alnuin': ['alnuin', 'unnail'],
 'alo': ['alo', 'lao', 'loa'],
 'alochia': ['acholia', 'alochia'],
 'alod': ['alod', 'dola', 'load', 'odal'],
 'aloe': ['aloe', 'olea'],
 'aloetic': ['aloetic', 'coalite'],
 'aloft': ['aloft', 'float', 'flota'],
 'alogian': ['alogian', 'logania'],
 'alogical': ['alogical', 'colalgia'],
 'aloid': ['aloid', 'dolia', 'idola'],
 'aloin': ['aloin', 'anoil', 'anoli'],
 'alois': ['aliso', 'alois'],
 'aloma': ['alamo', 'aloma'],
 'alone': ['alone', 'anole', 'olena'],
 'along': ['along', 'gonal', 'lango', 'longa', 'nogal'],
 'alonso': ['alonso', 'alsoon', 'saloon'],
 'alonzo': ['alonzo', 'zoonal'],
 'alop': ['alop', 'opal'],
 'alopecist': ['alopecist', 'altiscope', 'epicostal', 'scapolite'],
 'alosa': ['alosa', 'loasa', 'oasal'],
 'alose': ['alose', 'osela', 'solea'],
 'alow': ['alow', 'awol', 'lowa'],
 'aloxite': ['aloxite', 'oxalite'],
 'alp': ['alp', 'lap', 'pal'],
 'alpeen': ['alpeen', 'lenape', 'pelean'],
 'alpen': ['alpen', 'nepal', 'panel', 'penal', 'plane'],
 'alpestral': ['alpestral', 'palestral'],
 'alpestrian': ['alpestrian', 'palestrian', 'psalterian'],
 'alpestrine': ['alpestrine', 'episternal', 'interlapse', 'presential'],
 'alphenic': ['alphenic', 'cephalin'],
 'alphonse': ['alphonse', 'phenosal'],
 'alphos': ['alphos', 'pholas'],
 'alphosis': ['alphosis', 'haplosis'],
 'alpid': ['alpid', 'plaid'],
 'alpieu': ['alpieu', 'paulie'],
 'alpine': ['alpine', 'nepali', 'penial', 'pineal'],
 'alpinist': ['alpinist', 'antislip'],
 'alpist': ['alpist', 'pastil', 'spital'],
 'alraun': ['alraun', 'alruna', 'ranula'],
 'already': ['aleyard', 'already'],
 'alrighty': ['alrighty', 'arightly'],
 'alruna': ['alraun', 'alruna', 'ranula'],
 'alsine': ['alsine', 'neslia', 'saline', 'selina', 'silane'],
 'also': ['also', 'sola'],
 'alsoon': ['alonso', 'alsoon', 'saloon'],
 'alstonidine': ['alstonidine', 'nonidealist'],
 'alstonine': ['alstonine', 'tensional'],
 'alt': ['alt', 'lat', 'tal'],
 'altaian': ['altaian', 'latania', 'natalia'],
 'altaic': ['altaic', 'altica'],
 'altaid': ['adital', 'altaid'],
 'altair': ['altair', 'atrail', 'atrial', 'lariat', 'latria', 'talari'],
 'altamira': ['altamira', 'matralia'],
 'altar': ['altar', 'artal', 'ratal', 'talar'],
 'altared': ['altared', 'laterad'],
 'altarist': ['altarist', 'striatal'],
 'alter': ['alert', 'alter', 'artel', 'later', 'ratel', 'taler', 'telar'],
 'alterability': ['alterability', 'bilaterality', 'relatability'],
 'alterable': ['alterable', 'relatable'],
 'alterant': ['alterant', 'tarletan'],
 'alterer': ['alterer', 'realter', 'relater'],
 'altern': ['altern', 'antler', 'learnt', 'rental', 'ternal'],
 'alterne': ['alterne', 'enteral', 'eternal', 'teleran', 'teneral'],
 'althea': ['althea', 'elatha'],
 'altho': ['altho', 'lhota', 'loath'],
 'althorn': ['althorn', 'anthrol', 'thronal'],
 'altica': ['altaic', 'altica'],
 'altin': ['altin', 'latin'],
 'altiscope': ['alopecist', 'altiscope', 'epicostal', 'scapolite'],
 'altitude': ['altitude', 'latitude'],
 'altitudinal': ['altitudinal', 'latitudinal'],
 'altitudinarian': ['altitudinarian', 'latitudinarian'],
 'alto': ['alto', 'lota'],
 'altrices': ['altrices', 'selictar'],
 'altruism': ['altruism', 'muralist', 'traulism', 'ultraism'],
 'altruist': ['altruist', 'ultraist'],
 'altruistic': ['altruistic', 'truistical', 'ultraistic'],
 'aludel': ['allude', 'aludel'],
 'aludra': ['adular', 'aludra', 'radula'],
 'alulet': ['alulet', 'luteal'],
 'alulim': ['allium', 'alulim', 'muilla'],
 'alum': ['alum', 'maul'],
 'aluminate': ['aluminate', 'alumniate'],
 'aluminide': ['aluminide', 'unimedial'],
 'aluminosilicate': ['aluminosilicate', 'silicoaluminate'],
 'alumna': ['alumna', 'manual'],
 'alumni': ['alumni', 'unmail'],
 'alumniate': ['aluminate', 'alumniate'],
 'alur': ['alur', 'laur', 'lura', 'raul', 'ural'],
 'alure': ['alure', 'ureal'],
 'alurgite': ['alurgite', 'ligature'],
 'aluta': ['aluta', 'taula'],
 'alvan': ['alvan', 'naval'],
 'alvar': ['alvar', 'arval', 'larva'],
 'alveus': ['alveus', 'avulse'],
 'alvin': ['alvin', 'anvil', 'nival', 'vinal'],
 'alvine': ['alevin', 'alvine', 'valine', 'veinal', 'venial', 'vineal'],
 'aly': ['aly', 'lay'],
 'alypin': ['alypin', 'pialyn'],
 'alytes': ['alytes', 'astely', 'lysate', 'stealy'],
 'am': ['am', 'ma'],
 'ama': ['aam', 'ama'],
 'amacrine': ['amacrine', 'american', 'camerina', 'cinerama'],
 'amadi': ['amadi', 'damia', 'madia', 'maida'],
 'amaethon': ['amaethon', 'thomaean'],
 'amaga': ['agama', 'amaga'],
 'amah': ['amah', 'maha'],
 'amain': ['amain', 'amani', 'amnia', 'anima', 'mania'],
 'amakosa': ['akoasma', 'amakosa'],
 'amalgam': ['amalgam', 'malagma'],
 'amang': ['amang', 'ganam', 'manga'],
 'amani': ['amain', 'amani', 'amnia', 'anima', 'mania'],
 'amanist': ['amanist', 'stamina'],
 'amanitin': ['amanitin', 'maintain'],
 'amanitine': ['amanitine', 'inanimate'],
 'amanori': ['amanori', 'moarian'],
 'amapa': ['amapa', 'apama'],
 'amar': ['amar', 'amra', 'mara', 'rama'],
 'amarin': ['airman', 'amarin', 'marian', 'marina', 'mirana'],
 'amaroid': ['amaroid', 'diorama'],
 'amarth': ['amarth', 'martha'],
 'amass': ['amass', 'assam', 'massa', 'samas'],
 'amasser': ['amasser', 'reamass'],
 'amati': ['amati', 'amita', 'matai'],
 'amatorian': ['amatorian', 'inamorata'],
 'amaurosis': ['amaurosis', 'mosasauri'],
 'amazonite': ['amazonite', 'anatomize'],
 'amba': ['amba', 'maba'],
 'ambar': ['abram', 'ambar'],
 'ambarella': ['alarmable', 'ambarella'],
 'ambash': ['ambash', 'shamba'],
 'ambay': ['ambay', 'mbaya'],
 'ambeer': ['ambeer', 'beamer'],
 'amber': ['amber', 'bearm', 'bemar', 'bream', 'embar'],
 'ambier': ['ambier', 'bremia', 'embira'],
 'ambit': ['ambit', 'imbat'],
 'ambivert': ['ambivert', 'verbatim'],
 'amble': ['amble', 'belam', 'blame', 'mabel'],
 'ambler': ['ambler', 'blamer', 'lamber', 'marble', 'ramble'],
 'ambling': ['ambling', 'blaming'],
 'amblingly': ['amblingly', 'blamingly'],
 'ambo': ['ambo', 'boma'],
 'ambos': ['ambos', 'sambo'],
 'ambrein': ['ambrein', 'mirbane'],
 'ambrette': ['ambrette', 'tambreet'],
 'ambrose': ['ambrose', 'mesobar'],
 'ambrosia': ['ambrosia', 'saboraim'],
 'ambrosin': ['ambrosin', 'barosmin', 'sabromin'],
 'ambry': ['ambry', 'barmy'],
 'ambury': ['ambury', 'aumbry'],
 'ambush': ['ambush', 'shambu'],
 'amchoor': ['amchoor', 'ochroma'],
 'ame': ['ame', 'mae'],
 'ameed': ['adeem', 'ameed', 'edema'],
 'ameen': ['ameen', 'amene', 'enema'],
 'amelification': ['amelification', 'maleficiation'],
 'ameliorant': ['ameliorant', 'lomentaria'],
 'amellus': ['amellus', 'malleus'],
 'amelu': ['amelu', 'leuma', 'ulema'],
 'amelus': ['amelus', 'samuel'],
 'amen': ['amen', 'enam', 'mane', 'mean', 'name', 'nema'],
 'amenability': ['amenability', 'nameability'],
 'amenable': ['amenable', 'nameable'],
 'amend': ['amend', 'mande', 'maned'],
 'amende': ['amende', 'demean', 'meaned', 'nadeem'],
 'amender': ['amender', 'meander', 'reamend', 'reedman'],
 'amends': ['amends', 'desman'],
 'amene': ['ameen', 'amene', 'enema'],
 'amenia': ['amenia', 'anemia'],
 'amenism': ['amenism', 'immanes', 'misname'],
 'amenite': ['amenite', 'etamine', 'matinee'],
 'amenorrheal': ['amenorrheal', 'melanorrhea'],
 'ament': ['ament', 'meant', 'teman'],
 'amental': ['amental', 'leatman'],
 'amentia': ['amentia', 'aminate', 'anamite', 'animate'],
 'amerce': ['amerce', 'raceme'],
 'amercer': ['amercer', 'creamer'],
 'american': ['amacrine', 'american', 'camerina', 'cinerama'],
 'amerind': ['adermin', 'amerind', 'dimeran'],
 'amerism': ['amerism', 'asimmer', 'sammier'],
 'ameristic': ['ameristic', 'armistice', 'artemisic'],
 'amesite': ['amesite', 'mesitae', 'semitae'],
 'ametria': ['ametria', 'artemia', 'meratia', 'ramaite'],
 'ametrope': ['ametrope', 'metapore'],
 'amex': ['amex', 'exam', 'xema'],
 'amgarn': ['amgarn', 'mangar', 'marang', 'ragman'],
 'amhar': ['amhar', 'mahar', 'mahra'],
 'amherstite': ['amherstite', 'hemistater'],
 'amhran': ['amhran', 'harman', 'mahran'],
 'ami': ['aim', 'ami', 'ima'],
 'amia': ['amia', 'maia'],
 'amic': ['amic', 'mica'],
 'amical': ['amical', 'camail', 'lamaic'],
 'amiced': ['amiced', 'decima'],
 'amicicide': ['amicicide', 'cimicidae'],
 'amicron': ['amicron', 'marconi', 'minorca', 'romanic'],
 'amid': ['admi', 'amid', 'madi', 'maid'],
 'amidate': ['adamite', 'amidate'],
 'amide': ['amide', 'damie', 'media'],
 'amidide': ['amidide', 'diamide', 'mididae'],
 'amidin': ['amidin', 'damnii'],
 'amidine': ['amidine', 'diamine'],
 'amidism': ['amidism', 'maidism'],
 'amidist': ['amidist', 'dimatis'],
 'amidon': ['amidon', 'daimon', 'domain'],
 'amidophenol': ['amidophenol', 'monodelphia'],
 'amidst': ['amidst', 'datism'],
 'amigo': ['amigo', 'imago'],
 'amiidae': ['amiidae', 'maiidae'],
 'amil': ['amil', 'amli', 'lima', 'mail', 'mali', 'mila'],
 'amiles': ['amiles', 'asmile', 'mesail', 'mesial', 'samiel'],
 'amimia': ['amimia', 'miamia'],
 'amimide': ['amimide', 'mimidae'],
 'amin': ['amin', 'main', 'mani', 'mian', 'mina', 'naim'],
 'aminate': ['amentia', 'aminate', 'anamite', 'animate'],
 'amination': ['amination', 'animation'],
 'amine': ['amine', 'anime', 'maine', 'manei'],
 'amini': ['amini', 'animi'],
 'aminize': ['aminize', 'animize', 'azimine'],
 'amino': ['amino', 'inoma', 'naomi', 'omani', 'omina'],
 'aminoplast': ['aminoplast', 'plasmation'],
 'amintor': ['amintor', 'tormina'],
 'amir': ['amir', 'irma', 'mari', 'mira', 'rami', 'rima'],
 'amiranha': ['amiranha', 'maharani'],
 'amiray': ['amiray', 'myaria'],
 'amissness': ['amissness', 'massiness'],
 'amita': ['amati', 'amita', 'matai'],
 'amitosis': ['amitosis', 'omasitis'],
 'amixia': ['amixia', 'ixiama'],
 'amla': ['alma', 'amla', 'lama', 'mala'],
 'amli': ['amil', 'amli', 'lima', 'mail', 'mali', 'mila'],
 'amlong': ['amlong', 'logman'],
 'amma': ['amma', 'maam'],
 'ammelin': ['ammelin', 'limeman'],
 'ammeline': ['ammeline', 'melamine'],
 'ammeter': ['ammeter', 'metamer'],
 'ammi': ['ammi', 'imam', 'maim', 'mima'],
 'ammine': ['ammine', 'immane'],
 'ammo': ['ammo', 'mamo'],
 'ammonic': ['ammonic', 'mocmain'],
 'ammonolytic': ['ammonolytic', 'commonality'],
 'ammunition': ['ammunition', 'antimonium'],
 'amnestic': ['amnestic', 'semantic'],
 'amnia': ['amain', 'amani', 'amnia', 'anima', 'mania'],
 'amniac': ['amniac', 'caiman', 'maniac'],
 'amnic': ['amnic', 'manic'],
 'amnion': ['amnion', 'minoan', 'nomina'],
 'amnionata': ['amnionata', 'anamniota'],
 'amnionate': ['amnionate', 'anamniote', 'emanation'],
 'amniota': ['amniota', 'itonama'],
 'amniote': ['amniote', 'anomite'],
 'amniotic': ['amniotic', 'mication'],
 'amniotome': ['amniotome', 'momotinae'],
 'amok': ['amok', 'mako'],
 'amole': ['amole', 'maleo'],
 'amomis': ['amomis', 'mimosa'],
 'among': ['among', 'mango'],
 'amor': ['amor', 'maro', 'mora', 'omar', 'roam'],
 'amores': ['amores', 'ramose', 'sorema'],
 'amoret': ['amoret', 'morate'],
 'amorist': ['amorist', 'aortism', 'miastor'],
 'amoritic': ['amoritic', 'microtia'],
 'amorpha': ['amorpha', 'amphora'],
 'amorphic': ['amorphic', 'amphoric'],
 'amorphous': ['amorphous', 'amphorous'],
 'amort': ['amort', 'morat', 'torma'],
 'amortize': ['amortize', 'atomizer'],
 'amos': ['amos', 'soam', 'soma'],
 'amotion': ['amotion', 'otomian'],
 'amount': ['amount', 'moutan', 'outman'],
 'amoy': ['amoy', 'mayo'],
 'ampelis': ['ampelis', 'lepisma'],
 'ampelite': ['ampelite', 'pimelate'],
 'ampelitic': ['ampelitic', 'implicate'],
 'amper': ['amper', 'remap'],
 'amperemeter': ['amperemeter', 'permeameter'],
 'amperian': ['amperian', 'paramine', 'pearmain'],
 'amphicyon': ['amphicyon', 'hypomanic'],
 'amphigenous': ['amphigenous', 'musophagine'],
 'amphiphloic': ['amphiphloic', 'amphophilic'],
 'amphipodous': ['amphipodous', 'hippodamous'],
 'amphitropous': ['amphitropous', 'pastophorium'],
 'amphophilic': ['amphiphloic', 'amphophilic'],
 'amphora': ['amorpha', 'amphora'],
 'amphore': ['amphore', 'morphea'],
 'amphorette': ['amphorette', 'haptometer'],
 'amphoric': ['amorphic', 'amphoric'],
 'amphorous': ['amorphous', 'amphorous'],
 'amphoteric': ['amphoteric', 'metaphoric'],
 'ample': ['ample', 'maple'],
 'ampliate': ['ampliate', 'palamite'],
 'amplification': ['amplification', 'palmification'],
 'amply': ['amply', 'palmy'],
 'ampul': ['ampul', 'pluma'],
 'ampulla': ['ampulla', 'palmula'],
 'amra': ['amar', 'amra', 'mara', 'rama'],
 'amsath': ['amsath', 'asthma'],
 'amsel': ['amsel', 'melas', 'mesal', 'samel'],
 'amsonia': ['amsonia', 'anosmia'],
 'amt': ['amt', 'mat', 'tam'],
 'amulet': ['amulet', 'muleta'],
 'amunam': ['amunam', 'manuma'],
 'amuse': ['amuse', 'mesua'],
 'amused': ['amused', 'masdeu', 'medusa'],
 'amusee': ['amusee', 'saeume'],
 'amuser': ['amuser', 'mauser'],
 'amusgo': ['amusgo', 'sugamo'],
 'amvis': ['amvis', 'mavis'],
 'amy': ['amy', 'may', 'mya', 'yam'],
 'amyelic': ['amyelic', 'mycelia'],
 'amyl': ['amyl', 'lyam', 'myal'],
 'amylan': ['amylan', 'lamany', 'layman'],
 'amylenol': ['amylenol', 'myelonal'],
 'amylidene': ['amylidene', 'mydaleine'],
 'amylin': ['amylin', 'mainly'],
 'amylo': ['amylo', 'loamy'],
 'amylon': ['amylon', 'onymal'],
 'amyotrophy': ['amyotrophy', 'myoatrophy'],
 'amyrol': ['amyrol', 'molary'],
 'an': ['an', 'na'],
 'ana': ['ana', 'naa'],
 'anacara': ['anacara', 'aracana'],
 'anacard': ['anacard', 'caranda'],
 'anaces': ['anaces', 'scaean'],
 'anachorism': ['anachorism', 'chorasmian', 'maraschino'],
 'anacid': ['acnida', 'anacid', 'dacian'],
 'anacreontic': ['anacreontic', 'canceration'],
 'anacusis': ['anacusis', 'ascanius'],
 'anadem': ['anadem', 'maenad'],
 'anadenia': ['anadenia', 'danainae'],
 'anadrom': ['anadrom', 'madrona', 'mandora', 'monarda', 'roadman'],
 'anaemic': ['acnemia', 'anaemic'],
 'anaeretic': ['anaeretic', 'ecarinate'],
 'anal': ['alan', 'anal', 'lana'],
 'analcime': ['alcamine', 'analcime', 'calamine', 'camelina'],
 'analcite': ['analcite', 'anticlea', 'laitance'],
 'analcitite': ['analcitite', 'catalinite'],
 'analectic': ['acclinate', 'analectic'],
 'analeptical': ['analeptical', 'placentalia'],
 'anally': ['alanyl', 'anally'],
 'analogic': ['analogic', 'calinago'],
 'analogist': ['analogist', 'nostalgia'],
 'anam': ['anam', 'mana', 'naam', 'nama'],
 'anamesite': ['anamesite', 'seamanite'],
 'anamirta': ['anamirta', 'araminta'],
 'anamite': ['amentia', 'aminate', 'anamite', 'animate'],
 'anamniota': ['amnionata', 'anamniota'],
 'anamniote': ['amnionate', 'anamniote', 'emanation'],
 'anan': ['anan', 'anna', 'nana'],
 'ananda': ['ananda', 'danaan'],
 'anandria': ['anandria', 'andriana'],
 'anangioid': ['agoniadin', 'anangioid', 'ganoidian'],
 'ananism': ['ananism', 'samnani'],
 'ananite': ['ananite', 'anatine', 'taenian'],
 'anaphoric': ['anaphoric', 'pharaonic'],
 'anaphorical': ['anaphorical', 'pharaonical'],
 'anapnea': ['anapnea', 'napaean'],
 'anapsid': ['anapsid', 'sapinda'],
 'anapsida': ['anapsida', 'anaspida'],
 'anaptotic': ['anaptotic', 'captation'],
 'anarchic': ['anarchic', 'characin'],
 'anarchism': ['anarchism', 'arachnism'],
 'anarchist': ['anarchist', 'archsaint', 'cantharis'],
 'anarcotin': ['anarcotin', 'cantorian', 'carnation', 'narcotina'],
 'anaretic': ['anaretic', 'arcanite', 'carinate', 'craniate'],
 'anarthropod': ['anarthropod', 'arthropodan'],
 'anas': ['anas', 'ansa', 'saan'],
 'anasa': ['anasa', 'asana'],
 'anaspida': ['anapsida', 'anaspida'],
 'anastaltic': ['anastaltic', 'catalanist'],
 'anat': ['anat', 'anta', 'tana'],
 'anatidae': ['anatidae', 'taeniada'],
 'anatine': ['ananite', 'anatine', 'taenian'],
 'anatocism': ['anatocism', 'anosmatic'],
 'anatole': ['anatole', 'notaeal'],
 'anatolic': ['aconital', 'actional', 'anatolic'],
 'anatomicopathologic': ['anatomicopathologic', 'pathologicoanatomic'],
 'anatomicopathological': ['anatomicopathological', 'pathologicoanatomical'],
 'anatomicophysiologic': ['anatomicophysiologic', 'physiologicoanatomic'],
 'anatomism': ['anatomism', 'nomismata'],
 'anatomize': ['amazonite', 'anatomize'],
 'anatum': ['anatum', 'mantua', 'tamanu'],
 'anay': ['anay', 'yana'],
 'anba': ['anba', 'bana'],
 'ancestor': ['ancestor', 'entosarc'],
 'ancestral': ['ancestral', 'lancaster'],
 'anchat': ['acanth', 'anchat', 'tanach'],
 'anchietin': ['anchietin', 'cathinine'],
 'anchistea': ['anchistea', 'hanseatic'],
 'anchor': ['anchor', 'archon', 'charon', 'rancho'],
 'anchored': ['anchored', 'rondache'],
 'anchorer': ['anchorer', 'ranchero', 'reanchor'],
 'anchoretic': ['acherontic', 'anchoretic'],
 'anchoretical': ['acherontical', 'anchoretical'],
 'anchoretism': ['anchoretism', 'trichomanes'],
 'anchorite': ['anchorite', 'antechoir', 'heatronic', 'hectorian'],
 'anchoritism': ['anchoritism', 'chiromantis', 'chrismation', 'harmonistic'],
 'ancile': ['alcine', 'ancile'],
 'ancilla': ['aclinal', 'ancilla'],
 'ancillary': ['ancillary', 'carlylian', 'cranially'],
 'ancon': ['ancon', 'canon'],
 'anconitis': ['anconitis', 'antiscion', 'onanistic'],
 'ancony': ['ancony', 'canyon'],
 'ancoral': ['alcoran', 'ancoral', 'carolan'],
 'ancylus': ['ancylus', 'unscaly'],
 'ancyrene': ['ancyrene', 'cerynean'],
 'and': ['and', 'dan'],
 'anda': ['anda', 'dana'],
 'andante': ['andante', 'dantean'],
 'ande': ['ande', 'dane', 'dean', 'edna'],
 'andesitic': ['andesitic', 'dianetics'],
 'andhra': ['andhra', 'dharna'],
 'andi': ['adin', 'andi', 'dain', 'dani', 'dian', 'naid'],
 'andian': ['andian', 'danian', 'nidana'],
 'andine': ['aidenn', 'andine', 'dannie', 'indane'],
 'andira': ['adrian', 'andira', 'andria', 'radian', 'randia'],
 'andorite': ['andorite', 'nadorite', 'ordinate', 'rodentia'],
 'andre': ['andre', 'arend', 'daren', 'redan'],
 'andrew': ['andrew', 'redawn', 'wander', 'warden'],
 'andria': ['adrian', 'andira', 'andria', 'radian', 'randia'],
 'andriana': ['anandria', 'andriana'],
 'andrias': ['andrias', 'sardian', 'sarinda'],
 'andric': ['andric', 'cardin', 'rancid'],
 'andries': ['andries', 'isander', 'sardine'],
 'androgynus': ['androgynus', 'gynandrous'],
 'androl': ['androl', 'arnold', 'lardon', 'roland', 'ronald'],
 'andronicus': ['andronicus', 'unsardonic'],
 'androtomy': ['androtomy', 'dynamotor'],
 'anear': ['anear', 'arean', 'arena'],
 'aneath': ['ahtena', 'aneath', 'athena'],
 'anecdota': ['anecdota', 'coadnate'],
 'anele': ['anele', 'elean'],
 'anematosis': ['anematosis', 'menostasia'],
 'anemia': ['amenia', 'anemia'],
 'anemic': ['anemic', 'cinema', 'iceman'],
 'anemograph': ['anemograph', 'phanerogam'],
 'anemographic': ['anemographic', 'phanerogamic'],
 'anemography': ['anemography', 'phanerogamy'],
 'anemone': ['anemone', 'monaene'],
 'anent': ['anent', 'annet', 'nenta'],
 'anepia': ['anepia', 'apinae'],
 'aneretic': ['aneretic', 'centiare', 'creatine', 'increate', 'iterance'],
 'anergic': ['anergic', 'garnice', 'garniec', 'geranic', 'grecian'],
 'anergy': ['anergy', 'rangey'],
 'anerly': ['anerly', 'nearly'],
 'aneroid': ['aneroid', 'arenoid'],
 'anerotic': ['actioner', 'anerotic', 'ceration', 'creation', 'reaction'],
 'anes': ['anes', 'sane', 'sean'],
 'anesis': ['anesis', 'anseis', 'sanies', 'sansei', 'sasine'],
 'aneuric': ['aneuric', 'rinceau'],
 'aneurin': ['aneurin', 'uranine'],
 'aneurism': ['aneurism', 'arsenium', 'sumerian'],
 'anew': ['anew', 'wane', 'wean'],
 'angami': ['angami', 'magani', 'magian'],
 'angara': ['angara', 'aranga', 'nagara'],
 'angaria': ['agrania', 'angaria', 'niagara'],
 'angel': ['agnel', 'angel', 'angle', 'galen', 'genal', 'glean', 'lagen'],
 'angela': ['alnage', 'angela', 'galena', 'lagena'],
 'angeldom': ['angeldom', 'lodgeman'],
 'angelet': ['angelet', 'elegant'],
 'angelic': ['angelic', 'galenic'],
 'angelical': ['angelical', 'englacial', 'galenical'],
 'angelically': ['angelically', 'englacially'],
 'angelin': ['angelin', 'leaning'],
 'angelina': ['alangine', 'angelina', 'galenian'],
 'angelique': ['angelique', 'equiangle'],
 'angelo': ['angelo', 'engaol'],
 'angelot': ['angelot', 'tangelo'],
 'anger': ['anger', 'areng', 'grane', 'range'],
 'angerly': ['angerly', 'geranyl'],
 'angers': ['angers', 'sanger', 'serang'],
 'angico': ['agonic', 'angico', 'gaonic', 'goniac'],
 'angie': ['angie', 'gaine'],
 'angild': ['angild', 'lading'],
 'angili': ['ailing', 'angili', 'nilgai'],
 'angina': ['angina', 'inanga'],
 'anginal': ['alangin', 'anginal', 'anglian', 'nagnail'],
 'angiocholitis': ['angiocholitis', 'cholangioitis'],
 'angiochondroma': ['angiochondroma', 'chondroangioma'],
 'angiofibroma': ['angiofibroma', 'fibroangioma'],
 'angioid': ['angioid', 'gonidia'],
 'angiokeratoma': ['angiokeratoma', 'keratoangioma'],
 'angiometer': ['angiometer', 'ergotamine', 'geometrina'],
 'angka': ['angka', 'kanga'],
 'anglaise': ['anglaise', 'gelasian'],
 'angle': ['agnel', 'angel', 'angle', 'galen', 'genal', 'glean', 'lagen'],
 'angled': ['angled', 'dangle', 'englad', 'lagend'],
 'angler': ['angler', 'arleng', 'garnel', 'largen', 'rangle', 'regnal'],
 'angles': ['angles', 'gansel'],
 'angleworm': ['angleworm', 'lawmonger'],
 'anglian': ['alangin', 'anginal', 'anglian', 'nagnail'],
 'anglic': ['anglic', 'lacing'],
 'anglish': ['anglish', 'ashling'],
 'anglist': ['anglist', 'lasting', 'salting', 'slating', 'staling'],
 'angloid': ['angloid', 'loading'],
 'ango': ['agon', 'ango', 'gaon', 'goan', 'gona'],
 'angola': ['agonal', 'angola'],
 'angolar': ['angolar', 'organal'],
 'angor': ['angor',
           'argon',
           'goran',
           'grano',
           'groan',
           'nagor',
           'orang',
           'organ',
           'rogan',
           'ronga'],
 'angora': ['agroan', 'angora', 'anogra', 'arango', 'argoan', 'onagra'],
 'angriness': ['angriness', 'ranginess'],
 'angrite': ['angrite', 'granite', 'ingrate', 'tangier', 'tearing', 'tigrean'],
 'angry': ['angry', 'rangy'],
 'angst': ['angst', 'stang', 'tangs'],
 'angster': ['angster', 'garnets', 'nagster', 'strange'],
 'anguid': ['anguid', 'gaduin'],
 'anguine': ['anguine', 'guanine', 'guinean'],
 'angula': ['angula', 'nagual'],
 'angular': ['angular', 'granula'],
 'angulate': ['angulate', 'gaetulan'],
 'anguria': ['anguria', 'gaurian', 'guarani'],
 'angus': ['agnus', 'angus', 'sugan'],
 'anharmonic': ['anharmonic', 'monarchian'],
 'anhematosis': ['anhematosis', 'somasthenia'],
 'anhidrotic': ['anhidrotic', 'trachinoid'],
 'anhistous': ['anhistous', 'isanthous'],
 'anhydridize': ['anhydridize', 'hydrazidine'],
 'anhydrize': ['anhydrize', 'hydrazine'],
 'ani': ['ani', 'ian'],
 'anice': ['anice', 'eniac'],
 'aniconic': ['aniconic', 'ciconian'],
 'aniconism': ['aniconism', 'insomniac'],
 'anicular': ['anicular', 'caulinar'],
 'anicut': ['anicut', 'nautic', 'ticuna', 'tunica'],
 'anidian': ['anidian', 'indiana'],
 'aniente': ['aniente', 'itenean'],
 'anight': ['anight', 'athing'],
 'anil': ['alin', 'anil', 'lain', 'lina', 'nail'],
 'anile': ['alien', 'aline', 'anile', 'elain', 'elian', 'laine', 'linea'],
 'anilic': ['anilic', 'clinia'],
 'anilid': ['anilid', 'dialin', 'dianil', 'inlaid'],
 'anilide': ['anilide', 'elaidin'],
 'anilidic': ['anilidic', 'indicial'],
 'anima': ['amain', 'amani', 'amnia', 'anima', 'mania'],
 'animable': ['animable', 'maniable'],
 'animal': ['almain', 'animal', 'lamina', 'manila'],
 'animalic': ['animalic', 'limacina'],
 'animate': ['amentia', 'aminate', 'anamite', 'animate'],
 'animated': ['animated', 'mandaite', 'mantidae'],
 'animater': ['animater', 'marinate'],
 'animating': ['animating', 'imaginant'],
 'animation': ['amination', 'animation'],
 'animator': ['animator', 'tamanoir'],
 'anime': ['amine', 'anime', 'maine', 'manei'],
 'animi': ['amini', 'animi'],
 'animist': ['animist', 'santimi'],
 'animize': ['aminize', 'animize', 'azimine'],
 'animus': ['animus', 'anisum', 'anusim', 'manius'],
 'anionic': ['anionic', 'iconian'],
 'anis': ['anis', 'nais', 'nasi', 'nias', 'sain', 'sina'],
 'anisal': ['anisal', 'nasial', 'salian', 'salina'],
 'anisate': ['anisate', 'entasia'],
 'anise': ['anise', 'insea', 'siena', 'sinae'],
 'anisette': ['anisette', 'atestine', 'settaine'],
 'anisic': ['anisic', 'sicani', 'sinaic'],
 'anisilic': ['anisilic', 'sicilian'],
 'anisodont': ['anisodont', 'sondation'],
 'anisometric': ['anisometric',
                 'creationism',
                 'miscreation',
                 'ramisection',
                 'reactionism'],
 'anisopod': ['anisopod', 'isopodan'],
 'anisoptera': ['anisoptera', 'asperation', 'separation'],
 'anisum': ['animus', 'anisum', 'anusim', 'manius'],
 'anisuria': ['anisuria', 'isaurian'],
 'anisyl': ['anisyl', 'snaily'],
 'anita': ['anita', 'niata', 'tania'],
 'anither': ['anither', 'inearth', 'naither'],
 'ankee': ['aknee', 'ankee', 'keena'],
 'anker': ['anker', 'karen', 'naker'],
 'ankh': ['ankh', 'hank', 'khan'],
 'anklet': ['anklet', 'lanket', 'tankle'],
 'ankoli': ['ankoli', 'kaolin'],
 'ankus': ['ankus', 'kusan'],
 'ankyroid': ['ankyroid', 'dikaryon'],
 'anlace': ['anlace', 'calean'],
 'ann': ['ann', 'nan'],
 'anna': ['anan', 'anna', 'nana'],
 'annale': ['annale', 'anneal'],
 'annaline': ['annaline', 'linnaean'],
 'annalist': ['annalist', 'santalin'],
 'annalize': ['annalize', 'zelanian'],
 'annam': ['annam', 'manna'],
 'annamite': ['annamite', 'manatine'],
 'annard': ['annard', 'randan'],
 'annat': ['annat', 'tanan'],
 'annates': ['annates', 'tannase'],
 'anne': ['anne', 'nane'],
 'anneal': ['annale', 'anneal'],
 'annealer': ['annealer', 'lernaean', 'reanneal'],
 'annelid': ['annelid', 'lindane'],
 'annelism': ['annelism', 'linesman'],
 'anneloid': ['anneloid', 'nonideal'],
 'annet': ['anent', 'annet', 'nenta'],
 'annexer': ['annexer', 'reannex'],
 'annie': ['annie', 'inane'],
 'annite': ['annite', 'innate', 'tinean'],
 'annotine': ['annotine', 'tenonian'],
 'annoy': ['annoy', 'nonya'],
 'annoyancer': ['annoyancer', 'rayonnance'],
 'annoyer': ['annoyer', 'reannoy'],
 'annualist': ['annualist', 'sultanian'],
 'annulation': ['annulation', 'unnational'],
 'annulet': ['annulet', 'nauntle'],
 'annulment': ['annulment', 'tunnelman'],
 'annuloid': ['annuloid', 'uninodal'],
 'anodic': ['adonic', 'anodic'],
 'anodize': ['adonize', 'anodize'],
 'anodynic': ['anodynic', 'cydonian'],
 'anoestrous': ['anoestrous', 'treasonous'],
 'anoestrum': ['anoestrum', 'neuromast'],
 'anoetic': ['acetoin', 'aconite', 'anoetic', 'antoeci', 'cetonia'],
 'anogenic': ['anogenic', 'canoeing'],
 'anogra': ['agroan', 'angora', 'anogra', 'arango', 'argoan', 'onagra'],
 'anoil': ['aloin', 'anoil', 'anoli'],
 'anoint': ['anoint', 'nation'],
 'anointer': ['anointer', 'inornate', 'nonirate', 'reanoint'],
 'anole': ['alone', 'anole', 'olena'],
 'anoli': ['aloin', 'anoil', 'anoli'],
 'anolis': ['alison', 'anolis'],
 'anomaliped': ['anomaliped', 'palaemonid'],
 'anomalism': ['anomalism', 'malmaison'],
 'anomalist': ['anomalist', 'atonalism'],
 'anomiidae': ['anomiidae', 'maioidean'],
 'anomite': ['amniote', 'anomite'],
 'anomodont': ['anomodont', 'monodonta'],
 'anomural': ['anomural', 'monaural'],
 'anon': ['anon', 'nona', 'onan'],
 'anophyte': ['anophyte', 'typhoean'],
 'anopia': ['anopia', 'aponia', 'poiana'],
 'anorak': ['anorak', 'korana'],
 'anorchism': ['anorchism', 'harmonics'],
 'anorectic': ['accretion', 'anorectic', 'neoarctic'],
 'anorthic': ['anorthic', 'anthroic', 'tanchoir'],
 'anorthitite': ['anorthitite', 'trithionate'],
 'anorthose': ['anorthose', 'hoarstone'],
 'anosia': ['anosia', 'asonia'],
 'anosmatic': ['anatocism', 'anosmatic'],
 'anosmia': ['amsonia', 'anosmia'],
 'anosmic': ['anosmic', 'masonic'],
 'anoterite': ['anoterite', 'orientate'],
 'another': ['another', 'athenor', 'rheotan'],
 'anotia': ['anotia', 'atonia'],
 'anoxia': ['anoxia', 'axonia'],
 'anoxic': ['anoxic', 'oxanic'],
 'ansa': ['anas', 'ansa', 'saan'],
 'ansar': ['ansar', 'saran', 'sarna'],
 'ansation': ['ansation', 'sonatina'],
 'anseis': ['anesis', 'anseis', 'sanies', 'sansei', 'sasine'],
 'ansel': ['ansel', 'slane'],
 'anselm': ['anselm', 'mensal'],
 'anser': ['anser', 'nares', 'rasen', 'snare'],
 'anserine': ['anserine', 'reinsane'],
 'anserous': ['anserous', 'arsenous'],
 'ansu': ['ansu', 'anus'],
 'answerer': ['answerer', 'reanswer'],
 'ant': ['ant', 'nat', 'tan'],
 'anta': ['anat', 'anta', 'tana'],
 'antacrid': ['antacrid', 'cardiant', 'radicant', 'tridacna'],
 'antagonism': ['antagonism', 'montagnais'],
 'antagonist': ['antagonist', 'stagnation'],
 'antal': ['antal', 'natal'],
 'antanemic': ['antanemic', 'cinnamate'],
 'antar': ['antar', 'antra'],
 'antarchistical': ['antarchistical', 'charlatanistic'],
 'ante': ['ante', 'aten', 'etna', 'nate', 'neat', 'taen', 'tane', 'tean'],
 'anteact': ['anteact', 'cantate'],
 'anteal': ['anteal', 'lanate', 'teanal'],
 'antechoir': ['anchorite', 'antechoir', 'heatronic', 'hectorian'],
 'antecornu': ['antecornu', 'connature'],
 'antedate': ['antedate', 'edentata'],
 'antelabium': ['albuminate', 'antelabium'],
 'antelopian': ['antelopian', 'neapolitan', 'panelation'],
 'antelucan': ['antelucan', 'cannulate'],
 'antelude': ['antelude', 'unelated'],
 'anteluminary': ['anteluminary', 'unalimentary'],
 'antemedial': ['antemedial', 'delaminate'],
 'antenatal': ['antenatal', 'atlantean', 'tantalean'],
 'anteriad': ['anteriad', 'atridean', 'dentaria'],
 'anteroinferior': ['anteroinferior', 'inferoanterior'],
 'anterosuperior': ['anterosuperior', 'superoanterior'],
 'antes': ['antes', 'nates', 'stane', 'stean'],
 'anthela': ['anthela', 'ethanal'],
 'anthem': ['anthem', 'hetman', 'mentha'],
 'anthemwise': ['anthemwise', 'whitmanese'],
 'anther': ['anther', 'nather', 'tharen', 'thenar'],
 'anthericum': ['anthericum', 'narthecium'],
 'anthicidae': ['anthicidae', 'tachinidae'],
 'anthinae': ['anthinae', 'athenian'],
 'anthogenous': ['anthogenous', 'neognathous'],
 'anthonomus': ['anthonomus', 'monanthous'],
 'anthophile': ['anthophile', 'lithophane'],
 'anthracia': ['anthracia', 'antiarcha', 'catharina'],
 'anthroic': ['anorthic', 'anthroic', 'tanchoir'],
 'anthrol': ['althorn', 'anthrol', 'thronal'],
 'anthropic': ['anthropic', 'rhapontic'],
 'anti': ['aint', 'anti', 'tain', 'tina'],
 'antiabrin': ['antiabrin', 'britannia'],
 'antiae': ['aetian', 'antiae', 'taenia'],
 'antiager': ['antiager', 'trainage'],
 'antialbumid': ['antialbumid', 'balantidium'],
 'antialien': ['ailantine', 'antialien'],
 'antiarcha': ['anthracia', 'antiarcha', 'catharina'],
 'antiaris': ['antiaris', 'intarsia'],
 'antibenzaldoxime': ['antibenzaldoxime', 'benzantialdoxime'],
 'antiblue': ['antiblue', 'nubilate'],
 'antic': ['actin', 'antic'],
 'antical': ['actinal', 'alantic', 'alicant', 'antical'],
 'anticaste': ['anticaste', 'ataentsic'],
 'anticlea': ['analcite', 'anticlea', 'laitance'],
 'anticlinorium': ['anticlinorium', 'inclinatorium'],
 'anticly': ['anticly', 'cantily'],
 'anticness': ['anticness', 'cantiness', 'incessant'],
 'anticor': ['anticor', 'carotin', 'cortina', 'ontaric'],
 'anticouncil': ['anticouncil', 'inculcation'],
 'anticourt': ['anticourt', 'curtation', 'ructation'],
 'anticous': ['acontius', 'anticous'],
 'anticreative': ['anticreative', 'antireactive'],
 'anticreep': ['anticreep', 'apenteric', 'increpate'],
 'antidote': ['antidote', 'tetanoid'],
 'antidotical': ['antidotical', 'dictational'],
 'antietam': ['antietam', 'manettia'],
 'antiextreme': ['antiextreme', 'exterminate'],
 'antifouler': ['antifouler', 'fluorinate', 'uniflorate'],
 'antifriction': ['antifriction', 'nitrifaction'],
 'antifungin': ['antifungin', 'unfainting'],
 'antigen': ['antigen', 'gentian'],
 'antigenic': ['antigenic', 'gentianic'],
 'antiglare': ['antiglare', 'raglanite'],
 'antigod': ['antigod', 'doating'],
 'antigone': ['antigone', 'negation'],
 'antiheroic': ['antiheroic', 'theorician'],
 'antilemic': ['alimentic', 'antilemic', 'melanitic', 'metanilic'],
 'antilia': ['antilia', 'italian'],
 'antilipase': ['antilipase', 'sapiential'],
 'antilope': ['antilope', 'antipole'],
 'antimallein': ['antimallein', 'inalimental'],
 'antimasque': ['antimasque', 'squamatine'],
 'antimeric': ['antimeric', 'carminite', 'criminate', 'metrician'],
 'antimerina': ['antimerina', 'maintainer', 'remaintain'],
 'antimeter': ['antimeter', 'attermine', 'interteam', 'terminate', 'tetramine'],
 'antimodel': ['antimodel', 'maldonite', 'monilated'],
 'antimodern': ['antimodern', 'ordainment'],
 'antimonial': ['antimonial', 'lamination'],
 'antimonic': ['antimonic', 'antinomic'],
 'antimonium': ['ammunition', 'antimonium'],
 'antimony': ['antimony', 'antinomy'],
 'antimoral': ['antimoral', 'tailorman'],
 'antimosquito': ['antimosquito', 'misquotation'],
 'antinegro': ['antinegro', 'argentino', 'argention'],
 'antinepotic': ['antinepotic', 'pectination'],
 'antineutral': ['antineutral', 'triannulate'],
 'antinial': ['antinial', 'latinian'],
 'antinome': ['antinome', 'nominate'],
 'antinomian': ['antinomian', 'innominata'],
 'antinomic': ['antimonic', 'antinomic'],
 'antinomy': ['antimony', 'antinomy'],
 'antinormal': ['antinormal', 'nonmarital', 'nonmartial'],
 'antiphonetic': ['antiphonetic', 'pentathionic'],
 'antiphonic': ['antiphonic', 'napthionic'],
 'antiphony': ['antiphony', 'typhonian'],
 'antiphrasis': ['antiphrasis', 'artisanship'],
 'antipodic': ['antipodic', 'diapnotic'],
 'antipole': ['antilope', 'antipole'],
 'antipolo': ['antipolo', 'antipool', 'optional'],
 'antipool': ['antipolo', 'antipool', 'optional'],
 'antipope': ['antipope', 'appointe'],
 'antiprotease': ['antiprotease', 'entoparasite'],
 'antipsoric': ['antipsoric', 'ascription', 'crispation'],
 'antiptosis': ['antiptosis', 'panostitis'],
 'antiputrid': ['antiputrid', 'tripudiant'],
 'antipyretic': ['antipyretic', 'pertinacity'],
 'antique': ['antique', 'quinate'],
 'antiquer': ['antiquer', 'quartine'],
 'antirabies': ['antirabies', 'bestiarian'],
 'antiracer': ['antiracer', 'tarriance'],
 'antireactive': ['anticreative', 'antireactive'],
 'antired': ['antired', 'detrain', 'randite', 'trained'],
 'antireducer': ['antireducer', 'reincrudate', 'untraceried'],
 'antiroyalist': ['antiroyalist', 'stationarily'],
 'antirumor': ['antirumor', 'ruminator'],
 'antirun': ['antirun', 'untrain', 'urinant'],
 'antirust': ['antirust', 'naturist'],
 'antiscion': ['anconitis', 'antiscion', 'onanistic'],
 'antisepsin': ['antisepsin', 'paintiness'],
 'antisepsis': ['antisepsis', 'inspissate'],
 'antiseptic': ['antiseptic', 'psittacine'],
 'antiserum': ['antiserum', 'misaunter'],
 'antisi': ['antisi', 'isatin'],
 'antislip': ['alpinist', 'antislip'],
 'antisoporific': ['antisoporific', 'prosification', 'sporification'],
 'antispace': ['antispace', 'panaceist'],
 'antistes': ['antistes', 'titaness'],
 'antisun': ['antisun', 'unsaint', 'unsatin', 'unstain'],
 'antitheism': ['antitheism', 'themistian'],
 'antitonic': ['antitonic', 'nictation'],
 'antitorpedo': ['antitorpedo', 'deportation'],
 'antitrade': ['antitrade', 'attainder'],
 'antitrope': ['antitrope', 'patronite', 'tritanope'],
 'antitropic': ['antitropic', 'tritanopic'],
 'antitropical': ['antitropical', 'practitional'],
 'antivice': ['antivice', 'inactive', 'vineatic'],
 'antler': ['altern', 'antler', 'learnt', 'rental', 'ternal'],
 'antlia': ['antlia', 'latian', 'nalita'],
 'antliate': ['antliate', 'latinate'],
 'antlid': ['antlid', 'tindal'],
 'antling': ['antling', 'tanling'],
 'antoeci': ['acetoin', 'aconite', 'anoetic', 'antoeci', 'cetonia'],
 'antoecian': ['acetanion', 'antoecian'],
 'anton': ['anton', 'notan', 'tonna'],
 'antproof': ['antproof', 'tanproof'],
 'antra': ['antar', 'antra'],
 'antral': ['antral', 'tarnal'],
 'antre': ['antre', 'arent', 'retan', 'terna'],
 'antrocele': ['antrocele', 'coeternal', 'tolerance'],
 'antronasal': ['antronasal', 'nasoantral'],
 'antroscope': ['antroscope', 'contrapose'],
 'antroscopy': ['antroscopy', 'syncopator'],
 'antrotome': ['antrotome', 'nototrema'],
 'antrustion': ['antrustion', 'nasturtion'],
 'antu': ['antu', 'aunt', 'naut', 'taun', 'tuan', 'tuna'],
 'anubis': ['anubis', 'unbias'],
 'anura': ['anura', 'ruana'],
 'anuresis': ['anuresis', 'senarius'],
 'anuretic': ['anuretic', 'centauri', 'centuria', 'teucrian'],
 'anuria': ['anuria', 'urania'],
 'anuric': ['anuric', 'cinura', 'uranic'],
 'anurous': ['anurous', 'uranous'],
 'anury': ['anury', 'unary', 'unray'],
 'anus': ['ansu', 'anus'],
 'anusim': ['animus', 'anisum', 'anusim', 'manius'],
 'anvil': ['alvin', 'anvil', 'nival', 'vinal'],
 'any': ['any', 'nay', 'yan'],
 'aonach': ['aonach', 'choana'],
 'aoristic': ['aoristic', 'iscariot'],
 'aortal': ['aortal', 'rotala'],
 'aortectasis': ['aerostatics', 'aortectasis'],
 'aortism': ['amorist', 'aortism', 'miastor'],
 'aosmic': ['aosmic', 'mosaic'],
 'apachite': ['apachite', 'hepatica'],
 'apalachee': ['acalephae', 'apalachee'],
 'apama': ['amapa', 'apama'],
 'apanthropy': ['apanthropy', 'panatrophy'],
 'apar': ['apar', 'paar', 'para'],
 'apart': ['apart', 'trapa'],
 'apatetic': ['apatetic', 'capitate'],
 'ape': ['ape', 'pea'],
 'apedom': ['apedom', 'pomade'],
 'apeiron': ['apeiron', 'peorian'],
 'apelet': ['apelet', 'ptelea'],
 'apelike': ['apelike', 'pealike'],
 'apeling': ['apeling', 'leaping'],
 'apenteric': ['anticreep', 'apenteric', 'increpate'],
 'apeptic': ['apeptic', 'catpipe'],
 'aper': ['aper', 'pare', 'pear', 'rape', 'reap'],
 'aperch': ['aperch', 'eparch', 'percha', 'preach'],
 'aperitive': ['aperitive', 'petiveria'],
 'apert': ['apert', 'pater', 'peart', 'prate', 'taper', 'terap'],
 'apertly': ['apertly', 'peartly', 'platery', 'pteryla', 'taperly'],
 'apertness': ['apertness', 'peartness', 'taperness'],
 'apertured': ['apertured', 'departure'],
 'apery': ['apery', 'payer', 'repay'],
 'aphanes': ['aphanes', 'saphena'],
 'aphasia': ['aphasia', 'asaphia'],
 'aphasic': ['aphasic', 'asaphic'],
 'aphemic': ['aphemic', 'impeach'],
 'aphetic': ['aphetic', 'caphite', 'hepatic'],
 'aphetism': ['aphetism', 'mateship', 'shipmate', 'spithame'],
 'aphetize': ['aphetize', 'hepatize'],
 'aphides': ['aphides', 'diphase'],
 'aphidicolous': ['acidophilous', 'aphidicolous'],
 'aphis': ['aphis', 'apish', 'hispa', 'saiph', 'spahi'],
 'aphodian': ['adiaphon', 'aphodian'],
 'aphonic': ['aphonic', 'phocian'],
 'aphorismical': ['aphorismical', 'parochialism'],
 'aphotic': ['aphotic', 'picotah'],
 'aphra': ['aphra', 'harpa', 'parah'],
 'aphrodistic': ['aphrodistic', 'diastrophic'],
 'aphrodite': ['aphrodite', 'atrophied', 'diaporthe'],
 'apian': ['apian', 'apina'],
 'apiator': ['apiator', 'atropia', 'parotia'],
 'apical': ['apical', 'palaic'],
 'apicular': ['apicular', 'piacular'],
 'apina': ['apian', 'apina'],
 'apinae': ['anepia', 'apinae'],
 'apinage': ['aegipan', 'apinage'],
 'apinch': ['apinch', 'chapin', 'phanic'],
 'aping': ['aping', 'ngapi', 'pangi'],
 'apiole': ['apiole', 'leipoa'],
 'apiolin': ['apiolin', 'pinolia'],
 'apionol': ['apionol', 'polonia'],
 'apiose': ['apiose', 'apoise'],
 'apis': ['apis', 'pais', 'pasi', 'saip'],
 'apish': ['aphis', 'apish', 'hispa', 'saiph', 'spahi'],
 'apishly': ['apishly', 'layship'],
 'apism': ['apism', 'sampi'],
 'apitpat': ['apitpat', 'pitapat'],
 'apivorous': ['apivorous', 'oviparous'],
 'aplenty': ['aplenty', 'penalty'],
 'aplite': ['aplite', 'pilate'],
 'aplitic': ['aliptic', 'aplitic'],
 'aplodontia': ['adoptional', 'aplodontia'],
 'aplome': ['aplome', 'malope'],
 'apnea': ['apnea', 'paean'],
 'apneal': ['apneal', 'panela'],
 'apochromat': ['apochromat', 'archoptoma'],
 'apocrita': ['apocrita', 'aproctia'],
 'apocryph': ['apocryph', 'hypocarp'],
 'apod': ['apod', 'dopa'],
 'apoda': ['adpao', 'apoda'],
 'apogon': ['apogon', 'poonga'],
 'apoise': ['apiose', 'apoise'],
 'apolaustic': ['apolaustic', 'autopsical'],
 'apolistan': ['apolistan', 'lapsation'],
 'apollo': ['apollo', 'palolo'],
 'aponia': ['anopia', 'aponia', 'poiana'],
 'aponic': ['aponic', 'ponica'],
 'aporetic': ['aporetic', 'capriote', 'operatic'],
 'aporetical': ['aporetical', 'operatical'],
 'aporia': ['aporia', 'piaroa'],
 'aport': ['aport', 'parto', 'porta'],
 'aportoise': ['aportoise', 'esotropia'],
 'aposporous': ['aposporous', 'aprosopous'],
 'apostil': ['apostil', 'topsail'],
 'apostle': ['apostle', 'aseptol'],
 'apostrophus': ['apostrophus', 'pastophorus'],
 'apothesine': ['apothesine', 'isoheptane'],
 'apout': ['apout', 'taupo'],
 'appall': ['appall', 'palpal'],
 'apparent': ['apparent', 'trappean'],
 'appealer': ['appealer', 'reappeal'],
 'appealing': ['appealing', 'lagniappe', 'panplegia'],
 'appearer': ['appearer', 'rapparee', 'reappear'],
 'append': ['append', 'napped'],
 'applauder': ['applauder', 'reapplaud'],
 'applicator': ['applicator', 'procapital'],
 'applier': ['applier', 'aripple'],
 'appointe': ['antipope', 'appointe'],
 'appointer': ['appointer', 'reappoint'],
 'appointor': ['appointor', 'apportion'],
 'apportion': ['appointor', 'apportion'],
 'apportioner': ['apportioner', 'reapportion'],
 'appraisable': ['appraisable', 'parablepsia'],
 'apprehender': ['apprehender', 'reapprehend'],
 'approacher': ['approacher', 'reapproach'],
 'apricot': ['apricot', 'atropic', 'parotic', 'patrico'],
 'april': ['april', 'pilar', 'ripal'],
 'aprilis': ['aprilis', 'liparis'],
 'aproctia': ['apocrita', 'aproctia'],
 'apronless': ['apronless', 'responsal'],
 'aprosopous': ['aposporous', 'aprosopous'],
 'apse': ['apse', 'pesa', 'spae'],
 'apsidiole': ['apsidiole', 'episodial'],
 'apt': ['apt', 'pat', 'tap'],
 'aptal': ['aptal', 'palta', 'talpa'],
 'aptera': ['aptera', 'parate', 'patera'],
 'apterial': ['apterial', 'parietal'],
 'apteroid': ['apteroid', 'proteida'],
 'aptian': ['aptian', 'patina', 'taipan'],
 'aptly': ['aptly', 'patly', 'platy', 'typal'],
 'aptness': ['aptness', 'patness'],
 'aptote': ['aptote', 'optate', 'potate', 'teapot'],
 'apulian': ['apulian', 'paulian', 'paulina'],
 'apulse': ['apulse', 'upseal'],
 'apus': ['apus', 'supa', 'upas'],
 'aquabelle': ['aquabelle', 'equalable'],
 'aqueoigneous': ['aqueoigneous', 'igneoaqueous'],
 'aquicolous': ['aquicolous', 'loquacious'],
 'ar': ['ar', 'ra'],
 'arab': ['arab', 'arba', 'baar', 'bara'],
 'arabic': ['arabic', 'cairba'],
 'arabinic': ['arabinic', 'cabirian', 'carabini', 'cibarian'],
 'arabis': ['abaris', 'arabis'],
 'arabism': ['abramis', 'arabism'],
 'arabist': ['arabist', 'bartsia'],
 'arabit': ['arabit', 'tabira'],
 'arable': ['ablare', 'arable', 'arbela'],
 'araca': ['acara', 'araca'],
 'aracana': ['anacara', 'aracana'],
 'aracanga': ['aracanga', 'caragana'],
 'arachic': ['arachic', 'archaic'],
 'arachidonic': ['arachidonic', 'characinoid'],
 'arachis': ['arachis', 'asiarch', 'saharic'],
 'arachne': ['arachne', 'archean'],
 'arachnism': ['anarchism', 'arachnism'],
 'arachnitis': ['arachnitis', 'christiana'],
 'arad': ['adar', 'arad', 'raad', 'rada'],
 'arain': ['airan', 'arain', 'arian'],
 'arales': ['alares', 'arales'],
 'aralia': ['alaria', 'aralia'],
 'aralie': ['aerial', 'aralie'],
 'aramaic': ['aramaic', 'cariama'],
 'aramina': ['aramina', 'mariana'],
 'araminta': ['anamirta', 'araminta'],
 'aramus': ['aramus', 'asarum'],
 'araneid': ['araneid', 'ariadne', 'ranidae'],
 'aranein': ['aranein', 'raninae'],
 'aranga': ['angara', 'aranga', 'nagara'],
 'arango': ['agroan', 'angora', 'anogra', 'arango', 'argoan', 'onagra'],
 'arati': ['arati', 'atria', 'riata', 'tarai', 'tiara'],
 'aration': ['aration', 'otarian'],
 'arauan': ['arauan', 'arauna'],
 'arauna': ['arauan', 'arauna'],
 'arba': ['arab', 'arba', 'baar', 'bara'],
 'arbacin': ['arbacin', 'carabin', 'cariban'],
 'arbalester': ['arbalester', 'arbalestre', 'arrestable'],
 'arbalestre': ['arbalester', 'arbalestre', 'arrestable'],
 'arbalister': ['arbalister', 'breastrail'],
 'arbalo': ['aboral', 'arbalo'],
 'arbela': ['ablare', 'arable', 'arbela'],
 'arbiter': ['arbiter', 'rarebit'],
 'arbored': ['arbored', 'boarder', 'reboard'],
 'arboret': ['arboret', 'roberta', 'taborer'],
 'arboretum': ['arboretum', 'tambourer'],
 'arborist': ['arborist', 'ribroast'],
 'arbuscle': ['arbuscle', 'buscarle'],
 'arbutin': ['arbutin', 'tribuna'],
 'arc': ['arc', 'car'],
 'arca': ['arca', 'cara'],
 'arcadia': ['acardia', 'acarida', 'arcadia'],
 'arcadic': ['arcadic', 'cardiac'],
 'arcane': ['arcane', 'carane'],
 'arcanite': ['anaretic', 'arcanite', 'carinate', 'craniate'],
 'arcate': ['arcate', 'cerata'],
 'arch': ['arch', 'char', 'rach'],
 'archae': ['archae', 'areach'],
 'archaic': ['arachic', 'archaic'],
 'archaism': ['archaism', 'charisma'],
 'archapostle': ['archapostle', 'thecasporal'],
 'archcount': ['archcount', 'crouchant'],
 'arche': ['acher', 'arche', 'chare', 'chera', 'rache', 'reach'],
 'archeal': ['alchera', 'archeal'],
 'archean': ['arachne', 'archean'],
 'archer': ['archer', 'charer', 'rechar'],
 'arches': ['arches', 'chaser', 'eschar', 'recash', 'search'],
 'archidome': ['archidome', 'chromidae'],
 'archil': ['archil', 'chiral'],
 'arching': ['arching', 'chagrin'],
 'architis': ['architis', 'rachitis'],
 'archocele': ['archocele', 'cochleare'],
 'archon': ['anchor', 'archon', 'charon', 'rancho'],
 'archontia': ['archontia', 'tocharian'],
 'archoptoma': ['apochromat', 'archoptoma'],
 'archpoet': ['archpoet', 'protheca'],
 'archprelate': ['archprelate', 'pretracheal'],
 'archsaint': ['anarchist', 'archsaint', 'cantharis'],
 'archsee': ['archsee', 'rechase'],
 'archsin': ['archsin', 'incrash'],
 'archy': ['archy', 'chary'],
 'arcidae': ['arcidae', 'caridea'],
 'arcing': ['arcing', 'racing'],
 'arcite': ['acrite', 'arcite', 'tercia', 'triace', 'tricae'],
 'arcked': ['arcked', 'dacker'],
 'arcking': ['arcking', 'carking', 'racking'],
 'arcos': ['arcos', 'crosa', 'oscar', 'sacro'],
 'arctia': ['acrita', 'arctia'],
 'arctian': ['acritan', 'arctian'],
 'arcticize': ['arcticize', 'cicatrize'],
 'arctiid': ['arctiid', 'triacid', 'triadic'],
 'arctoid': ['arctoid', 'carotid', 'dartoic'],
 'arctoidean': ['arctoidean', 'carotidean', 'cordaitean', 'dinocerata'],
 'arctomys': ['arctomys', 'costmary', 'mascotry'],
 'arctos': ['arctos', 'castor', 'costar', 'scrota'],
 'arcual': ['arcual', 'arcula'],
 'arcuale': ['arcuale', 'caurale'],
 'arcubalist': ['arcubalist', 'ultrabasic'],
 'arcula': ['arcual', 'arcula'],
 'arculite': ['arculite', 'cutleria', 'lucretia', 'reticula', 'treculia'],
 'ardea': ['ardea', 'aread'],
 'ardeb': ['ardeb', 'beard', 'bread', 'debar'],
 'ardelia': ['ardelia', 'laridae', 'radiale'],
 'ardella': ['ardella', 'dareall'],
 'ardency': ['ardency', 'dancery'],
 'ardish': ['ardish', 'radish'],
 'ardoise': ['ardoise', 'aroides', 'soredia'],
 'ardu': ['ardu', 'daur', 'dura'],
 'are': ['aer', 'are', 'ear', 'era', 'rea'],
 'areach': ['archae', 'areach'],
 'aread': ['ardea', 'aread'],
 'areal': ['areal', 'reaal'],
 'arean': ['anear', 'arean', 'arena'],
 'arecaceae': ['aceraceae', 'arecaceae'],
 'arecaceous': ['aceraceous', 'arecaceous'],
 'arecain': ['acarine', 'acraein', 'arecain'],
 'arecolin': ['acrolein',
              'arecolin',
              'caroline',
              'colinear',
              'cornelia',
              'creolian',
              'lonicera'],
 'arecoline': ['arecoline', 'arenicole'],
 'arecuna': ['arecuna', 'aucaner'],
 'ared': ['ared', 'daer', 'dare', 'dear', 'read'],
 'areel': ['areel', 'earle'],
 'arena': ['anear', 'arean', 'arena'],
 'arenaria': ['aerarian', 'arenaria'],
 'arend': ['andre', 'arend', 'daren', 'redan'],
 'areng': ['anger', 'areng', 'grane', 'range'],
 'arenga': ['arenga', 'argean'],
 'arenicole': ['arecoline', 'arenicole'],
 'arenicolite': ['arenicolite', 'ricinoleate'],
 'arenig': ['arenig', 'earing', 'gainer', 'reagin', 'regain'],
 'arenoid': ['aneroid', 'arenoid'],
 'arenose': ['arenose', 'serenoa'],
 'arent': ['antre', 'arent', 'retan', 'terna'],
 'areographer': ['aerographer', 'areographer'],
 'areographic': ['aerographic', 'areographic'],
 'areographical': ['aerographical', 'areographical'],
 'areography': ['aerography', 'areography'],
 'areologic': ['aerologic', 'areologic'],
 'areological': ['aerological', 'areological'],
 'areologist': ['aerologist', 'areologist'],
 'areology': ['aerology', 'areology'],
 'areometer': ['aerometer', 'areometer'],
 'areometric': ['aerometric', 'areometric'],
 'areometry': ['aerometry', 'areometry'],
 'arete': ['arete', 'eater', 'teaer'],
 'argal': ['agral', 'argal'],
 'argali': ['argali', 'garial'],
 'argans': ['argans', 'sangar'],
 'argante': ['argante', 'granate', 'tanager'],
 'argas': ['argas', 'sagra'],
 'argean': ['arenga', 'argean'],
 'argeers': ['argeers', 'greaser', 'serrage'],
 'argel': ['argel', 'ergal', 'garle', 'glare', 'lager', 'large', 'regal'],
 'argenol': ['argenol', 'longear'],
 'argent': ['argent', 'garnet', 'garten', 'tanger'],
 'argentamid': ['argentamid', 'marginated'],
 'argenter': ['argenter', 'garneter'],
 'argenteum': ['argenteum', 'augmenter'],
 'argentic': ['argentic', 'citrange'],
 'argentide': ['argentide', 'denigrate', 'dinergate'],
 'argentiferous': ['argentiferous', 'garnetiferous'],
 'argentina': ['argentina', 'tanagrine'],
 'argentine': ['argentine', 'tangerine'],
 'argentino': ['antinegro', 'argentino', 'argention'],
 'argention': ['antinegro', 'argentino', 'argention'],
 'argentite': ['argentite', 'integrate'],
 'argentol': ['argentol', 'gerontal'],
 'argenton': ['argenton', 'negatron'],
 'argentous': ['argentous', 'neotragus'],
 'argentum': ['argentum', 'argument'],
 'arghan': ['arghan', 'hangar'],
 'argil': ['argil', 'glair', 'grail'],
 'arginine': ['arginine', 'nigerian'],
 'argive': ['argive', 'rivage'],
 'argo': ['argo', 'garo', 'gora'],
 'argoan': ['agroan', 'angora', 'anogra', 'arango', 'argoan', 'onagra'],
 'argol': ['algor', 'argol', 'goral', 'largo'],
 'argolet': ['argolet', 'gloater', 'legator'],
 'argolian': ['argolian', 'gloriana'],
 'argolic': ['argolic', 'cograil'],
 'argolid': ['argolid', 'goliard'],
 'argon': ['angor',
           'argon',
           'goran',
           'grano',
           'groan',
           'nagor',
           'orang',
           'organ',
           'rogan',
           'ronga'],
 'argot': ['argot', 'gator', 'gotra', 'groat'],
 'argue': ['argue', 'auger'],
 'argulus': ['argulus', 'lagurus'],
 'argument': ['argentum', 'argument'],
 'argus': ['argus', 'sugar'],
 'arguslike': ['arguslike', 'sugarlike'],
 'argute': ['argute', 'guetar', 'rugate', 'tuareg'],
 'argyle': ['argyle', 'gleary'],
 'arhar': ['arhar', 'arrah'],
 'arhat': ['arhat', 'artha', 'athar'],
 'aria': ['aira', 'aria', 'raia'],
 'ariadne': ['araneid', 'ariadne', 'ranidae'],
 'arian': ['airan', 'arain', 'arian'],
 'arianrhod': ['arianrhod', 'hordarian'],
 'aribine': ['aribine', 'bairnie', 'iberian'],
 'arician': ['arician', 'icarian'],
 'arid': ['arid', 'dari', 'raid'],
 'aridian': ['aridian', 'diarian'],
 'aridly': ['aridly', 'lyraid'],
 'ariegite': ['aegirite', 'ariegite'],
 'aries': ['aries', 'arise', 'raise', 'serai'],
 'arietid': ['arietid', 'iridate'],
 'arietta': ['arietta', 'ratitae'],
 'aright': ['aright', 'graith'],
 'arightly': ['alrighty', 'arightly'],
 'ariidae': ['ariidae', 'raiidae'],
 'aril': ['aril', 'lair', 'lari', 'liar', 'lira', 'rail', 'rial'],
 'ariled': ['ariled', 'derail', 'dialer'],
 'arillate': ['arillate', 'tiarella'],
 'arion': ['arion', 'noria'],
 'ariot': ['ariot', 'ratio'],
 'aripple': ['applier', 'aripple'],
 'arise': ['aries', 'arise', 'raise', 'serai'],
 'arisen': ['arisen', 'arsine', 'resina', 'serian'],
 'arist': ['arist',
           'astir',
           'sitar',
           'stair',
           'stria',
           'tarsi',
           'tisar',
           'trias'],
 'arista': ['arista', 'tarsia'],
 'aristeas': ['aristeas', 'asterias'],
 'aristol': ['aristol', 'oralist', 'ortalis', 'striola'],
 'aristulate': ['aristulate', 'australite'],
 'arite': ['arite', 'artie', 'irate', 'retia', 'tarie'],
 'arithmic': ['arithmic', 'mithraic', 'mithriac'],
 'arius': ['arius', 'asuri'],
 'arizona': ['arizona', 'azorian', 'zonaria'],
 'ark': ['ark', 'kra'],
 'arkab': ['abkar', 'arkab'],
 'arkite': ['arkite', 'karite'],
 'arkose': ['arkose', 'resoak', 'soaker'],
 'arlene': ['arlene', 'leaner'],
 'arleng': ['angler', 'arleng', 'garnel', 'largen', 'rangle', 'regnal'],
 'arles': ['arles', 'arsle', 'laser', 'seral', 'slare'],
 'arline': ['arline', 'larine', 'linear', 'nailer', 'renail'],
 'arm': ['arm', 'mar', 'ram'],
 'armada': ['armada', 'damara', 'ramada'],
 'armangite': ['armangite', 'marginate'],
 'armata': ['armata', 'matara', 'tamara'],
 'armed': ['armed', 'derma', 'dream', 'ramed'],
 'armenian': ['armenian', 'marianne'],
 'armenic': ['armenic', 'carmine', 'ceriman', 'crimean', 'mercian'],
 'armer': ['armer', 'rearm'],
 'armeria': ['armeria', 'mararie'],
 'armet': ['armet',
           'mater',
           'merat',
           'metra',
           'ramet',
           'tamer',
           'terma',
           'trame',
           'trema'],
 'armful': ['armful', 'fulmar'],
 'armgaunt': ['armgaunt', 'granatum'],
 'armied': ['admire', 'armied', 'damier', 'dimera', 'merida'],
 'armiferous': ['armiferous', 'ramiferous'],
 'armigerous': ['armigerous', 'ramigerous'],
 'armil': ['armil', 'marli', 'rimal'],
 'armilla': ['armilla', 'marilla'],
 'armillated': ['armillated', 'malladrite', 'mallardite'],
 'arming': ['arming', 'ingram', 'margin'],
 'armistice': ['ameristic', 'armistice', 'artemisic'],
 'armlet': ['armlet', 'malter', 'martel'],
 'armonica': ['armonica', 'macaroni', 'marocain'],
 'armoried': ['airdrome', 'armoried'],
 'armpit': ['armpit', 'impart'],
 'armplate': ['armplate', 'malapert'],
 'arms': ['arms', 'mars'],
 'armscye': ['armscye', 'screamy'],
 'army': ['army', 'mary', 'myra', 'yarm'],
 'arn': ['arn', 'nar', 'ran'],
 'arna': ['arna', 'rana'],
 'arnaut': ['arnaut', 'arunta'],
 'arne': ['arne', 'earn', 'rane'],
 'arneb': ['abner', 'arneb', 'reban'],
 'arni': ['arni', 'iran', 'nair', 'rain', 'rani'],
 'arnica': ['acinar',
            'arnica',
            'canari',
            'carian',
            'carina',
            'crania',
            'narica'],
 'arnold': ['androl', 'arnold', 'lardon', 'roland', 'ronald'],
 'arnotta': ['arnotta', 'natator'],
 'arnotto': ['arnotto', 'notator'],
 'arnut': ['arnut', 'tuarn', 'untar'],
 'aro': ['aro', 'oar', 'ora'],
 'aroast': ['aroast', 'ostara'],
 'arock': ['arock', 'croak'],
 'aroid': ['aroid', 'doria', 'radio'],
 'aroides': ['ardoise', 'aroides', 'soredia'],
 'aroint': ['aroint', 'ration'],
 'aromatic': ['aromatic', 'macrotia'],
 'aroon': ['aroon', 'oraon'],
 'arose': ['arose', 'oreas'],
 'around': ['around', 'arundo'],
 'arpen': ['arpen', 'paren'],
 'arpent': ['arpent',
            'enrapt',
            'entrap',
            'panter',
            'parent',
            'pretan',
            'trepan'],
 'arrah': ['arhar', 'arrah'],
 'arras': ['arras', 'sarra'],
 'arrau': ['arrau', 'aurar'],
 'arrayer': ['arrayer', 'rearray'],
 'arrect': ['arrect', 'carter', 'crater', 'recart', 'tracer'],
 'arrector': ['arrector', 'carroter'],
 'arrent': ['arrent', 'errant', 'ranter', 'ternar'],
 'arrest': ['arrest', 'astrer', 'raster', 'starer'],
 'arrestable': ['arbalester', 'arbalestre', 'arrestable'],
 'arrester': ['arrester', 'rearrest'],
 'arresting': ['arresting', 'astringer'],
 'arretine': ['arretine', 'eretrian', 'eritrean', 'retainer'],
 'arride': ['arride', 'raider'],
 'arrie': ['airer', 'arrie'],
 'arriet': ['arriet', 'tarrie'],
 'arrish': ['arrish', 'harris', 'rarish', 'sirrah'],
 'arrive': ['arrive', 'varier'],
 'arrogance': ['arrogance', 'coarrange'],
 'arrogant': ['arrogant', 'tarragon'],
 'arrogative': ['arrogative', 'variegator'],
 'arrowy': ['arrowy', 'yarrow'],
 'arry': ['arry', 'yarr'],
 'arsacid': ['arsacid', 'ascarid'],
 'arse': ['arse', 'rase', 'sare', 'sear', 'sera'],
 'arsedine': ['arsedine', 'arsenide', 'sedanier', 'siderean'],
 'arsenal': ['arsenal', 'ranales'],
 'arsenate': ['arsenate', 'serenata'],
 'arsenation': ['arsenation', 'senatorian', 'sonneratia'],
 'arseniate': ['arseniate', 'saernaite'],
 'arsenic': ['arsenic', 'cerasin', 'sarcine'],
 'arsenide': ['arsedine', 'arsenide', 'sedanier', 'siderean'],
 'arsenite': ['arsenite', 'resinate', 'teresian', 'teresina'],
 'arsenium': ['aneurism', 'arsenium', 'sumerian'],
 'arseniuret': ['arseniuret', 'uniserrate'],
 'arseno': ['arseno', 'reason'],
 'arsenopyrite': ['arsenopyrite', 'pyroarsenite'],
 'arsenous': ['anserous', 'arsenous'],
 'arses': ['arses', 'rasse'],
 'arshine': ['arshine', 'nearish', 'rhesian', 'sherani'],
 'arsine': ['arisen', 'arsine', 'resina', 'serian'],
 'arsino': ['arsino', 'rasion', 'sonrai'],
 'arsis': ['arsis', 'sarsi'],
 'arsle': ['arles', 'arsle', 'laser', 'seral', 'slare'],
 'arson': ['arson', 'saron', 'sonar'],
 'arsonic': ['arsonic', 'saronic'],
 'arsonite': ['arsonite', 'asterion', 'oestrian', 'rosinate', 'serotina'],
 'art': ['art', 'rat', 'tar', 'tra'],
 'artaba': ['artaba', 'batara'],
 'artabe': ['abater', 'artabe', 'eartab', 'trabea'],
 'artal': ['altar', 'artal', 'ratal', 'talar'],
 'artamus': ['artamus', 'sumatra'],
 'artarine': ['artarine', 'errantia'],
 'artefact': ['afteract', 'artefact', 'farcetta', 'farctate'],
 'artel': ['alert', 'alter', 'artel', 'later', 'ratel', 'taler', 'telar'],
 'artemas': ['artemas', 'astream'],
 'artemia': ['ametria', 'artemia', 'meratia', 'ramaite'],
 'artemis': ['artemis', 'maestri', 'misrate'],
 'artemisic': ['ameristic', 'armistice', 'artemisic'],
 'arterial': ['arterial', 'triareal'],
 'arterin': ['arterin', 'retrain', 'terrain', 'trainer'],
 'arterious': ['arterious', 'autoriser'],
 'artesian': ['artesian', 'asterina', 'asternia', 'erastian', 'seatrain'],
 'artgum': ['artgum', 'targum'],
 'artha': ['arhat', 'artha', 'athar'],
 'arthel': ['arthel', 'halter', 'lather', 'thaler'],
 'arthemis': ['arthemis', 'marshite', 'meharist'],
 'arthrochondritis': ['arthrochondritis', 'chondroarthritis'],
 'arthromere': ['arthromere', 'metrorrhea'],
 'arthropodan': ['anarthropod', 'arthropodan'],
 'arthrosteitis': ['arthrosteitis', 'ostearthritis'],
 'article': ['article', 'recital'],
 'articled': ['articled', 'lacertid'],
 'artie': ['arite', 'artie', 'irate', 'retia', 'tarie'],
 'artifice': ['actifier', 'artifice'],
 'artisan': ['artisan', 'astrain', 'sartain', 'tsarina'],
 'artisanship': ['antiphrasis', 'artisanship'],
 'artist': ['artist', 'strait', 'strati'],
 'artiste': ['artiste', 'striate'],
 'artlet': ['artlet', 'latter', 'rattle', 'tartle', 'tatler'],
 'artlike': ['artlike', 'ratlike', 'tarlike'],
 'arty': ['arty', 'atry', 'tray'],
 'aru': ['aru', 'rua', 'ura'],
 'aruac': ['aruac', 'carua'],
 'arui': ['arui', 'uria'],
 'arum': ['arum', 'maru', 'mura'],
 'arundo': ['around', 'arundo'],
 'arunta': ['arnaut', 'arunta'],
 'arusa': ['arusa', 'saura', 'usara'],
 'arusha': ['arusha', 'aushar'],
 'arustle': ['arustle', 'estrual', 'saluter', 'saulter'],
 'arval': ['alvar', 'arval', 'larva'],
 'arvel': ['arvel', 'larve', 'laver', 'ravel', 'velar'],
 'arx': ['arx', 'rax'],
 'ary': ['ary', 'ray', 'yar'],
 'arya': ['arya', 'raya'],
 'aryan': ['aryan', 'nayar', 'rayan'],
 'aryl': ['aryl', 'lyra', 'ryal', 'yarl'],
 'as': ['as', 'sa'],
 'asa': ['asa', 'saa'],
 'asak': ['asak', 'kasa', 'saka'],
 'asana': ['anasa', 'asana'],
 'asaph': ['asaph', 'pasha'],
 'asaphia': ['aphasia', 'asaphia'],
 'asaphic': ['aphasic', 'asaphic'],
 'asaprol': ['asaprol', 'parasol'],
 'asarh': ['asarh', 'raash', 'sarah'],
 'asarite': ['asarite', 'asteria', 'atresia', 'setaria'],
 'asarum': ['aramus', 'asarum'],
 'asbest': ['asbest', 'basset'],
 'ascanius': ['anacusis', 'ascanius'],
 'ascare': ['ascare', 'caesar', 'resaca'],
 'ascarid': ['arsacid', 'ascarid'],
 'ascaris': ['ascaris', 'carissa'],
 'ascendance': ['adnascence', 'ascendance'],
 'ascendant': ['adnascent', 'ascendant'],
 'ascender': ['ascender', 'reascend'],
 'ascent': ['ascent', 'secant', 'stance'],
 'ascertain': ['ascertain', 'cartesian', 'cartisane', 'sectarian'],
 'ascertainer': ['ascertainer', 'reascertain', 'secretarian'],
 'ascetic': ['ascetic', 'castice', 'siccate'],
 'ascham': ['ascham', 'chasma'],
 'asci': ['acis', 'asci', 'saic'],
 'ascian': ['ascian', 'sacian', 'scania', 'sicana'],
 'ascidia': ['ascidia', 'diascia'],
 'ascii': ['ascii', 'isiac'],
 'ascites': ['ascites', 'ectasis'],
 'ascitic': ['ascitic', 'sciatic'],
 'ascitical': ['ascitical', 'sciatical'],
 'asclent': ['asclent', 'scantle'],
 'asclepian': ['asclepian', 'spalacine'],
 'ascolichen': ['ascolichen', 'chalcosine'],
 'ascon': ['ascon', 'canso', 'oscan'],
 'ascot': ['ascot', 'coast', 'costa', 'tacso', 'tasco'],
 'ascription': ['antipsoric', 'ascription', 'crispation'],
 'ascry': ['ascry', 'scary', 'scray'],
 'ascula': ['ascula', 'calusa', 'casual', 'casula', 'causal'],
 'asdic': ['asdic', 'sadic'],
 'ase': ['aes', 'ase', 'sea'],
 'asearch': ['asearch', 'eschara'],
 'aselli': ['allies', 'aselli'],
 'asem': ['asem', 'mesa', 'same', 'seam'],
 'asemia': ['asemia', 'saeima'],
 'aseptic': ['aseptic', 'spicate'],
 'aseptol': ['apostle', 'aseptol'],
 'ash': ['ash', 'sah', 'sha'],
 'ashanti': ['ashanti', 'sanhita', 'shaitan', 'thasian'],
 'ashen': ['ashen', 'hanse', 'shane', 'shean'],
 'asher': ['asher', 'share', 'shear'],
 'ashet': ['ashet', 'haste', 'sheat'],
 'ashimmer': ['ashimmer', 'haremism'],
 'ashir': ['ashir', 'shari'],
 'ashling': ['anglish', 'ashling'],
 'ashman': ['ashman', 'shaman'],
 'ashore': ['ahorse', 'ashore', 'hoarse', 'shorea'],
 'ashraf': ['afshar', 'ashraf'],
 'ashur': ['ashur', 'surah'],
 'ashy': ['ashy', 'shay'],
 'asian': ['asian', 'naias', 'sanai'],
 'asiarch': ['arachis', 'asiarch', 'saharic'],
 'aside': ['aides', 'aside', 'sadie'],
 'asideu': ['asideu', 'suidae'],
 'asiento': ['aeonist', 'asiento', 'satieno'],
 'asilid': ['asilid', 'sialid'],
 'asilidae': ['asilidae', 'sialidae'],
 'asilus': ['asilus', 'lasius'],
 'asimen': ['asimen', 'inseam', 'mesian'],
 'asimmer': ['amerism', 'asimmer', 'sammier'],
 'asiphonate': ['asiphonate', 'asthenopia'],
 'ask': ['ask', 'sak'],
 'asker': ['asker', 'reask', 'saker', 'sekar'],
 'askew': ['askew', 'wakes'],
 'askip': ['askip', 'spaik'],
 'askr': ['askr', 'kras', 'sark'],
 'aslant': ['aslant', 'lansat', 'natals', 'santal'],
 'asleep': ['asleep', 'elapse', 'please'],
 'aslope': ['aslope', 'poales'],
 'asmalte': ['asmalte', 'maltase'],
 'asmile': ['amiles', 'asmile', 'mesail', 'mesial', 'samiel'],
 'asnort': ['asnort', 'satron'],
 'asoak': ['asoak', 'asoka'],
 'asok': ['asok', 'soak', 'soka'],
 'asoka': ['asoak', 'asoka'],
 'asonia': ['anosia', 'asonia'],
 'asop': ['asop', 'sapo', 'soap'],
 'asor': ['asor', 'rosa', 'soar', 'sora'],
 'asp': ['asp', 'sap', 'spa'],
 'aspartic': ['aspartic', 'satrapic'],
 'aspection': ['aspection', 'stenopaic'],
 'aspectual': ['aspectual', 'capsulate'],
 'aspen': ['aspen', 'panse', 'snape', 'sneap', 'spane', 'spean'],
 'asper': ['asper', 'parse', 'prase', 'spaer', 'spare', 'spear'],
 'asperate': ['asperate', 'separate'],
 'asperation': ['anisoptera', 'asperation', 'separation'],
 'asperge': ['asperge', 'presage'],
 'asperger': ['asperger', 'presager'],
 'aspergil': ['aspergil', 'splairge'],
 'asperite': ['asperite', 'parietes'],
 'aspermia': ['aspermia', 'sapremia'],
 'aspermic': ['aspermic', 'sapremic'],
 'asperser': ['asperser', 'repasser'],
 'asperulous': ['asperulous', 'pleasurous'],
 'asphalt': ['asphalt', 'spathal', 'taplash'],
 'aspic': ['aspic', 'spica'],
 'aspidinol': ['aspidinol', 'diplasion'],
 'aspirant': ['aspirant', 'partisan', 'spartina'],
 'aspirata': ['aspirata', 'parasita'],
 'aspirate': ['aspirate', 'parasite'],
 'aspire': ['aspire', 'paries', 'praise', 'sirpea', 'spirea'],
 'aspirer': ['aspirer', 'praiser', 'serpari'],
 'aspiring': ['aspiring', 'praising', 'singarip'],
 'aspiringly': ['aspiringly', 'praisingly'],
 'aspish': ['aspish', 'phasis'],
 'asporous': ['asporous', 'saporous'],
 'asport': ['asport', 'pastor', 'sproat'],
 'aspread': ['aspread', 'saperda'],
 'aspring': ['aspring', 'rasping', 'sparing'],
 'asquirm': ['asquirm', 'marquis'],
 'assagai': ['assagai', 'gaiassa'],
 'assailer': ['assailer', 'reassail'],
 'assam': ['amass', 'assam', 'massa', 'samas'],
 'assaulter': ['assaulter', 'reassault', 'saleratus'],
 'assayer': ['assayer', 'reassay'],
 'assemble': ['assemble', 'beamless'],
 'assent': ['assent', 'snaste'],
 'assenter': ['assenter', 'reassent', 'sarsenet'],
 'assentor': ['assentor', 'essorant', 'starnose'],
 'assert': ['assert', 'tasser'],
 'asserter': ['asserter', 'reassert'],
 'assertible': ['assertible', 'resistable'],
 'assertional': ['assertional', 'sensatorial'],
 'assertor': ['assertor', 'assorter', 'oratress', 'reassort'],
 'asset': ['asset', 'tasse'],
 'assets': ['assets', 'stases'],
 'assidean': ['assidean', 'nassidae'],
 'assiento': ['assiento', 'ossetian'],
 'assignee': ['agenesis', 'assignee'],
 'assigner': ['assigner', 'reassign'],
 'assist': ['assist', 'stasis'],
 'assister': ['assister', 'reassist'],
 'associationism': ['associationism', 'misassociation'],
 'assoilment': ['assoilment', 'salmonsite'],
 'assorter': ['assertor', 'assorter', 'oratress', 'reassort'],
 'assuage': ['assuage', 'sausage'],
 'assume': ['assume', 'seamus'],
 'assumer': ['assumer', 'erasmus', 'masseur'],
 'ast': ['ast', 'sat'],
 'astacus': ['acastus', 'astacus'],
 'astare': ['astare', 'satrae'],
 'astart': ['astart', 'strata'],
 'astartian': ['astartian', 'astrantia'],
 'astatine': ['astatine', 'sanitate'],
 'asteep': ['asteep', 'peseta'],
 'asteer': ['asteer',
            'easter',
            'eastre',
            'reseat',
            'saeter',
            'seater',
            'staree',
            'teaser',
            'teresa'],
 'astelic': ['astelic', 'elastic', 'latices'],
 'astely': ['alytes', 'astely', 'lysate', 'stealy'],
 'aster': ['aster', 'serta', 'stare', 'strae', 'tarse', 'teras'],
 'asteria': ['asarite', 'asteria', 'atresia', 'setaria'],
 'asterias': ['aristeas', 'asterias'],
 'asterikos': ['asterikos', 'keratosis'],
 'asterin': ['asterin', 'eranist', 'restain', 'stainer', 'starnie', 'stearin'],
 'asterina': ['artesian', 'asterina', 'asternia', 'erastian', 'seatrain'],
 'asterion': ['arsonite', 'asterion', 'oestrian', 'rosinate', 'serotina'],
 'astern': ['astern', 'enstar', 'stenar', 'sterna'],
 'asternia': ['artesian', 'asterina', 'asternia', 'erastian', 'seatrain'],
 'asteroid': ['asteroid', 'troiades'],
 'asterope': ['asterope', 'protease'],
 'asthenopia': ['asiphonate', 'asthenopia'],
 'asthma': ['amsath', 'asthma'],
 'asthmogenic': ['asthmogenic', 'mesognathic'],
 'asthore': ['asthore', 'earshot'],
 'astian': ['astian', 'tasian'],
 'astigmism': ['astigmism', 'sigmatism'],
 'astilbe': ['astilbe', 'bestial', 'blastie', 'stabile'],
 'astint': ['astint', 'tanist'],
 'astir': ['arist',
           'astir',
           'sitar',
           'stair',
           'stria',
           'tarsi',
           'tisar',
           'trias'],
 'astomous': ['astomous', 'somatous'],
 'astonied': ['astonied', 'sedation'],
 'astonisher': ['astonisher', 'reastonish', 'treasonish'],
 'astor': ['astor', 'roast'],
 'astragali': ['astragali', 'tarsalgia'],
 'astrain': ['artisan', 'astrain', 'sartain', 'tsarina'],
 'astral': ['astral', 'tarsal'],
 'astrantia': ['astartian', 'astrantia'],
 'astream': ['artemas', 'astream'],
 'astrer': ['arrest', 'astrer', 'raster', 'starer'],
 'astrict': ['astrict', 'cartist', 'stratic'],
 'astride': ['astride', 'diaster', 'disrate', 'restiad', 'staired'],
 'astrier': ['astrier', 'tarsier'],
 'astringe': ['astringe', 'ganister', 'gantries'],
 'astringent': ['astringent', 'transigent'],
 'astringer': ['arresting', 'astringer'],
 'astrodome': ['astrodome', 'roomstead'],
 'astrofel': ['astrofel', 'forestal'],
 'astroite': ['astroite', 'ostraite', 'storiate'],
 'astrolabe': ['astrolabe', 'roastable'],
 'astrut': ['astrut', 'rattus', 'stuart'],
 'astur': ['astur', 'surat', 'sutra'],
 'asturian': ['asturian', 'austrian', 'saturnia'],
 'astute': ['astute', 'statue'],
 'astylar': ['astylar', 'saltary'],
 'asunder': ['asunder', 'drusean'],
 'asuri': ['arius', 'asuri'],
 'aswail': ['aswail', 'sawali'],
 'asweat': ['asweat', 'awaste'],
 'aswim': ['aswim', 'swami'],
 'aswing': ['aswing', 'sawing'],
 'asyla': ['asyla', 'salay', 'sayal'],
 'asyllabic': ['asyllabic', 'basically'],
 'asyndetic': ['asyndetic', 'cystidean', 'syndicate'],
 'asynergia': ['asynergia', 'gainsayer'],
 'at': ['at', 'ta'],
 'ata': ['ata', 'taa'],
 'atabal': ['albata', 'atabal', 'balata'],
 'atabrine': ['atabrine', 'rabatine'],
 'atacaman': ['atacaman', 'tamanaca'],
 'ataentsic': ['anticaste', 'ataentsic'],
 'atalan': ['atalan', 'tanala'],
 'atap': ['atap', 'pata', 'tapa'],
 'atazir': ['atazir', 'ziarat'],
 'atchison': ['atchison', 'chitosan'],
 'ate': ['ate', 'eat', 'eta', 'tae', 'tea'],
 'ateba': ['abate', 'ateba', 'batea', 'beata'],
 'atebrin': ['atebrin', 'rabinet'],
 'atechnic': ['atechnic', 'catechin', 'technica'],
 'atechny': ['atechny', 'chantey'],
 'ateeter': ['ateeter', 'treatee'],
 'atef': ['atef', 'fate', 'feat'],
 'ateles': ['ateles', 'saltee', 'sealet', 'stelae', 'teasel'],
 'atelets': ['atelets', 'tsatlee'],
 'atelier': ['atelier', 'tiralee'],
 'aten': ['ante', 'aten', 'etna', 'nate', 'neat', 'taen', 'tane', 'tean'],
 'atenism': ['atenism', 'inmeats', 'insteam', 'samnite'],
 'atenist': ['atenist', 'instate', 'satient', 'steatin'],
 'ates': ['ates', 'east', 'eats', 'sate', 'seat', 'seta'],
 'atestine': ['anisette', 'atestine', 'settaine'],
 'athar': ['arhat', 'artha', 'athar'],
 'atheism': ['atheism', 'hamites'],
 'athena': ['ahtena', 'aneath', 'athena'],
 'athenian': ['anthinae', 'athenian'],
 'athenor': ['another', 'athenor', 'rheotan'],
 'athens': ['athens', 'hasten', 'snathe', 'sneath'],
 'atherine': ['atherine', 'herniate'],
 'atheris': ['atheris', 'sheriat'],
 'athermic': ['athermic', 'marchite', 'rhematic'],
 'athing': ['anight', 'athing'],
 'athirst': ['athirst', 'rattish', 'tartish'],
 'athletic': ['athletic', 'thetical'],
 'athletics': ['athletics', 'statelich'],
 'athort': ['athort', 'throat'],
 'athrive': ['athrive', 'hervati'],
 'ati': ['ait', 'ati', 'ita', 'tai'],
 'atik': ['atik', 'ikat'],
 'atimon': ['atimon', 'manito', 'montia'],
 'atingle': ['atingle', 'gelatin', 'genital', 'langite', 'telinga'],
 'atip': ['atip', 'pita'],
 'atis': ['atis', 'sita', 'tsia'],
 'atlantean': ['antenatal', 'atlantean', 'tantalean'],
 'atlantic': ['atlantic', 'tantalic'],
 'atlantid': ['atlantid', 'dilatant'],
 'atlantite': ['atlantite', 'tantalite'],
 'atlas': ['atlas', 'salat', 'salta'],
 'atle': ['atle', 'laet', 'late', 'leat', 'tael', 'tale', 'teal'],
 'atlee': ['atlee', 'elate'],
 'atloidean': ['atloidean', 'dealation'],
 'atma': ['atma', 'tama'],
 'atman': ['atman', 'manta'],
 'atmid': ['admit', 'atmid'],
 'atmo': ['atmo', 'atom', 'moat', 'toma'],
 'atmogenic': ['atmogenic', 'geomantic'],
 'atmos': ['atmos', 'stoma', 'tomas'],
 'atmosphere': ['atmosphere', 'shapometer'],
 'atmostea': ['atmostea', 'steatoma'],
 'atnah': ['atnah', 'tanha', 'thana'],
 'atocia': ['atocia', 'coaita'],
 'atokal': ['atokal', 'lakota'],
 'atoll': ['allot', 'atoll'],
 'atom': ['atmo', 'atom', 'moat', 'toma'],
 'atomic': ['atomic', 'matico'],
 'atomics': ['atomics', 'catoism', 'cosmati', 'osmatic', 'somatic'],
 'atomize': ['atomize', 'miaotze'],
 'atomizer': ['amortize', 'atomizer'],
 'atonal': ['atonal', 'latona'],
 'atonalism': ['anomalist', 'atonalism'],
 'atone': ['atone', 'oaten'],
 'atoner': ['atoner', 'norate', 'ornate'],
 'atonia': ['anotia', 'atonia'],
 'atonic': ['action', 'atonic', 'cation'],
 'atony': ['atony', 'ayont'],
 'atop': ['atop', 'pato'],
 'atopic': ['atopic', 'capito', 'copita'],
 'atorai': ['atorai', 'otaria'],
 'atrail': ['altair', 'atrail', 'atrial', 'lariat', 'latria', 'talari'],
 'atrepsy': ['atrepsy', 'yapster'],
 'atresia': ['asarite', 'asteria', 'atresia', 'setaria'],
 'atresic': ['atresic', 'stearic'],
 'atresy': ['atresy', 'estray', 'reasty', 'stayer'],
 'atretic': ['atretic', 'citrate'],
 'atria': ['arati', 'atria', 'riata', 'tarai', 'tiara'],
 'atrial': ['altair', 'atrail', 'atrial', 'lariat', 'latria', 'talari'],
 'atridean': ['anteriad', 'atridean', 'dentaria'],
 'atrip': ['atrip', 'tapir'],
 'atrocity': ['atrocity', 'citatory'],
 'atrophied': ['aphrodite', 'atrophied', 'diaporthe'],
 'atropia': ['apiator', 'atropia', 'parotia'],
 'atropic': ['apricot', 'atropic', 'parotic', 'patrico'],
 'atroscine': ['atroscine', 'certosina', 'ostracine', 'tinoceras', 'tricosane'],
 'atry': ['arty', 'atry', 'tray'],
 'attacco': ['attacco', 'toccata'],
 'attach': ['attach', 'chatta'],
 'attache': ['attache', 'thecata'],
 'attacher': ['attacher', 'reattach'],
 'attacker': ['attacker', 'reattack'],
 'attain': ['attain', 'tatian'],
 'attainder': ['antitrade', 'attainder'],
 'attainer': ['attainer', 'reattain', 'tertiana'],
 'attar': ['attar', 'tatar'],
 'attempter': ['attempter', 'reattempt'],
 'attender': ['attender', 'nattered', 'reattend'],
 'attention': ['attention', 'tentation'],
 'attentive': ['attentive', 'tentative'],
 'attentively': ['attentively', 'tentatively'],
 'attentiveness': ['attentiveness', 'tentativeness'],
 'atter': ['atter', 'tater', 'teart', 'tetra', 'treat'],
 'attermine': ['antimeter', 'attermine', 'interteam', 'terminate', 'tetramine'],
 'attern': ['attern', 'natter', 'ratten', 'tarten'],
 'attery': ['attery', 'treaty', 'yatter'],
 'attester': ['attester', 'reattest'],
 'attic': ['attic', 'catti', 'tacit'],
 'attical': ['attical', 'cattail'],
 'attinge': ['attinge', 'tintage'],
 'attire': ['attire', 'ratite', 'tertia'],
 'attired': ['attired', 'tradite'],
 'attorn': ['attorn', 'ratton', 'rottan'],
 'attracter': ['attracter', 'reattract'],
 'attractor': ['attractor', 'tractator'],
 'attrite': ['attrite', 'titrate'],
 'attrition': ['attrition', 'titration'],
 'attune': ['attune', 'nutate', 'tauten'],
 'atule': ['aleut', 'atule'],
 'atumble': ['atumble', 'mutable'],
 'atwin': ['atwin', 'twain', 'witan'],
 'atypic': ['atypic', 'typica'],
 'aube': ['aube', 'beau'],
 'aubrite': ['abiuret', 'aubrite', 'biurate', 'rubiate'],
 'aucan': ['acuan', 'aucan'],
 'aucaner': ['arecuna', 'aucaner'],
 'auchlet': ['auchlet', 'cutheal', 'taluche'],
 'auction': ['auction', 'caution'],
 'auctionary': ['auctionary', 'cautionary'],
 'audiencier': ['audiencier', 'enicuridae'],
 'auge': ['ague', 'auge'],
 'augen': ['augen', 'genua'],
 'augend': ['augend', 'engaud', 'unaged'],
 'auger': ['argue', 'auger'],
 'augerer': ['augerer', 'reargue'],
 'augh': ['augh', 'guha'],
 'augmenter': ['argenteum', 'augmenter'],
 'augustan': ['augustan', 'guatusan'],
 'auh': ['ahu', 'auh', 'hau'],
 'auk': ['aku', 'auk', 'kua'],
 'auld': ['auld', 'dual', 'laud', 'udal'],
 'aulete': ['aulete', 'eluate'],
 'auletic': ['aleutic', 'auletic', 'caulite', 'lutecia'],
 'auletris': ['auletris', 'lisuarte'],
 'aulic': ['aulic', 'lucia'],
 'aulostoma': ['aulostoma', 'autosomal'],
 'aulu': ['aulu', 'ulua'],
 'aum': ['aum', 'mau'],
 'aumbry': ['ambury', 'aumbry'],
 'aumil': ['aumil', 'miaul'],
 'aumrie': ['aumrie', 'uremia'],
 'auncel': ['auncel', 'cuneal', 'lacune', 'launce', 'unlace'],
 'aunt': ['antu', 'aunt', 'naut', 'taun', 'tuan', 'tuna'],
 'auntie': ['auntie', 'uniate'],
 'auntish': ['auntish', 'inhaust'],
 'auntly': ['auntly', 'lutany'],
 'auntsary': ['auntsary', 'unastray'],
 'aura': ['aaru', 'aura'],
 'aural': ['aural', 'laura'],
 'aurar': ['arrau', 'aurar'],
 'auresca': ['auresca', 'caesura'],
 'aureus': ['aureus', 'uraeus'],
 'auricle': ['auricle', 'ciruela'],
 'auricled': ['auricled', 'radicule'],
 'auride': ['auride', 'rideau'],
 'aurin': ['aurin', 'urian'],
 'aurir': ['aurir', 'urari'],
 'auriscalp': ['auriscalp', 'spiracula'],
 'auscult': ['auscult', 'scutula'],
 'aushar': ['arusha', 'aushar'],
 'auster': ['auster', 'reatus'],
 'austere': ['austere', 'euaster'],
 'australian': ['australian', 'saturnalia'],
 'australic': ['australic', 'lactarius'],
 'australite': ['aristulate', 'australite'],
 'austrian': ['asturian', 'austrian', 'saturnia'],
 'aute': ['aute', 'etua'],
 'autecism': ['autecism', 'musicate'],
 'authotype': ['authotype', 'autophyte'],
 'autoclave': ['autoclave', 'vacuolate'],
 'autocrat': ['actuator', 'autocrat'],
 'autoheterosis': ['autoheterosis', 'heteroousiast'],
 'autometric': ['autometric', 'tautomeric'],
 'autometry': ['autometry', 'tautomery'],
 'autophyte': ['authotype', 'autophyte'],
 'autoplast': ['autoplast', 'postulata'],
 'autopsic': ['autopsic', 'captious'],
 'autopsical': ['apolaustic', 'autopsical'],
 'autoradiograph': ['autoradiograph', 'radioautograph'],
 'autoradiographic': ['autoradiographic', 'radioautographic'],
 'autoradiography': ['autoradiography', 'radioautography'],
 'autoriser': ['arterious', 'autoriser'],
 'autosomal': ['aulostoma', 'autosomal'],
 'auxetic': ['auxetic', 'eutaxic'],
 'aval': ['aval', 'lava'],
 'avanti': ['avanti', 'vinata'],
 'avar': ['avar', 'vara'],
 'ave': ['ave', 'eva'],
 'avenge': ['avenge', 'geneva', 'vangee'],
 'avenger': ['avenger', 'engrave'],
 'avenin': ['avenin', 'vienna'],
 'aventine': ['aventine', 'venetian'],
 'aventurine': ['aventurine', 'uninervate'],
 'aver': ['aver', 'rave', 'vare', 'vera'],
 'avera': ['avera', 'erava'],
 'averil': ['averil', 'elvira'],
 'averin': ['averin', 'ravine'],
 'avert': ['avert', 'tarve', 'taver', 'trave'],
 'avertible': ['avertible', 'veritable'],
 'avertin': ['avertin', 'vitrean'],
 'aves': ['aves', 'save', 'vase'],
 'aviatic': ['aviatic', 'viatica'],
 'aviator': ['aviator', 'tovaria'],
 'avicular': ['avicular', 'varicula'],
 'avid': ['avid', 'diva'],
 'avidous': ['avidous', 'vaudois'],
 'avignonese': ['avignonese', 'ingaevones'],
 'avine': ['avine', 'naive', 'vinea'],
 'avirulence': ['acervuline', 'avirulence'],
 'avis': ['avis', 'siva', 'visa'],
 'avitic': ['avitic', 'viatic'],
 'avo': ['avo', 'ova'],
 'avocet': ['avocet', 'octave', 'vocate'],
 'avodire': ['avodire', 'avoider', 'reavoid'],
 'avoider': ['avodire', 'avoider', 'reavoid'],
 'avolation': ['avolation', 'ovational'],
 'avolitional': ['avolitional', 'violational'],
 'avoucher': ['avoucher', 'reavouch'],
 'avower': ['avower', 'reavow'],
 'avshar': ['avshar', 'varsha'],
 'avulse': ['alveus', 'avulse'],
 'aw': ['aw', 'wa'],
 'awag': ['awag', 'waag'],
 'awaiter': ['awaiter', 'reawait'],
 'awakener': ['awakener', 'reawaken'],
 'awarder': ['awarder', 'reaward'],
 'awash': ['awash', 'sawah'],
 'awaste': ['asweat', 'awaste'],
 'awat': ['awat', 'tawa'],
 'awd': ['awd', 'daw', 'wad'],
 'awe': ['awe', 'wae', 'wea'],
 'aweather': ['aweather', 'wheatear'],
 'aweek': ['aweek', 'keawe'],
 'awesome': ['awesome', 'waesome'],
 'awest': ['awest', 'sweat', 'tawse', 'waste'],
 'awfu': ['awfu', 'wauf'],
 'awful': ['awful', 'fulwa'],
 'awhet': ['awhet', 'wheat'],
 'awin': ['awin', 'wain'],
 'awing': ['awing', 'wigan'],
 'awl': ['awl', 'law'],
 'awn': ['awn', 'naw', 'wan'],
 'awned': ['awned', 'dewan', 'waned'],
 'awner': ['awner', 'newar'],
 'awning': ['awning', 'waning'],
 'awny': ['awny', 'wany', 'yawn'],
 'awol': ['alow', 'awol', 'lowa'],
 'awork': ['awork', 'korwa'],
 'awreck': ['awreck', 'wacker'],
 'awrong': ['awrong', 'growan'],
 'awry': ['awry', 'wary'],
 'axel': ['alex', 'axel', 'axle'],
 'axes': ['axes', 'saxe', 'seax'],
 'axil': ['alix', 'axil'],
 'axile': ['axile', 'lexia'],
 'axine': ['axine', 'xenia'],
 'axle': ['alex', 'axel', 'axle'],
 'axon': ['axon', 'noxa', 'oxan'],
 'axonal': ['axonal', 'oxalan'],
 'axonia': ['anoxia', 'axonia'],
 'ay': ['ay', 'ya'],
 'ayah': ['ayah', 'haya'],
 'aye': ['aye', 'yea'],
 'ayont': ['atony', 'ayont'],
 'azimine': ['aminize', 'animize', 'azimine'],
 'azo': ['azo', 'zoa'],
 'azole': ['azole', 'zoeal'],
 'azon': ['azon', 'onza', 'ozan'],
 'azorian': ['arizona', 'azorian', 'zonaria'],
 'azorite': ['azorite', 'zoarite'],
 'azoxine': ['azoxine', 'oxazine'],
 'azteca': ['azteca', 'zacate'],
 'azurine': ['azurine', 'urazine'],
 'ba': ['ab', 'ba'],
 'baa': ['aba', 'baa'],
 'baal': ['alba', 'baal', 'bala'],
 'baalath': ['baalath', 'bathala'],
 'baalite': ['baalite', 'bialate', 'labiate'],
 'baalshem': ['baalshem', 'shamable'],
 'baar': ['arab', 'arba', 'baar', 'bara'],
 'bab': ['abb', 'bab'],
 'baba': ['abba', 'baba'],
 'babbler': ['babbler', 'blabber', 'brabble'],
 'babery': ['babery', 'yabber'],
 'babhan': ['babhan', 'habnab'],
 'babishly': ['babishly', 'shabbily'],
 'babishness': ['babishness', 'shabbiness'],
 'babite': ['babite', 'bebait'],
 'babu': ['babu', 'buba'],
 'babul': ['babul', 'bubal'],
 'baby': ['abby', 'baby'],
 'babylonish': ['babylonish', 'nabobishly'],
 'bac': ['bac', 'cab'],
 'bacao': ['bacao', 'caoba'],
 'bach': ['bach', 'chab'],
 'bache': ['bache', 'beach'],
 'bachel': ['bachel', 'bleach'],
 'bachelor': ['bachelor', 'crabhole'],
 'bacillar': ['bacillar', 'cabrilla'],
 'bacis': ['bacis', 'basic'],
 'backblow': ['backblow', 'blowback'],
 'backen': ['backen', 'neback'],
 'backer': ['backer', 'reback'],
 'backfall': ['backfall', 'fallback'],
 'backfire': ['backfire', 'fireback'],
 'backlog': ['backlog', 'gablock'],
 'backrun': ['backrun', 'runback'],
 'backsaw': ['backsaw', 'sawback'],
 'backset': ['backset', 'setback'],
 'backstop': ['backstop', 'stopback'],
 'backswing': ['backswing', 'swingback'],
 'backward': ['backward', 'drawback'],
 'backway': ['backway', 'wayback'],
 'bacon': ['bacon', 'banco'],
 'bacterial': ['bacterial', 'calibrate'],
 'bacteriform': ['bacteriform', 'bracteiform'],
 'bacterin': ['bacterin', 'centibar'],
 'bacterioid': ['aborticide', 'bacterioid'],
 'bacterium': ['bacterium', 'cumbraite'],
 'bactrian': ['bactrian', 'cantabri'],
 'bacula': ['albuca', 'bacula'],
 'baculi': ['abulic', 'baculi'],
 'baculite': ['baculite', 'cubitale'],
 'baculites': ['baculites', 'bisulcate'],
 'baculoid': ['baculoid', 'cuboidal'],
 'bad': ['bad', 'dab'],
 'badaga': ['badaga', 'dagaba', 'gadaba'],
 'badan': ['badan', 'banda'],
 'bade': ['abed', 'bade', 'bead'],
 'badge': ['badge', 'begad'],
 'badian': ['badian', 'indaba'],
 'badigeon': ['badigeon', 'gabioned'],
 'badly': ['badly', 'baldy', 'blady'],
 'badon': ['badon', 'bando'],
 'bae': ['abe', 'bae', 'bea'],
 'baeria': ['aberia', 'baeria', 'baiera'],
 'baetulus': ['baetulus', 'subulate'],
 'baetyl': ['baetyl', 'baylet', 'bleaty'],
 'baetylic': ['baetylic', 'biacetyl'],
 'bafta': ['abaft', 'bafta'],
 'bag': ['bag', 'gab'],
 'bagani': ['bagani', 'bangia', 'ibanag'],
 'bagel': ['bagel', 'belga', 'gable', 'gleba'],
 'bagger': ['bagger', 'beggar'],
 'bagnio': ['bagnio', 'gabion', 'gobian'],
 'bago': ['bago', 'boga'],
 'bagre': ['bagre', 'barge', 'begar', 'rebag'],
 'bahar': ['bahar', 'bhara'],
 'bahoe': ['bahoe', 'bohea', 'obeah'],
 'baht': ['baht', 'bath', 'bhat'],
 'baiera': ['aberia', 'baeria', 'baiera'],
 'baignet': ['baignet', 'beating'],
 'bail': ['albi', 'bail', 'bali'],
 'bailage': ['algieba', 'bailage'],
 'bailer': ['bailer', 'barile'],
 'baillone': ['baillone', 'bonellia'],
 'bailment': ['bailment', 'libament'],
 'bailor': ['bailor', 'bioral'],
 'bailsman': ['bailsman', 'balanism', 'nabalism'],
 'bain': ['bain', 'bani', 'iban'],
 'baioc': ['baioc', 'cabio', 'cobia'],
 'bairam': ['bairam', 'bramia'],
 'bairn': ['abrin', 'bairn', 'brain', 'brian', 'rabin'],
 'bairnie': ['aribine', 'bairnie', 'iberian'],
 'bairnish': ['bairnish', 'bisharin'],
 'bais': ['absi', 'bais', 'bias', 'isba'],
 'baister': ['baister', 'tribase'],
 'baiter': ['baiter', 'barite', 'rebait', 'terbia'],
 'baith': ['baith', 'habit'],
 'bajocian': ['bajocian', 'jacobian'],
 'bakal': ['bakal', 'balak'],
 'bakatan': ['bakatan', 'batakan'],
 'bake': ['bake', 'beak'],
 'baker': ['baker', 'brake', 'break'],
 'bakerless': ['bakerless', 'brakeless', 'breakless'],
 'bakery': ['bakery', 'barkey'],
 'bakie': ['akebi', 'bakie'],
 'baku': ['baku', 'kuba'],
 'bal': ['alb', 'bal', 'lab'],
 'bala': ['alba', 'baal', 'bala'],
 'baladine': ['baladine', 'balaenid'],
 'balaenid': ['baladine', 'balaenid'],
 'balagan': ['balagan', 'bangala'],
 'balai': ['balai', 'labia'],
 'balak': ['bakal', 'balak'],
 'balan': ['alban', 'balan', 'banal', 'laban', 'nabal', 'nabla'],
 'balancer': ['balancer', 'barnacle'],
 'balangay': ['balangay', 'bangalay'],
 'balanic': ['balanic', 'caliban'],
 'balanid': ['balanid', 'banilad'],
 'balanism': ['bailsman', 'balanism', 'nabalism'],
 'balanite': ['albanite', 'balanite', 'nabalite'],
 'balanites': ['balanites', 'basaltine', 'stainable'],
 'balantidium': ['antialbumid', 'balantidium'],
 'balanus': ['balanus', 'nabalus', 'subanal'],
 'balas': ['balas', 'balsa', 'basal', 'sabal'],
 'balata': ['albata', 'atabal', 'balata'],
 'balatron': ['balatron', 'laborant'],
 'balaustine': ['balaustine', 'unsatiable'],
 'balaustre': ['balaustre', 'saturable'],
 'bald': ['bald', 'blad'],
 'balden': ['balden', 'bandle'],
 'balder': ['balder', 'bardel', 'bedlar', 'bedral', 'belard', 'blader'],
 'baldie': ['abdiel', 'baldie'],
 'baldish': ['baldish', 'bladish'],
 'baldmoney': ['baldmoney', 'molybdena'],
 'baldness': ['baldness', 'bandless'],
 'baldy': ['badly', 'baldy', 'blady'],
 'bale': ['abel', 'able', 'albe', 'bale', 'beal', 'bela', 'blae'],
 'balearic': ['balearic', 'cebalrai'],
 'baleen': ['baleen', 'enable'],
 'balefire': ['afebrile', 'balefire', 'fireable'],
 'baleise': ['baleise', 'besaiel'],
 'baler': ['abler', 'baler', 'belar', 'blare', 'blear'],
 'balete': ['balete', 'belate'],
 'bali': ['albi', 'bail', 'bali'],
 'baline': ['baline', 'blaine'],
 'balinger': ['balinger', 'ringable'],
 'balker': ['balker', 'barkle'],
 'ballaster': ['ballaster', 'reballast'],
 'ballate': ['ballate', 'tabella'],
 'balli': ['balli', 'billa'],
 'balloter': ['balloter', 'reballot'],
 'ballplayer': ['ballplayer', 'preallably'],
 'ballroom': ['ballroom', 'moorball'],
 'ballweed': ['ballweed', 'weldable'],
 'balm': ['balm', 'lamb'],
 'balminess': ['balminess', 'lambiness'],
 'balmlike': ['balmlike', 'lamblike'],
 'balmy': ['balmy', 'lamby'],
 'balolo': ['balolo', 'lobola'],
 'balonea': ['abalone', 'balonea'],
 'balor': ['balor', 'bolar', 'boral', 'labor', 'lobar'],
 'balow': ['ablow', 'balow', 'bowla'],
 'balsa': ['balas', 'balsa', 'basal', 'sabal'],
 'balsam': ['balsam', 'sambal'],
 'balsamic': ['balsamic', 'cabalism'],
 'balsamo': ['absalom', 'balsamo'],
 'balsamy': ['abysmal', 'balsamy'],
 'balt': ['balt', 'blat'],
 'baltei': ['albeit',
            'albite',
            'baltei',
            'belait',
            'betail',
            'bletia',
            'libate'],
 'balter': ['albert', 'balter', 'labret', 'tabler'],
 'balteus': ['balteus', 'sublate'],
 'baltis': ['baltis', 'bisalt'],
 'balu': ['balu', 'baul', 'bual', 'luba'],
 'balunda': ['balunda', 'bulanda'],
 'baluster': ['baluster', 'rustable'],
 'balut': ['balut', 'tubal'],
 'bam': ['bam', 'mab'],
 'ban': ['ban', 'nab'],
 'bana': ['anba', 'bana'],
 'banak': ['banak', 'nabak'],
 'banal': ['alban', 'balan', 'banal', 'laban', 'nabal', 'nabla'],
 'banat': ['banat', 'batan'],
 'banca': ['banca', 'caban'],
 'bancal': ['bancal', 'blanca'],
 'banco': ['bacon', 'banco'],
 'banda': ['badan', 'banda'],
 'bandage': ['bandage', 'dagbane'],
 'bandar': ['bandar', 'raband'],
 'bandarlog': ['bandarlog', 'langobard'],
 'bande': ['bande', 'benda'],
 'bander': ['bander', 'brenda'],
 'banderma': ['banderma', 'breadman'],
 'banderole': ['banderole', 'bandoleer'],
 'bandhook': ['bandhook', 'handbook'],
 'bandle': ['balden', 'bandle'],
 'bandless': ['baldness', 'bandless'],
 'bando': ['badon', 'bando'],
 'bandoleer': ['banderole', 'bandoleer'],
 'bandor': ['bandor', 'bondar', 'roband'],
 'bandore': ['bandore', 'broaden'],
 'bane': ['bane', 'bean', 'bena'],
 'bangala': ['balagan', 'bangala'],
 'bangalay': ['balangay', 'bangalay'],
 'bangash': ['bangash', 'nashgab'],
 'banger': ['banger', 'engarb', 'graben'],
 'banghy': ['banghy', 'hangby'],
 'bangia': ['bagani', 'bangia', 'ibanag'],
 'bangle': ['bangle', 'bengal'],
 'bani': ['bain', 'bani', 'iban'],
 'banilad': ['balanid', 'banilad'],
 'banisher': ['banisher', 'rebanish'],
 'baniva': ['baniva', 'bavian'],
 'baniya': ['baniya', 'banyai'],
 'banjoist': ['banjoist', 'bostanji'],
 'bank': ['bank', 'knab', 'nabk'],
 'banker': ['banker', 'barken'],
 'banshee': ['banshee', 'benshea'],
 'bantam': ['bantam', 'batman'],
 'banteng': ['banteng', 'bentang'],
 'banyai': ['baniya', 'banyai'],
 'banzai': ['banzai', 'zabian'],
 'bar': ['bar', 'bra', 'rab'],
 'bara': ['arab', 'arba', 'baar', 'bara'],
 'barabra': ['barabra', 'barbara'],
 'barad': ['barad', 'draba'],
 'barb': ['barb', 'brab'],
 'barbara': ['barabra', 'barbara'],
 'barbe': ['barbe', 'bebar', 'breba', 'rebab'],
 'barbed': ['barbed', 'dabber'],
 'barbel': ['barbel', 'labber', 'rabble'],
 'barbet': ['barbet', 'rabbet', 'tabber'],
 'barbette': ['barbette', 'bebatter'],
 'barbion': ['barbion', 'rabboni'],
 'barbitone': ['barbitone', 'barbotine'],
 'barbone': ['barbone', 'bebaron'],
 'barbotine': ['barbitone', 'barbotine'],
 'barcella': ['barcella', 'caballer'],
 'barcoo': ['barcoo', 'baroco'],
 'bard': ['bard', 'brad', 'drab'],
 'bardel': ['balder', 'bardel', 'bedlar', 'bedral', 'belard', 'blader'],
 'bardie': ['abider', 'bardie'],
 'bardily': ['bardily', 'rabidly', 'ridably'],
 'bardiness': ['bardiness', 'rabidness'],
 'barding': ['barding', 'brigand'],
 'bardo': ['abord', 'bardo', 'board', 'broad', 'dobra', 'dorab'],
 'bardy': ['bardy', 'darby'],
 'bare': ['bare', 'bear', 'brae'],
 'barefaced': ['barefaced', 'facebread'],
 'barefoot': ['barefoot', 'bearfoot'],
 'barehanded': ['barehanded', 'bradenhead', 'headbander'],
 'barehead': ['barehead', 'braehead'],
 'barely': ['barely', 'barley', 'bleary'],
 'barer': ['barer', 'rebar'],
 'baretta': ['baretta', 'rabatte', 'tabaret'],
 'bargainer': ['bargainer', 'rebargain'],
 'barge': ['bagre', 'barge', 'begar', 'rebag'],
 'bargeer': ['bargeer', 'gerbera'],
 'bargeese': ['bargeese', 'begrease'],
 'bari': ['abir', 'bari', 'rabi'],
 'baric': ['baric', 'carib', 'rabic'],
 'barid': ['barid', 'bidar', 'braid', 'rabid'],
 'barie': ['barie', 'beira', 'erbia', 'rebia'],
 'barile': ['bailer', 'barile'],
 'baris': ['baris', 'sabir'],
 'barish': ['barish', 'shibar'],
 'barit': ['barit', 'ribat'],
 'barite': ['baiter', 'barite', 'rebait', 'terbia'],
 'baritone': ['abrotine', 'baritone', 'obtainer', 'reobtain'],
 'barken': ['banker', 'barken'],
 'barker': ['barker', 'braker'],
 'barkey': ['bakery', 'barkey'],
 'barkle': ['balker', 'barkle'],
 'barky': ['barky', 'braky'],
 'barley': ['barely', 'barley', 'bleary'],
 'barling': ['barling', 'bringal'],
 'barm': ['barm', 'bram'],
 'barmbrack': ['barmbrack', 'brambrack'],
 'barmote': ['barmote', 'bromate'],
 'barmy': ['ambry', 'barmy'],
 'barn': ['barn', 'bran'],
 'barnabite': ['barnabite', 'rabbanite', 'rabbinate'],
 'barnacle': ['balancer', 'barnacle'],
 'barney': ['barney', 'nearby'],
 'barny': ['barny', 'bryan'],
 'baroco': ['barcoo', 'baroco'],
 'barolo': ['barolo', 'robalo'],
 'baron': ['baron', 'boran'],
 'baronet': ['baronet', 'reboant'],
 'barong': ['barong', 'brogan'],
 'barosmin': ['ambrosin', 'barosmin', 'sabromin'],
 'barothermograph': ['barothermograph', 'thermobarograph'],
 'barotse': ['barotse', 'boaster', 'reboast', 'sorbate'],
 'barpost': ['absorpt', 'barpost'],
 'barracan': ['barracan', 'barranca'],
 'barranca': ['barracan', 'barranca'],
 'barrelet': ['barrelet', 'terebral'],
 'barret': ['barret', 'barter'],
 'barrette': ['barrette', 'batterer'],
 'barrio': ['barrio', 'brairo'],
 'barsac': ['barsac', 'scarab'],
 'barse': ['barse', 'besra', 'saber', 'serab'],
 'bart': ['bart', 'brat'],
 'barter': ['barret', 'barter'],
 'barton': ['barton', 'brotan'],
 'bartsia': ['arabist', 'bartsia'],
 'barundi': ['barundi', 'unbraid'],
 'barvel': ['barvel', 'blaver', 'verbal'],
 'barwise': ['barwise', 'swarbie'],
 'barye': ['barye', 'beray', 'yerba'],
 'baryta': ['baryta', 'taryba'],
 'barytine': ['barytine', 'bryanite'],
 'baryton': ['baryton', 'brotany'],
 'bas': ['bas', 'sab'],
 'basal': ['balas', 'balsa', 'basal', 'sabal'],
 'basally': ['basally', 'salably'],
 'basaltic': ['basaltic', 'cabalist'],
 'basaltine': ['balanites', 'basaltine', 'stainable'],
 'base': ['base', 'besa', 'sabe', 'seba'],
 'basella': ['basella', 'sabella', 'salable'],
 'bash': ['bash', 'shab'],
 'basial': ['basial', 'blasia'],
 'basic': ['bacis', 'basic'],
 'basically': ['asyllabic', 'basically'],
 'basidium': ['basidium', 'diiambus'],
 'basil': ['basil', 'labis'],
 'basileus': ['basileus', 'issuable', 'suasible'],
 'basilweed': ['basilweed', 'bladewise'],
 'basinasal': ['basinasal', 'bassalian'],
 'basinet': ['basinet', 'besaint', 'bestain'],
 'basion': ['basion', 'bonsai', 'sabino'],
 'basiparachromatin': ['basiparachromatin', 'marsipobranchiata'],
 'basket': ['basket', 'betask'],
 'basketwork': ['basketwork', 'workbasket'],
 'basos': ['basos', 'basso'],
 'bassalian': ['basinasal', 'bassalian'],
 'bassanite': ['bassanite', 'sebastian'],
 'basset': ['asbest', 'basset'],
 'basso': ['basos', 'basso'],
 'bast': ['bast', 'bats', 'stab'],
 'basta': ['basta', 'staab'],
 'baste': ['baste', 'beast', 'tabes'],
 'basten': ['absent', 'basten'],
 'baster': ['baster', 'bestar', 'breast'],
 'bastille': ['bastille', 'listable'],
 'bastion': ['abiston', 'bastion'],
 'bastionet': ['bastionet', 'obstinate'],
 'bastite': ['bastite', 'batiste', 'bistate'],
 'basto': ['basto', 'boast', 'sabot'],
 'basuto': ['abouts', 'basuto'],
 'bat': ['bat', 'tab'],
 'batad': ['abdat', 'batad'],
 'batakan': ['bakatan', 'batakan'],
 'bataleur': ['bataleur', 'tabulare'],
 'batan': ['banat', 'batan'],
 'batara': ['artaba', 'batara'],
 'batcher': ['batcher', 'berchta', 'brachet'],
 'bate': ['abet', 'bate', 'beat', 'beta'],
 'batea': ['abate', 'ateba', 'batea', 'beata'],
 'batel': ['batel', 'blate', 'bleat', 'table'],
 'batement': ['abetment', 'batement'],
 'bater': ['abret', 'bater', 'berat'],
 'batfowler': ['afterblow', 'batfowler'],
 'bath': ['baht', 'bath', 'bhat'],
 'bathala': ['baalath', 'bathala'],
 'bathe': ['bathe', 'beath'],
 'bather': ['bather', 'bertha', 'breath'],
 'bathonian': ['bathonian', 'nabothian'],
 'batik': ['batik', 'kitab'],
 'batino': ['batino', 'oatbin', 'obtain'],
 'batis': ['absit', 'batis'],
 'batiste': ['bastite', 'batiste', 'bistate'],
 'batling': ['batling', 'tabling'],
 'batman': ['bantam', 'batman'],
 'batophobia': ['batophobia', 'tabophobia'],
 'batrachia': ['batrachia', 'brachiata'],
 'batrachian': ['batrachian', 'branchiata'],
 'bats': ['bast', 'bats', 'stab'],
 'battel': ['battel', 'battle', 'tablet'],
 'batteler': ['batteler', 'berattle'],
 'battening': ['battening', 'bitangent'],
 'batter': ['batter', 'bertat', 'tabret', 'tarbet'],
 'batterer': ['barrette', 'batterer'],
 'battle': ['battel', 'battle', 'tablet'],
 'battler': ['battler', 'blatter', 'brattle'],
 'battue': ['battue', 'tubate'],
 'batule': ['batule', 'betula', 'tabule'],
 'batyphone': ['batyphone', 'hypnobate'],
 'batzen': ['batzen', 'bezant', 'tanzeb'],
 'baud': ['baud', 'buda', 'daub'],
 'baul': ['balu', 'baul', 'bual', 'luba'],
 'baun': ['baun', 'buna', 'nabu', 'nuba'],
 'bauta': ['abuta', 'bauta'],
 'bavian': ['baniva', 'bavian'],
 'baw': ['baw', 'wab'],
 'bawl': ['bawl', 'blaw'],
 'bawler': ['bawler', 'brelaw', 'rebawl', 'warble'],
 'bay': ['aby', 'bay'],
 'baya': ['baya', 'yaba'],
 'bayed': ['bayed', 'beady', 'beday'],
 'baylet': ['baetyl', 'baylet', 'bleaty'],
 'bayonet': ['bayonet', 'betoyan'],
 'baze': ['baze', 'ezba'],
 'bea': ['abe', 'bae', 'bea'],
 'beach': ['bache', 'beach'],
 'bead': ['abed', 'bade', 'bead'],
 'beaded': ['beaded', 'bedead'],
 'beader': ['beader', 'bedare'],
 'beadleism': ['beadleism', 'demisable'],
 'beadlet': ['beadlet', 'belated'],
 'beady': ['bayed', 'beady', 'beday'],
 'beagle': ['beagle', 'belage', 'belgae'],
 'beak': ['bake', 'beak'],
 'beaker': ['beaker', 'berake', 'rebake'],
 'beal': ['abel', 'able', 'albe', 'bale', 'beal', 'bela', 'blae'],
 'bealing': ['algenib', 'bealing', 'belgian', 'bengali'],
 'beam': ['beam', 'bema'],
 'beamer': ['ambeer', 'beamer'],
 'beamless': ['assemble', 'beamless'],
 'beamster': ['beamster', 'bemaster', 'bestream'],
 'beamwork': ['beamwork', 'bowmaker'],
 'beamy': ['beamy', 'embay', 'maybe'],
 'bean': ['bane', 'bean', 'bena'],
 'beanfield': ['beanfield', 'definable'],
 'beant': ['abnet', 'beant'],
 'bear': ['bare', 'bear', 'brae'],
 'bearance': ['bearance', 'carabeen'],
 'beard': ['ardeb', 'beard', 'bread', 'debar'],
 'beardless': ['beardless', 'breadless'],
 'beardlessness': ['beardlessness', 'breadlessness'],
 'bearer': ['bearer', 'rebear'],
 'bearess': ['bearess', 'bessera'],
 'bearfoot': ['barefoot', 'bearfoot'],
 'bearing': ['bearing', 'begrain', 'brainge', 'rigbane'],
 'bearlet': ['bearlet', 'bleater', 'elberta', 'retable'],
 'bearm': ['amber', 'bearm', 'bemar', 'bream', 'embar'],
 'beast': ['baste', 'beast', 'tabes'],
 'beastlily': ['beastlily', 'bestially'],
 'beat': ['abet', 'bate', 'beat', 'beta'],
 'beata': ['abate', 'ateba', 'batea', 'beata'],
 'beater': ['beater', 'berate', 'betear', 'rebate', 'rebeat'],
 'beath': ['bathe', 'beath'],
 'beating': ['baignet', 'beating'],
 'beau': ['aube', 'beau'],
 'bebait': ['babite', 'bebait'],
 'bebar': ['barbe', 'bebar', 'breba', 'rebab'],
 'bebaron': ['barbone', 'bebaron'],
 'bebaste': ['bebaste', 'bebeast'],
 'bebatter': ['barbette', 'bebatter'],
 'bebay': ['abbey', 'bebay'],
 'bebeast': ['bebaste', 'bebeast'],
 'bebog': ['bebog', 'begob', 'gobbe'],
 'becard': ['becard', 'braced'],
 'becater': ['becater', 'betrace'],
 'because': ['because', 'besauce'],
 'becharm': ['becharm', 'brecham', 'chamber'],
 'becher': ['becher', 'breech'],
 'bechern': ['bechern', 'bencher'],
 'bechirp': ['bechirp', 'brephic'],
 'becker': ['becker', 'rebeck'],
 'beclad': ['beclad', 'cabled'],
 'beclart': ['beclart', 'crablet'],
 'becloud': ['becloud', 'obclude'],
 'becram': ['becram', 'camber', 'crambe'],
 'becrimson': ['becrimson', 'scombrine'],
 'becry': ['becry', 'bryce'],
 'bed': ['bed', 'deb'],
 'bedamn': ['bedamn', 'bedman'],
 'bedare': ['beader', 'bedare'],
 'bedark': ['bedark', 'debark'],
 'beday': ['bayed', 'beady', 'beday'],
 'bedead': ['beaded', 'bedead'],
 'bedel': ['bedel', 'bleed'],
 'beden': ['beden', 'deben', 'deneb'],
 'bedim': ['bedim', 'imbed'],
 'bedip': ['bedip', 'biped'],
 'bedismal': ['bedismal', 'semibald'],
 'bedlam': ['bedlam', 'beldam', 'blamed'],
 'bedlar': ['balder', 'bardel', 'bedlar', 'bedral', 'belard', 'blader'],
 'bedless': ['bedless', 'blessed'],
 'bedman': ['bedamn', 'bedman'],
 'bedoctor': ['bedoctor', 'codebtor'],
 'bedog': ['bedog', 'bodge'],
 'bedrail': ['bedrail', 'bridale', 'ridable'],
 'bedral': ['balder', 'bardel', 'bedlar', 'bedral', 'belard', 'blader'],
 'bedrid': ['bedrid', 'bidder'],
 'bedrip': ['bedrip', 'prebid'],
 'bedrock': ['bedrock', 'brocked'],
 'bedroom': ['bedroom', 'boerdom', 'boredom'],
 'bedrown': ['bedrown', 'browden'],
 'bedrug': ['bedrug', 'budger'],
 'bedsick': ['bedsick', 'sickbed'],
 'beduck': ['beduck', 'bucked'],
 'bedur': ['bedur', 'rebud', 'redub'],
 'bedusk': ['bedusk', 'busked'],
 'bedust': ['bedust', 'bestud', 'busted'],
 'beearn': ['beearn', 'berean'],
 'beeman': ['beeman', 'bemean', 'bename'],
 'been': ['been', 'bene', 'eben'],
 'beer': ['beer', 'bere', 'bree'],
 'beest': ['beest', 'beset'],
 'beeswing': ['beeswing', 'beswinge'],
 'befathered': ['befathered', 'featherbed'],
 'befile': ['befile', 'belief'],
 'befinger': ['befinger', 'befringe'],
 'beflea': ['beflea', 'beleaf'],
 'beflour': ['beflour', 'fourble'],
 'beflum': ['beflum', 'fumble'],
 'befret': ['befret', 'bereft'],
 'befringe': ['befinger', 'befringe'],
 'begad': ['badge', 'begad'],
 'begall': ['begall', 'glebal'],
 'begar': ['bagre', 'barge', 'begar', 'rebag'],
 'begash': ['begash', 'beshag'],
 'begat': ['begat', 'betag'],
 'begettal': ['begettal', 'gettable'],
 'beggar': ['bagger', 'beggar'],
 'beggarer': ['beggarer', 'rebeggar'],
 'begin': ['begin', 'being', 'binge'],
 'begird': ['begird', 'bridge'],
 'beglic': ['beglic', 'belgic'],
 'bego': ['bego', 'egbo'],
 'begob': ['bebog', 'begob', 'gobbe'],
 'begone': ['begone', 'engobe'],
 'begrain': ['bearing', 'begrain', 'brainge', 'rigbane'],
 'begrease': ['bargeese', 'begrease'],
 'behaviorism': ['behaviorism', 'misbehavior'],
 'behears': ['behears', 'beshear'],
 'behint': ['behint', 'henbit'],
 'beholder': ['beholder', 'rebehold'],
 'behorn': ['behorn', 'brehon'],
 'beid': ['beid', 'bide', 'debi', 'dieb'],
 'being': ['begin', 'being', 'binge'],
 'beira': ['barie', 'beira', 'erbia', 'rebia'],
 'beisa': ['abies', 'beisa'],
 'bel': ['bel', 'elb'],
 'bela': ['abel', 'able', 'albe', 'bale', 'beal', 'bela', 'blae'],
 'belabor': ['belabor', 'borable'],
 'belaced': ['belaced', 'debacle'],
 'belage': ['beagle', 'belage', 'belgae'],
 'belait': ['albeit',
            'albite',
            'baltei',
            'belait',
            'betail',
            'bletia',
            'libate'],
 'belam': ['amble', 'belam', 'blame', 'mabel'],
 'belar': ['abler', 'baler', 'belar', 'blare', 'blear'],
 'belard': ['balder', 'bardel', 'bedlar', 'bedral', 'belard', 'blader'],
 'belate': ['balete', 'belate'],
 'belated': ['beadlet', 'belated'],
 'belaud': ['ablude', 'belaud'],
 'beldam': ['bedlam', 'beldam', 'blamed'],
 'beleaf': ['beflea', 'beleaf'],
 'beleap': ['beleap', 'bepale'],
 'belga': ['bagel', 'belga', 'gable', 'gleba'],
 'belgae': ['beagle', 'belage', 'belgae'],
 'belgian': ['algenib', 'bealing', 'belgian', 'bengali'],
 'belgic': ['beglic', 'belgic'],
 'belial': ['alible', 'belial', 'labile', 'liable'],
 'belief': ['befile', 'belief'],
 'belili': ['belili', 'billie'],
 'belite': ['belite', 'beltie', 'bietle'],
 'belitter': ['belitter', 'tribelet'],
 'belive': ['belive', 'beveil'],
 'bella': ['bella', 'label'],
 'bellied': ['bellied', 'delible'],
 'bellona': ['allbone', 'bellona'],
 'bellonian': ['bellonian', 'nonliable'],
 'bellote': ['bellote', 'lobelet'],
 'bellower': ['bellower', 'rebellow'],
 'belltail': ['belltail', 'bletilla', 'tillable'],
 'bellyer': ['bellyer', 'rebelly'],
 'bellypinch': ['bellypinch', 'pinchbelly'],
 'beloid': ['beloid', 'boiled', 'bolide'],
 'belonger': ['belonger', 'rebelong'],
 'belonid': ['belonid', 'boldine'],
 'belord': ['belord', 'bordel', 'rebold'],
 'below': ['below', 'bowel', 'elbow'],
 'belt': ['belt', 'blet'],
 'beltane': ['beltane', 'tenable'],
 'belter': ['belter', 'elbert', 'treble'],
 'beltie': ['belite', 'beltie', 'bietle'],
 'beltine': ['beltine', 'tenible'],
 'beltir': ['beltir', 'riblet'],
 'beltman': ['beltman', 'lambent'],
 'belve': ['belve', 'bevel'],
 'bema': ['beam', 'bema'],
 'bemail': ['bemail', 'lambie'],
 'beman': ['beman', 'nambe'],
 'bemar': ['amber', 'bearm', 'bemar', 'bream', 'embar'],
 'bemaster': ['beamster', 'bemaster', 'bestream'],
 'bemaul': ['bemaul', 'blumea'],
 'bemeal': ['bemeal', 'meable'],
 'bemean': ['beeman', 'bemean', 'bename'],
 'bemire': ['bemire', 'bireme'],
 'bemitred': ['bemitred', 'timbered'],
 'bemoil': ['bemoil', 'mobile'],
 'bemole': ['bemole', 'embole'],
 'bemusk': ['bemusk', 'embusk'],
 'ben': ['ben', 'neb'],
 'bena': ['bane', 'bean', 'bena'],
 'benacus': ['acubens', 'benacus'],
 'bename': ['beeman', 'bemean', 'bename'],
 'benami': ['benami', 'bimane'],
 'bencher': ['bechern', 'bencher'],
 'benchwork': ['benchwork', 'workbench'],
 'benda': ['bande', 'benda'],
 'bender': ['bender', 'berend', 'rebend'],
 'bene': ['been', 'bene', 'eben'],
 'benedight': ['benedight', 'benighted'],
 'benefiter': ['benefiter', 'rebenefit'],
 'bengal': ['bangle', 'bengal'],
 'bengali': ['algenib', 'bealing', 'belgian', 'bengali'],
 'beni': ['beni', 'bien', 'bine', 'inbe'],
 'benighted': ['benedight', 'benighted'],
 'beno': ['beno', 'bone', 'ebon'],
 'benote': ['benote', 'betone'],
 'benshea': ['banshee', 'benshea'],
 'benshee': ['benshee', 'shebeen'],
 'bentang': ['banteng', 'bentang'],
 'benton': ['benton', 'bonnet'],
 'benu': ['benu', 'unbe'],
 'benward': ['benward', 'brawned'],
 'benzantialdoxime': ['antibenzaldoxime', 'benzantialdoxime'],
 'benzein': ['benzein', 'benzine'],
 'benzine': ['benzein', 'benzine'],
 'benzo': ['benzo', 'bonze'],
 'benzofluorene': ['benzofluorene', 'fluorobenzene'],
 'benzonitrol': ['benzonitrol', 'nitrobenzol'],
 'bepale': ['beleap', 'bepale'],
 'bepart': ['bepart', 'berapt', 'betrap'],
 'bepaste': ['bepaste', 'bespate'],
 'bepester': ['bepester', 'prebeset'],
 'beplaster': ['beplaster', 'prestable'],
 'ber': ['ber', 'reb'],
 'berake': ['beaker', 'berake', 'rebake'],
 'berapt': ['bepart', 'berapt', 'betrap'],
 'berat': ['abret', 'bater', 'berat'],
 'berate': ['beater', 'berate', 'betear', 'rebate', 'rebeat'],
 'berattle': ['batteler', 'berattle'],
 'beraunite': ['beraunite', 'unebriate'],
 'beray': ['barye', 'beray', 'yerba'],
 'berberi': ['berberi', 'rebribe'],
 'berchta': ['batcher', 'berchta', 'brachet'],
 'bere': ['beer', 'bere', 'bree'],
 'berean': ['beearn', 'berean'],
 'bereft': ['befret', 'bereft'],
 'berend': ['bender', 'berend', 'rebend'],
 'berg': ['berg', 'gerb'],
 'bergama': ['bergama', 'megabar'],
 'bergamo': ['bergamo', 'embargo'],
 'beri': ['beri', 'bier', 'brei', 'ribe'],
 'beringed': ['beringed', 'breeding'],
 'berinse': ['berinse', 'besiren'],
 'berley': ['berley', 'bleery'],
 'berlinite': ['berlinite', 'libertine'],
 'bermudite': ['bermudite', 'demibrute'],
 'bernard': ['bernard', 'brander', 'rebrand'],
 'bernese': ['bernese', 'besneer'],
 'beroe': ['beroe', 'boree'],
 'beroida': ['beroida', 'boreiad'],
 'beroll': ['beroll', 'boller'],
 'berossos': ['berossos', 'obsessor'],
 'beround': ['beround', 'bounder', 'rebound', 'unbored', 'unorbed', 'unrobed'],
 'berri': ['berri', 'brier'],
 'berried': ['berried', 'briered'],
 'berrybush': ['berrybush', 'shrubbery'],
 'bersil': ['bersil', 'birsle'],
 'bert': ['bert', 'bret'],
 'bertat': ['batter', 'bertat', 'tabret', 'tarbet'],
 'berth': ['berth', 'breth'],
 'bertha': ['bather', 'bertha', 'breath'],
 'berther': ['berther', 'herbert'],
 'berthing': ['berthing', 'brighten'],
 'bertie': ['bertie', 'betire', 'rebite'],
 'bertolonia': ['bertolonia', 'borolanite'],
 'berust': ['berust', 'buster', 'stuber'],
 'bervie': ['bervie', 'brieve'],
 'beryllia': ['beryllia', 'reliably'],
 'besa': ['base', 'besa', 'sabe', 'seba'],
 'besaiel': ['baleise', 'besaiel'],
 'besaint': ['basinet', 'besaint', 'bestain'],
 'besauce': ['because', 'besauce'],
 'bescour': ['bescour', 'buceros', 'obscure'],
 'beset': ['beest', 'beset'],
 'beshadow': ['beshadow', 'bodewash'],
 'beshag': ['begash', 'beshag'],
 'beshear': ['behears', 'beshear'],
 'beshod': ['beshod', 'debosh'],
 'besiren': ['berinse', 'besiren'],
 'besit': ['besit', 'betis'],
 'beslaver': ['beslaver', 'servable', 'versable'],
 'beslime': ['beslime', 'besmile'],
 'beslings': ['beslings', 'blessing', 'glibness'],
 'beslow': ['beslow', 'bowels'],
 'besmile': ['beslime', 'besmile'],
 'besneer': ['bernese', 'besneer'],
 'besoot': ['besoot', 'bootes'],
 'besot': ['besot', 'betso'],
 'besoul': ['besoul', 'blouse', 'obelus'],
 'besour': ['besour', 'boreus', 'bourse', 'bouser'],
 'bespate': ['bepaste', 'bespate'],
 'besra': ['barse', 'besra', 'saber', 'serab'],
 'bessera': ['bearess', 'bessera'],
 'bestain': ['basinet', 'besaint', 'bestain'],
 'bestar': ['baster', 'bestar', 'breast'],
 'besteer': ['besteer', 'rebeset'],
 'bestial': ['astilbe', 'bestial', 'blastie', 'stabile'],
 'bestially': ['beastlily', 'bestially'],
 'bestiarian': ['antirabies', 'bestiarian'],
 'bestiary': ['bestiary', 'sybarite'],
 'bestir': ['bestir', 'bister'],
 'bestorm': ['bestorm', 'mobster'],
 'bestowal': ['bestowal', 'stowable'],
 'bestower': ['bestower', 'rebestow'],
 'bestraw': ['bestraw', 'wabster'],
 'bestream': ['beamster', 'bemaster', 'bestream'],
 'bestrew': ['bestrew', 'webster'],
 'bestride': ['bestride', 'bistered'],
 'bestud': ['bedust', 'bestud', 'busted'],
 'beswinge': ['beeswing', 'beswinge'],
 'beta': ['abet', 'bate', 'beat', 'beta'],
 'betag': ['begat', 'betag'],
 'betail': ['albeit',
            'albite',
            'baltei',
            'belait',
            'betail',
            'bletia',
            'libate'],
 'betailor': ['betailor', 'laborite', 'orbitale'],
 'betask': ['basket', 'betask'],
 'betear': ['beater', 'berate', 'betear', 'rebate', 'rebeat'],
 'beth': ['beth', 'theb'],
 'betire': ['bertie', 'betire', 'rebite'],
 'betis': ['besit', 'betis'],
 'betone': ['benote', 'betone'],
 'betoss': ['betoss', 'bosset'],
 'betoya': ['betoya', 'teaboy'],
 'betoyan': ['bayonet', 'betoyan'],
 'betrace': ['becater', 'betrace'],
 'betrail': ['betrail', 'librate', 'triable', 'trilabe'],
 'betrap': ['bepart', 'berapt', 'betrap'],
 'betrayal': ['betrayal', 'tearably'],
 'betrayer': ['betrayer', 'eatberry', 'rebetray', 'teaberry'],
 'betread': ['betread', 'debater'],
 'betrim': ['betrim', 'timber', 'timbre'],
 'betso': ['besot', 'betso'],
 'betta': ['betta', 'tabet'],
 'bettina': ['bettina', 'tabinet', 'tibetan'],
 'betula': ['batule', 'betula', 'tabule'],
 'betulin': ['betulin', 'bluntie'],
 'beturbaned': ['beturbaned', 'unrabbeted'],
 'beveil': ['belive', 'beveil'],
 'bevel': ['belve', 'bevel'],
 'bever': ['bever', 'breve'],
 'bewailer': ['bewailer', 'rebewail'],
 'bework': ['bework', 'bowker'],
 'bey': ['bey', 'bye'],
 'beydom': ['beydom', 'embody'],
 'bezant': ['batzen', 'bezant', 'tanzeb'],
 'bezzo': ['bezzo', 'bozze'],
 'bhakti': ['bhakti', 'khatib'],
 'bhandari': ['bhandari', 'hairband'],
 'bhar': ['bhar', 'harb'],
 'bhara': ['bahar', 'bhara'],
 'bhat': ['baht', 'bath', 'bhat'],
 'bhima': ['bhima', 'biham'],
 'bhotia': ['bhotia', 'tobiah'],
 'bhutani': ['bhutani', 'unhabit'],
 'biacetyl': ['baetylic', 'biacetyl'],
 'bialate': ['baalite', 'bialate', 'labiate'],
 'bialveolar': ['bialveolar', 'labiovelar'],
 'bianca': ['abanic', 'bianca'],
 'bianco': ['bianco', 'bonaci'],
 'biangular': ['biangular', 'bulgarian'],
 'bias': ['absi', 'bais', 'bias', 'isba'],
 'biatomic': ['biatomic', 'moabitic'],
 'bible': ['bible', 'blibe'],
 'bicarpellary': ['bicarpellary', 'prebacillary'],
 'bickern': ['bickern', 'bricken'],
 'biclavate': ['activable', 'biclavate'],
 'bicorn': ['bicorn', 'bicron'],
 'bicornate': ['bicornate', 'carbonite', 'reboantic'],
 'bicrenate': ['abenteric', 'bicrenate'],
 'bicron': ['bicorn', 'bicron'],
 'bicrural': ['bicrural', 'rubrical'],
 'bid': ['bid', 'dib'],
 'bidar': ['barid', 'bidar', 'braid', 'rabid'],
 'bidder': ['bedrid', 'bidder'],
 'bide': ['beid', 'bide', 'debi', 'dieb'],
 'bident': ['bident', 'indebt'],
 'bidented': ['bidented', 'indebted'],
 'bider': ['bider', 'bredi', 'bride', 'rebid'],
 'bidet': ['bidet', 'debit'],
 'biduous': ['biduous', 'dubious'],
 'bien': ['beni', 'bien', 'bine', 'inbe'],
 'bier': ['beri', 'bier', 'brei', 'ribe'],
 'bietle': ['belite', 'beltie', 'bietle'],
 'bifer': ['bifer', 'brief', 'fiber'],
 'big': ['big', 'gib'],
 'biga': ['agib', 'biga', 'gabi'],
 'bigamous': ['bigamous', 'subimago'],
 'bigener': ['bigener', 'rebegin'],
 'bigential': ['bigential', 'tangibile'],
 'biggin': ['biggin', 'gibing'],
 'bigoted': ['bigoted', 'dogbite'],
 'biham': ['bhima', 'biham'],
 'bihari': ['bihari', 'habiri'],
 'bike': ['bike', 'kibe'],
 'bikram': ['bikram', 'imbark'],
 'bilaan': ['albian', 'bilaan'],
 'bilaterality': ['alterability', 'bilaterality', 'relatability'],
 'bilati': ['bilati', 'tibial'],
 'bilby': ['bilby', 'libby'],
 'bildar': ['bildar', 'bridal', 'ribald'],
 'bilge': ['bilge', 'gibel'],
 'biliate': ['biliate', 'tibiale'],
 'bilinear': ['bilinear', 'liberian'],
 'billa': ['balli', 'billa'],
 'billboard': ['billboard', 'broadbill'],
 'biller': ['biller', 'rebill'],
 'billeter': ['billeter', 'rebillet'],
 'billie': ['belili', 'billie'],
 'bilo': ['bilo', 'boil'],
 'bilobated': ['bilobated', 'bobtailed'],
 'biltong': ['biltong', 'bolting'],
 'bim': ['bim', 'mib'],
 'bimane': ['benami', 'bimane'],
 'bimodality': ['bimodality', 'myliobatid'],
 'bimotors': ['bimotors', 'robotism'],
 'bin': ['bin', 'nib'],
 'binal': ['albin', 'binal', 'blain'],
 'binary': ['binary', 'brainy'],
 'binder': ['binder', 'inbred', 'rebind'],
 'bindwood': ['bindwood', 'woodbind'],
 'bine': ['beni', 'bien', 'bine', 'inbe'],
 'binge': ['begin', 'being', 'binge'],
 'bino': ['bino', 'bion', 'boni'],
 'binocular': ['binocular', 'caliburno', 'colubrina'],
 'binomial': ['binomial', 'mobilian'],
 'binuclear': ['binuclear', 'incurable'],
 'biod': ['biod', 'boid'],
 'bion': ['bino', 'bion', 'boni'],
 'biopsychological': ['biopsychological', 'psychobiological'],
 'biopsychology': ['biopsychology', 'psychobiology'],
 'bioral': ['bailor', 'bioral'],
 'biorgan': ['biorgan', 'grobian'],
 'bios': ['bios', 'bois'],
 'biosociological': ['biosociological', 'sociobiological'],
 'biota': ['biota', 'ibota'],
 'biotics': ['biotics', 'cobitis'],
 'bipartile': ['bipartile', 'pretibial'],
 'biped': ['bedip', 'biped'],
 'bipedal': ['bipedal', 'piebald'],
 'bipersonal': ['bipersonal', 'prisonable'],
 'bipolar': ['bipolar', 'parboil'],
 'biracial': ['biracial', 'cibarial'],
 'birchen': ['birchen', 'brichen'],
 'bird': ['bird', 'drib'],
 'birdeen': ['birdeen', 'inbreed'],
 'birdlet': ['birdlet', 'driblet'],
 'birdling': ['birdling', 'bridling', 'lingbird'],
 'birdman': ['birdman', 'manbird'],
 'birdseed': ['birdseed', 'seedbird'],
 'birdstone': ['birdstone', 'stonebird'],
 'bireme': ['bemire', 'bireme'],
 'biretta': ['biretta', 'brattie', 'ratbite'],
 'birle': ['birle', 'liber'],
 'birma': ['abrim', 'birma'],
 'birn': ['birn', 'brin'],
 'birny': ['birny', 'briny'],
 'biron': ['biron', 'inorb', 'robin'],
 'birse': ['birse', 'ribes'],
 'birsle': ['bersil', 'birsle'],
 'birth': ['birth', 'brith'],
 'bis': ['bis', 'sib'],
 'bisalt': ['baltis', 'bisalt'],
 'bisaltae': ['bisaltae', 'satiable'],
 'bisharin': ['bairnish', 'bisharin'],
 'bistate': ['bastite', 'batiste', 'bistate'],
 'bister': ['bestir', 'bister'],
 'bistered': ['bestride', 'bistered'],
 'bisti': ['bisti', 'bitis'],
 'bisulcate': ['baculites', 'bisulcate'],
 'bit': ['bit', 'tib'],
 'bitangent': ['battening', 'bitangent'],
 'bitemporal': ['bitemporal', 'importable'],
 'biter': ['biter', 'tribe'],
 'bitis': ['bisti', 'bitis'],
 'bito': ['bito', 'obit'],
 'bitonality': ['bitonality', 'notability'],
 'bittern': ['bittern', 'britten'],
 'bitumed': ['bitumed', 'budtime'],
 'biurate': ['abiuret', 'aubrite', 'biurate', 'rubiate'],
 'biwa': ['biwa', 'wabi'],
 'bizarre': ['bizarre', 'brazier'],
 'bizet': ['bizet', 'zibet'],
 'blabber': ['babbler', 'blabber', 'brabble'],
 'blackacre': ['blackacre', 'crackable'],
 'blad': ['bald', 'blad'],
 'blader': ['balder', 'bardel', 'bedlar', 'bedral', 'belard', 'blader'],
 'bladewise': ['basilweed', 'bladewise'],
 'bladish': ['baldish', 'bladish'],
 'blady': ['badly', 'baldy', 'blady'],
 'blae': ['abel', 'able', 'albe', 'bale', 'beal', 'bela', 'blae'],
 'blaeberry': ['blaeberry', 'bleaberry'],
 'blaeness': ['ableness', 'blaeness', 'sensable'],
 'blain': ['albin', 'binal', 'blain'],
 'blaine': ['baline', 'blaine'],
 'blair': ['blair', 'brail', 'libra'],
 'blake': ['blake', 'bleak', 'kabel'],
 'blame': ['amble', 'belam', 'blame', 'mabel'],
 'blamed': ['bedlam', 'beldam', 'blamed'],
 'blamer': ['ambler', 'blamer', 'lamber', 'marble', 'ramble'],
 'blaming': ['ambling', 'blaming'],
 'blamingly': ['amblingly', 'blamingly'],
 'blanca': ['bancal', 'blanca'],
 'blare': ['abler', 'baler', 'belar', 'blare', 'blear'],
 'blarina': ['blarina', 'branial'],
 'blarney': ['blarney', 'renably'],
 'blas': ['blas', 'slab'],
 'blase': ['blase', 'sable'],
 'blasia': ['basial', 'blasia'],
 'blastema': ['blastema', 'lambaste'],
 'blastemic': ['blastemic', 'cembalist'],
 'blaster': ['blaster', 'reblast', 'stabler'],
 'blastie': ['astilbe', 'bestial', 'blastie', 'stabile'],
 'blasting': ['blasting', 'stabling'],
 'blastoderm': ['blastoderm', 'dermoblast'],
 'blastogenic': ['blastogenic', 'genoblastic'],
 'blastomeric': ['blastomeric', 'meroblastic'],
 'blastomycetic': ['blastomycetic', 'cytoblastemic'],
 'blastomycetous': ['blastomycetous', 'cytoblastemous'],
 'blasty': ['blasty', 'stably'],
 'blat': ['balt', 'blat'],
 'blate': ['batel', 'blate', 'bleat', 'table'],
 'blather': ['blather', 'halbert'],
 'blatter': ['battler', 'blatter', 'brattle'],
 'blaver': ['barvel', 'blaver', 'verbal'],
 'blaw': ['bawl', 'blaw'],
 'blay': ['ably', 'blay', 'yalb'],
 'blazoner': ['albronze', 'blazoner'],
 'bleaberry': ['blaeberry', 'bleaberry'],
 'bleach': ['bachel', 'bleach'],
 'bleacher': ['bleacher', 'rebleach'],
 'bleak': ['blake', 'bleak', 'kabel'],
 'bleaky': ['bleaky', 'kabyle'],
 'blear': ['abler', 'baler', 'belar', 'blare', 'blear'],
 'bleared': ['bleared', 'reblade'],
 'bleary': ['barely', 'barley', 'bleary'],
 'bleat': ['batel', 'blate', 'bleat', 'table'],
 'bleater': ['bearlet', 'bleater', 'elberta', 'retable'],
 'bleating': ['bleating', 'tangible'],
 'bleaty': ['baetyl', 'baylet', 'bleaty'],
 'bleed': ['bedel', 'bleed'],
 'bleery': ['berley', 'bleery'],
 'blender': ['blender', 'reblend'],
 'blendure': ['blendure', 'rebundle'],
 'blennoid': ['blennoid', 'blondine'],
 'blennoma': ['blennoma', 'nobleman'],
 'bleo': ['bleo', 'bole', 'lobe'],
 'blepharocera': ['blepharocera', 'reproachable'],
 'blessed': ['bedless', 'blessed'],
 'blesser': ['blesser', 'rebless'],
 'blessing': ['beslings', 'blessing', 'glibness'],
 'blet': ['belt', 'blet'],
 'bletia': ['albeit',
            'albite',
            'baltei',
            'belait',
            'betail',
            'bletia',
            'libate'],
 'bletilla': ['belltail', 'bletilla', 'tillable'],
 'blibe': ['bible', 'blibe'],
 'blighter': ['blighter', 'therblig'],
 'blimy': ['blimy', 'limby'],
 'blister': ['blister', 'bristle'],
 'blisterwort': ['blisterwort', 'bristlewort'],
 'blitter': ['blitter', 'brittle', 'triblet'],
 'blo': ['blo', 'lob'],
 'bloated': ['bloated', 'lobated'],
 'bloater': ['alberto', 'bloater', 'latrobe'],
 'bloating': ['bloating', 'obligant'],
 'blocker': ['blocker', 'brockle', 'reblock'],
 'blonde': ['blonde', 'bolden'],
 'blondine': ['blennoid', 'blondine'],
 'blood': ['blood', 'boldo'],
 'bloodleaf': ['bloodleaf', 'floodable'],
 'bloomer': ['bloomer', 'rebloom'],
 'bloomy': ['bloomy', 'lomboy'],
 'blore': ['blore', 'roble'],
 'blosmy': ['blosmy', 'symbol'],
 'blot': ['blot', 'bolt'],
 'blotless': ['blotless', 'boltless'],
 'blotter': ['blotter', 'bottler'],
 'blotting': ['blotting', 'bottling'],
 'blouse': ['besoul', 'blouse', 'obelus'],
 'blow': ['blow', 'bowl'],
 'blowback': ['backblow', 'blowback'],
 'blower': ['blower', 'bowler', 'reblow', 'worble'],
 'blowfly': ['blowfly', 'flyblow'],
 'blowing': ['blowing', 'bowling'],
 'blowout': ['blowout', 'outblow', 'outbowl'],
 'blowup': ['blowup', 'upblow'],
 'blowy': ['blowy', 'bowly'],
 'blub': ['blub', 'bulb'],
 'blubber': ['blubber', 'bubbler'],
 'blue': ['blue', 'lube'],
 'bluegill': ['bluegill', 'gullible'],
 'bluenose': ['bluenose', 'nebulose'],
 'bluer': ['bluer', 'brule', 'burel', 'ruble'],
 'blues': ['blues', 'bulse'],
 'bluffer': ['bluffer', 'rebluff'],
 'bluishness': ['bluishness', 'blushiness'],
 'bluism': ['bluism', 'limbus'],
 'blumea': ['bemaul', 'blumea'],
 'blunder': ['blunder', 'bundler'],
 'blunderer': ['blunderer', 'reblunder'],
 'blunge': ['blunge', 'bungle'],
 'blunger': ['blunger', 'bungler'],
 'bluntie': ['betulin', 'bluntie'],
 'blur': ['blur', 'burl'],
 'blushiness': ['bluishness', 'blushiness'],
 'bluster': ['bluster', 'brustle', 'bustler'],
 'boa': ['abo', 'boa'],
 'boar': ['boar', 'bora'],
 'board': ['abord', 'bardo', 'board', 'broad', 'dobra', 'dorab'],
 'boarder': ['arbored', 'boarder', 'reboard'],
 'boardly': ['boardly', 'broadly'],
 'boardy': ['boardy', 'boyard', 'byroad'],
 'boast': ['basto', 'boast', 'sabot'],
 'boaster': ['barotse', 'boaster', 'reboast', 'sorbate'],
 'boasting': ['boasting', 'bostangi'],
 'boat': ['boat', 'bota', 'toba'],
 'boater': ['boater', 'borate', 'rebato'],
 'boathouse': ['boathouse', 'houseboat'],
 'bobac': ['bobac', 'cabob'],
 'bobfly': ['bobfly', 'flobby'],
 'bobo': ['bobo', 'boob'],
 'bobtailed': ['bilobated', 'bobtailed'],
 'bocardo': ['bocardo', 'cordoba'],
 'boccale': ['boccale', 'cabocle'],
 'bocher': ['bocher', 'broche'],
 'bocking': ['bocking', 'kingcob'],
 'bod': ['bod', 'dob'],
 'bode': ['bode', 'dobe'],
 'boden': ['boden', 'boned'],
 'boder': ['boder', 'orbed'],
 'bodewash': ['beshadow', 'bodewash'],
 'bodge': ['bedog', 'bodge'],
 'bodhi': ['bodhi', 'dhobi'],
 'bodice': ['bodice', 'ceboid'],
 'bodier': ['bodier', 'boride', 'brodie'],
 'bodle': ['bodle', 'boled', 'lobed'],
 'bodo': ['bodo', 'bood', 'doob'],
 'body': ['body', 'boyd', 'doby'],
 'boer': ['boer', 'bore', 'robe'],
 'boerdom': ['bedroom', 'boerdom', 'boredom'],
 'boethian': ['boethian', 'nebaioth'],
 'bog': ['bog', 'gob'],
 'boga': ['bago', 'boga'],
 'bogan': ['bogan', 'goban'],
 'bogeyman': ['bogeyman', 'moneybag'],
 'boggler': ['boggler', 'broggle'],
 'boglander': ['boglander', 'longbeard'],
 'bogle': ['bogle', 'globe'],
 'boglet': ['boglet', 'goblet'],
 'bogo': ['bogo', 'gobo'],
 'bogue': ['bogue', 'bouge'],
 'bogum': ['bogum', 'gumbo'],
 'bogy': ['bogy', 'bygo', 'goby'],
 'bohea': ['bahoe', 'bohea', 'obeah'],
 'boho': ['boho', 'hobo'],
 'bohor': ['bohor', 'rohob'],
 'boid': ['biod', 'boid'],
 'boil': ['bilo', 'boil'],
 'boiled': ['beloid', 'boiled', 'bolide'],
 'boiler': ['boiler', 'reboil'],
 'boilover': ['boilover', 'overboil'],
 'bois': ['bios', 'bois'],
 'bojo': ['bojo', 'jobo'],
 'bolar': ['balor', 'bolar', 'boral', 'labor', 'lobar'],
 'bolden': ['blonde', 'bolden'],
 'bolderian': ['bolderian', 'ordinable'],
 'boldine': ['belonid', 'boldine'],
 'boldness': ['boldness', 'bondless'],
 'boldo': ['blood', 'boldo'],
 'bole': ['bleo', 'bole', 'lobe'],
 'boled': ['bodle', 'boled', 'lobed'],
 'bolelia': ['bolelia', 'lobelia', 'obelial'],
 'bolide': ['beloid', 'boiled', 'bolide'],
 'boller': ['beroll', 'boller'],
 'bolo': ['bolo', 'bool', 'lobo', 'obol'],
 'bolster': ['bolster', 'lobster'],
 'bolt': ['blot', 'bolt'],
 'boltage': ['boltage', 'globate'],
 'bolter': ['bolter', 'orblet', 'reblot', 'rebolt'],
 'bolthead': ['bolthead', 'theobald'],
 'bolting': ['biltong', 'bolting'],
 'boltless': ['blotless', 'boltless'],
 'boltonia': ['boltonia', 'lobation', 'oblation'],
 'bom': ['bom', 'mob'],
 'boma': ['ambo', 'boma'],
 'bombable': ['bombable', 'mobbable'],
 'bombacaceae': ['bombacaceae', 'cabombaceae'],
 'bomber': ['bomber', 'mobber'],
 'bon': ['bon', 'nob'],
 'bonaci': ['bianco', 'bonaci'],
 'bonair': ['bonair', 'borani'],
 'bondage': ['bondage', 'dogbane'],
 'bondar': ['bandor', 'bondar', 'roband'],
 'bondless': ['boldness', 'bondless'],
 'bone': ['beno', 'bone', 'ebon'],
 'boned': ['boden', 'boned'],
 'bonefish': ['bonefish', 'fishbone'],
 'boneless': ['boneless', 'noblesse'],
 'bonellia': ['baillone', 'bonellia'],
 'boner': ['boner', 'borne'],
 'boney': ['boney', 'ebony'],
 'boni': ['bino', 'bion', 'boni'],
 'bonitary': ['bonitary', 'trainboy'],
 'bonk': ['bonk', 'knob'],
 'bonnet': ['benton', 'bonnet'],
 'bonsai': ['basion', 'bonsai', 'sabino'],
 'bonus': ['bonus', 'bosun'],
 'bony': ['bony', 'byon'],
 'bonze': ['benzo', 'bonze'],
 'bonzer': ['bonzer', 'bronze'],
 'boob': ['bobo', 'boob'],
 'bood': ['bodo', 'bood', 'doob'],
 'booger': ['booger', 'goober'],
 'bookcase': ['bookcase', 'casebook'],
 'booker': ['booker', 'brooke', 'rebook'],
 'bookland': ['bookland', 'landbook'],
 'bookshop': ['bookshop', 'shopbook'],
 'bookward': ['bookward', 'woodbark'],
 'bookwork': ['bookwork', 'workbook'],
 'bool': ['bolo', 'bool', 'lobo', 'obol'],
 'booly': ['booly', 'looby'],
 'boomingly': ['boomingly', 'myoglobin'],
 'boopis': ['boopis', 'obispo'],
 'boor': ['boor', 'boro', 'broo'],
 'boort': ['boort', 'robot'],
 'boost': ['boost', 'boots'],
 'bootes': ['besoot', 'bootes'],
 'boother': ['boother', 'theorbo'],
 'boots': ['boost', 'boots'],
 'bop': ['bop', 'pob'],
 'bor': ['bor', 'orb', 'rob'],
 'bora': ['boar', 'bora'],
 'borable': ['belabor', 'borable'],
 'boracic': ['boracic', 'braccio'],
 'boral': ['balor', 'bolar', 'boral', 'labor', 'lobar'],
 'boran': ['baron', 'boran'],
 'borani': ['bonair', 'borani'],
 'borate': ['boater', 'borate', 'rebato'],
 'bord': ['bord', 'brod'],
 'bordel': ['belord', 'bordel', 'rebold'],
 'bordello': ['bordello', 'doorbell'],
 'border': ['border', 'roberd'],
 'borderer': ['borderer', 'broderer'],
 'bordure': ['bordure', 'bourder'],
 'bore': ['boer', 'bore', 'robe'],
 'boredom': ['bedroom', 'boerdom', 'boredom'],
 'boree': ['beroe', 'boree'],
 'boreen': ['boreen', 'enrobe', 'neebor', 'rebone'],
 'boreiad': ['beroida', 'boreiad'],
 'boreism': ['boreism', 'semiorb'],
 'borer': ['borer', 'rerob', 'rober'],
 'boreus': ['besour', 'boreus', 'bourse', 'bouser'],
 'borg': ['borg', 'brog', 'gorb'],
 'boric': ['boric', 'cribo', 'orbic'],
 'boride': ['bodier', 'boride', 'brodie'],
 'boring': ['boring', 'robing'],
 'boringly': ['boringly', 'goblinry'],
 'borlase': ['borlase', 'labrose', 'rosabel'],
 'borne': ['boner', 'borne'],
 'borneo': ['borneo', 'oberon'],
 'bornite': ['bornite', 'robinet'],
 'boro': ['boor', 'boro', 'broo'],
 'borocaine': ['borocaine', 'coenobiar'],
 'borofluohydric': ['borofluohydric', 'hydrofluoboric'],
 'borolanite': ['bertolonia', 'borolanite'],
 'boron': ['boron', 'broon'],
 'boronic': ['boronic', 'cobiron'],
 'borrower': ['borrower', 'reborrow'],
 'borscht': ['borscht', 'bortsch'],
 'bort': ['bort', 'brot'],
 'bortsch': ['borscht', 'bortsch'],
 'bos': ['bos', 'sob'],
 'bosc': ['bosc', 'scob'],
 'boser': ['boser', 'brose', 'sober'],
 'bosn': ['bosn', 'nobs', 'snob'],
 'bosselation': ['bosselation', 'eosinoblast'],
 'bosset': ['betoss', 'bosset'],
 'bostangi': ['boasting', 'bostangi'],
 'bostanji': ['banjoist', 'bostanji'],
 'bosun': ['bonus', 'bosun'],
 'bota': ['boat', 'bota', 'toba'],
 'botanical': ['botanical', 'catabolin'],
 'botanophilist': ['botanophilist', 'philobotanist'],
 'bote': ['bote', 'tobe'],
 'botein': ['botein', 'tobine'],
 'both': ['both', 'thob'],
 'bottler': ['blotter', 'bottler'],
 'bottling': ['blotting', 'bottling'],
 'bouge': ['bogue', 'bouge'],
 'bouget': ['bouget', 'outbeg'],
 'bouk': ['bouk', 'kobu'],
 'boulder': ['boulder', 'doubler'],
 'bouldering': ['bouldering', 'redoubling'],
 'boulter': ['boulter', 'trouble'],
 'bounden': ['bounden', 'unboned'],
 'bounder': ['beround', 'bounder', 'rebound', 'unbored', 'unorbed', 'unrobed'],
 'bounding': ['bounding', 'unboding'],
 'bourder': ['bordure', 'bourder'],
 'bourn': ['bourn', 'bruno'],
 'bourse': ['besour', 'boreus', 'bourse', 'bouser'],
 'bouser': ['besour', 'boreus', 'bourse', 'bouser'],
 'bousy': ['bousy', 'byous'],
 'bow': ['bow', 'wob'],
 'bowel': ['below', 'bowel', 'elbow'],
 'boweled': ['boweled', 'elbowed'],
 'bowels': ['beslow', 'bowels'],
 'bowery': ['bowery', 'bowyer', 'owerby'],
 'bowie': ['bowie', 'woibe'],
 'bowker': ['bework', 'bowker'],
 'bowl': ['blow', 'bowl'],
 'bowla': ['ablow', 'balow', 'bowla'],
 'bowler': ['blower', 'bowler', 'reblow', 'worble'],
 'bowling': ['blowing', 'bowling'],
 'bowly': ['blowy', 'bowly'],
 'bowmaker': ['beamwork', 'bowmaker'],
 'bowyer': ['bowery', 'bowyer', 'owerby'],
 'boxer': ['boxer', 'rebox'],
 'boxwork': ['boxwork', 'workbox'],
 'boyang': ['boyang', 'yagnob'],
 'boyard': ['boardy', 'boyard', 'byroad'],
 'boyd': ['body', 'boyd', 'doby'],
 'boyship': ['boyship', 'shipboy'],
 'bozo': ['bozo', 'zobo'],
 'bozze': ['bezzo', 'bozze'],
 'bra': ['bar', 'bra', 'rab'],
 'brab': ['barb', 'brab'],
 'brabble': ['babbler', 'blabber', 'brabble'],
 'braca': ['acrab', 'braca'],
 'braccio': ['boracic', 'braccio'],
 'brace': ['acerb', 'brace', 'caber'],
 'braced': ['becard', 'braced'],
 'braceleted': ['braceleted', 'celebrated'],
 'bracer': ['bracer', 'craber'],
 'braces': ['braces', 'scrabe'],
 'brachet': ['batcher', 'berchta', 'brachet'],
 'brachiata': ['batrachia', 'brachiata'],
 'brachiofacial': ['brachiofacial', 'faciobrachial'],
 'brachiopode': ['brachiopode', 'cardiophobe'],
 'bracon': ['bracon', 'carbon', 'corban'],
 'bractea': ['abreact', 'bractea', 'cabaret'],
 'bracteal': ['bracteal', 'cartable'],
 'bracteiform': ['bacteriform', 'bracteiform'],
 'bracteose': ['bracteose', 'obsecrate'],
 'brad': ['bard', 'brad', 'drab'],
 'bradenhead': ['barehanded', 'bradenhead', 'headbander'],
 'brae': ['bare', 'bear', 'brae'],
 'braehead': ['barehead', 'braehead'],
 'brag': ['brag', 'garb', 'grab'],
 'bragi': ['bragi', 'girba'],
 'bragless': ['bragless', 'garbless'],
 'brahmi': ['brahmi', 'mihrab'],
 'brahui': ['brahui', 'habiru'],
 'braid': ['barid', 'bidar', 'braid', 'rabid'],
 'braider': ['braider', 'rebraid'],
 'brail': ['blair', 'brail', 'libra'],
 'braille': ['braille', 'liberal'],
 'brain': ['abrin', 'bairn', 'brain', 'brian', 'rabin'],
 'brainache': ['brainache', 'branchiae'],
 'brainge': ['bearing', 'begrain', 'brainge', 'rigbane'],
 'brainwater': ['brainwater', 'waterbrain'],
 'brainy': ['binary', 'brainy'],
 'braird': ['braird', 'briard'],
 'brairo': ['barrio', 'brairo'],
 'braise': ['braise', 'rabies', 'rebias'],
 'brake': ['baker', 'brake', 'break'],
 'brakeage': ['brakeage', 'breakage'],
 'brakeless': ['bakerless', 'brakeless', 'breakless'],
 'braker': ['barker', 'braker'],
 'braky': ['barky', 'braky'],
 'bram': ['barm', 'bram'],
 'brambrack': ['barmbrack', 'brambrack'],
 'bramia': ['bairam', 'bramia'],
 'bran': ['barn', 'bran'],
 'brancher': ['brancher', 'rebranch'],
 'branchiae': ['brainache', 'branchiae'],
 'branchiata': ['batrachian', 'branchiata'],
 'branchiopoda': ['branchiopoda', 'podobranchia'],
 'brander': ['bernard', 'brander', 'rebrand'],
 'brandi': ['brandi', 'riband'],
 'brandisher': ['brandisher', 'rebrandish'],
 'branial': ['blarina', 'branial'],
 'brankie': ['brankie', 'inbreak'],
 'brash': ['brash', 'shrab'],
 'brasiletto': ['brasiletto', 'strobilate'],
 'brassie': ['brassie', 'rebasis'],
 'brat': ['bart', 'brat'],
 'brattie': ['biretta', 'brattie', 'ratbite'],
 'brattle': ['battler', 'blatter', 'brattle'],
 'braunite': ['braunite', 'urbanite', 'urbinate'],
 'brave': ['brave', 'breva'],
 'bravoite': ['abortive', 'bravoite'],
 'brawler': ['brawler', 'warbler'],
 'brawling': ['brawling', 'warbling'],
 'brawlingly': ['brawlingly', 'warblingly'],
 'brawly': ['brawly', 'byrlaw', 'warbly'],
 'brawned': ['benward', 'brawned'],
 'bray': ['bray', 'yarb'],
 'braza': ['braza', 'zabra'],
 'braze': ['braze', 'zebra'],
 'brazier': ['bizarre', 'brazier'],
 'bread': ['ardeb', 'beard', 'bread', 'debar'],
 'breadless': ['beardless', 'breadless'],
 'breadlessness': ['beardlessness', 'breadlessness'],
 'breadman': ['banderma', 'breadman'],
 'breadnut': ['breadnut', 'turbaned'],
 'breaghe': ['breaghe', 'herbage'],
 'break': ['baker', 'brake', 'break'],
 'breakage': ['brakeage', 'breakage'],
 'breakless': ['bakerless', 'brakeless', 'breakless'],
 'breakout': ['breakout', 'outbreak'],
 'breakover': ['breakover', 'overbreak'],
 'breakstone': ['breakstone', 'stonebreak'],
 'breakup': ['breakup', 'upbreak'],
 'breakwind': ['breakwind', 'windbreak'],
 'bream': ['amber', 'bearm', 'bemar', 'bream', 'embar'],
 'breast': ['baster', 'bestar', 'breast'],
 'breasting': ['breasting', 'brigantes'],
 'breastpin': ['breastpin', 'stepbairn'],
 'breastrail': ['arbalister', 'breastrail'],
 'breastweed': ['breastweed', 'sweetbread'],
 'breath': ['bather', 'bertha', 'breath'],
 'breathe': ['breathe', 'rebathe'],
 'breba': ['barbe', 'bebar', 'breba', 'rebab'],
 'breccia': ['acerbic', 'breccia'],
 'brecham': ['becharm', 'brecham', 'chamber'],
 'brede': ['brede', 'breed', 'rebed'],
 'bredi': ['bider', 'bredi', 'bride', 'rebid'],
 'bree': ['beer', 'bere', 'bree'],
 'breech': ['becher', 'breech'],
 'breed': ['brede', 'breed', 'rebed'],
 'breeder': ['breeder', 'rebreed'],
 'breeding': ['beringed', 'breeding'],
 'brehon': ['behorn', 'brehon'],
 'brei': ['beri', 'bier', 'brei', 'ribe'],
 'brelaw': ['bawler', 'brelaw', 'rebawl', 'warble'],
 'breme': ['breme', 'ember'],
 'bremia': ['ambier', 'bremia', 'embira'],
 'brenda': ['bander', 'brenda'],
 'brephic': ['bechirp', 'brephic'],
 'bret': ['bert', 'bret'],
 'breth': ['berth', 'breth'],
 'breva': ['brave', 'breva'],
 'breve': ['bever', 'breve'],
 'brewer': ['brewer', 'rebrew'],
 'brey': ['brey', 'byre', 'yerb'],
 'brian': ['abrin', 'bairn', 'brain', 'brian', 'rabin'],
 'briard': ['braird', 'briard'],
 'briber': ['briber', 'ribber'],
 'brichen': ['birchen', 'brichen'],
 'brickel': ['brickel', 'brickle'],
 'bricken': ['bickern', 'bricken'],
 'brickle': ['brickel', 'brickle'],
 'bricole': ['bricole', 'corbeil', 'orbicle'],
 'bridal': ['bildar', 'bridal', 'ribald'],
 'bridale': ['bedrail', 'bridale', 'ridable'],
 'bridally': ['bridally', 'ribaldly'],
 'bride': ['bider', 'bredi', 'bride', 'rebid'],
 'bridelace': ['bridelace', 'calibered'],
 'bridge': ['begird', 'bridge'],
 'bridgeward': ['bridgeward', 'drawbridge'],
 'bridling': ['birdling', 'bridling', 'lingbird'],
 'brief': ['bifer', 'brief', 'fiber'],
 'briefless': ['briefless', 'fiberless', 'fibreless'],
 'brier': ['berri', 'brier'],
 'briered': ['berried', 'briered'],
 'brieve': ['bervie', 'brieve'],
 'brigade': ['abridge', 'brigade'],
 'brigand': ['barding', 'brigand'],
 'brigantes': ['breasting', 'brigantes'],
 'brighten': ['berthing', 'brighten'],
 'brin': ['birn', 'brin'],
 'brine': ['brine', 'enrib'],
 'bringal': ['barling', 'bringal'],
 'bringer': ['bringer', 'rebring'],
 'briny': ['birny', 'briny'],
 'bristle': ['blister', 'bristle'],
 'bristlewort': ['blisterwort', 'bristlewort'],
 'brisure': ['brisure', 'bruiser'],
 'britannia': ['antiabrin', 'britannia'],
 'brith': ['birth', 'brith'],
 'brither': ['brither', 'rebirth'],
 'britten': ['bittern', 'britten'],
 'brittle': ['blitter', 'brittle', 'triblet'],
 'broacher': ['broacher', 'rebroach'],
 'broad': ['abord', 'bardo', 'board', 'broad', 'dobra', 'dorab'],
 'broadbill': ['billboard', 'broadbill'],
 'broadcaster': ['broadcaster', 'rebroadcast'],
 'broaden': ['bandore', 'broaden'],
 'broadhead': ['broadhead', 'headboard'],
 'broadly': ['boardly', 'broadly'],
 'broadside': ['broadside', 'sideboard'],
 'broadspread': ['broadspread', 'spreadboard'],
 'broadtail': ['broadtail', 'tailboard'],
 'brochan': ['brochan', 'charbon'],
 'broche': ['bocher', 'broche'],
 'brocho': ['brocho', 'brooch'],
 'brocked': ['bedrock', 'brocked'],
 'brockle': ['blocker', 'brockle', 'reblock'],
 'brod': ['bord', 'brod'],
 'broderer': ['borderer', 'broderer'],
 'brodie': ['bodier', 'boride', 'brodie'],
 'brog': ['borg', 'brog', 'gorb'],
 'brogan': ['barong', 'brogan'],
 'broggle': ['boggler', 'broggle'],
 'brolga': ['brolga', 'gorbal'],
 'broma': ['broma', 'rambo'],
 'bromate': ['barmote', 'bromate'],
 'brome': ['brome', 'omber'],
 'brominate': ['brominate', 'tribonema'],
 'bromohydrate': ['bromohydrate', 'hydrobromate'],
 'bronze': ['bonzer', 'bronze'],
 'broo': ['boor', 'boro', 'broo'],
 'brooch': ['brocho', 'brooch'],
 'brooke': ['booker', 'brooke', 'rebook'],
 'broon': ['boron', 'broon'],
 'brose': ['boser', 'brose', 'sober'],
 'brot': ['bort', 'brot'],
 'brotan': ['barton', 'brotan'],
 'brotany': ['baryton', 'brotany'],
 'broth': ['broth', 'throb'],
 'brothelry': ['brothelry', 'brotherly'],
 'brotherly': ['brothelry', 'brotherly'],
 'browden': ['bedrown', 'browden'],
 'browner': ['browner', 'rebrown'],
 'browntail': ['browntail', 'wrainbolt'],
 'bruce': ['bruce', 'cebur', 'cuber'],
 'brucina': ['brucina', 'rubican'],
 'bruckle': ['bruckle', 'buckler'],
 'brugh': ['brugh', 'burgh'],
 'bruin': ['bruin', 'burin', 'inrub'],
 'bruiser': ['brisure', 'bruiser'],
 'bruke': ['bruke', 'burke'],
 'brule': ['bluer', 'brule', 'burel', 'ruble'],
 'brulee': ['brulee', 'burele', 'reblue'],
 'brumal': ['brumal', 'labrum', 'lumbar', 'umbral'],
 'brumalia': ['albarium', 'brumalia'],
 'brume': ['brume', 'umber'],
 'brumous': ['brumous', 'umbrous'],
 'brunellia': ['brunellia', 'unliberal'],
 'brunet': ['brunet', 'bunter', 'burnet'],
 'bruno': ['bourn', 'bruno'],
 'brunt': ['brunt', 'burnt'],
 'brush': ['brush', 'shrub'],
 'brushed': ['brushed', 'subherd'],
 'brusher': ['brusher', 'rebrush'],
 'brushland': ['brushland', 'shrubland'],
 'brushless': ['brushless', 'shrubless'],
 'brushlet': ['brushlet', 'shrublet'],
 'brushlike': ['brushlike', 'shrublike'],
 'brushwood': ['brushwood', 'shrubwood'],
 'brustle': ['bluster', 'brustle', 'bustler'],
 'brut': ['brut', 'burt', 'trub', 'turb'],
 'bruta': ['bruta', 'tubar'],
 'brute': ['brute', 'buret', 'rebut', 'tuber'],
 'brutely': ['brutely', 'butlery'],
 'bryan': ['barny', 'bryan'],
 'bryanite': ['barytine', 'bryanite'],
 'bryce': ['becry', 'bryce'],
 'bual': ['balu', 'baul', 'bual', 'luba'],
 'buba': ['babu', 'buba'],
 'bubal': ['babul', 'bubal'],
 'bubbler': ['blubber', 'bubbler'],
 'buccocervical': ['buccocervical', 'cervicobuccal'],
 'bucconasal': ['bucconasal', 'nasobuccal'],
 'buceros': ['bescour', 'buceros', 'obscure'],
 'buckbush': ['buckbush', 'bushbuck'],
 'bucked': ['beduck', 'bucked'],
 'buckler': ['bruckle', 'buckler'],
 'bucksaw': ['bucksaw', 'sawbuck'],
 'bucrane': ['bucrane', 'unbrace'],
 'bud': ['bud', 'dub'],
 'buda': ['baud', 'buda', 'daub'],
 'budder': ['budder', 'redbud'],
 'budger': ['bedrug', 'budger'],
 'budgeter': ['budgeter', 'rebudget'],
 'budtime': ['bitumed', 'budtime'],
 'buffer': ['buffer', 'rebuff'],
 'buffeter': ['buffeter', 'rebuffet'],
 'bugan': ['bugan', 'bunga', 'unbag'],
 'bughouse': ['bughouse', 'housebug'],
 'bugi': ['bugi', 'guib'],
 'bugle': ['bugle', 'bulge'],
 'bugler': ['bugler', 'bulger', 'burgle'],
 'bugre': ['bugre', 'gebur'],
 'builder': ['builder', 'rebuild'],
 'buildup': ['buildup', 'upbuild'],
 'buirdly': ['buirdly', 'ludibry'],
 'bulanda': ['balunda', 'bulanda'],
 'bulb': ['blub', 'bulb'],
 'bulgarian': ['biangular', 'bulgarian'],
 'bulge': ['bugle', 'bulge'],
 'bulger': ['bugler', 'bulger', 'burgle'],
 'bulimic': ['bulimic', 'umbilic'],
 'bulimiform': ['bulimiform', 'umbiliform'],
 'bulker': ['bulker', 'rebulk'],
 'bulla': ['bulla', 'lulab'],
 'bullace': ['bullace', 'cueball'],
 'bulletin': ['bulletin', 'unbillet'],
 'bullfeast': ['bullfeast', 'stableful'],
 'bulse': ['blues', 'bulse'],
 'bulter': ['bulter', 'burlet', 'butler'],
 'bummler': ['bummler', 'mumbler'],
 'bun': ['bun', 'nub'],
 'buna': ['baun', 'buna', 'nabu', 'nuba'],
 'buncal': ['buncal', 'lucban'],
 'buncher': ['buncher', 'rebunch'],
 'bunder': ['bunder', 'burden', 'burned', 'unbred'],
 'bundle': ['bundle', 'unbled'],
 'bundler': ['blunder', 'bundler'],
 'bundu': ['bundu', 'unbud', 'undub'],
 'bunga': ['bugan', 'bunga', 'unbag'],
 'bungle': ['blunge', 'bungle'],
 'bungler': ['blunger', 'bungler'],
 'bungo': ['bungo', 'unbog'],
 'bunk': ['bunk', 'knub'],
 'bunter': ['brunet', 'bunter', 'burnet'],
 'bunty': ['bunty', 'butyn'],
 'bunya': ['bunya', 'unbay'],
 'bur': ['bur', 'rub'],
 'buran': ['buran', 'unbar', 'urban'],
 'burble': ['burble', 'lubber', 'rubble'],
 'burbler': ['burbler', 'rubbler'],
 'burbly': ['burbly', 'rubbly'],
 'burd': ['burd', 'drub'],
 'burdalone': ['burdalone', 'unlabored'],
 'burden': ['bunder', 'burden', 'burned', 'unbred'],
 'burdener': ['burdener', 'reburden'],
 'burdie': ['burdie', 'buried', 'rubied'],
 'bure': ['bure', 'reub', 'rube'],
 'burel': ['bluer', 'brule', 'burel', 'ruble'],
 'burele': ['brulee', 'burele', 'reblue'],
 'buret': ['brute', 'buret', 'rebut', 'tuber'],
 'burfish': ['burfish', 'furbish'],
 'burg': ['burg', 'grub'],
 'burgh': ['brugh', 'burgh'],
 'burgle': ['bugler', 'bulger', 'burgle'],
 'burian': ['burian', 'urbian'],
 'buried': ['burdie', 'buried', 'rubied'],
 'burin': ['bruin', 'burin', 'inrub'],
 'burke': ['bruke', 'burke'],
 'burl': ['blur', 'burl'],
 'burler': ['burler', 'burrel'],
 'burlet': ['bulter', 'burlet', 'butler'],
 'burletta': ['burletta', 'rebuttal'],
 'burmite': ['burmite', 'imbrute', 'terbium'],
 'burned': ['bunder', 'burden', 'burned', 'unbred'],
 'burner': ['burner', 'reburn'],
 'burnet': ['brunet', 'bunter', 'burnet'],
 'burnfire': ['burnfire', 'fireburn'],
 'burnie': ['burnie', 'rubine'],
 'burnisher': ['burnisher', 'reburnish'],
 'burnout': ['burnout', 'outburn'],
 'burnover': ['burnover', 'overburn'],
 'burnsides': ['burnsides', 'sideburns'],
 'burnt': ['brunt', 'burnt'],
 'burny': ['burny', 'runby'],
 'buro': ['buro', 'roub'],
 'burrel': ['burler', 'burrel'],
 'burro': ['burro', 'robur', 'rubor'],
 'bursa': ['abrus', 'bursa', 'subra'],
 'bursal': ['bursal', 'labrus'],
 'bursate': ['bursate', 'surbate'],
 'burse': ['burse', 'rebus', 'suber'],
 'burst': ['burst', 'strub'],
 'burster': ['burster', 'reburst'],
 'burt': ['brut', 'burt', 'trub', 'turb'],
 'burut': ['burut', 'trubu'],
 'bury': ['bury', 'ruby'],
 'bus': ['bus', 'sub'],
 'buscarle': ['arbuscle', 'buscarle'],
 'bushbuck': ['buckbush', 'bushbuck'],
 'busher': ['busher', 'rebush'],
 'bushwood': ['bushwood', 'woodbush'],
 'busied': ['busied', 'subdie'],
 'busked': ['bedusk', 'busked'],
 'busman': ['busman', 'subman'],
 'bust': ['bust', 'stub'],
 'busted': ['bedust', 'bestud', 'busted'],
 'buster': ['berust', 'buster', 'stuber'],
 'bustic': ['bustic', 'cubist'],
 'bustle': ['bustle', 'sublet', 'subtle'],
 'bustler': ['bluster', 'brustle', 'bustler'],
 'but': ['but', 'tub'],
 'bute': ['bute', 'tebu', 'tube'],
 'butea': ['butea', 'taube', 'tubae'],
 'butein': ['butein', 'butine', 'intube'],
 'butic': ['butic', 'cubit'],
 'butine': ['butein', 'butine', 'intube'],
 'butler': ['bulter', 'burlet', 'butler'],
 'butleress': ['butleress', 'tuberless'],
 'butlery': ['brutely', 'butlery'],
 'buttle': ['buttle', 'tublet'],
 'buttoner': ['buttoner', 'rebutton'],
 'butyn': ['bunty', 'butyn'],
 'buyer': ['buyer', 'rebuy'],
 'bye': ['bey', 'bye'],
 'byeman': ['byeman', 'byname'],
 'byerite': ['byerite', 'ebriety'],
 'bygo': ['bogy', 'bygo', 'goby'],
 'byname': ['byeman', 'byname'],
 'byon': ['bony', 'byon'],
 'byous': ['bousy', 'byous'],
 'byre': ['brey', 'byre', 'yerb'],
 'byrlaw': ['brawly', 'byrlaw', 'warbly'],
 'byroad': ['boardy', 'boyard', 'byroad'],
 'cab': ['bac', 'cab'],
 'caba': ['abac', 'caba'],
 'cabaan': ['cabaan', 'cabana', 'canaba'],
 'cabala': ['cabala', 'calaba'],
 'cabaletta': ['ablactate', 'cabaletta'],
 'cabalism': ['balsamic', 'cabalism'],
 'cabalist': ['basaltic', 'cabalist'],
 'caballer': ['barcella', 'caballer'],
 'caban': ['banca', 'caban'],
 'cabana': ['cabaan', 'cabana', 'canaba'],
 'cabaret': ['abreact', 'bractea', 'cabaret'],
 'cabbler': ['cabbler', 'clabber'],
 'caber': ['acerb', 'brace', 'caber'],
 'cabio': ['baioc', 'cabio', 'cobia'],
 'cabiri': ['cabiri', 'caribi'],
 'cabirian': ['arabinic', 'cabirian', 'carabini', 'cibarian'],
 'cable': ['cable', 'caleb'],
 'cabled': ['beclad', 'cabled'],
 'cabob': ['bobac', 'cabob'],
 'cabocle': ['boccale', 'cabocle'],
 'cabombaceae': ['bombacaceae', 'cabombaceae'],
 'cabrilla': ['bacillar', 'cabrilla'],
 'caca': ['acca', 'caca'],
 'cachet': ['cachet', 'chacte'],
 'cachou': ['cachou', 'caucho'],
 'cackler': ['cackler', 'clacker', 'crackle'],
 'cacodaemonic': ['cacodaemonic', 'cacodemoniac'],
 'cacodemoniac': ['cacodaemonic', 'cacodemoniac'],
 'cacomistle': ['cacomistle', 'cosmetical'],
 'cacoxenite': ['cacoxenite', 'excecation'],
 'cactaceae': ['cactaceae', 'taccaceae'],
 'cactaceous': ['cactaceous', 'taccaceous'],
 'cacti': ['cacti', 'ticca'],
 'cactoid': ['cactoid', 'octadic'],
 'caddice': ['caddice', 'decadic'],
 'caddie': ['caddie', 'eddaic'],
 'cade': ['cade', 'dace', 'ecad'],
 'cadent': ['cadent', 'canted', 'decant'],
 'cadential': ['cadential', 'dancalite'],
 'cader': ['acred', 'cader', 'cadre', 'cedar'],
 'cadet': ['cadet', 'ectad'],
 'cadge': ['cadge', 'caged'],
 'cadger': ['cadger', 'cradge'],
 'cadi': ['acid', 'cadi', 'caid'],
 'cadinene': ['cadinene', 'decennia', 'enneadic'],
 'cadmia': ['adamic', 'cadmia'],
 'cados': ['cados', 'scoad'],
 'cadre': ['acred', 'cader', 'cadre', 'cedar'],
 'cadua': ['cadua', 'cauda'],
 'caduac': ['caduac', 'caduca'],
 'caduca': ['caduac', 'caduca'],
 'cadus': ['cadus', 'dacus'],
 'caeciliae': ['caeciliae', 'ilicaceae'],
 'caedmonian': ['caedmonian', 'macedonian'],
 'caedmonic': ['caedmonic', 'macedonic'],
 'caelum': ['almuce', 'caelum', 'macule'],
 'caelus': ['caelus', 'caules', 'clause'],
 'caesar': ['ascare', 'caesar', 'resaca'],
 'caesarist': ['caesarist', 'staircase'],
 'caesura': ['auresca', 'caesura'],
 'caffeina': ['affiance', 'caffeina'],
 'caged': ['cadge', 'caged'],
 'cageling': ['cageling', 'glaceing'],
 'cager': ['cager', 'garce', 'grace'],
 'cahill': ['achill', 'cahill', 'chilla'],
 'cahita': ['cahita', 'ithaca'],
 'cahnite': ['cahnite', 'cathine'],
 'caid': ['acid', 'cadi', 'caid'],
 'caiman': ['amniac', 'caiman', 'maniac'],
 'caimito': ['caimito', 'comitia'],
 'cain': ['cain', 'inca'],
 'cainism': ['cainism', 'misniac'],
 'cairba': ['arabic', 'cairba'],
 'caird': ['acrid', 'caird', 'carid', 'darci', 'daric', 'dirca'],
 'cairene': ['cairene', 'cinerea'],
 'cairn': ['cairn', 'crain', 'naric'],
 'cairned': ['cairned', 'candier'],
 'cairny': ['cairny', 'riancy'],
 'cairo': ['cairo', 'oaric'],
 'caisson': ['caisson', 'cassino'],
 'cajoler': ['cajoler', 'jecoral'],
 'caker': ['acker', 'caker', 'crake', 'creak'],
 'cakey': ['ackey', 'cakey'],
 'cal': ['cal', 'lac'],
 'calaba': ['cabala', 'calaba'],
 'calamine': ['alcamine', 'analcime', 'calamine', 'camelina'],
 'calamint': ['calamint', 'claimant'],
 'calamitean': ['calamitean', 'catamenial'],
 'calander': ['calander', 'calendar'],
 'calandrinae': ['calandrinae', 'calendarian'],
 'calas': ['calas', 'casal', 'scala'],
 'calash': ['calash', 'lachsa'],
 'calathian': ['acanthial', 'calathian'],
 'calaverite': ['calaverite', 'lacerative'],
 'calcareocorneous': ['calcareocorneous', 'corneocalcareous'],
 'calcareosiliceous': ['calcareosiliceous', 'siliceocalcareous'],
 'calciner': ['calciner', 'larcenic'],
 'calculary': ['calculary', 'calycular'],
 'calculative': ['calculative', 'claviculate'],
 'calden': ['calden', 'candle', 'lanced'],
 'calean': ['anlace', 'calean'],
 'caleb': ['cable', 'caleb'],
 'caledonia': ['caledonia', 'laodicean'],
 'caledonite': ['caledonite', 'celadonite'],
 'calendar': ['calander', 'calendar'],
 'calendarial': ['calendarial', 'dalecarlian'],
 'calendarian': ['calandrinae', 'calendarian'],
 'calender': ['calender', 'encradle'],
 'calenture': ['calenture', 'crenulate'],
 'calepin': ['calepin', 'capelin', 'panicle', 'pelican', 'pinacle'],
 'calfkill': ['calfkill', 'killcalf'],
 'caliban': ['balanic', 'caliban'],
 'caliber': ['caliber', 'calibre'],
 'calibered': ['bridelace', 'calibered'],
 'calibrate': ['bacterial', 'calibrate'],
 'calibre': ['caliber', 'calibre'],
 'caliburno': ['binocular', 'caliburno', 'colubrina'],
 'calico': ['accoil', 'calico'],
 'calidity': ['calidity', 'dialytic'],
 'caliga': ['caliga', 'cigala'],
 'calinago': ['analogic', 'calinago'],
 'calinut': ['calinut', 'lunatic'],
 'caliper': ['caliper', 'picarel', 'replica'],
 'calipers': ['calipers', 'spiracle'],
 'caliphate': ['caliphate', 'hepatical'],
 'calite': ['calite', 'laetic', 'tecali'],
 'caliver': ['caliver', 'caviler', 'claiver', 'clavier', 'valeric', 'velaric'],
 'calk': ['calk', 'lack'],
 'calker': ['calker', 'lacker', 'rackle', 'recalk', 'reckla'],
 'callboy': ['callboy', 'collyba'],
 'caller': ['caller', 'cellar', 'recall'],
 'calli': ['calli', 'lilac'],
 'calligraphy': ['calligraphy', 'graphically'],
 'calliopsis': ['calliopsis', 'lipoclasis'],
 'callisection': ['callisection', 'clinoclasite'],
 'callitype': ['callitype', 'plicately'],
 'callo': ['callo', 'colla', 'local'],
 'callosal': ['callosal', 'scallola'],
 'callose': ['callose', 'oscella'],
 'callosity': ['callosity', 'stoically'],
 'callosum': ['callosum', 'mollusca'],
 'calluna': ['calluna', 'lacunal'],
 'callus': ['callus', 'sulcal'],
 'calm': ['calm', 'clam'],
 'calmant': ['calmant', 'clamant'],
 'calmative': ['calmative', 'clamative'],
 'calmer': ['calmer', 'carmel', 'clamer', 'marcel', 'mercal'],
 'calmierer': ['calmierer', 'reclaimer'],
 'calomba': ['calomba', 'cambalo'],
 'calonectria': ['calonectria', 'ectocranial'],
 'calor': ['alcor', 'calor', 'carlo', 'carol', 'claro', 'coral'],
 'calorie': ['calorie', 'cariole'],
 'calorist': ['calorist', 'coralist'],
 'calorite': ['calorite', 'erotical', 'loricate'],
 'calorize': ['calorize', 'coalizer'],
 'calotermes': ['calotermes', 'mesorectal', 'metacresol'],
 'calotermitid': ['calotermitid', 'dilatometric'],
 'calp': ['calp', 'clap'],
 'caltha': ['caltha', 'chalta'],
 'caltrop': ['caltrop', 'proctal'],
 'calusa': ['ascula', 'calusa', 'casual', 'casula', 'causal'],
 'calvaria': ['calvaria', 'clavaria'],
 'calvary': ['calvary', 'cavalry'],
 'calve': ['calve', 'cavel', 'clave'],
 'calver': ['calver', 'carvel', 'claver'],
 'calves': ['calves', 'scavel'],
 'calycular': ['calculary', 'calycular'],
 'calyptratae': ['acalyptrate', 'calyptratae'],
 'cam': ['cam', 'mac'],
 'camaca': ['camaca', 'macaca'],
 'camail': ['amical', 'camail', 'lamaic'],
 'caman': ['caman', 'macan'],
 'camara': ['acamar', 'camara', 'maraca'],
 'cambalo': ['calomba', 'cambalo'],
 'camber': ['becram', 'camber', 'crambe'],
 'cambrel': ['cambrel', 'clamber', 'cramble'],
 'came': ['acme', 'came', 'mace'],
 'cameist': ['cameist', 'etacism', 'sematic'],
 'camel': ['camel', 'clame', 'cleam', 'macle'],
 'camelid': ['camelid', 'decimal', 'declaim', 'medical'],
 'camelina': ['alcamine', 'analcime', 'calamine', 'camelina'],
 'camelish': ['camelish', 'schalmei'],
 'camellus': ['camellus', 'sacellum'],
 'cameloid': ['cameloid', 'comedial', 'melodica'],
 'cameograph': ['cameograph', 'macrophage'],
 'camera': ['acream', 'camera', 'mareca'],
 'cameral': ['cameral', 'caramel', 'carmela', 'ceramal', 'reclama'],
 'camerate': ['camerate', 'macerate', 'racemate'],
 'camerated': ['camerated', 'demarcate'],
 'cameration': ['aeromantic', 'cameration', 'maceration', 'racemation'],
 'camerina': ['amacrine', 'american', 'camerina', 'cinerama'],
 'camerist': ['camerist', 'ceramist', 'matrices'],
 'camion': ['camion', 'conima', 'manioc', 'monica'],
 'camisado': ['camisado', 'caodaism'],
 'camise': ['camise', 'macies'],
 'campaign': ['campaign', 'pangamic'],
 'campaigner': ['campaigner', 'recampaign'],
 'camphire': ['camphire', 'hemicarp'],
 'campine': ['campine', 'pemican'],
 'campoo': ['campoo', 'capomo'],
 'camptonite': ['camptonite', 'pentatomic'],
 'camus': ['camus', 'musca', 'scaum', 'sumac'],
 'camused': ['camused', 'muscade'],
 'canaba': ['cabaan', 'cabana', 'canaba'],
 'canadol': ['acnodal', 'canadol', 'locanda'],
 'canaille': ['alliance', 'canaille'],
 'canape': ['canape', 'panace'],
 'canari': ['acinar',
            'arnica',
            'canari',
            'carian',
            'carina',
            'crania',
            'narica'],
 'canarin': ['canarin', 'cranian'],
 'canariote': ['canariote', 'ceratonia'],
 'canary': ['canary', 'cynara'],
 'canaut': ['canaut', 'tucana'],
 'canceler': ['canceler', 'clarence', 'recancel'],
 'cancer': ['cancer', 'crance'],
 'cancerate': ['cancerate', 'reactance'],
 'canceration': ['anacreontic', 'canceration'],
 'cancri': ['cancri', 'carnic', 'cranic'],
 'cancroid': ['cancroid', 'draconic'],
 'candela': ['candela', 'decanal'],
 'candier': ['cairned', 'candier'],
 'candiru': ['candiru', 'iracund'],
 'candle': ['calden', 'candle', 'lanced'],
 'candor': ['candor', 'cardon', 'conrad'],
 'candroy': ['candroy', 'dacryon'],
 'cane': ['acne', 'cane', 'nace'],
 'canel': ['canel', 'clean', 'lance', 'lenca'],
 'canelo': ['canelo', 'colane'],
 'canephor': ['canephor', 'chaperno', 'chaperon'],
 'canephore': ['canephore', 'chaperone'],
 'canephroi': ['canephroi', 'parochine'],
 'caner': ['caner', 'crane', 'crena', 'nacre', 'rance'],
 'canful': ['canful', 'flucan'],
 'cangle': ['cangle', 'glance'],
 'cangler': ['cangler', 'glancer', 'reclang'],
 'cangue': ['cangue', 'uncage'],
 'canicola': ['canicola', 'laconica'],
 'canid': ['canid', 'cnida', 'danic'],
 'canidae': ['aidance', 'canidae'],
 'canine': ['canine', 'encina', 'neanic'],
 'canis': ['canis', 'scian'],
 'canister': ['canister', 'cestrian', 'cisterna', 'irascent'],
 'canker': ['canker', 'neckar'],
 'cankerworm': ['cankerworm', 'crownmaker'],
 'cannel': ['cannel', 'lencan'],
 'cannot': ['cannot', 'canton', 'conant', 'nonact'],
 'cannulate': ['antelucan', 'cannulate'],
 'canny': ['canny', 'nancy'],
 'canoe': ['acone', 'canoe', 'ocean'],
 'canoeing': ['anogenic', 'canoeing'],
 'canoeist': ['canoeist', 'cotesian'],
 'canon': ['ancon', 'canon'],
 'canonist': ['canonist', 'sanction', 'sonantic'],
 'canoodler': ['canoodler', 'coronaled'],
 'canroy': ['canroy', 'crayon', 'cyrano', 'nyroca'],
 'canso': ['ascon', 'canso', 'oscan'],
 'cantabri': ['bactrian', 'cantabri'],
 'cantala': ['cantala', 'catalan', 'lantaca'],
 'cantalite': ['cantalite', 'lactinate', 'tetanical'],
 'cantara': ['cantara', 'nacarat'],
 'cantaro': ['cantaro', 'croatan'],
 'cantate': ['anteact', 'cantate'],
 'canted': ['cadent', 'canted', 'decant'],
 'canteen': ['canteen', 'centena'],
 'canter': ['canter',
            'creant',
            'cretan',
            'nectar',
            'recant',
            'tanrec',
            'trance'],
 'canterer': ['canterer', 'recanter', 'recreant', 'terrance'],
 'cantharidae': ['acidanthera', 'cantharidae'],
 'cantharis': ['anarchist', 'archsaint', 'cantharis'],
 'canthus': ['canthus', 'staunch'],
 'cantico': ['cantico', 'catonic', 'taconic'],
 'cantilena': ['cantilena', 'lancinate'],
 'cantilever': ['cantilever', 'trivalence'],
 'cantily': ['anticly', 'cantily'],
 'cantina': ['cantina', 'tannaic'],
 'cantiness': ['anticness', 'cantiness', 'incessant'],
 'cantion': ['actinon', 'cantion', 'contain'],
 'cantle': ['cantle', 'cental', 'lancet', 'tancel'],
 'canto': ['acton', 'canto', 'octan'],
 'canton': ['cannot', 'canton', 'conant', 'nonact'],
 'cantonal': ['cantonal', 'connatal'],
 'cantor': ['cantor', 'carton', 'contra'],
 'cantorian': ['anarcotin', 'cantorian', 'carnation', 'narcotina'],
 'cantoris': ['cantoris', 'castorin', 'corsaint'],
 'cantred': ['cantred', 'centrad', 'tranced'],
 'cantus': ['cantus', 'tuscan', 'uncast'],
 'canun': ['canun', 'cunan'],
 'cany': ['cany', 'cyan'],
 'canyon': ['ancony', 'canyon'],
 'caoba': ['bacao', 'caoba'],
 'caodaism': ['camisado', 'caodaism'],
 'cap': ['cap', 'pac'],
 'capable': ['capable', 'pacable'],
 'caparison': ['caparison', 'paranosic'],
 'cape': ['cape', 'cepa', 'pace'],
 'caped': ['caped', 'decap', 'paced'],
 'capel': ['capel', 'place'],
 'capelin': ['calepin', 'capelin', 'panicle', 'pelican', 'pinacle'],
 'capeline': ['capeline', 'pelecani'],
 'caper': ['caper', 'crape', 'pacer', 'perca', 'recap'],
 'capernaite': ['capernaite', 'paraenetic'],
 'capernoited': ['capernoited', 'deprecation'],
 'capernoity': ['acetopyrin', 'capernoity'],
 'capes': ['capes', 'scape', 'space'],
 'caph': ['caph', 'chap'],
 'caphite': ['aphetic', 'caphite', 'hepatic'],
 'caphtor': ['caphtor', 'toparch'],
 'capias': ['capias', 'pisaca'],
 'capillament': ['capillament', 'implacental'],
 'capillarity': ['capillarity', 'piratically'],
 'capital': ['capital', 'palatic'],
 'capitan': ['capitan', 'captain'],
 'capitate': ['apatetic', 'capitate'],
 'capitellar': ['capitellar', 'prelatical'],
 'capito': ['atopic', 'capito', 'copita'],
 'capitol': ['capitol', 'coalpit', 'optical', 'topical'],
 'capomo': ['campoo', 'capomo'],
 'capon': ['capon', 'ponca'],
 'caponier': ['caponier', 'coprinae', 'procaine'],
 'capot': ['capot', 'coapt'],
 'capote': ['capote', 'toecap'],
 'capreol': ['capreol', 'polacre'],
 'capri': ['capri', 'picra', 'rapic'],
 'caprid': ['caprid', 'carpid', 'picard'],
 'capriote': ['aporetic', 'capriote', 'operatic'],
 'capsian': ['capsian', 'caspian', 'nascapi', 'panisca'],
 'capstone': ['capstone', 'opencast'],
 'capsula': ['capsula', 'pascual', 'scapula'],
 'capsular': ['capsular', 'scapular'],
 'capsulate': ['aspectual', 'capsulate'],
 'capsulated': ['capsulated', 'scapulated'],
 'capsule': ['capsule', 'specula', 'upscale'],
 'capsulectomy': ['capsulectomy', 'scapulectomy'],
 'capsuler': ['capsuler', 'specular'],
 'captain': ['capitan', 'captain'],
 'captation': ['anaptotic', 'captation'],
 'caption': ['caption', 'paction'],
 'captious': ['autopsic', 'captious'],
 'captor': ['captor', 'copart'],
 'capture': ['capture', 'uptrace'],
 'car': ['arc', 'car'],
 'cara': ['arca', 'cara'],
 'carabeen': ['bearance', 'carabeen'],
 'carabin': ['arbacin', 'carabin', 'cariban'],
 'carabini': ['arabinic', 'cabirian', 'carabini', 'cibarian'],
 'caracoli': ['caracoli', 'coracial'],
 'caracore': ['acrocera', 'caracore'],
 'caragana': ['aracanga', 'caragana'],
 'caramel': ['cameral', 'caramel', 'carmela', 'ceramal', 'reclama'],
 'caranda': ['anacard', 'caranda'],
 'carandas': ['carandas', 'sandarac'],
 'carane': ['arcane', 'carane'],
 'carangid': ['carangid', 'cardigan'],
 'carapine': ['carapine', 'carpaine'],
 'caravel': ['caravel', 'lavacre'],
 'carbamide': ['carbamide', 'crambidae'],
 'carbamine': ['carbamine', 'crambinae'],
 'carbamino': ['carbamino', 'macrobian'],
 'carbeen': ['carbeen', 'carbene'],
 'carbene': ['carbeen', 'carbene'],
 'carbo': ['carbo', 'carob', 'coarb', 'cobra'],
 'carbohydride': ['carbohydride', 'hydrocarbide'],
 'carbon': ['bracon', 'carbon', 'corban'],
 'carbonite': ['bicornate', 'carbonite', 'reboantic'],
 'carcel': ['carcel', 'cercal'],
 'carcinoma': ['carcinoma', 'macaronic'],
 'carcinosarcoma': ['carcinosarcoma', 'sarcocarcinoma'],
 'carcoon': ['carcoon', 'raccoon'],
 'cardel': ['cardel', 'cradle'],
 'cardia': ['acarid', 'cardia', 'carida'],
 'cardiac': ['arcadic', 'cardiac'],
 'cardial': ['cardial', 'radical'],
 'cardiant': ['antacrid', 'cardiant', 'radicant', 'tridacna'],
 'cardigan': ['carangid', 'cardigan'],
 'cardiidae': ['acrididae', 'cardiidae', 'cidaridae'],
 'cardin': ['andric', 'cardin', 'rancid'],
 'cardinal': ['cardinal', 'clarinda'],
 'cardioid': ['cardioid', 'caridoid'],
 'cardiophobe': ['brachiopode', 'cardiophobe'],
 'cardo': ['cardo', 'draco'],
 'cardon': ['candor', 'cardon', 'conrad'],
 'cardoon': ['cardoon', 'coronad'],
 'care': ['acer', 'acre', 'care', 'crea', 'race'],
 'careen': ['careen', 'carene', 'enrace'],
 'carene': ['careen', 'carene', 'enrace'],
 'carer': ['carer', 'crare', 'racer'],
 'carest': ['carest', 'caster', 'recast'],
 'caret': ['caret',
           'carte',
           'cater',
           'crate',
           'creat',
           'creta',
           'react',
           'recta',
           'trace'],
 'caretta': ['caretta', 'teacart', 'tearcat'],
 'carful': ['carful', 'furcal'],
 'carhop': ['carhop', 'paroch'],
 'cariama': ['aramaic', 'cariama'],
 'carian': ['acinar',
            'arnica',
            'canari',
            'carian',
            'carina',
            'crania',
            'narica'],
 'carib': ['baric', 'carib', 'rabic'],
 'cariban': ['arbacin', 'carabin', 'cariban'],
 'caribi': ['cabiri', 'caribi'],
 'carid': ['acrid', 'caird', 'carid', 'darci', 'daric', 'dirca'],
 'carida': ['acarid', 'cardia', 'carida'],
 'caridea': ['arcidae', 'caridea'],
 'caridean': ['caridean', 'dircaean', 'radiance'],
 'caridoid': ['cardioid', 'caridoid'],
 'carina': ['acinar',
            'arnica',
            'canari',
            'carian',
            'carina',
            'crania',
            'narica'],
 'carinal': ['carinal', 'carlina', 'clarain', 'cranial'],
 'carinatae': ['acraniate', 'carinatae'],
 'carinate': ['anaretic', 'arcanite', 'carinate', 'craniate'],
 'carinated': ['carinated', 'eradicant'],
 'cariole': ['calorie', 'cariole'],
 'carious': ['carious', 'curiosa'],
 'carisa': ['carisa', 'sciara'],
 'carissa': ['ascaris', 'carissa'],
 'cark': ['cark', 'rack'],
 'carking': ['arcking', 'carking', 'racking'],
 'carkingly': ['carkingly', 'rackingly'],
 'carless': ['carless', 'classer', 'reclass'],
 'carlet': ['carlet', 'cartel', 'claret', 'rectal', 'talcer'],
 'carlie': ['carlie', 'claire', 'eclair', 'erical'],
 'carlin': ['carlin', 'clarin', 'crinal'],
 'carlina': ['carinal', 'carlina', 'clarain', 'cranial'],
 'carlist': ['carlist', 'clarist'],
 'carlo': ['alcor', 'calor', 'carlo', 'carol', 'claro', 'coral'],
 'carlot': ['carlot', 'crotal'],
 'carlylian': ['ancillary', 'carlylian', 'cranially'],
 'carman': ['carman', 'marcan'],
 'carmel': ['calmer', 'carmel', 'clamer', 'marcel', 'mercal'],
 'carmela': ['cameral', 'caramel', 'carmela', 'ceramal', 'reclama'],
 'carmele': ['carmele', 'cleamer'],
 'carmelite': ['carmelite', 'melicerta'],
 'carmeloite': ['carmeloite', 'ectromelia', 'meteorical'],
 'carmine': ['armenic', 'carmine', 'ceriman', 'crimean', 'mercian'],
 'carminette': ['carminette', 'remittance'],
 'carminite': ['antimeric', 'carminite', 'criminate', 'metrician'],
 'carmot': ['carmot', 'comart'],
 'carnage': ['carnage', 'cranage', 'garance'],
 'carnalite': ['carnalite', 'claretian', 'lacertian', 'nectarial'],
 'carnate': ['carnate', 'cateran'],
 'carnation': ['anarcotin', 'cantorian', 'carnation', 'narcotina'],
 'carnationed': ['carnationed', 'dinoceratan'],
 'carnelian': ['carnelian', 'encranial'],
 'carneol': ['carneol', 'corneal'],
 'carneous': ['carneous', 'nacreous'],
 'carney': ['carney', 'craney'],
 'carnic': ['cancri', 'carnic', 'cranic'],
 'carniolan': ['carniolan', 'nonracial'],
 'carnose': ['carnose', 'coarsen', 'narcose'],
 'carnosity': ['carnosity', 'crayonist'],
 'carnotite': ['carnotite', 'cortinate'],
 'carnous': ['carnous', 'nacrous', 'narcous'],
 'caro': ['acor', 'caro', 'cora', 'orca'],
 'caroa': ['acroa', 'caroa'],
 'carob': ['carbo', 'carob', 'coarb', 'cobra'],
 'caroche': ['caroche', 'coacher', 'recoach'],
 'caroid': ['caroid', 'cordia'],
 'carol': ['alcor', 'calor', 'carlo', 'carol', 'claro', 'coral'],
 'carolan': ['alcoran', 'ancoral', 'carolan'],
 'carole': ['carole', 'coaler', 'coelar', 'oracle', 'recoal'],
 'carolean': ['carolean', 'lecanora'],
 'caroler': ['caroler', 'correal'],
 'caroli': ['caroli', 'corial', 'lorica'],
 'carolin': ['carolin', 'clarion', 'colarin', 'locrian'],
 'carolina': ['carolina', 'conarial'],
 'caroline': ['acrolein',
              'arecolin',
              'caroline',
              'colinear',
              'cornelia',
              'creolian',
              'lonicera'],
 'carolingian': ['carolingian', 'inorganical'],
 'carolus': ['carolus', 'oscular'],
 'carom': ['carom', 'coram', 'macro', 'marco'],
 'carone': ['carone', 'cornea'],
 'caroon': ['caroon', 'corona', 'racoon'],
 'carotenoid': ['carotenoid', 'coronadite', 'decoration'],
 'carotic': ['acrotic', 'carotic'],
 'carotid': ['arctoid', 'carotid', 'dartoic'],
 'carotidean': ['arctoidean', 'carotidean', 'cordaitean', 'dinocerata'],
 'carotin': ['anticor', 'carotin', 'cortina', 'ontaric'],
 'carouse': ['acerous', 'carouse', 'euscaro'],
 'carp': ['carp', 'crap'],
 'carpaine': ['carapine', 'carpaine'],
 'carpel': ['carpel', 'parcel', 'placer'],
 'carpellary': ['carpellary', 'parcellary'],
 'carpellate': ['carpellate', 'parcellate', 'prelacteal'],
 'carpent': ['carpent', 'precant'],
 'carpet': ['carpet', 'peract', 'preact'],
 'carpholite': ['carpholite', 'proethical'],
 'carpid': ['caprid', 'carpid', 'picard'],
 'carpiodes': ['carpiodes', 'scorpidae'],
 'carpocerite': ['carpocerite', 'reciprocate'],
 'carpogonial': ['carpogonial', 'coprolagnia'],
 'carpolite': ['carpolite', 'petricola'],
 'carpolith': ['carpolith', 'politarch', 'trophical'],
 'carposperm': ['carposperm', 'spermocarp'],
 'carrot': ['carrot', 'trocar'],
 'carroter': ['arrector', 'carroter'],
 'carse': ['carse', 'caser', 'ceras', 'scare', 'scrae'],
 'carsmith': ['carsmith', 'chartism'],
 'cartable': ['bracteal', 'cartable'],
 'carte': ['caret',
           'carte',
           'cater',
           'crate',
           'creat',
           'creta',
           'react',
           'recta',
           'trace'],
 'cartel': ['carlet', 'cartel', 'claret', 'rectal', 'talcer'],
 'cartelize': ['cartelize', 'zelatrice'],
 'carter': ['arrect', 'carter', 'crater', 'recart', 'tracer'],
 'cartesian': ['ascertain', 'cartesian', 'cartisane', 'sectarian'],
 'cartesianism': ['cartesianism', 'sectarianism'],
 'cartier': ['cartier', 'cirrate', 'erratic'],
 'cartilage': ['cartilage', 'rectalgia'],
 'cartisane': ['ascertain', 'cartesian', 'cartisane', 'sectarian'],
 'cartist': ['astrict', 'cartist', 'stratic'],
 'carton': ['cantor', 'carton', 'contra'],
 'cartoon': ['cartoon', 'coranto'],
 'cartoonist': ['cartoonist', 'scortation'],
 'carty': ['carty', 'tracy'],
 'carua': ['aruac', 'carua'],
 'carucal': ['accrual', 'carucal'],
 'carucate': ['accurate', 'carucate'],
 'carum': ['carum', 'cumar'],
 'carve': ['carve', 'crave', 'varec'],
 'carvel': ['calver', 'carvel', 'claver'],
 'carven': ['carven', 'cavern', 'craven'],
 'carver': ['carver', 'craver'],
 'carving': ['carving', 'craving'],
 'cary': ['cary', 'racy'],
 'caryl': ['acryl', 'caryl', 'clary'],
 'casabe': ['casabe', 'sabeca'],
 'casal': ['calas', 'casal', 'scala'],
 'cascade': ['cascade', 'saccade'],
 'case': ['case', 'esca'],
 'casebook': ['bookcase', 'casebook'],
 'caseful': ['caseful', 'fucales'],
 'casein': ['casein', 'incase'],
 'casel': ['alces', 'casel', 'scale'],
 'caser': ['carse', 'caser', 'ceras', 'scare', 'scrae'],
 'casern': ['casern', 'rescan'],
 'cashable': ['cashable', 'chasable'],
 'cashel': ['cashel', 'laches', 'sealch'],
 'cask': ['cask', 'sack'],
 'casket': ['casket', 'tesack'],
 'casking': ['casking', 'sacking'],
 'casklike': ['casklike', 'sacklike'],
 'casper': ['casper', 'escarp', 'parsec', 'scrape', 'secpar', 'spacer'],
 'caspian': ['capsian', 'caspian', 'nascapi', 'panisca'],
 'casque': ['casque', 'sacque'],
 'casquet': ['acquest', 'casquet'],
 'casse': ['casse', 'scase'],
 'cassian': ['cassian', 'cassina'],
 'cassina': ['cassian', 'cassina'],
 'cassino': ['caisson', 'cassino'],
 'cassock': ['cassock', 'cossack'],
 'cast': ['acts', 'cast', 'scat'],
 'castalia': ['castalia', 'sacalait'],
 'castalian': ['castalian', 'satanical'],
 'caste': ['caste', 'sceat'],
 'castelet': ['castelet', 'telecast'],
 'caster': ['carest', 'caster', 'recast'],
 'castice': ['ascetic', 'castice', 'siccate'],
 'castle': ['castle', 'sclate'],
 'castoff': ['castoff', 'offcast'],
 'castor': ['arctos', 'castor', 'costar', 'scrota'],
 'castores': ['castores', 'coassert'],
 'castoreum': ['castoreum', 'outscream'],
 'castoridae': ['castoridae', 'cestodaria'],
 'castorin': ['cantoris', 'castorin', 'corsaint'],
 'castra': ['castra', 'tarasc'],
 'casual': ['ascula', 'calusa', 'casual', 'casula', 'causal'],
 'casuality': ['casuality', 'causality'],
 'casually': ['casually', 'causally'],
 'casula': ['ascula', 'calusa', 'casual', 'casula', 'causal'],
 'cat': ['act', 'cat'],
 'catabolin': ['botanical', 'catabolin'],
 'catalan': ['cantala', 'catalan', 'lantaca'],
 'catalanist': ['anastaltic', 'catalanist'],
 'catalase': ['catalase', 'salaceta'],
 'catalinite': ['analcitite', 'catalinite'],
 'catalogue': ['catalogue', 'coagulate'],
 'catalyte': ['catalyte', 'cattleya'],
 'catamenial': ['calamitean', 'catamenial'],
 'catapultier': ['catapultier', 'particulate'],
 'cataria': ['acratia', 'cataria'],
 'catcher': ['catcher', 'recatch'],
 'catchup': ['catchup', 'upcatch'],
 'cate': ['cate', 'teca'],
 'catechin': ['atechnic', 'catechin', 'technica'],
 'catechism': ['catechism', 'schematic'],
 'catechol': ['catechol', 'coachlet'],
 'categoric': ['categoric', 'geocratic'],
 'catella': ['catella', 'lacteal'],
 'catenated': ['catenated', 'decantate'],
 'cater': ['caret',
           'carte',
           'cater',
           'crate',
           'creat',
           'creta',
           'react',
           'recta',
           'trace'],
 'cateran': ['carnate', 'cateran'],
 'caterer': ['caterer', 'recrate', 'retrace', 'terrace'],
 'cateress': ['cateress', 'cerastes'],
 'catfish': ['catfish', 'factish'],
 'cathari': ['cathari', 'chirata', 'cithara'],
 'catharina': ['anthracia', 'antiarcha', 'catharina'],
 'cathartae': ['cathartae', 'tracheata'],
 'cathepsin': ['cathepsin', 'stephanic'],
 'catherine': ['catherine', 'heritance'],
 'catheter': ['catheter', 'charette'],
 'cathine': ['cahnite', 'cathine'],
 'cathinine': ['anchietin', 'cathinine'],
 'cathion': ['cathion', 'chatino'],
 'cathograph': ['cathograph', 'tachograph'],
 'cathole': ['cathole', 'cholate'],
 'cathro': ['cathro', 'orchat'],
 'cathryn': ['cathryn', 'chantry'],
 'cathy': ['cathy', 'cyath', 'yacht'],
 'cation': ['action', 'atonic', 'cation'],
 'cationic': ['aconitic', 'cationic', 'itaconic'],
 'catkin': ['catkin', 'natick'],
 'catlin': ['catlin', 'tincal'],
 'catlinite': ['catlinite', 'intactile'],
 'catmalison': ['catmalison', 'monastical'],
 'catoism': ['atomics', 'catoism', 'cosmati', 'osmatic', 'somatic'],
 'catonian': ['catonian', 'taconian'],
 'catonic': ['cantico', 'catonic', 'taconic'],
 'catonism': ['catonism', 'monastic'],
 'catoptric': ['catoptric', 'protactic'],
 'catpipe': ['apeptic', 'catpipe'],
 'catstone': ['catstone', 'constate'],
 'catsup': ['catsup', 'upcast'],
 'cattail': ['attical', 'cattail'],
 'catti': ['attic', 'catti', 'tacit'],
 'cattily': ['cattily', 'tacitly'],
 'cattiness': ['cattiness', 'tacitness'],
 'cattle': ['cattle', 'tectal'],
 'cattleya': ['catalyte', 'cattleya'],
 'catvine': ['catvine', 'venatic'],
 'caucho': ['cachou', 'caucho'],
 'cauda': ['cadua', 'cauda'],
 'caudle': ['caudle', 'cedula', 'claude'],
 'caudodorsal': ['caudodorsal', 'dorsocaudal'],
 'caudofemoral': ['caudofemoral', 'femorocaudal'],
 'caudolateral': ['caudolateral', 'laterocaudal'],
 'caul': ['caul', 'ucal'],
 'cauld': ['cauld', 'ducal'],
 'caules': ['caelus', 'caules', 'clause'],
 'cauliform': ['cauliform', 'formulaic', 'fumarolic'],
 'caulinar': ['anicular', 'caulinar'],
 'caulis': ['caulis', 'clusia', 'sicula'],
 'caulite': ['aleutic', 'auletic', 'caulite', 'lutecia'],
 'caulome': ['caulome', 'leucoma'],
 'caulomic': ['caulomic', 'coumalic'],
 'caulote': ['caulote', 'colutea', 'oculate'],
 'caunch': ['caunch', 'cuchan'],
 'caurale': ['arcuale', 'caurale'],
 'causal': ['ascula', 'calusa', 'casual', 'casula', 'causal'],
 'causality': ['casuality', 'causality'],
 'causally': ['casually', 'causally'],
 'cause': ['cause', 'sauce'],
 'causeless': ['causeless', 'sauceless'],
 'causer': ['causer', 'saucer'],
 'causey': ['causey', 'cayuse'],
 'cautelous': ['cautelous', 'lutaceous'],
 'cauter': ['acture', 'cauter', 'curate'],
 'caution': ['auction', 'caution'],
 'cautionary': ['auctionary', 'cautionary'],
 'cautioner': ['cautioner', 'cointreau'],
 'caval': ['caval', 'clava'],
 'cavalry': ['calvary', 'cavalry'],
 'cavate': ['cavate', 'caveat', 'vacate'],
 'caveat': ['cavate', 'caveat', 'vacate'],
 'cavel': ['calve', 'cavel', 'clave'],
 'cavern': ['carven', 'cavern', 'craven'],
 'cavil': ['cavil', 'lavic'],
 'caviler': ['caliver', 'caviler', 'claiver', 'clavier', 'valeric', 'velaric'],
 'cavillation': ['cavillation', 'vacillation'],
 'cavitate': ['activate', 'cavitate'],
 'cavitation': ['activation', 'cavitation'],
 'cavitied': ['cavitied', 'vaticide'],
 'caw': ['caw', 'wac'],
 'cawk': ['cawk', 'wack'],
 'cawky': ['cawky', 'wacky'],
 'cayapa': ['cayapa', 'pacaya'],
 'cayuse': ['causey', 'cayuse'],
 'ccoya': ['accoy', 'ccoya'],
 'ceanothus': ['ceanothus', 'oecanthus'],
 'cearin': ['acerin', 'cearin'],
 'cebalrai': ['balearic', 'cebalrai'],
 'ceboid': ['bodice', 'ceboid'],
 'cebur': ['bruce', 'cebur', 'cuber'],
 'cecily': ['cecily', 'cicely'],
 'cedar': ['acred', 'cader', 'cadre', 'cedar'],
 'cedarn': ['cedarn', 'dancer', 'nacred'],
 'cedent': ['cedent', 'decent'],
 'ceder': ['ceder', 'cedre', 'cered', 'creed'],
 'cedrat': ['cedrat', 'decart', 'redact'],
 'cedrate': ['cedrate', 'cerated'],
 'cedre': ['ceder', 'cedre', 'cered', 'creed'],
 'cedrela': ['cedrela', 'creedal', 'declare'],
 'cedrin': ['cedrin', 'cinder', 'crined'],
 'cedriret': ['cedriret', 'directer', 'recredit', 'redirect'],
 'cedrol': ['cedrol', 'colder', 'cordel'],
 'cedron': ['cedron', 'conred'],
 'cedrus': ['cedrus', 'cursed'],
 'cedry': ['cedry', 'decry'],
 'cedula': ['caudle', 'cedula', 'claude'],
 'ceilinged': ['ceilinged', 'diligence'],
 'celadonite': ['caledonite', 'celadonite'],
 'celandine': ['celandine', 'decennial'],
 'celarent': ['celarent', 'centrale', 'enclaret'],
 'celature': ['celature', 'ulcerate'],
 'celebrate': ['celebrate', 'erectable'],
 'celebrated': ['braceleted', 'celebrated'],
 'celemin': ['celemin', 'melenic'],
 'celia': ['alice', 'celia', 'ileac'],
 'cellar': ['caller', 'cellar', 'recall'],
 'cellaret': ['allecret', 'cellaret'],
 'celloid': ['celloid', 'codille', 'collide', 'collied'],
 'celloidin': ['celloidin', 'collidine', 'decillion'],
 'celsian': ['celsian', 'escalin', 'sanicle', 'secalin'],
 'celtiberi': ['celtiberi', 'terebilic'],
 'celtis': ['celtis', 'clites'],
 'cembalist': ['blastemic', 'cembalist'],
 'cementer': ['cementer', 'cerement', 'recement'],
 'cendre': ['cendre', 'decern'],
 'cenosity': ['cenosity', 'cytosine'],
 'cense': ['cense', 'scene', 'sence'],
 'censer': ['censer', 'scerne', 'screen', 'secern'],
 'censerless': ['censerless', 'screenless'],
 'censorial': ['censorial', 'sarcoline'],
 'censual': ['censual', 'unscale'],
 'censureless': ['censureless', 'recluseness'],
 'cental': ['cantle', 'cental', 'lancet', 'tancel'],
 'centare': ['centare', 'crenate'],
 'centaur': ['centaur', 'untrace'],
 'centauri': ['anuretic', 'centauri', 'centuria', 'teucrian'],
 'centaury': ['centaury', 'cyanuret'],
 'centena': ['canteen', 'centena'],
 'centenar': ['centenar', 'entrance'],
 'centenier': ['centenier', 'renitence'],
 'center': ['center', 'recent', 'tenrec'],
 'centered': ['centered', 'decenter', 'decentre', 'recedent'],
 'centerer': ['centerer', 'recenter', 'recentre', 'terrence'],
 'centermost': ['centermost', 'escortment'],
 'centesimal': ['centesimal', 'lemniscate'],
 'centiar': ['centiar', 'certain', 'citrean', 'nacrite', 'nectria'],
 'centiare': ['aneretic', 'centiare', 'creatine', 'increate', 'iterance'],
 'centibar': ['bacterin', 'centibar'],
 'centimeter': ['centimeter', 'recitement', 'remittence'],
 'centimo': ['centimo', 'entomic', 'tecomin'],
 'centimolar': ['centimolar', 'melicraton'],
 'centinormal': ['centinormal', 'conterminal', 'nonmetrical'],
 'cento': ['cento', 'conte', 'tecon'],
 'centrad': ['cantred', 'centrad', 'tranced'],
 'centrale': ['celarent', 'centrale', 'enclaret'],
 'centranth': ['centranth', 'trenchant'],
 'centraxonia': ['centraxonia', 'excarnation'],
 'centriole': ['centriole', 'electrion', 'relection'],
 'centrodorsal': ['centrodorsal', 'dorsocentral'],
 'centroid': ['centroid', 'doctrine'],
 'centrolineal': ['centrolineal', 'crenellation'],
 'centunculus': ['centunculus', 'unsucculent'],
 'centuria': ['anuretic', 'centauri', 'centuria', 'teucrian'],
 'centurial': ['centurial', 'lucretian', 'ultranice'],
 'centuried': ['centuried', 'unrecited'],
 'centurion': ['centurion', 'continuer', 'cornutine'],
 'cepa': ['cape', 'cepa', 'pace'],
 'cephalin': ['alphenic', 'cephalin'],
 'cephalina': ['cephalina', 'epilachna'],
 'cephaloid': ['cephaloid', 'pholcidae'],
 'cephalomeningitis': ['cephalomeningitis', 'meningocephalitis'],
 'cephalometric': ['cephalometric', 'petrochemical'],
 'cephalopodous': ['cephalopodous', 'podocephalous'],
 'cephas': ['cephas', 'pesach'],
 'cepolidae': ['adipocele', 'cepolidae', 'ploceidae'],
 'ceps': ['ceps', 'spec'],
 'ceptor': ['ceptor', 'copter'],
 'ceral': ['ceral', 'clare', 'clear', 'lacer'],
 'ceramal': ['cameral', 'caramel', 'carmela', 'ceramal', 'reclama'],
 'ceramic': ['ceramic', 'racemic'],
 'ceramist': ['camerist', 'ceramist', 'matrices'],
 'ceras': ['carse', 'caser', 'ceras', 'scare', 'scrae'],
 'cerasein': ['cerasein', 'increase'],
 'cerasin': ['arsenic', 'cerasin', 'sarcine'],
 'cerastes': ['cateress', 'cerastes'],
 'cerata': ['arcate', 'cerata'],
 'cerate': ['cerate', 'create', 'ecarte'],
 'cerated': ['cedrate', 'cerated'],
 'ceratiid': ['ceratiid', 'raticide'],
 'ceration': ['actioner', 'anerotic', 'ceration', 'creation', 'reaction'],
 'ceratium': ['ceratium', 'muricate'],
 'ceratonia': ['canariote', 'ceratonia'],
 'ceratosa': ['ceratosa', 'ostracea'],
 'ceratothecal': ['ceratothecal', 'chloracetate'],
 'cerberic': ['cerberic', 'cerebric'],
 'cercal': ['carcel', 'cercal'],
 'cerci': ['cerci', 'ceric', 'cicer', 'circe'],
 'cercus': ['cercus', 'cruces'],
 'cerdonian': ['cerdonian', 'ordinance'],
 'cere': ['cere', 'cree'],
 'cereal': ['alerce', 'cereal', 'relace'],
 'cerealin': ['cerealin', 'cinereal', 'reliance'],
 'cerebra': ['cerebra', 'rebrace'],
 'cerebric': ['cerberic', 'cerebric'],
 'cerebroma': ['cerebroma', 'embraceor'],
 'cerebromeningitis': ['cerebromeningitis', 'meningocerebritis'],
 'cerebrum': ['cerebrum', 'cumberer'],
 'cered': ['ceder', 'cedre', 'cered', 'creed'],
 'cerement': ['cementer', 'cerement', 'recement'],
 'ceremonial': ['ceremonial', 'neomiracle'],
 'ceresin': ['ceresin', 'sincere'],
 'cereus': ['cereus', 'ceruse', 'recuse', 'rescue', 'secure'],
 'cerevis': ['cerevis', 'scrieve', 'service'],
 'ceria': ['acier', 'aeric', 'ceria', 'erica'],
 'ceric': ['cerci', 'ceric', 'cicer', 'circe'],
 'ceride': ['ceride', 'deicer'],
 'cerillo': ['cerillo', 'colleri', 'collier'],
 'ceriman': ['armenic', 'carmine', 'ceriman', 'crimean', 'mercian'],
 'cerin': ['cerin', 'crine'],
 'cerion': ['cerion', 'coiner', 'neroic', 'orcein', 'recoin'],
 'ceriops': ['ceriops', 'persico'],
 'cerite': ['cerite', 'certie', 'recite', 'tierce'],
 'cerium': ['cerium', 'uremic'],
 'cernuous': ['cernuous', 'coenurus'],
 'cero': ['cero', 'core'],
 'ceroma': ['ceroma', 'corema'],
 'ceroplast': ['ceroplast', 'precostal'],
 'ceroplastic': ['ceroplastic', 'cleistocarp', 'coreplastic'],
 'ceroplasty': ['ceroplasty', 'coreplasty'],
 'cerotic': ['cerotic', 'orectic'],
 'cerotin': ['cerotin', 'cointer', 'cotrine', 'cretion', 'noticer', 'rection'],
 'cerous': ['cerous', 'course', 'crouse', 'source'],
 'certain': ['centiar', 'certain', 'citrean', 'nacrite', 'nectria'],
 'certhia': ['certhia', 'rhaetic', 'theriac'],
 'certie': ['cerite', 'certie', 'recite', 'tierce'],
 'certifiable': ['certifiable', 'rectifiable'],
 'certification': ['certification', 'cretification', 'rectification'],
 'certificative': ['certificative', 'rectificative'],
 'certificator': ['certificator', 'rectificator'],
 'certificatory': ['certificatory', 'rectificatory'],
 'certified': ['certified', 'rectified'],
 'certifier': ['certifier', 'rectifier'],
 'certify': ['certify', 'cretify', 'rectify'],
 'certis': ['certis', 'steric'],
 'certitude': ['certitude', 'rectitude'],
 'certosina': ['atroscine', 'certosina', 'ostracine', 'tinoceras', 'tricosane'],
 'certosino': ['certosino', 'cortisone', 'socotrine'],
 'cerulean': ['cerulean', 'laurence'],
 'ceruminal': ['ceruminal', 'melanuric', 'numerical'],
 'ceruse': ['cereus', 'ceruse', 'recuse', 'rescue', 'secure'],
 'cervicobuccal': ['buccocervical', 'cervicobuccal'],
 'cervicodorsal': ['cervicodorsal', 'dorsocervical'],
 'cervicodynia': ['cervicodynia', 'corycavidine'],
 'cervicofacial': ['cervicofacial', 'faciocervical'],
 'cervicolabial': ['cervicolabial', 'labiocervical'],
 'cervicovesical': ['cervicovesical', 'vesicocervical'],
 'cervoid': ['cervoid', 'divorce'],
 'cervuline': ['cervuline', 'virulence'],
 'ceryl': ['ceryl', 'clyer'],
 'cerynean': ['ancyrene', 'cerynean'],
 'cesare': ['cesare', 'crease', 'recase', 'searce'],
 'cesarolite': ['cesarolite', 'esoterical'],
 'cesium': ['cesium', 'miscue'],
 'cesser': ['cesser', 'recess'],
 'cession': ['cession', 'oscines'],
 'cessor': ['cessor', 'crosse', 'scorse'],
 'cest': ['cest', 'sect'],
 'cestodaria': ['castoridae', 'cestodaria'],
 'cestrian': ['canister', 'cestrian', 'cisterna', 'irascent'],
 'cetane': ['cetane', 'tenace'],
 'cetene': ['cetene', 'ectene'],
 'ceti': ['ceti', 'cite', 'tice'],
 'cetid': ['cetid', 'edict'],
 'cetomorphic': ['cetomorphic', 'chemotropic', 'ectomorphic'],
 'cetonia': ['acetoin', 'aconite', 'anoetic', 'antoeci', 'cetonia'],
 'cetonian': ['cetonian', 'enaction'],
 'cetorhinus': ['cetorhinus', 'urosthenic'],
 'cetus': ['cetus', 'scute'],
 'cevenol': ['cevenol', 'clovene'],
 'cevine': ['cevine', 'evince', 'venice'],
 'cha': ['ach', 'cha'],
 'chab': ['bach', 'chab'],
 'chabouk': ['chabouk', 'chakobu'],
 'chaco': ['chaco', 'choca', 'coach'],
 'chacte': ['cachet', 'chacte'],
 'chaenolobus': ['chaenolobus', 'unchoosable'],
 'chaeta': ['achate', 'chaeta'],
 'chaetites': ['aesthetic', 'chaetites'],
 'chaetognath': ['chaetognath', 'gnathotheca'],
 'chaetopod': ['chaetopod', 'podotheca'],
 'chafer': ['chafer', 'frache'],
 'chagan': ['chagan', 'changa'],
 'chagrin': ['arching', 'chagrin'],
 'chai': ['chai', 'chia'],
 'chain': ['chain', 'chian', 'china'],
 'chained': ['chained', 'echidna'],
 'chainer': ['chainer', 'enchair', 'rechain'],
 'chainlet': ['chainlet', 'ethnical'],
 'chainman': ['chainman', 'chinaman'],
 'chair': ['chair', 'chria'],
 'chairer': ['chairer', 'charier'],
 'chait': ['aitch', 'chait', 'chati', 'chita', 'taich', 'tchai'],
 'chakar': ['chakar', 'chakra', 'charka'],
 'chakari': ['chakari', 'chikara', 'kachari'],
 'chakobu': ['chabouk', 'chakobu'],
 'chakra': ['chakar', 'chakra', 'charka'],
 'chalcon': ['chalcon', 'clochan', 'conchal'],
 'chalcosine': ['ascolichen', 'chalcosine'],
 'chaldron': ['chaldron', 'chlordan', 'chondral'],
 'chalet': ['achtel', 'chalet', 'thecal', 'thecla'],
 'chalker': ['chalker', 'hackler'],
 'chalky': ['chalky', 'hackly'],
 'challie': ['alichel', 'challie', 'helical'],
 'chalmer': ['chalmer', 'charmel'],
 'chalon': ['chalon', 'lochan'],
 'chalone': ['chalone', 'cholane'],
 'chalta': ['caltha', 'chalta'],
 'chamal': ['almach', 'chamal'],
 'chamar': ['chamar', 'machar'],
 'chamber': ['becharm', 'brecham', 'chamber'],
 'chamberer': ['chamberer', 'rechamber'],
 'chamian': ['chamian', 'mahican'],
 'chamisal': ['chamisal', 'chiasmal'],
 'chamiso': ['chamiso', 'chamois'],
 'chamite': ['chamite', 'hematic'],
 'chamois': ['chamiso', 'chamois'],
 'champa': ['champa', 'mapach'],
 'champain': ['champain', 'chinampa'],
 'chancer': ['chancer', 'chancre'],
 'chanchito': ['chanchito', 'nachitoch'],
 'chanco': ['chanco', 'concha'],
 'chancre': ['chancer', 'chancre'],
 'chandu': ['chandu', 'daunch'],
 'chane': ['achen', 'chane', 'chena', 'hance'],
 'chang': ['chang', 'ganch'],
 'changa': ['chagan', 'changa'],
 'changer': ['changer', 'genarch'],
 'chanidae': ['chanidae', 'hacienda'],
 'channeler': ['channeler', 'encharnel'],
 'chanst': ['chanst', 'snatch', 'stanch'],
 'chant': ['chant', 'natch'],
 'chanter': ['chanter', 'rechant'],
 'chantey': ['atechny', 'chantey'],
 'chantry': ['cathryn', 'chantry'],
 'chaos': ['chaos', 'oshac'],
 'chaotical': ['acatholic', 'chaotical'],
 'chap': ['caph', 'chap'],
 'chaparro': ['chaparro', 'parachor'],
 'chape': ['chape', 'cheap', 'peach'],
 'chaped': ['chaped', 'phecda'],
 'chapel': ['chapel', 'lepcha', 'pleach'],
 'chapelet': ['chapelet', 'peachlet'],
 'chapelmaster': ['chapelmaster', 'spermathecal'],
 'chaperno': ['canephor', 'chaperno', 'chaperon'],
 'chaperon': ['canephor', 'chaperno', 'chaperon'],
 'chaperone': ['canephore', 'chaperone'],
 'chaperonless': ['chaperonless', 'proseneschal'],
 'chapin': ['apinch', 'chapin', 'phanic'],
 'chapiter': ['chapiter', 'phreatic'],
 'chaps': ['chaps', 'pasch'],
 'chapt': ['chapt', 'pacht', 'patch'],
 'chapter': ['chapter', 'patcher', 'repatch'],
 'char': ['arch', 'char', 'rach'],
 'chara': ['achar', 'chara'],
 'charac': ['charac', 'charca'],
 'characin': ['anarchic', 'characin'],
 'characinoid': ['arachidonic', 'characinoid'],
 'charadrii': ['charadrii', 'richardia'],
 'charas': ['achras', 'charas'],
 'charbon': ['brochan', 'charbon'],
 'charca': ['charac', 'charca'],
 'chare': ['acher', 'arche', 'chare', 'chera', 'rache', 'reach'],
 'charer': ['archer', 'charer', 'rechar'],
 'charette': ['catheter', 'charette'],
 'charge': ['charge', 'creagh'],
 'charier': ['chairer', 'charier'],
 'chariot': ['chariot', 'haricot'],
 'charioted': ['charioted', 'trochidae'],
 'chariotman': ['achromatin', 'chariotman', 'machinator'],
 'charism': ['charism', 'chrisma'],
 'charisma': ['archaism', 'charisma'],
 'chark': ['chark', 'karch'],
 'charka': ['chakar', 'chakra', 'charka'],
 'charlatanistic': ['antarchistical', 'charlatanistic'],
 'charleen': ['charleen', 'charlene'],
 'charlene': ['charleen', 'charlene'],
 'charles': ['charles', 'clasher'],
 'charm': ['charm', 'march'],
 'charmel': ['chalmer', 'charmel'],
 'charmer': ['charmer', 'marcher', 'remarch'],
 'charnel': ['charnel', 'larchen'],
 'charon': ['anchor', 'archon', 'charon', 'rancho'],
 'charpoy': ['charpoy', 'corypha'],
 'chart': ['chart', 'ratch'],
 'charter': ['charter', 'ratcher'],
 'charterer': ['charterer', 'recharter'],
 'charting': ['charting', 'ratching'],
 'chartism': ['carsmith', 'chartism'],
 'charuk': ['charuk', 'chukar'],
 'chary': ['archy', 'chary'],
 'chasable': ['cashable', 'chasable'],
 'chaser': ['arches', 'chaser', 'eschar', 'recash', 'search'],
 'chasma': ['ascham', 'chasma'],
 'chaste': ['chaste', 'sachet', 'scathe', 'scheat'],
 'chasten': ['chasten', 'sanetch'],
 'chastener': ['chastener', 'rechasten'],
 'chastity': ['chastity', 'yachtist'],
 'chasuble': ['chasuble', 'subchela'],
 'chat': ['chat', 'tach'],
 'chatelainry': ['chatelainry', 'trachylinae'],
 'chati': ['aitch', 'chait', 'chati', 'chita', 'taich', 'tchai'],
 'chatino': ['cathion', 'chatino'],
 'chatsome': ['chatsome', 'moschate'],
 'chatta': ['attach', 'chatta'],
 'chattation': ['chattation', 'thanatotic'],
 'chattel': ['chattel', 'latchet'],
 'chatter': ['chatter', 'ratchet'],
 'chattery': ['chattery', 'ratchety', 'trachyte'],
 'chatti': ['chatti', 'hattic'],
 'chatty': ['chatty', 'tatchy'],
 'chatwood': ['chatwood', 'woodchat'],
 'chaute': ['chaute', 'chueta'],
 'chawan': ['chawan', 'chwana', 'wachna'],
 'chawer': ['chawer', 'rechaw'],
 'chawk': ['chawk', 'whack'],
 'chay': ['achy', 'chay'],
 'cheap': ['chape', 'cheap', 'peach'],
 'cheapen': ['cheapen', 'peachen'],
 'cheapery': ['cheapery', 'peachery'],
 'cheapside': ['cheapside', 'sphecidae'],
 'cheat': ['cheat', 'tache', 'teach', 'theca'],
 'cheatable': ['cheatable', 'teachable'],
 'cheatableness': ['cheatableness', 'teachableness'],
 'cheater': ['cheater', 'hectare', 'recheat', 'reteach', 'teacher'],
 'cheatery': ['cheatery', 'cytherea', 'teachery'],
 'cheating': ['cheating', 'teaching'],
 'cheatingly': ['cheatingly', 'teachingly'],
 'cheatrie': ['cheatrie', 'hetaeric'],
 'checker': ['checker', 'recheck'],
 'chee': ['chee', 'eche'],
 'cheek': ['cheek', 'cheke', 'keech'],
 'cheerer': ['cheerer', 'recheer'],
 'cheerly': ['cheerly', 'lechery'],
 'cheery': ['cheery', 'reechy'],
 'cheet': ['cheet', 'hecte'],
 'cheir': ['cheir', 'rheic'],
 'cheiropodist': ['cheiropodist', 'coeditorship'],
 'cheka': ['cheka', 'keach'],
 'cheke': ['cheek', 'cheke', 'keech'],
 'chela': ['chela', 'lache', 'leach'],
 'chelide': ['chelide', 'heliced'],
 'chelidon': ['chelidon', 'chelonid', 'delichon'],
 'chelidonate': ['chelidonate', 'endothecial'],
 'chelodina': ['chelodina', 'hedonical'],
 'chelone': ['chelone', 'echelon'],
 'chelonid': ['chelidon', 'chelonid', 'delichon'],
 'cheloniid': ['cheloniid', 'lichenoid'],
 'chemiatrist': ['chemiatrist', 'chrismatite', 'theatricism'],
 'chemical': ['alchemic', 'chemical'],
 'chemicomechanical': ['chemicomechanical', 'mechanicochemical'],
 'chemicophysical': ['chemicophysical', 'physicochemical'],
 'chemicovital': ['chemicovital', 'vitochemical'],
 'chemiloon': ['chemiloon', 'homocline'],
 'chemotaxy': ['chemotaxy', 'myxotheca'],
 'chemotropic': ['cetomorphic', 'chemotropic', 'ectomorphic'],
 'chena': ['achen', 'chane', 'chena', 'hance'],
 'chenica': ['chenica', 'chicane'],
 'chenille': ['chenille', 'hellenic'],
 'chenopod': ['chenopod', 'ponchoed'],
 'chera': ['acher', 'arche', 'chare', 'chera', 'rache', 'reach'],
 'chermes': ['chermes', 'schemer'],
 'chert': ['chert', 'retch'],
 'cherte': ['cherte', 'etcher'],
 'chervil': ['chervil', 'chilver'],
 'cheson': ['cheson', 'chosen', 'schone'],
 'chest': ['chest', 'stech'],
 'chestily': ['chestily', 'lecythis'],
 'chesty': ['chesty', 'scythe'],
 'chet': ['chet', 'etch', 'tche', 'tech'],
 'chettik': ['chettik', 'thicket'],
 'chetty': ['chetty', 'tetchy'],
 'chewer': ['chewer', 'rechew'],
 'chewink': ['chewink', 'whicken'],
 'chi': ['chi', 'hic', 'ich'],
 'chia': ['chai', 'chia'],
 'chiam': ['chiam', 'machi', 'micah'],
 'chian': ['chain', 'chian', 'china'],
 'chiasmal': ['chamisal', 'chiasmal'],
 'chiastolite': ['chiastolite', 'heliostatic'],
 'chicane': ['chenica', 'chicane'],
 'chicle': ['chicle', 'cliche'],
 'chid': ['chid', 'dich'],
 'chider': ['chider', 'herdic'],
 'chidra': ['chidra', 'diarch'],
 'chief': ['chief', 'fiche'],
 'chield': ['chield', 'childe'],
 'chien': ['chien', 'chine', 'niche'],
 'chikara': ['chakari', 'chikara', 'kachari'],
 'chil': ['chil', 'lich'],
 'childe': ['chield', 'childe'],
 'chilean': ['chilean', 'echinal', 'nichael'],
 'chili': ['chili', 'lichi'],
 'chiliasm': ['chiliasm', 'hilasmic', 'machilis'],
 'chilla': ['achill', 'cahill', 'chilla'],
 'chiloma': ['chiloma', 'malicho'],
 'chilopod': ['chilopod', 'pholcoid'],
 'chilopoda': ['chilopoda', 'haplodoci'],
 'chilostome': ['chilostome', 'schooltime'],
 'chilver': ['chervil', 'chilver'],
 'chimane': ['chimane', 'machine'],
 'chime': ['chime', 'hemic', 'miche'],
 'chimer': ['chimer', 'mechir', 'micher'],
 'chimera': ['chimera', 'hermaic'],
 'chimney': ['chimney', 'hymenic'],
 'chimu': ['chimu', 'humic'],
 'chin': ['chin', 'inch'],
 'china': ['chain', 'chian', 'china'],
 'chinaman': ['chainman', 'chinaman'],
 'chinampa': ['champain', 'chinampa'],
 'chinanta': ['acanthin', 'chinanta'],
 'chinar': ['chinar', 'inarch'],
 'chine': ['chien', 'chine', 'niche'],
 'chined': ['chined', 'inched'],
 'chink': ['chink', 'kinch'],
 'chinkle': ['chinkle', 'kelchin'],
 'chinks': ['chinks', 'skinch'],
 'chinoa': ['chinoa', 'noahic'],
 'chinotti': ['chinotti', 'tithonic'],
 'chint': ['chint', 'nitch'],
 'chiolite': ['chiolite', 'eolithic'],
 'chionididae': ['chionididae', 'onchidiidae'],
 'chiral': ['archil', 'chiral'],
 'chirapsia': ['chirapsia', 'pharisaic'],
 'chirata': ['cathari', 'chirata', 'cithara'],
 'chiro': ['chiro', 'choir', 'ichor'],
 'chiromancist': ['chiromancist', 'monarchistic'],
 'chiromant': ['chiromant', 'chromatin'],
 'chiromantic': ['chiromantic', 'chromatinic'],
 'chiromantis': ['anchoritism', 'chiromantis', 'chrismation', 'harmonistic'],
 'chirometer': ['chirometer', 'rheometric'],
 'chiroplasty': ['chiroplasty', 'polyarchist'],
 'chiropter': ['chiropter', 'peritroch'],
 'chirosophist': ['chirosophist', 'opisthorchis'],
 'chirotes': ['chirotes', 'theorics'],
 'chirotype': ['chirotype', 'hypocrite'],
 'chirp': ['chirp', 'prich'],
 'chirper': ['chirper', 'prerich'],
 'chiseler': ['chiseler', 'rechisel'],
 'chit': ['chit', 'itch', 'tchi'],
 'chita': ['aitch', 'chait', 'chati', 'chita', 'taich', 'tchai'],
 'chital': ['chital', 'claith'],
 'chitinoid': ['chitinoid', 'dithionic'],
 'chitosan': ['atchison', 'chitosan'],
 'chitose': ['chitose', 'echoist'],
 'chloe': ['chloe', 'choel'],
 'chloracetate': ['ceratothecal', 'chloracetate'],
 'chloranthy': ['chloranthy', 'rhynchotal'],
 'chlorate': ['chlorate', 'trochlea'],
 'chlordan': ['chaldron', 'chlordan', 'chondral'],
 'chlore': ['chlore', 'choler', 'orchel'],
 'chloremia': ['chloremia', 'homerical'],
 'chlorinate': ['chlorinate', 'ectorhinal', 'tornachile'],
 'chlorite': ['chlorite', 'clothier'],
 'chloritic': ['chloritic', 'trochilic'],
 'chloroamine': ['chloroamine', 'melanochroi'],
 'chloroanaemia': ['aeolharmonica', 'chloroanaemia'],
 'chloroanemia': ['chloroanemia', 'choleromania'],
 'chloroiodide': ['chloroiodide', 'iodochloride'],
 'chloroplatinic': ['chloroplatinic', 'platinochloric'],
 'cho': ['cho', 'och'],
 'choana': ['aonach', 'choana'],
 'choca': ['chaco', 'choca', 'coach'],
 'choco': ['choco', 'hocco'],
 'choel': ['chloe', 'choel'],
 'choenix': ['choenix', 'hexonic'],
 'choes': ['choes', 'chose'],
 'choiak': ['choiak', 'kochia'],
 'choice': ['choice', 'echoic'],
 'choil': ['choil', 'choli', 'olchi'],
 'choir': ['chiro', 'choir', 'ichor'],
 'choirman': ['choirman', 'harmonic', 'omniarch'],
 'choker': ['choker', 'hocker'],
 'choky': ['choky', 'hocky'],
 'chol': ['chol', 'loch'],
 'chola': ['chola', 'loach', 'olcha'],
 'cholane': ['chalone', 'cholane'],
 'cholangioitis': ['angiocholitis', 'cholangioitis'],
 'cholanic': ['cholanic', 'colchian'],
 'cholate': ['cathole', 'cholate'],
 'cholecystoduodenostomy': ['cholecystoduodenostomy', 'duodenocholecystostomy'],
 'choler': ['chlore', 'choler', 'orchel'],
 'cholera': ['cholera', 'choreal'],
 'cholerine': ['cholerine', 'rhinocele'],
 'choleromania': ['chloroanemia', 'choleromania'],
 'cholesteremia': ['cholesteremia', 'heteroecismal'],
 'choli': ['choil', 'choli', 'olchi'],
 'choline': ['choline', 'helicon'],
 'cholo': ['cholo', 'cohol'],
 'chondral': ['chaldron', 'chlordan', 'chondral'],
 'chondrite': ['chondrite', 'threnodic'],
 'chondroadenoma': ['adenochondroma', 'chondroadenoma'],
 'chondroangioma': ['angiochondroma', 'chondroangioma'],
 'chondroarthritis': ['arthrochondritis', 'chondroarthritis'],
 'chondrocostal': ['chondrocostal', 'costochondral'],
 'chondrofibroma': ['chondrofibroma', 'fibrochondroma'],
 'chondrolipoma': ['chondrolipoma', 'lipochondroma'],
 'chondromyxoma': ['chondromyxoma', 'myxochondroma'],
 'chondromyxosarcoma': ['chondromyxosarcoma', 'myxochondrosarcoma'],
 'choop': ['choop', 'pooch'],
 'chopa': ['chopa', 'phoca', 'poach'],
 'chopin': ['chopin', 'phonic'],
 'chopine': ['chopine', 'phocine'],
 'chora': ['achor', 'chora', 'corah', 'orach', 'roach'],
 'choral': ['choral', 'lorcha'],
 'chorasmian': ['anachorism', 'chorasmian', 'maraschino'],
 'chordal': ['chordal', 'dorlach'],
 'chorditis': ['chorditis', 'orchidist'],
 'chordotonal': ['chordotonal', 'notochordal'],
 'chore': ['chore', 'ocher'],
 'chorea': ['chorea', 'ochrea', 'rochea'],
 'choreal': ['cholera', 'choreal'],
 'choree': ['choree', 'cohere', 'echoer'],
 'choreoid': ['choreoid', 'ochidore'],
 'choreus': ['choreus', 'chouser', 'rhoecus'],
 'choric': ['choric', 'orchic'],
 'choriocele': ['choriocele', 'orchiocele'],
 'chorioidoretinitis': ['chorioidoretinitis', 'retinochorioiditis'],
 'chorism': ['chorism', 'chrisom'],
 'chorist': ['chorist', 'ostrich'],
 'choristate': ['choristate', 'rheostatic'],
 'chorization': ['chorization', 'rhizoctonia', 'zonotrichia'],
 'choroid': ['choroid', 'ochroid'],
 'chort': ['chort', 'rotch', 'torch'],
 'chorten': ['chorten', 'notcher'],
 'chorti': ['chorti', 'orthic', 'thoric', 'trochi'],
 'chose': ['choes', 'chose'],
 'chosen': ['cheson', 'chosen', 'schone'],
 'chou': ['chou', 'ouch'],
 'chough': ['chough', 'hughoc'],
 'choup': ['choup', 'pouch'],
 'chous': ['chous', 'hocus'],
 'chouser': ['choreus', 'chouser', 'rhoecus'],
 'chowder': ['chowder', 'cowherd'],
 'chria': ['chair', 'chria'],
 'chrism': ['chrism', 'smirch'],
 'chrisma': ['charism', 'chrisma'],
 'chrismation': ['anchoritism', 'chiromantis', 'chrismation', 'harmonistic'],
 'chrismatite': ['chemiatrist', 'chrismatite', 'theatricism'],
 'chrisom': ['chorism', 'chrisom'],
 'christ': ['christ', 'strich'],
 'christen': ['christen', 'snitcher'],
 'christener': ['christener', 'rechristen'],
 'christian': ['christian', 'christina'],
 'christiana': ['arachnitis', 'christiana'],
 'christina': ['christian', 'christina'],
 'christophe': ['christophe', 'hectorship'],
 'chromatician': ['achromatinic', 'chromatician'],
 'chromatid': ['chromatid', 'dichromat'],
 'chromatin': ['chiromant', 'chromatin'],
 'chromatinic': ['chiromantic', 'chromatinic'],
 'chromatocyte': ['chromatocyte', 'thoracectomy'],
 'chromatoid': ['chromatoid', 'tichodroma'],
 'chromatone': ['chromatone', 'enomotarch'],
 'chromid': ['chromid', 'richdom'],
 'chromidae': ['archidome', 'chromidae'],
 'chromite': ['chromite', 'trichome'],
 'chromocyte': ['chromocyte', 'cytochrome'],
 'chromolithography': ['chromolithography', 'lithochromography'],
 'chromophotography': ['chromophotography', 'photochromography'],
 'chromophotolithograph': ['chromophotolithograph', 'photochromolithograph'],
 'chromopsia': ['chromopsia', 'isocamphor'],
 'chromotype': ['chromotype', 'cormophyte', 'ectomorphy'],
 'chromotypic': ['chromotypic', 'cormophytic', 'mycotrophic'],
 'chronophotograph': ['chronophotograph', 'photochronograph'],
 'chronophotographic': ['chronophotographic', 'photochronographic'],
 'chronophotography': ['chronophotography', 'photochronography'],
 'chrysography': ['chrysography', 'psychorrhagy'],
 'chrysolite': ['chrysolite', 'chrysotile'],
 'chrysopid': ['chrysopid', 'dysphoric'],
 'chrysotile': ['chrysolite', 'chrysotile'],
 'chucker': ['chucker', 'rechuck'],
 'chueta': ['chaute', 'chueta'],
 'chukar': ['charuk', 'chukar'],
 'chulan': ['chulan', 'launch', 'nuchal'],
 'chum': ['chum', 'much'],
 'chumpish': ['chumpish', 'chumship'],
 'chumship': ['chumpish', 'chumship'],
 'chunari': ['chunari', 'unchair'],
 'chunnia': ['chunnia', 'unchain'],
 'chuprassie': ['chuprassie', 'haruspices'],
 'churl': ['churl', 'lurch'],
 'churn': ['churn', 'runch'],
 'chut': ['chut', 'tchu', 'utch'],
 'chwana': ['chawan', 'chwana', 'wachna'],
 'chyak': ['chyak', 'hacky'],
 'chylous': ['chylous', 'slouchy'],
 'cibarial': ['biracial', 'cibarial'],
 'cibarian': ['arabinic', 'cabirian', 'carabini', 'cibarian'],
 'cibolan': ['cibolan', 'coalbin'],
 'cicala': ['alcaic', 'cicala'],
 'cicatrize': ['arcticize', 'cicatrize'],
 'cicely': ['cecily', 'cicely'],
 'cicer': ['cerci', 'ceric', 'cicer', 'circe'],
 'cicerone': ['cicerone', 'croceine'],
 'cicindela': ['cicindela', 'cinclidae', 'icelandic'],
 'ciclatoun': ['ciclatoun', 'noctiluca'],
 'ciconian': ['aniconic', 'ciconian'],
 'ciconine': ['ciconine', 'conicine'],
 'cidaridae': ['acrididae', 'cardiidae', 'cidaridae'],
 'cidaris': ['cidaris', 'sciarid'],
 'cider': ['cider', 'cried', 'deric', 'dicer'],
 'cigala': ['caliga', 'cigala'],
 'cigar': ['cigar', 'craig'],
 'cilia': ['cilia', 'iliac'],
 'ciliation': ['ciliation', 'coinitial'],
 'cilice': ['cilice', 'icicle'],
 'cimbia': ['cimbia', 'iambic'],
 'cimicidae': ['amicicide', 'cimicidae'],
 'cinchonine': ['cinchonine', 'conchinine'],
 'cinclidae': ['cicindela', 'cinclidae', 'icelandic'],
 'cinder': ['cedrin', 'cinder', 'crined'],
 'cinderous': ['cinderous', 'decursion'],
 'cindie': ['cindie', 'incide'],
 'cine': ['cine', 'nice'],
 'cinel': ['cinel', 'cline'],
 'cinema': ['anemic', 'cinema', 'iceman'],
 'cinematographer': ['cinematographer', 'megachiropteran'],
 'cinene': ['cinene', 'nicene'],
 'cineole': ['cineole', 'coeline'],
 'cinerama': ['amacrine', 'american', 'camerina', 'cinerama'],
 'cineration': ['cineration', 'inceration'],
 'cinerea': ['cairene', 'cinerea'],
 'cinereal': ['cerealin', 'cinereal', 'reliance'],
 'cingulum': ['cingulum', 'glucinum'],
 'cinnamate': ['antanemic', 'cinnamate'],
 'cinnamol': ['cinnamol', 'nonclaim'],
 'cinnamon': ['cinnamon', 'mannonic'],
 'cinnamoned': ['cinnamoned', 'demicannon'],
 'cinque': ['cinque', 'quince'],
 'cinter': ['cinter', 'cretin', 'crinet'],
 'cinura': ['anuric', 'cinura', 'uranic'],
 'cion': ['cion', 'coin', 'icon'],
 'cipher': ['cipher', 'rechip'],
 'cipo': ['cipo', 'pico'],
 'circe': ['cerci', 'ceric', 'cicer', 'circe'],
 'circle': ['circle', 'cleric'],
 'cirrate': ['cartier', 'cirrate', 'erratic'],
 'cirrated': ['cirrated', 'craterid'],
 'cirrhotic': ['cirrhotic', 'trichroic'],
 'cirrose': ['cirrose', 'crosier'],
 'cirsectomy': ['cirsectomy', 'citromyces'],
 'cirsoid': ['cirsoid', 'soricid'],
 'ciruela': ['auricle', 'ciruela'],
 'cise': ['cise', 'sice'],
 'cisplatine': ['cisplatine', 'plasticine'],
 'cispontine': ['cispontine', 'inspection'],
 'cissoidal': ['cissoidal', 'dissocial'],
 'cistern': ['cistern', 'increst'],
 'cisterna': ['canister', 'cestrian', 'cisterna', 'irascent'],
 'cisternal': ['cisternal', 'larcenist'],
 'cistvaen': ['cistvaen', 'vesicant'],
 'cit': ['cit', 'tic'],
 'citadel': ['citadel', 'deltaic', 'dialect', 'edictal', 'lactide'],
 'citatory': ['atrocity', 'citatory'],
 'cite': ['ceti', 'cite', 'tice'],
 'citer': ['citer', 'recti', 'ticer', 'trice'],
 'cithara': ['cathari', 'chirata', 'cithara'],
 'citharist': ['citharist', 'trachitis'],
 'citharoedic': ['citharoedic', 'diachoretic'],
 'cither': ['cither', 'thrice'],
 'citied': ['citied', 'dietic'],
 'citizen': ['citizen', 'zincite'],
 'citral': ['citral', 'rictal'],
 'citramide': ['citramide', 'diametric', 'matricide'],
 'citrange': ['argentic', 'citrange'],
 'citrate': ['atretic', 'citrate'],
 'citrated': ['citrated', 'tetracid', 'tetradic'],
 'citrean': ['centiar', 'certain', 'citrean', 'nacrite', 'nectria'],
 'citrene': ['citrene', 'enteric', 'enticer', 'tercine'],
 'citreous': ['citreous', 'urticose'],
 'citric': ['citric', 'critic'],
 'citrin': ['citrin', 'nitric'],
 'citrination': ['citrination', 'intrication'],
 'citrine': ['citrine', 'crinite', 'inciter', 'neritic'],
 'citromyces': ['cirsectomy', 'citromyces'],
 'citron': ['citron', 'cortin', 'crotin'],
 'citronade': ['citronade', 'endaortic', 'redaction'],
 'citronella': ['citronella', 'interlocal'],
 'citrus': ['citrus', 'curtis', 'rictus', 'rustic'],
 'cive': ['cive', 'vice'],
 'civet': ['civet', 'evict'],
 'civetone': ['civetone', 'evection'],
 'civitan': ['activin', 'civitan'],
 'cixo': ['cixo', 'coix'],
 'clabber': ['cabbler', 'clabber'],
 'clacker': ['cackler', 'clacker', 'crackle'],
 'cladine': ['cladine', 'decalin', 'iceland'],
 'cladonia': ['cladonia', 'condalia', 'diaconal'],
 'cladophyll': ['cladophyll', 'phylloclad'],
 'claim': ['claim', 'clima', 'malic'],
 'claimant': ['calamint', 'claimant'],
 'claimer': ['claimer', 'miracle', 'reclaim'],
 'clairce': ['clairce', 'clarice'],
 'claire': ['carlie', 'claire', 'eclair', 'erical'],
 'claith': ['chital', 'claith'],
 'claiver': ['caliver', 'caviler', 'claiver', 'clavier', 'valeric', 'velaric'],
 'clam': ['calm', 'clam'],
 'clamant': ['calmant', 'clamant'],
 'clamative': ['calmative', 'clamative'],
 'clamatores': ['clamatores', 'scleromata'],
 'clamber': ['cambrel', 'clamber', 'cramble'],
 'clame': ['camel', 'clame', 'cleam', 'macle'],
 'clamer': ['calmer', 'carmel', 'clamer', 'marcel', 'mercal'],
 'clamor': ['clamor', 'colmar'],
 'clamorist': ['clamorist', 'crotalism'],
 'clangingly': ['clangingly', 'glancingly'],
 'clap': ['calp', 'clap'],
 'clapper': ['clapper', 'crapple'],
 'claquer': ['claquer', 'lacquer'],
 'clarain': ['carinal', 'carlina', 'clarain', 'cranial'],
 'clare': ['ceral', 'clare', 'clear', 'lacer'],
 'clarence': ['canceler', 'clarence', 'recancel'],
 'claret': ['carlet', 'cartel', 'claret', 'rectal', 'talcer'],
 'claretian': ['carnalite', 'claretian', 'lacertian', 'nectarial'],
 'clarice': ['clairce', 'clarice'],
 'clarin': ['carlin', 'clarin', 'crinal'],
 'clarinda': ['cardinal', 'clarinda'],
 'clarion': ['carolin', 'clarion', 'colarin', 'locrian'],
 'clarionet': ['alectrion', 'clarionet', 'crotaline', 'locarnite'],
 'clarist': ['carlist', 'clarist'],
 'claro': ['alcor', 'calor', 'carlo', 'carol', 'claro', 'coral'],
 'clary': ['acryl', 'caryl', 'clary'],
 'clasher': ['charles', 'clasher'],
 'clasp': ['clasp', 'scalp'],
 'clasper': ['clasper', 'reclasp', 'scalper'],
 'clasping': ['clasping', 'scalping'],
 'classed': ['classed', 'declass'],
 'classer': ['carless', 'classer', 'reclass'],
 'classism': ['classism', 'misclass'],
 'classwork': ['classwork', 'crosswalk'],
 'clat': ['clat', 'talc'],
 'clathrina': ['alchitran', 'clathrina'],
 'clathrose': ['clathrose', 'searcloth'],
 'clatterer': ['clatterer', 'craterlet'],
 'claude': ['caudle', 'cedula', 'claude'],
 'claudian': ['claudian', 'dulciana'],
 'claudicate': ['aciculated', 'claudicate'],
 'clause': ['caelus', 'caules', 'clause'],
 'claustral': ['claustral', 'lacustral'],
 'clava': ['caval', 'clava'],
 'clavacin': ['clavacin', 'vaccinal'],
 'clavaria': ['calvaria', 'clavaria'],
 'clave': ['calve', 'cavel', 'clave'],
 'claver': ['calver', 'carvel', 'claver'],
 'claviculate': ['calculative', 'claviculate'],
 'clavier': ['caliver', 'caviler', 'claiver', 'clavier', 'valeric', 'velaric'],
 'clavis': ['clavis', 'slavic'],
 'clay': ['acyl', 'clay', 'lacy'],
 'clayer': ['clayer', 'lacery'],
 'claytonia': ['acylation', 'claytonia'],
 'clead': ['clead', 'decal', 'laced'],
 'cleam': ['camel', 'clame', 'cleam', 'macle'],
 'cleamer': ['carmele', 'cleamer'],
 'clean': ['canel', 'clean', 'lance', 'lenca'],
 'cleaner': ['cleaner', 'reclean'],
 'cleanly': ['cleanly', 'lancely'],
 'cleanout': ['cleanout', 'outlance'],
 'cleanse': ['cleanse', 'scalene'],
 'cleanup': ['cleanup', 'unplace'],
 'clear': ['ceral', 'clare', 'clear', 'lacer'],
 'clearable': ['clearable', 'lacerable'],
 'clearer': ['clearer', 'reclear'],
 'cleat': ['cleat', 'eclat', 'ectal', 'lacet', 'tecla'],
 'clefted': ['clefted', 'deflect'],
 'cleistocarp': ['ceroplastic', 'cleistocarp', 'coreplastic'],
 'cleistogeny': ['cleistogeny', 'lysogenetic'],
 'cleoid': ['cleoid', 'coiled', 'docile'],
 'cleopatra': ['acropetal', 'cleopatra'],
 'cleric': ['circle', 'cleric'],
 'clericature': ['clericature', 'recirculate'],
 'clerkess': ['clerkess', 'reckless'],
 'clerking': ['clerking', 'reckling'],
 'clerus': ['clerus', 'cruels'],
 'clethra': ['clethra', 'latcher', 'ratchel', 'relatch', 'talcher', 'trachle'],
 'cleveite': ['cleveite', 'elective'],
 'cliche': ['chicle', 'cliche'],
 'clicker': ['clicker', 'crickle'],
 'clidastes': ['clidastes', 'discastle'],
 'clientage': ['clientage', 'genetical'],
 'cliented': ['cliented', 'denticle'],
 'cliftonia': ['cliftonia', 'fictional'],
 'clima': ['claim', 'clima', 'malic'],
 'climber': ['climber', 'reclimb'],
 'clime': ['clime', 'melic'],
 'cline': ['cinel', 'cline'],
 'clinger': ['clinger', 'cringle'],
 'clinia': ['anilic', 'clinia'],
 'clinicopathological': ['clinicopathological', 'pathologicoclinical'],
 'clinium': ['clinium', 'ulminic'],
 'clinker': ['clinker', 'crinkle'],
 'clinoclase': ['clinoclase', 'oscillance'],
 'clinoclasite': ['callisection', 'clinoclasite'],
 'clinodome': ['clinodome', 'melodicon', 'monocleid'],
 'clinology': ['clinology', 'coolingly'],
 'clinometer': ['clinometer', 'recoilment'],
 'clinospore': ['clinospore', 'necropolis'],
 'clio': ['clio', 'coil', 'coli', 'loci'],
 'cliona': ['alnico', 'cliona', 'oilcan'],
 'clione': ['clione', 'coelin', 'encoil', 'enolic'],
 'clipeus': ['clipeus', 'spicule'],
 'clipper': ['clipper', 'cripple'],
 'clipse': ['clipse', 'splice'],
 'clipsome': ['clipsome', 'polemics'],
 'clite': ['clite', 'telic'],
 'clites': ['celtis', 'clites'],
 'clitia': ['clitia', 'italic'],
 'clition': ['clition', 'nilotic'],
 'clitoria': ['clitoria', 'loricati'],
 'clitoridean': ['clitoridean', 'directional'],
 'clitoris': ['clitoris', 'coistril'],
 'clive': ['clive', 'velic'],
 'cloacinal': ['cloacinal', 'cocillana'],
 'cloam': ['cloam', 'comal'],
 'cloamen': ['aclemon', 'cloamen'],
 'clobber': ['clobber', 'cobbler'],
 'clochan': ['chalcon', 'clochan', 'conchal'],
 'clocked': ['clocked', 'cockled'],
 'clocker': ['clocker', 'cockler'],
 'clod': ['clod', 'cold'],
 'clodder': ['clodder', 'coddler'],
 'cloggy': ['cloggy', 'coggly'],
 'cloister': ['cloister', 'coistrel'],
 'cloisteral': ['cloisteral', 'sclerotial'],
 'cloit': ['cloit', 'lotic'],
 'clonicotonic': ['clonicotonic', 'tonicoclonic'],
 'clonus': ['clonus', 'consul'],
 'clop': ['clop', 'colp'],
 'close': ['close', 'socle'],
 'closer': ['closer', 'cresol', 'escrol'],
 'closter': ['closter', 'costrel'],
 'closterium': ['closterium', 'sclerotium'],
 'clot': ['clot', 'colt'],
 'clothier': ['chlorite', 'clothier'],
 'clotho': ['clotho', 'coolth'],
 'clotter': ['clotter', 'crottle'],
 'cloture': ['cloture', 'clouter'],
 'cloud': ['cloud', 'could'],
 'clouter': ['cloture', 'clouter'],
 'clovene': ['cevenol', 'clovene'],
 'clow': ['clow', 'cowl'],
 'cloy': ['cloy', 'coly'],
 'clue': ['clue', 'luce'],
 'clumse': ['clumse', 'muscle'],
 'clumsily': ['clumsily', 'scyllium'],
 'clumsy': ['clumsy', 'muscly'],
 'clunist': ['clunist', 'linctus'],
 'clupea': ['alecup', 'clupea'],
 'clupeine': ['clupeine', 'pulicene'],
 'clusia': ['caulis', 'clusia', 'sicula'],
 'clutch': ['clutch', 'cultch'],
 'clutter': ['clutter', 'cuttler'],
 'clyde': ['clyde', 'decyl'],
 'clyer': ['ceryl', 'clyer'],
 'clymenia': ['clymenia', 'mycelian'],
 'clypeolate': ['clypeolate', 'ptyalocele'],
 'clysis': ['clysis', 'lyssic'],
 'clysmic': ['clysmic', 'cyclism'],
 'cnemial': ['cnemial', 'melanic'],
 'cnemis': ['cnemis', 'mnesic'],
 'cneorum': ['cneorum', 'corneum'],
 'cnicus': ['cnicus', 'succin'],
 'cnida': ['canid', 'cnida', 'danic'],
 'cnidaria': ['acridian', 'cnidaria'],
 'cnidian': ['cnidian', 'indican'],
 'cnidophore': ['cnidophore', 'princehood'],
 'coach': ['chaco', 'choca', 'coach'],
 'coacher': ['caroche', 'coacher', 'recoach'],
 'coachlet': ['catechol', 'coachlet'],
 'coactor': ['coactor', 'tarocco'],
 'coadamite': ['acetamido', 'coadamite'],
 'coadnate': ['anecdota', 'coadnate'],
 'coadunite': ['coadunite', 'education', 'noctuidae'],
 'coagent': ['coagent', 'cognate'],
 'coagulate': ['catalogue', 'coagulate'],
 'coaita': ['atocia', 'coaita'],
 'coal': ['alco', 'coal', 'cola', 'loca'],
 'coalbin': ['cibolan', 'coalbin'],
 'coaler': ['carole', 'coaler', 'coelar', 'oracle', 'recoal'],
 'coalite': ['aloetic', 'coalite'],
 'coalition': ['coalition', 'lociation'],
 'coalitionist': ['coalitionist', 'solicitation'],
 'coalizer': ['calorize', 'coalizer'],
 'coalpit': ['capitol', 'coalpit', 'optical', 'topical'],
 'coaltitude': ['coaltitude', 'colatitude'],
 'coaming': ['coaming', 'macigno'],
 'coan': ['coan', 'onca'],
 'coapt': ['capot', 'coapt'],
 'coarb': ['carbo', 'carob', 'coarb', 'cobra'],
 'coarrange': ['arrogance', 'coarrange'],
 'coarse': ['acrose', 'coarse'],
 'coarsen': ['carnose', 'coarsen', 'narcose'],
 'coassert': ['castores', 'coassert'],
 'coast': ['ascot', 'coast', 'costa', 'tacso', 'tasco'],
 'coastal': ['coastal', 'salacot'],
 'coaster': ['coaster', 'recoast'],
 'coasting': ['agnostic', 'coasting'],
 'coated': ['coated', 'decoat'],
 'coater': ['coater', 'recoat'],
 'coating': ['coating', 'cotinga'],
 'coatroom': ['coatroom', 'morocota'],
 'coax': ['coax', 'coxa'],
 'cobbler': ['clobber', 'cobbler'],
 'cobia': ['baioc', 'cabio', 'cobia'],
 'cobiron': ['boronic', 'cobiron'],
 'cobitis': ['biotics', 'cobitis'],
 'cobra': ['carbo', 'carob', 'coarb', 'cobra'],
 'cocaine': ['cocaine', 'oceanic'],
 'cocainist': ['cocainist', 'siccation'],
 'cocama': ['cocama', 'macaco'],
 'cocamine': ['cocamine', 'comacine'],
 'cochleare': ['archocele', 'cochleare'],
 'cochleitis': ['cochleitis', 'ochlesitic'],
 'cocillana': ['cloacinal', 'cocillana'],
 'cocker': ['cocker', 'recock'],
 'cockily': ['cockily', 'colicky'],
 'cockled': ['clocked', 'cockled'],
 'cockler': ['clocker', 'cockler'],
 'cockup': ['cockup', 'upcock'],
 'cocreditor': ['cocreditor', 'codirector'],
 'cocurrent': ['cocurrent', 'occurrent', 'uncorrect'],
 'cod': ['cod', 'doc'],
 'codamine': ['codamine', 'comedian', 'daemonic', 'demoniac'],
 'codder': ['codder', 'corded'],
 'coddler': ['clodder', 'coddler'],
 'code': ['code', 'coed'],
 'codebtor': ['bedoctor', 'codebtor'],
 'coder': ['coder', 'cored', 'credo'],
 'coderive': ['coderive', 'divorcee'],
 'codicil': ['codicil', 'dicolic'],
 'codille': ['celloid', 'codille', 'collide', 'collied'],
 'codirector': ['cocreditor', 'codirector'],
 'codium': ['codium', 'mucoid'],
 'coed': ['code', 'coed'],
 'coeditorship': ['cheiropodist', 'coeditorship'],
 'coelar': ['carole', 'coaler', 'coelar', 'oracle', 'recoal'],
 'coelata': ['alcoate', 'coelata'],
 'coelection': ['coelection', 'entocoelic'],
 'coelia': ['aeolic', 'coelia'],
 'coelin': ['clione', 'coelin', 'encoil', 'enolic'],
 'coeline': ['cineole', 'coeline'],
 'coelogyne': ['coelogyne', 'gonyocele'],
 'coenactor': ['coenactor', 'croconate'],
 'coenobiar': ['borocaine', 'coenobiar'],
 'coenurus': ['cernuous', 'coenurus'],
 'coestate': ['coestate', 'ecostate'],
 'coeternal': ['antrocele', 'coeternal', 'tolerance'],
 'coeval': ['alcove', 'coeval', 'volcae'],
 'cofaster': ['cofaster', 'forecast'],
 'coferment': ['coferment', 'forcement'],
 'cogeneric': ['cogeneric', 'concierge'],
 'coggly': ['cloggy', 'coggly'],
 'cognate': ['coagent', 'cognate'],
 'cognatical': ['cognatical', 'galactonic'],
 'cognation': ['cognation', 'contagion'],
 'cognition': ['cognition', 'incognito'],
 'cognominal': ['cognominal', 'gnomonical'],
 'cogon': ['cogon', 'congo'],
 'cograil': ['argolic', 'cograil'],
 'coheir': ['coheir', 'heroic'],
 'cohen': ['cohen', 'enoch'],
 'cohere': ['choree', 'cohere', 'echoer'],
 'cohol': ['cholo', 'cohol'],
 'cohune': ['cohune', 'hounce'],
 'coif': ['coif', 'fico', 'foci'],
 'coign': ['coign', 'incog'],
 'coil': ['clio', 'coil', 'coli', 'loci'],
 'coiled': ['cleoid', 'coiled', 'docile'],
 'coiler': ['coiler', 'recoil'],
 'coin': ['cion', 'coin', 'icon'],
 'coinclude': ['coinclude', 'undecolic'],
 'coiner': ['cerion', 'coiner', 'neroic', 'orcein', 'recoin'],
 'coinfer': ['coinfer', 'conifer'],
 'coinherence': ['coinherence', 'incoherence'],
 'coinherent': ['coinherent', 'incoherent'],
 'coinitial': ['ciliation', 'coinitial'],
 'coinmate': ['coinmate', 'maconite'],
 'coinspire': ['coinspire', 'precision'],
 'coinsure': ['coinsure', 'corineus', 'cusinero'],
 'cointer': ['cerotin', 'cointer', 'cotrine', 'cretion', 'noticer', 'rection'],
 'cointreau': ['cautioner', 'cointreau'],
 'coistrel': ['cloister', 'coistrel'],
 'coistril': ['clitoris', 'coistril'],
 'coix': ['cixo', 'coix'],
 'coker': ['coker', 'corke', 'korec'],
 'coky': ['coky', 'yock'],
 'cola': ['alco', 'coal', 'cola', 'loca'],
 'colalgia': ['alogical', 'colalgia'],
 'colan': ['colan', 'conal'],
 'colane': ['canelo', 'colane'],
 'colarin': ['carolin', 'clarion', 'colarin', 'locrian'],
 'colate': ['acetol', 'colate', 'locate'],
 'colation': ['colation', 'coontail', 'location'],
 'colatitude': ['coaltitude', 'colatitude'],
 'colchian': ['cholanic', 'colchian'],
 'colcine': ['colcine', 'concile', 'conicle'],
 'colcothar': ['colcothar', 'ochlocrat'],
 'cold': ['clod', 'cold'],
 'colder': ['cedrol', 'colder', 'cordel'],
 'colectomy': ['colectomy', 'cyclotome'],
 'colemanite': ['colemanite', 'melaconite'],
 'coleur': ['coleur', 'colure'],
 'coleus': ['coleus', 'oscule'],
 'coli': ['clio', 'coil', 'coli', 'loci'],
 'colias': ['colias', 'scolia', 'social'],
 'colicky': ['cockily', 'colicky'],
 'colima': ['colima', 'olamic'],
 'colin': ['colin', 'nicol'],
 'colinear': ['acrolein',
              'arecolin',
              'caroline',
              'colinear',
              'cornelia',
              'creolian',
              'lonicera'],
 'colitis': ['colitis', 'solicit'],
 'colk': ['colk', 'lock'],
 'colla': ['callo', 'colla', 'local'],
 'collage': ['alcogel', 'collage'],
 'collare': ['collare', 'corella', 'ocellar'],
 'collaret': ['collaret', 'corallet'],
 'collarino': ['collarino', 'coronilla'],
 'collatee': ['collatee', 'ocellate'],
 'collationer': ['collationer', 'recollation'],
 'collectioner': ['collectioner', 'recollection'],
 'collegial': ['collegial', 'gallicole'],
 'collegian': ['allogenic', 'collegian'],
 'colleri': ['cerillo', 'colleri', 'collier'],
 'colleter': ['colleter', 'coteller', 'coterell', 'recollet'],
 'colletia': ['colletia', 'teocalli'],
 'collide': ['celloid', 'codille', 'collide', 'collied'],
 'collidine': ['celloidin', 'collidine', 'decillion'],
 'collie': ['collie', 'ocelli'],
 'collied': ['celloid', 'codille', 'collide', 'collied'],
 'collier': ['cerillo', 'colleri', 'collier'],
 'colligate': ['colligate', 'cotillage'],
 'colline': ['colline', 'lioncel'],
 'collinear': ['collinear', 'coralline'],
 'collinsia': ['collinsia', 'isoclinal'],
 'collotypy': ['collotypy', 'polycotyl'],
 'collusive': ['collusive', 'colluvies'],
 'colluvies': ['collusive', 'colluvies'],
 'collyba': ['callboy', 'collyba'],
 'colmar': ['clamor', 'colmar'],
 'colobus': ['colobus', 'subcool'],
 'coloenteritis': ['coloenteritis', 'enterocolitis'],
 'colombian': ['colombian', 'colombina'],
 'colombina': ['colombian', 'colombina'],
 'colonalgia': ['colonalgia', 'naological'],
 'colonate': ['colonate', 'ecotonal'],
 'colonialist': ['colonialist', 'oscillation'],
 'coloproctitis': ['coloproctitis', 'proctocolitis'],
 'color': ['color', 'corol', 'crool'],
 'colored': ['colored', 'croodle', 'decolor'],
 'colorer': ['colorer', 'recolor'],
 'colorin': ['colorin', 'orcinol'],
 'colorman': ['colorman', 'conormal'],
 'colp': ['clop', 'colp'],
 'colpeurynter': ['colpeurynter', 'counterreply'],
 'colpitis': ['colpitis', 'politics', 'psilotic'],
 'colporrhagia': ['colporrhagia', 'orographical'],
 'colt': ['clot', 'colt'],
 'colter': ['colter', 'lector', 'torcel'],
 'coltskin': ['coltskin', 'linstock'],
 'colubrina': ['binocular', 'caliburno', 'colubrina'],
 'columbo': ['columbo', 'coulomb'],
 'columbotitanate': ['columbotitanate', 'titanocolumbate'],
 'columnated': ['columnated', 'documental'],
 'columned': ['columned', 'uncledom'],
 'colunar': ['colunar', 'cornual', 'courlan'],
 'colure': ['coleur', 'colure'],
 'colutea': ['caulote', 'colutea', 'oculate'],
 'coly': ['cloy', 'coly'],
 'coma': ['coma', 'maco'],
 'comacine': ['cocamine', 'comacine'],
 'comal': ['cloam', 'comal'],
 'coman': ['coman', 'macon', 'manoc'],
 'comart': ['carmot', 'comart'],
 'comate': ['comate', 'metoac', 'tecoma'],
 'combat': ['combat', 'tombac'],
 'comber': ['comber', 'recomb'],
 'combinedly': ['combinedly', 'molybdenic'],
 'comedial': ['cameloid', 'comedial', 'melodica'],
 'comedian': ['codamine', 'comedian', 'daemonic', 'demoniac'],
 'comediant': ['comediant', 'metaconid'],
 'comedist': ['comedist', 'demotics', 'docetism', 'domestic'],
 'comedown': ['comedown', 'downcome'],
 'comeliness': ['comeliness', 'incomeless'],
 'comeling': ['comeling', 'comingle'],
 'comenic': ['comenic', 'encomic', 'meconic'],
 'comer': ['comer', 'crome'],
 'comforter': ['comforter', 'recomfort'],
 'comid': ['comid', 'domic'],
 'coming': ['coming', 'gnomic'],
 'comingle': ['comeling', 'comingle'],
 'comintern': ['comintern', 'nonmetric'],
 'comitia': ['caimito', 'comitia'],
 'comitragedy': ['comitragedy', 'tragicomedy'],
 'comity': ['comity', 'myotic'],
 'commander': ['commander', 'recommand'],
 'commation': ['commation', 'monatomic'],
 'commelina': ['commelina', 'melomanic'],
 'commender': ['commender', 'recommend'],
 'commentarial': ['commentarial', 'manometrical'],
 'commination': ['commination', 'monamniotic'],
 'commissioner': ['commissioner', 'recommission'],
 'commonality': ['ammonolytic', 'commonality'],
 'commorient': ['commorient', 'metronomic', 'monometric'],
 'compact': ['accompt', 'compact'],
 'compacter': ['compacter', 'recompact'],
 'company': ['company', 'copyman'],
 'compare': ['compare', 'compear'],
 'comparition': ['comparition', 'proamniotic'],
 'compasser': ['compasser', 'recompass'],
 'compear': ['compare', 'compear'],
 'compenetrate': ['compenetrate', 'contemperate'],
 'competitioner': ['competitioner', 'recompetition'],
 'compile': ['compile', 'polemic'],
 'compiler': ['compiler', 'complier'],
 'complainer': ['complainer', 'procnemial', 'recomplain'],
 'complaint': ['complaint', 'compliant'],
 'complanate': ['complanate', 'placentoma'],
 'compliant': ['complaint', 'compliant'],
 'complier': ['compiler', 'complier'],
 'compounder': ['compounder', 'recompound'],
 'comprehender': ['comprehender', 'recomprehend'],
 'compressed': ['compressed', 'decompress'],
 'comprise': ['comprise', 'perosmic'],
 'compromission': ['compromission', 'procommission'],
 'conal': ['colan', 'conal'],
 'conamed': ['conamed', 'macedon'],
 'conant': ['cannot', 'canton', 'conant', 'nonact'],
 'conarial': ['carolina', 'conarial'],
 'conarium': ['conarium', 'coumarin'],
 'conative': ['conative', 'invocate'],
 'concealer': ['concealer', 'reconceal'],
 'concent': ['concent', 'connect'],
 'concenter': ['concenter', 'reconnect'],
 'concentive': ['concentive', 'connective'],
 'concertize': ['concertize', 'concretize'],
 'concessioner': ['concessioner', 'reconcession'],
 'concha': ['chanco', 'concha'],
 'conchal': ['chalcon', 'clochan', 'conchal'],
 'conchinine': ['cinchonine', 'conchinine'],
 'concierge': ['cogeneric', 'concierge'],
 'concile': ['colcine', 'concile', 'conicle'],
 'concocter': ['concocter', 'reconcoct'],
 'concreter': ['concreter', 'reconcert'],
 'concretize': ['concertize', 'concretize'],
 'concretor': ['concretor', 'conrector'],
 'condalia': ['cladonia', 'condalia', 'diaconal'],
 'condemner': ['condemner', 'recondemn'],
 'condite': ['condite', 'ctenoid'],
 'conditioner': ['conditioner', 'recondition'],
 'condor': ['condor', 'cordon'],
 'conduit': ['conduit', 'duction', 'noctuid'],
 'condylomatous': ['condylomatous', 'monodactylous'],
 'cone': ['cone', 'once'],
 'conepate': ['conepate', 'tepecano'],
 'coner': ['coner', 'crone', 'recon'],
 'cones': ['cones', 'scone'],
 'confesser': ['confesser', 'reconfess'],
 'configurationism': ['configurationism', 'misconfiguration'],
 'confirmer': ['confirmer', 'reconfirm'],
 'confirmor': ['confirmor', 'corniform'],
 'conflate': ['conflate', 'falconet'],
 'conformer': ['conformer', 'reconform'],
 'confounder': ['confounder', 'reconfound'],
 'confrere': ['confrere', 'enforcer', 'reconfer'],
 'confronter': ['confronter', 'reconfront'],
 'congealer': ['congealer', 'recongeal'],
 'congeneric': ['congeneric', 'necrogenic'],
 'congenerous': ['congenerous', 'necrogenous'],
 'congenial': ['congenial', 'goclenian'],
 'congo': ['cogon', 'congo'],
 'congreet': ['congreet', 'coregent'],
 'congreve': ['congreve', 'converge'],
 'conical': ['conical', 'laconic'],
 'conicine': ['ciconine', 'conicine'],
 'conicle': ['colcine', 'concile', 'conicle'],
 'conicoid': ['conicoid', 'conoidic'],
 'conidium': ['conidium', 'mucinoid', 'oncidium'],
 'conifer': ['coinfer', 'conifer'],
 'conima': ['camion', 'conima', 'manioc', 'monica'],
 'conin': ['conin', 'nonic', 'oncin'],
 'conine': ['conine', 'connie', 'ennoic'],
 'conjoiner': ['conjoiner', 'reconjoin'],
 'conk': ['conk', 'nock'],
 'conker': ['conker', 'reckon'],
 'conkers': ['conkers', 'snocker'],
 'connarite': ['connarite', 'container', 'cotarnine', 'crenation', 'narcotine'],
 'connatal': ['cantonal', 'connatal'],
 'connation': ['connation', 'nonaction'],
 'connature': ['antecornu', 'connature'],
 'connect': ['concent', 'connect'],
 'connectival': ['connectival', 'conventical'],
 'connective': ['concentive', 'connective'],
 'connie': ['conine', 'connie', 'ennoic'],
 'connoissance': ['connoissance', 'nonaccession'],
 'conoidal': ['conoidal', 'dolciano'],
 'conoidic': ['conicoid', 'conoidic'],
 'conor': ['conor', 'croon', 'ronco'],
 'conormal': ['colorman', 'conormal'],
 'conoy': ['conoy', 'coony'],
 'conrad': ['candor', 'cardon', 'conrad'],
 'conrector': ['concretor', 'conrector'],
 'conred': ['cedron', 'conred'],
 'conringia': ['conringia', 'inorganic'],
 'consenter': ['consenter', 'nonsecret', 'reconsent'],
 'conservable': ['conservable', 'conversable'],
 'conservancy': ['conservancy', 'conversancy'],
 'conservant': ['conservant', 'conversant'],
 'conservation': ['conservation', 'conversation'],
 'conservational': ['conservational', 'conversational'],
 'conservationist': ['conservationist', 'conversationist'],
 'conservative': ['conservative', 'conversative'],
 'conserve': ['conserve', 'converse'],
 'conserver': ['conserver', 'converser'],
 'considerate': ['considerate', 'desecration'],
 'considerative': ['considerative', 'devisceration'],
 'considered': ['considered', 'deconsider'],
 'considerer': ['considerer', 'reconsider'],
 'consigner': ['consigner', 'reconsign'],
 'conspiracy': ['conspiracy', 'snipocracy'],
 'conspire': ['conspire', 'incorpse'],
 'constate': ['catstone', 'constate'],
 'constitutionalism': ['constitutionalism', 'misconstitutional'],
 'constitutioner': ['constitutioner', 'reconstitution'],
 'constrain': ['constrain', 'transonic'],
 'constructer': ['constructer', 'reconstruct'],
 'constructionism': ['constructionism', 'misconstruction'],
 'consul': ['clonus', 'consul'],
 'consulage': ['consulage', 'glucosane'],
 'consulary': ['consulary', 'cynosural'],
 'consulter': ['consulter', 'reconsult'],
 'consume': ['consume', 'muscone'],
 'consumer': ['consumer', 'mucrones'],
 'consute': ['consute', 'contuse'],
 'contagion': ['cognation', 'contagion'],
 'contain': ['actinon', 'cantion', 'contain'],
 'container': ['connarite', 'container', 'cotarnine', 'crenation', 'narcotine'],
 'conte': ['cento', 'conte', 'tecon'],
 'contemperate': ['compenetrate', 'contemperate'],
 'contender': ['contender', 'recontend'],
 'conter': ['conter', 'cornet', 'cronet', 'roncet'],
 'conterminal': ['centinormal', 'conterminal', 'nonmetrical'],
 'contester': ['contester', 'recontest'],
 'continual': ['continual', 'inoculant', 'unctional'],
 'continued': ['continued', 'unnoticed'],
 'continuer': ['centurion', 'continuer', 'cornutine'],
 'contise': ['contise', 'noetics', 'section'],
 'contline': ['contline', 'nonlicet'],
 'contortae': ['contortae', 'crotonate'],
 'contour': ['contour', 'cornuto', 'countor', 'crouton'],
 'contra': ['cantor', 'carton', 'contra'],
 'contracter': ['contracter', 'correctant', 'recontract'],
 'contrapose': ['antroscope', 'contrapose'],
 'contravene': ['contravene', 'covenanter'],
 'contrite': ['contrite', 'tetronic'],
 'contrive': ['contrive', 'invector'],
 'conturbation': ['conturbation', 'obtruncation'],
 'contuse': ['consute', 'contuse'],
 'conure': ['conure', 'rounce', 'uncore'],
 'conventical': ['connectival', 'conventical'],
 'conventioner': ['conventioner', 'reconvention'],
 'converge': ['congreve', 'converge'],
 'conversable': ['conservable', 'conversable'],
 'conversancy': ['conservancy', 'conversancy'],
 'conversant': ['conservant', 'conversant'],
 'conversation': ['conservation', 'conversation'],
 'conversational': ['conservational', 'conversational'],
 'conversationist': ['conservationist', 'conversationist'],
 'conversative': ['conservative', 'conversative'],
 'converse': ['conserve', 'converse'],
 'converser': ['conserver', 'converser'],
 'converter': ['converter', 'reconvert'],
 'convertise': ['convertise', 'ventricose'],
 'conveyer': ['conveyer', 'reconvey'],
 'conycatcher': ['conycatcher', 'technocracy'],
 'conyrine': ['conyrine', 'corynine'],
 'cooker': ['cooker', 'recook'],
 'cool': ['cool', 'loco'],
 'coolant': ['coolant', 'octonal'],
 'cooler': ['cooler', 'recool'],
 'coolingly': ['clinology', 'coolingly'],
 'coolth': ['clotho', 'coolth'],
 'coolweed': ['coolweed', 'locoweed'],
 'cooly': ['cooly', 'coyol'],
 'coonroot': ['coonroot', 'octoroon'],
 'coontail': ['colation', 'coontail', 'location'],
 'coony': ['conoy', 'coony'],
 'coop': ['coop', 'poco'],
 'coos': ['coos', 'soco'],
 'coost': ['coost', 'scoot'],
 'coot': ['coot', 'coto', 'toco'],
 'copa': ['copa', 'paco'],
 'copable': ['copable', 'placebo'],
 'copalite': ['copalite', 'poetical'],
 'coparent': ['coparent', 'portance'],
 'copart': ['captor', 'copart'],
 'copartner': ['copartner', 'procreant'],
 'copatain': ['copatain', 'pacation'],
 'copehan': ['copehan', 'panoche', 'phocean'],
 'copen': ['copen', 'ponce'],
 'coperta': ['coperta', 'pectora', 'porcate'],
 'copied': ['copied', 'epodic'],
 'copis': ['copis', 'pisco'],
 'copist': ['copist', 'coptis', 'optics', 'postic'],
 'copita': ['atopic', 'capito', 'copita'],
 'coplanar': ['coplanar', 'procanal'],
 'copleased': ['copleased', 'escaloped'],
 'copperer': ['copperer', 'recopper'],
 'coppery': ['coppery', 'precopy'],
 'copr': ['copr', 'corp', 'crop'],
 'coprinae': ['caponier', 'coprinae', 'procaine'],
 'coprinus': ['coprinus', 'poncirus'],
 'coprolagnia': ['carpogonial', 'coprolagnia'],
 'coprophagist': ['coprophagist', 'topographics'],
 'coprose': ['coprose', 'scooper'],
 'copse': ['copse', 'pecos', 'scope'],
 'copter': ['ceptor', 'copter'],
 'coptis': ['copist', 'coptis', 'optics', 'postic'],
 'copula': ['copula', 'cupola'],
 'copular': ['copular', 'croupal', 'cupolar', 'porcula'],
 'copulate': ['copulate', 'outplace'],
 'copulation': ['copulation', 'poculation'],
 'copus': ['copus', 'scoup'],
 'copyman': ['company', 'copyman'],
 'copyrighter': ['copyrighter', 'recopyright'],
 'coque': ['coque', 'ocque'],
 'coquitlam': ['coquitlam', 'quamoclit'],
 'cor': ['cor', 'cro', 'orc', 'roc'],
 'cora': ['acor', 'caro', 'cora', 'orca'],
 'coraciae': ['coraciae', 'icacorea'],
 'coracial': ['caracoli', 'coracial'],
 'coracias': ['coracias', 'rascacio'],
 'coradicate': ['coradicate', 'ectocardia'],
 'corah': ['achor', 'chora', 'corah', 'orach', 'roach'],
 'coraise': ['coraise', 'scoriae'],
 'coral': ['alcor', 'calor', 'carlo', 'carol', 'claro', 'coral'],
 'coraled': ['acerdol', 'coraled'],
 'coralist': ['calorist', 'coralist'],
 'corallet': ['collaret', 'corallet'],
 'corallian': ['corallian', 'corallina'],
 'corallina': ['corallian', 'corallina'],
 'coralline': ['collinear', 'coralline'],
 'corallite': ['corallite', 'lectorial'],
 'coram': ['carom', 'coram', 'macro', 'marco'],
 'coranto': ['cartoon', 'coranto'],
 'corban': ['bracon', 'carbon', 'corban'],
 'corbeil': ['bricole', 'corbeil', 'orbicle'],
 'cordaitean': ['arctoidean', 'carotidean', 'cordaitean', 'dinocerata'],
 'cordate': ['cordate', 'decator', 'redcoat'],
 'corded': ['codder', 'corded'],
 'cordel': ['cedrol', 'colder', 'cordel'],
 'corder': ['corder', 'record'],
 'cordia': ['caroid', 'cordia'],
 'cordial': ['cordial', 'dorical'],
 'cordicole': ['cordicole', 'crocodile'],
 'cordierite': ['cordierite', 'directoire'],
 'cordoba': ['bocardo', 'cordoba'],
 'cordon': ['condor', 'cordon'],
 'core': ['cero', 'core'],
 'cored': ['coder', 'cored', 'credo'],
 'coregent': ['congreet', 'coregent'],
 'coreless': ['coreless', 'sclerose'],
 'corella': ['collare', 'corella', 'ocellar'],
 'corema': ['ceroma', 'corema'],
 'coreplastic': ['ceroplastic', 'cleistocarp', 'coreplastic'],
 'coreplasty': ['ceroplasty', 'coreplasty'],
 'corer': ['corer', 'crore'],
 'coresidual': ['coresidual', 'radiculose'],
 'coresign': ['coresign', 'cosigner'],
 'corge': ['corge', 'gorce'],
 'corgi': ['corgi', 'goric', 'orgic'],
 'corial': ['caroli', 'corial', 'lorica'],
 'coriamyrtin': ['coriamyrtin', 'criminatory'],
 'corin': ['corin', 'noric', 'orcin'],
 'corindon': ['corindon', 'nodicorn'],
 'corineus': ['coinsure', 'corineus', 'cusinero'],
 'corinna': ['corinna', 'cronian'],
 'corinne': ['corinne', 'cornein', 'neronic'],
 'cork': ['cork', 'rock'],
 'corke': ['coker', 'corke', 'korec'],
 'corked': ['corked', 'docker', 'redock'],
 'corker': ['corker', 'recork', 'rocker'],
 'corkiness': ['corkiness', 'rockiness'],
 'corking': ['corking', 'rocking'],
 'corkish': ['corkish', 'rockish'],
 'corkwood': ['corkwood', 'rockwood', 'woodrock'],
 'corky': ['corky', 'rocky'],
 'corm': ['corm', 'crom'],
 'cormophyte': ['chromotype', 'cormophyte', 'ectomorphy'],
 'cormophytic': ['chromotypic', 'cormophytic', 'mycotrophic'],
 'cornage': ['acrogen', 'cornage'],
 'cornea': ['carone', 'cornea'],
 'corneal': ['carneol', 'corneal'],
 'cornein': ['corinne', 'cornein', 'neronic'],
 'cornelia': ['acrolein',
              'arecolin',
              'caroline',
              'colinear',
              'cornelia',
              'creolian',
              'lonicera'],
 'cornelius': ['cornelius', 'inclosure', 'reclusion'],
 'corneocalcareous': ['calcareocorneous', 'corneocalcareous'],
 'cornet': ['conter', 'cornet', 'cronet', 'roncet'],
 'corneum': ['cneorum', 'corneum'],
 'cornic': ['cornic', 'crocin'],
 'cornice': ['cornice', 'crocein'],
 'corniform': ['confirmor', 'corniform'],
 'cornin': ['cornin', 'rincon'],
 'cornish': ['cornish', 'cronish', 'sorchin'],
 'cornual': ['colunar', 'cornual', 'courlan'],
 'cornuate': ['cornuate', 'courante', 'cuneator', 'outrance'],
 'cornuated': ['cornuated', 'undercoat'],
 'cornucopiate': ['cornucopiate', 'reoccupation'],
 'cornulites': ['cornulites', 'uncloister'],
 'cornute': ['cornute', 'counter', 'recount', 'trounce'],
 'cornutine': ['centurion', 'continuer', 'cornutine'],
 'cornuto': ['contour', 'cornuto', 'countor', 'crouton'],
 'corny': ['corny', 'crony'],
 'corol': ['color', 'corol', 'crool'],
 'corollated': ['corollated', 'decollator'],
 'corona': ['caroon', 'corona', 'racoon'],
 'coronad': ['cardoon', 'coronad'],
 'coronadite': ['carotenoid', 'coronadite', 'decoration'],
 'coronal': ['coronal', 'locarno'],
 'coronaled': ['canoodler', 'coronaled'],
 'coronate': ['coronate', 'octonare', 'otocrane'],
 'coronated': ['coronated', 'creodonta'],
 'coroner': ['coroner', 'crooner', 'recroon'],
 'coronilla': ['collarino', 'coronilla'],
 'corp': ['copr', 'corp', 'crop'],
 'corporealist': ['corporealist', 'prosectorial'],
 'corradiate': ['corradiate', 'cortaderia', 'eradicator'],
 'correal': ['caroler', 'correal'],
 'correctant': ['contracter', 'correctant', 'recontract'],
 'correctioner': ['correctioner', 'recorrection'],
 'corrente': ['corrente', 'terceron'],
 'correption': ['correption', 'porrection'],
 'corrodentia': ['corrodentia', 'recordation'],
 'corrupter': ['corrupter', 'recorrupt'],
 'corsage': ['corsage', 'socager'],
 'corsaint': ['cantoris', 'castorin', 'corsaint'],
 'corse': ['corse', 'score'],
 'corselet': ['corselet', 'sclerote', 'selector'],
 'corset': ['corset', 'cortes', 'coster', 'escort', 'scoter', 'sector'],
 'corta': ['actor', 'corta', 'croat', 'rocta', 'taroc', 'troca'],
 'cortaderia': ['corradiate', 'cortaderia', 'eradicator'],
 'cortes': ['corset', 'cortes', 'coster', 'escort', 'scoter', 'sector'],
 'cortical': ['cortical', 'crotalic'],
 'cortices': ['cortices', 'cresotic'],
 'corticose': ['corticose', 'creosotic'],
 'cortin': ['citron', 'cortin', 'crotin'],
 'cortina': ['anticor', 'carotin', 'cortina', 'ontaric'],
 'cortinate': ['carnotite', 'cortinate'],
 'cortisone': ['certosino', 'cortisone', 'socotrine'],
 'corton': ['corton', 'croton'],
 'corvinae': ['corvinae', 'veronica'],
 'cory': ['cory', 'croy'],
 'corycavidine': ['cervicodynia', 'corycavidine'],
 'corydon': ['corydon', 'croydon'],
 'corynine': ['conyrine', 'corynine'],
 'corypha': ['charpoy', 'corypha'],
 'coryphene': ['coryphene', 'hypercone'],
 'cos': ['cos', 'osc', 'soc'],
 'cosalite': ['cosalite', 'societal'],
 'coset': ['coset', 'estoc', 'scote'],
 'cosh': ['cosh', 'scho'],
 'cosharer': ['cosharer', 'horsecar'],
 'cosigner': ['coresign', 'cosigner'],
 'cosine': ['cosine', 'oscine'],
 'cosmati': ['atomics', 'catoism', 'cosmati', 'osmatic', 'somatic'],
 'cosmetical': ['cacomistle', 'cosmetical'],
 'cosmetician': ['cosmetician', 'encomiastic'],
 'cosmist': ['cosmist', 'scotism'],
 'cossack': ['cassock', 'cossack'],
 'cosse': ['cosse', 'secos'],
 'cost': ['cost', 'scot'],
 'costa': ['ascot', 'coast', 'costa', 'tacso', 'tasco'],
 'costar': ['arctos', 'castor', 'costar', 'scrota'],
 'costean': ['costean', 'tsoneca'],
 'coster': ['corset', 'cortes', 'coster', 'escort', 'scoter', 'sector'],
 'costing': ['costing', 'gnostic'],
 'costispinal': ['costispinal', 'pansciolist'],
 'costmary': ['arctomys', 'costmary', 'mascotry'],
 'costochondral': ['chondrocostal', 'costochondral'],
 'costosternal': ['costosternal', 'sternocostal'],
 'costovertebral': ['costovertebral', 'vertebrocostal'],
 'costrel': ['closter', 'costrel'],
 'costula': ['costula', 'locusta', 'talcous'],
 'costumer': ['costumer', 'customer'],
 'cosurety': ['cosurety', 'courtesy'],
 'cosustain': ['cosustain', 'scusation'],
 'cotarius': ['cotarius', 'octarius', 'suctoria'],
 'cotarnine': ['connarite', 'container', 'cotarnine', 'crenation', 'narcotine'],
 'cote': ['cote', 'teco'],
 'coteline': ['coteline', 'election'],
 'coteller': ['colleter', 'coteller', 'coterell', 'recollet'],
 'coterell': ['colleter', 'coteller', 'coterell', 'recollet'],
 'cotesian': ['canoeist', 'cotesian'],
 'coth': ['coth', 'ocht'],
 'cotidal': ['cotidal', 'lactoid', 'talcoid'],
 'cotillage': ['colligate', 'cotillage'],
 'cotillion': ['cotillion', 'octillion'],
 'cotinga': ['coating', 'cotinga'],
 'cotinus': ['cotinus', 'suction', 'unstoic'],
 'cotise': ['cotise', 'oecist'],
 'coto': ['coot', 'coto', 'toco'],
 'cotranspire': ['cotranspire', 'pornerastic'],
 'cotrine': ['cerotin', 'cointer', 'cotrine', 'cretion', 'noticer', 'rection'],
 'cotripper': ['cotripper', 'periproct'],
 'cotte': ['cotte', 'octet'],
 'cotylosaur': ['cotylosaur', 'osculatory'],
 'cotype': ['cotype', 'ectopy'],
 'coude': ['coude', 'douce'],
 'could': ['cloud', 'could'],
 'coulisse': ['coulisse', 'leucosis', 'ossicule'],
 'coulomb': ['columbo', 'coulomb'],
 'coumalic': ['caulomic', 'coumalic'],
 'coumarin': ['conarium', 'coumarin'],
 'counsel': ['counsel', 'unclose'],
 'counter': ['cornute', 'counter', 'recount', 'trounce'],
 'counteracter': ['counteracter', 'countercarte'],
 'countercarte': ['counteracter', 'countercarte'],
 'countercharm': ['countercharm', 'countermarch'],
 'counterguard': ['counterguard', 'uncorrugated'],
 'counteridea': ['counteridea', 'decurionate'],
 'countermarch': ['countercharm', 'countermarch'],
 'counterpaled': ['counterpaled', 'counterplead', 'unpercolated'],
 'counterpaly': ['counterpaly', 'counterplay'],
 'counterplay': ['counterpaly', 'counterplay'],
 'counterplead': ['counterpaled', 'counterplead', 'unpercolated'],
 'counterreply': ['colpeurynter', 'counterreply'],
 'countersale': ['countersale', 'counterseal'],
 'countersea': ['countersea', 'nectareous'],
 'counterseal': ['countersale', 'counterseal'],
 'countershade': ['countershade', 'decantherous'],
 'counterstand': ['counterstand', 'uncontrasted'],
 'countertail': ['countertail', 'reluctation'],
 'countertrades': ['countertrades', 'unstercorated'],
 'countervail': ['countervail', 'involucrate'],
 'countervair': ['countervair', 'overcurtain', 'recurvation'],
 'countor': ['contour', 'cornuto', 'countor', 'crouton'],
 'coupe': ['coupe', 'pouce'],
 'couper': ['couper', 'croupe', 'poucer', 'recoup'],
 'couplement': ['couplement', 'uncomplete'],
 'couplet': ['couplet', 'octuple'],
 'coupon': ['coupon', 'uncoop'],
 'couponed': ['couponed', 'uncooped'],
 'courante': ['cornuate', 'courante', 'cuneator', 'outrance'],
 'courbaril': ['courbaril', 'orbicular'],
 'courlan': ['colunar', 'cornual', 'courlan'],
 'cours': ['cours', 'scour'],
 'course': ['cerous', 'course', 'crouse', 'source'],
 'coursed': ['coursed', 'scoured'],
 'courser': ['courser', 'scourer'],
 'coursing': ['coursing', 'scouring'],
 'court': ['court', 'crout', 'turco'],
 'courtesan': ['acentrous', 'courtesan', 'nectarous'],
 'courtesy': ['cosurety', 'courtesy'],
 'courtier': ['courtier', 'outcrier'],
 'courtiership': ['courtiership', 'peritrichous'],
 'courtin': ['courtin', 'ruction'],
 'courtman': ['courtman', 'turcoman'],
 'couter': ['couter', 'croute'],
 'couth': ['couth', 'thuoc', 'touch'],
 'couthily': ['couthily', 'touchily'],
 'couthiness': ['couthiness', 'touchiness'],
 'couthless': ['couthless', 'touchless'],
 'coutil': ['coutil', 'toluic'],
 'covenanter': ['contravene', 'covenanter'],
 'coverer': ['coverer', 'recover'],
 'coversine': ['coversine', 'vernicose'],
 'covert': ['covert', 'vector'],
 'covisit': ['covisit', 'ovistic'],
 'cowardy': ['cowardy', 'cowyard'],
 'cowherd': ['chowder', 'cowherd'],
 'cowl': ['clow', 'cowl'],
 'cowyard': ['cowardy', 'cowyard'],
 'coxa': ['coax', 'coxa'],
 'coxite': ['coxite', 'exotic'],
 'coyness': ['coyness', 'sycones'],
 'coyol': ['cooly', 'coyol'],
 'coyote': ['coyote', 'oocyte'],
 'craber': ['bracer', 'craber'],
 'crabhole': ['bachelor', 'crabhole'],
 'crablet': ['beclart', 'crablet'],
 'crackable': ['blackacre', 'crackable'],
 'crackle': ['cackler', 'clacker', 'crackle'],
 'crackmans': ['crackmans', 'cracksman'],
 'cracksman': ['crackmans', 'cracksman'],
 'cradge': ['cadger', 'cradge'],
 'cradle': ['cardel', 'cradle'],
 'cradlemate': ['cradlemate', 'malcreated'],
 'craig': ['cigar', 'craig'],
 'crain': ['cairn', 'crain', 'naric'],
 'crake': ['acker', 'caker', 'crake', 'creak'],
 'cram': ['cram', 'marc'],
 'cramasie': ['cramasie', 'mesaraic'],
 'crambe': ['becram', 'camber', 'crambe'],
 'crambidae': ['carbamide', 'crambidae'],
 'crambinae': ['carbamine', 'crambinae'],
 'cramble': ['cambrel', 'clamber', 'cramble'],
 'cramper': ['cramper', 'recramp'],
 'crampon': ['crampon', 'cropman'],
 'cranage': ['carnage', 'cranage', 'garance'],
 'crance': ['cancer', 'crance'],
 'crane': ['caner', 'crane', 'crena', 'nacre', 'rance'],
 'craner': ['craner', 'rancer'],
 'craney': ['carney', 'craney'],
 'crania': ['acinar',
            'arnica',
            'canari',
            'carian',
            'carina',
            'crania',
            'narica'],
 'craniad': ['acridan', 'craniad'],
 'cranial': ['carinal', 'carlina', 'clarain', 'cranial'],
 'cranially': ['ancillary', 'carlylian', 'cranially'],
 'cranian': ['canarin', 'cranian'],
 'craniate': ['anaretic', 'arcanite', 'carinate', 'craniate'],
 'cranic': ['cancri', 'carnic', 'cranic'],
 'craniectomy': ['craniectomy', 'cyanometric'],
 'craniognomy': ['craniognomy', 'organonymic'],
 'craniota': ['craniota', 'croatian', 'narcotia', 'raincoat'],
 'cranker': ['cranker', 'recrank'],
 'crap': ['carp', 'crap'],
 'crape': ['caper', 'crape', 'pacer', 'perca', 'recap'],
 'crappie': ['crappie', 'epicarp'],
 'crapple': ['clapper', 'crapple'],
 'crappo': ['crappo', 'croppa'],
 'craps': ['craps', 'scarp', 'scrap'],
 'crapulous': ['crapulous', 'opuscular'],
 'crare': ['carer', 'crare', 'racer'],
 'crate': ['caret',
           'carte',
           'cater',
           'crate',
           'creat',
           'creta',
           'react',
           'recta',
           'trace'],
 'crateful': ['crateful', 'fulcrate'],
 'crater': ['arrect', 'carter', 'crater', 'recart', 'tracer'],
 'craterid': ['cirrated', 'craterid'],
 'crateriform': ['crateriform', 'terraciform'],
 'crateris': ['crateris', 'serratic'],
 'craterlet': ['clatterer', 'craterlet'],
 'craterous': ['craterous', 'recusator'],
 'cratinean': ['cratinean', 'incarnate', 'nectarian'],
 'cratometric': ['cratometric', 'metrocratic'],
 'crave': ['carve', 'crave', 'varec'],
 'craven': ['carven', 'cavern', 'craven'],
 'craver': ['carver', 'craver'],
 'craving': ['carving', 'craving'],
 'crayon': ['canroy', 'crayon', 'cyrano', 'nyroca'],
 'crayonist': ['carnosity', 'crayonist'],
 'crea': ['acer', 'acre', 'care', 'crea', 'race'],
 'creagh': ['charge', 'creagh'],
 'creak': ['acker', 'caker', 'crake', 'creak'],
 'cream': ['cream', 'macer'],
 'creamer': ['amercer', 'creamer'],
 'creant': ['canter',
            'creant',
            'cretan',
            'nectar',
            'recant',
            'tanrec',
            'trance'],
 'crease': ['cesare', 'crease', 'recase', 'searce'],
 'creaser': ['creaser', 'searcer'],
 'creasing': ['creasing', 'scirenga'],
 'creat': ['caret',
           'carte',
           'cater',
           'crate',
           'creat',
           'creta',
           'react',
           'recta',
           'trace'],
 'creatable': ['creatable', 'traceable'],
 'create': ['cerate', 'create', 'ecarte'],
 'creatine': ['aneretic', 'centiare', 'creatine', 'increate', 'iterance'],
 'creatinine': ['creatinine', 'incinerate'],
 'creation': ['actioner', 'anerotic', 'ceration', 'creation', 'reaction'],
 'creational': ['creational', 'crotalinae', 'laceration', 'reactional'],
 'creationary': ['creationary', 'reactionary'],
 'creationism': ['anisometric',
                 'creationism',
                 'miscreation',
                 'ramisection',
                 'reactionism'],
 'creationist': ['creationist', 'reactionist'],
 'creative': ['creative', 'reactive'],
 'creatively': ['creatively', 'reactively'],
 'creativeness': ['creativeness', 'reactiveness'],
 'creativity': ['creativity', 'reactivity'],
 'creator': ['creator', 'reactor'],
 'crebrous': ['crebrous', 'obscurer'],
 'credential': ['credential', 'interlaced', 'reclinated'],
 'credit': ['credit', 'direct'],
 'creditable': ['creditable', 'directable'],
 'creditive': ['creditive', 'directive'],
 'creditor': ['creditor', 'director'],
 'creditorship': ['creditorship', 'directorship'],
 'creditress': ['creditress', 'directress'],
 'creditrix': ['creditrix', 'directrix'],
 'crednerite': ['crednerite', 'interceder'],
 'credo': ['coder', 'cored', 'credo'],
 'cree': ['cere', 'cree'],
 'creed': ['ceder', 'cedre', 'cered', 'creed'],
 'creedal': ['cedrela', 'creedal', 'declare'],
 'creedalism': ['creedalism', 'misdeclare'],
 'creedist': ['creedist', 'desertic', 'discreet', 'discrete'],
 'creep': ['creep', 'crepe'],
 'creepered': ['creepered', 'predecree'],
 'creepie': ['creepie', 'repiece'],
 'cremation': ['cremation', 'manticore'],
 'cremator': ['cremator', 'mercator'],
 'crematorial': ['crematorial', 'mercatorial'],
 'cremor': ['cremor', 'cromer'],
 'crena': ['caner', 'crane', 'crena', 'nacre', 'rance'],
 'crenate': ['centare', 'crenate'],
 'crenated': ['crenated', 'decanter', 'nectared'],
 'crenation': ['connarite', 'container', 'cotarnine', 'crenation', 'narcotine'],
 'crenelate': ['crenelate', 'lanceteer'],
 'crenelation': ['crenelation', 'intolerance'],
 'crenele': ['crenele', 'encreel'],
 'crenellation': ['centrolineal', 'crenellation'],
 'crenitic': ['crenitic', 'cretinic'],
 'crenology': ['crenology', 'necrology'],
 'crenula': ['crenula', 'lucarne', 'nuclear', 'unclear'],
 'crenulate': ['calenture', 'crenulate'],
 'creodonta': ['coronated', 'creodonta'],
 'creolian': ['acrolein',
              'arecolin',
              'caroline',
              'colinear',
              'cornelia',
              'creolian',
              'lonicera'],
 'creolin': ['creolin', 'licorne', 'locrine'],
 'creosotic': ['corticose', 'creosotic'],
 'crepe': ['creep', 'crepe'],
 'crepidula': ['crepidula', 'pedicular'],
 'crepine': ['crepine', 'increep'],
 'crepiness': ['crepiness', 'princesse'],
 'crepis': ['crepis', 'cripes', 'persic', 'precis', 'spicer'],
 'crepitant': ['crepitant', 'pittancer'],
 'crepitation': ['actinopteri', 'crepitation', 'precitation'],
 'crepitous': ['crepitous', 'euproctis', 'uroseptic'],
 'crepitus': ['crepitus', 'piecrust'],
 'crepon': ['crepon', 'procne'],
 'crepy': ['crepy', 'cypre', 'percy'],
 'cresol': ['closer', 'cresol', 'escrol'],
 'cresolin': ['cresolin', 'licensor'],
 'cresotic': ['cortices', 'cresotic'],
 'cresson': ['cresson', 'crosnes'],
 'crestline': ['crestline', 'stenciler'],
 'crestmoreite': ['crestmoreite', 'stereometric'],
 'creta': ['caret',
           'carte',
           'cater',
           'crate',
           'creat',
           'creta',
           'react',
           'recta',
           'trace'],
 'cretan': ['canter',
            'creant',
            'cretan',
            'nectar',
            'recant',
            'tanrec',
            'trance'],
 'crete': ['crete', 'erect'],
 'cretification': ['certification', 'cretification', 'rectification'],
 'cretify': ['certify', 'cretify', 'rectify'],
 'cretin': ['cinter', 'cretin', 'crinet'],
 'cretinic': ['crenitic', 'cretinic'],
 'cretinoid': ['cretinoid', 'direction'],
 'cretion': ['cerotin', 'cointer', 'cotrine', 'cretion', 'noticer', 'rection'],
 'cretism': ['cretism', 'metrics'],
 'crewer': ['crewer', 'recrew'],
 'cribo': ['boric', 'cribo', 'orbic'],
 'crickle': ['clicker', 'crickle'],
 'cricothyroid': ['cricothyroid', 'thyrocricoid'],
 'cried': ['cider', 'cried', 'deric', 'dicer'],
 'crier': ['crier', 'ricer'],
 'criey': ['criey', 'ricey'],
 'crile': ['crile', 'elric', 'relic'],
 'crimean': ['armenic', 'carmine', 'ceriman', 'crimean', 'mercian'],
 'crimeful': ['crimeful', 'merciful'],
 'crimeless': ['crimeless', 'merciless'],
 'crimelessness': ['crimelessness', 'mercilessness'],
 'criminalese': ['criminalese', 'misreliance'],
 'criminate': ['antimeric', 'carminite', 'criminate', 'metrician'],
 'criminatory': ['coriamyrtin', 'criminatory'],
 'crimpage': ['crimpage', 'pergamic'],
 'crinal': ['carlin', 'clarin', 'crinal'],
 'crinanite': ['crinanite', 'natricine'],
 'crinated': ['crinated', 'dicentra'],
 'crine': ['cerin', 'crine'],
 'crined': ['cedrin', 'cinder', 'crined'],
 'crinet': ['cinter', 'cretin', 'crinet'],
 'cringle': ['clinger', 'cringle'],
 'crinite': ['citrine', 'crinite', 'inciter', 'neritic'],
 'crinkle': ['clinker', 'crinkle'],
 'cripes': ['crepis', 'cripes', 'persic', 'precis', 'spicer'],
 'cripple': ['clipper', 'cripple'],
 'crisp': ['crisp', 'scrip'],
 'crispation': ['antipsoric', 'ascription', 'crispation'],
 'crisped': ['crisped', 'discerp'],
 'crispy': ['crispy', 'cypris'],
 'crista': ['crista', 'racist'],
 'cristopher': ['cristopher', 'rectorship'],
 'criteria': ['criteria', 'triceria'],
 'criterion': ['criterion', 'tricerion'],
 'criterium': ['criterium', 'tricerium'],
 'crith': ['crith', 'richt'],
 'critic': ['citric', 'critic'],
 'cro': ['cor', 'cro', 'orc', 'roc'],
 'croak': ['arock', 'croak'],
 'croat': ['actor', 'corta', 'croat', 'rocta', 'taroc', 'troca'],
 'croatan': ['cantaro', 'croatan'],
 'croatian': ['craniota', 'croatian', 'narcotia', 'raincoat'],
 'crocein': ['cornice', 'crocein'],
 'croceine': ['cicerone', 'croceine'],
 'crocetin': ['crocetin', 'necrotic'],
 'crocidolite': ['crocidolite', 'crocodilite'],
 'crocin': ['cornic', 'crocin'],
 'crocodile': ['cordicole', 'crocodile'],
 'crocodilite': ['crocidolite', 'crocodilite'],
 'croconate': ['coenactor', 'croconate'],
 'crocus': ['crocus', 'succor'],
 'crom': ['corm', 'crom'],
 'crome': ['comer', 'crome'],
 'cromer': ['cremor', 'cromer'],
 'crone': ['coner', 'crone', 'recon'],
 'cronet': ['conter', 'cornet', 'cronet', 'roncet'],
 'cronian': ['corinna', 'cronian'],
 'cronish': ['cornish', 'cronish', 'sorchin'],
 'crony': ['corny', 'crony'],
 'croodle': ['colored', 'croodle', 'decolor'],
 'crool': ['color', 'corol', 'crool'],
 'croon': ['conor', 'croon', 'ronco'],
 'crooner': ['coroner', 'crooner', 'recroon'],
 'crop': ['copr', 'corp', 'crop'],
 'cropman': ['crampon', 'cropman'],
 'croppa': ['crappo', 'croppa'],
 'crore': ['corer', 'crore'],
 'crosa': ['arcos', 'crosa', 'oscar', 'sacro'],
 'crosier': ['cirrose', 'crosier'],
 'crosnes': ['cresson', 'crosnes'],
 'crosse': ['cessor', 'crosse', 'scorse'],
 'crosser': ['crosser', 'recross'],
 'crossite': ['crossite', 'crosstie'],
 'crossover': ['crossover', 'overcross'],
 'crosstie': ['crossite', 'crosstie'],
 'crosstied': ['crosstied', 'dissector'],
 'crosstree': ['crosstree', 'rectoress'],
 'crosswalk': ['classwork', 'crosswalk'],
 'crotal': ['carlot', 'crotal'],
 'crotalic': ['cortical', 'crotalic'],
 'crotalinae': ['creational', 'crotalinae', 'laceration', 'reactional'],
 'crotaline': ['alectrion', 'clarionet', 'crotaline', 'locarnite'],
 'crotalism': ['clamorist', 'crotalism'],
 'crotalo': ['crotalo', 'locator'],
 'crotaloid': ['crotaloid', 'doctorial'],
 'crotin': ['citron', 'cortin', 'crotin'],
 'croton': ['corton', 'croton'],
 'crotonate': ['contortae', 'crotonate'],
 'crottle': ['clotter', 'crottle'],
 'crouchant': ['archcount', 'crouchant'],
 'croupal': ['copular', 'croupal', 'cupolar', 'porcula'],
 'croupe': ['couper', 'croupe', 'poucer', 'recoup'],
 'croupily': ['croupily', 'polyuric'],
 'croupiness': ['croupiness', 'percussion', 'supersonic'],
 'crouse': ['cerous', 'course', 'crouse', 'source'],
 'crout': ['court', 'crout', 'turco'],
 'croute': ['couter', 'croute'],
 'crouton': ['contour', 'cornuto', 'countor', 'crouton'],
 'crowder': ['crowder', 'recrowd'],
 'crowned': ['crowned', 'decrown'],
 'crowner': ['crowner', 'recrown'],
 'crownmaker': ['cankerworm', 'crownmaker'],
 'croy': ['cory', 'croy'],
 'croydon': ['corydon', 'croydon'],
 'cruces': ['cercus', 'cruces'],
 'cruciate': ['aceturic', 'cruciate'],
 'crudwort': ['crudwort', 'curdwort'],
 'cruel': ['cruel', 'lucre', 'ulcer'],
 'cruels': ['clerus', 'cruels'],
 'cruelty': ['cruelty', 'cutlery'],
 'cruet': ['cruet', 'eruct', 'recut', 'truce'],
 'cruise': ['cruise', 'crusie'],
 'cruisken': ['cruisken', 'unsicker'],
 'crunode': ['crunode', 'uncored'],
 'crureus': ['crureus', 'surcrue'],
 'crurogenital': ['crurogenital', 'genitocrural'],
 'cruroinguinal': ['cruroinguinal', 'inguinocrural'],
 'crus': ['crus', 'scur'],
 'crusado': ['acrodus', 'crusado'],
 'crusca': ['crusca', 'curcas'],
 'cruse': ['cruse', 'curse', 'sucre'],
 'crusher': ['crusher', 'recrush'],
 'crusie': ['cruise', 'crusie'],
 'crust': ['crust', 'curst'],
 'crustate': ['crustate', 'scrutate'],
 'crustation': ['crustation', 'scrutation'],
 'crustily': ['crustily', 'rusticly'],
 'crustiness': ['crustiness', 'rusticness'],
 'crusty': ['crusty', 'curtsy'],
 'cruth': ['cruth', 'rutch'],
 'cryosel': ['cryosel', 'scroyle'],
 'cryptodire': ['cryptodire', 'predictory'],
 'cryptomeria': ['cryptomeria', 'imprecatory'],
 'cryptostomate': ['cryptostomate', 'prostatectomy'],
 'ctenidial': ['ctenidial', 'identical'],
 'ctenoid': ['condite', 'ctenoid'],
 'ctenolium': ['ctenolium', 'monticule'],
 'ctenophore': ['ctenophore', 'nectophore'],
 'ctetology': ['ctetology', 'tectology'],
 'cuailnge': ['cuailnge', 'glaucine'],
 'cuarteron': ['cuarteron', 'raconteur'],
 'cubanite': ['cubanite', 'incubate'],
 'cuber': ['bruce', 'cebur', 'cuber'],
 'cubist': ['bustic', 'cubist'],
 'cubit': ['butic', 'cubit'],
 'cubitale': ['baculite', 'cubitale'],
 'cuboidal': ['baculoid', 'cuboidal'],
 'cuchan': ['caunch', 'cuchan'],
 'cueball': ['bullace', 'cueball'],
 'cueman': ['acumen', 'cueman'],
 'cuir': ['cuir', 'uric'],
 'culebra': ['culebra', 'curable'],
 'culet': ['culet', 'lucet'],
 'culinary': ['culinary', 'uranylic'],
 'culmy': ['culmy', 'cumyl'],
 'culpose': ['culpose', 'ploceus', 'upclose'],
 'cultch': ['clutch', 'cultch'],
 'cultivar': ['cultivar', 'curvital'],
 'culturine': ['culturine', 'inculture'],
 'cumaean': ['cumaean', 'encauma'],
 'cumar': ['carum', 'cumar'],
 'cumber': ['cumber', 'cumbre'],
 'cumberer': ['cerebrum', 'cumberer'],
 'cumbraite': ['bacterium', 'cumbraite'],
 'cumbre': ['cumber', 'cumbre'],
 'cumic': ['cumic', 'mucic'],
 'cumin': ['cumin', 'mucin'],
 'cumol': ['cumol', 'locum'],
 'cumulite': ['cumulite', 'lutecium'],
 'cumyl': ['culmy', 'cumyl'],
 'cuna': ['cuna', 'unca'],
 'cunan': ['canun', 'cunan'],
 'cuneal': ['auncel', 'cuneal', 'lacune', 'launce', 'unlace'],
 'cuneator': ['cornuate', 'courante', 'cuneator', 'outrance'],
 'cunila': ['cunila', 'lucian', 'lucina', 'uncial'],
 'cuon': ['cuon', 'unco'],
 'cuorin': ['cuorin', 'uronic'],
 'cupid': ['cupid', 'pudic'],
 'cupidity': ['cupidity', 'pudicity'],
 'cupidone': ['cupidone', 'uncopied'],
 'cupola': ['copula', 'cupola'],
 'cupolar': ['copular', 'croupal', 'cupolar', 'porcula'],
 'cupreous': ['cupreous', 'upcourse'],
 'cuprite': ['cuprite', 'picture'],
 'curable': ['culebra', 'curable'],
 'curate': ['acture', 'cauter', 'curate'],
 'curateship': ['curateship', 'pasticheur'],
 'curation': ['curation', 'nocturia'],
 'curatory': ['curatory', 'outcarry'],
 'curcas': ['crusca', 'curcas'],
 'curdle': ['curdle', 'curled'],
 'curdwort': ['crudwort', 'curdwort'],
 'cure': ['cure', 'ecru', 'eruc'],
 'curer': ['curer', 'recur'],
 'curial': ['curial', 'lauric', 'uracil', 'uralic'],
 'curialist': ['curialist', 'rusticial'],
 'curie': ['curie', 'ureic'],
 'curin': ['curin', 'incur', 'runic'],
 'curine': ['curine', 'erucin', 'neuric'],
 'curiosa': ['carious', 'curiosa'],
 'curite': ['curite', 'teucri', 'uretic'],
 'curled': ['curdle', 'curled'],
 'curler': ['curler', 'recurl'],
 'cursa': ['cursa', 'scaur'],
 'cursal': ['cursal', 'sulcar'],
 'curse': ['cruse', 'curse', 'sucre'],
 'cursed': ['cedrus', 'cursed'],
 'curst': ['crust', 'curst'],
 'cursus': ['cursus', 'ruscus'],
 'curtail': ['curtail', 'trucial'],
 'curtailer': ['curtailer', 'recruital', 'reticular'],
 'curtain': ['curtain', 'turacin', 'turcian'],
 'curtation': ['anticourt', 'curtation', 'ructation'],
 'curtilage': ['curtilage', 'cutigeral', 'graticule'],
 'curtis': ['citrus', 'curtis', 'rictus', 'rustic'],
 'curtise': ['curtise', 'icterus'],
 'curtsy': ['crusty', 'curtsy'],
 'curvital': ['cultivar', 'curvital'],
 'cush': ['cush', 'such'],
 'cushionless': ['cushionless', 'slouchiness'],
 'cusinero': ['coinsure', 'corineus', 'cusinero'],
 'cusk': ['cusk', 'suck'],
 'cusp': ['cusp', 'scup'],
 'cuspal': ['cuspal', 'placus'],
 'custom': ['custom', 'muscot'],
 'customer': ['costumer', 'customer'],
 'cutheal': ['auchlet', 'cutheal', 'taluche'],
 'cutigeral': ['curtilage', 'cutigeral', 'graticule'],
 'cutin': ['cutin', 'incut', 'tunic'],
 'cutis': ['cutis', 'ictus'],
 'cutler': ['cutler', 'reluct'],
 'cutleress': ['cutleress', 'lecturess', 'truceless'],
 'cutleria': ['arculite', 'cutleria', 'lucretia', 'reticula', 'treculia'],
 'cutlery': ['cruelty', 'cutlery'],
 'cutlet': ['cutlet', 'cuttle'],
 'cutoff': ['cutoff', 'offcut'],
 'cutout': ['cutout', 'outcut'],
 'cutover': ['cutover', 'overcut'],
 'cuttle': ['cutlet', 'cuttle'],
 'cuttler': ['clutter', 'cuttler'],
 'cutup': ['cutup', 'upcut'],
 'cuya': ['cuya', 'yuca'],
 'cyamus': ['cyamus', 'muysca'],
 'cyan': ['cany', 'cyan'],
 'cyanidine': ['cyanidine', 'dicyanine'],
 'cyanol': ['alcyon', 'cyanol'],
 'cyanole': ['alcyone', 'cyanole'],
 'cyanometric': ['craniectomy', 'cyanometric'],
 'cyanophycin': ['cyanophycin', 'phycocyanin'],
 'cyanuret': ['centaury', 'cyanuret'],
 'cyath': ['cathy', 'cyath', 'yacht'],
 'cyclamine': ['cyclamine', 'macilency'],
 'cyclian': ['cyclian', 'cynical'],
 'cyclide': ['cyclide', 'decylic', 'dicycle'],
 'cyclism': ['clysmic', 'cyclism'],
 'cyclotome': ['colectomy', 'cyclotome'],
 'cydonian': ['anodynic', 'cydonian'],
 'cylindrite': ['cylindrite', 'indirectly'],
 'cylix': ['cylix', 'xylic'],
 'cymation': ['cymation', 'myatonic', 'onymatic'],
 'cymoid': ['cymoid', 'mycoid'],
 'cymometer': ['cymometer', 'mecometry'],
 'cymose': ['cymose', 'mycose'],
 'cymule': ['cymule', 'lyceum'],
 'cynara': ['canary', 'cynara'],
 'cynaroid': ['cynaroid', 'dicaryon'],
 'cynical': ['cyclian', 'cynical'],
 'cynogale': ['acylogen', 'cynogale'],
 'cynophilic': ['cynophilic', 'philocynic'],
 'cynosural': ['consulary', 'cynosural'],
 'cyphonism': ['cyphonism', 'symphonic'],
 'cypre': ['crepy', 'cypre', 'percy'],
 'cypria': ['cypria', 'picary', 'piracy'],
 'cyprian': ['cyprian', 'cyprina'],
 'cyprina': ['cyprian', 'cyprina'],
 'cyprine': ['cyprine', 'pyrenic'],
 'cypris': ['crispy', 'cypris'],
 'cyrano': ['canroy', 'crayon', 'cyrano', 'nyroca'],
 'cyril': ['cyril', 'lyric'],
 'cyrilla': ['cyrilla', 'lyrical'],
 'cyrtopia': ['cyrtopia', 'poticary'],
 'cyst': ['cyst', 'scyt'],
 'cystidean': ['asyndetic', 'cystidean', 'syndicate'],
 'cystitis': ['cystitis', 'scytitis'],
 'cystoadenoma': ['adenocystoma', 'cystoadenoma'],
 'cystofibroma': ['cystofibroma', 'fibrocystoma'],
 'cystolith': ['cystolith', 'lithocyst'],
 'cystomyxoma': ['cystomyxoma', 'myxocystoma'],
 'cystonephrosis': ['cystonephrosis', 'nephrocystosis'],
 'cystopyelitis': ['cystopyelitis', 'pyelocystitis'],
 'cystotome': ['cystotome', 'cytostome', 'ostectomy'],
 'cystourethritis': ['cystourethritis', 'urethrocystitis'],
 'cytase': ['cytase', 'stacey'],
 'cytherea': ['cheatery', 'cytherea', 'teachery'],
 'cytherean': ['cytherean', 'enchytrae'],
 'cytisine': ['cytisine', 'syenitic'],
 'cytoblastemic': ['blastomycetic', 'cytoblastemic'],
 'cytoblastemous': ['blastomycetous', 'cytoblastemous'],
 'cytochrome': ['chromocyte', 'cytochrome'],
 'cytoid': ['cytoid', 'docity'],
 'cytomere': ['cytomere', 'merocyte'],
 'cytophil': ['cytophil', 'phycitol'],
 'cytosine': ['cenosity', 'cytosine'],
 'cytosome': ['cytosome', 'otomyces'],
 'cytost': ['cytost', 'scotty'],
 'cytostome': ['cystotome', 'cytostome', 'ostectomy'],
 'czarian': ['czarian', 'czarina'],
 'czarina': ['czarian', 'czarina'],
 'da': ['ad', 'da'],
 'dab': ['bad', 'dab'],
 'dabber': ['barbed', 'dabber'],
 'dabbler': ['dabbler', 'drabble'],
 'dabitis': ['dabitis', 'dibatis'],
 'dablet': ['dablet', 'tabled'],
 'dace': ['cade', 'dace', 'ecad'],
 'dacelo': ['alcedo', 'dacelo'],
 'dacian': ['acnida', 'anacid', 'dacian'],
 'dacker': ['arcked', 'dacker'],
 'dacryolith': ['dacryolith', 'hydrotical'],
 'dacryon': ['candroy', 'dacryon'],
 'dactylonomy': ['dactylonomy', 'monodactyly'],
 'dactylopteridae': ['dactylopteridae', 'pterodactylidae'],
 'dactylopterus': ['dactylopterus', 'pterodactylus'],
 'dacus': ['cadus', 'dacus'],
 'dad': ['add', 'dad'],
 'dada': ['adad', 'adda', 'dada'],
 'dadap': ['dadap', 'padda'],
 'dade': ['dade', 'dead', 'edda'],
 'dadu': ['addu', 'dadu', 'daud', 'duad'],
 'dae': ['ade', 'dae'],
 'daemon': ['daemon', 'damone', 'modena'],
 'daemonic': ['codamine', 'comedian', 'daemonic', 'demoniac'],
 'daer': ['ared', 'daer', 'dare', 'dear', 'read'],
 'dag': ['dag', 'gad'],
 'dagaba': ['badaga', 'dagaba', 'gadaba'],
 'dagame': ['dagame', 'damage'],
 'dagbane': ['bandage', 'dagbane'],
 'dagestan': ['dagestan', 'standage'],
 'dagger': ['dagger', 'gadger', 'ragged'],
 'daggers': ['daggers', 'seggard'],
 'daggle': ['daggle', 'lagged'],
 'dago': ['dago', 'goad'],
 'dagomba': ['dagomba', 'gambado'],
 'dags': ['dags', 'sgad'],
 'dah': ['dah', 'dha', 'had'],
 'daidle': ['daidle', 'laddie'],
 'daikon': ['daikon', 'nodiak'],
 'dail': ['dail', 'dali', 'dial', 'laid', 'lida'],
 'daily': ['daily', 'lydia'],
 'daimen': ['daimen', 'damine', 'maiden', 'median', 'medina'],
 'daimio': ['daimio', 'maioid'],
 'daimon': ['amidon', 'daimon', 'domain'],
 'dain': ['adin', 'andi', 'dain', 'dani', 'dian', 'naid'],
 'dairi': ['dairi', 'darii', 'radii'],
 'dairy': ['dairy', 'diary', 'yaird'],
 'dais': ['dais', 'dasi', 'disa', 'said', 'sida'],
 'daisy': ['daisy', 'sayid'],
 'daker': ['daker', 'drake', 'kedar', 'radek'],
 'dal': ['dal', 'lad'],
 'dale': ['dale', 'deal', 'lade', 'lead', 'leda'],
 'dalea': ['adela', 'dalea'],
 'dalecarlian': ['calendarial', 'dalecarlian'],
 'daleman': ['daleman', 'lademan', 'leadman'],
 'daler': ['alder', 'daler', 'lader'],
 'dalesman': ['dalesman', 'leadsman'],
 'dali': ['dail', 'dali', 'dial', 'laid', 'lida'],
 'dalle': ['dalle', 'della', 'ladle'],
 'dallying': ['dallying', 'ladyling'],
 'dalt': ['dalt', 'tald'],
 'dalteen': ['dalteen', 'dentale', 'edental'],
 'dam': ['dam', 'mad'],
 'dama': ['adam', 'dama'],
 'damage': ['dagame', 'damage'],
 'daman': ['adman', 'daman', 'namda'],
 'damara': ['armada', 'damara', 'ramada'],
 'dame': ['dame', 'made', 'mead'],
 'damewort': ['damewort', 'wardmote'],
 'damia': ['amadi', 'damia', 'madia', 'maida'],
 'damie': ['amide', 'damie', 'media'],
 'damier': ['admire', 'armied', 'damier', 'dimera', 'merida'],
 'damine': ['daimen', 'damine', 'maiden', 'median', 'medina'],
 'dammer': ['dammer', 'dramme'],
 'dammish': ['dammish', 'mahdism'],
 'damn': ['damn', 'mand'],
 'damnation': ['damnation', 'mandation'],
 'damnatory': ['damnatory', 'mandatory'],
 'damned': ['damned', 'demand', 'madden'],
 'damner': ['damner', 'manred', 'randem', 'remand'],
 'damnii': ['amidin', 'damnii'],
 'damnous': ['damnous', 'osmunda'],
 'damon': ['damon', 'monad', 'nomad'],
 'damone': ['daemon', 'damone', 'modena'],
 'damonico': ['damonico', 'monoacid'],
 'dampen': ['dampen', 'madnep'],
 'damper': ['damper', 'ramped'],
 'dampish': ['dampish', 'madship', 'phasmid'],
 'dan': ['and', 'dan'],
 'dana': ['anda', 'dana'],
 'danaan': ['ananda', 'danaan'],
 'danai': ['danai', 'diana', 'naiad'],
 'danainae': ['anadenia', 'danainae'],
 'danakil': ['danakil', 'dankali', 'kaldani', 'ladakin'],
 'danalite': ['danalite', 'detainal'],
 'dancalite': ['cadential', 'dancalite'],
 'dance': ['dance', 'decan'],
 'dancer': ['cedarn', 'dancer', 'nacred'],
 'dancery': ['ardency', 'dancery'],
 'dander': ['dander', 'darned', 'nadder'],
 'dandle': ['dandle', 'landed'],
 'dandler': ['dandler', 'dendral'],
 'dane': ['ande', 'dane', 'dean', 'edna'],
 'danewort': ['danewort', 'teardown'],
 'danger': ['danger', 'gander', 'garden', 'ranged'],
 'dangerful': ['dangerful', 'gardenful'],
 'dangerless': ['dangerless', 'gardenless'],
 'dangle': ['angled', 'dangle', 'englad', 'lagend'],
 'dangler': ['dangler', 'gnarled'],
 'danglin': ['danglin', 'landing'],
 'dani': ['adin', 'andi', 'dain', 'dani', 'dian', 'naid'],
 'danian': ['andian', 'danian', 'nidana'],
 'danic': ['canid', 'cnida', 'danic'],
 'daniel': ['aldine', 'daniel', 'delian', 'denial', 'enalid', 'leadin'],
 'daniele': ['adeline', 'daniele', 'delaine'],
 'danielic': ['alcidine', 'danielic', 'lecaniid'],
 'danio': ['adion', 'danio', 'doina', 'donia'],
 'danish': ['danish', 'sandhi'],
 'danism': ['danism', 'disman'],
 'danite': ['danite', 'detain'],
 'dankali': ['danakil', 'dankali', 'kaldani', 'ladakin'],
 'danli': ['danli', 'ladin', 'linda', 'nidal'],
 'dannie': ['aidenn', 'andine', 'dannie', 'indane'],
 'danseuse': ['danseuse', 'sudanese'],
 'dantean': ['andante', 'dantean'],
 'dantist': ['dantist', 'distant'],
 'danuri': ['danuri', 'diurna', 'dunair', 'durain', 'durani', 'durian'],
 'dao': ['ado', 'dao', 'oda'],
 'daoine': ['daoine', 'oneida'],
 'dap': ['dap', 'pad'],
 'daphnis': ['daphnis', 'dishpan'],
 'dapicho': ['dapicho', 'phacoid'],
 'dapple': ['dapple', 'lapped', 'palped'],
 'dar': ['dar', 'rad'],
 'daraf': ['daraf', 'farad'],
 'darby': ['bardy', 'darby'],
 'darci': ['acrid', 'caird', 'carid', 'darci', 'daric', 'dirca'],
 'dare': ['ared', 'daer', 'dare', 'dear', 'read'],
 'dareall': ['ardella', 'dareall'],
 'daren': ['andre', 'arend', 'daren', 'redan'],
 'darer': ['darer', 'drear'],
 'darg': ['darg', 'drag', 'grad'],
 'darger': ['darger', 'gerard', 'grader', 'redrag', 'regard'],
 'dargo': ['dargo', 'dogra', 'drago'],
 'dargsman': ['dargsman', 'dragsman'],
 'dari': ['arid', 'dari', 'raid'],
 'daric': ['acrid', 'caird', 'carid', 'darci', 'daric', 'dirca'],
 'darien': ['darien', 'draine'],
 'darii': ['dairi', 'darii', 'radii'],
 'darin': ['darin', 'dinar', 'drain', 'indra', 'nadir', 'ranid'],
 'daring': ['daring', 'dingar', 'gradin'],
 'darius': ['darius', 'radius'],
 'darken': ['darken', 'kanred', 'ranked'],
 'darkener': ['darkener', 'redarken'],
 'darn': ['darn', 'nard', 'rand'],
 'darned': ['dander', 'darned', 'nadder'],
 'darnel': ['aldern',
            'darnel',
            'enlard',
            'lander',
            'lenard',
            'randle',
            'reland'],
 'darner': ['darner', 'darren', 'errand', 'rander', 'redarn'],
 'darning': ['darning', 'randing'],
 'darrein': ['darrein', 'drainer'],
 'darren': ['darner', 'darren', 'errand', 'rander', 'redarn'],
 'darshana': ['darshana', 'shardana'],
 'darst': ['darst', 'darts', 'strad'],
 'dart': ['dart', 'drat'],
 'darter': ['darter',
            'dartre',
            'redart',
            'retard',
            'retrad',
            'tarred',
            'trader'],
 'darting': ['darting', 'trading'],
 'dartle': ['dartle', 'tardle'],
 'dartoic': ['arctoid', 'carotid', 'dartoic'],
 'dartre': ['darter',
            'dartre',
            'redart',
            'retard',
            'retrad',
            'tarred',
            'trader'],
 'dartrose': ['dartrose', 'roadster'],
 'darts': ['darst', 'darts', 'strad'],
 'daryl': ['daryl', 'lardy', 'lyard'],
 'das': ['das', 'sad'],
 'dash': ['dash', 'sadh', 'shad'],
 'dashed': ['dashed', 'shaded'],
 'dasheen': ['dasheen', 'enshade'],
 'dasher': ['dasher', 'shader', 'sheard'],
 'dashing': ['dashing', 'shading'],
 'dashnak': ['dashnak', 'shadkan'],
 'dashy': ['dashy', 'shady'],
 'dasi': ['dais', 'dasi', 'disa', 'said', 'sida'],
 'dasnt': ['dasnt', 'stand'],
 'dasturi': ['dasturi', 'rudista'],
 'dasya': ['adays', 'dasya'],
 'dasyurine': ['dasyurine', 'dysneuria'],
 'data': ['adat', 'data'],
 'datable': ['albetad', 'datable'],
 'dataria': ['dataria', 'radiata'],
 'date': ['adet', 'date', 'tade', 'tead', 'teda'],
 'dateless': ['dateless', 'detassel'],
 'dater': ['dater', 'derat', 'detar', 'drate', 'rated', 'trade', 'tread'],
 'datil': ['datil', 'dital', 'tidal', 'tilda'],
 'datism': ['amidst', 'datism'],
 'daub': ['baud', 'buda', 'daub'],
 'dauber': ['dauber', 'redaub'],
 'daubster': ['daubster', 'subtread'],
 'daud': ['addu', 'dadu', 'daud', 'duad'],
 'daunch': ['chandu', 'daunch'],
 'daunter': ['daunter', 'unarted', 'unrated', 'untread'],
 'dauntless': ['adultness', 'dauntless'],
 'daur': ['ardu', 'daur', 'dura'],
 'dave': ['dave', 'deva', 'vade', 'veda'],
 'daven': ['daven', 'vaned'],
 'davy': ['davy', 'vady'],
 'daw': ['awd', 'daw', 'wad'],
 'dawdler': ['dawdler', 'waddler'],
 'dawdling': ['dawdling', 'waddling'],
 'dawdlingly': ['dawdlingly', 'waddlingly'],
 'dawdy': ['dawdy', 'waddy'],
 'dawn': ['dawn', 'wand'],
 'dawnlike': ['dawnlike', 'wandlike'],
 'dawny': ['dawny', 'wandy'],
 'day': ['ady', 'day', 'yad'],
 'dayal': ['adlay', 'dayal'],
 'dayfly': ['dayfly', 'ladyfy'],
 'days': ['days', 'dyas'],
 'daysman': ['daysman', 'mandyas'],
 'daytime': ['daytime', 'maytide'],
 'daywork': ['daywork', 'workday'],
 'daze': ['adze', 'daze'],
 'de': ['de', 'ed'],
 'deacon': ['acnode', 'deacon'],
 'deaconship': ['deaconship', 'endophasic'],
 'dead': ['dade', 'dead', 'edda'],
 'deadborn': ['deadborn', 'endboard'],
 'deadener': ['deadener', 'endeared'],
 'deadlock': ['deadlock', 'deckload'],
 'deaf': ['deaf', 'fade'],
 'deair': ['aider', 'deair', 'irade', 'redia'],
 'deal': ['dale', 'deal', 'lade', 'lead', 'leda'],
 'dealable': ['dealable', 'leadable'],
 'dealation': ['atloidean', 'dealation'],
 'dealer': ['dealer', 'leader', 'redeal', 'relade', 'relead'],
 'dealership': ['dealership', 'leadership'],
 'dealing': ['adeling', 'dealing', 'leading'],
 'dealt': ['adlet', 'dealt', 'delta', 'lated', 'taled'],
 'deaminase': ['deaminase', 'mesadenia'],
 'dean': ['ande', 'dane', 'dean', 'edna'],
 'deaner': ['deaner', 'endear'],
 'deaness': ['deaness', 'edessan'],
 'deaquation': ['adequation', 'deaquation'],
 'dear': ['ared', 'daer', 'dare', 'dear', 'read'],
 'dearie': ['aeried', 'dearie'],
 'dearth': ['dearth', 'hatred', 'rathed', 'thread'],
 'deary': ['deary', 'deray', 'rayed', 'ready', 'yeard'],
 'deash': ['deash', 'hades', 'sadhe', 'shade'],
 'deasil': ['aisled', 'deasil', 'ladies', 'sailed'],
 'deave': ['deave', 'eaved', 'evade'],
 'deb': ['bed', 'deb'],
 'debacle': ['belaced', 'debacle'],
 'debar': ['ardeb', 'beard', 'bread', 'debar'],
 'debark': ['bedark', 'debark'],
 'debaser': ['debaser', 'sabered'],
 'debater': ['betread', 'debater'],
 'deben': ['beden', 'deben', 'deneb'],
 'debi': ['beid', 'bide', 'debi', 'dieb'],
 'debile': ['debile', 'edible'],
 'debit': ['bidet', 'debit'],
 'debosh': ['beshod', 'debosh'],
 'debrief': ['debrief', 'defiber', 'fibered'],
 'debutant': ['debutant', 'unbatted'],
 'debutante': ['debutante', 'unabetted'],
 'decachord': ['decachord', 'dodecarch'],
 'decadic': ['caddice', 'decadic'],
 'decal': ['clead', 'decal', 'laced'],
 'decalin': ['cladine', 'decalin', 'iceland'],
 'decaliter': ['decaliter', 'decalitre'],
 'decalitre': ['decaliter', 'decalitre'],
 'decameter': ['decameter', 'decametre'],
 'decametre': ['decameter', 'decametre'],
 'decan': ['dance', 'decan'],
 'decanal': ['candela', 'decanal'],
 'decani': ['decani', 'decian'],
 'decant': ['cadent', 'canted', 'decant'],
 'decantate': ['catenated', 'decantate'],
 'decanter': ['crenated', 'decanter', 'nectared'],
 'decantherous': ['countershade', 'decantherous'],
 'decap': ['caped', 'decap', 'paced'],
 'decart': ['cedrat', 'decart', 'redact'],
 'decastere': ['decastere', 'desecrate'],
 'decator': ['cordate', 'decator', 'redcoat'],
 'decay': ['acedy', 'decay'],
 'deceiver': ['deceiver', 'received'],
 'decennia': ['cadinene', 'decennia', 'enneadic'],
 'decennial': ['celandine', 'decennial'],
 'decent': ['cedent', 'decent'],
 'decenter': ['centered', 'decenter', 'decentre', 'recedent'],
 'decentre': ['centered', 'decenter', 'decentre', 'recedent'],
 'decern': ['cendre', 'decern'],
 'decian': ['decani', 'decian'],
 'deciatine': ['deciatine', 'diacetine', 'taenicide', 'teniacide'],
 'decider': ['decider', 'decried'],
 'decillion': ['celloidin', 'collidine', 'decillion'],
 'decima': ['amiced', 'decima'],
 'decimal': ['camelid', 'decimal', 'declaim', 'medical'],
 'decimally': ['decimally', 'medically'],
 'decimate': ['decimate', 'medicate'],
 'decimation': ['decimation', 'medication'],
 'decimator': ['decimator', 'medicator', 'mordicate'],
 'decimestrial': ['decimestrial', 'sedimetrical'],
 'decimosexto': ['decimosexto', 'sextodecimo'],
 'deckel': ['deckel', 'deckle'],
 'decker': ['decker', 'redeck'],
 'deckle': ['deckel', 'deckle'],
 'deckload': ['deadlock', 'deckload'],
 'declaim': ['camelid', 'decimal', 'declaim', 'medical'],
 'declaimer': ['declaimer', 'demiracle'],
 'declaration': ['declaration', 'redactional'],
 'declare': ['cedrela', 'creedal', 'declare'],
 'declass': ['classed', 'declass'],
 'declinate': ['declinate', 'encitadel'],
 'declinatory': ['adrenolytic', 'declinatory'],
 'decoat': ['coated', 'decoat'],
 'decollate': ['decollate', 'ocellated'],
 'decollator': ['corollated', 'decollator'],
 'decolor': ['colored', 'croodle', 'decolor'],
 'decompress': ['compressed', 'decompress'],
 'deconsider': ['considered', 'deconsider'],
 'decorate': ['decorate', 'ocreated'],
 'decoration': ['carotenoid', 'coronadite', 'decoration'],
 'decorist': ['decorist', 'sectroid'],
 'decream': ['decream', 'racemed'],
 'decree': ['decree', 'recede'],
 'decreer': ['decreer', 'receder'],
 'decreet': ['decreet', 'decrete'],
 'decrepit': ['decrepit', 'depicter', 'precited'],
 'decrete': ['decreet', 'decrete'],
 'decretist': ['decretist', 'trisected'],
 'decrial': ['decrial', 'radicel', 'radicle'],
 'decried': ['decider', 'decried'],
 'decrown': ['crowned', 'decrown'],
 'decry': ['cedry', 'decry'],
 'decurionate': ['counteridea', 'decurionate'],
 'decurrency': ['decurrency', 'recrudency'],
 'decursion': ['cinderous', 'decursion'],
 'decus': ['decus', 'duces'],
 'decyl': ['clyde', 'decyl'],
 'decylic': ['cyclide', 'decylic', 'dicycle'],
 'dedan': ['dedan', 'denda'],
 'dedicant': ['addicent', 'dedicant'],
 'dedo': ['dedo', 'dode', 'eddo'],
 'deduce': ['deduce', 'deuced'],
 'deduct': ['deduct', 'ducted'],
 'deem': ['deem', 'deme', 'mede', 'meed'],
 'deemer': ['deemer', 'meered', 'redeem', 'remede'],
 'deep': ['deep', 'peed'],
 'deer': ['deer', 'dere', 'dree', 'rede', 'reed'],
 'deerhair': ['deerhair', 'dehairer'],
 'deerhorn': ['deerhorn', 'dehorner'],
 'deerwood': ['deerwood', 'doorweed'],
 'defat': ['defat', 'fated'],
 'defaulter': ['defaulter', 'redefault'],
 'defeater': ['defeater', 'federate', 'redefeat'],
 'defensor': ['defensor', 'foresend'],
 'defer': ['defer', 'freed'],
 'defial': ['afield', 'defial'],
 'defiber': ['debrief', 'defiber', 'fibered'],
 'defile': ['defile', 'fidele'],
 'defiled': ['defiled', 'fielded'],
 'defiler': ['defiler', 'fielder'],
 'definable': ['beanfield', 'definable'],
 'define': ['define', 'infeed'],
 'definer': ['definer', 'refined'],
 'deflect': ['clefted', 'deflect'],
 'deflesh': ['deflesh', 'fleshed'],
 'deflex': ['deflex', 'flexed'],
 'deflower': ['deflower', 'flowered'],
 'defluent': ['defluent', 'unfelted'],
 'defog': ['defog', 'fodge'],
 'deforciant': ['deforciant', 'fornicated'],
 'deforest': ['deforest', 'forested'],
 'deform': ['deform', 'formed'],
 'deformer': ['deformer', 'reformed'],
 'defray': ['defray', 'frayed'],
 'defrost': ['defrost', 'frosted'],
 'deg': ['deg', 'ged'],
 'degarnish': ['degarnish', 'garnished'],
 'degasser': ['degasser', 'dressage'],
 'degelation': ['degelation', 'delegation'],
 'degrain': ['degrain', 'deraign', 'deringa', 'gradine', 'grained', 'reading'],
 'degu': ['degu', 'gude'],
 'dehair': ['dehair', 'haired'],
 'dehairer': ['deerhair', 'dehairer'],
 'dehorn': ['dehorn', 'horned'],
 'dehorner': ['deerhorn', 'dehorner'],
 'dehors': ['dehors', 'rhodes', 'shoder', 'shored'],
 'dehortation': ['dehortation', 'theriodonta'],
 'dehusk': ['dehusk', 'husked'],
 'deicer': ['ceride', 'deicer'],
 'deictical': ['deictical', 'dialectic'],
 'deification': ['deification', 'edification'],
 'deificatory': ['deificatory', 'edificatory'],
 'deifier': ['deifier', 'edifier'],
 'deify': ['deify', 'edify'],
 'deign': ['deign', 'dinge', 'nidge'],
 'deino': ['deino', 'dione', 'edoni'],
 'deinocephalia': ['deinocephalia', 'palaeechinoid'],
 'deinos': ['deinos', 'donsie', 'inodes', 'onside'],
 'deipara': ['deipara', 'paridae'],
 'deirdre': ['deirdre', 'derider', 'derride', 'ridered'],
 'deism': ['deism', 'disme'],
 'deist': ['deist', 'steid'],
 'deistic': ['deistic', 'dietics'],
 'deistically': ['deistically', 'dialystelic'],
 'deity': ['deity', 'tydie'],
 'deityship': ['deityship', 'diphysite'],
 'del': ['del', 'eld', 'led'],
 'delaine': ['adeline', 'daniele', 'delaine'],
 'delaminate': ['antemedial', 'delaminate'],
 'delapse': ['delapse', 'sepaled'],
 'delate': ['delate', 'elated'],
 'delater': ['delater', 'related', 'treadle'],
 'delator': ['delator', 'leotard'],
 'delawn': ['delawn', 'lawned', 'wandle'],
 'delay': ['delay', 'leady'],
 'delayer': ['delayer', 'layered', 'redelay'],
 'delayful': ['delayful', 'feudally'],
 'dele': ['dele', 'lede', 'leed'],
 'delead': ['delead', 'leaded'],
 'delegation': ['degelation', 'delegation'],
 'delegatory': ['delegatory', 'derogately'],
 'delete': ['delete', 'teedle'],
 'delf': ['delf', 'fled'],
 'delhi': ['delhi', 'hield'],
 'delia': ['adiel', 'delia', 'ideal'],
 'delian': ['aldine', 'daniel', 'delian', 'denial', 'enalid', 'leadin'],
 'delible': ['bellied', 'delible'],
 'delicateness': ['delicateness', 'delicatessen'],
 'delicatessen': ['delicateness', 'delicatessen'],
 'delichon': ['chelidon', 'chelonid', 'delichon'],
 'delict': ['delict', 'deltic'],
 'deligation': ['deligation', 'gadolinite', 'gelatinoid'],
 'delignate': ['delignate', 'gelatined'],
 'delimit': ['delimit', 'limited'],
 'delimitation': ['delimitation', 'mniotiltidae'],
 'delineator': ['delineator', 'rondeletia'],
 'delint': ['delint', 'dentil'],
 'delirament': ['delirament', 'derailment'],
 'deliriant': ['deliriant', 'draintile', 'interlaid'],
 'deliver': ['deliver', 'deviler', 'livered'],
 'deliverer': ['deliverer', 'redeliver'],
 'della': ['dalle', 'della', 'ladle'],
 'deloul': ['deloul', 'duello'],
 'delphinius': ['delphinius', 'sulphinide'],
 'delta': ['adlet', 'dealt', 'delta', 'lated', 'taled'],
 'deltaic': ['citadel', 'deltaic', 'dialect', 'edictal', 'lactide'],
 'deltic': ['delict', 'deltic'],
 'deluding': ['deluding', 'ungilded'],
 'delusion': ['delusion', 'unsoiled'],
 'delusionist': ['delusionist', 'indissolute'],
 'deluster': ['deluster', 'ulstered'],
 'demal': ['demal', 'medal'],
 'demand': ['damned', 'demand', 'madden'],
 'demander': ['demander', 'redemand'],
 'demanding': ['demanding', 'maddening'],
 'demandingly': ['demandingly', 'maddeningly'],
 'demantoid': ['demantoid', 'dominated'],
 'demarcate': ['camerated', 'demarcate'],
 'demarcation': ['demarcation', 'democratian'],
 'demark': ['demark', 'marked'],
 'demast': ['demast', 'masted'],
 'deme': ['deem', 'deme', 'mede', 'meed'],
 'demean': ['amende', 'demean', 'meaned', 'nadeem'],
 'demeanor': ['demeanor', 'enamored'],
 'dementia': ['dementia', 'mendaite'],
 'demerit': ['demerit', 'dimeter', 'merited', 'mitered'],
 'demerol': ['demerol', 'modeler', 'remodel'],
 'demetrian': ['demetrian', 'dermatine', 'meandrite', 'minareted'],
 'demi': ['demi', 'diem', 'dime', 'mide'],
 'demibrute': ['bermudite', 'demibrute'],
 'demicannon': ['cinnamoned', 'demicannon'],
 'demicanon': ['demicanon', 'dominance'],
 'demidog': ['demidog', 'demigod'],
 'demigod': ['demidog', 'demigod'],
 'demiluster': ['demiluster', 'demilustre'],
 'demilustre': ['demiluster', 'demilustre'],
 'demiparallel': ['demiparallel', 'imparalleled'],
 'demipronation': ['demipronation', 'preadmonition', 'predomination'],
 'demiracle': ['declaimer', 'demiracle'],
 'demiram': ['demiram', 'mermaid'],
 'demirep': ['demirep', 'epiderm', 'impeder', 'remiped'],
 'demirobe': ['demirobe', 'embodier'],
 'demisable': ['beadleism', 'demisable'],
 'demise': ['demise', 'diseme'],
 'demit': ['demit', 'timed'],
 'demiturned': ['demiturned', 'undertimed'],
 'demob': ['demob', 'mobed'],
 'democratian': ['demarcation', 'democratian'],
 'demolisher': ['demolisher', 'redemolish'],
 'demoniac': ['codamine', 'comedian', 'daemonic', 'demoniac'],
 'demoniacism': ['demoniacism', 'seminomadic'],
 'demonial': ['demonial', 'melanoid'],
 'demoniast': ['ademonist', 'demoniast', 'staminode'],
 'demonish': ['demonish', 'hedonism'],
 'demonism': ['demonism', 'medimnos', 'misnomed'],
 'demotics': ['comedist', 'demotics', 'docetism', 'domestic'],
 'demotion': ['demotion', 'entomoid', 'moontide'],
 'demount': ['demount', 'mounted'],
 'demurrer': ['demurrer', 'murderer'],
 'demurring': ['demurring', 'murdering'],
 'demurringly': ['demurringly', 'murderingly'],
 'demy': ['demy', 'emyd'],
 'den': ['den', 'end', 'ned'],
 'denarius': ['denarius', 'desaurin', 'unraised'],
 'denaro': ['denaro', 'orenda'],
 'denary': ['denary', 'yander'],
 'denat': ['denat', 'entad'],
 'denature': ['denature', 'undereat'],
 'denda': ['dedan', 'denda'],
 'dendral': ['dandler', 'dendral'],
 'dendrite': ['dendrite', 'tindered'],
 'dendrites': ['dendrites', 'distender', 'redistend'],
 'dene': ['dene', 'eden', 'need'],
 'deneb': ['beden', 'deben', 'deneb'],
 'dengue': ['dengue', 'unedge'],
 'denial': ['aldine', 'daniel', 'delian', 'denial', 'enalid', 'leadin'],
 'denier': ['denier', 'nereid'],
 'denierer': ['denierer', 'reindeer'],
 'denigrate': ['argentide', 'denigrate', 'dinergate'],
 'denim': ['denim', 'mendi'],
 'denis': ['denis', 'snide'],
 'denominate': ['denominate', 'emendation'],
 'denotable': ['denotable', 'detonable'],
 'denotation': ['denotation', 'detonation'],
 'denotative': ['denotative', 'detonative'],
 'denotive': ['denotive', 'devonite'],
 'denouncer': ['denouncer', 'unencored'],
 'dense': ['dense', 'needs'],
 'denshare': ['denshare', 'seerhand'],
 'denshire': ['denshire', 'drisheen'],
 'density': ['density', 'destiny'],
 'dent': ['dent', 'tend'],
 'dental': ['dental', 'tandle'],
 'dentale': ['dalteen', 'dentale', 'edental'],
 'dentalism': ['dentalism', 'dismantle'],
 'dentaria': ['anteriad', 'atridean', 'dentaria'],
 'dentatoserrate': ['dentatoserrate', 'serratodentate'],
 'dentatosinuate': ['dentatosinuate', 'sinuatodentate'],
 'denter': ['denter', 'rented', 'tender'],
 'dentex': ['dentex', 'extend'],
 'denticle': ['cliented', 'denticle'],
 'denticular': ['denticular', 'unarticled'],
 'dentil': ['delint', 'dentil'],
 'dentilingual': ['dentilingual', 'indulgential', 'linguidental'],
 'dentin': ['dentin', 'indent', 'intend', 'tinned'],
 'dentinal': ['dentinal', 'teinland', 'tendinal'],
 'dentine': ['dentine', 'nineted'],
 'dentinitis': ['dentinitis', 'tendinitis'],
 'dentinoma': ['dentinoma', 'nominated'],
 'dentist': ['dentist', 'distent', 'stinted'],
 'dentolabial': ['dentolabial', 'labiodental'],
 'dentolingual': ['dentolingual', 'linguodental'],
 'denture': ['denture', 'untreed'],
 'denudative': ['denudative', 'undeviated'],
 'denude': ['denude', 'dudeen'],
 'denumeral': ['denumeral', 'undermeal', 'unrealmed'],
 'denunciator': ['denunciator', 'underaction'],
 'deny': ['deny', 'dyne'],
 'deoppilant': ['deoppilant', 'pentaploid'],
 'deota': ['deota', 'todea'],
 'depa': ['depa', 'peda'],
 'depaint': ['depaint', 'inadept', 'painted', 'patined'],
 'depart': ['depart', 'parted', 'petard'],
 'departition': ['departition', 'partitioned', 'trepidation'],
 'departure': ['apertured', 'departure'],
 'depas': ['depas', 'sepad', 'spade'],
 'depencil': ['depencil', 'penciled', 'pendicle'],
 'depender': ['depender', 'redepend'],
 'depetticoat': ['depetticoat', 'petticoated'],
 'depicter': ['decrepit', 'depicter', 'precited'],
 'depiction': ['depiction', 'pectinoid'],
 'depilate': ['depilate', 'leptidae', 'pileated'],
 'depletion': ['depletion', 'diplotene'],
 'deploration': ['deploration', 'periodontal'],
 'deploy': ['deploy', 'podley'],
 'depoh': ['depoh', 'ephod', 'hoped'],
 'depolish': ['depolish', 'polished'],
 'deport': ['deport', 'ported', 'redtop'],
 'deportation': ['antitorpedo', 'deportation'],
 'deposal': ['adelops', 'deposal'],
 'deposer': ['deposer', 'reposed'],
 'deposit': ['deposit', 'topside'],
 'deposition': ['deposition', 'positioned'],
 'depositional': ['depositional', 'despoliation'],
 'depositure': ['depositure', 'pterideous'],
 'deprave': ['deprave', 'pervade'],
 'depraver': ['depraver', 'pervader'],
 'depravingly': ['depravingly', 'pervadingly'],
 'deprecable': ['deprecable', 'precedable'],
 'deprecation': ['capernoited', 'deprecation'],
 'depreciation': ['depreciation', 'predeication'],
 'depressant': ['depressant', 'partedness'],
 'deprint': ['deprint', 'printed'],
 'deprival': ['deprival', 'prevalid'],
 'deprivate': ['deprivate', 'predative'],
 'deprive': ['deprive', 'previde'],
 'depriver': ['depriver', 'predrive'],
 'depurant': ['depurant', 'unparted'],
 'depuration': ['depuration', 'portunidae'],
 'deraign': ['degrain', 'deraign', 'deringa', 'gradine', 'grained', 'reading'],
 'derail': ['ariled', 'derail', 'dialer'],
 'derailment': ['delirament', 'derailment'],
 'derange': ['derange', 'enraged', 'gardeen', 'gerenda', 'grandee', 'grenade'],
 'deranged': ['deranged', 'gardened'],
 'deranger': ['deranger', 'gardener'],
 'derat': ['dater', 'derat', 'detar', 'drate', 'rated', 'trade', 'tread'],
 'derate': ['derate', 'redate'],
 'derater': ['derater', 'retrade', 'retread', 'treader'],
 'deray': ['deary', 'deray', 'rayed', 'ready', 'yeard'],
 'dere': ['deer', 'dere', 'dree', 'rede', 'reed'],
 'deregister': ['deregister', 'registered'],
 'derelict': ['derelict', 'relicted'],
 'deric': ['cider', 'cried', 'deric', 'dicer'],
 'derider': ['deirdre', 'derider', 'derride', 'ridered'],
 'deringa': ['degrain', 'deraign', 'deringa', 'gradine', 'grained', 'reading'],
 'derision': ['derision', 'ironside', 'resinoid', 'sirenoid'],
 'derivation': ['derivation', 'ordinative'],
 'derivational': ['derivational', 'revalidation'],
 'derive': ['derive', 'redive'],
 'deriver': ['deriver', 'redrive', 'rivered'],
 'derma': ['armed', 'derma', 'dream', 'ramed'],
 'dermad': ['dermad', 'madder'],
 'dermal': ['dermal', 'marled', 'medlar'],
 'dermatic': ['dermatic', 'timecard'],
 'dermatine': ['demetrian', 'dermatine', 'meandrite', 'minareted'],
 'dermatoneurosis': ['dermatoneurosis', 'neurodermatosis'],
 'dermatophone': ['dermatophone', 'herpetomonad'],
 'dermoblast': ['blastoderm', 'dermoblast'],
 'dermol': ['dermol', 'molder', 'remold'],
 'dermosclerite': ['dermosclerite', 'sclerodermite'],
 'dern': ['dern', 'rend'],
 'derogately': ['delegatory', 'derogately'],
 'derogation': ['derogation', 'trogonidae'],
 'derout': ['derout', 'detour', 'douter'],
 'derride': ['deirdre', 'derider', 'derride', 'ridered'],
 'derries': ['derries', 'desirer', 'resider', 'serried'],
 'derringer': ['derringer', 'regrinder'],
 'derry': ['derry', 'redry', 'ryder'],
 'derust': ['derust', 'duster'],
 'desalt': ['desalt', 'salted'],
 'desand': ['desand', 'sadden', 'sanded'],
 'desaurin': ['denarius', 'desaurin', 'unraised'],
 'descendant': ['adscendent', 'descendant'],
 'descender': ['descender', 'redescend'],
 'descent': ['descent', 'scented'],
 'description': ['description', 'discerption'],
 'desecrate': ['decastere', 'desecrate'],
 'desecration': ['considerate', 'desecration'],
 'deseed': ['deseed', 'seeded'],
 'desertic': ['creedist', 'desertic', 'discreet', 'discrete'],
 'desertion': ['desertion', 'detersion'],
 'deserver': ['deserver', 'reserved', 'reversed'],
 'desex': ['desex', 'sexed'],
 'deshabille': ['deshabille', 'shieldable'],
 'desi': ['desi', 'ides', 'seid', 'side'],
 'desiccation': ['desiccation', 'discoactine'],
 'desight': ['desight', 'sighted'],
 'design': ['design', 'singed'],
 'designer': ['designer', 'redesign', 'resigned'],
 'desilver': ['desilver', 'silvered'],
 'desirable': ['desirable', 'redisable'],
 'desire': ['desire', 'reside'],
 'desirer': ['derries', 'desirer', 'resider', 'serried'],
 'desirous': ['desirous', 'siderous'],
 'desition': ['desition', 'sedition'],
 'desma': ['desma', 'mesad'],
 'desman': ['amends', 'desman'],
 'desmopathy': ['desmopathy', 'phymatodes'],
 'desorption': ['desorption', 'priodontes'],
 'despair': ['despair', 'pardesi'],
 'despairing': ['despairing', 'spinigrade'],
 'desperation': ['desperation', 'esperantido'],
 'despise': ['despise', 'pedesis'],
 'despiser': ['despiser', 'disperse'],
 'despoil': ['despoil', 'soliped', 'spoiled'],
 'despoiler': ['despoiler', 'leprosied'],
 'despoliation': ['depositional', 'despoliation'],
 'despot': ['despot', 'posted'],
 'despotat': ['despotat', 'postdate'],
 'dessert': ['dessert', 'tressed'],
 'destain': ['destain', 'instead', 'sainted', 'satined'],
 'destine': ['destine', 'edestin'],
 'destinism': ['destinism', 'timidness'],
 'destiny': ['density', 'destiny'],
 'desugar': ['desugar', 'sugared'],
 'detail': ['detail', 'dietal', 'dilate', 'edital', 'tailed'],
 'detailer': ['detailer', 'elaterid'],
 'detain': ['danite', 'detain'],
 'detainal': ['danalite', 'detainal'],
 'detar': ['dater', 'derat', 'detar', 'drate', 'rated', 'trade', 'tread'],
 'detassel': ['dateless', 'detassel'],
 'detax': ['detax', 'taxed'],
 'detecter': ['detecter', 'redetect'],
 'detent': ['detent', 'netted', 'tented'],
 'deter': ['deter', 'treed'],
 'determinant': ['determinant', 'detrainment'],
 'detersion': ['desertion', 'detersion'],
 'detest': ['detest', 'tested'],
 'dethrone': ['dethrone', 'threnode'],
 'detin': ['detin', 'teind', 'tined'],
 'detinet': ['detinet', 'dinette'],
 'detonable': ['denotable', 'detonable'],
 'detonation': ['denotation', 'detonation'],
 'detonative': ['denotative', 'detonative'],
 'detonator': ['detonator', 'tetraodon'],
 'detour': ['derout', 'detour', 'douter'],
 'detracter': ['detracter', 'retracted'],
 'detraction': ['detraction', 'doctrinate', 'tetarconid'],
 'detrain': ['antired', 'detrain', 'randite', 'trained'],
 'detrainment': ['determinant', 'detrainment'],
 'detrusion': ['detrusion', 'tinderous', 'unstoried'],
 'detrusive': ['detrusive', 'divesture', 'servitude'],
 'deuce': ['deuce', 'educe'],
 'deuced': ['deduce', 'deuced'],
 'deul': ['deul', 'duel', 'leud'],
 'deva': ['dave', 'deva', 'vade', 'veda'],
 'devance': ['devance', 'vendace'],
 'develin': ['develin', 'endevil'],
 'developer': ['developer', 'redevelop'],
 'devil': ['devil', 'divel', 'lived'],
 'deviler': ['deliver', 'deviler', 'livered'],
 'devisceration': ['considerative', 'devisceration'],
 'deviser': ['deviser', 'diverse', 'revised'],
 'devitrify': ['devitrify', 'fervidity'],
 'devoid': ['devoid', 'voided'],
 'devoir': ['devoir', 'voider'],
 'devonite': ['denotive', 'devonite'],
 'devourer': ['devourer', 'overdure', 'overrude'],
 'devow': ['devow', 'vowed'],
 'dew': ['dew', 'wed'],
 'dewan': ['awned', 'dewan', 'waned'],
 'dewater': ['dewater', 'tarweed', 'watered'],
 'dewer': ['dewer', 'ewder', 'rewed'],
 'dewey': ['dewey', 'weedy'],
 'dewily': ['dewily', 'widely', 'wieldy'],
 'dewiness': ['dewiness', 'wideness'],
 'dewool': ['dewool', 'elwood', 'wooled'],
 'deworm': ['deworm', 'wormed'],
 'dewy': ['dewy', 'wyde'],
 'dextraural': ['dextraural', 'extradural'],
 'dextrosinistral': ['dextrosinistral', 'sinistrodextral'],
 'dey': ['dey', 'dye', 'yed'],
 'deyhouse': ['deyhouse', 'dyehouse'],
 'deyship': ['deyship', 'diphyes'],
 'dezinc': ['dezinc', 'zendic'],
 'dha': ['dah', 'dha', 'had'],
 'dhamnoo': ['dhamnoo', 'hoodman', 'manhood'],
 'dhan': ['dhan', 'hand'],
 'dharna': ['andhra', 'dharna'],
 'dheri': ['dheri', 'hider', 'hired'],
 'dhobi': ['bodhi', 'dhobi'],
 'dhoon': ['dhoon', 'hondo'],
 'dhu': ['dhu', 'hud'],
 'di': ['di', 'id'],
 'diabolist': ['diabolist', 'idioblast'],
 'diacetin': ['diacetin', 'indicate'],
 'diacetine': ['deciatine', 'diacetine', 'taenicide', 'teniacide'],
 'diacetyl': ['diacetyl', 'lyctidae'],
 'diachoretic': ['citharoedic', 'diachoretic'],
 'diaclase': ['diaclase', 'sidalcea'],
 'diaconal': ['cladonia', 'condalia', 'diaconal'],
 'diact': ['diact', 'dicta'],
 'diadem': ['diadem', 'mediad'],
 'diaderm': ['admired', 'diaderm'],
 'diaeretic': ['diaeretic', 'icteridae'],
 'diagenetic': ['diagenetic', 'digenetica'],
 'diageotropism': ['diageotropism', 'geodiatropism'],
 'diagonal': ['diagonal', 'ganoidal', 'gonadial'],
 'dial': ['dail', 'dali', 'dial', 'laid', 'lida'],
 'dialect': ['citadel', 'deltaic', 'dialect', 'edictal', 'lactide'],
 'dialectic': ['deictical', 'dialectic'],
 'dialector': ['dialector', 'lacertoid'],
 'dialer': ['ariled', 'derail', 'dialer'],
 'dialin': ['anilid', 'dialin', 'dianil', 'inlaid'],
 'dialing': ['dialing', 'gliadin'],
 'dialister': ['dialister', 'trailside'],
 'diallelon': ['diallelon', 'llandeilo'],
 'dialogism': ['dialogism', 'sigmoidal'],
 'dialystelic': ['deistically', 'dialystelic'],
 'dialytic': ['calidity', 'dialytic'],
 'diamagnet': ['agminated', 'diamagnet'],
 'diamantine': ['diamantine', 'inanimated'],
 'diameter': ['diameter', 'diatreme'],
 'diametric': ['citramide', 'diametric', 'matricide'],
 'diamide': ['amidide', 'diamide', 'mididae'],
 'diamine': ['amidine', 'diamine'],
 'diamorphine': ['diamorphine', 'phronimidae'],
 'dian': ['adin', 'andi', 'dain', 'dani', 'dian', 'naid'],
 'diana': ['danai', 'diana', 'naiad'],
 'diander': ['diander', 'drained'],
 'diane': ['diane', 'idean'],
 'dianetics': ['andesitic', 'dianetics'],
 'dianil': ['anilid', 'dialin', 'dianil', 'inlaid'],
 'diapensia': ['diapensia', 'diaspinae'],
 'diaper': ['diaper', 'paired'],
 'diaphote': ['diaphote', 'hepatoid'],
 'diaphtherin': ['diaphtherin', 'diphtherian'],
 'diapnoic': ['diapnoic', 'pinacoid'],
 'diapnotic': ['antipodic', 'diapnotic'],
 'diaporthe': ['aphrodite', 'atrophied', 'diaporthe'],
 'diarch': ['chidra', 'diarch'],
 'diarchial': ['diarchial', 'rachidial'],
 'diarchy': ['diarchy', 'hyracid'],
 'diarian': ['aridian', 'diarian'],
 'diary': ['dairy', 'diary', 'yaird'],
 'diascia': ['ascidia', 'diascia'],
 'diascope': ['diascope', 'psocidae', 'scopidae'],
 'diaspinae': ['diapensia', 'diaspinae'],
 'diastem': ['diastem', 'misdate'],
 'diastema': ['adamsite', 'diastema'],
 'diaster': ['astride', 'diaster', 'disrate', 'restiad', 'staired'],
 'diastole': ['diastole', 'isolated', 'sodalite', 'solidate'],
 'diastrophic': ['aphrodistic', 'diastrophic'],
 'diastrophy': ['diastrophy', 'dystrophia'],
 'diatomales': ['diatomales', 'mastoidale', 'mastoideal'],
 'diatomean': ['diatomean', 'mantoidea'],
 'diatomin': ['diatomin', 'domitian'],
 'diatonic': ['actinoid', 'diatonic', 'naticoid'],
 'diatreme': ['diameter', 'diatreme'],
 'diatropism': ['diatropism', 'prismatoid'],
 'dib': ['bid', 'dib'],
 'dibatis': ['dabitis', 'dibatis'],
 'dibber': ['dibber', 'ribbed'],
 'dibbler': ['dibbler', 'dribble'],
 'dibrom': ['dibrom', 'morbid'],
 'dicaryon': ['cynaroid', 'dicaryon'],
 'dicast': ['dicast', 'stadic'],
 'dice': ['dice', 'iced'],
 'dicentra': ['crinated', 'dicentra'],
 'dicer': ['cider', 'cried', 'deric', 'dicer'],
 'diceras': ['diceras', 'radices', 'sidecar'],
 'dich': ['chid', 'dich'],
 'dichroite': ['dichroite', 'erichtoid', 'theriodic'],
 'dichromat': ['chromatid', 'dichromat'],
 'dichter': ['dichter', 'ditcher'],
 'dicolic': ['codicil', 'dicolic'],
 'dicolon': ['dicolon', 'dolcino'],
 'dicoumarin': ['acridonium', 'dicoumarin'],
 'dicta': ['diact', 'dicta'],
 'dictaphone': ['dictaphone', 'endopathic'],
 'dictational': ['antidotical', 'dictational'],
 'dictionary': ['dictionary', 'indicatory'],
 'dicyanine': ['cyanidine', 'dicyanine'],
 'dicycle': ['cyclide', 'decylic', 'dicycle'],
 'dicyema': ['dicyema', 'mediacy'],
 'diddle': ['diddle', 'lidded'],
 'diddler': ['diddler', 'driddle'],
 'didym': ['didym', 'middy'],
 'die': ['die', 'ide'],
 'dieb': ['beid', 'bide', 'debi', 'dieb'],
 'diego': ['diego', 'dogie', 'geoid'],
 'dielytra': ['dielytra', 'tileyard'],
 'diem': ['demi', 'diem', 'dime', 'mide'],
 'dier': ['dier', 'dire', 'reid', 'ride'],
 'diesel': ['diesel', 'sedile', 'seidel'],
 'diet': ['diet', 'dite', 'edit', 'tide', 'tied'],
 'dietal': ['detail', 'dietal', 'dilate', 'edital', 'tailed'],
 'dieter': ['dieter', 'tiered'],
 'dietic': ['citied', 'dietic'],
 'dietics': ['deistic', 'dietics'],
 'dig': ['dig', 'gid'],
 'digenetica': ['diagenetic', 'digenetica'],
 'digeny': ['digeny', 'dyeing'],
 'digester': ['digester', 'redigest'],
 'digitalein': ['digitalein', 'diligentia'],
 'digitation': ['digitation', 'goniatitid'],
 'digitonin': ['digitonin', 'indigotin'],
 'digredient': ['digredient', 'reddingite'],
 'dihalo': ['dihalo', 'haloid'],
 'diiambus': ['basidium', 'diiambus'],
 'dika': ['dika', 'kaid'],
 'dikaryon': ['ankyroid', 'dikaryon'],
 'dike': ['dike', 'keid'],
 'dilacerate': ['dilacerate', 'lacertidae'],
 'dilatant': ['atlantid', 'dilatant'],
 'dilate': ['detail', 'dietal', 'dilate', 'edital', 'tailed'],
 'dilater': ['dilater', 'lardite', 'redtail'],
 'dilatometric': ['calotermitid', 'dilatometric'],
 'dilator': ['dilator', 'ortalid'],
 'dilatory': ['adroitly', 'dilatory', 'idolatry'],
 'diligence': ['ceilinged', 'diligence'],
 'diligentia': ['digitalein', 'diligentia'],
 'dillue': ['dillue', 'illude'],
 'dilluer': ['dilluer', 'illuder'],
 'dilo': ['dilo', 'diol', 'doli', 'idol', 'olid'],
 'diluent': ['diluent', 'untiled'],
 'dilute': ['dilute', 'dultie'],
 'diluted': ['diluted', 'luddite'],
 'dilutent': ['dilutent', 'untilted', 'untitled'],
 'diluvian': ['diluvian', 'induvial'],
 'dim': ['dim', 'mid'],
 'dimatis': ['amidist', 'dimatis'],
 'dimble': ['dimble', 'limbed'],
 'dime': ['demi', 'diem', 'dime', 'mide'],
 'dimer': ['dimer', 'mider'],
 'dimera': ['admire', 'armied', 'damier', 'dimera', 'merida'],
 'dimeran': ['adermin', 'amerind', 'dimeran'],
 'dimerous': ['dimerous', 'soredium'],
 'dimeter': ['demerit', 'dimeter', 'merited', 'mitered'],
 'dimetria': ['dimetria', 'mitridae', 'tiremaid', 'triamide'],
 'diminisher': ['diminisher', 'rediminish'],
 'dimit': ['dimit', 'timid'],
 'dimmer': ['dimmer', 'immerd', 'rimmed'],
 'dimna': ['dimna', 'manid'],
 'dimyarian': ['dimyarian', 'myrianida'],
 'din': ['din', 'ind', 'nid'],
 'dinah': ['ahind', 'dinah'],
 'dinar': ['darin', 'dinar', 'drain', 'indra', 'nadir', 'ranid'],
 'dinder': ['dinder', 'ridden', 'rinded'],
 'dindle': ['dindle', 'niddle'],
 'dine': ['dine', 'enid', 'inde', 'nide'],
 'diner': ['diner', 'riden', 'rinde'],
 'dinergate': ['argentide', 'denigrate', 'dinergate'],
 'dinero': ['dinero', 'dorine'],
 'dinette': ['detinet', 'dinette'],
 'dineuric': ['dineuric', 'eurindic'],
 'dingar': ['daring', 'dingar', 'gradin'],
 'dinge': ['deign', 'dinge', 'nidge'],
 'dingle': ['dingle', 'elding', 'engild', 'gilden'],
 'dingo': ['dingo', 'doing', 'gondi', 'gonid'],
 'dingwall': ['dingwall', 'windgall'],
 'dingy': ['dingy', 'dying'],
 'dinheiro': ['dinheiro', 'hernioid'],
 'dinic': ['dinic', 'indic'],
 'dining': ['dining', 'indign', 'niding'],
 'dink': ['dink', 'kind'],
 'dinkey': ['dinkey', 'kidney'],
 'dinocerata': ['arctoidean', 'carotidean', 'cordaitean', 'dinocerata'],
 'dinoceratan': ['carnationed', 'dinoceratan'],
 'dinomic': ['dinomic', 'dominic'],
 'dint': ['dint', 'tind'],
 'dinus': ['dinus', 'indus', 'nidus'],
 'dioeciopolygamous': ['dioeciopolygamous', 'polygamodioecious'],
 'diogenite': ['diogenite', 'gideonite'],
 'diol': ['dilo', 'diol', 'doli', 'idol', 'olid'],
 'dion': ['dion', 'nodi', 'odin'],
 'dione': ['deino', 'dione', 'edoni'],
 'diopter': ['diopter', 'peridot', 'proetid', 'protide', 'pteroid'],
 'dioptra': ['dioptra', 'parotid'],
 'dioptral': ['dioptral', 'tripodal'],
 'dioptric': ['dioptric', 'tripodic'],
 'dioptrical': ['dioptrical', 'tripodical'],
 'dioptry': ['dioptry', 'tripody'],
 'diorama': ['amaroid', 'diorama'],
 'dioramic': ['dioramic', 'dromicia'],
 'dioscorein': ['dioscorein', 'dioscorine'],
 'dioscorine': ['dioscorein', 'dioscorine'],
 'dioscuri': ['dioscuri', 'sciuroid'],
 'diose': ['diose', 'idose', 'oside'],
 'diosmin': ['diosmin', 'odinism'],
 'diosmotic': ['diosmotic', 'sodomitic'],
 'diparentum': ['diparentum', 'unimparted'],
 'dipetto': ['dipetto', 'diptote'],
 'diphase': ['aphides', 'diphase'],
 'diphaser': ['diphaser', 'parished', 'raphides', 'sephardi'],
 'diphosphate': ['diphosphate', 'phosphatide'],
 'diphtherian': ['diaphtherin', 'diphtherian'],
 'diphyes': ['deyship', 'diphyes'],
 'diphysite': ['deityship', 'diphysite'],
 'dipicrate': ['dipicrate', 'patricide', 'pediatric'],
 'diplanar': ['diplanar', 'prandial'],
 'diplasion': ['aspidinol', 'diplasion'],
 'dipleura': ['dipleura', 'epidural'],
 'dipleural': ['dipleural', 'preludial'],
 'diplocephalus': ['diplocephalus', 'pseudophallic'],
 'diploe': ['diploe', 'dipole'],
 'diploetic': ['diploetic', 'lepidotic'],
 'diplotene': ['depletion', 'diplotene'],
 'dipnoan': ['dipnoan', 'nonpaid', 'pandion'],
 'dipolar': ['dipolar', 'polarid'],
 'dipole': ['diploe', 'dipole'],
 'dipsaceous': ['dipsaceous', 'spadiceous'],
 'dipter': ['dipter', 'trepid'],
 'dipteraceous': ['dipteraceous', 'epiceratodus'],
 'dipteral': ['dipteral', 'tripedal'],
 'dipterological': ['dipterological', 'pteridological'],
 'dipterologist': ['dipterologist', 'pteridologist'],
 'dipterology': ['dipterology', 'pteridology'],
 'dipteros': ['dipteros', 'portside'],
 'diptote': ['dipetto', 'diptote'],
 'dirca': ['acrid', 'caird', 'carid', 'darci', 'daric', 'dirca'],
 'dircaean': ['caridean', 'dircaean', 'radiance'],
 'dire': ['dier', 'dire', 'reid', 'ride'],
 'direct': ['credit', 'direct'],
 'directable': ['creditable', 'directable'],
 'directer': ['cedriret', 'directer', 'recredit', 'redirect'],
 'direction': ['cretinoid', 'direction'],
 'directional': ['clitoridean', 'directional'],
 'directive': ['creditive', 'directive'],
 'directly': ['directly', 'tridecyl'],
 'directoire': ['cordierite', 'directoire'],
 'director': ['creditor', 'director'],
 'directorship': ['creditorship', 'directorship'],
 'directress': ['creditress', 'directress'],
 'directrix': ['creditrix', 'directrix'],
 'direly': ['direly', 'idyler'],
 'direption': ['direption', 'perdition', 'tropidine'],
 'dirge': ['dirge', 'gride', 'redig', 'ridge'],
 'dirgelike': ['dirgelike', 'ridgelike'],
 'dirgeman': ['dirgeman', 'margined', 'midrange'],
 'dirgler': ['dirgler', 'girdler'],
 'dirten': ['dirten', 'rident', 'tinder'],
 'dis': ['dis', 'sid'],
 'disa': ['dais', 'dasi', 'disa', 'said', 'sida'],
 'disadventure': ['disadventure', 'unadvertised'],
 'disappearer': ['disappearer', 'redisappear'],
 'disarmed': ['disarmed', 'misdread'],
 'disastimeter': ['disastimeter', 'semistriated'],
 'disattire': ['disattire', 'distraite'],
 'disbud': ['disbud', 'disdub'],
 'disburse': ['disburse', 'subsider'],
 'discastle': ['clidastes', 'discastle'],
 'discern': ['discern', 'rescind'],
 'discerner': ['discerner', 'rescinder'],
 'discernment': ['discernment', 'rescindment'],
 'discerp': ['crisped', 'discerp'],
 'discerption': ['description', 'discerption'],
 'disclike': ['disclike', 'sicklied'],
 'discoactine': ['desiccation', 'discoactine'],
 'discoid': ['discoid', 'disodic'],
 'discontinuer': ['discontinuer', 'undiscretion'],
 'discounter': ['discounter', 'rediscount'],
 'discoverer': ['discoverer', 'rediscover'],
 'discreate': ['discreate', 'sericated'],
 'discreet': ['creedist', 'desertic', 'discreet', 'discrete'],
 'discreetly': ['discreetly', 'discretely'],
 'discreetness': ['discreetness', 'discreteness'],
 'discrepate': ['discrepate', 'pederastic'],
 'discrete': ['creedist', 'desertic', 'discreet', 'discrete'],
 'discretely': ['discreetly', 'discretely'],
 'discreteness': ['discreetness', 'discreteness'],
 'discretion': ['discretion', 'soricident'],
 'discriminator': ['discriminator', 'doctrinairism'],
 'disculpate': ['disculpate', 'spiculated'],
 'discusser': ['discusser', 'rediscuss'],
 'discutable': ['discutable', 'subdeltaic', 'subdialect'],
 'disdub': ['disbud', 'disdub'],
 'disease': ['disease', 'seaside'],
 'diseme': ['demise', 'diseme'],
 'disenact': ['disenact', 'distance'],
 'disendow': ['disendow', 'downside'],
 'disentwine': ['disentwine', 'indentwise'],
 'disharmony': ['disharmony', 'hydramnios'],
 'dishearten': ['dishearten', 'intershade'],
 'dished': ['dished', 'eddish'],
 'disherent': ['disherent', 'hinderest', 'tenderish'],
 'dishling': ['dishling', 'hidlings'],
 'dishonor': ['dishonor', 'ironshod'],
 'dishorn': ['dishorn', 'dronish'],
 'dishpan': ['daphnis', 'dishpan'],
 'disilicate': ['disilicate', 'idealistic'],
 'disimprove': ['disimprove', 'misprovide'],
 'disk': ['disk', 'kids', 'skid'],
 'dislocate': ['dislocate', 'lactoside'],
 'disman': ['danism', 'disman'],
 'dismantle': ['dentalism', 'dismantle'],
 'disme': ['deism', 'disme'],
 'dismemberer': ['dismemberer', 'disremember'],
 'disnature': ['disnature', 'sturnidae', 'truandise'],
 'disnest': ['disnest', 'dissent'],
 'disodic': ['discoid', 'disodic'],
 'disparage': ['disparage', 'grapsidae'],
 'disparation': ['disparation', 'tridiapason'],
 'dispatcher': ['dispatcher', 'redispatch'],
 'dispensable': ['dispensable', 'piebaldness'],
 'dispense': ['dispense', 'piedness'],
 'disperse': ['despiser', 'disperse'],
 'dispetal': ['dispetal', 'pedalist'],
 'dispireme': ['dispireme', 'epidermis'],
 'displayer': ['displayer', 'redisplay'],
 'displeaser': ['displeaser', 'pearlsides'],
 'disponee': ['disponee', 'openside'],
 'disporum': ['disporum', 'misproud'],
 'disprepare': ['disprepare', 'predespair'],
 'disrate': ['astride', 'diaster', 'disrate', 'restiad', 'staired'],
 'disremember': ['dismemberer', 'disremember'],
 'disrepute': ['disrepute', 'redispute'],
 'disrespect': ['disrespect', 'disscepter'],
 'disrupt': ['disrupt', 'prudist'],
 'disscepter': ['disrespect', 'disscepter'],
 'disseat': ['disseat', 'sestiad'],
 'dissector': ['crosstied', 'dissector'],
 'dissent': ['disnest', 'dissent'],
 'dissenter': ['dissenter', 'tiredness'],
 'dissertate': ['dissertate', 'statesider'],
 'disserve': ['disserve', 'dissever'],
 'dissever': ['disserve', 'dissever'],
 'dissocial': ['cissoidal', 'dissocial'],
 'dissolve': ['dissolve', 'voidless'],
 'dissoul': ['dissoul', 'dulosis', 'solidus'],
 'distale': ['distale', 'salited'],
 'distance': ['disenact', 'distance'],
 'distant': ['dantist', 'distant'],
 'distater': ['distater', 'striated'],
 'distender': ['dendrites', 'distender', 'redistend'],
 'distent': ['dentist', 'distent', 'stinted'],
 'distich': ['distich', 'stichid'],
 'distillage': ['distillage', 'sigillated'],
 'distiller': ['distiller', 'redistill'],
 'distinguisher': ['distinguisher', 'redistinguish'],
 'distoma': ['distoma', 'mastoid'],
 'distome': ['distome', 'modiste'],
 'distrainer': ['distrainer', 'redistrain'],
 'distrait': ['distrait', 'triadist'],
 'distraite': ['disattire', 'distraite'],
 'disturber': ['disturber', 'redisturb'],
 'disulphone': ['disulphone', 'unpolished'],
 'disuniform': ['disuniform', 'indusiform'],
 'dit': ['dit', 'tid'],
 'dita': ['adit', 'dita'],
 'dital': ['datil', 'dital', 'tidal', 'tilda'],
 'ditcher': ['dichter', 'ditcher'],
 'dite': ['diet', 'dite', 'edit', 'tide', 'tied'],
 'diter': ['diter', 'tired', 'tried'],
 'dithionic': ['chitinoid', 'dithionic'],
 'ditone': ['ditone', 'intoed'],
 'ditrochean': ['achondrite', 'ditrochean', 'ordanchite'],
 'diuranate': ['diuranate', 'untiaraed'],
 'diurna': ['danuri', 'diurna', 'dunair', 'durain', 'durani', 'durian'],
 'diurnation': ['diurnation', 'induration'],
 'diurne': ['diurne', 'inured', 'ruined', 'unride'],
 'diva': ['avid', 'diva'],
 'divan': ['divan', 'viand'],
 'divata': ['divata', 'dvaita'],
 'divel': ['devil', 'divel', 'lived'],
 'diver': ['diver', 'drive'],
 'diverge': ['diverge', 'grieved'],
 'diverse': ['deviser', 'diverse', 'revised'],
 'diverter': ['diverter', 'redivert', 'verditer'],
 'divest': ['divest', 'vedist'],
 'divesture': ['detrusive', 'divesture', 'servitude'],
 'divisionism': ['divisionism', 'misdivision'],
 'divorce': ['cervoid', 'divorce'],
 'divorcee': ['coderive', 'divorcee'],
 'do': ['do', 'od'],
 'doable': ['albedo', 'doable'],
 'doarium': ['doarium', 'uramido'],
 'doat': ['doat', 'toad', 'toda'],
 'doater': ['doater', 'toader'],
 'doating': ['antigod', 'doating'],
 'doatish': ['doatish', 'toadish'],
 'dob': ['bod', 'dob'],
 'dobe': ['bode', 'dobe'],
 'dobra': ['abord', 'bardo', 'board', 'broad', 'dobra', 'dorab'],
 'dobrao': ['dobrao', 'doorba'],
 'doby': ['body', 'boyd', 'doby'],
 'doc': ['cod', 'doc'],
 'docetism': ['comedist', 'demotics', 'docetism', 'domestic'],
 'docile': ['cleoid', 'coiled', 'docile'],
 'docity': ['cytoid', 'docity'],
 'docker': ['corked', 'docker', 'redock'],
 'doctorial': ['crotaloid', 'doctorial'],
 'doctorship': ['doctorship', 'trophodisc'],
 'doctrinairism': ['discriminator', 'doctrinairism'],
 'doctrinate': ['detraction', 'doctrinate', 'tetarconid'],
 'doctrine': ['centroid', 'doctrine'],
 'documental': ['columnated', 'documental'],
 'dod': ['dod', 'odd'],
 'dode': ['dedo', 'dode', 'eddo'],
 'dodecarch': ['decachord', 'dodecarch'],
 'dodlet': ['dodlet', 'toddle'],
 'dodman': ['dodman', 'oddman'],
 'doe': ['doe', 'edo', 'ode'],
 'doeg': ['doeg', 'doge', 'gode'],
 'doer': ['doer', 'redo', 'rode', 'roed'],
 'does': ['does', 'dose'],
 'doesnt': ['doesnt', 'stoned'],
 'dog': ['dog', 'god'],
 'dogate': ['dogate', 'dotage', 'togaed'],
 'dogbane': ['bondage', 'dogbane'],
 'dogbite': ['bigoted', 'dogbite'],
 'doge': ['doeg', 'doge', 'gode'],
 'dogger': ['dogger', 'gorged'],
 'doghead': ['doghead', 'godhead'],
 'doghood': ['doghood', 'godhood'],
 'dogie': ['diego', 'dogie', 'geoid'],
 'dogless': ['dogless', 'glossed', 'godless'],
 'doglike': ['doglike', 'godlike'],
 'dogly': ['dogly', 'godly', 'goldy'],
 'dogra': ['dargo', 'dogra', 'drago'],
 'dogship': ['dogship', 'godship'],
 'dogstone': ['dogstone', 'stegodon'],
 'dogwatch': ['dogwatch', 'watchdog'],
 'doina': ['adion', 'danio', 'doina', 'donia'],
 'doing': ['dingo', 'doing', 'gondi', 'gonid'],
 'doko': ['doko', 'dook'],
 'dol': ['dol', 'lod', 'old'],
 'dola': ['alod', 'dola', 'load', 'odal'],
 'dolcian': ['dolcian', 'nodical'],
 'dolciano': ['conoidal', 'dolciano'],
 'dolcino': ['dicolon', 'dolcino'],
 'dole': ['dole', 'elod', 'lode', 'odel'],
 'dolesman': ['dolesman', 'lodesman'],
 'doless': ['doless', 'dossel'],
 'doli': ['dilo', 'diol', 'doli', 'idol', 'olid'],
 'dolia': ['aloid', 'dolia', 'idola'],
 'dolina': ['dolina', 'ladino'],
 'doline': ['doline', 'indole', 'leonid', 'loined', 'olenid'],
 'dolium': ['dolium', 'idolum'],
 'dolly': ['dolly', 'lloyd'],
 'dolman': ['almond', 'dolman'],
 'dolor': ['dolor', 'drool'],
 'dolose': ['dolose', 'oodles', 'soodle'],
 'dolphin': ['dolphin', 'pinhold'],
 'dolt': ['dolt', 'told'],
 'dom': ['dom', 'mod'],
 'domain': ['amidon', 'daimon', 'domain'],
 'domainal': ['domainal', 'domanial'],
 'domal': ['domal', 'modal'],
 'domanial': ['domainal', 'domanial'],
 'dome': ['dome', 'mode', 'moed'],
 'domer': ['domer', 'drome'],
 'domestic': ['comedist', 'demotics', 'docetism', 'domestic'],
 'domic': ['comid', 'domic'],
 'domical': ['domical', 'lacmoid'],
 'dominance': ['demicanon', 'dominance'],
 'dominate': ['dominate', 'nematoid'],
 'dominated': ['demantoid', 'dominated'],
 'domination': ['admonition', 'domination'],
 'dominative': ['admonitive', 'dominative'],
 'dominator': ['admonitor', 'dominator'],
 'domine': ['domine', 'domnei', 'emodin', 'medino'],
 'dominial': ['dominial', 'imolinda', 'limoniad'],
 'dominic': ['dinomic', 'dominic'],
 'domino': ['domino', 'monoid'],
 'domitian': ['diatomin', 'domitian'],
 'domnei': ['domine', 'domnei', 'emodin', 'medino'],
 'don': ['don', 'nod'],
 'donal': ['donal', 'nodal'],
 'donar': ['adorn', 'donar', 'drona', 'radon'],
 'donated': ['donated', 'nodated'],
 'donatiaceae': ['actaeonidae', 'donatiaceae'],
 'donatism': ['donatism', 'saintdom'],
 'donator': ['donator', 'odorant', 'tornado'],
 'done': ['done', 'node'],
 'donet': ['donet', 'noted', 'toned'],
 'dong': ['dong', 'gond'],
 'donga': ['donga', 'gonad'],
 'dongola': ['dongola', 'gondola'],
 'dongon': ['dongon', 'nongod'],
 'donia': ['adion', 'danio', 'doina', 'donia'],
 'donna': ['donna', 'nonda'],
 'donnert': ['donnert', 'tendron'],
 'donnie': ['donnie', 'indone', 'ondine'],
 'donor': ['donor', 'rondo'],
 'donorship': ['donorship', 'rhodopsin'],
 'donsie': ['deinos', 'donsie', 'inodes', 'onside'],
 'donum': ['donum', 'mound'],
 'doob': ['bodo', 'bood', 'doob'],
 'dook': ['doko', 'dook'],
 'dool': ['dool', 'lood'],
 'dooli': ['dooli', 'iodol'],
 'doom': ['doom', 'mood'],
 'doomer': ['doomer', 'mooder', 'redoom', 'roomed'],
 'dooms': ['dooms', 'sodom'],
 'door': ['door', 'odor', 'oord', 'rood'],
 'doorba': ['dobrao', 'doorba'],
 'doorbell': ['bordello', 'doorbell'],
 'doored': ['doored', 'odored'],
 'doorframe': ['doorframe', 'reformado'],
 'doorless': ['doorless', 'odorless'],
 'doorplate': ['doorplate', 'leptodora'],
 'doorpost': ['doorpost', 'doorstop'],
 'doorstone': ['doorstone', 'roodstone'],
 'doorstop': ['doorpost', 'doorstop'],
 'doorweed': ['deerwood', 'doorweed'],
 'dop': ['dop', 'pod'],
 'dopa': ['apod', 'dopa'],
 'doper': ['doper', 'pedro', 'pored'],
 'dopplerite': ['dopplerite', 'lepidopter'],
 'dor': ['dor', 'rod'],
 'dora': ['dora', 'orad', 'road'],
 'dorab': ['abord', 'bardo', 'board', 'broad', 'dobra', 'dorab'],
 'doree': ['doree', 'erode'],
 'dori': ['dori', 'roid'],
 'doria': ['aroid', 'doria', 'radio'],
 'dorian': ['dorian', 'inroad', 'ordain'],
 'dorical': ['cordial', 'dorical'],
 'dorine': ['dinero', 'dorine'],
 'dorlach': ['chordal', 'dorlach'],
 'dormancy': ['dormancy', 'mordancy'],
 'dormant': ['dormant', 'mordant'],
 'dormer': ['dormer', 'remord'],
 'dormie': ['dormie', 'moider'],
 'dorn': ['dorn', 'rond'],
 'dornic': ['dornic', 'nordic'],
 'dorothea': ['dorothea', 'theodora'],
 'dorp': ['dorp', 'drop', 'prod'],
 'dorsel': ['dorsel', 'seldor', 'solder'],
 'dorsoapical': ['dorsoapical', 'prosodiacal'],
 'dorsocaudal': ['caudodorsal', 'dorsocaudal'],
 'dorsocentral': ['centrodorsal', 'dorsocentral'],
 'dorsocervical': ['cervicodorsal', 'dorsocervical'],
 'dorsolateral': ['dorsolateral', 'laterodorsal'],
 'dorsomedial': ['dorsomedial', 'mediodorsal'],
 'dorsosacral': ['dorsosacral', 'sacrodorsal'],
 'dorsoventrad': ['dorsoventrad', 'ventrodorsad'],
 'dorsoventral': ['dorsoventral', 'ventrodorsal'],
 'dorsoventrally': ['dorsoventrally', 'ventrodorsally'],
 'dos': ['dos', 'ods', 'sod'],
 'dosa': ['dosa', 'sado', 'soda'],
 'dosage': ['dosage', 'seadog'],
 'dose': ['does', 'dose'],
 'doser': ['doser', 'rosed'],
 'dosimetric': ['dosimetric', 'mediocrist'],
 'dossel': ['doless', 'dossel'],
 'dosser': ['dosser', 'sordes'],
 'dot': ['dot', 'tod'],
 'dotage': ['dogate', 'dotage', 'togaed'],
 'dote': ['dote', 'tode', 'toed'],
 'doter': ['doter', 'tored', 'trode'],
 'doty': ['doty', 'tody'],
 'doubler': ['boulder', 'doubler'],
 'doubter': ['doubter', 'obtrude', 'outbred', 'redoubt'],
 'douc': ['douc', 'duco'],
 'douce': ['coude', 'douce'],
 'doum': ['doum', 'moud', 'odum'],
 'doup': ['doup', 'updo'],
 'dour': ['dour', 'duro', 'ordu', 'roud'],
 'dourine': ['dourine', 'neuroid'],
 'dourly': ['dourly', 'lourdy'],
 'douser': ['douser', 'soured'],
 'douter': ['derout', 'detour', 'douter'],
 'dover': ['dover', 'drove', 'vedro'],
 'dow': ['dow', 'owd', 'wod'],
 'dowager': ['dowager', 'wordage'],
 'dower': ['dower', 'rowed'],
 'dowl': ['dowl', 'wold'],
 'dowlas': ['dowlas', 'oswald'],
 'downbear': ['downbear', 'rawboned'],
 'downcome': ['comedown', 'downcome'],
 'downer': ['downer', 'wonder', 'worden'],
 'downingia': ['downingia', 'godwinian'],
 'downset': ['downset', 'setdown'],
 'downside': ['disendow', 'downside'],
 'downtake': ['downtake', 'takedown'],
 'downthrow': ['downthrow', 'throwdown'],
 'downturn': ['downturn', 'turndown'],
 'downward': ['downward', 'drawdown'],
 'dowry': ['dowry', 'rowdy', 'wordy'],
 'dowser': ['dowser', 'drowse'],
 'doxa': ['doxa', 'odax'],
 'doyle': ['doyle', 'yodel'],
 'dozen': ['dozen', 'zoned'],
 'drab': ['bard', 'brad', 'drab'],
 'draba': ['barad', 'draba'],
 'drabble': ['dabbler', 'drabble'],
 'draco': ['cardo', 'draco'],
 'draconic': ['cancroid', 'draconic'],
 'draconis': ['draconis', 'sardonic'],
 'dracontian': ['dracontian', 'octandrian'],
 'drafter': ['drafter', 'redraft'],
 'drag': ['darg', 'drag', 'grad'],
 'draggle': ['draggle', 'raggled'],
 'dragline': ['dragline', 'reginald', 'ringlead'],
 'dragman': ['dragman', 'grandam', 'grandma'],
 'drago': ['dargo', 'dogra', 'drago'],
 'dragoman': ['dragoman', 'garamond', 'ondagram'],
 'dragonize': ['dragonize', 'organized'],
 'dragoon': ['dragoon', 'gadroon'],
 'dragoonage': ['dragoonage', 'gadroonage'],
 'dragsman': ['dargsman', 'dragsman'],
 'drail': ['drail', 'laird', 'larid', 'liard'],
 'drain': ['darin', 'dinar', 'drain', 'indra', 'nadir', 'ranid'],
 'drainable': ['albardine', 'drainable'],
 'drainage': ['drainage', 'gardenia'],
 'draine': ['darien', 'draine'],
 'drained': ['diander', 'drained'],
 'drainer': ['darrein', 'drainer'],
 'drainman': ['drainman', 'mandarin'],
 'draintile': ['deliriant', 'draintile', 'interlaid'],
 'drake': ['daker', 'drake', 'kedar', 'radek'],
 'dramme': ['dammer', 'dramme'],
 'drang': ['drang', 'grand'],
 'drape': ['drape', 'padre'],
 'drat': ['dart', 'drat'],
 'drate': ['dater', 'derat', 'detar', 'drate', 'rated', 'trade', 'tread'],
 'draw': ['draw', 'ward'],
 'drawable': ['drawable', 'wardable'],
 'drawback': ['backward', 'drawback'],
 'drawbore': ['drawbore', 'wardrobe'],
 'drawbridge': ['bridgeward', 'drawbridge'],
 'drawdown': ['downward', 'drawdown'],
 'drawee': ['drawee', 'rewade'],
 'drawer': ['drawer', 'redraw', 'reward', 'warder'],
 'drawers': ['drawers', 'resward'],
 'drawfile': ['drawfile', 'lifeward'],
 'drawgate': ['drawgate', 'gateward'],
 'drawhead': ['drawhead', 'headward'],
 'drawhorse': ['drawhorse', 'shoreward'],
 'drawing': ['drawing', 'ginward', 'warding'],
 'drawoff': ['drawoff', 'offward'],
 'drawout': ['drawout', 'outdraw', 'outward'],
 'drawsheet': ['drawsheet', 'watershed'],
 'drawstop': ['drawstop', 'postward'],
 'dray': ['adry', 'dray', 'yard'],
 'drayage': ['drayage', 'yardage'],
 'drayman': ['drayman', 'yardman'],
 'dread': ['adder', 'dread', 'readd'],
 'dreadly': ['dreadly', 'laddery'],
 'dream': ['armed', 'derma', 'dream', 'ramed'],
 'dreamage': ['dreamage', 'redamage'],
 'dreamer': ['dreamer', 'redream'],
 'dreamhole': ['dreamhole', 'heloderma'],
 'dreamish': ['dreamish', 'semihard'],
 'dreamland': ['dreamland', 'raddleman'],
 'drear': ['darer', 'drear'],
 'dreary': ['dreary', 'yarder'],
 'dredge': ['dredge', 'gedder'],
 'dree': ['deer', 'dere', 'dree', 'rede', 'reed'],
 'dreiling': ['dreiling', 'gridelin'],
 'dressage': ['degasser', 'dressage'],
 'dresser': ['dresser', 'redress'],
 'drib': ['bird', 'drib'],
 'dribble': ['dibbler', 'dribble'],
 'driblet': ['birdlet', 'driblet'],
 'driddle': ['diddler', 'driddle'],
 'drier': ['drier', 'rider'],
 'driest': ['driest', 'stride'],
 'driller': ['driller', 'redrill'],
 'drillman': ['drillman', 'mandrill'],
 'dringle': ['dringle', 'grindle'],
 'drisheen': ['denshire', 'drisheen'],
 'drive': ['diver', 'drive'],
 'driven': ['driven', 'nervid', 'verdin'],
 'drivescrew': ['drivescrew', 'screwdrive'],
 'drogue': ['drogue', 'gourde'],
 'drolly': ['drolly', 'lordly'],
 'drome': ['domer', 'drome'],
 'dromicia': ['dioramic', 'dromicia'],
 'drona': ['adorn', 'donar', 'drona', 'radon'],
 'drone': ['drone', 'ronde'],
 'drongo': ['drongo', 'gordon'],
 'dronish': ['dishorn', 'dronish'],
 'drool': ['dolor', 'drool'],
 'drop': ['dorp', 'drop', 'prod'],
 'dropsy': ['dropsy', 'dryops'],
 'drossel': ['drossel', 'rodless'],
 'drove': ['dover', 'drove', 'vedro'],
 'drow': ['drow', 'word'],
 'drowse': ['dowser', 'drowse'],
 'drub': ['burd', 'drub'],
 'drugger': ['drugger', 'grudger'],
 'druggery': ['druggery', 'grudgery'],
 'drungar': ['drungar', 'gurnard'],
 'drupe': ['drupe', 'duper', 'perdu', 'prude', 'pured'],
 'drusean': ['asunder', 'drusean'],
 'dryops': ['dropsy', 'dryops'],
 'duad': ['addu', 'dadu', 'daud', 'duad'],
 'dual': ['auld', 'dual', 'laud', 'udal'],
 'duali': ['duali', 'dulia'],
 'dualin': ['dualin', 'ludian', 'unlaid'],
 'dualism': ['dualism', 'laudism'],
 'dualist': ['dualist', 'laudist'],
 'dub': ['bud', 'dub'],
 'dubber': ['dubber', 'rubbed'],
 'dubious': ['biduous', 'dubious'],
 'dubitate': ['dubitate', 'tabitude'],
 'ducal': ['cauld', 'ducal'],
 'duces': ['decus', 'duces'],
 'duckstone': ['duckstone', 'unstocked'],
 'duco': ['douc', 'duco'],
 'ducted': ['deduct', 'ducted'],
 'duction': ['conduit', 'duction', 'noctuid'],
 'duculinae': ['duculinae', 'nuculidae'],
 'dudeen': ['denude', 'dudeen'],
 'dudler': ['dudler', 'ruddle'],
 'duel': ['deul', 'duel', 'leud'],
 'dueler': ['dueler', 'eluder'],
 'dueling': ['dueling', 'indulge'],
 'duello': ['deloul', 'duello'],
 'duenna': ['duenna', 'undean'],
 'duer': ['duer', 'dure', 'rude', 'urde'],
 'duffer': ['duffer', 'ruffed'],
 'dufter': ['dufter', 'turfed'],
 'dug': ['dug', 'gud'],
 'duim': ['duim', 'muid'],
 'dukery': ['dukery', 'duyker'],
 'dulat': ['adult', 'dulat'],
 'dulcian': ['dulcian', 'incudal', 'lucanid', 'lucinda'],
 'dulciana': ['claudian', 'dulciana'],
 'duler': ['duler', 'urled'],
 'dulia': ['duali', 'dulia'],
 'dullify': ['dullify', 'fluidly'],
 'dulosis': ['dissoul', 'dulosis', 'solidus'],
 'dulseman': ['dulseman', 'unalmsed'],
 'dultie': ['dilute', 'dultie'],
 'dum': ['dum', 'mud'],
 'duma': ['duma', 'maud'],
 'dumaist': ['dumaist', 'stadium'],
 'dumontite': ['dumontite', 'unomitted'],
 'dumple': ['dumple', 'plumed'],
 'dunair': ['danuri', 'diurna', 'dunair', 'durain', 'durani', 'durian'],
 'dunal': ['dunal', 'laund', 'lunda', 'ulnad'],
 'dunderpate': ['dunderpate', 'undeparted'],
 'dune': ['dune', 'nude', 'unde'],
 'dungaree': ['dungaree', 'guardeen', 'unagreed', 'underage', 'ungeared'],
 'dungeon': ['dungeon', 'negundo'],
 'dunger': ['dunger', 'gerund', 'greund', 'nudger'],
 'dungol': ['dungol', 'ungold'],
 'dungy': ['dungy', 'gundy'],
 'dunite': ['dunite', 'united', 'untied'],
 'dunlap': ['dunlap', 'upland'],
 'dunne': ['dunne', 'unden'],
 'dunner': ['dunner', 'undern'],
 'dunpickle': ['dunpickle', 'unpickled'],
 'dunstable': ['dunstable', 'unblasted', 'unstabled'],
 'dunt': ['dunt', 'tund'],
 'duny': ['duny', 'undy'],
 'duo': ['duo', 'udo'],
 'duodenal': ['duodenal', 'unloaded'],
 'duodenocholecystostomy': ['cholecystoduodenostomy', 'duodenocholecystostomy'],
 'duodenojejunal': ['duodenojejunal', 'jejunoduodenal'],
 'duodenopancreatectomy': ['duodenopancreatectomy', 'pancreatoduodenectomy'],
 'dup': ['dup', 'pud'],
 'duper': ['drupe', 'duper', 'perdu', 'prude', 'pured'],
 'dupion': ['dupion', 'unipod'],
 'dupla': ['dupla', 'plaud'],
 'duplone': ['duplone', 'unpoled'],
 'dura': ['ardu', 'daur', 'dura'],
 'durain': ['danuri', 'diurna', 'dunair', 'durain', 'durani', 'durian'],
 'duramen': ['duramen', 'maunder', 'unarmed'],
 'durance': ['durance', 'redunca', 'unraced'],
 'durango': ['aground', 'durango'],
 'durani': ['danuri', 'diurna', 'dunair', 'durain', 'durani', 'durian'],
 'durant': ['durant', 'tundra'],
 'durban': ['durban', 'undrab'],
 'durdenite': ['durdenite', 'undertide'],
 'dure': ['duer', 'dure', 'rude', 'urde'],
 'durene': ['durene', 'endure'],
 'durenol': ['durenol', 'lounder', 'roundel'],
 'durgan': ['durgan', 'undrag'],
 'durian': ['danuri', 'diurna', 'dunair', 'durain', 'durani', 'durian'],
 'during': ['during', 'ungird'],
 'durity': ['durity', 'rudity'],
 'durmast': ['durmast', 'mustard'],
 'duro': ['dour', 'duro', 'ordu', 'roud'],
 'dusken': ['dusken', 'sundek'],
 'dust': ['dust', 'stud'],
 'duster': ['derust', 'duster'],
 'dustin': ['dustin', 'nudist'],
 'dustpan': ['dustpan', 'upstand'],
 'dusty': ['dusty', 'study'],
 'duyker': ['dukery', 'duyker'],
 'dvaita': ['divata', 'dvaita'],
 'dwale': ['dwale', 'waled', 'weald'],
 'dwine': ['dwine', 'edwin', 'wendi', 'widen', 'wined'],
 'dyad': ['addy', 'dyad'],
 'dyas': ['days', 'dyas'],
 'dye': ['dey', 'dye', 'yed'],
 'dyehouse': ['deyhouse', 'dyehouse'],
 'dyeing': ['digeny', 'dyeing'],
 'dyer': ['dyer', 'yerd'],
 'dying': ['dingy', 'dying'],
 'dynamo': ['dynamo', 'monday'],
 'dynamoelectric': ['dynamoelectric', 'electrodynamic'],
 'dynamoelectrical': ['dynamoelectrical', 'electrodynamical'],
 'dynamotor': ['androtomy', 'dynamotor'],
 'dyne': ['deny', 'dyne'],
 'dyophone': ['dyophone', 'honeypod'],
 'dysluite': ['dysluite', 'sedulity'],
 'dysneuria': ['dasyurine', 'dysneuria'],
 'dysphoric': ['chrysopid', 'dysphoric'],
 'dysphrenia': ['dysphrenia', 'sphyraenid', 'sphyrnidae'],
 'dystome': ['dystome', 'modesty'],
 'dystrophia': ['diastrophy', 'dystrophia'],
 'ea': ['ae', 'ea'],
 'each': ['ache', 'each', 'haec'],
 'eager': ['agree', 'eager', 'eagre'],
 'eagle': ['aegle', 'eagle', 'galee'],
 'eagless': ['ageless', 'eagless'],
 'eaglet': ['eaglet', 'legate', 'teagle', 'telega'],
 'eagre': ['agree', 'eager', 'eagre'],
 'ean': ['ean', 'nae', 'nea'],
 'ear': ['aer', 'are', 'ear', 'era', 'rea'],
 'eared': ['eared', 'erade'],
 'earful': ['earful', 'farleu', 'ferula'],
 'earing': ['arenig', 'earing', 'gainer', 'reagin', 'regain'],
 'earl': ['earl', 'eral', 'lear', 'real'],
 'earlap': ['earlap', 'parale'],
 'earle': ['areel', 'earle'],
 'earlet': ['earlet', 'elater', 'relate'],
 'earliness': ['earliness', 'naileress'],
 'earlship': ['earlship', 'pearlish'],
 'early': ['early', 'layer', 'relay'],
 'earn': ['arne', 'earn', 'rane'],
 'earner': ['earner', 'ranere'],
 'earnest': ['earnest', 'eastern', 'nearest'],
 'earnestly': ['earnestly', 'easternly'],
 'earnful': ['earnful', 'funeral'],
 'earning': ['earning', 'engrain'],
 'earplug': ['earplug', 'graupel', 'plaguer'],
 'earring': ['earring', 'grainer'],
 'earringed': ['earringed', 'grenadier'],
 'earshot': ['asthore', 'earshot'],
 'eartab': ['abater', 'artabe', 'eartab', 'trabea'],
 'earth': ['earth', 'hater', 'heart', 'herat', 'rathe'],
 'earthborn': ['abhorrent', 'earthborn'],
 'earthed': ['earthed', 'hearted'],
 'earthen': ['earthen', 'enheart', 'hearten', 'naether', 'teheran', 'traheen'],
 'earthian': ['earthian', 'rhaetian'],
 'earthiness': ['earthiness', 'heartiness'],
 'earthless': ['earthless', 'heartless'],
 'earthling': ['earthling', 'heartling'],
 'earthly': ['earthly', 'heartly', 'lathery', 'rathely'],
 'earthnut': ['earthnut', 'heartnut'],
 'earthpea': ['earthpea', 'heartpea'],
 'earthquake': ['earthquake', 'heartquake'],
 'earthward': ['earthward', 'heartward'],
 'earthy': ['earthy', 'hearty', 'yearth'],
 'earwig': ['earwig', 'grewia'],
 'earwitness': ['earwitness', 'wateriness'],
 'easel': ['easel', 'lease'],
 'easement': ['easement', 'estamene'],
 'easer': ['easer', 'erase'],
 'easily': ['easily', 'elysia'],
 'easing': ['easing', 'sangei'],
 'east': ['ates', 'east', 'eats', 'sate', 'seat', 'seta'],
 'eastabout': ['aetobatus', 'eastabout'],
 'eastbound': ['eastbound', 'unboasted'],
 'easter': ['asteer',
            'easter',
            'eastre',
            'reseat',
            'saeter',
            'seater',
            'staree',
            'teaser',
            'teresa'],
 'easterling': ['easterling', 'generalist'],
 'eastern': ['earnest', 'eastern', 'nearest'],
 'easternly': ['earnestly', 'easternly'],
 'easting': ['easting',
             'gainset',
             'genista',
             'ingesta',
             'seating',
             'signate',
             'teasing'],
 'eastlake': ['alestake', 'eastlake'],
 'eastre': ['asteer',
            'easter',
            'eastre',
            'reseat',
            'saeter',
            'seater',
            'staree',
            'teaser',
            'teresa'],
 'easy': ['easy', 'eyas'],
 'eat': ['ate', 'eat', 'eta', 'tae', 'tea'],
 'eatberry': ['betrayer', 'eatberry', 'rebetray', 'teaberry'],
 'eaten': ['eaten', 'enate'],
 'eater': ['arete', 'eater', 'teaer'],
 'eating': ['eating', 'ingate', 'tangie'],
 'eats': ['ates', 'east', 'eats', 'sate', 'seat', 'seta'],
 'eave': ['eave', 'evea'],
 'eaved': ['deave', 'eaved', 'evade'],
 'eaver': ['eaver', 'reave'],
 'eaves': ['eaves', 'evase', 'seave'],
 'eben': ['been', 'bene', 'eben'],
 'ebenales': ['ebenales', 'lebanese'],
 'ebon': ['beno', 'bone', 'ebon'],
 'ebony': ['boney', 'ebony'],
 'ebriety': ['byerite', 'ebriety'],
 'eburna': ['eburna', 'unbare', 'unbear', 'urbane'],
 'eburnated': ['eburnated', 'underbeat', 'unrebated'],
 'eburnian': ['eburnian', 'inurbane'],
 'ecad': ['cade', 'dace', 'ecad'],
 'ecanda': ['adance', 'ecanda'],
 'ecardinal': ['ecardinal', 'lardacein'],
 'ecarinate': ['anaeretic', 'ecarinate'],
 'ecarte': ['cerate', 'create', 'ecarte'],
 'ecaudata': ['acaudate', 'ecaudata'],
 'ecclesiasticism': ['ecclesiasticism', 'misecclesiastic'],
 'eche': ['chee', 'eche'],
 'echelon': ['chelone', 'echelon'],
 'echeveria': ['echeveria', 'reachieve'],
 'echidna': ['chained', 'echidna'],
 'echinal': ['chilean', 'echinal', 'nichael'],
 'echinate': ['echinate', 'hecatine'],
 'echinital': ['echinital', 'inethical'],
 'echis': ['echis', 'shice'],
 'echoer': ['choree', 'cohere', 'echoer'],
 'echoic': ['choice', 'echoic'],
 'echoist': ['chitose', 'echoist'],
 'eciton': ['eciton', 'noetic', 'notice', 'octine'],
 'eckehart': ['eckehart', 'hacktree'],
 'eclair': ['carlie', 'claire', 'eclair', 'erical'],
 'eclat': ['cleat', 'eclat', 'ectal', 'lacet', 'tecla'],
 'eclipsable': ['eclipsable', 'spliceable'],
 'eclipser': ['eclipser', 'pericles', 'resplice'],
 'economics': ['economics', 'neocosmic'],
 'economism': ['economism', 'monoecism', 'monosemic'],
 'economist': ['economist', 'mesotonic'],
 'ecorticate': ['ecorticate', 'octaeteric'],
 'ecostate': ['coestate', 'ecostate'],
 'ecotonal': ['colonate', 'ecotonal'],
 'ecotype': ['ecotype', 'ocypete'],
 'ecrasite': ['ecrasite', 'sericate'],
 'ecru': ['cure', 'ecru', 'eruc'],
 'ectad': ['cadet', 'ectad'],
 'ectal': ['cleat', 'eclat', 'ectal', 'lacet', 'tecla'],
 'ectasis': ['ascites', 'ectasis'],
 'ectene': ['cetene', 'ectene'],
 'ectental': ['ectental', 'tentacle'],
 'ectiris': ['ectiris', 'eristic'],
 'ectocardia': ['coradicate', 'ectocardia'],
 'ectocranial': ['calonectria', 'ectocranial'],
 'ectoglia': ['ectoglia', 'geotical', 'goetical'],
 'ectomorph': ['ectomorph', 'topchrome'],
 'ectomorphic': ['cetomorphic', 'chemotropic', 'ectomorphic'],
 'ectomorphy': ['chromotype', 'cormophyte', 'ectomorphy'],
 'ectopia': ['ectopia', 'opacite'],
 'ectopy': ['cotype', 'ectopy'],
 'ectorhinal': ['chlorinate', 'ectorhinal', 'tornachile'],
 'ectosarc': ['ectosarc', 'reaccost'],
 'ectrogenic': ['ectrogenic', 'egocentric', 'geocentric'],
 'ectromelia': ['carmeloite', 'ectromelia', 'meteorical'],
 'ectropion': ['ectropion', 'neotropic'],
 'ed': ['de', 'ed'],
 'edda': ['dade', 'dead', 'edda'],
 'eddaic': ['caddie', 'eddaic'],
 'eddish': ['dished', 'eddish'],
 'eddo': ['dedo', 'dode', 'eddo'],
 'edema': ['adeem', 'ameed', 'edema'],
 'eden': ['dene', 'eden', 'need'],
 'edental': ['dalteen', 'dentale', 'edental'],
 'edentata': ['antedate', 'edentata'],
 'edessan': ['deaness', 'edessan'],
 'edestan': ['edestan', 'standee'],
 'edestin': ['destine', 'edestin'],
 'edgar': ['edgar', 'grade'],
 'edger': ['edger', 'greed'],
 'edgerman': ['edgerman', 'gendarme'],
 'edgrew': ['edgrew', 'wedger'],
 'edible': ['debile', 'edible'],
 'edict': ['cetid', 'edict'],
 'edictal': ['citadel', 'deltaic', 'dialect', 'edictal', 'lactide'],
 'edification': ['deification', 'edification'],
 'edificatory': ['deificatory', 'edificatory'],
 'edifier': ['deifier', 'edifier'],
 'edify': ['deify', 'edify'],
 'edit': ['diet', 'dite', 'edit', 'tide', 'tied'],
 'edital': ['detail', 'dietal', 'dilate', 'edital', 'tailed'],
 'edith': ['edith', 'ethid'],
 'edition': ['edition', 'odinite', 'otidine', 'tineoid'],
 'editor': ['editor', 'triode'],
 'editorial': ['editorial', 'radiolite'],
 'edmund': ['edmund', 'mudden'],
 'edna': ['ande', 'dane', 'dean', 'edna'],
 'edo': ['doe', 'edo', 'ode'],
 'edoni': ['deino', 'dione', 'edoni'],
 'education': ['coadunite', 'education', 'noctuidae'],
 'educe': ['deuce', 'educe'],
 'edward': ['edward', 'wadder', 'warded'],
 'edwin': ['dwine', 'edwin', 'wendi', 'widen', 'wined'],
 'eel': ['eel', 'lee'],
 'eelgrass': ['eelgrass', 'gearless', 'rageless'],
 'eelpot': ['eelpot', 'opelet'],
 'eelspear': ['eelspear', 'prelease'],
 'eely': ['eely', 'yeel'],
 'eer': ['eer', 'ere', 'ree'],
 'efik': ['efik', 'fike'],
 'eft': ['eft', 'fet'],
 'egad': ['aged', 'egad', 'gade'],
 'egba': ['egba', 'gabe'],
 'egbo': ['bego', 'egbo'],
 'egeran': ['egeran', 'enrage', 'ergane', 'genear', 'genera'],
 'egest': ['egest', 'geest', 'geste'],
 'egger': ['egger', 'grege'],
 'egghot': ['egghot', 'hogget'],
 'eggler': ['eggler', 'legger'],
 'eggy': ['eggy', 'yegg'],
 'eglantine': ['eglantine', 'inelegant', 'legantine'],
 'eglatere': ['eglatere', 'regelate', 'relegate'],
 'egma': ['egma', 'game', 'mage'],
 'ego': ['ego', 'geo'],
 'egocentric': ['ectrogenic', 'egocentric', 'geocentric'],
 'egoist': ['egoist', 'stogie'],
 'egol': ['egol', 'goel', 'loge', 'ogle', 'oleg'],
 'egotheism': ['egotheism', 'eightsome'],
 'egret': ['egret', 'greet', 'reget'],
 'eh': ['eh', 'he'],
 'ehretia': ['ehretia', 'etheria'],
 'eident': ['eident', 'endite'],
 'eidograph': ['eidograph', 'ideograph'],
 'eidology': ['eidology', 'ideology'],
 'eighth': ['eighth', 'height'],
 'eightsome': ['egotheism', 'eightsome'],
 'eigne': ['eigne', 'genie'],
 'eileen': ['eileen', 'lienee'],
 'ekaha': ['ekaha', 'hakea'],
 'eke': ['eke', 'kee'],
 'eker': ['eker', 'reek'],
 'ekoi': ['ekoi', 'okie'],
 'ekron': ['ekron', 'krone'],
 'ektene': ['ektene', 'ketene'],
 'elabrate': ['elabrate', 'tearable'],
 'elaidic': ['aedilic', 'elaidic'],
 'elaidin': ['anilide', 'elaidin'],
 'elain': ['alien', 'aline', 'anile', 'elain', 'elian', 'laine', 'linea'],
 'elaine': ['aileen', 'elaine'],
 'elamite': ['alemite', 'elamite'],
 'elance': ['elance', 'enlace'],
 'eland': ['eland', 'laden', 'lenad'],
 'elanet': ['elanet', 'lanete', 'lateen'],
 'elanus': ['elanus', 'unseal'],
 'elaphomyces': ['elaphomyces', 'mesocephaly'],
 'elaphurus': ['elaphurus', 'sulphurea'],
 'elapid': ['aliped', 'elapid'],
 'elapoid': ['elapoid', 'oedipal'],
 'elaps': ['elaps',
           'lapse',
           'lepas',
           'pales',
           'salep',
           'saple',
           'sepal',
           'slape',
           'spale',
           'speal'],
 'elapse': ['asleep', 'elapse', 'please'],
 'elastic': ['astelic', 'elastic', 'latices'],
 'elasticin': ['elasticin', 'inelastic', 'sciential'],
 'elastin': ['elastin', 'salient', 'saltine', 'slainte'],
 'elastomer': ['elastomer', 'salometer'],
 'elate': ['atlee', 'elate'],
 'elated': ['delate', 'elated'],
 'elater': ['earlet', 'elater', 'relate'],
 'elaterid': ['detailer', 'elaterid'],
 'elaterin': ['elaterin', 'entailer', 'treenail'],
 'elatha': ['althea', 'elatha'],
 'elatine': ['elatine', 'lineate'],
 'elation': ['alnoite', 'elation', 'toenail'],
 'elator': ['elator', 'lorate'],
 'elb': ['bel', 'elb'],
 'elbert': ['belter', 'elbert', 'treble'],
 'elberta': ['bearlet', 'bleater', 'elberta', 'retable'],
 'elbow': ['below', 'bowel', 'elbow'],
 'elbowed': ['boweled', 'elbowed'],
 'eld': ['del', 'eld', 'led'],
 'eldin': ['eldin', 'lined'],
 'elding': ['dingle', 'elding', 'engild', 'gilden'],
 'elean': ['anele', 'elean'],
 'election': ['coteline', 'election'],
 'elective': ['cleveite', 'elective'],
 'elector': ['elector', 'electro'],
 'electoral': ['electoral', 'recollate'],
 'electra': ['electra', 'treacle'],
 'electragy': ['electragy', 'glycerate'],
 'electret': ['electret', 'tercelet'],
 'electric': ['electric', 'lectrice'],
 'electrion': ['centriole', 'electrion', 'relection'],
 'electro': ['elector', 'electro'],
 'electrodynamic': ['dynamoelectric', 'electrodynamic'],
 'electrodynamical': ['dynamoelectrical', 'electrodynamical'],
 'electromagnetic': ['electromagnetic', 'magnetoelectric'],
 'electromagnetical': ['electromagnetical', 'magnetoelectrical'],
 'electrothermic': ['electrothermic', 'thermoelectric'],
 'electrothermometer': ['electrothermometer', 'thermoelectrometer'],
 'elegant': ['angelet', 'elegant'],
 'elegiambus': ['elegiambus', 'iambelegus'],
 'elegiast': ['elegiast', 'selagite'],
 'elemi': ['elemi', 'meile'],
 'elemin': ['elemin', 'meline'],
 'elephantic': ['elephantic', 'plancheite'],
 'elettaria': ['elettaria', 'retaliate'],
 'eleut': ['eleut', 'elute'],
 'elevator': ['elevator', 'overlate'],
 'elfin': ['elfin', 'nifle'],
 'elfishness': ['elfishness', 'fleshiness'],
 'elfkin': ['elfkin', 'finkel'],
 'elfwort': ['elfwort', 'felwort'],
 'eli': ['eli', 'lei', 'lie'],
 'elia': ['aiel', 'aile', 'elia'],
 'elian': ['alien', 'aline', 'anile', 'elain', 'elian', 'laine', 'linea'],
 'elias': ['aisle', 'elias'],
 'elicitor': ['elicitor', 'trioleic'],
 'eliminand': ['eliminand', 'mindelian'],
 'elinor': ['elinor', 'lienor', 'lorien', 'noiler'],
 'elinvar': ['elinvar', 'ravelin', 'reanvil', 'valerin'],
 'elisha': ['elisha', 'hailse', 'sheila'],
 'elisor': ['elisor', 'resoil'],
 'elissa': ['elissa', 'lassie'],
 'elite': ['elite', 'telei'],
 'eliza': ['aizle', 'eliza'],
 'elk': ['elk', 'lek'],
 'ella': ['alle', 'ella', 'leal'],
 'ellagate': ['allegate', 'ellagate'],
 'ellenyard': ['ellenyard', 'learnedly'],
 'ellick': ['ellick', 'illeck'],
 'elliot': ['elliot', 'oillet'],
 'elm': ['elm', 'mel'],
 'elmer': ['elmer', 'merel', 'merle'],
 'elmy': ['elmy', 'yelm'],
 'eloah': ['eloah', 'haole'],
 'elod': ['dole', 'elod', 'lode', 'odel'],
 'eloge': ['eloge', 'golee'],
 'elohimic': ['elohimic', 'hemiolic'],
 'elohist': ['elohist', 'hostile'],
 'eloign': ['eloign', 'gileno', 'legion'],
 'eloigner': ['eloigner', 'legioner'],
 'eloignment': ['eloignment', 'omnilegent'],
 'elon': ['elon', 'enol', 'leno', 'leon', 'lone', 'noel'],
 'elonite': ['elonite', 'leonite'],
 'elops': ['elops', 'slope', 'spole'],
 'elric': ['crile', 'elric', 'relic'],
 'els': ['els', 'les'],
 'elsa': ['elsa', 'sale', 'seal', 'slae'],
 'else': ['else', 'lees', 'seel', 'sele', 'slee'],
 'elsin': ['elsin', 'lenis', 'niels', 'silen', 'sline'],
 'elt': ['elt', 'let'],
 'eluate': ['aulete', 'eluate'],
 'eluder': ['dueler', 'eluder'],
 'elusion': ['elusion', 'luiseno'],
 'elusory': ['elusory', 'yoursel'],
 'elute': ['eleut', 'elute'],
 'elution': ['elution', 'outline'],
 'elutor': ['elutor', 'louter', 'outler'],
 'elvan': ['elvan', 'navel', 'venal'],
 'elvanite': ['elvanite', 'lavenite'],
 'elver': ['elver', 'lever', 'revel'],
 'elvet': ['elvet', 'velte'],
 'elvira': ['averil', 'elvira'],
 'elvis': ['elvis', 'levis', 'slive'],
 'elwood': ['dewool', 'elwood', 'wooled'],
 'elymi': ['elymi', 'emily', 'limey'],
 'elysia': ['easily', 'elysia'],
 'elytral': ['alertly', 'elytral'],
 'elytrin': ['elytrin', 'inertly', 'trinely'],
 'elytroposis': ['elytroposis', 'proteolysis'],
 'elytrous': ['elytrous', 'urostyle'],
 'em': ['em', 'me'],
 'emanate': ['emanate', 'manatee'],
 'emanation': ['amnionate', 'anamniote', 'emanation'],
 'emanatist': ['emanatist', 'staminate', 'tasmanite'],
 'embalmer': ['embalmer', 'emmarble'],
 'embar': ['amber', 'bearm', 'bemar', 'bream', 'embar'],
 'embargo': ['bergamo', 'embargo'],
 'embark': ['embark', 'markeb'],
 'embay': ['beamy', 'embay', 'maybe'],
 'ember': ['breme', 'ember'],
 'embind': ['embind', 'nimbed'],
 'embira': ['ambier', 'bremia', 'embira'],
 'embodier': ['demirobe', 'embodier'],
 'embody': ['beydom', 'embody'],
 'embole': ['bemole', 'embole'],
 'embraceor': ['cerebroma', 'embraceor'],
 'embrail': ['embrail', 'mirabel'],
 'embryoid': ['embryoid', 'reimbody'],
 'embus': ['embus', 'sebum'],
 'embusk': ['bemusk', 'embusk'],
 'emcee': ['emcee', 'meece'],
 'emeership': ['emeership', 'ephemeris'],
 'emend': ['emend', 'mende'],
 'emendation': ['denominate', 'emendation'],
 'emendator': ['emendator', 'ondameter'],
 'emerita': ['emerita', 'emirate'],
 'emerse': ['emerse', 'seemer'],
 'emersion': ['emersion', 'meriones'],
 'emersonian': ['emersonian', 'mansioneer'],
 'emesa': ['emesa', 'mease'],
 'emigrate': ['emigrate', 'remigate'],
 'emigration': ['emigration', 'remigation'],
 'emil': ['emil', 'lime', 'mile'],
 'emilia': ['emilia', 'mailie'],
 'emily': ['elymi', 'emily', 'limey'],
 'emim': ['emim', 'mime'],
 'emir': ['emir', 'imer', 'mire', 'reim', 'remi', 'riem', 'rime'],
 'emirate': ['emerita', 'emirate'],
 'emirship': ['emirship', 'imperish'],
 'emissary': ['emissary', 'missayer'],
 'emit': ['emit', 'item', 'mite', 'time'],
 'emitter': ['emitter', 'termite'],
 'emm': ['emm', 'mem'],
 'emmarble': ['embalmer', 'emmarble'],
 'emodin': ['domine', 'domnei', 'emodin', 'medino'],
 'emotion': ['emotion', 'moonite'],
 'empanel': ['empanel', 'emplane', 'peelman'],
 'empathic': ['empathic', 'emphatic'],
 'empathically': ['empathically', 'emphatically'],
 'emphasis': ['emphasis', 'misshape'],
 'emphatic': ['empathic', 'emphatic'],
 'emphatically': ['empathically', 'emphatically'],
 'empire': ['empire', 'epimer'],
 'empiricist': ['empiricist', 'empiristic'],
 'empiristic': ['empiricist', 'empiristic'],
 'emplane': ['empanel', 'emplane', 'peelman'],
 'employer': ['employer', 'polymere'],
 'emporia': ['emporia', 'meropia'],
 'emporial': ['emporial', 'proemial'],
 'emporium': ['emporium', 'pomerium', 'proemium'],
 'emprise': ['emprise', 'imprese', 'premise', 'spireme'],
 'empt': ['empt', 'temp'],
 'emptier': ['emptier', 'impetre'],
 'emption': ['emption', 'pimento'],
 'emptional': ['emptional', 'palmitone'],
 'emptor': ['emptor', 'trompe'],
 'empyesis': ['empyesis', 'pyemesis'],
 'emu': ['emu', 'ume'],
 'emulant': ['almuten', 'emulant'],
 'emulation': ['emulation', 'laumonite'],
 'emulsion': ['emulsion', 'solenium'],
 'emundation': ['emundation', 'mountained'],
 'emyd': ['demy', 'emyd'],
 'en': ['en', 'ne'],
 'enable': ['baleen', 'enable'],
 'enabler': ['enabler', 'renable'],
 'enaction': ['cetonian', 'enaction'],
 'enactor': ['enactor', 'necator', 'orcanet'],
 'enactory': ['enactory', 'octenary'],
 'enaena': ['aenean', 'enaena'],
 'enalid': ['aldine', 'daniel', 'delian', 'denial', 'enalid', 'leadin'],
 'enaliornis': ['enaliornis', 'rosaniline'],
 'enaluron': ['enaluron', 'neuronal'],
 'enam': ['amen', 'enam', 'mane', 'mean', 'name', 'nema'],
 'enamel': ['enamel', 'melena'],
 'enameling': ['enameling', 'malengine', 'meningeal'],
 'enamor': ['enamor', 'monera', 'oreman', 'romane'],
 'enamored': ['demeanor', 'enamored'],
 'enanthem': ['enanthem', 'menthane'],
 'enantiomer': ['enantiomer', 'renominate'],
 'enapt': ['enapt', 'paten', 'penta', 'tapen'],
 'enarch': ['enarch', 'ranche'],
 'enarm': ['enarm', 'namer', 'reman'],
 'enarme': ['enarme', 'meaner', 'rename'],
 'enarthrosis': ['enarthrosis', 'nearthrosis'],
 'enate': ['eaten', 'enate'],
 'enatic': ['acetin', 'actine', 'enatic'],
 'enation': ['enation', 'etonian'],
 'enbrave': ['enbrave', 'verbena'],
 'encapsule': ['encapsule', 'pelecanus'],
 'encase': ['encase', 'seance', 'seneca'],
 'encash': ['encash', 'sanche'],
 'encauma': ['cumaean', 'encauma'],
 'encaustes': ['acuteness', 'encaustes'],
 'encaustic': ['encaustic', 'succinate'],
 'encephalomeningitis': ['encephalomeningitis', 'meningoencephalitis'],
 'encephalomeningocele': ['encephalomeningocele', 'meningoencephalocele'],
 'encephalomyelitis': ['encephalomyelitis', 'myeloencephalitis'],
 'enchair': ['chainer', 'enchair', 'rechain'],
 'encharge': ['encharge', 'rechange'],
 'encharnel': ['channeler', 'encharnel'],
 'enchytrae': ['cytherean', 'enchytrae'],
 'encina': ['canine', 'encina', 'neanic'],
 'encinillo': ['encinillo', 'linolenic'],
 'encist': ['encist', 'incest', 'insect', 'scient'],
 'encitadel': ['declinate', 'encitadel'],
 'enclaret': ['celarent', 'centrale', 'enclaret'],
 'enclasp': ['enclasp', 'spancel'],
 'enclave': ['enclave', 'levance', 'valence'],
 'enclosure': ['enclosure', 'recounsel'],
 'encoignure': ['encoignure', 'neurogenic'],
 'encoil': ['clione', 'coelin', 'encoil', 'enolic'],
 'encomiastic': ['cosmetician', 'encomiastic'],
 'encomic': ['comenic', 'encomic', 'meconic'],
 'encomium': ['encomium', 'meconium'],
 'encoronal': ['encoronal', 'olecranon'],
 'encoronate': ['encoronate', 'entocornea'],
 'encradle': ['calender', 'encradle'],
 'encranial': ['carnelian', 'encranial'],
 'encratic': ['acentric', 'encratic', 'nearctic'],
 'encratism': ['encratism', 'miscreant'],
 'encraty': ['encraty', 'nectary'],
 'encreel': ['crenele', 'encreel'],
 'encrinital': ['encrinital', 'tricennial'],
 'encrisp': ['encrisp', 'pincers'],
 'encrust': ['encrust', 'uncrest'],
 'encurl': ['encurl', 'lucern'],
 'encurtain': ['encurtain', 'runcinate', 'uncertain'],
 'encyrtidae': ['encyrtidae', 'nycteridae'],
 'end': ['den', 'end', 'ned'],
 'endaortic': ['citronade', 'endaortic', 'redaction'],
 'endboard': ['deadborn', 'endboard'],
 'endear': ['deaner', 'endear'],
 'endeared': ['deadener', 'endeared'],
 'endearing': ['endearing', 'engrained', 'grenadine'],
 'endearingly': ['endearingly', 'engrainedly'],
 'endemial': ['endemial', 'madeline'],
 'endere': ['endere', 'needer', 'reeden'],
 'enderonic': ['enderonic', 'endocrine'],
 'endevil': ['develin', 'endevil'],
 'endew': ['endew', 'wende'],
 'ending': ['ending', 'ginned'],
 'endite': ['eident', 'endite'],
 'endive': ['endive', 'envied', 'veined'],
 'endoarteritis': ['endoarteritis', 'sideronatrite'],
 'endocline': ['endocline', 'indolence'],
 'endocrine': ['enderonic', 'endocrine'],
 'endome': ['endome', 'omened'],
 'endopathic': ['dictaphone', 'endopathic'],
 'endophasic': ['deaconship', 'endophasic'],
 'endoral': ['endoral', 'ladrone', 'leonard'],
 'endosarc': ['endosarc', 'secondar'],
 'endosome': ['endosome', 'moonseed'],
 'endosporium': ['endosporium', 'imponderous'],
 'endosteal': ['endosteal', 'leadstone'],
 'endothecial': ['chelidonate', 'endothecial'],
 'endothelia': ['endothelia', 'ethanediol', 'ethenoidal'],
 'endow': ['endow', 'nowed'],
 'endura': ['endura', 'neurad', 'undear', 'unread'],
 'endurably': ['endurably', 'undryable'],
 'endure': ['durene', 'endure'],
 'endurer': ['endurer', 'underer'],
 'enduring': ['enduring', 'unringed'],
 'enduringly': ['enduringly', 'underlying'],
 'endwise': ['endwise', 'sinewed'],
 'enema': ['ameen', 'amene', 'enema'],
 'enemy': ['enemy', 'yemen'],
 'energesis': ['energesis', 'regenesis'],
 'energeticist': ['energeticist', 'energetistic'],
 'energetistic': ['energeticist', 'energetistic'],
 'energic': ['energic', 'generic'],
 'energical': ['energical', 'generical'],
 'energid': ['energid', 'reeding'],
 'energist': ['energist', 'steering'],
 'energy': ['energy', 'greeny', 'gyrene'],
 'enervate': ['enervate', 'venerate'],
 'enervation': ['enervation', 'veneration'],
 'enervative': ['enervative', 'venerative'],
 'enervator': ['enervator', 'renovater', 'venerator'],
 'enfilade': ['alfenide', 'enfilade'],
 'enfile': ['enfile', 'enlief', 'enlife', 'feline'],
 'enflesh': ['enflesh', 'fleshen'],
 'enfoil': ['enfoil', 'olefin'],
 'enfold': ['enfold', 'folden', 'fondle'],
 'enforcer': ['confrere', 'enforcer', 'reconfer'],
 'enframe': ['enframe', 'freeman'],
 'engaol': ['angelo', 'engaol'],
 'engarb': ['banger', 'engarb', 'graben'],
 'engaud': ['augend', 'engaud', 'unaged'],
 'engild': ['dingle', 'elding', 'engild', 'gilden'],
 'engird': ['engird', 'ringed'],
 'engirdle': ['engirdle', 'reedling'],
 'engirt': ['engirt', 'tinger'],
 'englacial': ['angelical', 'englacial', 'galenical'],
 'englacially': ['angelically', 'englacially'],
 'englad': ['angled', 'dangle', 'englad', 'lagend'],
 'englander': ['englander', 'greenland'],
 'english': ['english', 'shingle'],
 'englisher': ['englisher', 'reshingle'],
 'englut': ['englut', 'gluten', 'ungelt'],
 'engobe': ['begone', 'engobe'],
 'engold': ['engold', 'golden'],
 'engrail': ['aligner', 'engrail', 'realign', 'reginal'],
 'engrailed': ['engrailed', 'geraldine'],
 'engrailment': ['engrailment', 'realignment'],
 'engrain': ['earning', 'engrain'],
 'engrained': ['endearing', 'engrained', 'grenadine'],
 'engrainedly': ['endearingly', 'engrainedly'],
 'engram': ['engram', 'german', 'manger'],
 'engraphic': ['engraphic', 'preaching'],
 'engrave': ['avenger', 'engrave'],
 'engross': ['engross', 'grossen'],
 'enhat': ['enhat', 'ethan', 'nathe', 'neath', 'thane'],
 'enheart': ['earthen', 'enheart', 'hearten', 'naether', 'teheran', 'traheen'],
 'enherit': ['enherit', 'etherin', 'neither', 'therein'],
 'enhydra': ['enhydra', 'henyard'],
 'eniac': ['anice', 'eniac'],
 'enicuridae': ['audiencier', 'enicuridae'],
 'enid': ['dine', 'enid', 'inde', 'nide'],
 'enif': ['enif', 'fine', 'neif', 'nife'],
 'enisle': ['enisle', 'ensile', 'senile', 'silene'],
 'enlace': ['elance', 'enlace'],
 'enlard': ['aldern',
            'darnel',
            'enlard',
            'lander',
            'lenard',
            'randle',
            'reland'],
 'enlarge': ['enlarge', 'general', 'gleaner'],
 'enleaf': ['enleaf', 'leafen'],
 'enlief': ['enfile', 'enlief', 'enlife', 'feline'],
 'enlife': ['enfile', 'enlief', 'enlife', 'feline'],
 'enlight': ['enlight', 'lighten'],
 'enlist': ['enlist', 'listen', 'silent', 'tinsel'],
 'enlisted': ['enlisted', 'lintseed'],
 'enlister': ['enlister', 'esterlin', 'listener', 'relisten'],
 'enmass': ['enmass', 'maness', 'messan'],
 'enneadic': ['cadinene', 'decennia', 'enneadic'],
 'ennobler': ['ennobler', 'nonrebel'],
 'ennoic': ['conine', 'connie', 'ennoic'],
 'ennomic': ['ennomic', 'meconin'],
 'enoch': ['cohen', 'enoch'],
 'enocyte': ['enocyte', 'neocyte'],
 'enodal': ['enodal', 'loaden'],
 'enoil': ['enoil', 'ileon', 'olein'],
 'enol': ['elon', 'enol', 'leno', 'leon', 'lone', 'noel'],
 'enolic': ['clione', 'coelin', 'encoil', 'enolic'],
 'enomania': ['enomania', 'maeonian'],
 'enomotarch': ['chromatone', 'enomotarch'],
 'enorganic': ['enorganic', 'ignorance'],
 'enorm': ['enorm', 'moner', 'morne'],
 'enormous': ['enormous', 'unmorose'],
 'enos': ['enos', 'nose'],
 'enostosis': ['enostosis', 'sootiness'],
 'enow': ['enow', 'owen', 'wone'],
 'enphytotic': ['enphytotic', 'entophytic'],
 'enrace': ['careen', 'carene', 'enrace'],
 'enrage': ['egeran', 'enrage', 'ergane', 'genear', 'genera'],
 'enraged': ['derange', 'enraged', 'gardeen', 'gerenda', 'grandee', 'grenade'],
 'enragedly': ['enragedly', 'legendary'],
 'enrapt': ['arpent',
            'enrapt',
            'entrap',
            'panter',
            'parent',
            'pretan',
            'trepan'],
 'enravish': ['enravish', 'ravenish', 'vanisher'],
 'enray': ['enray', 'yearn'],
 'enrib': ['brine', 'enrib'],
 'enrich': ['enrich', 'nicher', 'richen'],
 'enring': ['enring', 'ginner'],
 'enrive': ['enrive', 'envier', 'veiner', 'verine'],
 'enrobe': ['boreen', 'enrobe', 'neebor', 'rebone'],
 'enrol': ['enrol', 'loren'],
 'enrolled': ['enrolled', 'rondelle'],
 'enrough': ['enrough', 'roughen'],
 'enruin': ['enruin', 'neurin', 'unrein'],
 'enrut': ['enrut', 'tuner', 'urent'],
 'ens': ['ens', 'sen'],
 'ensaint': ['ensaint', 'stanine'],
 'ensate': ['ensate', 'enseat', 'santee', 'sateen', 'senate'],
 'ense': ['ense', 'esne', 'nese', 'seen', 'snee'],
 'enseam': ['enseam', 'semnae'],
 'enseat': ['ensate', 'enseat', 'santee', 'sateen', 'senate'],
 'ensepulcher': ['ensepulcher', 'ensepulchre'],
 'ensepulchre': ['ensepulcher', 'ensepulchre'],
 'enshade': ['dasheen', 'enshade'],
 'enshroud': ['enshroud', 'unshored'],
 'ensigncy': ['ensigncy', 'syngenic'],
 'ensilage': ['ensilage', 'genesial', 'signalee'],
 'ensile': ['enisle', 'ensile', 'senile', 'silene'],
 'ensilver': ['ensilver', 'sniveler'],
 'ensmall': ['ensmall', 'smallen'],
 'ensoul': ['ensoul', 'olenus', 'unsole'],
 'enspirit': ['enspirit', 'pristine'],
 'enstar': ['astern', 'enstar', 'stenar', 'sterna'],
 'enstatite': ['enstatite', 'intestate', 'satinette'],
 'enstool': ['enstool', 'olonets'],
 'enstore': ['enstore', 'estrone', 'storeen', 'tornese'],
 'ensue': ['ensue', 'seenu', 'unsee'],
 'ensuer': ['ensuer', 'ensure'],
 'ensure': ['ensuer', 'ensure'],
 'entablature': ['entablature', 'untreatable'],
 'entach': ['entach', 'netcha'],
 'entad': ['denat', 'entad'],
 'entada': ['adnate', 'entada'],
 'entail': ['entail', 'tineal'],
 'entailer': ['elaterin', 'entailer', 'treenail'],
 'ental': ['ental', 'laten', 'leant'],
 'entasia': ['anisate', 'entasia'],
 'entasis': ['entasis', 'sestian', 'sestina'],
 'entelam': ['entelam', 'leetman'],
 'enter': ['enter', 'neter', 'renet', 'terne', 'treen'],
 'enteral': ['alterne', 'enteral', 'eternal', 'teleran', 'teneral'],
 'enterer': ['enterer', 'terrene'],
 'enteria': ['enteria', 'trainee', 'triaene'],
 'enteric': ['citrene', 'enteric', 'enticer', 'tercine'],
 'enterocolitis': ['coloenteritis', 'enterocolitis'],
 'enterogastritis': ['enterogastritis', 'gastroenteritis'],
 'enteroid': ['enteroid', 'orendite'],
 'enteron': ['enteron', 'tenoner'],
 'enteropexy': ['enteropexy', 'oxyterpene'],
 'entertain': ['entertain', 'tarentine', 'terentian'],
 'entheal': ['entheal', 'lethean'],
 'enthraldom': ['enthraldom', 'motherland'],
 'enthuse': ['enthuse', 'unsheet'],
 'entia': ['entia', 'teian', 'tenai', 'tinea'],
 'enticer': ['citrene', 'enteric', 'enticer', 'tercine'],
 'entincture': ['entincture', 'unreticent'],
 'entire': ['entire', 'triene'],
 'entirely': ['entirely', 'lientery'],
 'entirety': ['entirety', 'eternity'],
 'entity': ['entity', 'tinety'],
 'entocoelic': ['coelection', 'entocoelic'],
 'entocornea': ['encoronate', 'entocornea'],
 'entohyal': ['entohyal', 'ethanoyl'],
 'entoil': ['entoil', 'lionet'],
 'entomeric': ['entomeric', 'intercome', 'morencite'],
 'entomic': ['centimo', 'entomic', 'tecomin'],
 'entomical': ['entomical', 'melanotic'],
 'entomion': ['entomion', 'noontime'],
 'entomoid': ['demotion', 'entomoid', 'moontide'],
 'entomophily': ['entomophily', 'monophylite'],
 'entomotomy': ['entomotomy', 'omentotomy'],
 'entoparasite': ['antiprotease', 'entoparasite'],
 'entophyte': ['entophyte', 'tenophyte'],
 'entophytic': ['enphytotic', 'entophytic'],
 'entopic': ['entopic', 'nepotic', 'pentoic'],
 'entoplastic': ['entoplastic', 'spinotectal', 'tectospinal', 'tenoplastic'],
 'entoretina': ['entoretina', 'tetraonine'],
 'entosarc': ['ancestor', 'entosarc'],
 'entotic': ['entotic', 'tonetic'],
 'entozoa': ['entozoa', 'ozonate'],
 'entozoic': ['entozoic', 'enzootic'],
 'entrail': ['entrail',
             'latiner',
             'latrine',
             'ratline',
             'reliant',
             'retinal',
             'trenail'],
 'entrain': ['entrain', 'teriann'],
 'entrance': ['centenar', 'entrance'],
 'entrap': ['arpent',
            'enrapt',
            'entrap',
            'panter',
            'parent',
            'pretan',
            'trepan'],
 'entreat': ['entreat', 'ratteen', 'tarente', 'ternate', 'tetrane'],
 'entreating': ['entreating', 'interagent'],
 'entree': ['entree', 'rentee', 'retene'],
 'entrepas': ['entrepas', 'septenar'],
 'entropion': ['entropion', 'pontonier', 'prenotion'],
 'entropium': ['entropium', 'importune'],
 'entrust': ['entrust', 'stunter', 'trusten'],
 'enumeration': ['enumeration', 'mountaineer'],
 'enunciation': ['enunciation', 'incuneation'],
 'enunciator': ['enunciator', 'uncreation'],
 'enure': ['enure', 'reune'],
 'envelope': ['envelope', 'ovenpeel'],
 'enverdure': ['enverdure', 'unrevered'],
 'envied': ['endive', 'envied', 'veined'],
 'envier': ['enrive', 'envier', 'veiner', 'verine'],
 'envious': ['envious', 'niveous', 'veinous'],
 'envoy': ['envoy', 'nevoy', 'yoven'],
 'enwood': ['enwood', 'wooden'],
 'enwound': ['enwound', 'unowned'],
 'enwrap': ['enwrap', 'pawner', 'repawn'],
 'enwrite': ['enwrite', 'retwine'],
 'enzootic': ['entozoic', 'enzootic'],
 'eoan': ['aeon', 'eoan'],
 'eogaean': ['eogaean', 'neogaea'],
 'eolithic': ['chiolite', 'eolithic'],
 'eon': ['eon', 'neo', 'one'],
 'eonism': ['eonism', 'mesion', 'oneism', 'simeon'],
 'eophyton': ['eophyton', 'honeypot'],
 'eosaurus': ['eosaurus', 'rousseau'],
 'eosin': ['eosin', 'noise'],
 'eosinoblast': ['bosselation', 'eosinoblast'],
 'epacrid': ['epacrid', 'peracid', 'preacid'],
 'epacris': ['epacris', 'scrapie', 'serapic'],
 'epactal': ['epactal', 'placate'],
 'eparch': ['aperch', 'eparch', 'percha', 'preach'],
 'eparchial': ['eparchial', 'raphaelic'],
 'eparchy': ['eparchy', 'preachy'],
 'epha': ['epha', 'heap'],
 'epharmonic': ['epharmonic', 'pinachrome'],
 'ephemeris': ['emeership', 'ephemeris'],
 'ephod': ['depoh', 'ephod', 'hoped'],
 'ephor': ['ephor', 'hoper'],
 'ephorus': ['ephorus', 'orpheus', 'upshore'],
 'epibasal': ['ablepsia', 'epibasal'],
 'epibole': ['epibole', 'epilobe'],
 'epic': ['epic', 'pice'],
 'epical': ['epical', 'piacle', 'plaice'],
 'epicarp': ['crappie', 'epicarp'],
 'epicentral': ['epicentral', 'parentelic'],
 'epiceratodus': ['dipteraceous', 'epiceratodus'],
 'epichorial': ['aerophilic', 'epichorial'],
 'epicly': ['epicly', 'pyelic'],
 'epicostal': ['alopecist', 'altiscope', 'epicostal', 'scapolite'],
 'epicotyl': ['epicotyl', 'lipocyte'],
 'epicranial': ['epicranial', 'periacinal'],
 'epiderm': ['demirep', 'epiderm', 'impeder', 'remiped'],
 'epiderma': ['epiderma', 'premedia'],
 'epidermal': ['epidermal', 'impleader', 'premedial'],
 'epidermis': ['dispireme', 'epidermis'],
 'epididymovasostomy': ['epididymovasostomy', 'vasoepididymostomy'],
 'epidural': ['dipleura', 'epidural'],
 'epigram': ['epigram', 'primage'],
 'epilabrum': ['epilabrum', 'impuberal'],
 'epilachna': ['cephalina', 'epilachna'],
 'epilate': ['epilate', 'epitela', 'pileate'],
 'epilation': ['epilation', 'polianite'],
 'epilatory': ['epilatory', 'petiolary'],
 'epilobe': ['epibole', 'epilobe'],
 'epimer': ['empire', 'epimer'],
 'epiotic': ['epiotic', 'poietic'],
 'epipactis': ['epipactis', 'epipastic'],
 'epipastic': ['epipactis', 'epipastic'],
 'epiplasm': ['epiplasm', 'palmipes'],
 'epiploic': ['epiploic', 'epipolic'],
 'epipolic': ['epiploic', 'epipolic'],
 'epirotic': ['epirotic', 'periotic'],
 'episclera': ['episclera', 'periclase'],
 'episematic': ['episematic', 'septicemia'],
 'episodal': ['episodal', 'lapidose', 'sepaloid'],
 'episodial': ['apsidiole', 'episodial'],
 'epistatic': ['epistatic', 'pistacite'],
 'episternal': ['alpestrine', 'episternal', 'interlapse', 'presential'],
 'episternum': ['episternum', 'uprisement'],
 'epistlar': ['epistlar', 'pilaster', 'plaister', 'priestal'],
 'epistle': ['epistle', 'septile'],
 'epistler': ['epistler', 'spirelet'],
 'epistoler': ['epistoler', 'peristole', 'perseitol', 'pistoleer'],
 'epistoma': ['epistoma', 'metopias'],
 'epistome': ['epistome', 'epsomite'],
 'epistroma': ['epistroma', 'peristoma'],
 'epitela': ['epilate', 'epitela', 'pileate'],
 'epithecal': ['epithecal', 'petechial', 'phacelite'],
 'epithecate': ['epithecate', 'petechiate'],
 'epithet': ['epithet', 'heptite'],
 'epithyme': ['epithyme', 'hemitype'],
 'epitomizer': ['epitomizer', 'peritomize'],
 'epizoal': ['epizoal', 'lopezia', 'opalize'],
 'epoch': ['epoch', 'poche'],
 'epodic': ['copied', 'epodic'],
 'epornitic': ['epornitic', 'proteinic'],
 'epos': ['epos', 'peso', 'pose', 'sope'],
 'epsilon': ['epsilon', 'sinople'],
 'epsomite': ['epistome', 'epsomite'],
 'epulis': ['epulis', 'pileus'],
 'epulo': ['epulo', 'loupe'],
 'epuloid': ['epuloid', 'euploid'],
 'epulosis': ['epulosis', 'pelusios'],
 'epulotic': ['epulotic', 'poultice'],
 'epural': ['epural', 'perula', 'pleura'],
 'epuration': ['epuration', 'eupatorin'],
 'equal': ['equal', 'quale', 'queal'],
 'equalable': ['aquabelle', 'equalable'],
 'equiangle': ['angelique', 'equiangle'],
 'equinity': ['equinity', 'inequity'],
 'equip': ['equip', 'pique'],
 'equitable': ['equitable', 'quietable'],
 'equitist': ['equitist', 'quietist'],
 'equus': ['equus', 'usque'],
 'er': ['er', 're'],
 'era': ['aer', 'are', 'ear', 'era', 'rea'],
 'erade': ['eared', 'erade'],
 'eradicant': ['carinated', 'eradicant'],
 'eradicator': ['corradiate', 'cortaderia', 'eradicator'],
 'eral': ['earl', 'eral', 'lear', 'real'],
 'eranist': ['asterin', 'eranist', 'restain', 'stainer', 'starnie', 'stearin'],
 'erase': ['easer', 'erase'],
 'erased': ['erased', 'reseda', 'seared'],
 'eraser': ['eraser', 'searer'],
 'erasmian': ['erasmian', 'raiseman'],
 'erasmus': ['assumer', 'erasmus', 'masseur'],
 'erastian': ['artesian', 'asterina', 'asternia', 'erastian', 'seatrain'],
 'erastus': ['erastus', 'ressaut'],
 'erava': ['avera', 'erava'],
 'erbia': ['barie', 'beira', 'erbia', 'rebia'],
 'erbium': ['erbium', 'imbrue'],
 'erd': ['erd', 'red'],
 'ere': ['eer', 'ere', 'ree'],
 'erect': ['crete', 'erect'],
 'erectable': ['celebrate', 'erectable'],
 'erecting': ['erecting', 'gentrice'],
 'erection': ['erection', 'neoteric', 'nocerite', 'renotice'],
 'eremic': ['eremic', 'merice'],
 'eremital': ['eremital', 'materiel'],
 'erept': ['erept', 'peter', 'petre'],
 'ereptic': ['ereptic', 'precite', 'receipt'],
 'ereption': ['ereption', 'tropeine'],
 'erethic': ['erethic', 'etheric', 'heretic', 'heteric', 'teicher'],
 'erethism': ['erethism', 'etherism', 'heterism'],
 'erethismic': ['erethismic', 'hetericism'],
 'erethistic': ['erethistic', 'hetericist'],
 'eretrian': ['arretine', 'eretrian', 'eritrean', 'retainer'],
 'erg': ['erg', 'ger', 'reg'],
 'ergal': ['argel', 'ergal', 'garle', 'glare', 'lager', 'large', 'regal'],
 'ergamine': ['ergamine', 'merginae'],
 'ergane': ['egeran', 'enrage', 'ergane', 'genear', 'genera'],
 'ergastic': ['agrestic', 'ergastic'],
 'ergates': ['ergates', 'gearset', 'geaster'],
 'ergoism': ['ergoism', 'ogreism'],
 'ergomaniac': ['ergomaniac', 'grecomania'],
 'ergon': ['ergon', 'genro', 'goner', 'negro'],
 'ergot': ['ergot', 'rotge'],
 'ergotamine': ['angiometer', 'ergotamine', 'geometrina'],
 'ergotin': ['ergotin', 'genitor', 'negrito', 'ogtiern', 'trigone'],
 'ergusia': ['ergusia', 'gerusia', 'sarigue'],
 'eria': ['aire', 'eria'],
 'erian': ['erian', 'irena', 'reina'],
 'eric': ['eric', 'rice'],
 'erica': ['acier', 'aeric', 'ceria', 'erica'],
 'ericad': ['acider', 'ericad'],
 'erical': ['carlie', 'claire', 'eclair', 'erical'],
 'erichtoid': ['dichroite', 'erichtoid', 'theriodic'],
 'erigenia': ['aegirine', 'erigenia'],
 'erigeron': ['erigeron', 'reignore'],
 'erik': ['erik', 'kier', 'reki'],
 'erineum': ['erineum', 'unireme'],
 'erinose': ['erinose', 'roseine'],
 'eristalis': ['eristalis', 'serialist'],
 'eristic': ['ectiris', 'eristic'],
 'eristical': ['eristical', 'realistic'],
 'erithacus': ['erithacus', 'eucharist'],
 'eritrean': ['arretine', 'eretrian', 'eritrean', 'retainer'],
 'erma': ['erma', 'mare', 'rame', 'ream'],
 'ermani': ['ermani', 'marine', 'remain'],
 'ermines': ['ermines', 'inermes'],
 'erne': ['erne', 'neer', 'reen'],
 'ernest': ['ernest', 'nester', 'resent', 'streen'],
 'ernie': ['ernie', 'ierne', 'irene'],
 'ernst': ['ernst', 'stern'],
 'erode': ['doree', 'erode'],
 'eros': ['eros', 'rose', 'sero', 'sore'],
 'erose': ['erose', 'soree'],
 'erotesis': ['erotesis', 'isostere'],
 'erotic': ['erotic', 'tercio'],
 'erotical': ['calorite', 'erotical', 'loricate'],
 'eroticism': ['eroticism', 'isometric', 'meroistic', 'trioecism'],
 'erotism': ['erotism', 'mortise', 'trisome'],
 'erotogenic': ['erotogenic', 'geocronite', 'orogenetic'],
 'errabund': ['errabund', 'unbarred'],
 'errand': ['darner', 'darren', 'errand', 'rander', 'redarn'],
 'errant': ['arrent', 'errant', 'ranter', 'ternar'],
 'errantia': ['artarine', 'errantia'],
 'erratic': ['cartier', 'cirrate', 'erratic'],
 'erratum': ['erratum', 'maturer'],
 'erring': ['erring', 'rering', 'ringer'],
 'errite': ['errite', 'reiter', 'retier', 'retire', 'tierer'],
 'ers': ['ers', 'ser'],
 'ersar': ['ersar', 'raser', 'serra'],
 'erse': ['erse', 'rees', 'seer', 'sere'],
 'erthen': ['erthen', 'henter', 'nether', 'threne'],
 'eruc': ['cure', 'ecru', 'eruc'],
 'eruciform': ['eruciform', 'urceiform'],
 'erucin': ['curine', 'erucin', 'neuric'],
 'erucivorous': ['erucivorous', 'overcurious'],
 'eruct': ['cruet', 'eruct', 'recut', 'truce'],
 'eruction': ['eruction', 'neurotic'],
 'erugate': ['erugate', 'guetare'],
 'erumpent': ['erumpent', 'untemper'],
 'eruption': ['eruption', 'unitrope'],
 'erwin': ['erwin', 'rewin', 'winer'],
 'eryngium': ['eryngium', 'gynerium'],
 'eryon': ['eryon', 'onery'],
 'eryops': ['eryops', 'osprey'],
 'erythea': ['erythea', 'hetaery', 'yeather'],
 'erythrin': ['erythrin', 'tyrrheni'],
 'erythrophage': ['erythrophage', 'heterography'],
 'erythrophyllin': ['erythrophyllin', 'phylloerythrin'],
 'erythropia': ['erythropia', 'pyrotheria'],
 'es': ['es', 'se'],
 'esca': ['case', 'esca'],
 'escalan': ['escalan', 'scalena'],
 'escalin': ['celsian', 'escalin', 'sanicle', 'secalin'],
 'escaloped': ['copleased', 'escaloped'],
 'escapement': ['escapement', 'espacement'],
 'escaper': ['escaper', 'respace'],
 'escarp': ['casper', 'escarp', 'parsec', 'scrape', 'secpar', 'spacer'],
 'eschar': ['arches', 'chaser', 'eschar', 'recash', 'search'],
 'eschara': ['asearch', 'eschara'],
 'escheator': ['escheator', 'tocharese'],
 'escobilla': ['escobilla', 'obeliscal'],
 'escolar': ['escolar', 'solacer'],
 'escort': ['corset', 'cortes', 'coster', 'escort', 'scoter', 'sector'],
 'escortment': ['centermost', 'escortment'],
 'escrol': ['closer', 'cresol', 'escrol'],
 'escropulo': ['escropulo', 'supercool'],
 'esculent': ['esculent', 'unselect'],
 'esculin': ['esculin', 'incluse'],
 'esere': ['esere', 'reese', 'resee'],
 'esexual': ['esexual', 'sexuale'],
 'eshin': ['eshin', 'shine'],
 'esiphonal': ['esiphonal', 'phaseolin'],
 'esker': ['esker', 'keres', 'reesk', 'seker', 'skeer', 'skere'],
 'eskualdun': ['eskualdun', 'euskaldun'],
 'eskuara': ['eskuara', 'euskara'],
 'esne': ['ense', 'esne', 'nese', 'seen', 'snee'],
 'esophagogastrostomy': ['esophagogastrostomy', 'gastroesophagostomy'],
 'esopus': ['esopus', 'spouse'],
 'esoterical': ['cesarolite', 'esoterical'],
 'esoterist': ['esoterist', 'trisetose'],
 'esotrope': ['esotrope', 'proteose'],
 'esotropia': ['aportoise', 'esotropia'],
 'espacement': ['escapement', 'espacement'],
 'espadon': ['espadon', 'spadone'],
 'esparto': ['esparto', 'petrosa', 'seaport'],
 'esperantic': ['esperantic', 'interspace'],
 'esperantido': ['desperation', 'esperantido'],
 'esperantism': ['esperantism', 'strepsinema'],
 'esperanto': ['esperanto', 'personate'],
 'espial': ['espial', 'lipase', 'pelias'],
 'espier': ['espier', 'peiser'],
 'espinal': ['espinal', 'pinales', 'spaniel'],
 'espino': ['espino', 'sepion'],
 'espringal': ['espringal', 'presignal', 'relapsing'],
 'esquire': ['esquire', 'risquee'],
 'essence': ['essence', 'senesce'],
 'essenism': ['essenism', 'messines'],
 'essie': ['essie', 'seise'],
 'essling': ['essling', 'singles'],
 'essoin': ['essoin', 'ossein'],
 'essonite': ['essonite', 'ossetine'],
 'essorant': ['assentor', 'essorant', 'starnose'],
 'estamene': ['easement', 'estamene'],
 'esteem': ['esteem', 'mestee'],
 'estella': ['estella', 'sellate'],
 'ester': ['ester',
           'estre',
           'reest',
           'reset',
           'steer',
           'stere',
           'stree',
           'terse',
           'tsere'],
 'esterlin': ['enlister', 'esterlin', 'listener', 'relisten'],
 'esterling': ['esterling', 'steerling'],
 'estevin': ['estevin', 'tensive'],
 'esth': ['esth', 'hest', 'seth'],
 'esther': ['esther', 'hester', 'theres'],
 'estivage': ['estivage', 'vegasite'],
 'estoc': ['coset', 'estoc', 'scote'],
 'estonian': ['estonian', 'nasonite'],
 'estop': ['estop', 'stoep', 'stope'],
 'estradiol': ['estradiol', 'idolaster'],
 'estrange': ['estrange', 'segreant', 'sergeant', 'sternage'],
 'estray': ['atresy', 'estray', 'reasty', 'stayer'],
 'estre': ['ester',
           'estre',
           'reest',
           'reset',
           'steer',
           'stere',
           'stree',
           'terse',
           'tsere'],
 'estreat': ['estreat', 'restate', 'retaste'],
 'estrepe': ['estrepe', 'resteep', 'steeper'],
 'estriate': ['estriate', 'treatise'],
 'estrin': ['estrin', 'insert', 'sinter', 'sterin', 'triens'],
 'estriol': ['estriol', 'torsile'],
 'estrogen': ['estrogen', 'gerontes'],
 'estrone': ['enstore', 'estrone', 'storeen', 'tornese'],
 'estrous': ['estrous', 'oestrus', 'sestuor', 'tussore'],
 'estrual': ['arustle', 'estrual', 'saluter', 'saulter'],
 'estufa': ['estufa', 'fusate'],
 'eta': ['ate', 'eat', 'eta', 'tae', 'tea'],
 'etacism': ['cameist', 'etacism', 'sematic'],
 'etacist': ['etacist', 'statice'],
 'etalon': ['etalon', 'tolane'],
 'etamin': ['etamin', 'inmate', 'taimen', 'tamein'],
 'etamine': ['amenite', 'etamine', 'matinee'],
 'etch': ['chet', 'etch', 'tche', 'tech'],
 'etcher': ['cherte', 'etcher'],
 'eternal': ['alterne', 'enteral', 'eternal', 'teleran', 'teneral'],
 'eternalism': ['eternalism', 'streamline'],
 'eternity': ['entirety', 'eternity'],
 'etesian': ['etesian', 'senaite'],
 'ethal': ['ethal', 'lathe', 'leath'],
 'ethan': ['enhat', 'ethan', 'nathe', 'neath', 'thane'],
 'ethanal': ['anthela', 'ethanal'],
 'ethane': ['ethane', 'taheen'],
 'ethanediol': ['endothelia', 'ethanediol', 'ethenoidal'],
 'ethanim': ['ethanim', 'hematin'],
 'ethanoyl': ['entohyal', 'ethanoyl'],
 'ethel': ['ethel', 'lethe'],
 'ethenoidal': ['endothelia', 'ethanediol', 'ethenoidal'],
 'ether': ['ether', 'rethe', 'theer', 'there', 'three'],
 'etheria': ['ehretia', 'etheria'],
 'etheric': ['erethic', 'etheric', 'heretic', 'heteric', 'teicher'],
 'etherin': ['enherit', 'etherin', 'neither', 'therein'],
 'etherion': ['etherion', 'hereinto', 'heronite'],
 'etherism': ['erethism', 'etherism', 'heterism'],
 'etherization': ['etherization', 'heterization'],
 'etherize': ['etherize', 'heterize'],
 'ethicism': ['ethicism', 'shemitic'],
 'ethicist': ['ethicist', 'thecitis', 'theistic'],
 'ethics': ['ethics', 'sethic'],
 'ethid': ['edith', 'ethid'],
 'ethine': ['ethine', 'theine'],
 'ethiop': ['ethiop', 'ophite', 'peitho'],
 'ethmoidal': ['ethmoidal', 'oldhamite'],
 'ethmosphenoid': ['ethmosphenoid', 'sphenoethmoid'],
 'ethmosphenoidal': ['ethmosphenoidal', 'sphenoethmoidal'],
 'ethnal': ['ethnal', 'hantle', 'lathen', 'thenal'],
 'ethnical': ['chainlet', 'ethnical'],
 'ethnological': ['allothogenic', 'ethnological'],
 'ethnos': ['ethnos', 'honest'],
 'ethography': ['ethography', 'hyetograph'],
 'ethologic': ['ethologic', 'theologic'],
 'ethological': ['ethological', 'lethologica', 'theological'],
 'ethology': ['ethology', 'theology'],
 'ethos': ['ethos', 'shote', 'those'],
 'ethylic': ['ethylic', 'techily'],
 'ethylin': ['ethylin', 'thienyl'],
 'etna': ['ante', 'aten', 'etna', 'nate', 'neat', 'taen', 'tane', 'tean'],
 'etnean': ['etnean', 'neaten'],
 'etonian': ['enation', 'etonian'],
 'etruscan': ['etruscan', 'recusant'],
 'etta': ['etta', 'tate', 'teat'],
 'ettarre': ['ettarre', 'retreat', 'treater'],
 'ettle': ['ettle', 'tetel'],
 'etua': ['aute', 'etua'],
 'euaster': ['austere', 'euaster'],
 'eucalypteol': ['eucalypteol', 'eucalyptole'],
 'eucalyptole': ['eucalypteol', 'eucalyptole'],
 'eucatropine': ['eucatropine', 'neurectopia'],
 'eucharis': ['acheirus', 'eucharis'],
 'eucharist': ['erithacus', 'eucharist'],
 'euchlaena': ['acheulean', 'euchlaena'],
 'eulogism': ['eulogism', 'uglisome'],
 'eumolpus': ['eumolpus', 'plumeous'],
 'eunomia': ['eunomia', 'moineau'],
 'eunomy': ['eunomy', 'euonym'],
 'euonym': ['eunomy', 'euonym'],
 'eupatorin': ['epuration', 'eupatorin'],
 'euplastic': ['euplastic', 'spiculate'],
 'euploid': ['epuloid', 'euploid'],
 'euproctis': ['crepitous', 'euproctis', 'uroseptic'],
 'eurindic': ['dineuric', 'eurindic'],
 'eurus': ['eurus', 'usure'],
 'euscaro': ['acerous', 'carouse', 'euscaro'],
 'euskaldun': ['eskualdun', 'euskaldun'],
 'euskara': ['eskuara', 'euskara'],
 'eusol': ['eusol', 'louse'],
 'eutannin': ['eutannin', 'uninnate'],
 'eutaxic': ['auxetic', 'eutaxic'],
 'eutheria': ['eutheria', 'hauerite'],
 'eutropic': ['eutropic', 'outprice'],
 'eva': ['ave', 'eva'],
 'evade': ['deave', 'eaved', 'evade'],
 'evader': ['evader', 'verdea'],
 'evadne': ['advene', 'evadne'],
 'evan': ['evan', 'nave', 'vane'],
 'evanish': ['evanish', 'inshave'],
 'evase': ['eaves', 'evase', 'seave'],
 'eve': ['eve', 'vee'],
 'evea': ['eave', 'evea'],
 'evection': ['civetone', 'evection'],
 'evejar': ['evejar', 'rajeev'],
 'evelyn': ['evelyn', 'evenly'],
 'even': ['even', 'neve', 'veen'],
 'evener': ['evener', 'veneer'],
 'evenly': ['evelyn', 'evenly'],
 'evens': ['evens', 'seven'],
 'eveque': ['eveque', 'queeve'],
 'ever': ['ever', 'reve', 'veer'],
 'evert': ['evert', 'revet'],
 'everwhich': ['everwhich', 'whichever'],
 'everwho': ['everwho', 'however', 'whoever'],
 'every': ['every', 'veery'],
 'evestar': ['evestar', 'versate'],
 'evict': ['civet', 'evict'],
 'evil': ['evil', 'levi', 'live', 'veil', 'vile', 'vlei'],
 'evildoer': ['evildoer', 'overidle'],
 'evilhearted': ['evilhearted', 'vilehearted'],
 'evilly': ['evilly', 'lively', 'vilely'],
 'evilness': ['evilness', 'liveness', 'veinless', 'vileness', 'vineless'],
 'evince': ['cevine', 'evince', 'venice'],
 'evisite': ['evisite', 'visitee'],
 'evitation': ['evitation', 'novitiate'],
 'evocator': ['evocator', 'overcoat'],
 'evodia': ['evodia', 'ovidae'],
 'evoker': ['evoker', 'revoke'],
 'evolver': ['evolver', 'revolve'],
 'ewder': ['dewer', 'ewder', 'rewed'],
 'ewe': ['ewe', 'wee'],
 'ewer': ['ewer', 'were'],
 'exacter': ['exacter', 'excreta'],
 'exalt': ['exalt', 'latex'],
 'exam': ['amex', 'exam', 'xema'],
 'examinate': ['examinate', 'exanimate', 'metaxenia'],
 'examination': ['examination', 'exanimation'],
 'exanimate': ['examinate', 'exanimate', 'metaxenia'],
 'exanimation': ['examination', 'exanimation'],
 'exasperation': ['exasperation', 'xenoparasite'],
 'exaudi': ['adieux', 'exaudi'],
 'excarnation': ['centraxonia', 'excarnation'],
 'excecation': ['cacoxenite', 'excecation'],
 'except': ['except', 'expect'],
 'exceptant': ['exceptant', 'expectant'],
 'exceptive': ['exceptive', 'expective'],
 'excitation': ['excitation', 'intoxicate'],
 'excitor': ['excitor', 'xerotic'],
 'excreta': ['exacter', 'excreta'],
 'excurse': ['excurse', 'excuser'],
 'excuser': ['excurse', 'excuser'],
 'exert': ['exert', 'exter'],
 'exhilarate': ['exhilarate', 'heteraxial'],
 'exist': ['exist', 'sixte'],
 'exocarp': ['exocarp', 'praecox'],
 'exon': ['exon', 'oxen'],
 'exordia': ['exordia', 'exradio'],
 'exotic': ['coxite', 'exotic'],
 'expatiater': ['expatiater', 'expatriate'],
 'expatriate': ['expatiater', 'expatriate'],
 'expect': ['except', 'expect'],
 'expectant': ['exceptant', 'expectant'],
 'expective': ['exceptive', 'expective'],
 'expirator': ['expirator', 'operatrix'],
 'expiree': ['expiree', 'peixere'],
 'explicator': ['explicator', 'extropical'],
 'expressionism': ['expressionism', 'misexpression'],
 'exradio': ['exordia', 'exradio'],
 'extend': ['dentex', 'extend'],
 'exter': ['exert', 'exter'],
 'exterminate': ['antiextreme', 'exterminate'],
 'extirpationist': ['extirpationist', 'sextipartition'],
 'extra': ['extra', 'retax', 'taxer'],
 'extradural': ['dextraural', 'extradural'],
 'extropical': ['explicator', 'extropical'],
 'exultancy': ['exultancy', 'unexactly'],
 'ey': ['ey', 'ye'],
 'eyah': ['ahey', 'eyah', 'yeah'],
 'eyas': ['easy', 'eyas'],
 'eye': ['eye', 'yee'],
 'eyed': ['eyed', 'yede'],
 'eyen': ['eyen', 'eyne'],
 'eyer': ['eyer', 'eyre', 'yere'],
 'eyn': ['eyn', 'nye', 'yen'],
 'eyne': ['eyen', 'eyne'],
 'eyot': ['eyot', 'yote'],
 'eyra': ['aery', 'eyra', 'yare', 'year'],
 'eyre': ['eyer', 'eyre', 'yere'],
 'ezba': ['baze', 'ezba'],
 'ezra': ['ezra', 'raze'],
 'facebread': ['barefaced', 'facebread'],
 'facer': ['facer', 'farce'],
 'faciend': ['faciend', 'fancied'],
 'facile': ['facile', 'filace'],
 'faciobrachial': ['brachiofacial', 'faciobrachial'],
 'faciocervical': ['cervicofacial', 'faciocervical'],
 'factable': ['factable', 'labefact'],
 'factional': ['factional', 'falcation'],
 'factish': ['catfish', 'factish'],
 'facture': ['facture', 'furcate'],
 'facula': ['facula', 'faucal'],
 'fade': ['deaf', 'fade'],
 'fader': ['fader', 'farde'],
 'faery': ['faery', 'freya'],
 'fagoter': ['aftergo', 'fagoter'],
 'faience': ['faience', 'fiancee'],
 'fail': ['alif', 'fail'],
 'fain': ['fain', 'naif'],
 'fainly': ['fainly', 'naifly'],
 'faint': ['faint', 'fanti'],
 'fair': ['fair', 'fiar', 'raif'],
 'fake': ['fake', 'feak'],
 'faker': ['faker', 'freak'],
 'fakery': ['fakery', 'freaky'],
 'fakir': ['fakir', 'fraik', 'kafir', 'rafik'],
 'falcation': ['factional', 'falcation'],
 'falco': ['falco', 'focal'],
 'falconet': ['conflate', 'falconet'],
 'fallback': ['backfall', 'fallback'],
 'faller': ['faller', 'refall'],
 'fallfish': ['fallfish', 'fishfall'],
 'fallible': ['fallible', 'fillable'],
 'falling': ['falling', 'fingall'],
 'falser': ['falser', 'flaser'],
 'faltboat': ['faltboat', 'flatboat'],
 'falutin': ['falutin', 'flutina'],
 'falx': ['falx', 'flax'],
 'fameless': ['fameless', 'selfsame'],
 'famelessness': ['famelessness', 'selfsameness'],
 'famine': ['famine', 'infame'],
 'fancied': ['faciend', 'fancied'],
 'fangle': ['fangle', 'flange'],
 'fannia': ['fannia', 'fianna'],
 'fanti': ['faint', 'fanti'],
 'far': ['far', 'fra'],
 'farad': ['daraf', 'farad'],
 'farce': ['facer', 'farce'],
 'farcetta': ['afteract', 'artefact', 'farcetta', 'farctate'],
 'farctate': ['afteract', 'artefact', 'farcetta', 'farctate'],
 'farde': ['fader', 'farde'],
 'fardel': ['alfred', 'fardel'],
 'fare': ['fare', 'fear', 'frae', 'rafe'],
 'farfel': ['farfel', 'raffle'],
 'faring': ['faring', 'frangi'],
 'farl': ['farl', 'ralf'],
 'farleu': ['earful', 'farleu', 'ferula'],
 'farm': ['farm', 'fram'],
 'farmable': ['farmable', 'framable'],
 'farmer': ['farmer', 'framer'],
 'farming': ['farming', 'framing'],
 'farnesol': ['farnesol', 'forensal'],
 'faro': ['faro', 'fora'],
 'farolito': ['farolito', 'footrail'],
 'farse': ['farse', 'frase'],
 'farset': ['farset', 'faster', 'strafe'],
 'farsi': ['farsi', 'sarif'],
 'fascio': ['fascio', 'fiasco'],
 'fasher': ['afresh', 'fasher', 'ferash'],
 'fashioner': ['fashioner', 'refashion'],
 'fast': ['fast', 'saft'],
 'fasten': ['fasten', 'nefast', 'stefan'],
 'fastener': ['fastener', 'fenestra', 'refasten'],
 'faster': ['farset', 'faster', 'strafe'],
 'fasthold': ['fasthold', 'holdfast'],
 'fastland': ['fastland', 'landfast'],
 'fat': ['aft', 'fat'],
 'fatal': ['aflat', 'fatal'],
 'fate': ['atef', 'fate', 'feat'],
 'fated': ['defat', 'fated'],
 'father': ['father', 'freath', 'hafter'],
 'faucal': ['facula', 'faucal'],
 'faucet': ['faucet', 'fucate'],
 'faulter': ['faulter', 'refutal', 'tearful'],
 'faultfind': ['faultfind', 'findfault'],
 'faunish': ['faunish', 'nusfiah'],
 'faunist': ['faunist', 'fustian', 'infaust'],
 'favorer': ['favorer', 'overfar', 'refavor'],
 'fayles': ['fayles', 'safely'],
 'feague': ['feague', 'feuage'],
 'feak': ['fake', 'feak'],
 'feal': ['alef', 'feal', 'flea', 'leaf'],
 'fealty': ['fealty', 'featly'],
 'fear': ['fare', 'fear', 'frae', 'rafe'],
 'feastful': ['feastful', 'sufflate'],
 'feat': ['atef', 'fate', 'feat'],
 'featherbed': ['befathered', 'featherbed'],
 'featherer': ['featherer', 'hereafter'],
 'featly': ['fealty', 'featly'],
 'feckly': ['feckly', 'flecky'],
 'fecundate': ['fecundate', 'unfaceted'],
 'fecundator': ['fecundator', 'unfactored'],
 'federate': ['defeater', 'federate', 'redefeat'],
 'feeder': ['feeder', 'refeed'],
 'feeding': ['feeding', 'feigned'],
 'feel': ['feel', 'flee'],
 'feeler': ['feeler', 'refeel', 'reflee'],
 'feer': ['feer', 'free', 'reef'],
 'feering': ['feering', 'feigner', 'freeing', 'reefing', 'refeign'],
 'feetless': ['feetless', 'feteless'],
 'fei': ['fei', 'fie', 'ife'],
 'feif': ['feif', 'fife'],
 'feigned': ['feeding', 'feigned'],
 'feigner': ['feering', 'feigner', 'freeing', 'reefing', 'refeign'],
 'feil': ['feil', 'file', 'leif', 'lief', 'life'],
 'feint': ['feint', 'fient'],
 'feis': ['feis', 'fise', 'sife'],
 'feist': ['feist', 'stife'],
 'felapton': ['felapton', 'pantofle'],
 'felid': ['felid', 'field'],
 'feline': ['enfile', 'enlief', 'enlife', 'feline'],
 'felinity': ['felinity', 'finitely'],
 'fels': ['fels', 'self'],
 'felt': ['felt', 'flet', 'left'],
 'felter': ['felter', 'telfer', 'trefle'],
 'felting': ['felting', 'neftgil'],
 'feltness': ['feltness', 'leftness'],
 'felwort': ['elfwort', 'felwort'],
 'feminal': ['feminal', 'inflame'],
 'femora': ['femora', 'foamer'],
 'femorocaudal': ['caudofemoral', 'femorocaudal'],
 'femorotibial': ['femorotibial', 'tibiofemoral'],
 'femur': ['femur', 'fumer'],
 'fen': ['fen', 'nef'],
 'fender': ['fender', 'ferned'],
 'fenestra': ['fastener', 'fenestra', 'refasten'],
 'feodary': ['feodary', 'foreday'],
 'feral': ['feral', 'flare'],
 'ferash': ['afresh', 'fasher', 'ferash'],
 'feria': ['afire', 'feria'],
 'ferine': ['ferine', 'refine'],
 'ferison': ['ferison', 'foresin'],
 'ferity': ['ferity', 'freity'],
 'ferk': ['ferk', 'kerf'],
 'ferling': ['ferling', 'flinger', 'refling'],
 'ferly': ['ferly', 'flyer', 'refly'],
 'fermail': ['fermail', 'fermila'],
 'fermenter': ['fermenter', 'referment'],
 'fermila': ['fermail', 'fermila'],
 'ferned': ['fender', 'ferned'],
 'ferri': ['ferri', 'firer', 'freir', 'frier'],
 'ferrihydrocyanic': ['ferrihydrocyanic', 'hydroferricyanic'],
 'ferrohydrocyanic': ['ferrohydrocyanic', 'hydroferrocyanic'],
 'ferry': ['ferry', 'freyr', 'fryer'],
 'fertil': ['fertil', 'filter', 'lifter', 'relift', 'trifle'],
 'ferula': ['earful', 'farleu', 'ferula'],
 'ferule': ['ferule', 'fueler', 'refuel'],
 'ferulic': ['ferulic', 'lucifer'],
 'fervidity': ['devitrify', 'fervidity'],
 'festination': ['festination', 'infestation', 'sinfonietta'],
 'fet': ['eft', 'fet'],
 'fetal': ['aleft', 'alfet', 'fetal', 'fleta'],
 'fetcher': ['fetcher', 'refetch'],
 'feteless': ['feetless', 'feteless'],
 'fetial': ['fetial', 'filate', 'lafite', 'leafit'],
 'fetish': ['fetish', 'fishet'],
 'fetor': ['fetor', 'forte', 'ofter'],
 'fetter': ['fetter', 'frette'],
 'feuage': ['feague', 'feuage'],
 'feudalism': ['feudalism', 'sulfamide'],
 'feudally': ['delayful', 'feudally'],
 'feulamort': ['feulamort', 'formulate'],
 'fi': ['fi', 'if'],
 'fiance': ['fiance', 'inface'],
 'fiancee': ['faience', 'fiancee'],
 'fianna': ['fannia', 'fianna'],
 'fiar': ['fair', 'fiar', 'raif'],
 'fiard': ['fiard', 'fraid'],
 'fiasco': ['fascio', 'fiasco'],
 'fiber': ['bifer', 'brief', 'fiber'],
 'fibered': ['debrief', 'defiber', 'fibered'],
 'fiberless': ['briefless', 'fiberless', 'fibreless'],
 'fiberware': ['fiberware', 'fibreware'],
 'fibreless': ['briefless', 'fiberless', 'fibreless'],
 'fibreware': ['fiberware', 'fibreware'],
 'fibroadenoma': ['adenofibroma', 'fibroadenoma'],
 'fibroangioma': ['angiofibroma', 'fibroangioma'],
 'fibrochondroma': ['chondrofibroma', 'fibrochondroma'],
 'fibrocystoma': ['cystofibroma', 'fibrocystoma'],
 'fibrolipoma': ['fibrolipoma', 'lipofibroma'],
 'fibromucous': ['fibromucous', 'mucofibrous'],
 'fibromyoma': ['fibromyoma', 'myofibroma'],
 'fibromyxoma': ['fibromyxoma', 'myxofibroma'],
 'fibromyxosarcoma': ['fibromyxosarcoma', 'myxofibrosarcoma'],
 'fibroneuroma': ['fibroneuroma', 'neurofibroma'],
 'fibroserous': ['fibroserous', 'serofibrous'],
 'fiche': ['chief', 'fiche'],
 'fickleness': ['fickleness', 'fleckiness'],
 'fickly': ['fickly', 'flicky'],
 'fico': ['coif', 'fico', 'foci'],
 'fictional': ['cliftonia', 'fictional'],
 'ficula': ['ficula', 'fulica'],
 'fiddler': ['fiddler', 'flidder'],
 'fidele': ['defile', 'fidele'],
 'fidget': ['fidget', 'gifted'],
 'fidicula': ['fidicula', 'fiducial'],
 'fiducial': ['fidicula', 'fiducial'],
 'fie': ['fei', 'fie', 'ife'],
 'fiedlerite': ['fiedlerite', 'friedelite'],
 'field': ['felid', 'field'],
 'fielded': ['defiled', 'fielded'],
 'fielder': ['defiler', 'fielder'],
 'fieldman': ['fieldman', 'inflamed'],
 'fiendish': ['fiendish', 'finished'],
 'fient': ['feint', 'fient'],
 'fiery': ['fiery', 'reify'],
 'fife': ['feif', 'fife'],
 'fifteener': ['fifteener', 'teneriffe'],
 'fifty': ['fifty', 'tiffy'],
 'fig': ['fig', 'gif'],
 'fighter': ['fighter', 'freight', 'refight'],
 'figurate': ['figurate', 'fruitage'],
 'fike': ['efik', 'fike'],
 'filace': ['facile', 'filace'],
 'filago': ['filago', 'gifola'],
 'filao': ['filao', 'folia'],
 'filar': ['filar', 'flair', 'frail'],
 'filate': ['fetial', 'filate', 'lafite', 'leafit'],
 'file': ['feil', 'file', 'leif', 'lief', 'life'],
 'filelike': ['filelike', 'lifelike'],
 'filer': ['filer', 'flier', 'lifer', 'rifle'],
 'filet': ['filet', 'flite'],
 'fillable': ['fallible', 'fillable'],
 'filler': ['filler', 'refill'],
 'filo': ['filo', 'foil', 'lifo'],
 'filter': ['fertil', 'filter', 'lifter', 'relift', 'trifle'],
 'filterer': ['filterer', 'refilter'],
 'filthless': ['filthless', 'shelflist'],
 'filtrable': ['filtrable', 'flirtable'],
 'filtration': ['filtration', 'flirtation'],
 'finale': ['afenil', 'finale'],
 'finder': ['finder', 'friend', 'redfin', 'refind'],
 'findfault': ['faultfind', 'findfault'],
 'fine': ['enif', 'fine', 'neif', 'nife'],
 'finely': ['finely', 'lenify'],
 'finer': ['finer', 'infer'],
 'finesser': ['finesser', 'rifeness'],
 'fingall': ['falling', 'fingall'],
 'finger': ['finger', 'fringe'],
 'fingerer': ['fingerer', 'refinger'],
 'fingerflower': ['fingerflower', 'fringeflower'],
 'fingerless': ['fingerless', 'fringeless'],
 'fingerlet': ['fingerlet', 'fringelet'],
 'fingu': ['fingu', 'fungi'],
 'finical': ['finical', 'lanific'],
 'finished': ['fiendish', 'finished'],
 'finisher': ['finisher', 'refinish'],
 'finitely': ['felinity', 'finitely'],
 'finkel': ['elfkin', 'finkel'],
 'finlet': ['finlet', 'infelt'],
 'finner': ['finner', 'infern'],
 'firca': ['afric', 'firca'],
 'fire': ['fire', 'reif', 'rife'],
 'fireable': ['afebrile', 'balefire', 'fireable'],
 'firearm': ['firearm', 'marfire'],
 'fireback': ['backfire', 'fireback'],
 'fireburn': ['burnfire', 'fireburn'],
 'fired': ['fired', 'fried'],
 'fireplug': ['fireplug', 'gripeful'],
 'firer': ['ferri', 'firer', 'freir', 'frier'],
 'fireshaft': ['fireshaft', 'tasheriff'],
 'firestone': ['firestone', 'forestine'],
 'firetop': ['firetop', 'potifer'],
 'firm': ['firm', 'frim'],
 'first': ['first', 'frist'],
 'firth': ['firth', 'frith'],
 'fise': ['feis', 'fise', 'sife'],
 'fishbone': ['bonefish', 'fishbone'],
 'fisheater': ['fisheater', 'sherifate'],
 'fisher': ['fisher', 'sherif'],
 'fishery': ['fishery', 'sherify'],
 'fishet': ['fetish', 'fishet'],
 'fishfall': ['fallfish', 'fishfall'],
 'fishlet': ['fishlet', 'leftish'],
 'fishpond': ['fishpond', 'pondfish'],
 'fishpool': ['fishpool', 'foolship'],
 'fishwood': ['fishwood', 'woodfish'],
 'fissury': ['fissury', 'russify'],
 'fist': ['fist', 'sift'],
 'fisted': ['fisted', 'sifted'],
 'fister': ['fister', 'resift', 'sifter', 'strife'],
 'fisting': ['fisting', 'sifting'],
 'fitout': ['fitout', 'outfit'],
 'fitter': ['fitter', 'tifter'],
 'fixer': ['fixer', 'refix'],
 'flageolet': ['flageolet', 'folletage'],
 'flair': ['filar', 'flair', 'frail'],
 'flamant': ['flamant', 'flatman'],
 'flame': ['flame', 'fleam'],
 'flamed': ['flamed', 'malfed'],
 'flandowser': ['flandowser', 'sandflower'],
 'flange': ['fangle', 'flange'],
 'flare': ['feral', 'flare'],
 'flaser': ['falser', 'flaser'],
 'flasher': ['flasher', 'reflash'],
 'flatboat': ['faltboat', 'flatboat'],
 'flatman': ['flamant', 'flatman'],
 'flatwise': ['flatwise', 'saltwife'],
 'flaunt': ['flaunt', 'unflat'],
 'flax': ['falx', 'flax'],
 'flea': ['alef', 'feal', 'flea', 'leaf'],
 'fleam': ['flame', 'fleam'],
 'fleay': ['fleay', 'leafy'],
 'fleche': ['fleche', 'fleech'],
 'flecker': ['flecker', 'freckle'],
 'fleckiness': ['fickleness', 'fleckiness'],
 'flecky': ['feckly', 'flecky'],
 'fled': ['delf', 'fled'],
 'flee': ['feel', 'flee'],
 'fleech': ['fleche', 'fleech'],
 'fleer': ['fleer', 'refel'],
 'flemish': ['flemish', 'himself'],
 'flenser': ['flenser', 'fresnel'],
 'flesh': ['flesh', 'shelf'],
 'fleshed': ['deflesh', 'fleshed'],
 'fleshen': ['enflesh', 'fleshen'],
 'flesher': ['flesher', 'herself'],
 'fleshful': ['fleshful', 'shelfful'],
 'fleshiness': ['elfishness', 'fleshiness'],
 'fleshy': ['fleshy', 'shelfy'],
 'flet': ['felt', 'flet', 'left'],
 'fleta': ['aleft', 'alfet', 'fetal', 'fleta'],
 'fleuret': ['fleuret', 'treeful'],
 'flew': ['flew', 'welf'],
 'flexed': ['deflex', 'flexed'],
 'flexured': ['flexured', 'refluxed'],
 'flicky': ['fickly', 'flicky'],
 'flidder': ['fiddler', 'flidder'],
 'flier': ['filer', 'flier', 'lifer', 'rifle'],
 'fligger': ['fligger', 'friggle'],
 'flinger': ['ferling', 'flinger', 'refling'],
 'flingy': ['flingy', 'flying'],
 'flirtable': ['filtrable', 'flirtable'],
 'flirtation': ['filtration', 'flirtation'],
 'flirter': ['flirter', 'trifler'],
 'flirting': ['flirting', 'trifling'],
 'flirtingly': ['flirtingly', 'triflingly'],
 'flit': ['flit', 'lift'],
 'flite': ['filet', 'flite'],
 'fliting': ['fliting', 'lifting'],
 'flitter': ['flitter', 'triflet'],
 'flo': ['flo', 'lof'],
 'float': ['aloft', 'float', 'flota'],
 'floater': ['floater', 'florate', 'refloat'],
 'flobby': ['bobfly', 'flobby'],
 'flodge': ['flodge', 'fodgel'],
 'floe': ['floe', 'fole'],
 'flog': ['flog', 'golf'],
 'flogger': ['flogger', 'frogleg'],
 'floodable': ['bloodleaf', 'floodable'],
 'flooder': ['flooder', 'reflood'],
 'floodwater': ['floodwater', 'toadflower', 'waterflood'],
 'floorer': ['floorer', 'refloor'],
 'florate': ['floater', 'florate', 'refloat'],
 'florentine': ['florentine', 'nonfertile'],
 'floret': ['floret', 'forlet', 'lofter', 'torfel'],
 'floria': ['floria', 'foliar'],
 'floriate': ['floriate', 'foralite'],
 'florican': ['florican', 'fornical'],
 'floridan': ['floridan', 'florinda'],
 'florinda': ['floridan', 'florinda'],
 'flot': ['flot', 'loft'],
 'flota': ['aloft', 'float', 'flota'],
 'flounder': ['flounder', 'reunfold', 'unfolder'],
 'flour': ['flour', 'fluor'],
 'flourisher': ['flourisher', 'reflourish'],
 'flouting': ['flouting', 'outfling'],
 'flow': ['flow', 'fowl', 'wolf'],
 'flower': ['flower', 'fowler', 'reflow', 'wolfer'],
 'flowered': ['deflower', 'flowered'],
 'flowerer': ['flowerer', 'reflower'],
 'flowery': ['flowery', 'fowlery'],
 'flowing': ['flowing', 'fowling'],
 'floyd': ['floyd', 'foldy'],
 'fluavil': ['fluavil', 'fluvial', 'vialful'],
 'flucan': ['canful', 'flucan'],
 'fluctuant': ['fluctuant', 'untactful'],
 'flue': ['flue', 'fuel'],
 'fluent': ['fluent', 'netful', 'unfelt', 'unleft'],
 'fluidly': ['dullify', 'fluidly'],
 'flukewort': ['flukewort', 'flutework'],
 'fluor': ['flour', 'fluor'],
 'fluorate': ['fluorate', 'outflare'],
 'fluorinate': ['antifouler', 'fluorinate', 'uniflorate'],
 'fluorine': ['fluorine', 'neurofil'],
 'fluorobenzene': ['benzofluorene', 'fluorobenzene'],
 'flusher': ['flusher', 'reflush'],
 'flushing': ['flushing', 'lungfish'],
 'fluster': ['fluster', 'restful'],
 'flustra': ['flustra', 'starful'],
 'flutework': ['flukewort', 'flutework'],
 'flutina': ['falutin', 'flutina'],
 'fluvial': ['fluavil', 'fluvial', 'vialful'],
 'fluxer': ['fluxer', 'reflux'],
 'flyblow': ['blowfly', 'flyblow'],
 'flyer': ['ferly', 'flyer', 'refly'],
 'flying': ['flingy', 'flying'],
 'fo': ['fo', 'of'],
 'foal': ['foal', 'loaf', 'olaf'],
 'foamer': ['femora', 'foamer'],
 'focal': ['falco', 'focal'],
 'foci': ['coif', 'fico', 'foci'],
 'focuser': ['focuser', 'refocus'],
 'fodge': ['defog', 'fodge'],
 'fodgel': ['flodge', 'fodgel'],
 'fogeater': ['fogeater', 'foregate'],
 'fogo': ['fogo', 'goof'],
 'foil': ['filo', 'foil', 'lifo'],
 'foister': ['foister', 'forties'],
 'folden': ['enfold', 'folden', 'fondle'],
 'folder': ['folder', 'refold'],
 'foldy': ['floyd', 'foldy'],
 'fole': ['floe', 'fole'],
 'folia': ['filao', 'folia'],
 'foliar': ['floria', 'foliar'],
 'foliature': ['foliature', 'toluifera'],
 'folletage': ['flageolet', 'folletage'],
 'fomenter': ['fomenter', 'refoment'],
 'fondle': ['enfold', 'folden', 'fondle'],
 'fondu': ['fondu', 'found'],
 'foo': ['foo', 'ofo'],
 'fool': ['fool', 'loof', 'olof'],
 'foolship': ['fishpool', 'foolship'],
 'footer': ['footer', 'refoot'],
 'foothot': ['foothot', 'hotfoot'],
 'footler': ['footler', 'rooflet'],
 'footpad': ['footpad', 'padfoot'],
 'footrail': ['farolito', 'footrail'],
 'foots': ['foots', 'sfoot', 'stoof'],
 'footsore': ['footsore', 'sorefoot'],
 'foppish': ['foppish', 'fopship'],
 'fopship': ['foppish', 'fopship'],
 'for': ['for', 'fro', 'orf'],
 'fora': ['faro', 'fora'],
 'foralite': ['floriate', 'foralite'],
 'foramen': ['foramen', 'foreman'],
 'forcemeat': ['aftercome', 'forcemeat'],
 'forcement': ['coferment', 'forcement'],
 'fore': ['fore', 'froe', 'ofer'],
 'forecast': ['cofaster', 'forecast'],
 'forecaster': ['forecaster', 'reforecast'],
 'forecover': ['forecover', 'overforce'],
 'foreday': ['feodary', 'foreday'],
 'forefit': ['forefit', 'forfeit'],
 'foregate': ['fogeater', 'foregate'],
 'foregirth': ['foregirth', 'foreright'],
 'forego': ['forego', 'goofer'],
 'forel': ['forel', 'rolfe'],
 'forelive': ['forelive', 'overfile'],
 'foreman': ['foramen', 'foreman'],
 'foremean': ['foremean', 'forename'],
 'forename': ['foremean', 'forename'],
 'forensal': ['farnesol', 'forensal'],
 'forensic': ['forensic', 'forinsec'],
 'forepart': ['forepart', 'prefator'],
 'foreright': ['foregirth', 'foreright'],
 'foresend': ['defensor', 'foresend'],
 'foresign': ['foresign', 'foresing'],
 'foresin': ['ferison', 'foresin'],
 'foresing': ['foresign', 'foresing'],
 'forest': ['forest', 'forset', 'foster'],
 'forestage': ['forestage', 'fosterage'],
 'forestal': ['astrofel', 'forestal'],
 'forestate': ['forestate', 'foretaste'],
 'forested': ['deforest', 'forested'],
 'forestem': ['forestem', 'fretsome'],
 'forester': ['forester', 'fosterer', 'reforest'],
 'forestine': ['firestone', 'forestine'],
 'foretaste': ['forestate', 'foretaste'],
 'foreutter': ['foreutter', 'outferret'],
 'forfeit': ['forefit', 'forfeit'],
 'forfeiter': ['forfeiter', 'reforfeit'],
 'forgeman': ['forgeman', 'formagen'],
 'forinsec': ['forensic', 'forinsec'],
 'forint': ['forint', 'fortin'],
 'forlet': ['floret', 'forlet', 'lofter', 'torfel'],
 'form': ['form', 'from'],
 'formagen': ['forgeman', 'formagen'],
 'formalin': ['formalin', 'informal', 'laniform'],
 'formally': ['formally', 'formylal'],
 'formed': ['deform', 'formed'],
 'former': ['former', 'reform'],
 'formica': ['aciform', 'formica'],
 'formicina': ['aciniform', 'formicina'],
 'formicoidea': ['aecidioform', 'formicoidea'],
 'formin': ['formin', 'inform'],
 'forminate': ['forminate', 'fremontia', 'taeniform'],
 'formulae': ['formulae', 'fumarole'],
 'formulaic': ['cauliform', 'formulaic', 'fumarolic'],
 'formulate': ['feulamort', 'formulate'],
 'formulator': ['formulator', 'torulaform'],
 'formylal': ['formally', 'formylal'],
 'fornical': ['florican', 'fornical'],
 'fornicated': ['deforciant', 'fornicated'],
 'forpit': ['forpit', 'profit'],
 'forritsome': ['forritsome', 'ostreiform'],
 'forrue': ['forrue', 'fourer', 'fourre', 'furore'],
 'forset': ['forest', 'forset', 'foster'],
 'forst': ['forst', 'frost'],
 'fort': ['fort', 'frot'],
 'forte': ['fetor', 'forte', 'ofter'],
 'forth': ['forth', 'froth'],
 'forthcome': ['forthcome', 'homecroft'],
 'forthy': ['forthy', 'frothy'],
 'forties': ['foister', 'forties'],
 'fortin': ['forint', 'fortin'],
 'forward': ['forward', 'froward'],
 'forwarder': ['forwarder', 'reforward'],
 'forwardly': ['forwardly', 'frowardly'],
 'forwardness': ['forwardness', 'frowardness'],
 'foster': ['forest', 'forset', 'foster'],
 'fosterage': ['forestage', 'fosterage'],
 'fosterer': ['forester', 'fosterer', 'reforest'],
 'fot': ['fot', 'oft'],
 'fou': ['fou', 'ouf'],
 'fouler': ['fouler', 'furole'],
 'found': ['fondu', 'found'],
 'foundationer': ['foundationer', 'refoundation'],
 'founder': ['founder', 'refound'],
 'foundling': ['foundling', 'unfolding'],
 'fourble': ['beflour', 'fourble'],
 'fourer': ['forrue', 'fourer', 'fourre', 'furore'],
 'fourre': ['forrue', 'fourer', 'fourre', 'furore'],
 'fowl': ['flow', 'fowl', 'wolf'],
 'fowler': ['flower', 'fowler', 'reflow', 'wolfer'],
 'fowlery': ['flowery', 'fowlery'],
 'fowling': ['flowing', 'fowling'],
 'fra': ['far', 'fra'],
 'frache': ['chafer', 'frache'],
 'frae': ['fare', 'fear', 'frae', 'rafe'],
 'fraghan': ['fraghan', 'harfang'],
 'fraid': ['fiard', 'fraid'],
 'fraik': ['fakir', 'fraik', 'kafir', 'rafik'],
 'frail': ['filar', 'flair', 'frail'],
 'fraiser': ['fraiser', 'frasier'],
 'fram': ['farm', 'fram'],
 'framable': ['farmable', 'framable'],
 'frame': ['frame', 'fream'],
 'framer': ['farmer', 'framer'],
 'framing': ['farming', 'framing'],
 'frangi': ['faring', 'frangi'],
 'frantic': ['frantic', 'infarct', 'infract'],
 'frase': ['farse', 'frase'],
 'frasier': ['fraiser', 'frasier'],
 'frat': ['frat', 'raft'],
 'fratcheous': ['fratcheous', 'housecraft'],
 'frater': ['frater', 'rafter'],
 'frayed': ['defray', 'frayed'],
 'freak': ['faker', 'freak'],
 'freaky': ['fakery', 'freaky'],
 'fream': ['frame', 'fream'],
 'freath': ['father', 'freath', 'hafter'],
 'freckle': ['flecker', 'freckle'],
 'free': ['feer', 'free', 'reef'],
 'freed': ['defer', 'freed'],
 'freeing': ['feering', 'feigner', 'freeing', 'reefing', 'refeign'],
 'freeman': ['enframe', 'freeman'],
 'freer': ['freer', 'refer'],
 'fregata': ['fregata', 'raftage'],
 'fregatae': ['afterage', 'fregatae'],
 'freight': ['fighter', 'freight', 'refight'],
 'freir': ['ferri', 'firer', 'freir', 'frier'],
 'freit': ['freit', 'refit'],
 'freity': ['ferity', 'freity'],
 'fremontia': ['forminate', 'fremontia', 'taeniform'],
 'frenetic': ['frenetic', 'infecter', 'reinfect'],
 'freshener': ['freshener', 'refreshen'],
 'fresnel': ['flenser', 'fresnel'],
 'fret': ['fret', 'reft', 'tref'],
 'fretful': ['fretful', 'truffle'],
 'fretsome': ['forestem', 'fretsome'],
 'frette': ['fetter', 'frette'],
 'freya': ['faery', 'freya'],
 'freyr': ['ferry', 'freyr', 'fryer'],
 'fried': ['fired', 'fried'],
 'friedelite': ['fiedlerite', 'friedelite'],
 'friend': ['finder', 'friend', 'redfin', 'refind'],
 'frier': ['ferri', 'firer', 'freir', 'frier'],
 'friesic': ['friesic', 'serific'],
 'friggle': ['fligger', 'friggle'],
 'frightener': ['frightener', 'refrighten'],
 'frigolabile': ['frigolabile', 'glorifiable'],
 'frike': ['frike', 'kefir'],
 'frim': ['firm', 'frim'],
 'fringe': ['finger', 'fringe'],
 'fringeflower': ['fingerflower', 'fringeflower'],
 'fringeless': ['fingerless', 'fringeless'],
 'fringelet': ['fingerlet', 'fringelet'],
 'frist': ['first', 'frist'],
 'frit': ['frit', 'rift'],
 'frith': ['firth', 'frith'],
 'friulian': ['friulian', 'unifilar'],
 'fro': ['for', 'fro', 'orf'],
 'froe': ['fore', 'froe', 'ofer'],
 'frogleg': ['flogger', 'frogleg'],
 'from': ['form', 'from'],
 'fronter': ['fronter', 'refront'],
 'frontonasal': ['frontonasal', 'nasofrontal'],
 'frontooccipital': ['frontooccipital', 'occipitofrontal'],
 'frontoorbital': ['frontoorbital', 'orbitofrontal'],
 'frontoparietal': ['frontoparietal', 'parietofrontal'],
 'frontotemporal': ['frontotemporal', 'temporofrontal'],
 'frontpiece': ['frontpiece', 'perfection'],
 'frost': ['forst', 'frost'],
 'frosted': ['defrost', 'frosted'],
 'frot': ['fort', 'frot'],
 'froth': ['forth', 'froth'],
 'frothy': ['forthy', 'frothy'],
 'froward': ['forward', 'froward'],
 'frowardly': ['forwardly', 'frowardly'],
 'frowardness': ['forwardness', 'frowardness'],
 'fruitage': ['figurate', 'fruitage'],
 'fruitless': ['fruitless', 'resistful'],
 'frush': ['frush', 'shurf'],
 'frustule': ['frustule', 'sulfuret'],
 'fruticulose': ['fruticulose', 'luctiferous'],
 'fryer': ['ferry', 'freyr', 'fryer'],
 'fucales': ['caseful', 'fucales'],
 'fucate': ['faucet', 'fucate'],
 'fuel': ['flue', 'fuel'],
 'fueler': ['ferule', 'fueler', 'refuel'],
 'fuerte': ['fuerte', 'refute'],
 'fuirena': ['fuirena', 'unafire'],
 'fulcrate': ['crateful', 'fulcrate'],
 'fulica': ['ficula', 'fulica'],
 'fulmar': ['armful', 'fulmar'],
 'fulminatory': ['fulminatory', 'unformality'],
 'fulminous': ['fulminous', 'sulfonium'],
 'fulwa': ['awful', 'fulwa'],
 'fumarole': ['formulae', 'fumarole'],
 'fumarolic': ['cauliform', 'formulaic', 'fumarolic'],
 'fumble': ['beflum', 'fumble'],
 'fumer': ['femur', 'fumer'],
 'fundable': ['fundable', 'unfabled'],
 'funder': ['funder', 'refund'],
 'funebrial': ['funebrial', 'unfriable'],
 'funeral': ['earnful', 'funeral'],
 'fungal': ['fungal', 'unflag'],
 'fungi': ['fingu', 'fungi'],
 'funori': ['funori', 'furoin'],
 'fur': ['fur', 'urf'],
 'fural': ['alfur', 'fural'],
 'furan': ['furan', 'unfar'],
 'furbish': ['burfish', 'furbish'],
 'furbisher': ['furbisher', 'refurbish'],
 'furcal': ['carful', 'furcal'],
 'furcate': ['facture', 'furcate'],
 'furler': ['furler', 'refurl'],
 'furnish': ['furnish', 'runfish'],
 'furnisher': ['furnisher', 'refurnish'],
 'furoin': ['funori', 'furoin'],
 'furole': ['fouler', 'furole'],
 'furore': ['forrue', 'fourer', 'fourre', 'furore'],
 'furstone': ['furstone', 'unforest'],
 'fusate': ['estufa', 'fusate'],
 'fusteric': ['fusteric', 'scutifer'],
 'fustian': ['faunist', 'fustian', 'infaust'],
 'gab': ['bag', 'gab'],
 'gabbler': ['gabbler', 'grabble'],
 'gabe': ['egba', 'gabe'],
 'gabelle': ['gabelle', 'gelable'],
 'gabelled': ['gabelled', 'geldable'],
 'gabi': ['agib', 'biga', 'gabi'],
 'gabion': ['bagnio', 'gabion', 'gobian'],
 'gabioned': ['badigeon', 'gabioned'],
 'gable': ['bagel', 'belga', 'gable', 'gleba'],
 'gablock': ['backlog', 'gablock'],
 'gaboon': ['abongo', 'gaboon'],
 'gad': ['dag', 'gad'],
 'gadaba': ['badaga', 'dagaba', 'gadaba'],
 'gadder': ['gadder', 'graded'],
 'gaddi': ['gaddi', 'gadid'],
 'gade': ['aged', 'egad', 'gade'],
 'gadger': ['dagger', 'gadger', 'ragged'],
 'gadget': ['gadget', 'tagged'],
 'gadid': ['gaddi', 'gadid'],
 'gadinine': ['gadinine', 'indigena'],
 'gadolinite': ['deligation', 'gadolinite', 'gelatinoid'],
 'gadroon': ['dragoon', 'gadroon'],
 'gadroonage': ['dragoonage', 'gadroonage'],
 'gaduin': ['anguid', 'gaduin'],
 'gael': ['gael', 'gale', 'geal'],
 'gaen': ['agen', 'gaen', 'gane', 'gean', 'gena'],
 'gaet': ['gaet', 'gate', 'geat', 'geta'],
 'gaetulan': ['angulate', 'gaetulan'],
 'gager': ['agger', 'gager', 'regga'],
 'gahnite': ['gahnite', 'heating'],
 'gahrwali': ['gahrwali', 'garhwali'],
 'gaiassa': ['assagai', 'gaiassa'],
 'gail': ['gail', 'gali', 'gila', 'glia'],
 'gain': ['gain', 'inga', 'naig', 'ngai'],
 'gaincall': ['gaincall', 'gallican'],
 'gaine': ['angie', 'gaine'],
 'gainer': ['arenig', 'earing', 'gainer', 'reagin', 'regain'],
 'gainless': ['gainless', 'glassine'],
 'gainly': ['gainly', 'laying'],
 'gainsayer': ['asynergia', 'gainsayer'],
 'gainset': ['easting',
             'gainset',
             'genista',
             'ingesta',
             'seating',
             'signate',
             'teasing'],
 'gainstrive': ['gainstrive', 'vinegarist'],
 'gainturn': ['gainturn', 'naturing'],
 'gaiter': ['gaiter', 'tairge', 'triage'],
 'gaize': ['gaize', 'ziega'],
 'gaj': ['gaj', 'jag'],
 'gal': ['gal', 'lag'],
 'gala': ['agal', 'agla', 'alga', 'gala'],
 'galactonic': ['cognatical', 'galactonic'],
 'galatae': ['galatae', 'galatea'],
 'galatea': ['galatae', 'galatea'],
 'gale': ['gael', 'gale', 'geal'],
 'galea': ['algae', 'galea'],
 'galee': ['aegle', 'eagle', 'galee'],
 'galei': ['agiel', 'agile', 'galei'],
 'galeid': ['algedi', 'galeid'],
 'galen': ['agnel', 'angel', 'angle', 'galen', 'genal', 'glean', 'lagen'],
 'galena': ['alnage', 'angela', 'galena', 'lagena'],
 'galenian': ['alangine', 'angelina', 'galenian'],
 'galenic': ['angelic', 'galenic'],
 'galenical': ['angelical', 'englacial', 'galenical'],
 'galenist': ['galenist', 'genitals', 'stealing'],
 'galenite': ['galenite', 'legatine'],
 'galeoid': ['galeoid', 'geoidal'],
 'galera': ['aglare', 'alegar', 'galera', 'laager'],
 'galet': ['aglet', 'galet'],
 'galewort': ['galewort', 'waterlog'],
 'galey': ['agley', 'galey'],
 'galga': ['galga', 'glaga'],
 'gali': ['gail', 'gali', 'gila', 'glia'],
 'galidia': ['agialid', 'galidia'],
 'galik': ['galik', 'glaik'],
 'galilean': ['galilean', 'gallinae'],
 'galiot': ['galiot', 'latigo'],
 'galla': ['algal', 'galla'],
 'gallate': ['gallate', 'tallage'],
 'gallein': ['gallein', 'galline', 'nigella'],
 'galleria': ['allergia', 'galleria'],
 'gallery': ['allergy', 'gallery', 'largely', 'regally'],
 'galli': ['galli', 'glial'],
 'gallican': ['gaincall', 'gallican'],
 'gallicole': ['collegial', 'gallicole'],
 'gallinae': ['galilean', 'gallinae'],
 'galline': ['gallein', 'galline', 'nigella'],
 'gallnut': ['gallnut', 'nutgall'],
 'galloper': ['galloper', 'regallop'],
 'gallotannate': ['gallotannate', 'tannogallate'],
 'gallotannic': ['gallotannic', 'tannogallic'],
 'gallstone': ['gallstone', 'stonegall'],
 'gallybagger': ['gallybagger', 'gallybeggar'],
 'gallybeggar': ['gallybagger', 'gallybeggar'],
 'galore': ['galore', 'gaoler'],
 'galtonia': ['galtonia', 'notalgia'],
 'galvanopsychic': ['galvanopsychic', 'psychogalvanic'],
 'galvanothermometer': ['galvanothermometer', 'thermogalvanometer'],
 'gam': ['gam', 'mag'],
 'gamaliel': ['gamaliel', 'melalgia'],
 'gamashes': ['gamashes', 'smashage'],
 'gamasid': ['gamasid', 'magadis'],
 'gambado': ['dagomba', 'gambado'],
 'gambier': ['gambier', 'imbarge'],
 'gambler': ['gambler', 'gambrel'],
 'gambrel': ['gambler', 'gambrel'],
 'game': ['egma', 'game', 'mage'],
 'gamely': ['gamely', 'gleamy', 'mygale'],
 'gamene': ['gamene', 'manege', 'menage'],
 'gamete': ['gamete', 'metage'],
 'gametogenic': ['gametogenic', 'gamogenetic', 'geomagnetic'],
 'gamic': ['gamic', 'magic'],
 'gamin': ['gamin', 'mangi'],
 'gaming': ['gaming', 'gigman'],
 'gamma': ['gamma', 'magma'],
 'gammer': ['gammer', 'gramme'],
 'gamogenetic': ['gametogenic', 'gamogenetic', 'geomagnetic'],
 'gamori': ['gamori', 'gomari', 'gromia'],
 'gan': ['gan', 'nag'],
 'ganam': ['amang', 'ganam', 'manga'],
 'ganch': ['chang', 'ganch'],
 'gander': ['danger', 'gander', 'garden', 'ranged'],
 'gandul': ['gandul', 'unglad'],
 'gane': ['agen', 'gaen', 'gane', 'gean', 'gena'],
 'gangan': ['gangan', 'nagnag'],
 'ganger': ['ganger', 'grange', 'nagger'],
 'ganging': ['ganging', 'nagging'],
 'gangism': ['gangism', 'gigsman'],
 'ganglioneuron': ['ganglioneuron', 'neuroganglion'],
 'gangly': ['gangly', 'naggly'],
 'ganguela': ['ganguela', 'language'],
 'gangway': ['gangway', 'waygang'],
 'ganister': ['astringe', 'ganister', 'gantries'],
 'ganoidal': ['diagonal', 'ganoidal', 'gonadial'],
 'ganoidean': ['ganoidean', 'indogaean'],
 'ganoidian': ['agoniadin', 'anangioid', 'ganoidian'],
 'ganosis': ['agnosis', 'ganosis'],
 'gansel': ['angles', 'gansel'],
 'gant': ['gant', 'gnat', 'tang'],
 'ganta': ['ganta', 'tanga'],
 'ganton': ['ganton', 'tongan'],
 'gantries': ['astringe', 'ganister', 'gantries'],
 'gantry': ['gantry', 'gyrant'],
 'ganymede': ['ganymede', 'megadyne'],
 'ganzie': ['agnize', 'ganzie'],
 'gaol': ['gaol', 'goal', 'gola', 'olga'],
 'gaoler': ['galore', 'gaoler'],
 'gaon': ['agon', 'ango', 'gaon', 'goan', 'gona'],
 'gaonic': ['agonic', 'angico', 'gaonic', 'goniac'],
 'gapa': ['gapa', 'paga'],
 'gape': ['gape', 'page', 'peag', 'pega'],
 'gaper': ['gaper', 'grape', 'pager', 'parge'],
 'gar': ['gar', 'gra', 'rag'],
 'gara': ['agar', 'agra', 'gara', 'raga'],
 'garamond': ['dragoman', 'garamond', 'ondagram'],
 'garance': ['carnage', 'cranage', 'garance'],
 'garb': ['brag', 'garb', 'grab'],
 'garbel': ['garbel', 'garble'],
 'garble': ['garbel', 'garble'],
 'garbless': ['bragless', 'garbless'],
 'garce': ['cager', 'garce', 'grace'],
 'garcinia': ['agaricin', 'garcinia'],
 'gardeen': ['derange', 'enraged', 'gardeen', 'gerenda', 'grandee', 'grenade'],
 'garden': ['danger', 'gander', 'garden', 'ranged'],
 'gardened': ['deranged', 'gardened'],
 'gardener': ['deranger', 'gardener'],
 'gardenful': ['dangerful', 'gardenful'],
 'gardenia': ['drainage', 'gardenia'],
 'gardenin': ['gardenin', 'grenadin'],
 'gardenless': ['dangerless', 'gardenless'],
 'gare': ['ager', 'agre', 'gare', 'gear', 'rage'],
 'gareh': ['gareh', 'gerah'],
 'garetta': ['garetta', 'rattage', 'regatta'],
 'garewaite': ['garewaite', 'waiterage'],
 'garfish': ['garfish', 'ragfish'],
 'garget': ['garget', 'tagger'],
 'gargety': ['gargety', 'raggety'],
 'gargle': ['gargle', 'gregal', 'lagger', 'raggle'],
 'garhwali': ['gahrwali', 'garhwali'],
 'garial': ['argali', 'garial'],
 'garle': ['argel', 'ergal', 'garle', 'glare', 'lager', 'large', 'regal'],
 'garment': ['garment', 'margent'],
 'garmenture': ['garmenture', 'reargument'],
 'garn': ['garn', 'gnar', 'rang'],
 'garnel': ['angler', 'arleng', 'garnel', 'largen', 'rangle', 'regnal'],
 'garner': ['garner', 'ranger'],
 'garnet': ['argent', 'garnet', 'garten', 'tanger'],
 'garneter': ['argenter', 'garneter'],
 'garnetiferous': ['argentiferous', 'garnetiferous'],
 'garnets': ['angster', 'garnets', 'nagster', 'strange'],
 'garnett': ['garnett', 'gnatter', 'gratten', 'tergant'],
 'garnice': ['anergic', 'garnice', 'garniec', 'geranic', 'grecian'],
 'garniec': ['anergic', 'garnice', 'garniec', 'geranic', 'grecian'],
 'garnish': ['garnish', 'rashing'],
 'garnished': ['degarnish', 'garnished'],
 'garnisher': ['garnisher', 'regarnish'],
 'garo': ['argo', 'garo', 'gora'],
 'garran': ['garran', 'ragnar'],
 'garret': ['garret', 'garter', 'grater', 'targer'],
 'garreted': ['garreted', 'gartered'],
 'garroter': ['garroter', 'regrator'],
 'garten': ['argent', 'garnet', 'garten', 'tanger'],
 'garter': ['garret', 'garter', 'grater', 'targer'],
 'gartered': ['garreted', 'gartered'],
 'gartering': ['gartering', 'regrating'],
 'garum': ['garum', 'murga'],
 'gary': ['gary', 'gray'],
 'gas': ['gas', 'sag'],
 'gasan': ['gasan', 'sanga'],
 'gash': ['gash', 'shag'],
 'gasless': ['gasless', 'glasses', 'sagless'],
 'gaslit': ['algist', 'gaslit'],
 'gasoliner': ['gasoliner', 'seignoral'],
 'gasper': ['gasper', 'sparge'],
 'gast': ['gast', 'stag'],
 'gaster': ['gaster', 'stager'],
 'gastrin': ['gastrin', 'staring'],
 'gastroenteritis': ['enterogastritis', 'gastroenteritis'],
 'gastroesophagostomy': ['esophagogastrostomy', 'gastroesophagostomy'],
 'gastrohepatic': ['gastrohepatic', 'hepatogastric'],
 'gastronomic': ['gastronomic', 'monogastric'],
 'gastropathic': ['gastropathic', 'graphostatic'],
 'gastrophrenic': ['gastrophrenic', 'nephrogastric', 'phrenogastric'],
 'gastrular': ['gastrular', 'stragular'],
 'gat': ['gat', 'tag'],
 'gate': ['gaet', 'gate', 'geat', 'geta'],
 'gateman': ['gateman', 'magenta', 'magnate', 'magneta'],
 'gater': ['gater', 'grate', 'great', 'greta', 'retag', 'targe'],
 'gateward': ['drawgate', 'gateward'],
 'gateway': ['gateway', 'getaway', 'waygate'],
 'gatherer': ['gatherer', 'regather'],
 'gator': ['argot', 'gator', 'gotra', 'groat'],
 'gatter': ['gatter', 'target'],
 'gaucho': ['gaucho', 'guacho'],
 'gaufer': ['agrufe', 'gaufer', 'gaufre'],
 'gauffer': ['gauffer', 'gauffre'],
 'gauffre': ['gauffer', 'gauffre'],
 'gaufre': ['agrufe', 'gaufer', 'gaufre'],
 'gaul': ['gaul', 'gula'],
 'gaulin': ['gaulin', 'lingua'],
 'gaulter': ['gaulter', 'tegular'],
 'gaum': ['gaum', 'muga'],
 'gaun': ['gaun', 'guan', 'guna', 'uang'],
 'gaunt': ['gaunt', 'tunga'],
 'gaur': ['gaur', 'guar', 'ruga'],
 'gaura': ['gaura', 'guara'],
 'gaurian': ['anguria', 'gaurian', 'guarani'],
 'gave': ['gave', 'vage', 'vega'],
 'gavyuti': ['gavyuti', 'vaguity'],
 'gaw': ['gaw', 'wag'],
 'gawn': ['gawn', 'gnaw', 'wang'],
 'gay': ['agy', 'gay'],
 'gaz': ['gaz', 'zag'],
 'gazel': ['gazel', 'glaze'],
 'gazer': ['gazer', 'graze'],
 'gazon': ['gazon', 'zogan'],
 'gazy': ['gazy', 'zyga'],
 'geal': ['gael', 'gale', 'geal'],
 'gean': ['agen', 'gaen', 'gane', 'gean', 'gena'],
 'gear': ['ager', 'agre', 'gare', 'gear', 'rage'],
 'geared': ['agreed', 'geared'],
 'gearless': ['eelgrass', 'gearless', 'rageless'],
 'gearman': ['gearman', 'manager'],
 'gearset': ['ergates', 'gearset', 'geaster'],
 'geaster': ['ergates', 'gearset', 'geaster'],
 'geat': ['gaet', 'gate', 'geat', 'geta'],
 'gebur': ['bugre', 'gebur'],
 'ged': ['deg', 'ged'],
 'gedder': ['dredge', 'gedder'],
 'geest': ['egest', 'geest', 'geste'],
 'gegger': ['gegger', 'gregge'],
 'geheimrat': ['geheimrat', 'hermitage'],
 'gein': ['gein', 'gien'],
 'geira': ['geira', 'regia'],
 'geison': ['geison', 'isogen'],
 'geissospermine': ['geissospermine', 'spermiogenesis'],
 'gel': ['gel', 'leg'],
 'gelable': ['gabelle', 'gelable'],
 'gelasian': ['anglaise', 'gelasian'],
 'gelastic': ['gelastic', 'gestical'],
 'gelatin': ['atingle', 'gelatin', 'genital', 'langite', 'telinga'],
 'gelatinate': ['gelatinate', 'nagatelite'],
 'gelatined': ['delignate', 'gelatined'],
 'gelatinizer': ['gelatinizer', 'integralize'],
 'gelatinoid': ['deligation', 'gadolinite', 'gelatinoid'],
 'gelation': ['gelation', 'lagonite', 'legation'],
 'gelatose': ['gelatose', 'segolate'],
 'geldable': ['gabelled', 'geldable'],
 'gelder': ['gelder', 'ledger', 'redleg'],
 'gelding': ['gelding', 'ledging'],
 'gelid': ['gelid', 'glide'],
 'gelidness': ['gelidness', 'glideness'],
 'gelosin': ['gelosin', 'lignose'],
 'gem': ['gem', 'meg'],
 'gemara': ['gemara', 'ramage'],
 'gemaric': ['gemaric', 'grimace', 'megaric'],
 'gemarist': ['gemarist', 'magister', 'sterigma'],
 'gematria': ['gematria', 'maritage'],
 'gemul': ['gemul', 'glume'],
 'gena': ['agen', 'gaen', 'gane', 'gean', 'gena'],
 'genal': ['agnel', 'angel', 'angle', 'galen', 'genal', 'glean', 'lagen'],
 'genarch': ['changer', 'genarch'],
 'gendarme': ['edgerman', 'gendarme'],
 'genear': ['egeran', 'enrage', 'ergane', 'genear', 'genera'],
 'geneat': ['geneat', 'negate', 'tegean'],
 'genera': ['egeran', 'enrage', 'ergane', 'genear', 'genera'],
 'generable': ['generable', 'greenable'],
 'general': ['enlarge', 'general', 'gleaner'],
 'generalist': ['easterling', 'generalist'],
 'generall': ['allergen', 'generall'],
 'generation': ['generation', 'renegation'],
 'generic': ['energic', 'generic'],
 'generical': ['energical', 'generical'],
 'genesiac': ['agenesic', 'genesiac'],
 'genesial': ['ensilage', 'genesial', 'signalee'],
 'genetical': ['clientage', 'genetical'],
 'genetta': ['genetta', 'tentage'],
 'geneura': ['geneura', 'uneager'],
 'geneva': ['avenge', 'geneva', 'vangee'],
 'genial': ['algine', 'genial', 'linage'],
 'genicular': ['genicular', 'neuralgic'],
 'genie': ['eigne', 'genie'],
 'genion': ['genion', 'inogen'],
 'genipa': ['genipa', 'piegan'],
 'genista': ['easting',
             'gainset',
             'genista',
             'ingesta',
             'seating',
             'signate',
             'teasing'],
 'genistein': ['genistein', 'gentisein'],
 'genital': ['atingle', 'gelatin', 'genital', 'langite', 'telinga'],
 'genitals': ['galenist', 'genitals', 'stealing'],
 'genitival': ['genitival', 'vigilante'],
 'genitocrural': ['crurogenital', 'genitocrural'],
 'genitor': ['ergotin', 'genitor', 'negrito', 'ogtiern', 'trigone'],
 'genitorial': ['genitorial', 'religation'],
 'genitory': ['genitory', 'ortygine'],
 'genitourinary': ['genitourinary', 'urinogenitary'],
 'geniture': ['geniture', 'guerinet'],
 'genizero': ['genizero', 'negroize'],
 'genoa': ['agone', 'genoa'],
 'genoblastic': ['blastogenic', 'genoblastic'],
 'genocidal': ['algedonic', 'genocidal'],
 'genom': ['genom', 'gnome'],
 'genotypical': ['genotypical', 'ptyalogenic'],
 'genre': ['genre', 'green', 'neger', 'reneg'],
 'genro': ['ergon', 'genro', 'goner', 'negro'],
 'gent': ['gent', 'teng'],
 'gentes': ['gentes', 'gesten'],
 'genthite': ['genthite', 'teething'],
 'gentian': ['antigen', 'gentian'],
 'gentianic': ['antigenic', 'gentianic'],
 'gentisein': ['genistein', 'gentisein'],
 'gentle': ['gentle', 'telegn'],
 'gentrice': ['erecting', 'gentrice'],
 'genua': ['augen', 'genua'],
 'genual': ['genual', 'leguan'],
 'genuine': ['genuine', 'ingenue'],
 'genus': ['genus', 'negus'],
 'geo': ['ego', 'geo'],
 'geocentric': ['ectrogenic', 'egocentric', 'geocentric'],
 'geocratic': ['categoric', 'geocratic'],
 'geocronite': ['erotogenic', 'geocronite', 'orogenetic'],
 'geodal': ['algedo', 'geodal'],
 'geode': ['geode', 'ogeed'],
 'geodiatropism': ['diageotropism', 'geodiatropism'],
 'geoduck': ['geoduck', 'goeduck'],
 'geohydrology': ['geohydrology', 'hydrogeology'],
 'geoid': ['diego', 'dogie', 'geoid'],
 'geoidal': ['galeoid', 'geoidal'],
 'geoisotherm': ['geoisotherm', 'isogeotherm'],
 'geomagnetic': ['gametogenic', 'gamogenetic', 'geomagnetic'],
 'geomant': ['geomant', 'magneto', 'megaton', 'montage'],
 'geomantic': ['atmogenic', 'geomantic'],
 'geometrical': ['geometrical', 'glaciometer'],
 'geometrina': ['angiometer', 'ergotamine', 'geometrina'],
 'geon': ['geon', 'gone'],
 'geonim': ['geonim', 'imogen'],
 'georama': ['georama', 'roamage'],
 'geotectonic': ['geotectonic', 'tocogenetic'],
 'geotic': ['geotic', 'goetic'],
 'geotical': ['ectoglia', 'geotical', 'goetical'],
 'geotonic': ['geotonic', 'otogenic'],
 'geoty': ['geoty', 'goety'],
 'ger': ['erg', 'ger', 'reg'],
 'gerah': ['gareh', 'gerah'],
 'geraldine': ['engrailed', 'geraldine'],
 'geranial': ['algerian', 'geranial', 'regalian'],
 'geranic': ['anergic', 'garnice', 'garniec', 'geranic', 'grecian'],
 'geraniol': ['geraniol', 'regional'],
 'geranomorph': ['geranomorph', 'monographer', 'nomographer'],
 'geranyl': ['angerly', 'geranyl'],
 'gerard': ['darger', 'gerard', 'grader', 'redrag', 'regard'],
 'gerastian': ['agrestian', 'gerastian', 'stangeria'],
 'geraty': ['geraty', 'gyrate'],
 'gerb': ['berg', 'gerb'],
 'gerbe': ['gerbe', 'grebe', 'rebeg'],
 'gerbera': ['bargeer', 'gerbera'],
 'gerenda': ['derange', 'enraged', 'gardeen', 'gerenda', 'grandee', 'grenade'],
 'gerendum': ['gerendum', 'unmerged'],
 'gerent': ['gerent', 'regent'],
 'gerenuk': ['gerenuk', 'greenuk'],
 'gerim': ['gerim', 'grime'],
 'gerip': ['gerip', 'gripe'],
 'german': ['engram', 'german', 'manger'],
 'germania': ['germania', 'megarian'],
 'germanics': ['germanics', 'screaming'],
 'germanification': ['germanification', 'remagnification'],
 'germanify': ['germanify', 'remagnify'],
 'germanious': ['germanious', 'gramineous', 'marigenous'],
 'germanist': ['germanist', 'streaming'],
 'germanite': ['germanite', 'germinate', 'gramenite', 'mangerite'],
 'germanly': ['germanly', 'germanyl'],
 'germanyl': ['germanly', 'germanyl'],
 'germinal': ['germinal', 'maligner', 'malinger'],
 'germinant': ['germinant', 'minargent'],
 'germinate': ['germanite', 'germinate', 'gramenite', 'mangerite'],
 'germon': ['germon', 'monger', 'morgen'],
 'geronomite': ['geronomite', 'goniometer'],
 'geront': ['geront', 'tonger'],
 'gerontal': ['argentol', 'gerontal'],
 'gerontes': ['estrogen', 'gerontes'],
 'gerontic': ['gerontic', 'negrotic'],
 'gerontism': ['gerontism', 'monergist'],
 'gerres': ['gerres', 'serger'],
 'gersum': ['gersum', 'mergus'],
 'gerund': ['dunger', 'gerund', 'greund', 'nudger'],
 'gerundive': ['gerundive', 'ungrieved'],
 'gerusia': ['ergusia', 'gerusia', 'sarigue'],
 'gervas': ['gervas', 'graves'],
 'gervase': ['gervase', 'greaves', 'servage'],
 'ges': ['ges', 'seg'],
 'gesan': ['agnes', 'gesan'],
 'gesith': ['gesith', 'steigh'],
 'gesning': ['gesning', 'ginseng'],
 'gest': ['gest', 'steg'],
 'gestapo': ['gestapo', 'postage'],
 'gestate': ['gestate', 'tagetes'],
 'geste': ['egest', 'geest', 'geste'],
 'gesten': ['gentes', 'gesten'],
 'gestical': ['gelastic', 'gestical'],
 'gesticular': ['gesticular', 'scutigeral'],
 'gesture': ['gesture', 'guester'],
 'get': ['get', 'teg'],
 'geta': ['gaet', 'gate', 'geat', 'geta'],
 'getaway': ['gateway', 'getaway', 'waygate'],
 'gettable': ['begettal', 'gettable'],
 'getup': ['getup', 'upget'],
 'geyerite': ['geyerite', 'tigereye'],
 'ghaist': ['ghaist', 'tagish'],
 'ghent': ['ghent', 'thegn'],
 'ghosty': ['ghosty', 'hogsty'],
 'ghoul': ['ghoul', 'lough'],
 'giansar': ['giansar', 'sarangi'],
 'giant': ['giant', 'tangi', 'tiang'],
 'gib': ['big', 'gib'],
 'gibbon': ['gibbon', 'gobbin'],
 'gibel': ['bilge', 'gibel'],
 'gibing': ['biggin', 'gibing'],
 'gid': ['dig', 'gid'],
 'gideonite': ['diogenite', 'gideonite'],
 'gien': ['gein', 'gien'],
 'gienah': ['gienah', 'hangie'],
 'gif': ['fig', 'gif'],
 'gifola': ['filago', 'gifola'],
 'gifted': ['fidget', 'gifted'],
 'gigman': ['gaming', 'gigman'],
 'gigsman': ['gangism', 'gigsman'],
 'gila': ['gail', 'gali', 'gila', 'glia'],
 'gilaki': ['gilaki', 'giliak'],
 'gilbertese': ['gilbertese', 'selbergite'],
 'gilden': ['dingle', 'elding', 'engild', 'gilden'],
 'gilder': ['gilder', 'girdle', 'glider', 'regild', 'ridgel'],
 'gilding': ['gilding', 'gliding'],
 'gileno': ['eloign', 'gileno', 'legion'],
 'giles': ['giles', 'gilse'],
 'giliak': ['gilaki', 'giliak'],
 'giller': ['giller', 'grille', 'regill'],
 'gilo': ['gilo', 'goli'],
 'gilpy': ['gilpy', 'pigly'],
 'gilse': ['giles', 'gilse'],
 'gim': ['gim', 'mig'],
 'gimel': ['gimel', 'glime'],
 'gimmer': ['gimmer', 'grimme', 'megrim'],
 'gimper': ['gimper', 'impreg'],
 'gin': ['gin', 'ing', 'nig'],
 'ginger': ['ginger', 'nigger'],
 'gingery': ['gingery', 'niggery'],
 'ginglymodi': ['ginglymodi', 'ginglymoid'],
 'ginglymoid': ['ginglymodi', 'ginglymoid'],
 'gink': ['gink', 'king'],
 'ginned': ['ending', 'ginned'],
 'ginner': ['enring', 'ginner'],
 'ginney': ['ginney', 'nignye'],
 'ginseng': ['gesning', 'ginseng'],
 'ginward': ['drawing', 'ginward', 'warding'],
 'gio': ['gio', 'goi'],
 'giornata': ['giornata', 'gratiano'],
 'giornatate': ['giornatate', 'tetragonia'],
 'gip': ['gip', 'pig'],
 'gipper': ['gipper', 'grippe'],
 'girandole': ['girandole', 'negroidal'],
 'girasole': ['girasole', 'seraglio'],
 'girba': ['bragi', 'girba'],
 'gird': ['gird', 'grid'],
 'girder': ['girder', 'ridger'],
 'girding': ['girding', 'ridging'],
 'girdingly': ['girdingly', 'ridgingly'],
 'girdle': ['gilder', 'girdle', 'glider', 'regild', 'ridgel'],
 'girdler': ['dirgler', 'girdler'],
 'girdling': ['girdling', 'ridgling'],
 'girling': ['girling', 'rigling'],
 'girn': ['girn', 'grin', 'ring'],
 'girny': ['girny', 'ringy'],
 'girondin': ['girondin', 'nonrigid'],
 'girsle': ['girsle', 'gisler', 'glires', 'grilse'],
 'girt': ['girt', 'grit', 'trig'],
 'girth': ['girth', 'grith', 'right'],
 'gish': ['gish', 'sigh'],
 'gisla': ['gisla', 'ligas', 'sigla'],
 'gisler': ['girsle', 'gisler', 'glires', 'grilse'],
 'git': ['git', 'tig'],
 'gitalin': ['gitalin', 'tailing'],
 'gith': ['gith', 'thig'],
 'gitksan': ['gitksan', 'skating', 'takings'],
 'gittern': ['gittern', 'gritten', 'retting'],
 'giustina': ['giustina', 'ignatius'],
 'giver': ['giver', 'vergi'],
 'glaceing': ['cageling', 'glaceing'],
 'glacier': ['glacier', 'gracile'],
 'glaciometer': ['geometrical', 'glaciometer'],
 'gladdener': ['gladdener', 'glandered', 'regladden'],
 'glaga': ['galga', 'glaga'],
 'glaik': ['galik', 'glaik'],
 'glaiket': ['glaiket', 'taglike'],
 'glair': ['argil', 'glair', 'grail'],
 'glaireous': ['aligerous', 'glaireous'],
 'glaister': ['glaister', 'regalist'],
 'glaive': ['glaive', 'vagile'],
 'glance': ['cangle', 'glance'],
 'glancer': ['cangler', 'glancer', 'reclang'],
 'glancingly': ['clangingly', 'glancingly'],
 'glandered': ['gladdener', 'glandered', 'regladden'],
 'glans': ['glans', 'slang'],
 'glare': ['argel', 'ergal', 'garle', 'glare', 'lager', 'large', 'regal'],
 'glariness': ['glariness', 'grainless'],
 'glary': ['glary', 'gyral'],
 'glasser': ['glasser', 'largess'],
 'glasses': ['gasless', 'glasses', 'sagless'],
 'glassie': ['algesis', 'glassie'],
 'glassine': ['gainless', 'glassine'],
 'glaucin': ['glaucin', 'glucina'],
 'glaucine': ['cuailnge', 'glaucine'],
 'glaum': ['algum', 'almug', 'glaum', 'gluma', 'mulga'],
 'glaur': ['glaur', 'gular'],
 'glaury': ['glaury', 'raguly'],
 'glaver': ['glaver', 'gravel'],
 'glaze': ['gazel', 'glaze'],
 'glazy': ['glazy', 'zygal'],
 'gleamy': ['gamely', 'gleamy', 'mygale'],
 'glean': ['agnel', 'angel', 'angle', 'galen', 'genal', 'glean', 'lagen'],
 'gleaner': ['enlarge', 'general', 'gleaner'],
 'gleary': ['argyle', 'gleary'],
 'gleba': ['bagel', 'belga', 'gable', 'gleba'],
 'glebal': ['begall', 'glebal'],
 'glede': ['glede', 'gleed', 'ledge'],
 'gledy': ['gledy', 'ledgy'],
 'gleed': ['glede', 'gleed', 'ledge'],
 'gleeman': ['gleeman', 'melange'],
 'glia': ['gail', 'gali', 'gila', 'glia'],
 'gliadin': ['dialing', 'gliadin'],
 'glial': ['galli', 'glial'],
 'glibness': ['beslings', 'blessing', 'glibness'],
 'glidder': ['glidder', 'griddle'],
 'glide': ['gelid', 'glide'],
 'glideness': ['gelidness', 'glideness'],
 'glider': ['gilder', 'girdle', 'glider', 'regild', 'ridgel'],
 'gliding': ['gilding', 'gliding'],
 'glime': ['gimel', 'glime'],
 'glink': ['glink', 'kling'],
 'glires': ['girsle', 'gisler', 'glires', 'grilse'],
 'glisten': ['glisten', 'singlet'],
 'glister': ['glister', 'gristle'],
 'glitnir': ['glitnir', 'ritling'],
 'glitter': ['glitter', 'grittle'],
 'gloater': ['argolet', 'gloater', 'legator'],
 'gloating': ['gloating', 'goatling'],
 'globate': ['boltage', 'globate'],
 'globe': ['bogle', 'globe'],
 'globin': ['globin', 'goblin', 'lobing'],
 'gloea': ['gloea', 'legoa'],
 'glome': ['glome', 'golem', 'molge'],
 'glomerate': ['algometer', 'glomerate'],
 'glore': ['glore', 'ogler'],
 'gloria': ['gloria', 'larigo', 'logria'],
 'gloriana': ['argolian', 'gloriana'],
 'gloriette': ['gloriette', 'rigolette'],
 'glorifiable': ['frigolabile', 'glorifiable'],
 'glossed': ['dogless', 'glossed', 'godless'],
 'glosser': ['glosser', 'regloss'],
 'glossitic': ['glossitic', 'logistics'],
 'glossohyal': ['glossohyal', 'hyoglossal'],
 'glossolabial': ['glossolabial', 'labioglossal'],
 'glossolabiolaryngeal': ['glossolabiolaryngeal', 'labioglossolaryngeal'],
 'glossolabiopharyngeal': ['glossolabiopharyngeal', 'labioglossopharyngeal'],
 'glottid': ['glottid', 'goldtit'],
 'glover': ['glover', 'grovel'],
 'gloveress': ['gloveress', 'groveless'],
 'glow': ['glow', 'gowl'],
 'glower': ['glower', 'reglow'],
 'gloy': ['gloy', 'logy'],
 'glucemia': ['glucemia', 'mucilage'],
 'glucina': ['glaucin', 'glucina'],
 'glucine': ['glucine', 'lucigen'],
 'glucinum': ['cingulum', 'glucinum'],
 'glucosane': ['consulage', 'glucosane'],
 'glue': ['glue', 'gule', 'luge'],
 'gluer': ['gluer', 'gruel', 'luger'],
 'gluma': ['algum', 'almug', 'glaum', 'gluma', 'mulga'],
 'glume': ['gemul', 'glume'],
 'glumose': ['glumose', 'lugsome'],
 'gluten': ['englut', 'gluten', 'ungelt'],
 'glutin': ['glutin', 'luting', 'ungilt'],
 'glutter': ['glutter', 'guttler'],
 'glycerate': ['electragy', 'glycerate'],
 'glycerinize': ['glycerinize', 'glycerizine'],
 'glycerizine': ['glycerinize', 'glycerizine'],
 'glycerophosphate': ['glycerophosphate', 'phosphoglycerate'],
 'glycocin': ['glycocin', 'glyconic'],
 'glyconic': ['glycocin', 'glyconic'],
 'glycosine': ['glycosine', 'lysogenic'],
 'glycosuria': ['glycosuria', 'graciously'],
 'gnaeus': ['gnaeus', 'unsage'],
 'gnaphalium': ['gnaphalium', 'phalangium'],
 'gnar': ['garn', 'gnar', 'rang'],
 'gnarled': ['dangler', 'gnarled'],
 'gnash': ['gnash', 'shang'],
 'gnat': ['gant', 'gnat', 'tang'],
 'gnatho': ['gnatho', 'thonga'],
 'gnathotheca': ['chaetognath', 'gnathotheca'],
 'gnatling': ['gnatling', 'tangling'],
 'gnatter': ['garnett', 'gnatter', 'gratten', 'tergant'],
 'gnaw': ['gawn', 'gnaw', 'wang'],
 'gnetum': ['gnetum', 'nutmeg'],
 'gnome': ['genom', 'gnome'],
 'gnomic': ['coming', 'gnomic'],
 'gnomist': ['gnomist', 'mosting'],
 'gnomonic': ['gnomonic', 'oncoming'],
 'gnomonical': ['cognominal', 'gnomonical'],
 'gnostic': ['costing', 'gnostic'],
 'gnostical': ['gnostical', 'nostalgic'],
 'gnu': ['gnu', 'gun'],
 'go': ['go', 'og'],
 'goa': ['ago', 'goa'],
 'goad': ['dago', 'goad'],
 'goal': ['gaol', 'goal', 'gola', 'olga'],
 'goan': ['agon', 'ango', 'gaon', 'goan', 'gona'],
 'goat': ['goat', 'toag', 'toga'],
 'goatee': ['goatee', 'goetae'],
 'goatlike': ['goatlike', 'togalike'],
 'goatling': ['gloating', 'goatling'],
 'goatly': ['goatly', 'otalgy'],
 'gob': ['bog', 'gob'],
 'goban': ['bogan', 'goban'],
 'gobbe': ['bebog', 'begob', 'gobbe'],
 'gobbin': ['gibbon', 'gobbin'],
 'gobelin': ['gobelin', 'gobline', 'ignoble', 'inglobe'],
 'gobian': ['bagnio', 'gabion', 'gobian'],
 'goblet': ['boglet', 'goblet'],
 'goblin': ['globin', 'goblin', 'lobing'],
 'gobline': ['gobelin', 'gobline', 'ignoble', 'inglobe'],
 'goblinry': ['boringly', 'goblinry'],
 'gobo': ['bogo', 'gobo'],
 'goby': ['bogy', 'bygo', 'goby'],
 'goclenian': ['congenial', 'goclenian'],
 'god': ['dog', 'god'],
 'goddam': ['goddam', 'mogdad'],
 'gode': ['doeg', 'doge', 'gode'],
 'godhead': ['doghead', 'godhead'],
 'godhood': ['doghood', 'godhood'],
 'godless': ['dogless', 'glossed', 'godless'],
 'godlike': ['doglike', 'godlike'],
 'godling': ['godling', 'lodging'],
 'godly': ['dogly', 'godly', 'goldy'],
 'godship': ['dogship', 'godship'],
 'godwinian': ['downingia', 'godwinian'],
 'goeduck': ['geoduck', 'goeduck'],
 'goel': ['egol', 'goel', 'loge', 'ogle', 'oleg'],
 'goer': ['goer', 'gore', 'ogre'],
 'goes': ['goes', 'sego'],
 'goetae': ['goatee', 'goetae'],
 'goetic': ['geotic', 'goetic'],
 'goetical': ['ectoglia', 'geotical', 'goetical'],
 'goety': ['geoty', 'goety'],
 'goglet': ['goglet', 'toggel', 'toggle'],
 'goi': ['gio', 'goi'],
 'goidel': ['goidel', 'goldie'],
 'goitral': ['goitral', 'larigot', 'ligator'],
 'gol': ['gol', 'log'],
 'gola': ['gaol', 'goal', 'gola', 'olga'],
 'golden': ['engold', 'golden'],
 'goldenmouth': ['goldenmouth', 'longmouthed'],
 'golder': ['golder', 'lodger'],
 'goldie': ['goidel', 'goldie'],
 'goldtit': ['glottid', 'goldtit'],
 'goldy': ['dogly', 'godly', 'goldy'],
 'golee': ['eloge', 'golee'],
 'golem': ['glome', 'golem', 'molge'],
 'golf': ['flog', 'golf'],
 'golfer': ['golfer', 'reflog'],
 'goli': ['gilo', 'goli'],
 'goliard': ['argolid', 'goliard'],
 'golo': ['golo', 'gool'],
 'goma': ['goma', 'ogam'],
 'gomari': ['gamori', 'gomari', 'gromia'],
 'gomart': ['gomart', 'margot'],
 'gomphrena': ['gomphrena', 'nephogram'],
 'gon': ['gon', 'nog'],
 'gona': ['agon', 'ango', 'gaon', 'goan', 'gona'],
 'gonad': ['donga', 'gonad'],
 'gonadial': ['diagonal', 'ganoidal', 'gonadial'],
 'gonal': ['along', 'gonal', 'lango', 'longa', 'nogal'],
 'gond': ['dong', 'gond'],
 'gondi': ['dingo', 'doing', 'gondi', 'gonid'],
 'gondola': ['dongola', 'gondola'],
 'gondolier': ['gondolier', 'negroloid'],
 'gone': ['geon', 'gone'],
 'goner': ['ergon', 'genro', 'goner', 'negro'],
 'gonesome': ['gonesome', 'osmogene'],
 'gongoresque': ['gongoresque', 'gorgonesque'],
 'gonia': ['gonia', 'ngaio', 'nogai'],
 'goniac': ['agonic', 'angico', 'gaonic', 'goniac'],
 'goniale': ['goniale', 'noilage'],
 'goniaster': ['goniaster', 'orangeist'],
 'goniatitid': ['digitation', 'goniatitid'],
 'gonid': ['dingo', 'doing', 'gondi', 'gonid'],
 'gonidia': ['angioid', 'gonidia'],
 'gonidiferous': ['gonidiferous', 'indigoferous'],
 'goniometer': ['geronomite', 'goniometer'],
 'gonomery': ['gonomery', 'merogony'],
 'gonosome': ['gonosome', 'mongoose'],
 'gonyocele': ['coelogyne', 'gonyocele'],
 'gonys': ['gonys', 'songy'],
 'goober': ['booger', 'goober'],
 'goodyear': ['goodyear', 'goodyera'],
 'goodyera': ['goodyear', 'goodyera'],
 'goof': ['fogo', 'goof'],
 'goofer': ['forego', 'goofer'],
 'gool': ['golo', 'gool'],
 'gools': ['gools', 'logos'],
 'goop': ['goop', 'pogo'],
 'gor': ['gor', 'rog'],
 'gora': ['argo', 'garo', 'gora'],
 'goral': ['algor', 'argol', 'goral', 'largo'],
 'goran': ['angor',
           'argon',
           'goran',
           'grano',
           'groan',
           'nagor',
           'orang',
           'organ',
           'rogan',
           'ronga'],
 'gorb': ['borg', 'brog', 'gorb'],
 'gorbal': ['brolga', 'gorbal'],
 'gorce': ['corge', 'gorce'],
 'gordian': ['gordian', 'idorgan', 'roading'],
 'gordon': ['drongo', 'gordon'],
 'gordonia': ['gordonia', 'organoid', 'rigadoon'],
 'gore': ['goer', 'gore', 'ogre'],
 'gorer': ['gorer', 'roger'],
 'gorge': ['gorge', 'grego'],
 'gorged': ['dogger', 'gorged'],
 'gorger': ['gorger', 'gregor'],
 'gorgerin': ['gorgerin', 'ringgoer'],
 'gorgonesque': ['gongoresque', 'gorgonesque'],
 'goric': ['corgi', 'goric', 'orgic'],
 'goring': ['goring', 'gringo'],
 'gorse': ['gorse', 'soger'],
 'gortonian': ['gortonian', 'organotin'],
 'gory': ['gory', 'gyro', 'orgy'],
 'gos': ['gos', 'sog'],
 'gosain': ['gosain', 'isagon', 'sagoin'],
 'gosh': ['gosh', 'shog'],
 'gospel': ['gospel', 'spogel'],
 'gossipry': ['gossipry', 'gryposis'],
 'got': ['got', 'tog'],
 'gotra': ['argot', 'gator', 'gotra', 'groat'],
 'goup': ['goup', 'ogpu', 'upgo'],
 'gourde': ['drogue', 'gourde'],
 'gout': ['gout', 'toug'],
 'goutish': ['goutish', 'outsigh'],
 'gowan': ['gowan', 'wagon', 'wonga'],
 'gowdnie': ['gowdnie', 'widgeon'],
 'gowl': ['glow', 'gowl'],
 'gown': ['gown', 'wong'],
 'goyin': ['goyin', 'yogin'],
 'gra': ['gar', 'gra', 'rag'],
 'grab': ['brag', 'garb', 'grab'],
 'grabble': ['gabbler', 'grabble'],
 'graben': ['banger', 'engarb', 'graben'],
 'grace': ['cager', 'garce', 'grace'],
 'gracile': ['glacier', 'gracile'],
 'graciously': ['glycosuria', 'graciously'],
 'grad': ['darg', 'drag', 'grad'],
 'gradation': ['gradation', 'indagator', 'tanagroid'],
 'grade': ['edgar', 'grade'],
 'graded': ['gadder', 'graded'],
 'grader': ['darger', 'gerard', 'grader', 'redrag', 'regard'],
 'gradient': ['gradient', 'treading'],
 'gradienter': ['gradienter', 'intergrade'],
 'gradientia': ['gradientia', 'grantiidae'],
 'gradin': ['daring', 'dingar', 'gradin'],
 'gradine': ['degrain', 'deraign', 'deringa', 'gradine', 'grained', 'reading'],
 'grading': ['grading', 'niggard'],
 'graeae': ['aerage', 'graeae'],
 'graeme': ['graeme', 'meager', 'meagre'],
 'grafter': ['grafter', 'regraft'],
 'graian': ['graian', 'nagari'],
 'grail': ['argil', 'glair', 'grail'],
 'grailer': ['grailer', 'reglair'],
 'grain': ['agrin', 'grain'],
 'grained': ['degrain', 'deraign', 'deringa', 'gradine', 'grained', 'reading'],
 'grainer': ['earring', 'grainer'],
 'grainless': ['glariness', 'grainless'],
 'graith': ['aright', 'graith'],
 'grallina': ['grallina', 'granilla'],
 'gralline': ['allergin', 'gralline'],
 'grame': ['grame', 'marge', 'regma'],
 'gramenite': ['germanite', 'germinate', 'gramenite', 'mangerite'],
 'gramineous': ['germanious', 'gramineous', 'marigenous'],
 'graminiform': ['graminiform', 'marginiform'],
 'graminous': ['graminous', 'ignoramus'],
 'gramme': ['gammer', 'gramme'],
 'gramophonic': ['gramophonic', 'monographic', 'nomographic', 'phonogramic'],
 'gramophonical': ['gramophonical', 'monographical', 'nomographical'],
 'gramophonically': ['gramophonically',
                     'monographically',
                     'nomographically',
                     'phonogramically'],
 'gramophonist': ['gramophonist', 'monographist'],
 'granadine': ['granadine', 'grenadian'],
 'granate': ['argante', 'granate', 'tanager'],
 'granatum': ['armgaunt', 'granatum'],
 'grand': ['drang', 'grand'],
 'grandam': ['dragman', 'grandam', 'grandma'],
 'grandee': ['derange', 'enraged', 'gardeen', 'gerenda', 'grandee', 'grenade'],
 'grandeeism': ['grandeeism', 'renegadism'],
 'grandeur': ['grandeur', 'unregard'],
 'grandeval': ['grandeval', 'landgrave'],
 'grandiose': ['grandiose', 'sargonide'],
 'grandma': ['dragman', 'grandam', 'grandma'],
 'grandparental': ['grandparental', 'grandpaternal'],
 'grandpaternal': ['grandparental', 'grandpaternal'],
 'grane': ['anger', 'areng', 'grane', 'range'],
 'grange': ['ganger', 'grange', 'nagger'],
 'grangousier': ['grangousier', 'gregarinous'],
 'granilla': ['grallina', 'granilla'],
 'granite': ['angrite', 'granite', 'ingrate', 'tangier', 'tearing', 'tigrean'],
 'granivore': ['granivore', 'overgrain'],
 'grano': ['angor',
           'argon',
           'goran',
           'grano',
           'groan',
           'nagor',
           'orang',
           'organ',
           'rogan',
           'ronga'],
 'granophyre': ['granophyre', 'renography'],
 'grantee': ['grantee', 'greaten', 'reagent', 'rentage'],
 'granter': ['granter', 'regrant'],
 'granth': ['granth', 'thrang'],
 'grantiidae': ['gradientia', 'grantiidae'],
 'granula': ['angular', 'granula'],
 'granule': ['granule', 'unlarge', 'unregal'],
 'granulite': ['granulite', 'traguline'],
 'grape': ['gaper', 'grape', 'pager', 'parge'],
 'graperoot': ['graperoot', 'prorogate'],
 'graphical': ['algraphic', 'graphical'],
 'graphically': ['calligraphy', 'graphically'],
 'graphologic': ['graphologic', 'logographic'],
 'graphological': ['graphological', 'logographical'],
 'graphology': ['graphology', 'logography'],
 'graphometer': ['graphometer', 'meteorgraph'],
 'graphophonic': ['graphophonic', 'phonographic'],
 'graphostatic': ['gastropathic', 'graphostatic'],
 'graphotypic': ['graphotypic', 'pictography', 'typographic'],
 'grapsidae': ['disparage', 'grapsidae'],
 'grasp': ['grasp', 'sprag'],
 'grasper': ['grasper', 'regrasp', 'sparger'],
 'grasser': ['grasser', 'regrass'],
 'grasshopper': ['grasshopper', 'hoppergrass'],
 'grassman': ['grassman', 'mangrass'],
 'grat': ['grat', 'trag'],
 'grate': ['gater', 'grate', 'great', 'greta', 'retag', 'targe'],
 'grateman': ['grateman', 'mangrate', 'mentagra', 'targeman'],
 'grater': ['garret', 'garter', 'grater', 'targer'],
 'gratiano': ['giornata', 'gratiano'],
 'graticule': ['curtilage', 'cutigeral', 'graticule'],
 'gratiolin': ['gratiolin', 'largition', 'tailoring'],
 'gratis': ['gratis', 'striga'],
 'gratten': ['garnett', 'gnatter', 'gratten', 'tergant'],
 'graupel': ['earplug', 'graupel', 'plaguer'],
 'gravamen': ['gravamen', 'graveman'],
 'gravel': ['glaver', 'gravel'],
 'graveman': ['gravamen', 'graveman'],
 'graves': ['gervas', 'graves'],
 'gravure': ['gravure', 'verruga'],
 'gray': ['gary', 'gray'],
 'grayling': ['grayling', 'ragingly'],
 'graze': ['gazer', 'graze'],
 'greaser': ['argeers', 'greaser', 'serrage'],
 'great': ['gater', 'grate', 'great', 'greta', 'retag', 'targe'],
 'greaten': ['grantee', 'greaten', 'reagent', 'rentage'],
 'greater': ['greater', 'regrate', 'terrage'],
 'greaves': ['gervase', 'greaves', 'servage'],
 'grebe': ['gerbe', 'grebe', 'rebeg'],
 'grecian': ['anergic', 'garnice', 'garniec', 'geranic', 'grecian'],
 'grecomania': ['ergomaniac', 'grecomania'],
 'greed': ['edger', 'greed'],
 'green': ['genre', 'green', 'neger', 'reneg'],
 'greenable': ['generable', 'greenable'],
 'greener': ['greener', 'regreen', 'reneger'],
 'greenish': ['greenish', 'sheering'],
 'greenland': ['englander', 'greenland'],
 'greenuk': ['gerenuk', 'greenuk'],
 'greeny': ['energy', 'greeny', 'gyrene'],
 'greet': ['egret', 'greet', 'reget'],
 'greeter': ['greeter', 'regreet'],
 'gregal': ['gargle', 'gregal', 'lagger', 'raggle'],
 'gregarian': ['gregarian', 'gregarina'],
 'gregarina': ['gregarian', 'gregarina'],
 'gregarinous': ['grangousier', 'gregarinous'],
 'grege': ['egger', 'grege'],
 'gregge': ['gegger', 'gregge'],
 'grego': ['gorge', 'grego'],
 'gregor': ['gorger', 'gregor'],
 'greige': ['greige', 'reggie'],
 'grein': ['grein', 'inger', 'nigre', 'regin', 'reign', 'ringe'],
 'gremial': ['gremial', 'lamiger'],
 'gremlin': ['gremlin', 'mingler'],
 'grenade': ['derange', 'enraged', 'gardeen', 'gerenda', 'grandee', 'grenade'],
 'grenadian': ['granadine', 'grenadian'],
 'grenadier': ['earringed', 'grenadier'],
 'grenadin': ['gardenin', 'grenadin'],
 'grenadine': ['endearing', 'engrained', 'grenadine'],
 'greta': ['gater', 'grate', 'great', 'greta', 'retag', 'targe'],
 'gretel': ['gretel', 'reglet'],
 'greund': ['dunger', 'gerund', 'greund', 'nudger'],
 'grewia': ['earwig', 'grewia'],
 'grey': ['grey', 'gyre'],
 'grid': ['gird', 'grid'],
 'griddle': ['glidder', 'griddle'],
 'gride': ['dirge', 'gride', 'redig', 'ridge'],
 'gridelin': ['dreiling', 'gridelin'],
 'grieve': ['grieve', 'regive'],
 'grieved': ['diverge', 'grieved'],
 'grille': ['giller', 'grille', 'regill'],
 'grilse': ['girsle', 'gisler', 'glires', 'grilse'],
 'grimace': ['gemaric', 'grimace', 'megaric'],
 'grime': ['gerim', 'grime'],
 'grimme': ['gimmer', 'grimme', 'megrim'],
 'grin': ['girn', 'grin', 'ring'],
 'grinder': ['grinder', 'regrind'],
 'grindle': ['dringle', 'grindle'],
 'gringo': ['goring', 'gringo'],
 'grip': ['grip', 'prig'],
 'gripe': ['gerip', 'gripe'],
 'gripeful': ['fireplug', 'gripeful'],
 'griper': ['griper', 'regrip'],
 'gripman': ['gripman', 'prigman', 'ramping'],
 'grippe': ['gipper', 'grippe'],
 'grisounite': ['grisounite', 'grisoutine', 'integrious'],
 'grisoutine': ['grisounite', 'grisoutine', 'integrious'],
 'grist': ['grist', 'grits', 'strig'],
 'gristle': ['glister', 'gristle'],
 'grit': ['girt', 'grit', 'trig'],
 'grith': ['girth', 'grith', 'right'],
 'grits': ['grist', 'grits', 'strig'],
 'gritten': ['gittern', 'gritten', 'retting'],
 'grittle': ['glitter', 'grittle'],
 'grivna': ['grivna', 'raving'],
 'grizzel': ['grizzel', 'grizzle'],
 'grizzle': ['grizzel', 'grizzle'],
 'groan': ['angor',
           'argon',
           'goran',
           'grano',
           'groan',
           'nagor',
           'orang',
           'organ',
           'rogan',
           'ronga'],
 'groaner': ['groaner', 'oranger', 'organer'],
 'groaning': ['groaning', 'organing'],
 'groat': ['argot', 'gator', 'gotra', 'groat'],
 'grobian': ['biorgan', 'grobian'],
 'groined': ['groined', 'negroid'],
 'gromia': ['gamori', 'gomari', 'gromia'],
 'groove': ['groove', 'overgo'],
 'grope': ['grope', 'porge'],
 'groper': ['groper', 'porger'],
 'groset': ['groset', 'storge'],
 'grossen': ['engross', 'grossen'],
 'grot': ['grot', 'trog'],
 'grotian': ['grotian', 'trigona'],
 'grotto': ['grotto', 'torgot'],
 'grounded': ['grounded', 'underdog', 'undergod'],
 'grouper': ['grouper', 'regroup'],
 'grouse': ['grouse', 'rugose'],
 'grousy': ['grousy', 'gyrous'],
 'grovel': ['glover', 'grovel'],
 'groveless': ['gloveress', 'groveless'],
 'growan': ['awrong', 'growan'],
 'grower': ['grower', 'regrow'],
 'grown': ['grown', 'wrong'],
 'grub': ['burg', 'grub'],
 'grudge': ['grudge', 'rugged'],
 'grudger': ['drugger', 'grudger'],
 'grudgery': ['druggery', 'grudgery'],
 'grue': ['grue', 'urge'],
 'gruel': ['gluer', 'gruel', 'luger'],
 'gruelly': ['gruelly', 'gullery'],
 'grues': ['grues', 'surge'],
 'grun': ['grun', 'rung'],
 'grush': ['grush', 'shrug'],
 'grusinian': ['grusinian', 'unarising'],
 'grutten': ['grutten', 'turgent'],
 'gryposis': ['gossipry', 'gryposis'],
 'guacho': ['gaucho', 'guacho'],
 'guan': ['gaun', 'guan', 'guna', 'uang'],
 'guanamine': ['guanamine', 'guineaman'],
 'guanine': ['anguine', 'guanine', 'guinean'],
 'guar': ['gaur', 'guar', 'ruga'],
 'guara': ['gaura', 'guara'],
 'guarani': ['anguria', 'gaurian', 'guarani'],
 'guarantorship': ['guarantorship', 'uranographist'],
 'guardeen': ['dungaree', 'guardeen', 'unagreed', 'underage', 'ungeared'],
 'guarder': ['guarder', 'reguard'],
 'guatusan': ['augustan', 'guatusan'],
 'gud': ['dug', 'gud'],
 'gude': ['degu', 'gude'],
 'guenon': ['guenon', 'ungone'],
 'guepard': ['guepard', 'upgrade'],
 'guerdon': ['guerdon', 'undergo', 'ungored'],
 'guerdoner': ['guerdoner', 'reundergo', 'undergoer', 'undergore'],
 'guerinet': ['geniture', 'guerinet'],
 'guester': ['gesture', 'guester'],
 'guetar': ['argute', 'guetar', 'rugate', 'tuareg'],
 'guetare': ['erugate', 'guetare'],
 'guha': ['augh', 'guha'],
 'guiana': ['guiana', 'iguana'],
 'guib': ['bugi', 'guib'],
 'guineaman': ['guanamine', 'guineaman'],
 'guinean': ['anguine', 'guanine', 'guinean'],
 'guiser': ['guiser', 'sergiu'],
 'gul': ['gul', 'lug'],
 'gula': ['gaul', 'gula'],
 'gulae': ['gulae', 'legua'],
 'gular': ['glaur', 'gular'],
 'gularis': ['agrilus', 'gularis'],
 'gulden': ['gulden', 'lunged'],
 'gule': ['glue', 'gule', 'luge'],
 'gules': ['gules', 'gusle'],
 'gullery': ['gruelly', 'gullery'],
 'gullible': ['bluegill', 'gullible'],
 'gulonic': ['gulonic', 'unlogic'],
 'gulp': ['gulp', 'plug'],
 'gulpin': ['gulpin', 'puling'],
 'gum': ['gum', 'mug'],
 'gumbo': ['bogum', 'gumbo'],
 'gumshoe': ['gumshoe', 'hugsome'],
 'gumweed': ['gumweed', 'mugweed'],
 'gun': ['gnu', 'gun'],
 'guna': ['gaun', 'guan', 'guna', 'uang'],
 'gunate': ['gunate', 'tangue'],
 'gundi': ['gundi', 'undig'],
 'gundy': ['dungy', 'gundy'],
 'gunk': ['gunk', 'kung'],
 'gunl': ['gunl', 'lung'],
 'gunnership': ['gunnership', 'unsphering'],
 'gunreach': ['gunreach', 'uncharge'],
 'gunsel': ['gunsel', 'selung', 'slunge'],
 'gunshot': ['gunshot', 'shotgun', 'uhtsong'],
 'gunster': ['gunster', 'surgent'],
 'gunter': ['gunter', 'gurnet', 'urgent'],
 'gup': ['gup', 'pug'],
 'gur': ['gur', 'rug'],
 'gurgeon': ['gurgeon', 'ungorge'],
 'gurgle': ['gurgle', 'lugger', 'ruggle'],
 'gurian': ['gurian', 'ugrian'],
 'guric': ['guric', 'ugric'],
 'gurl': ['gurl', 'lurg'],
 'gurnard': ['drungar', 'gurnard'],
 'gurnet': ['gunter', 'gurnet', 'urgent'],
 'gurt': ['gurt', 'trug'],
 'gush': ['gush', 'shug', 'sugh'],
 'gusher': ['gusher', 'regush'],
 'gusle': ['gules', 'gusle'],
 'gust': ['gust', 'stug'],
 'gut': ['gut', 'tug'],
 'gutless': ['gutless', 'tugless'],
 'gutlike': ['gutlike', 'tuglike'],
 'gutnish': ['gutnish', 'husting', 'unsight'],
 'guttler': ['glutter', 'guttler'],
 'guttular': ['guttular', 'guttural'],
 'guttural': ['guttular', 'guttural'],
 'gweed': ['gweed', 'wedge'],
 'gymnasic': ['gymnasic', 'syngamic'],
 'gymnastic': ['gymnastic', 'nystagmic'],
 'gynandrous': ['androgynus', 'gynandrous'],
 'gynerium': ['eryngium', 'gynerium'],
 'gynospore': ['gynospore', 'sporogeny'],
 'gypsine': ['gypsine', 'pigsney'],
 'gyral': ['glary', 'gyral'],
 'gyrant': ['gantry', 'gyrant'],
 'gyrate': ['geraty', 'gyrate'],
 'gyration': ['gyration', 'organity', 'ortygian'],
 'gyre': ['grey', 'gyre'],
 'gyrene': ['energy', 'greeny', 'gyrene'],
 'gyro': ['gory', 'gyro', 'orgy'],
 'gyroma': ['gyroma', 'morgay'],
 'gyromitra': ['gyromitra', 'migratory'],
 'gyrophora': ['gyrophora', 'orography'],
 'gyrous': ['grousy', 'gyrous'],
 'gyrus': ['gyrus', 'surgy'],
 'ha': ['ah', 'ha'],
 'haberdine': ['haberdine', 'hebridean'],
 'habile': ['habile', 'halebi'],
 'habiri': ['bihari', 'habiri'],
 'habiru': ['brahui', 'habiru'],
 'habit': ['baith', 'habit'],
 'habitan': ['abthain', 'habitan'],
 'habitat': ['habitat', 'tabitha'],
 'habited': ['habited', 'thebaid'],
 'habitus': ['habitus', 'ushabti'],
 'habnab': ['babhan', 'habnab'],
 'hacienda': ['chanidae', 'hacienda'],
 'hackin': ['hackin', 'kachin'],
 'hackle': ['hackle', 'lekach'],
 'hackler': ['chalker', 'hackler'],
 'hackly': ['chalky', 'hackly'],
 'hacktree': ['eckehart', 'hacktree'],
 'hackwood': ['hackwood', 'woodhack'],
 'hacky': ['chyak', 'hacky'],
 'had': ['dah', 'dha', 'had'],
 'hadden': ['hadden', 'handed'],
 'hade': ['hade', 'head'],
 'hades': ['deash', 'hades', 'sadhe', 'shade'],
 'hadji': ['hadji', 'jihad'],
 'haec': ['ache', 'each', 'haec'],
 'haem': ['ahem', 'haem', 'hame'],
 'haet': ['ahet', 'haet', 'hate', 'heat', 'thea'],
 'hafgan': ['afghan', 'hafgan'],
 'hafter': ['father', 'freath', 'hafter'],
 'hageen': ['hageen', 'hangee'],
 'hailse': ['elisha', 'hailse', 'sheila'],
 'hainan': ['hainan', 'nahani'],
 'hair': ['ahir', 'hair'],
 'hairband': ['bhandari', 'hairband'],
 'haired': ['dehair', 'haired'],
 'hairen': ['hairen', 'hernia'],
 'hairlet': ['hairlet', 'therial'],
 'hairstone': ['hairstone', 'hortensia'],
 'hairup': ['hairup', 'rupiah'],
 'hak': ['hak', 'kha'],
 'hakam': ['hakam', 'makah'],
 'hakea': ['ekaha', 'hakea'],
 'hakim': ['hakim', 'khami'],
 'haku': ['haku', 'kahu'],
 'halal': ['allah', 'halal'],
 'halbert': ['blather', 'halbert'],
 'hale': ['hale', 'heal', 'leah'],
 'halebi': ['habile', 'halebi'],
 'halenia': ['ainaleh', 'halenia'],
 'halesome': ['halesome', 'healsome'],
 'halicore': ['halicore', 'heroical'],
 'haliotidae': ['aethalioid', 'haliotidae'],
 'hallan': ['hallan', 'nallah'],
 'hallower': ['hallower', 'rehallow'],
 'halma': ['halma', 'hamal'],
 'halogeton': ['halogeton', 'theogonal'],
 'haloid': ['dihalo', 'haloid'],
 'halophile': ['halophile', 'philohela'],
 'halophytism': ['halophytism', 'hylopathism'],
 'hals': ['hals', 'lash'],
 'halse': ['halse', 'leash', 'selah', 'shale', 'sheal', 'shela'],
 'halsen': ['halsen', 'hansel', 'lanseh'],
 'halt': ['halt', 'lath'],
 'halter': ['arthel', 'halter', 'lather', 'thaler'],
 'halterbreak': ['halterbreak', 'leatherbark'],
 'halting': ['halting', 'lathing', 'thingal'],
 'halve': ['halve', 'havel'],
 'halver': ['halver', 'lavehr'],
 'ham': ['ham', 'mah'],
 'hamal': ['halma', 'hamal'],
 'hame': ['ahem', 'haem', 'hame'],
 'hameil': ['hameil', 'hiemal'],
 'hamel': ['hamel', 'hemal'],
 'hamfatter': ['aftermath', 'hamfatter'],
 'hami': ['hami', 'hima', 'mahi'],
 'hamital': ['hamital', 'thalami'],
 'hamites': ['atheism', 'hamites'],
 'hamlet': ['hamlet', 'malthe'],
 'hammerer': ['hammerer', 'rehammer'],
 'hamsa': ['hamsa', 'masha', 'shama'],
 'hamulites': ['hamulites', 'shulamite'],
 'hamus': ['hamus', 'musha'],
 'hanaster': ['hanaster', 'sheratan'],
 'hance': ['achen', 'chane', 'chena', 'hance'],
 'hand': ['dhan', 'hand'],
 'handbook': ['bandhook', 'handbook'],
 'handed': ['hadden', 'handed'],
 'hander': ['hander', 'harden'],
 'handicapper': ['handicapper', 'prehandicap'],
 'handscrape': ['handscrape', 'scaphander'],
 'handstone': ['handstone', 'stonehand'],
 'handwork': ['handwork', 'workhand'],
 'hangar': ['arghan', 'hangar'],
 'hangby': ['banghy', 'hangby'],
 'hangee': ['hageen', 'hangee'],
 'hanger': ['hanger', 'rehang'],
 'hangie': ['gienah', 'hangie'],
 'hangnail': ['hangnail', 'langhian'],
 'hangout': ['hangout', 'tohunga'],
 'hank': ['ankh', 'hank', 'khan'],
 'hano': ['hano', 'noah'],
 'hans': ['hans', 'nash', 'shan'],
 'hansa': ['ahsan', 'hansa', 'hasan'],
 'hanse': ['ashen', 'hanse', 'shane', 'shean'],
 'hanseatic': ['anchistea', 'hanseatic'],
 'hansel': ['halsen', 'hansel', 'lanseh'],
 'hant': ['hant', 'tanh', 'than'],
 'hantle': ['ethnal', 'hantle', 'lathen', 'thenal'],
 'hao': ['aho', 'hao'],
 'haole': ['eloah', 'haole'],
 'haoma': ['haoma', 'omaha'],
 'haori': ['haori', 'iroha'],
 'hap': ['hap', 'pah'],
 'hapalotis': ['hapalotis', 'sapotilha'],
 'hapi': ['hapi', 'pahi'],
 'haplodoci': ['chilopoda', 'haplodoci'],
 'haplont': ['haplont', 'naphtol'],
 'haplosis': ['alphosis', 'haplosis'],
 'haply': ['haply', 'phyla'],
 'happiest': ['happiest', 'peatship'],
 'haptene': ['haptene', 'heptane', 'phenate'],
 'haptenic': ['haptenic', 'pantheic', 'pithecan'],
 'haptere': ['haptere', 'preheat'],
 'haptic': ['haptic', 'pathic'],
 'haptics': ['haptics', 'spathic'],
 'haptometer': ['amphorette', 'haptometer'],
 'haptophoric': ['haptophoric', 'pathophoric'],
 'haptophorous': ['haptophorous', 'pathophorous'],
 'haptotropic': ['haptotropic', 'protopathic'],
 'hapu': ['hapu', 'hupa'],
 'harass': ['harass', 'hassar'],
 'harb': ['bhar', 'harb'],
 'harborer': ['abhorrer', 'harborer'],
 'harden': ['hander', 'harden'],
 'hardener': ['hardener', 'reharden'],
 'hardenite': ['hardenite', 'herniated'],
 'hardtail': ['hardtail', 'thaliard'],
 'hardy': ['hardy', 'hydra'],
 'hare': ['hare', 'hear', 'rhea'],
 'harebrain': ['harebrain', 'herbarian'],
 'harem': ['harem', 'herma', 'rhema'],
 'haremism': ['ashimmer', 'haremism'],
 'harfang': ['fraghan', 'harfang'],
 'haricot': ['chariot', 'haricot'],
 'hark': ['hark', 'khar', 'rakh'],
 'harka': ['harka', 'kahar'],
 'harlot': ['harlot', 'orthal', 'thoral'],
 'harmala': ['harmala', 'marhala'],
 'harman': ['amhran', 'harman', 'mahran'],
 'harmer': ['harmer', 'reharm'],
 'harmine': ['harmine', 'hireman'],
 'harmonic': ['choirman', 'harmonic', 'omniarch'],
 'harmonical': ['harmonical', 'monarchial'],
 'harmonics': ['anorchism', 'harmonics'],
 'harmonistic': ['anchoritism', 'chiromantis', 'chrismation', 'harmonistic'],
 'harnesser': ['harnesser', 'reharness'],
 'harold': ['harold', 'holard'],
 'harpa': ['aphra', 'harpa', 'parah'],
 'harpings': ['harpings', 'phrasing'],
 'harpist': ['harpist', 'traship'],
 'harpless': ['harpless', 'splasher'],
 'harris': ['arrish', 'harris', 'rarish', 'sirrah'],
 'harrower': ['harrower', 'reharrow'],
 'hart': ['hart', 'rath', 'tahr', 'thar', 'trah'],
 'hartin': ['hartin', 'thrain'],
 'hartite': ['hartite', 'rathite'],
 'haruspices': ['chuprassie', 'haruspices'],
 'harvester': ['harvester', 'reharvest'],
 'hasan': ['ahsan', 'hansa', 'hasan'],
 'hash': ['hash', 'sahh', 'shah'],
 'hasher': ['hasher', 'rehash'],
 'hasidic': ['hasidic', 'sahidic'],
 'hasidim': ['hasidim', 'maidish'],
 'hasky': ['hasky', 'shaky'],
 'haslet': ['haslet', 'lesath', 'shelta'],
 'hasp': ['hasp', 'pash', 'psha', 'shap'],
 'hassar': ['harass', 'hassar'],
 'hassel': ['hassel', 'hassle'],
 'hassle': ['hassel', 'hassle'],
 'haste': ['ashet', 'haste', 'sheat'],
 'hasten': ['athens', 'hasten', 'snathe', 'sneath'],
 'haster': ['haster', 'hearst', 'hearts'],
 'hastilude': ['hastilude', 'lustihead'],
 'hastler': ['hastler', 'slather'],
 'hasty': ['hasty', 'yasht'],
 'hat': ['aht', 'hat', 'tha'],
 'hatchery': ['hatchery', 'thearchy'],
 'hate': ['ahet', 'haet', 'hate', 'heat', 'thea'],
 'hateable': ['hateable', 'heatable'],
 'hateful': ['hateful', 'heatful'],
 'hateless': ['hateless', 'heatless'],
 'hater': ['earth', 'hater', 'heart', 'herat', 'rathe'],
 'hati': ['hati', 'thai'],
 'hatred': ['dearth', 'hatred', 'rathed', 'thread'],
 'hatress': ['hatress', 'shaster'],
 'hatt': ['hatt', 'tath', 'that'],
 'hattemist': ['hattemist', 'thematist'],
 'hatter': ['hatter', 'threat'],
 'hattery': ['hattery', 'theatry'],
 'hattic': ['chatti', 'hattic'],
 'hattock': ['hattock', 'totchka'],
 'hau': ['ahu', 'auh', 'hau'],
 'hauerite': ['eutheria', 'hauerite'],
 'haul': ['haul', 'hula'],
 'hauler': ['hauler', 'rehaul'],
 'haunt': ['ahunt', 'haunt', 'thuan', 'unhat'],
 'haunter': ['haunter', 'nauther', 'unearth', 'unheart', 'urethan'],
 'hauntingly': ['hauntingly', 'unhatingly'],
 'haurient': ['haurient', 'huterian'],
 'havel': ['halve', 'havel'],
 'havers': ['havers', 'shaver', 'shrave'],
 'haw': ['haw', 'hwa', 'wah', 'wha'],
 'hawer': ['hawer', 'whare'],
 'hawm': ['hawm', 'wham'],
 'hawse': ['hawse', 'shewa', 'whase'],
 'hawser': ['hawser', 'rewash', 'washer'],
 'hay': ['hay', 'yah'],
 'haya': ['ayah', 'haya'],
 'hayz': ['hayz', 'hazy'],
 'hazarder': ['hazarder', 'rehazard'],
 'hazel': ['hazel', 'hazle'],
 'hazle': ['hazel', 'hazle'],
 'hazy': ['hayz', 'hazy'],
 'he': ['eh', 'he'],
 'head': ['hade', 'head'],
 'headbander': ['barehanded', 'bradenhead', 'headbander'],
 'headboard': ['broadhead', 'headboard'],
 'header': ['adhere', 'header', 'hedera', 'rehead'],
 'headily': ['headily', 'hylidae'],
 'headlight': ['headlight', 'lighthead'],
 'headlong': ['headlong', 'longhead'],
 'headman': ['headman', 'manhead'],
 'headmaster': ['headmaster', 'headstream', 'streamhead'],
 'headnote': ['headnote', 'notehead'],
 'headrail': ['headrail', 'railhead'],
 'headrent': ['adherent', 'headrent', 'neatherd', 'threaden'],
 'headring': ['headring', 'ringhead'],
 'headset': ['headset', 'sethead'],
 'headskin': ['headskin', 'nakedish', 'sinkhead'],
 'headspring': ['headspring', 'springhead'],
 'headstone': ['headstone', 'stonehead'],
 'headstream': ['headmaster', 'headstream', 'streamhead'],
 'headstrong': ['headstrong', 'stronghead'],
 'headward': ['drawhead', 'headward'],
 'headwater': ['headwater', 'waterhead'],
 'heal': ['hale', 'heal', 'leah'],
 'healer': ['healer', 'rehale', 'reheal'],
 'healsome': ['halesome', 'healsome'],
 'heap': ['epha', 'heap'],
 'heaper': ['heaper', 'reheap'],
 'heaps': ['heaps', 'pesah', 'phase', 'shape'],
 'hear': ['hare', 'hear', 'rhea'],
 'hearer': ['hearer', 'rehear'],
 'hearken': ['hearken', 'kenareh'],
 'hearst': ['haster', 'hearst', 'hearts'],
 'heart': ['earth', 'hater', 'heart', 'herat', 'rathe'],
 'heartdeep': ['heartdeep', 'preheated'],
 'hearted': ['earthed', 'hearted'],
 'heartedness': ['heartedness', 'neatherdess'],
 'hearten': ['earthen', 'enheart', 'hearten', 'naether', 'teheran', 'traheen'],
 'heartener': ['heartener', 'rehearten'],
 'heartiness': ['earthiness', 'heartiness'],
 'hearting': ['hearting', 'ingather'],
 'heartless': ['earthless', 'heartless'],
 'heartling': ['earthling', 'heartling'],
 'heartly': ['earthly', 'heartly', 'lathery', 'rathely'],
 'heartnut': ['earthnut', 'heartnut'],
 'heartpea': ['earthpea', 'heartpea'],
 'heartquake': ['earthquake', 'heartquake'],
 'hearts': ['haster', 'hearst', 'hearts'],
 'heartsome': ['heartsome', 'samothere'],
 'heartward': ['earthward', 'heartward'],
 'heartweed': ['heartweed', 'weathered'],
 'hearty': ['earthy', 'hearty', 'yearth'],
 'heat': ['ahet', 'haet', 'hate', 'heat', 'thea'],
 'heatable': ['hateable', 'heatable'],
 'heater': ['heater', 'hereat', 'reheat'],
 'heatful': ['hateful', 'heatful'],
 'heath': ['heath', 'theah'],
 'heating': ['gahnite', 'heating'],
 'heatless': ['hateless', 'heatless'],
 'heatronic': ['anchorite', 'antechoir', 'heatronic', 'hectorian'],
 'heave': ['heave', 'hevea'],
 'hebraizer': ['hebraizer', 'herbarize'],
 'hebridean': ['haberdine', 'hebridean'],
 'hecate': ['achete', 'hecate', 'teache', 'thecae'],
 'hecatine': ['echinate', 'hecatine'],
 'heckle': ['heckle', 'kechel'],
 'hectare': ['cheater', 'hectare', 'recheat', 'reteach', 'teacher'],
 'hecte': ['cheet', 'hecte'],
 'hector': ['hector', 'rochet', 'tocher', 'troche'],
 'hectorian': ['anchorite', 'antechoir', 'heatronic', 'hectorian'],
 'hectorship': ['christophe', 'hectorship'],
 'hedera': ['adhere', 'header', 'hedera', 'rehead'],
 'hedonical': ['chelodina', 'hedonical'],
 'hedonism': ['demonish', 'hedonism'],
 'heehaw': ['heehaw', 'wahehe'],
 'heel': ['heel', 'hele'],
 'heeler': ['heeler', 'reheel'],
 'heelpost': ['heelpost', 'pesthole'],
 'heer': ['heer', 'here'],
 'hegari': ['hegari', 'hegira'],
 'hegemonic': ['hegemonic', 'hemogenic'],
 'hegira': ['hegari', 'hegira'],
 'hei': ['hei', 'hie'],
 'height': ['eighth', 'height'],
 'heightener': ['heightener', 'reheighten'],
 'heintzite': ['heintzite', 'hintzeite'],
 'heinz': ['heinz', 'hienz'],
 'heir': ['heir', 'hire'],
 'heirdom': ['heirdom', 'homerid'],
 'heirless': ['heirless', 'hireless'],
 'hejazi': ['hejazi', 'jeziah'],
 'helcosis': ['helcosis', 'ochlesis'],
 'helcotic': ['helcotic', 'lochetic', 'ochletic'],
 'hele': ['heel', 'hele'],
 'heliacal': ['achillea', 'heliacal'],
 'heliast': ['heliast', 'thesial'],
 'helical': ['alichel', 'challie', 'helical'],
 'heliced': ['chelide', 'heliced'],
 'helicon': ['choline', 'helicon'],
 'heling': ['heling', 'hingle'],
 'heliophotography': ['heliophotography', 'photoheliography'],
 'helios': ['helios', 'isohel'],
 'heliostatic': ['chiastolite', 'heliostatic'],
 'heliotactic': ['heliotactic', 'thiolacetic'],
 'helium': ['helium', 'humlie'],
 'hellcat': ['hellcat', 'tellach'],
 'helleborein': ['helleborein', 'helleborine'],
 'helleborine': ['helleborein', 'helleborine'],
 'hellenic': ['chenille', 'hellenic'],
 'helleri': ['helleri', 'hellier'],
 'hellicat': ['hellicat', 'lecithal'],
 'hellier': ['helleri', 'hellier'],
 'helm': ['helm', 'heml'],
 'heloderma': ['dreamhole', 'heloderma'],
 'helot': ['helot', 'hotel', 'thole'],
 'helotize': ['helotize', 'hotelize'],
 'helpmeet': ['helpmeet', 'meethelp'],
 'hemad': ['ahmed', 'hemad'],
 'hemal': ['hamel', 'hemal'],
 'hemapod': ['hemapod', 'mophead'],
 'hematic': ['chamite', 'hematic'],
 'hematin': ['ethanim', 'hematin'],
 'hematinic': ['hematinic', 'minchiate'],
 'hematolin': ['hematolin', 'maholtine'],
 'hematonic': ['hematonic', 'methanoic'],
 'hematosin': ['hematosin', 'thomasine'],
 'hematoxic': ['hematoxic', 'hexatomic'],
 'hematuric': ['hematuric', 'rheumatic'],
 'hemiasci': ['hemiasci', 'ischemia'],
 'hemiatrophy': ['hemiatrophy', 'hypothermia'],
 'hemic': ['chime', 'hemic', 'miche'],
 'hemicarp': ['camphire', 'hemicarp'],
 'hemicatalepsy': ['hemicatalepsy', 'mesaticephaly'],
 'hemiclastic': ['alchemistic', 'hemiclastic'],
 'hemicrany': ['hemicrany', 'machinery'],
 'hemiholohedral': ['hemiholohedral', 'holohemihedral'],
 'hemiolic': ['elohimic', 'hemiolic'],
 'hemiparesis': ['hemiparesis', 'phariseeism'],
 'hemistater': ['amherstite', 'hemistater'],
 'hemiterata': ['hemiterata', 'metatheria'],
 'hemitype': ['epithyme', 'hemitype'],
 'heml': ['helm', 'heml'],
 'hemogenic': ['hegemonic', 'hemogenic'],
 'hemol': ['hemol', 'mohel'],
 'hemologist': ['hemologist', 'theologism'],
 'hemopneumothorax': ['hemopneumothorax', 'pneumohemothorax'],
 'henbit': ['behint', 'henbit'],
 'hent': ['hent', 'neth', 'then'],
 'henter': ['erthen', 'henter', 'nether', 'threne'],
 'henyard': ['enhydra', 'henyard'],
 'hepar': ['hepar', 'phare', 'raphe'],
 'heparin': ['heparin', 'nephria'],
 'hepatic': ['aphetic', 'caphite', 'hepatic'],
 'hepatica': ['apachite', 'hepatica'],
 'hepatical': ['caliphate', 'hepatical'],
 'hepatize': ['aphetize', 'hepatize'],
 'hepatocolic': ['hepatocolic', 'otocephalic'],
 'hepatogastric': ['gastrohepatic', 'hepatogastric'],
 'hepatoid': ['diaphote', 'hepatoid'],
 'hepatomegalia': ['hepatomegalia', 'megalohepatia'],
 'hepatonephric': ['hepatonephric', 'phrenohepatic'],
 'hepatostomy': ['hepatostomy', 'somatophyte'],
 'hepialid': ['hepialid', 'phialide'],
 'heptace': ['heptace', 'tepache'],
 'heptad': ['heptad', 'pathed'],
 'heptagon': ['heptagon', 'pathogen'],
 'heptameron': ['heptameron', 'promethean'],
 'heptane': ['haptene', 'heptane', 'phenate'],
 'heptaploidy': ['heptaploidy', 'typhlopidae'],
 'hepteris': ['hepteris', 'treeship'],
 'heptine': ['heptine', 'nephite'],
 'heptite': ['epithet', 'heptite'],
 'heptorite': ['heptorite', 'tephroite'],
 'heptylic': ['heptylic', 'phyletic'],
 'her': ['her', 'reh', 'rhe'],
 'heraclid': ['heraclid', 'heraldic'],
 'heraldic': ['heraclid', 'heraldic'],
 'heraldist': ['heraldist', 'tehsildar'],
 'herat': ['earth', 'hater', 'heart', 'herat', 'rathe'],
 'herbage': ['breaghe', 'herbage'],
 'herbarian': ['harebrain', 'herbarian'],
 'herbarism': ['herbarism', 'shambrier'],
 'herbarize': ['hebraizer', 'herbarize'],
 'herbert': ['berther', 'herbert'],
 'herbous': ['herbous', 'subhero'],
 'herdic': ['chider', 'herdic'],
 'here': ['heer', 'here'],
 'hereafter': ['featherer', 'hereafter'],
 'hereat': ['heater', 'hereat', 'reheat'],
 'herein': ['herein', 'inhere'],
 'hereinto': ['etherion', 'hereinto', 'heronite'],
 'herem': ['herem', 'rheme'],
 'heretic': ['erethic', 'etheric', 'heretic', 'heteric', 'teicher'],
 'heretically': ['heretically', 'heterically'],
 'heretication': ['heretication', 'theoretician'],
 'hereto': ['hereto', 'hetero'],
 'heritance': ['catherine', 'heritance'],
 'herl': ['herl', 'hler', 'lehr'],
 'herma': ['harem', 'herma', 'rhema'],
 'hermaic': ['chimera', 'hermaic'],
 'hermitage': ['geheimrat', 'hermitage'],
 'hermo': ['hermo', 'homer', 'horme'],
 'herne': ['herne', 'rheen'],
 'hernia': ['hairen', 'hernia'],
 'hernial': ['hernial', 'inhaler'],
 'herniate': ['atherine', 'herniate'],
 'herniated': ['hardenite', 'herniated'],
 'hernioid': ['dinheiro', 'hernioid'],
 'hero': ['hero', 'hoer'],
 'herodian': ['herodian', 'ironhead'],
 'heroic': ['coheir', 'heroic'],
 'heroical': ['halicore', 'heroical'],
 'heroin': ['heroin', 'hieron', 'hornie'],
 'heroism': ['heroism', 'moreish'],
 'heronite': ['etherion', 'hereinto', 'heronite'],
 'herophile': ['herophile', 'rheophile'],
 'herpes': ['herpes', 'hesper', 'sphere'],
 'herpetism': ['herpetism', 'metership', 'metreship', 'temperish'],
 'herpetological': ['herpetological', 'pretheological'],
 'herpetomonad': ['dermatophone', 'herpetomonad'],
 'hers': ['hers', 'resh', 'sher'],
 'herse': ['herse', 'sereh', 'sheer', 'shree'],
 'hersed': ['hersed', 'sheder'],
 'herself': ['flesher', 'herself'],
 'hersir': ['hersir', 'sherri'],
 'herulian': ['herulian', 'inhauler'],
 'hervati': ['athrive', 'hervati'],
 'hesitater': ['hesitater', 'hetaerist'],
 'hesper': ['herpes', 'hesper', 'sphere'],
 'hespera': ['hespera', 'rephase', 'reshape'],
 'hesperia': ['hesperia', 'pharisee'],
 'hesperian': ['hesperian', 'phrenesia', 'seraphine'],
 'hesperid': ['hesperid', 'perished'],
 'hesperinon': ['hesperinon', 'prehension'],
 'hesperis': ['hesperis', 'seership'],
 'hest': ['esth', 'hest', 'seth'],
 'hester': ['esther', 'hester', 'theres'],
 'het': ['het', 'the'],
 'hetaeric': ['cheatrie', 'hetaeric'],
 'hetaerist': ['hesitater', 'hetaerist'],
 'hetaery': ['erythea', 'hetaery', 'yeather'],
 'heteratomic': ['heteratomic', 'theorematic'],
 'heteraxial': ['exhilarate', 'heteraxial'],
 'heteric': ['erethic', 'etheric', 'heretic', 'heteric', 'teicher'],
 'heterically': ['heretically', 'heterically'],
 'hetericism': ['erethismic', 'hetericism'],
 'hetericist': ['erethistic', 'hetericist'],
 'heterism': ['erethism', 'etherism', 'heterism'],
 'heterization': ['etherization', 'heterization'],
 'heterize': ['etherize', 'heterize'],
 'hetero': ['hereto', 'hetero'],
 'heterocarpus': ['heterocarpus', 'urethrascope'],
 'heteroclite': ['heteroclite', 'heterotelic'],
 'heterodromy': ['heterodromy', 'hydrometeor'],
 'heteroecismal': ['cholesteremia', 'heteroecismal'],
 'heterography': ['erythrophage', 'heterography'],
 'heterogynous': ['heterogynous', 'thyreogenous'],
 'heterology': ['heterology', 'thereology'],
 'heteromeri': ['heteromeri', 'moerithere'],
 'heteroousiast': ['autoheterosis', 'heteroousiast'],
 'heteropathy': ['heteropathy', 'theotherapy'],
 'heteropodal': ['heteropodal', 'prelatehood'],
 'heterotelic': ['heteroclite', 'heterotelic'],
 'heterotic': ['heterotic', 'theoretic'],
 'hetman': ['anthem', 'hetman', 'mentha'],
 'hetmanate': ['hetmanate', 'methanate'],
 'hetter': ['hetter', 'tether'],
 'hevea': ['heave', 'hevea'],
 'hevi': ['hevi', 'hive'],
 'hewel': ['hewel', 'wheel'],
 'hewer': ['hewer', 'wheer', 'where'],
 'hewn': ['hewn', 'when'],
 'hewt': ['hewt', 'thew', 'whet'],
 'hexacid': ['hexacid', 'hexadic'],
 'hexadic': ['hexacid', 'hexadic'],
 'hexakisoctahedron': ['hexakisoctahedron', 'octakishexahedron'],
 'hexakistetrahedron': ['hexakistetrahedron', 'tetrakishexahedron'],
 'hexatetrahedron': ['hexatetrahedron', 'tetrahexahedron'],
 'hexatomic': ['hematoxic', 'hexatomic'],
 'hexonic': ['choenix', 'hexonic'],
 'hiant': ['ahint', 'hiant', 'tahin'],
 'hiatal': ['hiatal', 'thalia'],
 'hibernate': ['hibernate', 'inbreathe'],
 'hic': ['chi', 'hic', 'ich'],
 'hickwall': ['hickwall', 'wallhick'],
 'hidage': ['adighe', 'hidage'],
 'hider': ['dheri', 'hider', 'hired'],
 'hidling': ['hidling', 'hilding'],
 'hidlings': ['dishling', 'hidlings'],
 'hidrotic': ['hidrotic', 'trichoid'],
 'hie': ['hei', 'hie'],
 'hield': ['delhi', 'hield'],
 'hiemal': ['hameil', 'hiemal'],
 'hienz': ['heinz', 'hienz'],
 'hieron': ['heroin', 'hieron', 'hornie'],
 'hieros': ['hieros', 'hosier'],
 'hight': ['hight', 'thigh'],
 'higuero': ['higuero', 'roughie'],
 'hilasmic': ['chiliasm', 'hilasmic', 'machilis'],
 'hilding': ['hidling', 'hilding'],
 'hillside': ['hillside', 'sidehill'],
 'hilsa': ['alish', 'hilsa'],
 'hilt': ['hilt', 'lith'],
 'hima': ['hami', 'hima', 'mahi'],
 'himself': ['flemish', 'himself'],
 'hinderest': ['disherent', 'hinderest', 'tenderish'],
 'hindu': ['hindu', 'hundi', 'unhid'],
 'hing': ['hing', 'nigh'],
 'hinge': ['hinge', 'neigh'],
 'hingle': ['heling', 'hingle'],
 'hint': ['hint', 'thin'],
 'hinter': ['hinter', 'nither', 'theirn'],
 'hintproof': ['hintproof', 'hoofprint'],
 'hintzeite': ['heintzite', 'hintzeite'],
 'hip': ['hip', 'phi'],
 'hipbone': ['hipbone', 'hopbine'],
 'hippodamous': ['amphipodous', 'hippodamous'],
 'hippolyte': ['hippolyte', 'typophile'],
 'hippus': ['hippus', 'uppish'],
 'hiram': ['hiram', 'ihram', 'mahri'],
 'hircine': ['hircine', 'rheinic'],
 'hire': ['heir', 'hire'],
 'hired': ['dheri', 'hider', 'hired'],
 'hireless': ['heirless', 'hireless'],
 'hireman': ['harmine', 'hireman'],
 'hiren': ['hiren', 'rhein', 'rhine'],
 'hirmos': ['hirmos', 'romish'],
 'hirse': ['hirse', 'shier', 'shire'],
 'hirsel': ['hirsel', 'hirsle', 'relish'],
 'hirsle': ['hirsel', 'hirsle', 'relish'],
 'his': ['his', 'hsi', 'shi'],
 'hish': ['hish', 'shih'],
 'hisn': ['hisn', 'shin', 'sinh'],
 'hispa': ['aphis', 'apish', 'hispa', 'saiph', 'spahi'],
 'hispanist': ['hispanist', 'saintship'],
 'hiss': ['hiss', 'sish'],
 'hist': ['hist', 'sith', 'this', 'tshi'],
 'histamine': ['histamine', 'semihiant'],
 'histie': ['histie', 'shiite'],
 'histioid': ['histioid', 'idiotish'],
 'histon': ['histon', 'shinto', 'tonish'],
 'histonal': ['histonal', 'toshnail'],
 'historic': ['historic', 'orchitis'],
 'historics': ['historics', 'trichosis'],
 'history': ['history', 'toryish'],
 'hittable': ['hittable', 'tithable'],
 'hitter': ['hitter', 'tither'],
 'hive': ['hevi', 'hive'],
 'hives': ['hives', 'shive'],
 'hler': ['herl', 'hler', 'lehr'],
 'ho': ['ho', 'oh'],
 'hoar': ['hoar', 'hora'],
 'hoard': ['hoard', 'rhoda'],
 'hoarse': ['ahorse', 'ashore', 'hoarse', 'shorea'],
 'hoarstone': ['anorthose', 'hoarstone'],
 'hoast': ['hoast', 'hosta', 'shoat'],
 'hobbism': ['hobbism', 'mobbish'],
 'hobo': ['boho', 'hobo'],
 'hocco': ['choco', 'hocco'],
 'hock': ['hock', 'koch'],
 'hocker': ['choker', 'hocker'],
 'hocky': ['choky', 'hocky'],
 'hocus': ['chous', 'hocus'],
 'hodiernal': ['hodiernal', 'rhodaline'],
 'hoer': ['hero', 'hoer'],
 'hogan': ['ahong', 'hogan'],
 'hogget': ['egghot', 'hogget'],
 'hogmanay': ['hogmanay', 'mahogany'],
 'hognut': ['hognut', 'nought'],
 'hogsty': ['ghosty', 'hogsty'],
 'hoister': ['hoister', 'rehoist'],
 'hoit': ['hoit', 'hoti', 'thio'],
 'holard': ['harold', 'holard'],
 'holconoti': ['holconoti', 'holotonic'],
 'holcus': ['holcus', 'lochus', 'slouch'],
 'holdfast': ['fasthold', 'holdfast'],
 'holdout': ['holdout', 'outhold'],
 'holdup': ['holdup', 'uphold'],
 'holeman': ['holeman', 'manhole'],
 'holey': ['holey', 'hoyle'],
 'holiday': ['holiday', 'hyaloid', 'hyoidal'],
 'hollandite': ['hollandite', 'hollantide'],
 'hollantide': ['hollandite', 'hollantide'],
 'hollower': ['hollower', 'rehollow'],
 'holmia': ['holmia', 'maholi'],
 'holocentrid': ['holocentrid', 'lechriodont'],
 'holohemihedral': ['hemiholohedral', 'holohemihedral'],
 'holosteric': ['holosteric', 'thiocresol'],
 'holotonic': ['holconoti', 'holotonic'],
 'holster': ['holster', 'hostler'],
 'homage': ['homage', 'ohmage'],
 'homarine': ['homarine', 'homerian'],
 'homecroft': ['forthcome', 'homecroft'],
 'homeogenous': ['homeogenous', 'homogeneous'],
 'homeotypic': ['homeotypic', 'mythopoeic'],
 'homeotypical': ['homeotypical', 'polymetochia'],
 'homer': ['hermo', 'homer', 'horme'],
 'homerian': ['homarine', 'homerian'],
 'homeric': ['homeric', 'moriche'],
 'homerical': ['chloremia', 'homerical'],
 'homerid': ['heirdom', 'homerid'],
 'homerist': ['homerist', 'isotherm', 'otherism', 'theorism'],
 'homiletics': ['homiletics', 'mesolithic'],
 'homo': ['homo', 'moho'],
 'homocline': ['chemiloon', 'homocline'],
 'homogeneous': ['homeogenous', 'homogeneous'],
 'homopolic': ['homopolic', 'lophocomi'],
 'homopteran': ['homopteran', 'trophonema'],
 'homrai': ['homrai', 'mahori', 'mohair'],
 'hondo': ['dhoon', 'hondo'],
 'honest': ['ethnos', 'honest'],
 'honeypod': ['dyophone', 'honeypod'],
 'honeypot': ['eophyton', 'honeypot'],
 'honorer': ['honorer', 'rehonor'],
 'hontous': ['hontous', 'nothous'],
 'hoodman': ['dhamnoo', 'hoodman', 'manhood'],
 'hoofprint': ['hintproof', 'hoofprint'],
 'hooker': ['hooker', 'rehook'],
 'hookweed': ['hookweed', 'weedhook'],
 'hoop': ['hoop', 'phoo', 'pooh'],
 'hooper': ['hooper', 'rehoop'],
 'hoot': ['hoot', 'thoo', 'toho'],
 'hop': ['hop', 'pho', 'poh'],
 'hopbine': ['hipbone', 'hopbine'],
 'hopcalite': ['hopcalite', 'phacolite'],
 'hope': ['hope', 'peho'],
 'hoped': ['depoh', 'ephod', 'hoped'],
 'hoper': ['ephor', 'hoper'],
 'hoplite': ['hoplite', 'pithole'],
 'hoppergrass': ['grasshopper', 'hoppergrass'],
 'hoppers': ['hoppers', 'shopper'],
 'hora': ['hoar', 'hora'],
 'horal': ['horal', 'lohar'],
 'hordarian': ['arianrhod', 'hordarian'],
 'horizontal': ['horizontal', 'notorhizal'],
 'horme': ['hermo', 'homer', 'horme'],
 'horned': ['dehorn', 'horned'],
 'hornet': ['hornet', 'nother', 'theron', 'throne'],
 'hornie': ['heroin', 'hieron', 'hornie'],
 'hornpipe': ['hornpipe', 'porphine'],
 'horopteric': ['horopteric', 'rheotropic', 'trichopore'],
 'horrent': ['horrent', 'norther'],
 'horse': ['horse', 'shoer', 'shore'],
 'horsecar': ['cosharer', 'horsecar'],
 'horseless': ['horseless', 'shoreless'],
 'horseman': ['horseman', 'rhamnose', 'shoreman'],
 'horser': ['horser', 'shorer'],
 'horsetail': ['horsetail', 'isotheral'],
 'horseweed': ['horseweed', 'shoreweed'],
 'horsewhip': ['horsewhip', 'whoreship'],
 'horsewood': ['horsewood', 'woodhorse'],
 'horsing': ['horsing', 'shoring'],
 'horst': ['horst', 'short'],
 'hortensia': ['hairstone', 'hortensia'],
 'hortite': ['hortite', 'orthite', 'thorite'],
 'hose': ['hose', 'shoe'],
 'hosed': ['hosed', 'shode'],
 'hosel': ['hosel', 'sheol', 'shole'],
 'hoseless': ['hoseless', 'shoeless'],
 'hoseman': ['hoseman', 'shoeman'],
 'hosier': ['hieros', 'hosier'],
 'hospitaler': ['hospitaler', 'trophesial'],
 'host': ['host', 'shot', 'thos', 'tosh'],
 'hosta': ['hoast', 'hosta', 'shoat'],
 'hostager': ['hostager', 'shortage'],
 'hoster': ['hoster', 'tosher'],
 'hostile': ['elohist', 'hostile'],
 'hosting': ['hosting', 'onsight'],
 'hostler': ['holster', 'hostler'],
 'hostless': ['hostless', 'shotless'],
 'hostly': ['hostly', 'toshly'],
 'hot': ['hot', 'tho'],
 'hotel': ['helot', 'hotel', 'thole'],
 'hotelize': ['helotize', 'hotelize'],
 'hotfoot': ['foothot', 'hotfoot'],
 'hoti': ['hoit', 'hoti', 'thio'],
 'hotter': ['hotter', 'tother'],
 'hounce': ['cohune', 'hounce'],
 'houseboat': ['boathouse', 'houseboat'],
 'housebug': ['bughouse', 'housebug'],
 'housecraft': ['fratcheous', 'housecraft'],
 'housetop': ['housetop', 'pothouse'],
 'housewarm': ['housewarm', 'warmhouse'],
 'housewear': ['housewear', 'warehouse'],
 'housework': ['housework', 'workhouse'],
 'hovering': ['hovering', 'overnigh'],
 'how': ['how', 'who'],
 'howel': ['howel', 'whole'],
 'however': ['everwho', 'however', 'whoever'],
 'howlet': ['howlet', 'thowel'],
 'howso': ['howso', 'woosh'],
 'howsomever': ['howsomever', 'whomsoever', 'whosomever'],
 'hoya': ['ahoy', 'hoya'],
 'hoyle': ['holey', 'hoyle'],
 'hsi': ['his', 'hsi', 'shi'],
 'huari': ['huari', 'uriah'],
 'hubert': ['hubert', 'turbeh'],
 'hud': ['dhu', 'hud'],
 'hudsonite': ['hudsonite', 'unhoisted'],
 'huer': ['huer', 'hure'],
 'hug': ['hug', 'ugh'],
 'hughes': ['hughes', 'sheugh'],
 'hughoc': ['chough', 'hughoc'],
 'hugo': ['hugo', 'ough'],
 'hugsome': ['gumshoe', 'hugsome'],
 'huk': ['huk', 'khu'],
 'hula': ['haul', 'hula'],
 'hulsean': ['hulsean', 'unleash'],
 'hulster': ['hulster', 'hustler', 'sluther'],
 'huma': ['ahum', 'huma'],
 'human': ['human', 'nahum'],
 'humane': ['humane', 'humean'],
 'humanics': ['humanics', 'inasmuch'],
 'humean': ['humane', 'humean'],
 'humeroradial': ['humeroradial', 'radiohumeral'],
 'humic': ['chimu', 'humic'],
 'humidor': ['humidor', 'rhodium'],
 'humlie': ['helium', 'humlie'],
 'humor': ['humor', 'mohur'],
 'humoralistic': ['humoralistic', 'humoristical'],
 'humoristical': ['humoralistic', 'humoristical'],
 'hump': ['hump', 'umph'],
 'hundi': ['hindu', 'hundi', 'unhid'],
 'hunger': ['hunger', 'rehung'],
 'hunterian': ['hunterian', 'ruthenian'],
 'hup': ['hup', 'phu'],
 'hupa': ['hapu', 'hupa'],
 'hurdis': ['hurdis', 'rudish'],
 'hurdle': ['hurdle', 'hurled'],
 'hure': ['huer', 'hure'],
 'hurled': ['hurdle', 'hurled'],
 'huron': ['huron', 'rohun'],
 'hurst': ['hurst', 'trush'],
 'hurt': ['hurt', 'ruth'],
 'hurter': ['hurter', 'ruther'],
 'hurtful': ['hurtful', 'ruthful'],
 'hurtfully': ['hurtfully', 'ruthfully'],
 'hurtfulness': ['hurtfulness', 'ruthfulness'],
 'hurting': ['hurting', 'ungirth', 'unright'],
 'hurtingest': ['hurtingest', 'shuttering'],
 'hurtle': ['hurtle', 'luther'],
 'hurtless': ['hurtless', 'ruthless'],
 'hurtlessly': ['hurtlessly', 'ruthlessly'],
 'hurtlessness': ['hurtlessness', 'ruthlessness'],
 'husbander': ['husbander', 'shabunder'],
 'husked': ['dehusk', 'husked'],
 'huso': ['huso', 'shou'],
 'huspil': ['huspil', 'pulish'],
 'husting': ['gutnish', 'husting', 'unsight'],
 'hustle': ['hustle', 'sleuth'],
 'hustler': ['hulster', 'hustler', 'sluther'],
 'huterian': ['haurient', 'huterian'],
 'hwa': ['haw', 'hwa', 'wah', 'wha'],
 'hyaloid': ['holiday', 'hyaloid', 'hyoidal'],
 'hydra': ['hardy', 'hydra'],
 'hydramnios': ['disharmony', 'hydramnios'],
 'hydrate': ['hydrate', 'thready'],
 'hydrazidine': ['anhydridize', 'hydrazidine'],
 'hydrazine': ['anhydrize', 'hydrazine'],
 'hydriodate': ['hydriodate', 'iodhydrate'],
 'hydriodic': ['hydriodic', 'iodhydric'],
 'hydriote': ['hydriote', 'thyreoid'],
 'hydrobromate': ['bromohydrate', 'hydrobromate'],
 'hydrocarbide': ['carbohydride', 'hydrocarbide'],
 'hydrocharis': ['hydrocharis', 'hydrorachis'],
 'hydroferricyanic': ['ferrihydrocyanic', 'hydroferricyanic'],
 'hydroferrocyanic': ['ferrohydrocyanic', 'hydroferrocyanic'],
 'hydrofluoboric': ['borofluohydric', 'hydrofluoboric'],
 'hydrogeology': ['geohydrology', 'hydrogeology'],
 'hydroiodic': ['hydroiodic', 'iodohydric'],
 'hydrometeor': ['heterodromy', 'hydrometeor'],
 'hydromotor': ['hydromotor', 'orthodromy'],
 'hydronephrosis': ['hydronephrosis', 'nephrohydrosis'],
 'hydropneumopericardium': ['hydropneumopericardium', 'pneumohydropericardium'],
 'hydropneumothorax': ['hydropneumothorax', 'pneumohydrothorax'],
 'hydrorachis': ['hydrocharis', 'hydrorachis'],
 'hydrosulphate': ['hydrosulphate', 'sulphohydrate'],
 'hydrotical': ['dacryolith', 'hydrotical'],
 'hydrous': ['hydrous', 'shroudy'],
 'hyetograph': ['ethography', 'hyetograph'],
 'hylidae': ['headily', 'hylidae'],
 'hylist': ['hylist', 'slithy'],
 'hyllus': ['hyllus', 'lushly'],
 'hylopathism': ['halophytism', 'hylopathism'],
 'hymenic': ['chimney', 'hymenic'],
 'hymettic': ['hymettic', 'thymetic'],
 'hymnologist': ['hymnologist', 'smoothingly'],
 'hyoglossal': ['glossohyal', 'hyoglossal'],
 'hyoidal': ['holiday', 'hyaloid', 'hyoidal'],
 'hyothyreoid': ['hyothyreoid', 'thyreohyoid'],
 'hyothyroid': ['hyothyroid', 'thyrohyoid'],
 'hypaethron': ['hypaethron', 'hypothenar'],
 'hypercone': ['coryphene', 'hypercone'],
 'hypergamous': ['hypergamous', 'museography'],
 'hypertoxic': ['hypertoxic', 'xerophytic'],
 'hypnobate': ['batyphone', 'hypnobate'],
 'hypnoetic': ['hypnoetic', 'neophytic'],
 'hypnotic': ['hypnotic', 'phytonic', 'pythonic', 'typhonic'],
 'hypnotism': ['hypnotism', 'pythonism'],
 'hypnotist': ['hypnotist', 'pythonist'],
 'hypnotize': ['hypnotize', 'pythonize'],
 'hypnotoid': ['hypnotoid', 'pythonoid'],
 'hypobole': ['hypobole', 'lyophobe'],
 'hypocarp': ['apocryph', 'hypocarp'],
 'hypocrite': ['chirotype', 'hypocrite'],
 'hypodorian': ['hypodorian', 'radiophony'],
 'hypoglottis': ['hypoglottis', 'phytologist'],
 'hypomanic': ['amphicyon', 'hypomanic'],
 'hypopteron': ['hypopteron', 'phonotyper'],
 'hyporadius': ['hyporadius', 'suprahyoid'],
 'hyposcleral': ['hyposcleral', 'phylloceras'],
 'hyposmia': ['hyposmia', 'phymosia'],
 'hypostomatic': ['hypostomatic', 'somatophytic'],
 'hypothec': ['hypothec', 'photechy'],
 'hypothenar': ['hypaethron', 'hypothenar'],
 'hypothermia': ['hemiatrophy', 'hypothermia'],
 'hypsiloid': ['hypsiloid', 'syphiloid'],
 'hyracid': ['diarchy', 'hyracid'],
 'hyssop': ['hyssop', 'phossy', 'sposhy'],
 'hysteresial': ['hysteresial', 'hysteriales'],
 'hysteria': ['hysteria', 'sheriyat'],
 'hysteriales': ['hysteresial', 'hysteriales'],
 'hysterolaparotomy': ['hysterolaparotomy', 'laparohysterotomy'],
 'hysteromyomectomy': ['hysteromyomectomy', 'myomohysterectomy'],
 'hysteropathy': ['hysteropathy', 'hysterophyta'],
 'hysterophyta': ['hysteropathy', 'hysterophyta'],
 'iamb': ['iamb', 'mabi'],
 'iambelegus': ['elegiambus', 'iambelegus'],
 'iambic': ['cimbia', 'iambic'],
 'ian': ['ani', 'ian'],
 'ianus': ['ianus', 'suina'],
 'iatraliptics': ['iatraliptics', 'partialistic'],
 'iatric': ['iatric', 'tricia'],
 'ibad': ['adib', 'ibad'],
 'iban': ['bain', 'bani', 'iban'],
 'ibanag': ['bagani', 'bangia', 'ibanag'],
 'iberian': ['aribine', 'bairnie', 'iberian'],
 'ibo': ['ibo', 'obi'],
 'ibota': ['biota', 'ibota'],
 'icacorea': ['coraciae', 'icacorea'],
 'icarian': ['arician', 'icarian'],
 'icecap': ['icecap', 'ipecac'],
 'iced': ['dice', 'iced'],
 'iceland': ['cladine', 'decalin', 'iceland'],
 'icelandic': ['cicindela', 'cinclidae', 'icelandic'],
 'iceman': ['anemic', 'cinema', 'iceman'],
 'ich': ['chi', 'hic', 'ich'],
 'ichnolite': ['ichnolite', 'neolithic'],
 'ichor': ['chiro', 'choir', 'ichor'],
 'icicle': ['cilice', 'icicle'],
 'icon': ['cion', 'coin', 'icon'],
 'iconian': ['anionic', 'iconian'],
 'iconism': ['iconism', 'imsonic', 'miscoin'],
 'iconolater': ['iconolater', 'relocation'],
 'iconomania': ['iconomania', 'oniomaniac'],
 'iconometrical': ['iconometrical', 'intracoelomic'],
 'icteridae': ['diaeretic', 'icteridae'],
 'icterine': ['icterine', 'reincite'],
 'icterus': ['curtise', 'icterus'],
 'ictonyx': ['ictonyx', 'oxyntic'],
 'ictus': ['cutis', 'ictus'],
 'id': ['di', 'id'],
 'ida': ['aid', 'ida'],
 'idaean': ['adenia', 'idaean'],
 'ide': ['die', 'ide'],
 'idea': ['aide', 'idea'],
 'ideal': ['adiel', 'delia', 'ideal'],
 'idealism': ['idealism', 'lamiides'],
 'idealistic': ['disilicate', 'idealistic'],
 'ideality': ['aedility', 'ideality'],
 'idealness': ['idealness', 'leadiness'],
 'idean': ['diane', 'idean'],
 'ideation': ['ideation', 'iodinate', 'taenioid'],
 'identical': ['ctenidial', 'identical'],
 'ideograph': ['eidograph', 'ideograph'],
 'ideology': ['eidology', 'ideology'],
 'ideoplasty': ['ideoplasty', 'stylopidae'],
 'ides': ['desi', 'ides', 'seid', 'side'],
 'idiasm': ['idiasm', 'simiad'],
 'idioblast': ['diabolist', 'idioblast'],
 'idiomology': ['idiomology', 'oligomyoid'],
 'idioretinal': ['idioretinal', 'litorinidae'],
 'idiotish': ['histioid', 'idiotish'],
 'idle': ['idle', 'lide', 'lied'],
 'idleman': ['idleman', 'melinda'],
 'idleset': ['idleset', 'isleted'],
 'idlety': ['idlety', 'lydite', 'tidely', 'tidley'],
 'idly': ['idly', 'idyl'],
 'idocrase': ['idocrase', 'radicose'],
 'idoism': ['idoism', 'iodism'],
 'idol': ['dilo', 'diol', 'doli', 'idol', 'olid'],
 'idola': ['aloid', 'dolia', 'idola'],
 'idolaster': ['estradiol', 'idolaster'],
 'idolatry': ['adroitly', 'dilatory', 'idolatry'],
 'idolum': ['dolium', 'idolum'],
 'idoneal': ['adinole', 'idoneal'],
 'idorgan': ['gordian', 'idorgan', 'roading'],
 'idose': ['diose', 'idose', 'oside'],
 'idotea': ['idotea', 'iodate', 'otidae'],
 'idryl': ['idryl', 'lyrid'],
 'idyl': ['idly', 'idyl'],
 'idyler': ['direly', 'idyler'],
 'ierne': ['ernie', 'ierne', 'irene'],
 'if': ['fi', 'if'],
 'ife': ['fei', 'fie', 'ife'],
 'igara': ['agria', 'igara'],
 'igdyr': ['igdyr', 'ridgy'],
 'igloo': ['igloo', 'logoi'],
 'ignatius': ['giustina', 'ignatius'],
 'igneoaqueous': ['aqueoigneous', 'igneoaqueous'],
 'ignicolist': ['ignicolist', 'soliciting'],
 'igniter': ['igniter', 'ringite', 'tigrine'],
 'ignitor': ['ignitor', 'rioting'],
 'ignoble': ['gobelin', 'gobline', 'ignoble', 'inglobe'],
 'ignoramus': ['graminous', 'ignoramus'],
 'ignorance': ['enorganic', 'ignorance'],
 'ignorant': ['ignorant', 'tongrian'],
 'ignore': ['ignore', 'region'],
 'ignorement': ['ignorement', 'omnigerent'],
 'iguana': ['guiana', 'iguana'],
 'ihlat': ['ihlat', 'tahil'],
 'ihram': ['hiram', 'ihram', 'mahri'],
 'ijma': ['ijma', 'jami'],
 'ikat': ['atik', 'ikat'],
 'ikona': ['ikona', 'konia'],
 'ikra': ['ikra', 'kari', 'raki'],
 'ila': ['ail', 'ila', 'lai'],
 'ileac': ['alice', 'celia', 'ileac'],
 'ileon': ['enoil', 'ileon', 'olein'],
 'iliac': ['cilia', 'iliac'],
 'iliacus': ['acilius', 'iliacus'],
 'ilian': ['ilian', 'inial'],
 'ilicaceae': ['caeciliae', 'ilicaceae'],
 'ilioischiac': ['ilioischiac', 'ischioiliac'],
 'iliosacral': ['iliosacral', 'oscillaria'],
 'ilk': ['ilk', 'kil'],
 'ilka': ['ilka', 'kail', 'kali'],
 'ilkane': ['alkine', 'ilkane', 'inlake', 'inleak'],
 'illative': ['illative', 'veiltail'],
 'illaudatory': ['illaudatory', 'laudatorily'],
 'illeck': ['ellick', 'illeck'],
 'illinois': ['illinois', 'illision'],
 'illision': ['illinois', 'illision'],
 'illium': ['illium', 'lilium'],
 'illoricated': ['illoricated', 'lacertiloid'],
 'illth': ['illth', 'thill'],
 'illude': ['dillue', 'illude'],
 'illuder': ['dilluer', 'illuder'],
 'illy': ['illy', 'lily', 'yill'],
 'ilmenite': ['ilmenite', 'melinite', 'menilite'],
 'ilongot': ['ilongot', 'tooling'],
 'ilot': ['ilot', 'toil'],
 'ilya': ['ilya', 'yali'],
 'ima': ['aim', 'ami', 'ima'],
 'imager': ['imager', 'maigre', 'margie', 'mirage'],
 'imaginant': ['animating', 'imaginant'],
 'imaginer': ['imaginer', 'migraine'],
 'imagist': ['imagist', 'stigmai'],
 'imago': ['amigo', 'imago'],
 'imam': ['ammi', 'imam', 'maim', 'mima'],
 'imaret': ['imaret', 'metria', 'mirate', 'rimate'],
 'imbarge': ['gambier', 'imbarge'],
 'imbark': ['bikram', 'imbark'],
 'imbat': ['ambit', 'imbat'],
 'imbed': ['bedim', 'imbed'],
 'imbrue': ['erbium', 'imbrue'],
 'imbrute': ['burmite', 'imbrute', 'terbium'],
 'imer': ['emir', 'imer', 'mire', 'reim', 'remi', 'riem', 'rime'],
 'imerina': ['imerina', 'inermia'],
 'imitancy': ['imitancy', 'intimacy', 'minacity'],
 'immane': ['ammine', 'immane'],
 'immanes': ['amenism', 'immanes', 'misname'],
 'immaterials': ['immaterials', 'materialism'],
 'immerd': ['dimmer', 'immerd', 'rimmed'],
 'immersible': ['immersible', 'semilimber'],
 'immersion': ['immersion', 'semiminor'],
 'immi': ['immi', 'mimi'],
 'imogen': ['geonim', 'imogen'],
 'imolinda': ['dominial', 'imolinda', 'limoniad'],
 'imp': ['imp', 'pim'],
 'impaction': ['impaction', 'ptomainic'],
 'impages': ['impages', 'mispage'],
 'impaint': ['impaint', 'timpani'],
 'impair': ['impair', 'pamiri'],
 'impala': ['impala', 'malapi'],
 'impaler': ['impaler', 'impearl', 'lempira', 'premial'],
 'impalsy': ['impalsy', 'misplay'],
 'impane': ['impane', 'pieman'],
 'impanel': ['impanel', 'maniple'],
 'impar': ['impar', 'pamir', 'prima'],
 'imparalleled': ['demiparallel', 'imparalleled'],
 'imparl': ['imparl', 'primal'],
 'impart': ['armpit', 'impart'],
 'imparter': ['imparter', 'reimpart'],
 'impartial': ['impartial', 'primatial'],
 'impaste': ['impaste', 'pastime'],
 'impasture': ['impasture', 'septarium'],
 'impeach': ['aphemic', 'impeach'],
 'impearl': ['impaler', 'impearl', 'lempira', 'premial'],
 'impeder': ['demirep', 'epiderm', 'impeder', 'remiped'],
 'impedient': ['impedient', 'mendipite'],
 'impenetrable': ['impenetrable', 'intemperable'],
 'impenetrably': ['impenetrably', 'intemperably'],
 'impenetrate': ['impenetrate', 'intemperate'],
 'imperant': ['imperant', 'pairment', 'partimen', 'premiant', 'tripeman'],
 'imperate': ['imperate', 'premiate'],
 'imperish': ['emirship', 'imperish'],
 'imperscriptible': ['imperscriptible', 'imprescriptible'],
 'impersonate': ['impersonate', 'proseminate'],
 'impersonation': ['impersonation', 'prosemination', 'semipronation'],
 'impeticos': ['impeticos', 'poeticism'],
 'impetre': ['emptier', 'impetre'],
 'impetus': ['impetus', 'upsmite'],
 'imphee': ['imphee', 'phemie'],
 'implacental': ['capillament', 'implacental'],
 'implanter': ['implanter', 'reimplant'],
 'implate': ['implate', 'palmite'],
 'impleader': ['epidermal', 'impleader', 'premedial'],
 'implicate': ['ampelitic', 'implicate'],
 'impling': ['impling', 'limping'],
 'imply': ['imply', 'limpy', 'pilmy'],
 'impollute': ['impollute', 'multipole'],
 'imponderous': ['endosporium', 'imponderous'],
 'imponent': ['imponent', 'pimenton'],
 'importable': ['bitemporal', 'importable'],
 'importancy': ['importancy', 'patronymic', 'pyromantic'],
 'importer': ['importer', 'promerit', 'reimport'],
 'importunance': ['importunance', 'unimportance'],
 'importunate': ['importunate', 'permutation'],
 'importune': ['entropium', 'importune'],
 'imposal': ['imposal', 'spiloma'],
 'imposer': ['imposer', 'promise', 'semipro'],
 'imposter': ['imposter', 'tripsome'],
 'imposure': ['imposure', 'premious'],
 'imprecatory': ['cryptomeria', 'imprecatory'],
 'impreg': ['gimper', 'impreg'],
 'imprescriptible': ['imperscriptible', 'imprescriptible'],
 'imprese': ['emprise', 'imprese', 'premise', 'spireme'],
 'impress': ['impress', 'persism', 'premiss'],
 'impresser': ['impresser', 'reimpress'],
 'impressibility': ['impressibility', 'permissibility'],
 'impressible': ['impressible', 'permissible'],
 'impressibleness': ['impressibleness', 'permissibleness'],
 'impressibly': ['impressibly', 'permissibly'],
 'impression': ['impression', 'permission'],
 'impressionism': ['impressionism', 'misimpression'],
 'impressive': ['impressive', 'permissive'],
 'impressively': ['impressively', 'permissively'],
 'impressiveness': ['impressiveness', 'permissiveness'],
 'impressure': ['impressure', 'presurmise'],
 'imprinter': ['imprinter', 'reimprint'],
 'imprisoner': ['imprisoner', 'reimprison'],
 'improcreant': ['improcreant', 'preromantic'],
 'impship': ['impship', 'pimpish'],
 'impuberal': ['epilabrum', 'impuberal'],
 'impugnable': ['impugnable', 'plumbagine'],
 'impure': ['impure', 'umpire'],
 'impuritan': ['impuritan', 'partinium'],
 'imputer': ['imputer', 'trumpie'],
 'imsonic': ['iconism', 'imsonic', 'miscoin'],
 'in': ['in', 'ni'],
 'inaction': ['aconitin', 'inaction', 'nicotian'],
 'inactivate': ['inactivate', 'vaticinate'],
 'inactivation': ['inactivation', 'vaticination'],
 'inactive': ['antivice', 'inactive', 'vineatic'],
 'inadept': ['depaint', 'inadept', 'painted', 'patined'],
 'inaja': ['inaja', 'jaina'],
 'inalimental': ['antimallein', 'inalimental'],
 'inamorata': ['amatorian', 'inamorata'],
 'inane': ['annie', 'inane'],
 'inanga': ['angina', 'inanga'],
 'inanimate': ['amanitine', 'inanimate'],
 'inanimated': ['diamantine', 'inanimated'],
 'inapt': ['inapt', 'paint', 'pinta'],
 'inaptly': ['inaptly', 'planity', 'ptyalin'],
 'inarch': ['chinar', 'inarch'],
 'inarm': ['inarm', 'minar'],
 'inasmuch': ['humanics', 'inasmuch'],
 'inaurate': ['inaurate', 'ituraean'],
 'inbe': ['beni', 'bien', 'bine', 'inbe'],
 'inbreak': ['brankie', 'inbreak'],
 'inbreathe': ['hibernate', 'inbreathe'],
 'inbred': ['binder', 'inbred', 'rebind'],
 'inbreed': ['birdeen', 'inbreed'],
 'inca': ['cain', 'inca'],
 'incaic': ['acinic', 'incaic'],
 'incarnate': ['cratinean', 'incarnate', 'nectarian'],
 'incase': ['casein', 'incase'],
 'incast': ['incast', 'nastic'],
 'incensation': ['incensation', 'inscenation'],
 'incept': ['incept', 'pectin'],
 'inceptor': ['inceptor', 'pretonic'],
 'inceration': ['cineration', 'inceration'],
 'incessant': ['anticness', 'cantiness', 'incessant'],
 'incest': ['encist', 'incest', 'insect', 'scient'],
 'inch': ['chin', 'inch'],
 'inched': ['chined', 'inched'],
 'inchoate': ['inchoate', 'noachite'],
 'incide': ['cindie', 'incide'],
 'incinerate': ['creatinine', 'incinerate'],
 'incisal': ['incisal', 'salicin'],
 'incision': ['incision', 'inosinic'],
 'incisure': ['incisure', 'sciurine'],
 'inciter': ['citrine', 'crinite', 'inciter', 'neritic'],
 'inclinatorium': ['anticlinorium', 'inclinatorium'],
 'inclosure': ['cornelius', 'inclosure', 'reclusion'],
 'include': ['include', 'nuclide'],
 'incluse': ['esculin', 'incluse'],
 'incog': ['coign', 'incog'],
 'incognito': ['cognition', 'incognito'],
 'incoherence': ['coinherence', 'incoherence'],
 'incoherent': ['coinherent', 'incoherent'],
 'incomeless': ['comeliness', 'incomeless'],
 'incomer': ['incomer', 'moneric'],
 'incomputable': ['incomputable', 'uncompatible'],
 'incondite': ['incondite', 'nicotined'],
 'inconglomerate': ['inconglomerate', 'nongeometrical'],
 'inconsistent': ['inconsistent', 'nonscientist'],
 'inconsonant': ['inconsonant', 'nonsanction'],
 'incontrovertibility': ['incontrovertibility', 'introconvertibility'],
 'incontrovertible': ['incontrovertible', 'introconvertible'],
 'incorporate': ['incorporate', 'procreation'],
 'incorporated': ['adrenotropic', 'incorporated'],
 'incorpse': ['conspire', 'incorpse'],
 'incrash': ['archsin', 'incrash'],
 'increase': ['cerasein', 'increase'],
 'increate': ['aneretic', 'centiare', 'creatine', 'increate', 'iterance'],
 'incredited': ['incredited', 'indirected'],
 'increep': ['crepine', 'increep'],
 'increpate': ['anticreep', 'apenteric', 'increpate'],
 'increst': ['cistern', 'increst'],
 'incruental': ['incruental', 'unicentral'],
 'incrustant': ['incrustant', 'scrutinant'],
 'incrustate': ['incrustate', 'scaturient', 'scrutinate'],
 'incubate': ['cubanite', 'incubate'],
 'incudal': ['dulcian', 'incudal', 'lucanid', 'lucinda'],
 'incudomalleal': ['incudomalleal', 'malleoincudal'],
 'inculcation': ['anticouncil', 'inculcation'],
 'inculture': ['culturine', 'inculture'],
 'incuneation': ['enunciation', 'incuneation'],
 'incur': ['curin', 'incur', 'runic'],
 'incurable': ['binuclear', 'incurable'],
 'incus': ['incus', 'usnic'],
 'incut': ['cutin', 'incut', 'tunic'],
 'ind': ['din', 'ind', 'nid'],
 'indaba': ['badian', 'indaba'],
 'indagator': ['gradation', 'indagator', 'tanagroid'],
 'indan': ['indan', 'nandi'],
 'indane': ['aidenn', 'andine', 'dannie', 'indane'],
 'inde': ['dine', 'enid', 'inde', 'nide'],
 'indebt': ['bident', 'indebt'],
 'indebted': ['bidented', 'indebted'],
 'indefinitude': ['indefinitude', 'unidentified'],
 'indent': ['dentin', 'indent', 'intend', 'tinned'],
 'indented': ['indented', 'intended'],
 'indentedly': ['indentedly', 'intendedly'],
 'indenter': ['indenter', 'intender', 'reintend'],
 'indentment': ['indentment', 'intendment'],
 'indentured': ['indentured', 'underntide'],
 'indentwise': ['disentwine', 'indentwise'],
 'indeprivable': ['indeprivable', 'predivinable'],
 'indesert': ['indesert', 'inserted', 'resident'],
 'indiana': ['anidian', 'indiana'],
 'indic': ['dinic', 'indic'],
 'indican': ['cnidian', 'indican'],
 'indicate': ['diacetin', 'indicate'],
 'indicatory': ['dictionary', 'indicatory'],
 'indicial': ['anilidic', 'indicial'],
 'indicter': ['indicter', 'indirect', 'reindict'],
 'indies': ['indies', 'inside'],
 'indigena': ['gadinine', 'indigena'],
 'indigitate': ['indigitate', 'tingitidae'],
 'indign': ['dining', 'indign', 'niding'],
 'indigoferous': ['gonidiferous', 'indigoferous'],
 'indigotin': ['digitonin', 'indigotin'],
 'indirect': ['indicter', 'indirect', 'reindict'],
 'indirected': ['incredited', 'indirected'],
 'indirectly': ['cylindrite', 'indirectly'],
 'indiscreet': ['indiscreet', 'indiscrete', 'iridescent'],
 'indiscreetly': ['indiscreetly', 'indiscretely', 'iridescently'],
 'indiscrete': ['indiscreet', 'indiscrete', 'iridescent'],
 'indiscretely': ['indiscreetly', 'indiscretely', 'iridescently'],
 'indissolute': ['delusionist', 'indissolute'],
 'indite': ['indite', 'tineid'],
 'inditer': ['inditer', 'nitride'],
 'indogaean': ['ganoidean', 'indogaean'],
 'indole': ['doline', 'indole', 'leonid', 'loined', 'olenid'],
 'indolence': ['endocline', 'indolence'],
 'indoles': ['indoles', 'sondeli'],
 'indologist': ['indologist', 'nidologist'],
 'indology': ['indology', 'nidology'],
 'indone': ['donnie', 'indone', 'ondine'],
 'indoors': ['indoors', 'sordino'],
 'indorse': ['indorse', 'ordines', 'siredon', 'sordine'],
 'indra': ['darin', 'dinar', 'drain', 'indra', 'nadir', 'ranid'],
 'indrawn': ['indrawn', 'winnard'],
 'induce': ['induce', 'uniced'],
 'inducer': ['inducer', 'uncried'],
 'indulge': ['dueling', 'indulge'],
 'indulgential': ['dentilingual', 'indulgential', 'linguidental'],
 'indulger': ['indulger', 'ungirdle'],
 'indument': ['indument', 'unminted'],
 'indurable': ['indurable', 'unbrailed', 'unridable'],
 'indurate': ['indurate', 'turdinae'],
 'induration': ['diurnation', 'induration'],
 'indus': ['dinus', 'indus', 'nidus'],
 'indusiform': ['disuniform', 'indusiform'],
 'induviae': ['induviae', 'viduinae'],
 'induvial': ['diluvian', 'induvial'],
 'inearth': ['anither', 'inearth', 'naither'],
 'inelastic': ['elasticin', 'inelastic', 'sciential'],
 'inelegant': ['eglantine', 'inelegant', 'legantine'],
 'ineludible': ['ineludible', 'unelidible'],
 'inept': ['inept', 'pinte'],
 'inequity': ['equinity', 'inequity'],
 'inerm': ['inerm', 'miner'],
 'inermes': ['ermines', 'inermes'],
 'inermia': ['imerina', 'inermia'],
 'inermous': ['inermous', 'monsieur'],
 'inert': ['inert', 'inter', 'niter', 'retin', 'trine'],
 'inertance': ['inertance', 'nectarine'],
 'inertial': ['inertial', 'linarite'],
 'inertly': ['elytrin', 'inertly', 'trinely'],
 'inethical': ['echinital', 'inethical'],
 'ineunt': ['ineunt', 'untine'],
 'inez': ['inez', 'zein'],
 'inface': ['fiance', 'inface'],
 'infame': ['famine', 'infame'],
 'infamy': ['infamy', 'manify'],
 'infarct': ['frantic', 'infarct', 'infract'],
 'infarction': ['infarction', 'infraction'],
 'infaust': ['faunist', 'fustian', 'infaust'],
 'infecter': ['frenetic', 'infecter', 'reinfect'],
 'infeed': ['define', 'infeed'],
 'infelt': ['finlet', 'infelt'],
 'infer': ['finer', 'infer'],
 'inferable': ['inferable', 'refinable'],
 'infern': ['finner', 'infern'],
 'inferoanterior': ['anteroinferior', 'inferoanterior'],
 'inferoposterior': ['inferoposterior', 'posteroinferior'],
 'infestation': ['festination', 'infestation', 'sinfonietta'],
 'infester': ['infester', 'reinfest'],
 'infidel': ['infidel', 'infield'],
 'infield': ['infidel', 'infield'],
 'inflame': ['feminal', 'inflame'],
 'inflamed': ['fieldman', 'inflamed'],
 'inflamer': ['inflamer', 'rifleman'],
 'inflatus': ['inflatus', 'stainful'],
 'inflicter': ['inflicter', 'reinflict'],
 'inform': ['formin', 'inform'],
 'informal': ['formalin', 'informal', 'laniform'],
 'informer': ['informer', 'reinform', 'reniform'],
 'infra': ['infra', 'irfan'],
 'infract': ['frantic', 'infarct', 'infract'],
 'infraction': ['infarction', 'infraction'],
 'infringe': ['infringe', 'refining'],
 'ing': ['gin', 'ing', 'nig'],
 'inga': ['gain', 'inga', 'naig', 'ngai'],
 'ingaevones': ['avignonese', 'ingaevones'],
 'ingate': ['eating', 'ingate', 'tangie'],
 'ingather': ['hearting', 'ingather'],
 'ingenue': ['genuine', 'ingenue'],
 'ingenuous': ['ingenuous', 'unigenous'],
 'inger': ['grein', 'inger', 'nigre', 'regin', 'reign', 'ringe'],
 'ingest': ['ingest', 'signet', 'stinge'],
 'ingesta': ['easting',
             'gainset',
             'genista',
             'ingesta',
             'seating',
             'signate',
             'teasing'],
 'ingle': ['ingle', 'ligne', 'linge', 'nigel'],
 'inglobe': ['gobelin', 'gobline', 'ignoble', 'inglobe'],
 'ingomar': ['ingomar', 'moringa', 'roaming'],
 'ingram': ['arming', 'ingram', 'margin'],
 'ingrate': ['angrite', 'granite', 'ingrate', 'tangier', 'tearing', 'tigrean'],
 'ingrow': ['ingrow', 'rowing'],
 'ingrowth': ['ingrowth', 'throwing'],
 'inguinal': ['inguinal', 'unailing'],
 'inguinocrural': ['cruroinguinal', 'inguinocrural'],
 'inhabiter': ['inhabiter', 'reinhabit'],
 'inhaler': ['hernial', 'inhaler'],
 'inhauler': ['herulian', 'inhauler'],
 'inhaust': ['auntish', 'inhaust'],
 'inhere': ['herein', 'inhere'],
 'inhumer': ['inhumer', 'rhenium'],
 'inial': ['ilian', 'inial'],
 'ink': ['ink', 'kin'],
 'inkle': ['inkle', 'liken'],
 'inkless': ['inkless', 'kinless'],
 'inkling': ['inkling', 'linking'],
 'inknot': ['inknot', 'tonkin'],
 'inkra': ['inkra', 'krina', 'nakir', 'rinka'],
 'inks': ['inks', 'sink', 'skin'],
 'inlaid': ['anilid', 'dialin', 'dianil', 'inlaid'],
 'inlake': ['alkine', 'ilkane', 'inlake', 'inleak'],
 'inlaut': ['inlaut', 'unital'],
 'inlaw': ['inlaw', 'liwan'],
 'inlay': ['inlay', 'naily'],
 'inlayer': ['inlayer', 'nailery'],
 'inleak': ['alkine', 'ilkane', 'inlake', 'inleak'],
 'inlet': ['inlet', 'linet'],
 'inlook': ['inlook', 'koilon'],
 'inly': ['inly', 'liny'],
 'inmate': ['etamin', 'inmate', 'taimen', 'tamein'],
 'inmeats': ['atenism', 'inmeats', 'insteam', 'samnite'],
 'inmost': ['inmost', 'monist', 'omnist'],
 'innate': ['annite', 'innate', 'tinean'],
 'innative': ['innative', 'invinate'],
 'innatural': ['innatural', 'triannual'],
 'inner': ['inner', 'renin'],
 'innerve': ['innerve', 'nervine', 'vernine'],
 'innest': ['innest', 'sennit', 'sinnet', 'tennis'],
 'innet': ['innet', 'tinne'],
 'innominata': ['antinomian', 'innominata'],
 'innovate': ['innovate', 'venation'],
 'innovationist': ['innovationist', 'nonvisitation'],
 'ino': ['ino', 'ion'],
 'inobtainable': ['inobtainable', 'nonbilabiate'],
 'inocarpus': ['inocarpus', 'unprosaic'],
 'inoculant': ['continual', 'inoculant', 'unctional'],
 'inocystoma': ['actomyosin', 'inocystoma'],
 'inodes': ['deinos', 'donsie', 'inodes', 'onside'],
 'inogen': ['genion', 'inogen'],
 'inoma': ['amino', 'inoma', 'naomi', 'omani', 'omina'],
 'inomyxoma': ['inomyxoma', 'myxoinoma'],
 'inone': ['inone', 'oenin'],
 'inoperculata': ['inoperculata', 'precautional'],
 'inorb': ['biron', 'inorb', 'robin'],
 'inorganic': ['conringia', 'inorganic'],
 'inorganical': ['carolingian', 'inorganical'],
 'inornate': ['anointer', 'inornate', 'nonirate', 'reanoint'],
 'inosic': ['inosic', 'sinico'],
 'inosinic': ['incision', 'inosinic'],
 'inosite': ['inosite', 'sionite'],
 'inphase': ['inphase', 'phineas'],
 'inpush': ['inpush', 'punish', 'unship'],
 'input': ['input', 'punti'],
 'inquiet': ['inquiet', 'quinite'],
 'inreality': ['inreality', 'linearity'],
 'inro': ['inro', 'iron', 'noir', 'nori'],
 'inroad': ['dorian', 'inroad', 'ordain'],
 'inroader': ['inroader', 'ordainer', 'reordain'],
 'inrub': ['bruin', 'burin', 'inrub'],
 'inrun': ['inrun', 'inurn'],
 'insane': ['insane', 'sienna'],
 'insatiably': ['insatiably', 'sanability'],
 'inscenation': ['incensation', 'inscenation'],
 'inscient': ['inscient', 'nicenist'],
 'insculp': ['insculp', 'sculpin'],
 'insea': ['anise', 'insea', 'siena', 'sinae'],
 'inseam': ['asimen', 'inseam', 'mesian'],
 'insect': ['encist', 'incest', 'insect', 'scient'],
 'insectan': ['insectan', 'instance'],
 'insectile': ['insectile', 'selenitic'],
 'insectivora': ['insectivora', 'visceration'],
 'insecure': ['insecure', 'sinecure'],
 'insee': ['insee', 'seine'],
 'inseer': ['inseer', 'nereis', 'seiner', 'serine', 'sirene'],
 'insert': ['estrin', 'insert', 'sinter', 'sterin', 'triens'],
 'inserted': ['indesert', 'inserted', 'resident'],
 'inserter': ['inserter', 'reinsert'],
 'insessor': ['insessor', 'rosiness'],
 'inset': ['inset', 'neist', 'snite', 'stein', 'stine', 'tsine'],
 'insetter': ['insetter', 'interest', 'interset', 'sternite'],
 'inshave': ['evanish', 'inshave'],
 'inshoot': ['inshoot', 'insooth'],
 'inside': ['indies', 'inside'],
 'insider': ['insider', 'siderin'],
 'insistent': ['insistent', 'tintiness'],
 'insister': ['insister', 'reinsist', 'sinister', 'sisterin'],
 'insole': ['insole', 'leonis', 'lesion', 'selion'],
 'insomnia': ['insomnia', 'simonian'],
 'insomniac': ['aniconism', 'insomniac'],
 'insooth': ['inshoot', 'insooth'],
 'insorb': ['insorb', 'sorbin'],
 'insoul': ['insoul', 'linous', 'nilous', 'unsoil'],
 'inspection': ['cispontine', 'inspection'],
 'inspiriter': ['inspiriter', 'reinspirit'],
 'inspissate': ['antisepsis', 'inspissate'],
 'inspreith': ['inspreith', 'nephritis', 'phrenitis'],
 'installer': ['installer', 'reinstall'],
 'instance': ['insectan', 'instance'],
 'instanter': ['instanter', 'transient'],
 'instar': ['instar', 'santir', 'strain'],
 'instate': ['atenist', 'instate', 'satient', 'steatin'],
 'instead': ['destain', 'instead', 'sainted', 'satined'],
 'insteam': ['atenism', 'inmeats', 'insteam', 'samnite'],
 'instep': ['instep', 'spinet'],
 'instiller': ['instiller', 'reinstill'],
 'instructer': ['instructer', 'intercrust', 'reinstruct'],
 'instructional': ['instructional', 'nonaltruistic'],
 'insula': ['insula', 'lanius', 'lusian'],
 'insulant': ['insulant', 'sultanin'],
 'insulse': ['insulse', 'silenus'],
 'insult': ['insult', 'sunlit', 'unlist', 'unslit'],
 'insulter': ['insulter', 'lustrine', 'reinsult'],
 'insunk': ['insunk', 'unskin'],
 'insurable': ['insurable', 'sublinear'],
 'insurance': ['insurance', 'nuisancer'],
 'insurant': ['insurant', 'unstrain'],
 'insure': ['insure', 'rusine', 'ursine'],
 'insurge': ['insurge', 'resuing'],
 'insurgent': ['insurgent', 'unresting'],
 'intactile': ['catlinite', 'intactile'],
 'intaglio': ['intaglio', 'ligation'],
 'intake': ['intake', 'kentia'],
 'intaker': ['intaker', 'katrine', 'keratin'],
 'intarsia': ['antiaris', 'intarsia'],
 'intarsiate': ['intarsiate', 'nestiatria'],
 'integral': ['integral', 'teraglin', 'triangle'],
 'integralize': ['gelatinizer', 'integralize'],
 'integrate': ['argentite', 'integrate'],
 'integrative': ['integrative', 'vertiginate', 'vinaigrette'],
 'integrious': ['grisounite', 'grisoutine', 'integrious'],
 'intemperable': ['impenetrable', 'intemperable'],
 'intemperably': ['impenetrably', 'intemperably'],
 'intemperate': ['impenetrate', 'intemperate'],
 'intemporal': ['intemporal', 'trampoline'],
 'intend': ['dentin', 'indent', 'intend', 'tinned'],
 'intended': ['indented', 'intended'],
 'intendedly': ['indentedly', 'intendedly'],
 'intender': ['indenter', 'intender', 'reintend'],
 'intendment': ['indentment', 'intendment'],
 'intense': ['intense', 'sennite'],
 'intent': ['intent', 'tinnet'],
 'intently': ['intently', 'nitently'],
 'inter': ['inert', 'inter', 'niter', 'retin', 'trine'],
 'interactional': ['interactional', 'intercalation'],
 'interagent': ['entreating', 'interagent'],
 'interally': ['interally', 'reliantly'],
 'interastral': ['interastral', 'intertarsal'],
 'intercalation': ['interactional', 'intercalation'],
 'intercale': ['intercale', 'interlace', 'lacertine', 'reclinate'],
 'intercede': ['intercede', 'tridecene'],
 'interceder': ['crednerite', 'interceder'],
 'intercession': ['intercession', 'recensionist'],
 'intercome': ['entomeric', 'intercome', 'morencite'],
 'interconal': ['interconal', 'nonrecital'],
 'intercrust': ['instructer', 'intercrust', 'reinstruct'],
 'interdome': ['interdome', 'mordenite', 'nemertoid'],
 'intereat': ['intereat', 'tinetare'],
 'interest': ['insetter', 'interest', 'interset', 'sternite'],
 'interester': ['interester', 'reinterest'],
 'interfering': ['interfering', 'interfinger'],
 'interfinger': ['interfering', 'interfinger'],
 'intergrade': ['gradienter', 'intergrade'],
 'interim': ['interim', 'termini'],
 'interimistic': ['interimistic', 'trimesitinic'],
 'interlace': ['intercale', 'interlace', 'lacertine', 'reclinate'],
 'interlaced': ['credential', 'interlaced', 'reclinated'],
 'interlaid': ['deliriant', 'draintile', 'interlaid'],
 'interlap': ['interlap', 'repliant', 'triplane'],
 'interlapse': ['alpestrine', 'episternal', 'interlapse', 'presential'],
 'interlay': ['interlay', 'lyterian'],
 'interleaf': ['interleaf', 'reinflate'],
 'interleaver': ['interleaver', 'reverential'],
 'interlocal': ['citronella', 'interlocal'],
 'interlope': ['interlope', 'interpole', 'repletion', 'terpineol'],
 'interlot': ['interlot', 'trotline'],
 'intermat': ['intermat', 'martinet', 'tetramin'],
 'intermatch': ['intermatch', 'thermantic'],
 'intermine': ['intermine', 'nemertini', 'terminine'],
 'intermorainic': ['intermorainic', 'recrimination'],
 'intermutual': ['intermutual', 'ultraminute'],
 'intern': ['intern', 'tinner'],
 'internality': ['internality', 'itinerantly'],
 'internecive': ['internecive', 'reincentive'],
 'internee': ['internee', 'retinene'],
 'interoceptor': ['interoceptor', 'reprotection'],
 'interpause': ['interpause', 'resupinate'],
 'interpave': ['interpave', 'prenative'],
 'interpeal': ['interpeal', 'interplea'],
 'interpellate': ['interpellate', 'pantellerite'],
 'interpellation': ['interpellation', 'interpollinate'],
 'interphone': ['interphone', 'pinnothere'],
 'interplay': ['interplay', 'painterly'],
 'interplea': ['interpeal', 'interplea'],
 'interplead': ['interplead', 'peridental'],
 'interpolar': ['interpolar', 'reniportal'],
 'interpolate': ['interpolate', 'triantelope'],
 'interpole': ['interlope', 'interpole', 'repletion', 'terpineol'],
 'interpollinate': ['interpellation', 'interpollinate'],
 'interpone': ['interpone', 'peritenon', 'pinnotere', 'preintone'],
 'interposal': ['interposal', 'psalterion'],
 'interposure': ['interposure', 'neuropteris'],
 'interpreter': ['interpreter', 'reinterpret'],
 'interproduce': ['interproduce', 'prereduction'],
 'interroom': ['interroom', 'remontoir'],
 'interrupter': ['interrupter', 'reinterrupt'],
 'intersale': ['intersale', 'larsenite'],
 'intersectional': ['intersectional', 'intraselection'],
 'interset': ['insetter', 'interest', 'interset', 'sternite'],
 'intershade': ['dishearten', 'intershade'],
 'intersituate': ['intersituate', 'tenuistriate'],
 'intersocial': ['intersocial', 'orleanistic', 'sclerotinia'],
 'interspace': ['esperantic', 'interspace'],
 'interspecific': ['interspecific', 'prescientific'],
 'interspiration': ['interspiration', 'repristination'],
 'intersporal': ['intersporal', 'tripersonal'],
 'interstation': ['interstation', 'strontianite'],
 'intertalk': ['intertalk', 'latterkin'],
 'intertarsal': ['interastral', 'intertarsal'],
 'interteam': ['antimeter', 'attermine', 'interteam', 'terminate', 'tetramine'],
 'intertie': ['intertie', 'retinite'],
 'intertone': ['intertone', 'retention'],
 'intervascular': ['intervascular', 'vernacularist'],
 'intervention': ['intervention', 'introvenient'],
 'interverbal': ['interverbal', 'invertebral'],
 'interviewer': ['interviewer', 'reinterview'],
 'interwed': ['interwed', 'wintered'],
 'interwish': ['interwish', 'winterish'],
 'interwork': ['interwork', 'tinworker'],
 'interwove': ['interwove', 'overtwine'],
 'intestate': ['enstatite', 'intestate', 'satinette'],
 'intestinovesical': ['intestinovesical', 'vesicointestinal'],
 'inthrong': ['inthrong', 'northing'],
 'intima': ['intima', 'timani'],
 'intimacy': ['imitancy', 'intimacy', 'minacity'],
 'intimater': ['intimater', 'traintime'],
 'into': ['into', 'nito', 'oint', 'tino'],
 'intoed': ['ditone', 'intoed'],
 'intolerance': ['crenelation', 'intolerance'],
 'intolerating': ['intolerating', 'nitrogelatin'],
 'intonate': ['intonate', 'totanine'],
 'intonator': ['intonator', 'tortonian'],
 'intone': ['intone', 'tenino'],
 'intonement': ['intonement', 'omnitenent'],
 'intoner': ['intoner', 'ternion'],
 'intort': ['intort', 'tornit', 'triton'],
 'intoxicate': ['excitation', 'intoxicate'],
 'intracoelomic': ['iconometrical', 'intracoelomic'],
 'intracosmic': ['intracosmic', 'narcoticism'],
 'intracostal': ['intracostal', 'stratonical'],
 'intractile': ['intractile', 'triclinate'],
 'intrada': ['intrada', 'radiant'],
 'intraselection': ['intersectional', 'intraselection'],
 'intraseptal': ['intraseptal', 'paternalist', 'prenatalist'],
 'intraspinal': ['intraspinal', 'pinnitarsal'],
 'intreat': ['intreat', 'iterant', 'nitrate', 'tertian'],
 'intrencher': ['intrencher', 'reintrench'],
 'intricate': ['intricate', 'triactine'],
 'intrication': ['citrination', 'intrication'],
 'intrigue': ['intrigue', 'tigurine'],
 'introconvertibility': ['incontrovertibility', 'introconvertibility'],
 'introconvertible': ['incontrovertible', 'introconvertible'],
 'introduce': ['introduce', 'reduction'],
 'introit': ['introit', 'nitriot'],
 'introitus': ['introitus', 'routinist'],
 'introvenient': ['intervention', 'introvenient'],
 'intrude': ['intrude', 'turdine', 'untired', 'untried'],
 'intruse': ['intruse', 'sturine'],
 'intrust': ['intrust', 'sturtin'],
 'intube': ['butein', 'butine', 'intube'],
 'intue': ['intue', 'unite', 'untie'],
 'inula': ['inula', 'luian', 'uinal'],
 'inurbane': ['eburnian', 'inurbane'],
 'inure': ['inure', 'urine'],
 'inured': ['diurne', 'inured', 'ruined', 'unride'],
 'inurn': ['inrun', 'inurn'],
 'inustion': ['inustion', 'unionist'],
 'invader': ['invader', 'ravined', 'viander'],
 'invaluable': ['invaluable', 'unvailable'],
 'invar': ['invar', 'ravin', 'vanir'],
 'invector': ['contrive', 'invector'],
 'inveigler': ['inveigler', 'relieving'],
 'inventer': ['inventer', 'reinvent', 'ventrine', 'vintener'],
 'inventress': ['inventress', 'vintneress'],
 'inverness': ['inverness', 'nerviness'],
 'inversatile': ['inversatile', 'serviential'],
 'inverse': ['inverse', 'versine'],
 'invert': ['invert', 'virent'],
 'invertase': ['invertase', 'servetian'],
 'invertebral': ['interverbal', 'invertebral'],
 'inverter': ['inverter', 'reinvert', 'trinerve'],
 'investigation': ['investigation', 'tenovaginitis'],
 'invinate': ['innative', 'invinate'],
 'inviter': ['inviter', 'vitrine'],
 'invocate': ['conative', 'invocate'],
 'invoker': ['invoker', 'overink'],
 'involucrate': ['countervail', 'involucrate'],
 'involucre': ['involucre', 'volucrine'],
 'inwards': ['inwards', 'sinward'],
 'inwith': ['inwith', 'within'],
 'iodate': ['idotea', 'iodate', 'otidae'],
 'iodhydrate': ['hydriodate', 'iodhydrate'],
 'iodhydric': ['hydriodic', 'iodhydric'],
 'iodinate': ['ideation', 'iodinate', 'taenioid'],
 'iodinium': ['iodinium', 'ionidium'],
 'iodism': ['idoism', 'iodism'],
 'iodite': ['iodite', 'teioid'],
 'iodo': ['iodo', 'ooid'],
 'iodocasein': ['iodocasein', 'oniscoidea'],
 'iodochloride': ['chloroiodide', 'iodochloride'],
 'iodohydric': ['hydroiodic', 'iodohydric'],
 'iodol': ['dooli', 'iodol'],
 'iodothyrin': ['iodothyrin', 'thyroiodin'],
 'iodous': ['iodous', 'odious'],
 'ion': ['ino', 'ion'],
 'ionidium': ['iodinium', 'ionidium'],
 'ionizer': ['ionizer', 'ironize'],
 'iota': ['iota', 'tiao'],
 'iotacist': ['iotacist', 'taoistic'],
 'ipecac': ['icecap', 'ipecac'],
 'ipil': ['ipil', 'pili'],
 'ipseand': ['ipseand', 'panside', 'pansied'],
 'ira': ['air', 'ira', 'ria'],
 'iracund': ['candiru', 'iracund'],
 'irade': ['aider', 'deair', 'irade', 'redia'],
 'iran': ['arni', 'iran', 'nair', 'rain', 'rani'],
 'irani': ['irani', 'irian'],
 'iranism': ['iranism', 'sirmian'],
 'iranist': ['iranist', 'istrian'],
 'irascent': ['canister', 'cestrian', 'cisterna', 'irascent'],
 'irate': ['arite', 'artie', 'irate', 'retia', 'tarie'],
 'irately': ['irately', 'reality'],
 'ire': ['ire', 'rie'],
 'irena': ['erian', 'irena', 'reina'],
 'irene': ['ernie', 'ierne', 'irene'],
 'irenic': ['irenic', 'ricine'],
 'irenics': ['irenics', 'resinic', 'sericin', 'sirenic'],
 'irenicum': ['irenicum', 'muricine'],
 'iresine': ['iresine', 'iserine'],
 'irfan': ['infra', 'irfan'],
 'irgun': ['irgun', 'ruing', 'unrig'],
 'irian': ['irani', 'irian'],
 'iridal': ['iridal', 'lariid'],
 'iridate': ['arietid', 'iridate'],
 'iridectomy': ['iridectomy', 'mediocrity'],
 'irides': ['irides', 'irised'],
 'iridescent': ['indiscreet', 'indiscrete', 'iridescent'],
 'iridescently': ['indiscreetly', 'indiscretely', 'iridescently'],
 'iridosmium': ['iridosmium', 'osmiridium'],
 'irised': ['irides', 'irised'],
 'irish': ['irish', 'rishi', 'sirih'],
 'irk': ['irk', 'rik'],
 'irma': ['amir', 'irma', 'mari', 'mira', 'rami', 'rima'],
 'iroha': ['haori', 'iroha'],
 'irok': ['irok', 'kori'],
 'iron': ['inro', 'iron', 'noir', 'nori'],
 'ironclad': ['ironclad', 'rolandic'],
 'irone': ['irone', 'norie'],
 'ironhead': ['herodian', 'ironhead'],
 'ironice': ['ironice', 'oneiric'],
 'ironize': ['ionizer', 'ironize'],
 'ironshod': ['dishonor', 'ironshod'],
 'ironside': ['derision', 'ironside', 'resinoid', 'sirenoid'],
 'irradiant': ['irradiant', 'triandria'],
 'irrationable': ['irrationable', 'orbitelarian'],
 'irredenta': ['irredenta', 'retainder'],
 'irrelate': ['irrelate', 'retailer'],
 'irrepentance': ['irrepentance', 'pretercanine'],
 'irving': ['irving', 'riving', 'virgin'],
 'irvingiana': ['irvingiana', 'viraginian'],
 'is': ['is', 'si'],
 'isabel': ['isabel', 'lesbia'],
 'isabella': ['isabella', 'sailable'],
 'isagogical': ['isagogical', 'sialagogic'],
 'isagon': ['gosain', 'isagon', 'sagoin'],
 'isander': ['andries', 'isander', 'sardine'],
 'isanthous': ['anhistous', 'isanthous'],
 'isatate': ['isatate', 'satiate', 'taetsia'],
 'isatic': ['isatic', 'saitic'],
 'isatin': ['antisi', 'isatin'],
 'isatinic': ['isatinic', 'sinaitic'],
 'isaurian': ['anisuria', 'isaurian'],
 'isawa': ['isawa', 'waasi'],
 'isba': ['absi', 'bais', 'bias', 'isba'],
 'iscariot': ['aoristic', 'iscariot'],
 'ischemia': ['hemiasci', 'ischemia'],
 'ischioiliac': ['ilioischiac', 'ischioiliac'],
 'ischiorectal': ['ischiorectal', 'sciotherical'],
 'iserine': ['iresine', 'iserine'],
 'iseum': ['iseum', 'musie'],
 'isiac': ['ascii', 'isiac'],
 'isidore': ['isidore', 'osiride'],
 'isis': ['isis', 'sisi'],
 'islam': ['islam', 'ismal', 'simal'],
 'islamic': ['islamic', 'laicism', 'silicam'],
 'islamitic': ['islamitic', 'italicism'],
 'islandy': ['islandy', 'lindsay'],
 'islay': ['islay', 'saily'],
 'isle': ['isle', 'lise', 'sile'],
 'islet': ['islet', 'istle', 'slite', 'stile'],
 'isleta': ['isleta', 'litsea', 'salite', 'stelai'],
 'isleted': ['idleset', 'isleted'],
 'ism': ['ism', 'sim'],
 'ismal': ['islam', 'ismal', 'simal'],
 'ismatic': ['ismatic', 'itacism'],
 'ismatical': ['ismatical', 'lamaistic'],
 'isocamphor': ['chromopsia', 'isocamphor'],
 'isoclinal': ['collinsia', 'isoclinal'],
 'isocline': ['isocline', 'silicone'],
 'isocoumarin': ['acrimonious', 'isocoumarin'],
 'isodulcite': ['isodulcite', 'solicitude'],
 'isogen': ['geison', 'isogen'],
 'isogeotherm': ['geoisotherm', 'isogeotherm'],
 'isogon': ['isogon', 'songoi'],
 'isogram': ['isogram', 'orgiasm'],
 'isohel': ['helios', 'isohel'],
 'isoheptane': ['apothesine', 'isoheptane'],
 'isolate': ['aeolist', 'isolate'],
 'isolated': ['diastole', 'isolated', 'sodalite', 'solidate'],
 'isolative': ['isolative', 'soliative'],
 'isolde': ['isolde', 'soiled'],
 'isomer': ['isomer', 'rimose'],
 'isometric': ['eroticism', 'isometric', 'meroistic', 'trioecism'],
 'isomorph': ['isomorph', 'moorship'],
 'isonitrile': ['isonitrile', 'resilition'],
 'isonym': ['isonym', 'myosin', 'simony'],
 'isophthalyl': ['isophthalyl', 'lithophysal'],
 'isopodan': ['anisopod', 'isopodan'],
 'isoptera': ['isoptera', 'septoria'],
 'isosaccharic': ['isosaccharic', 'sacroischiac'],
 'isostere': ['erotesis', 'isostere'],
 'isotac': ['isotac', 'scotia'],
 'isotheral': ['horsetail', 'isotheral'],
 'isotherm': ['homerist', 'isotherm', 'otherism', 'theorism'],
 'isotria': ['isotria', 'oaritis'],
 'isotron': ['isotron', 'torsion'],
 'isotrope': ['isotrope', 'portoise'],
 'isotropism': ['isotropism', 'promitosis'],
 'isotropy': ['isotropy', 'porosity'],
 'israel': ['israel', 'relais', 'resail', 'sailer', 'serail', 'serial'],
 'israeli': ['alisier', 'israeli'],
 'israelite': ['israelite', 'resiliate'],
 'issuable': ['basileus', 'issuable', 'suasible'],
 'issuant': ['issuant', 'sustain'],
 'issue': ['issue', 'susie'],
 'issuer': ['issuer', 'uresis'],
 'ist': ['ist', 'its', 'sit'],
 'isthmi': ['isthmi', 'timish'],
 'isthmian': ['isthmian', 'smithian'],
 'isthmoid': ['isthmoid', 'thomisid'],
 'istle': ['islet', 'istle', 'slite', 'stile'],
 'istrian': ['iranist', 'istrian'],
 'isuret': ['isuret', 'resuit'],
 'it': ['it', 'ti'],
 'ita': ['ait', 'ati', 'ita', 'tai'],
 'itacism': ['ismatic', 'itacism'],
 'itaconate': ['acetation', 'itaconate'],
 'itaconic': ['aconitic', 'cationic', 'itaconic'],
 'itali': ['itali', 'tilia'],
 'italian': ['antilia', 'italian'],
 'italic': ['clitia', 'italic'],
 'italicism': ['islamitic', 'italicism'],
 'italite': ['italite', 'letitia', 'tilaite'],
 'italon': ['italon', 'lation', 'talion'],
 'itaves': ['itaves', 'stevia'],
 'itch': ['chit', 'itch', 'tchi'],
 'item': ['emit', 'item', 'mite', 'time'],
 'iten': ['iten', 'neti', 'tien', 'tine'],
 'itenean': ['aniente', 'itenean'],
 'iter': ['iter', 'reit', 'rite', 'teri', 'tier', 'tire'],
 'iterable': ['iterable', 'liberate'],
 'iterance': ['aneretic', 'centiare', 'creatine', 'increate', 'iterance'],
 'iterant': ['intreat', 'iterant', 'nitrate', 'tertian'],
 'ithaca': ['cahita', 'ithaca'],
 'ithacan': ['ithacan', 'tachina'],
 'ither': ['ither', 'their'],
 'itinerant': ['itinerant', 'nitratine'],
 'itinerantly': ['internality', 'itinerantly'],
 'itmo': ['itmo', 'moit', 'omit', 'timo'],
 'ito': ['ito', 'toi'],
 'itoism': ['itoism', 'omitis'],
 'itoist': ['itoist', 'otitis'],
 'itoland': ['itoland', 'talonid', 'tindalo'],
 'itonama': ['amniota', 'itonama'],
 'itonia': ['aition', 'itonia'],
 'its': ['ist', 'its', 'sit'],
 'itself': ['itself', 'stifle'],
 'ituraean': ['inaurate', 'ituraean'],
 'itza': ['itza', 'tiza', 'zati'],
 'iva': ['iva', 'vai', 'via'],
 'ivan': ['ivan', 'vain', 'vina'],
 'ivorist': ['ivorist', 'visitor'],
 'iwaiwa': ['iwaiwa', 'waiwai'],
 'ixiama': ['amixia', 'ixiama'],
 'ixodic': ['ixodic', 'oxidic'],
 'iyo': ['iyo', 'yoi'],
 'izar': ['izar', 'zira'],
 'jacami': ['jacami', 'jicama'],
 'jacobian': ['bajocian', 'jacobian'],
 'jag': ['gaj', 'jag'],
 'jagir': ['jagir', 'jirga'],
 'jagua': ['ajuga', 'jagua'],
 'jail': ['jail', 'lija'],
 'jailer': ['jailer', 'rejail'],
 'jaime': ['jaime', 'jamie'],
 'jain': ['jain', 'jina'],
 'jaina': ['inaja', 'jaina'],
 'jalouse': ['jalouse', 'jealous'],
 'jama': ['jama', 'maja'],
 'jamesian': ['jamesian', 'jamesina'],
 'jamesina': ['jamesian', 'jamesina'],
 'jami': ['ijma', 'jami'],
 'jamie': ['jaime', 'jamie'],
 'jane': ['jane', 'jean'],
 'janos': ['janos', 'jason', 'jonas', 'sonja'],
 'jantu': ['jantu', 'jaunt', 'junta'],
 'januslike': ['januslike', 'seljukian'],
 'japonism': ['japonism', 'pajonism'],
 'jar': ['jar', 'raj'],
 'jara': ['ajar', 'jara', 'raja'],
 'jarmo': ['jarmo', 'major'],
 'jarnut': ['jarnut', 'jurant'],
 'jason': ['janos', 'jason', 'jonas', 'sonja'],
 'jat': ['jat', 'taj'],
 'jatki': ['jatki', 'tajik'],
 'jato': ['jato', 'jota'],
 'jaun': ['jaun', 'juan'],
 'jaunt': ['jantu', 'jaunt', 'junta'],
 'jaup': ['jaup', 'puja'],
 'jealous': ['jalouse', 'jealous'],
 'jean': ['jane', 'jean'],
 'jebusitical': ['jebusitical', 'justiciable'],
 'jecoral': ['cajoler', 'jecoral'],
 'jeffery': ['jeffery', 'jeffrey'],
 'jeffrey': ['jeffery', 'jeffrey'],
 'jejunoduodenal': ['duodenojejunal', 'jejunoduodenal'],
 'jenine': ['jenine', 'jennie'],
 'jennie': ['jenine', 'jennie'],
 'jerker': ['jerker', 'rejerk'],
 'jerkin': ['jerkin', 'jinker'],
 'jeziah': ['hejazi', 'jeziah'],
 'jicama': ['jacami', 'jicama'],
 'jihad': ['hadji', 'jihad'],
 'jina': ['jain', 'jina'],
 'jingoist': ['jingoist', 'joisting'],
 'jinker': ['jerkin', 'jinker'],
 'jirga': ['jagir', 'jirga'],
 'jobo': ['bojo', 'jobo'],
 'johan': ['johan', 'jonah'],
 'join': ['join', 'joni'],
 'joinant': ['joinant', 'jotnian'],
 'joiner': ['joiner', 'rejoin'],
 'jointless': ['jointless', 'joltiness'],
 'joisting': ['jingoist', 'joisting'],
 'jolter': ['jolter', 'rejolt'],
 'joltiness': ['jointless', 'joltiness'],
 'jonah': ['johan', 'jonah'],
 'jonas': ['janos', 'jason', 'jonas', 'sonja'],
 'joni': ['join', 'joni'],
 'joom': ['joom', 'mojo'],
 'joshi': ['joshi', 'shoji'],
 'jota': ['jato', 'jota'],
 'jotnian': ['joinant', 'jotnian'],
 'journeyer': ['journeyer', 'rejourney'],
 'joust': ['joust', 'justo'],
 'juan': ['jaun', 'juan'],
 'judaic': ['judaic', 'judica'],
 'judica': ['judaic', 'judica'],
 'jujitsu': ['jujitsu', 'jujuist'],
 'jujuist': ['jujitsu', 'jujuist'],
 'junta': ['jantu', 'jaunt', 'junta'],
 'jurant': ['jarnut', 'jurant'],
 'justiciable': ['jebusitical', 'justiciable'],
 'justo': ['joust', 'justo'],
 'jute': ['jute', 'teju'],
 'ka': ['ak', 'ka'],
 'kabel': ['blake', 'bleak', 'kabel'],
 'kaberu': ['kaberu', 'kubera'],
 'kabuli': ['kabuli', 'kiluba'],
 'kabyle': ['bleaky', 'kabyle'],
 'kachari': ['chakari', 'chikara', 'kachari'],
 'kachin': ['hackin', 'kachin'],
 'kafir': ['fakir', 'fraik', 'kafir', 'rafik'],
 'kaha': ['akha', 'kaha'],
 'kahar': ['harka', 'kahar'],
 'kahu': ['haku', 'kahu'],
 'kaid': ['dika', 'kaid'],
 'kaik': ['kaik', 'kaki'],
 'kail': ['ilka', 'kail', 'kali'],
 'kainga': ['kainga', 'kanagi'],
 'kaiwi': ['kaiwi', 'kiwai'],
 'kaka': ['akka', 'kaka'],
 'kaki': ['kaik', 'kaki'],
 'kala': ['akal', 'kala'],
 'kalamian': ['kalamian', 'malikana'],
 'kaldani': ['danakil', 'dankali', 'kaldani', 'ladakin'],
 'kale': ['kale', 'lake', 'leak'],
 'kali': ['ilka', 'kail', 'kali'],
 'kalo': ['kalo', 'kola', 'loka'],
 'kamansi': ['kamansi', 'kamasin'],
 'kamares': ['kamares', 'seamark'],
 'kamasin': ['kamansi', 'kamasin'],
 'kame': ['kame', 'make', 'meak'],
 'kamel': ['kamel', 'kemal'],
 'kamiya': ['kamiya', 'yakima'],
 'kan': ['kan', 'nak'],
 'kana': ['akan', 'kana'],
 'kanagi': ['kainga', 'kanagi'],
 'kanap': ['kanap', 'panak'],
 'kanat': ['kanat', 'tanak', 'tanka'],
 'kande': ['kande', 'knead', 'naked'],
 'kang': ['kang', 'knag'],
 'kanga': ['angka', 'kanga'],
 'kangani': ['kangani', 'kiangan'],
 'kangli': ['kangli', 'laking'],
 'kanred': ['darken', 'kanred', 'ranked'],
 'kans': ['kans', 'sank'],
 'kaolin': ['ankoli', 'kaolin'],
 'karch': ['chark', 'karch'],
 'karel': ['karel', 'laker'],
 'karen': ['anker', 'karen', 'naker'],
 'kari': ['ikra', 'kari', 'raki'],
 'karite': ['arkite', 'karite'],
 'karl': ['karl', 'kral', 'lark'],
 'karling': ['karling', 'larking'],
 'karma': ['karma', 'krama', 'marka'],
 'karo': ['karo', 'kora', 'okra', 'roka'],
 'karree': ['karree', 'rerake'],
 'karst': ['karst', 'skart', 'stark'],
 'karstenite': ['karstenite', 'kersantite'],
 'kartel': ['kartel', 'retalk', 'talker'],
 'kasa': ['asak', 'kasa', 'saka'],
 'kasbah': ['abkhas', 'kasbah'],
 'kasha': ['kasha', 'khasa', 'sakha', 'shaka'],
 'kashan': ['kashan', 'sankha'],
 'kasher': ['kasher', 'shaker'],
 'kashi': ['kashi', 'khasi'],
 'kasm': ['kasm', 'mask'],
 'katar': ['katar', 'takar'],
 'kate': ['kate', 'keta', 'take', 'teak'],
 'kath': ['kath', 'khat'],
 'katharsis': ['katharsis', 'shastraik'],
 'katie': ['katie', 'keita'],
 'katik': ['katik', 'tikka'],
 'katrine': ['intaker', 'katrine', 'keratin'],
 'katy': ['katy', 'kyat', 'taky'],
 'kavass': ['kavass', 'vakass'],
 'kavi': ['kavi', 'kiva'],
 'kay': ['kay', 'yak'],
 'kayak': ['kayak', 'yakka'],
 'kayan': ['kayan', 'yakan'],
 'kayo': ['kayo', 'oaky'],
 'kea': ['ake', 'kea'],
 'keach': ['cheka', 'keach'],
 'keawe': ['aweek', 'keawe'],
 'kechel': ['heckle', 'kechel'],
 'kedar': ['daker', 'drake', 'kedar', 'radek'],
 'kee': ['eke', 'kee'],
 'keech': ['cheek', 'cheke', 'keech'],
 'keel': ['keel', 'kele', 'leek'],
 'keen': ['keen', 'knee'],
 'keena': ['aknee', 'ankee', 'keena'],
 'keep': ['keep', 'peek'],
 'keepership': ['keepership', 'shipkeeper'],
 'kees': ['kees', 'seek', 'skee'],
 'keest': ['keest', 'skeet', 'skete', 'steek'],
 'kefir': ['frike', 'kefir'],
 'keid': ['dike', 'keid'],
 'keita': ['katie', 'keita'],
 'keith': ['keith', 'kithe'],
 'keitloa': ['keitloa', 'oatlike'],
 'kelchin': ['chinkle', 'kelchin'],
 'kele': ['keel', 'kele', 'leek'],
 'kelima': ['kelima', 'mikael'],
 'kelpie': ['kelpie', 'pelike'],
 'kelty': ['kelty', 'ketyl'],
 'kemal': ['kamel', 'kemal'],
 'kemalist': ['kemalist', 'mastlike'],
 'kenareh': ['hearken', 'kenareh'],
 'kennel': ['kennel', 'nelken'],
 'kenotic': ['kenotic', 'ketonic'],
 'kent': ['kent', 'knet'],
 'kentia': ['intake', 'kentia'],
 'kenton': ['kenton', 'nekton'],
 'kepi': ['kepi', 'kipe', 'pike'],
 'keralite': ['keralite', 'tearlike'],
 'kerasin': ['kerasin', 'sarkine'],
 'kerat': ['kerat', 'taker'],
 'keratin': ['intaker', 'katrine', 'keratin'],
 'keratoangioma': ['angiokeratoma', 'keratoangioma'],
 'keratosis': ['asterikos', 'keratosis'],
 'keres': ['esker', 'keres', 'reesk', 'seker', 'skeer', 'skere'],
 'keresan': ['keresan', 'sneaker'],
 'kerewa': ['kerewa', 'rewake'],
 'kerf': ['ferk', 'kerf'],
 'kern': ['kern', 'renk'],
 'kersantite': ['karstenite', 'kersantite'],
 'kersey': ['kersey', 'skeery'],
 'kestrel': ['kestrel', 'skelter'],
 'keta': ['kate', 'keta', 'take', 'teak'],
 'ketene': ['ektene', 'ketene'],
 'keto': ['keto', 'oket', 'toke'],
 'ketol': ['ketol', 'loket'],
 'ketonic': ['kenotic', 'ketonic'],
 'ketu': ['ketu', 'teuk', 'tuke'],
 'ketupa': ['ketupa', 'uptake'],
 'ketyl': ['kelty', 'ketyl'],
 'keup': ['keup', 'puke'],
 'keuper': ['keuper', 'peruke'],
 'kevan': ['kevan', 'knave'],
 'kha': ['hak', 'kha'],
 'khami': ['hakim', 'khami'],
 'khan': ['ankh', 'hank', 'khan'],
 'khar': ['hark', 'khar', 'rakh'],
 'khasa': ['kasha', 'khasa', 'sakha', 'shaka'],
 'khasi': ['kashi', 'khasi'],
 'khat': ['kath', 'khat'],
 'khatib': ['bhakti', 'khatib'],
 'khila': ['khila', 'kilah'],
 'khu': ['huk', 'khu'],
 'khula': ['khula', 'kulah'],
 'kiangan': ['kangani', 'kiangan'],
 'kibe': ['bike', 'kibe'],
 'kicker': ['kicker', 'rekick'],
 'kickout': ['kickout', 'outkick'],
 'kidney': ['dinkey', 'kidney'],
 'kids': ['disk', 'kids', 'skid'],
 'kiel': ['kiel', 'like'],
 'kier': ['erik', 'kier', 'reki'],
 'kiku': ['kiku', 'kuki'],
 'kikumon': ['kikumon', 'kokumin'],
 'kil': ['ilk', 'kil'],
 'kilah': ['khila', 'kilah'],
 'kiliare': ['airlike', 'kiliare'],
 'killcalf': ['calfkill', 'killcalf'],
 'killer': ['killer', 'rekill'],
 'kiln': ['kiln', 'link'],
 'kilnman': ['kilnman', 'linkman'],
 'kilo': ['kilo', 'koil', 'koli'],
 'kilp': ['kilp', 'klip'],
 'kilter': ['kilter', 'kirtle'],
 'kilting': ['kilting', 'kitling'],
 'kiluba': ['kabuli', 'kiluba'],
 'kimberlite': ['kimberlite', 'timberlike'],
 'kimnel': ['kimnel', 'milken'],
 'kin': ['ink', 'kin'],
 'kina': ['akin', 'kina', 'naik'],
 'kinase': ['kinase', 'sekani'],
 'kinch': ['chink', 'kinch'],
 'kind': ['dink', 'kind'],
 'kindle': ['kindle', 'linked'],
 'kinetomer': ['kinetomer', 'konimeter'],
 'king': ['gink', 'king'],
 'kingcob': ['bocking', 'kingcob'],
 'kingpin': ['kingpin', 'pinking'],
 'kingrow': ['kingrow', 'working'],
 'kinless': ['inkless', 'kinless'],
 'kinship': ['kinship', 'pinkish'],
 'kioko': ['kioko', 'kokio'],
 'kip': ['kip', 'pik'],
 'kipe': ['kepi', 'kipe', 'pike'],
 'kirk': ['kirk', 'rikk'],
 'kirktown': ['kirktown', 'knitwork'],
 'kirn': ['kirn', 'rink'],
 'kirsten': ['kirsten', 'kristen', 'stinker'],
 'kirsty': ['kirsty', 'skirty'],
 'kirtle': ['kilter', 'kirtle'],
 'kirve': ['kirve', 'kiver'],
 'kish': ['kish', 'shik', 'sikh'],
 'kishen': ['kishen', 'neskhi'],
 'kisra': ['kisra', 'sikar', 'skair'],
 'kissar': ['kissar', 'krasis'],
 'kisser': ['kisser', 'rekiss'],
 'kist': ['kist', 'skit'],
 'kistful': ['kistful', 'lutfisk'],
 'kitab': ['batik', 'kitab'],
 'kitan': ['kitan', 'takin'],
 'kitar': ['kitar', 'krait', 'rakit', 'traik'],
 'kitchen': ['kitchen', 'thicken'],
 'kitchener': ['kitchener', 'rethicken', 'thickener'],
 'kithe': ['keith', 'kithe'],
 'kitling': ['kilting', 'kitling'],
 'kitlope': ['kitlope', 'potlike', 'toplike'],
 'kittel': ['kittel', 'kittle'],
 'kittle': ['kittel', 'kittle'],
 'kittles': ['kittles', 'skittle'],
 'kiva': ['kavi', 'kiva'],
 'kiver': ['kirve', 'kiver'],
 'kiwai': ['kaiwi', 'kiwai'],
 'klan': ['klan', 'lank'],
 'klanism': ['klanism', 'silkman'],
 'klaus': ['klaus', 'lukas', 'sulka'],
 'kleistian': ['kleistian', 'saintlike', 'satinlike'],
 'klendusic': ['klendusic', 'unsickled'],
 'kling': ['glink', 'kling'],
 'klip': ['kilp', 'klip'],
 'klop': ['klop', 'polk'],
 'knab': ['bank', 'knab', 'nabk'],
 'knag': ['kang', 'knag'],
 'knap': ['knap', 'pank'],
 'knape': ['knape', 'pekan'],
 'knar': ['knar', 'kran', 'nark', 'rank'],
 'knave': ['kevan', 'knave'],
 'knawel': ['knawel', 'wankle'],
 'knead': ['kande', 'knead', 'naked'],
 'knee': ['keen', 'knee'],
 'knet': ['kent', 'knet'],
 'knit': ['knit', 'tink'],
 'knitter': ['knitter', 'trinket'],
 'knitwork': ['kirktown', 'knitwork'],
 'knob': ['bonk', 'knob'],
 'knot': ['knot', 'tonk'],
 'knottiness': ['knottiness', 'stinkstone'],
 'knower': ['knower', 'reknow', 'wroken'],
 'knub': ['bunk', 'knub'],
 'knurly': ['knurly', 'runkly'],
 'knut': ['knut', 'tunk'],
 'knute': ['knute', 'unket'],
 'ko': ['ko', 'ok'],
 'koa': ['ako', 'koa', 'oak', 'oka'],
 'koali': ['koali', 'koila'],
 'kobu': ['bouk', 'kobu'],
 'koch': ['hock', 'koch'],
 'kochia': ['choiak', 'kochia'],
 'koel': ['koel', 'loke'],
 'koi': ['koi', 'oki'],
 'koil': ['kilo', 'koil', 'koli'],
 'koila': ['koali', 'koila'],
 'koilon': ['inlook', 'koilon'],
 'kokan': ['kokan', 'konak'],
 'kokio': ['kioko', 'kokio'],
 'kokumin': ['kikumon', 'kokumin'],
 'kola': ['kalo', 'kola', 'loka'],
 'koli': ['kilo', 'koil', 'koli'],
 'kolo': ['kolo', 'look'],
 'kome': ['kome', 'moke'],
 'komi': ['komi', 'moki'],
 'kona': ['kona', 'nako'],
 'konak': ['kokan', 'konak'],
 'kongo': ['kongo', 'ngoko'],
 'kongoni': ['kongoni', 'nooking'],
 'konia': ['ikona', 'konia'],
 'konimeter': ['kinetomer', 'konimeter'],
 'kor': ['kor', 'rok'],
 'kora': ['karo', 'kora', 'okra', 'roka'],
 'korait': ['korait', 'troika'],
 'koran': ['koran', 'krona'],
 'korana': ['anorak', 'korana'],
 'kore': ['kore', 'roke'],
 'korec': ['coker', 'corke', 'korec'],
 'korero': ['korero', 'rooker'],
 'kori': ['irok', 'kori'],
 'korimako': ['korimako', 'koromika'],
 'koromika': ['korimako', 'koromika'],
 'korwa': ['awork', 'korwa'],
 'kory': ['kory', 'roky', 'york'],
 'kos': ['kos', 'sok'],
 'koso': ['koso', 'skoo', 'sook'],
 'kotar': ['kotar', 'tarok'],
 'koto': ['koto', 'toko', 'took'],
 'kra': ['ark', 'kra'],
 'krait': ['kitar', 'krait', 'rakit', 'traik'],
 'kraken': ['kraken', 'nekkar'],
 'kral': ['karl', 'kral', 'lark'],
 'krama': ['karma', 'krama', 'marka'],
 'kran': ['knar', 'kran', 'nark', 'rank'],
 'kras': ['askr', 'kras', 'sark'],
 'krasis': ['kissar', 'krasis'],
 'kraut': ['kraut', 'tukra'],
 'kreis': ['kreis', 'skier'],
 'kreistle': ['kreistle', 'triskele'],
 'krepi': ['krepi', 'piker'],
 'krina': ['inkra', 'krina', 'nakir', 'rinka'],
 'kris': ['kris', 'risk'],
 'krishna': ['krishna', 'rankish'],
 'kristen': ['kirsten', 'kristen', 'stinker'],
 'krona': ['koran', 'krona'],
 'krone': ['ekron', 'krone'],
 'kroo': ['kroo', 'rook'],
 'krosa': ['krosa', 'oskar'],
 'kua': ['aku', 'auk', 'kua'],
 'kuar': ['kuar', 'raku', 'rauk'],
 'kuba': ['baku', 'kuba'],
 'kubera': ['kaberu', 'kubera'],
 'kuki': ['kiku', 'kuki'],
 'kulah': ['khula', 'kulah'],
 'kulimit': ['kulimit', 'tilikum'],
 'kulm': ['kulm', 'mulk'],
 'kuman': ['kuman', 'naumk'],
 'kumhar': ['kumhar', 'kumrah'],
 'kumrah': ['kumhar', 'kumrah'],
 'kunai': ['kunai', 'nikau'],
 'kuneste': ['kuneste', 'netsuke'],
 'kung': ['gunk', 'kung'],
 'kurmi': ['kurmi', 'mukri'],
 'kurt': ['kurt', 'turk'],
 'kurus': ['kurus', 'ursuk'],
 'kusa': ['kusa', 'skua'],
 'kusam': ['kusam', 'sumak'],
 'kusan': ['ankus', 'kusan'],
 'kusha': ['kusha', 'shaku', 'ushak'],
 'kutchin': ['kutchin', 'unthick'],
 'kutenai': ['kutenai', 'unakite'],
 'kyar': ['kyar', 'yark'],
 'kyat': ['katy', 'kyat', 'taky'],
 'kyle': ['kyle', 'yelk'],
 'kylo': ['kylo', 'yolk'],
 'kyte': ['kyte', 'tyke'],
 'la': ['al', 'la'],
 'laager': ['aglare', 'alegar', 'galera', 'laager'],
 'laang': ['laang', 'lagan', 'lagna'],
 'lab': ['alb', 'bal', 'lab'],
 'laban': ['alban', 'balan', 'banal', 'laban', 'nabal', 'nabla'],
 'labber': ['barbel', 'labber', 'rabble'],
 'labefact': ['factable', 'labefact'],
 'label': ['bella', 'label'],
 'labeler': ['labeler', 'relabel'],
 'labia': ['balai', 'labia'],
 'labial': ['abilla', 'labial'],
 'labially': ['alliably', 'labially'],
 'labiate': ['baalite', 'bialate', 'labiate'],
 'labiella': ['alliable', 'labiella'],
 'labile': ['alible', 'belial', 'labile', 'liable'],
 'labiocervical': ['cervicolabial', 'labiocervical'],
 'labiodental': ['dentolabial', 'labiodental'],
 'labioglossal': ['glossolabial', 'labioglossal'],
 'labioglossolaryngeal': ['glossolabiolaryngeal', 'labioglossolaryngeal'],
 'labioglossopharyngeal': ['glossolabiopharyngeal', 'labioglossopharyngeal'],
 'labiomental': ['labiomental', 'mentolabial'],
 'labionasal': ['labionasal', 'nasolabial'],
 'labiovelar': ['bialveolar', 'labiovelar'],
 'labis': ['basil', 'labis'],
 'labor': ['balor', 'bolar', 'boral', 'labor', 'lobar'],
 'laborant': ['balatron', 'laborant'],
 'laborism': ['laborism', 'mislabor'],
 'laborist': ['laborist', 'strobila'],
 'laborite': ['betailor', 'laborite', 'orbitale'],
 'labrador': ['labrador', 'larboard'],
 'labret': ['albert', 'balter', 'labret', 'tabler'],
 'labridae': ['labridae', 'radiable'],
 'labrose': ['borlase', 'labrose', 'rosabel'],
 'labrum': ['brumal', 'labrum', 'lumbar', 'umbral'],
 'labrus': ['bursal', 'labrus'],
 'laburnum': ['alburnum', 'laburnum'],
 'lac': ['cal', 'lac'],
 'lace': ['acle', 'alec', 'lace'],
 'laced': ['clead', 'decal', 'laced'],
 'laceman': ['laceman', 'manacle'],
 'lacepod': ['lacepod', 'pedocal', 'placode'],
 'lacer': ['ceral', 'clare', 'clear', 'lacer'],
 'lacerable': ['clearable', 'lacerable'],
 'lacerate': ['lacerate', 'lacertae'],
 'laceration': ['creational', 'crotalinae', 'laceration', 'reactional'],
 'lacerative': ['calaverite', 'lacerative'],
 'lacertae': ['lacerate', 'lacertae'],
 'lacertian': ['carnalite', 'claretian', 'lacertian', 'nectarial'],
 'lacertid': ['articled', 'lacertid'],
 'lacertidae': ['dilacerate', 'lacertidae'],
 'lacertiloid': ['illoricated', 'lacertiloid'],
 'lacertine': ['intercale', 'interlace', 'lacertine', 'reclinate'],
 'lacertoid': ['dialector', 'lacertoid'],
 'lacery': ['clayer', 'lacery'],
 'lacet': ['cleat', 'eclat', 'ectal', 'lacet', 'tecla'],
 'lache': ['chela', 'lache', 'leach'],
 'laches': ['cashel', 'laches', 'sealch'],
 'lachrymonasal': ['lachrymonasal', 'nasolachrymal'],
 'lachsa': ['calash', 'lachsa'],
 'laciness': ['laciness', 'sensical'],
 'lacing': ['anglic', 'lacing'],
 'lacinia': ['lacinia', 'licania'],
 'laciniated': ['acetanilid', 'laciniated', 'teniacidal'],
 'lacis': ['lacis', 'salic'],
 'lack': ['calk', 'lack'],
 'lacker': ['calker', 'lacker', 'rackle', 'recalk', 'reckla'],
 'lacmoid': ['domical', 'lacmoid'],
 'laconic': ['conical', 'laconic'],
 'laconica': ['canicola', 'laconica'],
 'laconizer': ['laconizer', 'locarnize'],
 'lacquer': ['claquer', 'lacquer'],
 'lacquerer': ['lacquerer', 'relacquer'],
 'lactarene': ['lactarene', 'nectareal'],
 'lactarious': ['alacritous', 'lactarious', 'lactosuria'],
 'lactarium': ['lactarium', 'matricula'],
 'lactarius': ['australic', 'lactarius'],
 'lacteal': ['catella', 'lacteal'],
 'lacteous': ['lacteous', 'osculate'],
 'lactide': ['citadel', 'deltaic', 'dialect', 'edictal', 'lactide'],
 'lactinate': ['cantalite', 'lactinate', 'tetanical'],
 'lacto': ['lacto', 'tlaco'],
 'lactoid': ['cotidal', 'lactoid', 'talcoid'],
 'lactoprotein': ['lactoprotein', 'protectional'],
 'lactose': ['alecost', 'lactose', 'scotale', 'talcose'],
 'lactoside': ['dislocate', 'lactoside'],
 'lactosuria': ['alacritous', 'lactarious', 'lactosuria'],
 'lacunal': ['calluna', 'lacunal'],
 'lacune': ['auncel', 'cuneal', 'lacune', 'launce', 'unlace'],
 'lacustral': ['claustral', 'lacustral'],
 'lacwork': ['lacwork', 'warlock'],
 'lacy': ['acyl', 'clay', 'lacy'],
 'lad': ['dal', 'lad'],
 'ladakin': ['danakil', 'dankali', 'kaldani', 'ladakin'],
 'ladanum': ['ladanum', 'udalman'],
 'ladder': ['ladder', 'raddle'],
 'laddery': ['dreadly', 'laddery'],
 'laddie': ['daidle', 'laddie'],
 'lade': ['dale', 'deal', 'lade', 'lead', 'leda'],
 'lademan': ['daleman', 'lademan', 'leadman'],
 'laden': ['eland', 'laden', 'lenad'],
 'lader': ['alder', 'daler', 'lader'],
 'ladies': ['aisled', 'deasil', 'ladies', 'sailed'],
 'ladin': ['danli', 'ladin', 'linda', 'nidal'],
 'lading': ['angild', 'lading'],
 'ladino': ['dolina', 'ladino'],
 'ladle': ['dalle', 'della', 'ladle'],
 'ladrone': ['endoral', 'ladrone', 'leonard'],
 'ladyfy': ['dayfly', 'ladyfy'],
 'ladyish': ['ladyish', 'shadily'],
 'ladyling': ['dallying', 'ladyling'],
 'laet': ['atle', 'laet', 'late', 'leat', 'tael', 'tale', 'teal'],
 'laeti': ['alite', 'laeti'],
 'laetic': ['calite', 'laetic', 'tecali'],
 'lafite': ['fetial', 'filate', 'lafite', 'leafit'],
 'lag': ['gal', 'lag'],
 'lagan': ['laang', 'lagan', 'lagna'],
 'lagen': ['agnel', 'angel', 'angle', 'galen', 'genal', 'glean', 'lagen'],
 'lagena': ['alnage', 'angela', 'galena', 'lagena'],
 'lagend': ['angled', 'dangle', 'englad', 'lagend'],
 'lager': ['argel', 'ergal', 'garle', 'glare', 'lager', 'large', 'regal'],
 'lagetto': ['lagetto', 'tagetol'],
 'lagged': ['daggle', 'lagged'],
 'laggen': ['laggen', 'naggle'],
 'lagger': ['gargle', 'gregal', 'lagger', 'raggle'],
 'lagna': ['laang', 'lagan', 'lagna'],
 'lagniappe': ['appealing', 'lagniappe', 'panplegia'],
 'lagonite': ['gelation', 'lagonite', 'legation'],
 'lagunero': ['lagunero', 'organule', 'uroglena'],
 'lagurus': ['argulus', 'lagurus'],
 'lai': ['ail', 'ila', 'lai'],
 'laicism': ['islamic', 'laicism', 'silicam'],
 'laid': ['dail', 'dali', 'dial', 'laid', 'lida'],
 'lain': ['alin', 'anil', 'lain', 'lina', 'nail'],
 'laine': ['alien', 'aline', 'anile', 'elain', 'elian', 'laine', 'linea'],
 'laiose': ['aeolis', 'laiose'],
 'lair': ['aril', 'lair', 'lari', 'liar', 'lira', 'rail', 'rial'],
 'lairage': ['lairage', 'railage', 'regalia'],
 'laird': ['drail', 'laird', 'larid', 'liard'],
 'lairless': ['lairless', 'railless'],
 'lairman': ['lairman', 'laminar', 'malarin', 'railman'],
 'lairstone': ['lairstone', 'orleanist', 'serotinal'],
 'lairy': ['lairy', 'riyal'],
 'laitance': ['analcite', 'anticlea', 'laitance'],
 'laity': ['laity', 'taily'],
 'lak': ['alk', 'lak'],
 'lake': ['kale', 'lake', 'leak'],
 'lakeless': ['lakeless', 'leakless'],
 'laker': ['karel', 'laker'],
 'lakie': ['alike', 'lakie'],
 'laking': ['kangli', 'laking'],
 'lakish': ['lakish', 'shakil'],
 'lakota': ['atokal', 'lakota'],
 'laky': ['alky', 'laky'],
 'lalo': ['lalo', 'lola', 'olla'],
 'lalopathy': ['allopathy', 'lalopathy'],
 'lam': ['lam', 'mal'],
 'lama': ['alma', 'amla', 'lama', 'mala'],
 'lamaic': ['amical', 'camail', 'lamaic'],
 'lamaism': ['lamaism', 'miasmal'],
 'lamaist': ['lamaist', 'lamista'],
 'lamaistic': ['ismatical', 'lamaistic'],
 'lamanite': ['lamanite', 'laminate'],
 'lamany': ['amylan', 'lamany', 'layman'],
 'lamb': ['balm', 'lamb'],
 'lambaste': ['blastema', 'lambaste'],
 'lambent': ['beltman', 'lambent'],
 'lamber': ['ambler', 'blamer', 'lamber', 'marble', 'ramble'],
 'lambie': ['bemail', 'lambie'],
 'lambiness': ['balminess', 'lambiness'],
 'lamblike': ['balmlike', 'lamblike'],
 'lamby': ['balmy', 'lamby'],
 'lame': ['alem', 'alme', 'lame', 'leam', 'male', 'meal', 'mela'],
 'lamella': ['lamella', 'malella', 'malleal'],
 'lamellose': ['lamellose', 'semolella'],
 'lamely': ['lamely', 'mellay'],
 'lameness': ['lameness', 'maleness', 'maneless', 'nameless'],
 'lament': ['lament', 'manlet', 'mantel', 'mantle', 'mental'],
 'lamenter': ['lamenter', 'relament', 'remantle'],
 'lamenting': ['alignment', 'lamenting'],
 'lameter': ['lameter', 'metaler', 'remetal'],
 'lamia': ['alima', 'lamia'],
 'lamiger': ['gremial', 'lamiger'],
 'lamiides': ['idealism', 'lamiides'],
 'lamin': ['lamin', 'liman', 'milan'],
 'lamina': ['almain', 'animal', 'lamina', 'manila'],
 'laminae': ['laminae', 'melania'],
 'laminar': ['lairman', 'laminar', 'malarin', 'railman'],
 'laminarin': ['laminarin', 'linamarin'],
 'laminarite': ['laminarite', 'terminalia'],
 'laminate': ['lamanite', 'laminate'],
 'laminated': ['almandite', 'laminated'],
 'lamination': ['antimonial', 'lamination'],
 'laminboard': ['laminboard', 'lombardian'],
 'laminectomy': ['laminectomy', 'metonymical'],
 'laminose': ['laminose', 'lemonias', 'semolina'],
 'lamish': ['lamish', 'shimal'],
 'lamista': ['lamaist', 'lamista'],
 'lamiter': ['lamiter', 'marlite'],
 'lammer': ['lammer', 'rammel'],
 'lammy': ['lammy', 'malmy'],
 'lamna': ['alman', 'lamna', 'manal'],
 'lamnid': ['lamnid', 'mandil'],
 'lamnidae': ['aldamine', 'lamnidae'],
 'lamp': ['lamp', 'palm'],
 'lampad': ['lampad', 'palmad'],
 'lampas': ['lampas', 'plasma'],
 'lamper': ['lamper', 'palmer', 'relamp'],
 'lampers': ['lampers', 'sampler'],
 'lampful': ['lampful', 'palmful'],
 'lampist': ['lampist', 'palmist'],
 'lampistry': ['lampistry', 'palmistry'],
 'lampoon': ['lampoon', 'pomonal'],
 'lamprey': ['lamprey', 'palmery'],
 'lampyridae': ['lampyridae', 'pyramidale'],
 'lamus': ['lamus', 'malus', 'musal', 'slaum'],
 'lamut': ['lamut', 'tamul'],
 'lan': ['aln', 'lan'],
 'lana': ['alan', 'anal', 'lana'],
 'lanas': ['alans', 'lanas', 'nasal'],
 'lanate': ['anteal', 'lanate', 'teanal'],
 'lancaster': ['ancestral', 'lancaster'],
 'lancasterian': ['alcantarines', 'lancasterian'],
 'lance': ['canel', 'clean', 'lance', 'lenca'],
 'lanced': ['calden', 'candle', 'lanced'],
 'lancely': ['cleanly', 'lancely'],
 'lanceolar': ['lanceolar', 'olecranal'],
 'lancer': ['lancer', 'rancel'],
 'lances': ['lances', 'senlac'],
 'lancet': ['cantle', 'cental', 'lancet', 'tancel'],
 'lanceteer': ['crenelate', 'lanceteer'],
 'lancinate': ['cantilena', 'lancinate'],
 'landbook': ['bookland', 'landbook'],
 'landed': ['dandle', 'landed'],
 'lander': ['aldern',
            'darnel',
            'enlard',
            'lander',
            'lenard',
            'randle',
            'reland'],
 'landfast': ['fastland', 'landfast'],
 'landgrave': ['grandeval', 'landgrave'],
 'landimere': ['landimere', 'madrilene'],
 'landing': ['danglin', 'landing'],
 'landlubber': ['landlubber', 'lubberland'],
 'landreeve': ['landreeve', 'reeveland'],
 'landstorm': ['landstorm', 'transmold'],
 'landwash': ['landwash', 'washland'],
 'lane': ['alen', 'lane', 'lean', 'lena', 'nael', 'neal'],
 'lanete': ['elanet', 'lanete', 'lateen'],
 'laney': ['laney', 'layne'],
 'langhian': ['hangnail', 'langhian'],
 'langi': ['algin', 'align', 'langi', 'liang', 'linga'],
 'langite': ['atingle', 'gelatin', 'genital', 'langite', 'telinga'],
 'lango': ['along', 'gonal', 'lango', 'longa', 'nogal'],
 'langobard': ['bandarlog', 'langobard'],
 'language': ['ganguela', 'language'],
 'laniate': ['laniate', 'natalie', 'taenial'],
 'lanific': ['finical', 'lanific'],
 'laniform': ['formalin', 'informal', 'laniform'],
 'laniidae': ['aedilian', 'laniidae'],
 'lanista': ['lanista', 'santali'],
 'lanius': ['insula', 'lanius', 'lusian'],
 'lank': ['klan', 'lank'],
 'lanket': ['anklet', 'lanket', 'tankle'],
 'lanner': ['lanner', 'rannel'],
 'lansat': ['aslant', 'lansat', 'natals', 'santal'],
 'lanseh': ['halsen', 'hansel', 'lanseh'],
 'lantaca': ['cantala', 'catalan', 'lantaca'],
 'lanum': ['lanum', 'manul'],
 'lao': ['alo', 'lao', 'loa'],
 'laodicean': ['caledonia', 'laodicean'],
 'laotian': ['ailanto', 'alation', 'laotian', 'notalia'],
 'lap': ['alp', 'lap', 'pal'],
 'laparohysterotomy': ['hysterolaparotomy', 'laparohysterotomy'],
 'laparosplenotomy': ['laparosplenotomy', 'splenolaparotomy'],
 'lapidarist': ['lapidarist', 'triapsidal'],
 'lapidate': ['lapidate', 'talpidae'],
 'lapideon': ['lapideon', 'palinode', 'pedalion'],
 'lapidose': ['episodal', 'lapidose', 'sepaloid'],
 'lapith': ['lapith', 'tilpah'],
 'lapon': ['lapon', 'nopal'],
 'lapp': ['lapp', 'palp', 'plap'],
 'lappa': ['lappa', 'papal'],
 'lapped': ['dapple', 'lapped', 'palped'],
 'lapper': ['lapper', 'rappel'],
 'lappish': ['lappish', 'shiplap'],
 'lapsation': ['apolistan', 'lapsation'],
 'lapse': ['elaps',
           'lapse',
           'lepas',
           'pales',
           'salep',
           'saple',
           'sepal',
           'slape',
           'spale',
           'speal'],
 'lapsi': ['alisp', 'lapsi'],
 'lapsing': ['lapsing', 'sapling'],
 'lapstone': ['lapstone', 'pleonast'],
 'larboard': ['labrador', 'larboard'],
 'larcenic': ['calciner', 'larcenic'],
 'larcenist': ['cisternal', 'larcenist'],
 'larcenous': ['larcenous', 'senocular'],
 'larchen': ['charnel', 'larchen'],
 'lardacein': ['ecardinal', 'lardacein'],
 'lardite': ['dilater', 'lardite', 'redtail'],
 'lardon': ['androl', 'arnold', 'lardon', 'roland', 'ronald'],
 'lardy': ['daryl', 'lardy', 'lyard'],
 'large': ['argel', 'ergal', 'garle', 'glare', 'lager', 'large', 'regal'],
 'largely': ['allergy', 'gallery', 'largely', 'regally'],
 'largen': ['angler', 'arleng', 'garnel', 'largen', 'rangle', 'regnal'],
 'largeness': ['largeness', 'rangeless', 'regalness'],
 'largess': ['glasser', 'largess'],
 'largition': ['gratiolin', 'largition', 'tailoring'],
 'largo': ['algor', 'argol', 'goral', 'largo'],
 'lari': ['aril', 'lair', 'lari', 'liar', 'lira', 'rail', 'rial'],
 'lariat': ['altair', 'atrail', 'atrial', 'lariat', 'latria', 'talari'],
 'larid': ['drail', 'laird', 'larid', 'liard'],
 'laridae': ['ardelia', 'laridae', 'radiale'],
 'larigo': ['gloria', 'larigo', 'logria'],
 'larigot': ['goitral', 'larigot', 'ligator'],
 'lariid': ['iridal', 'lariid'],
 'larine': ['arline', 'larine', 'linear', 'nailer', 'renail'],
 'lark': ['karl', 'kral', 'lark'],
 'larking': ['karling', 'larking'],
 'larsenite': ['intersale', 'larsenite'],
 'larus': ['larus', 'sural', 'ursal'],
 'larva': ['alvar', 'arval', 'larva'],
 'larval': ['larval', 'vallar'],
 'larvate': ['larvate', 'lavaret', 'travale'],
 'larve': ['arvel', 'larve', 'laver', 'ravel', 'velar'],
 'larvicide': ['larvicide', 'veridical'],
 'laryngopharyngeal': ['laryngopharyngeal', 'pharyngolaryngeal'],
 'laryngopharyngitis': ['laryngopharyngitis', 'pharyngolaryngitis'],
 'laryngotome': ['laryngotome', 'maternology'],
 'laryngotracheotomy': ['laryngotracheotomy', 'tracheolaryngotomy'],
 'las': ['las', 'sal', 'sla'],
 'lasa': ['alas', 'lasa'],
 'lascar': ['lascar', 'rascal', 'sacral', 'scalar'],
 'laser': ['arles', 'arsle', 'laser', 'seral', 'slare'],
 'lash': ['hals', 'lash'],
 'lasi': ['lasi', 'lias', 'lisa', 'sail', 'sial'],
 'lasius': ['asilus', 'lasius'],
 'lask': ['lask', 'skal'],
 'lasket': ['lasket', 'sklate'],
 'laspring': ['laspring', 'sparling', 'springal'],
 'lasque': ['lasque', 'squeal'],
 'lasset': ['lasset', 'tassel'],
 'lassie': ['elissa', 'lassie'],
 'lasso': ['lasso', 'ossal'],
 'lassoer': ['lassoer', 'oarless', 'rosales'],
 'last': ['last', 'salt', 'slat'],
 'laster': ['laster',
            'lastre',
            'rastle',
            'relast',
            'resalt',
            'salter',
            'slater',
            'stelar'],
 'lasting': ['anglist', 'lasting', 'salting', 'slating', 'staling'],
 'lastly': ['lastly', 'saltly'],
 'lastness': ['lastness', 'saltness'],
 'lastre': ['laster',
            'lastre',
            'rastle',
            'relast',
            'resalt',
            'salter',
            'slater',
            'stelar'],
 'lasty': ['lasty', 'salty', 'slaty'],
 'lat': ['alt', 'lat', 'tal'],
 'lata': ['lata', 'taal', 'tala'],
 'latania': ['altaian', 'latania', 'natalia'],
 'latcher': ['clethra', 'latcher', 'ratchel', 'relatch', 'talcher', 'trachle'],
 'latchet': ['chattel', 'latchet'],
 'late': ['atle', 'laet', 'late', 'leat', 'tael', 'tale', 'teal'],
 'latebra': ['alberta', 'latebra', 'ratable'],
 'lated': ['adlet', 'dealt', 'delta', 'lated', 'taled'],
 'lateen': ['elanet', 'lanete', 'lateen'],
 'lately': ['lately', 'lealty'],
 'laten': ['ental', 'laten', 'leant'],
 'latent': ['latent', 'latten', 'nattle', 'talent', 'tantle'],
 'latentness': ['latentness', 'tenantless'],
 'later': ['alert', 'alter', 'artel', 'later', 'ratel', 'taler', 'telar'],
 'latera': ['latera', 'relata'],
 'laterad': ['altared', 'laterad'],
 'lateralis': ['lateralis', 'stellaria'],
 'lateran': ['alatern', 'lateran'],
 'laterite': ['laterite', 'literate', 'teretial'],
 'laterocaudal': ['caudolateral', 'laterocaudal'],
 'laterodorsal': ['dorsolateral', 'laterodorsal'],
 'lateroventral': ['lateroventral', 'ventrolateral'],
 'latest': ['latest', 'sattle', 'taslet'],
 'latex': ['exalt', 'latex'],
 'lath': ['halt', 'lath'],
 'lathe': ['ethal', 'lathe', 'leath'],
 'latheman': ['latheman', 'methanal'],
 'lathen': ['ethnal', 'hantle', 'lathen', 'thenal'],
 'lather': ['arthel', 'halter', 'lather', 'thaler'],
 'lathery': ['earthly', 'heartly', 'lathery', 'rathely'],
 'lathing': ['halting', 'lathing', 'thingal'],
 'latian': ['antlia', 'latian', 'nalita'],
 'latibulize': ['latibulize', 'utilizable'],
 'latices': ['astelic', 'elastic', 'latices'],
 'laticlave': ['laticlave', 'vacillate'],
 'latigo': ['galiot', 'latigo'],
 'latimeria': ['latimeria', 'marialite'],
 'latin': ['altin', 'latin'],
 'latinate': ['antliate', 'latinate'],
 'latiner': ['entrail',
             'latiner',
             'latrine',
             'ratline',
             'reliant',
             'retinal',
             'trenail'],
 'latinesque': ['latinesque', 'sequential'],
 'latinian': ['antinial', 'latinian'],
 'latinizer': ['latinizer', 'trinalize'],
 'latinus': ['latinus', 'tulisan', 'unalist'],
 'lation': ['italon', 'lation', 'talion'],
 'latirostres': ['latirostres', 'setirostral'],
 'latirus': ['latirus', 'trisula'],
 'latish': ['latish', 'tahsil'],
 'latite': ['latite', 'tailet', 'tailte', 'talite'],
 'latitude': ['altitude', 'latitude'],
 'latitudinal': ['altitudinal', 'latitudinal'],
 'latitudinarian': ['altitudinarian', 'latitudinarian'],
 'latomy': ['latomy', 'tyloma'],
 'latona': ['atonal', 'latona'],
 'latonian': ['latonian', 'nataloin', 'national'],
 'latria': ['altair', 'atrail', 'atrial', 'lariat', 'latria', 'talari'],
 'latrine': ['entrail',
             'latiner',
             'latrine',
             'ratline',
             'reliant',
             'retinal',
             'trenail'],
 'latris': ['latris', 'strial'],
 'latro': ['latro', 'rotal', 'toral'],
 'latrobe': ['alberto', 'bloater', 'latrobe'],
 'latrobite': ['latrobite', 'trilobate'],
 'latrocinium': ['latrocinium', 'tourmalinic'],
 'latron': ['latron', 'lontar', 'tornal'],
 'latten': ['latent', 'latten', 'nattle', 'talent', 'tantle'],
 'latter': ['artlet', 'latter', 'rattle', 'tartle', 'tatler'],
 'latterkin': ['intertalk', 'latterkin'],
 'lattice': ['lattice', 'tactile'],
 'latticinio': ['latticinio', 'licitation'],
 'latuka': ['latuka', 'taluka'],
 'latus': ['latus', 'sault', 'talus'],
 'latvian': ['latvian', 'valiant'],
 'laubanite': ['laubanite', 'unlabiate'],
 'laud': ['auld', 'dual', 'laud', 'udal'],
 'laudation': ['adulation', 'laudation'],
 'laudator': ['adulator', 'laudator'],
 'laudatorily': ['illaudatory', 'laudatorily'],
 'laudatory': ['adulatory', 'laudatory'],
 'lauder': ['lauder', 'udaler'],
 'laudism': ['dualism', 'laudism'],
 'laudist': ['dualist', 'laudist'],
 'laumonite': ['emulation', 'laumonite'],
 'laun': ['laun', 'luna', 'ulna', 'unal'],
 'launce': ['auncel', 'cuneal', 'lacune', 'launce', 'unlace'],
 'launch': ['chulan', 'launch', 'nuchal'],
 'launcher': ['launcher', 'relaunch'],
 'laund': ['dunal', 'laund', 'lunda', 'ulnad'],
 'launder': ['launder', 'rundale'],
 'laur': ['alur', 'laur', 'lura', 'raul', 'ural'],
 'laura': ['aural', 'laura'],
 'laurel': ['allure', 'laurel'],
 'laureled': ['laureled', 'reallude'],
 'laurence': ['cerulean', 'laurence'],
 'laurent': ['laurent', 'neutral', 'unalert'],
 'laurentide': ['adulterine', 'laurentide'],
 'lauric': ['curial', 'lauric', 'uracil', 'uralic'],
 'laurin': ['laurin', 'urinal'],
 'laurite': ['laurite', 'uralite'],
 'laurus': ['laurus', 'ursula'],
 'lava': ['aval', 'lava'],
 'lavacre': ['caravel', 'lavacre'],
 'lavaret': ['larvate', 'lavaret', 'travale'],
 'lave': ['lave', 'vale', 'veal', 'vela'],
 'laveer': ['laveer', 'leaver', 'reveal', 'vealer'],
 'lavehr': ['halver', 'lavehr'],
 'lavenite': ['elvanite', 'lavenite'],
 'laver': ['arvel', 'larve', 'laver', 'ravel', 'velar'],
 'laverania': ['laverania', 'valeriana'],
 'lavic': ['cavil', 'lavic'],
 'lavinia': ['lavinia', 'vinalia'],
 'lavish': ['lavish', 'vishal'],
 'lavisher': ['lavisher', 'shrieval'],
 'lavolta': ['lavolta', 'vallota'],
 'law': ['awl', 'law'],
 'lawing': ['lawing', 'waling'],
 'lawk': ['lawk', 'walk'],
 'lawmonger': ['angleworm', 'lawmonger'],
 'lawned': ['delawn', 'lawned', 'wandle'],
 'lawner': ['lawner', 'warnel'],
 'lawny': ['lawny', 'wanly'],
 'lawrie': ['lawrie', 'wailer'],
 'lawter': ['lawter', 'walter'],
 'lawyer': ['lawyer', 'yawler'],
 'laxism': ['laxism', 'smilax'],
 'lay': ['aly', 'lay'],
 'layer': ['early', 'layer', 'relay'],
 'layered': ['delayer', 'layered', 'redelay'],
 'layery': ['layery', 'yearly'],
 'laying': ['gainly', 'laying'],
 'layman': ['amylan', 'lamany', 'layman'],
 'layne': ['laney', 'layne'],
 'layout': ['layout', 'lutayo', 'outlay'],
 'layover': ['layover', 'overlay'],
 'layship': ['apishly', 'layship'],
 'lazarlike': ['alkalizer', 'lazarlike'],
 'laze': ['laze', 'zeal'],
 'lea': ['ale', 'lea'],
 'leach': ['chela', 'lache', 'leach'],
 'leachman': ['leachman', 'mechanal'],
 'lead': ['dale', 'deal', 'lade', 'lead', 'leda'],
 'leadable': ['dealable', 'leadable'],
 'leaded': ['delead', 'leaded'],
 'leader': ['dealer', 'leader', 'redeal', 'relade', 'relead'],
 'leadership': ['dealership', 'leadership'],
 'leadin': ['aldine', 'daniel', 'delian', 'denial', 'enalid', 'leadin'],
 'leadiness': ['idealness', 'leadiness'],
 'leading': ['adeling', 'dealing', 'leading'],
 'leadman': ['daleman', 'lademan', 'leadman'],
 'leads': ['leads', 'slade'],
 'leadsman': ['dalesman', 'leadsman'],
 'leadstone': ['endosteal', 'leadstone'],
 'leady': ['delay', 'leady'],
 'leaf': ['alef', 'feal', 'flea', 'leaf'],
 'leafen': ['enleaf', 'leafen'],
 'leafit': ['fetial', 'filate', 'lafite', 'leafit'],
 'leafy': ['fleay', 'leafy'],
 'leah': ['hale', 'heal', 'leah'],
 'leak': ['kale', 'lake', 'leak'],
 'leakiness': ['alikeness', 'leakiness'],
 'leakless': ['lakeless', 'leakless'],
 'leal': ['alle', 'ella', 'leal'],
 'lealty': ['lately', 'lealty'],
 'leam': ['alem', 'alme', 'lame', 'leam', 'male', 'meal', 'mela'],
 'leamer': ['leamer', 'mealer'],
 'lean': ['alen', 'lane', 'lean', 'lena', 'nael', 'neal'],
 'leander': ['leander', 'learned', 'reladen'],
 'leaner': ['arlene', 'leaner'],
 'leaning': ['angelin', 'leaning'],
 'leant': ['ental', 'laten', 'leant'],
 'leap': ['leap', 'lepa', 'pale', 'peal', 'plea'],
 'leaper': ['leaper', 'releap', 'repale', 'repeal'],
 'leaping': ['apeling', 'leaping'],
 'leapt': ['leapt',
           'palet',
           'patel',
           'pelta',
           'petal',
           'plate',
           'pleat',
           'tepal'],
 'lear': ['earl', 'eral', 'lear', 'real'],
 'learn': ['learn', 'renal'],
 'learned': ['leander', 'learned', 'reladen'],
 'learnedly': ['ellenyard', 'learnedly'],
 'learner': ['learner', 'relearn'],
 'learnt': ['altern', 'antler', 'learnt', 'rental', 'ternal'],
 'leasable': ['leasable', 'sealable'],
 'lease': ['easel', 'lease'],
 'leaser': ['alerse', 'leaser', 'reales', 'resale', 'reseal', 'sealer'],
 'leash': ['halse', 'leash', 'selah', 'shale', 'sheal', 'shela'],
 'leasing': ['leasing', 'sealing'],
 'least': ['least', 'setal', 'slate', 'stale', 'steal', 'stela', 'tales'],
 'leat': ['atle', 'laet', 'late', 'leat', 'tael', 'tale', 'teal'],
 'leath': ['ethal', 'lathe', 'leath'],
 'leather': ['leather', 'tarheel'],
 'leatherbark': ['halterbreak', 'leatherbark'],
 'leatherer': ['leatherer', 'releather', 'tarheeler'],
 'leatman': ['amental', 'leatman'],
 'leaver': ['laveer', 'leaver', 'reveal', 'vealer'],
 'leaves': ['leaves', 'sleave'],
 'leaving': ['leaving', 'vangeli'],
 'leavy': ['leavy', 'vealy'],
 'leban': ['leban', 'nable'],
 'lebanese': ['ebenales', 'lebanese'],
 'lebensraum': ['lebensraum', 'mensurable'],
 'lecaniid': ['alcidine', 'danielic', 'lecaniid'],
 'lecanora': ['carolean', 'lecanora'],
 'lecanoroid': ['lecanoroid', 'olecranoid'],
 'lechery': ['cheerly', 'lechery'],
 'lechriodont': ['holocentrid', 'lechriodont'],
 'lecithal': ['hellicat', 'lecithal'],
 'lecontite': ['lecontite', 'nicolette'],
 'lector': ['colter', 'lector', 'torcel'],
 'lectorial': ['corallite', 'lectorial'],
 'lectorship': ['lectorship', 'leptorchis'],
 'lectrice': ['electric', 'lectrice'],
 'lecturess': ['cutleress', 'lecturess', 'truceless'],
 'lecyth': ['lecyth', 'letchy'],
 'lecythis': ['chestily', 'lecythis'],
 'led': ['del', 'eld', 'led'],
 'leda': ['dale', 'deal', 'lade', 'lead', 'leda'],
 'lede': ['dele', 'lede', 'leed'],
 'leden': ['leden', 'neeld'],
 'ledge': ['glede', 'gleed', 'ledge'],
 'ledger': ['gelder', 'ledger', 'redleg'],
 'ledging': ['gelding', 'ledging'],
 'ledgy': ['gledy', 'ledgy'],
 'lee': ['eel', 'lee'],
 'leed': ['dele', 'lede', 'leed'],
 'leek': ['keel', 'kele', 'leek'],
 'leep': ['leep', 'peel', 'pele'],
 'leepit': ['leepit', 'pelite', 'pielet'],
 'leer': ['leer', 'reel'],
 'leeringly': ['leeringly', 'reelingly'],
 'leerness': ['leerness', 'lessener'],
 'lees': ['else', 'lees', 'seel', 'sele', 'slee'],
 'leet': ['leet', 'lete', 'teel', 'tele'],
 'leetman': ['entelam', 'leetman'],
 'leewan': ['leewan', 'weanel'],
 'left': ['felt', 'flet', 'left'],
 'leftish': ['fishlet', 'leftish'],
 'leftness': ['feltness', 'leftness'],
 'leg': ['gel', 'leg'],
 'legalist': ['legalist', 'stillage'],
 'legantine': ['eglantine', 'inelegant', 'legantine'],
 'legate': ['eaglet', 'legate', 'teagle', 'telega'],
 'legatine': ['galenite', 'legatine'],
 'legation': ['gelation', 'lagonite', 'legation'],
 'legative': ['legative', 'levigate'],
 'legator': ['argolet', 'gloater', 'legator'],
 'legendary': ['enragedly', 'legendary'],
 'leger': ['leger', 'regle'],
 'legger': ['eggler', 'legger'],
 'legion': ['eloign', 'gileno', 'legion'],
 'legioner': ['eloigner', 'legioner'],
 'legionry': ['legionry', 'yeorling'],
 'legislator': ['allegorist', 'legislator'],
 'legman': ['legman', 'mangel', 'mangle'],
 'legoa': ['gloea', 'legoa'],
 'legua': ['gulae', 'legua'],
 'leguan': ['genual', 'leguan'],
 'lehr': ['herl', 'hler', 'lehr'],
 'lei': ['eli', 'lei', 'lie'],
 'leif': ['feil', 'file', 'leif', 'lief', 'life'],
 'leila': ['allie', 'leila', 'lelia'],
 'leipoa': ['apiole', 'leipoa'],
 'leisten': ['leisten', 'setline', 'tensile'],
 'leister': ['leister', 'sterile'],
 'leith': ['leith', 'lithe'],
 'leitneria': ['leitneria', 'lienteria'],
 'lek': ['elk', 'lek'],
 'lekach': ['hackle', 'lekach'],
 'lekane': ['alkene', 'lekane'],
 'lelia': ['allie', 'leila', 'lelia'],
 'leman': ['leman', 'lemna'],
 'lemma': ['lemma', 'melam'],
 'lemna': ['leman', 'lemna'],
 'lemnad': ['lemnad', 'menald'],
 'lemnian': ['lemnian', 'lineman', 'melanin'],
 'lemniscate': ['centesimal', 'lemniscate'],
 'lemon': ['lemon', 'melon', 'monel'],
 'lemonias': ['laminose', 'lemonias', 'semolina'],
 'lemonlike': ['lemonlike', 'melonlike'],
 'lemony': ['lemony', 'myelon'],
 'lemosi': ['lemosi', 'limose', 'moiles'],
 'lempira': ['impaler', 'impearl', 'lempira', 'premial'],
 'lemuria': ['lemuria', 'miauler'],
 'lemurian': ['lemurian', 'malurine', 'rumelian'],
 'lemurinae': ['lemurinae', 'neurilema'],
 'lemurine': ['lemurine', 'meruline', 'relumine'],
 'lena': ['alen', 'lane', 'lean', 'lena', 'nael', 'neal'],
 'lenad': ['eland', 'laden', 'lenad'],
 'lenape': ['alpeen', 'lenape', 'pelean'],
 'lenard': ['aldern',
            'darnel',
            'enlard',
            'lander',
            'lenard',
            'randle',
            'reland'],
 'lenca': ['canel', 'clean', 'lance', 'lenca'],
 'lencan': ['cannel', 'lencan'],
 'lendee': ['lendee', 'needle'],
 'lender': ['lender', 'relend'],
 'lendu': ['lendu', 'unled'],
 'lengthy': ['lengthy', 'thegnly'],
 'lenient': ['lenient', 'tenline'],
 'lenify': ['finely', 'lenify'],
 'lenis': ['elsin', 'lenis', 'niels', 'silen', 'sline'],
 'lenity': ['lenity', 'yetlin'],
 'lenny': ['lenny', 'lynne'],
 'leno': ['elon', 'enol', 'leno', 'leon', 'lone', 'noel'],
 'lenora': ['lenora', 'loaner', 'orlean', 'reloan'],
 'lenticel': ['lenticel', 'lenticle'],
 'lenticle': ['lenticel', 'lenticle'],
 'lentil': ['lentil', 'lintel'],
 'lentisc': ['lentisc', 'scintle', 'stencil'],
 'lentisco': ['lentisco', 'telsonic'],
 'lentiscus': ['lentiscus', 'tunicless'],
 'lento': ['lento', 'olent'],
 'lentous': ['lentous', 'sultone'],
 'lenvoy': ['lenvoy', 'ovenly'],
 'leo': ['leo', 'ole'],
 'leon': ['elon', 'enol', 'leno', 'leon', 'lone', 'noel'],
 'leonard': ['endoral', 'ladrone', 'leonard'],
 'leonhardite': ['leonhardite', 'lionhearted'],
 'leonid': ['doline', 'indole', 'leonid', 'loined', 'olenid'],
 'leonines': ['leonines', 'selenion'],
 'leonis': ['insole', 'leonis', 'lesion', 'selion'],
 'leonist': ['leonist', 'onliest'],
 'leonite': ['elonite', 'leonite'],
 'leonotis': ['leonotis', 'oilstone'],
 'leoparde': ['leoparde', 'reapdole'],
 'leopardite': ['leopardite', 'protelidae'],
 'leotard': ['delator', 'leotard'],
 'lepa': ['leap', 'lepa', 'pale', 'peal', 'plea'],
 'lepanto': ['lepanto', 'nepotal', 'petalon', 'polenta'],
 'lepas': ['elaps',
           'lapse',
           'lepas',
           'pales',
           'salep',
           'saple',
           'sepal',
           'slape',
           'spale',
           'speal'],
 'lepcha': ['chapel', 'lepcha', 'pleach'],
 'leper': ['leper', 'perle', 'repel'],
 'leperdom': ['leperdom', 'premodel'],
 'lepidopter': ['dopplerite', 'lepidopter'],
 'lepidosauria': ['lepidosauria', 'pliosauridae'],
 'lepidote': ['lepidote', 'petioled'],
 'lepidotic': ['diploetic', 'lepidotic'],
 'lepisma': ['ampelis', 'lepisma'],
 'leporid': ['leporid', 'leproid'],
 'leporis': ['leporis', 'spoiler'],
 'lepra': ['lepra', 'paler', 'parel', 'parle', 'pearl', 'perla', 'relap'],
 'leproid': ['leporid', 'leproid'],
 'leproma': ['leproma', 'palermo', 'pleroma', 'polearm'],
 'leprosied': ['despoiler', 'leprosied'],
 'leprosis': ['leprosis', 'plerosis'],
 'leprous': ['leprous', 'pelorus', 'sporule'],
 'leptandra': ['leptandra', 'peltandra'],
 'leptidae': ['depilate', 'leptidae', 'pileated'],
 'leptiform': ['leptiform', 'peltiform'],
 'leptodora': ['doorplate', 'leptodora'],
 'leptome': ['leptome', 'poemlet'],
 'lepton': ['lepton', 'pentol'],
 'leptonema': ['leptonema', 'ptolemean'],
 'leptorchis': ['lectorship', 'leptorchis'],
 'lepus': ['lepus', 'pulse'],
 'ler': ['ler', 'rel'],
 'lernaean': ['annealer', 'lernaean', 'reanneal'],
 'lerot': ['lerot', 'orlet', 'relot'],
 'lerwa': ['lerwa', 'waler'],
 'les': ['els', 'les'],
 'lesath': ['haslet', 'lesath', 'shelta'],
 'lesbia': ['isabel', 'lesbia'],
 'lesche': ['lesche', 'sleech'],
 'lesion': ['insole', 'leonis', 'lesion', 'selion'],
 'lesional': ['lesional', 'solenial'],
 'leslie': ['leslie', 'sellie'],
 'lessener': ['leerness', 'lessener'],
 'lest': ['lest', 'selt'],
 'lester': ['lester', 'selter', 'streel'],
 'let': ['elt', 'let'],
 'letchy': ['lecyth', 'letchy'],
 'lete': ['leet', 'lete', 'teel', 'tele'],
 'lethargus': ['lethargus', 'slaughter'],
 'lethe': ['ethel', 'lethe'],
 'lethean': ['entheal', 'lethean'],
 'lethologica': ['ethological', 'lethologica', 'theological'],
 'letitia': ['italite', 'letitia', 'tilaite'],
 'leto': ['leto', 'lote', 'tole'],
 'letoff': ['letoff', 'offlet'],
 'lett': ['lett', 'telt'],
 'letten': ['letten', 'nettle'],
 'letterer': ['letterer', 'reletter'],
 'lettish': ['lettish', 'thistle'],
 'lettrin': ['lettrin', 'trintle'],
 'leu': ['leu', 'lue', 'ule'],
 'leucadian': ['leucadian', 'lucanidae'],
 'leucocism': ['leucocism', 'muscicole'],
 'leucoma': ['caulome', 'leucoma'],
 'leucosis': ['coulisse', 'leucosis', 'ossicule'],
 'leud': ['deul', 'duel', 'leud'],
 'leuk': ['leuk', 'luke'],
 'leuma': ['amelu', 'leuma', 'ulema'],
 'leung': ['leung', 'lunge'],
 'levance': ['enclave', 'levance', 'valence'],
 'levant': ['levant', 'valent'],
 'levanter': ['levanter', 'relevant', 'revelant'],
 'levantine': ['levantine', 'valentine'],
 'leveler': ['leveler', 'relevel'],
 'lever': ['elver', 'lever', 'revel'],
 'leverer': ['leverer', 'reveler'],
 'levi': ['evil', 'levi', 'live', 'veil', 'vile', 'vlei'],
 'levier': ['levier', 'relive', 'reveil', 'revile', 'veiler'],
 'levigate': ['legative', 'levigate'],
 'levin': ['levin', 'liven'],
 'levining': ['levining', 'nievling'],
 'levir': ['levir', 'liver', 'livre', 'rivel'],
 'levirate': ['levirate', 'relative'],
 'levis': ['elvis', 'levis', 'slive'],
 'levitation': ['levitation', 'tonalitive', 'velitation'],
 'levo': ['levo', 'love', 'velo', 'vole'],
 'levyist': ['levyist', 'sylvite'],
 'lewd': ['lewd', 'weld'],
 'lewis': ['lewis', 'swile'],
 'lexia': ['axile', 'lexia'],
 'ley': ['ley', 'lye'],
 'lhota': ['altho', 'lhota', 'loath'],
 'liability': ['alibility', 'liability'],
 'liable': ['alible', 'belial', 'labile', 'liable'],
 'liana': ['alain', 'alani', 'liana'],
 'liang': ['algin', 'align', 'langi', 'liang', 'linga'],
 'liar': ['aril', 'lair', 'lari', 'liar', 'lira', 'rail', 'rial'],
 'liard': ['drail', 'laird', 'larid', 'liard'],
 'lias': ['lasi', 'lias', 'lisa', 'sail', 'sial'],
 'liatris': ['liatris', 'trilisa'],
 'libament': ['bailment', 'libament'],
 'libate': ['albeit',
            'albite',
            'baltei',
            'belait',
            'betail',
            'bletia',
            'libate'],
 'libationer': ['libationer', 'liberation'],
 'libber': ['libber', 'ribble'],
 'libby': ['bilby', 'libby'],
 'libellary': ['libellary', 'liberally'],
 'liber': ['birle', 'liber'],
 'liberal': ['braille', 'liberal'],
 'liberally': ['libellary', 'liberally'],
 'liberate': ['iterable', 'liberate'],
 'liberation': ['libationer', 'liberation'],
 'liberator': ['liberator', 'orbitelar'],
 'liberian': ['bilinear', 'liberian'],
 'libertas': ['abristle', 'libertas'],
 'libertine': ['berlinite', 'libertine'],
 'libra': ['blair', 'brail', 'libra'],
 'librate': ['betrail', 'librate', 'triable', 'trilabe'],
 'licania': ['lacinia', 'licania'],
 'license': ['license', 'selenic', 'silence'],
 'licensed': ['licensed', 'silenced'],
 'licenser': ['licenser', 'silencer'],
 'licensor': ['cresolin', 'licensor'],
 'lich': ['chil', 'lich'],
 'lichanos': ['lichanos', 'nicholas'],
 'lichenoid': ['cheloniid', 'lichenoid'],
 'lichi': ['chili', 'lichi'],
 'licitation': ['latticinio', 'licitation'],
 'licker': ['licker', 'relick', 'rickle'],
 'lickspit': ['lickspit', 'lipstick'],
 'licorne': ['creolin', 'licorne', 'locrine'],
 'lida': ['dail', 'dali', 'dial', 'laid', 'lida'],
 'lidded': ['diddle', 'lidded'],
 'lidder': ['lidder', 'riddel', 'riddle'],
 'lide': ['idle', 'lide', 'lied'],
 'lie': ['eli', 'lei', 'lie'],
 'lied': ['idle', 'lide', 'lied'],
 'lief': ['feil', 'file', 'leif', 'lief', 'life'],
 'lien': ['lien', 'line', 'neil', 'nile'],
 'lienal': ['lienal', 'lineal'],
 'lienee': ['eileen', 'lienee'],
 'lienor': ['elinor', 'lienor', 'lorien', 'noiler'],
 'lienteria': ['leitneria', 'lienteria'],
 'lientery': ['entirely', 'lientery'],
 'lier': ['lier', 'lire', 'rile'],
 'lierne': ['lierne', 'reline'],
 'lierre': ['lierre', 'relier'],
 'liesh': ['liesh', 'shiel'],
 'lievaart': ['lievaart', 'varietal'],
 'life': ['feil', 'file', 'leif', 'lief', 'life'],
 'lifelike': ['filelike', 'lifelike'],
 'lifer': ['filer', 'flier', 'lifer', 'rifle'],
 'lifeward': ['drawfile', 'lifeward'],
 'lifo': ['filo', 'foil', 'lifo'],
 'lift': ['flit', 'lift'],
 'lifter': ['fertil', 'filter', 'lifter', 'relift', 'trifle'],
 'lifting': ['fliting', 'lifting'],
 'ligament': ['ligament', 'metaling', 'tegminal'],
 'ligas': ['gisla', 'ligas', 'sigla'],
 'ligate': ['aiglet', 'ligate', 'taigle', 'tailge'],
 'ligation': ['intaglio', 'ligation'],
 'ligator': ['goitral', 'larigot', 'ligator'],
 'ligature': ['alurgite', 'ligature'],
 'lighten': ['enlight', 'lighten'],
 'lightener': ['lightener', 'relighten', 'threeling'],
 'lighter': ['lighter', 'relight', 'rightle'],
 'lighthead': ['headlight', 'lighthead'],
 'lightness': ['lightness', 'nightless', 'thingless'],
 'ligne': ['ingle', 'ligne', 'linge', 'nigel'],
 'lignin': ['lignin', 'lining'],
 'lignitic': ['lignitic', 'tiglinic'],
 'lignose': ['gelosin', 'lignose'],
 'ligroine': ['ligroine', 'religion'],
 'ligure': ['ligure', 'reguli'],
 'lija': ['jail', 'lija'],
 'like': ['kiel', 'like'],
 'liken': ['inkle', 'liken'],
 'likewise': ['likewise', 'wiselike'],
 'lilac': ['calli', 'lilac'],
 'lilacky': ['alkylic', 'lilacky'],
 'lilium': ['illium', 'lilium'],
 'lilt': ['lilt', 'till'],
 'lily': ['illy', 'lily', 'yill'],
 'lim': ['lim', 'mil'],
 'lima': ['amil', 'amli', 'lima', 'mail', 'mali', 'mila'],
 'limacina': ['animalic', 'limacina'],
 'limacon': ['limacon', 'malonic'],
 'liman': ['lamin', 'liman', 'milan'],
 'limation': ['limation', 'miltonia'],
 'limbat': ['limbat', 'timbal'],
 'limbate': ['limbate', 'timable', 'timbale'],
 'limbed': ['dimble', 'limbed'],
 'limbus': ['bluism', 'limbus'],
 'limby': ['blimy', 'limby'],
 'lime': ['emil', 'lime', 'mile'],
 'limean': ['limean', 'maline', 'melian', 'menial'],
 'limeman': ['ammelin', 'limeman'],
 'limer': ['limer', 'meril', 'miler'],
 'limes': ['limes', 'miles', 'slime', 'smile'],
 'limestone': ['limestone', 'melonites', 'milestone'],
 'limey': ['elymi', 'emily', 'limey'],
 'liminess': ['liminess', 'senilism'],
 'limitary': ['limitary', 'military'],
 'limitate': ['limitate', 'militate'],
 'limitation': ['limitation', 'militation'],
 'limited': ['delimit', 'limited'],
 'limiter': ['limiter', 'relimit'],
 'limitless': ['limitless', 'semistill'],
 'limner': ['limner', 'merlin', 'milner'],
 'limnetic': ['limnetic', 'milicent'],
 'limoniad': ['dominial', 'imolinda', 'limoniad'],
 'limosa': ['limosa', 'somali'],
 'limose': ['lemosi', 'limose', 'moiles'],
 'limp': ['limp', 'pilm', 'plim'],
 'limper': ['limper', 'prelim', 'rimple'],
 'limping': ['impling', 'limping'],
 'limpsy': ['limpsy', 'simply'],
 'limpy': ['imply', 'limpy', 'pilmy'],
 'limsy': ['limsy', 'slimy', 'smily'],
 'lin': ['lin', 'nil'],
 'lina': ['alin', 'anil', 'lain', 'lina', 'nail'],
 'linaga': ['agnail', 'linaga'],
 'linage': ['algine', 'genial', 'linage'],
 'linamarin': ['laminarin', 'linamarin'],
 'linarite': ['inertial', 'linarite'],
 'linchet': ['linchet', 'tinchel'],
 'linctus': ['clunist', 'linctus'],
 'linda': ['danli', 'ladin', 'linda', 'nidal'],
 'lindane': ['annelid', 'lindane'],
 'linder': ['linder', 'rindle'],
 'lindoite': ['lindoite', 'tolidine'],
 'lindsay': ['islandy', 'lindsay'],
 'line': ['lien', 'line', 'neil', 'nile'],
 'linea': ['alien', 'aline', 'anile', 'elain', 'elian', 'laine', 'linea'],
 'lineal': ['lienal', 'lineal'],
 'lineament': ['lineament', 'manteline'],
 'linear': ['arline', 'larine', 'linear', 'nailer', 'renail'],
 'linearity': ['inreality', 'linearity'],
 'lineate': ['elatine', 'lineate'],
 'lineature': ['lineature', 'rutelinae'],
 'linecut': ['linecut', 'tunicle'],
 'lined': ['eldin', 'lined'],
 'lineman': ['lemnian', 'lineman', 'melanin'],
 'linen': ['linen', 'linne'],
 'linesman': ['annelism', 'linesman'],
 'linet': ['inlet', 'linet'],
 'linga': ['algin', 'align', 'langi', 'liang', 'linga'],
 'lingbird': ['birdling', 'bridling', 'lingbird'],
 'linge': ['ingle', 'ligne', 'linge', 'nigel'],
 'linger': ['linger', 'ringle'],
 'lingo': ['lingo', 'login'],
 'lingtow': ['lingtow', 'twoling'],
 'lingua': ['gaulin', 'lingua'],
 'lingual': ['lingual', 'lingula'],
 'linguidental': ['dentilingual', 'indulgential', 'linguidental'],
 'lingula': ['lingual', 'lingula'],
 'linguodental': ['dentolingual', 'linguodental'],
 'lingy': ['lingy', 'lying'],
 'linha': ['linha', 'nihal'],
 'lining': ['lignin', 'lining'],
 'link': ['kiln', 'link'],
 'linked': ['kindle', 'linked'],
 'linker': ['linker', 'relink'],
 'linking': ['inkling', 'linking'],
 'linkman': ['kilnman', 'linkman'],
 'links': ['links', 'slink'],
 'linnaea': ['alanine', 'linnaea'],
 'linnaean': ['annaline', 'linnaean'],
 'linne': ['linen', 'linne'],
 'linnet': ['linnet', 'linten'],
 'lino': ['lino', 'lion', 'loin', 'noil'],
 'linolenic': ['encinillo', 'linolenic'],
 'linometer': ['linometer', 'nilometer'],
 'linopteris': ['linopteris', 'prosilient'],
 'linous': ['insoul', 'linous', 'nilous', 'unsoil'],
 'linsey': ['linsey', 'lysine'],
 'linstock': ['coltskin', 'linstock'],
 'lintel': ['lentil', 'lintel'],
 'linten': ['linnet', 'linten'],
 'lintseed': ['enlisted', 'lintseed'],
 'linum': ['linum', 'ulmin'],
 'linus': ['linus', 'sunil'],
 'liny': ['inly', 'liny'],
 'lion': ['lino', 'lion', 'loin', 'noil'],
 'lioncel': ['colline', 'lioncel'],
 'lionel': ['lionel', 'niello'],
 'lionet': ['entoil', 'lionet'],
 'lionhearted': ['leonhardite', 'lionhearted'],
 'lipa': ['lipa', 'pail', 'pali', 'pial'],
 'lipan': ['lipan', 'pinal', 'plain'],
 'liparis': ['aprilis', 'liparis'],
 'liparite': ['liparite', 'reptilia'],
 'liparous': ['liparous', 'pliosaur'],
 'lipase': ['espial', 'lipase', 'pelias'],
 'lipin': ['lipin', 'pilin'],
 'liplet': ['liplet', 'pillet'],
 'lipochondroma': ['chondrolipoma', 'lipochondroma'],
 'lipoclasis': ['calliopsis', 'lipoclasis'],
 'lipocyte': ['epicotyl', 'lipocyte'],
 'lipofibroma': ['fibrolipoma', 'lipofibroma'],
 'lipolytic': ['lipolytic', 'politicly'],
 'lipoma': ['lipoma', 'pimola', 'ploima'],
 'lipomyoma': ['lipomyoma', 'myolipoma'],
 'lipomyxoma': ['lipomyxoma', 'myxolipoma'],
 'liposis': ['liposis', 'pilosis'],
 'lipotype': ['lipotype', 'polypite'],
 'lippen': ['lippen', 'nipple'],
 'lipper': ['lipper', 'ripple'],
 'lippia': ['lippia', 'pilpai'],
 'lipsanotheca': ['lipsanotheca', 'sphacelation'],
 'lipstick': ['lickspit', 'lipstick'],
 'liquate': ['liquate', 'tequila'],
 'liquidate': ['liquidate', 'qualitied'],
 'lira': ['aril', 'lair', 'lari', 'liar', 'lira', 'rail', 'rial'],
 'lirate': ['lirate', 'retail', 'retial', 'tailer'],
 'liration': ['liration', 'litorina'],
 'lire': ['lier', 'lire', 'rile'],
 'lis': ['lis', 'sil'],
 'lisa': ['lasi', 'lias', 'lisa', 'sail', 'sial'],
 'lise': ['isle', 'lise', 'sile'],
 'lisere': ['lisere', 'resile'],
 'lisk': ['lisk', 'silk', 'skil'],
 'lisle': ['lisle', 'selli'],
 'lisp': ['lisp', 'slip'],
 'lisper': ['lisper', 'pliers', 'sirple', 'spiler'],
 'list': ['list', 'silt', 'slit'],
 'listable': ['bastille', 'listable'],
 'listen': ['enlist', 'listen', 'silent', 'tinsel'],
 'listener': ['enlister', 'esterlin', 'listener', 'relisten'],
 'lister': ['lister', 'relist'],
 'listera': ['aletris', 'alister', 'listera', 'realist', 'saltier'],
 'listerian': ['listerian', 'trisilane'],
 'listerine': ['listerine', 'resilient'],
 'listerize': ['listerize', 'sterilize'],
 'listing': ['listing', 'silting'],
 'listless': ['listless', 'slitless'],
 'lisuarte': ['auletris', 'lisuarte'],
 'lit': ['lit', 'til'],
 'litas': ['alist', 'litas', 'slait', 'talis'],
 'litchi': ['litchi', 'lithic'],
 'lite': ['lite', 'teil', 'teli', 'tile'],
 'liter': ['liter', 'tiler'],
 'literal': ['literal', 'tallier'],
 'literary': ['literary', 'trailery'],
 'literate': ['laterite', 'literate', 'teretial'],
 'literose': ['literose', 'roselite', 'tirolese'],
 'lith': ['hilt', 'lith'],
 'litharge': ['litharge', 'thirlage'],
 'lithe': ['leith', 'lithe'],
 'lithectomy': ['lithectomy', 'methylotic'],
 'lithic': ['litchi', 'lithic'],
 'litho': ['litho', 'thiol', 'tholi'],
 'lithochromography': ['chromolithography', 'lithochromography'],
 'lithocyst': ['cystolith', 'lithocyst'],
 'lithonephria': ['lithonephria', 'philotherian'],
 'lithonephrotomy': ['lithonephrotomy', 'nephrolithotomy'],
 'lithophane': ['anthophile', 'lithophane'],
 'lithophone': ['lithophone', 'thiophenol'],
 'lithophotography': ['lithophotography', 'photolithography'],
 'lithophysal': ['isophthalyl', 'lithophysal'],
 'lithopone': ['lithopone', 'phonolite'],
 'lithous': ['lithous', 'loutish'],
 'litigate': ['litigate', 'tagilite'],
 'litmus': ['litmus', 'tilmus'],
 'litorina': ['liration', 'litorina'],
 'litorinidae': ['idioretinal', 'litorinidae'],
 'litra': ['litra', 'trail', 'trial'],
 'litsea': ['isleta', 'litsea', 'salite', 'stelai'],
 'litster': ['litster', 'slitter', 'stilter', 'testril'],
 'litten': ['litten', 'tinlet'],
 'litter': ['litter', 'tilter', 'titler'],
 'littery': ['littery', 'tritely'],
 'littoral': ['littoral', 'tortilla'],
 'lituiform': ['lituiform', 'trifolium'],
 'litus': ['litus', 'sluit', 'tulsi'],
 'live': ['evil', 'levi', 'live', 'veil', 'vile', 'vlei'],
 'lived': ['devil', 'divel', 'lived'],
 'livedo': ['livedo', 'olived'],
 'liveliness': ['liveliness', 'villeiness'],
 'livelong': ['livelong', 'loveling'],
 'lively': ['evilly', 'lively', 'vilely'],
 'liven': ['levin', 'liven'],
 'liveness': ['evilness', 'liveness', 'veinless', 'vileness', 'vineless'],
 'liver': ['levir', 'liver', 'livre', 'rivel'],
 'livered': ['deliver', 'deviler', 'livered'],
 'livery': ['livery', 'verily'],
 'livier': ['livier', 'virile'],
 'livonian': ['livonian', 'violanin'],
 'livre': ['levir', 'liver', 'livre', 'rivel'],
 'liwan': ['inlaw', 'liwan'],
 'llandeilo': ['diallelon', 'llandeilo'],
 'llew': ['llew', 'well'],
 'lloyd': ['dolly', 'lloyd'],
 'loa': ['alo', 'lao', 'loa'],
 'loach': ['chola', 'loach', 'olcha'],
 'load': ['alod', 'dola', 'load', 'odal'],
 'loaden': ['enodal', 'loaden'],
 'loader': ['loader', 'ordeal', 'reload'],
 'loading': ['angloid', 'loading'],
 'loaf': ['foal', 'loaf', 'olaf'],
 'loam': ['loam', 'loma', 'malo', 'mola', 'olam'],
 'loaminess': ['loaminess', 'melanosis'],
 'loaming': ['almoign', 'loaming'],
 'loamy': ['amylo', 'loamy'],
 'loaner': ['lenora', 'loaner', 'orlean', 'reloan'],
 'loasa': ['alosa', 'loasa', 'oasal'],
 'loath': ['altho', 'lhota', 'loath'],
 'loather': ['loather', 'rathole'],
 'loathly': ['loathly', 'tallyho'],
 'lob': ['blo', 'lob'],
 'lobar': ['balor', 'bolar', 'boral', 'labor', 'lobar'],
 'lobate': ['lobate', 'oblate'],
 'lobated': ['bloated', 'lobated'],
 'lobately': ['lobately', 'oblately'],
 'lobation': ['boltonia', 'lobation', 'oblation'],
 'lobe': ['bleo', 'bole', 'lobe'],
 'lobed': ['bodle', 'boled', 'lobed'],
 'lobelet': ['bellote', 'lobelet'],
 'lobelia': ['bolelia', 'lobelia', 'obelial'],
 'lobing': ['globin', 'goblin', 'lobing'],
 'lobo': ['bolo', 'bool', 'lobo', 'obol'],
 'lobola': ['balolo', 'lobola'],
 'lobscourse': ['lobscourse', 'lobscouser'],
 'lobscouser': ['lobscourse', 'lobscouser'],
 'lobster': ['bolster', 'lobster'],
 'loca': ['alco', 'coal', 'cola', 'loca'],
 'local': ['callo', 'colla', 'local'],
 'locanda': ['acnodal', 'canadol', 'locanda'],
 'locarnite': ['alectrion', 'clarionet', 'crotaline', 'locarnite'],
 'locarnize': ['laconizer', 'locarnize'],
 'locarno': ['coronal', 'locarno'],
 'locate': ['acetol', 'colate', 'locate'],
 'location': ['colation', 'coontail', 'location'],
 'locational': ['allocation', 'locational'],
 'locator': ['crotalo', 'locator'],
 'loch': ['chol', 'loch'],
 'lochan': ['chalon', 'lochan'],
 'lochetic': ['helcotic', 'lochetic', 'ochletic'],
 'lochus': ['holcus', 'lochus', 'slouch'],
 'loci': ['clio', 'coil', 'coli', 'loci'],
 'lociation': ['coalition', 'lociation'],
 'lock': ['colk', 'lock'],
 'locker': ['locker', 'relock'],
 'lockpin': ['lockpin', 'pinlock'],
 'lockram': ['lockram', 'marlock'],
 'lockspit': ['lockspit', 'lopstick'],
 'lockup': ['lockup', 'uplock'],
 'loco': ['cool', 'loco'],
 'locoweed': ['coolweed', 'locoweed'],
 'locrian': ['carolin', 'clarion', 'colarin', 'locrian'],
 'locrine': ['creolin', 'licorne', 'locrine'],
 'loculate': ['allocute', 'loculate'],
 'loculation': ['allocution', 'loculation'],
 'locum': ['cumol', 'locum'],
 'locusta': ['costula', 'locusta', 'talcous'],
 'lod': ['dol', 'lod', 'old'],
 'lode': ['dole', 'elod', 'lode', 'odel'],
 'lodesman': ['dolesman', 'lodesman'],
 'lodgeman': ['angeldom', 'lodgeman'],
 'lodger': ['golder', 'lodger'],
 'lodging': ['godling', 'lodging'],
 'loess': ['loess', 'soles'],
 'loessic': ['loessic', 'ossicle'],
 'lof': ['flo', 'lof'],
 'loft': ['flot', 'loft'],
 'lofter': ['floret', 'forlet', 'lofter', 'torfel'],
 'lofty': ['lofty', 'oftly'],
 'log': ['gol', 'log'],
 'logania': ['alogian', 'logania'],
 'logarithm': ['algorithm', 'logarithm'],
 'logarithmic': ['algorithmic', 'logarithmic'],
 'loge': ['egol', 'goel', 'loge', 'ogle', 'oleg'],
 'logger': ['logger', 'roggle'],
 'logicalist': ['logicalist', 'logistical'],
 'logicist': ['logicist', 'logistic'],
 'login': ['lingo', 'login'],
 'logistic': ['logicist', 'logistic'],
 'logistical': ['logicalist', 'logistical'],
 'logistics': ['glossitic', 'logistics'],
 'logman': ['amlong', 'logman'],
 'logographic': ['graphologic', 'logographic'],
 'logographical': ['graphological', 'logographical'],
 'logography': ['graphology', 'logography'],
 'logoi': ['igloo', 'logoi'],
 'logometrical': ['logometrical', 'metrological'],
 'logos': ['gools', 'logos'],
 'logotypy': ['logotypy', 'typology'],
 'logria': ['gloria', 'larigo', 'logria'],
 'logy': ['gloy', 'logy'],
 'lohar': ['horal', 'lohar'],
 'loin': ['lino', 'lion', 'loin', 'noil'],
 'loined': ['doline', 'indole', 'leonid', 'loined', 'olenid'],
 'loir': ['loir', 'lori', 'roil'],
 'lois': ['lois', 'silo', 'siol', 'soil', 'soli'],
 'loiter': ['loiter', 'toiler', 'triole'],
 'loka': ['kalo', 'kola', 'loka'],
 'lokao': ['lokao', 'oolak'],
 'loke': ['koel', 'loke'],
 'loket': ['ketol', 'loket'],
 'lola': ['lalo', 'lola', 'olla'],
 'loma': ['loam', 'loma', 'malo', 'mola', 'olam'],
 'lomatine': ['lomatine', 'tolamine'],
 'lombardian': ['laminboard', 'lombardian'],
 'lomboy': ['bloomy', 'lomboy'],
 'loment': ['loment', 'melton', 'molten'],
 'lomentaria': ['ameliorant', 'lomentaria'],
 'lomita': ['lomita', 'tomial'],
 'lone': ['elon', 'enol', 'leno', 'leon', 'lone', 'noel'],
 'longa': ['along', 'gonal', 'lango', 'longa', 'nogal'],
 'longanimous': ['longanimous', 'longimanous'],
 'longbeard': ['boglander', 'longbeard'],
 'longear': ['argenol', 'longear'],
 'longhead': ['headlong', 'longhead'],
 'longimanous': ['longanimous', 'longimanous'],
 'longimetry': ['longimetry', 'mongrelity'],
 'longmouthed': ['goldenmouth', 'longmouthed'],
 'longue': ['longue', 'lounge'],
 'lonicera': ['acrolein',
              'arecolin',
              'caroline',
              'colinear',
              'cornelia',
              'creolian',
              'lonicera'],
 'lontar': ['latron', 'lontar', 'tornal'],
 'looby': ['booly', 'looby'],
 'lood': ['dool', 'lood'],
 'loof': ['fool', 'loof', 'olof'],
 'look': ['kolo', 'look'],
 'looker': ['looker', 'relook'],
 'lookout': ['lookout', 'outlook'],
 'loom': ['loom', 'mool'],
 'loon': ['loon', 'nolo'],
 'loop': ['loop', 'polo', 'pool'],
 'looper': ['looper', 'pooler'],
 'loopist': ['loopist', 'poloist', 'topsoil'],
 'loopy': ['loopy', 'pooly'],
 'loosing': ['loosing', 'sinolog'],
 'loot': ['loot', 'tool'],
 'looter': ['looter', 'retool', 'rootle', 'tooler'],
 'lootie': ['lootie', 'oolite'],
 'lop': ['lop', 'pol'],
 'lope': ['lope', 'olpe', 'pole'],
 'loper': ['loper', 'poler'],
 'lopezia': ['epizoal', 'lopezia', 'opalize'],
 'lophine': ['lophine', 'pinhole'],
 'lophocomi': ['homopolic', 'lophocomi'],
 'loppet': ['loppet', 'topple'],
 'loppy': ['loppy', 'polyp'],
 'lopstick': ['lockspit', 'lopstick'],
 'loquacious': ['aquicolous', 'loquacious'],
 'lora': ['lora', 'oral'],
 'lorandite': ['lorandite', 'rodential'],
 'lorate': ['elator', 'lorate'],
 'lorcha': ['choral', 'lorcha'],
 'lordly': ['drolly', 'lordly'],
 'lore': ['lore', 'orle', 'role'],
 'lored': ['lored', 'older'],
 'loren': ['enrol', 'loren'],
 'lori': ['loir', 'lori', 'roil'],
 'lorica': ['caroli', 'corial', 'lorica'],
 'loricate': ['calorite', 'erotical', 'loricate'],
 'loricati': ['clitoria', 'loricati'],
 'lorien': ['elinor', 'lienor', 'lorien', 'noiler'],
 'loro': ['loro', 'olor', 'orlo', 'rool'],
 'lose': ['lose', 'sloe', 'sole'],
 'loser': ['loser', 'orsel', 'rosel', 'soler'],
 'lost': ['lost', 'lots', 'slot'],
 'lot': ['lot', 'tol'],
 'lota': ['alto', 'lota'],
 'lotase': ['lotase', 'osteal', 'solate', 'stolae', 'talose'],
 'lote': ['leto', 'lote', 'tole'],
 'lotic': ['cloit', 'lotic'],
 'lotrite': ['lotrite', 'tortile', 'triolet'],
 'lots': ['lost', 'lots', 'slot'],
 'lotta': ['lotta', 'total'],
 'lotter': ['lotter', 'rottle', 'tolter'],
 'lottie': ['lottie', 'toilet', 'tolite'],
 'lou': ['lou', 'luo'],
 'loud': ['loud', 'ludo'],
 'louden': ['louden', 'nodule'],
 'lough': ['ghoul', 'lough'],
 'lounder': ['durenol', 'lounder', 'roundel'],
 'lounge': ['longue', 'lounge'],
 'loupe': ['epulo', 'loupe'],
 'lourdy': ['dourly', 'lourdy'],
 'louse': ['eusol', 'louse'],
 'lousy': ['lousy', 'souly'],
 'lout': ['lout', 'tolu'],
 'louter': ['elutor', 'louter', 'outler'],
 'loutish': ['lithous', 'loutish'],
 'louty': ['louty', 'outly'],
 'louvar': ['louvar', 'ovular'],
 'louver': ['louver', 'louvre'],
 'louvre': ['louver', 'louvre'],
 'lovable': ['lovable', 'volable'],
 'lovage': ['lovage', 'volage'],
 'love': ['levo', 'love', 'velo', 'vole'],
 'loveling': ['livelong', 'loveling'],
 'lovely': ['lovely', 'volley'],
 'lovering': ['lovering', 'overling'],
 'low': ['low', 'lwo', 'owl'],
 'lowa': ['alow', 'awol', 'lowa'],
 'lowder': ['lowder', 'weldor', 'wordle'],
 'lower': ['lower', 'owler', 'rowel'],
 'lowerer': ['lowerer', 'relower'],
 'lowery': ['lowery', 'owlery', 'rowley', 'yowler'],
 'lowish': ['lowish', 'owlish'],
 'lowishly': ['lowishly', 'owlishly', 'sillyhow'],
 'lowishness': ['lowishness', 'owlishness'],
 'lowy': ['lowy', 'owly', 'yowl'],
 'loyal': ['alloy', 'loyal'],
 'loyalism': ['loyalism', 'lysiloma'],
 'loyd': ['loyd', 'odyl'],
 'luba': ['balu', 'baul', 'bual', 'luba'],
 'lubber': ['burble', 'lubber', 'rubble'],
 'lubberland': ['landlubber', 'lubberland'],
 'lube': ['blue', 'lube'],
 'lucan': ['lucan', 'nucal'],
 'lucania': ['lucania', 'luciana'],
 'lucanid': ['dulcian', 'incudal', 'lucanid', 'lucinda'],
 'lucanidae': ['leucadian', 'lucanidae'],
 'lucarne': ['crenula', 'lucarne', 'nuclear', 'unclear'],
 'lucban': ['buncal', 'lucban'],
 'luce': ['clue', 'luce'],
 'luceres': ['luceres', 'recluse'],
 'lucern': ['encurl', 'lucern'],
 'lucernal': ['lucernal', 'nucellar', 'uncellar'],
 'lucet': ['culet', 'lucet'],
 'lucia': ['aulic', 'lucia'],
 'lucian': ['cunila', 'lucian', 'lucina', 'uncial'],
 'luciana': ['lucania', 'luciana'],
 'lucifer': ['ferulic', 'lucifer'],
 'lucigen': ['glucine', 'lucigen'],
 'lucina': ['cunila', 'lucian', 'lucina', 'uncial'],
 'lucinda': ['dulcian', 'incudal', 'lucanid', 'lucinda'],
 'lucinoid': ['lucinoid', 'oculinid'],
 'lucite': ['lucite', 'luetic', 'uletic'],
 'lucrative': ['lucrative', 'revictual', 'victualer'],
 'lucre': ['cruel', 'lucre', 'ulcer'],
 'lucretia': ['arculite', 'cutleria', 'lucretia', 'reticula', 'treculia'],
 'lucretian': ['centurial', 'lucretian', 'ultranice'],
 'luctiferous': ['fruticulose', 'luctiferous'],
 'lucubrate': ['lucubrate', 'tubercula'],
 'ludden': ['ludden', 'nuddle'],
 'luddite': ['diluted', 'luddite'],
 'ludian': ['dualin', 'ludian', 'unlaid'],
 'ludibry': ['buirdly', 'ludibry'],
 'ludicroserious': ['ludicroserious', 'serioludicrous'],
 'ludo': ['loud', 'ludo'],
 'lue': ['leu', 'lue', 'ule'],
 'lues': ['lues', 'slue'],
 'luetic': ['lucite', 'luetic', 'uletic'],
 'lug': ['gul', 'lug'],
 'luge': ['glue', 'gule', 'luge'],
 'luger': ['gluer', 'gruel', 'luger'],
 'lugger': ['gurgle', 'lugger', 'ruggle'],
 'lugnas': ['lugnas', 'salung'],
 'lugsome': ['glumose', 'lugsome'],
 'luian': ['inula', 'luian', 'uinal'],
 'luiseno': ['elusion', 'luiseno'],
 'luite': ['luite', 'utile'],
 'lukas': ['klaus', 'lukas', 'sulka'],
 'luke': ['leuk', 'luke'],
 'lula': ['lula', 'ulla'],
 'lulab': ['bulla', 'lulab'],
 'lumbar': ['brumal', 'labrum', 'lumbar', 'umbral'],
 'lumber': ['lumber', 'rumble', 'umbrel'],
 'lumbosacral': ['lumbosacral', 'sacrolumbal'],
 'lumine': ['lumine', 'unlime'],
 'lump': ['lump', 'plum'],
 'lumper': ['lumper', 'plumer', 'replum', 'rumple'],
 'lumpet': ['lumpet', 'plumet'],
 'lumpiness': ['lumpiness', 'pluminess'],
 'lumpy': ['lumpy', 'plumy'],
 'luna': ['laun', 'luna', 'ulna', 'unal'],
 'lunacy': ['lunacy', 'unclay'],
 'lunar': ['lunar', 'ulnar', 'urnal'],
 'lunare': ['lunare', 'neural', 'ulnare', 'unreal'],
 'lunaria': ['lunaria', 'ulnaria', 'uralian'],
 'lunary': ['lunary', 'uranyl'],
 'lunatic': ['calinut', 'lunatic'],
 'lunation': ['lunation', 'ultonian'],
 'lunda': ['dunal', 'laund', 'lunda', 'ulnad'],
 'lung': ['gunl', 'lung'],
 'lunge': ['leung', 'lunge'],
 'lunged': ['gulden', 'lunged'],
 'lungfish': ['flushing', 'lungfish'],
 'lungsick': ['lungsick', 'suckling'],
 'lunisolar': ['lunisolar', 'solilunar'],
 'luo': ['lou', 'luo'],
 'lupe': ['lupe', 'pelu', 'peul', 'pule'],
 'luperci': ['luperci', 'pleuric'],
 'lupicide': ['lupicide', 'pediculi', 'pulicide'],
 'lupinaster': ['lupinaster', 'palustrine'],
 'lupine': ['lupine', 'unpile', 'upline'],
 'lupinus': ['lupinus', 'pinulus'],
 'lupis': ['lupis', 'pilus'],
 'lupous': ['lupous', 'opulus'],
 'lura': ['alur', 'laur', 'lura', 'raul', 'ural'],
 'lurch': ['churl', 'lurch'],
 'lure': ['lure', 'rule'],
 'lurer': ['lurer', 'ruler'],
 'lurg': ['gurl', 'lurg'],
 'luringly': ['luringly', 'rulingly'],
 'luscinia': ['luscinia', 'siculian'],
 'lush': ['lush', 'shlu', 'shul'],
 'lusher': ['lusher', 'shuler'],
 'lushly': ['hyllus', 'lushly'],
 'lushness': ['lushness', 'shunless'],
 'lusian': ['insula', 'lanius', 'lusian'],
 'lusk': ['lusk', 'sulk'],
 'lusky': ['lusky', 'sulky'],
 'lusory': ['lusory', 'sourly'],
 'lust': ['lust', 'slut'],
 'luster': ['luster', 'result', 'rustle', 'sutler', 'ulster'],
 'lusterless': ['lusterless', 'lustreless', 'resultless'],
 'lustihead': ['hastilude', 'lustihead'],
 'lustreless': ['lusterless', 'lustreless', 'resultless'],
 'lustrine': ['insulter', 'lustrine', 'reinsult'],
 'lustring': ['lustring', 'rustling'],
 'lusty': ['lusty', 'tylus'],
 'lutaceous': ['cautelous', 'lutaceous'],
 'lutany': ['auntly', 'lutany'],
 'lutayo': ['layout', 'lutayo', 'outlay'],
 'lute': ['lute', 'tule'],
 'luteal': ['alulet', 'luteal'],
 'lutecia': ['aleutic', 'auletic', 'caulite', 'lutecia'],
 'lutecium': ['cumulite', 'lutecium'],
 'lutein': ['lutein', 'untile'],
 'lutfisk': ['kistful', 'lutfisk'],
 'luther': ['hurtle', 'luther'],
 'lutheran': ['lutheran', 'unhalter'],
 'lutianoid': ['lutianoid', 'nautiloid'],
 'lutianus': ['lutianus', 'nautilus', 'ustulina'],
 'luting': ['glutin', 'luting', 'ungilt'],
 'lutose': ['lutose', 'solute', 'tousle'],
 'lutra': ['lutra', 'ultra'],
 'lutrinae': ['lutrinae', 'retinula', 'rutelian', 'tenurial'],
 'luxe': ['luxe', 'ulex'],
 'lwo': ['low', 'lwo', 'owl'],
 'lyam': ['amyl', 'lyam', 'myal'],
 'lyard': ['daryl', 'lardy', 'lyard'],
 'lyas': ['lyas', 'slay'],
 'lycaenid': ['adenylic', 'lycaenid'],
 'lyceum': ['cymule', 'lyceum'],
 'lycopodium': ['lycopodium', 'polycodium'],
 'lyctidae': ['diacetyl', 'lyctidae'],
 'lyddite': ['lyddite', 'tiddley'],
 'lydia': ['daily', 'lydia'],
 'lydite': ['idlety', 'lydite', 'tidely', 'tidley'],
 'lye': ['ley', 'lye'],
 'lying': ['lingy', 'lying'],
 'lymnaeid': ['lymnaeid', 'maidenly', 'medianly'],
 'lymphadenia': ['lymphadenia', 'nymphalidae'],
 'lymphectasia': ['lymphectasia', 'metaphysical'],
 'lymphopenia': ['lymphopenia', 'polyphemian'],
 'lynne': ['lenny', 'lynne'],
 'lyon': ['lyon', 'only'],
 'lyophobe': ['hypobole', 'lyophobe'],
 'lyra': ['aryl', 'lyra', 'ryal', 'yarl'],
 'lyraid': ['aridly', 'lyraid'],
 'lyrate': ['lyrate', 'raylet', 'realty', 'telary'],
 'lyre': ['lyre', 'rely'],
 'lyric': ['cyril', 'lyric'],
 'lyrical': ['cyrilla', 'lyrical'],
 'lyrid': ['idryl', 'lyrid'],
 'lys': ['lys', 'sly'],
 'lysander': ['lysander', 'synedral'],
 'lysate': ['alytes', 'astely', 'lysate', 'stealy'],
 'lyse': ['lyse', 'sley'],
 'lysiloma': ['loyalism', 'lysiloma'],
 'lysine': ['linsey', 'lysine'],
 'lysogenetic': ['cleistogeny', 'lysogenetic'],
 'lysogenic': ['glycosine', 'lysogenic'],
 'lyssic': ['clysis', 'lyssic'],
 'lyterian': ['interlay', 'lyterian'],
 'lyxose': ['lyxose', 'xylose'],
 'ma': ['am', 'ma'],
 'maam': ['amma', 'maam'],
 'mab': ['bam', 'mab'],
 'maba': ['amba', 'maba'],
 'mabel': ['amble', 'belam', 'blame', 'mabel'],
 'mabi': ['iamb', 'mabi'],
 'mabolo': ['abloom', 'mabolo'],
 'mac': ['cam', 'mac'],
 'macaca': ['camaca', 'macaca'],
 'macaco': ['cocama', 'macaco'],
 'macaglia': ['almaciga', 'macaglia'],
 'macan': ['caman', 'macan'],
 'macanese': ['macanese', 'maecenas'],
 'macao': ['acoma', 'macao'],
 'macarism': ['macarism', 'marasmic'],
 'macaroni': ['armonica', 'macaroni', 'marocain'],
 'macaronic': ['carcinoma', 'macaronic'],
 'mace': ['acme', 'came', 'mace'],
 'macedon': ['conamed', 'macedon'],
 'macedonian': ['caedmonian', 'macedonian'],
 'macedonic': ['caedmonic', 'macedonic'],
 'macer': ['cream', 'macer'],
 'macerate': ['camerate', 'macerate', 'racemate'],
 'maceration': ['aeromantic', 'cameration', 'maceration', 'racemation'],
 'machar': ['chamar', 'machar'],
 'machi': ['chiam', 'machi', 'micah'],
 'machilis': ['chiliasm', 'hilasmic', 'machilis'],
 'machinator': ['achromatin', 'chariotman', 'machinator'],
 'machine': ['chimane', 'machine'],
 'machinery': ['hemicrany', 'machinery'],
 'macies': ['camise', 'macies'],
 'macigno': ['coaming', 'macigno'],
 'macilency': ['cyclamine', 'macilency'],
 'macle': ['camel', 'clame', 'cleam', 'macle'],
 'maclura': ['maclura', 'macular'],
 'maco': ['coma', 'maco'],
 'macon': ['coman', 'macon', 'manoc'],
 'maconite': ['coinmate', 'maconite'],
 'macro': ['carom', 'coram', 'macro', 'marco'],
 'macrobian': ['carbamino', 'macrobian'],
 'macromazia': ['macromazia', 'macrozamia'],
 'macrophage': ['cameograph', 'macrophage'],
 'macrophotograph': ['macrophotograph', 'photomacrograph'],
 'macrotia': ['aromatic', 'macrotia'],
 'macrotin': ['macrotin', 'romantic'],
 'macrourid': ['macrourid', 'macruroid'],
 'macrourus': ['macrourus', 'macrurous'],
 'macrozamia': ['macromazia', 'macrozamia'],
 'macruroid': ['macrourid', 'macruroid'],
 'macrurous': ['macrourus', 'macrurous'],
 'mactra': ['mactra', 'tarmac'],
 'macular': ['maclura', 'macular'],
 'macule': ['almuce', 'caelum', 'macule'],
 'maculose': ['maculose', 'somacule'],
 'mad': ['dam', 'mad'],
 'madden': ['damned', 'demand', 'madden'],
 'maddening': ['demanding', 'maddening'],
 'maddeningly': ['demandingly', 'maddeningly'],
 'madder': ['dermad', 'madder'],
 'made': ['dame', 'made', 'mead'],
 'madeira': ['adermia', 'madeira'],
 'madeiran': ['madeiran', 'marinade'],
 'madeline': ['endemial', 'madeline'],
 'madi': ['admi', 'amid', 'madi', 'maid'],
 'madia': ['amadi', 'damia', 'madia', 'maida'],
 'madiga': ['agamid', 'madiga'],
 'madman': ['madman', 'nammad'],
 'madnep': ['dampen', 'madnep'],
 'madrid': ['madrid', 'riddam'],
 'madrier': ['admirer', 'madrier', 'married'],
 'madrilene': ['landimere', 'madrilene'],
 'madrona': ['anadrom', 'madrona', 'mandora', 'monarda', 'roadman'],
 'madship': ['dampish', 'madship', 'phasmid'],
 'madurese': ['madurese', 'measured'],
 'mae': ['ame', 'mae'],
 'maecenas': ['macanese', 'maecenas'],
 'maenad': ['anadem', 'maenad'],
 'maenadism': ['maenadism', 'mandaeism'],
 'maeonian': ['enomania', 'maeonian'],
 'maestri': ['artemis', 'maestri', 'misrate'],
 'maestro': ['maestro', 'tarsome'],
 'mag': ['gam', 'mag'],
 'magadis': ['gamasid', 'magadis'],
 'magani': ['angami', 'magani', 'magian'],
 'magas': ['agsam', 'magas'],
 'mage': ['egma', 'game', 'mage'],
 'magenta': ['gateman', 'magenta', 'magnate', 'magneta'],
 'magian': ['angami', 'magani', 'magian'],
 'magic': ['gamic', 'magic'],
 'magister': ['gemarist', 'magister', 'sterigma'],
 'magistrate': ['magistrate', 'sterigmata'],
 'magma': ['gamma', 'magma'],
 'magnate': ['gateman', 'magenta', 'magnate', 'magneta'],
 'magnes': ['magnes', 'semang'],
 'magneta': ['gateman', 'magenta', 'magnate', 'magneta'],
 'magnetist': ['agistment', 'magnetist'],
 'magneto': ['geomant', 'magneto', 'megaton', 'montage'],
 'magnetod': ['magnetod', 'megadont'],
 'magnetoelectric': ['electromagnetic', 'magnetoelectric'],
 'magnetoelectrical': ['electromagnetical', 'magnetoelectrical'],
 'magnolia': ['algomian', 'magnolia'],
 'magnus': ['magnus', 'musang'],
 'magpie': ['magpie', 'piemag'],
 'magyar': ['magyar', 'margay'],
 'mah': ['ham', 'mah'],
 'maha': ['amah', 'maha'],
 'mahar': ['amhar', 'mahar', 'mahra'],
 'maharani': ['amiranha', 'maharani'],
 'mahdism': ['dammish', 'mahdism'],
 'mahdist': ['adsmith', 'mahdist'],
 'mahi': ['hami', 'hima', 'mahi'],
 'mahican': ['chamian', 'mahican'],
 'mahogany': ['hogmanay', 'mahogany'],
 'maholi': ['holmia', 'maholi'],
 'maholtine': ['hematolin', 'maholtine'],
 'mahori': ['homrai', 'mahori', 'mohair'],
 'mahra': ['amhar', 'mahar', 'mahra'],
 'mahran': ['amhran', 'harman', 'mahran'],
 'mahri': ['hiram', 'ihram', 'mahri'],
 'maia': ['amia', 'maia'],
 'maid': ['admi', 'amid', 'madi', 'maid'],
 'maida': ['amadi', 'damia', 'madia', 'maida'],
 'maiden': ['daimen', 'damine', 'maiden', 'median', 'medina'],
 'maidenism': ['maidenism', 'medianism'],
 'maidenly': ['lymnaeid', 'maidenly', 'medianly'],
 'maidish': ['hasidim', 'maidish'],
 'maidism': ['amidism', 'maidism'],
 'maigre': ['imager', 'maigre', 'margie', 'mirage'],
 'maiidae': ['amiidae', 'maiidae'],
 'mail': ['amil', 'amli', 'lima', 'mail', 'mali', 'mila'],
 'mailed': ['aldime', 'mailed', 'medial'],
 'mailer': ['mailer', 'remail'],
 'mailie': ['emilia', 'mailie'],
 'maim': ['ammi', 'imam', 'maim', 'mima'],
 'main': ['amin', 'main', 'mani', 'mian', 'mina', 'naim'],
 'maine': ['amine', 'anime', 'maine', 'manei'],
 'mainly': ['amylin', 'mainly'],
 'mainour': ['mainour', 'uramino'],
 'mainpast': ['mainpast', 'mantispa', 'panamist', 'stampian'],
 'mainprise': ['mainprise', 'presimian'],
 'mains': ['mains', 'manis'],
 'maint': ['maint', 'matin'],
 'maintain': ['amanitin', 'maintain'],
 'maintainer': ['antimerina', 'maintainer', 'remaintain'],
 'maintop': ['maintop', 'ptomain', 'tampion', 'timpano'],
 'maioid': ['daimio', 'maioid'],
 'maioidean': ['anomiidae', 'maioidean'],
 'maire': ['aimer', 'maire', 'marie', 'ramie'],
 'maja': ['jama', 'maja'],
 'majoon': ['majoon', 'moonja'],
 'major': ['jarmo', 'major'],
 'makah': ['hakam', 'makah'],
 'makassar': ['makassar', 'samskara'],
 'make': ['kame', 'make', 'meak'],
 'maker': ['maker', 'marek', 'merak'],
 'maki': ['akim', 'maki'],
 'mako': ['amok', 'mako'],
 'mal': ['lam', 'mal'],
 'mala': ['alma', 'amla', 'lama', 'mala'],
 'malacologist': ['malacologist', 'mastological'],
 'malaga': ['agalma', 'malaga'],
 'malagma': ['amalgam', 'malagma'],
 'malakin': ['alkamin', 'malakin'],
 'malanga': ['malanga', 'nagmaal'],
 'malapert': ['armplate', 'malapert'],
 'malapi': ['impala', 'malapi'],
 'malar': ['alarm', 'malar', 'maral', 'marla', 'ramal'],
 'malarin': ['lairman', 'laminar', 'malarin', 'railman'],
 'malate': ['malate', 'meatal', 'tamale'],
 'malcreated': ['cradlemate', 'malcreated'],
 'maldonite': ['antimodel', 'maldonite', 'monilated'],
 'male': ['alem', 'alme', 'lame', 'leam', 'male', 'meal', 'mela'],
 'maleficiation': ['amelification', 'maleficiation'],
 'maleic': ['maleic', 'malice', 'melica'],
 'maleinoid': ['alimonied', 'maleinoid'],
 'malella': ['lamella', 'malella', 'malleal'],
 'maleness': ['lameness', 'maleness', 'maneless', 'nameless'],
 'malengine': ['enameling', 'malengine', 'meningeal'],
 'maleo': ['amole', 'maleo'],
 'malfed': ['flamed', 'malfed'],
 'malhonest': ['malhonest', 'mashelton'],
 'mali': ['amil', 'amli', 'lima', 'mail', 'mali', 'mila'],
 'malic': ['claim', 'clima', 'malic'],
 'malice': ['maleic', 'malice', 'melica'],
 'malicho': ['chiloma', 'malicho'],
 'maligner': ['germinal', 'maligner', 'malinger'],
 'malikana': ['kalamian', 'malikana'],
 'maline': ['limean', 'maline', 'melian', 'menial'],
 'malines': ['malines', 'salmine', 'selamin', 'seminal'],
 'malinger': ['germinal', 'maligner', 'malinger'],
 'malison': ['malison', 'manolis', 'osmanli', 'somnial'],
 'malladrite': ['armillated', 'malladrite', 'mallardite'],
 'mallardite': ['armillated', 'malladrite', 'mallardite'],
 'malleal': ['lamella', 'malella', 'malleal'],
 'mallein': ['mallein', 'manille'],
 'malleoincudal': ['incudomalleal', 'malleoincudal'],
 'malleus': ['amellus', 'malleus'],
 'malmaison': ['anomalism', 'malmaison'],
 'malmy': ['lammy', 'malmy'],
 'malo': ['loam', 'loma', 'malo', 'mola', 'olam'],
 'malonic': ['limacon', 'malonic'],
 'malonyl': ['allonym', 'malonyl'],
 'malope': ['aplome', 'malope'],
 'malpoise': ['malpoise', 'semiopal'],
 'malposed': ['malposed', 'plasmode'],
 'maltase': ['asmalte', 'maltase'],
 'malter': ['armlet', 'malter', 'martel'],
 'maltese': ['maltese', 'seamlet'],
 'malthe': ['hamlet', 'malthe'],
 'malurinae': ['malurinae', 'melanuria'],
 'malurine': ['lemurian', 'malurine', 'rumelian'],
 'malurus': ['malurus', 'ramulus'],
 'malus': ['lamus', 'malus', 'musal', 'slaum'],
 'mamers': ['mamers', 'sammer'],
 'mamo': ['ammo', 'mamo'],
 'man': ['man', 'nam'],
 'mana': ['anam', 'mana', 'naam', 'nama'],
 'manacle': ['laceman', 'manacle'],
 'manacus': ['manacus', 'samucan'],
 'manage': ['agname', 'manage'],
 'manager': ['gearman', 'manager'],
 'manal': ['alman', 'lamna', 'manal'],
 'manas': ['manas', 'saman'],
 'manatee': ['emanate', 'manatee'],
 'manatine': ['annamite', 'manatine'],
 'manbird': ['birdman', 'manbird'],
 'manchester': ['manchester', 'searchment'],
 'mand': ['damn', 'mand'],
 'mandaeism': ['maenadism', 'mandaeism'],
 'mandaite': ['animated', 'mandaite', 'mantidae'],
 'mandarin': ['drainman', 'mandarin'],
 'mandation': ['damnation', 'mandation'],
 'mandatory': ['damnatory', 'mandatory'],
 'mande': ['amend', 'mande', 'maned'],
 'mandelate': ['aldeament', 'mandelate'],
 'mandil': ['lamnid', 'mandil'],
 'mandola': ['mandola', 'odalman'],
 'mandora': ['anadrom', 'madrona', 'mandora', 'monarda', 'roadman'],
 'mandra': ['mandra', 'radman'],
 'mandrill': ['drillman', 'mandrill'],
 'mandyas': ['daysman', 'mandyas'],
 'mane': ['amen', 'enam', 'mane', 'mean', 'name', 'nema'],
 'maned': ['amend', 'mande', 'maned'],
 'manege': ['gamene', 'manege', 'menage'],
 'manei': ['amine', 'anime', 'maine', 'manei'],
 'maneless': ['lameness', 'maleness', 'maneless', 'nameless'],
 'manent': ['manent', 'netman'],
 'manerial': ['almerian', 'manerial'],
 'manes': ['manes', 'manse', 'mensa', 'samen', 'senam'],
 'maness': ['enmass', 'maness', 'messan'],
 'manettia': ['antietam', 'manettia'],
 'maney': ['maney', 'yamen'],
 'manga': ['amang', 'ganam', 'manga'],
 'mangar': ['amgarn', 'mangar', 'marang', 'ragman'],
 'mangel': ['legman', 'mangel', 'mangle'],
 'mangelin': ['mangelin', 'nameling'],
 'manger': ['engram', 'german', 'manger'],
 'mangerite': ['germanite', 'germinate', 'gramenite', 'mangerite'],
 'mangi': ['gamin', 'mangi'],
 'mangle': ['legman', 'mangel', 'mangle'],
 'mango': ['among', 'mango'],
 'mangrass': ['grassman', 'mangrass'],
 'mangrate': ['grateman', 'mangrate', 'mentagra', 'targeman'],
 'mangue': ['mangue', 'maunge'],
 'manhead': ['headman', 'manhead'],
 'manhole': ['holeman', 'manhole'],
 'manhood': ['dhamnoo', 'hoodman', 'manhood'],
 'mani': ['amin', 'main', 'mani', 'mian', 'mina', 'naim'],
 'mania': ['amain', 'amani', 'amnia', 'anima', 'mania'],
 'maniable': ['animable', 'maniable'],
 'maniac': ['amniac', 'caiman', 'maniac'],
 'manic': ['amnic', 'manic'],
 'manid': ['dimna', 'manid'],
 'manidae': ['adamine', 'manidae'],
 'manify': ['infamy', 'manify'],
 'manila': ['almain', 'animal', 'lamina', 'manila'],
 'manilla': ['alnilam', 'manilla'],
 'manille': ['mallein', 'manille'],
 'manioc': ['camion', 'conima', 'manioc', 'monica'],
 'maniple': ['impanel', 'maniple'],
 'manipuri': ['manipuri', 'unimpair'],
 'manis': ['mains', 'manis'],
 'manist': ['manist', 'mantis', 'matins', 'stamin'],
 'manistic': ['actinism', 'manistic'],
 'manito': ['atimon', 'manito', 'montia'],
 'maniu': ['maniu', 'munia', 'unami'],
 'manius': ['animus', 'anisum', 'anusim', 'manius'],
 'maniva': ['maniva', 'vimana'],
 'manlet': ['lament', 'manlet', 'mantel', 'mantle', 'mental'],
 'manna': ['annam', 'manna'],
 'mannite': ['mannite', 'tineman'],
 'mannonic': ['cinnamon', 'mannonic'],
 'mano': ['mano', 'moan', 'mona', 'noam', 'noma', 'oman'],
 'manoc': ['coman', 'macon', 'manoc'],
 'manolis': ['malison', 'manolis', 'osmanli', 'somnial'],
 'manometrical': ['commentarial', 'manometrical'],
 'manometry': ['manometry', 'momentary'],
 'manor': ['manor', 'moran', 'norma', 'ramon', 'roman'],
 'manorial': ['manorial', 'morainal'],
 'manorship': ['manorship', 'orphanism'],
 'manoscope': ['manoscope', 'moonscape'],
 'manred': ['damner', 'manred', 'randem', 'remand'],
 'manrent': ['manrent', 'remnant'],
 'manrope': ['manrope', 'ropeman'],
 'manse': ['manes', 'manse', 'mensa', 'samen', 'senam'],
 'manship': ['manship', 'shipman'],
 'mansion': ['mansion', 'onanism'],
 'mansioneer': ['emersonian', 'mansioneer'],
 'manslaughter': ['manslaughter', 'slaughterman'],
 'manso': ['manso', 'mason', 'monas'],
 'manta': ['atman', 'manta'],
 'mantel': ['lament', 'manlet', 'mantel', 'mantle', 'mental'],
 'manteline': ['lineament', 'manteline'],
 'manter': ['manter', 'marten', 'rament'],
 'mantes': ['mantes', 'stamen'],
 'manticore': ['cremation', 'manticore'],
 'mantidae': ['animated', 'mandaite', 'mantidae'],
 'mantis': ['manist', 'mantis', 'matins', 'stamin'],
 'mantispa': ['mainpast', 'mantispa', 'panamist', 'stampian'],
 'mantissa': ['mantissa', 'satanism'],
 'mantle': ['lament', 'manlet', 'mantel', 'mantle', 'mental'],
 'manto': ['manto', 'toman'],
 'mantodea': ['mantodea', 'nematoda'],
 'mantoidea': ['diatomean', 'mantoidea'],
 'mantra': ['mantra', 'tarman'],
 'mantrap': ['mantrap', 'rampant'],
 'mantua': ['anatum', 'mantua', 'tamanu'],
 'manual': ['alumna', 'manual'],
 'manualism': ['manualism', 'musalmani'],
 'manualiter': ['manualiter', 'unmaterial'],
 'manuel': ['manuel', 'unlame'],
 'manul': ['lanum', 'manul'],
 'manuma': ['amunam', 'manuma'],
 'manure': ['manure', 'menura'],
 'manward': ['manward', 'wardman'],
 'manwards': ['manwards', 'wardsman'],
 'manway': ['manway', 'wayman'],
 'manwise': ['manwise', 'wiseman'],
 'many': ['many', 'myna'],
 'mao': ['mao', 'oam'],
 'maori': ['maori', 'mario', 'moira'],
 'map': ['map', 'pam'],
 'mapach': ['champa', 'mapach'],
 'maple': ['ample', 'maple'],
 'mapper': ['mapper', 'pamper', 'pampre'],
 'mar': ['arm', 'mar', 'ram'],
 'mara': ['amar', 'amra', 'mara', 'rama'],
 'marabout': ['marabout', 'marabuto', 'tamboura'],
 'marabuto': ['marabout', 'marabuto', 'tamboura'],
 'maraca': ['acamar', 'camara', 'maraca'],
 'maral': ['alarm', 'malar', 'maral', 'marla', 'ramal'],
 'marang': ['amgarn', 'mangar', 'marang', 'ragman'],
 'mararie': ['armeria', 'mararie'],
 'marasca': ['marasca', 'mascara'],
 'maraschino': ['anachorism', 'chorasmian', 'maraschino'],
 'marasmic': ['macarism', 'marasmic'],
 'marbelize': ['marbelize', 'marbleize'],
 'marble': ['ambler', 'blamer', 'lamber', 'marble', 'ramble'],
 'marbleize': ['marbelize', 'marbleize'],
 'marbler': ['marbler', 'rambler'],
 'marbling': ['marbling', 'rambling'],
 'marc': ['cram', 'marc'],
 'marcan': ['carman', 'marcan'],
 'marcel': ['calmer', 'carmel', 'clamer', 'marcel', 'mercal'],
 'marcescent': ['marcescent', 'scarcement'],
 'march': ['charm', 'march'],
 'marcher': ['charmer', 'marcher', 'remarch'],
 'marchite': ['athermic', 'marchite', 'rhematic'],
 'marchpane': ['marchpane', 'preachman'],
 'marci': ['marci', 'mirac'],
 'marcionist': ['marcionist', 'romanistic'],
 'marcionite': ['marcionite', 'microtinae', 'remication'],
 'marco': ['carom', 'coram', 'macro', 'marco'],
 'marconi': ['amicron', 'marconi', 'minorca', 'romanic'],
 'mare': ['erma', 'mare', 'rame', 'ream'],
 'mareca': ['acream', 'camera', 'mareca'],
 'marek': ['maker', 'marek', 'merak'],
 'marengo': ['marengo', 'megaron'],
 'mareotid': ['mareotid', 'mediator'],
 'marfik': ['marfik', 'mirfak'],
 'marfire': ['firearm', 'marfire'],
 'margay': ['magyar', 'margay'],
 'marge': ['grame', 'marge', 'regma'],
 'margeline': ['margeline', 'regimenal'],
 'margent': ['garment', 'margent'],
 'margie': ['imager', 'maigre', 'margie', 'mirage'],
 'margin': ['arming', 'ingram', 'margin'],
 'marginal': ['alarming', 'marginal'],
 'marginally': ['alarmingly', 'marginally'],
 'marginate': ['armangite', 'marginate'],
 'marginated': ['argentamid', 'marginated'],
 'margined': ['dirgeman', 'margined', 'midrange'],
 'marginiform': ['graminiform', 'marginiform'],
 'margot': ['gomart', 'margot'],
 'marhala': ['harmala', 'marhala'],
 'mari': ['amir', 'irma', 'mari', 'mira', 'rami', 'rima'],
 'marialite': ['latimeria', 'marialite'],
 'marian': ['airman', 'amarin', 'marian', 'marina', 'mirana'],
 'mariana': ['aramina', 'mariana'],
 'marianne': ['armenian', 'marianne'],
 'marie': ['aimer', 'maire', 'marie', 'ramie'],
 'marigenous': ['germanious', 'gramineous', 'marigenous'],
 'marilla': ['armilla', 'marilla'],
 'marina': ['airman', 'amarin', 'marian', 'marina', 'mirana'],
 'marinade': ['madeiran', 'marinade'],
 'marinate': ['animater', 'marinate'],
 'marine': ['ermani', 'marine', 'remain'],
 'marinist': ['marinist', 'mistrain'],
 'mario': ['maori', 'mario', 'moira'],
 'marion': ['marion', 'romain'],
 'mariou': ['mariou', 'oarium'],
 'maris': ['maris', 'marsi', 'samir', 'simar'],
 'marish': ['marish', 'shamir'],
 'marishness': ['marishness', 'marshiness'],
 'marist': ['marist', 'matris', 'ramist'],
 'maritage': ['gematria', 'maritage'],
 'marital': ['marital', 'martial'],
 'maritality': ['maritality', 'martiality'],
 'maritally': ['maritally', 'martially'],
 'marka': ['karma', 'krama', 'marka'],
 'markeb': ['embark', 'markeb'],
 'marked': ['demark', 'marked'],
 'marker': ['marker', 'remark'],
 'marketable': ['marketable', 'tablemaker'],
 'marketeer': ['marketeer', 'treemaker'],
 'marketer': ['marketer', 'remarket'],
 'marko': ['marko', 'marok'],
 'marla': ['alarm', 'malar', 'maral', 'marla', 'ramal'],
 'marled': ['dermal', 'marled', 'medlar'],
 'marli': ['armil', 'marli', 'rimal'],
 'marline': ['marline', 'mineral', 'ramline'],
 'marlite': ['lamiter', 'marlite'],
 'marlock': ['lockram', 'marlock'],
 'maro': ['amor', 'maro', 'mora', 'omar', 'roam'],
 'marocain': ['armonica', 'macaroni', 'marocain'],
 'marok': ['marko', 'marok'],
 'maronian': ['maronian', 'romanian'],
 'maronist': ['maronist', 'romanist'],
 'maronite': ['maronite', 'martinoe', 'minorate', 'morenita', 'romanite'],
 'marquesan': ['marquesan', 'squareman'],
 'marquis': ['asquirm', 'marquis'],
 'marree': ['marree', 'reamer'],
 'married': ['admirer', 'madrier', 'married'],
 'marrot': ['marrot', 'mortar'],
 'marrowed': ['marrowed', 'romeward'],
 'marryer': ['marryer', 'remarry'],
 'mars': ['arms', 'mars'],
 'marsh': ['marsh', 'shram'],
 'marshaler': ['marshaler', 'remarshal'],
 'marshiness': ['marishness', 'marshiness'],
 'marshite': ['arthemis', 'marshite', 'meharist'],
 'marsi': ['maris', 'marsi', 'samir', 'simar'],
 'marsipobranchiata': ['basiparachromatin', 'marsipobranchiata'],
 'mart': ['mart', 'tram'],
 'martel': ['armlet', 'malter', 'martel'],
 'marteline': ['alimenter', 'marteline'],
 'marten': ['manter', 'marten', 'rament'],
 'martes': ['martes', 'master', 'remast', 'stream'],
 'martha': ['amarth', 'martha'],
 'martial': ['marital', 'martial'],
 'martiality': ['maritality', 'martiality'],
 'martially': ['maritally', 'martially'],
 'martian': ['martian', 'tamarin'],
 'martinet': ['intermat', 'martinet', 'tetramin'],
 'martinico': ['martinico', 'mortician'],
 'martinoe': ['maronite', 'martinoe', 'minorate', 'morenita', 'romanite'],
 'martite': ['martite', 'mitrate'],
 'martius': ['martius', 'matsuri', 'maurist'],
 'martu': ['martu', 'murat', 'turma'],
 'marty': ['marty', 'tryma'],
 'maru': ['arum', 'maru', 'mura'],
 'mary': ['army', 'mary', 'myra', 'yarm'],
 'marylander': ['aldermanry', 'marylander'],
 'marysole': ['marysole', 'ramosely'],
 'mas': ['mas', 'sam', 'sma'],
 'mascara': ['marasca', 'mascara'],
 'mascotry': ['arctomys', 'costmary', 'mascotry'],
 'masculine': ['masculine', 'semuncial', 'simulance'],
 'masculist': ['masculist', 'simulcast'],
 'masdeu': ['amused', 'masdeu', 'medusa'],
 'mash': ['mash', 'samh', 'sham'],
 'masha': ['hamsa', 'masha', 'shama'],
 'mashal': ['mashal', 'shamal'],
 'mashelton': ['malhonest', 'mashelton'],
 'masher': ['masher', 'ramesh', 'shamer'],
 'mashy': ['mashy', 'shyam'],
 'mask': ['kasm', 'mask'],
 'masker': ['masker', 'remask'],
 'mason': ['manso', 'mason', 'monas'],
 'masoner': ['masoner', 'romanes'],
 'masonic': ['anosmic', 'masonic'],
 'masonite': ['masonite', 'misatone'],
 'maspiter': ['maspiter', 'pastimer', 'primates'],
 'masque': ['masque', 'squame', 'squeam'],
 'massa': ['amass', 'assam', 'massa', 'samas'],
 'masse': ['masse', 'sesma'],
 'masser': ['masser', 'remass'],
 'masseter': ['masseter', 'seamster'],
 'masseur': ['assumer', 'erasmus', 'masseur'],
 'massicot': ['acosmist', 'massicot', 'somatics'],
 'massiness': ['amissness', 'massiness'],
 'masskanne': ['masskanne', 'sneaksman'],
 'mast': ['mast', 'mats', 'stam'],
 'masted': ['demast', 'masted'],
 'master': ['martes', 'master', 'remast', 'stream'],
 'masterate': ['masterate', 'metatarse'],
 'masterer': ['masterer', 'restream', 'streamer'],
 'masterful': ['masterful', 'streamful'],
 'masterless': ['masterless', 'streamless'],
 'masterlike': ['masterlike', 'streamlike'],
 'masterling': ['masterling', 'streamling'],
 'masterly': ['masterly', 'myrtales'],
 'mastership': ['mastership', 'shipmaster'],
 'masterwork': ['masterwork', 'workmaster'],
 'masterwort': ['masterwort', 'streamwort'],
 'mastery': ['mastery', 'streamy'],
 'mastic': ['mastic', 'misact'],
 'masticable': ['ablastemic', 'masticable'],
 'mastiche': ['mastiche', 'misteach'],
 'mastlike': ['kemalist', 'mastlike'],
 'mastoid': ['distoma', 'mastoid'],
 'mastoidale': ['diatomales', 'mastoidale', 'mastoideal'],
 'mastoideal': ['diatomales', 'mastoidale', 'mastoideal'],
 'mastological': ['malacologist', 'mastological'],
 'mastomenia': ['mastomenia', 'seminomata'],
 'mastotomy': ['mastotomy', 'stomatomy'],
 'masu': ['masu', 'musa', 'saum'],
 'mat': ['amt', 'mat', 'tam'],
 'matacan': ['matacan', 'tamanac'],
 'matai': ['amati', 'amita', 'matai'],
 'matar': ['matar', 'matra', 'trama'],
 'matara': ['armata', 'matara', 'tamara'],
 'matcher': ['matcher', 'rematch'],
 'mate': ['mate', 'meat', 'meta', 'tame', 'team', 'tema'],
 'mateless': ['mateless', 'meatless', 'tameless', 'teamless'],
 'matelessness': ['matelessness', 'tamelessness'],
 'mately': ['mately', 'tamely'],
 'mater': ['armet',
           'mater',
           'merat',
           'metra',
           'ramet',
           'tamer',
           'terma',
           'trame',
           'trema'],
 'materialism': ['immaterials', 'materialism'],
 'materiel': ['eremital', 'materiel'],
 'maternal': ['maternal', 'ramental'],
 'maternology': ['laryngotome', 'maternology'],
 'mateship': ['aphetism', 'mateship', 'shipmate', 'spithame'],
 'matey': ['matey', 'meaty'],
 'mathesis': ['mathesis', 'thamesis'],
 'mathetic': ['mathetic', 'thematic'],
 'matico': ['atomic', 'matico'],
 'matin': ['maint', 'matin'],
 'matinee': ['amenite', 'etamine', 'matinee'],
 'matins': ['manist', 'mantis', 'matins', 'stamin'],
 'matra': ['matar', 'matra', 'trama'],
 'matral': ['matral', 'tramal'],
 'matralia': ['altamira', 'matralia'],
 'matrices': ['camerist', 'ceramist', 'matrices'],
 'matricide': ['citramide', 'diametric', 'matricide'],
 'matricula': ['lactarium', 'matricula'],
 'matricular': ['matricular', 'trimacular'],
 'matris': ['marist', 'matris', 'ramist'],
 'matrocliny': ['matrocliny', 'romanticly'],
 'matronism': ['matronism', 'romantism'],
 'mats': ['mast', 'mats', 'stam'],
 'matsu': ['matsu', 'tamus', 'tsuma'],
 'matsuri': ['martius', 'matsuri', 'maurist'],
 'matter': ['matter', 'mettar'],
 'mattoir': ['mattoir', 'tritoma'],
 'maturable': ['maturable', 'metabular'],
 'maturation': ['maturation', 'natatorium'],
 'maturer': ['erratum', 'maturer'],
 'mau': ['aum', 'mau'],
 'maud': ['duma', 'maud'],
 'maudle': ['almude', 'maudle'],
 'mauger': ['mauger', 'murage'],
 'maul': ['alum', 'maul'],
 'mauler': ['mauler', 'merula', 'ramule'],
 'maun': ['maun', 'numa'],
 'maund': ['maund', 'munda', 'numda', 'undam', 'unmad'],
 'maunder': ['duramen', 'maunder', 'unarmed'],
 'maunderer': ['maunderer', 'underream'],
 'maunge': ['mangue', 'maunge'],
 'maureen': ['maureen', 'menurae'],
 'maurice': ['maurice', 'uraemic'],
 'maurist': ['martius', 'matsuri', 'maurist'],
 'mauser': ['amuser', 'mauser'],
 'mavis': ['amvis', 'mavis'],
 'maw': ['maw', 'mwa'],
 'mawp': ['mawp', 'wamp'],
 'may': ['amy', 'may', 'mya', 'yam'],
 'maybe': ['beamy', 'embay', 'maybe'],
 'mayer': ['mayer', 'reamy'],
 'maylike': ['maylike', 'yamilke'],
 'mayo': ['amoy', 'mayo'],
 'mayor': ['mayor', 'moray'],
 'maypoling': ['maypoling', 'pygmalion'],
 'maysin': ['maysin', 'minyas', 'mysian'],
 'maytide': ['daytime', 'maytide'],
 'mazer': ['mazer', 'zerma'],
 'mazur': ['mazur', 'murza'],
 'mbaya': ['ambay', 'mbaya'],
 'me': ['em', 'me'],
 'meable': ['bemeal', 'meable'],
 'mead': ['dame', 'made', 'mead'],
 'meader': ['meader', 'remade'],
 'meager': ['graeme', 'meager', 'meagre'],
 'meagre': ['graeme', 'meager', 'meagre'],
 'meak': ['kame', 'make', 'meak'],
 'meal': ['alem', 'alme', 'lame', 'leam', 'male', 'meal', 'mela'],
 'mealer': ['leamer', 'mealer'],
 'mealiness': ['mealiness', 'messaline'],
 'mealy': ['mealy', 'yamel'],
 'mean': ['amen', 'enam', 'mane', 'mean', 'name', 'nema'],
 'meander': ['amender', 'meander', 'reamend', 'reedman'],
 'meandrite': ['demetrian', 'dermatine', 'meandrite', 'minareted'],
 'meandrous': ['meandrous', 'roundseam'],
 'meaned': ['amende', 'demean', 'meaned', 'nadeem'],
 'meaner': ['enarme', 'meaner', 'rename'],
 'meanly': ['meanly', 'namely'],
 'meant': ['ament', 'meant', 'teman'],
 'mease': ['emesa', 'mease'],
 'measly': ['measly', 'samely'],
 'measuration': ['aeronautism', 'measuration'],
 'measure': ['measure', 'reamuse'],
 'measured': ['madurese', 'measured'],
 'meat': ['mate', 'meat', 'meta', 'tame', 'team', 'tema'],
 'meatal': ['malate', 'meatal', 'tamale'],
 'meatless': ['mateless', 'meatless', 'tameless', 'teamless'],
 'meatman': ['meatman', 'teamman'],
 'meatus': ['meatus', 'mutase'],
 'meaty': ['matey', 'meaty'],
 'mechanal': ['leachman', 'mechanal'],
 'mechanicochemical': ['chemicomechanical', 'mechanicochemical'],
 'mechanics': ['mechanics', 'mischance'],
 'mechir': ['chimer', 'mechir', 'micher'],
 'mecometry': ['cymometer', 'mecometry'],
 'meconic': ['comenic', 'encomic', 'meconic'],
 'meconin': ['ennomic', 'meconin'],
 'meconioid': ['meconioid', 'monoeidic'],
 'meconium': ['encomium', 'meconium'],
 'mecopteron': ['mecopteron', 'protocneme'],
 'medal': ['demal', 'medal'],
 'medallary': ['alarmedly', 'medallary'],
 'mede': ['deem', 'deme', 'mede', 'meed'],
 'media': ['amide', 'damie', 'media'],
 'mediacy': ['dicyema', 'mediacy'],
 'mediad': ['diadem', 'mediad'],
 'medial': ['aldime', 'mailed', 'medial'],
 'median': ['daimen', 'damine', 'maiden', 'median', 'medina'],
 'medianism': ['maidenism', 'medianism'],
 'medianly': ['lymnaeid', 'maidenly', 'medianly'],
 'mediator': ['mareotid', 'mediator'],
 'mediatress': ['mediatress', 'streamside'],
 'mediatrice': ['acidimeter', 'mediatrice'],
 'medical': ['camelid', 'decimal', 'declaim', 'medical'],
 'medically': ['decimally', 'medically'],
 'medicate': ['decimate', 'medicate'],
 'medication': ['decimation', 'medication'],
 'medicator': ['decimator', 'medicator', 'mordicate'],
 'medicatory': ['acidometry', 'medicatory', 'radiectomy'],
 'medicinal': ['adminicle', 'medicinal'],
 'medicophysical': ['medicophysical', 'physicomedical'],
 'medimnos': ['demonism', 'medimnos', 'misnomed'],
 'medina': ['daimen', 'damine', 'maiden', 'median', 'medina'],
 'medino': ['domine', 'domnei', 'emodin', 'medino'],
 'mediocrist': ['dosimetric', 'mediocrist'],
 'mediocrity': ['iridectomy', 'mediocrity'],
 'mediodorsal': ['dorsomedial', 'mediodorsal'],
 'medioventral': ['medioventral', 'ventromedial'],
 'meditate': ['admittee', 'meditate'],
 'meditator': ['meditator', 'trematoid'],
 'medlar': ['dermal', 'marled', 'medlar'],
 'medusa': ['amused', 'masdeu', 'medusa'],
 'medusan': ['medusan', 'sudamen'],
 'meece': ['emcee', 'meece'],
 'meed': ['deem', 'deme', 'mede', 'meed'],
 'meeks': ['meeks', 'smeek'],
 'meered': ['deemer', 'meered', 'redeem', 'remede'],
 'meet': ['meet', 'mete', 'teem'],
 'meeter': ['meeter', 'remeet', 'teemer'],
 'meethelp': ['helpmeet', 'meethelp'],
 'meeting': ['meeting', 'teeming', 'tegmine'],
 'meg': ['gem', 'meg'],
 'megabar': ['bergama', 'megabar'],
 'megachiropteran': ['cinematographer', 'megachiropteran'],
 'megadont': ['magnetod', 'megadont'],
 'megadyne': ['ganymede', 'megadyne'],
 'megaera': ['megaera', 'reamage'],
 'megalodon': ['megalodon', 'moonglade'],
 'megalohepatia': ['hepatomegalia', 'megalohepatia'],
 'megalophonous': ['megalophonous', 'omphalogenous'],
 'megalosplenia': ['megalosplenia', 'splenomegalia'],
 'megapod': ['megapod', 'pagedom'],
 'megapodius': ['megapodius', 'pseudimago'],
 'megarian': ['germania', 'megarian'],
 'megaric': ['gemaric', 'grimace', 'megaric'],
 'megaron': ['marengo', 'megaron'],
 'megaton': ['geomant', 'magneto', 'megaton', 'montage'],
 'megmho': ['megmho', 'megohm'],
 'megohm': ['megmho', 'megohm'],
 'megrim': ['gimmer', 'grimme', 'megrim'],
 'mehari': ['mehari', 'meriah'],
 'meharist': ['arthemis', 'marshite', 'meharist'],
 'meile': ['elemi', 'meile'],
 'mein': ['mein', 'mien', 'mine'],
 'meio': ['meio', 'oime'],
 'mel': ['elm', 'mel'],
 'mela': ['alem', 'alme', 'lame', 'leam', 'male', 'meal', 'mela'],
 'melaconite': ['colemanite', 'melaconite'],
 'melalgia': ['gamaliel', 'melalgia'],
 'melam': ['lemma', 'melam'],
 'melamine': ['ammeline', 'melamine'],
 'melange': ['gleeman', 'melange'],
 'melania': ['laminae', 'melania'],
 'melanian': ['alemanni', 'melanian'],
 'melanic': ['cnemial', 'melanic'],
 'melanilin': ['melanilin', 'millennia'],
 'melanin': ['lemnian', 'lineman', 'melanin'],
 'melanism': ['melanism', 'slimeman'],
 'melanite': ['melanite', 'meletian', 'metaline', 'nemalite'],
 'melanitic': ['alimentic', 'antilemic', 'melanitic', 'metanilic'],
 'melanochroi': ['chloroamine', 'melanochroi'],
 'melanogen': ['melanogen', 'melongena'],
 'melanoid': ['demonial', 'melanoid'],
 'melanorrhea': ['amenorrheal', 'melanorrhea'],
 'melanosis': ['loaminess', 'melanosis'],
 'melanotic': ['entomical', 'melanotic'],
 'melanuria': ['malurinae', 'melanuria'],
 'melanuric': ['ceruminal', 'melanuric', 'numerical'],
 'melas': ['amsel', 'melas', 'mesal', 'samel'],
 'melastoma': ['melastoma', 'metasomal'],
 'meldrop': ['meldrop', 'premold'],
 'melena': ['enamel', 'melena'],
 'melenic': ['celemin', 'melenic'],
 'meletian': ['melanite', 'meletian', 'metaline', 'nemalite'],
 'meletski': ['meletski', 'stemlike'],
 'melian': ['limean', 'maline', 'melian', 'menial'],
 'meliatin': ['meliatin', 'timaline'],
 'melic': ['clime', 'melic'],
 'melica': ['maleic', 'malice', 'melica'],
 'melicerta': ['carmelite', 'melicerta'],
 'melicraton': ['centimolar', 'melicraton'],
 'melinda': ['idleman', 'melinda'],
 'meline': ['elemin', 'meline'],
 'melinite': ['ilmenite', 'melinite', 'menilite'],
 'meliorant': ['meliorant', 'mentorial'],
 'melissa': ['aimless', 'melissa', 'seismal'],
 'melitose': ['melitose', 'mesolite'],
 'mellay': ['lamely', 'mellay'],
 'mellit': ['mellit', 'millet'],
 'melodia': ['melodia', 'molidae'],
 'melodica': ['cameloid', 'comedial', 'melodica'],
 'melodicon': ['clinodome', 'melodicon', 'monocleid'],
 'melodist': ['melodist', 'modelist'],
 'melomanic': ['commelina', 'melomanic'],
 'melon': ['lemon', 'melon', 'monel'],
 'melongena': ['melanogen', 'melongena'],
 'melonist': ['melonist', 'telonism'],
 'melonites': ['limestone', 'melonites', 'milestone'],
 'melonlike': ['lemonlike', 'melonlike'],
 'meloplasty': ['meloplasty', 'myeloplast'],
 'melosa': ['melosa', 'salome', 'semola'],
 'melotragic': ['algometric', 'melotragic'],
 'melotrope': ['melotrope', 'metropole'],
 'melter': ['melter', 'remelt'],
 'melters': ['melters', 'resmelt', 'smelter'],
 'melton': ['loment', 'melton', 'molten'],
 'melungeon': ['melungeon', 'nonlegume'],
 'mem': ['emm', 'mem'],
 'memnon': ['memnon', 'mennom'],
 'memo': ['memo', 'mome'],
 'memorandist': ['memorandist', 'moderantism', 'semidormant'],
 'menage': ['gamene', 'manege', 'menage'],
 'menald': ['lemnad', 'menald'],
 'menaspis': ['menaspis', 'semispan'],
 'mendaite': ['dementia', 'mendaite'],
 'mende': ['emend', 'mende'],
 'mender': ['mender', 'remend'],
 'mendi': ['denim', 'mendi'],
 'mendipite': ['impedient', 'mendipite'],
 'menial': ['limean', 'maline', 'melian', 'menial'],
 'menic': ['menic', 'mince'],
 'menilite': ['ilmenite', 'melinite', 'menilite'],
 'meningeal': ['enameling', 'malengine', 'meningeal'],
 'meningocephalitis': ['cephalomeningitis', 'meningocephalitis'],
 'meningocerebritis': ['cerebromeningitis', 'meningocerebritis'],
 'meningoencephalitis': ['encephalomeningitis', 'meningoencephalitis'],
 'meningoencephalocele': ['encephalomeningocele', 'meningoencephalocele'],
 'meningomyelitis': ['meningomyelitis', 'myelomeningitis'],
 'meningomyelocele': ['meningomyelocele', 'myelomeningocele'],
 'mennom': ['memnon', 'mennom'],
 'menostasia': ['anematosis', 'menostasia'],
 'mensa': ['manes', 'manse', 'mensa', 'samen', 'senam'],
 'mensal': ['anselm', 'mensal'],
 'mense': ['mense', 'mesne', 'semen'],
 'menstrual': ['menstrual', 'ulsterman'],
 'mensurable': ['lebensraum', 'mensurable'],
 'mentagra': ['grateman', 'mangrate', 'mentagra', 'targeman'],
 'mental': ['lament', 'manlet', 'mantel', 'mantle', 'mental'],
 'mentalis': ['mentalis', 'smaltine', 'stileman'],
 'mentalize': ['mentalize', 'mentzelia'],
 'mentation': ['mentation', 'montanite'],
 'mentha': ['anthem', 'hetman', 'mentha'],
 'menthane': ['enanthem', 'menthane'],
 'mentigerous': ['mentigerous', 'tergeminous'],
 'mentolabial': ['labiomental', 'mentolabial'],
 'mentor': ['mentor', 'merton', 'termon', 'tormen'],
 'mentorial': ['meliorant', 'mentorial'],
 'mentzelia': ['mentalize', 'mentzelia'],
 'menura': ['manure', 'menura'],
 'menurae': ['maureen', 'menurae'],
 'menyie': ['menyie', 'yemeni'],
 'meo': ['meo', 'moe'],
 'mephisto': ['mephisto', 'pithsome'],
 'merak': ['maker', 'marek', 'merak'],
 'merat': ['armet',
           'mater',
           'merat',
           'metra',
           'ramet',
           'tamer',
           'terma',
           'trame',
           'trema'],
 'meratia': ['ametria', 'artemia', 'meratia', 'ramaite'],
 'mercal': ['calmer', 'carmel', 'clamer', 'marcel', 'mercal'],
 'mercator': ['cremator', 'mercator'],
 'mercatorial': ['crematorial', 'mercatorial'],
 'mercian': ['armenic', 'carmine', 'ceriman', 'crimean', 'mercian'],
 'merciful': ['crimeful', 'merciful'],
 'merciless': ['crimeless', 'merciless'],
 'mercilessness': ['crimelessness', 'mercilessness'],
 'mere': ['mere', 'reem'],
 'merel': ['elmer', 'merel', 'merle'],
 'merely': ['merely', 'yelmer'],
 'merginae': ['ergamine', 'merginae'],
 'mergus': ['gersum', 'mergus'],
 'meriah': ['mehari', 'meriah'],
 'merice': ['eremic', 'merice'],
 'merida': ['admire', 'armied', 'damier', 'dimera', 'merida'],
 'meril': ['limer', 'meril', 'miler'],
 'meriones': ['emersion', 'meriones'],
 'merism': ['merism', 'mermis', 'simmer'],
 'merist': ['merist', 'mister', 'smiter'],
 'meristem': ['meristem', 'mimester'],
 'meristic': ['meristic', 'trimesic', 'trisemic'],
 'merit': ['merit', 'miter', 'mitre', 'remit', 'timer'],
 'merited': ['demerit', 'dimeter', 'merited', 'mitered'],
 'meriter': ['meriter', 'miterer', 'trireme'],
 'merle': ['elmer', 'merel', 'merle'],
 'merlin': ['limner', 'merlin', 'milner'],
 'mermaid': ['demiram', 'mermaid'],
 'mermis': ['merism', 'mermis', 'simmer'],
 'mero': ['mero', 'more', 'omer', 'rome'],
 'meroblastic': ['blastomeric', 'meroblastic'],
 'merocyte': ['cytomere', 'merocyte'],
 'merogony': ['gonomery', 'merogony'],
 'meroistic': ['eroticism', 'isometric', 'meroistic', 'trioecism'],
 'merop': ['merop', 'moper', 'proem', 'remop'],
 'meropia': ['emporia', 'meropia'],
 'meros': ['meros', 'mores', 'morse', 'sermo', 'smore'],
 'merosthenic': ['merosthenic', 'microsthene'],
 'merostome': ['merostome', 'osmometer'],
 'merrow': ['merrow', 'wormer'],
 'merse': ['merse', 'smeer'],
 'merton': ['mentor', 'merton', 'termon', 'tormen'],
 'merula': ['mauler', 'merula', 'ramule'],
 'meruline': ['lemurine', 'meruline', 'relumine'],
 'mesa': ['asem', 'mesa', 'same', 'seam'],
 'mesad': ['desma', 'mesad'],
 'mesadenia': ['deaminase', 'mesadenia'],
 'mesail': ['amiles', 'asmile', 'mesail', 'mesial', 'samiel'],
 'mesal': ['amsel', 'melas', 'mesal', 'samel'],
 'mesalike': ['mesalike', 'seamlike'],
 'mesaraic': ['cramasie', 'mesaraic'],
 'mesaticephaly': ['hemicatalepsy', 'mesaticephaly'],
 'mese': ['mese', 'seem', 'seme', 'smee'],
 'meshech': ['meshech', 'shechem'],
 'mesial': ['amiles', 'asmile', 'mesail', 'mesial', 'samiel'],
 'mesian': ['asimen', 'inseam', 'mesian'],
 'mesic': ['mesic', 'semic'],
 'mesion': ['eonism', 'mesion', 'oneism', 'simeon'],
 'mesitae': ['amesite', 'mesitae', 'semitae'],
 'mesne': ['mense', 'mesne', 'semen'],
 'meso': ['meso', 'mose', 'some'],
 'mesobar': ['ambrose', 'mesobar'],
 'mesocephaly': ['elaphomyces', 'mesocephaly'],
 'mesognathic': ['asthmogenic', 'mesognathic'],
 'mesohepar': ['mesohepar', 'semaphore'],
 'mesolite': ['melitose', 'mesolite'],
 'mesolithic': ['homiletics', 'mesolithic'],
 'mesological': ['mesological', 'semological'],
 'mesology': ['mesology', 'semology'],
 'mesomeric': ['mesomeric', 'microseme', 'semicrome'],
 'mesonotum': ['mesonotum', 'momentous'],
 'mesorectal': ['calotermes', 'mesorectal', 'metacresol'],
 'mesotonic': ['economist', 'mesotonic'],
 'mesoventral': ['mesoventral', 'ventromesal'],
 'mespil': ['mespil', 'simple'],
 'mesropian': ['mesropian', 'promnesia', 'spironema'],
 'messalian': ['messalian', 'seminasal'],
 'messaline': ['mealiness', 'messaline'],
 'messan': ['enmass', 'maness', 'messan'],
 'messelite': ['messelite', 'semisteel', 'teleseism'],
 'messines': ['essenism', 'messines'],
 'messor': ['messor', 'mosser', 'somers'],
 'mestee': ['esteem', 'mestee'],
 'mester': ['mester', 'restem', 'temser', 'termes'],
 'mesua': ['amuse', 'mesua'],
 'meta': ['mate', 'meat', 'meta', 'tame', 'team', 'tema'],
 'metabular': ['maturable', 'metabular'],
 'metaconid': ['comediant', 'metaconid'],
 'metacresol': ['calotermes', 'mesorectal', 'metacresol'],
 'metage': ['gamete', 'metage'],
 'metaler': ['lameter', 'metaler', 'remetal'],
 'metaline': ['melanite', 'meletian', 'metaline', 'nemalite'],
 'metaling': ['ligament', 'metaling', 'tegminal'],
 'metalist': ['metalist', 'smaltite'],
 'metallism': ['metallism', 'smalltime'],
 'metamer': ['ammeter', 'metamer'],
 'metanilic': ['alimentic', 'antilemic', 'melanitic', 'metanilic'],
 'metaphor': ['metaphor', 'trophema'],
 'metaphoric': ['amphoteric', 'metaphoric'],
 'metaphorical': ['metaphorical', 'pharmacolite'],
 'metaphysical': ['lymphectasia', 'metaphysical'],
 'metaplastic': ['metaplastic', 'palmatisect'],
 'metapore': ['ametrope', 'metapore'],
 'metasomal': ['melastoma', 'metasomal'],
 'metatarse': ['masterate', 'metatarse'],
 'metatheria': ['hemiterata', 'metatheria'],
 'metatrophic': ['metatrophic', 'metropathic'],
 'metaxenia': ['examinate', 'exanimate', 'metaxenia'],
 'mete': ['meet', 'mete', 'teem'],
 'meteor': ['meteor', 'remote'],
 'meteorgraph': ['graphometer', 'meteorgraph'],
 'meteorical': ['carmeloite', 'ectromelia', 'meteorical'],
 'meteoristic': ['meteoristic', 'meteoritics'],
 'meteoritics': ['meteoristic', 'meteoritics'],
 'meteoroid': ['meteoroid', 'odiometer'],
 'meter': ['meter', 'retem'],
 'meterless': ['meterless', 'metreless'],
 'metership': ['herpetism', 'metership', 'metreship', 'temperish'],
 'methanal': ['latheman', 'methanal'],
 'methanate': ['hetmanate', 'methanate'],
 'methanoic': ['hematonic', 'methanoic'],
 'mether': ['mether', 'themer'],
 'method': ['method', 'mothed'],
 'methylacetanilide': ['acetmethylanilide', 'methylacetanilide'],
 'methylic': ['methylic', 'thymelic'],
 'methylotic': ['lithectomy', 'methylotic'],
 'metier': ['metier', 'retime', 'tremie'],
 'metin': ['metin', 'temin', 'timne'],
 'metis': ['metis', 'smite', 'stime', 'times'],
 'metoac': ['comate', 'metoac', 'tecoma'],
 'metol': ['metol', 'motel'],
 'metonymical': ['laminectomy', 'metonymical'],
 'metope': ['metope', 'poemet'],
 'metopias': ['epistoma', 'metopias'],
 'metosteon': ['metosteon', 'tomentose'],
 'metra': ['armet',
           'mater',
           'merat',
           'metra',
           'ramet',
           'tamer',
           'terma',
           'trame',
           'trema'],
 'metrectasia': ['metrectasia', 'remasticate'],
 'metreless': ['meterless', 'metreless'],
 'metreship': ['herpetism', 'metership', 'metreship', 'temperish'],
 'metria': ['imaret', 'metria', 'mirate', 'rimate'],
 'metrician': ['antimeric', 'carminite', 'criminate', 'metrician'],
 'metrics': ['cretism', 'metrics'],
 'metrocratic': ['cratometric', 'metrocratic'],
 'metrological': ['logometrical', 'metrological'],
 'metronome': ['metronome', 'monometer', 'monotreme'],
 'metronomic': ['commorient', 'metronomic', 'monometric'],
 'metronomical': ['metronomical', 'monometrical'],
 'metropathic': ['metatrophic', 'metropathic'],
 'metrophlebitis': ['metrophlebitis', 'phlebometritis'],
 'metropole': ['melotrope', 'metropole'],
 'metroptosia': ['metroptosia', 'prostomiate'],
 'metrorrhea': ['arthromere', 'metrorrhea'],
 'metrostyle': ['metrostyle', 'stylometer'],
 'mettar': ['matter', 'mettar'],
 'metusia': ['metusia', 'suimate', 'timaeus'],
 'mew': ['mew', 'wem'],
 'meward': ['meward', 'warmed'],
 'mho': ['mho', 'ohm'],
 'mhometer': ['mhometer', 'ohmmeter'],
 'miamia': ['amimia', 'miamia'],
 'mian': ['amin', 'main', 'mani', 'mian', 'mina', 'naim'],
 'miaotse': ['miaotse', 'ostemia'],
 'miaotze': ['atomize', 'miaotze'],
 'mias': ['mias', 'saim', 'siam', 'sima'],
 'miasmal': ['lamaism', 'miasmal'],
 'miastor': ['amorist', 'aortism', 'miastor'],
 'miaul': ['aumil', 'miaul'],
 'miauler': ['lemuria', 'miauler'],
 'mib': ['bim', 'mib'],
 'mica': ['amic', 'mica'],
 'micah': ['chiam', 'machi', 'micah'],
 'micate': ['acmite', 'micate'],
 'mication': ['amniotic', 'mication'],
 'micellar': ['micellar', 'millrace'],
 'michael': ['michael', 'micheal'],
 'miche': ['chime', 'hemic', 'miche'],
 'micheal': ['michael', 'micheal'],
 'micher': ['chimer', 'mechir', 'micher'],
 'micht': ['micht', 'mitch'],
 'micranthropos': ['micranthropos', 'promonarchist'],
 'micro': ['micro', 'moric', 'romic'],
 'microcephal': ['microcephal', 'prochemical'],
 'microcephaly': ['microcephaly', 'pyrochemical'],
 'microcinema': ['microcinema', 'microcnemia'],
 'microcnemia': ['microcinema', 'microcnemia'],
 'microcrith': ['microcrith', 'trichromic'],
 'micropetalous': ['micropetalous', 'somatopleuric'],
 'microphagy': ['microphagy', 'myographic'],
 'microphone': ['microphone', 'neomorphic'],
 'microphot': ['microphot', 'morphotic'],
 'microphotograph': ['microphotograph', 'photomicrograph'],
 'microphotographic': ['microphotographic', 'photomicrographic'],
 'microphotography': ['microphotography', 'photomicrography'],
 'microphotoscope': ['microphotoscope', 'photomicroscope'],
 'micropterous': ['micropterous', 'prosectorium'],
 'micropyle': ['micropyle', 'polymeric'],
 'microradiometer': ['microradiometer', 'radiomicrometer'],
 'microseme': ['mesomeric', 'microseme', 'semicrome'],
 'microspectroscope': ['microspectroscope', 'spectromicroscope'],
 'microstat': ['microstat', 'stromatic'],
 'microsthene': ['merosthenic', 'microsthene'],
 'microstome': ['microstome', 'osmometric'],
 'microtia': ['amoritic', 'microtia'],
 'microtinae': ['marcionite', 'microtinae', 'remication'],
 'mid': ['dim', 'mid'],
 'midden': ['midden', 'minded'],
 'middler': ['middler', 'mildred'],
 'middy': ['didym', 'middy'],
 'mide': ['demi', 'diem', 'dime', 'mide'],
 'mider': ['dimer', 'mider'],
 'mididae': ['amidide', 'diamide', 'mididae'],
 'midrange': ['dirgeman', 'margined', 'midrange'],
 'midstory': ['midstory', 'modistry'],
 'miek': ['miek', 'mike'],
 'mien': ['mein', 'mien', 'mine'],
 'mig': ['gim', 'mig'],
 'migraine': ['imaginer', 'migraine'],
 'migrate': ['migrate', 'ragtime'],
 'migratory': ['gyromitra', 'migratory'],
 'mihrab': ['brahmi', 'mihrab'],
 'mikael': ['kelima', 'mikael'],
 'mike': ['miek', 'mike'],
 'mil': ['lim', 'mil'],
 'mila': ['amil', 'amli', 'lima', 'mail', 'mali', 'mila'],
 'milan': ['lamin', 'liman', 'milan'],
 'milden': ['milden', 'mindel'],
 'mildness': ['mildness', 'mindless'],
 'mildred': ['middler', 'mildred'],
 'mile': ['emil', 'lime', 'mile'],
 'milepost': ['milepost', 'polemist'],
 'miler': ['limer', 'meril', 'miler'],
 'miles': ['limes', 'miles', 'slime', 'smile'],
 'milesian': ['alienism', 'milesian'],
 'milestone': ['limestone', 'melonites', 'milestone'],
 'milicent': ['limnetic', 'milicent'],
 'military': ['limitary', 'military'],
 'militate': ['limitate', 'militate'],
 'militation': ['limitation', 'militation'],
 'milken': ['kimnel', 'milken'],
 'millennia': ['melanilin', 'millennia'],
 'miller': ['miller', 'remill'],
 'millet': ['mellit', 'millet'],
 'milliare': ['milliare', 'ramillie'],
 'millrace': ['micellar', 'millrace'],
 'milner': ['limner', 'merlin', 'milner'],
 'milo': ['milo', 'moil'],
 'milsie': ['milsie', 'simile'],
 'miltonia': ['limation', 'miltonia'],
 'mima': ['ammi', 'imam', 'maim', 'mima'],
 'mime': ['emim', 'mime'],
 'mimester': ['meristem', 'mimester'],
 'mimi': ['immi', 'mimi'],
 'mimidae': ['amimide', 'mimidae'],
 'mimosa': ['amomis', 'mimosa'],
 'min': ['min', 'nim'],
 'mina': ['amin', 'main', 'mani', 'mian', 'mina', 'naim'],
 'minacity': ['imitancy', 'intimacy', 'minacity'],
 'minar': ['inarm', 'minar'],
 'minaret': ['minaret', 'raiment', 'tireman'],
 'minareted': ['demetrian', 'dermatine', 'meandrite', 'minareted'],
 'minargent': ['germinant', 'minargent'],
 'minatory': ['minatory', 'romanity'],
 'mince': ['menic', 'mince'],
 'minchiate': ['hematinic', 'minchiate'],
 'mincopie': ['mincopie', 'poimenic'],
 'minded': ['midden', 'minded'],
 'mindel': ['milden', 'mindel'],
 'mindelian': ['eliminand', 'mindelian'],
 'minder': ['minder', 'remind'],
 'mindless': ['mildness', 'mindless'],
 'mine': ['mein', 'mien', 'mine'],
 'miner': ['inerm', 'miner'],
 'mineral': ['marline', 'mineral', 'ramline'],
 'minerva': ['minerva', 'vermian'],
 'minerval': ['minerval', 'verminal'],
 'mingler': ['gremlin', 'mingler'],
 'miniator': ['miniator', 'triamino'],
 'minish': ['minish', 'nimshi'],
 'minister': ['minister', 'misinter'],
 'ministry': ['ministry', 'myristin'],
 'minkish': ['minkish', 'nimkish'],
 'minnetaree': ['minnetaree', 'nemertinea'],
 'minoan': ['amnion', 'minoan', 'nomina'],
 'minometer': ['minometer', 'omnimeter'],
 'minor': ['minor', 'morin'],
 'minorate': ['maronite', 'martinoe', 'minorate', 'morenita', 'romanite'],
 'minorca': ['amicron', 'marconi', 'minorca', 'romanic'],
 'minos': ['minos', 'osmin', 'simon'],
 'minot': ['minot', 'timon', 'tomin'],
 'mintage': ['mintage', 'teaming', 'tegmina'],
 'minter': ['minter', 'remint', 'termin'],
 'minuend': ['minuend', 'unmined'],
 'minuet': ['minuet', 'minute'],
 'minute': ['minuet', 'minute'],
 'minutely': ['minutely', 'untimely'],
 'minuter': ['minuter', 'unmiter'],
 'minyas': ['maysin', 'minyas', 'mysian'],
 'mir': ['mir', 'rim'],
 'mira': ['amir', 'irma', 'mari', 'mira', 'rami', 'rima'],
 'mirabel': ['embrail', 'mirabel'],
 'mirac': ['marci', 'mirac'],
 'miracle': ['claimer', 'miracle', 'reclaim'],
 'mirage': ['imager', 'maigre', 'margie', 'mirage'],
 'mirana': ['airman', 'amarin', 'marian', 'marina', 'mirana'],
 'miranha': ['ahriman', 'miranha'],
 'mirate': ['imaret', 'metria', 'mirate', 'rimate'],
 'mirbane': ['ambrein', 'mirbane'],
 'mire': ['emir', 'imer', 'mire', 'reim', 'remi', 'riem', 'rime'],
 'mirfak': ['marfik', 'mirfak'],
 'mirounga': ['mirounga', 'moringua', 'origanum'],
 'miry': ['miry', 'rimy', 'yirm'],
 'mirza': ['mirza', 'mizar'],
 'misact': ['mastic', 'misact'],
 'misadvise': ['admissive', 'misadvise'],
 'misagent': ['misagent', 'steaming'],
 'misaim': ['misaim', 'misima'],
 'misandry': ['misandry', 'myrsinad'],
 'misassociation': ['associationism', 'misassociation'],
 'misatone': ['masonite', 'misatone'],
 'misattend': ['misattend', 'tandemist'],
 'misaunter': ['antiserum', 'misaunter'],
 'misbehavior': ['behaviorism', 'misbehavior'],
 'mischance': ['mechanics', 'mischance'],
 'misclass': ['classism', 'misclass'],
 'miscoin': ['iconism', 'imsonic', 'miscoin'],
 'misconfiguration': ['configurationism', 'misconfiguration'],
 'misconstitutional': ['constitutionalism', 'misconstitutional'],
 'misconstruction': ['constructionism', 'misconstruction'],
 'miscreant': ['encratism', 'miscreant'],
 'miscreation': ['anisometric',
                 'creationism',
                 'miscreation',
                 'ramisection',
                 'reactionism'],
 'miscue': ['cesium', 'miscue'],
 'misdate': ['diastem', 'misdate'],
 'misdaub': ['misdaub', 'submaid'],
 'misdeal': ['misdeal', 'mislead'],
 'misdealer': ['misdealer', 'misleader', 'misleared'],
 'misdeclare': ['creedalism', 'misdeclare'],
 'misdiet': ['misdiet', 'misedit', 'mistide'],
 'misdivision': ['divisionism', 'misdivision'],
 'misdread': ['disarmed', 'misdread'],
 'mise': ['mise', 'semi', 'sime'],
 'misease': ['misease', 'siamese'],
 'misecclesiastic': ['ecclesiasticism', 'misecclesiastic'],
 'misedit': ['misdiet', 'misedit', 'mistide'],
 'misexpression': ['expressionism', 'misexpression'],
 'mishmash': ['mishmash', 'shammish'],
 'misima': ['misaim', 'misima'],
 'misimpression': ['impressionism', 'misimpression'],
 'misinter': ['minister', 'misinter'],
 'mislabel': ['mislabel', 'semiball'],
 'mislabor': ['laborism', 'mislabor'],
 'mislead': ['misdeal', 'mislead'],
 'misleader': ['misdealer', 'misleader', 'misleared'],
 'mislear': ['mislear', 'realism'],
 'misleared': ['misdealer', 'misleader', 'misleared'],
 'misname': ['amenism', 'immanes', 'misname'],
 'misniac': ['cainism', 'misniac'],
 'misnomed': ['demonism', 'medimnos', 'misnomed'],
 'misoneism': ['misoneism', 'simeonism'],
 'mispage': ['impages', 'mispage'],
 'misperception': ['misperception', 'perceptionism'],
 'misperform': ['misperform', 'preformism'],
 'misphrase': ['misphrase', 'seraphism'],
 'misplay': ['impalsy', 'misplay'],
 'misplead': ['misplead', 'pedalism'],
 'misprisal': ['misprisal', 'spiralism'],
 'misproud': ['disporum', 'misproud'],
 'misprovide': ['disimprove', 'misprovide'],
 'misput': ['misput', 'sumpit'],
 'misquotation': ['antimosquito', 'misquotation'],
 'misrate': ['artemis', 'maestri', 'misrate'],
 'misread': ['misread', 'sidearm'],
 'misreform': ['misreform', 'reformism'],
 'misrelate': ['misrelate', 'salimeter'],
 'misrelation': ['misrelation', 'orientalism', 'relationism'],
 'misreliance': ['criminalese', 'misreliance'],
 'misreporter': ['misreporter', 'reporterism'],
 'misrepresentation': ['misrepresentation', 'representationism'],
 'misrepresenter': ['misrepresenter', 'remisrepresent'],
 'misrepute': ['misrepute', 'septerium'],
 'misrhyme': ['misrhyme', 'shimmery'],
 'misrule': ['misrule', 'simuler'],
 'missal': ['missal', 'salmis'],
 'missayer': ['emissary', 'missayer'],
 'misset': ['misset', 'tmesis'],
 'misshape': ['emphasis', 'misshape'],
 'missioner': ['missioner', 'remission'],
 'misspell': ['misspell', 'psellism'],
 'missuggestion': ['missuggestion', 'suggestionism'],
 'missy': ['missy', 'mysis'],
 'mist': ['mist', 'smit', 'stim'],
 'misteach': ['mastiche', 'misteach'],
 'mister': ['merist', 'mister', 'smiter'],
 'mistide': ['misdiet', 'misedit', 'mistide'],
 'mistle': ['mistle', 'smilet'],
 'mistone': ['mistone', 'moisten'],
 'mistradition': ['mistradition', 'traditionism'],
 'mistrain': ['marinist', 'mistrain'],
 'mistreat': ['mistreat', 'teratism'],
 'mistrial': ['mistrial', 'trialism'],
 'mistutor': ['mistutor', 'tutorism'],
 'misty': ['misty', 'stimy'],
 'misunderstander': ['misunderstander', 'remisunderstand'],
 'misura': ['misura', 'ramusi'],
 'misuser': ['misuser', 'surmise'],
 'mitannish': ['mitannish', 'sminthian'],
 'mitch': ['micht', 'mitch'],
 'mite': ['emit', 'item', 'mite', 'time'],
 'mitella': ['mitella', 'tellima'],
 'miteproof': ['miteproof', 'timeproof'],
 'miter': ['merit', 'miter', 'mitre', 'remit', 'timer'],
 'mitered': ['demerit', 'dimeter', 'merited', 'mitered'],
 'miterer': ['meriter', 'miterer', 'trireme'],
 'mithraic': ['arithmic', 'mithraic', 'mithriac'],
 'mithraicist': ['mithraicist', 'mithraistic'],
 'mithraistic': ['mithraicist', 'mithraistic'],
 'mithriac': ['arithmic', 'mithraic', 'mithriac'],
 'mitra': ['mitra', 'tarmi', 'timar', 'tirma'],
 'mitral': ['mitral', 'ramtil'],
 'mitrate': ['martite', 'mitrate'],
 'mitre': ['merit', 'miter', 'mitre', 'remit', 'timer'],
 'mitrer': ['mitrer', 'retrim', 'trimer'],
 'mitridae': ['dimetria', 'mitridae', 'tiremaid', 'triamide'],
 'mixer': ['mixer', 'remix'],
 'mizar': ['mirza', 'mizar'],
 'mnesic': ['cnemis', 'mnesic'],
 'mniaceous': ['acuminose', 'mniaceous'],
 'mniotiltidae': ['delimitation', 'mniotiltidae'],
 'mnium': ['mnium', 'nummi'],
 'mo': ['mo', 'om'],
 'moabitic': ['biatomic', 'moabitic'],
 'moan': ['mano', 'moan', 'mona', 'noam', 'noma', 'oman'],
 'moarian': ['amanori', 'moarian'],
 'moat': ['atmo', 'atom', 'moat', 'toma'],
 'mob': ['bom', 'mob'],
 'mobbable': ['bombable', 'mobbable'],
 'mobber': ['bomber', 'mobber'],
 'mobbish': ['hobbism', 'mobbish'],
 'mobed': ['demob', 'mobed'],
 'mobile': ['bemoil', 'mobile'],
 'mobilian': ['binomial', 'mobilian'],
 'mobocrat': ['mobocrat', 'motorcab'],
 'mobship': ['mobship', 'phobism'],
 'mobster': ['bestorm', 'mobster'],
 'mocker': ['mocker', 'remock'],
 'mocmain': ['ammonic', 'mocmain'],
 'mod': ['dom', 'mod'],
 'modal': ['domal', 'modal'],
 'mode': ['dome', 'mode', 'moed'],
 'modeler': ['demerol', 'modeler', 'remodel'],
 'modelist': ['melodist', 'modelist'],
 'modena': ['daemon', 'damone', 'modena'],
 'modenese': ['modenese', 'needsome'],
 'moderant': ['moderant', 'normated'],
 'moderantism': ['memorandist', 'moderantism', 'semidormant'],
 'modern': ['modern', 'morned'],
 'modernistic': ['modernistic', 'monstricide'],
 'modestly': ['modestly', 'styledom'],
 'modesty': ['dystome', 'modesty'],
 'modiste': ['distome', 'modiste'],
 'modistry': ['midstory', 'modistry'],
 'modius': ['modius', 'sodium'],
 'moe': ['meo', 'moe'],
 'moed': ['dome', 'mode', 'moed'],
 'moerithere': ['heteromeri', 'moerithere'],
 'mogdad': ['goddam', 'mogdad'],
 'moha': ['ahom', 'moha'],
 'mohair': ['homrai', 'mahori', 'mohair'],
 'mohel': ['hemol', 'mohel'],
 'mohican': ['mohican', 'monachi'],
 'moho': ['homo', 'moho'],
 'mohur': ['humor', 'mohur'],
 'moider': ['dormie', 'moider'],
 'moieter': ['moieter', 'romeite'],
 'moiety': ['moiety', 'moyite'],
 'moil': ['milo', 'moil'],
 'moiles': ['lemosi', 'limose', 'moiles'],
 'moineau': ['eunomia', 'moineau'],
 'moira': ['maori', 'mario', 'moira'],
 'moisten': ['mistone', 'moisten'],
 'moistener': ['moistener', 'neoterism'],
 'moisture': ['moisture', 'semitour'],
 'moit': ['itmo', 'moit', 'omit', 'timo'],
 'mojo': ['joom', 'mojo'],
 'moke': ['kome', 'moke'],
 'moki': ['komi', 'moki'],
 'mola': ['loam', 'loma', 'malo', 'mola', 'olam'],
 'molar': ['molar', 'moral', 'romal'],
 'molarity': ['molarity', 'morality'],
 'molary': ['amyrol', 'molary'],
 'molder': ['dermol', 'molder', 'remold'],
 'moler': ['moler', 'morel'],
 'molge': ['glome', 'golem', 'molge'],
 'molidae': ['melodia', 'molidae'],
 'molinia': ['molinia', 'monilia'],
 'mollusca': ['callosum', 'mollusca'],
 'moloid': ['moloid', 'oildom'],
 'molten': ['loment', 'melton', 'molten'],
 'molybdena': ['baldmoney', 'molybdena'],
 'molybdenic': ['combinedly', 'molybdenic'],
 'mome': ['memo', 'mome'],
 'moment': ['moment', 'montem'],
 'momentary': ['manometry', 'momentary'],
 'momentous': ['mesonotum', 'momentous'],
 'momotinae': ['amniotome', 'momotinae'],
 'mona': ['mano', 'moan', 'mona', 'noam', 'noma', 'oman'],
 'monachi': ['mohican', 'monachi'],
 'monactin': ['monactin', 'montanic'],
 'monad': ['damon', 'monad', 'nomad'],
 'monadic': ['monadic', 'nomadic'],
 'monadical': ['monadical', 'nomadical'],
 'monadically': ['monadically', 'nomadically'],
 'monadina': ['monadina', 'nomadian'],
 'monadism': ['monadism', 'nomadism'],
 'monaene': ['anemone', 'monaene'],
 'monal': ['almon', 'monal'],
 'monamniotic': ['commination', 'monamniotic'],
 'monanthous': ['anthonomus', 'monanthous'],
 'monarch': ['monarch', 'nomarch', 'onmarch'],
 'monarchial': ['harmonical', 'monarchial'],
 'monarchian': ['anharmonic', 'monarchian'],
 'monarchistic': ['chiromancist', 'monarchistic'],
 'monarchy': ['monarchy', 'nomarchy'],
 'monarda': ['anadrom', 'madrona', 'mandora', 'monarda', 'roadman'],
 'monas': ['manso', 'mason', 'monas'],
 'monasa': ['monasa', 'samoan'],
 'monase': ['monase', 'nosema'],
 'monaster': ['monaster', 'monstera', 'nearmost', 'storeman'],
 'monastery': ['monastery', 'oysterman'],
 'monastic': ['catonism', 'monastic'],
 'monastical': ['catmalison', 'monastical'],
 'monatomic': ['commation', 'monatomic'],
 'monaural': ['anomural', 'monaural'],
 'monday': ['dynamo', 'monday'],
 'mone': ['mone', 'nome', 'omen'],
 'monel': ['lemon', 'melon', 'monel'],
 'moner': ['enorm', 'moner', 'morne'],
 'monera': ['enamor', 'monera', 'oreman', 'romane'],
 'moneral': ['almoner', 'moneral', 'nemoral'],
 'monergist': ['gerontism', 'monergist'],
 'moneric': ['incomer', 'moneric'],
 'monesia': ['monesia', 'osamine', 'osmanie'],
 'monetary': ['monetary', 'myronate', 'naometry'],
 'money': ['money', 'moyen'],
 'moneybag': ['bogeyman', 'moneybag'],
 'moneyless': ['moneyless', 'moyenless'],
 'monger': ['germon', 'monger', 'morgen'],
 'mongler': ['mongler', 'mongrel'],
 'mongoose': ['gonosome', 'mongoose'],
 'mongrel': ['mongler', 'mongrel'],
 'mongrelity': ['longimetry', 'mongrelity'],
 'monial': ['monial', 'nomial', 'oilman'],
 'monias': ['monias', 'osamin', 'osmina'],
 'monica': ['camion', 'conima', 'manioc', 'monica'],
 'monilated': ['antimodel', 'maldonite', 'monilated'],
 'monilia': ['molinia', 'monilia'],
 'monism': ['monism', 'nomism', 'simmon'],
 'monist': ['inmost', 'monist', 'omnist'],
 'monistic': ['monistic', 'nicotism', 'nomistic'],
 'monitory': ['monitory', 'moronity'],
 'monitress': ['monitress', 'sermonist'],
 'mono': ['mono', 'moon'],
 'monoacid': ['damonico', 'monoacid'],
 'monoazo': ['monoazo', 'monozoa'],
 'monocleid': ['clinodome', 'melodicon', 'monocleid'],
 'monoclinous': ['monoclinous', 'monoclonius'],
 'monoclonius': ['monoclinous', 'monoclonius'],
 'monocracy': ['monocracy', 'nomocracy'],
 'monocystidae': ['monocystidae', 'monocystidea'],
 'monocystidea': ['monocystidae', 'monocystidea'],
 'monodactylous': ['condylomatous', 'monodactylous'],
 'monodactyly': ['dactylonomy', 'monodactyly'],
 'monodelphia': ['amidophenol', 'monodelphia'],
 'monodonta': ['anomodont', 'monodonta'],
 'monodram': ['monodram', 'romandom'],
 'monoecian': ['monoecian', 'neocomian'],
 'monoecism': ['economism', 'monoecism', 'monosemic'],
 'monoeidic': ['meconioid', 'monoeidic'],
 'monogastric': ['gastronomic', 'monogastric'],
 'monogenist': ['monogenist', 'nomogenist'],
 'monogenous': ['monogenous', 'nomogenous'],
 'monogeny': ['monogeny', 'nomogeny'],
 'monogram': ['monogram', 'nomogram'],
 'monograph': ['monograph', 'nomograph', 'phonogram'],
 'monographer': ['geranomorph', 'monographer', 'nomographer'],
 'monographic': ['gramophonic', 'monographic', 'nomographic', 'phonogramic'],
 'monographical': ['gramophonical', 'monographical', 'nomographical'],
 'monographically': ['gramophonically',
                     'monographically',
                     'nomographically',
                     'phonogramically'],
 'monographist': ['gramophonist', 'monographist'],
 'monography': ['monography', 'nomography'],
 'monoid': ['domino', 'monoid'],
 'monological': ['monological', 'nomological'],
 'monologist': ['monologist', 'nomologist', 'ontologism'],
 'monology': ['monology', 'nomology'],
 'monometer': ['metronome', 'monometer', 'monotreme'],
 'monometric': ['commorient', 'metronomic', 'monometric'],
 'monometrical': ['metronomical', 'monometrical'],
 'monomorphic': ['monomorphic', 'morphonomic'],
 'monont': ['monont', 'monton'],
 'monopathy': ['monopathy', 'pathonomy'],
 'monopersulphuric': ['monopersulphuric', 'permonosulphuric'],
 'monophote': ['monophote', 'motophone'],
 'monophylite': ['entomophily', 'monophylite'],
 'monophyllous': ['monophyllous', 'nomophyllous'],
 'monoplanist': ['monoplanist', 'postnominal'],
 'monopsychism': ['monopsychism', 'psychomonism'],
 'monopteral': ['monopteral', 'protonemal'],
 'monosemic': ['economism', 'monoecism', 'monosemic'],
 'monosodium': ['monosodium', 'omnimodous', 'onosmodium'],
 'monotheism': ['monotheism', 'nomotheism'],
 'monotheist': ['monotheist', 'thomsonite'],
 'monothetic': ['monothetic', 'nomothetic'],
 'monotreme': ['metronome', 'monometer', 'monotreme'],
 'monotypal': ['monotypal', 'toponymal'],
 'monotypic': ['monotypic', 'toponymic'],
 'monotypical': ['monotypical', 'toponymical'],
 'monozoa': ['monoazo', 'monozoa'],
 'monozoic': ['monozoic', 'zoonomic'],
 'monroeism': ['monroeism', 'semimoron'],
 'monsieur': ['inermous', 'monsieur'],
 'monstera': ['monaster', 'monstera', 'nearmost', 'storeman'],
 'monstricide': ['modernistic', 'monstricide'],
 'montage': ['geomant', 'magneto', 'megaton', 'montage'],
 'montagnais': ['antagonism', 'montagnais'],
 'montanic': ['monactin', 'montanic'],
 'montanite': ['mentation', 'montanite'],
 'montem': ['moment', 'montem'],
 'montes': ['montes', 'ostmen'],
 'montia': ['atimon', 'manito', 'montia'],
 'monticule': ['ctenolium', 'monticule'],
 'monton': ['monont', 'monton'],
 'montu': ['montu', 'mount', 'notum'],
 'monture': ['monture', 'mounter', 'remount'],
 'monumentary': ['monumentary', 'unmomentary'],
 'mood': ['doom', 'mood'],
 'mooder': ['doomer', 'mooder', 'redoom', 'roomed'],
 'mool': ['loom', 'mool'],
 'mools': ['mools', 'sloom'],
 'moon': ['mono', 'moon'],
 'moonglade': ['megalodon', 'moonglade'],
 'moonite': ['emotion', 'moonite'],
 'moonja': ['majoon', 'moonja'],
 'moonscape': ['manoscope', 'moonscape'],
 'moonseed': ['endosome', 'moonseed'],
 'moontide': ['demotion', 'entomoid', 'moontide'],
 'moop': ['moop', 'pomo'],
 'moor': ['moor', 'moro', 'room'],
 'moorage': ['moorage', 'roomage'],
 'moorball': ['ballroom', 'moorball'],
 'moore': ['moore', 'romeo'],
 'moorn': ['moorn', 'moron'],
 'moorship': ['isomorph', 'moorship'],
 'moorup': ['moorup', 'uproom'],
 'moorwort': ['moorwort', 'rootworm', 'tomorrow', 'wormroot'],
 'moory': ['moory', 'roomy'],
 'moost': ['moost', 'smoot'],
 'moot': ['moot', 'toom'],
 'mooth': ['mooth', 'thoom'],
 'mootstead': ['mootstead', 'stomatode'],
 'mop': ['mop', 'pom'],
 'mopane': ['mopane', 'pomane'],
 'mope': ['mope', 'poem', 'pome'],
 'moper': ['merop', 'moper', 'proem', 'remop'],
 'mophead': ['hemapod', 'mophead'],
 'mopish': ['mopish', 'ophism'],
 'mopla': ['mopla', 'palmo'],
 'mopsy': ['mopsy', 'myops'],
 'mora': ['amor', 'maro', 'mora', 'omar', 'roam'],
 'morainal': ['manorial', 'morainal'],
 'moraine': ['moraine', 'romaine'],
 'moral': ['molar', 'moral', 'romal'],
 'morality': ['molarity', 'morality'],
 'morals': ['morals', 'morsal'],
 'moran': ['manor', 'moran', 'norma', 'ramon', 'roman'],
 'morat': ['amort', 'morat', 'torma'],
 'morate': ['amoret', 'morate'],
 'moray': ['mayor', 'moray'],
 'morbid': ['dibrom', 'morbid'],
 'mordancy': ['dormancy', 'mordancy'],
 'mordant': ['dormant', 'mordant'],
 'mordenite': ['interdome', 'mordenite', 'nemertoid'],
 'mordicate': ['decimator', 'medicator', 'mordicate'],
 'more': ['mero', 'more', 'omer', 'rome'],
 'moreish': ['heroism', 'moreish'],
 'morel': ['moler', 'morel'],
 'morencite': ['entomeric', 'intercome', 'morencite'],
 'morenita': ['maronite', 'martinoe', 'minorate', 'morenita', 'romanite'],
 'moreote': ['moreote', 'oometer'],
 'mores': ['meros', 'mores', 'morse', 'sermo', 'smore'],
 'morga': ['agrom', 'morga'],
 'morganatic': ['actinogram', 'morganatic'],
 'morgay': ['gyroma', 'morgay'],
 'morgen': ['germon', 'monger', 'morgen'],
 'moribund': ['moribund', 'unmorbid'],
 'moric': ['micro', 'moric', 'romic'],
 'moriche': ['homeric', 'moriche'],
 'morin': ['minor', 'morin'],
 'moringa': ['ingomar', 'moringa', 'roaming'],
 'moringua': ['mirounga', 'moringua', 'origanum'],
 'morn': ['morn', 'norm'],
 'morne': ['enorm', 'moner', 'morne'],
 'morned': ['modern', 'morned'],
 'mornless': ['mornless', 'normless'],
 'moro': ['moor', 'moro', 'room'],
 'morocota': ['coatroom', 'morocota'],
 'moron': ['moorn', 'moron'],
 'moronic': ['moronic', 'omicron'],
 'moronity': ['monitory', 'moronity'],
 'morphea': ['amphore', 'morphea'],
 'morphonomic': ['monomorphic', 'morphonomic'],
 'morphotic': ['microphot', 'morphotic'],
 'morphotropic': ['morphotropic', 'protomorphic'],
 'morrisean': ['morrisean', 'rosmarine'],
 'morsal': ['morals', 'morsal'],
 'morse': ['meros', 'mores', 'morse', 'sermo', 'smore'],
 'mortacious': ['mortacious', 'urosomatic'],
 'mortar': ['marrot', 'mortar'],
 'mortician': ['martinico', 'mortician'],
 'mortise': ['erotism', 'mortise', 'trisome'],
 'morton': ['morton', 'tomorn'],
 'mortuarian': ['mortuarian', 'muratorian'],
 'mortuary': ['mortuary', 'outmarry'],
 'mortuous': ['mortuous', 'tumorous'],
 'morus': ['morus', 'mosur'],
 'mosaic': ['aosmic', 'mosaic'],
 'mosandrite': ['mosandrite', 'tarsonemid'],
 'mosasauri': ['amaurosis', 'mosasauri'],
 'moschate': ['chatsome', 'moschate'],
 'mose': ['meso', 'mose', 'some'],
 'mosker': ['mosker', 'smoker'],
 'mosser': ['messor', 'mosser', 'somers'],
 'moste': ['moste', 'smote'],
 'mosting': ['gnomist', 'mosting'],
 'mosul': ['mosul', 'mouls', 'solum'],
 'mosur': ['morus', 'mosur'],
 'mot': ['mot', 'tom'],
 'mote': ['mote', 'tome'],
 'motel': ['metol', 'motel'],
 'motet': ['motet', 'motte', 'totem'],
 'mothed': ['method', 'mothed'],
 'mother': ['mother', 'thermo'],
 'motherland': ['enthraldom', 'motherland'],
 'motherward': ['motherward', 'threadworm'],
 'motograph': ['motograph', 'photogram'],
 'motographic': ['motographic', 'tomographic'],
 'motophone': ['monophote', 'motophone'],
 'motorcab': ['mobocrat', 'motorcab'],
 'motte': ['motet', 'motte', 'totem'],
 'moud': ['doum', 'moud', 'odum'],
 'moudy': ['moudy', 'yomud'],
 'moul': ['moul', 'ulmo'],
 'mouls': ['mosul', 'mouls', 'solum'],
 'mound': ['donum', 'mound'],
 'mount': ['montu', 'mount', 'notum'],
 'mountained': ['emundation', 'mountained'],
 'mountaineer': ['enumeration', 'mountaineer'],
 'mounted': ['demount', 'mounted'],
 'mounter': ['monture', 'mounter', 'remount'],
 'mousery': ['mousery', 'seymour'],
 'mousoni': ['mousoni', 'ominous'],
 'mousse': ['mousse', 'smouse'],
 'moutan': ['amount', 'moutan', 'outman'],
 'mouther': ['mouther', 'theorum'],
 'mover': ['mover', 'vomer'],
 'moy': ['moy', 'yom'],
 'moyen': ['money', 'moyen'],
 'moyenless': ['moneyless', 'moyenless'],
 'moyite': ['moiety', 'moyite'],
 'mru': ['mru', 'rum'],
 'mu': ['mu', 'um'],
 'muang': ['muang', 'munga'],
 'much': ['chum', 'much'],
 'mucic': ['cumic', 'mucic'],
 'mucilage': ['glucemia', 'mucilage'],
 'mucin': ['cumin', 'mucin'],
 'mucinoid': ['conidium', 'mucinoid', 'oncidium'],
 'mucofibrous': ['fibromucous', 'mucofibrous'],
 'mucoid': ['codium', 'mucoid'],
 'muconic': ['muconic', 'uncomic'],
 'mucor': ['mucor', 'mucro'],
 'mucoserous': ['mucoserous', 'seromucous'],
 'mucro': ['mucor', 'mucro'],
 'mucrones': ['consumer', 'mucrones'],
 'mud': ['dum', 'mud'],
 'mudar': ['mudar', 'mudra'],
 'mudden': ['edmund', 'mudden'],
 'mudir': ['mudir', 'murid'],
 'mudra': ['mudar', 'mudra'],
 'mudstone': ['mudstone', 'unmodest'],
 'mug': ['gum', 'mug'],
 'muga': ['gaum', 'muga'],
 'muggles': ['muggles', 'smuggle'],
 'mugweed': ['gumweed', 'mugweed'],
 'muid': ['duim', 'muid'],
 'muilla': ['allium', 'alulim', 'muilla'],
 'muir': ['muir', 'rimu'],
 'muishond': ['muishond', 'unmodish'],
 'muist': ['muist', 'tuism'],
 'mukri': ['kurmi', 'mukri'],
 'muleta': ['amulet', 'muleta'],
 'mulga': ['algum', 'almug', 'glaum', 'gluma', 'mulga'],
 'mulier': ['mulier', 'muriel'],
 'mulita': ['mulita', 'ultima'],
 'mulk': ['kulm', 'mulk'],
 'multani': ['multani', 'talinum'],
 'multinervose': ['multinervose', 'volunteerism'],
 'multipole': ['impollute', 'multipole'],
 'mumbler': ['bummler', 'mumbler'],
 'munda': ['maund', 'munda', 'numda', 'undam', 'unmad'],
 'mundane': ['mundane', 'unamend', 'unmaned', 'unnamed'],
 'munga': ['muang', 'munga'],
 'mungo': ['mungo', 'muong'],
 'munia': ['maniu', 'munia', 'unami'],
 'munity': ['munity', 'mutiny'],
 'muong': ['mungo', 'muong'],
 'mura': ['arum', 'maru', 'mura'],
 'murage': ['mauger', 'murage'],
 'mural': ['mural', 'rumal'],
 'muralist': ['altruism', 'muralist', 'traulism', 'ultraism'],
 'muran': ['muran', 'ruman', 'unarm', 'unram', 'urman'],
 'murat': ['martu', 'murat', 'turma'],
 'muratorian': ['mortuarian', 'muratorian'],
 'murderer': ['demurrer', 'murderer'],
 'murdering': ['demurring', 'murdering'],
 'murderingly': ['demurringly', 'murderingly'],
 'murex': ['murex', 'rumex'],
 'murga': ['garum', 'murga'],
 'muricate': ['ceratium', 'muricate'],
 'muricine': ['irenicum', 'muricine'],
 'murid': ['mudir', 'murid'],
 'muriel': ['mulier', 'muriel'],
 'murine': ['murine', 'nerium'],
 'murly': ['murly', 'rumly'],
 'murmurer': ['murmurer', 'remurmur'],
 'murrain': ['murrain', 'murrina'],
 'murrina': ['murrain', 'murrina'],
 'murut': ['murut', 'utrum'],
 'murza': ['mazur', 'murza'],
 'mus': ['mus', 'sum'],
 'musa': ['masu', 'musa', 'saum'],
 'musal': ['lamus', 'malus', 'musal', 'slaum'],
 'musalmani': ['manualism', 'musalmani'],
 'musang': ['magnus', 'musang'],
 'musar': ['musar', 'ramus', 'rusma', 'surma'],
 'musca': ['camus', 'musca', 'scaum', 'sumac'],
 'muscade': ['camused', 'muscade'],
 'muscarine': ['muscarine', 'sucramine'],
 'musci': ['musci', 'music'],
 'muscicole': ['leucocism', 'muscicole'],
 'muscinae': ['muscinae', 'semuncia'],
 'muscle': ['clumse', 'muscle'],
 'muscly': ['clumsy', 'muscly'],
 'muscone': ['consume', 'muscone'],
 'muscot': ['custom', 'muscot'],
 'mused': ['mused', 'sedum'],
 'museography': ['hypergamous', 'museography'],
 'muser': ['muser', 'remus', 'serum'],
 'musha': ['hamus', 'musha'],
 'music': ['musci', 'music'],
 'musicate': ['autecism', 'musicate'],
 'musico': ['musico', 'suomic'],
 'musie': ['iseum', 'musie'],
 'musing': ['musing', 'signum'],
 'muslined': ['muslined', 'unmisled', 'unsmiled'],
 'musophagine': ['amphigenous', 'musophagine'],
 'mussaenda': ['mussaenda', 'unamassed'],
 'must': ['must', 'smut', 'stum'],
 'mustang': ['mustang', 'stagnum'],
 'mustard': ['durmast', 'mustard'],
 'muster': ['muster', 'sertum', 'stumer'],
 'musterer': ['musterer', 'remuster'],
 'mustily': ['mustily', 'mytilus'],
 'muta': ['muta', 'taum'],
 'mutable': ['atumble', 'mutable'],
 'mutant': ['mutant', 'tantum', 'tutman'],
 'mutase': ['meatus', 'mutase'],
 'mute': ['mute', 'tume'],
 'muteness': ['muteness', 'tenesmus'],
 'mutescence': ['mutescence', 'tumescence'],
 'mutilate': ['mutilate', 'ultimate'],
 'mutilation': ['mutilation', 'ultimation'],
 'mutiny': ['munity', 'mutiny'],
 'mutism': ['mutism', 'summit'],
 'mutual': ['mutual', 'umlaut'],
 'mutulary': ['mutulary', 'tumulary'],
 'muysca': ['cyamus', 'muysca'],
 'mwa': ['maw', 'mwa'],
 'my': ['my', 'ym'],
 'mya': ['amy', 'may', 'mya', 'yam'],
 'myal': ['amyl', 'lyam', 'myal'],
 'myaria': ['amiray', 'myaria'],
 'myatonic': ['cymation', 'myatonic', 'onymatic'],
 'mycelia': ['amyelic', 'mycelia'],
 'mycelian': ['clymenia', 'mycelian'],
 'mycoid': ['cymoid', 'mycoid'],
 'mycophagist': ['mycophagist', 'phagocytism'],
 'mycose': ['cymose', 'mycose'],
 'mycosterol': ['mycosterol', 'sclerotomy'],
 'mycotrophic': ['chromotypic', 'cormophytic', 'mycotrophic'],
 'mycterism': ['mycterism', 'symmetric'],
 'myctodera': ['myctodera', 'radectomy'],
 'mydaleine': ['amylidene', 'mydaleine'],
 'myeloencephalitis': ['encephalomyelitis', 'myeloencephalitis'],
 'myelomeningitis': ['meningomyelitis', 'myelomeningitis'],
 'myelomeningocele': ['meningomyelocele', 'myelomeningocele'],
 'myelon': ['lemony', 'myelon'],
 'myelonal': ['amylenol', 'myelonal'],
 'myeloneuritis': ['myeloneuritis', 'neuromyelitis'],
 'myeloplast': ['meloplasty', 'myeloplast'],
 'mygale': ['gamely', 'gleamy', 'mygale'],
 'myitis': ['myitis', 'simity'],
 'myliobatid': ['bimodality', 'myliobatid'],
 'mymar': ['mymar', 'rammy'],
 'myna': ['many', 'myna'],
 'myoatrophy': ['amyotrophy', 'myoatrophy'],
 'myocolpitis': ['myocolpitis', 'polysomitic'],
 'myofibroma': ['fibromyoma', 'myofibroma'],
 'myoglobin': ['boomingly', 'myoglobin'],
 'myographic': ['microphagy', 'myographic'],
 'myographist': ['myographist', 'pythagorism'],
 'myolipoma': ['lipomyoma', 'myolipoma'],
 'myomohysterectomy': ['hysteromyomectomy', 'myomohysterectomy'],
 'myope': ['myope', 'pomey'],
 'myoplastic': ['myoplastic', 'polymastic'],
 'myoplasty': ['myoplasty', 'polymasty'],
 'myopolar': ['myopolar', 'playroom'],
 'myops': ['mopsy', 'myops'],
 'myosin': ['isonym', 'myosin', 'simony'],
 'myosote': ['myosote', 'toysome'],
 'myotenotomy': ['myotenotomy', 'tenomyotomy'],
 'myotic': ['comity', 'myotic'],
 'myra': ['army', 'mary', 'myra', 'yarm'],
 'myrcia': ['myrcia', 'myrica'],
 'myrialiter': ['myrialiter', 'myrialitre'],
 'myrialitre': ['myrialiter', 'myrialitre'],
 'myriameter': ['myriameter', 'myriametre'],
 'myriametre': ['myriameter', 'myriametre'],
 'myrianida': ['dimyarian', 'myrianida'],
 'myrica': ['myrcia', 'myrica'],
 'myristate': ['myristate', 'tasimetry'],
 'myristin': ['ministry', 'myristin'],
 'myristone': ['myristone', 'smyrniote'],
 'myronate': ['monetary', 'myronate', 'naometry'],
 'myrsinad': ['misandry', 'myrsinad'],
 'myrtales': ['masterly', 'myrtales'],
 'myrtle': ['myrtle', 'termly'],
 'mysell': ['mysell', 'smelly'],
 'mysian': ['maysin', 'minyas', 'mysian'],
 'mysis': ['missy', 'mysis'],
 'mysterial': ['mysterial', 'salimetry'],
 'mystes': ['mystes', 'system'],
 'mythographer': ['mythographer', 'thermography'],
 'mythogreen': ['mythogreen', 'thermogeny'],
 'mythologer': ['mythologer', 'thermology'],
 'mythopoeic': ['homeotypic', 'mythopoeic'],
 'mythus': ['mythus', 'thymus'],
 'mytilid': ['mytilid', 'timidly'],
 'mytilus': ['mustily', 'mytilus'],
 'myxochondroma': ['chondromyxoma', 'myxochondroma'],
 'myxochondrosarcoma': ['chondromyxosarcoma', 'myxochondrosarcoma'],
 'myxocystoma': ['cystomyxoma', 'myxocystoma'],
 'myxofibroma': ['fibromyxoma', 'myxofibroma'],
 'myxofibrosarcoma': ['fibromyxosarcoma', 'myxofibrosarcoma'],
 'myxoinoma': ['inomyxoma', 'myxoinoma'],
 'myxolipoma': ['lipomyxoma', 'myxolipoma'],
 'myxotheca': ['chemotaxy', 'myxotheca'],
 'na': ['an', 'na'],
 'naa': ['ana', 'naa'],
 'naam': ['anam', 'mana', 'naam', 'nama'],
 'nab': ['ban', 'nab'],
 'nabak': ['banak', 'nabak'],
 'nabal': ['alban', 'balan', 'banal', 'laban', 'nabal', 'nabla'],
 'nabalism': ['bailsman', 'balanism', 'nabalism'],
 'nabalite': ['albanite', 'balanite', 'nabalite'],
 'nabalus': ['balanus', 'nabalus', 'subanal'],
 'nabk': ['bank', 'knab', 'nabk'],
 'nabla': ['alban', 'balan', 'banal', 'laban', 'nabal', 'nabla'],
 'nable': ['leban', 'nable'],
 'nabobishly': ['babylonish', 'nabobishly'],
 'nabothian': ['bathonian', 'nabothian'],
 'nabs': ['nabs', 'snab'],
 'nabu': ['baun', 'buna', 'nabu', 'nuba'],
 'nacarat': ['cantara', 'nacarat'],
 'nace': ['acne', 'cane', 'nace'],
 'nachitoch': ['chanchito', 'nachitoch'],
 'nacre': ['caner', 'crane', 'crena', 'nacre', 'rance'],
 'nacred': ['cedarn', 'dancer', 'nacred'],
 'nacreous': ['carneous', 'nacreous'],
 'nacrite': ['centiar', 'certain', 'citrean', 'nacrite', 'nectria'],
 'nacrous': ['carnous', 'nacrous', 'narcous'],
 'nadder': ['dander', 'darned', 'nadder'],
 'nadeem': ['amende', 'demean', 'meaned', 'nadeem'],
 'nadir': ['darin', 'dinar', 'drain', 'indra', 'nadir', 'ranid'],
 'nadorite': ['andorite', 'nadorite', 'ordinate', 'rodentia'],
 'nae': ['ean', 'nae', 'nea'],
 'nael': ['alen', 'lane', 'lean', 'lena', 'nael', 'neal'],
 'naether': ['earthen', 'enheart', 'hearten', 'naether', 'teheran', 'traheen'],
 'nag': ['gan', 'nag'],
 'nagara': ['angara', 'aranga', 'nagara'],
 'nagari': ['graian', 'nagari'],
 'nagatelite': ['gelatinate', 'nagatelite'],
 'nagger': ['ganger', 'grange', 'nagger'],
 'nagging': ['ganging', 'nagging'],
 'naggle': ['laggen', 'naggle'],
 'naggly': ['gangly', 'naggly'],
 'nagmaal': ['malanga', 'nagmaal'],
 'nagnag': ['gangan', 'nagnag'],
 'nagnail': ['alangin', 'anginal', 'anglian', 'nagnail'],
 'nagor': ['angor',
           'argon',
           'goran',
           'grano',
           'groan',
           'nagor',
           'orang',
           'organ',
           'rogan',
           'ronga'],
 'nagster': ['angster', 'garnets', 'nagster', 'strange'],
 'nagual': ['angula', 'nagual'],
 'nahani': ['hainan', 'nahani'],
 'nahor': ['nahor', 'norah', 'rohan'],
 'nahum': ['human', 'nahum'],
 'naiad': ['danai', 'diana', 'naiad'],
 'naiant': ['naiant', 'tainan'],
 'naias': ['asian', 'naias', 'sanai'],
 'naid': ['adin', 'andi', 'dain', 'dani', 'dian', 'naid'],
 'naif': ['fain', 'naif'],
 'naifly': ['fainly', 'naifly'],
 'naig': ['gain', 'inga', 'naig', 'ngai'],
 'naik': ['akin', 'kina', 'naik'],
 'nail': ['alin', 'anil', 'lain', 'lina', 'nail'],
 'nailer': ['arline', 'larine', 'linear', 'nailer', 'renail'],
 'naileress': ['earliness', 'naileress'],
 'nailery': ['inlayer', 'nailery'],
 'nailless': ['nailless', 'sensilla'],
 'nailrod': ['nailrod', 'ordinal', 'rinaldo', 'rodinal'],
 'nailshop': ['nailshop', 'siphonal'],
 'naily': ['inlay', 'naily'],
 'naim': ['amin', 'main', 'mani', 'mian', 'mina', 'naim'],
 'nain': ['nain', 'nina'],
 'naio': ['aion', 'naio'],
 'nair': ['arni', 'iran', 'nair', 'rain', 'rani'],
 'nairy': ['nairy', 'rainy'],
 'nais': ['anis', 'nais', 'nasi', 'nias', 'sain', 'sina'],
 'naish': ['naish', 'shina'],
 'naither': ['anither', 'inearth', 'naither'],
 'naive': ['avine', 'naive', 'vinea'],
 'naivete': ['naivete', 'nieveta'],
 'nak': ['kan', 'nak'],
 'naked': ['kande', 'knead', 'naked'],
 'nakedish': ['headskin', 'nakedish', 'sinkhead'],
 'naker': ['anker', 'karen', 'naker'],
 'nakir': ['inkra', 'krina', 'nakir', 'rinka'],
 'nako': ['kona', 'nako'],
 'nalita': ['antlia', 'latian', 'nalita'],
 'nallah': ['hallan', 'nallah'],
 'nam': ['man', 'nam'],
 'nama': ['anam', 'mana', 'naam', 'nama'],
 'namaz': ['namaz', 'zaman'],
 'nambe': ['beman', 'nambe'],
 'namda': ['adman', 'daman', 'namda'],
 'name': ['amen', 'enam', 'mane', 'mean', 'name', 'nema'],
 'nameability': ['amenability', 'nameability'],
 'nameable': ['amenable', 'nameable'],
 'nameless': ['lameness', 'maleness', 'maneless', 'nameless'],
 'nameling': ['mangelin', 'nameling'],
 'namely': ['meanly', 'namely'],
 'namer': ['enarm', 'namer', 'reman'],
 'nammad': ['madman', 'nammad'],
 'nan': ['ann', 'nan'],
 'nana': ['anan', 'anna', 'nana'],
 'nanaimo': ['nanaimo', 'omniana'],
 'nancy': ['canny', 'nancy'],
 'nandi': ['indan', 'nandi'],
 'nane': ['anne', 'nane'],
 'nanes': ['nanes', 'senna'],
 'nanoid': ['adonin', 'nanoid', 'nonaid'],
 'nanosomia': ['nanosomia', 'nosomania'],
 'nanpie': ['nanpie', 'pennia', 'pinnae'],
 'naological': ['colonalgia', 'naological'],
 'naometry': ['monetary', 'myronate', 'naometry'],
 'naomi': ['amino', 'inoma', 'naomi', 'omani', 'omina'],
 'naoto': ['naoto', 'toona'],
 'nap': ['nap', 'pan'],
 'napaean': ['anapnea', 'napaean'],
 'nape': ['nape', 'neap', 'nepa', 'pane', 'pean'],
 'napead': ['napead', 'panade'],
 'napery': ['napery', 'pyrena'],
 'naphthalize': ['naphthalize', 'phthalazine'],
 'naphtol': ['haplont', 'naphtol'],
 'napkin': ['napkin', 'pankin'],
 'napped': ['append', 'napped'],
 'napper': ['napper', 'papern'],
 'napron': ['napron', 'nonpar'],
 'napthionic': ['antiphonic', 'napthionic'],
 'napu': ['napu', 'puan', 'puna'],
 'nar': ['arn', 'nar', 'ran'],
 'narcaciontes': ['narcaciontes', 'transoceanic'],
 'narcose': ['carnose', 'coarsen', 'narcose'],
 'narcotia': ['craniota', 'croatian', 'narcotia', 'raincoat'],
 'narcoticism': ['intracosmic', 'narcoticism'],
 'narcotina': ['anarcotin', 'cantorian', 'carnation', 'narcotina'],
 'narcotine': ['connarite', 'container', 'cotarnine', 'crenation', 'narcotine'],
 'narcotism': ['narcotism', 'romancist'],
 'narcotist': ['narcotist', 'stratonic'],
 'narcotize': ['narcotize', 'zirconate'],
 'narcous': ['carnous', 'nacrous', 'narcous'],
 'nard': ['darn', 'nard', 'rand'],
 'nardine': ['adrenin', 'nardine'],
 'nardus': ['nardus', 'sundar', 'sundra'],
 'nares': ['anser', 'nares', 'rasen', 'snare'],
 'nargil': ['nargil', 'raglin'],
 'naric': ['cairn', 'crain', 'naric'],
 'narica': ['acinar',
            'arnica',
            'canari',
            'carian',
            'carina',
            'crania',
            'narica'],
 'nariform': ['nariform', 'raniform'],
 'narine': ['narine', 'ranine'],
 'nark': ['knar', 'kran', 'nark', 'rank'],
 'narration': ['narration', 'tornarian'],
 'narthecium': ['anthericum', 'narthecium'],
 'nary': ['nary', 'yarn'],
 'nasab': ['nasab', 'saban'],
 'nasal': ['alans', 'lanas', 'nasal'],
 'nasalism': ['nasalism', 'sailsman'],
 'nasard': ['nasard', 'sandra'],
 'nascapi': ['capsian', 'caspian', 'nascapi', 'panisca'],
 'nash': ['hans', 'nash', 'shan'],
 'nashgab': ['bangash', 'nashgab'],
 'nasi': ['anis', 'nais', 'nasi', 'nias', 'sain', 'sina'],
 'nasial': ['anisal', 'nasial', 'salian', 'salina'],
 'nasitis': ['nasitis', 'sistani'],
 'nasoantral': ['antronasal', 'nasoantral'],
 'nasobuccal': ['bucconasal', 'nasobuccal'],
 'nasofrontal': ['frontonasal', 'nasofrontal'],
 'nasolabial': ['labionasal', 'nasolabial'],
 'nasolachrymal': ['lachrymonasal', 'nasolachrymal'],
 'nasonite': ['estonian', 'nasonite'],
 'nasoorbital': ['nasoorbital', 'orbitonasal'],
 'nasopalatal': ['nasopalatal', 'palatonasal'],
 'nasoseptal': ['nasoseptal', 'septonasal'],
 'nassa': ['nassa', 'sasan'],
 'nassidae': ['assidean', 'nassidae'],
 'nast': ['nast', 'sant', 'stan'],
 'nastic': ['incast', 'nastic'],
 'nastily': ['nastily', 'saintly', 'staynil'],
 'nasturtion': ['antrustion', 'nasturtion'],
 'nasty': ['nasty', 'styan', 'tansy'],
 'nasua': ['nasua', 'sauna'],
 'nasus': ['nasus', 'susan'],
 'nasute': ['nasute', 'nauset', 'unseat'],
 'nat': ['ant', 'nat', 'tan'],
 'nataka': ['nataka', 'tanaka'],
 'natal': ['antal', 'natal'],
 'natalia': ['altaian', 'latania', 'natalia'],
 'natalie': ['laniate', 'natalie', 'taenial'],
 'nataloin': ['latonian', 'nataloin', 'national'],
 'natals': ['aslant', 'lansat', 'natals', 'santal'],
 'natator': ['arnotta', 'natator'],
 'natatorium': ['maturation', 'natatorium'],
 'natch': ['chant', 'natch'],
 'nate': ['ante', 'aten', 'etna', 'nate', 'neat', 'taen', 'tane', 'tean'],
 'nates': ['antes', 'nates', 'stane', 'stean'],
 'nathan': ['nathan', 'thanan'],
 'nathe': ['enhat', 'ethan', 'nathe', 'neath', 'thane'],
 'nather': ['anther', 'nather', 'tharen', 'thenar'],
 'natica': ['actian', 'natica', 'tanica'],
 'naticiform': ['actiniform', 'naticiform'],
 'naticine': ['actinine', 'naticine'],
 'natick': ['catkin', 'natick'],
 'naticoid': ['actinoid', 'diatonic', 'naticoid'],
 'nation': ['anoint', 'nation'],
 'national': ['latonian', 'nataloin', 'national'],
 'native': ['native', 'navite'],
 'natively': ['natively', 'venality'],
 'nativist': ['nativist', 'visitant'],
 'natr': ['natr', 'rant', 'tarn', 'tran'],
 'natricinae': ['natricinae', 'nectarinia'],
 'natricine': ['crinanite', 'natricine'],
 'natrolite': ['natrolite', 'tentorial'],
 'natter': ['attern', 'natter', 'ratten', 'tarten'],
 'nattered': ['attender', 'nattered', 'reattend'],
 'nattily': ['nattily', 'titanyl'],
 'nattle': ['latent', 'latten', 'nattle', 'talent', 'tantle'],
 'naturalistic': ['naturalistic', 'unartistical'],
 'naturing': ['gainturn', 'naturing'],
 'naturism': ['naturism', 'sturmian', 'turanism'],
 'naturist': ['antirust', 'naturist'],
 'naturistic': ['naturistic', 'unartistic'],
 'naturistically': ['naturistically', 'unartistically'],
 'nauger': ['nauger', 'raunge', 'ungear'],
 'naumk': ['kuman', 'naumk'],
 'naunt': ['naunt', 'tunna'],
 'nauntle': ['annulet', 'nauntle'],
 'nauplius': ['nauplius', 'paulinus'],
 'nauset': ['nasute', 'nauset', 'unseat'],
 'naut': ['antu', 'aunt', 'naut', 'taun', 'tuan', 'tuna'],
 'nauther': ['haunter', 'nauther', 'unearth', 'unheart', 'urethan'],
 'nautic': ['anicut', 'nautic', 'ticuna', 'tunica'],
 'nautical': ['actinula', 'nautical'],
 'nautiloid': ['lutianoid', 'nautiloid'],
 'nautilus': ['lutianus', 'nautilus', 'ustulina'],
 'naval': ['alvan', 'naval'],
 'navalist': ['navalist', 'salivant'],
 'navar': ['navar', 'varan', 'varna'],
 'nave': ['evan', 'nave', 'vane'],
 'navel': ['elvan', 'navel', 'venal'],
 'naviculare': ['naviculare', 'uncavalier'],
 'navigant': ['navigant', 'vaginant'],
 'navigate': ['navigate', 'vaginate'],
 'navite': ['native', 'navite'],
 'naw': ['awn', 'naw', 'wan'],
 'nawt': ['nawt', 'tawn', 'want'],
 'nay': ['any', 'nay', 'yan'],
 'nayar': ['aryan', 'nayar', 'rayan'],
 'nazarite': ['nazarite', 'nazirate', 'triazane'],
 'nazi': ['nazi', 'zain'],
 'nazim': ['nazim', 'nizam'],
 'nazirate': ['nazarite', 'nazirate', 'triazane'],
 'nazirite': ['nazirite', 'triazine'],
 'ne': ['en', 'ne'],
 'nea': ['ean', 'nae', 'nea'],
 'neal': ['alen', 'lane', 'lean', 'lena', 'nael', 'neal'],
 'neanic': ['canine', 'encina', 'neanic'],
 'neap': ['nape', 'neap', 'nepa', 'pane', 'pean'],
 'neapolitan': ['antelopian', 'neapolitan', 'panelation'],
 'nearby': ['barney', 'nearby'],
 'nearctic': ['acentric', 'encratic', 'nearctic'],
 'nearest': ['earnest', 'eastern', 'nearest'],
 'nearish': ['arshine', 'nearish', 'rhesian', 'sherani'],
 'nearly': ['anerly', 'nearly'],
 'nearmost': ['monaster', 'monstera', 'nearmost', 'storeman'],
 'nearthrosis': ['enarthrosis', 'nearthrosis'],
 'neat': ['ante', 'aten', 'etna', 'nate', 'neat', 'taen', 'tane', 'tean'],
 'neaten': ['etnean', 'neaten'],
 'neath': ['enhat', 'ethan', 'nathe', 'neath', 'thane'],
 'neatherd': ['adherent', 'headrent', 'neatherd', 'threaden'],
 'neatherdess': ['heartedness', 'neatherdess'],
 'neb': ['ben', 'neb'],
 'neback': ['backen', 'neback'],
 'nebaioth': ['boethian', 'nebaioth'],
 'nebalia': ['abelian', 'nebalia'],
 'nebelist': ['nebelist', 'stilbene', 'tensible'],
 'nebula': ['nebula', 'unable', 'unbale'],
 'nebulose': ['bluenose', 'nebulose'],
 'necator': ['enactor', 'necator', 'orcanet'],
 'necessarian': ['necessarian', 'renaissance'],
 'neckar': ['canker', 'neckar'],
 'necrogenic': ['congeneric', 'necrogenic'],
 'necrogenous': ['congenerous', 'necrogenous'],
 'necrology': ['crenology', 'necrology'],
 'necropoles': ['necropoles', 'preconsole'],
 'necropolis': ['clinospore', 'necropolis'],
 'necrotic': ['crocetin', 'necrotic'],
 'necrotomic': ['necrotomic', 'oncometric'],
 'necrotomy': ['necrotomy', 'normocyte', 'oncometry'],
 'nectar': ['canter',
            'creant',
            'cretan',
            'nectar',
            'recant',
            'tanrec',
            'trance'],
 'nectareal': ['lactarene', 'nectareal'],
 'nectared': ['crenated', 'decanter', 'nectared'],
 'nectareous': ['countersea', 'nectareous'],
 'nectarial': ['carnalite', 'claretian', 'lacertian', 'nectarial'],
 'nectarian': ['cratinean', 'incarnate', 'nectarian'],
 'nectaried': ['nectaried', 'tridecane'],
 'nectarine': ['inertance', 'nectarine'],
 'nectarinia': ['natricinae', 'nectarinia'],
 'nectarious': ['nectarious', 'recusation'],
 'nectarlike': ['nectarlike', 'trancelike'],
 'nectarous': ['acentrous', 'courtesan', 'nectarous'],
 'nectary': ['encraty', 'nectary'],
 'nectophore': ['ctenophore', 'nectophore'],
 'nectria': ['centiar', 'certain', 'citrean', 'nacrite', 'nectria'],
 'ned': ['den', 'end', 'ned'],
 'nedder': ['nedder', 'redden'],
 'neebor': ['boreen', 'enrobe', 'neebor', 'rebone'],
 'need': ['dene', 'eden', 'need'],
 'needer': ['endere', 'needer', 'reeden'],
 'needfire': ['needfire', 'redefine'],
 'needily': ['needily', 'yielden'],
 'needle': ['lendee', 'needle'],
 'needless': ['needless', 'seldseen'],
 'needs': ['dense', 'needs'],
 'needsome': ['modenese', 'needsome'],
 'neeger': ['neeger', 'reenge', 'renege'],
 'neeld': ['leden', 'neeld'],
 'neep': ['neep', 'peen'],
 'neepour': ['neepour', 'neurope'],
 'neer': ['erne', 'neer', 'reen'],
 'neet': ['neet', 'nete', 'teen'],
 'neetup': ['neetup', 'petune'],
 'nef': ['fen', 'nef'],
 'nefast': ['fasten', 'nefast', 'stefan'],
 'neftgil': ['felting', 'neftgil'],
 'negate': ['geneat', 'negate', 'tegean'],
 'negation': ['antigone', 'negation'],
 'negative': ['agentive', 'negative'],
 'negativism': ['negativism', 'timesaving'],
 'negator': ['negator', 'tronage'],
 'negatron': ['argenton', 'negatron'],
 'neger': ['genre', 'green', 'neger', 'reneg'],
 'neglecter': ['neglecter', 'reneglect'],
 'negritian': ['negritian', 'retaining'],
 'negrito': ['ergotin', 'genitor', 'negrito', 'ogtiern', 'trigone'],
 'negritoid': ['negritoid', 'rodingite'],
 'negro': ['ergon', 'genro', 'goner', 'negro'],
 'negroid': ['groined', 'negroid'],
 'negroidal': ['girandole', 'negroidal'],
 'negroize': ['genizero', 'negroize'],
 'negroloid': ['gondolier', 'negroloid'],
 'negrotic': ['gerontic', 'negrotic'],
 'negundo': ['dungeon', 'negundo'],
 'negus': ['genus', 'negus'],
 'neif': ['enif', 'fine', 'neif', 'nife'],
 'neigh': ['hinge', 'neigh'],
 'neil': ['lien', 'line', 'neil', 'nile'],
 'neiper': ['neiper', 'perine', 'pirene', 'repine'],
 'neist': ['inset', 'neist', 'snite', 'stein', 'stine', 'tsine'],
 'neither': ['enherit', 'etherin', 'neither', 'therein'],
 'nekkar': ['kraken', 'nekkar'],
 'nekton': ['kenton', 'nekton'],
 'nelken': ['kennel', 'nelken'],
 'nelsonite': ['nelsonite', 'solentine'],
 'nelumbian': ['nelumbian', 'unminable'],
 'nema': ['amen', 'enam', 'mane', 'mean', 'name', 'nema'],
 'nemalite': ['melanite', 'meletian', 'metaline', 'nemalite'],
 'nematoda': ['mantodea', 'nematoda'],
 'nematoid': ['dominate', 'nematoid'],
 'nematophyton': ['nematophyton', 'tenontophyma'],
 'nemertinea': ['minnetaree', 'nemertinea'],
 'nemertini': ['intermine', 'nemertini', 'terminine'],
 'nemertoid': ['interdome', 'mordenite', 'nemertoid'],
 'nemoral': ['almoner', 'moneral', 'nemoral'],
 'nenta': ['anent', 'annet', 'nenta'],
 'neo': ['eon', 'neo', 'one'],
 'neoarctic': ['accretion', 'anorectic', 'neoarctic'],
 'neocomian': ['monoecian', 'neocomian'],
 'neocosmic': ['economics', 'neocosmic'],
 'neocyte': ['enocyte', 'neocyte'],
 'neogaea': ['eogaean', 'neogaea'],
 'neogenesis': ['neogenesis', 'noegenesis'],
 'neogenetic': ['neogenetic', 'noegenetic'],
 'neognathous': ['anthogenous', 'neognathous'],
 'neolatry': ['neolatry', 'ornately', 'tyrolean'],
 'neolithic': ['ichnolite', 'neolithic'],
 'neomiracle': ['ceremonial', 'neomiracle'],
 'neomorphic': ['microphone', 'neomorphic'],
 'neon': ['neon', 'none'],
 'neophilism': ['neophilism', 'philoneism'],
 'neophytic': ['hypnoetic', 'neophytic'],
 'neoplasm': ['neoplasm', 'pleonasm', 'polesman', 'splenoma'],
 'neoplastic': ['neoplastic', 'pleonastic'],
 'neorama': ['neorama', 'romaean'],
 'neornithes': ['neornithes', 'rhinestone'],
 'neossin': ['neossin', 'sension'],
 'neoteric': ['erection', 'neoteric', 'nocerite', 'renotice'],
 'neoterism': ['moistener', 'neoterism'],
 'neotragus': ['argentous', 'neotragus'],
 'neotropic': ['ectropion', 'neotropic'],
 'neotropical': ['neotropical', 'percolation'],
 'neoza': ['neoza', 'ozena'],
 'nep': ['nep', 'pen'],
 'nepa': ['nape', 'neap', 'nepa', 'pane', 'pean'],
 'nepal': ['alpen', 'nepal', 'panel', 'penal', 'plane'],
 'nepali': ['alpine', 'nepali', 'penial', 'pineal'],
 'neper': ['neper', 'preen', 'repen'],
 'nepheloscope': ['nepheloscope', 'phonelescope'],
 'nephite': ['heptine', 'nephite'],
 'nephogram': ['gomphrena', 'nephogram'],
 'nephological': ['nephological', 'phenological'],
 'nephologist': ['nephologist', 'phenologist'],
 'nephology': ['nephology', 'phenology'],
 'nephria': ['heparin', 'nephria'],
 'nephric': ['nephric', 'phrenic', 'pincher'],
 'nephrite': ['nephrite', 'prehnite', 'trephine'],
 'nephritic': ['nephritic', 'phrenitic', 'prehnitic'],
 'nephritis': ['inspreith', 'nephritis', 'phrenitis'],
 'nephrocardiac': ['nephrocardiac', 'phrenocardiac'],
 'nephrocolic': ['nephrocolic', 'phrenocolic'],
 'nephrocystosis': ['cystonephrosis', 'nephrocystosis'],
 'nephrogastric': ['gastrophrenic', 'nephrogastric', 'phrenogastric'],
 'nephrohydrosis': ['hydronephrosis', 'nephrohydrosis'],
 'nephrolithotomy': ['lithonephrotomy', 'nephrolithotomy'],
 'nephrologist': ['nephrologist', 'phrenologist'],
 'nephrology': ['nephrology', 'phrenology'],
 'nephropathic': ['nephropathic', 'phrenopathic'],
 'nephropathy': ['nephropathy', 'phrenopathy'],
 'nephropsidae': ['nephropsidae', 'praesphenoid'],
 'nephroptosia': ['nephroptosia', 'prosiphonate'],
 'nephropyelitis': ['nephropyelitis', 'pyelonephritis'],
 'nephropyosis': ['nephropyosis', 'pyonephrosis'],
 'nephrosis': ['nephrosis', 'phronesis'],
 'nephrostoma': ['nephrostoma', 'strophomena'],
 'nephrotome': ['nephrotome', 'phonometer'],
 'nephrotomy': ['nephrotomy', 'phonometry'],
 'nepman': ['nepman', 'penman'],
 'nepotal': ['lepanto', 'nepotal', 'petalon', 'polenta'],
 'nepote': ['nepote', 'pontee', 'poteen'],
 'nepotic': ['entopic', 'nepotic', 'pentoic'],
 'nereid': ['denier', 'nereid'],
 'nereis': ['inseer', 'nereis', 'seiner', 'serine', 'sirene'],
 'neri': ['neri', 'rein', 'rine'],
 'nerita': ['nerita', 'ratine', 'retain', 'retina', 'tanier'],
 'neritic': ['citrine', 'crinite', 'inciter', 'neritic'],
 'neritina': ['neritina', 'retinian'],
 'neritoid': ['neritoid', 'retinoid'],
 'nerium': ['murine', 'nerium'],
 'neroic': ['cerion', 'coiner', 'neroic', 'orcein', 'recoin'],
 'neronic': ['corinne', 'cornein', 'neronic'],
 'nerval': ['nerval', 'vernal'],
 'nervate': ['nervate', 'veteran'],
 'nervation': ['nervation', 'vernation'],
 'nerve': ['nerve', 'never'],
 'nervid': ['driven', 'nervid', 'verdin'],
 'nervine': ['innerve', 'nervine', 'vernine'],
 'nerviness': ['inverness', 'nerviness'],
 'nervish': ['nervish', 'shriven'],
 'nervulose': ['nervulose', 'unresolve', 'vulnerose'],
 'nese': ['ense', 'esne', 'nese', 'seen', 'snee'],
 'nesh': ['nesh', 'shen'],
 'nesiot': ['nesiot', 'ostein'],
 'neskhi': ['kishen', 'neskhi'],
 'neslia': ['alsine', 'neslia', 'saline', 'selina', 'silane'],
 'nest': ['nest', 'sent', 'sten'],
 'nester': ['ernest', 'nester', 'resent', 'streen'],
 'nestiatria': ['intarsiate', 'nestiatria'],
 'nestlike': ['nestlike', 'skeletin'],
 'nestor': ['nestor', 'sterno', 'stoner', 'strone', 'tensor'],
 'net': ['net', 'ten'],
 'netcha': ['entach', 'netcha'],
 'nete': ['neet', 'nete', 'teen'],
 'neter': ['enter', 'neter', 'renet', 'terne', 'treen'],
 'netful': ['fluent', 'netful', 'unfelt', 'unleft'],
 'neth': ['hent', 'neth', 'then'],
 'nether': ['erthen', 'henter', 'nether', 'threne'],
 'neti': ['iten', 'neti', 'tien', 'tine'],
 'netman': ['manent', 'netman'],
 'netsuke': ['kuneste', 'netsuke'],
 'nettable': ['nettable', 'tentable'],
 'nettapus': ['nettapus', 'stepaunt'],
 'netted': ['detent', 'netted', 'tented'],
 'netter': ['netter', 'retent', 'tenter'],
 'nettion': ['nettion', 'tention', 'tontine'],
 'nettle': ['letten', 'nettle'],
 'nettler': ['nettler', 'ternlet'],
 'netty': ['netty', 'tenty'],
 'neurad': ['endura', 'neurad', 'undear', 'unread'],
 'neural': ['lunare', 'neural', 'ulnare', 'unreal'],
 'neuralgic': ['genicular', 'neuralgic'],
 'neuralist': ['neuralist', 'ulsterian', 'unrealist'],
 'neurectopia': ['eucatropine', 'neurectopia'],
 'neuric': ['curine', 'erucin', 'neuric'],
 'neurilema': ['lemurinae', 'neurilema'],
 'neurin': ['enruin', 'neurin', 'unrein'],
 'neurism': ['neurism', 'semiurn'],
 'neurite': ['neurite', 'retinue', 'reunite', 'uterine'],
 'neuroblast': ['neuroblast', 'unsortable'],
 'neurodermatosis': ['dermatoneurosis', 'neurodermatosis'],
 'neurofibroma': ['fibroneuroma', 'neurofibroma'],
 'neurofil': ['fluorine', 'neurofil'],
 'neuroganglion': ['ganglioneuron', 'neuroganglion'],
 'neurogenic': ['encoignure', 'neurogenic'],
 'neuroid': ['dourine', 'neuroid'],
 'neurolysis': ['neurolysis', 'resinously'],
 'neuromast': ['anoestrum', 'neuromast'],
 'neuromyelitis': ['myeloneuritis', 'neuromyelitis'],
 'neuronal': ['enaluron', 'neuronal'],
 'neurope': ['neepour', 'neurope'],
 'neuropsychological': ['neuropsychological', 'psychoneurological'],
 'neuropsychosis': ['neuropsychosis', 'psychoneurosis'],
 'neuropteris': ['interposure', 'neuropteris'],
 'neurosis': ['neurosis', 'resinous'],
 'neurotic': ['eruction', 'neurotic'],
 'neurotripsy': ['neurotripsy', 'tripyrenous'],
 'neustrian': ['neustrian', 'saturnine', 'sturninae'],
 'neuter': ['neuter', 'retune', 'runtee', 'tenure', 'tureen'],
 'neuterly': ['neuterly', 'rutylene'],
 'neutral': ['laurent', 'neutral', 'unalert'],
 'neutralism': ['neutralism', 'trimensual'],
 'neutrally': ['neutrally', 'unalertly'],
 'neutralness': ['neutralness', 'unalertness'],
 'nevada': ['nevada', 'vedana', 'venada'],
 'neve': ['even', 'neve', 'veen'],
 'never': ['nerve', 'never'],
 'nevo': ['nevo', 'oven'],
 'nevoy': ['envoy', 'nevoy', 'yoven'],
 'nevus': ['nevus', 'venus'],
 'new': ['new', 'wen'],
 'newar': ['awner', 'newar'],
 'newari': ['newari', 'wainer'],
 'news': ['news', 'sewn', 'snew'],
 'newt': ['newt', 'went'],
 'nexus': ['nexus', 'unsex'],
 'ngai': ['gain', 'inga', 'naig', 'ngai'],
 'ngaio': ['gonia', 'ngaio', 'nogai'],
 'ngapi': ['aping', 'ngapi', 'pangi'],
 'ngoko': ['kongo', 'ngoko'],
 'ni': ['in', 'ni'],
 'niagara': ['agrania', 'angaria', 'niagara'],
 'nias': ['anis', 'nais', 'nasi', 'nias', 'sain', 'sina'],
 'niata': ['anita', 'niata', 'tania'],
 'nib': ['bin', 'nib'],
 'nibs': ['nibs', 'snib'],
 'nibsome': ['nibsome', 'nimbose'],
 'nicarao': ['aaronic', 'nicarao', 'ocarina'],
 'niccolous': ['niccolous', 'occlusion'],
 'nice': ['cine', 'nice'],
 'nicene': ['cinene', 'nicene'],
 'nicenist': ['inscient', 'nicenist'],
 'nicesome': ['nicesome', 'semicone'],
 'nichael': ['chilean', 'echinal', 'nichael'],
 'niche': ['chien', 'chine', 'niche'],
 'nicher': ['enrich', 'nicher', 'richen'],
 'nicholas': ['lichanos', 'nicholas'],
 'nickel': ['nickel', 'nickle'],
 'nickle': ['nickel', 'nickle'],
 'nicol': ['colin', 'nicol'],
 'nicolas': ['nicolas', 'scaloni'],
 'nicolette': ['lecontite', 'nicolette'],
 'nicotian': ['aconitin', 'inaction', 'nicotian'],
 'nicotianin': ['nicotianin', 'nicotinian'],
 'nicotined': ['incondite', 'nicotined'],
 'nicotinian': ['nicotianin', 'nicotinian'],
 'nicotism': ['monistic', 'nicotism', 'nomistic'],
 'nicotize': ['nicotize', 'tonicize'],
 'nictate': ['nictate', 'tetanic'],
 'nictation': ['antitonic', 'nictation'],
 'nid': ['din', 'ind', 'nid'],
 'nidal': ['danli', 'ladin', 'linda', 'nidal'],
 'nidana': ['andian', 'danian', 'nidana'],
 'nidation': ['nidation', 'notidani'],
 'niddle': ['dindle', 'niddle'],
 'nide': ['dine', 'enid', 'inde', 'nide'],
 'nidge': ['deign', 'dinge', 'nidge'],
 'nidget': ['nidget', 'tinged'],
 'niding': ['dining', 'indign', 'niding'],
 'nidologist': ['indologist', 'nidologist'],
 'nidology': ['indology', 'nidology'],
 'nidularia': ['nidularia', 'uniradial'],
 'nidulate': ['nidulate', 'untailed'],
 'nidus': ['dinus', 'indus', 'nidus'],
 'niello': ['lionel', 'niello'],
 'niels': ['elsin', 'lenis', 'niels', 'silen', 'sline'],
 'nieve': ['nieve', 'venie'],
 'nieveta': ['naivete', 'nieveta'],
 'nievling': ['levining', 'nievling'],
 'nife': ['enif', 'fine', 'neif', 'nife'],
 'nifle': ['elfin', 'nifle'],
 'nig': ['gin', 'ing', 'nig'],
 'nigel': ['ingle', 'ligne', 'linge', 'nigel'],
 'nigella': ['gallein', 'galline', 'nigella'],
 'nigerian': ['arginine', 'nigerian'],
 'niggard': ['grading', 'niggard'],
 'nigger': ['ginger', 'nigger'],
 'niggery': ['gingery', 'niggery'],
 'nigh': ['hing', 'nigh'],
 'night': ['night', 'thing'],
 'nightless': ['lightness', 'nightless', 'thingless'],
 'nightlike': ['nightlike', 'thinglike'],
 'nightly': ['nightly', 'thingly'],
 'nightman': ['nightman', 'thingman'],
 'nignye': ['ginney', 'nignye'],
 'nigori': ['nigori', 'origin'],
 'nigre': ['grein', 'inger', 'nigre', 'regin', 'reign', 'ringe'],
 'nigrous': ['nigrous', 'rousing', 'souring'],
 'nihal': ['linha', 'nihal'],
 'nikau': ['kunai', 'nikau'],
 'nil': ['lin', 'nil'],
 'nile': ['lien', 'line', 'neil', 'nile'],
 'nilgai': ['ailing', 'angili', 'nilgai'],
 'nilometer': ['linometer', 'nilometer'],
 'niloscope': ['niloscope', 'scopoline'],
 'nilotic': ['clition', 'nilotic'],
 'nilous': ['insoul', 'linous', 'nilous', 'unsoil'],
 'nim': ['min', 'nim'],
 'nimbed': ['embind', 'nimbed'],
 'nimbose': ['nibsome', 'nimbose'],
 'nimkish': ['minkish', 'nimkish'],
 'nimshi': ['minish', 'nimshi'],
 'nina': ['nain', 'nina'],
 'ninescore': ['ninescore', 'recension'],
 'nineted': ['dentine', 'nineted'],
 'ninevite': ['ninevite', 'nivenite'],
 'ningpo': ['ningpo', 'pignon'],
 'nintu': ['nintu', 'ninut', 'untin'],
 'ninut': ['nintu', 'ninut', 'untin'],
 'niota': ['niota', 'taino'],
 'nip': ['nip', 'pin'],
 'nipa': ['nipa', 'pain', 'pani', 'pian', 'pina'],
 'nippers': ['nippers', 'snipper'],
 'nipple': ['lippen', 'nipple'],
 'nipter': ['nipter', 'terpin'],
 'nisaean': ['nisaean', 'sinaean'],
 'nisqualli': ['nisqualli', 'squillian'],
 'nisus': ['nisus', 'sinus'],
 'nit': ['nit', 'tin'],
 'nitch': ['chint', 'nitch'],
 'nitella': ['nitella', 'tellina'],
 'nitently': ['intently', 'nitently'],
 'niter': ['inert', 'inter', 'niter', 'retin', 'trine'],
 'nitered': ['nitered', 'redient', 'teinder'],
 'nither': ['hinter', 'nither', 'theirn'],
 'nito': ['into', 'nito', 'oint', 'tino'],
 'niton': ['niton', 'noint'],
 'nitrate': ['intreat', 'iterant', 'nitrate', 'tertian'],
 'nitratine': ['itinerant', 'nitratine'],
 'nitric': ['citrin', 'nitric'],
 'nitride': ['inditer', 'nitride'],
 'nitrifaction': ['antifriction', 'nitrifaction'],
 'nitriot': ['introit', 'nitriot'],
 'nitrobenzol': ['benzonitrol', 'nitrobenzol'],
 'nitrogelatin': ['intolerating', 'nitrogelatin'],
 'nitrosate': ['nitrosate', 'stationer'],
 'nitrous': ['nitrous', 'trusion'],
 'nitter': ['nitter', 'tinter'],
 'nitty': ['nitty', 'tinty'],
 'niue': ['niue', 'unie'],
 'nival': ['alvin', 'anvil', 'nival', 'vinal'],
 'nivenite': ['ninevite', 'nivenite'],
 'niveous': ['envious', 'niveous', 'veinous'],
 'nivosity': ['nivosity', 'vinosity'],
 'nizam': ['nazim', 'nizam'],
 'no': ['no', 'on'],
 'noa': ['noa', 'ona'],
 'noachite': ['inchoate', 'noachite'],
 'noah': ['hano', 'noah'],
 'noahic': ['chinoa', 'noahic'],
 'noam': ['mano', 'moan', 'mona', 'noam', 'noma', 'oman'],
 'nob': ['bon', 'nob'],
 'nobleman': ['blennoma', 'nobleman'],
 'noblesse': ['boneless', 'noblesse'],
 'nobs': ['bosn', 'nobs', 'snob'],
 'nocardia': ['nocardia', 'orcadian'],
 'nocent': ['nocent', 'nocten'],
 'nocerite': ['erection', 'neoteric', 'nocerite', 'renotice'],
 'nock': ['conk', 'nock'],
 'nocten': ['nocent', 'nocten'],
 'noctiluca': ['ciclatoun', 'noctiluca'],
 'noctuid': ['conduit', 'duction', 'noctuid'],
 'noctuidae': ['coadunite', 'education', 'noctuidae'],
 'nocturia': ['curation', 'nocturia'],
 'nod': ['don', 'nod'],
 'nodal': ['donal', 'nodal'],
 'nodated': ['donated', 'nodated'],
 'node': ['done', 'node'],
 'nodi': ['dion', 'nodi', 'odin'],
 'nodiak': ['daikon', 'nodiak'],
 'nodical': ['dolcian', 'nodical'],
 'nodicorn': ['corindon', 'nodicorn'],
 'nodule': ['louden', 'nodule'],
 'nodus': ['nodus', 'ounds', 'sound'],
 'noegenesis': ['neogenesis', 'noegenesis'],
 'noegenetic': ['neogenetic', 'noegenetic'],
 'noel': ['elon', 'enol', 'leno', 'leon', 'lone', 'noel'],
 'noetic': ['eciton', 'noetic', 'notice', 'octine'],
 'noetics': ['contise', 'noetics', 'section'],
 'nog': ['gon', 'nog'],
 'nogai': ['gonia', 'ngaio', 'nogai'],
 'nogal': ['along', 'gonal', 'lango', 'longa', 'nogal'],
 'noil': ['lino', 'lion', 'loin', 'noil'],
 'noilage': ['goniale', 'noilage'],
 'noiler': ['elinor', 'lienor', 'lorien', 'noiler'],
 'noint': ['niton', 'noint'],
 'noir': ['inro', 'iron', 'noir', 'nori'],
 'noise': ['eosin', 'noise'],
 'noiseless': ['noiseless', 'selenosis'],
 'noisette': ['noisette', 'teosinte'],
 'nolo': ['loon', 'nolo'],
 'noma': ['mano', 'moan', 'mona', 'noam', 'noma', 'oman'],
 'nomad': ['damon', 'monad', 'nomad'],
 'nomadian': ['monadina', 'nomadian'],
 'nomadic': ['monadic', 'nomadic'],
 'nomadical': ['monadical', 'nomadical'],
 'nomadically': ['monadically', 'nomadically'],
 'nomadism': ['monadism', 'nomadism'],
 'nomarch': ['monarch', 'nomarch', 'onmarch'],
 'nomarchy': ['monarchy', 'nomarchy'],
 'nome': ['mone', 'nome', 'omen'],
 'nomeus': ['nomeus', 'unsome'],
 'nomial': ['monial', 'nomial', 'oilman'],
 'nomina': ['amnion', 'minoan', 'nomina'],
 'nominate': ['antinome', 'nominate'],
 'nominated': ['dentinoma', 'nominated'],
 'nominature': ['nominature', 'numeration'],
 'nomism': ['monism', 'nomism', 'simmon'],
 'nomismata': ['anatomism', 'nomismata'],
 'nomistic': ['monistic', 'nicotism', 'nomistic'],
 'nomocracy': ['monocracy', 'nomocracy'],
 'nomogenist': ['monogenist', 'nomogenist'],
 'nomogenous': ['monogenous', 'nomogenous'],
 'nomogeny': ['monogeny', 'nomogeny'],
 'nomogram': ['monogram', 'nomogram'],
 'nomograph': ['monograph', 'nomograph', 'phonogram'],
 'nomographer': ['geranomorph', 'monographer', 'nomographer'],
 'nomographic': ['gramophonic', 'monographic', 'nomographic', 'phonogramic'],
 'nomographical': ['gramophonical', 'monographical', 'nomographical'],
 'nomographically': ['gramophonically',
                     'monographically',
                     'nomographically',
                     'phonogramically'],
 'nomography': ['monography', 'nomography'],
 'nomological': ['monological', 'nomological'],
 'nomologist': ['monologist', 'nomologist', 'ontologism'],
 'nomology': ['monology', 'nomology'],
 'nomophyllous': ['monophyllous', 'nomophyllous'],
 'nomotheism': ['monotheism', 'nomotheism'],
 'nomothetic': ['monothetic', 'nomothetic'],
 'nona': ['anon', 'nona', 'onan'],
 'nonaccession': ['connoissance', 'nonaccession'],
 'nonact': ['cannot', 'canton', 'conant', 'nonact'],
 'nonaction': ['connation', 'nonaction'],
 'nonagent': ['nonagent', 'tannogen'],
 'nonaid': ['adonin', 'nanoid', 'nonaid'],
 'nonaltruistic': ['instructional', 'nonaltruistic'],
 'nonanimal': ['nonanimal', 'nonmanila'],
 'nonbilabiate': ['inobtainable', 'nonbilabiate'],
 'noncaste': ['noncaste', 'tsonecan'],
 'noncereal': ['aleconner', 'noncereal'],
 'noncertified': ['noncertified', 'nonrectified'],
 'nonclaim': ['cinnamol', 'nonclaim'],
 'noncreation': ['noncreation', 'nonreaction'],
 'noncreative': ['noncreative', 'nonreactive'],
 'noncurantist': ['noncurantist', 'unconstraint'],
 'nonda': ['donna', 'nonda'],
 'nondesecration': ['nondesecration', 'recondensation'],
 'none': ['neon', 'none'],
 'nonempirical': ['nonempirical', 'prenominical'],
 'nonerudite': ['nonerudite', 'unoriented'],
 'nonesuch': ['nonesuch', 'unchosen'],
 'nonet': ['nonet', 'tenon'],
 'nonfertile': ['florentine', 'nonfertile'],
 'nongeometrical': ['inconglomerate', 'nongeometrical'],
 'nonglare': ['algernon', 'nonglare'],
 'nongod': ['dongon', 'nongod'],
 'nonhepatic': ['nonhepatic', 'pantheonic'],
 'nonic': ['conin', 'nonic', 'oncin'],
 'nonideal': ['anneloid', 'nonideal'],
 'nonidealist': ['alstonidine', 'nonidealist'],
 'nonirate': ['anointer', 'inornate', 'nonirate', 'reanoint'],
 'nonius': ['nonius', 'unison'],
 'nonlegato': ['nonlegato', 'ontogenal'],
 'nonlegume': ['melungeon', 'nonlegume'],
 'nonliable': ['bellonian', 'nonliable'],
 'nonlicet': ['contline', 'nonlicet'],
 'nonly': ['nonly', 'nonyl', 'nylon'],
 'nonmanila': ['nonanimal', 'nonmanila'],
 'nonmarital': ['antinormal', 'nonmarital', 'nonmartial'],
 'nonmartial': ['antinormal', 'nonmarital', 'nonmartial'],
 'nonmatter': ['nonmatter', 'remontant'],
 'nonmetric': ['comintern', 'nonmetric'],
 'nonmetrical': ['centinormal', 'conterminal', 'nonmetrical'],
 'nonmolar': ['nonmolar', 'nonmoral'],
 'nonmoral': ['nonmolar', 'nonmoral'],
 'nonnat': ['nonnat', 'nontan'],
 'nonoriental': ['nonoriental', 'nonrelation'],
 'nonpaid': ['dipnoan', 'nonpaid', 'pandion'],
 'nonpar': ['napron', 'nonpar'],
 'nonparental': ['nonparental', 'nonpaternal'],
 'nonpaternal': ['nonparental', 'nonpaternal'],
 'nonpearlitic': ['nonpearlitic', 'pratincoline'],
 'nonpenal': ['nonpenal', 'nonplane'],
 'nonplane': ['nonpenal', 'nonplane'],
 'nonracial': ['carniolan', 'nonracial'],
 'nonrated': ['nonrated', 'nontrade'],
 'nonreaction': ['noncreation', 'nonreaction'],
 'nonreactive': ['noncreative', 'nonreactive'],
 'nonrebel': ['ennobler', 'nonrebel'],
 'nonrecital': ['interconal', 'nonrecital'],
 'nonrectified': ['noncertified', 'nonrectified'],
 'nonrelation': ['nonoriental', 'nonrelation'],
 'nonreserve': ['nonreserve', 'nonreverse'],
 'nonreverse': ['nonreserve', 'nonreverse'],
 'nonrigid': ['girondin', 'nonrigid'],
 'nonsanction': ['inconsonant', 'nonsanction'],
 'nonscientist': ['inconsistent', 'nonscientist'],
 'nonsecret': ['consenter', 'nonsecret', 'reconsent'],
 'nontan': ['nonnat', 'nontan'],
 'nontrade': ['nonrated', 'nontrade'],
 'nonunited': ['nonunited', 'unintoned'],
 'nonuse': ['nonuse', 'unnose'],
 'nonvaginal': ['nonvaginal', 'novanglian'],
 'nonvisitation': ['innovationist', 'nonvisitation'],
 'nonya': ['annoy', 'nonya'],
 'nonyl': ['nonly', 'nonyl', 'nylon'],
 'nooking': ['kongoni', 'nooking'],
 'noontide': ['noontide', 'notioned'],
 'noontime': ['entomion', 'noontime'],
 'noop': ['noop', 'poon'],
 'noose': ['noose', 'osone'],
 'nooser': ['nooser', 'seroon', 'sooner'],
 'nopal': ['lapon', 'nopal'],
 'nope': ['nope', 'open', 'peon', 'pone'],
 'nor': ['nor', 'ron'],
 'nora': ['nora', 'orna', 'roan'],
 'norah': ['nahor', 'norah', 'rohan'],
 'norate': ['atoner', 'norate', 'ornate'],
 'noration': ['noration', 'ornation', 'orotinan'],
 'nordic': ['dornic', 'nordic'],
 'nordicity': ['nordicity', 'tyrocidin'],
 'noreast': ['noreast', 'rosetan', 'seatron', 'senator', 'treason'],
 'nori': ['inro', 'iron', 'noir', 'nori'],
 'noria': ['arion', 'noria'],
 'noric': ['corin', 'noric', 'orcin'],
 'norie': ['irone', 'norie'],
 'norite': ['norite', 'orient'],
 'norm': ['morn', 'norm'],
 'norma': ['manor', 'moran', 'norma', 'ramon', 'roman'],
 'normality': ['normality', 'trionymal'],
 'normated': ['moderant', 'normated'],
 'normless': ['mornless', 'normless'],
 'normocyte': ['necrotomy', 'normocyte', 'oncometry'],
 'norse': ['norse', 'noser', 'seron', 'snore'],
 'norsk': ['norsk', 'snork'],
 'north': ['north', 'thorn'],
 'norther': ['horrent', 'norther'],
 'northing': ['inthrong', 'northing'],
 'nosairi': ['nosairi', 'osirian'],
 'nose': ['enos', 'nose'],
 'nosean': ['nosean', 'oannes'],
 'noseless': ['noseless', 'soleness'],
 'noselite': ['noselite', 'solenite'],
 'nosema': ['monase', 'nosema'],
 'noser': ['norse', 'noser', 'seron', 'snore'],
 'nosesmart': ['nosesmart', 'storesman'],
 'nosism': ['nosism', 'simson'],
 'nosomania': ['nanosomia', 'nosomania'],
 'nostalgia': ['analogist', 'nostalgia'],
 'nostalgic': ['gnostical', 'nostalgic'],
 'nostic': ['nostic', 'sintoc', 'tocsin'],
 'nostoc': ['nostoc', 'oncost'],
 'nosu': ['nosu', 'nous', 'onus'],
 'not': ['not', 'ton'],
 'notability': ['bitonality', 'notability'],
 'notaeal': ['anatole', 'notaeal'],
 'notaeum': ['notaeum', 'outname'],
 'notal': ['notal', 'ontal', 'talon', 'tolan', 'tonal'],
 'notalgia': ['galtonia', 'notalgia'],
 'notalia': ['ailanto', 'alation', 'laotian', 'notalia'],
 'notan': ['anton', 'notan', 'tonna'],
 'notarial': ['notarial', 'rational', 'rotalian'],
 'notarially': ['notarially', 'rationally'],
 'notariate': ['notariate', 'rationate'],
 'notation': ['notation', 'tonation'],
 'notator': ['arnotto', 'notator'],
 'notcher': ['chorten', 'notcher'],
 'note': ['note', 'tone'],
 'noted': ['donet', 'noted', 'toned'],
 'notehead': ['headnote', 'notehead'],
 'noteless': ['noteless', 'toneless'],
 'notelessly': ['notelessly', 'tonelessly'],
 'notelessness': ['notelessness', 'tonelessness'],
 'noter': ['noter', 'tenor', 'toner', 'trone'],
 'nother': ['hornet', 'nother', 'theron', 'throne'],
 'nothous': ['hontous', 'nothous'],
 'notice': ['eciton', 'noetic', 'notice', 'octine'],
 'noticer': ['cerotin', 'cointer', 'cotrine', 'cretion', 'noticer', 'rection'],
 'notidani': ['nidation', 'notidani'],
 'notify': ['notify', 'tonify'],
 'notioned': ['noontide', 'notioned'],
 'notochordal': ['chordotonal', 'notochordal'],
 'notopterus': ['notopterus', 'portentous'],
 'notorhizal': ['horizontal', 'notorhizal'],
 'nototrema': ['antrotome', 'nototrema'],
 'notour': ['notour', 'unroot'],
 'notropis': ['notropis', 'positron', 'sorption'],
 'notum': ['montu', 'mount', 'notum'],
 'notus': ['notus', 'snout', 'stoun', 'tonus'],
 'nought': ['hognut', 'nought'],
 'noup': ['noup', 'puno', 'upon'],
 'nourisher': ['nourisher', 'renourish'],
 'nous': ['nosu', 'nous', 'onus'],
 'novalia': ['novalia', 'valonia'],
 'novanglian': ['nonvaginal', 'novanglian'],
 'novem': ['novem', 'venom'],
 'novitiate': ['evitation', 'novitiate'],
 'now': ['now', 'own', 'won'],
 'nowanights': ['nowanights', 'washington'],
 'nowed': ['endow', 'nowed'],
 'nowhere': ['nowhere', 'whereon'],
 'nowise': ['nowise', 'snowie'],
 'nowness': ['nowness', 'ownness'],
 'nowt': ['nowt', 'town', 'wont'],
 'noxa': ['axon', 'noxa', 'oxan'],
 'noy': ['noy', 'yon'],
 'nozi': ['nozi', 'zion'],
 'nu': ['nu', 'un'],
 'nub': ['bun', 'nub'],
 'nuba': ['baun', 'buna', 'nabu', 'nuba'],
 'nubian': ['nubian', 'unbain'],
 'nubilate': ['antiblue', 'nubilate'],
 'nubile': ['nubile', 'unible'],
 'nucal': ['lucan', 'nucal'],
 'nucellar': ['lucernal', 'nucellar', 'uncellar'],
 'nuchal': ['chulan', 'launch', 'nuchal'],
 'nuciferous': ['nuciferous', 'unciferous'],
 'nuciform': ['nuciform', 'unciform'],
 'nuclear': ['crenula', 'lucarne', 'nuclear', 'unclear'],
 'nucleator': ['nucleator', 'recountal'],
 'nucleoid': ['nucleoid', 'uncoiled'],
 'nuclide': ['include', 'nuclide'],
 'nuculid': ['nuculid', 'unlucid'],
 'nuculidae': ['duculinae', 'nuculidae'],
 'nudate': ['nudate', 'undate'],
 'nuddle': ['ludden', 'nuddle'],
 'nude': ['dune', 'nude', 'unde'],
 'nudeness': ['nudeness', 'unsensed'],
 'nudger': ['dunger', 'gerund', 'greund', 'nudger'],
 'nudist': ['dustin', 'nudist'],
 'nudity': ['nudity', 'untidy'],
 'nuisancer': ['insurance', 'nuisancer'],
 'numa': ['maun', 'numa'],
 'numberer': ['numberer', 'renumber'],
 'numda': ['maund', 'munda', 'numda', 'undam', 'unmad'],
 'numeration': ['nominature', 'numeration'],
 'numerical': ['ceruminal', 'melanuric', 'numerical'],
 'numerist': ['numerist', 'terminus'],
 'numida': ['numida', 'unmaid'],
 'numidae': ['numidae', 'unaimed'],
 'nummi': ['mnium', 'nummi'],
 'nunciate': ['nunciate', 'uncinate'],
 'nuncio': ['nuncio', 'uncoin'],
 'nuncioship': ['nuncioship', 'pincushion'],
 'nunki': ['nunki', 'unkin'],
 'nunlet': ['nunlet', 'tunnel', 'unlent'],
 'nunlike': ['nunlike', 'unliken'],
 'nunnated': ['nunnated', 'untanned'],
 'nunni': ['nunni', 'uninn'],
 'nuptial': ['nuptial', 'unplait'],
 'nurse': ['nurse', 'resun'],
 'nusfiah': ['faunish', 'nusfiah'],
 'nut': ['nut', 'tun'],
 'nutarian': ['nutarian', 'turanian'],
 'nutate': ['attune', 'nutate', 'tauten'],
 'nutgall': ['gallnut', 'nutgall'],
 'nuthatch': ['nuthatch', 'unthatch'],
 'nutlike': ['nutlike', 'tunlike'],
 'nutmeg': ['gnetum', 'nutmeg'],
 'nutramin': ['nutramin', 'ruminant'],
 'nutrice': ['nutrice', 'teucrin'],
 'nycteridae': ['encyrtidae', 'nycteridae'],
 'nycterine': ['nycterine', 'renitency'],
 'nycteris': ['nycteris', 'stycerin'],
 'nycturia': ['nycturia', 'tunicary'],
 'nye': ['eyn', 'nye', 'yen'],
 'nylast': ['nylast', 'stanly'],
 'nylon': ['nonly', 'nonyl', 'nylon'],
 'nymphalidae': ['lymphadenia', 'nymphalidae'],
 'nyroca': ['canroy', 'crayon', 'cyrano', 'nyroca'],
 'nystagmic': ['gymnastic', 'nystagmic'],
 'oak': ['ako', 'koa', 'oak', 'oka'],
 'oaky': ['kayo', 'oaky'],
 'oam': ['mao', 'oam'],
 'oannes': ['nosean', 'oannes'],
 'oar': ['aro', 'oar', 'ora'],
 'oared': ['adore', 'oared', 'oread'],
 'oaric': ['cairo', 'oaric'],
 'oaritis': ['isotria', 'oaritis'],
 'oarium': ['mariou', 'oarium'],
 'oarless': ['lassoer', 'oarless', 'rosales'],
 'oarman': ['oarman', 'ramona'],
 'oasal': ['alosa', 'loasa', 'oasal'],
 'oasis': ['oasis', 'sosia'],
 'oast': ['oast', 'stoa', 'taos'],
 'oat': ['oat', 'tao', 'toa'],
 'oatbin': ['batino', 'oatbin', 'obtain'],
 'oaten': ['atone', 'oaten'],
 'oatlike': ['keitloa', 'oatlike'],
 'obclude': ['becloud', 'obclude'],
 'obeah': ['bahoe', 'bohea', 'obeah'],
 'obeisant': ['obeisant', 'sabotine'],
 'obelial': ['bolelia', 'lobelia', 'obelial'],
 'obeliscal': ['escobilla', 'obeliscal'],
 'obelus': ['besoul', 'blouse', 'obelus'],
 'oberon': ['borneo', 'oberon'],
 'obi': ['ibo', 'obi'],
 'obispo': ['boopis', 'obispo'],
 'obit': ['bito', 'obit'],
 'objectative': ['objectative', 'objectivate'],
 'objectivate': ['objectative', 'objectivate'],
 'oblate': ['lobate', 'oblate'],
 'oblately': ['lobately', 'oblately'],
 'oblation': ['boltonia', 'lobation', 'oblation'],
 'obligant': ['bloating', 'obligant'],
 'obliviality': ['obliviality', 'violability'],
 'obol': ['bolo', 'bool', 'lobo', 'obol'],
 'obscurant': ['obscurant', 'subcantor'],
 'obscurantic': ['obscurantic', 'subnarcotic'],
 'obscurantist': ['obscurantist', 'substraction'],
 'obscure': ['bescour', 'buceros', 'obscure'],
 'obscurer': ['crebrous', 'obscurer'],
 'obsecrate': ['bracteose', 'obsecrate'],
 'observe': ['observe', 'obverse', 'verbose'],
 'obsessor': ['berossos', 'obsessor'],
 'obstinate': ['bastionet', 'obstinate'],
 'obtain': ['batino', 'oatbin', 'obtain'],
 'obtainal': ['ablation', 'obtainal'],
 'obtainer': ['abrotine', 'baritone', 'obtainer', 'reobtain'],
 'obtrude': ['doubter', 'obtrude', 'outbred', 'redoubt'],
 'obtruncation': ['conturbation', 'obtruncation'],
 'obturate': ['obturate', 'tabouret'],
 'obverse': ['observe', 'obverse', 'verbose'],
 'obversely': ['obversely', 'verbosely'],
 'ocarina': ['aaronic', 'nicarao', 'ocarina'],
 'occasioner': ['occasioner', 'reoccasion'],
 'occipitofrontal': ['frontooccipital', 'occipitofrontal'],
 'occipitotemporal': ['occipitotemporal', 'temporooccipital'],
 'occlusion': ['niccolous', 'occlusion'],
 'occurrent': ['cocurrent', 'occurrent', 'uncorrect'],
 'ocean': ['acone', 'canoe', 'ocean'],
 'oceanet': ['acetone', 'oceanet'],
 'oceanic': ['cocaine', 'oceanic'],
 'ocellar': ['collare', 'corella', 'ocellar'],
 'ocellate': ['collatee', 'ocellate'],
 'ocellated': ['decollate', 'ocellated'],
 'ocelli': ['collie', 'ocelli'],
 'och': ['cho', 'och'],
 'ocher': ['chore', 'ocher'],
 'ocherous': ['ocherous', 'ochreous'],
 'ochidore': ['choreoid', 'ochidore'],
 'ochlesis': ['helcosis', 'ochlesis'],
 'ochlesitic': ['cochleitis', 'ochlesitic'],
 'ochletic': ['helcotic', 'lochetic', 'ochletic'],
 'ochlocrat': ['colcothar', 'ochlocrat'],
 'ochrea': ['chorea', 'ochrea', 'rochea'],
 'ochreous': ['ocherous', 'ochreous'],
 'ochroid': ['choroid', 'ochroid'],
 'ochroma': ['amchoor', 'ochroma'],
 'ocht': ['coth', 'ocht'],
 'ocque': ['coque', 'ocque'],
 'ocreated': ['decorate', 'ocreated'],
 'octadic': ['cactoid', 'octadic'],
 'octaeteric': ['ecorticate', 'octaeteric'],
 'octakishexahedron': ['hexakisoctahedron', 'octakishexahedron'],
 'octan': ['acton', 'canto', 'octan'],
 'octandrian': ['dracontian', 'octandrian'],
 'octarius': ['cotarius', 'octarius', 'suctoria'],
 'octastrophic': ['octastrophic', 'postthoracic'],
 'octave': ['avocet', 'octave', 'vocate'],
 'octavian': ['octavian', 'octavina', 'vacation'],
 'octavina': ['octavian', 'octavina', 'vacation'],
 'octenary': ['enactory', 'octenary'],
 'octet': ['cotte', 'octet'],
 'octillion': ['cotillion', 'octillion'],
 'octine': ['eciton', 'noetic', 'notice', 'octine'],
 'octometer': ['octometer', 'rectotome', 'tocometer'],
 'octonal': ['coolant', 'octonal'],
 'octonare': ['coronate', 'octonare', 'otocrane'],
 'octonarius': ['acutorsion', 'octonarius'],
 'octoroon': ['coonroot', 'octoroon'],
 'octuple': ['couplet', 'octuple'],
 'ocularist': ['ocularist', 'suctorial'],
 'oculate': ['caulote', 'colutea', 'oculate'],
 'oculinid': ['lucinoid', 'oculinid'],
 'ocypete': ['ecotype', 'ocypete'],
 'od': ['do', 'od'],
 'oda': ['ado', 'dao', 'oda'],
 'odal': ['alod', 'dola', 'load', 'odal'],
 'odalman': ['mandola', 'odalman'],
 'odax': ['doxa', 'odax'],
 'odd': ['dod', 'odd'],
 'oddman': ['dodman', 'oddman'],
 'ode': ['doe', 'edo', 'ode'],
 'odel': ['dole', 'elod', 'lode', 'odel'],
 'odin': ['dion', 'nodi', 'odin'],
 'odinism': ['diosmin', 'odinism'],
 'odinite': ['edition', 'odinite', 'otidine', 'tineoid'],
 'odiometer': ['meteoroid', 'odiometer'],
 'odious': ['iodous', 'odious'],
 'odor': ['door', 'odor', 'oord', 'rood'],
 'odorant': ['donator', 'odorant', 'tornado'],
 'odored': ['doored', 'odored'],
 'odorless': ['doorless', 'odorless'],
 'ods': ['dos', 'ods', 'sod'],
 'odum': ['doum', 'moud', 'odum'],
 'odyl': ['loyd', 'odyl'],
 'odylist': ['odylist', 'styloid'],
 'oecanthus': ['ceanothus', 'oecanthus'],
 'oecist': ['cotise', 'oecist'],
 'oedipal': ['elapoid', 'oedipal'],
 'oenin': ['inone', 'oenin'],
 'oenocarpus': ['oenocarpus', 'uranoscope'],
 'oer': ['oer', 'ore', 'roe'],
 'oes': ['oes', 'ose', 'soe'],
 'oestrian': ['arsonite', 'asterion', 'oestrian', 'rosinate', 'serotina'],
 'oestrid': ['oestrid', 'steroid', 'storied'],
 'oestridae': ['oestridae', 'ostreidae', 'sorediate'],
 'oestrin': ['oestrin', 'tersion'],
 'oestriol': ['oestriol', 'rosolite'],
 'oestroid': ['oestroid', 'ordosite', 'ostreoid'],
 'oestrual': ['oestrual', 'rosulate'],
 'oestrum': ['oestrum', 'rosetum'],
 'oestrus': ['estrous', 'oestrus', 'sestuor', 'tussore'],
 'of': ['fo', 'of'],
 'ofer': ['fore', 'froe', 'ofer'],
 'offcast': ['castoff', 'offcast'],
 'offcut': ['cutoff', 'offcut'],
 'offender': ['offender', 'reoffend'],
 'offerer': ['offerer', 'reoffer'],
 'offlet': ['letoff', 'offlet'],
 'offset': ['offset', 'setoff'],
 'offuscate': ['offuscate', 'suffocate'],
 'offuscation': ['offuscation', 'suffocation'],
 'offward': ['drawoff', 'offward'],
 'ofo': ['foo', 'ofo'],
 'oft': ['fot', 'oft'],
 'oftens': ['oftens', 'soften'],
 'ofter': ['fetor', 'forte', 'ofter'],
 'oftly': ['lofty', 'oftly'],
 'og': ['go', 'og'],
 'ogam': ['goma', 'ogam'],
 'ogeed': ['geode', 'ogeed'],
 'ogle': ['egol', 'goel', 'loge', 'ogle', 'oleg'],
 'ogler': ['glore', 'ogler'],
 'ogpu': ['goup', 'ogpu', 'upgo'],
 'ogre': ['goer', 'gore', 'ogre'],
 'ogreism': ['ergoism', 'ogreism'],
 'ogtiern': ['ergotin', 'genitor', 'negrito', 'ogtiern', 'trigone'],
 'oh': ['ho', 'oh'],
 'ohm': ['mho', 'ohm'],
 'ohmage': ['homage', 'ohmage'],
 'ohmmeter': ['mhometer', 'ohmmeter'],
 'oilcan': ['alnico', 'cliona', 'oilcan'],
 'oilcup': ['oilcup', 'upcoil'],
 'oildom': ['moloid', 'oildom'],
 'oiler': ['oiler', 'oriel', 'reoil'],
 'oillet': ['elliot', 'oillet'],
 'oilman': ['monial', 'nomial', 'oilman'],
 'oilstone': ['leonotis', 'oilstone'],
 'oime': ['meio', 'oime'],
 'oinomania': ['oinomania', 'oniomania'],
 'oint': ['into', 'nito', 'oint', 'tino'],
 'oireachtas': ['oireachtas', 'theocrasia'],
 'ok': ['ko', 'ok'],
 'oka': ['ako', 'koa', 'oak', 'oka'],
 'oket': ['keto', 'oket', 'toke'],
 'oki': ['koi', 'oki'],
 'okie': ['ekoi', 'okie'],
 'okra': ['karo', 'kora', 'okra', 'roka'],
 'olaf': ['foal', 'loaf', 'olaf'],
 'olam': ['loam', 'loma', 'malo', 'mola', 'olam'],
 'olamic': ['colima', 'olamic'],
 'olcha': ['chola', 'loach', 'olcha'],
 'olchi': ['choil', 'choli', 'olchi'],
 'old': ['dol', 'lod', 'old'],
 'older': ['lored', 'older'],
 'oldhamite': ['ethmoidal', 'oldhamite'],
 'ole': ['leo', 'ole'],
 'olea': ['aloe', 'olea'],
 'olecranal': ['lanceolar', 'olecranal'],
 'olecranoid': ['lecanoroid', 'olecranoid'],
 'olecranon': ['encoronal', 'olecranon'],
 'olefin': ['enfoil', 'olefin'],
 'oleg': ['egol', 'goel', 'loge', 'ogle', 'oleg'],
 'olein': ['enoil', 'ileon', 'olein'],
 'olena': ['alone', 'anole', 'olena'],
 'olenid': ['doline', 'indole', 'leonid', 'loined', 'olenid'],
 'olent': ['lento', 'olent'],
 'olenus': ['ensoul', 'olenus', 'unsole'],
 'oleosity': ['oleosity', 'otiosely'],
 'olga': ['gaol', 'goal', 'gola', 'olga'],
 'oliban': ['albino', 'albion', 'alboin', 'oliban'],
 'olibanum': ['olibanum', 'umbonial'],
 'olid': ['dilo', 'diol', 'doli', 'idol', 'olid'],
 'oligoclase': ['oligoclase', 'sociolegal'],
 'oligomyoid': ['idiomology', 'oligomyoid'],
 'oligonephria': ['oligonephria', 'oligophrenia'],
 'oligonephric': ['oligonephric', 'oligophrenic'],
 'oligophrenia': ['oligonephria', 'oligophrenia'],
 'oligophrenic': ['oligonephric', 'oligophrenic'],
 'oliprance': ['oliprance', 'porcelain'],
 'oliva': ['oliva', 'viola'],
 'olivaceous': ['olivaceous', 'violaceous'],
 'olive': ['olive', 'ovile', 'voile'],
 'olived': ['livedo', 'olived'],
 'oliver': ['oliver', 'violer', 'virole'],
 'olivescent': ['olivescent', 'violescent'],
 'olivet': ['olivet', 'violet'],
 'olivetan': ['olivetan', 'velation'],
 'olivette': ['olivette', 'violette'],
 'olivine': ['olivine', 'violine'],
 'olla': ['lalo', 'lola', 'olla'],
 'olof': ['fool', 'loof', 'olof'],
 'olonets': ['enstool', 'olonets'],
 'olor': ['loro', 'olor', 'orlo', 'rool'],
 'olpe': ['lope', 'olpe', 'pole'],
 'olson': ['olson', 'solon'],
 'olympian': ['olympian', 'polymnia'],
 'om': ['mo', 'om'],
 'omaha': ['haoma', 'omaha'],
 'oman': ['mano', 'moan', 'mona', 'noam', 'noma', 'oman'],
 'omani': ['amino', 'inoma', 'naomi', 'omani', 'omina'],
 'omar': ['amor', 'maro', 'mora', 'omar', 'roam'],
 'omasitis': ['amitosis', 'omasitis'],
 'omber': ['brome', 'omber'],
 'omelet': ['omelet', 'telome'],
 'omen': ['mone', 'nome', 'omen'],
 'omened': ['endome', 'omened'],
 'omental': ['omental', 'telamon'],
 'omentotomy': ['entomotomy', 'omentotomy'],
 'omer': ['mero', 'more', 'omer', 'rome'],
 'omicron': ['moronic', 'omicron'],
 'omina': ['amino', 'inoma', 'naomi', 'omani', 'omina'],
 'ominous': ['mousoni', 'ominous'],
 'omit': ['itmo', 'moit', 'omit', 'timo'],
 'omitis': ['itoism', 'omitis'],
 'omniana': ['nanaimo', 'omniana'],
 'omniarch': ['choirman', 'harmonic', 'omniarch'],
 'omnigerent': ['ignorement', 'omnigerent'],
 'omnilegent': ['eloignment', 'omnilegent'],
 'omnimeter': ['minometer', 'omnimeter'],
 'omnimodous': ['monosodium', 'omnimodous', 'onosmodium'],
 'omnist': ['inmost', 'monist', 'omnist'],
 'omnitenent': ['intonement', 'omnitenent'],
 'omphalogenous': ['megalophonous', 'omphalogenous'],
 'on': ['no', 'on'],
 'ona': ['noa', 'ona'],
 'onager': ['onager', 'orange'],
 'onagra': ['agroan', 'angora', 'anogra', 'arango', 'argoan', 'onagra'],
 'onan': ['anon', 'nona', 'onan'],
 'onanism': ['mansion', 'onanism'],
 'onanistic': ['anconitis', 'antiscion', 'onanistic'],
 'onca': ['coan', 'onca'],
 'once': ['cone', 'once'],
 'oncetta': ['oncetta', 'tectona'],
 'onchidiidae': ['chionididae', 'onchidiidae'],
 'oncia': ['acoin', 'oncia'],
 'oncidium': ['conidium', 'mucinoid', 'oncidium'],
 'oncin': ['conin', 'nonic', 'oncin'],
 'oncometric': ['necrotomic', 'oncometric'],
 'oncometry': ['necrotomy', 'normocyte', 'oncometry'],
 'oncoming': ['gnomonic', 'oncoming'],
 'oncosimeter': ['oncosimeter', 'semicoronet'],
 'oncost': ['nostoc', 'oncost'],
 'ondagram': ['dragoman', 'garamond', 'ondagram'],
 'ondameter': ['emendator', 'ondameter'],
 'ondatra': ['adorant', 'ondatra'],
 'ondine': ['donnie', 'indone', 'ondine'],
 'ondy': ['ondy', 'yond'],
 'one': ['eon', 'neo', 'one'],
 'oneida': ['daoine', 'oneida'],
 'oneiric': ['ironice', 'oneiric'],
 'oneism': ['eonism', 'mesion', 'oneism', 'simeon'],
 'oneness': ['oneness', 'senones'],
 'oner': ['oner', 'rone'],
 'onery': ['eryon', 'onery'],
 'oniomania': ['oinomania', 'oniomania'],
 'oniomaniac': ['iconomania', 'oniomaniac'],
 'oniscidae': ['oniscidae', 'oscinidae', 'sciaenoid'],
 'onisciform': ['onisciform', 'somnorific'],
 'oniscoidea': ['iodocasein', 'oniscoidea'],
 'onkos': ['onkos', 'snook'],
 'onlepy': ['onlepy', 'openly'],
 'onliest': ['leonist', 'onliest'],
 'only': ['lyon', 'only'],
 'onmarch': ['monarch', 'nomarch', 'onmarch'],
 'onosmodium': ['monosodium', 'omnimodous', 'onosmodium'],
 'ons': ['ons', 'son'],
 'onset': ['onset', 'seton', 'steno', 'stone'],
 'onshore': ['onshore', 'sorehon'],
 'onside': ['deinos', 'donsie', 'inodes', 'onside'],
 'onsight': ['hosting', 'onsight'],
 'ontal': ['notal', 'ontal', 'talon', 'tolan', 'tonal'],
 'ontaric': ['anticor', 'carotin', 'cortina', 'ontaric'],
 'onto': ['onto', 'oont', 'toon'],
 'ontogenal': ['nonlegato', 'ontogenal'],
 'ontological': ['ontological', 'tonological'],
 'ontologism': ['monologist', 'nomologist', 'ontologism'],
 'ontology': ['ontology', 'tonology'],
 'onus': ['nosu', 'nous', 'onus'],
 'onymal': ['amylon', 'onymal'],
 'onymatic': ['cymation', 'myatonic', 'onymatic'],
 'onza': ['azon', 'onza', 'ozan'],
 'oocyte': ['coyote', 'oocyte'],
 'oodles': ['dolose', 'oodles', 'soodle'],
 'ooid': ['iodo', 'ooid'],
 'oolak': ['lokao', 'oolak'],
 'oolite': ['lootie', 'oolite'],
 'oometer': ['moreote', 'oometer'],
 'oons': ['oons', 'soon'],
 'oont': ['onto', 'oont', 'toon'],
 'oopak': ['oopak', 'pooka'],
 'oord': ['door', 'odor', 'oord', 'rood'],
 'opacate': ['opacate', 'peacoat'],
 'opacite': ['ectopia', 'opacite'],
 'opah': ['opah', 'paho', 'poha'],
 'opal': ['alop', 'opal'],
 'opalina': ['opalina', 'pianola'],
 'opalinine': ['opalinine', 'pleionian'],
 'opalize': ['epizoal', 'lopezia', 'opalize'],
 'opata': ['opata', 'patao', 'tapoa'],
 'opdalite': ['opdalite', 'petaloid'],
 'ope': ['ope', 'poe'],
 'opelet': ['eelpot', 'opelet'],
 'open': ['nope', 'open', 'peon', 'pone'],
 'opencast': ['capstone', 'opencast'],
 'opener': ['opener', 'reopen', 'repone'],
 'openly': ['onlepy', 'openly'],
 'openside': ['disponee', 'openside'],
 'operable': ['operable', 'ropeable'],
 'operae': ['aerope', 'operae'],
 'operant': ['operant', 'pronate', 'protean'],
 'operatic': ['aporetic', 'capriote', 'operatic'],
 'operatical': ['aporetical', 'operatical'],
 'operating': ['operating', 'pignorate'],
 'operatrix': ['expirator', 'operatrix'],
 'opercular': ['opercular', 'preocular'],
 'ophidion': ['ophidion', 'ophionid'],
 'ophionid': ['ophidion', 'ophionid'],
 'ophism': ['mopish', 'ophism'],
 'ophite': ['ethiop', 'ophite', 'peitho'],
 'opinant': ['opinant', 'pintano'],
 'opinator': ['opinator', 'tropaion'],
 'opiner': ['opiner', 'orpine', 'ponier'],
 'opiniaster': ['opiniaster', 'opiniastre'],
 'opiniastre': ['opiniaster', 'opiniastre'],
 'opiniatrety': ['opiniatrety', 'petitionary'],
 'opisometer': ['opisometer', 'opsiometer'],
 'opisthenar': ['opisthenar', 'spheration'],
 'opisthorchis': ['chirosophist', 'opisthorchis'],
 'oppian': ['oppian', 'papion', 'popian'],
 'opposer': ['opposer', 'propose'],
 'oppugn': ['oppugn', 'popgun'],
 'opsiometer': ['opisometer', 'opsiometer'],
 'opsonic': ['opsonic', 'pocosin'],
 'opsy': ['opsy', 'posy'],
 'opt': ['opt', 'pot', 'top'],
 'optable': ['optable', 'potable'],
 'optableness': ['optableness', 'potableness'],
 'optate': ['aptote', 'optate', 'potate', 'teapot'],
 'optation': ['optation', 'potation'],
 'optative': ['optative', 'potative'],
 'optic': ['optic', 'picot', 'topic'],
 'optical': ['capitol', 'coalpit', 'optical', 'topical'],
 'optically': ['optically', 'topically'],
 'optics': ['copist', 'coptis', 'optics', 'postic'],
 'optimal': ['optimal', 'palmito'],
 'option': ['option', 'potion'],
 'optional': ['antipolo', 'antipool', 'optional'],
 'optography': ['optography', 'topography'],
 'optological': ['optological', 'topological'],
 'optologist': ['optologist', 'topologist'],
 'optology': ['optology', 'topology'],
 'optometer': ['optometer', 'potometer'],
 'optophone': ['optophone', 'topophone'],
 'optotype': ['optotype', 'topotype'],
 'opulaster': ['opulaster', 'sportulae', 'sporulate'],
 'opulus': ['lupous', 'opulus'],
 'opuntia': ['opuntia', 'utopian'],
 'opus': ['opus', 'soup'],
 'opuscular': ['crapulous', 'opuscular'],
 'or': ['or', 'ro'],
 'ora': ['aro', 'oar', 'ora'],
 'orach': ['achor', 'chora', 'corah', 'orach', 'roach'],
 'oracle': ['carole', 'coaler', 'coelar', 'oracle', 'recoal'],
 'orad': ['dora', 'orad', 'road'],
 'oral': ['lora', 'oral'],
 'oralist': ['aristol', 'oralist', 'ortalis', 'striola'],
 'orality': ['orality', 'tailory'],
 'orang': ['angor',
           'argon',
           'goran',
           'grano',
           'groan',
           'nagor',
           'orang',
           'organ',
           'rogan',
           'ronga'],
 'orange': ['onager', 'orange'],
 'orangeist': ['goniaster', 'orangeist'],
 'oranger': ['groaner', 'oranger', 'organer'],
 'orangism': ['orangism', 'organism', 'sinogram'],
 'orangist': ['orangist', 'organist', 'roasting', 'signator'],
 'orangize': ['agonizer', 'orangize', 'organize'],
 'orant': ['orant', 'rotan', 'toran', 'trona'],
 'oraon': ['aroon', 'oraon'],
 'oratress': ['assertor', 'assorter', 'oratress', 'reassort'],
 'orb': ['bor', 'orb', 'rob'],
 'orbed': ['boder', 'orbed'],
 'orbic': ['boric', 'cribo', 'orbic'],
 'orbicle': ['bricole', 'corbeil', 'orbicle'],
 'orbicular': ['courbaril', 'orbicular'],
 'orbitale': ['betailor', 'laborite', 'orbitale'],
 'orbitelar': ['liberator', 'orbitelar'],
 'orbitelarian': ['irrationable', 'orbitelarian'],
 'orbitofrontal': ['frontoorbital', 'orbitofrontal'],
 'orbitonasal': ['nasoorbital', 'orbitonasal'],
 'orblet': ['bolter', 'orblet', 'reblot', 'rebolt'],
 'orbulina': ['orbulina', 'unilobar'],
 'orc': ['cor', 'cro', 'orc', 'roc'],
 'orca': ['acor', 'caro', 'cora', 'orca'],
 'orcadian': ['nocardia', 'orcadian'],
 'orcanet': ['enactor', 'necator', 'orcanet'],
 'orcein': ['cerion', 'coiner', 'neroic', 'orcein', 'recoin'],
 'orchat': ['cathro', 'orchat'],
 'orchel': ['chlore', 'choler', 'orchel'],
 'orchester': ['orchester', 'orchestre'],
 'orchestre': ['orchester', 'orchestre'],
 'orchic': ['choric', 'orchic'],
 'orchid': ['orchid', 'rhodic'],
 'orchidist': ['chorditis', 'orchidist'],
 'orchiocele': ['choriocele', 'orchiocele'],
 'orchitis': ['historic', 'orchitis'],
 'orcin': ['corin', 'noric', 'orcin'],
 'orcinol': ['colorin', 'orcinol'],
 'ordain': ['dorian', 'inroad', 'ordain'],
 'ordainer': ['inroader', 'ordainer', 'reordain'],
 'ordainment': ['antimodern', 'ordainment'],
 'ordanchite': ['achondrite', 'ditrochean', 'ordanchite'],
 'ordeal': ['loader', 'ordeal', 'reload'],
 'orderer': ['orderer', 'reorder'],
 'ordinable': ['bolderian', 'ordinable'],
 'ordinal': ['nailrod', 'ordinal', 'rinaldo', 'rodinal'],
 'ordinance': ['cerdonian', 'ordinance'],
 'ordinate': ['andorite', 'nadorite', 'ordinate', 'rodentia'],
 'ordinative': ['derivation', 'ordinative'],
 'ordinator': ['ordinator', 'radiotron'],
 'ordines': ['indorse', 'ordines', 'siredon', 'sordine'],
 'ordosite': ['oestroid', 'ordosite', 'ostreoid'],
 'ordu': ['dour', 'duro', 'ordu', 'roud'],
 'ore': ['oer', 'ore', 'roe'],
 'oread': ['adore', 'oared', 'oread'],
 'oreas': ['arose', 'oreas'],
 'orectic': ['cerotic', 'orectic'],
 'oreman': ['enamor', 'monera', 'oreman', 'romane'],
 'orenda': ['denaro', 'orenda'],
 'orendite': ['enteroid', 'orendite'],
 'orestean': ['orestean', 'resonate', 'stearone'],
 'orf': ['for', 'fro', 'orf'],
 'organ': ['angor',
           'argon',
           'goran',
           'grano',
           'groan',
           'nagor',
           'orang',
           'organ',
           'rogan',
           'ronga'],
 'organal': ['angolar', 'organal'],
 'organer': ['groaner', 'oranger', 'organer'],
 'organicism': ['organicism', 'organismic'],
 'organicist': ['organicist', 'organistic'],
 'organing': ['groaning', 'organing'],
 'organism': ['orangism', 'organism', 'sinogram'],
 'organismic': ['organicism', 'organismic'],
 'organist': ['orangist', 'organist', 'roasting', 'signator'],
 'organistic': ['organicist', 'organistic'],
 'organity': ['gyration', 'organity', 'ortygian'],
 'organize': ['agonizer', 'orangize', 'organize'],
 'organized': ['dragonize', 'organized'],
 'organoid': ['gordonia', 'organoid', 'rigadoon'],
 'organonymic': ['craniognomy', 'organonymic'],
 'organotin': ['gortonian', 'organotin'],
 'organule': ['lagunero', 'organule', 'uroglena'],
 'orgiasm': ['isogram', 'orgiasm'],
 'orgiast': ['agistor', 'agrotis', 'orgiast'],
 'orgic': ['corgi', 'goric', 'orgic'],
 'orgue': ['orgue', 'rogue', 'rouge'],
 'orgy': ['gory', 'gyro', 'orgy'],
 'oriel': ['oiler', 'oriel', 'reoil'],
 'orient': ['norite', 'orient'],
 'oriental': ['oriental', 'relation', 'tirolean'],
 'orientalism': ['misrelation', 'orientalism', 'relationism'],
 'orientalist': ['orientalist', 'relationist'],
 'orientate': ['anoterite', 'orientate'],
 'origanum': ['mirounga', 'moringua', 'origanum'],
 'origin': ['nigori', 'origin'],
 'orle': ['lore', 'orle', 'role'],
 'orlean': ['lenora', 'loaner', 'orlean', 'reloan'],
 'orleanist': ['lairstone', 'orleanist', 'serotinal'],
 'orleanistic': ['intersocial', 'orleanistic', 'sclerotinia'],
 'orlet': ['lerot', 'orlet', 'relot'],
 'orlo': ['loro', 'olor', 'orlo', 'rool'],
 'orna': ['nora', 'orna', 'roan'],
 'ornamenter': ['ornamenter', 'reornament'],
 'ornate': ['atoner', 'norate', 'ornate'],
 'ornately': ['neolatry', 'ornately', 'tyrolean'],
 'ornation': ['noration', 'ornation', 'orotinan'],
 'ornis': ['ornis', 'rosin'],
 'orniscopic': ['orniscopic', 'scorpionic'],
 'ornithomantic': ['ornithomantic', 'orthantimonic'],
 'ornithoptera': ['ornithoptera', 'prototherian'],
 'orogenetic': ['erotogenic', 'geocronite', 'orogenetic'],
 'orographical': ['colporrhagia', 'orographical'],
 'orography': ['gyrophora', 'orography'],
 'orotinan': ['noration', 'ornation', 'orotinan'],
 'orotund': ['orotund', 'rotundo'],
 'orphanism': ['manorship', 'orphanism'],
 'orpheon': ['orpheon', 'phorone'],
 'orpheus': ['ephorus', 'orpheus', 'upshore'],
 'orphical': ['orphical', 'rhopalic'],
 'orphism': ['orphism', 'rompish'],
 'orphize': ['orphize', 'phiroze'],
 'orpine': ['opiner', 'orpine', 'ponier'],
 'orsel': ['loser', 'orsel', 'rosel', 'soler'],
 'orselle': ['orselle', 'roselle'],
 'ort': ['ort', 'rot', 'tor'],
 'ortalid': ['dilator', 'ortalid'],
 'ortalis': ['aristol', 'oralist', 'ortalis', 'striola'],
 'ortet': ['ortet', 'otter', 'toter'],
 'orthal': ['harlot', 'orthal', 'thoral'],
 'orthantimonic': ['ornithomantic', 'orthantimonic'],
 'orthian': ['orthian', 'thorina'],
 'orthic': ['chorti', 'orthic', 'thoric', 'trochi'],
 'orthite': ['hortite', 'orthite', 'thorite'],
 'ortho': ['ortho', 'thoro'],
 'orthodromy': ['hydromotor', 'orthodromy'],
 'orthogamy': ['orthogamy', 'othygroma'],
 'orthogonial': ['orthogonial', 'orthologian'],
 'orthologian': ['orthogonial', 'orthologian'],
 'orthose': ['orthose', 'reshoot', 'shooter', 'soother'],
 'ortiga': ['agrito', 'ortiga'],
 'ortstein': ['ortstein', 'tenorist'],
 'ortygian': ['gyration', 'organity', 'ortygian'],
 'ortygine': ['genitory', 'ortygine'],
 'ory': ['ory', 'roy', 'yor'],
 'oryx': ['oryx', 'roxy'],
 'os': ['os', 'so'],
 'osamin': ['monias', 'osamin', 'osmina'],
 'osamine': ['monesia', 'osamine', 'osmanie'],
 'osc': ['cos', 'osc', 'soc'],
 'oscan': ['ascon', 'canso', 'oscan'],
 'oscar': ['arcos', 'crosa', 'oscar', 'sacro'],
 'oscella': ['callose', 'oscella'],
 'oscheal': ['oscheal', 'scholae'],
 'oscillance': ['clinoclase', 'oscillance'],
 'oscillaria': ['iliosacral', 'oscillaria'],
 'oscillation': ['colonialist', 'oscillation'],
 'oscin': ['oscin', 'scion', 'sonic'],
 'oscine': ['cosine', 'oscine'],
 'oscines': ['cession', 'oscines'],
 'oscinian': ['oscinian', 'socinian'],
 'oscinidae': ['oniscidae', 'oscinidae', 'sciaenoid'],
 'oscitant': ['actinost', 'oscitant'],
 'oscular': ['carolus', 'oscular'],
 'osculate': ['lacteous', 'osculate'],
 'osculatory': ['cotylosaur', 'osculatory'],
 'oscule': ['coleus', 'oscule'],
 'ose': ['oes', 'ose', 'soe'],
 'osela': ['alose', 'osela', 'solea'],
 'oshac': ['chaos', 'oshac'],
 'oside': ['diose', 'idose', 'oside'],
 'osier': ['osier', 'serio'],
 'osirian': ['nosairi', 'osirian'],
 'osiride': ['isidore', 'osiride'],
 'oskar': ['krosa', 'oskar'],
 'osmanie': ['monesia', 'osamine', 'osmanie'],
 'osmanli': ['malison', 'manolis', 'osmanli', 'somnial'],
 'osmatic': ['atomics', 'catoism', 'cosmati', 'osmatic', 'somatic'],
 'osmatism': ['osmatism', 'somatism'],
 'osmerus': ['osmerus', 'smouser'],
 'osmin': ['minos', 'osmin', 'simon'],
 'osmina': ['monias', 'osamin', 'osmina'],
 'osmiridium': ['iridosmium', 'osmiridium'],
 'osmogene': ['gonesome', 'osmogene'],
 'osmometer': ['merostome', 'osmometer'],
 'osmometric': ['microstome', 'osmometric'],
 'osmophore': ['osmophore', 'sophomore'],
 'osmotactic': ['osmotactic', 'scotomatic'],
 'osmunda': ['damnous', 'osmunda'],
 'osone': ['noose', 'osone'],
 'osprey': ['eryops', 'osprey'],
 'ossal': ['lasso', 'ossal'],
 'ossein': ['essoin', 'ossein'],
 'osselet': ['osselet', 'sestole', 'toeless'],
 'ossetian': ['assiento', 'ossetian'],
 'ossetine': ['essonite', 'ossetine'],
 'ossicle': ['loessic', 'ossicle'],
 'ossiculate': ['acleistous', 'ossiculate'],
 'ossicule': ['coulisse', 'leucosis', 'ossicule'],
 'ossuary': ['ossuary', 'suasory'],
 'ostara': ['aroast', 'ostara'],
 'osteal': ['lotase', 'osteal', 'solate', 'stolae', 'talose'],
 'ostearthritis': ['arthrosteitis', 'ostearthritis'],
 'ostectomy': ['cystotome', 'cytostome', 'ostectomy'],
 'ostein': ['nesiot', 'ostein'],
 'ostemia': ['miaotse', 'ostemia'],
 'ostent': ['ostent', 'teston'],
 'ostentation': ['ostentation', 'tionontates'],
 'ostentous': ['ostentous', 'sostenuto'],
 'osteometric': ['osteometric', 'stereotomic'],
 'osteometrical': ['osteometrical', 'stereotomical'],
 'osteometry': ['osteometry', 'stereotomy'],
 'ostic': ['ostic', 'sciot', 'stoic'],
 'ostmen': ['montes', 'ostmen'],
 'ostracea': ['ceratosa', 'ostracea'],
 'ostracean': ['ostracean', 'socratean'],
 'ostracine': ['atroscine', 'certosina', 'ostracine', 'tinoceras', 'tricosane'],
 'ostracism': ['ostracism', 'socratism'],
 'ostracize': ['ostracize', 'socratize'],
 'ostracon': ['ostracon', 'socotran'],
 'ostraite': ['astroite', 'ostraite', 'storiate'],
 'ostreidae': ['oestridae', 'ostreidae', 'sorediate'],
 'ostreiform': ['forritsome', 'ostreiform'],
 'ostreoid': ['oestroid', 'ordosite', 'ostreoid'],
 'ostrich': ['chorist', 'ostrich'],
 'oswald': ['dowlas', 'oswald'],
 'otalgy': ['goatly', 'otalgy'],
 'otaria': ['atorai', 'otaria'],
 'otarian': ['aration', 'otarian'],
 'otarine': ['otarine', 'torenia'],
 'other': ['other', 'thore', 'throe', 'toher'],
 'otherism': ['homerist', 'isotherm', 'otherism', 'theorism'],
 'otherist': ['otherist', 'theorist'],
 'othygroma': ['orthogamy', 'othygroma'],
 'otiant': ['otiant', 'titano'],
 'otidae': ['idotea', 'iodate', 'otidae'],
 'otidine': ['edition', 'odinite', 'otidine', 'tineoid'],
 'otiosely': ['oleosity', 'otiosely'],
 'otitis': ['itoist', 'otitis'],
 'oto': ['oto', 'too'],
 'otocephalic': ['hepatocolic', 'otocephalic'],
 'otocrane': ['coronate', 'octonare', 'otocrane'],
 'otogenic': ['geotonic', 'otogenic'],
 'otomian': ['amotion', 'otomian'],
 'otomyces': ['cytosome', 'otomyces'],
 'ottar': ['ottar', 'tarot', 'torta', 'troat'],
 'otter': ['ortet', 'otter', 'toter'],
 'otto': ['otto', 'toot', 'toto'],
 'otus': ['otus', 'oust', 'suto'],
 'otyak': ['otyak', 'tokay'],
 'ouch': ['chou', 'ouch'],
 'ouf': ['fou', 'ouf'],
 'ough': ['hugo', 'ough'],
 'ought': ['ought', 'tough'],
 'oughtness': ['oughtness', 'toughness'],
 'ounds': ['nodus', 'ounds', 'sound'],
 'our': ['our', 'uro'],
 'ours': ['ours', 'sour'],
 'oust': ['otus', 'oust', 'suto'],
 'ouster': ['ouster', 'souter', 'touser', 'trouse'],
 'out': ['out', 'tou'],
 'outarde': ['outarde', 'outdare', 'outread'],
 'outban': ['outban', 'unboat'],
 'outbar': ['outbar', 'rubato', 'tabour'],
 'outbeg': ['bouget', 'outbeg'],
 'outblow': ['blowout', 'outblow', 'outbowl'],
 'outblunder': ['outblunder', 'untroubled'],
 'outbowl': ['blowout', 'outblow', 'outbowl'],
 'outbreak': ['breakout', 'outbreak'],
 'outbred': ['doubter', 'obtrude', 'outbred', 'redoubt'],
 'outburn': ['burnout', 'outburn'],
 'outburst': ['outburst', 'subtutor'],
 'outbustle': ['outbustle', 'outsubtle'],
 'outcarol': ['outcarol', 'taurocol'],
 'outcarry': ['curatory', 'outcarry'],
 'outcase': ['acetous', 'outcase'],
 'outcharm': ['outcharm', 'outmarch'],
 'outcrier': ['courtier', 'outcrier'],
 'outcut': ['cutout', 'outcut'],
 'outdance': ['outdance', 'uncoated'],
 'outdare': ['outarde', 'outdare', 'outread'],
 'outdraw': ['drawout', 'outdraw', 'outward'],
 'outer': ['outer', 'outre', 'route'],
 'outerness': ['outerness', 'outreness'],
 'outferret': ['foreutter', 'outferret'],
 'outfit': ['fitout', 'outfit'],
 'outflare': ['fluorate', 'outflare'],
 'outfling': ['flouting', 'outfling'],
 'outfly': ['outfly', 'toyful'],
 'outgoer': ['outgoer', 'rougeot'],
 'outgrin': ['outgrin', 'outring', 'routing', 'touring'],
 'outhire': ['outhire', 'routhie'],
 'outhold': ['holdout', 'outhold'],
 'outkick': ['kickout', 'outkick'],
 'outlance': ['cleanout', 'outlance'],
 'outlay': ['layout', 'lutayo', 'outlay'],
 'outleap': ['outleap', 'outpeal'],
 'outler': ['elutor', 'louter', 'outler'],
 'outlet': ['outlet', 'tutelo'],
 'outline': ['elution', 'outline'],
 'outlinear': ['outlinear', 'uranolite'],
 'outlined': ['outlined', 'untoiled'],
 'outlook': ['lookout', 'outlook'],
 'outly': ['louty', 'outly'],
 'outman': ['amount', 'moutan', 'outman'],
 'outmarch': ['outcharm', 'outmarch'],
 'outmarry': ['mortuary', 'outmarry'],
 'outmaster': ['outmaster', 'outstream'],
 'outname': ['notaeum', 'outname'],
 'outpaint': ['outpaint', 'putation'],
 'outpass': ['outpass', 'passout'],
 'outpay': ['outpay', 'tapuyo'],
 'outpeal': ['outleap', 'outpeal'],
 'outpitch': ['outpitch', 'pitchout'],
 'outplace': ['copulate', 'outplace'],
 'outprice': ['eutropic', 'outprice'],
 'outpromise': ['outpromise', 'peritomous'],
 'outrance': ['cornuate', 'courante', 'cuneator', 'outrance'],
 'outrate': ['outrate', 'outtear', 'torteau'],
 'outre': ['outer', 'outre', 'route'],
 'outread': ['outarde', 'outdare', 'outread'],
 'outremer': ['outremer', 'urometer'],
 'outreness': ['outerness', 'outreness'],
 'outring': ['outgrin', 'outring', 'routing', 'touring'],
 'outrun': ['outrun', 'runout'],
 'outsaint': ['outsaint', 'titanous'],
 'outscream': ['castoreum', 'outscream'],
 'outsell': ['outsell', 'sellout'],
 'outset': ['outset', 'setout'],
 'outshake': ['outshake', 'shakeout'],
 'outshape': ['outshape', 'taphouse'],
 'outshine': ['outshine', 'tinhouse'],
 'outshut': ['outshut', 'shutout'],
 'outside': ['outside', 'tedious'],
 'outsideness': ['outsideness', 'tediousness'],
 'outsigh': ['goutish', 'outsigh'],
 'outsin': ['outsin', 'ustion'],
 'outslide': ['outslide', 'solitude'],
 'outsnore': ['outsnore', 'urosteon'],
 'outsoler': ['outsoler', 'torulose'],
 'outspend': ['outspend', 'unposted'],
 'outspit': ['outspit', 'utopist'],
 'outspring': ['outspring', 'sprouting'],
 'outspurn': ['outspurn', 'portunus'],
 'outstair': ['outstair', 'ratitous'],
 'outstand': ['outstand', 'standout'],
 'outstate': ['outstate', 'outtaste'],
 'outstream': ['outmaster', 'outstream'],
 'outstreet': ['outstreet', 'tetterous'],
 'outsubtle': ['outbustle', 'outsubtle'],
 'outtaste': ['outstate', 'outtaste'],
 'outtear': ['outrate', 'outtear', 'torteau'],
 'outthrough': ['outthrough', 'throughout'],
 'outthrow': ['outthrow', 'outworth', 'throwout'],
 'outtrail': ['outtrail', 'tutorial'],
 'outturn': ['outturn', 'turnout'],
 'outturned': ['outturned', 'untutored'],
 'outwalk': ['outwalk', 'walkout'],
 'outward': ['drawout', 'outdraw', 'outward'],
 'outwash': ['outwash', 'washout'],
 'outwatch': ['outwatch', 'watchout'],
 'outwith': ['outwith', 'without'],
 'outwork': ['outwork', 'workout'],
 'outworth': ['outthrow', 'outworth', 'throwout'],
 'ova': ['avo', 'ova'],
 'ovaloid': ['ovaloid', 'ovoidal'],
 'ovarial': ['ovarial', 'variola'],
 'ovariotubal': ['ovariotubal', 'tuboovarial'],
 'ovational': ['avolation', 'ovational'],
 'oven': ['nevo', 'oven'],
 'ovenly': ['lenvoy', 'ovenly'],
 'ovenpeel': ['envelope', 'ovenpeel'],
 'over': ['over', 'rove'],
 'overaction': ['overaction', 'revocation'],
 'overactive': ['overactive', 'revocative'],
 'overall': ['allover', 'overall'],
 'overblame': ['overblame', 'removable'],
 'overblow': ['overblow', 'overbowl'],
 'overboil': ['boilover', 'overboil'],
 'overbowl': ['overblow', 'overbowl'],
 'overbreak': ['breakover', 'overbreak'],
 'overburden': ['overburden', 'overburned'],
 'overburn': ['burnover', 'overburn'],
 'overburned': ['overburden', 'overburned'],
 'overcall': ['overcall', 'vocaller'],
 'overcare': ['overcare', 'overrace'],
 'overcirculate': ['overcirculate', 'uterocervical'],
 'overcoat': ['evocator', 'overcoat'],
 'overcross': ['crossover', 'overcross'],
 'overcup': ['overcup', 'upcover'],
 'overcurious': ['erucivorous', 'overcurious'],
 'overcurtain': ['countervair', 'overcurtain', 'recurvation'],
 'overcut': ['cutover', 'overcut'],
 'overdamn': ['overdamn', 'ravendom'],
 'overdare': ['overdare', 'overdear', 'overread'],
 'overdeal': ['overdeal', 'overlade', 'overlead'],
 'overdear': ['overdare', 'overdear', 'overread'],
 'overdraw': ['overdraw', 'overward'],
 'overdrawer': ['overdrawer', 'overreward'],
 'overdrip': ['overdrip', 'provider'],
 'overdure': ['devourer', 'overdure', 'overrude'],
 'overdust': ['overdust', 'overstud'],
 'overedit': ['overedit', 'overtide'],
 'overfar': ['favorer', 'overfar', 'refavor'],
 'overfile': ['forelive', 'overfile'],
 'overfilm': ['overfilm', 'veliform'],
 'overflower': ['overflower', 'reoverflow'],
 'overforce': ['forecover', 'overforce'],
 'overgaiter': ['overgaiter', 'revigorate'],
 'overglint': ['overglint', 'revolting'],
 'overgo': ['groove', 'overgo'],
 'overgrain': ['granivore', 'overgrain'],
 'overhate': ['overhate', 'overheat'],
 'overheat': ['overhate', 'overheat'],
 'overheld': ['overheld', 'verdelho'],
 'overidle': ['evildoer', 'overidle'],
 'overink': ['invoker', 'overink'],
 'overinsist': ['overinsist', 'versionist'],
 'overkeen': ['overkeen', 'overknee'],
 'overknee': ['overkeen', 'overknee'],
 'overlade': ['overdeal', 'overlade', 'overlead'],
 'overlast': ['overlast', 'oversalt'],
 'overlate': ['elevator', 'overlate'],
 'overlay': ['layover', 'overlay'],
 'overlead': ['overdeal', 'overlade', 'overlead'],
 'overlean': ['overlean', 'valerone'],
 'overleg': ['overleg', 'reglove'],
 'overlie': ['overlie', 'relievo'],
 'overling': ['lovering', 'overling'],
 'overlisten': ['overlisten', 'oversilent'],
 'overlive': ['overlive', 'overveil'],
 'overly': ['overly', 'volery'],
 'overmantel': ['overmantel', 'overmantle'],
 'overmantle': ['overmantel', 'overmantle'],
 'overmaster': ['overmaster', 'overstream'],
 'overmean': ['overmean', 'overname'],
 'overmerit': ['overmerit', 'overtimer'],
 'overname': ['overmean', 'overname'],
 'overneat': ['overneat', 'renovate'],
 'overnew': ['overnew', 'rewoven'],
 'overnigh': ['hovering', 'overnigh'],
 'overpaint': ['overpaint', 'pronative'],
 'overpass': ['overpass', 'passover'],
 'overpet': ['overpet', 'preveto', 'prevote'],
 'overpick': ['overpick', 'pickover'],
 'overplain': ['overplain', 'parvoline'],
 'overply': ['overply', 'plovery'],
 'overpointed': ['overpointed', 'predevotion'],
 'overpot': ['overpot', 'overtop'],
 'overrace': ['overcare', 'overrace'],
 'overrate': ['overrate', 'overtare'],
 'overread': ['overdare', 'overdear', 'overread'],
 'overreward': ['overdrawer', 'overreward'],
 'overrude': ['devourer', 'overdure', 'overrude'],
 'overrun': ['overrun', 'runover'],
 'oversad': ['oversad', 'savored'],
 'oversale': ['oversale', 'overseal'],
 'oversalt': ['overlast', 'oversalt'],
 'oversauciness': ['oversauciness', 'veraciousness'],
 'overseal': ['oversale', 'overseal'],
 'overseen': ['overseen', 'veronese'],
 'overset': ['overset', 'setover'],
 'oversilent': ['overlisten', 'oversilent'],
 'overslip': ['overslip', 'slipover'],
 'overspread': ['overspread', 'spreadover'],
 'overstain': ['overstain', 'servation', 'versation'],
 'overstir': ['overstir', 'servitor'],
 'overstrain': ['overstrain', 'traversion'],
 'overstream': ['overmaster', 'overstream'],
 'overstrew': ['overstrew', 'overwrest'],
 'overstud': ['overdust', 'overstud'],
 'overt': ['overt', 'rovet', 'torve', 'trove', 'voter'],
 'overtare': ['overrate', 'overtare'],
 'overthrow': ['overthrow', 'overwroth'],
 'overthwart': ['overthwart', 'thwartover'],
 'overtide': ['overedit', 'overtide'],
 'overtime': ['overtime', 'remotive'],
 'overtimer': ['overmerit', 'overtimer'],
 'overtip': ['overtip', 'pivoter'],
 'overtop': ['overpot', 'overtop'],
 'overtrade': ['overtrade', 'overtread'],
 'overtread': ['overtrade', 'overtread'],
 'overtrue': ['overtrue', 'overture', 'trouvere'],
 'overture': ['overtrue', 'overture', 'trouvere'],
 'overturn': ['overturn', 'turnover'],
 'overtwine': ['interwove', 'overtwine'],
 'overveil': ['overlive', 'overveil'],
 'overwalk': ['overwalk', 'walkover'],
 'overward': ['overdraw', 'overward'],
 'overwrest': ['overstrew', 'overwrest'],
 'overwroth': ['overthrow', 'overwroth'],
 'ovest': ['ovest', 'stove'],
 'ovidae': ['evodia', 'ovidae'],
 'ovidian': ['ovidian', 'vidonia'],
 'ovile': ['olive', 'ovile', 'voile'],
 'ovillus': ['ovillus', 'villous'],
 'oviparous': ['apivorous', 'oviparous'],
 'ovist': ['ovist', 'visto'],
 'ovistic': ['covisit', 'ovistic'],
 'ovoidal': ['ovaloid', 'ovoidal'],
 'ovular': ['louvar', 'ovular'],
 'ow': ['ow', 'wo'],
 'owd': ['dow', 'owd', 'wod'],
 'owe': ['owe', 'woe'],
 'owen': ['enow', 'owen', 'wone'],
 'owenism': ['owenism', 'winsome'],
 'ower': ['ower', 'wore'],
 'owerby': ['bowery', 'bowyer', 'owerby'],
 'owl': ['low', 'lwo', 'owl'],
 'owler': ['lower', 'owler', 'rowel'],
 'owlery': ['lowery', 'owlery', 'rowley', 'yowler'],
 'owlet': ['owlet', 'towel'],
 'owlish': ['lowish', 'owlish'],
 'owlishly': ['lowishly', 'owlishly', 'sillyhow'],
 'owlishness': ['lowishness', 'owlishness'],
 'owly': ['lowy', 'owly', 'yowl'],
 'own': ['now', 'own', 'won'],
 'owner': ['owner', 'reown', 'rowen'],
 'ownership': ['ownership', 'shipowner'],
 'ownness': ['nowness', 'ownness'],
 'owser': ['owser', 'resow', 'serow', 'sower', 'swore', 'worse'],
 'oxalan': ['axonal', 'oxalan'],
 'oxalite': ['aloxite', 'oxalite'],
 'oxan': ['axon', 'noxa', 'oxan'],
 'oxanic': ['anoxic', 'oxanic'],
 'oxazine': ['azoxine', 'oxazine'],
 'oxen': ['exon', 'oxen'],
 'oxidic': ['ixodic', 'oxidic'],
 'oximate': ['oximate', 'toxemia'],
 'oxy': ['oxy', 'yox'],
 'oxyntic': ['ictonyx', 'oxyntic'],
 'oxyphenol': ['oxyphenol', 'xylophone'],
 'oxyterpene': ['enteropexy', 'oxyterpene'],
 'oyer': ['oyer', 'roey', 'yore'],
 'oyster': ['oyster', 'rosety'],
 'oysterish': ['oysterish', 'thyreosis'],
 'oysterman': ['monastery', 'oysterman'],
 'ozan': ['azon', 'onza', 'ozan'],
 'ozena': ['neoza', 'ozena'],
 'ozonate': ['entozoa', 'ozonate'],
 'ozonic': ['ozonic', 'zoonic'],
 'ozotype': ['ozotype', 'zootype'],
 'paal': ['paal', 'pala'],
 'paar': ['apar', 'paar', 'para'],
 'pablo': ['pablo', 'polab'],
 'pac': ['cap', 'pac'],
 'pacable': ['capable', 'pacable'],
 'pacation': ['copatain', 'pacation'],
 'pacaya': ['cayapa', 'pacaya'],
 'pace': ['cape', 'cepa', 'pace'],
 'paced': ['caped', 'decap', 'paced'],
 'pacer': ['caper', 'crape', 'pacer', 'perca', 'recap'],
 'pachnolite': ['pachnolite', 'phonetical'],
 'pachometer': ['pachometer', 'phacometer'],
 'pacht': ['chapt', 'pacht', 'patch'],
 'pachylosis': ['pachylosis', 'phacolysis'],
 'pacificist': ['pacificist', 'pacifistic'],
 'pacifistic': ['pacificist', 'pacifistic'],
 'packer': ['packer', 'repack'],
 'paco': ['copa', 'paco'],
 'pacolet': ['pacolet', 'polecat'],
 'paction': ['caption', 'paction'],
 'pactional': ['pactional', 'pactolian', 'placation'],
 'pactionally': ['pactionally', 'polyactinal'],
 'pactolian': ['pactional', 'pactolian', 'placation'],
 'pad': ['dap', 'pad'],
 'padda': ['dadap', 'padda'],
 'padder': ['padder', 'parded'],
 'padfoot': ['footpad', 'padfoot'],
 'padle': ['padle', 'paled', 'pedal', 'plead'],
 'padre': ['drape', 'padre'],
 'padtree': ['padtree', 'predate', 'tapered'],
 'paean': ['apnea', 'paean'],
 'paeanism': ['paeanism', 'spanemia'],
 'paegel': ['paegel', 'paegle', 'pelage'],
 'paegle': ['paegel', 'paegle', 'pelage'],
 'paga': ['gapa', 'paga'],
 'page': ['gape', 'page', 'peag', 'pega'],
 'pagedom': ['megapod', 'pagedom'],
 'pager': ['gaper', 'grape', 'pager', 'parge'],
 'pageship': ['pageship', 'shippage'],
 'paginary': ['agrypnia', 'paginary'],
 'paguridae': ['paguridae', 'paguridea'],
 'paguridea': ['paguridae', 'paguridea'],
 'pagurine': ['pagurine', 'perugian'],
 'pah': ['hap', 'pah'],
 'pahari': ['pahari', 'pariah', 'raphia'],
 'pahi': ['hapi', 'pahi'],
 'paho': ['opah', 'paho', 'poha'],
 'paigle': ['paigle', 'pilage'],
 'paik': ['paik', 'pika'],
 'pail': ['lipa', 'pail', 'pali', 'pial'],
 'paillasse': ['paillasse', 'palliasse'],
 'pain': ['nipa', 'pain', 'pani', 'pian', 'pina'],
 'painless': ['painless', 'spinales'],
 'paint': ['inapt', 'paint', 'pinta'],
 'painted': ['depaint', 'inadept', 'painted', 'patined'],
 'painter': ['painter', 'pertain', 'pterian', 'repaint'],
 'painterly': ['interplay', 'painterly'],
 'paintiness': ['antisepsin', 'paintiness'],
 'paip': ['paip', 'pipa'],
 'pair': ['pair', 'pari', 'pria', 'ripa'],
 'paired': ['diaper', 'paired'],
 'pairer': ['pairer', 'rapier', 'repair'],
 'pairment': ['imperant', 'pairment', 'partimen', 'premiant', 'tripeman'],
 'pais': ['apis', 'pais', 'pasi', 'saip'],
 'pajonism': ['japonism', 'pajonism'],
 'pal': ['alp', 'lap', 'pal'],
 'pala': ['paal', 'pala'],
 'palaeechinoid': ['deinocephalia', 'palaeechinoid'],
 'palaemonid': ['anomaliped', 'palaemonid'],
 'palaemonoid': ['adenolipoma', 'palaemonoid'],
 'palaeornis': ['palaeornis', 'personalia'],
 'palaestrics': ['palaestrics', 'paracelsist'],
 'palaic': ['apical', 'palaic'],
 'palaite': ['palaite', 'petalia', 'pileata'],
 'palame': ['palame', 'palmae', 'pamela'],
 'palamite': ['ampliate', 'palamite'],
 'palas': ['palas', 'salpa'],
 'palate': ['aletap', 'palate', 'platea'],
 'palatial': ['palatial', 'palliata'],
 'palatic': ['capital', 'palatic'],
 'palation': ['palation', 'talapoin'],
 'palatonasal': ['nasopalatal', 'palatonasal'],
 'palau': ['palau', 'paula'],
 'palay': ['palay', 'playa'],
 'pale': ['leap', 'lepa', 'pale', 'peal', 'plea'],
 'paled': ['padle', 'paled', 'pedal', 'plead'],
 'paleness': ['paleness', 'paneless'],
 'paleolithy': ['paleolithy', 'polyhalite', 'polythelia'],
 'paler': ['lepra', 'paler', 'parel', 'parle', 'pearl', 'perla', 'relap'],
 'palermitan': ['palermitan', 'parliament'],
 'palermo': ['leproma', 'palermo', 'pleroma', 'polearm'],
 'pales': ['elaps',
           'lapse',
           'lepas',
           'pales',
           'salep',
           'saple',
           'sepal',
           'slape',
           'spale',
           'speal'],
 'palestral': ['alpestral', 'palestral'],
 'palestrian': ['alpestrian', 'palestrian', 'psalterian'],
 'palet': ['leapt',
           'palet',
           'patel',
           'pelta',
           'petal',
           'plate',
           'pleat',
           'tepal'],
 'palette': ['palette', 'peltate'],
 'pali': ['lipa', 'pail', 'pali', 'pial'],
 'palification': ['palification', 'pontificalia'],
 'palinode': ['lapideon', 'palinode', 'pedalion'],
 'palinodist': ['palinodist', 'plastinoid'],
 'palisade': ['palisade', 'salpidae'],
 'palish': ['palish', 'silpha'],
 'pallasite': ['aliseptal', 'pallasite'],
 'pallette': ['pallette', 'platelet'],
 'palliasse': ['paillasse', 'palliasse'],
 'palliata': ['palatial', 'palliata'],
 'pallone': ['pallone', 'pleonal'],
 'palluites': ['palluites', 'pulsatile'],
 'palm': ['lamp', 'palm'],
 'palmad': ['lampad', 'palmad'],
 'palmae': ['palame', 'palmae', 'pamela'],
 'palmary': ['palmary', 'palmyra'],
 'palmatilobed': ['palmatilobed', 'palmilobated'],
 'palmatisect': ['metaplastic', 'palmatisect'],
 'palmer': ['lamper', 'palmer', 'relamp'],
 'palmery': ['lamprey', 'palmery'],
 'palmette': ['palmette', 'template'],
 'palmful': ['lampful', 'palmful'],
 'palmification': ['amplification', 'palmification'],
 'palmilobated': ['palmatilobed', 'palmilobated'],
 'palmipes': ['epiplasm', 'palmipes'],
 'palmist': ['lampist', 'palmist'],
 'palmister': ['palmister', 'prelatism'],
 'palmistry': ['lampistry', 'palmistry'],
 'palmite': ['implate', 'palmite'],
 'palmito': ['optimal', 'palmito'],
 'palmitone': ['emptional', 'palmitone'],
 'palmo': ['mopla', 'palmo'],
 'palmula': ['ampulla', 'palmula'],
 'palmy': ['amply', 'palmy'],
 'palmyra': ['palmary', 'palmyra'],
 'palolo': ['apollo', 'palolo'],
 'palp': ['lapp', 'palp', 'plap'],
 'palpal': ['appall', 'palpal'],
 'palpatory': ['palpatory', 'papolatry'],
 'palped': ['dapple', 'lapped', 'palped'],
 'palpi': ['palpi', 'pipal'],
 'palster': ['palster', 'persalt', 'plaster', 'psalter', 'spartle', 'stapler'],
 'palsy': ['palsy', 'splay'],
 'palt': ['palt', 'plat'],
 'palta': ['aptal', 'palta', 'talpa'],
 'palter': ['palter', 'plater'],
 'palterer': ['palterer', 'platerer'],
 'paltry': ['paltry', 'partly', 'raptly'],
 'paludian': ['paludian', 'paludina'],
 'paludic': ['paludic', 'pudical'],
 'paludina': ['paludian', 'paludina'],
 'palus': ['palus', 'pasul'],
 'palustral': ['palustral', 'plaustral'],
 'palustrine': ['lupinaster', 'palustrine'],
 'paly': ['paly', 'play', 'pyal', 'pyla'],
 'pam': ['map', 'pam'],
 'pamela': ['palame', 'palmae', 'pamela'],
 'pamir': ['impar', 'pamir', 'prima'],
 'pamiri': ['impair', 'pamiri'],
 'pamper': ['mapper', 'pamper', 'pampre'],
 'pampre': ['mapper', 'pamper', 'pampre'],
 'pan': ['nap', 'pan'],
 'panace': ['canape', 'panace'],
 'panaceist': ['antispace', 'panaceist'],
 'panade': ['napead', 'panade'],
 'panak': ['kanap', 'panak'],
 'panamist': ['mainpast', 'mantispa', 'panamist', 'stampian'],
 'panary': ['panary', 'panyar'],
 'panatela': ['panatela', 'plataean'],
 'panatrophy': ['apanthropy', 'panatrophy'],
 'pancreatoduodenectomy': ['duodenopancreatectomy', 'pancreatoduodenectomy'],
 'pandean': ['pandean', 'pannade'],
 'pandemia': ['pandemia', 'pedimana'],
 'pander': ['pander', 'repand'],
 'panderly': ['panderly', 'repandly'],
 'pandermite': ['pandermite', 'pentamerid'],
 'panderous': ['panderous', 'repandous'],
 'pandion': ['dipnoan', 'nonpaid', 'pandion'],
 'pandour': ['pandour', 'poduran'],
 'pane': ['nape', 'neap', 'nepa', 'pane', 'pean'],
 'paned': ['paned', 'penda'],
 'panel': ['alpen', 'nepal', 'panel', 'penal', 'plane'],
 'panela': ['apneal', 'panela'],
 'panelation': ['antelopian', 'neapolitan', 'panelation'],
 'paneler': ['paneler', 'repanel', 'replane'],
 'paneless': ['paleness', 'paneless'],
 'panelist': ['panelist', 'pantelis', 'penalist', 'plastein'],
 'pangamic': ['campaign', 'pangamic'],
 'pangane': ['pangane', 'pannage'],
 'pangen': ['pangen', 'penang'],
 'pangene': ['pangene', 'pennage'],
 'pangi': ['aping', 'ngapi', 'pangi'],
 'pani': ['nipa', 'pain', 'pani', 'pian', 'pina'],
 'panicle': ['calepin', 'capelin', 'panicle', 'pelican', 'pinacle'],
 'paniculitis': ['paniculitis', 'paulinistic'],
 'panisca': ['capsian', 'caspian', 'nascapi', 'panisca'],
 'panisic': ['panisic', 'piscian', 'piscina', 'sinapic'],
 'pank': ['knap', 'pank'],
 'pankin': ['napkin', 'pankin'],
 'panman': ['panman', 'pannam'],
 'panmug': ['panmug', 'pugman'],
 'pannade': ['pandean', 'pannade'],
 'pannage': ['pangane', 'pannage'],
 'pannam': ['panman', 'pannam'],
 'panne': ['panne', 'penna'],
 'pannicle': ['pannicle', 'pinnacle'],
 'pannus': ['pannus', 'sannup', 'unsnap', 'unspan'],
 'panoche': ['copehan', 'panoche', 'phocean'],
 'panoistic': ['panoistic', 'piscation'],
 'panornithic': ['panornithic', 'rhaponticin'],
 'panostitis': ['antiptosis', 'panostitis'],
 'panplegia': ['appealing', 'lagniappe', 'panplegia'],
 'pansciolist': ['costispinal', 'pansciolist'],
 'panse': ['aspen', 'panse', 'snape', 'sneap', 'spane', 'spean'],
 'panside': ['ipseand', 'panside', 'pansied'],
 'pansied': ['ipseand', 'panside', 'pansied'],
 'pansy': ['pansy', 'snapy'],
 'pantaleon': ['pantaleon', 'pantalone'],
 'pantalone': ['pantaleon', 'pantalone'],
 'pantarchy': ['pantarchy', 'pyracanth'],
 'pantelis': ['panelist', 'pantelis', 'penalist', 'plastein'],
 'pantellerite': ['interpellate', 'pantellerite'],
 'panter': ['arpent',
            'enrapt',
            'entrap',
            'panter',
            'parent',
            'pretan',
            'trepan'],
 'pantheic': ['haptenic', 'pantheic', 'pithecan'],
 'pantheonic': ['nonhepatic', 'pantheonic'],
 'pantie': ['pantie', 'patine'],
 'panties': ['panties', 'sapient', 'spinate'],
 'pantile': ['pantile', 'pentail', 'platine', 'talpine'],
 'pantle': ['pantle', 'planet', 'platen'],
 'pantler': ['pantler', 'planter', 'replant'],
 'pantofle': ['felapton', 'pantofle'],
 'pantry': ['pantry', 'trypan'],
 'panyar': ['panary', 'panyar'],
 'papabot': ['papabot', 'papboat'],
 'papal': ['lappa', 'papal'],
 'papalistic': ['papalistic', 'papistical'],
 'papboat': ['papabot', 'papboat'],
 'paper': ['paper', 'rappe'],
 'papered': ['papered', 'pradeep'],
 'paperer': ['paperer', 'perpera', 'prepare', 'repaper'],
 'papern': ['napper', 'papern'],
 'papery': ['papery', 'prepay', 'yapper'],
 'papillote': ['papillote', 'popliteal'],
 'papion': ['oppian', 'papion', 'popian'],
 'papisher': ['papisher', 'sapphire'],
 'papistical': ['papalistic', 'papistical'],
 'papless': ['papless', 'sapples'],
 'papolatry': ['palpatory', 'papolatry'],
 'papule': ['papule', 'upleap'],
 'par': ['par', 'rap'],
 'para': ['apar', 'paar', 'para'],
 'parablepsia': ['appraisable', 'parablepsia'],
 'paracelsist': ['palaestrics', 'paracelsist'],
 'parachor': ['chaparro', 'parachor'],
 'paracolitis': ['paracolitis', 'piscatorial'],
 'paradisaic': ['paradisaic', 'paradisiac'],
 'paradisaically': ['paradisaically', 'paradisiacally'],
 'paradise': ['paradise', 'sparidae'],
 'paradisiac': ['paradisaic', 'paradisiac'],
 'paradisiacally': ['paradisaically', 'paradisiacally'],
 'parado': ['parado', 'pardao'],
 'paraenetic': ['capernaite', 'paraenetic'],
 'paragrapher': ['paragrapher', 'reparagraph'],
 'parah': ['aphra', 'harpa', 'parah'],
 'parale': ['earlap', 'parale'],
 'param': ['param', 'parma', 'praam'],
 'paramine': ['amperian', 'paramine', 'pearmain'],
 'paranephric': ['paranephric', 'paraphrenic'],
 'paranephritis': ['paranephritis', 'paraphrenitis'],
 'paranosic': ['caparison', 'paranosic'],
 'paraphrenic': ['paranephric', 'paraphrenic'],
 'paraphrenitis': ['paranephritis', 'paraphrenitis'],
 'parasita': ['aspirata', 'parasita'],
 'parasite': ['aspirate', 'parasite'],
 'parasol': ['asaprol', 'parasol'],
 'parasuchian': ['parasuchian', 'unpharasaic'],
 'parasyntheton': ['parasyntheton', 'thysanopteran'],
 'parate': ['aptera', 'parate', 'patera'],
 'parathion': ['parathion', 'phanariot'],
 'parazoan': ['parazoan', 'zaparoan'],
 'parboil': ['bipolar', 'parboil'],
 'parcel': ['carpel', 'parcel', 'placer'],
 'parcellary': ['carpellary', 'parcellary'],
 'parcellate': ['carpellate', 'parcellate', 'prelacteal'],
 'parchesi': ['parchesi', 'seraphic'],
 'pard': ['pard', 'prad'],
 'pardao': ['parado', 'pardao'],
 'parded': ['padder', 'parded'],
 'pardesi': ['despair', 'pardesi'],
 'pardo': ['adrop', 'pardo'],
 'pardoner': ['pardoner', 'preadorn'],
 'pare': ['aper', 'pare', 'pear', 'rape', 'reap'],
 'parel': ['lepra', 'paler', 'parel', 'parle', 'pearl', 'perla', 'relap'],
 'paren': ['arpen', 'paren'],
 'parent': ['arpent',
            'enrapt',
            'entrap',
            'panter',
            'parent',
            'pretan',
            'trepan'],
 'parental': ['parental', 'paternal', 'prenatal'],
 'parentalia': ['parentalia', 'planetaria'],
 'parentalism': ['parentalism', 'paternalism'],
 'parentality': ['parentality', 'paternality'],
 'parentally': ['parentally', 'paternally', 'prenatally'],
 'parentelic': ['epicentral', 'parentelic'],
 'parenticide': ['parenticide', 'preindicate'],
 'parer': ['parer', 'raper'],
 'paresis': ['paresis', 'serapis'],
 'paretic': ['paretic', 'patrice', 'picrate'],
 'parge': ['gaper', 'grape', 'pager', 'parge'],
 'pari': ['pair', 'pari', 'pria', 'ripa'],
 'pariah': ['pahari', 'pariah', 'raphia'],
 'paridae': ['deipara', 'paridae'],
 'paries': ['aspire', 'paries', 'praise', 'sirpea', 'spirea'],
 'parietal': ['apterial', 'parietal'],
 'parietes': ['asperite', 'parietes'],
 'parietofrontal': ['frontoparietal', 'parietofrontal'],
 'parietosquamosal': ['parietosquamosal', 'squamosoparietal'],
 'parietotemporal': ['parietotemporal', 'temporoparietal'],
 'parietovisceral': ['parietovisceral', 'visceroparietal'],
 'parine': ['parine', 'rapine'],
 'paring': ['paring', 'raping'],
 'paris': ['paris', 'parsi', 'sarip'],
 'parish': ['parish', 'raphis', 'rhapis'],
 'parished': ['diphaser', 'parished', 'raphides', 'sephardi'],
 'parison': ['parison', 'soprani'],
 'parity': ['parity', 'piraty'],
 'parkee': ['parkee', 'peaker'],
 'parker': ['parker', 'repark'],
 'parlatory': ['parlatory', 'portrayal'],
 'parle': ['lepra', 'paler', 'parel', 'parle', 'pearl', 'perla', 'relap'],
 'parley': ['parley', 'pearly', 'player', 'replay'],
 'parliament': ['palermitan', 'parliament'],
 'parly': ['parly', 'pylar', 'pyral'],
 'parma': ['param', 'parma', 'praam'],
 'parmesan': ['parmesan', 'spearman'],
 'parnel': ['parnel', 'planer', 'replan'],
 'paroch': ['carhop', 'paroch'],
 'parochialism': ['aphorismical', 'parochialism'],
 'parochine': ['canephroi', 'parochine'],
 'parodic': ['parodic', 'picador'],
 'paroecism': ['paroecism', 'premosaic'],
 'parol': ['parol', 'polar', 'poral', 'proal'],
 'parosela': ['parosela', 'psoralea'],
 'parosteal': ['parosteal', 'pastorale'],
 'parostotic': ['parostotic', 'postaortic'],
 'parotia': ['apiator', 'atropia', 'parotia'],
 'parotic': ['apricot', 'atropic', 'parotic', 'patrico'],
 'parotid': ['dioptra', 'parotid'],
 'parotitic': ['parotitic', 'patriotic'],
 'parotitis': ['parotitis', 'topiarist'],
 'parous': ['parous', 'upsoar'],
 'parovarium': ['parovarium', 'vaporarium'],
 'parrot': ['parrot', 'raptor'],
 'parroty': ['parroty', 'portray', 'tropary'],
 'parsable': ['parsable', 'prebasal', 'sparable'],
 'parse': ['asper', 'parse', 'prase', 'spaer', 'spare', 'spear'],
 'parsec': ['casper', 'escarp', 'parsec', 'scrape', 'secpar', 'spacer'],
 'parsee': ['parsee', 'persae', 'persea', 'serape'],
 'parser': ['parser', 'rasper', 'sparer'],
 'parsi': ['paris', 'parsi', 'sarip'],
 'parsley': ['parsley', 'pyrales', 'sparely', 'splayer'],
 'parsoned': ['parsoned', 'spadrone'],
 'parsonese': ['parsonese', 'preseason'],
 'parsonic': ['parsonic', 'scoparin'],
 'part': ['part', 'prat', 'rapt', 'tarp', 'trap'],
 'partan': ['partan', 'tarpan'],
 'parted': ['depart', 'parted', 'petard'],
 'partedness': ['depressant', 'partedness'],
 'parter': ['parter', 'prater'],
 'parthian': ['parthian', 'taphrina'],
 'partial': ['partial', 'patrial'],
 'partialistic': ['iatraliptics', 'partialistic'],
 'particle': ['particle', 'plicater', 'prelatic'],
 'particulate': ['catapultier', 'particulate'],
 'partigen': ['partigen', 'tapering'],
 'partile': ['partile', 'plaiter', 'replait'],
 'partimen': ['imperant', 'pairment', 'partimen', 'premiant', 'tripeman'],
 'partinium': ['impuritan', 'partinium'],
 'partisan': ['aspirant', 'partisan', 'spartina'],
 'partite': ['partite', 'tearpit'],
 'partitioned': ['departition', 'partitioned', 'trepidation'],
 'partitioner': ['partitioner', 'repartition'],
 'partlet': ['partlet', 'platter', 'prattle'],
 'partly': ['paltry', 'partly', 'raptly'],
 'parto': ['aport', 'parto', 'porta'],
 'parture': ['parture', 'rapture'],
 'party': ['party', 'trypa'],
 'parulis': ['parulis', 'spirula', 'uprisal'],
 'parure': ['parure', 'uprear'],
 'parvoline': ['overplain', 'parvoline'],
 'pasan': ['pasan', 'sapan'],
 'pasch': ['chaps', 'pasch'],
 'pascha': ['pascha', 'scapha'],
 'paschite': ['paschite', 'pastiche', 'pistache', 'scaphite'],
 'pascual': ['capsula', 'pascual', 'scapula'],
 'pash': ['hasp', 'pash', 'psha', 'shap'],
 'pasha': ['asaph', 'pasha'],
 'pashm': ['pashm', 'phasm'],
 'pashto': ['pashto', 'pathos', 'potash'],
 'pasi': ['apis', 'pais', 'pasi', 'saip'],
 'passer': ['passer', 'repass', 'sparse'],
 'passional': ['passional', 'sponsalia'],
 'passo': ['passo', 'psoas'],
 'passout': ['outpass', 'passout'],
 'passover': ['overpass', 'passover'],
 'past': ['past', 'spat', 'stap', 'taps'],
 'paste': ['paste', 'septa', 'spate'],
 'pastel': ['pastel', 'septal', 'staple'],
 'paster': ['paster', 'repast', 'trapes'],
 'pasterer': ['pasterer', 'strepera'],
 'pasteur': ['pasteur', 'pasture', 'upstare'],
 'pastiche': ['paschite', 'pastiche', 'pistache', 'scaphite'],
 'pasticheur': ['curateship', 'pasticheur'],
 'pastil': ['alpist', 'pastil', 'spital'],
 'pastile': ['aliptes', 'pastile', 'talipes'],
 'pastime': ['impaste', 'pastime'],
 'pastimer': ['maspiter', 'pastimer', 'primates'],
 'pastophorium': ['amphitropous', 'pastophorium'],
 'pastophorus': ['apostrophus', 'pastophorus'],
 'pastor': ['asport', 'pastor', 'sproat'],
 'pastoral': ['pastoral', 'proatlas'],
 'pastorale': ['parosteal', 'pastorale'],
 'pastose': ['pastose', 'petasos'],
 'pastural': ['pastural', 'spatular'],
 'pasture': ['pasteur', 'pasture', 'upstare'],
 'pasty': ['pasty', 'patsy'],
 'pasul': ['palus', 'pasul'],
 'pat': ['apt', 'pat', 'tap'],
 'pata': ['atap', 'pata', 'tapa'],
 'patao': ['opata', 'patao', 'tapoa'],
 'patarin': ['patarin', 'tarapin'],
 'patarine': ['patarine', 'tarpeian'],
 'patas': ['patas', 'tapas'],
 'patch': ['chapt', 'pacht', 'patch'],
 'patcher': ['chapter', 'patcher', 'repatch'],
 'patchery': ['patchery', 'petchary'],
 'pate': ['pate', 'peat', 'tape', 'teap'],
 'patel': ['leapt',
           'palet',
           'patel',
           'pelta',
           'petal',
           'plate',
           'pleat',
           'tepal'],
 'paten': ['enapt', 'paten', 'penta', 'tapen'],
 'patener': ['patener', 'pearten', 'petrean', 'terpane'],
 'patent': ['patent', 'patten', 'tapnet'],
 'pater': ['apert', 'pater', 'peart', 'prate', 'taper', 'terap'],
 'patera': ['aptera', 'parate', 'patera'],
 'paternal': ['parental', 'paternal', 'prenatal'],
 'paternalism': ['parentalism', 'paternalism'],
 'paternalist': ['intraseptal', 'paternalist', 'prenatalist'],
 'paternality': ['parentality', 'paternality'],
 'paternally': ['parentally', 'paternally', 'prenatally'],
 'paternoster': ['paternoster', 'prosternate', 'transportee'],
 'patesi': ['patesi', 'pietas'],
 'pathed': ['heptad', 'pathed'],
 'pathic': ['haptic', 'pathic'],
 'pathlet': ['pathlet', 'telpath'],
 'pathogen': ['heptagon', 'pathogen'],
 'pathologicoanatomic': ['anatomicopathologic', 'pathologicoanatomic'],
 'pathologicoanatomical': ['anatomicopathological', 'pathologicoanatomical'],
 'pathologicoclinical': ['clinicopathological', 'pathologicoclinical'],
 'pathonomy': ['monopathy', 'pathonomy'],
 'pathophoric': ['haptophoric', 'pathophoric'],
 'pathophorous': ['haptophorous', 'pathophorous'],
 'pathos': ['pashto', 'pathos', 'potash'],
 'pathy': ['pathy', 'typha'],
 'patiently': ['patiently', 'platynite'],
 'patina': ['aptian', 'patina', 'taipan'],
 'patine': ['pantie', 'patine'],
 'patined': ['depaint', 'inadept', 'painted', 'patined'],
 'patio': ['patio', 'taipo', 'topia'],
 'patly': ['aptly', 'patly', 'platy', 'typal'],
 'patness': ['aptness', 'patness'],
 'pato': ['atop', 'pato'],
 'patola': ['patola', 'tapalo'],
 'patrial': ['partial', 'patrial'],
 'patriarch': ['patriarch', 'phratriac'],
 'patrice': ['paretic', 'patrice', 'picrate'],
 'patricide': ['dipicrate', 'patricide', 'pediatric'],
 'patrico': ['apricot', 'atropic', 'parotic', 'patrico'],
 'patrilocal': ['allopatric', 'patrilocal'],
 'patriotic': ['parotitic', 'patriotic'],
 'patroclinous': ['patroclinous', 'pratincolous'],
 'patrol': ['patrol', 'portal', 'tropal'],
 'patron': ['patron', 'tarpon'],
 'patroness': ['patroness', 'transpose'],
 'patronite': ['antitrope', 'patronite', 'tritanope'],
 'patronymic': ['importancy', 'patronymic', 'pyromantic'],
 'patsy': ['pasty', 'patsy'],
 'patte': ['patte', 'tapet'],
 'pattee': ['pattee', 'tapete'],
 'patten': ['patent', 'patten', 'tapnet'],
 'pattener': ['pattener', 'repatent'],
 'patterer': ['patterer', 'pretreat'],
 'pattern': ['pattern', 'reptant'],
 'patterner': ['patterner', 'repattern'],
 'patu': ['patu', 'paut', 'tapu'],
 'patulent': ['patulent', 'petulant'],
 'pau': ['pau', 'pua'],
 'paul': ['paul', 'upla'],
 'paula': ['palau', 'paula'],
 'paulian': ['apulian', 'paulian', 'paulina'],
 'paulie': ['alpieu', 'paulie'],
 'paulin': ['paulin', 'pulian'],
 'paulina': ['apulian', 'paulian', 'paulina'],
 'paulinistic': ['paniculitis', 'paulinistic'],
 'paulinus': ['nauplius', 'paulinus'],
 'paulist': ['paulist', 'stipula'],
 'paup': ['paup', 'pupa'],
 'paut': ['patu', 'paut', 'tapu'],
 'paver': ['paver', 'verpa'],
 'pavid': ['pavid', 'vapid'],
 'pavidity': ['pavidity', 'vapidity'],
 'pavier': ['pavier', 'vipera'],
 'pavisor': ['pavisor', 'proavis'],
 'paw': ['paw', 'wap'],
 'pawner': ['enwrap', 'pawner', 'repawn'],
 'pay': ['pay', 'pya', 'yap'],
 'payer': ['apery', 'payer', 'repay'],
 'payroll': ['payroll', 'polarly'],
 'pea': ['ape', 'pea'],
 'peach': ['chape', 'cheap', 'peach'],
 'peachen': ['cheapen', 'peachen'],
 'peachery': ['cheapery', 'peachery'],
 'peachlet': ['chapelet', 'peachlet'],
 'peacoat': ['opacate', 'peacoat'],
 'peag': ['gape', 'page', 'peag', 'pega'],
 'peaker': ['parkee', 'peaker'],
 'peal': ['leap', 'lepa', 'pale', 'peal', 'plea'],
 'pealike': ['apelike', 'pealike'],
 'pean': ['nape', 'neap', 'nepa', 'pane', 'pean'],
 'pear': ['aper', 'pare', 'pear', 'rape', 'reap'],
 'pearl': ['lepra', 'paler', 'parel', 'parle', 'pearl', 'perla', 'relap'],
 'pearled': ['pearled', 'pedaler', 'pleader', 'replead'],
 'pearlet': ['pearlet', 'pleater', 'prelate', 'ptereal', 'replate', 'repleat'],
 'pearlin': ['pearlin', 'plainer', 'praline'],
 'pearlish': ['earlship', 'pearlish'],
 'pearlsides': ['displeaser', 'pearlsides'],
 'pearly': ['parley', 'pearly', 'player', 'replay'],
 'pearmain': ['amperian', 'paramine', 'pearmain'],
 'peart': ['apert', 'pater', 'peart', 'prate', 'taper', 'terap'],
 'pearten': ['patener', 'pearten', 'petrean', 'terpane'],
 'peartly': ['apertly', 'peartly', 'platery', 'pteryla', 'taperly'],
 'peartness': ['apertness', 'peartness', 'taperness'],
 'peasantry': ['peasantry', 'synaptera'],
 'peat': ['pate', 'peat', 'tape', 'teap'],
 'peatman': ['peatman', 'tapeman'],
 'peatship': ['happiest', 'peatship'],
 'peccation': ['acception', 'peccation'],
 'peckerwood': ['peckerwood', 'woodpecker'],
 'pecos': ['copse', 'pecos', 'scope'],
 'pectin': ['incept', 'pectin'],
 'pectinate': ['pectinate', 'pencatite'],
 'pectination': ['antinepotic', 'pectination'],
 'pectinatopinnate': ['pectinatopinnate', 'pinnatopectinate'],
 'pectinoid': ['depiction', 'pectinoid'],
 'pectora': ['coperta', 'pectora', 'porcate'],
 'pecunious': ['pecunious', 'puniceous'],
 'peda': ['depa', 'peda'],
 'pedal': ['padle', 'paled', 'pedal', 'plead'],
 'pedaler': ['pearled', 'pedaler', 'pleader', 'replead'],
 'pedalier': ['pedalier', 'perlidae'],
 'pedalion': ['lapideon', 'palinode', 'pedalion'],
 'pedalism': ['misplead', 'pedalism'],
 'pedalist': ['dispetal', 'pedalist'],
 'pedaliter': ['pedaliter', 'predetail'],
 'pedant': ['pedant', 'pentad'],
 'pedantess': ['adeptness', 'pedantess'],
 'pedantic': ['pedantic', 'pentacid'],
 'pedary': ['pedary', 'preday'],
 'pederastic': ['discrepate', 'pederastic'],
 'pedes': ['pedes', 'speed'],
 'pedesis': ['despise', 'pedesis'],
 'pedestrial': ['pedestrial', 'pilastered'],
 'pediatric': ['dipicrate', 'patricide', 'pediatric'],
 'pedicel': ['pedicel', 'pedicle'],
 'pedicle': ['pedicel', 'pedicle'],
 'pedicular': ['crepidula', 'pedicular'],
 'pediculi': ['lupicide', 'pediculi', 'pulicide'],
 'pedimana': ['pandemia', 'pedimana'],
 'pedocal': ['lacepod', 'pedocal', 'placode'],
 'pedometrician': ['pedometrician', 'premedication'],
 'pedrail': ['pedrail', 'predial'],
 'pedro': ['doper', 'pedro', 'pored'],
 'peed': ['deep', 'peed'],
 'peek': ['keep', 'peek'],
 'peel': ['leep', 'peel', 'pele'],
 'peelman': ['empanel', 'emplane', 'peelman'],
 'peen': ['neep', 'peen'],
 'peerly': ['peerly', 'yelper'],
 'pega': ['gape', 'page', 'peag', 'pega'],
 'peho': ['hope', 'peho'],
 'peiser': ['espier', 'peiser'],
 'peitho': ['ethiop', 'ophite', 'peitho'],
 'peixere': ['expiree', 'peixere'],
 'pekan': ['knape', 'pekan'],
 'pelage': ['paegel', 'paegle', 'pelage'],
 'pelasgoi': ['pelasgoi', 'spoilage'],
 'pele': ['leep', 'peel', 'pele'],
 'pelean': ['alpeen', 'lenape', 'pelean'],
 'pelecani': ['capeline', 'pelecani'],
 'pelecanus': ['encapsule', 'pelecanus'],
 'pelias': ['espial', 'lipase', 'pelias'],
 'pelican': ['calepin', 'capelin', 'panicle', 'pelican', 'pinacle'],
 'pelick': ['pelick', 'pickle'],
 'pelides': ['pelides', 'seedlip'],
 'pelidnota': ['pelidnota', 'planetoid'],
 'pelike': ['kelpie', 'pelike'],
 'pelisse': ['pelisse', 'pieless'],
 'pelite': ['leepit', 'pelite', 'pielet'],
 'pellation': ['pellation', 'pollinate'],
 'pellotine': ['pellotine', 'pollenite'],
 'pelmet': ['pelmet', 'temple'],
 'pelon': ['pelon', 'pleon'],
 'pelops': ['pelops', 'peplos'],
 'pelorian': ['pelorian', 'peronial', 'proalien'],
 'peloric': ['peloric', 'precoil'],
 'pelorus': ['leprous', 'pelorus', 'sporule'],
 'pelota': ['alepot', 'pelota'],
 'pelta': ['leapt',
           'palet',
           'patel',
           'pelta',
           'petal',
           'plate',
           'pleat',
           'tepal'],
 'peltandra': ['leptandra', 'peltandra'],
 'peltast': ['peltast', 'spattle'],
 'peltate': ['palette', 'peltate'],
 'peltation': ['peltation', 'potential'],
 'pelter': ['pelter', 'petrel'],
 'peltiform': ['leptiform', 'peltiform'],
 'pelting': ['pelting', 'petling'],
 'peltry': ['peltry', 'pertly'],
 'pelu': ['lupe', 'pelu', 'peul', 'pule'],
 'pelusios': ['epulosis', 'pelusios'],
 'pelycography': ['pelycography', 'pyrgocephaly'],
 'pemican': ['campine', 'pemican'],
 'pen': ['nep', 'pen'],
 'penal': ['alpen', 'nepal', 'panel', 'penal', 'plane'],
 'penalist': ['panelist', 'pantelis', 'penalist', 'plastein'],
 'penalty': ['aplenty', 'penalty'],
 'penang': ['pangen', 'penang'],
 'penates': ['penates', 'septane'],
 'pencatite': ['pectinate', 'pencatite'],
 'penciled': ['depencil', 'penciled', 'pendicle'],
 'pencilry': ['pencilry', 'princely'],
 'penda': ['paned', 'penda'],
 'pendicle': ['depencil', 'penciled', 'pendicle'],
 'pendulant': ['pendulant', 'unplanted'],
 'pendular': ['pendular', 'underlap', 'uplander'],
 'pendulate': ['pendulate', 'unpleated'],
 'pendulation': ['pendulation', 'pennatuloid'],
 'pendulum': ['pendulum', 'unlumped', 'unplumed'],
 'penetrable': ['penetrable', 'repentable'],
 'penetrance': ['penetrance', 'repentance'],
 'penetrant': ['penetrant', 'repentant'],
 'penial': ['alpine', 'nepali', 'penial', 'pineal'],
 'penis': ['penis', 'snipe', 'spine'],
 'penitencer': ['penitencer', 'pertinence'],
 'penman': ['nepman', 'penman'],
 'penna': ['panne', 'penna'],
 'pennage': ['pangene', 'pennage'],
 'pennate': ['pennate', 'pentane'],
 'pennatulid': ['pennatulid', 'pinnulated'],
 'pennatuloid': ['pendulation', 'pennatuloid'],
 'pennia': ['nanpie', 'pennia', 'pinnae'],
 'pennisetum': ['pennisetum', 'septennium'],
 'pensioner': ['pensioner', 'repension'],
 'pensive': ['pensive', 'vespine'],
 'penster': ['penster', 'present', 'serpent', 'strepen'],
 'penta': ['enapt', 'paten', 'penta', 'tapen'],
 'pentace': ['pentace', 'tepanec'],
 'pentacid': ['pedantic', 'pentacid'],
 'pentad': ['pedant', 'pentad'],
 'pentadecoic': ['adenectopic', 'pentadecoic'],
 'pentail': ['pantile', 'pentail', 'platine', 'talpine'],
 'pentamerid': ['pandermite', 'pentamerid'],
 'pentameroid': ['pentameroid', 'predominate'],
 'pentane': ['pennate', 'pentane'],
 'pentaploid': ['deoppilant', 'pentaploid'],
 'pentathionic': ['antiphonetic', 'pentathionic'],
 'pentatomic': ['camptonite', 'pentatomic'],
 'pentitol': ['pentitol', 'pointlet'],
 'pentoic': ['entopic', 'nepotic', 'pentoic'],
 'pentol': ['lepton', 'pentol'],
 'pentose': ['pentose', 'posteen'],
 'pentyl': ['pentyl', 'plenty'],
 'penult': ['penult', 'punlet', 'puntel'],
 'peon': ['nope', 'open', 'peon', 'pone'],
 'peony': ['peony', 'poney'],
 'peopler': ['peopler', 'popeler'],
 'peorian': ['apeiron', 'peorian'],
 'peplos': ['pelops', 'peplos'],
 'peplum': ['peplum', 'pumple'],
 'peplus': ['peplus', 'supple'],
 'pepo': ['pepo', 'pope'],
 'per': ['per', 'rep'],
 'peracid': ['epacrid', 'peracid', 'preacid'],
 'peract': ['carpet', 'peract', 'preact'],
 'peracute': ['peracute', 'preacute'],
 'peradventure': ['peradventure', 'preadventure'],
 'perakim': ['perakim', 'permiak', 'rampike'],
 'peramble': ['peramble', 'preamble'],
 'perambulate': ['perambulate', 'preambulate'],
 'perambulation': ['perambulation', 'preambulation'],
 'perambulatory': ['perambulatory', 'preambulatory'],
 'perates': ['perates', 'repaste', 'sperate'],
 'perbend': ['perbend', 'prebend'],
 'perborate': ['perborate', 'prorebate', 'reprobate'],
 'perca': ['caper', 'crape', 'pacer', 'perca', 'recap'],
 'percale': ['percale', 'replace'],
 'percaline': ['percaline', 'periclean'],
 'percent': ['percent', 'precent'],
 'percept': ['percept', 'precept'],
 'perception': ['perception', 'preception'],
 'perceptionism': ['misperception', 'perceptionism'],
 'perceptive': ['perceptive', 'preceptive'],
 'perceptively': ['perceptively', 'preceptively'],
 'perceptual': ['perceptual', 'preceptual'],
 'perceptually': ['perceptually', 'preceptually'],
 'percha': ['aperch', 'eparch', 'percha', 'preach'],
 'perchloric': ['perchloric', 'prechloric'],
 'percid': ['percid', 'priced'],
 'perclose': ['perclose', 'preclose'],
 'percoidea': ['adipocere', 'percoidea'],
 'percolate': ['percolate', 'prelocate'],
 'percolation': ['neotropical', 'percolation'],
 'percompound': ['percompound', 'precompound'],
 'percontation': ['percontation', 'pernoctation'],
 'perculsion': ['perculsion', 'preclusion'],
 'perculsive': ['perculsive', 'preclusive'],
 'percurrent': ['percurrent', 'precurrent'],
 'percursory': ['percursory', 'precursory'],
 'percussion': ['croupiness', 'percussion', 'supersonic'],
 'percussioner': ['percussioner', 'repercussion'],
 'percussor': ['percussor', 'procuress'],
 'percy': ['crepy', 'cypre', 'percy'],
 'perdicine': ['perdicine', 'recipiend'],
 'perdition': ['direption', 'perdition', 'tropidine'],
 'perdu': ['drupe', 'duper', 'perdu', 'prude', 'pured'],
 'peregrina': ['peregrina', 'pregainer'],
 'pereion': ['pereion', 'pioneer'],
 'perendure': ['perendure', 'underpeer'],
 'peres': ['peres', 'perse', 'speer', 'spree'],
 'perfect': ['perfect', 'prefect'],
 'perfected': ['perfected', 'predefect'],
 'perfection': ['frontpiece', 'perfection'],
 'perfectly': ['perfectly', 'prefectly'],
 'perfervid': ['perfervid', 'prefervid'],
 'perfoliation': ['perfoliation', 'prefoliation'],
 'perforative': ['perforative', 'prefavorite'],
 'perform': ['perform', 'preform'],
 'performant': ['performant', 'preformant'],
 'performative': ['performative', 'preformative'],
 'performer': ['performer', 'prereform', 'reperform'],
 'pergamic': ['crimpage', 'pergamic'],
 'perhaps': ['perhaps', 'prehaps'],
 'perhazard': ['perhazard', 'prehazard'],
 'peri': ['peri', 'pier', 'ripe'],
 'periacinal': ['epicranial', 'periacinal'],
 'perianal': ['airplane', 'perianal'],
 'perianth': ['perianth', 'triphane'],
 'periapt': ['periapt', 'rappite'],
 'periaster': ['periaster', 'sparterie'],
 'pericardiacophrenic': ['pericardiacophrenic', 'phrenicopericardiac'],
 'pericardiopleural': ['pericardiopleural', 'pleuropericardial'],
 'perichete': ['perichete', 'perithece'],
 'periclase': ['episclera', 'periclase'],
 'periclean': ['percaline', 'periclean'],
 'pericles': ['eclipser', 'pericles', 'resplice'],
 'pericopal': ['pericopal', 'periploca'],
 'periculant': ['periculant', 'unprelatic'],
 'peridental': ['interplead', 'peridental'],
 'peridiastolic': ['peridiastolic', 'periodicalist', 'proidealistic'],
 'peridot': ['diopter', 'peridot', 'proetid', 'protide', 'pteroid'],
 'perigone': ['perigone', 'pigeoner'],
 'peril': ['peril', 'piler', 'plier'],
 'perilous': ['perilous', 'uropsile'],
 'perimeter': ['perimeter', 'peritreme'],
 'perine': ['neiper', 'perine', 'pirene', 'repine'],
 'perineovaginal': ['perineovaginal', 'vaginoperineal'],
 'periodate': ['periodate', 'proetidae', 'proteidae'],
 'periodicalist': ['peridiastolic', 'periodicalist', 'proidealistic'],
 'periodontal': ['deploration', 'periodontal'],
 'periost': ['periost', 'porites', 'reposit', 'riposte'],
 'periosteal': ['periosteal', 'praseolite'],
 'periotic': ['epirotic', 'periotic'],
 'peripatetic': ['peripatetic', 'precipitate'],
 'peripatidae': ['peripatidae', 'peripatidea'],
 'peripatidea': ['peripatidae', 'peripatidea'],
 'periplaneta': ['periplaneta', 'prepalatine'],
 'periploca': ['pericopal', 'periploca'],
 'periplus': ['periplus', 'supplier'],
 'periportal': ['periportal', 'peritropal'],
 'periproct': ['cotripper', 'periproct'],
 'peripterous': ['peripterous', 'prepositure'],
 'perique': ['perique', 'repique'],
 'perirectal': ['perirectal', 'prerecital'],
 'periscian': ['periscian', 'precisian'],
 'periscopal': ['periscopal', 'sapropelic'],
 'perish': ['perish', 'reship'],
 'perished': ['hesperid', 'perished'],
 'perishment': ['perishment', 'reshipment'],
 'perisomal': ['perisomal', 'semipolar'],
 'perisome': ['perisome', 'promisee', 'reimpose'],
 'perispome': ['perispome', 'preimpose'],
 'peristole': ['epistoler', 'peristole', 'perseitol', 'pistoleer'],
 'peristoma': ['epistroma', 'peristoma'],
 'peristomal': ['peristomal', 'prestomial'],
 'peristylos': ['peristylos', 'pterylosis'],
 'perit': ['perit', 'retip', 'tripe'],
 'perite': ['perite', 'petrie', 'pieter'],
 'peritenon': ['interpone', 'peritenon', 'pinnotere', 'preintone'],
 'perithece': ['perichete', 'perithece'],
 'peritomize': ['epitomizer', 'peritomize'],
 'peritomous': ['outpromise', 'peritomous'],
 'peritreme': ['perimeter', 'peritreme'],
 'peritrichous': ['courtiership', 'peritrichous'],
 'peritroch': ['chiropter', 'peritroch'],
 'peritropal': ['periportal', 'peritropal'],
 'peritropous': ['peritropous', 'proprietous'],
 'perkin': ['perkin', 'pinker'],
 'perknite': ['perknite', 'peterkin'],
 'perla': ['lepra', 'paler', 'parel', 'parle', 'pearl', 'perla', 'relap'],
 'perle': ['leper', 'perle', 'repel'],
 'perlection': ['perlection', 'prelection'],
 'perlidae': ['pedalier', 'perlidae'],
 'perlingual': ['perlingual', 'prelingual'],
 'perlite': ['perlite', 'reptile'],
 'perlitic': ['perlitic', 'triplice'],
 'permeameter': ['amperemeter', 'permeameter'],
 'permeance': ['permeance', 'premenace'],
 'permeant': ['permeant', 'peterman'],
 'permeation': ['permeation', 'preominate'],
 'permiak': ['perakim', 'permiak', 'rampike'],
 'permissibility': ['impressibility', 'permissibility'],
 'permissible': ['impressible', 'permissible'],
 'permissibleness': ['impressibleness', 'permissibleness'],
 'permissibly': ['impressibly', 'permissibly'],
 'permission': ['impression', 'permission'],
 'permissive': ['impressive', 'permissive'],
 'permissively': ['impressively', 'permissively'],
 'permissiveness': ['impressiveness', 'permissiveness'],
 'permitter': ['permitter', 'pretermit'],
 'permixture': ['permixture', 'premixture'],
 'permonosulphuric': ['monopersulphuric', 'permonosulphuric'],
 'permutation': ['importunate', 'permutation'],
 'pernasal': ['pernasal', 'prenasal'],
 'pernis': ['pernis', 'respin', 'sniper'],
 'pernoctation': ['percontation', 'pernoctation'],
 'pernor': ['pernor', 'perron'],
 'pernyi': ['pernyi', 'pinery'],
 'peronial': ['pelorian', 'peronial', 'proalien'],
 'peropus': ['peropus', 'purpose'],
 'peroral': ['peroral', 'preoral'],
 'perorally': ['perorally', 'preorally'],
 'perorate': ['perorate', 'retepora'],
 'perosmate': ['perosmate', 'sematrope'],
 'perosmic': ['comprise', 'perosmic'],
 'perotic': ['perotic', 'proteic', 'tropeic'],
 'perpera': ['paperer', 'perpera', 'prepare', 'repaper'],
 'perpetualist': ['perpetualist', 'pluriseptate'],
 'perplexer': ['perplexer', 'reperplex'],
 'perron': ['pernor', 'perron'],
 'perry': ['perry', 'pryer'],
 'persae': ['parsee', 'persae', 'persea', 'serape'],
 'persalt': ['palster', 'persalt', 'plaster', 'psalter', 'spartle', 'stapler'],
 'perscribe': ['perscribe', 'prescribe'],
 'perse': ['peres', 'perse', 'speer', 'spree'],
 'persea': ['parsee', 'persae', 'persea', 'serape'],
 'perseid': ['perseid', 'preside'],
 'perseitol': ['epistoler', 'peristole', 'perseitol', 'pistoleer'],
 'perseity': ['perseity', 'speerity'],
 'persian': ['persian', 'prasine', 'saprine'],
 'persic': ['crepis', 'cripes', 'persic', 'precis', 'spicer'],
 'persico': ['ceriops', 'persico'],
 'persism': ['impress', 'persism', 'premiss'],
 'persist': ['persist', 'spriest'],
 'persistent': ['persistent', 'presentist', 'prettiness'],
 'personalia': ['palaeornis', 'personalia'],
 'personalistic': ['personalistic', 'pictorialness'],
 'personate': ['esperanto', 'personate'],
 'personed': ['personed', 'responde'],
 'pert': ['pert', 'petr', 'terp'],
 'pertain': ['painter', 'pertain', 'pterian', 'repaint'],
 'perten': ['perten', 'repent'],
 'perthite': ['perthite', 'tephrite'],
 'perthitic': ['perthitic', 'tephritic'],
 'pertinacity': ['antipyretic', 'pertinacity'],
 'pertinence': ['penitencer', 'pertinence'],
 'pertly': ['peltry', 'pertly'],
 'perturbational': ['perturbational', 'protuberantial'],
 'pertussal': ['pertussal', 'supersalt'],
 'perty': ['perty', 'typer'],
 'peru': ['peru', 'prue', 'pure'],
 'perugian': ['pagurine', 'perugian'],
 'peruke': ['keuper', 'peruke'],
 'perula': ['epural', 'perula', 'pleura'],
 'perun': ['perun', 'prune'],
 'perusable': ['perusable', 'superable'],
 'perusal': ['perusal', 'serpula'],
 'peruse': ['peruse', 'respue'],
 'pervade': ['deprave', 'pervade'],
 'pervader': ['depraver', 'pervader'],
 'pervadingly': ['depravingly', 'pervadingly'],
 'perverse': ['perverse', 'preserve'],
 'perversion': ['perversion', 'preversion'],
 'pervious': ['pervious', 'previous', 'viperous'],
 'perviously': ['perviously', 'previously', 'viperously'],
 'perviousness': ['perviousness', 'previousness', 'viperousness'],
 'pesa': ['apse', 'pesa', 'spae'],
 'pesach': ['cephas', 'pesach'],
 'pesah': ['heaps', 'pesah', 'phase', 'shape'],
 'peseta': ['asteep', 'peseta'],
 'peso': ['epos', 'peso', 'pose', 'sope'],
 'pess': ['pess', 'seps'],
 'pessoner': ['pessoner', 'response'],
 'pest': ['pest', 'sept', 'spet', 'step'],
 'peste': ['peste', 'steep'],
 'pester': ['pester', 'preset', 'restep', 'streep'],
 'pesthole': ['heelpost', 'pesthole'],
 'pesticidal': ['pesticidal', 'septicidal'],
 'pestiferous': ['pestiferous', 'septiferous'],
 'pestle': ['pestle', 'spleet'],
 'petal': ['leapt',
           'palet',
           'patel',
           'pelta',
           'petal',
           'plate',
           'pleat',
           'tepal'],
 'petalia': ['palaite', 'petalia', 'pileata'],
 'petaline': ['petaline', 'tapeline'],
 'petalism': ['petalism', 'septimal'],
 'petalless': ['petalless', 'plateless', 'pleatless'],
 'petallike': ['petallike', 'platelike'],
 'petaloid': ['opdalite', 'petaloid'],
 'petalon': ['lepanto', 'nepotal', 'petalon', 'polenta'],
 'petard': ['depart', 'parted', 'petard'],
 'petary': ['petary', 'pratey'],
 'petasos': ['pastose', 'petasos'],
 'petchary': ['patchery', 'petchary'],
 'petechial': ['epithecal', 'petechial', 'phacelite'],
 'petechiate': ['epithecate', 'petechiate'],
 'peteman': ['peteman', 'tempean'],
 'peter': ['erept', 'peter', 'petre'],
 'peterkin': ['perknite', 'peterkin'],
 'peterman': ['permeant', 'peterman'],
 'petiolary': ['epilatory', 'petiolary'],
 'petiole': ['petiole', 'pilotee'],
 'petioled': ['lepidote', 'petioled'],
 'petitionary': ['opiniatrety', 'petitionary'],
 'petitioner': ['petitioner', 'repetition'],
 'petiveria': ['aperitive', 'petiveria'],
 'petling': ['pelting', 'petling'],
 'peto': ['peto', 'poet', 'pote', 'tope'],
 'petr': ['pert', 'petr', 'terp'],
 'petre': ['erept', 'peter', 'petre'],
 'petrea': ['petrea', 'repeat', 'retape'],
 'petrean': ['patener', 'pearten', 'petrean', 'terpane'],
 'petrel': ['pelter', 'petrel'],
 'petricola': ['carpolite', 'petricola'],
 'petrie': ['perite', 'petrie', 'pieter'],
 'petrine': ['petrine', 'terpine'],
 'petrochemical': ['cephalometric', 'petrochemical'],
 'petrogale': ['petrogale', 'petrolage', 'prolegate'],
 'petrographer': ['petrographer', 'pterographer'],
 'petrographic': ['petrographic', 'pterographic'],
 'petrographical': ['petrographical', 'pterographical'],
 'petrographically': ['petrographically', 'pterylographical'],
 'petrography': ['petrography', 'pterography', 'typographer'],
 'petrol': ['petrol', 'replot'],
 'petrolage': ['petrogale', 'petrolage', 'prolegate'],
 'petrolean': ['petrolean', 'rantepole'],
 'petrolic': ['petrolic', 'plerotic'],
 'petrologically': ['petrologically', 'pterylological'],
 'petrosa': ['esparto', 'petrosa', 'seaport'],
 'petrosal': ['petrosal', 'polestar'],
 'petrosquamosal': ['petrosquamosal', 'squamopetrosal'],
 'petrous': ['petrous', 'posture', 'proetus', 'proteus', 'septuor', 'spouter'],
 'petticoated': ['depetticoat', 'petticoated'],
 'petulant': ['patulent', 'petulant'],
 'petune': ['neetup', 'petune'],
 'peul': ['lupe', 'pelu', 'peul', 'pule'],
 'pewy': ['pewy', 'wype'],
 'peyote': ['peyote', 'poteye'],
 'peyotl': ['peyotl', 'poetly'],
 'phacelia': ['acephali', 'phacelia'],
 'phacelite': ['epithecal', 'petechial', 'phacelite'],
 'phacoid': ['dapicho', 'phacoid'],
 'phacolite': ['hopcalite', 'phacolite'],
 'phacolysis': ['pachylosis', 'phacolysis'],
 'phacometer': ['pachometer', 'phacometer'],
 'phaethonic': ['phaethonic', 'theophanic'],
 'phaeton': ['phaeton', 'phonate'],
 'phagocytism': ['mycophagist', 'phagocytism'],
 'phalaecian': ['acephalina', 'phalaecian'],
 'phalangium': ['gnaphalium', 'phalangium'],
 'phalera': ['phalera', 'raphael'],
 'phanariot': ['parathion', 'phanariot'],
 'phanerogam': ['anemograph', 'phanerogam'],
 'phanerogamic': ['anemographic', 'phanerogamic'],
 'phanerogamy': ['anemography', 'phanerogamy'],
 'phanic': ['apinch', 'chapin', 'phanic'],
 'phano': ['phano', 'pohna'],
 'pharaonic': ['anaphoric', 'pharaonic'],
 'pharaonical': ['anaphorical', 'pharaonical'],
 'phare': ['hepar', 'phare', 'raphe'],
 'pharian': ['pharian', 'piranha'],
 'pharisaic': ['chirapsia', 'pharisaic'],
 'pharisean': ['pharisean', 'seraphina'],
 'pharisee': ['hesperia', 'pharisee'],
 'phariseeism': ['hemiparesis', 'phariseeism'],
 'pharmacolite': ['metaphorical', 'pharmacolite'],
 'pharyngolaryngeal': ['laryngopharyngeal', 'pharyngolaryngeal'],
 'pharyngolaryngitis': ['laryngopharyngitis', 'pharyngolaryngitis'],
 'pharyngorhinitis': ['pharyngorhinitis', 'rhinopharyngitis'],
 'phase': ['heaps', 'pesah', 'phase', 'shape'],
 'phaseless': ['phaseless', 'shapeless'],
 'phaseolin': ['esiphonal', 'phaseolin'],
 'phasis': ['aspish', 'phasis'],
 'phasm': ['pashm', 'phasm'],
 'phasmid': ['dampish', 'madship', 'phasmid'],
 'phasmoid': ['phasmoid', 'shopmaid'],
 'pheal': ['aleph', 'pheal'],
 'pheasant': ['pheasant', 'stephana'],
 'phecda': ['chaped', 'phecda'],
 'phemie': ['imphee', 'phemie'],
 'phenacite': ['phenacite', 'phenicate'],
 'phenate': ['haptene', 'heptane', 'phenate'],
 'phenetole': ['phenetole', 'telephone'],
 'phenic': ['phenic', 'pinche'],
 'phenicate': ['phenacite', 'phenicate'],
 'phenolic': ['phenolic', 'pinochle'],
 'phenological': ['nephological', 'phenological'],
 'phenologist': ['nephologist', 'phenologist'],
 'phenology': ['nephology', 'phenology'],
 'phenosal': ['alphonse', 'phenosal'],
 'pheon': ['pheon', 'phone'],
 'phi': ['hip', 'phi'],
 'phialide': ['hepialid', 'phialide'],
 'philobotanist': ['botanophilist', 'philobotanist'],
 'philocynic': ['cynophilic', 'philocynic'],
 'philohela': ['halophile', 'philohela'],
 'philoneism': ['neophilism', 'philoneism'],
 'philopoet': ['philopoet', 'photopile'],
 'philotheist': ['philotheist', 'theophilist'],
 'philotherian': ['lithonephria', 'philotherian'],
 'philozoic': ['philozoic', 'zoophilic'],
 'philozoist': ['philozoist', 'zoophilist'],
 'philter': ['philter', 'thripel'],
 'phineas': ['inphase', 'phineas'],
 'phiroze': ['orphize', 'phiroze'],
 'phit': ['phit', 'pith'],
 'phlebometritis': ['metrophlebitis', 'phlebometritis'],
 'phleum': ['phleum', 'uphelm'],
 'phloretic': ['phloretic', 'plethoric'],
 'pho': ['hop', 'pho', 'poh'],
 'phobism': ['mobship', 'phobism'],
 'phoca': ['chopa', 'phoca', 'poach'],
 'phocaean': ['phocaean', 'phocaena'],
 'phocaena': ['phocaean', 'phocaena'],
 'phocaenine': ['phocaenine', 'phoenicean'],
 'phocean': ['copehan', 'panoche', 'phocean'],
 'phocian': ['aphonic', 'phocian'],
 'phocine': ['chopine', 'phocine'],
 'phoenicean': ['phocaenine', 'phoenicean'],
 'pholad': ['adolph', 'pholad'],
 'pholas': ['alphos', 'pholas'],
 'pholcidae': ['cephaloid', 'pholcidae'],
 'pholcoid': ['chilopod', 'pholcoid'],
 'phonate': ['phaeton', 'phonate'],
 'phone': ['pheon', 'phone'],
 'phonelescope': ['nepheloscope', 'phonelescope'],
 'phonetical': ['pachnolite', 'phonetical'],
 'phonetics': ['phonetics', 'sphenotic'],
 'phoniatry': ['phoniatry', 'thiopyran'],
 'phonic': ['chopin', 'phonic'],
 'phonogram': ['monograph', 'nomograph', 'phonogram'],
 'phonogramic': ['gramophonic', 'monographic', 'nomographic', 'phonogramic'],
 'phonogramically': ['gramophonically',
                     'monographically',
                     'nomographically',
                     'phonogramically'],
 'phonographic': ['graphophonic', 'phonographic'],
 'phonolite': ['lithopone', 'phonolite'],
 'phonometer': ['nephrotome', 'phonometer'],
 'phonometry': ['nephrotomy', 'phonometry'],
 'phonophote': ['phonophote', 'photophone'],
 'phonotyper': ['hypopteron', 'phonotyper'],
 'phoo': ['hoop', 'phoo', 'pooh'],
 'phorone': ['orpheon', 'phorone'],
 'phoronidea': ['phoronidea', 'radiophone'],
 'phos': ['phos', 'posh', 'shop', 'soph'],
 'phosphatide': ['diphosphate', 'phosphatide'],
 'phosphoglycerate': ['glycerophosphate', 'phosphoglycerate'],
 'phossy': ['hyssop', 'phossy', 'sposhy'],
 'phot': ['phot', 'toph'],
 'photechy': ['hypothec', 'photechy'],
 'photochromography': ['chromophotography', 'photochromography'],
 'photochromolithograph': ['chromophotolithograph', 'photochromolithograph'],
 'photochronograph': ['chronophotograph', 'photochronograph'],
 'photochronographic': ['chronophotographic', 'photochronographic'],
 'photochronography': ['chronophotography', 'photochronography'],
 'photogram': ['motograph', 'photogram'],
 'photographer': ['photographer', 'rephotograph'],
 'photoheliography': ['heliophotography', 'photoheliography'],
 'photolithography': ['lithophotography', 'photolithography'],
 'photomacrograph': ['macrophotograph', 'photomacrograph'],
 'photometer': ['photometer', 'prototheme'],
 'photomicrograph': ['microphotograph', 'photomicrograph'],
 'photomicrographic': ['microphotographic', 'photomicrographic'],
 'photomicrography': ['microphotography', 'photomicrography'],
 'photomicroscope': ['microphotoscope', 'photomicroscope'],
 'photophone': ['phonophote', 'photophone'],
 'photopile': ['philopoet', 'photopile'],
 'photostereograph': ['photostereograph', 'stereophotograph'],
 'phototelegraph': ['phototelegraph', 'telephotograph'],
 'phototelegraphic': ['phototelegraphic', 'telephotographic'],
 'phototelegraphy': ['phototelegraphy', 'telephotography'],
 'phototypography': ['phototypography', 'phytotopography'],
 'phrase': ['phrase', 'seraph', 'shaper', 'sherpa'],
 'phraser': ['phraser', 'sharper'],
 'phrasing': ['harpings', 'phrasing'],
 'phrasy': ['phrasy', 'sharpy'],
 'phratriac': ['patriarch', 'phratriac'],
 'phreatic': ['chapiter', 'phreatic'],
 'phrenesia': ['hesperian', 'phrenesia', 'seraphine'],
 'phrenic': ['nephric', 'phrenic', 'pincher'],
 'phrenicopericardiac': ['pericardiacophrenic', 'phrenicopericardiac'],
 'phrenics': ['phrenics', 'pinscher'],
 'phrenitic': ['nephritic', 'phrenitic', 'prehnitic'],
 'phrenitis': ['inspreith', 'nephritis', 'phrenitis'],
 'phrenocardiac': ['nephrocardiac', 'phrenocardiac'],
 'phrenocolic': ['nephrocolic', 'phrenocolic'],
 'phrenocostal': ['phrenocostal', 'plastochrone'],
 'phrenogastric': ['gastrophrenic', 'nephrogastric', 'phrenogastric'],
 'phrenohepatic': ['hepatonephric', 'phrenohepatic'],
 'phrenologist': ['nephrologist', 'phrenologist'],
 'phrenology': ['nephrology', 'phrenology'],
 'phrenopathic': ['nephropathic', 'phrenopathic'],
 'phrenopathy': ['nephropathy', 'phrenopathy'],
 'phrenosplenic': ['phrenosplenic', 'splenonephric', 'splenophrenic'],
 'phronesis': ['nephrosis', 'phronesis'],
 'phronimidae': ['diamorphine', 'phronimidae'],
 'phthalazine': ['naphthalize', 'phthalazine'],
 'phu': ['hup', 'phu'],
 'phycitol': ['cytophil', 'phycitol'],
 'phycocyanin': ['cyanophycin', 'phycocyanin'],
 'phyla': ['haply', 'phyla'],
 'phyletic': ['heptylic', 'phyletic'],
 'phylloceras': ['hyposcleral', 'phylloceras'],
 'phylloclad': ['cladophyll', 'phylloclad'],
 'phyllodial': ['phyllodial', 'phylloidal'],
 'phylloerythrin': ['erythrophyllin', 'phylloerythrin'],
 'phylloidal': ['phyllodial', 'phylloidal'],
 'phyllopodous': ['phyllopodous', 'podophyllous'],
 'phyma': ['phyma', 'yamph'],
 'phymatodes': ['desmopathy', 'phymatodes'],
 'phymosia': ['hyposmia', 'phymosia'],
 'physa': ['physa', 'shapy'],
 'physalite': ['physalite', 'styphelia'],
 'physic': ['physic', 'scyphi'],
 'physicochemical': ['chemicophysical', 'physicochemical'],
 'physicomedical': ['medicophysical', 'physicomedical'],
 'physiocrat': ['physiocrat', 'psychotria'],
 'physiologicoanatomic': ['anatomicophysiologic', 'physiologicoanatomic'],
 'physiopsychological': ['physiopsychological', 'psychophysiological'],
 'physiopsychology': ['physiopsychology', 'psychophysiology'],
 'phytic': ['phytic', 'pitchy', 'pythic', 'typhic'],
 'phytogenesis': ['phytogenesis', 'pythogenesis'],
 'phytogenetic': ['phytogenetic', 'pythogenetic'],
 'phytogenic': ['phytogenic', 'pythogenic', 'typhogenic'],
 'phytogenous': ['phytogenous', 'pythogenous'],
 'phytoid': ['phytoid', 'typhoid'],
 'phytologist': ['hypoglottis', 'phytologist'],
 'phytometer': ['phytometer', 'thermotype'],
 'phytometric': ['phytometric', 'thermotypic'],
 'phytometry': ['phytometry', 'thermotypy'],
 'phytomonas': ['phytomonas', 'somnopathy'],
 'phyton': ['phyton', 'python'],
 'phytonic': ['hypnotic', 'phytonic', 'pythonic', 'typhonic'],
 'phytosis': ['phytosis', 'typhosis'],
 'phytotopography': ['phototypography', 'phytotopography'],
 'phytozoa': ['phytozoa', 'zoopathy', 'zoophyta'],
 'piacle': ['epical', 'piacle', 'plaice'],
 'piacular': ['apicular', 'piacular'],
 'pial': ['lipa', 'pail', 'pali', 'pial'],
 'pialyn': ['alypin', 'pialyn'],
 'pian': ['nipa', 'pain', 'pani', 'pian', 'pina'],
 'pianiste': ['pianiste', 'pisanite'],
 'piannet': ['piannet', 'pinnate'],
 'pianola': ['opalina', 'pianola'],
 'piaroa': ['aporia', 'piaroa'],
 'piast': ['piast', 'stipa', 'tapis'],
 'piaster': ['piaster', 'piastre', 'raspite', 'spirate', 'traipse'],
 'piastre': ['piaster', 'piastre', 'raspite', 'spirate', 'traipse'],
 'picador': ['parodic', 'picador'],
 'picae': ['picae', 'picea'],
 'pical': ['pical', 'plica'],
 'picard': ['caprid', 'carpid', 'picard'],
 'picarel': ['caliper', 'picarel', 'replica'],
 'picary': ['cypria', 'picary', 'piracy'],
 'pice': ['epic', 'pice'],
 'picea': ['picae', 'picea'],
 'picene': ['picene', 'piecen'],
 'picker': ['picker', 'repick'],
 'pickle': ['pelick', 'pickle'],
 'pickler': ['pickler', 'prickle'],
 'pickover': ['overpick', 'pickover'],
 'picktooth': ['picktooth', 'toothpick'],
 'pico': ['cipo', 'pico'],
 'picot': ['optic', 'picot', 'topic'],
 'picotah': ['aphotic', 'picotah'],
 'picra': ['capri', 'picra', 'rapic'],
 'picrate': ['paretic', 'patrice', 'picrate'],
 'pictography': ['graphotypic', 'pictography', 'typographic'],
 'pictorialness': ['personalistic', 'pictorialness'],
 'picture': ['cuprite', 'picture'],
 'picudilla': ['picudilla', 'pulicidal'],
 'pidan': ['pidan', 'pinda'],
 'piebald': ['bipedal', 'piebald'],
 'piebaldness': ['dispensable', 'piebaldness'],
 'piecen': ['picene', 'piecen'],
 'piecer': ['piecer', 'pierce', 'recipe'],
 'piecework': ['piecework', 'workpiece'],
 'piecrust': ['crepitus', 'piecrust'],
 'piedness': ['dispense', 'piedness'],
 'piegan': ['genipa', 'piegan'],
 'pieless': ['pelisse', 'pieless'],
 'pielet': ['leepit', 'pelite', 'pielet'],
 'piemag': ['magpie', 'piemag'],
 'pieman': ['impane', 'pieman'],
 'pien': ['pien', 'pine'],
 'piend': ['piend', 'pined'],
 'pier': ['peri', 'pier', 'ripe'],
 'pierce': ['piecer', 'pierce', 'recipe'],
 'piercent': ['piercent', 'prentice'],
 'piercer': ['piercer', 'reprice'],
 'pierlike': ['pierlike', 'ripelike'],
 'piet': ['piet', 'tipe'],
 'pietas': ['patesi', 'pietas'],
 'pieter': ['perite', 'petrie', 'pieter'],
 'pig': ['gip', 'pig'],
 'pigeoner': ['perigone', 'pigeoner'],
 'pigeontail': ['pigeontail', 'plagionite'],
 'pigly': ['gilpy', 'pigly'],
 'pignon': ['ningpo', 'pignon'],
 'pignorate': ['operating', 'pignorate'],
 'pigskin': ['pigskin', 'spiking'],
 'pigsney': ['gypsine', 'pigsney'],
 'pik': ['kip', 'pik'],
 'pika': ['paik', 'pika'],
 'pike': ['kepi', 'kipe', 'pike'],
 'pikel': ['pikel', 'pikle'],
 'piker': ['krepi', 'piker'],
 'pikle': ['pikel', 'pikle'],
 'pilage': ['paigle', 'pilage'],
 'pilar': ['april', 'pilar', 'ripal'],
 'pilaster': ['epistlar', 'pilaster', 'plaister', 'priestal'],
 'pilastered': ['pedestrial', 'pilastered'],
 'pilastric': ['pilastric', 'triplasic'],
 'pilate': ['aplite', 'pilate'],
 'pileata': ['palaite', 'petalia', 'pileata'],
 'pileate': ['epilate', 'epitela', 'pileate'],
 'pileated': ['depilate', 'leptidae', 'pileated'],
 'piled': ['piled', 'plied'],
 'piler': ['peril', 'piler', 'plier'],
 'piles': ['piles', 'plies', 'slipe', 'spiel', 'spile'],
 'pileus': ['epulis', 'pileus'],
 'pili': ['ipil', 'pili'],
 'pilin': ['lipin', 'pilin'],
 'pillarist': ['pillarist', 'pistillar'],
 'pillet': ['liplet', 'pillet'],
 'pilm': ['limp', 'pilm', 'plim'],
 'pilmy': ['imply', 'limpy', 'pilmy'],
 'pilosis': ['liposis', 'pilosis'],
 'pilotee': ['petiole', 'pilotee'],
 'pilpai': ['lippia', 'pilpai'],
 'pilus': ['lupis', 'pilus'],
 'pim': ['imp', 'pim'],
 'pimelate': ['ampelite', 'pimelate'],
 'pimento': ['emption', 'pimento'],
 'pimenton': ['imponent', 'pimenton'],
 'pimola': ['lipoma', 'pimola', 'ploima'],
 'pimpish': ['impship', 'pimpish'],
 'pimplous': ['pimplous', 'pompilus', 'populism'],
 'pin': ['nip', 'pin'],
 'pina': ['nipa', 'pain', 'pani', 'pian', 'pina'],
 'pinaces': ['pinaces', 'pincase'],
 'pinachrome': ['epharmonic', 'pinachrome'],
 'pinacle': ['calepin', 'capelin', 'panicle', 'pelican', 'pinacle'],
 'pinacoid': ['diapnoic', 'pinacoid'],
 'pinal': ['lipan', 'pinal', 'plain'],
 'pinales': ['espinal', 'pinales', 'spaniel'],
 'pinaster': ['pinaster', 'pristane'],
 'pincase': ['pinaces', 'pincase'],
 'pincer': ['pincer', 'prince'],
 'pincerlike': ['pincerlike', 'princelike'],
 'pincers': ['encrisp', 'pincers'],
 'pinchbelly': ['bellypinch', 'pinchbelly'],
 'pinche': ['phenic', 'pinche'],
 'pincher': ['nephric', 'phrenic', 'pincher'],
 'pincushion': ['nuncioship', 'pincushion'],
 'pinda': ['pidan', 'pinda'],
 'pindari': ['pindari', 'pridian'],
 'pine': ['pien', 'pine'],
 'pineal': ['alpine', 'nepali', 'penial', 'pineal'],
 'pined': ['piend', 'pined'],
 'piner': ['piner', 'prine', 'repin', 'ripen'],
 'pinery': ['pernyi', 'pinery'],
 'pingler': ['pingler', 'pringle'],
 'pinhold': ['dolphin', 'pinhold'],
 'pinhole': ['lophine', 'pinhole'],
 'pinite': ['pinite', 'tiepin'],
 'pinker': ['perkin', 'pinker'],
 'pinking': ['kingpin', 'pinking'],
 'pinkish': ['kinship', 'pinkish'],
 'pinlock': ['lockpin', 'pinlock'],
 'pinnacle': ['pannicle', 'pinnacle'],
 'pinnae': ['nanpie', 'pennia', 'pinnae'],
 'pinnate': ['piannet', 'pinnate'],
 'pinnatopectinate': ['pectinatopinnate', 'pinnatopectinate'],
 'pinnet': ['pinnet', 'tenpin'],
 'pinnitarsal': ['intraspinal', 'pinnitarsal'],
 'pinnotere': ['interpone', 'peritenon', 'pinnotere', 'preintone'],
 'pinnothere': ['interphone', 'pinnothere'],
 'pinnula': ['pinnula', 'unplain'],
 'pinnulated': ['pennatulid', 'pinnulated'],
 'pinochle': ['phenolic', 'pinochle'],
 'pinole': ['pinole', 'pleion'],
 'pinolia': ['apiolin', 'pinolia'],
 'pinscher': ['phrenics', 'pinscher'],
 'pinta': ['inapt', 'paint', 'pinta'],
 'pintail': ['pintail', 'tailpin'],
 'pintano': ['opinant', 'pintano'],
 'pinte': ['inept', 'pinte'],
 'pinto': ['pinto', 'point'],
 'pintura': ['pintura', 'puritan', 'uptrain'],
 'pinulus': ['lupinus', 'pinulus'],
 'piny': ['piny', 'pyin'],
 'pinyl': ['pinyl', 'pliny'],
 'pioneer': ['pereion', 'pioneer'],
 'pioted': ['pioted', 'podite'],
 'pipa': ['paip', 'pipa'],
 'pipal': ['palpi', 'pipal'],
 'piperno': ['piperno', 'propine'],
 'pique': ['equip', 'pique'],
 'pir': ['pir', 'rip'],
 'piracy': ['cypria', 'picary', 'piracy'],
 'piranha': ['pharian', 'piranha'],
 'piratess': ['piratess', 'serapist', 'tarsipes'],
 'piratically': ['capillarity', 'piratically'],
 'piraty': ['parity', 'piraty'],
 'pirene': ['neiper', 'perine', 'pirene', 'repine'],
 'pirssonite': ['pirssonite', 'trispinose'],
 'pisaca': ['capias', 'pisaca'],
 'pisan': ['pisan', 'sapin', 'spina'],
 'pisanite': ['pianiste', 'pisanite'],
 'piscation': ['panoistic', 'piscation'],
 'piscatorial': ['paracolitis', 'piscatorial'],
 'piscian': ['panisic', 'piscian', 'piscina', 'sinapic'],
 'pisciferous': ['pisciferous', 'spiciferous'],
 'pisciform': ['pisciform', 'spiciform'],
 'piscina': ['panisic', 'piscian', 'piscina', 'sinapic'],
 'pisco': ['copis', 'pisco'],
 'pise': ['pise', 'sipe'],
 'pish': ['pish', 'ship'],
 'pisk': ['pisk', 'skip'],
 'pisky': ['pisky', 'spiky'],
 'pismire': ['pismire', 'primsie'],
 'pisonia': ['pisonia', 'sinopia'],
 'pist': ['pist', 'spit'],
 'pistache': ['paschite', 'pastiche', 'pistache', 'scaphite'],
 'pistacite': ['epistatic', 'pistacite'],
 'pistareen': ['pistareen', 'sparteine'],
 'pistillar': ['pillarist', 'pistillar'],
 'pistillary': ['pistillary', 'spiritally'],
 'pistle': ['pistle', 'stipel'],
 'pistol': ['pistol', 'postil', 'spoilt'],
 'pistoleer': ['epistoler', 'peristole', 'perseitol', 'pistoleer'],
 'pit': ['pit', 'tip'],
 'pita': ['atip', 'pita'],
 'pitapat': ['apitpat', 'pitapat'],
 'pitarah': ['pitarah', 'taphria'],
 'pitcher': ['pitcher', 'repitch'],
 'pitchout': ['outpitch', 'pitchout'],
 'pitchy': ['phytic', 'pitchy', 'pythic', 'typhic'],
 'pith': ['phit', 'pith'],
 'pithecan': ['haptenic', 'pantheic', 'pithecan'],
 'pithole': ['hoplite', 'pithole'],
 'pithsome': ['mephisto', 'pithsome'],
 'pitless': ['pitless', 'tipless'],
 'pitman': ['pitman', 'tampin', 'tipman'],
 'pittancer': ['crepitant', 'pittancer'],
 'pittoid': ['pittoid', 'poditti'],
 'pityroid': ['pityroid', 'pyritoid'],
 'pivoter': ['overtip', 'pivoter'],
 'placate': ['epactal', 'placate'],
 'placation': ['pactional', 'pactolian', 'placation'],
 'place': ['capel', 'place'],
 'placebo': ['copable', 'placebo'],
 'placentalia': ['analeptical', 'placentalia'],
 'placentoma': ['complanate', 'placentoma'],
 'placer': ['carpel', 'parcel', 'placer'],
 'placode': ['lacepod', 'pedocal', 'placode'],
 'placoid': ['placoid', 'podalic', 'podical'],
 'placus': ['cuspal', 'placus'],
 'plagionite': ['pigeontail', 'plagionite'],
 'plague': ['plague', 'upgale'],
 'plaguer': ['earplug', 'graupel', 'plaguer'],
 'plaice': ['epical', 'piacle', 'plaice'],
 'plaid': ['alpid', 'plaid'],
 'plaidy': ['adipyl', 'plaidy'],
 'plain': ['lipan', 'pinal', 'plain'],
 'plainer': ['pearlin', 'plainer', 'praline'],
 'plaint': ['plaint', 'pliant'],
 'plaister': ['epistlar', 'pilaster', 'plaister', 'priestal'],
 'plaited': ['plaited', 'taliped'],
 'plaiter': ['partile', 'plaiter', 'replait'],
 'planate': ['planate', 'planeta', 'plantae', 'platane'],
 'planation': ['planation', 'platonian'],
 'plancheite': ['elephantic', 'plancheite'],
 'plane': ['alpen', 'nepal', 'panel', 'penal', 'plane'],
 'planer': ['parnel', 'planer', 'replan'],
 'planera': ['planera', 'preanal'],
 'planet': ['pantle', 'planet', 'platen'],
 'planeta': ['planate', 'planeta', 'plantae', 'platane'],
 'planetabler': ['planetabler', 'replantable'],
 'planetaria': ['parentalia', 'planetaria'],
 'planetoid': ['pelidnota', 'planetoid'],
 'planity': ['inaptly', 'planity', 'ptyalin'],
 'planker': ['planker', 'prankle'],
 'planta': ['planta', 'platan'],
 'plantae': ['planate', 'planeta', 'plantae', 'platane'],
 'planter': ['pantler', 'planter', 'replant'],
 'plap': ['lapp', 'palp', 'plap'],
 'plasher': ['plasher', 'spheral'],
 'plasm': ['plasm', 'psalm', 'slamp'],
 'plasma': ['lampas', 'plasma'],
 'plasmation': ['aminoplast', 'plasmation'],
 'plasmic': ['plasmic', 'psalmic'],
 'plasmode': ['malposed', 'plasmode'],
 'plasmodial': ['plasmodial', 'psalmodial'],
 'plasmodic': ['plasmodic', 'psalmodic'],
 'plasson': ['plasson', 'sponsal'],
 'plastein': ['panelist', 'pantelis', 'penalist', 'plastein'],
 'plaster': ['palster', 'persalt', 'plaster', 'psalter', 'spartle', 'stapler'],
 'plasterer': ['plasterer', 'replaster'],
 'plastery': ['plastery', 'psaltery'],
 'plasticine': ['cisplatine', 'plasticine'],
 'plastidome': ['plastidome', 'postmedial'],
 'plastinoid': ['palinodist', 'plastinoid'],
 'plastochrone': ['phrenocostal', 'plastochrone'],
 'plat': ['palt', 'plat'],
 'plataean': ['panatela', 'plataean'],
 'platan': ['planta', 'platan'],
 'platane': ['planate', 'planeta', 'plantae', 'platane'],
 'plate': ['leapt',
           'palet',
           'patel',
           'pelta',
           'petal',
           'plate',
           'pleat',
           'tepal'],
 'platea': ['aletap', 'palate', 'platea'],
 'plateless': ['petalless', 'plateless', 'pleatless'],
 'platelet': ['pallette', 'platelet'],
 'platelike': ['petallike', 'platelike'],
 'platen': ['pantle', 'planet', 'platen'],
 'plater': ['palter', 'plater'],
 'platerer': ['palterer', 'platerer'],
 'platery': ['apertly', 'peartly', 'platery', 'pteryla', 'taperly'],
 'platine': ['pantile', 'pentail', 'platine', 'talpine'],
 'platinochloric': ['chloroplatinic', 'platinochloric'],
 'platinous': ['platinous', 'pulsation'],
 'platode': ['platode', 'tadpole'],
 'platoid': ['platoid', 'talpoid'],
 'platonian': ['planation', 'platonian'],
 'platter': ['partlet', 'platter', 'prattle'],
 'platy': ['aptly', 'patly', 'platy', 'typal'],
 'platynite': ['patiently', 'platynite'],
 'platysternal': ['platysternal', 'transeptally'],
 'plaud': ['dupla', 'plaud'],
 'plaustral': ['palustral', 'plaustral'],
 'play': ['paly', 'play', 'pyal', 'pyla'],
 'playa': ['palay', 'playa'],
 'player': ['parley', 'pearly', 'player', 'replay'],
 'playgoer': ['playgoer', 'pylagore'],
 'playroom': ['myopolar', 'playroom'],
 'plea': ['leap', 'lepa', 'pale', 'peal', 'plea'],
 'pleach': ['chapel', 'lepcha', 'pleach'],
 'plead': ['padle', 'paled', 'pedal', 'plead'],
 'pleader': ['pearled', 'pedaler', 'pleader', 'replead'],
 'please': ['asleep', 'elapse', 'please'],
 'pleaser': ['pleaser', 'preseal', 'relapse'],
 'pleasure': ['pleasure', 'serpulae'],
 'pleasurer': ['pleasurer', 'reperusal'],
 'pleasurous': ['asperulous', 'pleasurous'],
 'pleat': ['leapt',
           'palet',
           'patel',
           'pelta',
           'petal',
           'plate',
           'pleat',
           'tepal'],
 'pleater': ['pearlet', 'pleater', 'prelate', 'ptereal', 'replate', 'repleat'],
 'pleatless': ['petalless', 'plateless', 'pleatless'],
 'plectre': ['plectre', 'prelect'],
 'pleion': ['pinole', 'pleion'],
 'pleionian': ['opalinine', 'pleionian'],
 'plenilunar': ['plenilunar', 'plurennial'],
 'plenty': ['pentyl', 'plenty'],
 'pleomorph': ['pleomorph', 'prophloem'],
 'pleon': ['pelon', 'pleon'],
 'pleonal': ['pallone', 'pleonal'],
 'pleonasm': ['neoplasm', 'pleonasm', 'polesman', 'splenoma'],
 'pleonast': ['lapstone', 'pleonast'],
 'pleonastic': ['neoplastic', 'pleonastic'],
 'pleroma': ['leproma', 'palermo', 'pleroma', 'polearm'],
 'plerosis': ['leprosis', 'plerosis'],
 'plerotic': ['petrolic', 'plerotic'],
 'plessor': ['plessor', 'preloss'],
 'plethora': ['plethora', 'traphole'],
 'plethoric': ['phloretic', 'plethoric'],
 'pleura': ['epural', 'perula', 'pleura'],
 'pleuric': ['luperci', 'pleuric'],
 'pleuropericardial': ['pericardiopleural', 'pleuropericardial'],
 'pleurotoma': ['pleurotoma', 'tropaeolum'],
 'pleurovisceral': ['pleurovisceral', 'visceropleural'],
 'pliancy': ['pliancy', 'pycnial'],
 'pliant': ['plaint', 'pliant'],
 'plica': ['pical', 'plica'],
 'plicately': ['callitype', 'plicately'],
 'plicater': ['particle', 'plicater', 'prelatic'],
 'plicator': ['plicator', 'tropical'],
 'plied': ['piled', 'plied'],
 'plier': ['peril', 'piler', 'plier'],
 'pliers': ['lisper', 'pliers', 'sirple', 'spiler'],
 'plies': ['piles', 'plies', 'slipe', 'spiel', 'spile'],
 'plighter': ['plighter', 'replight'],
 'plim': ['limp', 'pilm', 'plim'],
 'pliny': ['pinyl', 'pliny'],
 'pliosaur': ['liparous', 'pliosaur'],
 'pliosauridae': ['lepidosauria', 'pliosauridae'],
 'ploceidae': ['adipocele', 'cepolidae', 'ploceidae'],
 'ploceus': ['culpose', 'ploceus', 'upclose'],
 'plodder': ['plodder', 'proddle'],
 'ploima': ['lipoma', 'pimola', 'ploima'],
 'ploration': ['ploration', 'portional', 'prolation'],
 'plot': ['plot', 'polt'],
 'plotful': ['plotful', 'topfull'],
 'plotted': ['plotted', 'pottled'],
 'plotter': ['plotter', 'portlet'],
 'plousiocracy': ['plousiocracy', 'procaciously'],
 'plout': ['plout', 'pluto', 'poult'],
 'plouter': ['plouter', 'poulter'],
 'plovery': ['overply', 'plovery'],
 'plower': ['plower', 'replow'],
 'ploy': ['ploy', 'poly'],
 'plucker': ['plucker', 'puckrel'],
 'plug': ['gulp', 'plug'],
 'plum': ['lump', 'plum'],
 'pluma': ['ampul', 'pluma'],
 'plumbagine': ['impugnable', 'plumbagine'],
 'plumbic': ['plumbic', 'upclimb'],
 'plumed': ['dumple', 'plumed'],
 'plumeous': ['eumolpus', 'plumeous'],
 'plumer': ['lumper', 'plumer', 'replum', 'rumple'],
 'plumet': ['lumpet', 'plumet'],
 'pluminess': ['lumpiness', 'pluminess'],
 'plumy': ['lumpy', 'plumy'],
 'plunderer': ['plunderer', 'replunder'],
 'plunge': ['plunge', 'pungle'],
 'plup': ['plup', 'pulp'],
 'plurennial': ['plenilunar', 'plurennial'],
 'pluriseptate': ['perpetualist', 'pluriseptate'],
 'plutean': ['plutean', 'unpetal', 'unpleat'],
 'pluteus': ['pluteus', 'pustule'],
 'pluto': ['plout', 'pluto', 'poult'],
 'pluvine': ['pluvine', 'vulpine'],
 'plyer': ['plyer', 'reply'],
 'pneumatophony': ['pneumatophony', 'pneumonopathy'],
 'pneumohemothorax': ['hemopneumothorax', 'pneumohemothorax'],
 'pneumohydropericardium': ['hydropneumopericardium', 'pneumohydropericardium'],
 'pneumohydrothorax': ['hydropneumothorax', 'pneumohydrothorax'],
 'pneumonopathy': ['pneumatophony', 'pneumonopathy'],
 'pneumopyothorax': ['pneumopyothorax', 'pyopneumothorax'],
 'poach': ['chopa', 'phoca', 'poach'],
 'poachy': ['poachy', 'pochay'],
 'poales': ['aslope', 'poales'],
 'pob': ['bop', 'pob'],
 'pochay': ['poachy', 'pochay'],
 'poche': ['epoch', 'poche'],
 'pocketer': ['pocketer', 'repocket'],
 'poco': ['coop', 'poco'],
 'pocosin': ['opsonic', 'pocosin'],
 'poculation': ['copulation', 'poculation'],
 'pod': ['dop', 'pod'],
 'podalic': ['placoid', 'podalic', 'podical'],
 'podarthritis': ['podarthritis', 'traditorship'],
 'podial': ['podial', 'poliad'],
 'podical': ['placoid', 'podalic', 'podical'],
 'podiceps': ['podiceps', 'scopiped'],
 'podite': ['pioted', 'podite'],
 'poditti': ['pittoid', 'poditti'],
 'podler': ['podler', 'polder', 'replod'],
 'podley': ['deploy', 'podley'],
 'podobranchia': ['branchiopoda', 'podobranchia'],
 'podocephalous': ['cephalopodous', 'podocephalous'],
 'podophyllous': ['phyllopodous', 'podophyllous'],
 'podoscaph': ['podoscaph', 'scaphopod'],
 'podostomata': ['podostomata', 'stomatopoda'],
 'podostomatous': ['podostomatous', 'stomatopodous'],
 'podotheca': ['chaetopod', 'podotheca'],
 'podura': ['podura', 'uproad'],
 'poduran': ['pandour', 'poduran'],
 'poe': ['ope', 'poe'],
 'poem': ['mope', 'poem', 'pome'],
 'poemet': ['metope', 'poemet'],
 'poemlet': ['leptome', 'poemlet'],
 'poesy': ['poesy', 'posey', 'sepoy'],
 'poet': ['peto', 'poet', 'pote', 'tope'],
 'poetastrical': ['poetastrical', 'spectatorial'],
 'poetical': ['copalite', 'poetical'],
 'poeticism': ['impeticos', 'poeticism'],
 'poetics': ['poetics', 'septoic'],
 'poetly': ['peyotl', 'poetly'],
 'poetryless': ['poetryless', 'presystole'],
 'pogo': ['goop', 'pogo'],
 'poh': ['hop', 'pho', 'poh'],
 'poha': ['opah', 'paho', 'poha'],
 'pohna': ['phano', 'pohna'],
 'poiana': ['anopia', 'aponia', 'poiana'],
 'poietic': ['epiotic', 'poietic'],
 'poimenic': ['mincopie', 'poimenic'],
 'poinder': ['poinder', 'ponerid'],
 'point': ['pinto', 'point'],
 'pointel': ['pointel', 'pontile', 'topline'],
 'pointer': ['pointer', 'protein', 'pterion', 'repoint', 'tropine'],
 'pointlet': ['pentitol', 'pointlet'],
 'pointrel': ['pointrel', 'terpinol'],
 'poisonless': ['poisonless', 'solenopsis'],
 'poker': ['poker', 'proke'],
 'pol': ['lop', 'pol'],
 'polab': ['pablo', 'polab'],
 'polacre': ['capreol', 'polacre'],
 'polander': ['polander', 'ponderal', 'prenodal'],
 'polar': ['parol', 'polar', 'poral', 'proal'],
 'polarid': ['dipolar', 'polarid'],
 'polarimetry': ['polarimetry', 'premorality', 'temporarily'],
 'polaristic': ['polaristic', 'poristical', 'saprolitic'],
 'polarly': ['payroll', 'polarly'],
 'polder': ['podler', 'polder', 'replod'],
 'pole': ['lope', 'olpe', 'pole'],
 'polearm': ['leproma', 'palermo', 'pleroma', 'polearm'],
 'polecat': ['pacolet', 'polecat'],
 'polemic': ['compile', 'polemic'],
 'polemics': ['clipsome', 'polemics'],
 'polemist': ['milepost', 'polemist'],
 'polenta': ['lepanto', 'nepotal', 'petalon', 'polenta'],
 'poler': ['loper', 'poler'],
 'polesman': ['neoplasm', 'pleonasm', 'polesman', 'splenoma'],
 'polestar': ['petrosal', 'polestar'],
 'poliad': ['podial', 'poliad'],
 'polianite': ['epilation', 'polianite'],
 'policyholder': ['policyholder', 'polychloride'],
 'polio': ['polio', 'pooli'],
 'polis': ['polis', 'spoil'],
 'polished': ['depolish', 'polished'],
 'polisher': ['polisher', 'repolish'],
 'polistes': ['polistes', 'telopsis'],
 'politarch': ['carpolith', 'politarch', 'trophical'],
 'politicly': ['lipolytic', 'politicly'],
 'politics': ['colpitis', 'politics', 'psilotic'],
 'polk': ['klop', 'polk'],
 'pollenite': ['pellotine', 'pollenite'],
 'poller': ['poller', 'repoll'],
 'pollinate': ['pellation', 'pollinate'],
 'polo': ['loop', 'polo', 'pool'],
 'poloist': ['loopist', 'poloist', 'topsoil'],
 'polonia': ['apionol', 'polonia'],
 'polos': ['polos', 'sloop', 'spool'],
 'polt': ['plot', 'polt'],
 'poly': ['ploy', 'poly'],
 'polyacid': ['polyacid', 'polyadic'],
 'polyactinal': ['pactionally', 'polyactinal'],
 'polyadic': ['polyacid', 'polyadic'],
 'polyarchist': ['chiroplasty', 'polyarchist'],
 'polychloride': ['policyholder', 'polychloride'],
 'polychroism': ['polychroism', 'polyorchism'],
 'polycitral': ['polycitral', 'tropically'],
 'polycodium': ['lycopodium', 'polycodium'],
 'polycotyl': ['collotypy', 'polycotyl'],
 'polyeidic': ['polyeidic', 'polyideic'],
 'polyeidism': ['polyeidism', 'polyideism'],
 'polyester': ['polyester', 'proselyte'],
 'polygamodioecious': ['dioeciopolygamous', 'polygamodioecious'],
 'polyhalite': ['paleolithy', 'polyhalite', 'polythelia'],
 'polyideic': ['polyeidic', 'polyideic'],
 'polyideism': ['polyeidism', 'polyideism'],
 'polymastic': ['myoplastic', 'polymastic'],
 'polymasty': ['myoplasty', 'polymasty'],
 'polymere': ['employer', 'polymere'],
 'polymeric': ['micropyle', 'polymeric'],
 'polymetochia': ['homeotypical', 'polymetochia'],
 'polymnia': ['olympian', 'polymnia'],
 'polymyodi': ['polymyodi', 'polymyoid'],
 'polymyoid': ['polymyodi', 'polymyoid'],
 'polyorchism': ['polychroism', 'polyorchism'],
 'polyp': ['loppy', 'polyp'],
 'polyphemian': ['lymphopenia', 'polyphemian'],
 'polyphonist': ['polyphonist', 'psilophyton'],
 'polypite': ['lipotype', 'polypite'],
 'polyprene': ['polyprene', 'propylene'],
 'polypterus': ['polypterus', 'suppletory'],
 'polysomitic': ['myocolpitis', 'polysomitic'],
 'polyspore': ['polyspore', 'prosopyle'],
 'polythelia': ['paleolithy', 'polyhalite', 'polythelia'],
 'polythene': ['polythene', 'telephony'],
 'polyuric': ['croupily', 'polyuric'],
 'pom': ['mop', 'pom'],
 'pomade': ['apedom', 'pomade'],
 'pomane': ['mopane', 'pomane'],
 'pome': ['mope', 'poem', 'pome'],
 'pomeranian': ['pomeranian', 'praenomina'],
 'pomerium': ['emporium', 'pomerium', 'proemium'],
 'pomey': ['myope', 'pomey'],
 'pomo': ['moop', 'pomo'],
 'pomonal': ['lampoon', 'pomonal'],
 'pompilus': ['pimplous', 'pompilus', 'populism'],
 'pomster': ['pomster', 'stomper'],
 'ponca': ['capon', 'ponca'],
 'ponce': ['copen', 'ponce'],
 'ponchoed': ['chenopod', 'ponchoed'],
 'poncirus': ['coprinus', 'poncirus'],
 'ponderal': ['polander', 'ponderal', 'prenodal'],
 'ponderate': ['ponderate', 'predonate'],
 'ponderation': ['ponderation', 'predonation'],
 'ponderer': ['ponderer', 'reponder'],
 'pondfish': ['fishpond', 'pondfish'],
 'pone': ['nope', 'open', 'peon', 'pone'],
 'ponerid': ['poinder', 'ponerid'],
 'poneroid': ['poneroid', 'porodine'],
 'poney': ['peony', 'poney'],
 'ponica': ['aponic', 'ponica'],
 'ponier': ['opiner', 'orpine', 'ponier'],
 'pontederia': ['pontederia', 'proteidean'],
 'pontee': ['nepote', 'pontee', 'poteen'],
 'pontes': ['pontes', 'posnet'],
 'ponticular': ['ponticular', 'untropical'],
 'pontificalia': ['palification', 'pontificalia'],
 'pontile': ['pointel', 'pontile', 'topline'],
 'pontonier': ['entropion', 'pontonier', 'prenotion'],
 'pontus': ['pontus', 'unspot', 'unstop'],
 'pooch': ['choop', 'pooch'],
 'pooh': ['hoop', 'phoo', 'pooh'],
 'pooka': ['oopak', 'pooka'],
 'pool': ['loop', 'polo', 'pool'],
 'pooler': ['looper', 'pooler'],
 'pooli': ['polio', 'pooli'],
 'pooly': ['loopy', 'pooly'],
 'poon': ['noop', 'poon'],
 'poonac': ['acopon', 'poonac'],
 'poonga': ['apogon', 'poonga'],
 'poor': ['poor', 'proo'],
 'poot': ['poot', 'toop', 'topo'],
 'pope': ['pepo', 'pope'],
 'popeler': ['peopler', 'popeler'],
 'popery': ['popery', 'pyrope'],
 'popgun': ['oppugn', 'popgun'],
 'popian': ['oppian', 'papion', 'popian'],
 'popish': ['popish', 'shippo'],
 'popliteal': ['papillote', 'popliteal'],
 'poppel': ['poppel', 'popple'],
 'popple': ['poppel', 'popple'],
 'populism': ['pimplous', 'pompilus', 'populism'],
 'populus': ['populus', 'pulpous'],
 'poral': ['parol', 'polar', 'poral', 'proal'],
 'porcate': ['coperta', 'pectora', 'porcate'],
 'porcelain': ['oliprance', 'porcelain'],
 'porcelanite': ['porcelanite', 'praelection'],
 'porcula': ['copular', 'croupal', 'cupolar', 'porcula'],
 'pore': ['pore', 'rope'],
 'pored': ['doper', 'pedro', 'pored'],
 'porelike': ['porelike', 'ropelike'],
 'porer': ['porer', 'prore', 'roper'],
 'porge': ['grope', 'porge'],
 'porger': ['groper', 'porger'],
 'poriness': ['poriness', 'pression', 'ropiness'],
 'poring': ['poring', 'roping'],
 'poristical': ['polaristic', 'poristical', 'saprolitic'],
 'porites': ['periost', 'porites', 'reposit', 'riposte'],
 'poritidae': ['poritidae', 'triopidae'],
 'porker': ['porker', 'proker'],
 'pornerastic': ['cotranspire', 'pornerastic'],
 'porodine': ['poneroid', 'porodine'],
 'poros': ['poros', 'proso', 'sopor', 'spoor'],
 'porosity': ['isotropy', 'porosity'],
 'porotic': ['porotic', 'portico'],
 'porpentine': ['porpentine', 'prepontine'],
 'porphine': ['hornpipe', 'porphine'],
 'porphyrous': ['porphyrous', 'pyrophorus'],
 'porrection': ['correption', 'porrection'],
 'porret': ['porret', 'porter', 'report', 'troper'],
 'porta': ['aport', 'parto', 'porta'],
 'portail': ['portail', 'toprail'],
 'portal': ['patrol', 'portal', 'tropal'],
 'portance': ['coparent', 'portance'],
 'ported': ['deport', 'ported', 'redtop'],
 'portend': ['portend', 'protend'],
 'porteno': ['porteno', 'protone'],
 'portension': ['portension', 'protension'],
 'portent': ['portent', 'torpent'],
 'portentous': ['notopterus', 'portentous'],
 'porter': ['porret', 'porter', 'report', 'troper'],
 'porterage': ['porterage', 'reportage'],
 'portership': ['portership', 'pretorship'],
 'portfire': ['portfire', 'profiter'],
 'portia': ['portia', 'tapiro'],
 'portico': ['porotic', 'portico'],
 'portify': ['portify', 'torpify'],
 'portional': ['ploration', 'portional', 'prolation'],
 'portioner': ['portioner', 'reportion'],
 'portlet': ['plotter', 'portlet'],
 'portly': ['portly', 'protyl', 'tropyl'],
 'porto': ['porto', 'proto', 'troop'],
 'portoise': ['isotrope', 'portoise'],
 'portolan': ['portolan', 'pronotal'],
 'portor': ['portor', 'torpor'],
 'portray': ['parroty', 'portray', 'tropary'],
 'portrayal': ['parlatory', 'portrayal'],
 'portside': ['dipteros', 'portside'],
 'portsider': ['portsider', 'postrider'],
 'portunidae': ['depuration', 'portunidae'],
 'portunus': ['outspurn', 'portunus'],
 'pory': ['pory', 'pyro', 'ropy'],
 'posca': ['posca', 'scopa'],
 'pose': ['epos', 'peso', 'pose', 'sope'],
 'poser': ['poser', 'prose', 'ropes', 'spore'],
 'poseur': ['poseur', 'pouser', 'souper', 'uprose'],
 'posey': ['poesy', 'posey', 'sepoy'],
 'posh': ['phos', 'posh', 'shop', 'soph'],
 'posingly': ['posingly', 'spongily'],
 'position': ['position', 'sopition'],
 'positional': ['positional', 'spoilation', 'spoliation'],
 'positioned': ['deposition', 'positioned'],
 'positioner': ['positioner', 'reposition'],
 'positron': ['notropis', 'positron', 'sorption'],
 'positum': ['positum', 'utopism'],
 'posnet': ['pontes', 'posnet'],
 'posse': ['posse', 'speos'],
 'possessioner': ['possessioner', 'repossession'],
 'post': ['post', 'spot', 'stop', 'tops'],
 'postage': ['gestapo', 'postage'],
 'postaortic': ['parostotic', 'postaortic'],
 'postdate': ['despotat', 'postdate'],
 'posted': ['despot', 'posted'],
 'posteen': ['pentose', 'posteen'],
 'poster': ['poster', 'presto', 'repost', 'respot', 'stoper'],
 'posterial': ['posterial', 'saprolite'],
 'posterior': ['posterior', 'repositor'],
 'posterish': ['posterish', 'prothesis', 'sophister', 'storeship', 'tephrosis'],
 'posteroinferior': ['inferoposterior', 'posteroinferior'],
 'posterosuperior': ['posterosuperior', 'superoposterior'],
 'postgenial': ['postgenial', 'spangolite'],
 'posthaste': ['posthaste', 'tosephtas'],
 'postic': ['copist', 'coptis', 'optics', 'postic'],
 'postical': ['postical', 'slipcoat'],
 'postil': ['pistol', 'postil', 'spoilt'],
 'posting': ['posting', 'stoping'],
 'postischial': ['postischial', 'sophistical'],
 'postless': ['postless', 'spotless', 'stopless'],
 'postlike': ['postlike', 'spotlike'],
 'postman': ['postman', 'topsman'],
 'postmedial': ['plastidome', 'postmedial'],
 'postnaris': ['postnaris', 'sopranist'],
 'postnominal': ['monoplanist', 'postnominal'],
 'postrider': ['portsider', 'postrider'],
 'postsacral': ['postsacral', 'sarcoplast'],
 'postsign': ['postsign', 'signpost'],
 'postthoracic': ['octastrophic', 'postthoracic'],
 'postulata': ['autoplast', 'postulata'],
 'postural': ['postural', 'pulsator', 'sportula'],
 'posture': ['petrous', 'posture', 'proetus', 'proteus', 'septuor', 'spouter'],
 'posturer': ['posturer', 'resprout', 'sprouter'],
 'postuterine': ['postuterine', 'pretentious'],
 'postwar': ['postwar', 'sapwort'],
 'postward': ['drawstop', 'postward'],
 'postwoman': ['postwoman', 'womanpost'],
 'posy': ['opsy', 'posy'],
 'pot': ['opt', 'pot', 'top'],
 'potable': ['optable', 'potable'],
 'potableness': ['optableness', 'potableness'],
 'potash': ['pashto', 'pathos', 'potash'],
 'potass': ['potass', 'topass'],
 'potate': ['aptote', 'optate', 'potate', 'teapot'],
 'potation': ['optation', 'potation'],
 'potative': ['optative', 'potative'],
 'potator': ['potator', 'taproot'],
 'pote': ['peto', 'poet', 'pote', 'tope'],
 'poteen': ['nepote', 'pontee', 'poteen'],
 'potential': ['peltation', 'potential'],
 'poter': ['poter', 'prote', 'repot', 'tepor', 'toper', 'trope'],
 'poteye': ['peyote', 'poteye'],
 'pothouse': ['housetop', 'pothouse'],
 'poticary': ['cyrtopia', 'poticary'],
 'potifer': ['firetop', 'potifer'],
 'potion': ['option', 'potion'],
 'potlatch': ['potlatch', 'tolpatch'],
 'potlike': ['kitlope', 'potlike', 'toplike'],
 'potmaker': ['potmaker', 'topmaker'],
 'potmaking': ['potmaking', 'topmaking'],
 'potman': ['potman', 'tampon', 'topman'],
 'potometer': ['optometer', 'potometer'],
 'potstick': ['potstick', 'tipstock'],
 'potstone': ['potstone', 'topstone'],
 'pottled': ['plotted', 'pottled'],
 'pouce': ['coupe', 'pouce'],
 'poucer': ['couper', 'croupe', 'poucer', 'recoup'],
 'pouch': ['choup', 'pouch'],
 'poulpe': ['poulpe', 'pupelo'],
 'poult': ['plout', 'pluto', 'poult'],
 'poulter': ['plouter', 'poulter'],
 'poultice': ['epulotic', 'poultice'],
 'pounce': ['pounce', 'uncope'],
 'pounder': ['pounder', 'repound', 'unroped'],
 'pour': ['pour', 'roup'],
 'pourer': ['pourer', 'repour', 'rouper'],
 'pouser': ['poseur', 'pouser', 'souper', 'uprose'],
 'pout': ['pout', 'toup'],
 'pouter': ['pouter', 'roupet', 'troupe'],
 'pow': ['pow', 'wop'],
 'powder': ['powder', 'prowed'],
 'powderer': ['powderer', 'repowder'],
 'powerful': ['powerful', 'upflower'],
 'praam': ['param', 'parma', 'praam'],
 'practitional': ['antitropical', 'practitional'],
 'prad': ['pard', 'prad'],
 'pradeep': ['papered', 'pradeep'],
 'praecox': ['exocarp', 'praecox'],
 'praelabrum': ['praelabrum', 'preambular'],
 'praelection': ['porcelanite', 'praelection'],
 'praelector': ['praelector', 'receptoral'],
 'praenomina': ['pomeranian', 'praenomina'],
 'praepostor': ['praepostor', 'pterospora'],
 'praesphenoid': ['nephropsidae', 'praesphenoid'],
 'praetor': ['praetor', 'prorate'],
 'praetorian': ['praetorian', 'reparation'],
 'praise': ['aspire', 'paries', 'praise', 'sirpea', 'spirea'],
 'praiser': ['aspirer', 'praiser', 'serpari'],
 'praising': ['aspiring', 'praising', 'singarip'],
 'praisingly': ['aspiringly', 'praisingly'],
 'praline': ['pearlin', 'plainer', 'praline'],
 'pram': ['pram', 'ramp'],
 'prandial': ['diplanar', 'prandial'],
 'prankle': ['planker', 'prankle'],
 'prase': ['asper', 'parse', 'prase', 'spaer', 'spare', 'spear'],
 'praseolite': ['periosteal', 'praseolite'],
 'prasine': ['persian', 'prasine', 'saprine'],
 'prasinous': ['prasinous', 'psaronius'],
 'prasoid': ['prasoid', 'sparoid'],
 'prasophagous': ['prasophagous', 'saprophagous'],
 'prat': ['part', 'prat', 'rapt', 'tarp', 'trap'],
 'prate': ['apert', 'pater', 'peart', 'prate', 'taper', 'terap'],
 'prater': ['parter', 'prater'],
 'pratey': ['petary', 'pratey'],
 'pratfall': ['pratfall', 'trapfall'],
 'pratincoline': ['nonpearlitic', 'pratincoline'],
 'pratincolous': ['patroclinous', 'pratincolous'],
 'prattle': ['partlet', 'platter', 'prattle'],
 'prau': ['prau', 'rupa'],
 'prawner': ['prawner', 'prewarn'],
 'prayer': ['prayer', 'repray'],
 'preach': ['aperch', 'eparch', 'percha', 'preach'],
 'preacher': ['preacher', 'repreach'],
 'preaching': ['engraphic', 'preaching'],
 'preachman': ['marchpane', 'preachman'],
 'preachy': ['eparchy', 'preachy'],
 'preacid': ['epacrid', 'peracid', 'preacid'],
 'preact': ['carpet', 'peract', 'preact'],
 'preaction': ['preaction', 'precation', 'recaption'],
 'preactive': ['preactive', 'precative'],
 'preactively': ['preactively', 'precatively'],
 'preacute': ['peracute', 'preacute'],
 'preadmonition': ['demipronation', 'preadmonition', 'predomination'],
 'preadorn': ['pardoner', 'preadorn'],
 'preadventure': ['peradventure', 'preadventure'],
 'preallably': ['ballplayer', 'preallably'],
 'preallow': ['preallow', 'walloper'],
 'preamble': ['peramble', 'preamble'],
 'preambular': ['praelabrum', 'preambular'],
 'preambulate': ['perambulate', 'preambulate'],
 'preambulation': ['perambulation', 'preambulation'],
 'preambulatory': ['perambulatory', 'preambulatory'],
 'preanal': ['planera', 'preanal'],
 'prearm': ['prearm', 'ramper'],
 'preascitic': ['accipitres', 'preascitic'],
 'preauditory': ['preauditory', 'repudiatory'],
 'prebacillary': ['bicarpellary', 'prebacillary'],
 'prebasal': ['parsable', 'prebasal', 'sparable'],
 'prebend': ['perbend', 'prebend'],
 'prebeset': ['bepester', 'prebeset'],
 'prebid': ['bedrip', 'prebid'],
 'precant': ['carpent', 'precant'],
 'precantation': ['actinopteran', 'precantation'],
 'precast': ['precast', 'spectra'],
 'precation': ['preaction', 'precation', 'recaption'],
 'precative': ['preactive', 'precative'],
 'precatively': ['preactively', 'precatively'],
 'precaution': ['precaution', 'unoperatic'],
 'precautional': ['inoperculata', 'precautional'],
 'precedable': ['deprecable', 'precedable'],
 'preceder': ['preceder', 'precreed'],
 'precent': ['percent', 'precent'],
 'precept': ['percept', 'precept'],
 'preception': ['perception', 'preception'],
 'preceptive': ['perceptive', 'preceptive'],
 'preceptively': ['perceptively', 'preceptively'],
 'preceptual': ['perceptual', 'preceptual'],
 'preceptually': ['perceptually', 'preceptually'],
 'prechloric': ['perchloric', 'prechloric'],
 'prechoose': ['prechoose', 'rheoscope'],
 'precipitate': ['peripatetic', 'precipitate'],
 'precipitator': ['precipitator', 'prepatriotic'],
 'precis': ['crepis', 'cripes', 'persic', 'precis', 'spicer'],
 'precise': ['precise', 'scripee'],
 'precisian': ['periscian', 'precisian'],
 'precision': ['coinspire', 'precision'],
 'precitation': ['actinopteri', 'crepitation', 'precitation'],
 'precite': ['ereptic', 'precite', 'receipt'],
 'precited': ['decrepit', 'depicter', 'precited'],
 'preclose': ['perclose', 'preclose'],
 'preclusion': ['perculsion', 'preclusion'],
 'preclusive': ['perculsive', 'preclusive'],
 'precoil': ['peloric', 'precoil'],
 'precompound': ['percompound', 'precompound'],
 'precondense': ['precondense', 'respondence'],
 'preconnubial': ['preconnubial', 'pronunciable'],
 'preconsole': ['necropoles', 'preconsole'],
 'preconsultor': ['preconsultor', 'supercontrol'],
 'precontest': ['precontest', 'torpescent'],
 'precopy': ['coppery', 'precopy'],
 'precostal': ['ceroplast', 'precostal'],
 'precredit': ['precredit', 'predirect', 'repredict'],
 'precreditor': ['precreditor', 'predirector'],
 'precreed': ['preceder', 'precreed'],
 'precurrent': ['percurrent', 'precurrent'],
 'precursory': ['percursory', 'precursory'],
 'precyst': ['precyst', 'sceptry', 'spectry'],
 'predata': ['adapter', 'predata', 'readapt'],
 'predate': ['padtree', 'predate', 'tapered'],
 'predatism': ['predatism', 'spermatid'],
 'predative': ['deprivate', 'predative'],
 'predator': ['predator', 'protrade', 'teardrop'],
 'preday': ['pedary', 'preday'],
 'predealer': ['predealer', 'repleader'],
 'predecree': ['creepered', 'predecree'],
 'predefect': ['perfected', 'predefect'],
 'predeication': ['depreciation', 'predeication'],
 'prederive': ['prederive', 'redeprive'],
 'predespair': ['disprepare', 'predespair'],
 'predestine': ['predestine', 'presidente'],
 'predetail': ['pedaliter', 'predetail'],
 'predevotion': ['overpointed', 'predevotion'],
 'predial': ['pedrail', 'predial'],
 'prediastolic': ['prediastolic', 'psiloceratid'],
 'predication': ['predication', 'procidentia'],
 'predictory': ['cryptodire', 'predictory'],
 'predirect': ['precredit', 'predirect', 'repredict'],
 'predirector': ['precreditor', 'predirector'],
 'prediscretion': ['prediscretion', 'redescription'],
 'predislike': ['predislike', 'spiderlike'],
 'predivinable': ['indeprivable', 'predivinable'],
 'predominate': ['pentameroid', 'predominate'],
 'predomination': ['demipronation', 'preadmonition', 'predomination'],
 'predonate': ['ponderate', 'predonate'],
 'predonation': ['ponderation', 'predonation'],
 'predoubter': ['predoubter', 'preobtrude'],
 'predrive': ['depriver', 'predrive'],
 'preen': ['neper', 'preen', 'repen'],
 'prefactor': ['aftercrop', 'prefactor'],
 'prefator': ['forepart', 'prefator'],
 'prefavorite': ['perforative', 'prefavorite'],
 'prefect': ['perfect', 'prefect'],
 'prefectly': ['perfectly', 'prefectly'],
 'prefervid': ['perfervid', 'prefervid'],
 'prefiction': ['prefiction', 'proficient'],
 'prefoliation': ['perfoliation', 'prefoliation'],
 'preform': ['perform', 'preform'],
 'preformant': ['performant', 'preformant'],
 'preformative': ['performative', 'preformative'],
 'preformism': ['misperform', 'preformism'],
 'pregainer': ['peregrina', 'pregainer'],
 'prehandicap': ['handicapper', 'prehandicap'],
 'prehaps': ['perhaps', 'prehaps'],
 'prehazard': ['perhazard', 'prehazard'],
 'preheal': ['preheal', 'rephael'],
 'preheat': ['haptere', 'preheat'],
 'preheated': ['heartdeep', 'preheated'],
 'prehension': ['hesperinon', 'prehension'],
 'prehnite': ['nephrite', 'prehnite', 'trephine'],
 'prehnitic': ['nephritic', 'phrenitic', 'prehnitic'],
 'prehuman': ['prehuman', 'unhamper'],
 'preimpose': ['perispome', 'preimpose'],
 'preindicate': ['parenticide', 'preindicate'],
 'preinduce': ['preinduce', 'unpierced'],
 'preintone': ['interpone', 'peritenon', 'pinnotere', 'preintone'],
 'prelabrum': ['prelabrum', 'prelumbar'],
 'prelacteal': ['carpellate', 'parcellate', 'prelacteal'],
 'prelate': ['pearlet', 'pleater', 'prelate', 'ptereal', 'replate', 'repleat'],
 'prelatehood': ['heteropodal', 'prelatehood'],
 'prelatic': ['particle', 'plicater', 'prelatic'],
 'prelatical': ['capitellar', 'prelatical'],
 'prelation': ['prelation', 'rantipole'],
 'prelatism': ['palmister', 'prelatism'],
 'prelease': ['eelspear', 'prelease'],
 'prelect': ['plectre', 'prelect'],
 'prelection': ['perlection', 'prelection'],
 'prelim': ['limper', 'prelim', 'rimple'],
 'prelingual': ['perlingual', 'prelingual'],
 'prelocate': ['percolate', 'prelocate'],
 'preloss': ['plessor', 'preloss'],
 'preludial': ['dipleural', 'preludial'],
 'prelumbar': ['prelabrum', 'prelumbar'],
 'prelusion': ['prelusion', 'repulsion'],
 'prelusive': ['prelusive', 'repulsive'],
 'prelusively': ['prelusively', 'repulsively'],
 'prelusory': ['prelusory', 'repulsory'],
 'premate': ['premate', 'tempera'],
 'premedia': ['epiderma', 'premedia'],
 'premedial': ['epidermal', 'impleader', 'premedial'],
 'premedication': ['pedometrician', 'premedication'],
 'premenace': ['permeance', 'premenace'],
 'premerit': ['premerit', 'preremit', 'repermit'],
 'premial': ['impaler', 'impearl', 'lempira', 'premial'],
 'premiant': ['imperant', 'pairment', 'partimen', 'premiant', 'tripeman'],
 'premiate': ['imperate', 'premiate'],
 'premier': ['premier', 'reprime'],
 'premious': ['imposure', 'premious'],
 'premise': ['emprise', 'imprese', 'premise', 'spireme'],
 'premiss': ['impress', 'persism', 'premiss'],
 'premixture': ['permixture', 'premixture'],
 'premodel': ['leperdom', 'premodel'],
 'premolar': ['premolar', 'premoral'],
 'premold': ['meldrop', 'premold'],
 'premonetary': ['premonetary', 'pyranometer'],
 'premoral': ['premolar', 'premoral'],
 'premorality': ['polarimetry', 'premorality', 'temporarily'],
 'premosaic': ['paroecism', 'premosaic'],
 'premover': ['premover', 'prevomer'],
 'premusical': ['premusical', 'superclaim'],
 'prenasal': ['pernasal', 'prenasal'],
 'prenatal': ['parental', 'paternal', 'prenatal'],
 'prenatalist': ['intraseptal', 'paternalist', 'prenatalist'],
 'prenatally': ['parentally', 'paternally', 'prenatally'],
 'prenative': ['interpave', 'prenative'],
 'prender': ['prender', 'prendre'],
 'prendre': ['prender', 'prendre'],
 'prenodal': ['polander', 'ponderal', 'prenodal'],
 'prenominical': ['nonempirical', 'prenominical'],
 'prenotice': ['prenotice', 'reception'],
 'prenotion': ['entropion', 'pontonier', 'prenotion'],
 'prentice': ['piercent', 'prentice'],
 'preobtrude': ['predoubter', 'preobtrude'],
 'preocular': ['opercular', 'preocular'],
 'preominate': ['permeation', 'preominate'],
 'preopen': ['preopen', 'propene'],
 'preopinion': ['preopinion', 'prionopine'],
 'preoption': ['preoption', 'protopine'],
 'preoral': ['peroral', 'preoral'],
 'preorally': ['perorally', 'preorally'],
 'prep': ['prep', 'repp'],
 'prepalatine': ['periplaneta', 'prepalatine'],
 'prepare': ['paperer', 'perpera', 'prepare', 'repaper'],
 'prepatriotic': ['precipitator', 'prepatriotic'],
 'prepay': ['papery', 'prepay', 'yapper'],
 'preplot': ['preplot', 'toppler'],
 'prepollent': ['prepollent', 'propellent'],
 'prepontine': ['porpentine', 'prepontine'],
 'prepositure': ['peripterous', 'prepositure'],
 'prepuce': ['prepuce', 'upcreep'],
 'prerational': ['prerational', 'proletarian'],
 'prerealization': ['prerealization', 'proletarianize'],
 'prereceive': ['prereceive', 'reperceive'],
 'prerecital': ['perirectal', 'prerecital'],
 'prereduction': ['interproduce', 'prereduction'],
 'prerefer': ['prerefer', 'reprefer'],
 'prereform': ['performer', 'prereform', 'reperform'],
 'preremit': ['premerit', 'preremit', 'repermit'],
 'prerental': ['prerental', 'replanter'],
 'prerich': ['chirper', 'prerich'],
 'preromantic': ['improcreant', 'preromantic'],
 'presage': ['asperge', 'presage'],
 'presager': ['asperger', 'presager'],
 'presay': ['presay', 'speary'],
 'prescapular': ['prescapular', 'supercarpal'],
 'prescient': ['prescient', 'reinspect'],
 'prescientific': ['interspecific', 'prescientific'],
 'prescribe': ['perscribe', 'prescribe'],
 'prescutal': ['prescutal', 'scalpture'],
 'preseal': ['pleaser', 'preseal', 'relapse'],
 'preseason': ['parsonese', 'preseason'],
 'presell': ['presell', 'respell', 'speller'],
 'present': ['penster', 'present', 'serpent', 'strepen'],
 'presenter': ['presenter', 'represent'],
 'presential': ['alpestrine', 'episternal', 'interlapse', 'presential'],
 'presentist': ['persistent', 'presentist', 'prettiness'],
 'presentive': ['presentive', 'pretensive', 'vespertine'],
 'presentively': ['presentively', 'pretensively'],
 'presentiveness': ['presentiveness', 'pretensiveness'],
 'presently': ['presently', 'serpently'],
 'preserve': ['perverse', 'preserve'],
 'preset': ['pester', 'preset', 'restep', 'streep'],
 'preshare': ['preshare', 'rephrase'],
 'preship': ['preship', 'shipper'],
 'preshortage': ['preshortage', 'stereograph'],
 'preside': ['perseid', 'preside'],
 'presidencia': ['acipenserid', 'presidencia'],
 'president': ['president', 'serpentid'],
 'presidente': ['predestine', 'presidente'],
 'presider': ['presider', 'serriped'],
 'presign': ['presign', 'springe'],
 'presignal': ['espringal', 'presignal', 'relapsing'],
 'presimian': ['mainprise', 'presimian'],
 'presley': ['presley', 'sleepry'],
 'presser': ['presser', 'repress'],
 'pression': ['poriness', 'pression', 'ropiness'],
 'pressive': ['pressive', 'viperess'],
 'prest': ['prest', 'spret'],
 'prestable': ['beplaster', 'prestable'],
 'prestant': ['prestant', 'transept'],
 'prestate': ['prestate', 'pretaste'],
 'presto': ['poster', 'presto', 'repost', 'respot', 'stoper'],
 'prestock': ['prestock', 'sprocket'],
 'prestomial': ['peristomal', 'prestomial'],
 'prestrain': ['prestrain', 'transpire'],
 'presume': ['presume', 'supreme'],
 'presurmise': ['impressure', 'presurmise'],
 'presustain': ['presustain', 'puritaness', 'supersaint'],
 'presystole': ['poetryless', 'presystole'],
 'pretan': ['arpent',
            'enrapt',
            'entrap',
            'panter',
            'parent',
            'pretan',
            'trepan'],
 'pretaste': ['prestate', 'pretaste'],
 'pretensive': ['presentive', 'pretensive', 'vespertine'],
 'pretensively': ['presentively', 'pretensively'],
 'pretensiveness': ['presentiveness', 'pretensiveness'],
 'pretentious': ['postuterine', 'pretentious'],
 'pretercanine': ['irrepentance', 'pretercanine'],
 'preterient': ['preterient', 'triterpene'],
 'pretermit': ['permitter', 'pretermit'],
 'pretheological': ['herpetological', 'pretheological'],
 'pretibial': ['bipartile', 'pretibial'],
 'pretonic': ['inceptor', 'pretonic'],
 'pretorship': ['portership', 'pretorship'],
 'pretrace': ['pretrace', 'recarpet'],
 'pretracheal': ['archprelate', 'pretracheal'],
 'pretrain': ['pretrain', 'terrapin'],
 'pretransmission': ['pretransmission', 'transimpression'],
 'pretreat': ['patterer', 'pretreat'],
 'prettiness': ['persistent', 'presentist', 'prettiness'],
 'prevailer': ['prevailer', 'reprieval'],
 'prevalid': ['deprival', 'prevalid'],
 'prevention': ['prevention', 'provenient'],
 'preversion': ['perversion', 'preversion'],
 'preveto': ['overpet', 'preveto', 'prevote'],
 'previde': ['deprive', 'previde'],
 'previous': ['pervious', 'previous', 'viperous'],
 'previously': ['perviously', 'previously', 'viperously'],
 'previousness': ['perviousness', 'previousness', 'viperousness'],
 'prevoid': ['prevoid', 'provide'],
 'prevomer': ['premover', 'prevomer'],
 'prevote': ['overpet', 'preveto', 'prevote'],
 'prewar': ['prewar', 'rewrap', 'warper'],
 'prewarn': ['prawner', 'prewarn'],
 'prewhip': ['prewhip', 'whipper'],
 'prewrap': ['prewrap', 'wrapper'],
 'prexy': ['prexy', 'pyrex'],
 'prey': ['prey', 'pyre', 'rype'],
 'pria': ['pair', 'pari', 'pria', 'ripa'],
 'price': ['price', 'repic'],
 'priced': ['percid', 'priced'],
 'prich': ['chirp', 'prich'],
 'prickfoot': ['prickfoot', 'tickproof'],
 'prickle': ['pickler', 'prickle'],
 'pride': ['pride', 'pried', 'redip'],
 'pridian': ['pindari', 'pridian'],
 'pried': ['pride', 'pried', 'redip'],
 'prier': ['prier', 'riper'],
 'priest': ['priest', 'pteris', 'sprite', 'stripe'],
 'priestal': ['epistlar', 'pilaster', 'plaister', 'priestal'],
 'priesthood': ['priesthood', 'spritehood'],
 'priestless': ['priestless', 'stripeless'],
 'prig': ['grip', 'prig'],
 'prigman': ['gripman', 'prigman', 'ramping'],
 'prima': ['impar', 'pamir', 'prima'],
 'primage': ['epigram', 'primage'],
 'primal': ['imparl', 'primal'],
 'primates': ['maspiter', 'pastimer', 'primates'],
 'primatial': ['impartial', 'primatial'],
 'primely': ['primely', 'reimply'],
 'primeness': ['primeness', 'spenerism'],
 'primost': ['primost', 'tropism'],
 'primrose': ['primrose', 'promiser'],
 'primsie': ['pismire', 'primsie'],
 'primus': ['primus', 'purism'],
 'prince': ['pincer', 'prince'],
 'princehood': ['cnidophore', 'princehood'],
 'princeite': ['princeite', 'recipient'],
 'princelike': ['pincerlike', 'princelike'],
 'princely': ['pencilry', 'princely'],
 'princesse': ['crepiness', 'princesse'],
 'prine': ['piner', 'prine', 'repin', 'ripen'],
 'pringle': ['pingler', 'pringle'],
 'printed': ['deprint', 'printed'],
 'printer': ['printer', 'reprint'],
 'priodontes': ['desorption', 'priodontes'],
 'prionopine': ['preopinion', 'prionopine'],
 'prisage': ['prisage', 'spairge'],
 'prisal': ['prisal', 'spiral'],
 'prismatoid': ['diatropism', 'prismatoid'],
 'prisometer': ['prisometer', 'spirometer'],
 'prisonable': ['bipersonal', 'prisonable'],
 'pristane': ['pinaster', 'pristane'],
 'pristine': ['enspirit', 'pristine'],
 'pristis': ['pristis', 'tripsis'],
 'prius': ['prius', 'sirup'],
 'proadmission': ['adpromission', 'proadmission'],
 'proal': ['parol', 'polar', 'poral', 'proal'],
 'proalien': ['pelorian', 'peronial', 'proalien'],
 'proamniotic': ['comparition', 'proamniotic'],
 'proathletic': ['proathletic', 'prothetical'],
 'proatlas': ['pastoral', 'proatlas'],
 'proavis': ['pavisor', 'proavis'],
 'probationer': ['probationer', 'reprobation'],
 'probe': ['probe', 'rebop'],
 'procaciously': ['plousiocracy', 'procaciously'],
 'procaine': ['caponier', 'coprinae', 'procaine'],
 'procanal': ['coplanar', 'procanal'],
 'procapital': ['applicator', 'procapital'],
 'procedure': ['procedure', 'reproduce'],
 'proceeder': ['proceeder', 'reproceed'],
 'procellas': ['procellas', 'scalloper'],
 'procerite': ['procerite', 'receiptor'],
 'procession': ['procession', 'scorpiones'],
 'prochemical': ['microcephal', 'prochemical'],
 'procidentia': ['predication', 'procidentia'],
 'proclaimer': ['proclaimer', 'reproclaim'],
 'procne': ['crepon', 'procne'],
 'procnemial': ['complainer', 'procnemial', 'recomplain'],
 'procommission': ['compromission', 'procommission'],
 'procreant': ['copartner', 'procreant'],
 'procreate': ['procreate', 'pterocera'],
 'procreation': ['incorporate', 'procreation'],
 'proctal': ['caltrop', 'proctal'],
 'proctitis': ['proctitis', 'protistic', 'tropistic'],
 'proctocolitis': ['coloproctitis', 'proctocolitis'],
 'procuress': ['percussor', 'procuress'],
 'prod': ['dorp', 'drop', 'prod'],
 'proddle': ['plodder', 'proddle'],
 'proem': ['merop', 'moper', 'proem', 'remop'],
 'proemial': ['emporial', 'proemial'],
 'proemium': ['emporium', 'pomerium', 'proemium'],
 'proethical': ['carpholite', 'proethical'],
 'proetid': ['diopter', 'peridot', 'proetid', 'protide', 'pteroid'],
 'proetidae': ['periodate', 'proetidae', 'proteidae'],
 'proetus': ['petrous', 'posture', 'proetus', 'proteus', 'septuor', 'spouter'],
 'proficient': ['prefiction', 'proficient'],
 'profit': ['forpit', 'profit'],
 'profiter': ['portfire', 'profiter'],
 'progeny': ['progeny', 'pyrogen'],
 'prohibiter': ['prohibiter', 'reprohibit'],
 'proidealistic': ['peridiastolic', 'periodicalist', 'proidealistic'],
 'proke': ['poker', 'proke'],
 'proker': ['porker', 'proker'],
 'prolacrosse': ['prolacrosse', 'sclerospora'],
 'prolapse': ['prolapse', 'sapropel'],
 'prolation': ['ploration', 'portional', 'prolation'],
 'prolegate': ['petrogale', 'petrolage', 'prolegate'],
 'proletarian': ['prerational', 'proletarian'],
 'proletarianize': ['prerealization', 'proletarianize'],
 'proletariat': ['proletariat', 'reptatorial'],
 'proletary': ['proletary', 'pyrolater'],
 'prolicense': ['prolicense', 'proselenic'],
 'prolongate': ['prolongate', 'protogenal'],
 'promerit': ['importer', 'promerit', 'reimport'],
 'promethean': ['heptameron', 'promethean'],
 'promise': ['imposer', 'promise', 'semipro'],
 'promisee': ['perisome', 'promisee', 'reimpose'],
 'promiser': ['primrose', 'promiser'],
 'promitosis': ['isotropism', 'promitosis'],
 'promnesia': ['mesropian', 'promnesia', 'spironema'],
 'promonarchist': ['micranthropos', 'promonarchist'],
 'promote': ['promote', 'protome'],
 'pronaos': ['pronaos', 'soprano'],
 'pronate': ['operant', 'pronate', 'protean'],
 'pronative': ['overpaint', 'pronative'],
 'proneur': ['proneur', 'purrone'],
 'pronotal': ['portolan', 'pronotal'],
 'pronto': ['pronto', 'proton'],
 'pronunciable': ['preconnubial', 'pronunciable'],
 'proo': ['poor', 'proo'],
 'proofer': ['proofer', 'reproof'],
 'prop': ['prop', 'ropp'],
 'propellent': ['prepollent', 'propellent'],
 'propene': ['preopen', 'propene'],
 'prophloem': ['pleomorph', 'prophloem'],
 'propine': ['piperno', 'propine'],
 'propinoic': ['propinoic', 'propionic'],
 'propionic': ['propinoic', 'propionic'],
 'propitial': ['propitial', 'triplopia'],
 'proportioner': ['proportioner', 'reproportion'],
 'propose': ['opposer', 'propose'],
 'propraetorial': ['propraetorial', 'protoperlaria'],
 'proprietous': ['peritropous', 'proprietous'],
 'propylene': ['polyprene', 'propylene'],
 'propyne': ['propyne', 'pyropen'],
 'prorate': ['praetor', 'prorate'],
 'proration': ['proration', 'troparion'],
 'prore': ['porer', 'prore', 'roper'],
 'prorebate': ['perborate', 'prorebate', 'reprobate'],
 'proreduction': ['proreduction', 'reproduction'],
 'prorevision': ['prorevision', 'provisioner', 'reprovision'],
 'prorogate': ['graperoot', 'prorogate'],
 'prosaist': ['prosaist', 'protasis'],
 'prosateur': ['prosateur', 'pterosaur'],
 'prose': ['poser', 'prose', 'ropes', 'spore'],
 'prosecretin': ['prosecretin', 'reinspector'],
 'prosectorial': ['corporealist', 'prosectorial'],
 'prosectorium': ['micropterous', 'prosectorium'],
 'proselenic': ['prolicense', 'proselenic'],
 'proselyte': ['polyester', 'proselyte'],
 'proseminate': ['impersonate', 'proseminate'],
 'prosemination': ['impersonation', 'prosemination', 'semipronation'],
 'proseneschal': ['chaperonless', 'proseneschal'],
 'prosification': ['antisoporific', 'prosification', 'sporification'],
 'prosilient': ['linopteris', 'prosilient'],
 'prosiphonate': ['nephroptosia', 'prosiphonate'],
 'proso': ['poros', 'proso', 'sopor', 'spoor'],
 'prosodiacal': ['dorsoapical', 'prosodiacal'],
 'prosopyle': ['polyspore', 'prosopyle'],
 'prossy': ['prossy', 'spyros'],
 'prostatectomy': ['cryptostomate', 'prostatectomy'],
 'prosternate': ['paternoster', 'prosternate', 'transportee'],
 'prostomiate': ['metroptosia', 'prostomiate'],
 'protactic': ['catoptric', 'protactic'],
 'protasis': ['prosaist', 'protasis'],
 'prote': ['poter', 'prote', 'repot', 'tepor', 'toper', 'trope'],
 'protead': ['adopter', 'protead', 'readopt'],
 'protean': ['operant', 'pronate', 'protean'],
 'protease': ['asterope', 'protease'],
 'protectional': ['lactoprotein', 'protectional'],
 'proteic': ['perotic', 'proteic', 'tropeic'],
 'proteida': ['apteroid', 'proteida'],
 'proteidae': ['periodate', 'proetidae', 'proteidae'],
 'proteidean': ['pontederia', 'proteidean'],
 'protein': ['pointer', 'protein', 'pterion', 'repoint', 'tropine'],
 'proteinic': ['epornitic', 'proteinic'],
 'proteles': ['proteles', 'serpolet'],
 'protelidae': ['leopardite', 'protelidae'],
 'protend': ['portend', 'protend'],
 'protension': ['portension', 'protension'],
 'proteolysis': ['elytroposis', 'proteolysis'],
 'proteose': ['esotrope', 'proteose'],
 'protest': ['protest', 'spotter'],
 'protester': ['protester', 'reprotest'],
 'proteus': ['petrous', 'posture', 'proetus', 'proteus', 'septuor', 'spouter'],
 'protheca': ['archpoet', 'protheca'],
 'prothesis': ['posterish', 'prothesis', 'sophister', 'storeship', 'tephrosis'],
 'prothetical': ['proathletic', 'prothetical'],
 'prothoracic': ['acrotrophic', 'prothoracic'],
 'protide': ['diopter', 'peridot', 'proetid', 'protide', 'pteroid'],
 'protist': ['protist', 'tropist'],
 'protistic': ['proctitis', 'protistic', 'tropistic'],
 'proto': ['porto', 'proto', 'troop'],
 'protocneme': ['mecopteron', 'protocneme'],
 'protogenal': ['prolongate', 'protogenal'],
 'protoma': ['protoma', 'taproom'],
 'protomagnesium': ['protomagnesium', 'spermatogonium'],
 'protome': ['promote', 'protome'],
 'protomorphic': ['morphotropic', 'protomorphic'],
 'proton': ['pronto', 'proton'],
 'protone': ['porteno', 'protone'],
 'protonemal': ['monopteral', 'protonemal'],
 'protoparent': ['protoparent', 'protopteran'],
 'protopathic': ['haptotropic', 'protopathic'],
 'protopathy': ['protopathy', 'protophyta'],
 'protoperlaria': ['propraetorial', 'protoperlaria'],
 'protophyta': ['protopathy', 'protophyta'],
 'protophyte': ['protophyte', 'tropophyte'],
 'protophytic': ['protophytic', 'tropophytic'],
 'protopine': ['preoption', 'protopine'],
 'protopteran': ['protoparent', 'protopteran'],
 'protore': ['protore', 'trooper'],
 'protosulphide': ['protosulphide', 'sulphoproteid'],
 'prototheme': ['photometer', 'prototheme'],
 'prototherian': ['ornithoptera', 'prototherian'],
 'prototrophic': ['prototrophic', 'trophotropic'],
 'protrade': ['predator', 'protrade', 'teardrop'],
 'protreaty': ['protreaty', 'reptatory'],
 'protuberantial': ['perturbational', 'protuberantial'],
 'protyl': ['portly', 'protyl', 'tropyl'],
 'provenient': ['prevention', 'provenient'],
 'provide': ['prevoid', 'provide'],
 'provider': ['overdrip', 'provider'],
 'provisioner': ['prorevision', 'provisioner', 'reprovision'],
 'prowed': ['powder', 'prowed'],
 'prude': ['drupe', 'duper', 'perdu', 'prude', 'pured'],
 'prudent': ['prudent', 'prunted', 'uptrend'],
 'prudential': ['prudential', 'putredinal'],
 'prudist': ['disrupt', 'prudist'],
 'prudy': ['prudy', 'purdy', 'updry'],
 'prue': ['peru', 'prue', 'pure'],
 'prune': ['perun', 'prune'],
 'prunted': ['prudent', 'prunted', 'uptrend'],
 'prussic': ['prussic', 'scirpus'],
 'prut': ['prut', 'turp'],
 'pry': ['pry', 'pyr'],
 'pryer': ['perry', 'pryer'],
 'pryse': ['pryse', 'spyer'],
 'psalm': ['plasm', 'psalm', 'slamp'],
 'psalmic': ['plasmic', 'psalmic'],
 'psalmister': ['psalmister', 'spermalist'],
 'psalmodial': ['plasmodial', 'psalmodial'],
 'psalmodic': ['plasmodic', 'psalmodic'],
 'psaloid': ['psaloid', 'salpoid'],
 'psalter': ['palster', 'persalt', 'plaster', 'psalter', 'spartle', 'stapler'],
 'psalterian': ['alpestrian', 'palestrian', 'psalterian'],
 'psalterion': ['interposal', 'psalterion'],
 'psaltery': ['plastery', 'psaltery'],
 'psaltress': ['psaltress', 'strapless'],
 'psaronius': ['prasinous', 'psaronius'],
 'psedera': ['psedera', 'respade'],
 'psellism': ['misspell', 'psellism'],
 'pseudelytron': ['pseudelytron', 'unproselyted'],
 'pseudimago': ['megapodius', 'pseudimago'],
 'pseudophallic': ['diplocephalus', 'pseudophallic'],
 'psha': ['hasp', 'pash', 'psha', 'shap'],
 'psi': ['psi', 'sip'],
 'psiloceratid': ['prediastolic', 'psiloceratid'],
 'psilophyton': ['polyphonist', 'psilophyton'],
 'psilotic': ['colpitis', 'politics', 'psilotic'],
 'psittacine': ['antiseptic', 'psittacine'],
 'psoadic': ['psoadic', 'scapoid', 'sciapod'],
 'psoas': ['passo', 'psoas'],
 'psocidae': ['diascope', 'psocidae', 'scopidae'],
 'psocine': ['psocine', 'scopine'],
 'psora': ['psora', 'sapor', 'sarpo'],
 'psoralea': ['parosela', 'psoralea'],
 'psoroid': ['psoroid', 'sporoid'],
 'psorous': ['psorous', 'soursop', 'sporous'],
 'psychobiological': ['biopsychological', 'psychobiological'],
 'psychobiology': ['biopsychology', 'psychobiology'],
 'psychogalvanic': ['galvanopsychic', 'psychogalvanic'],
 'psychomancy': ['psychomancy', 'scyphomancy'],
 'psychomonism': ['monopsychism', 'psychomonism'],
 'psychoneurological': ['neuropsychological', 'psychoneurological'],
 'psychoneurosis': ['neuropsychosis', 'psychoneurosis'],
 'psychophysiological': ['physiopsychological', 'psychophysiological'],
 'psychophysiology': ['physiopsychology', 'psychophysiology'],
 'psychorrhagy': ['chrysography', 'psychorrhagy'],
 'psychosomatic': ['psychosomatic', 'somatopsychic'],
 'psychotechnology': ['psychotechnology', 'technopsychology'],
 'psychotheism': ['psychotheism', 'theopsychism'],
 'psychotria': ['physiocrat', 'psychotria'],
 'ptelea': ['apelet', 'ptelea'],
 'ptereal': ['pearlet', 'pleater', 'prelate', 'ptereal', 'replate', 'repleat'],
 'pterian': ['painter', 'pertain', 'pterian', 'repaint'],
 'pterideous': ['depositure', 'pterideous'],
 'pteridological': ['dipterological', 'pteridological'],
 'pteridologist': ['dipterologist', 'pteridologist'],
 'pteridology': ['dipterology', 'pteridology'],
 'pterion': ['pointer', 'protein', 'pterion', 'repoint', 'tropine'],
 'pteris': ['priest', 'pteris', 'sprite', 'stripe'],
 'pterocera': ['procreate', 'pterocera'],
 'pterodactylidae': ['dactylopteridae', 'pterodactylidae'],
 'pterodactylus': ['dactylopterus', 'pterodactylus'],
 'pterographer': ['petrographer', 'pterographer'],
 'pterographic': ['petrographic', 'pterographic'],
 'pterographical': ['petrographical', 'pterographical'],
 'pterography': ['petrography', 'pterography', 'typographer'],
 'pteroid': ['diopter', 'peridot', 'proetid', 'protide', 'pteroid'],
 'pteroma': ['pteroma', 'tempora'],
 'pteropus': ['pteropus', 'stoppeur'],
 'pterosaur': ['prosateur', 'pterosaur'],
 'pterospora': ['praepostor', 'pterospora'],
 'pteryla': ['apertly', 'peartly', 'platery', 'pteryla', 'taperly'],
 'pterylographical': ['petrographically', 'pterylographical'],
 'pterylological': ['petrologically', 'pterylological'],
 'pterylosis': ['peristylos', 'pterylosis'],
 'ptilota': ['ptilota', 'talipot', 'toptail'],
 'ptinus': ['ptinus', 'unspit'],
 'ptolemean': ['leptonema', 'ptolemean'],
 'ptomain': ['maintop', 'ptomain', 'tampion', 'timpano'],
 'ptomainic': ['impaction', 'ptomainic'],
 'ptyalin': ['inaptly', 'planity', 'ptyalin'],
 'ptyalocele': ['clypeolate', 'ptyalocele'],
 'ptyalogenic': ['genotypical', 'ptyalogenic'],
 'pu': ['pu', 'up'],
 'pua': ['pau', 'pua'],
 'puan': ['napu', 'puan', 'puna'],
 'publisher': ['publisher', 'republish'],
 'puckball': ['puckball', 'pullback'],
 'puckrel': ['plucker', 'puckrel'],
 'pud': ['dup', 'pud'],
 'pudendum': ['pudendum', 'undumped'],
 'pudent': ['pudent', 'uptend'],
 'pudic': ['cupid', 'pudic'],
 'pudical': ['paludic', 'pudical'],
 'pudicity': ['cupidity', 'pudicity'],
 'puerer': ['puerer', 'purree'],
 'puffer': ['puffer', 'repuff'],
 'pug': ['gup', 'pug'],
 'pugman': ['panmug', 'pugman'],
 'puisne': ['puisne', 'supine'],
 'puist': ['puist', 'upsit'],
 'puja': ['jaup', 'puja'],
 'puke': ['keup', 'puke'],
 'pule': ['lupe', 'pelu', 'peul', 'pule'],
 'pulian': ['paulin', 'pulian'],
 'pulicene': ['clupeine', 'pulicene'],
 'pulicidal': ['picudilla', 'pulicidal'],
 'pulicide': ['lupicide', 'pediculi', 'pulicide'],
 'puling': ['gulpin', 'puling'],
 'pulish': ['huspil', 'pulish'],
 'pullback': ['puckball', 'pullback'],
 'pulp': ['plup', 'pulp'],
 'pulper': ['pulper', 'purple'],
 'pulpiter': ['pulpiter', 'repulpit'],
 'pulpous': ['populus', 'pulpous'],
 'pulpstone': ['pulpstone', 'unstopple'],
 'pulsant': ['pulsant', 'upslant'],
 'pulsate': ['pulsate', 'spatule', 'upsteal'],
 'pulsatile': ['palluites', 'pulsatile'],
 'pulsation': ['platinous', 'pulsation'],
 'pulsator': ['postural', 'pulsator', 'sportula'],
 'pulse': ['lepus', 'pulse'],
 'pulsion': ['pulsion', 'unspoil', 'upsilon'],
 'pulvic': ['pulvic', 'vulpic'],
 'pumper': ['pumper', 'repump'],
 'pumple': ['peplum', 'pumple'],
 'puna': ['napu', 'puan', 'puna'],
 'puncher': ['puncher', 'unperch'],
 'punctilio': ['punctilio', 'unpolitic'],
 'puncturer': ['puncturer', 'upcurrent'],
 'punger': ['punger', 'repugn'],
 'pungle': ['plunge', 'pungle'],
 'puniceous': ['pecunious', 'puniceous'],
 'punish': ['inpush', 'punish', 'unship'],
 'punisher': ['punisher', 'repunish'],
 'punishment': ['punishment', 'unshipment'],
 'punlet': ['penult', 'punlet', 'puntel'],
 'punnet': ['punnet', 'unpent'],
 'puno': ['noup', 'puno', 'upon'],
 'punta': ['punta', 'unapt', 'untap'],
 'puntal': ['puntal', 'unplat'],
 'puntel': ['penult', 'punlet', 'puntel'],
 'punti': ['input', 'punti'],
 'punto': ['punto', 'unpot', 'untop'],
 'pupa': ['paup', 'pupa'],
 'pupelo': ['poulpe', 'pupelo'],
 'purana': ['purana', 'uparna'],
 'purdy': ['prudy', 'purdy', 'updry'],
 'pure': ['peru', 'prue', 'pure'],
 'pured': ['drupe', 'duper', 'perdu', 'prude', 'pured'],
 'puree': ['puree', 'rupee'],
 'purer': ['purer', 'purre'],
 'purine': ['purine', 'unripe', 'uprein'],
 'purism': ['primus', 'purism'],
 'purist': ['purist', 'spruit', 'uprist', 'upstir'],
 'puritan': ['pintura', 'puritan', 'uptrain'],
 'puritaness': ['presustain', 'puritaness', 'supersaint'],
 'purler': ['purler', 'purrel'],
 'purple': ['pulper', 'purple'],
 'purpose': ['peropus', 'purpose'],
 'purpuroxanthin': ['purpuroxanthin', 'xanthopurpurin'],
 'purre': ['purer', 'purre'],
 'purree': ['puerer', 'purree'],
 'purrel': ['purler', 'purrel'],
 'purrone': ['proneur', 'purrone'],
 'purse': ['purse', 'resup', 'sprue', 'super'],
 'purser': ['purser', 'spruer'],
 'purslane': ['purslane', 'serpulan', 'supernal'],
 'purslet': ['purslet', 'spurlet', 'spurtle'],
 'pursuer': ['pursuer', 'usurper'],
 'pursy': ['pursy', 'pyrus', 'syrup'],
 'pus': ['pus', 'sup'],
 'pushtu': ['pushtu', 'upshut'],
 'pustule': ['pluteus', 'pustule'],
 'pustulose': ['pustulose', 'stupulose'],
 'put': ['put', 'tup'],
 'putanism': ['putanism', 'sumpitan'],
 'putation': ['outpaint', 'putation'],
 'putredinal': ['prudential', 'putredinal'],
 'putrid': ['putrid', 'turpid'],
 'putridly': ['putridly', 'turpidly'],
 'pya': ['pay', 'pya', 'yap'],
 'pyal': ['paly', 'play', 'pyal', 'pyla'],
 'pycnial': ['pliancy', 'pycnial'],
 'pyelic': ['epicly', 'pyelic'],
 'pyelitis': ['pyelitis', 'sipylite'],
 'pyelocystitis': ['cystopyelitis', 'pyelocystitis'],
 'pyelonephritis': ['nephropyelitis', 'pyelonephritis'],
 'pyeloureterogram': ['pyeloureterogram', 'ureteropyelogram'],
 'pyemesis': ['empyesis', 'pyemesis'],
 'pygmalion': ['maypoling', 'pygmalion'],
 'pyin': ['piny', 'pyin'],
 'pyla': ['paly', 'play', 'pyal', 'pyla'],
 'pylades': ['pylades', 'splayed'],
 'pylagore': ['playgoer', 'pylagore'],
 'pylar': ['parly', 'pylar', 'pyral'],
 'pyonephrosis': ['nephropyosis', 'pyonephrosis'],
 'pyopneumothorax': ['pneumopyothorax', 'pyopneumothorax'],
 'pyosepticemia': ['pyosepticemia', 'septicopyemia'],
 'pyosepticemic': ['pyosepticemic', 'septicopyemic'],
 'pyr': ['pry', 'pyr'],
 'pyracanth': ['pantarchy', 'pyracanth'],
 'pyral': ['parly', 'pylar', 'pyral'],
 'pyrales': ['parsley', 'pyrales', 'sparely', 'splayer'],
 'pyralid': ['pyralid', 'rapidly'],
 'pyramidale': ['lampyridae', 'pyramidale'],
 'pyranometer': ['premonetary', 'pyranometer'],
 'pyre': ['prey', 'pyre', 'rype'],
 'pyrena': ['napery', 'pyrena'],
 'pyrenic': ['cyprine', 'pyrenic'],
 'pyrenoid': ['pyrenoid', 'pyridone', 'pyrodine'],
 'pyretogenic': ['pyretogenic', 'pyrogenetic'],
 'pyrex': ['prexy', 'pyrex'],
 'pyrgocephaly': ['pelycography', 'pyrgocephaly'],
 'pyridone': ['pyrenoid', 'pyridone', 'pyrodine'],
 'pyrites': ['pyrites', 'sperity'],
 'pyritoid': ['pityroid', 'pyritoid'],
 'pyro': ['pory', 'pyro', 'ropy'],
 'pyroarsenite': ['arsenopyrite', 'pyroarsenite'],
 'pyrochemical': ['microcephaly', 'pyrochemical'],
 'pyrocomenic': ['pyrocomenic', 'pyromeconic'],
 'pyrodine': ['pyrenoid', 'pyridone', 'pyrodine'],
 'pyrogen': ['progeny', 'pyrogen'],
 'pyrogenetic': ['pyretogenic', 'pyrogenetic'],
 'pyrolater': ['proletary', 'pyrolater'],
 'pyromantic': ['importancy', 'patronymic', 'pyromantic'],
 'pyromeconic': ['pyrocomenic', 'pyromeconic'],
 'pyrope': ['popery', 'pyrope'],
 'pyropen': ['propyne', 'pyropen'],
 'pyrophorus': ['porphyrous', 'pyrophorus'],
 'pyrotheria': ['erythropia', 'pyrotheria'],
 'pyruline': ['pyruline', 'unripely'],
 'pyrus': ['pursy', 'pyrus', 'syrup'],
 'pyruvil': ['pyruvil', 'pyvuril'],
 'pythagorism': ['myographist', 'pythagorism'],
 'pythia': ['pythia', 'typhia'],
 'pythic': ['phytic', 'pitchy', 'pythic', 'typhic'],
 'pythogenesis': ['phytogenesis', 'pythogenesis'],
 'pythogenetic': ['phytogenetic', 'pythogenetic'],
 'pythogenic': ['phytogenic', 'pythogenic', 'typhogenic'],
 'pythogenous': ['phytogenous', 'pythogenous'],
 'python': ['phyton', 'python'],
 'pythonic': ['hypnotic', 'phytonic', 'pythonic', 'typhonic'],
 'pythonism': ['hypnotism', 'pythonism'],
 'pythonist': ['hypnotist', 'pythonist'],
 'pythonize': ['hypnotize', 'pythonize'],
 'pythonoid': ['hypnotoid', 'pythonoid'],
 'pyvuril': ['pyruvil', 'pyvuril'],
 'quadrual': ['quadrual', 'quadrula'],
 'quadrula': ['quadrual', 'quadrula'],
 'quail': ['quail', 'quila'],
 'quake': ['quake', 'queak'],
 'quale': ['equal', 'quale', 'queal'],
 'qualitied': ['liquidate', 'qualitied'],
 'quamoclit': ['coquitlam', 'quamoclit'],
 'quannet': ['quannet', 'tanquen'],
 'quartile': ['quartile', 'requital', 'triequal'],
 'quartine': ['antiquer', 'quartine'],
 'quata': ['quata', 'taqua'],
 'quatrin': ['quatrin', 'tarquin'],
 'queak': ['quake', 'queak'],
 'queal': ['equal', 'quale', 'queal'],
 'queeve': ['eveque', 'queeve'],
 'quencher': ['quencher', 'requench'],
 'querier': ['querier', 'require'],
 'queriman': ['queriman', 'ramequin'],
 'querist': ['querist', 'squiret'],
 'quernal': ['quernal', 'ranquel'],
 'quester': ['quester', 'request'],
 'questioner': ['questioner', 'requestion'],
 'questor': ['questor', 'torques'],
 'quiet': ['quiet', 'quite'],
 'quietable': ['equitable', 'quietable'],
 'quieter': ['quieter', 'requite'],
 'quietist': ['equitist', 'quietist'],
 'quietsome': ['quietsome', 'semiquote'],
 'quiina': ['quiina', 'quinia'],
 'quila': ['quail', 'quila'],
 'quiles': ['quiles', 'quisle'],
 'quinate': ['antique', 'quinate'],
 'quince': ['cinque', 'quince'],
 'quinia': ['quiina', 'quinia'],
 'quinite': ['inquiet', 'quinite'],
 'quinnat': ['quinnat', 'quintan'],
 'quinse': ['quinse', 'sequin'],
 'quintan': ['quinnat', 'quintan'],
 'quintato': ['quintato', 'totaquin'],
 'quinze': ['quinze', 'zequin'],
 'quirt': ['quirt', 'qurti'],
 'quisle': ['quiles', 'quisle'],
 'quite': ['quiet', 'quite'],
 'quits': ['quits', 'squit'],
 'quote': ['quote', 'toque'],
 'quoter': ['quoter', 'roquet', 'torque'],
 'qurti': ['quirt', 'qurti'],
 'ra': ['ar', 'ra'],
 'raad': ['adar', 'arad', 'raad', 'rada'],
 'raash': ['asarh', 'raash', 'sarah'],
 'rab': ['bar', 'bra', 'rab'],
 'raband': ['bandar', 'raband'],
 'rabatine': ['atabrine', 'rabatine'],
 'rabatte': ['baretta', 'rabatte', 'tabaret'],
 'rabbanite': ['barnabite', 'rabbanite', 'rabbinate'],
 'rabbet': ['barbet', 'rabbet', 'tabber'],
 'rabbinate': ['barnabite', 'rabbanite', 'rabbinate'],
 'rabble': ['barbel', 'labber', 'rabble'],
 'rabboni': ['barbion', 'rabboni'],
 'rabi': ['abir', 'bari', 'rabi'],
 'rabic': ['baric', 'carib', 'rabic'],
 'rabid': ['barid', 'bidar', 'braid', 'rabid'],
 'rabidly': ['bardily', 'rabidly', 'ridably'],
 'rabidness': ['bardiness', 'rabidness'],
 'rabies': ['braise', 'rabies', 'rebias'],
 'rabin': ['abrin', 'bairn', 'brain', 'brian', 'rabin'],
 'rabinet': ['atebrin', 'rabinet'],
 'raccoon': ['carcoon', 'raccoon'],
 'race': ['acer', 'acre', 'care', 'crea', 'race'],
 'racemate': ['camerate', 'macerate', 'racemate'],
 'racemation': ['aeromantic', 'cameration', 'maceration', 'racemation'],
 'raceme': ['amerce', 'raceme'],
 'racemed': ['decream', 'racemed'],
 'racemic': ['ceramic', 'racemic'],
 'racer': ['carer', 'crare', 'racer'],
 'rach': ['arch', 'char', 'rach'],
 'rache': ['acher', 'arche', 'chare', 'chera', 'rache', 'reach'],
 'rachel': ['rachel', 'rechal'],
 'rachianectes': ['rachianectes', 'rhacianectes'],
 'rachidial': ['diarchial', 'rachidial'],
 'rachitis': ['architis', 'rachitis'],
 'racial': ['alaric', 'racial'],
 'racialist': ['racialist', 'satirical'],
 'racing': ['arcing', 'racing'],
 'racist': ['crista', 'racist'],
 'rack': ['cark', 'rack'],
 'racker': ['racker', 'rerack'],
 'racket': ['racket', 'retack', 'tacker'],
 'racking': ['arcking', 'carking', 'racking'],
 'rackingly': ['carkingly', 'rackingly'],
 'rackle': ['calker', 'lacker', 'rackle', 'recalk', 'reckla'],
 'racon': ['acorn', 'acron', 'racon'],
 'raconteur': ['cuarteron', 'raconteur'],
 'racoon': ['caroon', 'corona', 'racoon'],
 'racy': ['cary', 'racy'],
 'rad': ['dar', 'rad'],
 'rada': ['adar', 'arad', 'raad', 'rada'],
 'raddle': ['ladder', 'raddle'],
 'raddleman': ['dreamland', 'raddleman'],
 'radectomy': ['myctodera', 'radectomy'],
 'radek': ['daker', 'drake', 'kedar', 'radek'],
 'radiable': ['labridae', 'radiable'],
 'radiale': ['ardelia', 'laridae', 'radiale'],
 'radian': ['adrian', 'andira', 'andria', 'radian', 'randia'],
 'radiance': ['caridean', 'dircaean', 'radiance'],
 'radiant': ['intrada', 'radiant'],
 'radiata': ['dataria', 'radiata'],
 'radical': ['cardial', 'radical'],
 'radicant': ['antacrid', 'cardiant', 'radicant', 'tridacna'],
 'radicel': ['decrial', 'radicel', 'radicle'],
 'radices': ['diceras', 'radices', 'sidecar'],
 'radicle': ['decrial', 'radicel', 'radicle'],
 'radicose': ['idocrase', 'radicose'],
 'radicule': ['auricled', 'radicule'],
 'radiculose': ['coresidual', 'radiculose'],
 'radiectomy': ['acidometry', 'medicatory', 'radiectomy'],
 'radii': ['dairi', 'darii', 'radii'],
 'radio': ['aroid', 'doria', 'radio'],
 'radioautograph': ['autoradiograph', 'radioautograph'],
 'radioautographic': ['autoradiographic', 'radioautographic'],
 'radioautography': ['autoradiography', 'radioautography'],
 'radiohumeral': ['humeroradial', 'radiohumeral'],
 'radiolite': ['editorial', 'radiolite'],
 'radiolucent': ['radiolucent', 'reductional'],
 'radioman': ['adoniram', 'radioman'],
 'radiomicrometer': ['microradiometer', 'radiomicrometer'],
 'radiophone': ['phoronidea', 'radiophone'],
 'radiophony': ['hypodorian', 'radiophony'],
 'radiotelephone': ['radiotelephone', 'teleradiophone'],
 'radiotron': ['ordinator', 'radiotron'],
 'radish': ['ardish', 'radish'],
 'radius': ['darius', 'radius'],
 'radman': ['mandra', 'radman'],
 'radon': ['adorn', 'donar', 'drona', 'radon'],
 'radula': ['adular', 'aludra', 'radula'],
 'rafael': ['aflare', 'rafael'],
 'rafe': ['fare', 'fear', 'frae', 'rafe'],
 'raffee': ['affeer', 'raffee'],
 'raffia': ['affair', 'raffia'],
 'raffle': ['farfel', 'raffle'],
 'rafik': ['fakir', 'fraik', 'kafir', 'rafik'],
 'raft': ['frat', 'raft'],
 'raftage': ['fregata', 'raftage'],
 'rafter': ['frater', 'rafter'],
 'rag': ['gar', 'gra', 'rag'],
 'raga': ['agar', 'agra', 'gara', 'raga'],
 'rage': ['ager', 'agre', 'gare', 'gear', 'rage'],
 'rageless': ['eelgrass', 'gearless', 'rageless'],
 'ragfish': ['garfish', 'ragfish'],
 'ragged': ['dagger', 'gadger', 'ragged'],
 'raggee': ['agrege', 'raggee'],
 'raggety': ['gargety', 'raggety'],
 'raggle': ['gargle', 'gregal', 'lagger', 'raggle'],
 'raggled': ['draggle', 'raggled'],
 'raggy': ['aggry', 'raggy'],
 'ragingly': ['grayling', 'ragingly'],
 'raglanite': ['antiglare', 'raglanite'],
 'raglet': ['raglet', 'tergal'],
 'raglin': ['nargil', 'raglin'],
 'ragman': ['amgarn', 'mangar', 'marang', 'ragman'],
 'ragnar': ['garran', 'ragnar'],
 'ragshag': ['ragshag', 'shagrag'],
 'ragtag': ['ragtag', 'tagrag'],
 'ragtime': ['migrate', 'ragtime'],
 'ragule': ['ragule', 'regula'],
 'raguly': ['glaury', 'raguly'],
 'raia': ['aira', 'aria', 'raia'],
 'raid': ['arid', 'dari', 'raid'],
 'raider': ['arride', 'raider'],
 'raif': ['fair', 'fiar', 'raif'],
 'raiidae': ['ariidae', 'raiidae'],
 'rail': ['aril', 'lair', 'lari', 'liar', 'lira', 'rail', 'rial'],
 'railage': ['lairage', 'railage', 'regalia'],
 'railer': ['railer', 'rerail'],
 'railhead': ['headrail', 'railhead'],
 'railless': ['lairless', 'railless'],
 'railman': ['lairman', 'laminar', 'malarin', 'railman'],
 'raiment': ['minaret', 'raiment', 'tireman'],
 'rain': ['arni', 'iran', 'nair', 'rain', 'rani'],
 'raincoat': ['craniota', 'croatian', 'narcotia', 'raincoat'],
 'rainful': ['rainful', 'unfrail'],
 'rainspout': ['rainspout', 'supinator'],
 'rainy': ['nairy', 'rainy'],
 'rais': ['rais', 'sair', 'sari'],
 'raise': ['aries', 'arise', 'raise', 'serai'],
 'raiseman': ['erasmian', 'raiseman'],
 'raiser': ['raiser', 'sierra'],
 'raisin': ['raisin', 'sirian'],
 'raj': ['jar', 'raj'],
 'raja': ['ajar', 'jara', 'raja'],
 'rajah': ['ajhar', 'rajah'],
 'rajeev': ['evejar', 'rajeev'],
 'rake': ['rake', 'reak'],
 'rakesteel': ['rakesteel', 'rakestele'],
 'rakestele': ['rakesteel', 'rakestele'],
 'rakh': ['hark', 'khar', 'rakh'],
 'raki': ['ikra', 'kari', 'raki'],
 'rakish': ['rakish', 'riksha', 'shikar', 'shikra', 'sikhra'],
 'rakit': ['kitar', 'krait', 'rakit', 'traik'],
 'raku': ['kuar', 'raku', 'rauk'],
 'ralf': ['farl', 'ralf'],
 'ralliance': ['alliancer', 'ralliance'],
 'ralline': ['ralline', 'renilla'],
 'ram': ['arm', 'mar', 'ram'],
 'rama': ['amar', 'amra', 'mara', 'rama'],
 'ramada': ['armada', 'damara', 'ramada'],
 'ramage': ['gemara', 'ramage'],
 'ramaite': ['ametria', 'artemia', 'meratia', 'ramaite'],
 'ramal': ['alarm', 'malar', 'maral', 'marla', 'ramal'],
 'ramanas': ['ramanas', 'sramana'],
 'ramate': ['ramate', 'retama'],
 'ramble': ['ambler', 'blamer', 'lamber', 'marble', 'ramble'],
 'rambler': ['marbler', 'rambler'],
 'rambling': ['marbling', 'rambling'],
 'rambo': ['broma', 'rambo'],
 'rambutan': ['rambutan', 'tamburan'],
 'rame': ['erma', 'mare', 'rame', 'ream'],
 'ramed': ['armed', 'derma', 'dream', 'ramed'],
 'rament': ['manter', 'marten', 'rament'],
 'ramental': ['maternal', 'ramental'],
 'ramequin': ['queriman', 'ramequin'],
 'ramesh': ['masher', 'ramesh', 'shamer'],
 'ramet': ['armet',
           'mater',
           'merat',
           'metra',
           'ramet',
           'tamer',
           'terma',
           'trame',
           'trema'],
 'rami': ['amir', 'irma', 'mari', 'mira', 'rami', 'rima'],
 'ramie': ['aimer', 'maire', 'marie', 'ramie'],
 'ramiferous': ['armiferous', 'ramiferous'],
 'ramigerous': ['armigerous', 'ramigerous'],
 'ramillie': ['milliare', 'ramillie'],
 'ramisection': ['anisometric',
                 'creationism',
                 'miscreation',
                 'ramisection',
                 'reactionism'],
 'ramist': ['marist', 'matris', 'ramist'],
 'ramline': ['marline', 'mineral', 'ramline'],
 'rammel': ['lammer', 'rammel'],
 'rammy': ['mymar', 'rammy'],
 'ramon': ['manor', 'moran', 'norma', 'ramon', 'roman'],
 'ramona': ['oarman', 'ramona'],
 'ramose': ['amores', 'ramose', 'sorema'],
 'ramosely': ['marysole', 'ramosely'],
 'ramp': ['pram', 'ramp'],
 'rampant': ['mantrap', 'rampant'],
 'ramped': ['damper', 'ramped'],
 'ramper': ['prearm', 'ramper'],
 'rampike': ['perakim', 'permiak', 'rampike'],
 'ramping': ['gripman', 'prigman', 'ramping'],
 'ramsey': ['ramsey', 'smeary'],
 'ramson': ['ramson', 'ransom'],
 'ramtil': ['mitral', 'ramtil'],
 'ramule': ['mauler', 'merula', 'ramule'],
 'ramulus': ['malurus', 'ramulus'],
 'ramus': ['musar', 'ramus', 'rusma', 'surma'],
 'ramusi': ['misura', 'ramusi'],
 'ran': ['arn', 'nar', 'ran'],
 'rana': ['arna', 'rana'],
 'ranales': ['arsenal', 'ranales'],
 'rance': ['caner', 'crane', 'crena', 'nacre', 'rance'],
 'rancel': ['lancer', 'rancel'],
 'rancer': ['craner', 'rancer'],
 'ranche': ['enarch', 'ranche'],
 'ranchero': ['anchorer', 'ranchero', 'reanchor'],
 'rancho': ['anchor', 'archon', 'charon', 'rancho'],
 'rancid': ['andric', 'cardin', 'rancid'],
 'rand': ['darn', 'nard', 'rand'],
 'randan': ['annard', 'randan'],
 'randem': ['damner', 'manred', 'randem', 'remand'],
 'rander': ['darner', 'darren', 'errand', 'rander', 'redarn'],
 'randia': ['adrian', 'andira', 'andria', 'radian', 'randia'],
 'randing': ['darning', 'randing'],
 'randite': ['antired', 'detrain', 'randite', 'trained'],
 'randle': ['aldern',
            'darnel',
            'enlard',
            'lander',
            'lenard',
            'randle',
            'reland'],
 'random': ['random', 'rodman'],
 'rane': ['arne', 'earn', 'rane'],
 'ranere': ['earner', 'ranere'],
 'rang': ['garn', 'gnar', 'rang'],
 'range': ['anger', 'areng', 'grane', 'range'],
 'ranged': ['danger', 'gander', 'garden', 'ranged'],
 'rangeless': ['largeness', 'rangeless', 'regalness'],
 'ranger': ['garner', 'ranger'],
 'rangey': ['anergy', 'rangey'],
 'ranginess': ['angriness', 'ranginess'],
 'rangle': ['angler', 'arleng', 'garnel', 'largen', 'rangle', 'regnal'],
 'rangy': ['angry', 'rangy'],
 'rani': ['arni', 'iran', 'nair', 'rain', 'rani'],
 'ranid': ['darin', 'dinar', 'drain', 'indra', 'nadir', 'ranid'],
 'ranidae': ['araneid', 'ariadne', 'ranidae'],
 'raniform': ['nariform', 'raniform'],
 'raninae': ['aranein', 'raninae'],
 'ranine': ['narine', 'ranine'],
 'rank': ['knar', 'kran', 'nark', 'rank'],
 'ranked': ['darken', 'kanred', 'ranked'],
 'ranker': ['ranker', 'rerank'],
 'rankish': ['krishna', 'rankish'],
 'rannel': ['lanner', 'rannel'],
 'ranquel': ['quernal', 'ranquel'],
 'ransom': ['ramson', 'ransom'],
 'rant': ['natr', 'rant', 'tarn', 'tran'],
 'rantepole': ['petrolean', 'rantepole'],
 'ranter': ['arrent', 'errant', 'ranter', 'ternar'],
 'rantipole': ['prelation', 'rantipole'],
 'ranula': ['alraun', 'alruna', 'ranula'],
 'rap': ['par', 'rap'],
 'rape': ['aper', 'pare', 'pear', 'rape', 'reap'],
 'rapeful': ['rapeful', 'upflare'],
 'raper': ['parer', 'raper'],
 'raphael': ['phalera', 'raphael'],
 'raphaelic': ['eparchial', 'raphaelic'],
 'raphe': ['hepar', 'phare', 'raphe'],
 'raphia': ['pahari', 'pariah', 'raphia'],
 'raphides': ['diphaser', 'parished', 'raphides', 'sephardi'],
 'raphis': ['parish', 'raphis', 'rhapis'],
 'rapic': ['capri', 'picra', 'rapic'],
 'rapid': ['adrip', 'rapid'],
 'rapidly': ['pyralid', 'rapidly'],
 'rapier': ['pairer', 'rapier', 'repair'],
 'rapine': ['parine', 'rapine'],
 'raping': ['paring', 'raping'],
 'rapparee': ['appearer', 'rapparee', 'reappear'],
 'rappe': ['paper', 'rappe'],
 'rappel': ['lapper', 'rappel'],
 'rappite': ['periapt', 'rappite'],
 'rapt': ['part', 'prat', 'rapt', 'tarp', 'trap'],
 'raptly': ['paltry', 'partly', 'raptly'],
 'raptor': ['parrot', 'raptor'],
 'rapture': ['parture', 'rapture'],
 'rare': ['rare', 'rear'],
 'rarebit': ['arbiter', 'rarebit'],
 'rareripe': ['rareripe', 'repairer'],
 'rarish': ['arrish', 'harris', 'rarish', 'sirrah'],
 'ras': ['ras', 'sar'],
 'rasa': ['rasa', 'sara'],
 'rascacio': ['coracias', 'rascacio'],
 'rascal': ['lascar', 'rascal', 'sacral', 'scalar'],
 'rase': ['arse', 'rase', 'sare', 'sear', 'sera'],
 'rasen': ['anser', 'nares', 'rasen', 'snare'],
 'raser': ['ersar', 'raser', 'serra'],
 'rasher': ['rasher', 'sharer'],
 'rashing': ['garnish', 'rashing'],
 'rashti': ['rashti', 'tarish'],
 'rasion': ['arsino', 'rasion', 'sonrai'],
 'rasp': ['rasp', 'spar'],
 'rasped': ['rasped', 'spader', 'spread'],
 'rasper': ['parser', 'rasper', 'sparer'],
 'rasping': ['aspring', 'rasping', 'sparing'],
 'raspingly': ['raspingly', 'sparingly'],
 'raspingness': ['raspingness', 'sparingness'],
 'raspite': ['piaster', 'piastre', 'raspite', 'spirate', 'traipse'],
 'raspy': ['raspy', 'spary', 'spray'],
 'rasse': ['arses', 'rasse'],
 'raster': ['arrest', 'astrer', 'raster', 'starer'],
 'rastik': ['rastik', 'sarkit', 'straik'],
 'rastle': ['laster',
            'lastre',
            'rastle',
            'relast',
            'resalt',
            'salter',
            'slater',
            'stelar'],
 'rastus': ['rastus', 'tarsus'],
 'rat': ['art', 'rat', 'tar', 'tra'],
 'rata': ['rata', 'taar', 'tara'],
 'ratable': ['alberta', 'latebra', 'ratable'],
 'ratal': ['altar', 'artal', 'ratal', 'talar'],
 'ratanhia': ['ratanhia', 'rhatania'],
 'ratbite': ['biretta', 'brattie', 'ratbite'],
 'ratch': ['chart', 'ratch'],
 'ratchel': ['clethra', 'latcher', 'ratchel', 'relatch', 'talcher', 'trachle'],
 'ratcher': ['charter', 'ratcher'],
 'ratchet': ['chatter', 'ratchet'],
 'ratchety': ['chattery', 'ratchety', 'trachyte'],
 'ratching': ['charting', 'ratching'],
 'rate': ['rate', 'tare', 'tear', 'tera'],
 'rated': ['dater', 'derat', 'detar', 'drate', 'rated', 'trade', 'tread'],
 'ratel': ['alert', 'alter', 'artel', 'later', 'ratel', 'taler', 'telar'],
 'rateless': ['rateless', 'tasseler', 'tearless', 'tesseral'],
 'ratfish': ['ratfish', 'tashrif'],
 'rath': ['hart', 'rath', 'tahr', 'thar', 'trah'],
 'rathe': ['earth', 'hater', 'heart', 'herat', 'rathe'],
 'rathed': ['dearth', 'hatred', 'rathed', 'thread'],
 'rathely': ['earthly', 'heartly', 'lathery', 'rathely'],
 'ratherest': ['ratherest', 'shatterer'],
 'rathest': ['rathest', 'shatter'],
 'rathite': ['hartite', 'rathite'],
 'rathole': ['loather', 'rathole'],
 'raticidal': ['raticidal', 'triadical'],
 'raticide': ['ceratiid', 'raticide'],
 'ratine': ['nerita', 'ratine', 'retain', 'retina', 'tanier'],
 'rating': ['rating', 'tringa'],
 'ratio': ['ariot', 'ratio'],
 'ration': ['aroint', 'ration'],
 'rationable': ['alboranite', 'rationable'],
 'rational': ['notarial', 'rational', 'rotalian'],
 'rationale': ['alienator', 'rationale'],
 'rationalize': ['rationalize', 'realization'],
 'rationally': ['notarially', 'rationally'],
 'rationate': ['notariate', 'rationate'],
 'ratitae': ['arietta', 'ratitae'],
 'ratite': ['attire', 'ratite', 'tertia'],
 'ratitous': ['outstair', 'ratitous'],
 'ratlike': ['artlike', 'ratlike', 'tarlike'],
 'ratline': ['entrail',
             'latiner',
             'latrine',
             'ratline',
             'reliant',
             'retinal',
             'trenail'],
 'rattage': ['garetta', 'rattage', 'regatta'],
 'rattan': ['rattan', 'tantra', 'tartan'],
 'ratteen': ['entreat', 'ratteen', 'tarente', 'ternate', 'tetrane'],
 'ratten': ['attern', 'natter', 'ratten', 'tarten'],
 'ratti': ['ratti', 'titar', 'trait'],
 'rattish': ['athirst', 'rattish', 'tartish'],
 'rattle': ['artlet', 'latter', 'rattle', 'tartle', 'tatler'],
 'rattles': ['rattles', 'slatter', 'starlet', 'startle'],
 'rattlesome': ['rattlesome', 'saltometer'],
 'rattly': ['rattly', 'tartly'],
 'ratton': ['attorn', 'ratton', 'rottan'],
 'rattus': ['astrut', 'rattus', 'stuart'],
 'ratwood': ['ratwood', 'tarwood'],
 'raught': ['raught', 'tughra'],
 'rauk': ['kuar', 'raku', 'rauk'],
 'raul': ['alur', 'laur', 'lura', 'raul', 'ural'],
 'rauli': ['rauli', 'urali', 'urial'],
 'raun': ['raun', 'uran', 'urna'],
 'raunge': ['nauger', 'raunge', 'ungear'],
 'rave': ['aver', 'rave', 'vare', 'vera'],
 'ravel': ['arvel', 'larve', 'laver', 'ravel', 'velar'],
 'ravelin': ['elinvar', 'ravelin', 'reanvil', 'valerin'],
 'ravelly': ['ravelly', 'valeryl'],
 'ravendom': ['overdamn', 'ravendom'],
 'ravenelia': ['ravenelia', 'veneralia'],
 'ravenish': ['enravish', 'ravenish', 'vanisher'],
 'ravi': ['ravi', 'riva', 'vair', 'vari', 'vira'],
 'ravigote': ['ravigote', 'rogative'],
 'ravin': ['invar', 'ravin', 'vanir'],
 'ravine': ['averin', 'ravine'],
 'ravined': ['invader', 'ravined', 'viander'],
 'raving': ['grivna', 'raving'],
 'ravissant': ['ravissant', 'srivatsan'],
 'raw': ['raw', 'war'],
 'rawboned': ['downbear', 'rawboned'],
 'rawish': ['rawish', 'wairsh', 'warish'],
 'rax': ['arx', 'rax'],
 'ray': ['ary', 'ray', 'yar'],
 'raya': ['arya', 'raya'],
 'rayan': ['aryan', 'nayar', 'rayan'],
 'rayed': ['deary', 'deray', 'rayed', 'ready', 'yeard'],
 'raylet': ['lyrate', 'raylet', 'realty', 'telary'],
 'rayonnance': ['annoyancer', 'rayonnance'],
 'raze': ['ezra', 'raze'],
 're': ['er', 're'],
 'rea': ['aer', 'are', 'ear', 'era', 'rea'],
 'reaal': ['areal', 'reaal'],
 'reabandon': ['abandoner', 'reabandon'],
 'reabolish': ['abolisher', 'reabolish'],
 'reabsent': ['absenter', 'reabsent'],
 'reabsorb': ['absorber', 'reabsorb'],
 'reaccession': ['accessioner', 'reaccession'],
 'reaccomplish': ['accomplisher', 'reaccomplish'],
 'reaccord': ['accorder', 'reaccord'],
 'reaccost': ['ectosarc', 'reaccost'],
 'reach': ['acher', 'arche', 'chare', 'chera', 'rache', 'reach'],
 'reachieve': ['echeveria', 'reachieve'],
 'react': ['caret',
           'carte',
           'cater',
           'crate',
           'creat',
           'creta',
           'react',
           'recta',
           'trace'],
 'reactance': ['cancerate', 'reactance'],
 'reaction': ['actioner', 'anerotic', 'ceration', 'creation', 'reaction'],
 'reactional': ['creational', 'crotalinae', 'laceration', 'reactional'],
 'reactionary': ['creationary', 'reactionary'],
 'reactionism': ['anisometric',
                 'creationism',
                 'miscreation',
                 'ramisection',
                 'reactionism'],
 'reactionist': ['creationist', 'reactionist'],
 'reactive': ['creative', 'reactive'],
 'reactively': ['creatively', 'reactively'],
 'reactiveness': ['creativeness', 'reactiveness'],
 'reactivity': ['creativity', 'reactivity'],
 'reactor': ['creator', 'reactor'],
 'read': ['ared', 'daer', 'dare', 'dear', 'read'],
 'readapt': ['adapter', 'predata', 'readapt'],
 'readd': ['adder', 'dread', 'readd'],
 'readdress': ['addresser', 'readdress'],
 'reader': ['reader', 'redare', 'reread'],
 'reading': ['degrain', 'deraign', 'deringa', 'gradine', 'grained', 'reading'],
 'readjust': ['adjuster', 'readjust'],
 'readopt': ['adopter', 'protead', 'readopt'],
 'readorn': ['adorner', 'readorn'],
 'ready': ['deary', 'deray', 'rayed', 'ready', 'yeard'],
 'reaffect': ['affecter', 'reaffect'],
 'reaffirm': ['affirmer', 'reaffirm'],
 'reafflict': ['afflicter', 'reafflict'],
 'reagent': ['grantee', 'greaten', 'reagent', 'rentage'],
 'reagin': ['arenig', 'earing', 'gainer', 'reagin', 'regain'],
 'reak': ['rake', 'reak'],
 'real': ['earl', 'eral', 'lear', 'real'],
 'reales': ['alerse', 'leaser', 'reales', 'resale', 'reseal', 'sealer'],
 'realest': ['realest', 'reslate', 'resteal', 'stealer', 'teasler'],
 'realign': ['aligner', 'engrail', 'realign', 'reginal'],
 'realignment': ['engrailment', 'realignment'],
 'realism': ['mislear', 'realism'],
 'realist': ['aletris', 'alister', 'listera', 'realist', 'saltier'],
 'realistic': ['eristical', 'realistic'],
 'reality': ['irately', 'reality'],
 'realive': ['realive', 'valerie'],
 'realization': ['rationalize', 'realization'],
 'reallot': ['reallot', 'rotella', 'tallero'],
 'reallow': ['allower', 'reallow'],
 'reallude': ['laureled', 'reallude'],
 'realmlet': ['realmlet', 'tremella'],
 'realter': ['alterer', 'realter', 'relater'],
 'realtor': ['realtor', 'relator'],
 'realty': ['lyrate', 'raylet', 'realty', 'telary'],
 'ream': ['erma', 'mare', 'rame', 'ream'],
 'reamage': ['megaera', 'reamage'],
 'reamass': ['amasser', 'reamass'],
 'reamend': ['amender', 'meander', 'reamend', 'reedman'],
 'reamer': ['marree', 'reamer'],
 'reamuse': ['measure', 'reamuse'],
 'reamy': ['mayer', 'reamy'],
 'reanchor': ['anchorer', 'ranchero', 'reanchor'],
 'reanneal': ['annealer', 'lernaean', 'reanneal'],
 'reannex': ['annexer', 'reannex'],
 'reannoy': ['annoyer', 'reannoy'],
 'reanoint': ['anointer', 'inornate', 'nonirate', 'reanoint'],
 'reanswer': ['answerer', 'reanswer'],
 'reanvil': ['elinvar', 'ravelin', 'reanvil', 'valerin'],
 'reap': ['aper', 'pare', 'pear', 'rape', 'reap'],
 'reapdole': ['leoparde', 'reapdole'],
 'reappeal': ['appealer', 'reappeal'],
 'reappear': ['appearer', 'rapparee', 'reappear'],
 'reapplaud': ['applauder', 'reapplaud'],
 'reappoint': ['appointer', 'reappoint'],
 'reapportion': ['apportioner', 'reapportion'],
 'reapprehend': ['apprehender', 'reapprehend'],
 'reapproach': ['approacher', 'reapproach'],
 'rear': ['rare', 'rear'],
 'reargue': ['augerer', 'reargue'],
 'reargument': ['garmenture', 'reargument'],
 'rearise': ['rearise', 'reraise'],
 'rearm': ['armer', 'rearm'],
 'rearray': ['arrayer', 'rearray'],
 'rearrest': ['arrester', 'rearrest'],
 'reascend': ['ascender', 'reascend'],
 'reascent': ['reascent', 'sarcenet'],
 'reascertain': ['ascertainer', 'reascertain', 'secretarian'],
 'reask': ['asker', 'reask', 'saker', 'sekar'],
 'reason': ['arseno', 'reason'],
 'reassail': ['assailer', 'reassail'],
 'reassault': ['assaulter', 'reassault', 'saleratus'],
 'reassay': ['assayer', 'reassay'],
 'reassent': ['assenter', 'reassent', 'sarsenet'],
 'reassert': ['asserter', 'reassert'],
 'reassign': ['assigner', 'reassign'],
 'reassist': ['assister', 'reassist'],
 'reassort': ['assertor', 'assorter', 'oratress', 'reassort'],
 'reastonish': ['astonisher', 'reastonish', 'treasonish'],
 'reasty': ['atresy', 'estray', 'reasty', 'stayer'],
 'reasy': ['reasy', 'resay', 'sayer', 'seary'],
 'reattach': ['attacher', 'reattach'],
 'reattack': ['attacker', 'reattack'],
 'reattain': ['attainer', 'reattain', 'tertiana'],
 'reattempt': ['attempter', 'reattempt'],
 'reattend': ['attender', 'nattered', 'reattend'],
 'reattest': ['attester', 'reattest'],
 'reattract': ['attracter', 'reattract'],
 'reattraction': ['reattraction', 'retractation'],
 'reatus': ['auster', 'reatus'],
 'reavail': ['reavail', 'valeria'],
 'reave': ['eaver', 'reave'],
 'reavoid': ['avodire', 'avoider', 'reavoid'],
 'reavouch': ['avoucher', 'reavouch'],
 'reavow': ['avower', 'reavow'],
 'reawait': ['awaiter', 'reawait'],
 'reawaken': ['awakener', 'reawaken'],
 'reaward': ['awarder', 'reaward'],
 'reb': ['ber', 'reb'],
 'rebab': ['barbe', 'bebar', 'breba', 'rebab'],
 'reback': ['backer', 'reback'],
 'rebag': ['bagre', 'barge', 'begar', 'rebag'],
 'rebait': ['baiter', 'barite', 'rebait', 'terbia'],
 'rebake': ['beaker', 'berake', 'rebake'],
 'reballast': ['ballaster', 'reballast'],
 'reballot': ['balloter', 'reballot'],
 'reban': ['abner', 'arneb', 'reban'],
 'rebanish': ['banisher', 'rebanish'],
 'rebar': ['barer', 'rebar'],
 'rebargain': ['bargainer', 'rebargain'],
 'rebasis': ['brassie', 'rebasis'],
 'rebate': ['beater', 'berate', 'betear', 'rebate', 'rebeat'],
 'rebater': ['rebater', 'terebra'],
 'rebathe': ['breathe', 'rebathe'],
 'rebato': ['boater', 'borate', 'rebato'],
 'rebawl': ['bawler', 'brelaw', 'rebawl', 'warble'],
 'rebear': ['bearer', 'rebear'],
 'rebeat': ['beater', 'berate', 'betear', 'rebate', 'rebeat'],
 'rebeck': ['becker', 'rebeck'],
 'rebed': ['brede', 'breed', 'rebed'],
 'rebeg': ['gerbe', 'grebe', 'rebeg'],
 'rebeggar': ['beggarer', 'rebeggar'],
 'rebegin': ['bigener', 'rebegin'],
 'rebehold': ['beholder', 'rebehold'],
 'rebellow': ['bellower', 'rebellow'],
 'rebelly': ['bellyer', 'rebelly'],
 'rebelong': ['belonger', 'rebelong'],
 'rebend': ['bender', 'berend', 'rebend'],
 'rebenefit': ['benefiter', 'rebenefit'],
 'rebeset': ['besteer', 'rebeset'],
 'rebestow': ['bestower', 'rebestow'],
 'rebetray': ['betrayer', 'eatberry', 'rebetray', 'teaberry'],
 'rebewail': ['bewailer', 'rebewail'],
 'rebia': ['barie', 'beira', 'erbia', 'rebia'],
 'rebias': ['braise', 'rabies', 'rebias'],
 'rebid': ['bider', 'bredi', 'bride', 'rebid'],
 'rebill': ['biller', 'rebill'],
 'rebillet': ['billeter', 'rebillet'],
 'rebind': ['binder', 'inbred', 'rebind'],
 'rebirth': ['brither', 'rebirth'],
 'rebite': ['bertie', 'betire', 'rebite'],
 'reblade': ['bleared', 'reblade'],
 'reblast': ['blaster', 'reblast', 'stabler'],
 'rebleach': ['bleacher', 'rebleach'],
 'reblend': ['blender', 'reblend'],
 'rebless': ['blesser', 'rebless'],
 'reblock': ['blocker', 'brockle', 'reblock'],
 'rebloom': ['bloomer', 'rebloom'],
 'reblot': ['bolter', 'orblet', 'reblot', 'rebolt'],
 'reblow': ['blower', 'bowler', 'reblow', 'worble'],
 'reblue': ['brulee', 'burele', 'reblue'],
 'rebluff': ['bluffer', 'rebluff'],
 'reblunder': ['blunderer', 'reblunder'],
 'reboant': ['baronet', 'reboant'],
 'reboantic': ['bicornate', 'carbonite', 'reboantic'],
 'reboard': ['arbored', 'boarder', 'reboard'],
 'reboast': ['barotse', 'boaster', 'reboast', 'sorbate'],
 'reboil': ['boiler', 'reboil'],
 'rebold': ['belord', 'bordel', 'rebold'],
 'rebolt': ['bolter', 'orblet', 'reblot', 'rebolt'],
 'rebone': ['boreen', 'enrobe', 'neebor', 'rebone'],
 'rebook': ['booker', 'brooke', 'rebook'],
 'rebop': ['probe', 'rebop'],
 'rebore': ['rebore', 'rerobe'],
 'reborrow': ['borrower', 'reborrow'],
 'rebound': ['beround', 'bounder', 'rebound', 'unbored', 'unorbed', 'unrobed'],
 'rebounder': ['rebounder', 'underrobe'],
 'rebox': ['boxer', 'rebox'],
 'rebrace': ['cerebra', 'rebrace'],
 'rebraid': ['braider', 'rebraid'],
 'rebranch': ['brancher', 'rebranch'],
 'rebrand': ['bernard', 'brander', 'rebrand'],
 'rebrandish': ['brandisher', 'rebrandish'],
 'rebreed': ['breeder', 'rebreed'],
 'rebrew': ['brewer', 'rebrew'],
 'rebribe': ['berberi', 'rebribe'],
 'rebring': ['bringer', 'rebring'],
 'rebroach': ['broacher', 'rebroach'],
 'rebroadcast': ['broadcaster', 'rebroadcast'],
 'rebrown': ['browner', 'rebrown'],
 'rebrush': ['brusher', 'rebrush'],
 'rebud': ['bedur', 'rebud', 'redub'],
 'rebudget': ['budgeter', 'rebudget'],
 'rebuff': ['buffer', 'rebuff'],
 'rebuffet': ['buffeter', 'rebuffet'],
 'rebuild': ['builder', 'rebuild'],
 'rebulk': ['bulker', 'rebulk'],
 'rebunch': ['buncher', 'rebunch'],
 'rebundle': ['blendure', 'rebundle'],
 'reburden': ['burdener', 'reburden'],
 'reburn': ['burner', 'reburn'],
 'reburnish': ['burnisher', 'reburnish'],
 'reburst': ['burster', 'reburst'],
 'rebus': ['burse', 'rebus', 'suber'],
 'rebush': ['busher', 'rebush'],
 'rebut': ['brute', 'buret', 'rebut', 'tuber'],
 'rebute': ['rebute', 'retube'],
 'rebuttal': ['burletta', 'rebuttal'],
 'rebutton': ['buttoner', 'rebutton'],
 'rebuy': ['buyer', 'rebuy'],
 'recalk': ['calker', 'lacker', 'rackle', 'recalk', 'reckla'],
 'recall': ['caller', 'cellar', 'recall'],
 'recampaign': ['campaigner', 'recampaign'],
 'recancel': ['canceler', 'clarence', 'recancel'],
 'recant': ['canter',
            'creant',
            'cretan',
            'nectar',
            'recant',
            'tanrec',
            'trance'],
 'recantation': ['recantation', 'triacontane'],
 'recanter': ['canterer', 'recanter', 'recreant', 'terrance'],
 'recap': ['caper', 'crape', 'pacer', 'perca', 'recap'],
 'recaption': ['preaction', 'precation', 'recaption'],
 'recarpet': ['pretrace', 'recarpet'],
 'recart': ['arrect', 'carter', 'crater', 'recart', 'tracer'],
 'recase': ['cesare', 'crease', 'recase', 'searce'],
 'recash': ['arches', 'chaser', 'eschar', 'recash', 'search'],
 'recast': ['carest', 'caster', 'recast'],
 'recatch': ['catcher', 'recatch'],
 'recede': ['decree', 'recede'],
 'recedent': ['centered', 'decenter', 'decentre', 'recedent'],
 'receder': ['decreer', 'receder'],
 'receipt': ['ereptic', 'precite', 'receipt'],
 'receiptor': ['procerite', 'receiptor'],
 'receivables': ['receivables', 'serviceable'],
 'received': ['deceiver', 'received'],
 'recement': ['cementer', 'cerement', 'recement'],
 'recension': ['ninescore', 'recension'],
 'recensionist': ['intercession', 'recensionist'],
 'recent': ['center', 'recent', 'tenrec'],
 'recenter': ['centerer', 'recenter', 'recentre', 'terrence'],
 'recentre': ['centerer', 'recenter', 'recentre', 'terrence'],
 'reception': ['prenotice', 'reception'],
 'receptoral': ['praelector', 'receptoral'],
 'recess': ['cesser', 'recess'],
 'rechain': ['chainer', 'enchair', 'rechain'],
 'rechal': ['rachel', 'rechal'],
 'rechamber': ['chamberer', 'rechamber'],
 'rechange': ['encharge', 'rechange'],
 'rechant': ['chanter', 'rechant'],
 'rechar': ['archer', 'charer', 'rechar'],
 'recharter': ['charterer', 'recharter'],
 'rechase': ['archsee', 'rechase'],
 'rechaser': ['rechaser', 'research', 'searcher'],
 'rechasten': ['chastener', 'rechasten'],
 'rechaw': ['chawer', 'rechaw'],
 'recheat': ['cheater', 'hectare', 'recheat', 'reteach', 'teacher'],
 'recheck': ['checker', 'recheck'],
 'recheer': ['cheerer', 'recheer'],
 'rechew': ['chewer', 'rechew'],
 'rechip': ['cipher', 'rechip'],
 'rechisel': ['chiseler', 'rechisel'],
 'rechristen': ['christener', 'rechristen'],
 'rechuck': ['chucker', 'rechuck'],
 'recidivous': ['recidivous', 'veridicous'],
 'recipe': ['piecer', 'pierce', 'recipe'],
 'recipiend': ['perdicine', 'recipiend'],
 'recipient': ['princeite', 'recipient'],
 'reciprocate': ['carpocerite', 'reciprocate'],
 'recirculate': ['clericature', 'recirculate'],
 'recision': ['recision', 'soricine'],
 'recital': ['article', 'recital'],
 'recitativo': ['recitativo', 'victoriate'],
 'recite': ['cerite', 'certie', 'recite', 'tierce'],
 'recitement': ['centimeter', 'recitement', 'remittence'],
 'reckla': ['calker', 'lacker', 'rackle', 'recalk', 'reckla'],
 'reckless': ['clerkess', 'reckless'],
 'reckling': ['clerking', 'reckling'],
 'reckon': ['conker', 'reckon'],
 'reclaim': ['claimer', 'miracle', 'reclaim'],
 'reclaimer': ['calmierer', 'reclaimer'],
 'reclama': ['cameral', 'caramel', 'carmela', 'ceramal', 'reclama'],
 'reclang': ['cangler', 'glancer', 'reclang'],
 'reclasp': ['clasper', 'reclasp', 'scalper'],
 'reclass': ['carless', 'classer', 'reclass'],
 'reclean': ['cleaner', 'reclean'],
 'reclear': ['clearer', 'reclear'],
 'reclimb': ['climber', 'reclimb'],
 'reclinate': ['intercale', 'interlace', 'lacertine', 'reclinate'],
 'reclinated': ['credential', 'interlaced', 'reclinated'],
 'recluse': ['luceres', 'recluse'],
 'recluseness': ['censureless', 'recluseness'],
 'reclusion': ['cornelius', 'inclosure', 'reclusion'],
 'reclusive': ['reclusive', 'versicule'],
 'recoach': ['caroche', 'coacher', 'recoach'],
 'recoal': ['carole', 'coaler', 'coelar', 'oracle', 'recoal'],
 'recoast': ['coaster', 'recoast'],
 'recoat': ['coater', 'recoat'],
 'recock': ['cocker', 'recock'],
 'recoil': ['coiler', 'recoil'],
 'recoilment': ['clinometer', 'recoilment'],
 'recoin': ['cerion', 'coiner', 'neroic', 'orcein', 'recoin'],
 'recoinage': ['aerogenic', 'recoinage'],
 'recollate': ['electoral', 'recollate'],
 'recollation': ['collationer', 'recollation'],
 'recollection': ['collectioner', 'recollection'],
 'recollet': ['colleter', 'coteller', 'coterell', 'recollet'],
 'recolor': ['colorer', 'recolor'],
 'recomb': ['comber', 'recomb'],
 'recomfort': ['comforter', 'recomfort'],
 'recommand': ['commander', 'recommand'],
 'recommend': ['commender', 'recommend'],
 'recommission': ['commissioner', 'recommission'],
 'recompact': ['compacter', 'recompact'],
 'recompass': ['compasser', 'recompass'],
 'recompetition': ['competitioner', 'recompetition'],
 'recomplain': ['complainer', 'procnemial', 'recomplain'],
 'recompound': ['compounder', 'recompound'],
 'recomprehend': ['comprehender', 'recomprehend'],
 'recon': ['coner', 'crone', 'recon'],
 'reconceal': ['concealer', 'reconceal'],
 'reconcert': ['concreter', 'reconcert'],
 'reconcession': ['concessioner', 'reconcession'],
 'reconcoct': ['concocter', 'reconcoct'],
 'recondemn': ['condemner', 'recondemn'],
 'recondensation': ['nondesecration', 'recondensation'],
 'recondition': ['conditioner', 'recondition'],
 'reconfer': ['confrere', 'enforcer', 'reconfer'],
 'reconfess': ['confesser', 'reconfess'],
 'reconfirm': ['confirmer', 'reconfirm'],
 'reconform': ['conformer', 'reconform'],
 'reconfound': ['confounder', 'reconfound'],
 'reconfront': ['confronter', 'reconfront'],
 'recongeal': ['congealer', 'recongeal'],
 'reconjoin': ['conjoiner', 'reconjoin'],
 'reconnect': ['concenter', 'reconnect'],
 'reconnoiter': ['reconnoiter', 'reconnoitre'],
 'reconnoitre': ['reconnoiter', 'reconnoitre'],
 'reconsent': ['consenter', 'nonsecret', 'reconsent'],
 'reconsider': ['considerer', 'reconsider'],
 'reconsign': ['consigner', 'reconsign'],
 'reconstitution': ['constitutioner', 'reconstitution'],
 'reconstruct': ['constructer', 'reconstruct'],
 'reconsult': ['consulter', 'reconsult'],
 'recontend': ['contender', 'recontend'],
 'recontest': ['contester', 'recontest'],
 'recontinue': ['recontinue', 'unctioneer'],
 'recontract': ['contracter', 'correctant', 'recontract'],
 'reconvention': ['conventioner', 'reconvention'],
 'reconvert': ['converter', 'reconvert'],
 'reconvey': ['conveyer', 'reconvey'],
 'recook': ['cooker', 'recook'],
 'recool': ['cooler', 'recool'],
 'recopper': ['copperer', 'recopper'],
 'recopyright': ['copyrighter', 'recopyright'],
 'record': ['corder', 'record'],
 'recordation': ['corrodentia', 'recordation'],
 'recork': ['corker', 'recork', 'rocker'],
 'recorrection': ['correctioner', 'recorrection'],
 'recorrupt': ['corrupter', 'recorrupt'],
 'recounsel': ['enclosure', 'recounsel'],
 'recount': ['cornute', 'counter', 'recount', 'trounce'],
 'recountal': ['nucleator', 'recountal'],
 'recoup': ['couper', 'croupe', 'poucer', 'recoup'],
 'recourse': ['recourse', 'resource'],
 'recover': ['coverer', 'recover'],
 'recramp': ['cramper', 'recramp'],
 'recrank': ['cranker', 'recrank'],
 'recrate': ['caterer', 'recrate', 'retrace', 'terrace'],
 'recreant': ['canterer', 'recanter', 'recreant', 'terrance'],
 'recredit': ['cedriret', 'directer', 'recredit', 'redirect'],
 'recrew': ['crewer', 'recrew'],
 'recrimination': ['intermorainic', 'recrimination'],
 'recroon': ['coroner', 'crooner', 'recroon'],
 'recross': ['crosser', 'recross'],
 'recrowd': ['crowder', 'recrowd'],
 'recrown': ['crowner', 'recrown'],
 'recrudency': ['decurrency', 'recrudency'],
 'recruital': ['curtailer', 'recruital', 'reticular'],
 'recrush': ['crusher', 'recrush'],
 'recta': ['caret',
           'carte',
           'cater',
           'crate',
           'creat',
           'creta',
           'react',
           'recta',
           'trace'],
 'rectal': ['carlet', 'cartel', 'claret', 'rectal', 'talcer'],
 'rectalgia': ['cartilage', 'rectalgia'],
 'recti': ['citer', 'recti', 'ticer', 'trice'],
 'rectifiable': ['certifiable', 'rectifiable'],
 'rectification': ['certification', 'cretification', 'rectification'],
 'rectificative': ['certificative', 'rectificative'],
 'rectificator': ['certificator', 'rectificator'],
 'rectificatory': ['certificatory', 'rectificatory'],
 'rectified': ['certified', 'rectified'],
 'rectifier': ['certifier', 'rectifier'],
 'rectify': ['certify', 'cretify', 'rectify'],
 'rection': ['cerotin', 'cointer', 'cotrine', 'cretion', 'noticer', 'rection'],
 'rectitude': ['certitude', 'rectitude'],
 'rectoress': ['crosstree', 'rectoress'],
 'rectorship': ['cristopher', 'rectorship'],
 'rectotome': ['octometer', 'rectotome', 'tocometer'],
 'rectovesical': ['rectovesical', 'vesicorectal'],
 'recur': ['curer', 'recur'],
 'recurl': ['curler', 'recurl'],
 'recurse': ['recurse', 'rescuer', 'securer'],
 'recurtain': ['recurtain', 'unerratic'],
 'recurvation': ['countervair', 'overcurtain', 'recurvation'],
 'recurvous': ['recurvous', 'verrucous'],
 'recusance': ['recusance', 'securance'],
 'recusant': ['etruscan', 'recusant'],
 'recusation': ['nectarious', 'recusation'],
 'recusator': ['craterous', 'recusator'],
 'recuse': ['cereus', 'ceruse', 'recuse', 'rescue', 'secure'],
 'recut': ['cruet', 'eruct', 'recut', 'truce'],
 'red': ['erd', 'red'],
 'redact': ['cedrat', 'decart', 'redact'],
 'redaction': ['citronade', 'endaortic', 'redaction'],
 'redactional': ['declaration', 'redactional'],
 'redamage': ['dreamage', 'redamage'],
 'redan': ['andre', 'arend', 'daren', 'redan'],
 'redare': ['reader', 'redare', 'reread'],
 'redarken': ['darkener', 'redarken'],
 'redarn': ['darner', 'darren', 'errand', 'rander', 'redarn'],
 'redart': ['darter',
            'dartre',
            'redart',
            'retard',
            'retrad',
            'tarred',
            'trader'],
 'redate': ['derate', 'redate'],
 'redaub': ['dauber', 'redaub'],
 'redawn': ['andrew', 'redawn', 'wander', 'warden'],
 'redbait': ['redbait', 'tribade'],
 'redbud': ['budder', 'redbud'],
 'redcoat': ['cordate', 'decator', 'redcoat'],
 'redden': ['nedder', 'redden'],
 'reddingite': ['digredient', 'reddingite'],
 'rede': ['deer', 'dere', 'dree', 'rede', 'reed'],
 'redeal': ['dealer', 'leader', 'redeal', 'relade', 'relead'],
 'redeck': ['decker', 'redeck'],
 'redeed': ['redeed', 'reeded'],
 'redeem': ['deemer', 'meered', 'redeem', 'remede'],
 'redefault': ['defaulter', 'redefault'],
 'redefeat': ['defeater', 'federate', 'redefeat'],
 'redefine': ['needfire', 'redefine'],
 'redeflect': ['redeflect', 'reflected'],
 'redelay': ['delayer', 'layered', 'redelay'],
 'redeliver': ['deliverer', 'redeliver'],
 'redemand': ['demander', 'redemand'],
 'redemolish': ['demolisher', 'redemolish'],
 'redeny': ['redeny', 'yender'],
 'redepend': ['depender', 'redepend'],
 'redeprive': ['prederive', 'redeprive'],
 'rederivation': ['rederivation', 'veratroidine'],
 'redescend': ['descender', 'redescend'],
 'redescription': ['prediscretion', 'redescription'],
 'redesign': ['designer', 'redesign', 'resigned'],
 'redesman': ['redesman', 'seamrend'],
 'redetect': ['detecter', 'redetect'],
 'redevelop': ['developer', 'redevelop'],
 'redfin': ['finder', 'friend', 'redfin', 'refind'],
 'redhoop': ['redhoop', 'rhodope'],
 'redia': ['aider', 'deair', 'irade', 'redia'],
 'redient': ['nitered', 'redient', 'teinder'],
 'redig': ['dirge', 'gride', 'redig', 'ridge'],
 'redigest': ['digester', 'redigest'],
 'rediminish': ['diminisher', 'rediminish'],
 'redintegrator': ['redintegrator', 'retrogradient'],
 'redip': ['pride', 'pried', 'redip'],
 'redirect': ['cedriret', 'directer', 'recredit', 'redirect'],
 'redisable': ['desirable', 'redisable'],
 'redisappear': ['disappearer', 'redisappear'],
 'rediscount': ['discounter', 'rediscount'],
 'rediscover': ['discoverer', 'rediscover'],
 'rediscuss': ['discusser', 'rediscuss'],
 'redispatch': ['dispatcher', 'redispatch'],
 'redisplay': ['displayer', 'redisplay'],
 'redispute': ['disrepute', 'redispute'],
 'redistend': ['dendrites', 'distender', 'redistend'],
 'redistill': ['distiller', 'redistill'],
 'redistinguish': ['distinguisher', 'redistinguish'],
 'redistrain': ['distrainer', 'redistrain'],
 'redisturb': ['disturber', 'redisturb'],
 'redive': ['derive', 'redive'],
 'redivert': ['diverter', 'redivert', 'verditer'],
 'redleg': ['gelder', 'ledger', 'redleg'],
 'redlegs': ['redlegs', 'sledger'],
 'redo': ['doer', 'redo', 'rode', 'roed'],
 'redock': ['corked', 'docker', 'redock'],
 'redolent': ['redolent', 'rondelet'],
 'redoom': ['doomer', 'mooder', 'redoom', 'roomed'],
 'redoubling': ['bouldering', 'redoubling'],
 'redoubt': ['doubter', 'obtrude', 'outbred', 'redoubt'],
 'redound': ['redound', 'rounded', 'underdo'],
 'redowa': ['redowa', 'woader'],
 'redraft': ['drafter', 'redraft'],
 'redrag': ['darger', 'gerard', 'grader', 'redrag', 'regard'],
 'redraw': ['drawer', 'redraw', 'reward', 'warder'],
 'redrawer': ['redrawer', 'rewarder', 'warderer'],
 'redream': ['dreamer', 'redream'],
 'redress': ['dresser', 'redress'],
 'redrill': ['driller', 'redrill'],
 'redrive': ['deriver', 'redrive', 'rivered'],
 'redry': ['derry', 'redry', 'ryder'],
 'redtail': ['dilater', 'lardite', 'redtail'],
 'redtop': ['deport', 'ported', 'redtop'],
 'redub': ['bedur', 'rebud', 'redub'],
 'reductant': ['reductant', 'traducent', 'truncated'],
 'reduction': ['introduce', 'reduction'],
 'reductional': ['radiolucent', 'reductional'],
 'redue': ['redue', 'urdee'],
 'redunca': ['durance', 'redunca', 'unraced'],
 'redwithe': ['redwithe', 'withered'],
 'redye': ['redye', 'reedy'],
 'ree': ['eer', 'ere', 'ree'],
 'reechy': ['cheery', 'reechy'],
 'reed': ['deer', 'dere', 'dree', 'rede', 'reed'],
 'reeded': ['redeed', 'reeded'],
 'reeden': ['endere', 'needer', 'reeden'],
 'reedily': ['reedily', 'reyield', 'yielder'],
 'reeding': ['energid', 'reeding'],
 'reedling': ['engirdle', 'reedling'],
 'reedman': ['amender', 'meander', 'reamend', 'reedman'],
 'reedwork': ['reedwork', 'reworked'],
 'reedy': ['redye', 'reedy'],
 'reef': ['feer', 'free', 'reef'],
 'reefing': ['feering', 'feigner', 'freeing', 'reefing', 'refeign'],
 'reek': ['eker', 'reek'],
 'reel': ['leer', 'reel'],
 'reeler': ['reeler', 'rereel'],
 'reelingly': ['leeringly', 'reelingly'],
 'reem': ['mere', 'reem'],
 'reeming': ['reeming', 'regimen'],
 'reen': ['erne', 'neer', 'reen'],
 'reenge': ['neeger', 'reenge', 'renege'],
 'rees': ['erse', 'rees', 'seer', 'sere'],
 'reese': ['esere', 'reese', 'resee'],
 'reesk': ['esker', 'keres', 'reesk', 'seker', 'skeer', 'skere'],
 'reest': ['ester',
           'estre',
           'reest',
           'reset',
           'steer',
           'stere',
           'stree',
           'terse',
           'tsere'],
 'reester': ['reester', 'steerer'],
 'reestle': ['reestle', 'resteel', 'steeler'],
 'reesty': ['reesty', 'yester'],
 'reet': ['reet', 'teer', 'tree'],
 'reetam': ['reetam', 'retame', 'teamer'],
 'reeveland': ['landreeve', 'reeveland'],
 'refall': ['faller', 'refall'],
 'refashion': ['fashioner', 'refashion'],
 'refasten': ['fastener', 'fenestra', 'refasten'],
 'refavor': ['favorer', 'overfar', 'refavor'],
 'refeed': ['feeder', 'refeed'],
 'refeel': ['feeler', 'refeel', 'reflee'],
 'refeign': ['feering', 'feigner', 'freeing', 'reefing', 'refeign'],
 'refel': ['fleer', 'refel'],
 'refer': ['freer', 'refer'],
 'referment': ['fermenter', 'referment'],
 'refetch': ['fetcher', 'refetch'],
 'refight': ['fighter', 'freight', 'refight'],
 'refill': ['filler', 'refill'],
 'refilter': ['filterer', 'refilter'],
 'refinable': ['inferable', 'refinable'],
 'refind': ['finder', 'friend', 'redfin', 'refind'],
 'refine': ['ferine', 'refine'],
 'refined': ['definer', 'refined'],
 'refiner': ['refiner', 'reinfer'],
 'refinger': ['fingerer', 'refinger'],
 'refining': ['infringe', 'refining'],
 'refinish': ['finisher', 'refinish'],
 'refit': ['freit', 'refit'],
 'refix': ['fixer', 'refix'],
 'reflash': ['flasher', 'reflash'],
 'reflected': ['redeflect', 'reflected'],
 'reflee': ['feeler', 'refeel', 'reflee'],
 'refling': ['ferling', 'flinger', 'refling'],
 'refloat': ['floater', 'florate', 'refloat'],
 'reflog': ['golfer', 'reflog'],
 'reflood': ['flooder', 'reflood'],
 'refloor': ['floorer', 'refloor'],
 'reflourish': ['flourisher', 'reflourish'],
 'reflow': ['flower', 'fowler', 'reflow', 'wolfer'],
 'reflower': ['flowerer', 'reflower'],
 'reflush': ['flusher', 'reflush'],
 'reflux': ['fluxer', 'reflux'],
 'refluxed': ['flexured', 'refluxed'],
 'refly': ['ferly', 'flyer', 'refly'],
 'refocus': ['focuser', 'refocus'],
 'refold': ['folder', 'refold'],
 'refoment': ['fomenter', 'refoment'],
 'refoot': ['footer', 'refoot'],
 'reforecast': ['forecaster', 'reforecast'],
 'reforest': ['forester', 'fosterer', 'reforest'],
 'reforfeit': ['forfeiter', 'reforfeit'],
 'reform': ['former', 'reform'],
 'reformado': ['doorframe', 'reformado'],
 'reformed': ['deformer', 'reformed'],
 'reformism': ['misreform', 'reformism'],
 'reformist': ['reformist', 'restiform'],
 'reforward': ['forwarder', 'reforward'],
 'refound': ['founder', 'refound'],
 'refoundation': ['foundationer', 'refoundation'],
 'refreshen': ['freshener', 'refreshen'],
 'refrighten': ['frightener', 'refrighten'],
 'refront': ['fronter', 'refront'],
 'reft': ['fret', 'reft', 'tref'],
 'refuel': ['ferule', 'fueler', 'refuel'],
 'refund': ['funder', 'refund'],
 'refurbish': ['furbisher', 'refurbish'],
 'refurl': ['furler', 'refurl'],
 'refurnish': ['furnisher', 'refurnish'],
 'refusingly': ['refusingly', 'syringeful'],
 'refutal': ['faulter', 'refutal', 'tearful'],
 'refute': ['fuerte', 'refute'],
 'reg': ['erg', 'ger', 'reg'],
 'regain': ['arenig', 'earing', 'gainer', 'reagin', 'regain'],
 'regal': ['argel', 'ergal', 'garle', 'glare', 'lager', 'large', 'regal'],
 'regalia': ['lairage', 'railage', 'regalia'],
 'regalian': ['algerian', 'geranial', 'regalian'],
 'regalist': ['glaister', 'regalist'],
 'regallop': ['galloper', 'regallop'],
 'regally': ['allergy', 'gallery', 'largely', 'regally'],
 'regalness': ['largeness', 'rangeless', 'regalness'],
 'regard': ['darger', 'gerard', 'grader', 'redrag', 'regard'],
 'regarnish': ['garnisher', 'regarnish'],
 'regather': ['gatherer', 'regather'],
 'regatta': ['garetta', 'rattage', 'regatta'],
 'regelate': ['eglatere', 'regelate', 'relegate'],
 'regelation': ['regelation', 'relegation'],
 'regenesis': ['energesis', 'regenesis'],
 'regent': ['gerent', 'regent'],
 'reges': ['reges', 'serge'],
 'reget': ['egret', 'greet', 'reget'],
 'regga': ['agger', 'gager', 'regga'],
 'reggie': ['greige', 'reggie'],
 'regia': ['geira', 'regia'],
 'regild': ['gilder', 'girdle', 'glider', 'regild', 'ridgel'],
 'regill': ['giller', 'grille', 'regill'],
 'regimen': ['reeming', 'regimen'],
 'regimenal': ['margeline', 'regimenal'],
 'regin': ['grein', 'inger', 'nigre', 'regin', 'reign', 'ringe'],
 'reginal': ['aligner', 'engrail', 'realign', 'reginal'],
 'reginald': ['dragline', 'reginald', 'ringlead'],
 'region': ['ignore', 'region'],
 'regional': ['geraniol', 'regional'],
 'registered': ['deregister', 'registered'],
 'registerer': ['registerer', 'reregister'],
 'regive': ['grieve', 'regive'],
 'regladden': ['gladdener', 'glandered', 'regladden'],
 'reglair': ['grailer', 'reglair'],
 'regle': ['leger', 'regle'],
 'reglet': ['gretel', 'reglet'],
 'regloss': ['glosser', 'regloss'],
 'reglove': ['overleg', 'reglove'],
 'reglow': ['glower', 'reglow'],
 'regma': ['grame', 'marge', 'regma'],
 'regnal': ['angler', 'arleng', 'garnel', 'largen', 'rangle', 'regnal'],
 'regraft': ['grafter', 'regraft'],
 'regrant': ['granter', 'regrant'],
 'regrasp': ['grasper', 'regrasp', 'sparger'],
 'regrass': ['grasser', 'regrass'],
 'regrate': ['greater', 'regrate', 'terrage'],
 'regrating': ['gartering', 'regrating'],
 'regrator': ['garroter', 'regrator'],
 'regreen': ['greener', 'regreen', 'reneger'],
 'regreet': ['greeter', 'regreet'],
 'regrind': ['grinder', 'regrind'],
 'regrinder': ['derringer', 'regrinder'],
 'regrip': ['griper', 'regrip'],
 'regroup': ['grouper', 'regroup'],
 'regrow': ['grower', 'regrow'],
 'reguard': ['guarder', 'reguard'],
 'regula': ['ragule', 'regula'],
 'regulation': ['regulation', 'urogenital'],
 'reguli': ['ligure', 'reguli'],
 'regur': ['regur', 'urger'],
 'regush': ['gusher', 'regush'],
 'reh': ['her', 'reh', 'rhe'],
 'rehale': ['healer', 'rehale', 'reheal'],
 'rehallow': ['hallower', 'rehallow'],
 'rehammer': ['hammerer', 'rehammer'],
 'rehang': ['hanger', 'rehang'],
 'reharden': ['hardener', 'reharden'],
 'reharm': ['harmer', 'reharm'],
 'reharness': ['harnesser', 'reharness'],
 'reharrow': ['harrower', 'reharrow'],
 'reharvest': ['harvester', 'reharvest'],
 'rehash': ['hasher', 'rehash'],
 'rehaul': ['hauler', 'rehaul'],
 'rehazard': ['hazarder', 'rehazard'],
 'rehead': ['adhere', 'header', 'hedera', 'rehead'],
 'reheal': ['healer', 'rehale', 'reheal'],
 'reheap': ['heaper', 'reheap'],
 'rehear': ['hearer', 'rehear'],
 'rehearser': ['rehearser', 'reshearer'],
 'rehearten': ['heartener', 'rehearten'],
 'reheat': ['heater', 'hereat', 'reheat'],
 'reheel': ['heeler', 'reheel'],
 'reheighten': ['heightener', 'reheighten'],
 'rehoist': ['hoister', 'rehoist'],
 'rehollow': ['hollower', 'rehollow'],
 'rehonor': ['honorer', 'rehonor'],
 'rehook': ['hooker', 'rehook'],
 'rehoop': ['hooper', 'rehoop'],
 'rehung': ['hunger', 'rehung'],
 'reid': ['dier', 'dire', 'reid', 'ride'],
 'reif': ['fire', 'reif', 'rife'],
 'reify': ['fiery', 'reify'],
 'reign': ['grein', 'inger', 'nigre', 'regin', 'reign', 'ringe'],
 'reignore': ['erigeron', 'reignore'],
 'reillustrate': ['reillustrate', 'ultrasterile'],
 'reim': ['emir', 'imer', 'mire', 'reim', 'remi', 'riem', 'rime'],
 'reimbody': ['embryoid', 'reimbody'],
 'reimpart': ['imparter', 'reimpart'],
 'reimplant': ['implanter', 'reimplant'],
 'reimply': ['primely', 'reimply'],
 'reimport': ['importer', 'promerit', 'reimport'],
 'reimpose': ['perisome', 'promisee', 'reimpose'],
 'reimpress': ['impresser', 'reimpress'],
 'reimpression': ['reimpression', 'repermission'],
 'reimprint': ['imprinter', 'reimprint'],
 'reimprison': ['imprisoner', 'reimprison'],
 'rein': ['neri', 'rein', 'rine'],
 'reina': ['erian', 'irena', 'reina'],
 'reincentive': ['internecive', 'reincentive'],
 'reincite': ['icterine', 'reincite'],
 'reincrudate': ['antireducer', 'reincrudate', 'untraceried'],
 'reindeer': ['denierer', 'reindeer'],
 'reindict': ['indicter', 'indirect', 'reindict'],
 'reindue': ['reindue', 'uredine'],
 'reinfect': ['frenetic', 'infecter', 'reinfect'],
 'reinfer': ['refiner', 'reinfer'],
 'reinfest': ['infester', 'reinfest'],
 'reinflate': ['interleaf', 'reinflate'],
 'reinflict': ['inflicter', 'reinflict'],
 'reinform': ['informer', 'reinform', 'reniform'],
 'reinhabit': ['inhabiter', 'reinhabit'],
 'reins': ['reins', 'resin', 'rinse', 'risen', 'serin', 'siren'],
 'reinsane': ['anserine', 'reinsane'],
 'reinsert': ['inserter', 'reinsert'],
 'reinsist': ['insister', 'reinsist', 'sinister', 'sisterin'],
 'reinspect': ['prescient', 'reinspect'],
 'reinspector': ['prosecretin', 'reinspector'],
 'reinspirit': ['inspiriter', 'reinspirit'],
 'reinstall': ['installer', 'reinstall'],
 'reinstation': ['reinstation', 'santorinite'],
 'reinstill': ['instiller', 'reinstill'],
 'reinstruct': ['instructer', 'intercrust', 'reinstruct'],
 'reinsult': ['insulter', 'lustrine', 'reinsult'],
 'reintend': ['indenter', 'intender', 'reintend'],
 'reinter': ['reinter', 'terrine'],
 'reinterest': ['interester', 'reinterest'],
 'reinterpret': ['interpreter', 'reinterpret'],
 'reinterrupt': ['interrupter', 'reinterrupt'],
 'reinterview': ['interviewer', 'reinterview'],
 'reintrench': ['intrencher', 'reintrench'],
 'reintrude': ['reintrude', 'unretired'],
 'reinvent': ['inventer', 'reinvent', 'ventrine', 'vintener'],
 'reinvert': ['inverter', 'reinvert', 'trinerve'],
 'reinvest': ['reinvest', 'servient'],
 'reis': ['reis', 'rise', 'seri', 'sier', 'sire'],
 'reit': ['iter', 'reit', 'rite', 'teri', 'tier', 'tire'],
 'reiter': ['errite', 'reiter', 'retier', 'retire', 'tierer'],
 'reiterable': ['reiterable', 'reliberate'],
 'rejail': ['jailer', 'rejail'],
 'rejerk': ['jerker', 'rejerk'],
 'rejoin': ['joiner', 'rejoin'],
 'rejolt': ['jolter', 'rejolt'],
 'rejourney': ['journeyer', 'rejourney'],
 'reki': ['erik', 'kier', 'reki'],
 'rekick': ['kicker', 'rekick'],
 'rekill': ['killer', 'rekill'],
 'rekiss': ['kisser', 'rekiss'],
 'reknit': ['reknit', 'tinker'],
 'reknow': ['knower', 'reknow', 'wroken'],
 'rel': ['ler', 'rel'],
 'relabel': ['labeler', 'relabel'],
 'relace': ['alerce', 'cereal', 'relace'],
 'relacquer': ['lacquerer', 'relacquer'],
 'relade': ['dealer', 'leader', 'redeal', 'relade', 'relead'],
 'reladen': ['leander', 'learned', 'reladen'],
 'relais': ['israel', 'relais', 'resail', 'sailer', 'serail', 'serial'],
 'relament': ['lamenter', 'relament', 'remantle'],
 'relamp': ['lamper', 'palmer', 'relamp'],
 'reland': ['aldern',
            'darnel',
            'enlard',
            'lander',
            'lenard',
            'randle',
            'reland'],
 'relap': ['lepra', 'paler', 'parel', 'parle', 'pearl', 'perla', 'relap'],
 'relapse': ['pleaser', 'preseal', 'relapse'],
 'relapsing': ['espringal', 'presignal', 'relapsing'],
 'relast': ['laster',
            'lastre',
            'rastle',
            'relast',
            'resalt',
            'salter',
            'slater',
            'stelar'],
 'relata': ['latera', 'relata'],
 'relatability': ['alterability', 'bilaterality', 'relatability'],
 'relatable': ['alterable', 'relatable'],
 'relatch': ['clethra', 'latcher', 'ratchel', 'relatch', 'talcher', 'trachle'],
 'relate': ['earlet', 'elater', 'relate'],
 'related': ['delater', 'related', 'treadle'],
 'relater': ['alterer', 'realter', 'relater'],
 'relation': ['oriental', 'relation', 'tirolean'],
 'relationism': ['misrelation', 'orientalism', 'relationism'],
 'relationist': ['orientalist', 'relationist'],
 'relative': ['levirate', 'relative'],
 'relativization': ['relativization', 'revitalization'],
 'relativize': ['relativize', 'revitalize'],
 'relator': ['realtor', 'relator'],
 'relaunch': ['launcher', 'relaunch'],
 'relay': ['early', 'layer', 'relay'],
 'relead': ['dealer', 'leader', 'redeal', 'relade', 'relead'],
 'releap': ['leaper', 'releap', 'repale', 'repeal'],
 'relearn': ['learner', 'relearn'],
 'releather': ['leatherer', 'releather', 'tarheeler'],
 'relection': ['centriole', 'electrion', 'relection'],
 'relegate': ['eglatere', 'regelate', 'relegate'],
 'relegation': ['regelation', 'relegation'],
 'relend': ['lender', 'relend'],
 'reletter': ['letterer', 'reletter'],
 'relevant': ['levanter', 'relevant', 'revelant'],
 'relevation': ['relevation', 'revelation'],
 'relevator': ['relevator', 'revelator', 'veratrole'],
 'relevel': ['leveler', 'relevel'],
 'reliably': ['beryllia', 'reliably'],
 'reliance': ['cerealin', 'cinereal', 'reliance'],
 'reliant': ['entrail',
             'latiner',
             'latrine',
             'ratline',
             'reliant',
             'retinal',
             'trenail'],
 'reliantly': ['interally', 'reliantly'],
 'reliberate': ['reiterable', 'reliberate'],
 'relic': ['crile', 'elric', 'relic'],
 'relick': ['licker', 'relick', 'rickle'],
 'relicted': ['derelict', 'relicted'],
 'relier': ['lierre', 'relier'],
 'relieving': ['inveigler', 'relieving'],
 'relievo': ['overlie', 'relievo'],
 'relift': ['fertil', 'filter', 'lifter', 'relift', 'trifle'],
 'religation': ['genitorial', 'religation'],
 'relight': ['lighter', 'relight', 'rightle'],
 'relighten': ['lightener', 'relighten', 'threeling'],
 'religion': ['ligroine', 'religion'],
 'relimit': ['limiter', 'relimit'],
 'reline': ['lierne', 'reline'],
 'relink': ['linker', 'relink'],
 'relish': ['hirsel', 'hirsle', 'relish'],
 'relishy': ['relishy', 'shirley'],
 'relist': ['lister', 'relist'],
 'relisten': ['enlister', 'esterlin', 'listener', 'relisten'],
 'relive': ['levier', 'relive', 'reveil', 'revile', 'veiler'],
 'reload': ['loader', 'ordeal', 'reload'],
 'reloan': ['lenora', 'loaner', 'orlean', 'reloan'],
 'relocation': ['iconolater', 'relocation'],
 'relock': ['locker', 'relock'],
 'relook': ['looker', 'relook'],
 'relose': ['relose', 'resole'],
 'relost': ['relost', 'reslot', 'rostel', 'sterol', 'torsel'],
 'relot': ['lerot', 'orlet', 'relot'],
 'relower': ['lowerer', 'relower'],
 'reluct': ['cutler', 'reluct'],
 'reluctation': ['countertail', 'reluctation'],
 'relumine': ['lemurine', 'meruline', 'relumine'],
 'rely': ['lyre', 'rely'],
 'remade': ['meader', 'remade'],
 'remagnification': ['germanification', 'remagnification'],
 'remagnify': ['germanify', 'remagnify'],
 'remail': ['mailer', 'remail'],
 'remain': ['ermani', 'marine', 'remain'],
 'remains': ['remains', 'seminar'],
 'remaintain': ['antimerina', 'maintainer', 'remaintain'],
 'reman': ['enarm', 'namer', 'reman'],
 'remand': ['damner', 'manred', 'randem', 'remand'],
 'remanet': ['remanet', 'remeant', 'treeman'],
 'remantle': ['lamenter', 'relament', 'remantle'],
 'remap': ['amper', 'remap'],
 'remarch': ['charmer', 'marcher', 'remarch'],
 'remark': ['marker', 'remark'],
 'remarket': ['marketer', 'remarket'],
 'remarry': ['marryer', 'remarry'],
 'remarshal': ['marshaler', 'remarshal'],
 'remask': ['masker', 'remask'],
 'remass': ['masser', 'remass'],
 'remast': ['martes', 'master', 'remast', 'stream'],
 'remasticate': ['metrectasia', 'remasticate'],
 'rematch': ['matcher', 'rematch'],
 'remeant': ['remanet', 'remeant', 'treeman'],
 'remede': ['deemer', 'meered', 'redeem', 'remede'],
 'remeet': ['meeter', 'remeet', 'teemer'],
 'remelt': ['melter', 'remelt'],
 'remend': ['mender', 'remend'],
 'remetal': ['lameter', 'metaler', 'remetal'],
 'remi': ['emir', 'imer', 'mire', 'reim', 'remi', 'riem', 'rime'],
 'remication': ['marcionite', 'microtinae', 'remication'],
 'remigate': ['emigrate', 'remigate'],
 'remigation': ['emigration', 'remigation'],
 'remill': ['miller', 'remill'],
 'remind': ['minder', 'remind'],
 'remint': ['minter', 'remint', 'termin'],
 'remiped': ['demirep', 'epiderm', 'impeder', 'remiped'],
 'remisrepresent': ['misrepresenter', 'remisrepresent'],
 'remission': ['missioner', 'remission'],
 'remisunderstand': ['misunderstander', 'remisunderstand'],
 'remit': ['merit', 'miter', 'mitre', 'remit', 'timer'],
 'remittal': ['remittal', 'termital'],
 'remittance': ['carminette', 'remittance'],
 'remittence': ['centimeter', 'recitement', 'remittence'],
 'remitter': ['remitter', 'trimeter'],
 'remix': ['mixer', 'remix'],
 'remnant': ['manrent', 'remnant'],
 'remock': ['mocker', 'remock'],
 'remodel': ['demerol', 'modeler', 'remodel'],
 'remold': ['dermol', 'molder', 'remold'],
 'remontant': ['nonmatter', 'remontant'],
 'remontoir': ['interroom', 'remontoir'],
 'remop': ['merop', 'moper', 'proem', 'remop'],
 'remora': ['remora', 'roamer'],
 'remord': ['dormer', 'remord'],
 'remote': ['meteor', 'remote'],
 'remotive': ['overtime', 'remotive'],
 'remould': ['remould', 'ruledom'],
 'remount': ['monture', 'mounter', 'remount'],
 'removable': ['overblame', 'removable'],
 'remunerate': ['remunerate', 'renumerate'],
 'remuneration': ['remuneration', 'renumeration'],
 'remurmur': ['murmurer', 'remurmur'],
 'remus': ['muser', 'remus', 'serum'],
 'remuster': ['musterer', 'remuster'],
 'renable': ['enabler', 'renable'],
 'renably': ['blarney', 'renably'],
 'renail': ['arline', 'larine', 'linear', 'nailer', 'renail'],
 'renaissance': ['necessarian', 'renaissance'],
 'renal': ['learn', 'renal'],
 'rename': ['enarme', 'meaner', 'rename'],
 'renavigate': ['renavigate', 'vegetarian'],
 'rend': ['dern', 'rend'],
 'rendition': ['rendition', 'trinodine'],
 'reneg': ['genre', 'green', 'neger', 'reneg'],
 'renegadism': ['grandeeism', 'renegadism'],
 'renegation': ['generation', 'renegation'],
 'renege': ['neeger', 'reenge', 'renege'],
 'reneger': ['greener', 'regreen', 'reneger'],
 'reneglect': ['neglecter', 'reneglect'],
 'renerve': ['renerve', 'venerer'],
 'renes': ['renes', 'sneer'],
 'renet': ['enter', 'neter', 'renet', 'terne', 'treen'],
 'reniform': ['informer', 'reinform', 'reniform'],
 'renilla': ['ralline', 'renilla'],
 'renin': ['inner', 'renin'],
 'reniportal': ['interpolar', 'reniportal'],
 'renish': ['renish', 'shiner', 'shrine'],
 'renitence': ['centenier', 'renitence'],
 'renitency': ['nycterine', 'renitency'],
 'renitent': ['renitent', 'trentine'],
 'renk': ['kern', 'renk'],
 'rennet': ['rennet', 'tenner'],
 'renography': ['granophyre', 'renography'],
 'renominate': ['enantiomer', 'renominate'],
 'renotation': ['renotation', 'retonation'],
 'renotice': ['erection', 'neoteric', 'nocerite', 'renotice'],
 'renourish': ['nourisher', 'renourish'],
 'renovate': ['overneat', 'renovate'],
 'renovater': ['enervator', 'renovater', 'venerator'],
 'renown': ['renown', 'wonner'],
 'rent': ['rent', 'tern'],
 'rentage': ['grantee', 'greaten', 'reagent', 'rentage'],
 'rental': ['altern', 'antler', 'learnt', 'rental', 'ternal'],
 'rentaler': ['rentaler', 'rerental'],
 'rented': ['denter', 'rented', 'tender'],
 'rentee': ['entree', 'rentee', 'retene'],
 'renter': ['renter', 'rerent'],
 'renu': ['renu', 'ruen', 'rune'],
 'renumber': ['numberer', 'renumber'],
 'renumerate': ['remunerate', 'renumerate'],
 'renumeration': ['remuneration', 'renumeration'],
 'reobtain': ['abrotine', 'baritone', 'obtainer', 'reobtain'],
 'reoccasion': ['occasioner', 'reoccasion'],
 'reoccupation': ['cornucopiate', 'reoccupation'],
 'reoffend': ['offender', 'reoffend'],
 'reoffer': ['offerer', 'reoffer'],
 'reoil': ['oiler', 'oriel', 'reoil'],
 'reopen': ['opener', 'reopen', 'repone'],
 'reordain': ['inroader', 'ordainer', 'reordain'],
 'reorder': ['orderer', 'reorder'],
 'reordinate': ['reordinate', 'treronidae'],
 'reornament': ['ornamenter', 'reornament'],
 'reoverflow': ['overflower', 'reoverflow'],
 'reown': ['owner', 'reown', 'rowen'],
 'rep': ['per', 'rep'],
 'repack': ['packer', 'repack'],
 'repaint': ['painter', 'pertain', 'pterian', 'repaint'],
 'repair': ['pairer', 'rapier', 'repair'],
 'repairer': ['rareripe', 'repairer'],
 'repale': ['leaper', 'releap', 'repale', 'repeal'],
 'repand': ['pander', 'repand'],
 'repandly': ['panderly', 'repandly'],
 'repandous': ['panderous', 'repandous'],
 'repanel': ['paneler', 'repanel', 'replane'],
 'repaper': ['paperer', 'perpera', 'prepare', 'repaper'],
 'reparagraph': ['paragrapher', 'reparagraph'],
 'reparation': ['praetorian', 'reparation'],
 'repark': ['parker', 'repark'],
 'repartee': ['repartee', 'repeater'],
 'repartition': ['partitioner', 'repartition'],
 'repass': ['passer', 'repass', 'sparse'],
 'repasser': ['asperser', 'repasser'],
 'repast': ['paster', 'repast', 'trapes'],
 'repaste': ['perates', 'repaste', 'sperate'],
 'repasture': ['repasture', 'supertare'],
 'repatch': ['chapter', 'patcher', 'repatch'],
 'repatent': ['pattener', 'repatent'],
 'repattern': ['patterner', 'repattern'],
 'repawn': ['enwrap', 'pawner', 'repawn'],
 'repay': ['apery', 'payer', 'repay'],
 'repeal': ['leaper', 'releap', 'repale', 'repeal'],
 'repeat': ['petrea', 'repeat', 'retape'],
 'repeater': ['repartee', 'repeater'],
 'repel': ['leper', 'perle', 'repel'],
 'repen': ['neper', 'preen', 'repen'],
 'repension': ['pensioner', 'repension'],
 'repent': ['perten', 'repent'],
 'repentable': ['penetrable', 'repentable'],
 'repentance': ['penetrance', 'repentance'],
 'repentant': ['penetrant', 'repentant'],
 'reperceive': ['prereceive', 'reperceive'],
 'repercussion': ['percussioner', 'repercussion'],
 'repercussive': ['repercussive', 'superservice'],
 'reperform': ['performer', 'prereform', 'reperform'],
 'repermission': ['reimpression', 'repermission'],
 'repermit': ['premerit', 'preremit', 'repermit'],
 'reperplex': ['perplexer', 'reperplex'],
 'reperusal': ['pleasurer', 'reperusal'],
 'repetition': ['petitioner', 'repetition'],
 'rephael': ['preheal', 'rephael'],
 'rephase': ['hespera', 'rephase', 'reshape'],
 'rephotograph': ['photographer', 'rephotograph'],
 'rephrase': ['preshare', 'rephrase'],
 'repic': ['price', 'repic'],
 'repick': ['picker', 'repick'],
 'repiece': ['creepie', 'repiece'],
 'repin': ['piner', 'prine', 'repin', 'ripen'],
 'repine': ['neiper', 'perine', 'pirene', 'repine'],
 'repiner': ['repiner', 'ripener'],
 'repiningly': ['repiningly', 'ripeningly'],
 'repique': ['perique', 'repique'],
 'repitch': ['pitcher', 'repitch'],
 'replace': ['percale', 'replace'],
 'replait': ['partile', 'plaiter', 'replait'],
 'replan': ['parnel', 'planer', 'replan'],
 'replane': ['paneler', 'repanel', 'replane'],
 'replant': ['pantler', 'planter', 'replant'],
 'replantable': ['planetabler', 'replantable'],
 'replanter': ['prerental', 'replanter'],
 'replaster': ['plasterer', 'replaster'],
 'replate': ['pearlet', 'pleater', 'prelate', 'ptereal', 'replate', 'repleat'],
 'replay': ['parley', 'pearly', 'player', 'replay'],
 'replead': ['pearled', 'pedaler', 'pleader', 'replead'],
 'repleader': ['predealer', 'repleader'],
 'repleat': ['pearlet', 'pleater', 'prelate', 'ptereal', 'replate', 'repleat'],
 'repleteness': ['repleteness', 'terpeneless'],
 'repletion': ['interlope', 'interpole', 'repletion', 'terpineol'],
 'repliant': ['interlap', 'repliant', 'triplane'],
 'replica': ['caliper', 'picarel', 'replica'],
 'replight': ['plighter', 'replight'],
 'replod': ['podler', 'polder', 'replod'],
 'replot': ['petrol', 'replot'],
 'replow': ['plower', 'replow'],
 'replum': ['lumper', 'plumer', 'replum', 'rumple'],
 'replunder': ['plunderer', 'replunder'],
 'reply': ['plyer', 'reply'],
 'repocket': ['pocketer', 'repocket'],
 'repoint': ['pointer', 'protein', 'pterion', 'repoint', 'tropine'],
 'repolish': ['polisher', 'repolish'],
 'repoll': ['poller', 'repoll'],
 'reponder': ['ponderer', 'reponder'],
 'repone': ['opener', 'reopen', 'repone'],
 'report': ['porret', 'porter', 'report', 'troper'],
 'reportage': ['porterage', 'reportage'],
 'reporterism': ['misreporter', 'reporterism'],
 'reportion': ['portioner', 'reportion'],
 'reposed': ['deposer', 'reposed'],
 'reposit': ['periost', 'porites', 'reposit', 'riposte'],
 'reposition': ['positioner', 'reposition'],
 'repositor': ['posterior', 'repositor'],
 'repossession': ['possessioner', 'repossession'],
 'repost': ['poster', 'presto', 'repost', 'respot', 'stoper'],
 'repot': ['poter', 'prote', 'repot', 'tepor', 'toper', 'trope'],
 'repound': ['pounder', 'repound', 'unroped'],
 'repour': ['pourer', 'repour', 'rouper'],
 'repowder': ['powderer', 'repowder'],
 'repp': ['prep', 'repp'],
 'repray': ['prayer', 'repray'],
 'repreach': ['preacher', 'repreach'],
 'repredict': ['precredit', 'predirect', 'repredict'],
 'reprefer': ['prerefer', 'reprefer'],
 'represent': ['presenter', 'represent'],
 'representationism': ['misrepresentation', 'representationism'],
 'repress': ['presser', 'repress'],
 'repressive': ['repressive', 'respersive'],
 'reprice': ['piercer', 'reprice'],
 'reprieval': ['prevailer', 'reprieval'],
 'reprime': ['premier', 'reprime'],
 'reprint': ['printer', 'reprint'],
 'reprise': ['reprise', 'respire'],
 'repristination': ['interspiration', 'repristination'],
 'reproachable': ['blepharocera', 'reproachable'],
 'reprobate': ['perborate', 'prorebate', 'reprobate'],
 'reprobation': ['probationer', 'reprobation'],
 'reproceed': ['proceeder', 'reproceed'],
 'reproclaim': ['proclaimer', 'reproclaim'],
 'reproduce': ['procedure', 'reproduce'],
 'reproduction': ['proreduction', 'reproduction'],
 'reprohibit': ['prohibiter', 'reprohibit'],
 'reproof': ['proofer', 'reproof'],
 'reproportion': ['proportioner', 'reproportion'],
 'reprotection': ['interoceptor', 'reprotection'],
 'reprotest': ['protester', 'reprotest'],
 'reprovision': ['prorevision', 'provisioner', 'reprovision'],
 'reps': ['reps', 'resp'],
 'reptant': ['pattern', 'reptant'],
 'reptatorial': ['proletariat', 'reptatorial'],
 'reptatory': ['protreaty', 'reptatory'],
 'reptile': ['perlite', 'reptile'],
 'reptilia': ['liparite', 'reptilia'],
 'republish': ['publisher', 'republish'],
 'repudiatory': ['preauditory', 'repudiatory'],
 'repuff': ['puffer', 'repuff'],
 'repugn': ['punger', 'repugn'],
 'repulpit': ['pulpiter', 'repulpit'],
 'repulsion': ['prelusion', 'repulsion'],
 'repulsive': ['prelusive', 'repulsive'],
 'repulsively': ['prelusively', 'repulsively'],
 'repulsory': ['prelusory', 'repulsory'],
 'repump': ['pumper', 'repump'],
 'repunish': ['punisher', 'repunish'],
 'reputative': ['reputative', 'vituperate'],
 'repute': ['repute', 'uptree'],
 'requench': ['quencher', 'requench'],
 'request': ['quester', 'request'],
 'requestion': ['questioner', 'requestion'],
 'require': ['querier', 'require'],
 'requital': ['quartile', 'requital', 'triequal'],
 'requite': ['quieter', 'requite'],
 'rerack': ['racker', 'rerack'],
 'rerail': ['railer', 'rerail'],
 'reraise': ['rearise', 'reraise'],
 'rerake': ['karree', 'rerake'],
 'rerank': ['ranker', 'rerank'],
 'rerate': ['rerate', 'retare', 'tearer'],
 'reread': ['reader', 'redare', 'reread'],
 'rereel': ['reeler', 'rereel'],
 'reregister': ['registerer', 'reregister'],
 'rerent': ['renter', 'rerent'],
 'rerental': ['rentaler', 'rerental'],
 'rering': ['erring', 'rering', 'ringer'],
 'rerise': ['rerise', 'sirree'],
 'rerivet': ['rerivet', 'riveter'],
 'rerob': ['borer', 'rerob', 'rober'],
 'rerobe': ['rebore', 'rerobe'],
 'reroll': ['reroll', 'roller'],
 'reroof': ['reroof', 'roofer'],
 'reroot': ['reroot', 'rooter', 'torero'],
 'rerow': ['rerow', 'rower'],
 'rerun': ['rerun', 'runer'],
 'resaca': ['ascare', 'caesar', 'resaca'],
 'resack': ['resack', 'sacker', 'screak'],
 'resail': ['israel', 'relais', 'resail', 'sailer', 'serail', 'serial'],
 'resale': ['alerse', 'leaser', 'reales', 'resale', 'reseal', 'sealer'],
 'resalt': ['laster',
            'lastre',
            'rastle',
            'relast',
            'resalt',
            'salter',
            'slater',
            'stelar'],
 'resanction': ['resanction', 'sanctioner'],
 'resaw': ['resaw', 'sawer', 'seraw', 'sware', 'swear', 'warse'],
 'resawer': ['resawer', 'reswear', 'swearer'],
 'resay': ['reasy', 'resay', 'sayer', 'seary'],
 'rescan': ['casern', 'rescan'],
 'rescind': ['discern', 'rescind'],
 'rescinder': ['discerner', 'rescinder'],
 'rescindment': ['discernment', 'rescindment'],
 'rescratch': ['rescratch', 'scratcher'],
 'rescuable': ['rescuable', 'securable'],
 'rescue': ['cereus', 'ceruse', 'recuse', 'rescue', 'secure'],
 'rescuer': ['recurse', 'rescuer', 'securer'],
 'reseal': ['alerse', 'leaser', 'reales', 'resale', 'reseal', 'sealer'],
 'reseam': ['reseam', 'seamer'],
 'research': ['rechaser', 'research', 'searcher'],
 'reseat': ['asteer',
            'easter',
            'eastre',
            'reseat',
            'saeter',
            'seater',
            'staree',
            'teaser',
            'teresa'],
 'resect': ['resect', 'screet', 'secret'],
 'resection': ['resection', 'secretion'],
 'resectional': ['resectional', 'secretional'],
 'reseda': ['erased', 'reseda', 'seared'],
 'resee': ['esere', 'reese', 'resee'],
 'reseed': ['reseed', 'seeder'],
 'reseek': ['reseek', 'seeker'],
 'resell': ['resell', 'seller'],
 'resend': ['resend', 'sender'],
 'resene': ['resene', 'serene'],
 'resent': ['ernest', 'nester', 'resent', 'streen'],
 'reservable': ['reservable', 'reversable'],
 'reserval': ['reserval', 'reversal', 'slaverer'],
 'reserve': ['reserve', 'resever', 'reverse', 'severer'],
 'reserved': ['deserver', 'reserved', 'reversed'],
 'reservedly': ['reservedly', 'reversedly'],
 'reserveful': ['reserveful', 'reverseful'],
 'reserveless': ['reserveless', 'reverseless'],
 'reserver': ['reserver', 'reverser'],
 'reservist': ['reservist', 'reversist'],
 'reset': ['ester',
           'estre',
           'reest',
           'reset',
           'steer',
           'stere',
           'stree',
           'terse',
           'tsere'],
 'resever': ['reserve', 'resever', 'reverse', 'severer'],
 'resew': ['resew', 'sewer', 'sweer'],
 'resex': ['resex', 'xeres'],
 'resh': ['hers', 'resh', 'sher'],
 'reshape': ['hespera', 'rephase', 'reshape'],
 'reshare': ['reshare', 'reshear', 'shearer'],
 'resharpen': ['resharpen', 'sharpener'],
 'reshear': ['reshare', 'reshear', 'shearer'],
 'reshearer': ['rehearser', 'reshearer'],
 'reshift': ['reshift', 'shifter'],
 'reshingle': ['englisher', 'reshingle'],
 'reship': ['perish', 'reship'],
 'reshipment': ['perishment', 'reshipment'],
 'reshoot': ['orthose', 'reshoot', 'shooter', 'soother'],
 'reshoulder': ['reshoulder', 'shoulderer'],
 'reshower': ['reshower', 'showerer'],
 'reshun': ['reshun', 'rushen'],
 'reshunt': ['reshunt', 'shunter'],
 'reshut': ['reshut', 'suther', 'thurse', 'tusher'],
 'reside': ['desire', 'reside'],
 'resident': ['indesert', 'inserted', 'resident'],
 'resider': ['derries', 'desirer', 'resider', 'serried'],
 'residua': ['residua', 'ursidae'],
 'resift': ['fister', 'resift', 'sifter', 'strife'],
 'resigh': ['resigh', 'sigher'],
 'resign': ['resign', 'resing', 'signer', 'singer'],
 'resignal': ['resignal', 'seringal', 'signaler'],
 'resigned': ['designer', 'redesign', 'resigned'],
 'resile': ['lisere', 'resile'],
 'resiliate': ['israelite', 'resiliate'],
 'resilient': ['listerine', 'resilient'],
 'resilition': ['isonitrile', 'resilition'],
 'resilver': ['resilver', 'silverer', 'sliverer'],
 'resin': ['reins', 'resin', 'rinse', 'risen', 'serin', 'siren'],
 'resina': ['arisen', 'arsine', 'resina', 'serian'],
 'resinate': ['arsenite', 'resinate', 'teresian', 'teresina'],
 'resing': ['resign', 'resing', 'signer', 'singer'],
 'resinic': ['irenics', 'resinic', 'sericin', 'sirenic'],
 'resinize': ['resinize', 'sirenize'],
 'resink': ['resink', 'reskin', 'sinker'],
 'resinlike': ['resinlike', 'sirenlike'],
 'resinoid': ['derision', 'ironside', 'resinoid', 'sirenoid'],
 'resinol': ['resinol', 'serolin'],
 'resinous': ['neurosis', 'resinous'],
 'resinously': ['neurolysis', 'resinously'],
 'resiny': ['resiny', 'sireny'],
 'resist': ['resist', 'restis', 'sister'],
 'resistable': ['assertible', 'resistable'],
 'resistance': ['resistance', 'senatrices'],
 'resistful': ['fruitless', 'resistful'],
 'resisting': ['resisting', 'sistering'],
 'resistless': ['resistless', 'sisterless'],
 'resize': ['resize', 'seizer'],
 'resketch': ['resketch', 'sketcher'],
 'reskin': ['resink', 'reskin', 'sinker'],
 'reslash': ['reslash', 'slasher'],
 'reslate': ['realest', 'reslate', 'resteal', 'stealer', 'teasler'],
 'reslay': ['reslay', 'slayer'],
 'reslot': ['relost', 'reslot', 'rostel', 'sterol', 'torsel'],
 'resmell': ['resmell', 'smeller'],
 'resmelt': ['melters', 'resmelt', 'smelter'],
 'resmooth': ['resmooth', 'romeshot', 'smoother'],
 'resnap': ['resnap', 'respan', 'snaper'],
 'resnatch': ['resnatch', 'snatcher', 'stancher'],
 'resoak': ['arkose', 'resoak', 'soaker'],
 'resoap': ['resoap', 'soaper'],
 'resoften': ['resoften', 'softener'],
 'resoil': ['elisor', 'resoil'],
 'resojourn': ['resojourn', 'sojourner'],
 'resolder': ['resolder', 'solderer'],
 'resole': ['relose', 'resole'],
 'resolicit': ['resolicit', 'soliciter'],
 'resolution': ['resolution', 'solutioner'],
 'resonate': ['orestean', 'resonate', 'stearone'],
 'resort': ['resort', 'roster', 'sorter', 'storer'],
 'resorter': ['resorter', 'restorer', 'retrorse'],
 'resound': ['resound', 'sounder', 'unrosed'],
 'resource': ['recourse', 'resource'],
 'resow': ['owser', 'resow', 'serow', 'sower', 'swore', 'worse'],
 'resp': ['reps', 'resp'],
 'respace': ['escaper', 'respace'],
 'respade': ['psedera', 'respade'],
 'respan': ['resnap', 'respan', 'snaper'],
 'respeak': ['respeak', 'speaker'],
 'respect': ['respect', 'scepter', 'specter'],
 'respectless': ['respectless', 'scepterless'],
 'respell': ['presell', 'respell', 'speller'],
 'respersive': ['repressive', 'respersive'],
 'respin': ['pernis', 'respin', 'sniper'],
 'respiration': ['respiration', 'retinispora'],
 'respire': ['reprise', 'respire'],
 'respirit': ['respirit', 'spiriter'],
 'respite': ['respite', 'septier'],
 'resplend': ['resplend', 'splender'],
 'resplice': ['eclipser', 'pericles', 'resplice'],
 'responde': ['personed', 'responde'],
 'respondence': ['precondense', 'respondence'],
 'responsal': ['apronless', 'responsal'],
 'response': ['pessoner', 'response'],
 'respot': ['poster', 'presto', 'repost', 'respot', 'stoper'],
 'respray': ['respray', 'sprayer'],
 'respread': ['respread', 'spreader'],
 'respring': ['respring', 'springer'],
 'resprout': ['posturer', 'resprout', 'sprouter'],
 'respue': ['peruse', 'respue'],
 'resqueak': ['resqueak', 'squeaker'],
 'ressaut': ['erastus', 'ressaut'],
 'rest': ['rest', 'sert', 'stre'],
 'restack': ['restack', 'stacker'],
 'restaff': ['restaff', 'staffer'],
 'restain': ['asterin', 'eranist', 'restain', 'stainer', 'starnie', 'stearin'],
 'restake': ['restake', 'sakeret'],
 'restamp': ['restamp', 'stamper'],
 'restart': ['restart', 'starter'],
 'restate': ['estreat', 'restate', 'retaste'],
 'resteal': ['realest', 'reslate', 'resteal', 'stealer', 'teasler'],
 'resteel': ['reestle', 'resteel', 'steeler'],
 'resteep': ['estrepe', 'resteep', 'steeper'],
 'restem': ['mester', 'restem', 'temser', 'termes'],
 'restep': ['pester', 'preset', 'restep', 'streep'],
 'restful': ['fluster', 'restful'],
 'restiad': ['astride', 'diaster', 'disrate', 'restiad', 'staired'],
 'restiffen': ['restiffen', 'stiffener'],
 'restiform': ['reformist', 'restiform'],
 'resting': ['resting', 'stinger'],
 'restio': ['restio', 'sorite', 'sortie', 'triose'],
 'restis': ['resist', 'restis', 'sister'],
 'restitch': ['restitch', 'stitcher'],
 'restive': ['restive', 'servite'],
 'restock': ['restock', 'stocker'],
 'restorer': ['resorter', 'restorer', 'retrorse'],
 'restow': ['restow', 'stower', 'towser', 'worset'],
 'restowal': ['restowal', 'sealwort'],
 'restraighten': ['restraighten', 'straightener'],
 'restrain': ['restrain', 'strainer', 'transire'],
 'restraint': ['restraint', 'retransit', 'trainster', 'transiter'],
 'restream': ['masterer', 'restream', 'streamer'],
 'restrengthen': ['restrengthen', 'strengthener'],
 'restress': ['restress', 'stresser'],
 'restretch': ['restretch', 'stretcher'],
 'restring': ['restring', 'ringster', 'stringer'],
 'restrip': ['restrip', 'striper'],
 'restrive': ['restrive', 'reverist'],
 'restuff': ['restuff', 'stuffer'],
 'resty': ['resty', 'strey'],
 'restyle': ['restyle', 'tersely'],
 'resucceed': ['resucceed', 'succeeder'],
 'resuck': ['resuck', 'sucker'],
 'resue': ['resue', 'reuse'],
 'resuffer': ['resuffer', 'sufferer'],
 'resuggest': ['resuggest', 'suggester'],
 'resuing': ['insurge', 'resuing'],
 'resuit': ['isuret', 'resuit'],
 'result': ['luster', 'result', 'rustle', 'sutler', 'ulster'],
 'resulting': ['resulting', 'ulstering'],
 'resultless': ['lusterless', 'lustreless', 'resultless'],
 'resummon': ['resummon', 'summoner'],
 'resun': ['nurse', 'resun'],
 'resup': ['purse', 'resup', 'sprue', 'super'],
 'resuperheat': ['resuperheat', 'superheater'],
 'resupinate': ['interpause', 'resupinate'],
 'resupination': ['resupination', 'uranospinite'],
 'resupport': ['resupport', 'supporter'],
 'resuppose': ['resuppose', 'superpose'],
 'resupposition': ['resupposition', 'superposition'],
 'resuppress': ['resuppress', 'suppresser'],
 'resurrender': ['resurrender', 'surrenderer'],
 'resurround': ['resurround', 'surrounder'],
 'resuspect': ['resuspect', 'suspecter'],
 'resuspend': ['resuspend', 'suspender', 'unpressed'],
 'reswallow': ['reswallow', 'swallower'],
 'resward': ['drawers', 'resward'],
 'reswarm': ['reswarm', 'swarmer'],
 'reswear': ['resawer', 'reswear', 'swearer'],
 'resweat': ['resweat', 'sweater'],
 'resweep': ['resweep', 'sweeper'],
 'reswell': ['reswell', 'sweller'],
 'reswill': ['reswill', 'swiller'],
 'retable': ['bearlet', 'bleater', 'elberta', 'retable'],
 'retack': ['racket', 'retack', 'tacker'],
 'retag': ['gater', 'grate', 'great', 'greta', 'retag', 'targe'],
 'retail': ['lirate', 'retail', 'retial', 'tailer'],
 'retailer': ['irrelate', 'retailer'],
 'retain': ['nerita', 'ratine', 'retain', 'retina', 'tanier'],
 'retainal': ['retainal', 'telarian'],
 'retainder': ['irredenta', 'retainder'],
 'retainer': ['arretine', 'eretrian', 'eritrean', 'retainer'],
 'retaining': ['negritian', 'retaining'],
 'retaliate': ['elettaria', 'retaliate'],
 'retalk': ['kartel', 'retalk', 'talker'],
 'retama': ['ramate', 'retama'],
 'retame': ['reetam', 'retame', 'teamer'],
 'retan': ['antre', 'arent', 'retan', 'terna'],
 'retape': ['petrea', 'repeat', 'retape'],
 'retard': ['darter',
            'dartre',
            'redart',
            'retard',
            'retrad',
            'tarred',
            'trader'],
 'retardent': ['retardent', 'tetrander'],
 'retare': ['rerate', 'retare', 'tearer'],
 'retaste': ['estreat', 'restate', 'retaste'],
 'retax': ['extra', 'retax', 'taxer'],
 'retaxation': ['retaxation', 'tetraxonia'],
 'retch': ['chert', 'retch'],
 'reteach': ['cheater', 'hectare', 'recheat', 'reteach', 'teacher'],
 'retelegraph': ['retelegraph', 'telegrapher'],
 'retell': ['retell', 'teller'],
 'retem': ['meter', 'retem'],
 'retemper': ['retemper', 'temperer'],
 'retempt': ['retempt', 'tempter'],
 'retenant': ['retenant', 'tenanter'],
 'retender': ['retender', 'tenderer'],
 'retene': ['entree', 'rentee', 'retene'],
 'retent': ['netter', 'retent', 'tenter'],
 'retention': ['intertone', 'retention'],
 'retepora': ['perorate', 'retepora'],
 'retest': ['retest', 'setter', 'street', 'tester'],
 'rethank': ['rethank', 'thanker'],
 'rethatch': ['rethatch', 'thatcher'],
 'rethaw': ['rethaw', 'thawer', 'wreath'],
 'rethe': ['ether', 'rethe', 'theer', 'there', 'three'],
 'retheness': ['retheness', 'thereness', 'threeness'],
 'rethicken': ['kitchener', 'rethicken', 'thickener'],
 'rethink': ['rethink', 'thinker'],
 'rethrash': ['rethrash', 'thrasher'],
 'rethread': ['rethread', 'threader'],
 'rethreaten': ['rethreaten', 'threatener'],
 'rethresh': ['rethresh', 'thresher'],
 'rethrill': ['rethrill', 'thriller'],
 'rethrow': ['rethrow', 'thrower'],
 'rethrust': ['rethrust', 'thruster'],
 'rethunder': ['rethunder', 'thunderer'],
 'retia': ['arite', 'artie', 'irate', 'retia', 'tarie'],
 'retial': ['lirate', 'retail', 'retial', 'tailer'],
 'reticent': ['reticent', 'tencteri'],
 'reticket': ['reticket', 'ticketer'],
 'reticula': ['arculite', 'cutleria', 'lucretia', 'reticula', 'treculia'],
 'reticular': ['curtailer', 'recruital', 'reticular'],
 'retier': ['errite', 'reiter', 'retier', 'retire', 'tierer'],
 'retighten': ['retighten', 'tightener'],
 'retill': ['retill', 'rillet', 'tiller'],
 'retimber': ['retimber', 'timberer'],
 'retime': ['metier', 'retime', 'tremie'],
 'retin': ['inert', 'inter', 'niter', 'retin', 'trine'],
 'retina': ['nerita', 'ratine', 'retain', 'retina', 'tanier'],
 'retinal': ['entrail',
             'latiner',
             'latrine',
             'ratline',
             'reliant',
             'retinal',
             'trenail'],
 'retinalite': ['retinalite', 'trilineate'],
 'retinene': ['internee', 'retinene'],
 'retinian': ['neritina', 'retinian'],
 'retinispora': ['respiration', 'retinispora'],
 'retinite': ['intertie', 'retinite'],
 'retinker': ['retinker', 'tinkerer'],
 'retinochorioiditis': ['chorioidoretinitis', 'retinochorioiditis'],
 'retinoid': ['neritoid', 'retinoid'],
 'retinue': ['neurite', 'retinue', 'reunite', 'uterine'],
 'retinula': ['lutrinae', 'retinula', 'rutelian', 'tenurial'],
 'retinular': ['retinular', 'trineural'],
 'retip': ['perit', 'retip', 'tripe'],
 'retiral': ['retiral', 'retrial', 'trailer'],
 'retire': ['errite', 'reiter', 'retier', 'retire', 'tierer'],
 'retirer': ['retirer', 'terrier'],
 'retistene': ['retistene', 'serinette'],
 'retoast': ['retoast', 'rosetta', 'stoater', 'toaster'],
 'retold': ['retold', 'rodlet'],
 'retomb': ['retomb', 'trombe'],
 'retonation': ['renotation', 'retonation'],
 'retool': ['looter', 'retool', 'rootle', 'tooler'],
 'retooth': ['retooth', 'toother'],
 'retort': ['retort', 'retrot', 'rotter'],
 'retoss': ['retoss', 'tosser'],
 'retouch': ['retouch', 'toucher'],
 'retour': ['retour', 'router', 'tourer'],
 'retrace': ['caterer', 'recrate', 'retrace', 'terrace'],
 'retrack': ['retrack', 'tracker'],
 'retractation': ['reattraction', 'retractation'],
 'retracted': ['detracter', 'retracted'],
 'retraction': ['retraction', 'triaconter'],
 'retrad': ['darter',
            'dartre',
            'redart',
            'retard',
            'retrad',
            'tarred',
            'trader'],
 'retrade': ['derater', 'retrade', 'retread', 'treader'],
 'retradition': ['retradition', 'traditioner'],
 'retrain': ['arterin', 'retrain', 'terrain', 'trainer'],
 'retral': ['retral', 'terral'],
 'retramp': ['retramp', 'tramper'],
 'retransform': ['retransform', 'transformer'],
 'retransit': ['restraint', 'retransit', 'trainster', 'transiter'],
 'retransplant': ['retransplant', 'transplanter'],
 'retransport': ['retransport', 'transporter'],
 'retravel': ['retravel', 'revertal', 'traveler'],
 'retread': ['derater', 'retrade', 'retread', 'treader'],
 'retreat': ['ettarre', 'retreat', 'treater'],
 'retree': ['retree', 'teerer'],
 'retrench': ['retrench', 'trencher'],
 'retrial': ['retiral', 'retrial', 'trailer'],
 'retrim': ['mitrer', 'retrim', 'trimer'],
 'retrocaecal': ['accelerator', 'retrocaecal'],
 'retrogradient': ['redintegrator', 'retrogradient'],
 'retrorse': ['resorter', 'restorer', 'retrorse'],
 'retrot': ['retort', 'retrot', 'rotter'],
 'retrue': ['retrue', 'ureter'],
 'retrust': ['retrust', 'truster'],
 'retry': ['retry', 'terry'],
 'retter': ['retter', 'terret'],
 'retting': ['gittern', 'gritten', 'retting'],
 'retube': ['rebute', 'retube'],
 'retuck': ['retuck', 'tucker'],
 'retune': ['neuter', 'retune', 'runtee', 'tenure', 'tureen'],
 'returf': ['returf', 'rufter'],
 'return': ['return', 'turner'],
 'retuse': ['retuse', 'tereus'],
 'retwine': ['enwrite', 'retwine'],
 'retwist': ['retwist', 'twister'],
 'retzian': ['retzian', 'terzina'],
 'reub': ['bure', 'reub', 'rube'],
 'reundergo': ['guerdoner', 'reundergo', 'undergoer', 'undergore'],
 'reune': ['enure', 'reune'],
 'reunfold': ['flounder', 'reunfold', 'unfolder'],
 'reunify': ['reunify', 'unfiery'],
 'reunionist': ['reunionist', 'sturionine'],
 'reunite': ['neurite', 'retinue', 'reunite', 'uterine'],
 'reunpack': ['reunpack', 'unpacker'],
 'reuphold': ['reuphold', 'upholder'],
 'reupholster': ['reupholster', 'upholsterer'],
 'reuplift': ['reuplift', 'uplifter'],
 'reuse': ['resue', 'reuse'],
 'reutter': ['reutter', 'utterer'],
 'revacate': ['acervate', 'revacate'],
 'revalidation': ['derivational', 'revalidation'],
 'revamp': ['revamp', 'vamper'],
 'revarnish': ['revarnish', 'varnisher'],
 'reve': ['ever', 'reve', 'veer'],
 'reveal': ['laveer', 'leaver', 'reveal', 'vealer'],
 'reveil': ['levier', 'relive', 'reveil', 'revile', 'veiler'],
 'revel': ['elver', 'lever', 'revel'],
 'revelant': ['levanter', 'relevant', 'revelant'],
 'revelation': ['relevation', 'revelation'],
 'revelator': ['relevator', 'revelator', 'veratrole'],
 'reveler': ['leverer', 'reveler'],
 'revenant': ['revenant', 'venerant'],
 'revend': ['revend', 'vender'],
 'revender': ['revender', 'reverend'],
 'reveneer': ['reveneer', 'veneerer'],
 'revent': ['revent', 'venter'],
 'revenue': ['revenue', 'unreeve'],
 'rever': ['rever', 'verre'],
 'reverend': ['revender', 'reverend'],
 'reverential': ['interleaver', 'reverential'],
 'reverist': ['restrive', 'reverist'],
 'revers': ['revers', 'server', 'verser'],
 'reversable': ['reservable', 'reversable'],
 'reversal': ['reserval', 'reversal', 'slaverer'],
 'reverse': ['reserve', 'resever', 'reverse', 'severer'],
 'reversed': ['deserver', 'reserved', 'reversed'],
 'reversedly': ['reservedly', 'reversedly'],
 'reverseful': ['reserveful', 'reverseful'],
 'reverseless': ['reserveless', 'reverseless'],
 'reverser': ['reserver', 'reverser'],
 'reversewise': ['reversewise', 'revieweress'],
 'reversi': ['reversi', 'reviser'],
 'reversion': ['reversion', 'versioner'],
 'reversist': ['reservist', 'reversist'],
 'revertal': ['retravel', 'revertal', 'traveler'],
 'revest': ['revest', 'servet', 'sterve', 'verset', 'vester'],
 'revet': ['evert', 'revet'],
 'revete': ['revete', 'tervee'],
 'revictual': ['lucrative', 'revictual', 'victualer'],
 'review': ['review', 'viewer'],
 'revieweress': ['reversewise', 'revieweress'],
 'revigorate': ['overgaiter', 'revigorate'],
 'revile': ['levier', 'relive', 'reveil', 'revile', 'veiler'],
 'reviling': ['reviling', 'vierling'],
 'revisal': ['revisal', 'virales'],
 'revise': ['revise', 'siever'],
 'revised': ['deviser', 'diverse', 'revised'],
 'reviser': ['reversi', 'reviser'],
 'revision': ['revision', 'visioner'],
 'revisit': ['revisit', 'visiter'],
 'revisitant': ['revisitant', 'transitive'],
 'revitalization': ['relativization', 'revitalization'],
 'revitalize': ['relativize', 'revitalize'],
 'revocation': ['overaction', 'revocation'],
 'revocative': ['overactive', 'revocative'],
 'revoke': ['evoker', 'revoke'],
 'revolting': ['overglint', 'revolting'],
 'revolute': ['revolute', 'truelove'],
 'revolve': ['evolver', 'revolve'],
 'revomit': ['revomit', 'vomiter'],
 'revote': ['revote', 'vetoer'],
 'revuist': ['revuist', 'stuiver'],
 'rewade': ['drawee', 'rewade'],
 'rewager': ['rewager', 'wagerer'],
 'rewake': ['kerewa', 'rewake'],
 'rewaken': ['rewaken', 'wakener'],
 'rewall': ['rewall', 'waller'],
 'rewallow': ['rewallow', 'wallower'],
 'reward': ['drawer', 'redraw', 'reward', 'warder'],
 'rewarder': ['redrawer', 'rewarder', 'warderer'],
 'rewarm': ['rewarm', 'warmer'],
 'rewarn': ['rewarn', 'warner', 'warren'],
 'rewash': ['hawser', 'rewash', 'washer'],
 'rewater': ['rewater', 'waterer'],
 'rewave': ['rewave', 'weaver'],
 'rewax': ['rewax', 'waxer'],
 'reweaken': ['reweaken', 'weakener'],
 'rewear': ['rewear', 'warree', 'wearer'],
 'rewed': ['dewer', 'ewder', 'rewed'],
 'reweigh': ['reweigh', 'weigher'],
 'reweld': ['reweld', 'welder'],
 'rewet': ['rewet', 'tewer', 'twere'],
 'rewhirl': ['rewhirl', 'whirler'],
 'rewhisper': ['rewhisper', 'whisperer'],
 'rewhiten': ['rewhiten', 'whitener'],
 'rewiden': ['rewiden', 'widener'],
 'rewin': ['erwin', 'rewin', 'winer'],
 'rewind': ['rewind', 'winder'],
 'rewish': ['rewish', 'wisher'],
 'rewithdraw': ['rewithdraw', 'withdrawer'],
 'reword': ['reword', 'worder'],
 'rework': ['rework', 'worker'],
 'reworked': ['reedwork', 'reworked'],
 'rewound': ['rewound', 'unrowed', 'wounder'],
 'rewoven': ['overnew', 'rewoven'],
 'rewrap': ['prewar', 'rewrap', 'warper'],
 'reyield': ['reedily', 'reyield', 'yielder'],
 'rhacianectes': ['rachianectes', 'rhacianectes'],
 'rhaetian': ['earthian', 'rhaetian'],
 'rhaetic': ['certhia', 'rhaetic', 'theriac'],
 'rhamnose': ['horseman', 'rhamnose', 'shoreman'],
 'rhamnoside': ['admonisher', 'rhamnoside'],
 'rhapis': ['parish', 'raphis', 'rhapis'],
 'rhapontic': ['anthropic', 'rhapontic'],
 'rhaponticin': ['panornithic', 'rhaponticin'],
 'rhason': ['rhason', 'sharon', 'shoran'],
 'rhatania': ['ratanhia', 'rhatania'],
 'rhe': ['her', 'reh', 'rhe'],
 'rhea': ['hare', 'hear', 'rhea'],
 'rheen': ['herne', 'rheen'],
 'rheic': ['cheir', 'rheic'],
 'rhein': ['hiren', 'rhein', 'rhine'],
 'rheinic': ['hircine', 'rheinic'],
 'rhema': ['harem', 'herma', 'rhema'],
 'rhematic': ['athermic', 'marchite', 'rhematic'],
 'rheme': ['herem', 'rheme'],
 'rhemist': ['rhemist', 'smither'],
 'rhenium': ['inhumer', 'rhenium'],
 'rheometric': ['chirometer', 'rheometric'],
 'rheophile': ['herophile', 'rheophile'],
 'rheoscope': ['prechoose', 'rheoscope'],
 'rheostatic': ['choristate', 'rheostatic'],
 'rheotactic': ['rheotactic', 'theocratic'],
 'rheotan': ['another', 'athenor', 'rheotan'],
 'rheotropic': ['horopteric', 'rheotropic', 'trichopore'],
 'rhesian': ['arshine', 'nearish', 'rhesian', 'sherani'],
 'rhesus': ['rhesus', 'suresh'],
 'rhetor': ['rhetor', 'rother'],
 'rhetoricals': ['rhetoricals', 'trochlearis'],
 'rhetorize': ['rhetorize', 'theorizer'],
 'rheumatic': ['hematuric', 'rheumatic'],
 'rhine': ['hiren', 'rhein', 'rhine'],
 'rhinestone': ['neornithes', 'rhinestone'],
 'rhineura': ['rhineura', 'unhairer'],
 'rhinocele': ['cholerine', 'rhinocele'],
 'rhinopharyngitis': ['pharyngorhinitis', 'rhinopharyngitis'],
 'rhipidate': ['rhipidate', 'thripidae'],
 'rhizoctonia': ['chorization', 'rhizoctonia', 'zonotrichia'],
 'rhoda': ['hoard', 'rhoda'],
 'rhodaline': ['hodiernal', 'rhodaline'],
 'rhodanthe': ['rhodanthe', 'thornhead'],
 'rhodeose': ['rhodeose', 'seerhood'],
 'rhodes': ['dehors', 'rhodes', 'shoder', 'shored'],
 'rhodic': ['orchid', 'rhodic'],
 'rhodite': ['rhodite', 'theroid'],
 'rhodium': ['humidor', 'rhodium'],
 'rhodope': ['redhoop', 'rhodope'],
 'rhodopsin': ['donorship', 'rhodopsin'],
 'rhoecus': ['choreus', 'chouser', 'rhoecus'],
 'rhopalic': ['orphical', 'rhopalic'],
 'rhus': ['rhus', 'rush'],
 'rhynchotal': ['chloranthy', 'rhynchotal'],
 'rhyton': ['rhyton', 'thorny'],
 'ria': ['air', 'ira', 'ria'],
 'rial': ['aril', 'lair', 'lari', 'liar', 'lira', 'rail', 'rial'],
 'riancy': ['cairny', 'riancy'],
 'riant': ['riant', 'tairn', 'tarin', 'train'],
 'riata': ['arati', 'atria', 'riata', 'tarai', 'tiara'],
 'ribald': ['bildar', 'bridal', 'ribald'],
 'ribaldly': ['bridally', 'ribaldly'],
 'riband': ['brandi', 'riband'],
 'ribat': ['barit', 'ribat'],
 'ribbed': ['dibber', 'ribbed'],
 'ribber': ['briber', 'ribber'],
 'ribble': ['libber', 'ribble'],
 'ribbon': ['ribbon', 'robbin'],
 'ribe': ['beri', 'bier', 'brei', 'ribe'],
 'ribes': ['birse', 'ribes'],
 'riblet': ['beltir', 'riblet'],
 'ribroast': ['arborist', 'ribroast'],
 'ribspare': ['ribspare', 'sparerib'],
 'rice': ['eric', 'rice'],
 'ricer': ['crier', 'ricer'],
 'ricey': ['criey', 'ricey'],
 'richardia': ['charadrii', 'richardia'],
 'richdom': ['chromid', 'richdom'],
 'richen': ['enrich', 'nicher', 'richen'],
 'riches': ['riches', 'shicer'],
 'richt': ['crith', 'richt'],
 'ricine': ['irenic', 'ricine'],
 'ricinoleate': ['arenicolite', 'ricinoleate'],
 'rickets': ['rickets', 'sticker'],
 'rickle': ['licker', 'relick', 'rickle'],
 'rictal': ['citral', 'rictal'],
 'rictus': ['citrus', 'curtis', 'rictus', 'rustic'],
 'ridable': ['bedrail', 'bridale', 'ridable'],
 'ridably': ['bardily', 'rabidly', 'ridably'],
 'riddam': ['madrid', 'riddam'],
 'riddance': ['adendric', 'riddance'],
 'riddel': ['lidder', 'riddel', 'riddle'],
 'ridden': ['dinder', 'ridden', 'rinded'],
 'riddle': ['lidder', 'riddel', 'riddle'],
 'ride': ['dier', 'dire', 'reid', 'ride'],
 'rideau': ['auride', 'rideau'],
 'riden': ['diner', 'riden', 'rinde'],
 'rident': ['dirten', 'rident', 'tinder'],
 'rider': ['drier', 'rider'],
 'ridered': ['deirdre', 'derider', 'derride', 'ridered'],
 'ridge': ['dirge', 'gride', 'redig', 'ridge'],
 'ridgel': ['gilder', 'girdle', 'glider', 'regild', 'ridgel'],
 'ridgelike': ['dirgelike', 'ridgelike'],
 'ridger': ['girder', 'ridger'],
 'ridging': ['girding', 'ridging'],
 'ridgingly': ['girdingly', 'ridgingly'],
 'ridgling': ['girdling', 'ridgling'],
 'ridgy': ['igdyr', 'ridgy'],
 'rie': ['ire', 'rie'],
 'riem': ['emir', 'imer', 'mire', 'reim', 'remi', 'riem', 'rime'],
 'rife': ['fire', 'reif', 'rife'],
 'rifeness': ['finesser', 'rifeness'],
 'rifle': ['filer', 'flier', 'lifer', 'rifle'],
 'rifleman': ['inflamer', 'rifleman'],
 'rift': ['frit', 'rift'],
 'rigadoon': ['gordonia', 'organoid', 'rigadoon'],
 'rigation': ['rigation', 'trigonia'],
 'rigbane': ['bearing', 'begrain', 'brainge', 'rigbane'],
 'right': ['girth', 'grith', 'right'],
 'rightle': ['lighter', 'relight', 'rightle'],
 'rigling': ['girling', 'rigling'],
 'rigolette': ['gloriette', 'rigolette'],
 'rik': ['irk', 'rik'],
 'rikisha': ['rikisha', 'shikari'],
 'rikk': ['kirk', 'rikk'],
 'riksha': ['rakish', 'riksha', 'shikar', 'shikra', 'sikhra'],
 'rile': ['lier', 'lire', 'rile'],
 'rillet': ['retill', 'rillet', 'tiller'],
 'rillett': ['rillett', 'trillet'],
 'rillock': ['rillock', 'rollick'],
 'rim': ['mir', 'rim'],
 'rima': ['amir', 'irma', 'mari', 'mira', 'rami', 'rima'],
 'rimal': ['armil', 'marli', 'rimal'],
 'rimate': ['imaret', 'metria', 'mirate', 'rimate'],
 'rime': ['emir', 'imer', 'mire', 'reim', 'remi', 'riem', 'rime'],
 'rimmed': ['dimmer', 'immerd', 'rimmed'],
 'rimose': ['isomer', 'rimose'],
 'rimple': ['limper', 'prelim', 'rimple'],
 'rimu': ['muir', 'rimu'],
 'rimula': ['rimula', 'uramil'],
 'rimy': ['miry', 'rimy', 'yirm'],
 'rinaldo': ['nailrod', 'ordinal', 'rinaldo', 'rodinal'],
 'rinceau': ['aneuric', 'rinceau'],
 'rincon': ['cornin', 'rincon'],
 'rinde': ['diner', 'riden', 'rinde'],
 'rinded': ['dinder', 'ridden', 'rinded'],
 'rindle': ['linder', 'rindle'],
 'rine': ['neri', 'rein', 'rine'],
 'ring': ['girn', 'grin', 'ring'],
 'ringable': ['balinger', 'ringable'],
 'ringe': ['grein', 'inger', 'nigre', 'regin', 'reign', 'ringe'],
 'ringed': ['engird', 'ringed'],
 'ringer': ['erring', 'rering', 'ringer'],
 'ringgoer': ['gorgerin', 'ringgoer'],
 'ringhead': ['headring', 'ringhead'],
 'ringite': ['igniter', 'ringite', 'tigrine'],
 'ringle': ['linger', 'ringle'],
 'ringlead': ['dragline', 'reginald', 'ringlead'],
 'ringlet': ['ringlet', 'tingler', 'tringle'],
 'ringster': ['restring', 'ringster', 'stringer'],
 'ringtail': ['ringtail', 'trailing'],
 'ringy': ['girny', 'ringy'],
 'rink': ['kirn', 'rink'],
 'rinka': ['inkra', 'krina', 'nakir', 'rinka'],
 'rinse': ['reins', 'resin', 'rinse', 'risen', 'serin', 'siren'],
 'rio': ['rio', 'roi'],
 'riot': ['riot', 'roit', 'trio'],
 'rioting': ['ignitor', 'rioting'],
 'rip': ['pir', 'rip'],
 'ripa': ['pair', 'pari', 'pria', 'ripa'],
 'ripal': ['april', 'pilar', 'ripal'],
 'ripe': ['peri', 'pier', 'ripe'],
 'ripelike': ['pierlike', 'ripelike'],
 'ripen': ['piner', 'prine', 'repin', 'ripen'],
 'ripener': ['repiner', 'ripener'],
 'ripeningly': ['repiningly', 'ripeningly'],
 'riper': ['prier', 'riper'],
 'ripgut': ['ripgut', 'upgirt'],
 'ripost': ['ripost', 'triops', 'tripos'],
 'riposte': ['periost', 'porites', 'reposit', 'riposte'],
 'rippet': ['rippet', 'tipper'],
 'ripple': ['lipper', 'ripple'],
 'ripplet': ['ripplet', 'tippler', 'tripple'],
 'ripup': ['ripup', 'uprip'],
 'rise': ['reis', 'rise', 'seri', 'sier', 'sire'],
 'risen': ['reins', 'resin', 'rinse', 'risen', 'serin', 'siren'],
 'rishi': ['irish', 'rishi', 'sirih'],
 'risk': ['kris', 'risk'],
 'risky': ['risky', 'sirky'],
 'risper': ['risper', 'sprier'],
 'risque': ['risque', 'squire'],
 'risquee': ['esquire', 'risquee'],
 'rissel': ['rissel', 'rissle'],
 'rissle': ['rissel', 'rissle'],
 'rissoa': ['aissor', 'rissoa'],
 'rist': ['rist', 'stir'],
 'rit': ['rit', 'tri'],
 'rita': ['airt', 'rita', 'tari', 'tiar'],
 'rite': ['iter', 'reit', 'rite', 'teri', 'tier', 'tire'],
 'riteless': ['riteless', 'tireless'],
 'ritelessness': ['ritelessness', 'tirelessness'],
 'ritling': ['glitnir', 'ritling'],
 'ritualize': ['ritualize', 'uralitize'],
 'riva': ['ravi', 'riva', 'vair', 'vari', 'vira'],
 'rivage': ['argive', 'rivage'],
 'rival': ['rival', 'viral'],
 'rive': ['rive', 'veri', 'vier', 'vire'],
 'rivel': ['levir', 'liver', 'livre', 'rivel'],
 'riven': ['riven', 'viner'],
 'rivered': ['deriver', 'redrive', 'rivered'],
 'rivet': ['rivet', 'tirve', 'tiver'],
 'riveter': ['rerivet', 'riveter'],
 'rivetless': ['rivetless', 'silvester'],
 'riving': ['irving', 'riving', 'virgin'],
 'rivingly': ['rivingly', 'virginly'],
 'rivose': ['rivose', 'virose'],
 'riyal': ['lairy', 'riyal'],
 'ro': ['or', 'ro'],
 'roach': ['achor', 'chora', 'corah', 'orach', 'roach'],
 'road': ['dora', 'orad', 'road'],
 'roadability': ['adorability', 'roadability'],
 'roadable': ['adorable', 'roadable'],
 'roader': ['adorer', 'roader'],
 'roading': ['gordian', 'idorgan', 'roading'],
 'roadite': ['roadite', 'toadier'],
 'roadman': ['anadrom', 'madrona', 'mandora', 'monarda', 'roadman'],
 'roadster': ['dartrose', 'roadster'],
 'roam': ['amor', 'maro', 'mora', 'omar', 'roam'],
 'roamage': ['georama', 'roamage'],
 'roamer': ['remora', 'roamer'],
 'roaming': ['ingomar', 'moringa', 'roaming'],
 'roan': ['nora', 'orna', 'roan'],
 'roast': ['astor', 'roast'],
 'roastable': ['astrolabe', 'roastable'],
 'roasting': ['orangist', 'organist', 'roasting', 'signator'],
 'rob': ['bor', 'orb', 'rob'],
 'robalo': ['barolo', 'robalo'],
 'roband': ['bandor', 'bondar', 'roband'],
 'robbin': ['ribbon', 'robbin'],
 'robe': ['boer', 'bore', 'robe'],
 'rober': ['borer', 'rerob', 'rober'],
 'roberd': ['border', 'roberd'],
 'roberta': ['arboret', 'roberta', 'taborer'],
 'robin': ['biron', 'inorb', 'robin'],
 'robinet': ['bornite', 'robinet'],
 'robing': ['boring', 'robing'],
 'roble': ['blore', 'roble'],
 'robot': ['boort', 'robot'],
 'robotian': ['abortion', 'robotian'],
 'robotism': ['bimotors', 'robotism'],
 'robur': ['burro', 'robur', 'rubor'],
 'roc': ['cor', 'cro', 'orc', 'roc'],
 'rochea': ['chorea', 'ochrea', 'rochea'],
 'rochet': ['hector', 'rochet', 'tocher', 'troche'],
 'rock': ['cork', 'rock'],
 'rocker': ['corker', 'recork', 'rocker'],
 'rocketer': ['rocketer', 'rocktree'],
 'rockiness': ['corkiness', 'rockiness'],
 'rocking': ['corking', 'rocking'],
 'rockish': ['corkish', 'rockish'],
 'rocktree': ['rocketer', 'rocktree'],
 'rockwood': ['corkwood', 'rockwood', 'woodrock'],
 'rocky': ['corky', 'rocky'],
 'rocta': ['actor', 'corta', 'croat', 'rocta', 'taroc', 'troca'],
 'rod': ['dor', 'rod'],
 'rode': ['doer', 'redo', 'rode', 'roed'],
 'rodentia': ['andorite', 'nadorite', 'ordinate', 'rodentia'],
 'rodential': ['lorandite', 'rodential'],
 'rodinal': ['nailrod', 'ordinal', 'rinaldo', 'rodinal'],
 'rodingite': ['negritoid', 'rodingite'],
 'rodless': ['drossel', 'rodless'],
 'rodlet': ['retold', 'rodlet'],
 'rodman': ['random', 'rodman'],
 'rodney': ['rodney', 'yonder'],
 'roe': ['oer', 'ore', 'roe'],
 'roed': ['doer', 'redo', 'rode', 'roed'],
 'roey': ['oyer', 'roey', 'yore'],
 'rog': ['gor', 'rog'],
 'rogan': ['angor',
           'argon',
           'goran',
           'grano',
           'groan',
           'nagor',
           'orang',
           'organ',
           'rogan',
           'ronga'],
 'rogative': ['ravigote', 'rogative'],
 'roger': ['gorer', 'roger'],
 'roggle': ['logger', 'roggle'],
 'rogue': ['orgue', 'rogue', 'rouge'],
 'rohan': ['nahor', 'norah', 'rohan'],
 'rohob': ['bohor', 'rohob'],
 'rohun': ['huron', 'rohun'],
 'roi': ['rio', 'roi'],
 'roid': ['dori', 'roid'],
 'roil': ['loir', 'lori', 'roil'],
 'roister': ['roister', 'storier'],
 'roit': ['riot', 'roit', 'trio'],
 'rok': ['kor', 'rok'],
 'roka': ['karo', 'kora', 'okra', 'roka'],
 'roke': ['kore', 'roke'],
 'rokey': ['rokey', 'yoker'],
 'roky': ['kory', 'roky', 'york'],
 'roland': ['androl', 'arnold', 'lardon', 'roland', 'ronald'],
 'rolandic': ['ironclad', 'rolandic'],
 'role': ['lore', 'orle', 'role'],
 'rolfe': ['forel', 'rolfe'],
 'roller': ['reroll', 'roller'],
 'rollick': ['rillock', 'rollick'],
 'romaean': ['neorama', 'romaean'],
 'romain': ['marion', 'romain'],
 'romaine': ['moraine', 'romaine'],
 'romal': ['molar', 'moral', 'romal'],
 'roman': ['manor', 'moran', 'norma', 'ramon', 'roman'],
 'romancist': ['narcotism', 'romancist'],
 'romancy': ['acronym', 'romancy'],
 'romandom': ['monodram', 'romandom'],
 'romane': ['enamor', 'monera', 'oreman', 'romane'],
 'romanes': ['masoner', 'romanes'],
 'romanian': ['maronian', 'romanian'],
 'romanic': ['amicron', 'marconi', 'minorca', 'romanic'],
 'romanist': ['maronist', 'romanist'],
 'romanistic': ['marcionist', 'romanistic'],
 'romanite': ['maronite', 'martinoe', 'minorate', 'morenita', 'romanite'],
 'romanity': ['minatory', 'romanity'],
 'romanly': ['almonry', 'romanly'],
 'romantic': ['macrotin', 'romantic'],
 'romanticly': ['matrocliny', 'romanticly'],
 'romantism': ['matronism', 'romantism'],
 'rome': ['mero', 'more', 'omer', 'rome'],
 'romeite': ['moieter', 'romeite'],
 'romeo': ['moore', 'romeo'],
 'romero': ['romero', 'roomer'],
 'romeshot': ['resmooth', 'romeshot', 'smoother'],
 'romeward': ['marrowed', 'romeward'],
 'romic': ['micro', 'moric', 'romic'],
 'romish': ['hirmos', 'romish'],
 'rompish': ['orphism', 'rompish'],
 'ron': ['nor', 'ron'],
 'ronald': ['androl', 'arnold', 'lardon', 'roland', 'ronald'],
 'roncet': ['conter', 'cornet', 'cronet', 'roncet'],
 'ronco': ['conor', 'croon', 'ronco'],
 'rond': ['dorn', 'rond'],
 'rondache': ['anchored', 'rondache'],
 'ronde': ['drone', 'ronde'],
 'rondeau': ['rondeau', 'unoared'],
 'rondel': ['rondel', 'rondle'],
 'rondelet': ['redolent', 'rondelet'],
 'rondeletia': ['delineator', 'rondeletia'],
 'rondelle': ['enrolled', 'rondelle'],
 'rondle': ['rondel', 'rondle'],
 'rondo': ['donor', 'rondo'],
 'rondure': ['rondure', 'rounder', 'unorder'],
 'rone': ['oner', 'rone'],
 'ronga': ['angor',
           'argon',
           'goran',
           'grano',
           'groan',
           'nagor',
           'orang',
           'organ',
           'rogan',
           'ronga'],
 'rood': ['door', 'odor', 'oord', 'rood'],
 'roodstone': ['doorstone', 'roodstone'],
 'roofer': ['reroof', 'roofer'],
 'rooflet': ['footler', 'rooflet'],
 'rook': ['kroo', 'rook'],
 'rooker': ['korero', 'rooker'],
 'rool': ['loro', 'olor', 'orlo', 'rool'],
 'room': ['moor', 'moro', 'room'],
 'roomage': ['moorage', 'roomage'],
 'roomed': ['doomer', 'mooder', 'redoom', 'roomed'],
 'roomer': ['romero', 'roomer'],
 'roomlet': ['roomlet', 'tremolo'],
 'roomstead': ['astrodome', 'roomstead'],
 'roomward': ['roomward', 'wardroom'],
 'roomy': ['moory', 'roomy'],
 'roost': ['roost', 'torso'],
 'root': ['root', 'roto', 'toro'],
 'rooter': ['reroot', 'rooter', 'torero'],
 'rootle': ['looter', 'retool', 'rootle', 'tooler'],
 'rootlet': ['rootlet', 'tootler'],
 'rootworm': ['moorwort', 'rootworm', 'tomorrow', 'wormroot'],
 'rope': ['pore', 'rope'],
 'ropeable': ['operable', 'ropeable'],
 'ropelike': ['porelike', 'ropelike'],
 'ropeman': ['manrope', 'ropeman'],
 'roper': ['porer', 'prore', 'roper'],
 'ropes': ['poser', 'prose', 'ropes', 'spore'],
 'ropiness': ['poriness', 'pression', 'ropiness'],
 'roping': ['poring', 'roping'],
 'ropp': ['prop', 'ropp'],
 'ropy': ['pory', 'pyro', 'ropy'],
 'roquet': ['quoter', 'roquet', 'torque'],
 'rosa': ['asor', 'rosa', 'soar', 'sora'],
 'rosabel': ['borlase', 'labrose', 'rosabel'],
 'rosal': ['rosal', 'solar', 'soral'],
 'rosales': ['lassoer', 'oarless', 'rosales'],
 'rosalie': ['rosalie', 'seriola'],
 'rosaniline': ['enaliornis', 'rosaniline'],
 'rosated': ['rosated', 'torsade'],
 'rose': ['eros', 'rose', 'sero', 'sore'],
 'roseal': ['roseal', 'solera'],
 'rosed': ['doser', 'rosed'],
 'rosehead': ['rosehead', 'sorehead'],
 'roseine': ['erinose', 'roseine'],
 'rosel': ['loser', 'orsel', 'rosel', 'soler'],
 'roselite': ['literose', 'roselite', 'tirolese'],
 'roselle': ['orselle', 'roselle'],
 'roseola': ['aerosol', 'roseola'],
 'roset': ['roset', 'rotse', 'soter', 'stero', 'store', 'torse'],
 'rosetan': ['noreast', 'rosetan', 'seatron', 'senator', 'treason'],
 'rosetime': ['rosetime', 'timorese', 'tiresome'],
 'rosetta': ['retoast', 'rosetta', 'stoater', 'toaster'],
 'rosette': ['rosette', 'tetrose'],
 'rosetum': ['oestrum', 'rosetum'],
 'rosety': ['oyster', 'rosety'],
 'rosin': ['ornis', 'rosin'],
 'rosinate': ['arsonite', 'asterion', 'oestrian', 'rosinate', 'serotina'],
 'rosine': ['rosine', 'senior', 'soneri'],
 'rosiness': ['insessor', 'rosiness'],
 'rosmarine': ['morrisean', 'rosmarine'],
 'rosolite': ['oestriol', 'rosolite'],
 'rosorial': ['rosorial', 'sororial'],
 'rossite': ['rossite', 'sorites'],
 'rostel': ['relost', 'reslot', 'rostel', 'sterol', 'torsel'],
 'roster': ['resort', 'roster', 'sorter', 'storer'],
 'rostra': ['rostra', 'sartor'],
 'rostrate': ['rostrate', 'trostera'],
 'rosulate': ['oestrual', 'rosulate'],
 'rosy': ['rosy', 'sory'],
 'rot': ['ort', 'rot', 'tor'],
 'rota': ['rota', 'taro', 'tora'],
 'rotacism': ['acrotism', 'rotacism'],
 'rotal': ['latro', 'rotal', 'toral'],
 'rotala': ['aortal', 'rotala'],
 'rotalian': ['notarial', 'rational', 'rotalian'],
 'rotan': ['orant', 'rotan', 'toran', 'trona'],
 'rotanev': ['rotanev', 'venator'],
 'rotarian': ['rotarian', 'tornaria'],
 'rotate': ['rotate', 'tetrao'],
 'rotch': ['chort', 'rotch', 'torch'],
 'rote': ['rote', 'tore'],
 'rotella': ['reallot', 'rotella', 'tallero'],
 'rotge': ['ergot', 'rotge'],
 'rother': ['rhetor', 'rother'],
 'roto': ['root', 'roto', 'toro'],
 'rotse': ['roset', 'rotse', 'soter', 'stero', 'store', 'torse'],
 'rottan': ['attorn', 'ratton', 'rottan'],
 'rotten': ['rotten', 'terton'],
 'rotter': ['retort', 'retrot', 'rotter'],
 'rottle': ['lotter', 'rottle', 'tolter'],
 'rotula': ['rotula', 'torula'],
 'rotulian': ['rotulian', 'uranotil'],
 'rotuliform': ['rotuliform', 'toruliform'],
 'rotulus': ['rotulus', 'torulus'],
 'rotund': ['rotund', 'untrod'],
 'rotunda': ['rotunda', 'tandour'],
 'rotundate': ['rotundate', 'unrotated'],
 'rotundifoliate': ['rotundifoliate', 'titanofluoride'],
 'rotundo': ['orotund', 'rotundo'],
 'roub': ['buro', 'roub'],
 'roud': ['dour', 'duro', 'ordu', 'roud'],
 'rouge': ['orgue', 'rogue', 'rouge'],
 'rougeot': ['outgoer', 'rougeot'],
 'roughen': ['enrough', 'roughen'],
 'roughie': ['higuero', 'roughie'],
 'rouky': ['rouky', 'yurok'],
 'roulade': ['roulade', 'urodela'],
 'rounce': ['conure', 'rounce', 'uncore'],
 'rounded': ['redound', 'rounded', 'underdo'],
 'roundel': ['durenol', 'lounder', 'roundel'],
 'rounder': ['rondure', 'rounder', 'unorder'],
 'roundhead': ['roundhead', 'unhoarded'],
 'roundseam': ['meandrous', 'roundseam'],
 'roundup': ['roundup', 'unproud'],
 'roup': ['pour', 'roup'],
 'rouper': ['pourer', 'repour', 'rouper'],
 'roupet': ['pouter', 'roupet', 'troupe'],
 'rousedness': ['rousedness', 'souredness'],
 'rouser': ['rouser', 'sourer'],
 'rousing': ['nigrous', 'rousing', 'souring'],
 'rousseau': ['eosaurus', 'rousseau'],
 'roust': ['roust', 'rusot', 'stour', 'sutor', 'torus'],
 'rouster': ['rouster', 'trouser'],
 'rousting': ['rousting', 'stouring'],
 'rout': ['rout', 'toru', 'tour'],
 'route': ['outer', 'outre', 'route'],
 'router': ['retour', 'router', 'tourer'],
 'routh': ['routh', 'throu'],
 'routhie': ['outhire', 'routhie'],
 'routine': ['routine', 'tueiron'],
 'routing': ['outgrin', 'outring', 'routing', 'touring'],
 'routinist': ['introitus', 'routinist'],
 'rove': ['over', 'rove'],
 'rovet': ['overt', 'rovet', 'torve', 'trove', 'voter'],
 'row': ['row', 'wro'],
 'rowdily': ['rowdily', 'wordily'],
 'rowdiness': ['rowdiness', 'wordiness'],
 'rowdy': ['dowry', 'rowdy', 'wordy'],
 'rowed': ['dower', 'rowed'],
 'rowel': ['lower', 'owler', 'rowel'],
 'rowelhead': ['rowelhead', 'wheelroad'],
 'rowen': ['owner', 'reown', 'rowen'],
 'rower': ['rerow', 'rower'],
 'rowet': ['rowet', 'tower', 'wrote'],
 'rowing': ['ingrow', 'rowing'],
 'rowlet': ['rowlet', 'trowel', 'wolter'],
 'rowley': ['lowery', 'owlery', 'rowley', 'yowler'],
 'roxy': ['oryx', 'roxy'],
 'roy': ['ory', 'roy', 'yor'],
 'royalist': ['royalist', 'solitary'],
 'royet': ['royet', 'toyer'],
 'royt': ['royt', 'ryot', 'tory', 'troy', 'tyro'],
 'rua': ['aru', 'rua', 'ura'],
 'ruana': ['anura', 'ruana'],
 'rub': ['bur', 'rub'],
 'rubasse': ['rubasse', 'surbase'],
 'rubato': ['outbar', 'rubato', 'tabour'],
 'rubbed': ['dubber', 'rubbed'],
 'rubble': ['burble', 'lubber', 'rubble'],
 'rubbler': ['burbler', 'rubbler'],
 'rubbly': ['burbly', 'rubbly'],
 'rube': ['bure', 'reub', 'rube'],
 'rubella': ['rubella', 'rulable'],
 'rubescent': ['rubescent', 'subcenter'],
 'rubiate': ['abiuret', 'aubrite', 'biurate', 'rubiate'],
 'rubiator': ['rubiator', 'torrubia'],
 'rubican': ['brucina', 'rubican'],
 'rubied': ['burdie', 'buried', 'rubied'],
 'rubification': ['rubification', 'urbification'],
 'rubify': ['rubify', 'urbify'],
 'rubine': ['burnie', 'rubine'],
 'ruble': ['bluer', 'brule', 'burel', 'ruble'],
 'rubor': ['burro', 'robur', 'rubor'],
 'rubrical': ['bicrural', 'rubrical'],
 'ruby': ['bury', 'ruby'],
 'ructation': ['anticourt', 'curtation', 'ructation'],
 'ruction': ['courtin', 'ruction'],
 'rud': ['rud', 'urd'],
 'rudas': ['rudas', 'sudra'],
 'ruddle': ['dudler', 'ruddle'],
 'rude': ['duer', 'dure', 'rude', 'urde'],
 'rudish': ['hurdis', 'rudish'],
 'rudista': ['dasturi', 'rudista'],
 'rudity': ['durity', 'rudity'],
 'rue': ['rue', 'ure'],
 'ruen': ['renu', 'ruen', 'rune'],
 'ruffed': ['duffer', 'ruffed'],
 'rufter': ['returf', 'rufter'],
 'rug': ['gur', 'rug'],
 'ruga': ['gaur', 'guar', 'ruga'],
 'rugate': ['argute', 'guetar', 'rugate', 'tuareg'],
 'rugged': ['grudge', 'rugged'],
 'ruggle': ['gurgle', 'lugger', 'ruggle'],
 'rugose': ['grouse', 'rugose'],
 'ruinate': ['ruinate', 'taurine', 'uranite', 'urinate'],
 'ruination': ['ruination', 'urination'],
 'ruinator': ['ruinator', 'urinator'],
 'ruined': ['diurne', 'inured', 'ruined', 'unride'],
 'ruing': ['irgun', 'ruing', 'unrig'],
 'ruinous': ['ruinous', 'urinous'],
 'ruinousness': ['ruinousness', 'urinousness'],
 'rulable': ['rubella', 'rulable'],
 'rule': ['lure', 'rule'],
 'ruledom': ['remould', 'ruledom'],
 'ruler': ['lurer', 'ruler'],
 'ruling': ['ruling', 'urling'],
 'rulingly': ['luringly', 'rulingly'],
 'rum': ['mru', 'rum'],
 'rumal': ['mural', 'rumal'],
 'ruman': ['muran', 'ruman', 'unarm', 'unram', 'urman'],
 'rumble': ['lumber', 'rumble', 'umbrel'],
 'rumelian': ['lemurian', 'malurine', 'rumelian'],
 'rumex': ['murex', 'rumex'],
 'ruminant': ['nutramin', 'ruminant'],
 'ruminator': ['antirumor', 'ruminator'],
 'rumly': ['murly', 'rumly'],
 'rumple': ['lumper', 'plumer', 'replum', 'rumple'],
 'run': ['run', 'urn'],
 'runback': ['backrun', 'runback'],
 'runby': ['burny', 'runby'],
 'runch': ['churn', 'runch'],
 'runcinate': ['encurtain', 'runcinate', 'uncertain'],
 'rundale': ['launder', 'rundale'],
 'rundi': ['rundi', 'unrid'],
 'rundlet': ['rundlet', 'trundle'],
 'rune': ['renu', 'ruen', 'rune'],
 'runed': ['runed', 'under', 'unred'],
 'runer': ['rerun', 'runer'],
 'runfish': ['furnish', 'runfish'],
 'rung': ['grun', 'rung'],
 'runic': ['curin', 'incur', 'runic'],
 'runically': ['runically', 'unlyrical'],
 'runite': ['runite', 'triune', 'uniter', 'untire'],
 'runkly': ['knurly', 'runkly'],
 'runlet': ['runlet', 'turnel'],
 'runnet': ['runnet', 'tunner', 'unrent'],
 'runout': ['outrun', 'runout'],
 'runover': ['overrun', 'runover'],
 'runt': ['runt', 'trun', 'turn'],
 'runted': ['runted', 'tunder', 'turned'],
 'runtee': ['neuter', 'retune', 'runtee', 'tenure', 'tureen'],
 'runway': ['runway', 'unwary'],
 'rupa': ['prau', 'rupa'],
 'rupee': ['puree', 'rupee'],
 'rupestrian': ['rupestrian', 'supertrain'],
 'rupiah': ['hairup', 'rupiah'],
 'rural': ['rural', 'urlar'],
 'rus': ['rus', 'sur', 'urs'],
 'rusa': ['rusa', 'saur', 'sura', 'ursa', 'usar'],
 'ruscus': ['cursus', 'ruscus'],
 'ruse': ['ruse', 'suer', 'sure', 'user'],
 'rush': ['rhus', 'rush'],
 'rushen': ['reshun', 'rushen'],
 'rusine': ['insure', 'rusine', 'ursine'],
 'rusma': ['musar', 'ramus', 'rusma', 'surma'],
 'rusot': ['roust', 'rusot', 'stour', 'sutor', 'torus'],
 'russelia': ['russelia', 'siruelas'],
 'russet': ['russet', 'tusser'],
 'russify': ['fissury', 'russify'],
 'russine': ['russine', 'serinus', 'sunrise'],
 'rustable': ['baluster', 'rustable'],
 'rustic': ['citrus', 'curtis', 'rictus', 'rustic'],
 'rusticial': ['curialist', 'rusticial'],
 'rusticly': ['crustily', 'rusticly'],
 'rusticness': ['crustiness', 'rusticness'],
 'rustle': ['luster', 'result', 'rustle', 'sutler', 'ulster'],
 'rustling': ['lustring', 'rustling'],
 'rustly': ['rustly', 'sultry'],
 'rut': ['rut', 'tur'],
 'ruta': ['ruta', 'taur'],
 'rutch': ['cruth', 'rutch'],
 'rutelian': ['lutrinae', 'retinula', 'rutelian', 'tenurial'],
 'rutelinae': ['lineature', 'rutelinae'],
 'ruth': ['hurt', 'ruth'],
 'ruthenian': ['hunterian', 'ruthenian'],
 'ruther': ['hurter', 'ruther'],
 'ruthful': ['hurtful', 'ruthful'],
 'ruthfully': ['hurtfully', 'ruthfully'],
 'ruthfulness': ['hurtfulness', 'ruthfulness'],
 'ruthless': ['hurtless', 'ruthless'],
 'ruthlessly': ['hurtlessly', 'ruthlessly'],
 'ruthlessness': ['hurtlessness', 'ruthlessness'],
 'rutilant': ['rutilant', 'turntail'],
 'rutinose': ['rutinose', 'tursenoi'],
 'rutter': ['rutter', 'turret'],
 'rutyl': ['rutyl', 'truly'],
 'rutylene': ['neuterly', 'rutylene'],
 'ryal': ['aryl', 'lyra', 'ryal', 'yarl'],
 'ryder': ['derry', 'redry', 'ryder'],
 'rye': ['rye', 'yer'],
 'ryen': ['ryen', 'yern'],
 'ryot': ['royt', 'ryot', 'tory', 'troy', 'tyro'],
 'rype': ['prey', 'pyre', 'rype'],
 'rytina': ['rytina', 'trainy', 'tyrian'],
 'sa': ['as', 'sa'],
 'saa': ['asa', 'saa'],
 'saan': ['anas', 'ansa', 'saan'],
 'sab': ['bas', 'sab'],
 'saba': ['abas', 'saba'],
 'sabal': ['balas', 'balsa', 'basal', 'sabal'],
 'saban': ['nasab', 'saban'],
 'sabanut': ['sabanut', 'sabutan', 'tabanus'],
 'sabe': ['base', 'besa', 'sabe', 'seba'],
 'sabeca': ['casabe', 'sabeca'],
 'sabella': ['basella', 'sabella', 'salable'],
 'sabelli': ['sabelli', 'sebilla'],
 'sabellid': ['sabellid', 'slidable'],
 'saber': ['barse', 'besra', 'saber', 'serab'],
 'sabered': ['debaser', 'sabered'],
 'sabian': ['sabian', 'sabina'],
 'sabina': ['sabian', 'sabina'],
 'sabino': ['basion', 'bonsai', 'sabino'],
 'sabir': ['baris', 'sabir'],
 'sable': ['blase', 'sable'],
 'saboraim': ['ambrosia', 'saboraim'],
 'sabot': ['basto', 'boast', 'sabot'],
 'sabotine': ['obeisant', 'sabotine'],
 'sabromin': ['ambrosin', 'barosmin', 'sabromin'],
 'sabulite': ['sabulite', 'suitable'],
 'sabutan': ['sabanut', 'sabutan', 'tabanus'],
 'sacalait': ['castalia', 'sacalait'],
 'saccade': ['cascade', 'saccade'],
 'saccomyian': ['saccomyian', 'saccomyina'],
 'saccomyina': ['saccomyian', 'saccomyina'],
 'sacculoutricular': ['sacculoutricular', 'utriculosaccular'],
 'sacellum': ['camellus', 'sacellum'],
 'sachem': ['sachem', 'schema'],
 'sachet': ['chaste', 'sachet', 'scathe', 'scheat'],
 'sacian': ['ascian', 'sacian', 'scania', 'sicana'],
 'sack': ['cask', 'sack'],
 'sackbut': ['sackbut', 'subtack'],
 'sacken': ['sacken', 'skance'],
 'sacker': ['resack', 'sacker', 'screak'],
 'sacking': ['casking', 'sacking'],
 'sacklike': ['casklike', 'sacklike'],
 'sacque': ['casque', 'sacque'],
 'sacral': ['lascar', 'rascal', 'sacral', 'scalar'],
 'sacrification': ['sacrification', 'scarification'],
 'sacrificator': ['sacrificator', 'scarificator'],
 'sacripant': ['sacripant', 'spartanic'],
 'sacro': ['arcos', 'crosa', 'oscar', 'sacro'],
 'sacrodorsal': ['dorsosacral', 'sacrodorsal'],
 'sacroischiac': ['isosaccharic', 'sacroischiac'],
 'sacrolumbal': ['lumbosacral', 'sacrolumbal'],
 'sacrovertebral': ['sacrovertebral', 'vertebrosacral'],
 'sad': ['das', 'sad'],
 'sadden': ['desand', 'sadden', 'sanded'],
 'saddling': ['addlings', 'saddling'],
 'sadh': ['dash', 'sadh', 'shad'],
 'sadhe': ['deash', 'hades', 'sadhe', 'shade'],
 'sadic': ['asdic', 'sadic'],
 'sadie': ['aides', 'aside', 'sadie'],
 'sadiron': ['sadiron', 'sardoin'],
 'sado': ['dosa', 'sado', 'soda'],
 'sadr': ['sadr', 'sard'],
 'saeima': ['asemia', 'saeima'],
 'saernaite': ['arseniate', 'saernaite'],
 'saeter': ['asteer',
            'easter',
            'eastre',
            'reseat',
            'saeter',
            'seater',
            'staree',
            'teaser',
            'teresa'],
 'saeume': ['amusee', 'saeume'],
 'safar': ['safar', 'saraf'],
 'safely': ['fayles', 'safely'],
 'saft': ['fast', 'saft'],
 'sag': ['gas', 'sag'],
 'sagai': ['sagai', 'saiga'],
 'sagene': ['sagene', 'senega'],
 'sagger': ['sagger', 'seggar'],
 'sagless': ['gasless', 'glasses', 'sagless'],
 'sago': ['sago', 'soga'],
 'sagoin': ['gosain', 'isagon', 'sagoin'],
 'sagra': ['argas', 'sagra'],
 'sah': ['ash', 'sah', 'sha'],
 'saharic': ['arachis', 'asiarch', 'saharic'],
 'sahh': ['hash', 'sahh', 'shah'],
 'sahidic': ['hasidic', 'sahidic'],
 'sahme': ['sahme', 'shame'],
 'saho': ['saho', 'shoa'],
 'sai': ['sai', 'sia'],
 'saic': ['acis', 'asci', 'saic'],
 'said': ['dais', 'dasi', 'disa', 'said', 'sida'],
 'saidi': ['saidi', 'saiid'],
 'saiga': ['sagai', 'saiga'],
 'saiid': ['saidi', 'saiid'],
 'sail': ['lasi', 'lias', 'lisa', 'sail', 'sial'],
 'sailable': ['isabella', 'sailable'],
 'sailage': ['algesia', 'sailage'],
 'sailed': ['aisled', 'deasil', 'ladies', 'sailed'],
 'sailer': ['israel', 'relais', 'resail', 'sailer', 'serail', 'serial'],
 'sailing': ['aisling', 'sailing'],
 'sailoring': ['sailoring', 'signorial'],
 'sailsman': ['nasalism', 'sailsman'],
 'saily': ['islay', 'saily'],
 'saim': ['mias', 'saim', 'siam', 'sima'],
 'sain': ['anis', 'nais', 'nasi', 'nias', 'sain', 'sina'],
 'sainfoin': ['sainfoin', 'sinfonia'],
 'saint': ['saint', 'satin', 'stain'],
 'saintdom': ['donatism', 'saintdom'],
 'sainted': ['destain', 'instead', 'sainted', 'satined'],
 'saintless': ['saintless', 'saltiness', 'slatiness', 'stainless'],
 'saintlike': ['kleistian', 'saintlike', 'satinlike'],
 'saintly': ['nastily', 'saintly', 'staynil'],
 'saintship': ['hispanist', 'saintship'],
 'saip': ['apis', 'pais', 'pasi', 'saip'],
 'saiph': ['aphis', 'apish', 'hispa', 'saiph', 'spahi'],
 'sair': ['rais', 'sair', 'sari'],
 'saite': ['saite', 'taise'],
 'saithe': ['saithe', 'tashie', 'teaish'],
 'saitic': ['isatic', 'saitic'],
 'saivism': ['saivism', 'sivaism'],
 'sak': ['ask', 'sak'],
 'saka': ['asak', 'kasa', 'saka'],
 'sake': ['sake', 'seak'],
 'sakeen': ['sakeen', 'sekane'],
 'sakel': ['alkes', 'sakel', 'slake'],
 'saker': ['asker', 'reask', 'saker', 'sekar'],
 'sakeret': ['restake', 'sakeret'],
 'sakha': ['kasha', 'khasa', 'sakha', 'shaka'],
 'saki': ['saki', 'siak', 'sika'],
 'sal': ['las', 'sal', 'sla'],
 'salable': ['basella', 'sabella', 'salable'],
 'salably': ['basally', 'salably'],
 'salaceta': ['catalase', 'salaceta'],
 'salacot': ['coastal', 'salacot'],
 'salading': ['salading', 'salangid'],
 'salago': ['aglaos', 'salago'],
 'salamandarin': ['salamandarin', 'salamandrian', 'salamandrina'],
 'salamandrian': ['salamandarin', 'salamandrian', 'salamandrina'],
 'salamandrina': ['salamandarin', 'salamandrian', 'salamandrina'],
 'salangid': ['salading', 'salangid'],
 'salariat': ['alastair', 'salariat'],
 'salat': ['atlas', 'salat', 'salta'],
 'salay': ['asyla', 'salay', 'sayal'],
 'sale': ['elsa', 'sale', 'seal', 'slae'],
 'salele': ['salele', 'sallee'],
 'salep': ['elaps',
           'lapse',
           'lepas',
           'pales',
           'salep',
           'saple',
           'sepal',
           'slape',
           'spale',
           'speal'],
 'saleratus': ['assaulter', 'reassault', 'saleratus'],
 'salian': ['anisal', 'nasial', 'salian', 'salina'],
 'salic': ['lacis', 'salic'],
 'salicin': ['incisal', 'salicin'],
 'salicylide': ['salicylide', 'scylliidae'],
 'salience': ['salience', 'secaline'],
 'salient': ['elastin', 'salient', 'saltine', 'slainte'],
 'salimeter': ['misrelate', 'salimeter'],
 'salimetry': ['mysterial', 'salimetry'],
 'salina': ['anisal', 'nasial', 'salian', 'salina'],
 'saline': ['alsine', 'neslia', 'saline', 'selina', 'silane'],
 'salinoterreous': ['salinoterreous', 'soliterraneous'],
 'salite': ['isleta', 'litsea', 'salite', 'stelai'],
 'salited': ['distale', 'salited'],
 'saliva': ['saliva', 'salvia'],
 'salivan': ['salivan', 'slavian'],
 'salivant': ['navalist', 'salivant'],
 'salivate': ['salivate', 'vestalia'],
 'salle': ['salle', 'sella'],
 'sallee': ['salele', 'sallee'],
 'sallet': ['sallet', 'stella', 'talles'],
 'sallow': ['sallow', 'swallo'],
 'salm': ['alms', 'salm', 'slam'],
 'salma': ['salma', 'samal'],
 'salmine': ['malines', 'salmine', 'selamin', 'seminal'],
 'salmis': ['missal', 'salmis'],
 'salmo': ['salmo', 'somal'],
 'salmonsite': ['assoilment', 'salmonsite'],
 'salome': ['melosa', 'salome', 'semola'],
 'salometer': ['elastomer', 'salometer'],
 'salon': ['salon', 'sloan', 'solan'],
 'saloon': ['alonso', 'alsoon', 'saloon'],
 'salp': ['salp', 'slap'],
 'salpa': ['palas', 'salpa'],
 'salpidae': ['palisade', 'salpidae'],
 'salpoid': ['psaloid', 'salpoid'],
 'salt': ['last', 'salt', 'slat'],
 'salta': ['atlas', 'salat', 'salta'],
 'saltary': ['astylar', 'saltary'],
 'saltation': ['saltation', 'stational'],
 'salted': ['desalt', 'salted'],
 'saltee': ['ateles', 'saltee', 'sealet', 'stelae', 'teasel'],
 'salter': ['laster',
            'lastre',
            'rastle',
            'relast',
            'resalt',
            'salter',
            'slater',
            'stelar'],
 'saltern': ['saltern', 'starnel', 'sternal'],
 'saltery': ['saltery', 'stearyl'],
 'saltier': ['aletris', 'alister', 'listera', 'realist', 'saltier'],
 'saltine': ['elastin', 'salient', 'saltine', 'slainte'],
 'saltiness': ['saintless', 'saltiness', 'slatiness', 'stainless'],
 'salting': ['anglist', 'lasting', 'salting', 'slating', 'staling'],
 'saltish': ['saltish', 'slatish'],
 'saltly': ['lastly', 'saltly'],
 'saltness': ['lastness', 'saltness'],
 'saltometer': ['rattlesome', 'saltometer'],
 'saltus': ['saltus', 'tussal'],
 'saltwife': ['flatwise', 'saltwife'],
 'salty': ['lasty', 'salty', 'slaty'],
 'salung': ['lugnas', 'salung'],
 'salute': ['salute', 'setula'],
 'saluter': ['arustle', 'estrual', 'saluter', 'saulter'],
 'salva': ['salva', 'valsa', 'vasal'],
 'salve': ['salve', 'selva', 'slave', 'valse'],
 'salver': ['salver', 'serval', 'slaver', 'versal'],
 'salvia': ['saliva', 'salvia'],
 'salvy': ['salvy', 'sylva'],
 'sam': ['mas', 'sam', 'sma'],
 'samal': ['salma', 'samal'],
 'saman': ['manas', 'saman'],
 'samani': ['samani', 'samian'],
 'samaritan': ['samaritan', 'sarmatian'],
 'samas': ['amass', 'assam', 'massa', 'samas'],
 'sambal': ['balsam', 'sambal'],
 'sambo': ['ambos', 'sambo'],
 'same': ['asem', 'mesa', 'same', 'seam'],
 'samel': ['amsel', 'melas', 'mesal', 'samel'],
 'samely': ['measly', 'samely'],
 'samen': ['manes', 'manse', 'mensa', 'samen', 'senam'],
 'samh': ['mash', 'samh', 'sham'],
 'samian': ['samani', 'samian'],
 'samiel': ['amiles', 'asmile', 'mesail', 'mesial', 'samiel'],
 'samir': ['maris', 'marsi', 'samir', 'simar'],
 'samisen': ['samisen', 'samsien'],
 'samish': ['samish', 'sisham'],
 'samite': ['samite', 'semita', 'tamise', 'teaism'],
 'sammer': ['mamers', 'sammer'],
 'sammier': ['amerism', 'asimmer', 'sammier'],
 'samnani': ['ananism', 'samnani'],
 'samnite': ['atenism', 'inmeats', 'insteam', 'samnite'],
 'samoan': ['monasa', 'samoan'],
 'samothere': ['heartsome', 'samothere'],
 'samoyed': ['samoyed', 'someday'],
 'samphire': ['samphire', 'seraphim'],
 'sampi': ['apism', 'sampi'],
 'sampler': ['lampers', 'sampler'],
 'samsien': ['samisen', 'samsien'],
 'samskara': ['makassar', 'samskara'],
 'samucan': ['manacus', 'samucan'],
 'samuel': ['amelus', 'samuel'],
 'sanability': ['insatiably', 'sanability'],
 'sanai': ['asian', 'naias', 'sanai'],
 'sanand': ['sanand', 'sandan'],
 'sanche': ['encash', 'sanche'],
 'sanct': ['sanct', 'scant'],
 'sanction': ['canonist', 'sanction', 'sonantic'],
 'sanctioner': ['resanction', 'sanctioner'],
 'sanctity': ['sanctity', 'scantity'],
 'sandak': ['sandak', 'skanda'],
 'sandan': ['sanand', 'sandan'],
 'sandarac': ['carandas', 'sandarac'],
 'sandawe': ['sandawe', 'weasand'],
 'sanded': ['desand', 'sadden', 'sanded'],
 'sanderling': ['sanderling', 'slandering'],
 'sandflower': ['flandowser', 'sandflower'],
 'sandhi': ['danish', 'sandhi'],
 'sandra': ['nasard', 'sandra'],
 'sandworm': ['sandworm', 'swordman', 'wordsman'],
 'sane': ['anes', 'sane', 'sean'],
 'sanetch': ['chasten', 'sanetch'],
 'sang': ['sang', 'snag'],
 'sanga': ['gasan', 'sanga'],
 'sangar': ['argans', 'sangar'],
 'sangei': ['easing', 'sangei'],
 'sanger': ['angers', 'sanger', 'serang'],
 'sangrel': ['sangrel', 'snagrel'],
 'sanhita': ['ashanti', 'sanhita', 'shaitan', 'thasian'],
 'sanicle': ['celsian', 'escalin', 'sanicle', 'secalin'],
 'sanies': ['anesis', 'anseis', 'sanies', 'sansei', 'sasine'],
 'sanious': ['sanious', 'suasion'],
 'sanitate': ['astatine', 'sanitate'],
 'sanitize': ['sanitize', 'satinize'],
 'sanity': ['sanity', 'satiny'],
 'sank': ['kans', 'sank'],
 'sankha': ['kashan', 'sankha'],
 'sannup': ['pannus', 'sannup', 'unsnap', 'unspan'],
 'sanpoil': ['sanpoil', 'spaniol'],
 'sansei': ['anesis', 'anseis', 'sanies', 'sansei', 'sasine'],
 'sansi': ['sansi', 'sasin'],
 'sant': ['nast', 'sant', 'stan'],
 'santa': ['santa', 'satan'],
 'santal': ['aslant', 'lansat', 'natals', 'santal'],
 'santali': ['lanista', 'santali'],
 'santalin': ['annalist', 'santalin'],
 'santee': ['ensate', 'enseat', 'santee', 'sateen', 'senate'],
 'santiago': ['agonista', 'santiago'],
 'santimi': ['animist', 'santimi'],
 'santir': ['instar', 'santir', 'strain'],
 'santon': ['santon', 'sonant', 'stanno'],
 'santorinite': ['reinstation', 'santorinite'],
 'sap': ['asp', 'sap', 'spa'],
 'sapan': ['pasan', 'sapan'],
 'sapek': ['sapek', 'speak'],
 'saperda': ['aspread', 'saperda'],
 'saphena': ['aphanes', 'saphena'],
 'sapid': ['sapid', 'spaid'],
 'sapient': ['panties', 'sapient', 'spinate'],
 'sapiential': ['antilipase', 'sapiential'],
 'sapin': ['pisan', 'sapin', 'spina'],
 'sapinda': ['anapsid', 'sapinda'],
 'saple': ['elaps',
           'lapse',
           'lepas',
           'pales',
           'salep',
           'saple',
           'sepal',
           'slape',
           'spale',
           'speal'],
 'sapling': ['lapsing', 'sapling'],
 'sapo': ['asop', 'sapo', 'soap'],
 'sapor': ['psora', 'sapor', 'sarpo'],
 'saporous': ['asporous', 'saporous'],
 'sapota': ['sapota', 'taposa'],
 'sapotilha': ['hapalotis', 'sapotilha'],
 'sapphire': ['papisher', 'sapphire'],
 'sapples': ['papless', 'sapples'],
 'sapremia': ['aspermia', 'sapremia'],
 'sapremic': ['aspermic', 'sapremic'],
 'saprine': ['persian', 'prasine', 'saprine'],
 'saprolite': ['posterial', 'saprolite'],
 'saprolitic': ['polaristic', 'poristical', 'saprolitic'],
 'sapropel': ['prolapse', 'sapropel'],
 'sapropelic': ['periscopal', 'sapropelic'],
 'saprophagous': ['prasophagous', 'saprophagous'],
 'sapwort': ['postwar', 'sapwort'],
 'sar': ['ras', 'sar'],
 'sara': ['rasa', 'sara'],
 'saraad': ['saraad', 'sarada'],
 'sarada': ['saraad', 'sarada'],
 'saraf': ['safar', 'saraf'],
 'sarah': ['asarh', 'raash', 'sarah'],
 'saran': ['ansar', 'saran', 'sarna'],
 'sarangi': ['giansar', 'sarangi'],
 'sarcenet': ['reascent', 'sarcenet'],
 'sarcine': ['arsenic', 'cerasin', 'sarcine'],
 'sarcitis': ['sarcitis', 'triassic'],
 'sarcle': ['sarcle', 'scaler', 'sclera'],
 'sarcoadenoma': ['adenosarcoma', 'sarcoadenoma'],
 'sarcocarcinoma': ['carcinosarcoma', 'sarcocarcinoma'],
 'sarcoid': ['sarcoid', 'scaroid'],
 'sarcoline': ['censorial', 'sarcoline'],
 'sarcolite': ['alectoris', 'sarcolite', 'sclerotia', 'sectorial'],
 'sarcoplast': ['postsacral', 'sarcoplast'],
 'sarcotic': ['acrostic', 'sarcotic', 'socratic'],
 'sard': ['sadr', 'sard'],
 'sardian': ['andrias', 'sardian', 'sarinda'],
 'sardine': ['andries', 'isander', 'sardine'],
 'sardoin': ['sadiron', 'sardoin'],
 'sardonic': ['draconis', 'sardonic'],
 'sare': ['arse', 'rase', 'sare', 'sear', 'sera'],
 'sargonide': ['grandiose', 'sargonide'],
 'sari': ['rais', 'sair', 'sari'],
 'sarif': ['farsi', 'sarif'],
 'sarigue': ['ergusia', 'gerusia', 'sarigue'],
 'sarinda': ['andrias', 'sardian', 'sarinda'],
 'sarip': ['paris', 'parsi', 'sarip'],
 'sark': ['askr', 'kras', 'sark'],
 'sarkine': ['kerasin', 'sarkine'],
 'sarkit': ['rastik', 'sarkit', 'straik'],
 'sarmatian': ['samaritan', 'sarmatian'],
 'sarment': ['sarment', 'smarten'],
 'sarmentous': ['sarmentous', 'tarsonemus'],
 'sarna': ['ansar', 'saran', 'sarna'],
 'sarod': ['sarod', 'sorda'],
 'saron': ['arson', 'saron', 'sonar'],
 'saronic': ['arsonic', 'saronic'],
 'sarpo': ['psora', 'sapor', 'sarpo'],
 'sarra': ['arras', 'sarra'],
 'sarsenet': ['assenter', 'reassent', 'sarsenet'],
 'sarsi': ['arsis', 'sarsi'],
 'sart': ['sart', 'star', 'stra', 'tars', 'tsar'],
 'sartain': ['artisan', 'astrain', 'sartain', 'tsarina'],
 'sartish': ['sartish', 'shastri'],
 'sartor': ['rostra', 'sartor'],
 'sasan': ['nassa', 'sasan'],
 'sasin': ['sansi', 'sasin'],
 'sasine': ['anesis', 'anseis', 'sanies', 'sansei', 'sasine'],
 'sat': ['ast', 'sat'],
 'satan': ['santa', 'satan'],
 'satanical': ['castalian', 'satanical'],
 'satanism': ['mantissa', 'satanism'],
 'sate': ['ates', 'east', 'eats', 'sate', 'seat', 'seta'],
 'sateen': ['ensate', 'enseat', 'santee', 'sateen', 'senate'],
 'sateless': ['sateless', 'seatless'],
 'satelles': ['satelles', 'tessella'],
 'satellite': ['satellite', 'telestial'],
 'satiable': ['bisaltae', 'satiable'],
 'satiate': ['isatate', 'satiate', 'taetsia'],
 'satieno': ['aeonist', 'asiento', 'satieno'],
 'satient': ['atenist', 'instate', 'satient', 'steatin'],
 'satin': ['saint', 'satin', 'stain'],
 'satine': ['satine', 'tisane'],
 'satined': ['destain', 'instead', 'sainted', 'satined'],
 'satinette': ['enstatite', 'intestate', 'satinette'],
 'satinite': ['satinite', 'sittinae'],
 'satinize': ['sanitize', 'satinize'],
 'satinlike': ['kleistian', 'saintlike', 'satinlike'],
 'satiny': ['sanity', 'satiny'],
 'satire': ['satire', 'striae'],
 'satirical': ['racialist', 'satirical'],
 'satirist': ['satirist', 'tarsitis'],
 'satrae': ['astare', 'satrae'],
 'satrapic': ['aspartic', 'satrapic'],
 'satron': ['asnort', 'satron'],
 'sattle': ['latest', 'sattle', 'taslet'],
 'sattva': ['sattva', 'tavast'],
 'saturable': ['balaustre', 'saturable'],
 'saturation': ['saturation', 'titanosaur'],
 'saturator': ['saturator', 'tartarous'],
 'saturn': ['saturn', 'unstar'],
 'saturnale': ['alaternus', 'saturnale'],
 'saturnalia': ['australian', 'saturnalia'],
 'saturnia': ['asturian', 'austrian', 'saturnia'],
 'saturnine': ['neustrian', 'saturnine', 'sturninae'],
 'saturnism': ['saturnism', 'surmisant'],
 'satyr': ['satyr', 'stary', 'stray', 'trasy'],
 'satyrlike': ['satyrlike', 'streakily'],
 'sauce': ['cause', 'sauce'],
 'sauceless': ['causeless', 'sauceless'],
 'sauceline': ['sauceline', 'seleucian'],
 'saucer': ['causer', 'saucer'],
 'sauger': ['sauger', 'usager'],
 'saugh': ['agush', 'saugh'],
 'saul': ['saul', 'sula'],
 'sauld': ['aldus', 'sauld'],
 'sault': ['latus', 'sault', 'talus'],
 'saulter': ['arustle', 'estrual', 'saluter', 'saulter'],
 'saum': ['masu', 'musa', 'saum'],
 'sauna': ['nasua', 'sauna'],
 'saur': ['rusa', 'saur', 'sura', 'ursa', 'usar'],
 'saura': ['arusa', 'saura', 'usara'],
 'saurian': ['saurian', 'suriana'],
 'saury': ['saury', 'surya'],
 'sausage': ['assuage', 'sausage'],
 'saut': ['saut', 'tasu', 'utas'],
 'sauve': ['sauve', 'suave'],
 'save': ['aves', 'save', 'vase'],
 'savin': ['savin', 'sivan'],
 'saviour': ['saviour', 'various'],
 'savor': ['savor', 'sorva'],
 'savored': ['oversad', 'savored'],
 'saw': ['saw', 'swa', 'was'],
 'sawah': ['awash', 'sawah'],
 'sawali': ['aswail', 'sawali'],
 'sawback': ['backsaw', 'sawback'],
 'sawbuck': ['bucksaw', 'sawbuck'],
 'sawer': ['resaw', 'sawer', 'seraw', 'sware', 'swear', 'warse'],
 'sawing': ['aswing', 'sawing'],
 'sawish': ['sawish', 'siwash'],
 'sawn': ['sawn', 'snaw', 'swan'],
 'sawt': ['sawt', 'staw', 'swat', 'taws', 'twas', 'wast'],
 'sawyer': ['sawyer', 'swayer'],
 'saxe': ['axes', 'saxe', 'seax'],
 'saxten': ['saxten', 'sextan'],
 'say': ['say', 'yas'],
 'sayal': ['asyla', 'salay', 'sayal'],
 'sayer': ['reasy', 'resay', 'sayer', 'seary'],
 'sayid': ['daisy', 'sayid'],
 'scabbler': ['scabbler', 'scrabble'],
 'scabies': ['abscise', 'scabies'],
 'scaddle': ['scaddle', 'scalded'],
 'scaean': ['anaces', 'scaean'],
 'scala': ['calas', 'casal', 'scala'],
 'scalar': ['lascar', 'rascal', 'sacral', 'scalar'],
 'scalded': ['scaddle', 'scalded'],
 'scale': ['alces', 'casel', 'scale'],
 'scalena': ['escalan', 'scalena'],
 'scalene': ['cleanse', 'scalene'],
 'scaler': ['sarcle', 'scaler', 'sclera'],
 'scallola': ['callosal', 'scallola'],
 'scalloper': ['procellas', 'scalloper'],
 'scaloni': ['nicolas', 'scaloni'],
 'scalp': ['clasp', 'scalp'],
 'scalper': ['clasper', 'reclasp', 'scalper'],
 'scalping': ['clasping', 'scalping'],
 'scalpture': ['prescutal', 'scalpture'],
 'scaly': ['aclys', 'scaly'],
 'scambler': ['scambler', 'scramble'],
 'scampish': ['scampish', 'scaphism'],
 'scania': ['ascian', 'sacian', 'scania', 'sicana'],
 'scant': ['sanct', 'scant'],
 'scantity': ['sanctity', 'scantity'],
 'scantle': ['asclent', 'scantle'],
 'scape': ['capes', 'scape', 'space'],
 'scapeless': ['scapeless', 'spaceless'],
 'scapha': ['pascha', 'scapha'],
 'scaphander': ['handscrape', 'scaphander'],
 'scaphism': ['scampish', 'scaphism'],
 'scaphite': ['paschite', 'pastiche', 'pistache', 'scaphite'],
 'scaphopod': ['podoscaph', 'scaphopod'],
 'scapoid': ['psoadic', 'scapoid', 'sciapod'],
 'scapolite': ['alopecist', 'altiscope', 'epicostal', 'scapolite'],
 'scappler': ['scappler', 'scrapple'],
 'scapula': ['capsula', 'pascual', 'scapula'],
 'scapular': ['capsular', 'scapular'],
 'scapulated': ['capsulated', 'scapulated'],
 'scapulectomy': ['capsulectomy', 'scapulectomy'],
 'scarab': ['barsac', 'scarab'],
 'scarcement': ['marcescent', 'scarcement'],
 'scare': ['carse', 'caser', 'ceras', 'scare', 'scrae'],
 'scarification': ['sacrification', 'scarification'],
 'scarificator': ['sacrificator', 'scarificator'],
 'scarily': ['scarily', 'scraily'],
 'scarlet': ['scarlet', 'sclater'],
 'scarn': ['scarn', 'scran'],
 'scaroid': ['sarcoid', 'scaroid'],
 'scarp': ['craps', 'scarp', 'scrap'],
 'scarping': ['scarping', 'scraping'],
 'scart': ['scart', 'scrat'],
 'scarth': ['scarth', 'scrath', 'starch'],
 'scary': ['ascry', 'scary', 'scray'],
 'scase': ['casse', 'scase'],
 'scat': ['acts', 'cast', 'scat'],
 'scathe': ['chaste', 'sachet', 'scathe', 'scheat'],
 'scatterer': ['scatterer', 'streetcar'],
 'scaturient': ['incrustate', 'scaturient', 'scrutinate'],
 'scaum': ['camus', 'musca', 'scaum', 'sumac'],
 'scaur': ['cursa', 'scaur'],
 'scaut': ['scaut', 'scuta'],
 'scavel': ['calves', 'scavel'],
 'scawl': ['scawl', 'sclaw'],
 'sceat': ['caste', 'sceat'],
 'scene': ['cense', 'scene', 'sence'],
 'scenery': ['scenery', 'screeny'],
 'scented': ['descent', 'scented'],
 'scepter': ['respect', 'scepter', 'specter'],
 'sceptered': ['sceptered', 'spectered'],
 'scepterless': ['respectless', 'scepterless'],
 'sceptral': ['sceptral', 'scraplet', 'spectral'],
 'sceptry': ['precyst', 'sceptry', 'spectry'],
 'scerne': ['censer', 'scerne', 'screen', 'secern'],
 'schalmei': ['camelish', 'schalmei'],
 'scheat': ['chaste', 'sachet', 'scathe', 'scheat'],
 'schema': ['sachem', 'schema'],
 'schematic': ['catechism', 'schematic'],
 'scheme': ['scheme', 'smeech'],
 'schemer': ['chermes', 'schemer'],
 'scho': ['cosh', 'scho'],
 'scholae': ['oscheal', 'scholae'],
 'schone': ['cheson', 'chosen', 'schone'],
 'schooltime': ['chilostome', 'schooltime'],
 'schout': ['schout', 'scouth'],
 'schute': ['schute', 'tusche'],
 'sciaenoid': ['oniscidae', 'oscinidae', 'sciaenoid'],
 'scian': ['canis', 'scian'],
 'sciapod': ['psoadic', 'scapoid', 'sciapod'],
 'sciara': ['carisa', 'sciara'],
 'sciarid': ['cidaris', 'sciarid'],
 'sciatic': ['ascitic', 'sciatic'],
 'sciatical': ['ascitical', 'sciatical'],
 'scient': ['encist', 'incest', 'insect', 'scient'],
 'sciential': ['elasticin', 'inelastic', 'sciential'],
 'scillitan': ['scillitan', 'scintilla'],
 'scintilla': ['scillitan', 'scintilla'],
 'scintle': ['lentisc', 'scintle', 'stencil'],
 'scion': ['oscin', 'scion', 'sonic'],
 'sciot': ['ostic', 'sciot', 'stoic'],
 'sciotherical': ['ischiorectal', 'sciotherical'],
 'scious': ['scious', 'socius'],
 'scirenga': ['creasing', 'scirenga'],
 'scirpus': ['prussic', 'scirpus'],
 'scissortail': ['scissortail', 'solaristics'],
 'sciurine': ['incisure', 'sciurine'],
 'sciuroid': ['dioscuri', 'sciuroid'],
 'sclate': ['castle', 'sclate'],
 'sclater': ['scarlet', 'sclater'],
 'sclaw': ['scawl', 'sclaw'],
 'sclera': ['sarcle', 'scaler', 'sclera'],
 'sclere': ['sclere', 'screel'],
 'scleria': ['scleria', 'sercial'],
 'sclerite': ['sclerite', 'silcrete'],
 'sclerodermite': ['dermosclerite', 'sclerodermite'],
 'scleromata': ['clamatores', 'scleromata'],
 'sclerose': ['coreless', 'sclerose'],
 'sclerospora': ['prolacrosse', 'sclerospora'],
 'sclerote': ['corselet', 'sclerote', 'selector'],
 'sclerotia': ['alectoris', 'sarcolite', 'sclerotia', 'sectorial'],
 'sclerotial': ['cloisteral', 'sclerotial'],
 'sclerotinia': ['intersocial', 'orleanistic', 'sclerotinia'],
 'sclerotium': ['closterium', 'sclerotium'],
 'sclerotomy': ['mycosterol', 'sclerotomy'],
 'scoad': ['cados', 'scoad'],
 'scob': ['bosc', 'scob'],
 'scobicular': ['scobicular', 'scrobicula'],
 'scolia': ['colias', 'scolia', 'social'],
 'scolion': ['scolion', 'solonic'],
 'scombrine': ['becrimson', 'scombrine'],
 'scone': ['cones', 'scone'],
 'scooper': ['coprose', 'scooper'],
 'scoot': ['coost', 'scoot'],
 'scopa': ['posca', 'scopa'],
 'scoparin': ['parsonic', 'scoparin'],
 'scope': ['copse', 'pecos', 'scope'],
 'scopidae': ['diascope', 'psocidae', 'scopidae'],
 'scopine': ['psocine', 'scopine'],
 'scopiped': ['podiceps', 'scopiped'],
 'scopoline': ['niloscope', 'scopoline'],
 'scorbutical': ['scorbutical', 'subcortical'],
 'scorbutically': ['scorbutically', 'subcortically'],
 'score': ['corse', 'score'],
 'scorer': ['scorer', 'sorcer'],
 'scoriae': ['coraise', 'scoriae'],
 'scorpidae': ['carpiodes', 'scorpidae'],
 'scorpiones': ['procession', 'scorpiones'],
 'scorpionic': ['orniscopic', 'scorpionic'],
 'scorse': ['cessor', 'crosse', 'scorse'],
 'scortation': ['cartoonist', 'scortation'],
 'scot': ['cost', 'scot'],
 'scotale': ['alecost', 'lactose', 'scotale', 'talcose'],
 'scote': ['coset', 'estoc', 'scote'],
 'scoter': ['corset', 'cortes', 'coster', 'escort', 'scoter', 'sector'],
 'scotia': ['isotac', 'scotia'],
 'scotism': ['cosmist', 'scotism'],
 'scotomatic': ['osmotactic', 'scotomatic'],
 'scotty': ['cytost', 'scotty'],
 'scoup': ['copus', 'scoup'],
 'scour': ['cours', 'scour'],
 'scoured': ['coursed', 'scoured'],
 'scourer': ['courser', 'scourer'],
 'scourge': ['scourge', 'scrouge'],
 'scourger': ['scourger', 'scrouger'],
 'scouring': ['coursing', 'scouring'],
 'scouth': ['schout', 'scouth'],
 'scrabble': ['scabbler', 'scrabble'],
 'scrabe': ['braces', 'scrabe'],
 'scrae': ['carse', 'caser', 'ceras', 'scare', 'scrae'],
 'scraily': ['scarily', 'scraily'],
 'scramble': ['scambler', 'scramble'],
 'scran': ['scarn', 'scran'],
 'scrap': ['craps', 'scarp', 'scrap'],
 'scrape': ['casper', 'escarp', 'parsec', 'scrape', 'secpar', 'spacer'],
 'scrapie': ['epacris', 'scrapie', 'serapic'],
 'scraping': ['scarping', 'scraping'],
 'scraplet': ['sceptral', 'scraplet', 'spectral'],
 'scrapple': ['scappler', 'scrapple'],
 'scrat': ['scart', 'scrat'],
 'scratcher': ['rescratch', 'scratcher'],
 'scrath': ['scarth', 'scrath', 'starch'],
 'scray': ['ascry', 'scary', 'scray'],
 'screak': ['resack', 'sacker', 'screak'],
 'screaming': ['germanics', 'screaming'],
 'screamy': ['armscye', 'screamy'],
 'scree': ['scree', 'secre'],
 'screel': ['sclere', 'screel'],
 'screen': ['censer', 'scerne', 'screen', 'secern'],
 'screenless': ['censerless', 'screenless'],
 'screeny': ['scenery', 'screeny'],
 'screet': ['resect', 'screet', 'secret'],
 'screwdrive': ['drivescrew', 'screwdrive'],
 'scrieve': ['cerevis', 'scrieve', 'service'],
 'scrike': ['scrike', 'sicker'],
 'scrip': ['crisp', 'scrip'],
 'scripee': ['precise', 'scripee'],
 'scripula': ['scripula', 'spicular'],
 'scrobicula': ['scobicular', 'scrobicula'],
 'scrota': ['arctos', 'castor', 'costar', 'scrota'],
 'scrouge': ['scourge', 'scrouge'],
 'scrouger': ['scourger', 'scrouger'],
 'scrout': ['scrout', 'scruto'],
 'scroyle': ['cryosel', 'scroyle'],
 'scruf': ['scruf', 'scurf'],
 'scruffle': ['scruffle', 'scuffler'],
 'scruple': ['scruple', 'sculper'],
 'scrutate': ['crustate', 'scrutate'],
 'scrutation': ['crustation', 'scrutation'],
 'scrutinant': ['incrustant', 'scrutinant'],
 'scrutinate': ['incrustate', 'scaturient', 'scrutinate'],
 'scruto': ['scrout', 'scruto'],
 'scudi': ['scudi', 'sudic'],
 'scuffler': ['scruffle', 'scuffler'],
 'sculper': ['scruple', 'sculper'],
 'sculpin': ['insculp', 'sculpin'],
 'scup': ['cusp', 'scup'],
 'scur': ['crus', 'scur'],
 'scurf': ['scruf', 'scurf'],
 'scusation': ['cosustain', 'scusation'],
 'scuta': ['scaut', 'scuta'],
 'scutal': ['scutal', 'suclat'],
 'scute': ['cetus', 'scute'],
 'scutifer': ['fusteric', 'scutifer'],
 'scutigeral': ['gesticular', 'scutigeral'],
 'scutula': ['auscult', 'scutula'],
 'scye': ['scye', 'syce'],
 'scylliidae': ['salicylide', 'scylliidae'],
 'scyllium': ['clumsily', 'scyllium'],
 'scyphi': ['physic', 'scyphi'],
 'scyphomancy': ['psychomancy', 'scyphomancy'],
 'scyt': ['cyst', 'scyt'],
 'scythe': ['chesty', 'scythe'],
 'scytitis': ['cystitis', 'scytitis'],
 'se': ['es', 'se'],
 'sea': ['aes', 'ase', 'sea'],
 'seadog': ['dosage', 'seadog'],
 'seagirt': ['seagirt', 'strigae'],
 'seah': ['seah', 'shea'],
 'seak': ['sake', 'seak'],
 'seal': ['elsa', 'sale', 'seal', 'slae'],
 'sealable': ['leasable', 'sealable'],
 'sealch': ['cashel', 'laches', 'sealch'],
 'sealer': ['alerse', 'leaser', 'reales', 'resale', 'reseal', 'sealer'],
 'sealet': ['ateles', 'saltee', 'sealet', 'stelae', 'teasel'],
 'sealing': ['leasing', 'sealing'],
 'sealwort': ['restowal', 'sealwort'],
 'seam': ['asem', 'mesa', 'same', 'seam'],
 'seamanite': ['anamesite', 'seamanite'],
 'seamark': ['kamares', 'seamark'],
 'seamer': ['reseam', 'seamer'],
 'seamlet': ['maltese', 'seamlet'],
 'seamlike': ['mesalike', 'seamlike'],
 'seamrend': ['redesman', 'seamrend'],
 'seamster': ['masseter', 'seamster'],
 'seamus': ['assume', 'seamus'],
 'sean': ['anes', 'sane', 'sean'],
 'seance': ['encase', 'seance', 'seneca'],
 'seaplane': ['seaplane', 'spelaean'],
 'seaport': ['esparto', 'petrosa', 'seaport'],
 'sear': ['arse', 'rase', 'sare', 'sear', 'sera'],
 'searce': ['cesare', 'crease', 'recase', 'searce'],
 'searcer': ['creaser', 'searcer'],
 'search': ['arches', 'chaser', 'eschar', 'recash', 'search'],
 'searcher': ['rechaser', 'research', 'searcher'],
 'searchment': ['manchester', 'searchment'],
 'searcloth': ['clathrose', 'searcloth'],
 'seared': ['erased', 'reseda', 'seared'],
 'searer': ['eraser', 'searer'],
 'searing': ['searing', 'seringa'],
 'seary': ['reasy', 'resay', 'sayer', 'seary'],
 'seaside': ['disease', 'seaside'],
 'seat': ['ates', 'east', 'eats', 'sate', 'seat', 'seta'],
 'seated': ['seated', 'sedate'],
 'seater': ['asteer',
            'easter',
            'eastre',
            'reseat',
            'saeter',
            'seater',
            'staree',
            'teaser',
            'teresa'],
 'seating': ['easting',
             'gainset',
             'genista',
             'ingesta',
             'seating',
             'signate',
             'teasing'],
 'seatless': ['sateless', 'seatless'],
 'seatrain': ['artesian', 'asterina', 'asternia', 'erastian', 'seatrain'],
 'seatron': ['noreast', 'rosetan', 'seatron', 'senator', 'treason'],
 'seave': ['eaves', 'evase', 'seave'],
 'seax': ['axes', 'saxe', 'seax'],
 'seba': ['base', 'besa', 'sabe', 'seba'],
 'sebastian': ['bassanite', 'sebastian'],
 'sebilla': ['sabelli', 'sebilla'],
 'sebum': ['embus', 'sebum'],
 'secalin': ['celsian', 'escalin', 'sanicle', 'secalin'],
 'secaline': ['salience', 'secaline'],
 'secant': ['ascent', 'secant', 'stance'],
 'secern': ['censer', 'scerne', 'screen', 'secern'],
 'secernent': ['secernent', 'sentencer'],
 'secondar': ['endosarc', 'secondar'],
 'secos': ['cosse', 'secos'],
 'secpar': ['casper', 'escarp', 'parsec', 'scrape', 'secpar', 'spacer'],
 'secre': ['scree', 'secre'],
 'secret': ['resect', 'screet', 'secret'],
 'secretarian': ['ascertainer', 'reascertain', 'secretarian'],
 'secretion': ['resection', 'secretion'],
 'secretional': ['resectional', 'secretional'],
 'sect': ['cest', 'sect'],
 'sectarian': ['ascertain', 'cartesian', 'cartisane', 'sectarian'],
 'sectarianism': ['cartesianism', 'sectarianism'],
 'section': ['contise', 'noetics', 'section'],
 'sectism': ['sectism', 'smectis'],
 'sector': ['corset', 'cortes', 'coster', 'escort', 'scoter', 'sector'],
 'sectorial': ['alectoris', 'sarcolite', 'sclerotia', 'sectorial'],
 'sectroid': ['decorist', 'sectroid'],
 'securable': ['rescuable', 'securable'],
 'securance': ['recusance', 'securance'],
 'secure': ['cereus', 'ceruse', 'recuse', 'rescue', 'secure'],
 'securer': ['recurse', 'rescuer', 'securer'],
 'sedan': ['sedan', 'snead'],
 'sedanier': ['arsedine', 'arsenide', 'sedanier', 'siderean'],
 'sedat': ['sedat', 'stade', 'stead'],
 'sedate': ['seated', 'sedate'],
 'sedation': ['astonied', 'sedation'],
 'sederunt': ['sederunt', 'underset', 'undesert', 'unrested'],
 'sedile': ['diesel', 'sedile', 'seidel'],
 'sedimetric': ['sedimetric', 'semidirect'],
 'sedimetrical': ['decimestrial', 'sedimetrical'],
 'sedition': ['desition', 'sedition'],
 'sedulity': ['dysluite', 'sedulity'],
 'sedum': ['mused', 'sedum'],
 'seedbird': ['birdseed', 'seedbird'],
 'seeded': ['deseed', 'seeded'],
 'seeder': ['reseed', 'seeder'],
 'seedlip': ['pelides', 'seedlip'],
 'seeing': ['seeing', 'signee'],
 'seek': ['kees', 'seek', 'skee'],
 'seeker': ['reseek', 'seeker'],
 'seel': ['else', 'lees', 'seel', 'sele', 'slee'],
 'seem': ['mese', 'seem', 'seme', 'smee'],
 'seemer': ['emerse', 'seemer'],
 'seen': ['ense', 'esne', 'nese', 'seen', 'snee'],
 'seenu': ['ensue', 'seenu', 'unsee'],
 'seer': ['erse', 'rees', 'seer', 'sere'],
 'seerband': ['seerband', 'serabend'],
 'seerhand': ['denshare', 'seerhand'],
 'seerhood': ['rhodeose', 'seerhood'],
 'seership': ['hesperis', 'seership'],
 'seething': ['seething', 'sheeting'],
 'seg': ['ges', 'seg'],
 'seggar': ['sagger', 'seggar'],
 'seggard': ['daggers', 'seggard'],
 'sego': ['goes', 'sego'],
 'segolate': ['gelatose', 'segolate'],
 'segreant': ['estrange', 'segreant', 'sergeant', 'sternage'],
 'seid': ['desi', 'ides', 'seid', 'side'],
 'seidel': ['diesel', 'sedile', 'seidel'],
 'seignioral': ['seignioral', 'seignorial'],
 'seignoral': ['gasoliner', 'seignoral'],
 'seignorial': ['seignioral', 'seignorial'],
 'seine': ['insee', 'seine'],
 'seiner': ['inseer', 'nereis', 'seiner', 'serine', 'sirene'],
 'seise': ['essie', 'seise'],
 'seism': ['seism', 'semis'],
 'seismal': ['aimless', 'melissa', 'seismal'],
 'seismotic': ['seismotic', 'societism'],
 'seit': ['seit', 'site'],
 'seizable': ['seizable', 'sizeable'],
 'seizer': ['resize', 'seizer'],
 'sekane': ['sakeen', 'sekane'],
 'sekani': ['kinase', 'sekani'],
 'sekar': ['asker', 'reask', 'saker', 'sekar'],
 'seker': ['esker', 'keres', 'reesk', 'seker', 'skeer', 'skere'],
 'selagite': ['elegiast', 'selagite'],
 'selah': ['halse', 'leash', 'selah', 'shale', 'sheal', 'shela'],
 'selamin': ['malines', 'salmine', 'selamin', 'seminal'],
 'selbergite': ['gilbertese', 'selbergite'],
 'seldor': ['dorsel', 'seldor', 'solder'],
 'seldseen': ['needless', 'seldseen'],
 'sele': ['else', 'lees', 'seel', 'sele', 'slee'],
 'selector': ['corselet', 'sclerote', 'selector'],
 'selenic': ['license', 'selenic', 'silence'],
 'selenion': ['leonines', 'selenion'],
 'selenitic': ['insectile', 'selenitic'],
 'selenium': ['selenium', 'semilune', 'seminule'],
 'selenosis': ['noiseless', 'selenosis'],
 'seleucian': ['sauceline', 'seleucian'],
 'self': ['fels', 'self'],
 'selfsame': ['fameless', 'selfsame'],
 'selfsameness': ['famelessness', 'selfsameness'],
 'selictar': ['altrices', 'selictar'],
 'selina': ['alsine', 'neslia', 'saline', 'selina', 'silane'],
 'selion': ['insole', 'leonis', 'lesion', 'selion'],
 'seljukian': ['januslike', 'seljukian'],
 'sella': ['salle', 'sella'],
 'sellably': ['sellably', 'syllable'],
 'sellate': ['estella', 'sellate'],
 'seller': ['resell', 'seller'],
 'selli': ['lisle', 'selli'],
 'sellie': ['leslie', 'sellie'],
 'sellout': ['outsell', 'sellout'],
 'selt': ['lest', 'selt'],
 'selter': ['lester', 'selter', 'streel'],
 'selung': ['gunsel', 'selung', 'slunge'],
 'selva': ['salve', 'selva', 'slave', 'valse'],
 'semang': ['magnes', 'semang'],
 'semantic': ['amnestic', 'semantic'],
 'semaphore': ['mesohepar', 'semaphore'],
 'sematic': ['cameist', 'etacism', 'sematic'],
 'sematrope': ['perosmate', 'sematrope'],
 'seme': ['mese', 'seem', 'seme', 'smee'],
 'semen': ['mense', 'mesne', 'semen'],
 'semeostoma': ['semeostoma', 'semostomae'],
 'semi': ['mise', 'semi', 'sime'],
 'semiarch': ['semiarch', 'smachrie'],
 'semibald': ['bedismal', 'semibald'],
 'semiball': ['mislabel', 'semiball'],
 'semic': ['mesic', 'semic'],
 'semicircle': ['semicircle', 'semicleric'],
 'semicleric': ['semicircle', 'semicleric'],
 'semicone': ['nicesome', 'semicone'],
 'semicoronet': ['oncosimeter', 'semicoronet'],
 'semicrome': ['mesomeric', 'microseme', 'semicrome'],
 'semidirect': ['sedimetric', 'semidirect'],
 'semidormant': ['memorandist', 'moderantism', 'semidormant'],
 'semihard': ['dreamish', 'semihard'],
 'semihiant': ['histamine', 'semihiant'],
 'semilimber': ['immersible', 'semilimber'],
 'semilunar': ['semilunar', 'unrealism'],
 'semilune': ['selenium', 'semilune', 'seminule'],
 'semiminor': ['immersion', 'semiminor'],
 'semimoron': ['monroeism', 'semimoron'],
 'seminal': ['malines', 'salmine', 'selamin', 'seminal'],
 'seminar': ['remains', 'seminar'],
 'seminasal': ['messalian', 'seminasal'],
 'seminomadic': ['demoniacism', 'seminomadic'],
 'seminomata': ['mastomenia', 'seminomata'],
 'seminule': ['selenium', 'semilune', 'seminule'],
 'semiopal': ['malpoise', 'semiopal'],
 'semiorb': ['boreism', 'semiorb'],
 'semiovaloid': ['semiovaloid', 'semiovoidal'],
 'semiovoidal': ['semiovaloid', 'semiovoidal'],
 'semipolar': ['perisomal', 'semipolar'],
 'semipro': ['imposer', 'promise', 'semipro'],
 'semipronation': ['impersonation', 'prosemination', 'semipronation'],
 'semiquote': ['quietsome', 'semiquote'],
 'semirotund': ['semirotund', 'unmortised'],
 'semis': ['seism', 'semis'],
 'semispan': ['menaspis', 'semispan'],
 'semisteel': ['messelite', 'semisteel', 'teleseism'],
 'semistill': ['limitless', 'semistill'],
 'semistriated': ['disastimeter', 'semistriated'],
 'semita': ['samite', 'semita', 'tamise', 'teaism'],
 'semitae': ['amesite', 'mesitae', 'semitae'],
 'semitour': ['moisture', 'semitour'],
 'semiurban': ['semiurban', 'submarine'],
 'semiurn': ['neurism', 'semiurn'],
 'semivector': ['semivector', 'viscometer'],
 'semnae': ['enseam', 'semnae'],
 'semola': ['melosa', 'salome', 'semola'],
 'semolella': ['lamellose', 'semolella'],
 'semolina': ['laminose', 'lemonias', 'semolina'],
 'semological': ['mesological', 'semological'],
 'semology': ['mesology', 'semology'],
 'semostomae': ['semeostoma', 'semostomae'],
 'sempiternous': ['sempiternous', 'supermoisten'],
 'semuncia': ['muscinae', 'semuncia'],
 'semuncial': ['masculine', 'semuncial', 'simulance'],
 'sen': ['ens', 'sen'],
 'senaite': ['etesian', 'senaite'],
 'senam': ['manes', 'manse', 'mensa', 'samen', 'senam'],
 'senarius': ['anuresis', 'senarius'],
 'senate': ['ensate', 'enseat', 'santee', 'sateen', 'senate'],
 'senator': ['noreast', 'rosetan', 'seatron', 'senator', 'treason'],
 'senatorian': ['arsenation', 'senatorian', 'sonneratia'],
 'senatrices': ['resistance', 'senatrices'],
 'sence': ['cense', 'scene', 'sence'],
 'senci': ['senci', 'since'],
 'send': ['send', 'sned'],
 'sender': ['resend', 'sender'],
 'seneca': ['encase', 'seance', 'seneca'],
 'senega': ['sagene', 'senega'],
 'senesce': ['essence', 'senesce'],
 'senile': ['enisle', 'ensile', 'senile', 'silene'],
 'senilism': ['liminess', 'senilism'],
 'senior': ['rosine', 'senior', 'soneri'],
 'senlac': ['lances', 'senlac'],
 'senna': ['nanes', 'senna'],
 'sennit': ['innest', 'sennit', 'sinnet', 'tennis'],
 'sennite': ['intense', 'sennite'],
 'senocular': ['larcenous', 'senocular'],
 'senones': ['oneness', 'senones'],
 'sensable': ['ableness', 'blaeness', 'sensable'],
 'sensatorial': ['assertional', 'sensatorial'],
 'sensical': ['laciness', 'sensical'],
 'sensilia': ['sensilia', 'silesian'],
 'sensilla': ['nailless', 'sensilla'],
 'sension': ['neossin', 'sension'],
 'sensuism': ['sensuism', 'senusism'],
 'sent': ['nest', 'sent', 'sten'],
 'sentencer': ['secernent', 'sentencer'],
 'sentition': ['sentition', 'tenonitis'],
 'senusism': ['sensuism', 'senusism'],
 'sepad': ['depas', 'sepad', 'spade'],
 'sepal': ['elaps',
           'lapse',
           'lepas',
           'pales',
           'salep',
           'saple',
           'sepal',
           'slape',
           'spale',
           'speal'],
 'sepaled': ['delapse', 'sepaled'],
 'sepaloid': ['episodal', 'lapidose', 'sepaloid'],
 'separable': ['separable', 'spareable'],
 'separate': ['asperate', 'separate'],
 'separation': ['anisoptera', 'asperation', 'separation'],
 'sephardi': ['diphaser', 'parished', 'raphides', 'sephardi'],
 'sephen': ['sephen', 'sphene'],
 'sepian': ['sepian', 'spinae'],
 'sepic': ['sepic', 'spice'],
 'sepion': ['espino', 'sepion'],
 'sepoy': ['poesy', 'posey', 'sepoy'],
 'seps': ['pess', 'seps'],
 'sepsis': ['sepsis', 'speiss'],
 'sept': ['pest', 'sept', 'spet', 'step'],
 'septa': ['paste', 'septa', 'spate'],
 'septal': ['pastel', 'septal', 'staple'],
 'septane': ['penates', 'septane'],
 'septarium': ['impasture', 'septarium'],
 'septenar': ['entrepas', 'septenar'],
 'septennium': ['pennisetum', 'septennium'],
 'septentrio': ['septentrio', 'tripestone'],
 'septerium': ['misrepute', 'septerium'],
 'septi': ['septi', 'spite', 'stipe'],
 'septicemia': ['episematic', 'septicemia'],
 'septicidal': ['pesticidal', 'septicidal'],
 'septicopyemia': ['pyosepticemia', 'septicopyemia'],
 'septicopyemic': ['pyosepticemic', 'septicopyemic'],
 'septier': ['respite', 'septier'],
 'septiferous': ['pestiferous', 'septiferous'],
 'septile': ['epistle', 'septile'],
 'septimal': ['petalism', 'septimal'],
 'septocosta': ['septocosta', 'statoscope'],
 'septoic': ['poetics', 'septoic'],
 'septonasal': ['nasoseptal', 'septonasal'],
 'septoria': ['isoptera', 'septoria'],
 'septum': ['septum', 'upstem'],
 'septuor': ['petrous', 'posture', 'proetus', 'proteus', 'septuor', 'spouter'],
 'sequential': ['latinesque', 'sequential'],
 'sequin': ['quinse', 'sequin'],
 'ser': ['ers', 'ser'],
 'sera': ['arse', 'rase', 'sare', 'sear', 'sera'],
 'serab': ['barse', 'besra', 'saber', 'serab'],
 'serabend': ['seerband', 'serabend'],
 'seraglio': ['girasole', 'seraglio'],
 'serai': ['aries', 'arise', 'raise', 'serai'],
 'serail': ['israel', 'relais', 'resail', 'sailer', 'serail', 'serial'],
 'seral': ['arles', 'arsle', 'laser', 'seral', 'slare'],
 'serang': ['angers', 'sanger', 'serang'],
 'serape': ['parsee', 'persae', 'persea', 'serape'],
 'seraph': ['phrase', 'seraph', 'shaper', 'sherpa'],
 'seraphic': ['parchesi', 'seraphic'],
 'seraphim': ['samphire', 'seraphim'],
 'seraphina': ['pharisean', 'seraphina'],
 'seraphine': ['hesperian', 'phrenesia', 'seraphine'],
 'seraphism': ['misphrase', 'seraphism'],
 'serapic': ['epacris', 'scrapie', 'serapic'],
 'serapis': ['paresis', 'serapis'],
 'serapist': ['piratess', 'serapist', 'tarsipes'],
 'serau': ['serau', 'urase'],
 'seraw': ['resaw', 'sawer', 'seraw', 'sware', 'swear', 'warse'],
 'sercial': ['scleria', 'sercial'],
 'sere': ['erse', 'rees', 'seer', 'sere'],
 'serean': ['serean', 'serena'],
 'sereh': ['herse', 'sereh', 'sheer', 'shree'],
 'serena': ['serean', 'serena'],
 'serenata': ['arsenate', 'serenata'],
 'serene': ['resene', 'serene'],
 'serenoa': ['arenose', 'serenoa'],
 'serge': ['reges', 'serge'],
 'sergeant': ['estrange', 'segreant', 'sergeant', 'sternage'],
 'sergei': ['sergei', 'sieger'],
 'serger': ['gerres', 'serger'],
 'serging': ['serging', 'snigger'],
 'sergiu': ['guiser', 'sergiu'],
 'seri': ['reis', 'rise', 'seri', 'sier', 'sire'],
 'serial': ['israel', 'relais', 'resail', 'sailer', 'serail', 'serial'],
 'serialist': ['eristalis', 'serialist'],
 'serian': ['arisen', 'arsine', 'resina', 'serian'],
 'sericate': ['ecrasite', 'sericate'],
 'sericated': ['discreate', 'sericated'],
 'sericin': ['irenics', 'resinic', 'sericin', 'sirenic'],
 'serific': ['friesic', 'serific'],
 'serin': ['reins', 'resin', 'rinse', 'risen', 'serin', 'siren'],
 'serine': ['inseer', 'nereis', 'seiner', 'serine', 'sirene'],
 'serinette': ['retistene', 'serinette'],
 'seringa': ['searing', 'seringa'],
 'seringal': ['resignal', 'seringal', 'signaler'],
 'serinus': ['russine', 'serinus', 'sunrise'],
 'serio': ['osier', 'serio'],
 'seriola': ['rosalie', 'seriola'],
 'serioludicrous': ['ludicroserious', 'serioludicrous'],
 'sermo': ['meros', 'mores', 'morse', 'sermo', 'smore'],
 'sermonist': ['monitress', 'sermonist'],
 'sero': ['eros', 'rose', 'sero', 'sore'],
 'serofibrous': ['fibroserous', 'serofibrous'],
 'serolin': ['resinol', 'serolin'],
 'seromucous': ['mucoserous', 'seromucous'],
 'seron': ['norse', 'noser', 'seron', 'snore'],
 'seroon': ['nooser', 'seroon', 'sooner'],
 'seroot': ['seroot', 'sooter', 'torose'],
 'serotina': ['arsonite', 'asterion', 'oestrian', 'rosinate', 'serotina'],
 'serotinal': ['lairstone', 'orleanist', 'serotinal'],
 'serotine': ['serotine', 'torinese'],
 'serous': ['serous', 'souser'],
 'serow': ['owser', 'resow', 'serow', 'sower', 'swore', 'worse'],
 'serpari': ['aspirer', 'praiser', 'serpari'],
 'serpent': ['penster', 'present', 'serpent', 'strepen'],
 'serpentian': ['serpentian', 'serpentina'],
 'serpentid': ['president', 'serpentid'],
 'serpentina': ['serpentian', 'serpentina'],
 'serpentinous': ['serpentinous', 'supertension'],
 'serpently': ['presently', 'serpently'],
 'serpiginous': ['serpiginous', 'spinigerous'],
 'serpolet': ['proteles', 'serpolet'],
 'serpula': ['perusal', 'serpula'],
 'serpulae': ['pleasure', 'serpulae'],
 'serpulan': ['purslane', 'serpulan', 'supernal'],
 'serpulidae': ['serpulidae', 'superideal'],
 'serpuline': ['serpuline', 'superline'],
 'serra': ['ersar', 'raser', 'serra'],
 'serrage': ['argeers', 'greaser', 'serrage'],
 'serran': ['serran', 'snarer'],
 'serrano': ['serrano', 'sornare'],
 'serratic': ['crateris', 'serratic'],
 'serratodentate': ['dentatoserrate', 'serratodentate'],
 'serrature': ['serrature', 'treasurer'],
 'serried': ['derries', 'desirer', 'resider', 'serried'],
 'serriped': ['presider', 'serriped'],
 'sert': ['rest', 'sert', 'stre'],
 'serta': ['aster', 'serta', 'stare', 'strae', 'tarse', 'teras'],
 'sertum': ['muster', 'sertum', 'stumer'],
 'serum': ['muser', 'remus', 'serum'],
 'serut': ['serut', 'strue', 'turse', 'uster'],
 'servable': ['beslaver', 'servable', 'versable'],
 'servage': ['gervase', 'greaves', 'servage'],
 'serval': ['salver', 'serval', 'slaver', 'versal'],
 'servant': ['servant', 'versant'],
 'servation': ['overstain', 'servation', 'versation'],
 'serve': ['serve', 'sever', 'verse'],
 'server': ['revers', 'server', 'verser'],
 'servet': ['revest', 'servet', 'sterve', 'verset', 'vester'],
 'servetian': ['invertase', 'servetian'],
 'servian': ['servian', 'vansire'],
 'service': ['cerevis', 'scrieve', 'service'],
 'serviceable': ['receivables', 'serviceable'],
 'servient': ['reinvest', 'servient'],
 'serviential': ['inversatile', 'serviential'],
 'servilize': ['servilize', 'silverize'],
 'servite': ['restive', 'servite'],
 'servitor': ['overstir', 'servitor'],
 'servitude': ['detrusive', 'divesture', 'servitude'],
 'servo': ['servo', 'verso'],
 'sesma': ['masse', 'sesma'],
 'sestertium': ['sestertium', 'trusteeism'],
 'sestet': ['sestet', 'testes', 'tsetse'],
 'sestiad': ['disseat', 'sestiad'],
 'sestian': ['entasis', 'sestian', 'sestina'],
 'sestina': ['entasis', 'sestian', 'sestina'],
 'sestole': ['osselet', 'sestole', 'toeless'],
 'sestuor': ['estrous', 'oestrus', 'sestuor', 'tussore'],
 'sesuto': ['sesuto', 'setous'],
 'seta': ['ates', 'east', 'eats', 'sate', 'seat', 'seta'],
 'setae': ['setae', 'tease'],
 'setal': ['least', 'setal', 'slate', 'stale', 'steal', 'stela', 'tales'],
 'setaria': ['asarite', 'asteria', 'atresia', 'setaria'],
 'setback': ['backset', 'setback'],
 'setdown': ['downset', 'setdown'],
 'seth': ['esth', 'hest', 'seth'],
 'sethead': ['headset', 'sethead'],
 'sethian': ['sethian', 'sthenia'],
 'sethic': ['ethics', 'sethic'],
 'setibo': ['setibo', 'sobeit'],
 'setirostral': ['latirostres', 'setirostral'],
 'setline': ['leisten', 'setline', 'tensile'],
 'setoff': ['offset', 'setoff'],
 'seton': ['onset', 'seton', 'steno', 'stone'],
 'setous': ['sesuto', 'setous'],
 'setout': ['outset', 'setout'],
 'setover': ['overset', 'setover'],
 'sett': ['sett', 'stet', 'test'],
 'settable': ['settable', 'testable'],
 'settaine': ['anisette', 'atestine', 'settaine'],
 'settee': ['settee', 'testee'],
 'setter': ['retest', 'setter', 'street', 'tester'],
 'setting': ['setting', 'testing'],
 'settler': ['settler', 'sterlet', 'trestle'],
 'settlor': ['settlor', 'slotter'],
 'setula': ['salute', 'setula'],
 'setup': ['setup', 'stupe', 'upset'],
 'setwall': ['setwall', 'swallet'],
 'seven': ['evens', 'seven'],
 'sevener': ['sevener', 'veneres'],
 'sever': ['serve', 'sever', 'verse'],
 'severer': ['reserve', 'resever', 'reverse', 'severer'],
 'sew': ['sew', 'wes'],
 'sewed': ['sewed', 'swede'],
 'sewer': ['resew', 'sewer', 'sweer'],
 'sewered': ['sewered', 'sweered'],
 'sewing': ['sewing', 'swinge'],
 'sewn': ['news', 'sewn', 'snew'],
 'sewround': ['sewround', 'undersow'],
 'sexed': ['desex', 'sexed'],
 'sextan': ['saxten', 'sextan'],
 'sextipartition': ['extirpationist', 'sextipartition'],
 'sextodecimo': ['decimosexto', 'sextodecimo'],
 'sextry': ['sextry', 'xyster'],
 'sexuale': ['esexual', 'sexuale'],
 'sey': ['sey', 'sye', 'yes'],
 'seymour': ['mousery', 'seymour'],
 'sfoot': ['foots', 'sfoot', 'stoof'],
 'sgad': ['dags', 'sgad'],
 'sha': ['ash', 'sah', 'sha'],
 'shab': ['bash', 'shab'],
 'shabbily': ['babishly', 'shabbily'],
 'shabbiness': ['babishness', 'shabbiness'],
 'shabunder': ['husbander', 'shabunder'],
 'shad': ['dash', 'sadh', 'shad'],
 'shade': ['deash', 'hades', 'sadhe', 'shade'],
 'shaded': ['dashed', 'shaded'],
 'shader': ['dasher', 'shader', 'sheard'],
 'shadily': ['ladyish', 'shadily'],
 'shading': ['dashing', 'shading'],
 'shadkan': ['dashnak', 'shadkan'],
 'shady': ['dashy', 'shady'],
 'shafting': ['shafting', 'tangfish'],
 'shag': ['gash', 'shag'],
 'shagrag': ['ragshag', 'shagrag'],
 'shah': ['hash', 'sahh', 'shah'],
 'shahi': ['shahi', 'shiah'],
 'shaitan': ['ashanti', 'sanhita', 'shaitan', 'thasian'],
 'shaivism': ['shaivism', 'shivaism'],
 'shaka': ['kasha', 'khasa', 'sakha', 'shaka'],
 'shakeout': ['outshake', 'shakeout'],
 'shaker': ['kasher', 'shaker'],
 'shakil': ['lakish', 'shakil'],
 'shaku': ['kusha', 'shaku', 'ushak'],
 'shaky': ['hasky', 'shaky'],
 'shale': ['halse', 'leash', 'selah', 'shale', 'sheal', 'shela'],
 'shalt': ['shalt', 'slath'],
 'sham': ['mash', 'samh', 'sham'],
 'shama': ['hamsa', 'masha', 'shama'],
 'shamable': ['baalshem', 'shamable'],
 'shamal': ['mashal', 'shamal'],
 'shaman': ['ashman', 'shaman'],
 'shamba': ['ambash', 'shamba'],
 'shambrier': ['herbarism', 'shambrier'],
 'shambu': ['ambush', 'shambu'],
 'shame': ['sahme', 'shame'],
 'shamer': ['masher', 'ramesh', 'shamer'],
 'shamir': ['marish', 'shamir'],
 'shammish': ['mishmash', 'shammish'],
 'shan': ['hans', 'nash', 'shan'],
 'shane': ['ashen', 'hanse', 'shane', 'shean'],
 'shang': ['gnash', 'shang'],
 'shant': ['shant', 'snath'],
 'shap': ['hasp', 'pash', 'psha', 'shap'],
 'shape': ['heaps', 'pesah', 'phase', 'shape'],
 'shapeless': ['phaseless', 'shapeless'],
 'shaper': ['phrase', 'seraph', 'shaper', 'sherpa'],
 'shapometer': ['atmosphere', 'shapometer'],
 'shapy': ['physa', 'shapy'],
 'shardana': ['darshana', 'shardana'],
 'share': ['asher', 'share', 'shear'],
 'shareman': ['shareman', 'shearman'],
 'sharer': ['rasher', 'sharer'],
 'sharesman': ['sharesman', 'shearsman'],
 'shargar': ['shargar', 'sharrag'],
 'shari': ['ashir', 'shari'],
 'sharon': ['rhason', 'sharon', 'shoran'],
 'sharp': ['sharp', 'shrap'],
 'sharpener': ['resharpen', 'sharpener'],
 'sharper': ['phraser', 'sharper'],
 'sharpy': ['phrasy', 'sharpy'],
 'sharrag': ['shargar', 'sharrag'],
 'shasta': ['shasta', 'tassah'],
 'shaster': ['hatress', 'shaster'],
 'shastraik': ['katharsis', 'shastraik'],
 'shastri': ['sartish', 'shastri'],
 'shat': ['shat', 'tash'],
 'shatter': ['rathest', 'shatter'],
 'shatterer': ['ratherest', 'shatterer'],
 'shattering': ['shattering', 'straighten'],
 'shauri': ['shauri', 'surahi'],
 'shave': ['shave', 'sheva'],
 'shavee': ['shavee', 'sheave'],
 'shaver': ['havers', 'shaver', 'shrave'],
 'shavery': ['shavery', 'shravey'],
 'shaw': ['shaw', 'wash'],
 'shawano': ['shawano', 'washoan'],
 'shawl': ['shawl', 'walsh'],
 'shawy': ['shawy', 'washy'],
 'shay': ['ashy', 'shay'],
 'shea': ['seah', 'shea'],
 'sheal': ['halse', 'leash', 'selah', 'shale', 'sheal', 'shela'],
 'shean': ['ashen', 'hanse', 'shane', 'shean'],
 'shear': ['asher', 'share', 'shear'],
 'shearbill': ['shearbill', 'shillaber'],
 'sheard': ['dasher', 'shader', 'sheard'],
 'shearer': ['reshare', 'reshear', 'shearer'],
 'shearman': ['shareman', 'shearman'],
 'shearsman': ['sharesman', 'shearsman'],
 'sheat': ['ashet', 'haste', 'sheat'],
 'sheave': ['shavee', 'sheave'],
 'shebeen': ['benshee', 'shebeen'],
 'shechem': ['meshech', 'shechem'],
 'sheder': ['hersed', 'sheder'],
 'sheely': ['sheely', 'sheyle'],
 'sheer': ['herse', 'sereh', 'sheer', 'shree'],
 'sheering': ['greenish', 'sheering'],
 'sheet': ['sheet', 'these'],
 'sheeter': ['sheeter', 'therese'],
 'sheeting': ['seething', 'sheeting'],
 'sheila': ['elisha', 'hailse', 'sheila'],
 'shela': ['halse', 'leash', 'selah', 'shale', 'sheal', 'shela'],
 'shelf': ['flesh', 'shelf'],
 'shelfful': ['fleshful', 'shelfful'],
 'shelflist': ['filthless', 'shelflist'],
 'shelfy': ['fleshy', 'shelfy'],
 'shelta': ['haslet', 'lesath', 'shelta'],
 'shelty': ['shelty', 'thysel'],
 'shelve': ['shelve', 'shevel'],
 'shemitic': ['ethicism', 'shemitic'],
 'shen': ['nesh', 'shen'],
 'sheol': ['hosel', 'sheol', 'shole'],
 'sher': ['hers', 'resh', 'sher'],
 'sherani': ['arshine', 'nearish', 'rhesian', 'sherani'],
 'sheratan': ['hanaster', 'sheratan'],
 'sheriat': ['atheris', 'sheriat'],
 'sherif': ['fisher', 'sherif'],
 'sherifate': ['fisheater', 'sherifate'],
 'sherify': ['fishery', 'sherify'],
 'sheriyat': ['hysteria', 'sheriyat'],
 'sherpa': ['phrase', 'seraph', 'shaper', 'sherpa'],
 'sherri': ['hersir', 'sherri'],
 'sheugh': ['hughes', 'sheugh'],
 'sheva': ['shave', 'sheva'],
 'shevel': ['shelve', 'shevel'],
 'shevri': ['shevri', 'shiver', 'shrive'],
 'shewa': ['hawse', 'shewa', 'whase'],
 'sheyle': ['sheely', 'sheyle'],
 'shi': ['his', 'hsi', 'shi'],
 'shiah': ['shahi', 'shiah'],
 'shibar': ['barish', 'shibar'],
 'shice': ['echis', 'shice'],
 'shicer': ['riches', 'shicer'],
 'shide': ['shide', 'shied', 'sidhe'],
 'shied': ['shide', 'shied', 'sidhe'],
 'shiel': ['liesh', 'shiel'],
 'shieldable': ['deshabille', 'shieldable'],
 'shier': ['hirse', 'shier', 'shire'],
 'shiest': ['shiest', 'thesis'],
 'shifter': ['reshift', 'shifter'],
 'shih': ['hish', 'shih'],
 'shiite': ['histie', 'shiite'],
 'shik': ['kish', 'shik', 'sikh'],
 'shikar': ['rakish', 'riksha', 'shikar', 'shikra', 'sikhra'],
 'shikara': ['shikara', 'sikhara'],
 'shikari': ['rikisha', 'shikari'],
 'shikra': ['rakish', 'riksha', 'shikar', 'shikra', 'sikhra'],
 'shillaber': ['shearbill', 'shillaber'],
 'shimal': ['lamish', 'shimal'],
 'shimmery': ['misrhyme', 'shimmery'],
 'shin': ['hisn', 'shin', 'sinh'],
 'shina': ['naish', 'shina'],
 'shine': ['eshin', 'shine'],
 'shiner': ['renish', 'shiner', 'shrine'],
 'shingle': ['english', 'shingle'],
 'shinto': ['histon', 'shinto', 'tonish'],
 'shinty': ['shinty', 'snithy'],
 'ship': ['pish', 'ship'],
 'shipboy': ['boyship', 'shipboy'],
 'shipkeeper': ['keepership', 'shipkeeper'],
 'shiplap': ['lappish', 'shiplap'],
 'shipman': ['manship', 'shipman'],
 'shipmaster': ['mastership', 'shipmaster'],
 'shipmate': ['aphetism', 'mateship', 'shipmate', 'spithame'],
 'shipowner': ['ownership', 'shipowner'],
 'shippage': ['pageship', 'shippage'],
 'shipper': ['preship', 'shipper'],
 'shippo': ['popish', 'shippo'],
 'shipward': ['shipward', 'wardship'],
 'shipwork': ['shipwork', 'workship'],
 'shipworm': ['shipworm', 'wormship'],
 'shire': ['hirse', 'shier', 'shire'],
 'shirker': ['shirker', 'skirreh'],
 'shirley': ['relishy', 'shirley'],
 'shirty': ['shirty', 'thyris'],
 'shirvan': ['shirvan', 'varnish'],
 'shita': ['shita', 'thais'],
 'shivaism': ['shaivism', 'shivaism'],
 'shive': ['hives', 'shive'],
 'shiver': ['shevri', 'shiver', 'shrive'],
 'shlu': ['lush', 'shlu', 'shul'],
 'sho': ['sho', 'soh'],
 'shoa': ['saho', 'shoa'],
 'shoal': ['shoal', 'shola'],
 'shoat': ['hoast', 'hosta', 'shoat'],
 'shockable': ['shockable', 'shoeblack'],
 'shode': ['hosed', 'shode'],
 'shoder': ['dehors', 'rhodes', 'shoder', 'shored'],
 'shoe': ['hose', 'shoe'],
 'shoeblack': ['shockable', 'shoeblack'],
 'shoebrush': ['shoebrush', 'shorebush'],
 'shoeless': ['hoseless', 'shoeless'],
 'shoeman': ['hoseman', 'shoeman'],
 'shoer': ['horse', 'shoer', 'shore'],
 'shog': ['gosh', 'shog'],
 'shoji': ['joshi', 'shoji'],
 'shola': ['shoal', 'shola'],
 'shole': ['hosel', 'sheol', 'shole'],
 'shoo': ['shoo', 'soho'],
 'shoot': ['shoot', 'sooth', 'sotho', 'toosh'],
 'shooter': ['orthose', 'reshoot', 'shooter', 'soother'],
 'shooting': ['shooting', 'soothing'],
 'shop': ['phos', 'posh', 'shop', 'soph'],
 'shopbook': ['bookshop', 'shopbook'],
 'shopmaid': ['phasmoid', 'shopmaid'],
 'shopper': ['hoppers', 'shopper'],
 'shopwork': ['shopwork', 'workshop'],
 'shoran': ['rhason', 'sharon', 'shoran'],
 'shore': ['horse', 'shoer', 'shore'],
 'shorea': ['ahorse', 'ashore', 'hoarse', 'shorea'],
 'shorebush': ['shoebrush', 'shorebush'],
 'shored': ['dehors', 'rhodes', 'shoder', 'shored'],
 'shoreless': ['horseless', 'shoreless'],
 'shoreman': ['horseman', 'rhamnose', 'shoreman'],
 'shorer': ['horser', 'shorer'],
 'shoreward': ['drawhorse', 'shoreward'],
 'shoreweed': ['horseweed', 'shoreweed'],
 'shoring': ['horsing', 'shoring'],
 'short': ['horst', 'short'],
 'shortage': ['hostager', 'shortage'],
 'shorten': ['shorten', 'threnos'],
 'shot': ['host', 'shot', 'thos', 'tosh'],
 'shote': ['ethos', 'shote', 'those'],
 'shotgun': ['gunshot', 'shotgun', 'uhtsong'],
 'shotless': ['hostless', 'shotless'],
 'shotstar': ['shotstar', 'starshot'],
 'shou': ['huso', 'shou'],
 'shoulderer': ['reshoulder', 'shoulderer'],
 'shout': ['shout', 'south'],
 'shouter': ['shouter', 'souther'],
 'shouting': ['shouting', 'southing'],
 'shover': ['shover', 'shrove'],
 'showerer': ['reshower', 'showerer'],
 'shrab': ['brash', 'shrab'],
 'shram': ['marsh', 'shram'],
 'shrap': ['sharp', 'shrap'],
 'shrave': ['havers', 'shaver', 'shrave'],
 'shravey': ['shavery', 'shravey'],
 'shree': ['herse', 'sereh', 'sheer', 'shree'],
 'shrewly': ['shrewly', 'welshry'],
 'shriek': ['shriek', 'shrike'],
 'shrieval': ['lavisher', 'shrieval'],
 'shrike': ['shriek', 'shrike'],
 'shrine': ['renish', 'shiner', 'shrine'],
 'shrite': ['shrite', 'theirs'],
 'shrive': ['shevri', 'shiver', 'shrive'],
 'shriven': ['nervish', 'shriven'],
 'shroudy': ['hydrous', 'shroudy'],
 'shrove': ['shover', 'shrove'],
 'shrub': ['brush', 'shrub'],
 'shrubbery': ['berrybush', 'shrubbery'],
 'shrubland': ['brushland', 'shrubland'],
 'shrubless': ['brushless', 'shrubless'],
 'shrublet': ['brushlet', 'shrublet'],
 'shrublike': ['brushlike', 'shrublike'],
 'shrubwood': ['brushwood', 'shrubwood'],
 'shrug': ['grush', 'shrug'],
 'shu': ['shu', 'ush'],
 'shuba': ['shuba', 'subah'],
 'shug': ['gush', 'shug', 'sugh'],
 'shul': ['lush', 'shlu', 'shul'],
 'shulamite': ['hamulites', 'shulamite'],
 'shuler': ['lusher', 'shuler'],
 'shunless': ['lushness', 'shunless'],
 'shunter': ['reshunt', 'shunter'],
 'shure': ['shure', 'usher'],
 'shurf': ['frush', 'shurf'],
 'shut': ['shut', 'thus', 'tush'],
 'shutness': ['shutness', 'thusness'],
 'shutout': ['outshut', 'shutout'],
 'shuttering': ['hurtingest', 'shuttering'],
 'shyam': ['mashy', 'shyam'],
 'si': ['is', 'si'],
 'sia': ['sai', 'sia'],
 'siak': ['saki', 'siak', 'sika'],
 'sial': ['lasi', 'lias', 'lisa', 'sail', 'sial'],
 'sialagogic': ['isagogical', 'sialagogic'],
 'sialic': ['sialic', 'silica'],
 'sialid': ['asilid', 'sialid'],
 'sialidae': ['asilidae', 'sialidae'],
 'siam': ['mias', 'saim', 'siam', 'sima'],
 'siamese': ['misease', 'siamese'],
 'sib': ['bis', 'sib'],
 'sibyl': ['sibyl', 'sybil'],
 'sibylla': ['sibylla', 'syllabi'],
 'sicana': ['ascian', 'sacian', 'scania', 'sicana'],
 'sicani': ['anisic', 'sicani', 'sinaic'],
 'sicarius': ['acrisius', 'sicarius'],
 'siccate': ['ascetic', 'castice', 'siccate'],
 'siccation': ['cocainist', 'siccation'],
 'sice': ['cise', 'sice'],
 'sicel': ['sicel', 'slice'],
 'sicilian': ['anisilic', 'sicilian'],
 'sickbed': ['bedsick', 'sickbed'],
 'sicker': ['scrike', 'sicker'],
 'sickerly': ['sickerly', 'slickery'],
 'sickle': ['sickle', 'skelic'],
 'sickler': ['sickler', 'slicker'],
 'sicklied': ['disclike', 'sicklied'],
 'sickling': ['sickling', 'slicking'],
 'sicula': ['caulis', 'clusia', 'sicula'],
 'siculian': ['luscinia', 'siculian'],
 'sid': ['dis', 'sid'],
 'sida': ['dais', 'dasi', 'disa', 'said', 'sida'],
 'sidalcea': ['diaclase', 'sidalcea'],
 'side': ['desi', 'ides', 'seid', 'side'],
 'sidearm': ['misread', 'sidearm'],
 'sideboard': ['broadside', 'sideboard'],
 'sideburns': ['burnsides', 'sideburns'],
 'sidecar': ['diceras', 'radices', 'sidecar'],
 'sidehill': ['hillside', 'sidehill'],
 'siderean': ['arsedine', 'arsenide', 'sedanier', 'siderean'],
 'siderin': ['insider', 'siderin'],
 'sideronatrite': ['endoarteritis', 'sideronatrite'],
 'siderous': ['desirous', 'siderous'],
 'sidership': ['sidership', 'spiderish'],
 'sidesway': ['sidesway', 'sideways'],
 'sidetrack': ['sidetrack', 'trackside'],
 'sidewalk': ['sidewalk', 'walkside'],
 'sideway': ['sideway', 'wayside'],
 'sideways': ['sidesway', 'sideways'],
 'sidhe': ['shide', 'shied', 'sidhe'],
 'sidle': ['sidle', 'slide'],
 'sidler': ['sidler', 'slider'],
 'sidling': ['sidling', 'sliding'],
 'sidlingly': ['sidlingly', 'slidingly'],
 'sieger': ['sergei', 'sieger'],
 'siena': ['anise', 'insea', 'siena', 'sinae'],
 'sienna': ['insane', 'sienna'],
 'sier': ['reis', 'rise', 'seri', 'sier', 'sire'],
 'sierra': ['raiser', 'sierra'],
 'siesta': ['siesta', 'tassie'],
 'siever': ['revise', 'siever'],
 'sife': ['feis', 'fise', 'sife'],
 'sift': ['fist', 'sift'],
 'sifted': ['fisted', 'sifted'],
 'sifter': ['fister', 'resift', 'sifter', 'strife'],
 'sifting': ['fisting', 'sifting'],
 'sigh': ['gish', 'sigh'],
 'sigher': ['resigh', 'sigher'],
 'sighted': ['desight', 'sighted'],
 'sightlily': ['sightlily', 'slightily'],
 'sightliness': ['sightliness', 'slightiness'],
 'sightly': ['sightly', 'slighty'],
 'sigillated': ['distillage', 'sigillated'],
 'sigla': ['gisla', 'ligas', 'sigla'],
 'sigmatism': ['astigmism', 'sigmatism'],
 'sigmoidal': ['dialogism', 'sigmoidal'],
 'sign': ['sign', 'sing', 'snig'],
 'signable': ['signable', 'singable'],
 'signalee': ['ensilage', 'genesial', 'signalee'],
 'signaler': ['resignal', 'seringal', 'signaler'],
 'signalese': ['agileness', 'signalese'],
 'signally': ['signally', 'singally', 'slangily'],
 'signary': ['signary', 'syringa'],
 'signate': ['easting',
             'gainset',
             'genista',
             'ingesta',
             'seating',
             'signate',
             'teasing'],
 'signator': ['orangist', 'organist', 'roasting', 'signator'],
 'signee': ['seeing', 'signee'],
 'signer': ['resign', 'resing', 'signer', 'singer'],
 'signet': ['ingest', 'signet', 'stinge'],
 'signorial': ['sailoring', 'signorial'],
 'signpost': ['postsign', 'signpost'],
 'signum': ['musing', 'signum'],
 'sika': ['saki', 'siak', 'sika'],
 'sikar': ['kisra', 'sikar', 'skair'],
 'siket': ['siket', 'skite'],
 'sikh': ['kish', 'shik', 'sikh'],
 'sikhara': ['shikara', 'sikhara'],
 'sikhra': ['rakish', 'riksha', 'shikar', 'shikra', 'sikhra'],
 'sil': ['lis', 'sil'],
 'silane': ['alsine', 'neslia', 'saline', 'selina', 'silane'],
 'silas': ['silas', 'sisal'],
 'silcrete': ['sclerite', 'silcrete'],
 'sile': ['isle', 'lise', 'sile'],
 'silen': ['elsin', 'lenis', 'niels', 'silen', 'sline'],
 'silence': ['license', 'selenic', 'silence'],
 'silenced': ['licensed', 'silenced'],
 'silencer': ['licenser', 'silencer'],
 'silene': ['enisle', 'ensile', 'senile', 'silene'],
 'silent': ['enlist', 'listen', 'silent', 'tinsel'],
 'silently': ['silently', 'tinselly'],
 'silenus': ['insulse', 'silenus'],
 'silesian': ['sensilia', 'silesian'],
 'silica': ['sialic', 'silica'],
 'silicam': ['islamic', 'laicism', 'silicam'],
 'silicane': ['silicane', 'silicean'],
 'silicean': ['silicane', 'silicean'],
 'siliceocalcareous': ['calcareosiliceous', 'siliceocalcareous'],
 'silicoaluminate': ['aluminosilicate', 'silicoaluminate'],
 'silicone': ['isocline', 'silicone'],
 'silicotitanate': ['silicotitanate', 'titanosilicate'],
 'silicotungstate': ['silicotungstate', 'tungstosilicate'],
 'silicotungstic': ['silicotungstic', 'tungstosilicic'],
 'silk': ['lisk', 'silk', 'skil'],
 'silkaline': ['silkaline', 'snaillike'],
 'silkman': ['klanism', 'silkman'],
 'silkness': ['silkness', 'sinkless', 'skinless'],
 'silly': ['silly', 'silyl'],
 'sillyhow': ['lowishly', 'owlishly', 'sillyhow'],
 'silo': ['lois', 'silo', 'siol', 'soil', 'soli'],
 'silpha': ['palish', 'silpha'],
 'silt': ['list', 'silt', 'slit'],
 'silting': ['listing', 'silting'],
 'siltlike': ['siltlike', 'slitlike'],
 'silva': ['silva', 'slavi'],
 'silver': ['silver', 'sliver'],
 'silvered': ['desilver', 'silvered'],
 'silverer': ['resilver', 'silverer', 'sliverer'],
 'silverize': ['servilize', 'silverize'],
 'silverlike': ['silverlike', 'sliverlike'],
 'silverwood': ['silverwood', 'woodsilver'],
 'silvery': ['silvery', 'slivery'],
 'silvester': ['rivetless', 'silvester'],
 'silyl': ['silly', 'silyl'],
 'sim': ['ism', 'sim'],
 'sima': ['mias', 'saim', 'siam', 'sima'],
 'simal': ['islam', 'ismal', 'simal'],
 'simar': ['maris', 'marsi', 'samir', 'simar'],
 'sime': ['mise', 'semi', 'sime'],
 'simeon': ['eonism', 'mesion', 'oneism', 'simeon'],
 'simeonism': ['misoneism', 'simeonism'],
 'simiad': ['idiasm', 'simiad'],
 'simile': ['milsie', 'simile'],
 'simity': ['myitis', 'simity'],
 'simling': ['simling', 'smiling'],
 'simmer': ['merism', 'mermis', 'simmer'],
 'simmon': ['monism', 'nomism', 'simmon'],
 'simon': ['minos', 'osmin', 'simon'],
 'simonian': ['insomnia', 'simonian'],
 'simonist': ['simonist', 'sintoism'],
 'simony': ['isonym', 'myosin', 'simony'],
 'simple': ['mespil', 'simple'],
 'simpleton': ['simpleton', 'spoilment'],
 'simplicist': ['simplicist', 'simplistic'],
 'simplistic': ['simplicist', 'simplistic'],
 'simply': ['limpsy', 'simply'],
 'simson': ['nosism', 'simson'],
 'simulance': ['masculine', 'semuncial', 'simulance'],
 'simulcast': ['masculist', 'simulcast'],
 'simuler': ['misrule', 'simuler'],
 'sina': ['anis', 'nais', 'nasi', 'nias', 'sain', 'sina'],
 'sinae': ['anise', 'insea', 'siena', 'sinae'],
 'sinaean': ['nisaean', 'sinaean'],
 'sinaic': ['anisic', 'sicani', 'sinaic'],
 'sinaitic': ['isatinic', 'sinaitic'],
 'sinal': ['sinal', 'slain', 'snail'],
 'sinapic': ['panisic', 'piscian', 'piscina', 'sinapic'],
 'since': ['senci', 'since'],
 'sincere': ['ceresin', 'sincere'],
 'sinecure': ['insecure', 'sinecure'],
 'sinew': ['sinew', 'swine', 'wisen'],
 'sinewed': ['endwise', 'sinewed'],
 'sinewy': ['sinewy', 'swiney'],
 'sinfonia': ['sainfoin', 'sinfonia'],
 'sinfonietta': ['festination', 'infestation', 'sinfonietta'],
 'sing': ['sign', 'sing', 'snig'],
 'singable': ['signable', 'singable'],
 'singally': ['signally', 'singally', 'slangily'],
 'singarip': ['aspiring', 'praising', 'singarip'],
 'singed': ['design', 'singed'],
 'singer': ['resign', 'resing', 'signer', 'singer'],
 'single': ['single', 'slinge'],
 'singler': ['singler', 'slinger'],
 'singles': ['essling', 'singles'],
 'singlet': ['glisten', 'singlet'],
 'sinh': ['hisn', 'shin', 'sinh'],
 'sinico': ['inosic', 'sinico'],
 'sinister': ['insister', 'reinsist', 'sinister', 'sisterin'],
 'sinistrodextral': ['dextrosinistral', 'sinistrodextral'],
 'sink': ['inks', 'sink', 'skin'],
 'sinker': ['resink', 'reskin', 'sinker'],
 'sinkhead': ['headskin', 'nakedish', 'sinkhead'],
 'sinkless': ['silkness', 'sinkless', 'skinless'],
 'sinklike': ['sinklike', 'skinlike'],
 'sinnet': ['innest', 'sennit', 'sinnet', 'tennis'],
 'sinoatrial': ['sinoatrial', 'solitarian'],
 'sinogram': ['orangism', 'organism', 'sinogram'],
 'sinolog': ['loosing', 'sinolog'],
 'sinopia': ['pisonia', 'sinopia'],
 'sinople': ['epsilon', 'sinople'],
 'sinter': ['estrin', 'insert', 'sinter', 'sterin', 'triens'],
 'sinto': ['sinto', 'stion'],
 'sintoc': ['nostic', 'sintoc', 'tocsin'],
 'sintoism': ['simonist', 'sintoism'],
 'sintu': ['sintu', 'suint'],
 'sinuatodentate': ['dentatosinuate', 'sinuatodentate'],
 'sinuose': ['sinuose', 'suiones'],
 'sinus': ['nisus', 'sinus'],
 'sinward': ['inwards', 'sinward'],
 'siol': ['lois', 'silo', 'siol', 'soil', 'soli'],
 'sionite': ['inosite', 'sionite'],
 'sip': ['psi', 'sip'],
 'sipe': ['pise', 'sipe'],
 'siper': ['siper', 'spier', 'spire'],
 'siphonal': ['nailshop', 'siphonal'],
 'siphuncle': ['siphuncle', 'uncleship'],
 'sipling': ['sipling', 'spiling'],
 'sipylite': ['pyelitis', 'sipylite'],
 'sir': ['sir', 'sri'],
 'sire': ['reis', 'rise', 'seri', 'sier', 'sire'],
 'siredon': ['indorse', 'ordines', 'siredon', 'sordine'],
 'siren': ['reins', 'resin', 'rinse', 'risen', 'serin', 'siren'],
 'sirene': ['inseer', 'nereis', 'seiner', 'serine', 'sirene'],
 'sirenic': ['irenics', 'resinic', 'sericin', 'sirenic'],
 'sirenize': ['resinize', 'sirenize'],
 'sirenlike': ['resinlike', 'sirenlike'],
 'sirenoid': ['derision', 'ironside', 'resinoid', 'sirenoid'],
 'sireny': ['resiny', 'sireny'],
 'sirian': ['raisin', 'sirian'],
 'sirih': ['irish', 'rishi', 'sirih'],
 'sirky': ['risky', 'sirky'],
 'sirmian': ['iranism', 'sirmian'],
 'sirpea': ['aspire', 'paries', 'praise', 'sirpea', 'spirea'],
 'sirple': ['lisper', 'pliers', 'sirple', 'spiler'],
 'sirrah': ['arrish', 'harris', 'rarish', 'sirrah'],
 'sirree': ['rerise', 'sirree'],
 'siruelas': ['russelia', 'siruelas'],
 'sirup': ['prius', 'sirup'],
 'siruper': ['siruper', 'upriser'],
 'siryan': ['siryan', 'syrian'],
 'sis': ['sis', 'ssi'],
 'sisal': ['silas', 'sisal'],
 'sish': ['hiss', 'sish'],
 'sisham': ['samish', 'sisham'],
 'sisi': ['isis', 'sisi'],
 'sisseton': ['sisseton', 'stenosis'],
 'sistani': ['nasitis', 'sistani'],
 'sister': ['resist', 'restis', 'sister'],
 'sisterin': ['insister', 'reinsist', 'sinister', 'sisterin'],
 'sistering': ['resisting', 'sistering'],
 'sisterless': ['resistless', 'sisterless'],
 'sistrum': ['sistrum', 'trismus'],
 'sit': ['ist', 'its', 'sit'],
 'sita': ['atis', 'sita', 'tsia'],
 'sitao': ['sitao', 'staio'],
 'sitar': ['arist',
           'astir',
           'sitar',
           'stair',
           'stria',
           'tarsi',
           'tisar',
           'trias'],
 'sitch': ['sitch', 'stchi', 'stich'],
 'site': ['seit', 'site'],
 'sith': ['hist', 'sith', 'this', 'tshi'],
 'sithens': ['sithens', 'thissen'],
 'sitient': ['sitient', 'sittine'],
 'sitology': ['sitology', 'tsiology'],
 'sittinae': ['satinite', 'sittinae'],
 'sittine': ['sitient', 'sittine'],
 'situal': ['situal', 'situla', 'tulasi'],
 'situate': ['situate', 'usitate'],
 'situla': ['situal', 'situla', 'tulasi'],
 'situs': ['situs', 'suist'],
 'siva': ['avis', 'siva', 'visa'],
 'sivaism': ['saivism', 'sivaism'],
 'sivan': ['savin', 'sivan'],
 'siwan': ['siwan', 'swain'],
 'siwash': ['sawish', 'siwash'],
 'sixte': ['exist', 'sixte'],
 'sixty': ['sixty', 'xysti'],
 'sizeable': ['seizable', 'sizeable'],
 'sizeman': ['sizeman', 'zamenis'],
 'skair': ['kisra', 'sikar', 'skair'],
 'skal': ['lask', 'skal'],
 'skance': ['sacken', 'skance'],
 'skanda': ['sandak', 'skanda'],
 'skart': ['karst', 'skart', 'stark'],
 'skat': ['skat', 'task'],
 'skate': ['skate', 'stake', 'steak'],
 'skater': ['skater', 'staker', 'strake', 'streak', 'tasker'],
 'skating': ['gitksan', 'skating', 'takings'],
 'skean': ['skean', 'snake', 'sneak'],
 'skee': ['kees', 'seek', 'skee'],
 'skeel': ['skeel', 'sleek'],
 'skeeling': ['skeeling', 'sleeking'],
 'skeely': ['skeely', 'sleeky'],
 'skeen': ['skeen', 'skene'],
 'skeer': ['esker', 'keres', 'reesk', 'seker', 'skeer', 'skere'],
 'skeery': ['kersey', 'skeery'],
 'skeet': ['keest', 'skeet', 'skete', 'steek'],
 'skeeter': ['skeeter', 'teskere'],
 'skeletin': ['nestlike', 'skeletin'],
 'skelic': ['sickle', 'skelic'],
 'skelp': ['skelp', 'spelk'],
 'skelter': ['kestrel', 'skelter'],
 'skene': ['skeen', 'skene'],
 'skeo': ['skeo', 'soke'],
 'skeptic': ['skeptic', 'spicket'],
 'skere': ['esker', 'keres', 'reesk', 'seker', 'skeer', 'skere'],
 'sketcher': ['resketch', 'sketcher'],
 'skete': ['keest', 'skeet', 'skete', 'steek'],
 'skey': ['skey', 'skye'],
 'skid': ['disk', 'kids', 'skid'],
 'skier': ['kreis', 'skier'],
 'skil': ['lisk', 'silk', 'skil'],
 'skin': ['inks', 'sink', 'skin'],
 'skinch': ['chinks', 'skinch'],
 'skinless': ['silkness', 'sinkless', 'skinless'],
 'skinlike': ['sinklike', 'skinlike'],
 'skip': ['pisk', 'skip'],
 'skippel': ['skippel', 'skipple'],
 'skipple': ['skippel', 'skipple'],
 'skirmish': ['skirmish', 'smirkish'],
 'skirreh': ['shirker', 'skirreh'],
 'skirret': ['skirret', 'skirter', 'striker'],
 'skirt': ['skirt', 'stirk'],
 'skirter': ['skirret', 'skirter', 'striker'],
 'skirting': ['skirting', 'striking'],
 'skirtingly': ['skirtingly', 'strikingly'],
 'skirty': ['kirsty', 'skirty'],
 'skit': ['kist', 'skit'],
 'skite': ['siket', 'skite'],
 'skiter': ['skiter', 'strike'],
 'skittle': ['kittles', 'skittle'],
 'sklate': ['lasket', 'sklate'],
 'sklater': ['sklater', 'stalker'],
 'sklinter': ['sklinter', 'strinkle'],
 'skoal': ['skoal', 'sloka'],
 'skoo': ['koso', 'skoo', 'sook'],
 'skua': ['kusa', 'skua'],
 'skun': ['skun', 'sunk'],
 'skye': ['skey', 'skye'],
 'sla': ['las', 'sal', 'sla'],
 'slab': ['blas', 'slab'],
 'slacken': ['slacken', 'snackle'],
 'slade': ['leads', 'slade'],
 'slae': ['elsa', 'sale', 'seal', 'slae'],
 'slain': ['sinal', 'slain', 'snail'],
 'slainte': ['elastin', 'salient', 'saltine', 'slainte'],
 'slait': ['alist', 'litas', 'slait', 'talis'],
 'slake': ['alkes', 'sakel', 'slake'],
 'slam': ['alms', 'salm', 'slam'],
 'slamp': ['plasm', 'psalm', 'slamp'],
 'slandering': ['sanderling', 'slandering'],
 'slane': ['ansel', 'slane'],
 'slang': ['glans', 'slang'],
 'slangily': ['signally', 'singally', 'slangily'],
 'slangish': ['slangish', 'slashing'],
 'slangishly': ['slangishly', 'slashingly'],
 'slangster': ['slangster', 'strangles'],
 'slap': ['salp', 'slap'],
 'slape': ['elaps',
           'lapse',
           'lepas',
           'pales',
           'salep',
           'saple',
           'sepal',
           'slape',
           'spale',
           'speal'],
 'slare': ['arles', 'arsle', 'laser', 'seral', 'slare'],
 'slasher': ['reslash', 'slasher'],
 'slashing': ['slangish', 'slashing'],
 'slashingly': ['slangishly', 'slashingly'],
 'slat': ['last', 'salt', 'slat'],
 'slate': ['least', 'setal', 'slate', 'stale', 'steal', 'stela', 'tales'],
 'slater': ['laster',
            'lastre',
            'rastle',
            'relast',
            'resalt',
            'salter',
            'slater',
            'stelar'],
 'slath': ['shalt', 'slath'],
 'slather': ['hastler', 'slather'],
 'slatiness': ['saintless', 'saltiness', 'slatiness', 'stainless'],
 'slating': ['anglist', 'lasting', 'salting', 'slating', 'staling'],
 'slatish': ['saltish', 'slatish'],
 'slatter': ['rattles', 'slatter', 'starlet', 'startle'],
 'slaty': ['lasty', 'salty', 'slaty'],
 'slaughter': ['lethargus', 'slaughter'],
 'slaughterman': ['manslaughter', 'slaughterman'],
 'slaum': ['lamus', 'malus', 'musal', 'slaum'],
 'slave': ['salve', 'selva', 'slave', 'valse'],
 'slaver': ['salver', 'serval', 'slaver', 'versal'],
 'slaverer': ['reserval', 'reversal', 'slaverer'],
 'slavey': ['slavey', 'sylvae'],
 'slavi': ['silva', 'slavi'],
 'slavian': ['salivan', 'slavian'],
 'slavic': ['clavis', 'slavic'],
 'slavonic': ['slavonic', 'volscian'],
 'slay': ['lyas', 'slay'],
 'slayer': ['reslay', 'slayer'],
 'sleave': ['leaves', 'sleave'],
 'sledger': ['redlegs', 'sledger'],
 'slee': ['else', 'lees', 'seel', 'sele', 'slee'],
 'sleech': ['lesche', 'sleech'],
 'sleek': ['skeel', 'sleek'],
 'sleeking': ['skeeling', 'sleeking'],
 'sleeky': ['skeely', 'sleeky'],
 'sleep': ['sleep', 'speel'],
 'sleepless': ['sleepless', 'speelless'],
 'sleepry': ['presley', 'sleepry'],
 'sleet': ['sleet', 'slete', 'steel', 'stele'],
 'sleetiness': ['sleetiness', 'steeliness'],
 'sleeting': ['sleeting', 'steeling'],
 'sleetproof': ['sleetproof', 'steelproof'],
 'sleety': ['sleety', 'steely'],
 'slept': ['slept', 'spelt', 'splet'],
 'slete': ['sleet', 'slete', 'steel', 'stele'],
 'sleuth': ['hustle', 'sleuth'],
 'slew': ['slew', 'wels'],
 'slewing': ['slewing', 'swingle'],
 'sley': ['lyse', 'sley'],
 'slice': ['sicel', 'slice'],
 'slicht': ['slicht', 'slitch'],
 'slicken': ['slicken', 'snickle'],
 'slicker': ['sickler', 'slicker'],
 'slickery': ['sickerly', 'slickery'],
 'slicking': ['sickling', 'slicking'],
 'slidable': ['sabellid', 'slidable'],
 'slidden': ['slidden', 'sniddle'],
 'slide': ['sidle', 'slide'],
 'slider': ['sidler', 'slider'],
 'sliding': ['sidling', 'sliding'],
 'slidingly': ['sidlingly', 'slidingly'],
 'slifter': ['slifter', 'stifler'],
 'slightily': ['sightlily', 'slightily'],
 'slightiness': ['sightliness', 'slightiness'],
 'slighty': ['sightly', 'slighty'],
 'slime': ['limes', 'miles', 'slime', 'smile'],
 'slimeman': ['melanism', 'slimeman'],
 'slimer': ['slimer', 'smiler'],
 'slimy': ['limsy', 'slimy', 'smily'],
 'sline': ['elsin', 'lenis', 'niels', 'silen', 'sline'],
 'slinge': ['single', 'slinge'],
 'slinger': ['singler', 'slinger'],
 'slink': ['links', 'slink'],
 'slip': ['lisp', 'slip'],
 'slipcoat': ['postical', 'slipcoat'],
 'slipe': ['piles', 'plies', 'slipe', 'spiel', 'spile'],
 'slipover': ['overslip', 'slipover'],
 'slipway': ['slipway', 'waspily'],
 'slit': ['list', 'silt', 'slit'],
 'slitch': ['slicht', 'slitch'],
 'slite': ['islet', 'istle', 'slite', 'stile'],
 'slithy': ['hylist', 'slithy'],
 'slitless': ['listless', 'slitless'],
 'slitlike': ['siltlike', 'slitlike'],
 'slitted': ['slitted', 'stilted'],
 'slitter': ['litster', 'slitter', 'stilter', 'testril'],
 'slitty': ['slitty', 'stilty'],
 'slive': ['elvis', 'levis', 'slive'],
 'sliver': ['silver', 'sliver'],
 'sliverer': ['resilver', 'silverer', 'sliverer'],
 'sliverlike': ['silverlike', 'sliverlike'],
 'slivery': ['silvery', 'slivery'],
 'sloan': ['salon', 'sloan', 'solan'],
 'slod': ['slod', 'sold'],
 'sloe': ['lose', 'sloe', 'sole'],
 'sloka': ['skoal', 'sloka'],
 'slone': ['slone', 'solen'],
 'sloo': ['sloo', 'solo', 'sool'],
 'sloom': ['mools', 'sloom'],
 'sloop': ['polos', 'sloop', 'spool'],
 'slope': ['elops', 'slope', 'spole'],
 'sloper': ['sloper', 'splore'],
 'slot': ['lost', 'lots', 'slot'],
 'slote': ['slote', 'stole'],
 'sloted': ['sloted', 'stoled'],
 'slotter': ['settlor', 'slotter'],
 'slouch': ['holcus', 'lochus', 'slouch'],
 'slouchiness': ['cushionless', 'slouchiness'],
 'slouchy': ['chylous', 'slouchy'],
 'slovenian': ['slovenian', 'venosinal'],
 'slow': ['slow', 'sowl'],
 'slud': ['slud', 'suld'],
 'slue': ['lues', 'slue'],
 'sluit': ['litus', 'sluit', 'tulsi'],
 'slunge': ['gunsel', 'selung', 'slunge'],
 'slurp': ['slurp', 'spurl'],
 'slut': ['lust', 'slut'],
 'sluther': ['hulster', 'hustler', 'sluther'],
 'slutter': ['slutter', 'trustle'],
 'sly': ['lys', 'sly'],
 'sma': ['mas', 'sam', 'sma'],
 'smachrie': ['semiarch', 'smachrie'],
 'smallen': ['ensmall', 'smallen'],
 'smalltime': ['metallism', 'smalltime'],
 'smaltine': ['mentalis', 'smaltine', 'stileman'],
 'smaltite': ['metalist', 'smaltite'],
 'smart': ['smart', 'stram'],
 'smarten': ['sarment', 'smarten'],
 'smashage': ['gamashes', 'smashage'],
 'smeary': ['ramsey', 'smeary'],
 'smectis': ['sectism', 'smectis'],
 'smee': ['mese', 'seem', 'seme', 'smee'],
 'smeech': ['scheme', 'smeech'],
 'smeek': ['meeks', 'smeek'],
 'smeer': ['merse', 'smeer'],
 'smeeth': ['smeeth', 'smethe'],
 'smeller': ['resmell', 'smeller'],
 'smelly': ['mysell', 'smelly'],
 'smelter': ['melters', 'resmelt', 'smelter'],
 'smethe': ['smeeth', 'smethe'],
 'smilax': ['laxism', 'smilax'],
 'smile': ['limes', 'miles', 'slime', 'smile'],
 'smiler': ['slimer', 'smiler'],
 'smilet': ['mistle', 'smilet'],
 'smiling': ['simling', 'smiling'],
 'smily': ['limsy', 'slimy', 'smily'],
 'sminthian': ['mitannish', 'sminthian'],
 'smirch': ['chrism', 'smirch'],
 'smirkish': ['skirmish', 'smirkish'],
 'smit': ['mist', 'smit', 'stim'],
 'smite': ['metis', 'smite', 'stime', 'times'],
 'smiter': ['merist', 'mister', 'smiter'],
 'smither': ['rhemist', 'smither'],
 'smithian': ['isthmian', 'smithian'],
 'smoker': ['mosker', 'smoker'],
 'smoot': ['moost', 'smoot'],
 'smoother': ['resmooth', 'romeshot', 'smoother'],
 'smoothingly': ['hymnologist', 'smoothingly'],
 'smore': ['meros', 'mores', 'morse', 'sermo', 'smore'],
 'smote': ['moste', 'smote'],
 'smother': ['smother', 'thermos'],
 'smouse': ['mousse', 'smouse'],
 'smouser': ['osmerus', 'smouser'],
 'smuggle': ['muggles', 'smuggle'],
 'smut': ['must', 'smut', 'stum'],
 'smyrniot': ['smyrniot', 'tyronism'],
 'smyrniote': ['myristone', 'smyrniote'],
 'snab': ['nabs', 'snab'],
 'snackle': ['slacken', 'snackle'],
 'snag': ['sang', 'snag'],
 'snagrel': ['sangrel', 'snagrel'],
 'snail': ['sinal', 'slain', 'snail'],
 'snaillike': ['silkaline', 'snaillike'],
 'snaily': ['anisyl', 'snaily'],
 'snaith': ['snaith', 'tahsin'],
 'snake': ['skean', 'snake', 'sneak'],
 'snap': ['snap', 'span'],
 'snape': ['aspen', 'panse', 'snape', 'sneap', 'spane', 'spean'],
 'snaper': ['resnap', 'respan', 'snaper'],
 'snapless': ['snapless', 'spanless'],
 'snapy': ['pansy', 'snapy'],
 'snare': ['anser', 'nares', 'rasen', 'snare'],
 'snarer': ['serran', 'snarer'],
 'snaste': ['assent', 'snaste'],
 'snatch': ['chanst', 'snatch', 'stanch'],
 'snatchable': ['snatchable', 'stanchable'],
 'snatcher': ['resnatch', 'snatcher', 'stancher'],
 'snath': ['shant', 'snath'],
 'snathe': ['athens', 'hasten', 'snathe', 'sneath'],
 'snaw': ['sawn', 'snaw', 'swan'],
 'snead': ['sedan', 'snead'],
 'sneak': ['skean', 'snake', 'sneak'],
 'sneaker': ['keresan', 'sneaker'],
 'sneaksman': ['masskanne', 'sneaksman'],
 'sneap': ['aspen', 'panse', 'snape', 'sneap', 'spane', 'spean'],
 'sneath': ['athens', 'hasten', 'snathe', 'sneath'],
 'sneathe': ['sneathe', 'thesean'],
 'sned': ['send', 'sned'],
 'snee': ['ense', 'esne', 'nese', 'seen', 'snee'],
 'sneer': ['renes', 'sneer'],
 'snew': ['news', 'sewn', 'snew'],
 'snib': ['nibs', 'snib'],
 'snickle': ['slicken', 'snickle'],
 'sniddle': ['slidden', 'sniddle'],
 'snide': ['denis', 'snide'],
 'snig': ['sign', 'sing', 'snig'],
 'snigger': ['serging', 'snigger'],
 'snip': ['snip', 'spin'],
 'snipe': ['penis', 'snipe', 'spine'],
 'snipebill': ['snipebill', 'spinebill'],
 'snipelike': ['snipelike', 'spinelike'],
 'sniper': ['pernis', 'respin', 'sniper'],
 'snipocracy': ['conspiracy', 'snipocracy'],
 'snipper': ['nippers', 'snipper'],
 'snippet': ['snippet', 'stippen'],
 'snipy': ['snipy', 'spiny'],
 'snitcher': ['christen', 'snitcher'],
 'snite': ['inset', 'neist', 'snite', 'stein', 'stine', 'tsine'],
 'snithy': ['shinty', 'snithy'],
 'sniveler': ['ensilver', 'sniveler'],
 'snively': ['snively', 'sylvine'],
 'snob': ['bosn', 'nobs', 'snob'],
 'snocker': ['conkers', 'snocker'],
 'snod': ['snod', 'sond'],
 'snoek': ['snoek', 'snoke', 'soken'],
 'snog': ['snog', 'song'],
 'snoke': ['snoek', 'snoke', 'soken'],
 'snook': ['onkos', 'snook'],
 'snoop': ['snoop', 'spoon'],
 'snooper': ['snooper', 'spooner'],
 'snoopy': ['snoopy', 'spoony'],
 'snoot': ['snoot', 'stoon'],
 'snore': ['norse', 'noser', 'seron', 'snore'],
 'snorer': ['snorer', 'sorner'],
 'snoring': ['snoring', 'sorning'],
 'snork': ['norsk', 'snork'],
 'snotter': ['snotter', 'stentor', 'torsten'],
 'snout': ['notus', 'snout', 'stoun', 'tonus'],
 'snouter': ['snouter', 'tonsure', 'unstore'],
 'snow': ['snow', 'sown'],
 'snowie': ['nowise', 'snowie'],
 'snowish': ['snowish', 'whisson'],
 'snowy': ['snowy', 'wyson'],
 'snug': ['snug', 'sung'],
 'snup': ['snup', 'spun'],
 'snurp': ['snurp', 'spurn'],
 'snurt': ['snurt', 'turns'],
 'so': ['os', 'so'],
 'soak': ['asok', 'soak', 'soka'],
 'soaker': ['arkose', 'resoak', 'soaker'],
 'soally': ['soally', 'sollya'],
 'soam': ['amos', 'soam', 'soma'],
 'soap': ['asop', 'sapo', 'soap'],
 'soaper': ['resoap', 'soaper'],
 'soar': ['asor', 'rosa', 'soar', 'sora'],
 'sob': ['bos', 'sob'],
 'sobeit': ['setibo', 'sobeit'],
 'sober': ['boser', 'brose', 'sober'],
 'sobralite': ['sobralite', 'strobilae'],
 'soc': ['cos', 'osc', 'soc'],
 'socager': ['corsage', 'socager'],
 'social': ['colias', 'scolia', 'social'],
 'socialite': ['aeolistic', 'socialite'],
 'societal': ['cosalite', 'societal'],
 'societism': ['seismotic', 'societism'],
 'socinian': ['oscinian', 'socinian'],
 'sociobiological': ['biosociological', 'sociobiological'],
 'sociolegal': ['oligoclase', 'sociolegal'],
 'socius': ['scious', 'socius'],
 'socle': ['close', 'socle'],
 'soco': ['coos', 'soco'],
 'socotran': ['ostracon', 'socotran'],
 'socotrine': ['certosino', 'cortisone', 'socotrine'],
 'socratean': ['ostracean', 'socratean'],
 'socratic': ['acrostic', 'sarcotic', 'socratic'],
 'socratical': ['acrostical', 'socratical'],
 'socratically': ['acrostically', 'socratically'],
 'socraticism': ['acrosticism', 'socraticism'],
 'socratism': ['ostracism', 'socratism'],
 'socratize': ['ostracize', 'socratize'],
 'sod': ['dos', 'ods', 'sod'],
 'soda': ['dosa', 'sado', 'soda'],
 'sodalite': ['diastole', 'isolated', 'sodalite', 'solidate'],
 'sodium': ['modius', 'sodium'],
 'sodom': ['dooms', 'sodom'],
 'sodomitic': ['diosmotic', 'sodomitic'],
 'soe': ['oes', 'ose', 'soe'],
 'soft': ['soft', 'stof'],
 'soften': ['oftens', 'soften'],
 'softener': ['resoften', 'softener'],
 'sog': ['gos', 'sog'],
 'soga': ['sago', 'soga'],
 'soger': ['gorse', 'soger'],
 'soh': ['sho', 'soh'],
 'soho': ['shoo', 'soho'],
 'soil': ['lois', 'silo', 'siol', 'soil', 'soli'],
 'soiled': ['isolde', 'soiled'],
 'sojourner': ['resojourn', 'sojourner'],
 'sok': ['kos', 'sok'],
 'soka': ['asok', 'soak', 'soka'],
 'soke': ['skeo', 'soke'],
 'soken': ['snoek', 'snoke', 'soken'],
 'sola': ['also', 'sola'],
 'solacer': ['escolar', 'solacer'],
 'solan': ['salon', 'sloan', 'solan'],
 'solar': ['rosal', 'solar', 'soral'],
 'solaristics': ['scissortail', 'solaristics'],
 'solate': ['lotase', 'osteal', 'solate', 'stolae', 'talose'],
 'sold': ['slod', 'sold'],
 'solder': ['dorsel', 'seldor', 'solder'],
 'solderer': ['resolder', 'solderer'],
 'soldi': ['soldi', 'solid'],
 'soldo': ['soldo', 'solod'],
 'sole': ['lose', 'sloe', 'sole'],
 'solea': ['alose', 'osela', 'solea'],
 'solecist': ['solecist', 'solstice'],
 'solen': ['slone', 'solen'],
 'soleness': ['noseless', 'soleness'],
 'solenial': ['lesional', 'solenial'],
 'solenite': ['noselite', 'solenite'],
 'solenium': ['emulsion', 'solenium'],
 'solenopsis': ['poisonless', 'solenopsis'],
 'solent': ['solent', 'stolen', 'telson'],
 'solentine': ['nelsonite', 'solentine'],
 'soler': ['loser', 'orsel', 'rosel', 'soler'],
 'solera': ['roseal', 'solera'],
 'soles': ['loess', 'soles'],
 'soli': ['lois', 'silo', 'siol', 'soil', 'soli'],
 'soliative': ['isolative', 'soliative'],
 'solicit': ['colitis', 'solicit'],
 'solicitation': ['coalitionist', 'solicitation'],
 'soliciter': ['resolicit', 'soliciter'],
 'soliciting': ['ignicolist', 'soliciting'],
 'solicitude': ['isodulcite', 'solicitude'],
 'solid': ['soldi', 'solid'],
 'solidate': ['diastole', 'isolated', 'sodalite', 'solidate'],
 'solidus': ['dissoul', 'dulosis', 'solidus'],
 'solilunar': ['lunisolar', 'solilunar'],
 'soliped': ['despoil', 'soliped', 'spoiled'],
 'solitarian': ['sinoatrial', 'solitarian'],
 'solitary': ['royalist', 'solitary'],
 'soliterraneous': ['salinoterreous', 'soliterraneous'],
 'solitude': ['outslide', 'solitude'],
 'sollya': ['soally', 'sollya'],
 'solmizate': ['solmizate', 'zealotism'],
 'solo': ['sloo', 'solo', 'sool'],
 'solod': ['soldo', 'solod'],
 'solon': ['olson', 'solon'],
 'solonic': ['scolion', 'solonic'],
 'soloth': ['soloth', 'tholos'],
 'solotink': ['solotink', 'solotnik'],
 'solotnik': ['solotink', 'solotnik'],
 'solstice': ['solecist', 'solstice'],
 'solum': ['mosul', 'mouls', 'solum'],
 'solute': ['lutose', 'solute', 'tousle'],
 'solutioner': ['resolution', 'solutioner'],
 'soma': ['amos', 'soam', 'soma'],
 'somacule': ['maculose', 'somacule'],
 'somal': ['salmo', 'somal'],
 'somali': ['limosa', 'somali'],
 'somasthenia': ['anhematosis', 'somasthenia'],
 'somatic': ['atomics', 'catoism', 'cosmati', 'osmatic', 'somatic'],
 'somatics': ['acosmist', 'massicot', 'somatics'],
 'somatism': ['osmatism', 'somatism'],
 'somatophyte': ['hepatostomy', 'somatophyte'],
 'somatophytic': ['hypostomatic', 'somatophytic'],
 'somatopleuric': ['micropetalous', 'somatopleuric'],
 'somatopsychic': ['psychosomatic', 'somatopsychic'],
 'somatosplanchnic': ['somatosplanchnic', 'splanchnosomatic'],
 'somatous': ['astomous', 'somatous'],
 'somber': ['somber', 'sombre'],
 'sombre': ['somber', 'sombre'],
 'some': ['meso', 'mose', 'some'],
 'someday': ['samoyed', 'someday'],
 'somers': ['messor', 'mosser', 'somers'],
 'somnambule': ['somnambule', 'summonable'],
 'somnial': ['malison', 'manolis', 'osmanli', 'somnial'],
 'somnopathy': ['phytomonas', 'somnopathy'],
 'somnorific': ['onisciform', 'somnorific'],
 'son': ['ons', 'son'],
 'sonant': ['santon', 'sonant', 'stanno'],
 'sonantic': ['canonist', 'sanction', 'sonantic'],
 'sonar': ['arson', 'saron', 'sonar'],
 'sonatina': ['ansation', 'sonatina'],
 'sond': ['snod', 'sond'],
 'sondation': ['anisodont', 'sondation'],
 'sondeli': ['indoles', 'sondeli'],
 'soneri': ['rosine', 'senior', 'soneri'],
 'song': ['snog', 'song'],
 'songoi': ['isogon', 'songoi'],
 'songy': ['gonys', 'songy'],
 'sonic': ['oscin', 'scion', 'sonic'],
 'sonja': ['janos', 'jason', 'jonas', 'sonja'],
 'sonneratia': ['arsenation', 'senatorian', 'sonneratia'],
 'sonnet': ['sonnet', 'stonen', 'tenson'],
 'sonnetwise': ['sonnetwise', 'swinestone'],
 'sonrai': ['arsino', 'rasion', 'sonrai'],
 'sontag': ['sontag', 'tongas'],
 'soodle': ['dolose', 'oodles', 'soodle'],
 'sook': ['koso', 'skoo', 'sook'],
 'sool': ['sloo', 'solo', 'sool'],
 'soon': ['oons', 'soon'],
 'sooner': ['nooser', 'seroon', 'sooner'],
 'sooter': ['seroot', 'sooter', 'torose'],
 'sooth': ['shoot', 'sooth', 'sotho', 'toosh'],
 'soother': ['orthose', 'reshoot', 'shooter', 'soother'],
 'soothing': ['shooting', 'soothing'],
 'sootiness': ['enostosis', 'sootiness'],
 'sooty': ['sooty', 'soyot'],
 'sope': ['epos', 'peso', 'pose', 'sope'],
 'soph': ['phos', 'posh', 'shop', 'soph'],
 'sophister': ['posterish', 'prothesis', 'sophister', 'storeship', 'tephrosis'],
 'sophistical': ['postischial', 'sophistical'],
 'sophomore': ['osmophore', 'sophomore'],
 'sopition': ['position', 'sopition'],
 'sopor': ['poros', 'proso', 'sopor', 'spoor'],
 'soprani': ['parison', 'soprani'],
 'sopranist': ['postnaris', 'sopranist'],
 'soprano': ['pronaos', 'soprano'],
 'sora': ['asor', 'rosa', 'soar', 'sora'],
 'sorabian': ['abrasion', 'sorabian'],
 'soral': ['rosal', 'solar', 'soral'],
 'sorbate': ['barotse', 'boaster', 'reboast', 'sorbate'],
 'sorbin': ['insorb', 'sorbin'],
 'sorcer': ['scorer', 'sorcer'],
 'sorchin': ['cornish', 'cronish', 'sorchin'],
 'sorda': ['sarod', 'sorda'],
 'sordes': ['dosser', 'sordes'],
 'sordine': ['indorse', 'ordines', 'siredon', 'sordine'],
 'sordino': ['indoors', 'sordino'],
 'sore': ['eros', 'rose', 'sero', 'sore'],
 'soredia': ['ardoise', 'aroides', 'soredia'],
 'sorediate': ['oestridae', 'ostreidae', 'sorediate'],
 'soredium': ['dimerous', 'soredium'],
 'soree': ['erose', 'soree'],
 'sorefoot': ['footsore', 'sorefoot'],
 'sorehead': ['rosehead', 'sorehead'],
 'sorehon': ['onshore', 'sorehon'],
 'sorema': ['amores', 'ramose', 'sorema'],
 'soricid': ['cirsoid', 'soricid'],
 'soricident': ['discretion', 'soricident'],
 'soricine': ['recision', 'soricine'],
 'sorite': ['restio', 'sorite', 'sortie', 'triose'],
 'sorites': ['rossite', 'sorites'],
 'sornare': ['serrano', 'sornare'],
 'sorner': ['snorer', 'sorner'],
 'sorning': ['snoring', 'sorning'],
 'sororial': ['rosorial', 'sororial'],
 'sorption': ['notropis', 'positron', 'sorption'],
 'sortable': ['sortable', 'storable'],
 'sorted': ['sorted', 'strode'],
 'sorter': ['resort', 'roster', 'sorter', 'storer'],
 'sortie': ['restio', 'sorite', 'sortie', 'triose'],
 'sortilegus': ['sortilegus', 'strigulose'],
 'sortiment': ['sortiment', 'trimstone'],
 'sortly': ['sortly', 'styrol'],
 'sorty': ['sorty', 'story', 'stroy'],
 'sorva': ['savor', 'sorva'],
 'sory': ['rosy', 'sory'],
 'sosia': ['oasis', 'sosia'],
 'sostenuto': ['ostentous', 'sostenuto'],
 'soter': ['roset', 'rotse', 'soter', 'stero', 'store', 'torse'],
 'soterial': ['soterial', 'striolae'],
 'sotho': ['shoot', 'sooth', 'sotho', 'toosh'],
 'sotie': ['sotie', 'toise'],
 'sotnia': ['sotnia', 'tinosa'],
 'sotol': ['sotol', 'stool'],
 'sots': ['sots', 'toss'],
 'sotter': ['sotter', 'testor'],
 'soucar': ['acorus', 'soucar'],
 'souchet': ['souchet', 'techous', 'tousche'],
 'souly': ['lousy', 'souly'],
 'soum': ['soum', 'sumo'],
 'soumansite': ['soumansite', 'stamineous'],
 'sound': ['nodus', 'ounds', 'sound'],
 'sounder': ['resound', 'sounder', 'unrosed'],
 'soup': ['opus', 'soup'],
 'souper': ['poseur', 'pouser', 'souper', 'uprose'],
 'sour': ['ours', 'sour'],
 'source': ['cerous', 'course', 'crouse', 'source'],
 'soured': ['douser', 'soured'],
 'souredness': ['rousedness', 'souredness'],
 'souren': ['souren', 'unsore', 'ursone'],
 'sourer': ['rouser', 'sourer'],
 'souring': ['nigrous', 'rousing', 'souring'],
 'sourly': ['lusory', 'sourly'],
 'soursop': ['psorous', 'soursop', 'sporous'],
 'soury': ['soury', 'yours'],
 'souser': ['serous', 'souser'],
 'souter': ['ouster', 'souter', 'touser', 'trouse'],
 'souterrain': ['souterrain', 'ternarious', 'trouserian'],
 'south': ['shout', 'south'],
 'souther': ['shouter', 'souther'],
 'southerland': ['southerland', 'southlander'],
 'southing': ['shouting', 'southing'],
 'southlander': ['southerland', 'southlander'],
 'soviet': ['soviet', 'sovite'],
 'sovite': ['soviet', 'sovite'],
 'sowdones': ['sowdones', 'woodness'],
 'sowel': ['sowel', 'sowle'],
 'sower': ['owser', 'resow', 'serow', 'sower', 'swore', 'worse'],
 'sowl': ['slow', 'sowl'],
 'sowle': ['sowel', 'sowle'],
 'sown': ['snow', 'sown'],
 'sowt': ['sowt', 'stow', 'swot', 'wots'],
 'soyot': ['sooty', 'soyot'],
 'spa': ['asp', 'sap', 'spa'],
 'space': ['capes', 'scape', 'space'],
 'spaceless': ['scapeless', 'spaceless'],
 'spacer': ['casper', 'escarp', 'parsec', 'scrape', 'secpar', 'spacer'],
 'spade': ['depas', 'sepad', 'spade'],
 'spader': ['rasped', 'spader', 'spread'],
 'spadiceous': ['dipsaceous', 'spadiceous'],
 'spadone': ['espadon', 'spadone'],
 'spadonic': ['spadonic', 'spondaic', 'spondiac'],
 'spadrone': ['parsoned', 'spadrone'],
 'spae': ['apse', 'pesa', 'spae'],
 'spaer': ['asper', 'parse', 'prase', 'spaer', 'spare', 'spear'],
 'spahi': ['aphis', 'apish', 'hispa', 'saiph', 'spahi'],
 'spaid': ['sapid', 'spaid'],
 'spaik': ['askip', 'spaik'],
 'spairge': ['prisage', 'spairge'],
 'spalacine': ['asclepian', 'spalacine'],
 'spale': ['elaps',
           'lapse',
           'lepas',
           'pales',
           'salep',
           'saple',
           'sepal',
           'slape',
           'spale',
           'speal'],
 'spalt': ['spalt', 'splat'],
 'span': ['snap', 'span'],
 'spancel': ['enclasp', 'spancel'],
 'spane': ['aspen', 'panse', 'snape', 'sneap', 'spane', 'spean'],
 'spanemia': ['paeanism', 'spanemia'],
 'spangler': ['spangler', 'sprangle'],
 'spangolite': ['postgenial', 'spangolite'],
 'spaniel': ['espinal', 'pinales', 'spaniel'],
 'spaniol': ['sanpoil', 'spaniol'],
 'spanless': ['snapless', 'spanless'],
 'spar': ['rasp', 'spar'],
 'sparable': ['parsable', 'prebasal', 'sparable'],
 'spare': ['asper', 'parse', 'prase', 'spaer', 'spare', 'spear'],
 'spareable': ['separable', 'spareable'],
 'sparely': ['parsley', 'pyrales', 'sparely', 'splayer'],
 'sparer': ['parser', 'rasper', 'sparer'],
 'sparerib': ['ribspare', 'sparerib'],
 'sparge': ['gasper', 'sparge'],
 'sparger': ['grasper', 'regrasp', 'sparger'],
 'sparidae': ['paradise', 'sparidae'],
 'sparing': ['aspring', 'rasping', 'sparing'],
 'sparingly': ['raspingly', 'sparingly'],
 'sparingness': ['raspingness', 'sparingness'],
 'sparling': ['laspring', 'sparling', 'springal'],
 'sparoid': ['prasoid', 'sparoid'],
 'sparse': ['passer', 'repass', 'sparse'],
 'spart': ['spart', 'sprat', 'strap', 'traps'],
 'spartanic': ['sacripant', 'spartanic'],
 'sparteine': ['pistareen', 'sparteine'],
 'sparterie': ['periaster', 'sparterie'],
 'spartina': ['aspirant', 'partisan', 'spartina'],
 'spartle': ['palster', 'persalt', 'plaster', 'psalter', 'spartle', 'stapler'],
 'spary': ['raspy', 'spary', 'spray'],
 'spat': ['past', 'spat', 'stap', 'taps'],
 'spate': ['paste', 'septa', 'spate'],
 'spathal': ['asphalt', 'spathal', 'taplash'],
 'spathe': ['spathe', 'thapes'],
 'spathic': ['haptics', 'spathic'],
 'spatling': ['spatling', 'stapling'],
 'spatter': ['spatter', 'tapster'],
 'spattering': ['spattering', 'tapestring'],
 'spattle': ['peltast', 'spattle'],
 'spatular': ['pastural', 'spatular'],
 'spatule': ['pulsate', 'spatule', 'upsteal'],
 'spave': ['spave', 'vespa'],
 'speak': ['sapek', 'speak'],
 'speaker': ['respeak', 'speaker'],
 'speal': ['elaps',
           'lapse',
           'lepas',
           'pales',
           'salep',
           'saple',
           'sepal',
           'slape',
           'spale',
           'speal'],
 'spean': ['aspen', 'panse', 'snape', 'sneap', 'spane', 'spean'],
 'spear': ['asper', 'parse', 'prase', 'spaer', 'spare', 'spear'],
 'spearman': ['parmesan', 'spearman'],
 'spearmint': ['spearmint', 'spermatin'],
 'speary': ['presay', 'speary'],
 'spec': ['ceps', 'spec'],
 'spectatorial': ['poetastrical', 'spectatorial'],
 'specter': ['respect', 'scepter', 'specter'],
 'spectered': ['sceptered', 'spectered'],
 'spectra': ['precast', 'spectra'],
 'spectral': ['sceptral', 'scraplet', 'spectral'],
 'spectromicroscope': ['microspectroscope', 'spectromicroscope'],
 'spectrotelescope': ['spectrotelescope', 'telespectroscope'],
 'spectrous': ['spectrous', 'susceptor', 'suspector'],
 'spectry': ['precyst', 'sceptry', 'spectry'],
 'specula': ['capsule', 'specula', 'upscale'],
 'specular': ['capsuler', 'specular'],
 'speed': ['pedes', 'speed'],
 'speel': ['sleep', 'speel'],
 'speelless': ['sleepless', 'speelless'],
 'speer': ['peres', 'perse', 'speer', 'spree'],
 'speerity': ['perseity', 'speerity'],
 'speiss': ['sepsis', 'speiss'],
 'spelaean': ['seaplane', 'spelaean'],
 'spelk': ['skelp', 'spelk'],
 'speller': ['presell', 'respell', 'speller'],
 'spelt': ['slept', 'spelt', 'splet'],
 'spenerism': ['primeness', 'spenerism'],
 'speos': ['posse', 'speos'],
 'sperate': ['perates', 'repaste', 'sperate'],
 'sperity': ['pyrites', 'sperity'],
 'sperling': ['sperling', 'springle'],
 'spermalist': ['psalmister', 'spermalist'],
 'spermathecal': ['chapelmaster', 'spermathecal'],
 'spermatid': ['predatism', 'spermatid'],
 'spermatin': ['spearmint', 'spermatin'],
 'spermatogonium': ['protomagnesium', 'spermatogonium'],
 'spermatozoic': ['spermatozoic', 'zoospermatic'],
 'spermiogenesis': ['geissospermine', 'spermiogenesis'],
 'spermocarp': ['carposperm', 'spermocarp'],
 'spet': ['pest', 'sept', 'spet', 'step'],
 'spew': ['spew', 'swep'],
 'sphacelation': ['lipsanotheca', 'sphacelation'],
 'sphecidae': ['cheapside', 'sphecidae'],
 'sphene': ['sephen', 'sphene'],
 'sphenoethmoid': ['ethmosphenoid', 'sphenoethmoid'],
 'sphenoethmoidal': ['ethmosphenoidal', 'sphenoethmoidal'],
 'sphenotic': ['phonetics', 'sphenotic'],
 'spheral': ['plasher', 'spheral'],
 'spheration': ['opisthenar', 'spheration'],
 'sphere': ['herpes', 'hesper', 'sphere'],
 'sphery': ['sphery', 'sypher'],
 'sphyraenid': ['dysphrenia', 'sphyraenid', 'sphyrnidae'],
 'sphyrnidae': ['dysphrenia', 'sphyraenid', 'sphyrnidae'],
 'spica': ['aspic', 'spica'],
 'spicate': ['aseptic', 'spicate'],
 'spice': ['sepic', 'spice'],
 'spicer': ['crepis', 'cripes', 'persic', 'precis', 'spicer'],
 'spiciferous': ['pisciferous', 'spiciferous'],
 'spiciform': ['pisciform', 'spiciform'],
 'spicket': ['skeptic', 'spicket'],
 'spicular': ['scripula', 'spicular'],
 'spiculate': ['euplastic', 'spiculate'],
 'spiculated': ['disculpate', 'spiculated'],
 'spicule': ['clipeus', 'spicule'],
 'spider': ['spider', 'spired', 'spried'],
 'spiderish': ['sidership', 'spiderish'],
 'spiderlike': ['predislike', 'spiderlike'],
 'spiel': ['piles', 'plies', 'slipe', 'spiel', 'spile'],
 'spier': ['siper', 'spier', 'spire'],
 'spikelet': ['spikelet', 'steplike'],
 'spiking': ['pigskin', 'spiking'],
 'spiky': ['pisky', 'spiky'],
 'spile': ['piles', 'plies', 'slipe', 'spiel', 'spile'],
 'spiler': ['lisper', 'pliers', 'sirple', 'spiler'],
 'spiling': ['sipling', 'spiling'],
 'spiloma': ['imposal', 'spiloma'],
 'spilt': ['spilt', 'split'],
 'spin': ['snip', 'spin'],
 'spina': ['pisan', 'sapin', 'spina'],
 'spinae': ['sepian', 'spinae'],
 'spinales': ['painless', 'spinales'],
 'spinate': ['panties', 'sapient', 'spinate'],
 'spindled': ['spindled', 'splendid'],
 'spindler': ['spindler', 'splinder'],
 'spine': ['penis', 'snipe', 'spine'],
 'spinebill': ['snipebill', 'spinebill'],
 'spinel': ['spinel', 'spline'],
 'spinelike': ['snipelike', 'spinelike'],
 'spinet': ['instep', 'spinet'],
 'spinigerous': ['serpiginous', 'spinigerous'],
 'spinigrade': ['despairing', 'spinigrade'],
 'spinoid': ['spinoid', 'spionid'],
 'spinoneural': ['spinoneural', 'unipersonal'],
 'spinotectal': ['entoplastic', 'spinotectal', 'tectospinal', 'tenoplastic'],
 'spiny': ['snipy', 'spiny'],
 'spionid': ['spinoid', 'spionid'],
 'spiracle': ['calipers', 'spiracle'],
 'spiracula': ['auriscalp', 'spiracula'],
 'spiral': ['prisal', 'spiral'],
 'spiralism': ['misprisal', 'spiralism'],
 'spiraloid': ['spiraloid', 'sporidial'],
 'spiran': ['spiran', 'sprain'],
 'spirant': ['spirant', 'spraint'],
 'spirate': ['piaster', 'piastre', 'raspite', 'spirate', 'traipse'],
 'spire': ['siper', 'spier', 'spire'],
 'spirea': ['aspire', 'paries', 'praise', 'sirpea', 'spirea'],
 'spired': ['spider', 'spired', 'spried'],
 'spirelet': ['epistler', 'spirelet'],
 'spireme': ['emprise', 'imprese', 'premise', 'spireme'],
 'spiritally': ['pistillary', 'spiritally'],
 'spiriter': ['respirit', 'spiriter'],
 'spirometer': ['prisometer', 'spirometer'],
 'spironema': ['mesropian', 'promnesia', 'spironema'],
 'spirt': ['spirt', 'sprit', 'stirp', 'strip'],
 'spirula': ['parulis', 'spirula', 'uprisal'],
 'spit': ['pist', 'spit'],
 'spital': ['alpist', 'pastil', 'spital'],
 'spite': ['septi', 'spite', 'stipe'],
 'spithame': ['aphetism', 'mateship', 'shipmate', 'spithame'],
 'spitter': ['spitter', 'tipster'],
 'splairge': ['aspergil', 'splairge'],
 'splanchnosomatic': ['somatosplanchnic', 'splanchnosomatic'],
 'splasher': ['harpless', 'splasher'],
 'splat': ['spalt', 'splat'],
 'splay': ['palsy', 'splay'],
 'splayed': ['pylades', 'splayed'],
 'splayer': ['parsley', 'pyrales', 'sparely', 'splayer'],
 'spleet': ['pestle', 'spleet'],
 'splender': ['resplend', 'splender'],
 'splendid': ['spindled', 'splendid'],
 'splenium': ['splenium', 'unsimple'],
 'splenolaparotomy': ['laparosplenotomy', 'splenolaparotomy'],
 'splenoma': ['neoplasm', 'pleonasm', 'polesman', 'splenoma'],
 'splenomegalia': ['megalosplenia', 'splenomegalia'],
 'splenonephric': ['phrenosplenic', 'splenonephric', 'splenophrenic'],
 'splenophrenic': ['phrenosplenic', 'splenonephric', 'splenophrenic'],
 'splet': ['slept', 'spelt', 'splet'],
 'splice': ['clipse', 'splice'],
 'spliceable': ['eclipsable', 'spliceable'],
 'splinder': ['spindler', 'splinder'],
 'spline': ['spinel', 'spline'],
 'split': ['spilt', 'split'],
 'splitter': ['splitter', 'striplet'],
 'splore': ['sloper', 'splore'],
 'spogel': ['gospel', 'spogel'],
 'spoil': ['polis', 'spoil'],
 'spoilage': ['pelasgoi', 'spoilage'],
 'spoilation': ['positional', 'spoilation', 'spoliation'],
 'spoiled': ['despoil', 'soliped', 'spoiled'],
 'spoiler': ['leporis', 'spoiler'],
 'spoilment': ['simpleton', 'spoilment'],
 'spoilt': ['pistol', 'postil', 'spoilt'],
 'spole': ['elops', 'slope', 'spole'],
 'spoliation': ['positional', 'spoilation', 'spoliation'],
 'spondaic': ['spadonic', 'spondaic', 'spondiac'],
 'spondiac': ['spadonic', 'spondaic', 'spondiac'],
 'spongily': ['posingly', 'spongily'],
 'sponsal': ['plasson', 'sponsal'],
 'sponsalia': ['passional', 'sponsalia'],
 'spool': ['polos', 'sloop', 'spool'],
 'spoon': ['snoop', 'spoon'],
 'spooner': ['snooper', 'spooner'],
 'spoony': ['snoopy', 'spoony'],
 'spoonyism': ['spoonyism', 'symposion'],
 'spoor': ['poros', 'proso', 'sopor', 'spoor'],
 'spoot': ['spoot', 'stoop'],
 'sporangia': ['agapornis', 'sporangia'],
 'spore': ['poser', 'prose', 'ropes', 'spore'],
 'sporidial': ['spiraloid', 'sporidial'],
 'sporification': ['antisoporific', 'prosification', 'sporification'],
 'sporogeny': ['gynospore', 'sporogeny'],
 'sporoid': ['psoroid', 'sporoid'],
 'sporotrichum': ['sporotrichum', 'trichosporum'],
 'sporous': ['psorous', 'soursop', 'sporous'],
 'sporozoic': ['sporozoic', 'zoosporic'],
 'sport': ['sport', 'strop'],
 'sporter': ['sporter', 'strepor'],
 'sportula': ['postural', 'pulsator', 'sportula'],
 'sportulae': ['opulaster', 'sportulae', 'sporulate'],
 'sporulate': ['opulaster', 'sportulae', 'sporulate'],
 'sporule': ['leprous', 'pelorus', 'sporule'],
 'sposhy': ['hyssop', 'phossy', 'sposhy'],
 'spot': ['post', 'spot', 'stop', 'tops'],
 'spotless': ['postless', 'spotless', 'stopless'],
 'spotlessness': ['spotlessness', 'stoplessness'],
 'spotlike': ['postlike', 'spotlike'],
 'spottedly': ['spottedly', 'spotteldy'],
 'spotteldy': ['spottedly', 'spotteldy'],
 'spotter': ['protest', 'spotter'],
 'spouse': ['esopus', 'spouse'],
 'spout': ['spout', 'stoup'],
 'spouter': ['petrous', 'posture', 'proetus', 'proteus', 'septuor', 'spouter'],
 'sprag': ['grasp', 'sprag'],
 'sprain': ['spiran', 'sprain'],
 'spraint': ['spirant', 'spraint'],
 'sprangle': ['spangler', 'sprangle'],
 'sprat': ['spart', 'sprat', 'strap', 'traps'],
 'spray': ['raspy', 'spary', 'spray'],
 'sprayer': ['respray', 'sprayer'],
 'spread': ['rasped', 'spader', 'spread'],
 'spreadboard': ['broadspread', 'spreadboard'],
 'spreader': ['respread', 'spreader'],
 'spreadover': ['overspread', 'spreadover'],
 'spree': ['peres', 'perse', 'speer', 'spree'],
 'spret': ['prest', 'spret'],
 'spried': ['spider', 'spired', 'spried'],
 'sprier': ['risper', 'sprier'],
 'spriest': ['persist', 'spriest'],
 'springal': ['laspring', 'sparling', 'springal'],
 'springe': ['presign', 'springe'],
 'springer': ['respring', 'springer'],
 'springhead': ['headspring', 'springhead'],
 'springhouse': ['springhouse', 'surgeonship'],
 'springle': ['sperling', 'springle'],
 'sprit': ['spirt', 'sprit', 'stirp', 'strip'],
 'sprite': ['priest', 'pteris', 'sprite', 'stripe'],
 'spritehood': ['priesthood', 'spritehood'],
 'sproat': ['asport', 'pastor', 'sproat'],
 'sprocket': ['prestock', 'sprocket'],
 'sprout': ['sprout', 'stroup', 'stupor'],
 'sprouter': ['posturer', 'resprout', 'sprouter'],
 'sprouting': ['outspring', 'sprouting'],
 'sprue': ['purse', 'resup', 'sprue', 'super'],
 'spruer': ['purser', 'spruer'],
 'spruit': ['purist', 'spruit', 'uprist', 'upstir'],
 'spun': ['snup', 'spun'],
 'spunkie': ['spunkie', 'unspike'],
 'spuriae': ['spuriae', 'uparise', 'upraise'],
 'spurl': ['slurp', 'spurl'],
 'spurlet': ['purslet', 'spurlet', 'spurtle'],
 'spurn': ['snurp', 'spurn'],
 'spurt': ['spurt', 'turps'],
 'spurtive': ['spurtive', 'upstrive'],
 'spurtle': ['purslet', 'spurlet', 'spurtle'],
 'sputa': ['sputa', 'staup', 'stupa'],
 'sputumary': ['sputumary', 'sumptuary'],
 'sputumous': ['sputumous', 'sumptuous'],
 'spyer': ['pryse', 'spyer'],
 'spyros': ['prossy', 'spyros'],
 'squail': ['squail', 'squali'],
 'squali': ['squail', 'squali'],
 'squamatine': ['antimasque', 'squamatine'],
 'squame': ['masque', 'squame', 'squeam'],
 'squameous': ['squameous', 'squeamous'],
 'squamopetrosal': ['petrosquamosal', 'squamopetrosal'],
 'squamosoparietal': ['parietosquamosal', 'squamosoparietal'],
 'squareman': ['marquesan', 'squareman'],
 'squeaker': ['resqueak', 'squeaker'],
 'squeal': ['lasque', 'squeal'],
 'squeam': ['masque', 'squame', 'squeam'],
 'squeamous': ['squameous', 'squeamous'],
 'squillian': ['nisqualli', 'squillian'],
 'squire': ['risque', 'squire'],
 'squiret': ['querist', 'squiret'],
 'squit': ['quits', 'squit'],
 'sramana': ['ramanas', 'sramana'],
 'sri': ['sir', 'sri'],
 'srivatsan': ['ravissant', 'srivatsan'],
 'ssi': ['sis', 'ssi'],
 'ssu': ['ssu', 'sus'],
 'staab': ['basta', 'staab'],
 'stab': ['bast', 'bats', 'stab'],
 'stabile': ['astilbe', 'bestial', 'blastie', 'stabile'],
 'stable': ['ablest', 'stable', 'tables'],
 'stableful': ['bullfeast', 'stableful'],
 'stabler': ['blaster', 'reblast', 'stabler'],
 'stabling': ['blasting', 'stabling'],
 'stably': ['blasty', 'stably'],
 'staccato': ['staccato', 'stoccata'],
 'stacey': ['cytase', 'stacey'],
 'stacher': ['stacher', 'thraces'],
 'stacker': ['restack', 'stacker'],
 'stackman': ['stackman', 'tacksman'],
 'stacy': ['stacy', 'styca'],
 'stade': ['sedat', 'stade', 'stead'],
 'stadic': ['dicast', 'stadic'],
 'stadium': ['dumaist', 'stadium'],
 'staffer': ['restaff', 'staffer'],
 'stag': ['gast', 'stag'],
 'stager': ['gaster', 'stager'],
 'stagily': ['stagily', 'stygial'],
 'stagnation': ['antagonist', 'stagnation'],
 'stagnum': ['mustang', 'stagnum'],
 'stain': ['saint', 'satin', 'stain'],
 'stainable': ['balanites', 'basaltine', 'stainable'],
 'stainer': ['asterin', 'eranist', 'restain', 'stainer', 'starnie', 'stearin'],
 'stainful': ['inflatus', 'stainful'],
 'stainless': ['saintless', 'saltiness', 'slatiness', 'stainless'],
 'staio': ['sitao', 'staio'],
 'stair': ['arist',
           'astir',
           'sitar',
           'stair',
           'stria',
           'tarsi',
           'tisar',
           'trias'],
 'staircase': ['caesarist', 'staircase'],
 'staired': ['astride', 'diaster', 'disrate', 'restiad', 'staired'],
 'staithman': ['staithman', 'thanatism'],
 'staiver': ['staiver', 'taivers'],
 'stake': ['skate', 'stake', 'steak'],
 'staker': ['skater', 'staker', 'strake', 'streak', 'tasker'],
 'stalagmitic': ['stalagmitic', 'stigmatical'],
 'stale': ['least', 'setal', 'slate', 'stale', 'steal', 'stela', 'tales'],
 'staling': ['anglist', 'lasting', 'salting', 'slating', 'staling'],
 'stalker': ['sklater', 'stalker'],
 'staller': ['staller', 'stellar'],
 'stam': ['mast', 'mats', 'stam'],
 'stamen': ['mantes', 'stamen'],
 'stamin': ['manist', 'mantis', 'matins', 'stamin'],
 'stamina': ['amanist', 'stamina'],
 'staminal': ['staminal', 'tailsman', 'talisman'],
 'staminate': ['emanatist', 'staminate', 'tasmanite'],
 'stamineous': ['soumansite', 'stamineous'],
 'staminode': ['ademonist', 'demoniast', 'staminode'],
 'stammer': ['stammer', 'stremma'],
 'stampede': ['stampede', 'stepdame'],
 'stamper': ['restamp', 'stamper'],
 'stampian': ['mainpast', 'mantispa', 'panamist', 'stampian'],
 'stan': ['nast', 'sant', 'stan'],
 'stance': ['ascent', 'secant', 'stance'],
 'stanch': ['chanst', 'snatch', 'stanch'],
 'stanchable': ['snatchable', 'stanchable'],
 'stancher': ['resnatch', 'snatcher', 'stancher'],
 'stand': ['dasnt', 'stand'],
 'standage': ['dagestan', 'standage'],
 'standee': ['edestan', 'standee'],
 'stander': ['stander', 'sternad'],
 'standout': ['outstand', 'standout'],
 'standstill': ['standstill', 'stillstand'],
 'stane': ['antes', 'nates', 'stane', 'stean'],
 'stang': ['angst', 'stang', 'tangs'],
 'stangeria': ['agrestian', 'gerastian', 'stangeria'],
 'stanine': ['ensaint', 'stanine'],
 'stanly': ['nylast', 'stanly'],
 'stanno': ['santon', 'sonant', 'stanno'],
 'stap': ['past', 'spat', 'stap', 'taps'],
 'staple': ['pastel', 'septal', 'staple'],
 'stapler': ['palster', 'persalt', 'plaster', 'psalter', 'spartle', 'stapler'],
 'stapling': ['spatling', 'stapling'],
 'star': ['sart', 'star', 'stra', 'tars', 'tsar'],
 'starch': ['scarth', 'scrath', 'starch'],
 'stardom': ['stardom', 'tsardom'],
 'stare': ['aster', 'serta', 'stare', 'strae', 'tarse', 'teras'],
 'staree': ['asteer',
            'easter',
            'eastre',
            'reseat',
            'saeter',
            'seater',
            'staree',
            'teaser',
            'teresa'],
 'starer': ['arrest', 'astrer', 'raster', 'starer'],
 'starful': ['flustra', 'starful'],
 'staring': ['gastrin', 'staring'],
 'staringly': ['staringly', 'strayling'],
 'stark': ['karst', 'skart', 'stark'],
 'starky': ['starky', 'straky'],
 'starlet': ['rattles', 'slatter', 'starlet', 'startle'],
 'starlit': ['starlit', 'trisalt'],
 'starlite': ['starlite', 'taistrel'],
 'starnel': ['saltern', 'starnel', 'sternal'],
 'starnie': ['asterin', 'eranist', 'restain', 'stainer', 'starnie', 'stearin'],
 'starnose': ['assentor', 'essorant', 'starnose'],
 'starship': ['starship', 'tsarship'],
 'starshot': ['shotstar', 'starshot'],
 'starter': ['restart', 'starter'],
 'startle': ['rattles', 'slatter', 'starlet', 'startle'],
 'starve': ['starve', 'staver', 'strave', 'tavers', 'versta'],
 'starwise': ['starwise', 'waitress'],
 'stary': ['satyr', 'stary', 'stray', 'trasy'],
 'stases': ['assets', 'stases'],
 'stasis': ['assist', 'stasis'],
 'statable': ['statable', 'tastable'],
 'state': ['state', 'taste', 'tates', 'testa'],
 'stated': ['stated', 'tasted'],
 'stateful': ['stateful', 'tasteful'],
 'statefully': ['statefully', 'tastefully'],
 'statefulness': ['statefulness', 'tastefulness'],
 'stateless': ['stateless', 'tasteless'],
 'statelich': ['athletics', 'statelich'],
 'stately': ['stately', 'stylate'],
 'statement': ['statement', 'testament'],
 'stater': ['stater', 'taster', 'testar'],
 'statesider': ['dissertate', 'statesider'],
 'static': ['static', 'sticta'],
 'statice': ['etacist', 'statice'],
 'stational': ['saltation', 'stational'],
 'stationarily': ['antiroyalist', 'stationarily'],
 'stationer': ['nitrosate', 'stationer'],
 'statoscope': ['septocosta', 'statoscope'],
 'statue': ['astute', 'statue'],
 'stature': ['stature', 'stauter'],
 'staumer': ['staumer', 'strumae'],
 'staun': ['staun', 'suant'],
 'staunch': ['canthus', 'staunch'],
 'staup': ['sputa', 'staup', 'stupa'],
 'staurion': ['staurion', 'sutorian'],
 'stauter': ['stature', 'stauter'],
 'stave': ['stave', 'vesta'],
 'staver': ['starve', 'staver', 'strave', 'tavers', 'versta'],
 'staw': ['sawt', 'staw', 'swat', 'taws', 'twas', 'wast'],
 'stawn': ['stawn', 'wasnt'],
 'stayable': ['stayable', 'teasably'],
 'stayed': ['stayed', 'steady'],
 'stayer': ['atresy', 'estray', 'reasty', 'stayer'],
 'staynil': ['nastily', 'saintly', 'staynil'],
 'stchi': ['sitch', 'stchi', 'stich'],
 'stead': ['sedat', 'stade', 'stead'],
 'steady': ['stayed', 'steady'],
 'steak': ['skate', 'stake', 'steak'],
 'steal': ['least', 'setal', 'slate', 'stale', 'steal', 'stela', 'tales'],
 'stealer': ['realest', 'reslate', 'resteal', 'stealer', 'teasler'],
 'stealing': ['galenist', 'genitals', 'stealing'],
 'stealy': ['alytes', 'astely', 'lysate', 'stealy'],
 'steam': ['steam', 'stema'],
 'steaming': ['misagent', 'steaming'],
 'stean': ['antes', 'nates', 'stane', 'stean'],
 'stearic': ['atresic', 'stearic'],
 'stearin': ['asterin', 'eranist', 'restain', 'stainer', 'starnie', 'stearin'],
 'stearone': ['orestean', 'resonate', 'stearone'],
 'stearyl': ['saltery', 'stearyl'],
 'steatin': ['atenist', 'instate', 'satient', 'steatin'],
 'steatoma': ['atmostea', 'steatoma'],
 'steatornis': ['steatornis', 'treasonist'],
 'stech': ['chest', 'stech'],
 'steek': ['keest', 'skeet', 'skete', 'steek'],
 'steel': ['sleet', 'slete', 'steel', 'stele'],
 'steeler': ['reestle', 'resteel', 'steeler'],
 'steeliness': ['sleetiness', 'steeliness'],
 'steeling': ['sleeting', 'steeling'],
 'steelproof': ['sleetproof', 'steelproof'],
 'steely': ['sleety', 'steely'],
 'steen': ['steen', 'teens', 'tense'],
 'steep': ['peste', 'steep'],
 'steeper': ['estrepe', 'resteep', 'steeper'],
 'steepy': ['steepy', 'typees'],
 'steer': ['ester',
           'estre',
           'reest',
           'reset',
           'steer',
           'stere',
           'stree',
           'terse',
           'tsere'],
 'steerer': ['reester', 'steerer'],
 'steering': ['energist', 'steering'],
 'steerling': ['esterling', 'steerling'],
 'steeve': ['steeve', 'vestee'],
 'stefan': ['fasten', 'nefast', 'stefan'],
 'steg': ['gest', 'steg'],
 'stegodon': ['dogstone', 'stegodon'],
 'steid': ['deist', 'steid'],
 'steigh': ['gesith', 'steigh'],
 'stein': ['inset', 'neist', 'snite', 'stein', 'stine', 'tsine'],
 'stela': ['least', 'setal', 'slate', 'stale', 'steal', 'stela', 'tales'],
 'stelae': ['ateles', 'saltee', 'sealet', 'stelae', 'teasel'],
 'stelai': ['isleta', 'litsea', 'salite', 'stelai'],
 'stelar': ['laster',
            'lastre',
            'rastle',
            'relast',
            'resalt',
            'salter',
            'slater',
            'stelar'],
 'stele': ['sleet', 'slete', 'steel', 'stele'],
 'stella': ['sallet', 'stella', 'talles'],
 'stellar': ['staller', 'stellar'],
 'stellaria': ['lateralis', 'stellaria'],
 'stema': ['steam', 'stema'],
 'stemlike': ['meletski', 'stemlike'],
 'sten': ['nest', 'sent', 'sten'],
 'stenar': ['astern', 'enstar', 'stenar', 'sterna'],
 'stencil': ['lentisc', 'scintle', 'stencil'],
 'stenciler': ['crestline', 'stenciler'],
 'stenion': ['stenion', 'tension'],
 'steno': ['onset', 'seton', 'steno', 'stone'],
 'stenopaic': ['aspection', 'stenopaic'],
 'stenosis': ['sisseton', 'stenosis'],
 'stenotic': ['stenotic', 'tonetics'],
 'stentor': ['snotter', 'stentor', 'torsten'],
 'step': ['pest', 'sept', 'spet', 'step'],
 'stepaunt': ['nettapus', 'stepaunt'],
 'stepbairn': ['breastpin', 'stepbairn'],
 'stepdame': ['stampede', 'stepdame'],
 'stephana': ['pheasant', 'stephana'],
 'stephanic': ['cathepsin', 'stephanic'],
 'steplike': ['spikelet', 'steplike'],
 'sterculia': ['sterculia', 'urticales'],
 'stere': ['ester',
           'estre',
           'reest',
           'reset',
           'steer',
           'stere',
           'stree',
           'terse',
           'tsere'],
 'stereograph': ['preshortage', 'stereograph'],
 'stereometric': ['crestmoreite', 'stereometric'],
 'stereophotograph': ['photostereograph', 'stereophotograph'],
 'stereotelescope': ['stereotelescope', 'telestereoscope'],
 'stereotomic': ['osteometric', 'stereotomic'],
 'stereotomical': ['osteometrical', 'stereotomical'],
 'stereotomy': ['osteometry', 'stereotomy'],
 'steric': ['certis', 'steric'],
 'sterigma': ['gemarist', 'magister', 'sterigma'],
 'sterigmata': ['magistrate', 'sterigmata'],
 'sterile': ['leister', 'sterile'],
 'sterilize': ['listerize', 'sterilize'],
 'sterin': ['estrin', 'insert', 'sinter', 'sterin', 'triens'],
 'sterlet': ['settler', 'sterlet', 'trestle'],
 'stern': ['ernst', 'stern'],
 'sterna': ['astern', 'enstar', 'stenar', 'sterna'],
 'sternad': ['stander', 'sternad'],
 'sternage': ['estrange', 'segreant', 'sergeant', 'sternage'],
 'sternal': ['saltern', 'starnel', 'sternal'],
 'sternalis': ['sternalis', 'trainless'],
 'sternite': ['insetter', 'interest', 'interset', 'sternite'],
 'sterno': ['nestor', 'sterno', 'stoner', 'strone', 'tensor'],
 'sternocostal': ['costosternal', 'sternocostal'],
 'sternovertebral': ['sternovertebral', 'vertebrosternal'],
 'stero': ['roset', 'rotse', 'soter', 'stero', 'store', 'torse'],
 'steroid': ['oestrid', 'steroid', 'storied'],
 'sterol': ['relost', 'reslot', 'rostel', 'sterol', 'torsel'],
 'stert': ['stert', 'stret', 'trest'],
 'sterve': ['revest', 'servet', 'sterve', 'verset', 'vester'],
 'stet': ['sett', 'stet', 'test'],
 'stevan': ['stevan', 'svante'],
 'stevel': ['stevel', 'svelte'],
 'stevia': ['itaves', 'stevia'],
 'stew': ['stew', 'west'],
 'stewart': ['stewart', 'swatter'],
 'stewed': ['stewed', 'wedset'],
 'stewy': ['stewy', 'westy'],
 'stey': ['stey', 'yest'],
 'sthenia': ['sethian', 'sthenia'],
 'stich': ['sitch', 'stchi', 'stich'],
 'stichid': ['distich', 'stichid'],
 'sticker': ['rickets', 'sticker'],
 'stickler': ['stickler', 'strickle'],
 'sticta': ['static', 'sticta'],
 'stife': ['feist', 'stife'],
 'stiffener': ['restiffen', 'stiffener'],
 'stifle': ['itself', 'stifle'],
 'stifler': ['slifter', 'stifler'],
 'stigmai': ['imagist', 'stigmai'],
 'stigmatical': ['stalagmitic', 'stigmatical'],
 'stilbene': ['nebelist', 'stilbene', 'tensible'],
 'stile': ['islet', 'istle', 'slite', 'stile'],
 'stileman': ['mentalis', 'smaltine', 'stileman'],
 'stillage': ['legalist', 'stillage'],
 'stiller': ['stiller', 'trellis'],
 'stillstand': ['standstill', 'stillstand'],
 'stilted': ['slitted', 'stilted'],
 'stilter': ['litster', 'slitter', 'stilter', 'testril'],
 'stilty': ['slitty', 'stilty'],
 'stim': ['mist', 'smit', 'stim'],
 'stime': ['metis', 'smite', 'stime', 'times'],
 'stimulancy': ['stimulancy', 'unmystical'],
 'stimy': ['misty', 'stimy'],
 'stine': ['inset', 'neist', 'snite', 'stein', 'stine', 'tsine'],
 'stinge': ['ingest', 'signet', 'stinge'],
 'stinger': ['resting', 'stinger'],
 'stinker': ['kirsten', 'kristen', 'stinker'],
 'stinkstone': ['knottiness', 'stinkstone'],
 'stinted': ['dentist', 'distent', 'stinted'],
 'stion': ['sinto', 'stion'],
 'stipa': ['piast', 'stipa', 'tapis'],
 'stipe': ['septi', 'spite', 'stipe'],
 'stipel': ['pistle', 'stipel'],
 'stippen': ['snippet', 'stippen'],
 'stipula': ['paulist', 'stipula'],
 'stir': ['rist', 'stir'],
 'stirk': ['skirt', 'stirk'],
 'stirp': ['spirt', 'sprit', 'stirp', 'strip'],
 'stitcher': ['restitch', 'stitcher'],
 'stiver': ['stiver', 'strive', 'verist'],
 'stoa': ['oast', 'stoa', 'taos'],
 'stoach': ['stoach', 'stocah'],
 'stoat': ['stoat', 'toast'],
 'stoater': ['retoast', 'rosetta', 'stoater', 'toaster'],
 'stocah': ['stoach', 'stocah'],
 'stoccata': ['staccato', 'stoccata'],
 'stocker': ['restock', 'stocker'],
 'stoep': ['estop', 'stoep', 'stope'],
 'stof': ['soft', 'stof'],
 'stog': ['stog', 'togs'],
 'stogie': ['egoist', 'stogie'],
 'stoic': ['ostic', 'sciot', 'stoic'],
 'stoically': ['callosity', 'stoically'],
 'stoker': ['stoker', 'stroke'],
 'stolae': ['lotase', 'osteal', 'solate', 'stolae', 'talose'],
 'stole': ['slote', 'stole'],
 'stoled': ['sloted', 'stoled'],
 'stolen': ['solent', 'stolen', 'telson'],
 'stoma': ['atmos', 'stoma', 'tomas'],
 'stomatode': ['mootstead', 'stomatode'],
 'stomatomy': ['mastotomy', 'stomatomy'],
 'stomatopoda': ['podostomata', 'stomatopoda'],
 'stomatopodous': ['podostomatous', 'stomatopodous'],
 'stomper': ['pomster', 'stomper'],
 'stone': ['onset', 'seton', 'steno', 'stone'],
 'stonebird': ['birdstone', 'stonebird'],
 'stonebreak': ['breakstone', 'stonebreak'],
 'stoned': ['doesnt', 'stoned'],
 'stonegall': ['gallstone', 'stonegall'],
 'stonehand': ['handstone', 'stonehand'],
 'stonehead': ['headstone', 'stonehead'],
 'stonen': ['sonnet', 'stonen', 'tenson'],
 'stoner': ['nestor', 'sterno', 'stoner', 'strone', 'tensor'],
 'stonewood': ['stonewood', 'woodstone'],
 'stong': ['stong', 'tongs'],
 'stonker': ['stonker', 'storken'],
 'stoof': ['foots', 'sfoot', 'stoof'],
 'stool': ['sotol', 'stool'],
 'stoon': ['snoot', 'stoon'],
 'stoop': ['spoot', 'stoop'],
 'stop': ['post', 'spot', 'stop', 'tops'],
 'stopback': ['backstop', 'stopback'],
 'stope': ['estop', 'stoep', 'stope'],
 'stoper': ['poster', 'presto', 'repost', 'respot', 'stoper'],
 'stoping': ['posting', 'stoping'],
 'stopless': ['postless', 'spotless', 'stopless'],
 'stoplessness': ['spotlessness', 'stoplessness'],
 'stoppeur': ['pteropus', 'stoppeur'],
 'storable': ['sortable', 'storable'],
 'storage': ['storage', 'tagsore'],
 'store': ['roset', 'rotse', 'soter', 'stero', 'store', 'torse'],
 'storeen': ['enstore', 'estrone', 'storeen', 'tornese'],
 'storeman': ['monaster', 'monstera', 'nearmost', 'storeman'],
 'storer': ['resort', 'roster', 'sorter', 'storer'],
 'storeship': ['posterish', 'prothesis', 'sophister', 'storeship', 'tephrosis'],
 'storesman': ['nosesmart', 'storesman'],
 'storge': ['groset', 'storge'],
 'storiate': ['astroite', 'ostraite', 'storiate'],
 'storied': ['oestrid', 'steroid', 'storied'],
 'storier': ['roister', 'storier'],
 'stork': ['stork', 'torsk'],
 'storken': ['stonker', 'storken'],
 'storm': ['storm', 'strom'],
 'stormwind': ['stormwind', 'windstorm'],
 'story': ['sorty', 'story', 'stroy'],
 'stot': ['stot', 'tost'],
 'stotter': ['stotter', 'stretto'],
 'stoun': ['notus', 'snout', 'stoun', 'tonus'],
 'stoup': ['spout', 'stoup'],
 'stour': ['roust', 'rusot', 'stour', 'sutor', 'torus'],
 'stouring': ['rousting', 'stouring'],
 'stoutly': ['stoutly', 'tylotus'],
 'stove': ['ovest', 'stove'],
 'stover': ['stover', 'strove'],
 'stow': ['sowt', 'stow', 'swot', 'wots'],
 'stowable': ['bestowal', 'stowable'],
 'stower': ['restow', 'stower', 'towser', 'worset'],
 'stra': ['sart', 'star', 'stra', 'tars', 'tsar'],
 'strad': ['darst', 'darts', 'strad'],
 'stradine': ['stradine', 'strained', 'tarnside'],
 'strae': ['aster', 'serta', 'stare', 'strae', 'tarse', 'teras'],
 'strafe': ['farset', 'faster', 'strafe'],
 'stragular': ['gastrular', 'stragular'],
 'straighten': ['shattering', 'straighten'],
 'straightener': ['restraighten', 'straightener'],
 'straightup': ['straightup', 'upstraight'],
 'straik': ['rastik', 'sarkit', 'straik'],
 'strain': ['instar', 'santir', 'strain'],
 'strained': ['stradine', 'strained', 'tarnside'],
 'strainer': ['restrain', 'strainer', 'transire'],
 'strainerman': ['strainerman', 'transmarine'],
 'straint': ['straint', 'transit', 'tristan'],
 'strait': ['artist', 'strait', 'strati'],
 'strake': ['skater', 'staker', 'strake', 'streak', 'tasker'],
 'straky': ['starky', 'straky'],
 'stram': ['smart', 'stram'],
 'strange': ['angster', 'garnets', 'nagster', 'strange'],
 'strangles': ['slangster', 'strangles'],
 'strap': ['spart', 'sprat', 'strap', 'traps'],
 'strapless': ['psaltress', 'strapless'],
 'strata': ['astart', 'strata'],
 'strategi': ['strategi', 'strigate'],
 'strath': ['strath', 'thrast'],
 'strati': ['artist', 'strait', 'strati'],
 'stratic': ['astrict', 'cartist', 'stratic'],
 'stratonic': ['narcotist', 'stratonic'],
 'stratonical': ['intracostal', 'stratonical'],
 'strave': ['starve', 'staver', 'strave', 'tavers', 'versta'],
 'straw': ['straw', 'swart', 'warst'],
 'strawy': ['strawy', 'swarty'],
 'stray': ['satyr', 'stary', 'stray', 'trasy'],
 'strayling': ['staringly', 'strayling'],
 'stre': ['rest', 'sert', 'stre'],
 'streak': ['skater', 'staker', 'strake', 'streak', 'tasker'],
 'streakily': ['satyrlike', 'streakily'],
 'stream': ['martes', 'master', 'remast', 'stream'],
 'streamer': ['masterer', 'restream', 'streamer'],
 'streamful': ['masterful', 'streamful'],
 'streamhead': ['headmaster', 'headstream', 'streamhead'],
 'streaming': ['germanist', 'streaming'],
 'streamless': ['masterless', 'streamless'],
 'streamlike': ['masterlike', 'streamlike'],
 'streamline': ['eternalism', 'streamline'],
 'streamling': ['masterling', 'streamling'],
 'streamside': ['mediatress', 'streamside'],
 'streamwort': ['masterwort', 'streamwort'],
 'streamy': ['mastery', 'streamy'],
 'stree': ['ester',
           'estre',
           'reest',
           'reset',
           'steer',
           'stere',
           'stree',
           'terse',
           'tsere'],
 'streek': ['streek', 'streke'],
 'streel': ['lester', 'selter', 'streel'],
 'streen': ['ernest', 'nester', 'resent', 'streen'],
 'streep': ['pester', 'preset', 'restep', 'streep'],
 'street': ['retest', 'setter', 'street', 'tester'],
 'streetcar': ['scatterer', 'streetcar'],
 'streke': ['streek', 'streke'],
 'strelitz': ['strelitz', 'streltzi'],
 'streltzi': ['strelitz', 'streltzi'],
 'stremma': ['stammer', 'stremma'],
 'strengthener': ['restrengthen', 'strengthener'],
 'strepen': ['penster', 'present', 'serpent', 'strepen'],
 'strepera': ['pasterer', 'strepera'],
 'strepor': ['sporter', 'strepor'],
 'strepsinema': ['esperantism', 'strepsinema'],
 'streptothricosis': ['streptothricosis', 'streptotrichosis'],
 'streptotrichosis': ['streptothricosis', 'streptotrichosis'],
 'stresser': ['restress', 'stresser'],
 'stret': ['stert', 'stret', 'trest'],
 'stretcher': ['restretch', 'stretcher'],
 'stretcherman': ['stretcherman', 'trenchmaster'],
 'stretto': ['stotter', 'stretto'],
 'strew': ['strew', 'trews', 'wrest'],
 'strewer': ['strewer', 'wrester'],
 'strey': ['resty', 'strey'],
 'streyne': ['streyne', 'styrene', 'yestern'],
 'stria': ['arist',
           'astir',
           'sitar',
           'stair',
           'stria',
           'tarsi',
           'tisar',
           'trias'],
 'striae': ['satire', 'striae'],
 'strial': ['latris', 'strial'],
 'striatal': ['altarist', 'striatal'],
 'striate': ['artiste', 'striate'],
 'striated': ['distater', 'striated'],
 'strich': ['christ', 'strich'],
 'strickle': ['stickler', 'strickle'],
 'stride': ['driest', 'stride'],
 'strife': ['fister', 'resift', 'sifter', 'strife'],
 'strig': ['grist', 'grits', 'strig'],
 'striga': ['gratis', 'striga'],
 'strigae': ['seagirt', 'strigae'],
 'strigate': ['strategi', 'strigate'],
 'striges': ['striges', 'tigress'],
 'strigulose': ['sortilegus', 'strigulose'],
 'strike': ['skiter', 'strike'],
 'striker': ['skirret', 'skirter', 'striker'],
 'striking': ['skirting', 'striking'],
 'strikingly': ['skirtingly', 'strikingly'],
 'stringer': ['restring', 'ringster', 'stringer'],
 'strinkle': ['sklinter', 'strinkle'],
 'striola': ['aristol', 'oralist', 'ortalis', 'striola'],
 'striolae': ['soterial', 'striolae'],
 'strip': ['spirt', 'sprit', 'stirp', 'strip'],
 'stripe': ['priest', 'pteris', 'sprite', 'stripe'],
 'stripeless': ['priestless', 'stripeless'],
 'striper': ['restrip', 'striper'],
 'striplet': ['splitter', 'striplet'],
 'strippit': ['strippit', 'trippist'],
 'strit': ['strit', 'trist'],
 'strive': ['stiver', 'strive', 'verist'],
 'stroam': ['stroam', 'stroma'],
 'strobila': ['laborist', 'strobila'],
 'strobilae': ['sobralite', 'strobilae'],
 'strobilate': ['brasiletto', 'strobilate'],
 'strode': ['sorted', 'strode'],
 'stroke': ['stoker', 'stroke'],
 'strom': ['storm', 'strom'],
 'stroma': ['stroam', 'stroma'],
 'stromatic': ['microstat', 'stromatic'],
 'strone': ['nestor', 'sterno', 'stoner', 'strone', 'tensor'],
 'stronghead': ['headstrong', 'stronghead'],
 'strontian': ['strontian', 'trisonant'],
 'strontianite': ['interstation', 'strontianite'],
 'strop': ['sport', 'strop'],
 'strophaic': ['actorship', 'strophaic'],
 'strophiolate': ['strophiolate', 'theatropolis'],
 'strophomena': ['nephrostoma', 'strophomena'],
 'strounge': ['strounge', 'sturgeon'],
 'stroup': ['sprout', 'stroup', 'stupor'],
 'strove': ['stover', 'strove'],
 'strow': ['strow', 'worst'],
 'stroy': ['sorty', 'story', 'stroy'],
 'strub': ['burst', 'strub'],
 'struck': ['struck', 'trucks'],
 'strue': ['serut', 'strue', 'turse', 'uster'],
 'strumae': ['staumer', 'strumae'],
 'strut': ['strut', 'sturt', 'trust'],
 'struth': ['struth', 'thrust'],
 'struthian': ['struthian', 'unathirst'],
 'stu': ['stu', 'ust'],
 'stuart': ['astrut', 'rattus', 'stuart'],
 'stub': ['bust', 'stub'],
 'stuber': ['berust', 'buster', 'stuber'],
 'stud': ['dust', 'stud'],
 'studdie': ['studdie', 'studied'],
 'student': ['student', 'stunted'],
 'studia': ['aditus', 'studia'],
 'studied': ['studdie', 'studied'],
 'study': ['dusty', 'study'],
 'stue': ['stue', 'suet'],
 'stuffer': ['restuff', 'stuffer'],
 'stug': ['gust', 'stug'],
 'stuiver': ['revuist', 'stuiver'],
 'stum': ['must', 'smut', 'stum'],
 'stumer': ['muster', 'sertum', 'stumer'],
 'stumper': ['stumper', 'sumpter'],
 'stun': ['stun', 'sunt', 'tsun'],
 'stunner': ['stunner', 'unstern'],
 'stunted': ['student', 'stunted'],
 'stunter': ['entrust', 'stunter', 'trusten'],
 'stupa': ['sputa', 'staup', 'stupa'],
 'stupe': ['setup', 'stupe', 'upset'],
 'stupor': ['sprout', 'stroup', 'stupor'],
 'stuprate': ['stuprate', 'upstater'],
 'stupulose': ['pustulose', 'stupulose'],
 'sturdiness': ['sturdiness', 'undistress'],
 'sturgeon': ['strounge', 'sturgeon'],
 'sturine': ['intruse', 'sturine'],
 'sturionine': ['reunionist', 'sturionine'],
 'sturmian': ['naturism', 'sturmian', 'turanism'],
 'sturnidae': ['disnature', 'sturnidae', 'truandise'],
 'sturninae': ['neustrian', 'saturnine', 'sturninae'],
 'sturnus': ['sturnus', 'untruss'],
 'sturt': ['strut', 'sturt', 'trust'],
 'sturtin': ['intrust', 'sturtin'],
 'stut': ['stut', 'tuts'],
 'stutter': ['stutter', 'tutster'],
 'styan': ['nasty', 'styan', 'tansy'],
 'styca': ['stacy', 'styca'],
 'stycerin': ['nycteris', 'stycerin'],
 'stygial': ['stagily', 'stygial'],
 'stylate': ['stately', 'stylate'],
 'styledom': ['modestly', 'styledom'],
 'stylite': ['stylite', 'testily'],
 'styloid': ['odylist', 'styloid'],
 'stylometer': ['metrostyle', 'stylometer'],
 'stylopidae': ['ideoplasty', 'stylopidae'],
 'styphelia': ['physalite', 'styphelia'],
 'styrene': ['streyne', 'styrene', 'yestern'],
 'styrol': ['sortly', 'styrol'],
 'stythe': ['stythe', 'tethys'],
 'styx': ['styx', 'xyst'],
 'suability': ['suability', 'usability'],
 'suable': ['suable', 'usable'],
 'sualocin': ['sualocin', 'unsocial'],
 'suant': ['staun', 'suant'],
 'suasible': ['basileus', 'issuable', 'suasible'],
 'suasion': ['sanious', 'suasion'],
 'suasory': ['ossuary', 'suasory'],
 'suave': ['sauve', 'suave'],
 'sub': ['bus', 'sub'],
 'subah': ['shuba', 'subah'],
 'subalpine': ['subalpine', 'unspiable'],
 'subanal': ['balanus', 'nabalus', 'subanal'],
 'subcaecal': ['accusable', 'subcaecal'],
 'subcantor': ['obscurant', 'subcantor'],
 'subcapsular': ['subcapsular', 'subscapular'],
 'subcenter': ['rubescent', 'subcenter'],
 'subchela': ['chasuble', 'subchela'],
 'subcool': ['colobus', 'subcool'],
 'subcortical': ['scorbutical', 'subcortical'],
 'subcortically': ['scorbutically', 'subcortically'],
 'subdealer': ['subdealer', 'subleader'],
 'subdean': ['subdean', 'unbased'],
 'subdeltaic': ['discutable', 'subdeltaic', 'subdialect'],
 'subdialect': ['discutable', 'subdeltaic', 'subdialect'],
 'subdie': ['busied', 'subdie'],
 'suber': ['burse', 'rebus', 'suber'],
 'subessential': ['subessential', 'suitableness'],
 'subherd': ['brushed', 'subherd'],
 'subhero': ['herbous', 'subhero'],
 'subhuman': ['subhuman', 'unambush'],
 'subimago': ['bigamous', 'subimago'],
 'sublanate': ['sublanate', 'unsatable'],
 'sublate': ['balteus', 'sublate'],
 'sublative': ['sublative', 'vestibula'],
 'subleader': ['subdealer', 'subleader'],
 'sublet': ['bustle', 'sublet', 'subtle'],
 'sublinear': ['insurable', 'sublinear'],
 'sublumbar': ['sublumbar', 'subumbral'],
 'submaid': ['misdaub', 'submaid'],
 'subman': ['busman', 'subman'],
 'submarine': ['semiurban', 'submarine'],
 'subnarcotic': ['obscurantic', 'subnarcotic'],
 'subnitrate': ['subnitrate', 'subtertian'],
 'subnote': ['subnote', 'subtone', 'unbesot'],
 'suboval': ['suboval', 'subvola'],
 'subpeltate': ['subpeltate', 'upsettable'],
 'subplat': ['subplat', 'upblast'],
 'subra': ['abrus', 'bursa', 'subra'],
 'subscapular': ['subcapsular', 'subscapular'],
 'subserve': ['subserve', 'subverse'],
 'subsider': ['disburse', 'subsider'],
 'substernal': ['substernal', 'turbanless'],
 'substraction': ['obscurantist', 'substraction'],
 'subtack': ['sackbut', 'subtack'],
 'subterraneal': ['subterraneal', 'unarrestable'],
 'subtertian': ['subnitrate', 'subtertian'],
 'subtle': ['bustle', 'sublet', 'subtle'],
 'subtone': ['subnote', 'subtone', 'unbesot'],
 'subtread': ['daubster', 'subtread'],
 'subtutor': ['outburst', 'subtutor'],
 'subulate': ['baetulus', 'subulate'],
 'subumbral': ['sublumbar', 'subumbral'],
 'subverse': ['subserve', 'subverse'],
 'subvola': ['suboval', 'subvola'],
 'succade': ['accused', 'succade'],
 'succeeder': ['resucceed', 'succeeder'],
 'succin': ['cnicus', 'succin'],
 'succinate': ['encaustic', 'succinate'],
 'succor': ['crocus', 'succor'],
 'such': ['cush', 'such'],
 'suck': ['cusk', 'suck'],
 'sucker': ['resuck', 'sucker'],
 'suckling': ['lungsick', 'suckling'],
 'suclat': ['scutal', 'suclat'],
 'sucramine': ['muscarine', 'sucramine'],
 'sucre': ['cruse', 'curse', 'sucre'],
 'suction': ['cotinus', 'suction', 'unstoic'],
 'suctional': ['suctional', 'sulcation', 'unstoical'],
 'suctoria': ['cotarius', 'octarius', 'suctoria'],
 'suctorial': ['ocularist', 'suctorial'],
 'sud': ['sud', 'uds'],
 'sudamen': ['medusan', 'sudamen'],
 'sudan': ['sudan', 'unsad'],
 'sudanese': ['danseuse', 'sudanese'],
 'sudani': ['sudani', 'unsaid'],
 'sudation': ['adustion', 'sudation'],
 'sudic': ['scudi', 'sudic'],
 'sudra': ['rudas', 'sudra'],
 'sue': ['sue', 'use'],
 'suer': ['ruse', 'suer', 'sure', 'user'],
 'suet': ['stue', 'suet'],
 'sufferer': ['resuffer', 'sufferer'],
 'sufflate': ['feastful', 'sufflate'],
 'suffocate': ['offuscate', 'suffocate'],
 'suffocation': ['offuscation', 'suffocation'],
 'sugamo': ['amusgo', 'sugamo'],
 'sugan': ['agnus', 'angus', 'sugan'],
 'sugar': ['argus', 'sugar'],
 'sugared': ['desugar', 'sugared'],
 'sugarlike': ['arguslike', 'sugarlike'],
 'suggester': ['resuggest', 'suggester'],
 'suggestionism': ['missuggestion', 'suggestionism'],
 'sugh': ['gush', 'shug', 'sugh'],
 'suidae': ['asideu', 'suidae'],
 'suimate': ['metusia', 'suimate', 'timaeus'],
 'suina': ['ianus', 'suina'],
 'suint': ['sintu', 'suint'],
 'suiones': ['sinuose', 'suiones'],
 'suist': ['situs', 'suist'],
 'suitable': ['sabulite', 'suitable'],
 'suitableness': ['subessential', 'suitableness'],
 'suitor': ['suitor', 'tursio'],
 'sula': ['saul', 'sula'],
 'sulcal': ['callus', 'sulcal'],
 'sulcar': ['cursal', 'sulcar'],
 'sulcation': ['suctional', 'sulcation', 'unstoical'],
 'suld': ['slud', 'suld'],
 'sulfamide': ['feudalism', 'sulfamide'],
 'sulfonium': ['fulminous', 'sulfonium'],
 'sulfuret': ['frustule', 'sulfuret'],
 'sulk': ['lusk', 'sulk'],
 'sulka': ['klaus', 'lukas', 'sulka'],
 'sulky': ['lusky', 'sulky'],
 'sulphinide': ['delphinius', 'sulphinide'],
 'sulphohydrate': ['hydrosulphate', 'sulphohydrate'],
 'sulphoproteid': ['protosulphide', 'sulphoproteid'],
 'sulphurea': ['elaphurus', 'sulphurea'],
 'sultan': ['sultan', 'unsalt'],
 'sultane': ['sultane', 'unslate'],
 'sultanian': ['annualist', 'sultanian'],
 'sultanin': ['insulant', 'sultanin'],
 'sultone': ['lentous', 'sultone'],
 'sultry': ['rustly', 'sultry'],
 'sum': ['mus', 'sum'],
 'sumac': ['camus', 'musca', 'scaum', 'sumac'],
 'sumak': ['kusam', 'sumak'],
 'sumatra': ['artamus', 'sumatra'],
 'sumerian': ['aneurism', 'arsenium', 'sumerian'],
 'sumitro': ['sumitro', 'tourism'],
 'summit': ['mutism', 'summit'],
 'summonable': ['somnambule', 'summonable'],
 'summoner': ['resummon', 'summoner'],
 'sumo': ['soum', 'sumo'],
 'sumpit': ['misput', 'sumpit'],
 'sumpitan': ['putanism', 'sumpitan'],
 'sumpter': ['stumper', 'sumpter'],
 'sumptuary': ['sputumary', 'sumptuary'],
 'sumptuous': ['sputumous', 'sumptuous'],
 'sunbeamed': ['sunbeamed', 'unembased'],
 'sundar': ['nardus', 'sundar', 'sundra'],
 'sundek': ['dusken', 'sundek'],
 'sundra': ['nardus', 'sundar', 'sundra'],
 'sung': ['snug', 'sung'],
 'sunil': ['linus', 'sunil'],
 'sunk': ['skun', 'sunk'],
 'sunlighted': ['sunlighted', 'unslighted'],
 'sunlit': ['insult', 'sunlit', 'unlist', 'unslit'],
 'sunni': ['sunni', 'unsin'],
 'sunray': ['sunray', 'surnay', 'synura'],
 'sunrise': ['russine', 'serinus', 'sunrise'],
 'sunshade': ['sunshade', 'unsashed'],
 'sunt': ['stun', 'sunt', 'tsun'],
 'sunup': ['sunup', 'upsun'],
 'sunweed': ['sunweed', 'unsewed'],
 'suomic': ['musico', 'suomic'],
 'sup': ['pus', 'sup'],
 'supa': ['apus', 'supa', 'upas'],
 'super': ['purse', 'resup', 'sprue', 'super'],
 'superable': ['perusable', 'superable'],
 'supercarpal': ['prescapular', 'supercarpal'],
 'superclaim': ['premusical', 'superclaim'],
 'supercontrol': ['preconsultor', 'supercontrol'],
 'supercool': ['escropulo', 'supercool'],
 'superheater': ['resuperheat', 'superheater'],
 'superideal': ['serpulidae', 'superideal'],
 'superintender': ['superintender', 'unenterprised'],
 'superline': ['serpuline', 'superline'],
 'supermoisten': ['sempiternous', 'supermoisten'],
 'supernacular': ['supernacular', 'supranuclear'],
 'supernal': ['purslane', 'serpulan', 'supernal'],
 'superoanterior': ['anterosuperior', 'superoanterior'],
 'superoposterior': ['posterosuperior', 'superoposterior'],
 'superpose': ['resuppose', 'superpose'],
 'superposition': ['resupposition', 'superposition'],
 'supersaint': ['presustain', 'puritaness', 'supersaint'],
 'supersalt': ['pertussal', 'supersalt'],
 'superservice': ['repercussive', 'superservice'],
 'supersonic': ['croupiness', 'percussion', 'supersonic'],
 'supertare': ['repasture', 'supertare'],
 'supertension': ['serpentinous', 'supertension'],
 'supertotal': ['supertotal', 'tetraplous'],
 'supertrain': ['rupestrian', 'supertrain'],
 'supinator': ['rainspout', 'supinator'],
 'supine': ['puisne', 'supine'],
 'supper': ['supper', 'uppers'],
 'supple': ['peplus', 'supple'],
 'suppletory': ['polypterus', 'suppletory'],
 'supplier': ['periplus', 'supplier'],
 'supporter': ['resupport', 'supporter'],
 'suppresser': ['resuppress', 'suppresser'],
 'suprahyoid': ['hyporadius', 'suprahyoid'],
 'supranuclear': ['supernacular', 'supranuclear'],
 'supreme': ['presume', 'supreme'],
 'sur': ['rus', 'sur', 'urs'],
 'sura': ['rusa', 'saur', 'sura', 'ursa', 'usar'],
 'surah': ['ashur', 'surah'],
 'surahi': ['shauri', 'surahi'],
 'sural': ['larus', 'sural', 'ursal'],
 'surat': ['astur', 'surat', 'sutra'],
 'surbase': ['rubasse', 'surbase'],
 'surbate': ['bursate', 'surbate'],
 'surcrue': ['crureus', 'surcrue'],
 'sure': ['ruse', 'suer', 'sure', 'user'],
 'suresh': ['rhesus', 'suresh'],
 'surette': ['surette', 'trustee'],
 'surge': ['grues', 'surge'],
 'surgent': ['gunster', 'surgent'],
 'surgeonship': ['springhouse', 'surgeonship'],
 'surgy': ['gyrus', 'surgy'],
 'suriana': ['saurian', 'suriana'],
 'surinam': ['surinam', 'uranism'],
 'surma': ['musar', 'ramus', 'rusma', 'surma'],
 'surmisant': ['saturnism', 'surmisant'],
 'surmise': ['misuser', 'surmise'],
 'surnap': ['surnap', 'unspar'],
 'surnay': ['sunray', 'surnay', 'synura'],
 'surprisement': ['surprisement', 'trumperiness'],
 'surrenderer': ['resurrender', 'surrenderer'],
 'surrogate': ['surrogate', 'urogaster'],
 'surrounder': ['resurround', 'surrounder'],
 'surya': ['saury', 'surya'],
 'sus': ['ssu', 'sus'],
 'susan': ['nasus', 'susan'],
 'suscept': ['suscept', 'suspect'],
 'susceptible': ['susceptible', 'suspectible'],
 'susceptor': ['spectrous', 'susceptor', 'suspector'],
 'susie': ['issue', 'susie'],
 'suspect': ['suscept', 'suspect'],
 'suspecter': ['resuspect', 'suspecter'],
 'suspectible': ['susceptible', 'suspectible'],
 'suspector': ['spectrous', 'susceptor', 'suspector'],
 'suspender': ['resuspend', 'suspender', 'unpressed'],
 'sustain': ['issuant', 'sustain'],
 'suther': ['reshut', 'suther', 'thurse', 'tusher'],
 'sutler': ['luster', 'result', 'rustle', 'sutler', 'ulster'],
 'suto': ['otus', 'oust', 'suto'],
 'sutor': ['roust', 'rusot', 'stour', 'sutor', 'torus'],
 'sutorian': ['staurion', 'sutorian'],
 'sutorious': ['sutorious', 'ustorious'],
 'sutra': ['astur', 'surat', 'sutra'],
 'suttin': ['suttin', 'tunist'],
 'suture': ['suture', 'uterus'],
 'svante': ['stevan', 'svante'],
 'svelte': ['stevel', 'svelte'],
 'swa': ['saw', 'swa', 'was'],
 'swage': ['swage', 'wages'],
 'swain': ['siwan', 'swain'],
 'swale': ['swale', 'sweal', 'wasel'],
 'swaler': ['swaler', 'warsel', 'warsle'],
 'swallet': ['setwall', 'swallet'],
 'swallo': ['sallow', 'swallo'],
 'swallower': ['reswallow', 'swallower'],
 'swami': ['aswim', 'swami'],
 'swan': ['sawn', 'snaw', 'swan'],
 'swap': ['swap', 'wasp'],
 'swarbie': ['barwise', 'swarbie'],
 'sware': ['resaw', 'sawer', 'seraw', 'sware', 'swear', 'warse'],
 'swarmer': ['reswarm', 'swarmer'],
 'swart': ['straw', 'swart', 'warst'],
 'swarty': ['strawy', 'swarty'],
 'swarve': ['swarve', 'swaver'],
 'swat': ['sawt', 'staw', 'swat', 'taws', 'twas', 'wast'],
 'swath': ['swath', 'whats'],
 'swathe': ['swathe', 'sweath'],
 'swati': ['swati', 'waist'],
 'swatter': ['stewart', 'swatter'],
 'swaver': ['swarve', 'swaver'],
 'sway': ['sway', 'ways', 'yaws'],
 'swayer': ['sawyer', 'swayer'],
 'sweal': ['swale', 'sweal', 'wasel'],
 'swear': ['resaw', 'sawer', 'seraw', 'sware', 'swear', 'warse'],
 'swearer': ['resawer', 'reswear', 'swearer'],
 'sweat': ['awest', 'sweat', 'tawse', 'waste'],
 'sweater': ['resweat', 'sweater'],
 'sweatful': ['sweatful', 'wasteful'],
 'sweath': ['swathe', 'sweath'],
 'sweatily': ['sweatily', 'tileways'],
 'sweatless': ['sweatless', 'wasteless'],
 'sweatproof': ['sweatproof', 'wasteproof'],
 'swede': ['sewed', 'swede'],
 'sweep': ['sweep', 'weeps'],
 'sweeper': ['resweep', 'sweeper'],
 'sweer': ['resew', 'sewer', 'sweer'],
 'sweered': ['sewered', 'sweered'],
 'sweet': ['sweet', 'weste'],
 'sweetbread': ['breastweed', 'sweetbread'],
 'sweller': ['reswell', 'sweller'],
 'swelter': ['swelter', 'wrestle'],
 'swep': ['spew', 'swep'],
 'swertia': ['swertia', 'waister'],
 'swile': ['lewis', 'swile'],
 'swiller': ['reswill', 'swiller'],
 'swindle': ['swindle', 'windles'],
 'swine': ['sinew', 'swine', 'wisen'],
 'swinestone': ['sonnetwise', 'swinestone'],
 'swiney': ['sinewy', 'swiney'],
 'swingback': ['backswing', 'swingback'],
 'swinge': ['sewing', 'swinge'],
 'swingle': ['slewing', 'swingle'],
 'swingtree': ['swingtree', 'westering'],
 'swipy': ['swipy', 'wispy'],
 'swire': ['swire', 'wiser'],
 'swith': ['swith', 'whist', 'whits', 'wisht'],
 'swithe': ['swithe', 'whites'],
 'swither': ['swither', 'whister', 'withers'],
 'swoon': ['swoon', 'woons'],
 'swordman': ['sandworm', 'swordman', 'wordsman'],
 'swordmanship': ['swordmanship', 'wordsmanship'],
 'swore': ['owser', 'resow', 'serow', 'sower', 'swore', 'worse'],
 'swot': ['sowt', 'stow', 'swot', 'wots'],
 'sybarite': ['bestiary', 'sybarite'],
 'sybil': ['sibyl', 'sybil'],
 'syce': ['scye', 'syce'],
 'sycones': ['coyness', 'sycones'],
 'syconoid': ['syconoid', 'syodicon'],
 'sye': ['sey', 'sye', 'yes'],
 'syenitic': ['cytisine', 'syenitic'],
 'syllabi': ['sibylla', 'syllabi'],
 'syllable': ['sellably', 'syllable'],
 'sylva': ['salvy', 'sylva'],
 'sylvae': ['slavey', 'sylvae'],
 'sylvine': ['snively', 'sylvine'],
 'sylvite': ['levyist', 'sylvite'],
 'symbol': ['blosmy', 'symbol'],
 'symmetric': ['mycterism', 'symmetric'],
 'sympathy': ['sympathy', 'symphyta'],
 'symphonic': ['cyphonism', 'symphonic'],
 'symphyta': ['sympathy', 'symphyta'],
 'symposion': ['spoonyism', 'symposion'],
 'synapse': ['synapse', 'yapness'],
 'synaptera': ['peasantry', 'synaptera'],
 'syncopator': ['antroscopy', 'syncopator'],
 'syndicate': ['asyndetic', 'cystidean', 'syndicate'],
 'synedral': ['lysander', 'synedral'],
 'syngamic': ['gymnasic', 'syngamic'],
 'syngenic': ['ensigncy', 'syngenic'],
 'synura': ['sunray', 'surnay', 'synura'],
 'syodicon': ['syconoid', 'syodicon'],
 'sypher': ['sphery', 'sypher'],
 'syphiloid': ['hypsiloid', 'syphiloid'],
 'syrian': ['siryan', 'syrian'],
 'syringa': ['signary', 'syringa'],
 'syringeful': ['refusingly', 'syringeful'],
 'syrup': ['pursy', 'pyrus', 'syrup'],
 'system': ['mystes', 'system'],
 'systole': ['systole', 'toyless'],
 'ta': ['at', 'ta'],
 'taa': ['ata', 'taa'],
 'taal': ['lata', 'taal', 'tala'],
 'taar': ['rata', 'taar', 'tara'],
 'tab': ['bat', 'tab'],
 'tabanus': ['sabanut', 'sabutan', 'tabanus'],
 'tabaret': ['baretta', 'rabatte', 'tabaret'],
 'tabber': ['barbet', 'rabbet', 'tabber'],
 'tabella': ['ballate', 'tabella'],
 'tabes': ['baste', 'beast', 'tabes'],
 'tabet': ['betta', 'tabet'],
 'tabinet': ['bettina', 'tabinet', 'tibetan'],
 'tabira': ['arabit', 'tabira'],
 'tabitha': ['habitat', 'tabitha'],
 'tabitude': ['dubitate', 'tabitude'],
 'table': ['batel', 'blate', 'bleat', 'table'],
 'tabled': ['dablet', 'tabled'],
 'tablemaker': ['marketable', 'tablemaker'],
 'tabler': ['albert', 'balter', 'labret', 'tabler'],
 'tables': ['ablest', 'stable', 'tables'],
 'tablet': ['battel', 'battle', 'tablet'],
 'tabletary': ['tabletary', 'treatably'],
 'tabling': ['batling', 'tabling'],
 'tabophobia': ['batophobia', 'tabophobia'],
 'tabor': ['abort', 'tabor'],
 'taborer': ['arboret', 'roberta', 'taborer'],
 'taboret': ['abettor', 'taboret'],
 'taborin': ['abortin', 'taborin'],
 'tabour': ['outbar', 'rubato', 'tabour'],
 'tabouret': ['obturate', 'tabouret'],
 'tabret': ['batter', 'bertat', 'tabret', 'tarbet'],
 'tabu': ['abut', 'tabu', 'tuba'],
 'tabula': ['ablaut', 'tabula'],
 'tabulare': ['bataleur', 'tabulare'],
 'tabule': ['batule', 'betula', 'tabule'],
 'taccaceae': ['cactaceae', 'taccaceae'],
 'taccaceous': ['cactaceous', 'taccaceous'],
 'tach': ['chat', 'tach'],
 'tache': ['cheat', 'tache', 'teach', 'theca'],
 'tacheless': ['tacheless', 'teachless'],
 'tachina': ['ithacan', 'tachina'],
 'tachinidae': ['anthicidae', 'tachinidae'],
 'tachograph': ['cathograph', 'tachograph'],
 'tacit': ['attic', 'catti', 'tacit'],
 'tacitly': ['cattily', 'tacitly'],
 'tacitness': ['cattiness', 'tacitness'],
 'taciturn': ['taciturn', 'urticant'],
 'tacker': ['racket', 'retack', 'tacker'],
 'tacksman': ['stackman', 'tacksman'],
 'taconian': ['catonian', 'taconian'],
 'taconic': ['cantico', 'catonic', 'taconic'],
 'tacso': ['ascot', 'coast', 'costa', 'tacso', 'tasco'],
 'tacsonia': ['acontias', 'tacsonia'],
 'tactile': ['lattice', 'tactile'],
 'tade': ['adet', 'date', 'tade', 'tead', 'teda'],
 'tadpole': ['platode', 'tadpole'],
 'tae': ['ate', 'eat', 'eta', 'tae', 'tea'],
 'tael': ['atle', 'laet', 'late', 'leat', 'tael', 'tale', 'teal'],
 'taen': ['ante', 'aten', 'etna', 'nate', 'neat', 'taen', 'tane', 'tean'],
 'taenia': ['aetian', 'antiae', 'taenia'],
 'taeniada': ['anatidae', 'taeniada'],
 'taenial': ['laniate', 'natalie', 'taenial'],
 'taenian': ['ananite', 'anatine', 'taenian'],
 'taenicide': ['deciatine', 'diacetine', 'taenicide', 'teniacide'],
 'taeniform': ['forminate', 'fremontia', 'taeniform'],
 'taenioid': ['ideation', 'iodinate', 'taenioid'],
 'taetsia': ['isatate', 'satiate', 'taetsia'],
 'tag': ['gat', 'tag'],
 'tagetes': ['gestate', 'tagetes'],
 'tagetol': ['lagetto', 'tagetol'],
 'tagged': ['gadget', 'tagged'],
 'tagger': ['garget', 'tagger'],
 'tagilite': ['litigate', 'tagilite'],
 'tagish': ['ghaist', 'tagish'],
 'taglike': ['glaiket', 'taglike'],
 'tagrag': ['ragtag', 'tagrag'],
 'tagsore': ['storage', 'tagsore'],
 'taheen': ['ethane', 'taheen'],
 'tahil': ['ihlat', 'tahil'],
 'tahin': ['ahint', 'hiant', 'tahin'],
 'tahr': ['hart', 'rath', 'tahr', 'thar', 'trah'],
 'tahsil': ['latish', 'tahsil'],
 'tahsin': ['snaith', 'tahsin'],
 'tai': ['ait', 'ati', 'ita', 'tai'],
 'taich': ['aitch', 'chait', 'chati', 'chita', 'taich', 'tchai'],
 'taigle': ['aiglet', 'ligate', 'taigle', 'tailge'],
 'tail': ['alit', 'tail', 'tali'],
 'tailage': ['agalite', 'tailage', 'taliage'],
 'tailboard': ['broadtail', 'tailboard'],
 'tailed': ['detail', 'dietal', 'dilate', 'edital', 'tailed'],
 'tailer': ['lirate', 'retail', 'retial', 'tailer'],
 'tailet': ['latite', 'tailet', 'tailte', 'talite'],
 'tailge': ['aiglet', 'ligate', 'taigle', 'tailge'],
 'tailing': ['gitalin', 'tailing'],
 'taille': ['taille', 'telial'],
 'tailoring': ['gratiolin', 'largition', 'tailoring'],
 'tailorman': ['antimoral', 'tailorman'],
 'tailory': ['orality', 'tailory'],
 'tailpin': ['pintail', 'tailpin'],
 'tailsman': ['staminal', 'tailsman', 'talisman'],
 'tailte': ['latite', 'tailet', 'tailte', 'talite'],
 'taily': ['laity', 'taily'],
 'taimen': ['etamin', 'inmate', 'taimen', 'tamein'],
 'tain': ['aint', 'anti', 'tain', 'tina'],
 'tainan': ['naiant', 'tainan'],
 'taino': ['niota', 'taino'],
 'taint': ['taint', 'tanti', 'tinta', 'titan'],
 'tainture': ['tainture', 'unattire'],
 'taipan': ['aptian', 'patina', 'taipan'],
 'taipo': ['patio', 'taipo', 'topia'],
 'tairge': ['gaiter', 'tairge', 'triage'],
 'tairn': ['riant', 'tairn', 'tarin', 'train'],
 'taise': ['saite', 'taise'],
 'taistrel': ['starlite', 'taistrel'],
 'taistril': ['taistril', 'trialist'],
 'taivers': ['staiver', 'taivers'],
 'taj': ['jat', 'taj'],
 'tajik': ['jatki', 'tajik'],
 'takar': ['katar', 'takar'],
 'take': ['kate', 'keta', 'take', 'teak'],
 'takedown': ['downtake', 'takedown'],
 'taker': ['kerat', 'taker'],
 'takin': ['kitan', 'takin'],
 'takings': ['gitksan', 'skating', 'takings'],
 'taky': ['katy', 'kyat', 'taky'],
 'tal': ['alt', 'lat', 'tal'],
 'tala': ['lata', 'taal', 'tala'],
 'talapoin': ['palation', 'talapoin'],
 'talar': ['altar', 'artal', 'ratal', 'talar'],
 'talari': ['altair', 'atrail', 'atrial', 'lariat', 'latria', 'talari'],
 'talc': ['clat', 'talc'],
 'talcer': ['carlet', 'cartel', 'claret', 'rectal', 'talcer'],
 'talcher': ['clethra', 'latcher', 'ratchel', 'relatch', 'talcher', 'trachle'],
 'talcoid': ['cotidal', 'lactoid', 'talcoid'],
 'talcose': ['alecost', 'lactose', 'scotale', 'talcose'],
 'talcous': ['costula', 'locusta', 'talcous'],
 'tald': ['dalt', 'tald'],
 'tale': ['atle', 'laet', 'late', 'leat', 'tael', 'tale', 'teal'],
 'taled': ['adlet', 'dealt', 'delta', 'lated', 'taled'],
 'talent': ['latent', 'latten', 'nattle', 'talent', 'tantle'],
 'taler': ['alert', 'alter', 'artel', 'later', 'ratel', 'taler', 'telar'],
 'tales': ['least', 'setal', 'slate', 'stale', 'steal', 'stela', 'tales'],
 'tali': ['alit', 'tail', 'tali'],
 'taliage': ['agalite', 'tailage', 'taliage'],
 'taligrade': ['taligrade', 'tragedial'],
 'talinum': ['multani', 'talinum'],
 'talion': ['italon', 'lation', 'talion'],
 'taliped': ['plaited', 'taliped'],
 'talipedic': ['talipedic', 'talpicide'],
 'talipes': ['aliptes', 'pastile', 'talipes'],
 'talipot': ['ptilota', 'talipot', 'toptail'],
 'talis': ['alist', 'litas', 'slait', 'talis'],
 'talisman': ['staminal', 'tailsman', 'talisman'],
 'talite': ['latite', 'tailet', 'tailte', 'talite'],
 'talker': ['kartel', 'retalk', 'talker'],
 'tallage': ['gallate', 'tallage'],
 'tallero': ['reallot', 'rotella', 'tallero'],
 'talles': ['sallet', 'stella', 'talles'],
 'talliage': ['allagite', 'alligate', 'talliage'],
 'tallier': ['literal', 'tallier'],
 'tallyho': ['loathly', 'tallyho'],
 'talon': ['notal', 'ontal', 'talon', 'tolan', 'tonal'],
 'taloned': ['taloned', 'toledan'],
 'talonid': ['itoland', 'talonid', 'tindalo'],
 'talose': ['lotase', 'osteal', 'solate', 'stolae', 'talose'],
 'talpa': ['aptal', 'palta', 'talpa'],
 'talpicide': ['talipedic', 'talpicide'],
 'talpidae': ['lapidate', 'talpidae'],
 'talpine': ['pantile', 'pentail', 'platine', 'talpine'],
 'talpoid': ['platoid', 'talpoid'],
 'taluche': ['auchlet', 'cutheal', 'taluche'],
 'taluka': ['latuka', 'taluka'],
 'talus': ['latus', 'sault', 'talus'],
 'tam': ['amt', 'mat', 'tam'],
 'tama': ['atma', 'tama'],
 'tamale': ['malate', 'meatal', 'tamale'],
 'tamanac': ['matacan', 'tamanac'],
 'tamanaca': ['atacaman', 'tamanaca'],
 'tamanoir': ['animator', 'tamanoir'],
 'tamanu': ['anatum', 'mantua', 'tamanu'],
 'tamara': ['armata', 'matara', 'tamara'],
 'tamarao': ['tamarao', 'tamaroa'],
 'tamarin': ['martian', 'tamarin'],
 'tamaroa': ['tamarao', 'tamaroa'],
 'tambor': ['tambor', 'tromba'],
 'tamboura': ['marabout', 'marabuto', 'tamboura'],
 'tambourer': ['arboretum', 'tambourer'],
 'tambreet': ['ambrette', 'tambreet'],
 'tamburan': ['rambutan', 'tamburan'],
 'tame': ['mate', 'meat', 'meta', 'tame', 'team', 'tema'],
 'tamein': ['etamin', 'inmate', 'taimen', 'tamein'],
 'tameless': ['mateless', 'meatless', 'tameless', 'teamless'],
 'tamelessness': ['matelessness', 'tamelessness'],
 'tamely': ['mately', 'tamely'],
 'tamer': ['armet',
           'mater',
           'merat',
           'metra',
           'ramet',
           'tamer',
           'terma',
           'trame',
           'trema'],
 'tamise': ['samite', 'semita', 'tamise', 'teaism'],
 'tampin': ['pitman', 'tampin', 'tipman'],
 'tampion': ['maintop', 'ptomain', 'tampion', 'timpano'],
 'tampioned': ['ademption', 'tampioned'],
 'tampon': ['potman', 'tampon', 'topman'],
 'tamul': ['lamut', 'tamul'],
 'tamus': ['matsu', 'tamus', 'tsuma'],
 'tan': ['ant', 'nat', 'tan'],
 'tana': ['anat', 'anta', 'tana'],
 'tanach': ['acanth', 'anchat', 'tanach'],
 'tanager': ['argante', 'granate', 'tanager'],
 'tanagridae': ['tanagridae', 'tangaridae'],
 'tanagrine': ['argentina', 'tanagrine'],
 'tanagroid': ['gradation', 'indagator', 'tanagroid'],
 'tanak': ['kanat', 'tanak', 'tanka'],
 'tanaka': ['nataka', 'tanaka'],
 'tanala': ['atalan', 'tanala'],
 'tanan': ['annat', 'tanan'],
 'tanbur': ['tanbur', 'turban'],
 'tancel': ['cantle', 'cental', 'lancet', 'tancel'],
 'tanchoir': ['anorthic', 'anthroic', 'tanchoir'],
 'tandemist': ['misattend', 'tandemist'],
 'tandle': ['dental', 'tandle'],
 'tandour': ['rotunda', 'tandour'],
 'tane': ['ante', 'aten', 'etna', 'nate', 'neat', 'taen', 'tane', 'tean'],
 'tang': ['gant', 'gnat', 'tang'],
 'tanga': ['ganta', 'tanga'],
 'tangaridae': ['tanagridae', 'tangaridae'],
 'tangelo': ['angelot', 'tangelo'],
 'tanger': ['argent', 'garnet', 'garten', 'tanger'],
 'tangerine': ['argentine', 'tangerine'],
 'tangfish': ['shafting', 'tangfish'],
 'tangi': ['giant', 'tangi', 'tiang'],
 'tangibile': ['bigential', 'tangibile'],
 'tangible': ['bleating', 'tangible'],
 'tangie': ['eating', 'ingate', 'tangie'],
 'tangier': ['angrite', 'granite', 'ingrate', 'tangier', 'tearing', 'tigrean'],
 'tangle': ['tangle', 'telang'],
 'tangling': ['gnatling', 'tangling'],
 'tango': ['tango', 'tonga'],
 'tangs': ['angst', 'stang', 'tangs'],
 'tangue': ['gunate', 'tangue'],
 'tangum': ['tangum', 'tugman'],
 'tangun': ['tangun', 'tungan'],
 'tanh': ['hant', 'tanh', 'than'],
 'tanha': ['atnah', 'tanha', 'thana'],
 'tania': ['anita', 'niata', 'tania'],
 'tanica': ['actian', 'natica', 'tanica'],
 'tanier': ['nerita', 'ratine', 'retain', 'retina', 'tanier'],
 'tanist': ['astint', 'tanist'],
 'tanitic': ['tanitic', 'titanic'],
 'tanka': ['kanat', 'tanak', 'tanka'],
 'tankle': ['anklet', 'lanket', 'tankle'],
 'tanling': ['antling', 'tanling'],
 'tannaic': ['cantina', 'tannaic'],
 'tannase': ['annates', 'tannase'],
 'tannogallate': ['gallotannate', 'tannogallate'],
 'tannogallic': ['gallotannic', 'tannogallic'],
 'tannogen': ['nonagent', 'tannogen'],
 'tanproof': ['antproof', 'tanproof'],
 'tanquen': ['quannet', 'tanquen'],
 'tanrec': ['canter',
            'creant',
            'cretan',
            'nectar',
            'recant',
            'tanrec',
            'trance'],
 'tansy': ['nasty', 'styan', 'tansy'],
 'tantalean': ['antenatal', 'atlantean', 'tantalean'],
 'tantalic': ['atlantic', 'tantalic'],
 'tantalite': ['atlantite', 'tantalite'],
 'tantara': ['tantara', 'tartana'],
 'tantarara': ['tantarara', 'tarantara'],
 'tanti': ['taint', 'tanti', 'tinta', 'titan'],
 'tantle': ['latent', 'latten', 'nattle', 'talent', 'tantle'],
 'tantra': ['rattan', 'tantra', 'tartan'],
 'tantrism': ['tantrism', 'transmit'],
 'tantum': ['mutant', 'tantum', 'tutman'],
 'tanzeb': ['batzen', 'bezant', 'tanzeb'],
 'tao': ['oat', 'tao', 'toa'],
 'taoistic': ['iotacist', 'taoistic'],
 'taos': ['oast', 'stoa', 'taos'],
 'tap': ['apt', 'pat', 'tap'],
 'tapa': ['atap', 'pata', 'tapa'],
 'tapalo': ['patola', 'tapalo'],
 'tapas': ['patas', 'tapas'],
 'tape': ['pate', 'peat', 'tape', 'teap'],
 'tapeline': ['petaline', 'tapeline'],
 'tapeman': ['peatman', 'tapeman'],
 'tapen': ['enapt', 'paten', 'penta', 'tapen'],
 'taper': ['apert', 'pater', 'peart', 'prate', 'taper', 'terap'],
 'tapered': ['padtree', 'predate', 'tapered'],
 'tapering': ['partigen', 'tapering'],
 'taperly': ['apertly', 'peartly', 'platery', 'pteryla', 'taperly'],
 'taperness': ['apertness', 'peartness', 'taperness'],
 'tapestring': ['spattering', 'tapestring'],
 'tapestry': ['tapestry', 'tryptase'],
 'tapet': ['patte', 'tapet'],
 'tapete': ['pattee', 'tapete'],
 'taphouse': ['outshape', 'taphouse'],
 'taphria': ['pitarah', 'taphria'],
 'taphrina': ['parthian', 'taphrina'],
 'tapir': ['atrip', 'tapir'],
 'tapiro': ['portia', 'tapiro'],
 'tapirus': ['tapirus', 'upstair'],
 'tapis': ['piast', 'stipa', 'tapis'],
 'taplash': ['asphalt', 'spathal', 'taplash'],
 'tapmost': ['tapmost', 'topmast'],
 'tapnet': ['patent', 'patten', 'tapnet'],
 'tapoa': ['opata', 'patao', 'tapoa'],
 'taposa': ['sapota', 'taposa'],
 'taproom': ['protoma', 'taproom'],
 'taproot': ['potator', 'taproot'],
 'taps': ['past', 'spat', 'stap', 'taps'],
 'tapster': ['spatter', 'tapster'],
 'tapu': ['patu', 'paut', 'tapu'],
 'tapuyo': ['outpay', 'tapuyo'],
 'taqua': ['quata', 'taqua'],
 'tar': ['art', 'rat', 'tar', 'tra'],
 'tara': ['rata', 'taar', 'tara'],
 'taraf': ['taraf', 'tarfa'],
 'tarai': ['arati', 'atria', 'riata', 'tarai', 'tiara'],
 'taranchi': ['taranchi', 'thracian'],
 'tarantara': ['tantarara', 'tarantara'],
 'tarapin': ['patarin', 'tarapin'],
 'tarasc': ['castra', 'tarasc'],
 'tarbet': ['batter', 'bertat', 'tabret', 'tarbet'],
 'tardle': ['dartle', 'tardle'],
 'tardy': ['tardy', 'trady'],
 'tare': ['rate', 'tare', 'tear', 'tera'],
 'tarente': ['entreat', 'ratteen', 'tarente', 'ternate', 'tetrane'],
 'tarentine': ['entertain', 'tarentine', 'terentian'],
 'tarfa': ['taraf', 'tarfa'],
 'targe': ['gater', 'grate', 'great', 'greta', 'retag', 'targe'],
 'targeman': ['grateman', 'mangrate', 'mentagra', 'targeman'],
 'targer': ['garret', 'garter', 'grater', 'targer'],
 'target': ['gatter', 'target'],
 'targetman': ['targetman', 'termagant'],
 'targum': ['artgum', 'targum'],
 'tarheel': ['leather', 'tarheel'],
 'tarheeler': ['leatherer', 'releather', 'tarheeler'],
 'tari': ['airt', 'rita', 'tari', 'tiar'],
 'tarie': ['arite', 'artie', 'irate', 'retia', 'tarie'],
 'tarin': ['riant', 'tairn', 'tarin', 'train'],
 'tarish': ['rashti', 'tarish'],
 'tarletan': ['alterant', 'tarletan'],
 'tarlike': ['artlike', 'ratlike', 'tarlike'],
 'tarmac': ['mactra', 'tarmac'],
 'tarman': ['mantra', 'tarman'],
 'tarmi': ['mitra', 'tarmi', 'timar', 'tirma'],
 'tarn': ['natr', 'rant', 'tarn', 'tran'],
 'tarnal': ['antral', 'tarnal'],
 'tarnish': ['tarnish', 'trishna'],
 'tarnside': ['stradine', 'strained', 'tarnside'],
 'taro': ['rota', 'taro', 'tora'],
 'taroc': ['actor', 'corta', 'croat', 'rocta', 'taroc', 'troca'],
 'tarocco': ['coactor', 'tarocco'],
 'tarok': ['kotar', 'tarok'],
 'tarot': ['ottar', 'tarot', 'torta', 'troat'],
 'tarp': ['part', 'prat', 'rapt', 'tarp', 'trap'],
 'tarpan': ['partan', 'tarpan'],
 'tarpaulin': ['tarpaulin', 'unpartial'],
 'tarpeian': ['patarine', 'tarpeian'],
 'tarpon': ['patron', 'tarpon'],
 'tarquin': ['quatrin', 'tarquin'],
 'tarragon': ['arrogant', 'tarragon'],
 'tarred': ['darter',
            'dartre',
            'redart',
            'retard',
            'retrad',
            'tarred',
            'trader'],
 'tarrer': ['tarrer', 'terrar'],
 'tarriance': ['antiracer', 'tarriance'],
 'tarrie': ['arriet', 'tarrie'],
 'tars': ['sart', 'star', 'stra', 'tars', 'tsar'],
 'tarsal': ['astral', 'tarsal'],
 'tarsale': ['alaster', 'tarsale'],
 'tarsalgia': ['astragali', 'tarsalgia'],
 'tarse': ['aster', 'serta', 'stare', 'strae', 'tarse', 'teras'],
 'tarsi': ['arist',
           'astir',
           'sitar',
           'stair',
           'stria',
           'tarsi',
           'tisar',
           'trias'],
 'tarsia': ['arista', 'tarsia'],
 'tarsier': ['astrier', 'tarsier'],
 'tarsipes': ['piratess', 'serapist', 'tarsipes'],
 'tarsitis': ['satirist', 'tarsitis'],
 'tarsome': ['maestro', 'tarsome'],
 'tarsonemid': ['mosandrite', 'tarsonemid'],
 'tarsonemus': ['sarmentous', 'tarsonemus'],
 'tarsus': ['rastus', 'tarsus'],
 'tartan': ['rattan', 'tantra', 'tartan'],
 'tartana': ['tantara', 'tartana'],
 'tartaret': ['tartaret', 'tartrate'],
 'tartarin': ['tartarin', 'triratna'],
 'tartarous': ['saturator', 'tartarous'],
 'tarten': ['attern', 'natter', 'ratten', 'tarten'],
 'tartish': ['athirst', 'rattish', 'tartish'],
 'tartle': ['artlet', 'latter', 'rattle', 'tartle', 'tatler'],
 'tartlet': ['tartlet', 'tattler'],
 'tartly': ['rattly', 'tartly'],
 'tartrate': ['tartaret', 'tartrate'],
 'taruma': ['taruma', 'trauma'],
 'tarve': ['avert', 'tarve', 'taver', 'trave'],
 'tarweed': ['dewater', 'tarweed', 'watered'],
 'tarwood': ['ratwood', 'tarwood'],
 'taryba': ['baryta', 'taryba'],
 'tasco': ['ascot', 'coast', 'costa', 'tacso', 'tasco'],
 'tash': ['shat', 'tash'],
 'tasheriff': ['fireshaft', 'tasheriff'],
 'tashie': ['saithe', 'tashie', 'teaish'],
 'tashrif': ['ratfish', 'tashrif'],
 'tasian': ['astian', 'tasian'],
 'tasimetry': ['myristate', 'tasimetry'],
 'task': ['skat', 'task'],
 'tasker': ['skater', 'staker', 'strake', 'streak', 'tasker'],
 'taslet': ['latest', 'sattle', 'taslet'],
 'tasmanite': ['emanatist', 'staminate', 'tasmanite'],
 'tassah': ['shasta', 'tassah'],
 'tasse': ['asset', 'tasse'],
 'tassel': ['lasset', 'tassel'],
 'tasseler': ['rateless', 'tasseler', 'tearless', 'tesseral'],
 'tasser': ['assert', 'tasser'],
 'tassie': ['siesta', 'tassie'],
 'tastable': ['statable', 'tastable'],
 'taste': ['state', 'taste', 'tates', 'testa'],
 'tasted': ['stated', 'tasted'],
 'tasteful': ['stateful', 'tasteful'],
 'tastefully': ['statefully', 'tastefully'],
 'tastefulness': ['statefulness', 'tastefulness'],
 'tasteless': ['stateless', 'tasteless'],
 'taster': ['stater', 'taster', 'testar'],
 'tasu': ['saut', 'tasu', 'utas'],
 'tatar': ['attar', 'tatar'],
 'tatarize': ['tatarize', 'zaratite'],
 'tatchy': ['chatty', 'tatchy'],
 'tate': ['etta', 'tate', 'teat'],
 'tater': ['atter', 'tater', 'teart', 'tetra', 'treat'],
 'tates': ['state', 'taste', 'tates', 'testa'],
 'tath': ['hatt', 'tath', 'that'],
 'tatian': ['attain', 'tatian'],
 'tatler': ['artlet', 'latter', 'rattle', 'tartle', 'tatler'],
 'tatterly': ['tatterly', 'tattlery'],
 'tattler': ['tartlet', 'tattler'],
 'tattlery': ['tatterly', 'tattlery'],
 'tatu': ['tatu', 'taut'],
 'tau': ['tau', 'tua', 'uta'],
 'taube': ['butea', 'taube', 'tubae'],
 'taula': ['aluta', 'taula'],
 'taum': ['muta', 'taum'],
 'taun': ['antu', 'aunt', 'naut', 'taun', 'tuan', 'tuna'],
 'taungthu': ['taungthu', 'untaught'],
 'taupe': ['taupe', 'upeat'],
 'taupo': ['apout', 'taupo'],
 'taur': ['ruta', 'taur'],
 'taurean': ['taurean', 'uranate'],
 'tauric': ['tauric', 'uratic', 'urtica'],
 'taurine': ['ruinate', 'taurine', 'uranite', 'urinate'],
 'taurocol': ['outcarol', 'taurocol'],
 'taut': ['tatu', 'taut'],
 'tauten': ['attune', 'nutate', 'tauten'],
 'tautomeric': ['autometric', 'tautomeric'],
 'tautomery': ['autometry', 'tautomery'],
 'tav': ['tav', 'vat'],
 'tavast': ['sattva', 'tavast'],
 'tave': ['tave', 'veta'],
 'taver': ['avert', 'tarve', 'taver', 'trave'],
 'tavers': ['starve', 'staver', 'strave', 'tavers', 'versta'],
 'tavert': ['tavert', 'vatter'],
 'tavola': ['tavola', 'volata'],
 'taw': ['taw', 'twa', 'wat'],
 'tawa': ['awat', 'tawa'],
 'tawer': ['tawer', 'water', 'wreat'],
 'tawery': ['tawery', 'watery'],
 'tawite': ['tawite', 'tawtie', 'twaite'],
 'tawn': ['nawt', 'tawn', 'want'],
 'tawny': ['tawny', 'wanty'],
 'taws': ['sawt', 'staw', 'swat', 'taws', 'twas', 'wast'],
 'tawse': ['awest', 'sweat', 'tawse', 'waste'],
 'tawtie': ['tawite', 'tawtie', 'twaite'],
 'taxed': ['detax', 'taxed'],
 'taxer': ['extra', 'retax', 'taxer'],
 'tay': ['tay', 'yat'],
 'tayer': ['tayer', 'teary'],
 'tchai': ['aitch', 'chait', 'chati', 'chita', 'taich', 'tchai'],
 'tche': ['chet', 'etch', 'tche', 'tech'],
 'tchi': ['chit', 'itch', 'tchi'],
 'tchu': ['chut', 'tchu', 'utch'],
 'tchwi': ['tchwi', 'wicht', 'witch'],
 'tea': ['ate', 'eat', 'eta', 'tae', 'tea'],
 'teaberry': ['betrayer', 'eatberry', 'rebetray', 'teaberry'],
 'teaboy': ['betoya', 'teaboy'],
 'teacart': ['caretta', 'teacart', 'tearcat'],
 'teach': ['cheat', 'tache', 'teach', 'theca'],
 'teachable': ['cheatable', 'teachable'],
 'teachableness': ['cheatableness', 'teachableness'],
 'teache': ['achete', 'hecate', 'teache', 'thecae'],
 'teacher': ['cheater', 'hectare', 'recheat', 'reteach', 'teacher'],
 'teachery': ['cheatery', 'cytherea', 'teachery'],
 'teaching': ['cheating', 'teaching'],
 'teachingly': ['cheatingly', 'teachingly'],
 'teachless': ['tacheless', 'teachless'],
 'tead': ['adet', 'date', 'tade', 'tead', 'teda'],
 'teaer': ['arete', 'eater', 'teaer'],
 'teagle': ['eaglet', 'legate', 'teagle', 'telega'],
 'teaish': ['saithe', 'tashie', 'teaish'],
 'teaism': ['samite', 'semita', 'tamise', 'teaism'],
 'teak': ['kate', 'keta', 'take', 'teak'],
 'teal': ['atle', 'laet', 'late', 'leat', 'tael', 'tale', 'teal'],
 'team': ['mate', 'meat', 'meta', 'tame', 'team', 'tema'],
 'teamer': ['reetam', 'retame', 'teamer'],
 'teaming': ['mintage', 'teaming', 'tegmina'],
 'teamless': ['mateless', 'meatless', 'tameless', 'teamless'],
 'teamman': ['meatman', 'teamman'],
 'teamster': ['teamster', 'trametes'],
 'tean': ['ante', 'aten', 'etna', 'nate', 'neat', 'taen', 'tane', 'tean'],
 'teanal': ['anteal', 'lanate', 'teanal'],
 'teap': ['pate', 'peat', 'tape', 'teap'],
 'teapot': ['aptote', 'optate', 'potate', 'teapot'],
 'tear': ['rate', 'tare', 'tear', 'tera'],
 'tearable': ['elabrate', 'tearable'],
 'tearably': ['betrayal', 'tearably'],
 'tearcat': ['caretta', 'teacart', 'tearcat'],
 'teardown': ['danewort', 'teardown'],
 'teardrop': ['predator', 'protrade', 'teardrop'],
 'tearer': ['rerate', 'retare', 'tearer'],
 'tearful': ['faulter', 'refutal', 'tearful'],
 'tearing': ['angrite', 'granite', 'ingrate', 'tangier', 'tearing', 'tigrean'],
 'tearless': ['rateless', 'tasseler', 'tearless', 'tesseral'],
 'tearlike': ['keralite', 'tearlike'],
 'tearpit': ['partite', 'tearpit'],
 'teart': ['atter', 'tater', 'teart', 'tetra', 'treat'],
 'teary': ['tayer', 'teary'],
 'teasably': ['stayable', 'teasably'],
 'tease': ['setae', 'tease'],
 'teasel': ['ateles', 'saltee', 'sealet', 'stelae', 'teasel'],
 'teaser': ['asteer',
            'easter',
            'eastre',
            'reseat',
            'saeter',
            'seater',
            'staree',
            'teaser',
            'teresa'],
 'teasing': ['easting',
             'gainset',
             'genista',
             'ingesta',
             'seating',
             'signate',
             'teasing'],
 'teasler': ['realest', 'reslate', 'resteal', 'stealer', 'teasler'],
 'teasy': ['teasy', 'yeast'],
 'teat': ['etta', 'tate', 'teat'],
 'teather': ['teather', 'theater', 'thereat'],
 'tebu': ['bute', 'tebu', 'tube'],
 'teca': ['cate', 'teca'],
 'tecali': ['calite', 'laetic', 'tecali'],
 'tech': ['chet', 'etch', 'tche', 'tech'],
 'techily': ['ethylic', 'techily'],
 'technica': ['atechnic', 'catechin', 'technica'],
 'technocracy': ['conycatcher', 'technocracy'],
 'technopsychology': ['psychotechnology', 'technopsychology'],
 'techous': ['souchet', 'techous', 'tousche'],
 'techy': ['techy', 'tyche'],
 'tecla': ['cleat', 'eclat', 'ectal', 'lacet', 'tecla'],
 'teco': ['cote', 'teco'],
 'tecoma': ['comate', 'metoac', 'tecoma'],
 'tecomin': ['centimo', 'entomic', 'tecomin'],
 'tecon': ['cento', 'conte', 'tecon'],
 'tectal': ['cattle', 'tectal'],
 'tectology': ['ctetology', 'tectology'],
 'tectona': ['oncetta', 'tectona'],
 'tectospinal': ['entoplastic', 'spinotectal', 'tectospinal', 'tenoplastic'],
 'tecuma': ['acetum', 'tecuma'],
 'tecuna': ['tecuna', 'uncate'],
 'teda': ['adet', 'date', 'tade', 'tead', 'teda'],
 'tedious': ['outside', 'tedious'],
 'tediousness': ['outsideness', 'tediousness'],
 'teedle': ['delete', 'teedle'],
 'teel': ['leet', 'lete', 'teel', 'tele'],
 'teem': ['meet', 'mete', 'teem'],
 'teemer': ['meeter', 'remeet', 'teemer'],
 'teeming': ['meeting', 'teeming', 'tegmine'],
 'teems': ['teems', 'temse'],
 'teen': ['neet', 'nete', 'teen'],
 'teens': ['steen', 'teens', 'tense'],
 'teer': ['reet', 'teer', 'tree'],
 'teerer': ['retree', 'teerer'],
 'teest': ['teest', 'teste'],
 'teet': ['teet', 'tete'],
 'teeter': ['teeter', 'terete'],
 'teeth': ['teeth', 'theet'],
 'teething': ['genthite', 'teething'],
 'teg': ['get', 'teg'],
 'tegean': ['geneat', 'negate', 'tegean'],
 'tegmina': ['mintage', 'teaming', 'tegmina'],
 'tegminal': ['ligament', 'metaling', 'tegminal'],
 'tegmine': ['meeting', 'teeming', 'tegmine'],
 'tegular': ['gaulter', 'tegular'],
 'teheran': ['earthen', 'enheart', 'hearten', 'naether', 'teheran', 'traheen'],
 'tehsildar': ['heraldist', 'tehsildar'],
 'teian': ['entia', 'teian', 'tenai', 'tinea'],
 'teicher': ['erethic', 'etheric', 'heretic', 'heteric', 'teicher'],
 'teil': ['lite', 'teil', 'teli', 'tile'],
 'teind': ['detin', 'teind', 'tined'],
 'teinder': ['nitered', 'redient', 'teinder'],
 'teinland': ['dentinal', 'teinland', 'tendinal'],
 'teioid': ['iodite', 'teioid'],
 'teju': ['jute', 'teju'],
 'telamon': ['omental', 'telamon'],
 'telang': ['tangle', 'telang'],
 'telar': ['alert', 'alter', 'artel', 'later', 'ratel', 'taler', 'telar'],
 'telarian': ['retainal', 'telarian'],
 'telary': ['lyrate', 'raylet', 'realty', 'telary'],
 'tele': ['leet', 'lete', 'teel', 'tele'],
 'telecast': ['castelet', 'telecast'],
 'telega': ['eaglet', 'legate', 'teagle', 'telega'],
 'telegn': ['gentle', 'telegn'],
 'telegrapher': ['retelegraph', 'telegrapher'],
 'telei': ['elite', 'telei'],
 'telephone': ['phenetole', 'telephone'],
 'telephony': ['polythene', 'telephony'],
 'telephotograph': ['phototelegraph', 'telephotograph'],
 'telephotographic': ['phototelegraphic', 'telephotographic'],
 'telephotography': ['phototelegraphy', 'telephotography'],
 'teleradiophone': ['radiotelephone', 'teleradiophone'],
 'teleran': ['alterne', 'enteral', 'eternal', 'teleran', 'teneral'],
 'teleseism': ['messelite', 'semisteel', 'teleseism'],
 'telespectroscope': ['spectrotelescope', 'telespectroscope'],
 'telestereoscope': ['stereotelescope', 'telestereoscope'],
 'telestial': ['satellite', 'telestial'],
 'telestic': ['telestic', 'testicle'],
 'telfer': ['felter', 'telfer', 'trefle'],
 'teli': ['lite', 'teil', 'teli', 'tile'],
 'telial': ['taille', 'telial'],
 'telic': ['clite', 'telic'],
 'telinga': ['atingle', 'gelatin', 'genital', 'langite', 'telinga'],
 'tellach': ['hellcat', 'tellach'],
 'teller': ['retell', 'teller'],
 'tellima': ['mitella', 'tellima'],
 'tellina': ['nitella', 'tellina'],
 'tellurian': ['tellurian', 'unliteral'],
 'telome': ['omelet', 'telome'],
 'telonism': ['melonist', 'telonism'],
 'telopsis': ['polistes', 'telopsis'],
 'telpath': ['pathlet', 'telpath'],
 'telson': ['solent', 'stolen', 'telson'],
 'telsonic': ['lentisco', 'telsonic'],
 'telt': ['lett', 'telt'],
 'tema': ['mate', 'meat', 'meta', 'tame', 'team', 'tema'],
 'teman': ['ament', 'meant', 'teman'],
 'temin': ['metin', 'temin', 'timne'],
 'temp': ['empt', 'temp'],
 'tempean': ['peteman', 'tempean'],
 'temper': ['temper', 'tempre'],
 'tempera': ['premate', 'tempera'],
 'temperer': ['retemper', 'temperer'],
 'temperish': ['herpetism', 'metership', 'metreship', 'temperish'],
 'templar': ['templar', 'trample'],
 'template': ['palmette', 'template'],
 'temple': ['pelmet', 'temple'],
 'tempora': ['pteroma', 'tempora'],
 'temporarily': ['polarimetry', 'premorality', 'temporarily'],
 'temporofrontal': ['frontotemporal', 'temporofrontal'],
 'temporooccipital': ['occipitotemporal', 'temporooccipital'],
 'temporoparietal': ['parietotemporal', 'temporoparietal'],
 'tempre': ['temper', 'tempre'],
 'tempter': ['retempt', 'tempter'],
 'temse': ['teems', 'temse'],
 'temser': ['mester', 'restem', 'temser', 'termes'],
 'temulent': ['temulent', 'unmettle'],
 'ten': ['net', 'ten'],
 'tenable': ['beltane', 'tenable'],
 'tenace': ['cetane', 'tenace'],
 'tenai': ['entia', 'teian', 'tenai', 'tinea'],
 'tenanter': ['retenant', 'tenanter'],
 'tenantless': ['latentness', 'tenantless'],
 'tencteri': ['reticent', 'tencteri'],
 'tend': ['dent', 'tend'],
 'tender': ['denter', 'rented', 'tender'],
 'tenderer': ['retender', 'tenderer'],
 'tenderish': ['disherent', 'hinderest', 'tenderish'],
 'tendinal': ['dentinal', 'teinland', 'tendinal'],
 'tendinitis': ['dentinitis', 'tendinitis'],
 'tendour': ['tendour', 'unroted'],
 'tendril': ['tendril', 'trindle'],
 'tendron': ['donnert', 'tendron'],
 'teneral': ['alterne', 'enteral', 'eternal', 'teleran', 'teneral'],
 'teneriffe': ['fifteener', 'teneriffe'],
 'tenesmus': ['muteness', 'tenesmus'],
 'teng': ['gent', 'teng'],
 'tengu': ['tengu', 'unget'],
 'teniacidal': ['acetanilid', 'laciniated', 'teniacidal'],
 'teniacide': ['deciatine', 'diacetine', 'taenicide', 'teniacide'],
 'tenible': ['beltine', 'tenible'],
 'tenino': ['intone', 'tenino'],
 'tenline': ['lenient', 'tenline'],
 'tenner': ['rennet', 'tenner'],
 'tennis': ['innest', 'sennit', 'sinnet', 'tennis'],
 'tenomyotomy': ['myotenotomy', 'tenomyotomy'],
 'tenon': ['nonet', 'tenon'],
 'tenoner': ['enteron', 'tenoner'],
 'tenonian': ['annotine', 'tenonian'],
 'tenonitis': ['sentition', 'tenonitis'],
 'tenontophyma': ['nematophyton', 'tenontophyma'],
 'tenophyte': ['entophyte', 'tenophyte'],
 'tenoplastic': ['entoplastic', 'spinotectal', 'tectospinal', 'tenoplastic'],
 'tenor': ['noter', 'tenor', 'toner', 'trone'],
 'tenorist': ['ortstein', 'tenorist'],
 'tenovaginitis': ['investigation', 'tenovaginitis'],
 'tenpin': ['pinnet', 'tenpin'],
 'tenrec': ['center', 'recent', 'tenrec'],
 'tense': ['steen', 'teens', 'tense'],
 'tensible': ['nebelist', 'stilbene', 'tensible'],
 'tensile': ['leisten', 'setline', 'tensile'],
 'tension': ['stenion', 'tension'],
 'tensional': ['alstonine', 'tensional'],
 'tensive': ['estevin', 'tensive'],
 'tenson': ['sonnet', 'stonen', 'tenson'],
 'tensor': ['nestor', 'sterno', 'stoner', 'strone', 'tensor'],
 'tentable': ['nettable', 'tentable'],
 'tentacle': ['ectental', 'tentacle'],
 'tentage': ['genetta', 'tentage'],
 'tentation': ['attention', 'tentation'],
 'tentative': ['attentive', 'tentative'],
 'tentatively': ['attentively', 'tentatively'],
 'tentativeness': ['attentiveness', 'tentativeness'],
 'tented': ['detent', 'netted', 'tented'],
 'tenter': ['netter', 'retent', 'tenter'],
 'tention': ['nettion', 'tention', 'tontine'],
 'tentorial': ['natrolite', 'tentorial'],
 'tenty': ['netty', 'tenty'],
 'tenuiroster': ['tenuiroster', 'urosternite'],
 'tenuistriate': ['intersituate', 'tenuistriate'],
 'tenure': ['neuter', 'retune', 'runtee', 'tenure', 'tureen'],
 'tenurial': ['lutrinae', 'retinula', 'rutelian', 'tenurial'],
 'teocalli': ['colletia', 'teocalli'],
 'teosinte': ['noisette', 'teosinte'],
 'tepache': ['heptace', 'tepache'],
 'tepal': ['leapt',
           'palet',
           'patel',
           'pelta',
           'petal',
           'plate',
           'pleat',
           'tepal'],
 'tepanec': ['pentace', 'tepanec'],
 'tepecano': ['conepate', 'tepecano'],
 'tephrite': ['perthite', 'tephrite'],
 'tephritic': ['perthitic', 'tephritic'],
 'tephroite': ['heptorite', 'tephroite'],
 'tephrosis': ['posterish', 'prothesis', 'sophister', 'storeship', 'tephrosis'],
 'tepor': ['poter', 'prote', 'repot', 'tepor', 'toper', 'trope'],
 'tequila': ['liquate', 'tequila'],
 'tera': ['rate', 'tare', 'tear', 'tera'],
 'teraglin': ['integral', 'teraglin', 'triangle'],
 'terap': ['apert', 'pater', 'peart', 'prate', 'taper', 'terap'],
 'teras': ['aster', 'serta', 'stare', 'strae', 'tarse', 'teras'],
 'teratism': ['mistreat', 'teratism'],
 'terbia': ['baiter', 'barite', 'rebait', 'terbia'],
 'terbium': ['burmite', 'imbrute', 'terbium'],
 'tercelet': ['electret', 'tercelet'],
 'terceron': ['corrente', 'terceron'],
 'tercia': ['acrite', 'arcite', 'tercia', 'triace', 'tricae'],
 'tercine': ['citrene', 'enteric', 'enticer', 'tercine'],
 'tercio': ['erotic', 'tercio'],
 'terebilic': ['celtiberi', 'terebilic'],
 'terebinthian': ['terebinthian', 'terebinthina'],
 'terebinthina': ['terebinthian', 'terebinthina'],
 'terebra': ['rebater', 'terebra'],
 'terebral': ['barrelet', 'terebral'],
 'terentian': ['entertain', 'tarentine', 'terentian'],
 'teresa': ['asteer',
            'easter',
            'eastre',
            'reseat',
            'saeter',
            'seater',
            'staree',
            'teaser',
            'teresa'],
 'teresian': ['arsenite', 'resinate', 'teresian', 'teresina'],
 'teresina': ['arsenite', 'resinate', 'teresian', 'teresina'],
 'terete': ['teeter', 'terete'],
 'teretial': ['laterite', 'literate', 'teretial'],
 'tereus': ['retuse', 'tereus'],
 'tergal': ['raglet', 'tergal'],
 'tergant': ['garnett', 'gnatter', 'gratten', 'tergant'],
 'tergeminous': ['mentigerous', 'tergeminous'],
 'teri': ['iter', 'reit', 'rite', 'teri', 'tier', 'tire'],
 'teriann': ['entrain', 'teriann'],
 'terma': ['armet',
           'mater',
           'merat',
           'metra',
           'ramet',
           'tamer',
           'terma',
           'trame',
           'trema'],
 'termagant': ['targetman', 'termagant'],
 'termes': ['mester', 'restem', 'temser', 'termes'],
 'termin': ['minter', 'remint', 'termin'],
 'terminal': ['terminal', 'tramline'],
 'terminalia': ['laminarite', 'terminalia'],
 'terminate': ['antimeter', 'attermine', 'interteam', 'terminate', 'tetramine'],
 'termini': ['interim', 'termini'],
 'terminine': ['intermine', 'nemertini', 'terminine'],
 'terminus': ['numerist', 'terminus'],
 'termital': ['remittal', 'termital'],
 'termite': ['emitter', 'termite'],
 'termly': ['myrtle', 'termly'],
 'termon': ['mentor', 'merton', 'termon', 'tormen'],
 'termor': ['termor', 'tremor'],
 'tern': ['rent', 'tern'],
 'terna': ['antre', 'arent', 'retan', 'terna'],
 'ternal': ['altern', 'antler', 'learnt', 'rental', 'ternal'],
 'ternar': ['arrent', 'errant', 'ranter', 'ternar'],
 'ternarious': ['souterrain', 'ternarious', 'trouserian'],
 'ternate': ['entreat', 'ratteen', 'tarente', 'ternate', 'tetrane'],
 'terne': ['enter', 'neter', 'renet', 'terne', 'treen'],
 'ternion': ['intoner', 'ternion'],
 'ternlet': ['nettler', 'ternlet'],
 'terp': ['pert', 'petr', 'terp'],
 'terpane': ['patener', 'pearten', 'petrean', 'terpane'],
 'terpeneless': ['repleteness', 'terpeneless'],
 'terpin': ['nipter', 'terpin'],
 'terpine': ['petrine', 'terpine'],
 'terpineol': ['interlope', 'interpole', 'repletion', 'terpineol'],
 'terpinol': ['pointrel', 'terpinol'],
 'terrace': ['caterer', 'recrate', 'retrace', 'terrace'],
 'terraciform': ['crateriform', 'terraciform'],
 'terrage': ['greater', 'regrate', 'terrage'],
 'terrain': ['arterin', 'retrain', 'terrain', 'trainer'],
 'terral': ['retral', 'terral'],
 'terrance': ['canterer', 'recanter', 'recreant', 'terrance'],
 'terrapin': ['pretrain', 'terrapin'],
 'terrar': ['tarrer', 'terrar'],
 'terrence': ['centerer', 'recenter', 'recentre', 'terrence'],
 'terrene': ['enterer', 'terrene'],
 'terret': ['retter', 'terret'],
 'terri': ['terri', 'tirer', 'trier'],
 'terrier': ['retirer', 'terrier'],
 'terrine': ['reinter', 'terrine'],
 'terron': ['terron', 'treron', 'troner'],
 'terry': ['retry', 'terry'],
 'terse': ['ester',
           'estre',
           'reest',
           'reset',
           'steer',
           'stere',
           'stree',
           'terse',
           'tsere'],
 'tersely': ['restyle', 'tersely'],
 'tersion': ['oestrin', 'tersion'],
 'tertia': ['attire', 'ratite', 'tertia'],
 'tertian': ['intreat', 'iterant', 'nitrate', 'tertian'],
 'tertiana': ['attainer', 'reattain', 'tertiana'],
 'terton': ['rotten', 'terton'],
 'tervee': ['revete', 'tervee'],
 'terzina': ['retzian', 'terzina'],
 'terzo': ['terzo', 'tozer'],
 'tesack': ['casket', 'tesack'],
 'teskere': ['skeeter', 'teskere'],
 'tessella': ['satelles', 'tessella'],
 'tesseral': ['rateless', 'tasseler', 'tearless', 'tesseral'],
 'test': ['sett', 'stet', 'test'],
 'testa': ['state', 'taste', 'tates', 'testa'],
 'testable': ['settable', 'testable'],
 'testament': ['statement', 'testament'],
 'testar': ['stater', 'taster', 'testar'],
 'teste': ['teest', 'teste'],
 'tested': ['detest', 'tested'],
 'testee': ['settee', 'testee'],
 'tester': ['retest', 'setter', 'street', 'tester'],
 'testes': ['sestet', 'testes', 'tsetse'],
 'testicle': ['telestic', 'testicle'],
 'testicular': ['testicular', 'trisulcate'],
 'testily': ['stylite', 'testily'],
 'testing': ['setting', 'testing'],
 'teston': ['ostent', 'teston'],
 'testor': ['sotter', 'testor'],
 'testril': ['litster', 'slitter', 'stilter', 'testril'],
 'testy': ['testy', 'tyste'],
 'tetanic': ['nictate', 'tetanic'],
 'tetanical': ['cantalite', 'lactinate', 'tetanical'],
 'tetanoid': ['antidote', 'tetanoid'],
 'tetanus': ['tetanus', 'unstate', 'untaste'],
 'tetarconid': ['detraction', 'doctrinate', 'tetarconid'],
 'tetard': ['tetard', 'tetrad'],
 'tetchy': ['chetty', 'tetchy'],
 'tete': ['teet', 'tete'],
 'tetel': ['ettle', 'tetel'],
 'tether': ['hetter', 'tether'],
 'tethys': ['stythe', 'tethys'],
 'tetra': ['atter', 'tater', 'teart', 'tetra', 'treat'],
 'tetracid': ['citrated', 'tetracid', 'tetradic'],
 'tetrad': ['tetard', 'tetrad'],
 'tetradic': ['citrated', 'tetracid', 'tetradic'],
 'tetragonia': ['giornatate', 'tetragonia'],
 'tetrahexahedron': ['hexatetrahedron', 'tetrahexahedron'],
 'tetrakishexahedron': ['hexakistetrahedron', 'tetrakishexahedron'],
 'tetralin': ['tetralin', 'triental'],
 'tetramin': ['intermat', 'martinet', 'tetramin'],
 'tetramine': ['antimeter', 'attermine', 'interteam', 'terminate', 'tetramine'],
 'tetrander': ['retardent', 'tetrander'],
 'tetrandrous': ['tetrandrous', 'unrostrated'],
 'tetrane': ['entreat', 'ratteen', 'tarente', 'ternate', 'tetrane'],
 'tetrao': ['rotate', 'tetrao'],
 'tetraodon': ['detonator', 'tetraodon'],
 'tetraonine': ['entoretina', 'tetraonine'],
 'tetraplous': ['supertotal', 'tetraplous'],
 'tetrasporic': ['tetrasporic', 'triceratops'],
 'tetraxonia': ['retaxation', 'tetraxonia'],
 'tetrical': ['tetrical', 'tractile'],
 'tetricous': ['tetricous', 'toreutics'],
 'tetronic': ['contrite', 'tetronic'],
 'tetrose': ['rosette', 'tetrose'],
 'tetterous': ['outstreet', 'tetterous'],
 'teucri': ['curite', 'teucri', 'uretic'],
 'teucrian': ['anuretic', 'centauri', 'centuria', 'teucrian'],
 'teucrin': ['nutrice', 'teucrin'],
 'teuk': ['ketu', 'teuk', 'tuke'],
 'tew': ['tew', 'wet'],
 'tewa': ['tewa', 'twae', 'weta'],
 'tewel': ['tewel', 'tweel'],
 'tewer': ['rewet', 'tewer', 'twere'],
 'tewit': ['tewit', 'twite'],
 'tewly': ['tewly', 'wetly'],
 'tha': ['aht', 'hat', 'tha'],
 'thai': ['hati', 'thai'],
 'thais': ['shita', 'thais'],
 'thalami': ['hamital', 'thalami'],
 'thaler': ['arthel', 'halter', 'lather', 'thaler'],
 'thalia': ['hiatal', 'thalia'],
 'thaliard': ['hardtail', 'thaliard'],
 'thamesis': ['mathesis', 'thamesis'],
 'than': ['hant', 'tanh', 'than'],
 'thana': ['atnah', 'tanha', 'thana'],
 'thanan': ['nathan', 'thanan'],
 'thanatism': ['staithman', 'thanatism'],
 'thanatotic': ['chattation', 'thanatotic'],
 'thane': ['enhat', 'ethan', 'nathe', 'neath', 'thane'],
 'thanker': ['rethank', 'thanker'],
 'thapes': ['spathe', 'thapes'],
 'thar': ['hart', 'rath', 'tahr', 'thar', 'trah'],
 'tharen': ['anther', 'nather', 'tharen', 'thenar'],
 'tharm': ['tharm', 'thram'],
 'thasian': ['ashanti', 'sanhita', 'shaitan', 'thasian'],
 'that': ['hatt', 'tath', 'that'],
 'thatcher': ['rethatch', 'thatcher'],
 'thaw': ['thaw', 'wath', 'what'],
 'thawer': ['rethaw', 'thawer', 'wreath'],
 'the': ['het', 'the'],
 'thea': ['ahet', 'haet', 'hate', 'heat', 'thea'],
 'theah': ['heath', 'theah'],
 'thearchy': ['hatchery', 'thearchy'],
 'theat': ['theat', 'theta'],
 'theater': ['teather', 'theater', 'thereat'],
 'theatricism': ['chemiatrist', 'chrismatite', 'theatricism'],
 'theatropolis': ['strophiolate', 'theatropolis'],
 'theatry': ['hattery', 'theatry'],
 'theb': ['beth', 'theb'],
 'thebaid': ['habited', 'thebaid'],
 'theca': ['cheat', 'tache', 'teach', 'theca'],
 'thecae': ['achete', 'hecate', 'teache', 'thecae'],
 'thecal': ['achtel', 'chalet', 'thecal', 'thecla'],
 'thecasporal': ['archapostle', 'thecasporal'],
 'thecata': ['attache', 'thecata'],
 'thecitis': ['ethicist', 'thecitis', 'theistic'],
 'thecla': ['achtel', 'chalet', 'thecal', 'thecla'],
 'theer': ['ether', 'rethe', 'theer', 'there', 'three'],
 'theet': ['teeth', 'theet'],
 'thegn': ['ghent', 'thegn'],
 'thegnly': ['lengthy', 'thegnly'],
 'theine': ['ethine', 'theine'],
 'their': ['ither', 'their'],
 'theirn': ['hinter', 'nither', 'theirn'],
 'theirs': ['shrite', 'theirs'],
 'theism': ['theism', 'themis'],
 'theist': ['theist', 'thetis'],
 'theistic': ['ethicist', 'thecitis', 'theistic'],
 'thema': ['ahmet', 'thema'],
 'thematic': ['mathetic', 'thematic'],
 'thematist': ['hattemist', 'thematist'],
 'themer': ['mether', 'themer'],
 'themis': ['theism', 'themis'],
 'themistian': ['antitheism', 'themistian'],
 'then': ['hent', 'neth', 'then'],
 'thenal': ['ethnal', 'hantle', 'lathen', 'thenal'],
 'thenar': ['anther', 'nather', 'tharen', 'thenar'],
 'theobald': ['bolthead', 'theobald'],
 'theocrasia': ['oireachtas', 'theocrasia'],
 'theocrat': ['theocrat', 'trochate'],
 'theocratic': ['rheotactic', 'theocratic'],
 'theodora': ['dorothea', 'theodora'],
 'theodore': ['theodore', 'treehood'],
 'theogonal': ['halogeton', 'theogonal'],
 'theologic': ['ethologic', 'theologic'],
 'theological': ['ethological', 'lethologica', 'theological'],
 'theologism': ['hemologist', 'theologism'],
 'theology': ['ethology', 'theology'],
 'theophanic': ['phaethonic', 'theophanic'],
 'theophilist': ['philotheist', 'theophilist'],
 'theopsychism': ['psychotheism', 'theopsychism'],
 'theorbo': ['boother', 'theorbo'],
 'theorematic': ['heteratomic', 'theorematic'],
 'theoretic': ['heterotic', 'theoretic'],
 'theoretician': ['heretication', 'theoretician'],
 'theorician': ['antiheroic', 'theorician'],
 'theorics': ['chirotes', 'theorics'],
 'theorism': ['homerist', 'isotherm', 'otherism', 'theorism'],
 'theorist': ['otherist', 'theorist'],
 'theorizer': ['rhetorize', 'theorizer'],
 'theorum': ['mouther', 'theorum'],
 'theotherapy': ['heteropathy', 'theotherapy'],
 'therblig': ['blighter', 'therblig'],
 'there': ['ether', 'rethe', 'theer', 'there', 'three'],
 'thereas': ['thereas', 'theresa'],
 'thereat': ['teather', 'theater', 'thereat'],
 'therein': ['enherit', 'etherin', 'neither', 'therein'],
 'thereness': ['retheness', 'thereness', 'threeness'],
 'thereology': ['heterology', 'thereology'],
 'theres': ['esther', 'hester', 'theres'],
 'theresa': ['thereas', 'theresa'],
 'therese': ['sheeter', 'therese'],
 'therewithal': ['therewithal', 'whitleather'],
 'theriac': ['certhia', 'rhaetic', 'theriac'],
 'therial': ['hairlet', 'therial'],
 'theriodic': ['dichroite', 'erichtoid', 'theriodic'],
 'theriodonta': ['dehortation', 'theriodonta'],
 'thermantic': ['intermatch', 'thermantic'],
 'thermo': ['mother', 'thermo'],
 'thermobarograph': ['barothermograph', 'thermobarograph'],
 'thermoelectric': ['electrothermic', 'thermoelectric'],
 'thermoelectrometer': ['electrothermometer', 'thermoelectrometer'],
 'thermogalvanometer': ['galvanothermometer', 'thermogalvanometer'],
 'thermogeny': ['mythogreen', 'thermogeny'],
 'thermography': ['mythographer', 'thermography'],
 'thermology': ['mythologer', 'thermology'],
 'thermos': ['smother', 'thermos'],
 'thermotype': ['phytometer', 'thermotype'],
 'thermotypic': ['phytometric', 'thermotypic'],
 'thermotypy': ['phytometry', 'thermotypy'],
 'theroid': ['rhodite', 'theroid'],
 'theron': ['hornet', 'nother', 'theron', 'throne'],
 'thersitical': ['thersitical', 'trachelitis'],
 'these': ['sheet', 'these'],
 'thesean': ['sneathe', 'thesean'],
 'thesial': ['heliast', 'thesial'],
 'thesis': ['shiest', 'thesis'],
 'theta': ['theat', 'theta'],
 'thetical': ['athletic', 'thetical'],
 'thetis': ['theist', 'thetis'],
 'thew': ['hewt', 'thew', 'whet'],
 'they': ['they', 'yeth'],
 'theyre': ['theyre', 'yether'],
 'thicken': ['kitchen', 'thicken'],
 'thickener': ['kitchener', 'rethicken', 'thickener'],
 'thicket': ['chettik', 'thicket'],
 'thienyl': ['ethylin', 'thienyl'],
 'thig': ['gith', 'thig'],
 'thigh': ['hight', 'thigh'],
 'thill': ['illth', 'thill'],
 'thin': ['hint', 'thin'],
 'thing': ['night', 'thing'],
 'thingal': ['halting', 'lathing', 'thingal'],
 'thingless': ['lightness', 'nightless', 'thingless'],
 'thinglet': ['thinglet', 'thlinget'],
 'thinglike': ['nightlike', 'thinglike'],
 'thingly': ['nightly', 'thingly'],
 'thingman': ['nightman', 'thingman'],
 'thinker': ['rethink', 'thinker'],
 'thio': ['hoit', 'hoti', 'thio'],
 'thiocresol': ['holosteric', 'thiocresol'],
 'thiol': ['litho', 'thiol', 'tholi'],
 'thiolacetic': ['heliotactic', 'thiolacetic'],
 'thiophenol': ['lithophone', 'thiophenol'],
 'thiopyran': ['phoniatry', 'thiopyran'],
 'thirlage': ['litharge', 'thirlage'],
 'this': ['hist', 'sith', 'this', 'tshi'],
 'thissen': ['sithens', 'thissen'],
 'thistle': ['lettish', 'thistle'],
 'thlinget': ['thinglet', 'thlinget'],
 'tho': ['hot', 'tho'],
 'thob': ['both', 'thob'],
 'thole': ['helot', 'hotel', 'thole'],
 'tholi': ['litho', 'thiol', 'tholi'],
 'tholos': ['soloth', 'tholos'],
 'thomaean': ['amaethon', 'thomaean'],
 'thomasine': ['hematosin', 'thomasine'],
 'thomisid': ['isthmoid', 'thomisid'],
 'thomsonite': ['monotheist', 'thomsonite'],
 'thonder': ['thonder', 'thorned'],
 'thonga': ['gnatho', 'thonga'],
 'thoo': ['hoot', 'thoo', 'toho'],
 'thoom': ['mooth', 'thoom'],
 'thoracectomy': ['chromatocyte', 'thoracectomy'],
 'thoracic': ['thoracic', 'tocharic', 'trochaic'],
 'thoral': ['harlot', 'orthal', 'thoral'],
 'thore': ['other', 'thore', 'throe', 'toher'],
 'thoric': ['chorti', 'orthic', 'thoric', 'trochi'],
 'thorina': ['orthian', 'thorina'],
 'thorite': ['hortite', 'orthite', 'thorite'],
 'thorn': ['north', 'thorn'],
 'thorned': ['thonder', 'thorned'],
 'thornhead': ['rhodanthe', 'thornhead'],
 'thorny': ['rhyton', 'thorny'],
 'thoro': ['ortho', 'thoro'],
 'thort': ['thort', 'troth'],
 'thos': ['host', 'shot', 'thos', 'tosh'],
 'those': ['ethos', 'shote', 'those'],
 'thowel': ['howlet', 'thowel'],
 'thraces': ['stacher', 'thraces'],
 'thracian': ['taranchi', 'thracian'],
 'thraep': ['thraep', 'threap'],
 'thrain': ['hartin', 'thrain'],
 'thram': ['tharm', 'thram'],
 'thrang': ['granth', 'thrang'],
 'thrasher': ['rethrash', 'thrasher'],
 'thrast': ['strath', 'thrast'],
 'thraw': ['thraw', 'warth', 'whart', 'wrath'],
 'thread': ['dearth', 'hatred', 'rathed', 'thread'],
 'threaden': ['adherent', 'headrent', 'neatherd', 'threaden'],
 'threader': ['rethread', 'threader'],
 'threadworm': ['motherward', 'threadworm'],
 'thready': ['hydrate', 'thready'],
 'threap': ['thraep', 'threap'],
 'threat': ['hatter', 'threat'],
 'threatener': ['rethreaten', 'threatener'],
 'three': ['ether', 'rethe', 'theer', 'there', 'three'],
 'threeling': ['lightener', 'relighten', 'threeling'],
 'threeness': ['retheness', 'thereness', 'threeness'],
 'threne': ['erthen', 'henter', 'nether', 'threne'],
 'threnode': ['dethrone', 'threnode'],
 'threnodic': ['chondrite', 'threnodic'],
 'threnos': ['shorten', 'threnos'],
 'thresher': ['rethresh', 'thresher'],
 'thrice': ['cither', 'thrice'],
 'thriller': ['rethrill', 'thriller'],
 'thripel': ['philter', 'thripel'],
 'thripidae': ['rhipidate', 'thripidae'],
 'throat': ['athort', 'throat'],
 'throb': ['broth', 'throb'],
 'throe': ['other', 'thore', 'throe', 'toher'],
 'thronal': ['althorn', 'anthrol', 'thronal'],
 'throne': ['hornet', 'nother', 'theron', 'throne'],
 'throu': ['routh', 'throu'],
 'throughout': ['outthrough', 'throughout'],
 'throw': ['throw', 'whort', 'worth', 'wroth'],
 'throwdown': ['downthrow', 'throwdown'],
 'thrower': ['rethrow', 'thrower'],
 'throwing': ['ingrowth', 'throwing'],
 'throwout': ['outthrow', 'outworth', 'throwout'],
 'thrum': ['thrum', 'thurm'],
 'thrust': ['struth', 'thrust'],
 'thruster': ['rethrust', 'thruster'],
 'thuan': ['ahunt', 'haunt', 'thuan', 'unhat'],
 'thulr': ['thulr', 'thurl'],
 'thunderbearing': ['thunderbearing', 'underbreathing'],
 'thunderer': ['rethunder', 'thunderer'],
 'thundering': ['thundering', 'underthing'],
 'thuoc': ['couth', 'thuoc', 'touch'],
 'thurl': ['thulr', 'thurl'],
 'thurm': ['thrum', 'thurm'],
 'thurse': ['reshut', 'suther', 'thurse', 'tusher'],
 'thurt': ['thurt', 'truth'],
 'thus': ['shut', 'thus', 'tush'],
 'thusness': ['shutness', 'thusness'],
 'thwacker': ['thwacker', 'whatreck'],
 'thwartover': ['overthwart', 'thwartover'],
 'thymelic': ['methylic', 'thymelic'],
 'thymetic': ['hymettic', 'thymetic'],
 'thymus': ['mythus', 'thymus'],
 'thyreogenous': ['heterogynous', 'thyreogenous'],
 'thyreohyoid': ['hyothyreoid', 'thyreohyoid'],
 'thyreoid': ['hydriote', 'thyreoid'],
 'thyreoidal': ['thyreoidal', 'thyroideal'],
 'thyreosis': ['oysterish', 'thyreosis'],
 'thyris': ['shirty', 'thyris'],
 'thyrocricoid': ['cricothyroid', 'thyrocricoid'],
 'thyrogenic': ['thyrogenic', 'trichogyne'],
 'thyrohyoid': ['hyothyroid', 'thyrohyoid'],
 'thyroideal': ['thyreoidal', 'thyroideal'],
 'thyroiodin': ['iodothyrin', 'thyroiodin'],
 'thyroprivic': ['thyroprivic', 'vitrophyric'],
 'thysanopteran': ['parasyntheton', 'thysanopteran'],
 'thysel': ['shelty', 'thysel'],
 'ti': ['it', 'ti'],
 'tiang': ['giant', 'tangi', 'tiang'],
 'tiao': ['iota', 'tiao'],
 'tiar': ['airt', 'rita', 'tari', 'tiar'],
 'tiara': ['arati', 'atria', 'riata', 'tarai', 'tiara'],
 'tiarella': ['arillate', 'tiarella'],
 'tib': ['bit', 'tib'],
 'tibetan': ['bettina', 'tabinet', 'tibetan'],
 'tibial': ['bilati', 'tibial'],
 'tibiale': ['biliate', 'tibiale'],
 'tibiofemoral': ['femorotibial', 'tibiofemoral'],
 'tic': ['cit', 'tic'],
 'ticca': ['cacti', 'ticca'],
 'tice': ['ceti', 'cite', 'tice'],
 'ticer': ['citer', 'recti', 'ticer', 'trice'],
 'tichodroma': ['chromatoid', 'tichodroma'],
 'ticketer': ['reticket', 'ticketer'],
 'tickler': ['tickler', 'trickle'],
 'tickproof': ['prickfoot', 'tickproof'],
 'ticuna': ['anicut', 'nautic', 'ticuna', 'tunica'],
 'ticunan': ['ticunan', 'tunican'],
 'tid': ['dit', 'tid'],
 'tidal': ['datil', 'dital', 'tidal', 'tilda'],
 'tiddley': ['lyddite', 'tiddley'],
 'tide': ['diet', 'dite', 'edit', 'tide', 'tied'],
 'tidely': ['idlety', 'lydite', 'tidely', 'tidley'],
 'tiding': ['tiding', 'tingid'],
 'tidley': ['idlety', 'lydite', 'tidely', 'tidley'],
 'tied': ['diet', 'dite', 'edit', 'tide', 'tied'],
 'tien': ['iten', 'neti', 'tien', 'tine'],
 'tiepin': ['pinite', 'tiepin'],
 'tier': ['iter', 'reit', 'rite', 'teri', 'tier', 'tire'],
 'tierce': ['cerite', 'certie', 'recite', 'tierce'],
 'tiered': ['dieter', 'tiered'],
 'tierer': ['errite', 'reiter', 'retier', 'retire', 'tierer'],
 'tiffy': ['fifty', 'tiffy'],
 'tifter': ['fitter', 'tifter'],
 'tig': ['git', 'tig'],
 'tigella': ['tigella', 'tillage'],
 'tiger': ['tiger', 'tigre'],
 'tigereye': ['geyerite', 'tigereye'],
 'tightener': ['retighten', 'tightener'],
 'tiglinic': ['lignitic', 'tiglinic'],
 'tigre': ['tiger', 'tigre'],
 'tigrean': ['angrite', 'granite', 'ingrate', 'tangier', 'tearing', 'tigrean'],
 'tigress': ['striges', 'tigress'],
 'tigrine': ['igniter', 'ringite', 'tigrine'],
 'tigurine': ['intrigue', 'tigurine'],
 'tikka': ['katik', 'tikka'],
 'tikur': ['tikur', 'turki'],
 'til': ['lit', 'til'],
 'tilaite': ['italite', 'letitia', 'tilaite'],
 'tilda': ['datil', 'dital', 'tidal', 'tilda'],
 'tilde': ['tilde', 'tiled'],
 'tile': ['lite', 'teil', 'teli', 'tile'],
 'tiled': ['tilde', 'tiled'],
 'tiler': ['liter', 'tiler'],
 'tilery': ['tilery', 'tilyer'],
 'tileways': ['sweatily', 'tileways'],
 'tileyard': ['dielytra', 'tileyard'],
 'tilia': ['itali', 'tilia'],
 'tilikum': ['kulimit', 'tilikum'],
 'till': ['lilt', 'till'],
 'tillable': ['belltail', 'bletilla', 'tillable'],
 'tillaea': ['alalite', 'tillaea'],
 'tillage': ['tigella', 'tillage'],
 'tiller': ['retill', 'rillet', 'tiller'],
 'tilmus': ['litmus', 'tilmus'],
 'tilpah': ['lapith', 'tilpah'],
 'tilter': ['litter', 'tilter', 'titler'],
 'tilting': ['tilting', 'titling', 'tlingit'],
 'tiltup': ['tiltup', 'uptilt'],
 'tilyer': ['tilery', 'tilyer'],
 'timable': ['limbate', 'timable', 'timbale'],
 'timaeus': ['metusia', 'suimate', 'timaeus'],
 'timaline': ['meliatin', 'timaline'],
 'timani': ['intima', 'timani'],
 'timar': ['mitra', 'tarmi', 'timar', 'tirma'],
 'timbal': ['limbat', 'timbal'],
 'timbale': ['limbate', 'timable', 'timbale'],
 'timber': ['betrim', 'timber', 'timbre'],
 'timbered': ['bemitred', 'timbered'],
 'timberer': ['retimber', 'timberer'],
 'timberlike': ['kimberlite', 'timberlike'],
 'timbre': ['betrim', 'timber', 'timbre'],
 'time': ['emit', 'item', 'mite', 'time'],
 'timecard': ['dermatic', 'timecard'],
 'timed': ['demit', 'timed'],
 'timeproof': ['miteproof', 'timeproof'],
 'timer': ['merit', 'miter', 'mitre', 'remit', 'timer'],
 'times': ['metis', 'smite', 'stime', 'times'],
 'timesaving': ['negativism', 'timesaving'],
 'timework': ['timework', 'worktime'],
 'timid': ['dimit', 'timid'],
 'timidly': ['mytilid', 'timidly'],
 'timidness': ['destinism', 'timidness'],
 'timish': ['isthmi', 'timish'],
 'timne': ['metin', 'temin', 'timne'],
 'timo': ['itmo', 'moit', 'omit', 'timo'],
 'timon': ['minot', 'timon', 'tomin'],
 'timorese': ['rosetime', 'timorese', 'tiresome'],
 'timpani': ['impaint', 'timpani'],
 'timpano': ['maintop', 'ptomain', 'tampion', 'timpano'],
 'tin': ['nit', 'tin'],
 'tina': ['aint', 'anti', 'tain', 'tina'],
 'tincal': ['catlin', 'tincal'],
 'tinchel': ['linchet', 'tinchel'],
 'tinctorial': ['tinctorial', 'trinoctial'],
 'tind': ['dint', 'tind'],
 'tindal': ['antlid', 'tindal'],
 'tindalo': ['itoland', 'talonid', 'tindalo'],
 'tinder': ['dirten', 'rident', 'tinder'],
 'tindered': ['dendrite', 'tindered'],
 'tinderous': ['detrusion', 'tinderous', 'unstoried'],
 'tine': ['iten', 'neti', 'tien', 'tine'],
 'tinea': ['entia', 'teian', 'tenai', 'tinea'],
 'tineal': ['entail', 'tineal'],
 'tinean': ['annite', 'innate', 'tinean'],
 'tined': ['detin', 'teind', 'tined'],
 'tineid': ['indite', 'tineid'],
 'tineman': ['mannite', 'tineman'],
 'tineoid': ['edition', 'odinite', 'otidine', 'tineoid'],
 'tinetare': ['intereat', 'tinetare'],
 'tinety': ['entity', 'tinety'],
 'tinged': ['nidget', 'tinged'],
 'tinger': ['engirt', 'tinger'],
 'tingid': ['tiding', 'tingid'],
 'tingitidae': ['indigitate', 'tingitidae'],
 'tingler': ['ringlet', 'tingler', 'tringle'],
 'tinhouse': ['outshine', 'tinhouse'],
 'tink': ['knit', 'tink'],
 'tinker': ['reknit', 'tinker'],
 'tinkerer': ['retinker', 'tinkerer'],
 'tinkler': ['tinkler', 'trinkle'],
 'tinlet': ['litten', 'tinlet'],
 'tinne': ['innet', 'tinne'],
 'tinned': ['dentin', 'indent', 'intend', 'tinned'],
 'tinner': ['intern', 'tinner'],
 'tinnet': ['intent', 'tinnet'],
 'tino': ['into', 'nito', 'oint', 'tino'],
 'tinoceras': ['atroscine', 'certosina', 'ostracine', 'tinoceras', 'tricosane'],
 'tinosa': ['sotnia', 'tinosa'],
 'tinsel': ['enlist', 'listen', 'silent', 'tinsel'],
 'tinselly': ['silently', 'tinselly'],
 'tinta': ['taint', 'tanti', 'tinta', 'titan'],
 'tintage': ['attinge', 'tintage'],
 'tinter': ['nitter', 'tinter'],
 'tintie': ['tintie', 'titien'],
 'tintiness': ['insistent', 'tintiness'],
 'tinty': ['nitty', 'tinty'],
 'tinworker': ['interwork', 'tinworker'],
 'tionontates': ['ostentation', 'tionontates'],
 'tip': ['pit', 'tip'],
 'tipe': ['piet', 'tipe'],
 'tipful': ['tipful', 'uplift'],
 'tipless': ['pitless', 'tipless'],
 'tipman': ['pitman', 'tampin', 'tipman'],
 'tipper': ['rippet', 'tipper'],
 'tippler': ['ripplet', 'tippler', 'tripple'],
 'tipster': ['spitter', 'tipster'],
 'tipstock': ['potstick', 'tipstock'],
 'tipula': ['tipula', 'tulipa'],
 'tiralee': ['atelier', 'tiralee'],
 'tire': ['iter', 'reit', 'rite', 'teri', 'tier', 'tire'],
 'tired': ['diter', 'tired', 'tried'],
 'tiredly': ['tiredly', 'triedly'],
 'tiredness': ['dissenter', 'tiredness'],
 'tireless': ['riteless', 'tireless'],
 'tirelessness': ['ritelessness', 'tirelessness'],
 'tiremaid': ['dimetria', 'mitridae', 'tiremaid', 'triamide'],
 'tireman': ['minaret', 'raiment', 'tireman'],
 'tirer': ['terri', 'tirer', 'trier'],
 'tiresmith': ['tiresmith', 'tritheism'],
 'tiresome': ['rosetime', 'timorese', 'tiresome'],
 'tirma': ['mitra', 'tarmi', 'timar', 'tirma'],
 'tirolean': ['oriental', 'relation', 'tirolean'],
 'tirolese': ['literose', 'roselite', 'tirolese'],
 'tirve': ['rivet', 'tirve', 'tiver'],
 'tisane': ['satine', 'tisane'],
 'tisar': ['arist',
           'astir',
           'sitar',
           'stair',
           'stria',
           'tarsi',
           'tisar',
           'trias'],
 'titan': ['taint', 'tanti', 'tinta', 'titan'],
 'titaness': ['antistes', 'titaness'],
 'titanic': ['tanitic', 'titanic'],
 'titano': ['otiant', 'titano'],
 'titanocolumbate': ['columbotitanate', 'titanocolumbate'],
 'titanofluoride': ['rotundifoliate', 'titanofluoride'],
 'titanosaur': ['saturation', 'titanosaur'],
 'titanosilicate': ['silicotitanate', 'titanosilicate'],
 'titanous': ['outsaint', 'titanous'],
 'titanyl': ['nattily', 'titanyl'],
 'titar': ['ratti', 'titar', 'trait'],
 'titer': ['titer', 'titre', 'trite'],
 'tithable': ['hittable', 'tithable'],
 'tither': ['hitter', 'tither'],
 'tithonic': ['chinotti', 'tithonic'],
 'titien': ['tintie', 'titien'],
 'titleboard': ['titleboard', 'trilobated'],
 'titler': ['litter', 'tilter', 'titler'],
 'titling': ['tilting', 'titling', 'tlingit'],
 'titrate': ['attrite', 'titrate'],
 'titration': ['attrition', 'titration'],
 'titre': ['titer', 'titre', 'trite'],
 'tiver': ['rivet', 'tirve', 'tiver'],
 'tiza': ['itza', 'tiza', 'zati'],
 'tlaco': ['lacto', 'tlaco'],
 'tlingit': ['tilting', 'titling', 'tlingit'],
 'tmesis': ['misset', 'tmesis'],
 'toa': ['oat', 'tao', 'toa'],
 'toad': ['doat', 'toad', 'toda'],
 'toader': ['doater', 'toader'],
 'toadflower': ['floodwater', 'toadflower', 'waterflood'],
 'toadier': ['roadite', 'toadier'],
 'toadish': ['doatish', 'toadish'],
 'toady': ['toady', 'today'],
 'toadyish': ['toadyish', 'todayish'],
 'toag': ['goat', 'toag', 'toga'],
 'toast': ['stoat', 'toast'],
 'toaster': ['retoast', 'rosetta', 'stoater', 'toaster'],
 'toba': ['boat', 'bota', 'toba'],
 'tobe': ['bote', 'tobe'],
 'tobiah': ['bhotia', 'tobiah'],
 'tobine': ['botein', 'tobine'],
 'toccata': ['attacco', 'toccata'],
 'tocharese': ['escheator', 'tocharese'],
 'tocharian': ['archontia', 'tocharian'],
 'tocharic': ['thoracic', 'tocharic', 'trochaic'],
 'tocher': ['hector', 'rochet', 'tocher', 'troche'],
 'toco': ['coot', 'coto', 'toco'],
 'tocogenetic': ['geotectonic', 'tocogenetic'],
 'tocometer': ['octometer', 'rectotome', 'tocometer'],
 'tocsin': ['nostic', 'sintoc', 'tocsin'],
 'tod': ['dot', 'tod'],
 'toda': ['doat', 'toad', 'toda'],
 'today': ['toady', 'today'],
 'todayish': ['toadyish', 'todayish'],
 'toddle': ['dodlet', 'toddle'],
 'tode': ['dote', 'tode', 'toed'],
 'todea': ['deota', 'todea'],
 'tody': ['doty', 'tody'],
 'toecap': ['capote', 'toecap'],
 'toed': ['dote', 'tode', 'toed'],
 'toeless': ['osselet', 'sestole', 'toeless'],
 'toenail': ['alnoite', 'elation', 'toenail'],
 'tog': ['got', 'tog'],
 'toga': ['goat', 'toag', 'toga'],
 'togaed': ['dogate', 'dotage', 'togaed'],
 'togalike': ['goatlike', 'togalike'],
 'toggel': ['goglet', 'toggel', 'toggle'],
 'toggle': ['goglet', 'toggel', 'toggle'],
 'togs': ['stog', 'togs'],
 'toher': ['other', 'thore', 'throe', 'toher'],
 'toho': ['hoot', 'thoo', 'toho'],
 'tohunga': ['hangout', 'tohunga'],
 'toi': ['ito', 'toi'],
 'toil': ['ilot', 'toil'],
 'toiler': ['loiter', 'toiler', 'triole'],
 'toilet': ['lottie', 'toilet', 'tolite'],
 'toiletry': ['toiletry', 'tyrolite'],
 'toise': ['sotie', 'toise'],
 'tokay': ['otyak', 'tokay'],
 'toke': ['keto', 'oket', 'toke'],
 'toko': ['koto', 'toko', 'took'],
 'tol': ['lot', 'tol'],
 'tolamine': ['lomatine', 'tolamine'],
 'tolan': ['notal', 'ontal', 'talon', 'tolan', 'tonal'],
 'tolane': ['etalon', 'tolane'],
 'told': ['dolt', 'told'],
 'tole': ['leto', 'lote', 'tole'],
 'toledan': ['taloned', 'toledan'],
 'toledo': ['toledo', 'toodle'],
 'tolerance': ['antrocele', 'coeternal', 'tolerance'],
 'tolerancy': ['alectryon', 'tolerancy'],
 'tolidine': ['lindoite', 'tolidine'],
 'tolite': ['lottie', 'toilet', 'tolite'],
 'tollery': ['tollery', 'trolley'],
 'tolly': ['tolly', 'tolyl'],
 'tolpatch': ['potlatch', 'tolpatch'],
 'tolsey': ['tolsey', 'tylose'],
 'tolter': ['lotter', 'rottle', 'tolter'],
 'tolu': ['lout', 'tolu'],
 'toluic': ['coutil', 'toluic'],
 'toluifera': ['foliature', 'toluifera'],
 'tolyl': ['tolly', 'tolyl'],
 'tom': ['mot', 'tom'],
 'toma': ['atmo', 'atom', 'moat', 'toma'],
 'toman': ['manto', 'toman'],
 'tomas': ['atmos', 'stoma', 'tomas'],
 'tombac': ['combat', 'tombac'],
 'tome': ['mote', 'tome'],
 'tomentose': ['metosteon', 'tomentose'],
 'tomial': ['lomita', 'tomial'],
 'tomin': ['minot', 'timon', 'tomin'],
 'tomographic': ['motographic', 'tomographic'],
 'tomorn': ['morton', 'tomorn'],
 'tomorrow': ['moorwort', 'rootworm', 'tomorrow', 'wormroot'],
 'ton': ['not', 'ton'],
 'tonal': ['notal', 'ontal', 'talon', 'tolan', 'tonal'],
 'tonalitive': ['levitation', 'tonalitive', 'velitation'],
 'tonation': ['notation', 'tonation'],
 'tone': ['note', 'tone'],
 'toned': ['donet', 'noted', 'toned'],
 'toneless': ['noteless', 'toneless'],
 'tonelessly': ['notelessly', 'tonelessly'],
 'tonelessness': ['notelessness', 'tonelessness'],
 'toner': ['noter', 'tenor', 'toner', 'trone'],
 'tonetic': ['entotic', 'tonetic'],
 'tonetics': ['stenotic', 'tonetics'],
 'tonga': ['tango', 'tonga'],
 'tongan': ['ganton', 'tongan'],
 'tongas': ['sontag', 'tongas'],
 'tonger': ['geront', 'tonger'],
 'tongrian': ['ignorant', 'tongrian'],
 'tongs': ['stong', 'tongs'],
 'tonicize': ['nicotize', 'tonicize'],
 'tonicoclonic': ['clonicotonic', 'tonicoclonic'],
 'tonify': ['notify', 'tonify'],
 'tonish': ['histon', 'shinto', 'tonish'],
 'tonk': ['knot', 'tonk'],
 'tonkin': ['inknot', 'tonkin'],
 'tonna': ['anton', 'notan', 'tonna'],
 'tonological': ['ontological', 'tonological'],
 'tonology': ['ontology', 'tonology'],
 'tonsorial': ['tonsorial', 'torsional'],
 'tonsure': ['snouter', 'tonsure', 'unstore'],
 'tonsured': ['tonsured', 'unsorted', 'unstored'],
 'tontine': ['nettion', 'tention', 'tontine'],
 'tonus': ['notus', 'snout', 'stoun', 'tonus'],
 'tony': ['tony', 'yont'],
 'too': ['oto', 'too'],
 'toodle': ['toledo', 'toodle'],
 'took': ['koto', 'toko', 'took'],
 'tool': ['loot', 'tool'],
 'tooler': ['looter', 'retool', 'rootle', 'tooler'],
 'tooling': ['ilongot', 'tooling'],
 'toom': ['moot', 'toom'],
 'toon': ['onto', 'oont', 'toon'],
 'toona': ['naoto', 'toona'],
 'toop': ['poot', 'toop', 'topo'],
 'toosh': ['shoot', 'sooth', 'sotho', 'toosh'],
 'toot': ['otto', 'toot', 'toto'],
 'toother': ['retooth', 'toother'],
 'toothpick': ['picktooth', 'toothpick'],
 'tootler': ['rootlet', 'tootler'],
 'top': ['opt', 'pot', 'top'],
 'toparch': ['caphtor', 'toparch'],
 'topass': ['potass', 'topass'],
 'topchrome': ['ectomorph', 'topchrome'],
 'tope': ['peto', 'poet', 'pote', 'tope'],
 'toper': ['poter', 'prote', 'repot', 'tepor', 'toper', 'trope'],
 'topfull': ['plotful', 'topfull'],
 'toph': ['phot', 'toph'],
 'tophus': ['tophus', 'upshot'],
 'topia': ['patio', 'taipo', 'topia'],
 'topiarist': ['parotitis', 'topiarist'],
 'topic': ['optic', 'picot', 'topic'],
 'topical': ['capitol', 'coalpit', 'optical', 'topical'],
 'topically': ['optically', 'topically'],
 'toplike': ['kitlope', 'potlike', 'toplike'],
 'topline': ['pointel', 'pontile', 'topline'],
 'topmaker': ['potmaker', 'topmaker'],
 'topmaking': ['potmaking', 'topmaking'],
 'topman': ['potman', 'tampon', 'topman'],
 'topmast': ['tapmost', 'topmast'],
 'topo': ['poot', 'toop', 'topo'],
 'topographics': ['coprophagist', 'topographics'],
 'topography': ['optography', 'topography'],
 'topological': ['optological', 'topological'],
 'topologist': ['optologist', 'topologist'],
 'topology': ['optology', 'topology'],
 'toponymal': ['monotypal', 'toponymal'],
 'toponymic': ['monotypic', 'toponymic'],
 'toponymical': ['monotypical', 'toponymical'],
 'topophone': ['optophone', 'topophone'],
 'topotype': ['optotype', 'topotype'],
 'topple': ['loppet', 'topple'],
 'toppler': ['preplot', 'toppler'],
 'toprail': ['portail', 'toprail'],
 'tops': ['post', 'spot', 'stop', 'tops'],
 'topsail': ['apostil', 'topsail'],
 'topside': ['deposit', 'topside'],
 'topsman': ['postman', 'topsman'],
 'topsoil': ['loopist', 'poloist', 'topsoil'],
 'topstone': ['potstone', 'topstone'],
 'toptail': ['ptilota', 'talipot', 'toptail'],
 'toque': ['quote', 'toque'],
 'tor': ['ort', 'rot', 'tor'],
 'tora': ['rota', 'taro', 'tora'],
 'toral': ['latro', 'rotal', 'toral'],
 'toran': ['orant', 'rotan', 'toran', 'trona'],
 'torbanite': ['abortient', 'torbanite'],
 'torcel': ['colter', 'lector', 'torcel'],
 'torch': ['chort', 'rotch', 'torch'],
 'tore': ['rote', 'tore'],
 'tored': ['doter', 'tored', 'trode'],
 'torenia': ['otarine', 'torenia'],
 'torero': ['reroot', 'rooter', 'torero'],
 'toreutics': ['tetricous', 'toreutics'],
 'torfel': ['floret', 'forlet', 'lofter', 'torfel'],
 'torgot': ['grotto', 'torgot'],
 'toric': ['toric', 'troic'],
 'torinese': ['serotine', 'torinese'],
 'torma': ['amort', 'morat', 'torma'],
 'tormen': ['mentor', 'merton', 'termon', 'tormen'],
 'tormina': ['amintor', 'tormina'],
 'torn': ['torn', 'tron'],
 'tornachile': ['chlorinate', 'ectorhinal', 'tornachile'],
 'tornado': ['donator', 'odorant', 'tornado'],
 'tornal': ['latron', 'lontar', 'tornal'],
 'tornaria': ['rotarian', 'tornaria'],
 'tornarian': ['narration', 'tornarian'],
 'tornese': ['enstore', 'estrone', 'storeen', 'tornese'],
 'torney': ['torney', 'tyrone'],
 'tornit': ['intort', 'tornit', 'triton'],
 'tornus': ['tornus', 'unsort'],
 'toro': ['root', 'roto', 'toro'],
 'torose': ['seroot', 'sooter', 'torose'],
 'torpent': ['portent', 'torpent'],
 'torpescent': ['precontest', 'torpescent'],
 'torpid': ['torpid', 'tripod'],
 'torpify': ['portify', 'torpify'],
 'torpor': ['portor', 'torpor'],
 'torque': ['quoter', 'roquet', 'torque'],
 'torques': ['questor', 'torques'],
 'torrubia': ['rubiator', 'torrubia'],
 'torsade': ['rosated', 'torsade'],
 'torse': ['roset', 'rotse', 'soter', 'stero', 'store', 'torse'],
 'torsel': ['relost', 'reslot', 'rostel', 'sterol', 'torsel'],
 'torsile': ['estriol', 'torsile'],
 'torsion': ['isotron', 'torsion'],
 'torsional': ['tonsorial', 'torsional'],
 'torsk': ['stork', 'torsk'],
 'torso': ['roost', 'torso'],
 'torsten': ['snotter', 'stentor', 'torsten'],
 'tort': ['tort', 'trot'],
 'torta': ['ottar', 'tarot', 'torta', 'troat'],
 'torteau': ['outrate', 'outtear', 'torteau'],
 'torticone': ['torticone', 'tritocone'],
 'tortile': ['lotrite', 'tortile', 'triolet'],
 'tortilla': ['littoral', 'tortilla'],
 'tortonian': ['intonator', 'tortonian'],
 'tortrices': ['tortrices', 'trisector'],
 'torture': ['torture', 'trouter', 'tutorer'],
 'toru': ['rout', 'toru', 'tour'],
 'torula': ['rotula', 'torula'],
 'torulaform': ['formulator', 'torulaform'],
 'toruliform': ['rotuliform', 'toruliform'],
 'torulose': ['outsoler', 'torulose'],
 'torulus': ['rotulus', 'torulus'],
 'torus': ['roust', 'rusot', 'stour', 'sutor', 'torus'],
 'torve': ['overt', 'rovet', 'torve', 'trove', 'voter'],
 'tory': ['royt', 'ryot', 'tory', 'troy', 'tyro'],
 'toryish': ['history', 'toryish'],
 'toryism': ['toryism', 'trisomy'],
 'tosephtas': ['posthaste', 'tosephtas'],
 'tosh': ['host', 'shot', 'thos', 'tosh'],
 'tosher': ['hoster', 'tosher'],
 'toshly': ['hostly', 'toshly'],
 'toshnail': ['histonal', 'toshnail'],
 'toss': ['sots', 'toss'],
 'tosser': ['retoss', 'tosser'],
 'tossily': ['tossily', 'tylosis'],
 'tossup': ['tossup', 'uptoss'],
 'tost': ['stot', 'tost'],
 'total': ['lotta', 'total'],
 'totanine': ['intonate', 'totanine'],
 'totaquin': ['quintato', 'totaquin'],
 'totchka': ['hattock', 'totchka'],
 'totem': ['motet', 'motte', 'totem'],
 'toter': ['ortet', 'otter', 'toter'],
 'tother': ['hotter', 'tother'],
 'toto': ['otto', 'toot', 'toto'],
 'toty': ['toty', 'tyto'],
 'tou': ['out', 'tou'],
 'toucan': ['toucan', 'tucano', 'uncoat'],
 'touch': ['couth', 'thuoc', 'touch'],
 'toucher': ['retouch', 'toucher'],
 'touchily': ['couthily', 'touchily'],
 'touchiness': ['couthiness', 'touchiness'],
 'touching': ['touching', 'ungothic'],
 'touchless': ['couthless', 'touchless'],
 'toug': ['gout', 'toug'],
 'tough': ['ought', 'tough'],
 'toughness': ['oughtness', 'toughness'],
 'toup': ['pout', 'toup'],
 'tour': ['rout', 'toru', 'tour'],
 'tourer': ['retour', 'router', 'tourer'],
 'touring': ['outgrin', 'outring', 'routing', 'touring'],
 'tourism': ['sumitro', 'tourism'],
 'touristy': ['touristy', 'yttrious'],
 'tourmalinic': ['latrocinium', 'tourmalinic'],
 'tournamental': ['tournamental', 'ultramontane'],
 'tourte': ['tourte', 'touter'],
 'tousche': ['souchet', 'techous', 'tousche'],
 'touser': ['ouster', 'souter', 'touser', 'trouse'],
 'tousle': ['lutose', 'solute', 'tousle'],
 'touter': ['tourte', 'touter'],
 'tovaria': ['aviator', 'tovaria'],
 'tow': ['tow', 'two', 'wot'],
 'towel': ['owlet', 'towel'],
 'tower': ['rowet', 'tower', 'wrote'],
 'town': ['nowt', 'town', 'wont'],
 'towned': ['towned', 'wonted'],
 'towser': ['restow', 'stower', 'towser', 'worset'],
 'towy': ['towy', 'yowt'],
 'toxemia': ['oximate', 'toxemia'],
 'toy': ['toy', 'yot'],
 'toyer': ['royet', 'toyer'],
 'toyful': ['outfly', 'toyful'],
 'toyless': ['systole', 'toyless'],
 'toysome': ['myosote', 'toysome'],
 'tozer': ['terzo', 'tozer'],
 'tra': ['art', 'rat', 'tar', 'tra'],
 'trabea': ['abater', 'artabe', 'eartab', 'trabea'],
 'trace': ['caret',
           'carte',
           'cater',
           'crate',
           'creat',
           'creta',
           'react',
           'recta',
           'trace'],
 'traceable': ['creatable', 'traceable'],
 'tracer': ['arrect', 'carter', 'crater', 'recart', 'tracer'],
 'tracheata': ['cathartae', 'tracheata'],
 'trachelitis': ['thersitical', 'trachelitis'],
 'tracheolaryngotomy': ['laryngotracheotomy', 'tracheolaryngotomy'],
 'trachinoid': ['anhidrotic', 'trachinoid'],
 'trachitis': ['citharist', 'trachitis'],
 'trachle': ['clethra', 'latcher', 'ratchel', 'relatch', 'talcher', 'trachle'],
 'trachoma': ['achromat', 'trachoma'],
 'trachylinae': ['chatelainry', 'trachylinae'],
 'trachyte': ['chattery', 'ratchety', 'trachyte'],
 'tracker': ['retrack', 'tracker'],
 'trackside': ['sidetrack', 'trackside'],
 'tractator': ['attractor', 'tractator'],
 'tractile': ['tetrical', 'tractile'],
 'tracy': ['carty', 'tracy'],
 'trade': ['dater', 'derat', 'detar', 'drate', 'rated', 'trade', 'tread'],
 'trader': ['darter',
            'dartre',
            'redart',
            'retard',
            'retrad',
            'tarred',
            'trader'],
 'trading': ['darting', 'trading'],
 'tradite': ['attired', 'tradite'],
 'traditioner': ['retradition', 'traditioner'],
 'traditionism': ['mistradition', 'traditionism'],
 'traditorship': ['podarthritis', 'traditorship'],
 'traducent': ['reductant', 'traducent', 'truncated'],
 'trady': ['tardy', 'trady'],
 'trag': ['grat', 'trag'],
 'tragedial': ['taligrade', 'tragedial'],
 'tragicomedy': ['comitragedy', 'tragicomedy'],
 'tragulina': ['tragulina', 'triangula'],
 'traguline': ['granulite', 'traguline'],
 'trah': ['hart', 'rath', 'tahr', 'thar', 'trah'],
 'traheen': ['earthen', 'enheart', 'hearten', 'naether', 'teheran', 'traheen'],
 'traik': ['kitar', 'krait', 'rakit', 'traik'],
 'trail': ['litra', 'trail', 'trial'],
 'trailer': ['retiral', 'retrial', 'trailer'],
 'trailery': ['literary', 'trailery'],
 'trailing': ['ringtail', 'trailing'],
 'trailside': ['dialister', 'trailside'],
 'train': ['riant', 'tairn', 'tarin', 'train'],
 'trainable': ['albertina', 'trainable'],
 'trainage': ['antiager', 'trainage'],
 'trainboy': ['bonitary', 'trainboy'],
 'trained': ['antired', 'detrain', 'randite', 'trained'],
 'trainee': ['enteria', 'trainee', 'triaene'],
 'trainer': ['arterin', 'retrain', 'terrain', 'trainer'],
 'trainless': ['sternalis', 'trainless'],
 'trainster': ['restraint', 'retransit', 'trainster', 'transiter'],
 'traintime': ['intimater', 'traintime'],
 'trainy': ['rytina', 'trainy', 'tyrian'],
 'traipse': ['piaster', 'piastre', 'raspite', 'spirate', 'traipse'],
 'trait': ['ratti', 'titar', 'trait'],
 'tram': ['mart', 'tram'],
 'trama': ['matar', 'matra', 'trama'],
 'tramal': ['matral', 'tramal'],
 'trame': ['armet',
           'mater',
           'merat',
           'metra',
           'ramet',
           'tamer',
           'terma',
           'trame',
           'trema'],
 'trametes': ['teamster', 'trametes'],
 'tramline': ['terminal', 'tramline'],
 'tramper': ['retramp', 'tramper'],
 'trample': ['templar', 'trample'],
 'trampoline': ['intemporal', 'trampoline'],
 'tran': ['natr', 'rant', 'tarn', 'tran'],
 'trance': ['canter',
            'creant',
            'cretan',
            'nectar',
            'recant',
            'tanrec',
            'trance'],
 'tranced': ['cantred', 'centrad', 'tranced'],
 'trancelike': ['nectarlike', 'trancelike'],
 'trankum': ['trankum', 'turkman'],
 'transamination': ['transamination', 'transanimation'],
 'transanimation': ['transamination', 'transanimation'],
 'transept': ['prestant', 'transept'],
 'transeptally': ['platysternal', 'transeptally'],
 'transformer': ['retransform', 'transformer'],
 'transfuge': ['afterguns', 'transfuge'],
 'transient': ['instanter', 'transient'],
 'transigent': ['astringent', 'transigent'],
 'transimpression': ['pretransmission', 'transimpression'],
 'transire': ['restrain', 'strainer', 'transire'],
 'transit': ['straint', 'transit', 'tristan'],
 'transiter': ['restraint', 'retransit', 'trainster', 'transiter'],
 'transitive': ['revisitant', 'transitive'],
 'transmarine': ['strainerman', 'transmarine'],
 'transmit': ['tantrism', 'transmit'],
 'transmold': ['landstorm', 'transmold'],
 'transoceanic': ['narcaciontes', 'transoceanic'],
 'transonic': ['constrain', 'transonic'],
 'transpire': ['prestrain', 'transpire'],
 'transplanter': ['retransplant', 'transplanter'],
 'transportee': ['paternoster', 'prosternate', 'transportee'],
 'transporter': ['retransport', 'transporter'],
 'transpose': ['patroness', 'transpose'],
 'transposer': ['transposer', 'transprose'],
 'transprose': ['transposer', 'transprose'],
 'trap': ['part', 'prat', 'rapt', 'tarp', 'trap'],
 'trapa': ['apart', 'trapa'],
 'trapes': ['paster', 'repast', 'trapes'],
 'trapfall': ['pratfall', 'trapfall'],
 'traphole': ['plethora', 'traphole'],
 'trappean': ['apparent', 'trappean'],
 'traps': ['spart', 'sprat', 'strap', 'traps'],
 'traship': ['harpist', 'traship'],
 'trasy': ['satyr', 'stary', 'stray', 'trasy'],
 'traulism': ['altruism', 'muralist', 'traulism', 'ultraism'],
 'trauma': ['taruma', 'trauma'],
 'travale': ['larvate', 'lavaret', 'travale'],
 'trave': ['avert', 'tarve', 'taver', 'trave'],
 'travel': ['travel', 'varlet'],
 'traveler': ['retravel', 'revertal', 'traveler'],
 'traversion': ['overstrain', 'traversion'],
 'travertine': ['travertine', 'trinervate'],
 'travoy': ['travoy', 'votary'],
 'tray': ['arty', 'atry', 'tray'],
 'treacle': ['electra', 'treacle'],
 'tread': ['dater', 'derat', 'detar', 'drate', 'rated', 'trade', 'tread'],
 'treader': ['derater', 'retrade', 'retread', 'treader'],
 'treading': ['gradient', 'treading'],
 'treadle': ['delater', 'related', 'treadle'],
 'treason': ['noreast', 'rosetan', 'seatron', 'senator', 'treason'],
 'treasonish': ['astonisher', 'reastonish', 'treasonish'],
 'treasonist': ['steatornis', 'treasonist'],
 'treasonous': ['anoestrous', 'treasonous'],
 'treasurer': ['serrature', 'treasurer'],
 'treat': ['atter', 'tater', 'teart', 'tetra', 'treat'],
 'treatably': ['tabletary', 'treatably'],
 'treatee': ['ateeter', 'treatee'],
 'treater': ['ettarre', 'retreat', 'treater'],
 'treatise': ['estriate', 'treatise'],
 'treaty': ['attery', 'treaty', 'yatter'],
 'treble': ['belter', 'elbert', 'treble'],
 'treculia': ['arculite', 'cutleria', 'lucretia', 'reticula', 'treculia'],
 'tree': ['reet', 'teer', 'tree'],
 'treed': ['deter', 'treed'],
 'treeful': ['fleuret', 'treeful'],
 'treehood': ['theodore', 'treehood'],
 'treemaker': ['marketeer', 'treemaker'],
 'treeman': ['remanet', 'remeant', 'treeman'],
 'treen': ['enter', 'neter', 'renet', 'terne', 'treen'],
 'treenail': ['elaterin', 'entailer', 'treenail'],
 'treeship': ['hepteris', 'treeship'],
 'tref': ['fret', 'reft', 'tref'],
 'trefle': ['felter', 'telfer', 'trefle'],
 'trellis': ['stiller', 'trellis'],
 'trema': ['armet',
           'mater',
           'merat',
           'metra',
           'ramet',
           'tamer',
           'terma',
           'trame',
           'trema'],
 'trematoid': ['meditator', 'trematoid'],
 'tremella': ['realmlet', 'tremella'],
 'tremie': ['metier', 'retime', 'tremie'],
 'tremolo': ['roomlet', 'tremolo'],
 'tremor': ['termor', 'tremor'],
 'trenail': ['entrail',
             'latiner',
             'latrine',
             'ratline',
             'reliant',
             'retinal',
             'trenail'],
 'trenchant': ['centranth', 'trenchant'],
 'trencher': ['retrench', 'trencher'],
 'trenchmaster': ['stretcherman', 'trenchmaster'],
 'trenchwise': ['trenchwise', 'winchester'],
 'trentine': ['renitent', 'trentine'],
 'trepan': ['arpent',
            'enrapt',
            'entrap',
            'panter',
            'parent',
            'pretan',
            'trepan'],
 'trephine': ['nephrite', 'prehnite', 'trephine'],
 'trepid': ['dipter', 'trepid'],
 'trepidation': ['departition', 'partitioned', 'trepidation'],
 'treron': ['terron', 'treron', 'troner'],
 'treronidae': ['reordinate', 'treronidae'],
 'tressed': ['dessert', 'tressed'],
 'tressful': ['tressful', 'turfless'],
 'tressour': ['tressour', 'trousers'],
 'trest': ['stert', 'stret', 'trest'],
 'trestle': ['settler', 'sterlet', 'trestle'],
 'trevor': ['trevor', 'trover'],
 'trews': ['strew', 'trews', 'wrest'],
 'trey': ['trey', 'tyre'],
 'tri': ['rit', 'tri'],
 'triable': ['betrail', 'librate', 'triable', 'trilabe'],
 'triace': ['acrite', 'arcite', 'tercia', 'triace', 'tricae'],
 'triacid': ['arctiid', 'triacid', 'triadic'],
 'triacontane': ['recantation', 'triacontane'],
 'triaconter': ['retraction', 'triaconter'],
 'triactine': ['intricate', 'triactine'],
 'triadic': ['arctiid', 'triacid', 'triadic'],
 'triadical': ['raticidal', 'triadical'],
 'triadist': ['distrait', 'triadist'],
 'triaene': ['enteria', 'trainee', 'triaene'],
 'triage': ['gaiter', 'tairge', 'triage'],
 'trial': ['litra', 'trail', 'trial'],
 'trialism': ['mistrial', 'trialism'],
 'trialist': ['taistril', 'trialist'],
 'triamide': ['dimetria', 'mitridae', 'tiremaid', 'triamide'],
 'triamino': ['miniator', 'triamino'],
 'triandria': ['irradiant', 'triandria'],
 'triangle': ['integral', 'teraglin', 'triangle'],
 'triangula': ['tragulina', 'triangula'],
 'triannual': ['innatural', 'triannual'],
 'triannulate': ['antineutral', 'triannulate'],
 'triantelope': ['interpolate', 'triantelope'],
 'triapsidal': ['lapidarist', 'triapsidal'],
 'triareal': ['arterial', 'triareal'],
 'trias': ['arist',
           'astir',
           'sitar',
           'stair',
           'stria',
           'tarsi',
           'tisar',
           'trias'],
 'triassic': ['sarcitis', 'triassic'],
 'triazane': ['nazarite', 'nazirate', 'triazane'],
 'triazine': ['nazirite', 'triazine'],
 'tribade': ['redbait', 'tribade'],
 'tribase': ['baister', 'tribase'],
 'tribe': ['biter', 'tribe'],
 'tribelet': ['belitter', 'tribelet'],
 'triblet': ['blitter', 'brittle', 'triblet'],
 'tribonema': ['brominate', 'tribonema'],
 'tribuna': ['arbutin', 'tribuna'],
 'tribunal': ['tribunal', 'turbinal', 'untribal'],
 'tribunate': ['tribunate', 'turbinate'],
 'tribune': ['tribune', 'tuberin', 'turbine'],
 'tricae': ['acrite', 'arcite', 'tercia', 'triace', 'tricae'],
 'trice': ['citer', 'recti', 'ticer', 'trice'],
 'tricennial': ['encrinital', 'tricennial'],
 'triceratops': ['tetrasporic', 'triceratops'],
 'triceria': ['criteria', 'triceria'],
 'tricerion': ['criterion', 'tricerion'],
 'tricerium': ['criterium', 'tricerium'],
 'trichinous': ['trichinous', 'unhistoric'],
 'trichogyne': ['thyrogenic', 'trichogyne'],
 'trichoid': ['hidrotic', 'trichoid'],
 'trichomanes': ['anchoretism', 'trichomanes'],
 'trichome': ['chromite', 'trichome'],
 'trichopore': ['horopteric', 'rheotropic', 'trichopore'],
 'trichosis': ['historics', 'trichosis'],
 'trichosporum': ['sporotrichum', 'trichosporum'],
 'trichroic': ['cirrhotic', 'trichroic'],
 'trichroism': ['trichroism', 'triorchism'],
 'trichromic': ['microcrith', 'trichromic'],
 'tricia': ['iatric', 'tricia'],
 'trickle': ['tickler', 'trickle'],
 'triclinate': ['intractile', 'triclinate'],
 'tricolumnar': ['tricolumnar', 'ultramicron'],
 'tricosane': ['atroscine', 'certosina', 'ostracine', 'tinoceras', 'tricosane'],
 'tridacna': ['antacrid', 'cardiant', 'radicant', 'tridacna'],
 'tridecane': ['nectaried', 'tridecane'],
 'tridecene': ['intercede', 'tridecene'],
 'tridecyl': ['directly', 'tridecyl'],
 'tridiapason': ['disparation', 'tridiapason'],
 'tried': ['diter', 'tired', 'tried'],
 'triedly': ['tiredly', 'triedly'],
 'triene': ['entire', 'triene'],
 'triens': ['estrin', 'insert', 'sinter', 'sterin', 'triens'],
 'triental': ['tetralin', 'triental'],
 'triequal': ['quartile', 'requital', 'triequal'],
 'trier': ['terri', 'tirer', 'trier'],
 'trifle': ['fertil', 'filter', 'lifter', 'relift', 'trifle'],
 'trifler': ['flirter', 'trifler'],
 'triflet': ['flitter', 'triflet'],
 'trifling': ['flirting', 'trifling'],
 'triflingly': ['flirtingly', 'triflingly'],
 'trifolium': ['lituiform', 'trifolium'],
 'trig': ['girt', 'grit', 'trig'],
 'trigona': ['grotian', 'trigona'],
 'trigone': ['ergotin', 'genitor', 'negrito', 'ogtiern', 'trigone'],
 'trigonia': ['rigation', 'trigonia'],
 'trigonid': ['trigonid', 'tringoid'],
 'trigyn': ['trigyn', 'trying'],
 'trilabe': ['betrail', 'librate', 'triable', 'trilabe'],
 'trilineate': ['retinalite', 'trilineate'],
 'trilisa': ['liatris', 'trilisa'],
 'trillet': ['rillett', 'trillet'],
 'trilobate': ['latrobite', 'trilobate'],
 'trilobated': ['titleboard', 'trilobated'],
 'trimacular': ['matricular', 'trimacular'],
 'trimensual': ['neutralism', 'trimensual'],
 'trimer': ['mitrer', 'retrim', 'trimer'],
 'trimesic': ['meristic', 'trimesic', 'trisemic'],
 'trimesitinic': ['interimistic', 'trimesitinic'],
 'trimesyl': ['trimesyl', 'tylerism'],
 'trimeter': ['remitter', 'trimeter'],
 'trimstone': ['sortiment', 'trimstone'],
 'trinalize': ['latinizer', 'trinalize'],
 'trindle': ['tendril', 'trindle'],
 'trine': ['inert', 'inter', 'niter', 'retin', 'trine'],
 'trinely': ['elytrin', 'inertly', 'trinely'],
 'trinervate': ['travertine', 'trinervate'],
 'trinerve': ['inverter', 'reinvert', 'trinerve'],
 'trineural': ['retinular', 'trineural'],
 'tringa': ['rating', 'tringa'],
 'tringle': ['ringlet', 'tingler', 'tringle'],
 'tringoid': ['trigonid', 'tringoid'],
 'trinket': ['knitter', 'trinket'],
 'trinkle': ['tinkler', 'trinkle'],
 'trinoctial': ['tinctorial', 'trinoctial'],
 'trinodine': ['rendition', 'trinodine'],
 'trintle': ['lettrin', 'trintle'],
 'trio': ['riot', 'roit', 'trio'],
 'triode': ['editor', 'triode'],
 'trioecism': ['eroticism', 'isometric', 'meroistic', 'trioecism'],
 'triole': ['loiter', 'toiler', 'triole'],
 'trioleic': ['elicitor', 'trioleic'],
 'triolet': ['lotrite', 'tortile', 'triolet'],
 'trionymal': ['normality', 'trionymal'],
 'triopidae': ['poritidae', 'triopidae'],
 'triops': ['ripost', 'triops', 'tripos'],
 'triorchism': ['trichroism', 'triorchism'],
 'triose': ['restio', 'sorite', 'sortie', 'triose'],
 'tripe': ['perit', 'retip', 'tripe'],
 'tripedal': ['dipteral', 'tripedal'],
 'tripel': ['tripel', 'triple'],
 'tripeman': ['imperant', 'pairment', 'partimen', 'premiant', 'tripeman'],
 'tripersonal': ['intersporal', 'tripersonal'],
 'tripestone': ['septentrio', 'tripestone'],
 'triphane': ['perianth', 'triphane'],
 'triplane': ['interlap', 'repliant', 'triplane'],
 'triplasian': ['airplanist', 'triplasian'],
 'triplasic': ['pilastric', 'triplasic'],
 'triple': ['tripel', 'triple'],
 'triplice': ['perlitic', 'triplice'],
 'triplopia': ['propitial', 'triplopia'],
 'tripod': ['torpid', 'tripod'],
 'tripodal': ['dioptral', 'tripodal'],
 'tripodic': ['dioptric', 'tripodic'],
 'tripodical': ['dioptrical', 'tripodical'],
 'tripody': ['dioptry', 'tripody'],
 'tripos': ['ripost', 'triops', 'tripos'],
 'trippist': ['strippit', 'trippist'],
 'tripple': ['ripplet', 'tippler', 'tripple'],
 'tripsis': ['pristis', 'tripsis'],
 'tripsome': ['imposter', 'tripsome'],
 'tripudiant': ['antiputrid', 'tripudiant'],
 'tripyrenous': ['neurotripsy', 'tripyrenous'],
 'triratna': ['tartarin', 'triratna'],
 'trireme': ['meriter', 'miterer', 'trireme'],
 'trisalt': ['starlit', 'trisalt'],
 'trisected': ['decretist', 'trisected'],
 'trisector': ['tortrices', 'trisector'],
 'trisemic': ['meristic', 'trimesic', 'trisemic'],
 'trisetose': ['esoterist', 'trisetose'],
 'trishna': ['tarnish', 'trishna'],
 'trisilane': ['listerian', 'trisilane'],
 'triskele': ['kreistle', 'triskele'],
 'trismus': ['sistrum', 'trismus'],
 'trisome': ['erotism', 'mortise', 'trisome'],
 'trisomy': ['toryism', 'trisomy'],
 'trisonant': ['strontian', 'trisonant'],
 'trispinose': ['pirssonite', 'trispinose'],
 'trist': ['strit', 'trist'],
 'tristan': ['straint', 'transit', 'tristan'],
 'trisula': ['latirus', 'trisula'],
 'trisulcate': ['testicular', 'trisulcate'],
 'tritanope': ['antitrope', 'patronite', 'tritanope'],
 'tritanopic': ['antitropic', 'tritanopic'],
 'trite': ['titer', 'titre', 'trite'],
 'tritely': ['littery', 'tritely'],
 'triterpene': ['preterient', 'triterpene'],
 'tritheism': ['tiresmith', 'tritheism'],
 'trithionate': ['anorthitite', 'trithionate'],
 'tritocone': ['torticone', 'tritocone'],
 'tritoma': ['mattoir', 'tritoma'],
 'triton': ['intort', 'tornit', 'triton'],
 'triune': ['runite', 'triune', 'uniter', 'untire'],
 'trivalence': ['cantilever', 'trivalence'],
 'trivial': ['trivial', 'vitrail'],
 'trivialist': ['trivialist', 'vitrailist'],
 'troat': ['ottar', 'tarot', 'torta', 'troat'],
 'troca': ['actor', 'corta', 'croat', 'rocta', 'taroc', 'troca'],
 'trocar': ['carrot', 'trocar'],
 'trochaic': ['thoracic', 'tocharic', 'trochaic'],
 'trochate': ['theocrat', 'trochate'],
 'troche': ['hector', 'rochet', 'tocher', 'troche'],
 'trochi': ['chorti', 'orthic', 'thoric', 'trochi'],
 'trochidae': ['charioted', 'trochidae'],
 'trochila': ['acrolith', 'trochila'],
 'trochilic': ['chloritic', 'trochilic'],
 'trochlea': ['chlorate', 'trochlea'],
 'trochlearis': ['rhetoricals', 'trochlearis'],
 'trode': ['doter', 'tored', 'trode'],
 'trog': ['grot', 'trog'],
 'trogonidae': ['derogation', 'trogonidae'],
 'troiades': ['asteroid', 'troiades'],
 'troic': ['toric', 'troic'],
 'troika': ['korait', 'troika'],
 'trolley': ['tollery', 'trolley'],
 'tromba': ['tambor', 'tromba'],
 'trombe': ['retomb', 'trombe'],
 'trompe': ['emptor', 'trompe'],
 'tron': ['torn', 'tron'],
 'trona': ['orant', 'rotan', 'toran', 'trona'],
 'tronage': ['negator', 'tronage'],
 'trone': ['noter', 'tenor', 'toner', 'trone'],
 'troner': ['terron', 'treron', 'troner'],
 'troop': ['porto', 'proto', 'troop'],
 'trooper': ['protore', 'trooper'],
 'tropaeolum': ['pleurotoma', 'tropaeolum'],
 'tropaion': ['opinator', 'tropaion'],
 'tropal': ['patrol', 'portal', 'tropal'],
 'troparion': ['proration', 'troparion'],
 'tropary': ['parroty', 'portray', 'tropary'],
 'trope': ['poter', 'prote', 'repot', 'tepor', 'toper', 'trope'],
 'tropeic': ['perotic', 'proteic', 'tropeic'],
 'tropeine': ['ereption', 'tropeine'],
 'troper': ['porret', 'porter', 'report', 'troper'],
 'trophema': ['metaphor', 'trophema'],
 'trophesial': ['hospitaler', 'trophesial'],
 'trophical': ['carpolith', 'politarch', 'trophical'],
 'trophodisc': ['doctorship', 'trophodisc'],
 'trophonema': ['homopteran', 'trophonema'],
 'trophotropic': ['prototrophic', 'trophotropic'],
 'tropical': ['plicator', 'tropical'],
 'tropically': ['polycitral', 'tropically'],
 'tropidine': ['direption', 'perdition', 'tropidine'],
 'tropine': ['pointer', 'protein', 'pterion', 'repoint', 'tropine'],
 'tropism': ['primost', 'tropism'],
 'tropist': ['protist', 'tropist'],
 'tropistic': ['proctitis', 'protistic', 'tropistic'],
 'tropophyte': ['protophyte', 'tropophyte'],
 'tropophytic': ['protophytic', 'tropophytic'],
 'tropyl': ['portly', 'protyl', 'tropyl'],
 'trostera': ['rostrate', 'trostera'],
 'trot': ['tort', 'trot'],
 'troth': ['thort', 'troth'],
 'trotline': ['interlot', 'trotline'],
 'trouble': ['boulter', 'trouble'],
 'troughy': ['troughy', 'yoghurt'],
 'trounce': ['cornute', 'counter', 'recount', 'trounce'],
 'troupe': ['pouter', 'roupet', 'troupe'],
 'trouse': ['ouster', 'souter', 'touser', 'trouse'],
 'trouser': ['rouster', 'trouser'],
 'trouserian': ['souterrain', 'ternarious', 'trouserian'],
 'trousers': ['tressour', 'trousers'],
 'trout': ['trout', 'tutor'],
 'trouter': ['torture', 'trouter', 'tutorer'],
 'troutless': ['troutless', 'tutorless'],
 'trouty': ['trouty', 'tryout', 'tutory'],
 'trouvere': ['overtrue', 'overture', 'trouvere'],
 'trove': ['overt', 'rovet', 'torve', 'trove', 'voter'],
 'trover': ['trevor', 'trover'],
 'trow': ['trow', 'wort'],
 'trowel': ['rowlet', 'trowel', 'wolter'],
 'troy': ['royt', 'ryot', 'tory', 'troy', 'tyro'],
 'truandise': ['disnature', 'sturnidae', 'truandise'],
 'truant': ['truant', 'turtan'],
 'trub': ['brut', 'burt', 'trub', 'turb'],
 'trubu': ['burut', 'trubu'],
 'truce': ['cruet', 'eruct', 'recut', 'truce'],
 'truceless': ['cutleress', 'lecturess', 'truceless'],
 'trucial': ['curtail', 'trucial'],
 'trucks': ['struck', 'trucks'],
 'truculent': ['truculent', 'unclutter'],
 'truelove': ['revolute', 'truelove'],
 'truffle': ['fretful', 'truffle'],
 'trug': ['gurt', 'trug'],
 'truistical': ['altruistic', 'truistical', 'ultraistic'],
 'truly': ['rutyl', 'truly'],
 'trumperiness': ['surprisement', 'trumperiness'],
 'trumpie': ['imputer', 'trumpie'],
 'trun': ['runt', 'trun', 'turn'],
 'truncated': ['reductant', 'traducent', 'truncated'],
 'trundle': ['rundlet', 'trundle'],
 'trush': ['hurst', 'trush'],
 'trusion': ['nitrous', 'trusion'],
 'trust': ['strut', 'sturt', 'trust'],
 'trustee': ['surette', 'trustee'],
 'trusteeism': ['sestertium', 'trusteeism'],
 'trusten': ['entrust', 'stunter', 'trusten'],
 'truster': ['retrust', 'truster'],
 'trustle': ['slutter', 'trustle'],
 'truth': ['thurt', 'truth'],
 'trying': ['trigyn', 'trying'],
 'tryma': ['marty', 'tryma'],
 'tryout': ['trouty', 'tryout', 'tutory'],
 'trypa': ['party', 'trypa'],
 'trypan': ['pantry', 'trypan'],
 'tryptase': ['tapestry', 'tryptase'],
 'tsar': ['sart', 'star', 'stra', 'tars', 'tsar'],
 'tsardom': ['stardom', 'tsardom'],
 'tsarina': ['artisan', 'astrain', 'sartain', 'tsarina'],
 'tsarship': ['starship', 'tsarship'],
 'tsatlee': ['atelets', 'tsatlee'],
 'tsere': ['ester',
           'estre',
           'reest',
           'reset',
           'steer',
           'stere',
           'stree',
           'terse',
           'tsere'],
 'tsetse': ['sestet', 'testes', 'tsetse'],
 'tshi': ['hist', 'sith', 'this', 'tshi'],
 'tsia': ['atis', 'sita', 'tsia'],
 'tsine': ['inset', 'neist', 'snite', 'stein', 'stine', 'tsine'],
 'tsiology': ['sitology', 'tsiology'],
 'tsoneca': ['costean', 'tsoneca'],
 'tsonecan': ['noncaste', 'tsonecan'],
 'tsuga': ['agust', 'tsuga'],
 'tsuma': ['matsu', 'tamus', 'tsuma'],
 'tsun': ['stun', 'sunt', 'tsun'],
 'tu': ['tu', 'ut'],
 'tua': ['tau', 'tua', 'uta'],
 'tuan': ['antu', 'aunt', 'naut', 'taun', 'tuan', 'tuna'],
 'tuareg': ['argute', 'guetar', 'rugate', 'tuareg'],
 'tuarn': ['arnut', 'tuarn', 'untar'],
 'tub': ['but', 'tub'],
 'tuba': ['abut', 'tabu', 'tuba'],
 'tubae': ['butea', 'taube', 'tubae'],
 'tubal': ['balut', 'tubal'],
 'tubar': ['bruta', 'tubar'],
 'tubate': ['battue', 'tubate'],
 'tube': ['bute', 'tebu', 'tube'],
 'tuber': ['brute', 'buret', 'rebut', 'tuber'],
 'tubercula': ['lucubrate', 'tubercula'],
 'tuberin': ['tribune', 'tuberin', 'turbine'],
 'tuberless': ['butleress', 'tuberless'],
 'tublet': ['buttle', 'tublet'],
 'tuboovarial': ['ovariotubal', 'tuboovarial'],
 'tucana': ['canaut', 'tucana'],
 'tucano': ['toucan', 'tucano', 'uncoat'],
 'tuchun': ['tuchun', 'uncuth'],
 'tucker': ['retuck', 'tucker'],
 'tue': ['tue', 'ute'],
 'tueiron': ['routine', 'tueiron'],
 'tug': ['gut', 'tug'],
 'tughra': ['raught', 'tughra'],
 'tugless': ['gutless', 'tugless'],
 'tuglike': ['gutlike', 'tuglike'],
 'tugman': ['tangum', 'tugman'],
 'tuism': ['muist', 'tuism'],
 'tuke': ['ketu', 'teuk', 'tuke'],
 'tukra': ['kraut', 'tukra'],
 'tulare': ['tulare', 'uretal'],
 'tulasi': ['situal', 'situla', 'tulasi'],
 'tulchan': ['tulchan', 'unlatch'],
 'tule': ['lute', 'tule'],
 'tulipa': ['tipula', 'tulipa'],
 'tulisan': ['latinus', 'tulisan', 'unalist'],
 'tulsi': ['litus', 'sluit', 'tulsi'],
 'tumbler': ['tumbler', 'tumbrel'],
 'tumbrel': ['tumbler', 'tumbrel'],
 'tume': ['mute', 'tume'],
 'tumescence': ['mutescence', 'tumescence'],
 'tumorous': ['mortuous', 'tumorous'],
 'tumulary': ['mutulary', 'tumulary'],
 'tun': ['nut', 'tun'],
 'tuna': ['antu', 'aunt', 'naut', 'taun', 'tuan', 'tuna'],
 'tunable': ['abluent', 'tunable'],
 'tunbellied': ['tunbellied', 'unbilleted'],
 'tunca': ['tunca', 'unact'],
 'tund': ['dunt', 'tund'],
 'tunder': ['runted', 'tunder', 'turned'],
 'tundra': ['durant', 'tundra'],
 'tuner': ['enrut', 'tuner', 'urent'],
 'tunga': ['gaunt', 'tunga'],
 'tungan': ['tangun', 'tungan'],
 'tungate': ['tungate', 'tutenag'],
 'tungo': ['tungo', 'ungot'],
 'tungstosilicate': ['silicotungstate', 'tungstosilicate'],
 'tungstosilicic': ['silicotungstic', 'tungstosilicic'],
 'tunic': ['cutin', 'incut', 'tunic'],
 'tunica': ['anicut', 'nautic', 'ticuna', 'tunica'],
 'tunican': ['ticunan', 'tunican'],
 'tunicary': ['nycturia', 'tunicary'],
 'tunicle': ['linecut', 'tunicle'],
 'tunicless': ['lentiscus', 'tunicless'],
 'tunist': ['suttin', 'tunist'],
 'tunk': ['knut', 'tunk'],
 'tunker': ['tunker', 'turken'],
 'tunlike': ['nutlike', 'tunlike'],
 'tunna': ['naunt', 'tunna'],
 'tunnel': ['nunlet', 'tunnel', 'unlent'],
 'tunnelman': ['annulment', 'tunnelman'],
 'tunner': ['runnet', 'tunner', 'unrent'],
 'tunnor': ['tunnor', 'untorn'],
 'tuno': ['tuno', 'unto'],
 'tup': ['put', 'tup'],
 'tur': ['rut', 'tur'],
 'turacin': ['curtain', 'turacin', 'turcian'],
 'turanian': ['nutarian', 'turanian'],
 'turanism': ['naturism', 'sturmian', 'turanism'],
 'turb': ['brut', 'burt', 'trub', 'turb'],
 'turban': ['tanbur', 'turban'],
 'turbaned': ['breadnut', 'turbaned'],
 'turbanless': ['substernal', 'turbanless'],
 'turbeh': ['hubert', 'turbeh'],
 'turbinal': ['tribunal', 'turbinal', 'untribal'],
 'turbinate': ['tribunate', 'turbinate'],
 'turbine': ['tribune', 'tuberin', 'turbine'],
 'turbined': ['turbined', 'underbit'],
 'turcian': ['curtain', 'turacin', 'turcian'],
 'turco': ['court', 'crout', 'turco'],
 'turcoman': ['courtman', 'turcoman'],
 'turdinae': ['indurate', 'turdinae'],
 'turdine': ['intrude', 'turdine', 'untired', 'untried'],
 'tureen': ['neuter', 'retune', 'runtee', 'tenure', 'tureen'],
 'turfed': ['dufter', 'turfed'],
 'turfen': ['turfen', 'unfret'],
 'turfless': ['tressful', 'turfless'],
 'turgent': ['grutten', 'turgent'],
 'turk': ['kurt', 'turk'],
 'turken': ['tunker', 'turken'],
 'turki': ['tikur', 'turki'],
 'turkman': ['trankum', 'turkman'],
 'turma': ['martu', 'murat', 'turma'],
 'turn': ['runt', 'trun', 'turn'],
 'turndown': ['downturn', 'turndown'],
 'turned': ['runted', 'tunder', 'turned'],
 'turnel': ['runlet', 'turnel'],
 'turner': ['return', 'turner'],
 'turnhall': ['turnhall', 'unthrall'],
 'turnout': ['outturn', 'turnout'],
 'turnover': ['overturn', 'turnover'],
 'turnpin': ['turnpin', 'unprint'],
 'turns': ['snurt', 'turns'],
 'turntail': ['rutilant', 'turntail'],
 'turnup': ['turnup', 'upturn'],
 'turp': ['prut', 'turp'],
 'turpid': ['putrid', 'turpid'],
 'turpidly': ['putridly', 'turpidly'],
 'turps': ['spurt', 'turps'],
 'turret': ['rutter', 'turret'],
 'turricula': ['turricula', 'utricular'],
 'turse': ['serut', 'strue', 'turse', 'uster'],
 'tursenoi': ['rutinose', 'tursenoi'],
 'tursio': ['suitor', 'tursio'],
 'turtan': ['truant', 'turtan'],
 'tuscan': ['cantus', 'tuscan', 'uncast'],
 'tusche': ['schute', 'tusche'],
 'tush': ['shut', 'thus', 'tush'],
 'tusher': ['reshut', 'suther', 'thurse', 'tusher'],
 'tussal': ['saltus', 'tussal'],
 'tusser': ['russet', 'tusser'],
 'tussore': ['estrous', 'oestrus', 'sestuor', 'tussore'],
 'tutelo': ['outlet', 'tutelo'],
 'tutenag': ['tungate', 'tutenag'],
 'tutman': ['mutant', 'tantum', 'tutman'],
 'tutor': ['trout', 'tutor'],
 'tutorer': ['torture', 'trouter', 'tutorer'],
 'tutorial': ['outtrail', 'tutorial'],
 'tutorism': ['mistutor', 'tutorism'],
 'tutorless': ['troutless', 'tutorless'],
 'tutory': ['trouty', 'tryout', 'tutory'],
 'tuts': ['stut', 'tuts'],
 'tutster': ['stutter', 'tutster'],
 'twa': ['taw', 'twa', 'wat'],
 'twae': ['tewa', 'twae', 'weta'],
 'twain': ['atwin', 'twain', 'witan'],
 'twaite': ['tawite', 'tawtie', 'twaite'],
 'twal': ['twal', 'walt'],
 'twas': ['sawt', 'staw', 'swat', 'taws', 'twas', 'wast'],
 'twat': ['twat', 'watt'],
 'twee': ['twee', 'weet'],
 'tweel': ['tewel', 'tweel'],
 'twere': ['rewet', 'tewer', 'twere'],
 'twi': ['twi', 'wit'],
 'twigsome': ['twigsome', 'wegotism'],
 'twin': ['twin', 'wint'],
 'twiner': ['twiner', 'winter'],
 'twingle': ['twingle', 'welting', 'winglet'],
 'twinkle': ['twinkle', 'winklet'],
 'twinkler': ['twinkler', 'wrinklet'],
 'twinter': ['twinter', 'written'],
 'twire': ['twire', 'write'],
 'twister': ['retwist', 'twister'],
 'twitchety': ['twitchety', 'witchetty'],
 'twite': ['tewit', 'twite'],
 'two': ['tow', 'two', 'wot'],
 'twoling': ['lingtow', 'twoling'],
 'tyche': ['techy', 'tyche'],
 'tydie': ['deity', 'tydie'],
 'tye': ['tye', 'yet'],
 'tyke': ['kyte', 'tyke'],
 'tylerism': ['trimesyl', 'tylerism'],
 'tyloma': ['latomy', 'tyloma'],
 'tylose': ['tolsey', 'tylose'],
 'tylosis': ['tossily', 'tylosis'],
 'tylotus': ['stoutly', 'tylotus'],
 'tylus': ['lusty', 'tylus'],
 'typal': ['aptly', 'patly', 'platy', 'typal'],
 'typees': ['steepy', 'typees'],
 'typer': ['perty', 'typer'],
 'typha': ['pathy', 'typha'],
 'typhia': ['pythia', 'typhia'],
 'typhic': ['phytic', 'pitchy', 'pythic', 'typhic'],
 'typhlopidae': ['heptaploidy', 'typhlopidae'],
 'typhoean': ['anophyte', 'typhoean'],
 'typhogenic': ['phytogenic', 'pythogenic', 'typhogenic'],
 'typhoid': ['phytoid', 'typhoid'],
 'typhonian': ['antiphony', 'typhonian'],
 'typhonic': ['hypnotic', 'phytonic', 'pythonic', 'typhonic'],
 'typhosis': ['phytosis', 'typhosis'],
 'typica': ['atypic', 'typica'],
 'typographer': ['petrography', 'pterography', 'typographer'],
 'typographic': ['graphotypic', 'pictography', 'typographic'],
 'typology': ['logotypy', 'typology'],
 'typophile': ['hippolyte', 'typophile'],
 'tyre': ['trey', 'tyre'],
 'tyrian': ['rytina', 'trainy', 'tyrian'],
 'tyro': ['royt', 'ryot', 'tory', 'troy', 'tyro'],
 'tyrocidin': ['nordicity', 'tyrocidin'],
 'tyrolean': ['neolatry', 'ornately', 'tyrolean'],
 'tyrolite': ['toiletry', 'tyrolite'],
 'tyrone': ['torney', 'tyrone'],
 'tyronism': ['smyrniot', 'tyronism'],
 'tyrosine': ['tyrosine', 'tyrsenoi'],
 'tyrrheni': ['erythrin', 'tyrrheni'],
 'tyrsenoi': ['tyrosine', 'tyrsenoi'],
 'tyste': ['testy', 'tyste'],
 'tyto': ['toty', 'tyto'],
 'uang': ['gaun', 'guan', 'guna', 'uang'],
 'ucal': ['caul', 'ucal'],
 'udal': ['auld', 'dual', 'laud', 'udal'],
 'udaler': ['lauder', 'udaler'],
 'udalman': ['ladanum', 'udalman'],
 'udo': ['duo', 'udo'],
 'uds': ['sud', 'uds'],
 'ugh': ['hug', 'ugh'],
 'uglisome': ['eulogism', 'uglisome'],
 'ugrian': ['gurian', 'ugrian'],
 'ugric': ['guric', 'ugric'],
 'uhtsong': ['gunshot', 'shotgun', 'uhtsong'],
 'uinal': ['inula', 'luian', 'uinal'],
 'uinta': ['uinta', 'uniat'],
 'ulcer': ['cruel', 'lucre', 'ulcer'],
 'ulcerate': ['celature', 'ulcerate'],
 'ulcerous': ['ulcerous', 'urceolus'],
 'ule': ['leu', 'lue', 'ule'],
 'ulema': ['amelu', 'leuma', 'ulema'],
 'uletic': ['lucite', 'luetic', 'uletic'],
 'ulex': ['luxe', 'ulex'],
 'ulla': ['lula', 'ulla'],
 'ulling': ['ulling', 'ungill'],
 'ulmin': ['linum', 'ulmin'],
 'ulminic': ['clinium', 'ulminic'],
 'ulmo': ['moul', 'ulmo'],
 'ulna': ['laun', 'luna', 'ulna', 'unal'],
 'ulnad': ['dunal', 'laund', 'lunda', 'ulnad'],
 'ulnar': ['lunar', 'ulnar', 'urnal'],
 'ulnare': ['lunare', 'neural', 'ulnare', 'unreal'],
 'ulnaria': ['lunaria', 'ulnaria', 'uralian'],
 'ulotrichi': ['ulotrichi', 'urolithic'],
 'ulster': ['luster', 'result', 'rustle', 'sutler', 'ulster'],
 'ulstered': ['deluster', 'ulstered'],
 'ulsterian': ['neuralist', 'ulsterian', 'unrealist'],
 'ulstering': ['resulting', 'ulstering'],
 'ulsterman': ['menstrual', 'ulsterman'],
 'ultima': ['mulita', 'ultima'],
 'ultimate': ['mutilate', 'ultimate'],
 'ultimation': ['mutilation', 'ultimation'],
 'ultonian': ['lunation', 'ultonian'],
 'ultra': ['lutra', 'ultra'],
 'ultrabasic': ['arcubalist', 'ultrabasic'],
 'ultraism': ['altruism', 'muralist', 'traulism', 'ultraism'],
 'ultraist': ['altruist', 'ultraist'],
 'ultraistic': ['altruistic', 'truistical', 'ultraistic'],
 'ultramicron': ['tricolumnar', 'ultramicron'],
 'ultraminute': ['intermutual', 'ultraminute'],
 'ultramontane': ['tournamental', 'ultramontane'],
 'ultranice': ['centurial', 'lucretian', 'ultranice'],
 'ultrasterile': ['reillustrate', 'ultrasterile'],
 'ulua': ['aulu', 'ulua'],
 'ulva': ['ulva', 'uval'],
 'um': ['mu', 'um'],
 'umbel': ['umbel', 'umble'],
 'umbellar': ['umbellar', 'umbrella'],
 'umber': ['brume', 'umber'],
 'umbilic': ['bulimic', 'umbilic'],
 'umbiliform': ['bulimiform', 'umbiliform'],
 'umble': ['umbel', 'umble'],
 'umbonial': ['olibanum', 'umbonial'],
 'umbral': ['brumal', 'labrum', 'lumbar', 'umbral'],
 'umbrel': ['lumber', 'rumble', 'umbrel'],
 'umbrella': ['umbellar', 'umbrella'],
 'umbrous': ['brumous', 'umbrous'],
 'ume': ['emu', 'ume'],
 'umlaut': ['mutual', 'umlaut'],
 'umph': ['hump', 'umph'],
 'umpire': ['impure', 'umpire'],
 'un': ['nu', 'un'],
 'unabetted': ['debutante', 'unabetted'],
 'unabhorred': ['unabhorred', 'unharbored'],
 'unable': ['nebula', 'unable', 'unbale'],
 'unaccumulate': ['acutenaculum', 'unaccumulate'],
 'unact': ['tunca', 'unact'],
 'unadherent': ['unadherent', 'underneath', 'underthane'],
 'unadmire': ['unadmire', 'underaim'],
 'unadmired': ['unadmired', 'undermaid'],
 'unadored': ['unadored', 'unroaded'],
 'unadvertised': ['disadventure', 'unadvertised'],
 'unafire': ['fuirena', 'unafire'],
 'unaged': ['augend', 'engaud', 'unaged'],
 'unagreed': ['dungaree', 'guardeen', 'unagreed', 'underage', 'ungeared'],
 'unailing': ['inguinal', 'unailing'],
 'unaimed': ['numidae', 'unaimed'],
 'unaisled': ['unaisled', 'unsailed'],
 'unakite': ['kutenai', 'unakite'],
 'unal': ['laun', 'luna', 'ulna', 'unal'],
 'unalarming': ['unalarming', 'unmarginal'],
 'unalert': ['laurent', 'neutral', 'unalert'],
 'unalertly': ['neutrally', 'unalertly'],
 'unalertness': ['neutralness', 'unalertness'],
 'unalimentary': ['anteluminary', 'unalimentary'],
 'unalist': ['latinus', 'tulisan', 'unalist'],
 'unallotted': ['unallotted', 'untotalled'],
 'unalmsed': ['dulseman', 'unalmsed'],
 'unaltered': ['unaltered', 'unrelated'],
 'unaltering': ['unaltering', 'unrelating'],
 'unamassed': ['mussaenda', 'unamassed'],
 'unambush': ['subhuman', 'unambush'],
 'unamenability': ['unamenability', 'unnameability'],
 'unamenable': ['unamenable', 'unnameable'],
 'unamenableness': ['unamenableness', 'unnameableness'],
 'unamenably': ['unamenably', 'unnameably'],
 'unamend': ['mundane', 'unamend', 'unmaned', 'unnamed'],
 'unami': ['maniu', 'munia', 'unami'],
 'unapt': ['punta', 'unapt', 'untap'],
 'unarising': ['grusinian', 'unarising'],
 'unarm': ['muran', 'ruman', 'unarm', 'unram', 'urman'],
 'unarmed': ['duramen', 'maunder', 'unarmed'],
 'unarray': ['unarray', 'yaruran'],
 'unarrestable': ['subterraneal', 'unarrestable'],
 'unarrested': ['unarrested', 'unserrated'],
 'unarted': ['daunter', 'unarted', 'unrated', 'untread'],
 'unarticled': ['denticular', 'unarticled'],
 'unartistic': ['naturistic', 'unartistic'],
 'unartistical': ['naturalistic', 'unartistical'],
 'unartistically': ['naturistically', 'unartistically'],
 'unary': ['anury', 'unary', 'unray'],
 'unastray': ['auntsary', 'unastray'],
 'unathirst': ['struthian', 'unathirst'],
 'unattire': ['tainture', 'unattire'],
 'unattuned': ['unattuned', 'untaunted'],
 'unaverted': ['adventure', 'unaverted'],
 'unavertible': ['unavertible', 'unveritable'],
 'unbag': ['bugan', 'bunga', 'unbag'],
 'unbain': ['nubian', 'unbain'],
 'unbale': ['nebula', 'unable', 'unbale'],
 'unbar': ['buran', 'unbar', 'urban'],
 'unbare': ['eburna', 'unbare', 'unbear', 'urbane'],
 'unbarred': ['errabund', 'unbarred'],
 'unbased': ['subdean', 'unbased'],
 'unbaste': ['unbaste', 'unbeast'],
 'unbatted': ['debutant', 'unbatted'],
 'unbay': ['bunya', 'unbay'],
 'unbe': ['benu', 'unbe'],
 'unbear': ['eburna', 'unbare', 'unbear', 'urbane'],
 'unbearded': ['unbearded', 'unbreaded'],
 'unbeast': ['unbaste', 'unbeast'],
 'unbeavered': ['unbeavered', 'unbereaved'],
 'unbelied': ['unbelied', 'unedible'],
 'unbereaved': ['unbeavered', 'unbereaved'],
 'unbesot': ['subnote', 'subtone', 'unbesot'],
 'unbias': ['anubis', 'unbias'],
 'unbillet': ['bulletin', 'unbillet'],
 'unbilleted': ['tunbellied', 'unbilleted'],
 'unblasted': ['dunstable', 'unblasted', 'unstabled'],
 'unbled': ['bundle', 'unbled'],
 'unboasted': ['eastbound', 'unboasted'],
 'unboat': ['outban', 'unboat'],
 'unboding': ['bounding', 'unboding'],
 'unbog': ['bungo', 'unbog'],
 'unboiled': ['unboiled', 'unilobed'],
 'unboned': ['bounden', 'unboned'],
 'unborder': ['unborder', 'underorb'],
 'unbored': ['beround', 'bounder', 'rebound', 'unbored', 'unorbed', 'unrobed'],
 'unboweled': ['unboweled', 'unelbowed'],
 'unbrace': ['bucrane', 'unbrace'],
 'unbraceleted': ['unbraceleted', 'uncelebrated'],
 'unbraid': ['barundi', 'unbraid'],
 'unbrailed': ['indurable', 'unbrailed', 'unridable'],
 'unbreaded': ['unbearded', 'unbreaded'],
 'unbred': ['bunder', 'burden', 'burned', 'unbred'],
 'unbribed': ['unbribed', 'unribbed'],
 'unbrief': ['unbrief', 'unfiber'],
 'unbriefed': ['unbriefed', 'unfibered'],
 'unbroiled': ['unbroiled', 'underboil'],
 'unbrushed': ['unbrushed', 'underbush'],
 'unbud': ['bundu', 'unbud', 'undub'],
 'unburden': ['unburden', 'unburned'],
 'unburned': ['unburden', 'unburned'],
 'unbuttered': ['unbuttered', 'unrebutted'],
 'unca': ['cuna', 'unca'],
 'uncage': ['cangue', 'uncage'],
 'uncambered': ['uncambered', 'unembraced'],
 'uncamerated': ['uncamerated', 'unmacerated'],
 'uncapable': ['uncapable', 'unpacable'],
 'uncaptious': ['uncaptious', 'usucaption'],
 'uncarted': ['uncarted', 'uncrated', 'underact', 'untraced'],
 'uncartooned': ['uncartooned', 'uncoronated'],
 'uncase': ['uncase', 'usance'],
 'uncask': ['uncask', 'unsack'],
 'uncasked': ['uncasked', 'unsacked'],
 'uncast': ['cantus', 'tuscan', 'uncast'],
 'uncatalogued': ['uncatalogued', 'uncoagulated'],
 'uncate': ['tecuna', 'uncate'],
 'uncaused': ['uncaused', 'unsauced'],
 'uncavalier': ['naviculare', 'uncavalier'],
 'uncelebrated': ['unbraceleted', 'uncelebrated'],
 'uncellar': ['lucernal', 'nucellar', 'uncellar'],
 'uncenter': ['uncenter', 'unrecent'],
 'uncertain': ['encurtain', 'runcinate', 'uncertain'],
 'uncertifiable': ['uncertifiable', 'unrectifiable'],
 'uncertified': ['uncertified', 'unrectified'],
 'unchain': ['chunnia', 'unchain'],
 'unchair': ['chunari', 'unchair'],
 'unchalked': ['unchalked', 'unhackled'],
 'uncharge': ['gunreach', 'uncharge'],
 'uncharm': ['uncharm', 'unmarch'],
 'uncharming': ['uncharming', 'unmarching'],
 'uncharred': ['uncharred', 'underarch'],
 'uncheat': ['uncheat', 'unteach'],
 'uncheating': ['uncheating', 'unteaching'],
 'unchoked': ['unchoked', 'unhocked'],
 'unchoosable': ['chaenolobus', 'unchoosable'],
 'unchosen': ['nonesuch', 'unchosen'],
 'uncial': ['cunila', 'lucian', 'lucina', 'uncial'],
 'unciferous': ['nuciferous', 'unciferous'],
 'unciform': ['nuciform', 'unciform'],
 'uncinate': ['nunciate', 'uncinate'],
 'unclaimed': ['unclaimed', 'undecimal', 'unmedical'],
 'unclay': ['lunacy', 'unclay'],
 'unclead': ['unclead', 'unlaced'],
 'unclear': ['crenula', 'lucarne', 'nuclear', 'unclear'],
 'uncleared': ['uncleared', 'undeclare'],
 'uncledom': ['columned', 'uncledom'],
 'uncleship': ['siphuncle', 'uncleship'],
 'uncloister': ['cornulites', 'uncloister'],
 'unclose': ['counsel', 'unclose'],
 'unclutter': ['truculent', 'unclutter'],
 'unco': ['cuon', 'unco'],
 'uncoagulated': ['uncatalogued', 'uncoagulated'],
 'uncoat': ['toucan', 'tucano', 'uncoat'],
 'uncoated': ['outdance', 'uncoated'],
 'uncoiled': ['nucleoid', 'uncoiled'],
 'uncoin': ['nuncio', 'uncoin'],
 'uncollapsed': ['uncollapsed', 'unscalloped'],
 'uncolored': ['uncolored', 'undercool'],
 'uncomic': ['muconic', 'uncomic'],
 'uncompatible': ['incomputable', 'uncompatible'],
 'uncomplaint': ['uncomplaint', 'uncompliant'],
 'uncomplete': ['couplement', 'uncomplete'],
 'uncompliant': ['uncomplaint', 'uncompliant'],
 'unconcerted': ['unconcerted', 'unconcreted'],
 'unconcreted': ['unconcerted', 'unconcreted'],
 'unconservable': ['unconservable', 'unconversable'],
 'unconstraint': ['noncurantist', 'unconstraint'],
 'uncontrasted': ['counterstand', 'uncontrasted'],
 'unconversable': ['unconservable', 'unconversable'],
 'uncoop': ['coupon', 'uncoop'],
 'uncooped': ['couponed', 'uncooped'],
 'uncope': ['pounce', 'uncope'],
 'uncopied': ['cupidone', 'uncopied'],
 'uncore': ['conure', 'rounce', 'uncore'],
 'uncored': ['crunode', 'uncored'],
 'uncorked': ['uncorked', 'unrocked'],
 'uncoronated': ['uncartooned', 'uncoronated'],
 'uncorrect': ['cocurrent', 'occurrent', 'uncorrect'],
 'uncorrugated': ['counterguard', 'uncorrugated'],
 'uncorseted': ['uncorseted', 'unescorted'],
 'uncostumed': ['uncostumed', 'uncustomed'],
 'uncoursed': ['uncoursed', 'unscoured'],
 'uncouth': ['uncouth', 'untouch'],
 'uncoverable': ['uncoverable', 'unrevocable'],
 'uncradled': ['uncradled', 'underclad'],
 'uncrated': ['uncarted', 'uncrated', 'underact', 'untraced'],
 'uncreased': ['uncreased', 'undercase'],
 'uncreatable': ['uncreatable', 'untraceable'],
 'uncreatableness': ['uncreatableness', 'untraceableness'],
 'uncreation': ['enunciator', 'uncreation'],
 'uncreative': ['uncreative', 'unreactive'],
 'uncredited': ['uncredited', 'undirected'],
 'uncrest': ['encrust', 'uncrest'],
 'uncrested': ['uncrested', 'undersect'],
 'uncried': ['inducer', 'uncried'],
 'uncrooked': ['uncrooked', 'undercook'],
 'uncrude': ['uncrude', 'uncured'],
 'unctional': ['continual', 'inoculant', 'unctional'],
 'unctioneer': ['recontinue', 'unctioneer'],
 'uncured': ['uncrude', 'uncured'],
 'uncustomed': ['uncostumed', 'uncustomed'],
 'uncuth': ['tuchun', 'uncuth'],
 'undam': ['maund', 'munda', 'numda', 'undam', 'unmad'],
 'undangered': ['undangered', 'underanged', 'ungardened'],
 'undarken': ['undarken', 'unranked'],
 'undashed': ['undashed', 'unshaded'],
 'undate': ['nudate', 'undate'],
 'unde': ['dune', 'nude', 'unde'],
 'undean': ['duenna', 'undean'],
 'undear': ['endura', 'neurad', 'undear', 'unread'],
 'undeceiver': ['undeceiver', 'unreceived'],
 'undecimal': ['unclaimed', 'undecimal', 'unmedical'],
 'undeclare': ['uncleared', 'undeclare'],
 'undecolic': ['coinclude', 'undecolic'],
 'undecorated': ['undecorated', 'undercoated'],
 'undefiled': ['undefiled', 'unfielded'],
 'undeified': ['undeified', 'unedified'],
 'undelible': ['undelible', 'unlibeled'],
 'undelight': ['undelight', 'unlighted'],
 'undelude': ['undelude', 'uneluded'],
 'undeluding': ['undeluding', 'unindulged'],
 'undemanded': ['undemanded', 'unmaddened'],
 'unden': ['dunne', 'unden'],
 'undeparted': ['dunderpate', 'undeparted'],
 'undepraved': ['undepraved', 'unpervaded'],
 'under': ['runed', 'under', 'unred'],
 'underact': ['uncarted', 'uncrated', 'underact', 'untraced'],
 'underacted': ['underacted', 'unredacted'],
 'underaction': ['denunciator', 'underaction'],
 'underage': ['dungaree', 'guardeen', 'unagreed', 'underage', 'ungeared'],
 'underaid': ['underaid', 'unraided'],
 'underaim': ['unadmire', 'underaim'],
 'underanged': ['undangered', 'underanged', 'ungardened'],
 'underarch': ['uncharred', 'underarch'],
 'underarm': ['underarm', 'unmarred'],
 'underbake': ['underbake', 'underbeak'],
 'underbeak': ['underbake', 'underbeak'],
 'underbeat': ['eburnated', 'underbeat', 'unrebated'],
 'underbit': ['turbined', 'underbit'],
 'underboil': ['unbroiled', 'underboil'],
 'underbreathing': ['thunderbearing', 'underbreathing'],
 'underbrush': ['underbrush', 'undershrub'],
 'underbush': ['unbrushed', 'underbush'],
 'undercase': ['uncreased', 'undercase'],
 'underchap': ['underchap', 'unparched'],
 'underclad': ['uncradled', 'underclad'],
 'undercoat': ['cornuated', 'undercoat'],
 'undercoated': ['undecorated', 'undercoated'],
 'undercook': ['uncrooked', 'undercook'],
 'undercool': ['uncolored', 'undercool'],
 'undercut': ['undercut', 'unreduct'],
 'underdead': ['underdead', 'undreaded'],
 'underdig': ['underdig', 'ungirded', 'unridged'],
 'underdive': ['underdive', 'underived'],
 'underdo': ['redound', 'rounded', 'underdo'],
 'underdoer': ['underdoer', 'unordered'],
 'underdog': ['grounded', 'underdog', 'undergod'],
 'underdown': ['underdown', 'undrowned'],
 'underdrag': ['underdrag', 'undergrad'],
 'underdraw': ['underdraw', 'underward'],
 'undereat': ['denature', 'undereat'],
 'underer': ['endurer', 'underer'],
 'underfiend': ['underfiend', 'unfriended'],
 'underfill': ['underfill', 'unfrilled'],
 'underfire': ['underfire', 'unferried'],
 'underflow': ['underflow', 'wonderful'],
 'underfur': ['underfur', 'unfurred'],
 'undergo': ['guerdon', 'undergo', 'ungored'],
 'undergod': ['grounded', 'underdog', 'undergod'],
 'undergoer': ['guerdoner', 'reundergo', 'undergoer', 'undergore'],
 'undergore': ['guerdoner', 'reundergo', 'undergoer', 'undergore'],
 'undergown': ['undergown', 'unwronged'],
 'undergrad': ['underdrag', 'undergrad'],
 'undergrade': ['undergrade', 'unregarded'],
 'underheat': ['underheat', 'unearthed'],
 'underhonest': ['underhonest', 'unshortened'],
 'underhorse': ['underhorse', 'undershore'],
 'underived': ['underdive', 'underived'],
 'underkind': ['underkind', 'unkindred'],
 'underlap': ['pendular', 'underlap', 'uplander'],
 'underleaf': ['underleaf', 'unfederal'],
 'underlease': ['underlease', 'unreleased'],
 'underlegate': ['underlegate', 'unrelegated'],
 'underlid': ['underlid', 'unriddle'],
 'underlive': ['underlive', 'unreviled'],
 'underlying': ['enduringly', 'underlying'],
 'undermade': ['undermade', 'undreamed'],
 'undermaid': ['unadmired', 'undermaid'],
 'undermaker': ['undermaker', 'unremarked'],
 'undermaster': ['undermaster', 'understream'],
 'undermeal': ['denumeral', 'undermeal', 'unrealmed'],
 'undermine': ['undermine', 'unermined'],
 'undermost': ['undermost', 'unstormed'],
 'undermotion': ['undermotion', 'unmonitored'],
 'undern': ['dunner', 'undern'],
 'underneath': ['unadherent', 'underneath', 'underthane'],
 'undernote': ['undernote', 'undertone'],
 'undernoted': ['undernoted', 'undertoned'],
 'underntide': ['indentured', 'underntide'],
 'underorb': ['unborder', 'underorb'],
 'underpay': ['underpay', 'unprayed'],
 'underpeer': ['perendure', 'underpeer'],
 'underpick': ['underpick', 'unpricked'],
 'underpier': ['underpier', 'underripe'],
 'underpile': ['underpile', 'unreplied'],
 'underpose': ['underpose', 'unreposed'],
 'underpuke': ['underpuke', 'unperuked'],
 'underream': ['maunderer', 'underream'],
 'underripe': ['underpier', 'underripe'],
 'underrobe': ['rebounder', 'underrobe'],
 'undersap': ['undersap', 'unparsed', 'unrasped', 'unspared', 'unspread'],
 'undersea': ['undersea', 'unerased', 'unseared'],
 'underseam': ['underseam', 'unsmeared'],
 'undersect': ['uncrested', 'undersect'],
 'underserve': ['underserve',
                'underverse',
                'undeserver',
                'unreserved',
                'unreversed'],
 'underset': ['sederunt', 'underset', 'undesert', 'unrested'],
 'undershapen': ['undershapen', 'unsharpened'],
 'undershore': ['underhorse', 'undershore'],
 'undershrub': ['underbrush', 'undershrub'],
 'underside': ['underside', 'undesired'],
 'undersoil': ['undersoil', 'unsoldier'],
 'undersow': ['sewround', 'undersow'],
 'underspar': ['underspar', 'unsparred'],
 'understain': ['understain', 'unstrained'],
 'understand': ['understand', 'unstranded'],
 'understream': ['undermaster', 'understream'],
 'underthane': ['unadherent', 'underneath', 'underthane'],
 'underthing': ['thundering', 'underthing'],
 'undertide': ['durdenite', 'undertide'],
 'undertime': ['undertime', 'unmerited'],
 'undertimed': ['demiturned', 'undertimed'],
 'undertitle': ['undertitle', 'unlittered'],
 'undertone': ['undernote', 'undertone'],
 'undertoned': ['undernoted', 'undertoned'],
 'undertow': ['undertow', 'untrowed'],
 'undertread': ['undertread', 'unretarded'],
 'undertutor': ['undertutor', 'untortured'],
 'underverse': ['underserve',
                'underverse',
                'undeserver',
                'unreserved',
                'unreversed'],
 'underwage': ['underwage', 'unwagered'],
 'underward': ['underdraw', 'underward'],
 'underwarp': ['underwarp', 'underwrap'],
 'underwave': ['underwave', 'unwavered'],
 'underwrap': ['underwarp', 'underwrap'],
 'undesert': ['sederunt', 'underset', 'undesert', 'unrested'],
 'undeserve': ['undeserve', 'unsevered'],
 'undeserver': ['underserve',
                'underverse',
                'undeserver',
                'unreserved',
                'unreversed'],
 'undesign': ['undesign', 'unsigned', 'unsinged'],
 'undesired': ['underside', 'undesired'],
 'undeviated': ['denudative', 'undeviated'],
 'undieted': ['undieted', 'unedited'],
 'undig': ['gundi', 'undig'],
 'undirect': ['undirect', 'untriced'],
 'undirected': ['uncredited', 'undirected'],
 'undiscerned': ['undiscerned', 'unrescinded'],
 'undiscretion': ['discontinuer', 'undiscretion'],
 'undistress': ['sturdiness', 'undistress'],
 'undiverse': ['undiverse', 'unrevised'],
 'undog': ['undog', 'ungod'],
 'undrab': ['durban', 'undrab'],
 'undrag': ['durgan', 'undrag'],
 'undrape': ['undrape', 'unpared', 'unraped'],
 'undreaded': ['underdead', 'undreaded'],
 'undreamed': ['undermade', 'undreamed'],
 'undrowned': ['underdown', 'undrowned'],
 'undrugged': ['undrugged', 'ungrudged'],
 'undryable': ['endurably', 'undryable'],
 'undub': ['bundu', 'unbud', 'undub'],
 'undumped': ['pudendum', 'undumped'],
 'undy': ['duny', 'undy'],
 'uneager': ['geneura', 'uneager'],
 'unearned': ['unearned', 'unneared'],
 'unearnest': ['unearnest', 'uneastern'],
 'unearth': ['haunter', 'nauther', 'unearth', 'unheart', 'urethan'],
 'unearthed': ['underheat', 'unearthed'],
 'unearthly': ['unearthly', 'urethylan'],
 'uneastern': ['unearnest', 'uneastern'],
 'uneath': ['uneath', 'unhate'],
 'unebriate': ['beraunite', 'unebriate'],
 'unedge': ['dengue', 'unedge'],
 'unedible': ['unbelied', 'unedible'],
 'unedified': ['undeified', 'unedified'],
 'unedited': ['undieted', 'unedited'],
 'unelapsed': ['unelapsed', 'unpleased'],
 'unelated': ['antelude', 'unelated'],
 'unelbowed': ['unboweled', 'unelbowed'],
 'unelidible': ['ineludible', 'unelidible'],
 'uneluded': ['undelude', 'uneluded'],
 'unembased': ['sunbeamed', 'unembased'],
 'unembraced': ['uncambered', 'unembraced'],
 'unenabled': ['unenabled', 'unendable'],
 'unencored': ['denouncer', 'unencored'],
 'unendable': ['unenabled', 'unendable'],
 'unending': ['unending', 'unginned'],
 'unenervated': ['unenervated', 'unvenerated'],
 'unenlisted': ['unenlisted', 'unlistened', 'untinseled'],
 'unenterprised': ['superintender', 'unenterprised'],
 'unenviable': ['unenviable', 'unveniable'],
 'unenvied': ['unenvied', 'unveined'],
 'unequitable': ['unequitable', 'unquietable'],
 'unerased': ['undersea', 'unerased', 'unseared'],
 'unermined': ['undermine', 'unermined'],
 'unerratic': ['recurtain', 'unerratic'],
 'unerupted': ['unerupted', 'unreputed'],
 'unescorted': ['uncorseted', 'unescorted'],
 'unevil': ['unevil', 'unlive', 'unveil'],
 'unexactly': ['exultancy', 'unexactly'],
 'unexceptable': ['unexceptable', 'unexpectable'],
 'unexcepted': ['unexcepted', 'unexpected'],
 'unexcepting': ['unexcepting', 'unexpecting'],
 'unexpectable': ['unexceptable', 'unexpectable'],
 'unexpected': ['unexcepted', 'unexpected'],
 'unexpecting': ['unexcepting', 'unexpecting'],
 'unfabled': ['fundable', 'unfabled'],
 'unfaceted': ['fecundate', 'unfaceted'],
 'unfactional': ['afunctional', 'unfactional'],
 'unfactored': ['fecundator', 'unfactored'],
 'unfainting': ['antifungin', 'unfainting'],
 'unfallible': ['unfallible', 'unfillable'],
 'unfar': ['furan', 'unfar'],
 'unfarmed': ['unfarmed', 'unframed'],
 'unfederal': ['underleaf', 'unfederal'],
 'unfeeding': ['unfeeding', 'unfeigned'],
 'unfeeling': ['unfeeling', 'unfleeing'],
 'unfeigned': ['unfeeding', 'unfeigned'],
 'unfelt': ['fluent', 'netful', 'unfelt', 'unleft'],
 'unfelted': ['defluent', 'unfelted'],
 'unferried': ['underfire', 'unferried'],
 'unfiber': ['unbrief', 'unfiber'],
 'unfibered': ['unbriefed', 'unfibered'],
 'unfielded': ['undefiled', 'unfielded'],
 'unfiend': ['unfiend', 'unfined'],
 'unfiery': ['reunify', 'unfiery'],
 'unfillable': ['unfallible', 'unfillable'],
 'unfined': ['unfiend', 'unfined'],
 'unfired': ['unfired', 'unfried'],
 'unflag': ['fungal', 'unflag'],
 'unflat': ['flaunt', 'unflat'],
 'unfleeing': ['unfeeling', 'unfleeing'],
 'unfloured': ['unfloured', 'unfoldure'],
 'unfolder': ['flounder', 'reunfold', 'unfolder'],
 'unfolding': ['foundling', 'unfolding'],
 'unfoldure': ['unfloured', 'unfoldure'],
 'unforest': ['furstone', 'unforest'],
 'unforested': ['unforested', 'unfostered'],
 'unformality': ['fulminatory', 'unformality'],
 'unforward': ['unforward', 'unfroward'],
 'unfostered': ['unforested', 'unfostered'],
 'unfrail': ['rainful', 'unfrail'],
 'unframed': ['unfarmed', 'unframed'],
 'unfret': ['turfen', 'unfret'],
 'unfriable': ['funebrial', 'unfriable'],
 'unfried': ['unfired', 'unfried'],
 'unfriended': ['underfiend', 'unfriended'],
 'unfriending': ['unfriending', 'uninfringed'],
 'unfrilled': ['underfill', 'unfrilled'],
 'unfroward': ['unforward', 'unfroward'],
 'unfurl': ['unfurl', 'urnful'],
 'unfurred': ['underfur', 'unfurred'],
 'ungaite': ['ungaite', 'unitage'],
 'unganged': ['unganged', 'unnagged'],
 'ungardened': ['undangered', 'underanged', 'ungardened'],
 'ungarnish': ['ungarnish', 'unsharing'],
 'ungear': ['nauger', 'raunge', 'ungear'],
 'ungeared': ['dungaree', 'guardeen', 'unagreed', 'underage', 'ungeared'],
 'ungelt': ['englut', 'gluten', 'ungelt'],
 'ungenerable': ['ungenerable', 'ungreenable'],
 'unget': ['tengu', 'unget'],
 'ungilded': ['deluding', 'ungilded'],
 'ungill': ['ulling', 'ungill'],
 'ungilt': ['glutin', 'luting', 'ungilt'],
 'unginned': ['unending', 'unginned'],
 'ungird': ['during', 'ungird'],
 'ungirded': ['underdig', 'ungirded', 'unridged'],
 'ungirdle': ['indulger', 'ungirdle'],
 'ungirt': ['ungirt', 'untrig'],
 'ungirth': ['hurting', 'ungirth', 'unright'],
 'ungirthed': ['ungirthed', 'unrighted'],
 'unglad': ['gandul', 'unglad'],
 'unglued': ['unglued', 'unguled'],
 'ungod': ['undog', 'ungod'],
 'ungold': ['dungol', 'ungold'],
 'ungone': ['guenon', 'ungone'],
 'ungored': ['guerdon', 'undergo', 'ungored'],
 'ungorge': ['gurgeon', 'ungorge'],
 'ungot': ['tungo', 'ungot'],
 'ungothic': ['touching', 'ungothic'],
 'ungraphic': ['ungraphic', 'uparching'],
 'ungreenable': ['ungenerable', 'ungreenable'],
 'ungrieved': ['gerundive', 'ungrieved'],
 'ungroined': ['ungroined', 'unignored'],
 'ungrudged': ['undrugged', 'ungrudged'],
 'ungual': ['ungual', 'ungula'],
 'ungueal': ['ungueal', 'ungulae'],
 'ungula': ['ungual', 'ungula'],
 'ungulae': ['ungueal', 'ungulae'],
 'unguled': ['unglued', 'unguled'],
 'ungulp': ['ungulp', 'unplug'],
 'unhabit': ['bhutani', 'unhabit'],
 'unhackled': ['unchalked', 'unhackled'],
 'unhairer': ['rhineura', 'unhairer'],
 'unhalsed': ['unhalsed', 'unlashed', 'unshaled'],
 'unhalted': ['unhalted', 'unlathed'],
 'unhalter': ['lutheran', 'unhalter'],
 'unhaltered': ['unhaltered', 'unlathered'],
 'unhamper': ['prehuman', 'unhamper'],
 'unharbored': ['unabhorred', 'unharbored'],
 'unhasped': ['unhasped', 'unphased', 'unshaped'],
 'unhat': ['ahunt', 'haunt', 'thuan', 'unhat'],
 'unhate': ['uneath', 'unhate'],
 'unhatingly': ['hauntingly', 'unhatingly'],
 'unhayed': ['unhayed', 'unheady'],
 'unheady': ['unhayed', 'unheady'],
 'unhearsed': ['unhearsed', 'unsheared'],
 'unheart': ['haunter', 'nauther', 'unearth', 'unheart', 'urethan'],
 'unhid': ['hindu', 'hundi', 'unhid'],
 'unhistoric': ['trichinous', 'unhistoric'],
 'unhittable': ['unhittable', 'untithable'],
 'unhoarded': ['roundhead', 'unhoarded'],
 'unhocked': ['unchoked', 'unhocked'],
 'unhoisted': ['hudsonite', 'unhoisted'],
 'unhorse': ['unhorse', 'unshore'],
 'unhose': ['unhose', 'unshoe'],
 'unhosed': ['unhosed', 'unshoed'],
 'unhurt': ['unhurt', 'unruth'],
 'uniat': ['uinta', 'uniat'],
 'uniate': ['auntie', 'uniate'],
 'unible': ['nubile', 'unible'],
 'uniced': ['induce', 'uniced'],
 'unicentral': ['incruental', 'unicentral'],
 'unideal': ['aliunde', 'unideal'],
 'unidentified': ['indefinitude', 'unidentified'],
 'unidly': ['unidly', 'yildun'],
 'unie': ['niue', 'unie'],
 'unifilar': ['friulian', 'unifilar'],
 'uniflorate': ['antifouler', 'fluorinate', 'uniflorate'],
 'unigenous': ['ingenuous', 'unigenous'],
 'unignored': ['ungroined', 'unignored'],
 'unilobar': ['orbulina', 'unilobar'],
 'unilobed': ['unboiled', 'unilobed'],
 'unimedial': ['aluminide', 'unimedial'],
 'unimpair': ['manipuri', 'unimpair'],
 'unimparted': ['diparentum', 'unimparted'],
 'unimportance': ['importunance', 'unimportance'],
 'unimpressible': ['unimpressible', 'unpermissible'],
 'unimpressive': ['unimpressive', 'unpermissive'],
 'unindented': ['unindented', 'unintended'],
 'unindulged': ['undeluding', 'unindulged'],
 'uninervate': ['aventurine', 'uninervate'],
 'uninfringed': ['unfriending', 'uninfringed'],
 'uningested': ['uningested', 'unsigneted'],
 'uninn': ['nunni', 'uninn'],
 'uninnate': ['eutannin', 'uninnate'],
 'uninodal': ['annuloid', 'uninodal'],
 'unintended': ['unindented', 'unintended'],
 'unintoned': ['nonunited', 'unintoned'],
 'uninured': ['uninured', 'unruined'],
 'unionist': ['inustion', 'unionist'],
 'unipersonal': ['spinoneural', 'unipersonal'],
 'unipod': ['dupion', 'unipod'],
 'uniradial': ['nidularia', 'uniradial'],
 'unireme': ['erineum', 'unireme'],
 'uniserrate': ['arseniuret', 'uniserrate'],
 'unison': ['nonius', 'unison'],
 'unitage': ['ungaite', 'unitage'],
 'unital': ['inlaut', 'unital'],
 'unite': ['intue', 'unite', 'untie'],
 'united': ['dunite', 'united', 'untied'],
 'uniter': ['runite', 'triune', 'uniter', 'untire'],
 'unitiveness': ['unitiveness', 'unsensitive'],
 'unitrope': ['eruption', 'unitrope'],
 'univied': ['univied', 'viduine'],
 'unket': ['knute', 'unket'],
 'unkilned': ['unkilned', 'unlinked'],
 'unkin': ['nunki', 'unkin'],
 'unkindred': ['underkind', 'unkindred'],
 'unlabiate': ['laubanite', 'unlabiate'],
 'unlabored': ['burdalone', 'unlabored'],
 'unlace': ['auncel', 'cuneal', 'lacune', 'launce', 'unlace'],
 'unlaced': ['unclead', 'unlaced'],
 'unlade': ['unlade', 'unlead'],
 'unlaid': ['dualin', 'ludian', 'unlaid'],
 'unlame': ['manuel', 'unlame'],
 'unlapped': ['unlapped', 'unpalped'],
 'unlarge': ['granule', 'unlarge', 'unregal'],
 'unlashed': ['unhalsed', 'unlashed', 'unshaled'],
 'unlasting': ['unlasting', 'unslating'],
 'unlatch': ['tulchan', 'unlatch'],
 'unlathed': ['unhalted', 'unlathed'],
 'unlathered': ['unhaltered', 'unlathered'],
 'unlay': ['unlay', 'yulan'],
 'unlead': ['unlade', 'unlead'],
 'unleasable': ['unleasable', 'unsealable'],
 'unleased': ['unleased', 'unsealed'],
 'unleash': ['hulsean', 'unleash'],
 'unled': ['lendu', 'unled'],
 'unleft': ['fluent', 'netful', 'unfelt', 'unleft'],
 'unlent': ['nunlet', 'tunnel', 'unlent'],
 'unlevied': ['unlevied', 'unveiled'],
 'unlibeled': ['undelible', 'unlibeled'],
 'unliberal': ['brunellia', 'unliberal'],
 'unlicensed': ['unlicensed', 'unsilenced'],
 'unlighted': ['undelight', 'unlighted'],
 'unliken': ['nunlike', 'unliken'],
 'unlime': ['lumine', 'unlime'],
 'unlinked': ['unkilned', 'unlinked'],
 'unlist': ['insult', 'sunlit', 'unlist', 'unslit'],
 'unlistened': ['unenlisted', 'unlistened', 'untinseled'],
 'unlit': ['unlit', 'until'],
 'unliteral': ['tellurian', 'unliteral'],
 'unlittered': ['undertitle', 'unlittered'],
 'unlive': ['unevil', 'unlive', 'unveil'],
 'unloaded': ['duodenal', 'unloaded'],
 'unloaden': ['unloaden', 'unloaned'],
 'unloader': ['unloader', 'urodelan'],
 'unloaned': ['unloaden', 'unloaned'],
 'unlodge': ['unlodge', 'unogled'],
 'unlogic': ['gulonic', 'unlogic'],
 'unlooped': ['unlooped', 'unpooled'],
 'unlooted': ['unlooted', 'untooled'],
 'unlost': ['unlost', 'unslot'],
 'unlowered': ['unlowered', 'unroweled'],
 'unlucid': ['nuculid', 'unlucid'],
 'unlumped': ['pendulum', 'unlumped', 'unplumed'],
 'unlured': ['unlured', 'unruled'],
 'unlyrical': ['runically', 'unlyrical'],
 'unmacerated': ['uncamerated', 'unmacerated'],
 'unmad': ['maund', 'munda', 'numda', 'undam', 'unmad'],
 'unmadded': ['addendum', 'unmadded'],
 'unmaddened': ['undemanded', 'unmaddened'],
 'unmaid': ['numida', 'unmaid'],
 'unmail': ['alumni', 'unmail'],
 'unmailed': ['adlumine', 'unmailed'],
 'unmaned': ['mundane', 'unamend', 'unmaned', 'unnamed'],
 'unmantle': ['unmantle', 'unmental'],
 'unmarch': ['uncharm', 'unmarch'],
 'unmarching': ['uncharming', 'unmarching'],
 'unmarginal': ['unalarming', 'unmarginal'],
 'unmarred': ['underarm', 'unmarred'],
 'unmashed': ['unmashed', 'unshamed'],
 'unmate': ['unmate', 'untame', 'unteam'],
 'unmated': ['unmated', 'untamed'],
 'unmaterial': ['manualiter', 'unmaterial'],
 'unmeated': ['unmeated', 'unteamed'],
 'unmedical': ['unclaimed', 'undecimal', 'unmedical'],
 'unmeet': ['unmeet', 'unteem'],
 'unmemoired': ['unmemoired', 'unmemoried'],
 'unmemoried': ['unmemoired', 'unmemoried'],
 'unmental': ['unmantle', 'unmental'],
 'unmerged': ['gerendum', 'unmerged'],
 'unmerited': ['undertime', 'unmerited'],
 'unmettle': ['temulent', 'unmettle'],
 'unminable': ['nelumbian', 'unminable'],
 'unmined': ['minuend', 'unmined'],
 'unminted': ['indument', 'unminted'],
 'unmisled': ['muslined', 'unmisled', 'unsmiled'],
 'unmiter': ['minuter', 'unmiter'],
 'unmodest': ['mudstone', 'unmodest'],
 'unmodish': ['muishond', 'unmodish'],
 'unmomentary': ['monumentary', 'unmomentary'],
 'unmonitored': ['undermotion', 'unmonitored'],
 'unmorbid': ['moribund', 'unmorbid'],
 'unmorose': ['enormous', 'unmorose'],
 'unmortised': ['semirotund', 'unmortised'],
 'unmotived': ['unmotived', 'unvomited'],
 'unmystical': ['stimulancy', 'unmystical'],
 'unnagged': ['unganged', 'unnagged'],
 'unnail': ['alnuin', 'unnail'],
 'unnameability': ['unamenability', 'unnameability'],
 'unnameable': ['unamenable', 'unnameable'],
 'unnameableness': ['unamenableness', 'unnameableness'],
 'unnameably': ['unamenably', 'unnameably'],
 'unnamed': ['mundane', 'unamend', 'unmaned', 'unnamed'],
 'unnational': ['annulation', 'unnational'],
 'unnative': ['unnative', 'venutian'],
 'unneared': ['unearned', 'unneared'],
 'unnest': ['unnest', 'unsent'],
 'unnetted': ['unnetted', 'untented'],
 'unnose': ['nonuse', 'unnose'],
 'unnoted': ['unnoted', 'untoned'],
 'unnoticed': ['continued', 'unnoticed'],
 'unoared': ['rondeau', 'unoared'],
 'unogled': ['unlodge', 'unogled'],
 'unomitted': ['dumontite', 'unomitted'],
 'unoperatic': ['precaution', 'unoperatic'],
 'unorbed': ['beround', 'bounder', 'rebound', 'unbored', 'unorbed', 'unrobed'],
 'unorder': ['rondure', 'rounder', 'unorder'],
 'unordered': ['underdoer', 'unordered'],
 'unoriented': ['nonerudite', 'unoriented'],
 'unown': ['unown', 'unwon'],
 'unowned': ['enwound', 'unowned'],
 'unpacable': ['uncapable', 'unpacable'],
 'unpacker': ['reunpack', 'unpacker'],
 'unpaired': ['unpaired', 'unrepaid'],
 'unpale': ['unpale', 'uplane'],
 'unpalped': ['unlapped', 'unpalped'],
 'unpanel': ['unpanel', 'unpenal'],
 'unparceled': ['unparceled', 'unreplaced'],
 'unparched': ['underchap', 'unparched'],
 'unpared': ['undrape', 'unpared', 'unraped'],
 'unparsed': ['undersap', 'unparsed', 'unrasped', 'unspared', 'unspread'],
 'unparted': ['depurant', 'unparted'],
 'unpartial': ['tarpaulin', 'unpartial'],
 'unpenal': ['unpanel', 'unpenal'],
 'unpenetrable': ['unpenetrable', 'unrepentable'],
 'unpent': ['punnet', 'unpent'],
 'unperch': ['puncher', 'unperch'],
 'unpercolated': ['counterpaled', 'counterplead', 'unpercolated'],
 'unpermissible': ['unimpressible', 'unpermissible'],
 'unpermissive': ['unimpressive', 'unpermissive'],
 'unperuked': ['underpuke', 'unperuked'],
 'unpervaded': ['undepraved', 'unpervaded'],
 'unpetal': ['plutean', 'unpetal', 'unpleat'],
 'unpharasaic': ['parasuchian', 'unpharasaic'],
 'unphased': ['unhasped', 'unphased', 'unshaped'],
 'unphrased': ['unphrased', 'unsharped'],
 'unpickled': ['dunpickle', 'unpickled'],
 'unpierced': ['preinduce', 'unpierced'],
 'unpile': ['lupine', 'unpile', 'upline'],
 'unpiled': ['unpiled', 'unplied'],
 'unplace': ['cleanup', 'unplace'],
 'unplain': ['pinnula', 'unplain'],
 'unplait': ['nuptial', 'unplait'],
 'unplanted': ['pendulant', 'unplanted'],
 'unplat': ['puntal', 'unplat'],
 'unpleased': ['unelapsed', 'unpleased'],
 'unpleat': ['plutean', 'unpetal', 'unpleat'],
 'unpleated': ['pendulate', 'unpleated'],
 'unplied': ['unpiled', 'unplied'],
 'unplug': ['ungulp', 'unplug'],
 'unplumed': ['pendulum', 'unlumped', 'unplumed'],
 'unpoled': ['duplone', 'unpoled'],
 'unpolished': ['disulphone', 'unpolished'],
 'unpolitic': ['punctilio', 'unpolitic'],
 'unpooled': ['unlooped', 'unpooled'],
 'unposted': ['outspend', 'unposted'],
 'unpot': ['punto', 'unpot', 'untop'],
 'unprayed': ['underpay', 'unprayed'],
 'unprelatic': ['periculant', 'unprelatic'],
 'unpressed': ['resuspend', 'suspender', 'unpressed'],
 'unpricked': ['underpick', 'unpricked'],
 'unprint': ['turnpin', 'unprint'],
 'unprosaic': ['inocarpus', 'unprosaic'],
 'unproselyted': ['pseudelytron', 'unproselyted'],
 'unproud': ['roundup', 'unproud'],
 'unpursued': ['unpursued', 'unusurped'],
 'unpursuing': ['unpursuing', 'unusurping'],
 'unquietable': ['unequitable', 'unquietable'],
 'unrabbeted': ['beturbaned', 'unrabbeted'],
 'unraced': ['durance', 'redunca', 'unraced'],
 'unraided': ['underaid', 'unraided'],
 'unraised': ['denarius', 'desaurin', 'unraised'],
 'unram': ['muran', 'ruman', 'unarm', 'unram', 'urman'],
 'unranked': ['undarken', 'unranked'],
 'unraped': ['undrape', 'unpared', 'unraped'],
 'unrasped': ['undersap', 'unparsed', 'unrasped', 'unspared', 'unspread'],
 'unrated': ['daunter', 'unarted', 'unrated', 'untread'],
 'unravel': ['unravel', 'venular'],
 'unray': ['anury', 'unary', 'unray'],
 'unrayed': ['unrayed', 'unready'],
 'unreactive': ['uncreative', 'unreactive'],
 'unread': ['endura', 'neurad', 'undear', 'unread'],
 'unready': ['unrayed', 'unready'],
 'unreal': ['lunare', 'neural', 'ulnare', 'unreal'],
 'unrealism': ['semilunar', 'unrealism'],
 'unrealist': ['neuralist', 'ulsterian', 'unrealist'],
 'unrealmed': ['denumeral', 'undermeal', 'unrealmed'],
 'unrebated': ['eburnated', 'underbeat', 'unrebated'],
 'unrebutted': ['unbuttered', 'unrebutted'],
 'unreceived': ['undeceiver', 'unreceived'],
 'unrecent': ['uncenter', 'unrecent'],
 'unrecited': ['centuried', 'unrecited'],
 'unrectifiable': ['uncertifiable', 'unrectifiable'],
 'unrectified': ['uncertified', 'unrectified'],
 'unred': ['runed', 'under', 'unred'],
 'unredacted': ['underacted', 'unredacted'],
 'unreduct': ['undercut', 'unreduct'],
 'unreeve': ['revenue', 'unreeve'],
 'unreeving': ['unreeving', 'unveering'],
 'unregal': ['granule', 'unlarge', 'unregal'],
 'unregard': ['grandeur', 'unregard'],
 'unregarded': ['undergrade', 'unregarded'],
 'unrein': ['enruin', 'neurin', 'unrein'],
 'unreinstated': ['unreinstated', 'unstraitened'],
 'unrelated': ['unaltered', 'unrelated'],
 'unrelating': ['unaltering', 'unrelating'],
 'unreleased': ['underlease', 'unreleased'],
 'unrelegated': ['underlegate', 'unrelegated'],
 'unremarked': ['undermaker', 'unremarked'],
 'unrent': ['runnet', 'tunner', 'unrent'],
 'unrented': ['unrented', 'untender'],
 'unrepaid': ['unpaired', 'unrepaid'],
 'unrepentable': ['unpenetrable', 'unrepentable'],
 'unrepined': ['unrepined', 'unripened'],
 'unrepining': ['unrepining', 'unripening'],
 'unreplaced': ['unparceled', 'unreplaced'],
 'unreplied': ['underpile', 'unreplied'],
 'unreposed': ['underpose', 'unreposed'],
 'unreputed': ['unerupted', 'unreputed'],
 'unrescinded': ['undiscerned', 'unrescinded'],
 'unrescued': ['unrescued', 'unsecured'],
 'unreserved': ['underserve',
                'underverse',
                'undeserver',
                'unreserved',
                'unreversed'],
 'unresisted': ['unresisted', 'unsistered'],
 'unresolve': ['nervulose', 'unresolve', 'vulnerose'],
 'unrespect': ['unrespect', 'unscepter', 'unsceptre'],
 'unrespected': ['unrespected', 'unsceptered'],
 'unrested': ['sederunt', 'underset', 'undesert', 'unrested'],
 'unresting': ['insurgent', 'unresting'],
 'unretarded': ['undertread', 'unretarded'],
 'unreticent': ['entincture', 'unreticent'],
 'unretired': ['reintrude', 'unretired'],
 'unrevered': ['enverdure', 'unrevered'],
 'unreversed': ['underserve',
                'underverse',
                'undeserver',
                'unreserved',
                'unreversed'],
 'unreviled': ['underlive', 'unreviled'],
 'unrevised': ['undiverse', 'unrevised'],
 'unrevocable': ['uncoverable', 'unrevocable'],
 'unribbed': ['unbribed', 'unribbed'],
 'unrich': ['unrich', 'urchin'],
 'unrid': ['rundi', 'unrid'],
 'unridable': ['indurable', 'unbrailed', 'unridable'],
 'unriddle': ['underlid', 'unriddle'],
 'unride': ['diurne', 'inured', 'ruined', 'unride'],
 'unridged': ['underdig', 'ungirded', 'unridged'],
 'unrig': ['irgun', 'ruing', 'unrig'],
 'unright': ['hurting', 'ungirth', 'unright'],
 'unrighted': ['ungirthed', 'unrighted'],
 'unring': ['unring', 'urning'],
 'unringed': ['enduring', 'unringed'],
 'unripe': ['purine', 'unripe', 'uprein'],
 'unripely': ['pyruline', 'unripely'],
 'unripened': ['unrepined', 'unripened'],
 'unripening': ['unrepining', 'unripening'],
 'unroaded': ['unadored', 'unroaded'],
 'unrobed': ['beround', 'bounder', 'rebound', 'unbored', 'unorbed', 'unrobed'],
 'unrocked': ['uncorked', 'unrocked'],
 'unroot': ['notour', 'unroot'],
 'unroped': ['pounder', 'repound', 'unroped'],
 'unrosed': ['resound', 'sounder', 'unrosed'],
 'unrostrated': ['tetrandrous', 'unrostrated'],
 'unrotated': ['rotundate', 'unrotated'],
 'unroted': ['tendour', 'unroted'],
 'unroused': ['unroused', 'unsoured'],
 'unrouted': ['unrouted', 'untoured'],
 'unrowed': ['rewound', 'unrowed', 'wounder'],
 'unroweled': ['unlowered', 'unroweled'],
 'unroyalist': ['unroyalist', 'unsolitary'],
 'unruined': ['uninured', 'unruined'],
 'unruled': ['unlured', 'unruled'],
 'unrun': ['unrun', 'unurn'],
 'unruth': ['unhurt', 'unruth'],
 'unsack': ['uncask', 'unsack'],
 'unsacked': ['uncasked', 'unsacked'],
 'unsacred': ['unsacred', 'unscared'],
 'unsad': ['sudan', 'unsad'],
 'unsadden': ['unsadden', 'unsanded'],
 'unsage': ['gnaeus', 'unsage'],
 'unsaid': ['sudani', 'unsaid'],
 'unsailed': ['unaisled', 'unsailed'],
 'unsaint': ['antisun', 'unsaint', 'unsatin', 'unstain'],
 'unsainted': ['unsainted', 'unstained'],
 'unsalt': ['sultan', 'unsalt'],
 'unsalted': ['unsalted', 'unslated', 'unstaled'],
 'unsanded': ['unsadden', 'unsanded'],
 'unsardonic': ['andronicus', 'unsardonic'],
 'unsashed': ['sunshade', 'unsashed'],
 'unsatable': ['sublanate', 'unsatable'],
 'unsatiable': ['balaustine', 'unsatiable'],
 'unsatin': ['antisun', 'unsaint', 'unsatin', 'unstain'],
 'unsauced': ['uncaused', 'unsauced'],
 'unscale': ['censual', 'unscale'],
 'unscalloped': ['uncollapsed', 'unscalloped'],
 'unscaly': ['ancylus', 'unscaly'],
 'unscared': ['unsacred', 'unscared'],
 'unscepter': ['unrespect', 'unscepter', 'unsceptre'],
 'unsceptered': ['unrespected', 'unsceptered'],
 'unsceptre': ['unrespect', 'unscepter', 'unsceptre'],
 'unscoured': ['uncoursed', 'unscoured'],
 'unseal': ['elanus', 'unseal'],
 'unsealable': ['unleasable', 'unsealable'],
 'unsealed': ['unleased', 'unsealed'],
 'unseared': ['undersea', 'unerased', 'unseared'],
 'unseat': ['nasute', 'nauset', 'unseat'],
 'unseated': ['unseated', 'unsedate', 'unteased'],
 'unsecured': ['unrescued', 'unsecured'],
 'unsedate': ['unseated', 'unsedate', 'unteased'],
 'unsee': ['ensue', 'seenu', 'unsee'],
 'unseethed': ['unseethed', 'unsheeted'],
 'unseizable': ['unseizable', 'unsizeable'],
 'unselect': ['esculent', 'unselect'],
 'unsensed': ['nudeness', 'unsensed'],
 'unsensitive': ['unitiveness', 'unsensitive'],
 'unsent': ['unnest', 'unsent'],
 'unsepulcher': ['unsepulcher', 'unsepulchre'],
 'unsepulchre': ['unsepulcher', 'unsepulchre'],
 'unserrated': ['unarrested', 'unserrated'],
 'unserved': ['unserved', 'unversed'],
 'unset': ['unset', 'usent'],
 'unsevered': ['undeserve', 'unsevered'],
 'unsewed': ['sunweed', 'unsewed'],
 'unsex': ['nexus', 'unsex'],
 'unshaded': ['undashed', 'unshaded'],
 'unshaled': ['unhalsed', 'unlashed', 'unshaled'],
 'unshamed': ['unmashed', 'unshamed'],
 'unshaped': ['unhasped', 'unphased', 'unshaped'],
 'unsharing': ['ungarnish', 'unsharing'],
 'unsharped': ['unphrased', 'unsharped'],
 'unsharpened': ['undershapen', 'unsharpened'],
 'unsheared': ['unhearsed', 'unsheared'],
 'unsheet': ['enthuse', 'unsheet'],
 'unsheeted': ['unseethed', 'unsheeted'],
 'unship': ['inpush', 'punish', 'unship'],
 'unshipment': ['punishment', 'unshipment'],
 'unshoe': ['unhose', 'unshoe'],
 'unshoed': ['unhosed', 'unshoed'],
 'unshore': ['unhorse', 'unshore'],
 'unshored': ['enshroud', 'unshored'],
 'unshortened': ['underhonest', 'unshortened'],
 'unsicker': ['cruisken', 'unsicker'],
 'unsickled': ['klendusic', 'unsickled'],
 'unsight': ['gutnish', 'husting', 'unsight'],
 'unsignable': ['unsignable', 'unsingable'],
 'unsigned': ['undesign', 'unsigned', 'unsinged'],
 'unsigneted': ['uningested', 'unsigneted'],
 'unsilenced': ['unlicensed', 'unsilenced'],
 'unsimple': ['splenium', 'unsimple'],
 'unsin': ['sunni', 'unsin'],
 'unsingable': ['unsignable', 'unsingable'],
 'unsinged': ['undesign', 'unsigned', 'unsinged'],
 'unsistered': ['unresisted', 'unsistered'],
 'unsizeable': ['unseizable', 'unsizeable'],
 'unskin': ['insunk', 'unskin'],
 'unslate': ['sultane', 'unslate'],
 'unslated': ['unsalted', 'unslated', 'unstaled'],
 'unslating': ['unlasting', 'unslating'],
 'unslept': ['unslept', 'unspelt'],
 'unslighted': ['sunlighted', 'unslighted'],
 'unslit': ['insult', 'sunlit', 'unlist', 'unslit'],
 'unslot': ['unlost', 'unslot'],
 'unsmeared': ['underseam', 'unsmeared'],
 'unsmiled': ['muslined', 'unmisled', 'unsmiled'],
 'unsnap': ['pannus', 'sannup', 'unsnap', 'unspan'],
 'unsnatch': ['unsnatch', 'unstanch'],
 'unsnow': ['unsnow', 'unsown'],
 'unsocial': ['sualocin', 'unsocial'],
 'unsoil': ['insoul', 'linous', 'nilous', 'unsoil'],
 'unsoiled': ['delusion', 'unsoiled'],
 'unsoldier': ['undersoil', 'unsoldier'],
 'unsole': ['ensoul', 'olenus', 'unsole'],
 'unsolitary': ['unroyalist', 'unsolitary'],
 'unsomber': ['unsomber', 'unsombre'],
 'unsombre': ['unsomber', 'unsombre'],
 'unsome': ['nomeus', 'unsome'],
 'unsore': ['souren', 'unsore', 'ursone'],
 'unsort': ['tornus', 'unsort'],
 'unsortable': ['neuroblast', 'unsortable'],
 'unsorted': ['tonsured', 'unsorted', 'unstored'],
 'unsoured': ['unroused', 'unsoured'],
 'unsown': ['unsnow', 'unsown'],
 'unspan': ['pannus', 'sannup', 'unsnap', 'unspan'],
 'unspar': ['surnap', 'unspar'],
 'unspared': ['undersap', 'unparsed', 'unrasped', 'unspared', 'unspread'],
 'unsparred': ['underspar', 'unsparred'],
 'unspecterlike': ['unspecterlike', 'unspectrelike'],
 'unspectrelike': ['unspecterlike', 'unspectrelike'],
 'unsped': ['unsped', 'upsend'],
 'unspelt': ['unslept', 'unspelt'],
 'unsphering': ['gunnership', 'unsphering'],
 'unspiable': ['subalpine', 'unspiable'],
 'unspike': ['spunkie', 'unspike'],
 'unspit': ['ptinus', 'unspit'],
 'unspoil': ['pulsion', 'unspoil', 'upsilon'],
 'unspot': ['pontus', 'unspot', 'unstop'],
 'unspread': ['undersap', 'unparsed', 'unrasped', 'unspared', 'unspread'],
 'unstabled': ['dunstable', 'unblasted', 'unstabled'],
 'unstain': ['antisun', 'unsaint', 'unsatin', 'unstain'],
 'unstained': ['unsainted', 'unstained'],
 'unstaled': ['unsalted', 'unslated', 'unstaled'],
 'unstanch': ['unsnatch', 'unstanch'],
 'unstar': ['saturn', 'unstar'],
 'unstatable': ['unstatable', 'untastable'],
 'unstate': ['tetanus', 'unstate', 'untaste'],
 'unstateable': ['unstateable', 'untasteable'],
 'unstated': ['unstated', 'untasted'],
 'unstating': ['unstating', 'untasting'],
 'unstayed': ['unstayed', 'unsteady'],
 'unsteady': ['unstayed', 'unsteady'],
 'unstercorated': ['countertrades', 'unstercorated'],
 'unstern': ['stunner', 'unstern'],
 'unstocked': ['duckstone', 'unstocked'],
 'unstoic': ['cotinus', 'suction', 'unstoic'],
 'unstoical': ['suctional', 'sulcation', 'unstoical'],
 'unstop': ['pontus', 'unspot', 'unstop'],
 'unstopple': ['pulpstone', 'unstopple'],
 'unstore': ['snouter', 'tonsure', 'unstore'],
 'unstored': ['tonsured', 'unsorted', 'unstored'],
 'unstoried': ['detrusion', 'tinderous', 'unstoried'],
 'unstormed': ['undermost', 'unstormed'],
 'unstrain': ['insurant', 'unstrain'],
 'unstrained': ['understain', 'unstrained'],
 'unstraitened': ['unreinstated', 'unstraitened'],
 'unstranded': ['understand', 'unstranded'],
 'unstrewed': ['unstrewed', 'unwrested'],
 'unsucculent': ['centunculus', 'unsucculent'],
 'unsued': ['unsued', 'unused'],
 'unsusceptible': ['unsusceptible', 'unsuspectible'],
 'unsusceptive': ['unsusceptive', 'unsuspective'],
 'unsuspectible': ['unsusceptible', 'unsuspectible'],
 'unsuspective': ['unsusceptive', 'unsuspective'],
 'untactful': ['fluctuant', 'untactful'],
 'untailed': ['nidulate', 'untailed'],
 'untame': ['unmate', 'untame', 'unteam'],
 'untamed': ['unmated', 'untamed'],
 'untanned': ['nunnated', 'untanned'],
 'untap': ['punta', 'unapt', 'untap'],
 'untar': ['arnut', 'tuarn', 'untar'],
 'untastable': ['unstatable', 'untastable'],
 'untaste': ['tetanus', 'unstate', 'untaste'],
 'untasteable': ['unstateable', 'untasteable'],
 'untasted': ['unstated', 'untasted'],
 'untasting': ['unstating', 'untasting'],
 'untaught': ['taungthu', 'untaught'],
 'untaunted': ['unattuned', 'untaunted'],
 'unteach': ['uncheat', 'unteach'],
 'unteaching': ['uncheating', 'unteaching'],
 'unteam': ['unmate', 'untame', 'unteam'],
 'unteamed': ['unmeated', 'unteamed'],
 'unteased': ['unseated', 'unsedate', 'unteased'],
 'unteem': ['unmeet', 'unteem'],
 'untemper': ['erumpent', 'untemper'],
 'untender': ['unrented', 'untender'],
 'untented': ['unnetted', 'untented'],
 'unthatch': ['nuthatch', 'unthatch'],
 'unthick': ['kutchin', 'unthick'],
 'unthrall': ['turnhall', 'unthrall'],
 'untiaraed': ['diuranate', 'untiaraed'],
 'untidy': ['nudity', 'untidy'],
 'untie': ['intue', 'unite', 'untie'],
 'untied': ['dunite', 'united', 'untied'],
 'until': ['unlit', 'until'],
 'untile': ['lutein', 'untile'],
 'untiled': ['diluent', 'untiled'],
 'untilted': ['dilutent', 'untilted', 'untitled'],
 'untimely': ['minutely', 'untimely'],
 'untin': ['nintu', 'ninut', 'untin'],
 'untine': ['ineunt', 'untine'],
 'untinseled': ['unenlisted', 'unlistened', 'untinseled'],
 'untirable': ['untirable', 'untriable'],
 'untire': ['runite', 'triune', 'uniter', 'untire'],
 'untired': ['intrude', 'turdine', 'untired', 'untried'],
 'untithable': ['unhittable', 'untithable'],
 'untitled': ['dilutent', 'untilted', 'untitled'],
 'unto': ['tuno', 'unto'],
 'untoiled': ['outlined', 'untoiled'],
 'untoned': ['unnoted', 'untoned'],
 'untooled': ['unlooted', 'untooled'],
 'untop': ['punto', 'unpot', 'untop'],
 'untorn': ['tunnor', 'untorn'],
 'untortured': ['undertutor', 'untortured'],
 'untotalled': ['unallotted', 'untotalled'],
 'untouch': ['uncouth', 'untouch'],
 'untoured': ['unrouted', 'untoured'],
 'untrace': ['centaur', 'untrace'],
 'untraceable': ['uncreatable', 'untraceable'],
 'untraceableness': ['uncreatableness', 'untraceableness'],
 'untraced': ['uncarted', 'uncrated', 'underact', 'untraced'],
 'untraceried': ['antireducer', 'reincrudate', 'untraceried'],
 'untradeable': ['untradeable', 'untreadable'],
 'untrain': ['antirun', 'untrain', 'urinant'],
 'untread': ['daunter', 'unarted', 'unrated', 'untread'],
 'untreadable': ['untradeable', 'untreadable'],
 'untreatable': ['entablature', 'untreatable'],
 'untreed': ['denture', 'untreed'],
 'untriable': ['untirable', 'untriable'],
 'untribal': ['tribunal', 'turbinal', 'untribal'],
 'untriced': ['undirect', 'untriced'],
 'untried': ['intrude', 'turdine', 'untired', 'untried'],
 'untrig': ['ungirt', 'untrig'],
 'untrod': ['rotund', 'untrod'],
 'untropical': ['ponticular', 'untropical'],
 'untroubled': ['outblunder', 'untroubled'],
 'untrowed': ['undertow', 'untrowed'],
 'untruss': ['sturnus', 'untruss'],
 'untutored': ['outturned', 'untutored'],
 'unurn': ['unrun', 'unurn'],
 'unused': ['unsued', 'unused'],
 'unusurped': ['unpursued', 'unusurped'],
 'unusurping': ['unpursuing', 'unusurping'],
 'unvailable': ['invaluable', 'unvailable'],
 'unveering': ['unreeving', 'unveering'],
 'unveil': ['unevil', 'unlive', 'unveil'],
 'unveiled': ['unlevied', 'unveiled'],
 'unveined': ['unenvied', 'unveined'],
 'unvenerated': ['unenervated', 'unvenerated'],
 'unveniable': ['unenviable', 'unveniable'],
 'unveritable': ['unavertible', 'unveritable'],
 'unversed': ['unserved', 'unversed'],
 'unvessel': ['unvessel', 'usselven'],
 'unvest': ['unvest', 'venust'],
 'unvomited': ['unmotived', 'unvomited'],
 'unwagered': ['underwage', 'unwagered'],
 'unwan': ['unwan', 'wunna'],
 'unware': ['unware', 'wauner'],
 'unwarp': ['unwarp', 'unwrap'],
 'unwary': ['runway', 'unwary'],
 'unwavered': ['underwave', 'unwavered'],
 'unwept': ['unwept', 'upwent'],
 'unwon': ['unown', 'unwon'],
 'unwrap': ['unwarp', 'unwrap'],
 'unwrested': ['unstrewed', 'unwrested'],
 'unwronged': ['undergown', 'unwronged'],
 'unyoung': ['unyoung', 'youngun'],
 'unze': ['unze', 'zenu'],
 'up': ['pu', 'up'],
 'uparching': ['ungraphic', 'uparching'],
 'uparise': ['spuriae', 'uparise', 'upraise'],
 'uparna': ['purana', 'uparna'],
 'upas': ['apus', 'supa', 'upas'],
 'upblast': ['subplat', 'upblast'],
 'upblow': ['blowup', 'upblow'],
 'upbreak': ['breakup', 'upbreak'],
 'upbuild': ['buildup', 'upbuild'],
 'upcast': ['catsup', 'upcast'],
 'upcatch': ['catchup', 'upcatch'],
 'upclimb': ['plumbic', 'upclimb'],
 'upclose': ['culpose', 'ploceus', 'upclose'],
 'upcock': ['cockup', 'upcock'],
 'upcoil': ['oilcup', 'upcoil'],
 'upcourse': ['cupreous', 'upcourse'],
 'upcover': ['overcup', 'upcover'],
 'upcreep': ['prepuce', 'upcreep'],
 'upcurrent': ['puncturer', 'upcurrent'],
 'upcut': ['cutup', 'upcut'],
 'updo': ['doup', 'updo'],
 'updraw': ['updraw', 'upward'],
 'updry': ['prudy', 'purdy', 'updry'],
 'upeat': ['taupe', 'upeat'],
 'upflare': ['rapeful', 'upflare'],
 'upflower': ['powerful', 'upflower'],
 'upgale': ['plague', 'upgale'],
 'upget': ['getup', 'upget'],
 'upgirt': ['ripgut', 'upgirt'],
 'upgo': ['goup', 'ogpu', 'upgo'],
 'upgrade': ['guepard', 'upgrade'],
 'uphelm': ['phleum', 'uphelm'],
 'uphold': ['holdup', 'uphold'],
 'upholder': ['reuphold', 'upholder'],
 'upholsterer': ['reupholster', 'upholsterer'],
 'upla': ['paul', 'upla'],
 'upland': ['dunlap', 'upland'],
 'uplander': ['pendular', 'underlap', 'uplander'],
 'uplane': ['unpale', 'uplane'],
 'upleap': ['papule', 'upleap'],
 'uplift': ['tipful', 'uplift'],
 'uplifter': ['reuplift', 'uplifter'],
 'upline': ['lupine', 'unpile', 'upline'],
 'uplock': ['lockup', 'uplock'],
 'upon': ['noup', 'puno', 'upon'],
 'uppers': ['supper', 'uppers'],
 'uppish': ['hippus', 'uppish'],
 'upraise': ['spuriae', 'uparise', 'upraise'],
 'uprear': ['parure', 'uprear'],
 'uprein': ['purine', 'unripe', 'uprein'],
 'uprip': ['ripup', 'uprip'],
 'uprisal': ['parulis', 'spirula', 'uprisal'],
 'uprisement': ['episternum', 'uprisement'],
 'upriser': ['siruper', 'upriser'],
 'uprist': ['purist', 'spruit', 'uprist', 'upstir'],
 'uproad': ['podura', 'uproad'],
 'uproom': ['moorup', 'uproom'],
 'uprose': ['poseur', 'pouser', 'souper', 'uprose'],
 'upscale': ['capsule', 'specula', 'upscale'],
 'upseal': ['apulse', 'upseal'],
 'upsend': ['unsped', 'upsend'],
 'upset': ['setup', 'stupe', 'upset'],
 'upsettable': ['subpeltate', 'upsettable'],
 'upsetter': ['upsetter', 'upstreet'],
 'upshore': ['ephorus', 'orpheus', 'upshore'],
 'upshot': ['tophus', 'upshot'],
 'upshut': ['pushtu', 'upshut'],
 'upsilon': ['pulsion', 'unspoil', 'upsilon'],
 'upsit': ['puist', 'upsit'],
 'upslant': ['pulsant', 'upslant'],
 'upsmite': ['impetus', 'upsmite'],
 'upsoar': ['parous', 'upsoar'],
 'upstair': ['tapirus', 'upstair'],
 'upstand': ['dustpan', 'upstand'],
 'upstare': ['pasteur', 'pasture', 'upstare'],
 'upstater': ['stuprate', 'upstater'],
 'upsteal': ['pulsate', 'spatule', 'upsteal'],
 'upstem': ['septum', 'upstem'],
 'upstir': ['purist', 'spruit', 'uprist', 'upstir'],
 'upstraight': ['straightup', 'upstraight'],
 'upstreet': ['upsetter', 'upstreet'],
 'upstrive': ['spurtive', 'upstrive'],
 'upsun': ['sunup', 'upsun'],
 'upsway': ['upsway', 'upways'],
 'uptake': ['ketupa', 'uptake'],
 'uptend': ['pudent', 'uptend'],
 'uptilt': ['tiltup', 'uptilt'],
 'uptoss': ['tossup', 'uptoss'],
 'uptrace': ['capture', 'uptrace'],
 'uptrain': ['pintura', 'puritan', 'uptrain'],
 'uptree': ['repute', 'uptree'],
 'uptrend': ['prudent', 'prunted', 'uptrend'],
 'upturn': ['turnup', 'upturn'],
 'upward': ['updraw', 'upward'],
 'upwarp': ['upwarp', 'upwrap'],
 'upways': ['upsway', 'upways'],
 'upwent': ['unwept', 'upwent'],
 'upwind': ['upwind', 'windup'],
 'upwrap': ['upwarp', 'upwrap'],
 'ura': ['aru', 'rua', 'ura'],
 'uracil': ['curial', 'lauric', 'uracil', 'uralic'],
 'uraemic': ['maurice', 'uraemic'],
 'uraeus': ['aureus', 'uraeus'],
 'ural': ['alur', 'laur', 'lura', 'raul', 'ural'],
 'urali': ['rauli', 'urali', 'urial'],
 'uralian': ['lunaria', 'ulnaria', 'uralian'],
 'uralic': ['curial', 'lauric', 'uracil', 'uralic'],
 'uralite': ['laurite', 'uralite'],
 'uralitize': ['ritualize', 'uralitize'],
 'uramido': ['doarium', 'uramido'],
 'uramil': ['rimula', 'uramil'],
 'uramino': ['mainour', 'uramino'],
 'uran': ['raun', 'uran', 'urna'],
 'uranate': ['taurean', 'uranate'],
 'urania': ['anuria', 'urania'],
 'uranic': ['anuric', 'cinura', 'uranic'],
 'uranine': ['aneurin', 'uranine'],
 'uranism': ['surinam', 'uranism'],
 'uranite': ['ruinate', 'taurine', 'uranite', 'urinate'],
 'uranographist': ['guarantorship', 'uranographist'],
 'uranolite': ['outlinear', 'uranolite'],
 'uranoscope': ['oenocarpus', 'uranoscope'],
 'uranospinite': ['resupination', 'uranospinite'],
 'uranotil': ['rotulian', 'uranotil'],
 'uranous': ['anurous', 'uranous'],
 'uranyl': ['lunary', 'uranyl'],
 'uranylic': ['culinary', 'uranylic'],
 'urari': ['aurir', 'urari'],
 'urase': ['serau', 'urase'],
 'uratic': ['tauric', 'uratic', 'urtica'],
 'urazine': ['azurine', 'urazine'],
 'urban': ['buran', 'unbar', 'urban'],
 'urbane': ['eburna', 'unbare', 'unbear', 'urbane'],
 'urbanite': ['braunite', 'urbanite', 'urbinate'],
 'urbian': ['burian', 'urbian'],
 'urbification': ['rubification', 'urbification'],
 'urbify': ['rubify', 'urbify'],
 'urbinate': ['braunite', 'urbanite', 'urbinate'],
 'urceiform': ['eruciform', 'urceiform'],
 'urceole': ['urceole', 'urocele'],
 'urceolina': ['aleuronic', 'urceolina'],
 'urceolus': ['ulcerous', 'urceolus'],
 'urchin': ['unrich', 'urchin'],
 'urd': ['rud', 'urd'],
 'urde': ['duer', 'dure', 'rude', 'urde'],
 'urdee': ['redue', 'urdee'],
 'ure': ['rue', 'ure'],
 'ureal': ['alure', 'ureal'],
 'uredine': ['reindue', 'uredine'],
 'ureic': ['curie', 'ureic'],
 'uremia': ['aumrie', 'uremia'],
 'uremic': ['cerium', 'uremic'],
 'urena': ['urena', 'urnae'],
 'urent': ['enrut', 'tuner', 'urent'],
 'uresis': ['issuer', 'uresis'],
 'uretal': ['tulare', 'uretal'],
 'ureter': ['retrue', 'ureter'],
 'ureteropyelogram': ['pyeloureterogram', 'ureteropyelogram'],
 'urethan': ['haunter', 'nauther', 'unearth', 'unheart', 'urethan'],
 'urethrascope': ['heterocarpus', 'urethrascope'],
 'urethrocystitis': ['cystourethritis', 'urethrocystitis'],
 'urethylan': ['unearthly', 'urethylan'],
 'uretic': ['curite', 'teucri', 'uretic'],
 'urf': ['fur', 'urf'],
 'urge': ['grue', 'urge'],
 'urgent': ['gunter', 'gurnet', 'urgent'],
 'urger': ['regur', 'urger'],
 'uria': ['arui', 'uria'],
 'uriah': ['huari', 'uriah'],
 'urial': ['rauli', 'urali', 'urial'],
 'urian': ['aurin', 'urian'],
 'uric': ['cuir', 'uric'],
 'urinal': ['laurin', 'urinal'],
 'urinant': ['antirun', 'untrain', 'urinant'],
 'urinate': ['ruinate', 'taurine', 'uranite', 'urinate'],
 'urination': ['ruination', 'urination'],
 'urinator': ['ruinator', 'urinator'],
 'urine': ['inure', 'urine'],
 'urinogenitary': ['genitourinary', 'urinogenitary'],
 'urinous': ['ruinous', 'urinous'],
 'urinousness': ['ruinousness', 'urinousness'],
 'urite': ['urite', 'uteri'],
 'urlar': ['rural', 'urlar'],
 'urled': ['duler', 'urled'],
 'urling': ['ruling', 'urling'],
 'urman': ['muran', 'ruman', 'unarm', 'unram', 'urman'],
 'urn': ['run', 'urn'],
 'urna': ['raun', 'uran', 'urna'],
 'urnae': ['urena', 'urnae'],
 'urnal': ['lunar', 'ulnar', 'urnal'],
 'urnful': ['unfurl', 'urnful'],
 'urning': ['unring', 'urning'],
 'uro': ['our', 'uro'],
 'urocele': ['urceole', 'urocele'],
 'urodela': ['roulade', 'urodela'],
 'urodelan': ['unloader', 'urodelan'],
 'urogaster': ['surrogate', 'urogaster'],
 'urogenital': ['regulation', 'urogenital'],
 'uroglena': ['lagunero', 'organule', 'uroglena'],
 'urolithic': ['ulotrichi', 'urolithic'],
 'urometer': ['outremer', 'urometer'],
 'uronic': ['cuorin', 'uronic'],
 'uropsile': ['perilous', 'uropsile'],
 'uroseptic': ['crepitous', 'euproctis', 'uroseptic'],
 'urosomatic': ['mortacious', 'urosomatic'],
 'urosteon': ['outsnore', 'urosteon'],
 'urosternite': ['tenuiroster', 'urosternite'],
 'urosthenic': ['cetorhinus', 'urosthenic'],
 'urostyle': ['elytrous', 'urostyle'],
 'urs': ['rus', 'sur', 'urs'],
 'ursa': ['rusa', 'saur', 'sura', 'ursa', 'usar'],
 'ursal': ['larus', 'sural', 'ursal'],
 'ursidae': ['residua', 'ursidae'],
 'ursine': ['insure', 'rusine', 'ursine'],
 'ursone': ['souren', 'unsore', 'ursone'],
 'ursuk': ['kurus', 'ursuk'],
 'ursula': ['laurus', 'ursula'],
 'urtica': ['tauric', 'uratic', 'urtica'],
 'urticales': ['sterculia', 'urticales'],
 'urticant': ['taciturn', 'urticant'],
 'urticose': ['citreous', 'urticose'],
 'usability': ['suability', 'usability'],
 'usable': ['suable', 'usable'],
 'usager': ['sauger', 'usager'],
 'usance': ['uncase', 'usance'],
 'usar': ['rusa', 'saur', 'sura', 'ursa', 'usar'],
 'usara': ['arusa', 'saura', 'usara'],
 'use': ['sue', 'use'],
 'usent': ['unset', 'usent'],
 'user': ['ruse', 'suer', 'sure', 'user'],
 'ush': ['shu', 'ush'],
 'ushabti': ['habitus', 'ushabti'],
 'ushak': ['kusha', 'shaku', 'ushak'],
 'usher': ['shure', 'usher'],
 'usitate': ['situate', 'usitate'],
 'usnic': ['incus', 'usnic'],
 'usque': ['equus', 'usque'],
 'usselven': ['unvessel', 'usselven'],
 'ust': ['stu', 'ust'],
 'uster': ['serut', 'strue', 'turse', 'uster'],
 'ustion': ['outsin', 'ustion'],
 'ustorious': ['sutorious', 'ustorious'],
 'ustulina': ['lutianus', 'nautilus', 'ustulina'],
 'usucaption': ['uncaptious', 'usucaption'],
 'usure': ['eurus', 'usure'],
 'usurper': ['pursuer', 'usurper'],
 'ut': ['tu', 'ut'],
 'uta': ['tau', 'tua', 'uta'],
 'utas': ['saut', 'tasu', 'utas'],
 'utch': ['chut', 'tchu', 'utch'],
 'ute': ['tue', 'ute'],
 'uteri': ['urite', 'uteri'],
 'uterine': ['neurite', 'retinue', 'reunite', 'uterine'],
 'uterocervical': ['overcirculate', 'uterocervical'],
 'uterus': ['suture', 'uterus'],
 'utile': ['luite', 'utile'],
 'utilizable': ['latibulize', 'utilizable'],
 'utopian': ['opuntia', 'utopian'],
 'utopism': ['positum', 'utopism'],
 'utopist': ['outspit', 'utopist'],
 'utricular': ['turricula', 'utricular'],
 'utriculosaccular': ['sacculoutricular', 'utriculosaccular'],
 'utrum': ['murut', 'utrum'],
 'utterer': ['reutter', 'utterer'],
 'uva': ['uva', 'vau'],
 'uval': ['ulva', 'uval'],
 'uveal': ['uveal', 'value'],
 'uviol': ['uviol', 'vouli'],
 'vacate': ['cavate', 'caveat', 'vacate'],
 'vacation': ['octavian', 'octavina', 'vacation'],
 'vacationer': ['acervation', 'vacationer'],
 'vaccinal': ['clavacin', 'vaccinal'],
 'vacillate': ['laticlave', 'vacillate'],
 'vacillation': ['cavillation', 'vacillation'],
 'vacuolate': ['autoclave', 'vacuolate'],
 'vade': ['dave', 'deva', 'vade', 'veda'],
 'vady': ['davy', 'vady'],
 'vage': ['gave', 'vage', 'vega'],
 'vagile': ['glaive', 'vagile'],
 'vaginant': ['navigant', 'vaginant'],
 'vaginate': ['navigate', 'vaginate'],
 'vaginoabdominal': ['abdominovaginal', 'vaginoabdominal'],
 'vaginoperineal': ['perineovaginal', 'vaginoperineal'],
 'vaginovesical': ['vaginovesical', 'vesicovaginal'],
 'vaguity': ['gavyuti', 'vaguity'],
 'vai': ['iva', 'vai', 'via'],
 'vail': ['vail', 'vali', 'vial', 'vila'],
 'vain': ['ivan', 'vain', 'vina'],
 'vair': ['ravi', 'riva', 'vair', 'vari', 'vira'],
 'vakass': ['kavass', 'vakass'],
 'vale': ['lave', 'vale', 'veal', 'vela'],
 'valence': ['enclave', 'levance', 'valence'],
 'valencia': ['valencia', 'valiance'],
 'valent': ['levant', 'valent'],
 'valentine': ['levantine', 'valentine'],
 'valeria': ['reavail', 'valeria'],
 'valeriana': ['laverania', 'valeriana'],
 'valeric': ['caliver', 'caviler', 'claiver', 'clavier', 'valeric', 'velaric'],
 'valerie': ['realive', 'valerie'],
 'valerin': ['elinvar', 'ravelin', 'reanvil', 'valerin'],
 'valerone': ['overlean', 'valerone'],
 'valeryl': ['ravelly', 'valeryl'],
 'valeur': ['valeur', 'valuer'],
 'vali': ['vail', 'vali', 'vial', 'vila'],
 'valiance': ['valencia', 'valiance'],
 'valiant': ['latvian', 'valiant'],
 'valine': ['alevin', 'alvine', 'valine', 'veinal', 'venial', 'vineal'],
 'vallar': ['larval', 'vallar'],
 'vallidom': ['vallidom', 'villadom'],
 'vallota': ['lavolta', 'vallota'],
 'valonia': ['novalia', 'valonia'],
 'valor': ['valor', 'volar'],
 'valsa': ['salva', 'valsa', 'vasal'],
 'valse': ['salve', 'selva', 'slave', 'valse'],
 'value': ['uveal', 'value'],
 'valuer': ['valeur', 'valuer'],
 'vamper': ['revamp', 'vamper'],
 'vane': ['evan', 'nave', 'vane'],
 'vaned': ['daven', 'vaned'],
 'vangee': ['avenge', 'geneva', 'vangee'],
 'vangeli': ['leaving', 'vangeli'],
 'vanir': ['invar', 'ravin', 'vanir'],
 'vanisher': ['enravish', 'ravenish', 'vanisher'],
 'vansire': ['servian', 'vansire'],
 'vapid': ['pavid', 'vapid'],
 'vapidity': ['pavidity', 'vapidity'],
 'vaporarium': ['parovarium', 'vaporarium'],
 'vara': ['avar', 'vara'],
 'varan': ['navar', 'varan', 'varna'],
 'vare': ['aver', 'rave', 'vare', 'vera'],
 'varec': ['carve', 'crave', 'varec'],
 'vari': ['ravi', 'riva', 'vair', 'vari', 'vira'],
 'variate': ['variate', 'vateria'],
 'varices': ['varices', 'viscera'],
 'varicula': ['avicular', 'varicula'],
 'variegator': ['arrogative', 'variegator'],
 'varier': ['arrive', 'varier'],
 'varietal': ['lievaart', 'varietal'],
 'variola': ['ovarial', 'variola'],
 'various': ['saviour', 'various'],
 'varlet': ['travel', 'varlet'],
 'varletry': ['varletry', 'veratryl'],
 'varna': ['navar', 'varan', 'varna'],
 'varnish': ['shirvan', 'varnish'],
 'varnisher': ['revarnish', 'varnisher'],
 'varsha': ['avshar', 'varsha'],
 'vasal': ['salva', 'valsa', 'vasal'],
 'vase': ['aves', 'save', 'vase'],
 'vasoepididymostomy': ['epididymovasostomy', 'vasoepididymostomy'],
 'vat': ['tav', 'vat'],
 'vateria': ['variate', 'vateria'],
 'vaticide': ['cavitied', 'vaticide'],
 'vaticinate': ['inactivate', 'vaticinate'],
 'vaticination': ['inactivation', 'vaticination'],
 'vatter': ['tavert', 'vatter'],
 'vau': ['uva', 'vau'],
 'vaudois': ['avidous', 'vaudois'],
 'veal': ['lave', 'vale', 'veal', 'vela'],
 'vealer': ['laveer', 'leaver', 'reveal', 'vealer'],
 'vealiness': ['aliveness', 'vealiness'],
 'vealy': ['leavy', 'vealy'],
 'vector': ['covert', 'vector'],
 'veda': ['dave', 'deva', 'vade', 'veda'],
 'vedaic': ['advice', 'vedaic'],
 'vedaism': ['adevism', 'vedaism'],
 'vedana': ['nevada', 'vedana', 'venada'],
 'vedanta': ['vedanta', 'vetanda'],
 'vedantism': ['adventism', 'vedantism'],
 'vedantist': ['adventist', 'vedantist'],
 'vedist': ['divest', 'vedist'],
 'vedro': ['dover', 'drove', 'vedro'],
 'vee': ['eve', 'vee'],
 'veen': ['even', 'neve', 'veen'],
 'veer': ['ever', 'reve', 'veer'],
 'veery': ['every', 'veery'],
 'vega': ['gave', 'vage', 'vega'],
 'vegasite': ['estivage', 'vegasite'],
 'vegetarian': ['renavigate', 'vegetarian'],
 'vei': ['vei', 'vie'],
 'veil': ['evil', 'levi', 'live', 'veil', 'vile', 'vlei'],
 'veiler': ['levier', 'relive', 'reveil', 'revile', 'veiler'],
 'veiltail': ['illative', 'veiltail'],
 'vein': ['vein', 'vine'],
 'veinal': ['alevin', 'alvine', 'valine', 'veinal', 'venial', 'vineal'],
 'veined': ['endive', 'envied', 'veined'],
 'veiner': ['enrive', 'envier', 'veiner', 'verine'],
 'veinless': ['evilness', 'liveness', 'veinless', 'vileness', 'vineless'],
 'veinlet': ['veinlet', 'vinelet'],
 'veinous': ['envious', 'niveous', 'veinous'],
 'veinstone': ['veinstone', 'vonsenite'],
 'veinwise': ['veinwise', 'vinewise'],
 'vela': ['lave', 'vale', 'veal', 'vela'],
 'velar': ['arvel', 'larve', 'laver', 'ravel', 'velar'],
 'velaric': ['caliver', 'caviler', 'claiver', 'clavier', 'valeric', 'velaric'],
 'velation': ['olivetan', 'velation'],
 'velic': ['clive', 'velic'],
 'veliform': ['overfilm', 'veliform'],
 'velitation': ['levitation', 'tonalitive', 'velitation'],
 'velo': ['levo', 'love', 'velo', 'vole'],
 'velte': ['elvet', 'velte'],
 'venada': ['nevada', 'vedana', 'venada'],
 'venal': ['elvan', 'navel', 'venal'],
 'venality': ['natively', 'venality'],
 'venatic': ['catvine', 'venatic'],
 'venation': ['innovate', 'venation'],
 'venator': ['rotanev', 'venator'],
 'venatorial': ['venatorial', 'venoatrial'],
 'vendace': ['devance', 'vendace'],
 'vender': ['revend', 'vender'],
 'veneer': ['evener', 'veneer'],
 'veneerer': ['reveneer', 'veneerer'],
 'veneralia': ['ravenelia', 'veneralia'],
 'venerant': ['revenant', 'venerant'],
 'venerate': ['enervate', 'venerate'],
 'veneration': ['enervation', 'veneration'],
 'venerative': ['enervative', 'venerative'],
 'venerator': ['enervator', 'renovater', 'venerator'],
 'venerer': ['renerve', 'venerer'],
 'veneres': ['sevener', 'veneres'],
 'veneti': ['veneti', 'venite'],
 'venetian': ['aventine', 'venetian'],
 'venial': ['alevin', 'alvine', 'valine', 'veinal', 'venial', 'vineal'],
 'venice': ['cevine', 'evince', 'venice'],
 'venie': ['nieve', 'venie'],
 'venite': ['veneti', 'venite'],
 'venoatrial': ['venatorial', 'venoatrial'],
 'venom': ['novem', 'venom'],
 'venosinal': ['slovenian', 'venosinal'],
 'venter': ['revent', 'venter'],
 'ventrad': ['ventrad', 'verdant'],
 'ventricose': ['convertise', 'ventricose'],
 'ventrine': ['inventer', 'reinvent', 'ventrine', 'vintener'],
 'ventrodorsad': ['dorsoventrad', 'ventrodorsad'],
 'ventrodorsal': ['dorsoventral', 'ventrodorsal'],
 'ventrodorsally': ['dorsoventrally', 'ventrodorsally'],
 'ventrolateral': ['lateroventral', 'ventrolateral'],
 'ventromedial': ['medioventral', 'ventromedial'],
 'ventromesal': ['mesoventral', 'ventromesal'],
 'venular': ['unravel', 'venular'],
 'venus': ['nevus', 'venus'],
 'venust': ['unvest', 'venust'],
 'venutian': ['unnative', 'venutian'],
 'vera': ['aver', 'rave', 'vare', 'vera'],
 'veraciousness': ['oversauciness', 'veraciousness'],
 'veratroidine': ['rederivation', 'veratroidine'],
 'veratrole': ['relevator', 'revelator', 'veratrole'],
 'veratryl': ['varletry', 'veratryl'],
 'verbal': ['barvel', 'blaver', 'verbal'],
 'verbality': ['verbality', 'veritably'],
 'verbatim': ['ambivert', 'verbatim'],
 'verbena': ['enbrave', 'verbena'],
 'verberate': ['verberate', 'vertebrae'],
 'verbose': ['observe', 'obverse', 'verbose'],
 'verbosely': ['obversely', 'verbosely'],
 'verdant': ['ventrad', 'verdant'],
 'verdea': ['evader', 'verdea'],
 'verdelho': ['overheld', 'verdelho'],
 'verdin': ['driven', 'nervid', 'verdin'],
 'verditer': ['diverter', 'redivert', 'verditer'],
 'vergi': ['giver', 'vergi'],
 'veri': ['rive', 'veri', 'vier', 'vire'],
 'veridical': ['larvicide', 'veridical'],
 'veridicous': ['recidivous', 'veridicous'],
 'verily': ['livery', 'verily'],
 'verine': ['enrive', 'envier', 'veiner', 'verine'],
 'verism': ['verism', 'vermis'],
 'verist': ['stiver', 'strive', 'verist'],
 'veritable': ['avertible', 'veritable'],
 'veritably': ['verbality', 'veritably'],
 'vermian': ['minerva', 'vermian'],
 'verminal': ['minerval', 'verminal'],
 'vermis': ['verism', 'vermis'],
 'vernacularist': ['intervascular', 'vernacularist'],
 'vernal': ['nerval', 'vernal'],
 'vernation': ['nervation', 'vernation'],
 'vernicose': ['coversine', 'vernicose'],
 'vernine': ['innerve', 'nervine', 'vernine'],
 'veronese': ['overseen', 'veronese'],
 'veronica': ['corvinae', 'veronica'],
 'verpa': ['paver', 'verpa'],
 'verre': ['rever', 'verre'],
 'verrucous': ['recurvous', 'verrucous'],
 'verruga': ['gravure', 'verruga'],
 'versable': ['beslaver', 'servable', 'versable'],
 'versal': ['salver', 'serval', 'slaver', 'versal'],
 'versant': ['servant', 'versant'],
 'versate': ['evestar', 'versate'],
 'versation': ['overstain', 'servation', 'versation'],
 'verse': ['serve', 'sever', 'verse'],
 'verser': ['revers', 'server', 'verser'],
 'verset': ['revest', 'servet', 'sterve', 'verset', 'vester'],
 'versicule': ['reclusive', 'versicule'],
 'versine': ['inverse', 'versine'],
 'versioner': ['reversion', 'versioner'],
 'versionist': ['overinsist', 'versionist'],
 'verso': ['servo', 'verso'],
 'versta': ['starve', 'staver', 'strave', 'tavers', 'versta'],
 'vertebrae': ['verberate', 'vertebrae'],
 'vertebrocostal': ['costovertebral', 'vertebrocostal'],
 'vertebrosacral': ['sacrovertebral', 'vertebrosacral'],
 'vertebrosternal': ['sternovertebral', 'vertebrosternal'],
 'vertiginate': ['integrative', 'vertiginate', 'vinaigrette'],
 'vesicant': ['cistvaen', 'vesicant'],
 'vesicoabdominal': ['abdominovesical', 'vesicoabdominal'],
 'vesicocervical': ['cervicovesical', 'vesicocervical'],
 'vesicointestinal': ['intestinovesical', 'vesicointestinal'],
 'vesicorectal': ['rectovesical', 'vesicorectal'],
 'vesicovaginal': ['vaginovesical', 'vesicovaginal'],
 'vespa': ['spave', 'vespa'],
 'vespertine': ['presentive', 'pretensive', 'vespertine'],
 'vespine': ['pensive', 'vespine'],
 'vesta': ['stave', 'vesta'],
 'vestalia': ['salivate', 'vestalia'],
 'vestee': ['steeve', 'vestee'],
 'vester': ['revest', 'servet', 'sterve', 'verset', 'vester'],
 'vestibula': ['sublative', 'vestibula'],
 'veta': ['tave', 'veta'],
 'vetanda': ['vedanta', 'vetanda'],
 'veteran': ['nervate', 'veteran'],
 'veto': ['veto', 'voet', 'vote'],
 'vetoer': ['revote', 'vetoer'],
 'via': ['iva', 'vai', 'via'],
 'vial': ['vail', 'vali', 'vial', 'vila'],
 'vialful': ['fluavil', 'fluvial', 'vialful'],
 'viand': ['divan', 'viand'],
 'viander': ['invader', 'ravined', 'viander'],
 'viatic': ['avitic', 'viatic'],
 'viatica': ['aviatic', 'viatica'],
 'vibrate': ['vibrate', 'vrbaite'],
 'vicar': ['vicar', 'vraic'],
 'vice': ['cive', 'vice'],
 'vicegeral': ['vicegeral', 'viceregal'],
 'viceregal': ['vicegeral', 'viceregal'],
 'victoriate': ['recitativo', 'victoriate'],
 'victrola': ['victrola', 'vortical'],
 'victualer': ['lucrative', 'revictual', 'victualer'],
 'vidonia': ['ovidian', 'vidonia'],
 'viduinae': ['induviae', 'viduinae'],
 'viduine': ['univied', 'viduine'],
 'vie': ['vei', 'vie'],
 'vienna': ['avenin', 'vienna'],
 'vier': ['rive', 'veri', 'vier', 'vire'],
 'vierling': ['reviling', 'vierling'],
 'view': ['view', 'wive'],
 'viewer': ['review', 'viewer'],
 'vigilante': ['genitival', 'vigilante'],
 'vigor': ['vigor', 'virgo'],
 'vila': ['vail', 'vali', 'vial', 'vila'],
 'vile': ['evil', 'levi', 'live', 'veil', 'vile', 'vlei'],
 'vilehearted': ['evilhearted', 'vilehearted'],
 'vilely': ['evilly', 'lively', 'vilely'],
 'vileness': ['evilness', 'liveness', 'veinless', 'vileness', 'vineless'],
 'villadom': ['vallidom', 'villadom'],
 'villeiness': ['liveliness', 'villeiness'],
 'villous': ['ovillus', 'villous'],
 'vimana': ['maniva', 'vimana'],
 'vina': ['ivan', 'vain', 'vina'],
 'vinaigrette': ['integrative', 'vertiginate', 'vinaigrette'],
 'vinaigrous': ['vinaigrous', 'viraginous'],
 'vinal': ['alvin', 'anvil', 'nival', 'vinal'],
 'vinalia': ['lavinia', 'vinalia'],
 'vinata': ['avanti', 'vinata'],
 'vinculate': ['vinculate', 'vulcanite'],
 'vine': ['vein', 'vine'],
 'vinea': ['avine', 'naive', 'vinea'],
 'vineal': ['alevin', 'alvine', 'valine', 'veinal', 'venial', 'vineal'],
 'vineatic': ['antivice', 'inactive', 'vineatic'],
 'vinegarist': ['gainstrive', 'vinegarist'],
 'vineless': ['evilness', 'liveness', 'veinless', 'vileness', 'vineless'],
 'vinelet': ['veinlet', 'vinelet'],
 'viner': ['riven', 'viner'],
 'vinewise': ['veinwise', 'vinewise'],
 'vinosity': ['nivosity', 'vinosity'],
 'vintener': ['inventer', 'reinvent', 'ventrine', 'vintener'],
 'vintneress': ['inventress', 'vintneress'],
 'viola': ['oliva', 'viola'],
 'violability': ['obliviality', 'violability'],
 'violaceous': ['olivaceous', 'violaceous'],
 'violanin': ['livonian', 'violanin'],
 'violational': ['avolitional', 'violational'],
 'violer': ['oliver', 'violer', 'virole'],
 'violescent': ['olivescent', 'violescent'],
 'violet': ['olivet', 'violet'],
 'violette': ['olivette', 'violette'],
 'violine': ['olivine', 'violine'],
 'vipera': ['pavier', 'vipera'],
 'viperess': ['pressive', 'viperess'],
 'viperian': ['viperian', 'viperina'],
 'viperina': ['viperian', 'viperina'],
 'viperous': ['pervious', 'previous', 'viperous'],
 'viperously': ['perviously', 'previously', 'viperously'],
 'viperousness': ['perviousness', 'previousness', 'viperousness'],
 'vira': ['ravi', 'riva', 'vair', 'vari', 'vira'],
 'viraginian': ['irvingiana', 'viraginian'],
 'viraginous': ['vinaigrous', 'viraginous'],
 'viral': ['rival', 'viral'],
 'virales': ['revisal', 'virales'],
 'vire': ['rive', 'veri', 'vier', 'vire'],
 'virent': ['invert', 'virent'],
 'virgate': ['virgate', 'vitrage'],
 'virgin': ['irving', 'riving', 'virgin'],
 'virginly': ['rivingly', 'virginly'],
 'virgo': ['vigor', 'virgo'],
 'virile': ['livier', 'virile'],
 'virole': ['oliver', 'violer', 'virole'],
 'virose': ['rivose', 'virose'],
 'virtual': ['virtual', 'vitular'],
 'virtuose': ['virtuose', 'vitreous'],
 'virulence': ['cervuline', 'virulence'],
 'visa': ['avis', 'siva', 'visa'],
 'viscera': ['varices', 'viscera'],
 'visceration': ['insectivora', 'visceration'],
 'visceroparietal': ['parietovisceral', 'visceroparietal'],
 'visceropleural': ['pleurovisceral', 'visceropleural'],
 'viscometer': ['semivector', 'viscometer'],
 'viscontal': ['viscontal', 'volcanist'],
 'vishal': ['lavish', 'vishal'],
 'visioner': ['revision', 'visioner'],
 'visit': ['visit', 'vitis'],
 'visitant': ['nativist', 'visitant'],
 'visitee': ['evisite', 'visitee'],
 'visiter': ['revisit', 'visiter'],
 'visitor': ['ivorist', 'visitor'],
 'vistal': ['vistal', 'vitals'],
 'visto': ['ovist', 'visto'],
 'vitals': ['vistal', 'vitals'],
 'vitis': ['visit', 'vitis'],
 'vitochemical': ['chemicovital', 'vitochemical'],
 'vitrage': ['virgate', 'vitrage'],
 'vitrail': ['trivial', 'vitrail'],
 'vitrailist': ['trivialist', 'vitrailist'],
 'vitrain': ['vitrain', 'vitrina'],
 'vitrean': ['avertin', 'vitrean'],
 'vitreous': ['virtuose', 'vitreous'],
 'vitrina': ['vitrain', 'vitrina'],
 'vitrine': ['inviter', 'vitrine'],
 'vitrophyric': ['thyroprivic', 'vitrophyric'],
 'vitular': ['virtual', 'vitular'],
 'vituperate': ['reputative', 'vituperate'],
 'vlei': ['evil', 'levi', 'live', 'veil', 'vile', 'vlei'],
 'vocaller': ['overcall', 'vocaller'],
 'vocate': ['avocet', 'octave', 'vocate'],
 'voet': ['veto', 'voet', 'vote'],
 'voeten': ['voeten', 'voteen'],
 'vogue': ['vogue', 'vouge'],
 'voided': ['devoid', 'voided'],
 'voider': ['devoir', 'voider'],
 'voidless': ['dissolve', 'voidless'],
 'voile': ['olive', 'ovile', 'voile'],
 'volable': ['lovable', 'volable'],
 'volage': ['lovage', 'volage'],
 'volar': ['valor', 'volar'],
 'volata': ['tavola', 'volata'],
 'volatic': ['volatic', 'voltaic'],
 'volcae': ['alcove', 'coeval', 'volcae'],
 'volcanist': ['viscontal', 'volcanist'],
 'vole': ['levo', 'love', 'velo', 'vole'],
 'volery': ['overly', 'volery'],
 'volitate': ['volitate', 'voltaite'],
 'volley': ['lovely', 'volley'],
 'volscian': ['slavonic', 'volscian'],
 'volta': ['volta', 'votal'],
 'voltaic': ['volatic', 'voltaic'],
 'voltaite': ['volitate', 'voltaite'],
 'volucrine': ['involucre', 'volucrine'],
 'volunteerism': ['multinervose', 'volunteerism'],
 'vomer': ['mover', 'vomer'],
 'vomiter': ['revomit', 'vomiter'],
 'vonsenite': ['veinstone', 'vonsenite'],
 'vortical': ['victrola', 'vortical'],
 'votal': ['volta', 'votal'],
 'votary': ['travoy', 'votary'],
 'vote': ['veto', 'voet', 'vote'],
 'voteen': ['voeten', 'voteen'],
 'voter': ['overt', 'rovet', 'torve', 'trove', 'voter'],
 'vouge': ['vogue', 'vouge'],
 'vouli': ['uviol', 'vouli'],
 'vowed': ['devow', 'vowed'],
 'vowel': ['vowel', 'wolve'],
 'vraic': ['vicar', 'vraic'],
 'vrbaite': ['vibrate', 'vrbaite'],
 'vulcanite': ['vinculate', 'vulcanite'],
 'vulnerose': ['nervulose', 'unresolve', 'vulnerose'],
 'vulpic': ['pulvic', 'vulpic'],
 'vulpine': ['pluvine', 'vulpine'],
 'wa': ['aw', 'wa'],
 'waag': ['awag', 'waag'],
 'waasi': ['isawa', 'waasi'],
 'wab': ['baw', 'wab'],
 'wabi': ['biwa', 'wabi'],
 'wabster': ['bestraw', 'wabster'],
 'wac': ['caw', 'wac'],
 'wachna': ['chawan', 'chwana', 'wachna'],
 'wack': ['cawk', 'wack'],
 'wacker': ['awreck', 'wacker'],
 'wacky': ['cawky', 'wacky'],
 'wad': ['awd', 'daw', 'wad'],
 'wadder': ['edward', 'wadder', 'warded'],
 'waddler': ['dawdler', 'waddler'],
 'waddling': ['dawdling', 'waddling'],
 'waddlingly': ['dawdlingly', 'waddlingly'],
 'waddy': ['dawdy', 'waddy'],
 'wadna': ['adawn', 'wadna'],
 'wadset': ['wadset', 'wasted'],
 'wae': ['awe', 'wae', 'wea'],
 'waeg': ['waeg', 'wage', 'wega'],
 'waer': ['waer', 'ware', 'wear'],
 'waesome': ['awesome', 'waesome'],
 'wag': ['gaw', 'wag'],
 'wage': ['waeg', 'wage', 'wega'],
 'wagerer': ['rewager', 'wagerer'],
 'wages': ['swage', 'wages'],
 'waggel': ['waggel', 'waggle'],
 'waggle': ['waggel', 'waggle'],
 'wagnerite': ['wagnerite', 'winterage'],
 'wagon': ['gowan', 'wagon', 'wonga'],
 'wah': ['haw', 'hwa', 'wah', 'wha'],
 'wahehe': ['heehaw', 'wahehe'],
 'wail': ['wail', 'wali'],
 'wailer': ['lawrie', 'wailer'],
 'wain': ['awin', 'wain'],
 'wainer': ['newari', 'wainer'],
 'wairsh': ['rawish', 'wairsh', 'warish'],
 'waist': ['swati', 'waist'],
 'waister': ['swertia', 'waister'],
 'waiterage': ['garewaite', 'waiterage'],
 'waitress': ['starwise', 'waitress'],
 'waiwai': ['iwaiwa', 'waiwai'],
 'wake': ['wake', 'weak', 'weka'],
 'wakener': ['rewaken', 'wakener'],
 'waker': ['waker', 'wreak'],
 'wakes': ['askew', 'wakes'],
 'wale': ['wale', 'weal'],
 'waled': ['dwale', 'waled', 'weald'],
 'waler': ['lerwa', 'waler'],
 'wali': ['wail', 'wali'],
 'waling': ['lawing', 'waling'],
 'walk': ['lawk', 'walk'],
 'walkout': ['outwalk', 'walkout'],
 'walkover': ['overwalk', 'walkover'],
 'walkside': ['sidewalk', 'walkside'],
 'waller': ['rewall', 'waller'],
 'wallet': ['wallet', 'wellat'],
 'wallhick': ['hickwall', 'wallhick'],
 'walloper': ['preallow', 'walloper'],
 'wallower': ['rewallow', 'wallower'],
 'walsh': ['shawl', 'walsh'],
 'walt': ['twal', 'walt'],
 'walter': ['lawter', 'walter'],
 'wame': ['wame', 'weam'],
 'wamp': ['mawp', 'wamp'],
 'wan': ['awn', 'naw', 'wan'],
 'wand': ['dawn', 'wand'],
 'wander': ['andrew', 'redawn', 'wander', 'warden'],
 'wandle': ['delawn', 'lawned', 'wandle'],
 'wandlike': ['dawnlike', 'wandlike'],
 'wandy': ['dawny', 'wandy'],
 'wane': ['anew', 'wane', 'wean'],
 'waned': ['awned', 'dewan', 'waned'],
 'wang': ['gawn', 'gnaw', 'wang'],
 'wanghee': ['wanghee', 'whangee'],
 'wangler': ['wangler', 'wrangle'],
 'waning': ['awning', 'waning'],
 'wankle': ['knawel', 'wankle'],
 'wanly': ['lawny', 'wanly'],
 'want': ['nawt', 'tawn', 'want'],
 'wanty': ['tawny', 'wanty'],
 'wany': ['awny', 'wany', 'yawn'],
 'wap': ['paw', 'wap'],
 'war': ['raw', 'war'],
 'warble': ['bawler', 'brelaw', 'rebawl', 'warble'],
 'warbler': ['brawler', 'warbler'],
 'warbling': ['brawling', 'warbling'],
 'warblingly': ['brawlingly', 'warblingly'],
 'warbly': ['brawly', 'byrlaw', 'warbly'],
 'ward': ['draw', 'ward'],
 'wardable': ['drawable', 'wardable'],
 'warded': ['edward', 'wadder', 'warded'],
 'warden': ['andrew', 'redawn', 'wander', 'warden'],
 'warder': ['drawer', 'redraw', 'reward', 'warder'],
 'warderer': ['redrawer', 'rewarder', 'warderer'],
 'warding': ['drawing', 'ginward', 'warding'],
 'wardman': ['manward', 'wardman'],
 'wardmote': ['damewort', 'wardmote'],
 'wardrobe': ['drawbore', 'wardrobe'],
 'wardroom': ['roomward', 'wardroom'],
 'wardship': ['shipward', 'wardship'],
 'wardsman': ['manwards', 'wardsman'],
 'ware': ['waer', 'ware', 'wear'],
 'warehouse': ['housewear', 'warehouse'],
 'warf': ['warf', 'wraf'],
 'warish': ['rawish', 'wairsh', 'warish'],
 'warlock': ['lacwork', 'warlock'],
 'warmed': ['meward', 'warmed'],
 'warmer': ['rewarm', 'warmer'],
 'warmhouse': ['housewarm', 'warmhouse'],
 'warmish': ['warmish', 'wishram'],
 'warn': ['warn', 'wran'],
 'warnel': ['lawner', 'warnel'],
 'warner': ['rewarn', 'warner', 'warren'],
 'warp': ['warp', 'wrap'],
 'warper': ['prewar', 'rewrap', 'warper'],
 'warree': ['rewear', 'warree', 'wearer'],
 'warren': ['rewarn', 'warner', 'warren'],
 'warri': ['warri', 'wirra'],
 'warse': ['resaw', 'sawer', 'seraw', 'sware', 'swear', 'warse'],
 'warsel': ['swaler', 'warsel', 'warsle'],
 'warsle': ['swaler', 'warsel', 'warsle'],
 'warst': ['straw', 'swart', 'warst'],
 'warth': ['thraw', 'warth', 'whart', 'wrath'],
 'warua': ['warua', 'waura'],
 'warve': ['warve', 'waver'],
 'wary': ['awry', 'wary'],
 'was': ['saw', 'swa', 'was'],
 'wasel': ['swale', 'sweal', 'wasel'],
 'wash': ['shaw', 'wash'],
 'washen': ['washen', 'whenas'],
 'washer': ['hawser', 'rewash', 'washer'],
 'washington': ['nowanights', 'washington'],
 'washland': ['landwash', 'washland'],
 'washoan': ['shawano', 'washoan'],
 'washout': ['outwash', 'washout'],
 'washy': ['shawy', 'washy'],
 'wasnt': ['stawn', 'wasnt'],
 'wasp': ['swap', 'wasp'],
 'waspily': ['slipway', 'waspily'],
 'wast': ['sawt', 'staw', 'swat', 'taws', 'twas', 'wast'],
 'waste': ['awest', 'sweat', 'tawse', 'waste'],
 'wasted': ['wadset', 'wasted'],
 'wasteful': ['sweatful', 'wasteful'],
 'wasteless': ['sweatless', 'wasteless'],
 'wasteproof': ['sweatproof', 'wasteproof'],
 'wastrel': ['wastrel', 'wrastle'],
 'wat': ['taw', 'twa', 'wat'],
 'watchdog': ['dogwatch', 'watchdog'],
 'watchout': ['outwatch', 'watchout'],
 'water': ['tawer', 'water', 'wreat'],
 'waterbrain': ['brainwater', 'waterbrain'],
 'watered': ['dewater', 'tarweed', 'watered'],
 'waterer': ['rewater', 'waterer'],
 'waterflood': ['floodwater', 'toadflower', 'waterflood'],
 'waterhead': ['headwater', 'waterhead'],
 'wateriness': ['earwitness', 'wateriness'],
 'waterlog': ['galewort', 'waterlog'],
 'watershed': ['drawsheet', 'watershed'],
 'watery': ['tawery', 'watery'],
 'wath': ['thaw', 'wath', 'what'],
 'watt': ['twat', 'watt'],
 'wauf': ['awfu', 'wauf'],
 'wauner': ['unware', 'wauner'],
 'waura': ['warua', 'waura'],
 'waver': ['warve', 'waver'],
 'waxer': ['rewax', 'waxer'],
 'way': ['way', 'yaw'],
 'wayback': ['backway', 'wayback'],
 'waygang': ['gangway', 'waygang'],
 'waygate': ['gateway', 'getaway', 'waygate'],
 'wayman': ['manway', 'wayman'],
 'ways': ['sway', 'ways', 'yaws'],
 'wayside': ['sideway', 'wayside'],
 'wea': ['awe', 'wae', 'wea'],
 'weak': ['wake', 'weak', 'weka'],
 'weakener': ['reweaken', 'weakener'],
 'weakliness': ['weakliness', 'weaselskin'],
 'weal': ['wale', 'weal'],
 'weald': ['dwale', 'waled', 'weald'],
 'weam': ['wame', 'weam'],
 'wean': ['anew', 'wane', 'wean'],
 'weanel': ['leewan', 'weanel'],
 'wear': ['waer', 'ware', 'wear'],
 'wearer': ['rewear', 'warree', 'wearer'],
 'weasand': ['sandawe', 'weasand'],
 'weaselskin': ['weakliness', 'weaselskin'],
 'weather': ['weather', 'whereat', 'wreathe'],
 'weathered': ['heartweed', 'weathered'],
 'weaver': ['rewave', 'weaver'],
 'webster': ['bestrew', 'webster'],
 'wed': ['dew', 'wed'],
 'wede': ['wede', 'weed'],
 'wedge': ['gweed', 'wedge'],
 'wedger': ['edgrew', 'wedger'],
 'wedset': ['stewed', 'wedset'],
 'wee': ['ewe', 'wee'],
 'weed': ['wede', 'weed'],
 'weedhook': ['hookweed', 'weedhook'],
 'weedy': ['dewey', 'weedy'],
 'ween': ['ween', 'wene'],
 'weeps': ['sweep', 'weeps'],
 'weet': ['twee', 'weet'],
 'wega': ['waeg', 'wage', 'wega'],
 'wegotism': ['twigsome', 'wegotism'],
 'weigher': ['reweigh', 'weigher'],
 'weir': ['weir', 'weri', 'wire'],
 'weirangle': ['weirangle', 'wierangle'],
 'weird': ['weird', 'wired', 'wride', 'wried'],
 'weka': ['wake', 'weak', 'weka'],
 'weld': ['lewd', 'weld'],
 'weldable': ['ballweed', 'weldable'],
 'welder': ['reweld', 'welder'],
 'weldor': ['lowder', 'weldor', 'wordle'],
 'welf': ['flew', 'welf'],
 'welkin': ['welkin', 'winkel', 'winkle'],
 'well': ['llew', 'well'],
 'wellat': ['wallet', 'wellat'],
 'wels': ['slew', 'wels'],
 'welshry': ['shrewly', 'welshry'],
 'welting': ['twingle', 'welting', 'winglet'],
 'wem': ['mew', 'wem'],
 'wen': ['new', 'wen'],
 'wende': ['endew', 'wende'],
 'wendi': ['dwine', 'edwin', 'wendi', 'widen', 'wined'],
 'wene': ['ween', 'wene'],
 'went': ['newt', 'went'],
 'were': ['ewer', 'were'],
 'weri': ['weir', 'weri', 'wire'],
 'werther': ['werther', 'wherret'],
 'wes': ['sew', 'wes'],
 'weskit': ['weskit', 'wisket'],
 'west': ['stew', 'west'],
 'weste': ['sweet', 'weste'],
 'westering': ['swingtree', 'westering'],
 'westy': ['stewy', 'westy'],
 'wet': ['tew', 'wet'],
 'weta': ['tewa', 'twae', 'weta'],
 'wetly': ['tewly', 'wetly'],
 'wey': ['wey', 'wye', 'yew'],
 'wha': ['haw', 'hwa', 'wah', 'wha'],
 'whack': ['chawk', 'whack'],
 'whale': ['whale', 'wheal'],
 'wham': ['hawm', 'wham'],
 'whame': ['whame', 'wheam'],
 'whangee': ['wanghee', 'whangee'],
 'whare': ['hawer', 'whare'],
 'whart': ['thraw', 'warth', 'whart', 'wrath'],
 'whase': ['hawse', 'shewa', 'whase'],
 'what': ['thaw', 'wath', 'what'],
 'whatreck': ['thwacker', 'whatreck'],
 'whats': ['swath', 'whats'],
 'wheal': ['whale', 'wheal'],
 'wheam': ['whame', 'wheam'],
 'wheat': ['awhet', 'wheat'],
 'wheatear': ['aweather', 'wheatear'],
 'wheedle': ['wheedle', 'wheeled'],
 'wheel': ['hewel', 'wheel'],
 'wheeled': ['wheedle', 'wheeled'],
 'wheelroad': ['rowelhead', 'wheelroad'],
 'wheer': ['hewer', 'wheer', 'where'],
 'whein': ['whein', 'whine'],
 'when': ['hewn', 'when'],
 'whenas': ['washen', 'whenas'],
 'whenso': ['whenso', 'whosen'],
 'where': ['hewer', 'wheer', 'where'],
 'whereat': ['weather', 'whereat', 'wreathe'],
 'whereon': ['nowhere', 'whereon'],
 'wherret': ['werther', 'wherret'],
 'wherrit': ['wherrit', 'whirret', 'writher'],
 'whet': ['hewt', 'thew', 'whet'],
 'whichever': ['everwhich', 'whichever'],
 'whicken': ['chewink', 'whicken'],
 'whilter': ['whilter', 'whirtle'],
 'whine': ['whein', 'whine'],
 'whipper': ['prewhip', 'whipper'],
 'whirler': ['rewhirl', 'whirler'],
 'whirret': ['wherrit', 'whirret', 'writher'],
 'whirtle': ['whilter', 'whirtle'],
 'whisperer': ['rewhisper', 'whisperer'],
 'whisson': ['snowish', 'whisson'],
 'whist': ['swith', 'whist', 'whits', 'wisht'],
 'whister': ['swither', 'whister', 'withers'],
 'whit': ['whit', 'with'],
 'white': ['white', 'withe'],
 'whiten': ['whiten', 'withen'],
 'whitener': ['rewhiten', 'whitener'],
 'whitepot': ['whitepot', 'whitetop'],
 'whites': ['swithe', 'whites'],
 'whitetop': ['whitepot', 'whitetop'],
 'whitewood': ['whitewood', 'withewood'],
 'whitleather': ['therewithal', 'whitleather'],
 'whitmanese': ['anthemwise', 'whitmanese'],
 'whits': ['swith', 'whist', 'whits', 'wisht'],
 'whity': ['whity', 'withy'],
 'who': ['how', 'who'],
 'whoever': ['everwho', 'however', 'whoever'],
 'whole': ['howel', 'whole'],
 'whomsoever': ['howsomever', 'whomsoever', 'whosomever'],
 'whoreship': ['horsewhip', 'whoreship'],
 'whort': ['throw', 'whort', 'worth', 'wroth'],
 'whosen': ['whenso', 'whosen'],
 'whosomever': ['howsomever', 'whomsoever', 'whosomever'],
 'wicht': ['tchwi', 'wicht', 'witch'],
 'widdle': ['widdle', 'wilded'],
 'widely': ['dewily', 'widely', 'wieldy'],
 'widen': ['dwine', 'edwin', 'wendi', 'widen', 'wined'],
 'widener': ['rewiden', 'widener'],
 'wideness': ['dewiness', 'wideness'],
 'widgeon': ['gowdnie', 'widgeon'],
 'wieldy': ['dewily', 'widely', 'wieldy'],
 'wierangle': ['weirangle', 'wierangle'],
 'wigan': ['awing', 'wigan'],
 'wiggler': ['wiggler', 'wriggle'],
 'wilded': ['widdle', 'wilded'],
 'wildness': ['wildness', 'windless'],
 'winchester': ['trenchwise', 'winchester'],
 'windbreak': ['breakwind', 'windbreak'],
 'winder': ['rewind', 'winder'],
 'windgall': ['dingwall', 'windgall'],
 'windles': ['swindle', 'windles'],
 'windless': ['wildness', 'windless'],
 'windstorm': ['stormwind', 'windstorm'],
 'windup': ['upwind', 'windup'],
 'wined': ['dwine', 'edwin', 'wendi', 'widen', 'wined'],
 'winer': ['erwin', 'rewin', 'winer'],
 'winglet': ['twingle', 'welting', 'winglet'],
 'winkel': ['welkin', 'winkel', 'winkle'],
 'winkle': ['welkin', 'winkel', 'winkle'],
 'winklet': ['twinkle', 'winklet'],
 'winnard': ['indrawn', 'winnard'],
 'winnel': ['winnel', 'winnle'],
 'winnle': ['winnel', 'winnle'],
 'winsome': ['owenism', 'winsome'],
 'wint': ['twin', 'wint'],
 'winter': ['twiner', 'winter'],
 'winterage': ['wagnerite', 'winterage'],
 'wintered': ['interwed', 'wintered'],
 'winterish': ['interwish', 'winterish'],
 'winze': ['winze', 'wizen'],
 'wips': ['wips', 'wisp'],
 'wire': ['weir', 'weri', 'wire'],
 'wired': ['weird', 'wired', 'wride', 'wried'],
 'wirer': ['wirer', 'wrier'],
 'wirra': ['warri', 'wirra'],
 'wiselike': ['likewise', 'wiselike'],
 'wiseman': ['manwise', 'wiseman'],
 'wisen': ['sinew', 'swine', 'wisen'],
 'wiser': ['swire', 'wiser'],
 'wisewoman': ['wisewoman', 'womanwise'],
 'wisher': ['rewish', 'wisher'],
 'wishmay': ['wishmay', 'yahwism'],
 'wishram': ['warmish', 'wishram'],
 'wisht': ['swith', 'whist', 'whits', 'wisht'],
 'wisket': ['weskit', 'wisket'],
 'wisp': ['wips', 'wisp'],
 'wispy': ['swipy', 'wispy'],
 'wit': ['twi', 'wit'],
 'witan': ['atwin', 'twain', 'witan'],
 'witch': ['tchwi', 'wicht', 'witch'],
 'witchetty': ['twitchety', 'witchetty'],
 'with': ['whit', 'with'],
 'withdrawer': ['rewithdraw', 'withdrawer'],
 'withe': ['white', 'withe'],
 'withen': ['whiten', 'withen'],
 'wither': ['wither', 'writhe'],
 'withered': ['redwithe', 'withered'],
 'withering': ['withering', 'wrightine'],
 'withers': ['swither', 'whister', 'withers'],
 'withewood': ['whitewood', 'withewood'],
 'within': ['inwith', 'within'],
 'without': ['outwith', 'without'],
 'withy': ['whity', 'withy'],
 'wive': ['view', 'wive'],
 'wiver': ['wiver', 'wrive'],
 'wizen': ['winze', 'wizen'],
 'wo': ['ow', 'wo'],
 'woader': ['redowa', 'woader'],
 'wob': ['bow', 'wob'],
 'wod': ['dow', 'owd', 'wod'],
 'woe': ['owe', 'woe'],
 'woibe': ['bowie', 'woibe'],
 'wold': ['dowl', 'wold'],
 'wolf': ['flow', 'fowl', 'wolf'],
 'wolfer': ['flower', 'fowler', 'reflow', 'wolfer'],
 'wolter': ['rowlet', 'trowel', 'wolter'],
 'wolve': ['vowel', 'wolve'],
 'womanpost': ['postwoman', 'womanpost'],
 'womanwise': ['wisewoman', 'womanwise'],
 'won': ['now', 'own', 'won'],
 'wonder': ['downer', 'wonder', 'worden'],
 'wonderful': ['underflow', 'wonderful'],
 'wone': ['enow', 'owen', 'wone'],
 'wong': ['gown', 'wong'],
 'wonga': ['gowan', 'wagon', 'wonga'],
 'wonner': ['renown', 'wonner'],
 'wont': ['nowt', 'town', 'wont'],
 'wonted': ['towned', 'wonted'],
 'woodbark': ['bookward', 'woodbark'],
 'woodbind': ['bindwood', 'woodbind'],
 'woodbush': ['bushwood', 'woodbush'],
 'woodchat': ['chatwood', 'woodchat'],
 'wooden': ['enwood', 'wooden'],
 'woodfish': ['fishwood', 'woodfish'],
 'woodhack': ['hackwood', 'woodhack'],
 'woodhorse': ['horsewood', 'woodhorse'],
 'woodness': ['sowdones', 'woodness'],
 'woodpecker': ['peckerwood', 'woodpecker'],
 'woodrock': ['corkwood', 'rockwood', 'woodrock'],
 'woodsilver': ['silverwood', 'woodsilver'],
 'woodstone': ['stonewood', 'woodstone'],
 'woodworm': ['woodworm', 'wormwood'],
 'wooled': ['dewool', 'elwood', 'wooled'],
 'woons': ['swoon', 'woons'],
 'woosh': ['howso', 'woosh'],
 'wop': ['pow', 'wop'],
 'worble': ['blower', 'bowler', 'reblow', 'worble'],
 'word': ['drow', 'word'],
 'wordage': ['dowager', 'wordage'],
 'worden': ['downer', 'wonder', 'worden'],
 'worder': ['reword', 'worder'],
 'wordily': ['rowdily', 'wordily'],
 'wordiness': ['rowdiness', 'wordiness'],
 'wordle': ['lowder', 'weldor', 'wordle'],
 'wordsman': ['sandworm', 'swordman', 'wordsman'],
 'wordsmanship': ['swordmanship', 'wordsmanship'],
 'wordy': ['dowry', 'rowdy', 'wordy'],
 'wore': ['ower', 'wore'],
 'workbasket': ['basketwork', 'workbasket'],
 'workbench': ['benchwork', 'workbench'],
 'workbook': ['bookwork', 'workbook'],
 'workbox': ['boxwork', 'workbox'],
 'workday': ['daywork', 'workday'],
 'worker': ['rework', 'worker'],
 'workhand': ['handwork', 'workhand'],
 'workhouse': ['housework', 'workhouse'],
 'working': ['kingrow', 'working'],
 'workmaster': ['masterwork', 'workmaster'],
 'workout': ['outwork', 'workout'],
 'workpiece': ['piecework', 'workpiece'],
 'workship': ['shipwork', 'workship'],
 'workshop': ['shopwork', 'workshop'],
 'worktime': ['timework', 'worktime'],
 'wormed': ['deworm', 'wormed'],
 'wormer': ['merrow', 'wormer'],
 'wormroot': ['moorwort', 'rootworm', 'tomorrow', 'wormroot'],
 'wormship': ['shipworm', 'wormship'],
 'wormwood': ['woodworm', 'wormwood'],
 'worse': ['owser', 'resow', 'serow', 'sower', 'swore', 'worse'],
 'worset': ['restow', 'stower', 'towser', 'worset'],
 'worst': ['strow', 'worst'],
 'wort': ['trow', 'wort'],
 'worth': ['throw', 'whort', 'worth', 'wroth'],
 'worthful': ['worthful', 'wrothful'],
 'worthily': ['worthily', 'wrothily'],
 'worthiness': ['worthiness', 'wrothiness'],
 'worthy': ['worthy', 'wrothy'],
 'wot': ['tow', 'two', 'wot'],
 'wots': ['sowt', 'stow', 'swot', 'wots'],
 'wounder': ['rewound', 'unrowed', 'wounder'],
 'woy': ['woy', 'yow'],
 'wraf': ['warf', 'wraf'],
 'wrainbolt': ['browntail', 'wrainbolt'],
 'wraitly': ['wraitly', 'wrytail'],
 'wran': ['warn', 'wran'],
 'wrangle': ['wangler', 'wrangle'],
 'wrap': ['warp', 'wrap'],
 'wrapper': ['prewrap', 'wrapper'],
 'wrastle': ['wastrel', 'wrastle'],
 'wrath': ['thraw', 'warth', 'whart', 'wrath'],
 'wreak': ['waker', 'wreak'],
 'wreat': ['tawer', 'water', 'wreat'],
 'wreath': ['rethaw', 'thawer', 'wreath'],
 'wreathe': ['weather', 'whereat', 'wreathe'],
 'wrest': ['strew', 'trews', 'wrest'],
 'wrester': ['strewer', 'wrester'],
 'wrestle': ['swelter', 'wrestle'],
 'wride': ['weird', 'wired', 'wride', 'wried'],
 'wried': ['weird', 'wired', 'wride', 'wried'],
 'wrier': ['wirer', 'wrier'],
 'wriggle': ['wiggler', 'wriggle'],
 'wrightine': ['withering', 'wrightine'],
 'wrinklet': ['twinkler', 'wrinklet'],
 'write': ['twire', 'write'],
 'writhe': ['wither', 'writhe'],
 'writher': ['wherrit', 'whirret', 'writher'],
 'written': ['twinter', 'written'],
 'wrive': ['wiver', 'wrive'],
 'wro': ['row', 'wro'],
 'wroken': ['knower', 'reknow', 'wroken'],
 'wrong': ['grown', 'wrong'],
 'wrote': ['rowet', 'tower', 'wrote'],
 'wroth': ['throw', 'whort', 'worth', 'wroth'],
 'wrothful': ['worthful', 'wrothful'],
 'wrothily': ['worthily', 'wrothily'],
 'wrothiness': ['worthiness', 'wrothiness'],
 'wrothy': ['worthy', 'wrothy'],
 'wrytail': ['wraitly', 'wrytail'],
 'wunna': ['unwan', 'wunna'],
 'wyde': ['dewy', 'wyde'],
 'wye': ['wey', 'wye', 'yew'],
 'wype': ['pewy', 'wype'],
 'wyson': ['snowy', 'wyson'],
 'xanthein': ['xanthein', 'xanthine'],
 'xanthine': ['xanthein', 'xanthine'],
 'xanthopurpurin': ['purpuroxanthin', 'xanthopurpurin'],
 'xema': ['amex', 'exam', 'xema'],
 'xenia': ['axine', 'xenia'],
 'xenial': ['alexin', 'xenial'],
 'xenoparasite': ['exasperation', 'xenoparasite'],
 'xeres': ['resex', 'xeres'],
 'xerophytic': ['hypertoxic', 'xerophytic'],
 'xerotic': ['excitor', 'xerotic'],
 'xylic': ['cylix', 'xylic'],
 'xylitone': ['xylitone', 'xylonite'],
 'xylonite': ['xylitone', 'xylonite'],
 'xylophone': ['oxyphenol', 'xylophone'],
 'xylose': ['lyxose', 'xylose'],
 'xyst': ['styx', 'xyst'],
 'xyster': ['sextry', 'xyster'],
 'xysti': ['sixty', 'xysti'],
 'ya': ['ay', 'ya'],
 'yaba': ['baya', 'yaba'],
 'yabber': ['babery', 'yabber'],
 'yacht': ['cathy', 'cyath', 'yacht'],
 'yachtist': ['chastity', 'yachtist'],
 'yad': ['ady', 'day', 'yad'],
 'yaff': ['affy', 'yaff'],
 'yagnob': ['boyang', 'yagnob'],
 'yah': ['hay', 'yah'],
 'yahwism': ['wishmay', 'yahwism'],
 'yair': ['airy', 'yair'],
 'yaird': ['dairy', 'diary', 'yaird'],
 'yak': ['kay', 'yak'],
 'yakan': ['kayan', 'yakan'],
 'yakima': ['kamiya', 'yakima'],
 'yakka': ['kayak', 'yakka'],
 'yalb': ['ably', 'blay', 'yalb'],
 'yali': ['ilya', 'yali'],
 'yalla': ['allay', 'yalla'],
 'yallaer': ['allayer', 'yallaer'],
 'yam': ['amy', 'may', 'mya', 'yam'],
 'yamel': ['mealy', 'yamel'],
 'yamen': ['maney', 'yamen'],
 'yamilke': ['maylike', 'yamilke'],
 'yamph': ['phyma', 'yamph'],
 'yan': ['any', 'nay', 'yan'],
 'yana': ['anay', 'yana'],
 'yander': ['denary', 'yander'],
 'yap': ['pay', 'pya', 'yap'],
 'yapness': ['synapse', 'yapness'],
 'yapper': ['papery', 'prepay', 'yapper'],
 'yapster': ['atrepsy', 'yapster'],
 'yar': ['ary', 'ray', 'yar'],
 'yarb': ['bray', 'yarb'],
 'yard': ['adry', 'dray', 'yard'],
 'yardage': ['drayage', 'yardage'],
 'yarder': ['dreary', 'yarder'],
 'yardman': ['drayman', 'yardman'],
 'yare': ['aery', 'eyra', 'yare', 'year'],
 'yark': ['kyar', 'yark'],
 'yarl': ['aryl', 'lyra', 'ryal', 'yarl'],
 'yarm': ['army', 'mary', 'myra', 'yarm'],
 'yarn': ['nary', 'yarn'],
 'yarr': ['arry', 'yarr'],
 'yarrow': ['arrowy', 'yarrow'],
 'yaruran': ['unarray', 'yaruran'],
 'yas': ['say', 'yas'],
 'yasht': ['hasty', 'yasht'],
 'yat': ['tay', 'yat'],
 'yate': ['yate', 'yeat', 'yeta'],
 'yatter': ['attery', 'treaty', 'yatter'],
 'yaw': ['way', 'yaw'],
 'yawler': ['lawyer', 'yawler'],
 'yawn': ['awny', 'wany', 'yawn'],
 'yaws': ['sway', 'ways', 'yaws'],
 'ye': ['ey', 'ye'],
 'yea': ['aye', 'yea'],
 'yeah': ['ahey', 'eyah', 'yeah'],
 'year': ['aery', 'eyra', 'yare', 'year'],
 'yeard': ['deary', 'deray', 'rayed', 'ready', 'yeard'],
 'yearly': ['layery', 'yearly'],
 'yearn': ['enray', 'yearn'],
 'yearth': ['earthy', 'hearty', 'yearth'],
 'yeast': ['teasy', 'yeast'],
 'yeat': ['yate', 'yeat', 'yeta'],
 'yeather': ['erythea', 'hetaery', 'yeather'],
 'yed': ['dey', 'dye', 'yed'],
 'yede': ['eyed', 'yede'],
 'yee': ['eye', 'yee'],
 'yeel': ['eely', 'yeel'],
 'yees': ['yees', 'yese'],
 'yegg': ['eggy', 'yegg'],
 'yelk': ['kyle', 'yelk'],
 'yelm': ['elmy', 'yelm'],
 'yelmer': ['merely', 'yelmer'],
 'yelper': ['peerly', 'yelper'],
 'yemen': ['enemy', 'yemen'],
 'yemeni': ['menyie', 'yemeni'],
 'yen': ['eyn', 'nye', 'yen'],
 'yender': ['redeny', 'yender'],
 'yeo': ['yeo', 'yoe'],
 'yeorling': ['legionry', 'yeorling'],
 'yer': ['rye', 'yer'],
 'yerb': ['brey', 'byre', 'yerb'],
 'yerba': ['barye', 'beray', 'yerba'],
 'yerd': ['dyer', 'yerd'],
 'yere': ['eyer', 'eyre', 'yere'],
 'yern': ['ryen', 'yern'],
 'yes': ['sey', 'sye', 'yes'],
 'yese': ['yees', 'yese'],
 'yest': ['stey', 'yest'],
 'yester': ['reesty', 'yester'],
 'yestern': ['streyne', 'styrene', 'yestern'],
 'yet': ['tye', 'yet'],
 'yeta': ['yate', 'yeat', 'yeta'],
 'yeth': ['they', 'yeth'],
 'yether': ['theyre', 'yether'],
 'yetlin': ['lenity', 'yetlin'],
 'yew': ['wey', 'wye', 'yew'],
 'yielden': ['needily', 'yielden'],
 'yielder': ['reedily', 'reyield', 'yielder'],
 'yildun': ['unidly', 'yildun'],
 'yill': ['illy', 'lily', 'yill'],
 'yirm': ['miry', 'rimy', 'yirm'],
 'ym': ['my', 'ym'],
 'yock': ['coky', 'yock'],
 'yodel': ['doyle', 'yodel'],
 'yoe': ['yeo', 'yoe'],
 'yoghurt': ['troughy', 'yoghurt'],
 'yogin': ['goyin', 'yogin'],
 'yoi': ['iyo', 'yoi'],
 'yoker': ['rokey', 'yoker'],
 'yolk': ['kylo', 'yolk'],
 'yom': ['moy', 'yom'],
 'yomud': ['moudy', 'yomud'],
 'yon': ['noy', 'yon'],
 'yond': ['ondy', 'yond'],
 'yonder': ['rodney', 'yonder'],
 'yont': ['tony', 'yont'],
 'yor': ['ory', 'roy', 'yor'],
 'yore': ['oyer', 'roey', 'yore'],
 'york': ['kory', 'roky', 'york'],
 'yot': ['toy', 'yot'],
 'yote': ['eyot', 'yote'],
 'youngun': ['unyoung', 'youngun'],
 'yours': ['soury', 'yours'],
 'yoursel': ['elusory', 'yoursel'],
 'yoven': ['envoy', 'nevoy', 'yoven'],
 'yow': ['woy', 'yow'],
 'yowl': ['lowy', 'owly', 'yowl'],
 'yowler': ['lowery', 'owlery', 'rowley', 'yowler'],
 'yowt': ['towy', 'yowt'],
 'yox': ['oxy', 'yox'],
 'yttrious': ['touristy', 'yttrious'],
 'yuca': ['cuya', 'yuca'],
 'yuckel': ['yuckel', 'yuckle'],
 'yuckle': ['yuckel', 'yuckle'],
 'yulan': ['unlay', 'yulan'],
 'yurok': ['rouky', 'yurok'],
 'zabian': ['banzai', 'zabian'],
 'zabra': ['braza', 'zabra'],
 'zacate': ['azteca', 'zacate'],
 'zad': ['adz', 'zad'],
 'zag': ['gaz', 'zag'],
 'zain': ['nazi', 'zain'],
 'zaman': ['namaz', 'zaman'],
 'zamenis': ['sizeman', 'zamenis'],
 'zaparoan': ['parazoan', 'zaparoan'],
 'zaratite': ['tatarize', 'zaratite'],
 'zati': ['itza', 'tiza', 'zati'],
 'zeal': ['laze', 'zeal'],
 'zealotism': ['solmizate', 'zealotism'],
 'zebra': ['braze', 'zebra'],
 'zein': ['inez', 'zein'],
 'zelanian': ['annalize', 'zelanian'],
 'zelatrice': ['cartelize', 'zelatrice'],
 'zemmi': ['zemmi', 'zimme'],
 'zendic': ['dezinc', 'zendic'],
 'zenick': ['zenick', 'zincke'],
 'zenu': ['unze', 'zenu'],
 'zequin': ['quinze', 'zequin'],
 'zerda': ['adzer', 'zerda'],
 'zerma': ['mazer', 'zerma'],
 'ziarat': ['atazir', 'ziarat'],
 'zibet': ['bizet', 'zibet'],
 'ziega': ['gaize', 'ziega'],
 'zimme': ['zemmi', 'zimme'],
 'zincite': ['citizen', 'zincite'],
 'zincke': ['zenick', 'zincke'],
 'zinco': ['zinco', 'zonic'],
 'zion': ['nozi', 'zion'],
 'zira': ['izar', 'zira'],
 'zirconate': ['narcotize', 'zirconate'],
 'zoa': ['azo', 'zoa'],
 'zoanthidae': ['zoanthidae', 'zoanthidea'],
 'zoanthidea': ['zoanthidae', 'zoanthidea'],
 'zoarite': ['azorite', 'zoarite'],
 'zobo': ['bozo', 'zobo'],
 'zoeal': ['azole', 'zoeal'],
 'zogan': ['gazon', 'zogan'],
 'zolotink': ['zolotink', 'zolotnik'],
 'zolotnik': ['zolotink', 'zolotnik'],
 'zonaria': ['arizona', 'azorian', 'zonaria'],
 'zoned': ['dozen', 'zoned'],
 'zonic': ['zinco', 'zonic'],
 'zonotrichia': ['chorization', 'rhizoctonia', 'zonotrichia'],
 'zoonal': ['alonzo', 'zoonal'],
 'zoonic': ['ozonic', 'zoonic'],
 'zoonomic': ['monozoic', 'zoonomic'],
 'zoopathy': ['phytozoa', 'zoopathy', 'zoophyta'],
 'zoophilic': ['philozoic', 'zoophilic'],
 'zoophilist': ['philozoist', 'zoophilist'],
 'zoophyta': ['phytozoa', 'zoopathy', 'zoophyta'],
 'zoospermatic': ['spermatozoic', 'zoospermatic'],
 'zoosporic': ['sporozoic', 'zoosporic'],
 'zootype': ['ozotype', 'zootype'],
 'zyga': ['gazy', 'zyga'],
 'zygal': ['glazy', 'zygal']}

================================================
FILE: strings/autocomplete_using_trie.py
================================================
from __future__ import annotations

END = "#"


class Trie:
    def __init__(self) -> None:
        self._trie: dict = {}

    def insert_word(self, text: str) -> None:
        trie = self._trie
        for char in text:
            if char not in trie:
                trie[char] = {}
            trie = trie[char]
        trie[END] = True

    def find_word(self, prefix: str) -> tuple | list:
        trie = self._trie
        for char in prefix:
            if char in trie:
                trie = trie[char]
            else:
                return []
        return self._elements(trie)

    def _elements(self, d: dict) -> tuple:
        result = []
        for c, v in d.items():
            sub_result = [" "] if c == END else [(c + s) for s in self._elements(v)]
            result.extend(sub_result)
        return tuple(result)


trie = Trie()
words = ("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
    trie.insert_word(word)


def autocomplete_using_trie(string: str) -> tuple:
    """
    >>> trie = Trie()
    >>> for word in words:
    ...     trie.insert_word(word)
    ...
    >>> matches = autocomplete_using_trie("de")
    >>> "detergent " in matches
    True
    >>> "dog " in matches
    False
    """
    suffixes = trie.find_word(string)
    return tuple(string + word for word in suffixes)


def main() -> None:
    print(autocomplete_using_trie("de"))


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


================================================
FILE: strings/barcode_validator.py
================================================
"""
https://en.wikipedia.org/wiki/Check_digit#Algorithms
"""


def get_check_digit(barcode: int) -> int:
    """
    Returns the last digit of barcode by excluding the last digit first
    and then computing to reach the actual last digit from the remaining
    12 digits.

    >>> get_check_digit(8718452538119)
    9
    >>> get_check_digit(87184523)
    5
    >>> get_check_digit(87193425381086)
    9
    >>> [get_check_digit(x) for x in range(0, 100, 10)]
    [0, 7, 4, 1, 8, 5, 2, 9, 6, 3]
    """
    barcode //= 10  # exclude the last digit
    checker = False
    s = 0

    # extract and check each digit
    while barcode != 0:
        mult = 1 if checker else 3
        s += mult * (barcode % 10)
        barcode //= 10
        checker = not checker

    return (10 - (s % 10)) % 10


def is_valid(barcode: int) -> bool:
    """
    Checks for length of barcode and last-digit
    Returns boolean value of validity of barcode

    >>> is_valid(8718452538119)
    True
    >>> is_valid(87184525)
    False
    >>> is_valid(87193425381089)
    False
    >>> is_valid(0)
    False
    >>> is_valid(dwefgiweuf)
    Traceback (most recent call last):
        ...
    NameError: name 'dwefgiweuf' is not defined
    """
    return len(str(barcode)) == 13 and get_check_digit(barcode) == barcode % 10


def get_barcode(barcode: str) -> int:
    """
    Returns the barcode as an integer

    >>> get_barcode("8718452538119")
    8718452538119
    >>> get_barcode("dwefgiweuf")
    Traceback (most recent call last):
        ...
    ValueError: Barcode 'dwefgiweuf' has alphabetic characters.
    """
    if str(barcode).isalpha():
        msg = f"Barcode '{barcode}' has alphabetic characters."
        raise ValueError(msg)
    elif int(barcode) < 0:
        raise ValueError("The entered barcode has a negative value. Try again.")
    else:
        return int(barcode)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    """
    Enter a barcode.

    """
    barcode = get_barcode(input("Barcode: ").strip())

    if is_valid(barcode):
        print(f"'{barcode}' is a valid barcode.")
    else:
        print(f"'{barcode}' is NOT a valid barcode.")


================================================
FILE: strings/bitap_string_match.py
================================================
"""
Bitap exact string matching
https://en.wikipedia.org/wiki/Bitap_algorithm

Searches for a pattern inside text, and returns the index of the first occurrence
of the pattern. Both text and pattern consist of lowercase alphabetical characters only.

Complexity: O(m*n)
    n = length of text
    m = length of pattern

Python doctests can be run using this command:
python3 -m doctest -v bitap_string_match.py
"""


def bitap_string_match(text: str, pattern: str) -> int:
    """
    Retrieves the index of the first occurrence of pattern in text.

    Args:
        text: A string consisting only of lowercase alphabetical characters.
        pattern: A string consisting only of lowercase alphabetical characters.

    Returns:
        int: The index where pattern first occurs. Return -1  if not found.

    >>> bitap_string_match('abdabababc', 'ababc')
    5
    >>> bitap_string_match('aaaaaaaaaaaaaaaaaa', 'a')
    0
    >>> bitap_string_match('zxywsijdfosdfnso', 'zxywsijdfosdfnso')
    0
    >>> bitap_string_match('abdabababc', '')
    0
    >>> bitap_string_match('abdabababc', 'c')
    9
    >>> bitap_string_match('abdabababc', 'fofosdfo')
    -1
    >>> bitap_string_match('abdab', 'fofosdfo')
    -1
    """
    if not pattern:
        return 0
    m = len(pattern)
    if m > len(text):
        return -1

    # Initial state of bit string 1110
    state = ~1
    # Bit = 0 if character appears at index, and 1 otherwise
    pattern_mask: list[int] = [~0] * 27  # 1111

    for i, char in enumerate(pattern):
        # For the pattern mask for this character, set the bit to 0 for each i
        # the character appears.
        pattern_index: int = ord(char) - ord("a")
        pattern_mask[pattern_index] &= ~(1 << i)

    for i, char in enumerate(text):
        text_index = ord(char) - ord("a")
        # If this character does not appear in pattern, it's pattern mask is 1111.
        # Performing a bitwise OR between state and 1111 will reset the state to 1111
        # and start searching the start of pattern again.
        state |= pattern_mask[text_index]
        state <<= 1

        # If the mth bit (counting right to left) of the state is 0, then we have
        # found pattern in text
        if (state & (1 << m)) == 0:
            return i - m + 1

    return -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: strings/boyer_moore_search.py
================================================
"""
The algorithm finds the pattern in given text using following rule.

The bad-character rule considers the mismatched character in Text.
The next occurrence of that character to the left in Pattern is found,

If the mismatched character occurs to the left in Pattern,
a shift is proposed that aligns text block and pattern.

If the mismatched character does not occur to the left in Pattern,
a shift is proposed that moves the entirety of Pattern past
the point of mismatch in the text.

If there is no mismatch then the pattern matches with text block.

Time Complexity : O(n/m)
    n=length of main string
    m=length of pattern string
"""


class BoyerMooreSearch:
    """
    Example usage:

        bms = BoyerMooreSearch(text="ABAABA", pattern="AB")
        positions = bms.bad_character_heuristic()

    where 'positions' contain the locations where the pattern was matched.
    """

    def __init__(self, text: str, pattern: str):
        self.text, self.pattern = text, pattern
        self.textLen, self.patLen = len(text), len(pattern)

    def match_in_pattern(self, char: str) -> int:
        """
        Finds the index of char in pattern in reverse order.

        Parameters :
            char (chr): character to be searched

        Returns :
            i (int): index of char from last in pattern
            -1 (int): if char is not found in pattern

        >>> bms = BoyerMooreSearch(text="ABAABA", pattern="AB")
        >>> bms.match_in_pattern("B")
        1
        """

        for i in range(self.patLen - 1, -1, -1):
            if char == self.pattern[i]:
                return i
        return -1

    def mismatch_in_text(self, current_pos: int) -> int:
        """
        Find the index of mis-matched character in text when compared with pattern
        from last.

        Parameters :
            current_pos (int): current index position of text

        Returns :
            i (int): index of mismatched char from last in text
            -1 (int): if there is no mismatch between pattern and text block

        >>> bms = BoyerMooreSearch(text="ABAABA", pattern="AB")
        >>> bms.mismatch_in_text(2)
        3
        """

        for i in range(self.patLen - 1, -1, -1):
            if self.pattern[i] != self.text[current_pos + i]:
                return current_pos + i
        return -1

    def bad_character_heuristic(self) -> list[int]:
        """
        Finds the positions of the pattern location.

        >>> bms = BoyerMooreSearch(text="ABAABA", pattern="AB")
        >>> bms.bad_character_heuristic()
        [0, 3]
        """

        positions = []
        for i in range(self.textLen - self.patLen + 1):
            mismatch_index = self.mismatch_in_text(i)
            if mismatch_index == -1:
                positions.append(i)
            else:
                match_index = self.match_in_pattern(self.text[mismatch_index])
                i = (
                    mismatch_index - match_index
                )  # shifting index lgtm [py/multiple-definition]
        return positions


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: strings/camel_case_to_snake_case.py
================================================
def camel_to_snake_case(input_str: str) -> str:
    """
    Transforms a camelCase (or PascalCase) string to snake_case

    >>> camel_to_snake_case("someRandomString")
    'some_random_string'

    >>> camel_to_snake_case("SomeRandomStr#ng")
    'some_random_str_ng'

    >>> camel_to_snake_case("123someRandom123String123")
    '123_some_random_123_string_123'

    >>> camel_to_snake_case("123SomeRandom123String123")
    '123_some_random_123_string_123'

    >>> camel_to_snake_case(123)
    Traceback (most recent call last):
        ...
    ValueError: Expected string as input, found <class 'int'>

    """

    # check for invalid input type
    if not isinstance(input_str, str):
        msg = f"Expected string as input, found {type(input_str)}"
        raise ValueError(msg)

    snake_str = ""

    for index, char in enumerate(input_str):
        if char.isupper():
            snake_str += "_" + char.lower()

        # if char is lowercase but proceeded by a digit:
        elif input_str[index - 1].isdigit() and char.islower():
            snake_str += "_" + char

        # if char is a digit proceeded by a letter:
        elif input_str[index - 1].isalpha() and char.isnumeric():
            snake_str += "_" + char.lower()

        # if char is not alphanumeric:
        elif not char.isalnum():
            snake_str += "_"

        else:
            snake_str += char

    # remove leading underscore
    if snake_str[0] == "_":
        snake_str = snake_str[1:]

    return snake_str


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: strings/can_string_be_rearranged_as_palindrome.py
================================================
# Created by susmith98

from collections import Counter
from timeit import timeit

# Problem Description:
# Check if characters of the given string can be rearranged to form a palindrome.
# Counter is faster for long strings and non-Counter is faster for short strings.


def can_string_be_rearranged_as_palindrome_counter(
    input_str: str = "",
) -> bool:
    """
    A Palindrome is a String that reads the same forward as it does backwards.
    Examples of Palindromes mom, dad, malayalam
    >>> can_string_be_rearranged_as_palindrome_counter("Momo")
    True
    >>> can_string_be_rearranged_as_palindrome_counter("Mother")
    False
    >>> can_string_be_rearranged_as_palindrome_counter("Father")
    False
    >>> can_string_be_rearranged_as_palindrome_counter("A man a plan a canal Panama")
    True
    """
    return sum(c % 2 for c in Counter(input_str.replace(" ", "").lower()).values()) < 2


def can_string_be_rearranged_as_palindrome(input_str: str = "") -> bool:
    """
    A Palindrome is a String that reads the same forward as it does backwards.
    Examples of Palindromes mom, dad, malayalam
    >>> can_string_be_rearranged_as_palindrome("Momo")
    True
    >>> can_string_be_rearranged_as_palindrome("Mother")
    False
    >>> can_string_be_rearranged_as_palindrome("Father")
    False
    >>> can_string_be_rearranged_as_palindrome_counter("A man a plan a canal Panama")
    True
    """
    if len(input_str) == 0:
        return True
    lower_case_input_str = input_str.replace(" ", "").lower()
    # character_freq_dict: Stores the frequency of every character in the input string
    character_freq_dict: dict[str, int] = {}

    for character in lower_case_input_str:
        character_freq_dict[character] = character_freq_dict.get(character, 0) + 1
    """
    Above line of code is equivalent to:
    1) Getting the frequency of current character till previous index
    >>> character_freq =  character_freq_dict.get(character, 0)
    2) Incrementing the frequency of current character by 1
    >>> character_freq = character_freq + 1
    3) Updating the frequency of current character
    >>> character_freq_dict[character] = character_freq
    """
    """
    OBSERVATIONS:
    Even length palindrome
    -> Every character appears even no.of times.
    Odd length palindrome
    -> Every character appears even no.of times except for one character.
    LOGIC:
    Step 1: We'll count number of characters that appear odd number of times i.e oddChar
    Step 2:If we find more than 1 character that appears odd number of times,
    It is not possible to rearrange as a palindrome
    """
    odd_char = 0

    for character_count in character_freq_dict.values():
        if character_count % 2:
            odd_char += 1
    return not odd_char > 1


def benchmark(input_str: str = "") -> None:
    """
    Benchmark code for comparing above 2 functions
    """
    print("\nFor string = ", input_str, ":")
    print(
        "> can_string_be_rearranged_as_palindrome_counter()",
        "\tans =",
        can_string_be_rearranged_as_palindrome_counter(input_str),
        "\ttime =",
        timeit(
            "z.can_string_be_rearranged_as_palindrome_counter(z.check_str)",
            setup="import __main__ as z",
        ),
        "seconds",
    )
    print(
        "> can_string_be_rearranged_as_palindrome()",
        "\tans =",
        can_string_be_rearranged_as_palindrome(input_str),
        "\ttime =",
        timeit(
            "z.can_string_be_rearranged_as_palindrome(z.check_str)",
            setup="import __main__ as z",
        ),
        "seconds",
    )


if __name__ == "__main__":
    check_str = input(
        "Enter string to determine if it can be rearranged as a palindrome or not: "
    ).strip()
    benchmark(check_str)
    status = can_string_be_rearranged_as_palindrome_counter(check_str)
    print(f"{check_str} can {'' if status else 'not '}be rearranged as a palindrome")


================================================
FILE: strings/capitalize.py
================================================
def capitalize(sentence: str) -> str:
    """
    Capitalizes the first letter of a sentence or word.

    >>> capitalize("hello world")
    'Hello world'
    >>> capitalize("123 hello world")
    '123 hello world'
    >>> capitalize(" hello world")
    ' hello world'
    >>> capitalize("a")
    'A'
    >>> capitalize("")
    ''
    """
    if not sentence:
        return ""

    # Capitalize the first character if it's a lowercase letter
    # Concatenate the capitalized character with the rest of the string
    return sentence[0].upper() + sentence[1:]


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: strings/check_anagrams.py
================================================
"""
wiki: https://en.wikipedia.org/wiki/Anagram
"""

from collections import defaultdict


def check_anagrams(first_str: str, second_str: str) -> bool:
    """
    Two strings are anagrams if they are made up of the same letters but are
    arranged differently (ignoring the case).
    >>> check_anagrams('Silent', 'Listen')
    True
    >>> check_anagrams('This is a string', 'Is this a string')
    True
    >>> check_anagrams('This is    a      string', 'Is     this a string')
    True
    >>> check_anagrams('There', 'Their')
    False
    """
    first_str = first_str.lower().strip()
    second_str = second_str.lower().strip()

    # Remove whitespace
    first_str = first_str.replace(" ", "")
    second_str = second_str.replace(" ", "")

    # Strings of different lengths are not anagrams
    if len(first_str) != len(second_str):
        return False

    # Default values for count should be 0
    count: defaultdict[str, int] = defaultdict(int)

    # For each character in input strings,
    # increment count in the corresponding
    for i in range(len(first_str)):
        count[first_str[i]] += 1
        count[second_str[i]] -= 1

    return all(_count == 0 for _count in count.values())


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    input_a = input("Enter the first string ").strip()
    input_b = input("Enter the second string ").strip()

    status = check_anagrams(input_a, input_b)
    print(f"{input_a} and {input_b} are {'' if status else 'not '}anagrams.")


================================================
FILE: strings/count_vowels.py
================================================
def count_vowels(s: str) -> int:
    """
    Count the number of vowels in a given string.

    :param s: Input string to count vowels in.
    :return: Number of vowels in the input string.

    Examples:
    >>> count_vowels("hello world")
    3
    >>> count_vowels("HELLO WORLD")
    3
    >>> count_vowels("123 hello world")
    3
    >>> count_vowels("")
    0
    >>> count_vowels("a quick brown fox")
    5
    >>> count_vowels("the quick BROWN fox")
    5
    >>> count_vowels("PYTHON")
    1
    """
    if not isinstance(s, str):
        raise TypeError("Input must be a string")

    vowels = "aeiouAEIOU"
    return sum(1 for char in s if char in vowels)


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: strings/credit_card_validator.py
================================================
"""
Functions for testing the validity of credit card numbers.

https://en.wikipedia.org/wiki/Luhn_algorithm
"""


def validate_initial_digits(credit_card_number: str) -> bool:
    """
    Function to validate initial digits of a given credit card number.
    >>> valid = "4111111111111111 41111111111111 34 35 37 412345 523456 634567"
    >>> all(validate_initial_digits(cc) for cc in valid.split())
    True
    >>> invalid = "14 25 76 32323 36111111111111"
    >>> all(validate_initial_digits(cc) is False for cc in invalid.split())
    True
    """
    return credit_card_number.startswith(("34", "35", "37", "4", "5", "6"))


def luhn_validation(credit_card_number: str) -> bool:
    """
    Function to luhn algorithm validation for a given credit card number.
    >>> luhn_validation('4111111111111111')
    True
    >>> luhn_validation('36111111111111')
    True
    >>> luhn_validation('41111111111111')
    False
    """
    cc_number = credit_card_number
    total = 0
    half_len = len(cc_number) - 2
    for i in range(half_len, -1, -2):
        #  double the value of every second digit
        digit = int(cc_number[i])
        digit *= 2
        # If doubling of a number results in a two digit number
        # i.e greater than 9(e.g., 6 x 2 = 12),
        # then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
        # to get a single digit number.
        if digit > 9:
            digit %= 10
            digit += 1
        cc_number = cc_number[:i] + str(digit) + cc_number[i + 1 :]
        total += digit

    # Sum up the remaining digits
    for i in range(len(cc_number) - 1, -1, -2):
        total += int(cc_number[i])

    return total % 10 == 0


def validate_credit_card_number(credit_card_number: str) -> bool:
    """
    Function to validate the given credit card number.
    >>> validate_credit_card_number('4111111111111111')
    4111111111111111 is a valid credit card number.
    True
    >>> validate_credit_card_number('helloworld$')
    helloworld$ is an invalid credit card number because it has nonnumerical characters.
    False
    >>> validate_credit_card_number('32323')
    32323 is an invalid credit card number because of its length.
    False
    >>> validate_credit_card_number('32323323233232332323')
    32323323233232332323 is an invalid credit card number because of its length.
    False
    >>> validate_credit_card_number('36111111111111')
    36111111111111 is an invalid credit card number because of its first two digits.
    False
    >>> validate_credit_card_number('41111111111111')
    41111111111111 is an invalid credit card number because it fails the Luhn check.
    False
    """
    error_message = f"{credit_card_number} is an invalid credit card number because"
    if not credit_card_number.isdigit():
        print(f"{error_message} it has nonnumerical characters.")
        return False

    if not 13 <= len(credit_card_number) <= 16:
        print(f"{error_message} of its length.")
        return False

    if not validate_initial_digits(credit_card_number):
        print(f"{error_message} of its first two digits.")
        return False

    if not luhn_validation(credit_card_number):
        print(f"{error_message} it fails the Luhn check.")
        return False

    print(f"{credit_card_number} is a valid credit card number.")
    return True


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    validate_credit_card_number("4111111111111111")
    validate_credit_card_number("32323")


================================================
FILE: strings/damerau_levenshtein_distance.py
================================================
"""
This script is a implementation of the Damerau-Levenshtein distance algorithm.

It's an algorithm that measures the edit distance between two string sequences

More information about this algorithm can be found in this wikipedia article:
https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance
"""


def damerau_levenshtein_distance(first_string: str, second_string: str) -> int:
    """
    Implements the Damerau-Levenshtein distance algorithm that measures
    the edit distance between two strings.

    Parameters:
        first_string: The first string to compare
        second_string: The second string to compare

    Returns:
        distance: The edit distance between the first and second strings

    >>> damerau_levenshtein_distance("cat", "cut")
    1
    >>> damerau_levenshtein_distance("kitten", "sitting")
    3
    >>> damerau_levenshtein_distance("hello", "world")
    4
    >>> damerau_levenshtein_distance("book", "back")
    2
    >>> damerau_levenshtein_distance("container", "containment")
    3
    >>> damerau_levenshtein_distance("container", "containment")
    3
    """
    # Create a dynamic programming matrix to store the distances
    dp_matrix = [[0] * (len(second_string) + 1) for _ in range(len(first_string) + 1)]

    # Initialize the matrix
    for i in range(len(first_string) + 1):
        dp_matrix[i][0] = i
    for j in range(len(second_string) + 1):
        dp_matrix[0][j] = j

    # Fill the matrix
    for i, first_char in enumerate(first_string, start=1):
        for j, second_char in enumerate(second_string, start=1):
            cost = int(first_char != second_char)

            dp_matrix[i][j] = min(
                dp_matrix[i - 1][j] + 1,  # Deletion
                dp_matrix[i][j - 1] + 1,  # Insertion
                dp_matrix[i - 1][j - 1] + cost,  # Substitution
            )

            if (
                i > 1
                and j > 1
                and first_string[i - 1] == second_string[j - 2]
                and first_string[i - 2] == second_string[j - 1]
            ):
                # Transposition
                dp_matrix[i][j] = min(dp_matrix[i][j], dp_matrix[i - 2][j - 2] + cost)

    return dp_matrix[-1][-1]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: strings/detecting_english_programmatically.py
================================================
import os
from string import ascii_letters

LETTERS_AND_SPACE = ascii_letters + " \t\n"


def load_dictionary() -> dict[str, None]:
    path = os.path.split(os.path.realpath(__file__))
    english_words: dict[str, None] = {}
    with open(path[0] + "/dictionary.txt") as dictionary_file:
        for word in dictionary_file.read().split("\n"):
            english_words[word] = None
    return english_words


ENGLISH_WORDS = load_dictionary()


def get_english_count(message: str) -> float:
    message = message.upper()
    message = remove_non_letters(message)
    possible_words = message.split()
    matches = len([word for word in possible_words if word in ENGLISH_WORDS])
    return float(matches) / len(possible_words)


def remove_non_letters(message: str) -> str:
    """
    >>> remove_non_letters("Hi! how are you?")
    'Hi how are you'
    >>> remove_non_letters("P^y%t)h@o*n")
    'Python'
    >>> remove_non_letters("1+1=2")
    ''
    >>> remove_non_letters("www.google.com/")
    'wwwgooglecom'
    >>> remove_non_letters("")
    ''
    """
    return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE)


def is_english(
    message: str, word_percentage: int = 20, letter_percentage: int = 85
) -> bool:
    """
    >>> is_english('Hello World')
    True
    >>> is_english('llold HorWd')
    False
    """
    words_match = get_english_count(message) * 100 >= word_percentage
    num_letters = len(remove_non_letters(message))
    message_letters_percentage = (float(num_letters) / len(message)) * 100
    letters_match = message_letters_percentage >= letter_percentage
    return words_match and letters_match


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: strings/dictionary.txt
================================================
AARHUS
AARON
ABABA
ABACK
ABAFT
ABANDON
ABANDONED
ABANDONING
ABANDONMENT
ABANDONS
ABASE
ABASED
ABASEMENT
ABASEMENTS
ABASES
ABASH
ABASHED
ABASHES
ABASHING
ABASING
ABATE
ABATED
ABATEMENT
ABATEMENTS
ABATER
ABATES
ABATING
ABBA
ABBE
ABBEY
ABBEYS
ABBOT
ABBOTS
ABBOTT
ABBREVIATE
ABBREVIATED
ABBREVIATES
ABBREVIATING
ABBREVIATION
ABBREVIATIONS
ABBY
ABDOMEN
ABDOMENS
ABDOMINAL
ABDUCT
ABDUCTED
ABDUCTION
ABDUCTIONS
ABDUCTOR
ABDUCTORS
ABDUCTS
ABE
ABED
ABEL
ABELIAN
ABELSON
ABERDEEN
ABERNATHY
ABERRANT
ABERRATION
ABERRATIONS
ABET
ABETS
ABETTED
ABETTER
ABETTING
ABEYANCE
ABHOR
ABHORRED
ABHORRENT
ABHORRER
ABHORRING
ABHORS
ABIDE
ABIDED
ABIDES
ABIDING
ABIDJAN
ABIGAIL
ABILENE
ABILITIES
ABILITY
ABJECT
ABJECTION
ABJECTIONS
ABJECTLY
ABJECTNESS
ABJURE
ABJURED
ABJURES
ABJURING
ABLATE
ABLATED
ABLATES
ABLATING
ABLATION
ABLATIVE
ABLAZE
ABLE
ABLER
ABLEST
ABLY
ABNER
ABNORMAL
ABNORMALITIES
ABNORMALITY
ABNORMALLY
ABO
ABOARD
ABODE
ABODES
ABOLISH
ABOLISHED
ABOLISHER
ABOLISHERS
ABOLISHES
ABOLISHING
ABOLISHMENT
ABOLISHMENTS
ABOLITION
ABOLITIONIST
ABOLITIONISTS
ABOMINABLE
ABOMINATE
ABORIGINAL
ABORIGINE
ABORIGINES
ABORT
ABORTED
ABORTING
ABORTION
ABORTIONS
ABORTIVE
ABORTIVELY
ABORTS
ABOS
ABOUND
ABOUNDED
ABOUNDING
ABOUNDS
ABOUT
ABOVE
ABOVEBOARD
ABOVEGROUND
ABOVEMENTIONED
ABRADE
ABRADED
ABRADES
ABRADING
ABRAHAM
ABRAM
ABRAMS
ABRAMSON
ABRASION
ABRASIONS
ABRASIVE
ABREACTION
ABREACTIONS
ABREAST
ABRIDGE
ABRIDGED
ABRIDGES
ABRIDGING
ABRIDGMENT
ABROAD
ABROGATE
ABROGATED
ABROGATES
ABROGATING
ABRUPT
ABRUPTLY
ABRUPTNESS
ABSCESS
ABSCESSED
ABSCESSES
ABSCISSA
ABSCISSAS
ABSCOND
ABSCONDED
ABSCONDING
ABSCONDS
ABSENCE
ABSENCES
ABSENT
ABSENTED
ABSENTEE
ABSENTEEISM
ABSENTEES
ABSENTIA
ABSENTING
ABSENTLY
ABSENTMINDED
ABSENTS
ABSINTHE
ABSOLUTE
ABSOLUTELY
ABSOLUTENESS
ABSOLUTES
ABSOLUTION
ABSOLVE
ABSOLVED
ABSOLVES
ABSOLVING
ABSORB
ABSORBED
ABSORBENCY
ABSORBENT
ABSORBER
ABSORBING
ABSORBS
ABSORPTION
ABSORPTIONS
ABSORPTIVE
ABSTAIN
ABSTAINED
ABSTAINER
ABSTAINING
ABSTAINS
ABSTENTION
ABSTENTIONS
ABSTINENCE
ABSTRACT
ABSTRACTED
ABSTRACTING
ABSTRACTION
ABSTRACTIONISM
ABSTRACTIONIST
ABSTRACTIONS
ABSTRACTLY
ABSTRACTNESS
ABSTRACTOR
ABSTRACTORS
ABSTRACTS
ABSTRUSE
ABSTRUSENESS
ABSURD
ABSURDITIES
ABSURDITY
ABSURDLY
ABU
ABUNDANCE
ABUNDANT
ABUNDANTLY
ABUSE
ABUSED
ABUSES
ABUSING
ABUSIVE
ABUT
ABUTMENT
ABUTS
ABUTTED
ABUTTER
ABUTTERS
ABUTTING
ABYSMAL
ABYSMALLY
ABYSS
ABYSSES
ABYSSINIA
ABYSSINIAN
ABYSSINIANS
ACACIA
ACADEMIA
ACADEMIC
ACADEMICALLY
ACADEMICS
ACADEMIES
ACADEMY
ACADIA
ACAPULCO
ACCEDE
ACCEDED
ACCEDES
ACCELERATE
ACCELERATED
ACCELERATES
ACCELERATING
ACCELERATION
ACCELERATIONS
ACCELERATOR
ACCELERATORS
ACCELEROMETER
ACCELEROMETERS
ACCENT
ACCENTED
ACCENTING
ACCENTS
ACCENTUAL
ACCENTUATE
ACCENTUATED
ACCENTUATES
ACCENTUATING
ACCENTUATION
ACCEPT
ACCEPTABILITY
ACCEPTABLE
ACCEPTABLY
ACCEPTANCE
ACCEPTANCES
ACCEPTED
ACCEPTER
ACCEPTERS
ACCEPTING
ACCEPTOR
ACCEPTORS
ACCEPTS
ACCESS
ACCESSED
ACCESSES
ACCESSIBILITY
ACCESSIBLE
ACCESSIBLY
ACCESSING
ACCESSION
ACCESSIONS
ACCESSORIES
ACCESSORS
ACCESSORY
ACCIDENT
ACCIDENTAL
ACCIDENTALLY
ACCIDENTLY
ACCIDENTS
ACCLAIM
ACCLAIMED
ACCLAIMING
ACCLAIMS
ACCLAMATION
ACCLIMATE
ACCLIMATED
ACCLIMATES
ACCLIMATING
ACCLIMATIZATION
ACCLIMATIZED
ACCOLADE
ACCOLADES
ACCOMMODATE
ACCOMMODATED
ACCOMMODATES
ACCOMMODATING
ACCOMMODATION
ACCOMMODATIONS
ACCOMPANIED
ACCOMPANIES
ACCOMPANIMENT
ACCOMPANIMENTS
ACCOMPANIST
ACCOMPANISTS
ACCOMPANY
ACCOMPANYING
ACCOMPLICE
ACCOMPLICES
ACCOMPLISH
ACCOMPLISHED
ACCOMPLISHER
ACCOMPLISHERS
ACCOMPLISHES
ACCOMPLISHING
ACCOMPLISHMENT
ACCOMPLISHMENTS
ACCORD
ACCORDANCE
ACCORDED
ACCORDER
ACCORDERS
ACCORDING
ACCORDINGLY
ACCORDION
ACCORDIONS
ACCORDS
ACCOST
ACCOSTED
ACCOSTING
ACCOSTS
ACCOUNT
ACCOUNTABILITY
ACCOUNTABLE
ACCOUNTABLY
ACCOUNTANCY
ACCOUNTANT
ACCOUNTANTS
ACCOUNTED
ACCOUNTING
ACCOUNTS
ACCRA
ACCREDIT
ACCREDITATION
ACCREDITATIONS
ACCREDITED
ACCRETION
ACCRETIONS
ACCRUE
ACCRUED
ACCRUES
ACCRUING
ACCULTURATE
ACCULTURATED
ACCULTURATES
ACCULTURATING
ACCULTURATION
ACCUMULATE
ACCUMULATED
ACCUMULATES
ACCUMULATING
ACCUMULATION
ACCUMULATIONS
ACCUMULATOR
ACCUMULATORS
ACCURACIES
ACCURACY
ACCURATE
ACCURATELY
ACCURATENESS
ACCURSED
ACCUSAL
ACCUSATION
ACCUSATIONS
ACCUSATIVE
ACCUSE
ACCUSED
ACCUSER
ACCUSES
ACCUSING
ACCUSINGLY
ACCUSTOM
ACCUSTOMED
ACCUSTOMING
ACCUSTOMS
ACE
ACES
ACETATE
ACETONE
ACETYLENE
ACHAEAN
ACHAEANS
ACHE
ACHED
ACHES
ACHIEVABLE
ACHIEVE
ACHIEVED
ACHIEVEMENT
ACHIEVEMENTS
ACHIEVER
ACHIEVERS
ACHIEVES
ACHIEVING
ACHILLES
ACHING
ACID
ACIDIC
ACIDITIES
ACIDITY
ACIDLY
ACIDS
ACIDULOUS
ACKERMAN
ACKLEY
ACKNOWLEDGE
ACKNOWLEDGEABLE
ACKNOWLEDGED
ACKNOWLEDGEMENT
ACKNOWLEDGEMENTS
ACKNOWLEDGER
ACKNOWLEDGERS
ACKNOWLEDGES
ACKNOWLEDGING
ACKNOWLEDGMENT
ACKNOWLEDGMENTS
ACME
ACNE
ACOLYTE
ACOLYTES
ACORN
ACORNS
ACOUSTIC
ACOUSTICAL
ACOUSTICALLY
ACOUSTICIAN
ACOUSTICS
ACQUAINT
ACQUAINTANCE
ACQUAINTANCES
ACQUAINTED
ACQUAINTING
ACQUAINTS
ACQUIESCE
ACQUIESCED
ACQUIESCENCE
ACQUIESCENT
ACQUIESCES
ACQUIESCING
ACQUIRABLE
ACQUIRE
ACQUIRED
ACQUIRES
ACQUIRING
ACQUISITION
ACQUISITIONS
ACQUISITIVE
ACQUISITIVENESS
ACQUIT
ACQUITS
ACQUITTAL
ACQUITTED
ACQUITTER
ACQUITTING
ACRE
ACREAGE
ACRES
ACRID
ACRIMONIOUS
ACRIMONY
ACROBAT
ACROBATIC
ACROBATICS
ACROBATS
ACRONYM
ACRONYMS
ACROPOLIS
ACROSS
ACRYLIC
ACT
ACTA
ACTAEON
ACTED
ACTING
ACTINIUM
ACTINOMETER
ACTINOMETERS
ACTION
ACTIONS
ACTIVATE
ACTIVATED
ACTIVATES
ACTIVATING
ACTIVATION
ACTIVATIONS
ACTIVATOR
ACTIVATORS
ACTIVE
ACTIVELY
ACTIVISM
ACTIVIST
ACTIVISTS
ACTIVITIES
ACTIVITY
ACTON
ACTOR
ACTORS
ACTRESS
ACTRESSES
ACTS
ACTUAL
ACTUALITIES
ACTUALITY
ACTUALIZATION
ACTUALLY
ACTUALS
ACTUARIAL
ACTUARIALLY
ACTUATE
ACTUATED
ACTUATES
ACTUATING
ACTUATOR
ACTUATORS
ACUITY
ACUMEN
ACUTE
ACUTELY
ACUTENESS
ACYCLIC
ACYCLICALLY
ADA
ADAGE
ADAGES
ADAGIO
ADAGIOS
ADAIR
ADAM
ADAMANT
ADAMANTLY
ADAMS
ADAMSON
ADAPT
ADAPTABILITY
ADAPTABLE
ADAPTATION
ADAPTATIONS
ADAPTED
ADAPTER
ADAPTERS
ADAPTING
ADAPTIVE
ADAPTIVELY
ADAPTOR
ADAPTORS
ADAPTS
ADD
ADDED
ADDEND
ADDENDA
ADDENDUM
ADDER
ADDERS
ADDICT
ADDICTED
ADDICTING
ADDICTION
ADDICTIONS
ADDICTS
ADDING
ADDIS
ADDISON
ADDITION
ADDITIONAL
ADDITIONALLY
ADDITIONS
ADDITIVE
ADDITIVES
ADDITIVITY
ADDRESS
ADDRESSABILITY
ADDRESSABLE
ADDRESSED
ADDRESSEE
ADDRESSEES
ADDRESSER
ADDRESSERS
ADDRESSES
ADDRESSING
ADDRESSOGRAPH
ADDS
ADDUCE
ADDUCED
ADDUCES
ADDUCIBLE
ADDUCING
ADDUCT
ADDUCTED
ADDUCTING
ADDUCTION
ADDUCTOR
ADDUCTS
ADELAIDE
ADELE
ADELIA
ADEN
ADEPT
ADEQUACIES
ADEQUACY
ADEQUATE
ADEQUATELY
ADHERE
ADHERED
ADHERENCE
ADHERENT
ADHERENTS
ADHERER
ADHERERS
ADHERES
ADHERING
ADHESION
ADHESIONS
ADHESIVE
ADHESIVES
ADIABATIC
ADIABATICALLY
ADIEU
ADIRONDACK
ADIRONDACKS
ADJACENCY
ADJACENT
ADJECTIVE
ADJECTIVES
ADJOIN
ADJOINED
ADJOINING
ADJOINS
ADJOURN
ADJOURNED
ADJOURNING
ADJOURNMENT
ADJOURNS
ADJUDGE
ADJUDGED
ADJUDGES
ADJUDGING
ADJUDICATE
ADJUDICATED
ADJUDICATES
ADJUDICATING
ADJUDICATION
ADJUDICATIONS
ADJUNCT
ADJUNCTS
ADJURE
ADJURED
ADJURES
ADJURING
ADJUST
ADJUSTABLE
ADJUSTABLY
ADJUSTED
ADJUSTER
ADJUSTERS
ADJUSTING
ADJUSTMENT
ADJUSTMENTS
ADJUSTOR
ADJUSTORS
ADJUSTS
ADJUTANT
ADJUTANTS
ADKINS
ADLER
ADLERIAN
ADMINISTER
ADMINISTERED
ADMINISTERING
ADMINISTERINGS
ADMINISTERS
ADMINISTRABLE
ADMINISTRATE
ADMINISTRATION
ADMINISTRATIONS
ADMINISTRATIVE
ADMINISTRATIVELY
ADMINISTRATOR
ADMINISTRATORS
ADMIRABLE
ADMIRABLY
ADMIRAL
ADMIRALS
ADMIRALTY
ADMIRATION
ADMIRATIONS
ADMIRE
ADMIRED
ADMIRER
ADMIRERS
ADMIRES
ADMIRING
ADMIRINGLY
ADMISSIBILITY
ADMISSIBLE
ADMISSION
ADMISSIONS
ADMIT
ADMITS
ADMITTANCE
ADMITTED
ADMITTEDLY
ADMITTER
ADMITTERS
ADMITTING
ADMIX
ADMIXED
ADMIXES
ADMIXTURE
ADMONISH
ADMONISHED
ADMONISHES
ADMONISHING
ADMONISHMENT
ADMONISHMENTS
ADMONITION
ADMONITIONS
ADO
ADOBE
ADOLESCENCE
ADOLESCENT
ADOLESCENTS
ADOLPH
ADOLPHUS
ADONIS
ADOPT
ADOPTED
ADOPTER
ADOPTERS
ADOPTING
ADOPTION
ADOPTIONS
ADOPTIVE
ADOPTS
ADORABLE
ADORATION
ADORE
ADORED
ADORES
ADORN
ADORNED
ADORNMENT
ADORNMENTS
ADORNS
ADRENAL
ADRENALINE
ADRIAN
ADRIATIC
ADRIENNE
ADRIFT
ADROIT
ADROITNESS
ADS
ADSORB
ADSORBED
ADSORBING
ADSORBS
ADSORPTION
ADULATE
ADULATING
ADULATION
ADULT
ADULTERATE
ADULTERATED
ADULTERATES
ADULTERATING
ADULTERER
ADULTERERS
ADULTEROUS
ADULTEROUSLY
ADULTERY
ADULTHOOD
ADULTS
ADUMBRATE
ADUMBRATED
ADUMBRATES
ADUMBRATING
ADUMBRATION
ADVANCE
ADVANCED
ADVANCEMENT
ADVANCEMENTS
ADVANCES
ADVANCING
ADVANTAGE
ADVANTAGED
ADVANTAGEOUS
ADVANTAGEOUSLY
ADVANTAGES
ADVENT
ADVENTIST
ADVENTISTS
ADVENTITIOUS
ADVENTURE
ADVENTURED
ADVENTURER
ADVENTURERS
ADVENTURES
ADVENTURING
ADVENTUROUS
ADVERB
ADVERBIAL
ADVERBS
ADVERSARIES
ADVERSARY
ADVERSE
ADVERSELY
ADVERSITIES
ADVERSITY
ADVERT
ADVERTISE
ADVERTISED
ADVERTISEMENT
ADVERTISEMENTS
ADVERTISER
ADVERTISERS
ADVERTISES
ADVERTISING
ADVICE
ADVISABILITY
ADVISABLE
ADVISABLY
ADVISE
ADVISED
ADVISEDLY
ADVISEE
ADVISEES
ADVISEMENT
ADVISEMENTS
ADVISER
ADVISERS
ADVISES
ADVISING
ADVISOR
ADVISORS
ADVISORY
ADVOCACY
ADVOCATE
ADVOCATED
ADVOCATES
ADVOCATING
AEGEAN
AEGIS
AENEAS
AENEID
AEOLUS
AERATE
AERATED
AERATES
AERATING
AERATION
AERATOR
AERATORS
AERIAL
AERIALS
AEROACOUSTIC
AEROBACTER
AEROBIC
AEROBICS
AERODYNAMIC
AERODYNAMICS
AERONAUTIC
AERONAUTICAL
AERONAUTICS
AEROSOL
AEROSOLIZE
AEROSOLS
AEROSPACE
AESCHYLUS
AESOP
AESTHETIC
AESTHETICALLY
AESTHETICS
AFAR
AFFABLE
AFFAIR
AFFAIRS
AFFECT
AFFECTATION
AFFECTATIONS
AFFECTED
AFFECTING
AFFECTINGLY
AFFECTION
AFFECTIONATE
AFFECTIONATELY
AFFECTIONS
AFFECTIVE
AFFECTS
AFFERENT
AFFIANCED
AFFIDAVIT
AFFIDAVITS
AFFILIATE
AFFILIATED
AFFILIATES
AFFILIATING
AFFILIATION
AFFILIATIONS
AFFINITIES
AFFINITY
AFFIRM
AFFIRMATION
AFFIRMATIONS
AFFIRMATIVE
AFFIRMATIVELY
AFFIRMED
AFFIRMING
AFFIRMS
AFFIX
AFFIXED
AFFIXES
AFFIXING
AFFLICT
AFFLICTED
AFFLICTING
AFFLICTION
AFFLICTIONS
AFFLICTIVE
AFFLICTS
AFFLUENCE
AFFLUENT
AFFORD
AFFORDABLE
AFFORDED
AFFORDING
AFFORDS
AFFRICATE
AFFRICATES
AFFRIGHT
AFFRONT
AFFRONTED
AFFRONTING
AFFRONTS
AFGHAN
AFGHANISTAN
AFGHANS
AFICIONADO
AFIELD
AFIRE
AFLAME
AFLOAT
AFOOT
AFORE
AFOREMENTIONED
AFORESAID
AFORETHOUGHT
AFOUL
AFRAID
AFRESH
AFRICA
AFRICAN
AFRICANIZATION
AFRICANIZATIONS
AFRICANIZE
AFRICANIZED
AFRICANIZES
AFRICANIZING
AFRICANS
AFRIKAANS
AFRIKANER
AFRIKANERS
AFT
AFTER
AFTEREFFECT
AFTERGLOW
AFTERIMAGE
AFTERLIFE
AFTERMATH
AFTERMOST
AFTERNOON
AFTERNOONS
AFTERSHOCK
AFTERSHOCKS
AFTERTHOUGHT
AFTERTHOUGHTS
AFTERWARD
AFTERWARDS
AGAIN
AGAINST
AGAMEMNON
AGAPE
AGAR
AGATE
AGATES
AGATHA
AGE
AGED
AGEE
AGELESS
AGENCIES
AGENCY
AGENDA
AGENDAS
AGENT
AGENTS
AGER
AGERS
AGES
AGGIE
AGGIES
AGGLOMERATE
AGGLOMERATED
AGGLOMERATES
AGGLOMERATION
AGGLUTINATE
AGGLUTINATED
AGGLUTINATES
AGGLUTINATING
AGGLUTINATION
AGGLUTININ
AGGLUTININS
AGGRANDIZE
AGGRAVATE
AGGRAVATED
AGGRAVATES
AGGRAVATION
AGGREGATE
AGGREGATED
AGGREGATELY
AGGREGATES
AGGREGATING
AGGREGATION
AGGREGATIONS
AGGRESSION
AGGRESSIONS
AGGRESSIVE
AGGRESSIVELY
AGGRESSIVENESS
AGGRESSOR
AGGRESSORS
AGGRIEVE
AGGRIEVED
AGGRIEVES
AGGRIEVING
AGHAST
AGILE
AGILELY
AGILITY
AGING
AGITATE
AGITATED
AGITATES
AGITATING
AGITATION
AGITATIONS
AGITATOR
AGITATORS
AGLEAM
AGLOW
AGNES
AGNEW
AGNOSTIC
AGNOSTICS
AGO
AGOG
AGONIES
AGONIZE
AGONIZED
AGONIZES
AGONIZING
AGONIZINGLY
AGONY
AGRARIAN
AGREE
AGREEABLE
AGREEABLY
AGREED
AGREEING
AGREEMENT
AGREEMENTS
AGREER
AGREERS
AGREES
AGRICOLA
AGRICULTURAL
AGRICULTURALLY
AGRICULTURE
AGUE
AGWAY
AHEAD
AHMADABAD
AHMEDABAD
AID
AIDA
AIDE
AIDED
AIDES
AIDING
AIDS
AIKEN
AIL
AILEEN
AILERON
AILERONS
AILING
AILMENT
AILMENTS
AIM
AIMED
AIMER
AIMERS
AIMING
AIMLESS
AIMLESSLY
AIMS
AINU
AINUS
AIR
AIRBAG
AIRBAGS
AIRBORNE
AIRBUS
AIRCRAFT
AIRDROP
AIRDROPS
AIRED
AIREDALE
AIRER
AIRERS
AIRES
AIRFARE
AIRFIELD
AIRFIELDS
AIRFLOW
AIRFOIL
AIRFOILS
AIRFRAME
AIRFRAMES
AIRILY
AIRING
AIRINGS
AIRLESS
AIRLIFT
AIRLIFTS
AIRLINE
AIRLINER
AIRLINES
AIRLOCK
AIRLOCKS
AIRMAIL
AIRMAILS
AIRMAN
AIRMEN
AIRPLANE
AIRPLANES
AIRPORT
AIRPORTS
AIRS
AIRSHIP
AIRSHIPS
AIRSPACE
AIRSPEED
AIRSTRIP
AIRSTRIPS
AIRTIGHT
AIRWAY
AIRWAYS
AIRY
AISLE
AITKEN
AJAR
AJAX
AKERS
AKIMBO
AKIN
AKRON
ALABAMA
ALABAMANS
ALABAMIAN
ALABASTER
ALACRITY
ALADDIN
ALAMEDA
ALAMO
ALAMOS
ALAN
ALAR
ALARM
ALARMED
ALARMING
ALARMINGLY
ALARMIST
ALARMS
ALAS
ALASKA
ALASKAN
ALASTAIR
ALBA
ALBACORE
ALBANIA
ALBANIAN
ALBANIANS
ALBANY
ALBATROSS
ALBEIT
ALBERICH
ALBERT
ALBERTA
ALBERTO
ALBRECHT
ALBRIGHT
ALBUM
ALBUMIN
ALBUMS
ALBUQUERQUE
ALCESTIS
ALCHEMY
ALCIBIADES
ALCMENA
ALCOA
ALCOHOL
ALCOHOLIC
ALCOHOLICS
ALCOHOLISM
ALCOHOLS
ALCOTT
ALCOVE
ALCOVES
ALDEBARAN
ALDEN
ALDER
ALDERMAN
ALDERMEN
ALDRICH
ALE
ALEC
ALECK
ALEE
ALERT
ALERTED
ALERTEDLY
ALERTER
ALERTERS
ALERTING
ALERTLY
ALERTNESS
ALERTS
ALEUT
ALEUTIAN
ALEX
ALEXANDER
ALEXANDRA
ALEXANDRE
ALEXANDRIA
ALEXANDRINE
ALEXEI
ALEXIS
ALFA
ALFALFA
ALFONSO
ALFRED
ALFREDO
ALFRESCO
ALGA
ALGAE
ALGAECIDE
ALGEBRA
ALGEBRAIC
ALGEBRAICALLY
ALGEBRAS
ALGENIB
ALGER
ALGERIA
ALGERIAN
ALGIERS
ALGINATE
ALGOL
ALGOL
ALGONQUIAN
ALGONQUIN
ALGORITHM
ALGORITHMIC
ALGORITHMICALLY
ALGORITHMS
ALHAMBRA
ALI
ALIAS
ALIASED
ALIASES
ALIASING
ALIBI
ALIBIS
ALICE
ALICIA
ALIEN
ALIENATE
ALIENATED
ALIENATES
ALIENATING
ALIENATION
ALIENS
ALIGHT
ALIGN
ALIGNED
ALIGNING
ALIGNMENT
ALIGNMENTS
ALIGNS
ALIKE
ALIMENT
ALIMENTS
ALIMONY
ALISON
ALISTAIR
ALIVE
ALKALI
ALKALINE
ALKALIS
ALKALOID
ALKALOIDS
ALKYL
ALL
ALLAH
ALLAN
ALLAY
ALLAYED
ALLAYING
ALLAYS
ALLEGATION
ALLEGATIONS
ALLEGE
ALLEGED
ALLEGEDLY
ALLEGES
ALLEGHENIES
ALLEGHENY
ALLEGIANCE
ALLEGIANCES
ALLEGING
ALLEGORIC
ALLEGORICAL
ALLEGORICALLY
ALLEGORIES
ALLEGORY
ALLEGRA
ALLEGRETTO
ALLEGRETTOS
ALLELE
ALLELES
ALLEMANDE
ALLEN
ALLENDALE
ALLENTOWN
ALLERGIC
ALLERGIES
ALLERGY
ALLEVIATE
ALLEVIATED
ALLEVIATES
ALLEVIATING
ALLEVIATION
ALLEY
ALLEYS
ALLEYWAY
ALLEYWAYS
ALLIANCE
ALLIANCES
ALLIED
ALLIES
ALLIGATOR
ALLIGATORS
ALLIS
ALLISON
ALLITERATION
ALLITERATIONS
ALLITERATIVE
ALLOCATABLE
ALLOCATE
ALLOCATED
ALLOCATES
ALLOCATING
ALLOCATION
ALLOCATIONS
ALLOCATOR
ALLOCATORS
ALLOPHONE
ALLOPHONES
ALLOPHONIC
ALLOT
ALLOTMENT
ALLOTMENTS
ALLOTS
ALLOTTED
ALLOTTER
ALLOTTING
ALLOW
ALLOWABLE
ALLOWABLY
ALLOWANCE
ALLOWANCES
ALLOWED
ALLOWING
ALLOWS
ALLOY
ALLOYS
ALLSTATE
ALLUDE
ALLUDED
ALLUDES
ALLUDING
ALLURE
ALLUREMENT
ALLURING
ALLUSION
ALLUSIONS
ALLUSIVE
ALLUSIVENESS
ALLY
ALLYING
ALLYN
ALMA
ALMADEN
ALMANAC
ALMANACS
ALMIGHTY
ALMOND
ALMONDS
ALMONER
ALMOST
ALMS
ALMSMAN
ALNICO
ALOE
ALOES
ALOFT
ALOHA
ALONE
ALONENESS
ALONG
ALONGSIDE
ALOOF
ALOOFNESS
ALOUD
ALPERT
ALPHA
ALPHABET
ALPHABETIC
ALPHABETICAL
ALPHABETICALLY
ALPHABETICS
ALPHABETIZE
ALPHABETIZED
ALPHABETIZES
ALPHABETIZING
ALPHABETS
ALPHANUMERIC
ALPHERATZ
ALPHONSE
ALPINE
ALPS
ALREADY
ALSATIAN
ALSATIANS
ALSO
ALSOP
ALTAIR
ALTAR
ALTARS
ALTER
ALTERABLE
ALTERATION
ALTERATIONS
ALTERCATION
ALTERCATIONS
ALTERED
ALTERER
ALTERERS
ALTERING
ALTERNATE
ALTERNATED
ALTERNATELY
ALTERNATES
ALTERNATING
ALTERNATION
ALTERNATIONS
ALTERNATIVE
ALTERNATIVELY
ALTERNATIVES
ALTERNATOR
ALTERNATORS
ALTERS
ALTHAEA
ALTHOUGH
ALTITUDE
ALTITUDES
ALTOGETHER
ALTON
ALTOS
ALTRUISM
ALTRUIST
ALTRUISTIC
ALTRUISTICALLY
ALUM
ALUMINUM
ALUMNA
ALUMNAE
ALUMNI
ALUMNUS
ALUNDUM
ALVA
ALVAREZ
ALVEOLAR
ALVEOLI
ALVEOLUS
ALVIN
ALWAYS
ALYSSA
AMADEUS
AMAIN
AMALGAM
AMALGAMATE
AMALGAMATED
AMALGAMATES
AMALGAMATING
AMALGAMATION
AMALGAMS
AMANDA
AMANUENSIS
AMARETTO
AMARILLO
AMASS
AMASSED
AMASSES
AMASSING
AMATEUR
AMATEURISH
AMATEURISHNESS
AMATEURISM
AMATEURS
AMATORY
AMAZE
AMAZED
AMAZEDLY
AMAZEMENT
AMAZER
AMAZERS
AMAZES
AMAZING
AMAZINGLY
AMAZON
AMAZONS
AMBASSADOR
AMBASSADORS
AMBER
AMBIANCE
AMBIDEXTROUS
AMBIDEXTROUSLY
AMBIENT
AMBIGUITIES
AMBIGUITY
AMBIGUOUS
AMBIGUOUSLY
AMBITION
AMBITIONS
AMBITIOUS
AMBITIOUSLY
AMBIVALENCE
AMBIVALENT
AMBIVALENTLY
AMBLE
AMBLED
AMBLER
AMBLES
AMBLING
AMBROSIAL
AMBULANCE
AMBULANCES
AMBULATORY
AMBUSCADE
AMBUSH
AMBUSHED
AMBUSHES
AMDAHL
AMELIA
AMELIORATE
AMELIORATED
AMELIORATING
AMELIORATION
AMEN
AMENABLE
AMEND
AMENDED
AMENDING
AMENDMENT
AMENDMENTS
AMENDS
AMENITIES
AMENITY
AMENORRHEA
AMERADA
AMERICA
AMERICAN
AMERICANA
AMERICANISM
AMERICANIZATION
AMERICANIZATIONS
AMERICANIZE
AMERICANIZER
AMERICANIZERS
AMERICANIZES
AMERICANS
AMERICAS
AMERICIUM
AMES
AMHARIC
AMHERST
AMIABLE
AMICABLE
AMICABLY
AMID
AMIDE
AMIDST
AMIGA
AMIGO
AMINO
AMISS
AMITY
AMMAN
AMMERMAN
AMMO
AMMONIA
AMMONIAC
AMMONIUM
AMMUNITION
AMNESTY
AMOCO
AMOEBA
AMOEBAE
AMOEBAS
AMOK
AMONG
AMONGST
AMONTILLADO
AMORAL
AMORALITY
AMORIST
AMOROUS
AMORPHOUS
AMORPHOUSLY
AMORTIZE
AMORTIZED
AMORTIZES
AMORTIZING
AMOS
AMOUNT
AMOUNTED
AMOUNTER
AMOUNTERS
AMOUNTING
AMOUNTS
AMOUR
AMPERAGE
AMPERE
AMPERES
AMPERSAND
AMPERSANDS
AMPEX
AMPHETAMINE
AMPHETAMINES
AMPHIBIAN
AMPHIBIANS
AMPHIBIOUS
AMPHIBIOUSLY
AMPHIBOLOGY
AMPHITHEATER
AMPHITHEATERS
AMPLE
AMPLIFICATION
AMPLIFIED
AMPLIFIER
AMPLIFIERS
AMPLIFIES
AMPLIFY
AMPLIFYING
AMPLITUDE
AMPLITUDES
AMPLY
AMPOULE
AMPOULES
AMPUTATE
AMPUTATED
AMPUTATES
AMPUTATING
AMSTERDAM
AMTRAK
AMULET
AMULETS
AMUSE
AMUSED
AMUSEDLY
AMUSEMENT
AMUSEMENTS
AMUSER
AMUSERS
AMUSES
AMUSING
AMUSINGLY
AMY
AMYL
ANABAPTIST
ANABAPTISTS
ANABEL
ANACHRONISM
ANACHRONISMS
ANACHRONISTICALLY
ANACONDA
ANACONDAS
ANACREON
ANAEROBIC
ANAGRAM
ANAGRAMS
ANAHEIM
ANAL
ANALECTS
ANALOG
ANALOGICAL
ANALOGIES
ANALOGOUS
ANALOGOUSLY
ANALOGUE
ANALOGUES
ANALOGY
ANALYSES
ANALYSIS
ANALYST
ANALYSTS
ANALYTIC
ANALYTICAL
ANALYTICALLY
ANALYTICITIES
ANALYTICITY
ANALYZABLE
ANALYZE
ANALYZED
ANALYZER
ANALYZERS
ANALYZES
ANALYZING
ANAPHORA
ANAPHORIC
ANAPHORICALLY
ANAPLASMOSIS
ANARCHIC
ANARCHICAL
ANARCHISM
ANARCHIST
ANARCHISTS
ANARCHY
ANASTASIA
ANASTOMOSES
ANASTOMOSIS
ANASTOMOTIC
ANATHEMA
ANATOLE
ANATOLIA
ANATOLIAN
ANATOMIC
ANATOMICAL
ANATOMICALLY
ANATOMY
ANCESTOR
ANCESTORS
ANCESTRAL
ANCESTRY
ANCHOR
ANCHORAGE
ANCHORAGES
ANCHORED
ANCHORING
ANCHORITE
ANCHORITISM
ANCHORS
ANCHOVIES
ANCHOVY
ANCIENT
ANCIENTLY
ANCIENTS
ANCILLARY
AND
ANDALUSIA
ANDALUSIAN
ANDALUSIANS
ANDEAN
ANDERS
ANDERSEN
ANDERSON
ANDES
ANDING
ANDORRA
ANDOVER
ANDRE
ANDREA
ANDREI
ANDREW
ANDREWS
ANDROMACHE
ANDROMEDA
ANDY
ANECDOTAL
ANECDOTE
ANECDOTES
ANECHOIC
ANEMIA
ANEMIC
ANEMOMETER
ANEMOMETERS
ANEMOMETRY
ANEMONE
ANESTHESIA
ANESTHETIC
ANESTHETICALLY
ANESTHETICS
ANESTHETIZE
ANESTHETIZED
ANESTHETIZES
ANESTHETIZING
ANEW
ANGEL
ANGELA
ANGELENO
ANGELENOS
ANGELES
ANGELIC
ANGELICA
ANGELINA
ANGELINE
ANGELO
ANGELS
ANGER
ANGERED
ANGERING
ANGERS
ANGIE
ANGIOGRAPHY
ANGLE
ANGLED
ANGLER
ANGLERS
ANGLES
ANGLIA
ANGLICAN
ANGLICANISM
ANGLICANIZE
ANGLICANIZES
ANGLICANS
ANGLING
ANGLO
ANGLOPHILIA
ANGLOPHOBIA
ANGOLA
ANGORA
ANGRIER
ANGRIEST
ANGRILY
ANGRY
ANGST
ANGSTROM
ANGUISH
ANGUISHED
ANGULAR
ANGULARLY
ANGUS
ANHEUSER
ANHYDROUS
ANHYDROUSLY
ANILINE
ANIMAL
ANIMALS
ANIMATE
ANIMATED
ANIMATEDLY
ANIMATELY
ANIMATENESS
ANIMATES
ANIMATING
ANIMATION
ANIMATIONS
ANIMATOR
ANIMATORS
ANIMISM
ANIMIZED
ANIMOSITY
ANION
ANIONIC
ANIONS
ANISE
ANISEIKONIC
ANISOTROPIC
ANISOTROPY
ANITA
ANKARA
ANKLE
ANKLES
ANN
ANNA
ANNAL
ANNALIST
ANNALISTIC
ANNALS
ANNAPOLIS
ANNE
ANNETTE
ANNEX
ANNEXATION
ANNEXED
ANNEXES
ANNEXING
ANNIE
ANNIHILATE
ANNIHILATED
ANNIHILATES
ANNIHILATING
ANNIHILATION
ANNIVERSARIES
ANNIVERSARY
ANNOTATE
ANNOTATED
ANNOTATES
ANNOTATING
ANNOTATION
ANNOTATIONS
ANNOUNCE
ANNOUNCED
ANNOUNCEMENT
ANNOUNCEMENTS
ANNOUNCER
ANNOUNCERS
ANNOUNCES
ANNOUNCING
ANNOY
ANNOYANCE
ANNOYANCES
ANNOYED
ANNOYER
ANNOYERS
ANNOYING
ANNOYINGLY
ANNOYS
ANNUAL
ANNUALLY
ANNUALS
ANNUITY
ANNUL
ANNULAR
ANNULI
ANNULLED
ANNULLING
ANNULMENT
ANNULMENTS
ANNULS
ANNULUS
ANNUM
ANNUNCIATE
ANNUNCIATED
ANNUNCIATES
ANNUNCIATING
ANNUNCIATOR
ANNUNCIATORS
ANODE
ANODES
ANODIZE
ANODIZED
ANODIZES
ANOINT
ANOINTED
ANOINTING
ANOINTS
ANOMALIES
ANOMALOUS
ANOMALOUSLY
ANOMALY
ANOMIC
ANOMIE
ANON
ANONYMITY
ANONYMOUS
ANONYMOUSLY
ANOREXIA
ANOTHER
ANSELM
ANSELMO
ANSI
ANSWER
ANSWERABLE
ANSWERED
ANSWERER
ANSWERERS
ANSWERING
ANSWERS
ANT
ANTAEUS
ANTAGONISM
ANTAGONISMS
ANTAGONIST
ANTAGONISTIC
ANTAGONISTICALLY
ANTAGONISTS
ANTAGONIZE
ANTAGONIZED
ANTAGONIZES
ANTAGONIZING
ANTARCTIC
ANTARCTICA
ANTARES
ANTE
ANTEATER
ANTEATERS
ANTECEDENT
ANTECEDENTS
ANTEDATE
ANTELOPE
ANTELOPES
ANTENNA
ANTENNAE
ANTENNAS
ANTERIOR
ANTHEM
ANTHEMS
ANTHER
ANTHOLOGIES
ANTHOLOGY
ANTHONY
ANTHRACITE
ANTHROPOLOGICAL
ANTHROPOLOGICALLY
ANTHROPOLOGIST
ANTHROPOLOGISTS
ANTHROPOLOGY
ANTHROPOMORPHIC
ANTHROPOMORPHICALLY
ANTI
ANTIBACTERIAL
ANTIBIOTIC
ANTIBIOTICS
ANTIBODIES
ANTIBODY
ANTIC
ANTICIPATE
ANTICIPATED
ANTICIPATES
ANTICIPATING
ANTICIPATION
ANTICIPATIONS
ANTICIPATORY
ANTICOAGULATION
ANTICOMPETITIVE
ANTICS
ANTIDISESTABLISHMENTARIANISM
ANTIDOTE
ANTIDOTES
ANTIETAM
ANTIFORMANT
ANTIFUNDAMENTALIST
ANTIGEN
ANTIGENS
ANTIGONE
ANTIHISTORICAL
ANTILLES
ANTIMICROBIAL
ANTIMONY
ANTINOMIAN
ANTINOMY
ANTIOCH
ANTIPATHY
ANTIPHONAL
ANTIPODE
ANTIPODES
ANTIQUARIAN
ANTIQUARIANS
ANTIQUATE
ANTIQUATED
ANTIQUE
ANTIQUES
ANTIQUITIES
ANTIQUITY
ANTIREDEPOSITION
ANTIRESONANCE
ANTIRESONATOR
ANTISEMITIC
ANTISEMITISM
ANTISEPTIC
ANTISERA
ANTISERUM
ANTISLAVERY
ANTISOCIAL
ANTISUBMARINE
ANTISYMMETRIC
ANTISYMMETRY
ANTITHESIS
ANTITHETICAL
ANTITHYROID
ANTITOXIN
ANTITOXINS
ANTITRUST
ANTLER
ANTLERED
ANTOINE
ANTOINETTE
ANTON
ANTONIO
ANTONOVICS
ANTONY
ANTS
ANTWERP
ANUS
ANVIL
ANVILS
ANXIETIES
ANXIETY
ANXIOUS
ANXIOUSLY
ANY
ANYBODY
ANYHOW
ANYMORE
ANYONE
ANYPLACE
ANYTHING
ANYTIME
ANYWAY
ANYWHERE
AORTA
APACE
APACHES
APALACHICOLA
APART
APARTMENT
APARTMENTS
APATHETIC
APATHY
APE
APED
APERIODIC
APERIODICITY
APERTURE
APES
APETALOUS
APEX
APHASIA
APHASIC
APHELION
APHID
APHIDS
APHONIC
APHORISM
APHORISMS
APHRODITE
APIARIES
APIARY
APICAL
APIECE
APING
APISH
APLENTY
APLOMB
APOCALYPSE
APOCALYPTIC
APOCRYPHA
APOCRYPHAL
APOGEE
APOGEES
APOLLINAIRE
APOLLO
APOLLONIAN
APOLOGETIC
APOLOGETICALLY
APOLOGIA
APOLOGIES
APOLOGIST
APOLOGISTS
APOLOGIZE
APOLOGIZED
APOLOGIZES
APOLOGIZING
APOLOGY
APOSTATE
APOSTLE
APOSTLES
APOSTOLIC
APOSTROPHE
APOSTROPHES
APOTHECARY
APOTHEGM
APOTHEOSES
APOTHEOSIS
APPALACHIA
APPALACHIAN
APPALACHIANS
APPALL
APPALLED
APPALLING
APPALLINGLY
APPALOOSAS
APPANAGE
APPARATUS
APPAREL
APPARELED
APPARENT
APPARENTLY
APPARITION
APPARITIONS
APPEAL
APPEALED
APPEALER
APPEALERS
APPEALING
APPEALINGLY
APPEALS
APPEAR
APPEARANCE
APPEARANCES
APPEARED
APPEARER
APPEARERS
APPEARING
APPEARS
APPEASE
APPEASED
APPEASEMENT
APPEASES
APPEASING
APPELLANT
APPELLANTS
APPELLATE
APPELLATION
APPEND
APPENDAGE
APPENDAGES
APPENDED
APPENDER
APPENDERS
APPENDICES
APPENDICITIS
APPENDING
APPENDIX
APPENDIXES
APPENDS
APPERTAIN
APPERTAINS
APPETITE
APPETITES
APPETIZER
APPETIZING
APPIA
APPIAN
APPLAUD
APPLAUDED
APPLAUDING
APPLAUDS
APPLAUSE
APPLE
APPLEBY
APPLEJACK
APPLES
APPLETON
APPLIANCE
APPLIANCES
APPLICABILITY
APPLICABLE
APPLICANT
APPLICANTS
APPLICATION
APPLICATIONS
APPLICATIVE
APPLICATIVELY
APPLICATOR
APPLICATORS
APPLIED
APPLIER
APPLIERS
APPLIES
APPLIQUE
APPLY
APPLYING
APPOINT
APPOINTED
APPOINTEE
APPOINTEES
APPOINTER
APPOINTERS
APPOINTING
APPOINTIVE
APPOINTMENT
APPOINTMENTS
APPOINTS
APPOMATTOX
APPORTION
APPORTIONED
APPORTIONING
APPORTIONMENT
APPORTIONMENTS
APPORTIONS
APPOSITE
APPRAISAL
APPRAISALS
APPRAISE
APPRAISED
APPRAISER
APPRAISERS
APPRAISES
APPRAISING
APPRAISINGLY
APPRECIABLE
APPRECIABLY
APPRECIATE
APPRECIATED
APPRECIATES
APPRECIATING
APPRECIATION
APPRECIATIONS
APPRECIATIVE
APPRECIATIVELY
APPREHEND
APPREHENDED
APPREHENSIBLE
APPREHENSION
APPREHENSIONS
APPREHENSIVE
APPREHENSIVELY
APPREHENSIVENESS
APPRENTICE
APPRENTICED
APPRENTICES
APPRENTICESHIP
APPRISE
APPRISED
APPRISES
APPRISING
APPROACH
APPROACHABILITY
APPROACHABLE
APPROACHED
APPROACHER
APPROACHERS
APPROACHES
APPROACHING
APPROBATE
APPROBATION
APPROPRIATE
APPROPRIATED
APPROPRIATELY
APPROPRIATENESS
APPROPRIATES
APPROPRIATING
APPROPRIATION
APPROPRIATIONS
APPROPRIATOR
APPROPRIATORS
APPROVAL
APPROVALS
APPROVE
APPROVED
APPROVER
APPROVERS
APPROVES
APPROVING
APPROVINGLY
APPROXIMATE
APPROXIMATED
APPROXIMATELY
APPROXIMATES
APPROXIMATING
APPROXIMATION
APPROXIMATIONS
APPURTENANCE
APPURTENANCES
APRICOT
APRICOTS
APRIL
APRILS
APRON
APRONS
APROPOS
APSE
APSIS
APT
APTITUDE
APTITUDES
APTLY
APTNESS
AQUA
AQUARIA
AQUARIUM
AQUARIUS
AQUATIC
AQUEDUCT
AQUEDUCTS
AQUEOUS
AQUIFER
AQUIFERS
AQUILA
AQUINAS
ARAB
ARABESQUE
ARABIA
ARABIAN
ARABIANIZE
ARABIANIZES
ARABIANS
ARABIC
ARABICIZE
ARABICIZES
ARABLE
ARABS
ARABY
ARACHNE
ARACHNID
ARACHNIDS
ARAMCO
ARAPAHO
ARBITER
ARBITERS
ARBITRARILY
ARBITRARINESS
ARBITRARY
ARBITRATE
ARBITRATED
ARBITRATES
ARBITRATING
ARBITRATION
ARBITRATOR
ARBITRATORS
ARBOR
ARBOREAL
ARBORS
ARC
ARCADE
ARCADED
ARCADES
ARCADIA
ARCADIAN
ARCANE
ARCED
ARCH
ARCHAIC
ARCHAICALLY
ARCHAICNESS
ARCHAISM
ARCHAIZE
ARCHANGEL
ARCHANGELS
ARCHBISHOP
ARCHDIOCESE
ARCHDIOCESES
ARCHED
ARCHENEMY
ARCHEOLOGICAL
ARCHEOLOGIST
ARCHEOLOGY
ARCHER
ARCHERS
ARCHERY
ARCHES
ARCHETYPE
ARCHFOOL
ARCHIBALD
ARCHIE
ARCHIMEDES
ARCHING
ARCHIPELAGO
ARCHIPELAGOES
ARCHITECT
ARCHITECTONIC
ARCHITECTS
ARCHITECTURAL
ARCHITECTURALLY
ARCHITECTURE
ARCHITECTURES
ARCHIVAL
ARCHIVE
ARCHIVED
ARCHIVER
ARCHIVERS
ARCHIVES
ARCHIVING
ARCHIVIST
ARCHLY
ARCING
ARCLIKE
ARCO
ARCS
ARCSINE
ARCTANGENT
ARCTIC
ARCTURUS
ARDEN
ARDENT
ARDENTLY
ARDOR
ARDUOUS
ARDUOUSLY
ARDUOUSNESS
ARE
AREA
AREAS
ARENA
ARENAS
AREQUIPA
ARES
ARGENTINA
ARGENTINIAN
ARGIVE
ARGO
ARGON
ARGONAUT
ARGONAUTS
ARGONNE
ARGOS
ARGOT
ARGUABLE
ARGUABLY
ARGUE
ARGUED
ARGUER
ARGUERS
ARGUES
ARGUING
ARGUMENT
ARGUMENTATION
ARGUMENTATIVE
ARGUMENTS
ARGUS
ARIADNE
ARIANISM
ARIANIST
ARIANISTS
ARID
ARIDITY
ARIES
ARIGHT
ARISE
ARISEN
ARISER
ARISES
ARISING
ARISINGS
ARISTOCRACY
ARISTOCRAT
ARISTOCRATIC
ARISTOCRATICALLY
ARISTOCRATS
ARISTOTELIAN
ARISTOTLE
ARITHMETIC
ARITHMETICAL
ARITHMETICALLY
ARITHMETICS
ARITHMETIZE
ARITHMETIZED
ARITHMETIZES
ARIZONA
ARK
ARKANSAN
ARKANSAS
ARLEN
ARLENE
ARLINGTON
ARM
ARMADA
ARMADILLO
ARMADILLOS
ARMAGEDDON
ARMAGNAC
ARMAMENT
ARMAMENTS
ARMATA
ARMCHAIR
ARMCHAIRS
ARMCO
ARMED
ARMENIA
ARMENIAN
ARMER
ARMERS
ARMFUL
ARMHOLE
ARMIES
ARMING
ARMISTICE
ARMLOAD
ARMONK
ARMOR
ARMORED
ARMORER
ARMORY
ARMOUR
ARMPIT
ARMPITS
ARMS
ARMSTRONG
ARMY
ARNOLD
AROMA
AROMAS
AROMATIC
AROSE
AROUND
AROUSAL
AROUSE
AROUSED
AROUSES
AROUSING
ARPA
ARPANET
ARPANET
ARPEGGIO
ARPEGGIOS
ARRACK
ARRAGON
ARRAIGN
ARRAIGNED
ARRAIGNING
ARRAIGNMENT
ARRAIGNMENTS
ARRAIGNS
ARRANGE
ARRANGED
ARRANGEMENT
ARRANGEMENTS
ARRANGER
ARRANGERS
ARRANGES
ARRANGING
ARRANT
ARRAY
ARRAYED
ARRAYS
ARREARS
ARREST
ARRESTED
ARRESTER
ARRESTERS
ARRESTING
ARRESTINGLY
ARRESTOR
ARRESTORS
ARRESTS
ARRHENIUS
ARRIVAL
ARRIVALS
ARRIVE
ARRIVED
ARRIVES
ARRIVING
ARROGANCE
ARROGANT
ARROGANTLY
ARROGATE
ARROGATED
ARROGATES
ARROGATING
ARROGATION
ARROW
ARROWED
ARROWHEAD
ARROWHEADS
ARROWS
ARROYO
ARROYOS
ARSENAL
ARSENALS
ARSENIC
ARSINE
ARSON
ART
ARTEMIA
ARTEMIS
ARTERIAL
ARTERIES
ARTERIOLAR
ARTERIOLE
ARTERIOLES
ARTERIOSCLEROSIS
ARTERY
ARTFUL
ARTFULLY
ARTFULNESS
ARTHRITIS
ARTHROPOD
ARTHROPODS
ARTHUR
ARTICHOKE
ARTICHOKES
ARTICLE
ARTICLES
ARTICULATE
ARTICULATED
ARTICULATELY
ARTICULATENESS
ARTICULATES
ARTICULATING
ARTICULATION
ARTICULATIONS
ARTICULATOR
ARTICULATORS
ARTICULATORY
ARTIE
ARTIFACT
ARTIFACTS
ARTIFICE
ARTIFICER
ARTIFICES
ARTIFICIAL
ARTIFICIALITIES
ARTIFICIALITY
ARTIFICIALLY
ARTIFICIALNESS
ARTILLERIST
ARTILLERY
ARTISAN
ARTISANS
ARTIST
ARTISTIC
ARTISTICALLY
ARTISTRY
ARTISTS
ARTLESS
ARTS
ARTURO
ARTWORK
ARUBA
ARYAN
ARYANS
ASBESTOS
ASCEND
ASCENDANCY
ASCENDANT
ASCENDED
ASCENDENCY
ASCENDENT
ASCENDER
ASCENDERS
ASCENDING
ASCENDS
ASCENSION
ASCENSIONS
ASCENT
ASCERTAIN
ASCERTAINABLE
ASCERTAINED
ASCERTAINING
ASCERTAINS
ASCETIC
ASCETICISM
ASCETICS
ASCII
ASCOT
ASCRIBABLE
ASCRIBE
ASCRIBED
ASCRIBES
ASCRIBING
ASCRIPTION
ASEPTIC
ASH
ASHAMED
ASHAMEDLY
ASHEN
ASHER
ASHES
ASHEVILLE
ASHLAND
ASHLEY
ASHMAN
ASHMOLEAN
ASHORE
ASHTRAY
ASHTRAYS
ASIA
ASIAN
ASIANS
ASIATIC
ASIATICIZATION
ASIATICIZATIONS
ASIATICIZE
ASIATICIZES
ASIATICS
ASIDE
ASILOMAR
ASININE
ASK
ASKANCE
ASKED
ASKER
ASKERS
ASKEW
ASKING
ASKS
ASLEEP
ASOCIAL
ASP
ASPARAGUS
ASPECT
ASPECTS
ASPEN
ASPERSION
ASPERSIONS
ASPHALT
ASPHYXIA
ASPIC
ASPIRANT
ASPIRANTS
ASPIRATE
ASPIRATED
ASPIRATES
ASPIRATING
ASPIRATION
ASPIRATIONS
ASPIRATOR
ASPIRATORS
ASPIRE
ASPIRED
ASPIRES
ASPIRIN
ASPIRING
ASPIRINS
ASS
ASSAIL
ASSAILANT
ASSAILANTS
ASSAILED
ASSAILING
ASSAILS
ASSAM
ASSASSIN
ASSASSINATE
ASSASSINATED
ASSASSINATES
ASSASSINATING
ASSASSINATION
ASSASSINATIONS
ASSASSINS
ASSAULT
ASSAULTED
ASSAULTING
ASSAULTS
ASSAY
ASSAYED
ASSAYING
ASSEMBLAGE
ASSEMBLAGES
ASSEMBLE
ASSEMBLED
ASSEMBLER
ASSEMBLERS
ASSEMBLES
ASSEMBLIES
ASSEMBLING
ASSEMBLY
ASSENT
ASSENTED
ASSENTER
ASSENTING
ASSENTS
ASSERT
ASSERTED
ASSERTER
ASSERTERS
ASSERTING
ASSERTION
ASSERTIONS
ASSERTIVE
ASSERTIVELY
ASSERTIVENESS
ASSERTS
ASSES
ASSESS
ASSESSED
ASSESSES
ASSESSING
ASSESSMENT
ASSESSMENTS
ASSESSOR
ASSESSORS
ASSET
ASSETS
ASSIDUITY
ASSIDUOUS
ASSIDUOUSLY
ASSIGN
ASSIGNABLE
ASSIGNED
ASSIGNEE
ASSIGNEES
ASSIGNER
ASSIGNERS
ASSIGNING
ASSIGNMENT
ASSIGNMENTS
ASSIGNS
ASSIMILATE
ASSIMILATED
ASSIMILATES
ASSIMILATING
ASSIMILATION
ASSIMILATIONS
ASSIST
ASSISTANCE
ASSISTANCES
ASSISTANT
ASSISTANTS
ASSISTANTSHIP
ASSISTANTSHIPS
ASSISTED
ASSISTING
ASSISTS
ASSOCIATE
ASSOCIATED
ASSOCIATES
ASSOCIATING
ASSOCIATION
ASSOCIATIONAL
ASSOCIATIONS
ASSOCIATIVE
ASSOCIATIVELY
ASSOCIATIVITY
ASSOCIATOR
ASSOCIATORS
ASSONANCE
ASSONANT
ASSORT
ASSORTED
ASSORTMENT
ASSORTMENTS
ASSORTS
ASSUAGE
ASSUAGED
ASSUAGES
ASSUME
ASSUMED
ASSUMES
ASSUMING
ASSUMPTION
ASSUMPTIONS
ASSURANCE
ASSURANCES
ASSURE
ASSURED
ASSUREDLY
ASSURER
ASSURERS
ASSURES
ASSURING
ASSURINGLY
ASSYRIA
ASSYRIAN
ASSYRIANIZE
ASSYRIANIZES
ASSYRIOLOGY
ASTAIRE
ASTAIRES
ASTARTE
ASTATINE
ASTER
ASTERISK
ASTERISKS
ASTEROID
ASTEROIDAL
ASTEROIDS
ASTERS
ASTHMA
ASTON
ASTONISH
ASTONISHED
ASTONISHES
ASTONISHING
ASTONISHINGLY
ASTONISHMENT
ASTOR
ASTORIA
ASTOUND
ASTOUNDED
ASTOUNDING
ASTOUNDS
ASTRAL
ASTRAY
ASTRIDE
ASTRINGENCY
ASTRINGENT
ASTROLOGY
ASTRONAUT
ASTRONAUTICS
ASTRONAUTS
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BAD
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BANG
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BANSHEES
BANTAM
BANTER
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BANTERING
BANTERS
BANTU
BANTUS
BAPTISM
BAPTISMAL
BAPTISMS
BAPTIST
BAPTISTE
BAPTISTERY
BAPTISTRIES
BAPTISTRY
BAPTISTS
BAPTIZE
BAPTIZED
BAPTIZES
BAPTIZING
BAR
BARB
BARBADOS
BARBARA
BARBARIAN
BARBARIANS
BARBARIC
BARBARISM
BARBARITIES
BARBARITY
BARBAROUS
BARBAROUSLY
BARBECUE
BARBECUED
BARBECUES
BARBED
BARBELL
BARBELLS
BARBER
BARBITAL
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BARBITURATES
BARBOUR
BARBS
BARCELONA
BARCLAY
BARD
BARDS
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BARED
BAREFACED
BAREFOOT
BAREFOOTED
BARELY
BARENESS
BARER
BARES
BAREST
BARFLIES
BARFLY
BARGAIN
BARGAINED
BARGAINING
BARGAINS
BARGE
BARGES
BARGING
BARHOP
BARING
BARITONE
BARITONES
BARIUM
BARK
BARKED
BARKER
BARKERS
BARKING
BARKS
BARLEY
BARLOW
BARN
BARNABAS
BARNARD
BARNES
BARNET
BARNETT
BARNEY
BARNHARD
BARNS
BARNSTORM
BARNSTORMED
BARNSTORMING
BARNSTORMS
BARNUM
BARNYARD
BARNYARDS
BAROMETER
BAROMETERS
BAROMETRIC
BARON
BARONESS
BARONIAL
BARONIES
BARONS
BARONY
BAROQUE
BAROQUENESS
BARR
BARRACK
BARRACKS
BARRAGE
BARRAGES
BARRED
BARREL
BARRELLED
BARRELLING
BARRELS
BARREN
BARRENNESS
BARRETT
BARRICADE
BARRICADES
BARRIER
BARRIERS
BARRING
BARRINGER
BARRINGTON
BARRON
BARROW
BARRY
BARRYMORE
BARRYMORES
BARS
BARSTOW
BART
BARTENDER
BARTENDERS
BARTER
BARTERED
BARTERING
BARTERS
BARTH
BARTHOLOMEW
BARTLETT
BARTOK
BARTON
BASAL
BASALT
BASCOM
BASE
BASEBALL
BASEBALLS
BASEBAND
BASEBOARD
BASEBOARDS
BASED
BASEL
BASELESS
BASELINE
BASELINES
BASELY
BASEMAN
BASEMENT
BASEMENTS
BASENESS
BASER
BASES
BASH
BASHED
BASHES
BASHFUL
BASHFULNESS
BASHING
BASIC
BASIC
BASIC
BASICALLY
BASICS
BASIE
BASIL
BASIN
BASING
BASINS
BASIS
BASK
BASKED
BASKET
BASKETBALL
BASKETBALLS
BASKETS
BASKING
BASQUE
BASS
BASSES
BASSET
BASSETT
BASSINET
BASSINETS
BASTARD
BASTARDS
BASTE
BASTED
BASTES
BASTING
BASTION
BASTIONS
BAT
BATAVIA
BATCH
BATCHED
BATCHELDER
BATCHES
BATEMAN
BATES
BATH
BATHE
BATHED
BATHER
BATHERS
BATHES
BATHING
BATHOS
BATHROBE
BATHROBES
BATHROOM
BATHROOMS
BATHS
BATHTUB
BATHTUBS
BATHURST
BATISTA
BATON
BATONS
BATOR
BATS
BATTALION
BATTALIONS
BATTED
BATTELLE
BATTEN
BATTENS
BATTER
BATTERED
BATTERIES
BATTERING
BATTERS
BATTERY
BATTING
BATTLE
BATTLED
BATTLEFIELD
BATTLEFIELDS
BATTLEFRONT
BATTLEFRONTS
BATTLEGROUND
BATTLEGROUNDS
BATTLEMENT
BATTLEMENTS
BATTLER
BATTLERS
BATTLES
BATTLESHIP
BATTLESHIPS
BATTLING
BAUBLE
BAUBLES
BAUD
BAUDELAIRE
BAUER
BAUHAUS
BAUSCH
BAUXITE
BAVARIA
BAVARIAN
BAWDY
BAWL
BAWLED
BAWLING
BAWLS
BAXTER
BAY
BAYDA
BAYED
BAYES
BAYESIAN
BAYING
BAYLOR
BAYONET
BAYONETS
BAYONNE
BAYOU
BAYOUS
BAYPORT
BAYREUTH
BAYS
BAZAAR
BAZAARS
BEACH
BEACHED
BEACHES
BEACHHEAD
BEACHHEADS
BEACHING
BEACON
BEACONS
BEAD
BEADED
BEADING
BEADLE
BEADLES
BEADS
BEADY
BEAGLE
BEAGLES
BEAK
BEAKED
BEAKER
BEAKERS
BEAKS
BEAM
BEAMED
BEAMER
BEAMERS
BEAMING
BEAMS
BEAN
BEANBAG
BEANED
BEANER
BEANERS
BEANING
BEANS
BEAR
BEARABLE
BEARABLY
BEARD
BEARDED
BEARDLESS
BEARDS
BEARDSLEY
BEARER
BEARERS
BEARING
BEARINGS
BEARISH
BEARS
BEAST
BEASTLY
BEASTS
BEAT
BEATABLE
BEATABLY
BEATEN
BEATER
BEATERS
BEATIFIC
BEATIFICATION
BEATIFY
BEATING
BEATINGS
BEATITUDE
BEATITUDES
BEATNIK
BEATNIKS
BEATRICE
BEATS
BEAU
BEAUCHAMPS
BEAUJOLAIS
BEAUMONT
BEAUREGARD
BEAUS
BEAUTEOUS
BEAUTEOUSLY
BEAUTIES
BEAUTIFICATIONS
BEAUTIFIED
BEAUTIFIER
BEAUTIFIERS
BEAUTIFIES
BEAUTIFUL
BEAUTIFULLY
BEAUTIFY
BEAUTIFYING
BEAUTY
BEAVER
BEAVERS
BEAVERTON
BECALM
BECALMED
BECALMING
BECALMS
BECAME
BECAUSE
BECHTEL
BECK
BECKER
BECKMAN
BECKON
BECKONED
BECKONING
BECKONS
BECKY
BECOME
BECOMES
BECOMING
BECOMINGLY
BED
BEDAZZLE
BEDAZZLED
BEDAZZLEMENT
BEDAZZLES
BEDAZZLING
BEDBUG
BEDBUGS
BEDDED
BEDDER
BEDDERS
BEDDING
BEDEVIL
BEDEVILED
BEDEVILING
BEDEVILS
BEDFAST
BEDFORD
BEDLAM
BEDPOST
BEDPOSTS
BEDRAGGLE
BEDRAGGLED
BEDRIDDEN
BEDROCK
BEDROOM
BEDROOMS
BEDS
BEDSIDE
BEDSPREAD
BEDSPREADS
BEDSPRING
BEDSPRINGS
BEDSTEAD
BEDSTEADS
BEDTIME
BEE
BEEBE
BEECH
BEECHAM
BEECHEN
BEECHER
BEEF
BEEFED
BEEFER
BEEFERS
BEEFING
BEEFS
BEEFSTEAK
BEEFY
BEEHIVE
BEEHIVES
BEEN
BEEP
BEEPS
BEER
BEERS
BEES
BEET
BEETHOVEN
BEETLE
BEETLED
BEETLES
BEETLING
BEETS
BEFALL
BEFALLEN
BEFALLING
BEFALLS
BEFELL
BEFIT
BEFITS
BEFITTED
BEFITTING
BEFOG
BEFOGGED
BEFOGGING
BEFORE
BEFOREHAND
BEFOUL
BEFOULED
BEFOULING
BEFOULS
BEFRIEND
BEFRIENDED
BEFRIENDING
BEFRIENDS
BEFUDDLE
BEFUDDLED
BEFUDDLES
BEFUDDLING
BEG
BEGAN
BEGET
BEGETS
BEGETTING
BEGGAR
BEGGARLY
BEGGARS
BEGGARY
BEGGED
BEGGING
BEGIN
BEGINNER
BEGINNERS
BEGINNING
BEGINNINGS
BEGINS
BEGOT
BEGOTTEN
BEGRUDGE
BEGRUDGED
BEGRUDGES
BEGRUDGING
BEGRUDGINGLY
BEGS
BEGUILE
BEGUILED
BEGUILES
BEGUILING
BEGUN
BEHALF
BEHAVE
BEHAVED
BEHAVES
BEHAVING
BEHAVIOR
BEHAVIORAL
BEHAVIORALLY
BEHAVIORISM
BEHAVIORISTIC
BEHAVIORS
BEHEAD
BEHEADING
BEHELD
BEHEMOTH
BEHEMOTHS
BEHEST
BEHIND
BEHOLD
BEHOLDEN
BEHOLDER
BEHOLDERS
BEHOLDING
BEHOLDS
BEHOOVE
BEHOOVES
BEIGE
BEIJING
BEING
BEINGS
BEIRUT
BELA
BELABOR
BELABORED
BELABORING
BELABORS
BELATED
BELATEDLY
BELAY
BELAYED
BELAYING
BELAYS
BELCH
BELCHED
BELCHES
BELCHING
BELFAST
BELFRIES
BELFRY
BELGIAN
BELGIANS
BELGIUM
BELGRADE
BELIE
BELIED
BELIEF
BELIEFS
BELIES
BELIEVABLE
BELIEVABLY
BELIEVE
BELIEVED
BELIEVER
BELIEVERS
BELIEVES
BELIEVING
BELITTLE
BELITTLED
BELITTLES
BELITTLING
BELIZE
BELL
BELLA
BELLAMY
BELLATRIX
BELLBOY
BELLBOYS
BELLE
BELLES
BELLEVILLE
BELLHOP
BELLHOPS
BELLICOSE
BELLICOSITY
BELLIES
BELLIGERENCE
BELLIGERENT
BELLIGERENTLY
BELLIGERENTS
BELLINGHAM
BELLINI
BELLMAN
BELLMEN
BELLOVIN
BELLOW
BELLOWED
BELLOWING
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BELLS
BELLUM
BELLWETHER
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BELLWOOD
BELLY
BELLYACHE
BELLYFULL
BELMONT
BELOIT
BELONG
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BELSHAZZAR
BELT
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BELUSHI
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BENDABLE
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BENDING
BENDIX
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BENEDICT
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BENEDICTION
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BENEFACTOR
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BENEFICENCE
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BENSON
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BENTHAM
BENTLEY
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BENTON
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BEOGRAD
BEOWULF
BEQUEATH
BEQUEATHAL
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BEQUEATHS
BEQUEST
BEQUESTS
BERATE
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BERATES
BERATING
BEREA
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BEREAVEMENT
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BEREAVES
BEREAVING
BEREFT
BERENICES
BERESFORD
BERET
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BERGEN
BERGLAND
BERGLUND
BERGMAN
BERGSON
BERGSTEN
BERGSTROM
BERIBBONED
BERIBERI
BERINGER
BERKELEY
BERKELIUM
BERKOWITZ
BERKSHIRE
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BERLIN
BERLINER
BERLINERS
BERLINIZE
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BERLIOZ
BERLITZ
BERMAN
BERMUDA
BERN
BERNADINE
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BERNARDINO
BERNARDO
BERNE
BERNET
BERNHARD
BERNICE
BERNIE
BERNIECE
BERNINI
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BERNSTEIN
BERRA
BERRIES
BERRY
BERSERK
BERT
BERTH
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BERTHS
BERTIE
BERTRAM
BERTRAND
BERWICK
BERYL
BERYLLIUM
BESEECH
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BESEECHING
BESET
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BESETTING
BESIDE
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BESIEGE
BESIEGED
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BESIEGING
BESMIRCH
BESMIRCHED
BESMIRCHES
BESMIRCHING
BESOTTED
BESOTTER
BESOTTING
BESOUGHT
BESPEAK
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BESPECTACLED
BESPOKE
BESS
BESSEL
BESSEMER
BESSEMERIZE
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BESSIE
BEST
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BESTIAL
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BESTIR
BESTIRRING
BESTOW
BESTOWAL
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BET
BETA
BETATRON
BETEL
BETELGEUSE
BETHESDA
BETHLEHEM
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BETSY
BETTE
BETTER
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BETTERS
BETTIES
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BETWIXT
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BEVY
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BEWARE
BEWHISKERED
BEWILDER
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BEWILDERINGLY
BEWILDERMENT
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BEWITCH
BEWITCHED
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BEWITCHING
BEYOND
BHUTAN
BIALYSTOK
BIANCO
BIANNUAL
BIAS
BIASED
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BIASING
BIB
BIBBED
BIBBING
BIBLE
BIBLES
BIBLICAL
BIBLICALLY
BIBLIOGRAPHIC
BIBLIOGRAPHICAL
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BIBLIOGRAPHY
BIBLIOPHILE
BIBS
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BICENTENNIAL
BICEP
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BICKERING
BICKERS
BICONCAVE
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BICONVEX
BICYCLE
BICYCLED
BICYCLER
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BICYCLING
BID
BIDDABLE
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BIDDER
BIDDERS
BIDDIES
BIDDING
BIDDLE
BIDDY
BIDE
BIDIRECTIONAL
BIDS
BIEN
BIENNIAL
BIENNIUM
BIENVILLE
BIER
BIERCE
BIFOCAL
BIFOCALS
BIFURCATE
BIG
BIGELOW
BIGGER
BIGGEST
BIGGS
BIGHT
BIGHTS
BIGNESS
BIGOT
BIGOTED
BIGOTRY
BIGOTS
BIHARMONIC
BIJECTION
BIJECTIONS
BIJECTIVE
BIJECTIVELY
BIKE
BIKES
BIKING
BIKINI
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BILABIAL
BILATERAL
BILATERALLY
BILBAO
BILBO
BILE
BILGE
BILGES
BILINEAR
BILINGUAL
BILK
BILKED
BILKING
BILKS
BILL
BILLBOARD
BILLBOARDS
BILLED
BILLER
BILLERS
BILLET
BILLETED
BILLETING
BILLETS
BILLIARD
BILLIARDS
BILLIE
BILLIKEN
BILLIKENS
BILLING
BILLINGS
BILLION
BILLIONS
BILLIONTH
BILLOW
BILLOWED
BILLOWS
BILLS
BILTMORE
BIMETALLIC
BIMETALLISM
BIMINI
BIMODAL
BIMOLECULAR
BIMONTHLIES
BIMONTHLY
BIN
BINARIES
BINARY
BINAURAL
BIND
BINDER
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BINDING
BINDINGS
BINDS
BING
BINGE
BINGES
BINGHAM
BINGHAMTON
BINGO
BINI
BINOCULAR
BINOCULARS
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BINS
BINUCLEAR
BIOCHEMICAL
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BIOGRAPHER
BIOGRAPHERS
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BIOGRAPHICALLY
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BIOGRAPHY
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BIOLOGISTS
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BIOTA
BIOTIC
BIPARTISAN
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BIPEDS
BIPLANE
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BIRCH
BIRCHEN
BIRCHES
BIRD
BIRDBATH
BIRDBATHS
BIRDIE
BIRDIED
BIRDIES
BIRDLIKE
BIRDS
BIREFRINGENCE
BIREFRINGENT
BIRGIT
BIRMINGHAM
BIRMINGHAMIZE
BIRMINGHAMIZES
BIRTH
BIRTHDAY
BIRTHDAYS
BIRTHED
BIRTHPLACE
BIRTHPLACES
BIRTHRIGHT
BIRTHRIGHTS
BIRTHS
BISCAYNE
BISCUIT
BISCUITS
BISECT
BISECTED
BISECTING
BISECTION
BISECTIONS
BISECTOR
BISECTORS
BISECTS
BISHOP
BISHOPS
BISMARCK
BISMARK
BISMUTH
BISON
BISONS
BISQUE
BISQUES
BISSAU
BISTABLE
BISTATE
BIT
BITCH
BITCHES
BITE
BITER
BITERS
BITES
BITING
BITINGLY
BITMAP
BITNET
BITS
BITTEN
BITTER
BITTERER
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BITTERLY
BITTERNESS
BITTERNUT
BITTERROOT
BITTERS
BITTERSWEET
BITUMEN
BITUMINOUS
BITWISE
BIVALVE
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BIVARIATE
BIVOUAC
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BIWEEKLY
BIZARRE
BIZET
BLAB
BLABBED
BLABBERMOUTH
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BLABBING
BLABS
BLACK
BLACKBERRIES
BLACKBERRY
BLACKBIRD
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BLACKBURN
BLACKED
BLACKEN
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BLACKER
BLACKEST
BLACKFEET
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BLACKING
BLACKJACK
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BLACKLIST
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BLACKLY
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BLACKMAN
BLACKMER
BLACKNESS
BLACKOUT
BLACKOUTS
BLACKS
BLACKSMITH
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BLACKSTONE
BLACKWELL
BLACKWELLS
BLADDER
BLADDERS
BLADE
BLADES
BLAINE
BLAIR
BLAKE
BLAKEY
BLAMABLE
BLAME
BLAMED
BLAMELESS
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BLAMER
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BLAMES
BLAMEWORTHY
BLAMING
BLANCH
BLANCHARD
BLANCHE
BLANCHED
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BLANCHING
BLAND
BLANDLY
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BLANK
BLANKED
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BLANKET
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BLARE
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BLASE
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BLASPHEMING
BLASPHEMOUS
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BLASPHEMY
BLAST
BLASTED
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BLATANT
BLATANTLY
BLATZ
BLAZE
BLAZED
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BLAZING
BLEACH
BLEACHED
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BLEAT
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BLED
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BLEEDER
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BLESS
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BLIGHT
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BLINKED
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BLITHELY
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BLITZKRIEG
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BLOATER
BLOATING
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BLOB
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BLOC
BLOCH
BLOCK
BLOCKADE
BLOCKADED
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BLOCKADING
BLOCKAGE
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BLOCKER
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BLOCKHOUSE
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BLOCKING
BLOCKS
BLOCS
BLOKE
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BLOMBERG
BLOMQUIST
BLOND
BLONDE
BLONDES
BLONDS
BLOOD
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BLOODIED
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BLOODSHED
BLOODSHOT
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BLOODSTREAM
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BLOOM
BLOOMED
BLOOMERS
BLOOMFIELD
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BLOOMINGTON
BLOOMS
BLOOPER
BLOSSOM
BLOSSOMED
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BLOT
BLOTS
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BLOUSE
BLOUSES
BLOW
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BLOWFISH
BLOWING
BLOWN
BLOWOUT
BLOWS
BLOWUP
BLUBBER
BLUDGEON
BLUDGEONED
BLUDGEONING
BLUDGEONS
BLUE
BLUEBERRIES
BLUEBERRY
BLUEBIRD
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BLUEBONNETS
BLUEFISH
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BLUEPRINT
BLUEPRINTS
BLUER
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BLUEST
BLUESTOCKING
BLUFF
BLUFFING
BLUFFS
BLUING
BLUISH
BLUM
BLUMENTHAL
BLUNDER
BLUNDERBUSS
BLUNDERED
BLUNDERING
BLUNDERINGS
BLUNDERS
BLUNT
BLUNTED
BLUNTER
BLUNTEST
BLUNTING
BLUNTLY
BLUNTNESS
BLUNTS
BLUR
BLURB
BLURRED
BLURRING
BLURRY
BLURS
BLURT
BLURTED
BLURTING
BLURTS
BLUSH
BLUSHED
BLUSHES
BLUSHING
BLUSTER
BLUSTERED
BLUSTERING
BLUSTERS
BLUSTERY
BLYTHE
BOA
BOAR
BOARD
BOARDED
BOARDER
BOARDERS
BOARDING
BOARDINGHOUSE
BOARDINGHOUSES
BOARDS
BOARSH
BOAST
BOASTED
BOASTER
BOASTERS
BOASTFUL
BOASTFULLY
BOASTING
BOASTINGS
BOASTS
BOAT
BOATER
BOATERS
BOATHOUSE
BOATHOUSES
BOATING
BOATLOAD
BOATLOADS
BOATMAN
BOATMEN
BOATS
BOATSMAN
BOATSMEN
BOATSWAIN
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BOATYARD
BOATYARDS
BOB
BOBBED
BOBBIE
BOBBIN
BOBBING
BOBBINS
BOBBSEY
BOBBY
BOBOLINK
BOBOLINKS
BOBROW
BOBS
BOBWHITE
BOBWHITES
BOCA
BODE
BODENHEIM
BODES
BODICE
BODIED
BODIES
BODILY
BODLEIAN
BODY
BODYBUILDER
BODYBUILDERS
BODYBUILDING
BODYGUARD
BODYGUARDS
BODYWEIGHT
BOEING
BOEOTIA
BOEOTIAN
BOER
BOERS
BOG
BOGART
BOGARTIAN
BOGEYMEN
BOGGED
BOGGLE
BOGGLED
BOGGLES
BOGGLING
BOGOTA
BOGS
BOGUS
BOHEME
BOHEMIA
BOHEMIAN
BOHEMIANISM
BOHR
BOIL
BOILED
BOILER
BOILERPLATE
BOILERS
BOILING
BOILS
BOIS
BOISE
BOISTEROUS
BOISTEROUSLY
BOLD
BOLDER
BOLDEST
BOLDFACE
BOLDLY
BOLDNESS
BOLIVIA
BOLIVIAN
BOLL
BOLOGNA
BOLSHEVIK
BOLSHEVIKS
BOLSHEVISM
BOLSHEVIST
BOLSHEVISTIC
BOLSHOI
BOLSTER
BOLSTERED
BOLSTERING
BOLSTERS
BOLT
BOLTED
BOLTING
BOLTON
BOLTS
BOLTZMANN
BOMB
BOMBARD
BOMBARDED
BOMBARDING
BOMBARDMENT
BOMBARDS
BOMBAST
BOMBASTIC
BOMBAY
BOMBED
BOMBER
BOMBERS
BOMBING
BOMBINGS
BOMBPROOF
BOMBS
BONANZA
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BONAPARTE
BONAVENTURE
BOND
BONDAGE
BONDED
BONDER
BONDERS
BONDING
BONDS
BONDSMAN
BONDSMEN
BONE
BONED
BONER
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BONES
BONFIRE
BONFIRES
BONG
BONHAM
BONIFACE
BONING
BONN
BONNET
BONNETED
BONNETS
BONNEVILLE
BONNIE
BONNY
BONTEMPO
BONUS
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BONY
BOO
BOOB
BOOBOO
BOOBY
BOOK
BOOKCASE
BOOKCASES
BOOKED
BOOKER
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BOOKIE
BOOKIES
BOOKING
BOOKINGS
BOOKISH
BOOKKEEPER
BOOKKEEPERS
BOOKKEEPING
BOOKLET
BOOKLETS
BOOKMARK
BOOKS
BOOKSELLER
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BOOKSHELF
BOOKSHELVES
BOOKSTORE
BOOKSTORES
BOOKWORM
BOOLEAN
BOOLEANS
BOOM
BOOMED
BOOMERANG
BOOMERANGS
BOOMING
BOOMS
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BOONE
BOONTON
BOOR
BOORISH
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BOOS
BOOST
BOOSTED
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BOOSTING
BOOSTS
BOOT
BOOTABLE
BOOTED
BOOTES
BOOTH
BOOTHS
BOOTING
BOOTLE
BOOTLEG
BOOTLEGGED
BOOTLEGGER
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BOOTLEGGING
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BOOTS
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BOOTSTRAPPED
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BOOTY
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BORAX
BORDEAUX
BORDELLO
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BORDERED
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BORDERLAND
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BORDERLINE
BORDERS
BORE
BOREALIS
BOREAS
BORED
BOREDOM
BORER
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BORG
BORIC
BORING
BORIS
BORN
BORNE
BORNEO
BORON
BOROUGH
BOROUGHS
BORROUGHS
BORROW
BORROWED
BORROWER
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BOSE
BOSOM
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BOSPORUS
BOSS
BOSSED
BOSSES
BOSTITCH
BOSTON
BOSTONIAN
BOSTONIANS
BOSUN
BOSWELL
BOSWELLIZE
BOSWELLIZES
BOTANICAL
BOTANIST
BOTANISTS
BOTANY
BOTCH
BOTCHED
BOTCHER
BOTCHERS
BOTCHES
BOTCHING
BOTH
BOTHER
BOTHERED
BOTHERING
BOTHERS
BOTHERSOME
BOTSWANA
BOTTLE
BOTTLED
BOTTLENECK
BOTTLENECKS
BOTTLER
BOTTLERS
BOTTLES
BOTTLING
BOTTOM
BOTTOMED
BOTTOMING
BOTTOMLESS
BOTTOMS
BOTULINUS
BOTULISM
BOUCHER
BOUFFANT
BOUGH
BOUGHS
BOUGHT
BOULDER
BOULDERS
BOULEVARD
BOULEVARDS
BOUNCE
BOUNCED
BOUNCER
BOUNCES
BOUNCING
BOUNCY
BOUND
BOUNDARIES
BOUNDARY
BOUNDED
BOUNDEN
BOUNDING
BOUNDLESS
BOUNDLESSNESS
BOUNDS
BOUNTEOUS
BOUNTEOUSLY
BOUNTIES
BOUNTIFUL
BOUNTY
BOUQUET
BOUQUETS
BOURBAKI
BOURBON
BOURGEOIS
BOURGEOISIE
BOURNE
BOUSTROPHEDON
BOUSTROPHEDONIC
BOUT
BOUTIQUE
BOUTS
BOUVIER
BOVINE
BOVINES
BOW
BOWDITCH
BOWDLERIZE
BOWDLERIZED
BOWDLERIZES
BOWDLERIZING
BOWDOIN
BOWED
BOWEL
BOWELS
BOWEN
BOWER
BOWERS
BOWES
BOWING
BOWL
BOWLED
BOWLER
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BOWLINE
BOWLINES
BOWLING
BOWLS
BOWMAN
BOWS
BOWSTRING
BOWSTRINGS
BOX
BOXCAR
BOXCARS
BOXED
BOXER
BOXERS
BOXES
BOXFORD
BOXING
BOXTOP
BOXTOPS
BOXWOOD
BOY
BOYCE
BOYCOTT
BOYCOTTED
BOYCOTTS
BOYD
BOYFRIEND
BOYFRIENDS
BOYHOOD
BOYISH
BOYISHNESS
BOYLE
BOYLSTON
BOYS
BRA
BRACE
BRACED
BRACELET
BRACELETS
BRACES
BRACING
BRACKET
BRACKETED
BRACKETING
BRACKETS
BRACKISH
BRADBURY
BRADFORD
BRADLEY
BRADSHAW
BRADY
BRAE
BRAES
BRAG
BRAGG
BRAGGED
BRAGGER
BRAGGING
BRAGS
BRAHMAPUTRA
BRAHMS
BRAHMSIAN
BRAID
BRAIDED
BRAIDING
BRAIDS
BRAILLE
BRAIN
BRAINARD
BRAINARDS
BRAINCHILD
BRAINED
BRAINING
BRAINS
BRAINSTEM
BRAINSTEMS
BRAINSTORM
BRAINSTORMS
BRAINWASH
BRAINWASHED
BRAINWASHES
BRAINWASHING
BRAINY
BRAKE
BRAKED
BRAKEMAN
BRAKES
BRAKING
BRAMBLE
BRAMBLES
BRAMBLY
BRAN
BRANCH
BRANCHED
BRANCHES
BRANCHING
BRANCHINGS
BRANCHVILLE
BRAND
BRANDED
BRANDEIS
BRANDEL
BRANDENBURG
BRANDING
BRANDISH
BRANDISHES
BRANDISHING
BRANDON
BRANDS
BRANDT
BRANDY
BRANDYWINE
BRANIFF
BRANNON
BRAS
BRASH
BRASHLY
BRASHNESS
BRASILIA
BRASS
BRASSES
BRASSIERE
BRASSTOWN
BRASSY
BRAT
BRATS
BRAUN
BRAVADO
BRAVE
BRAVED
BRAVELY
BRAVENESS
BRAVER
BRAVERY
BRAVES
BRAVEST
BRAVING
BRAVO
BRAVOS
BRAWL
BRAWLER
BRAWLING
BRAWN
BRAY
BRAYED
BRAYER
BRAYING
BRAYS
BRAZE
BRAZED
BRAZEN
BRAZENLY
BRAZENNESS
BRAZES
BRAZIER
BRAZIERS
BRAZIL
BRAZILIAN
BRAZING
BRAZZAVILLE
BREACH
BREACHED
BREACHER
BREACHERS
BREACHES
BREACHING
BREAD
BREADBOARD
BREADBOARDS
BREADBOX
BREADBOXES
BREADED
BREADING
BREADS
BREADTH
BREADWINNER
BREADWINNERS
BREAK
BREAKABLE
BREAKABLES
BREAKAGE
BREAKAWAY
BREAKDOWN
BREAKDOWNS
BREAKER
BREAKERS
BREAKFAST
BREAKFASTED
BREAKFASTER
BREAKFASTERS
BREAKFASTING
BREAKFASTS
BREAKING
BREAKPOINT
BREAKPOINTS
BREAKS
BREAKTHROUGH
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BREAKTHROUGHS
BREAKUP
BREAKWATER
BREAKWATERS
BREAST
BREASTED
BREASTS
BREASTWORK
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BREATH
BREATHABLE
BREATHE
BREATHED
BREATHER
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BREATHES
BREATHING
BREATHLESS
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BREATHS
BREATHTAKING
BREATHTAKINGLY
BREATHY
BRED
BREECH
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BREEDER
BREEDING
BREEDS
BREEZE
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BREEZILY
BREEZY
BREMEN
BREMSSTRAHLUNG
BRENDA
BRENDAN
BRENNAN
BRENNER
BRENT
BRESENHAM
BREST
BRETHREN
BRETON
BRETONS
BRETT
BREVE
BREVET
BREVETED
BREVETING
BREVETS
BREVITY
BREW
BREWED
BREWER
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BREWERS
BREWERY
BREWING
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BREWSTER
BRIAN
BRIAR
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BRIBE
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BRIBERY
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BRIBING
BRICE
BRICK
BRICKBAT
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BRICKER
BRICKLAYER
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BRICKLAYING
BRICKS
BRIDAL
BRIDE
BRIDEGROOM
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BRIDESMAID
BRIDESMAIDS
BRIDEWELL
BRIDGE
BRIDGEABLE
BRIDGED
BRIDGEHEAD
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BRIDGEPORT
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BRIDGET
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BRIDGEWATER
BRIDGEWORK
BRIDGING
BRIDLE
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BRIDLING
BRIE
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BRIEFED
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BRIEFLY
BRIEFNESS
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BRIEN
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BRIG
BRIGADE
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BRIGADIER
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BRIGADOON
BRIGANTINE
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BRIGHT
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BRIGHTENED
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BRIGHTENS
BRIGHTER
BRIGHTEST
BRIGHTLY
BRIGHTNESS
BRIGHTON
BRIGS
BRILLIANCE
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BRILLIANT
BRILLIANTLY
BRILLOUIN
BRIM
BRIMFUL
BRIMMED
BRIMMING
BRIMSTONE
BRINDISI
BRINDLE
BRINDLED
BRINE
BRING
BRINGER
BRINGERS
BRINGING
BRINGS
BRINK
BRINKLEY
BRINKMANSHIP
BRINY
BRISBANE
BRISK
BRISKER
BRISKLY
BRISKNESS
BRISTLE
BRISTLED
BRISTLES
BRISTLING
BRISTOL
BRITAIN
BRITANNIC
BRITANNICA
BRITCHES
BRITISH
BRITISHER
BRITISHLY
BRITON
BRITONS
BRITTANY
BRITTEN
BRITTLE
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BROACH
BROACHED
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BROACHING
BROAD
BROADBAND
BROADCAST
BROADCASTED
BROADCASTER
BROADCASTERS
BROADCASTING
BROADCASTINGS
BROADCASTS
BROADEN
BROADENED
BROADENER
BROADENERS
BROADENING
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BROADENS
BROADER
BROADEST
BROADLY
BROADNESS
BROADSIDE
BROADWAY
BROCADE
BROCADED
BROCCOLI
BROCHURE
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BROCK
BROGLIE
BROIL
BROILED
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BROILING
BROILS
BROKE
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BROKENLY
BROKENNESS
BROKER
BROKERAGE
BROKERS
BROMFIELD
BROMIDE
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BROMINE
BROMLEY
BRONCHI
BRONCHIAL
BRONCHIOLE
BRONCHIOLES
BRONCHITIS
BRONCHUS
BRONTOSAURUS
BRONX
BRONZE
BRONZED
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BROOCH
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BROOD
BROODER
BROODING
BROODS
BROOK
BROOKDALE
BROOKE
BROOKED
BROOKFIELD
BROOKHAVEN
BROOKLINE
BROOKLYN
BROOKMONT
BROOKS
BROOM
BROOMS
BROOMSTICK
BROOMSTICKS
BROTH
BROTHEL
BROTHELS
BROTHER
BROTHERHOOD
BROTHERLINESS
BROTHERLY
BROTHERS
BROUGHT
BROW
BROWBEAT
BROWBEATEN
BROWBEATING
BROWBEATS
BROWN
BROWNE
BROWNED
BROWNELL
BROWNER
BROWNEST
BROWNIAN
BROWNIE
BROWNIES
BROWNING
BROWNISH
BROWNNESS
BROWNS
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BROWSE
BROWSING
BRUCE
BRUCKNER
BRUEGEL
BRUISE
BRUISED
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BRUISING
BRUMIDI
BRUNCH
BRUNCHES
BRUNETTE
BRUNHILDE
BRUNO
BRUNSWICK
BRUNT
BRUSH
BRUSHED
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BRUSHFIRE
BRUSHFIRES
BRUSHING
BRUSHLIKE
BRUSHY
BRUSQUE
BRUSQUELY
BRUSSELS
BRUTAL
BRUTALITIES
BRUTALITY
BRUTALIZE
BRUTALIZED
BRUTALIZES
BRUTALIZING
BRUTALLY
BRUTE
BRUTES
BRUTISH
BRUXELLES
BRYAN
BRYANT
BRYCE
BRYN
BUBBLE
BUBBLED
BUBBLES
BUBBLING
BUBBLY
BUCHANAN
BUCHAREST
BUCHENWALD
BUCHWALD
BUCK
BUCKBOARD
BUCKBOARDS
BUCKED
BUCKET
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BUCKING
BUCKLE
BUCKLED
BUCKLER
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BUCKLEY
BUCKLING
BUCKNELL
BUCKS
BUCKSHOT
BUCKSKIN
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BUCKWHEAT
BUCKY
BUCOLIC
BUD
BUDAPEST
BUDD
BUDDED
BUDDHA
BUDDHISM
BUDDHIST
BUDDHISTS
BUDDIES
BUDDING
BUDDY
BUDGE
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BUDGET
BUDGETARY
BUDGETED
BUDGETER
BUDGETERS
BUDGETING
BUDGETS
BUDGING
BUDS
BUDWEISER
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BUEHRING
BUENA
BUENOS
BUFF
BUFFALO
BUFFALOES
BUFFER
BUFFERED
BUFFERING
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BUFFET
BUFFETED
BUFFETING
BUFFETINGS
BUFFETS
BUFFOON
BUFFOONS
BUFFS
BUG
BUGABOO
BUGATTI
BUGEYED
BUGGED
BUGGER
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BUGGIES
BUGGING
BUGGY
BUGLE
BUGLED
BUGLER
BUGLES
BUGLING
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BUICK
BUILD
BUILDER
BUILDERS
BUILDING
BUILDINGS
BUILDS
BUILDUP
BUILDUPS
BUILT
BUILTIN
BUJUMBURA
BULB
BULBA
BULBS
BULGARIA
BULGARIAN
BULGE
BULGED
BULGING
BULK
BULKED
BULKHEAD
BULKHEADS
BULKS
BULKY
BULL
BULLDOG
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BULLDOZE
BULLDOZED
BULLDOZER
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BULLDOZING
BULLED
BULLET
BULLETIN
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BULLETS
BULLFROG
BULLIED
BULLIES
BULLING
BULLION
BULLISH
BULLOCK
BULLS
BULLSEYE
BULLY
BULLYING
BULWARK
BUM
BUMBLE
BUMBLEBEE
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BUMBLED
BUMBLER
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BUMBLING
BUMBRY
BUMMED
BUMMING
BUMP
BUMPED
BUMPER
BUMPERS
BUMPING
BUMPS
BUMPTIOUS
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BUMPTIOUSNESS
BUMS
BUN
BUNCH
BUNCHED
BUNCHES
BUNCHING
BUNDESTAG
BUNDLE
BUNDLED
BUNDLES
BUNDLING
BUNDOORA
BUNDY
BUNGALOW
BUNGALOWS
BUNGLE
BUNGLED
BUNGLER
BUNGLERS
BUNGLES
BUNGLING
BUNION
BUNIONS
BUNK
BUNKER
BUNKERED
BUNKERS
BUNKHOUSE
BUNKHOUSES
BUNKMATE
BUNKMATES
BUNKS
BUNNIES
BUNNY
BUNS
BUNSEN
BUNT
BUNTED
BUNTER
BUNTERS
BUNTING
BUNTS
BUNYAN
BUOY
BUOYANCY
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BURBANK
BURCH
BURDEN
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BURDENING
BURDENS
BURDENSOME
BUREAU
BUREAUCRACIES
BUREAUCRACY
BUREAUCRAT
BUREAUCRATIC
BUREAUCRATS
BUREAUS
BURGEON
BURGEONED
BURGEONING
BURGESS
BURGESSES
BURGHER
BURGHERS
BURGLAR
BURGLARIES
BURGLARIZE
BURGLARIZED
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BURGLARIZING
BURGLARPROOF
BURGLARPROOFED
BURGLARPROOFING
BURGLARPROOFS
BURGLARS
BURGLARY
BURGUNDIAN
BURGUNDIES
BURGUNDY
BURIAL
BURIED
BURIES
BURKE
BURKES
BURL
BURLESQUE
BURLESQUES
BURLINGAME
BURLINGTON
BURLY
BURMA
BURMESE
BURN
BURNE
BURNED
BURNER
BURNERS
BURNES
BURNETT
BURNHAM
BURNING
BURNINGLY
BURNINGS
BURNISH
BURNISHED
BURNISHES
BURNISHING
BURNS
BURNSIDE
BURNSIDES
BURNT
BURNTLY
BURNTNESS
BURP
BURPED
BURPING
BURPS
BURR
BURROUGHS
BURROW
BURROWED
BURROWER
BURROWING
BURROWS
BURRS
BURSA
BURSITIS
BURST
BURSTINESS
BURSTING
BURSTS
BURSTY
BURT
BURTON
BURTT
BURUNDI
BURY
BURYING
BUS
BUSBOY
BUSBOYS
BUSCH
BUSED
BUSES
BUSH
BUSHEL
BUSHELS
BUSHES
BUSHING
BUSHNELL
BUSHWHACK
BUSHWHACKED
BUSHWHACKING
BUSHWHACKS
BUSHY
BUSIED
BUSIER
BUSIEST
BUSILY
BUSINESS
BUSINESSES
BUSINESSLIKE
BUSINESSMAN
BUSINESSMEN
BUSING
BUSS
BUSSED
BUSSES
BUSSING
BUST
BUSTARD
BUSTARDS
BUSTED
BUSTER
BUSTLE
BUSTLING
BUSTS
BUSY
BUT
BUTANE
BUTCHER
BUTCHERED
BUTCHERS
BUTCHERY
BUTLER
BUTLERS
BUTT
BUTTE
BUTTED
BUTTER
BUTTERBALL
BUTTERCUP
BUTTERED
BUTTERER
BUTTERERS
BUTTERFAT
BUTTERFIELD
BUTTERFLIES
BUTTERFLY
BUTTERING
BUTTERMILK
BUTTERNUT
BUTTERS
BUTTERY
BUTTES
BUTTING
BUTTOCK
BUTTOCKS
BUTTON
BUTTONED
BUTTONHOLE
BUTTONHOLES
BUTTONING
BUTTONS
BUTTRESS
BUTTRESSED
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BUTTRESSING
BUTTRICK
BUTTS
BUTYL
BUTYRATE
BUXOM
BUXTEHUDE
BUXTON
BUY
BUYER
BUYERS
BUYING
BUYS
BUZZ
BUZZARD
BUZZARDS
BUZZED
BUZZER
BUZZES
BUZZING
BUZZWORD
BUZZWORDS
BUZZY
BYE
BYERS
BYGONE
BYLAW
BYLAWS
BYLINE
BYLINES
BYPASS
BYPASSED
BYPASSES
BYPASSING
BYPRODUCT
BYPRODUCTS
BYRD
BYRNE
BYRON
BYRONIC
BYRONISM
BYRONIZE
BYRONIZES
BYSTANDER
BYSTANDERS
BYTE
BYTES
BYWAY
BYWAYS
BYWORD
BYWORDS
BYZANTINE
BYZANTINIZE
BYZANTINIZES
BYZANTIUM
CAB
CABAL
CABANA
CABARET
CABBAGE
CABBAGES
CABDRIVER
CABIN
CABINET
CABINETS
CABINS
CABLE
CABLED
CABLES
CABLING
CABOOSE
CABOT
CABS
CACHE
CACHED
CACHES
CACHING
CACKLE
CACKLED
CACKLER
CACKLES
CACKLING
CACTI
CACTUS
CADAVER
CADENCE
CADENCED
CADILLAC
CADILLACS
CADRES
CADY
CAESAR
CAESARIAN
CAESARIZE
CAESARIZES
CAFE
CAFES
CAFETERIA
CAGE
CAGED
CAGER
CAGERS
CAGES
CAGING
CAHILL
CAIMAN
CAIN
CAINE
CAIRN
CAIRO
CAJOLE
CAJOLED
CAJOLES
CAJOLING
CAJUN
CAJUNS
CAKE
CAKED
CAKES
CAKING
CALAIS
CALAMITIES
CALAMITOUS
CALAMITY
CALCEOLARIA
CALCIFY
CALCIUM
CALCOMP
CALCOMP
CALCOMP
CALCULATE
CALCULATED
CALCULATES
CALCULATING
CALCULATION
CALCULATIONS
CALCULATIVE
CALCULATOR
CALCULATORS
CALCULI
CALCULUS
CALCUTTA
CALDER
CALDERA
CALDWELL
CALEB
CALENDAR
CALENDARS
CALF
CALFSKIN
CALGARY
CALHOUN
CALIBER
CALIBERS
CALIBRATE
CALIBRATED
CALIBRATES
CALIBRATING
CALIBRATION
CALIBRATIONS
CALICO
CALIFORNIA
CALIFORNIAN
CALIFORNIANS
CALIGULA
CALIPH
CALIPHS
CALKINS
CALL
CALLABLE
CALLAGHAN
CALLAHAN
CALLAN
CALLED
CALLER
CALLERS
CALLING
CALLIOPE
CALLISTO
CALLOUS
CALLOUSED
CALLOUSLY
CALLOUSNESS
CALLS
CALLUS
CALM
CALMED
CALMER
CALMEST
CALMING
CALMINGLY
CALMLY
CALMNESS
CALMS
CALORIC
CALORIE
CALORIES
CALORIMETER
CALORIMETRIC
CALORIMETRY
CALTECH
CALUMNY
CALVARY
CALVE
CALVERT
CALVES
CALVIN
CALVINIST
CALVINIZE
CALVINIZES
CALYPSO
CAM
CAMBODIA
CAMBRIAN
CAMBRIDGE
CAMDEN
CAME
CAMEL
CAMELOT
CAMELS
CAMEMBERT
CAMERA
CAMERAMAN
CAMERAMEN
CAMERAS
CAMERON
CAMEROON
CAMEROUN
CAMILLA
CAMILLE
CAMINO
CAMOUFLAGE
CAMOUFLAGED
CAMOUFLAGES
CAMOUFLAGING
CAMP
CAMPAIGN
CAMPAIGNED
CAMPAIGNER
CAMPAIGNERS
CAMPAIGNING
CAMPAIGNS
CAMPBELL
CAMPBELLSPORT
CAMPED
CAMPER
CAMPERS
CAMPFIRE
CAMPGROUND
CAMPING
CAMPS
CAMPSITE
CAMPUS
CAMPUSES
CAN
CANAAN
CANADA
CANADIAN
CANADIANIZATION
CANADIANIZATIONS
CANADIANIZE
CANADIANIZES
CANADIANS
CANAL
CANALS
CANARIES
CANARY
CANAVERAL
CANBERRA
CANCEL
CANCELED
CANCELING
CANCELLATION
CANCELLATIONS
CANCELS
CANCER
CANCEROUS
CANCERS
CANDACE
CANDID
CANDIDACY
CANDIDATE
CANDIDATES
CANDIDE
CANDIDLY
CANDIDNESS
CANDIED
CANDIES
CANDLE
CANDLELIGHT
CANDLER
CANDLES
CANDLESTICK
CANDLESTICKS
CANDLEWICK
CANDOR
CANDY
CANE
CANER
CANFIELD
CANINE
CANIS
CANISTER
CANKER
CANKERWORM
CANNABIS
CANNED
CANNEL
CANNER
CANNERS
CANNERY
CANNIBAL
CANNIBALIZE
CANNIBALIZED
CANNIBALIZES
CANNIBALIZING
CANNIBALS
CANNING
CANNISTER
CANNISTERS
CANNON
CANNONBALL
CANNONS
CANNOT
CANNY
CANOE
CANOES
CANOGA
CANON
CANONIC
CANONICAL
CANONICALIZATION
CANONICALIZE
CANONICALIZED
CANONICALIZES
CANONICALIZING
CANONICALLY
CANONICALS
CANONS
CANOPUS
CANOPY
CANS
CANT
CANTABRIGIAN
CANTALOUPE
CANTANKEROUS
CANTANKEROUSLY
CANTEEN
CANTERBURY
CANTILEVER
CANTO
CANTON
CANTONESE
CANTONS
CANTOR
CANTORS
CANUTE
CANVAS
CANVASES
CANVASS
CANVASSED
CANVASSER
CANVASSERS
CANVASSES
CANVASSING
CANYON
CANYONS
CAP
CAPABILITIES
CAPABILITY
CAPABLE
CAPABLY
CAPACIOUS
CAPACIOUSLY
CAPACIOUSNESS
CAPACITANCE
CAPACITANCES
CAPACITIES
CAPACITIVE
CAPACITOR
CAPACITORS
CAPACITY
CAPE
CAPER
CAPERS
CAPES
CAPET
CAPETOWN
CAPILLARY
CAPISTRANO
CAPITA
CAPITAL
CAPITALISM
CAPITALIST
CAPITALISTS
CAPITALIZATION
CAPITALIZATIONS
CAPITALIZE
CAPITALIZED
CAPITALIZER
CAPITALIZERS
CAPITALIZES
CAPITALIZING
CAPITALLY
CAPITALS
CAPITAN
CAPITOL
CAPITOLINE
CAPITOLS
CAPPED
CAPPING
CAPPY
CAPRICE
CAPRICIOUS
CAPRICIOUSLY
CAPRICIOUSNESS
CAPRICORN
CAPS
CAPSICUM
CAPSTAN
CAPSTONE
CAPSULE
CAPTAIN
CAPTAINED
CAPTAINING
CAPTAINS
CAPTION
CAPTIONS
CAPTIVATE
CAPTIVATED
CAPTIVATES
CAPTIVATING
CAPTIVATION
CAPTIVE
CAPTIVES
CAPTIVITY
CAPTOR
CAPTORS
CAPTURE
CAPTURED
CAPTURER
CAPTURERS
CAPTURES
CAPTURING
CAPUTO
CAPYBARA
CAR
CARACAS
CARAMEL
CARAVAN
CARAVANS
CARAWAY
CARBOHYDRATE
CARBOLIC
CARBOLOY
CARBON
CARBONATE
CARBONATES
CARBONATION
CARBONDALE
CARBONE
CARBONES
CARBONIC
CARBONIZATION
CARBONIZE
CARBONIZED
CARBONIZER
CARBONIZERS
CARBONIZES
CARBONIZING
CARBONS
CARBORUNDUM
CARBUNCLE
CARCASS
CARCASSES
CARCINOGEN
CARCINOGENIC
CARCINOMA
CARD
CARDBOARD
CARDER
CARDIAC
CARDIFF
CARDINAL
CARDINALITIES
CARDINALITY
CARDINALLY
CARDINALS
CARDIOD
CARDIOLOGY
CARDIOVASCULAR
CARDS
CARE
CARED
CAREEN
CAREER
CAREERS
CAREFREE
CAREFUL
CAREFULLY
CAREFULNESS
CARELESS
CARELESSLY
CARELESSNESS
CARES
CARESS
CARESSED
CARESSER
CARESSES
CARESSING
CARET
CARETAKER
CAREY
CARGILL
CARGO
CARGOES
CARIB
CARIBBEAN
CARIBOU
CARICATURE
CARING
CARL
CARLA
CARLETON
CARLETONIAN
CARLIN
CARLISLE
CARLO
CARLOAD
CARLSBAD
CARLSBADS
CARLSON
CARLTON
CARLYLE
CARMELA
CARMEN
CARMICHAEL
CARNAGE
CARNAL
CARNATION
CARNEGIE
CARNIVAL
CARNIVALS
CARNIVOROUS
CARNIVOROUSLY
CAROL
CAROLINA
CAROLINAS
CAROLINE
CAROLINGIAN
CAROLINIAN
CAROLINIANS
CAROLS
CAROLYN
CARP
CARPATHIA
CARPATHIANS
CARPENTER
CARPENTERS
CARPENTRY
CARPET
CARPETED
CARPETING
CARPETS
CARPORT
CARR
CARRARA
CARRIAGE
CARRIAGES
CARRIE
CARRIED
CARRIER
CARRIERS
CARRIES
CARRION
CARROLL
CARROT
CARROTS
CARRUTHERS
CARRY
CARRYING
CARRYOVER
CARRYOVERS
CARS
CARSON
CART
CARTED
CARTEL
CARTER
CARTERS
CARTESIAN
CARTHAGE
CARTHAGINIAN
CARTILAGE
CARTING
CARTOGRAPHER
CARTOGRAPHIC
CARTOGRAPHY
CARTON
CARTONS
CARTOON
CARTOONS
CARTRIDGE
CARTRIDGES
CARTS
CARTWHEEL
CARTY
CARUSO
CARVE
CARVED
CARVER
CARVES
CARVING
CARVINGS
CASANOVA
CASCADABLE
CASCADE
CASCADED
CASCADES
CASCADING
CASE
CASED
CASEMENT
CASEMENTS
CASES
CASEWORK
CASEY
CASH
CASHED
CASHER
CASHERS
CASHES
CASHEW
CASHIER
CASHIERS
CASHING
CASHMERE
CASING
CASINGS
CASINO
CASK
CASKET
CASKETS
CASKS
CASPIAN
CASSANDRA
CASSEROLE
CASSEROLES
CASSETTE
CASSIOPEIA
CASSITE
CASSITES
CASSIUS
CASSOCK
CAST
CASTE
CASTER
CASTERS
CASTES
CASTIGATE
CASTILLO
CASTING
CASTLE
CASTLED
CASTLES
CASTOR
CASTRO
CASTROISM
CASTS
CASUAL
CASUALLY
CASUALNESS
CASUALS
CASUALTIES
CASUALTY
CAT
CATACLYSMIC
CATALAN
CATALINA
CATALOG
CATALOGED
CATALOGER
CATALOGING
CATALOGS
CATALONIA
CATALYST
CATALYSTS
CATALYTIC
CATAPULT
CATARACT
CATASTROPHE
CATASTROPHES
CATASTROPHIC
CATAWBA
CATCH
CATCHABLE
CATCHER
CATCHERS
CATCHES
CATCHING
CATEGORICAL
CATEGORICALLY
CATEGORIES
CATEGORIZATION
CATEGORIZE
CATEGORIZED
CATEGORIZER
CATEGORIZERS
CATEGORIZES
CATEGORIZING
CATEGORY
CATER
CATERED
CATERER
CATERING
CATERPILLAR
CATERPILLARS
CATERS
CATHEDRAL
CATHEDRALS
CATHERINE
CATHERWOOD
CATHETER
CATHETERS
CATHODE
CATHODES
CATHOLIC
CATHOLICISM
CATHOLICISMS
CATHOLICS
CATHY
CATLIKE
CATNIP
CATS
CATSKILL
CATSKILLS
CATSUP
CATTAIL
CATTLE
CATTLEMAN
CATTLEMEN
CAUCASIAN
CAUCASIANS
CAUCASUS
CAUCHY
CAUCUS
CAUGHT
CAULDRON
CAULDRONS
CAULIFLOWER
CAULK
CAUSAL
CAUSALITY
CAUSALLY
CAUSATION
CAUSATIONS
CAUSE
CAUSED
CAUSER
CAUSES
CAUSEWAY
CAUSEWAYS
CAUSING
CAUSTIC
CAUSTICLY
CAUSTICS
CAUTION
CAUTIONED
CAUTIONER
CAUTIONERS
CAUTIONING
CAUTIONINGS
CAUTIONS
CAUTIOUS
CAUTIOUSLY
CAUTIOUSNESS
CAVALIER
CAVALIERLY
CAVALIERNESS
CAVALRY
CAVE
CAVEAT
CAVEATS
CAVED
CAVEMAN
CAVEMEN
CAVENDISH
CAVERN
CAVERNOUS
CAVERNS
CAVES
CAVIAR
CAVIL
CAVINESS
CAVING
CAVITIES
CAVITY
CAW
CAWING
CAYLEY
CAYUGA
CEASE
CEASED
CEASELESS
CEASELESSLY
CEASELESSNESS
CEASES
CEASING
CECIL
CECILIA
CECROPIA
CEDAR
CEDE
CEDED
CEDING
CEDRIC
CEILING
CEILINGS
CELANESE
CELEBES
CELEBRATE
CELEBRATED
CELEBRATES
CELEBRATING
CELEBRATION
CELEBRATIONS
CELEBRITIES
CELEBRITY
CELERITY
CELERY
CELESTE
CELESTIAL
CELESTIALLY
CELIA
CELL
CELLAR
CELLARS
CELLED
CELLIST
CELLISTS
CELLOPHANE
CELLS
CELLULAR
CELLULOSE
CELSIUS
CELT
CELTIC
CELTICIZE
CELTICIZES
CEMENT
CEMENTED
CEMENTING
CEMENTS
CEMETERIES
CEMETERY
CENOZOIC
CENSOR
CENSORED
CENSORING
CENSORS
CENSORSHIP
CENSURE
CENSURED
CENSURER
CENSURES
CENSUS
CENSUSES
CENT
CENTAUR
CENTENARY
CENTENNIAL
CENTER
CENTERED
CENTERING
CENTERPIECE
CENTERPIECES
CENTERS
CENTIGRADE
CENTIMETER
CENTIMETERS
CENTIPEDE
CENTIPEDES
CENTRAL
CENTRALIA
CENTRALISM
CENTRALIST
CENTRALIZATION
CENTRALIZE
CENTRALIZED
CENTRALIZES
CENTRALIZING
CENTRALLY
CENTREX
CENTREX
CENTRIFUGAL
CENTRIFUGE
CENTRIPETAL
CENTRIST
CENTROID
CENTS
CENTURIES
CENTURY
CEPHEUS
CERAMIC
CERBERUS
CEREAL
CEREALS
CEREBELLUM
CEREBRAL
CEREMONIAL
CEREMONIALLY
CEREMONIALNESS
CEREMONIES
CEREMONY
CERES
CERN
CERTAIN
CERTAINLY
CERTAINTIES
CERTAINTY
CERTIFIABLE
CERTIFICATE
CERTIFICATES
CERTIFICATION
CERTIFICATIONS
CERTIFIED
CERTIFIER
CERTIFIERS
CERTIFIES
CERTIFY
CERTIFYING
CERVANTES
CESARE
CESSATION
CESSATIONS
CESSNA
CETUS
CEYLON
CEZANNE
CEZANNES
CHABLIS
CHABLISES
CHAD
CHADWICK
CHAFE
CHAFER
CHAFF
CHAFFER
CHAFFEY
CHAFFING
CHAFING
CHAGRIN
CHAIN
CHAINED
CHAINING
CHAINS
CHAIR
CHAIRED
CHAIRING
CHAIRLADY
CHAIRMAN
CHAIRMEN
CHAIRPERSON
CHAIRPERSONS
CHAIRS
CHAIRWOMAN
CHAIRWOMEN
CHALICE
CHALICES
CHALK
CHALKED
CHALKING
CHALKS
CHALLENGE
CHALLENGED
CHALLENGER
CHALLENGERS
CHALLENGES
CHALLENGING
CHALMERS
CHAMBER
CHAMBERED
CHAMBERLAIN
CHAMBERLAINS
CHAMBERMAID
CHAMBERS
CHAMELEON
CHAMPAGNE
CHAMPAIGN
CHAMPION
CHAMPIONED
CHAMPIONING
CHAMPIONS
CHAMPIONSHIP
CHAMPIONSHIPS
CHAMPLAIN
CHANCE
CHANCED
CHANCELLOR
CHANCELLORSVILLE
CHANCERY
CHANCES
CHANCING
CHANDELIER
CHANDELIERS
CHANDIGARH
CHANG
CHANGE
CHANGEABILITY
CHANGEABLE
CHANGEABLY
CHANGED
CHANGEOVER
CHANGER
CHANGERS
CHANGES
CHANGING
CHANNEL
CHANNELED
CHANNELING
CHANNELLED
CHANNELLER
CHANNELLERS
CHANNELLING
CHANNELS
CHANNING
CHANT
CHANTED
CHANTER
CHANTICLEER
CHANTICLEERS
CHANTILLY
CHANTING
CHANTS
CHAO
CHAOS
CHAOTIC
CHAP
CHAPEL
CHAPELS
CHAPERON
CHAPERONE
CHAPERONED
CHAPLAIN
CHAPLAINS
CHAPLIN
CHAPMAN
CHAPS
CHAPTER
CHAPTERS
CHAR
CHARACTER
CHARACTERISTIC
CHARACTERISTICALLY
CHARACTERISTICS
CHARACTERIZABLE
CHARACTERIZATION
CHARACTERIZATIONS
CHARACTERIZE
CHARACTERIZED
CHARACTERIZER
CHARACTERIZERS
CHARACTERIZES
CHARACTERIZING
CHARACTERS
CHARCOAL
CHARCOALED
CHARGE
CHARGEABLE
CHARGED
CHARGER
CHARGERS
CHARGES
CHARGING
CHARIOT
CHARIOTS
CHARISMA
CHARISMATIC
CHARITABLE
CHARITABLENESS
CHARITIES
CHARITY
CHARLEMAGNE
CHARLEMAGNES
CHARLES
CHARLESTON
CHARLEY
CHARLIE
CHARLOTTE
CHARLOTTESVILLE
CHARM
CHARMED
CHARMER
CHARMERS
CHARMING
CHARMINGLY
CHARMS
CHARON
CHARS
CHART
CHARTA
CHARTABLE
CHARTED
CHARTER
CHARTERED
CHARTERING
CHARTERS
CHARTING
CHARTINGS
CHARTRES
CHARTREUSE
CHARTS
CHARYBDIS
CHASE
CHASED
CHASER
CHASERS
CHASES
CHASING
CHASM
CHASMS
CHASSIS
CHASTE
CHASTELY
CHASTENESS
CHASTISE
CHASTISED
CHASTISER
CHASTISERS
CHASTISES
CHASTISING
CHASTITY
CHAT
CHATEAU
CHATEAUS
CHATHAM
CHATTAHOOCHEE
CHATTANOOGA
CHATTEL
CHATTER
CHATTERED
CHATTERER
CHATTERING
CHATTERS
CHATTING
CHATTY
CHAUCER
CHAUFFEUR
CHAUFFEURED
CHAUNCEY
CHAUTAUQUA
CHEAP
CHEAPEN
CHEAPENED
CHEAPENING
CHEAPENS
CHEAPER
CHEAPEST
CHEAPLY
CHEAPNESS
CHEAT
CHEATED
CHEATER
CHEATERS
CHEATING
CHEATS
CHECK
CHECKABLE
CHECKBOOK
CHECKBOOKS
CHECKED
CHECKER
CHECKERBOARD
CHECKERBOARDED
CHECKERBOARDING
CHECKERS
CHECKING
CHECKLIST
CHECKOUT
CHECKPOINT
CHECKPOINTS
CHECKS
CHECKSUM
CHECKSUMMED
CHECKSUMMING
CHECKSUMS
CHECKUP
CHEEK
CHEEKBONE
CHEEKS
CHEEKY
CHEER
CHEERED
CHEERER
CHEERFUL
CHEERFULLY
CHEERFULNESS
CHEERILY
CHEERINESS
CHEERING
CHEERLEADER
CHEERLESS
CHEERLESSLY
CHEERLESSNESS
CHEERS
CHEERY
CHEESE
CHEESECLOTH
CHEESES
CHEESY
CHEETAH
CHEF
CHEFS
CHEKHOV
CHELSEA
CHEMICAL
CHEMICALLY
CHEMICALS
CHEMISE
CHEMIST
CHEMISTRIES
CHEMISTRY
CHEMISTS
CHEN
CHENEY
CHENG
CHERISH
CHERISHED
CHERISHES
CHERISHING
CHERITON
CHEROKEE
CHEROKEES
CHERRIES
CHERRY
CHERUB
CHERUBIM
CHERUBS
CHERYL
CHESAPEAKE
CHESHIRE
CHESS
CHEST
CHESTER
CHESTERFIELD
CHESTERTON
CHESTNUT
CHESTNUTS
CHESTS
CHEVROLET
CHEVY
CHEW
CHEWED
CHEWER
CHEWERS
CHEWING
CHEWS
CHEYENNE
CHEYENNES
CHIANG
CHIC
CHICAGO
CHICAGOAN
CHICAGOANS
CHICANA
CHICANAS
CHICANERY
CHICANO
CHICANOS
CHICK
CHICKADEE
CHICKADEES
CHICKASAWS
CHICKEN
CHICKENS
CHICKS
CHIDE
CHIDED
CHIDES
CHIDING
CHIEF
CHIEFLY
CHIEFS
CHIEFTAIN
CHIEFTAINS
CHIFFON
CHILD
CHILDBIRTH
CHILDHOOD
CHILDISH
CHILDISHLY
CHILDISHNESS
CHILDLIKE
CHILDREN
CHILE
CHILEAN
CHILES
CHILI
CHILL
CHILLED
CHILLER
CHILLERS
CHILLIER
CHILLINESS
CHILLING
CHILLINGLY
CHILLS
CHILLY
CHIME
CHIMERA
CHIMES
CHIMNEY
CHIMNEYS
CHIMPANZEE
CHIN
CHINA
CHINAMAN
CHINAMEN
CHINAS
CHINATOWN
CHINESE
CHING
CHINK
CHINKED
CHINKS
CHINNED
CHINNER
CHINNERS
CHINNING
CHINOOK
CHINS
CHINTZ
CHIP
CHIPMUNK
CHIPMUNKS
CHIPPENDALE
CHIPPEWA
CHIPS
CHIROPRACTOR
CHIRP
CHIRPED
CHIRPING
CHIRPS
CHISEL
CHISELED
CHISELER
CHISELS
CHISHOLM
CHIT
CHIVALROUS
CHIVALROUSLY
CHIVALROUSNESS
CHIVALRY
CHLOE
CHLORINE
CHLOROFORM
CHLOROPHYLL
CHLOROPLAST
CHLOROPLASTS
CHOCK
CHOCKS
CHOCOLATE
CHOCOLATES
CHOCTAW
CHOCTAWS
CHOICE
CHOICES
CHOICEST
CHOIR
CHOIRS
CHOKE
CHOKED
CHOKER
CHOKERS
CHOKES
CHOKING
CHOLERA
CHOMSKY
CHOOSE
CHOOSER
CHOOSERS
CHOOSES
CHOOSING
CHOP
CHOPIN
CHOPPED
CHOPPER
CHOPPERS
CHOPPING
CHOPPY
CHOPS
CHORAL
CHORD
CHORDATE
CHORDED
CHORDING
CHORDS
CHORE
CHOREOGRAPH
CHOREOGRAPHY
CHORES
CHORING
CHORTLE
CHORUS
CHORUSED
CHORUSES
CHOSE
CHOSEN
CHOU
CHOWDER
CHRIS
CHRIST
CHRISTEN
CHRISTENDOM
CHRISTENED
CHRISTENING
CHRISTENS
CHRISTENSEN
CHRISTENSON
CHRISTIAN
CHRISTIANA
CHRISTIANITY
CHRISTIANIZATION
CHRISTIANIZATIONS
CHRISTIANIZE
CHRISTIANIZER
CHRISTIANIZERS
CHRISTIANIZES
CHRISTIANIZING
CHRISTIANS
CHRISTIANSEN
CHRISTIANSON
CHRISTIE
CHRISTINA
CHRISTINE
CHRISTLIKE
CHRISTMAS
CHRISTOFFEL
CHRISTOPH
CHRISTOPHER
CHRISTY
CHROMATOGRAM
CHROMATOGRAPH
CHROMATOGRAPHY
CHROME
CHROMIUM
CHROMOSPHERE
CHRONIC
CHRONICLE
CHRONICLED
CHRONICLER
CHRONICLERS
CHRONICLES
CHRONOGRAPH
CHRONOGRAPHY
CHRONOLOGICAL
CHRONOLOGICALLY
CHRONOLOGIES
CHRONOLOGY
CHRYSANTHEMUM
CHRYSLER
CHUBBIER
CHUBBIEST
CHUBBINESS
CHUBBY
CHUCK
CHUCKLE
CHUCKLED
CHUCKLES
CHUCKS
CHUM
CHUNGKING
CHUNK
CHUNKS
CHUNKY
CHURCH
CHURCHES
CHURCHGOER
CHURCHGOING
CHURCHILL
CHURCHILLIAN
CHURCHLY
CHURCHMAN
CHURCHMEN
CHURCHWOMAN
CHURCHWOMEN
CHURCHYARD
CHURCHYARDS
CHURN
CHURNED
CHURNING
CHURNS
CHUTE
CHUTES
CHUTZPAH
CICADA
CICERO
CICERONIAN
CICERONIANIZE
CICERONIANIZES
CIDER
CIGAR
CIGARETTE
CIGARETTES
CIGARS
CILIA
CINCINNATI
CINDER
CINDERELLA
CINDERS
CINDY
CINEMA
CINEMATIC
CINERAMA
CINNAMON
CIPHER
CIPHERS
CIPHERTEXT
CIPHERTEXTS
CIRCA
CIRCE
CIRCLE
CIRCLED
CIRCLES
CIRCLET
CIRCLING
CIRCUIT
CIRCUITOUS
CIRCUITOUSLY
CIRCUITRY
CIRCUITS
CIRCULANT
CIRCULAR
CIRCULARITY
CIRCULARLY
CIRCULATE
CIRCULATED
CIRCULATES
CIRCULATING
CIRCULATION
CIRCUMCISE
CIRCUMCISION
CIRCUMFERENCE
CIRCUMFLEX
CIRCUMLOCUTION
CIRCUMLOCUTIONS
CIRCUMNAVIGATE
CIRCUMNAVIGATED
CIRCUMNAVIGATES
CIRCUMPOLAR
CIRCUMSCRIBE
CIRCUMSCRIBED
CIRCUMSCRIBING
CIRCUMSCRIPTION
CIRCUMSPECT
CIRCUMSPECTION
CIRCUMSPECTLY
CIRCUMSTANCE
CIRCUMSTANCED
CIRCUMSTANCES
CIRCUMSTANTIAL
CIRCUMSTANTIALLY
CIRCUMVENT
CIRCUMVENTABLE
CIRCUMVENTED
CIRCUMVENTING
CIRCUMVENTS
CIRCUS
CIRCUSES
CISTERN
CISTERNS
CITADEL
CITADELS
CITATION
CITATIONS
CITE
CITED
CITES
CITIES
CITING
CITIZEN
CITIZENS
CITIZENSHIP
CITROEN
CITRUS
CITY
CITYSCAPE
CITYWIDE
CIVET
CIVIC
CIVICS
CIVIL
CIVILIAN
CIVILIANS
CIVILITY
CIVILIZATION
CIVILIZATIONS
CIVILIZE
CIVILIZED
CIVILIZES
CIVILIZING
CIVILLY
CLAD
CLADDING
CLAIM
CLAIMABLE
CLAIMANT
CLAIMANTS
CLAIMED
CLAIMING
CLAIMS
CLAIRE
CLAIRVOYANT
CLAIRVOYANTLY
CLAM
CLAMBER
CLAMBERED
CLAMBERING
CLAMBERS
CLAMOR
CLAMORED
CLAMORING
CLAMOROUS
CLAMORS
CLAMP
CLAMPED
CLAMPING
CLAMPS
CLAMS
CLAN
CLANDESTINE
CLANG
CLANGED
CLANGING
CLANGS
CLANK
CLANNISH
CLAP
CLAPBOARD
CLAPEYRON
CLAPPING
CLAPS
CLARA
CLARE
CLAREMONT
CLARENCE
CLARENDON
CLARIFICATION
CLARIFICATIONS
CLARIFIED
CLARIFIES
CLARIFY
CLARIFYING
CLARINET
CLARITY
CLARK
CLARKE
CLARRIDGE
CLASH
CLASHED
CLASHES
CLASHING
CLASP
CLASPED
CLASPING
CLASPS
CLASS
CLASSED
CLASSES
CLASSIC
CLASSICAL
CLASSICALLY
CLASSICS
CLASSIFIABLE
CLASSIFICATION
CLASSIFICATIONS
CLASSIFIED
CLASSIFIER
CLASSIFIERS
CLASSIFIES
CLASSIFY
CLASSIFYING
CLASSMATE
CLASSMATES
CLASSROOM
CLASSROOMS
CLASSY
CLATTER
CLATTERED
CLATTERING
CLAUDE
CLAUDIA
CLAUDIO
CLAUS
CLAUSE
CLAUSEN
CLAUSES
CLAUSIUS
CLAUSTROPHOBIA
CLAUSTROPHOBIC
CLAW
CLAWED
CLAWING
CLAWS
CLAY
CLAYS
CLAYTON
CLEAN
CLEANED
CLEANER
CLEANERS
CLEANEST
CLEANING
CLEANLINESS
CLEANLY
CLEANNESS
CLEANS
CLEANSE
CLEANSED
CLEANSER
CLEANSERS
CLEANSES
CLEANSING
CLEANUP
CLEAR
CLEARANCE
CLEARANCES
CLEARED
CLEARER
CLEAREST
CLEARING
CLEARINGS
CLEARLY
CLEARNESS
CLEARS
CLEARWATER
CLEAVAGE
CLEAVE
CLEAVED
CLEAVER
CLEAVERS
CLEAVES
CLEAVING
CLEFT
CLEFTS
CLEMENCY
CLEMENS
CLEMENT
CLEMENTE
CLEMSON
CLENCH
CLENCHED
CLENCHES
CLERGY
CLERGYMAN
CLERGYMEN
CLERICAL
CLERK
CLERKED
CLERKING
CLERKS
CLEVELAND
CLEVER
CLEVERER
CLEVEREST
CLEVERLY
CLEVERNESS
CLICHE
CLICHES
CLICK
CLICKED
CLICKING
CLICKS
CLIENT
CLIENTELE
CLIENTS
CLIFF
CLIFFORD
CLIFFS
CLIFTON
CLIMATE
CLIMATES
CLIMATIC
CLIMATICALLY
CLIMATOLOGY
CLIMAX
CLIMAXED
CLIMAXES
CLIMB
CLIMBED
CLIMBER
CLIMBERS
CLIMBING
CLIMBS
CLIME
CLIMES
CLINCH
CLINCHED
CLINCHER
CLINCHES
CLING
CLINGING
CLINGS
CLINIC
CLINICAL
CLINICALLY
CLINICIAN
CLINICS
CLINK
CLINKED
CLINKER
CLINT
CLINTON
CLIO
CLIP
CLIPBOARD
CLIPPED
CLIPPER
CLIPPERS
CLIPPING
CLIPPINGS
CLIPS
CLIQUE
CLIQUES
CLITORIS
CLIVE
CLOAK
CLOAKROOM
CLOAKS
CLOBBER
CLOBBERED
CLOBBERING
CLOBBERS
CLOCK
CLOCKED
CLOCKER
CLOCKERS
CLOCKING
CLOCKINGS
CLOCKS
CLOCKWATCHER
CLOCKWISE
CLOCKWORK
CLOD
CLODS
CLOG
CLOGGED
CLOGGING
CLOGS
CLOISTER
CLOISTERS
CLONE
CLONED
CLONES
CLONING
CLOSE
CLOSED
CLOSELY
CLOSENESS
CLOSENESSES
CLOSER
CLOSERS
CLOSES
CLOSEST
CLOSET
CLOSETED
CLOSETS
CLOSEUP
CLOSING
CLOSURE
CLOSURES
CLOT
CLOTH
CLOTHE
CLOTHED
CLOTHES
CLOTHESHORSE
CLOTHESLINE
CLOTHING
CLOTHO
CLOTTING
CLOTURE
CLOUD
CLOUDBURST
CLOUDED
CLOUDIER
CLOUDIEST
CLOUDINESS
CLOUDING
CLOUDLESS
CLOUDS
CLOUDY
CLOUT
CLOVE
CLOVER
CLOVES
CLOWN
CLOWNING
CLOWNS
CLUB
CLUBBED
CLUBBING
CLUBHOUSE
CLUBROOM
CLUBS
CLUCK
CLUCKED
CLUCKING
CLUCKS
CLUE
CLUES
CLUJ
CLUMP
CLUMPED
CLUMPING
CLUMPS
CLUMSILY
CLUMSINESS
CLUMSY
CLUNG
CLUSTER
CLUSTERED
CLUSTERING
CLUSTERINGS
CLUSTERS
CLUTCH
CLUTCHED
CLUTCHES
CLUTCHING
CLUTTER
CLUTTERED
CLUTTERING
CLUTTERS
CLYDE
CLYTEMNESTRA
COACH
COACHED
COACHER
COACHES
COACHING
COACHMAN
COACHMEN
COAGULATE
COAL
COALESCE
COALESCED
COALESCES
COALESCING
COALITION
COALS
COARSE
COARSELY
COARSEN
COARSENED
COARSENESS
COARSER
COARSEST
COAST
COASTAL
COASTED
COASTER
COASTERS
COASTING
COASTLINE
COASTS
COAT
COATED
COATES
COATING
COATINGS
COATS
COATTAIL
COAUTHOR
COAX
COAXED
COAXER
COAXES
COAXIAL
COAXING
COBALT
COBB
COBBLE
COBBLER
COBBLERS
COBBLESTONE
COBOL
COBOL
COBRA
COBWEB
COBWEBS
COCA
COCAINE
COCHISE
COCHRAN
COCHRANE
COCK
COCKED
COCKING
COCKPIT
COCKROACH
COCKS
COCKTAIL
COCKTAILS
COCKY
COCO
COCOA
COCONUT
COCONUTS
COCOON
COCOONS
COD
CODDINGTON
CODDLE
CODE
CODED
CODEINE
CODER
CODERS
CODES
CODEWORD
CODEWORDS
CODFISH
CODICIL
CODIFICATION
CODIFICATIONS
CODIFIED
CODIFIER
CODIFIERS
CODIFIES
CODIFY
CODIFYING
CODING
CODINGS
CODPIECE
CODY
COED
COEDITOR
COEDUCATION
COEFFICIENT
COEFFICIENTS
COEQUAL
COERCE
COERCED
COERCES
COERCIBLE
COERCING
COERCION
COERCIVE
COEXIST
COEXISTED
COEXISTENCE
COEXISTING
COEXISTS
COFACTOR
COFFEE
COFFEECUP
COFFEEPOT
COFFEES
COFFER
COFFERS
COFFEY
COFFIN
COFFINS
COFFMAN
COG
COGENT
COGENTLY
COGITATE
COGITATED
COGITATES
COGITATING
COGITATION
COGNAC
COGNITION
COGNITIVE
COGNITIVELY
COGNIZANCE
COGNIZANT
COGS
COHABITATION
COHABITATIONS
COHEN
COHERE
COHERED
COHERENCE
COHERENT
COHERENTLY
COHERES
COHERING
COHESION
COHESIVE
COHESIVELY
COHESIVENESS
COHN
COHORT
COIL
COILED
COILING
COILS
COIN
COINAGE
COINCIDE
COINCIDED
COINCIDENCE
COINCIDENCES
COINCIDENT
COINCIDENTAL
COINCIDES
COINCIDING
COINED
COINER
COINING
COINS
COKE
COKES
COLANDER
COLBY
COLD
COLDER
COLDEST
COLDLY
COLDNESS
COLDS
COLE
COLEMAN
COLERIDGE
COLETTE
COLGATE
COLICKY
COLIFORM
COLISEUM
COLLABORATE
COLLABORATED
COLLABORATES
COLLABORATING
COLLABORATION
COLLABORATIONS
COLLABORATIVE
COLLABORATOR
COLLABORATORS
COLLAGEN
COLLAPSE
COLLAPSED
COLLAPSES
COLLAPSIBLE
COLLAPSING
COLLAR
COLLARBONE
COLLARED
COLLARING
COLLARS
COLLATE
COLLATERAL
COLLEAGUE
COLLEAGUES
COLLECT
COLLECTED
COLLECTIBLE
COLLECTING
COLLECTION
COLLECTIONS
COLLECTIVE
COLLECTIVELY
COLLECTIVES
COLLECTOR
COLLECTORS
COLLECTS
COLLEGE
COLLEGES
COLLEGIAN
COLLEGIATE
COLLIDE
COLLIDED
COLLIDES
COLLIDING
COLLIE
COLLIER
COLLIES
COLLINS
COLLISION
COLLISIONS
COLLOIDAL
COLLOQUIA
COLLOQUIAL
COLLOQUIUM
COLLOQUY
COLLUSION
COLOGNE
COLOMBIA
COLOMBIAN
COLOMBIANS
COLOMBO
COLON
COLONEL
COLONELS
COLONIAL
COLONIALLY
COLONIALS
COLONIES
COLONIST
COLONISTS
COLONIZATION
COLONIZE
COLONIZED
COLONIZER
COLONIZERS
COLONIZES
COLONIZING
COLONS
COLONY
COLOR
COLORADO
COLORED
COLORER
COLORERS
COLORFUL
COLORING
COLORINGS
COLORLESS
COLORS
COLOSSAL
COLOSSEUM
COLT
COLTS
COLUMBIA
COLUMBIAN
COLUMBUS
COLUMN
COLUMNIZE
COLUMNIZED
COLUMNIZES
COLUMNIZING
COLUMNS
COMANCHE
COMB
COMBAT
COMBATANT
COMBATANTS
COMBATED
COMBATING
COMBATIVE
COMBATS
COMBED
COMBER
COMBERS
COMBINATION
COMBINATIONAL
COMBINATIONS
COMBINATOR
COMBINATORIAL
COMBINATORIALLY
COMBINATORIC
COMBINATORICS
COMBINATORS
COMBINE
COMBINED
COMBINES
COMBING
COMBINGS
COMBINING
COMBS
COMBUSTIBLE
COMBUSTION
COMDEX
COME
COMEBACK
COMEDIAN
COMEDIANS
COMEDIC
COMEDIES
COMEDY
COMELINESS
COMELY
COMER
COMERS
COMES
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COMFORT
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COMMISSIONED
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COMPARISONS
COMPARTMENT
COMPARTMENTALIZE
COMPARTMENTALIZED
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COMPASSION
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COMPATIBILITY
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COMPELLED
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COMPETE
COMPETED
COMPETENCE
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COMPILED
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COMPILERS
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COMPOSURE
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COMPRISE
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COMPRISES
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COMPROMISERS
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COMPTROLLERS
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COMPULSIONS
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COMPUTERIZED
COMPUTERIZES
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CON
CONAKRY
CONANT
CONCATENATE
CONCATENATED
CONCATENATES
CONCATENATING
CONCATENATION
CONCATENATIONS
CONCAVE
CONCEAL
CONCEALED
CONCEALER
CONCEALERS
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CONCEALS
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CONCEDED
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CONCEIVED
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CONCENTRATED
CONCENTRATES
CONCENTRATING
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CONCENTRATIONS
CONCENTRATOR
CONCENTRATORS
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CONCEPT
CONCEPTION
CONCEPTIONS
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CONCEPTUALIZATIONS
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CONCERNEDLY
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CONCERTED
CONCERTMASTER
CONCERTO
CONCERTS
CONCESSION
CONCESSIONS
CONCILIATE
CONCILIATORY
CONCISE
CONCISELY
CONCISENESS
CONCLAVE
CONCLUDE
CONCLUDED
CONCLUDES
CONCLUDING
CONCLUSION
CONCLUSIONS
CONCLUSIVE
CONCLUSIVELY
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CONCOMITANT
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CONCURRED
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CONCURRENCIES
CONCURRENCY
CONCURRENT
CONCURRENTLY
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CONCURS
CONCUSSION
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CONDEMNATIONS
CONDEMNED
CONDEMNER
CONDEMNERS
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CONDEMNS
CONDENSATION
CONDENSE
CONDENSED
CONDENSER
CONDENSES
CONDENSING
CONDESCEND
CONDESCENDING
CONDITION
CONDITIONAL
CONDITIONALLY
CONDITIONALS
CONDITIONED
CONDITIONER
CONDITIONERS
CONDITIONING
CONDITIONS
CONDOM
CONDONE
CONDONED
CONDONES
CONDONING
CONDUCE
CONDUCIVE
CONDUCIVENESS
CONDUCT
CONDUCTANCE
CONDUCTED
CONDUCTING
CONDUCTION
CONDUCTIVE
CONDUCTIVITY
CONDUCTOR
CONDUCTORS
CONDUCTS
CONDUIT
CONE
CONES
CONESTOGA
CONFECTIONERY
CONFEDERACY
CONFEDERATE
CONFEDERATES
CONFEDERATION
CONFEDERATIONS
CONFER
CONFEREE
CONFERENCE
CONFERENCES
CONFERRED
CONFERRER
CONFERRERS
CONFERRING
CONFERS
CONFESS
CONFESSED
CONFESSES
CONFESSING
CONFESSION
CONFESSIONS
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CONFESSORS
CONFIDANT
CONFIDANTS
CONFIDE
CONFIDED
CONFIDENCE
CONFIDENCES
CONFIDENT
CONFIDENTIAL
CONFIDENTIALITY
CONFIDENTIALLY
CONFIDENTLY
CONFIDES
CONFIDING
CONFIDINGLY
CONFIGURABLE
CONFIGURATION
CONFIGURATIONS
CONFIGURE
CONFIGURED
CONFIGURES
CONFIGURING
CONFINE
CONFINED
CONFINEMENT
CONFINEMENTS
CONFINER
CONFINES
CONFINING
CONFIRM
CONFIRMATION
CONFIRMATIONS
CONFIRMATORY
CONFIRMED
CONFIRMING
CONFIRMS
CONFISCATE
CONFISCATED
CONFISCATES
CONFISCATING
CONFISCATION
CONFISCATIONS
CONFLAGRATION
CONFLICT
CONFLICTED
CONFLICTING
CONFLICTS
CONFLUENT
CONFOCAL
CONFORM
CONFORMAL
CONFORMANCE
CONFORMED
CONFORMING
CONFORMITY
CONFORMS
CONFOUND
CONFOUNDED
CONFOUNDING
CONFOUNDS
CONFRONT
CONFRONTATION
CONFRONTATIONS
CONFRONTED
CONFRONTER
CONFRONTERS
CONFRONTING
CONFRONTS
CONFUCIAN
CONFUCIANISM
CONFUCIUS
CONFUSE
CONFUSED
CONFUSER
CONFUSERS
CONFUSES
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CONFUSINGLY
CONFUSION
CONFUSIONS
CONGENIAL
CONGENIALLY
CONGENITAL
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CONGESTIVE
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CONGO
CONGOLESE
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CONGRATULATED
CONGRATULATION
CONGRATULATIONS
CONGRATULATORY
CONGREGATE
CONGREGATED
CONGREGATES
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CONGRESS
CONGRESSES
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CONGRESSIONALLY
CONGRESSMAN
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CONGRESSWOMAN
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CONGRUENCE
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CONIC
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CONIFEROUS
CONJECTURE
CONJECTURED
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CONJOINED
CONJUGAL
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CONJURE
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CONKLIN
CONLEY
CONNALLY
CONNECT
CONNECTED
CONNECTEDNESS
CONNECTICUT
CONNECTING
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CONNECTIONLESS
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CONNECTIVE
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CONNELLY
CONNER
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CONNIVANCE
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CONNOTATION
CONNOTATIVE
CONNOTE
CONNOTED
CONNOTES
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CONSCRIPT
CONSCRIPTION
CONSECRATE
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CONSECUTIVE
CONSECUTIVELY
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CONSEQUENCES
CONSEQUENT
CONSEQUENTIAL
CONSEQUENTIALITIES
CONSEQUENTIALITY
CONSEQUENTLY
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CONSERVATION
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CONSERVATIONISTS
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CONSERVATISM
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CONSERVED
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CONSIDER
CONSIDERABLE
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CONSIDERATIONS
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CONSISTENTLY
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CONSOLED
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CONSTABLE
CONSTABLES
CONSTANCE
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CONSTANTINE
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CONSTELLATIONS
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CONSTITUENCIES
CONSTITUENCY
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CONSTRICT
CONSTRUCT
CONSTRUCTED
CONSTRUCTIBILITY
CONSTRUCTIBLE
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CONTEMPLATIONS
CONTEMPLATIVE
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CONTEMPORARINESS
CONTEMPORARY
CONTEMPT
CONTEMPTIBLE
CONTEMPTUOUS
CONTEMPTUOUSLY
CONTEND
CONTENDED
CONTENDER
CONTENDERS
CONTENDING
CONTENDS
CONTENT
CONTENTED
CONTENTING
CONTENTION
CONTENTIONS
CONTENTLY
CONTENTMENT
CONTENTS
CONTEST
CONTESTABLE
CONTESTANT
CONTESTED
CONTESTER
CONTESTERS
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CONTESTS
CONTEXT
CONTEXTS
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CONTEXTUALLY
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CONTINENTALLY
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CONTINUE
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CONTINUES
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CONTINUUM
CONTORTIONS
CONTOUR
CONTOURED
CONTOURING
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CONTRABAND
CONTRACEPTION
CONTRACEPTIVE
CONTRACT
CONTRACTED
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CONTRACTION
CONTRACTIONS
CONTRACTOR
CONTRACTORS
CONTRACTS
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CONTRACTUALLY
CONTRADICT
CONTRADICTED
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CONTRADICTION
CONTRADICTIONS
CONTRADICTORY
CONTRADICTS
CONTRADISTINCTION
CONTRADISTINCTIONS
CONTRAPOSITIVE
CONTRAPOSITIVES
CONTRAPTION
CONTRAPTIONS
CONTRARINESS
CONTRARY
CONTRAST
CONTRASTED
CONTRASTER
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CONTRASTS
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CONTRIBUTORILY
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CONTRITE
CONTRITION
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CONTRIVER
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CONTROL
CONTROLLABILITY
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CONTROLLER
CONTROLLERS
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CONTROLS
CONTROVERSIAL
CONTROVERSIES
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CONTUMACIOUS
CONTUMACY
CONUNDRUM
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CONVENTION
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CONVERGED
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CONWAY
COO
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COOL
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COONS
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COOPER
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COOPERATIVELY
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COOPERATOR
COOPERATORS
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COOPS
COORDINATE
COORDINATED
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COORDINATING
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COORDINATOR
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COORS
COP
COPE
COPED
COPELAND
COPENHAGEN
COPERNICAN
COPERNICUS
COPES
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COPIER
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COPING
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COPPER
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COPY
COPYING
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COPYWRITER
COQUETTE
CORAL
CORBETT
CORCORAN
CORD
CORDED
CORDER
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CORDS
CORE
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CORER
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CORINTHIANIZES
CORINTHIANS
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CORK
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CORN
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CORNERSTONES
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CORNS
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CORRESPONDED
CORRESPONDENCE
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CORRIDOR
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CORRIGENDUM
CORRIGIBLE
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CORSET
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CORTICAL
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COSGROVE
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COSMIC
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COSTUMES
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COT
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COTTAGES
COTTON
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COTTONS
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COTYLEDONS
COUCH
COUCHED
COUCHES
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COUGAR
COUGH
COUGHED
COUGHING
COUGHS
COULD
COULOMB
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COUNCIL
COUNCILLOR
COUNCILLORS
COUNCILMAN
COUNCILMEN
COUNCILS
COUNCILWOMAN
COUNCILWOMEN
COUNSEL
COUNSELED
COUNSELING
COUNSELLED
COUNSELLING
COUNSELLOR
COUNSELLORS
COUNSELOR
COUNSELORS
COUNSELS
COUNT
COUNTABLE
COUNTABLY
COUNTED
COUNTENANCE
COUNTER
COUNTERACT
COUNTERACTED
COUNTERACTING
COUNTERACTIVE
COUNTERARGUMENT
COUNTERATTACK
COUNTERBALANCE
COUNTERCLOCKWISE
COUNTERED
COUNTEREXAMPLE
COUNTEREXAMPLES
COUNTERFEIT
COUNTERFEITED
COUNTERFEITER
COUNTERFEITING
COUNTERFLOW
COUNTERING
COUNTERINTUITIVE
COUNTERMAN
COUNTERMEASURE
COUNTERMEASURES
COUNTERMEN
COUNTERPART
COUNTERPARTS
COUNTERPOINT
COUNTERPOINTING
COUNTERPOISE
COUNTERPRODUCTIVE
COUNTERPROPOSAL
COUNTERREVOLUTION
COUNTERS
COUNTERSINK
COUNTERSUNK
COUNTESS
COUNTIES
COUNTING
COUNTLESS
COUNTRIES
COUNTRY
COUNTRYMAN
COUNTRYMEN
COUNTRYSIDE
COUNTRYWIDE
COUNTS
COUNTY
COUNTYWIDE
COUPLE
COUPLED
COUPLER
COUPLERS
COUPLES
COUPLING
COUPLINGS
COUPON
COUPONS
COURAGE
COURAGEOUS
COURAGEOUSLY
COURIER
COURIERS
COURSE
COURSED
COURSER
COURSES
COURSING
COURT
COURTED
COURTEOUS
COURTEOUSLY
COURTER
COURTERS
COURTESAN
COURTESIES
COURTESY
COURTHOUSE
COURTHOUSES
COURTIER
COURTIERS
COURTING
COURTLY
COURTNEY
COURTROOM
COURTROOMS
COURTS
COURTSHIP
COURTYARD
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COUSIN
COUSINS
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COVE
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COVENANTS
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COVENTRY
COVER
COVERABLE
COVERAGE
COVERED
COVERING
COVERINGS
COVERLET
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COVES
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COVETED
COVETING
COVETOUS
COVETOUSNESS
COVETS
COW
COWAN
COWARD
COWARDICE
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COWBOY
COWBOYS
COWED
COWER
COWERED
COWERER
COWERERS
COWERING
COWERINGLY
COWERS
COWHERD
COWHIDE
COWING
COWL
COWLICK
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COWLS
COWORKER
COWS
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COYOTE
COYOTES
COYPU
COZIER
COZINESS
COZY
CRAB
CRABAPPLE
CRABS
CRACK
CRACKED
CRACKER
CRACKERS
CRACKING
CRACKLE
CRACKLED
CRACKLES
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CRACKPOT
CRACKS
CRADLE
CRADLED
CRADLES
CRAFT
CRAFTED
CRAFTER
CRAFTINESS
CRAFTING
CRAFTS
CRAFTSMAN
CRAFTSMEN
CRAFTSPEOPLE
CRAFTSPERSON
CRAFTY
CRAG
CRAGGY
CRAGS
CRAIG
CRAM
CRAMER
CRAMMING
CRAMP
CRAMPS
CRAMS
CRANBERRIES
CRANBERRY
CRANDALL
CRANE
CRANES
CRANFORD
CRANIA
CRANIUM
CRANK
CRANKCASE
CRANKED
CRANKIER
CRANKIEST
CRANKILY
CRANKING
CRANKS
CRANKSHAFT
CRANKY
CRANNY
CRANSTON
CRASH
CRASHED
CRASHER
CRASHERS
CRASHES
CRASHING
CRASS
CRATE
CRATER
CRATERS
CRATES
CRAVAT
CRAVATS
CRAVE
CRAVED
CRAVEN
CRAVES
CRAVING
CRAWFORD
CRAWL
CRAWLED
CRAWLER
CRAWLERS
CRAWLING
CRAWLS
CRAY
CRAYON
CRAYS
CRAZE
CRAZED
CRAZES
CRAZIER
CRAZIEST
CRAZILY
CRAZINESS
CRAZING
CRAZY
CREAK
CREAKED
CREAKING
CREAKS
CREAKY
CREAM
CREAMED
CREAMER
CREAMERS
CREAMERY
CREAMING
CREAMS
CREAMY
CREASE
CREASED
CREASES
CREASING
CREATE
CREATED
CREATES
CREATING
CREATION
CREATIONS
CREATIVE
CREATIVELY
CREATIVENESS
CREATIVITY
CREATOR
CREATORS
CREATURE
CREATURES
CREDENCE
CREDENTIAL
CREDIBILITY
CREDIBLE
CREDIBLY
CREDIT
CREDITABLE
CREDITABLY
CREDITED
CREDITING
CREDITOR
CREDITORS
CREDITS
CREDULITY
CREDULOUS
CREDULOUSNESS
CREE
CREED
CREEDS
CREEK
CREEKS
CREEP
CREEPER
CREEPERS
CREEPING
CREEPS
CREEPY
CREIGHTON
CREMATE
CREMATED
CREMATES
CREMATING
CREMATION
CREMATIONS
CREMATORY
CREOLE
CREON
CREPE
CREPT
CRESCENT
CRESCENTS
CREST
CRESTED
CRESTFALLEN
CRESTS
CRESTVIEW
CRETACEOUS
CRETACEOUSLY
CRETAN
CRETE
CRETIN
CREVICE
CREVICES
CREW
CREWCUT
CREWED
CREWING
CREWS
CRIB
CRIBS
CRICKET
CRICKETS
CRIED
CRIER
CRIERS
CRIES
CRIME
CRIMEA
CRIMEAN
CRIMES
CRIMINAL
CRIMINALLY
CRIMINALS
CRIMINATE
CRIMSON
CRIMSONING
CRINGE
CRINGED
CRINGES
CRINGING
CRIPPLE
CRIPPLED
CRIPPLES
CRIPPLING
CRISES
CRISIS
CRISP
CRISPIN
CRISPLY
CRISPNESS
CRISSCROSS
CRITERIA
CRITERION
CRITIC
CRITICAL
CRITICALLY
CRITICISM
CRITICISMS
CRITICIZE
CRITICIZED
CRITICIZES
CRITICIZING
CRITICS
CRITIQUE
CRITIQUES
CRITIQUING
CRITTER
CROAK
CROAKED
CROAKING
CROAKS
CROATIA
CROATIAN
CROCHET
CROCHETS
CROCK
CROCKERY
CROCKETT
CROCKS
CROCODILE
CROCUS
CROFT
CROIX
CROMWELL
CROMWELLIAN
CROOK
CROOKED
CROOKS
CROP
CROPPED
CROPPER
CROPPERS
CROPPING
CROPS
CROSBY
CROSS
CROSSABLE
CROSSBAR
CROSSBARS
CROSSED
CROSSER
CROSSERS
CROSSES
CROSSING
CROSSINGS
CROSSLY
CROSSOVER
CROSSOVERS
CROSSPOINT
CROSSROAD
CROSSTALK
CROSSWALK
CROSSWORD
CROSSWORDS
CROTCH
CROTCHETY
CROUCH
CROUCHED
CROUCHING
CROW
CROWD
CROWDED
CROWDER
CROWDING
CROWDS
CROWED
CROWING
CROWLEY
CROWN
CROWNED
CROWNING
CROWNS
CROWS
CROYDON
CRUCIAL
CRUCIALLY
CRUCIBLE
CRUCIFIED
CRUCIFIES
CRUCIFIX
CRUCIFIXION
CRUCIFY
CRUCIFYING
CRUD
CRUDDY
CRUDE
CRUDELY
CRUDENESS
CRUDER
CRUDEST
CRUEL
CRUELER
CRUELEST
CRUELLY
CRUELTY
CRUICKSHANK
CRUISE
CRUISER
CRUISERS
CRUISES
CRUISING
CRUMB
CRUMBLE
CRUMBLED
CRUMBLES
CRUMBLING
CRUMBLY
CRUMBS
CRUMMY
CRUMPLE
CRUMPLED
CRUMPLES
CRUMPLING
CRUNCH
CRUNCHED
CRUNCHES
CRUNCHIER
CRUNCHIEST
CRUNCHING
CRUNCHY
CRUSADE
CRUSADER
CRUSADERS
CRUSADES
CRUSADING
CRUSH
CRUSHABLE
CRUSHED
CRUSHER
CRUSHERS
CRUSHES
CRUSHING
CRUSHINGLY
CRUSOE
CRUST
CRUSTACEAN
CRUSTACEANS
CRUSTS
CRUTCH
CRUTCHES
CRUX
CRUXES
CRUZ
CRY
CRYING
CRYOGENIC
CRYPT
CRYPTANALYSIS
CRYPTANALYST
CRYPTANALYTIC
CRYPTIC
CRYPTOGRAM
CRYPTOGRAPHER
CRYPTOGRAPHIC
CRYPTOGRAPHICALLY
CRYPTOGRAPHY
CRYPTOLOGIST
CRYPTOLOGY
CRYSTAL
CRYSTALLINE
CRYSTALLIZE
CRYSTALLIZED
CRYSTALLIZES
CRYSTALLIZING
CRYSTALS
CUB
CUBA
CUBAN
CUBANIZE
CUBANIZES
CUBANS
CUBBYHOLE
CUBE
CUBED
CUBES
CUBIC
CUBS
CUCKOO
CUCKOOS
CUCUMBER
CUCUMBERS
CUDDLE
CUDDLED
CUDDLY
CUDGEL
CUDGELS
CUE
CUED
CUES
CUFF
CUFFLINK
CUFFS
CUISINE
CULBERTSON
CULINARY
CULL
CULLED
CULLER
CULLING
CULLS
CULMINATE
CULMINATED
CULMINATES
CULMINATING
CULMINATION
CULPA
CULPABLE
CULPRIT
CULPRITS
CULT
CULTIVABLE
CULTIVATE
CULTIVATED
CULTIVATES
CULTIVATING
CULTIVATION
CULTIVATIONS
CULTIVATOR
CULTIVATORS
CULTS
CULTURAL
CULTURALLY
CULTURE
CULTURED
CULTURES
CULTURING
CULVER
CULVERS
CUMBERLAND
CUMBERSOME
CUMMINGS
CUMMINS
CUMULATIVE
CUMULATIVELY
CUNARD
CUNNILINGUS
CUNNING
CUNNINGHAM
CUNNINGLY
CUP
CUPBOARD
CUPBOARDS
CUPERTINO
CUPFUL
CUPID
CUPPED
CUPPING
CUPS
CURABLE
CURABLY
CURB
CURBING
CURBS
CURD
CURDLE
CURE
CURED
CURES
CURFEW
CURFEWS
CURING
CURIOSITIES
CURIOSITY
CURIOUS
CURIOUSER
CURIOUSEST
CURIOUSLY
CURL
CURLED
CURLER
CURLERS
CURLICUE
CURLING
CURLS
CURLY
CURRAN
CURRANT
CURRANTS
CURRENCIES
CURRENCY
CURRENT
CURRENTLY
CURRENTNESS
CURRENTS
CURRICULAR
CURRICULUM
CURRICULUMS
CURRIED
CURRIES
CURRY
CURRYING
CURS
CURSE
CURSED
CURSES
CURSING
CURSIVE
CURSOR
CURSORILY
CURSORS
CURSORY
CURT
CURTAIL
CURTAILED
CURTAILS
CURTAIN
CURTAINED
CURTAINS
CURTATE
CURTIS
CURTLY
CURTNESS
CURTSIES
CURTSY
CURVACEOUS
CURVATURE
CURVE
CURVED
CURVES
CURVILINEAR
CURVING
CUSHING
CUSHION
CUSHIONED
CUSHIONING
CUSHIONS
CUSHMAN
CUSP
CUSPS
CUSTARD
CUSTER
CUSTODIAL
CUSTODIAN
CUSTODIANS
CUSTODY
CUSTOM
CUSTOMARILY
CUSTOMARY
CUSTOMER
CUSTOMERS
CUSTOMIZABLE
CUSTOMIZATION
CUSTOMIZATIONS
CUSTOMIZE
CUSTOMIZED
CUSTOMIZER
CUSTOMIZERS
CUSTOMIZES
CUSTOMIZING
CUSTOMS
CUT
CUTANEOUS
CUTBACK
CUTE
CUTEST
CUTLASS
CUTLET
CUTOFF
CUTOUT
CUTOVER
CUTS
CUTTER
CUTTERS
CUTTHROAT
CUTTING
CUTTINGLY
CUTTINGS
CUTTLEFISH
CUVIER
CUZCO
CYANAMID
CYANIDE
CYBERNETIC
CYBERNETICS
CYBERSPACE
CYCLADES
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CYCLOIDAL
CYCLOIDS
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CYCLOTRONS
CYGNUS
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CYLINDERS
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CYMBALS
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CYRILLIC
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CYSTS
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CZECH
CZECHIZATION
CZECHIZATIONS
CZECHOSLOVAKIA
CZERNIAK
DABBLE
DABBLED
DABBLER
DABBLES
DABBLING
DACCA
DACRON
DACTYL
DACTYLIC
DAD
DADA
DADAISM
DADAIST
DADAISTIC
DADDY
DADE
DADS
DAEDALUS
DAEMON
DAEMONS
DAFFODIL
DAFFODILS
DAGGER
DAHL
DAHLIA
DAHOMEY
DAILEY
DAILIES
DAILY
DAIMLER
DAINTILY
DAINTINESS
DAINTY
DAIRY
DAIRYLEA
DAISIES
DAISY
DAKAR
DAKOTA
DALE
DALES
DALEY
DALHOUSIE
DALI
DALLAS
DALTON
DALY
DALZELL
DAM
DAMAGE
DAMAGED
DAMAGER
DAMAGERS
DAMAGES
DAMAGING
DAMASCUS
DAMASK
DAME
DAMMING
DAMN
DAMNATION
DAMNED
DAMNING
DAMNS
DAMOCLES
DAMON
DAMP
DAMPEN
DAMPENS
DAMPER
DAMPING
DAMPNESS
DAMS
DAMSEL
DAMSELS
DAN
DANA
DANBURY
DANCE
DANCED
DANCER
DANCERS
DANCES
DANCING
DANDELION
DANDELIONS
DANDY
DANE
DANES
DANGER
DANGEROUS
DANGEROUSLY
DANGERS
DANGLE
DANGLED
DANGLES
DANGLING
DANIEL
DANIELS
DANIELSON
DANISH
DANIZATION
DANIZATIONS
DANIZE
DANIZES
DANNY
DANTE
DANUBE
DANUBIAN
DANVILLE
DANZIG
DAPHNE
DAR
DARE
DARED
DARER
DARERS
DARES
DARESAY
DARING
DARINGLY
DARIUS
DARK
DARKEN
DARKER
DARKEST
DARKLY
DARKNESS
DARKROOM
DARLENE
DARLING
DARLINGS
DARLINGTON
DARN
DARNED
DARNER
DARNING
DARNS
DARPA
DARRELL
DARROW
DARRY
DART
DARTED
DARTER
DARTING
DARTMOUTH
DARTS
DARWIN
DARWINIAN
DARWINISM
DARWINISTIC
DARWINIZE
DARWINIZES
DASH
DASHBOARD
DASHED
DASHER
DASHERS
DASHES
DASHING
DASHINGLY
DATA
DATABASE
DATABASES
DATAGRAM
DATAGRAMS
DATAMATION
DATAMEDIA
DATE
DATED
DATELINE
DATER
DATES
DATING
DATIVE
DATSUN
DATUM
DAUGHERTY
DAUGHTER
DAUGHTERLY
DAUGHTERS
DAUNT
DAUNTED
DAUNTLESS
DAVE
DAVID
DAVIDSON
DAVIE
DAVIES
DAVINICH
DAVIS
DAVISON
DAVY
DAWN
DAWNED
DAWNING
DAWNS
DAWSON
DAY
DAYBREAK
DAYDREAM
DAYDREAMING
DAYDREAMS
DAYLIGHT
DAYLIGHTS
DAYS
DAYTIME
DAYTON
DAYTONA
DAZE
DAZED
DAZZLE
DAZZLED
DAZZLER
DAZZLES
DAZZLING
DAZZLINGLY
DEACON
DEACONS
DEACTIVATE
DEAD
DEADEN
DEADLINE
DEADLINES
DEADLOCK
DEADLOCKED
DEADLOCKING
DEADLOCKS
DEADLY
DEADNESS
DEADWOOD
DEAF
DEAFEN
DEAFER
DEAFEST
DEAFNESS
DEAL
DEALER
DEALERS
DEALERSHIP
DEALING
DEALINGS
DEALLOCATE
DEALLOCATED
DEALLOCATING
DEALLOCATION
DEALLOCATIONS
DEALS
DEALT
DEAN
DEANE
DEANNA
DEANS
DEAR
DEARBORN
DEARER
DEAREST
DEARLY
DEARNESS
DEARTH
DEARTHS
DEATH
DEATHBED
DEATHLY
DEATHS
DEBACLE
DEBAR
DEBASE
DEBATABLE
DEBATE
DEBATED
DEBATER
DEBATERS
DEBATES
DEBATING
DEBAUCH
DEBAUCHERY
DEBBIE
DEBBY
DEBILITATE
DEBILITATED
DEBILITATES
DEBILITATING
DEBILITY
DEBIT
DEBITED
DEBORAH
DEBRA
DEBRIEF
DEBRIS
DEBT
DEBTOR
DEBTS
DEBUG
DEBUGGED
DEBUGGER
DEBUGGERS
DEBUGGING
DEBUGS
DEBUNK
DEBUSSY
DEBUTANTE
DEC
DECADE
DECADENCE
DECADENT
DECADENTLY
DECADES
DECAL
DECATHLON
DECATUR
DECAY
DECAYED
DECAYING
DECAYS
DECCA
DECEASE
DECEASED
DECEASES
DECEASING
DECEDENT
DECEIT
DECEITFUL
DECEITFULLY
DECEITFULNESS
DECEIVE
DECEIVED
DECEIVER
DECEIVERS
DECEIVES
DECEIVING
DECELERATE
DECELERATED
DECELERATES
DECELERATING
DECELERATION
DECEMBER
DECEMBERS
DECENCIES
DECENCY
DECENNIAL
DECENT
DECENTLY
DECENTRALIZATION
DECENTRALIZED
DECEPTION
DECEPTIONS
DECEPTIVE
DECEPTIVELY
DECERTIFY
DECIBEL
DECIDABILITY
DECIDABLE
DECIDE
DECIDED
DECIDEDLY
DECIDES
DECIDING
DECIDUOUS
DECIMAL
DECIMALS
DECIMATE
DECIMATED
DECIMATES
DECIMATING
DECIMATION
DECIPHER
DECIPHERED
DECIPHERER
DECIPHERING
DECIPHERS
DECISION
DECISIONS
DECISIVE
DECISIVELY
DECISIVENESS
DECK
DECKED
DECKER
DECKING
DECKINGS
DECKS
DECLARATION
DECLARATIONS
DECLARATIVE
DECLARATIVELY
DECLARATIVES
DECLARATOR
DECLARATORY
DECLARE
DECLARED
DECLARER
DECLARERS
DECLARES
DECLARING
DECLASSIFY
DECLINATION
DECLINATIONS
DECLINE
DECLINED
DECLINER
DECLINERS
DECLINES
DECLINING
DECNET
DECODE
DECODED
DECODER
DECODERS
DECODES
DECODING
DECODINGS
DECOLLETAGE
DECOLLIMATE
DECOMPILE
DECOMPOSABILITY
DECOMPOSABLE
DECOMPOSE
DECOMPOSED
DECOMPOSES
DECOMPOSING
DECOMPOSITION
DECOMPOSITIONS
DECOMPRESS
DECOMPRESSION
DECORATE
DECORATED
DECORATES
DECORATING
DECORATION
DECORATIONS
DECORATIVE
DECORUM
DECOUPLE
DECOUPLED
DECOUPLES
DECOUPLING
DECOY
DECOYS
DECREASE
DECREASED
DECREASES
DECREASING
DECREASINGLY
DECREE
DECREED
DECREEING
DECREES
DECREMENT
DECREMENTED
DECREMENTING
DECREMENTS
DECRYPT
DECRYPTED
DECRYPTING
DECRYPTION
DECRYPTS
DECSTATION
DECSYSTEM
DECTAPE
DEDICATE
DEDICATED
DEDICATES
DEDICATING
DEDICATION
DEDUCE
DEDUCED
DEDUCER
DEDUCES
DEDUCIBLE
DEDUCING
DEDUCT
DEDUCTED
DEDUCTIBLE
DEDUCTING
DEDUCTION
DEDUCTIONS
DEDUCTIVE
DEE
DEED
DEEDED
DEEDING
DEEDS
DEEM
DEEMED
DEEMING
DEEMPHASIZE
DEEMPHASIZED
DEEMPHASIZES
DEEMPHASIZING
DEEMS
DEEP
DEEPEN
DEEPENED
DEEPENING
DEEPENS
DEEPER
DEEPEST
DEEPLY
DEEPS
DEER
DEERE
DEFACE
DEFAULT
DEFAULTED
DEFAULTER
DEFAULTING
DEFAULTS
DEFEAT
DEFEATED
DEFEATING
DEFEATS
DEFECATE
DEFECT
DEFECTED
DEFECTING
DEFECTION
DEFECTIONS
DEFECTIVE
DEFECTS
DEFEND
DEFENDANT
DEFENDANTS
DEFENDED
DEFENDER
DEFENDERS
DEFENDING
DEFENDS
DEFENESTRATE
DEFENESTRATED
DEFENESTRATES
DEFENESTRATING
DEFENESTRATION
DEFENSE
DEFENSELESS
DEFENSES
DEFENSIBLE
DEFENSIVE
DEFER
DEFERENCE
DEFERMENT
DEFERMENTS
DEFERRABLE
DEFERRED
DEFERRER
DEFERRERS
DEFERRING
DEFERS
DEFIANCE
DEFIANT
DEFIANTLY
DEFICIENCIES
DEFICIENCY
DEFICIENT
DEFICIT
DEFICITS
DEFIED
DEFIES
DEFILE
DEFILING
DEFINABLE
DEFINE
DEFINED
DEFINER
DEFINES
DEFINING
DEFINITE
DEFINITELY
DEFINITENESS
DEFINITION
DEFINITIONAL
DEFINITIONS
DEFINITIVE
DEFLATE
DEFLATER
DEFLECT
DEFOCUS
DEFOE
DEFOREST
DEFORESTATION
DEFORM
DEFORMATION
DEFORMATIONS
DEFORMED
DEFORMITIES
DEFORMITY
DEFRAUD
DEFRAY
DEFROST
DEFTLY
DEFUNCT
DEFY
DEFYING
DEGENERACY
DEGENERATE
DEGENERATED
DEGENERATES
DEGENERATING
DEGENERATION
DEGENERATIVE
DEGRADABLE
DEGRADATION
DEGRADATIONS
DEGRADE
DEGRADED
DEGRADES
DEGRADING
DEGREE
DEGREES
DEHUMIDIFY
DEHYDRATE
DEIFY
DEIGN
DEIGNED
DEIGNING
DEIGNS
DEIMOS
DEIRDRE
DEIRDRES
DEITIES
DEITY
DEJECTED
DEJECTEDLY
DEKALB
DEKASTERE
DEL
DELANEY
DELANO
DELAWARE
DELAY
DELAYED
DELAYING
DELAYS
DELEGATE
DELEGATED
DELEGATES
DELEGATING
DELEGATION
DELEGATIONS
DELETE
DELETED
DELETER
DELETERIOUS
DELETES
DELETING
DELETION
DELETIONS
DELFT
DELHI
DELIA
DELIBERATE
DELIBERATED
DELIBERATELY
DELIBERATENESS
DELIBERATES
DELIBERATING
DELIBERATION
DELIBERATIONS
DELIBERATIVE
DELIBERATOR
DELIBERATORS
DELICACIES
DELICACY
DELICATE
DELICATELY
DELICATESSEN
DELICIOUS
DELICIOUSLY
DELIGHT
DELIGHTED
DELIGHTEDLY
DELIGHTFUL
DELIGHTFULLY
DELIGHTING
DELIGHTS
DELILAH
DELIMIT
DELIMITATION
DELIMITED
DELIMITER
DELIMITERS
DELIMITING
DELIMITS
DELINEAMENT
DELINEATE
DELINEATED
DELINEATES
DELINEATING
DELINEATION
DELINQUENCY
DELINQUENT
DELIRIOUS
DELIRIOUSLY
DELIRIUM
DELIVER
DELIVERABLE
DELIVERABLES
DELIVERANCE
DELIVERED
DELIVERER
DELIVERERS
DELIVERIES
DELIVERING
DELIVERS
DELIVERY
DELL
DELLA
DELLS
DELLWOOD
DELMARVA
DELPHI
DELPHIC
DELPHICALLY
DELPHINUS
DELTA
DELTAS
DELUDE
DELUDED
DELUDES
DELUDING
DELUGE
DELUGED
DELUGES
DELUSION
DELUSIONS
DELUXE
DELVE
DELVES
DELVING
DEMAGNIFY
DEMAGOGUE
DEMAND
DEMANDED
DEMANDER
DEMANDING
DEMANDINGLY
DEMANDS
DEMARCATE
DEMEANOR
DEMENTED
DEMERIT
DEMETER
DEMIGOD
DEMISE
DEMO
DEMOCRACIES
DEMOCRACY
DEMOCRAT
DEMOCRATIC
DEMOCRATICALLY
DEMOCRATS
DEMODULATE
DEMODULATOR
DEMOGRAPHIC
DEMOLISH
DEMOLISHED
DEMOLISHES
DEMOLITION
DEMON
DEMONIAC
DEMONIC
DEMONS
DEMONSTRABLE
DEMONSTRATE
DEMONSTRATED
DEMONSTRATES
DEMONSTRATING
DEMONSTRATION
DEMONSTRATIONS
DEMONSTRATIVE
DEMONSTRATIVELY
DEMONSTRATOR
DEMONSTRATORS
DEMORALIZE
DEMORALIZED
DEMORALIZES
DEMORALIZING
DEMORGAN
DEMOTE
DEMOUNTABLE
DEMPSEY
DEMULTIPLEX
DEMULTIPLEXED
DEMULTIPLEXER
DEMULTIPLEXERS
DEMULTIPLEXING
DEMUR
DEMYTHOLOGIZE
DEN
DENATURE
DENEB
DENEBOLA
DENEEN
DENIABLE
DENIAL
DENIALS
DENIED
DENIER
DENIES
DENIGRATE
DENIGRATED
DENIGRATES
DENIGRATING
DENIZEN
DENMARK
DENNIS
DENNY
DENOMINATE
DENOMINATION
DENOMINATIONS
DENOMINATOR
DENOMINATORS
DENOTABLE
DENOTATION
DENOTATIONAL
DENOTATIONALLY
DENOTATIONS
DENOTATIVE
DENOTE
DENOTED
DENOTES
DENOTING
DENOUNCE
DENOUNCED
DENOUNCES
DENOUNCING
DENS
DENSE
DENSELY
DENSENESS
DENSER
DENSEST
DENSITIES
DENSITY
DENT
DENTAL
DENTALLY
DENTED
DENTING
DENTIST
DENTISTRY
DENTISTS
DENTON
DENTS
DENTURE
DENUDE
DENUMERABLE
DENUNCIATE
DENUNCIATION
DENVER
DENY
DENYING
DEODORANT
DEOXYRIBONUCLEIC
DEPART
DEPARTED
DEPARTING
DEPARTMENT
DEPARTMENTAL
DEPARTMENTS
DEPARTS
DEPARTURE
DEPARTURES
DEPEND
DEPENDABILITY
DEPENDABLE
DEPENDABLY
DEPENDED
DEPENDENCE
DEPENDENCIES
DEPENDENCY
DEPENDENT
DEPENDENTLY
DEPENDENTS
DEPENDING
DEPENDS
DEPICT
DEPICTED
DEPICTING
DEPICTS
DEPLETE
DEPLETED
DEPLETES
DEPLETING
DEPLETION
DEPLETIONS
DEPLORABLE
DEPLORE
DEPLORED
DEPLORES
DEPLORING
DEPLOY
DEPLOYED
DEPLOYING
DEPLOYMENT
DEPLOYMENTS
DEPLOYS
DEPORT
DEPORTATION
DEPORTEE
DEPORTMENT
DEPOSE
DEPOSED
DEPOSES
DEPOSIT
DEPOSITARY
DEPOSITED
DEPOSITING
DEPOSITION
DEPOSITIONS
DEPOSITOR
DEPOSITORS
DEPOSITORY
DEPOSITS
DEPOT
DEPOTS
DEPRAVE
DEPRAVED
DEPRAVITY
DEPRECATE
DEPRECIATE
DEPRECIATED
DEPRECIATES
DEPRECIATION
DEPRESS
DEPRESSED
DEPRESSES
DEPRESSING
DEPRESSION
DEPRESSIONS
DEPRIVATION
DEPRIVATIONS
DEPRIVE
DEPRIVED
DEPRIVES
DEPRIVING
DEPTH
DEPTHS
DEPUTIES
DEPUTY
DEQUEUE
DEQUEUED
DEQUEUES
DEQUEUING
DERAIL
DERAILED
DERAILING
DERAILS
DERBY
DERBYSHIRE
DEREFERENCE
DEREGULATE
DEREGULATED
DEREK
DERIDE
DERISION
DERIVABLE
DERIVATION
DERIVATIONS
DERIVATIVE
DERIVATIVES
DERIVE
DERIVED
DERIVES
DERIVING
DEROGATORY
DERRICK
DERRIERE
DERVISH
DES
DESCARTES
DESCEND
DESCENDANT
DESCENDANTS
DESCENDED
DESCENDENT
DESCENDER
DESCENDERS
DESCENDING
DESCENDS
DESCENT
DESCENTS
DESCRIBABLE
DESCRIBE
DESCRIBED
DESCRIBER
DESCRIBES
DESCRIBING
DESCRIPTION
DESCRIPTIONS
DESCRIPTIVE
DESCRIPTIVELY
DESCRIPTIVES
DESCRIPTOR
DESCRIPTORS
DESCRY
DESECRATE
DESEGREGATE
DESERT
DESERTED
DESERTER
DESERTERS
DESERTING
DESERTION
DESERTIONS
DESERTS
DESERVE
DESERVED
DESERVES
DESERVING
DESERVINGLY
DESERVINGS
DESIDERATA
DESIDERATUM
DESIGN
DESIGNATE
DESIGNATED
DESIGNATES
DESIGNATING
DESIGNATION
DESIGNATIONS
DESIGNATOR
DESIGNATORS
DESIGNED
DESIGNER
DESIGNERS
DESIGNING
DESIGNS
DESIRABILITY
DESIRABLE
DESIRABLY
DESIRE
DESIRED
DESIRES
DESIRING
DESIROUS
DESIST
DESK
DESKS
DESKTOP
DESMOND
DESOLATE
DESOLATELY
DESOLATION
DESOLATIONS
DESPAIR
DESPAIRED
DESPAIRING
DESPAIRINGLY
DESPAIRS
DESPATCH
DESPATCHED
DESPERADO
DESPERATE
DESPERATELY
DESPERATION
DESPICABLE
DESPISE
DESPISED
DESPISES
DESPISING
DESPITE
DESPOIL
DESPONDENT
DESPOT
DESPOTIC
DESPOTISM
DESPOTS
DESSERT
DESSERTS
DESSICATE
DESTABILIZE
DESTINATION
DESTINATIONS
DESTINE
DESTINED
DESTINIES
DESTINY
DESTITUTE
DESTITUTION
DESTROY
DESTROYED
DESTROYER
DESTROYERS
DESTROYING
DESTROYS
DESTRUCT
DESTRUCTION
DESTRUCTIONS
DESTRUCTIVE
DESTRUCTIVELY
DESTRUCTIVENESS
DESTRUCTOR
DESTUFF
DESTUFFING
DESTUFFS
DESUETUDE
DESULTORY
DESYNCHRONIZE
DETACH
DETACHED
DETACHER
DETACHES
DETACHING
DETACHMENT
DETACHMENTS
DETAIL
DETAILED
DETAILING
DETAILS
DETAIN
DETAINED
DETAINING
DETAINS
DETECT
DETECTABLE
DETECTABLY
DETECTED
DETECTING
DETECTION
DETECTIONS
DETECTIVE
DETECTIVES
DETECTOR
DETECTORS
DETECTS
DETENTE
DETENTION
DETER
DETERGENT
DETERIORATE
DETERIORATED
DETERIORATES
DETERIORATING
DETERIORATION
DETERMINABLE
DETERMINACY
DETERMINANT
DETERMINANTS
DETERMINATE
DETERMINATELY
DETERMINATION
DETERMINATIONS
DETERMINATIVE
DETERMINE
DETERMINED
DETERMINER
DETERMINERS
DETERMINES
DETERMINING
DETERMINISM
DETERMINISTIC
DETERMINISTICALLY
DETERRED
DETERRENT
DETERRING
DETEST
DETESTABLE
DETESTED
DETOUR
DETRACT
DETRACTOR
DETRACTORS
DETRACTS
DETRIMENT
DETRIMENTAL
DETROIT
DEUCE
DEUS
DEUTERIUM
DEUTSCH
DEVASTATE
DEVASTATED
DEVASTATES
DEVASTATING
DEVASTATION
DEVELOP
DEVELOPED
DEVELOPER
DEVELOPERS
DEVELOPING
DEVELOPMENT
DEVELOPMENTAL
DEVELOPMENTS
DEVELOPS
DEVIANT
DEVIANTS
DEVIATE
DEVIATED
DEVIATES
DEVIATING
DEVIATION
DEVIATIONS
DEVICE
DEVICES
DEVIL
DEVILISH
DEVILISHLY
DEVILS
DEVIOUS
DEVISE
DEVISED
DEVISES
DEVISING
DEVISINGS
DEVOID
DEVOLVE
DEVON
DEVONSHIRE
DEVOTE
DEVOTED
DEVOTEDLY
DEVOTEE
DEVOTEES
DEVOTES
DEVOTING
DEVOTION
DEVOTIONS
DEVOUR
DEVOURED
DEVOURER
DEVOURS
DEVOUT
DEVOUTLY
DEVOUTNESS
DEW
DEWDROP
DEWDROPS
DEWEY
DEWITT
DEWY
DEXEDRINE
DEXTERITY
DHABI
DIABETES
DIABETIC
DIABOLIC
DIACHRONIC
DIACRITICAL
DIADEM
DIAGNOSABLE
DIAGNOSE
DIAGNOSED
DIAGNOSES
DIAGNOSING
DIAGNOSIS
DIAGNOSTIC
DIAGNOSTICIAN
DIAGNOSTICS
DIAGONAL
DIAGONALLY
DIAGONALS
DIAGRAM
DIAGRAMMABLE
DIAGRAMMATIC
DIAGRAMMATICALLY
DIAGRAMMED
DIAGRAMMER
DIAGRAMMERS
DIAGRAMMING
DIAGRAMS
DIAL
DIALECT
DIALECTIC
DIALECTS
DIALED
DIALER
DIALERS
DIALING
DIALOG
DIALOGS
DIALOGUE
DIALOGUES
DIALS
DIALUP
DIALYSIS
DIAMAGNETIC
DIAMETER
DIAMETERS
DIAMETRIC
DIAMETRICALLY
DIAMOND
DIAMONDS
DIANA
DIANE
DIANNE
DIAPER
DIAPERS
DIAPHRAGM
DIAPHRAGMS
DIARIES
DIARRHEA
DIARY
DIATRIBE
DIATRIBES
DIBBLE
DICE
DICHOTOMIZE
DICHOTOMY
DICKENS
DICKERSON
DICKINSON
DICKSON
DICKY
DICTATE
DICTATED
DICTATES
DICTATING
DICTATION
DICTATIONS
DICTATOR
DICTATORIAL
DICTATORS
DICTATORSHIP
DICTION
DICTIONARIES
DICTIONARY
DICTUM
DICTUMS
DID
DIDACTIC
DIDDLE
DIDO
DIE
DIEBOLD
DIED
DIEGO
DIEHARD
DIELECTRIC
DIELECTRICS
DIEM
DIES
DIESEL
DIET
DIETARY
DIETER
DIETERS
DIETETIC
DIETICIAN
DIETITIAN
DIETITIANS
DIETRICH
DIETS
DIETZ
DIFFER
DIFFERED
DIFFERENCE
DIFFERENCES
DIFFERENT
DIFFERENTIABLE
DIFFERENTIAL
DIFFERENTIALS
DIFFERENTIATE
DIFFERENTIATED
DIFFERENTIATES
DIFFERENTIATING
DIFFERENTIATION
DIFFERENTIATIONS
DIFFERENTIATORS
DIFFERENTLY
DIFFERER
DIFFERERS
DIFFERING
DIFFERS
DIFFICULT
DIFFICULTIES
DIFFICULTLY
DIFFICULTY
DIFFRACT
DIFFUSE
DIFFUSED
DIFFUSELY
DIFFUSER
DIFFUSERS
DIFFUSES
DIFFUSIBLE
DIFFUSING
DIFFUSION
DIFFUSIONS
DIFFUSIVE
DIG
DIGEST
DIGESTED
DIGESTIBLE
DIGESTING
DIGESTION
DIGESTIVE
DIGESTS
DIGGER
DIGGERS
DIGGING
DIGGINGS
DIGIT
DIGITAL
DIGITALIS
DIGITALLY
DIGITIZATION
DIGITIZE
DIGITIZED
DIGITIZES
DIGITIZING
DIGITS
DIGNIFIED
DIGNIFY
DIGNITARY
DIGNITIES
DIGNITY
DIGRAM
DIGRESS
DIGRESSED
DIGRESSES
DIGRESSING
DIGRESSION
DIGRESSIONS
DIGRESSIVE
DIGS
DIHEDRAL
DIJKSTRA
DIJON
DIKE
DIKES
DILAPIDATE
DILATATION
DILATE
DILATED
DILATES
DILATING
DILATION
DILDO
DILEMMA
DILEMMAS
DILIGENCE
DILIGENT
DILIGENTLY
DILL
DILLON
DILOGARITHM
DILUTE
DILUTED
DILUTES
DILUTING
DILUTION
DIM
DIMAGGIO
DIME
DIMENSION
DIMENSIONAL
DIMENSIONALITY
DIMENSIONALLY
DIMENSIONED
DIMENSIONING
DIMENSIONS
DIMES
DIMINISH
DIMINISHED
DIMINISHES
DIMINISHING
DIMINUTION
DIMINUTIVE
DIMLY
DIMMED
DIMMER
DIMMERS
DIMMEST
DIMMING
DIMNESS
DIMPLE
DIMS
DIN
DINAH
DINE
DINED
DINER
DINERS
DINES
DING
DINGHY
DINGINESS
DINGO
DINGY
DINING
DINNER
DINNERS
DINNERTIME
DINNERWARE
DINOSAUR
DINT
DIOCLETIAN
DIODE
DIODES
DIOGENES
DION
DIONYSIAN
DIONYSUS
DIOPHANTINE
DIOPTER
DIORAMA
DIOXIDE
DIP
DIPHTHERIA
DIPHTHONG
DIPLOMA
DIPLOMACY
DIPLOMAS
DIPLOMAT
DIPLOMATIC
DIPLOMATS
DIPOLE
DIPPED
DIPPER
DIPPERS
DIPPING
DIPPINGS
DIPS
DIRAC
DIRE
DIRECT
DIRECTED
DIRECTING
DIRECTION
DIRECTIONAL
DIRECTIONALITY
DIRECTIONALLY
DIRECTIONS
DIRECTIVE
DIRECTIVES
DIRECTLY
DIRECTNESS
DIRECTOR
DIRECTORATE
DIRECTORIES
DIRECTORS
DIRECTORY
DIRECTRICES
DIRECTRIX
DIRECTS
DIRGE
DIRGES
DIRICHLET
DIRT
DIRTIER
DIRTIEST
DIRTILY
DIRTINESS
DIRTS
DIRTY
DIS
DISABILITIES
DISABILITY
DISABLE
DISABLED
DISABLER
DISABLERS
DISABLES
DISABLING
DISADVANTAGE
DISADVANTAGEOUS
DISADVANTAGES
DISAFFECTED
DISAFFECTION
DISAGREE
DISAGREEABLE
DISAGREED
DISAGREEING
DISAGREEMENT
DISAGREEMENTS
DISAGREES
DISALLOW
DISALLOWED
DISALLOWING
DISALLOWS
DISAMBIGUATE
DISAMBIGUATED
DISAMBIGUATES
DISAMBIGUATING
DISAMBIGUATION
DISAMBIGUATIONS
DISAPPEAR
DISAPPEARANCE
DISAPPEARANCES
DISAPPEARED
DISAPPEARING
DISAPPEARS
DISAPPOINT
DISAPPOINTED
DISAPPOINTING
DISAPPOINTMENT
DISAPPOINTMENTS
DISAPPROVAL
DISAPPROVE
DISAPPROVED
DISAPPROVES
DISARM
DISARMAMENT
DISARMED
DISARMING
DISARMS
DISASSEMBLE
DISASSEMBLED
DISASSEMBLES
DISASSEMBLING
DISASSEMBLY
DISASTER
DISASTERS
DISASTROUS
DISASTROUSLY
DISBAND
DISBANDED
DISBANDING
DISBANDS
DISBURSE
DISBURSED
DISBURSEMENT
DISBURSEMENTS
DISBURSES
DISBURSING
DISC
DISCARD
DISCARDED
DISCARDING
DISCARDS
DISCERN
DISCERNED
DISCERNIBILITY
DISCERNIBLE
DISCERNIBLY
DISCERNING
DISCERNINGLY
DISCERNMENT
DISCERNS
DISCHARGE
DISCHARGED
DISCHARGES
DISCHARGING
DISCIPLE
DISCIPLES
DISCIPLINARY
DISCIPLINE
DISCIPLINED
DISCIPLINES
DISCIPLINING
DISCLAIM
DISCLAIMED
DISCLAIMER
DISCLAIMS
DISCLOSE
DISCLOSED
DISCLOSES
DISCLOSING
DISCLOSURE
DISCLOSURES
DISCOMFORT
DISCONCERT
DISCONCERTING
DISCONCERTINGLY
DISCONNECT
DISCONNECTED
DISCONNECTING
DISCONNECTION
DISCONNECTS
DISCONTENT
DISCONTENTED
DISCONTINUANCE
DISCONTINUE
DISCONTINUED
DISCONTINUES
DISCONTINUITIES
DISCONTINUITY
DISCONTINUOUS
DISCORD
DISCORDANT
DISCOUNT
DISCOUNTED
DISCOUNTING
DISCOUNTS
DISCOURAGE
DISCOURAGED
DISCOURAGEMENT
DISCOURAGES
DISCOURAGING
DISCOURSE
DISCOURSES
DISCOVER
DISCOVERED
DISCOVERER
DISCOVERERS
DISCOVERIES
DISCOVERING
DISCOVERS
DISCOVERY
DISCREDIT
DISCREDITED
DISCREET
DISCREETLY
DISCREPANCIES
DISCREPANCY
DISCRETE
DISCRETELY
DISCRETENESS
DISCRETION
DISCRETIONARY
DISCRIMINANT
DISCRIMINATE
DISCRIMINATED
DISCRIMINATES
DISCRIMINATING
DISCRIMINATION
DISCRIMINATORY
DISCS
DISCUSS
DISCUSSANT
DISCUSSED
DISCUSSES
DISCUSSING
DISCUSSION
DISCUSSIONS
DISDAIN
DISDAINING
DISDAINS
DISEASE
DISEASED
DISEASES
DISEMBOWEL
DISENGAGE
DISENGAGED
DISENGAGES
DISENGAGING
DISENTANGLE
DISENTANGLING
DISFIGURE
DISFIGURED
DISFIGURES
DISFIGURING
DISGORGE
DISGRACE
DISGRACED
DISGRACEFUL
DISGRACEFULLY
DISGRACES
DISGRUNTLE
DISGRUNTLED
DISGUISE
DISGUISED
DISGUISES
DISGUST
DISGUSTED
DISGUSTEDLY
DISGUSTFUL
DISGUSTING
DISGUSTINGLY
DISGUSTS
DISH
DISHEARTEN
DISHEARTENING
DISHED
DISHES
DISHEVEL
DISHING
DISHONEST
DISHONESTLY
DISHONESTY
DISHONOR
DISHONORABLE
DISHONORED
DISHONORING
DISHONORS
DISHWASHER
DISHWASHERS
DISHWASHING
DISHWATER
DISILLUSION
DISILLUSIONED
DISILLUSIONING
DISILLUSIONMENT
DISILLUSIONMENTS
DISINCLINED
DISINGENUOUS
DISINTERESTED
DISINTERESTEDNESS
DISJOINT
DISJOINTED
DISJOINTLY
DISJOINTNESS
DISJUNCT
DISJUNCTION
DISJUNCTIONS
DISJUNCTIVE
DISJUNCTIVELY
DISJUNCTS
DISK
DISKETTE
DISKETTES
DISKS
DISLIKE
DISLIKED
DISLIKES
DISLIKING
DISLOCATE
DISLOCATED
DISLOCATES
DISLOCATING
DISLOCATION
DISLOCATIONS
DISLODGE
DISLODGED
DISMAL
DISMALLY
DISMAY
DISMAYED
DISMAYING
DISMEMBER
DISMEMBERED
DISMEMBERMENT
DISMEMBERS
DISMISS
DISMISSAL
DISMISSALS
DISMISSED
DISMISSER
DISMISSERS
DISMISSES
DISMISSING
DISMOUNT
DISMOUNTED
DISMOUNTING
DISMOUNTS
DISNEY
DISNEYLAND
DISOBEDIENCE
DISOBEDIENT
DISOBEY
DISOBEYED
DISOBEYING
DISOBEYS
DISORDER
DISORDERED
DISORDERLY
DISORDERS
DISORGANIZED
DISOWN
DISOWNED
DISOWNING
DISOWNS
DISPARAGE
DISPARATE
DISPARITIES
DISPARITY
DISPASSIONATE
DISPATCH
DISPATCHED
DISPATCHER
DISPATCHERS
DISPATCHES
DISPATCHING
DISPEL
DISPELL
DISPELLED
DISPELLING
DISPELS
DISPENSARY
DISPENSATION
DISPENSE
DISPENSED
DISPENSER
DISPENSERS
DISPENSES
DISPENSING
DISPERSAL
DISPERSE
DISPERSED
DISPERSES
DISPERSING
DISPERSION
DISPERSIONS
DISPLACE
DISPLACED
DISPLACEMENT
DISPLACEMENTS
DISPLACES
DISPLACING
DISPLAY
DISPLAYABLE
DISPLAYED
DISPLAYER
DISPLAYING
DISPLAYS
DISPLEASE
DISPLEASED
DISPLEASES
DISPLEASING
DISPLEASURE
DISPOSABLE
DISPOSAL
DISPOSALS
DISPOSE
DISPOSED
DISPOSER
DISPOSES
DISPOSING
DISPOSITION
DISPOSITIONS
DISPOSSESSED
DISPROPORTIONATE
DISPROVE
DISPROVED
DISPROVES
DISPROVING
DISPUTE
DISPUTED
DISPUTER
DISPUTERS
DISPUTES
DISPUTING
DISQUALIFICATION
DISQUALIFIED
DISQUALIFIES
DISQUALIFY
DISQUALIFYING
DISQUIET
DISQUIETING
DISRAELI
DISREGARD
DISREGARDED
DISREGARDING
DISREGARDS
DISRESPECTFUL
DISRUPT
DISRUPTED
DISRUPTING
DISRUPTION
DISRUPTIONS
DISRUPTIVE
DISRUPTS
DISSATISFACTION
DISSATISFACTIONS
DISSATISFACTORY
DISSATISFIED
DISSECT
DISSECTS
DISSEMBLE
DISSEMINATE
DISSEMINATED
DISSEMINATES
DISSEMINATING
DISSEMINATION
DISSENSION
DISSENSIONS
DISSENT
DISSENTED
DISSENTER
DISSENTERS
DISSENTING
DISSENTS
DISSERTATION
DISSERTATIONS
DISSERVICE
DISSIDENT
DISSIDENTS
DISSIMILAR
DISSIMILARITIES
DISSIMILARITY
DISSIPATE
DISSIPATED
DISSIPATES
DISSIPATING
DISSIPATION
DISSOCIATE
DISSOCIATED
DISSOCIATES
DISSOCIATING
DISSOCIATION
DISSOLUTION
DISSOLUTIONS
DISSOLVE
DISSOLVED
DISSOLVES
DISSOLVING
DISSONANT
DISSUADE
DISTAFF
DISTAL
DISTALLY
DISTANCE
DISTANCES
DISTANT
DISTANTLY
DISTASTE
DISTASTEFUL
DISTASTEFULLY
DISTASTES
DISTEMPER
DISTEMPERED
DISTEMPERS
DISTILL
DISTILLATION
DISTILLED
DISTILLER
DISTILLERS
DISTILLERY
DISTILLING
DISTILLS
DISTINCT
DISTINCTION
DISTINCTIONS
DISTINCTIVE
DISTINCTIVELY
DISTINCTIVENESS
DISTINCTLY
DISTINCTNESS
DISTINGUISH
DISTINGUISHABLE
DISTINGUISHED
DISTINGUISHES
DISTINGUISHING
DISTORT
DISTORTED
DISTORTING
DISTORTION
DISTORTIONS
DISTORTS
DISTRACT
DISTRACTED
DISTRACTING
DISTRACTION
DISTRACTIONS
DISTRACTS
DISTRAUGHT
DISTRESS
DISTRESSED
DISTRESSES
DISTRESSING
DISTRIBUTE
DISTRIBUTED
DISTRIBUTES
DISTRIBUTING
DISTRIBUTION
DISTRIBUTIONAL
DISTRIBUTIONS
DISTRIBUTIVE
DISTRIBUTIVITY
DISTRIBUTOR
DISTRIBUTORS
DISTRICT
DISTRICTS
DISTRUST
DISTRUSTED
DISTURB
DISTURBANCE
DISTURBANCES
DISTURBED
DISTURBER
DISTURBING
DISTURBINGLY
DISTURBS
DISUSE
DITCH
DITCHES
DITHER
DITTO
DITTY
DITZEL
DIURNAL
DIVAN
DIVANS
DIVE
DIVED
DIVER
DIVERGE
DIVERGED
DIVERGENCE
DIVERGENCES
DIVERGENT
DIVERGES
DIVERGING
DIVERS
DIVERSE
DIVERSELY
DIVERSIFICATION
DIVERSIFIED
DIVERSIFIES
DIVERSIFY
DIVERSIFYING
DIVERSION
DIVERSIONARY
DIVERSIONS
DIVERSITIES
DIVERSITY
DIVERT
DIVERTED
DIVERTING
DIVERTS
DIVES
DIVEST
DIVESTED
DIVESTING
DIVESTITURE
DIVESTS
DIVIDE
DIVIDED
DIVIDEND
DIVIDENDS
DIVIDER
DIVIDERS
DIVIDES
DIVIDING
DIVINE
DIVINELY
DIVINER
DIVING
DIVINING
DIVINITIES
DIVINITY
DIVISIBILITY
DIVISIBLE
DIVISION
DIVISIONAL
DIVISIONS
DIVISIVE
DIVISOR
DIVISORS
DIVORCE
DIVORCED
DIVORCEE
DIVULGE
DIVULGED
DIVULGES
DIVULGING
DIXIE
DIXIECRATS
DIXIELAND
DIXON
DIZZINESS
DIZZY
DJAKARTA
DMITRI
DNIEPER
DOBBIN
DOBBS
DOBERMAN
DOC
DOCILE
DOCK
DOCKED
DOCKET
DOCKS
DOCKSIDE
DOCKYARD
DOCTOR
DOCTORAL
DOCTORATE
DOCTORATES
DOCTORED
DOCTORS
DOCTRINAIRE
DOCTRINAL
DOCTRINE
DOCTRINES
DOCUMENT
DOCUMENTARIES
DOCUMENTARY
DOCUMENTATION
DOCUMENTATIONS
DOCUMENTED
DOCUMENTER
DOCUMENTERS
DOCUMENTING
DOCUMENTS
DODD
DODECAHEDRA
DODECAHEDRAL
DODECAHEDRON
DODGE
DODGED
DODGER
DODGERS
DODGING
DODINGTON
DODSON
DOE
DOER
DOERS
DOES
DOG
DOGE
DOGGED
DOGGEDLY
DOGGEDNESS
DOGGING
DOGHOUSE
DOGMA
DOGMAS
DOGMATIC
DOGMATISM
DOGS
DOGTOWN
DOHERTY
DOING
DOINGS
DOLAN
DOLDRUM
DOLE
DOLED
DOLEFUL
DOLEFULLY
DOLES
DOLL
DOLLAR
DOLLARS
DOLLIES
DOLLS
DOLLY
DOLORES
DOLPHIN
DOLPHINS
DOMAIN
DOMAINS
DOME
DOMED
DOMENICO
DOMES
DOMESDAY
DOMESTIC
DOMESTICALLY
DOMESTICATE
DOMESTICATED
DOMESTICATES
DOMESTICATING
DOMESTICATION
DOMICILE
DOMINANCE
DOMINANT
DOMINANTLY
DOMINATE
DOMINATED
DOMINATES
DOMINATING
DOMINATION
DOMINEER
DOMINEERING
DOMINGO
DOMINIC
DOMINICAN
DOMINICANS
DOMINICK
DOMINION
DOMINIQUE
DOMINO
DON
DONAHUE
DONALD
DONALDSON
DONATE
DONATED
DONATES
DONATING
DONATION
DONE
DONECK
DONKEY
DONKEYS
DONNA
DONNELLY
DONNER
DONNYBROOK
DONOR
DONOVAN
DONS
DOODLE
DOOLEY
DOOLITTLE
DOOM
DOOMED
DOOMING
DOOMS
DOOMSDAY
DOOR
DOORBELL
DOORKEEPER
DOORMAN
DOORMEN
DOORS
DOORSTEP
DOORSTEPS
DOORWAY
DOORWAYS
DOPE
DOPED
DOPER
DOPERS
DOPES
DOPING
DOPPLER
DORA
DORADO
DORCAS
DORCHESTER
DOREEN
DORIA
DORIC
DORICIZE
DORICIZES
DORIS
DORMANT
DORMITORIES
DORMITORY
DOROTHEA
DOROTHY
DORSET
DORTMUND
DOSAGE
DOSE
DOSED
DOSES
DOSSIER
DOSSIERS
DOSTOEVSKY
DOT
DOTE
DOTED
DOTES
DOTING
DOTINGLY
DOTS
DOTTED
DOTTING
DOUBLE
DOUBLED
DOUBLEDAY
DOUBLEHEADER
DOUBLER
DOUBLERS
DOUBLES
DOUBLET
DOUBLETON
DOUBLETS
DOUBLING
DOUBLOON
DOUBLY
DOUBT
DOUBTABLE
DOUBTED
DOUBTER
DOUBTERS
DOUBTFUL
DOUBTFULLY
DOUBTING
DOUBTLESS
DOUBTLESSLY
DOUBTS
DOUG
DOUGH
DOUGHERTY
DOUGHNUT
DOUGHNUTS
DOUGLAS
DOUGLASS
DOVE
DOVER
DOVES
DOVETAIL
DOW
DOWAGER
DOWEL
DOWLING
DOWN
DOWNCAST
DOWNED
DOWNERS
DOWNEY
DOWNFALL
DOWNFALLEN
DOWNGRADE
DOWNHILL
DOWNING
DOWNLINK
DOWNLINKS
DOWNLOAD
DOWNLOADED
DOWNLOADING
DOWNLOADS
DOWNPLAY
DOWNPLAYED
DOWNPLAYING
DOWNPLAYS
DOWNPOUR
DOWNRIGHT
DOWNS
DOWNSIDE
DOWNSTAIRS
DOWNSTREAM
DOWNTOWN
DOWNTOWNS
DOWNTRODDEN
DOWNTURN
DOWNWARD
DOWNWARDS
DOWNY
DOWRY
DOYLE
DOZE
DOZED
DOZEN
DOZENS
DOZENTH
DOZES
DOZING
DRAB
DRACO
DRACONIAN
DRAFT
DRAFTED
DRAFTEE
DRAFTER
DRAFTERS
DRAFTING
DRAFTS
DRAFTSMAN
DRAFTSMEN
DRAFTY
DRAG
DRAGGED
DRAGGING
DRAGNET
DRAGON
DRAGONFLY
DRAGONHEAD
DRAGONS
DRAGOON
DRAGOONED
DRAGOONS
DRAGS
DRAIN
DRAINAGE
DRAINED
DRAINER
DRAINING
DRAINS
DRAKE
DRAM
DRAMA
DRAMAMINE
DRAMAS
DRAMATIC
DRAMATICALLY
DRAMATICS
DRAMATIST
DRAMATISTS
DRANK
DRAPE
DRAPED
DRAPER
DRAPERIES
DRAPERS
DRAPERY
DRAPES
DRASTIC
DRASTICALLY
DRAUGHT
DRAUGHTS
DRAVIDIAN
DRAW
DRAWBACK
DRAWBACKS
DRAWBRIDGE
DRAWBRIDGES
DRAWER
DRAWERS
DRAWING
DRAWINGS
DRAWL
DRAWLED
DRAWLING
DRAWLS
DRAWN
DRAWNLY
DRAWNNESS
DRAWS
DREAD
DREADED
DREADFUL
DREADFULLY
DREADING
DREADNOUGHT
DREADS
DREAM
DREAMBOAT
DREAMED
DREAMER
DREAMERS
DREAMILY
DREAMING
DREAMLIKE
DREAMS
DREAMT
DREAMY
DREARINESS
DREARY
DREDGE
DREGS
DRENCH
DRENCHED
DRENCHES
DRENCHING
DRESS
DRESSED
DRESSER
DRESSERS
DRESSES
DRESSING
DRESSINGS
DRESSMAKER
DRESSMAKERS
DREW
DREXEL
DREYFUSS
DRIED
DRIER
DRIERS
DRIES
DRIEST
DRIFT
DRIFTED
DRIFTER
DRIFTERS
DRIFTING
DRIFTS
DRILL
DRILLED
DRILLER
DRILLING
DRILLS
DRILY
DRINK
DRINKABLE
DRINKER
DRINKERS
DRINKING
DRINKS
DRIP
DRIPPING
DRIPPY
DRIPS
DRISCOLL
DRIVE
DRIVEN
DRIVER
DRIVERS
DRIVES
DRIVEWAY
DRIVEWAYS
DRIVING
DRIZZLE
DRIZZLY
DROLL
DROMEDARY
DRONE
DRONES
DROOL
DROOP
DROOPED
DROOPING
DROOPS
DROOPY
DROP
DROPLET
DROPOUT
DROPPED
DROPPER
DROPPERS
DROPPING
DROPPINGS
DROPS
DROSOPHILA
DROUGHT
DROUGHTS
DROVE
DROVER
DROVERS
DROVES
DROWN
DROWNED
DROWNING
DROWNINGS
DROWNS
DROWSINESS
DROWSY
DRUBBING
DRUDGE
DRUDGERY
DRUG
DRUGGIST
DRUGGISTS
DRUGS
DRUGSTORE
DRUM
DRUMHEAD
DRUMMED
DRUMMER
DRUMMERS
DRUMMING
DRUMMOND
DRUMS
DRUNK
DRUNKARD
DRUNKARDS
DRUNKEN
DRUNKENNESS
DRUNKER
DRUNKLY
DRUNKS
DRURY
DRY
DRYDEN
DRYING
DRYLY
DUAL
DUALISM
DUALITIES
DUALITY
DUANE
DUB
DUBBED
DUBHE
DUBIOUS
DUBIOUSLY
DUBIOUSNESS
DUBLIN
DUBS
DUBUQUE
DUCHESS
DUCHESSES
DUCHY
DUCK
DUCKED
DUCKING
DUCKLING
DUCKS
DUCT
DUCTS
DUD
DUDLEY
DUE
DUEL
DUELING
DUELS
DUES
DUET
DUFFY
DUG
DUGAN
DUKE
DUKES
DULL
DULLED
DULLER
DULLES
DULLEST
DULLING
DULLNESS
DULLS
DULLY
DULUTH
DULY
DUMB
DUMBBELL
DUMBBELLS
DUMBER
DUMBEST
DUMBLY
DUMBNESS
DUMMIES
DUMMY
DUMP
DUMPED
DUMPER
DUMPING
DUMPS
DUMPTY
DUNBAR
DUNCAN
DUNCE
DUNCES
DUNDEE
DUNE
DUNEDIN
DUNES
DUNG
DUNGEON
DUNGEONS
DUNHAM
DUNK
DUNKIRK
DUNLAP
DUNLOP
DUNN
DUNNE
DUPE
DUPLEX
DUPLICABLE
DUPLICATE
DUPLICATED
DUPLICATES
DUPLICATING
DUPLICATION
DUPLICATIONS
DUPLICATOR
DUPLICATORS
DUPLICITY
DUPONT
DUPONT
DUPONTS
DUPONTS
DUQUESNE
DURABILITIES
DURABILITY
DURABLE
DURABLY
DURANGO
DURATION
DURATIONS
DURER
DURERS
DURESS
DURHAM
DURING
DURKEE
DURKIN
DURRELL
DURWARD
DUSENBERG
DUSENBURY
DUSK
DUSKINESS
DUSKY
DUSSELDORF
DUST
DUSTBIN
DUSTED
DUSTER
DUSTERS
DUSTIER
DUSTIEST
DUSTIN
DUSTING
DUSTS
DUSTY
DUTCH
DUTCHESS
DUTCHMAN
DUTCHMEN
DUTIES
DUTIFUL
DUTIFULLY
DUTIFULNESS
DUTTON
DUTY
DVORAK
DWARF
DWARFED
DWARFS
DWARVES
DWELL
DWELLED
DWELLER
DWELLERS
DWELLING
DWELLINGS
DWELLS
DWELT
DWIGHT
DWINDLE
DWINDLED
DWINDLING
DWYER
DYAD
DYADIC
DYE
DYED
DYEING
DYER
DYERS
DYES
DYING
DYKE
DYLAN
DYNAMIC
DYNAMICALLY
DYNAMICS
DYNAMISM
DYNAMITE
DYNAMITED
DYNAMITES
DYNAMITING
DYNAMO
DYNASTIC
DYNASTIES
DYNASTY
DYNE
DYSENTERY
DYSPEPTIC
DYSTROPHY
EACH
EAGAN
EAGER
EAGERLY
EAGERNESS
EAGLE
EAGLES
EAR
EARDRUM
EARED
EARL
EARLIER
EARLIEST
EARLINESS
EARLS
EARLY
EARMARK
EARMARKED
EARMARKING
EARMARKINGS
EARMARKS
EARN
EARNED
EARNER
EARNERS
EARNEST
EARNESTLY
EARNESTNESS
EARNING
EARNINGS
EARNS
EARP
EARPHONE
EARRING
EARRINGS
EARS
EARSPLITTING
EARTH
EARTHEN
EARTHENWARE
EARTHLINESS
EARTHLING
EARTHLY
EARTHMAN
EARTHMEN
EARTHMOVER
EARTHQUAKE
EARTHQUAKES
EARTHS
EARTHWORM
EARTHWORMS
EARTHY
EASE
EASED
EASEL
EASEMENT
EASEMENTS
EASES
EASIER
EASIEST
EASILY
EASINESS
EASING
EAST
EASTBOUND
EASTER
EASTERN
EASTERNER
EASTERNERS
EASTERNMOST
EASTHAMPTON
EASTLAND
EASTMAN
EASTWARD
EASTWARDS
EASTWICK
EASTWOOD
EASY
EASYGOING
EAT
EATEN
EATER
EATERS
EATING
EATINGS
EATON
EATS
EAVES
EAVESDROP
EAVESDROPPED
EAVESDROPPER
EAVESDROPPERS
EAVESDROPPING
EAVESDROPS
EBB
EBBING
EBBS
EBEN
EBONY
ECCENTRIC
ECCENTRICITIES
ECCENTRICITY
ECCENTRICS
ECCLES
ECCLESIASTICAL
ECHELON
ECHO
ECHOED
ECHOES
ECHOING
ECLECTIC
ECLIPSE
ECLIPSED
ECLIPSES
ECLIPSING
ECLIPTIC
ECOLE
ECOLOGY
ECONOMETRIC
ECONOMETRICA
ECONOMIC
ECONOMICAL
ECONOMICALLY
ECONOMICS
ECONOMIES
ECONOMIST
ECONOMISTS
ECONOMIZE
ECONOMIZED
ECONOMIZER
ECONOMIZERS
ECONOMIZES
ECONOMIZING
ECONOMY
ECOSYSTEM
ECSTASY
ECSTATIC
ECUADOR
ECUADORIAN
EDDIE
EDDIES
EDDY
EDEN
EDENIZATION
EDENIZATIONS
EDENIZE
EDENIZES
EDGAR
EDGE
EDGED
EDGERTON
EDGES
EDGEWATER
EDGEWOOD
EDGING
EDIBLE
EDICT
EDICTS
EDIFICE
EDIFICES
EDINBURGH
EDISON
EDIT
EDITED
EDITH
EDITING
EDITION
EDITIONS
EDITOR
EDITORIAL
EDITORIALLY
EDITORIALS
EDITORS
EDITS
EDMONDS
EDMONDSON
EDMONTON
EDMUND
EDNA
EDSGER
EDUARD
EDUARDO
EDUCABLE
EDUCATE
EDUCATED
EDUCATES
EDUCATING
EDUCATION
EDUCATIONAL
EDUCATIONALLY
EDUCATIONS
EDUCATOR
EDUCATORS
EDWARD
EDWARDIAN
EDWARDINE
EDWARDS
EDWIN
EDWINA
EEL
EELGRASS
EELS
EERIE
EERILY
EFFECT
EFFECTED
EFFECTING
EFFECTIVE
EFFECTIVELY
EFFECTIVENESS
EFFECTOR
EFFECTORS
EFFECTS
EFFECTUALLY
EFFECTUATE
EFFEMINATE
EFFICACY
EFFICIENCIES
EFFICIENCY
EFFICIENT
EFFICIENTLY
EFFIE
EFFIGY
EFFORT
EFFORTLESS
EFFORTLESSLY
EFFORTLESSNESS
EFFORTS
EGALITARIAN
EGAN
EGG
EGGED
EGGHEAD
EGGING
EGGPLANT
EGGS
EGGSHELL
EGO
EGOCENTRIC
EGOS
EGOTISM
EGOTIST
EGYPT
EGYPTIAN
EGYPTIANIZATION
EGYPTIANIZATIONS
EGYPTIANIZE
EGYPTIANIZES
EGYPTIANS
EGYPTIZE
EGYPTIZES
EGYPTOLOGY
EHRLICH
EICHMANN
EIFFEL
EIGENFUNCTION
EIGENSTATE
EIGENVALUE
EIGENVALUES
EIGENVECTOR
EIGHT
EIGHTEEN
EIGHTEENS
EIGHTEENTH
EIGHTFOLD
EIGHTH
EIGHTHES
EIGHTIES
EIGHTIETH
EIGHTS
EIGHTY
EILEEN
EINSTEIN
EINSTEINIAN
EIRE
EISENHOWER
EISNER
EITHER
EJACULATE
EJACULATED
EJACULATES
EJACULATING
EJACULATION
EJACULATIONS
EJECT
EJECTED
EJECTING
EJECTS
EKBERG
EKE
EKED
EKES
EKSTROM
EKTACHROME
ELABORATE
ELABORATED
ELABORATELY
ELABORATENESS
ELABORATES
ELABORATING
ELABORATION
ELABORATIONS
ELABORATORS
ELAINE
ELAPSE
ELAPSED
ELAPSES
ELAPSING
ELASTIC
ELASTICALLY
ELASTICITY
ELBA
ELBOW
ELBOWING
ELBOWS
ELDER
ELDERLY
ELDERS
ELDEST
ELDON
ELEANOR
ELEAZAR
ELECT
ELECTED
ELECTING
ELECTION
ELECTIONS
ELECTIVE
ELECTIVES
ELECTOR
ELECTORAL
ELECTORATE
ELECTORS
ELECTRA
ELECTRIC
ELECTRICAL
ELECTRICALLY
ELECTRICALNESS
ELECTRICIAN
ELECTRICITY
ELECTRIFICATION
ELECTRIFY
ELECTRIFYING
ELECTRO
ELECTROCARDIOGRAM
ELECTROCARDIOGRAPH
ELECTROCUTE
ELECTROCUTED
ELECTROCUTES
ELECTROCUTING
ELECTROCUTION
ELECTROCUTIONS
ELECTRODE
ELECTRODES
ELECTROENCEPHALOGRAM
ELECTROENCEPHALOGRAPH
ELECTROENCEPHALOGRAPHY
ELECTROLYSIS
ELECTROLYTE
ELECTROLYTES
ELECTROLYTIC
ELECTROMAGNETIC
ELECTROMECHANICAL
ELECTRON
ELECTRONIC
ELECTRONICALLY
ELECTRONICS
ELECTRONS
ELECTROPHORESIS
ELECTROPHORUS
ELECTS
ELEGANCE
ELEGANT
ELEGANTLY
ELEGY
ELEMENT
ELEMENTAL
ELEMENTALS
ELEMENTARY
ELEMENTS
ELENA
ELEPHANT
ELEPHANTS
ELEVATE
ELEVATED
ELEVATES
ELEVATION
ELEVATOR
ELEVATORS
ELEVEN
ELEVENS
ELEVENTH
ELF
ELGIN
ELI
ELICIT
ELICITED
ELICITING
ELICITS
ELIDE
ELIGIBILITY
ELIGIBLE
ELIJAH
ELIMINATE
ELIMINATED
ELIMINATES
ELIMINATING
ELIMINATION
ELIMINATIONS
ELIMINATOR
ELIMINATORS
ELINOR
ELIOT
ELISABETH
ELISHA
ELISION
ELITE
ELITIST
ELIZABETH
ELIZABETHAN
ELIZABETHANIZE
ELIZABETHANIZES
ELIZABETHANS
ELK
ELKHART
ELKS
ELLA
ELLEN
ELLIE
ELLIOT
ELLIOTT
ELLIPSE
ELLIPSES
ELLIPSIS
ELLIPSOID
ELLIPSOIDAL
ELLIPSOIDS
ELLIPTIC
ELLIPTICAL
ELLIPTICALLY
ELLIS
ELLISON
ELLSWORTH
ELLWOOD
ELM
ELMER
ELMHURST
ELMIRA
ELMS
ELMSFORD
ELOISE
ELOPE
ELOQUENCE
ELOQUENT
ELOQUENTLY
ELROY
ELSE
ELSEVIER
ELSEWHERE
ELSIE
ELSINORE
ELTON
ELUCIDATE
ELUCIDATED
ELUCIDATES
ELUCIDATING
ELUCIDATION
ELUDE
ELUDED
ELUDES
ELUDING
ELUSIVE
ELUSIVELY
ELUSIVENESS
ELVES
ELVIS
ELY
ELYSEE
ELYSEES
ELYSIUM
EMACIATE
EMACIATED
EMACS
EMANATE
EMANATING
EMANCIPATE
EMANCIPATION
EMANUEL
EMASCULATE
EMBALM
EMBARGO
EMBARGOES
EMBARK
EMBARKED
EMBARKS
EMBARRASS
EMBARRASSED
EMBARRASSES
EMBARRASSING
EMBARRASSMENT
EMBASSIES
EMBASSY
EMBED
EMBEDDED
EMBEDDING
EMBEDS
EMBELLISH
EMBELLISHED
EMBELLISHES
EMBELLISHING
EMBELLISHMENT
EMBELLISHMENTS
EMBER
EMBEZZLE
EMBLEM
EMBODIED
EMBODIES
EMBODIMENT
EMBODIMENTS
EMBODY
EMBODYING
EMBOLDEN
EMBRACE
EMBRACED
EMBRACES
EMBRACING
EMBROIDER
EMBROIDERED
EMBROIDERIES
EMBROIDERS
EMBROIDERY
EMBROIL
EMBRYO
EMBRYOLOGY
EMBRYOS
EMERALD
EMERALDS
EMERGE
EMERGED
EMERGENCE
EMERGENCIES
EMERGENCY
EMERGENT
EMERGES
EMERGING
EMERITUS
EMERSON
EMERY
EMIGRANT
EMIGRANTS
EMIGRATE
EMIGRATED
EMIGRATES
EMIGRATING
EMIGRATION
EMIL
EMILE
EMILIO
EMILY
EMINENCE
EMINENT
EMINENTLY
EMISSARY
EMISSION
EMIT
EMITS
EMITTED
EMITTER
EMITTING
EMMA
EMMANUEL
EMMETT
EMORY
EMOTION
EMOTIONAL
EMOTIONALLY
EMOTIONS
EMPATHY
EMPEROR
EMPERORS
EMPHASES
EMPHASIS
EMPHASIZE
EMPHASIZED
EMPHASIZES
EMPHASIZING
EMPHATIC
EMPHATICALLY
EMPIRE
EMPIRES
EMPIRICAL
EMPIRICALLY
EMPIRICIST
EMPIRICISTS
EMPLOY
EMPLOYABLE
EMPLOYED
EMPLOYEE
EMPLOYEES
EMPLOYER
EMPLOYERS
EMPLOYING
EMPLOYMENT
EMPLOYMENTS
EMPLOYS
EMPORIUM
EMPOWER
EMPOWERED
EMPOWERING
EMPOWERS
EMPRESS
EMPTIED
EMPTIER
EMPTIES
EMPTIEST
EMPTILY
EMPTINESS
EMPTY
EMPTYING
EMULATE
EMULATED
EMULATES
EMULATING
EMULATION
EMULATIONS
EMULATOR
EMULATORS
ENABLE
ENABLED
ENABLER
ENABLERS
ENABLES
ENABLING
ENACT
ENACTED
ENACTING
ENACTMENT
ENACTS
ENAMEL
ENAMELED
ENAMELING
ENAMELS
ENCAMP
ENCAMPED
ENCAMPING
ENCAMPS
ENCAPSULATE
ENCAPSULATED
ENCAPSULATES
ENCAPSULATING
ENCAPSULATION
ENCASED
ENCHANT
ENCHANTED
ENCHANTER
ENCHANTING
ENCHANTMENT
ENCHANTRESS
ENCHANTS
ENCIPHER
ENCIPHERED
ENCIPHERING
ENCIPHERS
ENCIRCLE
ENCIRCLED
ENCIRCLES
ENCLOSE
ENCLOSED
ENCLOSES
ENCLOSING
ENCLOSURE
ENCLOSURES
ENCODE
ENCODED
ENCODER
ENCODERS
ENCODES
ENCODING
ENCODINGS
ENCOMPASS
ENCOMPASSED
ENCOMPASSES
ENCOMPASSING
ENCORE
ENCOUNTER
ENCOUNTERED
ENCOUNTERING
ENCOUNTERS
ENCOURAGE
ENCOURAGED
ENCOURAGEMENT
ENCOURAGEMENTS
ENCOURAGES
ENCOURAGING
ENCOURAGINGLY
ENCROACH
ENCRUST
ENCRYPT
ENCRYPTED
ENCRYPTING
ENCRYPTION
ENCRYPTIONS
ENCRYPTS
ENCUMBER
ENCUMBERED
ENCUMBERING
ENCUMBERS
ENCYCLOPEDIA
ENCYCLOPEDIAS
ENCYCLOPEDIC
END
ENDANGER
ENDANGERED
ENDANGERING
ENDANGERS
ENDEAR
ENDEARED
ENDEARING
ENDEARS
ENDEAVOR
ENDEAVORED
ENDEAVORING
ENDEAVORS
ENDED
ENDEMIC
ENDER
ENDERS
ENDGAME
ENDICOTT
ENDING
ENDINGS
ENDLESS
ENDLESSLY
ENDLESSNESS
ENDORSE
ENDORSED
ENDORSEMENT
ENDORSES
ENDORSING
ENDOW
ENDOWED
ENDOWING
ENDOWMENT
ENDOWMENTS
ENDOWS
ENDPOINT
ENDS
ENDURABLE
ENDURABLY
ENDURANCE
ENDURE
ENDURED
ENDURES
ENDURING
ENDURINGLY
ENEMA
ENEMAS
ENEMIES
ENEMY
ENERGETIC
ENERGIES
ENERGIZE
ENERGY
ENERVATE
ENFEEBLE
ENFIELD
ENFORCE
ENFORCEABLE
ENFORCED
ENFORCEMENT
ENFORCER
ENFORCERS
ENFORCES
ENFORCING
ENFRANCHISE
ENG
ENGAGE
ENGAGED
ENGAGEMENT
ENGAGEMENTS
ENGAGES
ENGAGING
ENGAGINGLY
ENGEL
ENGELS
ENGENDER
ENGENDERED
ENGENDERING
ENGENDERS
ENGINE
ENGINEER
ENGINEERED
ENGINEERING
ENGINEERS
ENGINES
ENGLAND
ENGLANDER
ENGLANDERS
ENGLE
ENGLEWOOD
ENGLISH
ENGLISHIZE
ENGLISHIZES
ENGLISHMAN
ENGLISHMEN
ENGRAVE
ENGRAVED
ENGRAVER
ENGRAVES
ENGRAVING
ENGRAVINGS
ENGROSS
ENGROSSED
ENGROSSING
ENGULF
ENHANCE
ENHANCED
ENHANCEMENT
ENHANCEMENTS
ENHANCES
ENHANCING
ENID
ENIGMA
ENIGMATIC
ENJOIN
ENJOINED
ENJOINING
ENJOINS
ENJOY
ENJOYABLE
ENJOYABLY
ENJOYED
ENJOYING
ENJOYMENT
ENJOYS
ENLARGE
ENLARGED
ENLARGEMENT
ENLARGEMENTS
ENLARGER
ENLARGERS
ENLARGES
ENLARGING
ENLIGHTEN
ENLIGHTENED
ENLIGHTENING
ENLIGHTENMENT
ENLIST
ENLISTED
ENLISTMENT
ENLISTS
ENLIVEN
ENLIVENED
ENLIVENING
ENLIVENS
ENMITIES
ENMITY
ENNOBLE
ENNOBLED
ENNOBLES
ENNOBLING
ENNUI
ENOCH
ENORMITIES
ENORMITY
ENORMOUS
ENORMOUSLY
ENOS
ENOUGH
ENQUEUE
ENQUEUED
ENQUEUES
ENQUIRE
ENQUIRED
ENQUIRER
ENQUIRES
ENQUIRY
ENRAGE
ENRAGED
ENRAGES
ENRAGING
ENRAPTURE
ENRICH
ENRICHED
ENRICHES
ENRICHING
ENRICO
ENROLL
ENROLLED
ENROLLING
ENROLLMENT
ENROLLMENTS
ENROLLS
ENSEMBLE
ENSEMBLES
ENSIGN
ENSIGNS
ENSLAVE
ENSLAVED
ENSLAVES
ENSLAVING
ENSNARE
ENSNARED
ENSNARES
ENSNARING
ENSOLITE
ENSUE
ENSUED
ENSUES
ENSUING
ENSURE
ENSURED
ENSURER
ENSURERS
ENSURES
ENSURING
ENTAIL
ENTAILED
ENTAILING
ENTAILS
ENTANGLE
ENTER
ENTERED
ENTERING
ENTERPRISE
ENTERPRISES
ENTERPRISING
ENTERS
ENTERTAIN
ENTERTAINED
ENTERTAINER
ENTERTAINERS
ENTERTAINING
ENTERTAININGLY
ENTERTAINMENT
ENTERTAINMENTS
ENTERTAINS
ENTHUSIASM
ENTHUSIASMS
ENTHUSIAST
ENTHUSIASTIC
ENTHUSIASTICALLY
ENTHUSIASTS
ENTICE
ENTICED
ENTICER
ENTICERS
ENTICES
ENTICING
ENTIRE
ENTIRELY
ENTIRETIES
ENTIRETY
ENTITIES
ENTITLE
ENTITLED
ENTITLES
ENTITLING
ENTITY
ENTOMB
ENTRANCE
ENTRANCED
ENTRANCES
ENTRAP
ENTREAT
ENTREATED
ENTREATY
ENTREE
ENTRENCH
ENTRENCHED
ENTRENCHES
ENTRENCHING
ENTREPRENEUR
ENTREPRENEURIAL
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ENTRIES
ENTROPY
ENTRUST
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ENTRY
ENUMERABLE
ENUMERATE
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ENUMERATING
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ENUMERATIVE
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ENVELOP
ENVELOPE
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ENVIRON
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ENVIRONMENTAL
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ENVISION
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ENVY
ENZYME
EOCENE
EPAULET
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EPHEMERAL
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EPHESUS
EPHRAIM
EPIC
EPICENTER
EPICS
EPICUREAN
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EPICURUS
EPIDEMIC
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EPIGRAM
EPILEPTIC
EPILOGUE
EPIPHANY
EPISCOPAL
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EPISODE
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EPISTEMOLOGICAL
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EPISTLE
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EPITAPH
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EPITAXIAL
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EPITHET
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EPITOMIZE
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EPITOMIZING
EPOCH
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EPSILON
EPSOM
EPSTEIN
EQUAL
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EQUALING
EQUALITIES
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EQUALIZATION
EQUALIZE
EQUALIZED
EQUALIZER
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EQUALIZING
EQUALLY
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EQUATE
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EQUATING
EQUATION
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EQUATOR
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EQUESTRIAN
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EQUILATERAL
EQUILIBRATE
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EQUINOX
EQUIP
EQUIPMENT
EQUIPOISE
EQUIPPED
EQUIPPING
EQUIPS
EQUITABLE
EQUITABLY
EQUITY
EQUIVALENCE
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EQUIVALENT
EQUIVALENTLY
EQUIVALENTS
EQUIVOCAL
EQUIVOCALLY
ERA
ERADICATE
ERADICATED
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ERADICATING
ERADICATION
ERAS
ERASABLE
ERASE
ERASED
ERASER
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ERASING
ERASMUS
ERASTUS
ERASURE
ERATO
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ERE
ERECT
ERECTED
ERECTING
ERECTION
ERECTIONS
ERECTOR
ERECTORS
ERECTS
ERG
ERGO
ERGODIC
ERIC
ERICH
ERICKSON
ERICSSON
ERIE
ERIK
ERIKSON
ERIS
ERLANG
ERLENMEYER
ERLENMEYERS
ERMINE
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ERNE
ERNEST
ERNESTINE
ERNIE
ERNST
ERODE
EROS
EROSION
EROTIC
EROTICA
ERR
ERRAND
ERRANT
ERRATA
ERRATIC
ERRATUM
ERRED
ERRING
ERRINGLY
ERROL
ERRONEOUS
ERRONEOUSLY
ERRONEOUSNESS
ERROR
ERRORS
ERRS
ERSATZ
ERSKINE
ERUDITE
ERUPT
ERUPTION
ERVIN
ERWIN
ESCALATE
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ESCALATING
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ESCAPABLE
ESCAPADE
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ESCAPING
ESCHERICHIA
ESCHEW
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ESCORT
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ESCORTING
ESCORTS
ESCROW
ESKIMO
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ESKIMOS
ESMARK
ESOTERIC
ESPAGNOL
ESPECIAL
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ESPIONAGE
ESPOSITO
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ESPOUSED
ESPOUSES
ESPOUSING
ESPRIT
ESPY
ESQUIRE
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ESSAY
ESSAYED
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ESSEN
ESSENCE
ESSENCES
ESSENIZE
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ESSENTIAL
ESSENTIALLY
ESSENTIALS
ESSEX
ESTABLISH
ESTABLISHED
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ESTABLISHMENT
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ESTATE
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ESTEEM
ESTEEMED
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ESTEEMS
ESTELLA
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ESTHER
ESTHETICS
ESTIMATE
ESTIMATED
ESTIMATES
ESTIMATING
ESTIMATION
ESTIMATIONS
ESTONIA
ESTONIAN
ETCH
ETCHING
ETERNAL
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ETERNITIES
ETERNITY
ETHAN
ETHEL
ETHER
ETHEREAL
ETHEREALLY
ETHERNET
ETHERNETS
ETHERS
ETHIC
ETHICAL
ETHICALLY
ETHICS
ETHIOPIA
ETHIOPIANS
ETHNIC
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ETRURIA
ETRUSCAN
ETYMOLOGY
EUCALYPTUS
EUCHARIST
EUCLID
EUCLIDEAN
EUGENE
EUGENIA
EULER
EULERIAN
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EUNUCH
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EUPHEMISM
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EUPHORIA
EUPHORIC
EUPHRATES
EURASIA
EURASIAN
EUREKA
EURIPIDES
EUROPA
EUROPE
EUROPEAN
EUROPEANIZATION
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EUROPEANIZE
EUROPEANIZED
EUROPEANIZES
EUROPEANS
EURYDICE
EUTERPE
EUTHANASIA
EVA
EVACUATE
EVACUATED
EVACUATION
EVADE
EVADED
EVADES
EVADING
EVALUATE
EVALUATED
EVALUATES
EVALUATING
EVALUATION
EVALUATIONS
EVALUATIVE
EVALUATOR
EVALUATORS
EVANGELINE
EVANS
EVANSTON
EVANSVILLE
EVAPORATE
EVAPORATED
EVAPORATING
EVAPORATION
EVAPORATIVE
EVASION
EVASIVE
EVE
EVELYN
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EVENED
EVENHANDED
EVENHANDEDLY
EVENHANDEDNESS
EVENING
EVENINGS
EVENLY
EVENNESS
EVENS
EVENSEN
EVENT
EVENTFUL
EVENTFULLY
EVENTS
EVENTUAL
EVENTUALITIES
EVENTUALITY
EVENTUALLY
EVER
EVEREADY
EVEREST
EVERETT
EVERGLADE
EVERGLADES
EVERGREEN
EVERHART
EVERLASTING
EVERLASTINGLY
EVERMORE
EVERY
EVERYBODY
EVERYDAY
EVERYONE
EVERYTHING
EVERYWHERE
EVICT
EVICTED
EVICTING
EVICTION
EVICTIONS
EVICTS
EVIDENCE
EVIDENCED
EVIDENCES
EVIDENCING
EVIDENT
EVIDENTLY
EVIL
EVILLER
EVILLY
EVILS
EVINCE
EVINCED
EVINCES
EVOKE
EVOKED
EVOKES
EVOKING
EVOLUTE
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EVOLUTION
EVOLUTIONARY
EVOLUTIONS
EVOLVE
EVOLVED
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EVOLVING
EWE
EWEN
EWES
EWING
EXACERBATE
EXACERBATED
EXACERBATES
EXACERBATING
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EXACERBATIONS
EXACT
EXACTED
EXACTING
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EXACTION
EXACTIONS
EXACTITUDE
EXACTLY
EXACTNESS
EXACTS
EXAGGERATE
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EXAGGERATES
EXAGGERATING
EXAGGERATION
EXAGGERATIONS
EXALT
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EXALTING
EXALTS
EXAM
EXAMINATION
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EXAMINE
EXAMINED
EXAMINER
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EXAMINES
EXAMINING
EXAMPLE
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EXAMS
EXASPERATE
EXASPERATED
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EXASPERATING
EXASPERATION
EXCAVATE
EXCAVATED
EXCAVATES
EXCAVATING
EXCAVATION
EXCAVATIONS
EXCEED
EXCEEDED
EXCEEDING
EXCEEDINGLY
EXCEEDS
EXCEL
EXCELLED
EXCELLENCE
EXCELLENCES
EXCELLENCY
EXCELLENT
EXCELLENTLY
EXCELLING
EXCELS
EXCEPT
EXCEPTED
EXCEPTING
EXCEPTION
EXCEPTIONABLE
EXCEPTIONAL
EXCEPTIONALLY
EXCEPTIONS
EXCEPTS
EXCERPT
EXCERPTED
EXCERPTS
EXCESS
EXCESSES
EXCESSIVE
EXCESSIVELY
EXCHANGE
EXCHANGEABLE
EXCHANGED
EXCHANGES
EXCHANGING
EXCHEQUER
EXCHEQUERS
EXCISE
EXCISED
EXCISES
EXCISING
EXCISION
EXCITABLE
EXCITATION
EXCITATIONS
EXCITE
EXCITED
EXCITEDLY
EXCITEMENT
EXCITES
EXCITING
EXCITINGLY
EXCITON
EXCLAIM
EXCLAIMED
EXCLAIMER
EXCLAIMERS
EXCLAIMING
EXCLAIMS
EXCLAMATION
EXCLAMATIONS
EXCLAMATORY
EXCLUDE
EXCLUDED
EXCLUDES
EXCLUDING
EXCLUSION
EXCLUSIONARY
EXCLUSIONS
EXCLUSIVE
EXCLUSIVELY
EXCLUSIVENESS
EXCLUSIVITY
EXCOMMUNICATE
EXCOMMUNICATED
EXCOMMUNICATES
EXCOMMUNICATING
EXCOMMUNICATION
EXCRETE
EXCRETED
EXCRETES
EXCRETING
EXCRETION
EXCRETIONS
EXCRETORY
EXCRUCIATE
EXCURSION
EXCURSIONS
EXCUSABLE
EXCUSABLY
EXCUSE
EXCUSED
EXCUSES
EXCUSING
EXEC
EXECUTABLE
EXECUTE
EXECUTED
EXECUTES
EXECUTING
EXECUTION
EXECUTIONAL
EXECUTIONER
EXECUTIONS
EXECUTIVE
EXECUTIVES
EXECUTOR
EXECUTORS
EXEMPLAR
EXEMPLARY
EXEMPLIFICATION
EXEMPLIFIED
EXEMPLIFIER
EXEMPLIFIERS
EXEMPLIFIES
EXEMPLIFY
EXEMPLIFYING
EXEMPT
EXEMPTED
EXEMPTING
EXEMPTION
EXEMPTS
EXERCISE
EXERCISED
EXERCISER
EXERCISERS
EXERCISES
EXERCISING
EXERT
EXERTED
EXERTING
EXERTION
EXERTIONS
EXERTS
EXETER
EXHALE
EXHALED
EXHALES
EXHALING
EXHAUST
EXHAUSTED
EXHAUSTEDLY
EXHAUSTING
EXHAUSTION
EXHAUSTIVE
EXHAUSTIVELY
EXHAUSTS
EXHIBIT
EXHIBITED
EXHIBITING
EXHIBITION
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EXHIBITOR
EXHIBITORS
EXHIBITS
EXHILARATE
EXHORT
EXHORTATION
EXHORTATIONS
EXHUME
EXIGENCY
EXILE
EXILED
EXILES
EXILING
EXIST
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EXISTENCE
EXISTENT
EXISTENTIAL
EXISTENTIALISM
EXISTENTIALIST
EXISTENTIALISTS
EXISTENTIALLY
EXISTING
EXISTS
EXIT
EXITED
EXITING
EXITS
EXODUS
EXORBITANT
EXORBITANTLY
EXORCISM
EXORCIST
EXOSKELETON
EXOTIC
EXPAND
EXPANDABLE
EXPANDED
EXPANDER
EXPANDERS
EXPANDING
EXPANDS
EXPANSE
EXPANSES
EXPANSIBLE
EXPANSION
EXPANSIONISM
EXPANSIONS
EXPANSIVE
EXPECT
EXPECTANCY
EXPECTANT
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EXPECTATION
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EXPECTED
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EXPECTING
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EXPECTS
EXPEDIENCY
EXPEDIENT
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EXPEDITE
EXPEDITED
EXPEDITES
EXPEDITING
EXPEDITION
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EXPEDITIOUS
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EXPEL
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EXPELS
EXPEND
EXPENDABLE
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EXPENDING
EXPENDITURE
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EXPENDS
EXPENSE
EXPENSES
EXPENSIVE
EXPENSIVELY
EXPERIENCE
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EXPERIENCES
EXPERIENCING
EXPERIMENT
EXPERIMENTAL
EXPERIMENTALLY
EXPERIMENTATION
EXPERIMENTATIONS
EXPERIMENTED
EXPERIMENTER
EXPERIMENTERS
EXPERIMENTING
EXPERIMENTS
EXPERT
EXPERTISE
EXPERTLY
EXPERTNESS
EXPERTS
EXPIRATION
EXPIRATIONS
EXPIRE
EXPIRED
EXPIRES
EXPIRING
EXPLAIN
EXPLAINABLE
EXPLAINED
EXPLAINER
EXPLAINERS
EXPLAINING
EXPLAINS
EXPLANATION
EXPLANATIONS
EXPLANATORY
EXPLETIVE
EXPLICIT
EXPLICITLY
EXPLICITNESS
EXPLODE
EXPLODED
EXPLODES
EXPLODING
EXPLOIT
EXPLOITABLE
EXPLOITATION
EXPLOITATIONS
EXPLOITED
EXPLOITER
EXPLOITERS
EXPLOITING
EXPLOITS
EXPLORATION
EXPLORATIONS
EXPLORATORY
EXPLORE
EXPLORED
EXPLORER
EXPLORERS
EXPLORES
EXPLORING
EXPLOSION
EXPLOSIONS
EXPLOSIVE
EXPLOSIVELY
EXPLOSIVES
EXPONENT
EXPONENTIAL
EXPONENTIALLY
EXPONENTIALS
EXPONENTIATE
EXPONENTIATED
EXPONENTIATES
EXPONENTIATING
EXPONENTIATION
EXPONENTIATIONS
EXPONENTS
EXPORT
EXPORTATION
EXPORTED
EXPORTER
EXPORTERS
EXPORTING
EXPORTS
EXPOSE
EXPOSED
EXPOSER
EXPOSERS
EXPOSES
EXPOSING
EXPOSITION
EXPOSITIONS
EXPOSITORY
EXPOSURE
EXPOSURES
EXPOUND
EXPOUNDED
EXPOUNDER
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EXPOUNDS
EXPRESS
EXPRESSED
EXPRESSES
EXPRESSIBILITY
EXPRESSIBLE
EXPRESSIBLY
EXPRESSING
EXPRESSION
EXPRESSIONS
EXPRESSIVE
EXPRESSIVELY
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EXPRESSLY
EXPULSION
EXPUNGE
EXPUNGED
EXPUNGES
EXPUNGING
EXPURGATE
EXQUISITE
EXQUISITELY
EXQUISITENESS
EXTANT
EXTEMPORANEOUS
EXTEND
EXTENDABLE
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EXTENDING
EXTENDS
EXTENSIBILITY
EXTENSIBLE
EXTENSION
EXTENSIONS
EXTENSIVE
EXTENSIVELY
EXTENT
EXTENTS
EXTENUATE
EXTENUATED
EXTENUATING
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EXTERIOR
EXTERIORS
EXTERMINATE
EXTERMINATED
EXTERMINATES
EXTERMINATING
EXTERMINATION
EXTERNAL
EXTERNALLY
EXTINCT
EXTINCTION
EXTINGUISH
EXTINGUISHED
EXTINGUISHER
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EXTINGUISHING
EXTIRPATE
EXTOL
EXTORT
EXTORTED
EXTORTION
EXTRA
EXTRACT
EXTRACTED
EXTRACTING
EXTRACTION
EXTRACTIONS
EXTRACTOR
EXTRACTORS
EXTRACTS
EXTRACURRICULAR
EXTRAMARITAL
EXTRANEOUS
EXTRANEOUSLY
EXTRANEOUSNESS
EXTRAORDINARILY
EXTRAORDINARINESS
EXTRAORDINARY
EXTRAPOLATE
EXTRAPOLATED
EXTRAPOLATES
EXTRAPOLATING
EXTRAPOLATION
EXTRAPOLATIONS
EXTRAS
EXTRATERRESTRIAL
EXTRAVAGANCE
EXTRAVAGANT
EXTRAVAGANTLY
EXTRAVAGANZA
EXTREMAL
EXTREME
EXTREMELY
EXTREMES
EXTREMIST
EXTREMISTS
EXTREMITIES
EXTREMITY
EXTRICATE
EXTRINSIC
EXTROVERT
EXUBERANCE
EXULT
EXULTATION
EXXON
EYE
EYEBALL
EYEBROW
EYEBROWS
EYED
EYEFUL
EYEGLASS
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EYEING
EYELASH
EYELID
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EYEPIECE
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EYER
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EYES
EYESIGHT
EYEWITNESS
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EYING
EZEKIEL
EZRA
FABER
FABIAN
FABLE
FABLED
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FABRIC
FABRICATE
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FABRICATING
FABRICATION
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FABULOUS
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FACE
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FACET
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FACIAL
FACILE
FACILELY
FACILITATE
FACILITATED
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FACILITATING
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FACILITY
FACING
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FACSIMILE
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FACT
FACTION
FACTIONS
FACTIOUS
FACTO
FACTOR
FACTORED
FACTORIAL
FACTORIES
FACTORING
FACTORIZATION
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FACTORS
FACTORY
FACTS
FACTUAL
FACTUALLY
FACULTIES
FACULTY
FADE
FADED
FADEOUT
FADER
FADERS
FADES
FADING
FAFNIR
FAG
FAGIN
FAGS
FAHEY
FAHRENHEIT
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FAIL
FAILED
FAILING
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FAILS
FAILSOFT
FAILURE
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FAIN
FAINT
FAINTED
FAINTER
FAINTEST
FAINTING
FAINTLY
FAINTNESS
FAINTS
FAIR
FAIRBANKS
FAIRCHILD
FAIRER
FAIREST
FAIRFAX
FAIRFIELD
FAIRIES
FAIRING
FAIRLY
FAIRMONT
FAIRNESS
FAIRPORT
FAIRS
FAIRVIEW
FAIRY
FAIRYLAND
FAITH
FAITHFUL
FAITHFULLY
FAITHFULNESS
FAITHLESS
FAITHLESSLY
FAITHLESSNESS
FAITHS
FAKE
FAKED
FAKER
FAKES
FAKING
FALCON
FALCONER
FALCONS
FALK
FALKLAND
FALKLANDS
FALL
FALLACIES
FALLACIOUS
FALLACY
FALLEN
FALLIBILITY
FALLIBLE
FALLING
FALLOPIAN
FALLOUT
FALLOW
FALLS
FALMOUTH
FALSE
FALSEHOOD
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FALSELY
FALSENESS
FALSIFICATION
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FALSIFY
FALSIFYING
FALSITY
FALSTAFF
FALTER
FALTERED
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FAME
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FAMILIAL
FAMILIAR
FAMILIARITIES
FAMILIARITY
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FAMILIARIZE
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FAMILIARIZING
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FAMILIARNESS
FAMILIES
FAMILISM
FAMILY
FAMINE
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FAMISH
FAMOUS
FAMOUSLY
FAN
FANATIC
FANATICISM
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FANCIED
FANCIER
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FANCIEST
FANCIFUL
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FANCILY
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FANCYING
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FANFOLD
FANG
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FANGS
FANNED
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FANNING
FANNY
FANOUT
FANS
FANTASIES
FANTASIZE
FANTASTIC
FANTASY
FAQ
FAR
FARAD
FARADAY
FARAWAY
FARBER
FARCE
FARCES
FARE
FARED
FARES
FAREWELL
FAREWELLS
FARFETCHED
FARGO
FARINA
FARING
FARKAS
FARLEY
FARM
FARMED
FARMER
FARMERS
FARMHOUSE
FARMHOUSES
FARMING
FARMINGTON
FARMLAND
FARMS
FARMYARD
FARMYARDS
FARNSWORTH
FARRELL
FARSIGHTED
FARTHER
FARTHEST
FARTHING
FASCICLE
FASCINATE
FASCINATED
FASCINATES
FASCINATING
FASCINATION
FASCISM
FASCIST
FASHION
FASHIONABLE
FASHIONABLY
FASHIONED
FASHIONING
FASHIONS
FAST
FASTED
FASTEN
FASTENED
FASTENER
FASTENERS
FASTENING
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FASTENS
FASTER
FASTEST
FASTIDIOUS
FASTING
FASTNESS
FASTS
FAT
FATAL
FATALITIES
FATALITY
FATALLY
FATALS
FATE
FATED
FATEFUL
FATES
FATHER
FATHERED
FATHERLAND
FATHERLY
FATHERS
FATHOM
FATHOMED
FATHOMING
FATHOMS
FATIGUE
FATIGUED
FATIGUES
FATIGUING
FATIMA
FATNESS
FATS
FATTEN
FATTENED
FATTENER
FATTENERS
FATTENING
FATTENS
FATTER
FATTEST
FATTY
FAUCET
FAULKNER
FAULKNERIAN
FAULT
FAULTED
FAULTING
FAULTLESS
FAULTLESSLY
FAULTS
FAULTY
FAUN
FAUNA
FAUNTLEROY
FAUST
FAUSTIAN
FAUSTUS
FAVOR
FAVORABLE
FAVORABLY
FAVORED
FAVORER
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FAVORITE
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FAVORITISM
FAVORS
FAWKES
FAWN
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FAWNING
FAWNS
FAYETTE
FAYETTEVILLE
FAZE
FEAR
FEARED
FEARFUL
FEARFULLY
FEARING
FEARLESS
FEARLESSLY
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FEARS
FEARSOME
FEASIBILITY
FEASIBLE
FEAST
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FEASTING
FEASTS
FEAT
FEATHER
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FEATHERED
FEATHERER
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FEATHERMAN
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FEATHERY
FEATS
FEATURE
FEATURED
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FEATURING
FEBRUARIES
FEBRUARY
FECUND
FED
FEDDERS
FEDERAL
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FEDERALLY
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FEDORA
FEE
FEEBLE
FEEBLENESS
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FEEBLEST
FEEBLY
FEED
FEEDBACK
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FEEDING
FEEDINGS
FEEDS
FEEL
FEELER
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FEELING
FEELINGLY
FEELINGS
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FEENEY
FEES
FEET
FEIGN
FEIGNED
FEIGNING
FELDER
FELDMAN
FELICE
FELICIA
FELICITIES
FELICITY
FELINE
FELIX
FELL
FELLATIO
FELLED
FELLING
FELLINI
FELLOW
FELLOWS
FELLOWSHIP
FELLOWSHIPS
FELON
FELONIOUS
FELONY
FELT
FELTS
FEMALE
FEMALES
FEMININE
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FEMINISM
FEMINIST
FEMUR
FEMURS
FEN
FENCE
FENCED
FENCER
FENCERS
FENCES
FENCING
FEND
FENTON
FENWICK
FERBER
FERDINAND
FERDINANDO
FERGUSON
FERMAT
FERMENT
FERMENTATION
FERMENTATIONS
FERMENTED
FERMENTING
FERMENTS
FERMI
FERN
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FERNS
FEROCIOUS
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FEROCIOUSNESS
FEROCITY
FERREIRA
FERRER
FERRET
FERRIED
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FERRITE
FERRY
FERTILE
FERTILELY
FERTILITY
FERTILIZATION
FERTILIZE
FERTILIZED
FERTILIZER
FERTILIZERS
FERTILIZES
FERTILIZING
FERVENT
FERVENTLY
FERVOR
FERVORS
FESS
FESTIVAL
FESTIVALS
FESTIVE
FESTIVELY
FESTIVITIES
FESTIVITY
FETAL
FETCH
FETCHED
FETCHES
FETCHING
FETCHINGLY
FETID
FETISH
FETTER
FETTERED
FETTERS
FETTLE
FETUS
FEUD
FEUDAL
FEUDALISM
FEUDS
FEVER
FEVERED
FEVERISH
FEVERISHLY
FEVERS
FEW
FEWER
FEWEST
FEWNESS
FIANCE
FIANCEE
FIASCO
FIAT
FIB
FIBBING
FIBER
FIBERGLAS
FIBERS
FIBONACCI
FIBROSITIES
FIBROSITY
FIBROUS
FIBROUSLY
FICKLE
FICKLENESS
FICTION
FICTIONAL
FICTIONALLY
FICTIONS
FICTITIOUS
FICTITIOUSLY
FIDDLE
FIDDLED
FIDDLER
FIDDLES
FIDDLESTICK
FIDDLESTICKS
FIDDLING
FIDEL
FIDELITY
FIDGET
FIDUCIAL
FIEF
FIEFDOM
FIELD
FIELDED
FIELDER
FIELDERS
FIELDING
FIELDS
FIELDWORK
FIEND
FIENDISH
FIERCE
FIERCELY
FIERCENESS
FIERCER
FIERCEST
FIERY
FIFE
FIFTEEN
FIFTEENS
FIFTEENTH
FIFTH
FIFTIES
FIFTIETH
FIFTY
FIG
FIGARO
FIGHT
FIGHTER
FIGHTERS
FIGHTING
FIGHTS
FIGS
FIGURATIVE
FIGURATIVELY
FIGURE
FIGURED
FIGURES
FIGURING
FIGURINGS
FIJI
FIJIAN
FIJIANS
FILAMENT
FILAMENTS
FILE
FILED
FILENAME
FILENAMES
FILER
FILES
FILIAL
FILIBUSTER
FILING
FILINGS
FILIPINO
FILIPINOS
FILIPPO
FILL
FILLABLE
FILLED
FILLER
FILLERS
FILLING
FILLINGS
FILLMORE
FILLS
FILLY
FILM
FILMED
FILMING
FILMS
FILTER
FILTERED
FILTERING
FILTERS
FILTH
FILTHIER
FILTHIEST
FILTHINESS
FILTHY
FIN
FINAL
FINALITY
FINALIZATION
FINALIZE
FINALIZED
FINALIZES
FINALIZING
FINALLY
FINALS
FINANCE
FINANCED
FINANCES
FINANCIAL
FINANCIALLY
FINANCIER
FINANCIERS
FINANCING
FIND
FINDER
FINDERS
FINDING
FINDINGS
FINDS
FINE
FINED
FINELY
FINENESS
FINER
FINES
FINESSE
FINESSED
FINESSING
FINEST
FINGER
FINGERED
FINGERING
FINGERINGS
FINGERNAIL
FINGERPRINT
FINGERPRINTS
FINGERS
FINGERTIP
FINICKY
FINING
FINISH
FINISHED
FINISHER
FINISHERS
FINISHES
FINISHING
FINITE
FINITELY
FINITENESS
FINK
FINLAND
FINLEY
FINN
FINNEGAN
FINNISH
FINNS
FINNY
FINS
FIORELLO
FIORI
FIR
FIRE
FIREARM
FIREARMS
FIREBOAT
FIREBREAK
FIREBUG
FIRECRACKER
FIRED
FIREFLIES
FIREFLY
FIREHOUSE
FIRELIGHT
FIREMAN
FIREMEN
FIREPLACE
FIREPLACES
FIREPOWER
FIREPROOF
FIRER
FIRERS
FIRES
FIRESIDE
FIRESTONE
FIREWALL
FIREWOOD
FIREWORKS
FIRING
FIRINGS
FIRM
FIRMAMENT
FIRMED
FIRMER
FIRMEST
FIRMING
FIRMLY
FIRMNESS
FIRMS
FIRMWARE
FIRST
FIRSTHAND
FIRSTLY
FIRSTS
FISCAL
FISCALLY
FISCHBEIN
FISCHER
FISH
FISHED
FISHER
FISHERMAN
FISHERMEN
FISHERS
FISHERY
FISHES
FISHING
FISHKILL
FISHMONGER
FISHPOND
FISHY
FISK
FISKE
FISSION
FISSURE
FISSURED
FIST
FISTED
FISTICUFF
FISTS
FIT
FITCH
FITCHBURG
FITFUL
FITFULLY
FITLY
FITNESS
FITS
FITTED
FITTER
FITTERS
FITTING
FITTINGLY
FITTINGS
FITZGERALD
FITZPATRICK
FITZROY
FIVE
FIVEFOLD
FIVES
FIX
FIXATE
FIXATED
FIXATES
FIXATING
FIXATION
FIXATIONS
FIXED
FIXEDLY
FIXEDNESS
FIXER
FIXERS
FIXES
FIXING
FIXINGS
FIXTURE
FIXTURES
FIZEAU
FIZZLE
FIZZLED
FLABBERGAST
FLABBERGASTED
FLACK
FLAG
FLAGELLATE
FLAGGED
FLAGGING
FLAGLER
FLAGPOLE
FLAGRANT
FLAGRANTLY
FLAGS
FLAGSTAFF
FLAIL
FLAIR
FLAK
FLAKE
FLAKED
FLAKES
FLAKING
FLAKY
FLAM
FLAMBOYANT
FLAME
FLAMED
FLAMER
FLAMERS
FLAMES
FLAMING
FLAMMABLE
FLANAGAN
FLANDERS
FLANK
FLANKED
FLANKER
FLANKING
FLANKS
FLANNEL
FLANNELS
FLAP
FLAPS
FLARE
FLARED
FLARES
FLARING
FLASH
FLASHBACK
FLASHED
FLASHER
FLASHERS
FLASHES
FLASHING
FLASHLIGHT
FLASHLIGHTS
FLASHY
FLASK
FLAT
FLATBED
FLATLY
FLATNESS
FLATS
FLATTEN
FLATTENED
FLATTENING
FLATTER
FLATTERED
FLATTERER
FLATTERING
FLATTERY
FLATTEST
FLATULENT
FLATUS
FLATWORM
FLAUNT
FLAUNTED
FLAUNTING
FLAUNTS
FLAVOR
FLAVORED
FLAVORING
FLAVORINGS
FLAVORS
FLAW
FLAWED
FLAWLESS
FLAWLESSLY
FLAWS
FLAX
FLAXEN
FLEA
FLEAS
FLED
FLEDERMAUS
FLEDGED
FLEDGLING
FLEDGLINGS
FLEE
FLEECE
FLEECES
FLEECY
FLEEING
FLEES
FLEET
FLEETEST
FLEETING
FLEETLY
FLEETNESS
FLEETS
FLEISCHMAN
FLEISHER
FLEMING
FLEMINGS
FLEMISH
FLEMISHED
FLEMISHES
FLEMISHING
FLESH
FLESHED
FLESHES
FLESHING
FLESHLY
FLESHY
FLETCHER
FLETCHERIZE
FLETCHERIZES
FLEW
FLEX
FLEXIBILITIES
FLEXIBILITY
FLEXIBLE
FLEXIBLY
FLICK
FLICKED
FLICKER
FLICKERING
FLICKING
FLICKS
FLIER
FLIERS
FLIES
FLIGHT
FLIGHTS
FLIMSY
FLINCH
FLINCHED
FLINCHES
FLINCHING
FLING
FLINGS
FLINT
FLINTY
FLIP
FLIPFLOP
FLIPPED
FLIPS
FLIRT
FLIRTATION
FLIRTATIOUS
FLIRTED
FLIRTING
FLIRTS
FLIT
FLITTING
FLO
FLOAT
FLOATED
FLOATER
FLOATING
FLOATS
FLOCK
FLOCKED
FLOCKING
FLOCKS
FLOG
FLOGGING
FLOOD
FLOODED
FLOODING
FLOODLIGHT
FLOODLIT
FLOODS
FLOOR
FLOORED
FLOORING
FLOORINGS
FLOORS
FLOP
FLOPPIES
FLOPPILY
FLOPPING
FLOPPY
FLOPS
FLORA
FLORAL
FLORENCE
FLORENTINE
FLORID
FLORIDA
FLORIDIAN
FLORIDIANS
FLORIN
FLORIST
FLOSS
FLOSSED
FLOSSES
FLOSSING
FLOTATION
FLOTILLA
FLOUNDER
FLOUNDERED
FLOUNDERING
FLOUNDERS
FLOUR
FLOURED
FLOURISH
FLOURISHED
FLOURISHES
FLOURISHING
FLOW
FLOWCHART
FLOWCHARTING
FLOWCHARTS
FLOWED
FLOWER
FLOWERED
FLOWERINESS
FLOWERING
FLOWERPOT
FLOWERS
FLOWERY
FLOWING
FLOWN
FLOWS
FLOYD
FLU
FLUCTUATE
FLUCTUATES
FLUCTUATING
FLUCTUATION
FLUCTUATIONS
FLUE
FLUENCY
FLUENT
FLUENTLY
FLUFF
FLUFFIER
FLUFFIEST
FLUFFY
FLUID
FLUIDITY
FLUIDLY
FLUIDS
FLUKE
FLUNG
FLUNKED
FLUORESCE
FLUORESCENT
FLURRIED
FLURRY
FLUSH
FLUSHED
FLUSHES
FLUSHING
FLUTE
FLUTED
FLUTING
FLUTTER
FLUTTERED
FLUTTERING
FLUTTERS
FLUX
FLY
FLYABLE
FLYER
FLYERS
FLYING
FLYNN
FOAL
FOAM
FOAMED
FOAMING
FOAMS
FOAMY
FOB
FOBBING
FOCAL
FOCALLY
FOCI
FOCUS
FOCUSED
FOCUSES
FOCUSING
FOCUSSED
FODDER
FOE
FOES
FOG
FOGARTY
FOGGED
FOGGIER
FOGGIEST
FOGGILY
FOGGING
FOGGY
FOGS
FOGY
FOIBLE
FOIL
FOILED
FOILING
FOILS
FOIST
FOLD
FOLDED
FOLDER
FOLDERS
FOLDING
FOLDOUT
FOLDS
FOLEY
FOLIAGE
FOLK
FOLKLORE
FOLKS
FOLKSONG
FOLKSY
FOLLIES
FOLLOW
FOLLOWED
FOLLOWER
FOLLOWERS
FOLLOWING
FOLLOWINGS
FOLLOWS
FOLLY
FOLSOM
FOMALHAUT
FOND
FONDER
FONDLE
FONDLED
FONDLES
FONDLING
FONDLY
FONDNESS
FONT
FONTAINE
FONTAINEBLEAU
FONTANA
FONTS
FOOD
FOODS
FOODSTUFF
FOODSTUFFS
FOOL
FOOLED
FOOLHARDY
FOOLING
FOOLISH
FOOLISHLY
FOOLISHNESS
FOOLPROOF
FOOLS
FOOT
FOOTAGE
FOOTBALL
FOOTBALLS
FOOTBRIDGE
FOOTE
FOOTED
FOOTER
FOOTERS
FOOTFALL
FOOTHILL
FOOTHOLD
FOOTING
FOOTMAN
FOOTNOTE
FOOTNOTES
FOOTPATH
FOOTPRINT
FOOTPRINTS
FOOTSTEP
FOOTSTEPS
FOR
FORAGE
FORAGED
FORAGES
FORAGING
FORAY
FORAYS
FORBADE
FORBEAR
FORBEARANCE
FORBEARS
FORBES
FORBID
FORBIDDEN
FORBIDDING
FORBIDS
FORCE
FORCED
FORCEFUL
FORCEFULLY
FORCEFULNESS
FORCER
FORCES
FORCIBLE
FORCIBLY
FORCING
FORD
FORDHAM
FORDS
FORE
FOREARM
FOREARMS
FOREBODING
FORECAST
FORECASTED
FORECASTER
FORECASTERS
FORECASTING
FORECASTLE
FORECASTS
FOREFATHER
FOREFATHERS
FOREFINGER
FOREFINGERS
FOREGO
FOREGOES
FOREGOING
FOREGONE
FOREGROUND
FOREHEAD
FOREHEADS
FOREIGN
FOREIGNER
FOREIGNERS
FOREIGNS
FOREMAN
FOREMOST
FORENOON
FORENSIC
FORERUNNERS
FORESEE
FORESEEABLE
FORESEEN
FORESEES
FORESIGHT
FORESIGHTED
FOREST
FORESTALL
FORESTALLED
FORESTALLING
FORESTALLMENT
FORESTALLS
FORESTED
FORESTER
FORESTERS
FORESTRY
FORESTS
FORETELL
FORETELLING
FORETELLS
FORETOLD
FOREVER
FOREWARN
FOREWARNED
FOREWARNING
FOREWARNINGS
FOREWARNS
FORFEIT
FORFEITED
FORFEITURE
FORGAVE
FORGE
FORGED
FORGER
FORGERIES
FORGERY
FORGES
FORGET
FORGETFUL
FORGETFULNESS
FORGETS
FORGETTABLE
FORGETTABLY
FORGETTING
FORGING
FORGIVABLE
FORGIVABLY
FORGIVE
FORGIVEN
FORGIVENESS
FORGIVES
FORGIVING
FORGIVINGLY
FORGOT
FORGOTTEN
FORK
FORKED
FORKING
FORKLIFT
FORKS
FORLORN
FORLORNLY
FORM
FORMAL
FORMALISM
FORMALISMS
FORMALITIES
FORMALITY
FORMALIZATION
FORMALIZATIONS
FORMALIZE
FORMALIZED
FORMALIZES
FORMALIZING
FORMALLY
FORMANT
FORMANTS
FORMAT
FORMATION
FORMATIONS
FORMATIVE
FORMATIVELY
FORMATS
FORMATTED
FORMATTER
FORMATTERS
FORMATTING
FORMED
FORMER
FORMERLY
FORMICA
FORMICAS
FORMIDABLE
FORMING
FORMOSA
FORMOSAN
FORMS
FORMULA
FORMULAE
FORMULAS
FORMULATE
FORMULATED
FORMULATES
FORMULATING
FORMULATION
FORMULATIONS
FORMULATOR
FORMULATORS
FORNICATION
FORREST
FORSAKE
FORSAKEN
FORSAKES
FORSAKING
FORSYTHE
FORT
FORTE
FORTESCUE
FORTH
FORTHCOMING
FORTHRIGHT
FORTHWITH
FORTIER
FORTIES
FORTIETH
FORTIFICATION
FORTIFICATIONS
FORTIFIED
FORTIFIES
FORTIFY
FORTIFYING
FORTIORI
FORTITUDE
FORTNIGHT
FORTNIGHTLY
FORTRAN
FORTRAN
FORTRESS
FORTRESSES
FORTS
FORTUITOUS
FORTUITOUSLY
FORTUNATE
FORTUNATELY
FORTUNE
FORTUNES
FORTY
FORUM
FORUMS
FORWARD
FORWARDED
FORWARDER
FORWARDING
FORWARDNESS
FORWARDS
FOSS
FOSSIL
FOSTER
FOSTERED
FOSTERING
FOSTERS
FOUGHT
FOUL
FOULED
FOULEST
FOULING
FOULLY
FOULMOUTH
FOULNESS
FOULS
FOUND
FOUNDATION
FOUNDATIONS
FOUNDED
FOUNDER
FOUNDERED
FOUNDERS
FOUNDING
FOUNDLING
FOUNDRIES
FOUNDRY
FOUNDS
FOUNT
FOUNTAIN
FOUNTAINS
FOUNTS
FOUR
FOURFOLD
FOURIER
FOURS
FOURSCORE
FOURSOME
FOURSQUARE
FOURTEEN
FOURTEENS
FOURTEENTH
FOURTH
FOWL
FOWLER
FOWLS
FOX
FOXES
FOXHALL
FRACTION
FRACTIONAL
FRACTIONALLY
FRACTIONS
FRACTURE
FRACTURED
FRACTURES
FRACTURING
FRAGILE
FRAGMENT
FRAGMENTARY
FRAGMENTATION
FRAGMENTED
FRAGMENTING
FRAGMENTS
FRAGRANCE
FRAGRANCES
FRAGRANT
FRAGRANTLY
FRAIL
FRAILEST
FRAILTY
FRAME
FRAMED
FRAMER
FRAMES
FRAMEWORK
FRAMEWORKS
FRAMING
FRAN
FRANC
FRANCAISE
FRANCE
FRANCES
FRANCESCA
FRANCESCO
FRANCHISE
FRANCHISES
FRANCIE
FRANCINE
FRANCIS
FRANCISCAN
FRANCISCANS
FRANCISCO
FRANCIZE
FRANCIZES
FRANCO
FRANCOIS
FRANCOISE
FRANCS
FRANK
FRANKED
FRANKEL
FRANKER
FRANKEST
FRANKFORT
FRANKFURT
FRANKIE
FRANKING
FRANKLINIZATION
FRANKLINIZATIONS
FRANKLY
FRANKNESS
FRANKS
FRANNY
FRANTIC
FRANTICALLY
FRANZ
FRASER
FRATERNAL
FRATERNALLY
FRATERNITIES
FRATERNITY
FRAU
FRAUD
FRAUDS
FRAUDULENT
FRAUGHT
FRAY
FRAYED
FRAYING
FRAYNE
FRAYS
FRAZIER
FRAZZLE
FREAK
FREAKISH
FREAKS
FRECKLE
FRECKLED
FRECKLES
FRED
FREDDIE
FREDDY
FREDERIC
FREDERICK
FREDERICKS
FREDERICKSBURG
FREDERICO
FREDERICTON
FREDHOLM
FREDRICK
FREDRICKSON
FREE
FREED
FREEDMAN
FREEDOM
FREEDOMS
FREEING
FREEINGS
FREELY
FREEMAN
FREEMASON
FREEMASONRY
FREEMASONS
FREENESS
FREEPORT
FREER
FREES
FREEST
FREESTYLE
FREETOWN
FREEWAY
FREEWHEEL
FREEZE
FREEZER
FREEZERS
FREEZES
FREEZING
FREIDA
FREIGHT
FREIGHTED
FREIGHTER
FREIGHTERS
FREIGHTING
FREIGHTS
FRENCH
FRENCHIZE
FRENCHIZES
FRENCHMAN
FRENCHMEN
FRENETIC
FRENZIED
FRENZY
FREON
FREQUENCIES
FREQUENCY
FREQUENT
FREQUENTED
FREQUENTER
FREQUENTERS
FREQUENTING
FREQUENTLY
FREQUENTS
FRESCO
FRESCOES
FRESH
FRESHEN
FRESHENED
FRESHENER
FRESHENERS
FRESHENING
FRESHENS
FRESHER
FRESHEST
FRESHLY
FRESHMAN
FRESHMEN
FRESHNESS
FRESHWATER
FRESNEL
FRESNO
FRET
FRETFUL
FRETFULLY
FRETFULNESS
FREUD
FREUDIAN
FREUDIANISM
FREUDIANISMS
FREUDIANS
FREY
FREYA
FRIAR
FRIARS
FRICATIVE
FRICATIVES
FRICK
FRICTION
FRICTIONLESS
FRICTIONS
FRIDAY
FRIDAYS
FRIED
FRIEDMAN
FRIEDRICH
FRIEND
FRIENDLESS
FRIENDLIER
FRIENDLIEST
FRIENDLINESS
FRIENDLY
FRIENDS
FRIENDSHIP
FRIENDSHIPS
FRIES
FRIESLAND
FRIEZE
FRIEZES
FRIGATE
FRIGATES
FRIGGA
FRIGHT
FRIGHTEN
FRIGHTENED
FRIGHTENING
FRIGHTENINGLY
FRIGHTENS
FRIGHTFUL
FRIGHTFULLY
FRIGHTFULNESS
FRIGID
FRIGIDAIRE
FRILL
FRILLS
FRINGE
FRINGED
FRISBEE
FRISIA
FRISIAN
FRISK
FRISKED
FRISKING
FRISKS
FRISKY
FRITO
FRITTER
FRITZ
FRIVOLITY
FRIVOLOUS
FRIVOLOUSLY
FRO
FROCK
FROCKS
FROG
FROGS
FROLIC
FROLICS
FROM
FRONT
FRONTAGE
FRONTAL
FRONTED
FRONTIER
FRONTIERS
FRONTIERSMAN
FRONTIERSMEN
FRONTING
FRONTS
FROST
FROSTBELT
FROSTBITE
FROSTBITTEN
FROSTED
FROSTING
FROSTS
FROSTY
FROTH
FROTHING
FROTHY
FROWN
FROWNED
FROWNING
FROWNS
FROZE
FROZEN
FROZENLY
FRUEHAUF
FRUGAL
FRUGALLY
FRUIT
FRUITFUL
FRUITFULLY
FRUITFULNESS
FRUITION
FRUITLESS
FRUITLESSLY
FRUITS
FRUSTRATE
FRUSTRATED
FRUSTRATES
FRUSTRATING
FRUSTRATION
FRUSTRATIONS
FRY
FRYE
FUCHS
FUCHSIA
FUDGE
FUEL
FUELED
FUELING
FUELS
FUGITIVE
FUGITIVES
FUGUE
FUJI
FUJITSU
FULBRIGHT
FULBRIGHTS
FULCRUM
FULFILL
FULFILLED
FULFILLING
FULFILLMENT
FULFILLMENTS
FULFILLS
FULL
FULLER
FULLERTON
FULLEST
FULLNESS
FULLY
FULMINATE
FULTON
FUMBLE
FUMBLED
FUMBLING
FUME
FUMED
FUMES
FUMING
FUN
FUNCTION
FUNCTIONAL
FUNCTIONALITIES
FUNCTIONALITY
FUNCTIONALLY
FUNCTIONALS
FUNCTIONARY
FUNCTIONED
FUNCTIONING
FUNCTIONS
FUNCTOR
FUNCTORS
FUND
FUNDAMENTAL
FUNDAMENTALLY
FUNDAMENTALS
FUNDED
FUNDER
FUNDERS
FUNDING
FUNDS
FUNERAL
FUNERALS
FUNEREAL
FUNGAL
FUNGI
FUNGIBLE
FUNGICIDE
FUNGUS
FUNK
FUNNEL
FUNNELED
FUNNELING
FUNNELS
FUNNIER
FUNNIEST
FUNNILY
FUNNINESS
FUNNY
FUR
FURIES
FURIOUS
FURIOUSER
FURIOUSLY
FURLONG
FURLOUGH
FURMAN
FURNACE
FURNACES
FURNISH
FURNISHED
FURNISHES
FURNISHING
FURNISHINGS
FURNITURE
FURRIER
FURROW
FURROWED
FURROWS
FURRY
FURS
FURTHER
FURTHERED
FURTHERING
FURTHERMORE
FURTHERMOST
FURTHERS
FURTHEST
FURTIVE
FURTIVELY
FURTIVENESS
FURY
FUSE
FUSED
FUSES
FUSING
FUSION
FUSS
FUSSING
FUSSY
FUTILE
FUTILITY
FUTURE
FUTURES
FUTURISTIC
FUZZ
FUZZIER
FUZZINESS
FUZZY
GAB
GABARDINE
GABBING
GABERONES
GABLE
GABLED
GABLER
GABLES
GABON
GABORONE
GABRIEL
GABRIELLE
GAD
GADFLY
GADGET
GADGETRY
GADGETS
GAELIC
GAELICIZATION
GAELICIZATIONS
GAELICIZE
GAELICIZES
GAG
GAGGED
GAGGING
GAGING
GAGS
GAIETIES
GAIETY
GAIL
GAILY
GAIN
GAINED
GAINER
GAINERS
GAINES
GAINESVILLE
GAINFUL
GAINING
GAINS
GAIT
GAITED
GAITER
GAITERS
GAITHERSBURG
GALACTIC
GALAHAD
GALAPAGOS
GALATEA
GALATEAN
GALATEANS
GALATIA
GALATIANS
GALAXIES
GALAXY
GALBREATH
GALE
GALEN
GALILEAN
GALILEE
GALILEO
GALL
GALLAGHER
GALLANT
GALLANTLY
GALLANTRY
GALLANTS
GALLED
GALLERIED
GALLERIES
GALLERY
GALLEY
GALLEYS
GALLING
GALLON
GALLONS
GALLOP
GALLOPED
GALLOPER
GALLOPING
GALLOPS
GALLOWAY
GALLOWS
GALLS
GALLSTONE
GALLUP
GALOIS
GALT
GALVESTON
GALVIN
GALWAY
GAMBIA
GAMBIT
GAMBLE
GAMBLED
GAMBLER
GAMBLERS
GAMBLES
GAMBLING
GAMBOL
GAME
GAMED
GAMELY
GAMENESS
GAMES
GAMING
GAMMA
GANDER
GANDHI
GANDHIAN
GANG
GANGES
GANGLAND
GANGLING
GANGPLANK
GANGRENE
GANGS
GANGSTER
GANGSTERS
GANNETT
GANTRY
GANYMEDE
GAP
GAPE
GAPED
GAPES
GAPING
GAPS
GARAGE
GARAGED
GARAGES
GARB
GARBAGE
GARBAGES
GARBED
GARBLE
GARBLED
GARCIA
GARDEN
GARDENED
GARDENER
GARDENERS
GARDENING
GARDENS
GARDNER
GARFIELD
GARFUNKEL
GARGANTUAN
GARGLE
GARGLED
GARGLES
GARGLING
GARIBALDI
GARLAND
GARLANDED
GARLIC
GARMENT
GARMENTS
GARNER
GARNERED
GARNETT
GARNISH
GARRETT
GARRISON
GARRISONED
GARRISONIAN
GARRY
GARTER
GARTERS
GARTH
GARVEY
GARY
GAS
GASCONY
GASEOUS
GASEOUSLY
GASES
GASH
GASHES
GASKET
GASLIGHT
GASOLINE
GASP
GASPED
GASPEE
GASPING
GASPS
GASSED
GASSER
GASSET
GASSING
GASSINGS
GASSY
GASTON
GASTRIC
GASTROINTESTINAL
GASTRONOME
GASTRONOMY
GATE
GATED
GATES
GATEWAY
GATEWAYS
GATHER
GATHERED
GATHERER
GATHERERS
GATHERING
GATHERINGS
GATHERS
GATING
GATLINBURG
GATOR
GATSBY
GAUCHE
GAUDINESS
GAUDY
GAUGE
GAUGED
GAUGES
GAUGUIN
GAUL
GAULLE
GAULS
GAUNT
GAUNTLEY
GAUNTNESS
GAUSSIAN
GAUTAMA
GAUZE
GAVE
GAVEL
GAVIN
GAWK
GAWKY
GAY
GAYER
GAYEST
GAYETY
GAYLOR
GAYLORD
GAYLY
GAYNESS
GAYNOR
GAZE
GAZED
GAZELLE
GAZER
GAZERS
GAZES
GAZETTE
GAZING
GEAR
GEARED
GEARING
GEARS
GEARY
GECKO
GEESE
GEHRIG
GEIGER
GEIGY
GEISHA
GEL
GELATIN
GELATINE
GELATINOUS
GELD
GELLED
GELLING
GELS
GEM
GEMINI
GEMINID
GEMMA
GEMS
GENDER
GENDERS
GENE
GENEALOGY
GENERAL
GENERALIST
GENERALISTS
GENERALITIES
GENERALITY
GENERALIZATION
GENERALIZATIONS
GENERALIZE
GENERALIZED
GENERALIZER
GENERALIZERS
GENERALIZES
GENERALIZING
GENERALLY
GENERALS
GENERATE
GENERATED
GENERATES
GENERATING
GENERATION
GENERATIONS
GENERATIVE
GENERATOR
GENERATORS
GENERIC
GENERICALLY
GENEROSITIES
GENEROSITY
GENEROUS
GENEROUSLY
GENEROUSNESS
GENES
GENESCO
GENESIS
GENETIC
GENETICALLY
GENEVA
GENEVIEVE
GENIAL
GENIALLY
GENIE
GENIUS
GENIUSES
GENOA
GENRE
GENRES
GENT
GENTEEL
GENTILE
GENTLE
GENTLEMAN
GENTLEMANLY
GENTLEMEN
GENTLENESS
GENTLER
GENTLEST
GENTLEWOMAN
GENTLY
GENTRY
GENUINE
GENUINELY
GENUINENESS
GENUS
GEOCENTRIC
GEODESIC
GEODESY
GEODETIC
GEOFF
GEOFFREY
GEOGRAPHER
GEOGRAPHIC
GEOGRAPHICAL
GEOGRAPHICALLY
GEOGRAPHY
GEOLOGICAL
GEOLOGIST
GEOLOGISTS
GEOLOGY
GEOMETRIC
GEOMETRICAL
GEOMETRICALLY
GEOMETRICIAN
GEOMETRIES
GEOMETRY
GEOPHYSICAL
GEOPHYSICS
GEORGE
GEORGES
GEORGETOWN
GEORGIA
GEORGIAN
GEORGIANS
GEOSYNCHRONOUS
GERALD
GERALDINE
GERANIUM
GERARD
GERBER
GERBIL
GERHARD
GERHARDT
GERIATRIC
GERM
GERMAN
GERMANE
GERMANIA
GERMANIC
GERMANS
GERMANTOWN
GERMANY
GERMICIDE
GERMINAL
GERMINATE
GERMINATED
GERMINATES
GERMINATING
GERMINATION
GERMS
GEROME
GERRY
GERSHWIN
GERSHWINS
GERTRUDE
GERUND
GESTAPO
GESTURE
GESTURED
GESTURES
GESTURING
GET
GETAWAY
GETS
GETTER
GETTERS
GETTING
GETTY
GETTYSBURG
GEYSER
GHANA
GHANIAN
GHASTLY
GHENT
GHETTO
GHOST
GHOSTED
GHOSTLY
GHOSTS
GIACOMO
GIANT
GIANTS
GIBBERISH
GIBBONS
GIBBS
GIBBY
GIBRALTAR
GIBSON
GIDDINESS
GIDDINGS
GIDDY
GIDEON
GIFFORD
GIFT
GIFTED
GIFTS
GIG
GIGABIT
GIGABITS
GIGABYTE
GIGABYTES
GIGACYCLE
GIGAHERTZ
GIGANTIC
GIGAVOLT
GIGAWATT
GIGGLE
GIGGLED
GIGGLES
GIGGLING
GIL
GILBERTSON
GILCHRIST
GILD
GILDED
GILDING
GILDS
GILEAD
GILES
GILKSON
GILL
GILLESPIE
GILLETTE
GILLIGAN
GILLS
GILMORE
GILT
GIMBEL
GIMMICK
GIMMICKS
GIN
GINA
GINGER
GINGERBREAD
GINGERLY
GINGHAM
GINGHAMS
GINN
GINO
GINS
GINSBERG
GINSBURG
GIOCONDA
GIORGIO
GIOVANNI
GIPSIES
GIPSY
GIRAFFE
GIRAFFES
GIRD
GIRDER
GIRDERS
GIRDLE
GIRL
GIRLFRIEND
GIRLIE
GIRLISH
GIRLS
GIRT
GIRTH
GIST
GIULIANO
GIUSEPPE
GIVE
GIVEAWAY
GIVEN
GIVER
GIVERS
GIVES
GIVING
GLACIAL
GLACIER
GLACIERS
GLAD
GLADDEN
GLADDER
GLADDEST
GLADE
GLADIATOR
GLADLY
GLADNESS
GLADSTONE
GLADYS
GLAMOR
GLAMOROUS
GLAMOUR
GLANCE
GLANCED
GLANCES
GLANCING
GLAND
GLANDS
GLANDULAR
GLARE
GLARED
GLARES
GLARING
GLARINGLY
GLASGOW
GLASS
GLASSED
GLASSES
GLASSY
GLASWEGIAN
GLAUCOMA
GLAZE
GLAZED
GLAZER
GLAZES
GLAZING
GLEAM
GLEAMED
GLEAMING
GLEAMS
GLEAN
GLEANED
GLEANER
GLEANING
GLEANINGS
GLEANS
GLEASON
GLEE
GLEEFUL
GLEEFULLY
GLEES
GLEN
GLENDA
GLENDALE
GLENN
GLENS
GLIDDEN
GLIDE
GLIDED
GLIDER
GLIDERS
GLIDES
GLIMMER
GLIMMERED
GLIMMERING
GLIMMERS
GLIMPSE
GLIMPSED
GLIMPSES
GLINT
GLINTED
GLINTING
GLINTS
GLISTEN
GLISTENED
GLISTENING
GLISTENS
GLITCH
GLITTER
GLITTERED
GLITTERING
GLITTERS
GLOAT
GLOBAL
GLOBALLY
GLOBE
GLOBES
GLOBULAR
GLOBULARITY
GLOOM
GLOOMILY
GLOOMY
GLORIA
GLORIANA
GLORIES
GLORIFICATION
GLORIFIED
GLORIFIES
GLORIFY
GLORIOUS
GLORIOUSLY
GLORY
GLORYING
GLOSS
GLOSSARIES
GLOSSARY
GLOSSED
GLOSSES
GLOSSING
GLOSSY
GLOTTAL
GLOUCESTER
GLOVE
GLOVED
GLOVER
GLOVERS
GLOVES
GLOVING
GLOW
GLOWED
GLOWER
GLOWERS
GLOWING
GLOWINGLY
GLOWS
GLUE
GLUED
GLUES
GLUING
GLUT
GLUTTON
GLYNN
GNASH
GNAT
GNATS
GNAW
GNAWED
GNAWING
GNAWS
GNOME
GNOMON
GNU
GOA
GOAD
GOADED
GOAL
GOALS
GOAT
GOATEE
GOATEES
GOATS
GOBBLE
GOBBLED
GOBBLER
GOBBLERS
GOBBLES
GOBI
GOBLET
GOBLETS
GOBLIN
GOBLINS
GOD
GODDARD
GODDESS
GODDESSES
GODFATHER
GODFREY
GODHEAD
GODLIKE
GODLY
GODMOTHER
GODMOTHERS
GODOT
GODPARENT
GODS
GODSEND
GODSON
GODWIN
GODZILLA
GOES
GOETHE
GOFF
GOGGLES
GOGH
GOING
GOINGS
GOLD
GOLDA
GOLDBERG
GOLDEN
GOLDENLY
GOLDENNESS
GOLDENROD
GOLDFIELD
GOLDFISH
GOLDING
GOLDMAN
GOLDS
GOLDSMITH
GOLDSTEIN
GOLDSTINE
GOLDWATER
GOLETA
GOLF
GOLFER
GOLFERS
GOLFING
GOLIATH
GOLLY
GOMEZ
GONDOLA
GONE
GONER
GONG
GONGS
GONZALES
GONZALEZ
GOOD
GOODBY
GOODBYE
GOODE
GOODIES
GOODLY
GOODMAN
GOODNESS
GOODRICH
GOODS
GOODWILL
GOODWIN
GOODY
GOODYEAR
GOOF
GOOFED
GOOFS
GOOFY
GOOSE
GOPHER
GORDIAN
GORDON
GORE
GOREN
GORGE
GORGEOUS
GORGEOUSLY
GORGES
GORGING
GORHAM
GORILLA
GORILLAS
GORKY
GORTON
GORY
GOSH
GOSPEL
GOSPELERS
GOSPELS
GOSSIP
GOSSIPED
GOSSIPING
GOSSIPS
GOT
GOTHAM
GOTHIC
GOTHICALLY
GOTHICISM
GOTHICIZE
GOTHICIZED
GOTHICIZER
GOTHICIZERS
GOTHICIZES
GOTHICIZING
GOTO
GOTOS
GOTTEN
GOTTFRIED
GOUCHER
GOUDA
GOUGE
GOUGED
GOUGES
GOUGING
GOULD
GOURD
GOURMET
GOUT
GOVERN
GOVERNANCE
GOVERNED
GOVERNESS
GOVERNING
GOVERNMENT
GOVERNMENTAL
GOVERNMENTALLY
GOVERNMENTS
GOVERNOR
GOVERNORS
GOVERNS
GOWN
GOWNED
GOWNS
GRAB
GRABBED
GRABBER
GRABBERS
GRABBING
GRABBINGS
GRABS
GRACE
GRACED
GRACEFUL
GRACEFULLY
GRACEFULNESS
GRACES
GRACIE
GRACING
GRACIOUS
GRACIOUSLY
GRACIOUSNESS
GRAD
GRADATION
GRADATIONS
GRADE
GRADED
GRADER
GRADERS
GRADES
GRADIENT
GRADIENTS
GRADING
GRADINGS
GRADUAL
GRADUALLY
GRADUATE
GRADUATED
GRADUATES
GRADUATING
GRADUATION
GRADUATIONS
GRADY
GRAFF
GRAFT
GRAFTED
GRAFTER
GRAFTING
GRAFTON
GRAFTS
GRAHAM
GRAHAMS
GRAIL
GRAIN
GRAINED
GRAINING
GRAINS
GRAM
GRAMMAR
GRAMMARIAN
GRAMMARS
GRAMMATIC
GRAMMATICAL
GRAMMATICALLY
GRAMS
GRANARIES
GRANARY
GRAND
GRANDCHILD
GRANDCHILDREN
GRANDDAUGHTER
GRANDER
GRANDEST
GRANDEUR
GRANDFATHER
GRANDFATHERS
GRANDIOSE
GRANDLY
GRANDMA
GRANDMOTHER
GRANDMOTHERS
GRANDNEPHEW
GRANDNESS
GRANDNIECE
GRANDPA
GRANDPARENT
GRANDS
GRANDSON
GRANDSONS
GRANDSTAND
GRANGE
GRANITE
GRANNY
GRANOLA
GRANT
GRANTED
GRANTEE
GRANTER
GRANTING
GRANTOR
GRANTS
GRANULARITY
GRANULATE
GRANULATED
GRANULATES
GRANULATING
GRANVILLE
GRAPE
GRAPEFRUIT
GRAPES
GRAPEVINE
GRAPH
GRAPHED
GRAPHIC
GRAPHICAL
GRAPHICALLY
GRAPHICS
GRAPHING
GRAPHITE
GRAPHS
GRAPPLE
GRAPPLED
GRAPPLING
GRASP
GRASPABLE
GRASPED
GRASPING
GRASPINGLY
GRASPS
GRASS
GRASSED
GRASSERS
GRASSES
GRASSIER
GRASSIEST
GRASSLAND
GRASSY
GRATE
GRATED
GRATEFUL
GRATEFULLY
GRATEFULNESS
GRATER
GRATES
GRATIFICATION
GRATIFIED
GRATIFY
GRATIFYING
GRATING
GRATINGS
GRATIS
GRATITUDE
GRATUITIES
GRATUITOUS
GRATUITOUSLY
GRATUITOUSNESS
GRATUITY
GRAVE
GRAVEL
GRAVELLY
GRAVELY
GRAVEN
GRAVENESS
GRAVER
GRAVES
GRAVEST
GRAVESTONE
GRAVEYARD
GRAVITATE
GRAVITATION
GRAVITATIONAL
GRAVITY
GRAVY
GRAY
GRAYED
GRAYER
GRAYEST
GRAYING
GRAYNESS
GRAYSON
GRAZE
GRAZED
GRAZER
GRAZING
GREASE
GREASED
GREASES
GREASY
GREAT
GREATER
GREATEST
GREATLY
GREATNESS
GRECIAN
GRECIANIZE
GRECIANIZES
GREECE
GREED
GREEDILY
GREEDINESS
GREEDY
GREEK
GREEKIZE
GREEKIZES
GREEKS
GREEN
GREENBELT
GREENBERG
GREENBLATT
GREENBRIAR
GREENE
GREENER
GREENERY
GREENEST
GREENFELD
GREENFIELD
GREENGROCER
GREENHOUSE
GREENHOUSES
GREENING
GREENISH
GREENLAND
GREENLY
GREENNESS
GREENS
GREENSBORO
GREENSVILLE
GREENTREE
GREENVILLE
GREENWARE
GREENWICH
GREER
GREET
GREETED
GREETER
GREETING
GREETINGS
GREETS
GREG
GREGARIOUS
GREGG
GREGORIAN
GREGORY
GRENADE
GRENADES
GRENDEL
GRENIER
GRENOBLE
GRENVILLE
GRESHAM
GRETA
GRETCHEN
GREW
GREY
GREYEST
GREYHOUND
GREYING
GRID
GRIDDLE
GRIDIRON
GRIDS
GRIEF
GRIEFS
GRIEVANCE
GRIEVANCES
GRIEVE
GRIEVED
GRIEVER
GRIEVERS
GRIEVES
GRIEVING
GRIEVINGLY
GRIEVOUS
GRIEVOUSLY
GRIFFITH
GRILL
GRILLED
GRILLING
GRILLS
GRIM
GRIMACE
GRIMALDI
GRIME
GRIMED
GRIMES
GRIMLY
GRIMM
GRIMNESS
GRIN
GRIND
GRINDER
GRINDERS
GRINDING
GRINDINGS
GRINDS
GRINDSTONE
GRINDSTONES
GRINNING
GRINS
GRIP
GRIPE
GRIPED
GRIPES
GRIPING
GRIPPED
GRIPPING
GRIPPINGLY
GRIPS
GRIS
GRISLY
GRIST
GRISWOLD
GRIT
GRITS
GRITTY
GRIZZLY
GROAN
GROANED
GROANER
GROANERS
GROANING
GROANS
GROCER
GROCERIES
GROCERS
GROCERY
GROGGY
GROIN
GROOM
GROOMED
GROOMING
GROOMS
GROOT
GROOVE
GROOVED
GROOVES
GROPE
GROPED
GROPES
GROPING
GROSS
GROSSED
GROSSER
GROSSES
GROSSEST
GROSSET
GROSSING
GROSSLY
GROSSMAN
GROSSNESS
GROSVENOR
GROTESQUE
GROTESQUELY
GROTESQUES
GROTON
GROTTO
GROTTOS
GROUND
GROUNDED
GROUNDER
GROUNDERS
GROUNDING
GROUNDS
GROUNDWORK
GROUP
GROUPED
GROUPING
GROUPINGS
GROUPS
GROUSE
GROVE
GROVEL
GROVELED
GROVELING
GROVELS
GROVER
GROVERS
GROVES
GROW
GROWER
GROWERS
GROWING
GROWL
GROWLED
GROWLING
GROWLS
GROWN
GROWNUP
GROWNUPS
GROWS
GROWTH
GROWTHS
GRUB
GRUBBY
GRUBS
GRUDGE
GRUDGES
GRUDGINGLY
GRUESOME
GRUFF
GRUFFLY
GRUMBLE
GRUMBLED
GRUMBLES
GRUMBLING
GRUMMAN
GRUNT
GRUNTED
GRUNTING
GRUNTS
GRUSKY
GRUYERE
GUADALUPE
GUAM
GUANO
GUARANTEE
GUARANTEED
GUARANTEEING
GUARANTEER
GUARANTEERS
GUARANTEES
GUARANTY
GUARD
GUARDED
GUARDEDLY
GUARDHOUSE
GUARDIA
GUARDIAN
GUARDIANS
GUARDIANSHIP
GUARDING
GUARDS
GUATEMALA
GUATEMALAN
GUBERNATORIAL
GUELPH
GUENTHER
GUERRILLA
GUERRILLAS
GUESS
GUESSED
GUESSES
GUESSING
GUESSWORK
GUEST
GUESTS
GUGGENHEIM
GUHLEMAN
GUIANA
GUIDANCE
GUIDE
GUIDEBOOK
GUIDEBOOKS
GUIDED
GUIDELINE
GUIDELINES
GUIDES
GUIDING
GUILD
GUILDER
GUILDERS
GUILE
GUILFORD
GUILT
GUILTIER
GUILTIEST
GUILTILY
GUILTINESS
GUILTLESS
GUILTLESSLY
GUILTY
GUINEA
GUINEVERE
GUISE
GUISES
GUITAR
GUITARS
GUJARAT
GUJARATI
GULCH
GULCHES
GULF
GULFS
GULL
GULLAH
GULLED
GULLIES
GULLING
GULLS
GULLY
GULP
GULPED
GULPS
GUM
GUMMING
GUMPTION
GUMS
GUN
GUNDERSON
GUNFIRE
GUNMAN
GUNMEN
GUNNAR
GUNNED
GUNNER
GUNNERS
GUNNERY
GUNNING
GUNNY
GUNPLAY
GUNPOWDER
GUNS
GUNSHOT
GUNTHER
GURGLE
GURKHA
GURU
GUS
GUSH
GUSHED
GUSHER
GUSHES
GUSHING
GUST
GUSTAFSON
GUSTAV
GUSTAVE
GUSTAVUS
GUSTO
GUSTS
GUSTY
GUT
GUTENBERG
GUTHRIE
GUTS
GUTSY
GUTTER
GUTTERED
GUTTERS
GUTTING
GUTTURAL
GUY
GUYANA
GUYED
GUYER
GUYERS
GUYING
GUYS
GWEN
GWYN
GYMNASIUM
GYMNASIUMS
GYMNAST
GYMNASTIC
GYMNASTICS
GYMNASTS
GYPSIES
GYPSY
GYRO
GYROCOMPASS
GYROSCOPE
GYROSCOPES
HAAG
HAAS
HABEAS
HABERMAN
HABIB
HABIT
HABITAT
HABITATION
HABITATIONS
HABITATS
HABITS
HABITUAL
HABITUALLY
HABITUALNESS
HACK
HACKED
HACKER
HACKERS
HACKETT
HACKING
HACKNEYED
HACKS
HACKSAW
HAD
HADAMARD
HADDAD
HADDOCK
HADES
HADLEY
HADRIAN
HAFIZ
HAG
HAGEN
HAGER
HAGGARD
HAGGARDLY
HAGGLE
HAGSTROM
HAGUE
HAHN
HAIFA
HAIL
HAILED
HAILING
HAILS
HAILSTONE
HAILSTORM
HAINES
HAIR
HAIRCUT
HAIRCUTS
HAIRIER
HAIRINESS
HAIRLESS
HAIRPIN
HAIRS
HAIRY
HAITI
HAITIAN
HAL
HALCYON
HALE
HALER
HALEY
HALF
HALFHEARTED
HALFWAY
HALIFAX
HALL
HALLEY
HALLINAN
HALLMARK
HALLMARKS
HALLOW
HALLOWED
HALLOWEEN
HALLS
HALLUCINATE
HALLWAY
HALLWAYS
HALOGEN
HALPERN
HALSEY
HALSTEAD
HALT
HALTED
HALTER
HALTERS
HALTING
HALTINGLY
HALTS
HALVE
HALVED
HALVERS
HALVERSON
HALVES
HALVING
HAM
HAMAL
HAMBURG
HAMBURGER
HAMBURGERS
HAMEY
HAMILTON
HAMILTONIAN
HAMILTONIANS
HAMLET
HAMLETS
HAMLIN
HAMMER
HAMMERED
HAMMERING
HAMMERS
HAMMETT
HAMMING
HAMMOCK
HAMMOCKS
HAMMOND
HAMPER
HAMPERED
HAMPERS
HAMPSHIRE
HAMPTON
HAMS
HAMSTER
HAN
HANCOCK
HAND
HANDBAG
HANDBAGS
HANDBOOK
HANDBOOKS
HANDCUFF
HANDCUFFED
HANDCUFFING
HANDCUFFS
HANDED
HANDEL
HANDFUL
HANDFULS
HANDGUN
HANDICAP
HANDICAPPED
HANDICAPS
HANDIER
HANDIEST
HANDILY
HANDINESS
HANDING
HANDIWORK
HANDKERCHIEF
HANDKERCHIEFS
HANDLE
HANDLED
HANDLER
HANDLERS
HANDLES
HANDLING
HANDMAID
HANDOUT
HANDS
HANDSHAKE
HANDSHAKES
HANDSHAKING
HANDSOME
HANDSOMELY
HANDSOMENESS
HANDSOMER
HANDSOMEST
HANDWRITING
HANDWRITTEN
HANDY
HANEY
HANFORD
HANG
HANGAR
HANGARS
HANGED
HANGER
HANGERS
HANGING
HANGMAN
HANGMEN
HANGOUT
HANGOVER
HANGOVERS
HANGS
HANKEL
HANLEY
HANLON
HANNA
HANNAH
HANNIBAL
HANOI
HANOVER
HANOVERIAN
HANOVERIANIZE
HANOVERIANIZES
HANOVERIZE
HANOVERIZES
HANS
HANSEL
HANSEN
HANSON
HANUKKAH
HAP
HAPGOOD
HAPHAZARD
HAPHAZARDLY
HAPHAZARDNESS
HAPLESS
HAPLESSLY
HAPLESSNESS
HAPLY
HAPPEN
HAPPENED
HAPPENING
HAPPENINGS
HAPPENS
HAPPIER
HAPPIEST
HAPPILY
HAPPINESS
HAPPY
HAPSBURG
HARASS
HARASSED
HARASSES
HARASSING
HARASSMENT
HARBIN
HARBINGER
HARBOR
HARBORED
HARBORING
HARBORS
HARCOURT
HARD
HARDBOILED
HARDCOPY
HARDEN
HARDER
HARDEST
HARDHAT
HARDIN
HARDINESS
HARDING
HARDLY
HARDNESS
HARDSCRABBLE
HARDSHIP
HARDSHIPS
HARDWARE
HARDWIRED
HARDWORKING
HARDY
HARE
HARELIP
HAREM
HARES
HARK
HARKEN
HARLAN
HARLEM
HARLEY
HARLOT
HARLOTS
HARM
HARMED
HARMFUL
HARMFULLY
HARMFULNESS
HARMING
HARMLESS
HARMLESSLY
HARMLESSNESS
HARMON
HARMONIC
HARMONICS
HARMONIES
HARMONIOUS
HARMONIOUSLY
HARMONIOUSNESS
HARMONIST
HARMONISTIC
HARMONISTICALLY
HARMONIZE
HARMONY
HARMS
HARNESS
HARNESSED
HARNESSING
HAROLD
HARP
HARPER
HARPERS
HARPING
HARPY
HARRIED
HARRIER
HARRIET
HARRIMAN
HARRINGTON
HARRIS
HARRISBURG
HARRISON
HARRISONBURG
HARROW
HARROWED
HARROWING
HARROWS
HARRY
HARSH
HARSHER
HARSHLY
HARSHNESS
HART
HARTFORD
HARTLEY
HARTMAN
HARVARD
HARVARDIZE
HARVARDIZES
HARVEST
HARVESTED
HARVESTER
HARVESTING
HARVESTS
HARVEY
HARVEYIZE
HARVEYIZES
HARVEYS
HAS
HASH
HASHED
HASHER
HASHES
HASHING
HASHISH
HASKELL
HASKINS
HASSLE
HASTE
HASTEN
HASTENED
HASTENING
HASTENS
HASTILY
HASTINESS
HASTINGS
HASTY
HAT
HATCH
HATCHED
HATCHET
HATCHETS
HATCHING
HATCHURE
HATE
HATED
HATEFUL
HATEFULLY
HATEFULNESS
HATER
HATES
HATFIELD
HATHAWAY
HATING
HATRED
HATS
HATTERAS
HATTIE
HATTIESBURG
HATTIZE
HATTIZES
HAUGEN
HAUGHTILY
HAUGHTINESS
HAUGHTY
HAUL
HAULED
HAULER
HAULING
HAULS
HAUNCH
HAUNCHES
HAUNT
HAUNTED
HAUNTER
HAUNTING
HAUNTS
HAUSA
HAUSDORFF
HAUSER
HAVANA
HAVE
HAVEN
HAVENS
HAVES
HAVILLAND
HAVING
HAVOC
HAWAII
HAWAIIAN
HAWK
HAWKED
HAWKER
HAWKERS
HAWKINS
HAWKS
HAWLEY
HAWTHORNE
HAY
HAYDEN
HAYDN
HAYES
HAYING
HAYNES
HAYS
HAYSTACK
HAYWARD
HAYWOOD
HAZARD
HAZARDOUS
HAZARDS
HAZE
HAZEL
HAZES
HAZINESS
HAZY
HEAD
HEADACHE
HEADACHES
HEADED
HEADER
HEADERS
HEADGEAR
HEADING
HEADINGS
HEADLAND
HEADLANDS
HEADLIGHT
HEADLINE
HEADLINED
HEADLINES
HEADLINING
HEADLONG
HEADMASTER
HEADPHONE
HEADQUARTERS
HEADROOM
HEADS
HEADSET
HEADWAY
HEAL
HEALED
HEALER
HEALERS
HEALEY
HEALING
HEALS
HEALTH
HEALTHFUL
HEALTHFULLY
HEALTHFULNESS
HEALTHIER
HEALTHIEST
HEALTHILY
HEALTHINESS
HEALTHY
HEALY
HEAP
HEAPED
HEAPING
HEAPS
HEAR
HEARD
HEARER
HEARERS
HEARING
HEARINGS
HEARKEN
HEARS
HEARSAY
HEARST
HEART
HEARTBEAT
HEARTBREAK
HEARTEN
HEARTIEST
HEARTILY
HEARTINESS
HEARTLESS
HEARTS
HEARTWOOD
HEARTY
HEAT
HEATABLE
HEATED
HEATEDLY
HEATER
HEATERS
HEATH
HEATHEN
HEATHER
HEATHKIT
HEATHMAN
HEATING
HEATS
HEAVE
HEAVED
HEAVEN
HEAVENLY
HEAVENS
HEAVER
HEAVERS
HEAVES
HEAVIER
HEAVIEST
HEAVILY
HEAVINESS
HEAVING
HEAVY
HEAVYWEIGHT
HEBE
HEBRAIC
HEBRAICIZE
HEBRAICIZES
HEBREW
HEBREWS
HEBRIDES
HECATE
HECK
HECKLE
HECKMAN
HECTIC
HECUBA
HEDDA
HEDGE
HEDGED
HEDGEHOG
HEDGEHOGS
HEDGES
HEDONISM
HEDONIST
HEED
HEEDED
HEEDLESS
HEEDLESSLY
HEEDLESSNESS
HEEDS
HEEL
HEELED
HEELERS
HEELING
HEELS
HEFTY
HEGEL
HEGELIAN
HEGELIANIZE
HEGELIANIZES
HEGEMONY
HEIDEGGER
HEIDELBERG
HEIFER
HEIGHT
HEIGHTEN
HEIGHTENED
HEIGHTENING
HEIGHTENS
HEIGHTS
HEINE
HEINLEIN
HEINOUS
HEINOUSLY
HEINRICH
HEINZ
HEINZE
HEIR
HEIRESS
HEIRESSES
HEIRS
HEISENBERG
HEISER
HELD
HELEN
HELENA
HELENE
HELGA
HELICAL
HELICOPTER
HELIOCENTRIC
HELIOPOLIS
HELIUM
HELIX
HELL
HELLENIC
HELLENIZATION
HELLENIZATIONS
HELLENIZE
HELLENIZED
HELLENIZES
HELLENIZING
HELLESPONT
HELLFIRE
HELLISH
HELLMAN
HELLO
HELLS
HELM
HELMET
HELMETS
HELMHOLTZ
HELMSMAN
HELMUT
HELP
HELPED
HELPER
HELPERS
HELPFUL
HELPFULLY
HELPFULNESS
HELPING
HELPLESS
HELPLESSLY
HELPLESSNESS
HELPMATE
HELPS
HELSINKI
HELVETICA
HEM
HEMINGWAY
HEMISPHERE
HEMISPHERES
HEMLOCK
HEMLOCKS
HEMOGLOBIN
HEMORRHOID
HEMOSTAT
HEMOSTATS
HEMP
HEMPEN
HEMPSTEAD
HEMS
HEN
HENCE
HENCEFORTH
HENCHMAN
HENCHMEN
HENDERSON
HENDRICK
HENDRICKS
HENDRICKSON
HENDRIX
HENLEY
HENNESSEY
HENNESSY
HENNING
HENPECK
HENRI
HENRIETTA
HENS
HEPATITIS
HEPBURN
HER
HERA
HERACLITUS
HERALD
HERALDED
HERALDING
HERALDS
HERB
HERBERT
HERBIVORE
HERBIVOROUS
HERBS
HERCULEAN
HERCULES
HERD
HERDED
HERDER
HERDING
HERDS
HERE
HEREABOUT
HEREABOUTS
HEREAFTER
HEREBY
HEREDITARY
HEREDITY
HEREFORD
HEREIN
HEREINAFTER
HEREOF
HERES
HERESY
HERETIC
HERETICS
HERETO
HERETOFORE
HEREUNDER
HEREWITH
HERITAGE
HERITAGES
HERKIMER
HERMAN
HERMANN
HERMES
HERMETIC
HERMETICALLY
HERMIT
HERMITE
HERMITIAN
HERMITS
HERMOSA
HERNANDEZ
HERO
HERODOTUS
HEROES
HEROIC
HEROICALLY
HEROICS
HEROIN
HEROINE
HEROINES
HEROISM
HERON
HERONS
HERPES
HERR
HERRING
HERRINGS
HERRINGTON
HERS
HERSCHEL
HERSELF
HERSEY
HERSHEL
HERSHEY
HERTZ
HERTZOG
HESITANT
HESITANTLY
HESITATE
HESITATED
HESITATES
HESITATING
HESITATINGLY
HESITATION
HESITATIONS
HESPERUS
HESS
HESSE
HESSIAN
HESSIANS
HESTER
HETEROGENEITY
HETEROGENEOUS
HETEROGENEOUSLY
HETEROGENEOUSNESS
HETEROGENOUS
HETEROSEXUAL
HETMAN
HETTIE
HETTY
HEUBLEIN
HEURISTIC
HEURISTICALLY
HEURISTICS
HEUSEN
HEUSER
HEW
HEWED
HEWER
HEWETT
HEWITT
HEWLETT
HEWS
HEX
HEXADECIMAL
HEXAGON
HEXAGONAL
HEXAGONALLY
HEXAGONS
HEY
HEYWOOD
HIATT
HIAWATHA
HIBBARD
HIBERNATE
HIBERNIA
HICK
HICKEY
HICKEYS
HICKMAN
HICKOK
HICKORY
HICKS
HID
HIDDEN
HIDE
HIDEOUS
HIDEOUSLY
HIDEOUSNESS
HIDEOUT
HIDEOUTS
HIDES
HIDING
HIERARCHAL
HIERARCHIC
HIERARCHICAL
HIERARCHICALLY
HIERARCHIES
HIERARCHY
HIERONYMUS
HIGGINS
HIGH
HIGHER
HIGHEST
HIGHFIELD
HIGHLAND
HIGHLANDER
HIGHLANDS
HIGHLIGHT
HIGHLIGHTED
HIGHLIGHTING
HIGHLIGHTS
HIGHLY
HIGHNESS
HIGHNESSES
HIGHWAY
HIGHWAYMAN
HIGHWAYMEN
HIGHWAYS
HIJACK
HIJACKED
HIKE
HIKED
HIKER
HIKES
HIKING
HILARIOUS
HILARIOUSLY
HILARITY
HILBERT
HILDEBRAND
HILL
HILLARY
HILLBILLY
HILLCREST
HILLEL
HILLOCK
HILLS
HILLSBORO
HILLSDALE
HILLSIDE
HILLSIDES
HILLTOP
HILLTOPS
HILT
HILTON
HILTS
HIM
HIMALAYA
HIMALAYAS
HIMMLER
HIMSELF
HIND
HINDER
HINDERED
HINDERING
HINDERS
HINDI
HINDRANCE
HINDRANCES
HINDSIGHT
HINDU
HINDUISM
HINDUS
HINDUSTAN
HINES
HINGE
HINGED
HINGES
HINKLE
HINMAN
HINSDALE
HINT
HINTED
HINTING
HINTS
HIP
HIPPO
HIPPOCRATES
HIPPOCRATIC
HIPPOPOTAMUS
HIPS
HIRAM
HIRE
HIRED
HIRER
HIRERS
HIRES
HIREY
HIRING
HIRINGS
HIROSHI
HIROSHIMA
HIRSCH
HIS
HISPANIC
HISPANICIZE
HISPANICIZES
HISPANICS
HISS
HISSED
HISSES
HISSING
HISTOGRAM
HISTOGRAMS
HISTORIAN
HISTORIANS
HISTORIC
HISTORICAL
HISTORICALLY
HISTORIES
HISTORY
HIT
HITACHI
HITCH
HITCHCOCK
HITCHED
HITCHHIKE
HITCHHIKED
HITCHHIKER
HITCHHIKERS
HITCHHIKES
HITCHHIKING
HITCHING
HITHER
HITHERTO
HITLER
HITLERIAN
HITLERISM
HITLERITE
HITLERITES
HITS
HITTER
HITTERS
HITTING
HIVE
HOAGLAND
HOAR
HOARD
HOARDER
HOARDING
HOARINESS
HOARSE
HOARSELY
HOARSENESS
HOARY
HOBART
HOBBES
HOBBIES
HOBBLE
HOBBLED
HOBBLES
HOBBLING
HOBBS
HOBBY
HOBBYHORSE
HOBBYIST
HOBBYISTS
HOBDAY
HOBOKEN
HOCKEY
HODGEPODGE
HODGES
HODGKIN
HOE
HOES
HOFF
HOFFMAN
HOG
HOGGING
HOGS
HOIST
HOISTED
HOISTING
HOISTS
HOKAN
HOLBROOK
HOLCOMB
HOLD
HOLDEN
HOLDER
HOLDERS
HOLDING
HOLDINGS
HOLDS
HOLE
HOLED
HOLES
HOLIDAY
HOLIDAYS
HOLIES
HOLINESS
HOLISTIC
HOLLAND
HOLLANDAISE
HOLLANDER
HOLLERITH
HOLLINGSWORTH
HOLLISTER
HOLLOW
HOLLOWAY
HOLLOWED
HOLLOWING
HOLLOWLY
HOLLOWNESS
HOLLOWS
HOLLY
HOLLYWOOD
HOLLYWOODIZE
HOLLYWOODIZES
HOLM
HOLMAN
HOLMDEL
HOLMES
HOLOCAUST
HOLOCENE
HOLOGRAM
HOLOGRAMS
HOLST
HOLSTEIN
HOLY
HOLYOKE
HOLZMAN
HOM
HOMAGE
HOME
HOMED
HOMELESS
HOMELY
HOMEMADE
HOMEMAKER
HOMEMAKERS
HOMEOMORPHIC
HOMEOMORPHISM
HOMEOMORPHISMS
HOMEOPATH
HOMEOWNER
HOMER
HOMERIC
HOMERS
HOMES
HOMESICK
HOMESICKNESS
HOMESPUN
HOMESTEAD
HOMESTEADER
HOMESTEADERS
HOMESTEADS
HOMEWARD
HOMEWARDS
HOMEWORK
HOMICIDAL
HOMICIDE
HOMING
HOMO
HOMOGENEITIES
HOMOGENEITY
HOMOGENEOUS
HOMOGENEOUSLY
HOMOGENEOUSNESS
HOMOMORPHIC
HOMOMORPHISM
HOMOMORPHISMS
HOMOSEXUAL
HONDA
HONDO
HONDURAS
HONE
HONED
HONER
HONES
HONEST
HONESTLY
HONESTY
HONEY
HONEYBEE
HONEYCOMB
HONEYCOMBED
HONEYDEW
HONEYMOON
HONEYMOONED
HONEYMOONER
HONEYMOONERS
HONEYMOONING
HONEYMOONS
HONEYSUCKLE
HONEYWELL
HONING
HONOLULU
HONOR
HONORABLE
HONORABLENESS
HONORABLY
HONORARIES
HONORARIUM
HONORARY
HONORED
HONORER
HONORING
HONORS
HONSHU
HOOD
HOODED
HOODLUM
HOODS
HOODWINK
HOODWINKED
HOODWINKING
HOODWINKS
HOOF
HOOFS
HOOK
HOOKED
HOOKER
HOOKERS
HOOKING
HOOKS
HOOKUP
HOOKUPS
HOOP
HOOPER
HOOPS
HOOSIER
HOOSIERIZE
HOOSIERIZES
HOOT
HOOTED
HOOTER
HOOTING
HOOTS
HOOVER
HOOVERIZE
HOOVERIZES
HOOVES
HOP
HOPE
HOPED
HOPEFUL
HOPEFULLY
HOPEFULNESS
HOPEFULS
HOPELESS
HOPELESSLY
HOPELESSNESS
HOPES
HOPI
HOPING
HOPKINS
HOPKINSIAN
HOPPER
HOPPERS
HOPPING
HOPS
HORACE
HORATIO
HORDE
HORDES
HORIZON
HORIZONS
HORIZONTAL
HORIZONTALLY
HORMONE
HORMONES
HORN
HORNBLOWER
HORNED
HORNET
HORNETS
HORNS
HORNY
HOROWITZ
HORRENDOUS
HORRENDOUSLY
HORRIBLE
HORRIBLENESS
HORRIBLY
HORRID
HORRIDLY
HORRIFIED
HORRIFIES
HORRIFY
HORRIFYING
HORROR
HORRORS
HORSE
HORSEBACK
HORSEFLESH
HORSEFLY
HORSEMAN
HORSEPLAY
HORSEPOWER
HORSES
HORSESHOE
HORSESHOER
HORTICULTURE
HORTON
HORUS
HOSE
HOSES
HOSPITABLE
HOSPITABLY
HOSPITAL
HOSPITALITY
HOSPITALIZE
HOSPITALIZED
HOSPITALIZES
HOSPITALIZING
HOSPITALS
HOST
HOSTAGE
HOSTAGES
HOSTED
HOSTESS
HOSTESSES
HOSTILE
HOSTILELY
HOSTILITIES
HOSTILITY
HOSTING
HOSTS
HOT
HOTEL
HOTELS
HOTLY
HOTNESS
HOTTENTOT
HOTTER
HOTTEST
HOUDAILLE
HOUDINI
HOUGHTON
HOUND
HOUNDED
HOUNDING
HOUNDS
HOUR
HOURGLASS
HOURLY
HOURS
HOUSE
HOUSEBOAT
HOUSEBROKEN
HOUSED
HOUSEFLIES
HOUSEFLY
HOUSEHOLD
HOUSEHOLDER
HOUSEHOLDERS
HOUSEHOLDS
HOUSEKEEPER
HOUSEKEEPERS
HOUSEKEEPING
HOUSES
HOUSETOP
HOUSETOPS
HOUSEWIFE
HOUSEWIFELY
HOUSEWIVES
HOUSEWORK
HOUSING
HOUSTON
HOVEL
HOVELS
HOVER
HOVERED
HOVERING
HOVERS
HOW
HOWARD
HOWE
HOWELL
HOWEVER
HOWL
HOWLED
HOWLER
HOWLING
HOWLS
HOYT
HROTHGAR
HUB
HUBBARD
HUBBELL
HUBER
HUBERT
HUBRIS
HUBS
HUCK
HUDDLE
HUDDLED
HUDDLING
HUDSON
HUE
HUES
HUEY
HUFFMAN
HUG
HUGE
HUGELY
HUGENESS
HUGGING
HUGGINS
HUGH
HUGHES
HUGO
HUH
HULL
HULLS
HUM
HUMAN
HUMANE
HUMANELY
HUMANENESS
HUMANITARIAN
HUMANITIES
HUMANITY
HUMANLY
HUMANNESS
HUMANS
HUMBLE
HUMBLED
HUMBLENESS
HUMBLER
HUMBLEST
HUMBLING
HUMBLY
HUMBOLDT
HUMBUG
HUME
HUMERUS
HUMID
HUMIDIFICATION
HUMIDIFIED
HUMIDIFIER
HUMIDIFIERS
HUMIDIFIES
HUMIDIFY
HUMIDIFYING
HUMIDITY
HUMIDLY
HUMILIATE
HUMILIATED
HUMILIATES
HUMILIATING
HUMILIATION
HUMILIATIONS
HUMILITY
HUMMED
HUMMEL
HUMMING
HUMMINGBIRD
HUMOR
HUMORED
HUMORER
HUMORERS
HUMORING
HUMOROUS
HUMOROUSLY
HUMOROUSNESS
HUMORS
HUMP
HUMPBACK
HUMPED
HUMPHREY
HUMPTY
HUMS
HUN
HUNCH
HUNCHED
HUNCHES
HUNDRED
HUNDREDFOLD
HUNDREDS
HUNDREDTH
HUNG
HUNGARIAN
HUNGARY
HUNGER
HUNGERED
HUNGERING
HUNGERS
HUNGRIER
HUNGRIEST
HUNGRILY
HUNGRY
HUNK
HUNKS
HUNS
HUNT
HUNTED
HUNTER
HUNTERS
HUNTING
HUNTINGTON
HUNTLEY
HUNTS
HUNTSMAN
HUNTSVILLE
HURD
HURDLE
HURL
HURLED
HURLER
HURLERS
HURLING
HURON
HURONS
HURRAH
HURRICANE
HURRICANES
HURRIED
HURRIEDLY
HURRIES
HURRY
HURRYING
HURST
HURT
HURTING
HURTLE
HURTLING
HURTS
HURWITZ
HUSBAND
HUSBANDRY
HUSBANDS
HUSH
HUSHED
HUSHES
HUSHING
HUSK
HUSKED
HUSKER
HUSKINESS
HUSKING
HUSKS
HUSKY
HUSTLE
HUSTLED
HUSTLER
HUSTLES
HUSTLING
HUSTON
HUT
HUTCH
HUTCHINS
HUTCHINSON
HUTCHISON
HUTS
HUXLEY
HUXTABLE
HYACINTH
HYADES
HYANNIS
HYBRID
HYDE
HYDRA
HYDRANT
HYDRAULIC
HYDRO
HYDRODYNAMIC
HYDRODYNAMICS
HYDROGEN
HYDROGENS
HYENA
HYGIENE
HYMAN
HYMEN
HYMN
HYMNS
HYPER
HYPERBOLA
HYPERBOLIC
HYPERTEXT
HYPHEN
HYPHENATE
HYPHENS
HYPNOSIS
HYPNOTIC
HYPOCRISIES
HYPOCRISY
HYPOCRITE
HYPOCRITES
HYPODERMIC
HYPODERMICS
HYPOTHESES
HYPOTHESIS
HYPOTHESIZE
HYPOTHESIZED
HYPOTHESIZER
HYPOTHESIZES
HYPOTHESIZING
HYPOTHETICAL
HYPOTHETICALLY
HYSTERESIS
HYSTERICAL
HYSTERICALLY
IAN
IBERIA
IBERIAN
IBEX
IBID
IBIS
IBN
IBSEN
ICARUS
ICE
ICEBERG
ICEBERGS
ICEBOX
ICED
ICELAND
ICELANDIC
ICES
ICICLE
ICINESS
ICING
ICINGS
ICON
ICONOCLASM
ICONOCLAST
ICONS
ICOSAHEDRA
ICOSAHEDRAL
ICOSAHEDRON
ICY
IDA
IDAHO
IDEA
IDEAL
IDEALISM
IDEALISTIC
IDEALIZATION
IDEALIZATIONS
IDEALIZE
IDEALIZED
IDEALIZES
IDEALIZING
IDEALLY
IDEALS
IDEAS
IDEM
IDEMPOTENCY
IDEMPOTENT
IDENTICAL
IDENTICALLY
IDENTIFIABLE
IDENTIFIABLY
IDENTIFICATION
IDENTIFICATIONS
IDENTIFIED
IDENTIFIER
IDENTIFIERS
IDENTIFIES
IDENTIFY
IDENTIFYING
IDENTITIES
IDENTITY
IDEOLOGICAL
IDEOLOGICALLY
IDEOLOGY
IDIOCY
IDIOM
IDIOSYNCRASIES
IDIOSYNCRASY
IDIOSYNCRATIC
IDIOT
IDIOTIC
IDIOTS
IDLE
IDLED
IDLENESS
IDLER
IDLERS
IDLES
IDLEST
IDLING
IDLY
IDOL
IDOLATRY
IDOLS
IFNI
IGLOO
IGNITE
IGNITION
IGNOBLE
IGNOMINIOUS
IGNORAMUS
IGNORANCE
IGNORANT
IGNORANTLY
IGNORE
IGNORED
IGNORES
IGNORING
IGOR
IKE
ILIAD
ILIADIZE
ILIADIZES
ILL
ILLEGAL
ILLEGALITIES
ILLEGALITY
ILLEGALLY
ILLEGITIMATE
ILLICIT
ILLICITLY
ILLINOIS
ILLITERACY
ILLITERATE
ILLNESS
ILLNESSES
ILLOGICAL
ILLOGICALLY
ILLS
ILLUMINATE
ILLUMINATED
ILLUMINATES
ILLUMINATING
ILLUMINATION
ILLUMINATIONS
ILLUSION
ILLUSIONS
ILLUSIVE
ILLUSIVELY
ILLUSORY
ILLUSTRATE
ILLUSTRATED
ILLUSTRATES
ILLUSTRATING
ILLUSTRATION
ILLUSTRATIONS
ILLUSTRATIVE
ILLUSTRATIVELY
ILLUSTRATOR
ILLUSTRATORS
ILLUSTRIOUS
ILLUSTRIOUSNESS
ILLY
ILONA
ILYUSHIN
IMAGE
IMAGEN
IMAGERY
IMAGES
IMAGINABLE
IMAGINABLY
IMAGINARY
IMAGINATION
IMAGINATIONS
IMAGINATIVE
IMAGINATIVELY
IMAGINE
IMAGINED
IMAGINES
IMAGING
IMAGINING
IMAGININGS
IMBALANCE
IMBALANCES
IMBECILE
IMBIBE
IMBRIUM
IMITATE
IMITATED
IMITATES
IMITATING
IMITATION
IMITATIONS
IMITATIVE
IMMACULATE
IMMACULATELY
IMMATERIAL
IMMATERIALLY
IMMATURE
IMMATURITY
IMMEDIACIES
IMMEDIACY
IMMEDIATE
IMMEDIATELY
IMMEMORIAL
IMMENSE
IMMENSELY
IMMERSE
IMMERSED
IMMERSES
IMMERSION
IMMIGRANT
IMMIGRANTS
IMMIGRATE
IMMIGRATED
IMMIGRATES
IMMIGRATING
IMMIGRATION
IMMINENT
IMMINENTLY
IMMODERATE
IMMODEST
IMMORAL
IMMORTAL
IMMORTALITY
IMMORTALLY
IMMOVABILITY
IMMOVABLE
IMMOVABLY
IMMUNE
IMMUNITIES
IMMUNITY
IMMUNIZATION
IMMUTABLE
IMP
IMPACT
IMPACTED
IMPACTING
IMPACTION
IMPACTOR
IMPACTORS
IMPACTS
IMPAIR
IMPAIRED
IMPAIRING
IMPAIRS
IMPALE
IMPART
IMPARTED
IMPARTIAL
IMPARTIALLY
IMPARTS
IMPASSE
IMPASSIVE
IMPATIENCE
IMPATIENT
IMPATIENTLY
IMPEACH
IMPEACHABLE
IMPEACHED
IMPEACHMENT
IMPECCABLE
IMPEDANCE
IMPEDANCES
IMPEDE
IMPEDED
IMPEDES
IMPEDIMENT
IMPEDIMENTS
IMPEDING
IMPEL
IMPELLED
IMPELLING
IMPEND
IMPENDING
IMPENETRABILITY
IMPENETRABLE
IMPENETRABLY
IMPERATIVE
IMPERATIVELY
IMPERATIVES
IMPERCEIVABLE
IMPERCEPTIBLE
IMPERFECT
IMPERFECTION
IMPERFECTIONS
IMPERFECTLY
IMPERIAL
IMPERIALISM
IMPERIALIST
IMPERIALISTS
IMPERIL
IMPERILED
IMPERIOUS
IMPERIOUSLY
IMPERMANENCE
IMPERMANENT
IMPERMEABLE
IMPERMISSIBLE
IMPERSONAL
IMPERSONALLY
IMPERSONATE
IMPERSONATED
IMPERSONATES
IMPERSONATING
IMPERSONATION
IMPERSONATIONS
IMPERTINENT
IMPERTINENTLY
IMPERVIOUS
IMPERVIOUSLY
IMPETUOUS
IMPETUOUSLY
IMPETUS
IMPINGE
IMPINGED
IMPINGES
IMPINGING
IMPIOUS
IMPLACABLE
IMPLANT
IMPLANTED
IMPLANTING
IMPLANTS
IMPLAUSIBLE
IMPLEMENT
IMPLEMENTABLE
IMPLEMENTATION
IMPLEMENTATIONS
IMPLEMENTED
IMPLEMENTER
IMPLEMENTING
IMPLEMENTOR
IMPLEMENTORS
IMPLEMENTS
IMPLICANT
IMPLICANTS
IMPLICATE
IMPLICATED
IMPLICATES
IMPLICATING
IMPLICATION
IMPLICATIONS
IMPLICIT
IMPLICITLY
IMPLICITNESS
IMPLIED
IMPLIES
IMPLORE
IMPLORED
IMPLORING
IMPLY
IMPLYING
IMPOLITE
IMPORT
IMPORTANCE
IMPORTANT
IMPORTANTLY
IMPORTATION
IMPORTED
IMPORTER
IMPORTERS
IMPORTING
IMPORTS
IMPOSE
IMPOSED
IMPOSES
IMPOSING
IMPOSITION
IMPOSITIONS
IMPOSSIBILITIES
IMPOSSIBILITY
IMPOSSIBLE
IMPOSSIBLY
IMPOSTOR
IMPOSTORS
IMPOTENCE
IMPOTENCY
IMPOTENT
IMPOUND
IMPOVERISH
IMPOVERISHED
IMPOVERISHMENT
IMPRACTICABLE
IMPRACTICAL
IMPRACTICALITY
IMPRACTICALLY
IMPRECISE
IMPRECISELY
IMPRECISION
IMPREGNABLE
IMPREGNATE
IMPRESS
IMPRESSED
IMPRESSER
IMPRESSES
IMPRESSIBLE
IMPRESSING
IMPRESSION
IMPRESSIONABLE
IMPRESSIONIST
IMPRESSIONISTIC
IMPRESSIONS
IMPRESSIVE
IMPRESSIVELY
IMPRESSIVENESS
IMPRESSMENT
IMPRIMATUR
IMPRINT
IMPRINTED
IMPRINTING
IMPRINTS
IMPRISON
IMPRISONED
IMPRISONING
IMPRISONMENT
IMPRISONMENTS
IMPRISONS
IMPROBABILITY
IMPROBABLE
IMPROMPTU
IMPROPER
IMPROPERLY
IMPROPRIETY
IMPROVE
IMPROVED
IMPROVEMENT
IMPROVEMENTS
IMPROVES
IMPROVING
IMPROVISATION
IMPROVISATIONAL
IMPROVISATIONS
IMPROVISE
IMPROVISED
IMPROVISER
IMPROVISERS
IMPROVISES
IMPROVISING
IMPRUDENT
IMPS
IMPUDENT
IMPUDENTLY
IMPUGN
IMPULSE
IMPULSES
IMPULSION
IMPULSIVE
IMPUNITY
IMPURE
IMPURITIES
IMPURITY
IMPUTE
IMPUTED
INABILITY
INACCESSIBLE
INACCURACIES
INACCURACY
INACCURATE
INACTION
INACTIVATE
INACTIVE
INACTIVITY
INADEQUACIES
INADEQUACY
INADEQUATE
INADEQUATELY
INADEQUATENESS
INADMISSIBILITY
INADMISSIBLE
INADVERTENT
INADVERTENTLY
INADVISABLE
INALIENABLE
INALTERABLE
INANE
INANIMATE
INANIMATELY
INANNA
INAPPLICABLE
INAPPROACHABLE
INAPPROPRIATE
INAPPROPRIATENESS
INASMUCH
INATTENTION
INAUDIBLE
INAUGURAL
INAUGURATE
INAUGURATED
INAUGURATING
INAUGURATION
INAUSPICIOUS
INBOARD
INBOUND
INBREED
INCA
INCALCULABLE
INCANDESCENT
INCANTATION
INCAPABLE
INCAPACITATE
INCAPACITATING
INCARCERATE
INCARNATION
INCARNATIONS
INCAS
INCENDIARIES
INCENDIARY
INCENSE
INCENSED
INCENSES
INCENTIVE
INCENTIVES
INCEPTION
INCESSANT
INCESSANTLY
INCEST
INCESTUOUS
INCH
INCHED
INCHES
INCHING
INCIDENCE
INCIDENT
INCIDENTAL
INCIDENTALLY
INCIDENTALS
INCIDENTS
INCINERATE
INCIPIENT
INCISIVE
INCITE
INCITED
INCITEMENT
INCITES
INCITING
INCLEMENT
INCLINATION
INCLINATIONS
INCLINE
INCLINED
INCLINES
INCLINING
INCLOSE
INCLOSED
INCLOSES
INCLOSING
INCLUDE
INCLUDED
INCLUDES
INCLUDING
INCLUSION
INCLUSIONS
INCLUSIVE
INCLUSIVELY
INCLUSIVENESS
INCOHERENCE
INCOHERENT
INCOHERENTLY
INCOME
INCOMES
INCOMING
INCOMMENSURABLE
INCOMMENSURATE
INCOMMUNICABLE
INCOMPARABLE
INCOMPARABLY
INCOMPATIBILITIES
INCOMPATIBILITY
INCOMPATIBLE
INCOMPATIBLY
INCOMPETENCE
INCOMPETENT
INCOMPETENTS
INCOMPLETE
INCOMPLETELY
INCOMPLETENESS
INCOMPREHENSIBILITY
INCOMPREHENSIBLE
INCOMPREHENSIBLY
INCOMPREHENSION
INCOMPRESSIBLE
INCOMPUTABLE
INCONCEIVABLE
INCONCLUSIVE
INCONGRUITY
INCONGRUOUS
INCONSEQUENTIAL
INCONSEQUENTIALLY
INCONSIDERABLE
INCONSIDERATE
INCONSIDERATELY
INCONSIDERATENESS
INCONSISTENCIES
INCONSISTENCY
INCONSISTENT
INCONSISTENTLY
INCONSPICUOUS
INCONTESTABLE
INCONTROVERTIBLE
INCONTROVERTIBLY
INCONVENIENCE
INCONVENIENCED
INCONVENIENCES
INCONVENIENCING
INCONVENIENT
INCONVENIENTLY
INCONVERTIBLE
INCORPORATE
INCORPORATED
INCORPORATES
INCORPORATING
INCORPORATION
INCORRECT
INCORRECTLY
INCORRECTNESS
INCORRIGIBLE
INCREASE
INCREASED
INCREASES
INCREASING
INCREASINGLY
INCREDIBLE
INCREDIBLY
INCREDULITY
INCREDULOUS
INCREDULOUSLY
INCREMENT
INCREMENTAL
INCREMENTALLY
INCREMENTED
INCREMENTER
INCREMENTING
INCREMENTS
INCRIMINATE
INCUBATE
INCUBATED
INCUBATES
INCUBATING
INCUBATION
INCUBATOR
INCUBATORS
INCULCATE
INCUMBENT
INCUR
INCURABLE
INCURRED
INCURRING
INCURS
INCURSION
INDEBTED
INDEBTEDNESS
INDECENT
INDECIPHERABLE
INDECISION
INDECISIVE
INDEED
INDEFATIGABLE
INDEFENSIBLE
INDEFINITE
INDEFINITELY
INDEFINITENESS
INDELIBLE
INDEMNIFY
INDEMNITY
INDENT
INDENTATION
INDENTATIONS
INDENTED
INDENTING
INDENTS
INDENTURE
INDEPENDENCE
INDEPENDENT
INDEPENDENTLY
INDESCRIBABLE
INDESTRUCTIBLE
INDETERMINACIES
INDETERMINACY
INDETERMINATE
INDETERMINATELY
INDEX
INDEXABLE
INDEXED
INDEXES
INDEXING
INDIA
INDIAN
INDIANA
INDIANAPOLIS
INDIANS
INDICATE
INDICATED
INDICATES
INDICATING
INDICATION
INDICATIONS
INDICATIVE
INDICATOR
INDICATORS
INDICES
INDICT
INDICTMENT
INDICTMENTS
INDIES
INDIFFERENCE
INDIFFERENT
INDIFFERENTLY
INDIGENOUS
INDIGENOUSLY
INDIGENOUSNESS
INDIGESTIBLE
INDIGESTION
INDIGNANT
INDIGNANTLY
INDIGNATION
INDIGNITIES
INDIGNITY
INDIGO
INDIRA
INDIRECT
INDIRECTED
INDIRECTING
INDIRECTION
INDIRECTIONS
INDIRECTLY
INDIRECTS
INDISCREET
INDISCRETION
INDISCRIMINATE
INDISCRIMINATELY
INDISPENSABILITY
INDISPENSABLE
INDISPENSABLY
INDISPUTABLE
INDISTINCT
INDISTINGUISHABLE
INDIVIDUAL
INDIVIDUALISM
INDIVIDUALISTIC
INDIVIDUALITY
INDIVIDUALIZE
INDIVIDUALIZED
INDIVIDUALIZES
INDIVIDUALIZING
INDIVIDUALLY
INDIVIDUALS
INDIVISIBILITY
INDIVISIBLE
INDO
INDOCHINA
INDOCHINESE
INDOCTRINATE
INDOCTRINATED
INDOCTRINATES
INDOCTRINATING
INDOCTRINATION
INDOEUROPEAN
INDOLENT
INDOLENTLY
INDOMITABLE
INDONESIA
INDONESIAN
INDOOR
INDOORS
INDUBITABLE
INDUCE
INDUCED
INDUCEMENT
INDUCEMENTS
INDUCER
INDUCES
INDUCING
INDUCT
INDUCTANCE
INDUCTANCES
INDUCTED
INDUCTEE
INDUCTING
INDUCTION
INDUCTIONS
INDUCTIVE
INDUCTIVELY
INDUCTOR
INDUCTORS
INDUCTS
INDULGE
INDULGED
INDULGENCE
INDULGENCES
INDULGENT
INDULGING
INDUS
INDUSTRIAL
INDUSTRIALISM
INDUSTRIALIST
INDUSTRIALISTS
INDUSTRIALIZATION
INDUSTRIALIZED
INDUSTRIALLY
INDUSTRIALS
INDUSTRIES
INDUSTRIOUS
INDUSTRIOUSLY
INDUSTRIOUSNESS
INDUSTRY
INDY
INEFFECTIVE
INEFFECTIVELY
INEFFECTIVENESS
INEFFECTUAL
INEFFICIENCIES
INEFFICIENCY
INEFFICIENT
INEFFICIENTLY
INELEGANT
INELIGIBLE
INEPT
INEQUALITIES
INEQUALITY
INEQUITABLE
INEQUITY
INERT
INERTIA
INERTIAL
INERTLY
INERTNESS
INESCAPABLE
INESCAPABLY
INESSENTIAL
INESTIMABLE
INEVITABILITIES
INEVITABILITY
INEVITABLE
INEVITABLY
INEXACT
INEXCUSABLE
INEXCUSABLY
INEXHAUSTIBLE
INEXORABLE
INEXORABLY
INEXPENSIVE
INEXPENSIVELY
INEXPERIENCE
INEXPERIENCED
INEXPLICABLE
INFALLIBILITY
INFALLIBLE
INFALLIBLY
INFAMOUS
INFAMOUSLY
INFAMY
INFANCY
INFANT
INFANTILE
INFANTRY
INFANTRYMAN
INFANTRYMEN
INFANTS
INFARCT
INFATUATE
INFEASIBLE
INFECT
INFECTED
INFECTING
INFECTION
INFECTIONS
INFECTIOUS
INFECTIOUSLY
INFECTIVE
INFECTS
INFER
INFERENCE
INFERENCES
INFERENTIAL
INFERIOR
INFERIORITY
INFERIORS
INFERNAL
INFERNALLY
INFERNO
INFERNOS
INFERRED
INFERRING
INFERS
INFERTILE
INFEST
INFESTED
INFESTING
INFESTS
INFIDEL
INFIDELITY
INFIDELS
INFIGHTING
INFILTRATE
INFINITE
INFINITELY
INFINITENESS
INFINITESIMAL
INFINITIVE
INFINITIVES
INFINITUDE
INFINITUM
INFINITY
INFIRM
INFIRMARY
INFIRMITY
INFIX
INFLAME
INFLAMED
INFLAMMABLE
INFLAMMATION
INFLAMMATORY
INFLATABLE
INFLATE
INFLATED
INFLATER
INFLATES
INFLATING
INFLATION
INFLATIONARY
INFLEXIBILITY
INFLEXIBLE
INFLICT
INFLICTED
INFLICTING
INFLICTS
INFLOW
INFLUENCE
INFLUENCED
INFLUENCES
INFLUENCING
INFLUENTIAL
INFLUENTIALLY
INFLUENZA
INFORM
INFORMAL
INFORMALITY
INFORMALLY
INFORMANT
INFORMANTS
INFORMATICA
INFORMATION
INFORMATIONAL
INFORMATIVE
INFORMATIVELY
INFORMED
INFORMER
INFORMERS
INFORMING
INFORMS
INFRA
INFRARED
INFRASTRUCTURE
INFREQUENT
INFREQUENTLY
INFRINGE
INFRINGED
INFRINGEMENT
INFRINGEMENTS
INFRINGES
INFRINGING
INFURIATE
INFURIATED
INFURIATES
INFURIATING
INFURIATION
INFUSE
INFUSED
INFUSES
INFUSING
INFUSION
INFUSIONS
INGENIOUS
INGENIOUSLY
INGENIOUSNESS
INGENUITY
INGENUOUS
INGERSOLL
INGEST
INGESTION
INGLORIOUS
INGOT
INGRAM
INGRATE
INGRATIATE
INGRATITUDE
INGREDIENT
INGREDIENTS
INGROWN
INHABIT
INHABITABLE
INHABITANCE
INHABITANT
INHABITANTS
INHABITED
INHABITING
INHABITS
INHALE
INHALED
INHALER
INHALES
INHALING
INHERE
INHERENT
INHERENTLY
INHERES
INHERIT
INHERITABLE
INHERITANCE
INHERITANCES
INHERITED
INHERITING
INHERITOR
INHERITORS
INHERITRESS
INHERITRESSES
INHERITRICES
INHERITRIX
INHERITS
INHIBIT
INHIBITED
INHIBITING
INHIBITION
INHIBITIONS
INHIBITOR
INHIBITORS
INHIBITORY
INHIBITS
INHOMOGENEITIES
INHOMOGENEITY
INHOMOGENEOUS
INHOSPITABLE
INHUMAN
INHUMANE
INIMICAL
INIMITABLE
INIQUITIES
INIQUITY
INITIAL
INITIALED
INITIALING
INITIALIZATION
INITIALIZATIONS
INITIALIZE
INITIALIZED
INITIALIZER
INITIALIZERS
INITIALIZES
INITIALIZING
INITIALLY
INITIALS
INITIATE
INITIATED
INITIATES
INITIATING
INITIATION
INITIATIONS
INITIATIVE
INITIATIVES
INITIATOR
INITIATORS
INJECT
INJECTED
INJECTING
INJECTION
INJECTIONS
INJECTIVE
INJECTS
INJUDICIOUS
INJUN
INJUNCTION
INJUNCTIONS
INJUNS
INJURE
INJURED
INJURES
INJURIES
INJURING
INJURIOUS
INJURY
INJUSTICE
INJUSTICES
INK
INKED
INKER
INKERS
INKING
INKINGS
INKLING
INKLINGS
INKS
INLAID
INLAND
INLAY
INLET
INLETS
INLINE
INMAN
INMATE
INMATES
INN
INNARDS
INNATE
INNATELY
INNER
INNERMOST
INNING
INNINGS
INNOCENCE
INNOCENT
INNOCENTLY
INNOCENTS
INNOCUOUS
INNOCUOUSLY
INNOCUOUSNESS
INNOVATE
INNOVATION
INNOVATIONS
INNOVATIVE
INNS
INNUENDO
INNUMERABILITY
INNUMERABLE
INNUMERABLY
INOCULATE
INOPERABLE
INOPERATIVE
INOPPORTUNE
INORDINATE
INORDINATELY
INORGANIC
INPUT
INPUTS
INQUEST
INQUIRE
INQUIRED
INQUIRER
INQUIRERS
INQUIRES
INQUIRIES
INQUIRING
INQUIRY
INQUISITION
INQUISITIONS
INQUISITIVE
INQUISITIVELY
INQUISITIVENESS
INROAD
INROADS
INSANE
INSANELY
INSANITY
INSATIABLE
INSCRIBE
INSCRIBED
INSCRIBES
INSCRIBING
INSCRIPTION
INSCRIPTIONS
INSCRUTABLE
INSECT
INSECTICIDE
INSECTS
INSECURE
INSECURELY
INSEMINATE
INSENSIBLE
INSENSITIVE
INSENSITIVELY
INSENSITIVITY
INSEPARABLE
INSERT
INSERTED
INSERTING
INSERTION
INSERTIONS
INSERTS
INSET
INSIDE
INSIDER
INSIDERS
INSIDES
INSIDIOUS
INSIDIOUSLY
INSIDIOUSNESS
INSIGHT
INSIGHTFUL
INSIGHTS
INSIGNIA
INSIGNIFICANCE
INSIGNIFICANT
INSINCERE
INSINCERITY
INSINUATE
INSINUATED
INSINUATES
INSINUATING
INSINUATION
INSINUATIONS
INSIPID
INSIST
INSISTED
INSISTENCE
INSISTENT
INSISTENTLY
INSISTING
INSISTS
INSOFAR
INSOLENCE
INSOLENT
INSOLENTLY
INSOLUBLE
INSOLVABLE
INSOLVENT
INSOMNIA
INSOMNIAC
INSPECT
INSPECTED
INSPECTING
INSPECTION
INSPECTIONS
INSPECTOR
INSPECTORS
INSPECTS
INSPIRATION
INSPIRATIONS
INSPIRE
INSPIRED
INSPIRER
INSPIRES
INSPIRING
INSTABILITIES
INSTABILITY
INSTALL
INSTALLATION
INSTALLATIONS
INSTALLED
INSTALLER
INSTALLERS
INSTALLING
INSTALLMENT
INSTALLMENTS
INSTALLS
INSTANCE
INSTANCES
INSTANT
INSTANTANEOUS
INSTANTANEOUSLY
INSTANTER
INSTANTIATE
INSTANTIATED
INSTANTIATES
INSTANTIATING
INSTANTIATION
INSTANTIATIONS
INSTANTLY
INSTANTS
INSTEAD
INSTIGATE
INSTIGATED
INSTIGATES
INSTIGATING
INSTIGATOR
INSTIGATORS
INSTILL
INSTINCT
INSTINCTIVE
INSTINCTIVELY
INSTINCTS
INSTINCTUAL
INSTITUTE
INSTITUTED
INSTITUTER
INSTITUTERS
INSTITUTES
INSTITUTING
INSTITUTION
INSTITUTIONAL
INSTITUTIONALIZE
INSTITUTIONALIZED
INSTITUTIONALIZES
INSTITUTIONALIZING
INSTITUTIONALLY
INSTITUTIONS
INSTRUCT
INSTRUCTED
INSTRUCTING
INSTRUCTION
INSTRUCTIONAL
INSTRUCTIONS
INSTRUCTIVE
INSTRUCTIVELY
INSTRUCTOR
INSTRUCTORS
INSTRUCTS
INSTRUMENT
INSTRUMENTAL
INSTRUMENTALIST
INSTRUMENTALISTS
INSTRUMENTALLY
INSTRUMENTALS
INSTRUMENTATION
INSTRUMENTED
INSTRUMENTING
INSTRUMENTS
INSUBORDINATE
INSUFFERABLE
INSUFFICIENT
INSUFFICIENTLY
INSULAR
INSULATE
INSULATED
INSULATES
INSULATING
INSULATION
INSULATOR
INSULATORS
INSULIN
INSULT
INSULTED
INSULTING
INSULTS
INSUPERABLE
INSUPPORTABLE
INSURANCE
INSURE
INSURED
INSURER
INSURERS
INSURES
INSURGENT
INSURGENTS
INSURING
INSURMOUNTABLE
INSURRECTION
INSURRECTIONS
INTACT
INTANGIBLE
INTANGIBLES
INTEGER
INTEGERS
INTEGRABLE
INTEGRAL
INTEGRALS
INTEGRAND
INTEGRATE
INTEGRATED
INTEGRATES
INTEGRATING
INTEGRATION
INTEGRATIONS
INTEGRATIVE
INTEGRITY
INTEL
INTELLECT
INTELLECTS
INTELLECTUAL
INTELLECTUALLY
INTELLECTUALS
INTELLIGENCE
INTELLIGENT
INTELLIGENTLY
INTELLIGENTSIA
INTELLIGIBILITY
INTELLIGIBLE
INTELLIGIBLY
INTELSAT
INTEMPERATE
INTEND
INTENDED
INTENDING
INTENDS
INTENSE
INTENSELY
INTENSIFICATION
INTENSIFIED
INTENSIFIER
INTENSIFIERS
INTENSIFIES
INTENSIFY
INTENSIFYING
INTENSITIES
INTENSITY
INTENSIVE
INTENSIVELY
INTENT
INTENTION
INTENTIONAL
INTENTIONALLY
INTENTIONED
INTENTIONS
INTENTLY
INTENTNESS
INTENTS
INTER
INTERACT
INTERACTED
INTERACTING
INTERACTION
INTERACTIONS
INTERACTIVE
INTERACTIVELY
INTERACTIVITY
INTERACTS
INTERCEPT
INTERCEPTED
INTERCEPTING
INTERCEPTION
INTERCEPTOR
INTERCEPTS
INTERCHANGE
INTERCHANGEABILITY
INTERCHANGEABLE
INTERCHANGEABLY
INTERCHANGED
INTERCHANGER
INTERCHANGES
INTERCHANGING
INTERCHANGINGS
INTERCHANNEL
INTERCITY
INTERCOM
INTERCOMMUNICATE
INTERCOMMUNICATED
INTERCOMMUNICATES
INTERCOMMUNICATING
INTERCOMMUNICATION
INTERCONNECT
INTERCONNECTED
INTERCONNECTING
INTERCONNECTION
INTERCONNECTIONS
INTERCONNECTS
INTERCONTINENTAL
INTERCOURSE
INTERDATA
INTERDEPENDENCE
INTERDEPENDENCIES
INTERDEPENDENCY
INTERDEPENDENT
INTERDICT
INTERDICTION
INTERDISCIPLINARY
INTEREST
INTERESTED
INTERESTING
INTERESTINGLY
INTERESTS
INTERFACE
INTERFACED
INTERFACER
INTERFACES
INTERFACING
INTERFERE
INTERFERED
INTERFERENCE
INTERFERENCES
INTERFERES
INTERFERING
INTERFERINGLY
INTERFEROMETER
INTERFEROMETRIC
INTERFEROMETRY
INTERFRAME
INTERGROUP
INTERIM
INTERIOR
INTERIORS
INTERJECT
INTERLACE
INTERLACED
INTERLACES
INTERLACING
INTERLEAVE
INTERLEAVED
INTERLEAVES
INTERLEAVING
INTERLINK
INTERLINKED
INTERLINKS
INTERLISP
INTERMEDIARY
INTERMEDIATE
INTERMEDIATES
INTERMINABLE
INTERMINGLE
INTERMINGLED
INTERMINGLES
INTERMINGLING
INTERMISSION
INTERMITTENT
INTERMITTENTLY
INTERMIX
INTERMIXED
INTERMODULE
INTERN
INTERNAL
INTERNALIZE
INTERNALIZED
INTERNALIZES
INTERNALIZING
INTERNALLY
INTERNALS
INTERNATIONAL
INTERNATIONALITY
INTERNATIONALLY
INTERNED
INTERNET
INTERNET
INTERNETWORK
INTERNING
INTERNS
INTERNSHIP
INTEROFFICE
INTERPERSONAL
INTERPLAY
INTERPOL
INTERPOLATE
INTERPOLATED
INTERPOLATES
INTERPOLATING
INTERPOLATION
INTERPOLATIONS
INTERPOSE
INTERPOSED
INTERPOSES
INTERPOSING
INTERPRET
INTERPRETABLE
INTERPRETATION
INTERPRETATIONS
INTERPRETED
INTERPRETER
INTERPRETERS
INTERPRETING
INTERPRETIVE
INTERPRETIVELY
INTERPRETS
INTERPROCESS
INTERRELATE
INTERRELATED
INTERRELATES
INTERRELATING
INTERRELATION
INTERRELATIONS
INTERRELATIONSHIP
INTERRELATIONSHIPS
INTERROGATE
INTERROGATED
INTERROGATES
INTERROGATING
INTERROGATION
INTERROGATIONS
INTERROGATIVE
INTERRUPT
INTERRUPTED
INTERRUPTIBLE
INTERRUPTING
INTERRUPTION
INTERRUPTIONS
INTERRUPTIVE
INTERRUPTS
INTERSECT
INTERSECTED
INTERSECTING
INTERSECTION
INTERSECTIONS
INTERSECTS
INTERSPERSE
INTERSPERSED
INTERSPERSES
INTERSPERSING
INTERSPERSION
INTERSTAGE
INTERSTATE
INTERTWINE
INTERTWINED
INTERTWINES
INTERTWINING
INTERVAL
INTERVALS
INTERVENE
INTERVENED
INTERVENES
INTERVENING
INTERVENTION
INTERVENTIONS
INTERVIEW
INTERVIEWED
INTERVIEWEE
INTERVIEWER
INTERVIEWERS
INTERVIEWING
INTERVIEWS
INTERWOVEN
INTESTATE
INTESTINAL
INTESTINE
INTESTINES
INTIMACY
INTIMATE
INTIMATED
INTIMATELY
INTIMATING
INTIMATION
INTIMATIONS
INTIMIDATE
INTIMIDATED
INTIMIDATES
INTIMIDATING
INTIMIDATION
INTO
INTOLERABLE
INTOLERABLY
INTOLERANCE
INTOLERANT
INTONATION
INTONATIONS
INTONE
INTOXICANT
INTOXICATE
INTOXICATED
INTOXICATING
INTOXICATION
INTRACTABILITY
INTRACTABLE
INTRACTABLY
INTRAGROUP
INTRALINE
INTRAMURAL
INTRAMUSCULAR
INTRANSIGENT
INTRANSITIVE
INTRANSITIVELY
INTRAOFFICE
INTRAPROCESS
INTRASTATE
INTRAVENOUS
INTREPID
INTRICACIES
INTRICACY
INTRICATE
INTRICATELY
INTRIGUE
INTRIGUED
INTRIGUES
INTRIGUING
INTRINSIC
INTRINSICALLY
INTRODUCE
INTRODUCED
INTRODUCES
INTRODUCING
INTRODUCTION
INTRODUCTIONS
INTRODUCTORY
INTROSPECT
INTROSPECTION
INTROSPECTIONS
INTROSPECTIVE
INTROVERT
INTROVERTED
INTRUDE
INTRUDED
INTRUDER
INTRUDERS
INTRUDES
INTRUDING
INTRUSION
INTRUSIONS
INTRUST
INTUBATE
INTUBATED
INTUBATES
INTUBATION
INTUITION
INTUITIONIST
INTUITIONS
INTUITIVE
INTUITIVELY
INUNDATE
INVADE
INVADED
INVADER
INVADERS
INVADES
INVADING
INVALID
INVALIDATE
INVALIDATED
INVALIDATES
INVALIDATING
INVALIDATION
INVALIDATIONS
INVALIDITIES
INVALIDITY
INVALIDLY
INVALIDS
INVALUABLE
INVARIABLE
INVARIABLY
INVARIANCE
INVARIANT
INVARIANTLY
INVARIANTS
INVASION
INVASIONS
INVECTIVE
INVENT
INVENTED
INVENTING
INVENTION
INVENTIONS
INVENTIVE
INVENTIVELY
INVENTIVENESS
INVENTOR
INVENTORIES
INVENTORS
INVENTORY
INVENTS
INVERNESS
INVERSE
INVERSELY
INVERSES
INVERSION
INVERSIONS
INVERT
INVERTEBRATE
INVERTEBRATES
INVERTED
INVERTER
INVERTERS
INVERTIBLE
INVERTING
INVERTS
INVEST
INVESTED
INVESTIGATE
INVESTIGATED
INVESTIGATES
INVESTIGATING
INVESTIGATION
INVESTIGATIONS
INVESTIGATIVE
INVESTIGATOR
INVESTIGATORS
INVESTIGATORY
INVESTING
INVESTMENT
INVESTMENTS
INVESTOR
INVESTORS
INVESTS
INVETERATE
INVIGORATE
INVINCIBLE
INVISIBILITY
INVISIBLE
INVISIBLY
INVITATION
INVITATIONS
INVITE
INVITED
INVITES
INVITING
INVOCABLE
INVOCATION
INVOCATIONS
INVOICE
INVOICED
INVOICES
INVOICING
INVOKE
INVOKED
INVOKER
INVOKES
INVOKING
INVOLUNTARILY
INVOLUNTARY
INVOLVE
INVOLVED
INVOLVEMENT
INVOLVEMENTS
INVOLVES
INVOLVING
INWARD
INWARDLY
INWARDNESS
INWARDS
IODINE
ION
IONIAN
IONIANS
IONICIZATION
IONICIZATIONS
IONICIZE
IONICIZES
IONOSPHERE
IONOSPHERIC
IONS
IOTA
IOWA
IRA
IRAN
IRANIAN
IRANIANS
IRANIZE
IRANIZES
IRAQ
IRAQI
IRAQIS
IRATE
IRATELY
IRATENESS
IRE
IRELAND
IRENE
IRES
IRIS
IRISH
IRISHIZE
IRISHIZES
IRISHMAN
IRISHMEN
IRK
IRKED
IRKING
IRKS
IRKSOME
IRMA
IRON
IRONED
IRONIC
IRONICAL
IRONICALLY
IRONIES
IRONING
IRONINGS
IRONS
IRONY
IROQUOIS
IRRADIATE
IRRATIONAL
IRRATIONALLY
IRRATIONALS
IRRAWADDY
IRRECONCILABLE
IRRECOVERABLE
IRREDUCIBLE
IRREDUCIBLY
IRREFLEXIVE
IRREFUTABLE
IRREGULAR
IRREGULARITIES
IRREGULARITY
IRREGULARLY
IRREGULARS
IRRELEVANCE
IRRELEVANCES
IRRELEVANT
IRRELEVANTLY
IRREPLACEABLE
IRREPRESSIBLE
IRREPRODUCIBILITY
IRREPRODUCIBLE
IRRESISTIBLE
IRRESPECTIVE
IRRESPECTIVELY
IRRESPONSIBLE
IRRESPONSIBLY
IRRETRIEVABLY
IRREVERENT
IRREVERSIBILITY
IRREVERSIBLE
IRREVERSIBLY
IRREVOCABLE
IRREVOCABLY
IRRIGATE
IRRIGATED
IRRIGATES
IRRIGATING
IRRIGATION
IRRITABLE
IRRITANT
IRRITATE
IRRITATED
IRRITATES
IRRITATING
IRRITATION
IRRITATIONS
IRVIN
IRVINE
IRVING
IRWIN
ISAAC
ISAACS
ISAACSON
ISABEL
ISABELLA
ISADORE
ISAIAH
ISFAHAN
ISING
ISIS
ISLAM
ISLAMABAD
ISLAMIC
ISLAMIZATION
ISLAMIZATIONS
ISLAMIZE
ISLAMIZES
ISLAND
ISLANDER
ISLANDERS
ISLANDIA
ISLANDS
ISLE
ISLES
ISLET
ISLETS
ISOLATE
ISOLATED
ISOLATES
ISOLATING
ISOLATION
ISOLATIONS
ISOLDE
ISOMETRIC
ISOMORPHIC
ISOMORPHICALLY
ISOMORPHISM
ISOMORPHISMS
ISOTOPE
ISOTOPES
ISRAEL
ISRAELI
ISRAELIS
ISRAELITE
ISRAELITES
ISRAELITIZE
ISRAELITIZES
ISSUANCE
ISSUE
ISSUED
ISSUER
ISSUERS
ISSUES
ISSUING
ISTANBUL
ISTHMUS
ISTVAN
ITALIAN
ITALIANIZATION
ITALIANIZATIONS
ITALIANIZE
ITALIANIZER
ITALIANIZERS
ITALIANIZES
ITALIANS
ITALIC
ITALICIZE
ITALICIZED
ITALICS
ITALY
ITCH
ITCHES
ITCHING
ITEL
ITEM
ITEMIZATION
ITEMIZATIONS
ITEMIZE
ITEMIZED
ITEMIZES
ITEMIZING
ITEMS
ITERATE
ITERATED
ITERATES
ITERATING
ITERATION
ITERATIONS
ITERATIVE
ITERATIVELY
ITERATOR
ITERATORS
ITHACA
ITHACAN
ITINERARIES
ITINERARY
ITO
ITS
ITSELF
IVAN
IVANHOE
IVERSON
IVIES
IVORY
IVY
IZAAK
IZVESTIA
JAB
JABBED
JABBING
JABLONSKY
JABS
JACK
JACKASS
JACKET
JACKETED
JACKETS
JACKIE
JACKING
JACKKNIFE
JACKMAN
JACKPOT
JACKSON
JACKSONIAN
JACKSONS
JACKSONVILLE
JACKY
JACOB
JACOBEAN
JACOBI
JACOBIAN
JACOBINIZE
JACOBITE
JACOBS
JACOBSEN
JACOBSON
JACOBUS
JACOBY
JACQUELINE
JACQUES
JADE
JADED
JAEGER
JAGUAR
JAIL
JAILED
JAILER
JAILERS
JAILING
JAILS
JAIME
JAKARTA
JAKE
JAKES
JAM
JAMAICA
JAMAICAN
JAMES
JAMESON
JAMESTOWN
JAMMED
JAMMING
JAMS
JANE
JANEIRO
JANESVILLE
JANET
JANICE
JANIS
JANITOR
JANITORS
JANOS
JANSEN
JANSENIST
JANUARIES
JANUARY
JANUS
JAPAN
JAPANESE
JAPANIZATION
JAPANIZATIONS
JAPANIZE
JAPANIZED
JAPANIZES
JAPANIZING
JAR
JARGON
JARRED
JARRING
JARRINGLY
JARS
JARVIN
JASON
JASTROW
JAUNDICE
JAUNT
JAUNTINESS
JAUNTS
JAUNTY
JAVA
JAVANESE
JAVELIN
JAVELINS
JAW
JAWBONE
JAWS
JAY
JAYCEE
JAYCEES
JAZZ
JAZZY
JEALOUS
JEALOUSIES
JEALOUSLY
JEALOUSY
JEAN
JEANNE
JEANNIE
JEANS
JED
JEEP
JEEPS
JEER
JEERS
JEFF
JEFFERSON
JEFFERSONIAN
JEFFERSONIANS
JEFFREY
JEHOVAH
JELLIES
JELLO
JELLY
JELLYFISH
JENKINS
JENNIE
JENNIFER
JENNINGS
JENNY
JENSEN
JEOPARDIZE
JEOPARDIZED
JEOPARDIZES
JEOPARDIZING
JEOPARDY
JEREMIAH
JEREMY
JERES
JERICHO
JERK
JERKED
JERKINESS
JERKING
JERKINGS
JERKS
JERKY
JEROBOAM
JEROME
JERRY
JERSEY
JERSEYS
JERUSALEM
JESSE
JESSICA
JESSIE
JESSY
JEST
JESTED
JESTER
JESTING
JESTS
JESUIT
JESUITISM
JESUITIZE
JESUITIZED
JESUITIZES
JESUITIZING
JESUITS
JESUS
JET
JETLINER
JETS
JETTED
JETTING
JEW
JEWEL
JEWELED
JEWELER
JEWELL
JEWELLED
JEWELRIES
JEWELRY
JEWELS
JEWETT
JEWISH
JEWISHNESS
JEWS
JIFFY
JIG
JIGS
JIGSAW
JILL
JIM
JIMENEZ
JIMMIE
JINGLE
JINGLED
JINGLING
JINNY
JITTER
JITTERBUG
JITTERY
JOAN
JOANNA
JOANNE
JOAQUIN
JOB
JOBREL
JOBS
JOCKEY
JOCKSTRAP
JOCUND
JODY
JOE
JOEL
JOES
JOG
JOGGING
JOGS
JOHANN
JOHANNA
JOHANNES
JOHANNESBURG
JOHANSEN
JOHANSON
JOHN
JOHNNIE
JOHNNY
JOHNS
JOHNSEN
JOHNSON
JOHNSTON
JOHNSTOWN
JOIN
JOINED
JOINER
JOINERS
JOINING
JOINS
JOINT
JOINTLY
JOINTS
JOKE
JOKED
JOKER
JOKERS
JOKES
JOKING
JOKINGLY
JOLIET
JOLLA
JOLLY
JOLT
JOLTED
JOLTING
JOLTS
JON
JONAS
JONATHAN
JONATHANIZATION
JONATHANIZATIONS
JONES
JONESES
JONQUIL
JOPLIN
JORDAN
JORDANIAN
JORGE
JORGENSEN
JORGENSON
JOSE
JOSEF
JOSEPH
JOSEPHINE
JOSEPHSON
JOSEPHUS
JOSHUA
JOSIAH
JOSTLE
JOSTLED
JOSTLES
JOSTLING
JOT
JOTS
JOTTED
JOTTING
JOULE
JOURNAL
JOURNALISM
JOURNALIST
JOURNALISTS
JOURNALIZE
JOURNALIZED
JOURNALIZES
JOURNALIZING
JOURNALS
JOURNEY
JOURNEYED
JOURNEYING
JOURNEYINGS
JOURNEYMAN
JOURNEYMEN
JOURNEYS
JOUST
JOUSTED
JOUSTING
JOUSTS
JOVANOVICH
JOVE
JOVIAL
JOVIAN
JOY
JOYCE
JOYFUL
JOYFULLY
JOYOUS
JOYOUSLY
JOYOUSNESS
JOYRIDE
JOYS
JOYSTICK
JUAN
JUANITA
JUBAL
JUBILEE
JUDAICA
JUDAISM
JUDAS
JUDD
JUDDER
JUDDERED
JUDDERING
JUDDERS
JUDE
JUDEA
JUDGE
JUDGED
JUDGES
JUDGING
JUDGMENT
JUDGMENTS
JUDICIAL
JUDICIARY
JUDICIOUS
JUDICIOUSLY
JUDITH
JUDO
JUDSON
JUDY
JUG
JUGGLE
JUGGLER
JUGGLERS
JUGGLES
JUGGLING
JUGOSLAVIA
JUGS
JUICE
JUICES
JUICIEST
JUICY
JUKES
JULES
JULIA
JULIAN
JULIE
JULIES
JULIET
JULIO
JULIUS
JULY
JUMBLE
JUMBLED
JUMBLES
JUMBO
JUMP
JUMPED
JUMPER
JUMPERS
JUMPING
JUMPS
JUMPY
JUNCTION
JUNCTIONS
JUNCTURE
JUNCTURES
JUNE
JUNEAU
JUNES
JUNG
JUNGIAN
JUNGLE
JUNGLES
JUNIOR
JUNIORS
JUNIPER
JUNK
JUNKER
JUNKERS
JUNKS
JUNKY
JUNO
JUNTA
JUPITER
JURA
JURAS
JURASSIC
JURE
JURIES
JURISDICTION
JURISDICTIONS
JURISPRUDENCE
JURIST
JUROR
JURORS
JURY
JUST
JUSTICE
JUSTICES
JUSTIFIABLE
JUSTIFIABLY
JUSTIFICATION
JUSTIFICATIONS
JUSTIFIED
JUSTIFIER
JUSTIFIERS
JUSTIFIES
JUSTIFY
JUSTIFYING
JUSTINE
JUSTINIAN
JUSTLY
JUSTNESS
JUT
JUTISH
JUTLAND
JUTTING
JUVENILE
JUVENILES
JUXTAPOSE
JUXTAPOSED
JUXTAPOSES
JUXTAPOSING
KABUKI
KABUL
KADDISH
KAFKA
KAFKAESQUE
KAHN
KAJAR
KALAMAZOO
KALI
KALMUK
KAMCHATKA
KAMIKAZE
KAMIKAZES
KAMPALA
KAMPUCHEA
KANARESE
KANE
KANGAROO
KANJI
KANKAKEE
KANNADA
KANSAS
KANT
KANTIAN
KAPLAN
KAPPA
KARACHI
KARAMAZOV
KARATE
KAREN
KARL
KAROL
KARP
KASHMIR
KASKASKIA
KATE
KATHARINE
KATHERINE
KATHLEEN
KATHY
KATIE
KATMANDU
KATOWICE
KATZ
KAUFFMAN
KAUFMAN
KAY
KEATON
KEATS
KEEGAN
KEEL
KEELED
KEELING
KEELS
KEEN
KEENAN
KEENER
KEENEST
KEENLY
KEENNESS
KEEP
KEEPER
KEEPERS
KEEPING
KEEPS
KEITH
KELLER
KELLEY
KELLOGG
KELLY
KELSEY
KELVIN
KEMP
KEN
KENDALL
KENILWORTH
KENNAN
KENNECOTT
KENNEDY
KENNEL
KENNELS
KENNETH
KENNEY
KENNING
KENNY
KENOSHA
KENSINGTON
KENT
KENTON
KENTUCKY
KENYA
KENYON
KEPLER
KEPT
KERCHIEF
KERCHIEFS
KERMIT
KERN
KERNEL
KERNELS
KERNIGHAN
KEROSENE
KEROUAC
KERR
KESSLER
KETCHUP
KETTERING
KETTLE
KETTLES
KEVIN
KEWASKUM
KEWAUNEE
KEY
KEYBOARD
KEYBOARDS
KEYED
KEYES
KEYHOLE
KEYING
KEYNES
KEYNESIAN
KEYNOTE
KEYPAD
KEYPADS
KEYS
KEYSTROKE
KEYSTROKES
KEYWORD
KEYWORDS
KHARTOUM
KHMER
KHRUSHCHEV
KHRUSHCHEVS
KICK
KICKAPOO
KICKED
KICKER
KICKERS
KICKING
KICKOFF
KICKS
KID
KIDDE
KIDDED
KIDDIE
KIDDING
KIDNAP
KIDNAPPER
KIDNAPPERS
KIDNAPPING
KIDNAPPINGS
KIDNAPS
KIDNEY
KIDNEYS
KIDS
KIEFFER
KIEL
KIEV
KIEWIT
KIGALI
KIKUYU
KILGORE
KILIMANJARO
KILL
KILLEBREW
KILLED
KILLER
KILLERS
KILLING
KILLINGLY
KILLINGS
KILLJOY
KILLS
KILOBIT
KILOBITS
KILOBLOCK
KILOBYTE
KILOBYTES
KILOGRAM
KILOGRAMS
KILOHERTZ
KILOHM
KILOJOULE
KILOMETER
KILOMETERS
KILOTON
KILOVOLT
KILOWATT
KILOWORD
KIM
KIMBALL
KIMBERLY
KIMONO
KIN
KIND
KINDER
KINDERGARTEN
KINDEST
KINDHEARTED
KINDLE
KINDLED
KINDLES
KINDLING
KINDLY
KINDNESS
KINDRED
KINDS
KINETIC
KING
KINGDOM
KINGDOMS
KINGLY
KINGPIN
KINGS
KINGSBURY
KINGSLEY
KINGSTON
KINGSTOWN
KINGWOOD
KINK
KINKY
KINNEY
KINNICKINNIC
KINSEY
KINSHASHA
KINSHIP
KINSMAN
KIOSK
KIOWA
KIPLING
KIRBY
KIRCHNER
KIRCHOFF
KIRK
KIRKLAND
KIRKPATRICK
KIRKWOOD
KIROV
KISS
KISSED
KISSER
KISSERS
KISSES
KISSING
KIT
KITAKYUSHU
KITCHEN
KITCHENETTE
KITCHENS
KITE
KITED
KITES
KITING
KITS
KITTEN
KITTENISH
KITTENS
KITTY
KIWANIS
KLAN
KLAUS
KLAXON
KLEIN
KLEINROCK
KLINE
KLUDGE
KLUDGES
KLUX
KLYSTRON
KNACK
KNAPP
KNAPSACK
KNAPSACKS
KNAUER
KNAVE
KNAVES
KNEAD
KNEADS
KNEE
KNEECAP
KNEED
KNEEING
KNEEL
KNEELED
KNEELING
KNEELS
KNEES
KNELL
KNELLS
KNELT
KNEW
KNICKERBOCKER
KNICKERBOCKERS
KNIFE
KNIFED
KNIFES
KNIFING
KNIGHT
KNIGHTED
KNIGHTHOOD
KNIGHTING
KNIGHTLY
KNIGHTS
KNIGHTSBRIDGE
KNIT
KNITS
KNIVES
KNOB
KNOBELOCH
KNOBS
KNOCK
KNOCKDOWN
KNOCKED
KNOCKER
KNOCKERS
KNOCKING
KNOCKOUT
KNOCKS
KNOLL
KNOLLS
KNOSSOS
KNOT
KNOTS
KNOTT
KNOTTED
KNOTTING
KNOW
KNOWABLE
KNOWER
KNOWHOW
KNOWING
KNOWINGLY
KNOWLEDGE
KNOWLEDGEABLE
KNOWLES
KNOWLTON
KNOWN
KNOWS
KNOX
KNOXVILLE
KNUCKLE
KNUCKLED
KNUCKLES
KNUDSEN
KNUDSON
KNUTH
KNUTSEN
KNUTSON
KOALA
KOBAYASHI
KOCH
KOCHAB
KODACHROME
KODAK
KODIAK
KOENIG
KOENIGSBERG
KOHLER
KONG
KONRAD
KOPPERS
KORAN
KOREA
KOREAN
KOREANS
KOSHER
KOVACS
KOWALEWSKI
KOWALSKI
KOWLOON
KOWTOW
KRAEMER
KRAKATOA
KRAKOW
KRAMER
KRAUSE
KREBS
KREMLIN
KRESGE
KRIEGER
KRISHNA
KRISTIN
KRONECKER
KRUEGER
KRUGER
KRUSE
KUALA
KUDO
KUENNING
KUHN
KUMAR
KURD
KURDISH
KURT
KUWAIT
KUWAITI
KYOTO
LAB
LABAN
LABEL
LABELED
LABELING
LABELLED
LABELLER
LABELLERS
LABELLING
LABELS
LABOR
LABORATORIES
LABORATORY
LABORED
LABORER
LABORERS
LABORING
LABORINGS
LABORIOUS
LABORIOUSLY
LABORS
LABRADOR
LABS
LABYRINTH
LABYRINTHS
LAC
LACE
LACED
LACERATE
LACERATED
LACERATES
LACERATING
LACERATION
LACERATIONS
LACERTA
LACES
LACEY
LACHESIS
LACING
LACK
LACKAWANNA
LACKED
LACKEY
LACKING
LACKS
LACQUER
LACQUERED
LACQUERS
LACROSSE
LACY
LAD
LADDER
LADEN
LADIES
LADING
LADLE
LADS
LADY
LADYLIKE
LAFAYETTE
LAG
LAGER
LAGERS
LAGOON
LAGOONS
LAGOS
LAGRANGE
LAGRANGIAN
LAGS
LAGUERRE
LAGUNA
LAHORE
LAID
LAIDLAW
LAIN
LAIR
LAIRS
LAISSEZ
LAKE
LAKEHURST
LAKES
LAKEWOOD
LAMAR
LAMARCK
LAMB
LAMBDA
LAMBDAS
LAMBERT
LAMBS
LAME
LAMED
LAMELY
LAMENESS
LAMENT
LAMENTABLE
LAMENTATION
LAMENTATIONS
LAMENTED
LAMENTING
LAMENTS
LAMES
LAMINAR
LAMING
LAMP
LAMPLIGHT
LAMPOON
LAMPORT
LAMPREY
LAMPS
LANA
LANCASHIRE
LANCASTER
LANCE
LANCED
LANCELOT
LANCER
LANCES
LAND
LANDED
LANDER
LANDERS
LANDFILL
LANDING
LANDINGS
LANDIS
LANDLADIES
LANDLADY
LANDLORD
LANDLORDS
LANDMARK
LANDMARKS
LANDOWNER
LANDOWNERS
LANDS
LANDSCAPE
LANDSCAPED
LANDSCAPES
LANDSCAPING
LANDSLIDE
LANDWEHR
LANE
LANES
LANG
LANGE
LANGELAND
LANGFORD
LANGLEY
LANGMUIR
LANGUAGE
LANGUAGES
LANGUID
LANGUIDLY
LANGUIDNESS
LANGUISH
LANGUISHED
LANGUISHES
LANGUISHING
LANKA
LANSING
LANTERN
LANTERNS
LAO
LAOCOON
LAOS
LAOTIAN
LAOTIANS
LAP
LAPEL
LAPELS
LAPLACE
LAPLACIAN
LAPPING
LAPS
LAPSE
LAPSED
LAPSES
LAPSING
LARAMIE
LARD
LARDER
LAREDO
LARES
LARGE
LARGELY
LARGENESS
LARGER
LARGEST
LARK
LARKIN
LARKS
LARRY
LARS
LARSEN
LARSON
LARVA
LARVAE
LARYNX
LASCIVIOUS
LASER
LASERS
LASH
LASHED
LASHES
LASHING
LASHINGS
LASS
LASSES
LASSO
LAST
LASTED
LASTING
LASTLY
LASTS
LASZLO
LATCH
LATCHED
LATCHES
LATCHING
LATE
LATELY
LATENCY
LATENESS
LATENT
LATER
LATERAL
LATERALLY
LATERAN
LATEST
LATEX
LATHE
LATHROP
LATIN
LATINATE
LATINITY
LATINIZATION
LATINIZATIONS
LATINIZE
LATINIZED
LATINIZER
LATINIZERS
LATINIZES
LATINIZING
LATITUDE
LATITUDES
LATRINE
LATRINES
LATROBE
LATTER
LATTERLY
LATTICE
LATTICES
LATTIMER
LATVIA
LAUDABLE
LAUDERDALE
LAUE
LAUGH
LAUGHABLE
LAUGHABLY
LAUGHED
LAUGHING
LAUGHINGLY
LAUGHINGSTOCK
LAUGHLIN
LAUGHS
LAUGHTER
LAUNCH
LAUNCHED
LAUNCHER
LAUNCHES
LAUNCHING
LAUNCHINGS
LAUNDER
LAUNDERED
LAUNDERER
LAUNDERING
LAUNDERINGS
LAUNDERS
LAUNDROMAT
LAUNDROMATS
LAUNDRY
LAUREATE
LAUREL
LAURELS
LAUREN
LAURENCE
LAURENT
LAURENTIAN
LAURIE
LAUSANNE
LAVA
LAVATORIES
LAVATORY
LAVENDER
LAVISH
LAVISHED
LAVISHING
LAVISHLY
LAVOISIER
LAW
LAWBREAKER
LAWFORD
LAWFUL
LAWFULLY
LAWGIVER
LAWLESS
LAWLESSNESS
LAWN
LAWNS
LAWRENCE
LAWRENCEVILLE
LAWS
LAWSON
LAWSUIT
LAWSUITS
LAWYER
LAWYERS
LAX
LAXATIVE
LAY
LAYER
LAYERED
LAYERING
LAYERS
LAYING
LAYMAN
LAYMEN
LAYOFF
LAYOFFS
LAYOUT
LAYOUTS
LAYS
LAYTON
LAZARUS
LAZED
LAZIER
LAZIEST
LAZILY
LAZINESS
LAZING
LAZY
LAZYBONES
LEAD
LEADED
LEADEN
LEADER
LEADERS
LEADERSHIP
LEADERSHIPS
LEADING
LEADINGS
LEADS
LEAF
LEAFED
LEAFIEST
LEAFING
LEAFLESS
LEAFLET
LEAFLETS
LEAFY
LEAGUE
LEAGUED
LEAGUER
LEAGUERS
LEAGUES
LEAK
LEAKAGE
LEAKAGES
LEAKED
LEAKING
LEAKS
LEAKY
LEAN
LEANDER
LEANED
LEANER
LEANEST
LEANING
LEANNESS
LEANS
LEAP
LEAPED
LEAPFROG
LEAPING
LEAPS
LEAPT
LEAR
LEARN
LEARNED
LEARNER
LEARNERS
LEARNING
LEARNS
LEARY
LEASE
LEASED
LEASES
LEASH
LEASHES
LEASING
LEAST
LEATHER
LEATHERED
LEATHERN
LEATHERNECK
LEATHERS
LEAVE
LEAVED
LEAVEN
LEAVENED
LEAVENING
LEAVENWORTH
LEAVES
LEAVING
LEAVINGS
LEBANESE
LEBANON
LEBESGUE
LECHERY
LECTURE
LECTURED
LECTURER
LECTURERS
LECTURES
LECTURING
LED
LEDGE
LEDGER
LEDGERS
LEDGES
LEE
LEECH
LEECHES
LEEDS
LEEK
LEER
LEERY
LEES
LEEUWENHOEK
LEEWARD
LEEWAY
LEFT
LEFTIST
LEFTISTS
LEFTMOST
LEFTOVER
LEFTOVERS
LEFTWARD
LEG
LEGACIES
LEGACY
LEGAL
LEGALITY
LEGALIZATION
LEGALIZE
LEGALIZED
LEGALIZES
LEGALIZING
LEGALLY
LEGEND
LEGENDARY
LEGENDRE
LEGENDS
LEGER
LEGERS
LEGGED
LEGGINGS
LEGIBILITY
LEGIBLE
LEGIBLY
LEGION
LEGIONS
LEGISLATE
LEGISLATED
LEGISLATES
LEGISLATING
LEGISLATION
LEGISLATIVE
LEGISLATOR
LEGISLATORS
LEGISLATURE
LEGISLATURES
LEGITIMACY
LEGITIMATE
LEGITIMATELY
LEGS
LEGUME
LEHIGH
LEHMAN
LEIBNIZ
LEIDEN
LEIGH
LEIGHTON
LEILA
LEIPZIG
LEISURE
LEISURELY
LELAND
LEMKE
LEMMA
LEMMAS
LEMMING
LEMMINGS
LEMON
LEMONADE
LEMONS
LEMUEL
LEN
LENA
LEND
LENDER
LENDERS
LENDING
LENDS
LENGTH
LENGTHEN
LENGTHENED
LENGTHENING
LENGTHENS
LENGTHLY
LENGTHS
LENGTHWISE
LENGTHY
LENIENCY
LENIENT
LENIENTLY
LENIN
LENINGRAD
LENINISM
LENINIST
LENNOX
LENNY
LENORE
LENS
LENSES
LENT
LENTEN
LENTIL
LENTILS
LEO
LEON
LEONA
LEONARD
LEONARDO
LEONE
LEONID
LEOPARD
LEOPARDS
LEOPOLD
LEOPOLDVILLE
LEPER
LEPROSY
LEROY
LESBIAN
LESBIANS
LESLIE
LESOTHO
LESS
LESSEN
LESSENED
LESSENING
LESSENS
LESSER
LESSON
LESSONS
LESSOR
LEST
LESTER
LET
LETHAL
LETHE
LETITIA
LETS
LETTER
LETTERED
LETTERER
LETTERHEAD
LETTERING
LETTERS
LETTING
LETTUCE
LEUKEMIA
LEV
LEVEE
LEVEES
LEVEL
LEVELED
LEVELER
LEVELING
LEVELLED
LEVELLER
LEVELLEST
LEVELLING
LEVELLY
LEVELNESS
LEVELS
LEVER
LEVERAGE
LEVERS
LEVI
LEVIABLE
LEVIED
LEVIES
LEVIN
LEVINE
LEVIS
LEVITICUS
LEVITT
LEVITY
LEVY
LEVYING
LEW
LEWD
LEWDLY
LEWDNESS
LEWELLYN
LEXICAL
LEXICALLY
LEXICOGRAPHIC
LEXICOGRAPHICAL
LEXICOGRAPHICALLY
LEXICON
LEXICONS
LEXINGTON
LEYDEN
LIABILITIES
LIABILITY
LIABLE
LIAISON
LIAISONS
LIAR
LIARS
LIBEL
LIBELOUS
LIBERACE
LIBERAL
LIBERALIZE
LIBERALIZED
LIBERALIZES
LIBERALIZING
LIBERALLY
LIBERALS
LIBERATE
LIBERATED
LIBERATES
LIBERATING
LIBERATION
LIBERATOR
LIBERATORS
LIBERIA
LIBERTARIAN
LIBERTIES
LIBERTY
LIBIDO
LIBRARIAN
LIBRARIANS
LIBRARIES
LIBRARY
LIBRETTO
LIBREVILLE
LIBYA
LIBYAN
LICE
LICENSE
LICENSED
LICENSEE
LICENSES
LICENSING
LICENSOR
LICENTIOUS
LICHEN
LICHENS
LICHTER
LICK
LICKED
LICKING
LICKS
LICORICE
LID
LIDS
LIE
LIEBERMAN
LIECHTENSTEIN
LIED
LIEGE
LIEN
LIENS
LIES
LIEU
LIEUTENANT
LIEUTENANTS
LIFE
LIFEBLOOD
LIFEBOAT
LIFEGUARD
LIFELESS
LIFELESSNESS
LIFELIKE
LIFELONG
LIFER
LIFESPAN
LIFESTYLE
LIFESTYLES
LIFETIME
LIFETIMES
LIFT
LIFTED
LIFTER
LIFTERS
LIFTING
LIFTS
LIGAMENT
LIGATURE
LIGGET
LIGGETT
LIGHT
LIGHTED
LIGHTEN
LIGHTENS
LIGHTER
LIGHTERS
LIGHTEST
LIGHTFACE
LIGHTHEARTED
LIGHTHOUSE
LIGHTHOUSES
LIGHTING
LIGHTLY
LIGHTNESS
LIGHTNING
LIGHTNINGS
LIGHTS
LIGHTWEIGHT
LIKE
LIKED
LIKELIER
LIKELIEST
LIKELIHOOD
LIKELIHOODS
LIKELINESS
LIKELY
LIKEN
LIKENED
LIKENESS
LIKENESSES
LIKENING
LIKENS
LIKES
LIKEWISE
LIKING
LILA
LILAC
LILACS
LILIAN
LILIES
LILLIAN
LILLIPUT
LILLIPUTIAN
LILLIPUTIANIZE
LILLIPUTIANIZES
LILLY
LILY
LIMA
LIMAN
LIMB
LIMBER
LIMBO
LIMBS
LIME
LIMELIGHT
LIMERICK
LIMES
LIMESTONE
LIMIT
LIMITABILITY
LIMITABLY
LIMITATION
LIMITATIONS
LIMITED
LIMITER
LIMITERS
LIMITING
LIMITLESS
LIMITS
LIMOUSINE
LIMP
LIMPED
LIMPING
LIMPLY
LIMPNESS
LIMPS
LIN
LINCOLN
LIND
LINDA
LINDBERG
LINDBERGH
LINDEN
LINDHOLM
LINDQUIST
LINDSAY
LINDSEY
LINDSTROM
LINDY
LINE
LINEAR
LINEARITIES
LINEARITY
LINEARIZABLE
LINEARIZE
LINEARIZED
LINEARIZES
LINEARIZING
LINEARLY
LINED
LINEN
LINENS
LINER
LINERS
LINES
LINEUP
LINGER
LINGERED
LINGERIE
LINGERING
LINGERS
LINGO
LINGUA
LINGUIST
LINGUISTIC
LINGUISTICALLY
LINGUISTICS
LINGUISTS
LINING
LININGS
LINK
LINKAGE
LINKAGES
LINKED
LINKER
LINKERS
LINKING
LINKS
LINNAEUS
LINOLEUM
LINOTYPE
LINSEED
LINT
LINTON
LINUS
LINUX
LION
LIONEL
LIONESS
LIONESSES
LIONS
LIP
LIPPINCOTT
LIPS
LIPSCHITZ
LIPSCOMB
LIPSTICK
LIPTON
LIQUID
LIQUIDATE
LIQUIDATION
LIQUIDATIONS
LIQUIDITY
LIQUIDS
LIQUOR
LIQUORS
LISA
LISBON
LISE
LISP
LISPED
LISPING
LISPS
LISS
LISSAJOUS
LIST
LISTED
LISTEN
LISTENED
LISTENER
LISTENERS
LISTENING
LISTENS
LISTER
LISTERIZE
LISTERIZES
LISTERS
LISTING
LISTINGS
LISTLESS
LISTON
LISTS
LIT
LITANY
LITER
LITERACY
LITERAL
LITERALLY
LITERALNESS
LITERALS
LITERARY
LITERATE
LITERATURE
LITERATURES
LITERS
LITHE
LITHOGRAPH
LITHOGRAPHY
LITHUANIA
LITHUANIAN
LITIGANT
LITIGATE
LITIGATION
LITIGIOUS
LITMUS
LITTER
LITTERBUG
LITTERED
LITTERING
LITTERS
LITTLE
LITTLENESS
LITTLER
LITTLEST
LITTLETON
LITTON
LIVABLE
LIVABLY
LIVE
LIVED
LIVELIHOOD
LIVELY
LIVENESS
LIVER
LIVERIED
LIVERMORE
LIVERPOOL
LIVERPUDLIAN
LIVERS
LIVERY
LIVES
LIVESTOCK
LIVID
LIVING
LIVINGSTON
LIZ
LIZARD
LIZARDS
LIZZIE
LIZZY
LLOYD
LOAD
LOADED
LOADER
LOADERS
LOADING
LOADINGS
LOADS
LOAF
LOAFED
LOAFER
LOAN
LOANED
LOANING
LOANS
LOATH
LOATHE
LOATHED
LOATHING
LOATHLY
LOATHSOME
LOAVES
LOBBIED
LOBBIES
LOBBY
LOBBYING
LOBE
LOBES
LOBSTER
LOBSTERS
LOCAL
LOCALITIES
LOCALITY
LOCALIZATION
LOCALIZE
LOCALIZED
LOCALIZES
LOCALIZING
LOCALLY
LOCALS
LOCATE
LOCATED
LOCATES
LOCATING
LOCATION
LOCATIONS
LOCATIVE
LOCATIVES
LOCATOR
LOCATORS
LOCI
LOCK
LOCKE
LOCKED
LOCKER
LOCKERS
LOCKHART
LOCKHEED
LOCKIAN
LOCKING
LOCKINGS
LOCKOUT
LOCKOUTS
LOCKS
LOCKSMITH
LOCKSTEP
LOCKUP
LOCKUPS
LOCKWOOD
LOCOMOTION
LOCOMOTIVE
LOCOMOTIVES
LOCUS
LOCUST
LOCUSTS
LODGE
LODGED
LODGER
LODGES
LODGING
LODGINGS
LODOWICK
LOEB
LOFT
LOFTINESS
LOFTS
LOFTY
LOGAN
LOGARITHM
LOGARITHMIC
LOGARITHMICALLY
LOGARITHMS
LOGGED
LOGGER
LOGGERS
LOGGING
LOGIC
LOGICAL
LOGICALLY
LOGICIAN
LOGICIANS
LOGICS
LOGIN
LOGINS
LOGISTIC
LOGISTICS
LOGJAM
LOGO
LOGS
LOIN
LOINCLOTH
LOINS
LOIRE
LOIS
LOITER
LOITERED
LOITERER
LOITERING
LOITERS
LOKI
LOLA
LOMB
LOMBARD
LOMBARDY
LOME
LONDON
LONDONDERRY
LONDONER
LONDONIZATION
LONDONIZATIONS
LONDONIZE
LONDONIZES
LONE
LONELIER
LONELIEST
LONELINESS
LONELY
LONER
LONERS
LONESOME
LONG
LONGED
LONGER
LONGEST
LONGEVITY
LONGFELLOW
LONGHAND
LONGING
LONGINGS
LONGITUDE
LONGITUDES
LONGS
LONGSTANDING
LONGSTREET
LOOK
LOOKAHEAD
LOOKED
LOOKER
LOOKERS
LOOKING
LOOKOUT
LOOKS
LOOKUP
LOOKUPS
LOOM
LOOMED
LOOMING
LOOMIS
LOOMS
LOON
LOOP
LOOPED
LOOPHOLE
LOOPHOLES
LOOPING
LOOPS
LOOSE
LOOSED
LOOSELEAF
LOOSELY
LOOSEN
LOOSENED
LOOSENESS
LOOSENING
LOOSENS
LOOSER
LOOSES
LOOSEST
LOOSING
LOOT
LOOTED
LOOTER
LOOTING
LOOTS
LOPEZ
LOPSIDED
LORD
LORDLY
LORDS
LORDSHIP
LORE
LORELEI
LOREN
LORENTZIAN
LORENZ
LORETTA
LORINDA
LORRAINE
LORRY
LOS
LOSE
LOSER
LOSERS
LOSES
LOSING
LOSS
LOSSES
LOSSIER
LOSSIEST
LOSSY
LOST
LOT
LOTHARIO
LOTION
LOTS
LOTTE
LOTTERY
LOTTIE
LOTUS
LOU
LOUD
LOUDER
LOUDEST
LOUDLY
LOUDNESS
LOUDSPEAKER
LOUDSPEAKERS
LOUIS
LOUISA
LOUISE
LOUISIANA
LOUISIANAN
LOUISVILLE
LOUNGE
LOUNGED
LOUNGES
LOUNGING
LOUNSBURY
LOURDES
LOUSE
LOUSY
LOUT
LOUVRE
LOVABLE
LOVABLY
LOVE
LOVED
LOVEJOY
LOVELACE
LOVELAND
LOVELIER
LOVELIES
LOVELIEST
LOVELINESS
LOVELORN
LOVELY
LOVER
LOVERS
LOVES
LOVING
LOVINGLY
LOW
LOWE
LOWELL
LOWER
LOWERED
LOWERING
LOWERS
LOWEST
LOWLAND
LOWLANDS
LOWLIEST
LOWLY
LOWNESS
LOWRY
LOWS
LOY
LOYAL
LOYALLY
LOYALTIES
LOYALTY
LOYOLA
LUBBOCK
LUBELL
LUBRICANT
LUBRICATE
LUBRICATION
LUCAS
LUCERNE
LUCIA
LUCIAN
LUCID
LUCIEN
LUCIFER
LUCILLE
LUCIUS
LUCK
LUCKED
LUCKIER
LUCKIEST
LUCKILY
LUCKLESS
LUCKS
LUCKY
LUCRATIVE
LUCRETIA
LUCRETIUS
LUCY
LUDICROUS
LUDICROUSLY
LUDICROUSNESS
LUDLOW
LUDMILLA
LUDWIG
LUFTHANSA
LUFTWAFFE
LUGGAGE
LUIS
LUKE
LUKEWARM
LULL
LULLABY
LULLED
LULLS
LUMBER
LUMBERED
LUMBERING
LUMINOUS
LUMINOUSLY
LUMMOX
LUMP
LUMPED
LUMPING
LUMPS
LUMPUR
LUMPY
LUNAR
LUNATIC
LUNCH
LUNCHED
LUNCHEON
LUNCHEONS
LUNCHES
LUNCHING
LUND
LUNDBERG
LUNDQUIST
LUNG
LUNGED
LUNGS
LURA
LURCH
LURCHED
LURCHES
LURCHING
LURE
LURED
LURES
LURING
LURK
LURKED
LURKING
LURKS
LUSAKA
LUSCIOUS
LUSCIOUSLY
LUSCIOUSNESS
LUSH
LUST
LUSTER
LUSTFUL
LUSTILY
LUSTINESS
LUSTROUS
LUSTS
LUSTY
LUTE
LUTES
LUTHER
LUTHERAN
LUTHERANIZE
LUTHERANIZER
LUTHERANIZERS
LUTHERANIZES
LUTZ
LUXEMBOURG
LUXEMBURG
LUXURIANT
LUXURIANTLY
LUXURIES
LUXURIOUS
LUXURIOUSLY
LUXURY
LUZON
LYDIA
LYING
LYKES
LYLE
LYMAN
LYMPH
LYNCH
LYNCHBURG
LYNCHED
LYNCHER
LYNCHES
LYNDON
LYNN
LYNX
LYNXES
LYON
LYONS
LYRA
LYRE
LYRIC
LYRICS
LYSENKO
MABEL
MAC
MACADAMIA
MACARTHUR
MACARTHUR
MACASSAR
MACAULAY
MACAULAYAN
MACAULAYISM
MACAULAYISMS
MACBETH
MACDONALD
MACDONALD
MACDOUGALL
MACDOUGALL
MACDRAW
MACE
MACED
MACEDON
MACEDONIA
MACEDONIAN
MACES
MACGREGOR
MACGREGOR
MACH
MACHIAVELLI
MACHIAVELLIAN
MACHINATION
MACHINE
MACHINED
MACHINELIKE
MACHINERY
MACHINES
MACHINING
MACHO
MACINTOSH
MACINTOSH
MACINTOSH
MACKENZIE
MACKENZIE
MACKEREL
MACKEY
MACKINAC
MACKINAW
MACMAHON
MACMILLAN
MACMILLAN
MACON
MACPAINT
MACRO
MACROECONOMICS
MACROMOLECULE
MACROMOLECULES
MACROPHAGE
MACROS
MACROSCOPIC
MAD
MADAGASCAR
MADAM
MADAME
MADAMES
MADDEN
MADDENING
MADDER
MADDEST
MADDOX
MADE
MADEIRA
MADELEINE
MADELINE
MADHOUSE
MADHYA
MADISON
MADLY
MADMAN
MADMEN
MADNESS
MADONNA
MADONNAS
MADRAS
MADRID
MADSEN
MAE
MAELSTROM
MAESTRO
MAFIA
MAFIOSI
MAGAZINE
MAGAZINES
MAGDALENE
MAGELLAN
MAGELLANIC
MAGENTA
MAGGIE
MAGGOT
MAGGOTS
MAGIC
MAGICAL
MAGICALLY
MAGICIAN
MAGICIANS
MAGILL
MAGISTRATE
MAGISTRATES
MAGNA
MAGNESIUM
MAGNET
MAGNETIC
MAGNETICALLY
MAGNETISM
MAGNETISMS
MAGNETIZABLE
MAGNETIZED
MAGNETO
MAGNIFICATION
MAGNIFICENCE
MAGNIFICENT
MAGNIFICENTLY
MAGNIFIED
MAGNIFIER
MAGNIFIES
MAGNIFY
MAGNIFYING
MAGNITUDE
MAGNITUDES
MAGNOLIA
MAGNUM
MAGNUSON
MAGOG
MAGPIE
MAGRUDER
MAGUIRE
MAGUIRES
MAHARASHTRA
MAHAYANA
MAHAYANIST
MAHOGANY
MAHONEY
MAID
MAIDEN
MAIDENS
MAIDS
MAIER
MAIL
MAILABLE
MAILBOX
MAILBOXES
MAILED
MAILER
MAILING
MAILINGS
MAILMAN
MAILMEN
MAILS
MAIM
MAIMED
MAIMING
MAIMS
MAIN
MAINE
MAINFRAME
MAINFRAMES
MAINLAND
MAINLINE
MAINLY
MAINS
MAINSTAY
MAINSTREAM
MAINTAIN
MAINTAINABILITY
MAINTAINABLE
MAINTAINED
MAINTAINER
MAINTAINERS
MAINTAINING
MAINTAINS
MAINTENANCE
MAINTENANCES
MAIZE
MAJESTIC
MAJESTIES
MAJESTY
MAJOR
MAJORCA
MAJORED
MAJORING
MAJORITIES
MAJORITY
MAJORS
MAKABLE
MAKE
MAKER
MAKERS
MAKES
MAKESHIFT
MAKEUP
MAKEUPS
MAKING
MAKINGS
MALABAR
MALADIES
MALADY
MALAGASY
MALAMUD
MALARIA
MALAWI
MALAY
MALAYIZE
MALAYIZES
MALAYSIA
MALAYSIAN
MALCOLM
MALCONTENT
MALDEN
MALDIVE
MALE
MALEFACTOR
MALEFACTORS
MALENESS
MALES
MALEVOLENT
MALFORMED
MALFUNCTION
MALFUNCTIONED
MALFUNCTIONING
MALFUNCTIONS
MALI
MALIBU
MALICE
MALICIOUS
MALICIOUSLY
MALICIOUSNESS
MALIGN
MALIGNANT
MALIGNANTLY
MALL
MALLARD
MALLET
MALLETS
MALLORY
MALNUTRITION
MALONE
MALONEY
MALPRACTICE
MALRAUX
MALT
MALTA
MALTED
MALTESE
MALTHUS
MALTHUSIAN
MALTON
MALTS
MAMA
MAMMA
MAMMAL
MAMMALIAN
MAMMALS
MAMMAS
MAMMOTH
MAN
MANAGE
MANAGEABLE
MANAGEABLENESS
MANAGED
MANAGEMENT
MANAGEMENTS
MANAGER
MANAGERIAL
MANAGERS
MANAGES
MANAGING
MANAGUA
MANAMA
MANCHESTER
MANCHURIA
MANDARIN
MANDATE
MANDATED
MANDATES
MANDATING
MANDATORY
MANDELBROT
MANDIBLE
MANE
MANES
MANEUVER
MANEUVERED
MANEUVERING
MANEUVERS
MANFRED
MANGER
MANGERS
MANGLE
MANGLED
MANGLER
MANGLES
MANGLING
MANHATTAN
MANHATTANIZE
MANHATTANIZES
MANHOLE
MANHOOD
MANIA
MANIAC
MANIACAL
MANIACS
MANIC
MANICURE
MANICURED
MANICURES
MANICURING
MANIFEST
MANIFESTATION
MANIFESTATIONS
MANIFESTED
MANIFESTING
MANIFESTLY
MANIFESTS
MANIFOLD
MANIFOLDS
MANILA
MANIPULABILITY
MANIPULABLE
MANIPULATABLE
MANIPULATE
MANIPULATED
MANIPULATES
MANIPULATING
MANIPULATION
MANIPULATIONS
MANIPULATIVE
MANIPULATOR
MANIPULATORS
MANIPULATORY
MANITOBA
MANITOWOC
MANKIND
MANKOWSKI
MANLEY
MANLY
MANN
MANNED
MANNER
MANNERED
MANNERLY
MANNERS
MANNING
MANOMETER
MANOMETERS
MANOR
MANORS
MANPOWER
MANS
MANSFIELD
MANSION
MANSIONS
MANSLAUGHTER
MANTEL
MANTELS
MANTIS
MANTISSA
MANTISSAS
MANTLE
MANTLEPIECE
MANTLES
MANUAL
MANUALLY
MANUALS
MANUEL
MANUFACTURE
MANUFACTURED
MANUFACTURER
MANUFACTURERS
MANUFACTURES
MANUFACTURING
MANURE
MANUSCRIPT
MANUSCRIPTS
MANVILLE
MANY
MAO
MAORI
MAP
MAPLE
MAPLECREST
MAPLES
MAPPABLE
MAPPED
MAPPING
MAPPINGS
MAPS
MARATHON
MARBLE
MARBLES
MARBLING
MARC
MARCEAU
MARCEL
MARCELLO
MARCH
MARCHED
MARCHER
MARCHES
MARCHING
MARCIA
MARCO
MARCOTTE
MARCUS
MARCY
MARDI
MARDIS
MARE
MARES
MARGARET
MARGARINE
MARGERY
MARGIN
MARGINAL
MARGINALLY
MARGINS
MARGO
MARGUERITE
MARIANNE
MARIE
MARIETTA
MARIGOLD
MARIJUANA
MARILYN
MARIN
MARINA
MARINADE
MARINATE
MARINE
MARINER
MARINES
MARINO
MARIO
MARION
MARIONETTE
MARITAL
MARITIME
MARJORIE
MARJORY
MARK
MARKABLE
MARKED
MARKEDLY
MARKER
MARKERS
MARKET
MARKETABILITY
MARKETABLE
MARKETED
MARKETING
MARKETINGS
MARKETPLACE
MARKETPLACES
MARKETS
MARKHAM
MARKING
MARKINGS
MARKISM
MARKOV
MARKOVIAN
MARKOVITZ
MARKS
MARLBORO
MARLBOROUGH
MARLENE
MARLOWE
MARMALADE
MARMOT
MAROON
MARQUETTE
MARQUIS
MARRIAGE
MARRIAGEABLE
MARRIAGES
MARRIED
MARRIES
MARRIOTT
MARROW
MARRY
MARRYING
MARS
MARSEILLES
MARSH
MARSHA
MARSHAL
MARSHALED
MARSHALING
MARSHALL
MARSHALLED
MARSHALLING
MARSHALS
MARSHES
MARSHMALLOW
MART
MARTEN
MARTHA
MARTIAL
MARTIAN
MARTIANS
MARTINEZ
MARTINGALE
MARTINI
MARTINIQUE
MARTINSON
MARTS
MARTY
MARTYR
MARTYRDOM
MARTYRS
MARVEL
MARVELED
MARVELLED
MARVELLING
MARVELOUS
MARVELOUSLY
MARVELOUSNESS
MARVELS
MARVIN
MARX
MARXIAN
MARXISM
MARXISMS
MARXIST
MARY
MARYLAND
MARYLANDERS
MASCARA
MASCULINE
MASCULINELY
MASCULINITY
MASERU
MASH
MASHED
MASHES
MASHING
MASK
MASKABLE
MASKED
MASKER
MASKING
MASKINGS
MASKS
MASOCHIST
MASOCHISTS
MASON
MASONIC
MASONITE
MASONRY
MASONS
MASQUERADE
MASQUERADER
MASQUERADES
MASQUERADING
MASS
MASSACHUSETTS
MASSACRE
MASSACRED
MASSACRES
MASSAGE
MASSAGES
MASSAGING
MASSED
MASSES
MASSEY
MASSING
MASSIVE
MAST
MASTED
MASTER
MASTERED
MASTERFUL
MASTERFULLY
MASTERING
MASTERINGS
MASTERLY
MASTERMIND
MASTERPIECE
MASTERPIECES
MASTERS
MASTERY
MASTODON
MASTS
MASTURBATE
MASTURBATED
MASTURBATES
MASTURBATING
MASTURBATION
MAT
MATCH
MATCHABLE
MATCHED
MATCHER
MATCHERS
MATCHES
MATCHING
MATCHINGS
MATCHLESS
MATE
MATED
MATEO
MATER
MATERIAL
MATERIALIST
MATERIALIZE
MATERIALIZED
MATERIALIZES
MATERIALIZING
MATERIALLY
MATERIALS
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MOTORIST
MOTORISTS
MOTORIZE
MOTORIZED
MOTORIZES
MOTORIZING
MOTOROLA
MOTORS
MOTTO
MOTTOES
MOULD
MOULDING
MOULTON
MOUND
MOUNDED
MOUNDS
MOUNT
MOUNTABLE
MOUNTAIN
MOUNTAINEER
MOUNTAINEERING
MOUNTAINEERS
MOUNTAINOUS
MOUNTAINOUSLY
MOUNTAINS
MOUNTED
MOUNTER
MOUNTING
MOUNTINGS
MOUNTS
MOURN
MOURNED
MOURNER
MOURNERS
MOURNFUL
MOURNFULLY
MOURNFULNESS
MOURNING
MOURNS
MOUSE
MOUSER
MOUSES
MOUSETRAP
MOUSY
MOUTH
MOUTHE
MOUTHED
MOUTHES
MOUTHFUL
MOUTHING
MOUTHPIECE
MOUTHS
MOUTON
MOVABLE
MOVE
MOVED
MOVEMENT
MOVEMENTS
MOVER
MOVERS
MOVES
MOVIE
MOVIES
MOVING
MOVINGS
MOW
MOWED
MOWER
MOWS
MOYER
MOZART
MUCH
MUCK
MUCKER
MUCKING
MUCUS
MUD
MUDD
MUDDIED
MUDDINESS
MUDDLE
MUDDLED
MUDDLEHEAD
MUDDLER
MUDDLERS
MUDDLES
MUDDLING
MUDDY
MUELLER
MUENSTER
MUFF
MUFFIN
MUFFINS
MUFFLE
MUFFLED
MUFFLER
MUFFLES
MUFFLING
MUFFS
MUG
MUGGING
MUGS
MUHAMMAD
MUIR
MUKDEN
MULATTO
MULBERRIES
MULBERRY
MULE
MULES
MULL
MULLAH
MULLEN
MULTI
MULTIBIT
MULTIBUS
MULTIBYTE
MULTICAST
MULTICASTING
MULTICASTS
MULTICELLULAR
MULTICOMPUTER
MULTICS
MULTICS
MULTIDIMENSIONAL
MULTILATERAL
MULTILAYER
MULTILAYERED
MULTILEVEL
MULTIMEDIA
MULTINATIONAL
MULTIPLE
MULTIPLES
MULTIPLEX
MULTIPLEXED
MULTIPLEXER
MULTIPLEXERS
MULTIPLEXES
MULTIPLEXING
MULTIPLEXOR
MULTIPLEXORS
MULTIPLICAND
MULTIPLICANDS
MULTIPLICATION
MULTIPLICATIONS
MULTIPLICATIVE
MULTIPLICATIVES
MULTIPLICITY
MULTIPLIED
MULTIPLIER
MULTIPLIERS
MULTIPLIES
MULTIPLY
MULTIPLYING
MULTIPROCESS
MULTIPROCESSING
MULTIPROCESSOR
MULTIPROCESSORS
MULTIPROGRAM
MULTIPROGRAMMED
MULTIPROGRAMMING
MULTISTAGE
MULTITUDE
MULTITUDES
MULTIUSER
MULTIVARIATE
MULTIWORD
MUMBLE
MUMBLED
MUMBLER
MUMBLERS
MUMBLES
MUMBLING
MUMBLINGS
MUMFORD
MUMMIES
MUMMY
MUNCH
MUNCHED
MUNCHING
MUNCIE
MUNDANE
MUNDANELY
MUNDT
MUNG
MUNICH
MUNICIPAL
MUNICIPALITIES
MUNICIPALITY
MUNICIPALLY
MUNITION
MUNITIONS
MUNROE
MUNSEY
MUNSON
MUONG
MURAL
MURDER
MURDERED
MURDERER
MURDERERS
MURDERING
MURDEROUS
MURDEROUSLY
MURDERS
MURIEL
MURKY
MURMUR
MURMURED
MURMURER
MURMURING
MURMURS
MURPHY
MURRAY
MURROW
MUSCAT
MUSCLE
MUSCLED
MUSCLES
MUSCLING
MUSCOVITE
MUSCOVY
MUSCULAR
MUSCULATURE
MUSE
MUSED
MUSES
MUSEUM
MUSEUMS
MUSH
MUSHROOM
MUSHROOMED
MUSHROOMING
MUSHROOMS
MUSHY
MUSIC
MUSICAL
MUSICALLY
MUSICALS
MUSICIAN
MUSICIANLY
MUSICIANS
MUSICOLOGY
MUSING
MUSINGS
MUSK
MUSKEGON
MUSKET
MUSKETS
MUSKOX
MUSKOXEN
MUSKRAT
MUSKRATS
MUSKS
MUSLIM
MUSLIMS
MUSLIN
MUSSEL
MUSSELS
MUSSOLINI
MUSSOLINIS
MUSSORGSKY
MUST
MUSTACHE
MUSTACHED
MUSTACHES
MUSTARD
MUSTER
MUSTINESS
MUSTS
MUSTY
MUTABILITY
MUTABLE
MUTABLENESS
MUTANDIS
MUTANT
MUTATE
MUTATED
MUTATES
MUTATING
MUTATION
MUTATIONS
MUTATIS
MUTATIVE
MUTE
MUTED
MUTELY
MUTENESS
MUTILATE
MUTILATED
MUTILATES
MUTILATING
MUTILATION
MUTINIES
MUTINY
MUTT
MUTTER
MUTTERED
MUTTERER
MUTTERERS
MUTTERING
MUTTERS
MUTTON
MUTUAL
MUTUALLY
MUZAK
MUZO
MUZZLE
MUZZLES
MYCENAE
MYCENAEAN
MYERS
MYNHEER
MYRA
MYRIAD
MYRON
MYRTLE
MYSELF
MYSORE
MYSTERIES
MYSTERIOUS
MYSTERIOUSLY
MYSTERIOUSNESS
MYSTERY
MYSTIC
MYSTICAL
MYSTICS
MYSTIFY
MYTH
MYTHICAL
MYTHOLOGIES
MYTHOLOGY
NAB
NABISCO
NABLA
NABLAS
NADIA
NADINE
NADIR
NAG
NAGASAKI
NAGGED
NAGGING
NAGOYA
NAGS
NAGY
NAIL
NAILED
NAILING
NAILS
NAIR
NAIROBI
NAIVE
NAIVELY
NAIVENESS
NAIVETE
NAKAMURA
NAKAYAMA
NAKED
NAKEDLY
NAKEDNESS
NAKOMA
NAME
NAMEABLE
NAMED
NAMELESS
NAMELESSLY
NAMELY
NAMER
NAMERS
NAMES
NAMESAKE
NAMESAKES
NAMING
NAN
NANCY
NANETTE
NANKING
NANOINSTRUCTION
NANOINSTRUCTIONS
NANOOK
NANOPROGRAM
NANOPROGRAMMING
NANOSECOND
NANOSECONDS
NANOSTORE
NANOSTORES
NANTUCKET
NAOMI
NAP
NAPKIN
NAPKINS
NAPLES
NAPOLEON
NAPOLEONIC
NAPOLEONIZE
NAPOLEONIZES
NAPS
NARBONNE
NARCISSUS
NARCOTIC
NARCOTICS
NARRAGANSETT
NARRATE
NARRATION
NARRATIVE
NARRATIVES
NARROW
NARROWED
NARROWER
NARROWEST
NARROWING
NARROWLY
NARROWNESS
NARROWS
NARY
NASA
NASAL
NASALLY
NASAS
NASH
NASHUA
NASHVILLE
NASSAU
NASTIER
NASTIEST
NASTILY
NASTINESS
NASTY
NAT
NATAL
NATALIE
NATCHEZ
NATE
NATHAN
NATHANIEL
NATION
NATIONAL
NATIONALIST
NATIONALISTS
NATIONALITIES
NATIONALITY
NATIONALIZATION
NATIONALIZE
NATIONALIZED
NATIONALIZES
NATIONALIZING
NATIONALLY
NATIONALS
NATIONHOOD
NATIONS
NATIONWIDE
NATIVE
NATIVELY
NATIVES
NATIVITY
NATO
NATOS
NATURAL
NATURALISM
NATURALIST
NATURALIZATION
NATURALLY
NATURALNESS
NATURALS
NATURE
NATURED
NATURES
NAUGHT
NAUGHTIER
NAUGHTINESS
NAUGHTY
NAUR
NAUSEA
NAUSEATE
NAUSEUM
NAVAHO
NAVAJO
NAVAL
NAVALLY
NAVEL
NAVIES
NAVIGABLE
NAVIGATE
NAVIGATED
NAVIGATES
NAVIGATING
NAVIGATION
NAVIGATOR
NAVIGATORS
NAVONA
NAVY
NAY
NAZARENE
NAZARETH
NAZI
NAZIS
NAZISM
NDJAMENA
NEAL
NEANDERTHAL
NEAPOLITAN
NEAR
NEARBY
NEARED
NEARER
NEAREST
NEARING
NEARLY
NEARNESS
NEARS
NEARSIGHTED
NEAT
NEATER
NEATEST
NEATLY
NEATNESS
NEBRASKA
NEBRASKAN
NEBUCHADNEZZAR
NEBULA
NEBULAR
NEBULOUS
NECESSARIES
NECESSARILY
NECESSARY
NECESSITATE
NECESSITATED
NECESSITATES
NECESSITATING
NECESSITATION
NECESSITIES
NECESSITY
NECK
NECKING
NECKLACE
NECKLACES
NECKLINE
NECKS
NECKTIE
NECKTIES
NECROSIS
NECTAR
NED
NEED
NEEDED
NEEDFUL
NEEDHAM
NEEDING
NEEDLE
NEEDLED
NEEDLER
NEEDLERS
NEEDLES
NEEDLESS
NEEDLESSLY
NEEDLESSNESS
NEEDLEWORK
NEEDLING
NEEDS
NEEDY
NEFF
NEGATE
NEGATED
NEGATES
NEGATING
NEGATION
NEGATIONS
NEGATIVE
NEGATIVELY
NEGATIVES
NEGATOR
NEGATORS
NEGLECT
NEGLECTED
NEGLECTING
NEGLECTS
NEGLIGEE
NEGLIGENCE
NEGLIGENT
NEGLIGIBLE
NEGOTIABLE
NEGOTIATE
NEGOTIATED
NEGOTIATES
NEGOTIATING
NEGOTIATION
NEGOTIATIONS
NEGRO
NEGROES
NEGROID
NEGROIZATION
NEGROIZATIONS
NEGROIZE
NEGROIZES
NEHRU
NEIGH
NEIGHBOR
NEIGHBORHOOD
NEIGHBORHOODS
NEIGHBORING
NEIGHBORLY
NEIGHBORS
NEIL
NEITHER
NELL
NELLIE
NELSEN
NELSON
NEMESIS
NEOCLASSIC
NEON
NEONATAL
NEOPHYTE
NEOPHYTES
NEPAL
NEPALI
NEPHEW
NEPHEWS
NEPTUNE
NERO
NERVE
NERVES
NERVOUS
NERVOUSLY
NERVOUSNESS
NESS
NEST
NESTED
NESTER
NESTING
NESTLE
NESTLED
NESTLES
NESTLING
NESTOR
NESTS
NET
NETHER
NETHERLANDS
NETS
NETTED
NETTING
NETTLE
NETTLED
NETWORK
NETWORKED
NETWORKING
NETWORKS
NEUMANN
NEURAL
NEURITIS
NEUROLOGICAL
NEUROLOGISTS
NEURON
NEURONS
NEUROSES
NEUROSIS
NEUROTIC
NEUTER
NEUTRAL
NEUTRALITIES
NEUTRALITY
NEUTRALIZE
NEUTRALIZED
NEUTRALIZING
NEUTRALLY
NEUTRINO
NEUTRINOS
NEUTRON
NEVA
NEVADA
NEVER
NEVERTHELESS
NEVINS
NEW
NEWARK
NEWBOLD
NEWBORN
NEWBURY
NEWBURYPORT
NEWCASTLE
NEWCOMER
NEWCOMERS
NEWELL
NEWER
NEWEST
NEWFOUNDLAND
NEWLY
NEWLYWED
NEWMAN
NEWMANIZE
NEWMANIZES
NEWNESS
NEWPORT
NEWS
NEWSCAST
NEWSGROUP
NEWSLETTER
NEWSLETTERS
NEWSMAN
NEWSMEN
NEWSPAPER
NEWSPAPERS
NEWSSTAND
NEWSWEEK
NEWSWEEKLY
NEWT
NEWTON
NEWTONIAN
NEXT
NGUYEN
NIAGARA
NIAMEY
NIBBLE
NIBBLED
NIBBLER
NIBBLERS
NIBBLES
NIBBLING
NIBELUNG
NICARAGUA
NICCOLO
NICE
NICELY
NICENESS
NICER
NICEST
NICHE
NICHOLAS
NICHOLLS
NICHOLS
NICHOLSON
NICK
NICKED
NICKEL
NICKELS
NICKER
NICKING
NICKLAUS
NICKNAME
NICKNAMED
NICKNAMES
NICKS
NICODEMUS
NICOSIA
NICOTINE
NIECE
NIECES
NIELSEN
NIELSON
NIETZSCHE
NIFTY
NIGER
NIGERIA
NIGERIAN
NIGH
NIGHT
NIGHTCAP
NIGHTCLUB
NIGHTFALL
NIGHTGOWN
NIGHTINGALE
NIGHTINGALES
NIGHTLY
NIGHTMARE
NIGHTMARES
NIGHTMARISH
NIGHTS
NIGHTTIME
NIHILISM
NIJINSKY
NIKKO
NIKOLAI
NIL
NILE
NILSEN
NILSSON
NIMBLE
NIMBLENESS
NIMBLER
NIMBLY
NIMBUS
NINA
NINE
NINEFOLD
NINES
NINETEEN
NINETEENS
NINETEENTH
NINETIES
NINETIETH
NINETY
NINEVEH
NINTH
NIOBE
NIP
NIPPLE
NIPPON
NIPPONIZE
NIPPONIZES
NIPS
NITRIC
NITROGEN
NITROUS
NITTY
NIXON
NOAH
NOBEL
NOBILITY
NOBLE
NOBLEMAN
NOBLENESS
NOBLER
NOBLES
NOBLEST
NOBLY
NOBODY
NOCTURNAL
NOCTURNALLY
NOD
NODAL
NODDED
NODDING
NODE
NODES
NODS
NODULAR
NODULE
NOEL
NOETHERIAN
NOISE
NOISELESS
NOISELESSLY
NOISES
NOISIER
NOISILY
NOISINESS
NOISY
NOLAN
NOLL
NOMENCLATURE
NOMINAL
NOMINALLY
NOMINATE
NOMINATED
NOMINATING
NOMINATION
NOMINATIVE
NOMINEE
NON
NONADAPTIVE
NONBIODEGRADABLE
NONBLOCKING
NONCE
NONCHALANT
NONCOMMERCIAL
NONCOMMUNICATION
NONCONSECUTIVELY
NONCONSERVATIVE
NONCRITICAL
NONCYCLIC
NONDECREASING
NONDESCRIPT
NONDESCRIPTLY
NONDESTRUCTIVELY
NONDETERMINACY
NONDETERMINATE
NONDETERMINATELY
NONDETERMINISM
NONDETERMINISTIC
NONDETERMINISTICALLY
NONE
NONEMPTY
NONETHELESS
NONEXISTENCE
NONEXISTENT
NONEXTENSIBLE
NONFUNCTIONAL
NONGOVERNMENTAL
NONIDEMPOTENT
NONINTERACTING
NONINTERFERENCE
NONINTERLEAVED
NONINTRUSIVE
NONINTUITIVE
NONINVERTING
NONLINEAR
NONLINEARITIES
NONLINEARITY
NONLINEARLY
NONLOCAL
NONMASKABLE
NONMATHEMATICAL
NONMILITARY
NONNEGATIVE
NONNEGLIGIBLE
NONNUMERICAL
NONOGENARIAN
NONORTHOGONAL
NONORTHOGONALITY
NONPERISHABLE
NONPERSISTENT
NONPORTABLE
NONPROCEDURAL
NONPROCEDURALLY
NONPROFIT
NONPROGRAMMABLE
NONPROGRAMMER
NONSEGMENTED
NONSENSE
NONSENSICAL
NONSEQUENTIAL
NONSPECIALIST
NONSPECIALISTS
NONSTANDARD
NONSYNCHRONOUS
NONTECHNICAL
NONTERMINAL
NONTERMINALS
NONTERMINATING
NONTERMINATION
NONTHERMAL
NONTRANSPARENT
NONTRIVIAL
NONUNIFORM
NONUNIFORMITY
NONZERO
NOODLE
NOOK
NOOKS
NOON
NOONDAY
NOONS
NOONTIDE
NOONTIME
NOOSE
NOR
NORA
NORDHOFF
NORDIC
NORDSTROM
NOREEN
NORFOLK
NORM
NORMA
NORMAL
NORMALCY
NORMALITY
NORMALIZATION
NORMALIZE
NORMALIZED
NORMALIZES
NORMALIZING
NORMALLY
NORMALS
NORMAN
NORMANDY
NORMANIZATION
NORMANIZATIONS
NORMANIZE
NORMANIZER
NORMANIZERS
NORMANIZES
NORMATIVE
NORMS
NORRIS
NORRISTOWN
NORSE
NORTH
NORTHAMPTON
NORTHBOUND
NORTHEAST
NORTHEASTER
NORTHEASTERN
NORTHERLY
NORTHERN
NORTHERNER
NORTHERNERS
NORTHERNLY
NORTHFIELD
NORTHROP
NORTHRUP
NORTHUMBERLAND
NORTHWARD
NORTHWARDS
NORTHWEST
NORTHWESTERN
NORTON
NORWALK
NORWAY
NORWEGIAN
NORWICH
NOSE
NOSED
NOSES
NOSING
NOSTALGIA
NOSTALGIC
NOSTRADAMUS
NOSTRAND
NOSTRIL
NOSTRILS
NOT
NOTABLE
NOTABLES
NOTABLY
NOTARIZE
NOTARIZED
NOTARIZES
NOTARIZING
NOTARY
NOTATION
NOTATIONAL
NOTATIONS
NOTCH
NOTCHED
NOTCHES
NOTCHING
NOTE
NOTEBOOK
NOTEBOOKS
NOTED
NOTES
NOTEWORTHY
NOTHING
NOTHINGNESS
NOTHINGS
NOTICE
NOTICEABLE
NOTICEABLY
NOTICED
NOTICES
NOTICING
NOTIFICATION
NOTIFICATIONS
NOTIFIED
NOTIFIER
NOTIFIERS
NOTIFIES
NOTIFY
NOTIFYING
NOTING
NOTION
NOTIONS
NOTORIETY
NOTORIOUS
NOTORIOUSLY
NOTRE
NOTTINGHAM
NOTWITHSTANDING
NOUAKCHOTT
NOUN
NOUNS
NOURISH
NOURISHED
NOURISHES
NOURISHING
NOURISHMENT
NOVAK
NOVEL
NOVELIST
NOVELISTS
NOVELS
NOVELTIES
NOVELTY
NOVEMBER
NOVEMBERS
NOVICE
NOVICES
NOVOSIBIRSK
NOW
NOWADAYS
NOWHERE
NOXIOUS
NOYES
NOZZLE
NUANCE
NUANCES
NUBIA
NUBIAN
NUBILE
NUCLEAR
NUCLEI
NUCLEIC
NUCLEOTIDE
NUCLEOTIDES
NUCLEUS
NUCLIDE
NUDE
NUDGE
NUDGED
NUDITY
NUGENT
NUGGET
NUISANCE
NUISANCES
NULL
NULLARY
NULLED
NULLIFIED
NULLIFIERS
NULLIFIES
NULLIFY
NULLIFYING
NULLS
NUMB
NUMBED
NUMBER
NUMBERED
NUMBERER
NUMBERING
NUMBERLESS
NUMBERS
NUMBING
NUMBLY
NUMBNESS
NUMBS
NUMERABLE
NUMERAL
NUMERALS
NUMERATOR
NUMERATORS
NUMERIC
NUMERICAL
NUMERICALLY
NUMERICS
NUMEROUS
NUMISMATIC
NUMISMATIST
NUN
NUNS
NUPTIAL
NURSE
NURSED
NURSERIES
NURSERY
NURSES
NURSING
NURTURE
NURTURED
NURTURES
NURTURING
NUT
NUTATE
NUTRIA
NUTRIENT
NUTRITION
NUTRITIOUS
NUTS
NUTSHELL
NUTSHELLS
NUZZLE
NYLON
NYMPH
NYMPHOMANIA
NYMPHOMANIAC
NYMPHS
NYQUIST
OAF
OAK
OAKEN
OAKLAND
OAKLEY
OAKMONT
OAKS
OAR
OARS
OASES
OASIS
OAT
OATEN
OATH
OATHS
OATMEAL
OATS
OBEDIENCE
OBEDIENCES
OBEDIENT
OBEDIENTLY
OBELISK
OBERLIN
OBERON
OBESE
OBEY
OBEYED
OBEYING
OBEYS
OBFUSCATE
OBFUSCATORY
OBITUARY
OBJECT
OBJECTED
OBJECTING
OBJECTION
OBJECTIONABLE
OBJECTIONS
OBJECTIVE
OBJECTIVELY
OBJECTIVES
OBJECTOR
OBJECTORS
OBJECTS
OBLIGATED
OBLIGATION
OBLIGATIONS
OBLIGATORY
OBLIGE
OBLIGED
OBLIGES
OBLIGING
OBLIGINGLY
OBLIQUE
OBLIQUELY
OBLIQUENESS
OBLITERATE
OBLITERATED
OBLITERATES
OBLITERATING
OBLITERATION
OBLIVION
OBLIVIOUS
OBLIVIOUSLY
OBLIVIOUSNESS
OBLONG
OBNOXIOUS
OBOE
OBSCENE
OBSCURE
OBSCURED
OBSCURELY
OBSCURER
OBSCURES
OBSCURING
OBSCURITIES
OBSCURITY
OBSEQUIOUS
OBSERVABLE
OBSERVANCE
OBSERVANCES
OBSERVANT
OBSERVATION
OBSERVATIONS
OBSERVATORY
OBSERVE
OBSERVED
OBSERVER
OBSERVERS
OBSERVES
OBSERVING
OBSESSION
OBSESSIONS
OBSESSIVE
OBSOLESCENCE
OBSOLESCENT
OBSOLETE
OBSOLETED
OBSOLETES
OBSOLETING
OBSTACLE
OBSTACLES
OBSTINACY
OBSTINATE
OBSTINATELY
OBSTRUCT
OBSTRUCTED
OBSTRUCTING
OBSTRUCTION
OBSTRUCTIONS
OBSTRUCTIVE
OBTAIN
OBTAINABLE
OBTAINABLY
OBTAINED
OBTAINING
OBTAINS
OBVIATE
OBVIATED
OBVIATES
OBVIATING
OBVIATION
OBVIATIONS
OBVIOUS
OBVIOUSLY
OBVIOUSNESS
OCCAM
OCCASION
OCCASIONAL
OCCASIONALLY
OCCASIONED
OCCASIONING
OCCASIONINGS
OCCASIONS
OCCIDENT
OCCIDENTAL
OCCIDENTALIZATION
OCCIDENTALIZATIONS
OCCIDENTALIZE
OCCIDENTALIZED
OCCIDENTALIZES
OCCIDENTALIZING
OCCIDENTALS
OCCIPITAL
OCCLUDE
OCCLUDED
OCCLUDES
OCCLUSION
OCCLUSIONS
OCCULT
OCCUPANCIES
OCCUPANCY
OCCUPANT
OCCUPANTS
OCCUPATION
OCCUPATIONAL
OCCUPATIONALLY
OCCUPATIONS
OCCUPIED
OCCUPIER
OCCUPIES
OCCUPY
OCCUPYING
OCCUR
OCCURRED
OCCURRENCE
OCCURRENCES
OCCURRING
OCCURS
OCEAN
OCEANIA
OCEANIC
OCEANOGRAPHY
OCEANS
OCONOMOWOC
OCTAGON
OCTAGONAL
OCTAHEDRA
OCTAHEDRAL
OCTAHEDRON
OCTAL
OCTANE
OCTAVE
OCTAVES
OCTAVIA
OCTET
OCTETS
OCTOBER
OCTOBERS
OCTOGENARIAN
OCTOPUS
ODD
ODDER
ODDEST
ODDITIES
ODDITY
ODDLY
ODDNESS
ODDS
ODE
ODERBERG
ODERBERGS
ODES
ODESSA
ODIN
ODIOUS
ODIOUSLY
ODIOUSNESS
ODIUM
ODOR
ODOROUS
ODOROUSLY
ODOROUSNESS
ODORS
ODYSSEUS
ODYSSEY
OEDIPAL
OEDIPALLY
OEDIPUS
OFF
OFFENBACH
OFFEND
OFFENDED
OFFENDER
OFFENDERS
OFFENDING
OFFENDS
OFFENSE
OFFENSES
OFFENSIVE
OFFENSIVELY
OFFENSIVENESS
OFFER
OFFERED
OFFERER
OFFERERS
OFFERING
OFFERINGS
OFFERS
OFFHAND
OFFICE
OFFICEMATE
OFFICER
OFFICERS
OFFICES
OFFICIAL
OFFICIALDOM
OFFICIALLY
OFFICIALS
OFFICIATE
OFFICIO
OFFICIOUS
OFFICIOUSLY
OFFICIOUSNESS
OFFING
OFFLOAD
OFFS
OFFSET
OFFSETS
OFFSETTING
OFFSHORE
OFFSPRING
OFT
OFTEN
OFTENTIMES
OGDEN
OHIO
OHM
OHMMETER
OIL
OILCLOTH
OILED
OILER
OILERS
OILIER
OILIEST
OILING
OILS
OILY
OINTMENT
OJIBWA
OKAMOTO
OKAY
OKINAWA
OKLAHOMA
OKLAHOMAN
OLAF
OLAV
OLD
OLDEN
OLDENBURG
OLDER
OLDEST
OLDNESS
OLDSMOBILE
OLDUVAI
OLDY
OLEANDER
OLEG
OLEOMARGARINE
OLGA
OLIGARCHY
OLIGOCENE
OLIN
OLIVE
OLIVER
OLIVERS
OLIVES
OLIVETTI
OLIVIA
OLIVIER
OLSEN
OLSON
OLYMPIA
OLYMPIAN
OLYMPIANIZE
OLYMPIANIZES
OLYMPIC
OLYMPICS
OLYMPUS
OMAHA
OMAN
OMEGA
OMELET
OMEN
OMENS
OMICRON
OMINOUS
OMINOUSLY
OMINOUSNESS
OMISSION
OMISSIONS
OMIT
OMITS
OMITTED
OMITTING
OMNIBUS
OMNIDIRECTIONAL
OMNIPOTENT
OMNIPRESENT
OMNISCIENT
OMNISCIENTLY
OMNIVORE
ONANISM
ONCE
ONCOLOGY
ONE
ONEIDA
ONENESS
ONEROUS
ONES
ONESELF
ONETIME
ONGOING
ONION
ONIONS
ONLINE
ONLOOKER
ONLY
ONONDAGA
ONRUSH
ONSET
ONSETS
ONSLAUGHT
ONTARIO
ONTO
ONTOLOGY
ONUS
ONWARD
ONWARDS
ONYX
OOZE
OOZED
OPACITY
OPAL
OPALS
OPAQUE
OPAQUELY
OPAQUENESS
OPCODE
OPEC
OPEL
OPEN
OPENED
OPENER
OPENERS
OPENING
OPENINGS
OPENLY
OPENNESS
OPENS
OPERA
OPERABLE
OPERAND
OPERANDI
OPERANDS
OPERAS
OPERATE
OPERATED
OPERATES
OPERATING
OPERATION
OPERATIONAL
OPERATIONALLY
OPERATIONS
OPERATIVE
OPERATIVES
OPERATOR
OPERATORS
OPERETTA
OPHIUCHUS
OPHIUCUS
OPIATE
OPINION
OPINIONS
OPIUM
OPOSSUM
OPPENHEIMER
OPPONENT
OPPONENTS
OPPORTUNE
OPPORTUNELY
OPPORTUNISM
OPPORTUNISTIC
OPPORTUNITIES
OPPORTUNITY
OPPOSABLE
OPPOSE
OPPOSED
OPPOSES
OPPOSING
OPPOSITE
OPPOSITELY
OPPOSITENESS
OPPOSITES
OPPOSITION
OPPRESS
OPPRESSED
OPPRESSES
OPPRESSING
OPPRESSION
OPPRESSIVE
OPPRESSOR
OPPRESSORS
OPPROBRIUM
OPT
OPTED
OPTHALMIC
OPTIC
OPTICAL
OPTICALLY
OPTICS
OPTIMA
OPTIMAL
OPTIMALITY
OPTIMALLY
OPTIMISM
OPTIMIST
OPTIMISTIC
OPTIMISTICALLY
OPTIMIZATION
OPTIMIZATIONS
OPTIMIZE
OPTIMIZED
OPTIMIZER
OPTIMIZERS
OPTIMIZES
OPTIMIZING
OPTIMUM
OPTING
OPTION
OPTIONAL
OPTIONALLY
OPTIONS
OPTOACOUSTIC
OPTOMETRIST
OPTOMETRY
OPTS
OPULENCE
OPULENT
OPUS
ORACLE
ORACLES
ORAL
ORALLY
ORANGE
ORANGES
ORANGUTAN
ORATION
ORATIONS
ORATOR
ORATORIES
ORATORS
ORATORY
ORB
ORBIT
ORBITAL
ORBITALLY
ORBITED
ORBITER
ORBITERS
ORBITING
ORBITS
ORCHARD
ORCHARDS
ORCHESTRA
ORCHESTRAL
ORCHESTRAS
ORCHESTRATE
ORCHID
ORCHIDS
ORDAIN
ORDAINED
ORDAINING
ORDAINS
ORDEAL
ORDER
ORDERED
ORDERING
ORDERINGS
ORDERLIES
ORDERLY
ORDERS
ORDINAL
ORDINANCE
ORDINANCES
ORDINARILY
ORDINARINESS
ORDINARY
ORDINATE
ORDINATES
ORDINATION
ORE
OREGANO
OREGON
OREGONIANS
ORES
ORESTEIA
ORESTES
ORGAN
ORGANIC
ORGANISM
ORGANISMS
ORGANIST
ORGANISTS
ORGANIZABLE
ORGANIZATION
ORGANIZATIONAL
ORGANIZATIONALLY
ORGANIZATIONS
ORGANIZE
ORGANIZED
ORGANIZER
ORGANIZERS
ORGANIZES
ORGANIZING
ORGANS
ORGASM
ORGIASTIC
ORGIES
ORGY
ORIENT
ORIENTAL
ORIENTALIZATION
ORIENTALIZATIONS
ORIENTALIZE
ORIENTALIZED
ORIENTALIZES
ORIENTALIZING
ORIENTALS
ORIENTATION
ORIENTATIONS
ORIENTED
ORIENTING
ORIENTS
ORIFICE
ORIFICES
ORIGIN
ORIGINAL
ORIGINALITY
ORIGINALLY
ORIGINALS
ORIGINATE
ORIGINATED
ORIGINATES
ORIGINATING
ORIGINATION
ORIGINATOR
ORIGINATORS
ORIGINS
ORIN
ORINOCO
ORIOLE
ORION
ORKNEY
ORLANDO
ORLEANS
ORLICK
ORLY
ORNAMENT
ORNAMENTAL
ORNAMENTALLY
ORNAMENTATION
ORNAMENTED
ORNAMENTING
ORNAMENTS
ORNATE
ORNERY
ORONO
ORPHAN
ORPHANAGE
ORPHANED
ORPHANS
ORPHEUS
ORPHIC
ORPHICALLY
ORR
ORTEGA
ORTHANT
ORTHODONTIST
ORTHODOX
ORTHODOXY
ORTHOGONAL
ORTHOGONALITY
ORTHOGONALLY
ORTHOPEDIC
ORVILLE
ORWELL
ORWELLIAN
OSAKA
OSBERT
OSBORN
OSBORNE
OSCAR
OSCILLATE
OSCILLATED
OSCILLATES
OSCILLATING
OSCILLATION
OSCILLATIONS
OSCILLATOR
OSCILLATORS
OSCILLATORY
OSCILLOSCOPE
OSCILLOSCOPES
OSGOOD
OSHKOSH
OSIRIS
OSLO
OSMOSIS
OSMOTIC
OSSIFY
OSTENSIBLE
OSTENSIBLY
OSTENTATIOUS
OSTEOPATH
OSTEOPATHIC
OSTEOPATHY
OSTEOPOROSIS
OSTRACISM
OSTRANDER
OSTRICH
OSTRICHES
OSWALD
OTHELLO
OTHER
OTHERS
OTHERWISE
OTHERWORLDLY
OTIS
OTT
OTTAWA
OTTER
OTTERS
OTTO
OTTOMAN
OTTOMANIZATION
OTTOMANIZATIONS
OTTOMANIZE
OTTOMANIZES
OUAGADOUGOU
OUCH
OUGHT
OUNCE
OUNCES
OUR
OURS
OURSELF
OURSELVES
OUST
OUT
OUTBOUND
OUTBREAK
OUTBREAKS
OUTBURST
OUTBURSTS
OUTCAST
OUTCASTS
OUTCOME
OUTCOMES
OUTCRIES
OUTCRY
OUTDATED
OUTDO
OUTDOOR
OUTDOORS
OUTER
OUTERMOST
OUTFIT
OUTFITS
OUTFITTED
OUTGOING
OUTGREW
OUTGROW
OUTGROWING
OUTGROWN
OUTGROWS
OUTGROWTH
OUTING
OUTLANDISH
OUTLAST
OUTLASTS
OUTLAW
OUTLAWED
OUTLAWING
OUTLAWS
OUTLAY
OUTLAYS
OUTLET
OUTLETS
OUTLINE
OUTLINED
OUTLINES
OUTLINING
OUTLIVE
OUTLIVED
OUTLIVES
OUTLIVING
OUTLOOK
OUTLYING
OUTNUMBERED
OUTPERFORM
OUTPERFORMED
OUTPERFORMING
OUTPERFORMS
OUTPOST
OUTPOSTS
OUTPUT
OUTPUTS
OUTPUTTING
OUTRAGE
OUTRAGED
OUTRAGEOUS
OUTRAGEOUSLY
OUTRAGES
OUTRIGHT
OUTRUN
OUTRUNS
OUTS
OUTSET
OUTSIDE
OUTSIDER
OUTSIDERS
OUTSKIRTS
OUTSTANDING
OUTSTANDINGLY
OUTSTRETCHED
OUTSTRIP
OUTSTRIPPED
OUTSTRIPPING
OUTSTRIPS
OUTVOTE
OUTVOTED
OUTVOTES
OUTVOTING
OUTWARD
OUTWARDLY
OUTWEIGH
OUTWEIGHED
OUTWEIGHING
OUTWEIGHS
OUTWIT
OUTWITS
OUTWITTED
OUTWITTING
OVAL
OVALS
OVARIES
OVARY
OVEN
OVENS
OVER
OVERALL
OVERALLS
OVERBOARD
OVERCAME
OVERCOAT
OVERCOATS
OVERCOME
OVERCOMES
OVERCOMING
OVERCROWD
OVERCROWDED
OVERCROWDING
OVERCROWDS
OVERDONE
OVERDOSE
OVERDRAFT
OVERDRAFTS
OVERDUE
OVEREMPHASIS
OVEREMPHASIZED
OVERESTIMATE
OVERESTIMATED
OVERESTIMATES
OVERESTIMATING
OVERESTIMATION
OVERFLOW
OVERFLOWED
OVERFLOWING
OVERFLOWS
OVERGROWN
OVERHANG
OVERHANGING
OVERHANGS
OVERHAUL
OVERHAULING
OVERHEAD
OVERHEADS
OVERHEAR
OVERHEARD
OVERHEARING
OVERHEARS
OVERJOY
OVERJOYED
OVERKILL
OVERLAND
OVERLAP
OVERLAPPED
OVERLAPPING
OVERLAPS
OVERLAY
OVERLAYING
OVERLAYS
OVERLOAD
OVERLOADED
OVERLOADING
OVERLOADS
OVERLOOK
OVERLOOKED
OVERLOOKING
OVERLOOKS
OVERLY
OVERNIGHT
OVERNIGHTER
OVERNIGHTERS
OVERPOWER
OVERPOWERED
OVERPOWERING
OVERPOWERS
OVERPRINT
OVERPRINTED
OVERPRINTING
OVERPRINTS
OVERPRODUCTION
OVERRIDDEN
OVERRIDE
OVERRIDES
OVERRIDING
OVERRODE
OVERRULE
OVERRULED
OVERRULES
OVERRUN
OVERRUNNING
OVERRUNS
OVERSEAS
OVERSEE
OVERSEEING
OVERSEER
OVERSEERS
OVERSEES
OVERSHADOW
OVERSHADOWED
OVERSHADOWING
OVERSHADOWS
OVERSHOOT
OVERSHOT
OVERSIGHT
OVERSIGHTS
OVERSIMPLIFIED
OVERSIMPLIFIES
OVERSIMPLIFY
OVERSIMPLIFYING
OVERSIZED
OVERSTATE
OVERSTATED
OVERSTATEMENT
OVERSTATEMENTS
OVERSTATES
OVERSTATING
OVERSTOCKS
OVERSUBSCRIBED
OVERT
OVERTAKE
OVERTAKEN
OVERTAKER
OVERTAKERS
OVERTAKES
OVERTAKING
OVERTHREW
OVERTHROW
OVERTHROWN
OVERTIME
OVERTLY
OVERTONE
OVERTONES
OVERTOOK
OVERTURE
OVERTURES
OVERTURN
OVERTURNED
OVERTURNING
OVERTURNS
OVERUSE
OVERVIEW
OVERVIEWS
OVERWHELM
OVERWHELMED
OVERWHELMING
OVERWHELMINGLY
OVERWHELMS
OVERWORK
OVERWORKED
OVERWORKING
OVERWORKS
OVERWRITE
OVERWRITES
OVERWRITING
OVERWRITTEN
OVERZEALOUS
OVID
OWE
OWED
OWEN
OWENS
OWES
OWING
OWL
OWLS
OWN
OWNED
OWNER
OWNERS
OWNERSHIP
OWNERSHIPS
OWNING
OWNS
OXEN
OXFORD
OXIDE
OXIDES
OXIDIZE
OXIDIZED
OXNARD
OXONIAN
OXYGEN
OYSTER
OYSTERS
OZARK
OZARKS
OZONE
OZZIE
PABLO
PABST
PACE
PACED
PACEMAKER
PACER
PACERS
PACES
PACIFIC
PACIFICATION
PACIFIED
PACIFIER
PACIFIES
PACIFISM
PACIFIST
PACIFY
PACING
PACK
PACKAGE
PACKAGED
PACKAGER
PACKAGERS
PACKAGES
PACKAGING
PACKAGINGS
PACKARD
PACKARDS
PACKED
PACKER
PACKERS
PACKET
PACKETS
PACKING
PACKS
PACKWOOD
PACT
PACTS
PAD
PADDED
PADDING
PADDLE
PADDOCK
PADDY
PADLOCK
PADS
PAGAN
PAGANINI
PAGANS
PAGE
PAGEANT
PAGEANTRY
PAGEANTS
PAGED
PAGER
PAGERS
PAGES
PAGINATE
PAGINATED
PAGINATES
PAGINATING
PAGINATION
PAGING
PAGODA
PAID
PAIL
PAILS
PAIN
PAINE
PAINED
PAINFUL
PAINFULLY
PAINLESS
PAINS
PAINSTAKING
PAINSTAKINGLY
PAINT
PAINTED
PAINTER
PAINTERS
PAINTING
PAINTINGS
PAINTS
PAIR
PAIRED
PAIRING
PAIRINGS
PAIRS
PAIRWISE
PAJAMA
PAJAMAS
PAKISTAN
PAKISTANI
PAKISTANIS
PAL
PALACE
PALACES
PALATE
PALATES
PALATINE
PALE
PALED
PALELY
PALENESS
PALEOLITHIC
PALEOZOIC
PALER
PALERMO
PALES
PALEST
PALESTINE
PALESTINIAN
PALFREY
PALINDROME
PALINDROMIC
PALING
PALL
PALLADIAN
PALLADIUM
PALLIATE
PALLIATIVE
PALLID
PALM
PALMED
PALMER
PALMING
PALMOLIVE
PALMS
PALMYRA
PALO
PALOMAR
PALPABLE
PALS
PALSY
PAM
PAMELA
PAMPER
PAMPHLET
PAMPHLETS
PAN
PANACEA
PANACEAS
PANAMA
PANAMANIAN
PANCAKE
PANCAKES
PANCHO
PANDA
PANDANUS
PANDAS
PANDEMIC
PANDEMONIUM
PANDER
PANDORA
PANE
PANEL
PANELED
PANELING
PANELIST
PANELISTS
PANELS
PANES
PANG
PANGAEA
PANGS
PANIC
PANICKED
PANICKING
PANICKY
PANICS
PANNED
PANNING
PANORAMA
PANORAMIC
PANS
PANSIES
PANSY
PANT
PANTED
PANTHEISM
PANTHEIST
PANTHEON
PANTHER
PANTHERS
PANTIES
PANTING
PANTOMIME
PANTRIES
PANTRY
PANTS
PANTY
PANTYHOSE
PAOLI
PAPA
PAPAL
PAPER
PAPERBACK
PAPERBACKS
PAPERED
PAPERER
PAPERERS
PAPERING
PAPERINGS
PAPERS
PAPERWEIGHT
PAPERWORK
PAPOOSE
PAPPAS
PAPUA
PAPYRUS
PAR
PARABOLA
PARABOLIC
PARABOLOID
PARABOLOIDAL
PARACHUTE
PARACHUTED
PARACHUTES
PARADE
PARADED
PARADES
PARADIGM
PARADIGMS
PARADING
PARADISE
PARADOX
PARADOXES
PARADOXICAL
PARADOXICALLY
PARAFFIN
PARAGON
PARAGONS
PARAGRAPH
PARAGRAPHING
PARAGRAPHS
PARAGUAY
PARAGUAYAN
PARAGUAYANS
PARAKEET
PARALLAX
PARALLEL
PARALLELED
PARALLELING
PARALLELISM
PARALLELIZE
PARALLELIZED
PARALLELIZES
PARALLELIZING
PARALLELOGRAM
PARALLELOGRAMS
PARALLELS
PARALYSIS
PARALYZE
PARALYZED
PARALYZES
PARALYZING
PARAMETER
PARAMETERIZABLE
PARAMETERIZATION
PARAMETERIZATIONS
PARAMETERIZE
PARAMETERIZED
PARAMETERIZES
PARAMETERIZING
PARAMETERLESS
PARAMETERS
PARAMETRIC
PARAMETRIZED
PARAMILITARY
PARAMOUNT
PARAMUS
PARANOIA
PARANOIAC
PARANOID
PARANORMAL
PARAPET
PARAPETS
PARAPHERNALIA
PARAPHRASE
PARAPHRASED
PARAPHRASES
PARAPHRASING
PARAPSYCHOLOGY
PARASITE
PARASITES
PARASITIC
PARASITICS
PARASOL
PARBOIL
PARC
PARCEL
PARCELED
PARCELING
PARCELS
PARCH
PARCHED
PARCHMENT
PARDON
PARDONABLE
PARDONABLY
PARDONED
PARDONER
PARDONERS
PARDONING
PARDONS
PARE
PAREGORIC
PARENT
PARENTAGE
PARENTAL
PARENTHESES
PARENTHESIS
PARENTHESIZED
PARENTHESIZES
PARENTHESIZING
PARENTHETIC
PARENTHETICAL
PARENTHETICALLY
PARENTHOOD
PARENTS
PARES
PARETO
PARIAH
PARIMUTUEL
PARING
PARINGS
PARIS
PARISH
PARISHES
PARISHIONER
PARISIAN
PARISIANIZATION
PARISIANIZATIONS
PARISIANIZE
PARISIANIZES
PARITY
PARK
PARKE
PARKED
PARKER
PARKERS
PARKERSBURG
PARKHOUSE
PARKING
PARKINSON
PARKINSONIAN
PARKLAND
PARKLIKE
PARKS
PARKWAY
PARLAY
PARLEY
PARLIAMENT
PARLIAMENTARIAN
PARLIAMENTARY
PARLIAMENTS
PARLOR
PARLORS
PARMESAN
PAROCHIAL
PARODY
PAROLE
PAROLED
PAROLES
PAROLING
PARR
PARRIED
PARRISH
PARROT
PARROTING
PARROTS
PARRS
PARRY
PARS
PARSE
PARSED
PARSER
PARSERS
PARSES
PARSI
PARSIFAL
PARSIMONY
PARSING
PARSINGS
PARSLEY
PARSON
PARSONS
PART
PARTAKE
PARTAKER
PARTAKES
PARTAKING
PARTED
PARTER
PARTERS
PARTHENON
PARTHIA
PARTIAL
PARTIALITY
PARTIALLY
PARTICIPANT
PARTICIPANTS
PARTICIPATE
PARTICIPATED
PARTICIPATES
PARTICIPATING
PARTICIPATION
PARTICIPLE
PARTICLE
PARTICLES
PARTICULAR
PARTICULARLY
PARTICULARS
PARTICULATE
PARTIES
PARTING
PARTINGS
PARTISAN
PARTISANS
PARTITION
PARTITIONED
PARTITIONING
PARTITIONS
PARTLY
PARTNER
PARTNERED
PARTNERS
PARTNERSHIP
PARTOOK
PARTRIDGE
PARTRIDGES
PARTS
PARTY
PASADENA
PASCAL
PASCAL
PASO
PASS
PASSAGE
PASSAGES
PASSAGEWAY
PASSAIC
PASSE
PASSED
PASSENGER
PASSENGERS
PASSER
PASSERS
PASSES
PASSING
PASSION
PASSIONATE
PASSIONATELY
PASSIONS
PASSIVATE
PASSIVE
PASSIVELY
PASSIVENESS
PASSIVITY
PASSOVER
PASSPORT
PASSPORTS
PASSWORD
PASSWORDS
PAST
PASTE
PASTED
PASTEL
PASTERNAK
PASTES
PASTEUR
PASTIME
PASTIMES
PASTING
PASTNESS
PASTOR
PASTORAL
PASTORS
PASTRY
PASTS
PASTURE
PASTURES
PAT
PATAGONIA
PATAGONIANS
PATCH
PATCHED
PATCHES
PATCHING
PATCHWORK
PATCHY
PATE
PATEN
PATENT
PATENTABLE
PATENTED
PATENTER
PATENTERS
PATENTING
PATENTLY
PATENTS
PATERNAL
PATERNALLY
PATERNOSTER
PATERSON
PATH
PATHETIC
PATHNAME
PATHNAMES
PATHOGEN
PATHOGENESIS
PATHOLOGICAL
PATHOLOGY
PATHOS
PATHS
PATHWAY
PATHWAYS
PATIENCE
PATIENT
PATIENTLY
PATIENTS
PATINA
PATIO
PATRIARCH
PATRIARCHAL
PATRIARCHS
PATRIARCHY
PATRICE
PATRICIA
PATRICIAN
PATRICIANS
PATRICK
PATRIMONIAL
PATRIMONY
PATRIOT
PATRIOTIC
PATRIOTISM
PATRIOTS
PATROL
PATROLLED
PATROLLING
PATROLMAN
PATROLMEN
PATROLS
PATRON
PATRONAGE
PATRONIZE
PATRONIZED
PATRONIZES
PATRONIZING
PATRONS
PATS
PATSIES
PATSY
PATTER
PATTERED
PATTERING
PATTERINGS
PATTERN
PATTERNED
PATTERNING
PATTERNS
PATTERS
PATTERSON
PATTI
PATTIES
PATTON
PATTY
PAUCITY
PAUL
PAULA
PAULETTE
PAULI
PAULINE
PAULING
PAULINIZE
PAULINIZES
PAULO
PAULSEN
PAULSON
PAULUS
PAUNCH
PAUNCHY
PAUPER
PAUSE
PAUSED
PAUSES
PAUSING
PAVE
PAVED
PAVEMENT
PAVEMENTS
PAVES
PAVILION
PAVILIONS
PAVING
PAVLOV
PAVLOVIAN
PAW
PAWING
PAWN
PAWNS
PAWNSHOP
PAWS
PAWTUCKET
PAY
PAYABLE
PAYCHECK
PAYCHECKS
PAYED
PAYER
PAYERS
PAYING
PAYMENT
PAYMENTS
PAYNE
PAYNES
PAYNIZE
PAYNIZES
PAYOFF
PAYOFFS
PAYROLL
PAYS
PAYSON
PAZ
PEA
PEABODY
PEACE
PEACEABLE
PEACEFUL
PEACEFULLY
PEACEFULNESS
PEACETIME
PEACH
PEACHES
PEACHTREE
PEACOCK
PEACOCKS
PEAK
PEAKED
PEAKS
PEAL
PEALE
PEALED
PEALING
PEALS
PEANUT
PEANUTS
PEAR
PEARCE
PEARL
PEARLS
PEARLY
PEARS
PEARSON
PEAS
PEASANT
PEASANTRY
PEASANTS
PEASE
PEAT
PEBBLE
PEBBLES
PECCARY
PECK
PECKED
PECKING
PECKS
PECOS
PECTORAL
PECULIAR
PECULIARITIES
PECULIARITY
PECULIARLY
PECUNIARY
PEDAGOGIC
PEDAGOGICAL
PEDAGOGICALLY
PEDAGOGY
PEDAL
PEDANT
PEDANTIC
PEDANTRY
PEDDLE
PEDDLER
PEDDLERS
PEDESTAL
PEDESTRIAN
PEDESTRIANS
PEDIATRIC
PEDIATRICIAN
PEDIATRICS
PEDIGREE
PEDRO
PEEK
PEEKED
PEEKING
PEEKS
PEEL
PEELED
PEELING
PEELS
PEEP
PEEPED
PEEPER
PEEPHOLE
PEEPING
PEEPS
PEER
PEERED
PEERING
PEERLESS
PEERS
PEG
PEGASUS
PEGBOARD
PEGGY
PEGS
PEIPING
PEJORATIVE
PEKING
PELHAM
PELICAN
PELLAGRA
PELOPONNESE
PELT
PELTING
PELTS
PELVIC
PELVIS
PEMBROKE
PEN
PENAL
PENALIZE
PENALIZED
PENALIZES
PENALIZING
PENALTIES
PENALTY
PENANCE
PENCE
PENCHANT
PENCIL
PENCILED
PENCILS
PEND
PENDANT
PENDED
PENDING
PENDLETON
PENDS
PENDULUM
PENDULUMS
PENELOPE
PENETRABLE
PENETRATE
PENETRATED
PENETRATES
PENETRATING
PENETRATINGLY
PENETRATION
PENETRATIONS
PENETRATIVE
PENETRATOR
PENETRATORS
PENGUIN
PENGUINS
PENH
PENICILLIN
PENINSULA
PENINSULAS
PENIS
PENISES
PENITENT
PENITENTIARY
PENN
PENNED
PENNIES
PENNILESS
PENNING
PENNSYLVANIA
PENNY
PENROSE
PENS
PENSACOLA
PENSION
PENSIONER
PENSIONS
PENSIVE
PENT
PENTAGON
PENTAGONS
PENTATEUCH
PENTECOST
PENTECOSTAL
PENTHOUSE
PENULTIMATE
PENUMBRA
PEONY
PEOPLE
PEOPLED
PEOPLES
PEORIA
PEP
PEPPER
PEPPERED
PEPPERING
PEPPERMINT
PEPPERONI
PEPPERS
PEPPERY
PEPPY
PEPSI
PEPSICO
PEPSICO
PEPTIDE
PER
PERCEIVABLE
PERCEIVABLY
PERCEIVE
PERCEIVED
PERCEIVER
PERCEIVERS
PERCEIVES
PERCEIVING
PERCENT
PERCENTAGE
PERCENTAGES
PERCENTILE
PERCENTILES
PERCENTS
PERCEPTIBLE
PERCEPTIBLY
PERCEPTION
PERCEPTIONS
PERCEPTIVE
PERCEPTIVELY
PERCEPTUAL
PERCEPTUALLY
PERCH
PERCHANCE
PERCHED
PERCHES
PERCHING
PERCIVAL
PERCUSSION
PERCUTANEOUS
PERCY
PEREMPTORY
PERENNIAL
PERENNIALLY
PEREZ
PERFECT
PERFECTED
PERFECTIBLE
PERFECTING
PERFECTION
PERFECTIONIST
PERFECTIONISTS
PERFECTLY
PERFECTNESS
PERFECTS
PERFORCE
PERFORM
PERFORMANCE
PERFORMANCES
PERFORMED
PERFORMER
PERFORMERS
PERFORMING
PERFORMS
PERFUME
PERFUMED
PERFUMES
PERFUMING
PERFUNCTORY
PERGAMON
PERHAPS
PERICLEAN
PERICLES
PERIHELION
PERIL
PERILLA
PERILOUS
PERILOUSLY
PERILS
PERIMETER
PERIOD
PERIODIC
PERIODICAL
PERIODICALLY
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PERIODS
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PERPETRATES
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PERPETRATIONS
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PERPETRATORS
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PERSIA
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PERSIANIZATION
PERSIANIZATIONS
PERSIANIZE
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PERSIST
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PERSISTENTLY
PERSISTING
PERSISTS
PERSON
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PERSONS
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PERSUADER
PERSUADERS
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PERSUASIVELY
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PERTAIN
PERTAINED
PERTAINING
PERTAINS
PERTH
PERTINENT
PERTURB
PERTURBATION
PERTURBATIONS
PERTURBED
PERU
PERUSAL
PERUSE
PERUSED
PERUSER
PERUSERS
PERUSES
PERUSING
PERUVIAN
PERUVIANIZE
PERUVIANIZES
PERUVIANS
PERVADE
PERVADED
PERVADES
PERVADING
PERVASIVE
PERVASIVELY
PERVERSION
PERVERT
PERVERTED
PERVERTS
PESSIMISM
PESSIMIST
PESSIMISTIC
PEST
PESTER
PESTICIDE
PESTILENCE
PESTILENT
PESTS
PET
PETAL
PETALS
PETE
PETER
PETERS
PETERSBURG
PETERSEN
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PETITIONED
PETITIONER
PETITIONING
PETITIONS
PETKIEWICZ
PETRI
PETROLEUM
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PETTER
PETTERS
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PEUGEOT
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PHAEDRA
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PHEASANTS
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PHENOMENALLY
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PHENOMENOLOGICALLY
PHENOMENOLOGIES
PHENOMENOLOGY
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PHILISTINIZES
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PHILOSOPHICAL
PHILOSOPHICALLY
PHILOSOPHIES
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PHOSPHORUS
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PHOTOCOPIER
PHOTOCOPIERS
PHOTOCOPIES
PHOTOCOPY
PHOTOCOPYING
PHOTODIODE
PHOTODIODES
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PHOTOGRAPHER
PHOTOGRAPHERS
PHOTOGRAPHIC
PHOTOGRAPHING
PHOTOGRAPHS
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PHOTOS
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PHOTOTYPESETTERS
PHRASE
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PHRASES
PHRASING
PHRASINGS
PHYLA
PHYLLIS
PHYLUM
PHYSIC
PHYSICAL
PHYSICALLY
PHYSICALNESS
PHYSICALS
PHYSICIAN
PHYSICIANS
PHYSICIST
PHYSICISTS
PHYSICS
PHYSIOLOGICAL
PHYSIOLOGICALLY
PHYSIOLOGY
PHYSIOTHERAPIST
PHYSIOTHERAPY
PHYSIQUE
PHYTOPLANKTON
PIANIST
PIANO
PIANOS
PICA
PICAS
PICASSO
PICAYUNE
PICCADILLY
PICCOLO
PICK
PICKAXE
PICKED
PICKER
PICKERING
PICKERS
PICKET
PICKETED
PICKETER
PICKETERS
PICKETING
PICKETS
PICKETT
PICKFORD
PICKING
PICKINGS
PICKLE
PICKLED
PICKLES
PICKLING
PICKMAN
PICKS
PICKUP
PICKUPS
PICKY
PICNIC
PICNICKED
PICNICKING
PICNICS
PICOFARAD
PICOJOULE
PICOSECOND
PICT
PICTORIAL
PICTORIALLY
PICTURE
PICTURED
PICTURES
PICTURESQUE
PICTURESQUENESS
PICTURING
PIDDLE
PIDGIN
PIE
PIECE
PIECED
PIECEMEAL
PIECES
PIECEWISE
PIECING
PIEDFORT
PIEDMONT
PIER
PIERCE
PIERCED
PIERCES
PIERCING
PIERRE
PIERS
PIERSON
PIES
PIETY
PIEZOELECTRIC
PIG
PIGEON
PIGEONHOLE
PIGEONS
PIGGISH
PIGGY
PIGGYBACK
PIGGYBACKED
PIGGYBACKING
PIGGYBACKS
PIGMENT
PIGMENTATION
PIGMENTED
PIGMENTS
PIGPEN
PIGS
PIGSKIN
PIGTAIL
PIKE
PIKER
PIKES
PILATE
PILE
PILED
PILERS
PILES
PILFER
PILFERAGE
PILGRIM
PILGRIMAGE
PILGRIMAGES
PILGRIMS
PILING
PILINGS
PILL
PILLAGE
PILLAGED
PILLAR
PILLARED
PILLARS
PILLORY
PILLOW
PILLOWS
PILLS
PILLSBURY
PILOT
PILOTING
PILOTS
PIMP
PIMPLE
PIN
PINAFORE
PINBALL
PINCH
PINCHED
PINCHES
PINCHING
PINCUSHION
PINE
PINEAPPLE
PINEAPPLES
PINED
PINEHURST
PINES
PING
PINHEAD
PINHOLE
PINING
PINION
PINK
PINKER
PINKEST
PINKIE
PINKISH
PINKLY
PINKNESS
PINKS
PINNACLE
PINNACLES
PINNED
PINNING
PINNINGS
PINOCHLE
PINPOINT
PINPOINTING
PINPOINTS
PINS
PINSCHER
PINSKY
PINT
PINTO
PINTS
PINWHEEL
PION
PIONEER
PIONEERED
PIONEERING
PIONEERS
PIOTR
PIOUS
PIOUSLY
PIP
PIPE
PIPED
PIPELINE
PIPELINED
PIPELINES
PIPELINING
PIPER
PIPERS
PIPES
PIPESTONE
PIPETTE
PIPING
PIQUE
PIRACY
PIRAEUS
PIRATE
PIRATES
PISA
PISCATAWAY
PISCES
PISS
PISTACHIO
PISTIL
PISTILS
PISTOL
PISTOLS
PISTON
PISTONS
PIT
PITCH
PITCHED
PITCHER
PITCHERS
PITCHES
PITCHFORK
PITCHING
PITEOUS
PITEOUSLY
PITFALL
PITFALLS
PITH
PITHED
PITHES
PITHIER
PITHIEST
PITHINESS
PITHING
PITHY
PITIABLE
PITIED
PITIER
PITIERS
PITIES
PITIFUL
PITIFULLY
PITILESS
PITILESSLY
PITNEY
PITS
PITT
PITTED
PITTSBURGH
PITTSBURGHERS
PITTSFIELD
PITTSTON
PITUITARY
PITY
PITYING
PITYINGLY
PIUS
PIVOT
PIVOTAL
PIVOTING
PIVOTS
PIXEL
PIXELS
PIZARRO
PIZZA
PLACARD
PLACARDS
PLACATE
PLACE
PLACEBO
PLACED
PLACEHOLDER
PLACEMENT
PLACEMENTS
PLACENTA
PLACENTAL
PLACER
PLACES
PLACID
PLACIDLY
PLACING
PLAGIARISM
PLAGIARIST
PLAGUE
PLAGUED
PLAGUES
PLAGUING
PLAID
PLAIDS
PLAIN
PLAINER
PLAINEST
PLAINFIELD
PLAINLY
PLAINNESS
PLAINS
PLAINTEXT
PLAINTEXTS
PLAINTIFF
PLAINTIFFS
PLAINTIVE
PLAINTIVELY
PLAINTIVENESS
PLAINVIEW
PLAIT
PLAITS
PLAN
PLANAR
PLANARITY
PLANCK
PLANE
PLANED
PLANELOAD
PLANER
PLANERS
PLANES
PLANET
PLANETARIA
PLANETARIUM
PLANETARY
PLANETESIMAL
PLANETOID
PLANETS
PLANING
PLANK
PLANKING
PLANKS
PLANKTON
PLANNED
PLANNER
PLANNERS
PLANNING
PLANOCONCAVE
PLANOCONVEX
PLANS
PLANT
PLANTATION
PLANTATIONS
PLANTED
PLANTER
PLANTERS
PLANTING
PLANTINGS
PLANTS
PLAQUE
PLASMA
PLASTER
PLASTERED
PLASTERER
PLASTERING
PLASTERS
PLASTIC
PLASTICITY
PLASTICS
PLATE
PLATEAU
PLATEAUS
PLATED
PLATELET
PLATELETS
PLATEN
PLATENS
PLATES
PLATFORM
PLATFORMS
PLATING
PLATINUM
PLATITUDE
PLATO
PLATONIC
PLATONISM
PLATONIST
PLATOON
PLATTE
PLATTER
PLATTERS
PLATTEVILLE
PLAUSIBILITY
PLAUSIBLE
PLAY
PLAYABLE
PLAYBACK
PLAYBOY
PLAYED
PLAYER
PLAYERS
PLAYFUL
PLAYFULLY
PLAYFULNESS
PLAYGROUND
PLAYGROUNDS
PLAYHOUSE
PLAYING
PLAYMATE
PLAYMATES
PLAYOFF
PLAYROOM
PLAYS
PLAYTHING
PLAYTHINGS
PLAYTIME
PLAYWRIGHT
PLAYWRIGHTS
PLAYWRITING
PLAZA
PLEA
PLEAD
PLEADED
PLEADER
PLEADING
PLEADS
PLEAS
PLEASANT
PLEASANTLY
PLEASANTNESS
PLEASE
PLEASED
PLEASES
PLEASING
PLEASINGLY
PLEASURE
PLEASURES
PLEAT
PLEBEIAN
PLEBIAN
PLEBISCITE
PLEBISCITES
PLEDGE
PLEDGED
PLEDGES
PLEIADES
PLEISTOCENE
PLENARY
PLENIPOTENTIARY
PLENTEOUS
PLENTIFUL
PLENTIFULLY
PLENTY
PLETHORA
PLEURISY
PLEXIGLAS
PLIABLE
PLIANT
PLIED
PLIERS
PLIES
PLIGHT
PLINY
PLIOCENE
PLOD
PLODDING
PLOT
PLOTS
PLOTTED
PLOTTER
PLOTTERS
PLOTTING
PLOW
PLOWED
PLOWER
PLOWING
PLOWMAN
PLOWS
PLOWSHARE
PLOY
PLOYS
PLUCK
PLUCKED
PLUCKING
PLUCKS
PLUCKY
PLUG
PLUGGABLE
PLUGGED
PLUGGING
PLUGS
PLUM
PLUMAGE
PLUMB
PLUMBED
PLUMBING
PLUMBS
PLUME
PLUMED
PLUMES
PLUMMET
PLUMMETING
PLUMP
PLUMPED
PLUMPNESS
PLUMS
PLUNDER
PLUNDERED
PLUNDERER
PLUNDERERS
PLUNDERING
PLUNDERS
PLUNGE
PLUNGED
PLUNGER
PLUNGERS
PLUNGES
PLUNGING
PLUNK
PLURAL
PLURALITY
PLURALS
PLUS
PLUSES
PLUSH
PLUTARCH
PLUTO
PLUTONIUM
PLY
PLYMOUTH
PLYWOOD
PNEUMATIC
PNEUMONIA
POACH
POACHER
POACHES
POCAHONTAS
POCKET
POCKETBOOK
POCKETBOOKS
POCKETED
POCKETFUL
POCKETING
POCKETS
POCONO
POCONOS
POD
PODIA
PODIUM
PODS
PODUNK
POE
POEM
POEMS
POET
POETIC
POETICAL
POETICALLY
POETICS
POETRIES
POETRY
POETS
POGO
POGROM
POIGNANCY
POIGNANT
POINCARE
POINDEXTER
POINT
POINTED
POINTEDLY
POINTER
POINTERS
POINTING
POINTLESS
POINTS
POINTY
POISE
POISED
POISES
POISON
POISONED
POISONER
POISONING
POISONOUS
POISONOUSNESS
POISONS
POISSON
POKE
POKED
POKER
POKERFACE
POKES
POKING
POLAND
POLAR
POLARIS
POLARITIES
POLARITY
POLAROID
POLE
POLECAT
POLED
POLEMIC
POLEMICS
POLES
POLICE
POLICED
POLICEMAN
POLICEMEN
POLICES
POLICIES
POLICING
POLICY
POLING
POLIO
POLISH
POLISHED
POLISHER
POLISHERS
POLISHES
POLISHING
POLITBURO
POLITE
POLITELY
POLITENESS
POLITER
POLITEST
POLITIC
POLITICAL
POLITICALLY
POLITICIAN
POLITICIANS
POLITICKING
POLITICS
POLK
POLKA
POLL
POLLARD
POLLED
POLLEN
POLLING
POLLOI
POLLS
POLLUTANT
POLLUTE
POLLUTED
POLLUTES
POLLUTING
POLLUTION
POLLUX
POLO
POLYALPHABETIC
POLYGON
POLYGONS
POLYHYMNIA
POLYMER
POLYMERS
POLYMORPHIC
POLYNESIA
POLYNESIAN
POLYNOMIAL
POLYNOMIALS
POLYPHEMUS
POLYTECHNIC
POLYTHEIST
POMERANIA
POMERANIAN
POMONA
POMP
POMPADOUR
POMPEII
POMPEY
POMPOSITY
POMPOUS
POMPOUSLY
POMPOUSNESS
PONCE
PONCHARTRAIN
PONCHO
POND
PONDER
PONDERED
PONDERING
PONDEROUS
PONDERS
PONDS
PONG
PONIES
PONTIAC
PONTIFF
PONTIFIC
PONTIFICATE
PONY
POOCH
POODLE
POOL
POOLE
POOLED
POOLING
POOLS
POOR
POORER
POOREST
POORLY
POORNESS
POP
POPCORN
POPE
POPEK
POPEKS
POPISH
POPLAR
POPLIN
POPPED
POPPIES
POPPING
POPPY
POPS
POPSICLE
POPSICLES
POPULACE
POPULAR
POPULARITY
POPULARIZATION
POPULARIZE
POPULARIZED
POPULARIZES
POPULARIZING
POPULARLY
POPULATE
POPULATED
POPULATES
POPULATING
POPULATION
POPULATIONS
POPULOUS
POPULOUSNESS
PORCELAIN
PORCH
PORCHES
PORCINE
PORCUPINE
PORCUPINES
PORE
PORED
PORES
PORING
PORK
PORKER
PORNOGRAPHER
PORNOGRAPHIC
PORNOGRAPHY
POROUS
PORPOISE
PORRIDGE
PORT
PORTABILITY
PORTABLE
PORTAGE
PORTAL
PORTALS
PORTE
PORTED
PORTEND
PORTENDED
PORTENDING
PORTENDS
PORTENT
PORTENTOUS
PORTER
PORTERHOUSE
PORTERS
PORTFOLIO
PORTFOLIOS
PORTIA
PORTICO
PORTING
PORTION
PORTIONS
PORTLAND
PORTLY
PORTMANTEAU
PORTO
PORTRAIT
PORTRAITS
PORTRAY
PORTRAYAL
PORTRAYED
PORTRAYING
PORTRAYS
PORTS
PORTSMOUTH
PORTUGAL
PORTUGUESE
POSE
POSED
POSEIDON
POSER
POSERS
POSES
POSH
POSING
POSIT
POSITED
POSITING
POSITION
POSITIONAL
POSITIONED
POSITIONING
POSITIONS
POSITIVE
POSITIVELY
POSITIVENESS
POSITIVES
POSITRON
POSITS
POSNER
POSSE
POSSESS
POSSESSED
POSSESSES
POSSESSING
POSSESSION
POSSESSIONAL
POSSESSIONS
POSSESSIVE
POSSESSIVELY
POSSESSIVENESS
POSSESSOR
POSSESSORS
POSSIBILITIES
POSSIBILITY
POSSIBLE
POSSIBLY
POSSUM
POSSUMS
POST
POSTAGE
POSTAL
POSTCARD
POSTCONDITION
POSTDOCTORAL
POSTED
POSTER
POSTERIOR
POSTERIORI
POSTERITY
POSTERS
POSTFIX
POSTGRADUATE
POSTING
POSTLUDE
POSTMAN
POSTMARK
POSTMASTER
POSTMASTERS
POSTMORTEM
POSTOPERATIVE
POSTORDER
POSTPONE
POSTPONED
POSTPONING
POSTPROCESS
POSTPROCESSOR
POSTS
POSTSCRIPT
POSTSCRIPTS
POSTULATE
POSTULATED
POSTULATES
POSTULATING
POSTULATION
POSTULATIONS
POSTURE
POSTURES
POT
POTABLE
POTASH
POTASSIUM
POTATO
POTATOES
POTBELLY
POTEMKIN
POTENT
POTENTATE
POTENTATES
POTENTIAL
POTENTIALITIES
POTENTIALITY
POTENTIALLY
POTENTIALS
POTENTIATING
POTENTIOMETER
POTENTIOMETERS
POTHOLE
POTION
POTLATCH
POTOMAC
POTPOURRI
POTS
POTSDAM
POTTAWATOMIE
POTTED
POTTER
POTTERS
POTTERY
POTTING
POTTS
POUCH
POUCHES
POUGHKEEPSIE
POULTICE
POULTRY
POUNCE
POUNCED
POUNCES
POUNCING
POUND
POUNDED
POUNDER
POUNDERS
POUNDING
POUNDS
POUR
POURED
POURER
POURERS
POURING
POURS
POUSSIN
POUSSINS
POUT
POUTED
POUTING
POUTS
POVERTY
POWDER
POWDERED
POWDERING
POWDERPUFF
POWDERS
POWDERY
POWELL
POWER
POWERED
POWERFUL
POWERFULLY
POWERFULNESS
POWERING
POWERLESS
POWERLESSLY
POWERLESSNESS
POWERS
POX
POYNTING
PRACTICABLE
PRACTICABLY
PRACTICAL
PRACTICALITY
PRACTICALLY
PRACTICE
PRACTICED
PRACTICES
PRACTICING
PRACTITIONER
PRACTITIONERS
PRADESH
PRADO
PRAGMATIC
PRAGMATICALLY
PRAGMATICS
PRAGMATISM
PRAGMATIST
PRAGUE
PRAIRIE
PRAISE
PRAISED
PRAISER
PRAISERS
PRAISES
PRAISEWORTHY
PRAISING
PRAISINGLY
PRANCE
PRANCED
PRANCER
PRANCING
PRANK
PRANKS
PRATE
PRATT
PRATTVILLE
PRAVDA
PRAY
PRAYED
PRAYER
PRAYERS
PRAYING
PREACH
PREACHED
PREACHER
PREACHERS
PREACHES
PREACHING
PREALLOCATE
PREALLOCATED
PREALLOCATING
PREAMBLE
PREAMBLES
PREASSIGN
PREASSIGNED
PREASSIGNING
PREASSIGNS
PRECAMBRIAN
PRECARIOUS
PRECARIOUSLY
PRECARIOUSNESS
PRECAUTION
PRECAUTIONS
PRECEDE
PRECEDED
PRECEDENCE
PRECEDENCES
PRECEDENT
PRECEDENTED
PRECEDENTS
PRECEDES
PRECEDING
PRECEPT
PRECEPTS
PRECESS
PRECESSION
PRECINCT
PRECINCTS
PRECIOUS
PRECIOUSLY
PRECIOUSNESS
PRECIPICE
PRECIPITABLE
PRECIPITATE
PRECIPITATED
PRECIPITATELY
PRECIPITATENESS
PRECIPITATES
PRECIPITATING
PRECIPITATION
PRECIPITOUS
PRECIPITOUSLY
PRECISE
PRECISELY
PRECISENESS
PRECISION
PRECISIONS
PRECLUDE
PRECLUDED
PRECLUDES
PRECLUDING
PRECOCIOUS
PRECOCIOUSLY
PRECOCITY
PRECOMPUTE
PRECOMPUTED
PRECOMPUTING
PRECONCEIVE
PRECONCEIVED
PRECONCEPTION
PRECONCEPTIONS
PRECONDITION
PRECONDITIONED
PRECONDITIONS
PRECURSOR
PRECURSORS
PREDATE
PREDATED
PREDATES
PREDATING
PREDATORY
PREDECESSOR
PREDECESSORS
PREDEFINE
PREDEFINED
PREDEFINES
PREDEFINING
PREDEFINITION
PREDEFINITIONS
PREDETERMINATION
PREDETERMINE
PREDETERMINED
PREDETERMINES
PREDETERMINING
PREDICAMENT
PREDICATE
PREDICATED
PREDICATES
PREDICATING
PREDICATION
PREDICATIONS
PREDICT
PREDICTABILITY
PREDICTABLE
PREDICTABLY
PREDICTED
PREDICTING
PREDICTION
PREDICTIONS
PREDICTIVE
PREDICTOR
PREDICTS
PREDILECTION
PREDILECTIONS
PREDISPOSITION
PREDOMINANT
PREDOMINANTLY
PREDOMINATE
PREDOMINATED
PREDOMINATELY
PREDOMINATES
PREDOMINATING
PREDOMINATION
PREEMINENCE
PREEMINENT
PREEMPT
PREEMPTED
PREEMPTING
PREEMPTION
PREEMPTIVE
PREEMPTOR
PREEMPTS
PREEN
PREEXISTING
PREFAB
PREFABRICATE
PREFACE
PREFACED
PREFACES
PREFACING
PREFER
PREFERABLE
PREFERABLY
PREFERENCE
PREFERENCES
PREFERENTIAL
PREFERENTIALLY
PREFERRED
PREFERRING
PREFERS
PREFIX
PREFIXED
PREFIXES
PREFIXING
PREGNANCY
PREGNANT
PREHISTORIC
PREINITIALIZE
PREINITIALIZED
PREINITIALIZES
PREINITIALIZING
PREJUDGE
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PREJUDICE
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PREJUDICES
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PRELATE
PRELIMINARIES
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PREOCCUPATION
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PREP
PREPARATION
PREPARATIONS
PREPARATIVE
PREPARATIVES
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PREPEND
PREPENDED
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PREPROCESSORS
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PREREQUISITE
PREREQUISITES
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PRESBYTERIANISM
PRESBYTERIANIZE
PRESBYTERIANIZES
PRESCOTT
PRESCRIBE
PRESCRIBED
PRESCRIBES
PRESCRIPTION
PRESCRIPTIONS
PRESCRIPTIVE
PRESELECT
PRESELECTED
PRESELECTING
PRESELECTS
PRESENCE
PRESENCES
PRESENT
PRESENTATION
PRESENTATIONS
PRESENTED
PRESENTER
PRESENTING
PRESENTLY
PRESENTNESS
PRESENTS
PRESERVATION
PRESERVATIONS
PRESERVE
PRESERVED
PRESERVER
PRESERVERS
PRESERVES
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PRESET
PRESIDE
PRESIDED
PRESIDENCY
PRESIDENT
PRESIDENTIAL
PRESIDENTS
PRESIDES
PRESIDING
PRESLEY
PRESS
PRESSED
PRESSER
PRESSES
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PRESSINGS
PRESSURE
PRESSURED
PRESSURES
PRESSURING
PRESSURIZE
PRESSURIZED
PRESTIDIGITATE
PRESTIGE
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PRESTON
PRESUMABLY
PRESUME
PRESUMED
PRESUMES
PRESUMING
PRESUMPTION
PRESUMPTIONS
PRESUMPTIVE
PRESUMPTUOUS
PRESUMPTUOUSNESS
PRESUPPOSE
PRESUPPOSED
PRESUPPOSES
PRESUPPOSING
PRESUPPOSITION
PRETEND
PRETENDED
PRETENDER
PRETENDERS
PRETENDING
PRETENDS
PRETENSE
PRETENSES
PRETENSION
PRETENSIONS
PRETENTIOUS
PRETENTIOUSLY
PRETENTIOUSNESS
PRETEXT
PRETEXTS
PRETORIA
PRETORIAN
PRETTIER
PRETTIEST
PRETTILY
PRETTINESS
PRETTY
PREVAIL
PREVAILED
PREVAILING
PREVAILINGLY
PREVAILS
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PREVENTABLE
PREVENTABLY
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PREVENTION
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PREVENTIVES
PREVENTS
PREVIEW
PREVIEWED
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PREVIEWS
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PREVIOUSLY
PREY
PREYED
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PREYS
PRIAM
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PRICK
PRICKED
PRICKING
PRICKLY
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PRIDES
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PRIESTLEY
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PRIM
PRIMA
PRIMACY
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PRIMARIES
PRIMARILY
PRIMARY
PRIMATE
PRIME
PRIMED
PRIMENESS
PRIMER
PRIMERS
PRIMES
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PRIMING
PRIMITIVE
PRIMITIVELY
PRIMITIVENESS
PRIMITIVES
PRIMROSE
PRINCE
PRINCELY
PRINCES
PRINCESS
PRINCESSES
PRINCETON
PRINCIPAL
PRINCIPALITIES
PRINCIPALITY
PRINCIPALLY
PRINCIPALS
PRINCIPIA
PRINCIPLE
PRINCIPLED
PRINCIPLES
PRINT
PRINTABLE
PRINTABLY
PRINTED
PRINTER
PRINTERS
PRINTING
PRINTOUT
PRINTS
PRIOR
PRIORI
PRIORITIES
PRIORITY
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PRISCILLA
PRISM
PRISMS
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PRISONERS
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PRIVACY
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PRIVATELY
PRIVATES
PRIVATION
PRIVATIONS
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PRIZERS
PRIZES
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PRIZING
PRO
PROBABILISTIC
PROBABILISTICALLY
PROBABILITIES
PROBABILITY
PROBABLE
PROBABLY
PROBATE
PROBATED
PROBATES
PROBATING
PROBATION
PROBATIVE
PROBE
PROBED
PROBES
PROBING
PROBINGS
PROBITY
PROBLEM
PROBLEMATIC
PROBLEMATICAL
PROBLEMATICALLY
PROBLEMS
PROCAINE
PROCEDURAL
PROCEDURALLY
PROCEDURE
PROCEDURES
PROCEED
PROCEEDED
PROCEEDING
PROCEEDINGS
PROCEEDS
PROCESS
PROCESSED
PROCESSES
PROCESSING
PROCESSION
PROCESSOR
PROCESSORS
PROCLAIM
PROCLAIMED
PROCLAIMER
PROCLAIMERS
PROCLAIMING
PROCLAIMS
PROCLAMATION
PROCLAMATIONS
PROCLIVITIES
PROCLIVITY
PROCOTOLS
PROCRASTINATE
PROCRASTINATED
PROCRASTINATES
PROCRASTINATING
PROCRASTINATION
PROCREATE
PROCRUSTEAN
PROCRUSTEANIZE
PROCRUSTEANIZES
PROCRUSTES
PROCTER
PROCURE
PROCURED
PROCUREMENT
PROCUREMENTS
PROCURER
PROCURERS
PROCURES
PROCURING
PROCYON
PROD
PRODIGAL
PRODIGALLY
PRODIGIOUS
PRODIGY
PRODUCE
PRODUCED
PRODUCER
PRODUCERS
PRODUCES
PRODUCIBLE
PRODUCING
PRODUCT
PRODUCTION
PRODUCTIONS
PRODUCTIVE
PRODUCTIVELY
PRODUCTIVITY
PRODUCTS
PROFANE
PROFANELY
PROFESS
PROFESSED
PROFESSES
PROFESSING
PROFESSION
PROFESSIONAL
PROFESSIONALISM
PROFESSIONALLY
PROFESSIONALS
PROFESSIONS
PROFESSOR
PROFESSORIAL
PROFESSORS
PROFFER
PROFFERED
PROFFERS
PROFICIENCY
PROFICIENT
PROFICIENTLY
PROFILE
PROFILED
PROFILES
PROFILING
PROFIT
PROFITABILITY
PROFITABLE
PROFITABLY
PROFITED
PROFITEER
PROFITEERS
PROFITING
PROFITS
PROFITTED
PROFLIGATE
PROFOUND
PROFOUNDEST
PROFOUNDLY
PROFUNDITY
PROFUSE
PROFUSION
PROGENITOR
PROGENY
PROGNOSIS
PROGNOSTICATE
PROGRAM
PROGRAMMABILITY
PROGRAMMABLE
PROGRAMMED
PROGRAMMER
PROGRAMMERS
PROGRAMMING
PROGRAMS
PROGRESS
PROGRESSED
PROGRESSES
PROGRESSING
PROGRESSION
PROGRESSIONS
PROGRESSIVE
PROGRESSIVELY
PROHIBIT
PROHIBITED
PROHIBITING
PROHIBITION
PROHIBITIONS
PROHIBITIVE
PROHIBITIVELY
PROHIBITORY
PROHIBITS
PROJECT
PROJECTED
PROJECTILE
PROJECTING
PROJECTION
PROJECTIONS
PROJECTIVE
PROJECTIVELY
PROJECTOR
PROJECTORS
PROJECTS
PROKOFIEFF
PROKOFIEV
PROLATE
PROLEGOMENA
PROLETARIAT
PROLIFERATE
PROLIFERATED
PROLIFERATES
PROLIFERATING
PROLIFERATION
PROLIFIC
PROLIX
PROLOG
PROLOGUE
PROLONG
PROLONGATE
PROLONGED
PROLONGING
PROLONGS
PROMENADE
PROMENADES
PROMETHEAN
PROMETHEUS
PROMINENCE
PROMINENT
PROMINENTLY
PROMISCUOUS
PROMISE
PROMISED
PROMISES
PROMISING
PROMONTORY
PROMOTE
PROMOTED
PROMOTER
PROMOTERS
PROMOTES
PROMOTING
PROMOTION
PROMOTIONAL
PROMOTIONS
PROMPT
PROMPTED
PROMPTER
PROMPTEST
PROMPTING
PROMPTINGS
PROMPTLY
PROMPTNESS
PROMPTS
PROMULGATE
PROMULGATED
PROMULGATES
PROMULGATING
PROMULGATION
PRONE
PRONENESS
PRONG
PRONGED
PRONGS
PRONOUN
PRONOUNCE
PRONOUNCEABLE
PRONOUNCED
PRONOUNCEMENT
PRONOUNCEMENTS
PRONOUNCES
PRONOUNCING
PRONOUNS
PRONUNCIATION
PRONUNCIATIONS
PROOF
PROOFREAD
PROOFREADER
PROOFS
PROP
PROPAGANDA
PROPAGANDIST
PROPAGATE
PROPAGATED
PROPAGATES
PROPAGATING
PROPAGATION
PROPAGATIONS
PROPANE
PROPEL
PROPELLANT
PROPELLED
PROPELLER
PROPELLERS
PROPELLING
PROPELS
PROPENSITY
PROPER
PROPERLY
PROPERNESS
PROPERTIED
PROPERTIES
PROPERTY
PROPHECIES
PROPHECY
PROPHESIED
PROPHESIER
PROPHESIES
PROPHESY
PROPHET
PROPHETIC
PROPHETS
PROPITIOUS
PROPONENT
PROPONENTS
PROPORTION
PROPORTIONAL
PROPORTIONALLY
PROPORTIONATELY
PROPORTIONED
PROPORTIONING
PROPORTIONMENT
PROPORTIONS
PROPOS
PROPOSAL
PROPOSALS
PROPOSE
PROPOSED
PROPOSER
PROPOSES
PROPOSING
PROPOSITION
PROPOSITIONAL
PROPOSITIONALLY
PROPOSITIONED
PROPOSITIONING
PROPOSITIONS
PROPOUND
PROPOUNDED
PROPOUNDING
PROPOUNDS
PROPRIETARY
PROPRIETOR
PROPRIETORS
PROPRIETY
PROPS
PROPULSION
PROPULSIONS
PRORATE
PRORATED
PRORATES
PROS
PROSCENIUM
PROSCRIBE
PROSCRIPTION
PROSE
PROSECUTE
PROSECUTED
PROSECUTES
PROSECUTING
PROSECUTION
PROSECUTIONS
PROSECUTOR
PROSELYTIZE
PROSELYTIZED
PROSELYTIZES
PROSELYTIZING
PROSERPINE
PROSODIC
PROSODICS
PROSPECT
PROSPECTED
PROSPECTING
PROSPECTION
PROSPECTIONS
PROSPECTIVE
PROSPECTIVELY
PROSPECTIVES
PROSPECTOR
PROSPECTORS
PROSPECTS
PROSPECTUS
PROSPER
PROSPERED
PROSPERING
PROSPERITY
PROSPEROUS
PROSPERS
PROSTATE
PROSTHETIC
PROSTITUTE
PROSTITUTION
PROSTRATE
PROSTRATION
PROTAGONIST
PROTEAN
PROTECT
PROTECTED
PROTECTING
PROTECTION
PROTECTIONS
PROTECTIVE
PROTECTIVELY
PROTECTIVENESS
PROTECTOR
PROTECTORATE
PROTECTORS
PROTECTS
PROTEGE
PROTEGES
PROTEIN
PROTEINS
PROTEST
PROTESTANT
PROTESTANTISM
PROTESTANTIZE
PROTESTANTIZES
PROTESTATION
PROTESTATIONS
PROTESTED
PROTESTING
PROTESTINGLY
PROTESTOR
PROTESTS
PROTISTA
PROTOCOL
PROTOCOLS
PROTON
PROTONS
PROTOPHYTA
PROTOPLASM
PROTOTYPE
PROTOTYPED
PROTOTYPES
PROTOTYPICAL
PROTOTYPICALLY
PROTOTYPING
PROTOZOA
PROTOZOAN
PROTRACT
PROTRUDE
PROTRUDED
PROTRUDES
PROTRUDING
PROTRUSION
PROTRUSIONS
PROTUBERANT
PROUD
PROUDER
PROUDEST
PROUDLY
PROUST
PROVABILITY
PROVABLE
PROVABLY
PROVE
PROVED
PROVEN
PROVENANCE
PROVENCE
PROVER
PROVERB
PROVERBIAL
PROVERBS
PROVERS
PROVES
PROVIDE
PROVIDED
PROVIDENCE
PROVIDENT
PROVIDER
PROVIDERS
PROVIDES
PROVIDING
PROVINCE
PROVINCES
PROVINCIAL
PROVING
PROVISION
PROVISIONAL
PROVISIONALLY
PROVISIONED
PROVISIONING
PROVISIONS
PROVISO
PROVOCATION
PROVOKE
PROVOKED
PROVOKES
PROVOST
PROW
PROWESS
PROWL
PROWLED
PROWLER
PROWLERS
PROWLING
PROWS
PROXIMAL
PROXIMATE
PROXIMITY
PROXMIRE
PROXY
PRUDENCE
PRUDENT
PRUDENTIAL
PRUDENTLY
PRUNE
PRUNED
PRUNER
PRUNERS
PRUNES
PRUNING
PRURIENT
PRUSSIA
PRUSSIAN
PRUSSIANIZATION
PRUSSIANIZATIONS
PRUSSIANIZE
PRUSSIANIZER
PRUSSIANIZERS
PRUSSIANIZES
PRY
PRYING
PSALM
PSALMS
PSEUDO
PSEUDOFILES
PSEUDOINSTRUCTION
PSEUDOINSTRUCTIONS
PSEUDONYM
PSEUDOPARALLELISM
PSILOCYBIN
PSYCH
PSYCHE
PSYCHEDELIC
PSYCHES
PSYCHIATRIC
PSYCHIATRIST
PSYCHIATRISTS
PSYCHIATRY
PSYCHIC
PSYCHO
PSYCHOANALYSIS
PSYCHOANALYST
PSYCHOANALYTIC
PSYCHOBIOLOGY
PSYCHOLOGICAL
PSYCHOLOGICALLY
PSYCHOLOGIST
PSYCHOLOGISTS
PSYCHOLOGY
PSYCHOPATH
PSYCHOPATHIC
PSYCHOPHYSIC
PSYCHOSES
PSYCHOSIS
PSYCHOSOCIAL
PSYCHOSOMATIC
PSYCHOTHERAPEUTIC
PSYCHOTHERAPIST
PSYCHOTHERAPY
PSYCHOTIC
PTOLEMAIC
PTOLEMAISTS
PTOLEMY
PUB
PUBERTY
PUBLIC
PUBLICATION
PUBLICATIONS
PUBLICITY
PUBLICIZE
PUBLICIZED
PUBLICIZES
PUBLICIZING
PUBLICLY
PUBLISH
PUBLISHED
PUBLISHER
PUBLISHERS
PUBLISHES
PUBLISHING
PUBS
PUCCINI
PUCKER
PUCKERED
PUCKERING
PUCKERS
PUDDING
PUDDINGS
PUDDLE
PUDDLES
PUDDLING
PUERTO
PUFF
PUFFED
PUFFIN
PUFFING
PUFFS
PUGH
PUKE
PULASKI
PULITZER
PULL
PULLED
PULLER
PULLEY
PULLEYS
PULLING
PULLINGS
PULLMAN
PULLMANIZE
PULLMANIZES
PULLMANS
PULLOVER
PULLS
PULMONARY
PULP
PULPING
PULPIT
PULPITS
PULSAR
PULSATE
PULSATION
PULSATIONS
PULSE
PULSED
PULSES
PULSING
PUMA
PUMICE
PUMMEL
PUMP
PUMPED
PUMPING
PUMPKIN
PUMPKINS
PUMPS
PUN
PUNCH
PUNCHED
PUNCHER
PUNCHES
PUNCHING
PUNCTUAL
PUNCTUALLY
PUNCTUATION
PUNCTURE
PUNCTURED
PUNCTURES
PUNCTURING
PUNDIT
PUNGENT
PUNIC
PUNISH
PUNISHABLE
PUNISHED
PUNISHES
PUNISHING
PUNISHMENT
PUNISHMENTS
PUNITIVE
PUNJAB
PUNJABI
PUNS
PUNT
PUNTED
PUNTING
PUNTS
PUNY
PUP
PUPA
PUPIL
PUPILS
PUPPET
PUPPETEER
PUPPETS
PUPPIES
PUPPY
PUPS
PURCELL
PURCHASE
PURCHASED
PURCHASER
PURCHASERS
PURCHASES
PURCHASING
PURDUE
PURE
PURELY
PURER
PUREST
PURGATORY
PURGE
PURGED
PURGES
PURGING
PURIFICATION
PURIFICATIONS
PURIFIED
PURIFIER
PURIFIERS
PURIFIES
PURIFY
PURIFYING
PURINA
PURIST
PURITAN
PURITANIC
PURITANIZE
PURITANIZER
PURITANIZERS
PURITANIZES
PURITY
PURPLE
PURPLER
PURPLEST
PURPORT
PURPORTED
PURPORTEDLY
PURPORTER
PURPORTERS
PURPORTING
PURPORTS
PURPOSE
PURPOSED
PURPOSEFUL
PURPOSEFULLY
PURPOSELY
PURPOSES
PURPOSIVE
PURR
PURRED
PURRING
PURRS
PURSE
PURSED
PURSER
PURSES
PURSUANT
PURSUE
PURSUED
PURSUER
PURSUERS
PURSUES
PURSUING
PURSUIT
PURSUITS
PURVEYOR
PURVIEW
PUS
PUSAN
PUSEY
PUSH
PUSHBUTTON
PUSHDOWN
PUSHED
PUSHER
PUSHERS
PUSHES
PUSHING
PUSS
PUSSY
PUSSYCAT
PUT
PUTNAM
PUTS
PUTT
PUTTER
PUTTERING
PUTTERS
PUTTING
PUTTY
PUZZLE
PUZZLED
PUZZLEMENT
PUZZLER
PUZZLERS
PUZZLES
PUZZLING
PUZZLINGS
PYGMALION
PYGMIES
PYGMY
PYLE
PYONGYANG
PYOTR
PYRAMID
PYRAMIDS
PYRE
PYREX
PYRRHIC
PYTHAGORAS
PYTHAGOREAN
PYTHAGOREANIZE
PYTHAGOREANIZES
PYTHAGOREANS
PYTHON
QATAR
QUA
QUACK
QUACKED
QUACKERY
QUACKS
QUAD
QUADRANGLE
QUADRANGULAR
QUADRANT
QUADRANTS
QUADRATIC
QUADRATICAL
QUADRATICALLY
QUADRATICS
QUADRATURE
QUADRATURES
QUADRENNIAL
QUADRILATERAL
QUADRILLION
QUADRUPLE
QUADRUPLED
QUADRUPLES
QUADRUPLING
QUADRUPOLE
QUAFF
QUAGMIRE
QUAGMIRES
QUAHOG
QUAIL
QUAILS
QUAINT
QUAINTLY
QUAINTNESS
QUAKE
QUAKED
QUAKER
QUAKERESS
QUAKERIZATION
QUAKERIZATIONS
QUAKERIZE
QUAKERIZES
QUAKERS
QUAKES
QUAKING
QUALIFICATION
QUALIFICATIONS
QUALIFIED
QUALIFIER
QUALIFIERS
QUALIFIES
QUALIFY
QUALIFYING
QUALITATIVE
QUALITATIVELY
QUALITIES
QUALITY
QUALM
QUANDARIES
QUANDARY
QUANTA
QUANTICO
QUANTIFIABLE
QUANTIFICATION
QUANTIFICATIONS
QUANTIFIED
QUANTIFIER
QUANTIFIERS
QUANTIFIES
QUANTIFY
QUANTIFYING
QUANTILE
QUANTITATIVE
QUANTITATIVELY
QUANTITIES
QUANTITY
QUANTIZATION
QUANTIZE
QUANTIZED
QUANTIZES
QUANTIZING
QUANTUM
QUARANTINE
QUARANTINES
QUARANTINING
QUARK
QUARREL
QUARRELED
QUARRELING
QUARRELS
QUARRELSOME
QUARRIES
QUARRY
QUART
QUARTER
QUARTERBACK
QUARTERED
QUARTERING
QUARTERLY
QUARTERMASTER
QUARTERS
QUARTET
QUARTETS
QUARTILE
QUARTS
QUARTZ
QUARTZITE
QUASAR
QUASH
QUASHED
QUASHES
QUASHING
QUASI
QUASIMODO
QUATERNARY
QUAVER
QUAVERED
QUAVERING
QUAVERS
QUAY
QUEASY
QUEBEC
QUEEN
QUEENLY
QUEENS
QUEENSLAND
QUEER
QUEERER
QUEEREST
QUEERLY
QUEERNESS
QUELL
QUELLING
QUENCH
QUENCHED
QUENCHES
QUENCHING
QUERIED
QUERIES
QUERY
QUERYING
QUEST
QUESTED
QUESTER
QUESTERS
QUESTING
QUESTION
QUESTIONABLE
QUESTIONABLY
QUESTIONED
QUESTIONER
QUESTIONERS
QUESTIONING
QUESTIONINGLY
QUESTIONINGS
QUESTIONNAIRE
QUESTIONNAIRES
QUESTIONS
QUESTS
QUEUE
QUEUED
QUEUEING
QUEUER
QUEUERS
QUEUES
QUEUING
QUEZON
QUIBBLE
QUICHUA
QUICK
QUICKEN
QUICKENED
QUICKENING
QUICKENS
QUICKER
QUICKEST
QUICKIE
QUICKLIME
QUICKLY
QUICKNESS
QUICKSAND
QUICKSILVER
QUIESCENT
QUIET
QUIETED
QUIETER
QUIETEST
QUIETING
QUIETLY
QUIETNESS
QUIETS
QUIETUDE
QUILL
QUILT
QUILTED
QUILTING
QUILTS
QUINCE
QUININE
QUINN
QUINT
QUINTET
QUINTILLION
QUIP
QUIRINAL
QUIRK
QUIRKY
QUIT
QUITE
QUITO
QUITS
QUITTER
QUITTERS
QUITTING
QUIVER
QUIVERED
QUIVERING
QUIVERS
QUIXOTE
QUIXOTIC
QUIXOTISM
QUIZ
QUIZZED
QUIZZES
QUIZZICAL
QUIZZING
QUO
QUONSET
QUORUM
QUOTA
QUOTAS
QUOTATION
QUOTATIONS
QUOTE
QUOTED
QUOTES
QUOTH
QUOTIENT
QUOTIENTS
QUOTING
RABAT
RABBI
RABBIT
RABBITS
RABBLE
RABID
RABIES
RABIN
RACCOON
RACCOONS
RACE
RACED
RACER
RACERS
RACES
RACETRACK
RACHEL
RACHMANINOFF
RACIAL
RACIALLY
RACINE
RACING
RACK
RACKED
RACKET
RACKETEER
RACKETEERING
RACKETEERS
RACKETS
RACKING
RACKS
RADAR
RADARS
RADCLIFFE
RADIAL
RADIALLY
RADIAN
RADIANCE
RADIANT
RADIANTLY
RADIATE
RADIATED
RADIATES
RADIATING
RADIATION
RADIATIONS
RADIATOR
RADIATORS
RADICAL
RADICALLY
RADICALS
RADICES
RADII
RADIO
RADIOACTIVE
RADIOASTRONOMY
RADIOED
RADIOGRAPHY
RADIOING
RADIOLOGY
RADIOS
RADISH
RADISHES
RADIUM
RADIUS
RADIX
RADON
RAE
RAFAEL
RAFFERTY
RAFT
RAFTER
RAFTERS
RAFTS
RAG
RAGE
RAGED
RAGES
RAGGED
RAGGEDLY
RAGGEDNESS
RAGING
RAGS
RAGUSAN
RAGWEED
RAID
RAIDED
RAIDER
RAIDERS
RAIDING
RAIDS
RAIL
RAILED
RAILER
RAILERS
RAILING
RAILROAD
RAILROADED
RAILROADER
RAILROADERS
RAILROADING
RAILROADS
RAILS
RAILWAY
RAILWAYS
RAIMENT
RAIN
RAINBOW
RAINCOAT
RAINCOATS
RAINDROP
RAINDROPS
RAINED
RAINFALL
RAINIER
RAINIEST
RAINING
RAINS
RAINSTORM
RAINY
RAISE
RAISED
RAISER
RAISERS
RAISES
RAISIN
RAISING
RAKE
RAKED
RAKES
RAKING
RALEIGH
RALLIED
RALLIES
RALLY
RALLYING
RALPH
RALSTON
RAM
RAMADA
RAMAN
RAMBLE
RAMBLER
RAMBLES
RAMBLING
RAMBLINGS
RAMIFICATION
RAMIFICATIONS
RAMIREZ
RAMO
RAMONA
RAMP
RAMPAGE
RAMPANT
RAMPART
RAMPS
RAMROD
RAMS
RAMSEY
RAN
RANCH
RANCHED
RANCHER
RANCHERS
RANCHES
RANCHING
RANCID
RAND
RANDALL
RANDOLPH
RANDOM
RANDOMIZATION
RANDOMIZE
RANDOMIZED
RANDOMIZES
RANDOMLY
RANDOMNESS
RANDY
RANG
RANGE
RANGED
RANGELAND
RANGER
RANGERS
RANGES
RANGING
RANGOON
RANGY
RANIER
RANK
RANKED
RANKER
RANKERS
RANKEST
RANKIN
RANKINE
RANKING
RANKINGS
RANKLE
RANKLY
RANKNESS
RANKS
RANSACK
RANSACKED
RANSACKING
RANSACKS
RANSOM
RANSOMER
RANSOMING
RANSOMS
RANT
RANTED
RANTER
RANTERS
RANTING
RANTS
RAOUL
RAP
RAPACIOUS
RAPE
RAPED
RAPER
RAPES
RAPHAEL
RAPID
RAPIDITY
RAPIDLY
RAPIDS
RAPIER
RAPING
RAPPORT
RAPPROCHEMENT
RAPS
RAPT
RAPTLY
RAPTURE
RAPTURES
RAPTUROUS
RAPUNZEL
RARE
RARELY
RARENESS
RARER
RAREST
RARITAN
RARITY
RASCAL
RASCALLY
RASCALS
RASH
RASHER
RASHLY
RASHNESS
RASMUSSEN
RASP
RASPBERRY
RASPED
RASPING
RASPS
RASTER
RASTUS
RAT
RATE
RATED
RATER
RATERS
RATES
RATFOR
RATHER
RATIFICATION
RATIFIED
RATIFIES
RATIFY
RATIFYING
RATING
RATINGS
RATIO
RATION
RATIONAL
RATIONALE
RATIONALES
RATIONALITIES
RATIONALITY
RATIONALIZATION
RATIONALIZATIONS
RATIONALIZE
RATIONALIZED
RATIONALIZES
RATIONALIZING
RATIONALLY
RATIONALS
RATIONING
RATIONS
RATIOS
RATS
RATTLE
RATTLED
RATTLER
RATTLERS
RATTLES
RATTLESNAKE
RATTLESNAKES
RATTLING
RAUCOUS
RAUL
RAVAGE
RAVAGED
RAVAGER
RAVAGERS
RAVAGES
RAVAGING
RAVE
RAVED
RAVEN
RAVENING
RAVENOUS
RAVENOUSLY
RAVENS
RAVES
RAVINE
RAVINES
RAVING
RAVINGS
RAW
RAWER
RAWEST
RAWLINGS
RAWLINS
RAWLINSON
RAWLY
RAWNESS
RAWSON
RAY
RAYBURN
RAYLEIGH
RAYMOND
RAYMONDVILLE
RAYS
RAYTHEON
RAZE
RAZOR
RAZORS
REABBREVIATE
REABBREVIATED
REABBREVIATES
REABBREVIATING
REACH
REACHABILITY
REACHABLE
REACHABLY
REACHED
REACHER
REACHES
REACHING
REACQUIRED
REACT
REACTED
REACTING
REACTION
REACTIONARIES
REACTIONARY
REACTIONS
REACTIVATE
REACTIVATED
REACTIVATES
REACTIVATING
REACTIVATION
REACTIVE
REACTIVELY
REACTIVITY
REACTOR
REACTORS
REACTS
READ
READABILITY
READABLE
READER
READERS
READIED
READIER
READIES
READIEST
READILY
READINESS
READING
READINGS
READJUSTED
READOUT
READOUTS
READS
READY
READYING
REAGAN
REAL
REALEST
REALIGN
REALIGNED
REALIGNING
REALIGNS
REALISM
REALIST
REALISTIC
REALISTICALLY
REALISTS
REALITIES
REALITY
REALIZABLE
REALIZABLY
REALIZATION
REALIZATIONS
REALIZE
REALIZED
REALIZES
REALIZING
REALLOCATE
REALLY
REALM
REALMS
REALNESS
REALS
REALTOR
REAM
REANALYZE
REANALYZES
REANALYZING
REAP
REAPED
REAPER
REAPING
REAPPEAR
REAPPEARED
REAPPEARING
REAPPEARS
REAPPRAISAL
REAPPRAISALS
REAPS
REAR
REARED
REARING
REARRANGE
REARRANGEABLE
REARRANGED
REARRANGEMENT
REARRANGEMENTS
REARRANGES
REARRANGING
REARREST
REARRESTED
REARS
REASON
REASONABLE
REASONABLENESS
REASONABLY
REASONED
REASONER
REASONING
REASONINGS
REASONS
REASSEMBLE
REASSEMBLED
REASSEMBLES
REASSEMBLING
REASSEMBLY
REASSESSMENT
REASSESSMENTS
REASSIGN
REASSIGNED
REASSIGNING
REASSIGNMENT
REASSIGNMENTS
REASSIGNS
REASSURE
REASSURED
REASSURES
REASSURING
REAWAKEN
REAWAKENED
REAWAKENING
REAWAKENS
REBATE
REBATES
REBECCA
REBEL
REBELLED
REBELLING
REBELLION
REBELLIONS
REBELLIOUS
REBELLIOUSLY
REBELLIOUSNESS
REBELS
REBIND
REBINDING
REBINDS
REBOOT
REBOOTED
REBOOTING
REBOOTS
REBOUND
REBOUNDED
REBOUNDING
REBOUNDS
REBROADCAST
REBROADCASTING
REBROADCASTS
REBUFF
REBUFFED
REBUILD
REBUILDING
REBUILDS
REBUILT
REBUKE
REBUKED
REBUKES
REBUKING
REBUTTAL
REBUTTED
REBUTTING
RECALCITRANT
RECALCULATE
RECALCULATED
RECALCULATES
RECALCULATING
RECALCULATION
RECALCULATIONS
RECALIBRATE
RECALIBRATED
RECALIBRATES
RECALIBRATING
RECALL
RECALLED
RECALLING
RECALLS
RECANT
RECAPITULATE
RECAPITULATED
RECAPITULATES
RECAPITULATION
RECAPTURE
RECAPTURED
RECAPTURES
RECAPTURING
RECAST
RECASTING
RECASTS
RECEDE
RECEDED
RECEDES
RECEDING
RECEIPT
RECEIPTS
RECEIVABLE
RECEIVE
RECEIVED
RECEIVER
RECEIVERS
RECEIVES
RECEIVING
RECENT
RECENTLY
RECENTNESS
RECEPTACLE
RECEPTACLES
RECEPTION
RECEPTIONIST
RECEPTIONS
RECEPTIVE
RECEPTIVELY
RECEPTIVENESS
RECEPTIVITY
RECEPTOR
RECESS
RECESSED
RECESSES
RECESSION
RECESSIVE
RECIFE
RECIPE
RECIPES
RECIPIENT
RECIPIENTS
RECIPROCAL
RECIPROCALLY
RECIPROCATE
RECIPROCATED
RECIPROCATES
RECIPROCATING
RECIPROCATION
RECIPROCITY
RECIRCULATE
RECIRCULATED
RECIRCULATES
RECIRCULATING
RECITAL
RECITALS
RECITATION
RECITATIONS
RECITE
RECITED
RECITER
RECITES
RECITING
RECKLESS
RECKLESSLY
RECKLESSNESS
RECKON
RECKONED
RECKONER
RECKONING
RECKONINGS
RECKONS
RECLAIM
RECLAIMABLE
RECLAIMED
RECLAIMER
RECLAIMERS
RECLAIMING
RECLAIMS
RECLAMATION
RECLAMATIONS
RECLASSIFICATION
RECLASSIFIED
RECLASSIFIES
RECLASSIFY
RECLASSIFYING
RECLINE
RECLINING
RECODE
RECODED
RECODES
RECODING
RECOGNITION
RECOGNITIONS
RECOGNIZABILITY
RECOGNIZABLE
RECOGNIZABLY
RECOGNIZE
RECOGNIZED
RECOGNIZER
RECOGNIZERS
RECOGNIZES
RECOGNIZING
RECOIL
RECOILED
RECOILING
RECOILS
RECOLLECT
RECOLLECTED
RECOLLECTING
RECOLLECTION
RECOLLECTIONS
RECOMBINATION
RECOMBINE
RECOMBINED
RECOMBINES
RECOMBINING
RECOMMEND
RECOMMENDATION
RECOMMENDATIONS
RECOMMENDED
RECOMMENDER
RECOMMENDING
RECOMMENDS
RECOMPENSE
RECOMPILE
RECOMPILED
RECOMPILES
RECOMPILING
RECOMPUTE
RECOMPUTED
RECOMPUTES
RECOMPUTING
RECONCILE
RECONCILED
RECONCILER
RECONCILES
RECONCILIATION
RECONCILING
RECONFIGURABLE
RECONFIGURATION
RECONFIGURATIONS
RECONFIGURE
RECONFIGURED
RECONFIGURER
RECONFIGURES
RECONFIGURING
RECONNECT
RECONNECTED
RECONNECTING
RECONNECTION
RECONNECTS
RECONSIDER
RECONSIDERATION
RECONSIDERED
RECONSIDERING
RECONSIDERS
RECONSTITUTED
RECONSTRUCT
RECONSTRUCTED
RECONSTRUCTING
RECONSTRUCTION
RECONSTRUCTS
RECONVERTED
RECONVERTS
RECORD
RECORDED
RECORDER
RECORDERS
RECORDING
RECORDINGS
RECORDS
RECOUNT
RECOUNTED
RECOUNTING
RECOUNTS
RECOURSE
RECOVER
RECOVERABLE
RECOVERED
RECOVERIES
RECOVERING
RECOVERS
RECOVERY
RECREATE
RECREATED
RECREATES
RECREATING
RECREATION
RECREATIONAL
RECREATIONS
RECREATIVE
RECRUIT
RECRUITED
RECRUITER
RECRUITING
RECRUITS
RECTA
RECTANGLE
RECTANGLES
RECTANGULAR
RECTIFY
RECTOR
RECTORS
RECTUM
RECTUMS
RECUPERATE
RECUR
RECURRENCE
RECURRENCES
RECURRENT
RECURRENTLY
RECURRING
RECURS
RECURSE
RECURSED
RECURSES
RECURSING
RECURSION
RECURSIONS
RECURSIVE
RECURSIVELY
RECYCLABLE
RECYCLE
RECYCLED
RECYCLES
RECYCLING
RED
REDBREAST
REDCOAT
REDDEN
REDDENED
REDDER
REDDEST
REDDISH
REDDISHNESS
REDECLARE
REDECLARED
REDECLARES
REDECLARING
REDEEM
REDEEMED
REDEEMER
REDEEMERS
REDEEMING
REDEEMS
REDEFINE
REDEFINED
REDEFINES
REDEFINING
REDEFINITION
REDEFINITIONS
REDEMPTION
REDESIGN
REDESIGNED
REDESIGNING
REDESIGNS
REDEVELOPMENT
REDFORD
REDHEAD
REDHOOK
REDIRECT
REDIRECTED
REDIRECTING
REDIRECTION
REDIRECTIONS
REDISPLAY
REDISPLAYED
REDISPLAYING
REDISPLAYS
REDISTRIBUTE
REDISTRIBUTED
REDISTRIBUTES
REDISTRIBUTING
REDLY
REDMOND
REDNECK
REDNESS
REDO
REDONE
REDOUBLE
REDOUBLED
REDRAW
REDRAWN
REDRESS
REDRESSED
REDRESSES
REDRESSING
REDS
REDSTONE
REDUCE
REDUCED
REDUCER
REDUCERS
REDUCES
REDUCIBILITY
REDUCIBLE
REDUCIBLY
REDUCING
REDUCTION
REDUCTIONS
REDUNDANCIES
REDUNDANCY
REDUNDANT
REDUNDANTLY
REDWOOD
REED
REEDS
REEDUCATION
REEDVILLE
REEF
REEFER
REEFS
REEL
REELECT
REELECTED
REELECTING
REELECTS
REELED
REELER
REELING
REELS
REEMPHASIZE
REEMPHASIZED
REEMPHASIZES
REEMPHASIZING
REENABLED
REENFORCEMENT
REENTER
REENTERED
REENTERING
REENTERS
REENTRANT
REESE
REESTABLISH
REESTABLISHED
REESTABLISHES
REESTABLISHING
REEVALUATE
REEVALUATED
REEVALUATES
REEVALUATING
REEVALUATION
REEVES
REEXAMINE
REEXAMINED
REEXAMINES
REEXAMINING
REEXECUTED
REFER
REFEREE
REFEREED
REFEREEING
REFEREES
REFERENCE
REFERENCED
REFERENCER
REFERENCES
REFERENCING
REFERENDA
REFERENDUM
REFERENDUMS
REFERENT
REFERENTIAL
REFERENTIALITY
REFERENTIALLY
REFERENTS
REFERRAL
REFERRALS
REFERRED
REFERRING
REFERS
REFILL
REFILLABLE
REFILLED
REFILLING
REFILLS
REFINE
REFINED
REFINEMENT
REFINEMENTS
REFINER
REFINERY
REFINES
REFINING
REFLECT
REFLECTED
REFLECTING
REFLECTION
REFLECTIONS
REFLECTIVE
REFLECTIVELY
REFLECTIVITY
REFLECTOR
REFLECTORS
REFLECTS
REFLEX
REFLEXES
REFLEXIVE
REFLEXIVELY
REFLEXIVENESS
REFLEXIVITY
REFORESTATION
REFORM
REFORMABLE
REFORMAT
REFORMATION
REFORMATORY
REFORMATS
REFORMATTED
REFORMATTING
REFORMED
REFORMER
REFORMERS
REFORMING
REFORMS
REFORMULATE
REFORMULATED
REFORMULATES
REFORMULATING
REFORMULATION
REFRACT
REFRACTED
REFRACTION
REFRACTORY
REFRAGMENT
REFRAIN
REFRAINED
REFRAINING
REFRAINS
REFRESH
REFRESHED
REFRESHER
REFRESHERS
REFRESHES
REFRESHING
REFRESHINGLY
REFRESHMENT
REFRESHMENTS
REFRIGERATE
REFRIGERATOR
REFRIGERATORS
REFUEL
REFUELED
REFUELING
REFUELS
REFUGE
REFUGEE
REFUGEES
REFUSAL
REFUSE
REFUSED
REFUSES
REFUSING
REFUTABLE
REFUTATION
REFUTE
REFUTED
REFUTER
REFUTES
REFUTING
REGAIN
REGAINED
REGAINING
REGAINS
REGAL
REGALED
REGALLY
REGARD
REGARDED
REGARDING
REGARDLESS
REGARDS
REGATTA
REGENERATE
REGENERATED
REGENERATES
REGENERATING
REGENERATION
REGENERATIVE
REGENERATOR
REGENERATORS
REGENT
REGENTS
REGIME
REGIMEN
REGIMENT
REGIMENTATION
REGIMENTED
REGIMENTS
REGIMES
REGINA
REGINALD
REGION
REGIONAL
REGIONALLY
REGIONS
REGIS
REGISTER
REGISTERED
REGISTERING
REGISTERS
REGISTRAR
REGISTRATION
REGISTRATIONS
REGISTRY
REGRESS
REGRESSED
REGRESSES
REGRESSING
REGRESSION
REGRESSIONS
REGRESSIVE
REGRET
REGRETFUL
REGRETFULLY
REGRETS
REGRETTABLE
REGRETTABLY
REGRETTED
REGRETTING
REGROUP
REGROUPED
REGROUPING
REGULAR
REGULARITIES
REGULARITY
REGULARLY
REGULARS
REGULATE
REGULATED
REGULATES
REGULATING
REGULATION
REGULATIONS
REGULATIVE
REGULATOR
REGULATORS
REGULATORY
REGULUS
REHABILITATE
REHEARSAL
REHEARSALS
REHEARSE
REHEARSED
REHEARSER
REHEARSES
REHEARSING
REICH
REICHENBERG
REICHSTAG
REID
REIGN
REIGNED
REIGNING
REIGNS
REILLY
REIMBURSABLE
REIMBURSE
REIMBURSED
REIMBURSEMENT
REIMBURSEMENTS
REIN
REINCARNATE
REINCARNATED
REINCARNATION
REINDEER
REINED
REINFORCE
REINFORCED
REINFORCEMENT
REINFORCEMENTS
REINFORCER
REINFORCES
REINFORCING
REINHARD
REINHARDT
REINHOLD
REINITIALIZE
REINITIALIZED
REINITIALIZING
REINS
REINSERT
REINSERTED
REINSERTING
REINSERTS
REINSTATE
REINSTATED
REINSTATEMENT
REINSTATES
REINSTATING
REINTERPRET
REINTERPRETED
REINTERPRETING
REINTERPRETS
REINTRODUCE
REINTRODUCED
REINTRODUCES
REINTRODUCING
REINVENT
REINVENTED
REINVENTING
REINVENTS
REITERATE
REITERATED
REITERATES
REITERATING
REITERATION
REJECT
REJECTED
REJECTING
REJECTION
REJECTIONS
REJECTOR
REJECTORS
REJECTS
REJOICE
REJOICED
REJOICER
REJOICES
REJOICING
REJOIN
REJOINDER
REJOINED
REJOINING
REJOINS
RELABEL
RELABELED
RELABELING
RELABELLED
RELABELLING
RELABELS
RELAPSE
RELATE
RELATED
RELATER
RELATES
RELATING
RELATION
RELATIONAL
RELATIONALLY
RELATIONS
RELATIONSHIP
RELATIONSHIPS
RELATIVE
RELATIVELY
RELATIVENESS
RELATIVES
RELATIVISM
RELATIVISTIC
RELATIVISTICALLY
RELATIVITY
RELAX
RELAXATION
RELAXATIONS
RELAXED
RELAXER
RELAXES
RELAXING
RELAY
RELAYED
RELAYING
RELAYS
RELEASE
RELEASED
RELEASES
RELEASING
RELEGATE
RELEGATED
RELEGATES
RELEGATING
RELENT
RELENTED
RELENTING
RELENTLESS
RELENTLESSLY
RELENTLESSNESS
RELENTS
RELEVANCE
RELEVANCES
RELEVANT
RELEVANTLY
RELIABILITY
RELIABLE
RELIABLY
RELIANCE
RELIANT
RELIC
RELICS
RELIED
RELIEF
RELIES
RELIEVE
RELIEVED
RELIEVER
RELIEVERS
RELIEVES
RELIEVING
RELIGION
RELIGIONS
RELIGIOUS
RELIGIOUSLY
RELIGIOUSNESS
RELINK
RELINQUISH
RELINQUISHED
RELINQUISHES
RELINQUISHING
RELISH
RELISHED
RELISHES
RELISHING
RELIVE
RELIVES
RELIVING
RELOAD
RELOADED
RELOADER
RELOADING
RELOADS
RELOCATABLE
RELOCATE
RELOCATED
RELOCATES
RELOCATING
RELOCATION
RELOCATIONS
RELUCTANCE
RELUCTANT
RELUCTANTLY
RELY
RELYING
REMAIN
REMAINDER
REMAINDERS
REMAINED
REMAINING
REMAINS
REMARK
REMARKABLE
REMARKABLENESS
REMARKABLY
REMARKED
REMARKING
REMARKS
REMBRANDT
REMEDIAL
REMEDIED
REMEDIES
REMEDY
REMEDYING
REMEMBER
REMEMBERED
REMEMBERING
REMEMBERS
REMEMBRANCE
REMEMBRANCES
REMIND
REMINDED
REMINDER
REMINDERS
REMINDING
REMINDS
REMINGTON
REMINISCENCE
REMINISCENCES
REMINISCENT
REMINISCENTLY
REMISS
REMISSION
REMIT
REMITTANCE
REMNANT
REMNANTS
REMODEL
REMODELED
REMODELING
REMODELS
REMONSTRATE
REMONSTRATED
REMONSTRATES
REMONSTRATING
REMONSTRATION
REMONSTRATIVE
REMORSE
REMORSEFUL
REMOTE
REMOTELY
REMOTENESS
REMOTEST
REMOVABLE
REMOVAL
REMOVALS
REMOVE
REMOVED
REMOVER
REMOVES
REMOVING
REMUNERATE
REMUNERATION
REMUS
REMY
RENA
RENAISSANCE
RENAL
RENAME
RENAMED
RENAMES
RENAMING
RENAULT
RENAULTS
REND
RENDER
RENDERED
RENDERING
RENDERINGS
RENDERS
RENDEZVOUS
RENDING
RENDITION
RENDITIONS
RENDS
RENE
RENEE
RENEGADE
RENEGOTIABLE
RENEW
RENEWABLE
RENEWAL
RENEWED
RENEWER
RENEWING
RENEWS
RENO
RENOIR
RENOUNCE
RENOUNCES
RENOUNCING
RENOVATE
RENOVATED
RENOVATION
RENOWN
RENOWNED
RENSSELAER
RENT
RENTAL
RENTALS
RENTED
RENTING
RENTS
RENUMBER
RENUMBERING
RENUMBERS
RENUNCIATE
RENUNCIATION
RENVILLE
REOCCUR
REOPEN
REOPENED
REOPENING
REOPENS
REORDER
REORDERED
REORDERING
REORDERS
REORGANIZATION
REORGANIZATIONS
REORGANIZE
REORGANIZED
REORGANIZES
REORGANIZING
REPACKAGE
REPAID
REPAIR
REPAIRED
REPAIRER
REPAIRING
REPAIRMAN
REPAIRMEN
REPAIRS
REPARATION
REPARATIONS
REPARTEE
REPARTITION
REPAST
REPASTS
REPAY
REPAYING
REPAYS
REPEAL
REPEALED
REPEALER
REPEALING
REPEALS
REPEAT
REPEATABLE
REPEATED
REPEATEDLY
REPEATER
REPEATERS
REPEATING
REPEATS
REPEL
REPELLED
REPELLENT
REPELS
REPENT
REPENTANCE
REPENTED
REPENTING
REPENTS
REPERCUSSION
REPERCUSSIONS
REPERTOIRE
REPERTORY
REPETITION
REPETITIONS
REPETITIOUS
REPETITIVE
REPETITIVELY
REPETITIVENESS
REPHRASE
REPHRASED
REPHRASES
REPHRASING
REPINE
REPLACE
REPLACEABLE
REPLACED
REPLACEMENT
REPLACEMENTS
REPLACER
REPLACES
REPLACING
REPLAY
REPLAYED
REPLAYING
REPLAYS
REPLENISH
REPLENISHED
REPLENISHES
REPLENISHING
REPLETE
REPLETENESS
REPLETION
REPLICA
REPLICAS
REPLICATE
REPLICATED
REPLICATES
REPLICATING
REPLICATION
REPLICATIONS
REPLIED
REPLIES
REPLY
REPLYING
REPORT
REPORTED
REPORTEDLY
REPORTER
REPORTERS
REPORTING
REPORTS
REPOSE
REPOSED
REPOSES
REPOSING
REPOSITION
REPOSITIONED
REPOSITIONING
REPOSITIONS
REPOSITORIES
REPOSITORY
REPREHENSIBLE
REPRESENT
REPRESENTABLE
REPRESENTABLY
REPRESENTATION
REPRESENTATIONAL
REPRESENTATIONALLY
REPRESENTATIONS
REPRESENTATIVE
REPRESENTATIVELY
REPRESENTATIVENESS
REPRESENTATIVES
REPRESENTED
REPRESENTING
REPRESENTS
REPRESS
REPRESSED
REPRESSES
REPRESSING
REPRESSION
REPRESSIONS
REPRESSIVE
REPRIEVE
REPRIEVED
REPRIEVES
REPRIEVING
REPRIMAND
REPRINT
REPRINTED
REPRINTING
REPRINTS
REPRISAL
REPRISALS
REPROACH
REPROACHED
REPROACHES
REPROACHING
REPROBATE
REPRODUCE
REPRODUCED
REPRODUCER
REPRODUCERS
REPRODUCES
REPRODUCIBILITIES
REPRODUCIBILITY
REPRODUCIBLE
REPRODUCIBLY
REPRODUCING
REPRODUCTION
REPRODUCTIONS
REPROGRAM
REPROGRAMMED
REPROGRAMMING
REPROGRAMS
REPROOF
REPROVE
REPROVER
REPTILE
REPTILES
REPTILIAN
REPUBLIC
REPUBLICAN
REPUBLICANS
REPUBLICS
REPUDIATE
REPUDIATED
REPUDIATES
REPUDIATING
REPUDIATION
REPUDIATIONS
REPUGNANT
REPULSE
REPULSED
REPULSES
REPULSING
REPULSION
REPULSIONS
REPULSIVE
REPUTABLE
REPUTABLY
REPUTATION
REPUTATIONS
REPUTE
REPUTED
REPUTEDLY
REPUTES
REQUEST
REQUESTED
REQUESTER
REQUESTERS
REQUESTING
REQUESTS
REQUIRE
REQUIRED
REQUIREMENT
REQUIREMENTS
REQUIRES
REQUIRING
REQUISITE
REQUISITES
REQUISITION
REQUISITIONED
REQUISITIONING
REQUISITIONS
REREAD
REREGISTER
REROUTE
REROUTED
REROUTES
REROUTING
RERUN
RERUNS
RESCHEDULE
RESCIND
RESCUE
RESCUED
RESCUER
RESCUERS
RESCUES
RESCUING
RESEARCH
RESEARCHED
RESEARCHER
RESEARCHERS
RESEARCHES
RESEARCHING
RESELECT
RESELECTED
RESELECTING
RESELECTS
RESELL
RESELLING
RESEMBLANCE
RESEMBLANCES
RESEMBLE
RESEMBLED
RESEMBLES
RESEMBLING
RESENT
RESENTED
RESENTFUL
RESENTFULLY
RESENTING
RESENTMENT
RESENTS
RESERPINE
RESERVATION
RESERVATIONS
RESERVE
RESERVED
RESERVER
RESERVES
RESERVING
RESERVOIR
RESERVOIRS
RESET
RESETS
RESETTING
RESETTINGS
RESIDE
RESIDED
RESIDENCE
RESIDENCES
RESIDENT
RESIDENTIAL
RESIDENTIALLY
RESIDENTS
RESIDES
RESIDING
RESIDUAL
RESIDUE
RESIDUES
RESIGN
RESIGNATION
RESIGNATIONS
RESIGNED
RESIGNING
RESIGNS
RESILIENT
RESIN
RESINS
RESIST
RESISTABLE
RESISTANCE
RESISTANCES
RESISTANT
RESISTANTLY
RESISTED
RESISTIBLE
RESISTING
RESISTIVE
RESISTIVITY
RESISTOR
RESISTORS
RESISTS
RESOLUTE
RESOLUTELY
RESOLUTENESS
RESOLUTION
RESOLUTIONS
RESOLVABLE
RESOLVE
RESOLVED
RESOLVER
RESOLVERS
RESOLVES
RESOLVING
RESONANCE
RESONANCES
RESONANT
RESONATE
RESORT
RESORTED
RESORTING
RESORTS
RESOUND
RESOUNDING
RESOUNDS
RESOURCE
RESOURCEFUL
RESOURCEFULLY
RESOURCEFULNESS
RESOURCES
RESPECT
RESPECTABILITY
RESPECTABLE
RESPECTABLY
RESPECTED
RESPECTER
RESPECTFUL
RESPECTFULLY
RESPECTFULNESS
RESPECTING
RESPECTIVE
RESPECTIVELY
RESPECTS
RESPIRATION
RESPIRATOR
RESPIRATORY
RESPITE
RESPLENDENT
RESPLENDENTLY
RESPOND
RESPONDED
RESPONDENT
RESPONDENTS
RESPONDER
RESPONDING
RESPONDS
RESPONSE
RESPONSES
RESPONSIBILITIES
RESPONSIBILITY
RESPONSIBLE
RESPONSIBLENESS
RESPONSIBLY
RESPONSIVE
RESPONSIVELY
RESPONSIVENESS
REST
RESTART
RESTARTED
RESTARTING
RESTARTS
RESTATE
RESTATED
RESTATEMENT
RESTATES
RESTATING
RESTAURANT
RESTAURANTS
RESTAURATEUR
RESTED
RESTFUL
RESTFULLY
RESTFULNESS
RESTING
RESTITUTION
RESTIVE
RESTLESS
RESTLESSLY
RESTLESSNESS
RESTORATION
RESTORATIONS
RESTORE
RESTORED
RESTORER
RESTORERS
RESTORES
RESTORING
RESTRAIN
RESTRAINED
RESTRAINER
RESTRAINERS
RESTRAINING
RESTRAINS
RESTRAINT
RESTRAINTS
RESTRICT
RESTRICTED
RESTRICTING
RESTRICTION
RESTRICTIONS
RESTRICTIVE
RESTRICTIVELY
RESTRICTS
RESTROOM
RESTRUCTURE
RESTRUCTURED
RESTRUCTURES
RESTRUCTURING
RESTS
RESULT
RESULTANT
RESULTANTLY
RESULTANTS
RESULTED
RESULTING
RESULTS
RESUMABLE
RESUME
RESUMED
RESUMES
RESUMING
RESUMPTION
RESUMPTIONS
RESURGENT
RESURRECT
RESURRECTED
RESURRECTING
RESURRECTION
RESURRECTIONS
RESURRECTOR
RESURRECTORS
RESURRECTS
RESUSCITATE
RESYNCHRONIZATION
RESYNCHRONIZE
RESYNCHRONIZED
RESYNCHRONIZING
RETAIL
RETAILER
RETAILERS
RETAILING
RETAIN
RETAINED
RETAINER
RETAINERS
RETAINING
RETAINMENT
RETAINS
RETALIATE
RETALIATION
RETALIATORY
RETARD
RETARDED
RETARDER
RETARDING
RETCH
RETENTION
RETENTIONS
RETENTIVE
RETENTIVELY
RETENTIVENESS
RETICLE
RETICLES
RETICULAR
RETICULATE
RETICULATED
RETICULATELY
RETICULATES
RETICULATING
RETICULATION
RETINA
RETINAL
RETINAS
RETINUE
RETIRE
RETIRED
RETIREE
RETIREMENT
RETIREMENTS
RETIRES
RETIRING
RETORT
RETORTED
RETORTS
RETRACE
RETRACED
RETRACES
RETRACING
RETRACT
RETRACTED
RETRACTING
RETRACTION
RETRACTIONS
RETRACTS
RETRAIN
RETRAINED
RETRAINING
RETRAINS
RETRANSLATE
RETRANSLATED
RETRANSMISSION
RETRANSMISSIONS
RETRANSMIT
RETRANSMITS
RETRANSMITTED
RETRANSMITTING
RETREAT
RETREATED
RETREATING
RETREATS
RETRIBUTION
RETRIED
RETRIER
RETRIERS
RETRIES
RETRIEVABLE
RETRIEVAL
RETRIEVALS
RETRIEVE
RETRIEVED
RETRIEVER
RETRIEVERS
RETRIEVES
RETRIEVING
RETROACTIVE
RETROACTIVELY
RETROFIT
RETROFITTING
RETROGRADE
RETROSPECT
RETROSPECTION
RETROSPECTIVE
RETRY
RETRYING
RETURN
RETURNABLE
RETURNED
RETURNER
RETURNING
RETURNS
RETYPE
RETYPED
RETYPES
RETYPING
REUB
REUBEN
REUNION
REUNIONS
REUNITE
REUNITED
REUNITING
REUSABLE
REUSE
REUSED
REUSES
REUSING
REUTERS
REUTHER
REVAMP
REVAMPED
REVAMPING
REVAMPS
REVEAL
REVEALED
REVEALING
REVEALS
REVEL
REVELATION
REVELATIONS
REVELED
REVELER
REVELING
REVELRY
REVELS
REVENGE
REVENGER
REVENUE
REVENUERS
REVENUES
REVERBERATE
REVERE
REVERED
REVERENCE
REVEREND
REVERENDS
REVERENT
REVERENTLY
REVERES
REVERIE
REVERIFIED
REVERIFIES
REVERIFY
REVERIFYING
REVERING
REVERSAL
REVERSALS
REVERSE
REVERSED
REVERSELY
REVERSER
REVERSES
REVERSIBLE
REVERSING
REVERSION
REVERT
REVERTED
REVERTING
REVERTS
REVIEW
REVIEWED
REVIEWER
REVIEWERS
REVIEWING
REVIEWS
REVILE
REVILED
REVILER
REVILING
REVISE
REVISED
REVISER
REVISES
REVISING
REVISION
REVISIONARY
REVISIONS
REVISIT
REVISITED
REVISITING
REVISITS
REVIVAL
REVIVALS
REVIVE
REVIVED
REVIVER
REVIVES
REVIVING
REVOCABLE
REVOCATION
REVOKE
REVOKED
REVOKER
REVOKES
REVOKING
REVOLT
REVOLTED
REVOLTER
REVOLTING
REVOLTINGLY
REVOLTS
REVOLUTION
REVOLUTIONARIES
REVOLUTIONARY
REVOLUTIONIZE
REVOLUTIONIZED
REVOLUTIONIZER
REVOLUTIONS
REVOLVE
REVOLVED
REVOLVER
REVOLVERS
REVOLVES
REVOLVING
REVULSION
REWARD
REWARDED
REWARDING
REWARDINGLY
REWARDS
REWIND
REWINDING
REWINDS
REWIRE
REWORK
REWORKED
REWORKING
REWORKS
REWOUND
REWRITE
REWRITES
REWRITING
REWRITTEN
REX
REYKJAVIK
REYNOLDS
RHAPSODY
RHEA
RHEIMS
RHEINHOLDT
RHENISH
RHESUS
RHETORIC
RHEUMATIC
RHEUMATISM
RHINE
RHINESTONE
RHINO
RHINOCEROS
RHO
RHODA
RHODE
RHODES
RHODESIA
RHODODENDRON
RHOMBIC
RHOMBUS
RHUBARB
RHYME
RHYMED
RHYMES
RHYMING
RHYTHM
RHYTHMIC
RHYTHMICALLY
RHYTHMS
RIB
RIBALD
RIBBED
RIBBING
RIBBON
RIBBONS
RIBOFLAVIN
RIBONUCLEIC
RIBS
RICA
RICAN
RICANISM
RICANS
RICE
RICH
RICHARD
RICHARDS
RICHARDSON
RICHER
RICHES
RICHEST
RICHEY
RICHFIELD
RICHLAND
RICHLY
RICHMOND
RICHNESS
RICHTER
RICK
RICKENBAUGH
RICKETS
RICKETTSIA
RICKETY
RICKSHAW
RICKSHAWS
RICO
RICOCHET
RID
RIDDANCE
RIDDEN
RIDDING
RIDDLE
RIDDLED
RIDDLES
RIDDLING
RIDE
RIDER
RIDERS
RIDES
RIDGE
RIDGEFIELD
RIDGEPOLE
RIDGES
RIDGWAY
RIDICULE
RIDICULED
RIDICULES
RIDICULING
RIDICULOUS
RIDICULOUSLY
RIDICULOUSNESS
RIDING
RIDS
RIEMANN
RIEMANNIAN
RIFLE
RIFLED
RIFLEMAN
RIFLER
RIFLES
RIFLING
RIFT
RIG
RIGA
RIGEL
RIGGING
RIGGS
RIGHT
RIGHTED
RIGHTEOUS
RIGHTEOUSLY
RIGHTEOUSNESS
RIGHTER
RIGHTFUL
RIGHTFULLY
RIGHTFULNESS
RIGHTING
RIGHTLY
RIGHTMOST
RIGHTNESS
RIGHTS
RIGHTWARD
RIGID
RIGIDITY
RIGIDLY
RIGOR
RIGOROUS
RIGOROUSLY
RIGORS
RIGS
RILEY
RILKE
RILL
RIM
RIME
RIMS
RIND
RINDS
RINEHART
RING
RINGED
RINGER
RINGERS
RINGING
RINGINGLY
RINGINGS
RINGS
RINGSIDE
RINK
RINSE
RINSED
RINSER
RINSES
RINSING
RIO
RIORDAN
RIOT
RIOTED
RIOTER
RIOTERS
RIOTING
RIOTOUS
RIOTS
RIP
RIPE
RIPELY
RIPEN
RIPENESS
RIPLEY
RIPOFF
RIPPED
RIPPING
RIPPLE
RIPPLED
RIPPLES
RIPPLING
RIPS
RISC
RISE
RISEN
RISER
RISERS
RISES
RISING
RISINGS
RISK
RISKED
RISKING
RISKS
RISKY
RITCHIE
RITE
RITES
RITTER
RITUAL
RITUALLY
RITUALS
RITZ
RIVAL
RIVALED
RIVALLED
RIVALLING
RIVALRIES
RIVALRY
RIVALS
RIVER
RIVERBANK
RIVERFRONT
RIVERS
RIVERSIDE
RIVERVIEW
RIVET
RIVETER
RIVETS
RIVIERA
RIVULET
RIVULETS
RIYADH
ROACH
ROAD
ROADBED
ROADBLOCK
ROADS
ROADSIDE
ROADSTER
ROADSTERS
ROADWAY
ROADWAYS
ROAM
ROAMED
ROAMING
ROAMS
ROAR
ROARED
ROARER
ROARING
ROARS
ROAST
ROASTED
ROASTER
ROASTING
ROASTS
ROB
ROBBED
ROBBER
ROBBERIES
ROBBERS
ROBBERY
ROBBIE
ROBBIN
ROBBING
ROBBINS
ROBE
ROBED
ROBERT
ROBERTA
ROBERTO
ROBERTS
ROBERTSON
ROBERTSONS
ROBES
ROBIN
ROBING
ROBINS
ROBINSON
ROBINSONVILLE
ROBOT
ROBOTIC
ROBOTICS
ROBOTS
ROBS
ROBUST
ROBUSTLY
ROBUSTNESS
ROCCO
ROCHESTER
ROCHFORD
ROCK
ROCKABYE
ROCKAWAY
ROCKAWAYS
ROCKED
ROCKEFELLER
ROCKER
ROCKERS
ROCKET
ROCKETED
ROCKETING
ROCKETS
ROCKFORD
ROCKIES
ROCKING
ROCKLAND
ROCKS
ROCKVILLE
ROCKWELL
ROCKY
ROD
RODE
RODENT
RODENTS
RODEO
RODGERS
RODNEY
RODRIGUEZ
RODS
ROE
ROENTGEN
ROGER
ROGERS
ROGUE
ROGUES
ROLAND
ROLE
ROLES
ROLL
ROLLBACK
ROLLED
ROLLER
ROLLERS
ROLLIE
ROLLING
ROLLINS
ROLLS
ROMAN
ROMANCE
ROMANCER
ROMANCERS
ROMANCES
ROMANCING
ROMANESQUE
ROMANIA
ROMANIZATIONS
ROMANIZER
ROMANIZERS
ROMANIZES
ROMANO
ROMANS
ROMANTIC
ROMANTICS
ROME
ROMELDALE
ROMEO
ROMP
ROMPED
ROMPER
ROMPING
ROMPS
ROMULUS
RON
RONALD
RONNIE
ROOF
ROOFED
ROOFER
ROOFING
ROOFS
ROOFTOP
ROOK
ROOKIE
ROOM
ROOMED
ROOMER
ROOMERS
ROOMFUL
ROOMING
ROOMMATE
ROOMS
ROOMY
ROONEY
ROOSEVELT
ROOSEVELTIAN
ROOST
ROOSTER
ROOSTERS
ROOT
ROOTED
ROOTER
ROOTING
ROOTS
ROPE
ROPED
ROPER
ROPERS
ROPES
ROPING
ROQUEMORE
RORSCHACH
ROSA
ROSABELLE
ROSALIE
ROSARY
ROSE
ROSEBUD
ROSEBUDS
ROSEBUSH
ROSELAND
ROSELLA
ROSEMARY
ROSEN
ROSENBERG
ROSENBLUM
ROSENTHAL
ROSENZWEIG
ROSES
ROSETTA
ROSETTE
ROSIE
ROSINESS
ROSS
ROSSI
ROSTER
ROSTRUM
ROSWELL
ROSY
ROT
ROTARIAN
ROTARIANS
ROTARY
ROTATE
ROTATED
ROTATES
ROTATING
ROTATION
ROTATIONAL
ROTATIONS
ROTATOR
ROTH
ROTHSCHILD
ROTOR
ROTS
ROTTEN
ROTTENNESS
ROTTERDAM
ROTTING
ROTUND
ROTUNDA
ROUGE
ROUGH
ROUGHED
ROUGHEN
ROUGHER
ROUGHEST
ROUGHLY
ROUGHNECK
ROUGHNESS
ROULETTE
ROUND
ROUNDABOUT
ROUNDED
ROUNDEDNESS
ROUNDER
ROUNDEST
ROUNDHEAD
ROUNDHOUSE
ROUNDING
ROUNDLY
ROUNDNESS
ROUNDOFF
ROUNDS
ROUNDTABLE
ROUNDUP
ROUNDWORM
ROURKE
ROUSE
ROUSED
ROUSES
ROUSING
ROUSSEAU
ROUSTABOUT
ROUT
ROUTE
ROUTED
ROUTER
ROUTERS
ROUTES
ROUTINE
ROUTINELY
ROUTINES
ROUTING
ROUTINGS
ROVE
ROVED
ROVER
ROVES
ROVING
ROW
ROWBOAT
ROWDY
ROWE
ROWED
ROWENA
ROWER
ROWING
ROWLAND
ROWLEY
ROWS
ROXBURY
ROXY
ROY
ROYAL
ROYALIST
ROYALISTS
ROYALLY
ROYALTIES
ROYALTY
ROYCE
ROZELLE
RUANDA
RUB
RUBAIYAT
RUBBED
RUBBER
RUBBERS
RUBBERY
RUBBING
RUBBISH
RUBBLE
RUBDOWN
RUBE
RUBEN
RUBENS
RUBIES
RUBIN
RUBLE
RUBLES
RUBOUT
RUBS
RUBY
RUDDER
RUDDERS
RUDDINESS
RUDDY
RUDE
RUDELY
RUDENESS
RUDIMENT
RUDIMENTARY
RUDIMENTS
RUDOLF
RUDOLPH
RUDY
RUDYARD
RUE
RUEFULLY
RUFFIAN
RUFFIANLY
RUFFIANS
RUFFLE
RUFFLED
RUFFLES
RUFUS
RUG
RUGGED
RUGGEDLY
RUGGEDNESS
RUGS
RUIN
RUINATION
RUINATIONS
RUINED
RUINING
RUINOUS
RUINOUSLY
RUINS
RULE
RULED
RULER
RULERS
RULES
RULING
RULINGS
RUM
RUMANIA
RUMANIAN
RUMANIANS
RUMBLE
RUMBLED
RUMBLER
RUMBLES
RUMBLING
RUMEN
RUMFORD
RUMMAGE
RUMMEL
RUMMY
RUMOR
RUMORED
RUMORS
RUMP
RUMPLE
RUMPLED
RUMPLY
RUMPUS
RUN
RUNAWAY
RUNDOWN
RUNG
RUNGE
RUNGS
RUNNABLE
RUNNER
RUNNERS
RUNNING
RUNNYMEDE
RUNOFF
RUNS
RUNT
RUNTIME
RUNYON
RUPEE
RUPPERT
RUPTURE
RUPTURED
RUPTURES
RUPTURING
RURAL
RURALLY
RUSH
RUSHED
RUSHER
RUSHES
RUSHING
RUSHMORE
RUSS
RUSSELL
RUSSET
RUSSIA
RUSSIAN
RUSSIANIZATIONS
RUSSIANIZES
RUSSIANS
RUSSO
RUST
RUSTED
RUSTIC
RUSTICATE
RUSTICATED
RUSTICATES
RUSTICATING
RUSTICATION
RUSTING
RUSTLE
RUSTLED
RUSTLER
RUSTLERS
RUSTLING
RUSTS
RUSTY
RUT
RUTGERS
RUTH
RUTHERFORD
RUTHLESS
RUTHLESSLY
RUTHLESSNESS
RUTLAND
RUTLEDGE
RUTS
RWANDA
RYAN
RYDBERG
RYDER
RYE
SABBATH
SABBATHIZE
SABBATHIZES
SABBATICAL
SABER
SABERS
SABINA
SABINE
SABLE
SABLES
SABOTAGE
SACHS
SACK
SACKER
SACKING
SACKS
SACRAMENT
SACRAMENTO
SACRED
SACREDLY
SACREDNESS
SACRIFICE
SACRIFICED
SACRIFICER
SACRIFICERS
SACRIFICES
SACRIFICIAL
SACRIFICIALLY
SACRIFICING
SACRILEGE
SACRILEGIOUS
SACROSANCT
SAD
SADDEN
SADDENED
SADDENS
SADDER
SADDEST
SADDLE
SADDLEBAG
SADDLED
SADDLES
SADIE
SADISM
SADIST
SADISTIC
SADISTICALLY
SADISTS
SADLER
SADLY
SADNESS
SAFARI
SAFE
SAFEGUARD
SAFEGUARDED
SAFEGUARDING
SAFEGUARDS
SAFEKEEPING
SAFELY
SAFENESS
SAFER
SAFES
SAFEST
SAFETIES
SAFETY
SAFFRON
SAG
SAGA
SAGACIOUS
SAGACITY
SAGE
SAGEBRUSH
SAGELY
SAGES
SAGGING
SAGINAW
SAGITTAL
SAGITTARIUS
SAGS
SAGUARO
SAHARA
SAID
SAIGON
SAIL
SAILBOAT
SAILED
SAILFISH
SAILING
SAILOR
SAILORLY
SAILORS
SAILS
SAINT
SAINTED
SAINTHOOD
SAINTLY
SAINTS
SAKE
SAKES
SAL
SALAAM
SALABLE
SALAD
SALADS
SALAMANDER
SALAMI
SALARIED
SALARIES
SALARY
SALE
SALEM
SALERNO
SALES
SALESGIRL
SALESIAN
SALESLADY
SALESMAN
SALESMEN
SALESPERSON
SALIENT
SALINA
SALINE
SALISBURY
SALISH
SALIVA
SALIVARY
SALIVATE
SALK
SALLE
SALLIES
SALLOW
SALLY
SALLYING
SALMON
SALON
SALONS
SALOON
SALOONS
SALT
SALTED
SALTER
SALTERS
SALTIER
SALTIEST
SALTINESS
SALTING
SALTON
SALTS
SALTY
SALUTARY
SALUTATION
SALUTATIONS
SALUTE
SALUTED
SALUTES
SALUTING
SALVADOR
SALVADORAN
SALVAGE
SALVAGED
SALVAGER
SALVAGES
SALVAGING
SALVATION
SALVATORE
SALVE
SALVER
SALVES
SALZ
SAM
SAMARITAN
SAME
SAMENESS
SAMMY
SAMOA
SAMOAN
SAMPLE
SAMPLED
SAMPLER
SAMPLERS
SAMPLES
SAMPLING
SAMPLINGS
SAMPSON
SAMSON
SAMUEL
SAMUELS
SAMUELSON
SAN
SANA
SANATORIA
SANATORIUM
SANBORN
SANCHEZ
SANCHO
SANCTIFICATION
SANCTIFIED
SANCTIFY
SANCTIMONIOUS
SANCTION
SANCTIONED
SANCTIONING
SANCTIONS
SANCTITY
SANCTUARIES
SANCTUARY
SANCTUM
SAND
SANDAL
SANDALS
SANDBAG
SANDBURG
SANDED
SANDER
SANDERLING
SANDERS
SANDERSON
SANDIA
SANDING
SANDMAN
SANDPAPER
SANDRA
SANDS
SANDSTONE
SANDUSKY
SANDWICH
SANDWICHES
SANDY
SANE
SANELY
SANER
SANEST
SANFORD
SANG
SANGUINE
SANHEDRIN
SANITARIUM
SANITARY
SANITATION
SANITY
SANK
SANSKRIT
SANSKRITIC
SANSKRITIZE
SANTA
SANTAYANA
SANTIAGO
SANTO
SAO
SAP
SAPIENS
SAPLING
SAPLINGS
SAPPHIRE
SAPPHO
SAPS
SAPSUCKER
SARA
SARACEN
SARACENS
SARAH
SARAN
SARASOTA
SARATOGA
SARCASM
SARCASMS
SARCASTIC
SARDINE
SARDINIA
SARDONIC
SARGENT
SARI
SARTRE
SASH
SASKATCHEWAN
SASKATOON
SAT
SATAN
SATANIC
SATANISM
SATANIST
SATCHEL
SATCHELS
SATE
SATED
SATELLITE
SATELLITES
SATES
SATIN
SATING
SATIRE
SATIRES
SATIRIC
SATISFACTION
SATISFACTIONS
SATISFACTORILY
SATISFACTORY
SATISFIABILITY
SATISFIABLE
SATISFIED
SATISFIES
SATISFY
SATISFYING
SATURATE
SATURATED
SATURATES
SATURATING
SATURATION
SATURDAY
SATURDAYS
SATURN
SATURNALIA
SATURNISM
SATYR
SAUCE
SAUCEPAN
SAUCEPANS
SAUCER
SAUCERS
SAUCES
SAUCY
SAUD
SAUDI
SAUKVILLE
SAUL
SAULT
SAUNDERS
SAUNTER
SAUSAGE
SAUSAGES
SAVAGE
SAVAGED
SAVAGELY
SAVAGENESS
SAVAGER
SAVAGERS
SAVAGES
SAVAGING
SAVANNAH
SAVE
SAVED
SAVER
SAVERS
SAVES
SAVING
SAVINGS
SAVIOR
SAVIORS
SAVIOUR
SAVONAROLA
SAVOR
SAVORED
SAVORING
SAVORS
SAVORY
SAVOY
SAVOYARD
SAVOYARDS
SAW
SAWDUST
SAWED
SAWFISH
SAWING
SAWMILL
SAWMILLS
SAWS
SAWTOOTH
SAX
SAXON
SAXONIZATION
SAXONIZATIONS
SAXONIZE
SAXONIZES
SAXONS
SAXONY
SAXOPHONE
SAXTON
SAY
SAYER
SAYERS
SAYING
SAYINGS
SAYS
SCAB
SCABBARD
SCABBARDS
SCABROUS
SCAFFOLD
SCAFFOLDING
SCAFFOLDINGS
SCAFFOLDS
SCALA
SCALABLE
SCALAR
SCALARS
SCALD
SCALDED
SCALDING
SCALE
SCALED
SCALES
SCALING
SCALINGS
SCALLOP
SCALLOPED
SCALLOPS
SCALP
SCALPS
SCALY
SCAMPER
SCAMPERING
SCAMPERS
SCAN
SCANDAL
SCANDALOUS
SCANDALS
SCANDINAVIA
SCANDINAVIAN
SCANDINAVIANS
SCANNED
SCANNER
SCANNERS
SCANNING
SCANS
SCANT
SCANTIER
SCANTIEST
SCANTILY
SCANTINESS
SCANTLY
SCANTY
SCAPEGOAT
SCAR
SCARBOROUGH
SCARCE
SCARCELY
SCARCENESS
SCARCER
SCARCITY
SCARE
SCARECROW
SCARED
SCARES
SCARF
SCARING
SCARLATTI
SCARLET
SCARS
SCARSDALE
SCARVES
SCARY
SCATTER
SCATTERBRAIN
SCATTERED
SCATTERING
SCATTERS
SCENARIO
SCENARIOS
SCENE
SCENERY
SCENES
SCENIC
SCENT
SCENTED
SCENTS
SCEPTER
SCEPTERS
SCHAEFER
SCHAEFFER
SCHAFER
SCHAFFNER
SCHANTZ
SCHAPIRO
SCHEDULABLE
SCHEDULE
SCHEDULED
SCHEDULER
SCHEDULERS
SCHEDULES
SCHEDULING
SCHEHERAZADE
SCHELLING
SCHEMA
SCHEMAS
SCHEMATA
SCHEMATIC
SCHEMATICALLY
SCHEMATICS
SCHEME
SCHEMED
SCHEMER
SCHEMERS
SCHEMES
SCHEMING
SCHILLER
SCHISM
SCHIZOPHRENIA
SCHLESINGER
SCHLITZ
SCHLOSS
SCHMIDT
SCHMITT
SCHNABEL
SCHNEIDER
SCHOENBERG
SCHOFIELD
SCHOLAR
SCHOLARLY
SCHOLARS
SCHOLARSHIP
SCHOLARSHIPS
SCHOLASTIC
SCHOLASTICALLY
SCHOLASTICS
SCHOOL
SCHOOLBOY
SCHOOLBOYS
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SCHOOLER
SCHOOLERS
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SCHOOLHOUSES
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SCHOOLMASTER
SCHOOLMASTERS
SCHOOLROOM
SCHOOLROOMS
SCHOOLS
SCHOONER
SCHOPENHAUER
SCHOTTKY
SCHROEDER
SCHROEDINGER
SCHUBERT
SCHULTZ
SCHULZ
SCHUMACHER
SCHUMAN
SCHUMANN
SCHUSTER
SCHUYLER
SCHUYLKILL
SCHWAB
SCHWARTZ
SCHWEITZER
SCIENCE
SCIENCES
SCIENTIFIC
SCIENTIFICALLY
SCIENTIST
SCIENTISTS
SCISSOR
SCISSORED
SCISSORING
SCISSORS
SCLEROSIS
SCLEROTIC
SCOFF
SCOFFED
SCOFFER
SCOFFING
SCOFFS
SCOLD
SCOLDED
SCOLDING
SCOLDS
SCOOP
SCOOPED
SCOOPING
SCOOPS
SCOOT
SCOPE
SCOPED
SCOPES
SCOPING
SCORCH
SCORCHED
SCORCHER
SCORCHES
SCORCHING
SCORE
SCOREBOARD
SCORECARD
SCORED
SCORER
SCORERS
SCORES
SCORING
SCORINGS
SCORN
SCORNED
SCORNER
SCORNFUL
SCORNFULLY
SCORNING
SCORNS
SCORPIO
SCORPION
SCORPIONS
SCOT
SCOTCH
SCOTCHGARD
SCOTCHMAN
SCOTIA
SCOTIAN
SCOTLAND
SCOTS
SCOTSMAN
SCOTSMEN
SCOTT
SCOTTISH
SCOTTSDALE
SCOTTY
SCOUNDREL
SCOUNDRELS
SCOUR
SCOURED
SCOURGE
SCOURING
SCOURS
SCOUT
SCOUTED
SCOUTING
SCOUTS
SCOW
SCOWL
SCOWLED
SCOWLING
SCOWLS
SCRAM
SCRAMBLE
SCRAMBLED
SCRAMBLER
SCRAMBLES
SCRAMBLING
SCRANTON
SCRAP
SCRAPE
SCRAPED
SCRAPER
SCRAPERS
SCRAPES
SCRAPING
SCRAPINGS
SCRAPPED
SCRAPS
SCRATCH
SCRATCHED
SCRATCHER
SCRATCHERS
SCRATCHES
SCRATCHING
SCRATCHY
SCRAWL
SCRAWLED
SCRAWLING
SCRAWLS
SCRAWNY
SCREAM
SCREAMED
SCREAMER
SCREAMERS
SCREAMING
SCREAMS
SCREECH
SCREECHED
SCREECHES
SCREECHING
SCREEN
SCREENED
SCREENING
SCREENINGS
SCREENPLAY
SCREENS
SCREW
SCREWBALL
SCREWDRIVER
SCREWED
SCREWING
SCREWS
SCRIBBLE
SCRIBBLED
SCRIBBLER
SCRIBBLES
SCRIBE
SCRIBES
SCRIBING
SCRIBNERS
SCRIMMAGE
SCRIPPS
SCRIPT
SCRIPTS
SCRIPTURE
SCRIPTURES
SCROLL
SCROLLED
SCROLLING
SCROLLS
SCROOGE
SCROUNGE
SCRUB
SCRUMPTIOUS
SCRUPLE
SCRUPULOUS
SCRUPULOUSLY
SCRUTINIZE
SCRUTINIZED
SCRUTINIZING
SCRUTINY
SCUBA
SCUD
SCUFFLE
SCUFFLED
SCUFFLES
SCUFFLING
SCULPT
SCULPTED
SCULPTOR
SCULPTORS
SCULPTS
SCULPTURE
SCULPTURED
SCULPTURES
SCURRIED
SCURRY
SCURVY
SCUTTLE
SCUTTLED
SCUTTLES
SCUTTLING
SCYLLA
SCYTHE
SCYTHES
SCYTHIA
SEA
SEABOARD
SEABORG
SEABROOK
SEACOAST
SEACOASTS
SEAFOOD
SEAGATE
SEAGRAM
SEAGULL
SEAHORSE
SEAL
SEALED
SEALER
SEALING
SEALS
SEALY
SEAM
SEAMAN
SEAMED
SEAMEN
SEAMING
SEAMS
SEAMY
SEAN
SEAPORT
SEAPORTS
SEAQUARIUM
SEAR
SEARCH
SEARCHED
SEARCHER
SEARCHERS
SEARCHES
SEARCHING
SEARCHINGLY
SEARCHINGS
SEARCHLIGHT
SEARED
SEARING
SEARINGLY
SEARS
SEAS
SEASHORE
SEASHORES
SEASIDE
SEASON
SEASONABLE
SEASONABLY
SEASONAL
SEASONALLY
SEASONED
SEASONER
SEASONERS
SEASONING
SEASONINGS
SEASONS
SEAT
SEATED
SEATING
SEATS
SEATTLE
SEAWARD
SEAWEED
SEBASTIAN
SECANT
SECEDE
SECEDED
SECEDES
SECEDING
SECESSION
SECLUDE
SECLUDED
SECLUSION
SECOND
SECONDARIES
SECONDARILY
SECONDARY
SECONDED
SECONDER
SECONDERS
SECONDHAND
SECONDING
SECONDLY
SECONDS
SECRECY
SECRET
SECRETARIAL
SECRETARIAT
SECRETARIES
SECRETARY
SECRETE
SECRETED
SECRETES
SECRETING
SECRETION
SECRETIONS
SECRETIVE
SECRETIVELY
SECRETLY
SECRETS
SECT
SECTARIAN
SECTION
SECTIONAL
SECTIONED
SECTIONING
SECTIONS
SECTOR
SECTORS
SECTS
SECULAR
SECURE
SECURED
SECURELY
SECURES
SECURING
SECURINGS
SECURITIES
SECURITY
SEDAN
SEDATE
SEDGE
SEDGWICK
SEDIMENT
SEDIMENTARY
SEDIMENTS
SEDITION
SEDITIOUS
SEDUCE
SEDUCED
SEDUCER
SEDUCERS
SEDUCES
SEDUCING
SEDUCTION
SEDUCTIVE
SEE
SEED
SEEDED
SEEDER
SEEDERS
SEEDING
SEEDINGS
SEEDLING
SEEDLINGS
SEEDS
SEEDY
SEEING
SEEK
SEEKER
SEEKERS
SEEKING
SEEKS
SEELEY
SEEM
SEEMED
SEEMING
SEEMINGLY
SEEMLY
SEEMS
SEEN
SEEP
SEEPAGE
SEEPED
SEEPING
SEEPS
SEER
SEERS
SEERSUCKER
SEES
SEETHE
SEETHED
SEETHES
SEETHING
SEGMENT
SEGMENTATION
SEGMENTATIONS
SEGMENTED
SEGMENTING
SEGMENTS
SEGOVIA
SEGREGATE
SEGREGATED
SEGREGATES
SEGREGATING
SEGREGATION
SEGUNDO
SEIDEL
SEISMIC
SEISMOGRAPH
SEISMOLOGY
SEIZE
SEIZED
SEIZES
SEIZING
SEIZURE
SEIZURES
SELDOM
SELECT
SELECTED
SELECTING
SELECTION
SELECTIONS
SELECTIVE
SELECTIVELY
SELECTIVITY
SELECTMAN
SELECTMEN
SELECTOR
SELECTORS
SELECTRIC
SELECTS
SELENA
SELENIUM
SELF
SELFISH
SELFISHLY
SELFISHNESS
SELFRIDGE
SELFSAME
SELKIRK
SELL
SELLER
SELLERS
SELLING
SELLOUT
SELLS
SELMA
SELTZER
SELVES
SELWYN
SEMANTIC
SEMANTICAL
SEMANTICALLY
SEMANTICIST
SEMANTICISTS
SEMANTICS
SEMAPHORE
SEMAPHORES
SEMBLANCE
SEMESTER
SEMESTERS
SEMI
SEMIAUTOMATED
SEMICOLON
SEMICOLONS
SEMICONDUCTOR
SEMICONDUCTORS
SEMINAL
SEMINAR
SEMINARIAN
SEMINARIES
SEMINARS
SEMINARY
SEMINOLE
SEMIPERMANENT
SEMIPERMANENTLY
SEMIRAMIS
SEMITE
SEMITIC
SEMITICIZE
SEMITICIZES
SEMITIZATION
SEMITIZATIONS
SEMITIZE
SEMITIZES
SENATE
SENATES
SENATOR
SENATORIAL
SENATORS
SEND
SENDER
SENDERS
SENDING
SENDS
SENECA
SENEGAL
SENILE
SENIOR
SENIORITY
SENIORS
SENSATION
SENSATIONAL
SENSATIONALLY
SENSATIONS
SENSE
SENSED
SENSELESS
SENSELESSLY
SENSELESSNESS
SENSES
SENSIBILITIES
SENSIBILITY
SENSIBLE
SENSIBLY
SENSING
SENSITIVE
SENSITIVELY
SENSITIVENESS
SENSITIVES
SENSITIVITIES
SENSITIVITY
SENSOR
SENSORS
SENSORY
SENSUAL
SENSUOUS
SENT
SENTENCE
SENTENCED
SENTENCES
SENTENCING
SENTENTIAL
SENTIMENT
SENTIMENTAL
SENTIMENTALLY
SENTIMENTS
SENTINEL
SENTINELS
SENTRIES
SENTRY
SEOUL
SEPARABLE
SEPARATE
SEPARATED
SEPARATELY
SEPARATENESS
SEPARATES
SEPARATING
SEPARATION
SEPARATIONS
SEPARATOR
SEPARATORS
SEPIA
SEPOY
SEPT
SEPTEMBER
SEPTEMBERS
SEPULCHER
SEPULCHERS
SEQUEL
SEQUELS
SEQUENCE
SEQUENCED
SEQUENCER
SEQUENCERS
SEQUENCES
SEQUENCING
SEQUENCINGS
SEQUENTIAL
SEQUENTIALITY
SEQUENTIALIZE
SEQUENTIALIZED
SEQUENTIALIZES
SEQUENTIALIZING
SEQUENTIALLY
SEQUESTER
SEQUOIA
SERAFIN
SERBIA
SERBIAN
SERBIANS
SERENDIPITOUS
SERENDIPITY
SERENE
SERENELY
SERENITY
SERF
SERFS
SERGEANT
SERGEANTS
SERGEI
SERIAL
SERIALIZABILITY
SERIALIZABLE
SERIALIZATION
SERIALIZATIONS
SERIALIZE
SERIALIZED
SERIALIZES
SERIALIZING
SERIALLY
SERIALS
SERIES
SERIF
SERIOUS
SERIOUSLY
SERIOUSNESS
SERMON
SERMONS
SERPENS
SERPENT
SERPENTINE
SERPENTS
SERRA
SERUM
SERUMS
SERVANT
SERVANTS
SERVE
SERVED
SERVER
SERVERS
SERVES
SERVICE
SERVICEABILITY
SERVICEABLE
SERVICED
SERVICEMAN
SERVICEMEN
SERVICES
SERVICING
SERVILE
SERVING
SERVINGS
SERVITUDE
SERVO
SERVOMECHANISM
SESAME
SESSION
SESSIONS
SET
SETBACK
SETH
SETS
SETTABLE
SETTER
SETTERS
SETTING
SETTINGS
SETTLE
SETTLED
SETTLEMENT
SETTLEMENTS
SETTLER
SETTLERS
SETTLES
SETTLING
SETUP
SETUPS
SEVEN
SEVENFOLD
SEVENS
SEVENTEEN
SEVENTEENS
SEVENTEENTH
SEVENTH
SEVENTIES
SEVENTIETH
SEVENTY
SEVER
SEVERAL
SEVERALFOLD
SEVERALLY
SEVERANCE
SEVERE
SEVERED
SEVERELY
SEVERER
SEVEREST
SEVERING
SEVERITIES
SEVERITY
SEVERN
SEVERS
SEVILLE
SEW
SEWAGE
SEWARD
SEWED
SEWER
SEWERS
SEWING
SEWS
SEX
SEXED
SEXES
SEXIST
SEXTANS
SEXTET
SEXTILLION
SEXTON
SEXTUPLE
SEXTUPLET
SEXUAL
SEXUALITY
SEXUALLY
SEXY
SEYCHELLES
SEYMOUR
SHABBY
SHACK
SHACKED
SHACKLE
SHACKLED
SHACKLES
SHACKLING
SHACKS
SHADE
SHADED
SHADES
SHADIER
SHADIEST
SHADILY
SHADINESS
SHADING
SHADINGS
SHADOW
SHADOWED
SHADOWING
SHADOWS
SHADOWY
SHADY
SHAFER
SHAFFER
SHAFT
SHAFTS
SHAGGY
SHAKABLE
SHAKABLY
SHAKE
SHAKEDOWN
SHAKEN
SHAKER
SHAKERS
SHAKES
SHAKESPEARE
SHAKESPEAREAN
SHAKESPEARIAN
SHAKESPEARIZE
SHAKESPEARIZES
SHAKINESS
SHAKING
SHAKY
SHALE
SHALL
SHALLOW
SHALLOWER
SHALLOWLY
SHALLOWNESS
SHAM
SHAMBLES
SHAME
SHAMED
SHAMEFUL
SHAMEFULLY
SHAMELESS
SHAMELESSLY
SHAMES
SHAMING
SHAMPOO
SHAMROCK
SHAMS
SHANGHAI
SHANGHAIED
SHANGHAIING
SHANGHAIINGS
SHANGHAIS
SHANNON
SHANTIES
SHANTUNG
SHANTY
SHAPE
SHAPED
SHAPELESS
SHAPELESSLY
SHAPELESSNESS
SHAPELY
SHAPER
SHAPERS
SHAPES
SHAPING
SHAPIRO
SHARABLE
SHARD
SHARE
SHAREABLE
SHARECROPPER
SHARECROPPERS
SHARED
SHAREHOLDER
SHAREHOLDERS
SHARER
SHARERS
SHARES
SHARI
SHARING
SHARK
SHARKS
SHARON
SHARP
SHARPE
SHARPEN
SHARPENED
SHARPENING
SHARPENS
SHARPER
SHARPEST
SHARPLY
SHARPNESS
SHARPSHOOT
SHASTA
SHATTER
SHATTERED
SHATTERING
SHATTERPROOF
SHATTERS
SHATTUCK
SHAVE
SHAVED
SHAVEN
SHAVES
SHAVING
SHAVINGS
SHAWANO
SHAWL
SHAWLS
SHAWNEE
SHE
SHEA
SHEAF
SHEAR
SHEARED
SHEARER
SHEARING
SHEARS
SHEATH
SHEATHING
SHEATHS
SHEAVES
SHEBOYGAN
SHED
SHEDDING
SHEDIR
SHEDS
SHEEHAN
SHEEN
SHEEP
SHEEPSKIN
SHEER
SHEERED
SHEET
SHEETED
SHEETING
SHEETS
SHEFFIELD
SHEIK
SHEILA
SHELBY
SHELDON
SHELF
SHELL
SHELLED
SHELLER
SHELLEY
SHELLING
SHELLS
SHELTER
SHELTERED
SHELTERING
SHELTERS
SHELTON
SHELVE
SHELVED
SHELVES
SHELVING
SHENANDOAH
SHENANIGAN
SHEPARD
SHEPHERD
SHEPHERDS
SHEPPARD
SHERATON
SHERBET
SHERIDAN
SHERIFF
SHERIFFS
SHERLOCK
SHERMAN
SHERRILL
SHERRY
SHERWIN
SHERWOOD
SHIBBOLETH
SHIED
SHIELD
SHIELDED
SHIELDING
SHIELDS
SHIES
SHIFT
SHIFTED
SHIFTER
SHIFTERS
SHIFTIER
SHIFTIEST
SHIFTILY
SHIFTINESS
SHIFTING
SHIFTS
SHIFTY
SHIITE
SHIITES
SHILL
SHILLING
SHILLINGS
SHILLONG
SHILOH
SHIMMER
SHIMMERING
SHIN
SHINBONE
SHINE
SHINED
SHINER
SHINERS
SHINES
SHINGLE
SHINGLES
SHINING
SHININGLY
SHINTO
SHINTOISM
SHINTOIZE
SHINTOIZES
SHINY
SHIP
SHIPBOARD
SHIPBUILDING
SHIPLEY
SHIPMATE
SHIPMENT
SHIPMENTS
SHIPPED
SHIPPER
SHIPPERS
SHIPPING
SHIPS
SHIPSHAPE
SHIPWRECK
SHIPWRECKED
SHIPWRECKS
SHIPYARD
SHIRE
SHIRK
SHIRKER
SHIRKING
SHIRKS
SHIRLEY
SHIRT
SHIRTING
SHIRTS
SHIT
SHIVA
SHIVER
SHIVERED
SHIVERER
SHIVERING
SHIVERS
SHMUEL
SHOAL
SHOALS
SHOCK
SHOCKED
SHOCKER
SHOCKERS
SHOCKING
SHOCKINGLY
SHOCKLEY
SHOCKS
SHOD
SHODDY
SHOE
SHOED
SHOEHORN
SHOEING
SHOELACE
SHOEMAKER
SHOES
SHOESTRING
SHOJI
SHONE
SHOOK
SHOOT
SHOOTER
SHOOTERS
SHOOTING
SHOOTINGS
SHOOTS
SHOP
SHOPKEEPER
SHOPKEEPERS
SHOPPED
SHOPPER
SHOPPERS
SHOPPING
SHOPS
SHOPWORN
SHORE
SHORELINE
SHORES
SHOREWOOD
SHORN
SHORT
SHORTAGE
SHORTAGES
SHORTCOMING
SHORTCOMINGS
SHORTCUT
SHORTCUTS
SHORTED
SHORTEN
SHORTENED
SHORTENING
SHORTENS
SHORTER
SHORTEST
SHORTFALL
SHORTHAND
SHORTHANDED
SHORTING
SHORTISH
SHORTLY
SHORTNESS
SHORTS
SHORTSIGHTED
SHORTSTOP
SHOSHONE
SHOT
SHOTGUN
SHOTGUNS
SHOTS
SHOULD
SHOULDER
SHOULDERED
SHOULDERING
SHOULDERS
SHOUT
SHOUTED
SHOUTER
SHOUTERS
SHOUTING
SHOUTS
SHOVE
SHOVED
SHOVEL
SHOVELED
SHOVELS
SHOVES
SHOVING
SHOW
SHOWBOAT
SHOWCASE
SHOWDOWN
SHOWED
SHOWER
SHOWERED
SHOWERING
SHOWERS
SHOWING
SHOWINGS
SHOWN
SHOWPIECE
SHOWROOM
SHOWS
SHOWY
SHRANK
SHRAPNEL
SHRED
SHREDDER
SHREDDING
SHREDS
SHREVEPORT
SHREW
SHREWD
SHREWDEST
SHREWDLY
SHREWDNESS
SHREWS
SHRIEK
SHRIEKED
SHRIEKING
SHRIEKS
SHRILL
SHRILLED
SHRILLING
SHRILLNESS
SHRILLY
SHRIMP
SHRINE
SHRINES
SHRINK
SHRINKABLE
SHRINKAGE
SHRINKING
SHRINKS
SHRIVEL
SHRIVELED
SHROUD
SHROUDED
SHRUB
SHRUBBERY
SHRUBS
SHRUG
SHRUGS
SHRUNK
SHRUNKEN
SHU
SHUDDER
SHUDDERED
SHUDDERING
SHUDDERS
SHUFFLE
SHUFFLEBOARD
SHUFFLED
SHUFFLES
SHUFFLING
SHULMAN
SHUN
SHUNS
SHUNT
SHUT
SHUTDOWN
SHUTDOWNS
SHUTOFF
SHUTOUT
SHUTS
SHUTTER
SHUTTERED
SHUTTERS
SHUTTING
SHUTTLE
SHUTTLECOCK
SHUTTLED
SHUTTLES
SHUTTLING
SHY
SHYLOCK
SHYLOCKIAN
SHYLY
SHYNESS
SIAM
SIAMESE
SIAN
SIBERIA
SIBERIAN
SIBLEY
SIBLING
SIBLINGS
SICILIAN
SICILIANA
SICILIANS
SICILY
SICK
SICKEN
SICKER
SICKEST
SICKLE
SICKLY
SICKNESS
SICKNESSES
SICKROOM
SIDE
SIDEARM
SIDEBAND
SIDEBOARD
SIDEBOARDS
SIDEBURNS
SIDECAR
SIDED
SIDELIGHT
SIDELIGHTS
SIDELINE
SIDEREAL
SIDES
SIDESADDLE
SIDESHOW
SIDESTEP
SIDETRACK
SIDEWALK
SIDEWALKS
SIDEWAYS
SIDEWISE
SIDING
SIDINGS
SIDNEY
SIEGE
SIEGEL
SIEGES
SIEGFRIED
SIEGLINDA
SIEGMUND
SIEMENS
SIENA
SIERRA
SIEVE
SIEVES
SIFFORD
SIFT
SIFTED
SIFTER
SIFTING
SIGGRAPH
SIGH
SIGHED
SIGHING
SIGHS
SIGHT
SIGHTED
SIGHTING
SIGHTINGS
SIGHTLY
SIGHTS
SIGHTSEEING
SIGMA
SIGMUND
SIGN
SIGNAL
SIGNALED
SIGNALING
SIGNALLED
SIGNALLING
SIGNALLY
SIGNALS
SIGNATURE
SIGNATURES
SIGNED
SIGNER
SIGNERS
SIGNET
SIGNIFICANCE
SIGNIFICANT
SIGNIFICANTLY
SIGNIFICANTS
SIGNIFICATION
SIGNIFIED
SIGNIFIES
SIGNIFY
SIGNIFYING
SIGNING
SIGNS
SIKH
SIKHES
SIKHS
SIKKIM
SIKKIMESE
SIKORSKY
SILAS
SILENCE
SILENCED
SILENCER
SILENCERS
SILENCES
SILENCING
SILENT
SILENTLY
SILHOUETTE
SILHOUETTED
SILHOUETTES
SILICA
SILICATE
SILICON
SILICONE
SILK
SILKEN
SILKIER
SILKIEST
SILKILY
SILKINE
SILKS
SILKY
SILL
SILLIEST
SILLINESS
SILLS
SILLY
SILO
SILT
SILTED
SILTING
SILTS
SILVER
SILVERED
SILVERING
SILVERMAN
SILVERS
SILVERSMITH
SILVERSTEIN
SILVERWARE
SILVERY
SIMILAR
SIMILARITIES
SIMILARITY
SIMILARLY
SIMILE
SIMILITUDE
SIMLA
SIMMER
SIMMERED
SIMMERING
SIMMERS
SIMMONS
SIMMONSVILLE
SIMMS
SIMON
SIMONS
SIMONSON
SIMPLE
SIMPLEMINDED
SIMPLENESS
SIMPLER
SIMPLEST
SIMPLETON
SIMPLEX
SIMPLICITIES
SIMPLICITY
SIMPLIFICATION
SIMPLIFICATIONS
SIMPLIFIED
SIMPLIFIER
SIMPLIFIERS
SIMPLIFIES
SIMPLIFY
SIMPLIFYING
SIMPLISTIC
SIMPLY
SIMPSON
SIMS
SIMULA
SIMULA
SIMULATE
SIMULATED
SIMULATES
SIMULATING
SIMULATION
SIMULATIONS
SIMULATOR
SIMULATORS
SIMULCAST
SIMULTANEITY
SIMULTANEOUS
SIMULTANEOUSLY
SINAI
SINATRA
SINBAD
SINCE
SINCERE
SINCERELY
SINCEREST
SINCERITY
SINCLAIR
SINE
SINES
SINEW
SINEWS
SINEWY
SINFUL
SINFULLY
SINFULNESS
SING
SINGABLE
SINGAPORE
SINGBORG
SINGE
SINGED
SINGER
SINGERS
SINGING
SINGINGLY
SINGLE
SINGLED
SINGLEHANDED
SINGLENESS
SINGLES
SINGLET
SINGLETON
SINGLETONS
SINGLING
SINGLY
SINGS
SINGSONG
SINGULAR
SINGULARITIES
SINGULARITY
SINGULARLY
SINISTER
SINK
SINKED
SINKER
SINKERS
SINKHOLE
SINKING
SINKS
SINNED
SINNER
SINNERS
SINNING
SINS
SINUOUS
SINUS
SINUSOID
SINUSOIDAL
SINUSOIDS
SIOUX
SIP
SIPHON
SIPHONING
SIPPING
SIPS
SIR
SIRE
SIRED
SIREN
SIRENS
SIRES
SIRIUS
SIRS
SIRUP
SISTER
SISTERLY
SISTERS
SISTINE
SISYPHEAN
SISYPHUS
SIT
SITE
SITED
SITES
SITING
SITS
SITTER
SITTERS
SITTING
SITTINGS
SITU
SITUATE
SITUATED
SITUATES
SITUATING
SITUATION
SITUATIONAL
SITUATIONALLY
SITUATIONS
SIVA
SIX
SIXES
SIXFOLD
SIXGUN
SIXPENCE
SIXTEEN
SIXTEENS
SIXTEENTH
SIXTH
SIXTIES
SIXTIETH
SIXTY
SIZABLE
SIZE
SIZED
SIZES
SIZING
SIZINGS
SIZZLE
SKATE
SKATED
SKATER
SKATERS
SKATES
SKATING
SKELETAL
SKELETON
SKELETONS
SKEPTIC
SKEPTICAL
SKEPTICALLY
SKEPTICISM
SKEPTICS
SKETCH
SKETCHBOOK
SKETCHED
SKETCHES
SKETCHILY
SKETCHING
SKETCHPAD
SKETCHY
SKEW
SKEWED
SKEWER
SKEWERS
SKEWING
SKEWS
SKI
SKID
SKIDDING
SKIED
SKIES
SKIFF
SKIING
SKILL
SKILLED
SKILLET
SKILLFUL
SKILLFULLY
SKILLFULNESS
SKILLS
SKIM
SKIMMED
SKIMMING
SKIMP
SKIMPED
SKIMPING
SKIMPS
SKIMPY
SKIMS
SKIN
SKINDIVE
SKINNED
SKINNER
SKINNERS
SKINNING
SKINNY
SKINS
SKIP
SKIPPED
SKIPPER
SKIPPERS
SKIPPING
SKIPPY
SKIPS
SKIRMISH
SKIRMISHED
SKIRMISHER
SKIRMISHERS
SKIRMISHES
SKIRMISHING
SKIRT
SKIRTED
SKIRTING
SKIRTS
SKIS
SKIT
SKOPJE
SKULK
SKULKED
SKULKER
SKULKING
SKULKS
SKULL
SKULLCAP
SKULLDUGGERY
SKULLS
SKUNK
SKUNKS
SKY
SKYE
SKYHOOK
SKYJACK
SKYLARK
SKYLARKING
SKYLARKS
SKYLIGHT
SKYLIGHTS
SKYLINE
SKYROCKETS
SKYSCRAPER
SKYSCRAPERS
SLAB
SLACK
SLACKEN
SLACKER
SLACKING
SLACKLY
SLACKNESS
SLACKS
SLAIN
SLAM
SLAMMED
SLAMMING
SLAMS
SLANDER
SLANDERER
SLANDEROUS
SLANDERS
SLANG
SLANT
SLANTED
SLANTING
SLANTS
SLAP
SLAPPED
SLAPPING
SLAPS
SLAPSTICK
SLASH
SLASHED
SLASHES
SLASHING
SLAT
SLATE
SLATED
SLATER
SLATES
SLATS
SLAUGHTER
SLAUGHTERED
SLAUGHTERHOUSE
SLAUGHTERING
SLAUGHTERS
SLAV
SLAVE
SLAVER
SLAVERY
SLAVES
SLAVIC
SLAVICIZE
SLAVICIZES
SLAVISH
SLAVIZATION
SLAVIZATIONS
SLAVIZE
SLAVIZES
SLAVONIC
SLAVONICIZE
SLAVONICIZES
SLAVS
SLAY
SLAYER
SLAYERS
SLAYING
SLAYS
SLED
SLEDDING
SLEDGE
SLEDGEHAMMER
SLEDGES
SLEDS
SLEEK
SLEEP
SLEEPER
SLEEPERS
SLEEPILY
SLEEPINESS
SLEEPING
SLEEPLESS
SLEEPLESSLY
SLEEPLESSNESS
SLEEPS
SLEEPWALK
SLEEPY
SLEET
SLEEVE
SLEEVES
SLEIGH
SLEIGHS
SLEIGHT
SLENDER
SLENDERER
SLEPT
SLESINGER
SLEUTH
SLEW
SLEWING
SLICE
SLICED
SLICER
SLICERS
SLICES
SLICING
SLICK
SLICKER
SLICKERS
SLICKS
SLID
SLIDE
SLIDER
SLIDERS
SLIDES
SLIDING
SLIGHT
SLIGHTED
SLIGHTER
SLIGHTEST
SLIGHTING
SLIGHTLY
SLIGHTNESS
SLIGHTS
SLIM
SLIME
SLIMED
SLIMLY
SLIMY
SLING
SLINGING
SLINGS
SLINGSHOT
SLIP
SLIPPAGE
SLIPPED
SLIPPER
SLIPPERINESS
SLIPPERS
SLIPPERY
SLIPPING
SLIPS
SLIT
SLITHER
SLITS
SLIVER
SLOAN
SLOANE
SLOB
SLOCUM
SLOGAN
SLOGANS
SLOOP
SLOP
SLOPE
SLOPED
SLOPER
SLOPERS
SLOPES
SLOPING
SLOPPED
SLOPPINESS
SLOPPING
SLOPPY
SLOPS
SLOT
SLOTH
SLOTHFUL
SLOTHS
SLOTS
SLOTTED
SLOTTING
SLOUCH
SLOUCHED
SLOUCHES
SLOUCHING
SLOVAKIA
SLOVENIA
SLOW
SLOWDOWN
SLOWED
SLOWER
SLOWEST
SLOWING
SLOWLY
SLOWNESS
SLOWS
SLUDGE
SLUG
SLUGGISH
SLUGGISHLY
SLUGGISHNESS
SLUGS
SLUICE
SLUM
SLUMBER
SLUMBERED
SLUMMING
SLUMP
SLUMPED
SLUMPS
SLUMS
SLUNG
SLUR
SLURP
SLURRING
SLURRY
SLURS
SLY
SLYLY
SMACK
SMACKED
SMACKING
SMACKS
SMALL
SMALLER
SMALLEST
SMALLEY
SMALLISH
SMALLNESS
SMALLPOX
SMALLTIME
SMALLWOOD
SMART
SMARTED
SMARTER
SMARTEST
SMARTLY
SMARTNESS
SMASH
SMASHED
SMASHER
SMASHERS
SMASHES
SMASHING
SMASHINGLY
SMATTERING
SMEAR
SMEARED
SMEARING
SMEARS
SMELL
SMELLED
SMELLING
SMELLS
SMELLY
SMELT
SMELTER
SMELTS
SMILE
SMILED
SMILES
SMILING
SMILINGLY
SMIRK
SMITE
SMITH
SMITHEREENS
SMITHFIELD
SMITHS
SMITHSON
SMITHSONIAN
SMITHTOWN
SMITHY
SMITTEN
SMOCK
SMOCKING
SMOCKS
SMOG
SMOKABLE
SMOKE
SMOKED
SMOKER
SMOKERS
SMOKES
SMOKESCREEN
SMOKESTACK
SMOKIES
SMOKING
SMOKY
SMOLDER
SMOLDERED
SMOLDERING
SMOLDERS
SMOOCH
SMOOTH
SMOOTHBORE
SMOOTHED
SMOOTHER
SMOOTHES
SMOOTHEST
SMOOTHING
SMOOTHLY
SMOOTHNESS
SMOTE
SMOTHER
SMOTHERED
SMOTHERING
SMOTHERS
SMUCKER
SMUDGE
SMUG
SMUGGLE
SMUGGLED
SMUGGLER
SMUGGLERS
SMUGGLES
SMUGGLING
SMUT
SMUTTY
SMYRNA
SMYTHE
SNACK
SNAFU
SNAG
SNAIL
SNAILS
SNAKE
SNAKED
SNAKELIKE
SNAKES
SNAP
SNAPDRAGON
SNAPPED
SNAPPER
SNAPPERS
SNAPPILY
SNAPPING
SNAPPY
SNAPS
SNAPSHOT
SNAPSHOTS
SNARE
SNARED
SNARES
SNARING
SNARK
SNARL
SNARLED
SNARLING
SNATCH
SNATCHED
SNATCHES
SNATCHING
SNAZZY
SNEAD
SNEAK
SNEAKED
SNEAKER
SNEAKERS
SNEAKIER
SNEAKIEST
SNEAKILY
SNEAKINESS
SNEAKING
SNEAKS
SNEAKY
SNEED
SNEER
SNEERED
SNEERING
SNEERS
SNEEZE
SNEEZED
SNEEZES
SNEEZING
SNIDER
SNIFF
SNIFFED
SNIFFING
SNIFFLE
SNIFFS
SNIFTER
SNIGGER
SNIP
SNIPE
SNIPPET
SNIVEL
SNOB
SNOBBERY
SNOBBISH
SNODGRASS
SNOOP
SNOOPED
SNOOPING
SNOOPS
SNOOPY
SNORE
SNORED
SNORES
SNORING
SNORKEL
SNORT
SNORTED
SNORTING
SNORTS
SNOTTY
SNOUT
SNOUTS
SNOW
SNOWBALL
SNOWBELT
SNOWED
SNOWFALL
SNOWFLAKE
SNOWIER
SNOWIEST
SNOWILY
SNOWING
SNOWMAN
SNOWMEN
SNOWS
SNOWSHOE
SNOWSHOES
SNOWSTORM
SNOWY
SNUB
SNUFF
SNUFFED
SNUFFER
SNUFFING
SNUFFS
SNUG
SNUGGLE
SNUGGLED
SNUGGLES
SNUGGLING
SNUGLY
SNUGNESS
SNYDER
SOAK
SOAKED
SOAKING
SOAKS
SOAP
SOAPED
SOAPING
SOAPS
SOAPY
SOAR
SOARED
SOARING
SOARS
SOB
SOBBING
SOBER
SOBERED
SOBERING
SOBERLY
SOBERNESS
SOBERS
SOBRIETY
SOBS
SOCCER
SOCIABILITY
SOCIABLE
SOCIABLY
SOCIAL
SOCIALISM
SOCIALIST
SOCIALISTS
SOCIALIZE
SOCIALIZED
SOCIALIZES
SOCIALIZING
SOCIALLY
SOCIETAL
SOCIETIES
SOCIETY
SOCIOECONOMIC
SOCIOLOGICAL
SOCIOLOGICALLY
SOCIOLOGIST
SOCIOLOGISTS
SOCIOLOGY
SOCK
SOCKED
SOCKET
SOCKETS
SOCKING
SOCKS
SOCRATES
SOCRATIC
SOD
SODA
SODDY
SODIUM
SODOMY
SODS
SOFA
SOFAS
SOFIA
SOFT
SOFTBALL
SOFTEN
SOFTENED
SOFTENING
SOFTENS
SOFTER
SOFTEST
SOFTLY
SOFTNESS
SOFTWARE
SOFTWARES
SOGGY
SOIL
SOILED
SOILING
SOILS
SOIREE
SOJOURN
SOJOURNER
SOJOURNERS
SOL
SOLACE
SOLACED
SOLAR
SOLD
SOLDER
SOLDERED
SOLDIER
SOLDIERING
SOLDIERLY
SOLDIERS
SOLE
SOLELY
SOLEMN
SOLEMNITY
SOLEMNLY
SOLEMNNESS
SOLENOID
SOLES
SOLICIT
SOLICITATION
SOLICITED
SOLICITING
SOLICITOR
SOLICITOUS
SOLICITS
SOLICITUDE
SOLID
SOLIDARITY
SOLIDIFICATION
SOLIDIFIED
SOLIDIFIES
SOLIDIFY
SOLIDIFYING
SOLIDITY
SOLIDLY
SOLIDNESS
SOLIDS
SOLILOQUY
SOLITAIRE
SOLITARY
SOLITUDE
SOLITUDES
SOLLY
SOLO
SOLOMON
SOLON
SOLOS
SOLOVIEV
SOLSTICE
SOLUBILITY
SOLUBLE
SOLUTION
SOLUTIONS
SOLVABLE
SOLVE
SOLVED
SOLVENT
SOLVENTS
SOLVER
SOLVERS
SOLVES
SOLVING
SOMALI
SOMALIA
SOMALIS
SOMATIC
SOMBER
SOMBERLY
SOME
SOMEBODY
SOMEDAY
SOMEHOW
SOMEONE
SOMEPLACE
SOMERS
SOMERSAULT
SOMERSET
SOMERVILLE
SOMETHING
SOMETIME
SOMETIMES
SOMEWHAT
SOMEWHERE
SOMMELIER
SOMMERFELD
SOMNOLENT
SON
SONAR
SONATA
SONENBERG
SONG
SONGBOOK
SONGS
SONIC
SONNET
SONNETS
SONNY
SONOMA
SONORA
SONS
SONY
SOON
SOONER
SOONEST
SOOT
SOOTH
SOOTHE
SOOTHED
SOOTHER
SOOTHES
SOOTHING
SOOTHSAYER
SOPHIA
SOPHIAS
SOPHIE
SOPHISTICATED
SOPHISTICATION
SOPHISTRY
SOPHOCLEAN
SOPHOCLES
SOPHOMORE
SOPHOMORES
SOPRANO
SORCERER
SORCERERS
SORCERY
SORDID
SORDIDLY
SORDIDNESS
SORE
SORELY
SORENESS
SORENSEN
SORENSON
SORER
SORES
SOREST
SORGHUM
SORORITY
SORREL
SORRENTINE
SORRIER
SORRIEST
SORROW
SORROWFUL
SORROWFULLY
SORROWS
SORRY
SORT
SORTED
SORTER
SORTERS
SORTIE
SORTING
SORTS
SOUGHT
SOUL
SOULFUL
SOULS
SOUND
SOUNDED
SOUNDER
SOUNDEST
SOUNDING
SOUNDINGS
SOUNDLY
SOUNDNESS
SOUNDPROOF
SOUNDS
SOUP
SOUPED
SOUPS
SOUR
SOURCE
SOURCES
SOURDOUGH
SOURED
SOURER
SOUREST
SOURING
SOURLY
SOURNESS
SOURS
SOUSA
SOUTH
SOUTHAMPTON
SOUTHBOUND
SOUTHEAST
SOUTHEASTERN
SOUTHERN
SOUTHERNER
SOUTHERNERS
SOUTHERNMOST
SOUTHERNWOOD
SOUTHEY
SOUTHFIELD
SOUTHLAND
SOUTHPAW
SOUTHWARD
SOUTHWEST
SOUTHWESTERN
SOUVENIR
SOVEREIGN
SOVEREIGNS
SOVEREIGNTY
SOVIET
SOVIETS
SOW
SOWN
SOY
SOYA
SOYBEAN
SPA
SPACE
SPACECRAFT
SPACED
SPACER
SPACERS
SPACES
SPACESHIP
SPACESHIPS
SPACESUIT
SPACEWAR
SPACING
SPACINGS
SPACIOUS
SPADED
SPADES
SPADING
SPAFFORD
SPAHN
SPAIN
SPALDING
SPAN
SPANDREL
SPANIARD
SPANIARDIZATION
SPANIARDIZATIONS
SPANIARDIZE
SPANIARDIZES
SPANIARDS
SPANIEL
SPANISH
SPANISHIZE
SPANISHIZES
SPANK
SPANKED
SPANKING
SPANKS
SPANNED
SPANNER
SPANNERS
SPANNING
SPANS
SPARC
SPARCSTATION
SPARE
SPARED
SPARELY
SPARENESS
SPARER
SPARES
SPAREST
SPARING
SPARINGLY
SPARK
SPARKED
SPARKING
SPARKLE
SPARKLING
SPARKMAN
SPARKS
SPARRING
SPARROW
SPARROWS
SPARSE
SPARSELY
SPARSENESS
SPARSER
SPARSEST
SPARTA
SPARTAN
SPARTANIZE
SPARTANIZES
SPASM
SPASTIC
SPAT
SPATE
SPATES
SPATIAL
SPATIALLY
SPATTER
SPATTERED
SPATULA
SPAULDING
SPAWN
SPAWNED
SPAWNING
SPAWNS
SPAYED
SPEAK
SPEAKABLE
SPEAKEASY
SPEAKER
SPEAKERPHONE
SPEAKERPHONES
SPEAKERS
SPEAKING
SPEAKS
SPEAR
SPEARED
SPEARMINT
SPEARS
SPEC
SPECIAL
SPECIALIST
SPECIALISTS
SPECIALIZATION
SPECIALIZATIONS
SPECIALIZE
SPECIALIZED
SPECIALIZES
SPECIALIZING
SPECIALLY
SPECIALS
SPECIALTIES
SPECIALTY
SPECIE
SPECIES
SPECIFIABLE
SPECIFIC
SPECIFICALLY
SPECIFICATION
SPECIFICATIONS
SPECIFICITY
SPECIFICS
SPECIFIED
SPECIFIER
SPECIFIERS
SPECIFIES
SPECIFY
SPECIFYING
SPECIMEN
SPECIMENS
SPECIOUS
SPECK
SPECKLE
SPECKLED
SPECKLES
SPECKS
SPECTACLE
SPECTACLED
SPECTACLES
SPECTACULAR
SPECTACULARLY
SPECTATOR
SPECTATORS
SPECTER
SPECTERS
SPECTOR
SPECTRA
SPECTRAL
SPECTROGRAM
SPECTROGRAMS
SPECTROGRAPH
SPECTROGRAPHIC
SPECTROGRAPHY
SPECTROMETER
SPECTROPHOTOMETER
SPECTROPHOTOMETRY
SPECTROSCOPE
SPECTROSCOPIC
SPECTROSCOPY
SPECTRUM
SPECULATE
SPECULATED
SPECULATES
SPECULATING
SPECULATION
SPECULATIONS
SPECULATIVE
SPECULATOR
SPECULATORS
SPED
SPEECH
SPEECHES
SPEECHLESS
SPEECHLESSNESS
SPEED
SPEEDBOAT
SPEEDED
SPEEDER
SPEEDERS
SPEEDILY
SPEEDING
SPEEDOMETER
SPEEDS
SPEEDUP
SPEEDUPS
SPEEDY
SPELL
SPELLBOUND
SPELLED
SPELLER
SPELLERS
SPELLING
SPELLINGS
SPELLS
SPENCER
SPENCERIAN
SPEND
SPENDER
SPENDERS
SPENDING
SPENDS
SPENGLERIAN
SPENT
SPERM
SPERRY
SPHERE
SPHERES
SPHERICAL
SPHERICALLY
SPHEROID
SPHEROIDAL
SPHINX
SPICA
SPICE
SPICED
SPICES
SPICINESS
SPICY
SPIDER
SPIDERS
SPIDERY
SPIEGEL
SPIES
SPIGOT
SPIKE
SPIKED
SPIKES
SPILL
SPILLED
SPILLER
SPILLING
SPILLS
SPILT
SPIN
SPINACH
SPINAL
SPINALLY
SPINDLE
SPINDLED
SPINDLING
SPINE
SPINNAKER
SPINNER
SPINNERS
SPINNING
SPINOFF
SPINS
SPINSTER
SPINY
SPIRAL
SPIRALED
SPIRALING
SPIRALLY
SPIRE
SPIRES
SPIRIT
SPIRITED
SPIRITEDLY
SPIRITING
SPIRITS
SPIRITUAL
SPIRITUALLY
SPIRITUALS
SPIRO
SPIT
SPITE
SPITED
SPITEFUL
SPITEFULLY
SPITEFULNESS
SPITES
SPITFIRE
SPITING
SPITS
SPITTING
SPITTLE
SPITZ
SPLASH
SPLASHED
SPLASHES
SPLASHING
SPLASHY
SPLEEN
SPLENDID
SPLENDIDLY
SPLENDOR
SPLENETIC
SPLICE
SPLICED
SPLICER
SPLICERS
SPLICES
SPLICING
SPLICINGS
SPLINE
SPLINES
SPLINT
SPLINTER
SPLINTERED
SPLINTERS
SPLINTERY
SPLIT
SPLITS
SPLITTER
SPLITTERS
SPLITTING
SPLURGE
SPOIL
SPOILAGE
SPOILED
SPOILER
SPOILERS
SPOILING
SPOILS
SPOKANE
SPOKE
SPOKED
SPOKEN
SPOKES
SPOKESMAN
SPOKESMEN
SPONGE
SPONGED
SPONGER
SPONGERS
SPONGES
SPONGING
SPONGY
SPONSOR
SPONSORED
SPONSORING
SPONSORS
SPONSORSHIP
SPONTANEITY
SPONTANEOUS
SPONTANEOUSLY
SPOOF
SPOOK
SPOOKY
SPOOL
SPOOLED
SPOOLER
SPOOLERS
SPOOLING
SPOOLS
SPOON
SPOONED
SPOONFUL
SPOONING
SPOONS
SPORADIC
SPORE
SPORES
SPORT
SPORTED
SPORTING
SPORTINGLY
SPORTIVE
SPORTS
SPORTSMAN
SPORTSMEN
SPORTSWEAR
SPORTSWRITER
SPORTSWRITING
SPORTY
SPOSATO
SPOT
SPOTLESS
SPOTLESSLY
SPOTLIGHT
SPOTS
SPOTTED
SPOTTER
SPOTTERS
SPOTTING
SPOTTY
SPOUSE
SPOUSES
SPOUT
SPOUTED
SPOUTING
SPOUTS
SPRAGUE
SPRAIN
SPRANG
SPRAWL
SPRAWLED
SPRAWLING
SPRAWLS
SPRAY
SPRAYED
SPRAYER
SPRAYING
SPRAYS
SPREAD
SPREADER
SPREADERS
SPREADING
SPREADINGS
SPREADS
SPREADSHEET
SPREE
SPREES
SPRIG
SPRIGHTLY
SPRING
SPRINGBOARD
SPRINGER
SPRINGERS
SPRINGFIELD
SPRINGIER
SPRINGIEST
SPRINGINESS
SPRINGING
SPRINGS
SPRINGTIME
SPRINGY
SPRINKLE
SPRINKLED
SPRINKLER
SPRINKLES
SPRINKLING
SPRINT
SPRINTED
SPRINTER
SPRINTERS
SPRINTING
SPRINTS
SPRITE
SPROCKET
SPROUL
SPROUT
SPROUTED
SPROUTING
SPRUCE
SPRUCED
SPRUNG
SPUDS
SPUN
SPUNK
SPUR
SPURIOUS
SPURN
SPURNED
SPURNING
SPURNS
SPURS
SPURT
SPURTED
SPURTING
SPURTS
SPUTTER
SPUTTERED
SPY
SPYGLASS
SPYING
SQUABBLE
SQUABBLED
SQUABBLES
SQUABBLING
SQUAD
SQUADRON
SQUADRONS
SQUADS
SQUALID
SQUALL
SQUALLS
SQUANDER
SQUARE
SQUARED
SQUARELY
SQUARENESS
SQUARER
SQUARES
SQUAREST
SQUARESVILLE
SQUARING
SQUASH
SQUASHED
SQUASHING
SQUAT
SQUATS
SQUATTING
SQUAW
SQUAWK
SQUAWKED
SQUAWKING
SQUAWKS
SQUEAK
SQUEAKED
SQUEAKING
SQUEAKS
SQUEAKY
SQUEAL
SQUEALED
SQUEALING
SQUEALS
SQUEAMISH
SQUEEZE
SQUEEZED
SQUEEZER
SQUEEZES
SQUEEZING
SQUELCH
SQUIBB
SQUID
SQUINT
SQUINTED
SQUINTING
SQUIRE
SQUIRES
SQUIRM
SQUIRMED
SQUIRMS
SQUIRMY
SQUIRREL
SQUIRRELED
SQUIRRELING
SQUIRRELS
SQUIRT
SQUISHY
SRI
STAB
STABBED
STABBING
STABILE
STABILITIES
STABILITY
STABILIZE
STABILIZED
STABILIZER
STABILIZERS
STABILIZES
STABILIZING
STABLE
STABLED
STABLER
STABLES
STABLING
STABLY
STABS
STACK
STACKED
STACKING
STACKS
STACY
STADIA
STADIUM
STAFF
STAFFED
STAFFER
STAFFERS
STAFFING
STAFFORD
STAFFORDSHIRE
STAFFS
STAG
STAGE
STAGECOACH
STAGECOACHES
STAGED
STAGER
STAGERS
STAGES
STAGGER
STAGGERED
STAGGERING
STAGGERS
STAGING
STAGNANT
STAGNATE
STAGNATION
STAGS
STAHL
STAID
STAIN
STAINED
STAINING
STAINLESS
STAINS
STAIR
STAIRCASE
STAIRCASES
STAIRS
STAIRWAY
STAIRWAYS
STAIRWELL
STAKE
STAKED
STAKES
STALACTITE
STALE
STALEMATE
STALEY
STALIN
STALINIST
STALINS
STALK
STALKED
STALKING
STALL
STALLED
STALLING
STALLINGS
STALLION
STALLS
STALWART
STALWARTLY
STAMEN
STAMENS
STAMFORD
STAMINA
STAMMER
STAMMERED
STAMMERER
STAMMERING
STAMMERS
STAMP
STAMPED
STAMPEDE
STAMPEDED
STAMPEDES
STAMPEDING
STAMPER
STAMPERS
STAMPING
STAMPS
STAN
STANCH
STANCHEST
STANCHION
STAND
STANDARD
STANDARDIZATION
STANDARDIZE
STANDARDIZED
STANDARDIZES
STANDARDIZING
STANDARDLY
STANDARDS
STANDBY
STANDING
STANDINGS
STANDISH
STANDOFF
STANDPOINT
STANDPOINTS
STANDS
STANDSTILL
STANFORD
STANHOPE
STANLEY
STANS
STANTON
STANZA
STANZAS
STAPHYLOCOCCUS
STAPLE
STAPLER
STAPLES
STAPLETON
STAPLING
STAR
STARBOARD
STARCH
STARCHED
STARDOM
STARE
STARED
STARER
STARES
STARFISH
STARGATE
STARING
STARK
STARKEY
STARKLY
STARLET
STARLIGHT
STARLING
STARR
STARRED
STARRING
STARRY
STARS
START
STARTED
STARTER
STARTERS
STARTING
STARTLE
STARTLED
STARTLES
STARTLING
STARTS
STARTUP
STARTUPS
STARVATION
STARVE
STARVED
STARVES
STARVING
STATE
STATED
STATELY
STATEMENT
STATEMENTS
STATEN
STATES
STATESMAN
STATESMANLIKE
STATESMEN
STATEWIDE
STATIC
STATICALLY
STATING
STATION
STATIONARY
STATIONED
STATIONER
STATIONERY
STATIONING
STATIONMASTER
STATIONS
STATISTIC
STATISTICAL
STATISTICALLY
STATISTICIAN
STATISTICIANS
STATISTICS
STATLER
STATUE
STATUES
STATUESQUE
STATUESQUELY
STATUESQUENESS
STATUETTE
STATURE
STATUS
STATUSES
STATUTE
STATUTES
STATUTORILY
STATUTORINESS
STATUTORY
STAUFFER
STAUNCH
STAUNCHEST
STAUNCHLY
STAUNTON
STAVE
STAVED
STAVES
STAY
STAYED
STAYING
STAYS
STEAD
STEADFAST
STEADFASTLY
STEADFASTNESS
STEADIED
STEADIER
STEADIES
STEADIEST
STEADILY
STEADINESS
STEADY
STEADYING
STEAK
STEAKS
STEAL
STEALER
STEALING
STEALS
STEALTH
STEALTHILY
STEALTHY
STEAM
STEAMBOAT
STEAMBOATS
STEAMED
STEAMER
STEAMERS
STEAMING
STEAMS
STEAMSHIP
STEAMSHIPS
STEAMY
STEARNS
STEED
STEEL
STEELE
STEELED
STEELERS
STEELING
STEELMAKER
STEELS
STEELY
STEEN
STEEP
STEEPED
STEEPER
STEEPEST
STEEPING
STEEPLE
STEEPLES
STEEPLY
STEEPNESS
STEEPS
STEER
STEERABLE
STEERED
STEERING
STEERS
STEFAN
STEGOSAURUS
STEINBECK
STEINBERG
STEINER
STELLA
STELLAR
STEM
STEMMED
STEMMING
STEMS
STENCH
STENCHES
STENCIL
STENCILS
STENDHAL
STENDLER
STENOGRAPHER
STENOGRAPHERS
STENOTYPE
STEP
STEPCHILD
STEPHAN
STEPHANIE
STEPHEN
STEPHENS
STEPHENSON
STEPMOTHER
STEPMOTHERS
STEPPED
STEPPER
STEPPING
STEPS
STEPSON
STEPWISE
STEREO
STEREOS
STEREOSCOPIC
STEREOTYPE
STEREOTYPED
STEREOTYPES
STEREOTYPICAL
STERILE
STERILIZATION
STERILIZATIONS
STERILIZE
STERILIZED
STERILIZER
STERILIZES
STERILIZING
STERLING
STERN
STERNBERG
STERNLY
STERNNESS
STERNO
STERNS
STETHOSCOPE
STETSON
STETSONS
STEUBEN
STEVE
STEVEDORE
STEVEN
STEVENS
STEVENSON
STEVIE
STEW
STEWARD
STEWARDESS
STEWARDS
STEWART
STEWED
STEWS
STICK
STICKER
STICKERS
STICKIER
STICKIEST
STICKILY
STICKINESS
STICKING
STICKLEBACK
STICKS
STICKY
STIFF
STIFFEN
STIFFENS
STIFFER
STIFFEST
STIFFLY
STIFFNESS
STIFFS
STIFLE
STIFLED
STIFLES
STIFLING
STIGMA
STIGMATA
STILE
STILES
STILETTO
STILL
STILLBIRTH
STILLBORN
STILLED
STILLER
STILLEST
STILLING
STILLNESS
STILLS
STILLWELL
STILT
STILTS
STIMSON
STIMULANT
STIMULANTS
STIMULATE
STIMULATED
STIMULATES
STIMULATING
STIMULATION
STIMULATIONS
STIMULATIVE
STIMULI
STIMULUS
STING
STINGING
STINGS
STINGY
STINK
STINKER
STINKERS
STINKING
STINKS
STINT
STIPEND
STIPENDS
STIPULATE
STIPULATED
STIPULATES
STIPULATING
STIPULATION
STIPULATIONS
STIR
STIRLING
STIRRED
STIRRER
STIRRERS
STIRRING
STIRRINGLY
STIRRINGS
STIRRUP
STIRS
STITCH
STITCHED
STITCHES
STITCHING
STOCHASTIC
STOCHASTICALLY
STOCK
STOCKADE
STOCKADES
STOCKBROKER
STOCKED
STOCKER
STOCKERS
STOCKHOLDER
STOCKHOLDERS
STOCKHOLM
STOCKING
STOCKINGS
STOCKPILE
STOCKROOM
STOCKS
STOCKTON
STOCKY
STODGY
STOICHIOMETRY
STOKE
STOKES
STOLE
STOLEN
STOLES
STOLID
STOMACH
STOMACHED
STOMACHER
STOMACHES
STOMACHING
STOMP
STONE
STONED
STONEHENGE
STONES
STONING
STONY
STOOD
STOOGE
STOOL
STOOP
STOOPED
STOOPING
STOOPS
STOP
STOPCOCK
STOPCOCKS
STOPGAP
STOPOVER
STOPPABLE
STOPPAGE
STOPPED
STOPPER
STOPPERS
STOPPING
STOPS
STOPWATCH
STORAGE
STORAGES
STORE
STORED
STOREHOUSE
STOREHOUSES
STOREKEEPER
STOREROOM
STORES
STOREY
STOREYED
STOREYS
STORIED
STORIES
STORING
STORK
STORKS
STORM
STORMED
STORMIER
STORMIEST
STORMINESS
STORMING
STORMS
STORMY
STORY
STORYBOARD
STORYTELLER
STOUFFER
STOUT
STOUTER
STOUTEST
STOUTLY
STOUTNESS
STOVE
STOVES
STOW
STOWE
STOWED
STRADDLE
STRAFE
STRAGGLE
STRAGGLED
STRAGGLER
STRAGGLERS
STRAGGLES
STRAGGLING
STRAIGHT
STRAIGHTAWAY
STRAIGHTEN
STRAIGHTENED
STRAIGHTENS
STRAIGHTER
STRAIGHTEST
STRAIGHTFORWARD
STRAIGHTFORWARDLY
STRAIGHTFORWARDNESS
STRAIGHTNESS
STRAIGHTWAY
STRAIN
STRAINED
STRAINER
STRAINERS
STRAINING
STRAINS
STRAIT
STRAITEN
STRAITS
STRAND
STRANDED
STRANDING
STRANDS
STRANGE
STRANGELY
STRANGENESS
STRANGER
STRANGERS
STRANGEST
STRANGLE
STRANGLED
STRANGLER
STRANGLERS
STRANGLES
STRANGLING
STRANGLINGS
STRANGULATION
STRANGULATIONS
STRAP
STRAPS
STRASBOURG
STRATAGEM
STRATAGEMS
STRATEGIC
STRATEGIES
STRATEGIST
STRATEGY
STRATFORD
STRATIFICATION
STRATIFICATIONS
STRATIFIED
STRATIFIES
STRATIFY
STRATOSPHERE
STRATOSPHERIC
STRATTON
STRATUM
STRAUSS
STRAVINSKY
STRAW
STRAWBERRIES
STRAWBERRY
STRAWS
STRAY
STRAYED
STRAYS
STREAK
STREAKED
STREAKS
STREAM
STREAMED
STREAMER
STREAMERS
STREAMING
STREAMLINE
STREAMLINED
STREAMLINER
STREAMLINES
STREAMLINING
STREAMS
STREET
STREETCAR
STREETCARS
STREETERS
STREETS
STRENGTH
STRENGTHEN
STRENGTHENED
STRENGTHENER
STRENGTHENING
STRENGTHENS
STRENGTHS
STRENUOUS
STRENUOUSLY
STREPTOCOCCUS
STRESS
STRESSED
STRESSES
STRESSFUL
STRESSING
STRETCH
STRETCHED
STRETCHER
STRETCHERS
STRETCHES
STRETCHING
STREW
STREWN
STREWS
STRICKEN
STRICKLAND
STRICT
STRICTER
STRICTEST
STRICTLY
STRICTNESS
STRICTURE
STRIDE
STRIDER
STRIDES
STRIDING
STRIFE
STRIKE
STRIKEBREAKER
STRIKER
STRIKERS
STRIKES
STRIKING
STRIKINGLY
STRINDBERG
STRING
STRINGED
STRINGENT
STRINGENTLY
STRINGER
STRINGERS
STRINGIER
STRINGIEST
STRINGINESS
STRINGING
STRINGS
STRINGY
STRIP
STRIPE
STRIPED
STRIPES
STRIPPED
STRIPPER
STRIPPERS
STRIPPING
STRIPS
STRIPTEASE
STRIVE
STRIVEN
STRIVES
STRIVING
STRIVINGS
STROBE
STROBED
STROBES
STROBOSCOPIC
STRODE
STROKE
STROKED
STROKER
STROKERS
STROKES
STROKING
STROLL
STROLLED
STROLLER
STROLLING
STROLLS
STROM
STROMBERG
STRONG
STRONGER
STRONGEST
STRONGHEART
STRONGHOLD
STRONGLY
STRONTIUM
STROVE
STRUCK
STRUCTURAL
STRUCTURALLY
STRUCTURE
STRUCTURED
STRUCTURER
STRUCTURES
STRUCTURING
STRUGGLE
STRUGGLED
STRUGGLES
STRUGGLING
STRUNG
STRUT
STRUTS
STRUTTING
STRYCHNINE
STU
STUART
STUB
STUBBLE
STUBBLEFIELD
STUBBLEFIELDS
STUBBORN
STUBBORNLY
STUBBORNNESS
STUBBY
STUBS
STUCCO
STUCK
STUD
STUDEBAKER
STUDENT
STUDENTS
STUDIED
STUDIES
STUDIO
STUDIOS
STUDIOUS
STUDIOUSLY
STUDS
STUDY
STUDYING
STUFF
STUFFED
STUFFIER
STUFFIEST
STUFFING
STUFFS
STUFFY
STUMBLE
STUMBLED
STUMBLES
STUMBLING
STUMP
STUMPED
STUMPING
STUMPS
STUN
STUNG
STUNNING
STUNNINGLY
STUNT
STUNTS
STUPEFY
STUPEFYING
STUPENDOUS
STUPENDOUSLY
STUPID
STUPIDEST
STUPIDITIES
STUPIDITY
STUPIDLY
STUPOR
STURBRIDGE
STURDINESS
STURDY
STURGEON
STURM
STUTTER
STUTTGART
STUYVESANT
STYGIAN
STYLE
STYLED
STYLER
STYLERS
STYLES
STYLI
STYLING
STYLISH
STYLISHLY
STYLISHNESS
STYLISTIC
STYLISTICALLY
STYLIZED
STYLUS
STYROFOAM
STYX
SUAVE
SUB
SUBATOMIC
SUBCHANNEL
SUBCHANNELS
SUBCLASS
SUBCLASSES
SUBCOMMITTEES
SUBCOMPONENT
SUBCOMPONENTS
SUBCOMPUTATION
SUBCOMPUTATIONS
SUBCONSCIOUS
SUBCONSCIOUSLY
SUBCULTURE
SUBCULTURES
SUBCYCLE
SUBCYCLES
SUBDIRECTORIES
SUBDIRECTORY
SUBDIVIDE
SUBDIVIDED
SUBDIVIDES
SUBDIVIDING
SUBDIVISION
SUBDIVISIONS
SUBDOMAINS
SUBDUE
SUBDUED
SUBDUES
SUBDUING
SUBEXPRESSION
SUBEXPRESSIONS
SUBFIELD
SUBFIELDS
SUBFILE
SUBFILES
SUBGOAL
SUBGOALS
SUBGRAPH
SUBGRAPHS
SUBGROUP
SUBGROUPS
SUBINTERVAL
SUBINTERVALS
SUBJECT
SUBJECTED
SUBJECTING
SUBJECTION
SUBJECTIVE
SUBJECTIVELY
SUBJECTIVITY
SUBJECTS
SUBLANGUAGE
SUBLANGUAGES
SUBLAYER
SUBLAYERS
SUBLIMATION
SUBLIMATIONS
SUBLIME
SUBLIMED
SUBLIST
SUBLISTS
SUBMARINE
SUBMARINER
SUBMARINERS
SUBMARINES
SUBMERGE
SUBMERGED
SUBMERGES
SUBMERGING
SUBMISSION
SUBMISSIONS
SUBMISSIVE
SUBMIT
SUBMITS
SUBMITTAL
SUBMITTED
SUBMITTING
SUBMODE
SUBMODES
SUBMODULE
SUBMODULES
SUBMULTIPLEXED
SUBNET
SUBNETS
SUBNETWORK
SUBNETWORKS
SUBOPTIMAL
SUBORDINATE
SUBORDINATED
SUBORDINATES
SUBORDINATION
SUBPARTS
SUBPHASES
SUBPOENA
SUBPROBLEM
SUBPROBLEMS
SUBPROCESSES
SUBPROGRAM
SUBPROGRAMS
SUBPROJECT
SUBPROOF
SUBPROOFS
SUBRANGE
SUBRANGES
SUBROUTINE
SUBROUTINES
SUBS
SUBSCHEMA
SUBSCHEMAS
SUBSCRIBE
SUBSCRIBED
SUBSCRIBER
SUBSCRIBERS
SUBSCRIBES
SUBSCRIBING
SUBSCRIPT
SUBSCRIPTED
SUBSCRIPTING
SUBSCRIPTION
SUBSCRIPTIONS
SUBSCRIPTS
SUBSECTION
SUBSECTIONS
SUBSEGMENT
SUBSEGMENTS
SUBSEQUENCE
SUBSEQUENCES
SUBSEQUENT
SUBSEQUENTLY
SUBSERVIENT
SUBSET
SUBSETS
SUBSIDE
SUBSIDED
SUBSIDES
SUBSIDIARIES
SUBSIDIARY
SUBSIDIES
SUBSIDING
SUBSIDIZE
SUBSIDIZED
SUBSIDIZES
SUBSIDIZING
SUBSIDY
SUBSIST
SUBSISTED
SUBSISTENCE
SUBSISTENT
SUBSISTING
SUBSISTS
SUBSLOT
SUBSLOTS
SUBSPACE
SUBSPACES
SUBSTANCE
SUBSTANCES
SUBSTANTIAL
SUBSTANTIALLY
SUBSTANTIATE
SUBSTANTIATED
SUBSTANTIATES
SUBSTANTIATING
SUBSTANTIATION
SUBSTANTIATIONS
SUBSTANTIVE
SUBSTANTIVELY
SUBSTANTIVITY
SUBSTATION
SUBSTATIONS
SUBSTITUTABILITY
SUBSTITUTABLE
SUBSTITUTE
SUBSTITUTED
SUBSTITUTES
SUBSTITUTING
SUBSTITUTION
SUBSTITUTIONS
SUBSTRATE
SUBSTRATES
SUBSTRING
SUBSTRINGS
SUBSTRUCTURE
SUBSTRUCTURES
SUBSUME
SUBSUMED
SUBSUMES
SUBSUMING
SUBSYSTEM
SUBSYSTEMS
SUBTASK
SUBTASKS
SUBTERFUGE
SUBTERRANEAN
SUBTITLE
SUBTITLED
SUBTITLES
SUBTLE
SUBTLENESS
SUBTLER
SUBTLEST
SUBTLETIES
SUBTLETY
SUBTLY
SUBTOTAL
SUBTRACT
SUBTRACTED
SUBTRACTING
SUBTRACTION
SUBTRACTIONS
SUBTRACTOR
SUBTRACTORS
SUBTRACTS
SUBTRAHEND
SUBTRAHENDS
SUBTREE
SUBTREES
SUBUNIT
SUBUNITS
SUBURB
SUBURBAN
SUBURBIA
SUBURBS
SUBVERSION
SUBVERSIVE
SUBVERT
SUBVERTED
SUBVERTER
SUBVERTING
SUBVERTS
SUBWAY
SUBWAYS
SUCCEED
SUCCEEDED
SUCCEEDING
SUCCEEDS
SUCCESS
SUCCESSES
SUCCESSFUL
SUCCESSFULLY
SUCCESSION
SUCCESSIONS
SUCCESSIVE
SUCCESSIVELY
SUCCESSOR
SUCCESSORS
SUCCINCT
SUCCINCTLY
SUCCINCTNESS
SUCCOR
SUCCUMB
SUCCUMBED
SUCCUMBING
SUCCUMBS
SUCH
SUCK
SUCKED
SUCKER
SUCKERS
SUCKING
SUCKLE
SUCKLING
SUCKS
SUCTION
SUDAN
SUDANESE
SUDANIC
SUDDEN
SUDDENLY
SUDDENNESS
SUDS
SUDSING
SUE
SUED
SUES
SUEZ
SUFFER
SUFFERANCE
SUFFERED
SUFFERER
SUFFERERS
SUFFERING
SUFFERINGS
SUFFERS
SUFFICE
SUFFICED
SUFFICES
SUFFICIENCY
SUFFICIENT
SUFFICIENTLY
SUFFICING
SUFFIX
SUFFIXED
SUFFIXER
SUFFIXES
SUFFIXING
SUFFOCATE
SUFFOCATED
SUFFOCATES
SUFFOCATING
SUFFOCATION
SUFFOLK
SUFFRAGE
SUFFRAGETTE
SUGAR
SUGARED
SUGARING
SUGARINGS
SUGARS
SUGGEST
SUGGESTED
SUGGESTIBLE
SUGGESTING
SUGGESTION
SUGGESTIONS
SUGGESTIVE
SUGGESTIVELY
SUGGESTS
SUICIDAL
SUICIDALLY
SUICIDE
SUICIDES
SUING
SUIT
SUITABILITY
SUITABLE
SUITABLENESS
SUITABLY
SUITCASE
SUITCASES
SUITE
SUITED
SUITERS
SUITES
SUITING
SUITOR
SUITORS
SUITS
SUKARNO
SULFA
SULFUR
SULFURIC
SULFUROUS
SULK
SULKED
SULKINESS
SULKING
SULKS
SULKY
SULLEN
SULLENLY
SULLENNESS
SULLIVAN
SULPHATE
SULPHUR
SULPHURED
SULPHURIC
SULTAN
SULTANS
SULTRY
SULZBERGER
SUM
SUMAC
SUMATRA
SUMERIA
SUMERIAN
SUMMAND
SUMMANDS
SUMMARIES
SUMMARILY
SUMMARIZATION
SUMMARIZATIONS
SUMMARIZE
SUMMARIZED
SUMMARIZES
SUMMARIZING
SUMMARY
SUMMATION
SUMMATIONS
SUMMED
SUMMER
SUMMERDALE
SUMMERS
SUMMERTIME
SUMMING
SUMMIT
SUMMITRY
SUMMON
SUMMONED
SUMMONER
SUMMONERS
SUMMONING
SUMMONS
SUMMONSES
SUMNER
SUMPTUOUS
SUMS
SUMTER
SUN
SUNBEAM
SUNBEAMS
SUNBELT
SUNBONNET
SUNBURN
SUNBURNT
SUNDAY
SUNDAYS
SUNDER
SUNDIAL
SUNDOWN
SUNDRIES
SUNDRY
SUNFLOWER
SUNG
SUNGLASS
SUNGLASSES
SUNK
SUNKEN
SUNLIGHT
SUNLIT
SUNNED
SUNNING
SUNNY
SUNNYVALE
SUNRISE
SUNS
SUNSET
SUNSHINE
SUNSPOT
SUNTAN
SUNTANNED
SUNTANNING
SUPER
SUPERB
SUPERBLOCK
SUPERBLY
SUPERCOMPUTER
SUPERCOMPUTERS
SUPEREGO
SUPEREGOS
SUPERFICIAL
SUPERFICIALLY
SUPERFLUITIES
SUPERFLUITY
SUPERFLUOUS
SUPERFLUOUSLY
SUPERGROUP
SUPERGROUPS
SUPERHUMAN
SUPERHUMANLY
SUPERIMPOSE
SUPERIMPOSED
SUPERIMPOSES
SUPERIMPOSING
SUPERINTEND
SUPERINTENDENT
SUPERINTENDENTS
SUPERIOR
SUPERIORITY
SUPERIORS
SUPERLATIVE
SUPERLATIVELY
SUPERLATIVES
SUPERMARKET
SUPERMARKETS
SUPERMINI
SUPERMINIS
SUPERNATURAL
SUPERPOSE
SUPERPOSED
SUPERPOSES
SUPERPOSING
SUPERPOSITION
SUPERSCRIPT
SUPERSCRIPTED
SUPERSCRIPTING
SUPERSCRIPTS
SUPERSEDE
SUPERSEDED
SUPERSEDES
SUPERSEDING
SUPERSET
SUPERSETS
SUPERSTITION
SUPERSTITIONS
SUPERSTITIOUS
SUPERUSER
SUPERVISE
SUPERVISED
SUPERVISES
SUPERVISING
SUPERVISION
SUPERVISOR
SUPERVISORS
SUPERVISORY
SUPINE
SUPPER
SUPPERS
SUPPLANT
SUPPLANTED
SUPPLANTING
SUPPLANTS
SUPPLE
SUPPLEMENT
SUPPLEMENTAL
SUPPLEMENTARY
SUPPLEMENTED
SUPPLEMENTING
SUPPLEMENTS
SUPPLENESS
SUPPLICATION
SUPPLIED
SUPPLIER
SUPPLIERS
SUPPLIES
SUPPLY
SUPPLYING
SUPPORT
SUPPORTABLE
SUPPORTED
SUPPORTER
SUPPORTERS
SUPPORTING
SUPPORTINGLY
SUPPORTIVE
SUPPORTIVELY
SUPPORTS
SUPPOSE
SUPPOSED
SUPPOSEDLY
SUPPOSES
SUPPOSING
SUPPOSITION
SUPPOSITIONS
SUPPRESS
SUPPRESSED
SUPPRESSES
SUPPRESSING
SUPPRESSION
SUPPRESSOR
SUPPRESSORS
SUPRANATIONAL
SUPREMACY
SUPREME
SUPREMELY
SURCHARGE
SURE
SURELY
SURENESS
SURETIES
SURETY
SURF
SURFACE
SURFACED
SURFACENESS
SURFACES
SURFACING
SURGE
SURGED
SURGEON
SURGEONS
SURGERY
SURGES
SURGICAL
SURGICALLY
SURGING
SURLINESS
SURLY
SURMISE
SURMISED
SURMISES
SURMOUNT
SURMOUNTED
SURMOUNTING
SURMOUNTS
SURNAME
SURNAMES
SURPASS
SURPASSED
SURPASSES
SURPASSING
SURPLUS
SURPLUSES
SURPRISE
SURPRISED
SURPRISES
SURPRISING
SURPRISINGLY
SURREAL
SURRENDER
SURRENDERED
SURRENDERING
SURRENDERS
SURREPTITIOUS
SURREY
SURROGATE
SURROGATES
SURROUND
SURROUNDED
SURROUNDING
SURROUNDINGS
SURROUNDS
SURTAX
SURVEY
SURVEYED
SURVEYING
SURVEYOR
SURVEYORS
SURVEYS
SURVIVAL
SURVIVALS
SURVIVE
SURVIVED
SURVIVES
SURVIVING
SURVIVOR
SURVIVORS
SUS
SUSAN
SUSANNE
SUSCEPTIBLE
SUSIE
SUSPECT
SUSPECTED
SUSPECTING
SUSPECTS
SUSPEND
SUSPENDED
SUSPENDER
SUSPENDERS
SUSPENDING
SUSPENDS
SUSPENSE
SUSPENSES
SUSPENSION
SUSPENSIONS
SUSPICION
SUSPICIONS
SUSPICIOUS
SUSPICIOUSLY
SUSQUEHANNA
SUSSEX
SUSTAIN
SUSTAINED
SUSTAINING
SUSTAINS
SUSTENANCE
SUTHERLAND
SUTTON
SUTURE
SUTURES
SUWANEE
SUZANNE
SUZERAINTY
SUZUKI
SVELTE
SVETLANA
SWAB
SWABBING
SWAGGER
SWAGGERED
SWAGGERING
SWAHILI
SWAIN
SWAINS
SWALLOW
SWALLOWED
SWALLOWING
SWALLOWS
SWALLOWTAIL
SWAM
SWAMI
SWAMP
SWAMPED
SWAMPING
SWAMPS
SWAMPY
SWAN
SWANK
SWANKY
SWANLIKE
SWANS
SWANSEA
SWANSON
SWAP
SWAPPED
SWAPPING
SWAPS
SWARM
SWARMED
SWARMING
SWARMS
SWARTHMORE
SWARTHOUT
SWARTHY
SWARTZ
SWASTIKA
SWAT
SWATTED
SWAY
SWAYED
SWAYING
SWAZILAND
SWEAR
SWEARER
SWEARING
SWEARS
SWEAT
SWEATED
SWEATER
SWEATERS
SWEATING
SWEATS
SWEATSHIRT
SWEATY
SWEDE
SWEDEN
SWEDES
SWEDISH
SWEENEY
SWEENEYS
SWEEP
SWEEPER
SWEEPERS
SWEEPING
SWEEPINGS
SWEEPS
SWEEPSTAKES
SWEET
SWEETEN
SWEETENED
SWEETENER
SWEETENERS
SWEETENING
SWEETENINGS
SWEETENS
SWEETER
SWEETEST
SWEETHEART
SWEETHEARTS
SWEETISH
SWEETLY
SWEETNESS
SWEETS
SWELL
SWELLED
SWELLING
SWELLINGS
SWELLS
SWELTER
SWENSON
SWEPT
SWERVE
SWERVED
SWERVES
SWERVING
SWIFT
SWIFTER
SWIFTEST
SWIFTLY
SWIFTNESS
SWIM
SWIMMER
SWIMMERS
SWIMMING
SWIMMINGLY
SWIMS
SWIMSUIT
SWINBURNE
SWINDLE
SWINE
SWING
SWINGER
SWINGERS
SWINGING
SWINGS
SWINK
SWIPE
SWIRL
SWIRLED
SWIRLING
SWISH
SWISHED
SWISS
SWITCH
SWITCHBLADE
SWITCHBOARD
SWITCHBOARDS
SWITCHED
SWITCHER
SWITCHERS
SWITCHES
SWITCHING
SWITCHINGS
SWITCHMAN
SWITZER
SWITZERLAND
SWIVEL
SWIZZLE
SWOLLEN
SWOON
SWOOP
SWOOPED
SWOOPING
SWOOPS
SWORD
SWORDFISH
SWORDS
SWORE
SWORN
SWUM
SWUNG
SYBIL
SYCAMORE
SYCOPHANT
SYCOPHANTIC
SYDNEY
SYKES
SYLLABLE
SYLLABLES
SYLLOGISM
SYLLOGISMS
SYLLOGISTIC
SYLOW
SYLVAN
SYLVANIA
SYLVESTER
SYLVIA
SYLVIE
SYMBIOSIS
SYMBIOTIC
SYMBOL
SYMBOLIC
SYMBOLICALLY
SYMBOLICS
SYMBOLISM
SYMBOLIZATION
SYMBOLIZE
SYMBOLIZED
SYMBOLIZES
SYMBOLIZING
SYMBOLS
SYMINGTON
SYMMETRIC
SYMMETRICAL
SYMMETRICALLY
SYMMETRIES
SYMMETRY
SYMPATHETIC
SYMPATHIES
SYMPATHIZE
SYMPATHIZED
SYMPATHIZER
SYMPATHIZERS
SYMPATHIZES
SYMPATHIZING
SYMPATHIZINGLY
SYMPATHY
SYMPHONIC
SYMPHONIES
SYMPHONY
SYMPOSIA
SYMPOSIUM
SYMPOSIUMS
SYMPTOM
SYMPTOMATIC
SYMPTOMS
SYNAGOGUE
SYNAPSE
SYNAPSES
SYNAPTIC
SYNCHRONISM
SYNCHRONIZATION
SYNCHRONIZE
SYNCHRONIZED
SYNCHRONIZER
SYNCHRONIZERS
SYNCHRONIZES
SYNCHRONIZING
SYNCHRONOUS
SYNCHRONOUSLY
SYNCHRONY
SYNCHROTRON
SYNCOPATE
SYNDICATE
SYNDICATED
SYNDICATES
SYNDICATION
SYNDROME
SYNDROMES
SYNERGISM
SYNERGISTIC
SYNERGY
SYNGE
SYNOD
SYNONYM
SYNONYMOUS
SYNONYMOUSLY
SYNONYMS
SYNOPSES
SYNOPSIS
SYNTACTIC
SYNTACTICAL
SYNTACTICALLY
SYNTAX
SYNTAXES
SYNTHESIS
SYNTHESIZE
SYNTHESIZED
SYNTHESIZER
SYNTHESIZERS
SYNTHESIZES
SYNTHESIZING
SYNTHETIC
SYNTHETICS
SYRACUSE
SYRIA
SYRIAN
SYRIANIZE
SYRIANIZES
SYRIANS
SYRINGE
SYRINGES
SYRUP
SYRUPY
SYSTEM
SYSTEMATIC
SYSTEMATICALLY
SYSTEMATIZE
SYSTEMATIZED
SYSTEMATIZES
SYSTEMATIZING
SYSTEMIC
SYSTEMS
SYSTEMWIDE
SZILARD
TAB
TABERNACLE
TABERNACLES
TABLE
TABLEAU
TABLEAUS
TABLECLOTH
TABLECLOTHS
TABLED
TABLES
TABLESPOON
TABLESPOONFUL
TABLESPOONFULS
TABLESPOONS
TABLET
TABLETS
TABLING
TABOO
TABOOS
TABS
TABULAR
TABULATE
TABULATED
TABULATES
TABULATING
TABULATION
TABULATIONS
TABULATOR
TABULATORS
TACHOMETER
TACHOMETERS
TACIT
TACITLY
TACITUS
TACK
TACKED
TACKING
TACKLE
TACKLES
TACOMA
TACT
TACTIC
TACTICS
TACTILE
TAFT
TAG
TAGGED
TAGGING
TAGS
TAHITI
TAHOE
TAIL
TAILED
TAILING
TAILOR
TAILORED
TAILORING
TAILORS
TAILS
TAINT
TAINTED
TAIPEI
TAIWAN
TAIWANESE
TAKE
TAKEN
TAKER
TAKERS
TAKES
TAKING
TAKINGS
TALE
TALENT
TALENTED
TALENTS
TALES
TALK
TALKATIVE
TALKATIVELY
TALKATIVENESS
TALKED
TALKER
TALKERS
TALKIE
TALKING
TALKS
TALL
TALLADEGA
TALLAHASSEE
TALLAHATCHIE
TALLAHOOSA
TALLCHIEF
TALLER
TALLEST
TALLEYRAND
TALLNESS
TALLOW
TALLY
TALMUD
TALMUDISM
TALMUDIZATION
TALMUDIZATIONS
TALMUDIZE
TALMUDIZES
TAME
TAMED
TAMELY
TAMENESS
TAMER
TAMES
TAMIL
TAMING
TAMMANY
TAMMANYIZE
TAMMANYIZES
TAMPA
TAMPER
TAMPERED
TAMPERING
TAMPERS
TAN
TANAKA
TANANARIVE
TANDEM
TANG
TANGANYIKA
TANGENT
TANGENTIAL
TANGENTS
TANGIBLE
TANGIBLY
TANGLE
TANGLED
TANGY
TANK
TANKER
TANKERS
TANKS
TANNENBAUM
TANNER
TANNERS
TANTALIZING
TANTALIZINGLY
TANTALUS
TANTAMOUNT
TANTRUM
TANTRUMS
TANYA
TANZANIA
TAOISM
TAOIST
TAOS
TAP
TAPE
TAPED
TAPER
TAPERED
TAPERING
TAPERS
TAPES
TAPESTRIES
TAPESTRY
TAPING
TAPINGS
TAPPED
TAPPER
TAPPERS
TAPPING
TAPROOT
TAPROOTS
TAPS
TAR
TARA
TARBELL
TARDINESS
TARDY
TARGET
TARGETED
TARGETING
TARGETS
TARIFF
TARIFFS
TARRY
TARRYTOWN
TART
TARTARY
TARTLY
TARTNESS
TARTUFFE
TARZAN
TASK
TASKED
TASKING
TASKS
TASMANIA
TASS
TASSEL
TASSELS
TASTE
TASTED
TASTEFUL
TASTEFULLY
TASTEFULNESS
TASTELESS
TASTELESSLY
TASTER
TASTERS
TASTES
TASTING
TATE
TATTER
TATTERED
TATTOO
TATTOOED
TATTOOS
TAU
TAUGHT
TAUNT
TAUNTED
TAUNTER
TAUNTING
TAUNTS
TAURUS
TAUT
TAUTLY
TAUTNESS
TAUTOLOGICAL
TAUTOLOGICALLY
TAUTOLOGIES
TAUTOLOGY
TAVERN
TAVERNS
TAWNEY
TAWNY
TAX
TAXABLE
TAXATION
TAXED
TAXES
TAXI
TAXICAB
TAXICABS
TAXIED
TAXIING
TAXING
TAXIS
TAXONOMIC
TAXONOMICALLY
TAXONOMY
TAXPAYER
TAXPAYERS
TAYLOR
TAYLORIZE
TAYLORIZES
TAYLORS
TCHAIKOVSKY
TEA
TEACH
TEACHABLE
TEACHER
TEACHERS
TEACHES
TEACHING
TEACHINGS
TEACUP
TEAM
TEAMED
TEAMING
TEAMS
TEAR
TEARED
TEARFUL
TEARFULLY
TEARING
TEARS
TEAS
TEASE
TEASED
TEASES
TEASING
TEASPOON
TEASPOONFUL
TEASPOONFULS
TEASPOONS
TECHNICAL
TECHNICALITIES
TECHNICALITY
TECHNICALLY
TECHNICIAN
TECHNICIANS
TECHNION
TECHNIQUE
TECHNIQUES
TECHNOLOGICAL
TECHNOLOGICALLY
TECHNOLOGIES
TECHNOLOGIST
TECHNOLOGISTS
TECHNOLOGY
TED
TEDDY
TEDIOUS
TEDIOUSLY
TEDIOUSNESS
TEDIUM
TEEM
TEEMED
TEEMING
TEEMS
TEEN
TEENAGE
TEENAGED
TEENAGER
TEENAGERS
TEENS
TEETH
TEETHE
TEETHED
TEETHES
TEETHING
TEFLON
TEGUCIGALPA
TEHERAN
TEHRAN
TEKTRONIX
TELECOMMUNICATION
TELECOMMUNICATIONS
TELEDYNE
TELEFUNKEN
TELEGRAM
TELEGRAMS
TELEGRAPH
TELEGRAPHED
TELEGRAPHER
TELEGRAPHERS
TELEGRAPHIC
TELEGRAPHING
TELEGRAPHS
TELEMANN
TELEMETRY
TELEOLOGICAL
TELEOLOGICALLY
TELEOLOGY
TELEPATHY
TELEPHONE
TELEPHONED
TELEPHONER
TELEPHONERS
TELEPHONES
TELEPHONIC
TELEPHONING
TELEPHONY
TELEPROCESSING
TELESCOPE
TELESCOPED
TELESCOPES
TELESCOPING
TELETEX
TELETEXT
TELETYPE
TELETYPES
TELEVISE
TELEVISED
TELEVISES
TELEVISING
TELEVISION
TELEVISIONS
TELEVISOR
TELEVISORS
TELEX
TELL
TELLER
TELLERS
TELLING
TELLS
TELNET
TELNET
TEMPER
TEMPERAMENT
TEMPERAMENTAL
TEMPERAMENTS
TEMPERANCE
TEMPERATE
TEMPERATELY
TEMPERATENESS
TEMPERATURE
TEMPERATURES
TEMPERED
TEMPERING
TEMPERS
TEMPEST
TEMPESTUOUS
TEMPESTUOUSLY
TEMPLATE
TEMPLATES
TEMPLE
TEMPLEMAN
TEMPLES
TEMPLETON
TEMPORAL
TEMPORALLY
TEMPORARIES
TEMPORARILY
TEMPORARY
TEMPT
TEMPTATION
TEMPTATIONS
TEMPTED
TEMPTER
TEMPTERS
TEMPTING
TEMPTINGLY
TEMPTS
TEN
TENACIOUS
TENACIOUSLY
TENANT
TENANTS
TEND
TENDED
TENDENCIES
TENDENCY
TENDER
TENDERLY
TENDERNESS
TENDERS
TENDING
TENDS
TENEMENT
TENEMENTS
TENEX
TENEX
TENFOLD
TENNECO
TENNESSEE
TENNEY
TENNIS
TENNYSON
TENOR
TENORS
TENS
TENSE
TENSED
TENSELY
TENSENESS
TENSER
TENSES
TENSEST
TENSING
TENSION
TENSIONS
TENT
TENTACLE
TENTACLED
TENTACLES
TENTATIVE
TENTATIVELY
TENTED
TENTH
TENTING
TENTS
TENURE
TERESA
TERM
TERMED
TERMINAL
TERMINALLY
TERMINALS
TERMINATE
TERMINATED
TERMINATES
TERMINATING
TERMINATION
TERMINATIONS
TERMINATOR
TERMINATORS
TERMING
TERMINOLOGIES
TERMINOLOGY
TERMINUS
TERMS
TERMWISE
TERNARY
TERPSICHORE
TERRA
TERRACE
TERRACED
TERRACES
TERRAIN
TERRAINS
TERRAN
TERRE
TERRESTRIAL
TERRESTRIALS
TERRIBLE
TERRIBLY
TERRIER
TERRIERS
TERRIFIC
TERRIFIED
TERRIFIES
TERRIFY
TERRIFYING
TERRITORIAL
TERRITORIES
TERRITORY
TERROR
TERRORISM
TERRORIST
TERRORISTIC
TERRORISTS
TERRORIZE
TERRORIZED
TERRORIZES
TERRORIZING
TERRORS
TERTIARY
TESS
TESSIE
TEST
TESTABILITY
TESTABLE
TESTAMENT
TESTAMENTS
TESTED
TESTER
TESTERS
TESTICLE
TESTICLES
TESTIFIED
TESTIFIER
TESTIFIERS
TESTIFIES
TESTIFY
TESTIFYING
TESTIMONIES
TESTIMONY
TESTING
TESTINGS
TESTS
TEUTONIC
TEX
TEX
TEXACO
TEXAN
TEXANS
TEXAS
TEXASES
TEXT
TEXTBOOK
TEXTBOOKS
TEXTILE
TEXTILES
TEXTRON
TEXTS
TEXTUAL
TEXTUALLY
TEXTURE
TEXTURED
TEXTURES
THAI
THAILAND
THALIA
THAMES
THAN
THANK
THANKED
THANKFUL
THANKFULLY
THANKFULNESS
THANKING
THANKLESS
THANKLESSLY
THANKLESSNESS
THANKS
THANKSGIVING
THANKSGIVINGS
THAT
THATCH
THATCHES
THATS
THAW
THAWED
THAWING
THAWS
THAYER
THE
THEA
THEATER
THEATERS
THEATRICAL
THEATRICALLY
THEATRICALS
THEBES
THEFT
THEFTS
THEIR
THEIRS
THELMA
THEM
THEMATIC
THEME
THEMES
THEMSELVES
THEN
THENCE
THENCEFORTH
THEODORE
THEODOSIAN
THEODOSIUS
THEOLOGICAL
THEOLOGY
THEOREM
THEOREMS
THEORETIC
THEORETICAL
THEORETICALLY
THEORETICIANS
THEORIES
THEORIST
THEORISTS
THEORIZATION
THEORIZATIONS
THEORIZE
THEORIZED
THEORIZER
THEORIZERS
THEORIZES
THEORIZING
THEORY
THERAPEUTIC
THERAPIES
THERAPIST
THERAPISTS
THERAPY
THERE
THEREABOUTS
THEREAFTER
THEREBY
THEREFORE
THEREIN
THEREOF
THEREON
THERESA
THERETO
THEREUPON
THEREWITH
THERMAL
THERMODYNAMIC
THERMODYNAMICS
THERMOFAX
THERMOMETER
THERMOMETERS
THERMOSTAT
THERMOSTATS
THESE
THESES
THESEUS
THESIS
THESSALONIAN
THESSALY
THETIS
THEY
THICK
THICKEN
THICKENS
THICKER
THICKEST
THICKET
THICKETS
THICKLY
THICKNESS
THIEF
THIENSVILLE
THIEVE
THIEVES
THIEVING
THIGH
THIGHS
THIMBLE
THIMBLES
THIMBU
THIN
THING
THINGS
THINK
THINKABLE
THINKABLY
THINKER
THINKERS
THINKING
THINKS
THINLY
THINNER
THINNESS
THINNEST
THIRD
THIRDLY
THIRDS
THIRST
THIRSTED
THIRSTS
THIRSTY
THIRTEEN
THIRTEENS
THIRTEENTH
THIRTIES
THIRTIETH
THIRTY
THIS
THISTLE
THOMAS
THOMISTIC
THOMPSON
THOMSON
THONG
THOR
THOREAU
THORN
THORNBURG
THORNS
THORNTON
THORNY
THOROUGH
THOROUGHFARE
THOROUGHFARES
THOROUGHLY
THOROUGHNESS
THORPE
THORSTEIN
THOSE
THOUGH
THOUGHT
THOUGHTFUL
THOUGHTFULLY
THOUGHTFULNESS
THOUGHTLESS
THOUGHTLESSLY
THOUGHTLESSNESS
THOUGHTS
THOUSAND
THOUSANDS
THOUSANDTH
THRACE
THRACIAN
THRASH
THRASHED
THRASHER
THRASHES
THRASHING
THREAD
THREADED
THREADER
THREADERS
THREADING
THREADS
THREAT
THREATEN
THREATENED
THREATENING
THREATENS
THREATS
THREE
THREEFOLD
THREES
THREESCORE
THRESHOLD
THRESHOLDS
THREW
THRICE
THRIFT
THRIFTY
THRILL
THRILLED
THRILLER
THRILLERS
THRILLING
THRILLINGLY
THRILLS
THRIVE
THRIVED
THRIVES
THRIVING
THROAT
THROATED
THROATS
THROB
THROBBED
THROBBING
THROBS
THRONE
THRONEBERRY
THRONES
THRONG
THRONGS
THROTTLE
THROTTLED
THROTTLES
THROTTLING
THROUGH
THROUGHOUT
THROUGHPUT
THROW
THROWER
THROWING
THROWN
THROWS
THRUSH
THRUST
THRUSTER
THRUSTERS
THRUSTING
THRUSTS
THUBAN
THUD
THUDS
THUG
THUGS
THULE
THUMB
THUMBED
THUMBING
THUMBS
THUMP
THUMPED
THUMPING
THUNDER
THUNDERBOLT
THUNDERBOLTS
THUNDERED
THUNDERER
THUNDERERS
THUNDERING
THUNDERS
THUNDERSTORM
THUNDERSTORMS
THURBER
THURMAN
THURSDAY
THURSDAYS
THUS
THUSLY
THWART
THWARTED
THWARTING
THWARTS
THYSELF
TIBER
TIBET
TIBETAN
TIBURON
TICK
TICKED
TICKER
TICKERS
TICKET
TICKETS
TICKING
TICKLE
TICKLED
TICKLES
TICKLING
TICKLISH
TICKS
TICONDEROGA
TIDAL
TIDALLY
TIDE
TIDED
TIDES
TIDIED
TIDINESS
TIDING
TIDINGS
TIDY
TIDYING
TIE
TIECK
TIED
TIENTSIN
TIER
TIERS
TIES
TIFFANY
TIGER
TIGERS
TIGHT
TIGHTEN
TIGHTENED
TIGHTENER
TIGHTENERS
TIGHTENING
TIGHTENINGS
TIGHTENS
TIGHTER
TIGHTEST
TIGHTLY
TIGHTNESS
TIGRIS
TIJUANA
TILDE
TILE
TILED
TILES
TILING
TILL
TILLABLE
TILLED
TILLER
TILLERS
TILLICH
TILLIE
TILLING
TILLS
TILT
TILTED
TILTING
TILTS
TIM
TIMBER
TIMBERED
TIMBERING
TIMBERS
TIME
TIMED
TIMELESS
TIMELESSLY
TIMELESSNESS
TIMELY
TIMEOUT
TIMEOUTS
TIMER
TIMERS
TIMES
TIMESHARE
TIMESHARES
TIMESHARING
TIMESTAMP
TIMESTAMPS
TIMETABLE
TIMETABLES
TIMEX
TIMID
TIMIDITY
TIMIDLY
TIMING
TIMINGS
TIMMY
TIMON
TIMONIZE
TIMONIZES
TIMS
TIN
TINA
TINCTURE
TINGE
TINGED
TINGLE
TINGLED
TINGLES
TINGLING
TINIER
TINIEST
TINILY
TININESS
TINKER
TINKERED
TINKERING
TINKERS
TINKLE
TINKLED
TINKLES
TINKLING
TINNIER
TINNIEST
TINNILY
TINNINESS
TINNY
TINS
TINSELTOWN
TINT
TINTED
TINTING
TINTS
TINY
TIOGA
TIP
TIPPECANOE
TIPPED
TIPPER
TIPPERARY
TIPPERS
TIPPING
TIPS
TIPTOE
TIRANA
TIRE
TIRED
TIREDLY
TIRELESS
TIRELESSLY
TIRELESSNESS
TIRES
TIRESOME
TIRESOMELY
TIRESOMENESS
TIRING
TISSUE
TISSUES
TIT
TITAN
TITHE
TITHER
TITHES
TITHING
TITLE
TITLED
TITLES
TITO
TITS
TITTER
TITTERS
TITUS
TOAD
TOADS
TOAST
TOASTED
TOASTER
TOASTING
TOASTS
TOBACCO
TOBAGO
TOBY
TODAY
TODAYS
TODD
TOE
TOES
TOGETHER
TOGETHERNESS
TOGGLE
TOGGLED
TOGGLES
TOGGLING
TOGO
TOIL
TOILED
TOILER
TOILET
TOILETS
TOILING
TOILS
TOKEN
TOKENS
TOKYO
TOLAND
TOLD
TOLEDO
TOLERABILITY
TOLERABLE
TOLERABLY
TOLERANCE
TOLERANCES
TOLERANT
TOLERANTLY
TOLERATE
TOLERATED
TOLERATES
TOLERATING
TOLERATION
TOLL
TOLLED
TOLLEY
TOLLS
TOLSTOY
TOM
TOMAHAWK
TOMAHAWKS
TOMATO
TOMATOES
TOMB
TOMBIGBEE
TOMBS
TOMLINSON
TOMMIE
TOMOGRAPHY
TOMORROW
TOMORROWS
TOMPKINS
TON
TONE
TONED
TONER
TONES
TONGS
TONGUE
TONGUED
TONGUES
TONI
TONIC
TONICS
TONIGHT
TONING
TONIO
TONNAGE
TONS
TONSIL
TOO
TOOK
TOOL
TOOLED
TOOLER
TOOLERS
TOOLING
TOOLS
TOOMEY
TOOTH
TOOTHBRUSH
TOOTHBRUSHES
TOOTHPASTE
TOOTHPICK
TOOTHPICKS
TOP
TOPEKA
TOPER
TOPIC
TOPICAL
TOPICALLY
TOPICS
TOPMOST
TOPOGRAPHY
TOPOLOGICAL
TOPOLOGIES
TOPOLOGY
TOPPLE
TOPPLED
TOPPLES
TOPPLING
TOPS
TOPSY
TORAH
TORCH
TORCHES
TORE
TORIES
TORMENT
TORMENTED
TORMENTER
TORMENTERS
TORMENTING
TORN
TORNADO
TORNADOES
TORONTO
TORPEDO
TORPEDOES
TORQUE
TORQUEMADA
TORRANCE
TORRENT
TORRENTS
TORRID
TORTOISE
TORTOISES
TORTURE
TORTURED
TORTURER
TORTURERS
TORTURES
TORTURING
TORUS
TORUSES
TORY
TORYIZE
TORYIZES
TOSCA
TOSCANINI
TOSHIBA
TOSS
TOSSED
TOSSES
TOSSING
TOTAL
TOTALED
TOTALING
TOTALITIES
TOTALITY
TOTALLED
TOTALLER
TOTALLERS
TOTALLING
TOTALLY
TOTALS
TOTO
TOTTER
TOTTERED
TOTTERING
TOTTERS
TOUCH
TOUCHABLE
TOUCHED
TOUCHES
TOUCHIER
TOUCHIEST
TOUCHILY
TOUCHINESS
TOUCHING
TOUCHINGLY
TOUCHY
TOUGH
TOUGHEN
TOUGHER
TOUGHEST
TOUGHLY
TOUGHNESS
TOULOUSE
TOUR
TOURED
TOURING
TOURIST
TOURISTS
TOURNAMENT
TOURNAMENTS
TOURS
TOW
TOWARD
TOWARDS
TOWED
TOWEL
TOWELING
TOWELLED
TOWELLING
TOWELS
TOWER
TOWERED
TOWERING
TOWERS
TOWN
TOWNLEY
TOWNS
TOWNSEND
TOWNSHIP
TOWNSHIPS
TOWSLEY
TOY
TOYED
TOYING
TOYNBEE
TOYOTA
TOYS
TRACE
TRACEABLE
TRACED
TRACER
TRACERS
TRACES
TRACING
TRACINGS
TRACK
TRACKED
TRACKER
TRACKERS
TRACKING
TRACKS
TRACT
TRACTABILITY
TRACTABLE
TRACTARIANS
TRACTIVE
TRACTOR
TRACTORS
TRACTS
TRACY
TRADE
TRADED
TRADEMARK
TRADEMARKS
TRADEOFF
TRADEOFFS
TRADER
TRADERS
TRADES
TRADESMAN
TRADING
TRADITION
TRADITIONAL
TRADITIONALLY
TRADITIONS
TRAFFIC
TRAFFICKED
TRAFFICKER
TRAFFICKERS
TRAFFICKING
TRAFFICS
TRAGEDIES
TRAGEDY
TRAGIC
TRAGICALLY
TRAIL
TRAILED
TRAILER
TRAILERS
TRAILING
TRAILINGS
TRAILS
TRAIN
TRAINED
TRAINEE
TRAINEES
TRAINER
TRAINERS
TRAINING
TRAINS
TRAIT
TRAITOR
TRAITORS
TRAITS
TRAJECTORIES
TRAJECTORY
TRAMP
TRAMPED
TRAMPING
TRAMPLE
TRAMPLED
TRAMPLER
TRAMPLES
TRAMPLING
TRAMPS
TRANCE
TRANCES
TRANQUIL
TRANQUILITY
TRANQUILLY
TRANSACT
TRANSACTION
TRANSACTIONS
TRANSATLANTIC
TRANSCEIVE
TRANSCEIVER
TRANSCEIVERS
TRANSCEND
TRANSCENDED
TRANSCENDENT
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TRIO
TRIP
TRIPLE
TRIPLED
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TRIPLET
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TRIUMPHAL
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TRIUMPHANTLY
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TRIVIA
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TRIVIALLY
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TROD
TROJAN
TROLL
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TROOPER
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TROOPS
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TROPHIES
TROPHY
TROPIC
TROPICAL
TROPICS
TROT
TROTS
TROTSKY
TROUBLE
TROUBLED
TROUBLEMAKER
TROUBLEMAKERS
TROUBLES
TROUBLESHOOT
TROUBLESHOOTER
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TROUBLESOME
TROUBLESOMELY
TROUBLING
TROUGH
TROUSER
TROUSERS
TROUT
TROUTMAN
TROWEL
TROWELS
TROY
TRUANT
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TRUCK
TRUCKED
TRUCKEE
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TRUCKERS
TRUCKING
TRUCKS
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TRUDGED
TRUDY
TRUE
TRUED
TRUER
TRUES
TRUEST
TRUING
TRUISM
TRUISMS
TRUJILLO
TRUK
TRULY
TRUMAN
TRUMBULL
TRUMP
TRUMPED
TRUMPET
TRUMPETER
TRUMPS
TRUNCATE
TRUNCATED
TRUNCATES
TRUNCATING
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TRUST
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TRUSTEE
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TRUSTFUL
TRUSTFULLY
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TRUSTING
TRUSTINGLY
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TRUSTY
TRUTH
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TRY
TRYING
TSUNEMATSU
TUB
TUBE
TUBER
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TUBERS
TUBES
TUBING
TUBS
TUCK
TUCKED
TUCKER
TUCKING
TUCKS
TUCSON
TUDOR
TUESDAY
TUESDAYS
TUFT
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TUG
TUGS
TUITION
TULANE
TULIP
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TULSA
TUMBLE
TUMBLED
TUMBLER
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TUMBLING
TUMOR
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TUMULT
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TUMULTUOUS
TUNABLE
TUNE
TUNED
TUNER
TUNERS
TUNES
TUNIC
TUNICS
TUNING
TUNIS
TUNISIA
TUNISIAN
TUNNEL
TUNNELED
TUNNELS
TUPLE
TUPLES
TURBAN
TURBANS
TURBULENCE
TURBULENT
TURBULENTLY
TURF
TURGID
TURGIDLY
TURIN
TURING
TURKEY
TURKEYS
TURKISH
TURKIZE
TURKIZES
TURMOIL
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TURN
TURNABLE
TURNAROUND
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TURNER
TURNERS
TURNING
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TURNIP
TURNIPS
TURNOVER
TURNS
TURPENTINE
TURQUOISE
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TURTLE
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TUSCAN
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TUSCARORA
TUSKEGEE
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TUTANKHAMON
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TUTORIAL
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TWANG
TWAS
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TWELFTH
TWELVE
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TWENTIETH
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TYPED
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TYPHON
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VACATION
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WEB
WEBB
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WEBSTERVILLE
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WEDDING
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WEE
WEED
WEEDS
WEEK
WEEKEND
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WEHR
WEI
WEIBULL
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WEIDMAN
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WEIGHED
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WEIGHINGS
WEIGHS
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WEIGHTED
WEIGHTING
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WEISS
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WELLES
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WEPT
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WHACK
WHACKED
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WHAT
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WHATLEY
WHATSOEVER
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WHEREABOUTS
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WHEREBY
WHEREIN
WHEREUPON
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WHIM
WHIMPER
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WHIT
WHITAKER
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WHITE
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WHITENED
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WHITMAN
WHITMANIZE
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WHITNEY
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WHITTIER
WHITTLE
WHITTLED
WHITTLES
WHITTLING
WHIZ
WHIZZED
WHIZZES
WHIZZING
WHO
WHOEVER
WHOLE
WHOLEHEARTED
WHOLEHEARTEDLY
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WHOLESALE
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WHOLESALERS
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WHOLLY
WHOM
WHOMEVER
WHOOP
WHOOPED
WHOOPING
WHOOPS
WHORE
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WHORLS
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WICHITA
WICK
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WIDTH
WIDTHS
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WIELDED
WIELDER
WIELDING
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WIER
WIFE
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WIG
WIGGINS
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WIGS
WIGWAM
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WILCOX
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WILDCAT
WILDCATS
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WILES
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WILKIE
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WILL
WILLA
WILLAMETTE
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WILLEM
WILLFUL
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WILLIAM
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WILLIE
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WILLIS
WILLISSON
WILLOUGHBY
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WILLOWS
WILLS
WILLY
WILMA
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WILMINGTON
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WILSON
WILSONIAN
WILT
WILTED
WILTING
WILTS
WILTSHIRE
WILY
WIN
WINCE
WINCED
WINCES
WINCHELL
WINCHESTER
WINCING
WIND
WINDED
WINDER
WINDERS
WINDING
WINDMILL
WINDMILLS
WINDOW
WINDOWS
WINDS
WINDSOR
WINDY
WINE
WINED
WINEHEAD
WINER
WINERS
WINES
WINFIELD
WING
WINGED
WINGING
WINGS
WINIFRED
WINING
WINK
WINKED
WINKER
WINKING
WINKS
WINNEBAGO
WINNER
WINNERS
WINNETKA
WINNIE
WINNING
WINNINGLY
WINNINGS
WINNIPEG
WINNIPESAUKEE
WINOGRAD
WINOOSKI
WINS
WINSBOROUGH
WINSETT
WINSLOW
WINSTON
WINTER
WINTERED
WINTERING
WINTERS
WINTHROP
WINTRY
WIPE
WIPED
WIPER
WIPERS
WIPES
WIPING
WIRE
WIRED
WIRELESS
WIRES
WIRETAP
WIRETAPPERS
WIRETAPPING
WIRETAPS
WIRINESS
WIRING
WIRY
WISCONSIN
WISDOM
WISDOMS
WISE
WISED
WISELY
WISENHEIMER
WISER
WISEST
WISH
WISHED
WISHER
WISHERS
WISHES
WISHFUL
WISHING
WISP
WISPS
WISTFUL
WISTFULLY
WISTFULNESS
WIT
WITCH
WITCHCRAFT
WITCHES
WITCHING
WITH
WITHAL
WITHDRAW
WITHDRAWAL
WITHDRAWALS
WITHDRAWING
WITHDRAWN
WITHDRAWS
WITHDREW
WITHER
WITHERS
WITHERSPOON
WITHHELD
WITHHOLD
WITHHOLDER
WITHHOLDERS
WITHHOLDING
WITHHOLDINGS
WITHHOLDS
WITHIN
WITHOUT
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WITHSTANDING
WITHSTANDS
WITHSTOOD
WITNESS
WITNESSED
WITNESSES
WITNESSING
WITS
WITT
WITTGENSTEIN
WITTY
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WIZARDS
WOE
WOEFUL
WOEFULLY
WOKE
WOLCOTT
WOLF
WOLFE
WOLFF
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WOLVES
WOMAN
WOMANHOOD
WOMANLY
WOMB
WOMBS
WOMEN
WON
WONDER
WONDERED
WONDERFUL
WONDERFULLY
WONDERFULNESS
WONDERING
WONDERINGLY
WONDERMENT
WONDERS
WONDROUS
WONDROUSLY
WONG
WONT
WONTED
WOO
WOOD
WOODARD
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WOODBURY
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WOODCOCKS
WOODED
WOODEN
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WOODPECKERS
WOODROW
WOODS
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WOODWARDS
WOODWORK
WOODWORKING
WOODY
WOOED
WOOER
WOOF
WOOFED
WOOFER
WOOFERS
WOOFING
WOOFS
WOOING
WOOL
WOOLEN
WOOLLY
WOOLS
WOOLWORTH
WOONSOCKET
WOOS
WOOSTER
WORCESTER
WORCESTERSHIRE
WORD
WORDED
WORDILY
WORDINESS
WORDING
WORDS
WORDSWORTH
WORDY
WORE
WORK
WORKABLE
WORKABLY
WORKBENCH
WORKBENCHES
WORKBOOK
WORKBOOKS
WORKED
WORKER
WORKERS
WORKHORSE
WORKHORSES
WORKING
WORKINGMAN
WORKINGS
WORKLOAD
WORKMAN
WORKMANSHIP
WORKMEN
WORKS
WORKSHOP
WORKSHOPS
WORKSPACE
WORKSTATION
WORKSTATIONS
WORLD
WORLDLINESS
WORLDLY
WORLDS
WORLDWIDE
WORM
WORMED
WORMING
WORMS
WORN
WORRIED
WORRIER
WORRIERS
WORRIES
WORRISOME
WORRY
WORRYING
WORRYINGLY
WORSE
WORSHIP
WORSHIPED
WORSHIPER
WORSHIPFUL
WORSHIPING
WORSHIPS
WORST
WORSTED
WORTH
WORTHIEST
WORTHINESS
WORTHINGTON
WORTHLESS
WORTHLESSNESS
WORTHS
WORTHWHILE
WORTHWHILENESS
WORTHY
WOTAN
WOULD
WOUND
WOUNDED
WOUNDING
WOUNDS
WOVE
WOVEN
WRANGLE
WRANGLED
WRANGLER
WRAP
WRAPAROUND
WRAPPED
WRAPPER
WRAPPERS
WRAPPING
WRAPPINGS
WRAPS
WRATH
WREAK
WREAKS
WREATH
WREATHED
WREATHES
WRECK
WRECKAGE
WRECKED
WRECKER
WRECKERS
WRECKING
WRECKS
WREN
WRENCH
WRENCHED
WRENCHES
WRENCHING
WRENS
WREST
WRESTLE
WRESTLER
WRESTLES
WRESTLING
WRESTLINGS
WRETCH
WRETCHED
WRETCHEDNESS
WRETCHES
WRIGGLE
WRIGGLED
WRIGGLER
WRIGGLES
WRIGGLING
WRIGLEY
WRING
WRINGER
WRINGS
WRINKLE
WRINKLED
WRINKLES
WRIST
WRISTS
WRISTWATCH
WRISTWATCHES
WRIT
WRITABLE
WRITE
WRITER
WRITERS
WRITES
WRITHE
WRITHED
WRITHES
WRITHING
WRITING
WRITINGS
WRITS
WRITTEN
WRONG
WRONGED
WRONGING
WRONGLY
WRONGS
WRONSKIAN
WROTE
WROUGHT
WRUNG
WUHAN
WYANDOTTE
WYATT
WYETH
WYLIE
WYMAN
WYNER
WYNN
WYOMING
XANTHUS
XAVIER
XEBEC
XENAKIS
XENIA
XENIX
XEROX
XEROXED
XEROXES
XEROXING
XERXES
XHOSA
YAGI
YAKIMA
YALE
YALIES
YALTA
YAMAHA
YANK
YANKED
YANKEE
YANKEES
YANKING
YANKS
YANKTON
YAOUNDE
YAQUI
YARD
YARDS
YARDSTICK
YARDSTICKS
YARMOUTH
YARN
YARNS
YATES
YAUNDE
YAWN
YAWNER
YAWNING
YEA
YEAGER
YEAR
YEARLY
YEARN
YEARNED
YEARNING
YEARNINGS
YEARS
YEAS
YEAST
YEASTS
YEATS
YELL
YELLED
YELLER
YELLING
YELLOW
YELLOWED
YELLOWER
YELLOWEST
YELLOWING
YELLOWISH
YELLOWKNIFE
YELLOWNESS
YELLOWS
YELLOWSTONE
YELP
YELPED
YELPING
YELPS
YEMEN
YENTL
YEOMAN
YEOMEN
YERKES
YES
YESTERDAY
YESTERDAYS
YET
YIDDISH
YIELD
YIELDED
YIELDING
YIELDS
YODER
YOKE
YOKES
YOKNAPATAWPHA
YOKOHAMA
YOKUTS
YON
YONDER
YONKERS
YORICK
YORK
YORKER
YORKERS
YORKSHIRE
YORKTOWN
YOSEMITE
YOST
YOU
YOUNG
YOUNGER
YOUNGEST
YOUNGLY
YOUNGSTER
YOUNGSTERS
YOUNGSTOWN
YOUR
YOURS
YOURSELF
YOURSELVES
YOUTH
YOUTHES
YOUTHFUL
YOUTHFULLY
YOUTHFULNESS
YPSILANTI
YUBA
YUCATAN
YUGOSLAV
YUGOSLAVIA
YUGOSLAVIAN
YUGOSLAVIANS
YUH
YUKI
YUKON
YURI
YVES
YVETTE
ZACHARY
ZAGREB
ZAIRE
ZAMBIA
ZAN
ZANZIBAR
ZEAL
ZEALAND
ZEALOUS
ZEALOUSLY
ZEALOUSNESS
ZEBRA
ZEBRAS
ZEFFIRELLI
ZEISS
ZELLERBACH
ZEN
ZENITH
ZENNIST
ZERO
ZEROED
ZEROES
ZEROING
ZEROS
ZEROTH
ZEST
ZEUS
ZIEGFELD
ZIEGFELDS
ZIEGLER
ZIGGY
ZIGZAG
ZILLIONS
ZIMMERMAN
ZINC
ZION
ZIONISM
ZIONIST
ZIONISTS
ZIONS
ZODIAC
ZOE
ZOMBA
ZONAL
ZONALLY
ZONE
ZONED
ZONES
ZONING
ZOO
ZOOLOGICAL
ZOOLOGICALLY
ZOOM
ZOOMS
ZOOS
ZORN
ZOROASTER
ZOROASTRIAN
ZULU
ZULUS
ZURICH


================================================
FILE: strings/dna.py
================================================
import re


def dna(dna: str) -> str:
    """
    https://en.wikipedia.org/wiki/DNA
    Returns the second side of a DNA strand

    >>> dna("GCTA")
    'CGAT'
    >>> dna("ATGC")
    'TACG'
    >>> dna("CTGA")
    'GACT'
    >>> dna("GFGG")
    Traceback (most recent call last):
        ...
    ValueError: Invalid Strand
    """

    if len(re.findall("[ATCG]", dna)) != len(dna):
        raise ValueError("Invalid Strand")

    return dna.translate(dna.maketrans("ATCG", "TAGC"))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: strings/edit_distance.py
================================================
def edit_distance(source: str, target: str) -> int:
    """
    Edit distance algorithm is a string metric, i.e., it is a way of quantifying how
    dissimilar two strings are to one another. It is measured by counting the minimum
    number of operations required to transform one string into another.

    This implementation assumes that the cost of operations (insertion, deletion and
    substitution) is always 1

    Args:
    source: the initial string with respect to which we are calculating the edit
        distance for the target
    target: the target string, formed after performing n operations on the source string

    >>> edit_distance("GATTIC", "GALTIC")
    1
    >>> edit_distance("NUM3", "HUM2")
    2
    >>> edit_distance("cap", "CAP")
    3
    >>> edit_distance("Cat", "")
    3
    >>> edit_distance("cat", "cat")
    0
    >>> edit_distance("", "123456789")
    9
    >>> edit_distance("Be@uty", "Beautyyyy!")
    5
    >>> edit_distance("lstring", "lsstring")
    1
    """
    if len(source) == 0:
        return len(target)
    elif len(target) == 0:
        return len(source)

    delta = int(source[-1] != target[-1])  # Substitution
    return min(
        edit_distance(source[:-1], target[:-1]) + delta,
        edit_distance(source, target[:-1]) + 1,
        edit_distance(source[:-1], target) + 1,
    )


if __name__ == "__main__":
    print(edit_distance("ATCGCTG", "TAGCTAA"))  # Answer is 4


================================================
FILE: strings/frequency_finder.py
================================================
# Frequency Finder

import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
english_letter_freq = {
    "E": 12.70,
    "T": 9.06,
    "A": 8.17,
    "O": 7.51,
    "I": 6.97,
    "N": 6.75,
    "S": 6.33,
    "H": 6.09,
    "R": 5.99,
    "D": 4.25,
    "L": 4.03,
    "C": 2.78,
    "U": 2.76,
    "M": 2.41,
    "W": 2.36,
    "F": 2.23,
    "G": 2.02,
    "Y": 1.97,
    "P": 1.93,
    "B": 1.29,
    "V": 0.98,
    "K": 0.77,
    "J": 0.15,
    "X": 0.15,
    "Q": 0.10,
    "Z": 0.07,
}
ETAOIN = "ETAOINSHRDLCUMWFGYPBVKJXQZ"
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"


def get_letter_count(message: str) -> dict[str, int]:
    letter_count = dict.fromkeys(string.ascii_uppercase, 0)
    for letter in message.upper():
        if letter in LETTERS:
            letter_count[letter] += 1

    return letter_count


def get_item_at_index_zero(x: tuple) -> str:
    return x[0]


def get_frequency_order(message: str) -> str:
    """
    Get the frequency order of the letters in the given string
    >>> get_frequency_order('Hello World')
    'LOWDRHEZQXJKVBPYGFMUCSNIAT'
    >>> get_frequency_order('Hello@')
    'LHOEZQXJKVBPYGFWMUCDRSNIAT'
    >>> get_frequency_order('h')
    'HZQXJKVBPYGFWMUCLDRSNIOATE'
    """
    letter_to_freq = get_letter_count(message)
    freq_to_letter: dict[int, list[str]] = {
        freq: [] for letter, freq in letter_to_freq.items()
    }
    for letter in LETTERS:
        freq_to_letter[letter_to_freq[letter]].append(letter)

    freq_to_letter_str: dict[int, str] = {}

    for freq in freq_to_letter:  # noqa: PLC0206
        freq_to_letter[freq].sort(key=ETAOIN.find, reverse=True)
        freq_to_letter_str[freq] = "".join(freq_to_letter[freq])

    freq_pairs = list(freq_to_letter_str.items())
    freq_pairs.sort(key=get_item_at_index_zero, reverse=True)

    freq_order: list[str] = [freq_pair[1] for freq_pair in freq_pairs]

    return "".join(freq_order)


def english_freq_match_score(message: str) -> int:
    """
    >>> english_freq_match_score('Hello World')
    1
    """
    freq_order = get_frequency_order(message)
    match_score = 0
    for common_letter in ETAOIN[:6]:
        if common_letter in freq_order[:6]:
            match_score += 1

    for uncommon_letter in ETAOIN[-6:]:
        if uncommon_letter in freq_order[-6:]:
            match_score += 1

    return match_score


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: strings/hamming_distance.py
================================================
def hamming_distance(string1: str, string2: str) -> int:
    """Calculate the Hamming distance between two equal length strings
    In information theory, the Hamming distance between two strings of equal
    length is the number of positions at which the corresponding symbols are
    different. https://en.wikipedia.org/wiki/Hamming_distance

    Args:
        string1 (str): Sequence 1
        string2 (str): Sequence 2

    Returns:
        int: Hamming distance

    >>> hamming_distance("python", "python")
    0
    >>> hamming_distance("karolin", "kathrin")
    3
    >>> hamming_distance("00000", "11111")
    5
    >>> hamming_distance("karolin", "kath")
    Traceback (most recent call last):
      ...
    ValueError: String lengths must match!
    """
    if len(string1) != len(string2):
        raise ValueError("String lengths must match!")

    count = 0

    for char1, char2 in zip(string1, string2):
        if char1 != char2:
            count += 1

    return count


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: strings/indian_phone_validator.py
================================================
import re


def indian_phone_validator(phone: str) -> bool:
    """
    Determine whether the string is a valid phone number or not
    :param phone:
    :return: Boolean
    >>> indian_phone_validator("+91123456789")
    False
    >>> indian_phone_validator("+919876543210")
    True
    >>> indian_phone_validator("01234567896")
    False
    >>> indian_phone_validator("919876543218")
    True
    >>> indian_phone_validator("+91-1234567899")
    False
    >>> indian_phone_validator("+91-9876543218")
    True
    """
    pat = re.compile(r"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$")
    if match := re.search(pat, phone):
        return match.string == phone
    return False


if __name__ == "__main__":
    print(indian_phone_validator("+918827897895"))


================================================
FILE: strings/is_contains_unique_chars.py
================================================
def is_contains_unique_chars(input_str: str) -> bool:
    """
    Check if all characters in the string is unique or not.
    >>> is_contains_unique_chars("I_love.py")
    True
    >>> is_contains_unique_chars("I don't love Python")
    False

    Time complexity: O(n)
    Space complexity: O(1) 19320 bytes as we are having 144697 characters in unicode
    """

    # Each bit will represent each unicode character
    # For example 65th bit representing 'A'
    # https://stackoverflow.com/a/12811293
    bitmap = 0
    for ch in input_str:
        ch_unicode = ord(ch)
        ch_bit_index_on = pow(2, ch_unicode)

        # If we already turned on bit for current character's unicode
        if bitmap >> ch_unicode & 1 == 1:
            return False
        bitmap |= ch_bit_index_on
    return True


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: strings/is_isogram.py
================================================
"""
wiki: https://en.wikipedia.org/wiki/Heterogram_(literature)#Isograms
"""


def is_isogram(string: str) -> bool:
    """
    An isogram is a word in which no letter is repeated.
    Examples of isograms are uncopyrightable and ambidextrously.
    >>> is_isogram('Uncopyrightable')
    True
    >>> is_isogram('allowance')
    False
    >>> is_isogram('copy1')
    Traceback (most recent call last):
     ...
    ValueError: String must only contain alphabetic characters.
    """
    if not all(x.isalpha() for x in string):
        raise ValueError("String must only contain alphabetic characters.")

    letters = sorted(string.lower())
    return len(letters) == len(set(letters))


if __name__ == "__main__":
    input_str = input("Enter a string ").strip()

    isogram = is_isogram(input_str)
    print(f"{input_str} is {'an' if isogram else 'not an'} isogram.")


================================================
FILE: strings/is_pangram.py
================================================
"""
wiki: https://en.wikipedia.org/wiki/Pangram
"""


def is_pangram(
    input_str: str = "The quick brown fox jumps over the lazy dog",
) -> bool:
    """
    A Pangram String contains all the alphabets at least once.
    >>> is_pangram("The quick brown fox jumps over the lazy dog")
    True
    >>> is_pangram("Waltz, bad nymph, for quick jigs vex.")
    True
    >>> is_pangram("Jived fox nymph grabs quick waltz.")
    True
    >>> is_pangram("My name is Unknown")
    False
    >>> is_pangram("The quick brown fox jumps over the la_y dog")
    False
    >>> is_pangram()
    True
    """
    # Declare frequency as a set to have unique occurrences of letters
    frequency = set()

    # Replace all the whitespace in our sentence
    input_str = input_str.replace(" ", "")
    for alpha in input_str:
        if "a" <= alpha.lower() <= "z":
            frequency.add(alpha.lower())
    return len(frequency) == 26


def is_pangram_faster(
    input_str: str = "The quick brown fox jumps over the lazy dog",
) -> bool:
    """
    >>> is_pangram_faster("The quick brown fox jumps over the lazy dog")
    True
    >>> is_pangram_faster("Waltz, bad nymph, for quick jigs vex.")
    True
    >>> is_pangram_faster("Jived fox nymph grabs quick waltz.")
    True
    >>> is_pangram_faster("The quick brown fox jumps over the la_y dog")
    False
    >>> is_pangram_faster()
    True
    """
    flag = [False] * 26
    for char in input_str:
        if char.islower():
            flag[ord(char) - 97] = True
        elif char.isupper():
            flag[ord(char) - 65] = True
    return all(flag)


def is_pangram_fastest(
    input_str: str = "The quick brown fox jumps over the lazy dog",
) -> bool:
    """
    >>> is_pangram_fastest("The quick brown fox jumps over the lazy dog")
    True
    >>> is_pangram_fastest("Waltz, bad nymph, for quick jigs vex.")
    True
    >>> is_pangram_fastest("Jived fox nymph grabs quick waltz.")
    True
    >>> is_pangram_fastest("The quick brown fox jumps over the la_y dog")
    False
    >>> is_pangram_fastest()
    True
    """
    return len({char for char in input_str.lower() if char.isalpha()}) == 26


def benchmark() -> None:
    """
    Benchmark code comparing different version.
    """
    from timeit import timeit

    setup = "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
    print(timeit("is_pangram()", setup=setup))
    print(timeit("is_pangram_faster()", setup=setup))
    print(timeit("is_pangram_fastest()", setup=setup))
    # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
    # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()


================================================
FILE: strings/is_polish_national_id.py
================================================
def is_polish_national_id(input_str: str) -> bool:
    """
    Verification of the correctness of the PESEL number.
    www-gov-pl.translate.goog/web/gov/czym-jest-numer-pesel?_x_tr_sl=auto&_x_tr_tl=en

    PESEL can start with 0, that's why we take str as input,
    but convert it to int for some calculations.


    >>> is_polish_national_id(123)
    Traceback (most recent call last):
        ...
    ValueError: Expected str as input, found <class 'int'>

    >>> is_polish_national_id("abc")
    Traceback (most recent call last):
        ...
    ValueError: Expected number as input

    >>> is_polish_national_id("02070803628") # correct PESEL
    True

    >>> is_polish_national_id("02150803629") # wrong month
    False

    >>> is_polish_national_id("02075503622") # wrong day
    False

    >>> is_polish_national_id("-99012212349") # wrong range
    False

    >>> is_polish_national_id("990122123499999") # wrong range
    False

    >>> is_polish_national_id("02070803621") # wrong checksum
    False
    """

    # check for invalid input type
    if not isinstance(input_str, str):
        msg = f"Expected str as input, found {type(input_str)}"
        raise ValueError(msg)

    # check if input can be converted to int
    try:
        input_int = int(input_str)
    except ValueError:
        msg = "Expected number as input"
        raise ValueError(msg)

    # check number range
    if not 10100000 <= input_int <= 99923199999:
        return False

    # check month correctness
    month = int(input_str[2:4])

    if (
        month not in range(1, 13)  # year 1900-1999
        and month not in range(21, 33)  # 2000-2099
        and month not in range(41, 53)  # 2100-2199
        and month not in range(61, 73)  # 2200-2299
        and month not in range(81, 93)  # 1800-1899
    ):
        return False

    # check day correctness
    day = int(input_str[4:6])

    if day not in range(1, 32):
        return False

    # check the checksum
    multipliers = [1, 3, 7, 9, 1, 3, 7, 9, 1, 3]
    subtotal = 0

    digits_to_check = str(input_str)[:-1]  # cut off the checksum

    for index, digit in enumerate(digits_to_check):
        # Multiply corresponding digits and multipliers.
        # In case of a double-digit result, add only the last digit.
        subtotal += (int(digit) * multipliers[index]) % 10

    checksum = 10 - subtotal % 10

    return checksum == input_int % 10


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: strings/is_spain_national_id.py
================================================
NUMBERS_PLUS_LETTER = "Input must be a string of 8 numbers plus letter"
LOOKUP_LETTERS = "TRWAGMYFPDXBNJZSQVHLCKE"


def is_spain_national_id(spanish_id: str) -> bool:
    """
    Spain National Id is a string composed by 8 numbers plus a letter
    The letter in fact is not part of the ID, it acts as a validator,
    checking you didn't do a mistake when entering it on a system or
    are giving a fake one.

    https://en.wikipedia.org/wiki/Documento_Nacional_de_Identidad_(Spain)#Number

    >>> is_spain_national_id("12345678Z")
    True
    >>> is_spain_national_id("12345678z")  # It is case-insensitive
    True
    >>> is_spain_national_id("12345678x")
    False
    >>> is_spain_national_id("12345678I")
    False
    >>> is_spain_national_id("12345678-Z")  # Some systems add a dash
    True
    >>> is_spain_national_id("12345678")
    Traceback (most recent call last):
        ...
    ValueError: Input must be a string of 8 numbers plus letter
    >>> is_spain_national_id("123456709")
    Traceback (most recent call last):
        ...
    ValueError: Input must be a string of 8 numbers plus letter
    >>> is_spain_national_id("1234567--Z")
    Traceback (most recent call last):
        ...
    ValueError: Input must be a string of 8 numbers plus letter
    >>> is_spain_national_id("1234Z")
    Traceback (most recent call last):
        ...
    ValueError: Input must be a string of 8 numbers plus letter
    >>> is_spain_national_id("1234ZzZZ")
    Traceback (most recent call last):
        ...
    ValueError: Input must be a string of 8 numbers plus letter
    >>> is_spain_national_id(12345678)
    Traceback (most recent call last):
        ...
    TypeError: Expected string as input, found int
    """

    if not isinstance(spanish_id, str):
        msg = f"Expected string as input, found {type(spanish_id).__name__}"
        raise TypeError(msg)

    spanish_id_clean = spanish_id.replace("-", "").upper()
    if len(spanish_id_clean) != 9:
        raise ValueError(NUMBERS_PLUS_LETTER)

    try:
        number = int(spanish_id_clean[0:8])
        letter = spanish_id_clean[8]
    except ValueError as ex:
        raise ValueError(NUMBERS_PLUS_LETTER) from ex

    if letter.isdigit():
        raise ValueError(NUMBERS_PLUS_LETTER)

    return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: strings/is_srilankan_phone_number.py
================================================
import re


def is_sri_lankan_phone_number(phone: str) -> bool:
    """
    Determine whether the string is a valid sri lankan mobile phone number or not
    References: https://aye.sh/blog/sri-lankan-phone-number-regex

    >>> is_sri_lankan_phone_number("+94773283048")
    True
    >>> is_sri_lankan_phone_number("+9477-3283048")
    True
    >>> is_sri_lankan_phone_number("0718382399")
    True
    >>> is_sri_lankan_phone_number("0094702343221")
    True
    >>> is_sri_lankan_phone_number("075 3201568")
    True
    >>> is_sri_lankan_phone_number("07779209245")
    False
    >>> is_sri_lankan_phone_number("0957651234")
    False
    """

    pattern = re.compile(r"^(?:0|94|\+94|0{2}94)7(0|1|2|4|5|6|7|8)(-| |)\d{7}$")

    return bool(re.search(pattern, phone))


if __name__ == "__main__":
    phone = "0094702343221"

    print(is_sri_lankan_phone_number(phone))


================================================
FILE: strings/is_valid_email_address.py
================================================
"""
Implements an is valid email address algorithm

@ https://en.wikipedia.org/wiki/Email_address
"""

import string

email_tests: tuple[tuple[str, bool], ...] = (
    ("simple@example.com", True),
    ("very.common@example.com", True),
    ("disposable.style.email.with+symbol@example.com", True),
    ("other-email-with-hyphen@and.subdomains.example.com", True),
    ("fully-qualified-domain@example.com", True),
    ("user.name+tag+sorting@example.com", True),
    ("x@example.com", True),
    ("example-indeed@strange-example.com", True),
    ("test/test@test.com", True),
    (
        "123456789012345678901234567890123456789012345678901234567890123@example.com",
        True,
    ),
    ("admin@mailserver1", True),
    ("example@s.example", True),
    ("Abc.example.com", False),
    ("A@b@c@example.com", False),
    ("abc@example..com", False),
    ("a(c)d,e:f;g<h>i[j\\k]l@example.com", False),
    (
        "12345678901234567890123456789012345678901234567890123456789012345@example.com",
        False,
    ),
    ("i.like.underscores@but_its_not_allowed_in_this_part", False),
    ("", False),
)

# The maximum octets (one character as a standard unicode character is one byte)
# that the local part and the domain part can have
MAX_LOCAL_PART_OCTETS = 64
MAX_DOMAIN_OCTETS = 255


def is_valid_email_address(email: str) -> bool:
    """
    Returns True if the passed email address is valid.

    The local part of the email precedes the singular @ symbol and
    is associated with a display-name. For example, "john.smith"
    The domain is stricter than the local part and follows the @ symbol.

    Global email checks:
     1. There can only be one @ symbol in the email address. Technically if the
        @ symbol is quoted in the local-part, then it is valid, however this
        implementation ignores "" for now.
        (See https://en.wikipedia.org/wiki/Email_address#:~:text=If%20quoted,)
     2. The local-part and the domain are limited to a certain number of octets. With
        unicode storing a single character in one byte, each octet is equivalent to
        a character. Hence, we can just check the length of the string.
    Checks for the local-part:
     3. The local-part may contain: upper and lowercase latin letters, digits 0 to 9,
        and printable characters (!#$%&'*+-/=?^_`{|}~)
     4. The local-part may also contain a "." in any place that is not the first or
        last character, and may not have more than one "." consecutively.

    Checks for the domain:
     5. The domain may contain: upper and lowercase latin letters and digits 0 to 9
     6. Hyphen "-", provided that it is not the first or last character
     7. The domain may also contain a "." in any place that is not the first or
        last character, and may not have more than one "." consecutively.

    >>> for email, valid in email_tests:
    ...     assert is_valid_email_address(email) == valid
    """

    # (1.) Make sure that there is only one @ symbol in the email address
    if email.count("@") != 1:
        return False

    local_part, domain = email.split("@")
    # (2.) Check octet length of the local part and domain
    if len(local_part) > MAX_LOCAL_PART_OCTETS or len(domain) > MAX_DOMAIN_OCTETS:
        return False

    # (3.) Validate the characters in the local-part
    if any(
        char not in string.ascii_letters + string.digits + ".(!#$%&'*+-/=?^_`{|}~)"
        for char in local_part
    ):
        return False

    # (4.) Validate the placement of "." characters in the local-part
    if local_part.startswith(".") or local_part.endswith(".") or ".." in local_part:
        return False

    # (5.) Validate the characters in the domain
    if any(char not in string.ascii_letters + string.digits + ".-" for char in domain):
        return False

    # (6.) Validate the placement of "-" characters
    if domain.startswith("-") or domain.endswith("."):
        return False

    # (7.) Validate the placement of "." characters
    return not (domain.startswith(".") or domain.endswith(".") or ".." in domain)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    for email, valid in email_tests:
        is_valid = is_valid_email_address(email)
        assert is_valid == valid, f"{email} is {is_valid}"
        print(f"Email address {email} is {'not ' if not is_valid else ''}valid")


================================================
FILE: strings/jaro_winkler.py
================================================
"""https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance"""


def jaro_winkler(str1: str, str2: str) -> float:
    """
    Jaro-Winkler distance is a string metric measuring an edit distance between two
    sequences.
    Output value is between 0.0 and 1.0.

    >>> jaro_winkler("martha", "marhta")
    0.9611111111111111
    >>> jaro_winkler("CRATE", "TRACE")
    0.7333333333333334
    >>> jaro_winkler("test", "dbdbdbdb")
    0.0
    >>> jaro_winkler("test", "test")
    1.0
    >>> jaro_winkler("hello world", "HeLLo W0rlD")
    0.6363636363636364
    >>> jaro_winkler("test", "")
    0.0
    >>> jaro_winkler("hello", "world")
    0.4666666666666666
    >>> jaro_winkler("hell**o", "*world")
    0.4365079365079365
    """

    def get_matched_characters(_str1: str, _str2: str) -> str:
        matched = []
        limit = min(len(_str1), len(_str2)) // 2
        for i, char in enumerate(_str1):
            left = int(max(0, i - limit))
            right = int(min(i + limit + 1, len(_str2)))
            if char in _str2[left:right]:
                matched.append(char)
                _str2 = (
                    f"{_str2[0 : _str2.index(char)]} {_str2[_str2.index(char) + 1 :]}"
                )

        return "".join(matched)

    # matching characters
    matching_1 = get_matched_characters(str1, str2)
    matching_2 = get_matched_characters(str2, str1)
    match_count = len(matching_1)

    # transposition
    transpositions = (
        len([(c1, c2) for c1, c2 in zip(matching_1, matching_2) if c1 != c2]) // 2
    )

    if not match_count:
        jaro = 0.0
    else:
        jaro = (
            1
            / 3
            * (
                match_count / len(str1)
                + match_count / len(str2)
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    prefix_len = 0
    for c1, c2 in zip(str1[:4], str2[:4]):
        if c1 == c2:
            prefix_len += 1
        else:
            break

    return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(jaro_winkler("hello", "world"))


================================================
FILE: strings/join.py
================================================
"""
Program to join a list of strings with a separator
"""


def join(separator: str, separated: list[str]) -> str:
    """
    Joins a list of strings using a separator
    and returns the result.

    :param separator: Separator to be used
                for joining the strings.
    :param separated: List of strings to be joined.

    :return: Joined string with the specified separator.

    Examples:

    >>> join("", ["a", "b", "c", "d"])
    'abcd'
    >>> join("#", ["a", "b", "c", "d"])
    'a#b#c#d'
    >>> join("#", "a")
    'a'
    >>> join(" ", ["You", "are", "amazing!"])
    'You are amazing!'
    >>> join(",", ["", "", ""])
    ',,'

    This example should raise an
    exception for non-string elements:
    >>> join("#", ["a", "b", "c", 1])
    Traceback (most recent call last):
        ...
    Exception: join() accepts only strings

    Additional test case with a different separator:
    >>> join("-", ["apple", "banana", "cherry"])
    'apple-banana-cherry'
    """

    # Check that all elements are strings
    for word_or_phrase in separated:
        # If the element is not a string, raise an exception
        if not isinstance(word_or_phrase, str):
            raise Exception("join() accepts only strings")

    joined: str = ""
    """
    The last element of the list is not followed by the separator.
    So, we need to iterate through the list and join each element
    with the separator except the last element.
    """
    last_index: int = len(separated) - 1
    """
    Iterate through the list and join each element with the separator.
    Except the last element, all other elements are followed by the separator.
    """
    for word_or_phrase in separated[:last_index]:
        # join the element with the separator.
        joined += word_or_phrase + separator

    # If the list is not empty, join the last element.
    if separated != []:
        joined += separated[last_index]

    # Return the joined string.
    return joined


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: strings/knuth_morris_pratt.py
================================================
from __future__ import annotations


def knuth_morris_pratt(text: str, pattern: str) -> int:
    """
    The Knuth-Morris-Pratt Algorithm for finding a pattern within a piece of text
    with complexity O(n + m)

    1) Preprocess pattern to identify any suffixes that are identical to prefixes

        This tells us where to continue from if we get a mismatch between a character
        in our pattern and the text.

    2) Step through the text one character at a time and compare it to a character in
        the pattern updating our location within the pattern if necessary

    >>> kmp = "knuth_morris_pratt"
    >>> all(
    ...    knuth_morris_pratt(kmp, s) == kmp.find(s)
    ...    for s in ("kn", "h_m", "rr", "tt", "not there")
    ... )
    True
    """

    # 1) Construct the failure array
    failure = get_failure_array(pattern)

    # 2) Step through text searching for pattern
    i, j = 0, 0  # index into text, pattern
    while i < len(text):
        if pattern[j] == text[i]:
            if j == (len(pattern) - 1):
                return i - j
            j += 1

        # if this is a prefix in our pattern
        # just go back far enough to continue
        elif j > 0:
            j = failure[j - 1]
            continue
        i += 1
    return -1


def get_failure_array(pattern: str) -> list[int]:
    """
    Calculates the new index we should go to if we fail a comparison
    :param pattern:
    :return:
    """
    failure = [0]
    i = 0
    j = 1
    while j < len(pattern):
        if pattern[i] == pattern[j]:
            i += 1
        elif i > 0:
            i = failure[i - 1]
            continue
        j += 1
        failure.append(i)
    return failure


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Test 1)
    pattern = "abc1abc12"
    text1 = "alskfjaldsabc1abc1abc12k23adsfabcabc"
    text2 = "alskfjaldsk23adsfabcabc"
    assert knuth_morris_pratt(text1, pattern)
    assert knuth_morris_pratt(text2, pattern)

    # Test 2)
    pattern = "ABABX"
    text = "ABABZABABYABABX"
    assert knuth_morris_pratt(text, pattern)

    # Test 3)
    pattern = "AAAB"
    text = "ABAAAAAB"
    assert knuth_morris_pratt(text, pattern)

    # Test 4)
    pattern = "abcdabcy"
    text = "abcxabcdabxabcdabcdabcy"
    assert knuth_morris_pratt(text, pattern)

    # Test 5) -> Doctests
    kmp = "knuth_morris_pratt"
    assert all(
        knuth_morris_pratt(kmp, s) == kmp.find(s)
        for s in ("kn", "h_m", "rr", "tt", "not there")
    )

    # Test 6)
    pattern = "aabaabaaa"
    assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]


================================================
FILE: strings/levenshtein_distance.py
================================================
from collections.abc import Callable


def levenshtein_distance(first_word: str, second_word: str) -> int:
    """
    Implementation of the Levenshtein distance in Python.
    :param first_word: the first word to measure the difference.
    :param second_word: the second word to measure the difference.
    :return: the levenshtein distance between the two words.
    Examples:
    >>> levenshtein_distance("planet", "planetary")
    3
    >>> levenshtein_distance("", "test")
    4
    >>> levenshtein_distance("book", "back")
    2
    >>> levenshtein_distance("book", "book")
    0
    >>> levenshtein_distance("test", "")
    4
    >>> levenshtein_distance("", "")
    0
    >>> levenshtein_distance("orchestration", "container")
    10
    """
    # The longer word should come first
    if len(first_word) < len(second_word):
        return levenshtein_distance(second_word, first_word)

    if len(second_word) == 0:
        return len(first_word)

    previous_row = list(range(len(second_word) + 1))

    for i, c1 in enumerate(first_word):
        current_row = [i + 1]

        for j, c2 in enumerate(second_word):
            # Calculate insertions, deletions, and substitutions
            insertions = previous_row[j + 1] + 1
            deletions = current_row[j] + 1
            substitutions = previous_row[j] + (c1 != c2)

            # Get the minimum to append to the current row
            current_row.append(min(insertions, deletions, substitutions))

        # Store the previous row
        previous_row = current_row

    # Returns the last element (distance)
    return previous_row[-1]


def levenshtein_distance_optimized(first_word: str, second_word: str) -> int:
    """
    Compute the Levenshtein distance between two words (strings).
    The function is optimized for efficiency by modifying rows in place.
    :param first_word: the first word to measure the difference.
    :param second_word: the second word to measure the difference.
    :return: the Levenshtein distance between the two words.
    Examples:
    >>> levenshtein_distance_optimized("planet", "planetary")
    3
    >>> levenshtein_distance_optimized("", "test")
    4
    >>> levenshtein_distance_optimized("book", "back")
    2
    >>> levenshtein_distance_optimized("book", "book")
    0
    >>> levenshtein_distance_optimized("test", "")
    4
    >>> levenshtein_distance_optimized("", "")
    0
    >>> levenshtein_distance_optimized("orchestration", "container")
    10
    """
    if len(first_word) < len(second_word):
        return levenshtein_distance_optimized(second_word, first_word)

    if len(second_word) == 0:
        return len(first_word)

    previous_row = list(range(len(second_word) + 1))

    for i, c1 in enumerate(first_word):
        current_row = [i + 1] + [0] * len(second_word)

        for j, c2 in enumerate(second_word):
            insertions = previous_row[j + 1] + 1
            deletions = current_row[j] + 1
            substitutions = previous_row[j] + (c1 != c2)
            current_row[j + 1] = min(insertions, deletions, substitutions)

        previous_row = current_row

    return previous_row[-1]


def benchmark_levenshtein_distance(func: Callable) -> None:
    """
    Benchmark the Levenshtein distance function.
    :param str: The name of the function being benchmarked.
    :param func: The function to be benchmarked.
    """
    from timeit import timeit

    stmt = f"{func.__name__}('sitting', 'kitten')"
    setup = f"from __main__ import {func.__name__}"
    number = 25_000
    result = timeit(stmt=stmt, setup=setup, number=number)
    print(f"{func.__name__:<30} finished {number:,} runs in {result:.5f} seconds")


if __name__ == "__main__":
    # Get user input for words
    first_word = input("Enter the first word for Levenshtein distance:\n").strip()
    second_word = input("Enter the second word for Levenshtein distance:\n").strip()

    # Calculate and print Levenshtein distances
    print(f"{levenshtein_distance(first_word, second_word) = }")
    print(f"{levenshtein_distance_optimized(first_word, second_word) = }")

    # Benchmark the Levenshtein distance functions
    benchmark_levenshtein_distance(levenshtein_distance)
    benchmark_levenshtein_distance(levenshtein_distance_optimized)


================================================
FILE: strings/lower.py
================================================
def lower(word: str) -> str:
    """
    Will convert the entire string to lowercase letters

    >>> lower("wow")
    'wow'
    >>> lower("HellZo")
    'hellzo'
    >>> lower("WHAT")
    'what'
    >>> lower("wh[]32")
    'wh[]32'
    >>> lower("whAT")
    'what'
    """

    # Converting to ASCII value, obtaining the integer representation
    # and checking to see if the character is a capital letter.
    # If it is a capital letter, it is shifted by 32, making it a lowercase letter.
    return "".join(chr(ord(char) + 32) if "A" <= char <= "Z" else char for char in word)


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: strings/manacher.py
================================================
def palindromic_string(input_string: str) -> str:
    """
    >>> palindromic_string('abbbaba')
    'abbba'
    >>> palindromic_string('ababa')
    'ababa'

    Manacher's algorithm which finds Longest palindromic Substring in linear time.

    1. first this convert input_string("xyx") into new_string("x|y|x") where odd
        positions are actual input characters.
    2. for each character in new_string it find corresponding length and
        store the length and left,right to store previously calculated info.
        (please look the explanation for details)

    3. return corresponding output_string by removing all "|"
    """
    max_length = 0

    # if input_string is "aba" than new_input_string become "a|b|a"
    new_input_string = ""
    output_string = ""

    # append each character + "|" in new_string for range(0, length-1)
    for i in input_string[: len(input_string) - 1]:
        new_input_string += i + "|"
    # append last character
    new_input_string += input_string[-1]

    # we will store the starting and ending of previous furthest ending palindromic
    # substring
    left, right = 0, 0

    # length[i] shows the length of palindromic substring with center i
    length = [1 for i in range(len(new_input_string))]

    # for each character in new_string find corresponding palindromic string
    start = 0
    for j in range(len(new_input_string)):
        k = 1 if j > right else min(length[left + right - j] // 2, right - j + 1)
        while (
            j - k >= 0
            and j + k < len(new_input_string)
            and new_input_string[k + j] == new_input_string[j - k]
        ):
            k += 1

        length[j] = 2 * k - 1

        # does this string is ending after the previously explored end (that is right) ?
        # if yes the update the new right to the last index of this
        if j + k - 1 > right:
            left = j - k + 1
            right = j + k - 1

        # update max_length and start position
        if max_length < length[j]:
            max_length = length[j]
            start = j

    # create that string
    s = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
    for i in s:
        if i != "|":
            output_string += i

    return output_string


if __name__ == "__main__":
    import doctest

    doctest.testmod()

"""
...a0...a1...a2.....a3......a4...a5...a6....

consider the string for which we are calculating the longest palindromic substring is
shown above where ... are some characters in between and right now we are calculating
the length of palindromic substring with center at a5 with following conditions :
i) we have stored the length of palindromic substring which has center at a3
    (starts at left ends at right) and it is the furthest ending till now,
    and it has ending after a6
ii) a2 and a4 are equally distant from a3 so char(a2) == char(a4)
iii) a0 and a6 are equally distant from a3 so char(a0) == char(a6)
iv) a1 is corresponding equal character of a5 in palindrome with center a3 (remember
    that in below derivation of a4==a6)

now for a5 we will calculate the length of palindromic substring with center as a5 but
can we use previously calculated information in some way?
Yes, look the above string we know that a5 is inside the palindrome with center a3 and
previously we have calculated that
a0==a2 (palindrome of center a1)
a2==a4 (palindrome of center a3)
a0==a6 (palindrome of center a3)
so a4==a6

so we can say that palindrome at center a5 is at least as long as palindrome at center
a1 but this only holds if a0 and a6 are inside the limits of palindrome centered at a3
so finally ..

len_of_palindrome__at(a5) = min(len_of_palindrome_at(a1), right-a5)
where a3 lies from left to right and we have to keep updating that

and if the a5 lies outside of left,right boundary we calculate length of palindrome with
bruteforce and update left,right.

it gives the linear time complexity just like z-function
"""


================================================
FILE: strings/min_cost_string_conversion.py
================================================
"""
Algorithm for calculating the most cost-efficient sequence for converting one string
into another.
The only allowed operations are
--- Cost to copy a character is copy_cost
--- Cost to replace a character is replace_cost
--- Cost to delete a character is delete_cost
--- Cost to insert a character is insert_cost
"""


def compute_transform_tables(
    source_string: str,
    destination_string: str,
    copy_cost: int,
    replace_cost: int,
    delete_cost: int,
    insert_cost: int,
) -> tuple[list[list[int]], list[list[str]]]:
    """
    Finds the most cost efficient sequence
    for converting one string into another.

    >>> costs, operations = compute_transform_tables("cat", "cut", 1, 2, 3, 3)
    >>> costs[0][:4]
    [0, 3, 6, 9]
    >>> costs[2][:4]
    [6, 4, 3, 6]
    >>> operations[0][:4]
    ['0', 'Ic', 'Iu', 'It']
    >>> operations[3][:4]
    ['Dt', 'Dt', 'Rtu', 'Ct']

    >>> compute_transform_tables("", "", 1, 2, 3, 3)
    ([[0]], [['0']])
    """
    source_seq = list(source_string)
    destination_seq = list(destination_string)
    len_source_seq = len(source_seq)
    len_destination_seq = len(destination_seq)
    costs = [
        [0 for _ in range(len_destination_seq + 1)] for _ in range(len_source_seq + 1)
    ]
    ops = [
        ["0" for _ in range(len_destination_seq + 1)] for _ in range(len_source_seq + 1)
    ]

    for i in range(1, len_source_seq + 1):
        costs[i][0] = i * delete_cost
        ops[i][0] = f"D{source_seq[i - 1]}"

    for i in range(1, len_destination_seq + 1):
        costs[0][i] = i * insert_cost
        ops[0][i] = f"I{destination_seq[i - 1]}"

    for i in range(1, len_source_seq + 1):
        for j in range(1, len_destination_seq + 1):
            if source_seq[i - 1] == destination_seq[j - 1]:
                costs[i][j] = costs[i - 1][j - 1] + copy_cost
                ops[i][j] = f"C{source_seq[i - 1]}"
            else:
                costs[i][j] = costs[i - 1][j - 1] + replace_cost
                ops[i][j] = f"R{source_seq[i - 1]}" + str(destination_seq[j - 1])

            if costs[i - 1][j] + delete_cost < costs[i][j]:
                costs[i][j] = costs[i - 1][j] + delete_cost
                ops[i][j] = f"D{source_seq[i - 1]}"

            if costs[i][j - 1] + insert_cost < costs[i][j]:
                costs[i][j] = costs[i][j - 1] + insert_cost
                ops[i][j] = f"I{destination_seq[j - 1]}"

    return costs, ops


def assemble_transformation(ops: list[list[str]], i: int, j: int) -> list[str]:
    """
    Assembles the transformations based on the ops table.

    >>> ops = [['0', 'Ic', 'Iu', 'It'],
    ...        ['Dc', 'Cc', 'Iu', 'It'],
    ...        ['Da', 'Da', 'Rau', 'Rat'],
    ...        ['Dt', 'Dt', 'Rtu', 'Ct']]
    >>> x = len(ops) - 1
    >>> y = len(ops[0]) - 1
    >>> assemble_transformation(ops, x, y)
    ['Cc', 'Rau', 'Ct']

    >>> ops1 = [['0']]
    >>> x1 = len(ops1) - 1
    >>> y1 = len(ops1[0]) - 1
    >>> assemble_transformation(ops1, x1, y1)
    []

    >>> ops2 = [['0', 'I1', 'I2', 'I3'],
    ...         ['D1', 'C1', 'I2', 'I3'],
    ...         ['D2', 'D2', 'R23', 'R23']]
    >>> x2 = len(ops2) - 1
    >>> y2 = len(ops2[0]) - 1
    >>> assemble_transformation(ops2, x2, y2)
    ['C1', 'I2', 'R23']
    """
    if i == 0 and j == 0:
        return []
    elif ops[i][j][0] in {"C", "R"}:
        seq = assemble_transformation(ops, i - 1, j - 1)
        seq.append(ops[i][j])
        return seq
    elif ops[i][j][0] == "D":
        seq = assemble_transformation(ops, i - 1, j)
        seq.append(ops[i][j])
        return seq
    else:
        seq = assemble_transformation(ops, i, j - 1)
        seq.append(ops[i][j])
        return seq


if __name__ == "__main__":
    _, operations = compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

    m = len(operations)
    n = len(operations[0])
    sequence = assemble_transformation(operations, m - 1, n - 1)

    string = list("Python")
    i = 0
    cost = 0

    with open("min_cost.txt", "w") as file:
        for op in sequence:
            print("".join(string))

            if op[0] == "C":
                file.write("%-16s" % "Copy %c" % op[1])  # noqa: UP031
                file.write("\t\t\t" + "".join(string))
                file.write("\r\n")

                cost -= 1
            elif op[0] == "R":
                string[i] = op[2]

                file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2])))  # noqa: UP031
                file.write("\t\t" + "".join(string))
                file.write("\r\n")

                cost += 1
            elif op[0] == "D":
                string.pop(i)

                file.write("%-16s" % "Delete %c" % op[1])  # noqa: UP031
                file.write("\t\t\t" + "".join(string))
                file.write("\r\n")

                cost += 2
            else:
                string.insert(i, op[1])

                file.write("%-16s" % "Insert %c" % op[1])  # noqa: UP031
                file.write("\t\t\t" + "".join(string))
                file.write("\r\n")

                cost += 2

            i += 1

        print("".join(string))
        print("Cost: ", cost)

        file.write("\r\nMinimum cost: " + str(cost))


================================================
FILE: strings/naive_string_search.py
================================================
"""
https://en.wikipedia.org/wiki/String-searching_algorithm#Na%C3%AFve_string_search
this algorithm tries to find the pattern from every position of
the mainString if pattern is found from position i it add it to
the answer and does the same for position i+1
Complexity : O(n*m)
    n=length of main string
    m=length of pattern string
"""


def naive_pattern_search(s: str, pattern: str) -> list:
    """
    >>> naive_pattern_search("ABAAABCDBBABCDDEBCABC", "ABC")
    [4, 10, 18]
    >>> naive_pattern_search("ABC", "ABAAABCDBBABCDDEBCABC")
    []
    >>> naive_pattern_search("", "ABC")
    []
    >>> naive_pattern_search("TEST", "TEST")
    [0]
    >>> naive_pattern_search("ABCDEGFTEST", "TEST")
    [7]
    """
    pat_len = len(pattern)
    position = []
    for i in range(len(s) - pat_len + 1):
        match_found = True
        for j in range(pat_len):
            if s[i + j] != pattern[j]:
                match_found = False
                break
        if match_found:
            position.append(i)
    return position


if __name__ == "__main__":
    assert naive_pattern_search("ABCDEFG", "DE") == [3]
    print(naive_pattern_search("ABAAABCDBBABCDDEBCABC", "ABC"))


================================================
FILE: strings/ngram.py
================================================
"""
https://en.wikipedia.org/wiki/N-gram
"""


def create_ngram(sentence: str, ngram_size: int) -> list[str]:
    """
    Create ngrams from a sentence

    >>> create_ngram("I am a sentence", 2)
    ['I ', ' a', 'am', 'm ', ' a', 'a ', ' s', 'se', 'en', 'nt', 'te', 'en', 'nc', 'ce']
    >>> create_ngram("I am an NLPer", 2)
    ['I ', ' a', 'am', 'm ', ' a', 'an', 'n ', ' N', 'NL', 'LP', 'Pe', 'er']
    >>> create_ngram("This is short", 50)
    []
    """
    return [sentence[i : i + ngram_size] for i in range(len(sentence) - ngram_size + 1)]


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: strings/palindrome.py
================================================
# Algorithms to determine if a string is palindrome

from timeit import timeit

test_data = {
    "MALAYALAM": True,
    "String": False,
    "rotor": True,
    "level": True,
    "A": True,
    "BB": True,
    "ABC": False,
    "amanaplanacanalpanama": True,  # "a man a plan a canal panama"
    "abcdba": False,
    "AB": False,
}
# Ensure our test data is valid
assert all((key == key[::-1]) == value for key, value in test_data.items())


def is_palindrome(s: str) -> bool:
    """
    Return True if s is a palindrome otherwise return False.

    >>> all(is_palindrome(key) == value for key, value in test_data.items())
    True
    """

    start_i = 0
    end_i = len(s) - 1
    while start_i < end_i:
        if s[start_i] == s[end_i]:
            start_i += 1
            end_i -= 1
        else:
            return False
    return True


def is_palindrome_traversal(s: str) -> bool:
    """
    Return True if s is a palindrome otherwise return False.

    >>> all(is_palindrome_traversal(key) == value for key, value in test_data.items())
    True
    """
    end = len(s) // 2
    n = len(s)

    # We need to traverse till half of the length of string
    # as we can get access of the i'th last element from
    # i'th index.
    # eg: [0,1,2,3,4,5] => 4th index can be accessed
    # with the help of 1st index (i==n-i-1)
    # where n is length of string
    return all(s[i] == s[n - i - 1] for i in range(end))


def is_palindrome_recursive(s: str) -> bool:
    """
    Return True if s is a palindrome otherwise return False.

    >>> all(is_palindrome_recursive(key) == value for key, value in test_data.items())
    True
    """
    if len(s) <= 1:
        return True
    if s[0] == s[len(s) - 1]:
        return is_palindrome_recursive(s[1:-1])
    else:
        return False


def is_palindrome_slice(s: str) -> bool:
    """
    Return True if s is a palindrome otherwise return False.

    >>> all(is_palindrome_slice(key) == value for key, value in test_data.items())
    True
    """
    return s == s[::-1]


def benchmark_function(name: str) -> None:
    stmt = f"all({name}(key) == value for key, value in test_data.items())"
    setup = f"from __main__ import test_data, {name}"
    number = 500000
    result = timeit(stmt=stmt, setup=setup, number=number)
    print(f"{name:<35} finished {number:,} runs in {result:.5f} seconds")


if __name__ == "__main__":
    for key, value in test_data.items():
        assert is_palindrome(key) == is_palindrome_recursive(key)
        assert is_palindrome(key) == is_palindrome_slice(key)
        print(f"{key:21} {value}")
    print("a man a plan a canal panama")

    # finished 500,000 runs in 0.46793 seconds
    benchmark_function("is_palindrome_slice")
    # finished 500,000 runs in 0.85234 seconds
    benchmark_function("is_palindrome")
    # finished 500,000 runs in 1.32028 seconds
    benchmark_function("is_palindrome_recursive")
    # finished 500,000 runs in 2.08679 seconds
    benchmark_function("is_palindrome_traversal")


================================================
FILE: strings/pig_latin.py
================================================
def pig_latin(word: str) -> str:
    """Compute the piglatin of a given string.

    https://en.wikipedia.org/wiki/Pig_Latin

    Usage examples:
    >>> pig_latin("pig")
    'igpay'
    >>> pig_latin("latin")
    'atinlay'
    >>> pig_latin("banana")
    'ananabay'
    >>> pig_latin("friends")
    'iendsfray'
    >>> pig_latin("smile")
    'ilesmay'
    >>> pig_latin("string")
    'ingstray'
    >>> pig_latin("eat")
    'eatway'
    >>> pig_latin("omelet")
    'omeletway'
    >>> pig_latin("are")
    'areway'
    >>> pig_latin(" ")
    ''
    >>> pig_latin(None)
    ''
    """
    if not (word or "").strip():
        return ""
    word = word.lower()
    if word[0] in "aeiou":
        return f"{word}way"
    for i, char in enumerate(word):  # noqa: B007
        if char in "aeiou":
            break
    return f"{word[i:]}{word[:i]}ay"


if __name__ == "__main__":
    print(f"{pig_latin('friends') = }")
    word = input("Enter a word: ")
    print(f"{pig_latin(word) = }")


================================================
FILE: strings/prefix_function.py
================================================
"""
https://cp-algorithms.com/string/prefix-function.html

Prefix function Knuth-Morris-Pratt algorithm

Different algorithm than Knuth-Morris-Pratt pattern finding

E.x. Finding longest prefix which is also suffix

Time Complexity: O(n) - where n is the length of the string
"""


def prefix_function(input_string: str) -> list:
    """
    For the given string this function computes value for each index(i),
    which represents the longest coincidence of prefix and suffix
    for given substring (input_str[0...i])

    For the value of the first element the algorithm always returns 0

    >>> prefix_function("aabcdaabc")
    [0, 1, 0, 0, 0, 1, 2, 3, 4]
    >>> prefix_function("asdasdad")
    [0, 0, 0, 1, 2, 3, 4, 0]
    """

    # list for the result values
    prefix_result = [0] * len(input_string)

    for i in range(1, len(input_string)):
        # use last results for better performance - dynamic programming
        j = prefix_result[i - 1]
        while j > 0 and input_string[i] != input_string[j]:
            j = prefix_result[j - 1]

        if input_string[i] == input_string[j]:
            j += 1
        prefix_result[i] = j

    return prefix_result


def longest_prefix(input_str: str) -> int:
    """
    Prefix-function use case
    Finding longest prefix which is suffix as well

    >>> longest_prefix("aabcdaabc")
    4
    >>> longest_prefix("asdasdad")
    4
    >>> longest_prefix("abcab")
    2
    """

    # just returning maximum value of the array gives us answer
    return max(prefix_function(input_str))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: strings/rabin_karp.py
================================================
# Numbers of alphabet which we call base
alphabet_size = 256
# Modulus to hash a string
modulus = 1000003


def rabin_karp(pattern: str, text: str) -> bool:
    """
    The Rabin-Karp Algorithm for finding a pattern within a piece of text
    with complexity O(nm), most efficient when it is used with multiple patterns
    as it is able to check if any of a set of patterns match a section of text in o(1)
    given the precomputed hashes.

    This will be the simple version which only assumes one pattern is being searched
    for but it's not hard to modify

    1) Calculate pattern hash

    2) Step through the text one character at a time passing a window with the same
        length as the pattern
        calculating the hash of the text within the window compare it with the hash
        of the pattern. Only testing equality if the hashes match
    """
    p_len = len(pattern)
    t_len = len(text)
    if p_len > t_len:
        return False

    p_hash = 0
    text_hash = 0
    modulus_power = 1

    # Calculating the hash of pattern and substring of text
    for i in range(p_len):
        p_hash = (ord(pattern[i]) + p_hash * alphabet_size) % modulus
        text_hash = (ord(text[i]) + text_hash * alphabet_size) % modulus
        if i == p_len - 1:
            continue
        modulus_power = (modulus_power * alphabet_size) % modulus

    for i in range(t_len - p_len + 1):
        if text_hash == p_hash and text[i : i + p_len] == pattern:
            return True
        if i == t_len - p_len:
            continue
        # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
        text_hash = (
            (text_hash - ord(text[i]) * modulus_power) * alphabet_size
            + ord(text[i + p_len])
        ) % modulus
    return False


def test_rabin_karp() -> None:
    """
    >>> test_rabin_karp()
    Success.
    """
    # Test 1)
    pattern = "abc1abc12"
    text1 = "alskfjaldsabc1abc1abc12k23adsfabcabc"
    text2 = "alskfjaldsk23adsfabcabc"
    assert rabin_karp(pattern, text1)
    assert not rabin_karp(pattern, text2)

    # Test 2)
    pattern = "ABABX"
    text = "ABABZABABYABABX"
    assert rabin_karp(pattern, text)

    # Test 3)
    pattern = "AAAB"
    text = "ABAAAAAB"
    assert rabin_karp(pattern, text)

    # Test 4)
    pattern = "abcdabcy"
    text = "abcxabcdabxabcdabcdabcy"
    assert rabin_karp(pattern, text)

    # Test 5)
    pattern = "Lü"
    text = "Lüsai"
    assert rabin_karp(pattern, text)
    pattern = "Lue"
    assert not rabin_karp(pattern, text)
    print("Success.")


if __name__ == "__main__":
    test_rabin_karp()


================================================
FILE: strings/remove_duplicate.py
================================================
def remove_duplicates(sentence: str) -> str:
    """
    Remove duplicates from sentence
    >>> remove_duplicates("Python is great and Java is also great")
    'Java Python also and great is'
    >>> remove_duplicates("Python   is      great and Java is also great")
    'Java Python also and great is'
    """
    return " ".join(sorted(set(sentence.split())))


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: strings/reverse_letters.py
================================================
def reverse_letters(sentence: str, length: int = 0) -> str:
    """
    Reverse all words that are longer than the given length of characters in a sentence.
    If ``length`` is not specified, it defaults to 0.

    >>> reverse_letters("Hey wollef sroirraw", 3)
    'Hey fellow warriors'
    >>> reverse_letters("nohtyP is nohtyP", 2)
    'Python is Python'
    >>> reverse_letters("1 12 123 1234 54321 654321", 0)
    '1 21 321 4321 12345 123456'
    >>> reverse_letters("racecar")
    'racecar'
    """
    return " ".join(
        word[::-1] if len(word) > length else word for word in sentence.split()
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(reverse_letters("Hey wollef sroirraw"))


================================================
FILE: strings/reverse_words.py
================================================
def reverse_words(sentence: str) -> str:
    """Reverse the order of words in a given string.

    Extra whitespace between words is ignored.

    >>> reverse_words("I love Python")
    'Python love I'
    >>> reverse_words("I     Love          Python")
    'Python Love I'
    """
    return " ".join(sentence.split()[::-1])


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: strings/snake_case_to_camel_pascal_case.py
================================================
def snake_to_camel_case(input_str: str, use_pascal: bool = False) -> str:
    """
    Transforms a snake_case given string to camelCase (or PascalCase if indicated)
    (defaults to not use Pascal)

    >>> snake_to_camel_case("some_random_string")
    'someRandomString'

    >>> snake_to_camel_case("some_random_string", use_pascal=True)
    'SomeRandomString'

    >>> snake_to_camel_case("some_random_string_with_numbers_123")
    'someRandomStringWithNumbers123'

    >>> snake_to_camel_case("some_random_string_with_numbers_123", use_pascal=True)
    'SomeRandomStringWithNumbers123'

    >>> snake_to_camel_case(123)
    Traceback (most recent call last):
        ...
    ValueError: Expected string as input, found <class 'int'>

    >>> snake_to_camel_case("some_string", use_pascal="True")
    Traceback (most recent call last):
        ...
    ValueError: Expected boolean as use_pascal parameter, found <class 'str'>
    """

    if not isinstance(input_str, str):
        msg = f"Expected string as input, found {type(input_str)}"
        raise ValueError(msg)
    if not isinstance(use_pascal, bool):
        msg = f"Expected boolean as use_pascal parameter, found {type(use_pascal)}"
        raise ValueError(msg)

    words = input_str.split("_")

    start_index = 0 if use_pascal else 1

    words_to_capitalize = words[start_index:]

    capitalized_words = [word[0].upper() + word[1:] for word in words_to_capitalize]

    initial_word = "" if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words])


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: strings/split.py
================================================
def split(string: str, separator: str = " ") -> list:
    """
    Will split the string up into all the values separated by the separator
    (defaults to spaces)

    >>> split("apple#banana#cherry#orange",separator='#')
    ['apple', 'banana', 'cherry', 'orange']

    >>> split("Hello there")
    ['Hello', 'there']

    >>> split("11/22/63",separator = '/')
    ['11', '22', '63']

    >>> split("12:43:39",separator = ":")
    ['12', '43', '39']

    >>> split(";abbb;;c;", separator=';')
    ['', 'abbb', '', 'c', '']
    """

    split_words = []

    last_index = 0
    for index, char in enumerate(string):
        if char == separator:
            split_words.append(string[last_index:index])
            last_index = index + 1
        if index + 1 == len(string):
            split_words.append(string[last_index : index + 1])
    return split_words


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: strings/string_switch_case.py
================================================
import re

"""
general info:
https://en.wikipedia.org/wiki/Naming_convention_(programming)#Python_and_Ruby

pascal case [ an upper Camel Case ]: https://en.wikipedia.org/wiki/Camel_case

camel case: https://en.wikipedia.org/wiki/Camel_case

kebab case [ can be found in general info ]:
https://en.wikipedia.org/wiki/Naming_convention_(programming)#Python_and_Ruby

snake case: https://en.wikipedia.org/wiki/Snake_case
"""


# assistant functions
def split_input(str_: str) -> list:
    """
    >>> split_input("one two 31235three4four")
    [['one', 'two', '31235three4four']]
    """
    return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]", str_)]


def to_simple_case(str_: str) -> str:
    """
    >>> to_simple_case("one two 31235three4four")
    'OneTwo31235three4four'
    >>> to_simple_case("This should be combined")
    'ThisShouldBeCombined'
    >>> to_simple_case("The first letters are capitalized, then string is merged")
    'TheFirstLettersAreCapitalizedThenStringIsMerged'
    >>> to_simple_case("special characters :, ', %, ^, $, are ignored")
    'SpecialCharactersAreIgnored'
    """
    string_split = split_input(str_)
    return "".join(
        ["".join([char.capitalize() for char in sub_str]) for sub_str in string_split]
    )


def to_complex_case(text: str, upper: bool, separator: str) -> str:
    """
    Returns the string concatenated with the delimiter we provide.

    Parameters:
    @text: The string on which we want to perform operation
    @upper: Boolean value to determine whether we want capitalized result or not
    @separator: The delimiter with which we want to concatenate words

    Examples:
    >>> to_complex_case("one two 31235three4four", True, "_")
    'ONE_TWO_31235THREE4FOUR'
    >>> to_complex_case("one two 31235three4four", False, "-")
    'one-two-31235three4four'
    """
    try:
        string_split = split_input(text)
        if upper:
            res_str = "".join(
                [
                    separator.join([char.upper() for char in sub_str])
                    for sub_str in string_split
                ]
            )
        else:
            res_str = "".join(
                [
                    separator.join([char.lower() for char in sub_str])
                    for sub_str in string_split
                ]
            )
        return res_str
    except IndexError:
        return "not valid string"


# main content
def to_pascal_case(text: str) -> str:
    """
    >>> to_pascal_case("one two 31235three4four")
    'OneTwo31235three4four'
    """
    return to_simple_case(text)


def to_camel_case(text: str) -> str:
    """
    >>> to_camel_case("one two 31235three4four")
    'oneTwo31235three4four'
    """
    try:
        res_str = to_simple_case(text)
        return res_str[0].lower() + res_str[1:]
    except IndexError:
        return "not valid string"


def to_snake_case(text: str, upper: bool) -> str:
    """
    >>> to_snake_case("one two 31235three4four", True)
    'ONE_TWO_31235THREE4FOUR'
    >>> to_snake_case("one two 31235three4four", False)
    'one_two_31235three4four'
    """
    return to_complex_case(text, upper, "_")


def to_kebab_case(text: str, upper: bool) -> str:
    """
    >>> to_kebab_case("one two 31235three4four", True)
    'ONE-TWO-31235THREE4FOUR'
    >>> to_kebab_case("one two 31235three4four", False)
    'one-two-31235three4four'
    """
    return to_complex_case(text, upper, "-")


if __name__ == "__main__":
    __import__("doctest").testmod()


================================================
FILE: strings/strip.py
================================================
def strip(user_string: str, characters: str = " \t\n\r") -> str:
    """
    Remove leading and trailing characters (whitespace by default) from a string.

    Args:
        user_string (str): The input string to be stripped.
        characters (str, optional): Optional characters to be removed
                (default is whitespace).

    Returns:
        str: The stripped string.

    Examples:
        >>> strip("   hello   ")
        'hello'
        >>> strip("...world...", ".")
        'world'
        >>> strip("123hello123", "123")
        'hello'
        >>> strip("")
        ''
    """

    start = 0
    end = len(user_string)

    while start < end and user_string[start] in characters:
        start += 1

    while end > start and user_string[end - 1] in characters:
        end -= 1

    return user_string[start:end]


================================================
FILE: strings/text_justification.py
================================================
def text_justification(word: str, max_width: int) -> list:
    """
    Will format the string such that each line has exactly
    (max_width) characters and is fully (left and right) justified,
    and return the list of justified text.

    example 1:
    string = "This is an example of text justification."
    max_width = 16

    output = ['This    is    an',
              'example  of text',
              'justification.  ']

    >>> text_justification("This is an example of text justification.", 16)
    ['This    is    an', 'example  of text', 'justification.  ']

    example 2:
    string = "Two roads diverged in a yellow wood"
    max_width = 16
    output = ['Two        roads',
              'diverged   in  a',
              'yellow wood     ']

    >>> text_justification("Two roads diverged in a yellow wood", 16)
    ['Two        roads', 'diverged   in  a', 'yellow wood     ']

    Time complexity: O(m*n)
    Space complexity: O(m*n)
    """

    # Converting string into list of strings split by a space
    words = word.split()

    def justify(line: list, width: int, max_width: int) -> str:
        overall_spaces_count = max_width - width
        words_count = len(line)
        if len(line) == 1:
            # if there is only word in line
            # just insert overall_spaces_count for the remainder of line
            return line[0] + " " * overall_spaces_count
        else:
            spaces_to_insert_between_words = words_count - 1
            # num_spaces_between_words_list[i] : tells you to insert
            # num_spaces_between_words_list[i] spaces
            # after word on line[i]
            num_spaces_between_words_list = spaces_to_insert_between_words * [
                overall_spaces_count // spaces_to_insert_between_words
            ]
            spaces_count_in_locations = (
                overall_spaces_count % spaces_to_insert_between_words
            )
            # distribute spaces via round robin to the left words
            for i in range(spaces_count_in_locations):
                num_spaces_between_words_list[i] += 1
            aligned_words_list = []
            for i in range(spaces_to_insert_between_words):
                # add the word
                aligned_words_list.append(line[i])
                # add the spaces to insert
                aligned_words_list.append(num_spaces_between_words_list[i] * " ")
            # just add the last word to the sentence
            aligned_words_list.append(line[-1])
            # join the aligned words list to form a justified line
            return "".join(aligned_words_list)

    answer = []
    line: list[str] = []
    width = 0
    for inner_word in words:
        if width + len(inner_word) + len(line) <= max_width:
            # keep adding words until we can fill out max_width
            # width = sum of length of all words (without overall_spaces_count)
            # len(inner_word) = length of current inner_word
            # len(line) = number of overall_spaces_count to insert between words
            line.append(inner_word)
            width += len(inner_word)
        else:
            # justify the line and add it to result
            answer.append(justify(line, width, max_width))
            # reset new line and new width
            line, width = [inner_word], len(inner_word)
    remaining_spaces = max_width - width - len(line)
    answer.append(" ".join(line) + (remaining_spaces + 1) * " ")
    return answer


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: strings/title.py
================================================
def to_title_case(word: str) -> str:
    """
    Converts a string to capitalized case, preserving the input as is

    >>> to_title_case("Aakash")
    'Aakash'

    >>> to_title_case("aakash")
    'Aakash'

    >>> to_title_case("AAKASH")
    'Aakash'

    >>> to_title_case("aAkAsH")
    'Aakash'
    """

    """
    Convert the first character to uppercase if it's lowercase
    """
    if "a" <= word[0] <= "z":
        word = chr(ord(word[0]) - 32) + word[1:]

    """
    Convert the remaining characters to lowercase if they are uppercase
    """
    for i in range(1, len(word)):
        if "A" <= word[i] <= "Z":
            word = word[:i] + chr(ord(word[i]) + 32) + word[i + 1 :]

    return word


def sentence_to_title_case(input_str: str) -> str:
    """
    Converts a string to title case, preserving the input as is

    >>> sentence_to_title_case("Aakash Giri")
    'Aakash Giri'

    >>> sentence_to_title_case("aakash giri")
    'Aakash Giri'

    >>> sentence_to_title_case("AAKASH GIRI")
    'Aakash Giri'

    >>> sentence_to_title_case("aAkAsH gIrI")
    'Aakash Giri'
    """

    return " ".join(to_title_case(word) for word in input_str.split())


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: strings/top_k_frequent_words.py
================================================
"""
Finds the top K most frequent words from the provided word list.

This implementation aims to show how to solve the problem using the Heap class
already present in this repository.
Computing order statistics is, in fact, a typical usage of heaps.

This is mostly shown for educational purposes, since the problem can be solved
in a few lines using collections.Counter from the Python standard library:

from collections import Counter
def top_k_frequent_words(words, k_value):
    return [x[0] for x in Counter(words).most_common(k_value)]
"""

from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap


@total_ordering
class WordCount:
    def __init__(self, word: str, count: int) -> None:
        self.word = word
        self.count = count

    def __eq__(self, other: object) -> bool:
        """
        >>> WordCount('a', 1).__eq__(WordCount('b', 1))
        True
        >>> WordCount('a', 1).__eq__(WordCount('a', 1))
        True
        >>> WordCount('a', 1).__eq__(WordCount('a', 2))
        False
        >>> WordCount('a', 1).__eq__(WordCount('b', 2))
        False
        >>> WordCount('a', 1).__eq__(1)
        NotImplemented
        """
        if not isinstance(other, WordCount):
            return NotImplemented
        return self.count == other.count

    def __lt__(self, other: object) -> bool:
        """
        >>> WordCount('a', 1).__lt__(WordCount('b', 1))
        False
        >>> WordCount('a', 1).__lt__(WordCount('a', 1))
        False
        >>> WordCount('a', 1).__lt__(WordCount('a', 2))
        True
        >>> WordCount('a', 1).__lt__(WordCount('b', 2))
        True
        >>> WordCount('a', 2).__lt__(WordCount('a', 1))
        False
        >>> WordCount('a', 2).__lt__(WordCount('b', 1))
        False
        >>> WordCount('a', 1).__lt__(1)
        NotImplemented
        """
        if not isinstance(other, WordCount):
            return NotImplemented
        return self.count < other.count


def top_k_frequent_words(words: list[str], k_value: int) -> list[str]:
    """
    Returns the `k_value` most frequently occurring words,
    in non-increasing order of occurrence.
    In this context, a word is defined as an element in the provided list.

    In case `k_value` is greater than the number of distinct words, a value of k equal
    to the number of distinct words will be considered, instead.

    >>> top_k_frequent_words(['a', 'b', 'c', 'a', 'c', 'c'], 3)
    ['c', 'a', 'b']
    >>> top_k_frequent_words(['a', 'b', 'c', 'a', 'c', 'c'], 2)
    ['c', 'a']
    >>> top_k_frequent_words(['a', 'b', 'c', 'a', 'c', 'c'], 1)
    ['c']
    >>> top_k_frequent_words(['a', 'b', 'c', 'a', 'c', 'c'], 0)
    []
    >>> top_k_frequent_words([], 1)
    []
    >>> top_k_frequent_words(['a', 'a'], 2)
    ['a']
    """
    heap: Heap[WordCount] = Heap()
    count_by_word = Counter(words)
    heap.build_max_heap(
        [WordCount(word, count) for word, count in count_by_word.items()]
    )
    return [heap.extract_max().word for _ in range(min(k_value, len(count_by_word)))]


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: strings/upper.py
================================================
def upper(word: str) -> str:
    """
    Convert an entire string to ASCII uppercase letters by looking for lowercase ASCII
    letters and subtracting 32 from their integer representation to get the uppercase
    letter.

    >>> upper("wow")
    'WOW'
    >>> upper("Hello")
    'HELLO'
    >>> upper("WHAT")
    'WHAT'
    >>> upper("wh[]32")
    'WH[]32'
    """
    return "".join(chr(ord(char) - 32) if "a" <= char <= "z" else char for char in word)


if __name__ == "__main__":
    from doctest import testmod

    testmod()


================================================
FILE: strings/wave_string.py
================================================
def wave(txt: str) -> list:
    """
    Returns a so called 'wave' of a given string
    >>> wave('cat')
    ['Cat', 'cAt', 'caT']
    >>> wave('one')
    ['One', 'oNe', 'onE']
    >>> wave('book')
    ['Book', 'bOok', 'boOk', 'booK']
    """

    return [
        txt[:a] + txt[a].upper() + txt[a + 1 :]
        for a in range(len(txt))
        if txt[a].isalpha()
    ]


if __name__ == "__main__":
    __import__("doctest").testmod()


================================================
FILE: strings/wildcard_pattern_matching.py
================================================
"""
Implementation of regular expression matching with support for '.' and '*'.
'.' Matches any single character.
'*' Matches zero or more of the preceding element.
The matching should cover the entire input string (not partial).

"""


def match_pattern(input_string: str, pattern: str) -> bool:
    """
    uses bottom-up dynamic programming solution for matching the input
    string with a given pattern.

    Runtime: O(len(input_string)*len(pattern))

    Arguments
    --------
    input_string: str, any string which should be compared with the pattern
    pattern: str, the string that represents a pattern and may contain
    '.' for single character matches and '*' for zero or more of preceding character
    matches

    Note
    ----
    the pattern cannot start with a '*',
    because there should be at least one character before *

    Returns
    -------
    A Boolean denoting whether the given string follows the pattern

    Examples
    -------
    >>> match_pattern("aab", "c*a*b")
    True
    >>> match_pattern("dabc", "*abc")
    False
    >>> match_pattern("aaa", "aa")
    False
    >>> match_pattern("aaa", "a.a")
    True
    >>> match_pattern("aaab", "aa*")
    False
    >>> match_pattern("aaab", ".*")
    True
    >>> match_pattern("a", "bbbb")
    False
    >>> match_pattern("", "bbbb")
    False
    >>> match_pattern("a", "")
    False
    >>> match_pattern("", "")
    True
    """

    len_string = len(input_string) + 1
    len_pattern = len(pattern) + 1

    # dp is a 2d matrix where dp[i][j] denotes whether prefix string of
    # length i of input_string matches with prefix string of length j of
    # given pattern.
    # "dp" stands for dynamic programming.
    dp = [[0 for i in range(len_pattern)] for j in range(len_string)]

    # since string of zero length match pattern of zero length
    dp[0][0] = 1

    # since pattern of zero length will never match with string of non-zero length
    for i in range(1, len_string):
        dp[i][0] = 0

    # since string of zero length will match with pattern where there
    # is at least one * alternatively
    for j in range(1, len_pattern):
        dp[0][j] = dp[0][j - 2] if pattern[j - 1] == "*" else 0

    # now using bottom-up approach to find for all remaining lengths
    for i in range(1, len_string):
        for j in range(1, len_pattern):
            if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
                dp[i][j] = dp[i - 1][j - 1]

            elif pattern[j - 1] == "*":
                if dp[i][j - 2] == 1:
                    dp[i][j] = 1
                elif pattern[j - 2] in (input_string[i - 1], "."):
                    dp[i][j] = dp[i - 1][j]
                else:
                    dp[i][j] = 0
            else:
                dp[i][j] = 0

    return bool(dp[-1][-1])


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    # inputing the strings
    # input_string = input("input a string :")
    # pattern = input("input a pattern :")

    input_string = "aab"
    pattern = "c*a*b"

    # using function to check whether given string matches the given pattern
    if match_pattern(input_string, pattern):
        print(f"{input_string} matches the given pattern {pattern}")
    else:
        print(f"{input_string} does not match with the given pattern {pattern}")


================================================
FILE: strings/word_occurrence.py
================================================
# Created by sarathkaul on 17/11/19
# Modified by Arkadip Bhattacharya(@darkmatter18) on 20/04/2020
from collections import defaultdict


def word_occurrence(sentence: str) -> dict:
    """
    >>> from collections import Counter
    >>> SENTENCE = "a b A b c b d b d e f e g e h e i e j e 0"
    >>> occurence_dict = word_occurrence(SENTENCE)
    >>> all(occurence_dict[word] == count for word, count
    ...     in Counter(SENTENCE.split()).items())
    True
    >>> dict(word_occurrence("Two  spaces"))
    {'Two': 1, 'spaces': 1}
    """
    occurrence: defaultdict[str, int] = defaultdict(int)
    # Creating a dictionary containing count of each word
    for word in sentence.split():
        occurrence[word] += 1
    return occurrence


if __name__ == "__main__":
    for word, count in word_occurrence("INPUT STRING").items():
        print(f"{word}: {count}")


================================================
FILE: strings/word_patterns.py
================================================
def get_word_pattern(word: str) -> str:
    """
    Returns numerical pattern of character appearances in given word
    >>> get_word_pattern("")
    ''
    >>> get_word_pattern(" ")
    '0'
    >>> get_word_pattern("pattern")
    '0.1.2.2.3.4.5'
    >>> get_word_pattern("word pattern")
    '0.1.2.3.4.5.6.7.7.8.2.9'
    >>> get_word_pattern("get word pattern")
    '0.1.2.3.4.5.6.7.3.8.9.2.2.1.6.10'
    >>> get_word_pattern()
    Traceback (most recent call last):
    ...
    TypeError: get_word_pattern() missing 1 required positional argument: 'word'
    >>> get_word_pattern(1)
    Traceback (most recent call last):
    ...
    AttributeError: 'int' object has no attribute 'upper'
    >>> get_word_pattern(1.1)
    Traceback (most recent call last):
    ...
    AttributeError: 'float' object has no attribute 'upper'
    >>> get_word_pattern([])
    Traceback (most recent call last):
    ...
    AttributeError: 'list' object has no attribute 'upper'
    """
    word = word.upper()
    next_num = 0
    letter_nums = {}
    word_pattern = []

    for letter in word:
        if letter not in letter_nums:
            letter_nums[letter] = str(next_num)
            next_num += 1
        word_pattern.append(letter_nums[letter])
    return ".".join(word_pattern)


if __name__ == "__main__":
    import pprint
    import time

    start_time = time.time()
    with open("dictionary.txt") as in_file:
        word_list = in_file.read().splitlines()

    all_patterns: dict = {}
    for word in word_list:
        pattern = get_word_pattern(word)
        if pattern in all_patterns:
            all_patterns[pattern].append(word)
        else:
            all_patterns[pattern] = [word]

    with open("word_patterns.txt", "w") as out_file:
        out_file.write(pprint.pformat(all_patterns))

    total_time = round(time.time() - start_time, 2)
    print(f"Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.")
    # Done!  9,581 word patterns found in 0.58 seconds.


================================================
FILE: strings/words.txt
================================================
A
a
aa
aal
aalii
aam
Aani
aardvark
aardwolf
Aaron
Aaronic
Aaronical
Aaronite
Aaronitic
Aaru
Ab
aba
Ababdeh
Ababua
abac
abaca
abacate
abacay
abacinate
abacination
abaciscus
abacist
aback
abactinal
abactinally
abaction
abactor
abaculus
abacus
Abadite
abaff
abaft
abaisance
abaiser
abaissed
abalienate
abalienation
abalone
Abama
abampere
abandon
abandonable
abandoned
abandonedly
abandonee
abandoner
abandonment
Abanic
Abantes
abaptiston
Abarambo
Abaris
abarthrosis
abarticular
abarticulation
abas
abase
abased
abasedly
abasedness
abasement
abaser
Abasgi
abash
abashed
abashedly
abashedness
abashless
abashlessly
abashment
abasia
abasic
abask
Abassin
abastardize
abatable
abate
abatement
abater
abatis
abatised
abaton
abator
abattoir
Abatua
abature
abave
abaxial
abaxile
abaze
abb
Abba
abbacomes
abbacy
Abbadide
abbas
abbasi
abbassi
Abbasside
abbatial
abbatical
abbess
abbey
abbeystede
Abbie
abbot
abbotcy
abbotnullius
abbotship
abbreviate
abbreviately
abbreviation
abbreviator
abbreviatory
abbreviature
Abby
abcoulomb
abdal
abdat
Abderian
Abderite
abdest
abdicable
abdicant
abdicate
abdication
abdicative
abdicator
Abdiel
abditive
abditory
abdomen
abdominal
Abdominales
abdominalian
abdominally
abdominoanterior
abdominocardiac
abdominocentesis
abdominocystic
abdominogenital
abdominohysterectomy
abdominohysterotomy
abdominoposterior
abdominoscope
abdominoscopy
abdominothoracic
abdominous
abdominovaginal
abdominovesical
abduce
abducens
abducent
abduct
abduction
abductor
Abe
abeam
abear
abearance
abecedarian
abecedarium
abecedary
abed
abeigh
Abel
abele
Abelia
Abelian
Abelicea
Abelite
abelite
Abelmoschus
abelmosk
Abelonian
abeltree
Abencerrages
abenteric
abepithymia
Aberdeen
aberdevine
Aberdonian
Aberia
aberrance
aberrancy
aberrant
aberrate
aberration
aberrational
aberrator
aberrometer
aberroscope
aberuncator
abet
abetment
abettal
abettor
abevacuation
abey
abeyance
abeyancy
abeyant
abfarad
abhenry
abhiseka
abhominable
abhor
abhorrence
abhorrency
abhorrent
abhorrently
abhorrer
abhorrible
abhorring
Abhorson
abidal
abidance
abide
abider
abidi
abiding
abidingly
abidingness
Abie
Abies
abietate
abietene
abietic
abietin
Abietineae
abietineous
abietinic
Abiezer
Abigail
abigail
abigailship
abigeat
abigeus
abilao
ability
abilla
abilo
abintestate
abiogenesis
abiogenesist
abiogenetic
abiogenetical
abiogenetically
abiogenist
abiogenous
abiogeny
abiological
abiologically
abiology
abiosis
abiotic
abiotrophic
abiotrophy
Abipon
abir
abirritant
abirritate
abirritation
abirritative
abiston
Abitibi
abiuret
abject
abjectedness
abjection
abjective
abjectly
abjectness
abjoint
abjudge
abjudicate
abjudication
abjunction
abjunctive
abjuration
abjuratory
abjure
abjurement
abjurer
abkar
abkari
Abkhas
Abkhasian
ablach
ablactate
ablactation
ablare
ablastemic
ablastous
ablate
ablation
ablatitious
ablatival
ablative
ablator
ablaut
ablaze
able
ableeze
ablegate
ableness
ablepharia
ablepharon
ablepharous
Ablepharus
ablepsia
ableptical
ableptically
abler
ablest
ablewhackets
ablins
abloom
ablow
ablude
abluent
ablush
ablution
ablutionary
abluvion
ably
abmho
Abnaki
abnegate
abnegation
abnegative
abnegator
Abner
abnerval
abnet
abneural
abnormal
abnormalism
abnormalist
abnormality
abnormalize
abnormally
abnormalness
abnormity
abnormous
abnumerable
Abo
aboard
Abobra
abode
abodement
abody
abohm
aboil
abolish
abolisher
abolishment
abolition
abolitionary
abolitionism
abolitionist
abolitionize
abolla
aboma
abomasum
abomasus
abominable
abominableness
abominably
abominate
abomination
abominator
abomine
Abongo
aboon
aborad
aboral
aborally
abord
aboriginal
aboriginality
aboriginally
aboriginary
aborigine
abort
aborted
aborticide
abortient
abortifacient
abortin
abortion
abortional
abortionist
abortive
abortively
abortiveness
abortus
abouchement
abound
abounder
abounding
aboundingly
about
abouts
above
aboveboard
abovedeck
aboveground
aboveproof
abovestairs
abox
abracadabra
abrachia
abradant
abrade
abrader
Abraham
Abrahamic
Abrahamidae
Abrahamite
Abrahamitic
abraid
Abram
Abramis
abranchial
abranchialism
abranchian
Abranchiata
abranchiate
abranchious
abrasax
abrase
abrash
abrasiometer
abrasion
abrasive
abrastol
abraum
abraxas
abreact
abreaction
abreast
abrenounce
abret
abrico
abridge
abridgeable
abridged
abridgedly
abridger
abridgment
abrim
abrin
abristle
abroach
abroad
Abrocoma
abrocome
abrogable
abrogate
abrogation
abrogative
abrogator
Abroma
Abronia
abrook
abrotanum
abrotine
abrupt
abruptedly
abruption
abruptly
abruptness
Abrus
Absalom
absampere
Absaroka
absarokite
abscess
abscessed
abscession
abscessroot
abscind
abscise
abscision
absciss
abscissa
abscissae
abscisse
abscission
absconce
abscond
absconded
abscondedly
abscondence
absconder
absconsa
abscoulomb
absence
absent
absentation
absentee
absenteeism
absenteeship
absenter
absently
absentment
absentmindedly
absentness
absfarad
abshenry
Absi
absinthe
absinthial
absinthian
absinthiate
absinthic
absinthin
absinthine
absinthism
absinthismic
absinthium
absinthol
absit
absmho
absohm
absolute
absolutely
absoluteness
absolution
absolutism
absolutist
absolutistic
absolutistically
absolutive
absolutization
absolutize
absolutory
absolvable
absolvatory
absolve
absolvent
absolver
absolvitor
absolvitory
absonant
absonous
absorb
absorbability
absorbable
absorbed
absorbedly
absorbedness
absorbefacient
absorbency
absorbent
absorber
absorbing
absorbingly
absorbition
absorpt
absorptance
absorptiometer
absorptiometric
absorption
absorptive
absorptively
absorptiveness
absorptivity
absquatulate
abstain
abstainer
abstainment
abstemious
abstemiously
abstemiousness
abstention
abstentionist
abstentious
absterge
abstergent
abstersion
abstersive
abstersiveness
abstinence
abstinency
abstinent
abstinential
abstinently
abstract
abstracted
abstractedly
abstractedness
abstracter
abstraction
abstractional
abstractionism
abstractionist
abstractitious
abstractive
abstractively
abstractiveness
abstractly
abstractness
abstractor
abstrahent
abstricted
abstriction
abstruse
abstrusely
abstruseness
abstrusion
abstrusity
absume
absumption
absurd
absurdity
absurdly
absurdness
absvolt
Absyrtus
abterminal
abthain
abthainrie
abthainry
abthanage
Abu
abu
abucco
abulia
abulic
abulomania
abuna
abundance
abundancy
abundant
Abundantia
abundantly
abura
aburabozu
aburban
aburst
aburton
abusable
abuse
abusedly
abusee
abuseful
abusefully
abusefulness
abuser
abusion
abusious
abusive
abusively
abusiveness
abut
Abuta
Abutilon
abutment
abuttal
abutter
abutting
abuzz
abvolt
abwab
aby
abysm
abysmal
abysmally
abyss
abyssal
Abyssinian
abyssobenthonic
abyssolith
abyssopelagic
acacatechin
acacatechol
acacetin
Acacia
Acacian
acaciin
acacin
academe
academial
academian
Academic
academic
academical
academically
academicals
academician
academicism
academism
academist
academite
academization
academize
Academus
academy
Acadia
acadialite
Acadian
Acadie
Acaena
acajou
acaleph
Acalepha
Acalephae
acalephan
acalephoid
acalycal
acalycine
acalycinous
acalyculate
Acalypha
Acalypterae
Acalyptrata
Acalyptratae
acalyptrate
Acamar
acampsia
acana
acanaceous
acanonical
acanth
acantha
Acanthaceae
acanthaceous
acanthad
Acantharia
Acanthia
acanthial
acanthin
acanthine
acanthion
acanthite
acanthocarpous
Acanthocephala
acanthocephalan
Acanthocephali
acanthocephalous
Acanthocereus
acanthocladous
Acanthodea
acanthodean
Acanthodei
Acanthodes
acanthodian
Acanthodidae
Acanthodii
Acanthodini
acanthoid
Acantholimon
acanthological
acanthology
acantholysis
acanthoma
Acanthomeridae
acanthon
Acanthopanax
Acanthophis
acanthophorous
acanthopod
acanthopodous
acanthopomatous
acanthopore
acanthopteran
Acanthopteri
acanthopterous
acanthopterygian
Acanthopterygii
acanthosis
acanthous
Acanthuridae
Acanthurus
acanthus
acapnia
acapnial
acapsular
acapu
acapulco
acara
Acarapis
acardia
acardiac
acari
acarian
acariasis
acaricidal
acaricide
acarid
Acarida
Acaridea
acaridean
acaridomatium
acariform
Acarina
acarine
acarinosis
acarocecidium
acarodermatitis
acaroid
acarol
acarologist
acarology
acarophilous
acarophobia
acarotoxic
acarpelous
acarpous
Acarus
Acastus
acatalectic
acatalepsia
acatalepsy
acataleptic
acatallactic
acatamathesia
acataphasia
acataposis
acatastasia
acatastatic
acate
acategorical
acatery
acatharsia
acatharsy
acatholic
acaudal
acaudate
acaulescent
acauline
acaulose
acaulous
acca
accede
accedence
acceder
accelerable
accelerando
accelerant
accelerate
accelerated
acceleratedly
acceleration
accelerative
accelerator
acceleratory
accelerograph
accelerometer
accend
accendibility
accendible
accension
accensor
accent
accentless
accentor
accentuable
accentual
accentuality
accentually
accentuate
accentuation
accentuator
accentus
accept
acceptability
acceptable
acceptableness
acceptably
acceptance
acceptancy
acceptant
acceptation
accepted
acceptedly
accepter
acceptilate
acceptilation
acception
acceptive
acceptor
acceptress
accerse
accersition
accersitor
access
accessarily
accessariness
accessary
accessaryship
accessibility
accessible
accessibly
accession
accessional
accessioner
accessive
accessively
accessless
accessorial
accessorily
accessoriness
accessorius
accessory
accidence
accidency
accident
accidental
accidentalism
accidentalist
accidentality
accidentally
accidentalness
accidented
accidential
accidentiality
accidently
accidia
accidie
accinge
accipient
Accipiter
accipitral
accipitrary
Accipitres
accipitrine
accismus
accite
acclaim
acclaimable
acclaimer
acclamation
acclamator
acclamatory
acclimatable
acclimatation
acclimate
acclimatement
acclimation
acclimatizable
acclimatization
acclimatize
acclimatizer
acclimature
acclinal
acclinate
acclivitous
acclivity
acclivous
accloy
accoast
accoil
accolade
accoladed
accolated
accolent
accolle
accombination
accommodable
accommodableness
accommodate
accommodately
accommodateness
accommodating
accommodatingly
accommodation
accommodational
accommodative
accommodativeness
accommodator
accompanier
accompaniment
accompanimental
accompanist
accompany
accompanyist
accompletive
accomplice
accompliceship
accomplicity
accomplish
accomplishable
accomplished
accomplisher
accomplishment
accomplisht
accompt
accord
accordable
accordance
accordancy
accordant
accordantly
accorder
according
accordingly
accordion
accordionist
accorporate
accorporation
accost
accostable
accosted
accouche
accouchement
accoucheur
accoucheuse
account
accountability
accountable
accountableness
accountably
accountancy
accountant
accountantship
accounting
accountment
accouple
accouplement
accouter
accouterment
accoy
accredit
accreditate
accreditation
accredited
accreditment
accrementitial
accrementition
accresce
accrescence
accrescent
accretal
accrete
accretion
accretionary
accretive
accroach
accroides
accrual
accrue
accruement
accruer
accubation
accubitum
accubitus
accultural
acculturate
acculturation
acculturize
accumbency
accumbent
accumber
accumulable
accumulate
accumulation
accumulativ
accumulative
accumulatively
accumulativeness
accumulator
accuracy
accurate
accurately
accurateness
accurse
accursed
accursedly
accursedness
accusable
accusably
accusal
accusant
accusation
accusatival
accusative
accusatively
accusatorial
accusatorially
accusatory
accusatrix
accuse
accused
accuser
accusingly
accusive
accustom
accustomed
accustomedly
accustomedness
ace
aceacenaphthene
aceanthrene
aceanthrenequinone
acecaffine
aceconitic
acedia
acediamine
acediast
acedy
Aceldama
Acemetae
Acemetic
acenaphthene
acenaphthenyl
acenaphthylene
acentric
acentrous
aceologic
aceology
acephal
Acephala
acephalan
Acephali
acephalia
Acephalina
acephaline
acephalism
acephalist
Acephalite
acephalocyst
acephalous
acephalus
Acer
Aceraceae
aceraceous
Acerae
Acerata
acerate
Acerates
acerathere
Aceratherium
aceratosis
acerb
Acerbas
acerbate
acerbic
acerbity
acerdol
acerin
acerose
acerous
acerra
acertannin
acervate
acervately
acervation
acervative
acervose
acervuline
acervulus
acescence
acescency
acescent
aceship
acesodyne
Acestes
acetabular
Acetabularia
acetabuliferous
acetabuliform
acetabulous
acetabulum
acetacetic
acetal
acetaldehydase
acetaldehyde
acetaldehydrase
acetalization
acetalize
acetamide
acetamidin
acetamidine
acetamido
acetaminol
acetanilid
acetanilide
acetanion
acetaniside
acetanisidide
acetannin
acetarious
acetarsone
acetate
acetated
acetation
acetbromamide
acetenyl
acethydrazide
acetic
acetification
acetifier
acetify
acetimeter
acetimetry
acetin
acetize
acetmethylanilide
acetnaphthalide
acetoacetanilide
acetoacetate
acetoacetic
acetoamidophenol
acetoarsenite
Acetobacter
acetobenzoic
acetobromanilide
acetochloral
acetocinnamene
acetoin
acetol
acetolysis
acetolytic
acetometer
acetometrical
acetometrically
acetometry
acetomorphine
acetonaphthone
acetonate
acetonation
acetone
acetonemia
acetonemic
acetonic
acetonitrile
acetonization
acetonize
acetonuria
acetonurometer
acetonyl
acetonylacetone
acetonylidene
acetophenetide
acetophenin
acetophenine
acetophenone
acetopiperone
acetopyrin
acetosalicylic
acetose
acetosity
acetosoluble
acetothienone
acetotoluide
acetotoluidine
acetous
acetoveratrone
acetoxime
acetoxyl
acetoxyphthalide
acetphenetid
acetphenetidin
acetract
acettoluide
acetum
aceturic
acetyl
acetylacetonates
acetylacetone
acetylamine
acetylate
acetylation
acetylator
acetylbenzene
acetylbenzoate
acetylbenzoic
acetylbiuret
acetylcarbazole
acetylcellulose
acetylcholine
acetylcyanide
acetylenation
acetylene
acetylenediurein
acetylenic
acetylenyl
acetylfluoride
acetylglycine
acetylhydrazine
acetylic
acetylide
acetyliodide
acetylizable
acetylization
acetylize
acetylizer
acetylmethylcarbinol
acetylperoxide
acetylphenol
acetylphenylhydrazine
acetylrosaniline
acetylsalicylate
acetylsalol
acetyltannin
acetylthymol
acetyltropeine
acetylurea
ach
Achaean
Achaemenian
Achaemenid
Achaemenidae
Achaemenidian
Achaenodon
Achaeta
achaetous
achage
Achagua
Achakzai
achalasia
Achamoth
Achango
achar
Achariaceae
Achariaceous
achate
Achates
Achatina
Achatinella
Achatinidae
ache
acheilia
acheilous
acheiria
acheirous
acheirus
Achen
achene
achenial
achenium
achenocarp
achenodium
acher
Achernar
Acheronian
Acherontic
Acherontical
achete
Achetidae
Acheulean
acheweed
achievable
achieve
achievement
achiever
achigan
achilary
achill
Achillea
Achillean
Achilleid
achilleine
Achillize
achillobursitis
achillodynia
achime
Achimenes
Achinese
aching
achingly
achira
Achitophel
achlamydate
Achlamydeae
achlamydeous
achlorhydria
achlorophyllous
achloropsia
Achmetha
acholia
acholic
Acholoe
acholous
acholuria
acholuric
Achomawi
achondrite
achondritic
achondroplasia
achondroplastic
achor
achordal
Achordata
achordate
Achorion
Achras
achree
achroacyte
Achroanthes
achrodextrin
achrodextrinase
achroglobin
achroiocythaemia
achroiocythemia
achroite
achroma
achromacyte
achromasia
achromat
achromate
Achromatiaceae
achromatic
achromatically
achromaticity
achromatin
achromatinic
achromatism
Achromatium
achromatizable
achromatization
achromatize
achromatocyte
achromatolysis
achromatope
achromatophile
achromatopia
achromatopsia
achromatopsy
achromatosis
achromatous
achromaturia
achromia
achromic
Achromobacter
Achromobacterieae
achromoderma
achromophilous
achromotrichia
achromous
achronical
achroodextrin
achroodextrinase
achroous
achropsia
achtehalber
achtel
achtelthaler
Achuas
achy
achylia
achylous
achymia
achymous
Achyranthes
Achyrodes
acichloride
acicula
acicular
acicularly
aciculate
aciculated
aciculum
acid
Acidanthera
Acidaspis
acidemia
acider
acidic
acidiferous
acidifiable
acidifiant
acidific
acidification
acidifier
acidify
acidimeter
acidimetric
acidimetrical
acidimetrically
acidimetry
acidite
acidity
acidize
acidly
acidness
acidoid
acidology
acidometer
acidometry
acidophile
acidophilic
acidophilous
acidoproteolytic
acidosis
acidosteophyte
acidotic
acidproof
acidulate
acidulent
acidulous
aciduric
acidyl
acier
acierage
Acieral
acierate
acieration
aciform
aciliate
aciliated
Acilius
acinaceous
acinaces
acinacifolious
acinaciform
acinar
acinarious
acinary
Acineta
Acinetae
acinetan
Acinetaria
acinetarian
acinetic
acinetiform
Acinetina
acinetinan
acinic
aciniform
acinose
acinotubular
acinous
acinus
Acipenser
Acipenseres
acipenserid
Acipenseridae
acipenserine
acipenseroid
Acipenseroidei
Acis
aciurgy
acker
ackey
ackman
acknow
acknowledge
acknowledgeable
acknowledged
acknowledgedly
acknowledger
aclastic
acle
acleidian
acleistous
Aclemon
aclidian
aclinal
aclinic
acloud
aclys
Acmaea
Acmaeidae
acmatic
acme
acmesthesia
acmic
Acmispon
acmite
acne
acneform
acneiform
acnemia
Acnida
acnodal
acnode
Acocanthera
acocantherin
acock
acockbill
acocotl
Acoela
Acoelomata
acoelomate
acoelomatous
Acoelomi
acoelomous
acoelous
Acoemetae
Acoemeti
Acoemetic
acoin
acoine
Acolapissa
acold
Acolhua
Acolhuan
acologic
acology
acolous
acoluthic
acolyte
acolythate
Acoma
acoma
acomia
acomous
aconative
acondylose
acondylous
acone
aconic
aconin
aconine
aconital
aconite
aconitia
aconitic
aconitin
aconitine
Aconitum
Acontias
acontium
Acontius
aconuresis
acopic
acopon
acopyrin
acopyrine
acor
acorea
acoria
acorn
acorned
Acorus
acosmic
acosmism
acosmist
acosmistic
acotyledon
acotyledonous
acouasm
acouchi
acouchy
acoumeter
acoumetry
acouometer
acouophonia
acoupa
acousmata
acousmatic
acoustic
acoustical
acoustically
acoustician
acousticolateral
Acousticon
acoustics
acquaint
acquaintance
acquaintanceship
acquaintancy
acquaintant
acquainted
acquaintedness
acquest
acquiesce
acquiescement
acquiescence
acquiescency
acquiescent
acquiescently
acquiescer
acquiescingly
acquirability
acquirable
acquire
acquired
acquirement
acquirenda
acquirer
acquisible
acquisite
acquisited
acquisition
acquisitive
acquisitively
acquisitiveness
acquisitor
acquisitum
acquist
acquit
acquitment
acquittal
acquittance
acquitter
Acrab
acracy
acraein
Acraeinae
acraldehyde
Acrania
acranial
acraniate
acrasia
Acrasiaceae
Acrasiales
Acrasida
Acrasieae
Acraspeda
acraspedote
acratia
acraturesis
acrawl
acraze
acre
acreable
acreage
acreak
acream
acred
Acredula
acreman
acrestaff
acrid
acridan
acridian
acridic
Acrididae
Acridiidae
acridine
acridinic
acridinium
acridity
Acridium
acridly
acridness
acridone
acridonium
acridophagus
acridyl
acriflavin
acriflavine
acrimonious
acrimoniously
acrimoniousness
acrimony
acrindoline
acrinyl
acrisia
Acrisius
Acrita
acritan
acrite
acritical
acritol
Acroa
acroaesthesia
acroama
acroamatic
acroamatics
acroanesthesia
acroarthritis
acroasphyxia
acroataxia
acroatic
acrobacy
acrobat
Acrobates
acrobatholithic
acrobatic
acrobatical
acrobatically
acrobatics
acrobatism
acroblast
acrobryous
acrobystitis
Acrocarpi
acrocarpous
acrocephalia
acrocephalic
acrocephalous
acrocephaly
Acrocera
Acroceratidae
Acroceraunian
Acroceridae
Acrochordidae
Acrochordinae
acrochordon
Acroclinium
Acrocomia
acroconidium
acrocontracture
acrocoracoid
acrocyanosis
acrocyst
acrodactylum
acrodermatitis
acrodont
acrodontism
acrodrome
acrodromous
Acrodus
acrodynia
acroesthesia
acrogamous
acrogamy
acrogen
acrogenic
acrogenous
acrogenously
acrography
Acrogynae
acrogynae
acrogynous
acrolein
acrolith
acrolithan
acrolithic
acrologic
acrologically
acrologism
acrologue
acrology
acromania
acromastitis
acromegalia
acromegalic
acromegaly
acromelalgia
acrometer
acromial
acromicria
acromioclavicular
acromiocoracoid
acromiodeltoid
acromiohumeral
acromiohyoid
acromion
acromioscapular
acromiosternal
acromiothoracic
acromonogrammatic
acromphalus
Acromyodi
acromyodian
acromyodic
acromyodous
acromyotonia
acromyotonus
acron
acronarcotic
acroneurosis
acronical
acronically
acronyc
acronych
Acronycta
acronyctous
acronym
acronymic
acronymize
acronymous
acronyx
acrook
acroparalysis
acroparesthesia
acropathology
acropathy
acropetal
acropetally
acrophobia
acrophonetic
acrophonic
acrophony
acropodium
acropoleis
acropolis
acropolitan
Acropora
acrorhagus
acrorrheuma
acrosarc
acrosarcum
acroscleriasis
acroscleroderma
acroscopic
acrose
acrosome
acrosphacelus
acrospire
acrospore
acrosporous
across
acrostic
acrostical
acrostically
acrostichal
Acrosticheae
acrostichic
acrostichoid
Acrostichum
acrosticism
acrostolion
acrostolium
acrotarsial
acrotarsium
acroteleutic
acroterial
acroteric
acroterium
Acrothoracica
acrotic
acrotism
acrotomous
Acrotreta
Acrotretidae
acrotrophic
acrotrophoneurosis
Acrux
Acrydium
acryl
acrylaldehyde
acrylate
acrylic
acrylonitrile
acrylyl
act
acta
actability
actable
Actaea
Actaeaceae
Actaeon
Actaeonidae
Actiad
Actian
actification
actifier
actify
actin
actinal
actinally
actinautographic
actinautography
actine
actinenchyma
acting
Actinia
actinian
Actiniaria
actiniarian
actinic
actinically
Actinidia
Actinidiaceae
actiniferous
actiniform
actinine
actiniochrome
actiniohematin
Actiniomorpha
actinism
Actinistia
actinium
actinobacillosis
Actinobacillus
actinoblast
actinobranch
actinobranchia
actinocarp
actinocarpic
actinocarpous
actinochemistry
actinocrinid
Actinocrinidae
actinocrinite
Actinocrinus
actinocutitis
actinodermatitis
actinodielectric
actinodrome
actinodromous
actinoelectric
actinoelectrically
actinoelectricity
actinogonidiate
actinogram
actinograph
actinography
actinoid
Actinoida
Actinoidea
actinolite
actinolitic
actinologous
actinologue
actinology
actinomere
actinomeric
actinometer
actinometric
actinometrical
actinometry
actinomorphic
actinomorphous
actinomorphy
Actinomyces
Actinomycetaceae
Actinomycetales
actinomycete
actinomycetous
actinomycin
actinomycoma
actinomycosis
actinomycotic
Actinomyxidia
Actinomyxidiida
actinon
Actinonema
actinoneuritis
actinophone
actinophonic
actinophore
actinophorous
actinophryan
Actinophrys
Actinopoda
actinopraxis
actinopteran
Actinopteri
actinopterous
actinopterygian
Actinopterygii
actinopterygious
actinoscopy
actinosoma
actinosome
Actinosphaerium
actinost
actinostereoscopy
actinostomal
actinostome
actinotherapeutic
actinotherapeutics
actinotherapy
actinotoxemia
actinotrichium
actinotrocha
actinouranium
Actinozoa
actinozoal
actinozoan
actinozoon
actinula
action
actionable
actionably
actional
actionary
actioner
actionize
actionless
Actipylea
Actium
activable
activate
activation
activator
active
actively
activeness
activin
activism
activist
activital
activity
activize
actless
actomyosin
acton
actor
actorship
actress
Acts
actu
actual
actualism
actualist
actualistic
actuality
actualization
actualize
actually
actualness
actuarial
actuarially
actuarian
actuary
actuaryship
actuation
actuator
acture
acturience
actutate
acuaesthesia
Acuan
acuate
acuation
Acubens
acuclosure
acuductor
acuesthesia
acuity
aculea
Aculeata
aculeate
aculeated
aculeiform
aculeolate
aculeolus
aculeus
acumen
acuminate
acumination
acuminose
acuminous
acuminulate
acupress
acupressure
acupunctuate
acupunctuation
acupuncturation
acupuncturator
acupuncture
acurative
acushla
acutangular
acutate
acute
acutely
acutenaculum
acuteness
acutiator
acutifoliate
Acutilinguae
acutilingual
acutilobate
acutiplantar
acutish
acutograve
acutonodose
acutorsion
acyanoblepsia
acyanopsia
acyclic
acyesis
acyetic
acyl
acylamido
acylamidobenzene
acylamino
acylate
acylation
acylogen
acyloin
acyloxy
acyloxymethane
acyrological
acyrology
acystia
ad
Ada
adactyl
adactylia
adactylism
adactylous
Adad
adad
adage
adagial
adagietto
adagio
Adai
Adaize
Adam
adamant
adamantean
adamantine
adamantinoma
adamantoblast
adamantoblastoma
adamantoid
adamantoma
adamas
Adamastor
adambulacral
adamellite
Adamhood
Adamic
Adamical
Adamically
adamine
Adamite
adamite
Adamitic
Adamitical
Adamitism
Adamsia
adamsite
adance
adangle
Adansonia
Adapa
adapid
Adapis
adapt
adaptability
adaptable
adaptation
adaptational
adaptationally
adaptative
adaptedness
adapter
adaption
adaptional
adaptionism
adaptitude
adaptive
adaptively
adaptiveness
adaptometer
adaptor
adaptorial
Adar
adarme
adat
adati
adatom
adaunt
adaw
adawe
adawlut
adawn
adaxial
aday
adays
adazzle
adcraft
add
Adda
adda
addability
addable
addax
addebted
added
addedly
addend
addenda
addendum
adder
adderbolt
adderfish
adderspit
adderwort
addibility
addible
addicent
addict
addicted
addictedness
addiction
Addie
addiment
Addisonian
Addisoniana
additament
additamentary
addition
additional
additionally
additionary
additionist
addititious
additive
additively
additivity
additory
addle
addlebrain
addlebrained
addlehead
addleheaded
addleheadedly
addleheadedness
addlement
addleness
addlepate
addlepated
addlepatedness
addleplot
addlings
addlins
addorsed
address
addressee
addresser
addressful
Addressograph
addressor
addrest
Addu
adduce
adducent
adducer
adducible
adduct
adduction
adductive
adductor
Addy
Ade
ade
adead
adeem
adeep
Adela
Adelaide
Adelarthra
Adelarthrosomata
adelarthrosomatous
Adelbert
Adelea
Adeleidae
Adelges
Adelia
Adelina
Adeline
adeling
adelite
Adeliza
adelocerous
Adelochorda
adelocodonic
adelomorphic
adelomorphous
adelopod
Adelops
Adelphi
Adelphian
adelphogamy
Adelphoi
adelpholite
adelphophagy
ademonist
adempted
ademption
adenalgia
adenalgy
Adenanthera
adenase
adenasthenia
adendric
adendritic
adenectomy
adenectopia
adenectopic
adenemphractic
adenemphraxis
adenia
adeniform
adenine
adenitis
adenization
adenoacanthoma
adenoblast
adenocancroid
adenocarcinoma
adenocarcinomatous
adenocele
adenocellulitis
adenochondroma
adenochondrosarcoma
adenochrome
adenocyst
adenocystoma
adenocystomatous
adenodermia
adenodiastasis
adenodynia
adenofibroma
adenofibrosis
adenogenesis
adenogenous
adenographer
adenographic
adenographical
adenography
adenohypersthenia
adenoid
adenoidal
adenoidism
adenoliomyofibroma
adenolipoma
adenolipomatosis
adenologaditis
adenological
adenology
adenolymphocele
adenolymphoma
adenoma
adenomalacia
adenomatome
adenomatous
adenomeningeal
adenometritis
adenomycosis
adenomyofibroma
adenomyoma
adenomyxoma
adenomyxosarcoma
adenoncus
adenoneural
adenoneure
adenopathy
adenopharyngeal
adenopharyngitis
adenophlegmon
Adenophora
adenophore
adenophorous
adenophthalmia
adenophyllous
adenophyma
adenopodous
adenosarcoma
adenosclerosis
adenose
adenosine
adenosis
adenostemonous
Adenostoma
adenotome
adenotomic
adenotomy
adenotyphoid
adenotyphus
adenyl
adenylic
Adeodatus
Adeona
Adephaga
adephagan
adephagia
adephagous
adept
adeptness
adeptship
adequacy
adequate
adequately
adequateness
adequation
adequative
adermia
adermin
Adessenarian
adet
adevism
adfected
adfix
adfluxion
adglutinate
Adhafera
adhaka
adhamant
Adhara
adharma
adhere
adherence
adherency
adherent
adherently
adherer
adherescence
adherescent
adhesion
adhesional
adhesive
adhesively
adhesivemeter
adhesiveness
adhibit
adhibition
adiabatic
adiabatically
adiabolist
adiactinic
adiadochokinesis
adiagnostic
adiantiform
Adiantum
adiaphon
adiaphonon
adiaphoral
adiaphoresis
adiaphoretic
adiaphorism
adiaphorist
adiaphoristic
adiaphorite
adiaphoron
adiaphorous
adiate
adiathermal
adiathermancy
adiathermanous
adiathermic
adiathetic
adiation
Adib
Adicea
adicity
Adiel
adieu
adieux
Adigei
Adighe
Adigranth
adigranth
Adin
Adinida
adinidan
adinole
adion
adipate
adipescent
adipic
adipinic
adipocele
adipocellulose
adipocere
adipoceriform
adipocerous
adipocyte
adipofibroma
adipogenic
adipogenous
adipoid
adipolysis
adipolytic
adipoma
adipomatous
adipometer
adipopexia
adipopexis
adipose
adiposeness
adiposis
adiposity
adiposogenital
adiposuria
adipous
adipsia
adipsic
adipsous
adipsy
adipyl
Adirondack
adit
adital
aditus
adjacency
adjacent
adjacently
adjag
adject
adjection
adjectional
adjectival
adjectivally
adjective
adjectively
adjectivism
adjectivitis
adjiger
adjoin
adjoined
adjoinedly
adjoining
adjoint
adjourn
adjournal
adjournment
adjudge
adjudgeable
adjudger
adjudgment
adjudicate
adjudication
adjudicative
adjudicator
adjudicature
adjunct
adjunction
adjunctive
adjunctively
adjunctly
adjuration
adjuratory
adjure
adjurer
adjust
adjustable
adjustably
adjustage
adjustation
adjuster
adjustive
adjustment
adjutage
adjutancy
adjutant
adjutantship
adjutorious
adjutory
adjutrice
adjuvant
Adlai
adlay
adless
adlet
Adlumia
adlumidine
adlumine
adman
admarginate
admaxillary
admeasure
admeasurement
admeasurer
admedial
admedian
admensuration
admi
adminicle
adminicula
adminicular
adminiculary
adminiculate
adminiculation
adminiculum
administer
administerd
administerial
administrable
administrant
administrate
administration
administrational
administrative
administratively
administrator
administratorship
administratress
administratrices
administratrix
admirability
admirable
admirableness
admirably
admiral
admiralship
admiralty
admiration
admirative
admirator
admire
admired
admiredly
admirer
admiring
admiringly
admissibility
admissible
admissibleness
admissibly
admission
admissive
admissory
admit
admittable
admittance
admitted
admittedly
admittee
admitter
admittible
admix
admixtion
admixture
admonish
admonisher
admonishingly
admonishment
admonition
admonitioner
admonitionist
admonitive
admonitively
admonitor
admonitorial
admonitorily
admonitory
admonitrix
admortization
adnascence
adnascent
adnate
adnation
adnephrine
adnerval
adneural
adnex
adnexal
adnexed
adnexitis
adnexopexy
adnominal
adnominally
adnomination
adnoun
ado
adobe
adolesce
adolescence
adolescency
adolescent
adolescently
Adolph
Adolphus
Adonai
Adonean
Adonia
Adoniad
Adonian
Adonic
adonidin
adonin
Adoniram
Adonis
adonite
adonitol
adonize
adoperate
adoperation
adopt
adoptability
adoptable
adoptant
adoptative
adopted
adoptedly
adoptee
adopter
adoptian
adoptianism
adoptianist
adoption
adoptional
adoptionism
adoptionist
adoptious
adoptive
adoptively
adorability
adorable
adorableness
adorably
adoral
adorally
adorant
Adorantes
adoration
adoratory
adore
adorer
Adoretus
adoringly
adorn
adorner
adorningly
adornment
adosculation
adossed
adoulie
adown
Adoxa
Adoxaceae
adoxaceous
adoxography
adoxy
adoze
adpao
adpress
adpromission
adradial
adradially
adradius
Adramelech
Adrammelech
adread
adream
adreamed
adreamt
adrectal
adrenal
adrenalectomize
adrenalectomy
Adrenalin
adrenaline
adrenalize
adrenalone
adrenergic
adrenin
adrenine
adrenochrome
adrenocortical
adrenocorticotropic
adrenolysis
adrenolytic
adrenotropic
Adrian
Adriana
Adriatic
Adrienne
adrift
adrip
adroit
adroitly
adroitness
adroop
adrop
adrostral
adrowse
adrue
adry
adsbud
adscendent
adscititious
adscititiously
adscript
adscripted
adscription
adscriptitious
adscriptitius
adscriptive
adsessor
adsheart
adsignification
adsignify
adsmith
adsmithing
adsorb
adsorbable
adsorbate
adsorbent
adsorption
adsorptive
adstipulate
adstipulation
adstipulator
adterminal
adtevac
adular
adularescence
adularia
adulate
adulation
adulator
adulatory
adulatress
Adullam
Adullamite
adult
adulter
adulterant
adulterate
adulterately
adulterateness
adulteration
adulterator
adulterer
adulteress
adulterine
adulterize
adulterous
adulterously
adultery
adulthood
adulticidal
adulticide
adultness
adultoid
adumbral
adumbrant
adumbrate
adumbration
adumbrative
adumbratively
adunc
aduncate
aduncated
aduncity
aduncous
adusk
adust
adustion
adustiosis
Advaita
advance
advanceable
advanced
advancedness
advancement
advancer
advancing
advancingly
advancive
advantage
advantageous
advantageously
advantageousness
advection
advectitious
advective
advehent
advene
advenience
advenient
Advent
advential
Adventism
Adventist
adventitia
adventitious
adventitiously
adventitiousness
adventive
adventual
adventure
adventureful
adventurement
adventurer
adventureship
adventuresome
adventuresomely
adventuresomeness
adventuress
adventurish
adventurous
adventurously
adventurousness
adverb
adverbial
adverbiality
adverbialize
adverbially
adverbiation
adversant
adversaria
adversarious
adversary
adversative
adversatively
adverse
adversely
adverseness
adversifoliate
adversifolious
adversity
advert
advertence
advertency
advertent
advertently
advertisable
advertise
advertisee
advertisement
advertiser
advertising
advice
adviceful
advisability
advisable
advisableness
advisably
advisal
advisatory
advise
advised
advisedly
advisedness
advisee
advisement
adviser
advisership
advisive
advisiveness
advisor
advisorily
advisory
advocacy
advocate
advocateship
advocatess
advocation
advocator
advocatory
advocatress
advocatrice
advocatrix
advolution
advowee
advowson
ady
adynamia
adynamic
adynamy
adyta
adyton
adytum
adz
adze
adzer
adzooks
ae
Aeacides
Aeacus
Aeaean
Aechmophorus
aecial
Aecidiaceae
aecidial
aecidioform
Aecidiomycetes
aecidiospore
aecidiostage
aecidium
aeciospore
aeciostage
aecioteliospore
aeciotelium
aecium
aedeagus
Aedes
aedicula
aedile
aedileship
aedilian
aedilic
aedilitian
aedility
aedoeagus
aefald
aefaldness
aefaldy
aefauld
aegagropila
aegagropile
aegagrus
Aegean
aegerian
aegeriid
Aegeriidae
Aegialitis
aegicrania
Aegina
Aeginetan
Aeginetic
Aegipan
aegirine
aegirinolite
aegirite
aegis
Aegisthus
Aegithalos
Aegithognathae
aegithognathism
aegithognathous
Aegle
Aegopodium
aegrotant
aegyptilla
aegyrite
aeluroid
Aeluroidea
aelurophobe
aelurophobia
aeluropodous
aenach
aenean
aeneolithic
aeneous
aenigmatite
aeolharmonica
Aeolia
Aeolian
Aeolic
Aeolicism
aeolid
Aeolidae
Aeolididae
aeolina
aeoline
aeolipile
Aeolis
Aeolism
Aeolist
aeolistic
aeolodicon
aeolodion
aeolomelodicon
aeolopantalon
aeolotropic
aeolotropism
aeolotropy
aeolsklavier
aeon
aeonial
aeonian
aeonist
Aepyceros
Aepyornis
Aepyornithidae
Aepyornithiformes
Aequi
Aequian
Aequiculi
Aequipalpia
aequoreal
aer
aerage
aerarian
aerarium
aerate
aeration
aerator
aerenchyma
aerenterectasia
aerial
aerialist
aeriality
aerially
aerialness
aeric
aerical
Aerides
aerie
aeried
aerifaction
aeriferous
aerification
aeriform
aerify
aero
Aerobacter
aerobate
aerobatic
aerobatics
aerobe
aerobian
aerobic
aerobically
aerobiologic
aerobiological
aerobiologically
aerobiologist
aerobiology
aerobion
aerobiont
aerobioscope
aerobiosis
aerobiotic
aerobiotically
aerobious
aerobium
aeroboat
Aerobranchia
aerobranchiate
aerobus
aerocamera
aerocartograph
Aerocharidae
aerocolpos
aerocraft
aerocurve
aerocyst
aerodermectasia
aerodone
aerodonetic
aerodonetics
aerodrome
aerodromics
aerodynamic
aerodynamical
aerodynamicist
aerodynamics
aerodyne
aeroembolism
aeroenterectasia
aerofoil
aerogel
aerogen
aerogenes
aerogenesis
aerogenic
aerogenically
aerogenous
aerogeologist
aerogeology
aerognosy
aerogram
aerograph
aerographer
aerographic
aerographical
aerographics
aerography
aerogun
aerohydrodynamic
aerohydropathy
aerohydroplane
aerohydrotherapy
aerohydrous
aeroides
aerolite
aerolith
aerolithology
aerolitic
aerolitics
aerologic
aerological
aerologist
aerology
aeromaechanic
aeromancer
aeromancy
aeromantic
aeromarine
aeromechanical
aeromechanics
aerometeorograph
aerometer
aerometric
aerometry
aeromotor
aeronat
aeronaut
aeronautic
aeronautical
aeronautically
aeronautics
aeronautism
aeronef
aeroneurosis
aeropathy
Aerope
aeroperitoneum
aeroperitonia
aerophagia
aerophagist
aerophagy
aerophane
aerophilatelic
aerophilatelist
aerophilately
aerophile
aerophilic
aerophilous
aerophobia
aerophobic
aerophone
aerophor
aerophore
aerophotography
aerophysical
aerophysics
aerophyte
aeroplane
aeroplaner
aeroplanist
aeropleustic
aeroporotomy
aeroscepsis
aeroscepsy
aeroscope
aeroscopic
aeroscopically
aeroscopy
aerose
aerosiderite
aerosiderolite
Aerosol
aerosol
aerosphere
aerosporin
aerostat
aerostatic
aerostatical
aerostatics
aerostation
aerosteam
aerotactic
aerotaxis
aerotechnical
aerotherapeutics
aerotherapy
aerotonometer
aerotonometric
aerotonometry
aerotropic
aerotropism
aeroyacht
aeruginous
aerugo
aery
aes
Aeschylean
Aeschynanthus
Aeschynomene
aeschynomenous
Aesculaceae
aesculaceous
Aesculapian
Aesculapius
Aesculus
Aesopian
Aesopic
aesthete
aesthetic
aesthetical
aesthetically
aesthetician
aestheticism
aestheticist
aestheticize
aesthetics
aesthiology
aesthophysiology
Aestii
aethalioid
aethalium
aetheogam
aetheogamic
aetheogamous
aethered
Aethionema
aethogen
aethrioscope
Aethusa
Aetian
aetiogenic
aetiotropic
aetiotropically
Aetobatidae
Aetobatus
Aetolian
Aetomorphae
aetosaur
aetosaurian
Aetosaurus
aevia
aface
afaint
Afar
afar
afara
afear
afeard
afeared
afebrile
Afenil
afernan
afetal
affa
affability
affable
affableness
affably
affabrous
affair
affaite
affect
affectable
affectate
affectation
affectationist
affected
affectedly
affectedness
affecter
affectibility
affectible
affecting
affectingly
affection
affectional
affectionally
affectionate
affectionately
affectionateness
affectioned
affectious
affective
affectively
affectivity
affeer
affeerer
affeerment
affeir
affenpinscher
affenspalte
afferent
affettuoso
affiance
affiancer
affiant
affidation
affidavit
affidavy
affiliable
affiliate
affiliation
affinal
affination
affine
affined
affinely
affinitative
affinitatively
affinite
affinition
affinitive
affinity
affirm
affirmable
affirmably
affirmance
affirmant
affirmation
affirmative
affirmatively
affirmatory
affirmer
affirmingly
affix
affixal
affixation
affixer
affixion
affixture
afflation
afflatus
afflict
afflicted
afflictedness
afflicter
afflicting
afflictingly
affliction
afflictionless
afflictive
afflictively
affluence
affluent
affluently
affluentness
afflux
affluxion
afforce
afforcement
afford
affordable
afforest
afforestable
afforestation
afforestment
afformative
affranchise
affranchisement
affray
affrayer
affreight
affreighter
affreightment
affricate
affricated
affrication
affricative
affright
affrighted
affrightedly
affrighter
affrightful
affrightfully
affrightingly
affrightment
affront
affronte
affronted
affrontedly
affrontedness
affronter
affronting
affrontingly
affrontingness
affrontive
affrontiveness
affrontment
affuse
affusion
affy
Afghan
afghani
afield
Afifi
afikomen
afire
aflagellar
aflame
aflare
aflat
aflaunt
aflicker
aflight
afloat
aflow
aflower
afluking
aflush
aflutter
afoam
afoot
afore
aforehand
aforenamed
aforesaid
aforethought
aforetime
aforetimes
afortiori
afoul
afraid
afraidness
Aframerican
Afrasia
Afrasian
afreet
afresh
afret
Afric
African
Africana
Africanism
Africanist
Africanization
Africanize
Africanoid
Africanthropus
Afridi
Afrikaans
Afrikander
Afrikanderdom
Afrikanderism
Afrikaner
Afrogaea
Afrogaean
afront
afrown
Afshah
Afshar
aft
aftaba
after
afteract
afterage
afterattack
afterband
afterbeat
afterbirth
afterblow
afterbody
afterbrain
afterbreach
afterbreast
afterburner
afterburning
aftercare
aftercareer
aftercast
aftercataract
aftercause
afterchance
afterchrome
afterchurch
afterclap
afterclause
aftercome
aftercomer
aftercoming
aftercooler
aftercost
aftercourse
aftercrop
aftercure
afterdamp
afterdate
afterdays
afterdeck
afterdinner
afterdrain
afterdrops
aftereffect
afterend
aftereye
afterfall
afterfame
afterfeed
afterfermentation
afterform
afterfriend
afterfruits
afterfuture
aftergame
aftergas
afterglide
afterglow
aftergo
aftergood
aftergrass
aftergrave
aftergrief
aftergrind
aftergrowth
afterguard
afterguns
afterhand
afterharm
afterhatch
afterhelp
afterhend
afterhold
afterhope
afterhours
afterimage
afterimpression
afterings
afterking
afterknowledge
afterlife
afterlifetime
afterlight
afterloss
afterlove
aftermark
aftermarriage
aftermass
aftermast
aftermath
aftermatter
aftermeal
aftermilk
aftermost
afternight
afternoon
afternoons
afternose
afternote
afteroar
afterpain
afterpart
afterpast
afterpeak
afterpiece
afterplanting
afterplay
afterpressure
afterproof
afterrake
afterreckoning
afterrider
afterripening
afterroll
afterschool
aftersend
aftersensation
aftershaft
aftershafted
aftershine
aftership
aftershock
aftersong
aftersound
afterspeech
afterspring
afterstain
afterstate
afterstorm
afterstrain
afterstretch
afterstudy
afterswarm
afterswarming
afterswell
aftertan
aftertask
aftertaste
afterthinker
afterthought
afterthoughted
afterthrift
aftertime
aftertimes
aftertouch
aftertreatment
aftertrial
afterturn
aftervision
afterwale
afterwar
afterward
afterwards
afterwash
afterwhile
afterwisdom
afterwise
afterwit
afterwitted
afterwork
afterworking
afterworld
afterwrath
afterwrist
aftmost
Aftonian
aftosa
aftward
aftwards
afunction
afunctional
afwillite
Afzelia
aga
agabanee
agacante
agacella
Agaces
Agade
Agag
again
against
againstand
agal
agalactia
agalactic
agalactous
agalawood
agalaxia
agalaxy
Agalena
Agalenidae
Agalinis
agalite
agalloch
agallochum
agallop
agalma
agalmatolite
agalwood
Agama
agama
Agamae
Agamemnon
agamete
agami
agamian
agamic
agamically
agamid
Agamidae
agamobium
agamogenesis
agamogenetic
agamogenetically
agamogony
agamoid
agamont
agamospore
agamous
agamy
aganglionic
Aganice
Aganippe
Agao
Agaonidae
Agapanthus
agape
Agapemone
Agapemonian
Agapemonist
Agapemonite
agapetae
agapeti
agapetid
Agapetidae
Agapornis
agar
agaric
agaricaceae
agaricaceous
Agaricales
agaricic
agariciform
agaricin
agaricine
agaricoid
Agaricus
Agaristidae
agarita
Agarum
agarwal
agasp
Agastache
Agastreae
agastric
agastroneuria
agate
agateware
Agatha
Agathaea
Agathaumas
agathin
Agathis
agathism
agathist
agathodaemon
agathodaemonic
agathokakological
agathology
Agathosma
agatiferous
agatiform
agatine
agatize
agatoid
agaty
Agau
Agave
agavose
Agawam
Agaz
agaze
agazed
Agdistis
age
aged
agedly
agedness
agee
Agelacrinites
Agelacrinitidae
Agelaius
Agelaus
ageless
agelessness
agelong
agen
Agena
agency
agenda
agendum
agenesia
agenesic
agenesis
agennetic
agent
agentess
agential
agentival
agentive
agentry
agentship
ageometrical
ager
Ageratum
ageusia
ageusic
ageustia
agger
aggerate
aggeration
aggerose
Aggie
agglomerant
agglomerate
agglomerated
agglomeratic
agglomeration
agglomerative
agglomerator
agglutinability
agglutinable
agglutinant
agglutinate
agglutination
agglutinationist
agglutinative
agglutinator
agglutinin
agglutinize
agglutinogen
agglutinogenic
agglutinoid
agglutinoscope
agglutogenic
aggradation
aggradational
aggrade
aggrandizable
aggrandize
aggrandizement
aggrandizer
aggrate
aggravate
aggravating
aggravatingly
aggravation
aggravative
aggravator
aggregable
aggregant
Aggregata
Aggregatae
aggregate
aggregately
aggregateness
aggregation
aggregative
aggregator
aggregatory
aggress
aggressin
aggression
aggressionist
aggressive
aggressively
aggressiveness
aggressor
aggrievance
aggrieve
aggrieved
aggrievedly
aggrievedness
aggrievement
aggroup
aggroupment
aggry
aggur
agha
Aghan
aghanee
aghast
aghastness
Aghlabite
Aghorapanthi
Aghori
Agialid
Agib
Agiel
agilawood
agile
agilely
agileness
agility
agillawood
aging
agio
agiotage
agist
agistator
agistment
agistor
agitable
agitant
agitate
agitatedly
agitation
agitational
agitationist
agitative
agitator
agitatorial
agitatrix
agitprop
Agkistrodon
agla
Aglaia
aglance
Aglaonema
Aglaos
aglaozonia
aglare
Aglaspis
Aglauros
agleaf
agleam
aglet
aglethead
agley
aglimmer
aglint
Aglipayan
Aglipayano
aglitter
aglobulia
Aglossa
aglossal
aglossate
aglossia
aglow
aglucon
aglutition
aglycosuric
Aglypha
aglyphodont
Aglyphodonta
Aglyphodontia
aglyphous
agmatine
agmatology
agminate
agminated
agnail
agname
agnamed
agnate
Agnatha
agnathia
agnathic
Agnathostomata
agnathostomatous
agnathous
agnatic
agnatically
agnation
agnel
Agnes
agnification
agnize
Agnoetae
Agnoete
Agnoetism
agnoiology
Agnoite
agnomen
agnomical
agnominal
agnomination
agnosia
agnosis
agnostic
agnostically
agnosticism
Agnostus
agnosy
Agnotozoic
agnus
ago
agog
agoge
agogic
agogics
agoho
agoing
agomensin
agomphiasis
agomphious
agomphosis
agon
agonal
agone
agoniada
agoniadin
agoniatite
Agoniatites
agonic
agonied
agonist
Agonista
agonistarch
agonistic
agonistically
agonistics
agonium
agonize
agonizedly
agonizer
agonizingly
Agonostomus
agonothete
agonothetic
agony
agora
agoranome
agoraphobia
agouara
agouta
agouti
agpaite
agpaitic
Agra
agraffee
agrah
agral
agrammatical
agrammatism
Agrania
agranulocyte
agranulocytosis
agranuloplastic
Agrapha
agraphia
agraphic
agrarian
agrarianism
agrarianize
agrarianly
Agrauleum
agre
agree
agreeability
agreeable
agreeableness
agreeably
agreed
agreeing
agreeingly
agreement
agreer
agregation
agrege
agrestal
agrestial
agrestian
agrestic
agria
agricere
agricole
agricolist
agricolite
agricolous
agricultor
agricultural
agriculturalist
agriculturally
agriculture
agriculturer
agriculturist
Agrilus
Agrimonia
agrimony
agrimotor
agrin
Agriochoeridae
Agriochoerus
agriological
agriologist
agriology
Agrionia
agrionid
Agrionidae
Agriotes
Agriotypidae
Agriotypus
agrise
agrito
agroan
agrobiologic
agrobiological
agrobiologically
agrobiologist
agrobiology
agrogeological
agrogeologically
agrogeology
agrologic
agrological
agrologically
agrology
agrom
Agromyza
agromyzid
Agromyzidae
agronome
agronomial
agronomic
agronomical
agronomics
agronomist
agronomy
agroof
agrope
Agropyron
Agrostemma
agrosteral
Agrostis
agrostographer
agrostographic
agrostographical
agrostography
agrostologic
agrostological
agrostologist
agrostology
agrotechny
Agrotis
aground
agrufe
agruif
agrypnia
agrypnotic
agsam
agua
aguacate
Aguacateca
aguavina
Agudist
ague
aguelike
agueproof
agueweed
aguey
aguilarite
aguilawood
aguinaldo
aguirage
aguish
aguishly
aguishness
agunah
agush
agust
agy
Agyieus
agynarious
agynary
agynous
agyrate
agyria
Ah
ah
aha
ahaaina
ahankara
Ahantchuyuk
ahartalav
ahaunch
ahead
aheap
ahem
Ahepatokla
Ahet
ahey
ahimsa
ahind
ahint
Ahir
ahluwalia
ahmadi
Ahmadiya
Ahmed
Ahmet
Ahnfeltia
aho
Ahom
ahong
ahorse
ahorseback
Ahousaht
ahoy
Ahrendahronon
Ahriman
Ahrimanian
ahsan
Aht
Ahtena
ahu
ahuatle
ahuehuete
ahull
ahum
ahungered
ahungry
ahunt
ahura
ahush
ahwal
ahypnia
ai
Aias
Aiawong
aichmophobia
aid
aidable
aidance
aidant
aide
Aidenn
aider
Aides
aidful
aidless
aiel
aigialosaur
Aigialosauridae
Aigialosaurus
aiglet
aigremore
aigrette
aiguille
aiguillesque
aiguillette
aiguilletted
aikinite
ail
ailantery
ailanthic
Ailanthus
ailantine
ailanto
aile
Aileen
aileron
ailette
Ailie
ailing
aillt
ailment
ailsyte
Ailuridae
ailuro
ailuroid
Ailuroidea
Ailuropoda
Ailuropus
Ailurus
ailweed
aim
Aimak
aimara
Aimee
aimer
aimful
aimfully
aiming
aimless
aimlessly
aimlessness
Aimore
aimworthiness
ainaleh
ainhum
ainoi
ainsell
aint
Ainu
aion
aionial
air
Aira
airable
airampo
airan
airbound
airbrained
airbrush
aircraft
aircraftman
aircraftsman
aircraftswoman
aircraftwoman
aircrew
aircrewman
airdock
airdrome
airdrop
aire
Airedale
airedale
airer
airfield
airfoil
airframe
airfreight
airfreighter
airgraphics
airhead
airiferous
airified
airily
airiness
airing
airish
airless
airlift
airlike
airliner
airmail
airman
airmanship
airmark
airmarker
airmonger
airohydrogen
airometer
airpark
airphobia
airplane
airplanist
airport
airproof
airscape
airscrew
airship
airsick
airsickness
airstrip
airt
airtight
airtightly
airtightness
airward
airwards
airway
airwayman
airwoman
airworthiness
airworthy
airy
aischrolatreia
aiseweed
aisle
aisled
aisleless
aisling
Aissaoua
Aissor
aisteoir
Aistopoda
Aistopodes
ait
aitch
aitchbone
aitchless
aitchpiece
aitesis
aithochroi
aition
aitiotropic
Aitkenite
Aitutakian
aiwan
Aix
aizle
Aizoaceae
aizoaceous
Aizoon
Ajaja
ajaja
ajangle
ajar
ajari
Ajatasatru
ajava
ajhar
ajivika
ajog
ajoint
ajowan
Ajuga
ajutment
ak
Aka
aka
Akal
akala
Akali
akalimba
akamatsu
Akamnik
Akan
Akanekunik
Akania
Akaniaceae
akaroa
akasa
Akawai
akazga
akazgine
akcheh
ake
akeake
akebi
Akebia
akee
akeki
akeley
akenobeite
akepiro
akerite
akey
Akha
Akhissar
Akhlame
Akhmimic
akhoond
akhrot
akhyana
akia
Akim
akimbo
akin
akindle
akinesia
akinesic
akinesis
akinete
akinetic
Akiskemikinik
Akiyenik
Akka
Akkad
Akkadian
Akkadist
akmudar
akmuddar
aknee
ako
akoasm
akoasma
akoluthia
akonge
Akontae
Akoulalion
akov
akpek
Akra
akra
Akrabattine
akroasis
akrochordite
akroterion
Aktistetae
Aktistete
Aktivismus
Aktivist
aku
akuammine
akule
akund
Akwapim
Al
al
ala
Alabama
Alabaman
Alabamian
alabamide
alabamine
alabandite
alabarch
alabaster
alabastos
alabastrian
alabastrine
alabastrites
alabastron
alabastrum
alacha
alack
alackaday
alacreatine
alacreatinine
alacrify
alacritous
alacrity
Alactaga
alada
Aladdin
Aladdinize
Aladfar
Aladinist
alaihi
Alain
alaite
Alaki
Alala
alala
alalite
alalonga
alalunga
alalus
Alamanni
Alamannian
Alamannic
alameda
alamo
alamodality
alamonti
alamosite
alamoth
Alan
alan
aland
Alangiaceae
alangin
alangine
Alangium
alani
alanine
alannah
Alans
alantic
alantin
alantol
alantolactone
alantolic
alanyl
alar
Alarbus
alares
Alaria
Alaric
alarm
alarmable
alarmed
alarmedly
alarming
alarmingly
alarmism
alarmist
Alarodian
alarum
alary
alas
Alascan
Alaska
alaskaite
Alaskan
alaskite
Alastair
Alaster
alastrim
alate
alated
alatern
alaternus
alation
Alauda
Alaudidae
alaudine
Alaunian
Alawi
Alb
alb
alba
albacore
albahaca
Albainn
Alban
alban
Albanenses
Albanensian
Albania
Albanian
albanite
Albany
albarco
albardine
albarello
albarium
albaspidin
albata
Albatros
albatross
albe
albedo
albedograph
albee
albeit
Alberene
Albert
Alberta
albertin
Albertina
Albertine
Albertinian
Albertist
albertite
Alberto
albertustaler
albertype
albescence
albescent
albespine
albetad
Albi
Albian
albicans
albicant
albication
albiculi
albification
albificative
albiflorous
albify
Albigenses
Albigensian
Albigensianism
Albin
albinal
albiness
albinic
albinism
albinistic
albino
albinoism
albinotic
albinuria
Albion
Albireo
albite
albitic
albitite
albitization
albitophyre
Albizzia
albocarbon
albocinereous
Albococcus
albocracy
Alboin
albolite
albolith
albopannin
albopruinose
alboranite
Albrecht
Albright
albronze
Albruna
Albuca
Albuginaceae
albuginea
albugineous
albuginitis
albugo
album
albumean
albumen
albumenization
albumenize
albumenizer
albumimeter
albumin
albuminate
albuminaturia
albuminiferous
albuminiform
albuminimeter
albuminimetry
albuminiparous
albuminization
albuminize
albuminocholia
albuminofibrin
albuminogenous
albuminoid
albuminoidal
albuminolysis
albuminometer
albuminometry
albuminone
albuminorrhea
albuminoscope
albuminose
albuminosis
albuminous
albuminousness
albuminuria
albuminuric
albumoid
albumoscope
albumose
albumosuria
alburn
alburnous
alburnum
albus
albutannin
Albyn
Alca
Alcaaba
Alcae
Alcaic
alcaide
alcalde
alcaldeship
alcaldia
Alcaligenes
alcalizate
Alcalzar
alcamine
alcanna
Alcantara
Alcantarines
alcarraza
alcatras
alcazar
Alcedines
Alcedinidae
Alcedininae
Alcedo
alcelaphine
Alcelaphus
Alces
alchemic
alchemical
alchemically
Alchemilla
alchemist
alchemistic
alchemistical
alchemistry
alchemize
alchemy
alchera
alcheringa
alchimy
alchitran
alchochoden
Alchornea
alchymy
Alcibiadean
Alcicornium
Alcidae
alcidine
alcine
Alcippe
alclad
alco
alcoate
alcogel
alcogene
alcohate
alcohol
alcoholate
alcoholature
alcoholdom
alcoholemia
alcoholic
alcoholically
alcoholicity
alcoholimeter
alcoholism
alcoholist
alcoholizable
alcoholization
alcoholize
alcoholmeter
alcoholmetric
alcoholomania
alcoholometer
alcoholometric
alcoholometrical
alcoholometry
alcoholophilia
alcoholuria
alcoholysis
alcoholytic
Alcor
Alcoran
Alcoranic
Alcoranist
alcornoco
alcornoque
alcosol
Alcotate
alcove
alcovinometer
Alcuinian
alcyon
Alcyonacea
alcyonacean
Alcyonaria
alcyonarian
Alcyone
Alcyones
Alcyoniaceae
alcyonic
alcyoniform
Alcyonium
alcyonoid
aldamine
aldane
aldazin
aldazine
aldeament
Aldebaran
aldebaranium
aldehol
aldehydase
aldehyde
aldehydic
aldehydine
aldehydrol
alder
Alderamin
alderman
aldermanate
aldermancy
aldermaness
aldermanic
aldermanical
aldermanity
aldermanlike
aldermanly
aldermanry
aldermanship
aldern
Alderney
alderwoman
Aldhafara
Aldhafera
aldim
aldime
aldimine
Aldine
aldine
aldoheptose
aldohexose
aldoketene
aldol
aldolization
aldolize
aldononose
aldopentose
aldose
aldoside
aldoxime
Aldrovanda
Aldus
ale
Alea
aleak
aleatory
alebench
aleberry
Alebion
alec
alecithal
alecize
Aleck
aleconner
alecost
Alectoria
alectoria
Alectorides
alectoridine
alectorioid
Alectoris
alectoromachy
alectoromancy
Alectoromorphae
alectoromorphous
Alectoropodes
alectoropodous
Alectrion
Alectrionidae
alectryomachy
alectryomancy
Alectryon
alecup
alee
alef
alefnull
aleft
alefzero
alegar
alehoof
alehouse
Alejandro
alem
alemana
Alemanni
Alemannian
Alemannic
Alemannish
alembic
alembicate
alembroth
Alemite
alemite
alemmal
alemonger
alen
Alencon
Aleochara
aleph
alephs
alephzero
alepidote
alepole
alepot
Aleppine
Aleppo
alerce
alerse
alert
alertly
alertness
alesan
alestake
aletap
aletaster
Alethea
alethiology
alethopteis
alethopteroid
alethoscope
aletocyte
Aletris
alette
aleukemic
Aleurites
aleuritic
Aleurobius
Aleurodes
Aleurodidae
aleuromancy
aleurometer
aleuronat
aleurone
aleuronic
aleuroscope
Aleut
Aleutian
Aleutic
aleutite
alevin
alewife
Alex
Alexander
alexanders
Alexandra
Alexandreid
Alexandrian
Alexandrianism
Alexandrina
Alexandrine
alexandrite
Alexas
Alexia
alexia
Alexian
alexic
alexin
alexinic
alexipharmacon
alexipharmacum
alexipharmic
alexipharmical
alexipyretic
Alexis
alexiteric
alexiterical
Alexius
aleyard
Aleyrodes
aleyrodid
Aleyrodidae
Alf
alf
alfa
alfaje
alfalfa
alfaqui
alfaquin
alfenide
alfet
alfilaria
alfileria
alfilerilla
alfilerillo
alfiona
Alfirk
alfonsin
alfonso
alforja
Alfred
Alfreda
alfresco
alfridaric
alfridary
Alfur
Alfurese
Alfuro
alga
algae
algaecide
algaeological
algaeologist
algaeology
algaesthesia
algaesthesis
algal
algalia
Algaroth
algarroba
algarrobilla
algarrobin
Algarsife
Algarsyf
algate
Algebar
algebra
algebraic
algebraical
algebraically
algebraist
algebraization
algebraize
Algedi
algedo
algedonic
algedonics
algefacient
Algenib
Algerian
Algerine
algerine
Algernon
algesia
algesic
algesis
algesthesis
algetic
Algic
algic
algid
algidity
algidness
Algieba
algific
algin
alginate
algine
alginic
alginuresis
algiomuscular
algist
algivorous
algocyan
algodoncillo
algodonite
algoesthesiometer
algogenic
algoid
Algol
algolagnia
algolagnic
algolagnist
algolagny
algological
algologist
algology
Algoman
algometer
algometric
algometrical
algometrically
algometry
Algomian
Algomic
Algonkian
Algonquian
Algonquin
algophilia
algophilist
algophobia
algor
Algorab
Algores
algorism
algorismic
algorist
algoristic
algorithm
algorithmic
algosis
algous
algovite
algraphic
algraphy
alguazil
algum
Algy
Alhagi
Alhambra
Alhambraic
Alhambresque
Alhena
alhenna
alias
Alibamu
alibangbang
alibi
alibility
alible
Alicant
Alice
alichel
Alichino
Alicia
Alick
alicoche
alictisal
alicyclic
Alida
alidade
Alids
alien
alienability
alienable
alienage
alienate
alienation
alienator
aliency
alienee
aliener
alienicola
alienigenate
alienism
alienist
alienize
alienor
alienship
aliethmoid
aliethmoidal
alif
aliferous
aliform
aligerous
alight
align
aligner
alignment
aligreek
aliipoe
alike
alikeness
alikewise
Alikuluf
Alikulufan
alilonghi
alima
aliment
alimental
alimentally
alimentariness
alimentary
alimentation
alimentative
alimentatively
alimentativeness
alimenter
alimentic
alimentive
alimentiveness
alimentotherapy
alimentum
alimonied
alimony
alin
alinasal
Aline
alineation
alintatao
aliofar
Alioth
alipata
aliped
aliphatic
alipterion
aliptes
aliptic
aliquant
aliquot
aliseptal
alish
alisier
Alisma
Alismaceae
alismaceous
alismad
alismal
Alismales
Alismataceae
alismoid
aliso
Alison
alison
alisonite
alisp
alisphenoid
alisphenoidal
alist
Alister
alit
alite
alitrunk
aliturgic
aliturgical
aliunde
alive
aliveness
alivincular
Alix
aliyah
alizarate
alizari
alizarin
aljoba
alk
alkahest
alkahestic
alkahestica
alkahestical
Alkaid
alkalamide
alkalemia
alkalescence
alkalescency
alkalescent
alkali
alkalic
alkaliferous
alkalifiable
alkalify
alkaligen
alkaligenous
alkalimeter
alkalimetric
alkalimetrical
alkalimetrically
alkalimetry
alkaline
alkalinity
alkalinization
alkalinize
alkalinuria
alkalizable
alkalizate
alkalization
alkalize
alkalizer
alkaloid
alkaloidal
alkalometry
alkalosis
alkalous
Alkalurops
alkamin
alkamine
alkane
alkanet
Alkanna
alkannin
Alkaphrah
alkapton
alkaptonuria
alkaptonuric
alkargen
alkarsin
alkekengi
alkene
alkenna
alkenyl
alkermes
Alkes
alkide
alkine
alkool
Alkoran
Alkoranic
alkoxide
alkoxy
alkoxyl
alky
alkyd
alkyl
alkylamine
alkylate
alkylation
alkylene
alkylic
alkylidene
alkylize
alkylogen
alkyloxy
alkyne
all
allabuta
allactite
allaeanthus
allagite
allagophyllous
allagostemonous
Allah
allalinite
Allamanda
allamotti
Allan
allan
allanite
allanitic
allantiasis
allantochorion
allantoic
allantoid
allantoidal
Allantoidea
allantoidean
allantoidian
allantoin
allantoinase
allantoinuria
allantois
allantoxaidin
allanturic
Allasch
allassotonic
allative
allatrate
allay
allayer
allayment
allbone
Alle
allecret
allectory
allegate
allegation
allegator
allege
allegeable
allegedly
allegement
alleger
Alleghenian
Allegheny
allegiance
allegiancy
allegiant
allegoric
allegorical
allegorically
allegoricalness
allegorism
allegorist
allegorister
allegoristic
allegorization
allegorize
allegorizer
allegory
allegretto
allegro
allele
allelic
allelism
allelocatalytic
allelomorph
allelomorphic
allelomorphism
allelotropic
allelotropism
allelotropy
alleluia
alleluiatic
allemand
allemande
allemontite
Allen
allenarly
allene
Allentiac
Allentiacan
aller
allergen
allergenic
allergia
allergic
allergin
allergist
allergy
allerion
allesthesia
alleviate
alleviatingly
alleviation
alleviative
alleviator
alleviatory
alley
alleyed
alleyite
alleyway
allgood
Allhallow
Allhallowtide
allheal
alliable
alliably
Alliaceae
alliaceous
alliance
alliancer
Alliaria
allicampane
allice
allicholly
alliciency
allicient
Allie
allied
Allies
allies
alligate
alligator
alligatored
allineate
allineation
Allionia
Allioniaceae
allision
alliteral
alliterate
alliteration
alliterational
alliterationist
alliterative
alliteratively
alliterativeness
alliterator
Allium
allivalite
allmouth
allness
Allobroges
allocable
allocaffeine
allocatable
allocate
allocatee
allocation
allocator
allochetia
allochetite
allochezia
allochiral
allochirally
allochiria
allochlorophyll
allochroic
allochroite
allochromatic
allochroous
allochthonous
allocinnamic
alloclase
alloclasite
allocochick
allocrotonic
allocryptic
allocute
allocution
allocutive
allocyanine
allodelphite
allodesmism
alloeosis
alloeostropha
alloeotic
alloerotic
alloerotism
allogamous
allogamy
allogene
allogeneity
allogeneous
allogenic
allogenically
allograph
alloiogenesis
alloisomer
alloisomeric
alloisomerism
allokinesis
allokinetic
allokurtic
allomerism
allomerous
allometric
allometry
allomorph
allomorphic
allomorphism
allomorphite
allomucic
allonomous
allonym
allonymous
allopalladium
allopath
allopathetic
allopathetically
allopathic
allopathically
allopathist
allopathy
allopatric
allopatrically
allopatry
allopelagic
allophanamide
allophanates
allophane
allophanic
allophone
allophyle
allophylian
allophylic
Allophylus
allophytoid
alloplasm
alloplasmatic
alloplasmic
alloplast
alloplastic
alloplasty
alloploidy
allopolyploid
allopsychic
alloquial
alloquialism
alloquy
allorhythmia
allorrhyhmia
allorrhythmic
allosaur
Allosaurus
allose
allosematic
allosome
allosyndesis
allosyndetic
allot
allotee
allotelluric
allotheism
Allotheria
allothigene
allothigenetic
allothigenetically
allothigenic
allothigenous
allothimorph
allothimorphic
allothogenic
allothogenous
allotment
allotriodontia
Allotriognathi
allotriomorphic
allotriophagia
allotriophagy
allotriuria
allotrope
allotrophic
allotropic
allotropical
allotropically
allotropicity
allotropism
allotropize
allotropous
allotropy
allotrylic
allottable
allottee
allotter
allotype
allotypical
allover
allow
allowable
allowableness
allowably
allowance
allowedly
allower
alloxan
alloxanate
alloxanic
alloxantin
alloxuraemia
alloxuremia
alloxuric
alloxyproteic
alloy
alloyage
allozooid
allseed
allspice
allthing
allthorn
alltud
allude
allure
allurement
allurer
alluring
alluringly
alluringness
allusion
allusive
allusively
allusiveness
alluvia
alluvial
alluviate
alluviation
alluvion
alluvious
alluvium
allwhere
allwhither
allwork
Allworthy
Ally
ally
allyl
allylamine
allylate
allylation
allylene
allylic
allylthiourea
Alma
alma
Almach
almaciga
almacigo
almadia
almadie
almagest
almagra
Almain
Alman
almanac
almandine
almandite
alme
almeidina
almemar
Almerian
almeriite
Almida
almightily
almightiness
almighty
almique
Almira
almirah
almochoden
Almohad
Almohade
Almohades
almoign
Almon
almon
almond
almondy
almoner
almonership
almonry
Almoravid
Almoravide
Almoravides
almost
almous
alms
almsdeed
almsfolk
almsful
almsgiver
almsgiving
almshouse
almsman
almswoman
almucantar
almuce
almud
almude
almug
Almuredin
almuten
aln
alnage
alnager
alnagership
Alnaschar
Alnascharism
alnein
alnico
Alnilam
alniresinol
Alnitak
Alnitham
alniviridol
alnoite
alnuin
Alnus
alo
Aloadae
Alocasia
alochia
alod
alodial
alodialism
alodialist
alodiality
alodially
alodian
alodiary
alodification
alodium
alody
aloe
aloed
aloelike
aloemodin
aloeroot
aloesol
aloeswood
aloetic
aloetical
aloewood
aloft
alogia
Alogian
alogical
alogically
alogism
alogy
aloid
aloin
Alois
aloisiite
aloma
alomancy
alone
aloneness
along
alongshore
alongshoreman
alongside
alongst
Alonso
Alonsoa
Alonzo
aloof
aloofly
aloofness
aloose
alop
alopecia
Alopecias
alopecist
alopecoid
Alopecurus
alopeke
Alopias
Alopiidae
Alosa
alose
Alouatta
alouatte
aloud
alow
alowe
Aloxite
Aloysia
Aloysius
alp
alpaca
alpasotes
Alpax
alpeen
Alpen
alpenglow
alpenhorn
alpenstock
alpenstocker
alpestral
alpestrian
alpestrine
alpha
alphabet
alphabetarian
alphabetic
alphabetical
alphabetically
alphabetics
alphabetiform
alphabetism
alphabetist
alphabetization
alphabetize
alphabetizer
Alphard
alphatoluic
Alphean
Alphecca
alphenic
Alpheratz
alphitomancy
alphitomorphous
alphol
Alphonist
Alphonse
Alphonsine
Alphonsism
Alphonso
alphorn
alphos
alphosis
alphyl
Alpian
Alpid
alpieu
alpigene
Alpine
alpine
alpinely
alpinery
alpinesque
Alpinia
Alpiniaceae
Alpinism
Alpinist
alpist
Alpujarra
alqueire
alquier
alquifou
alraun
alreadiness
already
alright
alrighty
alroot
alruna
Alsatia
Alsatian
alsbachite
Alshain
Alsinaceae
alsinaceous
Alsine
also
alsoon
Alsophila
Alstonia
alstonidine
alstonine
alstonite
Alstroemeria
alsweill
alt
Altaian
Altaic
Altaid
Altair
altaite
Altamira
altar
altarage
altared
altarist
altarlet
altarpiece
altarwise
altazimuth
alter
alterability
alterable
alterableness
alterably
alterant
alterate
alteration
alterative
altercate
altercation
altercative
alteregoism
alteregoistic
alterer
alterity
altern
alternacy
alternance
alternant
Alternanthera
Alternaria
alternariose
alternate
alternately
alternateness
alternating
alternatingly
alternation
alternationist
alternative
alternatively
alternativeness
alternativity
alternator
alterne
alternifoliate
alternipetalous
alternipinnate
alternisepalous
alternize
alterocentric
Althaea
althaein
Althea
althea
althein
altheine
althionic
altho
althorn
although
Altica
Alticamelus
altigraph
altilik
altiloquence
altiloquent
altimeter
altimetrical
altimetrically
altimetry
altin
altincar
Altingiaceae
altingiaceous
altininck
altiplano
altiscope
altisonant
altisonous
altissimo
altitude
altitudinal
altitudinarian
alto
altogether
altogetherness
altometer
altoun
altrices
altricial
altropathy
altrose
altruism
altruist
altruistic
altruistically
altschin
altun
Aluco
Aluconidae
Aluconinae
aludel
Aludra
alula
alular
alulet
Alulim
alum
alumbloom
Alumel
alumic
alumiferous
alumina
aluminaphone
aluminate
alumine
aluminic
aluminide
aluminiferous
aluminiform
aluminish
aluminite
aluminium
aluminize
aluminoferric
aluminographic
aluminography
aluminose
aluminosilicate
aluminosis
aluminosity
aluminothermic
aluminothermics
aluminothermy
aluminotype
aluminous
aluminum
aluminyl
alumish
alumite
alumium
alumna
alumnae
alumnal
alumni
alumniate
Alumnol
alumnus
alumohydrocalcite
alumroot
Alundum
aluniferous
alunite
alunogen
alupag
Alur
alure
alurgite
alushtite
aluta
alutaceous
Alvah
Alvan
alvar
alvearium
alveary
alveloz
alveola
alveolar
alveolariform
alveolary
alveolate
alveolated
alveolation
alveole
alveolectomy
alveoli
alveoliform
alveolite
Alveolites
alveolitis
alveoloclasia
alveolocondylean
alveolodental
alveololabial
alveololingual
alveolonasal
alveolosubnasal
alveolotomy
alveolus
alveus
alviducous
Alvin
Alvina
alvine
Alvissmal
alvite
alvus
alway
always
aly
Alya
alycompaine
alymphia
alymphopotent
alypin
alysson
Alyssum
alytarch
Alytes
am
ama
amaas
Amabel
amability
amacratic
amacrinal
amacrine
amadavat
amadelphous
Amadi
Amadis
amadou
Amaethon
Amafingo
amaga
amah
Amahuaca
amain
amaister
amakebe
Amakosa
amala
amalaita
amalaka
Amalfian
Amalfitan
amalgam
amalgamable
amalgamate
amalgamation
amalgamationist
amalgamative
amalgamatize
amalgamator
amalgamist
amalgamization
amalgamize
Amalings
Amalrician
amaltas
amamau
Amampondo
Amanda
amandin
Amandus
amang
amani
amania
Amanist
Amanita
amanitin
amanitine
Amanitopsis
amanori
amanous
amantillo
amanuenses
amanuensis
amapa
Amapondo
amar
Amara
Amarantaceae
amarantaceous
amaranth
Amaranthaceae
amaranthaceous
amaranthine
amaranthoid
Amaranthus
amarantite
Amarantus
amarelle
amarevole
amargoso
amarillo
amarin
amarine
amaritude
amarity
amaroid
amaroidal
Amarth
amarthritis
amaryllid
Amaryllidaceae
amaryllidaceous
amaryllideous
Amaryllis
amasesis
amass
amassable
amasser
amassment
Amasta
amasthenic
amastia
amasty
Amatembu
amaterialistic
amateur
amateurish
amateurishly
amateurishness
amateurism
amateurship
Amati
amative
amatively
amativeness
amatol
amatorial
amatorially
amatorian
amatorious
amatory
amatrice
amatungula
amaurosis
amaurotic
amaze
amazed
amazedly
amazedness
amazeful
amazement
amazia
Amazilia
amazing
amazingly
Amazon
Amazona
Amazonian
Amazonism
amazonite
Amazulu
amba
ambage
ambagiosity
ambagious
ambagiously
ambagiousness
ambagitory
ambalam
amban
ambar
ambaree
ambarella
ambary
ambash
ambassade
Ambassadeur
ambassador
ambassadorial
ambassadorially
ambassadorship
ambassadress
ambassage
ambassy
ambatch
ambatoarinite
ambay
ambeer
amber
amberfish
ambergris
amberiferous
amberite
amberoid
amberous
ambery
ambicolorate
ambicoloration
ambidexter
ambidexterity
ambidextral
ambidextrous
ambidextrously
ambidextrousness
ambience
ambiency
ambiens
ambient
ambier
ambigenous
ambiguity
ambiguous
ambiguously
ambiguousness
ambilateral
ambilateralaterally
ambilaterality
ambilevous
ambilian
ambilogy
ambiopia
ambiparous
ambisinister
ambisinistrous
ambisporangiate
ambisyllabic
ambit
ambital
ambitendency
ambition
ambitionist
ambitionless
ambitionlessly
ambitious
ambitiously
ambitiousness
ambitty
ambitus
ambivalence
ambivalency
ambivalent
ambivert
amble
ambler
ambling
amblingly
amblotic
amblyacousia
amblyaphia
Amblycephalidae
Amblycephalus
amblychromatic
Amblydactyla
amblygeusia
amblygon
amblygonal
amblygonite
amblyocarpous
Amblyomma
amblyope
amblyopia
amblyopic
Amblyopsidae
Amblyopsis
amblyoscope
amblypod
Amblypoda
amblypodous
Amblyrhynchus
amblystegite
Amblystoma
ambo
amboceptoid
amboceptor
Ambocoelia
Amboina
Amboinese
ambomalleal
ambon
ambonite
Ambonnay
ambos
ambosexous
ambosexual
ambrain
ambrein
ambrette
Ambrica
ambrite
ambroid
ambrology
Ambrose
ambrose
ambrosia
ambrosiac
Ambrosiaceae
ambrosiaceous
ambrosial
ambrosially
Ambrosian
ambrosian
ambrosiate
ambrosin
ambrosine
Ambrosio
ambrosterol
ambrotype
ambry
ambsace
ambulacral
ambulacriform
ambulacrum
ambulance
ambulancer
ambulant
ambulate
ambulatio
ambulation
ambulative
ambulator
Ambulatoria
ambulatorial
ambulatorium
ambulatory
ambuling
ambulomancy
amburbial
ambury
ambuscade
ambuscader
ambush
ambusher
ambushment
Ambystoma
Ambystomidae
amchoor
ame
amebiform
Amedeo
ameed
ameen
Ameiuridae
Ameiurus
Ameiva
Amelanchier
amelcorn
Amelia
amelia
amelification
ameliorable
ameliorableness
ameliorant
ameliorate
amelioration
ameliorativ
ameliorative
ameliorator
amellus
ameloblast
ameloblastic
amelu
amelus
Amen
amen
amenability
amenable
amenableness
amenably
amend
amendable
amendableness
amendatory
amende
amender
amendment
amends
amene
amenia
Amenism
Amenite
amenity
amenorrhea
amenorrheal
amenorrheic
amenorrhoea
ament
amentaceous
amental
amentia
Amentiferae
amentiferous
amentiform
amentulum
amentum
amerce
amerceable
amercement
amercer
amerciament
America
American
Americana
Americanese
Americanism
Americanist
Americanistic
Americanitis
Americanization
Americanize
Americanizer
Americanly
Americanoid
Americaward
Americawards
americium
Americomania
Americophobe
Amerimnon
Amerind
Amerindian
Amerindic
amerism
ameristic
amesite
Ametabola
ametabole
ametabolia
ametabolian
ametabolic
ametabolism
ametabolous
ametaboly
ametallous
amethodical
amethodically
amethyst
amethystine
ametoecious
ametria
ametrometer
ametrope
ametropia
ametropic
ametrous
Amex
amgarn
amhar
amherstite
amhran
Ami
ami
Amia
amiability
amiable
amiableness
amiably
amianth
amianthiform
amianthine
Amianthium
amianthoid
amianthoidal
amianthus
amic
amicability
amicable
amicableness
amicably
amical
amice
amiced
amicicide
amicrobic
amicron
amicronucleate
amid
amidase
amidate
amidation
amide
amidic
amidid
amidide
amidin
amidine
Amidism
Amidist
amido
amidoacetal
amidoacetic
amidoacetophenone
amidoaldehyde
amidoazo
amidoazobenzene
amidoazobenzol
amidocaffeine
amidocapric
amidofluorid
amidofluoride
amidogen
amidoguaiacol
amidohexose
amidoketone
amidol
amidomyelin
amidon
amidophenol
amidophosphoric
amidoplast
amidoplastid
amidopyrine
amidosuccinamic
amidosulphonal
amidothiazole
amidoxime
amidoxy
amidoxyl
amidrazone
amidship
amidships
amidst
amidstream
amidulin
Amigo
Amiidae
amil
Amiles
Amiloun
amimia
amimide
amin
aminate
amination
amine
amini
aminic
aminity
aminization
aminize
amino
aminoacetal
aminoacetanilide
aminoacetic
aminoacetone
aminoacetophenetidine
aminoacetophenone
aminoacidemia
aminoaciduria
aminoanthraquinone
aminoazobenzene
aminobarbituric
aminobenzaldehyde
aminobenzamide
aminobenzene
aminobenzoic
aminocaproic
aminodiphenyl
aminoethionic
aminoformic
aminogen
aminoglutaric
aminoguanidine
aminoid
aminoketone
aminolipin
aminolysis
aminolytic
aminomalonic
aminomyelin
aminophenol
aminoplast
aminoplastic
aminopropionic
aminopurine
aminopyrine
aminoquinoline
aminosis
aminosuccinamic
aminosulphonic
aminothiophen
aminovaleric
aminoxylol
Aminta
Amintor
Amioidei
Amir
amir
Amiranha
amiray
amirship
Amish
Amishgo
amiss
amissibility
amissible
amissness
Amita
Amitabha
amitosis
amitotic
amitotically
amity
amixia
Amizilis
amla
amli
amlikar
amlong
Amma
amma
amman
Ammanite
ammelide
ammelin
ammeline
ammer
ammeter
Ammi
Ammiaceae
ammiaceous
ammine
amminochloride
amminolysis
amminolytic
ammiolite
ammo
Ammobium
ammochaeta
ammochryse
ammocoete
ammocoetes
ammocoetid
Ammocoetidae
ammocoetiform
ammocoetoid
Ammodytes
Ammodytidae
ammodytoid
ammonal
ammonate
ammonation
Ammonea
ammonia
ammoniacal
ammoniacum
ammoniate
ammoniation
ammonic
ammonical
ammoniemia
ammonification
ammonifier
ammonify
ammoniojarosite
ammonion
ammonionitrate
Ammonite
ammonite
Ammonites
Ammonitess
ammonitic
ammoniticone
ammonitiferous
Ammonitish
ammonitoid
Ammonitoidea
ammonium
ammoniuria
ammonization
ammono
ammonobasic
ammonocarbonic
ammonocarbonous
ammonoid
Ammonoidea
ammonoidean
ammonolysis
ammonolytic
ammonolyze
Ammophila
ammophilous
ammoresinol
ammotherapy
ammu
ammunition
amnemonic
amnesia
amnesic
amnestic
amnesty
amnia
amniac
amniatic
amnic
Amnigenia
amnioallantoic
amniocentesis
amniochorial
amnioclepsis
amniomancy
amnion
Amnionata
amnionate
amnionic
amniorrhea
Amniota
amniote
amniotic
amniotitis
amniotome
amober
amobyr
amoeba
amoebae
Amoebaea
amoebaean
amoebaeum
amoebalike
amoeban
amoebian
amoebiasis
amoebic
amoebicide
amoebid
Amoebida
Amoebidae
amoebiform
Amoebobacter
Amoebobacterieae
amoebocyte
Amoebogeniae
amoeboid
amoeboidism
amoebous
amoebula
amok
amoke
amole
amolilla
amomal
Amomales
Amomis
amomum
among
amongst
amontillado
amor
amorado
amoraic
amoraim
amoral
amoralism
amoralist
amorality
amoralize
Amores
amoret
amoretto
Amoreuxia
amorism
amorist
amoristic
Amorite
Amoritic
Amoritish
amorosity
amoroso
amorous
amorously
amorousness
Amorpha
amorphia
amorphic
amorphinism
amorphism
Amorphophallus
amorphophyte
amorphotae
amorphous
amorphously
amorphousness
amorphus
amorphy
amort
amortisseur
amortizable
amortization
amortize
amortizement
Amorua
Amos
Amoskeag
amotion
amotus
amount
amour
amourette
amovability
amovable
amove
Amoy
Amoyan
Amoyese
ampalaya
ampalea
ampangabeite
ampasimenite
Ampelidaceae
ampelidaceous
Ampelidae
ampelideous
Ampelis
ampelite
ampelitic
ampelographist
ampelography
ampelopsidin
ampelopsin
Ampelopsis
Ampelosicyos
ampelotherapy
amper
amperage
ampere
amperemeter
Amperian
amperometer
ampersand
ampery
amphanthium
ampheclexis
ampherotokous
ampherotoky
amphetamine
amphiarthrodial
amphiarthrosis
amphiaster
amphibalus
Amphibia
amphibial
amphibian
amphibichnite
amphibiety
amphibiological
amphibiology
amphibion
amphibiotic
Amphibiotica
amphibious
amphibiously
amphibiousness
amphibium
amphiblastic
amphiblastula
amphiblestritis
Amphibola
amphibole
amphibolia
amphibolic
amphiboliferous
amphiboline
amphibolite
amphibolitic
amphibological
amphibologically
amphibologism
amphibology
amphibolous
amphiboly
amphibrach
amphibrachic
amphibryous
Amphicarpa
Amphicarpaea
amphicarpic
amphicarpium
amphicarpogenous
amphicarpous
amphicentric
amphichroic
amphichrom
amphichromatic
amphichrome
amphicoelian
amphicoelous
Amphicondyla
amphicondylous
amphicrania
amphicreatinine
amphicribral
amphictyon
amphictyonian
amphictyonic
amphictyony
Amphicyon
Amphicyonidae
amphicyrtic
amphicyrtous
amphicytula
amphid
amphide
amphidesmous
amphidetic
amphidiarthrosis
amphidiploid
amphidiploidy
amphidisc
Amphidiscophora
amphidiscophoran
amphierotic
amphierotism
Amphigaea
amphigam
Amphigamae
amphigamous
amphigastrium
amphigastrula
amphigean
amphigen
amphigene
amphigenesis
amphigenetic
amphigenous
amphigenously
amphigonic
amphigonium
amphigonous
amphigony
amphigoric
amphigory
amphigouri
amphikaryon
amphilogism
amphilogy
amphimacer
amphimictic
amphimictical
amphimictically
amphimixis
amphimorula
Amphinesian
Amphineura
amphineurous
amphinucleus
Amphion
Amphionic
Amphioxi
Amphioxidae
Amphioxides
Amphioxididae
amphioxus
amphipeptone
amphiphloic
amphiplatyan
Amphipleura
amphiploid
amphiploidy
amphipneust
Amphipneusta
amphipneustic
Amphipnous
amphipod
Amphipoda
amphipodal
amphipodan
amphipodiform
amphipodous
amphiprostylar
amphiprostyle
amphiprotic
amphipyrenin
Amphirhina
amphirhinal
amphirhine
amphisarca
amphisbaena
amphisbaenian
amphisbaenic
Amphisbaenidae
amphisbaenoid
amphisbaenous
amphiscians
amphiscii
Amphisile
Amphisilidae
amphispermous
amphisporangiate
amphispore
Amphistoma
amphistomatic
amphistome
amphistomoid
amphistomous
Amphistomum
amphistylar
amphistylic
amphistyly
amphitene
amphitheater
amphitheatered
amphitheatral
amphitheatric
amphitheatrical
amphitheatrically
amphithecial
amphithecium
amphithect
amphithyron
amphitokal
amphitokous
amphitoky
amphitriaene
amphitrichous
Amphitrite
amphitropal
amphitropous
Amphitruo
Amphitryon
Amphiuma
Amphiumidae
amphivasal
amphivorous
Amphizoidae
amphodarch
amphodelite
amphodiplopia
amphogenous
ampholyte
amphopeptone
amphophil
amphophile
amphophilic
amphophilous
amphora
amphoral
amphore
amphorette
amphoric
amphoricity
amphoriloquy
amphorophony
amphorous
amphoteric
Amphrysian
ample
amplectant
ampleness
amplexation
amplexicaudate
amplexicaul
amplexicauline
amplexifoliate
amplexus
ampliate
ampliation
ampliative
amplicative
amplidyne
amplification
amplificative
amplificator
amplificatory
amplifier
amplify
amplitude
amply
ampollosity
ampongue
ampoule
ampul
ampulla
ampullaceous
ampullar
Ampullaria
Ampullariidae
ampullary
ampullate
ampullated
ampulliform
ampullitis
ampullula
amputate
amputation
amputational
amputative
amputator
amputee
ampyx
amra
amreeta
amrita
Amritsar
amsath
amsel
Amsonia
Amsterdamer
amt
amtman
Amuchco
amuck
Amueixa
amuguis
amula
amulet
amuletic
amulla
amunam
amurca
amurcosity
amurcous
Amurru
amusable
amuse
amused
amusedly
amusee
amusement
amuser
amusette
Amusgo
amusia
amusing
amusingly
amusingness
amusive
amusively
amusiveness
amutter
amuyon
amuyong
amuze
amvis
Amy
amy
Amyclaean
Amyclas
amyelencephalia
amyelencephalic
amyelencephalous
amyelia
amyelic
amyelinic
amyelonic
amyelous
amygdal
amygdala
Amygdalaceae
amygdalaceous
amygdalase
amygdalate
amygdalectomy
amygdalic
amygdaliferous
amygdaliform
amygdalin
amygdaline
amygdalinic
amygdalitis
amygdaloid
amygdaloidal
amygdalolith
amygdaloncus
amygdalopathy
amygdalothripsis
amygdalotome
amygdalotomy
Amygdalus
amygdonitrile
amygdophenin
amygdule
amyl
amylaceous
amylamine
amylan
amylase
amylate
amylemia
amylene
amylenol
amylic
amylidene
amyliferous
amylin
amylo
amylocellulose
amyloclastic
amylocoagulase
amylodextrin
amylodyspepsia
amylogen
amylogenesis
amylogenic
amylohydrolysis
amylohydrolytic
amyloid
amyloidal
amyloidosis
amyloleucite
amylolysis
amylolytic
amylom
amylometer
amylon
amylopectin
amylophagia
amylophosphate
amylophosphoric
amyloplast
amyloplastic
amyloplastid
amylopsin
amylose
amylosis
amylosynthesis
amylum
amyluria
Amynodon
amynodont
amyosthenia
amyosthenic
amyotaxia
amyotonia
amyotrophia
amyotrophic
amyotrophy
amyous
Amyraldism
Amyraldist
Amyridaceae
amyrin
Amyris
amyrol
amyroot
Amytal
amyxorrhea
amyxorrhoea
an
Ana
ana
Anabaena
Anabantidae
Anabaptism
Anabaptist
Anabaptistic
Anabaptistical
Anabaptistically
Anabaptistry
anabaptize
Anabas
anabasine
anabasis
anabasse
anabata
anabathmos
anabatic
anaberoga
anabibazon
anabiosis
anabiotic
Anablepidae
Anableps
anabo
anabohitsite
anabolic
anabolin
anabolism
anabolite
anabolize
anabong
anabranch
anabrosis
anabrotic
anacahuita
anacahuite
anacalypsis
anacampsis
anacamptic
anacamptically
anacamptics
anacamptometer
anacanth
anacanthine
Anacanthini
anacanthous
anacara
anacard
Anacardiaceae
anacardiaceous
anacardic
Anacardium
anacatadidymus
anacatharsis
anacathartic
anacephalaeosis
anacephalize
Anaces
Anacharis
anachorism
anachromasis
anachronic
anachronical
anachronically
anachronism
anachronismatical
anachronist
anachronistic
anachronistical
anachronistically
anachronize
anachronous
anachronously
anachueta
anacid
anacidity
anaclasis
anaclastic
anaclastics
Anaclete
anacleticum
anaclinal
anaclisis
anaclitic
anacoenosis
anacoluthia
anacoluthic
anacoluthically
anacoluthon
anaconda
Anacreon
Anacreontic
Anacreontically
anacrisis
Anacrogynae
anacrogynae
anacrogynous
anacromyodian
anacrotic
anacrotism
anacrusis
anacrustic
anacrustically
anaculture
anacusia
anacusic
anacusis
Anacyclus
anadem
anadenia
anadicrotic
anadicrotism
anadidymus
anadiplosis
anadipsia
anadipsic
anadrom
anadromous
Anadyomene
anaematosis
anaemia
anaemic
anaeretic
anaerobation
anaerobe
anaerobia
anaerobian
anaerobic
anaerobically
anaerobies
anaerobion
anaerobiont
anaerobiosis
anaerobiotic
anaerobiotically
anaerobious
anaerobism
anaerobium
anaerophyte
anaeroplastic
anaeroplasty
anaesthesia
anaesthesiant
anaesthetically
anaesthetizer
anaetiological
anagalactic
Anagallis
anagap
anagenesis
anagenetic
anagep
anagignoskomena
anaglyph
anaglyphic
anaglyphical
anaglyphics
anaglyphoscope
anaglyphy
anaglyptic
anaglyptical
anaglyptics
anaglyptograph
anaglyptographic
anaglyptography
anaglypton
anagnorisis
anagnost
anagoge
anagogic
anagogical
anagogically
anagogics
anagogy
anagram
anagrammatic
anagrammatical
anagrammatically
anagrammatism
anagrammatist
anagrammatize
anagrams
anagraph
anagua
anagyrin
anagyrine
Anagyris
anahau
Anahita
Anaitis
Anakes
anakinesis
anakinetic
anakinetomer
anakinetomeric
anakoluthia
anakrousis
anaktoron
anal
analabos
analav
analcime
analcimite
analcite
analcitite
analecta
analectic
analects
analemma
analemmatic
analepsis
analepsy
analeptic
analeptical
analgen
analgesia
analgesic
Analgesidae
analgesis
analgesist
analgetic
analgia
analgic
analgize
analkalinity
anallagmatic
anallantoic
Anallantoidea
anallantoidean
anallergic
anally
analogic
analogical
analogically
analogicalness
analogion
analogism
analogist
analogistic
analogize
analogon
analogous
analogously
analogousness
analogue
analogy
analphabet
analphabete
analphabetic
analphabetical
analphabetism
analysability
analysable
analysand
analysation
analyse
analyser
analyses
analysis
analyst
analytic
analytical
analytically
analytics
analyzability
analyzable
analyzation
analyze
analyzer
Anam
anam
anama
anamesite
anametadromous
Anamirta
anamirtin
Anamite
anamite
anammonid
anammonide
anamnesis
anamnestic
anamnestically
Anamnia
Anamniata
Anamnionata
anamnionic
Anamniota
anamniote
anamniotic
anamorphic
anamorphism
anamorphoscope
anamorphose
anamorphosis
anamorphote
anamorphous
anan
anana
ananaplas
ananaples
ananas
ananda
anandrarious
anandria
anandrous
ananepionic
anangioid
anangular
Ananias
Ananism
Ananite
anankastic
Anansi
Ananta
anantherate
anantherous
ananthous
ananym
anapaest
anapaestic
anapaestical
anapaestically
anapaganize
anapaite
anapanapa
anapeiratic
anaphalantiasis
Anaphalis
anaphase
Anaphe
anaphia
anaphora
anaphoral
anaphoria
anaphoric
anaphorical
anaphrodisia
anaphrodisiac
anaphroditic
anaphroditous
anaphylactic
anaphylactin
anaphylactogen
anaphylactogenic
anaphylactoid
anaphylatoxin
anaphylaxis
anaphyte
anaplasia
anaplasis
anaplasm
Anaplasma
anaplasmosis
anaplastic
anaplasty
anaplerosis
anaplerotic
anapnea
anapneic
anapnoeic
anapnograph
anapnoic
anapnometer
anapodeictic
anapophysial
anapophysis
anapsid
Anapsida
anapsidan
Anapterygota
anapterygote
anapterygotism
anapterygotous
Anaptomorphidae
Anaptomorphus
anaptotic
anaptychus
anaptyctic
anaptyctical
anaptyxis
anaqua
anarcestean
Anarcestes
anarch
anarchal
anarchial
anarchic
anarchical
anarchically
anarchism
anarchist
anarchistic
anarchize
anarchoindividualist
anarchosocialist
anarchosyndicalism
anarchosyndicalist
anarchy
anarcotin
anareta
anaretic
anaretical
anargyros
anarthria
anarthric
anarthropod
Anarthropoda
anarthropodous
anarthrosis
anarthrous
anarthrously
anarthrousness
anartismos
anarya
Anaryan
Anas
Anasa
anasarca
anasarcous
Anasazi
anaschistic
anaseismic
Anasitch
anaspadias
anaspalin
Anaspida
Anaspidacea
Anaspides
anastalsis
anastaltic
Anastasia
Anastasian
anastasimon
anastasimos
anastasis
Anastasius
anastate
anastatic
Anastatica
Anastatus
anastigmat
anastigmatic
anastomose
anastomosis
anastomotic
Anastomus
anastrophe
Anastrophia
Anat
anatase
anatexis
anathema
anathematic
anathematical
anathematically
anathematism
anathematization
anathematize
anathematizer
anatheme
anathemize
Anatherum
Anatidae
anatifa
Anatifae
anatifer
anatiferous
Anatinacea
Anatinae
anatine
anatocism
Anatole
Anatolian
Anatolic
Anatoly
anatomic
anatomical
anatomically
anatomicobiological
anatomicochirurgical
anatomicomedical
anatomicopathologic
anatomicopathological
anatomicophysiologic
anatomicophysiological
anatomicosurgical
anatomism
anatomist
anatomization
anatomize
anatomizer
anatomopathologic
anatomopathological
anatomy
anatopism
anatox
anatoxin
anatreptic
anatripsis
anatripsology
anatriptic
anatron
anatropal
anatropia
anatropous
Anatum
anaudia
anaunter
anaunters
Anax
Anaxagorean
Anaxagorize
anaxial
Anaximandrian
anaxon
anaxone
Anaxonia
anay
anazoturia
anba
anbury
Ancerata
ancestor
ancestorial
ancestorially
ancestral
ancestrally
ancestress
ancestrial
ancestrian
ancestry
Ancha
Anchat
Anchietea
anchietin
anchietine
anchieutectic
anchimonomineral
Anchisaurus
Anchises
Anchistea
Anchistopoda
anchithere
anchitherioid
anchor
anchorable
anchorage
anchorate
anchored
anchorer
anchoress
anchoret
anchoretic
anchoretical
anchoretish
anchoretism
anchorhold
anchorite
anchoritess
anchoritic
anchoritical
anchoritish
anchoritism
anchorless
anchorlike
anchorwise
anchovy
Anchtherium
Anchusa
anchusin
anchusine
anchylose
anchylosis
ancience
anciency
ancient
ancientism
anciently
ancientness
ancientry
ancienty
ancile
ancilla
ancillary
ancipital
ancipitous
Ancistrocladaceae
ancistrocladaceous
Ancistrocladus
ancistroid
ancon
Ancona
anconad
anconagra
anconal
ancone
anconeal
anconeous
anconeus
anconitis
anconoid
ancony
ancora
ancoral
Ancyloceras
Ancylocladus
Ancylodactyla
ancylopod
Ancylopoda
Ancylostoma
ancylostome
ancylostomiasis
Ancylostomum
Ancylus
Ancyrean
Ancyrene
and
anda
andabatarian
Andalusian
andalusite
Andaman
Andamanese
andante
andantino
Andaqui
Andaquian
Andarko
Andaste
Ande
Andean
Anderson
Andesic
andesine
andesinite
andesite
andesitic
Andevo
Andhra
Andi
Andian
Andine
Andira
andirin
andirine
andiroba
andiron
Andoke
andorite
Andorobo
Andorran
andouillet
andradite
andranatomy
andrarchy
Andre
Andrea
Andreaea
Andreaeaceae
Andreaeales
Andreas
Andrena
andrenid
Andrenidae
Andrew
andrewsite
Andria
Andriana
Andrias
andric
Andries
androcentric
androcephalous
androcephalum
androclinium
Androclus
androconium
androcracy
androcratic
androcyte
androdioecious
androdioecism
androdynamous
androecial
androecium
androgametangium
androgametophore
androgen
androgenesis
androgenetic
androgenic
androgenous
androginous
androgone
androgonia
androgonial
androgonidium
androgonium
Andrographis
andrographolide
androgynal
androgynary
androgyne
androgyneity
androgynia
androgynism
androgynous
androgynus
androgyny
android
androidal
androkinin
androl
androlepsia
androlepsy
Andromache
andromania
Andromaque
Andromeda
Andromede
andromedotoxin
andromonoecious
andromonoecism
andromorphous
andron
Andronicus
andronitis
andropetalar
andropetalous
androphagous
androphobia
androphonomania
androphore
androphorous
androphorum
androphyll
Andropogon
Androsace
Androscoggin
androseme
androsin
androsphinx
androsporangium
androspore
androsterone
androtauric
androtomy
Andy
anear
aneath
anecdota
anecdotage
anecdotal
anecdotalism
anecdote
anecdotic
anecdotical
anecdotically
anecdotist
anele
anelectric
anelectrode
anelectrotonic
anelectrotonus
anelytrous
anematosis
Anemia
anemia
anemic
anemobiagraph
anemochord
anemoclastic
anemogram
anemograph
anemographic
anemographically
anemography
anemological
anemology
anemometer
anemometric
anemometrical
anemometrically
anemometrograph
anemometrographic
anemometrographically
anemometry
anemonal
anemone
Anemonella
anemonin
anemonol
anemony
anemopathy
anemophile
anemophilous
anemophily
Anemopsis
anemoscope
anemosis
anemotaxis
anemotropic
anemotropism
anencephalia
anencephalic
anencephalotrophia
anencephalous
anencephalus
anencephaly
anend
anenergia
anenst
anent
anenterous
anepia
anepigraphic
anepigraphous
anepiploic
anepithymia
anerethisia
aneretic
anergia
anergic
anergy
anerly
aneroid
aneroidograph
anerotic
anerythroplasia
anerythroplastic
anes
anesis
anesthesia
anesthesiant
anesthesimeter
anesthesiologist
anesthesiology
anesthesis
anesthetic
anesthetically
anesthetist
anesthetization
anesthetize
anesthetizer
anesthyl
anethole
Anethum
anetiological
aneuploid
aneuploidy
aneuria
aneuric
aneurilemmic
aneurin
aneurism
aneurismally
aneurysm
aneurysmal
aneurysmally
aneurysmatic
anew
Anezeh
anfractuose
anfractuosity
anfractuous
anfractuousness
anfracture
Angami
Angara
angaralite
angaria
angary
Angdistis
angekok
angel
Angela
angelate
angeldom
Angeleno
angelet
angeleyes
angelfish
angelhood
angelic
Angelica
angelica
Angelical
angelical
angelically
angelicalness
Angelican
angelicic
angelicize
angelico
angelin
Angelina
angeline
angelique
angelize
angellike
Angelo
angelocracy
angelographer
angelolater
angelolatry
angelologic
angelological
angelology
angelomachy
Angelonia
angelophany
angelot
angelship
Angelus
anger
angerly
Angerona
Angeronalia
Angers
Angetenar
Angevin
angeyok
angiasthenia
angico
Angie
angiectasis
angiectopia
angiemphraxis
angiitis
angild
angili
angina
anginal
anginiform
anginoid
anginose
anginous
angioasthenia
angioataxia
angioblast
angioblastic
angiocarditis
angiocarp
angiocarpian
angiocarpic
angiocarpous
angiocavernous
angiocholecystitis
angiocholitis
angiochondroma
angioclast
angiocyst
angiodermatitis
angiodiascopy
angioelephantiasis
angiofibroma
angiogenesis
angiogenic
angiogeny
angioglioma
angiograph
angiography
angiohyalinosis
angiohydrotomy
angiohypertonia
angiohypotonia
angioid
angiokeratoma
angiokinesis
angiokinetic
angioleucitis
angiolipoma
angiolith
angiology
angiolymphitis
angiolymphoma
angioma
angiomalacia
angiomatosis
angiomatous
angiomegaly
angiometer
angiomyocardiac
angiomyoma
angiomyosarcoma
angioneoplasm
angioneurosis
angioneurotic
angionoma
angionosis
angioparalysis
angioparalytic
angioparesis
angiopathy
angiophorous
angioplany
angioplasty
angioplerosis
angiopoietic
angiopressure
angiorrhagia
angiorrhaphy
angiorrhea
angiorrhexis
angiosarcoma
angiosclerosis
angiosclerotic
angioscope
angiosis
angiospasm
angiospastic
angiosperm
Angiospermae
angiospermal
angiospermatous
angiospermic
angiospermous
angiosporous
angiostegnosis
angiostenosis
angiosteosis
angiostomize
angiostomy
angiostrophy
angiosymphysis
angiotasis
angiotelectasia
angiothlipsis
angiotome
angiotomy
angiotonic
angiotonin
angiotribe
angiotripsy
angiotrophic
Angka
anglaise
angle
angleberry
angled
anglehook
anglepod
angler
Angles
anglesite
anglesmith
angletouch
angletwitch
anglewing
anglewise
angleworm
Anglian
Anglic
Anglican
Anglicanism
Anglicanize
Anglicanly
Anglicanum
Anglicism
Anglicist
Anglicization
anglicization
Anglicize
anglicize
Anglification
Anglify
anglimaniac
angling
Anglish
Anglist
Anglistics
Anglogaea
Anglogaean
angloid
Angloman
Anglomane
Anglomania
Anglomaniac
Anglophile
Anglophobe
Anglophobia
Anglophobiac
Anglophobic
Anglophobist
ango
Angola
angolar
Angolese
angor
Angora
angostura
Angouleme
Angoumian
Angraecum
angrily
angriness
angrite
angry
angst
angster
Angstrom
angstrom
anguid
Anguidae
anguiform
Anguilla
Anguillaria
Anguillidae
anguilliform
anguilloid
Anguillula
Anguillulidae
Anguimorpha
anguine
anguineal
anguineous
Anguinidae
anguiped
Anguis
anguis
anguish
anguished
anguishful
anguishous
anguishously
angula
angular
angulare
angularity
angularization
angularize
angularly
angularness
angulate
angulated
angulately
angulateness
angulation
angulatogibbous
angulatosinuous
anguliferous
angulinerved
Anguloa
angulodentate
angulometer
angulosity
angulosplenial
angulous
anguria
Angus
angusticlave
angustifoliate
angustifolious
angustirostrate
angustisellate
angustiseptal
angustiseptate
angwantibo
anhalamine
anhaline
anhalonine
Anhalonium
anhalouidine
anhang
Anhanga
anharmonic
anhedonia
anhedral
anhedron
anhelation
anhelous
anhematosis
anhemolytic
anhidrosis
anhidrotic
anhima
Anhimae
Anhimidae
anhinga
anhistic
anhistous
anhungered
anhungry
anhydrate
anhydration
anhydremia
anhydremic
anhydric
anhydride
anhydridization
anhydridize
anhydrite
anhydrization
anhydrize
anhydroglocose
anhydromyelia
anhydrous
anhydroxime
anhysteretic
ani
Aniba
Anice
aniconic
aniconism
anicular
anicut
anidian
anidiomatic
anidiomatical
anidrosis
Aniellidae
aniente
anigh
anight
anights
anil
anilao
anilau
anile
anileness
anilic
anilid
anilide
anilidic
anilidoxime
aniline
anilinism
anilinophile
anilinophilous
anility
anilla
anilopyrin
anilopyrine
anima
animability
animable
animableness
animadversion
animadversional
animadversive
animadversiveness
animadvert
animadverter
animal
animalcula
animalculae
animalcular
animalcule
animalculine
animalculism
animalculist
animalculous
animalculum
animalhood
Animalia
animalian
animalic
animalier
animalish
animalism
animalist
animalistic
animality
Animalivora
animalivore
animalivorous
animalization
animalize
animally
animastic
animastical
animate
animated
animatedly
animately
animateness
animater
animating
animatingly
animation
animatism
animatistic
animative
animatograph
animator
anime
animi
Animikean
animikite
animism
animist
animistic
animize
animosity
animotheism
animous
animus
anion
anionic
aniridia
anis
anisal
anisalcohol
anisaldehyde
anisaldoxime
anisamide
anisandrous
anisanilide
anisate
anischuria
anise
aniseed
aniseikonia
aniseikonic
aniselike
aniseroot
anisette
anisic
anisidin
anisidine
anisil
anisilic
anisobranchiate
anisocarpic
anisocarpous
anisocercal
anisochromatic
anisochromia
anisocoria
anisocotyledonous
anisocotyly
anisocratic
anisocycle
anisocytosis
anisodactyl
Anisodactyla
Anisodactyli
anisodactylic
anisodactylous
anisodont
anisogamete
anisogamous
anisogamy
anisogenous
anisogeny
anisognathism
anisognathous
anisogynous
anisoin
anisole
anisoleucocytosis
Anisomeles
anisomelia
anisomelus
anisomeric
anisomerous
anisometric
anisometrope
anisometropia
anisometropic
anisomyarian
Anisomyodi
anisomyodian
anisomyodous
anisopetalous
anisophyllous
anisophylly
anisopia
anisopleural
anisopleurous
anisopod
Anisopoda
anisopodal
anisopodous
anisopogonous
Anisoptera
anisopterous
anisosepalous
anisospore
anisostaminous
anisostemonous
anisosthenic
anisostichous
Anisostichus
anisostomous
anisotonic
anisotropal
anisotrope
anisotropic
anisotropical
anisotropically
anisotropism
anisotropous
anisotropy
anisoyl
anisum
anisuria
anisyl
anisylidene
Anita
anither
anitrogenous
anjan
Anjou
ankaramite
ankaratrite
ankee
anker
ankerite
ankh
ankle
anklebone
anklejack
anklet
anklong
Ankoli
Ankou
ankus
ankusha
ankylenteron
ankyloblepharon
ankylocheilia
ankylodactylia
ankylodontia
ankyloglossia
ankylomele
ankylomerism
ankylophobia
ankylopodia
ankylopoietic
ankyloproctia
ankylorrhinia
Ankylosaurus
ankylose
ankylosis
ankylostoma
ankylotia
ankylotic
ankylotome
ankylotomy
ankylurethria
ankyroid
anlace
anlaut
Ann
ann
Anna
anna
Annabel
annabergite
annal
annale
annaline
annalism
annalist
annalistic
annalize
annals
Annam
Annamese
Annamite
Annamitic
Annapurna
Annard
annat
annates
annatto
Anne
anneal
annealer
annectent
annection
annelid
Annelida
annelidan
Annelides
annelidian
annelidous
annelism
Annellata
anneloid
annerodite
Anneslia
annet
Annette
annex
annexa
annexable
annexal
annexation
annexational
annexationist
annexer
annexion
annexionist
annexitis
annexive
annexment
annexure
annidalin
Annie
Anniellidae
annihilability
annihilable
annihilate
annihilation
annihilationism
annihilationist
annihilative
annihilator
annihilatory
Annist
annite
anniversarily
anniversariness
anniversary
anniverse
annodated
Annona
annona
Annonaceae
annonaceous
annotate
annotater
annotation
annotative
annotator
annotatory
annotine
annotinous
announce
announceable
announcement
announcer
annoy
annoyance
annoyancer
annoyer
annoyful
annoying
annoyingly
annoyingness
annoyment
annual
annualist
annualize
annually
annuary
annueler
annuent
annuitant
annuity
annul
annular
Annularia
annularity
annularly
annulary
Annulata
annulate
annulated
annulation
annulet
annulettee
annulism
annullable
annullate
annullation
annuller
annulment
annuloid
Annuloida
Annulosa
annulosan
annulose
annulus
annunciable
annunciate
annunciation
annunciative
annunciator
annunciatory
anoa
Anobiidae
anocarpous
anociassociation
anococcygeal
anodal
anode
anodendron
anodic
anodically
anodize
Anodon
Anodonta
anodontia
anodos
anodyne
anodynia
anodynic
anodynous
anoegenetic
anoesia
anoesis
anoestrous
anoestrum
anoestrus
anoetic
anogenic
anogenital
Anogra
anoil
anoine
anoint
anointer
anointment
anole
anoli
anolian
Anolis
Anolympiad
anolyte
Anomala
anomaliflorous
anomaliped
anomalism
anomalist
anomalistic
anomalistical
anomalistically
anomalocephalus
anomaloflorous
Anomalogonatae
anomalogonatous
Anomalon
anomalonomy
Anomalopteryx
anomaloscope
anomalotrophy
anomalous
anomalously
anomalousness
anomalure
Anomaluridae
Anomalurus
anomaly
Anomatheca
Anomia
Anomiacea
Anomiidae
anomite
anomocarpous
anomodont
Anomodontia
Anomoean
Anomoeanism
anomophyllous
anomorhomboid
anomorhomboidal
anomphalous
Anomura
anomural
anomuran
anomurous
anomy
anon
anonang
anoncillo
anonol
anonychia
anonym
anonyma
anonymity
anonymous
anonymously
anonymousness
anonymuncule
anoopsia
anoperineal
anophele
Anopheles
Anophelinae
anopheline
anophoria
anophthalmia
anophthalmos
Anophthalmus
anophyte
anopia
anopisthographic
Anopla
Anoplanthus
anoplocephalic
anoplonemertean
Anoplonemertini
anoplothere
Anoplotheriidae
anoplotherioid
Anoplotherium
anoplotheroid
Anoplura
anopluriform
anopsia
anopubic
anorak
anorchia
anorchism
anorchous
anorchus
anorectal
anorectic
anorectous
anorexia
anorexy
anorgana
anorganic
anorganism
anorganology
anormal
anormality
anorogenic
anorth
anorthic
anorthite
anorthitic
anorthitite
anorthoclase
anorthographic
anorthographical
anorthographically
anorthography
anorthophyre
anorthopia
anorthoscope
anorthose
anorthosite
anoscope
anoscopy
Anosia
anosmatic
anosmia
anosmic
anosphrasia
anosphresia
anospinal
anostosis
Anostraca
anoterite
another
anotherkins
anotia
anotropia
anotta
anotto
anotus
anounou
Anous
anovesical
anoxemia
anoxemic
anoxia
anoxic
anoxidative
anoxybiosis
anoxybiotic
anoxyscope
ansa
ansar
ansarian
Ansarie
ansate
ansation
Anseis
Ansel
Anselm
Anselmian
Anser
anserated
Anseres
Anseriformes
Anserinae
anserine
anserous
anspessade
ansu
ansulate
answer
answerability
answerable
answerableness
answerably
answerer
answeringly
answerless
answerlessly
ant
Anta
anta
antacid
antacrid
antadiform
Antaean
Antaeus
antagonism
antagonist
antagonistic
antagonistical
antagonistically
antagonization
antagonize
antagonizer
antagony
Antaimerina
Antaios
Antaiva
antal
antalgesic
antalgol
antalkali
antalkaline
antambulacral
antanacathartic
antanaclasis
Antanandro
antanemic
antapex
antaphrodisiac
antaphroditic
antapocha
antapodosis
antapology
antapoplectic
Antar
Antara
antarchism
antarchist
antarchistic
antarchistical
antarchy
Antarctalia
Antarctalian
antarctic
Antarctica
antarctica
antarctical
antarctically
Antarctogaea
Antarctogaean
Antares
antarthritic
antasphyctic
antasthenic
antasthmatic
antatrophic
antdom
ante
anteact
anteal
anteambulate
anteambulation
anteater
antebaptismal
antebath
antebrachial
antebrachium
antebridal
antecabinet
antecaecal
antecardium
antecavern
antecedaneous
antecedaneously
antecede
antecedence
antecedency
antecedent
antecedental
antecedently
antecessor
antechamber
antechapel
Antechinomys
antechoir
antechurch
anteclassical
antecloset
antecolic
antecommunion
anteconsonantal
antecornu
antecourt
antecoxal
antecubital
antecurvature
antedate
antedawn
antediluvial
antediluvially
antediluvian
Antedon
antedonin
antedorsal
antefebrile
antefix
antefixal
anteflected
anteflexed
anteflexion
antefurca
antefurcal
antefuture
antegarden
antegrade
antehall
antehistoric
antehuman
antehypophysis
anteinitial
antejentacular
antejudiciary
antejuramentum
antelabium
antelegal
antelocation
antelope
antelopian
antelucan
antelude
anteluminary
antemarginal
antemarital
antemedial
antemeridian
antemetallic
antemetic
antemillennial
antemingent
antemortal
antemundane
antemural
antenarial
antenatal
antenatalitial
antenati
antenave
antenna
antennae
antennal
Antennaria
antennariid
Antennariidae
Antennarius
antennary
Antennata
antennate
antenniferous
antenniform
antennula
antennular
antennulary
antennule
antenodal
antenoon
Antenor
antenumber
anteoccupation
anteocular
anteopercle
anteoperculum
anteorbital
antepagmenta
antepagments
antepalatal
antepaschal
antepast
antepatriarchal
antepectoral
antepectus
antependium
antepenult
antepenultima
antepenultimate
antephialtic
antepileptic
antepirrhema
anteporch
anteportico
anteposition
anteposthumous
anteprandial
antepredicament
antepredicamental
antepreterit
antepretonic
anteprohibition
anteprostate
anteprostatic
antepyretic
antequalm
antereformation
antereformational
anteresurrection
anterethic
anterevolutional
anterevolutionary
anteriad
anterior
anteriority
anteriorly
anteriorness
anteroclusion
anterodorsal
anteroexternal
anterofixation
anteroflexion
anterofrontal
anterograde
anteroinferior
anterointerior
anterointernal
anterolateral
anterolaterally
anteromedial
anteromedian
anteroom
anteroparietal
anteroposterior
anteroposteriorly
anteropygal
anterospinal
anterosuperior
anteroventral
anteroventrally
antes
antescript
antesignanus
antespring
antestature
antesternal
antesternum
antesunrise
antesuperior
antetemple
antetype
Anteva
antevenient
anteversion
antevert
antevocalic
antewar
anthecological
anthecologist
anthecology
Antheia
anthela
anthelion
anthelmintic
anthem
anthema
anthemene
anthemia
Anthemideae
anthemion
Anthemis
anthemwise
anthemy
anther
Antheraea
antheral
Anthericum
antherid
antheridial
antheridiophore
antheridium
antheriferous
antheriform
antherless
antherogenous
antheroid
antherozoid
antherozoidal
antherozooid
antherozooidal
anthesis
Anthesteria
Anthesteriac
anthesterin
Anthesterion
anthesterol
antheximeter
Anthicidae
Anthidium
anthill
Anthinae
anthine
anthobiology
anthocarp
anthocarpous
anthocephalous
Anthoceros
Anthocerotaceae
Anthocerotales
anthocerote
anthochlor
anthochlorine
anthoclinium
anthocyan
anthocyanidin
anthocyanin
anthodium
anthoecological
anthoecologist
anthoecology
anthogenesis
anthogenetic
anthogenous
anthography
anthoid
anthokyan
antholite
anthological
anthologically
anthologion
anthologist
anthologize
anthology
antholysis
Antholyza
anthomania
anthomaniac
Anthomedusae
anthomedusan
Anthomyia
anthomyiid
Anthomyiidae
Anthonin
Anthonomus
Anthony
anthood
anthophagous
Anthophila
anthophile
anthophilian
anthophilous
anthophobia
Anthophora
anthophore
Anthophoridae
anthophorous
anthophyllite
anthophyllitic
Anthophyta
anthophyte
anthorine
anthosiderite
Anthospermum
anthotaxis
anthotaxy
anthotropic
anthotropism
anthoxanthin
Anthoxanthum
Anthozoa
anthozoan
anthozoic
anthozooid
anthozoon
anthracemia
anthracene
anthraceniferous
anthrachrysone
anthracia
anthracic
anthraciferous
anthracin
anthracite
anthracitic
anthracitiferous
anthracitious
anthracitism
anthracitization
anthracnose
anthracnosis
anthracocide
anthracoid
anthracolithic
anthracomancy
Anthracomarti
anthracomartian
Anthracomartus
anthracometer
anthracometric
anthraconecrosis
anthraconite
Anthracosaurus
anthracosis
anthracothere
Anthracotheriidae
Anthracotherium
anthracotic
anthracyl
anthradiol
anthradiquinone
anthraflavic
anthragallol
anthrahydroquinone
anthramine
anthranil
anthranilate
anthranilic
anthranol
anthranone
anthranoyl
anthranyl
anthraphenone
anthrapurpurin
anthrapyridine
anthraquinol
anthraquinone
anthraquinonyl
anthrarufin
anthratetrol
anthrathiophene
anthratriol
anthrax
anthraxolite
anthraxylon
Anthrenus
anthribid
Anthribidae
Anthriscus
anthrohopobiological
anthroic
anthrol
anthrone
anthropic
anthropical
Anthropidae
anthropobiologist
anthropobiology
anthropocentric
anthropocentrism
anthropoclimatologist
anthropoclimatology
anthropocosmic
anthropodeoxycholic
Anthropodus
anthropogenesis
anthropogenetic
anthropogenic
anthropogenist
anthropogenous
anthropogeny
anthropogeographer
anthropogeographical
anthropogeography
anthropoglot
anthropogony
anthropography
anthropoid
anthropoidal
Anthropoidea
anthropoidean
anthropolater
anthropolatric
anthropolatry
anthropolite
anthropolithic
anthropolitic
anthropological
anthropologically
anthropologist
anthropology
anthropomancy
anthropomantic
anthropomantist
anthropometer
anthropometric
anthropometrical
anthropometrically
anthropometrist
anthropometry
anthropomorph
Anthropomorpha
anthropomorphic
anthropomorphical
anthropomorphically
Anthropomorphidae
anthropomorphism
anthropomorphist
anthropomorphite
anthropomorphitic
anthropomorphitical
anthropomorphitism
anthropomorphization
anthropomorphize
anthropomorphological
anthropomorphologically
anthropomorphology
anthropomorphosis
anthropomorphotheist
anthropomorphous
anthropomorphously
anthroponomical
anthroponomics
anthroponomist
anthroponomy
anthropopathia
anthropopathic
anthropopathically
anthropopathism
anthropopathite
anthropopathy
anthropophagi
anthropophagic
anthropophagical
anthropophaginian
anthropophagism
anthropophagist
anthropophagistic
anthropophagite
anthropophagize
anthropophagous
anthropophagously
anthropophagy
anthropophilous
anthropophobia
anthropophuism
anthropophuistic
anthropophysiography
anthropophysite
Anthropopithecus
anthropopsychic
anthropopsychism
Anthropos
anthroposcopy
anthroposociologist
anthroposociology
anthroposomatology
anthroposophical
anthroposophist
anthroposophy
anthropoteleoclogy
anthropoteleological
anthropotheism
anthropotomical
anthropotomist
anthropotomy
anthropotoxin
Anthropozoic
anthropurgic
anthroropolith
anthroxan
anthroxanic
anthryl
anthrylene
Anthurium
Anthus
Anthyllis
anthypophora
anthypophoretic
Anti
anti
antiabolitionist
antiabrasion
antiabrin
antiabsolutist
antiacid
antiadiaphorist
antiaditis
antiadministration
antiae
antiaesthetic
antiager
antiagglutinating
antiagglutinin
antiaggression
antiaggressionist
antiaggressive
antiaircraft
antialbumid
antialbumin
antialbumose
antialcoholic
antialcoholism
antialcoholist
antialdoxime
antialexin
antialien
antiamboceptor
antiamusement
antiamylase
antianaphylactogen
antianaphylaxis
antianarchic
antianarchist
antiangular
antiannexation
antiannexationist
antianopheline
antianthrax
antianthropocentric
antianthropomorphism
antiantibody
antiantidote
antiantienzyme
antiantitoxin
antiaphrodisiac
antiaphthic
antiapoplectic
antiapostle
antiaquatic
antiar
Antiarcha
Antiarchi
antiarin
Antiaris
antiaristocrat
antiarthritic
antiascetic
antiasthmatic
antiastronomical
antiatheism
antiatheist
antiatonement
antiattrition
antiautolysin
antibacchic
antibacchius
antibacterial
antibacteriolytic
antiballooner
antibalm
antibank
antibasilican
antibenzaldoxime
antiberiberin
antibibliolatry
antibigotry
antibilious
antibiont
antibiosis
antibiotic
antibishop
antiblastic
antiblennorrhagic
antiblock
antiblue
antibody
antiboxing
antibreakage
antibridal
antibromic
antibubonic
Antiburgher
antic
anticachectic
antical
anticalcimine
anticalculous
anticalligraphic
anticancer
anticapital
anticapitalism
anticapitalist
anticardiac
anticardium
anticarious
anticarnivorous
anticaste
anticatalase
anticatalyst
anticatalytic
anticatalyzer
anticatarrhal
anticathexis
anticathode
anticaustic
anticensorship
anticentralization
anticephalalgic
anticeremonial
anticeremonialism
anticeremonialist
anticheater
antichlor
antichlorine
antichloristic
antichlorotic
anticholagogue
anticholinergic
antichoromanic
antichorus
antichresis
antichretic
antichrist
antichristian
antichristianity
antichristianly
antichrome
antichronical
antichronically
antichthon
antichurch
antichurchian
antichymosin
anticipant
anticipatable
anticipate
anticipation
anticipative
anticipatively
anticipator
anticipatorily
anticipatory
anticivic
anticivism
anticize
anticker
anticlactic
anticlassical
anticlassicist
Anticlea
anticlergy
anticlerical
anticlericalism
anticlimactic
anticlimax
anticlinal
anticline
anticlinorium
anticlockwise
anticlogging
anticly
anticnemion
anticness
anticoagulant
anticoagulating
anticoagulative
anticoagulin
anticogitative
anticolic
anticombination
anticomet
anticomment
anticommercial
anticommunist
anticomplement
anticomplementary
anticomplex
anticonceptionist
anticonductor
anticonfederationist
anticonformist
anticonscience
anticonscription
anticonscriptive
anticonstitutional
anticonstitutionalist
anticonstitutionally
anticontagion
anticontagionist
anticontagious
anticonventional
anticonventionalism
anticonvulsive
anticor
anticorn
anticorrosion
anticorrosive
anticorset
anticosine
anticosmetic
anticouncil
anticourt
anticourtier
anticous
anticovenanter
anticovenanting
anticreation
anticreative
anticreator
anticreep
anticreeper
anticreeping
anticrepuscular
anticrepuscule
anticrisis
anticritic
anticritique
anticrochet
anticrotalic
anticryptic
anticum
anticyclic
anticyclone
anticyclonic
anticyclonically
anticynic
anticytolysin
anticytotoxin
antidactyl
antidancing
antidecalogue
antideflation
antidemocrat
antidemocratic
antidemocratical
antidemoniac
antidetonant
antidetonating
antidiabetic
antidiastase
Antidicomarian
Antidicomarianite
antidictionary
antidiffuser
antidinic
antidiphtheria
antidiphtheric
antidiphtherin
antidiphtheritic
antidisciplinarian
antidivine
antidivorce
antidogmatic
antidomestic
antidominican
Antidorcas
antidoron
antidotal
antidotally
antidotary
antidote
antidotical
antidotically
antidotism
antidraft
antidrag
antidromal
antidromic
antidromically
antidromous
antidromy
antidrug
antiduke
antidumping
antidynamic
antidynastic
antidyscratic
antidysenteric
antidysuric
antiecclesiastic
antiecclesiastical
antiedemic
antieducation
antieducational
antiegotism
antiejaculation
antiemetic
antiemperor
antiempirical
antiendotoxin
antiendowment
antienergistic
antienthusiastic
antienzyme
antienzymic
antiepicenter
antiepileptic
antiepiscopal
antiepiscopist
antiepithelial
antierosion
antierysipelas
Antietam
antiethnic
antieugenic
antievangelical
antievolution
antievolutionist
antiexpansionist
antiexporting
antiextreme
antieyestrain
antiface
antifaction
antifame
antifanatic
antifat
antifatigue
antifebrile
antifederal
antifederalism
antifederalist
antifelon
antifelony
antifeminism
antifeminist
antiferment
antifermentative
antifertilizer
antifeudal
antifeudalism
antifibrinolysin
antifibrinolysis
antifideism
antifire
antiflash
antiflattering
antiflatulent
antiflux
antifoam
antifoaming
antifogmatic
antiforeign
antiforeignism
antiformin
antifouler
antifouling
antifowl
antifreeze
antifreezing
antifriction
antifrictional
antifrost
antifundamentalist
antifungin
antigalactagogue
antigalactic
antigambling
antiganting
antigen
antigenic
antigenicity
antighostism
antigigmanic
antiglare
antiglyoxalase
antigod
Antigone
antigonococcic
Antigonon
antigonorrheic
Antigonus
antigorite
antigovernment
antigraft
antigrammatical
antigraph
antigravitate
antigravitational
antigropelos
antigrowth
Antiguan
antiguggler
antigyrous
antihalation
antiharmonist
antihectic
antihelix
antihelminthic
antihemagglutinin
antihemisphere
antihemoglobin
antihemolysin
antihemolytic
antihemorrhagic
antihemorrheidal
antihero
antiheroic
antiheroism
antiheterolysin
antihidrotic
antihierarchical
antihierarchist
antihistamine
antihistaminic
antiholiday
antihormone
antihuff
antihum
antihuman
antihumbuggist
antihunting
antihydrophobic
antihydropic
antihydropin
antihygienic
antihylist
antihypnotic
antihypochondriac
antihypophora
antihysteric
Antikamnia
antikathode
antikenotoxin
antiketogen
antiketogenesis
antiketogenic
antikinase
antiking
antiknock
antilabor
antilaborist
antilacrosse
antilacrosser
antilactase
antilapsarian
antileague
antilegalist
antilegomena
antilemic
antilens
antilepsis
antileptic
antilethargic
antileveling
Antilia
antiliberal
antilibration
antilift
antilipase
antilipoid
antiliquor
antilithic
antiliturgical
antiliturgist
Antillean
antilobium
Antilocapra
Antilocapridae
Antilochus
antiloemic
antilogarithm
antilogic
antilogical
antilogism
antilogous
antilogy
antiloimic
Antilope
Antilopinae
antilottery
antiluetin
antilynching
antilysin
antilysis
antilyssic
antilytic
antimacassar
antimachine
antimachinery
antimagistratical
antimalaria
antimalarial
antimallein
antimaniac
antimaniacal
Antimarian
antimark
antimartyr
antimask
antimasker
Antimason
Antimasonic
Antimasonry
antimasque
antimasquer
antimasquerade
antimaterialist
antimaterialistic
antimatrimonial
antimatrimonialist
antimedical
antimedieval
antimelancholic
antimellin
antimeningococcic
antimension
antimensium
antimephitic
antimere
antimerger
antimeric
Antimerina
antimerism
antimeristem
antimetabole
antimetathesis
antimetathetic
antimeter
antimethod
antimetrical
antimetropia
antimetropic
antimiasmatic
antimicrobic
antimilitarism
antimilitarist
antimilitary
antiministerial
antiministerialist
antiminsion
antimiscegenation
antimission
antimissionary
antimissioner
antimixing
antimnemonic
antimodel
antimodern
antimonarchial
antimonarchic
antimonarchical
antimonarchically
antimonarchicalness
antimonarchist
antimonate
antimonial
antimoniate
antimoniated
antimonic
antimonid
antimonide
antimoniferous
antimonious
antimonite
antimonium
antimoniuret
antimoniureted
antimoniuretted
antimonopolist
antimonopoly
antimonsoon
antimony
antimonyl
antimoral
antimoralism
antimoralist
antimosquito
antimusical
antimycotic
antimythic
antimythical
antinarcotic
antinarrative
antinational
antinationalist
antinationalistic
antinatural
antinegro
antinegroism
antineologian
antinephritic
antinepotic
antineuralgic
antineuritic
antineurotoxin
antineutral
antinial
antinicotine
antinion
antinode
antinoise
antinome
antinomian
antinomianism
antinomic
antinomical
antinomist
antinomy
antinormal
antinosarian
Antinous
Antiochene
Antiochian
Antiochianism
antiodont
antiodontalgic
Antiope
antiopelmous
antiophthalmic
antiopium
antiopiumist
antiopiumite
antioptimist
antioptionist
antiorgastic
antiorthodox
antioxidant
antioxidase
antioxidizer
antioxidizing
antioxygen
antioxygenation
antioxygenator
antioxygenic
antipacifist
antipapacy
antipapal
antipapalist
antipapism
antipapist
antipapistical
antiparabema
antiparagraphe
antiparagraphic
antiparallel
antiparallelogram
antiparalytic
antiparalytical
antiparasitic
antiparastatitis
antiparliament
antiparliamental
antiparliamentarist
antiparliamentary
antipart
Antipasch
Antipascha
antipass
antipastic
Antipatharia
antipatharian
antipathetic
antipathetical
antipathetically
antipatheticalness
antipathic
Antipathida
antipathist
antipathize
antipathogen
antipathy
antipatriarch
antipatriarchal
antipatriot
antipatriotic
antipatriotism
antipedal
Antipedobaptism
Antipedobaptist
antipeduncular
antipellagric
antipepsin
antipeptone
antiperiodic
antiperistalsis
antiperistaltic
antiperistasis
antiperistatic
antiperistatical
antiperistatically
antipersonnel
antiperthite
antipestilential
antipetalous
antipewism
antiphagocytic
antipharisaic
antipharmic
antiphase
antiphilosophic
antiphilosophical
antiphlogistian
antiphlogistic
antiphon
antiphonal
antiphonally
antiphonary
antiphoner
antiphonetic
antiphonic
antiphonical
antiphonically
antiphonon
antiphony
antiphrasis
antiphrastic
antiphrastical
antiphrastically
antiphthisic
antiphthisical
antiphylloxeric
antiphysic
antiphysical
antiphysician
antiplague
antiplanet
antiplastic
antiplatelet
antipleion
antiplenist
antiplethoric
antipleuritic
antiplurality
antipneumococcic
antipodagric
antipodagron
antipodal
antipode
antipodean
antipodes
antipodic
antipodism
antipodist
antipoetic
antipoints
antipolar
antipole
antipolemist
antipolitical
antipollution
antipolo
antipolygamy
antipolyneuritic
antipool
antipooling
antipope
antipopery
antipopular
antipopulationist
antiportable
antiposition
antipoverty
antipragmatic
antipragmatist
antiprecipitin
antipredeterminant
antiprelate
antiprelatic
antiprelatist
antipreparedness
antiprestidigitation
antipriest
antipriestcraft
antiprime
antiprimer
antipriming
antiprinciple
antiprism
antiproductionist
antiprofiteering
antiprohibition
antiprohibitionist
antiprojectivity
antiprophet
antiprostate
antiprostatic
antiprotease
antiproteolysis
antiprotozoal
antiprudential
antipruritic
antipsalmist
antipsoric
antiptosis
antipudic
antipuritan
antiputrefaction
antiputrefactive
antiputrescent
antiputrid
antipyic
antipyonin
antipyresis
antipyretic
Antipyrine
antipyrotic
antipyryl
antiqua
antiquarian
antiquarianism
antiquarianize
antiquarianly
antiquarism
antiquartan
antiquary
antiquate
antiquated
antiquatedness
antiquation
antique
antiquely
antiqueness
antiquer
antiquing
antiquist
antiquitarian
antiquity
antirabic
antirabies
antiracemate
antiracer
antirachitic
antirachitically
antiracing
antiradiating
antiradiation
antiradical
antirailwayist
antirational
antirationalism
antirationalist
antirationalistic
antirattler
antireactive
antirealism
antirealistic
antirebating
antirecruiting
antired
antireducer
antireform
antireformer
antireforming
antireformist
antireligion
antireligious
antiremonstrant
antirennet
antirennin
antirent
antirenter
antirentism
antirepublican
antireservationist
antirestoration
antireticular
antirevisionist
antirevolutionary
antirevolutionist
antirheumatic
antiricin
antirickets
antiritual
antiritualistic
antirobin
antiromance
antiromantic
antiromanticism
antiroyal
antiroyalist
Antirrhinum
antirumor
antirun
antirust
antisacerdotal
antisacerdotalist
antisaloon
antisalooner
antisavage
antiscabious
antiscale
antischolastic
antischool
antiscians
antiscientific
antiscion
antiscolic
antiscorbutic
antiscorbutical
antiscrofulous
antiseismic
antiselene
antisensitizer
antisensuous
antisensuousness
antisepalous
antisepsin
antisepsis
antiseptic
antiseptical
antiseptically
antisepticism
antisepticist
antisepticize
antiseption
antiseptize
antiserum
antishipping
Antisi
antisialagogue
antisialic
antisiccative
antisideric
antisilverite
antisimoniacal
antisine
antisiphon
antisiphonal
antiskeptical
antiskid
antiskidding
antislavery
antislaveryism
antislickens
antislip
antismoking
antisnapper
antisocial
antisocialist
antisocialistic
antisocialistically
antisociality
antisolar
antisophist
antisoporific
antispace
antispadix
antispasis
antispasmodic
antispast
antispastic
antispectroscopic
antispermotoxin
antispiritual
antispirochetic
antisplasher
antisplenetic
antisplitting
antispreader
antispreading
antisquama
antisquatting
antistadholder
antistadholderian
antistalling
antistaphylococcic
antistate
antistatism
antistatist
antisteapsin
antisterility
antistes
antistimulant
antistock
antistreptococcal
antistreptococcic
antistreptococcin
antistreptococcus
antistrike
antistrophal
antistrophe
antistrophic
antistrophically
antistrophize
antistrophon
antistrumatic
antistrumous
antisubmarine
antisubstance
antisudoral
antisudorific
antisuffrage
antisuffragist
antisun
antisupernaturalism
antisupernaturalist
antisurplician
antisymmetrical
antisyndicalism
antisyndicalist
antisynod
antisyphilitic
antitabetic
antitabloid
antitangent
antitank
antitarnish
antitartaric
antitax
antiteetotalism
antitegula
antitemperance
antitetanic
antitetanolysin
antithalian
antitheft
antitheism
antitheist
antitheistic
antitheistical
antitheistically
antithenar
antitheologian
antitheological
antithermic
antithermin
antitheses
antithesis
antithesism
antithesize
antithet
antithetic
antithetical
antithetically
antithetics
antithrombic
antithrombin
antitintinnabularian
antitobacco
antitobacconal
antitobacconist
antitonic
antitorpedo
antitoxic
antitoxin
antitrade
antitrades
antitraditional
antitragal
antitragic
antitragicus
antitragus
antitrismus
antitrochanter
antitropal
antitrope
antitropic
antitropical
antitropous
antitropy
antitrust
antitrypsin
antitryptic
antituberculin
antituberculosis
antituberculotic
antituberculous
antiturnpikeism
antitwilight
antitypal
antitype
antityphoid
antitypic
antitypical
antitypically
antitypy
antityrosinase
antiunion
antiunionist
antiuratic
antiurease
antiusurious
antiutilitarian
antivaccination
antivaccinationist
antivaccinator
antivaccinist
antivariolous
antivenefic
antivenereal
antivenin
antivenom
antivenomous
antivermicular
antivibrating
antivibrator
antivibratory
antivice
antiviral
antivirus
antivitalist
antivitalistic
antivitamin
antivivisection
antivivisectionist
antivolition
antiwar
antiwarlike
antiwaste
antiwedge
antiweed
antiwit
antixerophthalmic
antizealot
antizymic
antizymotic
antler
antlered
antlerite
antlerless
antlia
antliate
Antlid
antling
antluetic
antodontalgic
antoeci
antoecian
antoecians
Antoinette
Anton
Antonella
Antonia
Antonina
antoninianus
Antonio
antonomasia
antonomastic
antonomastical
antonomastically
antonomasy
Antony
antonym
antonymous
antonymy
antorbital
antproof
antra
antral
antralgia
antre
antrectomy
antrin
antritis
antrocele
antronasal
antrophore
antrophose
antrorse
antrorsely
antroscope
antroscopy
Antrostomus
antrotome
antrotomy
antrotympanic
antrotympanitis
antrum
antrustion
antrustionship
antship
Antu
antu
Antum
Antwerp
antwise
anubing
Anubis
anucleate
anukabiet
Anukit
anuloma
Anura
anuran
anuresis
anuretic
anuria
anuric
anurous
anury
anus
anusim
anusvara
anutraminosa
anvasser
anvil
anvilsmith
anxietude
anxiety
anxious
anxiously
anxiousness
any
anybody
Anychia
anyhow
anyone
anyplace
Anystidae
anything
anythingarian
anythingarianism
anyway
anyways
anywhen
anywhere
anywhereness
anywheres
anywhy
anywise
anywither
Anzac
Anzanian
Ao
aogiri
Aoife
aonach
Aonian
aorist
aoristic
aoristically
aorta
aortal
aortarctia
aortectasia
aortectasis
aortic
aorticorenal
aortism
aortitis
aortoclasia
aortoclasis
aortolith
aortomalacia
aortomalaxis
aortopathy
aortoptosia
aortoptosis
aortorrhaphy
aortosclerosis
aortostenosis
aortotomy
aosmic
Aotea
Aotearoa
Aotes
Aotus
aoudad
Aouellimiden
Aoul
apa
apabhramsa
apace
Apache
apache
Apachette
apachism
apachite
apadana
apagoge
apagogic
apagogical
apagogically
apaid
Apalachee
apalit
Apama
apandry
Apanteles
Apantesis
apanthropia
apanthropy
apar
Aparai
aparaphysate
aparejo
Apargia
aparithmesis
apart
apartheid
aparthrosis
apartment
apartmental
apartness
apasote
apastron
apatan
Apatela
apatetic
apathetic
apathetical
apathetically
apathic
apathism
apathist
apathistical
apathogenic
Apathus
apathy
apatite
Apatornis
Apatosaurus
Apaturia
Apayao
ape
apeak
apectomy
apedom
apehood
apeiron
apelet
apelike
apeling
apellous
Apemantus
Apennine
apenteric
apepsia
apepsinia
apepsy
apeptic
aper
aperch
aperea
aperient
aperiodic
aperiodically
aperiodicity
aperispermic
aperistalsis
aperitive
apert
apertly
apertness
apertometer
apertural
aperture
apertured
Aperu
apery
apesthesia
apesthetic
apesthetize
Apetalae
apetaloid
apetalose
apetalous
apetalousness
apetaly
apex
apexed
aphaeresis
aphaeretic
aphagia
aphakia
aphakial
aphakic
Aphanapteryx
Aphanes
aphanesite
Aphaniptera
aphanipterous
aphanite
aphanitic
aphanitism
Aphanomyces
aphanophyre
aphanozygous
Apharsathacites
aphasia
aphasiac
aphasic
Aphelandra
Aphelenchus
aphelian
Aphelinus
aphelion
apheliotropic
apheliotropically
apheliotropism
Aphelops
aphemia
aphemic
aphengescope
aphengoscope
aphenoscope
apheresis
apheretic
aphesis
apheta
aphetic
aphetically
aphetism
aphetize
aphicidal
aphicide
aphid
aphides
aphidian
aphidicide
aphidicolous
aphidid
Aphididae
Aphidiinae
aphidious
Aphidius
aphidivorous
aphidolysin
aphidophagous
aphidozer
aphilanthropy
Aphis
aphlaston
aphlebia
aphlogistic
aphnology
aphodal
aphodian
Aphodius
aphodus
aphonia
aphonic
aphonous
aphony
aphoria
aphorism
aphorismatic
aphorismer
aphorismic
aphorismical
aphorismos
aphorist
aphoristic
aphoristically
aphorize
aphorizer
Aphoruridae
aphotic
aphototactic
aphototaxis
aphototropic
aphototropism
Aphra
aphrasia
aphrite
aphrizite
aphrodisia
aphrodisiac
aphrodisiacal
aphrodisian
Aphrodision
Aphrodistic
Aphrodite
Aphroditeum
aphroditic
Aphroditidae
aphroditous
aphrolite
aphronia
aphrosiderite
aphtha
Aphthartodocetae
Aphthartodocetic
Aphthartodocetism
aphthic
aphthitalite
aphthoid
aphthong
aphthongal
aphthongia
aphthous
aphydrotropic
aphydrotropism
aphyllose
aphyllous
aphylly
aphyric
Apiaca
Apiaceae
apiaceous
Apiales
apian
apiarian
apiarist
apiary
apiator
apicad
apical
apically
apices
Apician
apicifixed
apicilar
apicillary
apicitis
apickaback
apicoectomy
apicolysis
apicula
apicular
apiculate
apiculated
apiculation
apicultural
apiculture
apiculturist
apiculus
Apidae
apiece
apieces
apigenin
apii
apiin
apikoros
apilary
Apina
Apinae
Apinage
apinch
aping
apinoid
apio
Apioceridae
apioid
apioidal
apiole
apiolin
apiologist
apiology
apionol
Apios
apiose
Apiosoma
apiphobia
Apis
apish
apishamore
apishly
apishness
apism
apitong
apitpat
Apium
apivorous
apjohnite
aplacental
Aplacentalia
Aplacentaria
Aplacophora
aplacophoran
aplacophorous
aplanat
aplanatic
aplanatically
aplanatism
Aplanobacter
aplanogamete
aplanospore
aplasia
aplastic
Aplectrum
aplenty
aplite
aplitic
aplobasalt
aplodiorite
Aplodontia
Aplodontiidae
aplomb
aplome
Aplopappus
aploperistomatous
aplostemonous
aplotaxene
aplotomy
Apluda
aplustre
Aplysia
apnea
apneal
apneic
apneumatic
apneumatosis
Apneumona
apneumonous
apneustic
apoaconitine
apoatropine
apobiotic
apoblast
apocaffeine
apocalypse
apocalypst
apocalypt
apocalyptic
apocalyptical
apocalyptically
apocalypticism
apocalyptism
apocalyptist
apocamphoric
apocarp
apocarpous
apocarpy
apocatastasis
apocatastatic
apocatharsis
apocenter
apocentric
apocentricity
apocha
apocholic
apochromat
apochromatic
apochromatism
apocinchonine
apocodeine
apocopate
apocopated
apocopation
apocope
apocopic
apocrenic
apocrisiary
Apocrita
apocrustic
apocryph
Apocrypha
apocryphal
apocryphalist
apocryphally
apocryphalness
apocryphate
apocryphon
Apocynaceae
apocynaceous
apocyneous
Apocynum
apod
Apoda
apodal
apodan
apodeipnon
apodeixis
apodema
apodemal
apodematal
apodeme
Apodes
Apodia
apodia
apodictic
apodictical
apodictically
apodictive
Apodidae
apodixis
apodosis
apodous
apodyterium
apoembryony
apofenchene
apogaeic
apogalacteum
apogamic
apogamically
apogamous
apogamously
apogamy
apogeal
apogean
apogee
apogeic
apogenous
apogeny
apogeotropic
apogeotropically
apogeotropism
Apogon
Apogonidae
apograph
apographal
apoharmine
apohyal
Apoidea
apoise
apojove
apokrea
apokreos
apolar
apolarity
apolaustic
apolegamic
Apolista
Apolistan
Apollinarian
Apollinarianism
Apolline
Apollo
Apollonia
Apollonian
Apollonic
apollonicon
Apollonistic
Apolloship
Apollyon
apologal
apologete
apologetic
apologetical
apologetically
apologetics
apologia
apologist
apologize
apologizer
apologue
apology
apolousis
Apolysin
apolysis
apolytikion
apomecometer
apomecometry
apometabolic
apometabolism
apometabolous
apometaboly
apomictic
apomictical
apomixis
apomorphia
apomorphine
aponeurology
aponeurorrhaphy
aponeurosis
aponeurositis
aponeurotic
aponeurotome
aponeurotomy
aponia
aponic
Aponogeton
Aponogetonaceae
aponogetonaceous
apoop
apopenptic
apopetalous
apophantic
apophasis
apophatic
Apophis
apophlegmatic
apophonia
apophony
apophorometer
apophthegm
apophthegmatist
apophyge
apophylactic
apophylaxis
apophyllite
apophyllous
apophysary
apophysate
apophyseal
apophysis
apophysitis
apoplasmodial
apoplastogamous
apoplectic
apoplectical
apoplectically
apoplectiform
apoplectoid
apoplex
apoplexy
apopyle
apoquinamine
apoquinine
aporetic
aporetical
aporhyolite
aporia
Aporobranchia
aporobranchian
Aporobranchiata
Aporocactus
Aporosa
aporose
aporphin
aporphine
Aporrhaidae
Aporrhais
aporrhaoid
aporrhegma
aport
aportoise
aposafranine
aposaturn
aposaturnium
aposematic
aposematically
aposepalous
aposia
aposiopesis
aposiopetic
apositia
apositic
aposoro
aposporogony
aposporous
apospory
apostasis
apostasy
apostate
apostatic
apostatical
apostatically
apostatism
apostatize
apostaxis
apostemate
apostematic
apostemation
apostematous
aposteme
aposteriori
aposthia
apostil
apostle
apostlehood
apostleship
apostolate
apostoless
apostoli
Apostolian
Apostolic
apostolic
apostolical
apostolically
apostolicalness
Apostolici
apostolicism
apostolicity
apostolize
Apostolos
apostrophal
apostrophation
apostrophe
apostrophic
apostrophied
apostrophize
apostrophus
Apotactic
Apotactici
apotelesm
apotelesmatic
apotelesmatical
apothecal
apothecary
apothecaryship
apothece
apothecial
apothecium
apothegm
apothegmatic
apothegmatical
apothegmatically
apothegmatist
apothegmatize
apothem
apotheose
apotheoses
apotheosis
apotheosize
apothesine
apothesis
apotome
apotracheal
apotropaic
apotropaion
apotropaism
apotropous
apoturmeric
apotype
apotypic
apout
apoxesis
Apoxyomenos
apozem
apozema
apozemical
apozymase
Appalachia
Appalachian
appall
appalling
appallingly
appallment
appalment
appanage
appanagist
apparatus
apparel
apparelment
apparence
apparency
apparent
apparently
apparentness
apparition
apparitional
apparitor
appassionata
appassionato
appay
appeal
appealability
appealable
appealer
appealing
appealingly
appealingness
appear
appearance
appearanced
appearer
appeasable
appeasableness
appeasably
appease
appeasement
appeaser
appeasing
appeasingly
appeasive
appellability
appellable
appellancy
appellant
appellate
appellation
appellational
appellative
appellatived
appellatively
appellativeness
appellatory
appellee
appellor
append
appendage
appendaged
appendalgia
appendance
appendancy
appendant
appendectomy
appendical
appendicalgia
appendice
appendicectasis
appendicectomy
appendices
appendicial
appendicious
appendicitis
appendicle
appendicocaecostomy
appendicostomy
appendicular
Appendicularia
appendicularian
Appendiculariidae
Appendiculata
appendiculate
appendiculated
appenditious
appendix
appendorontgenography
appendotome
appentice
apperceive
apperception
apperceptionism
apperceptionist
apperceptionistic
apperceptive
apperceptively
appercipient
appersonation
appertain
appertainment
appertinent
appet
appete
appetence
appetency
appetent
appetently
appetibility
appetible
appetibleness
appetite
appetition
appetitional
appetitious
appetitive
appetize
appetizement
appetizer
appetizingly
appinite
Appius
applanate
applanation
applaud
applaudable
applaudably
applauder
applaudingly
applause
applausive
applausively
apple
appleberry
appleblossom
applecart
appledrane
applegrower
applejack
applejohn
applemonger
applenut
appleringy
appleroot
applesauce
applewife
applewoman
appliable
appliableness
appliably
appliance
appliant
applicability
applicable
applicableness
applicably
applicancy
applicant
applicate
application
applicative
applicatively
applicator
applicatorily
applicatory
applied
appliedly
applier
applique
applosion
applosive
applot
applotment
apply
applyingly
applyment
appoggiatura
appoint
appointable
appointe
appointee
appointer
appointive
appointment
appointor
Appomatox
Appomattoc
apport
apportion
apportionable
apportioner
apportionment
apposability
apposable
appose
apposer
apposiopestic
apposite
appositely
appositeness
apposition
appositional
appositionally
appositive
appositively
appraisable
appraisal
appraise
appraisement
appraiser
appraising
appraisingly
appraisive
appreciable
appreciably
appreciant
appreciate
appreciatingly
appreciation
appreciational
appreciativ
appreciative
appreciatively
appreciativeness
appreciator
appreciatorily
appreciatory
appredicate
apprehend
apprehender
apprehendingly
apprehensibility
apprehensible
apprehensibly
apprehension
apprehensive
apprehensively
apprehensiveness
apprend
apprense
apprentice
apprenticehood
apprenticement
apprenticeship
appressed
appressor
appressorial
appressorium
appreteur
apprise
apprize
apprizement
apprizer
approach
approachability
approachabl
approachable
approachableness
approacher
approaching
approachless
approachment
approbate
approbation
approbative
approbativeness
approbator
approbatory
approof
appropinquate
appropinquation
appropinquity
appropre
appropriable
appropriate
appropriately
appropriateness
appropriation
appropriative
appropriativeness
appropriator
approvable
approvableness
approval
approvance
approve
approvedly
approvedness
approvement
approver
approvingly
approximal
approximate
approximately
approximation
approximative
approximatively
approximativeness
approximator
appulse
appulsion
appulsive
appulsively
appurtenance
appurtenant
apractic
apraxia
apraxic
apricate
aprication
aprickle
apricot
April
Aprilesque
Apriline
Aprilis
apriori
apriorism
apriorist
aprioristic
apriority
Aprocta
aproctia
aproctous
apron
aproneer
apronful
apronless
apronlike
apropos
aprosexia
aprosopia
aprosopous
aproterodont
apse
apselaphesia
apselaphesis
apsidal
apsidally
apsides
apsidiole
apsis
apsychia
apsychical
apt
Aptal
Aptenodytes
Aptera
apteral
apteran
apterial
apterium
apteroid
apterous
Apteryges
apterygial
Apterygidae
Apterygiformes
Apterygogenea
Apterygota
apterygote
apterygotous
Apteryx
Aptian
Aptiana
aptitude
aptitudinal
aptitudinally
aptly
aptness
aptote
aptotic
aptyalia
aptyalism
aptychus
Apulian
apulmonic
apulse
apurpose
Apus
apyonin
apyrene
apyretic
apyrexia
apyrexial
apyrexy
apyrotype
apyrous
aqua
aquabelle
aquabib
aquacade
aquacultural
aquaculture
aquaemanale
aquafortist
aquage
aquagreen
aquamarine
aquameter
aquaplane
aquapuncture
aquarelle
aquarellist
aquaria
aquarial
Aquarian
aquarian
Aquarid
Aquarii
aquariist
aquarium
Aquarius
aquarter
aquascutum
aquatic
aquatical
aquatically
aquatile
aquatint
aquatinta
aquatinter
aquation
aquativeness
aquatone
aquavalent
aquavit
aqueduct
aqueoglacial
aqueoigneous
aqueomercurial
aqueous
aqueously
aqueousness
aquicolous
aquicultural
aquiculture
aquiculturist
aquifer
aquiferous
Aquifoliaceae
aquifoliaceous
aquiform
Aquila
Aquilaria
aquilawood
aquilege
Aquilegia
Aquilian
Aquilid
aquiline
aquilino
aquincubital
aquincubitalism
Aquinist
aquintocubital
aquintocubitalism
aquiparous
Aquitanian
aquiver
aquo
aquocapsulitis
aquocarbonic
aquocellolitis
aquopentamminecobaltic
aquose
aquosity
aquotization
aquotize
ar
ara
Arab
araba
araban
arabana
Arabella
arabesque
arabesquely
arabesquerie
Arabian
Arabianize
Arabic
Arabicism
Arabicize
Arabidopsis
arability
arabin
arabinic
arabinose
arabinosic
Arabis
Arabism
Arabist
arabit
arabitol
arabiyeh
Arabize
arable
Arabophil
Araby
araca
Aracana
aracanga
aracari
Araceae
araceous
arachic
arachidonic
arachin
Arachis
arachnactis
Arachne
arachnean
arachnid
Arachnida
arachnidan
arachnidial
arachnidism
arachnidium
arachnism
Arachnites
arachnitis
arachnoid
arachnoidal
Arachnoidea
arachnoidea
arachnoidean
arachnoiditis
arachnological
arachnologist
arachnology
Arachnomorphae
arachnophagous
arachnopia
arad
Aradidae
arado
araeostyle
araeosystyle
Aragallus
Aragonese
Aragonian
aragonite
araguato
arain
Arains
Arakanese
arakawaite
arake
Arales
Aralia
Araliaceae
araliaceous
araliad
Araliaephyllum
aralie
Araliophyllum
aralkyl
aralkylated
Aramaean
Aramaic
Aramaicize
Aramaism
aramayoite
Aramidae
aramina
Araminta
Aramis
Aramitess
Aramu
Aramus
Aranea
Araneae
araneid
Araneida
araneidan
araneiform
Araneiformes
Araneiformia
aranein
Araneina
Araneoidea
araneologist
araneology
araneous
aranga
arango
Aranyaka
aranzada
arapahite
Arapaho
arapaima
araphorostic
arapunga
Araquaju
arar
Arara
arara
araracanga
ararao
ararauna
arariba
araroba
arati
aration
aratory
Araua
Arauan
Araucan
Araucanian
Araucano
Araucaria
Araucariaceae
araucarian
Araucarioxylon
Araujia
Arauna
Arawa
Arawak
Arawakan
Arawakian
arba
Arbacia
arbacin
arbalest
arbalester
arbalestre
arbalestrier
arbalist
arbalister
arbalo
Arbela
arbiter
arbitrable
arbitrager
arbitragist
arbitral
arbitrament
arbitrarily
arbitrariness
arbitrary
arbitrate
arbitration
arbitrational
arbitrationist
arbitrative
arbitrator
arbitratorship
arbitratrix
arbitrement
arbitrer
arbitress
arboloco
arbor
arboraceous
arboral
arborary
arborator
arboreal
arboreally
arborean
arbored
arboreous
arborescence
arborescent
arborescently
arboresque
arboret
arboreta
arboretum
arborical
arboricole
arboricoline
arboricolous
arboricultural
arboriculture
arboriculturist
arboriform
arborist
arborization
arborize
arboroid
arborolatry
arborous
arborvitae
arborway
arbuscle
arbuscula
arbuscular
arbuscule
arbusterol
arbustum
arbutase
arbute
arbutean
arbutin
arbutinase
arbutus
arc
arca
Arcacea
arcade
Arcadia
Arcadian
arcadian
Arcadianism
Arcadianly
Arcadic
Arcady
arcana
arcanal
arcane
arcanite
arcanum
arcate
arcature
Arcella
Arceuthobium
arch
archabomination
archae
archaecraniate
Archaeoceti
Archaeocyathidae
Archaeocyathus
archaeogeology
archaeographic
archaeographical
archaeography
archaeolatry
archaeolith
archaeolithic
archaeologer
archaeologian
archaeologic
archaeological
archaeologically
archaeologist
archaeology
Archaeopithecus
Archaeopteris
Archaeopterygiformes
Archaeopteryx
Archaeornis
Archaeornithes
archaeostoma
Archaeostomata
archaeostomatous
archagitator
archaic
archaical
archaically
archaicism
archaism
archaist
archaistic
archaize
archaizer
archangel
archangelic
Archangelica
archangelical
archangelship
archantagonist
archantiquary
archapostate
archapostle
archarchitect
archarios
archartist
archband
archbeacon
archbeadle
archbishop
archbishopess
archbishopric
archbishopry
archbotcher
archboutefeu
archbuffoon
archbuilder
archchampion
archchaplain
archcharlatan
archcheater
archchemic
archchief
archchronicler
archcity
archconfraternity
archconsoler
archconspirator
archcorrupter
archcorsair
archcount
archcozener
archcriminal
archcritic
archcrown
archcupbearer
archdapifer
archdapifership
archdeacon
archdeaconate
archdeaconess
archdeaconry
archdeaconship
archdean
archdeanery
archdeceiver
archdefender
archdemon
archdepredator
archdespot
archdetective
archdevil
archdiocesan
archdiocese
archdiplomatist
archdissembler
archdisturber
archdivine
archdogmatist
archdolt
archdruid
archducal
archduchess
archduchy
archduke
archdukedom
arche
archeal
Archean
archearl
archebiosis
archecclesiastic
archecentric
arched
archegone
archegonial
Archegoniata
Archegoniatae
archegoniate
archegoniophore
archegonium
archegony
Archegosaurus
archeion
Archelaus
Archelenis
archelogy
Archelon
archemperor
Archencephala
archencephalic
archenemy
archengineer
archenteric
archenteron
archeocyte
Archeozoic
Archer
archer
archeress
archerfish
archership
archery
arches
archespore
archesporial
archesporium
archetypal
archetypally
archetype
archetypic
archetypical
archetypically
archetypist
archeunuch
archeus
archexorcist
archfelon
archfiend
archfire
archflamen
archflatterer
archfoe
archfool
archform
archfounder
archfriend
archgenethliac
archgod
archgomeral
archgovernor
archgunner
archhead
archheart
archheresy
archheretic
archhost
archhouse
archhumbug
archhypocrisy
archhypocrite
Archiannelida
archiater
Archibald
archibenthal
archibenthic
archibenthos
archiblast
archiblastic
archiblastoma
archiblastula
Archibuteo
archicantor
archicarp
archicerebrum
Archichlamydeae
archichlamydeous
archicleistogamous
archicleistogamy
archicoele
archicontinent
archicyte
archicytula
Archidamus
Archidiaceae
archidiaconal
archidiaconate
archididascalian
archididascalos
Archidiskodon
Archidium
archidome
Archie
archiepiscopacy
archiepiscopal
archiepiscopally
archiepiscopate
archiereus
archigaster
archigastrula
archigenesis
archigonic
archigonocyte
archigony
archiheretical
archikaryon
archil
archilithic
Archilochian
archilowe
archimage
Archimago
archimagus
archimandrite
Archimedean
Archimedes
archimime
archimorphic
archimorula
archimperial
archimperialism
archimperialist
archimperialistic
archimpressionist
Archimycetes
archineuron
archinfamy
archinformer
arching
archipallial
archipallium
archipelagian
archipelagic
archipelago
archipin
archiplasm
archiplasmic
Archiplata
archiprelatical
archipresbyter
archipterygial
archipterygium
archisperm
Archispermae
archisphere
archispore
archistome
archisupreme
archisymbolical
architect
architective
architectonic
Architectonica
architectonically
architectonics
architectress
architectural
architecturalist
architecturally
architecture
architecturesque
Architeuthis
architis
architraval
architrave
architraved
architypographer
archival
archive
archivist
archivolt
archizoic
archjockey
archking
archknave
archleader
archlecher
archleveler
archlexicographer
archliar
archlute
archly
archmachine
archmagician
archmagirist
archmarshal
archmediocrity
archmessenger
archmilitarist
archmime
archminister
archmock
archmocker
archmockery
archmonarch
archmonarchist
archmonarchy
archmugwump
archmurderer
archmystagogue
archness
archocele
archocystosyrinx
archology
archon
archonship
archont
archontate
Archontia
archontic
archoplasm
archoplasmic
archoptoma
archoptosis
archorrhagia
archorrhea
archostegnosis
archostenosis
archosyrinx
archoverseer
archpall
archpapist
archpastor
archpatriarch
archpatron
archphilosopher
archphylarch
archpiece
archpilferer
archpillar
archpirate
archplagiarist
archplagiary
archplayer
archplotter
archplunderer
archplutocrat
archpoet
archpolitician
archpontiff
archpractice
archprelate
archprelatic
archprelatical
archpresbyter
archpresbyterate
archpresbytery
archpretender
archpriest
archpriesthood
archpriestship
archprimate
archprince
archprophet
archprotopope
archprototype
archpublican
archpuritan
archradical
archrascal
archreactionary
archrebel
archregent
archrepresentative
archrobber
archrogue
archruler
archsacrificator
archsacrificer
archsaint
archsatrap
archscoundrel
archseducer
archsee
archsewer
archshepherd
archsin
archsnob
archspirit
archspy
archsteward
archswindler
archsynagogue
archtempter
archthief
archtraitor
archtreasurer
archtreasurership
archturncoat
archtyrant
archurger
archvagabond
archvampire
archvestryman
archvillain
archvillainy
archvisitor
archwag
archway
archwench
archwise
archworker
archworkmaster
Archy
archy
Arcidae
Arcifera
arciferous
arcifinious
arciform
arcing
Arcite
arcked
arcking
arcocentrous
arcocentrum
arcograph
Arcos
Arctalia
Arctalian
Arctamerican
arctation
Arctia
arctian
arctic
arctically
arctician
arcticize
arcticward
arcticwards
arctiid
Arctiidae
Arctisca
Arctium
Arctocephalus
Arctogaea
Arctogaeal
Arctogaean
arctoid
Arctoidea
arctoidean
Arctomys
Arctos
Arctosis
Arctostaphylos
Arcturia
Arcturus
arcual
arcuale
arcuate
arcuated
arcuately
arcuation
arcubalist
arcubalister
arcula
arculite
ardassine
Ardea
Ardeae
ardeb
Ardeidae
Ardelia
ardella
ardency
ardennite
ardent
ardently
ardentness
Ardhamagadhi
Ardhanari
ardish
Ardisia
Ardisiaceae
ardoise
ardor
ardri
ardu
arduinite
arduous
arduously
arduousness
ardurous
are
area
areach
aread
areal
areality
Arean
arear
areasoner
areaway
Areca
Arecaceae
arecaceous
arecaidin
arecaidine
arecain
arecaine
Arecales
arecolidin
arecolidine
arecolin
arecoline
Arecuna
ared
areek
areel
arefact
arefaction
aregenerative
aregeneratory
areito
arena
arenaceous
arenae
Arenaria
arenariae
arenarious
arenation
arend
arendalite
areng
Arenga
Arenicola
arenicole
arenicolite
arenicolous
Arenig
arenilitic
arenoid
arenose
arenosity
arent
areocentric
areographer
areographic
areographical
areographically
areography
areola
areolar
areolate
areolated
areolation
areole
areolet
areologic
areological
areologically
areologist
areology
areometer
areometric
areometrical
areometry
Areopagist
Areopagite
Areopagitic
Areopagitica
Areopagus
areotectonics
areroscope
aretaics
arete
Arethusa
Arethuse
Aretinian
arfvedsonite
argal
argala
argali
argans
Argante
Argas
argasid
Argasidae
Argean
argeers
argel
Argemone
argemony
argenol
argent
argental
argentamid
argentamide
argentamin
argentamine
argentate
argentation
argenteous
argenter
argenteum
argentic
argenticyanide
argentide
argentiferous
Argentina
Argentine
argentine
Argentinean
Argentinian
Argentinidae
argentinitrate
Argentinize
Argentino
argention
argentite
argentojarosite
argentol
argentometric
argentometrically
argentometry
argenton
argentoproteinum
argentose
argentous
argentum
Argestes
arghan
arghel
arghool
Argid
argil
argillaceous
argilliferous
argillite
argillitic
argilloarenaceous
argillocalcareous
argillocalcite
argilloferruginous
argilloid
argillomagnesian
argillous
arginine
argininephosphoric
Argiope
Argiopidae
Argiopoidea
Argive
Argo
argo
Argoan
argol
argolet
Argolian
Argolic
Argolid
argon
Argonaut
Argonauta
Argonautic
Argonne
argosy
argot
argotic
Argovian
arguable
argue
arguer
argufier
argufy
Argulus
argument
argumental
argumentation
argumentatious
argumentative
argumentatively
argumentativeness
argumentator
argumentatory
Argus
argusfish
Argusianus
Arguslike
argute
argutely
arguteness
Argyle
Argyll
Argynnis
argyranthemous
argyranthous
Argyraspides
argyria
argyric
argyrite
argyrocephalous
argyrodite
Argyrol
Argyroneta
Argyropelecus
argyrose
argyrosis
Argyrosomus
argyrythrose
arhar
arhat
arhatship
Arhauaco
arhythmic
aria
Ariadne
Arian
Ariana
Arianism
Arianistic
Arianistical
Arianize
Arianizer
Arianrhod
aribine
Arician
aricine
arid
Arided
aridge
aridian
aridity
aridly
aridness
ariegite
Ariel
ariel
arienzo
Aries
arietation
Arietid
arietinous
arietta
aright
arightly
arigue
Ariidae
Arikara
aril
ariled
arillary
arillate
arillated
arilliform
arillode
arillodium
arilloid
arillus
Arimasp
Arimaspian
Arimathaean
Ariocarpus
Arioi
Arioian
Arion
ariose
arioso
ariot
aripple
Arisaema
arisard
arise
arisen
arist
arista
Aristarch
Aristarchian
aristarchy
aristate
Aristeas
Aristida
Aristides
Aristippus
aristocracy
aristocrat
aristocratic
aristocratical
aristocratically
aristocraticalness
aristocraticism
aristocraticness
aristocratism
aristodemocracy
aristodemocratical
aristogenesis
aristogenetic
aristogenic
aristogenics
Aristol
Aristolochia
Aristolochiaceae
aristolochiaceous
Aristolochiales
aristolochin
aristolochine
aristological
aristologist
aristology
aristomonarchy
Aristophanic
aristorepublicanism
Aristotelian
Aristotelianism
Aristotelic
Aristotelism
aristotype
aristulate
arite
arithmetic
arithmetical
arithmetically
arithmetician
arithmetization
arithmetize
arithmic
arithmocracy
arithmocratic
arithmogram
arithmograph
arithmography
arithmomania
arithmometer
Arius
Arivaipa
Arizona
Arizonan
Arizonian
arizonite
arjun
ark
Arkab
Arkansan
Arkansas
Arkansawyer
arkansite
Arkite
arkite
arkose
arkosic
arksutite
Arlene
Arleng
arles
Arline
arm
armada
armadilla
Armadillididae
Armadillidium
armadillo
Armado
Armageddon
Armageddonist
armagnac
armament
armamentarium
armamentary
armangite
armariolum
armarium
Armata
Armatoles
Armatoli
armature
armbone
armchair
armchaired
armed
armeniaceous
Armenian
Armenic
Armenize
Armenoid
armer
Armeria
Armeriaceae
armet
armful
armgaunt
armhole
armhoop
Armida
armied
armiferous
armiger
armigeral
armigerous
armil
armilla
Armillaria
armillary
armillate
armillated
arming
Arminian
Arminianism
Arminianize
Arminianizer
armipotence
armipotent
armisonant
armisonous
armistice
armless
armlet
armload
armoire
armonica
armor
Armoracia
armored
armorer
armorial
Armoric
Armorican
Armorician
armoried
armorist
armorproof
armorwise
armory
Armouchiquois
armozeen
armpiece
armpit
armplate
armrack
armrest
arms
armscye
armure
army
arn
arna
Arnaut
arnberry
Arne
Arneb
Arnebia
arnee
arni
arnica
Arnold
Arnoldist
Arnoseris
arnotta
arnotto
Arnusian
arnut
Aro
aroar
aroast
arock
aroeira
aroid
aroideous
Aroides
aroint
arolium
arolla
aroma
aromacity
aromadendrin
aromatic
aromatically
aromaticness
aromatite
aromatites
aromatization
aromatize
aromatizer
aromatophor
aromatophore
Aronia
aroon
Aroras
Arosaguntacook
arose
around
arousal
arouse
arousement
arouser
arow
aroxyl
arpeggiando
arpeggiated
arpeggiation
arpeggio
arpeggioed
arpen
arpent
arquerite
arquifoux
arracach
arracacha
Arracacia
arrack
arrah
arraign
arraigner
arraignment
arrame
arrange
arrangeable
arrangement
arranger
arrant
arrantly
Arras
arras
arrased
arrasene
arrastra
arrastre
arratel
arrau
array
arrayal
arrayer
arrayment
arrear
arrearage
arrect
arrector
arrendation
arrenotokous
arrenotoky
arrent
arrentable
arrentation
arreptitious
arrest
arrestable
arrestation
arrestee
arrester
arresting
arrestingly
arrestive
arrestment
arrestor
Arretine
arrhenal
Arrhenatherum
arrhenoid
arrhenotokous
arrhenotoky
arrhinia
arrhizal
arrhizous
arrhythmia
arrhythmic
arrhythmical
arrhythmically
arrhythmous
arrhythmy
arriage
arriba
arride
arridge
arrie
arriere
Arriet
arrimby
arris
arrish
arrisways
arriswise
arrival
arrive
arriver
arroba
arrogance
arrogancy
arrogant
arrogantly
arrogantness
arrogate
arrogatingly
arrogation
arrogative
arrogator
arrojadite
arrope
arrosive
arrow
arrowbush
arrowed
arrowhead
arrowheaded
arrowleaf
arrowless
arrowlet
arrowlike
arrowplate
arrowroot
arrowsmith
arrowstone
arrowweed
arrowwood
arrowworm
arrowy
arroyo
Arruague
Arry
Arryish
Arsacid
Arsacidan
arsanilic
arse
arsedine
arsenal
arsenate
arsenation
arseneted
arsenetted
arsenfast
arsenferratose
arsenhemol
arseniasis
arseniate
arsenic
arsenical
arsenicalism
arsenicate
arsenicism
arsenicize
arsenicophagy
arsenide
arseniferous
arsenillo
arseniopleite
arseniosiderite
arsenious
arsenism
arsenite
arsenium
arseniuret
arseniureted
arsenization
arseno
arsenobenzene
arsenobenzol
arsenobismite
arsenoferratin
arsenofuran
arsenohemol
arsenolite
arsenophagy
arsenophen
arsenophenol
arsenophenylglycin
arsenopyrite
arsenostyracol
arsenotherapy
arsenotungstates
arsenotungstic
arsenous
arsenoxide
arsenyl
arses
arsesmart
arsheen
arshin
arshine
arsine
arsinic
arsino
Arsinoitherium
arsis
arsle
arsmetrik
arsmetrike
arsnicker
arsoite
arson
arsonate
arsonation
arsonic
arsonist
arsonite
arsonium
arsono
arsonvalization
arsphenamine
arsyl
arsylene
Art
art
artaba
artabe
artal
Artamidae
Artamus
artar
artarine
artcraft
artefact
artel
Artemas
Artemia
Artemis
Artemisia
artemisic
artemisin
Artemision
Artemisium
arteriagra
arterial
arterialization
arterialize
arterially
arteriarctia
arteriasis
arteriectasia
arteriectasis
arteriectopia
arterin
arterioarctia
arteriocapillary
arteriococcygeal
arteriodialysis
arteriodiastasis
arteriofibrosis
arteriogenesis
arteriogram
arteriograph
arteriography
arteriole
arteriolith
arteriology
arteriolosclerosis
arteriomalacia
arteriometer
arteriomotor
arterionecrosis
arteriopalmus
arteriopathy
arteriophlebotomy
arterioplania
arterioplasty
arteriopressor
arteriorenal
arteriorrhagia
arteriorrhaphy
arteriorrhexis
arteriosclerosis
arteriosclerotic
arteriospasm
arteriostenosis
arteriostosis
arteriostrepsis
arteriosympathectomy
arteriotome
arteriotomy
arteriotrepsis
arterious
arteriovenous
arterioversion
arterioverter
arteritis
artery
Artesian
artesian
artful
artfully
artfulness
Artgum
artha
arthel
arthemis
arthragra
arthral
arthralgia
arthralgic
arthrectomy
arthredema
arthrempyesis
arthresthesia
arthritic
arthritical
arthriticine
arthritis
arthritism
arthrobacterium
arthrobranch
arthrobranchia
arthrocace
arthrocarcinoma
arthrocele
arthrochondritis
arthroclasia
arthrocleisis
arthroclisis
arthroderm
arthrodesis
arthrodia
arthrodial
arthrodic
Arthrodira
arthrodiran
arthrodire
arthrodirous
Arthrodonteae
arthrodynia
arthrodynic
arthroempyema
arthroempyesis
arthroendoscopy
Arthrogastra
arthrogastran
arthrogenous
arthrography
arthrogryposis
arthrolite
arthrolith
arthrolithiasis
arthrology
arthromeningitis
arthromere
arthromeric
arthrometer
arthrometry
arthroncus
arthroneuralgia
arthropathic
arthropathology
arthropathy
arthrophlogosis
arthrophyma
arthroplastic
arthroplasty
arthropleura
arthropleure
arthropod
Arthropoda
arthropodal
arthropodan
arthropodous
Arthropomata
arthropomatous
arthropterous
arthropyosis
arthrorheumatism
arthrorrhagia
arthrosclerosis
arthrosia
arthrosis
arthrospore
arthrosporic
arthrosporous
arthrosteitis
arthrosterigma
arthrostome
arthrostomy
Arthrostraca
arthrosynovitis
arthrosyrinx
arthrotome
arthrotomy
arthrotrauma
arthrotropic
arthrotyphoid
arthrous
arthroxerosis
Arthrozoa
arthrozoan
arthrozoic
Arthur
Arthurian
Arthuriana
artiad
artichoke
article
articled
articulability
articulable
articulacy
articulant
articular
articulare
articularly
articulary
Articulata
articulate
articulated
articulately
articulateness
articulation
articulationist
articulative
articulator
articulatory
articulite
articulus
Artie
artifact
artifactitious
artifice
artificer
artificership
artificial
artificialism
artificiality
artificialize
artificially
artificialness
artiller
artillerist
artillery
artilleryman
artilleryship
artiness
artinite
Artinskian
artiodactyl
Artiodactyla
artiodactylous
artiphyllous
artisan
artisanship
artist
artistdom
artiste
artistic
artistical
artistically
artistry
artless
artlessly
artlessness
artlet
artlike
Artocarpaceae
artocarpad
artocarpeous
artocarpous
Artocarpus
artolater
artophagous
artophorion
artotype
artotypy
Artotyrite
artware
arty
aru
Aruac
arui
aruke
Arulo
Arum
arumin
Aruncus
arundiferous
arundinaceous
Arundinaria
arundineous
Arundo
Arunta
arupa
arusa
arusha
arustle
arval
arvel
Arverni
Arvicola
arvicole
Arvicolinae
arvicoline
arvicolous
arviculture
arx
ary
Arya
Aryan
Aryanism
Aryanization
Aryanize
aryballoid
aryballus
aryepiglottic
aryl
arylamine
arylamino
arylate
arytenoid
arytenoidal
arzan
Arzava
Arzawa
arzrunite
arzun
As
as
Asa
asaddle
asafetida
Asahel
asak
asale
asana
Asaph
asaphia
Asaphic
asaphid
Asaphidae
Asaphus
asaprol
asarabacca
Asaraceae
Asarh
asarite
asaron
asarone
asarotum
Asarum
asbest
asbestic
asbestiform
asbestine
asbestinize
asbestoid
asbestoidal
asbestos
asbestosis
asbestous
asbestus
asbolin
asbolite
Ascabart
Ascalabota
ascan
Ascanian
Ascanius
ascare
ascariasis
ascaricidal
ascaricide
ascarid
Ascaridae
ascarides
Ascaridia
ascaridiasis
ascaridole
Ascaris
ascaron
Ascella
ascellus
ascend
ascendable
ascendance
ascendancy
ascendant
ascendence
ascendency
ascendent
ascender
ascendible
ascending
ascendingly
ascension
ascensional
ascensionist
Ascensiontide
ascensive
ascent
ascertain
ascertainable
ascertainableness
ascertainably
ascertainer
ascertainment
ascescency
ascescent
ascetic
ascetical
ascetically
asceticism
Ascetta
aschaffite
ascham
aschistic
asci
ascian
Ascidia
Ascidiacea
Ascidiae
ascidian
ascidiate
ascidicolous
ascidiferous
ascidiform
ascidioid
Ascidioida
Ascidioidea
Ascidiozoa
ascidiozooid
ascidium
asciferous
ascigerous
ascii
ascites
ascitic
ascitical
ascititious
asclent
Asclepiad
asclepiad
Asclepiadaceae
asclepiadaceous
Asclepiadae
Asclepiadean
asclepiadeous
Asclepiadic
Asclepian
Asclepias
asclepidin
asclepidoid
Asclepieion
asclepin
Asclepius
ascocarp
ascocarpous
Ascochyta
ascogenous
ascogone
ascogonial
ascogonidium
ascogonium
ascolichen
Ascolichenes
ascoma
ascomycetal
ascomycete
Ascomycetes
ascomycetous
ascon
Ascones
ascophore
ascophorous
Ascophyllum
ascorbic
ascospore
ascosporic
ascosporous
Ascot
ascot
Ascothoracica
ascribable
ascribe
ascript
ascription
ascriptitii
ascriptitious
ascriptitius
ascry
ascula
Ascupart
ascus
ascyphous
Ascyrum
asdic
ase
asearch
asecretory
aseethe
aseismatic
aseismic
aseismicity
aseity
aselgeia
asellate
Aselli
Asellidae
Aselline
Asellus
asem
asemasia
asemia
asepsis
aseptate
aseptic
aseptically
asepticism
asepticize
aseptify
aseptol
aseptolin
asexual
asexuality
asexualization
asexualize
asexually
asfetida
ash
Asha
ashake
ashame
ashamed
ashamedly
ashamedness
ashamnu
Ashangos
Ashantee
Ashanti
Asharasi
ashberry
ashcake
ashen
Asher
asherah
Asherites
ashery
ashes
ashet
ashily
ashimmer
ashine
ashiness
ashipboard
Ashir
ashiver
Ashkenazic
Ashkenazim
ashkoko
ashlar
ashlared
ashlaring
ashless
ashling
Ashluslay
ashman
Ashmolean
Ashochimi
ashore
ashpan
ashpit
ashplant
ashraf
ashrafi
ashthroat
Ashur
ashur
ashweed
ashwort
ashy
asialia
Asian
Asianic
Asianism
Asiarch
Asiarchate
Asiatic
Asiatical
Asiatically
Asiatican
Asiaticism
Asiaticization
Asiaticize
Asiatize
aside
asidehand
asideness
asiderite
asideu
asiento
asilid
Asilidae
Asilus
asimen
Asimina
asimmer
asinego
asinine
asininely
asininity
asiphonate
asiphonogama
asitia
ask
askable
askance
askant
askar
askari
asker
askew
askingly
askip
asklent
Asklepios
askos
Askr
aslant
aslantwise
aslaver
asleep
aslop
aslope
aslumber
asmack
asmalte
asmear
asmile
asmoke
asmolder
asniffle
asnort
asoak
asocial
asok
asoka
asomatophyte
asomatous
asonant
asonia
asop
asor
asouth
asp
aspace
aspalathus
Aspalax
asparagic
asparagine
asparaginic
asparaginous
asparagus
asparagyl
asparkle
aspartate
aspartic
aspartyl
Aspasia
Aspatia
aspect
aspectable
aspectant
aspection
aspectual
aspen
asper
asperate
asperation
aspergation
asperge
asperger
Asperges
aspergil
aspergill
Aspergillaceae
Aspergillales
aspergilliform
aspergillin
aspergillosis
aspergillum
aspergillus
Asperifoliae
asperifoliate
asperifolious
asperite
asperity
aspermatic
aspermatism
aspermatous
aspermia
aspermic
aspermous
asperous
asperously
asperse
aspersed
asperser
aspersion
aspersive
aspersively
aspersor
aspersorium
aspersory
Asperugo
Asperula
asperuloside
asperulous
asphalt
asphaltene
asphalter
asphaltic
asphaltite
asphaltum
aspheterism
aspheterize
asphodel
Asphodelaceae
Asphodeline
Asphodelus
asphyctic
asphyctous
asphyxia
asphyxial
asphyxiant
asphyxiate
asphyxiation
asphyxiative
asphyxiator
asphyxied
asphyxy
aspic
aspiculate
aspiculous
aspidate
aspidiaria
aspidinol
Aspidiotus
Aspidiske
Aspidistra
aspidium
Aspidobranchia
Aspidobranchiata
aspidobranchiate
Aspidocephali
Aspidochirota
Aspidoganoidei
aspidomancy
Aspidosperma
aspidospermine
aspirant
aspirata
aspirate
aspiration
aspirator
aspiratory
aspire
aspirer
aspirin
aspiring
aspiringly
aspiringness
aspish
asplanchnic
Asplenieae
asplenioid
Asplenium
asporogenic
asporogenous
asporous
asport
asportation
asporulate
aspout
asprawl
aspread
Aspredinidae
Aspredo
aspring
asprout
asquare
asquat
asqueal
asquint
asquirm
ass
assacu
assagai
assai
assail
assailable
assailableness
assailant
assailer
assailment
Assam
Assamese
Assamites
assapan
assapanic
assarion
assart
assary
assassin
assassinate
assassination
assassinative
assassinator
assassinatress
assassinist
assate
assation
assault
assaultable
assaulter
assaut
assay
assayable
assayer
assaying
assbaa
asse
assecuration
assecurator
assedation
assegai
asself
assemblable
assemblage
assemble
assembler
assembly
assemblyman
assent
assentaneous
assentation
assentatious
assentator
assentatorily
assentatory
assented
assenter
assentient
assenting
assentingly
assentive
assentiveness
assentor
assert
assertable
assertative
asserter
assertible
assertion
assertional
assertive
assertively
assertiveness
assertor
assertorial
assertorially
assertoric
assertorical
assertorically
assertorily
assertory
assertress
assertrix
assertum
assess
assessable
assessably
assessed
assessee
assession
assessionary
assessment
assessor
assessorial
assessorship
assessory
asset
assets
assever
asseverate
asseveratingly
asseveration
asseverative
asseveratively
asseveratory
asshead
assi
assibilate
assibilation
Assidean
assident
assidual
assidually
assiduity
assiduous
assiduously
assiduousness
assientist
assiento
assify
assign
assignability
assignable
assignably
assignat
assignation
assigned
assignee
assigneeship
assigner
assignment
assignor
assilag
assimilability
assimilable
assimilate
assimilation
assimilationist
assimilative
assimilativeness
assimilator
assimilatory
Assiniboin
assis
Assisan
assise
assish
assishly
assishness
assist
assistance
assistant
assistanted
assistantship
assistency
assister
assistful
assistive
assistless
assistor
assize
assizement
assizer
assizes
asslike
assman
Assmannshauser
assmanship
associability
associable
associableness
associate
associated
associatedness
associateship
association
associational
associationalism
associationalist
associationism
associationist
associationistic
associative
associatively
associativeness
associator
associatory
assoil
assoilment
assoilzie
assonance
assonanced
assonant
assonantal
assonantic
assonate
Assonia
assort
assortative
assorted
assortedness
assorter
assortive
assortment
assuade
assuage
assuagement
assuager
assuasive
assubjugate
assuetude
assumable
assumably
assume
assumed
assumedly
assumer
assuming
assumingly
assumingness
assumpsit
assumption
Assumptionist
assumptious
assumptiousness
assumptive
assumptively
assurable
assurance
assurant
assure
assured
assuredly
assuredness
assurer
assurge
assurgency
assurgent
assuring
assuringly
assyntite
Assyrian
Assyrianize
Assyriological
Assyriologist
Assyriologue
Assyriology
Assyroid
assythment
ast
asta
Astacidae
Astacus
Astakiwi
astalk
astarboard
astare
astart
Astarte
Astartian
Astartidae
astasia
astatic
astatically
astaticism
astatine
astatize
astatizer
astay
asteam
asteatosis
asteep
asteer
asteism
astelic
astely
aster
Asteraceae
asteraceous
Asterales
Asterella
astereognosis
asteria
asterial
Asterias
asteriated
Asteriidae
asterikos
asterin
Asterina
Asterinidae
asterioid
Asterion
asterion
Asterionella
asterisk
asterism
asterismal
astern
asternal
Asternata
asternia
Asterochiton
asteroid
asteroidal
Asteroidea
asteroidean
Asterolepidae
Asterolepis
Asterope
asterophyllite
Asterophyllites
Asterospondyli
asterospondylic
asterospondylous
Asteroxylaceae
Asteroxylon
Asterozoa
asterwort
asthenia
asthenic
asthenical
asthenobiosis
asthenobiotic
asthenolith
asthenology
asthenopia
asthenopic
asthenosphere
astheny
asthma
asthmatic
asthmatical
asthmatically
asthmatoid
asthmogenic
asthore
asthorin
Astian
astichous
astigmatic
astigmatical
astigmatically
astigmatism
astigmatizer
astigmatometer
astigmatoscope
astigmatoscopy
astigmia
astigmism
astigmometer
astigmometry
Astilbe
astilbe
astint
astipulate
astir
astite
astomatal
astomatous
astomia
astomous
astonied
astonish
astonishedly
astonisher
astonishing
astonishingly
astonishingness
astonishment
astony
astoop
astor
astound
astoundable
astounding
astoundingly
astoundment
Astrachan
astraddle
Astraea
Astraean
astraean
astraeid
Astraeidae
astraeiform
astragal
astragalar
astragalectomy
astragali
astragalocalcaneal
astragalocentral
astragalomancy
astragalonavicular
astragaloscaphoid
astragalotibial
Astragalus
astragalus
astrain
astrakanite
astrakhan
astral
astrally
astrand
Astrantia
astraphobia
astrapophobia
astray
astream
astrer
astrict
astriction
astrictive
astrictively
astrictiveness
Astrid
astride
astrier
astriferous
astrild
astringe
astringency
astringent
astringently
astringer
astroalchemist
astroblast
Astrocaryum
astrochemist
astrochemistry
astrochronological
astrocyte
astrocytoma
astrocytomata
astrodiagnosis
astrodome
astrofel
astrogeny
astroglia
astrognosy
astrogonic
astrogony
astrograph
astrographic
astrography
astroid
astroite
astrolabe
astrolabical
astrolater
astrolatry
astrolithology
astrologaster
astrologer
astrologian
astrologic
astrological
astrologically
astrologistic
astrologize
astrologous
astrology
astromancer
astromancy
astromantic
astrometeorological
astrometeorologist
astrometeorology
astrometer
astrometrical
astrometry
astronaut
astronautics
astronomer
astronomic
astronomical
astronomically
astronomics
astronomize
astronomy
Astropecten
Astropectinidae
astrophil
astrophobia
astrophotographic
astrophotography
astrophotometer
astrophotometrical
astrophotometry
astrophyllite
astrophysical
astrophysicist
astrophysics
Astrophyton
astroscope
Astroscopus
astroscopy
astrospectral
astrospectroscopic
astrosphere
astrotheology
astrut
astucious
astuciously
astucity
Astur
Asturian
astute
astutely
astuteness
astylar
Astylospongia
Astylosternus
asudden
asunder
Asuri
aswail
aswarm
asway
asweat
aswell
aswim
aswing
aswirl
aswoon
aswooned
asyla
asyllabia
asyllabic
asyllabical
asylum
asymbiotic
asymbolia
asymbolic
asymbolical
asymmetric
asymmetrical
asymmetrically
Asymmetron
asymmetry
asymptomatic
asymptote
asymptotic
asymptotical
asymptotically
asynapsis
asynaptic
asynartete
asynartetic
asynchronism
asynchronous
asyndesis
asyndetic
asyndetically
asyndeton
asynergia
asynergy
asyngamic
asyngamy
asyntactic
asyntrophy
asystole
asystolic
asystolism
asyzygetic
at
Ata
atabal
atabeg
atabek
Atabrine
Atacaman
Atacamenan
Atacamenian
Atacameno
atacamite
atactic
atactiform
Ataentsic
atafter
Ataigal
Ataiyal
Atalan
ataman
atamasco
Atamosco
atangle
atap
ataraxia
ataraxy
atatschite
ataunt
atavi
atavic
atavism
atavist
atavistic
atavistically
atavus
ataxaphasia
ataxia
ataxiagram
ataxiagraph
ataxiameter
ataxiaphasia
ataxic
ataxinomic
ataxite
ataxonomic
ataxophemia
ataxy
atazir
atbash
atchison
ate
Ateba
atebrin
atechnic
atechnical
atechny
ateeter
atef
atelectasis
atelectatic
ateleological
Ateles
atelestite
atelets
atelier
ateliosis
Atellan
atelo
atelocardia
atelocephalous
ateloglossia
atelognathia
atelomitic
atelomyelia
atelopodia
ateloprosopia
atelorachidia
atelostomia
atemporal
Aten
Atenism
Atenist
Aterian
ates
Atestine
ateuchi
ateuchus
Atfalati
Athabasca
Athabascan
athalamous
athalline
Athamantid
athanasia
Athanasian
Athanasianism
Athanasianist
athanasy
athanor
Athapascan
athar
Atharvan
Athecae
Athecata
athecate
atheism
atheist
atheistic
atheistical
atheistically
atheisticalness
atheize
atheizer
athelia
atheling
athematic
Athena
Athenaea
athenaeum
athenee
Athenian
Athenianly
athenor
Athens
atheological
atheologically
atheology
atheous
Athericera
athericeran
athericerous
atherine
Atherinidae
Atheriogaea
Atheriogaean
Atheris
athermancy
athermanous
athermic
athermous
atheroma
atheromasia
atheromata
atheromatosis
atheromatous
atherosclerosis
Atherosperma
Atherurus
athetesis
athetize
athetoid
athetosic
athetosis
athing
athirst
athlete
athletehood
athletic
athletical
athletically
athleticism
athletics
athletism
athletocracy
athlothete
athlothetes
athodyd
athort
athrepsia
athreptic
athrill
athrive
athrob
athrocyte
athrocytosis
athrogenic
athrong
athrough
athwart
athwarthawse
athwartship
athwartships
athwartwise
athymia
athymic
athymy
athyreosis
athyria
athyrid
Athyridae
Athyris
Athyrium
athyroid
athyroidism
athyrosis
Ati
Atik
Atikokania
atilt
atimon
atinga
atingle
atinkle
atip
atis
Atka
Atlanta
atlantad
atlantal
Atlantean
atlantes
Atlantic
atlantic
Atlantica
Atlantid
Atlantides
atlantite
atlantoaxial
atlantodidymus
atlantomastoid
atlantoodontoid
Atlantosaurus
Atlas
atlas
Atlaslike
atlatl
atle
atlee
atloaxoid
atloid
atloidean
atloidoaxoid
atma
atman
atmiatrics
atmiatry
atmid
atmidalbumin
atmidometer
atmidometry
atmo
atmocausis
atmocautery
atmoclastic
atmogenic
atmograph
atmologic
atmological
atmologist
atmology
atmolysis
atmolyzation
atmolyze
atmolyzer
atmometer
atmometric
atmometry
atmos
atmosphere
atmosphereful
atmosphereless
atmospheric
atmospherical
atmospherically
atmospherics
atmospherology
atmostea
atmosteal
atmosteon
Atnah
atocha
atocia
atokal
atoke
atokous
atoll
atom
atomatic
atomechanics
atomerg
atomic
atomical
atomically
atomician
atomicism
atomicity
atomics
atomiferous
atomism
atomist
atomistic
atomistical
atomistically
atomistics
atomity
atomization
atomize
atomizer
atomology
atomy
atonable
atonal
atonalism
atonalistic
atonality
atonally
atone
atonement
atoneness
atoner
atonia
atonic
atonicity
atoningly
atony
atop
Atophan
atophan
atopic
atopite
atopy
Atorai
Atossa
atour
atoxic
Atoxyl
atoxyl
atrabilarian
atrabilarious
atrabiliar
atrabiliarious
atrabiliary
atrabilious
atrabiliousness
atracheate
Atractaspis
Atragene
atragene
atrail
atrament
atramental
atramentary
atramentous
atraumatic
Atrebates
Atremata
atrematous
atremble
atrepsy
atreptic
atresia
atresic
atresy
atretic
atria
atrial
atrichia
atrichosis
atrichous
atrickle
Atridean
atrienses
atriensis
atriocoelomic
atrioporal
atriopore
atrioventricular
atrip
Atriplex
atrium
atrocha
atrochal
atrochous
atrocious
atrociously
atrociousness
atrocity
atrolactic
Atropa
atropaceous
atropal
atropamine
atrophia
atrophiated
atrophic
atrophied
atrophoderma
atrophy
atropia
atropic
Atropidae
atropine
atropinism
atropinization
atropinize
atropism
atropous
atrorubent
atrosanguineous
atroscine
atrous
atry
Atrypa
Atta
atta
Attacapan
attacco
attach
attachable
attachableness
attache
attached
attachedly
attacher
attacheship
attachment
attack
attackable
attacker
attacolite
Attacus
attacus
attagen
attaghan
attain
attainability
attainable
attainableness
attainder
attainer
attainment
attaint
attaintment
attainture
Attalea
attaleh
Attalid
attar
attargul
attask
attemper
attemperament
attemperance
attemperate
attemperately
attemperation
attemperator
attempt
attemptability
attemptable
attempter
attemptless
attend
attendance
attendancy
attendant
attendantly
attender
attendingly
attendment
attendress
attensity
attent
attention
attentional
attentive
attentively
attentiveness
attently
attenuable
attenuant
attenuate
attenuation
attenuative
attenuator
atter
attercop
attercrop
atterminal
attermine
atterminement
attern
attery
attest
attestable
attestant
attestation
attestative
attestator
attester
attestive
Attic
attic
Attical
Atticism
atticism
Atticist
Atticize
atticize
atticomastoid
attid
Attidae
attinge
attingence
attingency
attingent
attire
attired
attirement
attirer
attitude
attitudinal
attitudinarian
attitudinarianism
attitudinize
attitudinizer
Attiwendaronk
attorn
attorney
attorneydom
attorneyism
attorneyship
attornment
attract
attractability
attractable
attractableness
attractant
attracter
attractile
attractingly
attraction
attractionally
attractive
attractively
attractiveness
attractivity
attractor
attrahent
attrap
attributable
attributal
attribute
attributer
attribution
attributive
attributively
attributiveness
attrist
attrite
attrited
attriteness
attrition
attritive
attritus
attune
attunely
attunement
Atuami
atule
atumble
atune
atwain
atweel
atween
atwin
atwirl
atwist
atwitch
atwitter
atwixt
atwo
atypic
atypical
atypically
atypy
auantic
aube
aubepine
Aubrey
Aubrietia
aubrietia
aubrite
auburn
aubusson
Auca
auca
Aucan
Aucaner
Aucanian
Auchenia
auchenia
auchenium
auchlet
auction
auctionary
auctioneer
auctorial
Aucuba
aucuba
aucupate
audacious
audaciously
audaciousness
audacity
Audaean
Audian
Audibertia
audibility
audible
audibleness
audibly
audience
audiencier
audient
audile
audio
audiogenic
audiogram
audiologist
audiology
audiometer
audiometric
audiometry
Audion
audion
audiophile
audiphone
audit
audition
auditive
auditor
auditoria
auditorial
auditorially
auditorily
auditorium
auditorship
auditory
auditress
auditual
audivise
audiviser
audivision
Audrey
Audubonistic
Aueto
auganite
auge
Augean
augelite
augen
augend
auger
augerer
augh
aught
aughtlins
augite
augitic
augitite
augitophyre
augment
augmentable
augmentation
augmentationer
augmentative
augmentatively
augmented
augmentedly
augmenter
augmentive
augur
augural
augurate
augurial
augurous
augurship
augury
August
august
Augusta
augustal
Augustan
Augusti
Augustin
Augustinian
Augustinianism
Augustinism
augustly
augustness
Augustus
auh
auhuhu
Auk
auk
auklet
aula
aulacocarpous
Aulacodus
Aulacomniaceae
Aulacomnium
aulae
aularian
auld
auldfarrantlike
auletai
aulete
auletes
auletic
auletrides
auletris
aulic
aulicism
auloi
aulophyte
aulos
Aulostoma
Aulostomatidae
Aulostomi
aulostomid
Aulostomidae
Aulostomus
aulu
aum
aumaga
aumail
aumbry
aumery
aumil
aumildar
aumous
aumrie
auncel
aune
Aunjetitz
aunt
aunthood
auntie
auntish
auntlike
auntly
auntsary
auntship
aupaka
aura
aurae
aural
aurally
auramine
Aurantiaceae
aurantiaceous
Aurantium
aurantium
aurar
aurate
aurated
aureate
aureately
aureateness
aureation
aureity
Aurelia
aurelia
aurelian
Aurelius
Aureocasidium
aureola
aureole
aureolin
aureoline
aureomycin
aureous
aureously
auresca
aureus
auribromide
auric
aurichalcite
aurichalcum
aurichloride
aurichlorohydric
auricle
auricled
auricomous
Auricula
auricula
auriculae
auricular
auriculare
auriculares
Auricularia
auricularia
Auriculariaceae
auriculariae
Auriculariales
auricularian
auricularis
auricularly
auriculate
auriculated
auriculately
Auriculidae
auriculocranial
auriculoparietal
auriculotemporal
auriculoventricular
auriculovertical
auricyanhydric
auricyanic
auricyanide
auride
auriferous
aurific
aurification
auriform
aurify
Auriga
aurigal
aurigation
aurigerous
Aurigid
Aurignacian
aurilave
aurin
aurinasal
auriphone
auriphrygia
auriphrygiate
auripuncture
aurir
auriscalp
auriscalpia
auriscalpium
auriscope
auriscopy
aurist
aurite
aurivorous
auroauric
aurobromide
aurochloride
aurochs
aurocyanide
aurodiamine
auronal
aurophobia
aurophore
aurora
aurorae
auroral
aurorally
aurore
aurorean
Aurorian
aurorium
aurotellurite
aurothiosulphate
aurothiosulphuric
aurous
aurrescu
aurulent
aurum
aurure
auryl
Aus
auscult
auscultascope
auscultate
auscultation
auscultative
auscultator
auscultatory
Auscultoscope
auscultoscope
Aushar
auslaut
auslaute
Ausones
Ausonian
auspex
auspicate
auspice
auspices
auspicial
auspicious
auspiciously
auspiciousness
auspicy
Aussie
Austafrican
austenite
austenitic
Auster
austere
austerely
austereness
austerity
Austerlitz
Austin
Austral
austral
Australasian
australene
Australia
Australian
Australianism
Australianize
Australic
Australioid
australite
Australoid
Australopithecinae
australopithecine
Australopithecus
Australorp
Austrasian
Austrian
Austrianize
Austric
austrium
Austroasiatic
Austrogaea
Austrogaean
austromancy
Austronesian
Austrophil
Austrophile
Austrophilism
Austroriparian
ausu
ausubo
autacoid
autacoidal
autallotriomorphic
autantitypy
autarch
autarchic
autarchical
Autarchoglossa
autarchy
autarkic
autarkical
autarkist
autarky
aute
autechoscope
autecious
auteciously
auteciousness
autecism
autecologic
autecological
autecologically
autecologist
autecology
autecy
autem
authentic
authentical
authentically
authenticalness
authenticate
authentication
authenticator
authenticity
authenticly
authenticness
authigene
authigenetic
authigenic
authigenous
author
authorcraft
authoress
authorhood
authorial
authorially
authorish
authorism
authoritarian
authoritarianism
authoritative
authoritatively
authoritativeness
authority
authorizable
authorization
authorize
authorized
authorizer
authorless
authorling
authorly
authorship
authotype
autism
autist
autistic
auto
autoabstract
autoactivation
autoactive
autoaddress
autoagglutinating
autoagglutination
autoagglutinin
autoalarm
autoalkylation
autoallogamous
autoallogamy
autoanalysis
autoanalytic
autoantibody
autoanticomplement
autoantitoxin
autoasphyxiation
autoaspiration
autoassimilation
autobahn
autobasidia
Autobasidiomycetes
autobasidiomycetous
autobasidium
Autobasisii
autobiographal
autobiographer
autobiographic
autobiographical
autobiographically
autobiographist
autobiography
autobiology
autoblast
autoboat
autoboating
autobolide
autobus
autocab
autocade
autocall
autocamp
autocamper
autocamping
autocar
autocarist
autocarpian
autocarpic
autocarpous
autocatalepsy
autocatalysis
autocatalytic
autocatalytically
autocatalyze
autocatheterism
autocephalia
autocephality
autocephalous
autocephaly
autoceptive
autochemical
autocholecystectomy
autochrome
autochromy
autochronograph
autochthon
autochthonal
autochthonic
autochthonism
autochthonous
autochthonously
autochthonousness
autochthony
autocide
autocinesis
autoclasis
autoclastic
autoclave
autocoenobium
autocoherer
autocoid
autocollimation
autocollimator
autocolony
autocombustible
autocombustion
autocomplexes
autocondensation
autoconduction
autoconvection
autoconverter
autocopist
autocoprophagous
autocorrosion
autocracy
autocrat
autocratic
autocratical
autocratically
autocrator
autocratoric
autocratorical
autocratrix
autocratship
autocremation
autocriticism
autocystoplasty
autocytolysis
autocytolytic
autodecomposition
autodepolymerization
autodermic
autodestruction
autodetector
autodiagnosis
autodiagnostic
autodiagrammatic
autodidact
autodidactic
autodifferentiation
autodiffusion
autodigestion
autodigestive
autodrainage
autodrome
autodynamic
autodyne
autoecholalia
autoecic
autoecious
autoeciously
autoeciousness
autoecism
autoecous
autoecy
autoeducation
autoeducative
autoelectrolysis
autoelectrolytic
autoelectronic
autoelevation
autoepigraph
autoepilation
autoerotic
autoerotically
autoeroticism
autoerotism
autoexcitation
autofecundation
autofermentation
autoformation
autofrettage
autogamic
autogamous
autogamy
autogauge
autogeneal
autogenesis
autogenetic
autogenetically
autogenic
autogenous
autogenously
autogeny
Autogiro
autogiro
autognosis
autognostic
autograft
autografting
autogram
autograph
autographal
autographer
autographic
autographical
autographically
autographism
autographist
autographometer
autography
autogravure
Autoharp
autoharp
autoheader
autohemic
autohemolysin
autohemolysis
autohemolytic
autohemorrhage
autohemotherapy
autoheterodyne
autoheterosis
autohexaploid
autohybridization
autohypnosis
autohypnotic
autohypnotism
autohypnotization
autoicous
autoignition
autoimmunity
autoimmunization
autoinduction
autoinductive
autoinfection
autoinfusion
autoinhibited
autoinoculable
autoinoculation
autointellectual
autointoxicant
autointoxication
autoirrigation
autoist
autojigger
autojuggernaut
autokinesis
autokinetic
autokrator
autolaryngoscope
autolaryngoscopic
autolaryngoscopy
autolater
autolatry
autolavage
autolesion
autolimnetic
autolith
autoloading
autological
autologist
autologous
autology
autoluminescence
autoluminescent
autolysate
autolysin
autolysis
autolytic
Autolytus
autolyzate
autolyze
automa
automacy
automanual
automat
automata
automatic
automatical
automatically
automaticity
automatin
automatism
automatist
automatization
automatize
automatograph
automaton
automatonlike
automatous
automechanical
automelon
autometamorphosis
autometric
autometry
automobile
automobilism
automobilist
automobilistic
automobility
automolite
automonstration
automorph
automorphic
automorphically
automorphism
automotive
automotor
automower
automysophobia
autonegation
autonephrectomy
autonephrotoxin
autoneurotoxin
autonitridation
autonoetic
autonomasy
autonomic
autonomical
autonomically
autonomist
autonomize
autonomous
autonomously
autonomy
autonym
autoparasitism
autopathic
autopathography
autopathy
autopelagic
autopepsia
autophagi
autophagia
autophagous
autophagy
autophobia
autophoby
autophon
autophone
autophonoscope
autophonous
autophony
autophotoelectric
autophotograph
autophotometry
autophthalmoscope
autophyllogeny
autophyte
autophytic
autophytically
autophytograph
autophytography
autopilot
autoplagiarism
autoplasmotherapy
autoplast
autoplastic
autoplasty
autopneumatic
autopoint
autopoisonous
autopolar
autopolo
autopoloist
autopolyploid
autopore
autoportrait
autoportraiture
autopositive
autopotent
autoprogressive
autoproteolysis
autoprothesis
autopsic
autopsical
autopsy
autopsychic
autopsychoanalysis
autopsychology
autopsychorhythmia
autopsychosis
autoptic
autoptical
autoptically
autopticity
autopyotherapy
autoracemization
autoradiograph
autoradiographic
autoradiography
autoreduction
autoregenerator
autoregulation
autoreinfusion
autoretardation
autorhythmic
autorhythmus
autoriser
autorotation
autorrhaphy
Autosauri
Autosauria
autoschediasm
autoschediastic
autoschediastical
autoschediastically
autoschediaze
autoscience
autoscope
autoscopic
autoscopy
autosender
autosensitization
autosensitized
autosepticemia
autoserotherapy
autoserum
autosexing
autosight
autosign
autosite
autositic
autoskeleton
autosled
autoslip
autosomal
autosomatognosis
autosomatognostic
autosome
autosoteric
autosoterism
autospore
autosporic
autospray
autostability
autostage
autostandardization
autostarter
autostethoscope
autostylic
autostylism
autostyly
autosuggestibility
autosuggestible
autosuggestion
autosuggestionist
autosuggestive
autosuppression
autosymbiontic
autosymbolic
autosymbolical
autosymbolically
autosymnoia
Autosyn
autosyndesis
autotelegraph
autotelic
autotetraploid
autotetraploidy
autothaumaturgist
autotheater
autotheism
autotheist
autotherapeutic
autotherapy
autothermy
autotomic
autotomize
autotomous
autotomy
autotoxaemia
autotoxic
autotoxication
autotoxicity
autotoxicosis
autotoxin
autotoxis
autotractor
autotransformer
autotransfusion
autotransplant
autotransplantation
autotrepanation
autotriploid
autotriploidy
autotroph
autotrophic
autotrophy
autotropic
autotropically
autotropism
autotruck
autotuberculin
autoturning
autotype
autotyphization
autotypic
autotypography
autotypy
autourine
autovaccination
autovaccine
autovalet
autovalve
autovivisection
autoxeny
autoxidation
autoxidator
autoxidizability
autoxidizable
autoxidize
autoxidizer
autozooid
autrefois
autumn
autumnal
autumnally
autumnian
autumnity
Autunian
autunite
auxamylase
auxanogram
auxanology
auxanometer
auxesis
auxetic
auxetical
auxetically
auxiliar
auxiliarly
auxiliary
auxiliate
auxiliation
auxiliator
auxiliatory
auxilium
auximone
auxin
auxinic
auxinically
auxoaction
auxoamylase
auxoblast
auxobody
auxocardia
auxochrome
auxochromic
auxochromism
auxochromous
auxocyte
auxoflore
auxofluor
auxograph
auxographic
auxohormone
auxology
auxometer
auxospore
auxosubstance
auxotonic
auxotox
ava
avadana
avadavat
avadhuta
avahi
avail
availability
available
availableness
availably
availingly
availment
aval
avalanche
avalent
avalvular
Avanguardisti
avania
avanious
Avanti
avanturine
Avar
Avaradrano
avaremotemo
Avarian
avarice
avaricious
avariciously
avariciousness
Avarish
Avars
avascular
avast
avaunt
Ave
ave
avellan
avellane
avellaneous
avellano
avelonge
aveloz
Avena
avenaceous
avenage
avenalin
avener
avenge
avengeful
avengement
avenger
avengeress
avenging
avengingly
avenin
avenolith
avenous
avens
aventail
Aventine
aventurine
avenue
aver
avera
average
averagely
averager
averah
averil
averin
averment
Avernal
Avernus
averrable
averral
Averrhoa
Averroism
Averroist
Averroistic
averruncate
averruncation
averruncator
aversant
aversation
averse
aversely
averseness
aversion
aversive
avert
avertable
averted
avertedly
averter
avertible
Avertin
Avery
Aves
Avesta
Avestan
avian
avianization
avianize
aviarist
aviary
aviate
aviatic
aviation
aviator
aviatorial
aviatoriality
aviatory
aviatress
aviatrices
aviatrix
Avicennia
Avicenniaceae
Avicennism
avichi
avicide
avick
avicolous
Avicula
avicular
Avicularia
avicularia
avicularian
Aviculariidae
Avicularimorphae
avicularium
Aviculidae
aviculture
aviculturist
avid
avidious
avidiously
avidity
avidly
avidous
avidya
avifauna
avifaunal
avigate
avigation
avigator
Avignonese
avijja
Avikom
avine
aviolite
avirulence
avirulent
Avis
aviso
avital
avitaminosis
avitaminotic
avitic
avives
avizandum
avo
avocado
avocate
avocation
avocative
avocatory
avocet
avodire
avogadrite
avoid
avoidable
avoidably
avoidance
avoider
avoidless
avoidment
avoirdupois
avolate
avolation
avolitional
avondbloem
avouch
avouchable
avoucher
avouchment
avourneen
avow
avowable
avowableness
avowably
avowal
avowance
avowant
avowed
avowedly
avowedness
avower
avowry
avoyer
avoyership
Avshar
avulse
avulsion
avuncular
avunculate
aw
awa
Awabakal
awabi
Awadhi
awaft
awag
await
awaiter
Awaitlala
awakable
awake
awaken
awakenable
awakener
awakening
awakeningly
awakenment
awald
awalim
awalt
Awan
awane
awanting
awapuhi
award
awardable
awarder
awardment
aware
awaredom
awareness
awaruite
awash
awaste
awat
awatch
awater
awave
away
awayness
awber
awd
awe
awearied
aweary
aweather
aweband
awedness
awee
aweek
aweel
aweigh
Awellimiden
awesome
awesomely
awesomeness
awest
aweto
awfu
awful
awfully
awfulness
awheel
awheft
awhet
awhile
awhir
awhirl
awide
awiggle
awikiwiki
awin
awing
awink
awiwi
awkward
awkwardish
awkwardly
awkwardness
awl
awless
awlessness
awlwort
awmous
awn
awned
awner
awning
awninged
awnless
awnlike
awny
awoke
Awol
awork
awreck
awrist
awrong
awry
Awshar
ax
axal
axbreaker
axe
axed
Axel
axenic
axes
axfetch
axhammer
axhammered
axhead
axial
axiality
axially
axiate
axiation
Axifera
axiform
axifugal
axil
axile
axilemma
axilemmata
axilla
axillae
axillant
axillar
axillary
axine
axinite
axinomancy
axiolite
axiolitic
axiological
axiologically
axiologist
axiology
axiom
axiomatic
axiomatical
axiomatically
axiomatization
axiomatize
axion
axiopisty
Axis
axis
axised
axisymmetric
axisymmetrical
axite
axle
axled
axlesmith
axletree
axmaker
axmaking
axman
axmanship
axmaster
Axminster
axodendrite
axofugal
axogamy
axoid
axoidean
axolemma
axolotl
axolysis
axometer
axometric
axometry
axon
axonal
axoneure
axoneuron
Axonia
Axonolipa
axonolipous
axonometric
axonometry
Axonophora
axonophorous
Axonopus
axonost
axopetal
axophyte
axoplasm
axopodia
axopodium
axospermous
axostyle
axseed
axstone
axtree
Axumite
axunge
axweed
axwise
axwort
Ay
ay
ayacahuite
ayah
Ayahuca
Aydendron
aye
ayegreen
ayelp
ayenbite
ayin
Aylesbury
ayless
aylet
ayllu
Aymara
Aymaran
Aymoro
ayond
ayont
ayous
Ayrshire
Aythya
ayu
Ayubite
Ayyubid
azadrachta
azafrin
Azalea
azalea
Azande
azarole
azedarach
azelaic
azelate
Azelfafage
azeotrope
azeotropic
azeotropism
azeotropy
Azerbaijanese
Azerbaijani
Azerbaijanian
Azha
azide
aziethane
Azilian
azilut
Azimech
azimene
azimethylene
azimide
azimine
azimino
aziminobenzene
azimuth
azimuthal
azimuthally
azine
aziola
azlactone
azo
azobacter
azobenzene
azobenzil
azobenzoic
azobenzol
azoblack
azoch
azocochineal
azocoralline
azocorinth
azocyanide
azocyclic
azodicarboxylic
azodiphenyl
azodisulphonic
azoeosin
azoerythrin
azofication
azofier
azoflavine
azoformamide
azoformic
azofy
azogallein
azogreen
azogrenadine
azohumic
azoic
azoimide
azoisobutyronitrile
azole
azolitmin
Azolla
azomethine
azon
azonal
azonaphthalene
azonic
azonium
azoospermia
azoparaffin
azophen
azophenetole
azophenine
azophenol
azophenyl
azophenylene
azophosphin
azophosphore
azoprotein
Azorian
azorite
azorubine
azosulphine
azosulphonic
azotate
azote
azoted
azotemia
azotenesis
azotetrazole
azoth
azothionium
azotic
azotine
azotite
azotize
Azotobacter
Azotobacterieae
azotoluene
azotometer
azotorrhoea
azotous
azoturia
azovernine
azox
azoxazole
azoxime
azoxine
azoxonium
azoxy
azoxyanisole
azoxybenzene
azoxybenzoic
azoxynaphthalene
azoxyphenetole
azoxytoluidine
Aztec
Azteca
azteca
Aztecan
azthionium
azulene
azulite
azulmic
azumbre
azure
azurean
azured
azureous
azurine
azurite
azurmalachite
azurous
azury
Azygobranchia
Azygobranchiata
azygobranchiate
azygomatous
azygos
azygosperm
azygospore
azygous
azyme
azymite
azymous
B
b
ba
baa
baahling
Baal
baal
Baalath
Baalish
Baalism
Baalist
Baalite
Baalitical
Baalize
Baalshem
baar
Bab
baba
babacoote
babai
babasco
babassu
babaylan
Babbie
Babbitt
babbitt
babbitter
Babbittess
Babbittian
Babbittism
Babbittry
babblative
babble
babblement
babbler
babblesome
babbling
babblingly
babblish
babblishly
babbly
babby
Babcock
babe
babehood
Babel
Babeldom
babelet
Babelic
babelike
Babelish
Babelism
Babelize
babery
babeship
Babesia
babesiasis
Babhan
Babi
Babiana
babiche
babied
Babiism
babillard
Babine
babingtonite
babirusa
babish
babished
babishly
babishness
Babism
Babist
Babite
bablah
babloh
baboen
Babongo
baboo
baboodom
babooism
baboon
baboonery
baboonish
baboonroot
baboot
babouche
Babouvism
Babouvist
babroot
Babs
babu
Babua
babudom
babuina
babuism
babul
Babuma
Babungera
babushka
baby
babydom
babyfied
babyhood
babyhouse
babyish
babyishly
babyishness
babyism
babylike
Babylon
Babylonian
Babylonic
Babylonish
Babylonism
Babylonite
Babylonize
babyolatry
babyship
bac
bacaba
bacach
bacalao
bacao
bacbakiri
bacca
baccaceous
baccae
baccalaurean
baccalaureate
baccara
baccarat
baccate
baccated
Bacchae
bacchanal
Bacchanalia
bacchanalian
bacchanalianism
bacchanalianly
bacchanalism
bacchanalization
bacchanalize
bacchant
bacchante
bacchantes
bacchantic
bacchar
baccharis
baccharoid
baccheion
bacchiac
bacchian
Bacchic
bacchic
Bacchical
Bacchides
bacchii
bacchius
Bacchus
Bacchuslike
bacciferous
bacciform
baccivorous
bach
Bacharach
bache
bachel
bachelor
bachelordom
bachelorhood
bachelorism
bachelorize
bachelorlike
bachelorly
bachelorship
bachelorwise
bachelry
Bachichi
Bacillaceae
bacillar
Bacillariaceae
bacillariaceous
Bacillariales
Bacillarieae
Bacillariophyta
bacillary
bacillemia
bacilli
bacillian
bacillicidal
bacillicide
bacillicidic
bacilliculture
bacilliform
bacilligenic
bacilliparous
bacillite
bacillogenic
bacillogenous
bacillophobia
bacillosis
bacilluria
bacillus
Bacis
bacitracin
back
backache
backaching
backachy
backage
backband
backbearing
backbencher
backbite
backbiter
backbitingly
backblow
backboard
backbone
backboned
backboneless
backbonelessness
backbrand
backbreaker
backbreaking
backcap
backcast
backchain
backchat
backcourt
backcross
backdoor
backdown
backdrop
backed
backen
backer
backet
backfall
backfatter
backfield
backfill
backfiller
backfilling
backfire
backfiring
backflap
backflash
backflow
backfold
backframe
backfriend
backfurrow
backgame
backgammon
background
backhand
backhanded
backhandedly
backhandedness
backhander
backhatch
backheel
backhooker
backhouse
backie
backiebird
backing
backjaw
backjoint
backlands
backlash
backlashing
backless
backlet
backlings
backlog
backlotter
backmost
backpedal
backpiece
backplate
backrope
backrun
backsaw
backscraper
backset
backsetting
backsettler
backshift
backside
backsight
backslap
backslapper
backslapping
backslide
backslider
backslidingness
backspace
backspacer
backspang
backspier
backspierer
backspin
backspread
backspringing
backstaff
backstage
backstamp
backstay
backster
backstick
backstitch
backstone
backstop
backstrap
backstretch
backstring
backstrip
backstroke
backstromite
backswept
backswing
backsword
backswording
backswordman
backswordsman
backtack
backtender
backtenter
backtrack
backtracker
backtrick
backup
backveld
backvelder
backwall
backward
backwardation
backwardly
backwardness
backwards
backwash
backwasher
backwashing
backwater
backwatered
backway
backwood
backwoods
backwoodsiness
backwoodsman
backwoodsy
backword
backworm
backwort
backyarder
baclin
bacon
baconer
Baconian
Baconianism
Baconic
Baconism
Baconist
baconize
baconweed
bacony
Bacopa
bacteremia
bacteria
Bacteriaceae
bacteriaceous
bacterial
bacterially
bacterian
bacteric
bactericholia
bactericidal
bactericide
bactericidin
bacterid
bacteriemia
bacteriform
bacterin
bacterioagglutinin
bacterioblast
bacteriocyte
bacteriodiagnosis
bacteriofluorescin
bacteriogenic
bacteriogenous
bacteriohemolysin
bacterioid
bacterioidal
bacteriologic
bacteriological
bacteriologically
bacteriologist
bacteriology
bacteriolysin
bacteriolysis
bacteriolytic
bacteriolyze
bacteriopathology
bacteriophage
bacteriophagia
bacteriophagic
bacteriophagous
bacteriophagy
bacteriophobia
bacterioprecipitin
bacterioprotein
bacteriopsonic
bacteriopsonin
bacteriopurpurin
bacterioscopic
bacterioscopical
bacterioscopically
bacterioscopist
bacterioscopy
bacteriosis
bacteriosolvent
bacteriostasis
bacteriostat
bacteriostatic
bacteriotherapeutic
bacteriotherapy
bacteriotoxic
bacteriotoxin
bacteriotropic
bacteriotropin
bacteriotrypsin
bacterious
bacteritic
bacterium
bacteriuria
bacterization
bacterize
bacteroid
bacteroidal
Bacteroideae
Bacteroides
Bactrian
Bactris
Bactrites
bactriticone
bactritoid
bacula
bacule
baculi
baculiferous
baculiform
baculine
baculite
Baculites
baculitic
baculiticone
baculoid
baculum
baculus
bacury
bad
Badaga
badan
Badarian
badarrah
Badawi
baddeleyite
badderlocks
baddish
baddishly
baddishness
baddock
bade
badenite
badge
badgeless
badgeman
badger
badgerbrush
badgerer
badgeringly
badgerlike
badgerly
badgerweed
badiaga
badian
badigeon
badinage
badious
badland
badlands
badly
badminton
badness
Badon
Baduhenna
bae
Baedeker
Baedekerian
Baeria
baetuli
baetulus
baetyl
baetylic
baetylus
baetzner
bafaro
baff
baffeta
baffle
bafflement
baffler
baffling
bafflingly
bafflingness
baffy
baft
bafta
Bafyot
bag
baga
Baganda
bagani
bagasse
bagataway
bagatelle
bagatine
bagattini
bagattino
Bagaudae
Bagdad
Bagdi
bagel
bagful
baggage
baggageman
baggagemaster
baggager
baggala
bagganet
Baggara
bagged
bagger
baggie
baggily
bagginess
bagging
baggit
baggy
Bagheli
baghouse
Baginda
Bagirmi
bagleaves
baglike
bagmaker
bagmaking
bagman
bagnio
bagnut
bago
Bagobo
bagonet
bagpipe
bagpiper
bagpipes
bagplant
bagrationite
bagre
bagreef
bagroom
baguette
bagwig
bagwigged
bagworm
bagwyn
bah
Bahai
Bahaism
Bahaist
Baham
Bahama
Bahamian
bahan
bahar
Bahaullah
bahawder
bahay
bahera
bahiaite
Bahima
bahisti
Bahmani
Bahmanid
bahnung
baho
bahoe
bahoo
baht
Bahuma
bahur
bahut
Bahutu
bahuvrihi
Baianism
baidarka
Baidya
Baiera
baiginet
baignet
baikalite
baikerinite
baikerite
baikie
bail
bailable
bailage
bailee
bailer
bailey
bailie
bailiery
bailieship
bailiff
bailiffry
bailiffship
bailiwick
bailliage
baillone
Baillonella
bailment
bailor
bailpiece
bailsman
bailwood
bain
bainie
Baining
baioc
baiocchi
baiocco
bairagi
Bairam
bairn
bairnie
bairnish
bairnishness
bairnliness
bairnly
bairnteam
bairntime
bairnwort
Bais
Baisakh
baister
bait
baiter
baith
baittle
baitylos
baize
bajada
bajan
Bajardo
bajarigar
Bajau
Bajocian
bajra
bajree
bajri
bajury
baka
Bakairi
bakal
Bakalai
Bakalei
Bakatan
bake
bakeboard
baked
bakehouse
Bakelite
bakelite
bakelize
baken
bakeoven
bakepan
baker
bakerdom
bakeress
bakerite
bakerless
bakerly
bakership
bakery
bakeshop
bakestone
Bakhtiari
bakie
baking
bakingly
bakli
Bakongo
Bakshaish
baksheesh
baktun
Baku
baku
Bakuba
bakula
Bakunda
Bakuninism
Bakuninist
bakupari
Bakutu
Bakwiri
Bal
bal
Bala
Balaam
Balaamite
Balaamitical
balachong
balaclava
baladine
Balaena
Balaenicipites
balaenid
Balaenidae
balaenoid
Balaenoidea
balaenoidean
Balaenoptera
Balaenopteridae
balafo
balagan
balaghat
balai
Balaic
Balak
Balaklava
balalaika
Balan
balance
balanceable
balanced
balancedness
balancelle
balanceman
balancement
balancer
balancewise
balancing
balander
balandra
balandrana
balaneutics
balangay
balanic
balanid
Balanidae
balaniferous
balanism
balanite
Balanites
balanitis
balanoblennorrhea
balanocele
Balanoglossida
Balanoglossus
balanoid
Balanophora
Balanophoraceae
balanophoraceous
balanophore
balanophorin
balanoplasty
balanoposthitis
balanopreputial
Balanops
Balanopsidaceae
Balanopsidales
balanorrhagia
Balanta
Balante
balantidial
balantidiasis
balantidic
balantidiosis
Balantidium
Balanus
Balao
balao
Balarama
balas
balata
balatong
balatron
balatronic
balausta
balaustine
balaustre
Balawa
Balawu
balboa
balbriggan
balbutiate
balbutient
balbuties
balconet
balconied
balcony
bald
baldachin
baldachined
baldachini
baldachino
baldberry
baldcrown
balden
balder
balderdash
baldhead
baldicoot
Baldie
baldish
baldling
baldly
baldmoney
baldness
baldpate
baldrib
baldric
baldricked
baldricwise
balductum
Baldwin
baldy
bale
Balearian
Balearic
Balearica
baleen
balefire
baleful
balefully
balefulness
balei
baleise
baleless
baler
balete
Bali
bali
balibago
Balija
Balilla
baline
Balinese
balinger
balinghasay
balisaur
balistarius
Balistes
balistid
Balistidae
balistraria
balita
balk
Balkan
Balkanic
Balkanization
Balkanize
Balkar
balker
balkingly
Balkis
balky
ball
ballad
ballade
balladeer
ballader
balladeroyal
balladic
balladical
balladier
balladism
balladist
balladize
balladlike
balladling
balladmonger
balladmongering
balladry
balladwise
ballahoo
ballam
ballan
ballant
ballast
ballastage
ballaster
ballasting
ballata
ballate
ballatoon
balldom
balled
baller
ballerina
ballet
balletic
balletomane
Ballhausplatz
balli
ballist
ballista
ballistae
ballistic
ballistically
ballistician
ballistics
Ballistite
ballistocardiograph
ballium
ballmine
ballogan
ballonet
balloon
balloonation
ballooner
balloonery
balloonet
balloonfish
balloonflower
balloonful
ballooning
balloonish
balloonist
balloonlike
ballot
Ballota
ballotade
ballotage
balloter
balloting
ballotist
ballottement
ballow
Ballplatz
ballplayer
ballproof
ballroom
ballstock
ballup
ballweed
bally
ballyhack
ballyhoo
ballyhooer
ballywack
ballywrack
balm
balmacaan
Balmarcodes
Balmawhapple
balmily
balminess
balmlike
balmony
Balmoral
balmy
balneal
balneary
balneation
balneatory
balneographer
balneography
balneologic
balneological
balneologist
balneology
balneophysiology
balneotechnics
balneotherapeutics
balneotherapia
balneotherapy
Balnibarbi
Baloch
Baloghia
Balolo
balonea
baloney
baloo
Balopticon
Balor
Baloskion
Baloskionaceae
balow
balsa
balsam
balsamation
Balsamea
Balsameaceae
balsameaceous
balsamer
balsamic
balsamical
balsamically
balsamiferous
balsamina
Balsaminaceae
balsaminaceous
balsamine
balsamitic
balsamiticness
balsamize
balsamo
Balsamodendron
Balsamorrhiza
balsamous
balsamroot
balsamum
balsamweed
balsamy
Balt
baltei
balter
balteus
Balthasar
Balti
Baltic
Baltimore
Baltimorean
baltimorite
Baltis
balu
Baluba
Baluch
Baluchi
Baluchistan
baluchithere
baluchitheria
Baluchitherium
baluchitherium
Baluga
Balunda
balushai
baluster
balustered
balustrade
balustraded
balustrading
balut
balwarra
balza
Balzacian
balzarine
bam
Bamalip
Bamangwato
bamban
Bambara
bambini
bambino
bambocciade
bamboo
bamboozle
bamboozlement
bamboozler
Bambos
bamboula
Bambuba
Bambusa
Bambuseae
Bambute
bamoth
Ban
ban
Bana
banaba
banago
banak
banakite
banal
banality
banally
banana
Bananaland
Bananalander
Banande
bananist
bananivorous
banat
Banate
banatite
banausic
Banba
Banbury
banc
banca
bancal
banchi
banco
bancus
band
Banda
banda
bandage
bandager
bandagist
bandaite
bandaka
bandala
bandalore
bandanna
bandannaed
bandar
bandarlog
bandbox
bandboxical
bandboxy
bandcase
bandcutter
bande
bandeau
banded
bandelet
bander
Banderma
banderole
bandersnatch
bandfish
bandhava
bandhook
Bandhor
bandhu
bandi
bandicoot
bandicoy
bandie
bandikai
bandiness
banding
bandit
banditism
banditry
banditti
bandle
bandless
bandlessly
bandlessness
bandlet
bandman
bandmaster
bando
bandog
bandoleer
bandoleered
bandoline
bandonion
Bandor
bandore
bandrol
bandsman
bandstand
bandster
bandstring
Bandusia
Bandusian
bandwork
bandy
bandyball
bandyman
bane
baneberry
baneful
banefully
banefulness
banewort
Banff
bang
banga
Bangala
bangalay
bangalow
Bangash
bangboard
bange
banger
banghy
Bangia
Bangiaceae
bangiaceous
Bangiales
banging
bangkok
bangle
bangled
bangling
bangster
bangtail
Bangwaketsi
bani
banian
banig
banilad
banish
banisher
banishment
banister
Baniva
baniwa
baniya
banjo
banjoist
banjore
banjorine
banjuke
bank
bankable
Bankalachi
bankbook
banked
banker
bankera
bankerdom
bankeress
banket
bankfull
banking
bankman
bankrider
bankrupt
bankruptcy
bankruptism
bankruptlike
bankruptly
bankruptship
bankrupture
bankshall
Banksia
Banksian
bankside
banksman
bankweed
banky
banner
bannered
bannerer
banneret
bannerfish
bannerless
bannerlike
bannerman
bannerol
bannerwise
bannet
banning
bannister
Bannock
bannock
Bannockburn
banns
bannut
banovina
banquet
banqueteer
banqueteering
banqueter
banquette
bansalague
banshee
banstickle
bant
Bantam
bantam
bantamize
bantamweight
bantay
bantayan
banteng
banter
banterer
banteringly
bantery
Bantingism
bantingize
bantling
Bantoid
Bantu
banty
banuyo
banxring
banya
Banyai
banyan
Banyoro
Banyuls
banzai
baobab
bap
Baphia
Baphomet
Baphometic
Baptanodon
Baptisia
baptisin
baptism
baptismal
baptismally
Baptist
baptistery
baptistic
baptizable
baptize
baptizee
baptizement
baptizer
Baptornis
bar
bara
barabara
barabora
Barabra
Baraca
barad
baragnosis
baragouin
baragouinish
Baraithas
barajillo
Baralipton
Baramika
barandos
barangay
barasingha
barathea
barathra
barathrum
barauna
barb
Barbacoa
Barbacoan
barbacou
Barbadian
Barbados
barbal
barbaloin
Barbara
barbaralalia
Barbarea
barbaresque
Barbarian
barbarian
barbarianism
barbarianize
barbaric
barbarical
barbarically
barbarious
barbariousness
barbarism
barbarity
barbarization
barbarize
barbarous
barbarously
barbarousness
Barbary
barbary
barbas
barbasco
barbastel
barbate
barbated
barbatimao
barbe
barbecue
barbed
barbeiro
barbel
barbellate
barbellula
barbellulate
barber
barberess
barberfish
barberish
barberry
barbershop
barbet
barbette
Barbeyaceae
barbican
barbicel
barbigerous
barbion
barbital
barbitalism
barbiton
barbitone
barbitos
barbiturate
barbituric
barbless
barblet
barbone
barbotine
Barbra
barbudo
Barbula
barbulate
barbule
barbulyie
barbwire
Barcan
barcarole
barcella
barcelona
Barcoo
bard
bardane
bardash
bardcraft
bardel
Bardesanism
Bardesanist
Bardesanite
bardess
bardic
bardie
bardiglio
bardily
bardiness
barding
bardish
bardism
bardlet
bardlike
bardling
bardo
Bardolater
Bardolatry
Bardolph
Bardolphian
bardship
Bardulph
bardy
Bare
bare
bareback
barebacked
bareboat
barebone
bareboned
bareca
barefaced
barefacedly
barefacedness
barefit
barefoot
barefooted
barehanded
barehead
bareheaded
bareheadedness
barelegged
barely
barenecked
bareness
barer
baresark
baresma
baretta
barff
barfish
barfly
barful
bargain
bargainee
bargainer
bargainor
bargainwise
bargander
barge
bargeboard
bargee
bargeer
bargeese
bargehouse
bargelike
bargeload
bargeman
bargemaster
barger
bargh
bargham
barghest
bargoose
Bari
bari
baria
baric
barid
barie
barile
barilla
baring
baris
barish
barit
barite
baritone
barium
bark
barkbound
barkcutter
barkeeper
barken
barkentine
barker
barkery
barkevikite
barkevikitic
barkey
barkhan
barking
barkingly
Barkinji
barkle
barkless
barklyite
barkometer
barkpeel
barkpeeler
barkpeeling
barksome
barky
barlafumble
barlafummil
barless
barley
barleybird
barleybreak
barleycorn
barleyhood
barleymow
barleysick
barling
barlock
barlow
barm
barmaid
barman
barmaster
barmbrack
barmcloth
Barmecidal
Barmecide
barmkin
barmote
barmskin
barmy
barmybrained
barn
Barnabas
Barnabite
Barnaby
barnacle
Barnard
barnard
barnbrack
Barnburner
Barney
barney
barnful
barnhardtite
barnman
barnstorm
barnstormer
barnstorming
Barnumism
Barnumize
barny
barnyard
Baroco
barocyclonometer
barodynamic
barodynamics
barognosis
barogram
barograph
barographic
baroi
barolo
barology
Barolong
barometer
barometric
barometrical
barometrically
barometrograph
barometrography
barometry
barometz
baromotor
baron
baronage
baroness
baronet
baronetage
baronetcy
baronethood
baronetical
baronetship
barong
Baronga
baronial
baronize
baronry
baronship
barony
Baroque
baroque
baroscope
baroscopic
baroscopical
Barosma
barosmin
barotactic
barotaxis
barotaxy
barothermograph
barothermohygrograph
baroto
Barotse
barouche
barouchet
Barouni
baroxyton
barpost
barquantine
barra
barrabkie
barrable
barrabora
barracan
barrack
barracker
barraclade
barracoon
barracouta
barracuda
barrad
barragan
barrage
barragon
barramunda
barramundi
barranca
barrandite
barras
barrator
barratrous
barratrously
barratry
barred
barrel
barrelage
barreled
barreler
barrelet
barrelful
barrelhead
barrelmaker
barrelmaking
barrelwise
barren
barrenly
barrenness
barrenwort
barrer
barret
Barrett
barrette
barretter
barricade
barricader
barricado
barrico
barrier
barriguda
barrigudo
barrikin
barriness
barring
Barrington
Barringtonia
Barrio
barrio
barrister
barristerial
barristership
barristress
barroom
barrow
barrowful
Barrowist
barrowman
barrulee
barrulet
barrulety
barruly
Barry
barry
Barsac
barse
barsom
Bart
bartender
bartending
barter
barterer
barth
barthite
bartholinitis
Bartholomean
Bartholomew
Bartholomewtide
Bartholomite
bartizan
bartizaned
Bartlemy
Bartlett
Barton
barton
Bartonella
Bartonia
Bartram
Bartramia
Bartramiaceae
Bartramian
Bartsia
baru
Baruch
Barundi
baruria
barvel
barwal
barway
barways
barwise
barwood
barycenter
barycentric
barye
baryecoia
baryglossia
barylalia
barylite
baryphonia
baryphonic
baryphony
barysilite
barysphere
baryta
barytes
barythymia
barytic
barytine
barytocalcite
barytocelestine
barytocelestite
baryton
barytone
barytophyllite
barytostrontianite
barytosulphate
bas
basal
basale
basalia
basally
basalt
basaltes
basaltic
basaltiform
basaltine
basaltoid
basanite
basaree
Bascology
bascule
base
baseball
baseballdom
baseballer
baseboard
baseborn
basebred
based
basehearted
baseheartedness
baselard
baseless
baselessly
baselessness
baselike
baseliner
Basella
Basellaceae
basellaceous
basely
baseman
basement
basementward
baseness
basenji
bases
bash
bashaw
bashawdom
bashawism
bashawship
bashful
bashfully
bashfulness
Bashilange
Bashkir
bashlyk
Bashmuric
basial
basialveolar
basiarachnitis
basiarachnoiditis
basiate
basiation
Basibracteolate
basibranchial
basibranchiate
basibregmatic
basic
basically
basichromatic
basichromatin
basichromatinic
basichromiole
basicity
basicranial
basicytoparaplastin
basidia
basidial
basidigital
basidigitale
basidiogenetic
basidiolichen
Basidiolichenes
basidiomycete
Basidiomycetes
basidiomycetous
basidiophore
basidiospore
basidiosporous
basidium
basidorsal
basifacial
basification
basifier
basifixed
basifugal
basify
basigamous
basigamy
basigenic
basigenous
basiglandular
basigynium
basihyal
basihyoid
Basil
basil
basilar
Basilarchia
basilary
basilateral
basilemma
basileus
Basilian
basilic
Basilica
basilica
Basilicae
basilical
basilican
basilicate
basilicon
Basilics
Basilidian
Basilidianism
basilinna
basiliscan
basiliscine
Basiliscus
basilisk
basilissa
Basilosauridae
Basilosaurus
basilweed
basilysis
basilyst
basimesostasis
basin
basinasal
basinasial
basined
basinerved
basinet
basinlike
basioccipital
basion
basiophitic
basiophthalmite
basiophthalmous
basiotribe
basiotripsy
basiparachromatin
basiparaplastin
basipetal
basiphobia
basipodite
basipoditic
basipterygial
basipterygium
basipterygoid
basiradial
basirhinal
basirostral
basis
basiscopic
basisphenoid
basisphenoidal
basitemporal
basiventral
basivertebral
bask
basker
Baskerville
basket
basketball
basketballer
basketful
basketing
basketmaker
basketmaking
basketry
basketware
basketwoman
basketwood
basketwork
basketworm
Baskish
Baskonize
Basoche
Basoga
basoid
Basoko
Basommatophora
basommatophorous
bason
Basongo
basophile
basophilia
basophilic
basophilous
basophobia
basos
basote
Basque
basque
basqued
basquine
bass
Bassa
Bassalia
Bassalian
bassan
bassanello
bassanite
bassara
bassarid
Bassaris
Bassariscus
bassarisk
basset
bassetite
bassetta
Bassia
bassie
bassine
bassinet
bassist
bassness
basso
bassoon
bassoonist
bassorin
bassus
basswood
Bast
bast
basta
Bastaard
Bastard
bastard
bastardism
bastardization
bastardize
bastardliness
bastardly
bastardy
baste
basten
baster
bastide
bastille
bastinade
bastinado
basting
bastion
bastionary
bastioned
bastionet
bastite
bastnasite
basto
baston
basurale
Basuto
Bat
bat
bataan
batad
Batak
batakan
bataleur
Batan
batara
batata
Batatas
batatilla
Batavi
Batavian
batch
batcher
bate
batea
bateau
bateaux
bated
Batekes
batel
bateman
batement
bater
Batetela
batfish
batfowl
batfowler
batfowling
Bath
bath
Bathala
bathe
batheable
bather
bathetic
bathflower
bathhouse
bathic
bathing
bathless
bathman
bathmic
bathmism
bathmotropic
bathmotropism
bathochromatic
bathochromatism
bathochrome
bathochromic
bathochromy
bathoflore
bathofloric
batholite
batholith
batholithic
batholitic
bathometer
Bathonian
bathophobia
bathorse
bathos
bathrobe
bathroom
bathroomed
bathroot
bathtub
bathukolpian
bathukolpic
bathvillite
bathwort
bathyal
bathyanesthesia
bathybian
bathybic
bathybius
bathycentesis
bathychrome
bathycolpian
bathycolpic
bathycurrent
bathyesthesia
bathygraphic
bathyhyperesthesia
bathyhypesthesia
bathylimnetic
bathylite
bathylith
bathylithic
bathylitic
bathymeter
bathymetric
bathymetrical
bathymetrically
bathymetry
bathyorographical
bathypelagic
bathyplankton
bathyseism
bathysmal
bathysophic
bathysophical
bathysphere
bathythermograph
Batidaceae
batidaceous
batik
batiker
batikulin
batikuling
bating
batino
Batis
batiste
batitinan
batlan
batlike
batling
batlon
batman
Batocrinidae
Batocrinus
Batodendron
batoid
Batoidei
Batoka
baton
Batonga
batonistic
batonne
batophobia
Batrachia
batrachian
batrachiate
Batrachidae
Batrachium
batrachoid
Batrachoididae
batrachophagous
Batrachophidia
batrachophobia
batrachoplasty
Batrachospermum
bats
batsman
batsmanship
batster
batswing
batt
Batta
batta
battailous
Battak
Battakhin
battalia
battalion
battarism
battarismus
battel
batteler
batten
battener
battening
batter
batterable
battercake
batterdock
battered
batterer
batterfang
batteried
batterman
battery
batteryman
battik
batting
battish
battle
battled
battledore
battlefield
battleful
battleground
battlement
battlemented
battleplane
battler
battleship
battlesome
battlestead
battlewagon
battleward
battlewise
battological
battologist
battologize
battology
battue
batty
batukite
batule
Batussi
Batwa
batwing
batyphone
batz
batzen
bauble
baublery
baubling
Baubo
bauch
bauchle
bauckie
bauckiebird
baud
baudekin
baudrons
Bauera
Bauhinia
baul
bauleah
Baume
baumhauerite
baun
bauno
Baure
bauson
bausond
bauta
bauxite
bauxitite
Bavarian
bavaroy
bavary
bavenite
baviaantje
Bavian
bavian
baviere
bavin
Bavius
bavoso
baw
bawarchi
bawbee
bawcock
bawd
bawdily
bawdiness
bawdry
bawdship
bawdyhouse
bawl
bawler
bawley
bawn
Bawra
bawtie
baxter
Baxterian
Baxterianism
baxtone
bay
Baya
baya
bayadere
bayal
bayamo
Bayard
bayard
bayardly
bayberry
baybolt
baybush
baycuru
bayed
bayeta
baygall
bayhead
bayish
bayldonite
baylet
baylike
bayman
bayness
Bayogoula
bayok
bayonet
bayoneted
bayoneteer
bayou
baywood
bazaar
baze
Bazigar
bazoo
bazooka
bazzite
bdellid
Bdellidae
bdellium
bdelloid
Bdelloida
Bdellostoma
Bdellostomatidae
Bdellostomidae
bdellotomy
Bdelloura
Bdellouridae
be
Bea
beach
beachcomb
beachcomber
beachcombing
beached
beachhead
beachlamar
beachless
beachman
beachmaster
beachward
beachy
beacon
beaconage
beaconless
beaconwise
bead
beaded
beader
beadflush
beadhouse
beadily
beadiness
beading
beadle
beadledom
beadlehood
beadleism
beadlery
beadleship
beadlet
beadlike
beadman
beadroll
beadrow
beadsman
beadswoman
beadwork
beady
Beagle
beagle
beagling
beak
beaked
beaker
beakerful
beakerman
beakermen
beakful
beakhead
beakiron
beaklike
beaky
beal
beala
bealing
beallach
bealtared
Bealtine
Bealtuinn
beam
beamage
beambird
beamed
beamer
beamfilling
beamful
beamhouse
beamily
beaminess
beaming
beamingly
beamish
beamless
beamlet
beamlike
beamman
beamsman
beamster
beamwork
beamy
bean
beanbag
beanbags
beancod
beanery
beanfeast
beanfeaster
beanfield
beanie
beano
beansetter
beanshooter
beanstalk
beant
beanweed
beany
beaproned
bear
bearable
bearableness
bearably
bearance
bearbaiter
bearbaiting
bearbane
bearberry
bearbind
bearbine
bearcoot
beard
bearded
bearder
beardie
bearding
beardless
beardlessness
beardom
beardtongue
beardy
bearer
bearess
bearfoot
bearherd
bearhide
bearhound
bearing
bearish
bearishly
bearishness
bearlet
bearlike
bearm
bearship
bearskin
beartongue
bearward
bearwood
bearwort
beast
beastbane
beastdom
beasthood
beastie
beastily
beastish
beastishness
beastlike
beastlily
beastliness
beastling
beastlings
beastly
beastman
beastship
beat
Beata
beata
beatable
beatae
beatee
beaten
beater
beaterman
beath
beatific
beatifical
beatifically
beatificate
beatification
beatify
beatinest
beating
beatitude
Beatrice
Beatrix
beatster
beatus
beau
Beauclerc
beaufin
Beaufort
beauish
beauism
Beaujolais
Beaumontia
Beaune
beaupere
beauseant
beauship
beauteous
beauteously
beauteousness
beauti
beautician
beautied
beautification
beautifier
beautiful
beautifully
beautifulness
beautify
beautihood
beauty
beautydom
beautyship
beaux
beaver
Beaverboard
beaverboard
beavered
beaverette
beaverish
beaverism
beaverite
beaverize
Beaverkill
beaverkin
beaverlike
beaverpelt
beaverroot
beaverteen
beaverwood
beavery
beback
bebait
beballed
bebang
bebannered
bebar
bebaron
bebaste
bebat
bebathe
bebatter
bebay
bebeast
bebed
bebeerine
bebeeru
bebelted
bebilya
bebite
bebization
beblain
beblear
bebled
bebless
beblister
beblood
bebloom
beblotch
beblubber
bebog
bebop
beboss
bebotch
bebothered
bebouldered
bebrave
bebreech
bebrine
bebrother
bebrush
bebump
bebusy
bebuttoned
becall
becalm
becalmment
becap
becard
becarpet
becarve
becassocked
becater
because
beccafico
becense
bechained
bechalk
bechance
becharm
bechase
bechatter
bechauffeur
becheck
becher
bechern
bechignoned
bechirp
Bechtler
Bechuana
becircled
becivet
Beck
beck
beckelite
becker
becket
Beckie
beckiron
beckon
beckoner
beckoning
beckoningly
Becky
beclad
beclamor
beclamour
beclang
beclart
beclasp
beclatter
beclaw
becloak
beclog
beclothe
becloud
beclout
beclown
becluster
becobweb
becoiffed
becollier
becolme
becolor
becombed
become
becomes
becoming
becomingly
becomingness
becomma
becompass
becompliment
becoom
becoresh
becost
becousined
becovet
becoward
becquerelite
becram
becramp
becrampon
becrawl
becreep
becrime
becrimson
becrinolined
becripple
becroak
becross
becrowd
becrown
becrush
becrust
becry
becudgel
becuffed
becuiba
becumber
becuna
becurl
becurry
becurse
becurtained
becushioned
becut
bed
bedabble
bedad
bedaggered
bedamn
bedamp
bedangled
bedare
bedark
bedarken
bedash
bedaub
bedawn
beday
bedaze
bedazement
bedazzle
bedazzlement
bedazzling
bedazzlingly
bedboard
bedbug
bedcap
bedcase
bedchair
bedchamber
bedclothes
bedcord
bedcover
bedded
bedder
bedding
bedead
bedeaf
bedeafen
bedebt
bedeck
bedecorate
bedeguar
bedel
beden
bedene
bedesman
bedevil
bedevilment
bedew
bedewer
bedewoman
bedfast
bedfellow
bedfellowship
bedflower
bedfoot
Bedford
bedframe
bedgery
bedgoer
bedgown
bediademed
bediamonded
bediaper
bedight
bedikah
bedim
bedimple
bedin
bedip
bedirt
bedirter
bedirty
bedismal
bedizen
bedizenment
bedkey
bedlam
bedlamer
Bedlamic
bedlamism
bedlamite
bedlamitish
bedlamize
bedlar
bedless
bedlids
bedmaker
bedmaking
bedman
bedmate
bedoctor
bedog
bedolt
bedot
bedote
Bedouin
Bedouinism
bedouse
bedown
bedoyo
bedpan
bedplate
bedpost
bedquilt
bedrabble
bedraggle
bedragglement
bedrail
bedral
bedrape
bedravel
bedrench
bedress
bedribble
bedrid
bedridden
bedriddenness
bedrift
bedright
bedrip
bedrivel
bedrizzle
bedrock
bedroll
bedroom
bedrop
bedrown
bedrowse
bedrug
bedscrew
bedsick
bedside
bedsite
bedsock
bedsore
bedspread
bedspring
bedstaff
bedstand
bedstaves
bedstead
bedstock
bedstraw
bedstring
bedtick
bedticking
bedtime
bedub
beduchess
beduck
beduke
bedull
bedumb
bedunce
bedunch
bedung
bedur
bedusk
bedust
bedwarf
bedway
bedways
bedwell
bedye
Bee
bee
beearn
beebread
beech
beechdrops
beechen
beechnut
beechwood
beechwoods
beechy
beedged
beedom
beef
beefeater
beefer
beefhead
beefheaded
beefily
beefin
beefiness
beefish
beefishness
beefless
beeflower
beefsteak
beeftongue
beefwood
beefy
beegerite
beehead
beeheaded
beeherd
beehive
beehouse
beeish
beeishness
beek
beekeeper
beekeeping
beekite
Beekmantown
beelbow
beelike
beeline
beelol
Beelzebub
Beelzebubian
Beelzebul
beeman
beemaster
been
beennut
beer
beerage
beerbachite
beerbibber
beerhouse
beerily
beeriness
beerish
beerishly
beermaker
beermaking
beermonger
beerocracy
Beerothite
beerpull
beery
bees
beest
beestings
beeswax
beeswing
beeswinged
beet
beeth
Beethovenian
Beethovenish
Beethovian
beetle
beetled
beetlehead
beetleheaded
beetler
beetlestock
beetlestone
beetleweed
beetmister
beetrave
beetroot
beetrooty
beety
beeve
beevish
beeware
beeway
beeweed
beewise
beewort
befall
befame
befamilied
befamine
befan
befancy
befanned
befathered
befavor
befavour
befeather
beferned
befetished
befetter
befezzed
befiddle
befilch
befile
befilleted
befilmed
befilth
befinger
befire
befist
befit
befitting
befittingly
befittingness
beflag
beflannel
beflap
beflatter
beflea
befleck
beflounce
beflour
beflout
beflower
beflum
befluster
befoam
befog
befool
befoolment
befop
before
beforehand
beforeness
beforested
beforetime
beforetimes
befortune
befoul
befouler
befoulment
befountained
befraught
befreckle
befreeze
befreight
befret
befriend
befriender
befriendment
befrill
befringe
befriz
befrocked
befrogged
befrounce
befrumple
befuddle
befuddlement
befuddler
befume
befurbelowed
befurred
beg
begabled
begad
begall
begani
begar
begari
begarlanded
begarnish
begartered
begash
begat
begaud
begaudy
begay
begaze
begeck
begem
beget
begettal
begetter
beggable
beggar
beggardom
beggarer
beggaress
beggarhood
beggarism
beggarlike
beggarliness
beggarly
beggarman
beggarweed
beggarwise
beggarwoman
beggary
Beggiatoa
Beggiatoaceae
beggiatoaceous
begging
beggingly
beggingwise
Beghard
begift
begiggle
begild
begin
beginger
beginner
beginning
begird
begirdle
beglad
beglamour
beglare
beglerbeg
beglerbeglic
beglerbegluc
beglerbegship
beglerbey
beglic
beglide
beglitter
beglobed
begloom
begloze
begluc
beglue
begnaw
bego
begob
begobs
begoggled
begohm
begone
begonia
Begoniaceae
begoniaceous
Begoniales
begorra
begorry
begotten
begottenness
begoud
begowk
begowned
begrace
begrain
begrave
begray
begrease
begreen
begrett
begrim
begrime
begrimer
begroan
begrown
begrudge
begrudgingly
begruntle
begrutch
begrutten
beguard
beguess
beguile
beguileful
beguilement
beguiler
beguiling
beguilingly
Beguin
Beguine
beguine
begulf
begum
begun
begunk
begut
behale
behalf
behallow
behammer
behap
behatted
behave
behavior
behavioral
behaviored
behaviorism
behaviorist
behavioristic
behavioristically
behead
beheadal
beheader
beheadlined
behear
behears
behearse
behedge
beheld
behelp
behemoth
behen
behenate
behenic
behest
behind
behinder
behindhand
behindsight
behint
behn
behold
beholdable
beholden
beholder
beholding
beholdingness
behoney
behoof
behooped
behoot
behoove
behooveful
behoovefully
behoovefulness
behooves
behooving
behoovingly
behorn
behorror
behowl
behung
behusband
behymn
behypocrite
beice
Beid
beige
being
beingless
beingness
beinked
beira
beisa
Beja
bejabers
bejade
bejan
bejant
bejaundice
bejazz
bejel
bejewel
bejezebel
bejig
bejuggle
bejumble
bekah
bekerchief
bekick
bekilted
beking
bekinkinite
bekiss
bekko
beknave
beknight
beknit
beknived
beknotted
beknottedly
beknottedness
beknow
beknown
Bel
bel
bela
belabor
belaced
beladle
belady
belage
belah
Belait
Belaites
belam
Belamcanda
belanda
belar
belard
belash
belate
belated
belatedly
belatedness
belatticed
belaud
belauder
belavendered
belay
belayer
belch
belcher
beld
beldam
beldamship
belderroot
belduque
beleaf
beleaguer
beleaguerer
beleaguerment
beleap
beleave
belecture
beledgered
belee
belemnid
belemnite
Belemnites
belemnitic
Belemnitidae
belemnoid
Belemnoidea
beletter
belfried
belfry
belga
Belgae
Belgian
Belgic
Belgophile
Belgrade
Belgravia
Belgravian
Belial
Belialic
Belialist
belibel
belick
belie
belief
beliefful
belieffulness
beliefless
belier
believability
believable
believableness
believe
believer
believing
believingly
belight
beliked
Belili
belimousined
Belinda
Belinuridae
Belinurus
belion
beliquor
Belis
belite
belitter
belittle
belittlement
belittler
belive
bell
Bella
Bellabella
Bellacoola
belladonna
bellarmine
Bellatrix
bellbind
bellbird
bellbottle
bellboy
belle
belled
belledom
Belleek
bellehood
belleric
Bellerophon
Bellerophontidae
belletrist
belletristic
bellflower
bellhanger
bellhanging
bellhop
bellhouse
bellicism
bellicose
bellicosely
bellicoseness
bellicosity
bellied
belliferous
belligerence
belligerency
belligerent
belligerently
belling
bellipotent
Bellis
bellite
bellmaker
bellmaking
bellman
bellmanship
bellmaster
bellmouth
bellmouthed
Bellona
Bellonian
bellonion
bellote
Bellovaci
bellow
bellower
bellows
bellowsful
bellowslike
bellowsmaker
bellowsmaking
bellowsman
bellpull
belltail
belltopper
belltopperdom
bellware
bellwaver
bellweed
bellwether
bellwind
bellwine
bellwood
bellwort
belly
bellyache
bellyband
bellyer
bellyfish
bellyflaught
bellyful
bellying
bellyland
bellylike
bellyman
bellypiece
bellypinch
beloam
beloeilite
beloid
belomancy
Belone
belonesite
belong
belonger
belonging
belonid
Belonidae
belonite
belonoid
belonosphaerite
belord
Belostoma
Belostomatidae
Belostomidae
belout
belove
beloved
below
belowstairs
belozenged
Belshazzar
Belshazzaresque
belsire
belt
Beltane
belted
Beltene
belter
Beltian
beltie
beltine
belting
Beltir
Beltis
beltmaker
beltmaking
beltman
belton
beltwise
Beluchi
Belucki
beluga
belugite
belute
belve
belvedere
Belverdian
bely
belying
belyingly
belzebuth
bema
bemad
bemadam
bemaddening
bemail
bemaim
bemajesty
beman
bemangle
bemantle
bemar
bemartyr
bemask
bemaster
bemat
bemata
bemaul
bemazed
Bemba
Bembecidae
Bembex
bemeal
bemean
bemedaled
bemedalled
bementite
bemercy
bemingle
beminstrel
bemire
bemirement
bemirror
bemirrorment
bemist
bemistress
bemitered
bemitred
bemix
bemoan
bemoanable
bemoaner
bemoaning
bemoaningly
bemoat
bemock
bemoil
bemoisten
bemole
bemolt
bemonster
bemoon
bemotto
bemoult
bemouth
bemuck
bemud
bemuddle
bemuddlement
bemuddy
bemuffle
bemurmur
bemuse
bemused
bemusedly
bemusement
bemusk
bemuslined
bemuzzle
Ben
ben
bena
benab
Benacus
bename
benami
benamidar
benasty
benben
bench
benchboard
bencher
benchership
benchfellow
benchful
benching
benchland
benchlet
benchman
benchwork
benchy
bencite
bend
benda
bendability
bendable
bended
bender
bending
bendingly
bendlet
bendsome
bendwise
bendy
bene
beneaped
beneath
beneception
beneceptive
beneceptor
benedicite
Benedict
benedict
Benedicta
Benedictine
Benedictinism
benediction
benedictional
benedictionary
benedictive
benedictively
benedictory
Benedictus
benedight
benefaction
benefactive
benefactor
benefactorship
benefactory
benefactress
benefic
benefice
beneficed
beneficeless
beneficence
beneficent
beneficential
beneficently
beneficial
beneficially
beneficialness
beneficiary
beneficiaryship
beneficiate
beneficiation
benefit
benefiter
beneighbored
Benelux
benempt
benempted
beneplacito
benet
Benetnasch
benettle
Beneventan
Beneventana
benevolence
benevolent
benevolently
benevolentness
benevolist
beng
Bengal
Bengalese
Bengali
Bengalic
bengaline
Bengola
Beni
beni
benight
benighted
benightedness
benighten
benighter
benightmare
benightment
benign
benignancy
benignant
benignantly
benignity
benignly
Benin
Benincasa
benison
benitoite
benj
Benjamin
benjamin
benjaminite
Benjamite
Benjy
benjy
Benkulen
benmost
benn
benne
bennel
Bennet
bennet
Bennettitaceae
bennettitaceous
Bennettitales
Bennettites
bennetweed
Benny
benny
beno
benorth
benote
bensel
bensh
benshea
benshee
benshi
Benson
bent
bentang
benthal
Benthamic
Benthamism
Benthamite
benthic
benthon
benthonic
benthos
Bentincks
bentiness
benting
Benton
bentonite
bentstar
bentwood
benty
Benu
benumb
benumbed
benumbedness
benumbing
benumbingly
benumbment
benward
benweed
benzacridine
benzal
benzalacetone
benzalacetophenone
benzalaniline
benzalazine
benzalcohol
benzalcyanhydrin
benzaldehyde
benzaldiphenyl
benzaldoxime
benzalethylamine
benzalhydrazine
benzalphenylhydrazone
benzalphthalide
benzamide
benzamido
benzamine
benzaminic
benzamino
benzanalgen
benzanilide
benzanthrone
benzantialdoxime
benzazide
benzazimide
benzazine
benzazole
benzbitriazole
benzdiazine
benzdifuran
benzdioxazine
benzdioxdiazine
benzdioxtriazine
Benzedrine
benzein
benzene
benzenediazonium
benzenoid
benzenyl
benzhydrol
benzhydroxamic
benzidine
benzidino
benzil
benzilic
benzimidazole
benziminazole
benzinduline
benzine
benzo
benzoate
benzoated
benzoazurine
benzobis
benzocaine
benzocoumaran
benzodiazine
benzodiazole
benzoflavine
benzofluorene
benzofulvene
benzofuran
benzofuroquinoxaline
benzofuryl
benzoglycolic
benzoglyoxaline
benzohydrol
benzoic
benzoid
benzoin
benzoinated
benzoiodohydrin
benzol
benzolate
benzole
benzolize
benzomorpholine
benzonaphthol
benzonitrile
benzonitrol
benzoperoxide
benzophenanthrazine
benzophenanthroline
benzophenazine
benzophenol
benzophenone
benzophenothiazine
benzophenoxazine
benzophloroglucinol
benzophosphinic
benzophthalazine
benzopinacone
benzopyran
benzopyranyl
benzopyrazolone
benzopyrylium
benzoquinoline
benzoquinone
benzoquinoxaline
benzosulphimide
benzotetrazine
benzotetrazole
benzothiazine
benzothiazole
benzothiazoline
benzothiodiazole
benzothiofuran
benzothiophene
benzothiopyran
benzotoluide
benzotriazine
benzotriazole
benzotrichloride
benzotrifuran
benzoxate
benzoxy
benzoxyacetic
benzoxycamphor
benzoxyphenanthrene
benzoyl
benzoylate
benzoylation
benzoylformic
benzoylglycine
benzpinacone
benzthiophen
benztrioxazine
benzyl
benzylamine
benzylic
benzylidene
benzylpenicillin
beode
Beothuk
Beothukan
Beowulf
bepaid
Bepaint
bepale
bepaper
beparch
beparody
beparse
bepart
bepaste
bepastured
bepat
bepatched
bepaw
bepearl
bepelt
bepen
bepepper
beperiwigged
bepester
bepewed
bephilter
bephrase
bepicture
bepiece
bepierce
bepile
bepill
bepillared
bepimple
bepinch
bepistoled
bepity
beplague
beplaided
beplaster
beplumed
bepommel
bepowder
bepraise
bepraisement
bepraiser
beprank
bepray
bepreach
bepress
bepretty
bepride
beprose
bepuddle
bepuff
bepun
bepurple
bepuzzle
bepuzzlement
bequalm
bequeath
bequeathable
bequeathal
bequeather
bequeathment
bequest
bequirtle
bequote
ber
berain
berairou
berakah
berake
berakoth
berapt
berascal
berat
berate
berattle
beraunite
beray
berbamine
Berber
Berberi
Berberian
berberid
Berberidaceae
berberidaceous
berberine
Berberis
berberry
Berchemia
Berchta
berdache
bere
Berean
bereason
bereave
bereavement
bereaven
bereaver
bereft
berend
Berengaria
Berengarian
Berengarianism
berengelite
Berenice
Bereshith
beresite
beret
berewick
berg
bergalith
Bergama
Bergamask
bergamiol
Bergamo
Bergamot
bergamot
bergander
bergaptene
berger
berghaan
berginization
berginize
berglet
bergschrund
Bergsonian
Bergsonism
bergut
bergy
bergylt
berhyme
Beri
beribanded
beribboned
beriberi
beriberic
beride
berigora
beringed
beringite
beringleted
berinse
berith
Berkeleian
Berkeleianism
Berkeleyism
Berkeleyite
berkelium
berkovets
berkowitz
Berkshire
berley
berlin
berline
Berliner
berlinite
Berlinize
berm
Bermuda
Bermudian
bermudite
Bern
Bernard
Bernardina
Bernardine
berne
Bernese
Bernice
Bernicia
bernicle
Bernie
Berninesque
Bernoullian
berobed
Beroe
Beroida
Beroidae
beroll
Berossos
berouged
beround
berrendo
berret
berri
berried
berrier
berrigan
berrugate
berry
berrybush
berryless
berrylike
berrypicker
berrypicking
berseem
berserk
berserker
Bersiamite
Bersil
Bert
Bertat
Berteroa
berth
Bertha
berthage
berthed
berther
berthierite
berthing
Berthold
Bertholletia
Bertie
Bertolonia
Bertram
bertram
Bertrand
bertrandite
bertrum
beruffed
beruffled
berust
bervie
berycid
Berycidae
beryciform
berycine
berycoid
Berycoidea
berycoidean
Berycoidei
Berycomorphi
beryl
berylate
beryllia
berylline
berylliosis
beryllium
berylloid
beryllonate
beryllonite
beryllosis
Berytidae
Beryx
berzelianite
berzeliite
bes
besa
besagne
besaiel
besaint
besan
besanctify
besauce
bescab
bescarf
bescatter
bescent
bescorch
bescorn
bescoundrel
bescour
bescourge
bescramble
bescrape
bescratch
bescrawl
bescreen
bescribble
bescurf
bescurvy
bescutcheon
beseam
besee
beseech
beseecher
beseeching
beseechingly
beseechingness
beseechment
beseem
beseeming
beseemingly
beseemingness
beseemliness
beseemly
beseen
beset
besetment
besetter
besetting
beshackle
beshade
beshadow
beshag
beshake
beshame
beshawled
beshear
beshell
beshield
beshine
beshiver
beshlik
beshod
beshout
beshow
beshower
beshrew
beshriek
beshrivel
beshroud
besiclometer
beside
besides
besiege
besieged
besiegement
besieger
besieging
besiegingly
besigh
besilver
besin
besing
besiren
besit
beslab
beslap
beslash
beslave
beslaver
besleeve
beslime
beslimer
beslings
beslipper
beslobber
beslow
beslubber
beslur
beslushed
besmear
besmearer
besmell
besmile
besmirch
besmircher
besmirchment
besmoke
besmooth
besmother
besmouch
besmudge
besmut
besmutch
besnare
besneer
besnivel
besnow
besnuff
besodden
besogne
besognier
besoil
besom
besomer
besonnet
besoot
besoothe
besoothement
besot
besotment
besotted
besottedly
besottedness
besotting
besottingly
besought
besoul
besour
bespangle
bespate
bespatter
bespatterer
bespatterment
bespawl
bespeak
bespeakable
bespeaker
bespecked
bespeckle
bespecklement
bespectacled
besped
bespeech
bespeed
bespell
bespelled
bespend
bespete
bespew
bespice
bespill
bespin
bespirit
bespit
besplash
besplatter
besplit
bespoke
bespoken
bespot
bespottedness
bespouse
bespout
bespray
bespread
besprent
besprinkle
besprinkler
bespurred
besputter
bespy
besqueeze
besquib
besra
Bess
Bessarabian
Besselian
Bessemer
bessemer
Bessemerize
bessemerize
Bessera
Bessi
Bessie
Bessy
best
bestab
bestain
bestamp
bestar
bestare
bestarve
bestatued
bestay
bestayed
bestead
besteer
bestench
bester
bestial
bestialism
bestialist
bestiality
bestialize
bestially
bestiarian
bestiarianism
bestiary
bestick
bestill
bestink
bestir
bestness
bestock
bestore
bestorm
bestove
bestow
bestowable
bestowage
bestowal
bestower
bestowing
bestowment
bestraddle
bestrapped
bestraught
bestraw
bestreak
bestream
bestrew
bestrewment
bestride
bestripe
bestrode
bestubbled
bestuck
bestud
besugar
besuit
besully
beswarm
besweatered
besweeten
beswelter
beswim
beswinge
beswitch
bet
Beta
beta
betacism
betacismus
betafite
betag
betail
betailor
betaine
betainogen
betalk
betallow
betangle
betanglement
betask
betassel
betatron
betattered
betaxed
betear
beteela
beteem
betel
Betelgeuse
Beth
beth
bethabara
bethankit
bethel
Bethesda
bethflower
bethink
Bethlehem
Bethlehemite
bethought
bethrall
bethreaten
bethroot
Bethuel
bethumb
bethump
bethunder
bethwack
Bethylidae
betide
betimber
betimes
betinge
betipple
betire
betis
betitle
betocsin
betoil
betoken
betokener
betone
betongue
Betonica
betony
betorcin
betorcinol
betoss
betowel
betowered
Betoya
Betoyan
betrace
betrail
betrample
betrap
betravel
betray
betrayal
betrayer
betrayment
betread
betrend
betrim
betrinket
betroth
betrothal
betrothed
betrothment
betrough
betrousered
betrumpet
betrunk
Betsey
Betsileos
Betsimisaraka
betso
Betsy
Betta
betted
better
betterer
bettergates
bettering
betterly
betterment
bettermost
betterness
betters
Bettina
Bettine
betting
bettong
bettonga
Bettongia
bettor
Betty
betty
betuckered
Betula
Betulaceae
betulaceous
betulin
betulinamaric
betulinic
betulinol
Betulites
beturbaned
betusked
betutor
betutored
betwattled
between
betweenbrain
betweenity
betweenmaid
betweenness
betweenwhiles
betwine
betwit
betwixen
betwixt
beudantite
Beulah
beuniformed
bevatron
beveil
bevel
beveled
beveler
bevelled
bevelment
bevenom
bever
beverage
Beverly
beverse
bevesseled
bevesselled
beveto
bevillain
bevined
bevoiled
bevomit
bevue
bevy
bewail
bewailable
bewailer
bewailing
bewailingly
bewailment
bewaitered
bewall
beware
bewash
bewaste
bewater
beweary
beweep
beweeper
bewelcome
bewelter
bewept
bewest
bewet
bewhig
bewhiskered
bewhisper
bewhistle
bewhite
bewhiten
bewidow
bewig
bewigged
bewilder
bewildered
bewilderedly
bewilderedness
bewildering
bewilderingly
bewilderment
bewimple
bewinged
bewinter
bewired
bewitch
bewitchedness
bewitcher
bewitchery
bewitchful
bewitching
bewitchingly
bewitchingness
bewitchment
bewith
bewizard
bework
beworm
beworn
beworry
beworship
bewrap
bewrathed
bewray
bewrayer
bewrayingly
bewrayment
bewreath
bewreck
bewrite
bey
beydom
beylic
beylical
beyond
beyrichite
beyship
Bezaleel
Bezaleelian
bezant
bezantee
bezanty
bezel
bezesteen
bezetta
bezique
bezoar
bezoardic
bezonian
Bezpopovets
bezzi
bezzle
bezzo
bhabar
Bhadon
Bhaga
bhagavat
bhagavata
bhaiachari
bhaiyachara
bhakta
bhakti
bhalu
bhandar
bhandari
bhang
bhangi
Bhar
bhara
bharal
Bharata
bhat
bhava
Bhavani
bheesty
bhikku
bhikshu
Bhil
Bhili
Bhima
Bhojpuri
bhoosa
Bhotia
Bhotiya
Bhowani
bhoy
Bhumij
bhungi
bhungini
bhut
Bhutanese
Bhutani
bhutatathata
Bhutia
biabo
biacetyl
biacetylene
biacid
biacromial
biacuminate
biacuru
bialate
biallyl
bialveolar
Bianca
Bianchi
bianchite
bianco
biangular
biangulate
biangulated
biangulous
bianisidine
biannual
biannually
biannulate
biarchy
biarcuate
biarcuated
biarticular
biarticulate
biarticulated
bias
biasness
biasteric
biaswise
biatomic
biauricular
biauriculate
biaxal
biaxial
biaxiality
biaxially
biaxillary
bib
bibacious
bibacity
bibasic
bibation
bibb
bibber
bibble
bibbler
bibbons
bibcock
bibenzyl
bibi
Bibio
bibionid
Bibionidae
bibiri
bibitory
Bible
bibless
Biblic
Biblical
Biblicality
Biblically
Biblicism
Biblicist
Biblicistic
Biblicolegal
Biblicoliterary
Biblicopsychological
biblioclasm
biblioclast
bibliofilm
bibliogenesis
bibliognost
bibliognostic
bibliogony
bibliograph
bibliographer
bibliographic
bibliographical
bibliographically
bibliographize
bibliography
biblioklept
bibliokleptomania
bibliokleptomaniac
bibliolater
bibliolatrous
bibliolatry
bibliological
bibliologist
bibliology
bibliomancy
bibliomane
bibliomania
bibliomaniac
bibliomaniacal
bibliomanian
bibliomanianism
bibliomanism
bibliomanist
bibliopegic
bibliopegist
bibliopegistic
bibliopegy
bibliophage
bibliophagic
bibliophagist
bibliophagous
bibliophile
bibliophilic
bibliophilism
bibliophilist
bibliophilistic
bibliophily
bibliophobia
bibliopolar
bibliopole
bibliopolery
bibliopolic
bibliopolical
bibliopolically
bibliopolism
bibliopolist
bibliopolistic
bibliopoly
bibliosoph
bibliotaph
bibliotaphic
bibliothec
bibliotheca
bibliothecal
bibliothecarial
bibliothecarian
bibliothecary
bibliotherapeutic
bibliotherapist
bibliotherapy
bibliothetic
bibliotic
bibliotics
bibliotist
Biblism
Biblist
biblus
biborate
bibracteate
bibracteolate
bibulosity
bibulous
bibulously
bibulousness
Bibulus
bicalcarate
bicameral
bicameralism
bicamerist
bicapitate
bicapsular
bicarbonate
bicarbureted
bicarinate
bicarpellary
bicarpellate
bicaudal
bicaudate
Bice
bice
bicellular
bicentenary
bicentennial
bicephalic
bicephalous
biceps
bicetyl
bichir
bichloride
bichord
bichromate
bichromatic
bichromatize
bichrome
bichromic
bichy
biciliate
biciliated
bicipital
bicipitous
bicircular
bicirrose
bick
bicker
bickerer
bickern
biclavate
biclinium
bicollateral
bicollaterality
bicolligate
bicolor
bicolored
bicolorous
biconcave
biconcavity
bicondylar
bicone
biconic
biconical
biconically
biconjugate
biconsonantal
biconvex
bicorn
bicornate
bicorne
bicorned
bicornous
bicornuate
bicornuous
bicornute
bicorporal
bicorporate
bicorporeal
bicostate
bicrenate
bicrescentic
bicrofarad
bicron
bicrural
bicursal
bicuspid
bicuspidate
bicyanide
bicycle
bicycler
bicyclic
bicyclism
bicyclist
bicyclo
bicycloheptane
bicylindrical
bid
bidactyl
bidactyle
bidactylous
bidar
bidarka
bidcock
biddable
biddableness
biddably
biddance
Biddelian
bidder
bidding
Biddulphia
Biddulphiaceae
Biddy
biddy
bide
Bidens
bident
bidental
bidentate
bidented
bidential
bidenticulate
bider
bidet
bidigitate
bidimensional
biding
bidirectional
bidiurnal
Bidpai
bidri
biduous
bieberite
Biedermeier
bield
bieldy
bielectrolysis
bielenite
Bielid
Bielorouss
bien
bienly
bienness
biennia
biennial
biennially
biennium
bier
bierbalk
biethnic
bietle
bifacial
bifanged
bifara
bifarious
bifariously
bifer
biferous
biff
biffin
bifid
bifidate
bifidated
bifidity
bifidly
bifilar
bifilarly
bifistular
biflabellate
biflagellate
biflecnode
biflected
biflex
biflorate
biflorous
bifluoride
bifocal
bifoil
bifold
bifolia
bifoliate
bifoliolate
bifolium
biforked
biform
biformed
biformity
biforous
bifront
bifrontal
bifronted
bifurcal
bifurcate
bifurcated
bifurcately
bifurcation
big
biga
bigamic
bigamist
bigamistic
bigamize
bigamous
bigamously
bigamy
bigarade
bigaroon
bigarreau
bigbloom
bigemina
bigeminal
bigeminate
bigeminated
bigeminum
bigener
bigeneric
bigential
bigeye
bigg
biggah
biggen
bigger
biggest
biggin
biggish
biggonet
bigha
bighead
bighearted
bigheartedness
bighorn
bight
biglandular
biglenoid
biglot
bigmouth
bigmouthed
bigness
Bignonia
Bignoniaceae
bignoniaceous
bignoniad
bignou
bigoniac
bigonial
bigot
bigoted
bigotedly
bigotish
bigotry
bigotty
bigroot
bigthatch
biguanide
biguttate
biguttulate
bigwig
bigwigged
bigwiggedness
bigwiggery
bigwiggism
Bihai
Biham
bihamate
Bihari
biharmonic
bihourly
bihydrazine
bija
bijasal
bijou
bijouterie
bijoux
bijugate
bijugular
bike
bikh
bikhaconitine
bikini
Bikol
Bikram
Bikukulla
Bilaan
bilabe
bilabial
bilabiate
bilalo
bilamellar
bilamellate
bilamellated
bilaminar
bilaminate
bilaminated
bilander
bilateral
bilateralism
bilaterality
bilaterally
bilateralness
Bilati
bilberry
bilbie
bilbo
bilboquet
bilby
bilch
bilcock
bildar
bilders
bile
bilestone
bilge
bilgy
Bilharzia
bilharzial
bilharziasis
bilharzic
bilharziosis
bilianic
biliary
biliate
biliation
bilic
bilicyanin
bilifaction
biliferous
bilification
bilifuscin
bilify
bilihumin
bilimbi
bilimbing
biliment
Bilin
bilinear
bilineate
bilingual
bilingualism
bilingually
bilinguar
bilinguist
bilinigrin
bilinite
bilio
bilious
biliously
biliousness
biliprasin
bilipurpurin
bilipyrrhin
bilirubin
bilirubinemia
bilirubinic
bilirubinuria
biliteral
biliteralism
bilith
bilithon
biliverdic
biliverdin
bilixanthin
bilk
bilker
Bill
bill
billa
billable
billabong
billback
billbeetle
Billbergia
billboard
billbroking
billbug
billed
biller
billet
billeter
billethead
billeting
billetwood
billety
billfish
billfold
billhead
billheading
billholder
billhook
billian
billiard
billiardist
billiardly
billiards
Billie
Billiken
billikin
billing
billingsgate
billion
billionaire
billionism
billionth
billitonite
Billjim
billman
billon
billot
billow
billowiness
billowy
billposter
billposting
billsticker
billsticking
Billy
billy
billyboy
billycan
billycock
billyer
billyhood
billywix
bilo
bilobated
bilobe
bilobed
bilobiate
bilobular
bilocation
bilocellate
bilocular
biloculate
Biloculina
biloculine
bilophodont
Biloxi
bilsh
Bilskirnir
bilsted
biltong
biltongue
Bim
bimaculate
bimaculated
bimalar
Bimana
bimanal
bimane
bimanous
bimanual
bimanually
bimarginate
bimarine
bimastic
bimastism
bimastoid
bimasty
bimaxillary
bimbil
Bimbisara
bimeby
bimensal
bimester
bimestrial
bimetalic
bimetallism
bimetallist
bimetallistic
bimillenary
bimillennium
bimillionaire
Bimini
Bimmeler
bimodal
bimodality
bimolecular
bimonthly
bimotored
bimotors
bimucronate
bimuscular
bin
binal
binaphthyl
binarium
binary
binate
binately
bination
binational
binaural
binauricular
binbashi
bind
binder
bindery
bindheimite
binding
bindingly
bindingness
bindle
bindlet
bindoree
bindweb
bindweed
bindwith
bindwood
bine
binervate
bineweed
bing
binge
bingey
binghi
bingle
bingo
bingy
binh
Bini
biniodide
Binitarian
Binitarianism
bink
binman
binna
binnacle
binning
binnite
binnogue
bino
binocle
binocular
binocularity
binocularly
binoculate
binodal
binode
binodose
binodous
binomenclature
binomial
binomialism
binomially
binominal
binominated
binominous
binormal
binotic
binotonous
binous
binoxalate
binoxide
bint
bintangor
binturong
binuclear
binucleate
binucleated
binucleolate
binukau
Binzuru
biobibliographical
biobibliography
bioblast
bioblastic
biocatalyst
biocellate
biocentric
biochemic
biochemical
biochemically
biochemics
biochemist
biochemistry
biochemy
biochore
bioclimatic
bioclimatology
biocoenose
biocoenosis
biocoenotic
biocycle
biod
biodynamic
biodynamical
biodynamics
biodyne
bioecologic
bioecological
bioecologically
bioecologist
bioecology
biogen
biogenase
biogenesis
biogenesist
biogenetic
biogenetical
biogenetically
biogenetics
biogenous
biogeny
biogeochemistry
biogeographic
biogeographical
biogeographically
biogeography
biognosis
biograph
biographee
biographer
biographic
biographical
biographically
biographist
biographize
biography
bioherm
biokinetics
biolinguistics
biolith
biologese
biologic
biological
biologically
biologicohumanistic
biologism
biologist
biologize
biology
bioluminescence
bioluminescent
biolysis
biolytic
biomagnetic
biomagnetism
biomathematics
biome
biomechanical
biomechanics
biometeorology
biometer
biometric
biometrical
biometrically
biometrician
biometricist
biometrics
biometry
biomicroscopy
bion
bionergy
bionomic
bionomical
bionomically
bionomics
bionomist
bionomy
biophagism
biophagous
biophagy
biophilous
biophore
biophotophone
biophysical
biophysicochemical
biophysics
biophysiography
biophysiological
biophysiologist
biophysiology
biophyte
bioplasm
bioplasmic
bioplast
bioplastic
bioprecipitation
biopsic
biopsy
biopsychic
biopsychical
biopsychological
biopsychologist
biopsychology
biopyribole
bioral
biorbital
biordinal
bioreaction
biorgan
bios
bioscope
bioscopic
bioscopy
biose
biosis
biosocial
biosociological
biosphere
biostatic
biostatical
biostatics
biostatistics
biosterin
biosterol
biostratigraphy
biosynthesis
biosynthetic
biosystematic
biosystematics
biosystematist
biosystematy
Biota
biota
biotaxy
biotechnics
biotic
biotical
biotics
biotin
biotite
biotitic
biotome
biotomy
biotope
biotype
biotypic
biovular
biovulate
bioxalate
bioxide
bipack
bipaleolate
Bipaliidae
Bipalium
bipalmate
biparasitic
biparental
biparietal
biparous
biparted
bipartible
bipartient
bipartile
bipartisan
bipartisanship
bipartite
bipartitely
bipartition
biparty
bipaschal
bipectinate
bipectinated
biped
bipedal
bipedality
bipedism
bipeltate
bipennate
bipennated
bipenniform
biperforate
bipersonal
bipetalous
biphase
biphasic
biphenol
biphenyl
biphenylene
bipinnaria
bipinnate
bipinnated
bipinnately
bipinnatifid
bipinnatiparted
bipinnatipartite
bipinnatisect
bipinnatisected
biplanal
biplanar
biplane
biplicate
biplicity
biplosion
biplosive
bipod
bipolar
bipolarity
bipolarize
Bipont
Bipontine
biporose
biporous
biprism
biprong
bipunctal
bipunctate
bipunctual
bipupillate
bipyramid
bipyramidal
bipyridine
bipyridyl
biquadrantal
biquadrate
biquadratic
biquarterly
biquartz
biquintile
biracial
biracialism
biradial
biradiate
biradiated
biramous
birational
birch
birchbark
birchen
birching
birchman
birchwood
bird
birdbander
birdbanding
birdbath
birdberry
birdcall
birdcatcher
birdcatching
birdclapper
birdcraft
birddom
birdeen
birder
birdglue
birdhood
birdhouse
birdie
birdikin
birding
birdland
birdless
birdlet
birdlike
birdlime
birdling
birdlore
birdman
birdmouthed
birdnest
birdnester
birdseed
birdstone
birdweed
birdwise
birdwoman
birdy
birectangular
birefracting
birefraction
birefractive
birefringence
birefringent
bireme
biretta
Birgus
biri
biriba
birimose
birk
birken
Birkenhead
Birkenia
Birkeniidae
birkie
birkremite
birl
birle
birler
birlie
birlieman
birlinn
birma
Birmingham
Birminghamize
birn
birny
Biron
birostrate
birostrated
birotation
birotatory
birr
birse
birsle
birsy
birth
birthbed
birthday
birthland
birthless
birthmark
birthmate
birthnight
birthplace
birthright
birthroot
birthstone
birthstool
birthwort
birthy
bis
bisabol
bisaccate
bisacromial
bisalt
Bisaltae
bisantler
bisaxillary
bisbeeite
biscacha
Biscanism
Biscayan
Biscayanism
biscayen
Biscayner
bischofite
biscotin
biscuit
biscuiting
biscuitlike
biscuitmaker
biscuitmaking
biscuitroot
biscuitry
bisdiapason
bisdimethylamino
bisect
bisection
bisectional
bisectionally
bisector
bisectrices
bisectrix
bisegment
biseptate
biserial
biserially
biseriate
biseriately
biserrate
bisetose
bisetous
bisexed
bisext
bisexual
bisexualism
bisexuality
bisexually
bisexuous
bisglyoxaline
Bishareen
Bishari
Bisharin
bishop
bishopdom
bishopess
bishopful
bishophood
bishopless
bishoplet
bishoplike
bishopling
bishopric
bishopship
bishopweed
bisiliac
bisilicate
bisiliquous
bisimine
bisinuate
bisinuation
bisischiadic
bisischiatic
Bisley
bislings
bismar
Bismarck
Bismarckian
Bismarckianism
bismarine
bismerpund
bismillah
bismite
Bismosol
bismuth
bismuthal
bismuthate
bismuthic
bismuthide
bismuthiferous
bismuthine
bismuthinite
bismuthite
bismuthous
bismuthyl
bismutite
bismutoplagionite
bismutosmaltite
bismutosphaerite
bisnaga
bison
bisonant
bisontine
bisphenoid
bispinose
bispinous
bispore
bisporous
bisque
bisquette
bissext
bissextile
bisson
bistate
bistephanic
bister
bistered
bistetrazole
bisti
bistipular
bistipulate
bistipuled
bistort
Bistorta
bistournage
bistoury
bistratal
bistratose
bistriate
bistriazole
bistro
bisubstituted
bisubstitution
bisulcate
bisulfid
bisulphate
bisulphide
bisulphite
bisyllabic
bisyllabism
bisymmetric
bisymmetrical
bisymmetrically
bisymmetry
bit
bitable
bitangent
bitangential
bitanhol
bitartrate
bitbrace
bitch
bite
bitemporal
bitentaculate
biter
biternate
biternately
bitesheep
bitewing
bitheism
Bithynian
biti
biting
bitingly
bitingness
Bitis
bitless
bito
bitolyl
bitonality
bitreadle
bitripartite
bitripinnatifid
bitriseptate
bitrochanteric
bitstock
bitstone
bitt
bitted
bitten
bitter
bitterbark
bitterblain
bitterbloom
bitterbur
bitterbush
bitterful
bitterhead
bitterhearted
bitterheartedness
bittering
bitterish
bitterishness
bitterless
bitterling
bitterly
bittern
bitterness
bitternut
bitterroot
bitters
bittersweet
bitterweed
bitterwood
bitterworm
bitterwort
bitthead
bittie
Bittium
bittock
bitty
bitubercular
bituberculate
bituberculated
Bitulithic
bitulithic
bitume
bitumed
bitumen
bituminate
bituminiferous
bituminization
bituminize
bituminoid
bituminous
bitwise
bityite
bitypic
biune
biunial
biunity
biunivocal
biurate
biurea
biuret
bivalence
bivalency
bivalent
bivalve
bivalved
Bivalvia
bivalvian
bivalvous
bivalvular
bivariant
bivariate
bivascular
bivaulted
bivector
biventer
biventral
biverbal
bivinyl
bivious
bivittate
bivocal
bivocalized
bivoltine
bivoluminous
bivouac
biwa
biweekly
biwinter
Bixa
Bixaceae
bixaceous
bixbyite
bixin
biyearly
biz
bizardite
bizarre
bizarrely
bizarreness
Bizen
bizet
bizonal
bizone
Bizonia
bizygomatic
bizz
Bjorne
blab
blabber
blabberer
blachong
black
blackacre
blackamoor
blackback
blackball
blackballer
blackband
Blackbeard
blackbelly
blackberry
blackbine
blackbird
blackbirder
blackbirding
blackboard
blackboy
blackbreast
blackbush
blackbutt
blackcap
blackcoat
blackcock
blackdamp
blacken
blackener
blackening
blacker
blacketeer
blackey
blackeyes
blackface
Blackfeet
blackfellow
blackfellows
blackfin
blackfire
blackfish
blackfisher
blackfishing
Blackfoot
blackfoot
Blackfriars
blackguard
blackguardism
blackguardize
blackguardly
blackguardry
Blackhander
blackhead
blackheads
blackheart
blackhearted
blackheartedness
blackie
blacking
blackish
blackishly
blackishness
blackit
blackjack
blackland
blackleg
blackleggery
blacklegism
blacklegs
blackly
blackmail
blackmailer
blackneb
blackneck
blackness
blacknob
blackout
blackpoll
blackroot
blackseed
blackshirted
blacksmith
blacksmithing
blackstick
blackstrap
blacktail
blackthorn
blacktongue
blacktree
blackwash
blackwasher
blackwater
blackwood
blackwork
blackwort
blacky
blad
bladder
bladderet
bladderless
bladderlike
bladdernose
bladdernut
bladderpod
bladderseed
bladderweed
bladderwort
bladdery
blade
bladebone
bladed
bladelet
bladelike
blader
bladesmith
bladewise
blading
bladish
blady
bladygrass
blae
blaeberry
blaeness
blaewort
blaff
blaffert
blaflum
blah
blahlaut
blain
Blaine
Blair
blair
blairmorite
Blake
blake
blakeberyed
blamable
blamableness
blamably
blame
blamed
blameful
blamefully
blamefulness
blameless
blamelessly
blamelessness
blamer
blameworthiness
blameworthy
blaming
blamingly
blan
blanc
blanca
blancard
Blanch
blanch
blancher
blanching
blanchingly
blancmange
blancmanger
blanco
bland
blanda
Blandfordia
blandiloquence
blandiloquious
blandiloquous
blandish
blandisher
blandishing
blandishingly
blandishment
blandly
blandness
blank
blankard
blankbook
blanked
blankeel
blanket
blanketed
blanketeer
blanketflower
blanketing
blanketless
blanketmaker
blanketmaking
blanketry
blanketweed
blankety
blanking
blankish
Blankit
blankite
blankly
blankness
blanky
blanque
blanquillo
blare
Blarina
blarney
blarneyer
blarnid
blarny
blart
blas
blase
blash
blashy
Blasia
blaspheme
blasphemer
blasphemous
blasphemously
blasphemousness
blasphemy
blast
blasted
blastema
blastemal
blastematic
blastemic
blaster
blastful
blasthole
blastid
blastie
blasting
blastment
blastocarpous
blastocheme
blastochyle
blastocoele
blastocolla
blastocyst
blastocyte
blastoderm
blastodermatic
blastodermic
blastodisk
blastogenesis
blastogenetic
blastogenic
blastogeny
blastogranitic
blastoid
Blastoidea
blastoma
blastomata
blastomere
blastomeric
Blastomyces
blastomycete
Blastomycetes
blastomycetic
blastomycetous
blastomycosis
blastomycotic
blastoneuropore
Blastophaga
blastophitic
blastophoral
blastophore
blastophoric
blastophthoria
blastophthoric
blastophyllum
blastoporal
blastopore
blastoporic
blastoporphyritic
blastosphere
blastospheric
blastostylar
blastostyle
blastozooid
blastplate
blastula
blastulae
blastular
blastulation
blastule
blasty
blat
blatancy
blatant
blatantly
blate
blately
blateness
blather
blatherer
blatherskite
blathery
blatjang
Blatta
blatta
Blattariae
blatter
blatterer
blatti
blattid
Blattidae
blattiform
Blattodea
blattoid
Blattoidea
blaubok
Blaugas
blauwbok
blaver
blaw
blawort
blay
Blayne
blaze
blazer
blazing
blazingly
blazon
blazoner
blazoning
blazonment
blazonry
blazy
bleaberry
bleach
bleachability
bleachable
bleached
bleacher
bleacherite
bleacherman
bleachery
bleachfield
bleachground
bleachhouse
bleaching
bleachman
bleachworks
bleachyard
bleak
bleakish
bleakly
bleakness
bleaky
blear
bleared
blearedness
bleareye
bleariness
blearness
bleary
bleat
bleater
bleating
bleatingly
bleaty
bleb
blebby
blechnoid
Blechnum
bleck
blee
bleed
bleeder
bleeding
bleekbok
bleery
bleeze
bleezy
blellum
blemish
blemisher
blemishment
Blemmyes
blench
blencher
blenching
blenchingly
blencorn
blend
blendcorn
blende
blended
blender
blending
blendor
blendure
blendwater
blennadenitis
blennemesis
blennenteria
blennenteritis
blenniid
Blenniidae
blenniiform
Blenniiformes
blennioid
Blennioidea
blennocele
blennocystitis
blennoemesis
blennogenic
blennogenous
blennoid
blennoma
blennometritis
blennophlogisma
blennophlogosis
blennophthalmia
blennoptysis
blennorrhagia
blennorrhagic
blennorrhea
blennorrheal
blennorrhinia
blennosis
blennostasis
blennostatic
blennothorax
blennotorrhea
blennuria
blenny
blennymenitis
blent
bleo
blephara
blepharadenitis
blepharal
blepharanthracosis
blepharedema
blepharelcosis
blepharemphysema
Blephariglottis
blepharism
blepharitic
blepharitis
blepharoadenitis
blepharoadenoma
blepharoatheroma
blepharoblennorrhea
blepharocarcinoma
Blepharocera
Blepharoceridae
blepharochalasis
blepharochromidrosis
blepharoclonus
blepharocoloboma
blepharoconjunctivitis
blepharodiastasis
blepharodyschroia
blepharohematidrosis
blepharolithiasis
blepharomelasma
blepharoncosis
blepharoncus
blepharophimosis
blepharophryplasty
blepharophthalmia
blepharophyma
blepharoplast
blepharoplastic
blepharoplasty
blepharoplegia
blepharoptosis
blepharopyorrhea
blepharorrhaphy
blepharospasm
blepharospath
blepharosphincterectomy
blepharostat
blepharostenosis
blepharosymphysis
blepharosyndesmitis
blepharosynechia
blepharotomy
blepharydatis
Blephillia
blesbok
blesbuck
bless
blessed
blessedly
blessedness
blesser
blessing
blessingly
blest
blet
bletheration
Bletia
Bletilla
blewits
blibe
blick
blickey
Blighia
blight
blightbird
blighted
blighter
blighting
blightingly
blighty
blimbing
blimp
blimy
blind
blindage
blindball
blinded
blindedly
blinder
blindeyes
blindfast
blindfish
blindfold
blindfolded
blindfoldedness
blindfolder
blindfoldly
blinding
blindingly
blindish
blindless
blindling
blindly
blindness
blindstory
blindweed
blindworm
blink
blinkard
blinked
blinker
blinkered
blinking
blinkingly
blinks
blinky
blinter
blintze
blip
bliss
blissful
blissfully
blissfulness
blissless
blissom
blister
blistered
blistering
blisteringly
blisterweed
blisterwort
blistery
blite
blithe
blithebread
blitheful
blithefully
blithehearted
blithelike
blithely
blithemeat
blithen
blitheness
blither
blithering
blithesome
blithesomely
blithesomeness
blitter
Blitum
blitz
blitzbuggy
blitzkrieg
blizz
blizzard
blizzardly
blizzardous
blizzardy
blo
bloat
bloated
bloatedness
bloater
bloating
blob
blobbed
blobber
blobby
bloc
block
blockade
blockader
blockage
blockbuster
blocked
blocker
blockhead
blockheaded
blockheadedly
blockheadedness
blockheadish
blockheadishness
blockheadism
blockholer
blockhouse
blockiness
blocking
blockish
blockishly
blockishness
blocklayer
blocklike
blockmaker
blockmaking
blockman
blockpate
blockship
blocky
blodite
bloke
blolly
blomstrandine
blonde
blondeness
blondine
blood
bloodalley
bloodalp
bloodbeat
bloodberry
bloodbird
bloodcurdler
bloodcurdling
blooddrop
blooddrops
blooded
bloodfin
bloodflower
bloodguilt
bloodguiltiness
bloodguiltless
bloodguilty
bloodhound
bloodied
bloodily
bloodiness
bloodleaf
bloodless
bloodlessly
bloodlessness
bloodletter
bloodletting
bloodline
bloodmobile
bloodmonger
bloodnoun
bloodripe
bloodripeness
bloodroot
bloodshed
bloodshedder
bloodshedding
bloodshot
bloodshotten
bloodspiller
bloodspilling
bloodstain
bloodstained
bloodstainedness
bloodstanch
bloodstock
bloodstone
bloodstroke
bloodsuck
bloodsucker
bloodsucking
bloodthirst
bloodthirster
bloodthirstily
bloodthirstiness
bloodthirsting
bloodthirsty
bloodweed
bloodwite
bloodwood
bloodworm
bloodwort
bloodworthy
bloody
bloodybones
blooey
bloom
bloomage
bloomer
Bloomeria
bloomerism
bloomers
bloomery
bloomfell
blooming
bloomingly
bloomingness
bloomkin
bloomless
Bloomsburian
Bloomsbury
bloomy
bloop
blooper
blooping
blore
blosmy
blossom
blossombill
blossomed
blossomhead
blossomless
blossomry
blossomtime
blossomy
blot
blotch
blotched
blotchy
blotless
blotter
blottesque
blottesquely
blotting
blottingly
blotto
blotty
bloubiskop
blouse
bloused
blousing
blout
blow
blowback
blowball
blowcock
blowdown
blowen
blower
blowfish
blowfly
blowgun
blowhard
blowhole
blowiness
blowing
blowings
blowiron
blowlamp
blowline
blown
blowoff
blowout
blowpipe
blowpoint
blowproof
blowspray
blowth
blowtorch
blowtube
blowup
blowy
blowze
blowzed
blowzing
blowzy
blub
blubber
blubberer
blubbering
blubberingly
blubberman
blubberous
blubbery
blucher
bludgeon
bludgeoned
bludgeoneer
bludgeoner
blue
blueback
bluebead
Bluebeard
bluebeard
Bluebeardism
bluebell
bluebelled
blueberry
bluebill
bluebird
blueblaw
bluebonnet
bluebook
bluebottle
bluebreast
bluebuck
bluebush
bluebutton
bluecap
bluecoat
bluecup
bluefish
bluegill
bluegown
bluegrass
bluehearted
bluehearts
blueing
bluejack
bluejacket
bluejoint
blueleg
bluelegs
bluely
blueness
bluenose
Bluenoser
blueprint
blueprinter
bluer
blues
bluesides
bluestem
bluestocking
bluestockingish
bluestockingism
bluestone
bluestoner
bluet
bluethroat
bluetongue
bluetop
blueweed
bluewing
bluewood
bluey
bluff
bluffable
bluffer
bluffly
bluffness
bluffy
bluggy
bluing
bluish
bluishness
bluism
Blumea
blunder
blunderbuss
blunderer
blunderful
blunderhead
blunderheaded
blunderheadedness
blundering
blunderingly
blundersome
blunge
blunger
blunk
blunker
blunks
blunnen
blunt
blunter
blunthead
blunthearted
bluntie
bluntish
bluntly
bluntness
blup
blur
blurb
blurbist
blurred
blurredness
blurrer
blurry
blurt
blush
blusher
blushful
blushfully
blushfulness
blushiness
blushing
blushingly
blushless
blushwort
blushy
bluster
blusteration
blusterer
blustering
blusteringly
blusterous
blusterously
blustery
blype
bo
boa
Boaedon
boagane
Boanbura
Boanerges
boanergism
boar
boarcite
board
boardable
boarder
boarding
boardinghouse
boardlike
boardly
boardman
boardwalk
boardy
boarfish
boarhound
boarish
boarishly
boarishness
boarship
boarskin
boarspear
boarstaff
boarwood
boast
boaster
boastful
boastfully
boastfulness
boasting
boastive
boastless
boat
boatable
boatage
boatbill
boatbuilder
boatbuilding
boater
boatfalls
boatful
boathead
boatheader
boathouse
boatie
boating
boatkeeper
boatless
boatlike
boatlip
boatload
boatloader
boatloading
boatly
boatman
boatmanship
boatmaster
boatowner
boatsetter
boatshop
boatside
boatsman
boatswain
boattail
boatward
boatwise
boatwoman
boatwright
Bob
bob
boba
bobac
Bobadil
Bobadilian
Bobadilish
Bobadilism
bobbed
bobber
bobbery
Bobbie
bobbin
bobbiner
bobbinet
bobbing
Bobbinite
bobbinwork
bobbish
bobbishly
bobble
Bobby
bobby
bobcat
bobcoat
bobeche
bobfly
bobierrite
bobization
bobjerom
bobo
bobolink
bobotie
bobsled
bobsleigh
bobstay
bobtail
bobtailed
bobwhite
bobwood
bocaccio
bocal
bocardo
bocasine
bocca
boccale
boccarella
boccaro
bocce
Bocconia
boce
bocedization
Boche
bocher
Bochism
bock
bockerel
bockeret
bocking
bocoy
bod
bodach
bodacious
bodaciously
bode
bodeful
bodega
bodement
boden
bodenbenderite
boder
bodewash
bodge
bodger
bodgery
bodhi
bodhisattva
bodice
bodiced
bodicemaker
bodicemaking
bodied
bodier
bodieron
bodikin
bodiless
bodilessness
bodiliness
bodily
bodiment
boding
bodingly
bodkin
bodkinwise
bodle
Bodleian
Bodo
bodock
Bodoni
body
bodybending
bodybuilder
bodyguard
bodyhood
bodyless
bodymaker
bodymaking
bodyplate
bodywise
bodywood
bodywork
Boebera
Boedromion
Boehmenism
Boehmenist
Boehmenite
Boehmeria
boeotarch
Boeotian
Boeotic
Boer
Boerdom
Boerhavia
Boethian
Boethusian
bog
boga
bogan
bogard
bogart
bogberry
bogey
bogeyman
boggart
boggin
bogginess
boggish
boggle
bogglebo
boggler
boggy
boghole
bogie
bogieman
bogier
Bogijiab
bogland
boglander
bogle
bogledom
boglet
bogman
bogmire
Bogo
bogo
Bogomil
Bogomile
Bogomilian
bogong
Bogota
bogsucker
bogtrot
bogtrotter
bogtrotting
bogue
bogum
bogus
bogusness
bogway
bogwood
bogwort
bogy
bogydom
bogyism
bogyland
Bohairic
bohawn
bohea
Bohemia
Bohemian
Bohemianism
bohemium
bohereen
bohireen
boho
bohor
bohunk
boid
Boidae
Boii
Boiko
boil
boilable
boildown
boiled
boiler
boilerful
boilerhouse
boilerless
boilermaker
boilermaking
boilerman
boilersmith
boilerworks
boilery
boiling
boilinglike
boilingly
boilover
boily
Bois
boist
boisterous
boisterously
boisterousness
bojite
bojo
bokadam
bokard
bokark
boke
Bokhara
Bokharan
bokom
bola
Bolag
bolar
Bolboxalis
bold
bolden
Bolderian
boldhearted
boldine
boldly
boldness
boldo
Boldu
bole
bolection
bolectioned
boled
boleite
Bolelia
bolelike
bolero
Boletaceae
boletaceous
bolete
Boletus
boleweed
bolewort
bolide
bolimba
bolis
bolivar
bolivarite
bolivia
Bolivian
boliviano
bolk
boll
Bollandist
bollard
bolled
boller
bolling
bollock
bollworm
bolly
Bolo
bolo
Bologna
Bolognan
Bolognese
bolograph
bolographic
bolographically
bolography
Boloism
boloman
bolometer
bolometric
boloney
boloroot
Bolshevik
Bolsheviki
Bolshevikian
Bolshevism
Bolshevist
Bolshevistic
Bolshevistically
Bolshevize
Bolshie
bolson
bolster
bolsterer
bolsterwork
bolt
boltage
boltant
boltcutter
boltel
bolter
bolthead
boltheader
boltheading
bolthole
bolti
bolting
boltless
boltlike
boltmaker
boltmaking
Boltonia
boltonite
boltrope
boltsmith
boltstrake
boltuprightness
boltwork
bolus
Bolyaian
bom
boma
Bomarea
bomb
bombable
Bombacaceae
bombacaceous
bombard
bombarde
bombardelle
bombarder
bombardier
bombardment
bombardon
bombast
bombaster
bombastic
bombastically
bombastry
Bombax
Bombay
bombazet
bombazine
bombed
bomber
bombiccite
Bombidae
bombilate
bombilation
Bombinae
bombinate
bombination
bombo
bombola
bombonne
bombous
bombproof
bombshell
bombsight
Bombus
bombycid
Bombycidae
bombyciform
Bombycilla
Bombycillidae
Bombycina
bombycine
Bombyliidae
Bombyx
Bon
bon
bonaci
bonagh
bonaght
bonair
bonairly
bonairness
bonally
bonang
bonanza
Bonapartean
Bonapartism
Bonapartist
Bonasa
bonasus
bonaventure
Bonaveria
bonavist
Bonbo
bonbon
bonce
bond
bondage
bondager
bondar
bonded
Bondelswarts
bonder
bonderman
bondfolk
bondholder
bondholding
bonding
bondless
bondman
bondmanship
bondsman
bondstone
bondswoman
bonduc
bondwoman
bone
boneache
bonebinder
boneblack
bonebreaker
boned
bonedog
bonefish
boneflower
bonehead
boneheaded
boneless
bonelessly
bonelessness
bonelet
bonelike
Bonellia
boner
boneset
bonesetter
bonesetting
boneshaker
boneshaw
bonetail
bonewood
bonework
bonewort
Boney
bonfire
bong
Bongo
bongo
bonhomie
Boni
boniata
Boniface
bonification
boniform
bonify
boniness
boninite
bonitarian
bonitary
bonito
bonk
bonnaz
bonnet
bonneted
bonneter
bonnethead
bonnetless
bonnetlike
bonnetman
bonnibel
Bonnie
bonnily
bonniness
Bonny
bonny
bonnyclabber
bonnyish
bonnyvis
Bononian
bonsai
bonspiel
bontebok
bontebuck
bontequagga
Bontok
bonus
bonxie
bony
bonyfish
bonze
bonzer
bonzery
bonzian
boo
boob
boobery
boobily
boobook
booby
boobyalla
boobyish
boobyism
bood
boodie
boodle
boodledom
boodleism
boodleize
boodler
boody
boof
booger
boogiewoogie
boohoo
boojum
book
bookable
bookbinder
bookbindery
bookbinding
bookboard
bookcase
bookcraft
bookdealer
bookdom
booked
booker
bookery
bookfold
bookful
bookholder
bookhood
bookie
bookiness
booking
bookish
bookishly
bookishness
bookism
bookkeeper
bookkeeping
bookland
bookless
booklet
booklike
bookling
booklore
booklover
bookmaker
bookmaking
Bookman
bookman
bookmark
bookmarker
bookmate
bookmobile
bookmonger
bookplate
bookpress
bookrack
bookrest
bookroom
bookseller
booksellerish
booksellerism
bookselling
bookshelf
bookshop
bookstack
bookstall
bookstand
bookstore
bookward
bookwards
bookways
bookwise
bookwork
bookworm
bookwright
booky
bool
Boolian
booly
boolya
boom
boomable
boomage
boomah
boomboat
boomdas
boomer
boomerang
booming
boomingly
boomless
boomlet
boomorah
boomslang
boomslange
boomster
boomy
boon
boondock
boondocks
boondoggle
boondoggler
Boone
boonfellow
boongary
boonk
boonless
Boophilus
boopis
boor
boorish
boorishly
boorishness
boort
boose
boost
booster
boosterism
boosy
boot
bootblack
bootboy
booted
bootee
booter
bootery
Bootes
bootful
booth
boother
Boothian
boothite
bootholder
boothose
Bootid
bootied
bootikin
booting
bootjack
bootlace
bootleg
bootlegger
bootlegging
bootless
bootlessly
bootlessness
bootlick
bootlicker
bootmaker
bootmaking
boots
bootstrap
booty
bootyless
booze
boozed
boozer
boozily
booziness
boozy
bop
bopeep
boppist
bopyrid
Bopyridae
bopyridian
Bopyrus
bor
bora
borable
borachio
boracic
boraciferous
boracous
borage
Boraginaceae
boraginaceous
Borago
Borak
borak
boral
Boran
Borana
Borani
borasca
borasque
Borassus
borate
borax
Borboridae
Borborus
borborygmic
borborygmus
bord
bordage
bordar
bordarius
Bordeaux
bordel
bordello
border
bordered
borderer
Borderies
bordering
borderism
borderland
borderlander
borderless
borderline
bordermark
Borderside
bordroom
bordure
bordured
bore
boreable
boread
Boreades
boreal
borealis
borean
Boreas
borecole
boredom
boree
boreen
boregat
borehole
Boreiad
boreism
borele
borer
boresome
Boreus
borg
borgh
borghalpenny
Borghese
borh
boric
borickite
boride
borine
boring
boringly
boringness
Borinqueno
Boris
borish
borism
bority
borize
borlase
born
borne
Bornean
Borneo
borneol
borning
bornite
bornitic
bornyl
Boro
boro
Borocaine
borocalcite
borocarbide
borocitrate
borofluohydric
borofluoric
borofluoride
borofluorin
boroglycerate
boroglyceride
boroglycerine
borolanite
boron
boronatrocalcite
Boronia
boronic
borophenol
borophenylic
Bororo
Bororoan
borosalicylate
borosalicylic
borosilicate
borosilicic
borotungstate
borotungstic
borough
boroughlet
boroughmaster
boroughmonger
boroughmongering
boroughmongery
boroughship
borowolframic
borracha
borrel
Borrelia
Borrelomycetaceae
Borreria
Borrichia
Borromean
Borrovian
borrow
borrowable
borrower
borrowing
borsch
borscht
borsholder
borsht
borstall
bort
bortsch
borty
bortz
Boruca
Borussian
borwort
boryl
Borzicactus
borzoi
Bos
Bosc
boscage
bosch
boschbok
Boschneger
boschvark
boschveld
bose
Boselaphus
boser
bosh
Boshas
bosher
Bosjesman
bosjesman
bosk
bosker
bosket
boskiness
bosky
bosn
Bosniac
Bosniak
Bosnian
Bosnisch
bosom
bosomed
bosomer
bosomy
Bosporan
Bosporanic
Bosporian
bosporus
boss
bossage
bossdom
bossed
bosselated
bosselation
bosser
bosset
bossiness
bossing
bossism
bosslet
bossship
bossy
bostangi
bostanji
bosthoon
Boston
boston
Bostonese
Bostonian
bostonite
bostrychid
Bostrychidae
bostrychoid
bostrychoidal
bostryx
bosun
Boswellia
Boswellian
Boswelliana
Boswellism
Boswellize
bot
bota
botanic
botanical
botanically
botanist
botanize
botanizer
botanomancy
botanophile
botanophilist
botany
botargo
Botaurinae
Botaurus
botch
botched
botchedly
botcher
botcherly
botchery
botchily
botchiness
botchka
botchy
bote
Botein
botella
boterol
botfly
both
bother
botheration
botherer
botherheaded
botherment
bothersome
bothlike
Bothnian
Bothnic
bothrenchyma
Bothriocephalus
Bothriocidaris
Bothriolepis
bothrium
Bothrodendron
bothropic
Bothrops
bothros
bothsided
bothsidedness
bothway
bothy
Botocudo
botonee
botong
Botrychium
Botrydium
Botryllidae
Botryllus
botryogen
botryoid
botryoidal
botryoidally
botryolite
Botryomyces
botryomycoma
botryomycosis
botryomycotic
Botryopteriaceae
botryopterid
Botryopteris
botryose
botryotherapy
Botrytis
bott
bottekin
Botticellian
bottine
bottle
bottlebird
bottled
bottleflower
bottleful
bottlehead
bottleholder
bottlelike
bottlemaker
bottlemaking
bottleman
bottleneck
bottlenest
bottlenose
bottler
bottling
bottom
bottomchrome
bottomed
bottomer
bottoming
bottomless
bottomlessly
bottomlessness
bottommost
bottomry
bottstick
botuliform
botulin
botulinum
botulism
botulismus
bouchal
bouchaleen
boucharde
bouche
boucher
boucherism
boucherize
bouchette
boud
boudoir
bouffancy
bouffant
Bougainvillaea
Bougainvillea
Bougainvillia
Bougainvilliidae
bougar
bouge
bouget
bough
boughed
boughless
boughpot
bought
boughten
boughy
bougie
bouillabaisse
bouillon
bouk
boukit
boulangerite
Boulangism
Boulangist
boulder
boulderhead
bouldering
bouldery
boule
boulevard
boulevardize
boultel
boulter
boulterer
boun
bounce
bounceable
bounceably
bouncer
bouncing
bouncingly
bound
boundable
boundary
bounded
boundedly
boundedness
bounden
bounder
bounding
boundingly
boundless
boundlessly
boundlessness
boundly
boundness
bounteous
bounteously
bounteousness
bountied
bountiful
bountifully
bountifulness
bountith
bountree
bounty
bountyless
bouquet
bourasque
Bourbon
bourbon
Bourbonesque
Bourbonian
Bourbonism
Bourbonist
bourbonize
bourd
bourder
bourdon
bourette
bourg
bourgeois
bourgeoise
bourgeoisie
bourgeoisitic
Bourignian
Bourignianism
Bourignianist
Bourignonism
Bourignonist
bourn
bournless
bournonite
bourock
Bourout
bourse
bourtree
bouse
bouser
Boussingaultia
boussingaultite
boustrophedon
boustrophedonic
bousy
bout
boutade
Bouteloua
bouto
boutonniere
boutylka
Bouvardia
bouw
bovarism
bovarysm
bovate
bovenland
bovicide
boviculture
bovid
Bovidae
boviform
bovine
bovinely
bovinity
Bovista
bovoid
bovovaccination
bovovaccine
bow
bowable
bowback
bowbells
bowbent
bowboy
Bowdichia
bowdlerism
bowdlerization
bowdlerize
bowed
bowedness
bowel
boweled
bowelless
bowellike
bowels
bowenite
bower
bowerbird
bowerlet
bowermaiden
bowermay
bowerwoman
Bowery
bowery
Boweryish
bowet
bowfin
bowgrace
bowhead
bowie
bowieful
bowing
bowingly
bowk
bowkail
bowker
bowknot
bowl
bowla
bowleg
bowlegged
bowleggedness
bowler
bowless
bowlful
bowlike
bowline
bowling
bowllike
bowlmaker
bowls
bowly
bowmaker
bowmaking
bowman
bowpin
bowralite
bowshot
bowsprit
bowstave
bowstring
bowstringed
bowwoman
bowwood
bowwort
bowwow
bowyer
boxberry
boxboard
boxbush
boxcar
boxen
Boxer
boxer
Boxerism
boxfish
boxful
boxhaul
boxhead
boxing
boxkeeper
boxlike
boxmaker
boxmaking
boxman
boxthorn
boxty
boxwallah
boxwood
boxwork
boxy
boy
boyang
boyar
boyard
boyardism
boyardom
boyarism
Boyce
boycott
boycottage
boycotter
boycottism
Boyd
boydom
boyer
boyhood
boyish
boyishly
boyishness
boyism
boyla
boylike
boyology
boysenberry
boyship
boza
bozal
bozo
bozze
bra
brab
brabagious
brabant
Brabanter
Brabantine
brabble
brabblement
brabbler
brabblingly
Brabejum
braca
braccate
braccia
bracciale
braccianite
braccio
brace
braced
bracelet
braceleted
bracer
bracero
braces
brach
Brachelytra
brachelytrous
bracherer
brachering
brachet
brachial
brachialgia
brachialis
Brachiata
brachiate
brachiation
brachiator
brachiferous
brachigerous
Brachinus
brachiocephalic
brachiocrural
brachiocubital
brachiocyllosis
brachiofacial
brachiofaciolingual
brachioganoid
Brachioganoidei
brachiolaria
brachiolarian
brachiopod
Brachiopoda
brachiopode
brachiopodist
brachiopodous
brachioradial
brachioradialis
brachiorrhachidian
brachiorrheuma
brachiosaur
Brachiosaurus
brachiostrophosis
brachiotomy
brachistocephali
brachistocephalic
brachistocephalous
brachistocephaly
brachistochrone
brachistochronic
brachistochronous
brachium
brachtmema
brachyaxis
brachycardia
brachycatalectic
brachycephal
brachycephalic
brachycephalism
brachycephalization
brachycephalize
brachycephalous
brachycephaly
Brachycera
brachyceral
brachyceric
brachycerous
brachychronic
brachycnemic
Brachycome
brachycranial
brachydactyl
brachydactylic
brachydactylism
brachydactylous
brachydactyly
brachydiagonal
brachydodrome
brachydodromous
brachydomal
brachydomatic
brachydome
brachydont
brachydontism
brachyfacial
brachyglossal
brachygnathia
brachygnathism
brachygnathous
brachygrapher
brachygraphic
brachygraphical
brachygraphy
brachyhieric
brachylogy
brachymetropia
brachymetropic
Brachyoura
brachyphalangia
Brachyphyllum
brachypinacoid
brachypinacoidal
brachypleural
brachypnea
brachypodine
brachypodous
brachyprism
brachyprosopic
brachypterous
brachypyramid
brachyrrhinia
brachysclereid
brachyskelic
brachysm
brachystaphylic
Brachystegia
brachystochrone
Brachystomata
brachystomatous
brachystomous
brachytic
brachytypous
Brachyura
brachyural
brachyuran
brachyuranic
brachyure
brachyurous
Brachyurus
bracing
bracingly
bracingness
brack
brackebuschite
bracken
brackened
bracker
bracket
bracketing
bracketwise
brackish
brackishness
brackmard
bracky
Bracon
braconid
Braconidae
bract
bractea
bracteal
bracteate
bracted
bracteiform
bracteolate
bracteole
bracteose
bractless
bractlet
Brad
brad
bradawl
Bradbury
Bradburya
bradenhead
Bradford
Bradley
bradmaker
Bradshaw
bradsot
bradyacousia
bradycardia
bradycauma
bradycinesia
bradycrotic
bradydactylia
bradyesthesia
bradyglossia
bradykinesia
bradykinetic
bradylalia
bradylexia
bradylogia
bradynosus
bradypepsia
bradypeptic
bradyphagia
bradyphasia
bradyphemia
bradyphrasia
bradyphrenia
bradypnea
bradypnoea
bradypod
bradypode
Bradypodidae
bradypodoid
Bradypus
bradyseism
bradyseismal
bradyseismic
bradyseismical
bradyseismism
bradyspermatism
bradysphygmia
bradystalsis
bradyteleocinesia
bradyteleokinesis
bradytocia
bradytrophic
bradyuria
brae
braeface
braehead
braeman
braeside
brag
braggardism
braggart
braggartism
braggartly
braggartry
braggat
bragger
braggery
bragget
bragging
braggingly
braggish
braggishly
Bragi
bragite
bragless
braguette
Brahm
Brahma
brahmachari
Brahmahood
Brahmaic
Brahman
Brahmana
Brahmanaspati
Brahmanda
Brahmaness
Brahmanhood
Brahmani
Brahmanic
Brahmanical
Brahmanism
Brahmanist
Brahmanistic
Brahmanize
Brahmany
Brahmi
Brahmic
Brahmin
Brahminic
Brahminism
Brahmoism
Brahmsian
Brahmsite
Brahui
braid
braided
braider
braiding
Braidism
Braidist
brail
Braille
Braillist
brain
brainache
braincap
braincraft
brainer
brainfag
brainge
braininess
brainless
brainlessly
brainlessness
brainlike
brainpan
brains
brainsick
brainsickly
brainsickness
brainstone
brainward
brainwash
brainwasher
brainwashing
brainwater
brainwood
brainwork
brainworker
brainy
braird
braireau
brairo
braise
brake
brakeage
brakehand
brakehead
brakeless
brakeload
brakemaker
brakemaking
brakeman
braker
brakeroot
brakesman
brakie
braky
Bram
Bramantesque
Bramantip
bramble
brambleberry
bramblebush
brambled
brambling
brambly
brambrack
Bramia
bran
brancard
branch
branchage
branched
Branchellion
brancher
branchery
branchful
branchi
branchia
branchiae
branchial
Branchiata
branchiate
branchicolous
branchiferous
branchiform
branchihyal
branchiness
branching
Branchiobdella
branchiocardiac
branchiogenous
branchiomere
branchiomeric
branchiomerism
branchiopallial
branchiopod
Branchiopoda
branchiopodan
branchiopodous
Branchiopulmonata
branchiopulmonate
branchiosaur
Branchiosauria
branchiosaurian
Branchiosaurus
branchiostegal
Branchiostegidae
branchiostegite
branchiostegous
Branchiostoma
branchiostomid
Branchiostomidae
Branchipodidae
Branchipus
branchireme
Branchiura
branchiurous
branchless
branchlet
branchlike
branchling
branchman
branchstand
branchway
branchy
brand
branded
Brandenburg
Brandenburger
brander
brandering
Brandi
brandied
brandify
brandise
brandish
brandisher
brandisite
brandless
brandling
Brandon
brandreth
Brandy
brandy
brandyball
brandyman
brandywine
brangle
brangled
branglement
brangler
brangling
branial
brank
brankie
brankursine
branle
branner
brannerite
branny
bransle
bransolder
brant
Branta
brantail
brantness
Brasenia
brash
brashiness
brashness
brashy
brasiletto
brasque
brass
brassage
brassard
brassart
Brassavola
brassbound
brassbounder
brasse
brasser
brasset
Brassia
brassic
Brassica
Brassicaceae
brassicaceous
brassidic
brassie
brassiere
brassily
brassiness
brassish
brasslike
brassware
brasswork
brassworker
brassworks
brassy
brassylic
brat
bratling
bratstvo
brattach
brattice
bratticer
bratticing
brattie
brattish
brattishing
brattle
brauna
Brauneberger
Brauneria
braunite
Brauronia
Brauronian
Brava
bravade
bravado
bravadoism
brave
bravehearted
bravely
braveness
braver
bravery
braving
bravish
bravo
bravoite
bravura
bravuraish
braw
brawl
brawler
brawling
brawlingly
brawlsome
brawly
brawlys
brawn
brawned
brawnedness
brawner
brawnily
brawniness
brawny
braws
braxy
bray
brayer
brayera
brayerin
braystone
braza
braze
brazen
brazenface
brazenfaced
brazenfacedly
brazenly
brazenness
brazer
brazera
brazier
braziery
brazil
brazilein
brazilette
Brazilian
brazilin
brazilite
brazilwood
breach
breacher
breachful
breachy
bread
breadbasket
breadberry
breadboard
breadbox
breadearner
breadearning
breaden
breadfruit
breadless
breadlessness
breadmaker
breadmaking
breadman
breadnut
breadroot
breadseller
breadstuff
breadth
breadthen
breadthless
breadthriders
breadthways
breadthwise
breadwinner
breadwinning
breaghe
break
breakable
breakableness
breakably
breakage
breakaway
breakax
breakback
breakbones
breakdown
breaker
breakerman
breakfast
breakfaster
breakfastless
breaking
breakless
breakneck
breakoff
breakout
breakover
breakshugh
breakstone
breakthrough
breakup
breakwater
breakwind
bream
breards
breast
breastband
breastbeam
breastbone
breasted
breaster
breastfeeding
breastful
breastheight
breasthook
breastie
breasting
breastless
breastmark
breastpiece
breastpin
breastplate
breastplow
breastrail
breastrope
breastsummer
breastweed
breastwise
breastwood
breastwork
breath
breathable
breathableness
breathe
breathed
breather
breathful
breathiness
breathing
breathingly
breathless
breathlessly
breathlessness
breathseller
breathy
breba
breccia
breccial
brecciated
brecciation
brecham
Brechites
breck
brecken
bred
bredbergite
brede
bredi
bree
breech
breechblock
breechcloth
breechclout
breeched
breeches
breechesflower
breechesless
breeching
breechless
breechloader
breed
breedable
breedbate
breeder
breediness
breeding
breedy
breek
breekless
breekums
breeze
breezeful
breezeless
breezelike
breezeway
breezily
breeziness
breezy
bregma
bregmata
bregmate
bregmatic
brehon
brehonship
brei
breislakite
breithauptite
brekkle
brelaw
breloque
breme
bremely
bremeness
Bremia
bremsstrahlung
Brenda
Brendan
Brender
brennage
Brent
brent
Brenthis
brephic
Brescian
Bret
bret
bretelle
bretesse
breth
brethren
Breton
Bretonian
Bretschneideraceae
Brett
brett
brettice
Bretwalda
Bretwaldadom
Bretwaldaship
breunnerite
breva
breve
brevet
brevetcy
breviary
breviate
breviature
brevicaudate
brevicipitid
Brevicipitidae
breviconic
brevier
brevifoliate
breviger
brevilingual
breviloquence
breviloquent
breviped
brevipen
brevipennate
breviradiate
brevirostral
brevirostrate
Brevirostrines
brevit
brevity
brew
brewage
brewer
brewership
brewery
brewhouse
brewing
brewis
brewmaster
brewst
brewster
brewsterite
brey
Brian
briar
briarberry
Briard
Briarean
Briareus
briarroot
bribe
bribee
bribegiver
bribegiving
bribemonger
briber
bribery
bribetaker
bribetaking
bribeworthy
Bribri
brichen
brichette
brick
brickbat
brickcroft
brickel
bricken
brickfield
brickfielder
brickhood
bricking
brickish
brickkiln
bricklayer
bricklaying
brickle
brickleness
bricklike
brickliner
bricklining
brickly
brickmaker
brickmaking
brickmason
brickset
bricksetter
bricktimber
brickwise
brickwork
bricky
brickyard
bricole
bridal
bridale
bridaler
bridally
Bride
bride
bridebed
bridebowl
bridecake
bridechamber
bridecup
bridegod
bridegroom
bridegroomship
bridehead
bridehood
brideknot
bridelace
brideless
bridelike
bridely
bridemaid
bridemaiden
bridemaidship
brideship
bridesmaid
bridesmaiding
bridesman
bridestake
bridewain
brideweed
bridewell
bridewort
bridge
bridgeable
bridgeboard
bridgebote
bridgebuilder
bridgebuilding
bridged
bridgehead
bridgekeeper
bridgeless
bridgelike
bridgemaker
bridgemaking
bridgeman
bridgemaster
bridgepot
Bridger
bridger
Bridget
bridgetree
bridgeward
bridgewards
bridgeway
bridgework
bridging
bridle
bridled
bridleless
bridleman
bridler
bridling
bridoon
brief
briefing
briefless
brieflessly
brieflessness
briefly
briefness
briefs
brier
brierberry
briered
brierroot
brierwood
briery
brieve
brig
brigade
brigadier
brigadiership
brigalow
brigand
brigandage
brigander
brigandine
brigandish
brigandishly
brigandism
Brigantes
Brigantia
brigantine
brigatry
brigbote
brigetty
Briggs
Briggsian
Brighella
Brighid
bright
brighten
brightener
brightening
Brighteyes
brighteyes
brightish
brightly
brightness
brightsmith
brightsome
brightsomeness
brightwork
Brigid
Brigittine
brill
brilliance
brilliancy
brilliandeer
brilliant
brilliantine
brilliantly
brilliantness
brilliantwise
brilliolette
brillolette
brills
brim
brimborion
brimborium
brimful
brimfully
brimfulness
briming
brimless
brimmed
brimmer
brimming
brimmingly
brimstone
brimstonewort
brimstony
brin
brindlish
brine
brinehouse
brineless
brineman
briner
bring
bringal
bringall
bringer
brininess
brinish
brinishness
brinjal
brinjarry
brink
brinkless
briny
brioche
briolette
brique
briquette
brisk
brisken
brisket
briskish
briskly
briskness
brisling
brisque
briss
Brissotin
Brissotine
bristle
bristlebird
bristlecone
bristled
bristleless
bristlelike
bristler
bristletail
bristlewort
bristliness
bristly
Bristol
brisure
brit
Britain
Britannia
Britannian
Britannic
Britannically
britchka
brith
brither
Briticism
British
Britisher
Britishhood
Britishism
Britishly
Britishness
Briton
Britoness
britska
Brittany
britten
brittle
brittlebush
brittlely
brittleness
brittlestem
brittlewood
brittlewort
brittling
Briza
brizz
broach
broacher
broad
broadacre
broadax
broadbill
Broadbrim
broadbrim
broadcast
broadcaster
broadcloth
broaden
broadhead
broadhearted
broadhorn
broadish
broadleaf
broadloom
broadly
broadmouth
broadness
broadpiece
broadshare
broadsheet
broadside
broadspread
broadsword
broadtail
broadthroat
Broadway
broadway
Broadwayite
broadways
broadwife
broadwise
brob
Brobdingnag
Brobdingnagian
brocade
brocaded
brocard
brocardic
brocatel
brocatello
broccoli
broch
brochan
brochant
brochantite
broche
brochette
brochidodromous
brocho
brochure
brock
brockage
brocked
brocket
brockle
brod
brodder
brodeglass
brodequin
broderer
Brodiaea
Brodie
brog
brogan
brogger
broggerite
broggle
brogue
brogueful
brogueneer
broguer
broguery
broguish
broider
broiderer
broideress
broidery
broigne
broil
broiler
broiling
broilingly
brokage
broke
broken
brokenhearted
brokenheartedly
brokenheartedness
brokenly
brokenness
broker
brokerage
brokeress
brokership
broking
brolga
broll
brolly
broma
bromacetanilide
bromacetate
bromacetic
bromacetone
bromal
bromalbumin
bromamide
bromargyrite
bromate
bromaurate
bromauric
brombenzamide
brombenzene
brombenzyl
bromcamphor
bromcresol
brome
bromeigon
Bromeikon
bromeikon
Bromelia
Bromeliaceae
bromeliaceous
bromeliad
bromelin
bromellite
bromethyl
bromethylene
bromgelatin
bromhidrosis
bromhydrate
bromhydric
Bromian
bromic
bromide
bromidic
bromidically
bromidrosis
brominate
bromination
bromindigo
bromine
brominism
brominize
bromiodide
Bromios
bromism
bromite
Bromius
bromization
bromize
bromizer
bromlite
bromoacetone
bromoaurate
bromoauric
bromobenzene
bromobenzyl
bromocamphor
bromochlorophenol
bromocresol
bromocyanidation
bromocyanide
bromocyanogen
bromoethylene
bromoform
bromogelatin
bromohydrate
bromohydrin
bromoil
bromoiodide
bromoiodism
bromoiodized
bromoketone
bromol
bromomania
bromomenorrhea
bromomethane
bromometric
bromometrical
bromometrically
bromometry
bromonaphthalene
bromophenol
bromopicrin
bromopnea
bromoprotein
bromothymol
bromous
bromphenol
brompicrin
bromthymol
bromuret
Bromus
bromvogel
bromyrite
bronc
bronchadenitis
bronchi
bronchia
bronchial
bronchially
bronchiarctia
bronchiectasis
bronchiectatic
bronchiloquy
bronchiocele
bronchiocrisis
bronchiogenic
bronchiolar
bronchiole
bronchioli
bronchiolitis
bronchiolus
bronchiospasm
bronchiostenosis
bronchitic
bronchitis
bronchium
bronchoadenitis
bronchoalveolar
bronchoaspergillosis
bronchoblennorrhea
bronchocavernous
bronchocele
bronchocephalitis
bronchoconstriction
bronchoconstrictor
bronchodilatation
bronchodilator
bronchoegophony
bronchoesophagoscopy
bronchogenic
bronchohemorrhagia
broncholemmitis
broncholith
broncholithiasis
bronchomotor
bronchomucormycosis
bronchomycosis
bronchopathy
bronchophonic
bronchophony
bronchophthisis
bronchoplasty
bronchoplegia
bronchopleurisy
bronchopneumonia
bronchopneumonic
bronchopulmonary
bronchorrhagia
bronchorrhaphy
bronchorrhea
bronchoscope
bronchoscopic
bronchoscopist
bronchoscopy
bronchospasm
bronchostenosis
bronchostomy
bronchotetany
bronchotome
bronchotomist
bronchotomy
bronchotracheal
bronchotyphoid
bronchotyphus
bronchovesicular
bronchus
bronco
broncobuster
brongniardite
bronk
Bronteana
bronteon
brontephobia
Brontesque
bronteum
brontide
brontogram
brontograph
brontolite
brontology
brontometer
brontophobia
Brontops
Brontosaurus
brontoscopy
Brontotherium
Brontozoum
Bronx
bronze
bronzed
bronzelike
bronzen
bronzer
bronzesmith
bronzewing
bronzify
bronzine
bronzing
bronzite
bronzitite
bronzy
broo
brooch
brood
brooder
broodiness
brooding
broodingly
broodless
broodlet
broodling
broody
brook
brookable
Brooke
brooked
brookflower
brookie
brookite
brookless
brooklet
brooklike
brooklime
Brooklynite
brookside
brookweed
brooky
brool
broom
broombush
broomcorn
broomer
broommaker
broommaking
broomrape
broomroot
broomshank
broomstaff
broomstick
broomstraw
broomtail
broomweed
broomwood
broomwort
broomy
broon
broose
broozled
brose
Brosimum
brosot
brosy
brot
brotan
brotany
broth
brothel
brotheler
brothellike
brothelry
brother
brotherhood
brotherless
brotherlike
brotherliness
brotherly
brothership
Brotherton
brotherwort
brothy
brotocrystal
Brotula
brotulid
Brotulidae
brotuliform
brough
brougham
brought
Broussonetia
brow
browache
Browallia
browallia
browband
browbeat
browbeater
browbound
browden
browed
browis
browless
browman
brown
brownback
browner
Brownian
brownie
browniness
browning
Browningesque
brownish
Brownism
Brownist
Brownistic
Brownistical
brownly
brownness
brownout
brownstone
browntail
browntop
brownweed
brownwort
browny
browpiece
browpost
browse
browser
browsick
browsing
browst
bruang
Bruce
Brucella
brucellosis
Bruchidae
Bruchus
brucia
brucina
brucine
brucite
bruckle
bruckled
bruckleness
Bructeri
brugh
brugnatellite
bruin
bruise
bruiser
bruisewort
bruising
bruit
bruiter
bruke
Brule
brulee
brulyie
brulyiement
brumal
Brumalia
brumby
brume
Brummagem
brummagem
brumous
brumstane
brumstone
brunch
Brunella
Brunellia
Brunelliaceae
brunelliaceous
brunet
brunetness
brunette
brunetteness
Brunfelsia
brunissure
Brunistic
brunneous
Brunnichia
Bruno
Brunonia
Brunoniaceae
Brunonian
Brunonism
Brunswick
brunswick
brunt
bruscus
brush
brushable
brushball
brushbird
brushbush
brushed
brusher
brushes
brushet
brushful
brushiness
brushing
brushite
brushland
brushless
brushlessness
brushlet
brushlike
brushmaker
brushmaking
brushman
brushoff
brushproof
brushwood
brushwork
brushy
brusque
brusquely
brusqueness
Brussels
brustle
brut
Bruta
brutage
brutal
brutalism
brutalist
brutalitarian
brutality
brutalization
brutalize
brutally
brute
brutedom
brutelike
brutely
bruteness
brutification
brutify
bruting
brutish
brutishly
brutishness
brutism
brutter
Brutus
bruzz
Bryaceae
bryaceous
Bryales
Bryan
Bryanism
Bryanite
Bryanthus
Bryce
bryogenin
bryological
bryologist
bryology
Bryonia
bryonidin
bryonin
bryony
Bryophyllum
Bryophyta
bryophyte
bryophytic
Bryozoa
bryozoan
bryozoon
bryozoum
Brython
Brythonic
Bryum
Bu
bu
bual
buaze
bub
buba
bubal
bubaline
Bubalis
bubalis
Bubastid
Bubastite
bubble
bubbleless
bubblement
bubbler
bubbling
bubblingly
bubblish
bubbly
bubby
bubbybush
Bube
bubinga
Bubo
bubo
buboed
bubonalgia
bubonic
Bubonidae
bubonocele
bubukle
bucare
bucca
buccal
buccally
buccan
buccaneer
buccaneerish
buccate
Buccellarius
buccina
buccinal
buccinator
buccinatory
Buccinidae
bucciniform
buccinoid
Buccinum
Bucco
buccobranchial
buccocervical
buccogingival
buccolabial
buccolingual
bucconasal
Bucconidae
Bucconinae
buccopharyngeal
buccula
Bucculatrix
bucentaur
Bucephala
Bucephalus
Buceros
Bucerotes
Bucerotidae
Bucerotinae
Buchanan
Buchanite
buchite
Buchloe
Buchmanism
Buchmanite
Buchnera
buchnerite
buchonite
buchu
buck
buckaroo
buckberry
buckboard
buckbrush
buckbush
bucked
buckeen
bucker
bucket
bucketer
bucketful
bucketing
bucketmaker
bucketmaking
bucketman
buckety
buckeye
buckhorn
buckhound
buckie
bucking
buckish
buckishly
buckishness
buckjump
buckjumper
bucklandite
buckle
buckled
buckleless
buckler
Buckleya
buckling
bucklum
bucko
buckplate
buckpot
buckra
buckram
bucksaw
buckshee
buckshot
buckskin
buckskinned
buckstall
buckstay
buckstone
bucktail
buckthorn
bucktooth
buckwagon
buckwash
buckwasher
buckwashing
buckwheat
buckwheater
buckwheatlike
Bucky
bucky
bucoliast
bucolic
bucolical
bucolically
bucolicism
Bucorvinae
Bucorvus
bucrane
bucranium
Bud
bud
buda
buddage
budder
Buddh
Buddha
Buddhahood
Buddhaship
buddhi
Buddhic
Buddhism
Buddhist
Buddhistic
Buddhistical
Buddhology
budding
buddle
Buddleia
buddleman
buddler
buddy
budge
budger
budgeree
budgereegah
budgerigar
budgerow
budget
budgetary
budgeteer
budgeter
budgetful
Budh
budless
budlet
budlike
budmash
Budorcas
budtime
Budukha
Buduma
budwood
budworm
budzat
Buettneria
Buettneriaceae
bufagin
buff
buffable
buffalo
buffaloback
buffball
buffcoat
buffed
buffer
buffet
buffeter
buffing
buffle
bufflehead
bufflehorn
buffont
buffoon
buffoonery
buffoonesque
buffoonish
buffoonism
buffware
buffy
bufidin
bufo
Bufonidae
bufonite
bufotalin
bug
bugaboo
bugan
bugbane
bugbear
bugbeardom
bugbearish
bugbite
bugdom
bugfish
bugger
buggery
bugginess
buggy
buggyman
bughead
bughouse
Bugi
Buginese
Buginvillaea
bugle
bugled
bugler
buglet
bugleweed
buglewort
bugloss
bugologist
bugology
bugproof
bugre
bugseed
bugweed
bugwort
buhl
buhr
buhrstone
build
buildable
builder
building
buildingless
buildress
buildup
built
buirdly
buisson
buist
Bukat
Bukeyef
bukh
Bukidnon
bukshi
bulak
Bulanda
bulb
bulbaceous
bulbar
bulbed
bulbiferous
bulbiform
bulbil
Bulbilis
bulbilla
bulbless
bulblet
bulblike
bulbocapnin
bulbocapnine
bulbocavernosus
bulbocavernous
Bulbochaete
Bulbocodium
bulbomedullary
bulbomembranous
bulbonuclear
Bulbophyllum
bulborectal
bulbose
bulbospinal
bulbotuber
bulbous
bulbul
bulbule
bulby
bulchin
Bulgar
Bulgari
Bulgarian
Bulgaric
Bulgarophil
bulge
bulger
bulginess
bulgy
bulimia
bulimiac
bulimic
bulimiform
bulimoid
Bulimulidae
Bulimus
bulimy
bulk
bulked
bulker
bulkhead
bulkheaded
bulkily
bulkiness
bulkish
bulky
bull
bulla
bullace
bullamacow
bullan
bullary
bullate
bullated
bullation
bullback
bullbaiting
bullbat
bullbeggar
bullberry
bullbird
bullboat
bullcart
bullcomber
bulldog
bulldogged
bulldoggedness
bulldoggy
bulldogism
bulldoze
bulldozer
buller
bullet
bulleted
bullethead
bulletheaded
bulletheadedness
bulletin
bulletless
bulletlike
bulletmaker
bulletmaking
bulletproof
bulletwood
bullety
bullfeast
bullfight
bullfighter
bullfighting
bullfinch
bullfist
bullflower
bullfoot
bullfrog
bullhead
bullheaded
bullheadedly
bullheadedness
bullhide
bullhoof
bullhorn
Bullidae
bulliform
bullimong
bulling
bullion
bullionism
bullionist
bullionless
bullish
bullishly
bullishness
bullism
bullit
bullneck
bullnose
bullnut
bullock
bullocker
Bullockite
bullockman
bullocky
Bullom
bullous
bullpates
bullpoll
bullpout
bullskin
bullsticker
bullsucker
bullswool
bulltoad
bullule
bullweed
bullwhack
bullwhacker
bullwhip
bullwort
bully
bullyable
bullydom
bullyhuff
bullying
bullyism
bullyrag
bullyragger
bullyragging
bullyrook
bulrush
bulrushlike
bulrushy
bulse
bult
bulter
bultey
bultong
bultow
bulwand
bulwark
bum
bumbailiff
bumbailiffship
bumbarge
bumbaste
bumbaze
bumbee
bumbershoot
bumble
bumblebee
bumbleberry
Bumbledom
bumblefoot
bumblekite
bumblepuppy
bumbler
bumbo
bumboat
bumboatman
bumboatwoman
bumclock
Bumelia
bumicky
bummalo
bummaree
bummed
bummer
bummerish
bummie
bumming
bummler
bummock
bump
bumpee
bumper
bumperette
bumpily
bumpiness
bumping
bumpingly
bumpkin
bumpkinet
bumpkinish
bumpkinly
bumpology
bumptious
bumptiously
bumptiousness
bumpy
bumtrap
bumwood
bun
Buna
buna
buncal
bunce
bunch
bunchberry
buncher
bunchflower
bunchily
bunchiness
bunchy
buncombe
bund
Bunda
Bundahish
Bundeli
bunder
Bundestag
bundle
bundler
bundlerooted
bundlet
bundobust
bundook
Bundu
bundweed
bundy
bunemost
bung
Bunga
bungaloid
bungalow
bungarum
Bungarus
bungee
bungerly
bungey
bungfu
bungfull
bunghole
bungle
bungler
bunglesome
bungling
bunglingly
bungmaker
bungo
bungwall
bungy
Buninahua
bunion
bunk
bunker
bunkerman
bunkery
bunkhouse
bunkie
bunkload
bunko
bunkum
bunnell
bunny
bunnymouth
bunodont
Bunodonta
bunolophodont
Bunomastodontidae
bunoselenodont
bunsenite
bunt
buntal
bunted
Bunter
bunter
bunting
buntline
bunton
bunty
bunya
bunyah
bunyip
Bunyoro
buoy
buoyage
buoyance
buoyancy
buoyant
buoyantly
buoyantness
Buphaga
buphthalmia
buphthalmic
Buphthalmum
bupleurol
Bupleurum
buplever
buprestid
Buprestidae
buprestidan
Buprestis
bur
buran
burao
Burbank
burbank
burbankian
Burbankism
burbark
Burberry
burble
burbler
burbly
burbot
burbush
burd
burdalone
burden
burdener
burdenless
burdenous
burdensome
burdensomely
burdensomeness
burdie
Burdigalian
burdock
burdon
bure
bureau
bureaucracy
bureaucrat
bureaucratic
bureaucratical
bureaucratically
bureaucratism
bureaucratist
bureaucratization
bureaucratize
bureaux
burel
burele
buret
burette
burfish
burg
burgage
burgality
burgall
burgee
burgensic
burgeon
burgess
burgessdom
burggrave
burgh
burghal
burghalpenny
burghbote
burghemot
burgher
burgherage
burgherdom
burgheress
burgherhood
burghermaster
burghership
burghmaster
burghmoot
burglar
burglarious
burglariously
burglarize
burglarproof
burglary
burgle
burgomaster
burgomastership
burgonet
burgoo
burgoyne
burgrave
burgraviate
burgul
Burgundian
Burgundy
burgus
burgware
burhead
Burhinidae
Burhinus
Buri
buri
burial
burian
Buriat
buried
burier
burin
burinist
burion
buriti
burka
burke
burker
burkundaz
burl
burlap
burled
burler
burlesque
burlesquely
burlesquer
burlet
burletta
Burley
burlily
burliness
Burlington
burly
Burman
Burmannia
Burmanniaceae
burmanniaceous
Burmese
burmite
burn
burnable
burnbeat
burned
burner
burnet
burnetize
burnfire
burnie
burniebee
burning
burningly
burnish
burnishable
burnisher
burnishing
burnishment
burnoose
burnoosed
burnous
burnout
burnover
Burnsian
burnside
burnsides
burnt
burntweed
burnut
burnwood
burny
buro
burp
burr
burrah
burrawang
burred
burrel
burrer
burrgrailer
burring
burrish
burrito
burrknot
burro
burrobrush
burrow
burroweed
burrower
burrowstown
burry
bursa
bursal
bursar
bursarial
bursarship
bursary
bursate
bursattee
bursautee
burse
burseed
Bursera
Burseraceae
Burseraceous
bursicle
bursiculate
bursiform
bursitis
burst
burster
burstwort
burt
burthenman
burton
burtonization
burtonize
burucha
Burushaski
Burut
burweed
bury
burying
bus
Busaos
busby
buscarl
buscarle
bush
bushbeater
bushbuck
bushcraft
bushed
bushel
busheler
bushelful
bushelman
bushelwoman
busher
bushfighter
bushfighting
bushful
bushhammer
bushi
bushily
bushiness
bushing
bushland
bushless
bushlet
bushlike
bushmaker
bushmaking
Bushman
bushmanship
bushmaster
bushment
Bushongo
bushranger
bushranging
bushrope
bushveld
bushwa
bushwhack
bushwhacker
bushwhacking
bushwife
bushwoman
bushwood
bushy
busied
busily
busine
business
businesslike
businesslikeness
businessman
businesswoman
busk
busked
busker
busket
buskin
buskined
buskle
busky
busman
buss
busser
bussock
bussu
bust
bustard
busted
bustee
buster
busthead
bustic
busticate
bustle
bustled
bustler
bustling
bustlingly
busy
busybodied
busybody
busybodyish
busybodyism
busybodyness
Busycon
busyhead
busying
busyish
busyness
busywork
but
butadiene
butadiyne
butanal
butane
butanoic
butanol
butanolid
butanolide
butanone
butch
butcher
butcherbird
butcherdom
butcherer
butcheress
butchering
butcherless
butcherliness
butcherly
butcherous
butchery
Bute
Butea
butein
butene
butenyl
Buteo
buteonine
butic
butine
Butler
butler
butlerage
butlerdom
butleress
butlerism
butlerlike
butlership
butlery
butment
Butomaceae
butomaceous
Butomus
butoxy
butoxyl
Butsu
butt
butte
butter
butteraceous
butterback
butterball
butterbill
butterbird
butterbox
butterbump
butterbur
butterbush
buttercup
buttered
butterfat
butterfingered
butterfingers
butterfish
butterflower
butterfly
butterflylike
butterhead
butterine
butteriness
butteris
butterjags
butterless
butterlike
buttermaker
buttermaking
butterman
buttermilk
buttermonger
buttermouth
butternose
butternut
butterroot
butterscotch
butterweed
butterwife
butterwoman
butterworker
butterwort
butterwright
buttery
butteryfingered
buttgenbachite
butting
buttinsky
buttle
buttock
buttocked
buttocker
button
buttonball
buttonbur
buttonbush
buttoned
buttoner
buttonhold
buttonholder
buttonhole
buttonholer
buttonhook
buttonless
buttonlike
buttonmold
buttons
buttonweed
buttonwood
buttony
buttress
buttressless
buttresslike
buttstock
buttwoman
buttwood
butty
buttyman
butyl
butylamine
butylation
butylene
butylic
Butyn
butyne
butyr
butyraceous
butyral
butyraldehyde
butyrate
butyric
butyrically
butyrin
butyrinase
butyrochloral
butyrolactone
butyrometer
butyrometric
butyrone
butyrous
butyrousness
butyryl
Buxaceae
buxaceous
Buxbaumia
Buxbaumiaceae
buxerry
buxom
buxomly
buxomness
Buxus
buy
buyable
buyer
Buyides
buzane
buzylene
buzz
buzzard
buzzardlike
buzzardly
buzzer
buzzerphone
buzzgloak
buzzies
buzzing
buzzingly
buzzle
buzzwig
buzzy
by
Byblidaceae
Byblis
bycoket
bye
byee
byegaein
byeman
byepath
byerite
byerlite
byestreet
byeworker
byeworkman
bygane
byganging
bygo
bygoing
bygone
byhand
bylaw
bylawman
byname
bynedestin
Bynin
byon
byordinar
byordinary
byous
byously
bypass
bypasser
bypast
bypath
byplay
byre
byreman
byrewards
byrewoman
byrlaw
byrlawman
byrnie
byroad
Byron
Byronesque
Byronian
Byroniana
Byronic
Byronically
Byronics
Byronish
Byronism
Byronist
Byronite
Byronize
byrrus
Byrsonima
byrthynsak
Bysacki
bysen
bysmalith
byspell
byssaceous
byssal
byssiferous
byssin
byssine
byssinosis
byssogenous
byssoid
byssolite
byssus
bystander
bystreet
byth
bytime
bytownite
bytownitite
bywalk
bywalker
byway
bywoner
byword
bywork
Byzantian
Byzantine
Byzantinesque
Byzantinism
Byzantinize
C
c
ca
caam
caama
caaming
caapeba
caatinga
cab
caba
cabaan
caback
cabaho
cabal
cabala
cabalassou
cabaletta
cabalic
cabalism
cabalist
cabalistic
cabalistical
cabalistically
caballer
caballine
caban
cabana
cabaret
cabas
cabasset
cabassou
cabbage
cabbagehead
cabbagewood
cabbagy
cabber
cabble
cabbler
cabby
cabda
cabdriver
cabdriving
cabellerote
caber
cabernet
cabestro
cabezon
cabilliau
cabin
Cabinda
cabinet
cabinetmaker
cabinetmaking
cabinetry
cabinetwork
cabinetworker
cabinetworking
cabio
Cabirean
Cabiri
Cabiria
Cabirian
Cabiric
Cabiritic
cable
cabled
cablegram
cableless
cablelike
cableman
cabler
cablet
cableway
cabling
cabman
cabob
caboceer
cabochon
cabocle
Cabomba
Cabombaceae
caboodle
cabook
caboose
caboshed
cabot
cabotage
cabree
cabrerite
cabreuva
cabrilla
cabriole
cabriolet
cabrit
cabstand
cabureiba
cabuya
Caca
Cacajao
Cacalia
cacam
Cacan
Cacana
cacanthrax
cacao
Cacara
Cacatua
Cacatuidae
Cacatuinae
Caccabis
cacesthesia
cacesthesis
cachalot
cachaza
cache
cachectic
cachemia
cachemic
cachet
cachexia
cachexic
cachexy
cachibou
cachinnate
cachinnation
cachinnator
cachinnatory
cacholong
cachou
cachrys
cachucha
cachunde
Cacicus
cacidrosis
caciocavallo
cacique
caciqueship
caciquism
cack
cackerel
cackle
cackler
cacocholia
cacochroia
cacochylia
cacochymia
cacochymic
cacochymical
cacochymy
cacocnemia
cacodaemoniac
cacodaemonial
cacodaemonic
cacodemon
cacodemonia
cacodemoniac
cacodemonial
cacodemonic
cacodemonize
cacodemonomania
cacodontia
cacodorous
cacodoxian
cacodoxical
cacodoxy
cacodyl
cacodylate
cacodylic
cacoeconomy
cacoepist
cacoepistic
cacoepy
cacoethes
cacoethic
cacogalactia
cacogastric
cacogenesis
cacogenic
cacogenics
cacogeusia
cacoglossia
cacographer
cacographic
cacographical
cacography
cacology
cacomagician
cacomelia
cacomistle
cacomixl
cacomixle
cacomorphia
cacomorphosis
caconychia
caconym
caconymic
cacoon
cacopathy
cacopharyngia
cacophonia
cacophonic
cacophonical
cacophonically
cacophonist
cacophonize
cacophonous
cacophonously
cacophony
cacophthalmia
cacoplasia
cacoplastic
cacoproctia
cacorhythmic
cacorrhachis
cacorrhinia
cacosmia
cacospermia
cacosplanchnia
cacostomia
cacothansia
cacotheline
cacothesis
cacothymia
cacotrichia
cacotrophia
cacotrophic
cacotrophy
cacotype
cacoxene
cacoxenite
cacozeal
cacozealous
cacozyme
Cactaceae
cactaceous
Cactales
cacti
cactiform
cactoid
Cactus
cacuminal
cacuminate
cacumination
cacuminous
cacur
cad
cadalene
cadamba
cadastral
cadastration
cadastre
cadaver
cadaveric
cadaverine
cadaverize
cadaverous
cadaverously
cadaverousness
cadbait
cadbit
cadbote
caddice
caddiced
Caddie
caddie
caddis
caddised
caddish
caddishly
caddishness
caddle
Caddo
Caddoan
caddow
caddy
cade
cadelle
cadence
cadenced
cadency
cadent
cadential
cadenza
cader
caderas
Cadet
cadet
cadetcy
cadetship
cadette
cadew
cadge
cadger
cadgily
cadginess
cadgy
cadi
cadilesker
cadinene
cadism
cadiueio
cadjan
cadlock
Cadmean
cadmia
cadmic
cadmide
cadmiferous
cadmium
cadmiumize
Cadmopone
Cadmus
cados
cadrans
cadre
cadua
caduac
caduca
caducary
caducean
caduceus
caduciary
caducibranch
Caducibranchiata
caducibranchiate
caducicorn
caducity
caducous
cadus
Cadwal
Cadwallader
cadweed
caeca
caecal
caecally
caecectomy
caeciform
Caecilia
Caeciliae
caecilian
Caeciliidae
caecitis
caecocolic
caecostomy
caecotomy
caecum
Caedmonian
Caedmonic
Caelian
caelometer
Caelum
Caelus
Caenogaea
Caenogaean
Caenolestes
caenostylic
caenostyly
caeoma
caeremoniarius
Caerphilly
Caesalpinia
Caesalpiniaceae
caesalpiniaceous
Caesar
Caesardom
Caesarean
Caesareanize
Caesarian
Caesarism
Caesarist
Caesarize
caesaropapacy
caesaropapism
caesaropopism
Caesarotomy
Caesarship
caesious
caesura
caesural
caesuric
cafeneh
cafenet
cafeteria
caffa
caffeate
caffeic
caffeina
caffeine
caffeinic
caffeinism
caffeism
caffeol
caffeone
caffetannic
caffetannin
caffiso
caffle
caffoline
caffoy
cafh
cafiz
caftan
caftaned
cag
Cagayan
cage
caged
cageful
cageless
cagelike
cageling
cageman
cager
cagester
cagework
cagey
caggy
cagily
cagit
cagmag
Cagn
Cahenslyism
Cahill
cahincic
Cahita
cahiz
Cahnite
Cahokia
cahoot
cahot
cahow
Cahuapana
Cahuilla
caickle
caid
cailcedra
cailleach
caimacam
caimakam
caiman
caimitillo
caimito
Cain
cain
Caingang
Caingua
Cainian
Cainish
Cainism
Cainite
Cainitic
caique
caiquejee
Cairba
caird
Cairene
cairn
cairned
cairngorm
cairngorum
cairny
Cairo
caisson
caissoned
Caitanyas
Caite
caitiff
Cajan
Cajanus
cajeput
cajole
cajolement
cajoler
cajolery
cajoling
cajolingly
cajuela
Cajun
cajun
cajuput
cajuputene
cajuputol
Cakavci
Cakchikel
cake
cakebox
cakebread
cakehouse
cakemaker
cakemaking
caker
cakette
cakewalk
cakewalker
cakey
Cakile
caky
cal
calaba
Calabar
Calabari
calabash
calabaza
calabazilla
calaber
calaboose
calabrasella
Calabrese
calabrese
Calabrian
calade
Caladium
calais
calalu
Calamagrostis
calamanco
calamansi
Calamariaceae
calamariaceous
Calamariales
calamarian
calamarioid
calamaroid
calamary
calambac
calambour
calamiferous
calamiform
calaminary
calamine
calamint
Calamintha
calamistral
calamistrum
calamite
calamitean
Calamites
calamitoid
calamitous
calamitously
calamitousness
calamity
Calamodendron
calamondin
Calamopitys
Calamospermae
Calamostachys
calamus
calander
Calandra
calandria
Calandridae
Calandrinae
Calandrinia
calangay
calantas
Calanthe
calapite
Calappa
Calappidae
Calas
calascione
calash
Calathea
calathian
calathidium
calathiform
calathiscus
calathus
Calatrava
calaverite
calbroben
calcaneal
calcaneoastragalar
calcaneoastragaloid
calcaneocuboid
calcaneofibular
calcaneonavicular
calcaneoplantar
calcaneoscaphoid
calcaneotibial
calcaneum
calcaneus
calcar
calcarate
Calcarea
calcareoargillaceous
calcareobituminous
calcareocorneous
calcareosiliceous
calcareosulphurous
calcareous
calcareously
calcareousness
calcariferous
calcariform
calcarine
calced
calceiform
calcemia
Calceolaria
calceolate
Calchaqui
Calchaquian
calcic
calciclase
calcicole
calcicolous
calcicosis
calciferol
Calciferous
calciferous
calcific
calcification
calcified
calciform
calcifugal
calcifuge
calcifugous
calcify
calcigenous
calcigerous
calcimeter
calcimine
calciminer
calcinable
calcination
calcinatory
calcine
calcined
calciner
calcinize
calciobiotite
calciocarnotite
calcioferrite
calcioscheelite
calciovolborthite
calcipexy
calciphile
calciphilia
calciphilous
calciphobe
calciphobous
calciphyre
calciprivic
calcisponge
Calcispongiae
calcite
calcitestaceous
calcitic
calcitrant
calcitrate
calcitreation
calcium
calcivorous
calcographer
calcographic
calcography
calcrete
calculability
calculable
Calculagraph
calculary
calculate
calculated
calculatedly
calculating
calculatingly
calculation
calculational
calculative
calculator
calculatory
calculi
calculiform
calculist
calculous
calculus
Calcydon
calden
caldron
calean
Caleb
Caledonia
Caledonian
caledonite
calefacient
calefaction
calefactive
calefactor
calefactory
calelectric
calelectrical
calelectricity
Calemes
calendal
calendar
calendarer
calendarial
calendarian
calendaric
calender
calenderer
calendric
calendrical
calendry
calends
Calendula
calendulin
calentural
calenture
calenturist
calepin
calescence
calescent
calf
calfbound
calfhood
calfish
calfkill
calfless
calflike
calfling
calfskin
Caliban
Calibanism
caliber
calibered
calibogus
calibrate
calibration
calibrator
calibre
Caliburn
Caliburno
calicate
calices
caliciform
calicle
calico
calicoback
calicoed
calicular
caliculate
Calicut
calid
calidity
caliduct
California
Californian
californite
californium
caliga
caligated
caliginous
caliginously
caligo
Calimeris
Calinago
calinda
calinut
caliological
caliologist
caliology
calipash
calipee
caliper
caliperer
calipers
caliph
caliphal
caliphate
caliphship
Calista
calistheneum
calisthenic
calisthenical
calisthenics
Calite
caliver
calix
Calixtin
Calixtus
calk
calkage
calker
calkin
calking
call
Calla
callable
callainite
callant
callboy
caller
callet
calli
Callianassa
Callianassidae
Calliandra
Callicarpa
Callicebus
callid
callidity
callidness
calligraph
calligrapha
calligrapher
calligraphic
calligraphical
calligraphically
calligraphist
calligraphy
calling
Callionymidae
Callionymus
Calliope
calliophone
Calliopsis
calliper
calliperer
Calliphora
calliphorid
Calliphoridae
calliphorine
callipygian
callipygous
Callirrhoe
Callisaurus
callisection
callisteia
Callistemon
Callistephus
Callithrix
callithump
callithumpian
Callitrichaceae
callitrichaceous
Callitriche
Callitrichidae
Callitris
callitype
callo
Callorhynchidae
Callorhynchus
callosal
callose
callosity
callosomarginal
callosum
callous
callously
callousness
Callovian
callow
callower
callowman
callowness
Calluna
callus
Callynteria
calm
calmant
calmative
calmer
calmierer
calmingly
calmly
calmness
calmy
Calocarpum
Calochortaceae
Calochortus
calodemon
calography
calomba
calomel
calomorphic
Calonectria
Calonyction
calool
Calophyllum
Calopogon
calor
calorescence
calorescent
caloric
caloricity
calorie
calorifacient
calorific
calorifical
calorifically
calorification
calorifics
calorifier
calorify
calorigenic
calorimeter
calorimetric
calorimetrical
calorimetrically
calorimetry
calorimotor
caloris
calorisator
calorist
Calorite
calorize
calorizer
Calosoma
Calotermes
calotermitid
Calotermitidae
Calothrix
calotte
calotype
calotypic
calotypist
caloyer
calp
calpac
calpack
calpacked
calpulli
Caltha
caltrap
caltrop
calumba
calumet
calumniate
calumniation
calumniative
calumniator
calumniatory
calumnious
calumniously
calumniousness
calumny
Calusa
calutron
Calvados
calvaria
calvarium
Calvary
Calvatia
calve
calved
calver
calves
Calvin
Calvinian
Calvinism
Calvinist
Calvinistic
Calvinistical
Calvinistically
Calvinize
calvish
calvities
calvity
calvous
calx
calycanth
Calycanthaceae
calycanthaceous
calycanthemous
calycanthemy
calycanthine
Calycanthus
calycate
Calyceraceae
calyceraceous
calyces
calyciferous
calycifloral
calyciflorate
calyciflorous
calyciform
calycinal
calycine
calycle
calycled
Calycocarpum
calycoid
calycoideous
Calycophora
Calycophorae
calycophoran
Calycozoa
calycozoan
calycozoic
calycozoon
calycular
calyculate
calyculated
calycule
calyculus
Calydon
Calydonian
Calymene
calymma
calyphyomy
calypsist
Calypso
calypso
calypsonian
calypter
Calypterae
Calyptoblastea
calyptoblastic
Calyptorhynchus
calyptra
Calyptraea
Calyptranthes
Calyptrata
Calyptratae
calyptrate
calyptriform
calyptrimorphous
calyptro
calyptrogen
Calyptrogyne
Calystegia
calyx
cam
camaca
Camacan
camagon
camail
camailed
Camaldolensian
Camaldolese
Camaldolesian
Camaldolite
Camaldule
Camaldulian
camalote
caman
camansi
camara
camaraderie
Camarasaurus
camarilla
camass
Camassia
camata
camatina
Camaxtli
camb
Camball
Cambalo
Cambarus
cambaye
camber
Cambeva
cambial
cambiform
cambiogenetic
cambism
cambist
cambistry
cambium
Cambodian
cambogia
cambrel
cambresine
Cambrian
Cambric
cambricleaf
cambuca
Cambuscan
Cambyuskan
Came
came
cameist
camel
camelback
cameleer
Camelid
Camelidae
Camelina
cameline
camelish
camelishness
camelkeeper
Camellia
Camelliaceae
camellike
camellin
Camellus
camelman
cameloid
Cameloidea
camelopard
Camelopardalis
Camelopardid
Camelopardidae
Camelopardus
camelry
Camelus
Camembert
Camenae
Camenes
cameo
cameograph
cameography
camera
cameral
cameralism
cameralist
cameralistic
cameralistics
cameraman
Camerata
camerate
camerated
cameration
camerier
Camerina
Camerinidae
camerist
camerlingo
Cameronian
Camestres
camilla
camillus
camion
camisado
Camisard
camise
camisia
camisole
camlet
camleteen
Cammarum
cammed
cammock
cammocky
camomile
camoodi
camoodie
Camorra
Camorrism
Camorrist
Camorrista
camouflage
camouflager
camp
Campa
campagna
campagnol
campaign
campaigner
campana
campane
campanero
Campanian
campaniform
campanile
campaniliform
campanilla
campanini
campanist
campanistic
campanologer
campanological
campanologically
campanologist
campanology
Campanula
Campanulaceae
campanulaceous
Campanulales
campanular
Campanularia
Campanulariae
campanularian
Campanularidae
Campanulatae
campanulate
campanulated
campanulous
Campaspe
Campbellism
Campbellite
campbellite
campcraft
Campe
Campephagidae
campephagine
Campephilus
camper
campestral
campfight
campfire
campground
camphane
camphanic
camphanone
camphanyl
camphene
camphine
camphire
campho
camphocarboxylic
camphoid
camphol
campholic
campholide
campholytic
camphor
camphoraceous
camphorate
camphoric
camphorize
camphorone
camphoronic
camphoroyl
camphorphorone
camphorwood
camphory
camphoryl
camphylene
Campignian
campimeter
campimetrical
campimetry
Campine
campion
cample
campmaster
campo
Campodea
campodeid
Campodeidae
campodeiform
campodeoid
campody
Camponotus
campoo
camporee
campshed
campshedding
campsheeting
campshot
campstool
camptodrome
camptonite
Camptosorus
campulitropal
campulitropous
campus
campward
campylite
campylodrome
campylometer
Campyloneuron
campylospermous
campylotropal
campylotropous
camshach
camshachle
camshaft
camstane
camstone
camuning
camus
camused
camwood
can
Cana
Canaan
Canaanite
Canaanitess
Canaanitic
Canaanitish
canaba
Canacee
Canada
canada
Canadian
Canadianism
Canadianization
Canadianize
canadine
canadite
canadol
canaigre
canaille
canajong
canal
canalage
canalboat
canalicular
canaliculate
canaliculated
canaliculation
canaliculi
canaliculization
canaliculus
canaliferous
canaliform
canalization
canalize
canaller
canalling
canalman
canalside
Canamary
canamo
Cananaean
Cananga
Canangium
canape
canapina
canard
Canari
canari
Canarian
canarin
Canariote
Canarium
Canarsee
canary
canasta
canaster
canaut
Canavali
Canavalia
canavalin
Canberra
cancan
cancel
cancelable
cancelation
canceleer
canceler
cancellarian
cancellate
cancellated
cancellation
cancelli
cancellous
cancellus
cancelment
cancer
cancerate
canceration
cancerdrops
cancered
cancerigenic
cancerism
cancerophobe
cancerophobia
cancerous
cancerously
cancerousness
cancerroot
cancerweed
cancerwort
canch
canchalagua
Canchi
Cancri
Cancrid
cancriform
cancrinite
cancrisocial
cancrivorous
cancrizans
cancroid
cancrophagous
cancrum
cand
Candace
candareen
candela
candelabra
candelabrum
candelilla
candent
candescence
candescent
candescently
candid
candidacy
candidate
candidateship
candidature
candidly
candidness
candied
candier
candify
Candiot
candiru
candle
candleball
candlebeam
candleberry
candlebomb
candlebox
candlefish
candleholder
candlelight
candlelighted
candlelighter
candlelighting
candlelit
candlemaker
candlemaking
Candlemas
candlenut
candlepin
candler
candlerent
candleshine
candleshrift
candlestand
candlestick
candlesticked
candlestickward
candlewaster
candlewasting
candlewick
candlewood
candlewright
candock
Candollea
Candolleaceae
candolleaceous
candor
candroy
candy
candymaker
candymaking
candys
candystick
candytuft
candyweed
cane
canebrake
canel
canelike
canella
Canellaceae
canellaceous
Canelo
canelo
caneology
canephor
canephore
canephoros
canephroi
caner
canescence
canescent
canette
canewise
canework
Canfield
canfieldite
canful
cangan
cangia
cangle
cangler
cangue
canhoop
Canichana
Canichanan
canicola
Canicula
canicular
canicule
canid
Canidae
Canidia
canille
caninal
canine
caniniform
caninity
caninus
canioned
canions
Canis
Canisiana
canistel
canister
canities
canjac
cank
canker
cankerberry
cankerbird
cankereat
cankered
cankeredly
cankeredness
cankerflower
cankerous
cankerroot
cankerweed
cankerworm
cankerwort
cankery
canmaker
canmaking
canman
Canna
canna
cannabic
Cannabinaceae
cannabinaceous
cannabine
cannabinol
Cannabis
cannabism
Cannaceae
cannaceous
cannach
canned
cannel
cannelated
cannelure
cannelured
cannequin
canner
cannery
cannet
cannibal
cannibalean
cannibalic
cannibalish
cannibalism
cannibalistic
cannibalistically
cannibality
cannibalization
cannibalize
cannibally
cannikin
cannily
canniness
canning
cannon
cannonade
cannoned
cannoneer
cannoneering
Cannonism
cannonproof
cannonry
cannot
Cannstatt
cannula
cannular
cannulate
cannulated
canny
canoe
canoeing
Canoeiro
canoeist
canoeload
canoeman
canoewood
canon
canoncito
canoness
canonic
canonical
canonically
canonicalness
canonicals
canonicate
canonicity
canonics
canonist
canonistic
canonistical
canonizant
canonization
canonize
canonizer
canonlike
canonry
canonship
canoodle
canoodler
Canopic
canopic
Canopus
canopy
canorous
canorously
canorousness
Canossa
canroy
canroyer
canso
cant
Cantab
cantabank
cantabile
Cantabri
Cantabrian
Cantabrigian
Cantabrize
cantala
cantalite
cantaloupe
cantankerous
cantankerously
cantankerousness
cantar
cantara
cantaro
cantata
Cantate
cantation
cantative
cantatory
cantboard
canted
canteen
cantefable
canter
Canterburian
Canterburianism
Canterbury
canterer
canthal
Cantharellus
Cantharidae
cantharidal
cantharidate
cantharides
cantharidian
cantharidin
cantharidism
cantharidize
cantharis
cantharophilous
cantharus
canthectomy
canthitis
cantholysis
canthoplasty
canthorrhaphy
canthotomy
canthus
cantic
canticle
cantico
cantilena
cantilene
cantilever
cantilevered
cantillate
cantillation
cantily
cantina
cantiness
canting
cantingly
cantingness
cantion
cantish
cantle
cantlet
canto
Canton
canton
cantonal
cantonalism
cantoned
cantoner
Cantonese
cantonment
cantoon
cantor
cantoral
Cantorian
cantoris
cantorous
cantorship
cantred
cantref
cantrip
cantus
cantwise
canty
Canuck
canun
canvas
canvasback
canvasman
canvass
canvassy
cany
canyon
canzon
canzonet
caoba
Caodaism
Caodaist
caoutchouc
caoutchoucin
cap
capability
capable
capableness
capably
capacious
capaciously
capaciousness
capacitance
capacitate
capacitation
capacitative
capacitativly
capacitive
capacitor
capacity
capanna
capanne
caparison
capax
capcase
Cape
cape
caped
capel
capelet
capelin
capeline
Capella
capellet
caper
caperbush
capercaillie
capercally
capercut
caperer
capering
caperingly
Capernaism
Capernaite
Capernaitic
Capernaitical
Capernaitically
Capernaitish
capernoited
capernoitie
capernoity
capersome
caperwort
capes
capeskin
Capetian
Capetonian
capeweed
capewise
capful
Caph
caph
caphar
caphite
Caphtor
Caphtorim
capias
capicha
capillaceous
capillaire
capillament
capillarectasia
capillarily
capillarimeter
capillariness
capillariomotor
capillarity
capillary
capillation
capilliculture
capilliform
capillitial
capillitium
capillose
capistrate
capital
capitaldom
capitaled
capitalism
capitalist
capitalistic
capitalistically
capitalizable
capitalization
capitalize
capitally
capitalness
capitan
capitate
capitated
capitatim
capitation
capitative
capitatum
capitellar
capitellate
capitelliform
capitellum
Capito
Capitol
Capitolian
Capitoline
Capitolium
Capitonidae
Capitoninae
capitoul
capitoulate
capitulant
capitular
capitularly
capitulary
capitulate
capitulation
capitulator
capitulatory
capituliform
capitulum
capivi
capkin
capless
caplin
capmaker
capmaking
capman
capmint
Capnodium
Capnoides
capnomancy
capocchia
capomo
capon
caponier
caponize
caponizer
caporal
capot
capote
cappadine
Cappadocian
Capparidaceae
capparidaceous
Capparis
capped
cappelenite
capper
cappie
capping
capple
cappy
Capra
caprate
Caprella
Caprellidae
caprelline
capreol
capreolar
capreolary
capreolate
capreoline
Capreolus
Capri
capric
capriccetto
capricci
capriccio
caprice
capricious
capriciously
capriciousness
Capricorn
Capricornid
Capricornus
caprid
caprificate
caprification
caprificator
caprifig
Caprifoliaceae
caprifoliaceous
Caprifolium
caprifolium
capriform
caprigenous
Caprimulgi
Caprimulgidae
Caprimulgiformes
caprimulgine
Caprimulgus
caprin
caprine
caprinic
Capriola
capriole
Capriote
capriped
capripede
caprizant
caproate
caproic
caproin
Capromys
caprone
capronic
capronyl
caproyl
capryl
caprylate
caprylene
caprylic
caprylin
caprylone
caprylyl
capsa
capsaicin
Capsella
capsheaf
capshore
Capsian
capsicin
Capsicum
capsicum
capsid
Capsidae
capsizal
capsize
capstan
capstone
capsula
capsulae
capsular
capsulate
capsulated
capsulation
capsule
capsulectomy
capsuler
capsuliferous
capsuliform
capsuligerous
capsulitis
capsulociliary
capsulogenous
capsulolenticular
capsulopupillary
capsulorrhaphy
capsulotome
capsulotomy
capsumin
captaculum
captain
captaincy
captainess
captainly
captainry
captainship
captance
captation
caption
captious
captiously
captiousness
captivate
captivately
captivating
captivatingly
captivation
captivative
captivator
captivatrix
captive
captivity
captor
captress
capturable
capture
capturer
Capuan
capuche
capuched
Capuchin
capuchin
capucine
capulet
capulin
capybara
Caquetio
car
Cara
carabao
carabeen
carabid
Carabidae
carabidan
carabideous
carabidoid
carabin
carabineer
Carabini
caraboid
Carabus
carabus
caracal
caracara
caracol
caracole
caracoler
caracoli
caracolite
caracoller
caracore
caract
Caractacus
caracter
Caradoc
carafe
Caragana
Caraguata
caraguata
Caraho
caraibe
Caraipa
caraipi
Caraja
Carajas
carajura
caramba
carambola
carambole
caramel
caramelan
caramelen
caramelin
caramelization
caramelize
caramoussal
carancha
caranda
Carandas
caranday
carane
Caranga
carangid
Carangidae
carangoid
Carangus
caranna
Caranx
Carapa
carapace
carapaced
Carapache
Carapacho
carapacic
carapato
carapax
Carapidae
carapine
carapo
Carapus
Carara
carat
caratch
caraunda
caravan
caravaneer
caravanist
caravanner
caravansary
caravanserai
caravanserial
caravel
caraway
Carayan
carbacidometer
carbamate
carbamic
carbamide
carbamido
carbamine
carbamino
carbamyl
carbanil
carbanilic
carbanilide
carbarn
carbasus
carbazic
carbazide
carbazine
carbazole
carbazylic
carbeen
carbene
carberry
carbethoxy
carbethoxyl
carbide
carbimide
carbine
carbinol
carbinyl
carbo
carboazotine
carbocinchomeronic
carbodiimide
carbodynamite
carbogelatin
carbohemoglobin
carbohydrase
carbohydrate
carbohydraturia
carbohydrazide
carbohydride
carbohydrogen
carbolate
carbolated
carbolfuchsin
carbolic
carbolineate
Carbolineum
carbolize
Carboloy
carboluria
carbolxylol
carbomethene
carbomethoxy
carbomethoxyl
carbon
carbona
carbonaceous
carbonade
carbonado
Carbonari
Carbonarism
Carbonarist
carbonatation
carbonate
carbonation
carbonatization
carbonator
carbonemia
carbonero
carbonic
carbonide
Carboniferous
carboniferous
carbonification
carbonify
carbonigenous
carbonimeter
carbonimide
carbonite
carbonitride
carbonium
carbonizable
carbonization
carbonize
carbonizer
carbonless
Carbonnieux
carbonometer
carbonometry
carbonous
carbonuria
carbonyl
carbonylene
carbonylic
carbophilous
carbora
Carborundum
carborundum
carbosilicate
carbostyril
carboxide
carboxy
Carboxydomonas
carboxyhemoglobin
carboxyl
carboxylase
carboxylate
carboxylation
carboxylic
carboy
carboyed
carbro
carbromal
carbuilder
carbuncle
carbuncled
carbuncular
carbungi
carburant
carburate
carburation
carburator
carbure
carburet
carburetant
carburetor
carburization
carburize
carburizer
carburometer
carbyl
carbylamine
carcajou
carcake
carcanet
carcaneted
carcass
Carcavelhos
carceag
carcel
carceral
carcerate
carceration
Carcharhinus
Carcharias
carchariid
Carchariidae
carcharioid
Carcharodon
carcharodont
carcinemia
carcinogen
carcinogenesis
carcinogenic
carcinoid
carcinological
carcinologist
carcinology
carcinolysin
carcinolytic
carcinoma
carcinomata
carcinomatoid
carcinomatosis
carcinomatous
carcinomorphic
carcinophagous
carcinopolypus
carcinosarcoma
carcinosarcomata
Carcinoscorpius
carcinosis
carcoon
card
cardaissin
Cardamine
cardamom
Cardanic
cardboard
cardcase
cardecu
carded
cardel
carder
cardholder
cardia
cardiac
cardiacal
Cardiacea
cardiacean
cardiagra
cardiagram
cardiagraph
cardiagraphy
cardial
cardialgia
cardialgy
cardiameter
cardiamorphia
cardianesthesia
cardianeuria
cardiant
cardiaplegia
cardiarctia
cardiasthenia
cardiasthma
cardiataxia
cardiatomy
cardiatrophia
cardiauxe
Cardiazol
cardicentesis
cardiectasis
cardiectomize
cardiectomy
cardielcosis
cardiemphraxia
cardiform
Cardigan
cardigan
Cardiidae
cardin
cardinal
cardinalate
cardinalic
Cardinalis
cardinalism
cardinalist
cardinalitial
cardinalitian
cardinally
cardinalship
cardines
carding
cardioaccelerator
cardioarterial
cardioblast
cardiocarpum
cardiocele
cardiocentesis
cardiocirrhosis
cardioclasia
cardioclasis
cardiodilator
cardiodynamics
cardiodynia
cardiodysesthesia
cardiodysneuria
cardiogenesis
cardiogenic
cardiogram
cardiograph
cardiographic
cardiography
cardiohepatic
cardioid
cardiokinetic
cardiolith
cardiological
cardiologist
cardiology
cardiolysis
cardiomalacia
cardiomegaly
cardiomelanosis
cardiometer
cardiometric
cardiometry
cardiomotility
cardiomyoliposis
cardiomyomalacia
cardioncus
cardionecrosis
cardionephric
cardioneural
cardioneurosis
cardionosus
cardioparplasis
cardiopathic
cardiopathy
cardiopericarditis
cardiophobe
cardiophobia
cardiophrenia
cardioplasty
cardioplegia
cardiopneumatic
cardiopneumograph
cardioptosis
cardiopulmonary
cardiopuncture
cardiopyloric
cardiorenal
cardiorespiratory
cardiorrhaphy
cardiorrheuma
cardiorrhexis
cardioschisis
cardiosclerosis
cardioscope
cardiospasm
Cardiospermum
cardiosphygmogram
cardiosphygmograph
cardiosymphysis
cardiotherapy
cardiotomy
cardiotonic
cardiotoxic
cardiotrophia
cardiotrophotherapy
cardiovascular
cardiovisceral
cardipaludism
cardipericarditis
cardisophistical
carditic
carditis
Cardium
cardlike
cardmaker
cardmaking
cardo
cardol
cardon
cardona
cardoncillo
cardooer
cardoon
cardophagus
cardplayer
cardroom
cardsharp
cardsharping
cardstock
Carduaceae
carduaceous
Carduelis
Carduus
care
carecloth
careen
careenage
careener
career
careerer
careering
careeringly
careerist
carefree
careful
carefully
carefulness
careless
carelessly
carelessness
carene
carer
caress
caressant
caresser
caressing
caressingly
caressive
caressively
carest
caret
caretaker
caretaking
Caretta
Carettochelydidae
careworn
Carex
carfare
carfax
carfuffle
carful
carga
cargo
cargoose
carhop
carhouse
cariacine
Cariacus
cariama
Cariamae
Carian
Carib
Caribal
Cariban
Caribbean
Caribbee
Caribi
Caribisi
caribou
Carica
Caricaceae
caricaceous
caricatura
caricaturable
caricatural
caricature
caricaturist
caricetum
caricographer
caricography
caricologist
caricology
caricous
carid
Carida
Caridea
caridean
caridoid
Caridomorpha
caries
Carijona
carillon
carillonneur
carina
carinal
Carinaria
Carinatae
carinate
carinated
carination
Cariniana
cariniform
Carinthian
cariole
carioling
cariosity
carious
cariousness
Caripuna
Cariri
Caririan
Carisa
Carissa
caritative
caritive
Cariyo
cark
carking
carkingly
carkled
Carl
carl
carless
carlet
carlie
carlin
Carlina
carline
carling
carlings
carlish
carlishness
Carlisle
Carlism
Carlist
Carlo
carload
carloading
carloadings
Carlos
carlot
Carlovingian
carls
Carludovica
Carlylean
Carlyleian
Carlylese
Carlylesque
Carlylian
Carlylism
carmagnole
carmalum
Carman
carman
Carmanians
Carmel
Carmela
carmele
Carmelite
Carmelitess
carmeloite
Carmen
carminative
Carmine
carmine
carminette
carminic
carminite
carminophilous
carmoisin
carmot
Carnacian
carnage
carnaged
carnal
carnalism
carnalite
carnality
carnalize
carnallite
carnally
carnalness
carnaptious
Carnaria
carnassial
carnate
carnation
carnationed
carnationist
carnauba
carnaubic
carnaubyl
Carnegie
Carnegiea
carnelian
carneol
carneole
carneous
carney
carnic
carniferous
carniferrin
carnifex
carnification
carnifices
carnificial
carniform
carnify
Carniolan
carnival
carnivaler
carnivalesque
Carnivora
carnivoracity
carnivoral
carnivore
carnivorism
carnivorous
carnivorously
carnivorousness
carnose
carnosine
carnosity
carnotite
carnous
Caro
caroa
carob
caroba
caroche
Caroid
Carol
carol
Carolan
Carole
Carolean
caroler
caroli
carolin
Carolina
Caroline
caroline
Caroling
Carolingian
Carolinian
carolus
Carolyn
carom
carombolette
carone
caronic
caroome
caroon
carotene
carotenoid
carotic
carotid
carotidal
carotidean
carotin
carotinemia
carotinoid
caroubier
carousal
carouse
carouser
carousing
carousingly
carp
carpaine
carpal
carpale
carpalia
Carpathian
carpel
carpellary
carpellate
carpent
carpenter
Carpenteria
carpentering
carpentership
carpentry
carper
carpet
carpetbag
carpetbagger
carpetbaggery
carpetbaggism
carpetbagism
carpetbeater
carpeting
carpetlayer
carpetless
carpetmaker
carpetmaking
carpetmonger
carpetweb
carpetweed
carpetwork
carpetwoven
Carphiophiops
carpholite
Carphophis
carphosiderite
carpid
carpidium
carpincho
carping
carpingly
carpintero
Carpinus
Carpiodes
carpitis
carpium
carpocace
Carpocapsa
carpocarpal
carpocephala
carpocephalum
carpocerite
carpocervical
Carpocratian
Carpodacus
Carpodetus
carpogam
carpogamy
carpogenic
carpogenous
carpogone
carpogonial
carpogonium
Carpoidea
carpolite
carpolith
carpological
carpologically
carpologist
carpology
carpomania
carpometacarpal
carpometacarpus
carpopedal
Carpophaga
carpophagous
carpophalangeal
carpophore
carpophyll
carpophyte
carpopodite
carpopoditic
carpoptosia
carpoptosis
carport
carpos
carposperm
carposporangia
carposporangial
carposporangium
carpospore
carposporic
carposporous
carpostome
carpus
carquaise
carr
carrack
carrageen
carrageenin
Carrara
Carraran
carrel
carriable
carriage
carriageable
carriageful
carriageless
carriagesmith
carriageway
Carrick
carrick
Carrie
carried
carrier
carrion
carritch
carritches
carriwitchet
Carrizo
carrizo
carroch
carrollite
carronade
carrot
carrotage
carroter
carrotiness
carrottop
carrotweed
carrotwood
carroty
carrousel
carrow
Carry
carry
carryall
carrying
carrytale
carse
carshop
carsick
carsmith
Carsten
cart
cartable
cartaceous
cartage
cartboot
cartbote
carte
cartel
cartelism
cartelist
cartelization
cartelize
Carter
carter
Cartesian
Cartesianism
cartful
Carthaginian
carthame
carthamic
carthamin
Carthamus
Carthusian
Cartier
cartilage
cartilaginean
Cartilaginei
cartilagineous
Cartilagines
cartilaginification
cartilaginoid
cartilaginous
cartisane
Cartist
cartload
cartmaker
cartmaking
cartman
cartobibliography
cartogram
cartograph
cartographer
cartographic
cartographical
cartographically
cartography
cartomancy
carton
cartonnage
cartoon
cartoonist
cartouche
cartridge
cartsale
cartulary
cartway
cartwright
cartwrighting
carty
carua
carucage
carucal
carucate
carucated
Carum
caruncle
caruncula
carunculae
caruncular
carunculate
carunculated
carunculous
carvacrol
carvacryl
carval
carve
carvel
carven
carvene
carver
carvership
carvestrene
carving
carvoepra
carvol
carvomenthene
carvone
carvyl
carwitchet
Cary
Carya
caryatic
caryatid
caryatidal
caryatidean
caryatidic
caryl
Caryocar
Caryocaraceae
caryocaraceous
Caryophyllaceae
caryophyllaceous
caryophyllene
caryophylleous
caryophyllin
caryophyllous
Caryophyllus
caryopilite
caryopses
caryopsides
caryopsis
Caryopteris
Caryota
casaba
casabe
casal
casalty
Casamarca
Casanovanic
Casasia
casate
casaun
casava
casave
casavi
casbah
cascabel
cascade
Cascadia
Cascadian
cascadite
cascado
cascalho
cascalote
cascara
cascarilla
cascaron
casco
cascol
Case
case
Casearia
casease
caseate
caseation
casebook
casebox
cased
caseful
casefy
caseharden
caseic
casein
caseinate
caseinogen
casekeeper
Casel
caseless
caselessly
casemaker
casemaking
casemate
casemated
casement
casemented
caseolysis
caseose
caseous
caser
casern
caseum
caseweed
casewood
casework
caseworker
caseworm
Casey
cash
casha
cashable
cashableness
cashaw
cashbook
cashbox
cashboy
cashcuttee
cashel
cashew
cashgirl
Cashibo
cashier
cashierer
cashierment
cashkeeper
cashment
Cashmere
cashmere
cashmerette
Cashmirian
Casimir
Casimiroa
casing
casino
casiri
cask
casket
casking
casklike
Caslon
Caspar
Casparian
Casper
Caspian
casque
casqued
casquet
casquetel
casquette
cass
cassabanana
cassabully
cassady
Cassandra
cassareep
cassation
casse
Cassegrain
Cassegrainian
casselty
cassena
casserole
Cassia
cassia
Cassiaceae
Cassian
cassican
Cassicus
Cassida
cassideous
cassidid
Cassididae
Cassidinae
cassidony
Cassidulina
cassiduloid
Cassiduloidea
Cassie
cassie
Cassiepeia
cassimere
cassina
cassine
Cassinese
cassinette
Cassinian
cassino
cassinoid
cassioberry
Cassiope
Cassiopeia
Cassiopeian
Cassiopeid
cassiopeium
Cassis
cassis
cassiterite
Cassius
cassock
cassolette
casson
cassonade
cassoon
cassowary
cassumunar
Cassytha
Cassythaceae
cast
castable
castagnole
Castalia
Castalian
Castalides
Castalio
Castanea
castanean
castaneous
castanet
Castanopsis
Castanospermum
castaway
caste
casteless
castelet
castellan
castellano
castellanship
castellany
castellar
castellate
castellated
castellation
caster
casterless
casthouse
castice
castigable
castigate
castigation
castigative
castigator
castigatory
Castilian
Castilla
Castilleja
Castilloa
casting
castle
castled
castlelike
castlet
castlewards
castlewise
castling
castock
castoff
Castor
castor
Castores
castoreum
castorial
Castoridae
castorin
castorite
castorized
Castoroides
castory
castra
castral
castrametation
castrate
castrater
castration
castrator
castrensial
castrensian
castrum
castuli
casual
casualism
casualist
casuality
casually
casualness
casualty
Casuariidae
Casuariiformes
Casuarina
Casuarinaceae
casuarinaceous
Casuarinales
Casuarius
casuary
casuist
casuistess
casuistic
casuistical
casuistically
casuistry
casula
caswellite
Casziel
Cat
cat
catabaptist
catabases
catabasis
catabatic
catabibazon
catabiotic
catabolic
catabolically
catabolin
catabolism
catabolite
catabolize
catacaustic
catachreses
catachresis
catachrestic
catachrestical
catachrestically
catachthonian
cataclasm
cataclasmic
cataclastic
cataclinal
cataclysm
cataclysmal
cataclysmatic
cataclysmatist
cataclysmic
cataclysmically
cataclysmist
catacomb
catacorolla
catacoustics
catacromyodian
catacrotic
catacrotism
catacumbal
catadicrotic
catadicrotism
catadioptric
catadioptrical
catadioptrics
catadromous
catafalco
catafalque
catagenesis
catagenetic
catagmatic
Cataian
catakinesis
catakinetic
catakinetomer
catakinomeric
Catalan
Catalanganes
Catalanist
catalase
Catalaunian
catalecta
catalectic
catalecticant
catalepsis
catalepsy
cataleptic
cataleptiform
cataleptize
cataleptoid
catalexis
catalina
catalineta
catalinite
catallactic
catallactically
catallactics
catallum
catalogia
catalogic
catalogical
catalogist
catalogistic
catalogue
cataloguer
cataloguish
cataloguist
cataloguize
Catalonian
catalowne
Catalpa
catalpa
catalufa
catalyses
catalysis
catalyst
catalyte
catalytic
catalytical
catalytically
catalyzator
catalyze
catalyzer
catamaran
Catamarcan
Catamarenan
catamenia
catamenial
catamite
catamited
catamiting
catamount
catamountain
catan
Catananche
catapan
catapasm
catapetalous
cataphasia
cataphatic
cataphora
cataphoresis
cataphoretic
cataphoria
cataphoric
cataphract
Cataphracta
Cataphracti
cataphrenia
cataphrenic
Cataphrygian
cataphrygianism
cataphyll
cataphylla
cataphyllary
cataphyllum
cataphysical
cataplasia
cataplasis
cataplasm
catapleiite
cataplexy
catapult
catapultic
catapultier
cataract
cataractal
cataracted
cataractine
cataractous
cataractwise
cataria
catarinite
catarrh
catarrhal
catarrhally
catarrhed
Catarrhina
catarrhine
catarrhinian
catarrhous
catasarka
Catasetum
catasta
catastaltic
catastasis
catastate
catastatic
catasterism
catastrophal
catastrophe
catastrophic
catastrophical
catastrophically
catastrophism
catastrophist
catathymic
catatonia
catatoniac
catatonic
catawampous
catawampously
catawamptious
catawamptiously
catawampus
Catawba
catberry
catbird
catboat
catcall
catch
catchable
catchall
catchcry
catcher
catchfly
catchiness
catching
catchingly
catchingness
catchland
catchment
catchpenny
catchplate
catchpole
catchpolery
catchpoleship
catchpoll
catchpollery
catchup
catchwater
catchweed
catchweight
catchword
catchwork
catchy
catclaw
catdom
cate
catechesis
catechetic
catechetical
catechetically
catechin
catechism
catechismal
catechist
catechistic
catechistical
catechistically
catechizable
catechization
catechize
catechizer
catechol
catechu
catechumen
catechumenal
catechumenate
catechumenical
catechumenically
catechumenism
catechumenship
catechutannic
categorem
categorematic
categorematical
categorematically
categorial
categoric
categorical
categorically
categoricalness
categorist
categorization
categorize
category
catelectrotonic
catelectrotonus
catella
catena
catenae
catenarian
catenary
catenate
catenated
catenation
catenoid
catenulate
catepuce
cater
cateran
catercap
catercorner
caterer
caterership
cateress
caterpillar
caterpillared
caterpillarlike
caterva
caterwaul
caterwauler
caterwauling
Catesbaea
cateye
catface
catfaced
catfacing
catfall
catfish
catfoot
catfooted
catgut
Catha
Cathari
Catharina
Catharine
Catharism
Catharist
Catharistic
catharization
catharize
catharpin
catharping
Cathars
catharsis
Cathartae
Cathartes
cathartic
cathartical
cathartically
catharticalness
Cathartidae
Cathartides
Cathartolinum
Cathay
Cathayan
cathead
cathect
cathectic
cathection
cathedra
cathedral
cathedraled
cathedralesque
cathedralic
cathedrallike
cathedralwise
cathedratic
cathedratica
cathedratical
cathedratically
cathedraticum
cathepsin
Catherine
catheter
catheterism
catheterization
catheterize
catheti
cathetometer
cathetometric
cathetus
cathexion
cathexis
cathidine
cathin
cathine
cathinine
cathion
cathisma
cathodal
cathode
cathodic
cathodical
cathodically
cathodofluorescence
cathodograph
cathodography
cathodoluminescence
cathograph
cathography
cathole
catholic
catholical
catholically
catholicalness
catholicate
catholicism
catholicist
catholicity
catholicize
catholicizer
catholicly
catholicness
catholicon
catholicos
catholicus
catholyte
cathood
cathop
Cathrin
cathro
Cathryn
Cathy
Catilinarian
cation
cationic
cativo
catjang
catkin
catkinate
catlap
catlike
catlin
catling
catlinite
catmalison
catmint
catnip
catoblepas
Catocala
catocalid
catocathartic
catoctin
Catodon
catodont
catogene
catogenic
Catoism
Catonian
Catonic
Catonically
Catonism
catoptric
catoptrical
catoptrically
catoptrics
catoptrite
catoptromancy
catoptromantic
Catoquina
catostomid
Catostomidae
catostomoid
Catostomus
catpiece
catpipe
catproof
Catskill
catskin
catstep
catstick
catstitch
catstitcher
catstone
catsup
cattabu
cattail
cattalo
cattery
Catti
cattily
cattimandoo
cattiness
catting
cattish
cattishly
cattishness
cattle
cattlebush
cattlegate
cattleless
cattleman
Cattleya
cattleya
cattleyak
Catty
catty
cattyman
Catullian
catvine
catwalk
catwise
catwood
catwort
caubeen
cauboge
Caucasian
Caucasic
Caucasoid
cauch
cauchillo
caucho
caucus
cauda
caudad
caudae
caudal
caudally
caudalward
Caudata
caudata
caudate
caudated
caudation
caudatolenticular
caudatory
caudatum
caudex
caudices
caudicle
caudiform
caudillism
caudle
caudocephalad
caudodorsal
caudofemoral
caudolateral
caudotibial
caudotibialis
Caughnawaga
caught
cauk
caul
cauld
cauldrife
cauldrifeness
Caulerpa
Caulerpaceae
caulerpaceous
caules
caulescent
caulicle
caulicole
caulicolous
caulicule
cauliculus
cauliferous
cauliflorous
cauliflory
cauliflower
cauliform
cauligenous
caulinar
caulinary
cauline
caulis
Caulite
caulivorous
caulocarpic
caulocarpous
caulome
caulomer
caulomic
caulophylline
Caulophyllum
Caulopteris
caulopteris
caulosarc
caulotaxis
caulotaxy
caulote
caum
cauma
caumatic
caunch
Caunos
Caunus
caup
caupo
caupones
Cauqui
caurale
Caurus
causability
causable
causal
causalgia
causality
causally
causate
causation
causational
causationism
causationist
causative
causatively
causativeness
causativity
cause
causeful
causeless
causelessly
causelessness
causer
causerie
causeway
causewayman
causey
causidical
causing
causingness
causse
causson
caustic
caustical
caustically
causticiser
causticism
causticity
causticization
causticize
causticizer
causticly
causticness
caustification
caustify
Causus
cautel
cautelous
cautelously
cautelousness
cauter
cauterant
cauterization
cauterize
cautery
caution
cautionary
cautioner
cautionry
cautious
cautiously
cautiousness
cautivo
cava
cavae
caval
cavalcade
cavalero
cavalier
cavalierish
cavalierishness
cavalierism
cavalierly
cavalierness
cavaliero
cavaliership
cavalla
cavalry
cavalryman
cavascope
cavate
cavatina
cave
caveat
caveator
cavekeeper
cavel
cavelet
cavelike
cavendish
cavern
cavernal
caverned
cavernicolous
cavernitis
cavernlike
cavernoma
cavernous
cavernously
cavernulous
cavesson
cavetto
Cavia
caviar
cavicorn
Cavicornia
Cavidae
cavie
cavil
caviler
caviling
cavilingly
cavilingness
cavillation
Cavina
caving
cavings
cavish
cavitary
cavitate
cavitation
cavitied
cavity
caviya
cavort
cavus
cavy
caw
cawk
cawky
cawney
cawquaw
caxiri
caxon
Caxton
Caxtonian
cay
Cayapa
Cayapo
Cayenne
cayenne
cayenned
Cayleyan
cayman
Cayubaba
Cayubaban
Cayuga
Cayugan
Cayuse
Cayuvava
caza
cazimi
Ccoya
ce
Ceanothus
cearin
cease
ceaseless
ceaselessly
ceaselessness
ceasmic
Cebalrai
Cebatha
cebell
cebian
cebid
Cebidae
cebil
cebine
ceboid
cebollite
cebur
Cebus
cecidiologist
cecidiology
cecidium
cecidogenous
cecidologist
cecidology
cecidomyian
cecidomyiid
Cecidomyiidae
cecidomyiidous
Cecil
Cecile
Cecilia
cecilite
cecils
Cecily
cecity
cecograph
Cecomorphae
cecomorphic
cecostomy
Cecropia
Cecrops
cecutiency
cedar
cedarbird
cedared
cedarn
cedarware
cedarwood
cedary
cede
cedent
ceder
cedilla
cedrat
cedrate
cedre
Cedrela
cedrene
Cedric
cedrin
cedrine
cedriret
cedrium
cedrol
cedron
Cedrus
cedry
cedula
cee
Ceiba
ceibo
ceil
ceile
ceiler
ceilidh
ceiling
ceilinged
ceilingward
ceilingwards
ceilometer
Celadon
celadon
celadonite
Celaeno
celandine
Celanese
Celarent
Celastraceae
celastraceous
Celastrus
celation
celative
celature
Celebesian
celebrant
celebrate
celebrated
celebratedness
celebrater
celebration
celebrative
celebrator
celebratory
celebrity
celemin
celemines
celeomorph
Celeomorphae
celeomorphic
celeriac
celerity
celery
celesta
Celeste
celeste
celestial
celestiality
celestialize
celestially
celestialness
celestina
Celestine
celestine
Celestinian
celestite
celestitude
Celia
celiac
celiadelphus
celiagra
celialgia
celibacy
celibatarian
celibate
celibatic
celibatist
celibatory
celidographer
celidography
celiectasia
celiectomy
celiemia
celiitis
celiocele
celiocentesis
celiocolpotomy
celiocyesis
celiodynia
celioelytrotomy
celioenterotomy
celiogastrotomy
celiohysterotomy
celiolymph
celiomyalgia
celiomyodynia
celiomyomectomy
celiomyomotomy
celiomyositis
celioncus
celioparacentesis
celiopyosis
celiorrhaphy
celiorrhea
celiosalpingectomy
celiosalpingotomy
celioschisis
celioscope
celioscopy
celiotomy
celite
cell
cella
cellae
cellar
cellarage
cellarer
cellaress
cellaret
cellaring
cellarless
cellarman
cellarous
cellarway
cellarwoman
cellated
celled
Cellepora
cellepore
Cellfalcicula
celliferous
celliform
cellifugal
cellipetal
cellist
Cellite
cello
cellobiose
celloid
celloidin
celloist
cellophane
cellose
Cellucotton
cellular
cellularity
cellularly
cellulase
cellulate
cellulated
cellulation
cellule
cellulicidal
celluliferous
cellulifugal
cellulifugally
cellulin
cellulipetal
cellulipetally
cellulitis
cellulocutaneous
cellulofibrous
Celluloid
celluloid
celluloided
Cellulomonadeae
Cellulomonas
cellulose
cellulosic
cellulosity
cellulotoxic
cellulous
Cellvibrio
Celosia
Celotex
celotomy
Celsia
celsian
Celsius
Celt
celt
Celtdom
Celtiberi
Celtiberian
Celtic
Celtically
Celticism
Celticist
Celticize
Celtidaceae
celtiform
Celtillyrians
Celtis
Celtish
Celtism
Celtist
celtium
Celtization
Celtologist
Celtologue
Celtomaniac
Celtophil
Celtophobe
Celtophobia
celtuce
cembalist
cembalo
cement
cemental
cementation
cementatory
cementer
cementification
cementin
cementite
cementitious
cementless
cementmaker
cementmaking
cementoblast
cementoma
cementum
cemeterial
cemetery
cenacle
cenaculum
cenanthous
cenanthy
cencerro
Cenchrus
cendre
cenobian
cenobite
cenobitic
cenobitical
cenobitically
cenobitism
cenobium
cenoby
cenogenesis
cenogenetic
cenogenetically
cenogonous
Cenomanian
cenosite
cenosity
cenospecies
cenospecific
cenospecifically
cenotaph
cenotaphic
cenotaphy
Cenozoic
cenozoology
cense
censer
censerless
censive
censor
censorable
censorate
censorial
censorious
censoriously
censoriousness
censorship
censual
censurability
censurable
censurableness
censurably
censure
censureless
censurer
censureship
census
cent
centage
cental
centare
centaur
centaurdom
Centaurea
centauress
centauri
centaurial
centaurian
centauric
Centaurid
Centauridium
Centaurium
centauromachia
centauromachy
Centaurus
centaurus
centaury
centavo
centena
centenar
centenarian
centenarianism
centenary
centenier
centenionalis
centennial
centennially
center
centerable
centerboard
centered
centerer
centering
centerless
centermost
centerpiece
centervelic
centerward
centerwise
centesimal
centesimally
centesimate
centesimation
centesimi
centesimo
centesis
Centetes
centetid
Centetidae
centgener
centiar
centiare
centibar
centifolious
centigrade
centigram
centile
centiliter
centillion
centillionth
Centiloquy
centime
centimeter
centimo
centimolar
centinormal
centipedal
centipede
centiplume
centipoise
centistere
centistoke
centner
cento
centonical
centonism
centrad
central
centrale
Centrales
centralism
centralist
centralistic
centrality
centralization
centralize
centralizer
centrally
centralness
centranth
Centranthus
centrarchid
Centrarchidae
centrarchoid
Centraxonia
centraxonial
Centrechinoida
centric
Centricae
centrical
centricality
centrically
centricalness
centricipital
centriciput
centricity
centriffed
centrifugal
centrifugalization
centrifugalize
centrifugaller
centrifugally
centrifugate
centrifugation
centrifuge
centrifugence
centriole
centripetal
centripetalism
centripetally
centripetence
centripetency
centriscid
Centriscidae
centrisciform
centriscoid
Centriscus
centrist
centroacinar
centrobaric
centrobarical
centroclinal
centrode
centrodesmose
centrodesmus
centrodorsal
centrodorsally
centroid
centroidal
centrolecithal
Centrolepidaceae
centrolepidaceous
centrolinead
centrolineal
centromere
centronucleus
centroplasm
Centropomidae
Centropomus
Centrosema
centrosome
centrosomic
Centrosoyus
Centrospermae
centrosphere
centrosymmetric
centrosymmetry
Centrotus
centrum
centry
centum
centumvir
centumviral
centumvirate
Centunculus
centuple
centuplicate
centuplication
centuply
centuria
centurial
centuriate
centuriation
centuriator
centuried
centurion
century
ceorl
ceorlish
cep
cepa
cepaceous
cepe
cephaeline
Cephaelis
Cephalacanthidae
Cephalacanthus
cephalad
cephalagra
cephalalgia
cephalalgic
cephalalgy
cephalanthium
cephalanthous
Cephalanthus
Cephalaspis
Cephalata
cephalate
cephaldemae
cephalemia
cephaletron
Cephaleuros
cephalhematoma
cephalhydrocele
cephalic
cephalin
Cephalina
cephaline
cephalism
cephalitis
cephalization
cephaloauricular
Cephalobranchiata
cephalobranchiate
cephalocathartic
cephalocaudal
cephalocele
cephalocentesis
cephalocercal
Cephalocereus
cephalochord
Cephalochorda
cephalochordal
Cephalochordata
cephalochordate
cephaloclasia
cephaloclast
cephalocone
cephaloconic
cephalocyst
cephalodiscid
Cephalodiscida
Cephalodiscus
cephalodymia
cephalodymus
cephalodynia
cephalofacial
cephalogenesis
cephalogram
cephalograph
cephalohumeral
cephalohumeralis
cephaloid
cephalology
cephalomancy
cephalomant
cephalomelus
cephalomenia
cephalomeningitis
cephalomere
cephalometer
cephalometric
cephalometry
cephalomotor
cephalomyitis
cephalon
cephalonasal
cephalopagus
cephalopathy
cephalopharyngeal
cephalophine
cephalophorous
Cephalophus
cephalophyma
cephaloplegia
cephaloplegic
cephalopod
Cephalopoda
cephalopodan
cephalopodic
cephalopodous
Cephalopterus
cephalorachidian
cephalorhachidian
cephalosome
cephalospinal
Cephalosporium
cephalostyle
Cephalotaceae
cephalotaceous
Cephalotaxus
cephalotheca
cephalothecal
cephalothoracic
cephalothoracopagus
cephalothorax
cephalotome
cephalotomy
cephalotractor
cephalotribe
cephalotripsy
cephalotrocha
Cephalotus
cephalous
Cephas
Cepheid
cephid
Cephidae
Cephus
Cepolidae
ceps
ceptor
cequi
ceraceous
cerago
ceral
ceramal
cerambycid
Cerambycidae
Ceramiaceae
ceramiaceous
ceramic
ceramicite
ceramics
ceramidium
ceramist
Ceramium
ceramographic
ceramography
cerargyrite
ceras
cerasein
cerasin
cerastes
Cerastium
Cerasus
cerata
cerate
ceratectomy
cerated
ceratiasis
ceratiid
Ceratiidae
ceratioid
ceration
ceratite
Ceratites
ceratitic
Ceratitidae
Ceratitis
ceratitoid
Ceratitoidea
Ceratium
Ceratobatrachinae
ceratoblast
ceratobranchial
ceratocricoid
Ceratodidae
Ceratodontidae
Ceratodus
ceratofibrous
ceratoglossal
ceratoglossus
ceratohyal
ceratohyoid
ceratoid
ceratomandibular
ceratomania
Ceratonia
Ceratophrys
Ceratophyllaceae
ceratophyllaceous
Ceratophyllum
Ceratophyta
ceratophyte
Ceratops
Ceratopsia
ceratopsian
ceratopsid
Ceratopsidae
Ceratopteridaceae
ceratopteridaceous
Ceratopteris
ceratorhine
Ceratosa
Ceratosaurus
Ceratospongiae
ceratospongian
Ceratostomataceae
Ceratostomella
ceratotheca
ceratothecal
Ceratozamia
ceraunia
ceraunics
ceraunogram
ceraunograph
ceraunomancy
ceraunophone
ceraunoscope
ceraunoscopy
Cerberean
Cerberic
Cerberus
cercal
cercaria
cercarial
cercarian
cercariform
cercelee
cerci
Cercidiphyllaceae
Cercis
Cercocebus
Cercolabes
Cercolabidae
cercomonad
Cercomonadidae
Cercomonas
cercopid
Cercopidae
cercopithecid
Cercopithecidae
cercopithecoid
Cercopithecus
cercopod
Cercospora
Cercosporella
cercus
Cerdonian
cere
cereal
cerealian
cerealin
cerealism
cerealist
cerealose
cerebella
cerebellar
cerebellifugal
cerebellipetal
cerebellocortex
cerebellopontile
cerebellopontine
cerebellorubral
cerebellospinal
cerebellum
cerebra
cerebral
cerebralgia
cerebralism
cerebralist
cerebralization
cerebralize
cerebrally
cerebrasthenia
cerebrasthenic
cerebrate
cerebration
cerebrational
Cerebratulus
cerebric
cerebricity
cerebriform
cerebriformly
cerebrifugal
cerebrin
cerebripetal
cerebritis
cerebrize
cerebrocardiac
cerebrogalactose
cerebroganglion
cerebroganglionic
cerebroid
cerebrology
cerebroma
cerebromalacia
cerebromedullary
cerebromeningeal
cerebromeningitis
cerebrometer
cerebron
cerebronic
cerebroparietal
cerebropathy
cerebropedal
cerebrophysiology
cerebropontile
cerebropsychosis
cerebrorachidian
cerebrosclerosis
cerebroscope
cerebroscopy
cerebrose
cerebrosensorial
cerebroside
cerebrosis
cerebrospinal
cerebrospinant
cerebrosuria
cerebrotomy
cerebrotonia
cerebrotonic
cerebrovisceral
cerebrum
cerecloth
cered
cereless
cerement
ceremonial
ceremonialism
ceremonialist
ceremonialize
ceremonially
ceremonious
ceremoniously
ceremoniousness
ceremony
cereous
cerer
ceresin
Cereus
cerevis
ceria
Cerialia
cerianthid
Cerianthidae
cerianthoid
Cerianthus
ceric
ceride
ceriferous
cerigerous
cerillo
ceriman
cerin
cerine
Cerinthe
Cerinthian
Ceriomyces
Cerion
Cerionidae
ceriops
Ceriornis
cerise
cerite
Cerithiidae
cerithioid
Cerithium
cerium
cermet
cern
cerniture
cernuous
cero
cerograph
cerographic
cerographist
cerography
ceroline
cerolite
ceroma
ceromancy
cerophilous
ceroplast
ceroplastic
ceroplastics
ceroplasty
cerotate
cerote
cerotene
cerotic
cerotin
cerotype
cerous
ceroxyle
Ceroxylon
cerrero
cerrial
cerris
certain
certainly
certainty
Certhia
Certhiidae
certie
certifiable
certifiableness
certifiably
certificate
certification
certificative
certificator
certificatory
certified
certifier
certify
certiorari
certiorate
certioration
certis
certitude
certosina
certosino
certy
cerule
cerulean
cerulein
ceruleite
ceruleolactite
ceruleous
cerulescent
ceruleum
cerulignol
cerulignone
cerumen
ceruminal
ceruminiferous
ceruminous
cerumniparous
ceruse
cerussite
Cervantist
cervantite
cervical
Cervicapra
cervicaprine
cervicectomy
cervicicardiac
cervicide
cerviciplex
cervicispinal
cervicitis
cervicoauricular
cervicoaxillary
cervicobasilar
cervicobrachial
cervicobregmatic
cervicobuccal
cervicodorsal
cervicodynia
cervicofacial
cervicohumeral
cervicolabial
cervicolingual
cervicolumbar
cervicomuscular
cerviconasal
cervicorn
cervicoscapular
cervicothoracic
cervicovaginal
cervicovesical
cervid
Cervidae
Cervinae
cervine
cervisia
cervisial
cervix
cervoid
cervuline
Cervulus
Cervus
ceryl
Cerynean
Cesare
cesarevitch
cesarolite
cesious
cesium
cespititous
cespitose
cespitosely
cespitulose
cess
cessantly
cessation
cessative
cessavit
cesser
cession
cessionaire
cessionary
cessor
cesspipe
cesspit
cesspool
cest
Cestida
Cestidae
Cestoda
Cestodaria
cestode
cestoid
Cestoidea
cestoidean
Cestracion
cestraciont
Cestraciontes
Cestraciontidae
Cestrian
Cestrum
cestrum
cestus
Cetacea
cetacean
cetaceous
cetaceum
cetane
Cete
cetene
ceterach
ceti
cetic
ceticide
Cetid
cetin
Cetiosauria
cetiosaurian
Cetiosaurus
cetological
cetologist
cetology
Cetomorpha
cetomorphic
Cetonia
cetonian
Cetoniides
Cetoniinae
cetorhinid
Cetorhinidae
cetorhinoid
Cetorhinus
cetotolite
Cetraria
cetraric
cetrarin
Cetus
cetyl
cetylene
cetylic
cevadilla
cevadilline
cevadine
Cevennian
Cevenol
Cevenole
cevine
cevitamic
ceylanite
Ceylon
Ceylonese
ceylonite
ceyssatite
Ceyx
Cezannesque
cha
chaa
chab
chabasie
chabazite
Chablis
chabot
chabouk
chabuk
chabutra
Chac
chacate
chachalaca
Chachapuya
chack
Chackchiuma
chacker
chackle
chackler
chacma
Chaco
chacona
chacte
chad
chadacryst
Chaenactis
Chaenolobus
Chaenomeles
chaeta
Chaetangiaceae
Chaetangium
Chaetetes
Chaetetidae
Chaetifera
chaetiferous
Chaetites
Chaetitidae
Chaetochloa
Chaetodon
chaetodont
chaetodontid
Chaetodontidae
chaetognath
Chaetognatha
chaetognathan
chaetognathous
Chaetophora
Chaetophoraceae
chaetophoraceous
Chaetophorales
chaetophorous
chaetopod
Chaetopoda
chaetopodan
chaetopodous
chaetopterin
Chaetopterus
chaetosema
Chaetosoma
Chaetosomatidae
Chaetosomidae
chaetotactic
chaetotaxy
Chaetura
chafe
chafer
chafery
chafewax
chafeweed
chaff
chaffcutter
chaffer
chafferer
chaffinch
chaffiness
chaffing
chaffingly
chaffless
chafflike
chaffman
chaffseed
chaffwax
chaffweed
chaffy
chaft
chafted
Chaga
chagan
Chagga
chagrin
chaguar
chagul
chahar
chai
Chailletiaceae
chain
chainage
chained
chainer
chainette
chainless
chainlet
chainmaker
chainmaking
chainman
chainon
chainsmith
chainwale
chainwork
chair
chairer
chairless
chairmaker
chairmaking
chairman
chairmanship
chairmender
chairmending
chairwarmer
chairwoman
chais
chaise
chaiseless
Chait
chaitya
chaja
chaka
chakar
chakari
Chakavski
chakazi
chakdar
chakobu
chakra
chakram
chakravartin
chaksi
chal
chalaco
chalana
chalastic
Chalastogastra
chalaza
chalazal
chalaze
chalazian
chalaziferous
chalazion
chalazogam
chalazogamic
chalazogamy
chalazoidite
chalcanthite
Chalcedonian
chalcedonic
chalcedonous
chalcedony
chalcedonyx
chalchuite
chalcid
Chalcidian
Chalcidic
chalcidicum
chalcidid
Chalcididae
chalcidiform
chalcidoid
Chalcidoidea
Chalcioecus
Chalcis
chalcites
chalcocite
chalcograph
chalcographer
chalcographic
chalcographical
chalcographist
chalcography
chalcolite
chalcolithic
chalcomancy
chalcomenite
chalcon
chalcone
chalcophanite
chalcophyllite
chalcopyrite
chalcosiderite
chalcosine
chalcostibite
chalcotrichite
chalcotript
chalcus
Chaldaei
Chaldaic
Chaldaical
Chaldaism
Chaldean
Chaldee
chalder
chaldron
chalet
chalice
chaliced
chalicosis
chalicothere
chalicotheriid
Chalicotheriidae
chalicotherioid
Chalicotherium
Chalina
Chalinidae
chalinine
Chalinitis
chalk
chalkcutter
chalker
chalkiness
chalklike
chalkography
chalkosideric
chalkstone
chalkstony
chalkworker
chalky
challah
challenge
challengeable
challengee
challengeful
challenger
challengingly
challie
challis
challote
chalmer
chalon
chalone
Chalons
chalque
chalta
Chalukya
Chalukyan
chalumeau
chalutz
chalutzim
Chalybean
chalybeate
chalybeous
Chalybes
chalybite
Cham
cham
Chama
Chamacea
Chamacoco
Chamaebatia
Chamaecistus
chamaecranial
Chamaecrista
Chamaecyparis
Chamaedaphne
Chamaeleo
Chamaeleon
Chamaeleontidae
Chamaelirium
Chamaenerion
Chamaepericlymenum
chamaeprosopic
Chamaerops
chamaerrhine
Chamaesaura
Chamaesiphon
Chamaesiphonaceae
Chamaesiphonaceous
Chamaesiphonales
Chamaesyce
chamal
Chamar
chamar
chamber
chamberdeacon
chambered
chamberer
chambering
chamberlain
chamberlainry
chamberlainship
chamberlet
chamberleted
chamberletted
chambermaid
Chambertin
chamberwoman
Chambioa
chambray
chambrel
chambul
chamecephalic
chamecephalous
chamecephalus
chamecephaly
chameleon
chameleonic
chameleonize
chameleonlike
chamfer
chamferer
chamfron
Chamian
Chamicuro
Chamidae
chamisal
chamiso
Chamite
chamite
Chamkanni
chamma
chamois
Chamoisette
chamoisite
chamoline
Chamomilla
Chamorro
Chamos
champ
Champa
champac
champaca
champacol
champagne
champagneless
champagnize
champaign
champain
champaka
champer
champertor
champertous
champerty
champignon
champion
championess
championize
championless
championlike
championship
Champlain
Champlainic
champleve
champy
Chanabal
Chanca
chance
chanceful
chancefully
chancefulness
chancel
chanceled
chanceless
chancellery
chancellor
chancellorate
chancelloress
chancellorism
chancellorship
chancer
chancery
chancewise
chanche
chanchito
chanco
chancre
chancriform
chancroid
chancroidal
chancrous
chancy
chandala
chandam
chandelier
Chandi
chandi
chandler
chandleress
chandlering
chandlery
chandoo
chandu
chandul
Chane
chanfrin
Chang
chang
changa
changar
change
changeability
changeable
changeableness
changeably
changedale
changedness
changeful
changefully
changefulness
changeless
changelessly
changelessness
changeling
changement
changer
Changoan
Changos
Changuina
Changuinan
Chanidae
chank
chankings
channel
channelbill
channeled
channeler
channeling
channelization
channelize
channelled
channeller
channelling
channelwards
channer
chanson
chansonnette
chanst
chant
chantable
chanter
chanterelle
chantership
chantey
chanteyman
chanticleer
chanting
chantingly
chantlate
chantress
chantry
chao
chaogenous
chaology
chaos
chaotic
chaotical
chaotically
chaoticness
Chaouia
chap
Chapacura
Chapacuran
chapah
Chapanec
chaparral
chaparro
chapatty
chapbook
chape
chapeau
chapeaux
chaped
chapel
chapeless
chapelet
chapelgoer
chapelgoing
chapellage
chapellany
chapelman
chapelmaster
chapelry
chapelward
chaperno
chaperon
chaperonage
chaperone
chaperonless
chapfallen
chapin
chapiter
chapitral
chaplain
chaplaincy
chaplainry
chaplainship
chapless
chaplet
chapleted
chapman
chapmanship
chapournet
chapournetted
chappaul
chapped
chapper
chappie
chappin
chapping
chappow
chappy
chaps
chapt
chaptalization
chaptalize
chapter
chapteral
chapterful
chapwoman
char
Chara
charabanc
charabancer
charac
Characeae
characeous
characetum
characin
characine
characinid
Characinidae
characinoid
character
characterful
characterial
characterical
characterism
characterist
characteristic
characteristical
characteristically
characteristicalness
characteristicness
characterizable
characterization
characterize
characterizer
characterless
characterlessness
characterological
characterologist
characterology
charactery
charade
Charadrii
Charadriidae
charadriiform
Charadriiformes
charadrine
charadrioid
Charadriomorphae
Charadrius
Charales
charas
charbon
Charca
charcoal
charcoaly
charcutier
chard
chardock
chare
charer
charet
charette
charge
chargeability
chargeable
chargeableness
chargeably
chargee
chargeless
chargeling
chargeman
charger
chargeship
charging
Charicleia
charier
charily
chariness
chariot
charioted
chariotee
charioteer
charioteership
chariotlike
chariotman
chariotry
chariotway
charism
charisma
charismatic
Charissa
charisticary
charitable
charitableness
charitably
Charites
charity
charityless
charivari
chark
charka
charkha
charkhana
charlady
charlatan
charlatanic
charlatanical
charlatanically
charlatanish
charlatanism
charlatanistic
charlatanry
charlatanship
Charleen
Charlene
Charles
Charleston
Charley
Charlie
charlock
Charlotte
charm
charmedly
charmel
charmer
charmful
charmfully
charmfulness
charming
charmingly
charmingness
charmless
charmlessly
charmwise
charnel
charnockite
Charon
Charonian
Charonic
Charontas
Charophyta
charpit
charpoy
charqued
charqui
charr
Charruan
Charruas
charry
charshaf
charsingha
chart
chartaceous
charter
charterable
charterage
chartered
charterer
charterhouse
Charterist
charterless
chartermaster
charthouse
charting
Chartism
Chartist
chartist
chartless
chartographist
chartology
chartometer
chartophylax
chartreuse
Chartreux
chartroom
chartula
chartulary
charuk
charwoman
chary
Charybdian
Charybdis
chasable
chase
chaseable
chaser
Chasidim
chasing
chasm
chasma
chasmal
chasmed
chasmic
chasmogamic
chasmogamous
chasmogamy
chasmophyte
chasmy
chasse
Chasselas
chassepot
chasseur
chassignite
chassis
Chastacosta
chaste
chastely
chasten
chastener
chasteness
chasteningly
chastenment
chasteweed
chastisable
chastise
chastisement
chastiser
chastity
chasuble
chasubled
chat
chataka
Chateau
chateau
chateaux
chatelain
chatelaine
chatelainry
chatellany
chathamite
chati
Chatillon
Chatino
Chatot
chatoyance
chatoyancy
chatoyant
chatsome
chatta
chattable
Chattanooga
Chattanoogan
chattation
chattel
chattelhood
chattelism
chattelization
chattelize
chattelship
chatter
chatteration
chatterbag
chatterbox
chatterer
chattering
chatteringly
chattermag
chattermagging
Chattertonian
chattery
Chatti
chattily
chattiness
chatting
chattingly
chatty
chatwood
Chaucerian
Chauceriana
Chaucerianism
Chaucerism
Chauchat
chaudron
chauffer
chauffeur
chauffeurship
Chaui
chauk
chaukidari
Chauliodes
chaulmoogra
chaulmoograte
chaulmoogric
Chauna
chaus
chausseemeile
Chautauqua
Chautauquan
chaute
chauth
chauvinism
chauvinist
chauvinistic
chauvinistically
Chavante
Chavantean
chavender
chavibetol
chavicin
chavicine
chavicol
chavish
chaw
chawan
chawbacon
chawer
Chawia
chawk
chawl
chawstick
chay
chaya
chayaroot
Chayma
Chayota
chayote
chayroot
chazan
Chazy
che
cheap
cheapen
cheapener
cheapery
cheaping
cheapish
cheaply
cheapness
Cheapside
cheat
cheatable
cheatableness
cheatee
cheater
cheatery
cheating
cheatingly
cheatrie
Chebacco
chebec
chebel
chebog
chebule
chebulinic
Chechehet
Chechen
check
checkable
checkage
checkbird
checkbite
checkbook
checked
checker
checkerbelly
checkerberry
checkerbloom
checkerboard
checkerbreast
checkered
checkerist
checkers
checkerwise
checkerwork
checkhook
checkless
checkman
checkmate
checkoff
checkrack
checkrein
checkroll
checkroom
checkrope
checkrow
checkrowed
checkrower
checkstone
checkstrap
checkstring
checkup
checkweigher
checkwork
checky
cheddaring
cheddite
cheder
chedlock
chee
cheecha
cheechako
cheek
cheekbone
cheeker
cheekily
cheekiness
cheekish
cheekless
cheekpiece
cheeky
cheep
cheeper
cheepily
cheepiness
cheepy
cheer
cheered
cheerer
cheerful
cheerfulize
cheerfully
cheerfulness
cheerfulsome
cheerily
cheeriness
cheering
cheeringly
cheerio
cheerleader
cheerless
cheerlessly
cheerlessness
cheerly
cheery
cheese
cheeseboard
cheesebox
cheeseburger
cheesecake
cheesecloth
cheesecurd
cheesecutter
cheeseflower
cheeselip
cheesemonger
cheesemongering
cheesemongerly
cheesemongery
cheeseparer
cheeseparing
cheeser
cheesery
cheesewood
cheesiness
cheesy
cheet
cheetah
cheeter
cheetie
chef
Chefrinia
chegoe
chegre
Chehalis
Cheilanthes
cheilitis
Cheilodipteridae
Cheilodipterus
Cheilostomata
cheilostomatous
cheir
cheiragra
Cheiranthus
Cheirogaleus
Cheiroglossa
cheirognomy
cheirography
cheirolin
cheirology
cheiromancy
cheiromegaly
cheiropatagium
cheiropodist
cheiropody
cheiropompholyx
Cheiroptera
cheiropterygium
cheirosophy
cheirospasm
Cheirotherium
Cheka
chekan
cheke
cheki
Chekist
chekmak
chela
chelaship
chelate
chelation
chelem
chelerythrine
chelicer
chelicera
cheliceral
chelicerate
chelicere
chelide
chelidon
chelidonate
chelidonian
chelidonic
chelidonine
Chelidonium
Chelidosaurus
Cheliferidea
cheliferous
cheliform
chelingo
cheliped
Chellean
chello
Chelodina
chelodine
chelone
Chelonia
chelonian
chelonid
Chelonidae
cheloniid
Cheloniidae
chelonin
chelophore
chelp
Cheltenham
Chelura
Chelydidae
Chelydra
Chelydridae
chelydroid
chelys
Chemakuan
chemasthenia
chemawinite
Chemehuevi
chemesthesis
chemiatric
chemiatrist
chemiatry
chemic
chemical
chemicalization
chemicalize
chemically
chemicker
chemicoastrological
chemicobiologic
chemicobiology
chemicocautery
chemicodynamic
chemicoengineering
chemicoluminescence
chemicomechanical
chemicomineralogical
chemicopharmaceutical
chemicophysical
chemicophysics
chemicophysiological
chemicovital
chemigraph
chemigraphic
chemigraphy
chemiloon
chemiluminescence
chemiotactic
chemiotaxic
chemiotaxis
chemiotropic
chemiotropism
chemiphotic
chemis
chemise
chemisette
chemism
chemisorb
chemisorption
chemist
chemistry
chemitype
chemitypy
chemoceptor
chemokinesis
chemokinetic
chemolysis
chemolytic
chemolyze
chemoreception
chemoreceptor
chemoreflex
chemoresistance
chemoserotherapy
chemosis
chemosmosis
chemosmotic
chemosynthesis
chemosynthetic
chemotactic
chemotactically
chemotaxis
chemotaxy
chemotherapeutic
chemotherapeutics
chemotherapist
chemotherapy
chemotic
chemotropic
chemotropically
chemotropism
Chemung
chemurgic
chemurgical
chemurgy
Chen
chena
chende
chenevixite
Cheney
cheng
chenica
chenille
cheniller
chenopod
Chenopodiaceae
chenopodiaceous
Chenopodiales
Chenopodium
cheoplastic
chepster
cheque
Chequers
Chera
chercock
cherem
Cheremiss
Cheremissian
cherimoya
cherish
cherishable
cherisher
cherishing
cherishingly
cherishment
Cherkess
Cherkesser
Chermes
Chermidae
Chermish
Chernomorish
chernozem
Cherokee
cheroot
cherried
cherry
cherryblossom
cherrylike
chersonese
Chersydridae
chert
cherte
cherty
cherub
cherubic
cherubical
cherubically
cherubim
cherubimic
cherubimical
cherubin
Cherusci
Chervante
chervil
chervonets
Chesapeake
Cheshire
cheson
chess
chessboard
chessdom
chessel
chesser
chessist
chessman
chessmen
chesstree
chessylite
chest
Chester
chester
chesterfield
Chesterfieldian
chesterlite
chestful
chestily
chestiness
chestnut
chestnutty
chesty
Chet
cheth
chettik
chetty
chetverik
chetvert
chevage
cheval
chevalier
chevaline
chevance
cheve
cheven
chevener
chevesaile
chevin
Cheviot
chevisance
chevise
chevon
chevrette
chevron
chevrone
chevronel
chevronelly
chevronwise
chevrony
chevrotain
chevy
chew
chewbark
chewer
chewink
chewstick
chewy
Cheyenne
cheyney
chhatri
chi
chia
Chiam
Chian
Chianti
Chiapanec
Chiapanecan
chiaroscurist
chiaroscuro
chiasm
chiasma
chiasmal
chiasmatype
chiasmatypy
chiasmic
Chiasmodon
chiasmodontid
Chiasmodontidae
chiasmus
chiastic
chiastolite
chiastoneural
chiastoneurous
chiastoneury
chiaus
Chibcha
Chibchan
chibinite
chibouk
chibrit
chic
chicane
chicaner
chicanery
chicaric
chicayote
Chicha
chichi
chichicaste
Chichimec
chichimecan
chichipate
chichipe
chichituna
chick
chickabiddy
chickadee
Chickahominy
Chickamauga
chickaree
Chickasaw
chickasaw
chickell
chicken
chickenberry
chickenbill
chickenbreasted
chickenhearted
chickenheartedly
chickenheartedness
chickenhood
chickenweed
chickenwort
chicker
chickhood
chickling
chickstone
chickweed
chickwit
chicky
chicle
chicness
Chico
chico
Chicomecoatl
chicory
chicot
chicote
chicqued
chicquer
chicquest
chicquing
chid
chidden
chide
chider
chiding
chidingly
chidingness
chidra
chief
chiefdom
chiefery
chiefess
chiefest
chiefish
chiefless
chiefling
chiefly
chiefship
chieftain
chieftaincy
chieftainess
chieftainry
chieftainship
chieftess
chield
Chien
chien
chiffer
chiffon
chiffonade
chiffonier
chiffony
chifforobe
chigetai
chiggak
chigger
chiggerweed
chignon
chignoned
chigoe
chih
chihfu
Chihuahua
chikara
chil
chilacavote
chilalgia
chilarium
chilblain
Chilcat
child
childbearing
childbed
childbirth
childcrowing
childe
childed
Childermas
childhood
childing
childish
childishly
childishness
childkind
childless
childlessness
childlike
childlikeness
childly
childness
childrenite
childridden
childship
childward
chile
Chilean
Chileanization
Chileanize
chilectropion
chilenite
chili
chiliad
chiliadal
chiliadic
chiliagon
chiliahedron
chiliarch
chiliarchia
chiliarchy
chiliasm
chiliast
chiliastic
chilicote
chilicothe
chilidium
Chilina
Chilinidae
chiliomb
Chilion
chilitis
Chilkat
chill
chilla
chillagite
chilled
chiller
chillily
chilliness
chilling
chillingly
chillish
Chilliwack
chillness
chillo
chillroom
chillsome
chillum
chillumchee
chilly
chilognath
Chilognatha
chilognathan
chilognathous
chilogrammo
chiloma
Chilomastix
chiloncus
chiloplasty
chilopod
Chilopoda
chilopodan
chilopodous
Chilopsis
Chilostoma
Chilostomata
chilostomatous
chilostome
chilotomy
Chiltern
chilver
chimaera
chimaerid
Chimaeridae
chimaeroid
Chimaeroidei
Chimakuan
Chimakum
Chimalakwe
Chimalapa
Chimane
chimango
Chimaphila
Chimarikan
Chimariko
chimble
chime
chimer
chimera
chimeric
chimerical
chimerically
chimericalness
chimesmaster
chiminage
Chimmesyan
chimney
chimneyhead
chimneyless
chimneyman
Chimonanthus
chimopeelagic
chimpanzee
Chimu
Chin
chin
china
chinaberry
chinalike
Chinaman
chinamania
chinamaniac
chinampa
chinanta
Chinantecan
Chinantecs
chinaphthol
chinar
chinaroot
Chinatown
chinaware
chinawoman
chinband
chinch
chincha
Chinchasuyu
chinchayote
chinche
chincherinchee
chinchilla
chinching
chincloth
chincough
chine
chined
Chinee
Chinese
Chinesery
ching
chingma
Chingpaw
Chinhwan
chinik
chinin
Chink
chink
chinkara
chinker
chinkerinchee
chinking
chinkle
chinks
chinky
chinless
chinnam
chinned
chinny
chino
chinoa
chinol
Chinook
Chinookan
chinotoxine
chinotti
chinpiece
chinquapin
chinse
chint
chintz
chinwood
Chiococca
chiococcine
Chiogenes
chiolite
chionablepsia
Chionanthus
Chionaspis
Chionididae
Chionis
Chionodoxa
Chiot
chiotilla
Chip
chip
chipchap
chipchop
Chipewyan
chiplet
chipling
chipmunk
chippable
chippage
chipped
Chippendale
chipper
chipping
chippy
chips
chipwood
Chiquitan
Chiquito
chiragra
chiral
chiralgia
chirality
chirapsia
chirarthritis
chirata
Chiriana
Chiricahua
Chiriguano
chirimen
Chirino
chirinola
chiripa
chirivita
chirk
chirm
chiro
chirocosmetics
chirogale
chirognomic
chirognomically
chirognomist
chirognomy
chirognostic
chirograph
chirographary
chirographer
chirographic
chirographical
chirography
chirogymnast
chirological
chirologically
chirologist
chirology
chiromance
chiromancer
chiromancist
chiromancy
chiromant
chiromantic
chiromantical
Chiromantis
chiromegaly
chirometer
Chiromyidae
Chiromys
Chiron
chironomic
chironomid
Chironomidae
Chironomus
chironomy
chironym
chiropatagium
chiroplasty
chiropod
chiropodial
chiropodic
chiropodical
chiropodist
chiropodistry
chiropodous
chiropody
chiropompholyx
chiropractic
chiropractor
chiropraxis
chiropter
Chiroptera
chiropteran
chiropterite
chiropterophilous
chiropterous
chiropterygian
chiropterygious
chiropterygium
chirosophist
chirospasm
Chirotes
chirotherian
Chirotherium
chirothesia
chirotonsor
chirotonsory
chirotony
chirotype
chirp
chirper
chirpily
chirpiness
chirping
chirpingly
chirpling
chirpy
chirr
chirrup
chirruper
chirrupy
chirurgeon
chirurgery
Chisedec
chisel
chiseled
chiseler
chisellike
chiselly
chiselmouth
chit
Chita
chitak
chital
chitchat
chitchatty
Chitimacha
Chitimachan
chitin
chitinization
chitinized
chitinocalcareous
chitinogenous
chitinoid
chitinous
chiton
chitosamine
chitosan
chitose
chitra
Chitrali
chittamwood
chitter
chitterling
chitty
chivalresque
chivalric
chivalrous
chivalrously
chivalrousness
chivalry
chive
chivey
chiviatite
Chiwere
chkalik
chladnite
chlamyd
chlamydate
chlamydeous
Chlamydobacteriaceae
chlamydobacteriaceous
Chlamydobacteriales
Chlamydomonadaceae
Chlamydomonadidae
Chlamydomonas
Chlamydosaurus
Chlamydoselachidae
Chlamydoselachus
chlamydospore
Chlamydozoa
chlamydozoan
chlamyphore
Chlamyphorus
chlamys
Chleuh
chloanthite
chloasma
Chloe
chlor
chloracetate
chloragogen
chloral
chloralformamide
chloralide
chloralism
chloralization
chloralize
chloralose
chloralum
chloramide
chloramine
chloramphenicol
chloranemia
chloranemic
chloranhydride
chloranil
Chloranthaceae
chloranthaceous
Chloranthus
chloranthy
chlorapatite
chlorastrolite
chlorate
chlorazide
chlorcosane
chlordan
chlordane
chlore
Chlorella
Chlorellaceae
chlorellaceous
chloremia
chlorenchyma
chlorhydrate
chlorhydric
chloric
chloridate
chloridation
chloride
Chloridella
Chloridellidae
chlorider
chloridize
chlorimeter
chlorimetric
chlorimetry
chlorinate
chlorination
chlorinator
chlorine
chlorinize
chlorinous
chloriodide
Chlorion
Chlorioninae
chlorite
chloritic
chloritization
chloritize
chloritoid
chlorize
chlormethane
chlormethylic
chloroacetate
chloroacetic
chloroacetone
chloroacetophenone
chloroamide
chloroamine
chloroanaemia
chloroanemia
chloroaurate
chloroauric
chloroaurite
chlorobenzene
chlorobromide
chlorocalcite
chlorocarbonate
chlorochromates
chlorochromic
chlorochrous
Chlorococcaceae
Chlorococcales
Chlorococcum
Chlorococcus
chlorocresol
chlorocruorin
chlorodize
chloroform
chloroformate
chloroformic
chloroformism
chloroformist
chloroformization
chloroformize
chlorogenic
chlorogenine
chlorohydrin
chlorohydrocarbon
chloroiodide
chloroleucite
chloroma
chloromelanite
chlorometer
chloromethane
chlorometric
chlorometry
Chloromycetin
chloronitrate
chloropal
chloropalladates
chloropalladic
chlorophane
chlorophenol
chlorophoenicite
Chlorophora
Chlorophyceae
chlorophyceous
chlorophyl
chlorophyll
chlorophyllaceous
chlorophyllan
chlorophyllase
chlorophyllian
chlorophyllide
chlorophylliferous
chlorophylligenous
chlorophylligerous
chlorophyllin
chlorophyllite
chlorophylloid
chlorophyllose
chlorophyllous
chloropia
chloropicrin
chloroplast
chloroplastic
chloroplastid
chloroplatinate
chloroplatinic
chloroplatinite
chloroplatinous
chloroprene
chloropsia
chloroquine
chlorosilicate
chlorosis
chlorospinel
chlorosulphonic
chlorotic
chlorous
chlorozincate
chlorsalol
chloryl
Chnuphis
cho
choachyte
choana
choanate
Choanephora
choanocytal
choanocyte
Choanoflagellata
choanoflagellate
Choanoflagellida
Choanoflagellidae
choanoid
choanophorous
choanosomal
choanosome
choate
choaty
chob
choca
chocard
Chocho
chocho
chock
chockablock
chocker
chockler
chockman
Choco
Chocoan
chocolate
Choctaw
choel
choenix
Choeropsis
Choes
choffer
choga
chogak
chogset
Choiak
choice
choiceful
choiceless
choicelessness
choicely
choiceness
choicy
choil
choiler
choir
choirboy
choirlike
choirman
choirmaster
choirwise
Choisya
chokage
choke
chokeberry
chokebore
chokecherry
chokedamp
choker
chokered
chokerman
chokestrap
chokeweed
chokidar
choking
chokingly
chokra
choky
Chol
chol
Chola
chola
cholagogic
cholagogue
cholalic
cholane
cholangioitis
cholangitis
cholanic
cholanthrene
cholate
chold
choleate
cholecyanine
cholecyst
cholecystalgia
cholecystectasia
cholecystectomy
cholecystenterorrhaphy
cholecystenterostomy
cholecystgastrostomy
cholecystic
cholecystitis
cholecystnephrostomy
cholecystocolostomy
cholecystocolotomy
cholecystoduodenostomy
cholecystogastrostomy
cholecystogram
cholecystography
cholecystoileostomy
cholecystojejunostomy
cholecystokinin
cholecystolithiasis
cholecystolithotripsy
cholecystonephrostomy
cholecystopexy
cholecystorrhaphy
cholecystostomy
cholecystotomy
choledoch
choledochal
choledochectomy
choledochitis
choledochoduodenostomy
choledochoenterostomy
choledocholithiasis
choledocholithotomy
choledocholithotripsy
choledochoplasty
choledochorrhaphy
choledochostomy
choledochotomy
cholehematin
choleic
choleine
choleinic
cholelith
cholelithiasis
cholelithic
cholelithotomy
cholelithotripsy
cholelithotrity
cholemia
choleokinase
cholepoietic
choler
cholera
choleraic
choleric
cholericly
cholericness
choleriform
cholerigenous
cholerine
choleroid
choleromania
cholerophobia
cholerrhagia
cholestane
cholestanol
cholesteatoma
cholesteatomatous
cholestene
cholesterate
cholesteremia
cholesteric
cholesterin
cholesterinemia
cholesterinic
cholesterinuria
cholesterol
cholesterolemia
cholesteroluria
cholesterosis
cholesteryl
choletelin
choletherapy
choleuria
choli
choliamb
choliambic
choliambist
cholic
choline
cholinergic
cholinesterase
cholinic
cholla
choller
Cholo
cholochrome
cholocyanine
Choloepus
chologenetic
choloidic
choloidinic
chololith
chololithic
Cholonan
Cholones
cholophein
cholorrhea
choloscopy
cholterheaded
cholum
choluria
Choluteca
chomp
chondral
chondralgia
chondrarsenite
chondre
chondrectomy
chondrenchyma
chondric
chondrification
chondrify
chondrigen
chondrigenous
Chondrilla
chondrin
chondrinous
chondriocont
chondriome
chondriomere
chondriomite
chondriosomal
chondriosome
chondriosphere
chondrite
chondritic
chondritis
chondroadenoma
chondroalbuminoid
chondroangioma
chondroarthritis
chondroblast
chondroblastoma
chondrocarcinoma
chondrocele
chondroclasis
chondroclast
chondrocoracoid
chondrocostal
chondrocranial
chondrocranium
chondrocyte
chondrodite
chondroditic
chondrodynia
chondrodystrophia
chondrodystrophy
chondroendothelioma
chondroepiphysis
chondrofetal
chondrofibroma
chondrofibromatous
Chondroganoidei
chondrogen
chondrogenesis
chondrogenetic
chondrogenous
chondrogeny
chondroglossal
chondroglossus
chondrography
chondroid
chondroitic
chondroitin
chondrolipoma
chondrology
chondroma
chondromalacia
chondromatous
chondromucoid
Chondromyces
chondromyoma
chondromyxoma
chondromyxosarcoma
chondropharyngeal
chondropharyngeus
chondrophore
chondrophyte
chondroplast
chondroplastic
chondroplasty
chondroprotein
chondropterygian
Chondropterygii
chondropterygious
chondrosamine
chondrosarcoma
chondrosarcomatous
chondroseptum
chondrosin
chondrosis
chondroskeleton
chondrostean
Chondrostei
chondrosteoma
chondrosteous
chondrosternal
chondrotome
chondrotomy
chondroxiphoid
chondrule
chondrus
chonolith
chonta
Chontal
Chontalan
Chontaquiro
chontawood
choop
choosable
choosableness
choose
chooser
choosing
choosingly
choosy
chop
chopa
chopboat
chopfallen
chophouse
chopin
chopine
choplogic
chopped
chopper
choppered
chopping
choppy
chopstick
Chopunnish
Chora
choragic
choragion
choragium
choragus
choragy
Chorai
choral
choralcelo
choraleon
choralist
chorally
Chorasmian
chord
chorda
Chordaceae
chordacentrous
chordacentrum
chordaceous
chordal
chordally
chordamesoderm
Chordata
chordate
chorded
Chordeiles
chorditis
chordoid
chordomesoderm
chordotomy
chordotonal
chore
chorea
choreal
choreatic
choree
choregic
choregus
choregy
choreic
choreiform
choreograph
choreographer
choreographic
choreographical
choreography
choreoid
choreomania
chorepiscopal
chorepiscopus
choreus
choreutic
chorial
choriamb
choriambic
choriambize
choriambus
choric
chorine
chorioadenoma
chorioallantoic
chorioallantoid
chorioallantois
choriocapillaris
choriocapillary
choriocarcinoma
choriocele
chorioepithelioma
chorioid
chorioidal
chorioiditis
chorioidocyclitis
chorioidoiritis
chorioidoretinitis
chorioma
chorion
chorionepithelioma
chorionic
Chorioptes
chorioptic
chorioretinal
chorioretinitis
Choripetalae
choripetalous
choriphyllous
chorisepalous
chorisis
chorism
chorist
choristate
chorister
choristership
choristic
choristoblastoma
choristoma
choristry
chorization
chorizont
chorizontal
chorizontes
chorizontic
chorizontist
chorogi
chorograph
chorographer
chorographic
chorographical
chorographically
chorography
choroid
choroidal
choroidea
choroiditis
choroidocyclitis
choroidoiritis
choroidoretinitis
chorological
chorologist
chorology
choromania
choromanic
chorometry
chorook
Chorotega
Choroti
chort
chorten
Chorti
chortle
chortler
chortosterol
chorus
choruser
choruslike
Chorwat
choryos
chose
chosen
chott
Chou
Chouan
Chouanize
chouette
chough
chouka
choultry
choup
chouquette
chous
chouse
chouser
chousingha
chow
Chowanoc
chowchow
chowder
chowderhead
chowderheaded
chowk
chowry
choya
choyroot
Chozar
chrematheism
chrematist
chrematistic
chrematistics
chreotechnics
chresmology
chrestomathic
chrestomathics
chrestomathy
chria
chrimsel
Chris
chrism
chrisma
chrismal
chrismary
chrismatine
chrismation
chrismatite
chrismatize
chrismatory
chrismon
chrisom
chrisomloosing
chrisroot
Chrissie
Christ
Christabel
Christadelphian
Christadelphianism
christcross
Christdom
Christed
christen
Christendie
Christendom
christened
christener
christening
Christenmas
Christhood
Christiad
Christian
Christiana
Christiania
Christianiadeal
Christianism
christianite
Christianity
Christianization
Christianize
Christianizer
Christianlike
Christianly
Christianness
Christianogentilism
Christianography
Christianomastix
Christianopaganism
Christicide
Christie
Christiform
Christina
Christine
Christless
Christlessness
Christlike
Christlikeness
Christliness
Christly
Christmas
Christmasberry
Christmasing
Christmastide
Christmasy
Christocentric
Christofer
Christogram
Christolatry
Christological
Christologist
Christology
Christophany
Christophe
Christopher
Christos
chroatol
Chrobat
chroma
chromaffin
chromaffinic
chromammine
chromaphil
chromaphore
chromascope
chromate
chromatic
chromatical
chromatically
chromatician
chromaticism
chromaticity
chromatics
chromatid
chromatin
chromatinic
Chromatioideae
chromatism
chromatist
Chromatium
chromatize
chromatocyte
chromatodysopia
chromatogenous
chromatogram
chromatograph
chromatographic
chromatography
chromatoid
chromatology
chromatolysis
chromatolytic
chromatometer
chromatone
chromatopathia
chromatopathic
chromatopathy
chromatophil
chromatophile
chromatophilia
chromatophilic
chromatophilous
chromatophobia
chromatophore
chromatophoric
chromatophorous
chromatoplasm
chromatopsia
chromatoptometer
chromatoptometry
chromatoscope
chromatoscopy
chromatosis
chromatosphere
chromatospheric
chromatrope
chromaturia
chromatype
chromazurine
chromdiagnosis
chrome
chromene
chromesthesia
chromic
chromicize
chromid
Chromidae
Chromides
chromidial
Chromididae
chromidiogamy
chromidiosome
chromidium
chromidrosis
chromiferous
chromiole
chromism
chromite
chromitite
chromium
chromo
Chromobacterieae
Chromobacterium
chromoblast
chromocenter
chromocentral
chromochalcographic
chromochalcography
chromocollograph
chromocollographic
chromocollography
chromocollotype
chromocollotypy
chromocratic
chromocyte
chromocytometer
chromodermatosis
chromodiascope
chromogen
chromogene
chromogenesis
chromogenetic
chromogenic
chromogenous
chromogram
chromograph
chromoisomer
chromoisomeric
chromoisomerism
chromoleucite
chromolipoid
chromolith
chromolithic
chromolithograph
chromolithographer
chromolithographic
chromolithography
chromolysis
chromomere
chromometer
chromone
chromonema
chromoparous
chromophage
chromophane
chromophile
chromophilic
chromophilous
chromophobic
chromophore
chromophoric
chromophorous
chromophotograph
chromophotographic
chromophotography
chromophotolithograph
chromophyll
chromoplasm
chromoplasmic
chromoplast
chromoplastid
chromoprotein
chromopsia
chromoptometer
chromoptometrical
chromosantonin
chromoscope
chromoscopic
chromoscopy
chromosomal
chromosome
chromosphere
chromospheric
chromotherapist
chromotherapy
chromotrope
chromotropic
chromotropism
chromotropy
chromotype
chromotypic
chromotypographic
chromotypography
chromotypy
chromous
chromoxylograph
chromoxylography
chromule
chromy
chromyl
chronal
chronanagram
chronaxia
chronaxie
chronaxy
chronic
chronical
chronically
chronicity
chronicle
chronicler
chronicon
chronisotherm
chronist
chronobarometer
chronocinematography
chronocrator
chronocyclegraph
chronodeik
chronogeneous
chronogenesis
chronogenetic
chronogram
chronogrammatic
chronogrammatical
chronogrammatically
chronogrammatist
chronogrammic
chronograph
chronographer
chronographic
chronographical
chronographically
chronography
chronoisothermal
chronologer
chronologic
chronological
chronologically
chronologist
chronologize
chronology
chronomancy
chronomantic
chronometer
chronometric
chronometrical
chronometrically
chronometry
chrononomy
chronopher
chronophotograph
chronophotographic
chronophotography
Chronos
chronoscope
chronoscopic
chronoscopically
chronoscopy
chronosemic
chronostichon
chronothermal
chronothermometer
chronotropic
chronotropism
Chroococcaceae
chroococcaceous
Chroococcales
chroococcoid
Chroococcus
Chrosperma
chrotta
chrysal
chrysalid
chrysalidal
chrysalides
chrysalidian
chrysaline
chrysalis
chrysaloid
chrysamine
chrysammic
chrysamminic
Chrysamphora
chrysaniline
chrysanisic
chrysanthemin
chrysanthemum
chrysanthous
Chrysaor
chrysarobin
chrysatropic
chrysazin
chrysazol
chryselectrum
chryselephantine
Chrysemys
chrysene
chrysenic
chrysid
Chrysidella
chrysidid
Chrysididae
chrysin
Chrysippus
Chrysis
chrysoaristocracy
Chrysobalanaceae
Chrysobalanus
chrysoberyl
chrysobull
chrysocarpous
chrysochlore
Chrysochloridae
Chrysochloris
chrysochlorous
chrysochrous
chrysocolla
chrysocracy
chrysoeriol
chrysogen
chrysograph
chrysographer
chrysography
chrysohermidin
chrysoidine
chrysolite
chrysolitic
chrysology
Chrysolophus
chrysomelid
Chrysomelidae
chrysomonad
Chrysomonadales
Chrysomonadina
chrysomonadine
Chrysomyia
Chrysopa
chrysopal
chrysopee
chrysophan
chrysophanic
Chrysophanus
chrysophenine
chrysophilist
chrysophilite
Chrysophlyctis
chrysophyll
Chrysophyllum
chrysopid
Chrysopidae
chrysopoeia
chrysopoetic
chrysopoetics
chrysoprase
Chrysops
Chrysopsis
chrysorin
chrysosperm
Chrysosplenium
Chrysothamnus
Chrysothrix
chrysotile
Chrysotis
chrystocrene
chthonian
chthonic
chthonophagia
chthonophagy
chub
chubbed
chubbedness
chubbily
chubbiness
chubby
Chuchona
Chuck
chuck
chucker
chuckhole
chuckies
chucking
chuckingly
chuckle
chucklehead
chuckleheaded
chuckler
chucklingly
chuckrum
chuckstone
chuckwalla
chucky
Chud
chuddar
Chude
Chudic
Chueta
chufa
chuff
chuffy
chug
chugger
chuhra
Chuje
chukar
Chukchi
chukker
chukor
chulan
chullpa
chum
Chumashan
Chumawi
chummage
chummer
chummery
chummily
chummy
chump
chumpaka
chumpish
chumpishness
Chumpivilca
chumpy
chumship
Chumulu
Chun
chun
chunari
Chuncho
chunga
chunk
chunkhead
chunkily
chunkiness
chunky
chunner
chunnia
chunter
chupak
chupon
chuprassie
chuprassy
church
churchanity
churchcraft
churchdom
churchful
churchgoer
churchgoing
churchgrith
churchianity
churchified
churchiness
churching
churchish
churchism
churchite
churchless
churchlet
churchlike
churchliness
churchly
churchman
churchmanly
churchmanship
churchmaster
churchscot
churchward
churchwarden
churchwardenism
churchwardenize
churchwardenship
churchwards
churchway
churchwise
churchwoman
churchy
churchyard
churel
churinga
churl
churled
churlhood
churlish
churlishly
churlishness
churly
churm
churn
churnability
churnful
churning
churnmilk
churnstaff
Churoya
Churoyan
churr
Churrigueresque
churruck
churrus
churrworm
chut
chute
chuter
chutney
Chuvash
Chwana
chyack
chyak
chylaceous
chylangioma
chylaqueous
chyle
chylemia
chylidrosis
chylifaction
chylifactive
chylifactory
chyliferous
chylific
chylification
chylificatory
chyliform
chylify
chylocaulous
chylocauly
chylocele
chylocyst
chyloid
chylomicron
chylopericardium
chylophyllous
chylophylly
chylopoiesis
chylopoietic
chylosis
chylothorax
chylous
chyluria
chymaqueous
chymase
chyme
chymia
chymic
chymiferous
chymification
chymify
chymosin
chymosinogen
chymotrypsin
chymotrypsinogen
chymous
chypre
chytra
chytrid
Chytridiaceae
chytridiaceous
chytridial
Chytridiales
chytridiose
chytridiosis
Chytridium
Chytroi
cibarial
cibarian
cibarious
cibation
cibol
Cibola
Cibolan
Ciboney
cibophobia
ciborium
cibory
ciboule
cicad
cicada
Cicadellidae
cicadid
Cicadidae
cicala
cicatrice
cicatrices
cicatricial
cicatricle
cicatricose
cicatricula
cicatricule
cicatrisive
cicatrix
cicatrizant
cicatrizate
cicatrization
cicatrize
cicatrizer
cicatrose
Cicely
cicely
cicer
ciceronage
cicerone
ciceroni
Ciceronian
Ciceronianism
Ciceronianize
Ciceronic
Ciceronically
ciceronism
ciceronize
cichlid
Cichlidae
cichloid
cichoraceous
Cichoriaceae
cichoriaceous
Cichorium
Cicindela
cicindelid
cicindelidae
cicisbeism
ciclatoun
Ciconia
Ciconiae
ciconian
ciconiid
Ciconiidae
ciconiiform
Ciconiiformes
ciconine
ciconioid
Cicuta
cicutoxin
Cid
cidarid
Cidaridae
cidaris
Cidaroida
cider
ciderish
ciderist
ciderkin
cig
cigala
cigar
cigaresque
cigarette
cigarfish
cigarillo
cigarito
cigarless
cigua
ciguatera
cilectomy
cilia
ciliary
Ciliata
ciliate
ciliated
ciliately
ciliation
cilice
Cilician
cilicious
Cilicism
ciliella
ciliferous
ciliform
ciliiferous
ciliiform
Cilioflagellata
cilioflagellate
ciliograde
ciliolate
ciliolum
Ciliophora
cilioretinal
cilioscleral
ciliospinal
ciliotomy
cilium
cillosis
cimbia
Cimbri
Cimbrian
Cimbric
cimelia
cimex
cimicid
Cimicidae
cimicide
cimiciform
Cimicifuga
cimicifugin
cimicoid
ciminite
cimline
Cimmeria
Cimmerian
Cimmerianism
cimolite
cinch
cincher
cincholoipon
cincholoiponic
cinchomeronic
Cinchona
Cinchonaceae
cinchonaceous
cinchonamine
cinchonate
cinchonia
cinchonic
cinchonicine
cinchonidia
cinchonidine
cinchonine
cinchoninic
cinchonism
cinchonization
cinchonize
cinchonology
cinchophen
cinchotine
cinchotoxine
cincinnal
Cincinnati
Cincinnatia
Cincinnatian
cincinnus
Cinclidae
Cinclidotus
cinclis
Cinclus
cinct
cincture
cinder
Cinderella
cinderlike
cinderman
cinderous
cindery
Cindie
Cindy
cine
cinecamera
cinefilm
cinel
cinema
Cinemascope
cinematic
cinematical
cinematically
cinematize
cinematograph
cinematographer
cinematographic
cinematographical
cinematographically
cinematographist
cinematography
cinemelodrama
cinemize
cinemograph
cinenchyma
cinenchymatous
cinene
cinenegative
cineole
cineolic
cinephone
cinephotomicrography
cineplastics
cineplasty
cineraceous
Cinerama
Cineraria
cinerarium
cinerary
cineration
cinerator
cinerea
cinereal
cinereous
cineritious
cinevariety
cingle
cingular
cingulate
cingulated
cingulum
cinnabar
cinnabaric
cinnabarine
cinnamal
cinnamaldehyde
cinnamate
cinnamein
cinnamene
cinnamenyl
cinnamic
Cinnamodendron
cinnamol
cinnamomic
Cinnamomum
cinnamon
cinnamoned
cinnamonic
cinnamonlike
cinnamonroot
cinnamonwood
cinnamyl
cinnamylidene
cinnoline
cinnyl
cinquain
cinque
cinquecentism
cinquecentist
cinquecento
cinquefoil
cinquefoiled
cinquepace
cinter
Cinura
cinuran
cinurous
cion
cionectomy
cionitis
cionocranial
cionocranian
cionoptosis
cionorrhaphia
cionotome
cionotomy
Cipango
cipher
cipherable
cipherdom
cipherer
cipherhood
cipo
cipolin
cippus
circa
Circaea
Circaeaceae
Circaetus
Circassian
Circassic
Circe
Circean
Circensian
circinal
circinate
circinately
circination
Circinus
circiter
circle
circled
circler
circlet
circlewise
circling
circovarian
circuit
circuitable
circuital
circuiteer
circuiter
circuition
circuitman
circuitor
circuitous
circuitously
circuitousness
circuity
circulable
circulant
circular
circularism
circularity
circularization
circularize
circularizer
circularly
circularness
circularwise
circulate
circulation
circulative
circulator
circulatory
circumagitate
circumagitation
circumambages
circumambagious
circumambience
circumambiency
circumambient
circumambulate
circumambulation
circumambulator
circumambulatory
circumanal
circumantarctic
circumarctic
circumarticular
circumaviate
circumaviation
circumaviator
circumaxial
circumaxile
circumaxillary
circumbasal
circumbendibus
circumboreal
circumbuccal
circumbulbar
circumcallosal
Circumcellion
circumcenter
circumcentral
circumcinct
circumcincture
circumcircle
circumcise
circumciser
circumcision
circumclude
circumclusion
circumcolumnar
circumcone
circumconic
circumcorneal
circumcrescence
circumcrescent
circumdenudation
circumdiction
circumduce
circumduct
circumduction
circumesophagal
circumesophageal
circumference
circumferential
circumferentially
circumferentor
circumflant
circumflect
circumflex
circumflexion
circumfluence
circumfluent
circumfluous
circumforaneous
circumfulgent
circumfuse
circumfusile
circumfusion
circumgenital
circumgyrate
circumgyration
circumgyratory
circumhorizontal
circumincession
circuminsession
circuminsular
circumintestinal
circumitineration
circumjacence
circumjacency
circumjacent
circumlental
circumlitio
circumlittoral
circumlocute
circumlocution
circumlocutional
circumlocutionary
circumlocutionist
circumlocutory
circummeridian
circummeridional
circummigration
circummundane
circummure
circumnatant
circumnavigable
circumnavigate
circumnavigation
circumnavigator
circumnavigatory
circumneutral
circumnuclear
circumnutate
circumnutation
circumnutatory
circumocular
circumoesophagal
circumoral
circumorbital
circumpacific
circumpallial
circumparallelogram
circumpentagon
circumplicate
circumplication
circumpolar
circumpolygon
circumpose
circumposition
circumradius
circumrenal
circumrotate
circumrotation
circumrotatory
circumsail
circumscissile
circumscribable
circumscribe
circumscribed
circumscriber
circumscript
circumscription
circumscriptive
circumscriptively
circumscriptly
circumsinous
circumspangle
circumspatial
circumspect
circumspection
circumspective
circumspectively
circumspectly
circumspectness
circumspheral
circumstance
circumstanced
circumstantiability
circumstantiable
circumstantial
circumstantiality
circumstantially
circumstantialness
circumstantiate
circumstantiation
circumtabular
circumterraneous
circumterrestrial
circumtonsillar
circumtropical
circumumbilical
circumundulate
circumundulation
circumvallate
circumvallation
circumvascular
circumvent
circumventer
circumvention
circumventive
circumventor
circumviate
circumvolant
circumvolute
circumvolution
circumvolutory
circumvolve
circumzenithal
circus
circusy
cirque
cirrate
cirrated
Cirratulidae
Cirratulus
Cirrhopetalum
cirrhosed
cirrhosis
cirrhotic
cirrhous
cirri
cirribranch
cirriferous
cirriform
cirrigerous
cirrigrade
cirriped
Cirripedia
cirripedial
cirrolite
cirropodous
cirrose
Cirrostomi
cirrous
cirrus
cirsectomy
Cirsium
cirsocele
cirsoid
cirsomphalos
cirsophthalmia
cirsotome
cirsotomy
ciruela
cirurgian
Cisalpine
cisalpine
Cisalpinism
cisandine
cisatlantic
cisco
cise
cisele
cisgangetic
cisjurane
cisleithan
cismarine
Cismontane
cismontane
Cismontanism
cisoceanic
cispadane
cisplatine
cispontine
cisrhenane
Cissampelos
cissing
cissoid
cissoidal
Cissus
cist
cista
Cistaceae
cistaceous
cistae
cisted
Cistercian
Cistercianism
cistern
cisterna
cisternal
cistic
cistophoric
cistophorus
Cistudo
Cistus
cistvaen
cit
citable
citadel
citation
citator
citatory
cite
citee
Citellus
citer
citess
cithara
Citharexylum
citharist
citharista
citharoedi
citharoedic
citharoedus
cither
citied
citification
citified
citify
Citigradae
citigrade
citizen
citizendom
citizeness
citizenhood
citizenish
citizenism
citizenize
citizenly
citizenry
citizenship
citole
citraconate
citraconic
citral
citramide
citramontane
citrange
citrangeade
citrate
citrated
citrean
citrene
citreous
citric
citriculture
citriculturist
citril
citrin
citrination
citrine
citrinin
citrinous
citrometer
Citromyces
citron
citronade
citronella
citronellal
citronelle
citronellic
citronellol
citronin
citronwood
Citropsis
citropten
citrous
citrullin
Citrullus
Citrus
citrus
citrylidene
cittern
citua
city
citycism
citydom
cityfolk
cityful
cityish
cityless
cityness
cityscape
cityward
citywards
cive
civet
civetlike
civetone
civic
civically
civicism
civics
civil
civilian
civility
civilizable
civilization
civilizational
civilizatory
civilize
civilized
civilizedness
civilizee
civilizer
civilly
civilness
civism
Civitan
civvy
cixiid
Cixiidae
Cixo
clabber
clabbery
clachan
clack
Clackama
clackdish
clacker
clacket
clackety
clad
cladanthous
cladautoicous
cladding
cladine
cladocarpous
Cladocera
cladoceran
cladocerous
cladode
cladodial
cladodont
cladodontid
Cladodontidae
Cladodus
cladogenous
Cladonia
Cladoniaceae
cladoniaceous
cladonioid
Cladophora
Cladophoraceae
cladophoraceous
Cladophorales
cladophyll
cladophyllum
cladoptosis
cladose
Cladoselache
Cladoselachea
cladoselachian
Cladoselachidae
cladosiphonic
Cladosporium
Cladothrix
Cladrastis
cladus
clag
claggum
claggy
Claiborne
Claibornian
claim
claimable
claimant
claimer
claimless
clairaudience
clairaudient
clairaudiently
clairce
Claire
clairecole
clairecolle
clairschach
clairschacher
clairsentience
clairsentient
clairvoyance
clairvoyancy
clairvoyant
clairvoyantly
claith
claithes
claiver
Clallam
clam
clamant
clamantly
clamative
Clamatores
clamatorial
clamatory
clamb
clambake
clamber
clamberer
clamcracker
clame
clamer
clammed
clammer
clammily
clamminess
clamming
clammish
clammy
clammyweed
clamor
clamorer
clamorist
clamorous
clamorously
clamorousness
clamorsome
clamp
clamper
clamshell
clamworm
clan
clancular
clancularly
clandestine
clandestinely
clandestineness
clandestinity
clanfellow
clang
clangful
clangingly
clangor
clangorous
clangorously
Clangula
clanjamfray
clanjamfrey
clanjamfrie
clanjamphrey
clank
clankety
clanking
clankingly
clankingness
clankless
clanless
clanned
clanning
clannishly
clannishness
clansfolk
clanship
clansman
clansmanship
clanswoman
Claosaurus
clap
clapboard
clapbread
clapmatch
clapnet
clapped
clapper
clapperclaw
clapperclawer
clapperdudgeon
clappermaclaw
clapping
clapt
claptrap
clapwort
claque
claquer
Clara
clarabella
clarain
Clare
Clarence
Clarenceux
Clarenceuxship
Clarencieux
clarendon
claret
Claretian
Claribel
claribella
Clarice
clarifiant
clarification
clarifier
clarify
clarigation
clarin
Clarinda
clarinet
clarinetist
clarinettist
clarion
clarionet
Clarissa
Clarisse
Clarist
clarity
Clark
clark
clarkeite
Clarkia
claro
Claromontane
clarshech
clart
clarty
clary
clash
clasher
clashingly
clashy
clasmatocyte
clasmatosis
clasp
clasper
clasping
claspt
class
classable
classbook
classed
classer
classes
classfellow
classic
classical
classicalism
classicalist
classicality
classicalize
classically
classicalness
classicism
classicist
classicistic
classicize
classicolatry
classifiable
classific
classifically
classification
classificational
classificator
classificatory
classified
classifier
classis
classism
classman
classmanship
classmate
classroom
classwise
classwork
classy
clastic
clat
clatch
Clathraceae
clathraceous
Clathraria
clathrarian
clathrate
Clathrina
Clathrinidae
clathroid
clathrose
clathrulate
Clathrus
Clatsop
clatter
clatterer
clatteringly
clattertrap
clattery
clatty
Claude
claudent
claudetite
Claudia
Claudian
claudicant
claudicate
claudication
Claudio
Claudius
claught
clausal
clause
Clausilia
Clausiliidae
clausthalite
claustra
claustral
claustration
claustrophobia
claustrum
clausula
clausular
clausule
clausure
claut
clava
clavacin
claval
Clavaria
Clavariaceae
clavariaceous
clavate
clavated
clavately
clavation
clave
clavecin
clavecinist
clavel
clavelization
clavelize
clavellate
clavellated
claver
clavial
claviature
clavicembalo
Claviceps
clavichord
clavichordist
clavicithern
clavicle
clavicorn
clavicornate
Clavicornes
Clavicornia
clavicotomy
clavicular
clavicularium
claviculate
claviculus
clavicylinder
clavicymbal
clavicytherium
clavier
clavierist
claviform
claviger
clavigerous
claviharp
clavilux
claviol
clavipectoral
clavis
clavodeltoid
clavodeltoideus
clavola
clavolae
clavolet
clavus
clavy
claw
clawed
clawer
clawk
clawker
clawless
Clay
clay
claybank
claybrained
clayen
clayer
clayey
clayiness
clayish
claylike
clayman
claymore
Clayoquot
claypan
Clayton
Claytonia
clayware
clayweed
cleach
clead
cleaded
cleading
cleam
cleamer
clean
cleanable
cleaner
cleanhanded
cleanhandedness
cleanhearted
cleaning
cleanish
cleanlily
cleanliness
cleanly
cleanness
cleanout
cleansable
cleanse
cleanser
cleansing
cleanskins
cleanup
clear
clearable
clearage
clearance
clearcole
clearedness
clearer
clearheaded
clearheadedly
clearheadedness
clearhearted
clearing
clearinghouse
clearish
clearly
clearness
clearskins
clearstarch
clearweed
clearwing
cleat
cleavability
cleavable
cleavage
cleave
cleaveful
cleavelandite
cleaver
cleavers
cleaverwort
cleaving
cleavingly
cleche
cleck
cled
cledge
cledgy
cledonism
clee
cleek
cleeked
cleeky
clef
cleft
clefted
cleg
cleidagra
cleidarthritis
cleidocostal
cleidocranial
cleidohyoid
cleidomancy
cleidomastoid
cleidorrhexis
cleidoscapular
cleidosternal
cleidotomy
cleidotripsy
cleistocarp
cleistocarpous
cleistogamic
cleistogamically
cleistogamous
cleistogamously
cleistogamy
cleistogene
cleistogenous
cleistogeny
cleistothecium
Cleistothecopsis
cleithral
cleithrum
Clem
clem
Clematis
clematite
Clemclemalats
clemence
clemency
Clement
clement
Clementina
Clementine
clemently
clench
cleoid
Cleome
Cleopatra
clep
Clepsine
clepsydra
cleptobiosis
cleptobiotic
clerestoried
clerestory
clergy
clergyable
clergylike
clergyman
clergywoman
cleric
clerical
clericalism
clericalist
clericality
clericalize
clerically
clericate
clericature
clericism
clericity
clerid
Cleridae
clerihew
clerisy
clerk
clerkage
clerkdom
clerkery
clerkess
clerkhood
clerking
clerkish
clerkless
clerklike
clerkliness
clerkly
clerkship
Clerodendron
cleromancy
cleronomy
cleruch
cleruchial
cleruchic
cleruchy
Clerus
cletch
Clethra
Clethraceae
clethraceous
cleuch
cleve
cleveite
clever
cleverality
cleverish
cleverishly
cleverly
cleverness
clevis
clew
cliack
clianthus
cliche
click
clicker
clicket
clickless
clicky
Clidastes
cliency
client
clientage
cliental
cliented
clientelage
clientele
clientless
clientry
clientship
Cliff
cliff
cliffed
cliffless
clifflet
clifflike
Clifford
cliffside
cliffsman
cliffweed
cliffy
clift
Cliftonia
cliftonite
clifty
clima
Climaciaceae
climaciaceous
Climacium
climacteric
climacterical
climacterically
climactic
climactical
climactically
climacus
climata
climatal
climate
climath
climatic
climatical
climatically
Climatius
climatize
climatographical
climatography
climatologic
climatological
climatologically
climatologist
climatology
climatometer
climatotherapeutics
climatotherapy
climature
climax
climb
climbable
climber
climbing
clime
climograph
clinal
clinamen
clinamina
clinandria
clinandrium
clinanthia
clinanthium
clinch
clincher
clinchingly
clinchingness
cline
cling
clinger
clingfish
clinging
clingingly
clingingness
clingstone
clingy
clinia
clinic
clinical
clinically
clinician
clinicist
clinicopathological
clinium
clink
clinker
clinkerer
clinkery
clinking
clinkstone
clinkum
clinoaxis
clinocephalic
clinocephalism
clinocephalous
clinocephalus
clinocephaly
clinochlore
clinoclase
clinoclasite
clinodiagonal
clinodomatic
clinodome
clinograph
clinographic
clinohedral
clinohedrite
clinohumite
clinoid
clinologic
clinology
clinometer
clinometric
clinometrical
clinometry
clinopinacoid
clinopinacoidal
Clinopodium
clinoprism
clinopyramid
clinopyroxene
clinorhombic
clinospore
clinostat
clinquant
clint
clinting
Clinton
Clintonia
clintonite
clinty
Clio
Cliona
Clione
clip
clipei
clipeus
clippable
clipped
clipper
clipperman
clipping
clips
clipse
clipsheet
clipsome
clipt
clique
cliquedom
cliqueless
cliquish
cliquishly
cliquishness
cliquism
cliquy
cliseometer
clisere
clishmaclaver
Clisiocampa
Clistogastra
clit
clitch
clite
clitella
clitellar
clitelliferous
clitelline
clitellum
clitellus
clites
clithe
clithral
clithridiate
clitia
clition
Clitocybe
Clitoria
clitoridauxe
clitoridean
clitoridectomy
clitoriditis
clitoridotomy
clitoris
clitorism
clitoritis
clitter
clitterclatter
clival
clive
clivers
Clivia
clivis
clivus
cloaca
cloacal
cloacaline
cloacean
cloacinal
cloacinean
cloacitis
cloak
cloakage
cloaked
cloakedly
cloaking
cloakless
cloaklet
cloakmaker
cloakmaking
cloakroom
cloakwise
cloam
cloamen
cloamer
clobber
clobberer
clochan
cloche
clocher
clochette
clock
clockbird
clockcase
clocked
clocker
clockface
clockhouse
clockkeeper
clockless
clocklike
clockmaker
clockmaking
clockmutch
clockroom
clocksmith
clockwise
clockwork
clod
clodbreaker
clodder
cloddily
cloddiness
cloddish
cloddishly
cloddishness
cloddy
clodhead
clodhopper
clodhopping
clodlet
clodpate
clodpated
clodpoll
cloff
clog
clogdogdo
clogger
cloggily
clogginess
cloggy
cloghad
cloglike
clogmaker
clogmaking
clogwood
clogwyn
cloiochoanitic
cloisonless
cloisonne
cloister
cloisteral
cloistered
cloisterer
cloisterless
cloisterlike
cloisterliness
cloisterly
cloisterwise
cloistral
cloistress
cloit
clomb
clomben
clonal
clone
clonic
clonicity
clonicotonic
clonism
clonorchiasis
Clonorchis
Clonothrix
clonus
cloof
cloop
cloot
clootie
clop
cloragen
clorargyrite
cloriodid
closable
close
closecross
closed
closefisted
closefistedly
closefistedness
closehanded
closehearted
closely
closemouth
closemouthed
closen
closeness
closer
closestool
closet
closewing
closh
closish
closter
Closterium
clostridial
Clostridium
closure
clot
clotbur
clote
cloth
clothbound
clothe
clothes
clothesbag
clothesbasket
clothesbrush
clotheshorse
clothesline
clothesman
clothesmonger
clothespin
clothespress
clothesyard
clothier
clothify
Clothilda
clothing
clothmaker
clothmaking
Clotho
clothworker
clothy
clottage
clottedness
clotter
clotty
cloture
clotweed
cloud
cloudage
cloudberry
cloudburst
cloudcap
clouded
cloudful
cloudily
cloudiness
clouding
cloudland
cloudless
cloudlessly
cloudlessness
cloudlet
cloudlike
cloudling
cloudology
cloudscape
cloudship
cloudward
cloudwards
cloudy
clough
clour
clout
clouted
clouter
clouterly
clouty
clove
cloven
clovene
clover
clovered
cloverlay
cloverleaf
cloveroot
cloverroot
clovery
clow
clown
clownade
clownage
clownery
clownheal
clownish
clownishly
clownishness
clownship
clowring
cloy
cloyedness
cloyer
cloying
cloyingly
cloyingness
cloyless
cloysome
club
clubbability
clubbable
clubbed
clubber
clubbily
clubbing
clubbish
clubbism
clubbist
clubby
clubdom
clubfellow
clubfisted
clubfoot
clubfooted
clubhand
clubhaul
clubhouse
clubionid
Clubionidae
clubland
clubman
clubmate
clubmobile
clubmonger
clubridden
clubroom
clubroot
clubstart
clubster
clubweed
clubwoman
clubwood
cluck
clue
cluff
clump
clumpish
clumproot
clumpy
clumse
clumsily
clumsiness
clumsy
clunch
clung
Cluniac
Cluniacensian
Clunisian
Clunist
clunk
clupanodonic
Clupea
clupeid
Clupeidae
clupeiform
clupeine
Clupeodei
clupeoid
cluricaune
Clusia
Clusiaceae
clusiaceous
cluster
clusterberry
clustered
clusterfist
clustering
clusteringly
clustery
clutch
clutchman
cluther
clutter
clutterer
clutterment
cluttery
cly
Clyde
Clydesdale
Clydeside
Clydesider
clyer
clyfaker
clyfaking
Clymenia
clype
clypeal
Clypeaster
Clypeastridea
Clypeastrina
clypeastroid
Clypeastroida
Clypeastroidea
clypeate
clypeiform
clypeolar
clypeolate
clypeole
clypeus
clysis
clysma
clysmian
clysmic
clyster
clysterize
Clytemnestra
cnemapophysis
cnemial
cnemidium
Cnemidophorus
cnemis
Cneoraceae
cneoraceous
Cneorum
cnicin
Cnicus
cnida
Cnidaria
cnidarian
Cnidian
cnidoblast
cnidocell
cnidocil
cnidocyst
cnidophore
cnidophorous
cnidopod
cnidosac
Cnidoscolus
cnidosis
coabode
coabound
coabsume
coacceptor
coacervate
coacervation
coach
coachability
coachable
coachbuilder
coachbuilding
coachee
coacher
coachfellow
coachful
coaching
coachlet
coachmaker
coachmaking
coachman
coachmanship
coachmaster
coachsmith
coachsmithing
coachway
coachwhip
coachwise
coachwoman
coachwork
coachwright
coachy
coact
coaction
coactive
coactively
coactivity
coactor
coadamite
coadapt
coadaptation
coadequate
coadjacence
coadjacency
coadjacent
coadjacently
coadjudicator
coadjust
coadjustment
coadjutant
coadjutator
coadjute
coadjutement
coadjutive
coadjutor
coadjutorship
coadjutress
coadjutrix
coadjuvancy
coadjuvant
coadjuvate
coadminister
coadministration
coadministrator
coadministratrix
coadmiration
coadmire
coadmit
coadnate
coadore
coadsorbent
coadunate
coadunation
coadunative
coadunatively
coadunite
coadventure
coadventurer
coadvice
coaffirmation
coafforest
coaged
coagency
coagent
coaggregate
coaggregated
coaggregation
coagitate
coagitator
coagment
coagonize
coagriculturist
coagula
coagulability
coagulable
coagulant
coagulase
coagulate
coagulation
coagulative
coagulator
coagulatory
coagulin
coagulometer
coagulose
coagulum
Coahuiltecan
coaid
coaita
coak
coakum
coal
coalbag
coalbagger
coalbin
coalbox
coaldealer
coaler
coalesce
coalescence
coalescency
coalescent
coalfish
coalfitter
coalhole
coalification
coalify
Coalite
coalition
coalitional
coalitioner
coalitionist
coalize
coalizer
coalless
coalmonger
coalmouse
coalpit
coalrake
coalsack
coalternate
coalternation
coalternative
coaltitude
coaly
coalyard
coambassador
coambulant
coamiable
coaming
Coan
coanimate
coannex
coannihilate
coapostate
coapparition
coappear
coappearance
coapprehend
coapprentice
coappriser
coapprover
coapt
coaptate
coaptation
coaration
coarb
coarbiter
coarbitrator
coarctate
coarctation
coardent
coarrange
coarrangement
coarse
coarsely
coarsen
coarseness
coarsish
coascend
coassert
coasserter
coassession
coassessor
coassignee
coassist
coassistance
coassistant
coassume
coast
coastal
coastally
coaster
Coastguard
coastguardman
coasting
coastland
coastman
coastside
coastwaiter
coastward
coastwards
coastways
coastwise
coat
coated
coatee
coater
coati
coatie
coatimondie
coatimundi
coating
coatless
coatroom
coattail
coattailed
coattend
coattest
coattestation
coattestator
coaudience
coauditor
coaugment
coauthor
coauthority
coauthorship
coawareness
coax
coaxal
coaxation
coaxer
coaxial
coaxially
coaxing
coaxingly
coaxy
cob
cobaea
cobalt
cobaltammine
cobaltic
cobalticyanic
cobalticyanides
cobaltiferous
cobaltinitrite
cobaltite
cobaltocyanic
cobaltocyanide
cobaltous
cobang
cobbed
cobber
cobberer
cobbing
cobble
cobbler
cobblerfish
cobblerism
cobblerless
cobblership
cobblery
cobblestone
cobbling
cobbly
cobbra
cobby
cobcab
Cobdenism
Cobdenite
cobego
cobelief
cobeliever
cobelligerent
cobenignity
coberger
cobewail
cobhead
cobia
cobiron
cobishop
Cobitidae
Cobitis
coble
cobleman
Coblentzian
Cobleskill
cobless
cobloaf
cobnut
cobola
coboundless
cobourg
cobra
cobreathe
cobridgehead
cobriform
cobrother
cobstone
coburg
coburgess
coburgher
coburghership
Cobus
cobweb
cobwebbery
cobwebbing
cobwebby
cobwork
coca
cocaceous
cocaine
cocainism
cocainist
cocainization
cocainize
cocainomania
cocainomaniac
Cocama
Cocamama
cocamine
Cocanucos
cocarboxylase
cocash
cocashweed
cocause
cocautioner
Coccaceae
coccagee
coccal
Cocceian
Cocceianism
coccerin
cocci
coccid
Coccidae
coccidia
coccidial
coccidian
Coccidiidea
coccidioidal
Coccidioides
Coccidiomorpha
coccidiosis
coccidium
coccidology
cocciferous
cocciform
coccigenic
coccinella
coccinellid
Coccinellidae
coccionella
cocco
coccobacillus
coccochromatic
Coccogonales
coccogone
Coccogoneae
coccogonium
coccoid
coccolite
coccolith
coccolithophorid
Coccolithophoridae
Coccoloba
Coccolobis
Coccomyces
coccosphere
coccostean
coccosteid
Coccosteidae
Coccosteus
Coccothraustes
coccothraustine
Coccothrinax
coccous
coccule
cocculiferous
Cocculus
cocculus
coccus
coccydynia
coccygalgia
coccygeal
coccygean
coccygectomy
coccygerector
coccyges
coccygeus
coccygine
coccygodynia
coccygomorph
Coccygomorphae
coccygomorphic
coccygotomy
coccyodynia
coccyx
Coccyzus
cocentric
cochairman
cochal
cochief
Cochin
cochineal
cochlea
cochlear
cochleare
Cochlearia
cochlearifoliate
cochleariform
cochleate
cochleated
cochleiform
cochleitis
cochleous
cochlidiid
Cochlidiidae
cochliodont
Cochliodontidae
Cochliodus
Cochlospermaceae
cochlospermaceous
Cochlospermum
Cochranea
cochurchwarden
cocillana
cocircular
cocircularity
cocitizen
cocitizenship
cock
cockade
cockaded
Cockaigne
cockal
cockalorum
cockamaroo
cockarouse
cockateel
cockatoo
cockatrice
cockawee
cockbell
cockbill
cockbird
cockboat
cockbrain
cockchafer
cockcrow
cockcrower
cockcrowing
cocked
Cocker
cocker
cockerel
cockermeg
cockernony
cocket
cockeye
cockeyed
cockfight
cockfighting
cockhead
cockhorse
cockieleekie
cockily
cockiness
cocking
cockish
cockle
cockleboat
cocklebur
cockled
cockler
cockleshell
cocklet
cocklewife
cocklight
cockling
cockloft
cockly
cockmaster
cockmatch
cockmate
cockneian
cockneity
cockney
cockneybred
cockneydom
cockneyese
cockneyess
cockneyfication
cockneyfy
cockneyish
cockneyishly
cockneyism
cockneyize
cockneyland
cockneyship
cockpit
cockroach
cockscomb
cockscombed
cocksfoot
cockshead
cockshot
cockshut
cockshy
cockshying
cockspur
cockstone
cocksure
cocksuredom
cocksureism
cocksurely
cocksureness
cocksurety
cocktail
cockthrowing
cockup
cockweed
cocky
Cocle
coco
cocoa
cocoach
cocobolo
Coconino
coconnection
coconqueror
coconscious
coconsciously
coconsciousness
coconsecrator
coconspirator
coconstituent
cocontractor
Coconucan
Coconuco
coconut
cocoon
cocoonery
cocorico
cocoroot
Cocos
cocotte
cocovenantor
cocowood
cocowort
cocozelle
cocreate
cocreator
cocreatorship
cocreditor
cocrucify
coctile
coction
coctoantigen
coctoprecipitin
cocuisa
cocullo
cocurator
cocurrent
cocuswood
cocuyo
Cocytean
Cocytus
cod
coda
codamine
codbank
codder
codding
coddle
coddler
code
codebtor
codeclination
codecree
codefendant
codeine
codeless
codelight
codelinquency
codelinquent
codenization
codeposit
coder
coderive
codescendant
codespairer
codex
codfish
codfisher
codfishery
codger
codhead
codheaded
Codiaceae
codiaceous
Codiaeum
Codiales
codical
codices
codicil
codicilic
codicillary
codictatorship
codification
codifier
codify
codilla
codille
codiniac
codirectional
codirector
codiscoverer
codisjunct
codist
Codium
codivine
codling
codman
codo
codol
codomestication
codominant
codon
codpiece
codpitchings
Codrus
codshead
codworm
coe
coecal
coecum
coed
coeditor
coeditorship
coeducate
coeducation
coeducational
coeducationalism
coeducationalize
coeducationally
coeffect
coefficacy
coefficient
coefficiently
coeffluent
coeffluential
coelacanth
coelacanthid
Coelacanthidae
coelacanthine
Coelacanthini
coelacanthoid
coelacanthous
coelanaglyphic
coelar
coelarium
Coelastraceae
coelastraceous
Coelastrum
Coelata
coelder
coeldership
Coelebogyne
coelect
coelection
coelector
coelectron
coelelminth
Coelelminthes
coelelminthic
Coelentera
Coelenterata
coelenterate
coelenteric
coelenteron
coelestine
coelevate
coelho
coelia
coeliac
coelialgia
coelian
Coelicolae
Coelicolist
coeligenous
coelin
coeline
coeliomyalgia
coeliorrhea
coeliorrhoea
coelioscopy
coeliotomy
coeloblastic
coeloblastula
Coelococcus
coelodont
coelogastrula
Coeloglossum
Coelogyne
coelom
coeloma
Coelomata
coelomate
coelomatic
coelomatous
coelomesoblast
coelomic
Coelomocoela
coelomopore
coelonavigation
coelongated
coeloplanula
coelosperm
coelospermous
coelostat
coelozoic
coemanate
coembedded
coembody
coembrace
coeminency
coemperor
coemploy
coemployee
coemployment
coempt
coemption
coemptional
coemptionator
coemptive
coemptor
coenact
coenactor
coenaculous
coenamor
coenamorment
coenamourment
coenanthium
coendear
Coendidae
Coendou
coendure
coenenchym
coenenchyma
coenenchymal
coenenchymatous
coenenchyme
coenesthesia
coenesthesis
coenflame
coengage
coengager
coenjoy
coenobe
coenobiar
coenobic
coenobioid
coenobium
coenoblast
coenoblastic
coenocentrum
coenocyte
coenocytic
coenodioecism
coenoecial
coenoecic
coenoecium
coenogamete
coenomonoecism
coenosarc
coenosarcal
coenosarcous
coenosite
coenospecies
coenospecific
coenospecifically
coenosteal
coenosteum
coenotrope
coenotype
coenotypic
coenthrone
coenurus
coenzyme
coequal
coequality
coequalize
coequally
coequalness
coequate
coequated
coequation
coerce
coercement
coercer
coercibility
coercible
coercibleness
coercibly
coercion
coercionary
coercionist
coercitive
coercive
coercively
coerciveness
coercivity
Coerebidae
coeruleolactite
coessential
coessentiality
coessentially
coessentialness
coestablishment
coestate
coetaneity
coetaneous
coetaneously
coetaneousness
coeternal
coeternally
coeternity
coetus
coeval
coevality
coevally
coexchangeable
coexclusive
coexecutant
coexecutor
coexecutrix
coexert
coexertion
coexist
coexistence
coexistency
coexistent
coexpand
coexpanded
coexperiencer
coexpire
coexplosion
coextend
coextension
coextensive
coextensively
coextensiveness
coextent
cofactor
Cofane
cofaster
cofather
cofathership
cofeature
cofeoffee
coferment
cofermentation
coff
Coffea
coffee
coffeebush
coffeecake
coffeegrower
coffeegrowing
coffeehouse
coffeeleaf
coffeepot
coffeeroom
coffeetime
coffeeweed
coffeewood
coffer
cofferdam
cofferer
cofferfish
coffering
cofferlike
cofferwork
coffin
coffinless
coffinmaker
coffinmaking
coffle
coffret
cofighter
coforeknown
coformulator
cofounder
cofoundress
cofreighter
coft
cofunction
cog
cogence
cogency
cogener
cogeneric
cogent
cogently
cogged
cogger
coggie
cogging
coggle
coggledy
cogglety
coggly
coghle
cogitability
cogitable
cogitabund
cogitabundity
cogitabundly
cogitabundous
cogitant
cogitantly
cogitate
cogitatingly
cogitation
cogitative
cogitatively
cogitativeness
cogitativity
cogitator
coglorify
coglorious
cogman
cognac
cognate
cognateness
cognatic
cognatical
cognation
cognisable
cognisance
cognition
cognitional
cognitive
cognitively
cognitum
cognizability
cognizable
cognizableness
cognizably
cognizance
cognizant
cognize
cognizee
cognizer
cognizor
cognomen
cognominal
cognominate
cognomination
cognosce
cognoscent
cognoscibility
cognoscible
cognoscitive
cognoscitively
cogon
cogonal
cogovernment
cogovernor
cogracious
cograil
cogrediency
cogredient
cogroad
Cogswellia
coguarantor
coguardian
cogue
cogway
cogwheel
cogwood
cohabit
cohabitancy
cohabitant
cohabitation
coharmonious
coharmoniously
coharmonize
coheartedness
coheir
coheiress
coheirship
cohelper
cohelpership
Cohen
cohenite
coherald
cohere
coherence
coherency
coherent
coherently
coherer
coheretic
coheritage
coheritor
cohesibility
cohesible
cohesion
cohesive
cohesively
cohesiveness
cohibit
cohibition
cohibitive
cohibitor
coho
cohoba
cohobate
cohobation
cohobator
cohol
cohort
cohortation
cohortative
cohosh
cohune
cohusband
coidentity
coif
coifed
coiffure
coign
coigue
coil
coiled
coiler
coiling
coilsmith
coimmense
coimplicant
coimplicate
coimplore
coin
coinable
coinage
coincide
coincidence
coincidency
coincident
coincidental
coincidentally
coincidently
coincider
coinclination
coincline
coinclude
coincorporate
coindicant
coindicate
coindication
coindwelling
coiner
coinfeftment
coinfer
coinfinite
coinfinity
coinhabit
coinhabitant
coinhabitor
coinhere
coinherence
coinherent
coinheritance
coinheritor
coining
coinitial
coinmaker
coinmaking
coinmate
coinspire
coinstantaneity
coinstantaneous
coinstantaneously
coinstantaneousness
coinsurance
coinsure
cointense
cointension
cointensity
cointer
cointerest
cointersecting
cointise
Cointreau
coinventor
coinvolve
coiny
coir
coislander
coistrel
coistril
coital
coition
coiture
coitus
Coix
cojudge
cojuror
cojusticiar
coke
cokelike
cokeman
coker
cokernut
cokery
coking
coky
col
Cola
cola
colaborer
Colada
colalgia
Colan
colander
colane
colarin
colate
colation
colatitude
colatorium
colature
colauxe
colback
colberter
colbertine
Colbertism
colcannon
Colchian
Colchicaceae
colchicine
Colchicum
Colchis
colchyte
Colcine
colcothar
cold
colder
coldfinch
coldhearted
coldheartedly
coldheartedness
coldish
coldly
coldness
coldproof
coldslaw
Cole
cole
coleader
colecannon
colectomy
Coleen
colegatee
colegislator
colemanite
colemouse
Coleochaetaceae
coleochaetaceous
Coleochaete
Coleophora
Coleophoridae
coleopter
Coleoptera
coleopteral
coleopteran
coleopterist
coleopteroid
coleopterological
coleopterology
coleopteron
coleopterous
coleoptile
coleoptilum
coleorhiza
Coleosporiaceae
Coleosporium
coleplant
coleseed
coleslaw
colessee
colessor
coletit
coleur
Coleus
colewort
coli
Colias
colibacillosis
colibacterin
colibri
colic
colical
colichemarde
colicky
colicolitis
colicroot
colicweed
colicwort
colicystitis
colicystopyelitis
coliform
Coliidae
Coliiformes
colilysin
Colima
colima
Colin
colin
colinear
colinephritis
coling
Colinus
coliplication
colipuncture
colipyelitis
colipyuria
colisepsis
Coliseum
coliseum
colitic
colitis
colitoxemia
coliuria
Colius
colk
coll
Colla
collaborate
collaboration
collaborationism
collaborationist
collaborative
collaboratively
collaborator
collage
collagen
collagenic
collagenous
collapse
collapsibility
collapsible
collar
collarband
collarbird
collarbone
collard
collare
collared
collaret
collarino
collarless
collarman
collatable
collate
collatee
collateral
collaterality
collaterally
collateralness
collation
collationer
collatitious
collative
collator
collatress
collaud
collaudation
colleague
colleagueship
collect
collectability
collectable
collectanea
collectarium
collected
collectedly
collectedness
collectibility
collectible
collection
collectional
collectioner
collective
collectively
collectiveness
collectivism
collectivist
collectivistic
collectivistically
collectivity
collectivization
collectivize
collector
collectorate
collectorship
collectress
colleen
collegatary
college
colleger
collegial
collegialism
collegiality
collegian
collegianer
Collegiant
collegiate
collegiately
collegiateness
collegiation
collegium
Collembola
collembolan
collembole
collembolic
collembolous
collenchyma
collenchymatic
collenchymatous
collenchyme
collencytal
collencyte
Colleri
Colleries
Collery
collery
collet
colleter
colleterial
colleterium
Colletes
Colletia
colletic
Colletidae
colletin
Colletotrichum
colletside
colley
collibert
colliculate
colliculus
collide
collidine
collie
collied
collier
colliery
collieshangie
colliform
colligate
colligation
colligative
colligible
collimate
collimation
collimator
Collin
collin
collinal
colline
collinear
collinearity
collinearly
collineate
collineation
colling
collingly
collingual
Collins
collins
Collinsia
collinsite
Collinsonia
colliquate
colliquation
colliquative
colliquativeness
collision
collisional
collisive
colloblast
collobrierite
collocal
Collocalia
collocate
collocation
collocationable
collocative
collocatory
collochemistry
collochromate
collock
collocution
collocutor
collocutory
collodiochloride
collodion
collodionization
collodionize
collodiotype
collodium
collogue
colloid
colloidal
colloidality
colloidize
colloidochemical
Collomia
collop
colloped
collophanite
collophore
colloque
colloquia
colloquial
colloquialism
colloquialist
colloquiality
colloquialize
colloquially
colloquialness
colloquist
colloquium
colloquize
colloquy
collothun
collotype
collotypic
collotypy
colloxylin
colluctation
collude
colluder
collum
collumelliaceous
collusion
collusive
collusively
collusiveness
collutorium
collutory
colluvial
colluvies
colly
collyba
Collybia
Collyridian
collyrite
collyrium
collywest
collyweston
collywobbles
colmar
colobin
colobium
coloboma
Colobus
Colocasia
colocentesis
Colocephali
colocephalous
coloclysis
colocola
colocolic
colocynth
colocynthin
colodyspepsia
coloenteritis
cologarithm
Cologne
cololite
Colombian
colombier
colombin
Colombina
colometric
colometrically
colometry
colon
colonalgia
colonate
colonel
colonelcy
colonelship
colongitude
colonial
colonialism
colonialist
colonialize
colonially
colonialness
colonic
colonist
colonitis
colonizability
colonizable
colonization
colonizationist
colonize
colonizer
colonnade
colonnaded
colonnette
colonopathy
colonopexy
colonoscope
colonoscopy
colony
colopexia
colopexotomy
colopexy
colophane
colophany
colophene
colophenic
colophon
colophonate
Colophonian
colophonic
colophonist
colophonite
colophonium
colophony
coloplication
coloproctitis
coloptosis
colopuncture
coloquintid
coloquintida
color
colorability
colorable
colorableness
colorably
Coloradan
Colorado
colorado
coloradoite
colorant
colorate
coloration
colorational
colorationally
colorative
coloratura
colorature
colorcast
colorectitis
colorectostomy
colored
colorer
colorfast
colorful
colorfully
colorfulness
colorific
colorifics
colorimeter
colorimetric
colorimetrical
colorimetrically
colorimetrics
colorimetrist
colorimetry
colorin
coloring
colorist
coloristic
colorization
colorize
colorless
colorlessly
colorlessness
colormaker
colormaking
colorman
colorrhaphy
colors
colortype
Colorum
colory
coloss
colossal
colossality
colossally
colossean
Colosseum
colossi
Colossian
Colossochelys
colossus
Colossuswise
colostomy
colostral
colostration
colostric
colostrous
colostrum
colotomy
colotyphoid
colove
colp
colpenchyma
colpeo
colpeurynter
colpeurysis
colpindach
colpitis
colpocele
colpocystocele
colpohyperplasia
colpohysterotomy
colpoperineoplasty
colpoperineorrhaphy
colpoplastic
colpoplasty
colpoptosis
colporrhagia
colporrhaphy
colporrhea
colporrhexis
colport
colportage
colporter
colporteur
colposcope
colposcopy
colpotomy
colpus
Colt
colt
colter
colthood
coltish
coltishly
coltishness
coltpixie
coltpixy
coltsfoot
coltskin
Coluber
colubrid
Colubridae
colubriform
Colubriformes
Colubriformia
Colubrina
Colubrinae
colubrine
colubroid
colugo
Columba
columbaceous
Columbae
Columban
Columbanian
columbarium
columbary
columbate
columbeion
Columbella
Columbia
columbiad
Columbian
columbic
Columbid
Columbidae
columbier
columbiferous
Columbiformes
columbin
Columbine
columbine
columbite
columbium
columbo
columboid
columbotantalate
columbotitanate
columella
columellar
columellate
Columellia
Columelliaceae
columelliform
column
columnal
columnar
columnarian
columnarity
columnated
columned
columner
columniation
columniferous
columniform
columning
columnist
columnization
columnwise
colunar
colure
Colutea
Colville
coly
Colymbidae
colymbiform
colymbion
Colymbriformes
Colymbus
colyone
colyonic
colytic
colyum
colyumist
colza
coma
comacine
comagistracy
comagmatic
comaker
comal
comamie
Coman
Comanche
Comanchean
Comandra
comanic
comart
Comarum
comate
comatose
comatosely
comatoseness
comatosity
comatous
comatula
comatulid
comb
combaron
combat
combatable
combatant
combater
combative
combatively
combativeness
combativity
combed
comber
combfish
combflower
combinable
combinableness
combinant
combinantive
combinate
combination
combinational
combinative
combinator
combinatorial
combinatory
combine
combined
combinedly
combinedness
combinement
combiner
combing
combining
comble
combless
comblessness
combmaker
combmaking
comboloio
comboy
Combretaceae
combretaceous
Combretum
combure
comburendo
comburent
comburgess
comburimeter
comburimetry
comburivorous
combust
combustibility
combustible
combustibleness
combustibly
combustion
combustive
combustor
combwise
combwright
comby
come
comeback
Comecrudo
comedial
comedian
comediant
comedic
comedical
comedienne
comedietta
comedist
comedo
comedown
comedy
comelily
comeliness
comeling
comely
comendite
comenic
comephorous
comer
comes
comestible
comet
cometarium
cometary
comether
cometic
cometical
cometlike
cometographer
cometographical
cometography
cometoid
cometology
cometwise
comeuppance
comfit
comfiture
comfort
comfortable
comfortableness
comfortably
comforter
comfortful
comforting
comfortingly
comfortless
comfortlessly
comfortlessness
comfortress
comfortroot
comfrey
comfy
Comiakin
comic
comical
comicality
comically
comicalness
comicocratic
comicocynical
comicodidactic
comicography
comicoprosaic
comicotragedy
comicotragic
comicotragical
comicry
Comid
comiferous
Cominform
coming
comingle
comino
Comintern
comism
comital
comitant
comitatensian
comitative
comitatus
comitia
comitial
Comitium
comitragedy
comity
comma
command
commandable
commandant
commandedness
commandeer
commander
commandership
commandery
commanding
commandingly
commandingness
commandless
commandment
commando
commandoman
commandress
commassation
commassee
commatic
commation
commatism
commeasurable
commeasure
commeddle
Commelina
Commelinaceae
commelinaceous
commemorable
commemorate
commemoration
commemorational
commemorative
commemoratively
commemorativeness
commemorator
commemoratory
commemorize
commence
commenceable
commencement
commencer
commend
commendable
commendableness
commendably
commendador
commendam
commendatary
commendation
commendator
commendatory
commender
commendingly
commendment
commensal
commensalism
commensalist
commensalistic
commensality
commensally
commensurability
commensurable
commensurableness
commensurably
commensurate
commensurately
commensurateness
commensuration
comment
commentarial
commentarialism
commentary
commentate
commentation
commentator
commentatorial
commentatorially
commentatorship
commenter
commerce
commerceless
commercer
commerciable
commercial
commercialism
commercialist
commercialistic
commerciality
commercialization
commercialize
commercially
commercium
commerge
commie
comminate
commination
comminative
comminator
comminatory
commingle
comminglement
commingler
comminister
comminuate
comminute
comminution
comminutor
Commiphora
commiserable
commiserate
commiseratingly
commiseration
commiserative
commiseratively
commiserator
commissar
commissarial
commissariat
commissary
commissaryship
commission
commissionaire
commissional
commissionate
commissioner
commissionership
commissionship
commissive
commissively
commissural
commissure
commissurotomy
commit
commitment
committable
committal
committee
committeeism
committeeman
committeeship
committeewoman
committent
committer
committible
committor
commix
commixt
commixtion
commixture
commodatary
commodate
commodation
commodatum
commode
commodious
commodiously
commodiousness
commoditable
commodity
commodore
common
commonable
commonage
commonality
commonalty
commoner
commonership
commoney
commonish
commonition
commonize
commonly
commonness
commonplace
commonplaceism
commonplacely
commonplaceness
commonplacer
commons
commonsensible
commonsensibly
commonsensical
commonsensically
commonty
commonweal
commonwealth
commonwealthism
commorancy
commorant
commorient
commorth
commot
commotion
commotional
commotive
commove
communa
communal
communalism
communalist
communalistic
communality
communalization
communalize
communalizer
communally
communard
commune
communer
communicability
communicable
communicableness
communicably
communicant
communicate
communicatee
communicating
communication
communicative
communicatively
communicativeness
communicator
communicatory
communion
communionist
communique
communism
communist
communistery
communistic
communistically
communital
communitarian
communitary
communitive
communitorium
community
communization
communize
commutability
commutable
commutableness
commutant
commutate
commutation
commutative
commutatively
commutator
commute
commuter
commuting
commutual
commutuality
Comnenian
comoid
comolecule
comortgagee
comose
comourn
comourner
comournful
comous
Comox
compact
compacted
compactedly
compactedness
compacter
compactible
compaction
compactly
compactness
compactor
compacture
compages
compaginate
compagination
companator
companion
companionability
companionable
companionableness
companionably
companionage
companionate
companionize
companionless
companionship
companionway
company
comparability
comparable
comparableness
comparably
comparascope
comparate
comparatival
comparative
comparatively
comparativeness
comparativist
comparator
compare
comparer
comparison
comparition
comparograph
compart
compartition
compartment
compartmental
compartmentalization
compartmentalize
compartmentally
compartmentize
compass
compassable
compasser
compasses
compassing
compassion
compassionable
compassionate
compassionately
compassionateness
compassionless
compassive
compassivity
compassless
compaternity
compatibility
compatible
compatibleness
compatibly
compatriot
compatriotic
compatriotism
compear
compearance
compearant
compeer
compel
compellable
compellably
compellation
compellative
compellent
compeller
compelling
compellingly
compend
compendency
compendent
compendia
compendiary
compendiate
compendious
compendiously
compendiousness
compendium
compenetrate
compenetration
compensable
compensate
compensating
compensatingly
compensation
compensational
compensative
compensativeness
compensator
compensatory
compense
compenser
compesce
compete
competence
competency
competent
competently
competentness
competition
competitioner
competitive
competitively
competitiveness
competitor
competitorship
competitory
competitress
competitrix
compilation
compilator
compilatory
compile
compilement
compiler
compital
Compitalia
compitum
complacence
complacency
complacent
complacential
complacentially
complacently
complain
complainable
complainant
complainer
complainingly
complainingness
complaint
complaintive
complaintiveness
complaisance
complaisant
complaisantly
complaisantness
complanar
complanate
complanation
complect
complected
complement
complemental
complementally
complementalness
complementariness
complementarism
complementary
complementation
complementative
complementer
complementoid
complete
completedness
completely
completement
completeness
completer
completion
completive
completively
completory
complex
complexedness
complexification
complexify
complexion
complexionably
complexional
complexionally
complexioned
complexionist
complexionless
complexity
complexively
complexly
complexness
complexus
compliable
compliableness
compliably
compliance
compliancy
compliant
compliantly
complicacy
complicant
complicate
complicated
complicatedly
complicatedness
complication
complicative
complice
complicitous
complicity
complier
compliment
complimentable
complimental
complimentally
complimentalness
complimentarily
complimentariness
complimentary
complimentation
complimentative
complimenter
complimentingly
complin
complot
complotter
Complutensian
compluvium
comply
compo
compoer
compole
compone
componed
componency
componendo
component
componental
componented
compony
comport
comportment
compos
compose
composed
composedly
composedness
composer
composita
Compositae
composite
compositely
compositeness
composition
compositional
compositionally
compositive
compositively
compositor
compositorial
compositous
composograph
compossibility
compossible
compost
composture
composure
compotation
compotationship
compotator
compotatory
compote
compotor
compound
compoundable
compoundedness
compounder
compounding
compoundness
comprachico
comprador
comprecation
compreg
compregnate
comprehend
comprehender
comprehendible
comprehendingly
comprehense
comprehensibility
comprehensible
comprehensibleness
comprehensibly
comprehension
comprehensive
comprehensively
comprehensiveness
comprehensor
compresbyter
compresbyterial
compresence
compresent
compress
compressed
compressedly
compressibility
compressible
compressibleness
compressingly
compression
compressional
compressive
compressively
compressometer
compressor
compressure
comprest
compriest
comprisable
comprisal
comprise
comprised
compromise
compromiser
compromising
compromisingly
compromissary
compromission
compromissorial
compromit
compromitment
comprovincial
Compsilura
Compsoa
Compsognathus
Compsothlypidae
compter
Comptometer
Comptonia
comptroller
comptrollership
compulsative
compulsatively
compulsatorily
compulsatory
compulsed
compulsion
compulsitor
compulsive
compulsively
compulsiveness
compulsorily
compulsoriness
compulsory
compunction
compunctionary
compunctionless
compunctious
compunctiously
compunctive
compurgation
compurgator
compurgatorial
compurgatory
compursion
computability
computable
computably
computation
computational
computative
computativeness
compute
computer
computist
computus
comrade
comradely
comradery
comradeship
Comsomol
comstockery
Comtian
Comtism
Comtist
comurmurer
Comus
con
conacaste
conacre
conal
conalbumin
conamed
Conant
conarial
conarium
conation
conational
conationalistic
conative
conatus
conaxial
concamerate
concamerated
concameration
concanavalin
concaptive
concassation
concatenary
concatenate
concatenation
concatenator
concausal
concause
concavation
concave
concavely
concaveness
concaver
concavity
conceal
concealable
concealed
concealedly
concealedness
concealer
concealment
concede
conceded
concededly
conceder
conceit
conceited
conceitedly
conceitedness
conceitless
conceity
conceivability
conceivable
conceivableness
conceivably
conceive
conceiver
concelebrate
concelebration
concent
concenter
concentive
concentralization
concentrate
concentrated
concentration
concentrative
concentrativeness
concentrator
concentric
concentrically
concentricity
concentual
concentus
concept
conceptacle
conceptacular
conceptaculum
conception
conceptional
conceptionist
conceptism
conceptive
conceptiveness
conceptual
conceptualism
conceptualist
conceptualistic
conceptuality
conceptualization
conceptualize
conceptually
conceptus
concern
concerned
concernedly
concernedness
concerning
concerningly
concerningness
concernment
concert
concerted
concertedly
concertgoer
concertina
concertinist
concertist
concertize
concertizer
concertmaster
concertmeister
concertment
concerto
concertstuck
concessible
concession
concessionaire
concessional
concessionary
concessioner
concessionist
concessive
concessively
concessiveness
concessor
concettism
concettist
conch
concha
conchal
conchate
conche
conched
concher
Conchifera
conchiferous
conchiform
conchinine
conchiolin
conchitic
conchitis
Conchobor
conchoid
conchoidal
conchoidally
conchological
conchologically
conchologist
conchologize
conchology
conchometer
conchometry
Conchostraca
conchotome
Conchubar
Conchucu
conchuela
conchy
conchyliated
conchyliferous
conchylium
concierge
concile
conciliable
conciliabule
conciliabulum
conciliar
conciliate
conciliating
conciliatingly
conciliation
conciliationist
conciliative
conciliator
conciliatorily
conciliatoriness
conciliatory
concilium
concinnity
concinnous
concionator
concipiency
concipient
concise
concisely
conciseness
concision
conclamant
conclamation
conclave
conclavist
concludable
conclude
concluder
concluding
concludingly
conclusion
conclusional
conclusionally
conclusive
conclusively
conclusiveness
conclusory
concoagulate
concoagulation
concoct
concocter
concoction
concoctive
concoctor
concolor
concolorous
concomitance
concomitancy
concomitant
concomitantly
conconscious
Concord
concord
concordal
concordance
concordancer
concordant
concordantial
concordantly
concordat
concordatory
concorder
concordial
concordist
concordity
concorporate
Concorrezanes
concourse
concreate
concremation
concrement
concresce
concrescence
concrescible
concrescive
concrete
concretely
concreteness
concreter
concretion
concretional
concretionary
concretism
concretive
concretively
concretize
concretor
concubinage
concubinal
concubinarian
concubinary
concubinate
concubine
concubinehood
concubitancy
concubitant
concubitous
concubitus
concupiscence
concupiscent
concupiscible
concupiscibleness
concupy
concur
concurrence
concurrency
concurrent
concurrently
concurrentness
concurring
concurringly
concursion
concurso
concursus
concuss
concussant
concussion
concussional
concussive
concutient
concyclic
concyclically
cond
Condalia
condemn
condemnable
condemnably
condemnate
condemnation
condemnatory
condemned
condemner
condemning
condemningly
condensability
condensable
condensance
condensary
condensate
condensation
condensational
condensative
condensator
condense
condensed
condensedly
condensedness
condenser
condensery
condensity
condescend
condescendence
condescendent
condescender
condescending
condescendingly
condescendingness
condescension
condescensive
condescensively
condescensiveness
condiction
condictious
condiddle
condiddlement
condign
condigness
condignity
condignly
condiment
condimental
condimentary
condisciple
condistillation
condite
condition
conditional
conditionalism
conditionalist
conditionality
conditionalize
conditionally
conditionate
conditioned
conditioner
condivision
condolatory
condole
condolement
condolence
condolent
condoler
condoling
condolingly
condominate
condominium
condonable
condonance
condonation
condonative
condone
condonement
condoner
condor
conduce
conducer
conducing
conducingly
conducive
conduciveness
conduct
conductance
conductibility
conductible
conductility
conductimeter
conductio
conduction
conductional
conductitious
conductive
conductively
conductivity
conductometer
conductometric
conductor
conductorial
conductorless
conductorship
conductory
conductress
conductus
conduit
conduplicate
conduplicated
conduplication
condurangin
condurango
condylar
condylarth
Condylarthra
condylarthrosis
condylarthrous
condyle
condylectomy
condylion
condyloid
condyloma
condylomatous
condylome
condylopod
Condylopoda
condylopodous
condylos
condylotomy
Condylura
condylure
cone
coned
coneen
coneflower
conehead
coneighboring
coneine
conelet
conemaker
conemaking
Conemaugh
conenose
conepate
coner
cones
conessine
Conestoga
confab
confabular
confabulate
confabulation
confabulator
confabulatory
confact
confarreate
confarreation
confated
confect
confection
confectionary
confectioner
confectionery
Confed
confederacy
confederal
confederalist
confederate
confederater
confederatio
confederation
confederationist
confederatism
confederative
confederatize
confederator
confelicity
conferee
conference
conferential
conferment
conferrable
conferral
conferrer
conferruminate
conferted
Conferva
Confervaceae
confervaceous
conferval
Confervales
confervoid
Confervoideae
confervous
confess
confessable
confessant
confessarius
confessary
confessedly
confesser
confessing
confessingly
confession
confessional
confessionalian
confessionalism
confessionalist
confessionary
confessionist
confessor
confessorship
confessory
confidant
confide
confidence
confidency
confident
confidential
confidentiality
confidentially
confidentialness
confidentiary
confidently
confidentness
confider
confiding
confidingly
confidingness
configural
configurate
configuration
configurational
configurationally
configurationism
configurationist
configurative
configure
confinable
confine
confineable
confined
confinedly
confinedness
confineless
confinement
confiner
confining
confinity
confirm
confirmable
confirmand
confirmation
confirmative
confirmatively
confirmatorily
confirmatory
confirmed
confirmedly
confirmedness
confirmee
confirmer
confirming
confirmingly
confirmity
confirmment
confirmor
confiscable
confiscatable
confiscate
confiscation
confiscator
confiscatory
confitent
confiteor
confiture
confix
conflagrant
conflagrate
conflagration
conflagrative
conflagrator
conflagratory
conflate
conflated
conflation
conflict
conflicting
conflictingly
confliction
conflictive
conflictory
conflow
confluence
confluent
confluently
conflux
confluxibility
confluxible
confluxibleness
confocal
conform
conformability
conformable
conformableness
conformably
conformal
conformance
conformant
conformate
conformation
conformator
conformer
conformist
conformity
confound
confoundable
confounded
confoundedly
confoundedness
confounder
confounding
confoundingly
confrater
confraternal
confraternity
confraternization
confrere
confriar
confrication
confront
confrontal
confrontation
confronte
confronter
confrontment
Confucian
Confucianism
Confucianist
confusability
confusable
confusably
confuse
confused
confusedly
confusedness
confusingly
confusion
confusional
confusticate
confustication
confutable
confutation
confutative
confutator
confute
confuter
conga
congeable
congeal
congealability
congealable
congealableness
congealedness
congealer
congealment
congee
congelation
congelative
congelifraction
congeliturbate
congeliturbation
congener
congeneracy
congeneric
congenerical
congenerous
congenerousness
congenetic
congenial
congeniality
congenialize
congenially
congenialness
congenital
congenitally
congenitalness
conger
congeree
congest
congested
congestible
congestion
congestive
congiary
congius
conglobate
conglobately
conglobation
conglobe
conglobulate
conglomerate
conglomeratic
conglomeration
conglutin
conglutinant
conglutinate
conglutination
conglutinative
Congo
Congoese
Congolese
Congoleum
congou
congratulable
congratulant
congratulate
congratulation
congratulational
congratulator
congratulatory
congredient
congreet
congregable
congreganist
congregant
congregate
congregation
congregational
congregationalism
Congregationalist
congregationalize
congregationally
Congregationer
congregationist
congregative
congregativeness
congregator
Congreso
congress
congresser
congressional
congressionalist
congressionally
congressionist
congressist
congressive
congressman
Congresso
congresswoman
Congreve
Congridae
congroid
congruence
congruency
congruent
congruential
congruently
congruism
congruist
congruistic
congruity
congruous
congruously
congruousness
conhydrine
Coniacian
conic
conical
conicality
conically
conicalness
coniceine
conichalcite
conicine
conicity
conicle
conicoid
conicopoly
conics
Conidae
conidia
conidial
conidian
conidiiferous
conidioid
conidiophore
conidiophorous
conidiospore
conidium
conifer
Coniferae
coniferin
coniferophyte
coniferous
conification
coniform
Conilurus
conima
conimene
conin
conine
Coniogramme
Coniophora
Coniopterygidae
Conioselinum
coniosis
Coniothyrium
coniroster
conirostral
Conirostres
Conium
conject
conjective
conjecturable
conjecturably
conjectural
conjecturalist
conjecturality
conjecturally
conjecture
conjecturer
conjobble
conjoin
conjoined
conjoinedly
conjoiner
conjoint
conjointly
conjointment
conjointness
conjubilant
conjugable
conjugacy
conjugal
Conjugales
conjugality
conjugally
conjugant
conjugata
Conjugatae
conjugate
conjugated
conjugately
conjugateness
conjugation
conjugational
conjugationally
conjugative
conjugator
conjugial
conjugium
conjunct
conjunction
conjunctional
conjunctionally
conjunctiva
conjunctival
conjunctive
conjunctively
conjunctiveness
conjunctivitis
conjunctly
conjunctur
conjunctural
conjuncture
conjuration
conjurator
conjure
conjurement
conjurer
conjurership
conjuror
conjury
conk
conkanee
conker
conkers
conky
conn
connach
Connaraceae
connaraceous
connarite
Connarus
connascency
connascent
connatal
connate
connately
connateness
connation
connatural
connaturality
connaturalize
connaturally
connaturalness
connature
connaught
connect
connectable
connectant
connected
connectedly
connectedness
connectible
connection
connectional
connectival
connective
connectively
connectivity
connector
connellite
conner
connex
connexion
connexionalism
connexity
connexive
connexivum
connexus
Connie
conning
conniption
connivance
connivancy
connivant
connivantly
connive
connivent
conniver
Connochaetes
connoissance
connoisseur
connoisseurship
connotation
connotative
connotatively
connote
connotive
connotively
connubial
connubiality
connubially
connubiate
connubium
connumerate
connumeration
Conocarpus
Conocephalum
Conocephalus
conoclinium
conocuneus
conodont
conoid
conoidal
conoidally
conoidic
conoidical
conoidically
Conolophus
conominee
cononintelligent
Conopholis
conopid
Conopidae
conoplain
conopodium
Conopophaga
Conopophagidae
Conor
Conorhinus
conormal
conoscope
conourish
Conoy
conphaseolin
conplane
conquedle
conquer
conquerable
conquerableness
conqueress
conquering
conqueringly
conquerment
conqueror
conquest
conquian
conquinamine
conquinine
conquistador
Conrad
conrector
conrectorship
conred
Conringia
consanguine
consanguineal
consanguinean
consanguineous
consanguineously
consanguinity
conscience
conscienceless
consciencelessly
consciencelessness
consciencewise
conscient
conscientious
conscientiously
conscientiousness
conscionable
conscionableness
conscionably
conscious
consciously
consciousness
conscribe
conscript
conscription
conscriptional
conscriptionist
conscriptive
consecrate
consecrated
consecratedness
consecrater
consecration
consecrative
consecrator
consecratory
consectary
consecute
consecution
consecutive
consecutively
consecutiveness
consecutives
consenescence
consenescency
consension
consensual
consensually
consensus
consent
consentable
consentaneity
consentaneous
consentaneously
consentaneousness
consentant
consenter
consentful
consentfully
consentience
consentient
consentiently
consenting
consentingly
consentingness
consentive
consentively
consentment
consequence
consequency
consequent
consequential
consequentiality
consequentially
consequentialness
consequently
consertal
conservable
conservacy
conservancy
conservant
conservate
conservation
conservational
conservationist
conservatism
conservatist
conservative
conservatively
conservativeness
conservatize
conservatoire
conservator
conservatorio
conservatorium
conservatorship
conservatory
conservatrix
conserve
conserver
consider
considerability
considerable
considerableness
considerably
considerance
considerate
considerately
considerateness
consideration
considerative
consideratively
considerativeness
considerator
considered
considerer
considering
consideringly
consign
consignable
consignatary
consignation
consignatory
consignee
consigneeship
consigner
consignificant
consignificate
consignification
consignificative
consignificator
consignify
consignment
consignor
consiliary
consilience
consilient
consimilar
consimilarity
consimilate
consist
consistence
consistency
consistent
consistently
consistorial
consistorian
consistory
consociate
consociation
consociational
consociationism
consociative
consocies
consol
consolable
consolableness
consolably
Consolamentum
consolation
Consolato
consolatorily
consolatoriness
consolatory
consolatrix
console
consolement
consoler
consolidant
consolidate
consolidated
consolidation
consolidationist
consolidative
consolidator
consoling
consolingly
consolute
consomme
consonance
consonancy
consonant
consonantal
consonantic
consonantism
consonantize
consonantly
consonantness
consonate
consonous
consort
consortable
consorter
consortial
consortion
consortism
consortium
consortship
consound
conspecies
conspecific
conspectus
consperse
conspersion
conspicuity
conspicuous
conspicuously
conspicuousness
conspiracy
conspirant
conspiration
conspirative
conspirator
conspiratorial
conspiratorially
conspiratory
conspiratress
conspire
conspirer
conspiring
conspiringly
conspue
constable
constablery
constableship
constabless
constablewick
constabular
constabulary
Constance
constancy
constant
constantan
Constantine
Constantinian
Constantinopolitan
constantly
constantness
constat
constatation
constate
constatory
constellate
constellation
constellatory
consternate
consternation
constipate
constipation
constituency
constituent
constituently
constitute
constituter
constitution
constitutional
constitutionalism
constitutionalist
constitutionality
constitutionalization
constitutionalize
constitutionally
constitutionary
constitutioner
constitutionist
constitutive
constitutively
constitutiveness
constitutor
constrain
constrainable
constrained
constrainedly
constrainedness
constrainer
constraining
constrainingly
constrainment
constraint
constrict
constricted
constriction
constrictive
constrictor
constringe
constringency
constringent
construability
construable
construct
constructer
constructible
construction
constructional
constructionally
constructionism
constructionist
constructive
constructively
constructiveness
constructivism
constructivist
constructor
constructorship
constructure
construe
construer
constuprate
constupration
consubsist
consubsistency
consubstantial
consubstantialism
consubstantialist
consubstantiality
consubstantially
consubstantiate
consubstantiation
consubstantiationist
consubstantive
consuete
consuetitude
consuetude
consuetudinal
consuetudinary
consul
consulage
consular
consularity
consulary
consulate
consulship
consult
consultable
consultant
consultary
consultation
consultative
consultatory
consultee
consulter
consulting
consultive
consultively
consultor
consultory
consumable
consume
consumedly
consumeless
consumer
consuming
consumingly
consumingness
consummate
consummately
consummation
consummative
consummatively
consummativeness
consummator
consummatory
consumpt
consumpted
consumptible
consumption
consumptional
consumptive
consumptively
consumptiveness
consumptivity
consute
contabescence
contabescent
contact
contactor
contactual
contactually
contagion
contagioned
contagionist
contagiosity
contagious
contagiously
contagiousness
contagium
contain
containable
container
containment
contakion
contaminable
contaminant
contaminate
contamination
contaminative
contaminator
contaminous
contangential
contango
conte
contect
contection
contemn
contemner
contemnible
contemnibly
contemning
contemningly
contemnor
contemper
contemperate
contemperature
contemplable
contemplamen
contemplant
contemplate
contemplatingly
contemplation
contemplatist
contemplative
contemplatively
contemplativeness
contemplator
contemplature
contemporanean
contemporaneity
contemporaneous
contemporaneously
contemporaneousness
contemporarily
contemporariness
contemporary
contemporize
contempt
contemptful
contemptibility
contemptible
contemptibleness
contemptibly
contemptuous
contemptuously
contemptuousness
contendent
contender
contending
contendingly
contendress
content
contentable
contented
contentedly
contentedness
contentful
contention
contentional
contentious
contentiously
contentiousness
contentless
contently
contentment
contentness
contents
conter
conterminal
conterminant
contermine
conterminous
conterminously
conterminousness
contest
contestable
contestableness
contestably
contestant
contestation
contestee
contester
contestingly
contestless
context
contextive
contextual
contextually
contextural
contexture
contextured
conticent
contignation
contiguity
contiguous
contiguously
contiguousness
continence
continency
continent
continental
Continentaler
continentalism
continentalist
continentality
Continentalize
continentally
continently
contingence
contingency
contingent
contingential
contingentialness
contingently
contingentness
continuable
continual
continuality
continually
continualness
continuance
continuancy
continuando
continuant
continuantly
continuate
continuately
continuateness
continuation
continuative
continuatively
continuativeness
continuator
continue
continued
continuedly
continuedness
continuer
continuingly
continuist
continuity
continuous
continuously
continuousness
continuum
contise
contline
conto
contorniate
contorsive
contort
Contortae
contorted
contortedly
contortedness
contortion
contortional
contortionate
contortioned
contortionist
contortionistic
contortive
contour
contourne
contra
contraband
contrabandage
contrabandery
contrabandism
contrabandist
contrabandista
contrabass
contrabassist
contrabasso
contracapitalist
contraception
contraceptionist
contraceptive
contracivil
contraclockwise
contract
contractable
contractant
contractation
contracted
contractedly
contractedness
contractee
contracter
contractibility
contractible
contractibleness
contractibly
contractile
contractility
contraction
contractional
contractionist
contractive
contractively
contractiveness
contractor
contractual
contractually
contracture
contractured
contradebt
contradict
contradictable
contradictedness
contradicter
contradiction
contradictional
contradictious
contradictiously
contradictiousness
contradictive
contradictively
contradictiveness
contradictor
contradictorily
contradictoriness
contradictory
contradiscriminate
contradistinct
contradistinction
contradistinctive
contradistinctively
contradistinctly
contradistinguish
contradivide
contrafacture
contrafagotto
contrafissura
contraflexure
contraflow
contrafocal
contragredience
contragredient
contrahent
contrail
contraindicate
contraindication
contraindicative
contralateral
contralto
contramarque
contranatural
contrantiscion
contraoctave
contraparallelogram
contraplex
contrapolarization
contrapone
contraponend
Contraposaune
contrapose
contraposit
contraposita
contraposition
contrapositive
contraprogressist
contraprop
contraproposal
contraption
contraptious
contrapuntal
contrapuntalist
contrapuntally
contrapuntist
contrapunto
contrarational
contraregular
contraregularity
contraremonstrance
contraremonstrant
contrarevolutionary
contrariant
contrariantly
contrariety
contrarily
contrariness
contrarious
contrariously
contrariousness
contrariwise
contrarotation
contrary
contrascriptural
contrast
contrastable
contrastably
contrastedly
contrastimulant
contrastimulation
contrastimulus
contrastingly
contrastive
contrastively
contrastment
contrasty
contrasuggestible
contratabular
contrate
contratempo
contratenor
contravalence
contravallation
contravariant
contravene
contravener
contravention
contraversion
contravindicate
contravindication
contrawise
contrayerva
contrectation
contreface
contrefort
contretemps
contributable
contribute
contribution
contributional
contributive
contributively
contributiveness
contributor
contributorial
contributorship
contributory
contrite
contritely
contriteness
contrition
contriturate
contrivance
contrivancy
contrive
contrivement
contriver
control
controllability
controllable
controllableness
controllably
controller
controllership
controlless
controllingly
controlment
controversial
controversialism
controversialist
controversialize
controversially
controversion
controversional
controversionalism
controversionalist
controversy
controvert
controverter
controvertible
controvertibly
controvertist
contubernal
contubernial
contubernium
contumacious
contumaciously
contumaciousness
contumacity
contumacy
contumelious
contumeliously
contumeliousness
contumely
contund
conturbation
contuse
contusion
contusioned
contusive
conubium
Conularia
conumerary
conumerous
conundrum
conundrumize
conurbation
conure
Conuropsis
Conurus
conus
conusable
conusance
conusant
conusee
conusor
conutrition
conuzee
conuzor
convalesce
convalescence
convalescency
convalescent
convalescently
convallamarin
Convallaria
Convallariaceae
convallariaceous
convallarin
convect
convection
convectional
convective
convectively
convector
convenable
convenably
convene
convenee
convener
convenership
convenience
conveniency
convenient
conveniently
convenientness
convent
conventical
conventically
conventicle
conventicler
conventicular
convention
conventional
conventionalism
conventionalist
conventionality
conventionalization
conventionalize
conventionally
conventionary
conventioner
conventionism
conventionist
conventionize
conventual
conventually
converge
convergement
convergence
convergency
convergent
convergescence
converging
conversable
conversableness
conversably
conversance
conversancy
conversant
conversantly
conversation
conversationable
conversational
conversationalist
conversationally
conversationism
conversationist
conversationize
conversative
converse
conversely
converser
conversibility
conversible
conversion
conversional
conversionism
conversionist
conversive
convert
converted
convertend
converter
convertibility
convertible
convertibleness
convertibly
converting
convertingness
convertise
convertism
convertite
convertive
convertor
conveth
convex
convexed
convexedly
convexedness
convexity
convexly
convexness
convey
conveyable
conveyal
conveyance
conveyancer
conveyancing
conveyer
convict
convictable
conviction
convictional
convictism
convictive
convictively
convictiveness
convictment
convictor
convince
convinced
convincedly
convincedness
convincement
convincer
convincibility
convincible
convincing
convincingly
convincingness
convival
convive
convivial
convivialist
conviviality
convivialize
convivially
convocant
convocate
convocation
convocational
convocationally
convocationist
convocative
convocator
convoke
convoker
Convoluta
convolute
convoluted
convolutely
convolution
convolutional
convolutionary
convolutive
convolve
convolvement
Convolvulaceae
convolvulaceous
convolvulad
convolvuli
convolvulic
convolvulin
convolvulinic
convolvulinolic
Convolvulus
convoy
convulsant
convulse
convulsedly
convulsibility
convulsible
convulsion
convulsional
convulsionary
convulsionism
convulsionist
convulsive
convulsively
convulsiveness
cony
conycatcher
conyrine
coo
cooba
coodle
cooee
cooer
coof
Coohee
cooing
cooingly
cooja
cook
cookable
cookbook
cookdom
cookee
cookeite
cooker
cookery
cookhouse
cooking
cookish
cookishly
cookless
cookmaid
cookout
cookroom
cookshack
cookshop
cookstove
cooky
cool
coolant
coolen
cooler
coolerman
coolheaded
coolheadedly
coolheadedness
coolhouse
coolibah
coolie
cooling
coolingly
coolingness
coolish
coolly
coolness
coolth
coolung
coolweed
coolwort
cooly
coom
coomb
coomy
coon
cooncan
coonily
cooniness
coonroot
coonskin
coontail
coontie
coony
coop
cooper
cooperage
Cooperia
coopering
coopery
cooree
Coorg
coorie
cooruptibly
Coos
cooser
coost
Coosuc
coot
cooter
cootfoot
coothay
cootie
cop
copa
copable
copacetic
copaene
copaiba
copaibic
Copaifera
Copaiva
copaivic
copaiye
copal
copalche
copalcocote
copaliferous
copalite
copalm
coparallel
coparcenary
coparcener
coparceny
coparent
copart
copartaker
copartner
copartnership
copartnery
coparty
copassionate
copastor
copastorate
copatain
copatentee
copatriot
copatron
copatroness
cope
Copehan
copei
Copelata
Copelatae
copelate
copellidine
copeman
copemate
copen
copending
copenetrate
Copeognatha
copepod
Copepoda
copepodan
copepodous
coper
coperception
coperiodic
Copernican
Copernicanism
Copernicia
coperta
copesman
copesmate
copestone
copetitioner
cophasal
Cophetua
cophosis
copiability
copiable
copiapite
copied
copier
copilot
coping
copiopia
copiopsia
copiosity
copious
copiously
copiousness
copis
copist
copita
coplaintiff
coplanar
coplanarity
copleased
coplotter
coploughing
coplowing
copolar
copolymer
copolymerization
copolymerize
coppaelite
copped
copper
copperas
copperbottom
copperer
copperhead
copperheadism
coppering
copperish
copperization
copperize
copperleaf
coppernose
coppernosed
copperplate
copperproof
coppersidesman
copperskin
coppersmith
coppersmithing
copperware
copperwing
copperworks
coppery
copperytailed
coppet
coppice
coppiced
coppicing
coppin
copping
copple
copplecrown
coppled
coppy
copr
copra
coprecipitate
coprecipitation
copremia
copremic
copresbyter
copresence
copresent
Coprides
Coprinae
coprincipal
coprincipate
Coprinus
coprisoner
coprodaeum
coproduce
coproducer
coprojector
coprolagnia
coprolagnist
coprolalia
coprolaliac
coprolite
coprolith
coprolitic
coprology
copromisor
copromoter
coprophagan
coprophagia
coprophagist
coprophagous
coprophagy
coprophilia
coprophiliac
coprophilic
coprophilism
coprophilous
coprophyte
coproprietor
coproprietorship
coprose
Coprosma
coprostasis
coprosterol
coprozoic
copse
copsewood
copsewooded
copsing
copsy
Copt
copter
Coptic
Coptis
copula
copulable
copular
copularium
copulate
copulation
copulative
copulatively
copulatory
copunctal
copurchaser
copus
copy
copybook
copycat
copygraph
copygraphed
copyhold
copyholder
copyholding
copyism
copyist
copyman
copyreader
copyright
copyrightable
copyrighter
copywise
coque
coquecigrue
coquelicot
coqueluche
coquet
coquetoon
coquetry
coquette
coquettish
coquettishly
coquettishness
coquicken
coquilla
Coquille
coquille
coquimbite
coquina
coquita
Coquitlam
coquito
cor
Cora
cora
Corabeca
Corabecan
corach
Coraciae
coracial
Coracias
Coracii
Coraciidae
coraciiform
Coraciiformes
coracine
coracle
coracler
coracoacromial
coracobrachial
coracobrachialis
coracoclavicular
coracocostal
coracohumeral
coracohyoid
coracoid
coracoidal
coracomandibular
coracomorph
Coracomorphae
coracomorphic
coracopectoral
coracoprocoracoid
coracoradialis
coracoscapular
coracovertebral
coradical
coradicate
corah
coraise
coral
coralberry
coralbush
coraled
coralflower
coralist
corallet
Corallian
corallic
Corallidae
corallidomous
coralliferous
coralliform
Coralligena
coralligenous
coralligerous
corallike
Corallina
Corallinaceae
corallinaceous
coralline
corallite
Corallium
coralloid
coralloidal
Corallorhiza
corallum
Corallus
coralroot
coralwort
coram
Corambis
coranto
corban
corbeau
corbeil
corbel
corbeling
corbicula
corbiculate
corbiculum
corbie
corbiestep
corbovinum
corbula
corcass
Corchorus
corcir
corcopali
Corcyraean
cord
cordage
Cordaitaceae
cordaitaceous
cordaitalean
Cordaitales
cordaitean
Cordaites
cordant
cordate
cordately
cordax
Cordeau
corded
cordel
Cordelia
Cordelier
cordeliere
cordelle
corder
Cordery
cordewane
Cordia
cordial
cordiality
cordialize
cordially
cordialness
cordiceps
cordicole
cordierite
cordies
cordiform
cordigeri
cordillera
cordilleran
cordiner
cording
cordite
corditis
cordleaf
cordmaker
cordoba
cordon
cordonnet
Cordovan
Cordula
corduroy
corduroyed
cordwain
cordwainer
cordwainery
cordwood
cordy
Cordyceps
cordyl
Cordylanthus
Cordyline
core
corebel
coreceiver
coreciprocal
corectome
corectomy
corector
cored
coredeem
coredeemer
coredemptress
coreductase
Coree
coreflexed
coregence
coregency
coregent
coregnancy
coregnant
coregonid
Coregonidae
coregonine
coregonoid
Coregonus
coreid
Coreidae
coreign
coreigner
corejoice
coreless
coreligionist
corella
corelysis
Corema
coremaker
coremaking
coremium
coremorphosis
corenounce
coreometer
Coreopsis
coreplastic
coreplasty
corer
coresidence
coresidual
coresign
coresonant
coresort
corespect
corespondency
corespondent
coretomy
coreveler
coreveller
corevolve
Corey
corf
Corfiote
Corflambo
corge
corgi
coriaceous
corial
coriamyrtin
coriander
coriandrol
Coriandrum
Coriaria
Coriariaceae
coriariaceous
coriin
Corimelaena
Corimelaenidae
Corin
corindon
Corineus
coring
Corinna
corinne
Corinth
Corinthian
Corinthianesque
Corinthianism
Corinthianize
Coriolanus
coriparian
corium
Corixa
Corixidae
cork
corkage
corkboard
corke
corked
corker
corkiness
corking
corkish
corkite
corkmaker
corkmaking
corkscrew
corkscrewy
corkwing
corkwood
corky
corm
Cormac
cormel
cormidium
cormoid
Cormophyta
cormophyte
cormophytic
cormorant
cormous
cormus
corn
Cornaceae
cornaceous
cornage
cornbell
cornberry
cornbin
cornbinks
cornbird
cornbole
cornbottle
cornbrash
corncake
corncob
corncracker
corncrib
corncrusher
corndodger
cornea
corneagen
corneal
cornein
corneitis
cornel
Cornelia
cornelian
Cornelius
cornemuse
corneocalcareous
corneosclerotic
corneosiliceous
corneous
corner
cornerbind
cornered
cornerer
cornerpiece
cornerstone
cornerways
cornerwise
cornet
cornetcy
cornettino
cornettist
corneule
corneum
cornfield
cornfloor
cornflower
corngrower
cornhouse
cornhusk
cornhusker
cornhusking
cornic
cornice
cornicle
corniculate
corniculer
corniculum
Corniferous
cornific
cornification
cornified
corniform
cornigerous
cornin
corning
corniplume
Cornish
Cornishman
cornland
cornless
cornloft
cornmaster
cornmonger
cornopean
cornpipe
cornrick
cornroot
cornstalk
cornstarch
cornstook
cornu
cornual
cornuate
cornuated
cornubianite
cornucopia
Cornucopiae
cornucopian
cornucopiate
cornule
cornulite
Cornulites
cornupete
Cornus
cornute
cornuted
cornutine
cornuto
cornwallis
cornwallite
corny
coroa
Coroado
corocleisis
corodiary
corodiastasis
corodiastole
corody
corol
corolla
corollaceous
corollarial
corollarially
corollary
corollate
corollated
corolliferous
corolliform
corollike
corolline
corollitic
corometer
corona
coronach
coronad
coronadite
coronae
coronagraph
coronagraphic
coronal
coronale
coronaled
coronally
coronamen
coronary
coronate
coronated
coronation
coronatorial
coroner
coronership
coronet
coroneted
coronetted
coronetty
coroniform
Coronilla
coronillin
coronion
coronitis
coronium
coronize
coronobasilar
coronofacial
coronofrontal
coronoid
Coronopus
coronule
coroparelcysis
coroplast
coroplasta
coroplastic
Coropo
coroscopy
corotomy
corozo
corp
corpora
corporal
corporalism
corporality
corporally
corporalship
corporas
corporate
corporately
corporateness
corporation
corporational
corporationer
corporationism
corporative
corporator
corporature
corporeal
corporealist
corporeality
corporealization
corporealize
corporeally
corporealness
corporeals
corporeity
corporeous
corporification
corporify
corporosity
corposant
corps
corpsbruder
corpse
corpsman
corpulence
corpulency
corpulent
corpulently
corpulentness
corpus
corpuscle
corpuscular
corpuscularian
corpuscularity
corpusculated
corpuscule
corpusculous
corpusculum
corrade
corradial
corradiate
corradiation
corral
corrasion
corrasive
Correa
correal
correality
correct
correctable
correctant
corrected
correctedness
correctible
correcting
correctingly
correction
correctional
correctionalist
correctioner
correctitude
corrective
correctively
correctiveness
correctly
correctness
corrector
correctorship
correctress
correctrice
corregidor
correlatable
correlate
correlated
correlation
correlational
correlative
correlatively
correlativeness
correlativism
correlativity
correligionist
corrente
correption
corresol
correspond
correspondence
correspondency
correspondent
correspondential
correspondentially
correspondently
correspondentship
corresponder
corresponding
correspondingly
corresponsion
corresponsive
corresponsively
corridor
corridored
corrie
Corriedale
corrige
corrigenda
corrigendum
corrigent
corrigibility
corrigible
corrigibleness
corrigibly
Corrigiola
Corrigiolaceae
corrival
corrivality
corrivalry
corrivalship
corrivate
corrivation
corrobboree
corroborant
corroborate
corroboration
corroborative
corroboratively
corroborator
corroboratorily
corroboratory
corroboree
corrode
corrodent
Corrodentia
corroder
corrodiary
corrodibility
corrodible
corrodier
corroding
corrosibility
corrosible
corrosibleness
corrosion
corrosional
corrosive
corrosively
corrosiveness
corrosivity
corrugate
corrugated
corrugation
corrugator
corrupt
corrupted
corruptedly
corruptedness
corrupter
corruptful
corruptibility
corruptible
corruptibleness
corrupting
corruptingly
corruption
corruptionist
corruptive
corruptively
corruptly
corruptness
corruptor
corruptress
corsac
corsage
corsaint
corsair
corse
corselet
corsepresent
corsesque
corset
corseting
corsetless
corsetry
Corsican
corsie
corsite
corta
Cortaderia
cortege
Cortes
cortex
cortez
cortical
cortically
corticate
corticated
corticating
cortication
cortices
corticiferous
corticiform
corticifugal
corticifugally
corticipetal
corticipetally
Corticium
corticoafferent
corticoefferent
corticoline
corticopeduncular
corticose
corticospinal
corticosterone
corticostriate
corticous
cortin
cortina
cortinarious
Cortinarius
cortinate
cortisone
cortlandtite
Corton
coruco
coruler
Coruminacan
corundophilite
corundum
corupay
coruscant
coruscate
coruscation
corver
corvette
corvetto
Corvidae
corviform
corvillosum
corvina
Corvinae
corvine
corvoid
Corvus
Cory
Corybant
Corybantian
corybantiasm
Corybantic
corybantic
Corybantine
corybantish
corybulbin
corybulbine
corycavamine
corycavidin
corycavidine
corycavine
Corycia
Corycian
corydalin
corydaline
Corydalis
corydine
Corydon
coryl
Corylaceae
corylaceous
corylin
Corylopsis
Corylus
corymb
corymbed
corymbiate
corymbiated
corymbiferous
corymbiform
corymbose
corymbous
corynebacterial
Corynebacterium
Coryneum
corynine
Corynocarpaceae
corynocarpaceous
Corynocarpus
Corypha
Coryphaena
coryphaenid
Coryphaenidae
coryphaenoid
Coryphaenoididae
coryphaeus
coryphee
coryphene
Coryphodon
coryphodont
coryphylly
corytuberine
coryza
cos
cosalite
cosaque
cosavior
coscet
Coscinodiscaceae
Coscinodiscus
coscinomancy
coscoroba
coseasonal
coseat
cosec
cosecant
cosech
cosectarian
cosectional
cosegment
coseism
coseismal
coseismic
cosenator
cosentiency
cosentient
coservant
cosession
coset
cosettler
cosh
cosharer
cosheath
cosher
cosherer
coshering
coshery
cosignatory
cosigner
cosignitary
cosily
cosinage
cosine
cosiness
cosingular
cosinusoid
Cosmati
cosmecology
cosmesis
cosmetic
cosmetical
cosmetically
cosmetician
cosmetiste
cosmetological
cosmetologist
cosmetology
cosmic
cosmical
cosmicality
cosmically
cosmism
cosmist
cosmocracy
cosmocrat
cosmocratic
cosmogenesis
cosmogenetic
cosmogenic
cosmogeny
cosmogonal
cosmogoner
cosmogonic
cosmogonical
cosmogonist
cosmogonize
cosmogony
cosmographer
cosmographic
cosmographical
cosmographically
cosmographist
cosmography
cosmolabe
cosmolatry
cosmologic
cosmological
cosmologically
cosmologist
cosmology
cosmometry
cosmopathic
cosmoplastic
cosmopoietic
cosmopolicy
cosmopolis
cosmopolitan
cosmopolitanism
cosmopolitanization
cosmopolitanize
cosmopolitanly
cosmopolite
cosmopolitic
cosmopolitical
cosmopolitics
cosmopolitism
cosmorama
cosmoramic
cosmorganic
cosmos
cosmoscope
cosmosophy
cosmosphere
cosmotellurian
cosmotheism
cosmotheist
cosmotheistic
cosmothetic
cosmotron
cosmozoan
cosmozoic
cosmozoism
cosonant
cosounding
cosovereign
cosovereignty
cospecies
cospecific
cosphered
cosplendor
cosplendour
coss
Cossack
Cossaean
cossas
cosse
cosset
cossette
cossid
Cossidae
cossnent
cossyrite
cost
costa
Costaea
costal
costalgia
costally
costander
Costanoan
costar
costard
Costata
costate
costated
costean
costeaning
costectomy
costellate
coster
costerdom
costermonger
costicartilage
costicartilaginous
costicervical
costiferous
costiform
costing
costipulator
costispinal
costive
costively
costiveness
costless
costlessness
costliness
costly
costmary
costoabdominal
costoapical
costocentral
costochondral
costoclavicular
costocolic
costocoracoid
costodiaphragmatic
costogenic
costoinferior
costophrenic
costopleural
costopneumopexy
costopulmonary
costoscapular
costosternal
costosuperior
costothoracic
costotome
costotomy
costotrachelian
costotransversal
costotransverse
costovertebral
costoxiphoid
costraight
costrel
costula
costulation
costume
costumer
costumery
costumic
costumier
costumiere
costuming
costumist
costusroot
cosubject
cosubordinate
cosuffer
cosufferer
cosuggestion
cosuitor
cosurety
cosustain
coswearer
cosy
cosymmedian
cot
cotangent
cotangential
cotarius
cotarnine
cotch
cote
coteful
coteline
coteller
cotemporane
cotemporanean
cotemporaneous
cotemporaneously
cotemporary
cotenancy
cotenant
cotenure
coterell
coterie
coterminous
Cotesian
coth
cothamore
cothe
cotheorist
cothish
cothon
cothurn
cothurnal
cothurnate
cothurned
cothurnian
cothurnus
cothy
cotidal
cotillage
cotillion
Cotinga
cotingid
Cotingidae
cotingoid
Cotinus
cotise
cotitular
cotland
cotman
coto
cotoin
Cotonam
Cotoneaster
cotonier
cotorment
cotoro
cotorture
Cotoxo
cotquean
cotraitor
cotransfuse
cotranslator
cotranspire
cotransubstantiate
cotrine
cotripper
cotrustee
cotset
cotsetla
cotsetle
cotta
cottabus
cottage
cottaged
cottager
cottagers
cottagey
cotte
cotted
cotter
cotterel
cotterite
cotterway
cottid
Cottidae
cottier
cottierism
cottiform
cottoid
cotton
cottonade
cottonbush
cottonee
cottoneer
cottoner
Cottonian
cottonization
cottonize
cottonless
cottonmouth
cottonocracy
Cottonopolis
cottonseed
cottontail
cottontop
cottonweed
cottonwood
cottony
Cottus
cotty
cotuit
cotula
cotunnite
Coturnix
cotutor
cotwin
cotwinned
cotwist
cotyla
cotylar
cotyledon
cotyledonal
cotyledonar
cotyledonary
cotyledonous
cotyliform
cotyligerous
cotyliscus
cotyloid
Cotylophora
cotylophorous
cotylopubic
cotylosacral
cotylosaur
Cotylosauria
cotylosaurian
cotype
Cotys
Cotyttia
couac
coucal
couch
couchancy
couchant
couched
couchee
coucher
couching
couchmaker
couchmaking
couchmate
couchy
coude
coudee
coue
Coueism
cougar
cough
cougher
coughroot
coughweed
coughwort
cougnar
coul
could
couldron
coulee
coulisse
coulomb
coulometer
coulterneb
coulure
couma
coumalic
coumalin
coumara
coumaran
coumarate
coumaric
coumarilic
coumarin
coumarinic
coumarone
coumarou
Coumarouna
council
councilist
councilman
councilmanic
councilor
councilorship
councilwoman
counderstand
counite
couniversal
counsel
counselable
counselee
counselful
counselor
counselorship
count
countable
countableness
countably
countdom
countenance
countenancer
counter
counterabut
counteraccusation
counteracquittance
counteract
counteractant
counteracter
counteracting
counteractingly
counteraction
counteractive
counteractively
counteractivity
counteractor
counteraddress
counteradvance
counteradvantage
counteradvice
counteradvise
counteraffirm
counteraffirmation
counteragency
counteragent
counteragitate
counteragitation
counteralliance
counterambush
counterannouncement
counteranswer
counterappeal
counterappellant
counterapproach
counterapse
counterarch
counterargue
counterargument
counterartillery
counterassertion
counterassociation
counterassurance
counterattack
counterattestation
counterattired
counterattraction
counterattractive
counterattractively
counteraverment
counteravouch
counteravouchment
counterbalance
counterbarrage
counterbase
counterbattery
counterbeating
counterbend
counterbewitch
counterbid
counterblast
counterblow
counterbond
counterborder
counterbore
counterboycott
counterbrace
counterbranch
counterbrand
counterbreastwork
counterbuff
counterbuilding
countercampaign
countercarte
countercause
counterchange
counterchanged
countercharge
countercharm
countercheck
countercheer
counterclaim
counterclaimant
counterclockwise
countercolored
countercommand
countercompetition
countercomplaint
countercompony
countercondemnation
counterconquest
counterconversion
countercouchant
countercoupe
countercourant
countercraft
countercriticism
countercross
countercry
countercurrent
countercurrently
countercurrentwise
counterdance
counterdash
counterdecision
counterdeclaration
counterdecree
counterdefender
counterdemand
counterdemonstration
counterdeputation
counterdesire
counterdevelopment
counterdifficulty
counterdigged
counterdike
counterdiscipline
counterdisengage
counterdisengagement
counterdistinction
counterdistinguish
counterdoctrine
counterdogmatism
counterdraft
counterdrain
counterdrive
counterearth
counterefficiency
countereffort
counterembattled
counterembowed
counterenamel
counterend
counterenergy
counterengagement
counterengine
counterenthusiasm
counterentry
counterequivalent
counterermine
counterespionage
counterestablishment
counterevidence
counterexaggeration
counterexcitement
counterexcommunication
counterexercise
counterexplanation
counterexposition
counterexpostulation
counterextend
counterextension
counterfact
counterfallacy
counterfaller
counterfeit
counterfeiter
counterfeitly
counterfeitment
counterfeitness
counterferment
counterfessed
counterfire
counterfix
counterflange
counterflashing
counterflight
counterflory
counterflow
counterflux
counterfoil
counterforce
counterformula
counterfort
counterfugue
countergabble
countergabion
countergambit
countergarrison
countergauge
countergauger
countergift
countergirded
counterglow
counterguard
counterhaft
counterhammering
counterhypothesis
counteridea
counterideal
counterimagination
counterimitate
counterimitation
counterimpulse
counterindentation
counterindented
counterindicate
counterindication
counterinfluence
counterinsult
counterintelligence
counterinterest
counterinterpretation
counterintrigue
counterinvective
counterirritant
counterirritate
counterirritation
counterjudging
counterjumper
counterlath
counterlathing
counterlatration
counterlaw
counterleague
counterlegislation
counterlife
counterlocking
counterlode
counterlove
counterly
countermachination
counterman
countermand
countermandable
countermaneuver
countermanifesto
countermarch
countermark
countermarriage
countermeasure
countermeet
countermessage
countermigration
countermine
countermission
countermotion
countermount
countermove
countermovement
countermure
countermutiny
counternaiant
counternarrative
counternatural
counternecromancy
counternoise
counternotice
counterobjection
counterobligation
counteroffensive
counteroffer
counteropening
counteropponent
counteropposite
counterorator
counterorder
counterorganization
counterpaled
counterpaly
counterpane
counterpaned
counterparadox
counterparallel
counterparole
counterparry
counterpart
counterpassant
counterpassion
counterpenalty
counterpendent
counterpetition
counterpicture
counterpillar
counterplan
counterplay
counterplayer
counterplea
counterplead
counterpleading
counterplease
counterplot
counterpoint
counterpointe
counterpointed
counterpoise
counterpoison
counterpole
counterponderate
counterpose
counterposition
counterposting
counterpotence
counterpotency
counterpotent
counterpractice
counterpray
counterpreach
counterpreparation
counterpressure
counterprick
counterprinciple
counterprocess
counterproject
counterpronunciamento
counterproof
counterpropaganda
counterpropagandize
counterprophet
counterproposal
counterproposition
counterprotection
counterprotest
counterprove
counterpull
counterpunch
counterpuncture
counterpush
counterquartered
counterquarterly
counterquery
counterquestion
counterquip
counterradiation
counterraid
counterraising
counterrampant
counterrate
counterreaction
counterreason
counterreckoning
counterrecoil
counterreconnaissance
counterrefer
counterreflected
counterreform
counterreformation
counterreligion
counterremonstrant
counterreply
counterreprisal
counterresolution
counterrestoration
counterretreat
counterrevolution
counterrevolutionary
counterrevolutionist
counterrevolutionize
counterriposte
counterroll
counterround
counterruin
countersale
countersalient
counterscale
counterscalloped
counterscarp
counterscoff
countersconce
counterscrutiny
countersea
counterseal
countersecure
countersecurity
counterselection
countersense
counterservice
countershade
countershaft
countershafting
countershear
countershine
countershout
counterside
countersiege
countersign
countersignal
countersignature
countersink
countersleight
counterslope
countersmile
countersnarl
counterspying
counterstain
counterstamp
counterstand
counterstatant
counterstatement
counterstatute
counterstep
counterstimulate
counterstimulation
counterstimulus
counterstock
counterstratagem
counterstream
counterstrike
counterstroke
counterstruggle
countersubject
countersuggestion
countersuit
countersun
countersunk
countersurprise
counterswing
countersworn
countersympathy
countersynod
countertack
countertail
countertally
countertaste
countertechnicality
countertendency
countertenor
counterterm
counterterror
countertheme
countertheory
counterthought
counterthreat
counterthrust
counterthwarting
countertierce
countertime
countertouch
countertraction
countertrades
countertransference
countertranslation
countertraverse
countertreason
countertree
countertrench
countertrespass
countertrippant
countertripping
countertruth
countertug
counterturn
counterturned
countertype
countervail
countervair
countervairy
countervallation
countervaunt
countervene
countervengeance
countervenom
countervibration
counterview
countervindication
countervolition
countervolley
countervote
counterwager
counterwall
counterwarmth
counterwave
counterweigh
counterweight
counterweighted
counterwheel
counterwill
counterwilling
counterwind
counterwitness
counterword
counterwork
counterworker
counterwrite
countess
countfish
counting
countinghouse
countless
countor
countrified
countrifiedness
country
countryfolk
countryman
countrypeople
countryseat
countryside
countryward
countrywoman
countship
county
coup
coupage
coupe
couped
coupee
coupelet
couper
couple
coupled
couplement
coupler
coupleress
couplet
coupleteer
coupling
coupon
couponed
couponless
coupstick
coupure
courage
courageous
courageously
courageousness
courager
courant
courante
courap
couratari
courb
courbache
courbaril
courbash
courge
courida
courier
couril
courlan
Cours
course
coursed
courser
coursing
court
courtbred
courtcraft
courteous
courteously
courteousness
courtepy
courter
courtesan
courtesanry
courtesanship
courtesy
courtezanry
courtezanship
courthouse
courtier
courtierism
courtierly
courtiership
courtin
courtless
courtlet
courtlike
courtliness
courtling
courtly
courtman
Courtney
courtroom
courtship
courtyard
courtzilite
couscous
couscousou
couseranite
cousin
cousinage
cousiness
cousinhood
cousinly
cousinry
cousinship
cousiny
coussinet
coustumier
coutel
coutelle
couter
Coutet
couth
couthie
couthily
couthiness
couthless
coutil
coutumier
couvade
couxia
covado
covalence
covalent
Covarecan
Covarecas
covariable
covariance
covariant
covariation
covassal
cove
coved
covelline
covellite
covenant
covenantal
covenanted
covenantee
Covenanter
covenanter
covenanting
covenantor
covent
coventrate
coventrize
Coventry
cover
coverage
coveralls
coverchief
covercle
covered
coverer
covering
coverless
coverlet
coverlid
coversed
coverside
coversine
coverslut
covert
covertical
covertly
covertness
coverture
covet
covetable
coveter
coveting
covetingly
covetiveness
covetous
covetously
covetousness
covey
covibrate
covibration
covid
Coviello
covillager
Covillea
covin
coving
covinous
covinously
covisit
covisitor
covite
covolume
covotary
cow
cowal
Cowan
coward
cowardice
cowardliness
cowardly
cowardness
cowardy
cowbane
cowbell
cowberry
cowbind
cowbird
cowboy
cowcatcher
cowdie
coween
cower
cowfish
cowgate
cowgram
cowhage
cowheart
cowhearted
cowheel
cowherb
cowherd
cowhide
cowhiding
cowhorn
Cowichan
cowish
cowitch
cowkeeper
cowl
cowle
cowled
cowleech
cowleeching
cowlick
cowlicks
cowlike
cowling
Cowlitz
cowlstaff
cowman
cowpath
cowpea
cowpen
Cowperian
cowperitis
cowpock
cowpox
cowpuncher
cowquake
cowrie
cowroid
cowshed
cowskin
cowslip
cowslipped
cowsucker
cowtail
cowthwort
cowtongue
cowweed
cowwheat
cowy
cowyard
cox
coxa
coxal
coxalgia
coxalgic
coxankylometer
coxarthritis
coxarthrocace
coxarthropathy
coxbones
coxcomb
coxcombess
coxcombhood
coxcombic
coxcombical
coxcombicality
coxcombically
coxcombity
coxcombry
coxcomby
coxcomical
coxcomically
coxite
coxitis
coxocerite
coxoceritic
coxodynia
coxofemoral
coxopodite
coxswain
coxy
coy
coyan
coydog
coyish
coyishness
coyly
coyness
coynye
coyo
coyol
coyote
Coyotero
coyotillo
coyoting
coypu
coyure
coz
coze
cozen
cozenage
cozener
cozening
cozeningly
cozier
cozily
coziness
cozy
crab
crabbed
crabbedly
crabbedness
crabber
crabbery
crabbing
crabby
crabcatcher
crabeater
craber
crabhole
crablet
crablike
crabman
crabmill
crabsidle
crabstick
crabweed
crabwise
crabwood
Cracca
Cracidae
Cracinae
crack
crackable
crackajack
crackbrain
crackbrained
crackbrainedness
crackdown
cracked
crackedness
cracker
crackerberry
crackerjack
crackers
crackhemp
crackiness
cracking
crackjaw
crackle
crackled
crackless
crackleware
crackling
crackly
crackmans
cracknel
crackpot
crackskull
cracksman
cracky
cracovienne
craddy
cradge
cradle
cradleboard
cradlechild
cradlefellow
cradleland
cradlelike
cradlemaker
cradlemaking
cradleman
cradlemate
cradler
cradleside
cradlesong
cradletime
cradling
Cradock
craft
craftily
craftiness
craftless
craftsman
craftsmanship
craftsmaster
craftswoman
craftwork
craftworker
crafty
crag
craggan
cragged
craggedness
craggily
cragginess
craggy
craglike
cragsman
cragwork
craichy
Craig
craigmontite
crain
craisey
craizey
crajuru
crake
crakefeet
crakow
cram
cramasie
crambambulee
crambambuli
Crambe
crambe
cramberry
crambid
Crambidae
Crambinae
cramble
crambly
crambo
Crambus
crammer
cramp
cramped
crampedness
cramper
crampet
crampfish
cramping
crampingly
crampon
cramponnee
crampy
cran
cranage
cranberry
crance
crandall
crandallite
crane
cranelike
craneman
craner
cranesman
craneway
craney
Crania
crania
craniacromial
craniad
cranial
cranially
cranian
Craniata
craniate
cranic
craniectomy
craniocele
craniocerebral
cranioclasis
cranioclasm
cranioclast
cranioclasty
craniodidymus
craniofacial
craniognomic
craniognomy
craniognosy
craniograph
craniographer
craniography
craniological
craniologically
craniologist
craniology
craniomalacia
craniomaxillary
craniometer
craniometric
craniometrical
craniometrically
craniometrist
craniometry
craniopagus
craniopathic
craniopathy
craniopharyngeal
craniophore
cranioplasty
craniopuncture
craniorhachischisis
craniosacral
cranioschisis
cranioscopical
cranioscopist
cranioscopy
craniospinal
craniostenosis
craniostosis
Craniota
craniotabes
craniotome
craniotomy
craniotopography
craniotympanic
craniovertebral
cranium
crank
crankbird
crankcase
cranked
cranker
crankery
crankily
crankiness
crankle
crankless
crankly
crankman
crankous
crankpin
crankshaft
crankum
cranky
crannage
crannied
crannock
crannog
crannoger
cranny
cranreuch
crantara
crants
crap
crapaud
crapaudine
crape
crapefish
crapehanger
crapelike
crappie
crappin
crapple
crappo
craps
crapshooter
crapulate
crapulence
crapulent
crapulous
crapulously
crapulousness
crapy
craquelure
crare
crash
crasher
crasis
craspedal
craspedodromous
craspedon
Craspedota
craspedotal
craspedote
crass
crassamentum
crassier
crassilingual
Crassina
crassitude
crassly
crassness
Crassula
Crassulaceae
crassulaceous
Crataegus
Crataeva
cratch
cratchens
cratches
crate
crateful
cratemaker
cratemaking
crateman
crater
crateral
cratered
Craterellus
Craterid
crateriform
crateris
craterkin
craterless
craterlet
craterlike
craterous
craticular
Cratinean
cratometer
cratometric
cratometry
craunch
craunching
craunchingly
cravat
crave
craven
Cravenette
cravenette
cravenhearted
cravenly
cravenness
craver
craving
cravingly
cravingness
cravo
craw
crawberry
crawdad
crawfish
crawfoot
crawful
crawl
crawler
crawlerize
crawley
crawleyroot
crawling
crawlingly
crawlsome
crawly
crawm
crawtae
Crawthumper
Crax
crayer
crayfish
crayon
crayonist
crayonstone
craze
crazed
crazedly
crazedness
crazily
craziness
crazingmill
crazy
crazycat
crazyweed
crea
creagh
creaght
creak
creaker
creakily
creakiness
creakingly
creaky
cream
creambush
creamcake
creamcup
creamer
creamery
creameryman
creamfruit
creamily
creaminess
creamless
creamlike
creammaker
creammaking
creamometer
creamsacs
creamware
creamy
creance
creancer
creant
crease
creaseless
creaser
creashaks
creasing
creasy
creat
creatable
create
createdness
creatic
creatine
creatinephosphoric
creatinine
creatininemia
creatinuria
creation
creational
creationary
creationism
creationist
creationistic
creative
creatively
creativeness
creativity
creatophagous
creator
creatorhood
creatorrhea
creatorship
creatotoxism
creatress
creatrix
creatural
creature
creaturehood
creatureless
creatureliness
creatureling
creaturely
creatureship
creaturize
crebricostate
crebrisulcate
crebrity
crebrous
creche
creddock
credence
credencive
credenciveness
credenda
credensive
credensiveness
credent
credential
credently
credenza
credibility
credible
credibleness
credibly
credit
creditability
creditable
creditableness
creditably
creditive
creditless
creditor
creditorship
creditress
creditrix
crednerite
Credo
credulity
credulous
credulously
credulousness
Cree
cree
creed
creedal
creedalism
creedalist
creeded
creedist
creedite
creedless
creedlessness
creedmore
creedsman
Creek
creek
creeker
creekfish
creekside
creekstuff
creeky
creel
creeler
creem
creen
creep
creepage
creeper
creepered
creeperless
creephole
creepie
creepiness
creeping
creepingly
creepmouse
creepmousy
creepy
creese
creesh
creeshie
creeshy
creirgist
cremaster
cremasterial
cremasteric
cremate
cremation
cremationism
cremationist
cremator
crematorial
crematorium
crematory
crembalum
cremnophobia
cremocarp
cremometer
cremone
cremor
cremorne
cremule
crena
crenate
crenated
crenately
crenation
crenature
crenel
crenelate
crenelated
crenelation
crenele
creneled
crenelet
crenellate
crenellation
crenic
crenitic
crenology
crenotherapy
Crenothrix
crenula
crenulate
crenulated
crenulation
creodont
Creodonta
creole
creoleize
creolian
Creolin
creolism
creolization
creolize
creophagia
creophagism
creophagist
creophagous
creophagy
creosol
creosote
creosoter
creosotic
crepance
crepe
crepehanger
Crepidula
crepine
crepiness
Crepis
crepitaculum
crepitant
crepitate
crepitation
crepitous
crepitus
crepon
crept
crepuscle
crepuscular
crepuscule
crepusculine
crepusculum
crepy
cresamine
crescendo
crescent
crescentade
crescentader
Crescentia
crescentic
crescentiform
crescentlike
crescentoid
crescentwise
crescive
crescograph
crescographic
cresegol
cresol
cresolin
cresorcinol
cresotate
cresotic
cresotinic
cresoxide
cresoxy
cresphontes
cress
cressed
cresselle
cresset
Cressida
cresson
cressweed
cresswort
cressy
crest
crested
crestfallen
crestfallenly
crestfallenness
cresting
crestless
crestline
crestmoreite
cresyl
cresylate
cresylene
cresylic
cresylite
creta
Cretaceous
cretaceous
cretaceously
Cretacic
Cretan
Crete
cretefaction
Cretic
cretic
cretification
cretify
cretin
cretinic
cretinism
cretinization
cretinize
cretinoid
cretinous
cretion
cretionary
Cretism
cretonne
crevalle
crevasse
crevice
creviced
crew
crewel
crewelist
crewellery
crewelwork
crewer
crewless
crewman
Crex
crib
cribbage
cribber
cribbing
cribble
cribellum
cribo
cribral
cribrate
cribrately
cribration
cribriform
cribrose
cribwork
cric
Cricetidae
cricetine
Cricetus
crick
cricket
cricketer
cricketing
crickety
crickey
crickle
cricoarytenoid
cricoid
cricopharyngeal
cricothyreoid
cricothyreotomy
cricothyroid
cricothyroidean
cricotomy
cricotracheotomy
Cricotus
cried
crier
criey
crig
crile
crime
Crimean
crimeful
crimeless
crimelessness
crimeproof
criminal
criminaldom
criminalese
criminalism
criminalist
criminalistic
criminalistician
criminalistics
criminality
criminally
criminalness
criminaloid
criminate
crimination
criminative
criminator
criminatory
crimine
criminogenesis
criminogenic
criminologic
criminological
criminologist
criminology
criminosis
criminous
criminously
criminousness
crimogenic
crimp
crimpage
crimper
crimping
crimple
crimpness
crimpy
crimson
crimsonly
crimsonness
crimsony
crin
crinal
crinanite
crinated
crinatory
crine
crined
crinet
cringe
cringeling
cringer
cringing
cringingly
cringingness
cringle
crinicultural
criniculture
criniferous
Criniger
crinigerous
criniparous
crinite
crinitory
crinivorous
crink
crinkle
crinkleroot
crinkly
crinoid
crinoidal
Crinoidea
crinoidean
crinoline
crinose
crinosity
crinula
Crinum
criobolium
criocephalus
Crioceras
crioceratite
crioceratitic
Crioceris
criophore
Criophoros
criosphinx
cripes
crippingly
cripple
crippledom
crippleness
crippler
crippling
cripply
Cris
crises
crisic
crisis
crisp
crispate
crispated
crispation
crispature
crisped
crisper
crispily
Crispin
crispine
crispiness
crisping
crisply
crispness
crispy
criss
crissal
crisscross
crissum
crista
cristate
Cristatella
Cristi
cristiform
Cristina
Cristineaux
Cristino
Cristispira
Cristivomer
cristobalite
Cristopher
critch
criteria
criteriology
criterion
criterional
criterium
crith
Crithidia
crithmene
crithomancy
critic
critical
criticality
critically
criticalness
criticaster
criticasterism
criticastry
criticisable
criticism
criticist
criticizable
criticize
criticizer
criticizingly
critickin
criticship
criticule
critique
critling
crizzle
cro
croak
Croaker
croaker
croakily
croakiness
croaky
Croat
Croatan
Croatian
croc
Crocanthemum
crocard
croceic
crocein
croceine
croceous
crocetin
croche
crochet
crocheter
crocheting
croci
crocidolite
Crocidura
crocin
crock
crocker
crockery
crockeryware
crocket
crocketed
crocky
crocodile
Crocodilia
crocodilian
Crocodilidae
crocodiline
crocodilite
crocodiloid
Crocodilus
Crocodylidae
Crocodylus
crocoisite
crocoite
croconate
croconic
Crocosmia
Crocus
crocus
crocused
croft
crofter
crofterization
crofterize
crofting
croftland
croisette
croissante
Crokinole
Crom
cromaltite
crome
Cromer
Cromerian
cromfordite
cromlech
cromorna
cromorne
Cromwell
Cromwellian
Cronartium
crone
croneberry
cronet
Cronian
cronish
cronk
cronkness
cronstedtite
crony
crood
croodle
crook
crookback
crookbacked
crookbill
crookbilled
crooked
crookedly
crookedness
crooken
crookesite
crookfingered
crookheaded
crookkneed
crookle
crooklegged
crookneck
crooknecked
crooknosed
crookshouldered
crooksided
crooksterned
crooktoothed
crool
Croomia
croon
crooner
crooning
crooningly
crop
crophead
cropland
cropman
croppa
cropper
croppie
cropplecrown
croppy
cropshin
cropsick
cropsickness
cropweed
croquet
croquette
crore
crosa
Crosby
crosier
crosiered
crosnes
cross
crossability
crossable
crossarm
crossband
crossbar
crossbeak
crossbeam
crossbelt
crossbill
crossbolt
crossbolted
crossbones
crossbow
crossbowman
crossbred
crossbreed
crosscurrent
crosscurrented
crosscut
crosscutter
crosscutting
crosse
crossed
crosser
crossette
crossfall
crossfish
crossflow
crossflower
crossfoot
crosshackle
crosshand
crosshatch
crosshaul
crosshauling
crosshead
crossing
crossite
crossjack
crosslegs
crosslet
crossleted
crosslight
crosslighted
crossline
crossly
crossness
crossopodia
crossopterygian
Crossopterygii
Crossosoma
Crossosomataceae
crossosomataceous
crossover
crosspatch
crosspath
crosspiece
crosspoint
crossrail
crossroad
crossroads
crossrow
crossruff
crosstail
crosstie
crosstied
crosstoes
crosstrack
crosstree
crosswalk
crossway
crossways
crossweb
crossweed
crosswise
crossword
crosswort
crostarie
crotal
Crotalaria
crotalic
Crotalidae
crotaliform
Crotalinae
crotaline
crotalism
crotalo
crotaloid
crotalum
Crotalus
crotaphic
crotaphion
crotaphite
crotaphitic
Crotaphytus
crotch
crotched
crotchet
crotcheteer
crotchetiness
crotchety
crotchy
crotin
Croton
crotonaldehyde
crotonate
crotonic
crotonization
crotonyl
crotonylene
Crotophaga
crottels
crottle
crotyl
crouch
crouchant
crouched
croucher
crouching
crouchingly
crounotherapy
croup
croupade
croupal
croupe
crouperbush
croupier
croupily
croupiness
croupous
croupy
crouse
crousely
crout
croute
crouton
crow
crowbait
crowbar
crowberry
crowbill
crowd
crowded
crowdedly
crowdedness
crowder
crowdweed
crowdy
crower
crowflower
crowfoot
crowfooted
crowhop
crowing
crowingly
crowkeeper
crowl
crown
crownbeard
crowned
crowner
crownless
crownlet
crownling
crownmaker
crownwork
crownwort
crowshay
crowstep
crowstepped
crowstick
crowstone
crowtoe
croy
croyden
croydon
croze
crozer
crozzle
crozzly
crubeen
cruce
cruces
crucethouse
cruche
crucial
cruciality
crucially
crucian
Crucianella
cruciate
cruciately
cruciation
crucible
Crucibulum
crucifer
Cruciferae
cruciferous
crucificial
crucified
crucifier
crucifix
crucifixion
cruciform
cruciformity
cruciformly
crucify
crucigerous
crucilly
crucily
cruck
crude
crudely
crudeness
crudity
crudwort
cruel
cruelhearted
cruelize
cruelly
cruelness
cruels
cruelty
cruent
cruentation
cruet
cruety
cruise
cruiser
cruisken
cruive
cruller
crum
crumb
crumbable
crumbcloth
crumber
crumble
crumblement
crumblet
crumbliness
crumblingness
crumblings
crumbly
crumby
crumen
crumenal
crumlet
crummie
crummier
crummiest
crummock
crummy
crump
crumper
crumpet
crumple
crumpled
crumpler
crumpling
crumply
crumpy
crunch
crunchable
crunchiness
crunching
crunchingly
crunchingness
crunchweed
crunchy
crunk
crunkle
crunodal
crunode
crunt
cruor
crupper
crural
crureus
crurogenital
cruroinguinal
crurotarsal
crus
crusade
crusader
crusado
Crusca
cruse
crush
crushability
crushable
crushed
crusher
crushing
crushingly
crusie
crusily
crust
crusta
Crustacea
crustaceal
crustacean
crustaceological
crustaceologist
crustaceology
crustaceous
crustade
crustal
crustalogical
crustalogist
crustalogy
crustate
crustated
crustation
crusted
crustedly
cruster
crustific
crustification
crustily
crustiness
crustless
crustose
crustosis
crusty
crutch
crutched
crutcher
crutching
crutchlike
cruth
crutter
crux
cruzeiro
cry
cryable
cryaesthesia
cryalgesia
cryanesthesia
crybaby
cryesthesia
crying
cryingly
crymodynia
crymotherapy
cryoconite
cryogen
cryogenic
cryogenics
cryogeny
cryohydrate
cryohydric
cryolite
cryometer
cryophile
cryophilic
cryophoric
cryophorus
cryophyllite
cryophyte
cryoplankton
cryoscope
cryoscopic
cryoscopy
cryosel
cryostase
cryostat
crypt
crypta
cryptal
cryptamnesia
cryptamnesic
cryptanalysis
cryptanalyst
cryptarch
cryptarchy
crypted
Crypteronia
Crypteroniaceae
cryptesthesia
cryptesthetic
cryptic
cryptical
cryptically
cryptoagnostic
cryptobatholithic
cryptobranch
Cryptobranchia
Cryptobranchiata
cryptobranchiate
Cryptobranchidae
Cryptobranchus
cryptocarp
cryptocarpic
cryptocarpous
Cryptocarya
Cryptocephala
cryptocephalous
Cryptocerata
cryptocerous
cryptoclastic
Cryptocleidus
cryptococci
cryptococcic
Cryptococcus
cryptococcus
cryptocommercial
cryptocrystalline
cryptocrystallization
cryptodeist
Cryptodira
cryptodiran
cryptodire
cryptodirous
cryptodouble
cryptodynamic
cryptogam
Cryptogamia
cryptogamian
cryptogamic
cryptogamical
cryptogamist
cryptogamous
cryptogamy
cryptogenetic
cryptogenic
cryptogenous
Cryptoglaux
cryptoglioma
cryptogram
Cryptogramma
cryptogrammatic
cryptogrammatical
cryptogrammatist
cryptogrammic
cryptograph
cryptographal
cryptographer
cryptographic
cryptographical
cryptographically
cryptographist
cryptography
cryptoheresy
cryptoheretic
cryptoinflationist
cryptolite
cryptologist
cryptology
cryptolunatic
cryptomere
Cryptomeria
cryptomerous
cryptomnesia
cryptomnesic
cryptomonad
Cryptomonadales
Cryptomonadina
cryptonema
Cryptonemiales
cryptoneurous
cryptonym
cryptonymous
cryptopapist
cryptoperthite
Cryptophagidae
cryptophthalmos
Cryptophyceae
cryptophyte
cryptopine
cryptoporticus
Cryptoprocta
cryptoproselyte
cryptoproselytism
cryptopyic
cryptopyrrole
cryptorchid
cryptorchidism
cryptorchis
Cryptorhynchus
cryptorrhesis
cryptorrhetic
cryptoscope
cryptoscopy
cryptosplenetic
Cryptostegia
cryptostoma
Cryptostomata
cryptostomate
cryptostome
Cryptotaenia
cryptous
cryptovalence
cryptovalency
cryptozonate
Cryptozonia
cryptozygosity
cryptozygous
Crypturi
Crypturidae
crystal
crystallic
crystalliferous
crystalliform
crystalligerous
crystallin
crystalline
crystallinity
crystallite
crystallitic
crystallitis
crystallizability
crystallizable
crystallization
crystallize
crystallized
crystallizer
crystalloblastic
crystallochemical
crystallochemistry
crystallogenesis
crystallogenetic
crystallogenic
crystallogenical
crystallogeny
crystallogram
crystallographer
crystallographic
crystallographical
crystallographically
crystallography
crystalloid
crystalloidal
crystallology
crystalloluminescence
crystallomagnetic
crystallomancy
crystallometric
crystallometry
crystallophyllian
crystallose
crystallurgy
crystalwort
crystic
crystograph
crystoleum
Crystolon
crystosphene
csardas
Ctenacanthus
ctene
ctenidial
ctenidium
cteniform
Ctenocephalus
ctenocyst
ctenodactyl
Ctenodipterini
ctenodont
Ctenodontidae
Ctenodus
ctenoid
ctenoidean
Ctenoidei
ctenoidian
ctenolium
Ctenophora
ctenophoral
ctenophoran
ctenophore
ctenophoric
ctenophorous
Ctenoplana
Ctenostomata
ctenostomatous
ctenostome
ctetology
cuadra
Cuailnge
cuapinole
cuarenta
cuarta
cuarteron
cuartilla
cuartillo
cub
Cuba
cubage
Cuban
cubangle
cubanite
Cubanize
cubatory
cubature
cubbing
cubbish
cubbishly
cubbishness
cubby
cubbyhole
cubbyhouse
cubbyyew
cubdom
cube
cubeb
cubelet
Cubelium
cuber
cubhood
cubi
cubic
cubica
cubical
cubically
cubicalness
cubicity
cubicle
cubicly
cubicone
cubicontravariant
cubicovariant
cubicular
cubiculum
cubiform
cubism
cubist
cubit
cubital
cubitale
cubited
cubitiere
cubito
cubitocarpal
cubitocutaneous
cubitodigital
cubitometacarpal
cubitopalmar
cubitoplantar
cubitoradial
cubitus
cubmaster
cubocalcaneal
cuboctahedron
cubocube
cubocuneiform
cubododecahedral
cuboid
cuboidal
cuboides
cubomancy
Cubomedusae
cubomedusan
cubometatarsal
cubonavicular
Cuchan
Cuchulainn
cuck
cuckhold
cuckold
cuckoldom
cuckoldry
cuckoldy
cuckoo
cuckooflower
cuckoomaid
cuckoopint
cuckoopintle
cuckstool
cucoline
Cucujid
Cucujidae
Cucujus
Cuculi
Cuculidae
cuculiform
Cuculiformes
cuculine
cuculla
cucullaris
cucullate
cucullately
cuculliform
cucullus
cuculoid
Cuculus
Cucumaria
Cucumariidae
cucumber
cucumiform
Cucumis
cucurbit
Cucurbita
Cucurbitaceae
cucurbitaceous
cucurbite
cucurbitine
cud
cudava
cudbear
cudden
cuddle
cuddleable
cuddlesome
cuddly
Cuddy
cuddy
cuddyhole
cudgel
cudgeler
cudgerie
cudweed
cue
cueball
cueca
cueist
cueman
cuemanship
cuerda
cuesta
Cueva
cuff
cuffer
cuffin
cuffy
cuffyism
cuggermugger
cuichunchulli
cuinage
cuir
cuirass
cuirassed
cuirassier
cuisinary
cuisine
cuissard
cuissart
cuisse
cuissen
cuisten
Cuitlateco
cuittikin
Cujam
cuke
Culavamsa
culbut
Culdee
culebra
culet
culeus
Culex
culgee
culicid
Culicidae
culicidal
culicide
culiciform
culicifugal
culicifuge
Culicinae
culicine
Culicoides
culilawan
culinarily
culinary
cull
culla
cullage
Cullen
culler
cullet
culling
cullion
cullis
cully
culm
culmen
culmicolous
culmiferous
culmigenous
culminal
culminant
culminate
culmination
culmy
culotte
culottes
culottic
culottism
culpa
culpability
culpable
culpableness
culpably
culpatory
culpose
culprit
cult
cultch
cultellation
cultellus
culteranismo
cultic
cultigen
cultirostral
Cultirostres
cultish
cultism
cultismo
cultist
cultivability
cultivable
cultivably
cultivar
cultivatability
cultivatable
cultivate
cultivated
cultivation
cultivator
cultrate
cultrated
cultriform
cultrirostral
Cultrirostres
cultual
culturable
cultural
culturally
culture
cultured
culturine
culturist
culturization
culturize
culturological
culturologically
culturologist
culturology
cultus
culver
culverfoot
culverhouse
culverin
culverineer
culverkey
culvert
culvertage
culverwort
cum
Cumacea
cumacean
cumaceous
Cumaean
cumal
cumaldehyde
Cumanagoto
cumaphyte
cumaphytic
cumaphytism
Cumar
cumay
cumbent
cumber
cumberer
cumberlandite
cumberless
cumberment
cumbersome
cumbersomely
cumbersomeness
cumberworld
cumbha
cumbly
cumbraite
cumbrance
cumbre
Cumbrian
cumbrous
cumbrously
cumbrousness
cumbu
cumene
cumengite
cumenyl
cumflutter
cumhal
cumic
cumidin
cumidine
cumin
cuminal
cuminic
cuminoin
cuminol
cuminole
cuminseed
cuminyl
cummer
cummerbund
cummin
cummingtonite
cumol
cump
cumshaw
cumulant
cumular
cumulate
cumulately
cumulation
cumulatist
cumulative
cumulatively
cumulativeness
cumuli
cumuliform
cumulite
cumulophyric
cumulose
cumulous
cumulus
cumyl
Cuna
cunabular
Cunan
Cunarder
Cunas
cunctation
cunctatious
cunctative
cunctator
cunctatorship
cunctatury
cunctipotent
cundeamor
cuneal
cuneate
cuneately
cuneatic
cuneator
cuneiform
cuneiformist
cuneocuboid
cuneonavicular
cuneoscaphoid
cunette
cuneus
cungeboi
cunicular
cuniculus
cunila
cunjah
cunjer
cunjevoi
cunner
cunnilinctus
cunnilingus
cunning
Cunninghamia
cunningly
cunningness
Cunonia
Cunoniaceae
cunoniaceous
cunye
Cunza
Cuon
cuorin
cup
Cupania
cupay
cupbearer
cupboard
cupcake
cupel
cupeler
cupellation
cupflower
cupful
Cuphea
cuphead
cupholder
Cupid
cupidinous
cupidity
cupidon
cupidone
cupless
cupmaker
cupmaking
cupman
cupmate
cupola
cupolaman
cupolar
cupolated
cupped
cupper
cupping
cuppy
cuprammonia
cuprammonium
cupreine
cuprene
cupreous
Cupressaceae
cupressineous
Cupressinoxylon
Cupressus
cupric
cupride
cupriferous
cuprite
cuproammonium
cuprobismutite
cuprocyanide
cuprodescloizite
cuproid
cuproiodargyrite
cupromanganese
cupronickel
cuproplumbite
cuproscheelite
cuprose
cuprosilicon
cuprotungstite
cuprous
cuprum
cupseed
cupstone
cupula
cupulate
cupule
Cupuliferae
cupuliferous
cupuliform
cur
curability
curable
curableness
curably
curacao
curacy
curare
curarine
curarization
curarize
curassow
curatage
curate
curatel
curateship
curatess
curatial
curatic
curation
curative
curatively
curativeness
curatize
curatolatry
curator
curatorial
curatorium
curatorship
curatory
curatrix
Curavecan
curb
curbable
curber
curbing
curbless
curblike
curbstone
curbstoner
curby
curcas
curch
curcuddoch
Curculio
curculionid
Curculionidae
curculionist
Curcuma
curcumin
curd
curdiness
curdle
curdler
curdly
curdwort
curdy
cure
cureless
curelessly
curemaster
curer
curettage
curette
curettement
curfew
curial
curialism
curialist
curialistic
curiality
curiate
Curiatii
curiboca
curie
curiescopy
curietherapy
curin
curine
curing
curio
curiologic
curiologically
curiologics
curiology
curiomaniac
curiosa
curiosity
curioso
curious
curiously
curiousness
curite
Curitis
curium
curl
curled
curledly
curledness
curler
curlew
curlewberry
curlicue
curliewurly
curlike
curlily
curliness
curling
curlingly
curlpaper
curly
curlycue
curlyhead
curlylocks
curmudgeon
curmudgeonery
curmudgeonish
curmudgeonly
curmurring
curn
curney
curnock
curple
curr
currach
currack
curragh
currant
curratow
currawang
currency
current
currently
currentness
currentwise
curricle
curricula
curricular
curricularization
curricularize
curriculum
curried
currier
curriery
currish
currishly
currishness
curry
currycomb
curryfavel
Cursa
cursal
curse
cursed
cursedly
cursedness
curser
curship
cursitor
cursive
cursively
cursiveness
cursor
cursorary
Cursores
Cursoria
cursorial
Cursoriidae
cursorily
cursoriness
cursorious
Cursorius
cursory
curst
curstful
curstfully
curstly
curstness
cursus
Curt
curt
curtail
curtailed
curtailedly
curtailer
curtailment
curtain
curtaining
curtainless
curtainwise
curtal
Curtana
curtate
curtation
curtesy
curtilage
Curtis
Curtise
curtly
curtness
curtsy
curua
curuba
Curucaneca
Curucanecan
curucucu
curule
Curuminaca
Curuminacan
Curupira
cururo
curvaceous
curvaceousness
curvacious
curvant
curvate
curvation
curvature
curve
curved
curvedly
curvedness
curver
curvesome
curvesomeness
curvet
curvicaudate
curvicostate
curvidentate
curvifoliate
curviform
curvilineal
curvilinear
curvilinearity
curvilinearly
curvimeter
curvinervate
curvinerved
curvirostral
Curvirostres
curviserial
curvital
curvity
curvograph
curvometer
curvous
curvulate
curvy
curwhibble
curwillet
cuscohygrine
cusconine
Cuscus
cuscus
Cuscuta
Cuscutaceae
cuscutaceous
cusec
cuselite
cush
cushag
cushat
cushaw
cushewbird
cushion
cushioned
cushionflower
cushionless
cushionlike
cushiony
Cushite
Cushitic
cushlamochree
cushy
cusie
cusinero
cusk
cusp
cuspal
cusparidine
cusparine
cuspate
cusped
cuspid
cuspidal
cuspidate
cuspidation
cuspidine
cuspidor
cuspule
cuss
cussed
cussedly
cussedness
cusser
cusso
custard
custerite
custodee
custodes
custodial
custodiam
custodian
custodianship
custodier
custody
custom
customable
customarily
customariness
customary
customer
customhouse
customs
custumal
cut
cutaneal
cutaneous
cutaneously
cutaway
cutback
cutch
cutcher
cutcherry
cute
cutely
cuteness
Cuterebra
Cuthbert
cutheal
cuticle
cuticolor
cuticula
cuticular
cuticularization
cuticularize
cuticulate
cutidure
cutie
cutification
cutigeral
cutin
cutinization
cutinize
cutireaction
cutis
cutisector
Cutiterebra
cutitis
cutization
cutlass
cutler
cutleress
Cutleria
Cutleriaceae
cutleriaceous
Cutleriales
cutlery
cutlet
cutling
cutlips
cutocellulose
cutoff
cutout
cutover
cutpurse
cuttable
cuttage
cuttail
cuttanee
cutted
cutter
cutterhead
cutterman
cutthroat
cutting
cuttingly
cuttingness
cuttle
cuttlebone
cuttlefish
cuttler
cuttoo
cutty
cuttyhunk
cutup
cutwater
cutweed
cutwork
cutworm
cuvette
Cuvierian
cuvy
cuya
Cuzceno
cwierc
cwm
cyamelide
Cyamus
cyan
cyanacetic
cyanamide
cyananthrol
Cyanastraceae
Cyanastrum
cyanate
cyanaurate
cyanauric
cyanbenzyl
cyancarbonic
Cyanea
cyanean
cyanemia
cyaneous
cyanephidrosis
cyanformate
cyanformic
cyanhidrosis
cyanhydrate
cyanhydric
cyanhydrin
cyanic
cyanicide
cyanidation
cyanide
cyanidin
cyanidine
cyanidrosis
cyanimide
cyanin
cyanine
cyanite
cyanize
cyanmethemoglobin
cyanoacetate
cyanoacetic
cyanoaurate
cyanoauric
cyanobenzene
cyanocarbonic
cyanochlorous
cyanochroia
cyanochroic
Cyanocitta
cyanocrystallin
cyanoderma
cyanogen
cyanogenesis
cyanogenetic
cyanogenic
cyanoguanidine
cyanohermidin
cyanohydrin
cyanol
cyanole
cyanomaclurin
cyanometer
cyanomethaemoglobin
cyanomethemoglobin
cyanometric
cyanometry
cyanopathic
cyanopathy
cyanophile
cyanophilous
cyanophoric
cyanophose
Cyanophyceae
cyanophycean
cyanophyceous
cyanophycin
cyanopia
cyanoplastid
cyanoplatinite
cyanoplatinous
cyanopsia
cyanose
cyanosed
cyanosis
Cyanospiza
cyanotic
cyanotrichite
cyanotype
cyanuramide
cyanurate
cyanuret
cyanuric
cyanurine
cyanus
cyaphenine
cyath
Cyathaspis
Cyathea
Cyatheaceae
cyatheaceous
cyathiform
cyathium
cyathoid
cyatholith
Cyathophyllidae
cyathophylline
cyathophylloid
Cyathophyllum
cyathos
cyathozooid
cyathus
cybernetic
cyberneticist
cybernetics
Cybister
cycad
Cycadaceae
cycadaceous
Cycadales
cycadean
cycadeoid
Cycadeoidea
cycadeous
cycadiform
cycadlike
cycadofilicale
Cycadofilicales
Cycadofilices
cycadofilicinean
Cycadophyta
Cycas
Cycladic
cyclamen
cyclamin
cyclamine
cyclammonium
cyclane
Cyclanthaceae
cyclanthaceous
Cyclanthales
Cyclanthus
cyclar
cyclarthrodial
cyclarthrsis
cyclas
cycle
cyclecar
cycledom
cyclene
cycler
cyclesmith
Cycliae
cyclian
cyclic
cyclical
cyclically
cyclicism
cyclide
cycling
cyclism
cyclist
cyclistic
cyclitic
cyclitis
cyclization
cyclize
cycloalkane
Cyclobothra
cyclobutane
cyclocoelic
cyclocoelous
Cycloconium
cyclodiolefin
cycloganoid
Cycloganoidei
cyclogram
cyclograph
cyclographer
cycloheptane
cycloheptanone
cyclohexane
cyclohexanol
cyclohexanone
cyclohexene
cyclohexyl
cycloid
cycloidal
cycloidally
cycloidean
Cycloidei
cycloidian
cycloidotrope
cyclolith
Cycloloma
cyclomania
cyclometer
cyclometric
cyclometrical
cyclometry
Cyclomyaria
cyclomyarian
cyclonal
cyclone
cyclonic
cyclonical
cyclonically
cyclonist
cyclonite
cyclonologist
cyclonology
cyclonometer
cyclonoscope
cycloolefin
cycloparaffin
cyclope
Cyclopean
cyclopean
cyclopedia
cyclopedic
cyclopedical
cyclopedically
cyclopedist
cyclopentadiene
cyclopentane
cyclopentanone
cyclopentene
Cyclopes
cyclopes
cyclophoria
cyclophoric
Cyclophorus
cyclophrenia
cyclopia
Cyclopic
cyclopism
cyclopite
cycloplegia
cycloplegic
cyclopoid
cyclopropane
Cyclops
Cyclopteridae
cyclopteroid
cyclopterous
cyclopy
cyclorama
cycloramic
Cyclorrhapha
cyclorrhaphous
cycloscope
cyclose
cyclosis
cyclospermous
Cyclospondyli
cyclospondylic
cyclospondylous
Cyclosporales
Cyclosporeae
Cyclosporinae
cyclosporous
Cyclostoma
Cyclostomata
cyclostomate
Cyclostomatidae
cyclostomatous
cyclostome
Cyclostomes
Cyclostomi
Cyclostomidae
cyclostomous
cyclostrophic
cyclostyle
Cyclotella
cyclothem
cyclothure
cyclothurine
Cyclothurus
cyclothyme
cyclothymia
cyclothymiac
cyclothymic
cyclotome
cyclotomic
cyclotomy
Cyclotosaurus
cyclotron
cyclovertebral
cyclus
Cydippe
cydippian
cydippid
Cydippida
Cydonia
Cydonian
cydonium
cyesiology
cyesis
cygneous
cygnet
Cygnid
Cygninae
cygnine
Cygnus
cyke
cylinder
cylindered
cylinderer
cylinderlike
cylindraceous
cylindrarthrosis
Cylindrella
cylindrelloid
cylindrenchyma
cylindric
cylindrical
cylindricality
cylindrically
cylindricalness
cylindricity
cylindricule
cylindriform
cylindrite
cylindrocellular
cylindrocephalic
cylindroconical
cylindroconoidal
cylindrocylindric
cylindrodendrite
cylindrograph
cylindroid
cylindroidal
cylindroma
cylindromatous
cylindrometric
cylindroogival
Cylindrophis
Cylindrosporium
cylindruria
cylix
Cyllenian
Cyllenius
cyllosis
cyma
cymagraph
cymaphen
cymaphyte
cymaphytic
cymaphytism
cymar
cymation
cymatium
cymba
cymbaeform
cymbal
Cymbalaria
cymbaleer
cymbaler
cymbaline
cymbalist
cymballike
cymbalo
cymbalon
cymbate
Cymbella
cymbiform
Cymbium
cymbling
cymbocephalic
cymbocephalous
cymbocephaly
Cymbopogon
cyme
cymelet
cymene
cymiferous
cymling
Cymodoceaceae
cymogene
cymograph
cymographic
cymoid
Cymoidium
cymometer
cymophane
cymophanous
cymophenol
cymoscope
cymose
cymosely
cymotrichous
cymotrichy
cymous
Cymraeg
Cymric
Cymry
cymule
cymulose
cynanche
Cynanchum
cynanthropy
Cynara
cynaraceous
cynarctomachy
cynareous
cynaroid
cynebot
cynegetic
cynegetics
cynegild
cynhyena
Cynias
cyniatria
cyniatrics
cynic
cynical
cynically
cynicalness
cynicism
cynicist
cynipid
Cynipidae
cynipidous
cynipoid
Cynipoidea
Cynips
cynism
cynocephalic
cynocephalous
cynocephalus
cynoclept
Cynocrambaceae
cynocrambaceous
Cynocrambe
Cynodon
cynodont
Cynodontia
Cynogale
cynogenealogist
cynogenealogy
Cynoglossum
Cynognathus
cynography
cynoid
Cynoidea
cynology
Cynomoriaceae
cynomoriaceous
Cynomorium
Cynomorpha
cynomorphic
cynomorphous
Cynomys
cynophile
cynophilic
cynophilist
cynophobe
cynophobia
Cynopithecidae
cynopithecoid
cynopodous
cynorrhodon
Cynosarges
Cynoscion
Cynosura
cynosural
cynosure
Cynosurus
cynotherapy
Cynoxylon
Cynthia
Cynthian
Cynthiidae
Cynthius
cyp
Cyperaceae
cyperaceous
Cyperus
cyphella
cyphellate
Cyphomandra
cyphonautes
cyphonism
Cypraea
cypraeid
Cypraeidae
cypraeiform
cypraeoid
cypre
cypres
cypress
cypressed
cypressroot
Cypria
Cyprian
Cyprididae
Cypridina
Cypridinidae
cypridinoid
Cyprina
cyprine
cyprinid
Cyprinidae
cypriniform
cyprinine
cyprinodont
Cyprinodontes
Cyprinodontidae
cyprinodontoid
cyprinoid
Cyprinoidea
cyprinoidean
Cyprinus
Cypriote
Cypripedium
Cypris
cypsela
Cypseli
Cypselid
Cypselidae
cypseliform
Cypseliformes
cypseline
cypseloid
cypselomorph
Cypselomorphae
cypselomorphic
cypselous
Cypselus
cyptozoic
Cyrano
Cyrenaic
Cyrenaicism
Cyrenian
Cyril
Cyrilla
Cyrillaceae
cyrillaceous
Cyrillian
Cyrillianism
Cyrillic
cyriologic
cyriological
Cyrtandraceae
Cyrtidae
cyrtoceracone
Cyrtoceras
cyrtoceratite
cyrtoceratitic
cyrtograph
cyrtolite
cyrtometer
Cyrtomium
cyrtopia
cyrtosis
Cyrus
cyrus
cyst
cystadenoma
cystadenosarcoma
cystal
cystalgia
cystamine
cystaster
cystatrophia
cystatrophy
cystectasia
cystectasy
cystectomy
cysted
cysteine
cysteinic
cystelcosis
cystenchyma
cystenchymatous
cystencyte
cysterethism
cystic
cysticarpic
cysticarpium
cysticercoid
cysticercoidal
cysticercosis
cysticercus
cysticolous
cystid
Cystidea
cystidean
cystidicolous
cystidium
cystiferous
cystiform
cystigerous
Cystignathidae
cystignathine
cystine
cystinuria
cystirrhea
cystis
cystitis
cystitome
cystoadenoma
cystocarcinoma
cystocarp
cystocarpic
cystocele
cystocolostomy
cystocyte
cystodynia
cystoelytroplasty
cystoenterocele
cystoepiplocele
cystoepithelioma
cystofibroma
Cystoflagellata
cystoflagellate
cystogenesis
cystogenous
cystogram
cystoid
Cystoidea
cystoidean
cystolith
cystolithectomy
cystolithiasis
cystolithic
cystoma
cystomatous
cystomorphous
cystomyoma
cystomyxoma
Cystonectae
cystonectous
cystonephrosis
cystoneuralgia
cystoparalysis
Cystophora
cystophore
cystophotography
cystophthisis
cystoplasty
cystoplegia
cystoproctostomy
Cystopteris
cystoptosis
Cystopus
cystopyelitis
cystopyelography
cystopyelonephritis
cystoradiography
cystorrhagia
cystorrhaphy
cystorrhea
cystosarcoma
cystoschisis
cystoscope
cystoscopic
cystoscopy
cystose
cystospasm
cystospastic
cystospore
cystostomy
cystosyrinx
cystotome
cystotomy
cystotrachelotomy
cystoureteritis
cystourethritis
cystous
cytase
cytasic
Cytherea
Cytherean
Cytherella
Cytherellidae
Cytinaceae
cytinaceous
Cytinus
cytioderm
cytisine
Cytisus
cytitis
cytoblast
cytoblastema
cytoblastemal
cytoblastematous
cytoblastemic
cytoblastemous
cytochemistry
cytochrome
cytochylema
cytocide
cytoclasis
cytoclastic
cytococcus
cytocyst
cytode
cytodendrite
cytoderm
cytodiagnosis
cytodieresis
cytodieretic
cytogamy
cytogene
cytogenesis
cytogenetic
cytogenetical
cytogenetically
cytogeneticist
cytogenetics
cytogenic
cytogenous
cytogeny
cytoglobin
cytohyaloplasm
cytoid
cytokinesis
cytolist
cytologic
cytological
cytologically
cytologist
cytology
cytolymph
cytolysin
cytolysis
cytolytic
cytoma
cytomere
cytometer
cytomicrosome
cytomitome
cytomorphosis
cyton
cytoparaplastin
cytopathologic
cytopathological
cytopathologically
cytopathology
Cytophaga
cytophagous
cytophagy
cytopharynx
cytophil
cytophysics
cytophysiology
cytoplasm
cytoplasmic
cytoplast
cytoplastic
cytoproct
cytopyge
cytoreticulum
cytoryctes
cytosine
cytosome
Cytospora
Cytosporina
cytost
cytostomal
cytostome
cytostroma
cytostromatic
cytotactic
cytotaxis
cytotoxic
cytotoxin
cytotrophoblast
cytotrophy
cytotropic
cytotropism
cytozoic
cytozoon
cytozymase
cytozyme
cytula
Cyzicene
cyzicene
czar
czardas
czardom
czarevitch
czarevna
czarian
czaric
czarina
czarinian
czarish
czarism
czarist
czaristic
czaritza
czarowitch
czarowitz
czarship
Czech
Czechic
Czechish
Czechization
Czechoslovak
Czechoslovakian
D
d
da
daalder
dab
dabb
dabba
dabber
dabble
dabbler
dabbling
dabblingly
dabblingness
dabby
dabchick
Dabih
Dabitis
dablet
daboia
daboya
dabster
dace
Dacelo
Daceloninae
dacelonine
dachshound
dachshund
Dacian
dacite
dacitic
dacker
dacoit
dacoitage
dacoity
dacryadenalgia
dacryadenitis
dacryagogue
dacrycystalgia
Dacrydium
dacryelcosis
dacryoadenalgia
dacryoadenitis
dacryoblenorrhea
dacryocele
dacryocyst
dacryocystalgia
dacryocystitis
dacryocystoblennorrhea
dacryocystocele
dacryocystoptosis
dacryocystorhinostomy
dacryocystosyringotomy
dacryocystotome
dacryocystotomy
dacryohelcosis
dacryohemorrhea
dacryolite
dacryolith
dacryolithiasis
dacryoma
dacryon
dacryops
dacryopyorrhea
dacryopyosis
dacryosolenitis
dacryostenosis
dacryosyrinx
dacryuria
Dactyl
dactyl
dactylar
dactylate
dactylic
dactylically
dactylioglyph
dactylioglyphic
dactylioglyphist
dactylioglyphtic
dactylioglyphy
dactyliographer
dactyliographic
dactyliography
dactyliology
dactyliomancy
dactylion
dactyliotheca
Dactylis
dactylist
dactylitic
dactylitis
dactylogram
dactylograph
dactylographic
dactylography
dactyloid
dactylology
dactylomegaly
dactylonomy
dactylopatagium
Dactylopius
dactylopodite
dactylopore
Dactylopteridae
Dactylopterus
dactylorhiza
dactyloscopic
dactyloscopy
dactylose
dactylosternal
dactylosymphysis
dactylotheca
dactylous
dactylozooid
dactylus
Dacus
dacyorrhea
dad
Dada
dada
Dadaism
Dadaist
dadap
Dadayag
dadder
daddle
daddock
daddocky
daddy
daddynut
dade
dadenhudd
dado
Dadoxylon
Dadu
daduchus
Dadupanthi
dae
Daedal
daedal
Daedalea
Daedalean
Daedalian
Daedalic
Daedalidae
Daedalist
daedaloid
Daedalus
daemon
Daemonelix
daemonic
daemonurgist
daemonurgy
daemony
daer
daff
daffery
daffing
daffish
daffle
daffodil
daffodilly
daffy
daffydowndilly
Dafla
daft
daftberry
daftlike
daftly
daftness
dag
dagaba
dagame
dagassa
Dagbamba
Dagbane
dagesh
Dagestan
dagga
dagger
daggerbush
daggered
daggerlike
daggerproof
daggers
daggle
daggletail
daggletailed
daggly
daggy
daghesh
daglock
Dagmar
Dago
dagoba
Dagomba
dags
Daguerrean
daguerreotype
daguerreotyper
daguerreotypic
daguerreotypist
daguerreotypy
dah
dahabeah
Dahlia
Dahoman
Dahomeyan
dahoon
Daibutsu
daidle
daidly
Daijo
daiker
daikon
Dail
Dailamite
dailiness
daily
daimen
daimiate
daimio
daimon
daimonic
daimonion
daimonistic
daimonology
dain
daincha
dainteth
daintify
daintihood
daintily
daintiness
daintith
dainty
Daira
daira
dairi
dairy
dairying
dairymaid
dairyman
dairywoman
dais
daisied
daisy
daisybush
daitya
daiva
dak
daker
Dakhini
dakir
Dakota
daktylon
daktylos
dal
dalar
Dalarnian
Dalbergia
Dalcassian
Dale
dale
Dalea
Dalecarlian
daleman
daler
dalesfolk
dalesman
dalespeople
daleswoman
daleth
dali
Dalibarda
dalk
dallack
dalle
dalles
dalliance
dallier
dally
dallying
dallyingly
Dalmania
Dalmanites
Dalmatian
Dalmatic
dalmatic
Dalradian
dalt
dalteen
Dalton
dalton
Daltonian
Daltonic
Daltonism
Daltonist
dam
dama
damage
damageability
damageable
damageableness
damageably
damagement
damager
damages
damagingly
daman
Damara
Damascene
damascene
damascened
damascener
damascenine
Damascus
damask
damaskeen
damasse
damassin
Damayanti
dambonitol
dambose
dambrod
dame
damenization
damewort
Damgalnunna
Damia
damiana
Damianist
damie
damier
damine
damkjernite
damlike
dammar
Dammara
damme
dammer
dammish
damn
damnability
damnable
damnableness
damnably
damnation
damnatory
damned
damner
damnification
damnify
Damnii
damning
damningly
damningness
damnonians
Damnonii
damnous
damnously
Damoclean
Damocles
Damoetas
damoiseau
Damon
Damone
damonico
damourite
damp
dampang
damped
dampen
dampener
damper
damping
dampish
dampishly
dampishness
damply
dampness
dampproof
dampproofer
dampproofing
dampy
damsel
damselfish
damselhood
damson
Dan
dan
Dana
Danaan
Danagla
Danai
Danaid
danaid
Danaidae
danaide
Danaidean
Danainae
danaine
Danais
danaite
Danakil
danalite
danburite
dancalite
dance
dancer
danceress
dancery
dancette
dancing
dancingly
dand
danda
dandelion
dander
dandiacal
dandiacally
dandically
dandification
dandify
dandilly
dandily
dandiprat
dandizette
dandle
dandler
dandling
dandlingly
dandruff
dandruffy
dandy
dandydom
dandyish
dandyism
dandyize
dandyling
Dane
Daneball
Daneflower
Danegeld
Danelaw
Daneweed
Danewort
dang
danger
dangerful
dangerfully
dangerless
dangerous
dangerously
dangerousness
dangersome
dangle
dangleberry
danglement
dangler
danglin
dangling
danglingly
Dani
Danian
Danic
danicism
Daniel
Daniele
Danielic
Danielle
Daniglacial
danio
Danish
Danism
Danite
Danization
Danize
dank
Dankali
dankish
dankishness
dankly
dankness
danli
Dannebrog
dannemorite
danner
Dannie
dannock
Danny
danoranja
dansant
danseuse
danta
Dantean
Dantesque
Danthonia
Dantist
Dantology
Dantomania
danton
Dantonesque
Dantonist
Dantophilist
Dantophily
Danube
Danubian
Danuri
Danzig
Danziger
dao
daoine
dap
Dapedium
Dapedius
Daphnaceae
Daphne
Daphnean
Daphnephoria
daphnetin
Daphnia
daphnin
daphnioid
Daphnis
daphnoid
dapicho
dapico
dapifer
dapper
dapperling
dapperly
dapperness
dapple
dappled
dar
darabukka
darac
daraf
Darapti
darat
darbha
darby
Darbyism
Darbyite
Darci
Dard
Dardan
dardanarius
Dardani
dardanium
dardaol
Dardic
Dardistan
dare
dareall
daredevil
daredevilism
daredevilry
daredeviltry
dareful
Daren
darer
Dares
daresay
darg
dargah
darger
Darghin
Dargo
dargsman
dargue
dari
daribah
daric
Darien
Darii
Darin
daring
daringly
daringness
dariole
Darius
Darjeeling
dark
darken
darkener
darkening
darkful
darkhearted
darkheartedness
darkish
darkishness
darkle
darkling
darklings
darkly
darkmans
darkness
darkroom
darkskin
darksome
darksomeness
darky
darling
darlingly
darlingness
Darlingtonia
darn
darnation
darned
darnel
darner
darnex
darning
daroga
daroo
darr
darrein
Darrell
Darren
Darryl
darshana
Darsonval
Darsonvalism
darst
dart
Dartagnan
dartars
dartboard
darter
darting
dartingly
dartingness
dartle
dartlike
dartman
Dartmoor
dartoic
dartoid
dartos
dartre
dartrose
dartrous
darts
dartsman
Darwinian
Darwinical
Darwinically
Darwinism
Darwinist
Darwinistic
Darwinite
Darwinize
Daryl
darzee
das
Daschagga
dash
dashboard
dashed
dashedly
dashee
dasheen
dasher
dashing
dashingly
dashmaker
Dashnak
Dashnakist
Dashnaktzutiun
dashplate
dashpot
dashwheel
dashy
dasi
Dasiphora
dasnt
dassie
dassy
dastard
dastardize
dastardliness
dastardly
dastur
dasturi
Dasya
Dasyatidae
Dasyatis
Dasycladaceae
dasycladaceous
Dasylirion
dasymeter
dasypaedal
dasypaedes
dasypaedic
Dasypeltis
dasyphyllous
Dasypodidae
dasypodoid
Dasyprocta
Dasyproctidae
dasyproctine
Dasypus
Dasystephana
dasyure
Dasyuridae
dasyurine
dasyuroid
Dasyurus
Dasyus
data
datable
datableness
datably
dataria
datary
datch
datcha
date
dateless
datemark
dater
datil
dating
dation
Datisca
Datiscaceae
datiscaceous
datiscetin
datiscin
datiscoside
Datisi
Datism
datival
dative
datively
dativogerundial
datolite
datolitic
dattock
datum
Datura
daturic
daturism
daub
daube
Daubentonia
Daubentoniidae
dauber
daubery
daubing
daubingly
daubreeite
daubreelite
daubster
dauby
Daucus
daud
daughter
daughterhood
daughterkin
daughterless
daughterlike
daughterliness
daughterling
daughterly
daughtership
Daulias
daunch
dauncy
Daunii
daunt
daunter
daunting
dauntingly
dauntingness
dauntless
dauntlessly
dauntlessness
daunton
dauphin
dauphine
dauphiness
Daur
Dauri
daut
dautie
dauw
davach
Davallia
Dave
daven
davenport
daver
daverdy
David
Davidian
Davidic
Davidical
Davidist
davidsonite
Daviesia
daviesite
davit
davoch
Davy
davy
davyne
daw
dawdle
dawdler
dawdling
dawdlingly
dawdy
dawish
dawkin
Dawn
dawn
dawning
dawnlight
dawnlike
dawnstreak
dawnward
dawny
Dawson
Dawsonia
Dawsoniaceae
dawsoniaceous
dawsonite
dawtet
dawtit
dawut
day
dayabhaga
Dayakker
dayal
daybeam
dayberry
dayblush
daybook
daybreak
daydawn
daydream
daydreamer
daydreamy
daydrudge
dayflower
dayfly
daygoing
dayless
daylight
daylit
daylong
dayman
daymare
daymark
dayroom
days
dayshine
daysman
dayspring
daystar
daystreak
daytale
daytide
daytime
daytimes
dayward
daywork
dayworker
daywrit
Daza
daze
dazed
dazedly
dazedness
dazement
dazingly
dazy
dazzle
dazzlement
dazzler
dazzlingly
de
deacetylate
deacetylation
deacidification
deacidify
deacon
deaconal
deaconate
deaconess
deaconhood
deaconize
deaconry
deaconship
deactivate
deactivation
dead
deadbeat
deadborn
deadcenter
deaden
deadener
deadening
deader
deadeye
deadfall
deadhead
deadheadism
deadhearted
deadheartedly
deadheartedness
deadhouse
deading
deadish
deadishly
deadishness
deadlatch
deadlight
deadlily
deadline
deadliness
deadlock
deadly
deadman
deadmelt
deadness
deadpan
deadpay
deadtongue
deadwood
deadwort
deaerate
deaeration
deaerator
deaf
deafen
deafening
deafeningly
deafforest
deafforestation
deafish
deafly
deafness
deair
deal
dealable
dealate
dealated
dealation
dealbate
dealbation
dealbuminize
dealcoholist
dealcoholization
dealcoholize
dealer
dealerdom
dealership
dealfish
dealing
dealkalize
dealkylate
dealkylation
dealt
deambulation
deambulatory
deamidase
deamidate
deamidation
deamidization
deamidize
deaminase
deaminate
deamination
deaminization
deaminize
deammonation
Dean
dean
deanathematize
deaner
deanery
deaness
deanimalize
deanship
deanthropomorphic
deanthropomorphism
deanthropomorphization
deanthropomorphize
deappetizing
deaquation
dear
dearborn
dearie
dearly
dearness
dearomatize
dearsenicate
dearsenicator
dearsenicize
dearth
dearthfu
dearticulation
dearworth
dearworthily
dearworthiness
deary
deash
deasil
deaspirate
deaspiration
deassimilation
death
deathbed
deathblow
deathday
deathful
deathfully
deathfulness
deathify
deathin
deathiness
deathless
deathlessly
deathlessness
deathlike
deathliness
deathling
deathly
deathroot
deathshot
deathsman
deathtrap
deathward
deathwards
deathwatch
deathweed
deathworm
deathy
deave
deavely
Deb
deb
debacle
debadge
debamboozle
debar
debarbarization
debarbarize
debark
debarkation
debarkment
debarment
debarrance
debarrass
debarration
debase
debasedness
debasement
debaser
debasingly
debatable
debate
debateful
debatefully
debatement
debater
debating
debatingly
debauch
debauched
debauchedly
debauchedness
debauchee
debaucher
debauchery
debauchment
Debbie
Debby
debby
debeige
debellate
debellation
debellator
deben
debenture
debentured
debenzolize
Debi
debile
debilissima
debilitant
debilitate
debilitated
debilitation
debilitative
debility
debind
debit
debiteuse
debituminization
debituminize
deblaterate
deblateration
deboistly
deboistness
debonair
debonaire
debonairity
debonairly
debonairness
debonnaire
Deborah
debord
debordment
debosh
deboshed
debouch
debouchment
debride
debrief
debris
debrominate
debromination
debruise
debt
debtee
debtful
debtless
debtor
debtorship
debullition
debunk
debunker
debunkment
debus
Debussyan
Debussyanize
debut
debutant
debutante
decachord
decad
decadactylous
decadal
decadally
decadarch
decadarchy
decadary
decadation
decade
decadence
decadency
decadent
decadentism
decadently
decadescent
decadianome
decadic
decadist
decadrachm
decadrachma
decaesarize
decaffeinate
decaffeinize
decafid
decagon
decagonal
decagram
decagramme
decahedral
decahedron
decahydrate
decahydrated
decahydronaphthalene
Decaisnea
decal
decalcification
decalcifier
decalcify
decalcomania
decalcomaniac
decalescence
decalescent
Decalin
decaliter
decalitre
decalobate
Decalogist
Decalogue
decalvant
decalvation
decameral
Decameron
Decameronic
decamerous
decameter
decametre
decamp
decampment
decan
decanal
decanally
decanate
decane
decangular
decani
decanically
decannulation
decanonization
decanonize
decant
decantate
decantation
decanter
decantherous
decap
decapetalous
decaphyllous
decapitable
decapitalization
decapitalize
decapitate
decapitation
decapitator
decapod
Decapoda
decapodal
decapodan
decapodiform
decapodous
decapper
decapsulate
decapsulation
decarbonate
decarbonator
decarbonization
decarbonize
decarbonized
decarbonizer
decarboxylate
decarboxylation
decarboxylization
decarboxylize
decarburation
decarburization
decarburize
decarch
decarchy
decardinalize
decare
decarhinus
decarnate
decarnated
decart
decasemic
decasepalous
decaspermal
decaspermous
decast
decastellate
decastere
decastich
decastyle
decasualization
decasualize
decasyllabic
decasyllable
decasyllabon
decate
decathlon
decatholicize
decatize
decatizer
decatoic
decator
decatyl
decaudate
decaudation
decay
decayable
decayed
decayedness
decayer
decayless
decease
deceased
decedent
deceit
deceitful
deceitfully
deceitfulness
deceivability
deceivable
deceivableness
deceivably
deceive
deceiver
deceiving
deceivingly
decelerate
deceleration
decelerator
decelerometer
December
Decemberish
Decemberly
Decembrist
decemcostate
decemdentate
decemfid
decemflorous
decemfoliate
decemfoliolate
decemjugate
decemlocular
decempartite
decempeda
decempedal
decempedate
decempennate
decemplex
decemplicate
decempunctate
decemstriate
decemuiri
decemvir
decemviral
decemvirate
decemvirship
decenary
decence
decency
decene
decennal
decennary
decennia
decenniad
decennial
decennially
decennium
decennoval
decent
decenter
decently
decentness
decentralism
decentralist
decentralization
decentralize
decentration
decentre
decenyl
decephalization
deceptibility
deceptible
deception
deceptious
deceptiously
deceptitious
deceptive
deceptively
deceptiveness
deceptivity
decerebrate
decerebration
decerebrize
decern
decerniture
decernment
decess
decession
dechemicalization
dechemicalize
dechenite
Dechlog
dechlore
dechlorination
dechoralize
dechristianization
dechristianize
Decian
deciare
deciatine
decibel
deciceronize
decidable
decide
decided
decidedly
decidedness
decider
decidingly
decidua
decidual
deciduary
Deciduata
deciduate
deciduitis
deciduoma
deciduous
deciduously
deciduousness
decigram
decigramme
decil
decile
deciliter
decillion
decillionth
decima
decimal
decimalism
decimalist
decimalization
decimalize
decimally
decimate
decimation
decimator
decimestrial
decimeter
decimolar
decimole
decimosexto
Decimus
decinormal
decipher
decipherability
decipherable
decipherably
decipherer
decipherment
decipium
decipolar
decision
decisional
decisive
decisively
decisiveness
decistere
decitizenize
Decius
decivilization
decivilize
deck
decke
decked
deckel
decker
deckhead
deckhouse
deckie
decking
deckle
deckload
deckswabber
declaim
declaimant
declaimer
declamation
declamatoriness
declamatory
declarable
declarant
declaration
declarative
declaratively
declarator
declaratorily
declaratory
declare
declared
declaredly
declaredness
declarer
declass
declassicize
declassify
declension
declensional
declensionally
declericalize
declimatize
declinable
declinal
declinate
declination
declinational
declinatory
declinature
decline
declined
declinedness
decliner
declinograph
declinometer
declivate
declive
declivitous
declivity
declivous
declutch
decoagulate
decoagulation
decoat
decocainize
decoct
decoctible
decoction
decoctive
decoctum
decode
Decodon
decohere
decoherence
decoherer
decohesion
decoic
decoke
decollate
decollated
decollation
decollator
decolletage
decollete
decolor
decolorant
decolorate
decoloration
decolorimeter
decolorization
decolorize
decolorizer
decolour
decommission
decompensate
decompensation
decomplex
decomponible
decomposability
decomposable
decompose
decomposed
decomposer
decomposite
decomposition
decomposure
decompound
decompoundable
decompoundly
decompress
decompressing
decompression
decompressive
deconcatenate
deconcentrate
deconcentration
deconcentrator
decongestive
deconsecrate
deconsecration
deconsider
deconsideration
decontaminate
decontamination
decontrol
deconventionalize
decopperization
decopperize
decorability
decorable
decorably
decorament
decorate
decorated
decoration
decorationist
decorative
decoratively
decorativeness
decorator
decoratory
decorist
decorous
decorously
decorousness
decorrugative
decorticate
decortication
decorticator
decorticosis
decorum
decostate
decoy
decoyer
decoyman
decrassify
decream
decrease
decreaseless
decreasing
decreasingly
decreation
decreative
decree
decreeable
decreement
decreer
decreet
decrement
decrementless
decremeter
decrepit
decrepitate
decrepitation
decrepitly
decrepitness
decrepitude
decrescence
decrescendo
decrescent
decretal
decretalist
decrete
decretist
decretive
decretively
decretorial
decretorily
decretory
decretum
decrew
decrial
decried
decrier
decrown
decrudescence
decrustation
decry
decrystallization
decubital
decubitus
decultivate
deculturate
decuman
decumana
decumanus
Decumaria
decumary
decumbence
decumbency
decumbent
decumbently
decumbiture
decuple
decuplet
decuria
decurion
decurionate
decurrence
decurrency
decurrent
decurrently
decurring
decursion
decursive
decursively
decurtate
decurvation
decurvature
decurve
decury
decus
decussate
decussated
decussately
decussation
decussis
decussorium
decyl
decylene
decylenic
decylic
decyne
Dedan
Dedanim
Dedanite
dedecorate
dedecoration
dedecorous
dedendum
dedentition
dedicant
dedicate
dedicatee
dedication
dedicational
dedicative
dedicator
dedicatorial
dedicatorily
dedicatory
dedicature
dedifferentiate
dedifferentiation
dedimus
deditician
dediticiancy
dedition
dedo
dedoggerelize
dedogmatize
dedolation
deduce
deducement
deducibility
deducible
deducibleness
deducibly
deducive
deduct
deductible
deduction
deductive
deductively
deductory
deduplication
dee
deed
deedbox
deedeed
deedful
deedfully
deedily
deediness
deedless
deedy
deem
deemer
deemie
deemster
deemstership
deep
deepen
deepener
deepening
deepeningly
Deepfreeze
deeping
deepish
deeplier
deeply
deepmost
deepmouthed
deepness
deepsome
deepwater
deepwaterman
deer
deerberry
deerdog
deerdrive
deerfood
deerhair
deerherd
deerhorn
deerhound
deerlet
deermeat
deerskin
deerstalker
deerstalking
deerstand
deerstealer
deertongue
deerweed
deerwood
deeryard
deevey
deevilick
deface
defaceable
defacement
defacer
defacing
defacingly
defalcate
defalcation
defalcator
defalk
defamation
defamatory
defame
defamed
defamer
defamingly
defassa
defat
default
defaultant
defaulter
defaultless
defaulture
defeasance
defeasanced
defease
defeasibility
defeasible
defeasibleness
defeat
defeater
defeatism
defeatist
defeatment
defeature
defecant
defecate
defecation
defecator
defect
defectibility
defectible
defection
defectionist
defectious
defective
defectively
defectiveness
defectless
defectology
defector
defectoscope
defedation
defeminize
defence
defend
defendable
defendant
defender
defendress
defenestration
defensative
defense
defenseless
defenselessly
defenselessness
defensibility
defensible
defensibleness
defensibly
defension
defensive
defensively
defensiveness
defensor
defensorship
defensory
defer
deferable
deference
deferent
deferentectomy
deferential
deferentiality
deferentially
deferentitis
deferment
deferrable
deferral
deferred
deferrer
deferrization
deferrize
defervesce
defervescence
defervescent
defeudalize
defiable
defial
defiance
defiant
defiantly
defiantness
defiber
defibrinate
defibrination
defibrinize
deficience
deficiency
deficient
deficiently
deficit
defier
defiguration
defilade
defile
defiled
defiledness
defilement
defiler
defiliation
defiling
defilingly
definability
definable
definably
define
defined
definedly
definement
definer
definiendum
definiens
definite
definitely
definiteness
definition
definitional
definitiones
definitive
definitively
definitiveness
definitization
definitize
definitor
definitude
deflagrability
deflagrable
deflagrate
deflagration
deflagrator
deflate
deflation
deflationary
deflationist
deflator
deflect
deflectable
deflected
deflection
deflectionization
deflectionize
deflective
deflectometer
deflector
deflesh
deflex
deflexibility
deflexible
deflexion
deflexure
deflocculant
deflocculate
deflocculation
deflocculator
deflorate
defloration
deflorescence
deflower
deflowerer
defluent
defluous
defluvium
defluxion
defoedation
defog
defoliage
defoliate
defoliated
defoliation
defoliator
deforce
deforcement
deforceor
deforcer
deforciant
deforest
deforestation
deforester
deform
deformability
deformable
deformalize
deformation
deformational
deformative
deformed
deformedly
deformedness
deformer
deformeter
deformism
deformity
defortify
defoul
defraud
defraudation
defrauder
defraudment
defray
defrayable
defrayal
defrayer
defrayment
defreeze
defrication
defrock
defrost
defroster
deft
defterdar
deftly
deftness
defunct
defunction
defunctionalization
defunctionalize
defunctness
defuse
defusion
defy
defyingly
deg
deganglionate
degarnish
degas
degasification
degasifier
degasify
degasser
degauss
degelatinize
degelation
degeneracy
degeneralize
degenerate
degenerately
degenerateness
degeneration
degenerationist
degenerative
degenerescence
degenerescent
degentilize
degerm
degerminate
degerminator
degged
degger
deglaciation
deglaze
deglutinate
deglutination
deglutition
deglutitious
deglutitive
deglutitory
deglycerin
deglycerine
degorge
degradable
degradand
degradation
degradational
degradative
degrade
degraded
degradedly
degradedness
degradement
degrader
degrading
degradingly
degradingness
degraduate
degraduation
degrain
degrease
degreaser
degree
degreeless
degreewise
degression
degressive
degressively
degu
Deguelia
deguelin
degum
degummer
degust
degustation
dehair
dehairer
Dehaites
deheathenize
dehematize
dehepatize
Dehgan
dehisce
dehiscence
dehiscent
dehistoricize
Dehkan
dehnstufe
dehonestate
dehonestation
dehorn
dehorner
dehors
dehort
dehortation
dehortative
dehortatory
dehorter
dehull
dehumanization
dehumanize
dehumidification
dehumidifier
dehumidify
dehusk
Dehwar
dehydrant
dehydrase
dehydrate
dehydration
dehydrator
dehydroascorbic
dehydrocorydaline
dehydrofreezing
dehydrogenase
dehydrogenate
dehydrogenation
dehydrogenization
dehydrogenize
dehydromucic
dehydrosparteine
dehypnotize
deice
deicer
deicidal
deicide
deictic
deictical
deictically
deidealize
Deidesheimer
deific
deifical
deification
deificatory
deifier
deiform
deiformity
deify
deign
Deimos
deincrustant
deindividualization
deindividualize
deindividuate
deindustrialization
deindustrialize
deink
Deino
Deinocephalia
Deinoceras
Deinodon
Deinodontidae
deinos
Deinosauria
Deinotherium
deinsularize
deintellectualization
deintellectualize
deionize
Deipara
deiparous
Deiphobus
deipnodiplomatic
deipnophobia
deipnosophism
deipnosophist
deipnosophistic
deipotent
Deirdre
deiseal
deisidaimonia
deism
deist
deistic
deistical
deistically
deisticalness
deity
deityship
deject
dejecta
dejected
dejectedly
dejectedness
dejectile
dejection
dejectly
dejectory
dejecture
dejerate
dejeration
dejerator
dejeune
dejeuner
dejunkerize
Dekabrist
dekaparsec
dekapode
dekko
dekle
deknight
Del
delabialization
delabialize
delacrimation
delactation
delaine
delaminate
delamination
delapse
delapsion
delate
delater
delatinization
delatinize
delation
delator
delatorian
Delaware
Delawarean
delawn
delay
delayable
delayage
delayer
delayful
delaying
delayingly
Delbert
dele
delead
delectability
delectable
delectableness
delectably
delectate
delectation
delectus
delegable
delegacy
delegalize
delegant
delegate
delegatee
delegateship
delegation
delegative
delegator
delegatory
delenda
Delesseria
Delesseriaceae
delesseriaceous
delete
deleterious
deleteriously
deleteriousness
deletion
deletive
deletory
delf
delft
delftware
Delhi
Delia
Delian
deliberalization
deliberalize
deliberant
deliberate
deliberately
deliberateness
deliberation
deliberative
deliberatively
deliberativeness
deliberator
delible
delicacy
delicate
delicately
delicateness
delicatesse
delicatessen
delicense
Delichon
delicioso
Delicious
delicious
deliciously
deliciousness
delict
delictum
deligated
deligation
delight
delightable
delighted
delightedly
delightedness
delighter
delightful
delightfully
delightfulness
delighting
delightingly
delightless
delightsome
delightsomely
delightsomeness
delignate
delignification
Delilah
delime
delimit
delimitate
delimitation
delimitative
delimiter
delimitize
delineable
delineament
delineate
delineation
delineative
delineator
delineatory
delineature
delinquence
delinquency
delinquent
delinquently
delint
delinter
deliquesce
deliquescence
deliquescent
deliquium
deliracy
delirament
deliration
deliriant
delirifacient
delirious
deliriously
deliriousness
delirium
delitescence
delitescency
delitescent
deliver
deliverable
deliverance
deliverer
deliveress
deliveror
delivery
deliveryman
dell
Della
dellenite
Delobranchiata
delocalization
delocalize
delomorphic
delomorphous
deloul
delouse
delphacid
Delphacidae
Delphian
Delphin
Delphinapterus
delphine
delphinic
Delphinid
Delphinidae
delphinin
delphinine
delphinite
Delphinium
Delphinius
delphinoid
Delphinoidea
delphinoidine
Delphinus
delphocurarine
Delsarte
Delsartean
Delsartian
Delta
delta
deltafication
deltaic
deltal
deltarium
deltation
delthyrial
delthyrium
deltic
deltidial
deltidium
deltiology
deltohedron
deltoid
deltoidal
delubrum
deludable
delude
deluder
deludher
deluding
deludingly
deluge
deluminize
delundung
delusion
delusional
delusionist
delusive
delusively
delusiveness
delusory
deluster
deluxe
delve
delver
demagnetizable
demagnetization
demagnetize
demagnetizer
demagog
demagogic
demagogical
demagogically
demagogism
demagogue
demagoguery
demagogy
demal
demand
demandable
demandant
demander
demanding
demandingly
demanganization
demanganize
demantoid
demarcate
demarcation
demarcator
demarch
demarchy
demargarinate
demark
demarkation
demast
dematerialization
dematerialize
Dematiaceae
dematiaceous
deme
demean
demeanor
demegoric
demency
dement
dementate
dementation
demented
dementedly
dementedness
dementholize
dementia
demephitize
demerit
demeritorious
demeritoriously
Demerol
demersal
demersed
demersion
demesman
demesmerize
demesne
demesnial
demetallize
demethylate
demethylation
Demetrian
demetricize
demi
demiadult
demiangel
demiassignation
demiatheism
demiatheist
demibarrel
demibastion
demibastioned
demibath
demibeast
demibelt
demibob
demibombard
demibrassart
demibrigade
demibrute
demibuckram
demicadence
demicannon
demicanon
demicanton
demicaponier
demichamfron
demicircle
demicircular
demicivilized
demicolumn
demicoronal
demicritic
demicuirass
demiculverin
demicylinder
demicylindrical
demidandiprat
demideify
demideity
demidevil
demidigested
demidistance
demiditone
demidoctor
demidog
demidolmen
demidome
demieagle
demifarthing
demifigure
demiflouncing
demifusion
demigardebras
demigauntlet
demigentleman
demiglobe
demigod
demigoddess
demigoddessship
demigorge
demigriffin
demigroat
demihag
demihearse
demiheavenly
demihigh
demihogshead
demihorse
demihuman
demijambe
demijohn
demikindred
demiking
demilance
demilancer
demilawyer
demilegato
demilion
demilitarization
demilitarize
demiliterate
demilune
demiluster
demilustre
demiman
demimark
demimentoniere
demimetope
demimillionaire
demimondaine
demimonde
demimonk
deminatured
demineralization
demineralize
deminude
deminudity
demioctagonal
demioctangular
demiofficial
demiorbit
demiourgoi
demiowl
demiox
demipagan
demiparallel
demipauldron
demipectinate
demipesade
demipike
demipillar
demipique
demiplacate
demiplate
demipomada
demipremise
demipremiss
demipriest
demipronation
demipuppet
demiquaver
demiracle
demiram
demirelief
demirep
demirevetment
demirhumb
demirilievo
demirobe
demisability
demisable
demisacrilege
demisang
demisangue
demisavage
demise
demiseason
demisecond
demisemiquaver
demisemitone
demisheath
demishirt
demisovereign
demisphere
demiss
demission
demissionary
demissly
demissness
demissory
demisuit
demit
demitasse
demitint
demitoilet
demitone
demitrain
demitranslucence
demitube
demiturned
demiurge
demiurgeous
demiurgic
demiurgical
demiurgically
demiurgism
demivambrace
demivirgin
demivoice
demivol
demivolt
demivotary
demiwivern
demiwolf
demnition
demob
demobilization
demobilize
democracy
democrat
democratian
democratic
democratical
democratically
democratifiable
democratism
democratist
democratization
democratize
demodectic
demoded
Demodex
Demodicidae
Demodocus
demodulation
demodulator
demogenic
Demogorgon
demographer
demographic
demographical
demographically
demographist
demography
demoid
demoiselle
demolish
demolisher
demolishment
demolition
demolitionary
demolitionist
demological
demology
Demon
demon
demonastery
demoness
demonetization
demonetize
demoniac
demoniacal
demoniacally
demoniacism
demonial
demonian
demonianism
demoniast
demonic
demonical
demonifuge
demonish
demonism
demonist
demonize
demonkind
demonland
demonlike
demonocracy
demonograph
demonographer
demonography
demonolater
demonolatrous
demonolatrously
demonolatry
demonologer
demonologic
demonological
demonologically
demonologist
demonology
demonomancy
demonophobia
demonry
demonship
demonstrability
demonstrable
demonstrableness
demonstrably
demonstrant
demonstratable
demonstrate
demonstratedly
demonstrater
demonstration
demonstrational
demonstrationist
demonstrative
demonstratively
demonstrativeness
demonstrator
demonstratorship
demonstratory
demophil
demophilism
demophobe
Demophon
Demophoon
demoralization
demoralize
demoralizer
demorphinization
demorphism
demos
Demospongiae
Demosthenean
Demosthenic
demote
demotic
demotics
demotion
demotist
demount
demountability
demountable
dempster
demulce
demulcent
demulsibility
demulsify
demulsion
demure
demurely
demureness
demurity
demurrable
demurrage
demurral
demurrant
demurrer
demurring
demurringly
demutization
demy
demyship
den
denarcotization
denarcotize
denarius
denaro
denary
denat
denationalization
denationalize
denaturalization
denaturalize
denaturant
denaturate
denaturation
denature
denaturization
denaturize
denaturizer
denazify
denda
dendrachate
dendral
Dendraspis
dendraxon
dendric
dendriform
dendrite
Dendrites
dendritic
dendritical
dendritically
dendritiform
Dendrium
Dendrobates
Dendrobatinae
dendrobe
Dendrobium
Dendrocalamus
Dendroceratina
dendroceratine
Dendrochirota
dendrochronological
dendrochronologist
dendrochronology
dendroclastic
Dendrocoela
dendrocoelan
dendrocoele
dendrocoelous
Dendrocolaptidae
dendrocolaptine
Dendroctonus
Dendrocygna
dendrodont
Dendrodus
Dendroeca
Dendrogaea
Dendrogaean
dendrograph
dendrography
Dendrohyrax
Dendroica
dendroid
dendroidal
Dendroidea
Dendrolagus
dendrolatry
Dendrolene
dendrolite
dendrologic
dendrological
dendrologist
dendrologous
dendrology
Dendromecon
dendrometer
dendron
dendrophil
dendrophile
dendrophilous
Dendropogon
Dene
dene
Deneb
Denebola
denegate
denegation
denehole
denervate
denervation
deneutralization
dengue
deniable
denial
denicotinize
denier
denierage
denierer
denigrate
denigration
denigrator
denim
Denis
denitrate
denitration
denitrator
denitrificant
denitrification
denitrificator
denitrifier
denitrify
denitrize
denization
denizen
denizenation
denizenize
denizenship
Denmark
dennet
Dennis
Dennstaedtia
denominable
denominate
denomination
denominational
denominationalism
denominationalist
denominationalize
denominationally
denominative
denominatively
denominator
denotable
denotation
denotative
denotatively
denotativeness
denotatum
denote
denotement
denotive
denouement
denounce
denouncement
denouncer
dense
densely
densen
denseness
denshare
densher
denshire
densification
densifier
densify
densimeter
densimetric
densimetrically
densimetry
densitometer
density
dent
dentagra
dental
dentale
dentalgia
Dentaliidae
dentalism
dentality
Dentalium
dentalization
dentalize
dentally
dentaphone
Dentaria
dentary
dentata
dentate
dentated
dentately
dentation
dentatoangulate
dentatocillitate
dentatocostate
dentatocrenate
dentatoserrate
dentatosetaceous
dentatosinuate
dentel
dentelated
dentelle
dentelure
denter
dentex
dentical
denticate
Denticeti
denticle
denticular
denticulate
denticulately
denticulation
denticule
dentiferous
dentification
dentiform
dentifrice
dentigerous
dentil
dentilabial
dentilated
dentilation
dentile
dentilingual
dentiloquist
dentiloquy
dentimeter
dentin
dentinal
dentinalgia
dentinasal
dentine
dentinitis
dentinoblast
dentinocemental
dentinoid
dentinoma
dentiparous
dentiphone
dentiroster
dentirostral
dentirostrate
Dentirostres
dentiscalp
dentist
dentistic
dentistical
dentistry
dentition
dentoid
dentolabial
dentolingual
dentonasal
dentosurgical
dentural
denture
denty
denucleate
denudant
denudate
denudation
denudative
denude
denuder
denumerable
denumerably
denumeral
denumerant
denumerantive
denumeration
denumerative
denunciable
denunciant
denunciate
denunciation
denunciative
denunciatively
denunciator
denunciatory
denutrition
deny
denyingly
deobstruct
deobstruent
deoccidentalize
deoculate
deodand
deodara
deodorant
deodorization
deodorize
deodorizer
deontological
deontologist
deontology
deoperculate
deoppilant
deoppilate
deoppilation
deoppilative
deordination
deorganization
deorganize
deorientalize
deorsumvergence
deorsumversion
deorusumduction
deossification
deossify
deota
deoxidant
deoxidate
deoxidation
deoxidative
deoxidator
deoxidization
deoxidize
deoxidizer
deoxygenate
deoxygenation
deoxygenization
deozonization
deozonize
deozonizer
depa
depaganize
depaint
depancreatization
depancreatize
depark
deparliament
depart
departed
departer
departisanize
departition
department
departmental
departmentalism
departmentalization
departmentalize
departmentally
departmentization
departmentize
departure
depas
depascent
depass
depasturable
depasturage
depasturation
depasture
depatriate
depauperate
depauperation
depauperization
depauperize
depencil
depend
dependability
dependable
dependableness
dependably
dependence
dependency
dependent
dependently
depender
depending
dependingly
depeople
deperdite
deperditely
deperition
depersonalization
depersonalize
depersonize
depetalize
depeter
depetticoat
dephase
dephilosophize
dephlegmate
dephlegmation
dephlegmatize
dephlegmator
dephlegmatory
dephlegmedness
dephlogisticate
dephlogisticated
dephlogistication
dephosphorization
dephosphorize
dephysicalization
dephysicalize
depickle
depict
depicter
depiction
depictive
depicture
depiedmontize
depigment
depigmentate
depigmentation
depigmentize
depilate
depilation
depilator
depilatory
depilitant
depilous
deplaceable
deplane
deplasmolysis
deplaster
deplenish
deplete
deplethoric
depletion
depletive
depletory
deploitation
deplorability
deplorable
deplorableness
deplorably
deploration
deplore
deplored
deploredly
deploredness
deplorer
deploringly
deploy
deployment
deplumate
deplumated
deplumation
deplume
deplump
depoetize
depoh
depolarization
depolarize
depolarizer
depolish
depolishing
depolymerization
depolymerize
depone
deponent
depopularize
depopulate
depopulation
depopulative
depopulator
deport
deportable
deportation
deportee
deporter
deportment
deposable
deposal
depose
deposer
deposit
depositary
depositation
depositee
deposition
depositional
depositive
depositor
depository
depositum
depositure
depot
depotentiate
depotentiation
depravation
deprave
depraved
depravedly
depravedness
depraver
depravingly
depravity
deprecable
deprecate
deprecatingly
deprecation
deprecative
deprecator
deprecatorily
deprecatoriness
deprecatory
depreciable
depreciant
depreciate
depreciatingly
depreciation
depreciative
depreciatively
depreciator
depreciatoriness
depreciatory
depredate
depredation
depredationist
depredator
depredatory
depress
depressant
depressed
depressibility
depressible
depressing
depressingly
depressingness
depression
depressive
depressively
depressiveness
depressomotor
depressor
depreter
deprint
depriorize
deprivable
deprival
deprivate
deprivation
deprivative
deprive
deprivement
depriver
deprovincialize
depside
depth
depthen
depthing
depthless
depthometer
depthwise
depullulation
depurant
depurate
depuration
depurative
depurator
depuratory
depursement
deputable
deputation
deputational
deputationist
deputationize
deputative
deputatively
deputator
depute
deputize
deputy
deputyship
dequeen
derabbinize
deracialize
deracinate
deracination
deradelphus
deradenitis
deradenoncus
derah
deraign
derail
derailer
derailment
derange
derangeable
deranged
derangement
deranger
derat
derate
derater
derationalization
derationalize
deratization
deray
Derbend
Derby
derby
derbylite
dere
deregister
deregulationize
dereism
dereistic
dereistically
Derek
derelict
dereliction
derelictly
derelictness
dereligion
dereligionize
derencephalocele
derencephalus
deresinate
deresinize
deric
deride
derider
deridingly
Deringa
Deripia
derisible
derision
derisive
derisively
derisiveness
derisory
derivability
derivable
derivably
derival
derivant
derivate
derivately
derivation
derivational
derivationally
derivationist
derivatist
derivative
derivatively
derivativeness
derive
derived
derivedly
derivedness
deriver
derm
derma
Dermacentor
dermad
dermahemia
dermal
dermalgia
dermalith
dermamyiasis
dermanaplasty
dermapostasis
Dermaptera
dermapteran
dermapterous
dermaskeleton
dermasurgery
dermatagra
dermatalgia
dermataneuria
dermatatrophia
dermatauxe
dermathemia
dermatic
dermatine
dermatitis
Dermatobia
dermatocele
dermatocellulitis
dermatoconiosis
Dermatocoptes
dermatocoptic
dermatocyst
dermatodynia
dermatogen
dermatoglyphics
dermatograph
dermatographia
dermatography
dermatoheteroplasty
dermatoid
dermatological
dermatologist
dermatology
dermatolysis
dermatoma
dermatome
dermatomere
dermatomic
dermatomuscular
dermatomyces
dermatomycosis
dermatomyoma
dermatoneural
dermatoneurology
dermatoneurosis
dermatonosus
dermatopathia
dermatopathic
dermatopathology
dermatopathophobia
Dermatophagus
dermatophobia
dermatophone
dermatophony
dermatophyte
dermatophytic
dermatophytosis
dermatoplasm
dermatoplast
dermatoplastic
dermatoplasty
dermatopnagic
dermatopsy
Dermatoptera
dermatoptic
dermatorrhagia
dermatorrhea
dermatorrhoea
dermatosclerosis
dermatoscopy
dermatosis
dermatoskeleton
dermatotherapy
dermatotome
dermatotomy
dermatotropic
dermatoxerasia
dermatozoon
dermatozoonosis
dermatrophia
dermatrophy
dermenchysis
Dermestes
dermestid
Dermestidae
dermestoid
dermic
dermis
dermitis
dermoblast
Dermobranchia
dermobranchiata
dermobranchiate
Dermochelys
dermochrome
dermococcus
dermogastric
dermographia
dermographic
dermographism
dermography
dermohemal
dermohemia
dermohumeral
dermoid
dermoidal
dermoidectomy
dermol
dermolysis
dermomuscular
dermomycosis
dermoneural
dermoneurosis
dermonosology
dermoosseous
dermoossification
dermopathic
dermopathy
dermophlebitis
dermophobe
dermophyte
dermophytic
dermoplasty
Dermoptera
dermopteran
dermopterous
dermoreaction
Dermorhynchi
dermorhynchous
dermosclerite
dermoskeletal
dermoskeleton
dermostenosis
dermostosis
dermosynovitis
dermotropic
dermovaccine
dermutation
dern
dernier
derodidymus
derogate
derogately
derogation
derogative
derogatively
derogator
derogatorily
derogatoriness
derogatory
Derotrema
Derotremata
derotremate
derotrematous
derotreme
derout
Derrick
derrick
derricking
derrickman
derride
derries
derringer
Derris
derry
dertrotheca
dertrum
deruinate
deruralize
derust
dervish
dervishhood
dervishism
dervishlike
desaccharification
desacralization
desacralize
desalt
desamidization
desand
desaturate
desaturation
desaurin
descale
descant
descanter
descantist
descend
descendable
descendance
descendant
descendence
descendent
descendental
descendentalism
descendentalist
descendentalistic
descender
descendibility
descendible
descending
descendingly
descension
descensional
descensionist
descensive
descent
Deschampsia
descloizite
descort
describability
describable
describably
describe
describer
descrier
descript
description
descriptionist
descriptionless
descriptive
descriptively
descriptiveness
descriptory
descrive
descry
deseasonalize
desecrate
desecrater
desecration
desectionalize
deseed
desegmentation
desegmented
desensitization
desensitize
desensitizer
desentimentalize
deseret
desert
deserted
desertedly
desertedness
deserter
desertful
desertfully
desertic
deserticolous
desertion
desertism
desertless
desertlessly
desertlike
desertness
desertress
desertrice
desertward
deserve
deserved
deservedly
deservedness
deserveless
deserver
deserving
deservingly
deservingness
desex
desexualization
desexualize
deshabille
desi
desiccant
desiccate
desiccation
desiccative
desiccator
desiccatory
desiderant
desiderata
desiderate
desideration
desiderative
desideratum
desight
desightment
design
designable
designate
designation
designative
designator
designatory
designatum
designed
designedly
designedness
designee
designer
designful
designfully
designfulness
designing
designingly
designless
designlessly
designlessness
desilicate
desilicification
desilicify
desiliconization
desiliconize
desilver
desilverization
desilverize
desilverizer
desinence
desinent
desiodothyroxine
desipience
desipiency
desipient
desirability
desirable
desirableness
desirably
desire
desired
desiredly
desiredness
desireful
desirefulness
desireless
desirer
desiringly
desirous
desirously
desirousness
desist
desistance
desistive
desition
desize
desk
desklike
deslime
desma
desmachymatous
desmachyme
desmacyte
desman
Desmanthus
Desmarestia
Desmarestiaceae
desmarestiaceous
Desmatippus
desmectasia
desmepithelium
desmic
desmid
Desmidiaceae
desmidiaceous
Desmidiales
desmidiologist
desmidiology
desmine
desmitis
desmocyte
desmocytoma
Desmodactyli
Desmodium
desmodont
Desmodontidae
Desmodus
desmodynia
desmogen
desmogenous
Desmognathae
desmognathism
desmognathous
desmography
desmohemoblast
desmoid
desmology
desmoma
Desmomyaria
desmon
Desmoncus
desmoneoplasm
desmonosology
desmopathologist
desmopathology
desmopathy
desmopelmous
desmopexia
desmopyknosis
desmorrhexis
Desmoscolecidae
Desmoscolex
desmosis
desmosite
Desmothoraca
desmotomy
desmotrope
desmotropic
desmotropism
desocialization
desocialize
desolate
desolately
desolateness
desolater
desolating
desolatingly
desolation
desolative
desonation
desophisticate
desophistication
desorption
desoxalate
desoxyanisoin
desoxybenzoin
desoxycinchonine
desoxycorticosterone
desoxymorphine
desoxyribonucleic
despair
despairer
despairful
despairfully
despairfulness
despairing
despairingly
despairingness
despecialization
despecialize
despecificate
despecification
despect
desperacy
desperado
desperadoism
desperate
desperately
desperateness
desperation
despicability
despicable
despicableness
despicably
despiritualization
despiritualize
despisable
despisableness
despisal
despise
despisedness
despisement
despiser
despisingly
despite
despiteful
despitefully
despitefulness
despiteous
despiteously
despoil
despoiler
despoilment
despoliation
despond
despondence
despondency
despondent
despondently
desponder
desponding
despondingly
despot
despotat
Despotes
despotic
despotically
despoticalness
despoticly
despotism
despotist
despotize
despumate
despumation
desquamate
desquamation
desquamative
desquamatory
dess
dessa
dessert
dessertspoon
dessertspoonful
dessiatine
dessil
destabilize
destain
destandardize
desterilization
desterilize
destinate
destination
destine
destinezite
destinism
destinist
destiny
destitute
destitutely
destituteness
destitution
destour
destress
destrier
destroy
destroyable
destroyer
destroyingly
destructibility
destructible
destructibleness
destruction
destructional
destructionism
destructionist
destructive
destructively
destructiveness
destructivism
destructivity
destructor
destructuralize
desubstantiate
desucration
desuete
desuetude
desugar
desugarize
Desulfovibrio
desulphur
desulphurate
desulphuration
desulphurization
desulphurize
desulphurizer
desultor
desultorily
desultoriness
desultorious
desultory
desuperheater
desyatin
desyl
desynapsis
desynaptic
desynonymization
desynonymize
detach
detachability
detachable
detachableness
detachably
detached
detachedly
detachedness
detacher
detachment
detail
detailed
detailedly
detailedness
detailer
detailism
detailist
detain
detainable
detainal
detainer
detainingly
detainment
detar
detassel
detax
detect
detectability
detectable
detectably
detectaphone
detecter
detectible
detection
detective
detectivism
detector
detenant
detent
detention
detentive
deter
deterge
detergence
detergency
detergent
detergible
deteriorate
deterioration
deteriorationist
deteriorative
deteriorator
deteriorism
deteriority
determent
determinability
determinable
determinableness
determinably
determinacy
determinant
determinantal
determinate
determinately
determinateness
determination
determinative
determinatively
determinativeness
determinator
determine
determined
determinedly
determinedness
determiner
determinism
determinist
deterministic
determinoid
deterrence
deterrent
detersion
detersive
detersively
detersiveness
detest
detestability
detestable
detestableness
detestably
detestation
detester
dethronable
dethrone
dethronement
dethroner
dethyroidism
detin
detinet
detinue
detonable
detonate
detonation
detonative
detonator
detorsion
detour
detoxicant
detoxicate
detoxication
detoxicator
detoxification
detoxify
detract
detracter
detractingly
detraction
detractive
detractively
detractiveness
detractor
detractory
detractress
detrain
detrainment
detribalization
detribalize
detriment
detrimental
detrimentality
detrimentally
detrimentalness
detrital
detrited
detrition
detritus
Detroiter
detrude
detruncate
detruncation
detrusion
detrusive
detrusor
detubation
detumescence
detune
detur
deuce
deuced
deucedly
deul
deurbanize
deutencephalic
deutencephalon
deuteragonist
deuteranomal
deuteranomalous
deuteranope
deuteranopia
deuteranopic
deuteric
deuteride
deuterium
deuteroalbumose
deuterocanonical
deuterocasease
deuterocone
deuteroconid
deuterodome
deuteroelastose
deuterofibrinose
deuterogamist
deuterogamy
deuterogelatose
deuterogenic
deuteroglobulose
deuteromorphic
Deuteromycetes
deuteromyosinose
deuteron
Deuteronomic
Deuteronomical
Deuteronomist
Deuteronomistic
Deuteronomy
deuteropathic
deuteropathy
deuteroplasm
deuteroprism
deuteroproteose
deuteroscopic
deuteroscopy
deuterostoma
Deuterostomata
deuterostomatous
deuterotokous
deuterotoky
deuterotype
deuterovitellose
deuterozooid
deutobromide
deutocarbonate
deutochloride
deutomala
deutomalal
deutomalar
deutomerite
deuton
deutonephron
deutonymph
deutonymphal
deutoplasm
deutoplasmic
deutoplastic
deutoscolex
deutoxide
Deutzia
dev
deva
devachan
devadasi
devall
devaloka
devalorize
devaluate
devaluation
devalue
devance
devaporate
devaporation
devast
devastate
devastating
devastatingly
devastation
devastative
devastator
devastavit
devaster
devata
develin
develop
developability
developable
developedness
developer
developist
development
developmental
developmentalist
developmentally
developmentarian
developmentary
developmentist
developoid
devertebrated
devest
deviability
deviable
deviancy
deviant
deviate
deviation
deviationism
deviationist
deviative
deviator
deviatory
device
deviceful
devicefully
devicefulness
devil
devilbird
devildom
deviled
deviler
deviless
devilet
devilfish
devilhood
deviling
devilish
devilishly
devilishness
devilism
devilize
devilkin
devillike
devilman
devilment
devilmonger
devilry
devilship
deviltry
devilward
devilwise
devilwood
devily
devious
deviously
deviousness
devirginate
devirgination
devirginator
devirilize
devisable
devisal
deviscerate
devisceration
devise
devisee
deviser
devisor
devitalization
devitalize
devitalized
devitaminize
devitrification
devitrify
devocalization
devocalize
devoice
devoid
devoir
devolatilize
devolute
devolution
devolutionary
devolutionist
devolve
devolvement
Devon
Devonian
Devonic
devonite
devonport
devonshire
devorative
devote
devoted
devotedly
devotedness
devotee
devoteeism
devotement
devoter
devotion
devotional
devotionalism
devotionalist
devotionality
devotionally
devotionalness
devotionate
devotionist
devour
devourable
devourer
devouress
devouring
devouringly
devouringness
devourment
devout
devoutless
devoutlessly
devoutlessness
devoutly
devoutness
devow
devulcanization
devulcanize
devulgarize
devvel
dew
dewan
dewanee
dewanship
dewater
dewaterer
dewax
dewbeam
dewberry
dewclaw
dewclawed
dewcup
dewdamp
dewdrop
dewdropper
dewer
Dewey
deweylite
dewfall
dewflower
dewily
dewiness
dewlap
dewlapped
dewless
dewlight
dewlike
dewool
deworm
dewret
dewtry
dewworm
dewy
dexiocardia
dexiotrope
dexiotropic
dexiotropism
dexiotropous
Dexter
dexter
dexterical
dexterity
dexterous
dexterously
dexterousness
dextrad
dextral
dextrality
dextrally
dextran
dextraural
dextrin
dextrinase
dextrinate
dextrinize
dextrinous
dextro
dextroaural
dextrocardia
dextrocardial
dextrocerebral
dextrocular
dextrocularity
dextroduction
dextroglucose
dextrogyrate
dextrogyration
dextrogyratory
dextrogyrous
dextrolactic
dextrolimonene
dextropinene
dextrorotary
dextrorotatary
dextrorotation
dextrorsal
dextrorse
dextrorsely
dextrosazone
dextrose
dextrosinistral
dextrosinistrally
dextrosuria
dextrotartaric
dextrotropic
dextrotropous
dextrous
dextrously
dextrousness
dextroversion
dey
deyhouse
deyship
deywoman
Dezaley
dezinc
dezincation
dezincification
dezincify
dezymotize
dha
dhabb
dhai
dhak
dhamnoo
dhan
dhangar
dhanuk
dhanush
Dhanvantari
dharana
dharani
dharma
dharmakaya
dharmashastra
dharmasmriti
dharmasutra
dharmsala
dharna
dhaura
dhauri
dhava
dhaw
Dheneb
dheri
dhobi
dhole
dhoni
dhoon
dhoti
dhoul
dhow
Dhritarashtra
dhu
dhunchee
dhunchi
Dhundia
dhurra
dhyal
dhyana
di
diabase
diabasic
diabetes
diabetic
diabetogenic
diabetogenous
diabetometer
diablerie
diabolarch
diabolarchy
diabolatry
diabolepsy
diaboleptic
diabolic
diabolical
diabolically
diabolicalness
diabolification
diabolify
diabolism
diabolist
diabolization
diabolize
diabological
diabology
diabolology
diabrosis
diabrotic
Diabrotica
diacanthous
diacaustic
diacetamide
diacetate
diacetic
diacetin
diacetine
diacetonuria
diaceturia
diacetyl
diacetylene
diachoretic
diachronic
diachylon
diachylum
diacid
diacipiperazine
diaclase
diaclasis
diaclastic
diacle
diaclinal
diacodion
diacoele
diacoelia
diaconal
diaconate
diaconia
diaconicon
diaconicum
diacope
diacranterian
diacranteric
diacrisis
diacritic
diacritical
diacritically
Diacromyodi
diacromyodian
diact
diactin
diactinal
diactinic
diactinism
Diadelphia
diadelphian
diadelphic
diadelphous
diadem
Diadema
Diadematoida
diaderm
diadermic
diadoche
Diadochi
Diadochian
diadochite
diadochokinesia
diadochokinetic
diadromous
diadumenus
diaene
diaereses
diaeresis
diaeretic
diaetetae
diagenesis
diagenetic
diageotropic
diageotropism
diaglyph
diaglyphic
diagnosable
diagnose
diagnoseable
diagnoses
diagnosis
diagnostic
diagnostically
diagnosticate
diagnostication
diagnostician
diagnostics
diagometer
diagonal
diagonality
diagonalize
diagonally
diagonalwise
diagonic
diagram
diagrammatic
diagrammatical
diagrammatician
diagrammatize
diagrammeter
diagrammitically
diagraph
diagraphic
diagraphical
diagraphics
diagredium
diagrydium
Diaguitas
Diaguite
diaheliotropic
diaheliotropically
diaheliotropism
diakinesis
dial
dialcohol
dialdehyde
dialect
dialectal
dialectalize
dialectally
dialectic
dialectical
dialectically
dialectician
dialecticism
dialecticize
dialectics
dialectologer
dialectological
dialectologist
dialectology
dialector
dialer
dialin
dialing
dialist
Dialister
dialkyl
dialkylamine
diallage
diallagic
diallagite
diallagoid
diallel
diallelon
diallelus
diallyl
dialogic
dialogical
dialogically
dialogism
dialogist
dialogistic
dialogistical
dialogistically
dialogite
dialogize
dialogue
dialoguer
Dialonian
dialuric
dialycarpous
Dialypetalae
dialypetalous
dialyphyllous
dialysepalous
dialysis
dialystaminous
dialystelic
dialystely
dialytic
dialytically
dialyzability
dialyzable
dialyzate
dialyzation
dialyzator
dialyze
dialyzer
diamagnet
diamagnetic
diamagnetically
diamagnetism
diamantiferous
diamantine
diamantoid
diamb
diambic
diamesogamous
diameter
diametral
diametrally
diametric
diametrical
diametrically
diamicton
diamide
diamidogen
diamine
diaminogen
diaminogene
diammine
diamminobromide
diamminonitrate
diammonium
diamond
diamondback
diamonded
diamondiferous
diamondize
diamondlike
diamondwise
diamondwork
diamorphine
diamylose
Dian
dian
Diana
Diancecht
diander
Diandria
diandrian
diandrous
Diane
dianetics
Dianil
dianilid
dianilide
dianisidin
dianisidine
dianite
dianodal
dianoetic
dianoetical
dianoetically
Dianthaceae
Dianthera
Dianthus
diapalma
diapase
diapasm
diapason
diapasonal
diapause
diapedesis
diapedetic
Diapensia
Diapensiaceae
diapensiaceous
diapente
diaper
diapering
diaphane
diaphaneity
diaphanie
diaphanometer
diaphanometric
diaphanometry
diaphanoscope
diaphanoscopy
diaphanotype
diaphanous
diaphanously
diaphanousness
diaphany
diaphone
diaphonia
diaphonic
diaphonical
diaphony
diaphoresis
diaphoretic
diaphoretical
diaphorite
diaphote
diaphototropic
diaphototropism
diaphragm
diaphragmal
diaphragmatic
diaphragmatically
diaphtherin
diaphysial
diaphysis
diaplasma
diaplex
diaplexal
diaplexus
diapnoic
diapnotic
diapophysial
diapophysis
Diaporthe
diapositive
diapsid
Diapsida
diapsidan
diapyesis
diapyetic
diarch
diarchial
diarchic
diarchy
diarhemia
diarial
diarian
diarist
diaristic
diarize
diarrhea
diarrheal
diarrheic
diarrhetic
diarsenide
diarthric
diarthrodial
diarthrosis
diarticular
diary
diaschisis
diaschisma
diaschistic
Diascia
diascope
diascopy
diascord
diascordium
diaskeuasis
diaskeuast
Diaspidinae
diaspidine
Diaspinae
diaspine
diaspirin
Diaspora
diaspore
diastaltic
diastase
diastasic
diastasimetry
diastasis
diastataxic
diastataxy
diastatic
diastatically
diastem
diastema
diastematic
diastematomyelia
diaster
diastole
diastolic
diastomatic
diastral
diastrophe
diastrophic
diastrophism
diastrophy
diasynthesis
diasyrm
diatessaron
diathermacy
diathermal
diathermancy
diathermaneity
diathermanous
diathermic
diathermize
diathermometer
diathermotherapy
diathermous
diathermy
diathesic
diathesis
diathetic
diatom
Diatoma
Diatomaceae
diatomacean
diatomaceoid
diatomaceous
Diatomales
Diatomeae
diatomean
diatomic
diatomicity
diatomiferous
diatomin
diatomist
diatomite
diatomous
diatonic
diatonical
diatonically
diatonous
diatoric
diatreme
diatribe
diatribist
diatropic
diatropism
Diatryma
Diatrymiformes
Diau
diaulic
diaulos
diaxial
diaxon
diazenithal
diazeuctic
diazeuxis
diazide
diazine
diazoamine
diazoamino
diazoaminobenzene
diazoanhydride
diazoate
diazobenzene
diazohydroxide
diazoic
diazoimide
diazoimido
diazole
diazoma
diazomethane
diazonium
diazotate
diazotic
diazotizability
diazotizable
diazotization
diazotize
diazotype
dib
dibase
dibasic
dibasicity
dibatag
Dibatis
dibber
dibble
dibbler
dibbuk
dibenzophenazine
dibenzopyrrole
dibenzoyl
dibenzyl
dibhole
diblastula
diborate
Dibothriocephalus
dibrach
dibranch
Dibranchia
Dibranchiata
dibranchiate
dibranchious
dibrom
dibromid
dibromide
dibromoacetaldehyde
dibromobenzene
dibs
dibstone
dibutyrate
dibutyrin
dicacodyl
Dicaeidae
dicaeology
dicalcic
dicalcium
dicarbonate
dicarbonic
dicarboxylate
dicarboxylic
dicarpellary
dicaryon
dicaryophase
dicaryophyte
dicaryotic
dicast
dicastery
dicastic
dicatalectic
dicatalexis
Diccon
dice
diceboard
dicebox
dicecup
dicellate
diceman
Dicentra
dicentrine
dicephalism
dicephalous
dicephalus
diceplay
dicer
Diceras
Diceratidae
dicerion
dicerous
dicetyl
dich
Dichapetalaceae
Dichapetalum
dichas
dichasial
dichasium
dichastic
Dichelyma
dichlamydeous
dichloramine
dichlorhydrin
dichloride
dichloroacetic
dichlorohydrin
dichloromethane
dichocarpism
dichocarpous
dichogamous
dichogamy
Dichondra
Dichondraceae
dichopodial
dichoptic
dichord
dichoree
Dichorisandra
dichotic
dichotomal
dichotomic
dichotomically
dichotomist
dichotomistic
dichotomization
dichotomize
dichotomous
dichotomously
dichotomy
dichroic
dichroiscope
dichroism
dichroite
dichroitic
dichromasy
dichromat
dichromate
dichromatic
dichromatism
dichromic
dichromism
dichronous
dichrooscope
dichroous
dichroscope
dichroscopic
Dichter
dicing
Dick
dick
dickcissel
dickens
Dickensian
Dickensiana
dicker
dickey
dickeybird
dickinsonite
Dicksonia
dicky
Diclidantheraceae
diclinic
diclinism
diclinous
Diclytra
dicoccous
dicodeine
dicoelious
dicolic
dicolon
dicondylian
dicot
dicotyl
dicotyledon
dicotyledonary
Dicotyledones
dicotyledonous
Dicotyles
Dicotylidae
dicotylous
dicoumarin
Dicranaceae
dicranaceous
dicranoid
dicranterian
Dicranum
Dicrostonyx
dicrotal
dicrotic
dicrotism
dicrotous
Dicruridae
dicta
Dictaen
Dictamnus
Dictaphone
dictate
dictatingly
dictation
dictational
dictative
dictator
dictatorial
dictatorialism
dictatorially
dictatorialness
dictatorship
dictatory
dictatress
dictatrix
dictature
dictic
diction
dictionary
Dictograph
dictum
dictynid
Dictynidae
Dictyoceratina
dictyoceratine
dictyodromous
dictyogen
dictyogenous
Dictyograptus
dictyoid
Dictyonema
Dictyonina
dictyonine
Dictyophora
dictyopteran
Dictyopteris
Dictyosiphon
Dictyosiphonaceae
dictyosiphonaceous
dictyosome
dictyostele
dictyostelic
Dictyota
Dictyotaceae
dictyotaceous
Dictyotales
dictyotic
Dictyoxylon
dicyanide
dicyanine
dicyanodiamide
dicyanogen
dicycle
dicyclic
Dicyclica
dicyclist
Dicyema
Dicyemata
dicyemid
Dicyemida
Dicyemidae
Dicynodon
dicynodont
Dicynodontia
Dicynodontidae
did
Didache
Didachist
didactic
didactical
didacticality
didactically
didactician
didacticism
didacticity
didactics
didactive
didactyl
didactylism
didactylous
didapper
didascalar
didascaliae
didascalic
didascalos
didascaly
didder
diddle
diddler
diddy
didelph
Didelphia
didelphian
didelphic
didelphid
Didelphidae
didelphine
Didelphis
didelphoid
didelphous
Didelphyidae
didepsid
didepside
Dididae
didie
didine
Didinium
didle
didna
didnt
Dido
didodecahedral
didodecahedron
didrachma
didrachmal
didromy
didst
diductor
Didunculidae
Didunculinae
Didunculus
Didus
didym
didymate
didymia
didymitis
didymium
didymoid
didymolite
didymous
didymus
Didynamia
didynamian
didynamic
didynamous
didynamy
die
dieb
dieback
diectasis
diedral
diedric
Dieffenbachia
Diego
Diegueno
diehard
dielectric
dielectrically
dielike
Dielytra
diem
diemaker
diemaking
diencephalic
diencephalon
diene
dier
Dieri
Diervilla
diesel
dieselization
dieselize
diesinker
diesinking
diesis
diestock
diet
dietal
dietarian
dietary
Dieter
dieter
dietetic
dietetically
dietetics
dietetist
diethanolamine
diethyl
diethylamine
diethylenediamine
diethylstilbestrol
dietic
dietician
dietics
dietine
dietist
dietitian
dietotherapeutics
dietotherapy
dietotoxic
dietotoxicity
dietrichite
dietzeite
diewise
Dieyerie
diezeugmenon
Difda
diferrion
diffame
diffarreation
differ
difference
differencingly
different
differentia
differentiable
differential
differentialize
differentially
differentiant
differentiate
differentiation
differentiator
differently
differentness
differingly
difficile
difficileness
difficult
difficultly
difficultness
difficulty
diffidation
diffide
diffidence
diffident
diffidently
diffidentness
diffinity
diffluence
diffluent
Difflugia
difform
difformed
difformity
diffract
diffraction
diffractive
diffractively
diffractiveness
diffractometer
diffrangibility
diffrangible
diffugient
diffusate
diffuse
diffused
diffusedly
diffusely
diffuseness
diffuser
diffusibility
diffusible
diffusibleness
diffusibly
diffusimeter
diffusiometer
diffusion
diffusionism
diffusionist
diffusive
diffusively
diffusiveness
diffusivity
diffusor
diformin
dig
digallate
digallic
digametic
digamist
digamma
digammated
digammic
digamous
digamy
digastric
Digenea
digeneous
digenesis
digenetic
Digenetica
digenic
digenous
digeny
digerent
digest
digestant
digested
digestedly
digestedness
digester
digestibility
digestible
digestibleness
digestibly
digestion
digestional
digestive
digestively
digestiveness
digestment
diggable
digger
digging
diggings
dight
dighter
digit
digital
digitalein
digitalin
digitalis
digitalism
digitalization
digitalize
digitally
Digitaria
digitate
digitated
digitately
digitation
digitiform
Digitigrada
digitigrade
digitigradism
digitinervate
digitinerved
digitipinnate
digitize
digitizer
digitogenin
digitonin
digitoplantar
digitorium
digitoxin
digitoxose
digitule
digitus
digladiate
digladiation
digladiator
diglossia
diglot
diglottic
diglottism
diglottist
diglucoside
diglyceride
diglyph
diglyphic
digmeat
dignification
dignified
dignifiedly
dignifiedness
dignify
dignitarial
dignitarian
dignitary
dignity
digoneutic
digoneutism
digonoporous
digonous
Digor
digram
digraph
digraphic
digredience
digrediency
digredient
digress
digressingly
digression
digressional
digressionary
digressive
digressively
digressiveness
digressory
digs
diguanide
Digynia
digynian
digynous
dihalide
dihalo
dihalogen
dihedral
dihedron
dihexagonal
dihexahedral
dihexahedron
dihybrid
dihybridism
dihydrate
dihydrated
dihydrazone
dihydric
dihydride
dihydrite
dihydrocupreine
dihydrocuprin
dihydrogen
dihydrol
dihydronaphthalene
dihydronicotine
dihydrotachysterol
dihydroxy
dihydroxysuccinic
dihydroxytoluene
dihysteria
diiamb
diiambus
diiodide
diiodo
diiodoform
diipenates
Diipolia
diisatogen
dijudicate
dijudication
dika
dikage
dikamali
dikaryon
dikaryophase
dikaryophasic
dikaryophyte
dikaryophytic
dikaryotic
Dike
dike
dikegrave
dikelocephalid
Dikelocephalus
diker
dikereeve
dikeside
diketo
diketone
dikkop
diktyonite
dilacerate
dilaceration
dilambdodont
dilamination
Dilantin
dilapidate
dilapidated
dilapidation
dilapidator
dilatability
dilatable
dilatableness
dilatably
dilatancy
dilatant
dilatate
dilatation
dilatative
dilatator
dilatatory
dilate
dilated
dilatedly
dilatedness
dilater
dilatingly
dilation
dilative
dilatometer
dilatometric
dilatometry
dilator
dilatorily
dilatoriness
dilatory
dildo
dilection
Dilemi
Dilemite
dilemma
dilemmatic
dilemmatical
dilemmatically
dilettant
dilettante
dilettanteish
dilettanteism
dilettanteship
dilettanti
dilettantish
dilettantism
dilettantist
diligence
diligency
diligent
diligentia
diligently
diligentness
dilker
dill
Dillenia
Dilleniaceae
dilleniaceous
dilleniad
dilli
dillier
dilligrout
dilling
dillseed
dillue
dilluer
dillweed
dilly
dillydallier
dillydally
dillyman
dilo
dilogy
diluent
dilute
diluted
dilutedly
dilutedness
dilutee
dilutely
diluteness
dilutent
diluter
dilution
dilutive
dilutor
diluvia
diluvial
diluvialist
diluvian
diluvianism
diluvion
diluvium
dim
dimagnesic
dimanganion
dimanganous
Dimaris
dimastigate
Dimatis
dimber
dimberdamber
dimble
dime
dimensible
dimension
dimensional
dimensionality
dimensionally
dimensioned
dimensionless
dimensive
dimer
Dimera
dimeran
dimercuric
dimercurion
dimercury
dimeric
dimeride
dimerism
dimerization
dimerlie
dimerous
dimetallic
dimeter
dimethoxy
dimethyl
dimethylamine
dimethylamino
dimethylaniline
dimethylbenzene
dimetria
dimetric
Dimetry
dimication
dimidiate
dimidiation
diminish
diminishable
diminishableness
diminisher
diminishingly
diminishment
diminuendo
diminutal
diminute
diminution
diminutival
diminutive
diminutively
diminutiveness
diminutivize
dimiss
dimission
dimissorial
dimissory
dimit
Dimitry
Dimittis
dimity
dimly
dimmed
dimmedness
dimmer
dimmest
dimmet
dimmish
Dimna
dimness
dimolecular
dimoric
dimorph
dimorphic
dimorphism
Dimorphotheca
dimorphous
dimple
dimplement
dimply
dimps
dimpsy
Dimyaria
dimyarian
dimyaric
din
Dinah
dinamode
Dinantian
dinaphthyl
dinar
Dinaric
Dinarzade
dinder
dindle
Dindymene
Dindymus
dine
diner
dinergate
dineric
dinero
dinette
dineuric
ding
dingar
dingbat
dingdong
dinge
dingee
dinghee
dinghy
dingily
dinginess
dingle
dingleberry
dinglebird
dingledangle
dingly
dingmaul
dingo
dingus
Dingwall
dingy
dinheiro
dinic
dinical
Dinichthys
dining
dinitrate
dinitril
dinitrile
dinitro
dinitrobenzene
dinitrocellulose
dinitrophenol
dinitrotoluene
dink
Dinka
dinkey
dinkum
dinky
dinmont
dinner
dinnerless
dinnerly
dinnertime
dinnerware
dinnery
Dinobryon
Dinoceras
Dinocerata
dinoceratan
dinoceratid
Dinoceratidae
Dinoflagellata
Dinoflagellatae
dinoflagellate
Dinoflagellida
dinomic
Dinomys
Dinophilea
Dinophilus
Dinophyceae
Dinornis
Dinornithes
dinornithic
dinornithid
Dinornithidae
Dinornithiformes
dinornithine
dinornithoid
dinosaur
Dinosauria
dinosaurian
dinothere
Dinotheres
dinotherian
Dinotheriidae
Dinotherium
dinsome
dint
dintless
dinus
diobely
diobol
diocesan
diocese
Diocletian
dioctahedral
Dioctophyme
diode
Diodia
Diodon
diodont
Diodontidae
Dioecia
dioecian
dioeciodimorphous
dioeciopolygamous
dioecious
dioeciously
dioeciousness
dioecism
dioecy
dioestrous
dioestrum
dioestrus
Diogenean
Diogenic
diogenite
dioicous
diol
diolefin
diolefinic
Diomedea
Diomedeidae
Dion
Dionaea
Dionaeaceae
Dione
dionise
dionym
dionymal
Dionysia
Dionysiac
Dionysiacal
Dionysiacally
Dioon
Diophantine
Diopsidae
diopside
Diopsis
dioptase
diopter
Dioptidae
dioptograph
dioptometer
dioptometry
dioptoscopy
dioptra
dioptral
dioptrate
dioptric
dioptrical
dioptrically
dioptrics
dioptrometer
dioptrometry
dioptroscopy
dioptry
diorama
dioramic
diordinal
diorite
dioritic
diorthosis
diorthotic
Dioscorea
Dioscoreaceae
dioscoreaceous
dioscorein
dioscorine
Dioscuri
Dioscurian
diose
Diosma
diosmin
diosmose
diosmosis
diosmotic
diosphenol
Diospyraceae
diospyraceous
Diospyros
diota
diotic
Diotocardia
diovular
dioxane
dioxide
dioxime
dioxindole
dioxy
dip
Dipala
diparentum
dipartite
dipartition
dipaschal
dipentene
dipeptid
dipeptide
dipetalous
dipetto
diphase
diphaser
diphasic
diphead
diphenol
diphenyl
diphenylamine
diphenylchloroarsine
diphenylene
diphenylenimide
diphenylguanidine
diphenylmethane
diphenylquinomethane
diphenylthiourea
diphosgene
diphosphate
diphosphide
diphosphoric
diphosphothiamine
diphrelatic
diphtheria
diphtherial
diphtherian
diphtheric
diphtheritic
diphtheritically
diphtheritis
diphtheroid
diphtheroidal
diphtherotoxin
diphthong
diphthongal
diphthongalize
diphthongally
diphthongation
diphthongic
diphthongization
diphthongize
diphycercal
diphycercy
Diphyes
diphygenic
diphyletic
Diphylla
Diphylleia
Diphyllobothrium
diphyllous
diphyodont
diphyozooid
Diphysite
Diphysitism
diphyzooid
dipicrate
dipicrylamin
dipicrylamine
Diplacanthidae
Diplacanthus
diplacusis
Dipladenia
diplanar
diplanetic
diplanetism
diplantidian
diplarthrism
diplarthrous
diplasiasmus
diplasic
diplasion
diplegia
dipleidoscope
dipleura
dipleural
dipleurogenesis
dipleurogenetic
diplex
diplobacillus
diplobacterium
diploblastic
diplocardia
diplocardiac
Diplocarpon
diplocaulescent
diplocephalous
diplocephalus
diplocephaly
diplochlamydeous
diplococcal
diplococcemia
diplococcic
diplococcoid
diplococcus
diploconical
diplocoria
Diplodia
Diplodocus
Diplodus
diploe
diploetic
diplogangliate
diplogenesis
diplogenetic
diplogenic
Diploglossata
diploglossate
diplograph
diplographic
diplographical
diplography
diplohedral
diplohedron
diploic
diploid
diploidic
diploidion
diploidy
diplois
diplokaryon
diploma
diplomacy
diplomat
diplomate
diplomatic
diplomatical
diplomatically
diplomatics
diplomatism
diplomatist
diplomatize
diplomatology
diplomyelia
diplonema
diplonephridia
diploneural
diplont
diploperistomic
diplophase
diplophyte
diplopia
diplopic
diploplacula
diploplacular
diploplaculate
diplopod
Diplopoda
diplopodic
Diploptera
diplopterous
Diplopteryga
diplopy
diplosis
diplosome
diplosphenal
diplosphene
Diplospondyli
diplospondylic
diplospondylism
diplostemonous
diplostemony
diplostichous
Diplotaxis
diplotegia
diplotene
Diplozoon
diplumbic
Dipneumona
Dipneumones
dipneumonous
dipneustal
Dipneusti
dipnoan
Dipnoi
dipnoid
dipnoous
dipode
dipodic
Dipodidae
Dipodomyinae
Dipodomys
dipody
dipolar
dipolarization
dipolarize
dipole
diporpa
dipotassic
dipotassium
dipped
dipper
dipperful
dipping
diprimary
diprismatic
dipropargyl
dipropyl
Diprotodon
diprotodont
Diprotodontia
Dipsacaceae
dipsacaceous
Dipsaceae
dipsaceous
Dipsacus
Dipsadinae
dipsas
dipsetic
dipsey
dipsomania
dipsomaniac
dipsomaniacal
Dipsosaurus
dipsosis
dipter
Diptera
Dipteraceae
dipteraceous
dipterad
dipteral
dipteran
dipterist
dipterocarp
Dipterocarpaceae
dipterocarpaceous
dipterocarpous
Dipterocarpus
dipterocecidium
dipterological
dipterologist
dipterology
dipteron
dipteros
dipterous
Dipteryx
diptote
diptych
Dipus
dipware
dipygus
dipylon
dipyre
dipyrenous
dipyridyl
Dirca
Dircaean
dird
dirdum
dire
direct
directable
directed
directer
direction
directional
directionally
directionless
directitude
directive
directively
directiveness
directivity
directly
directness
Directoire
director
directoral
directorate
directorial
directorially
directorship
directory
directress
directrices
directrix
direful
direfully
direfulness
direly
dirempt
diremption
direness
direption
dirge
dirgeful
dirgelike
dirgeman
dirgler
dirhem
Dirian
Dirichletian
dirigent
dirigibility
dirigible
dirigomotor
diriment
Dirk
dirk
dirl
dirndl
dirt
dirtbird
dirtboard
dirten
dirtily
dirtiness
dirtplate
dirty
dis
Disa
disability
disable
disabled
disablement
disabusal
disabuse
disacceptance
disaccharide
disaccharose
disaccommodate
disaccommodation
disaccord
disaccordance
disaccordant
disaccustom
disaccustomed
disaccustomedness
disacidify
disacknowledge
disacknowledgement
disacquaint
disacquaintance
disadjust
disadorn
disadvance
disadvantage
disadvantageous
disadvantageously
disadvantageousness
disadventure
disadventurous
disadvise
disaffect
disaffectation
disaffected
disaffectedly
disaffectedness
disaffection
disaffectionate
disaffiliate
disaffiliation
disaffirm
disaffirmance
disaffirmation
disaffirmative
disafforest
disafforestation
disafforestment
disagglomeration
disaggregate
disaggregation
disaggregative
disagio
disagree
disagreeability
disagreeable
disagreeableness
disagreeably
disagreed
disagreement
disagreer
disalicylide
disalign
disalignment
disalike
disallow
disallowable
disallowableness
disallowance
disally
disamenity
Disamis
disanagrammatize
disanalogous
disangularize
disanimal
disanimate
disanimation
disannex
disannexation
disannul
disannuller
disannulment
disanoint
disanswerable
disapostle
disapparel
disappear
disappearance
disappearer
disappearing
disappoint
disappointed
disappointedly
disappointer
disappointing
disappointingly
disappointingness
disappointment
disappreciate
disappreciation
disapprobation
disapprobative
disapprobatory
disappropriate
disappropriation
disapprovable
disapproval
disapprove
disapprover
disapprovingly
disaproned
disarchbishop
disarm
disarmament
disarmature
disarmed
disarmer
disarming
disarmingly
disarrange
disarrangement
disarray
disarticulate
disarticulation
disarticulator
disasinate
disasinize
disassemble
disassembly
disassimilate
disassimilation
disassimilative
disassociate
disassociation
disaster
disastimeter
disastrous
disastrously
disastrousness
disattaint
disattire
disattune
disauthenticate
disauthorize
disavow
disavowable
disavowal
disavowedly
disavower
disavowment
disawa
disazo
disbalance
disbalancement
disband
disbandment
disbar
disbark
disbarment
disbelief
disbelieve
disbeliever
disbelieving
disbelievingly
disbench
disbenchment
disbloom
disbody
disbosom
disbowel
disbrain
disbranch
disbud
disbudder
disburden
disburdenment
disbursable
disburse
disbursement
disburser
disburthen
disbury
disbutton
disc
discage
discal
discalceate
discalced
discanonization
discanonize
discanter
discantus
discapacitate
discard
discardable
discarder
discardment
discarnate
discarnation
discase
discastle
discept
disceptation
disceptator
discern
discerner
discernible
discernibleness
discernibly
discerning
discerningly
discernment
discerp
discerpibility
discerpible
discerpibleness
discerptibility
discerptible
discerptibleness
discerption
discharacter
discharge
dischargeable
dischargee
discharger
discharging
discharity
discharm
dischase
Disciflorae
discifloral
disciform
discigerous
Discina
discinct
discinoid
disciple
disciplelike
discipleship
disciplinability
disciplinable
disciplinableness
disciplinal
disciplinant
disciplinarian
disciplinarianism
disciplinarily
disciplinary
disciplinative
disciplinatory
discipline
discipliner
discipular
discircumspection
discission
discitis
disclaim
disclaimant
disclaimer
disclamation
disclamatory
disclass
disclassify
disclike
disclimax
discloister
disclose
disclosed
discloser
disclosive
disclosure
discloud
discoach
discoactine
discoblastic
discoblastula
discobolus
discocarp
discocarpium
discocarpous
discocephalous
discodactyl
discodactylous
discogastrula
discoglossid
Discoglossidae
discoglossoid
discographical
discography
discohexaster
discoid
discoidal
Discoidea
Discoideae
discolichen
discolith
discolor
discolorate
discoloration
discolored
discoloredness
discolorization
discolorment
discolourization
Discomedusae
discomedusan
discomedusoid
discomfit
discomfiter
discomfiture
discomfort
discomfortable
discomfortableness
discomforting
discomfortingly
discommend
discommendable
discommendableness
discommendably
discommendation
discommender
discommode
discommodious
discommodiously
discommodiousness
discommodity
discommon
discommons
discommunity
discomorula
discompliance
discompose
discomposed
discomposedly
discomposedness
discomposing
discomposingly
discomposure
discomycete
Discomycetes
discomycetous
Disconanthae
disconanthous
disconcert
disconcerted
disconcertedly
disconcertedness
disconcerting
disconcertingly
disconcertingness
disconcertion
disconcertment
disconcord
disconduce
disconducive
Disconectae
disconform
disconformable
disconformity
discongruity
disconjure
disconnect
disconnected
disconnectedly
disconnectedness
disconnecter
disconnection
disconnective
disconnectiveness
disconnector
disconsider
disconsideration
disconsolate
disconsolately
disconsolateness
disconsolation
disconsonancy
disconsonant
discontent
discontented
discontentedly
discontentedness
discontentful
discontenting
discontentive
discontentment
discontiguity
discontiguous
discontiguousness
discontinuable
discontinuance
discontinuation
discontinue
discontinuee
discontinuer
discontinuity
discontinuor
discontinuous
discontinuously
discontinuousness
disconula
disconvenience
disconvenient
disconventicle
discophile
Discophora
discophoran
discophore
discophorous
discoplacenta
discoplacental
Discoplacentalia
discoplacentalian
discoplasm
discopodous
discord
discordance
discordancy
discordant
discordantly
discordantness
discordful
Discordia
discording
discorporate
discorrespondency
discorrespondent
discount
discountable
discountenance
discountenancer
discounter
discouple
discourage
discourageable
discouragement
discourager
discouraging
discouragingly
discouragingness
discourse
discourseless
discourser
discoursive
discoursively
discoursiveness
discourteous
discourteously
discourteousness
discourtesy
discous
discovenant
discover
discoverability
discoverable
discoverably
discovered
discoverer
discovert
discoverture
discovery
discreate
discreation
discredence
discredit
discreditability
discreditable
discreet
discreetly
discreetness
discrepance
discrepancy
discrepant
discrepantly
discrepate
discrepation
discrested
discrete
discretely
discreteness
discretion
discretional
discretionally
discretionarily
discretionary
discretive
discretively
discretiveness
discriminability
discriminable
discriminal
discriminant
discriminantal
discriminate
discriminately
discriminateness
discriminating
discriminatingly
discrimination
discriminational
discriminative
discriminatively
discriminator
discriminatory
discrown
disculpate
disculpation
disculpatory
discumber
discursative
discursativeness
discursify
discursion
discursive
discursively
discursiveness
discursory
discursus
discurtain
discus
discuss
discussable
discussant
discusser
discussible
discussion
discussional
discussionism
discussionist
discussive
discussment
discutable
discutient
disdain
disdainable
disdainer
disdainful
disdainfully
disdainfulness
disdainly
disdeceive
disdenominationalize
disdiaclast
disdiaclastic
disdiapason
disdiazo
disdiplomatize
disdodecahedroid
disdub
disease
diseased
diseasedly
diseasedness
diseaseful
diseasefulness
disecondary
disedge
disedification
disedify
diseducate
diselder
diselectrification
diselectrify
diselenide
disematism
disembargo
disembark
disembarkation
disembarkment
disembarrass
disembarrassment
disembattle
disembed
disembellish
disembitter
disembocation
disembodiment
disembody
disembogue
disemboguement
disembosom
disembowel
disembowelment
disembower
disembroil
disemburden
diseme
disemic
disemplane
disemploy
disemployment
disempower
disenable
disenablement
disenact
disenactment
disenamor
disenamour
disenchain
disenchant
disenchanter
disenchantingly
disenchantment
disenchantress
disencharm
disenclose
disencumber
disencumberment
disencumbrance
disendow
disendower
disendowment
disenfranchise
disenfranchisement
disengage
disengaged
disengagedness
disengagement
disengirdle
disenjoy
disenjoyment
disenmesh
disennoble
disennui
disenshroud
disenslave
disensoul
disensure
disentail
disentailment
disentangle
disentanglement
disentangler
disenthral
disenthrall
disenthrallment
disenthralment
disenthrone
disenthronement
disentitle
disentomb
disentombment
disentrain
disentrainment
disentrammel
disentrance
disentrancement
disentwine
disenvelop
disepalous
disequalize
disequalizer
disequilibrate
disequilibration
disequilibrium
disestablish
disestablisher
disestablishment
disestablishmentarian
disesteem
disesteemer
disestimation
disexcommunicate
disfaith
disfame
disfashion
disfavor
disfavorer
disfeature
disfeaturement
disfellowship
disfen
disfiguration
disfigurative
disfigure
disfigurement
disfigurer
disfiguringly
disflesh
disfoliage
disforest
disforestation
disfranchise
disfranchisement
disfranchiser
disfrequent
disfriar
disfrock
disfurnish
disfurnishment
disgarland
disgarnish
disgarrison
disgavel
disgeneric
disgenius
disgig
disglorify
disglut
disgood
disgorge
disgorgement
disgorger
disgospel
disgown
disgrace
disgraceful
disgracefully
disgracefulness
disgracement
disgracer
disgracious
disgradation
disgrade
disgregate
disgregation
disgruntle
disgruntlement
disguisable
disguisal
disguise
disguised
disguisedly
disguisedness
disguiseless
disguisement
disguiser
disguising
disgulf
disgust
disgusted
disgustedly
disgustedness
disguster
disgustful
disgustfully
disgustfulness
disgusting
disgustingly
disgustingness
dish
dishabilitate
dishabilitation
dishabille
dishabituate
dishallow
dishallucination
disharmonic
disharmonical
disharmonious
disharmonism
disharmonize
disharmony
dishboard
dishcloth
dishclout
disheart
dishearten
disheartener
disheartening
dishearteningly
disheartenment
disheaven
dished
dishellenize
dishelm
disher
disherent
disherison
disherit
disheritment
dishevel
disheveled
dishevelment
dishexecontahedroid
dishful
Dishley
dishlike
dishling
dishmaker
dishmaking
dishmonger
dishome
dishonest
dishonestly
dishonor
dishonorable
dishonorableness
dishonorably
dishonorary
dishonorer
dishorn
dishorner
dishorse
dishouse
dishpan
dishpanful
dishrag
dishumanize
dishwasher
dishwashing
dishwashings
dishwater
dishwatery
dishwiper
dishwiping
disidentify
disilane
disilicane
disilicate
disilicic
disilicid
disilicide
disillude
disilluminate
disillusion
disillusionist
disillusionize
disillusionizer
disillusionment
disillusive
disimagine
disimbitter
disimitate
disimitation
disimmure
disimpark
disimpassioned
disimprison
disimprisonment
disimprove
disimprovement
disincarcerate
disincarceration
disincarnate
disincarnation
disinclination
disincline
disincorporate
disincorporation
disincrust
disincrustant
disincrustion
disindividualize
disinfect
disinfectant
disinfecter
disinfection
disinfective
disinfector
disinfest
disinfestation
disinfeudation
disinflame
disinflate
disinflation
disingenuity
disingenuous
disingenuously
disingenuousness
disinherison
disinherit
disinheritable
disinheritance
disinhume
disinsulation
disinsure
disintegrable
disintegrant
disintegrate
disintegration
disintegrationist
disintegrative
disintegrator
disintegratory
disintegrity
disintegrous
disintensify
disinter
disinterest
disinterested
disinterestedly
disinterestedness
disinteresting
disinterment
disintertwine
disintrench
disintricate
disinvagination
disinvest
disinvestiture
disinvigorate
disinvite
disinvolve
disjasked
disject
disjection
disjoin
disjoinable
disjoint
disjointed
disjointedly
disjointedness
disjointly
disjointure
disjunct
disjunction
disjunctive
disjunctively
disjunctor
disjuncture
disjune
disk
diskelion
diskless
disklike
dislaurel
disleaf
dislegitimate
dislevelment
dislicense
dislikable
dislike
dislikelihood
disliker
disliking
dislimn
dislink
dislip
disload
dislocability
dislocable
dislocate
dislocated
dislocatedly
dislocatedness
dislocation
dislocator
dislocatory
dislodge
dislodgeable
dislodgement
dislove
disloyal
disloyalist
disloyally
disloyalty
disluster
dismain
dismal
dismality
dismalize
dismally
dismalness
disman
dismantle
dismantlement
dismantler
dismarble
dismark
dismarket
dismask
dismast
dismastment
dismay
dismayable
dismayed
dismayedness
dismayful
dismayfully
dismayingly
disme
dismember
dismembered
dismemberer
dismemberment
dismembrate
dismembrator
disminion
disminister
dismiss
dismissable
dismissal
dismissible
dismissingly
dismission
dismissive
dismissory
dismoded
dismount
dismountable
dismutation
disna
disnaturalization
disnaturalize
disnature
disnest
disnew
disniche
disnosed
disnumber
disobedience
disobedient
disobediently
disobey
disobeyal
disobeyer
disobligation
disoblige
disobliger
disobliging
disobligingly
disobligingness
disoccupation
disoccupy
disodic
disodium
disomatic
disomatous
disomic
disomus
disoperculate
disorb
disorchard
disordained
disorder
disordered
disorderedly
disorderedness
disorderer
disorderliness
disorderly
disordinated
disordination
disorganic
disorganization
disorganize
disorganizer
disorient
disorientate
disorientation
disown
disownable
disownment
disoxygenate
disoxygenation
disozonize
dispapalize
disparage
disparageable
disparagement
disparager
disparaging
disparagingly
disparate
disparately
disparateness
disparation
disparity
dispark
dispart
dispartment
dispassionate
dispassionately
dispassionateness
dispassioned
dispatch
dispatcher
dispatchful
dispatriated
dispauper
dispauperize
dispeace
dispeaceful
dispel
dispeller
dispend
dispender
dispendious
dispendiously
dispenditure
dispensability
dispensable
dispensableness
dispensary
dispensate
dispensation
dispensational
dispensative
dispensatively
dispensator
dispensatorily
dispensatory
dispensatress
dispensatrix
dispense
dispenser
dispensingly
dispeople
dispeoplement
dispeopler
dispergate
dispergation
dispergator
dispericraniate
disperiwig
dispermic
dispermous
dispermy
dispersal
dispersant
disperse
dispersed
dispersedly
dispersedness
dispersement
disperser
dispersibility
dispersible
dispersion
dispersity
dispersive
dispersively
dispersiveness
dispersoid
dispersoidological
dispersoidology
dispersonalize
dispersonate
dispersonification
dispersonify
dispetal
disphenoid
dispiece
dispireme
dispirit
dispirited
dispiritedly
dispiritedness
dispiritingly
dispiritment
dispiteous
dispiteously
dispiteousness
displace
displaceability
displaceable
displacement
displacency
displacer
displant
display
displayable
displayed
displayer
displease
displeased
displeasedly
displeaser
displeasing
displeasingly
displeasingness
displeasurable
displeasurably
displeasure
displeasurement
displenish
displicency
displume
displuviate
dispondaic
dispondee
dispone
disponee
disponent
disponer
dispope
dispopularize
disporous
disport
disportive
disportment
Disporum
disposability
disposable
disposableness
disposal
dispose
disposed
disposedly
disposedness
disposer
disposingly
disposition
dispositional
dispositioned
dispositive
dispositively
dispossess
dispossession
dispossessor
dispossessory
dispost
disposure
dispowder
dispractice
dispraise
dispraiser
dispraisingly
dispread
dispreader
disprejudice
disprepare
disprince
disprison
disprivacied
disprivilege
disprize
disprobabilization
disprobabilize
disprobative
dispromise
disproof
disproportion
disproportionable
disproportionableness
disproportionably
disproportional
disproportionality
disproportionally
disproportionalness
disproportionate
disproportionately
disproportionateness
disproportionation
disprovable
disproval
disprove
disprovement
disproven
disprover
dispulp
dispunct
dispunishable
dispunitive
disputability
disputable
disputableness
disputably
disputant
disputation
disputatious
disputatiously
disputatiousness
disputative
disputatively
disputativeness
disputator
dispute
disputeless
disputer
disqualification
disqualify
disquantity
disquiet
disquieted
disquietedly
disquietedness
disquieten
disquieter
disquieting
disquietingly
disquietly
disquietness
disquietude
disquiparancy
disquiparant
disquiparation
disquisite
disquisition
disquisitional
disquisitionary
disquisitive
disquisitively
disquisitor
disquisitorial
disquisitory
disquixote
disrank
disrate
disrealize
disrecommendation
disregard
disregardable
disregardance
disregardant
disregarder
disregardful
disregardfully
disregardfulness
disrelated
disrelation
disrelish
disrelishable
disremember
disrepair
disreputability
disreputable
disreputableness
disreputably
disreputation
disrepute
disrespect
disrespecter
disrespectful
disrespectfully
disrespectfulness
disrestore
disring
disrobe
disrobement
disrober
disroof
disroost
disroot
disrudder
disrump
disrupt
disruptability
disruptable
disrupter
disruption
disruptionist
disruptive
disruptively
disruptiveness
disruptment
disruptor
disrupture
diss
dissatisfaction
dissatisfactoriness
dissatisfactory
dissatisfied
dissatisfiedly
dissatisfiedness
dissatisfy
dissaturate
disscepter
disseat
dissect
dissected
dissectible
dissecting
dissection
dissectional
dissective
dissector
disseize
disseizee
disseizin
disseizor
disseizoress
disselboom
dissemblance
dissemble
dissembler
dissemblingly
dissembly
dissemilative
disseminate
dissemination
disseminative
disseminator
disseminule
dissension
dissensualize
dissent
dissentaneous
dissentaneousness
dissenter
dissenterism
dissentience
dissentiency
dissentient
dissenting
dissentingly
dissentious
dissentiously
dissentism
dissentment
dissepiment
dissepimental
dissert
dissertate
dissertation
dissertational
dissertationist
dissertative
dissertator
disserve
disservice
disserviceable
disserviceableness
disserviceably
dissettlement
dissever
disseverance
disseverment
disshadow
dissheathe
disshroud
dissidence
dissident
dissidently
dissight
dissightly
dissiliency
dissilient
dissimilar
dissimilarity
dissimilarly
dissimilars
dissimilate
dissimilation
dissimilatory
dissimile
dissimilitude
dissimulate
dissimulation
dissimulative
dissimulator
dissimule
dissimuler
dissipable
dissipate
dissipated
dissipatedly
dissipatedness
dissipater
dissipation
dissipative
dissipativity
dissipator
dissociability
dissociable
dissociableness
dissocial
dissociality
dissocialize
dissociant
dissociate
dissociation
dissociative
dissoconch
dissogeny
dissogony
dissolubility
dissoluble
dissolubleness
dissolute
dissolutely
dissoluteness
dissolution
dissolutional
dissolutionism
dissolutionist
dissolutive
dissolvable
dissolvableness
dissolve
dissolveability
dissolvent
dissolver
dissolving
dissolvingly
dissonance
dissonancy
dissonant
dissonantly
dissonous
dissoul
dissuade
dissuader
dissuasion
dissuasive
dissuasively
dissuasiveness
dissuasory
dissuit
dissuitable
dissuited
dissyllabic
dissyllabification
dissyllabify
dissyllabism
dissyllabize
dissyllable
dissymmetric
dissymmetrical
dissymmetrically
dissymmetry
dissympathize
dissympathy
distad
distaff
distain
distal
distale
distally
distalwards
distance
distanceless
distancy
distannic
distant
distantly
distantness
distaste
distasted
distasteful
distastefully
distastefulness
distater
distemonous
distemper
distemperature
distempered
distemperedly
distemperedness
distemperer
distenant
distend
distendedly
distender
distensibility
distensible
distensive
distent
distention
disthene
disthrall
disthrone
distich
Distichlis
distichous
distichously
distill
distillable
distillage
distilland
distillate
distillation
distillatory
distilled
distiller
distillery
distilling
distillmint
distinct
distinctify
distinction
distinctional
distinctionless
distinctive
distinctively
distinctiveness
distinctly
distinctness
distingue
distinguish
distinguishability
distinguishable
distinguishableness
distinguishably
distinguished
distinguishedly
distinguisher
distinguishing
distinguishingly
distinguishment
distoclusion
Distoma
Distomatidae
distomatosis
distomatous
distome
distomian
distomiasis
Distomidae
Distomum
distort
distorted
distortedly
distortedness
distorter
distortion
distortional
distortionist
distortionless
distortive
distract
distracted
distractedly
distractedness
distracter
distractibility
distractible
distractingly
distraction
distractive
distractively
distrain
distrainable
distrainee
distrainer
distrainment
distrainor
distraint
distrait
distraite
distraught
distress
distressed
distressedly
distressedness
distressful
distressfully
distressfulness
distressing
distressingly
distributable
distributary
distribute
distributed
distributedly
distributee
distributer
distribution
distributional
distributionist
distributival
distributive
distributively
distributiveness
distributor
distributress
district
distrouser
distrust
distruster
distrustful
distrustfully
distrustfulness
distrustingly
distune
disturb
disturbance
disturbative
disturbed
disturbedly
disturber
disturbing
disturbingly
disturn
disturnpike
disubstituted
disubstitution
disulfonic
disulfuric
disulphate
disulphide
disulphonate
disulphone
disulphonic
disulphoxide
disulphuret
disulphuric
disuniform
disuniformity
disunify
disunion
disunionism
disunionist
disunite
disuniter
disunity
disusage
disusance
disuse
disutility
disutilize
disvaluation
disvalue
disvertebrate
disvisage
disvoice
disvulnerability
diswarren
diswench
diswood
disworth
disyllabic
disyllable
disyoke
dit
dita
dital
ditch
ditchbank
ditchbur
ditchdigger
ditchdown
ditcher
ditchless
ditchside
ditchwater
dite
diter
diterpene
ditertiary
ditetragonal
dithalous
dithecal
ditheism
ditheist
ditheistic
ditheistical
dithematic
dither
dithery
dithiobenzoic
dithioglycol
dithioic
dithion
dithionate
dithionic
dithionite
dithionous
dithymol
dithyramb
dithyrambic
dithyrambically
Dithyrambos
Dithyrambus
ditokous
ditolyl
ditone
ditrematous
ditremid
Ditremidae
ditrichotomous
ditriglyph
ditriglyphic
ditrigonal
ditrigonally
Ditrocha
ditrochean
ditrochee
ditrochous
ditroite
dittamy
dittander
dittany
dittay
dittied
ditto
dittogram
dittograph
dittographic
dittography
dittology
ditty
diumvirate
diuranate
diureide
diuresis
diuretic
diuretically
diureticalness
Diurna
diurnal
diurnally
diurnalness
diurnation
diurne
diurnule
diuturnal
diuturnity
div
diva
divagate
divagation
divalence
divalent
divan
divariant
divaricate
divaricately
divaricating
divaricatingly
divarication
divaricator
divata
dive
divekeeper
divel
divellent
divellicate
diver
diverge
divergement
divergence
divergency
divergent
divergently
diverging
divergingly
divers
diverse
diversely
diverseness
diversicolored
diversifiability
diversifiable
diversification
diversified
diversifier
diversiflorate
diversiflorous
diversifoliate
diversifolious
diversiform
diversify
diversion
diversional
diversionary
diversipedate
diversisporous
diversity
diversly
diversory
divert
divertedly
diverter
divertibility
divertible
diverticle
diverticular
diverticulate
diverticulitis
diverticulosis
diverticulum
diverting
divertingly
divertingness
divertisement
divertive
divertor
divest
divestible
divestitive
divestiture
divestment
divesture
dividable
dividableness
divide
divided
dividedly
dividedness
dividend
divider
dividing
dividingly
dividual
dividualism
dividually
dividuity
dividuous
divinable
divinail
divination
divinator
divinatory
divine
divinely
divineness
diviner
divineress
diving
divinify
divining
diviningly
divinity
divinityship
divinization
divinize
divinyl
divisibility
divisible
divisibleness
divisibly
division
divisional
divisionally
divisionary
divisionism
divisionist
divisionistic
divisive
divisively
divisiveness
divisor
divisorial
divisory
divisural
divorce
divorceable
divorcee
divorcement
divorcer
divorcible
divorcive
divot
divoto
divulgate
divulgater
divulgation
divulgatory
divulge
divulgement
divulgence
divulger
divulse
divulsion
divulsive
divulsor
divus
Divvers
divvy
diwata
dixenite
Dixie
dixie
Dixiecrat
dixit
dixy
dizain
dizen
dizenment
dizoic
dizygotic
dizzard
dizzily
dizziness
dizzy
Djagatay
djasakid
djave
djehad
djerib
djersa
Djuka
do
doab
doable
doarium
doat
doated
doater
doating
doatish
Dob
dob
dobbed
dobber
dobbin
dobbing
dobby
dobe
dobla
doblon
dobra
dobrao
dobson
doby
doc
docent
docentship
Docetae
Docetic
Docetically
Docetism
Docetist
Docetistic
Docetize
dochmiac
dochmiacal
dochmiasis
dochmius
docibility
docible
docibleness
docile
docilely
docility
docimasia
docimastic
docimastical
docimasy
docimology
docity
dock
dockage
docken
docker
docket
dockhead
dockhouse
dockization
dockize
dockland
dockmackie
dockman
dockmaster
dockside
dockyard
dockyardman
docmac
Docoglossa
docoglossan
docoglossate
docosane
doctor
doctoral
doctorally
doctorate
doctorbird
doctordom
doctoress
doctorfish
doctorhood
doctorial
doctorially
doctorization
doctorize
doctorless
doctorlike
doctorly
doctorship
doctress
doctrinaire
doctrinairism
doctrinal
doctrinalism
doctrinalist
doctrinality
doctrinally
doctrinarian
doctrinarianism
doctrinarily
doctrinarity
doctrinary
doctrinate
doctrine
doctrinism
doctrinist
doctrinization
doctrinize
doctrix
document
documental
documentalist
documentarily
documentary
documentation
documentize
dod
dodd
doddart
dodded
dodder
doddered
dodderer
doddering
doddery
doddie
dodding
doddle
doddy
doddypoll
Dode
dodecade
dodecadrachm
dodecafid
dodecagon
dodecagonal
dodecahedral
dodecahedric
dodecahedron
dodecahydrate
dodecahydrated
dodecamerous
dodecane
Dodecanesian
dodecanoic
dodecant
dodecapartite
dodecapetalous
dodecarch
dodecarchy
dodecasemic
dodecastyle
dodecastylos
dodecasyllabic
dodecasyllable
dodecatemory
Dodecatheon
dodecatoic
dodecatyl
dodecatylic
dodecuplet
dodecyl
dodecylene
dodecylic
dodge
dodgeful
dodger
dodgery
dodgily
dodginess
dodgy
dodkin
dodlet
dodman
dodo
dodoism
Dodona
Dodonaea
Dodonaeaceae
Dodonaean
Dodonean
Dodonian
dodrans
doe
doebird
Doedicurus
Doeg
doeglic
doegling
doer
does
doeskin
doesnt
doest
doff
doffer
doftberry
dog
dogal
dogate
dogbane
Dogberry
dogberry
Dogberrydom
Dogberryism
dogbite
dogblow
dogboat
dogbolt
dogbush
dogcart
dogcatcher
dogdom
doge
dogedom
dogeless
dogeship
dogface
dogfall
dogfight
dogfish
dogfoot
dogged
doggedly
doggedness
dogger
doggerel
doggereler
doggerelism
doggerelist
doggerelize
doggerelizer
doggery
doggess
doggish
doggishly
doggishness
doggo
doggone
doggoned
doggrel
doggrelize
doggy
doghead
doghearted
doghole
doghood
doghouse
dogie
dogless
doglike
dogly
dogma
dogman
dogmata
dogmatic
dogmatical
dogmatically
dogmaticalness
dogmatician
dogmatics
dogmatism
dogmatist
dogmatization
dogmatize
dogmatizer
dogmouth
dogplate
dogproof
Dogra
Dogrib
dogs
dogship
dogshore
dogskin
dogsleep
dogstone
dogtail
dogtie
dogtooth
dogtoothing
dogtrick
dogtrot
dogvane
dogwatch
dogwood
dogy
doigt
doiled
doily
doina
doing
doings
doit
doited
doitkin
doitrified
doke
Doketic
Doketism
dokhma
dokimastic
Dokmarok
Doko
Dol
dola
dolabra
dolabrate
dolabriform
dolcan
dolcian
dolciano
dolcino
doldrum
doldrums
dole
dolefish
doleful
dolefully
dolefulness
dolefuls
dolent
dolently
dolerite
doleritic
dolerophanite
dolesman
dolesome
dolesomely
dolesomeness
doless
doli
dolia
dolichoblond
dolichocephal
dolichocephali
dolichocephalic
dolichocephalism
dolichocephalize
dolichocephalous
dolichocephaly
dolichocercic
dolichocnemic
dolichocranial
dolichofacial
Dolichoglossus
dolichohieric
Dolicholus
dolichopellic
dolichopodous
dolichoprosopic
Dolichopsyllidae
Dolichos
dolichos
dolichosaur
Dolichosauri
Dolichosauria
Dolichosaurus
Dolichosoma
dolichostylous
dolichotmema
dolichuric
dolichurus
Doliidae
dolina
doline
dolioform
Doliolidae
Doliolum
dolium
doll
dollar
dollarbird
dollardee
dollardom
dollarfish
dollarleaf
dollbeer
dolldom
dollface
dollfish
dollhood
dollhouse
dollier
dolliness
dollish
dollishly
dollishness
dollmaker
dollmaking
dollop
dollship
dolly
dollyman
dollyway
dolman
dolmen
dolmenic
Dolomedes
dolomite
dolomitic
dolomitization
dolomitize
dolomization
dolomize
dolor
Dolores
doloriferous
dolorific
dolorifuge
dolorous
dolorously
dolorousness
dolose
dolous
Dolph
dolphin
dolphinlike
Dolphus
dolt
dolthead
doltish
doltishly
doltishness
dom
domain
domainal
domal
domanial
domatium
domatophobia
domba
Dombeya
Domdaniel
dome
domelike
doment
domer
domesday
domestic
domesticable
domesticality
domestically
domesticate
domestication
domesticative
domesticator
domesticity
domesticize
domett
domeykite
domic
domical
domically
Domicella
domicile
domicilement
domiciliar
domiciliary
domiciliate
domiciliation
dominance
dominancy
dominant
dominantly
dominate
dominated
dominatingly
domination
dominative
dominator
domine
domineer
domineerer
domineering
domineeringly
domineeringness
dominial
Dominic
dominical
dominicale
Dominican
Dominick
dominie
dominion
dominionism
dominionist
Dominique
dominium
domino
dominus
domitable
domite
Domitian
domitic
domn
domnei
domoid
dompt
domy
Don
don
donable
Donacidae
donaciform
Donal
Donald
Donar
donary
donatary
donate
donated
donatee
Donatiaceae
donation
Donatism
Donatist
Donatistic
Donatistical
donative
donatively
donator
donatory
donatress
donax
doncella
Dondia
done
donee
Donet
doney
dong
donga
Dongola
Dongolese
dongon
Donia
donjon
donkey
donkeyback
donkeyish
donkeyism
donkeyman
donkeywork
Donmeh
Donn
Donna
donna
Donne
donnered
donnert
Donnie
donnish
donnishness
donnism
donnot
donor
donorship
donought
Donovan
donship
donsie
dont
donum
doob
doocot
doodab
doodad
Doodia
doodle
doodlebug
doodler
doodlesack
doohickey
doohickus
doohinkey
doohinkus
dooja
dook
dooket
dookit
dool
doolee
dooley
dooli
doolie
dooly
doom
doomage
doombook
doomer
doomful
dooms
doomsday
doomsman
doomstead
doon
door
doorba
doorbell
doorboy
doorbrand
doorcase
doorcheek
doored
doorframe
doorhead
doorjamb
doorkeeper
doorknob
doorless
doorlike
doormaid
doormaker
doormaking
doorman
doornail
doorplate
doorpost
doorsill
doorstead
doorstep
doorstone
doorstop
doorward
doorway
doorweed
doorwise
dooryard
dop
dopa
dopamelanin
dopaoxidase
dopatta
dope
dopebook
doper
dopester
dopey
doppelkummel
Dopper
dopper
doppia
Doppler
dopplerite
Dor
dor
Dora
dorab
dorad
Doradidae
dorado
doraphobia
Dorask
Doraskean
dorbeetle
Dorcas
dorcastry
Dorcatherium
Dorcopsis
doree
dorestane
dorhawk
Dori
doria
Dorian
Doric
Dorical
Doricism
Doricize
Dorididae
Dorine
Doris
Dorism
Dorize
dorje
Dorking
dorlach
dorlot
dorm
dormancy
dormant
dormer
dormered
dormie
dormient
dormilona
dormition
dormitive
dormitory
dormouse
dormy
dorn
dorneck
dornic
dornick
dornock
Dorobo
Doronicum
Dorosoma
Dorothea
Dorothy
dorp
dorsabdominal
dorsabdominally
dorsad
dorsal
dorsale
dorsalgia
dorsalis
dorsally
dorsalmost
dorsalward
dorsalwards
dorsel
dorser
dorsibranch
Dorsibranchiata
dorsibranchiate
dorsicollar
dorsicolumn
dorsicommissure
dorsicornu
dorsiduct
dorsiferous
dorsifixed
dorsiflex
dorsiflexion
dorsiflexor
dorsigrade
dorsilateral
dorsilumbar
dorsimedian
dorsimesal
dorsimeson
dorsiparous
dorsispinal
dorsiventral
dorsiventrality
dorsiventrally
dorsoabdominal
dorsoanterior
dorsoapical
Dorsobranchiata
dorsocaudad
dorsocaudal
dorsocentral
dorsocephalad
dorsocephalic
dorsocervical
dorsocervically
dorsodynia
dorsoepitrochlear
dorsointercostal
dorsointestinal
dorsolateral
dorsolumbar
dorsomedial
dorsomedian
dorsomesal
dorsonasal
dorsonuchal
dorsopleural
dorsoposteriad
dorsoposterior
dorsoradial
dorsosacral
dorsoscapular
dorsosternal
dorsothoracic
dorsoventrad
dorsoventral
dorsoventrally
Dorstenia
dorsulum
dorsum
dorsumbonal
dorter
dortiness
dortiship
dorts
dorty
doruck
Dory
dory
Doryanthes
Dorylinae
doryphorus
dos
dosa
dosadh
dosage
dose
doser
dosimeter
dosimetric
dosimetrician
dosimetrist
dosimetry
Dosinia
dosiology
dosis
Dositheans
dosology
doss
dossal
dossel
dosser
dosseret
dossier
dossil
dossman
Dot
dot
dotage
dotal
dotard
dotardism
dotardly
dotardy
dotate
dotation
dotchin
dote
doted
doter
Dothideacea
dothideaceous
Dothideales
Dothidella
dothienenteritis
Dothiorella
dotiness
doting
dotingly
dotingness
dotish
dotishness
dotkin
dotless
dotlike
Doto
Dotonidae
dotriacontane
dotted
dotter
dotterel
dottily
dottiness
dotting
dottle
dottler
Dottore
Dotty
dotty
doty
douar
double
doubled
doubledamn
doubleganger
doublegear
doublehanded
doublehandedly
doublehandedness
doublehatching
doublehearted
doubleheartedness
doublehorned
doubleleaf
doublelunged
doubleness
doubler
doublet
doubleted
doubleton
doubletone
doubletree
doublets
doubling
doubloon
doubly
doubt
doubtable
doubtably
doubtedly
doubter
doubtful
doubtfully
doubtfulness
doubting
doubtingly
doubtingness
doubtless
doubtlessly
doubtlessness
doubtmonger
doubtous
doubtsome
douc
douce
doucely
douceness
doucet
douche
doucin
doucine
doudle
Doug
dough
doughbird
doughboy
doughface
doughfaceism
doughfoot
doughhead
doughiness
doughlike
doughmaker
doughmaking
doughman
doughnut
dought
doughtily
doughtiness
doughty
doughy
Douglas
doulocracy
doum
doundake
doup
douping
dour
dourine
dourly
dourness
douse
douser
dout
douter
doutous
douzepers
douzieme
dove
dovecot
doveflower
dovefoot
dovehouse
dovekey
dovekie
dovelet
dovelike
doveling
dover
dovetail
dovetailed
dovetailer
dovetailwise
doveweed
dovewood
dovish
Dovyalis
dow
dowable
dowager
dowagerism
dowcet
dowd
dowdily
dowdiness
dowdy
dowdyish
dowdyism
dowed
dowel
dower
doweral
doweress
dowerless
dowery
dowf
dowie
Dowieism
Dowieite
dowily
dowiness
dowitch
dowitcher
dowl
dowlas
dowless
down
downbear
downbeard
downbeat
downby
downcast
downcastly
downcastness
downcome
downcomer
downcoming
downcry
downcurved
downcut
downdale
downdraft
downer
downface
downfall
downfallen
downfalling
downfeed
downflow
downfold
downfolded
downgate
downgone
downgrade
downgrowth
downhanging
downhaul
downheaded
downhearted
downheartedly
downheartedness
downhill
downily
downiness
Downing
Downingia
downland
downless
downlie
downlier
downligging
downlike
downline
downlooked
downlooker
downlying
downmost
downness
downpour
downpouring
downright
downrightly
downrightness
downrush
downrushing
downset
downshare
downshore
downside
downsinking
downsitting
downsliding
downslip
downslope
downsman
downspout
downstage
downstairs
downstate
downstater
downstream
downstreet
downstroke
downswing
downtake
downthrow
downthrown
downthrust
Downton
downtown
downtrampling
downtreading
downtrend
downtrodden
downtroddenness
downturn
downward
downwardly
downwardness
downway
downweed
downweigh
downweight
downweighted
downwind
downwith
downy
dowp
dowry
dowsabel
dowse
dowser
dowset
doxa
Doxantha
doxastic
doxasticon
doxographer
doxographical
doxography
doxological
doxologically
doxologize
doxology
doxy
Doyle
doze
dozed
dozen
dozener
dozenth
dozer
dozily
doziness
dozy
dozzled
drab
Draba
drabbet
drabbish
drabble
drabbler
drabbletail
drabbletailed
drabby
drably
drabness
Dracaena
Dracaenaceae
drachm
drachma
drachmae
drachmai
drachmal
dracma
Draco
Dracocephalum
Draconian
Draconianism
Draconic
draconic
Draconically
Draconid
Draconis
Draconism
draconites
draconitic
dracontian
dracontiasis
dracontic
dracontine
dracontites
Dracontium
dracunculus
draegerman
draff
draffman
draffy
draft
draftage
draftee
drafter
draftily
draftiness
drafting
draftman
draftmanship
draftproof
draftsman
draftsmanship
draftswoman
draftswomanship
draftwoman
drafty
drag
dragade
dragbar
dragbolt
dragged
dragger
draggily
dragginess
dragging
draggingly
draggle
draggletail
draggletailed
draggletailedly
draggletailedness
draggly
draggy
draghound
dragline
dragman
dragnet
drago
dragoman
dragomanate
dragomanic
dragomanish
dragon
dragonesque
dragoness
dragonet
dragonfish
dragonfly
dragonhead
dragonhood
dragonish
dragonism
dragonize
dragonkind
dragonlike
dragonnade
dragonroot
dragontail
dragonwort
dragoon
dragoonable
dragoonade
dragoonage
dragooner
dragrope
dragsaw
dragsawing
dragsman
dragstaff
drail
drain
drainable
drainage
drainboard
draine
drained
drainer
drainerman
drainless
drainman
drainpipe
draintile
draisine
drake
drakestone
drakonite
dram
drama
dramalogue
Dramamine
dramatic
dramatical
dramatically
dramaticism
dramatics
dramaticule
dramatism
dramatist
dramatizable
dramatization
dramatize
dramatizer
dramaturge
dramaturgic
dramaturgical
dramaturgist
dramaturgy
dramm
drammage
dramme
drammed
drammer
dramming
drammock
dramseller
dramshop
drang
drank
drant
drapable
Draparnaldia
drape
drapeable
draper
draperess
draperied
drapery
drapetomania
drapping
drassid
Drassidae
drastic
drastically
drat
dratchell
drate
dratted
dratting
draught
draughtboard
draughthouse
draughtman
draughtmanship
draughts
draughtsman
draughtsmanship
draughtswoman
draughtswomanship
Dravida
Dravidian
Dravidic
dravya
draw
drawable
drawarm
drawback
drawbar
drawbeam
drawbench
drawboard
drawbolt
drawbore
drawboy
drawbridge
Drawcansir
drawcut
drawdown
drawee
drawer
drawers
drawfile
drawfiling
drawgate
drawgear
drawglove
drawhead
drawhorse
drawing
drawk
drawknife
drawknot
drawl
drawlatch
drawler
drawling
drawlingly
drawlingness
drawlink
drawloom
drawly
drawn
drawnet
drawoff
drawout
drawplate
drawpoint
drawrod
drawshave
drawsheet
drawspan
drawspring
drawstop
drawstring
drawtongs
drawtube
dray
drayage
drayman
drazel
dread
dreadable
dreader
dreadful
dreadfully
dreadfulness
dreadingly
dreadless
dreadlessly
dreadlessness
dreadly
dreadness
dreadnought
dream
dreamage
dreamer
dreamery
dreamful
dreamfully
dreamfulness
dreamhole
dreamily
dreaminess
dreamingly
dreamish
dreamland
dreamless
dreamlessly
dreamlessness
dreamlet
dreamlike
dreamlit
dreamlore
dreamsily
dreamsiness
dreamsy
dreamt
dreamtide
dreamwhile
dreamwise
dreamworld
dreamy
drear
drearfully
drearily
dreariment
dreariness
drearisome
drearly
drearness
dreary
dredge
dredgeful
dredger
dredging
dree
dreep
dreepiness
dreepy
dreg
dreggily
dregginess
dreggish
dreggy
dregless
dregs
dreiling
Dreissensia
dreissiger
drench
drencher
drenching
drenchingly
dreng
drengage
Drepanaspis
Drepanidae
Drepanididae
drepaniform
Drepanis
drepanium
drepanoid
Dreparnaudia
dress
dressage
dressed
dresser
dressership
dressily
dressiness
dressing
dressline
dressmaker
dressmakership
dressmakery
dressmaking
dressy
drest
Drew
drew
drewite
Dreyfusism
Dreyfusist
drias
drib
dribble
dribblement
dribbler
driblet
driddle
dried
drier
drierman
driest
drift
driftage
driftbolt
drifter
drifting
driftingly
driftland
driftless
driftlessness
driftlet
driftman
driftpiece
driftpin
driftway
driftweed
driftwind
driftwood
drifty
drightin
drill
driller
drillet
drilling
drillman
drillmaster
drillstock
Drimys
dringle
drink
drinkability
drinkable
drinkableness
drinkably
drinker
drinking
drinkless
drinkproof
drinn
drip
dripper
dripping
dripple
dripproof
drippy
dripstick
dripstone
drisheen
drisk
drivable
drivage
drive
driveaway
driveboat
drivebolt
drivehead
drivel
driveler
drivelingly
driven
drivepipe
driver
driverless
drivership
drivescrew
driveway
drivewell
driving
drivingly
drizzle
drizzly
drochuil
droddum
drofland
drogh
drogher
drogherman
drogue
droit
droitsman
droitural
droiturel
Drokpa
droll
drollery
drollingly
drollish
drollishness
drollist
drollness
drolly
Dromaeognathae
dromaeognathism
dromaeognathous
Dromaeus
drome
dromedarian
dromedarist
dromedary
drometer
Dromiacea
dromic
Dromiceiidae
Dromiceius
Dromicia
dromograph
dromomania
dromometer
dromond
Dromornis
dromos
dromotropic
drona
dronage
drone
dronepipe
droner
drongo
droningly
dronish
dronishly
dronishness
dronkgrass
drony
drool
droop
drooper
drooping
droopingly
droopingness
droopt
droopy
drop
dropberry
dropcloth
dropflower
drophead
droplet
droplight
droplike
dropling
dropman
dropout
dropper
dropping
droppingly
droppy
dropseed
dropsical
dropsically
dropsicalness
dropsied
dropsy
dropsywort
dropt
dropwise
dropworm
dropwort
Droschken
Drosera
Droseraceae
droseraceous
droshky
drosky
drosograph
drosometer
Drosophila
Drosophilidae
Drosophyllum
dross
drossel
drosser
drossiness
drossless
drossy
drostdy
droud
drought
droughtiness
droughty
drouk
drove
drover
drovy
drow
drown
drowner
drowningly
drowse
drowsily
drowsiness
drowsy
drub
drubber
drubbing
drubbly
drucken
drudge
drudger
drudgery
drudgingly
drudgism
druery
drug
drugeteria
drugger
druggery
drugget
druggeting
druggist
druggister
druggy
drugless
drugman
drugshop
drugstore
druid
druidess
druidic
druidical
druidism
druidry
druith
Drukpa
drum
drumbeat
drumble
drumbledore
drumbler
drumfire
drumfish
drumhead
drumheads
drumlike
drumlin
drumline
drumlinoid
drumloid
drumloidal
drumly
drummer
drumming
drummy
drumskin
drumstick
drumwood
drung
drungar
drunk
drunkard
drunken
drunkenly
drunkenness
drunkensome
drunkenwise
drunkery
Drupa
Drupaceae
drupaceous
drupal
drupe
drupel
drupelet
drupeole
drupetum
drupiferous
Druse
druse
Drusean
Drusedom
drusy
druxiness
druxy
dry
dryad
dryadetum
dryadic
dryas
dryasdust
drybeard
drybrained
drycoal
Drydenian
Drydenism
dryfoot
drygoodsman
dryhouse
drying
dryish
dryly
Drynaria
dryness
Dryobalanops
Dryope
Dryopes
Dryophyllum
Dryopians
dryopithecid
Dryopithecinae
dryopithecine
Dryopithecus
Dryops
Dryopteris
dryopteroid
drysalter
drysaltery
dryster
dryth
dryworker
Dschubba
duad
duadic
dual
Duala
duali
dualin
dualism
dualist
dualistic
dualistically
duality
dualization
dualize
dually
Dualmutef
dualogue
Duane
duarch
duarchy
dub
dubash
dubb
dubba
dubbah
dubbeltje
dubber
dubbing
dubby
Dubhe
Dubhgall
dubiety
dubiocrystalline
dubiosity
dubious
dubiously
dubiousness
dubitable
dubitably
dubitancy
dubitant
dubitate
dubitatingly
dubitation
dubitative
dubitatively
Duboisia
duboisin
duboisine
Dubonnet
dubs
ducal
ducally
ducamara
ducape
ducat
ducato
ducatoon
ducdame
duces
Duchesnea
Duchess
duchess
duchesse
duchesslike
duchy
duck
duckbill
duckblind
duckboard
duckboat
ducker
duckery
duckfoot
duckhearted
duckhood
duckhouse
duckhunting
duckie
ducking
duckling
ducklingship
duckmeat
duckpin
duckpond
duckstone
duckweed
duckwife
duckwing
Duco
duct
ducted
ductibility
ductible
ductile
ductilely
ductileness
ductilimeter
ductility
ductilize
duction
ductless
ductor
ductule
Ducula
Duculinae
dud
dudaim
dudder
duddery
duddies
dude
dudeen
dudgeon
dudine
dudish
dudishness
dudism
dudler
dudley
Dudleya
dudleyite
dudman
due
duel
dueler
dueling
duelist
duelistic
duello
dueness
duenna
duennadom
duennaship
duer
Duessa
duet
duettist
duff
duffadar
duffel
duffer
dufferdom
duffing
dufoil
dufrenite
dufrenoysite
dufter
dufterdar
duftery
dug
dugal
dugdug
duggler
dugong
Dugongidae
dugout
dugway
duhat
Duhr
duiker
duikerbok
duim
Duit
duit
dujan
Duke
duke
dukedom
dukeling
dukely
dukery
dukeship
dukhn
dukker
dukkeripen
Dulanganes
Dulat
dulbert
dulcet
dulcetly
dulcetness
dulcian
dulciana
dulcification
dulcifluous
dulcify
dulcigenic
dulcimer
Dulcin
Dulcinea
Dulcinist
dulcitol
dulcitude
dulcose
duledge
duler
dulia
dull
dullard
dullardism
dullardness
dullbrained
duller
dullery
dullhead
dullhearted
dullification
dullify
dullish
dullity
dullness
dullpate
dullsome
dully
dulosis
dulotic
dulse
dulseman
dult
dultie
dulwilly
duly
dum
duma
dumaist
dumb
dumba
dumbbell
dumbbeller
dumbcow
dumbfounder
dumbfounderment
dumbhead
dumbledore
dumbly
dumbness
dumdum
dumetose
dumfound
dumfounder
dumfounderment
dummel
dummered
dumminess
dummy
dummyism
dummyweed
Dumontia
Dumontiaceae
dumontite
dumortierite
dumose
dumosity
dump
dumpage
dumpcart
dumper
dumpily
dumpiness
dumping
dumpish
dumpishly
dumpishness
dumple
dumpling
dumpoke
dumpy
dumsola
dun
dunair
dunal
dunbird
Duncan
dunce
duncedom
duncehood
duncery
dunch
Dunciad
duncical
duncify
duncish
duncishly
duncishness
dundasite
dunder
dunderhead
dunderheaded
dunderheadedness
dunderpate
dune
dunelike
dunfish
dung
Dungan
dungannonite
dungaree
dungbeck
dungbird
dungbred
dungeon
dungeoner
dungeonlike
dunger
dunghill
dunghilly
dungol
dungon
dungy
dungyard
dunite
dunk
dunkadoo
Dunkard
Dunker
dunker
Dunkirk
Dunkirker
Dunlap
dunlin
Dunlop
dunnage
dunne
dunner
dunness
dunnish
dunnite
dunnock
dunny
dunpickle
Duns
dunst
dunstable
dunt
duntle
duny
dunziekte
duo
duocosane
duodecahedral
duodecahedron
duodecane
duodecennial
duodecillion
duodecimal
duodecimality
duodecimally
duodecimfid
duodecimo
duodecimole
duodecuple
duodena
duodenal
duodenary
duodenate
duodenation
duodene
duodenectomy
duodenitis
duodenocholangitis
duodenocholecystostomy
duodenocholedochotomy
duodenocystostomy
duodenoenterostomy
duodenogram
duodenojejunal
duodenojejunostomy
duodenopancreatectomy
duodenoscopy
duodenostomy
duodenotomy
duodenum
duodrama
duograph
duogravure
duole
duoliteral
duologue
duomachy
duopod
duopolistic
duopoly
duopsonistic
duopsony
duosecant
duotone
duotriacontane
duotype
dup
dupability
dupable
dupe
dupedom
duper
dupery
dupion
dupla
duplation
duple
duplet
duplex
duplexity
duplicability
duplicable
duplicand
duplicate
duplication
duplicative
duplicator
duplicature
duplicia
duplicident
Duplicidentata
duplicidentate
duplicipennate
duplicitas
duplicity
duplification
duplify
duplone
dupondius
duppy
dura
durability
durable
durableness
durably
durain
dural
Duralumin
duramatral
duramen
durance
Durandarte
durangite
Durango
Durani
durant
Duranta
duraplasty
duraquara
duraspinalis
duration
durational
durationless
durative
durax
durbachite
Durban
durbar
durdenite
dure
durene
durenol
duress
duressor
durgan
Durham
durian
duridine
Durindana
during
duringly
Durio
durity
durmast
durn
duro
Duroc
durometer
duroquinone
durra
durrie
durrin
durry
durst
durukuli
durwaun
duryl
Duryodhana
Durzada
dusack
duscle
dush
dusio
dusk
dusken
duskily
duskiness
duskingtide
duskish
duskishly
duskishness
duskly
duskness
dusky
dust
dustbin
dustbox
dustcloth
dustee
duster
dusterman
dustfall
dustily
Dustin
dustiness
dusting
dustless
dustlessness
dustman
dustpan
dustproof
dustuck
dustwoman
dusty
dustyfoot
Dusun
Dutch
dutch
Dutcher
Dutchify
Dutchman
Dutchy
duteous
duteously
duteousness
dutiability
dutiable
dutied
dutiful
dutifully
dutifulness
dutra
duty
dutymonger
duumvir
duumviral
duumvirate
duvet
duvetyn
dux
duyker
dvaita
dvandva
dwale
dwalm
Dwamish
dwang
dwarf
dwarfish
dwarfishly
dwarfishness
dwarfism
dwarfling
dwarfness
dwarfy
dwayberry
Dwayne
dwell
dwelled
dweller
dwelling
dwelt
Dwight
dwindle
dwindlement
dwine
Dwyka
dyad
dyadic
Dyak
dyakisdodecahedron
Dyakish
dyarchic
dyarchical
dyarchy
Dyas
Dyassic
dyaster
Dyaus
dyce
dye
dyeable
dyehouse
dyeing
dyeleaves
dyemaker
dyemaking
dyer
dyester
dyestuff
dyeware
dyeweed
dyewood
dygogram
dying
dyingly
dyingness
dyke
dykehopper
dyker
dykereeve
Dylan
dynagraph
dynameter
dynametric
dynametrical
dynamic
dynamical
dynamically
dynamics
dynamis
dynamism
dynamist
dynamistic
dynamitard
dynamite
dynamiter
dynamitic
dynamitical
dynamitically
dynamiting
dynamitish
dynamitism
dynamitist
dynamization
dynamize
dynamo
dynamoelectric
dynamoelectrical
dynamogenesis
dynamogenic
dynamogenous
dynamogenously
dynamogeny
dynamometamorphic
dynamometamorphism
dynamometamorphosed
dynamometer
dynamometric
dynamometrical
dynamometry
dynamomorphic
dynamoneure
dynamophone
dynamostatic
dynamotor
dynast
Dynastes
dynastical
dynastically
dynasticism
dynastid
dynastidan
Dynastides
Dynastinae
dynasty
dynatron
dyne
dyophone
Dyophysite
Dyophysitic
Dyophysitical
Dyophysitism
dyotheism
Dyothelete
Dyotheletian
Dyotheletic
Dyotheletical
Dyotheletism
Dyothelism
dyphone
dysacousia
dysacousis
dysanalyte
dysaphia
dysarthria
dysarthric
dysarthrosis
dysbulia
dysbulic
dyschiria
dyschroa
dyschroia
dyschromatopsia
dyschromatoptic
dyschronous
dyscrasia
dyscrasial
dyscrasic
dyscrasite
dyscratic
dyscrystalline
dysenteric
dysenterical
dysentery
dysepulotic
dysepulotical
dyserethisia
dysergasia
dysergia
dysesthesia
dysesthetic
dysfunction
dysgenesic
dysgenesis
dysgenetic
dysgenic
dysgenical
dysgenics
dysgeogenous
dysgnosia
dysgraphia
dysidrosis
dyskeratosis
dyskinesia
dyskinetic
dyslalia
dyslexia
dyslogia
dyslogistic
dyslogistically
dyslogy
dysluite
dyslysin
dysmenorrhea
dysmenorrheal
dysmerism
dysmeristic
dysmerogenesis
dysmerogenetic
dysmeromorph
dysmeromorphic
dysmetria
dysmnesia
dysmorphism
dysmorphophobia
dysneuria
dysnomy
dysodile
dysodontiasis
dysorexia
dysorexy
dysoxidation
dysoxidizable
dysoxidize
dyspathetic
dyspathy
dyspepsia
dyspepsy
dyspeptic
dyspeptical
dyspeptically
dysphagia
dysphagic
dysphasia
dysphasic
dysphemia
dysphonia
dysphonic
dysphoria
dysphoric
dysphotic
dysphrasia
dysphrenia
dyspituitarism
dysplasia
dysplastic
dyspnea
dyspneal
dyspneic
dyspnoic
dysprosia
dysprosium
dysraphia
dyssnite
Dyssodia
dysspermatism
dyssynergia
dyssystole
dystaxia
dystectic
dysteleological
dysteleologist
dysteleology
dysthyroidism
dystocia
dystocial
dystome
dystomic
dystomous
dystrophia
dystrophic
dystrophy
dysuria
dysuric
dysyntribite
dytiscid
Dytiscidae
Dytiscus
dzeren
Dzungar
E
e
ea
each
eachwhere
eager
eagerly
eagerness
eagle
eaglelike
eagless
eaglestone
eaglet
eaglewood
eagre
ean
ear
earache
earbob
earcap
earcockle
eardrop
eardropper
eardrum
eared
earflower
earful
earhole
earing
earjewel
Earl
earl
earlap
earldom
Earle
earless
earlet
earlike
earliness
earlish
earlock
earlship
early
earmark
earn
earner
earnest
earnestly
earnestness
earnful
Earnie
earning
earnings
earphone
earpick
earpiece
earplug
earreach
earring
earringed
earscrew
earshot
earsore
earsplitting
eartab
earth
earthboard
earthborn
earthbred
earthdrake
earthed
earthen
earthenhearted
earthenware
earthfall
earthfast
earthgall
earthgrubber
earthian
earthiness
earthkin
earthless
earthlight
earthlike
earthliness
earthling
earthly
earthmaker
earthmaking
earthnut
earthpea
earthquake
earthquaked
earthquaken
earthquaking
Earthshaker
earthshine
earthshock
earthslide
earthsmoke
earthstar
earthtongue
earthwall
earthward
earthwards
earthwork
earthworm
earthy
earwax
earwig
earwigginess
earwiggy
earwitness
earworm
earwort
ease
easeful
easefully
easefulness
easel
easeless
easement
easer
easier
easiest
easily
easiness
easing
east
eastabout
eastbound
Easter
easter
easterling
easterly
Eastern
eastern
easterner
Easternism
Easternly
easternmost
Eastertide
easting
Eastlake
eastland
eastmost
Eastre
eastward
eastwardly
easy
easygoing
easygoingness
eat
eatability
eatable
eatableness
eatage
Eatanswill
eatberry
eaten
eater
eatery
eating
eats
eave
eaved
eavedrop
eaver
eaves
eavesdrop
eavesdropper
eavesdropping
ebb
ebbman
Eben
Ebenaceae
ebenaceous
Ebenales
ebeneous
Ebenezer
Eberthella
Ebionism
Ebionite
Ebionitic
Ebionitism
Ebionize
Eboe
eboe
ebon
ebonist
ebonite
ebonize
ebony
ebracteate
ebracteolate
ebriate
ebriety
ebriosity
ebrious
ebriously
ebullate
ebullience
ebulliency
ebullient
ebulliently
ebulliometer
ebullioscope
ebullioscopic
ebullioscopy
ebullition
ebullitive
ebulus
eburated
eburine
Eburna
eburnated
eburnation
eburnean
eburneoid
eburneous
eburnian
eburnification
ecad
ecalcarate
ecanda
ecardinal
Ecardines
ecarinate
ecarte
Ecaudata
ecaudate
Ecballium
ecbatic
ecblastesis
ecbole
ecbolic
Ecca
eccaleobion
eccentrate
eccentric
eccentrical
eccentrically
eccentricity
eccentring
eccentrometer
ecchondroma
ecchondrosis
ecchondrotome
ecchymoma
ecchymose
ecchymosis
ecclesia
ecclesial
ecclesiarch
ecclesiarchy
ecclesiast
Ecclesiastes
ecclesiastic
ecclesiastical
ecclesiastically
ecclesiasticism
ecclesiasticize
ecclesiastics
Ecclesiasticus
ecclesiastry
ecclesioclastic
ecclesiography
ecclesiolater
ecclesiolatry
ecclesiologic
ecclesiological
ecclesiologically
ecclesiologist
ecclesiology
ecclesiophobia
eccoprotic
eccoproticophoric
eccrinology
eccrisis
eccritic
eccyclema
eccyesis
ecdemic
ecdemite
ecderon
ecderonic
ecdysiast
ecdysis
ecesic
ecesis
ecgonine
eche
echea
echelette
echelon
echelonment
Echeloot
Echeneidae
echeneidid
Echeneididae
echeneidoid
Echeneis
Echeveria
echidna
Echidnidae
Echimys
Echinacea
echinal
echinate
echinid
Echinidea
echinital
echinite
Echinocactus
Echinocaris
Echinocereus
Echinochloa
echinochrome
echinococcus
Echinoderes
Echinoderidae
echinoderm
Echinoderma
echinodermal
Echinodermata
echinodermatous
echinodermic
Echinodorus
echinoid
Echinoidea
echinologist
echinology
Echinomys
Echinopanax
Echinops
echinopsine
Echinorhinidae
Echinorhinus
Echinorhynchus
Echinospermum
Echinosphaerites
Echinosphaeritidae
Echinostoma
Echinostomatidae
echinostome
echinostomiasis
Echinozoa
echinulate
echinulated
echinulation
echinuliform
echinus
Echis
echitamine
Echites
Echium
echiurid
Echiurida
echiuroid
Echiuroidea
Echiurus
echo
echoer
echoic
echoingly
echoism
echoist
echoize
echolalia
echolalic
echoless
echometer
echopractic
echopraxia
echowise
Echuca
eciliate
Eciton
ecize
Eckehart
ecklein
eclair
eclampsia
eclamptic
eclat
eclectic
eclectical
eclectically
eclecticism
eclecticize
Eclectics
eclectism
eclectist
eclegm
eclegma
eclipsable
eclipsareon
eclipsation
eclipse
eclipser
eclipsis
ecliptic
ecliptical
ecliptically
eclogite
eclogue
eclosion
ecmnesia
ecoid
ecole
ecologic
ecological
ecologically
ecologist
ecology
econometer
econometric
econometrician
econometrics
economic
economical
economically
economics
economism
economist
Economite
economization
economize
economizer
economy
ecophene
ecophobia
ecorticate
ecospecies
ecospecific
ecospecifically
ecostate
ecosystem
ecotonal
ecotone
ecotype
ecotypic
ecotypically
ecphonesis
ecphorable
ecphore
ecphoria
ecphorization
ecphorize
ecphrasis
ecrasite
ecru
ecrustaceous
ecstasis
ecstasize
ecstasy
ecstatic
ecstatica
ecstatical
ecstatically
ecstaticize
ecstrophy
ectad
ectadenia
ectal
ectally
ectasia
ectasis
ectatic
ectene
ectental
ectepicondylar
ectethmoid
ectethmoidal
Ecthesis
ecthetically
ecthlipsis
ecthyma
ectiris
ectobatic
ectoblast
ectoblastic
ectobronchium
ectocardia
Ectocarpaceae
ectocarpaceous
Ectocarpales
ectocarpic
ectocarpous
Ectocarpus
ectocinerea
ectocinereal
ectocoelic
ectocondylar
ectocondyle
ectocondyloid
ectocornea
ectocranial
ectocuneiform
ectocuniform
ectocyst
ectodactylism
ectoderm
ectodermal
ectodermic
ectodermoidal
ectodermosis
ectodynamomorphic
ectoentad
ectoenzyme
ectoethmoid
ectogenesis
ectogenic
ectogenous
ectoglia
Ectognatha
ectolecithal
ectoloph
ectomere
ectomeric
ectomesoblast
ectomorph
ectomorphic
ectomorphy
ectonephridium
ectoparasite
ectoparasitic
Ectoparasitica
ectopatagium
ectophloic
ectophyte
ectophytic
ectopia
ectopic
Ectopistes
ectoplacenta
ectoplasm
ectoplasmatic
ectoplasmic
ectoplastic
ectoplasy
Ectoprocta
ectoproctan
ectoproctous
ectopterygoid
ectopy
ectoretina
ectorganism
ectorhinal
ectosarc
ectosarcous
ectoskeleton
ectosomal
ectosome
ectosphenoid
ectosphenotic
ectosphere
ectosteal
ectosteally
ectostosis
ectotheca
ectotoxin
Ectotrophi
ectotrophic
ectozoa
ectozoan
ectozoic
ectozoon
ectrodactylia
ectrodactylism
ectrodactyly
ectrogenic
ectrogeny
ectromelia
ectromelian
ectromelic
ectromelus
ectropion
ectropium
ectropometer
ectrosyndactyly
ectypal
ectype
ectypography
Ecuadoran
Ecuadorian
ecuelling
ecumenic
ecumenical
ecumenicalism
ecumenicality
ecumenically
ecumenicity
ecyphellate
eczema
eczematization
eczematoid
eczematosis
eczematous
Ed
edacious
edaciously
edaciousness
edacity
Edana
edaphic
edaphology
edaphon
Edaphosauria
Edaphosaurus
Edda
Eddaic
edder
Eddic
Eddie
eddish
eddo
Eddy
eddy
eddyroot
edea
edeagra
edeitis
edelweiss
edema
edematous
edemic
Eden
Edenic
edenite
Edenization
Edenize
edental
edentalous
Edentata
edentate
edentulate
edentulous
edeodynia
edeology
edeomania
edeoscopy
edeotomy
Edessan
edestan
edestin
Edestosaurus
Edgar
edge
edgebone
edged
edgeless
edgemaker
edgemaking
edgeman
edger
edgerman
edgeshot
edgestone
edgeways
edgeweed
edgewise
edginess
edging
edgingly
edgrew
edgy
edh
edibility
edible
edibleness
edict
edictal
edictally
edicule
edificable
edification
edificator
edificatory
edifice
edificial
edifier
edify
edifying
edifyingly
edifyingness
edingtonite
edit
edital
Edith
edition
editor
editorial
editorialize
editorially
editorship
editress
Ediya
Edmond
Edmund
Edna
Edo
Edomite
Edomitish
Edoni
Edriasteroidea
Edrioasteroid
Edrioasteroidea
Edriophthalma
edriophthalmatous
edriophthalmian
edriophthalmic
edriophthalmous
Eduardo
Educabilia
educabilian
educability
educable
educand
educatable
educate
educated
educatee
education
educationable
educational
educationalism
educationalist
educationally
educationary
educationist
educative
educator
educatory
educatress
educe
educement
educible
educive
educt
eduction
eductive
eductor
edulcorate
edulcoration
edulcorative
edulcorator
Eduskunta
Edward
Edwardean
Edwardeanism
Edwardian
Edwardine
Edwardsia
Edwardsiidae
Edwin
Edwina
eegrass
eel
eelboat
eelbob
eelbobber
eelcake
eelcatcher
eeler
eelery
eelfare
eelfish
eelgrass
eellike
eelpot
eelpout
eelshop
eelskin
eelspear
eelware
eelworm
eely
eer
eerie
eerily
eeriness
eerisome
effable
efface
effaceable
effacement
effacer
effect
effecter
effectful
effectible
effective
effectively
effectiveness
effectivity
effectless
effector
effects
effectual
effectuality
effectualize
effectually
effectualness
effectuate
effectuation
effeminacy
effeminate
effeminately
effeminateness
effemination
effeminatize
effeminization
effeminize
effendi
efferent
effervesce
effervescence
effervescency
effervescent
effervescible
effervescingly
effervescive
effete
effeteness
effetman
efficacious
efficaciously
efficaciousness
efficacity
efficacy
efficience
efficiency
efficient
efficiently
Effie
effigial
effigiate
effigiation
effigurate
effiguration
effigy
efflate
efflation
effloresce
efflorescence
efflorescency
efflorescent
efflower
effluence
effluency
effluent
effluvia
effluvial
effluviate
effluviography
effluvious
effluvium
efflux
effluxion
effodient
Effodientia
efform
efformation
efformative
effort
effortful
effortless
effortlessly
effossion
effraction
effranchise
effranchisement
effrontery
effulge
effulgence
effulgent
effulgently
effund
effuse
effusiometer
effusion
effusive
effusively
effusiveness
Efik
eflagelliferous
efoliolate
efoliose
efoveolate
eft
eftest
eftsoons
egad
egalitarian
egalitarianism
egality
Egba
Egbert
Egbo
egence
egeran
Egeria
egest
egesta
egestion
egestive
egg
eggberry
eggcup
eggcupful
eggeater
egger
eggfish
eggfruit
egghead
egghot
egging
eggler
eggless
egglike
eggnog
eggplant
eggshell
eggy
egilops
egipto
Eglamore
eglandular
eglandulose
eglantine
eglatere
eglestonite
egma
ego
egocentric
egocentricity
egocentrism
Egocerus
egohood
egoism
egoist
egoistic
egoistical
egoistically
egoity
egoize
egoizer
egol
egolatrous
egomania
egomaniac
egomaniacal
egomism
egophonic
egophony
egosyntonic
egotheism
egotism
egotist
egotistic
egotistical
egotistically
egotize
egregious
egregiously
egregiousness
egress
egression
egressive
egressor
egret
Egretta
egrimony
egueiite
egurgitate
eguttulate
Egypt
Egyptian
Egyptianism
Egyptianization
Egyptianize
Egyptize
Egyptologer
Egyptologic
Egyptological
Egyptologist
Egyptology
eh
Ehatisaht
eheu
ehlite
Ehretia
Ehretiaceae
ehrwaldite
ehuawa
eichbergite
Eichhornia
eichwaldite
eicosane
eident
eidently
eider
eidetic
eidograph
eidolic
eidolism
eidology
eidolology
eidolon
eidoptometry
eidouranion
eigenfunction
eigenvalue
eight
eighteen
eighteenfold
eighteenmo
eighteenth
eighteenthly
eightfoil
eightfold
eighth
eighthly
eightieth
eightling
eightpenny
eightscore
eightsman
eightsome
eighty
eightyfold
eigne
Eikonogen
eikonology
Eileen
Eimak
eimer
Eimeria
einkorn
Einsteinian
Eireannach
Eirene
eiresione
eisegesis
eisegetical
eisodic
eisteddfod
eisteddfodic
eisteddfodism
either
ejaculate
ejaculation
ejaculative
ejaculator
ejaculatory
Ejam
eject
ejecta
ejectable
ejection
ejective
ejectively
ejectivity
ejectment
ejector
ejicient
ejoo
ekaboron
ekacaesium
ekaha
ekamanganese
ekasilicon
ekatantalum
eke
ekebergite
eker
ekerite
eking
ekka
Ekoi
ekphore
Ekron
Ekronite
ektene
ektenes
ektodynamorphic
el
elaborate
elaborately
elaborateness
elaboration
elaborative
elaborator
elaboratory
elabrate
Elachista
Elachistaceae
elachistaceous
Elaeagnaceae
elaeagnaceous
Elaeagnus
Elaeis
elaeoblast
elaeoblastic
Elaeocarpaceae
elaeocarpaceous
Elaeocarpus
Elaeococca
Elaeodendron
elaeodochon
elaeomargaric
elaeometer
elaeoptene
elaeosaccharum
elaeothesium
elaidate
elaidic
elaidin
elaidinic
elain
Elaine
elaine
elaioleucite
elaioplast
elaiosome
Elamite
Elamitic
Elamitish
elance
eland
elanet
Elanus
Elaphe
Elaphebolion
elaphine
Elaphodus
Elaphoglossum
Elaphomyces
Elaphomycetaceae
Elaphrium
elaphure
elaphurine
Elaphurus
elapid
Elapidae
Elapinae
elapine
elapoid
Elaps
elapse
Elapsoidea
elasmobranch
elasmobranchian
elasmobranchiate
Elasmobranchii
elasmosaur
Elasmosaurus
elasmothere
Elasmotherium
elastance
elastic
elastica
elastically
elastician
elasticin
elasticity
elasticize
elasticizer
elasticness
elastin
elastivity
elastomer
elastomeric
elastometer
elastometry
elastose
elatcha
elate
elated
elatedly
elatedness
elater
elaterid
Elateridae
elaterin
elaterite
elaterium
elateroid
Elatha
Elatinaceae
elatinaceous
Elatine
elation
elative
elator
elatrometer
elb
Elbert
Elberta
elbow
elbowboard
elbowbush
elbowchair
elbowed
elbower
elbowpiece
elbowroom
elbowy
elcaja
elchee
eld
elder
elderberry
elderbrotherhood
elderbrotherish
elderbrotherly
elderbush
elderhood
elderliness
elderly
elderman
eldership
eldersisterly
elderwoman
elderwood
elderwort
eldest
eldin
elding
Eldred
eldress
eldritch
Elean
Eleanor
Eleatic
Eleaticism
Eleazar
elecampane
elect
electable
electee
electicism
election
electionary
electioneer
electioneerer
elective
electively
electiveness
electivism
electivity
electly
elector
electoral
electorally
electorate
electorial
electorship
Electra
electragist
electragy
electralize
electrepeter
electress
electret
electric
electrical
electricalize
electrically
electricalness
electrician
electricity
electricize
electrics
electriferous
electrifiable
electrification
electrifier
electrify
electrion
electrionic
electrizable
electrization
electrize
electrizer
electro
electroacoustic
electroaffinity
electroamalgamation
electroanalysis
electroanalytic
electroanalytical
electroanesthesia
electroballistic
electroballistics
electrobath
electrobiological
electrobiologist
electrobiology
electrobioscopy
electroblasting
electrobrasser
electrobus
electrocapillarity
electrocapillary
electrocardiogram
electrocardiograph
electrocardiographic
electrocardiography
electrocatalysis
electrocatalytic
electrocataphoresis
electrocataphoretic
electrocauterization
electrocautery
electroceramic
electrochemical
electrochemically
electrochemist
electrochemistry
electrochronograph
electrochronographic
electrochronometer
electrochronometric
electrocoagulation
electrocoating
electrocolloidal
electrocontractility
electrocorticogram
electroculture
electrocute
electrocution
electrocutional
electrocutioner
electrocystoscope
electrode
electrodeless
electrodentistry
electrodeposit
electrodepositable
electrodeposition
electrodepositor
electrodesiccate
electrodesiccation
electrodiagnosis
electrodialysis
electrodialyze
electrodialyzer
electrodiplomatic
electrodispersive
electrodissolution
electrodynamic
electrodynamical
electrodynamics
electrodynamism
electrodynamometer
electroencephalogram
electroencephalograph
electroencephalography
electroendosmose
electroendosmosis
electroendosmotic
electroengrave
electroengraving
electroergometer
electroetching
electroethereal
electroextraction
electroform
electroforming
electrofuse
electrofused
electrofusion
electrogalvanic
electrogalvanize
electrogenesis
electrogenetic
electrogild
electrogilding
electrogilt
electrograph
electrographic
electrographite
electrography
electroharmonic
electrohemostasis
electrohomeopathy
electrohorticulture
electrohydraulic
electroimpulse
electroindustrial
electroionic
electroirrigation
electrokinematics
electrokinetic
electrokinetics
electrolier
electrolithotrity
electrologic
electrological
electrologist
electrology
electroluminescence
electroluminescent
electrolysis
electrolyte
electrolytic
electrolytical
electrolytically
electrolyzability
electrolyzable
electrolyzation
electrolyze
electrolyzer
electromagnet
electromagnetic
electromagnetical
electromagnetically
electromagnetics
electromagnetism
electromagnetist
electromassage
electromechanical
electromechanics
electromedical
electromer
electromeric
electromerism
electrometallurgical
electrometallurgist
electrometallurgy
electrometer
electrometric
electrometrical
electrometrically
electrometry
electromobile
electromobilism
electromotion
electromotive
electromotivity
electromotograph
electromotor
electromuscular
electromyographic
electron
electronarcosis
electronegative
electronervous
electronic
electronics
electronographic
electrooptic
electrooptical
electrooptically
electrooptics
electroosmosis
electroosmotic
electroosmotically
electrootiatrics
electropathic
electropathology
electropathy
electropercussive
electrophobia
electrophone
electrophore
electrophoresis
electrophoretic
electrophoric
Electrophoridae
electrophorus
electrophotometer
electrophotometry
electrophototherapy
electrophrenic
electrophysics
electrophysiological
electrophysiologist
electrophysiology
electropism
electroplate
electroplater
electroplating
electroplax
electropneumatic
electropneumatically
electropoion
electropolar
electropositive
electropotential
electropower
electropsychrometer
electropult
electropuncturation
electropuncture
electropuncturing
electropyrometer
electroreceptive
electroreduction
electrorefine
electroscission
electroscope
electroscopic
electrosherardizing
electroshock
electrosmosis
electrostatic
electrostatical
electrostatically
electrostatics
electrosteel
electrostenolysis
electrostenolytic
electrostereotype
electrostriction
electrosurgery
electrosurgical
electrosynthesis
electrosynthetic
electrosynthetically
electrotactic
electrotautomerism
electrotaxis
electrotechnic
electrotechnical
electrotechnician
electrotechnics
electrotechnology
electrotelegraphic
electrotelegraphy
electrotelethermometer
electrotellurograph
electrotest
electrothanasia
electrothanatosis
electrotherapeutic
electrotherapeutical
electrotherapeutics
electrotherapeutist
electrotherapist
electrotherapy
electrothermal
electrothermancy
electrothermic
electrothermics
electrothermometer
electrothermostat
electrothermostatic
electrothermotic
electrotitration
electrotonic
electrotonicity
electrotonize
electrotonus
electrotrephine
electrotropic
electrotropism
electrotype
electrotyper
electrotypic
electrotyping
electrotypist
electrotypy
electrovalence
electrovalency
electrovection
electroviscous
electrovital
electrowin
electrum
electuary
eleemosynarily
eleemosynariness
eleemosynary
elegance
elegancy
elegant
elegantly
elegiac
elegiacal
elegiambic
elegiambus
elegiast
elegist
elegit
elegize
elegy
eleidin
element
elemental
elementalism
elementalist
elementalistic
elementalistically
elementality
elementalize
elementally
elementarily
elementariness
elementary
elementoid
elemi
elemicin
elemin
elench
elenchi
elenchic
elenchical
elenchically
elenchize
elenchtic
elenchtical
elenctic
elenge
eleoblast
Eleocharis
eleolite
eleomargaric
eleometer
eleonorite
eleoptene
eleostearate
eleostearic
elephant
elephanta
elephantiac
elephantiasic
elephantiasis
elephantic
elephanticide
Elephantidae
elephantine
elephantlike
elephantoid
elephantoidal
Elephantopus
elephantous
elephantry
Elephas
Elettaria
Eleusine
Eleusinia
Eleusinian
Eleusinion
Eleut
eleutherarch
Eleutheri
Eleutheria
Eleutherian
Eleutherios
eleutherism
eleutherodactyl
Eleutherodactyli
Eleutherodactylus
eleutheromania
eleutheromaniac
eleutheromorph
eleutheropetalous
eleutherophyllous
eleutherosepalous
Eleutherozoa
eleutherozoan
elevate
elevated
elevatedly
elevatedness
elevating
elevatingly
elevation
elevational
elevator
elevatory
eleven
elevener
elevenfold
eleventh
eleventhly
elevon
elf
elfenfolk
elfhood
elfic
elfin
elfinwood
elfish
elfishly
elfishness
elfkin
elfland
elflike
elflock
elfship
elfwife
elfwort
Eli
Elia
Elian
Elianic
Elias
eliasite
elicit
elicitable
elicitate
elicitation
elicitor
elicitory
elide
elidible
eligibility
eligible
eligibleness
eligibly
Elihu
Elijah
eliminable
eliminand
eliminant
eliminate
elimination
eliminative
eliminator
eliminatory
Elinor
Elinvar
Eliot
Eliphalet
eliquate
eliquation
Elisabeth
Elisha
Elishah
elision
elisor
Elissa
elite
elixir
Eliza
Elizabeth
Elizabethan
Elizabethanism
Elizabethanize
elk
Elkanah
Elkdom
Elkesaite
elkhorn
elkhound
Elkoshite
elkslip
Elkuma
elkwood
ell
Ella
ellachick
ellagate
ellagic
ellagitannin
Ellasar
elle
elleck
Ellen
ellenyard
Ellerian
ellfish
Ellice
Ellick
Elliot
Elliott
ellipse
ellipses
ellipsis
ellipsograph
ellipsoid
ellipsoidal
ellipsone
ellipsonic
elliptic
elliptical
elliptically
ellipticalness
ellipticity
elliptograph
elliptoid
ellops
ellwand
elm
Elmer
elmy
Eloah
elocular
elocute
elocution
elocutionary
elocutioner
elocutionist
elocutionize
elod
Elodea
Elodeaceae
Elodes
eloge
elogium
Elohim
Elohimic
Elohism
Elohist
Elohistic
eloign
eloigner
eloignment
Eloise
Elon
elongate
elongated
elongation
elongative
Elonite
elope
elopement
eloper
Elopidae
elops
eloquence
eloquent
eloquential
eloquently
eloquentness
Elotherium
elotillo
elpasolite
elpidite
Elric
els
Elsa
else
elsehow
elsewards
elseways
elsewhen
elsewhere
elsewheres
elsewhither
elsewise
Elsholtzia
elsin
elt
eluate
elucidate
elucidation
elucidative
elucidator
elucidatory
elucubrate
elucubration
elude
eluder
elusion
elusive
elusively
elusiveness
elusoriness
elusory
elute
elution
elutor
elutriate
elutriation
elutriator
eluvial
eluviate
eluviation
eluvium
elvan
elvanite
elvanitic
elver
elves
elvet
Elvira
Elvis
elvish
elvishly
Elwood
elydoric
Elymi
Elymus
Elysee
Elysia
elysia
Elysian
Elysiidae
Elysium
elytral
elytriferous
elytriform
elytrigerous
elytrin
elytrocele
elytroclasia
elytroid
elytron
elytroplastic
elytropolypus
elytroposis
elytrorhagia
elytrorrhagia
elytrorrhaphy
elytrostenosis
elytrotomy
elytrous
elytrum
Elzevir
Elzevirian
Em
em
emaciate
emaciation
emajagua
emanant
emanate
emanation
emanational
emanationism
emanationist
emanatism
emanatist
emanatistic
emanativ
emanative
emanatively
emanator
emanatory
emancipate
emancipation
emancipationist
emancipatist
emancipative
emancipator
emancipatory
emancipatress
emancipist
emandibulate
emanium
emarcid
emarginate
emarginately
emargination
Emarginula
emasculate
emasculation
emasculative
emasculator
emasculatory
Embadomonas
emball
emballonurid
Emballonuridae
emballonurine
embalm
embalmer
embalmment
embank
embankment
embannered
embar
embargo
embargoist
embark
embarkation
embarkment
embarras
embarrass
embarrassed
embarrassedly
embarrassing
embarrassingly
embarrassment
embarrel
embassage
embassy
embastioned
embathe
embatholithic
embattle
embattled
embattlement
embay
embayment
Embden
embed
embedment
embeggar
Embelia
embelic
embellish
embellisher
embellishment
ember
embergoose
Emberiza
emberizidae
Emberizinae
emberizine
embezzle
embezzlement
embezzler
Embiidae
Embiidina
embind
Embiodea
Embioptera
embiotocid
Embiotocidae
embiotocoid
embira
embitter
embitterer
embitterment
emblaze
emblazer
emblazon
emblazoner
emblazonment
emblazonry
emblem
emblema
emblematic
emblematical
emblematically
emblematicalness
emblematicize
emblematist
emblematize
emblematology
emblement
emblemist
emblemize
emblemology
emblic
emblossom
embodier
embodiment
embody
embog
emboitement
embolden
emboldener
embole
embolectomy
embolemia
embolic
emboliform
embolism
embolismic
embolismus
embolite
embolium
embolize
embolo
embololalia
Embolomeri
embolomerism
embolomerous
embolomycotic
embolum
embolus
emboly
emborder
emboscata
embosom
emboss
embossage
embosser
embossing
embossman
embossment
embosture
embottle
embouchure
embound
embow
embowed
embowel
emboweler
embowelment
embower
embowerment
embowment
embox
embrace
embraceable
embraceably
embracement
embraceor
embracer
embracery
embracing
embracingly
embracingness
embracive
embrail
embranchment
embrangle
embranglement
embrasure
embreathe
embreathement
Embrica
embright
embrittle
embrittlement
embroaden
embrocate
embrocation
embroider
embroiderer
embroideress
embroidery
embroil
embroiler
embroilment
embronze
embrown
embryectomy
embryo
embryocardia
embryoctonic
embryoctony
embryoferous
embryogenesis
embryogenetic
embryogenic
embryogeny
embryogony
embryographer
embryographic
embryography
embryoid
embryoism
embryologic
embryological
embryologically
embryologist
embryology
embryoma
embryon
embryonal
embryonary
embryonate
embryonated
embryonic
embryonically
embryoniferous
embryoniform
embryony
embryopathology
embryophagous
embryophore
Embryophyta
embryophyte
embryoplastic
embryoscope
embryoscopic
embryotega
embryotic
embryotome
embryotomy
embryotrophic
embryotrophy
embryous
embryulcia
embryulcus
embubble
embuia
embus
embusk
embuskin
emcee
eme
emeer
emeership
Emeline
emend
emendable
emendandum
emendate
emendation
emendator
emendatory
emender
emerald
emeraldine
emeraude
emerge
emergence
emergency
emergent
emergently
emergentness
Emerita
emerited
emeritus
emerize
emerse
emersed
emersion
Emersonian
Emersonianism
Emery
emery
Emesa
Emesidae
emesis
emetatrophia
emetic
emetically
emetine
emetocathartic
emetology
emetomorphine
emgalla
emication
emiction
emictory
emigrant
emigrate
emigration
emigrational
emigrationist
emigrative
emigrator
emigratory
emigree
Emil
Emilia
Emily
Emim
eminence
eminency
eminent
eminently
emir
emirate
emirship
emissarium
emissary
emissaryship
emissile
emission
emissive
emissivity
emit
emittent
emitter
Emm
Emma
emma
Emmanuel
emmarble
emmarvel
emmenagogic
emmenagogue
emmenic
emmeniopathy
emmenology
emmensite
Emmental
emmer
emmergoose
emmet
emmetrope
emmetropia
emmetropic
emmetropism
emmetropy
Emmett
emodin
emollescence
emolliate
emollient
emoloa
emolument
emolumental
emolumentary
emote
emotion
emotionable
emotional
emotionalism
emotionalist
emotionality
emotionalization
emotionalize
emotionally
emotioned
emotionist
emotionize
emotionless
emotionlessness
emotive
emotively
emotiveness
emotivity
empacket
empaistic
empall
empanel
empanelment
empanoply
empaper
emparadise
emparchment
empark
empasm
empathic
empathically
empathize
empathy
Empedoclean
empeirema
Empeo
emperor
emperorship
empery
Empetraceae
empetraceous
Empetrum
emphases
emphasis
emphasize
emphatic
emphatical
emphatically
emphaticalness
emphlysis
emphractic
emphraxis
emphysema
emphysematous
emphyteusis
emphyteuta
emphyteutic
empicture
Empididae
Empidonax
empiecement
Empire
empire
empirema
empiric
empirical
empiricalness
empiricism
empiricist
empirics
empiriocritcism
empiriocritical
empiriological
empirism
empiristic
emplace
emplacement
emplane
emplastic
emplastration
emplastrum
emplectite
empleomania
employ
employability
employable
employed
employee
employer
employless
employment
emplume
empocket
empodium
empoison
empoisonment
emporetic
emporeutic
emporia
emporial
emporium
empower
empowerment
empress
emprise
emprosthotonic
emprosthotonos
emprosthotonus
empt
emptier
emptily
emptiness
emptings
emptins
emption
emptional
emptor
empty
emptyhearted
emptysis
empurple
Empusa
empyema
empyemic
empyesis
empyocele
empyreal
empyrean
empyreuma
empyreumatic
empyreumatical
empyreumatize
empyromancy
emu
emulable
emulant
emulate
emulation
emulative
emulatively
emulator
emulatory
emulatress
emulgence
emulgent
emulous
emulously
emulousness
emulsibility
emulsible
emulsifiability
emulsifiable
emulsification
emulsifier
emulsify
emulsin
emulsion
emulsionize
emulsive
emulsoid
emulsor
emunctory
emundation
emyd
Emydea
emydian
Emydidae
Emydinae
Emydosauria
emydosaurian
Emys
en
enable
enablement
enabler
enact
enactable
enaction
enactive
enactment
enactor
enactory
enaena
enage
Enajim
enalid
Enaliornis
enaliosaur
Enaliosauria
enaliosaurian
enallachrome
enallage
enaluron
enam
enamber
enambush
enamdar
enamel
enameler
enameling
enamelist
enamelless
enamellist
enameloma
enamelware
enamor
enamorato
enamored
enamoredness
enamorment
enamourment
enanguish
enanthem
enanthema
enanthematous
enanthesis
enantiobiosis
enantioblastic
enantioblastous
enantiomer
enantiomeride
enantiomorph
enantiomorphic
enantiomorphism
enantiomorphous
enantiomorphously
enantiomorphy
enantiopathia
enantiopathic
enantiopathy
enantiosis
enantiotropic
enantiotropy
enantobiosis
enapt
enarbor
enarbour
enarch
enarched
enargite
enarm
enarme
enarthrodia
enarthrodial
enarthrosis
enate
enatic
enation
enbrave
encaenia
encage
encake
encalendar
encallow
encamp
encampment
encanker
encanthis
encapsulate
encapsulation
encapsule
encarditis
encarnadine
encarnalize
encarpium
encarpus
encase
encasement
encash
encashable
encashment
encasserole
encastage
encatarrhaphy
encauma
encaustes
encaustic
encaustically
encave
encefalon
Encelia
encell
encenter
encephala
encephalalgia
Encephalartos
encephalasthenia
encephalic
encephalin
encephalitic
encephalitis
encephalocele
encephalocoele
encephalodialysis
encephalogram
encephalograph
encephalography
encephaloid
encephalolith
encephalology
encephaloma
encephalomalacia
encephalomalacosis
encephalomalaxis
encephalomeningitis
encephalomeningocele
encephalomere
encephalomeric
encephalometer
encephalometric
encephalomyelitis
encephalomyelopathy
encephalon
encephalonarcosis
encephalopathia
encephalopathic
encephalopathy
encephalophyma
encephalopsychesis
encephalopyosis
encephalorrhagia
encephalosclerosis
encephaloscope
encephaloscopy
encephalosepsis
encephalospinal
encephalothlipsis
encephalotome
encephalotomy
encephalous
enchain
enchainment
enchair
enchalice
enchannel
enchant
enchanter
enchanting
enchantingly
enchantingness
enchantment
enchantress
encharge
encharnel
enchase
enchaser
enchasten
Enchelycephali
enchequer
enchest
enchilada
enchiridion
Enchodontid
Enchodontidae
Enchodontoid
Enchodus
enchondroma
enchondromatous
enchondrosis
enchorial
enchurch
enchylema
enchylematous
enchymatous
enchytrae
enchytraeid
Enchytraeidae
Enchytraeus
encina
encinal
encincture
encinder
encinillo
encipher
encircle
encirclement
encircler
encist
encitadel
enclaret
enclasp
enclave
enclavement
enclisis
enclitic
enclitical
enclitically
encloak
encloister
enclose
encloser
enclosure
enclothe
encloud
encoach
encode
encoffin
encoignure
encoil
encolden
encollar
encolor
encolpion
encolumn
encomendero
encomia
encomiast
encomiastic
encomiastical
encomiastically
encomic
encomienda
encomiologic
encomium
encommon
encompass
encompasser
encompassment
encoop
encorbelment
encore
encoronal
encoronate
encoronet
encounter
encounterable
encounterer
encourage
encouragement
encourager
encouraging
encouragingly
encowl
encraal
encradle
encranial
encratic
Encratism
Encratite
encraty
encreel
encrimson
encrinal
encrinic
Encrinidae
encrinidae
encrinital
encrinite
encrinitic
encrinitical
encrinoid
Encrinoidea
Encrinus
encrisp
encroach
encroacher
encroachingly
encroachment
encrotchet
encrown
encrownment
encrust
encrustment
encrypt
encryption
encuirassed
encumber
encumberer
encumberingly
encumberment
encumbrance
encumbrancer
encup
encurl
encurtain
encushion
encyclic
encyclical
encyclopedia
encyclopediac
encyclopediacal
encyclopedial
encyclopedian
encyclopediast
encyclopedic
encyclopedically
encyclopedism
encyclopedist
encyclopedize
encyrtid
Encyrtidae
encyst
encystation
encystment
end
endable
endamage
endamageable
endamagement
endamask
endameba
endamebic
Endamoeba
endamoebiasis
endamoebic
Endamoebidae
endanger
endangerer
endangerment
endangium
endaortic
endaortitis
endarch
endarchy
endarterial
endarteritis
endarterium
endaspidean
endaze
endboard
endbrain
endear
endearance
endeared
endearedly
endearedness
endearing
endearingly
endearingness
endearment
endeavor
endeavorer
ended
endeictic
endellionite
endemial
endemic
endemically
endemicity
endemiological
endemiology
endemism
endenizen
ender
endere
endermatic
endermic
endermically
enderon
enderonic
endevil
endew
endgate
endiadem
endiaper
ending
endite
endive
endless
endlessly
endlessness
endlichite
endlong
endmatcher
endmost
endoabdominal
endoangiitis
endoaortitis
endoappendicitis
endoarteritis
endoauscultation
endobatholithic
endobiotic
endoblast
endoblastic
endobronchial
endobronchially
endobronchitis
endocannibalism
endocardiac
endocardial
endocarditic
endocarditis
endocardium
endocarp
endocarpal
endocarpic
endocarpoid
endocellular
endocentric
Endoceras
Endoceratidae
endoceratite
endoceratitic
endocervical
endocervicitis
endochondral
endochorion
endochorionic
endochrome
endochylous
endoclinal
endocline
endocoelar
endocoele
endocoeliac
endocolitis
endocolpitis
endocondensation
endocone
endoconidium
endocorpuscular
endocortex
endocranial
endocranium
endocrinal
endocrine
endocrinic
endocrinism
endocrinological
endocrinologist
endocrinology
endocrinopathic
endocrinopathy
endocrinotherapy
endocrinous
endocritic
endocycle
endocyclic
endocyemate
endocyst
endocystitis
endoderm
endodermal
endodermic
endodermis
endodontia
endodontic
endodontist
endodynamomorphic
endoenteritis
endoenzyme
endoesophagitis
endofaradism
endogalvanism
endogamic
endogamous
endogamy
endogastric
endogastrically
endogastritis
endogen
Endogenae
endogenesis
endogenetic
endogenic
endogenous
endogenously
endogeny
endoglobular
endognath
endognathal
endognathion
endogonidium
endointoxication
endokaryogamy
endolabyrinthitis
endolaryngeal
endolemma
endolumbar
endolymph
endolymphangial
endolymphatic
endolymphic
endolysin
endomastoiditis
endome
endomesoderm
endometrial
endometritis
endometrium
endometry
endomitosis
endomitotic
endomixis
endomorph
endomorphic
endomorphism
endomorphy
Endomyces
Endomycetaceae
endomysial
endomysium
endoneurial
endoneurium
endonuclear
endonucleolus
endoparasite
endoparasitic
Endoparasitica
endopathic
endopelvic
endopericarditis
endoperidial
endoperidium
endoperitonitis
endophagous
endophagy
endophasia
endophasic
endophlebitis
endophragm
endophragmal
Endophyllaceae
endophyllous
Endophyllum
endophytal
endophyte
endophytic
endophytically
endophytous
endoplasm
endoplasma
endoplasmic
endoplast
endoplastron
endoplastular
endoplastule
endopleura
endopleural
endopleurite
endopleuritic
endopod
endopodite
endopoditic
endoproct
Endoprocta
endoproctous
endopsychic
Endopterygota
endopterygote
endopterygotic
endopterygotism
endopterygotous
endorachis
endoral
endore
endorhinitis
endorsable
endorsation
endorse
endorsed
endorsee
endorsement
endorser
endorsingly
endosalpingitis
endosarc
endosarcode
endosarcous
endosclerite
endoscope
endoscopic
endoscopy
endosecretory
endosepsis
endosiphon
endosiphonal
endosiphonate
endosiphuncle
endoskeletal
endoskeleton
endosmometer
endosmometric
endosmosic
endosmosis
endosmotic
endosmotically
endosome
endosperm
endospermic
endospore
endosporium
endosporous
endoss
endosteal
endosteally
endosteitis
endosteoma
endosternite
endosternum
endosteum
endostitis
endostoma
endostome
endostosis
endostracal
endostracum
endostylar
endostyle
endostylic
endotheca
endothecal
endothecate
endothecial
endothecium
endothelia
endothelial
endothelioblastoma
endotheliocyte
endothelioid
endotheliolysin
endotheliolytic
endothelioma
endotheliomyoma
endotheliomyxoma
endotheliotoxin
endothelium
endothermal
endothermic
endothermous
endothermy
Endothia
endothoracic
endothorax
Endothrix
endothys
endotoxic
endotoxin
endotoxoid
endotracheitis
endotrachelitis
Endotrophi
endotrophic
endotys
endovaccination
endovasculitis
endovenous
endow
endower
endowment
endozoa
endpiece
Endromididae
Endromis
endue
enduement
endungeon
endura
endurability
endurable
endurableness
endurably
endurance
endurant
endure
endurer
enduring
enduringly
enduringness
endways
endwise
endyma
endymal
Endymion
endysis
Eneas
eneclann
enema
enemy
enemylike
enemyship
enepidermic
energeia
energesis
energetic
energetical
energetically
energeticalness
energeticist
energetics
energetistic
energic
energical
energid
energism
energist
energize
energizer
energumen
energumenon
energy
enervate
enervation
enervative
enervator
eneuch
eneugh
enface
enfacement
enfamous
enfasten
enfatico
enfeature
enfeeble
enfeeblement
enfeebler
enfelon
enfeoff
enfeoffment
enfester
enfetter
enfever
enfigure
enfilade
enfilading
enfile
enfiled
enflagellate
enflagellation
enflesh
enfleurage
enflower
enfoil
enfold
enfolden
enfolder
enfoldment
enfonced
enforce
enforceability
enforceable
enforced
enforcedly
enforcement
enforcer
enforcibility
enforcible
enforcingly
enfork
enfoul
enframe
enframement
enfranchisable
enfranchise
enfranchisement
enfranchiser
enfree
enfrenzy
enfuddle
enfurrow
engage
engaged
engagedly
engagedness
engagement
engager
engaging
engagingly
engagingness
engaol
engarb
engarble
engarland
engarment
engarrison
engastrimyth
engastrimythic
engaud
engaze
Engelmannia
engem
engender
engenderer
engenderment
engerminate
enghosted
engild
engine
engineer
engineering
engineership
enginehouse
engineless
enginelike
engineman
enginery
enginous
engird
engirdle
engirt
engjateigur
englacial
englacially
englad
engladden
Englander
Engler
Englerophoenix
Englifier
Englify
English
Englishable
Englisher
Englishhood
Englishism
Englishize
Englishly
Englishman
Englishness
Englishry
Englishwoman
englobe
englobement
engloom
englory
englut
englyn
engnessang
engobe
engold
engolden
engore
engorge
engorgement
engouled
engrace
engraff
engraft
engraftation
engrafter
engraftment
engrail
engrailed
engrailment
engrain
engrained
engrainedly
engrainer
engram
engramma
engrammatic
engrammic
engrandize
engrandizement
engraphia
engraphic
engraphically
engraphy
engrapple
engrasp
Engraulidae
Engraulis
engrave
engraved
engravement
engraver
engraving
engreen
engrieve
engroove
engross
engrossed
engrossedly
engrosser
engrossing
engrossingly
engrossingness
engrossment
enguard
engulf
engulfment
engyscope
engysseismology
Engystomatidae
enhallow
enhalo
enhamper
enhance
enhanced
enhancement
enhancer
enhancive
enharmonic
enharmonical
enharmonically
enhat
enhaunt
enhearse
enheart
enhearten
enhedge
enhelm
enhemospore
enherit
enheritage
enheritance
enhorror
enhunger
enhusk
Enhydra
Enhydrinae
Enhydris
enhydrite
enhydritic
enhydros
enhydrous
enhypostasia
enhypostasis
enhypostatic
enhypostatize
eniac
Enicuridae
Enid
Enif
enigma
enigmatic
enigmatical
enigmatically
enigmaticalness
enigmatist
enigmatization
enigmatize
enigmatographer
enigmatography
enigmatology
enisle
enjail
enjamb
enjambed
enjambment
enjelly
enjeopard
enjeopardy
enjewel
enjoin
enjoinder
enjoiner
enjoinment
enjoy
enjoyable
enjoyableness
enjoyably
enjoyer
enjoying
enjoyingly
enjoyment
enkerchief
enkernel
Enki
Enkidu
enkindle
enkindler
enkraal
enlace
enlacement
enlard
enlarge
enlargeable
enlargeableness
enlarged
enlargedly
enlargedness
enlargement
enlarger
enlarging
enlargingly
enlaurel
enleaf
enleague
enlevement
enlief
enlife
enlight
enlighten
enlightened
enlightenedly
enlightenedness
enlightener
enlightening
enlighteningly
enlightenment
enlink
enlinkment
enlist
enlisted
enlister
enlistment
enliven
enlivener
enlivening
enliveningly
enlivenment
enlock
enlodge
enlodgement
enmarble
enmask
enmass
enmesh
enmeshment
enmist
enmity
enmoss
enmuffle
enneacontahedral
enneacontahedron
ennead
enneadianome
enneadic
enneagon
enneagynous
enneahedral
enneahedria
enneahedron
enneapetalous
enneaphyllous
enneasemic
enneasepalous
enneaspermous
enneastyle
enneastylos
enneasyllabic
enneateric
enneatic
enneatical
ennerve
enniche
ennoble
ennoblement
ennobler
ennobling
ennoblingly
ennoic
ennomic
ennui
Enoch
Enochic
enocyte
enodal
enodally
enoil
enol
enolate
enolic
enolizable
enolization
enolize
enomania
enomaniac
enomotarch
enomoty
enophthalmos
enophthalmus
Enopla
enoplan
enoptromancy
enorganic
enorm
enormity
enormous
enormously
enormousness
Enos
enostosis
enough
enounce
enouncement
enow
enphytotic
enplane
enquicken
enquire
enquirer
enquiry
enrace
enrage
enraged
enragedly
enragement
enrange
enrank
enrapt
enrapture
enrapturer
enravish
enravishingly
enravishment
enray
enregiment
enregister
enregistration
enregistry
enrib
enrich
enricher
enriching
enrichingly
enrichment
enring
enrive
enrobe
enrobement
enrober
enrockment
enrol
enroll
enrolled
enrollee
enroller
enrollment
enrolment
enroot
enrough
enruin
enrut
ens
ensaffron
ensaint
ensample
ensand
ensandal
ensanguine
ensate
enscene
ensconce
enscroll
ensculpture
ense
enseam
enseat
enseem
ensellure
ensemble
ensepulcher
ensepulchre
enseraph
enserf
ensete
enshade
enshadow
enshawl
ensheathe
enshell
enshelter
enshield
enshrine
enshrinement
enshroud
Ensiferi
ensiform
ensign
ensigncy
ensignhood
ensignment
ensignry
ensignship
ensilage
ensilate
ensilation
ensile
ensilist
ensilver
ensisternum
ensky
enslave
enslavedness
enslavement
enslaver
ensmall
ensnare
ensnarement
ensnarer
ensnaring
ensnaringly
ensnarl
ensnow
ensorcelize
ensorcell
ensoul
enspell
ensphere
enspirit
enstamp
enstar
enstate
enstatite
enstatitic
enstatolite
ensteel
enstool
enstore
enstrengthen
ensuable
ensuance
ensuant
ensue
ensuer
ensuingly
ensulphur
ensure
ensurer
enswathe
enswathement
ensweep
entablature
entablatured
entablement
entach
entad
Entada
entail
entailable
entailer
entailment
ental
entame
Entamoeba
entamoebiasis
entamoebic
entangle
entangled
entangledly
entangledness
entanglement
entangler
entangling
entanglingly
entapophysial
entapophysis
entarthrotic
entasia
entasis
entelam
entelechy
entellus
Entelodon
entelodont
entempest
entemple
entente
Ententophil
entepicondylar
enter
enterable
enteraden
enteradenographic
enteradenography
enteradenological
enteradenology
enteral
enteralgia
enterate
enterauxe
enterclose
enterectomy
enterer
entergogenic
enteria
enteric
entericoid
entering
enteritidis
enteritis
entermete
enteroanastomosis
enterobiliary
enterocele
enterocentesis
enterochirurgia
enterochlorophyll
enterocholecystostomy
enterocinesia
enterocinetic
enterocleisis
enteroclisis
enteroclysis
Enterocoela
enterocoele
enterocoelic
enterocoelous
enterocolitis
enterocolostomy
enterocrinin
enterocyst
enterocystoma
enterodynia
enteroepiplocele
enterogastritis
enterogastrone
enterogenous
enterogram
enterograph
enterography
enterohelcosis
enterohemorrhage
enterohepatitis
enterohydrocele
enteroid
enterointestinal
enteroischiocele
enterokinase
enterokinesia
enterokinetic
enterolith
enterolithiasis
Enterolobium
enterology
enteromegalia
enteromegaly
enteromere
enteromesenteric
Enteromorpha
enteromycosis
enteromyiasis
enteron
enteroneuritis
enteroparalysis
enteroparesis
enteropathy
enteropexia
enteropexy
enterophthisis
enteroplasty
enteroplegia
enteropneust
Enteropneusta
enteropneustan
enteroptosis
enteroptotic
enterorrhagia
enterorrhaphy
enterorrhea
enteroscope
enterosepsis
enterospasm
enterostasis
enterostenosis
enterostomy
enterosyphilis
enterotome
enterotomy
enterotoxemia
enterotoxication
enterozoa
enterozoan
enterozoic
enterprise
enterpriseless
enterpriser
enterprising
enterprisingly
enterritoriality
entertain
entertainable
entertainer
entertaining
entertainingly
entertainingness
entertainment
enthalpy
entheal
enthelmintha
enthelminthes
enthelminthic
enthetic
enthral
enthraldom
enthrall
enthralldom
enthraller
enthralling
enthrallingly
enthrallment
enthralment
enthrone
enthronement
enthronization
enthronize
enthuse
enthusiasm
enthusiast
enthusiastic
enthusiastical
enthusiastically
enthusiastly
enthymematic
enthymematical
enthymeme
entia
entice
enticeable
enticeful
enticement
enticer
enticing
enticingly
enticingness
entifical
entification
entify
entincture
entire
entirely
entireness
entirety
entiris
entitative
entitatively
entitle
entitlement
entity
entoblast
entoblastic
entobranchiate
entobronchium
entocalcaneal
entocarotid
entocele
entocnemial
entocoele
entocoelic
entocondylar
entocondyle
entocondyloid
entocone
entoconid
entocornea
entocranial
entocuneiform
entocuniform
entocyemate
entocyst
entoderm
entodermal
entodermic
entogastric
entogenous
entoglossal
entohyal
entoil
entoilment
Entoloma
entomb
entombment
entomere
entomeric
entomic
entomical
entomion
entomogenous
entomoid
entomologic
entomological
entomologically
entomologist
entomologize
entomology
Entomophaga
entomophagan
entomophagous
Entomophila
entomophilous
entomophily
Entomophthora
Entomophthoraceae
entomophthoraceous
Entomophthorales
entomophthorous
entomophytous
Entomosporium
Entomostraca
entomostracan
entomostracous
entomotaxy
entomotomist
entomotomy
entone
entonement
entoolitic
entoparasite
entoparasitic
entoperipheral
entophytal
entophyte
entophytic
entophytically
entophytous
entopic
entopical
entoplasm
entoplastic
entoplastral
entoplastron
entopopliteal
Entoprocta
entoproctous
entopterygoid
entoptic
entoptical
entoptically
entoptics
entoptoscope
entoptoscopic
entoptoscopy
entoretina
entorganism
entosarc
entosclerite
entosphenal
entosphenoid
entosphere
entosternal
entosternite
entosternum
entothorax
entotic
Entotrophi
entotympanic
entourage
entozoa
entozoal
entozoan
entozoarian
entozoic
entozoological
entozoologically
entozoologist
entozoology
entozoon
entracte
entrail
entrails
entrain
entrainer
entrainment
entrammel
entrance
entrancedly
entrancement
entranceway
entrancing
entrancingly
entrant
entrap
entrapment
entrapper
entrappingly
entreasure
entreat
entreating
entreatingly
entreatment
entreaty
entree
entremets
entrench
entrenchment
entrepas
entrepot
entrepreneur
entrepreneurial
entrepreneurship
entresol
entrochite
entrochus
entropion
entropionize
entropium
entropy
entrough
entrust
entrustment
entry
entryman
entryway
enturret
entwine
entwinement
entwist
Entyloma
enucleate
enucleation
enucleator
Enukki
enumerable
enumerate
enumeration
enumerative
enumerator
enunciability
enunciable
enunciate
enunciation
enunciative
enunciatively
enunciator
enunciatory
enure
enuresis
enuretic
enurny
envapor
envapour
envassal
envassalage
envault
enveil
envelop
envelope
enveloper
envelopment
envenom
envenomation
enverdure
envermeil
enviable
enviableness
enviably
envied
envier
envineyard
envious
enviously
enviousness
environ
environage
environal
environic
environment
environmental
environmentalism
environmentalist
environmentally
environs
envisage
envisagement
envision
envolume
envoy
envoyship
envy
envying
envyingly
enwallow
enwiden
enwind
enwisen
enwoman
enwomb
enwood
enworthed
enwound
enwrap
enwrapment
enwreathe
enwrite
enwrought
enzone
enzootic
enzooty
enzym
enzymatic
enzyme
enzymic
enzymically
enzymologist
enzymology
enzymolysis
enzymolytic
enzymosis
enzymotic
eoan
Eoanthropus
Eocarboniferous
Eocene
Eodevonian
Eogaea
Eogaean
Eoghanacht
Eohippus
eolation
eolith
eolithic
Eomecon
eon
eonism
Eopalaeozoic
Eopaleozoic
eophyte
eophytic
eophyton
eorhyolite
eosate
Eosaurus
eoside
eosin
eosinate
eosinic
eosinoblast
eosinophile
eosinophilia
eosinophilic
eosinophilous
eosphorite
Eozoic
eozoon
eozoonal
epacmaic
epacme
epacrid
Epacridaceae
epacridaceous
Epacris
epact
epactal
epagoge
epagogic
epagomenae
epagomenal
epagomenic
epagomenous
epaleaceous
epalpate
epanadiplosis
Epanagoge
epanalepsis
epanaleptic
epanaphora
epanaphoral
epanastrophe
epanisognathism
epanisognathous
epanodos
epanody
Epanorthidae
epanorthosis
epanorthotic
epanthous
epapillate
epappose
eparch
eparchate
Eparchean
eparchial
eparchy
eparcuale
eparterial
epaule
epaulement
epaulet
epauleted
epauletted
epauliere
epaxial
epaxially
epedaphic
epee
epeeist
Epeira
epeiric
epeirid
Epeiridae
epeirogenesis
epeirogenetic
epeirogenic
epeirogeny
epeisodion
epembryonic
epencephal
epencephalic
epencephalon
ependyma
ependymal
ependyme
ependymitis
ependymoma
ependytes
epenthesis
epenthesize
epenthetic
epephragmal
epepophysial
epepophysis
epergne
eperotesis
Eperua
epexegesis
epexegetic
epexegetical
epexegetically
epha
ephah
epharmonic
epharmony
ephebe
ephebeion
ephebeum
ephebic
ephebos
ephebus
ephectic
Ephedra
Ephedraceae
ephedrine
ephelcystic
ephelis
Ephemera
ephemera
ephemerae
ephemeral
ephemerality
ephemerally
ephemeralness
ephemeran
ephemerid
Ephemerida
Ephemeridae
ephemerides
ephemeris
ephemerist
ephemeromorph
ephemeromorphic
ephemeron
Ephemeroptera
ephemerous
Ephesian
Ephesine
ephetae
ephete
ephetic
ephialtes
ephidrosis
ephippial
ephippium
ephod
ephor
ephoral
ephoralty
ephorate
ephoric
ephorship
ephorus
ephphatha
Ephraim
Ephraimite
Ephraimitic
Ephraimitish
Ephraitic
Ephrathite
Ephthalite
Ephthianura
ephthianure
Ephydra
ephydriad
ephydrid
Ephydridae
ephymnium
ephyra
ephyrula
epibasal
Epibaterium
epibatholithic
epibenthic
epibenthos
epiblast
epiblastema
epiblastic
epiblema
epibole
epibolic
epibolism
epiboly
epiboulangerite
epibranchial
epic
epical
epically
epicalyx
epicanthic
epicanthus
epicardia
epicardiac
epicardial
epicardium
epicarid
epicaridan
Epicaridea
Epicarides
epicarp
Epicauta
epicede
epicedial
epicedian
epicedium
epicele
epicene
epicenism
epicenity
epicenter
epicentral
epicentrum
Epiceratodus
epicerebral
epicheirema
epichil
epichile
epichilium
epichindrotic
epichirema
epichondrosis
epichordal
epichorial
epichoric
epichorion
epichoristic
Epichristian
epicism
epicist
epiclastic
epicleidian
epicleidium
epiclesis
epiclidal
epiclinal
epicly
epicnemial
Epicoela
epicoelar
epicoele
epicoelia
epicoeliac
epicoelian
epicoeloma
epicoelous
epicolic
epicondylar
epicondyle
epicondylian
epicondylic
epicontinental
epicoracohumeral
epicoracoid
epicoracoidal
epicormic
epicorolline
epicortical
epicostal
epicotyl
epicotyleal
epicotyledonary
epicranial
epicranium
epicranius
Epicrates
epicrisis
epicritic
epicrystalline
Epictetian
epicure
Epicurean
Epicureanism
epicurish
epicurishly
Epicurism
Epicurize
epicycle
epicyclic
epicyclical
epicycloid
epicycloidal
epicyemate
epicyesis
epicystotomy
epicyte
epideictic
epideictical
epideistic
epidemic
epidemical
epidemically
epidemicalness
epidemicity
epidemiographist
epidemiography
epidemiological
epidemiologist
epidemiology
epidemy
epidendral
epidendric
Epidendron
Epidendrum
epiderm
epiderma
epidermal
epidermatic
epidermatoid
epidermatous
epidermic
epidermical
epidermically
epidermidalization
epidermis
epidermization
epidermoid
epidermoidal
epidermolysis
epidermomycosis
Epidermophyton
epidermophytosis
epidermose
epidermous
epidesmine
epidialogue
epidiascope
epidiascopic
epidictic
epidictical
epididymal
epididymectomy
epididymis
epididymite
epididymitis
epididymodeferentectomy
epididymodeferential
epididymovasostomy
epidiorite
epidiorthosis
epidosite
epidote
epidotic
epidotiferous
epidotization
epidural
epidymides
epifascial
epifocal
epifolliculitis
Epigaea
epigamic
epigaster
epigastraeum
epigastral
epigastrial
epigastric
epigastrical
epigastriocele
epigastrium
epigastrocele
epigeal
epigean
epigeic
epigene
epigenesis
epigenesist
epigenetic
epigenetically
epigenic
epigenist
epigenous
epigeous
epiglottal
epiglottic
epiglottidean
epiglottiditis
epiglottis
epiglottitis
epignathous
epigonal
epigonation
epigone
Epigoni
epigonic
Epigonichthyidae
Epigonichthys
epigonium
epigonos
epigonous
Epigonus
epigram
epigrammatic
epigrammatical
epigrammatically
epigrammatism
epigrammatist
epigrammatize
epigrammatizer
epigraph
epigrapher
epigraphic
epigraphical
epigraphically
epigraphist
epigraphy
epiguanine
epigyne
epigynous
epigynum
epigyny
Epihippus
epihyal
epihydric
epihydrinic
epikeia
epiklesis
Epikouros
epilabrum
Epilachna
Epilachnides
epilamellar
epilaryngeal
epilate
epilation
epilatory
epilegomenon
epilemma
epilemmal
epilepsy
epileptic
epileptically
epileptiform
epileptogenic
epileptogenous
epileptoid
epileptologist
epileptology
epilimnion
epilobe
Epilobiaceae
Epilobium
epilogation
epilogic
epilogical
epilogist
epilogistic
epilogize
epilogue
Epimachinae
epimacus
epimandibular
epimanikia
Epimedium
Epimenidean
epimer
epimeral
epimere
epimeric
epimeride
epimerite
epimeritic
epimeron
epimerum
epimorphic
epimorphosis
epimysium
epimyth
epinaos
epinastic
epinastically
epinasty
epineolithic
Epinephelidae
Epinephelus
epinephrine
epinette
epineural
epineurial
epineurium
epinglette
epinicial
epinician
epinicion
epinine
epiopticon
epiotic
Epipactis
epipaleolithic
epiparasite
epiparodos
epipastic
epiperipheral
epipetalous
epiphanous
Epiphany
epipharyngeal
epipharynx
Epiphegus
epiphenomenal
epiphenomenalism
epiphenomenalist
epiphenomenon
epiphloedal
epiphloedic
epiphloeum
epiphonema
epiphora
epiphragm
epiphylline
epiphyllous
Epiphyllum
epiphysary
epiphyseal
epiphyseolysis
epiphysial
epiphysis
epiphysitis
epiphytal
epiphyte
epiphytic
epiphytical
epiphytically
epiphytism
epiphytology
epiphytotic
epiphytous
epipial
epiplankton
epiplanktonic
epiplasm
epiplasmic
epiplastral
epiplastron
epiplectic
epipleura
epipleural
epiplexis
epiploce
epiplocele
epiploic
epiploitis
epiploon
epiplopexy
epipodial
epipodiale
epipodite
epipoditic
epipodium
epipolic
epipolism
epipolize
epiprecoracoid
Epipsychidion
epipteric
epipterous
epipterygoid
epipubic
epipubis
epirhizous
epirogenic
epirogeny
Epirote
Epirotic
epirotulian
epirrhema
epirrhematic
epirrheme
episarcine
episcenium
episclera
episcleral
episcleritis
episcopable
episcopacy
Episcopal
episcopal
episcopalian
Episcopalianism
Episcopalianize
episcopalism
episcopality
Episcopally
episcopally
episcopate
episcopature
episcope
episcopicide
episcopization
episcopize
episcopolatry
episcotister
episematic
episepalous
episiocele
episiohematoma
episioplasty
episiorrhagia
episiorrhaphy
episiostenosis
episiotomy
episkeletal
episkotister
episodal
episode
episodial
episodic
episodical
episodically
epispadiac
epispadias
epispastic
episperm
epispermic
epispinal
episplenitis
episporangium
epispore
episporium
epistapedial
epistasis
epistatic
epistaxis
epistemic
epistemolog
epistemological
epistemologically
epistemologist
epistemology
epistemonic
epistemonical
epistemophilia
epistemophiliac
epistemophilic
episternal
episternalia
episternite
episternum
epistilbite
epistlar
epistle
epistler
epistolarian
epistolarily
epistolary
epistolatory
epistoler
epistolet
epistolic
epistolical
epistolist
epistolizable
epistolization
epistolize
epistolizer
epistolographer
epistolographic
epistolographist
epistolography
epistoma
epistomal
epistome
epistomian
epistroma
epistrophe
epistropheal
epistropheus
epistrophic
epistrophy
epistylar
epistyle
Epistylis
episyllogism
episynaloephe
episynthetic
episyntheton
epitactic
epitaph
epitapher
epitaphial
epitaphian
epitaphic
epitaphical
epitaphist
epitaphize
epitaphless
epitasis
epitela
epitendineum
epitenon
epithalamia
epithalamial
epithalamiast
epithalamic
epithalamion
epithalamium
epithalamize
epithalamus
epithalamy
epithalline
epitheca
epithecal
epithecate
epithecium
epithelia
epithelial
epithelioblastoma
epithelioceptor
epitheliogenetic
epithelioglandular
epithelioid
epitheliolysin
epitheliolysis
epitheliolytic
epithelioma
epitheliomatous
epitheliomuscular
epitheliosis
epitheliotoxin
epithelium
epithelization
epithelize
epitheloid
epithem
epithesis
epithet
epithetic
epithetical
epithetically
epithetician
epithetize
epitheton
epithumetic
epithyme
epithymetic
epithymetical
epitimesis
epitoke
epitomator
epitomatory
epitome
epitomic
epitomical
epitomically
epitomist
epitomization
epitomize
epitomizer
epitonic
Epitoniidae
epitonion
Epitonium
epitoxoid
epitrachelion
epitrichial
epitrichium
epitrite
epitritic
epitrochlea
epitrochlear
epitrochoid
epitrochoidal
epitrope
epitrophic
epitrophy
epituberculosis
epituberculous
epitympanic
epitympanum
epityphlitis
epityphlon
epiural
epivalve
epixylous
epizeuxis
Epizoa
epizoa
epizoal
epizoan
epizoarian
epizoic
epizoicide
epizoon
epizootic
epizootiology
epoch
epocha
epochal
epochally
epochism
epochist
epode
epodic
epollicate
Epomophorus
eponychium
eponym
eponymic
eponymism
eponymist
eponymize
eponymous
eponymus
eponymy
epoophoron
epopee
epopoean
epopoeia
epopoeist
epopt
epoptes
epoptic
epoptist
epornitic
epornitically
epos
Eppie
Eppy
Eproboscidea
epruinose
epsilon
Epsom
epsomite
Eptatretidae
Eptatretus
epulary
epulation
epulis
epulo
epuloid
epulosis
epulotic
epupillate
epural
epurate
epuration
epyllion
equability
equable
equableness
equably
equaeval
equal
equalable
equaling
equalist
equalitarian
equalitarianism
equality
equalization
equalize
equalizer
equalizing
equalling
equally
equalness
equangular
equanimity
equanimous
equanimously
equanimousness
equant
equatable
equate
equation
equational
equationally
equationism
equationist
equator
equatorial
equatorially
equatorward
equatorwards
equerry
equerryship
equestrial
equestrian
equestrianism
equestrianize
equestrianship
equestrienne
equianchorate
equiangle
equiangular
equiangularity
equianharmonic
equiarticulate
equiatomic
equiaxed
equiaxial
equibalance
equibiradiate
equicellular
equichangeable
equicohesive
equiconvex
equicostate
equicrural
equicurve
equid
equidense
equidensity
equidiagonal
equidifferent
equidimensional
equidistance
equidistant
equidistantial
equidistantly
equidistribution
equidiurnal
equidivision
equidominant
equidurable
equielliptical
equiexcellency
equiform
equiformal
equiformity
equiglacial
equigranular
equijacent
equilateral
equilaterally
equilibrant
equilibrate
equilibration
equilibrative
equilibrator
equilibratory
equilibria
equilibrial
equilibriate
equilibrio
equilibrious
equilibrist
equilibristat
equilibristic
equilibrity
equilibrium
equilibrize
equilobate
equilobed
equilocation
equilucent
equimodal
equimolar
equimolecular
equimomental
equimultiple
equinate
equine
equinecessary
equinely
equinia
equinity
equinoctial
equinoctially
equinovarus
equinox
equinumerally
equinus
equiomnipotent
equip
equipaga
equipage
equiparant
equiparate
equiparation
equipartile
equipartisan
equipartition
equiped
equipedal
equiperiodic
equipluve
equipment
equipoise
equipollence
equipollency
equipollent
equipollently
equipollentness
equiponderance
equiponderancy
equiponderant
equiponderate
equiponderation
equipostile
equipotent
equipotential
equipotentiality
equipper
equiprobabilism
equiprobabilist
equiprobability
equiproducing
equiproportional
equiproportionality
equiradial
equiradiate
equiradical
equirotal
equisegmented
Equisetaceae
equisetaceous
Equisetales
equisetic
Equisetum
equisided
equisignal
equisized
equison
equisonance
equisonant
equispaced
equispatial
equisufficiency
equisurface
equitable
equitableness
equitably
equitangential
equitant
equitation
equitative
equitemporal
equitemporaneous
equites
equitist
equitriangular
equity
equivalence
equivalenced
equivalency
equivalent
equivalently
equivaliant
equivalue
equivaluer
equivalve
equivalved
equivalvular
equivelocity
equivocacy
equivocal
equivocality
equivocally
equivocalness
equivocate
equivocatingly
equivocation
equivocator
equivocatory
equivoluminal
equivoque
equivorous
equivote
equoid
equoidean
equuleus
Equus
er
era
erade
eradiate
eradiation
eradicable
eradicant
eradicate
eradication
eradicative
eradicator
eradicatory
eradiculose
Eragrostis
eral
eranist
Eranthemum
Eranthis
erasable
erase
erased
erasement
eraser
erasion
Erasmian
Erasmus
Erastian
Erastianism
Erastianize
Erastus
erasure
Erava
erbia
erbium
erd
erdvark
ere
Erechtheum
Erechtheus
Erechtites
erect
erectable
erecter
erectile
erectility
erecting
erection
erective
erectly
erectness
erectopatent
erector
erelong
eremacausis
Eremian
eremic
eremital
eremite
eremiteship
eremitic
eremitical
eremitish
eremitism
Eremochaeta
eremochaetous
eremology
eremophyte
Eremopteris
Eremurus
erenach
erenow
erepsin
erept
ereptase
ereptic
ereption
erethic
erethisia
erethism
erethismic
erethistic
erethitic
Erethizon
Erethizontidae
Eretrian
erewhile
erewhiles
erg
ergal
ergamine
Ergane
ergasia
ergasterion
ergastic
ergastoplasm
ergastoplasmic
ergastulum
ergatandromorph
ergatandromorphic
ergatandrous
ergatandry
ergates
ergatocracy
ergatocrat
ergatogyne
ergatogynous
ergatogyny
ergatoid
ergatomorph
ergatomorphic
ergatomorphism
ergmeter
ergodic
ergogram
ergograph
ergographic
ergoism
ergology
ergomaniac
ergometer
ergometric
ergometrine
ergon
ergonovine
ergophile
ergophobia
ergophobiac
ergoplasm
ergostat
ergosterin
ergosterol
ergot
ergotamine
ergotaminine
ergoted
ergothioneine
ergotic
ergotin
ergotinine
ergotism
ergotist
ergotization
ergotize
ergotoxin
ergotoxine
ergusia
eria
Erian
Erianthus
Eric
eric
Erica
Ericaceae
ericaceous
ericad
erical
Ericales
ericetal
ericeticolous
ericetum
erichthus
erichtoid
ericineous
ericius
Erick
ericoid
ericolin
ericophyte
Eridanid
Erie
Erigenia
Erigeron
erigible
Eriglossa
eriglossate
Erik
erika
erikite
Erinaceidae
erinaceous
Erinaceus
erineum
erinite
Erinize
erinose
Eriobotrya
Eriocaulaceae
eriocaulaceous
Eriocaulon
Eriocomi
Eriodendron
Eriodictyon
erioglaucine
Eriogonum
eriometer
erionite
Eriophorum
Eriophyes
Eriophyidae
eriophyllous
Eriosoma
Eriphyle
Eristalis
eristic
eristical
eristically
Erithacus
Eritrean
erizo
erlking
Erma
Ermanaric
Ermani
Ermanrich
ermelin
ermine
ermined
erminee
ermines
erminites
erminois
erne
Ernest
Ernestine
Ernie
Ernst
erode
eroded
erodent
erodible
Erodium
erogeneity
erogenesis
erogenetic
erogenic
erogenous
erogeny
Eros
eros
erose
erosely
erosible
erosion
erosional
erosionist
erosive
erostrate
eroteme
erotesis
erotetic
erotic
erotica
erotical
erotically
eroticism
eroticize
eroticomania
erotism
erotogenesis
erotogenetic
erotogenic
erotogenicity
erotomania
erotomaniac
erotopath
erotopathic
erotopathy
Erotylidae
Erpetoichthys
erpetologist
err
errability
errable
errableness
errabund
errancy
errand
errant
Errantia
errantly
errantness
errantry
errata
erratic
erratical
erratically
erraticalness
erraticism
erraticness
erratum
errhine
erring
erringly
errite
erroneous
erroneously
erroneousness
error
errorful
errorist
errorless
ers
Ersar
ersatz
Erse
Ertebolle
erth
erthen
erthling
erthly
erubescence
erubescent
erubescite
eruc
Eruca
eruca
erucic
eruciform
erucin
erucivorous
eruct
eructance
eructation
eructative
eruction
erudit
erudite
eruditely
eruditeness
eruditical
erudition
eruditional
eruditionist
erugate
erugation
erugatory
erumpent
erupt
eruption
eruptional
eruptive
eruptively
eruptiveness
eruptivity
ervenholder
Ervipiame
Ervum
Erwin
Erwinia
eryhtrism
Erymanthian
Eryngium
eryngo
Eryon
Eryops
Erysibe
Erysimum
erysipelas
erysipelatoid
erysipelatous
erysipeloid
Erysipelothrix
erysipelous
Erysiphaceae
Erysiphe
Erythea
erythema
erythematic
erythematous
erythemic
Erythraea
Erythraean
Erythraeidae
erythrasma
erythrean
erythremia
erythremomelalgia
erythrene
erythrin
Erythrina
erythrine
Erythrinidae
Erythrinus
erythrismal
erythristic
erythrite
erythritic
erythritol
erythroblast
erythroblastic
erythroblastosis
erythrocarpous
erythrocatalysis
Erythrochaete
erythrochroic
erythrochroism
erythroclasis
erythroclastic
erythrocyte
erythrocytic
erythrocytoblast
erythrocytolysin
erythrocytolysis
erythrocytolytic
erythrocytometer
erythrocytorrhexis
erythrocytoschisis
erythrocytosis
erythrodegenerative
erythrodermia
erythrodextrin
erythrogenesis
erythrogenic
erythroglucin
erythrogonium
erythroid
erythrol
erythrolein
erythrolitmin
erythrolysin
erythrolysis
erythrolytic
erythromelalgia
erythron
erythroneocytosis
Erythronium
erythronium
erythropenia
erythrophage
erythrophagous
erythrophilous
erythrophleine
erythrophobia
erythrophore
erythrophyll
erythrophyllin
erythropia
erythroplastid
erythropoiesis
erythropoietic
erythropsia
erythropsin
erythrorrhexis
erythroscope
erythrose
erythrosiderite
erythrosin
erythrosinophile
erythrosis
Erythroxylaceae
erythroxylaceous
erythroxyline
Erythroxylon
Erythroxylum
erythrozincite
erythrozyme
erythrulose
Eryx
es
esca
escadrille
escalade
escalader
escalado
escalan
escalate
Escalator
escalator
escalin
Escallonia
Escalloniaceae
escalloniaceous
escalop
escaloped
escambio
escambron
escapable
escapade
escapage
escape
escapee
escapeful
escapeless
escapement
escaper
escapingly
escapism
escapist
escarbuncle
escargatoire
escarole
escarp
escarpment
eschalot
eschar
eschara
escharine
escharoid
escharotic
eschatocol
eschatological
eschatologist
eschatology
escheat
escheatable
escheatage
escheatment
escheator
escheatorship
Escherichia
eschew
eschewal
eschewance
eschewer
Eschscholtzia
eschynite
esclavage
escoba
escobadura
escobilla
escobita
escolar
esconson
escopette
Escorial
escort
escortage
escortee
escortment
escribe
escritoire
escritorial
escrol
escropulo
escrow
escruage
escudo
Esculapian
esculent
esculetin
esculin
escutcheon
escutcheoned
escutellate
esdragol
Esdras
Esebrias
esemplastic
esemplasy
eseptate
esere
eserine
esexual
eshin
esiphonal
esker
Eskimauan
Eskimo
Eskimoic
Eskimoid
Eskimoized
Eskualdun
Eskuara
Esmeralda
Esmeraldan
esmeraldite
esne
esoanhydride
esocataphoria
Esocidae
esociform
esocyclic
esodic
esoenteritis
esoethmoiditis
esogastritis
esonarthex
esoneural
esophagal
esophagalgia
esophageal
esophagean
esophagectasia
esophagectomy
esophagi
esophagism
esophagismus
esophagitis
esophago
esophagocele
esophagodynia
esophagogastroscopy
esophagogastrostomy
esophagomalacia
esophagometer
esophagomycosis
esophagopathy
esophagoplasty
esophagoplegia
esophagoplication
esophagoptosis
esophagorrhagia
esophagoscope
esophagoscopy
esophagospasm
esophagostenosis
esophagostomy
esophagotome
esophagotomy
esophagus
esophoria
esophoric
Esopus
esoteric
esoterica
esoterical
esoterically
esotericism
esotericist
esoterics
esoterism
esoterist
esoterize
esotery
esothyropexy
esotrope
esotropia
esotropic
Esox
espacement
espadon
espalier
espantoon
esparcet
esparsette
esparto
espathate
espave
especial
especially
especialness
esperance
Esperantic
Esperantidist
Esperantido
Esperantism
Esperantist
Esperanto
espial
espichellite
espier
espinal
espingole
espinillo
espino
espionage
esplanade
esplees
esponton
espousal
espouse
espousement
espouser
Espriella
espringal
espundia
espy
esquamate
esquamulose
Esquiline
esquire
esquirearchy
esquiredom
esquireship
ess
essang
essay
essayer
essayette
essayical
essayish
essayism
essayist
essayistic
essayistical
essaylet
essed
Essedones
Esselen
Esselenian
essence
essency
Essene
Essenian
Essenianism
Essenic
Essenical
Essenis
Essenism
Essenize
essentia
essential
essentialism
essentialist
essentiality
essentialize
essentially
essentialness
essenwood
Essex
essexite
Essie
essling
essoin
essoinee
essoiner
essoinment
essonite
essorant
establish
establishable
established
establisher
establishment
establishmentarian
establishmentarianism
establishmentism
estacade
estadal
estadio
estado
estafette
estafetted
estamene
estamp
estampage
estampede
estampedero
estate
estatesman
esteem
esteemable
esteemer
Estella
ester
esterase
esterellite
esteriferous
esterification
esterify
esterization
esterize
esterlin
esterling
estevin
Esth
Esthacyte
esthematology
Esther
Estheria
estherian
Estheriidae
esthesia
esthesio
esthesioblast
esthesiogen
esthesiogenic
esthesiogeny
esthesiography
esthesiology
esthesiometer
esthesiometric
esthesiometry
esthesioneurosis
esthesiophysiology
esthesis
esthetology
esthetophore
esthiomene
estimable
estimableness
estimably
estimate
estimatingly
estimation
estimative
estimator
estipulate
estivage
estival
estivate
estivation
estivator
estmark
estoc
estoile
Estonian
estop
estoppage
estoppel
Estotiland
estovers
estrade
estradiol
estradiot
estragole
estrange
estrangedness
estrangement
estranger
estrapade
estray
estre
estreat
estrepe
estrepement
estriate
estriche
estrin
estriol
estrogen
estrogenic
estrone
estrous
estrual
estruate
estruation
estuarial
estuarine
estuary
estufa
estuous
estus
esugarization
esurience
esurient
esuriently
eta
etaballi
etacism
etacist
etalon
Etamin
etamine
etch
Etchareottine
etcher
Etchimin
etching
Eteoclus
Eteocretes
Eteocreton
eternal
eternalism
eternalist
eternalization
eternalize
eternally
eternalness
eternity
eternization
eternize
etesian
ethal
ethaldehyde
Ethan
ethanal
ethanamide
ethane
ethanedial
ethanediol
ethanedithiol
ethanethial
ethanethiol
Ethanim
ethanol
ethanolamine
ethanolysis
ethanoyl
Ethel
ethel
ethene
Etheneldeli
ethenic
ethenoid
ethenoidal
ethenol
ethenyl
Etheostoma
Etheostomidae
Etheostominae
etheostomoid
ether
etherate
ethereal
etherealism
ethereality
etherealization
etherealize
ethereally
etherealness
etherean
ethered
ethereous
Etheria
etheric
etherification
etheriform
etherify
Etheriidae
etherin
etherion
etherism
etherization
etherize
etherizer
etherolate
etherous
ethic
ethical
ethicalism
ethicality
ethically
ethicalness
ethician
ethicism
ethicist
ethicize
ethicoaesthetic
ethicophysical
ethicopolitical
ethicoreligious
ethicosocial
ethics
ethid
ethide
ethidene
ethine
ethiodide
ethionic
Ethiop
Ethiopia
Ethiopian
Ethiopic
ethiops
ethmofrontal
ethmoid
ethmoidal
ethmoiditis
ethmolachrymal
ethmolith
ethmomaxillary
ethmonasal
ethmopalatal
ethmopalatine
ethmophysal
ethmopresphenoidal
ethmosphenoid
ethmosphenoidal
ethmoturbinal
ethmoturbinate
ethmovomer
ethmovomerine
ethmyphitis
ethnal
ethnarch
ethnarchy
ethnic
ethnical
ethnically
ethnicism
ethnicist
ethnicize
ethnicon
ethnize
ethnobiological
ethnobiology
ethnobotanic
ethnobotanical
ethnobotanist
ethnobotany
ethnocentric
ethnocentrism
ethnocracy
ethnodicy
ethnoflora
ethnogenic
ethnogeny
ethnogeographer
ethnogeographic
ethnogeographical
ethnogeographically
ethnogeography
ethnographer
ethnographic
ethnographical
ethnographically
ethnographist
ethnography
ethnologer
ethnologic
ethnological
ethnologically
ethnologist
ethnology
ethnomaniac
ethnopsychic
ethnopsychological
ethnopsychology
ethnos
ethnotechnics
ethnotechnography
ethnozoological
ethnozoology
ethography
etholide
ethologic
ethological
ethology
ethonomic
ethonomics
ethopoeia
ethos
ethoxide
ethoxycaffeine
ethoxyl
ethrog
ethyl
ethylamide
ethylamine
ethylate
ethylation
ethylene
ethylenediamine
ethylenic
ethylenimine
ethylenoid
ethylhydrocupreine
ethylic
ethylidene
ethylidyne
ethylin
ethylmorphine
ethylsulphuric
ethyne
ethynyl
etiogenic
etiolate
etiolation
etiolin
etiolize
etiological
etiologically
etiologist
etiologue
etiology
etiophyllin
etioporphyrin
etiotropic
etiotropically
etiquette
etiquettical
etna
Etnean
Etonian
Etrurian
Etruscan
Etruscologist
Etruscology
Etta
Ettarre
ettle
etua
etude
etui
etym
etymic
etymography
etymologer
etymologic
etymological
etymologically
etymologicon
etymologist
etymologization
etymologize
etymology
etymon
etymonic
etypic
etypical
etypically
eu
Euahlayi
euangiotic
Euascomycetes
euaster
Eubacteriales
eubacterium
Eubasidii
Euboean
Euboic
Eubranchipus
eucaine
eucairite
eucalypt
eucalypteol
eucalyptian
eucalyptic
eucalyptography
eucalyptol
eucalyptole
Eucalyptus
eucalyptus
Eucarida
eucatropine
eucephalous
Eucharis
Eucharist
eucharistial
eucharistic
eucharistical
Eucharistically
eucharistically
eucharistize
Eucharitidae
Euchite
Euchlaena
euchlorhydria
euchloric
euchlorine
Euchlorophyceae
euchological
euchologion
euchology
Euchorda
euchre
euchred
euchroic
euchroite
euchromatic
euchromatin
euchrome
euchromosome
euchrone
Eucirripedia
euclase
Euclea
Eucleidae
Euclid
Euclidean
Euclideanism
Eucnemidae
eucolite
Eucommia
Eucommiaceae
eucone
euconic
Euconjugatae
Eucopepoda
Eucosia
eucosmid
Eucosmidae
eucrasia
eucrasite
eucrasy
eucrite
Eucryphia
Eucryphiaceae
eucryphiaceous
eucryptite
eucrystalline
euctical
eucyclic
eudaemon
eudaemonia
eudaemonic
eudaemonical
eudaemonics
eudaemonism
eudaemonist
eudaemonistic
eudaemonistical
eudaemonistically
eudaemonize
eudaemony
eudaimonia
eudaimonism
eudaimonist
Eudemian
Eudendrium
Eudeve
eudiagnostic
eudialyte
eudiaphoresis
eudidymite
eudiometer
eudiometric
eudiometrical
eudiometrically
eudiometry
eudipleural
Eudist
Eudora
Eudorina
Eudoxian
Eudromias
Eudyptes
Euergetes
euge
Eugene
eugenesic
eugenesis
eugenetic
Eugenia
eugenic
eugenical
eugenically
eugenicist
eugenics
Eugenie
eugenism
eugenist
eugenol
eugenolate
eugeny
Euglandina
Euglena
Euglenaceae
Euglenales
Euglenida
Euglenidae
Euglenineae
euglenoid
Euglenoidina
euglobulin
eugranitic
Eugregarinida
Eugubine
Eugubium
euharmonic
euhedral
euhemerism
euhemerist
euhemeristic
euhemeristically
euhemerize
euhyostylic
euhyostyly
euktolite
eulachon
Eulalia
eulalia
eulamellibranch
Eulamellibranchia
Eulamellibranchiata
Eulima
Eulimidae
eulogia
eulogic
eulogical
eulogically
eulogious
eulogism
eulogist
eulogistic
eulogistical
eulogistically
eulogium
eulogization
eulogize
eulogizer
eulogy
eulysite
eulytine
eulytite
Eumenes
eumenid
Eumenidae
Eumenidean
Eumenides
eumenorrhea
eumerism
eumeristic
eumerogenesis
eumerogenetic
eumeromorph
eumeromorphic
eumitosis
eumitotic
eumoiriety
eumoirous
Eumolpides
Eumolpus
eumorphous
eumycete
Eumycetes
eumycetic
Eunectes
Eunice
eunicid
Eunicidae
Eunomia
Eunomian
Eunomianism
eunomy
eunuch
eunuchal
eunuchism
eunuchize
eunuchoid
eunuchoidism
eunuchry
euomphalid
Euomphalus
euonym
euonymin
euonymous
Euonymus
euonymy
Euornithes
euornithic
Euorthoptera
euosmite
euouae
eupad
Eupanorthidae
Eupanorthus
eupathy
eupatoriaceous
eupatorin
Eupatorium
eupatory
eupatrid
eupatridae
eupepsia
eupepsy
eupeptic
eupepticism
eupepticity
Euphausia
Euphausiacea
euphausiid
Euphausiidae
Euphemia
euphemian
euphemious
euphemiously
euphemism
euphemist
euphemistic
euphemistical
euphemistically
euphemize
euphemizer
euphemous
euphemy
euphon
euphone
euphonetic
euphonetics
euphonia
euphonic
euphonical
euphonically
euphonicalness
euphonious
euphoniously
euphoniousness
euphonism
euphonium
euphonize
euphonon
euphonous
euphony
euphonym
Euphorbia
Euphorbiaceae
euphorbiaceous
euphorbium
euphoria
euphoric
euphory
Euphrasia
euphrasy
Euphratean
euphroe
Euphrosyne
Euphues
euphuism
euphuist
euphuistic
euphuistical
euphuistically
euphuize
Euphyllopoda
eupione
eupittonic
euplastic
Euplectella
Euplexoptera
Euplocomi
Euploeinae
euploid
euploidy
eupnea
Eupolidean
Eupolyzoa
eupolyzoan
Eupomatia
Eupomatiaceae
eupractic
eupraxia
Euprepia
Euproctis
eupsychics
Euptelea
Eupterotidae
eupyrchroite
eupyrene
eupyrion
Eurafric
Eurafrican
Euraquilo
Eurasian
Eurasianism
Eurasiatic
eureka
eurhodine
eurhodol
Eurindic
Euripidean
euripus
eurite
Euroaquilo
eurobin
Euroclydon
Europa
Europasian
European
Europeanism
Europeanization
Europeanize
Europeanly
Europeward
europium
Europocentric
Eurus
Euryalae
Euryale
Euryaleae
euryalean
Euryalida
euryalidan
Euryalus
eurybathic
eurybenthic
eurycephalic
eurycephalous
Eurycerotidae
Euryclea
Eurydice
Eurygaea
Eurygaean
eurygnathic
eurygnathism
eurygnathous
euryhaline
Eurylaimi
Eurylaimidae
eurylaimoid
Eurylaimus
Eurymus
euryon
Eurypelma
Eurypharyngidae
Eurypharynx
euryprognathous
euryprosopic
eurypterid
Eurypterida
eurypteroid
Eurypteroidea
Eurypterus
Eurypyga
Eurypygae
Eurypygidae
eurypylous
euryscope
Eurystheus
eurystomatous
eurythermal
eurythermic
eurythmic
eurythmical
eurythmics
eurythmy
eurytomid
Eurytomidae
Eurytus
euryzygous
Euscaro
Eusebian
Euselachii
Euskaldun
Euskara
Euskarian
Euskaric
Euskera
eusol
Euspongia
eusporangiate
Eustace
Eustachian
eustachium
Eustathian
eustatic
Eusthenopteron
eustomatous
eustyle
Eusuchia
eusuchian
eusynchite
Eutaenia
eutannin
eutaxic
eutaxite
eutaxitic
eutaxy
eutechnic
eutechnics
eutectic
eutectoid
Euterpe
Euterpean
eutexia
Euthamia
euthanasia
euthanasy
euthenics
euthenist
Eutheria
eutherian
euthermic
Euthycomi
euthycomic
Euthyneura
euthyneural
euthyneurous
euthytatic
euthytropic
eutomous
eutony
Eutopia
Eutopian
eutrophic
eutrophy
eutropic
eutropous
Eutychian
Eutychianism
euxanthate
euxanthic
euxanthone
euxenite
Euxine
Eva
evacuant
evacuate
evacuation
evacuative
evacuator
evacue
evacuee
evadable
evade
evader
evadingly
Evadne
evagation
evaginable
evaginate
evagination
evaluable
evaluate
evaluation
evaluative
evalue
Evan
evanesce
evanescence
evanescency
evanescent
evanescently
evanescible
evangel
evangelary
evangelian
evangeliarium
evangeliary
evangelical
evangelicalism
evangelicality
evangelically
evangelicalness
evangelican
evangelicism
evangelicity
Evangeline
evangelion
evangelism
evangelist
evangelistarion
evangelistarium
evangelistary
evangelistic
evangelistically
evangelistics
evangelistship
evangelium
evangelization
evangelize
evangelizer
Evaniidae
evanish
evanishment
evanition
evansite
evaporability
evaporable
evaporate
evaporation
evaporative
evaporativity
evaporator
evaporimeter
evaporize
evaporometer
evase
evasible
evasion
evasional
evasive
evasively
evasiveness
Eve
eve
Evea
evechurr
evection
evectional
Evehood
evejar
Eveless
evelight
Evelina
Eveline
evelong
Evelyn
even
evenblush
evendown
evener
evenfall
evenforth
evenglow
evenhanded
evenhandedly
evenhandedness
evening
evenlight
evenlong
evenly
evenmete
evenminded
evenmindedness
evenness
evens
evensong
event
eventful
eventfully
eventfulness
eventide
eventime
eventless
eventlessly
eventlessness
eventognath
Eventognathi
eventognathous
eventration
eventual
eventuality
eventualize
eventually
eventuate
eventuation
evenwise
evenworthy
eveque
ever
Everard
everbearer
everbearing
everbloomer
everblooming
everduring
Everett
everglade
evergreen
evergreenery
evergreenite
everlasting
everlastingly
everlastingness
everliving
evermore
Evernia
evernioid
eversible
eversion
eversive
eversporting
evert
evertebral
Evertebrata
evertebrate
evertile
evertor
everwhich
everwho
every
everybody
everyday
everydayness
everyhow
everylike
Everyman
everyman
everyness
everyone
everything
everywhen
everywhence
everywhere
everywhereness
everywheres
everywhither
evestar
evetide
eveweed
evict
eviction
evictor
evidence
evidencive
evident
evidential
evidentially
evidentiary
evidently
evidentness
evil
evildoer
evilhearted
evilly
evilmouthed
evilness
evilproof
evilsayer
evilspeaker
evilspeaking
evilwishing
evince
evincement
evincible
evincibly
evincingly
evincive
evirate
eviration
eviscerate
evisceration
evisite
evitable
evitate
evitation
evittate
evocable
evocate
evocation
evocative
evocatively
evocator
evocatory
evocatrix
Evodia
evoe
evoke
evoker
evolute
evolution
evolutional
evolutionally
evolutionary
evolutionism
evolutionist
evolutionize
evolutive
evolutoid
evolvable
evolve
evolvement
evolvent
evolver
Evonymus
evovae
evulgate
evulgation
evulse
evulsion
evzone
ewder
Ewe
ewe
ewelease
ewer
ewerer
ewery
ewry
ex
exacerbate
exacerbation
exacerbescence
exacerbescent
exact
exactable
exacter
exacting
exactingly
exactingness
exaction
exactitude
exactive
exactiveness
exactly
exactment
exactness
exactor
exactress
exadversum
exaggerate
exaggerated
exaggeratedly
exaggerating
exaggeratingly
exaggeration
exaggerative
exaggeratively
exaggerativeness
exaggerator
exaggeratory
exagitate
exagitation
exairesis
exalate
exalbuminose
exalbuminous
exallotriote
exalt
exaltation
exaltative
exalted
exaltedly
exaltedness
exalter
exam
examen
examinability
examinable
examinant
examinate
examination
examinational
examinationism
examinationist
examinative
examinator
examinatorial
examinatory
examine
examinee
examiner
examinership
examining
examiningly
example
exampleless
exampleship
exanimate
exanimation
exanthem
exanthema
exanthematic
exanthematous
exappendiculate
exarate
exaration
exarch
exarchal
exarchate
exarchateship
Exarchic
Exarchist
exarchist
exarchy
exareolate
exarillate
exaristate
exarteritis
exarticulate
exarticulation
exasperate
exasperated
exasperatedly
exasperater
exasperating
exasperatingly
exasperation
exasperative
exaspidean
Exaudi
exaugurate
exauguration
excalate
excalation
excalcarate
excalceate
excalceation
Excalibur
excamb
excamber
excambion
excandescence
excandescency
excandescent
excantation
excarnate
excarnation
excathedral
excaudate
excavate
excavation
excavationist
excavator
excavatorial
excavatory
excave
excecate
excecation
excedent
exceed
exceeder
exceeding
exceedingly
exceedingness
excel
excelente
excellence
excellency
excellent
excellently
excelsin
Excelsior
excelsior
excelsitude
excentral
excentric
excentrical
excentricity
except
exceptant
excepting
exception
exceptionable
exceptionableness
exceptionably
exceptional
exceptionality
exceptionally
exceptionalness
exceptionary
exceptionless
exceptious
exceptiousness
exceptive
exceptively
exceptiveness
exceptor
excerebration
excerpt
excerptible
excerption
excerptive
excerptor
excess
excessive
excessively
excessiveness
excessman
exchange
exchangeability
exchangeable
exchangeably
exchanger
Exchangite
Exchequer
exchequer
excide
excipient
exciple
Excipulaceae
excipular
excipule
excipuliform
excipulum
excircle
excisable
excise
exciseman
excisemanship
excision
excisor
excitability
excitable
excitableness
excitancy
excitant
excitation
excitative
excitator
excitatory
excite
excited
excitedly
excitedness
excitement
exciter
exciting
excitingly
excitive
excitoglandular
excitometabolic
excitomotion
excitomotor
excitomotory
excitomuscular
excitonutrient
excitor
excitory
excitosecretory
excitovascular
exclaim
exclaimer
exclaiming
exclaimingly
exclamation
exclamational
exclamative
exclamatively
exclamatorily
exclamatory
exclave
exclosure
excludable
exclude
excluder
excluding
excludingly
exclusion
exclusionary
exclusioner
exclusionism
exclusionist
exclusive
exclusively
exclusiveness
exclusivism
exclusivist
exclusivity
exclusory
Excoecaria
excogitable
excogitate
excogitation
excogitative
excogitator
excommunicable
excommunicant
excommunicate
excommunication
excommunicative
excommunicator
excommunicatory
exconjugant
excoriable
excoriate
excoriation
excoriator
excorticate
excortication
excrement
excremental
excrementary
excrementitial
excrementitious
excrementitiously
excrementitiousness
excrementive
excresce
excrescence
excrescency
excrescent
excrescential
excreta
excretal
excrete
excreter
excretes
excretion
excretionary
excretitious
excretive
excretory
excriminate
excruciable
excruciate
excruciating
excruciatingly
excruciation
excruciator
excubant
excudate
exculpable
exculpate
exculpation
exculpative
exculpatorily
exculpatory
excurrent
excurse
excursion
excursional
excursionary
excursioner
excursionism
excursionist
excursionize
excursive
excursively
excursiveness
excursory
excursus
excurvate
excurvated
excurvation
excurvature
excurved
excusability
excusable
excusableness
excusably
excusal
excusative
excusator
excusatory
excuse
excuseful
excusefully
excuseless
excuser
excusing
excusingly
excusive
excuss
excyst
excystation
excysted
excystment
exdelicto
exdie
exeat
execrable
execrableness
execrably
execrate
execration
execrative
execratively
execrator
execratory
executable
executancy
executant
execute
executed
executer
execution
executional
executioneering
executioner
executioneress
executionist
executive
executively
executiveness
executiveship
executor
executorial
executorship
executory
executress
executrices
executrix
executrixship
executry
exedent
exedra
exegeses
exegesis
exegesist
exegete
exegetic
exegetical
exegetically
exegetics
exegetist
exemplar
exemplaric
exemplarily
exemplariness
exemplarism
exemplarity
exemplary
exemplifiable
exemplification
exemplificational
exemplificative
exemplificator
exemplifier
exemplify
exempt
exemptible
exemptile
exemption
exemptionist
exemptive
exencephalia
exencephalic
exencephalous
exencephalus
exendospermic
exendospermous
exenterate
exenteration
exequatur
exequial
exequy
exercisable
exercise
exerciser
exercitant
exercitation
exercitor
exercitorial
exercitorian
exeresis
exergual
exergue
exert
exertion
exertionless
exertive
exes
exeunt
exfiguration
exfigure
exfiltration
exflagellate
exflagellation
exflect
exfodiate
exfodiation
exfoliate
exfoliation
exfoliative
exfoliatory
exgorgitation
exhalable
exhalant
exhalation
exhalatory
exhale
exhaust
exhausted
exhaustedly
exhaustedness
exhauster
exhaustibility
exhaustible
exhausting
exhaustingly
exhaustion
exhaustive
exhaustively
exhaustiveness
exhaustless
exhaustlessly
exhaustlessness
exheredate
exheredation
exhibit
exhibitable
exhibitant
exhibiter
exhibition
exhibitional
exhibitioner
exhibitionism
exhibitionist
exhibitionistic
exhibitionize
exhibitive
exhibitively
exhibitor
exhibitorial
exhibitorship
exhibitory
exhilarant
exhilarate
exhilarating
exhilaratingly
exhilaration
exhilarative
exhilarator
exhilaratory
exhort
exhortation
exhortative
exhortatively
exhortator
exhortatory
exhorter
exhortingly
exhumate
exhumation
exhumator
exhumatory
exhume
exhumer
exigence
exigency
exigent
exigenter
exigently
exigible
exiguity
exiguous
exiguously
exiguousness
exilarch
exilarchate
exile
exiledom
exilement
exiler
exilian
exilic
exility
eximious
eximiously
eximiousness
exinanite
exinanition
exindusiate
exinguinal
exist
existability
existence
existent
existential
existentialism
existentialist
existentialistic
existentialize
existentially
existently
exister
existibility
existible
existlessness
exit
exite
exition
exitus
exlex
exmeridian
Exmoor
exoarteritis
Exoascaceae
exoascaceous
Exoascales
Exoascus
Exobasidiaceae
Exobasidiales
Exobasidium
exocannibalism
exocardia
exocardiac
exocardial
exocarp
exocataphoria
exoccipital
exocentric
Exochorda
exochorion
exoclinal
exocline
exocoelar
exocoele
exocoelic
exocoelom
Exocoetidae
Exocoetus
exocolitis
exocone
exocrine
exoculate
exoculation
exocyclic
Exocyclica
Exocycloida
exode
exoderm
exodermis
exodic
exodist
exodontia
exodontist
exodos
exodromic
exodromy
exodus
exody
exoenzyme
exoenzymic
exoerythrocytic
exogamic
exogamous
exogamy
exogastric
exogastrically
exogastritis
exogen
Exogenae
exogenetic
exogenic
exogenous
exogenously
exogeny
exognathion
exognathite
Exogonium
Exogyra
exolemma
exometritis
exomion
exomis
exomologesis
exomorphic
exomorphism
exomphalos
exomphalous
exomphalus
Exon
exon
exonarthex
exoner
exonerate
exoneration
exonerative
exonerator
exoneural
Exonian
exonship
exopathic
exoperidium
exophagous
exophagy
exophasia
exophasic
exophoria
exophoric
exophthalmic
exophthalmos
exoplasm
exopod
exopodite
exopoditic
Exopterygota
exopterygotic
exopterygotism
exopterygotous
exorability
exorable
exorableness
exorbital
exorbitance
exorbitancy
exorbitant
exorbitantly
exorbitate
exorbitation
exorcisation
exorcise
exorcisement
exorciser
exorcism
exorcismal
exorcisory
exorcist
exorcistic
exorcistical
exordia
exordial
exordium
exordize
exorganic
exorhason
exormia
exornation
exosepsis
exoskeletal
exoskeleton
exosmic
exosmose
exosmosis
exosmotic
exosperm
exosporal
exospore
exosporium
exosporous
Exostema
exostome
exostosed
exostosis
exostotic
exostra
exostracism
exostracize
exoteric
exoterical
exoterically
exotericism
exoterics
exotheca
exothecal
exothecate
exothecium
exothermal
exothermic
exothermous
exotic
exotically
exoticalness
exoticism
exoticist
exoticity
exoticness
exotism
exotospore
exotoxic
exotoxin
exotropia
exotropic
exotropism
expalpate
expand
expanded
expandedly
expandedness
expander
expanding
expandingly
expanse
expansibility
expansible
expansibleness
expansibly
expansile
expansion
expansional
expansionary
expansionism
expansionist
expansive
expansively
expansiveness
expansivity
expansometer
expansure
expatiate
expatiater
expatiatingly
expatiation
expatiative
expatiator
expatiatory
expatriate
expatriation
expect
expectable
expectance
expectancy
expectant
expectantly
expectation
expectative
expectedly
expecter
expectingly
expective
expectorant
expectorate
expectoration
expectorative
expectorator
expede
expediate
expedience
expediency
expedient
expediential
expedientially
expedientist
expediently
expeditate
expeditation
expedite
expedited
expeditely
expediteness
expediter
expedition
expeditionary
expeditionist
expeditious
expeditiously
expeditiousness
expel
expellable
expellant
expellee
expeller
expend
expendability
expendable
expender
expendible
expenditor
expenditrix
expenditure
expense
expenseful
expensefully
expensefulness
expenseless
expensilation
expensive
expensively
expensiveness
expenthesis
expergefacient
expergefaction
experience
experienceable
experienced
experienceless
experiencer
experiencible
experient
experiential
experientialism
experientialist
experientially
experiment
experimental
experimentalism
experimentalist
experimentalize
experimentally
experimentarian
experimentation
experimentative
experimentator
experimented
experimentee
experimenter
experimentist
experimentize
experimently
expert
expertism
expertize
expertly
expertness
expertship
expiable
expiate
expiation
expiational
expiatist
expiative
expiator
expiatoriness
expiatory
expilate
expilation
expilator
expirable
expirant
expirate
expiration
expirator
expiratory
expire
expiree
expirer
expiring
expiringly
expiry
expiscate
expiscation
expiscator
expiscatory
explain
explainable
explainer
explaining
explainingly
explanate
explanation
explanative
explanatively
explanator
explanatorily
explanatoriness
explanatory
explant
explantation
explement
explemental
expletive
expletively
expletiveness
expletory
explicable
explicableness
explicate
explication
explicative
explicatively
explicator
explicatory
explicit
explicitly
explicitness
explodable
explode
exploded
explodent
exploder
exploit
exploitable
exploitage
exploitation
exploitationist
exploitative
exploiter
exploitive
exploiture
explorable
exploration
explorational
explorative
exploratively
explorativeness
explorator
exploratory
explore
explorement
explorer
exploring
exploringly
explosibility
explosible
explosion
explosionist
explosive
explosively
explosiveness
expone
exponence
exponency
exponent
exponential
exponentially
exponentiation
exponible
export
exportability
exportable
exportation
exporter
exposal
expose
exposed
exposedness
exposer
exposit
exposition
expositional
expositionary
expositive
expositively
expositor
expositorial
expositorially
expositorily
expositoriness
expository
expositress
expostulate
expostulating
expostulatingly
expostulation
expostulative
expostulatively
expostulator
expostulatory
exposure
expound
expoundable
expounder
express
expressable
expressage
expressed
expresser
expressibility
expressible
expressibly
expression
expressionable
expressional
expressionful
expressionism
expressionist
expressionistic
expressionless
expressionlessly
expressionlessness
expressive
expressively
expressiveness
expressivism
expressivity
expressless
expressly
expressman
expressness
expressway
exprimable
exprobrate
exprobration
exprobratory
expromission
expromissor
expropriable
expropriate
expropriation
expropriator
expugn
expugnable
expuition
expulsatory
expulse
expulser
expulsion
expulsionist
expulsive
expulsory
expunction
expunge
expungeable
expungement
expunger
expurgate
expurgation
expurgative
expurgator
expurgatorial
expurgatory
expurge
exquisite
exquisitely
exquisiteness
exquisitism
exquisitively
exradio
exradius
exrupeal
exsanguinate
exsanguination
exsanguine
exsanguineous
exsanguinity
exsanguinous
exsanguious
exscind
exscissor
exscriptural
exsculptate
exscutellate
exsect
exsectile
exsection
exsector
exsequatur
exsert
exserted
exsertile
exsertion
exship
exsibilate
exsibilation
exsiccant
exsiccatae
exsiccate
exsiccation
exsiccative
exsiccator
exsiliency
exsomatic
exspuition
exsputory
exstipulate
exstrophy
exsuccous
exsuction
exsufflate
exsufflation
exsufflicate
exsurge
exsurgent
extant
extemporal
extemporally
extemporalness
extemporaneity
extemporaneous
extemporaneously
extemporaneousness
extemporarily
extemporariness
extemporary
extempore
extemporization
extemporize
extemporizer
extend
extended
extendedly
extendedness
extender
extendibility
extendible
extending
extense
extensibility
extensible
extensibleness
extensile
extensimeter
extension
extensional
extensionist
extensity
extensive
extensively
extensiveness
extensometer
extensor
extensory
extensum
extent
extenuate
extenuating
extenuatingly
extenuation
extenuative
extenuator
extenuatory
exter
exterior
exteriorate
exterioration
exteriority
exteriorization
exteriorize
exteriorly
exteriorness
exterminable
exterminate
extermination
exterminative
exterminator
exterminatory
exterminatress
exterminatrix
exterminist
extern
external
externalism
externalist
externalistic
externality
externalization
externalize
externally
externals
externate
externation
externe
externity
externization
externize
externomedian
externum
exteroceptist
exteroceptive
exteroceptor
exterraneous
exterrestrial
exterritorial
exterritoriality
exterritorialize
exterritorially
extima
extinct
extinction
extinctionist
extinctive
extinctor
extine
extinguish
extinguishable
extinguishant
extinguished
extinguisher
extinguishment
extipulate
extirpate
extirpation
extirpationist
extirpative
extirpator
extirpatory
extispex
extispicious
extispicy
extogenous
extol
extoll
extollation
extoller
extollingly
extollment
extolment
extoolitic
extorsive
extorsively
extort
extorter
extortion
extortionary
extortionate
extortionately
extortioner
extortionist
extortive
extra
extrabold
extrabranchial
extrabronchial
extrabuccal
extrabulbar
extrabureau
extraburghal
extracalendar
extracalicular
extracanonical
extracapsular
extracardial
extracarpal
extracathedral
extracellular
extracellularly
extracerebral
extracivic
extracivically
extraclassroom
extraclaustral
extracloacal
extracollegiate
extracolumella
extraconscious
extraconstellated
extraconstitutional
extracorporeal
extracorpuscular
extracosmic
extracosmical
extracostal
extracranial
extract
extractable
extractant
extracted
extractible
extractiform
extraction
extractive
extractor
extractorship
extracultural
extracurial
extracurricular
extracurriculum
extracutaneous
extracystic
extradecretal
extradialectal
extraditable
extradite
extradition
extradomestic
extrados
extradosed
extradotal
extraduction
extradural
extraembryonic
extraenteric
extraepiphyseal
extraequilibrium
extraessential
extraessentially
extrafascicular
extrafloral
extrafocal
extrafoliaceous
extraforaneous
extraformal
extragalactic
extragastric
extrait
extrajudicial
extrajudicially
extralateral
extralite
extrality
extramarginal
extramatrical
extramedullary
extramental
extrameridian
extrameridional
extrametaphysical
extrametrical
extrametropolitan
extramodal
extramolecular
extramorainal
extramorainic
extramoral
extramoralist
extramundane
extramural
extramurally
extramusical
extranational
extranatural
extranean
extraneity
extraneous
extraneously
extraneousness
extranidal
extranormal
extranuclear
extraocular
extraofficial
extraoral
extraorbital
extraorbitally
extraordinarily
extraordinariness
extraordinary
extraorganismal
extraovate
extraovular
extraparenchymal
extraparental
extraparietal
extraparliamentary
extraparochial
extraparochially
extrapatriarchal
extrapelvic
extraperineal
extraperiodic
extraperiosteal
extraperitoneal
extraphenomenal
extraphysical
extraphysiological
extrapituitary
extraplacental
extraplanetary
extrapleural
extrapoetical
extrapolar
extrapolate
extrapolation
extrapolative
extrapolator
extrapopular
extraprofessional
extraprostatic
extraprovincial
extrapulmonary
extrapyramidal
extraquiz
extrared
extraregarding
extraregular
extraregularly
extrarenal
extraretinal
extrarhythmical
extrasacerdotal
extrascholastic
extraschool
extrascientific
extrascriptural
extrascripturality
extrasensible
extrasensory
extrasensuous
extraserous
extrasocial
extrasolar
extrasomatic
extraspectral
extraspherical
extraspinal
extrastapedial
extrastate
extrasterile
extrastomachal
extrasyllabic
extrasyllogistic
extrasyphilitic
extrasystole
extrasystolic
extratabular
extratarsal
extratellurian
extratelluric
extratemporal
extratension
extratensive
extraterrene
extraterrestrial
extraterritorial
extraterritoriality
extraterritorially
extrathecal
extratheistic
extrathermodynamic
extrathoracic
extratorrid
extratracheal
extratribal
extratropical
extratubal
extratympanic
extrauterine
extravagance
extravagancy
extravagant
Extravagantes
extravagantly
extravagantness
extravaganza
extravagate
extravaginal
extravasate
extravasation
extravascular
extraventricular
extraversion
extravert
extravillar
extraviolet
extravisceral
extrazodiacal
extreme
extremeless
extremely
extremeness
extremism
extremist
extremistic
extremital
extremity
extricable
extricably
extricate
extricated
extrication
extrinsic
extrinsical
extrinsicality
extrinsically
extrinsicalness
extrinsicate
extrinsication
extroitive
extropical
extrorsal
extrorse
extrorsely
extrospect
extrospection
extrospective
extroversion
extroversive
extrovert
extrovertish
extrude
extruder
extruding
extrusile
extrusion
extrusive
extrusory
extubate
extubation
extumescence
extund
extusion
exuberance
exuberancy
exuberant
exuberantly
exuberantness
exuberate
exuberation
exudate
exudation
exudative
exude
exudence
exulcerate
exulceration
exulcerative
exulceratory
exult
exultance
exultancy
exultant
exultantly
exultation
exultet
exultingly
exululate
exumbral
exumbrella
exumbrellar
exundance
exundancy
exundate
exundation
exuviability
exuviable
exuviae
exuvial
exuviate
exuviation
exzodiacal
ey
eyah
eyalet
eyas
eye
eyeball
eyebalm
eyebar
eyebeam
eyeberry
eyeblink
eyebolt
eyebree
eyebridled
eyebright
eyebrow
eyecup
eyed
eyedness
eyedot
eyedrop
eyeflap
eyeful
eyeglance
eyeglass
eyehole
Eyeish
eyelash
eyeless
eyelessness
eyelet
eyeleteer
eyeletter
eyelid
eyelight
eyelike
eyeline
eyemark
eyen
eyepiece
eyepit
eyepoint
eyer
eyereach
eyeroot
eyesalve
eyeseed
eyeservant
eyeserver
eyeservice
eyeshade
eyeshield
eyeshot
eyesight
eyesome
eyesore
eyespot
eyestalk
eyestone
eyestrain
eyestring
eyetooth
eyewaiter
eyewash
eyewater
eyewear
eyewink
eyewinker
eyewitness
eyewort
eyey
eying
eyn
eyne
eyot
eyoty
eyra
eyre
eyrie
eyrir
ezba
Ezekiel
Ezra
F
f
fa
Faba
Fabaceae
fabaceous
fabella
fabes
Fabian
Fabianism
Fabianist
fabiform
fable
fabled
fabledom
fableist
fableland
fablemaker
fablemonger
fablemongering
fabler
fabliau
fabling
Fabraea
fabric
fabricant
fabricate
fabrication
fabricative
fabricator
fabricatress
Fabrikoid
fabrikoid
Fabronia
Fabroniaceae
fabular
fabulist
fabulosity
fabulous
fabulously
fabulousness
faburden
facadal
facade
face
faceable
facebread
facecloth
faced
faceless
facellite
facemaker
facemaking
faceman
facemark
facepiece
faceplate
facer
facet
facete
faceted
facetely
faceteness
facetiae
facetiation
facetious
facetiously
facetiousness
facewise
facework
facia
facial
facially
faciation
faciend
facient
facies
facile
facilely
facileness
facilitate
facilitation
facilitative
facilitator
facility
facing
facingly
facinorous
facinorousness
faciobrachial
faciocervical
faciolingual
facioplegia
facioscapulohumeral
fack
fackeltanz
fackings
fackins
facks
facsimile
facsimilist
facsimilize
fact
factable
factabling
factful
Factice
facticide
faction
factional
factionalism
factionary
factioneer
factionist
factionistism
factious
factiously
factiousness
factish
factitial
factitious
factitiously
factitive
factitively
factitude
factive
factor
factorability
factorable
factorage
factordom
factoress
factorial
factorially
factorist
factorization
factorize
factorship
factory
factoryship
factotum
factrix
factual
factuality
factually
factualness
factum
facture
facty
facula
facular
faculous
facultate
facultative
facultatively
facultied
facultize
faculty
facund
facy
fad
fadable
faddiness
faddish
faddishness
faddism
faddist
faddle
faddy
fade
fadeaway
faded
fadedly
fadedness
fadeless
faden
fader
fadge
fading
fadingly
fadingness
fadmonger
fadmongering
fadmongery
fadridden
fady
fae
faerie
Faeroe
faery
faeryland
faff
faffle
faffy
fag
Fagaceae
fagaceous
fagald
Fagales
Fagara
fage
Fagelia
fager
fagger
faggery
fagging
faggingly
fagine
fagopyrism
fagopyrismus
Fagopyrum
fagot
fagoter
fagoting
fagottino
fagottist
fagoty
Fagus
faham
fahlerz
fahlore
fahlunite
Fahrenheit
faience
fail
failing
failingly
failingness
faille
failure
fain
fainaigue
fainaiguer
faineance
faineancy
faineant
faineantism
fainly
fainness
fains
faint
fainter
faintful
faintheart
fainthearted
faintheartedly
faintheartedness
fainting
faintingly
faintish
faintishness
faintly
faintness
faints
fainty
faipule
fair
fairer
fairfieldite
fairgoer
fairgoing
fairgrass
fairground
fairily
fairing
fairish
fairishly
fairkeeper
fairlike
fairling
fairly
fairm
fairness
fairstead
fairtime
fairwater
fairway
fairy
fairydom
fairyfolk
fairyhood
fairyish
fairyism
fairyland
fairylike
fairyologist
fairyology
fairyship
faith
faithbreach
faithbreaker
faithful
faithfully
faithfulness
faithless
faithlessly
faithlessness
faithwise
faithworthiness
faithworthy
faitour
fake
fakement
faker
fakery
fakiness
fakir
fakirism
Fakofo
faky
falanaka
Falange
Falangism
Falangist
Falasha
falbala
falcade
Falcata
falcate
falcated
falcation
falcer
falces
falchion
falcial
Falcidian
falciform
Falcinellus
falciparum
Falco
falcon
falconbill
falconelle
falconer
Falcones
falconet
Falconidae
Falconiformes
Falconinae
falconine
falconlike
falconoid
falconry
falcopern
falcula
falcular
falculate
Falcunculus
faldage
falderal
faldfee
faldstool
Falerian
Falernian
Falerno
Faliscan
Falisci
Falkland
fall
fallace
fallacious
fallaciously
fallaciousness
fallacy
fallage
fallation
fallaway
fallback
fallectomy
fallen
fallenness
faller
fallfish
fallibility
fallible
fallibleness
fallibly
falling
Fallopian
fallostomy
fallotomy
fallow
fallowist
fallowness
falltime
fallway
fally
falsary
false
falsehearted
falseheartedly
falseheartedness
falsehood
falsely
falsen
falseness
falser
falsettist
falsetto
falsework
falsidical
falsie
falsifiable
falsificate
falsification
falsificator
falsifier
falsify
falsism
Falstaffian
faltboat
faltche
falter
falterer
faltering
falteringly
Falunian
Faluns
falutin
falx
fam
Fama
famatinite
famble
fame
fameflower
fameful
fameless
famelessly
famelessness
Fameuse
fameworthy
familia
familial
familiar
familiarism
familiarity
familiarization
familiarize
familiarizer
familiarizingly
familiarly
familiarness
familism
familist
familistery
familistic
familistical
family
familyish
famine
famish
famishment
famous
famously
famousness
famulary
famulus
Fan
fan
fana
fanal
fanam
fanatic
fanatical
fanatically
fanaticalness
fanaticism
fanaticize
fanback
fanbearer
fanciable
fancical
fancied
fancier
fanciful
fancifully
fancifulness
fancify
fanciless
fancy
fancymonger
fancysick
fancywork
fand
fandangle
fandango
fandom
fanega
fanegada
fanfarade
Fanfare
fanfare
fanfaron
fanfaronade
fanfaronading
fanflower
fanfoot
fang
fanged
fangle
fangled
fanglement
fangless
fanglet
fanglomerate
fangot
fangy
fanhouse
faniente
fanion
fanioned
fanlight
fanlike
fanmaker
fanmaking
fanman
fannel
fanner
Fannia
fannier
fanning
Fanny
fanon
fant
fantail
fantasia
fantasie
fantasied
fantasist
fantasque
fantassin
fantast
fantastic
fantastical
fantasticality
fantastically
fantasticalness
fantasticate
fantastication
fantasticism
fantasticly
fantasticness
fantastico
fantastry
fantasy
Fanti
fantigue
fantoccini
fantocine
fantod
fantoddish
Fanwe
fanweed
fanwise
fanwork
fanwort
fanwright
Fany
faon
Fapesmo
far
farad
faradaic
faraday
faradic
faradism
faradization
faradize
faradizer
faradmeter
faradocontractility
faradomuscular
faradonervous
faradopalpation
farandole
farasula
faraway
farawayness
farce
farcelike
farcer
farcetta
farcial
farcialize
farcical
farcicality
farcically
farcicalness
farcied
farcify
farcing
farcinoma
farcist
farctate
farcy
farde
fardel
fardelet
fardh
fardo
fare
farer
farewell
farfara
farfel
farfetched
farfetchedness
Farfugium
fargoing
fargood
farina
farinaceous
farinaceously
faring
farinometer
farinose
farinosely
farinulent
Farish
farish
farkleberry
farl
farleu
farm
farmable
farmage
farmer
farmeress
farmerette
farmerlike
farmership
farmery
farmhold
farmhouse
farmhousey
farming
farmost
farmplace
farmstead
farmsteading
farmtown
farmy
farmyard
farmyardy
farnesol
farness
Farnovian
faro
Faroeish
Faroese
farolito
Farouk
farraginous
farrago
farrand
farrandly
farrantly
farreate
farreation
farrier
farrierlike
farriery
farrisite
farrow
farruca
farsalah
farse
farseeing
farseeingness
farseer
farset
Farsi
farsighted
farsightedly
farsightedness
farther
farthermost
farthest
farthing
farthingale
farthingless
farweltered
fasces
fascet
fascia
fascial
fasciate
fasciated
fasciately
fasciation
fascicle
fascicled
fascicular
fascicularly
fasciculate
fasciculated
fasciculately
fasciculation
fascicule
fasciculus
fascinate
fascinated
fascinatedly
fascinating
fascinatingly
fascination
fascinative
fascinator
fascinatress
fascine
fascinery
Fascio
fasciodesis
fasciola
fasciolar
Fasciolaria
Fasciolariidae
fasciole
fasciolet
fascioliasis
Fasciolidae
fascioloid
fascioplasty
fasciotomy
fascis
fascism
fascist
Fascista
Fascisti
fascisticization
fascisticize
fascistization
fascistize
fash
fasher
fashery
fashion
fashionability
fashionable
fashionableness
fashionably
fashioned
fashioner
fashionist
fashionize
fashionless
fashionmonger
fashionmonging
fashious
fashiousness
fasibitikite
fasinite
fass
fassalite
fast
fasten
fastener
fastening
faster
fastgoing
fasthold
fastidiosity
fastidious
fastidiously
fastidiousness
fastidium
fastigate
fastigated
fastigiate
fastigium
fasting
fastingly
fastish
fastland
fastness
fastuous
fastuously
fastuousness
fastus
fat
Fatagaga
fatal
fatalism
fatalist
fatalistic
fatalistically
fatality
fatalize
fatally
fatalness
fatbird
fatbrained
fate
fated
fateful
fatefully
fatefulness
fatelike
fathead
fatheaded
fatheadedness
fathearted
father
fathercraft
fathered
fatherhood
fatherland
fatherlandish
fatherless
fatherlessness
fatherlike
fatherliness
fatherling
fatherly
fathership
fathmur
fathom
fathomable
fathomage
fathomer
Fathometer
fathomless
fathomlessly
fathomlessness
fatidic
fatidical
fatidically
fatiferous
fatigability
fatigable
fatigableness
fatigue
fatigueless
fatiguesome
fatiguing
fatiguingly
fatiha
fatil
fatiloquent
Fatima
Fatimid
fatiscence
fatiscent
fatless
fatling
fatly
fatness
fatsia
fattable
fatten
fattenable
fattener
fatter
fattily
fattiness
fattish
fattishness
fattrels
fatty
fatuism
fatuitous
fatuitousness
fatuity
fatuoid
fatuous
fatuously
fatuousness
fatwood
faucal
faucalize
fauces
faucet
fauchard
faucial
faucitis
faucre
faugh
faujasite
fauld
Faulkland
fault
faultage
faulter
faultfind
faultfinder
faultfinding
faultful
faultfully
faultily
faultiness
faulting
faultless
faultlessly
faultlessness
faultsman
faulty
faun
Fauna
faunal
faunally
faunated
faunish
faunist
faunistic
faunistical
faunistically
faunlike
faunological
faunology
faunule
fause
faussebraie
faussebrayed
faust
Faustian
fauterer
fautor
fautorship
fauve
Fauvism
Fauvist
favaginous
favella
favellidium
favelloid
Faventine
faveolate
faveolus
faviform
favilla
favillous
favism
favissa
favn
favonian
Favonius
favor
favorable
favorableness
favorably
favored
favoredly
favoredness
favorer
favoress
favoring
favoringly
favorite
favoritism
favorless
favose
favosely
favosite
Favosites
Favositidae
favositoid
favous
favus
fawn
fawner
fawnery
fawning
fawningly
fawningness
fawnlike
fawnskin
fawny
Fay
fay
Fayal
fayalite
Fayettism
fayles
Fayumic
faze
fazenda
fe
feaberry
feague
feak
feal
fealty
fear
fearable
feared
fearedly
fearedness
fearer
fearful
fearfully
fearfulness
fearingly
fearless
fearlessly
fearlessness
fearnought
fearsome
fearsomely
fearsomeness
feasance
feasibility
feasible
feasibleness
feasibly
feasor
feast
feasten
feaster
feastful
feastfully
feastless
feat
feather
featherback
featherbed
featherbedding
featherbird
featherbone
featherbrain
featherbrained
featherdom
feathered
featheredge
featheredged
featherer
featherfew
featherfoil
featherhead
featherheaded
featheriness
feathering
featherleaf
featherless
featherlessness
featherlet
featherlike
featherman
feathermonger
featherpate
featherpated
featherstitch
featherstitching
feathertop
featherway
featherweed
featherweight
featherwing
featherwise
featherwood
featherwork
featherworker
feathery
featliness
featly
featness
featous
featural
featurally
feature
featured
featureful
featureless
featureliness
featurely
featy
feaze
feazings
febricant
febricide
febricity
febricula
febrifacient
febriferous
febrific
febrifugal
febrifuge
febrile
febrility
Febronian
Febronianism
Februarius
February
februation
fecal
fecalith
fecaloid
feces
Fechnerian
feck
feckful
feckfully
feckless
fecklessly
fecklessness
feckly
fecula
feculence
feculency
feculent
fecund
fecundate
fecundation
fecundative
fecundator
fecundatory
fecundify
fecundity
fecundize
fed
feddan
federacy
Federal
federal
federalism
federalist
federalization
federalize
federally
federalness
federate
federation
federationist
federatist
federative
federatively
federator
Fedia
Fedora
fee
feeable
feeble
feeblebrained
feeblehearted
feebleheartedly
feebleheartedness
feebleness
feebling
feeblish
feebly
feed
feedable
feedback
feedbin
feedboard
feedbox
feeder
feedhead
feeding
feedman
feedsman
feedstuff
feedway
feedy
feel
feelable
feeler
feeless
feeling
feelingful
feelingless
feelinglessly
feelingly
feelingness
feer
feere
feering
feetage
feetless
feeze
fefnicute
fegary
Fegatella
Fehmic
fei
feif
feigher
feign
feigned
feignedly
feignedness
feigner
feigning
feigningly
Feijoa
feil
feint
feis
feist
feisty
Felapton
feldsher
feldspar
feldsparphyre
feldspathic
feldspathization
feldspathoid
Felichthys
felicide
felicific
felicitate
felicitation
felicitator
felicitous
felicitously
felicitousness
felicity
felid
Felidae
feliform
Felinae
feline
felinely
felineness
felinity
felinophile
felinophobe
Felis
Felix
fell
fellable
fellage
fellah
fellaheen
fellahin
Fellani
Fellata
Fellatah
fellatio
fellation
fellen
feller
fellic
felliducous
fellifluous
felling
fellingbird
fellinic
fellmonger
fellmongering
fellmongery
fellness
felloe
fellow
fellowcraft
fellowess
fellowheirship
fellowless
fellowlike
fellowship
fellside
fellsman
felly
feloid
felon
feloness
felonious
feloniously
feloniousness
felonry
felonsetter
felonsetting
felonweed
felonwood
felonwort
felony
fels
felsite
felsitic
felsobanyite
felsophyre
felsophyric
felsosphaerite
felstone
felt
felted
felter
felting
feltlike
feltmaker
feltmaking
feltmonger
feltness
feltwork
feltwort
felty
feltyfare
felucca
Felup
felwort
female
femalely
femaleness
femality
femalize
Feme
feme
femerell
femic
femicide
feminacy
feminal
feminality
feminate
femineity
feminie
feminility
feminin
feminine
femininely
feminineness
femininism
femininity
feminism
feminist
feministic
feministics
feminity
feminization
feminize
feminologist
feminology
feminophobe
femora
femoral
femorocaudal
femorocele
femorococcygeal
femorofibular
femoropopliteal
femororotulian
femorotibial
femur
fen
fenbank
fenberry
fence
fenceful
fenceless
fencelessness
fencelet
fenceplay
fencer
fenceress
fenchene
fenchone
fenchyl
fencible
fencing
fend
fendable
fender
fendering
fenderless
fendillate
fendillation
fendy
feneration
fenestella
Fenestellidae
fenestra
fenestral
fenestrate
fenestrated
fenestration
fenestrato
fenestrule
Fenian
Fenianism
fenite
fenks
fenland
fenlander
fenman
fennec
fennel
fennelflower
fennig
fennish
Fennoman
fenny
fenouillet
Fenrir
fensive
fent
fenter
fenugreek
Fenzelia
feod
feodal
feodality
feodary
feodatory
feoff
feoffee
feoffeeship
feoffment
feoffor
feower
feracious
feracity
Ferae
Ferahan
feral
feralin
Feramorz
ferash
ferberite
Ferdiad
ferdwit
feretory
feretrum
ferfathmur
ferfet
ferganite
Fergus
fergusite
Ferguson
fergusonite
feria
ferial
feridgi
ferie
ferine
ferinely
ferineness
Feringi
Ferio
Ferison
ferity
ferk
ferling
ferly
fermail
Fermatian
ferme
ferment
fermentability
fermentable
fermentarian
fermentation
fermentative
fermentatively
fermentativeness
fermentatory
fermenter
fermentescible
fermentitious
fermentive
fermentology
fermentor
fermentum
fermerer
fermery
fermila
fermorite
fern
fernandinite
Fernando
fernbird
fernbrake
ferned
fernery
ferngale
ferngrower
fernland
fernleaf
fernless
fernlike
fernshaw
fernsick
ferntickle
ferntickled
fernwort
ferny
Ferocactus
ferocious
ferociously
ferociousness
ferocity
feroher
Feronia
ferrado
ferrament
Ferrara
Ferrarese
ferrate
ferrated
ferrateen
ferratin
ferrean
ferreous
ferret
ferreter
ferreting
ferretto
ferrety
ferri
ferriage
ferric
ferrichloride
ferricyanate
ferricyanhydric
ferricyanic
ferricyanide
ferricyanogen
ferrier
ferriferous
ferrihydrocyanic
ferriprussiate
ferriprussic
ferrite
ferritization
ferritungstite
ferrivorous
ferroalloy
ferroaluminum
ferroboron
ferrocalcite
ferrocerium
ferrochrome
ferrochromium
ferroconcrete
ferroconcretor
ferrocyanate
ferrocyanhydric
ferrocyanic
ferrocyanide
ferrocyanogen
ferroglass
ferrogoslarite
ferrohydrocyanic
ferroinclave
ferromagnesian
ferromagnetic
ferromagnetism
ferromanganese
ferromolybdenum
ferronatrite
ferronickel
ferrophosphorus
ferroprint
ferroprussiate
ferroprussic
ferrosilicon
ferrotitanium
ferrotungsten
ferrotype
ferrotyper
ferrous
ferrovanadium
ferrozirconium
ferruginate
ferrugination
ferruginean
ferruginous
ferrule
ferruler
ferrum
ferruminate
ferrumination
ferry
ferryboat
ferryhouse
ferryman
ferryway
ferthumlungur
Fertil
fertile
fertilely
fertileness
fertility
fertilizable
fertilization
fertilizational
fertilize
fertilizer
feru
ferula
ferulaceous
ferule
ferulic
fervanite
fervency
fervent
fervently
ferventness
fervescence
fervescent
fervid
fervidity
fervidly
fervidness
Fervidor
fervor
fervorless
Fesapo
Fescennine
fescenninity
fescue
fess
fessely
fesswise
fest
festal
festally
Feste
fester
festerment
festilogy
festinance
festinate
festinately
festination
festine
Festino
festival
festivally
festive
festively
festiveness
festivity
festivous
festology
festoon
festoonery
festoony
festuca
festucine
fet
fetal
fetalism
fetalization
fetation
fetch
fetched
fetcher
fetching
fetchingly
feteless
feterita
fetial
fetiales
fetichmonger
feticidal
feticide
fetid
fetidity
fetidly
fetidness
fetiferous
fetiparous
fetish
fetisheer
fetishic
fetishism
fetishist
fetishistic
fetishization
fetishize
fetishmonger
fetishry
fetlock
fetlocked
fetlow
fetography
fetometry
fetoplacental
fetor
fetter
fetterbush
fetterer
fetterless
fetterlock
fetticus
fettle
fettler
fettling
fetus
feu
feuage
feuar
feucht
feud
feudal
feudalism
feudalist
feudalistic
feudality
feudalizable
feudalization
feudalize
feudally
feudatorial
feudatory
feudee
feudist
feudovassalism
feued
Feuillants
feuille
feuilletonism
feuilletonist
feuilletonistic
feulamort
fever
feverberry
feverbush
fevercup
feveret
feverfew
fevergum
feverish
feverishly
feverishness
feverless
feverlike
feverous
feverously
feverroot
fevertrap
fevertwig
fevertwitch
feverweed
feverwort
few
fewness
fewsome
fewter
fewterer
fewtrils
fey
feyness
fez
Fezzan
fezzed
Fezziwig
fezzy
fi
fiacre
fiance
fiancee
fianchetto
Fianna
fiar
fiard
fiasco
fiat
fiatconfirmatio
fib
fibber
fibbery
fibdom
Fiber
fiber
fiberboard
fibered
Fiberglas
fiberize
fiberizer
fiberless
fiberware
fibration
fibreless
fibreware
fibriform
fibril
fibrilla
fibrillar
fibrillary
fibrillate
fibrillated
fibrillation
fibrilled
fibrilliferous
fibrilliform
fibrillose
fibrillous
fibrin
fibrinate
fibrination
fibrine
fibrinemia
fibrinoalbuminous
fibrinocellular
fibrinogen
fibrinogenetic
fibrinogenic
fibrinogenous
fibrinolysin
fibrinolysis
fibrinolytic
fibrinoplastic
fibrinoplastin
fibrinopurulent
fibrinose
fibrinosis
fibrinous
fibrinuria
fibroadenia
fibroadenoma
fibroadipose
fibroangioma
fibroareolar
fibroblast
fibroblastic
fibrobronchitis
fibrocalcareous
fibrocarcinoma
fibrocartilage
fibrocartilaginous
fibrocaseose
fibrocaseous
fibrocellular
fibrochondritis
fibrochondroma
fibrochondrosteal
fibrocrystalline
fibrocyst
fibrocystic
fibrocystoma
fibrocyte
fibroelastic
fibroenchondroma
fibrofatty
fibroferrite
fibroglia
fibroglioma
fibrohemorrhagic
fibroid
fibroin
fibrointestinal
fibroligamentous
fibrolipoma
fibrolipomatous
fibrolite
fibrolitic
fibroma
fibromata
fibromatoid
fibromatosis
fibromatous
fibromembrane
fibromembranous
fibromucous
fibromuscular
fibromyectomy
fibromyitis
fibromyoma
fibromyomatous
fibromyomectomy
fibromyositis
fibromyotomy
fibromyxoma
fibromyxosarcoma
fibroneuroma
fibronuclear
fibronucleated
fibropapilloma
fibropericarditis
fibroplastic
fibropolypus
fibropsammoma
fibropurulent
fibroreticulate
fibrosarcoma
fibrose
fibroserous
fibrosis
fibrositis
Fibrospongiae
fibrotic
fibrotuberculosis
fibrous
fibrously
fibrousness
fibrovasal
fibrovascular
fibry
fibster
fibula
fibulae
fibular
fibulare
fibulocalcaneal
Ficaria
ficary
fice
ficelle
fiche
Fichtean
Fichteanism
fichtelite
fichu
ficiform
fickle
ficklehearted
fickleness
ficklety
ficklewise
fickly
fico
ficoid
Ficoidaceae
Ficoideae
ficoides
fictation
fictile
fictileness
fictility
fiction
fictional
fictionalize
fictionally
fictionary
fictioneer
fictioner
fictionist
fictionistic
fictionization
fictionize
fictionmonger
fictious
fictitious
fictitiously
fictitiousness
fictive
fictively
Ficula
Ficus
fid
Fidac
fidalgo
fidate
fidation
fiddle
fiddleback
fiddlebrained
fiddlecome
fiddledeedee
fiddlefaced
fiddlehead
fiddleheaded
fiddler
fiddlerfish
fiddlery
fiddlestick
fiddlestring
fiddlewood
fiddley
fiddling
fide
fideicommiss
fideicommissary
fideicommission
fideicommissioner
fideicommissor
fideicommissum
fideism
fideist
fidejussion
fidejussionary
fidejussor
fidejussory
Fidele
Fidelia
Fidelio
fidelity
fidepromission
fidepromissor
Fides
Fidessa
fidfad
fidge
fidget
fidgeter
fidgetily
fidgetiness
fidgeting
fidgetingly
fidgety
Fidia
fidicinal
fidicinales
fidicula
Fido
fiducia
fiducial
fiducially
fiduciarily
fiduciary
fiducinales
fie
fiedlerite
fiefdom
field
fieldball
fieldbird
fielded
fielder
fieldfare
fieldish
fieldman
fieldpiece
fieldsman
fieldward
fieldwards
fieldwork
fieldworker
fieldwort
fieldy
fiend
fiendful
fiendfully
fiendhead
fiendish
fiendishly
fiendishness
fiendism
fiendlike
fiendliness
fiendly
fiendship
fient
Fierabras
Fierasfer
fierasferid
Fierasferidae
fierasferoid
fierce
fiercehearted
fiercely
fiercen
fierceness
fierding
fierily
fieriness
fiery
fiesta
fieulamort
Fife
fife
fifer
fifie
fifish
fifo
fifteen
fifteener
fifteenfold
fifteenth
fifteenthly
fifth
fifthly
fiftieth
fifty
fiftyfold
fig
figaro
figbird
figeater
figent
figged
figgery
figging
figgle
figgy
fight
fightable
fighter
fighteress
fighting
fightingly
fightwite
Figitidae
figless
figlike
figment
figmental
figpecker
figshell
figulate
figulated
figuline
figurability
figurable
figural
figurant
figurante
figurate
figurately
figuration
figurative
figuratively
figurativeness
figure
figured
figuredly
figurehead
figureheadless
figureheadship
figureless
figurer
figuresome
figurette
figurial
figurine
figurism
figurist
figurize
figury
figworm
figwort
Fiji
Fijian
fike
fikie
filace
filaceous
filacer
Filago
filament
filamentar
filamentary
filamented
filamentiferous
filamentoid
filamentose
filamentous
filamentule
filander
filanders
filao
filar
Filaria
filaria
filarial
filarian
filariasis
filaricidal
filariform
filariid
Filariidae
filarious
filasse
filate
filator
filature
filbert
filch
filcher
filchery
filching
filchingly
file
filefish
filelike
filemaker
filemaking
filemot
filer
filesmith
filet
filial
filiality
filially
filialness
filiate
filiation
filibeg
filibranch
Filibranchia
filibranchiate
filibuster
filibusterer
filibusterism
filibusterous
filical
Filicales
filicauline
Filices
filicic
filicidal
filicide
filiciform
filicin
Filicineae
filicinean
filicite
Filicites
filicologist
filicology
Filicornia
filiety
filiferous
filiform
filiformed
Filigera
filigerous
filigree
filing
filings
filionymic
filiopietistic
filioque
Filipendula
filipendulous
Filipina
Filipiniana
Filipinization
Filipinize
Filipino
filippo
filipuncture
filite
Filix
fill
fillable
filled
fillemot
filler
fillercap
fillet
filleter
filleting
filletlike
filletster
filleul
filling
fillingly
fillingness
fillip
fillipeen
fillister
fillmass
fillock
fillowite
filly
film
filmable
filmdom
filmet
filmgoer
filmgoing
filmic
filmiform
filmily
filminess
filmish
filmist
filmize
filmland
filmlike
filmogen
filmslide
filmstrip
filmy
filo
filoplumaceous
filoplume
filopodium
Filosa
filose
filoselle
fils
filter
filterability
filterable
filterableness
filterer
filtering
filterman
filth
filthify
filthily
filthiness
filthless
filthy
filtrability
filtrable
filtratable
filtrate
filtration
fimble
fimbria
fimbrial
fimbriate
fimbriated
fimbriation
fimbriatum
fimbricate
fimbricated
fimbrilla
fimbrillate
fimbrilliferous
fimbrillose
fimbriodentate
Fimbristylis
fimetarious
fimicolous
Fin
fin
finable
finableness
finagle
finagler
final
finale
finalism
finalist
finality
finalize
finally
finance
financial
financialist
financially
financier
financiery
financist
finback
finch
finchbacked
finched
finchery
find
findability
findable
findal
finder
findfault
finding
findjan
fine
fineable
finebent
fineish
fineleaf
fineless
finely
finement
fineness
finer
finery
finespun
finesse
finesser
finestill
finestiller
finetop
finfish
finfoot
Fingal
Fingall
Fingallian
fingent
finger
fingerable
fingerberry
fingerbreadth
fingered
fingerer
fingerfish
fingerflower
fingerhold
fingerhook
fingering
fingerleaf
fingerless
fingerlet
fingerlike
fingerling
fingernail
fingerparted
fingerprint
fingerprinting
fingerroot
fingersmith
fingerspin
fingerstall
fingerstone
fingertip
fingerwise
fingerwork
fingery
fingrigo
Fingu
finial
finialed
finical
finicality
finically
finicalness
finicism
finick
finickily
finickiness
finicking
finickingly
finickingness
finific
finify
Finiglacial
finikin
finiking
fining
finis
finish
finishable
finished
finisher
finishing
finite
finitely
finiteness
finitesimal
finitive
finitude
finity
finjan
fink
finkel
finland
Finlander
finless
finlet
finlike
Finmark
Finn
finnac
finned
finner
finnesko
Finnic
Finnicize
finnip
Finnish
finny
finochio
Fionnuala
fiord
fiorded
Fioretti
fiorin
fiorite
Fiot
fip
fipenny
fipple
fique
fir
Firbolg
firca
fire
fireable
firearm
firearmed
fireback
fireball
firebird
fireblende
fireboard
fireboat
firebolt
firebolted
firebote
firebox
fireboy
firebrand
firebrat
firebreak
firebrick
firebug
fireburn
firecoat
firecracker
firecrest
fired
firedamp
firedog
firedrake
firefall
firefang
firefanged
fireflaught
fireflirt
fireflower
firefly
fireguard
firehouse
fireless
firelight
firelike
fireling
firelit
firelock
fireman
firemanship
firemaster
fireplace
fireplug
firepower
fireproof
fireproofing
fireproofness
firer
fireroom
firesafe
firesafeness
firesafety
fireshaft
fireshine
fireside
firesider
firesideship
firespout
firestone
firestopping
firetail
firetop
firetrap
firewarden
firewater
fireweed
firewood
firework
fireworkless
fireworky
fireworm
firing
firk
firker
firkin
firlot
firm
firmament
firmamental
firman
firmance
firmer
firmhearted
firmisternal
Firmisternia
firmisternial
firmisternous
firmly
firmness
firn
Firnismalerei
Firoloida
firring
firry
first
firstcomer
firsthand
firstling
firstly
firstness
firstship
firth
fisc
fiscal
fiscalify
fiscalism
fiscalization
fiscalize
fiscally
fischerite
fise
fisetin
fish
fishable
fishback
fishbed
fishberry
fishbolt
fishbone
fisheater
fished
fisher
fisherboat
fisherboy
fisheress
fisherfolk
fishergirl
fisherman
fisherpeople
fisherwoman
fishery
fishet
fisheye
fishfall
fishful
fishgarth
fishgig
fishhood
fishhook
fishhooks
fishhouse
fishify
fishily
fishiness
fishing
fishingly
fishless
fishlet
fishlike
fishline
fishling
fishman
fishmonger
fishmouth
fishplate
fishpond
fishpool
fishpot
fishpotter
fishpound
fishskin
fishtail
fishway
fishweed
fishweir
fishwife
fishwoman
fishwood
fishworker
fishworks
fishworm
fishy
fishyard
fisnoga
fissate
fissicostate
fissidactyl
Fissidens
Fissidentaceae
fissidentaceous
fissile
fissileness
fissilingual
Fissilinguia
fissility
fission
fissionable
fissipalmate
fissipalmation
fissiparation
fissiparism
fissiparity
fissiparous
fissiparously
fissiparousness
fissiped
Fissipeda
fissipedal
fissipedate
Fissipedia
fissipedial
Fissipes
fissirostral
fissirostrate
Fissirostres
fissive
fissural
fissuration
fissure
fissureless
Fissurella
Fissurellidae
fissuriform
fissury
fist
fisted
fister
fistful
fistiana
fistic
fistical
fisticuff
fisticuffer
fisticuffery
fistify
fistiness
fisting
fistlike
fistmele
fistnote
fistuca
fistula
Fistulana
fistular
Fistularia
Fistulariidae
fistularioid
fistulate
fistulated
fistulatome
fistulatous
fistule
fistuliform
Fistulina
fistulize
fistulose
fistulous
fistwise
fisty
fit
fitch
fitched
fitchee
fitcher
fitchery
fitchet
fitchew
fitful
fitfully
fitfulness
fitly
fitment
fitness
fitout
fitroot
fittable
fittage
fitted
fittedness
fitten
fitter
fitters
fittily
fittiness
fitting
fittingly
fittingness
Fittonia
fitty
fittyfied
fittyways
fittywise
fitweed
Fitzclarence
Fitzroy
Fitzroya
Fiuman
five
fivebar
fivefold
fivefoldness
fiveling
fivepence
fivepenny
fivepins
fiver
fives
fivescore
fivesome
fivestones
fix
fixable
fixage
fixate
fixatif
fixation
fixative
fixator
fixature
fixed
fixedly
fixedness
fixer
fixidity
fixing
fixity
fixture
fixtureless
fixure
fizelyite
fizgig
fizz
fizzer
fizzle
fizzy
fjarding
fjeld
fjerding
Fjorgyn
flabbergast
flabbergastation
flabbily
flabbiness
flabby
flabellarium
flabellate
flabellation
flabellifoliate
flabelliform
flabellinerved
flabellum
flabrum
flaccid
flaccidity
flaccidly
flaccidness
flacherie
Flacian
Flacianism
Flacianist
flack
flacked
flacker
flacket
Flacourtia
Flacourtiaceae
flacourtiaceous
flaff
flaffer
flag
flagboat
flagellant
flagellantism
flagellar
Flagellaria
Flagellariaceae
flagellariaceous
Flagellata
Flagellatae
flagellate
flagellated
flagellation
flagellative
flagellator
flagellatory
flagelliferous
flagelliform
flagellist
flagellosis
flagellula
flagellum
flageolet
flagfall
flagger
flaggery
flaggily
flagginess
flagging
flaggingly
flaggish
flaggy
flagitate
flagitation
flagitious
flagitiously
flagitiousness
flagleaf
flagless
flaglet
flaglike
flagmaker
flagmaking
flagman
flagon
flagonet
flagonless
flagpole
flagrance
flagrancy
flagrant
flagrantly
flagrantness
flagroot
flagship
flagstaff
flagstick
flagstone
flagworm
flail
flaillike
flair
flaith
flaithship
flajolotite
flak
flakage
flake
flakeless
flakelet
flaker
flakily
flakiness
flaky
flam
Flamandization
Flamandize
flamant
flamb
flambeau
flambeaux
flamberg
flamboyance
flamboyancy
flamboyant
flamboyantism
flamboyantize
flamboyantly
flamboyer
flame
flamed
flameflower
flameless
flamelet
flamelike
flamen
flamenco
flamenship
flameproof
flamer
flamfew
flamineous
flaming
Flamingant
flamingly
flamingo
Flaminian
flaminica
flaminical
flammability
flammable
flammeous
flammiferous
flammulated
flammulation
flammule
flamy
flan
flancard
flanch
flanched
flanconade
flandan
flandowser
flane
flange
flangeless
flanger
flangeway
flank
flankard
flanked
flanker
flanking
flankwise
flanky
flannel
flannelbush
flanneled
flannelette
flannelflower
flannelleaf
flannelly
flannelmouth
flannelmouthed
flannels
flanque
flap
flapcake
flapdock
flapdoodle
flapdragon
flapjack
flapmouthed
flapper
flapperdom
flapperhood
flapperish
flapperism
flare
flareback
flareboard
flareless
flaring
flaringly
flary
flaser
flash
flashboard
flasher
flashet
flashily
flashiness
flashing
flashingly
flashlight
flashlike
flashly
flashness
flashover
flashpan
flashproof
flashtester
flashy
flask
flasker
flasket
flasklet
flasque
flat
flatboat
flatbottom
flatcap
flatcar
flatdom
flated
flatfish
flatfoot
flathat
flathead
flatiron
flatland
flatlet
flatling
flatly
flatman
flatness
flatnose
flatten
flattener
flattening
flatter
flatterable
flattercap
flatterdock
flatterer
flattering
flatteringly
flatteringness
flattery
flattie
flatting
flattish
flattop
flatulence
flatulency
flatulent
flatulently
flatulentness
flatus
flatware
flatway
flatways
flatweed
flatwise
flatwoods
flatwork
flatworm
Flaubertian
flaught
flaughter
flaunt
flaunter
flauntily
flauntiness
flaunting
flauntingly
flaunty
flautino
flautist
flavanilin
flavaniline
flavanthrene
flavanthrone
flavedo
Flaveria
flavescence
flavescent
Flavia
Flavian
flavic
flavicant
flavid
flavin
flavine
Flavius
flavo
Flavobacterium
flavone
flavoprotein
flavopurpurin
flavor
flavored
flavorer
flavorful
flavoring
flavorless
flavorous
flavorsome
flavory
flavour
flaw
flawed
flawflower
flawful
flawless
flawlessly
flawlessness
flawn
flawy
flax
flaxboard
flaxbush
flaxdrop
flaxen
flaxlike
flaxman
flaxseed
flaxtail
flaxweed
flaxwench
flaxwife
flaxwoman
flaxwort
flaxy
flay
flayer
flayflint
flea
fleabane
fleabite
fleadock
fleam
fleaseed
fleaweed
fleawood
fleawort
fleay
flebile
fleche
flechette
fleck
flecken
flecker
fleckiness
fleckled
fleckless
flecklessly
flecky
flecnodal
flecnode
flection
flectional
flectionless
flector
fled
fledge
fledgeless
fledgling
fledgy
flee
fleece
fleeceable
fleeced
fleeceflower
fleeceless
fleecelike
fleecer
fleech
fleechment
fleecily
fleeciness
fleecy
fleer
fleerer
fleering
fleeringly
fleet
fleeter
fleetful
fleeting
fleetingly
fleetingness
fleetings
fleetly
fleetness
fleetwing
Flem
Fleming
Flemish
flemish
flench
flense
flenser
flerry
flesh
fleshbrush
fleshed
fleshen
flesher
fleshful
fleshhood
fleshhook
fleshiness
fleshing
fleshings
fleshless
fleshlike
fleshlily
fleshliness
fleshly
fleshment
fleshmonger
fleshpot
fleshy
flet
Fleta
fletch
Fletcher
fletcher
Fletcherism
Fletcherite
Fletcherize
flether
fleuret
fleurettee
fleuronnee
fleury
flew
flewed
flewit
flews
flex
flexanimous
flexed
flexibility
flexible
flexibleness
flexibly
flexile
flexility
flexion
flexionless
flexor
flexuose
flexuosity
flexuous
flexuously
flexuousness
flexural
flexure
flexured
fley
fleyedly
fleyedness
fleyland
fleysome
flibbertigibbet
flicflac
flick
flicker
flickering
flickeringly
flickerproof
flickertail
flickery
flicky
flidder
flier
fligger
flight
flighted
flighter
flightful
flightily
flightiness
flighting
flightless
flightshot
flighty
flimflam
flimflammer
flimflammery
flimmer
flimp
flimsily
flimsiness
flimsy
flinch
flincher
flinching
flinchingly
flinder
Flindersia
flindosa
flindosy
fling
flinger
flingy
flinkite
flint
flinter
flinthearted
flintify
flintily
flintiness
flintless
flintlike
flintlock
flintwood
flintwork
flintworker
flinty
flioma
flip
flipe
flipjack
flippancy
flippant
flippantly
flippantness
flipper
flipperling
flippery
flirt
flirtable
flirtation
flirtational
flirtationless
flirtatious
flirtatiously
flirtatiousness
flirter
flirtigig
flirting
flirtingly
flirtish
flirtishness
flirtling
flirty
flisk
flisky
flit
flitch
flitchen
flite
flitfold
fliting
flitter
flitterbat
flittermouse
flittern
flitting
flittingly
flitwite
flivver
flix
flixweed
Flo
float
floatability
floatable
floatage
floatation
floatative
floatboard
floater
floatiness
floating
floatingly
floative
floatless
floatmaker
floatman
floatplane
floatsman
floatstone
floaty
flob
flobby
floc
floccillation
floccipend
floccose
floccosely
flocculable
flocculant
floccular
flocculate
flocculation
flocculator
floccule
flocculence
flocculency
flocculent
flocculently
flocculose
flocculus
floccus
flock
flocker
flocking
flockless
flocklike
flockman
flockmaster
flockowner
flockwise
flocky
flocoon
flodge
floe
floeberg
Floerkea
floey
flog
floggable
flogger
flogging
floggingly
flogmaster
flogster
flokite
flong
flood
floodable
floodage
floodboard
floodcock
flooded
flooder
floodgate
flooding
floodless
floodlet
floodlight
floodlighting
floodlike
floodmark
floodometer
floodproof
floodtime
floodwater
floodway
floodwood
floody
floor
floorage
floorcloth
floorer
floorhead
flooring
floorless
floorman
floorwalker
floorward
floorway
floorwise
floozy
flop
flophouse
flopover
flopper
floppers
floppily
floppiness
floppy
flopwing
Flora
flora
floral
Floralia
floralize
florally
floramor
floran
florate
floreal
floreate
Florence
florence
florent
Florentine
Florentinism
florentium
flores
florescence
florescent
floressence
floret
floreted
floretum
Floria
Florian
floriate
floriated
floriation
florican
floricin
floricultural
floriculturally
floriculture
floriculturist
florid
Florida
Floridan
Florideae
floridean
florideous
Floridian
floridity
floridly
floridness
floriferous
floriferously
floriferousness
florification
floriform
florigen
florigenic
florigraphy
florikan
floriken
florilegium
florimania
florimanist
florin
Florinda
floriparous
floripondio
floriscope
Florissant
florist
floristic
floristically
floristics
floristry
florisugent
florivorous
floroon
floroscope
florula
florulent
flory
floscular
Floscularia
floscularian
Flosculariidae
floscule
flosculose
flosculous
flosh
floss
flosser
flossflower
Flossie
flossification
flossing
flossy
flot
flota
flotage
flotant
flotation
flotative
flotilla
flotorial
flotsam
flounce
flouncey
flouncing
flounder
floundering
flounderingly
flour
flourish
flourishable
flourisher
flourishing
flourishingly
flourishment
flourishy
flourlike
floury
flouse
flout
flouter
flouting
floutingly
flow
flowable
flowage
flower
flowerage
flowered
flowerer
floweret
flowerful
flowerily
floweriness
flowering
flowerist
flowerless
flowerlessness
flowerlet
flowerlike
flowerpecker
flowerpot
flowerwork
flowery
flowing
flowingly
flowingness
flowmanostat
flowmeter
flown
flowoff
Floyd
flu
fluate
fluavil
flub
flubdub
flubdubbery
flucan
fluctiferous
fluctigerous
fluctisonant
fluctisonous
fluctuability
fluctuable
fluctuant
fluctuate
fluctuation
fluctuosity
fluctuous
flue
flued
flueless
fluellen
fluellite
flueman
fluency
fluent
fluently
fluentness
fluer
fluework
fluey
fluff
fluffer
fluffily
fluffiness
fluffy
Flugelhorn
flugelman
fluible
fluid
fluidacetextract
fluidal
fluidally
fluidextract
fluidglycerate
fluidible
fluidic
fluidification
fluidifier
fluidify
fluidimeter
fluidism
fluidist
fluidity
fluidization
fluidize
fluidly
fluidness
fluidram
fluigram
fluitant
fluke
fluked
flukeless
flukeworm
flukewort
flukily
flukiness
fluking
fluky
flumdiddle
flume
flumerin
fluminose
flummadiddle
flummer
flummery
flummox
flummydiddle
flump
flung
flunk
flunker
flunkeydom
flunkeyhood
flunkeyish
flunkeyize
flunky
flunkydom
flunkyhood
flunkyish
flunkyism
flunkyistic
flunkyite
flunkyize
fluoaluminate
fluoaluminic
fluoarsenate
fluoborate
fluoboric
fluoborid
fluoboride
fluoborite
fluobromide
fluocarbonate
fluocerine
fluocerite
fluochloride
fluohydric
fluophosphate
fluor
fluoran
fluoranthene
fluorapatite
fluorate
fluorbenzene
fluorene
fluorenyl
fluoresage
fluoresce
fluorescein
fluorescence
fluorescent
fluorescigenic
fluorescigenous
fluorescin
fluorhydric
fluoric
fluoridate
fluoridation
fluoride
fluoridization
fluoridize
fluorimeter
fluorinate
fluorination
fluorindine
fluorine
fluorite
fluormeter
fluorobenzene
fluoroborate
fluoroform
fluoroformol
fluorogen
fluorogenic
fluorography
fluoroid
fluorometer
fluoroscope
fluoroscopic
fluoroscopy
fluorosis
fluorotype
fluorspar
fluoryl
fluosilicate
fluosilicic
fluotantalate
fluotantalic
fluotitanate
fluotitanic
fluozirconic
flurn
flurr
flurried
flurriedly
flurriment
flurry
flush
flushboard
flusher
flusherman
flushgate
flushing
flushingly
flushness
flushy
flusk
flusker
fluster
flusterate
flusteration
flusterer
flusterment
flustery
Flustra
flustrine
flustroid
flustrum
flute
flutebird
fluted
flutelike
flutemouth
fluter
flutework
Flutidae
flutina
fluting
flutist
flutter
flutterable
flutteration
flutterer
fluttering
flutteringly
flutterless
flutterment
fluttersome
fluttery
fluty
fluvial
fluvialist
fluviatic
fluviatile
fluvicoline
fluvioglacial
fluviograph
fluviolacustrine
fluviology
fluviomarine
fluviometer
fluviose
fluvioterrestrial
fluviovolcanic
flux
fluxation
fluxer
fluxibility
fluxible
fluxibleness
fluxibly
fluxile
fluxility
fluxion
fluxional
fluxionally
fluxionary
fluxionist
fluxmeter
fluxroot
fluxweed
fly
flyable
flyaway
flyback
flyball
flybane
flybelt
flyblow
flyblown
flyboat
flyboy
flycatcher
flyeater
flyer
flyflap
flyflapper
flyflower
flying
flyingly
flyleaf
flyless
flyman
flyness
flypaper
flype
flyproof
Flysch
flyspeck
flytail
flytier
flytrap
flyway
flyweight
flywheel
flywinch
flywort
Fo
foal
foalfoot
foalhood
foaly
foam
foambow
foamer
foamflower
foamily
foaminess
foaming
foamingly
foamless
foamlike
foamy
fob
focal
focalization
focalize
focally
focaloid
foci
focimeter
focimetry
focoids
focometer
focometry
focsle
focus
focusable
focuser
focusless
fod
fodda
fodder
fodderer
foddering
fodderless
foder
fodge
fodgel
fodient
Fodientia
foe
foehn
foehnlike
foeish
foeless
foelike
foeman
foemanship
Foeniculum
foenngreek
foeship
foetalization
fog
fogbound
fogbow
fogdog
fogdom
fogeater
fogey
fogfruit
foggage
fogged
fogger
foggily
fogginess
foggish
foggy
foghorn
fogle
fogless
fogman
fogo
fogon
fogou
fogproof
fogram
fogramite
fogramity
fogscoffer
fogus
fogy
fogydom
fogyish
fogyism
fohat
foible
foil
foilable
foiler
foiling
foilsman
foining
foiningly
Foism
foison
foisonless
Foist
foist
foister
foistiness
foisty
foiter
Fokker
fold
foldable
foldage
foldboat
foldcourse
folded
foldedly
folden
folder
folding
foldless
foldskirt
foldure
foldwards
foldy
fole
folgerite
folia
foliaceous
foliaceousness
foliage
foliaged
foliageous
folial
foliar
foliary
foliate
foliated
foliation
foliature
folie
foliicolous
foliiferous
foliiform
folio
foliobranch
foliobranchiate
foliocellosis
foliolate
foliole
folioliferous
foliolose
foliose
foliosity
foliot
folious
foliously
folium
folk
folkcraft
folkfree
folkland
folklore
folkloric
folklorish
folklorism
folklorist
folkloristic
folkmoot
folkmooter
folkmot
folkmote
folkmoter
folkright
folksiness
folksy
Folkvang
Folkvangr
folkway
folky
folles
folletage
follicle
follicular
folliculate
folliculated
follicule
folliculin
Folliculina
folliculitis
folliculose
folliculosis
folliculous
folliful
follis
follow
followable
follower
followership
following
followingly
folly
follyproof
Fomalhaut
foment
fomentation
fomenter
fomes
fomites
Fon
fondak
fondant
fondish
fondle
fondler
fondlesome
fondlike
fondling
fondlingly
fondly
fondness
fondu
fondue
fonduk
fonly
fonnish
fono
fons
font
Fontainea
fontal
fontally
fontanel
fontange
fonted
fontful
fonticulus
fontinal
Fontinalaceae
fontinalaceous
Fontinalis
fontlet
foo
Foochow
Foochowese
food
fooder
foodful
foodless
foodlessness
foodstuff
foody
foofaraw
fool
fooldom
foolery
fooless
foolfish
foolhardihood
foolhardily
foolhardiness
foolhardiship
foolhardy
fooling
foolish
foolishly
foolishness
foollike
foolocracy
foolproof
foolproofness
foolscap
foolship
fooner
fooster
foosterer
foot
footage
footback
football
footballer
footballist
footband
footblower
footboard
footboy
footbreadth
footbridge
footcloth
footed
footeite
footer
footfall
footfarer
footfault
footfolk
footful
footganger
footgear
footgeld
foothalt
foothill
foothold
foothook
foothot
footing
footingly
footings
footle
footler
footless
footlicker
footlight
footlights
footling
footlining
footlock
footmaker
footman
footmanhood
footmanry
footmanship
footmark
footnote
footnoted
footpace
footpad
footpaddery
footpath
footpick
footplate
footprint
footrail
footrest
footrill
footroom
footrope
foots
footscald
footslog
footslogger
footsore
footsoreness
footstalk
footstall
footstep
footstick
footstock
footstone
footstool
footwalk
footwall
footway
footwear
footwork
footworn
footy
fooyoung
foozle
foozler
fop
fopling
foppery
foppish
foppishly
foppishness
foppy
fopship
For
for
fora
forage
foragement
forager
foralite
foramen
foraminated
foramination
foraminifer
Foraminifera
foraminiferal
foraminiferan
foraminiferous
foraminose
foraminous
foraminulate
foraminule
foraminulose
foraminulous
forane
foraneen
foraneous
forasmuch
foray
forayer
forb
forbade
forbar
forbathe
forbear
forbearable
forbearance
forbearant
forbearantly
forbearer
forbearing
forbearingly
forbearingness
forbesite
forbid
forbiddable
forbiddal
forbiddance
forbidden
forbiddenly
forbiddenness
forbidder
forbidding
forbiddingly
forbiddingness
forbit
forbled
forblow
forbore
forborne
forbow
forby
force
forceable
forced
forcedly
forcedness
forceful
forcefully
forcefulness
forceless
forcemeat
forcement
forceps
forcepslike
forcer
forchase
forche
forcibility
forcible
forcibleness
forcibly
forcing
forcingly
forcipate
forcipated
forcipes
forcipiform
forcipressure
Forcipulata
forcipulate
forcleave
forconceit
ford
fordable
fordableness
fordays
Fordicidia
fording
fordless
fordo
fordone
fordwine
fordy
fore
foreaccounting
foreaccustom
foreacquaint
foreact
foreadapt
foreadmonish
foreadvertise
foreadvice
foreadvise
foreallege
foreallot
foreannounce
foreannouncement
foreanswer
foreappoint
foreappointment
forearm
foreassign
foreassurance
forebackwardly
forebay
forebear
forebemoan
forebemoaned
forebespeak
forebitt
forebitten
forebitter
forebless
foreboard
forebode
forebodement
foreboder
foreboding
forebodingly
forebodingness
forebody
foreboot
forebowels
forebowline
forebrace
forebrain
forebreast
forebridge
foreburton
forebush
forecar
forecarriage
forecast
forecaster
forecasting
forecastingly
forecastle
forecastlehead
forecastleman
forecatching
forecatharping
forechamber
forechase
forechoice
forechoose
forechurch
forecited
foreclaw
foreclosable
foreclose
foreclosure
forecome
forecomingness
forecommend
foreconceive
foreconclude
forecondemn
foreconscious
foreconsent
foreconsider
forecontrive
forecool
forecooler
forecounsel
forecount
forecourse
forecourt
forecover
forecovert
foredate
foredawn
foreday
foredeck
foredeclare
foredecree
foredeep
foredefeated
foredefine
foredenounce
foredescribe
foredeserved
foredesign
foredesignment
foredesk
foredestine
foredestiny
foredetermination
foredetermine
foredevised
foredevote
forediscern
foredispose
foredivine
foredone
foredoom
foredoomer
foredoor
foreface
forefather
forefatherly
forefault
forefeel
forefeeling
forefeelingly
forefelt
forefield
forefigure
forefin
forefinger
forefit
foreflank
foreflap
foreflipper
forefoot
forefront
foregallery
foregame
foreganger
foregate
foregift
foregirth
foreglance
foregleam
foreglimpse
foreglow
forego
foregoer
foregoing
foregone
foregoneness
foreground
foreguess
foreguidance
forehalf
forehall
forehammer
forehand
forehanded
forehandedness
forehandsel
forehard
forehatch
forehatchway
forehead
foreheaded
forehear
forehearth
foreheater
forehill
forehinting
forehold
forehood
forehoof
forehook
foreign
foreigneering
foreigner
foreignership
foreignism
foreignization
foreignize
foreignly
foreignness
foreimagination
foreimagine
foreimpressed
foreimpression
foreinclined
foreinstruct
foreintend
foreiron
forejudge
forejudgment
forekeel
foreking
foreknee
foreknow
foreknowable
foreknower
foreknowing
foreknowingly
foreknowledge
forel
forelady
foreland
forelay
foreleech
foreleg
forelimb
forelive
forellenstein
forelock
forelook
foreloop
forelooper
foreloper
foremade
foreman
foremanship
foremarch
foremark
foremartyr
foremast
foremasthand
foremastman
foremean
foremeant
foremelt
foremention
forementioned
foremessenger
foremilk
foremisgiving
foremistress
foremost
foremostly
foremother
forename
forenamed
forenews
forenight
forenoon
forenote
forenoted
forenotice
forenotion
forensal
forensic
forensical
forensicality
forensically
foreordain
foreordainment
foreorder
foreordinate
foreordination
foreorlop
forepad
forepale
foreparents
forepart
forepassed
forepast
forepaw
forepayment
forepeak
foreperiod
forepiece
foreplace
foreplan
foreplanting
forepole
foreporch
forepossessed
forepost
forepredicament
forepreparation
foreprepare
forepretended
foreproduct
foreproffer
forepromise
forepromised
foreprovided
foreprovision
forepurpose
forequarter
forequoted
foreran
forerank
forereach
forereaching
foreread
forereading
forerecited
forereckon
forerehearsed
foreremembered
forereport
forerequest
forerevelation
forerib
forerigging
foreright
foreroom
foreroyal
forerun
forerunner
forerunnership
forerunnings
foresaddle
foresaid
foresail
foresay
forescene
forescent
foreschool
foreschooling
forescript
foreseason
foreseat
foresee
foreseeability
foreseeable
foreseeingly
foreseer
foreseize
foresend
foresense
foresentence
foreset
foresettle
foresettled
foreshadow
foreshadower
foreshaft
foreshank
foreshape
foresheet
foreshift
foreship
foreshock
foreshoe
foreshop
foreshore
foreshorten
foreshortening
foreshot
foreshoulder
foreshow
foreshower
foreshroud
foreside
foresight
foresighted
foresightedness
foresightful
foresightless
foresign
foresignify
foresin
foresing
foresinger
foreskin
foreskirt
foresleeve
foresound
forespeak
forespecified
forespeed
forespencer
forest
forestaff
forestage
forestair
forestal
forestall
forestaller
forestallment
forestarling
forestate
forestation
forestay
forestaysail
forestcraft
forested
foresteep
forestem
forestep
forester
forestership
forestful
forestial
Forestian
forestick
Forestiera
forestine
forestish
forestless
forestlike
forestology
forestral
forestress
forestry
forestside
forestudy
forestwards
foresty
foresummer
foresummon
foresweat
foretack
foretackle
foretalk
foretalking
foretaste
foretaster
foretell
foretellable
foreteller
forethink
forethinker
forethought
forethoughted
forethoughtful
forethoughtfully
forethoughtfulness
forethoughtless
forethrift
foretime
foretimed
foretoken
foretold
foretop
foretopman
foretrace
foretrysail
foreturn
foretype
foretypified
foreuse
foreutter
forevalue
forever
forevermore
foreview
forevision
forevouch
forevouched
forevow
forewarm
forewarmer
forewarn
forewarner
forewarning
forewarningly
forewaters
foreween
foreweep
foreweigh
forewing
forewinning
forewisdom
forewish
forewoman
forewonted
foreword
foreworld
foreworn
forewritten
forewrought
foreyard
foreyear
forfairn
forfar
forfare
forfars
forfault
forfaulture
forfeit
forfeiter
forfeits
forfeiture
forfend
forficate
forficated
forfication
forficiform
Forficula
forficulate
Forficulidae
forfouchten
forfoughen
forfoughten
forgainst
forgather
forge
forgeability
forgeable
forged
forgedly
forgeful
forgeman
forger
forgery
forget
forgetful
forgetfully
forgetfulness
forgetive
forgetness
forgettable
forgetter
forgetting
forgettingly
forgie
forging
forgivable
forgivableness
forgivably
forgive
forgiveless
forgiveness
forgiver
forgiving
forgivingly
forgivingness
forgo
forgoer
forgot
forgotten
forgottenness
forgrow
forgrown
forhoo
forhooy
forhow
forinsec
forint
forisfamiliate
forisfamiliation
forjesket
forjudge
forjudger
fork
forkable
forkbeard
forked
forkedly
forkedness
forker
forkful
forkhead
forkiness
forkless
forklike
forkman
forksmith
forktail
forkwise
forky
forleft
forlet
forlorn
forlornity
forlornly
forlornness
form
formability
formable
formably
formagen
formagenic
formal
formalazine
formaldehyde
formaldehydesulphoxylate
formaldehydesulphoxylic
formaldoxime
formalesque
Formalin
formalism
formalist
formalistic
formalith
formality
formalization
formalize
formalizer
formally
formalness
formamide
formamidine
formamido
formamidoxime
formanilide
formant
format
formate
formation
formational
formative
formatively
formativeness
formature
formazyl
forme
formed
formedon
formee
formel
formene
formenic
former
formeret
formerly
formerness
formful
formiate
formic
Formica
formican
Formicariae
formicarian
Formicariidae
formicarioid
formicarium
formicaroid
formicary
formicate
formication
formicative
formicicide
formicid
Formicidae
formicide
Formicina
Formicinae
formicine
Formicivora
formicivorous
Formicoidea
formidability
formidable
formidableness
formidably
formin
forminate
forming
formless
formlessly
formlessness
Formol
formolite
formonitrile
Formosan
formose
formoxime
formula
formulable
formulae
formulaic
formular
formularism
formularist
formularistic
formularization
formularize
formulary
formulate
formulation
formulator
formulatory
formule
formulism
formulist
formulistic
formulization
formulize
formulizer
formwork
formy
formyl
formylal
formylate
formylation
fornacic
Fornax
fornaxid
fornenst
fornent
fornical
fornicate
fornicated
fornication
fornicator
fornicatress
fornicatrix
forniciform
forninst
fornix
forpet
forpine
forpit
forprise
forrad
forrard
forride
forrit
forritsome
forrue
forsake
forsaken
forsakenly
forsakenness
forsaker
forset
forslow
forsooth
forspeak
forspend
forspread
Forst
forsterite
forswear
forswearer
forsworn
forswornness
Forsythia
fort
fortalice
forte
fortescue
fortescure
forth
forthbring
forthbringer
forthcome
forthcomer
forthcoming
forthcomingness
forthcut
forthfare
forthfigured
forthgaze
forthgo
forthgoing
forthink
forthputting
forthright
forthrightly
forthrightness
forthrights
forthtell
forthteller
forthwith
forthy
forties
fortieth
fortifiable
fortification
fortifier
fortify
fortifying
fortifyingly
fortin
fortis
fortissimo
fortitude
fortitudinous
fortlet
fortnight
fortnightly
fortravail
fortread
fortress
fortuitism
fortuitist
fortuitous
fortuitously
fortuitousness
fortuity
fortunate
fortunately
fortunateness
fortune
fortuned
fortuneless
Fortunella
fortunetell
fortuneteller
fortunetelling
fortunite
forty
fortyfold
forum
forumize
forwander
forward
forwardal
forwardation
forwarder
forwarding
forwardly
forwardness
forwards
forwean
forweend
forwent
forwoden
forworden
fosh
fosie
Fosite
fossa
fossage
fossane
fossarian
fosse
fossed
fossette
fossick
fossicker
fossiform
fossil
fossilage
fossilated
fossilation
fossildom
fossiled
fossiliferous
fossilification
fossilify
fossilism
fossilist
fossilizable
fossilization
fossilize
fossillike
fossilogist
fossilogy
fossilological
fossilologist
fossilology
fossor
Fossores
Fossoria
fossorial
fossorious
fossula
fossulate
fossule
fossulet
fostell
Foster
foster
fosterable
fosterage
fosterer
fosterhood
fostering
fosteringly
fosterite
fosterland
fosterling
fostership
fostress
fot
fotch
fother
Fothergilla
fotmal
fotui
fou
foud
foudroyant
fouette
fougade
fougasse
fought
foughten
foughty
foujdar
foujdary
foul
foulage
foulard
fouler
fouling
foulish
foully
foulmouthed
foulmouthedly
foulmouthedness
foulness
foulsome
foumart
foun
found
foundation
foundational
foundationally
foundationary
foundationed
foundationer
foundationless
foundationlessness
founder
founderous
foundership
foundery
founding
foundling
foundress
foundry
foundryman
fount
fountain
fountained
fountaineer
fountainhead
fountainless
fountainlet
fountainous
fountainously
fountainwise
fountful
Fouquieria
Fouquieriaceae
fouquieriaceous
four
fourble
fourche
fourchee
fourcher
fourchette
fourchite
fourer
fourflusher
fourfold
Fourierian
Fourierism
Fourierist
Fourieristic
Fourierite
fourling
fourpence
fourpenny
fourpounder
fourre
fourrier
fourscore
foursome
foursquare
foursquarely
foursquareness
fourstrand
fourteen
fourteener
fourteenfold
fourteenth
fourteenthly
fourth
fourther
fourthly
foussa
foute
fouter
fouth
fovea
foveal
foveate
foveated
foveation
foveiform
foveola
foveolarious
foveolate
foveolated
foveole
foveolet
fow
fowk
fowl
fowler
fowlerite
fowlery
fowlfoot
fowling
fox
foxbane
foxberry
foxchop
foxer
foxery
foxfeet
foxfinger
foxfish
foxglove
foxhole
foxhound
foxily
foxiness
foxing
foxish
foxlike
foxproof
foxship
foxskin
foxtail
foxtailed
foxtongue
foxwood
foxy
foy
foyaite
foyaitic
foyboat
foyer
foziness
fozy
fra
frab
frabbit
frabjous
frabjously
frabous
fracas
fracedinous
frache
frack
fractable
fractabling
fracted
Fracticipita
fractile
fraction
fractional
fractionalism
fractionalize
fractionally
fractionary
fractionate
fractionating
fractionation
fractionator
fractionization
fractionize
fractionlet
fractious
fractiously
fractiousness
fractocumulus
fractonimbus
fractostratus
fractuosity
fracturable
fractural
fracture
fractureproof
frae
Fragaria
fraghan
Fragilaria
Fragilariaceae
fragile
fragilely
fragileness
fragility
fragment
fragmental
fragmentally
fragmentarily
fragmentariness
fragmentary
fragmentation
fragmented
fragmentist
fragmentitious
fragmentize
fragrance
fragrancy
fragrant
fragrantly
fragrantness
fraid
fraik
frail
frailejon
frailish
frailly
frailness
frailty
fraise
fraiser
Fram
framable
framableness
frambesia
frame
framea
frameable
frameableness
framed
frameless
framer
framesmith
framework
framing
frammit
frampler
frampold
franc
Frances
franchisal
franchise
franchisement
franchiser
Francic
Francis
francisc
francisca
Franciscan
Franciscanism
Francisco
francium
Francize
franco
Francois
francolin
francolite
Francomania
Franconian
Francophile
Francophilism
Francophobe
Francophobia
frangent
Frangi
frangibility
frangible
frangibleness
frangipane
frangipani
frangula
Frangulaceae
frangulic
frangulin
frangulinic
Frank
frank
frankability
frankable
frankalmoign
Frankenia
Frankeniaceae
frankeniaceous
Frankenstein
franker
frankfurter
frankhearted
frankheartedly
frankheartedness
Frankify
frankincense
frankincensed
franking
Frankish
Frankist
franklandite
Franklin
franklin
Franklinia
Franklinian
Frankliniana
Franklinic
Franklinism
Franklinist
franklinite
Franklinization
frankly
frankmarriage
frankness
frankpledge
frantic
frantically
franticly
franticness
franzy
frap
frappe
frapping
frasco
frase
Frasera
frasier
frass
frat
fratch
fratched
fratcheous
fratcher
fratchety
fratchy
frater
Fratercula
fraternal
fraternalism
fraternalist
fraternality
fraternally
fraternate
fraternation
fraternism
fraternity
fraternization
fraternize
fraternizer
fratery
Fraticelli
Fraticellian
fratority
Fratricelli
fratricidal
fratricide
fratry
fraud
fraudful
fraudfully
fraudless
fraudlessly
fraudlessness
fraudproof
fraudulence
fraudulency
fraudulent
fraudulently
fraudulentness
fraughan
fraught
frawn
fraxetin
fraxin
fraxinella
Fraxinus
fray
frayed
frayedly
frayedness
fraying
frayn
frayproof
fraze
frazer
frazil
frazzle
frazzling
freak
freakdom
freakery
freakful
freakily
freakiness
freakish
freakishly
freakishness
freaky
fream
freath
freck
frecken
freckened
frecket
freckle
freckled
freckledness
freckleproof
freckling
frecklish
freckly
Fred
Freddie
Freddy
Frederic
Frederica
Frederick
frederik
fredricite
free
freeboard
freeboot
freebooter
freebootery
freebooting
freeborn
Freechurchism
freed
freedman
freedom
freedwoman
freehand
freehanded
freehandedly
freehandedness
freehearted
freeheartedly
freeheartedness
freehold
freeholder
freeholdership
freeholding
freeing
freeish
Freekirker
freelage
freeloving
freelovism
freely
freeman
freemanship
freemartin
freemason
freemasonic
freemasonical
freemasonism
freemasonry
freeness
freer
Freesia
freesilverism
freesilverite
freestanding
freestone
freet
freethinker
freethinking
freetrader
freety
freeward
freeway
freewheel
freewheeler
freewheeling
freewill
freewoman
freezable
freeze
freezer
freezing
freezingly
Fregata
Fregatae
Fregatidae
freibergite
freieslebenite
freight
freightage
freighter
freightless
freightment
freir
freit
freity
fremd
fremdly
fremdness
fremescence
fremescent
fremitus
Fremontia
Fremontodendron
frenal
Frenatae
frenate
French
frenched
Frenchification
frenchification
Frenchify
frenchify
Frenchily
Frenchiness
frenching
Frenchism
Frenchize
Frenchless
Frenchly
Frenchman
Frenchness
Frenchwise
Frenchwoman
Frenchy
frenetic
frenetical
frenetically
Frenghi
frenular
frenulum
frenum
frenzelite
frenzied
frenziedly
frenzy
Freon
frequence
frequency
frequent
frequentable
frequentage
frequentation
frequentative
frequenter
frequently
frequentness
frescade
fresco
frescoer
frescoist
fresh
freshen
freshener
freshet
freshhearted
freshish
freshly
freshman
freshmanhood
freshmanic
freshmanship
freshness
freshwoman
Fresison
fresnel
fresno
fret
fretful
fretfully
fretfulness
fretless
fretsome
frett
frettage
frettation
frette
fretted
fretter
fretting
frettingly
fretty
fretum
fretways
fretwise
fretwork
fretworked
Freudian
Freudianism
Freudism
Freudist
Freya
freyalite
Freycinetia
Freyja
Freyr
friability
friable
friableness
friand
friandise
friar
friarbird
friarhood
friarling
friarly
friary
frib
fribble
fribbleism
fribbler
fribblery
fribbling
fribblish
fribby
fricandeau
fricandel
fricassee
frication
fricative
fricatrice
friction
frictionable
frictional
frictionally
frictionize
frictionless
frictionlessly
frictionproof
Friday
Fridila
fridstool
fried
Frieda
friedcake
friedelite
friedrichsdor
friend
friended
friendless
friendlessness
friendlike
friendlily
friendliness
friendliwise
friendly
friendship
frier
frieseite
Friesian
Friesic
Friesish
frieze
friezer
friezy
frig
frigate
frigatoon
friggle
fright
frightable
frighten
frightenable
frightened
frightenedly
frightenedness
frightener
frightening
frighteningly
frighter
frightful
frightfully
frightfulness
frightless
frightment
frighty
frigid
Frigidaire
frigidarium
frigidity
frigidly
frigidness
frigiferous
frigolabile
frigoric
frigorific
frigorifical
frigorify
frigorimeter
frigostable
frigotherapy
Frija
frijol
frijolillo
frijolito
frike
frill
frillback
frilled
friller
frillery
frillily
frilliness
frilling
frilly
frim
Frimaire
fringe
fringed
fringeflower
fringeless
fringelet
fringent
fringepod
Fringetail
Fringilla
fringillaceous
Fringillidae
fringilliform
Fringilliformes
fringilline
fringilloid
fringing
fringy
fripperer
frippery
frisca
Frisesomorum
frisette
Frisian
Frisii
frisk
frisker
frisket
friskful
friskily
friskiness
frisking
friskingly
frisky
frisolee
frison
frist
frisure
frit
frith
frithborh
frithbot
frithles
frithsoken
frithstool
frithwork
Fritillaria
fritillary
fritt
fritter
fritterer
Fritz
Friulian
frivol
frivoler
frivolism
frivolist
frivolity
frivolize
frivolous
frivolously
frivolousness
frixion
friz
frize
frizer
frizz
frizzer
frizzily
frizziness
frizzing
frizzle
frizzler
frizzly
frizzy
fro
frock
frocking
frockless
frocklike
frockmaker
froe
Froebelian
Froebelism
Froebelist
frog
frogbit
frogeater
frogeye
frogface
frogfish
frogflower
frogfoot
frogged
froggery
frogginess
frogging
froggish
froggy
froghood
froghopper
frogland
frogleaf
frogleg
froglet
froglike
frogling
frogman
frogmouth
frognose
frogskin
frogstool
frogtongue
frogwort
froise
frolic
frolicful
frolicker
frolicky
frolicly
frolicness
frolicsome
frolicsomely
frolicsomeness
from
fromward
fromwards
frond
frondage
fronded
frondent
frondesce
frondescence
frondescent
frondiferous
frondiform
frondigerous
frondivorous
frondlet
frondose
frondosely
frondous
front
frontad
frontage
frontager
frontal
frontalis
frontality
frontally
frontbencher
fronted
fronter
frontier
frontierlike
frontierman
frontiersman
Frontignan
fronting
frontingly
Frontirostria
frontispiece
frontless
frontlessly
frontlessness
frontlet
frontoauricular
frontoethmoid
frontogenesis
frontolysis
frontomallar
frontomaxillary
frontomental
frontonasal
frontooccipital
frontoorbital
frontoparietal
frontopontine
frontosphenoidal
frontosquamosal
frontotemporal
frontozygomatic
frontpiece
frontsman
frontstall
frontward
frontways
frontwise
froom
frore
frory
frosh
frost
frostation
frostbird
frostbite
frostbow
frosted
froster
frostfish
frostflower
frostily
frostiness
frosting
frostless
frostlike
frostproof
frostproofing
frostroot
frostweed
frostwork
frostwort
frosty
frot
froth
frother
Frothi
frothily
frothiness
frothing
frothless
frothsome
frothy
frotton
froufrou
frough
froughy
frounce
frounceless
frow
froward
frowardly
frowardness
frower
frowl
frown
frowner
frownful
frowning
frowningly
frownless
frowny
frowst
frowstily
frowstiness
frowsty
frowy
frowze
frowzily
frowziness
frowzled
frowzly
frowzy
froze
frozen
frozenhearted
frozenly
frozenness
fruchtschiefer
fructed
fructescence
fructescent
fructicultural
fructiculture
Fructidor
fructiferous
fructiferously
fructification
fructificative
fructifier
fructiform
fructify
fructiparous
fructivorous
fructose
fructoside
fructuary
fructuosity
fructuous
fructuously
fructuousness
frugal
frugalism
frugalist
frugality
frugally
frugalness
fruggan
Frugivora
frugivorous
fruit
fruitade
fruitage
fruitarian
fruitarianism
fruitcake
fruited
fruiter
fruiterer
fruiteress
fruitery
fruitful
fruitfullness
fruitfully
fruitgrower
fruitgrowing
fruitiness
fruiting
fruition
fruitist
fruitive
fruitless
fruitlessly
fruitlessness
fruitlet
fruitling
fruitstalk
fruittime
fruitwise
fruitwoman
fruitwood
fruitworm
fruity
frumentaceous
frumentarious
frumentation
frumenty
frump
frumpery
frumpily
frumpiness
frumpish
frumpishly
frumpishness
frumple
frumpy
frush
frustrate
frustrately
frustrater
frustration
frustrative
frustratory
frustule
frustulent
frustulose
frustum
frutescence
frutescent
fruticetum
fruticose
fruticous
fruticulose
frutify
fry
fryer
fu
fub
fubby
fubsy
Fucaceae
fucaceous
Fucales
fucate
fucation
fucatious
Fuchsia
Fuchsian
fuchsin
fuchsine
fuchsinophil
fuchsinophilous
fuchsite
fuchsone
fuci
fucinita
fuciphagous
fucoid
fucoidal
Fucoideae
fucosan
fucose
fucous
fucoxanthin
fucus
fud
fuddle
fuddler
fuder
fudge
fudger
fudgy
Fuegian
fuel
fueler
fuelizer
fuerte
fuff
fuffy
fugacious
fugaciously
fugaciousness
fugacity
fugal
fugally
fuggy
fugient
fugitate
fugitation
fugitive
fugitively
fugitiveness
fugitivism
fugitivity
fugle
fugleman
fuglemanship
fugler
fugu
fugue
fuguist
fuidhir
fuirdays
Fuirena
fuji
Fulah
fulciform
fulcral
fulcrate
fulcrum
fulcrumage
fulfill
fulfiller
fulfillment
Fulfulde
fulgent
fulgently
fulgentness
fulgid
fulgide
fulgidity
fulgor
Fulgora
fulgorid
Fulgoridae
Fulgoroidea
fulgorous
Fulgur
fulgural
fulgurant
fulgurantly
fulgurata
fulgurate
fulgurating
fulguration
fulgurator
fulgurite
fulgurous
fulham
Fulica
Fulicinae
fulicine
fuliginosity
fuliginous
fuliginously
fuliginousness
Fuligula
Fuligulinae
fuliguline
fulk
full
fullam
fullback
fuller
fullering
fullery
fullface
fullhearted
fulling
fullish
fullmouth
fullmouthed
fullmouthedly
fullness
fullom
Fullonian
fully
fulmar
Fulmarus
fulmicotton
fulminancy
fulminant
fulminate
fulminating
fulmination
fulminator
fulminatory
fulmine
fulmineous
fulminic
fulminous
fulminurate
fulminuric
fulsome
fulsomely
fulsomeness
fulth
Fultz
Fulup
fulvene
fulvescent
fulvid
fulvidness
fulvous
fulwa
fulyie
fulzie
fum
fumacious
fumado
fumage
fumagine
Fumago
fumarate
Fumaria
Fumariaceae
fumariaceous
fumaric
fumarine
fumarium
fumaroid
fumaroidal
fumarole
fumarolic
fumaryl
fumatorium
fumatory
fumble
fumbler
fumbling
fume
fumeless
fumer
fumeroot
fumet
fumette
fumewort
fumiduct
fumiferous
fumigant
fumigate
fumigation
fumigator
fumigatorium
fumigatory
fumily
fuminess
fuming
fumingly
fumistery
fumitory
fumose
fumosity
fumous
fumously
fumy
fun
funambulate
funambulation
funambulator
funambulatory
funambulic
funambulism
funambulist
funambulo
Funaria
Funariaceae
funariaceous
function
functional
functionalism
functionalist
functionality
functionalize
functionally
functionarism
functionary
functionate
functionation
functionize
functionless
fund
fundable
fundal
fundament
fundamental
fundamentalism
fundamentalist
fundamentality
fundamentally
fundamentalness
fundatorial
fundatrix
funded
funder
fundholder
fundi
fundic
fundiform
funditor
fundless
fundmonger
fundmongering
funds
Fundulinae
funduline
Fundulus
fundungi
fundus
funebrial
funeral
funeralize
funerary
funereal
funereally
funest
fungaceous
fungal
Fungales
fungate
fungation
fungi
Fungia
fungian
fungibility
fungible
fungic
fungicidal
fungicide
fungicolous
fungiferous
fungiform
fungilliform
fungin
fungistatic
fungivorous
fungo
fungoid
fungoidal
fungological
fungologist
fungology
fungose
fungosity
fungous
fungus
fungused
funguslike
fungusy
funicle
funicular
funiculate
funicule
funiculitis
funiculus
funiform
funipendulous
funis
Funje
funk
funker
Funkia
funkiness
funky
funmaker
funmaking
funnel
funneled
funnelform
funnellike
funnelwise
funnily
funniment
funniness
funny
funnyman
funori
funt
Funtumia
Fur
fur
furacious
furaciousness
furacity
fural
furaldehyde
furan
furanoid
furazan
furazane
furbelow
furbish
furbishable
furbisher
furbishment
furca
furcal
furcate
furcately
furcation
Furcellaria
furcellate
furciferine
furciferous
furciform
Furcraea
furcula
furcular
furculum
furdel
Furfooz
furfur
furfuraceous
furfuraceously
furfural
furfuralcohol
furfuraldehyde
furfuramide
furfuran
furfuration
furfurine
furfuroid
furfurole
furfurous
furfuryl
furfurylidene
furiant
furibund
furied
Furies
furify
furil
furilic
furiosa
furiosity
furioso
furious
furiously
furiousness
furison
furl
furlable
Furlan
furler
furless
furlong
furlough
furnace
furnacelike
furnaceman
furnacer
furnacite
furnage
Furnariidae
Furnariides
Furnarius
furner
furnish
furnishable
furnished
furnisher
furnishing
furnishment
furniture
furnitureless
furodiazole
furoic
furoid
furoin
furole
furomethyl
furomonazole
furor
furore
furphy
furred
furrier
furriered
furriery
furrily
furriness
furring
furrow
furrower
furrowless
furrowlike
furrowy
furry
furstone
further
furtherance
furtherer
furtherest
furtherly
furthermore
furthermost
furthersome
furthest
furtive
furtively
furtiveness
Furud
furuncle
furuncular
furunculoid
furunculosis
furunculous
fury
furyl
furze
furzechat
furzed
furzeling
furzery
furzetop
furzy
fusain
fusarial
fusariose
fusariosis
Fusarium
fusarole
fusate
fusc
fuscescent
fuscin
fuscohyaline
fuscous
fuse
fuseboard
fused
fusee
fuselage
fuseplug
fusht
fusibility
fusible
fusibleness
fusibly
Fusicladium
Fusicoccum
fusiform
Fusiformis
fusil
fusilier
fusillade
fusilly
fusinist
fusion
fusional
fusionism
fusionist
fusionless
fusoid
fuss
fusser
fussification
fussify
fussily
fussiness
fussock
fussy
fust
fustanella
fustee
fusteric
fustet
fustian
fustianish
fustianist
fustianize
fustic
fustigate
fustigation
fustigator
fustigatory
fustilugs
fustily
fustin
fustiness
fustle
fusty
Fusulina
fusuma
fusure
Fusus
fut
futchel
fute
futhorc
futile
futilely
futileness
futilitarian
futilitarianism
futility
futilize
futtermassel
futtock
futural
future
futureless
futureness
futuric
futurism
futurist
futuristic
futurition
futurity
futurize
futwa
fuye
fuze
fuzz
fuzzball
fuzzily
fuzziness
fuzzy
fyke
fylfot
fyrd
G
g
Ga
ga
gab
gabardine
gabbard
gabber
gabble
gabblement
gabbler
gabbro
gabbroic
gabbroid
gabbroitic
gabby
Gabe
gabelle
gabelled
gabelleman
gabeller
gaberdine
gaberlunzie
gabgab
gabi
gabion
gabionade
gabionage
gabioned
gablatores
gable
gableboard
gablelike
gablet
gablewise
gablock
Gaboon
Gabriel
Gabriella
Gabrielrache
Gabunese
gaby
Gad
gad
Gadaba
gadabout
Gadarene
Gadaria
gadbee
gadbush
Gaddang
gadded
gadder
Gaddi
gaddi
gadding
gaddingly
gaddish
gaddishness
gade
gadfly
gadge
gadger
gadget
gadid
Gadidae
gadinine
Gaditan
gadling
gadman
gadoid
Gadoidea
gadolinia
gadolinic
gadolinite
gadolinium
gadroon
gadroonage
Gadsbodikins
Gadsbud
Gadslid
gadsman
Gadswoons
gaduin
Gadus
gadwall
Gadzooks
Gael
Gaeldom
Gaelic
Gaelicism
Gaelicist
Gaelicization
Gaelicize
Gaeltacht
gaen
Gaertnerian
gaet
Gaetulan
Gaetuli
Gaetulian
gaff
gaffe
gaffer
Gaffkya
gaffle
gaffsman
gag
gagate
gage
gageable
gagee
gageite
gagelike
gager
gagership
gagger
gaggery
gaggle
gaggler
gagman
gagor
gagroot
gagtooth
gahnite
Gahrwali
Gaia
gaiassa
Gaidropsaridae
gaiety
Gail
Gaillardia
gaily
gain
gainable
gainage
gainbirth
gaincall
gaincome
gaine
gainer
gainful
gainfully
gainfulness
gaining
gainless
gainlessness
gainliness
gainly
gains
gainsay
gainsayer
gainset
gainsome
gainspeaker
gainspeaking
gainst
gainstrive
gainturn
gaintwist
gainyield
gair
gairfish
gaisling
gait
gaited
gaiter
gaiterless
gaiting
gaize
gaj
gal
gala
Galacaceae
galactagogue
galactagoguic
galactan
galactase
galactemia
galacthidrosis
Galactia
galactic
galactidrosis
galactite
galactocele
galactodendron
galactodensimeter
galactogenetic
galactohemia
galactoid
galactolipide
galactolipin
galactolysis
galactolytic
galactoma
galactometer
galactometry
galactonic
galactopathy
galactophagist
galactophagous
galactophlebitis
galactophlysis
galactophore
galactophoritis
galactophorous
galactophthysis
galactophygous
galactopoiesis
galactopoietic
galactopyra
galactorrhea
galactorrhoea
galactoscope
galactose
galactoside
galactosis
galactostasis
galactosuria
galactotherapy
galactotrophy
galacturia
galagala
Galaginae
Galago
galah
galanas
galanga
galangin
galant
Galanthus
galantine
galany
galapago
Galatae
galatea
Galatian
Galatic
galatotrophic
Galax
galaxian
Galaxias
Galaxiidae
galaxy
galban
galbanum
Galbula
Galbulae
Galbulidae
Galbulinae
galbulus
Galcha
Galchic
Gale
gale
galea
galeage
galeate
galeated
galee
galeeny
Galega
galegine
Galei
galeid
Galeidae
galeiform
galempung
Galen
galena
Galenian
Galenic
galenic
Galenical
galenical
Galenism
Galenist
galenite
galenobismutite
galenoid
Galeodes
Galeodidae
galeoid
Galeopithecus
Galeopsis
Galeorchis
Galeorhinidae
Galeorhinus
galeproof
galera
galericulate
galerum
galerus
Galesaurus
galet
Galeus
galewort
galey
Galga
galgal
Galgulidae
gali
Galibi
Galician
Galictis
Galidia
Galidictis
Galik
Galilean
galilee
galimatias
galingale
Galinsoga
galiongee
galiot
galipidine
galipine
galipoidin
galipoidine
galipoipin
galipot
Galium
gall
Galla
galla
gallacetophenone
gallah
gallanilide
gallant
gallantize
gallantly
gallantness
gallantry
gallate
gallature
gallberry
gallbush
galleass
galled
Gallegan
gallein
galleon
galler
Galleria
gallerian
galleried
Galleriidae
gallery
gallerylike
gallet
galley
galleylike
galleyman
galleyworm
gallflower
gallfly
Galli
galliambic
galliambus
Gallian
galliard
galliardise
galliardly
galliardness
Gallic
gallic
Gallican
Gallicanism
Gallicism
Gallicization
Gallicize
Gallicizer
gallicola
Gallicolae
gallicole
gallicolous
galliferous
Gallification
gallification
galliform
Galliformes
Gallify
galligaskin
gallimaufry
Gallinaceae
gallinacean
Gallinacei
gallinaceous
Gallinae
Gallinago
gallinazo
galline
galling
gallingly
gallingness
gallinipper
Gallinula
gallinule
Gallinulinae
gallinuline
gallipot
Gallirallus
gallisin
gallium
gallivant
gallivanter
gallivat
gallivorous
galliwasp
gallnut
gallocyanin
gallocyanine
galloflavine
galloglass
Galloman
Gallomania
Gallomaniac
gallon
gallonage
galloner
galloon
gallooned
gallop
gallopade
galloper
Galloperdix
Gallophile
Gallophilism
Gallophobe
Gallophobia
galloping
galloptious
gallotannate
gallotannic
gallotannin
gallous
Gallovidian
Galloway
galloway
gallowglass
gallows
gallowsmaker
gallowsness
gallowsward
gallstone
Gallus
galluses
gallweed
gallwort
gally
gallybagger
gallybeggar
gallycrow
Galoisian
galoot
galop
galore
galosh
galp
galravage
galravitch
galt
Galtonia
Galtonian
galuchat
galumph
galumptious
Galusha
galuth
galvanic
galvanical
galvanically
galvanism
galvanist
galvanization
galvanize
galvanized
galvanizer
galvanocauterization
galvanocautery
galvanocontractility
galvanofaradization
galvanoglyph
galvanoglyphy
galvanograph
galvanographic
galvanography
galvanologist
galvanology
galvanolysis
galvanomagnet
galvanomagnetic
galvanomagnetism
galvanometer
galvanometric
galvanometrical
galvanometrically
galvanometry
galvanoplastic
galvanoplastical
galvanoplastically
galvanoplastics
galvanoplasty
galvanopsychic
galvanopuncture
galvanoscope
galvanoscopic
galvanoscopy
galvanosurgery
galvanotactic
galvanotaxis
galvanotherapy
galvanothermometer
galvanothermy
galvanotonic
galvanotropic
galvanotropism
galvayne
galvayning
Galways
Galwegian
galyac
galyak
galziekte
gam
gamahe
Gamaliel
gamashes
gamasid
Gamasidae
Gamasoidea
gamb
gamba
gambade
gambado
gambang
gambeer
gambeson
gambet
gambette
gambia
gambier
gambist
gambit
gamble
gambler
gamblesome
gamblesomeness
gambling
gambodic
gamboge
gambogian
gambogic
gamboised
gambol
gambrel
gambreled
gambroon
Gambusia
gamdeboo
game
gamebag
gameball
gamecock
gamecraft
gameful
gamekeeper
gamekeeping
gamelang
gameless
gamelike
Gamelion
gamelotte
gamely
gamene
gameness
gamesome
gamesomely
gamesomeness
gamester
gamestress
gametal
gametange
gametangium
gamete
gametic
gametically
gametocyst
gametocyte
gametogenesis
gametogenic
gametogenous
gametogeny
gametogonium
gametogony
gametoid
gametophagia
gametophore
gametophyll
gametophyte
gametophytic
gamic
gamily
gamin
gaminesque
gaminess
gaming
gaminish
gamma
gammacism
gammacismus
gammadion
gammarid
Gammaridae
gammarine
gammaroid
Gammarus
gammation
gammelost
gammer
gammerel
gammerstang
Gammexane
gammick
gammock
gammon
gammoner
gammoning
gammy
gamobium
gamodesmic
gamodesmy
gamogenesis
gamogenetic
gamogenetical
gamogenetically
gamogony
Gamolepis
gamomania
gamont
Gamopetalae
gamopetalous
gamophagia
gamophagy
gamophyllous
gamori
gamosepalous
gamostele
gamostelic
gamostely
gamotropic
gamotropism
gamp
gamphrel
gamut
gamy
gan
ganam
ganancial
Ganapati
ganch
Ganda
gander
ganderess
gandergoose
gandermooner
ganderteeth
Gandhara
Gandharva
Gandhiism
Gandhism
Gandhist
gandul
gandum
gandurah
gane
ganef
gang
Ganga
ganga
Gangamopteris
gangan
gangava
gangboard
gangdom
gange
ganger
Gangetic
ganggang
ganging
gangism
gangland
ganglander
ganglia
gangliac
ganglial
gangliar
gangliasthenia
gangliate
gangliated
gangliectomy
gangliform
gangliitis
gangling
ganglioblast
gangliocyte
ganglioform
ganglioid
ganglioma
ganglion
ganglionary
ganglionate
ganglionectomy
ganglioneural
ganglioneure
ganglioneuroma
ganglioneuron
ganglionic
ganglionitis
ganglionless
ganglioplexus
gangly
gangman
gangmaster
gangplank
gangrel
gangrene
gangrenescent
gangrenous
gangsman
gangster
gangsterism
gangtide
gangue
Ganguela
gangway
gangwayman
ganister
ganja
ganner
gannet
Ganocephala
ganocephalan
ganocephalous
ganodont
Ganodonta
Ganodus
ganoid
ganoidal
ganoidean
Ganoidei
ganoidian
ganoin
ganomalite
ganophyllite
ganosis
Ganowanian
gansel
gansey
gansy
gant
ganta
gantang
gantlet
gantline
ganton
gantries
gantry
gantryman
gantsl
Ganymede
Ganymedes
ganza
ganzie
gaol
gaolbird
gaoler
Gaon
Gaonate
Gaonic
gap
Gapa
gapa
gape
gaper
gapes
gapeseed
gapeworm
gaping
gapingly
gapingstock
gapo
gappy
gapy
gar
gara
garabato
garad
garage
garageman
Garamond
garance
garancine
garapata
garava
garavance
garawi
garb
garbage
garbardine
garbel
garbell
garbill
garble
garbleable
garbler
garbless
garbling
garboard
garboil
garbure
garce
Garcinia
gardant
gardeen
garden
gardenable
gardencraft
gardened
gardener
gardenership
gardenesque
gardenful
gardenhood
Gardenia
gardenin
gardening
gardenize
gardenless
gardenlike
gardenly
gardenmaker
gardenmaking
gardenwards
gardenwise
gardeny
garderobe
gardevin
gardy
gardyloo
gare
garefowl
gareh
garetta
garewaite
garfish
garganey
Gargantua
Gargantuan
garget
gargety
gargle
gargol
gargoyle
gargoyled
gargoyley
gargoylish
gargoylishly
gargoylism
Garhwali
garial
gariba
garibaldi
Garibaldian
garish
garishly
garishness
garland
garlandage
garlandless
garlandlike
garlandry
garlandwise
garle
garlic
garlicky
garliclike
garlicmonger
garlicwort
garment
garmentless
garmentmaker
garmenture
garmentworker
garn
garnel
garner
garnerage
garnet
garnetberry
garneter
garnetiferous
garnets
garnett
garnetter
garnetwork
garnetz
garnice
garniec
garnierite
garnish
garnishable
garnished
garnishee
garnisheement
garnisher
garnishment
garnishry
garniture
Garo
garoo
garookuh
garrafa
garran
Garret
garret
garreted
garreteer
garretmaster
garrison
Garrisonian
Garrisonism
garrot
garrote
garroter
Garrulinae
garruline
garrulity
garrulous
garrulously
garrulousness
Garrulus
garrupa
Garrya
Garryaceae
garse
Garshuni
garsil
garston
garten
garter
gartered
gartering
garterless
garth
garthman
Garuda
garum
garvanzo
garvey
garvock
Gary
gas
Gasan
gasbag
gascoigny
Gascon
gasconade
gasconader
Gasconism
gascromh
gaseity
gaselier
gaseosity
gaseous
gaseousness
gasfiring
gash
gashes
gashful
gashliness
gashly
gasholder
gashouse
gashy
gasifiable
gasification
gasifier
gasiform
gasify
gasket
gaskin
gasking
gaskins
gasless
gaslight
gaslighted
gaslighting
gaslit
gaslock
gasmaker
gasman
gasogenic
gasoliery
gasoline
gasolineless
gasoliner
gasometer
gasometric
gasometrical
gasometry
gasp
Gaspar
gasparillo
gasper
gaspereau
gaspergou
gaspiness
gasping
gaspingly
gasproof
gaspy
gasser
Gasserian
gassiness
gassing
gassy
gast
gastaldite
gastaldo
gaster
gasteralgia
Gasterolichenes
gasteromycete
Gasteromycetes
gasteromycetous
Gasterophilus
gasteropod
Gasteropoda
gasterosteid
Gasterosteidae
gasterosteiform
gasterosteoid
Gasterosteus
gasterotheca
gasterothecal
Gasterotricha
gasterotrichan
gasterozooid
gastight
gastightness
Gastornis
Gastornithidae
gastradenitis
gastraea
gastraead
Gastraeadae
gastraeal
gastraeum
gastral
gastralgia
gastralgic
gastralgy
gastraneuria
gastrasthenia
gastratrophia
gastrectasia
gastrectasis
gastrectomy
gastrelcosis
gastric
gastricism
gastrilegous
gastriloquial
gastriloquism
gastriloquist
gastriloquous
gastriloquy
gastrin
gastritic
gastritis
gastroadenitis
gastroadynamic
gastroalbuminorrhea
gastroanastomosis
gastroarthritis
gastroatonia
gastroatrophia
gastroblennorrhea
gastrocatarrhal
gastrocele
gastrocentrous
Gastrochaena
Gastrochaenidae
gastrocnemial
gastrocnemian
gastrocnemius
gastrocoel
gastrocolic
gastrocoloptosis
gastrocolostomy
gastrocolotomy
gastrocolpotomy
gastrocystic
gastrocystis
gastrodialysis
gastrodiaphanoscopy
gastrodidymus
gastrodisk
gastroduodenal
gastroduodenitis
gastroduodenoscopy
gastroduodenotomy
gastrodynia
gastroelytrotomy
gastroenteralgia
gastroenteric
gastroenteritic
gastroenteritis
gastroenteroanastomosis
gastroenterocolitis
gastroenterocolostomy
gastroenterological
gastroenterologist
gastroenterology
gastroenteroptosis
gastroenterostomy
gastroenterotomy
gastroepiploic
gastroesophageal
gastroesophagostomy
gastrogastrotomy
gastrogenital
gastrograph
gastrohelcosis
gastrohepatic
gastrohepatitis
gastrohydrorrhea
gastrohyperneuria
gastrohypertonic
gastrohysterectomy
gastrohysteropexy
gastrohysterorrhaphy
gastrohysterotomy
gastroid
gastrointestinal
gastrojejunal
gastrojejunostomy
gastrolater
gastrolatrous
gastrolienal
gastrolith
Gastrolobium
gastrologer
gastrological
gastrologist
gastrology
gastrolysis
gastrolytic
gastromalacia
gastromancy
gastromelus
gastromenia
gastromyces
gastromycosis
gastromyxorrhea
gastronephritis
gastronome
gastronomer
gastronomic
gastronomical
gastronomically
gastronomist
gastronomy
gastronosus
gastropancreatic
gastropancreatitis
gastroparalysis
gastroparesis
gastroparietal
gastropathic
gastropathy
gastroperiodynia
gastropexy
gastrophile
gastrophilism
gastrophilist
gastrophilite
Gastrophilus
gastrophrenic
gastrophthisis
gastroplasty
gastroplenic
gastropleuritis
gastroplication
gastropneumatic
gastropneumonic
gastropod
Gastropoda
gastropodan
gastropodous
gastropore
gastroptosia
gastroptosis
gastropulmonary
gastropulmonic
gastropyloric
gastrorrhagia
gastrorrhaphy
gastrorrhea
gastroschisis
gastroscope
gastroscopic
gastroscopy
gastrosoph
gastrosopher
gastrosophy
gastrospasm
gastrosplenic
gastrostaxis
gastrostegal
gastrostege
gastrostenosis
gastrostomize
Gastrostomus
gastrostomy
gastrosuccorrhea
gastrotheca
gastrothecal
gastrotome
gastrotomic
gastrotomy
Gastrotricha
gastrotrichan
gastrotubotomy
gastrotympanites
gastrovascular
gastroxynsis
gastrozooid
gastrula
gastrular
gastrulate
gastrulation
gasworker
gasworks
gat
gata
gatch
gatchwork
gate
gateado
gateage
gated
gatehouse
gatekeeper
gateless
gatelike
gatemaker
gateman
gatepost
gater
gatetender
gateward
gatewards
gateway
gatewayman
gatewise
gatewoman
gateworks
gatewright
Gatha
gather
gatherable
gatherer
gathering
Gathic
gating
gator
gatter
gatteridge
gau
gaub
gauby
gauche
gauchely
gaucheness
gaucherie
Gaucho
gaud
gaudery
Gaudete
gaudful
gaudily
gaudiness
gaudless
gaudsman
gaudy
gaufer
gauffer
gauffered
gauffre
gaufre
gaufrette
gauge
gaugeable
gauger
gaugership
gauging
Gaul
gaulding
gauleiter
Gaulic
gaulin
Gaulish
Gaullism
Gaullist
Gault
gault
gaulter
gaultherase
Gaultheria
gaultherin
gaum
gaumish
gaumless
gaumlike
gaumy
gaun
gaunt
gaunted
gauntlet
gauntleted
gauntly
gauntness
gauntry
gaunty
gaup
gaupus
gaur
Gaura
Gaurian
gaus
gauss
gaussage
gaussbergite
Gaussian
gauster
gausterer
gaut
gauteite
gauze
gauzelike
gauzewing
gauzily
gauziness
gauzy
gavall
gave
gavel
gaveler
gavelkind
gavelkinder
gavelman
gavelock
Gavia
Gaviae
gavial
Gavialis
gavialoid
Gaviiformes
gavotte
gavyuti
gaw
gawby
gawcie
gawk
gawkhammer
gawkihood
gawkily
gawkiness
gawkish
gawkishly
gawkishness
gawky
gawm
gawn
gawney
gawsie
gay
gayal
gayatri
gaybine
gaycat
gaydiang
gayish
Gaylussacia
gaylussite
gayment
gayness
Gaypoo
gaysome
gaywings
gayyou
gaz
gazabo
gazangabin
Gazania
gaze
gazebo
gazee
gazehound
gazel
gazeless
Gazella
gazelle
gazelline
gazement
gazer
gazettal
gazette
gazetteer
gazetteerage
gazetteerish
gazetteership
gazi
gazing
gazingly
gazingstock
gazogene
gazon
gazophylacium
gazy
gazzetta
Ge
ge
Geadephaga
geadephagous
geal
gean
geanticlinal
geanticline
gear
gearbox
geared
gearing
gearksutite
gearless
gearman
gearset
gearshift
gearwheel
gease
geason
Geaster
Geat
geat
Geatas
gebang
gebanga
gebbie
gebur
Gecarcinidae
Gecarcinus
geck
gecko
geckoid
geckotian
geckotid
Geckotidae
geckotoid
Ged
ged
gedackt
gedanite
gedder
gedeckt
gedecktwork
Gederathite
Gederite
gedrite
Gee
gee
geebong
geebung
Geechee
geejee
geek
geelbec
geeldikkop
geelhout
geepound
geerah
geest
geet
Geez
geezer
Gegenschein
gegg
geggee
gegger
geggery
Geheimrat
Gehenna
gehlenite
Geikia
geikielite
gein
geira
Geisenheimer
geisha
geison
geisotherm
geisothermal
Geissoloma
Geissolomataceae
Geissolomataceous
Geissorhiza
geissospermin
geissospermine
geitjie
geitonogamous
geitonogamy
Gekko
Gekkones
gekkonid
Gekkonidae
gekkonoid
Gekkota
gel
gelable
gelada
gelandejump
gelandelaufer
gelandesprung
Gelasian
Gelasimus
gelastic
Gelastocoridae
gelatification
gelatigenous
gelatin
gelatinate
gelatination
gelatined
gelatiniferous
gelatiniform
gelatinify
gelatinigerous
gelatinity
gelatinizability
gelatinizable
gelatinization
gelatinize
gelatinizer
gelatinobromide
gelatinochloride
gelatinoid
gelatinotype
gelatinous
gelatinously
gelatinousness
gelation
gelatose
geld
geldability
geldable
geldant
gelder
gelding
Gelechia
gelechiid
Gelechiidae
Gelfomino
gelid
Gelidiaceae
gelidity
Gelidium
gelidly
gelidness
gelignite
gelilah
gelinotte
gell
Gellert
gelly
gelogenic
gelong
geloscopy
gelose
gelosin
gelotherapy
gelotometer
gelotoscopy
gelototherapy
gelsemic
gelsemine
gelseminic
gelseminine
Gelsemium
gelt
gem
Gemara
Gemaric
Gemarist
gematria
gematrical
gemauve
gemel
gemeled
gemellione
gemellus
geminate
geminated
geminately
gemination
geminative
Gemini
Geminid
geminiflorous
geminiform
geminous
Gemitores
gemitorial
gemless
gemlike
Gemma
gemma
gemmaceous
gemmae
gemmate
gemmation
gemmative
gemmeous
gemmer
gemmiferous
gemmiferousness
gemmification
gemmiform
gemmily
gemminess
Gemmingia
gemmipara
gemmipares
gemmiparity
gemmiparous
gemmiparously
gemmoid
gemmology
gemmula
gemmulation
gemmule
gemmuliferous
gemmy
gemot
gemsbok
gemsbuck
gemshorn
gemul
gemuti
gemwork
gen
gena
genal
genapp
genapper
genarch
genarcha
genarchaship
genarchship
gendarme
gendarmery
gender
genderer
genderless
Gene
gene
genealogic
genealogical
genealogically
genealogist
genealogize
genealogizer
genealogy
genear
geneat
genecologic
genecological
genecologically
genecologist
genecology
geneki
genep
genera
generability
generable
generableness
general
generalate
generalcy
generale
generalia
Generalidad
generalific
generalism
generalissima
generalissimo
generalist
generalistic
generality
generalizable
generalization
generalize
generalized
generalizer
generall
generally
generalness
generalship
generalty
generant
generate
generating
generation
generational
generationism
generative
generatively
generativeness
generator
generatrix
generic
generical
generically
genericalness
generification
generosity
generous
generously
generousness
Genesee
geneserine
Genesiac
Genesiacal
genesial
genesic
genesiology
genesis
Genesitic
genesiurgic
genet
genethliac
genethliacal
genethliacally
genethliacon
genethliacs
genethlialogic
genethlialogical
genethlialogy
genethlic
genetic
genetical
genetically
geneticism
geneticist
genetics
genetmoil
genetous
Genetrix
genetrix
Genetta
Geneura
Geneva
geneva
Genevan
Genevese
Genevieve
Genevois
genevoise
genial
geniality
genialize
genially
genialness
genian
genic
genicular
geniculate
geniculated
geniculately
geniculation
geniculum
genie
genii
genin
genioglossal
genioglossi
genioglossus
geniohyoglossal
geniohyoglossus
geniohyoid
geniolatry
genion
genioplasty
genip
Genipa
genipa
genipap
genipapada
genisaro
Genista
genista
genistein
genital
genitalia
genitals
genitival
genitivally
genitive
genitocrural
genitofemoral
genitor
genitorial
genitory
genitourinary
geniture
genius
genizah
genizero
Genny
Genoa
genoblast
genoblastic
genocidal
genocide
Genoese
genoese
genom
genome
genomic
genonema
genos
genotype
genotypic
genotypical
genotypically
Genoveva
genovino
genre
genro
gens
genson
gent
genteel
genteelish
genteelism
genteelize
genteelly
genteelness
gentes
genthite
gentian
Gentiana
Gentianaceae
gentianaceous
Gentianales
gentianella
gentianic
gentianin
gentianose
gentianwort
gentile
gentiledom
gentilesse
gentilic
gentilism
gentilitial
gentilitian
gentilitious
gentility
gentilization
gentilize
gentiobiose
gentiopicrin
gentisein
gentisic
gentisin
gentle
gentlefolk
gentlehearted
gentleheartedly
gentleheartedness
gentlehood
gentleman
gentlemanhood
gentlemanism
gentlemanize
gentlemanlike
gentlemanlikeness
gentlemanliness
gentlemanly
gentlemanship
gentlemens
gentlemouthed
gentleness
gentlepeople
gentleship
gentlewoman
gentlewomanhood
gentlewomanish
gentlewomanlike
gentlewomanliness
gentlewomanly
gently
gentman
Gentoo
gentrice
gentry
genty
genu
genua
genual
genuclast
genuflect
genuflection
genuflector
genuflectory
genuflex
genuflexuous
genuine
genuinely
genuineness
genus
genyantrum
Genyophrynidae
genyoplasty
genys
geo
geoaesthesia
geoagronomic
geobiologic
geobiology
geobiont
geobios
geoblast
geobotanic
geobotanical
geobotanist
geobotany
geocarpic
geocentric
geocentrical
geocentrically
geocentricism
geocerite
geochemical
geochemist
geochemistry
geochronic
geochronology
geochrony
Geococcyx
geocoronium
geocratic
geocronite
geocyclic
geodaesia
geodal
geode
geodesic
geodesical
geodesist
geodesy
geodete
geodetic
geodetical
geodetically
geodetician
geodetics
geodiatropism
geodic
geodiferous
geodist
geoduck
geodynamic
geodynamical
geodynamics
geoethnic
Geoff
Geoffrey
geoffroyin
geoffroyine
geoform
geogenesis
geogenetic
geogenic
geogenous
geogeny
Geoglossaceae
Geoglossum
geoglyphic
geognosis
geognosist
geognost
geognostic
geognostical
geognostically
geognosy
geogonic
geogonical
geogony
geographer
geographic
geographical
geographically
geographics
geographism
geographize
geography
geohydrologist
geohydrology
geoid
geoidal
geoisotherm
geolatry
geologer
geologian
geologic
geological
geologically
geologician
geologist
geologize
geology
geomagnetic
geomagnetician
geomagnetics
geomagnetist
geomalic
geomalism
geomaly
geomance
geomancer
geomancy
geomant
geomantic
geomantical
geomantically
geometer
geometric
geometrical
geometrically
geometrician
geometricize
geometrid
Geometridae
geometriform
Geometrina
geometrine
geometrize
geometroid
Geometroidea
geometry
geomoroi
geomorphic
geomorphist
geomorphogenic
geomorphogenist
geomorphogeny
geomorphological
geomorphology
geomorphy
geomyid
Geomyidae
Geomys
Geon
geonavigation
geonegative
Geonic
Geonim
Geonoma
geonoma
geonyctinastic
geonyctitropic
geoparallelotropic
geophagia
geophagism
geophagist
geophagous
geophagy
Geophila
geophilid
Geophilidae
geophilous
Geophilus
Geophone
geophone
geophysical
geophysicist
geophysics
geophyte
geophytic
geoplagiotropism
Geoplana
Geoplanidae
geopolar
geopolitic
geopolitical
geopolitically
geopolitician
geopolitics
Geopolitik
geoponic
geoponical
geoponics
geopony
geopositive
Geoprumnon
georama
Geordie
George
Georgemas
Georgette
Georgia
georgiadesite
Georgian
Georgiana
georgic
Georgie
geoscopic
geoscopy
geoselenic
geosid
geoside
geosphere
Geospiza
geostatic
geostatics
geostrategic
geostrategist
geostrategy
geostrophic
geosynclinal
geosyncline
geotactic
geotactically
geotaxis
geotaxy
geotechnic
geotechnics
geotectology
geotectonic
geotectonics
Geoteuthis
geotherm
geothermal
geothermic
geothermometer
Geothlypis
geotic
geotical
geotilla
geotonic
geotonus
geotropic
geotropically
geotropism
geotropy
geoty
Gepeoo
Gephyrea
gephyrean
gephyrocercal
gephyrocercy
Gepidae
ger
gerah
Gerald
Geraldine
Geraniaceae
geraniaceous
geranial
Geraniales
geranic
geraniol
Geranium
geranium
geranomorph
Geranomorphae
geranomorphic
geranyl
Gerard
gerardia
Gerasene
gerastian
gerate
gerated
geratic
geratologic
geratologous
geratology
geraty
gerb
gerbe
Gerbera
Gerberia
gerbil
Gerbillinae
Gerbillus
gercrow
gereagle
gerefa
gerenda
gerendum
gerent
gerenuk
gerfalcon
gerhardtite
geriatric
geriatrician
geriatrics
gerim
gerip
germ
germal
German
german
germander
germane
germanely
germaneness
Germanesque
Germanhood
Germania
Germanic
germanic
Germanical
Germanically
Germanics
Germanification
Germanify
germanious
Germanish
Germanism
Germanist
Germanistic
germanite
Germanity
germanity
germanium
Germanization
germanization
Germanize
germanize
Germanizer
Germanly
Germanness
Germanocentric
Germanomania
Germanomaniac
Germanophile
Germanophilist
Germanophobe
Germanophobia
Germanophobic
Germanophobist
germanous
Germantown
germanyl
germarium
germen
germfree
germicidal
germicide
germifuge
germigenous
germin
germina
germinability
germinable
Germinal
germinal
germinally
germinance
germinancy
germinant
germinate
germination
germinative
germinatively
germinator
germing
germinogony
germiparity
germless
germlike
germling
germon
germproof
germule
germy
gernitz
gerocomia
gerocomical
gerocomy
geromorphism
Geronomite
geront
gerontal
gerontes
gerontic
gerontine
gerontism
geronto
gerontocracy
gerontocrat
gerontocratic
gerontogeous
gerontology
gerontophilia
gerontoxon
Gerres
gerrhosaurid
Gerrhosauridae
Gerridae
gerrymander
gerrymanderer
gers
gersdorffite
Gershom
Gershon
Gershonite
gersum
Gertie
Gertrude
gerund
gerundial
gerundially
gerundival
gerundive
gerundively
gerusia
Gervais
gervao
Gervas
Gervase
Gerygone
gerygone
Geryonia
geryonid
Geryonidae
Geryoniidae
Ges
Gesan
Geshurites
gesith
gesithcund
gesithcundman
Gesnera
Gesneraceae
gesneraceous
Gesneria
gesneria
Gesneriaceae
gesneriaceous
Gesnerian
gesning
gessamine
gesso
gest
Gestalt
gestalter
gestaltist
gestant
Gestapo
gestate
gestation
gestational
gestative
gestatorial
gestatorium
gestatory
geste
gested
gesten
gestening
gestic
gestical
gesticulacious
gesticulant
gesticular
gesticularious
gesticulate
gesticulation
gesticulative
gesticulatively
gesticulator
gesticulatory
gestion
gestning
gestural
gesture
gestureless
gesturer
get
geta
Getae
getah
getaway
gether
Gethsemane
gethsemane
Gethsemanic
gethsemanic
Getic
getling
getpenny
Getsul
gettable
getter
getting
getup
Geullah
Geum
geum
gewgaw
gewgawed
gewgawish
gewgawry
gewgawy
gey
geyan
geyerite
geyser
geyseral
geyseric
geyserine
geyserish
geyserite
gez
ghafir
ghaist
ghalva
Ghan
gharial
gharnao
gharry
Ghassanid
ghastily
ghastlily
ghastliness
ghastly
ghat
ghatti
ghatwal
ghatwazi
ghazi
ghazism
Ghaznevid
Gheber
ghebeta
Ghedda
ghee
Gheg
Ghegish
gheleem
Ghent
gherkin
ghetchoo
ghetti
ghetto
ghettoization
ghettoize
Ghibelline
Ghibellinism
Ghilzai
Ghiordes
ghizite
ghoom
ghost
ghostcraft
ghostdom
ghoster
ghostess
ghostfish
ghostflower
ghosthood
ghostified
ghostily
ghostish
ghostism
ghostland
ghostless
ghostlet
ghostlify
ghostlike
ghostlily
ghostliness
ghostly
ghostmonger
ghostology
ghostship
ghostweed
ghostwrite
ghosty
ghoul
ghoulery
ghoulish
ghoulishly
ghoulishness
ghrush
ghurry
Ghuz
Gi
Giansar
giant
giantesque
giantess
gianthood
giantish
giantism
giantize
giantkind
giantlike
giantly
giantry
giantship
Giardia
giardia
giardiasis
giarra
giarre
Gib
gib
gibaro
gibbals
gibbed
gibber
Gibberella
gibbergunyah
gibberish
gibberose
gibberosity
gibbet
gibbetwise
Gibbi
gibblegabble
gibblegabbler
gibbles
gibbon
gibbose
gibbosity
gibbous
gibbously
gibbousness
gibbsite
gibbus
gibby
gibe
gibel
gibelite
Gibeonite
giber
gibing
gibingly
gibleh
giblet
giblets
Gibraltar
Gibson
gibstaff
gibus
gid
giddap
giddea
giddify
giddily
giddiness
giddy
giddyberry
giddybrain
giddyhead
giddyish
Gideon
Gideonite
gidgee
gie
gied
gien
Gienah
gieseckite
gif
giffgaff
Gifola
gift
gifted
giftedly
giftedness
giftie
giftless
giftling
giftware
gig
gigantean
gigantesque
gigantic
gigantical
gigantically
giganticidal
giganticide
giganticness
gigantism
gigantize
gigantoblast
gigantocyte
gigantolite
gigantological
gigantology
gigantomachy
Gigantopithecus
Gigantosaurus
Gigantostraca
gigantostracan
gigantostracous
Gigartina
Gigartinaceae
gigartinaceous
Gigartinales
gigback
gigelira
gigeria
gigerium
gigful
gigger
giggish
giggit
giggle
giggledom
gigglement
giggler
gigglesome
giggling
gigglingly
gigglish
giggly
Gigi
giglet
gigliato
giglot
gigman
gigmaness
gigmanhood
gigmania
gigmanic
gigmanically
gigmanism
gigmanity
gignate
gignitive
gigolo
gigot
gigsman
gigster
gigtree
gigunu
Gil
Gila
Gilaki
Gilbert
gilbert
gilbertage
Gilbertese
Gilbertian
Gilbertianism
gilbertite
gild
gildable
gilded
gilden
gilder
gilding
Gileadite
Gileno
Giles
gilguy
Gilia
gilia
Giliak
gilim
Gill
gill
gillaroo
gillbird
gilled
Gillenia
giller
Gilles
gillflirt
gillhooter
Gillian
gillie
gilliflirt
gilling
gilliver
gillotage
gillotype
gillstoup
gilly
gillyflower
gillygaupus
gilo
gilpy
gilravage
gilravager
gilse
gilsonite
gilt
giltcup
gilthead
gilttail
gim
gimbal
gimbaled
gimbaljawed
gimberjawed
gimble
gimcrack
gimcrackery
gimcrackiness
gimcracky
gimel
Gimirrai
gimlet
gimleteyed
gimlety
gimmal
gimmer
gimmerpet
gimmick
gimp
gimped
gimper
gimping
gin
ging
ginger
gingerade
gingerberry
gingerbread
gingerbready
gingerin
gingerleaf
gingerline
gingerliness
gingerly
gingerness
gingernut
gingerol
gingerous
gingerroot
gingersnap
gingerspice
gingerwork
gingerwort
gingery
gingham
ginghamed
gingili
gingiva
gingivae
gingival
gingivalgia
gingivectomy
gingivitis
gingivoglossitis
gingivolabial
ginglyform
ginglymoarthrodia
ginglymoarthrodial
Ginglymodi
ginglymodian
ginglymoid
ginglymoidal
Ginglymostoma
ginglymostomoid
ginglymus
ginglyni
ginhouse
gink
Ginkgo
ginkgo
Ginkgoaceae
ginkgoaceous
Ginkgoales
ginned
ginner
ginners
ginnery
ginney
ginning
ginnle
Ginny
ginny
ginseng
ginward
gio
giobertite
giornata
giornatate
Giottesque
Giovanni
gip
gipon
gipper
Gippy
gipser
gipsire
gipsyweed
Giraffa
giraffe
giraffesque
Giraffidae
giraffine
giraffoid
girandola
girandole
girasol
girasole
girba
gird
girder
girderage
girderless
girding
girdingly
girdle
girdlecake
girdlelike
girdler
girdlestead
girdling
girdlingly
Girella
Girellidae
Girgashite
Girgasite
girl
girleen
girlery
girlfully
girlhood
girlie
girliness
girling
girlish
girlishly
girlishness
girlism
girllike
girly
girn
girny
giro
giroflore
Girondin
Girondism
Girondist
girouette
girouettism
girr
girse
girsh
girsle
girt
girth
girtline
gisarme
gish
gisla
gisler
gismondine
gismondite
gist
git
gitaligenin
gitalin
Gitanemuck
gith
Gitksan
gitonin
gitoxigenin
gitoxin
gittern
Gittite
gittith
Giuseppe
giustina
give
giveable
giveaway
given
givenness
giver
givey
giving
gizz
gizzard
gizzen
gizzern
glabella
glabellae
glabellar
glabellous
glabellum
glabrate
glabrescent
glabrous
glace
glaceed
glaceing
glaciable
glacial
glacialism
glacialist
glacialize
glacially
glaciaria
glaciarium
glaciate
glaciation
glacier
glaciered
glacieret
glacierist
glacification
glacioaqueous
glaciolacustrine
glaciological
glaciologist
glaciology
glaciomarine
glaciometer
glacionatant
glacis
glack
glad
gladden
gladdener
gladdon
gladdy
glade
gladelike
gladeye
gladful
gladfully
gladfulness
gladhearted
gladiate
gladiator
gladiatorial
gladiatorism
gladiatorship
gladiatrix
gladify
gladii
gladiola
gladiolar
gladiole
gladioli
gladiolus
gladius
gladkaite
gladless
gladly
gladness
gladsome
gladsomely
gladsomeness
Gladstone
Gladstonian
Gladstonianism
glady
Gladys
glaga
Glagol
Glagolic
Glagolitic
Glagolitsa
glaieul
glaik
glaiket
glaiketness
glair
glaireous
glairiness
glairy
glaister
glaive
glaived
glaked
glaky
glam
glamberry
glamorize
glamorous
glamorously
glamour
glamoury
glance
glancer
glancing
glancingly
gland
glandaceous
glandarious
glandered
glanderous
glanders
glandes
glandiferous
glandiform
glandless
glandlike
glandular
glandularly
glandule
glanduliferous
glanduliform
glanduligerous
glandulose
glandulosity
glandulous
glandulousness
Glaniostomi
glans
glar
glare
glareless
Glareola
glareole
Glareolidae
glareous
glareproof
glareworm
glarily
glariness
glaring
glaringly
glaringness
glarry
glary
Glaserian
glaserite
glashan
glass
glassen
glasser
glasses
glassfish
glassful
glasshouse
glassie
glassily
glassine
glassiness
Glassite
glassless
glasslike
glassmaker
glassmaking
glassman
glassophone
glassrope
glassteel
glassware
glassweed
glasswork
glassworker
glassworking
glassworks
glasswort
glassy
Glaswegian
Glathsheim
Glathsheimr
glauberite
glaucescence
glaucescent
Glaucidium
glaucin
glaucine
Glaucionetta
Glaucium
glaucochroite
glaucodot
glaucolite
glaucoma
glaucomatous
Glaucomys
Glauconia
glauconiferous
Glauconiidae
glauconite
glauconitic
glauconitization
glaucophane
glaucophanite
glaucophanization
glaucophanize
glaucophyllous
Glaucopis
glaucosuria
glaucous
glaucously
Glauke
glaum
glaumrie
glaur
glaury
Glaux
glaver
glaze
glazed
glazen
glazer
glazework
glazier
glaziery
glazily
glaziness
glazing
glazy
gleam
gleamily
gleaminess
gleaming
gleamingly
gleamless
gleamy
glean
gleanable
gleaner
gleaning
gleary
gleba
glebal
glebe
glebeless
glebous
Glecoma
glede
Gleditsia
gledy
glee
gleed
gleeful
gleefully
gleefulness
gleeishly
gleek
gleemaiden
gleeman
gleesome
gleesomely
gleesomeness
gleet
gleety
gleewoman
gleg
glegly
glegness
Glen
glen
Glengarry
Glenn
glenohumeral
glenoid
glenoidal
glent
glessite
gleyde
glia
gliadin
glial
glib
glibbery
glibly
glibness
glidder
gliddery
glide
glideless
glideness
glider
gliderport
glidewort
gliding
glidingly
gliff
gliffing
glime
glimmer
glimmering
glimmeringly
glimmerite
glimmerous
glimmery
glimpse
glimpser
glink
glint
glioma
gliomatous
gliosa
gliosis
Glires
Gliridae
gliriform
Gliriformia
glirine
Glis
glisk
glisky
glissade
glissader
glissando
glissette
glisten
glistening
glisteningly
glister
glisteringly
Glitnir
glitter
glitterance
glittering
glitteringly
glittersome
glittery
gloam
gloaming
gloat
gloater
gloating
gloatingly
global
globally
globate
globated
globe
globed
globefish
globeflower
globeholder
globelet
Globicephala
globiferous
Globigerina
globigerine
Globigerinidae
globin
Globiocephalus
globoid
globose
globosely
globoseness
globosite
globosity
globosphaerite
globous
globously
globousness
globular
Globularia
Globulariaceae
globulariaceous
globularity
globularly
globularness
globule
globulet
globulicidal
globulicide
globuliferous
globuliform
globulimeter
globulin
globulinuria
globulite
globulitic
globuloid
globulolysis
globulose
globulous
globulousness
globulysis
globy
glochid
glochideous
glochidia
glochidial
glochidian
glochidiate
glochidium
glochis
glockenspiel
gloea
gloeal
Gloeocapsa
gloeocapsoid
gloeosporiose
Gloeosporium
Gloiopeltis
Gloiosiphonia
Gloiosiphoniaceae
glom
glome
glomerate
glomeration
Glomerella
glomeroporphyritic
glomerular
glomerulate
glomerule
glomerulitis
glomerulonephritis
glomerulose
glomerulus
glommox
glomus
glonoin
glonoine
gloom
gloomful
gloomfully
gloomily
gloominess
glooming
gloomingly
gloomless
gloomth
gloomy
glop
gloppen
glor
glore
Gloria
Gloriana
gloriation
gloriette
glorifiable
glorification
glorifier
glorify
gloriole
Gloriosa
gloriosity
glorious
gloriously
gloriousness
glory
gloryful
glorying
gloryingly
gloryless
gloss
glossa
glossagra
glossal
glossalgia
glossalgy
glossanthrax
glossarial
glossarially
glossarian
glossarist
glossarize
glossary
Glossata
glossate
glossator
glossatorial
glossectomy
glossed
glosser
glossic
glossily
Glossina
glossiness
glossing
glossingly
Glossiphonia
Glossiphonidae
glossist
glossitic
glossitis
glossless
glossmeter
glossocarcinoma
glossocele
glossocoma
glossocomon
glossodynamometer
glossodynia
glossoepiglottic
glossoepiglottidean
glossograph
glossographer
glossographical
glossography
glossohyal
glossoid
glossokinesthetic
glossolabial
glossolabiolaryngeal
glossolabiopharyngeal
glossolalia
glossolalist
glossolaly
glossolaryngeal
glossological
glossologist
glossology
glossolysis
glossoncus
glossopalatine
glossopalatinus
glossopathy
glossopetra
Glossophaga
glossophagine
glossopharyngeal
glossopharyngeus
Glossophora
glossophorous
glossophytia
glossoplasty
glossoplegia
glossopode
glossopodium
Glossopteris
glossoptosis
glossopyrosis
glossorrhaphy
glossoscopia
glossoscopy
glossospasm
glossosteresis
Glossotherium
glossotomy
glossotype
glossy
glost
glottal
glottalite
glottalize
glottic
glottid
glottidean
glottis
glottiscope
glottogonic
glottogonist
glottogony
glottologic
glottological
glottologist
glottology
Gloucester
glout
glove
gloveless
glovelike
glovemaker
glovemaking
glover
gloveress
glovey
gloving
glow
glower
glowerer
glowering
gloweringly
glowfly
glowing
glowingly
glowworm
Gloxinia
gloy
gloze
glozing
glozingly
glub
glucase
glucemia
glucid
glucide
glucidic
glucina
glucine
glucinic
glucinium
glucinum
gluck
glucofrangulin
glucokinin
glucolipid
glucolipide
glucolipin
glucolipine
glucolysis
glucosaemia
glucosamine
glucosan
glucosane
glucosazone
glucose
glucosemia
glucosic
glucosid
glucosidal
glucosidase
glucoside
glucosidic
glucosidically
glucosin
glucosine
glucosone
glucosuria
glucuronic
glue
glued
gluemaker
gluemaking
gluepot
gluer
gluey
glueyness
glug
gluish
gluishness
glum
gluma
Glumaceae
glumaceous
glumal
Glumales
glume
glumiferous
Glumiflorae
glumly
glummy
glumness
glumose
glumosity
glump
glumpily
glumpiness
glumpish
glumpy
glunch
Gluneamie
glusid
gluside
glut
glutamic
glutamine
glutaminic
glutaric
glutathione
glutch
gluteal
glutelin
gluten
glutenin
glutenous
gluteofemoral
gluteoinguinal
gluteoperineal
gluteus
glutin
glutinate
glutination
glutinative
glutinize
glutinose
glutinosity
glutinous
glutinously
glutinousness
glutition
glutoid
glutose
glutter
gluttery
glutting
gluttingly
glutton
gluttoness
gluttonish
gluttonism
gluttonize
gluttonous
gluttonously
gluttonousness
gluttony
glyceraldehyde
glycerate
Glyceria
glyceric
glyceride
glycerin
glycerinate
glycerination
glycerine
glycerinize
glycerite
glycerize
glycerizin
glycerizine
glycerogel
glycerogelatin
glycerol
glycerolate
glycerole
glycerolize
glycerophosphate
glycerophosphoric
glycerose
glyceroxide
glyceryl
glycid
glycide
glycidic
glycidol
Glycine
glycine
glycinin
glycocholate
glycocholic
glycocin
glycocoll
glycogelatin
glycogen
glycogenesis
glycogenetic
glycogenic
glycogenize
glycogenolysis
glycogenous
glycogeny
glycohaemia
glycohemia
glycol
glycolaldehyde
glycolate
glycolic
glycolide
glycolipid
glycolipide
glycolipin
glycolipine
glycoluric
glycoluril
glycolyl
glycolylurea
glycolysis
glycolytic
glycolytically
Glyconian
Glyconic
glyconic
glyconin
glycoproteid
glycoprotein
glycosaemia
glycose
glycosemia
glycosin
glycosine
glycosuria
glycosuric
glycuresis
glycuronic
glycyl
glycyphyllin
Glycyrrhiza
glycyrrhizin
Glynn
glyoxal
glyoxalase
glyoxalic
glyoxalin
glyoxaline
glyoxim
glyoxime
glyoxyl
glyoxylic
glyph
glyphic
glyphograph
glyphographer
glyphographic
glyphography
glyptic
glyptical
glyptician
Glyptodon
glyptodont
Glyptodontidae
glyptodontoid
glyptograph
glyptographer
glyptographic
glyptography
glyptolith
glyptological
glyptologist
glyptology
glyptotheca
Glyptotherium
glyster
Gmelina
gmelinite
gnabble
Gnaeus
gnaphalioid
Gnaphalium
gnar
gnarl
gnarled
gnarliness
gnarly
gnash
gnashingly
gnat
gnatcatcher
gnatflower
gnathal
gnathalgia
gnathic
gnathidium
gnathion
gnathism
gnathite
gnathitis
Gnatho
gnathobase
gnathobasic
Gnathobdellae
Gnathobdellida
gnathometer
gnathonic
gnathonical
gnathonically
gnathonism
gnathonize
gnathophorous
gnathoplasty
gnathopod
Gnathopoda
gnathopodite
gnathopodous
gnathostegite
Gnathostoma
Gnathostomata
gnathostomatous
gnathostome
Gnathostomi
gnathostomous
gnathotheca
gnatling
gnatproof
gnatsnap
gnatsnapper
gnatter
gnatty
gnatworm
gnaw
gnawable
gnawer
gnawing
gnawingly
gnawn
gneiss
gneissic
gneissitic
gneissoid
gneissose
gneissy
Gnetaceae
gnetaceous
Gnetales
Gnetum
gnocchetti
gnome
gnomed
gnomesque
gnomic
gnomical
gnomically
gnomide
gnomish
gnomist
gnomologic
gnomological
gnomologist
gnomology
gnomon
Gnomonia
Gnomoniaceae
gnomonic
gnomonical
gnomonics
gnomonological
gnomonologically
gnomonology
gnosiological
gnosiology
gnosis
Gnostic
gnostic
gnostical
gnostically
Gnosticism
gnosticity
gnosticize
gnosticizer
gnostology
gnu
go
goa
goad
goadsman
goadster
goaf
Goajiro
goal
Goala
goalage
goalee
goalie
goalkeeper
goalkeeping
goalless
goalmouth
Goan
Goanese
goanna
Goasila
goat
goatbeard
goatbrush
goatbush
goatee
goateed
goatfish
goatherd
goatherdess
goatish
goatishly
goatishness
goatland
goatlike
goatling
goatly
goatroot
goatsbane
goatsbeard
goatsfoot
goatskin
goatstone
goatsucker
goatweed
goaty
goave
gob
goback
goban
gobang
gobbe
gobber
gobbet
gobbin
gobbing
gobble
gobbledygook
gobbler
gobby
Gobelin
gobelin
gobernadora
gobi
Gobia
Gobian
gobiesocid
Gobiesocidae
gobiesociform
Gobiesox
gobiid
Gobiidae
gobiiform
Gobiiformes
Gobinism
Gobinist
Gobio
gobioid
Gobioidea
Gobioidei
goblet
gobleted
gobletful
goblin
gobline
goblinesque
goblinish
goblinism
goblinize
goblinry
gobmouthed
gobo
gobonated
gobony
gobstick
goburra
goby
gobylike
gocart
Goclenian
God
god
godchild
Goddam
Goddard
goddard
goddaughter
godded
goddess
goddesshood
goddessship
goddikin
goddize
gode
godet
Godetia
godfather
godfatherhood
godfathership
Godforsaken
Godfrey
Godful
godhead
godhood
Godiva
godkin
godless
godlessly
godlessness
godlet
godlike
godlikeness
godlily
godliness
godling
godly
godmaker
godmaking
godmamma
godmother
godmotherhood
godmothership
godown
godpapa
godparent
Godsake
godsend
godship
godson
godsonship
Godspeed
Godward
Godwin
Godwinian
godwit
goeduck
goel
goelism
Goemagot
Goemot
goer
goes
Goetae
Goethian
goetia
goetic
goetical
goety
goff
goffer
goffered
gofferer
goffering
goffle
gog
gogga
goggan
goggle
goggled
goggler
gogglers
goggly
goglet
Gogo
gogo
Gohila
goi
goiabada
Goidel
Goidelic
going
goitcho
goiter
goitered
goitral
goitrogen
goitrogenic
goitrous
Gokuraku
gol
gola
golach
goladar
golandaas
golandause
Golaseccan
Golconda
Gold
gold
goldbeater
goldbeating
Goldbird
goldbrick
goldbricker
goldbug
goldcrest
goldcup
golden
goldenback
goldeneye
goldenfleece
goldenhair
goldenknop
goldenlocks
goldenly
Goldenmouth
goldenmouthed
goldenness
goldenpert
goldenrod
goldenseal
goldentop
goldenwing
golder
goldfielder
goldfinch
goldfinny
goldfish
goldflower
goldhammer
goldhead
Goldi
Goldic
goldie
goldilocks
goldin
goldish
goldless
goldlike
Goldonian
goldseed
goldsinny
goldsmith
goldsmithery
goldsmithing
goldspink
goldstone
goldtail
goldtit
goldwater
goldweed
goldwork
goldworker
Goldy
goldy
golee
golem
golf
golfdom
golfer
Golgi
Golgotha
goli
goliard
goliardery
goliardic
Goliath
goliath
goliathize
golkakra
Goll
golland
gollar
golliwogg
golly
Golo
goloe
golpe
Goma
gomari
Gomarian
Gomarist
Gomarite
gomart
gomashta
gomavel
gombay
gombeen
gombeenism
gombroon
Gomeisa
gomer
gomeral
gomlah
gommelin
Gomontia
Gomorrhean
Gomphocarpus
gomphodont
Gompholobium
gomphosis
Gomphrena
gomuti
gon
Gona
gonad
gonadal
gonadial
gonadic
gonadotropic
gonadotropin
gonaduct
gonagra
gonakie
gonal
gonalgia
gonangial
gonangium
gonapod
gonapophysal
gonapophysial
gonapophysis
gonarthritis
Gond
gondang
Gondi
gondite
gondola
gondolet
gondolier
gone
goneness
goneoclinic
gonepoiesis
gonepoietic
goner
Goneril
gonesome
gonfalcon
gonfalonier
gonfalonierate
gonfaloniership
gonfanon
gong
gongman
Gongoresque
Gongorism
Gongorist
gongoristic
gonia
goniac
gonial
goniale
Goniaster
goniatite
Goniatites
goniatitic
goniatitid
Goniatitidae
goniatitoid
gonid
gonidangium
gonidia
gonidial
gonidic
gonidiferous
gonidiogenous
gonidioid
gonidiophore
gonidiose
gonidiospore
gonidium
gonimic
gonimium
gonimolobe
gonimous
goniocraniometry
Goniodoridae
Goniodorididae
Goniodoris
goniometer
goniometric
goniometrical
goniometrically
goniometry
gonion
Goniopholidae
Goniopholis
goniostat
goniotropous
gonitis
Gonium
gonium
gonnardite
gonne
gonoblast
gonoblastic
gonoblastidial
gonoblastidium
gonocalycine
gonocalyx
gonocheme
gonochorism
gonochorismal
gonochorismus
gonochoristic
gonococcal
gonococcic
gonococcoid
gonococcus
gonocoel
gonocyte
gonoecium
Gonolobus
gonomere
gonomery
gonophore
gonophoric
gonophorous
gonoplasm
gonopoietic
gonorrhea
gonorrheal
gonorrheic
gonosomal
gonosome
gonosphere
gonostyle
gonotheca
gonothecal
gonotokont
gonotome
gonotype
gonozooid
gony
gonyalgia
gonydeal
gonydial
gonyocele
gonyoncus
gonys
Gonystylaceae
gonystylaceous
Gonystylus
gonytheca
Gonzalo
goo
goober
good
Goodenia
Goodeniaceae
goodeniaceous
Goodenoviaceae
goodhearted
goodheartedly
goodheartedness
gooding
goodish
goodishness
goodlihead
goodlike
goodliness
goodly
goodman
goodmanship
goodness
goods
goodsome
goodwife
goodwill
goodwillit
goodwilly
goody
goodyear
Goodyera
goodyish
goodyism
goodyness
goodyship
goof
goofer
goofily
goofiness
goofy
googly
googol
googolplex
googul
gook
gool
goolah
gools
gooma
goon
goondie
goonie
Goop
goosander
goose
goosebeak
gooseberry
goosebill
goosebird
goosebone
gooseboy
goosecap
goosefish
gooseflower
goosefoot
goosegirl
goosegog
gooseherd
goosehouse
gooselike
goosemouth
gooseneck
goosenecked
gooserumped
goosery
goosetongue
gooseweed
goosewing
goosewinged
goosish
goosishly
goosishness
goosy
gopher
gopherberry
gopherroot
gopherwood
gopura
Gor
gor
gora
goracco
goral
goran
gorb
gorbal
gorbellied
gorbelly
gorbet
gorble
gorblimy
gorce
gorcock
gorcrow
Gordiacea
gordiacean
gordiaceous
Gordian
Gordiidae
Gordioidea
Gordius
gordolobo
Gordon
Gordonia
gordunite
Gordyaean
gore
gorer
gorevan
gorfly
gorge
gorgeable
gorged
gorgedly
gorgelet
gorgeous
gorgeously
gorgeousness
gorger
gorgerin
gorget
gorgeted
gorglin
Gorgon
Gorgonacea
gorgonacean
gorgonaceous
gorgonesque
gorgoneum
Gorgonia
Gorgoniacea
gorgoniacean
gorgoniaceous
Gorgonian
gorgonian
gorgonin
gorgonize
gorgonlike
Gorgonzola
Gorgosaurus
gorhen
goric
gorilla
gorillaship
gorillian
gorilline
gorilloid
gorily
goriness
goring
Gorkhali
Gorkiesque
gorlin
gorlois
gormandize
gormandizer
gormaw
gormed
gorra
gorraf
gorry
gorse
gorsebird
gorsechat
gorsedd
gorsehatch
gorsy
Gortonian
Gortonite
gory
gos
gosain
goschen
gosh
goshawk
Goshen
goshenite
goslarite
goslet
gosling
gosmore
gospel
gospeler
gospelist
gospelize
gospellike
gospelly
gospelmonger
gospelwards
Gosplan
gospodar
gosport
gossamer
gossamered
gossamery
gossampine
gossan
gossaniferous
gossard
gossip
gossipdom
gossipee
gossiper
gossiphood
gossipiness
gossiping
gossipingly
gossipmonger
gossipred
gossipry
gossipy
gossoon
gossy
gossypine
Gossypium
gossypol
gossypose
got
gotch
gote
Goth
Gotha
Gotham
Gothamite
Gothic
Gothically
Gothicism
Gothicist
Gothicity
Gothicize
Gothicizer
Gothicness
Gothish
Gothism
gothite
Gothlander
Gothonic
Gotiglacial
gotra
gotraja
gotten
Gottfried
Gottlieb
gouaree
Gouda
Goudy
gouge
gouger
goujon
goulash
goumi
goup
Goura
gourami
gourd
gourde
gourdful
gourdhead
gourdiness
gourdlike
gourdworm
gourdy
Gourinae
gourmand
gourmander
gourmanderie
gourmandism
gourmet
gourmetism
gourounut
goustrous
gousty
gout
goutify
goutily
goutiness
goutish
goutte
goutweed
goutwort
gouty
gove
govern
governability
governable
governableness
governably
governail
governance
governess
governessdom
governesshood
governessy
governing
governingly
government
governmental
governmentalism
governmentalist
governmentalize
governmentally
governmentish
governor
governorate
governorship
gowan
gowdnie
gowf
gowfer
gowiddie
gowk
gowked
gowkedly
gowkedness
gowkit
gowl
gown
gownlet
gownsman
gowpen
goy
Goyana
goyazite
Goyetian
goyim
goyin
goyle
gozell
gozzard
gra
Graafian
grab
grabbable
grabber
grabble
grabbler
grabbling
grabbots
graben
grabhook
grabouche
Grace
grace
graceful
gracefully
gracefulness
graceless
gracelessly
gracelessness
gracelike
gracer
Gracilaria
gracilariid
Gracilariidae
gracile
gracileness
gracilescent
gracilis
gracility
graciosity
gracioso
gracious
graciously
graciousness
grackle
Graculus
grad
gradable
gradal
gradate
gradation
gradational
gradationally
gradationately
gradative
gradatively
gradatory
graddan
grade
graded
gradefinder
gradely
grader
Gradgrind
gradgrind
Gradgrindian
Gradgrindish
Gradgrindism
gradient
gradienter
Gradientia
gradin
gradine
grading
gradiometer
gradiometric
gradometer
gradual
gradualism
gradualist
gradualistic
graduality
gradually
gradualness
graduand
graduate
graduated
graduateship
graduatical
graduating
graduation
graduator
gradus
Graeae
Graeculus
Graeme
graff
graffage
graffer
Graffias
graffito
grafship
graft
graftage
graftdom
grafted
grafter
grafting
graftonite
graftproof
Graham
graham
grahamite
Graian
grail
grailer
grailing
grain
grainage
grained
grainedness
grainer
grainering
grainery
grainfield
graininess
graining
grainland
grainless
grainman
grainsick
grainsickness
grainsman
grainways
grainy
graip
graisse
graith
Grallae
Grallatores
grallatorial
grallatory
grallic
Grallina
gralline
gralloch
gram
grama
gramarye
gramashes
grame
gramenite
gramicidin
Graminaceae
graminaceous
Gramineae
gramineal
gramineous
gramineousness
graminicolous
graminiferous
graminifolious
graminiform
graminin
graminivore
graminivorous
graminological
graminology
graminous
grammalogue
grammar
grammarian
grammarianism
grammarless
grammatic
grammatical
grammatically
grammaticalness
grammaticaster
grammaticism
grammaticize
grammatics
grammatist
grammatistical
grammatite
grammatolator
grammatolatry
Grammatophyllum
gramme
Grammontine
gramoches
Gramophone
gramophone
gramophonic
gramophonical
gramophonically
gramophonist
gramp
grampa
grampus
granada
granadilla
granadillo
Granadine
granage
granary
granate
granatum
granch
grand
grandam
grandame
grandaunt
grandchild
granddad
granddaddy
granddaughter
granddaughterly
grandee
grandeeism
grandeeship
grandesque
grandeur
grandeval
grandfather
grandfatherhood
grandfatherish
grandfatherless
grandfatherly
grandfathership
grandfer
grandfilial
grandiloquence
grandiloquent
grandiloquently
grandiloquous
grandiose
grandiosely
grandiosity
grandisonant
Grandisonian
Grandisonianism
grandisonous
grandly
grandma
grandmaternal
Grandmontine
grandmother
grandmotherhood
grandmotherism
grandmotherliness
grandmotherly
grandnephew
grandness
grandniece
grandpa
grandparent
grandparentage
grandparental
grandpaternal
grandsire
grandson
grandsonship
grandstand
grandstander
granduncle
grane
grange
granger
grangerism
grangerite
grangerization
grangerize
grangerizer
Grangousier
graniform
granilla
granite
granitelike
graniteware
granitic
granitical
graniticoline
granitiferous
granitification
granitiform
granitite
granitization
granitize
granitoid
granivore
granivorous
granjeno
grank
grannom
granny
grannybush
grano
granoblastic
granodiorite
granogabbro
granolite
granolith
granolithic
granomerite
granophyre
granophyric
granose
granospherite
Grant
grant
grantable
grantedly
grantee
granter
Granth
Grantha
Grantia
Grantiidae
grantor
granula
granular
granularity
granularly
granulary
granulate
granulated
granulater
granulation
granulative
granulator
granule
granulet
granuliferous
granuliform
granulite
granulitic
granulitis
granulitization
granulitize
granulize
granuloadipose
granulocyte
granuloma
granulomatous
granulometric
granulosa
granulose
granulous
Granville
granza
granzita
grape
graped
grapeflower
grapefruit
grapeful
grapeless
grapelet
grapelike
grapenuts
graperoot
grapery
grapeshot
grapeskin
grapestalk
grapestone
grapevine
grapewise
grapewort
graph
graphalloy
graphic
graphical
graphically
graphicalness
graphicly
graphicness
graphics
Graphidiaceae
Graphiola
graphiological
graphiologist
graphiology
Graphis
graphite
graphiter
graphitic
graphitization
graphitize
graphitoid
graphitoidal
Graphium
graphologic
graphological
graphologist
graphology
graphomania
graphomaniac
graphometer
graphometric
graphometrical
graphometry
graphomotor
Graphophone
graphophone
graphophonic
graphorrhea
graphoscope
graphospasm
graphostatic
graphostatical
graphostatics
graphotype
graphotypic
graphy
graping
grapnel
grappa
grapple
grappler
grappling
Grapsidae
grapsoid
Grapsus
Grapta
graptolite
Graptolitha
Graptolithida
Graptolithina
graptolitic
Graptolitoidea
Graptoloidea
graptomancy
grapy
grasp
graspable
grasper
grasping
graspingly
graspingness
graspless
grass
grassant
grassation
grassbird
grasschat
grasscut
grasscutter
grassed
grasser
grasset
grassflat
grassflower
grasshop
grasshopper
grasshopperdom
grasshopperish
grasshouse
grassiness
grassing
grassland
grassless
grasslike
grassman
grassnut
grassplot
grassquit
grasswards
grassweed
grasswidowhood
grasswork
grassworm
grassy
grat
grate
grateful
gratefully
gratefulness
grateless
grateman
grater
gratewise
grather
Gratia
Gratiano
graticulate
graticulation
graticule
gratification
gratified
gratifiedly
gratifier
gratify
gratifying
gratifyingly
gratility
gratillity
gratinate
grating
Gratiola
gratiolin
gratiosolin
gratis
gratitude
gratten
grattoir
gratuitant
gratuitous
gratuitously
gratuitousness
gratuity
gratulant
gratulate
gratulation
gratulatorily
gratulatory
graupel
gravamen
gravamina
grave
graveclod
gravecloth
graveclothes
graved
gravedigger
gravegarth
gravel
graveless
gravelike
graveling
gravelish
gravelliness
gravelly
gravelroot
gravelstone
gravelweed
gravely
gravemaker
gravemaking
graveman
gravemaster
graven
graveness
Gravenstein
graveolence
graveolency
graveolent
graver
Graves
graveship
graveside
gravestead
gravestone
graveward
gravewards
graveyard
gravic
gravicembalo
gravid
gravidity
gravidly
gravidness
Gravigrada
gravigrade
gravimeter
gravimetric
gravimetrical
gravimetrically
gravimetry
graving
gravitate
gravitater
gravitation
gravitational
gravitationally
gravitative
gravitometer
gravity
gravure
gravy
grawls
gray
grayback
graybeard
graycoat
grayfish
grayfly
grayhead
grayish
graylag
grayling
grayly
graymalkin
graymill
grayness
graypate
graywacke
grayware
graywether
grazable
graze
grazeable
grazer
grazier
grazierdom
graziery
grazing
grazingly
grease
greasebush
greasehorn
greaseless
greaselessness
greaseproof
greaseproofness
greaser
greasewood
greasily
greasiness
greasy
great
greatcoat
greatcoated
greaten
greater
greathead
greatheart
greathearted
greatheartedness
greatish
greatly
greatmouthed
greatness
greave
greaved
greaves
grebe
Grebo
grece
Grecian
Grecianize
Grecism
Grecize
Grecomania
Grecomaniac
Grecophil
gree
greed
greedily
greediness
greedless
greedsome
greedy
greedygut
greedyguts
Greek
Greekdom
Greekery
Greekess
Greekish
Greekism
Greekist
Greekize
Greekless
Greekling
green
greenable
greenage
greenalite
greenback
Greenbacker
Greenbackism
greenbark
greenbone
greenbrier
Greencloth
greencoat
greener
greenery
greeney
greenfinch
greenfish
greengage
greengill
greengrocer
greengrocery
greenhead
greenheaded
greenheart
greenhearted
greenhew
greenhide
greenhood
greenhorn
greenhornism
greenhouse
greening
greenish
greenishness
greenkeeper
greenkeeping
Greenland
Greenlander
Greenlandic
Greenlandish
greenlandite
Greenlandman
greenleek
greenless
greenlet
greenling
greenly
greenness
greenockite
greenovite
greenroom
greensand
greensauce
greenshank
greensick
greensickness
greenside
greenstone
greenstuff
greensward
greenswarded
greentail
greenth
greenuk
greenweed
Greenwich
greenwing
greenwithe
greenwood
greenwort
greeny
greenyard
greet
greeter
greeting
greetingless
greetingly
greffier
greffotome
Greg
gregal
gregale
gregaloid
gregarian
gregarianism
Gregarina
Gregarinae
Gregarinaria
gregarine
Gregarinida
gregarinidal
gregariniform
Gregarinina
Gregarinoidea
gregarinosis
gregarinous
gregarious
gregariously
gregariousness
gregaritic
grege
Gregg
Gregge
greggle
grego
Gregor
Gregorian
Gregorianist
Gregorianize
Gregorianizer
Gregory
greige
grein
greisen
gremial
gremlin
grenade
Grenadian
grenadier
grenadierial
grenadierly
grenadiership
grenadin
grenadine
Grendel
Grenelle
Gressoria
gressorial
gressorious
Greta
Gretchen
Gretel
greund
Grevillea
grew
grewhound
Grewia
grey
greyhound
Greyiaceae
greyly
greyness
gribble
grice
grid
griddle
griddlecake
griddler
gride
gridelin
gridiron
griece
grieced
grief
griefful
grieffully
griefless
grieflessness
grieshoch
grievance
grieve
grieved
grievedly
griever
grieveship
grieving
grievingly
grievous
grievously
grievousness
Griff
griff
griffade
griffado
griffaun
griffe
griffin
griffinage
griffinesque
griffinhood
griffinish
griffinism
Griffith
griffithite
Griffon
griffon
griffonage
griffonne
grift
grifter
grig
griggles
grignet
grigri
grihastha
grihyasutra
grike
grill
grillade
grillage
grille
grilled
griller
grillroom
grillwork
grilse
grim
grimace
grimacer
grimacier
grimacing
grimacingly
grimalkin
grime
grimful
grimgribber
grimily
griminess
grimliness
grimly
grimme
Grimmia
Grimmiaceae
grimmiaceous
grimmish
grimness
grimp
grimy
grin
grinagog
grinch
grind
grindable
Grindelia
grinder
grinderman
grindery
grinding
grindingly
grindle
grindstone
gringo
gringolee
gringophobia
Grinnellia
grinner
grinning
grinningly
grinny
grintern
grip
gripe
gripeful
griper
gripgrass
griphite
Griphosaurus
griping
gripingly
gripless
gripman
gripment
grippal
grippe
gripper
grippiness
gripping
grippingly
grippingness
gripple
grippleness
grippotoxin
grippy
gripsack
gripy
Griqua
griquaite
Griqualander
gris
grisaille
grisard
Griselda
griseous
grisette
grisettish
grisgris
griskin
grisliness
grisly
Grison
grison
grisounite
grisoutine
Grissel
grissens
grissons
grist
gristbite
grister
Gristhorbia
gristle
gristliness
gristly
gristmill
gristmiller
gristmilling
gristy
grit
grith
grithbreach
grithman
gritless
gritrock
grits
gritstone
gritten
gritter
grittily
grittiness
grittle
gritty
grivet
grivna
Grizel
Grizzel
grizzle
grizzled
grizzler
grizzly
grizzlyman
groan
groaner
groanful
groaning
groaningly
groat
groats
groatsworth
grobian
grobianism
grocer
grocerdom
groceress
grocerly
grocerwise
grocery
groceryman
Groenendael
groff
grog
groggery
groggily
grogginess
groggy
grogram
grogshop
groin
groined
groinery
groining
Grolier
Grolieresque
gromatic
gromatics
Gromia
grommet
gromwell
groom
groomer
groomish
groomishly
groomlet
groomling
groomsman
groomy
groop
groose
groot
grooty
groove
grooveless
groovelike
groover
grooverhead
grooviness
grooving
groovy
grope
groper
groping
gropingly
gropple
grorudite
gros
grosbeak
groschen
groser
groset
grosgrain
grosgrained
gross
grossart
grossen
grosser
grossification
grossify
grossly
grossness
grosso
grossulaceous
grossular
Grossularia
grossularia
Grossulariaceae
grossulariaceous
grossularious
grossularite
grosz
groszy
grot
grotesque
grotesquely
grotesqueness
grotesquerie
grothine
grothite
Grotian
Grotianism
grottesco
grotto
grottoed
grottolike
grottowork
grouch
grouchily
grouchiness
grouchingly
grouchy
grouf
grough
ground
groundable
groundably
groundage
groundberry
groundbird
grounded
groundedly
groundedness
groundenell
grounder
groundflower
grounding
groundless
groundlessly
groundlessness
groundliness
groundling
groundly
groundman
groundmass
groundneedle
groundnut
groundplot
grounds
groundsel
groundsill
groundsman
groundward
groundwood
groundwork
groundy
group
groupage
groupageness
grouped
grouper
grouping
groupist
grouplet
groupment
groupwise
grouse
grouseberry
grouseless
grouser
grouseward
grousewards
grousy
grout
grouter
grouthead
grouts
grouty
grouze
grove
groved
grovel
groveler
groveless
groveling
grovelingly
grovelings
grovy
grow
growable
growan
growed
grower
growing
growingly
growingupness
growl
growler
growlery
growling
growlingly
growly
grown
grownup
growse
growsome
growth
growthful
growthiness
growthless
growthy
grozart
grozet
grr
grub
grubbed
grubber
grubbery
grubbily
grubbiness
grubby
grubhood
grubless
grubroot
grubs
grubstake
grubstaker
Grubstreet
grubstreet
grubworm
grudge
grudgeful
grudgefully
grudgekin
grudgeless
grudger
grudgery
grudging
grudgingly
grudgingness
grudgment
grue
gruel
grueler
grueling
gruelly
Grues
gruesome
gruesomely
gruesomeness
gruff
gruffily
gruffiness
gruffish
gruffly
gruffness
gruffs
gruffy
grufted
grugru
Gruidae
gruiform
Gruiformes
gruine
Gruis
grum
grumble
grumbler
grumblesome
Grumbletonian
grumbling
grumblingly
grumbly
grume
Grumium
grumly
grummel
grummels
grummet
grummeter
grumness
grumose
grumous
grumousness
grump
grumph
grumphie
grumphy
grumpily
grumpiness
grumpish
grumpy
grun
Grundified
Grundlov
grundy
Grundyism
Grundyist
Grundyite
grunerite
gruneritization
grunion
grunt
grunter
Grunth
grunting
gruntingly
gruntle
gruntled
gruntling
Grus
grush
grushie
Grusian
Grusinian
gruss
grutch
grutten
gryde
grylli
gryllid
Gryllidae
gryllos
Gryllotalpa
Gryllus
gryllus
grypanian
Gryphaea
Gryphosaurus
gryposis
Grypotherium
grysbok
guaba
guacacoa
guachamaca
guacharo
guachipilin
Guacho
Guacico
guacimo
guacin
guaco
guaconize
Guadagnini
guadalcazarite
Guaharibo
Guahiban
Guahibo
Guahivo
guaiac
guaiacol
guaiacolize
guaiaconic
guaiacum
guaiaretic
guaiasanol
guaiol
guaka
Gualaca
guama
guan
Guana
guana
guanabana
guanabano
guanaco
guanajuatite
guanamine
guanase
guanay
Guanche
guaneide
guango
guanidine
guanidopropionic
guaniferous
guanine
guanize
guano
guanophore
guanosine
guanyl
guanylic
guao
guapena
guapilla
guapinol
Guaque
guar
guara
guarabu
guaracha
guaraguao
guarana
Guarani
guarani
Guaranian
guaranine
guarantee
guaranteeship
guarantor
guarantorship
guaranty
guarapucu
Guaraunan
Guarauno
guard
guardable
guardant
guarded
guardedly
guardedness
guardeen
guarder
guardfish
guardful
guardfully
guardhouse
guardian
guardiancy
guardianess
guardianless
guardianly
guardianship
guarding
guardingly
guardless
guardlike
guardo
guardrail
guardroom
guardship
guardsman
guardstone
Guarea
guariba
guarinite
guarneri
Guarnerius
Guarnieri
Guarrau
guarri
Guaruan
guasa
Guastalline
guatambu
Guatemalan
Guatemaltecan
guativere
Guato
Guatoan
Guatusan
Guatuso
Guauaenok
guava
guavaberry
guavina
guayaba
guayabi
guayabo
guayacan
Guayaqui
Guaycuru
Guaycuruan
Guaymie
guayroto
guayule
guaza
Guazuma
gubbertush
Gubbin
gubbo
gubernacula
gubernacular
gubernaculum
gubernative
gubernator
gubernatorial
gubernatrix
guberniya
gucki
gud
gudame
guddle
gude
gudebrother
gudefather
gudemother
gudesake
gudesakes
gudesire
gudewife
gudge
gudgeon
gudget
gudok
gue
guebucu
guejarite
Guelph
Guelphic
Guelphish
Guelphism
guemal
guenepe
guenon
guepard
guerdon
guerdonable
guerdoner
guerdonless
guereza
Guerickian
Guerinet
Guernsey
guernsey
guernseyed
guerrilla
guerrillaism
guerrillaship
Guesdism
Guesdist
guess
guessable
guesser
guessing
guessingly
guesswork
guessworker
guest
guestchamber
guesten
guester
guesthouse
guesting
guestive
guestless
Guestling
guestling
guestmaster
guestship
guestwise
Guetar
Guetare
gufa
guff
guffaw
guffer
guffin
guffy
gugal
guggle
gugglet
guglet
guglia
guglio
gugu
Guha
Guhayna
guhr
Guiana
Guianan
Guianese
guib
guiba
guidable
guidage
guidance
guide
guideboard
guidebook
guidebookish
guidecraft
guideless
guideline
guidepost
guider
guideress
guidership
guideship
guideway
guidman
Guido
guidon
Guidonian
guidwilly
guige
Guignardia
guignol
guijo
Guilandina
guild
guilder
guildhall
guildic
guildry
guildship
guildsman
guile
guileful
guilefully
guilefulness
guileless
guilelessly
guilelessness
guilery
guillemet
guillemot
Guillermo
guillevat
guilloche
guillochee
guillotinade
guillotine
guillotinement
guillotiner
guillotinism
guillotinist
guilt
guiltily
guiltiness
guiltless
guiltlessly
guiltlessness
guiltsick
guilty
guily
guimbard
guimpe
Guinea
guinea
Guineaman
Guinean
Guinevere
guipure
Guisard
guisard
guise
guiser
Guisian
guising
guitar
guitarfish
guitarist
guitermanite
guitguit
Guittonian
Gujar
Gujarati
Gujrati
gul
gula
gulae
gulaman
gulancha
Gulanganes
gular
gularis
gulch
gulden
guldengroschen
gule
gules
Gulf
gulf
gulflike
gulfside
gulfwards
gulfweed
gulfy
gulgul
gulinula
gulinulae
gulinular
gulix
gull
Gullah
gullery
gullet
gulleting
gullibility
gullible
gullibly
gullion
gullish
gullishly
gullishness
gully
gullyhole
Gulo
gulonic
gulose
gulosity
gulp
gulper
gulpin
gulping
gulpingly
gulpy
gulravage
gulsach
Gum
gum
gumbo
gumboil
gumbotil
gumby
gumchewer
gumdigger
gumdigging
gumdrop
gumfield
gumflower
gumihan
gumless
gumlike
gumly
gumma
gummage
gummaker
gummaking
gummata
gummatous
gummed
gummer
gummiferous
gumminess
gumming
gummite
gummose
gummosis
gummosity
gummous
gummy
gump
gumphion
gumption
gumptionless
gumptious
gumpus
gumshoe
gumweed
gumwood
gun
guna
gunate
gunation
gunbearer
gunboat
gunbright
gunbuilder
guncotton
gundi
gundy
gunebo
gunfire
gunflint
gunge
gunhouse
Gunite
gunite
gunj
gunk
gunl
gunless
gunlock
gunmaker
gunmaking
gunman
gunmanship
gunnage
Gunnar
gunne
gunnel
gunner
Gunnera
Gunneraceae
gunneress
gunnership
gunnery
gunnies
gunning
gunnung
gunny
gunocracy
gunong
gunpaper
gunplay
gunpowder
gunpowderous
gunpowdery
gunpower
gunrack
gunreach
gunrunner
gunrunning
gunsel
gunshop
gunshot
gunsman
gunsmith
gunsmithery
gunsmithing
gunster
gunstick
gunstock
gunstocker
gunstocking
gunstone
Gunter
gunter
Gunther
gunwale
gunyah
gunyang
gunyeh
Gunz
Gunzian
gup
guppy
guptavidya
gur
Guran
gurdfish
gurdle
gurdwara
gurge
gurgeon
gurgeons
gurges
gurgitation
gurgle
gurglet
gurgling
gurglingly
gurgly
gurgoyle
gurgulation
Gurian
Guric
Gurish
Gurjara
gurjun
gurk
Gurkha
gurl
gurly
Gurmukhi
gurnard
gurnet
gurnetty
Gurneyite
gurniad
gurr
gurrah
gurry
gurt
guru
guruship
Gus
gush
gusher
gushet
gushily
gushiness
gushing
gushingly
gushingness
gushy
gusla
gusle
guss
gusset
Gussie
gussie
gust
gustable
gustation
gustative
gustativeness
gustatory
Gustavus
gustful
gustfully
gustfulness
gustily
gustiness
gustless
gusto
gustoish
Gustus
gusty
gut
Guti
Gutium
gutless
gutlike
gutling
Gutnic
Gutnish
gutt
gutta
guttable
guttate
guttated
guttatim
guttation
gutte
gutter
Guttera
gutterblood
guttering
gutterlike
gutterling
gutterman
guttersnipe
guttersnipish
gutterspout
gutterwise
guttery
gutti
guttide
guttie
Guttiferae
guttiferal
Guttiferales
guttiferous
guttiform
guttiness
guttle
guttler
guttula
guttulae
guttular
guttulate
guttule
guttural
gutturalism
gutturality
gutturalization
gutturalize
gutturally
gutturalness
gutturize
gutturonasal
gutturopalatal
gutturopalatine
gutturotetany
guttus
gutty
gutweed
gutwise
gutwort
guvacine
guvacoline
Guy
guy
Guyandot
guydom
guyer
guytrash
guz
guze
Guzmania
guzmania
Guzul
guzzle
guzzledom
guzzler
gwag
gweduc
gweed
gweeon
gwely
Gwen
Gwendolen
gwine
gwyniad
Gyarung
gyascutus
Gyges
Gygis
gyle
gym
gymel
gymkhana
Gymnadenia
Gymnadeniopsis
Gymnanthes
gymnanthous
Gymnarchidae
Gymnarchus
gymnasia
gymnasial
gymnasiarch
gymnasiarchy
gymnasiast
gymnasic
gymnasium
gymnast
gymnastic
gymnastically
gymnastics
gymnemic
gymnetrous
gymnic
gymnical
gymnics
gymnite
Gymnoblastea
gymnoblastic
Gymnocalycium
gymnocarpic
gymnocarpous
Gymnocerata
gymnoceratous
gymnocidium
Gymnocladus
Gymnoconia
Gymnoderinae
Gymnodiniaceae
gymnodiniaceous
Gymnodiniidae
Gymnodinium
gymnodont
Gymnodontes
gymnogen
gymnogenous
Gymnoglossa
gymnoglossate
gymnogynous
Gymnogyps
Gymnolaema
Gymnolaemata
gymnolaematous
Gymnonoti
Gymnopaedes
gymnopaedic
gymnophiona
gymnoplast
Gymnorhina
gymnorhinal
Gymnorhininae
gymnosoph
gymnosophist
gymnosophy
gymnosperm
Gymnospermae
gymnospermal
gymnospermic
gymnospermism
Gymnospermous
gymnospermy
Gymnosporangium
gymnospore
gymnosporous
Gymnostomata
Gymnostomina
gymnostomous
Gymnothorax
gymnotid
Gymnotidae
Gymnotoka
gymnotokous
Gymnotus
Gymnura
gymnure
Gymnurinae
gymnurine
gympie
gyn
gynaecea
gynaeceum
gynaecocoenic
gynander
gynandrarchic
gynandrarchy
Gynandria
gynandria
gynandrian
gynandrism
gynandroid
gynandromorph
gynandromorphic
gynandromorphism
gynandromorphous
gynandromorphy
gynandrophore
gynandrosporous
gynandrous
gynandry
gynantherous
gynarchic
gynarchy
gyne
gynecic
gynecidal
gynecide
gynecocentric
gynecocracy
gynecocrat
gynecocratic
gynecocratical
gynecoid
gynecolatry
gynecologic
gynecological
gynecologist
gynecology
gynecomania
gynecomastia
gynecomastism
gynecomasty
gynecomazia
gynecomorphous
gyneconitis
gynecopathic
gynecopathy
gynecophore
gynecophoric
gynecophorous
gynecotelic
gynecratic
gyneocracy
gyneolater
gyneolatry
gynephobia
Gynerium
gynethusia
gyniatrics
gyniatry
gynic
gynics
gynobase
gynobaseous
gynobasic
gynocardia
gynocardic
gynocracy
gynocratic
gynodioecious
gynodioeciously
gynodioecism
gynoecia
gynoecium
gynogenesis
gynomonecious
gynomonoeciously
gynomonoecism
gynophagite
gynophore
gynophoric
gynosporangium
gynospore
gynostegia
gynostegium
gynostemium
Gynura
gyp
Gypaetus
gype
gypper
Gyppo
Gyps
gyps
gypseian
gypseous
gypsiferous
gypsine
gypsiologist
gypsite
gypsography
gypsologist
gypsology
Gypsophila
gypsophila
gypsophilous
gypsophily
gypsoplast
gypsous
gypster
gypsum
Gypsy
gypsy
gypsydom
gypsyesque
gypsyfy
gypsyhead
gypsyhood
gypsyish
gypsyism
gypsylike
gypsyry
gypsyweed
gypsywise
gypsywort
Gyracanthus
gyral
gyrally
gyrant
gyrate
gyration
gyrational
gyrator
gyratory
gyre
Gyrencephala
gyrencephalate
gyrencephalic
gyrencephalous
gyrene
gyrfalcon
gyri
gyric
gyrinid
Gyrinidae
Gyrinus
gyro
gyrocar
gyroceracone
gyroceran
Gyroceras
gyrochrome
gyrocompass
Gyrodactylidae
Gyrodactylus
gyrogonite
gyrograph
gyroidal
gyroidally
gyrolite
gyrolith
gyroma
gyromagnetic
gyromancy
gyromele
gyrometer
Gyromitra
gyron
gyronny
Gyrophora
Gyrophoraceae
Gyrophoraceous
gyrophoric
gyropigeon
gyroplane
gyroscope
gyroscopic
gyroscopically
gyroscopics
gyrose
gyrostabilizer
Gyrostachys
gyrostat
gyrostatic
gyrostatically
gyrostatics
Gyrotheca
gyrous
gyrovagi
gyrovagues
gyrowheel
gyrus
gyte
gytling
gyve
H
h
ha
haab
haaf
Habab
habanera
Habbe
habble
habdalah
Habe
habeas
habena
habenal
habenar
Habenaria
habendum
habenula
habenular
haberdash
haberdasher
haberdasheress
haberdashery
haberdine
habergeon
habilable
habilatory
habile
habiliment
habilimentation
habilimented
habilitate
habilitation
habilitator
hability
habille
Habiri
Habiru
habit
habitability
habitable
habitableness
habitably
habitacle
habitacule
habitally
habitan
habitance
habitancy
habitant
habitat
habitate
habitation
habitational
habitative
habited
habitual
habituality
habitualize
habitually
habitualness
habituate
habituation
habitude
habitudinal
habitue
habitus
habnab
haboob
Habronema
habronemiasis
habronemic
habu
habutai
habutaye
hache
Hachiman
hachure
hacienda
hack
hackamatak
hackamore
hackbarrow
hackberry
hackbolt
hackbush
hackbut
hackbuteer
hacked
hackee
hacker
hackery
hackin
hacking
hackingly
hackle
hackleback
hackler
hacklog
hackly
hackmack
hackman
hackmatack
hackney
hackneyed
hackneyer
hackneyism
hackneyman
hacksaw
hacksilber
hackster
hackthorn
hacktree
hackwood
hacky
had
Hadassah
hadbot
hadden
haddie
haddo
haddock
haddocker
hade
Hadean
Hadendoa
Hadendowa
hadentomoid
Hadentomoidea
Hades
Hadhramautian
hading
Hadith
hadj
Hadjemi
hadji
hadland
Hadramautian
hadrome
Hadromerina
hadromycosis
hadrosaur
Hadrosaurus
haec
haecceity
Haeckelian
Haeckelism
haem
Haemamoeba
Haemanthus
Haemaphysalis
haemaspectroscope
haematherm
haemathermal
haemathermous
haematinon
haematinum
haematite
Haematobranchia
haematobranchiate
Haematocrya
haematocryal
Haematophilina
haematophiline
Haematopus
haematorrhachis
haematosepsis
Haematotherma
haematothermal
haematoxylic
haematoxylin
Haematoxylon
haemoconcentration
haemodilution
Haemodoraceae
haemodoraceous
haemoglobin
haemogram
Haemogregarina
Haemogregarinidae
haemonchiasis
haemonchosis
Haemonchus
haemony
haemophile
Haemoproteus
haemorrhage
haemorrhagia
haemorrhagic
haemorrhoid
haemorrhoidal
haemosporid
Haemosporidia
haemosporidian
Haemosporidium
Haemulidae
haemuloid
haeremai
haet
haff
haffet
haffkinize
haffle
Hafgan
hafiz
hafnium
hafnyl
haft
hafter
hag
Haganah
Hagarite
hagberry
hagboat
hagborn
hagbush
hagdon
hageen
Hagenia
hagfish
haggada
haggaday
haggadic
haggadical
haggadist
haggadistic
haggard
haggardly
haggardness
hagged
hagger
haggis
haggish
haggishly
haggishness
haggister
haggle
haggler
haggly
haggy
hagi
hagia
hagiarchy
hagiocracy
Hagiographa
hagiographal
hagiographer
hagiographic
hagiographical
hagiographist
hagiography
hagiolater
hagiolatrous
hagiolatry
hagiologic
hagiological
hagiologist
hagiology
hagiophobia
hagioscope
hagioscopic
haglet
haglike
haglin
hagride
hagrope
hagseed
hagship
hagstone
hagtaper
hagweed
hagworm
hah
Hahnemannian
Hahnemannism
Haiathalah
Haida
Haidan
Haidee
haidingerite
Haiduk
haik
haikai
haikal
Haikh
haikwan
hail
hailer
hailproof
hailse
hailshot
hailstone
hailstorm
hailweed
haily
Haimavati
hain
Hainai
Hainan
Hainanese
hainberry
haine
hair
hairband
hairbeard
hairbird
hairbrain
hairbreadth
hairbrush
haircloth
haircut
haircutter
haircutting
hairdo
hairdress
hairdresser
hairdressing
haire
haired
hairen
hairhoof
hairhound
hairif
hairiness
hairlace
hairless
hairlessness
hairlet
hairline
hairlock
hairmeal
hairmonger
hairpin
hairsplitter
hairsplitting
hairspring
hairstone
hairstreak
hairtail
hairup
hairweed
hairwood
hairwork
hairworm
hairy
Haisla
Haithal
Haitian
haje
hajib
hajilij
hak
hakam
hakdar
hake
Hakea
hakeem
hakenkreuz
Hakenkreuzler
hakim
Hakka
hako
haku
Hal
hala
halakah
halakic
halakist
halakistic
halal
halalcor
halation
Halawi
halazone
halberd
halberdier
halberdman
halberdsman
halbert
halch
halcyon
halcyonian
halcyonic
Halcyonidae
Halcyoninae
halcyonine
Haldanite
hale
halebi
Halecomorphi
haleness
Halenia
haler
halerz
Halesia
halesome
half
halfback
halfbeak
halfer
halfheaded
halfhearted
halfheartedly
halfheartedness
halfling
halfman
halfness
halfpace
halfpaced
halfpenny
halfpennyworth
halfway
halfwise
Haliaeetus
halibios
halibiotic
halibiu
halibut
halibuter
Halicarnassean
Halicarnassian
Halichondriae
halichondrine
halichondroid
Halicore
Halicoridae
halide
halidom
halieutic
halieutically
halieutics
Haligonian
Halimeda
halimous
halinous
haliographer
haliography
Haliotidae
Haliotis
haliotoid
haliplankton
haliplid
Haliplidae
Haliserites
halisteresis
halisteretic
halite
Halitheriidae
Halitherium
halitosis
halituosity
halituous
halitus
hall
hallabaloo
hallage
hallah
hallan
hallanshaker
hallebardier
hallecret
halleflinta
halleflintoid
hallel
hallelujah
hallelujatic
hallex
Halleyan
halliblash
halling
hallman
hallmark
hallmarked
hallmarker
hallmoot
halloo
Hallopididae
hallopodous
Hallopus
hallow
Hallowday
hallowed
hallowedly
hallowedness
Halloween
hallower
Hallowmas
Hallowtide
halloysite
Hallstatt
Hallstattian
hallucal
hallucinate
hallucination
hallucinational
hallucinative
hallucinator
hallucinatory
hallucined
hallucinosis
hallux
hallway
halma
halmalille
halmawise
halo
Haloa
Halobates
halobios
halobiotic
halochromism
halochromy
Halocynthiidae
haloesque
halogen
halogenate
halogenation
halogenoid
halogenous
Halogeton
halohydrin
haloid
halolike
halolimnic
halomancy
halometer
halomorphic
halophile
halophilism
halophilous
halophyte
halophytic
halophytism
Halopsyche
Halopsychidae
Haloragidaceae
haloragidaceous
Halosauridae
Halosaurus
haloscope
Halosphaera
halotrichite
haloxene
hals
halse
halsen
halsfang
halt
halter
halterbreak
halteres
Halteridium
halterproof
Haltica
halting
haltingly
haltingness
haltless
halucket
halukkah
halurgist
halurgy
halutz
halvaner
halvans
halve
halved
halvelings
halver
halves
halyard
Halysites
ham
hamacratic
Hamadan
hamadryad
Hamal
hamal
hamald
Hamamelidaceae
hamamelidaceous
Hamamelidanthemum
hamamelidin
Hamamelidoxylon
hamamelin
Hamamelis
Hamamelites
hamartiologist
hamartiology
hamartite
hamate
hamated
Hamathite
hamatum
hambergite
hamble
hambroline
hamburger
hame
hameil
hamel
Hamelia
hamesucken
hamewith
hamfat
hamfatter
hami
Hamidian
Hamidieh
hamiform
Hamilton
Hamiltonian
Hamiltonianism
Hamiltonism
hamingja
hamirostrate
Hamital
Hamite
Hamites
Hamitic
Hamiticized
Hamitism
Hamitoid
hamlah
hamlet
hamleted
hamleteer
hamletization
hamletize
hamlinite
hammada
hammam
hammer
hammerable
hammerbird
hammercloth
hammerdress
hammerer
hammerfish
hammerhead
hammerheaded
hammering
hammeringly
hammerkop
hammerless
hammerlike
hammerman
hammersmith
hammerstone
hammertoe
hammerwise
hammerwork
hammerwort
hammochrysos
hammock
hammy
hamose
hamous
hamper
hamperedly
hamperedness
hamperer
hamperman
Hampshire
hamrongite
hamsa
hamshackle
hamster
hamstring
hamular
hamulate
hamule
Hamulites
hamulose
hamulus
hamus
hamza
han
Hanafi
Hanafite
hanaper
hanaster
Hanbalite
hanbury
hance
hanced
hanch
hancockite
hand
handbag
handball
handballer
handbank
handbanker
handbarrow
handbill
handblow
handbolt
handbook
handbow
handbreadth
handcar
handcart
handclap
handclasp
handcloth
handcraft
handcraftman
handcraftsman
handcuff
handed
handedness
Handelian
hander
handersome
handfast
handfasting
handfastly
handfastness
handflower
handful
handgrasp
handgravure
handgrip
handgriping
handgun
handhaving
handhold
handhole
handicap
handicapped
handicapper
handicraft
handicraftship
handicraftsman
handicraftsmanship
handicraftswoman
handicuff
handily
handiness
handistroke
handiwork
handkercher
handkerchief
handkerchiefful
handlaid
handle
handleable
handled
handleless
handler
handless
handlike
handling
handmade
handmaid
handmaiden
handmaidenly
handout
handpost
handprint
handrail
handrailing
handreader
handreading
handsale
handsaw
handsbreadth
handscrape
handsel
handseller
handset
handshake
handshaker
handshaking
handsmooth
handsome
handsomeish
handsomely
handsomeness
handspade
handspike
handspoke
handspring
handstaff
handstand
handstone
handstroke
handwear
handwheel
handwhile
handwork
handworkman
handwrist
handwrite
handwriting
handy
handyblow
handybook
handygrip
hangability
hangable
hangalai
hangar
hangbird
hangby
hangdog
hange
hangee
hanger
hangfire
hangie
hanging
hangingly
hangkang
hangle
hangman
hangmanship
hangment
hangnail
hangnest
hangout
hangul
hangwoman
hangworm
hangworthy
hanif
hanifism
hanifite
hanifiya
Hank
hank
hanker
hankerer
hankering
hankeringly
hankie
hankle
hanksite
hanky
hanna
hannayite
Hannibal
Hannibalian
Hannibalic
Hano
Hanoverian
Hanoverianize
Hanoverize
Hans
hansa
Hansard
Hansardization
Hansardize
Hanse
hanse
Hanseatic
hansel
hansgrave
hansom
hant
hantle
Hanukkah
Hanuman
hao
haole
haoma
haori
hap
Hapale
Hapalidae
hapalote
Hapalotis
hapaxanthous
haphazard
haphazardly
haphazardness
haphtarah
Hapi
hapless
haplessly
haplessness
haplite
haplocaulescent
haplochlamydeous
Haplodoci
Haplodon
haplodont
haplodonty
haplography
haploid
haploidic
haploidy
haplolaly
haplologic
haplology
haploma
Haplomi
haplomid
haplomous
haplont
haploperistomic
haploperistomous
haplopetalous
haplophase
haplophyte
haploscope
haploscopic
haplosis
haplostemonous
haplotype
haply
happen
happening
happenstance
happier
happiest
happify
happiless
happily
happiness
happing
happy
hapten
haptene
haptenic
haptere
hapteron
haptic
haptics
haptometer
haptophor
haptophoric
haptophorous
haptotropic
haptotropically
haptotropism
hapu
hapuku
haqueton
harakeke
harangue
harangueful
haranguer
Hararese
Harari
harass
harassable
harassedly
harasser
harassingly
harassment
haratch
Haratin
Haraya
Harb
harbergage
harbi
harbinge
harbinger
harbingership
harbingery
harbor
harborage
harborer
harborless
harborous
harborside
harborward
hard
hardanger
hardback
hardbake
hardbeam
hardberry
harden
hardenable
Hardenbergia
hardener
hardening
hardenite
harder
Harderian
hardfern
hardfist
hardfisted
hardfistedness
hardhack
hardhanded
hardhandedness
hardhead
hardheaded
hardheadedly
hardheadedness
hardhearted
hardheartedly
hardheartedness
hardihood
hardily
hardim
hardiment
hardiness
hardish
hardishrew
hardly
hardmouth
hardmouthed
hardness
hardock
hardpan
hardship
hardstand
hardstanding
hardtack
hardtail
hardware
hardwareman
Hardwickia
hardwood
hardy
hardystonite
hare
harebell
harebottle
harebrain
harebrained
harebrainedly
harebrainedness
harebur
harefoot
harefooted
harehearted
harehound
Harelda
harelike
harelip
harelipped
harem
haremism
haremlik
harengiform
harfang
haricot
harigalds
hariolate
hariolation
hariolize
harish
hark
harka
harl
Harleian
Harlemese
Harlemite
harlequin
harlequina
harlequinade
harlequinery
harlequinesque
harlequinic
harlequinism
harlequinize
harling
harlock
harlot
harlotry
harm
Harmachis
harmal
harmala
harmaline
harman
harmattan
harmel
harmer
harmful
harmfully
harmfulness
harmine
harminic
harmless
harmlessly
harmlessness
Harmon
harmonia
harmoniacal
harmonial
harmonic
harmonica
harmonical
harmonically
harmonicalness
harmonichord
harmonici
harmonicism
harmonicon
harmonics
harmonious
harmoniously
harmoniousness
harmoniphon
harmoniphone
harmonist
harmonistic
harmonistically
Harmonite
harmonium
harmonizable
harmonization
harmonize
harmonizer
harmonogram
harmonograph
harmonometer
harmony
harmost
harmotome
harmotomic
harmproof
harn
harness
harnesser
harnessry
harnpan
Harold
harp
Harpa
harpago
harpagon
Harpagornis
Harpalides
Harpalinae
Harpalus
harper
harperess
Harpidae
harpier
harpings
harpist
harpless
harplike
Harpocrates
harpoon
harpooner
Harporhynchus
harpress
harpsichord
harpsichordist
harpula
Harpullia
harpwaytuning
harpwise
Harpy
Harpyia
harpylike
harquebus
harquebusade
harquebusier
harr
harrateen
harridan
harrier
Harris
Harrisia
harrisite
Harrovian
harrow
harrower
harrowing
harrowingly
harrowingness
harrowment
Harry
harry
harsh
harshen
harshish
harshly
harshness
harshweed
harstigite
hart
hartal
hartberry
hartebeest
hartin
hartite
Hartleian
Hartleyan
Hartmann
Hartmannia
Hartogia
hartshorn
hartstongue
harttite
Hartungen
haruspex
haruspical
haruspicate
haruspication
haruspice
haruspices
haruspicy
Harv
Harvard
Harvardian
Harvardize
Harveian
harvest
harvestbug
harvester
harvestless
harvestman
harvestry
harvesttime
Harvey
Harveyize
harzburgite
hasan
hasenpfeffer
hash
hashab
hasher
Hashimite
hashish
Hashiya
hashy
Hasidean
Hasidic
Hasidim
Hasidism
Hasinai
hask
Haskalah
haskness
hasky
haslet
haslock
Hasmonaean
hasp
hassar
hassel
hassle
hassock
hassocky
hasta
hastate
hastately
hastati
hastatolanceolate
hastatosagittate
haste
hasteful
hastefully
hasteless
hastelessness
hasten
hastener
hasteproof
haster
hastilude
hastily
hastiness
hastings
hastingsite
hastish
hastler
hasty
hat
hatable
hatband
hatbox
hatbrim
hatbrush
hatch
hatchability
hatchable
hatchel
hatcheler
hatcher
hatchery
hatcheryman
hatchet
hatchetback
hatchetfish
hatchetlike
hatchetman
hatchettine
hatchettolite
hatchety
hatchgate
hatching
hatchling
hatchman
hatchment
hatchminder
hatchway
hatchwayman
hate
hateable
hateful
hatefully
hatefulness
hateless
hatelessness
hater
hatful
hath
hatherlite
hathi
Hathor
Hathoric
Hati
Hatikvah
hatless
hatlessness
hatlike
hatmaker
hatmaking
hatpin
hatrack
hatrail
hatred
hatress
hatstand
hatt
hatted
Hattemist
hatter
Hatteria
hattery
Hatti
Hattic
Hattie
hatting
Hattism
Hattize
hattock
Hatty
hatty
hau
hauberget
hauberk
hauchecornite
hauerite
haugh
haughland
haught
haughtily
haughtiness
haughtly
haughtness
haughtonite
haughty
haul
haulabout
haulage
haulageway
haulback
hauld
hauler
haulier
haulm
haulmy
haulster
haunch
haunched
hauncher
haunching
haunchless
haunchy
haunt
haunter
hauntingly
haunty
Hauranitic
hauriant
haurient
Hausa
hause
hausen
hausmannite
hausse
Haussmannization
Haussmannize
haustellate
haustellated
haustellous
haustellum
haustement
haustorial
haustorium
haustral
haustrum
hautboy
hautboyist
hauteur
hauynite
hauynophyre
havage
Havaiki
Havaikian
Havana
Havanese
have
haveable
haveage
havel
haveless
havelock
haven
havenage
havener
havenership
havenet
havenful
havenless
havent
havenward
haver
havercake
haverel
haverer
havergrass
havermeal
havers
haversack
Haversian
haversine
havier
havildar
havingness
havoc
havocker
haw
Hawaiian
hawaiite
hawbuck
hawcubite
hawer
hawfinch
Hawiya
hawk
hawkbill
hawkbit
hawked
hawker
hawkery
Hawkeye
hawkie
hawking
hawkish
hawklike
hawknut
hawkweed
hawkwise
hawky
hawm
hawok
Haworthia
hawse
hawsehole
hawseman
hawsepiece
hawsepipe
hawser
hawserwise
hawthorn
hawthorned
hawthorny
hay
haya
hayband
haybird
haybote
haycap
haycart
haycock
haydenite
hayey
hayfield
hayfork
haygrower
haylift
hayloft
haymaker
haymaking
haymarket
haymow
hayrack
hayrake
hayraker
hayrick
hayseed
haysel
haystack
haysuck
haytime
hayward
hayweed
haywire
hayz
Hazara
hazard
hazardable
hazarder
hazardful
hazardize
hazardless
hazardous
hazardously
hazardousness
hazardry
haze
Hazel
hazel
hazeled
hazeless
hazelly
hazelnut
hazelwood
hazelwort
hazen
hazer
hazily
haziness
hazing
hazle
haznadar
hazy
hazzan
he
head
headache
headachy
headband
headbander
headboard
headborough
headcap
headchair
headcheese
headchute
headcloth
headdress
headed
headender
header
headfirst
headforemost
headframe
headful
headgear
headily
headiness
heading
headkerchief
headland
headledge
headless
headlessness
headlight
headlighting
headlike
headline
headliner
headlock
headlong
headlongly
headlongs
headlongwise
headman
headmark
headmaster
headmasterly
headmastership
headmistress
headmistressship
headmold
headmost
headnote
headpenny
headphone
headpiece
headplate
headpost
headquarter
headquarters
headrace
headrail
headreach
headrent
headrest
headright
headring
headroom
headrope
headsail
headset
headshake
headship
headsill
headskin
headsman
headspring
headstall
headstand
headstick
headstock
headstone
headstream
headstrong
headstrongly
headstrongness
headwaiter
headwall
headward
headwark
headwater
headway
headwear
headwork
headworker
headworking
heady
heaf
heal
healable
heald
healder
healer
healful
healing
healingly
healless
healsome
healsomeness
health
healthcraft
healthful
healthfully
healthfulness
healthguard
healthily
healthiness
healthless
healthlessness
healthsome
healthsomely
healthsomeness
healthward
healthy
heap
heaper
heaps
heapstead
heapy
hear
hearable
hearer
hearing
hearingless
hearken
hearkener
hearsay
hearse
hearsecloth
hearselike
hearst
heart
heartache
heartaching
heartbeat
heartbird
heartblood
heartbreak
heartbreaker
heartbreaking
heartbreakingly
heartbroken
heartbrokenly
heartbrokenness
heartburn
heartburning
heartdeep
heartease
hearted
heartedly
heartedness
hearten
heartener
heartening
hearteningly
heartfelt
heartful
heartfully
heartfulness
heartgrief
hearth
hearthless
hearthman
hearthpenny
hearthrug
hearthstead
hearthstone
hearthward
hearthwarming
heartikin
heartily
heartiness
hearting
heartland
heartleaf
heartless
heartlessly
heartlessness
heartlet
heartling
heartly
heartnut
heartpea
heartquake
heartroot
hearts
heartscald
heartsease
heartseed
heartsette
heartsick
heartsickening
heartsickness
heartsome
heartsomely
heartsomeness
heartsore
heartstring
heartthrob
heartward
heartwater
heartweed
heartwise
heartwood
heartwort
hearty
heat
heatable
heatdrop
heatedly
heater
heaterman
heatful
heath
heathberry
heathbird
heathen
heathendom
heatheness
heathenesse
heathenhood
heathenish
heathenishly
heathenishness
heathenism
heathenize
heathenness
heathenry
heathenship
Heather
heather
heathered
heatheriness
heathery
heathless
heathlike
heathwort
heathy
heating
heatingly
heatless
heatlike
heatmaker
heatmaking
heatproof
heatronic
heatsman
heatstroke
heaume
heaumer
heautarit
heautomorphism
Heautontimorumenos
heautophany
heave
heaveless
heaven
Heavenese
heavenful
heavenhood
heavenish
heavenishly
heavenize
heavenless
heavenlike
heavenliness
heavenly
heavens
heavenward
heavenwardly
heavenwardness
heavenwards
heaver
heavies
heavily
heaviness
heaving
heavisome
heavity
heavy
heavyback
heavyhanded
heavyhandedness
heavyheaded
heavyhearted
heavyheartedness
heavyweight
hebamic
hebdomad
hebdomadal
hebdomadally
hebdomadary
hebdomader
hebdomarian
hebdomary
hebeanthous
hebecarpous
hebecladous
hebegynous
hebenon
hebeosteotomy
hebepetalous
hebephrenia
hebephrenic
hebetate
hebetation
hebetative
hebete
hebetic
hebetomy
hebetude
hebetudinous
Hebraean
Hebraic
Hebraica
Hebraical
Hebraically
Hebraicize
Hebraism
Hebraist
Hebraistic
Hebraistical
Hebraistically
Hebraization
Hebraize
Hebraizer
Hebrew
Hebrewdom
Hebrewess
Hebrewism
Hebrician
Hebridean
Hebronite
hebronite
hecastotheism
Hecate
Hecatean
Hecatic
Hecatine
hecatomb
Hecatombaeon
hecatomped
hecatompedon
hecatonstylon
hecatontarchy
hecatontome
hecatophyllous
hech
Hechtia
heck
heckelphone
Heckerism
heckimal
heckle
heckler
hectare
hecte
hectic
hectical
hectically
hecticly
hecticness
hectocotyl
hectocotyle
hectocotyliferous
hectocotylization
hectocotylize
hectocotylus
hectogram
hectograph
hectographic
hectography
hectoliter
hectometer
Hector
hector
Hectorean
Hectorian
hectoringly
hectorism
hectorly
hectorship
hectostere
hectowatt
heddle
heddlemaker
heddler
hedebo
hedenbergite
Hedeoma
heder
Hedera
hederaceous
hederaceously
hederated
hederic
hederiferous
hederiform
hederigerent
hederin
hederose
hedge
hedgeberry
hedgeborn
hedgebote
hedgebreaker
hedgehog
hedgehoggy
hedgehop
hedgehopper
hedgeless
hedgemaker
hedgemaking
hedger
hedgerow
hedgesmith
hedgeweed
hedgewise
hedgewood
hedging
hedgingly
hedgy
hedonic
hedonical
hedonically
hedonics
hedonism
hedonist
hedonistic
hedonistically
hedonology
hedriophthalmous
hedrocele
hedrumite
Hedychium
hedyphane
Hedysarum
heed
heeder
heedful
heedfully
heedfulness
heedily
heediness
heedless
heedlessly
heedlessness
heedy
heehaw
heel
heelball
heelband
heelcap
heeled
heeler
heelgrip
heelless
heelmaker
heelmaking
heelpath
heelpiece
heelplate
heelpost
heelprint
heelstrap
heeltap
heeltree
heemraad
heer
heeze
heezie
heezy
heft
hefter
heftily
heftiness
hefty
hegari
Hegelian
Hegelianism
Hegelianize
Hegelizer
hegemon
hegemonic
hegemonical
hegemonist
hegemonizer
hegemony
hegira
hegumen
hegumene
Hehe
hei
heiau
Heidi
heifer
heiferhood
heigh
heighday
height
heighten
heightener
heii
Heikum
Heiltsuk
heimin
Hein
Heinesque
Heinie
heinous
heinously
heinousness
Heinrich
heintzite
Heinz
heir
heirdom
heiress
heiressdom
heiresshood
heirless
heirloom
heirship
heirskip
heitiki
Hejazi
Hejazian
hekteus
helbeh
helcoid
helcology
helcoplasty
helcosis
helcotic
heldentenor
helder
Helderbergian
hele
Helen
Helena
helenin
helenioid
Helenium
Helenus
helepole
Helge
heliacal
heliacally
Heliaea
heliaean
Heliamphora
Heliand
helianthaceous
Helianthemum
helianthic
helianthin
Helianthium
Helianthoidea
Helianthoidean
Helianthus
heliast
heliastic
heliazophyte
helical
helically
heliced
helices
helichryse
helichrysum
Helicidae
heliciform
helicin
Helicina
helicine
Helicinidae
helicitic
helicline
helicograph
helicogyrate
helicogyre
helicoid
helicoidal
helicoidally
helicometry
helicon
Heliconia
Heliconian
Heliconiidae
Heliconiinae
heliconist
Heliconius
helicoprotein
helicopter
helicorubin
helicotrema
Helicteres
helictite
helide
Heligmus
heling
helio
heliocentric
heliocentrical
heliocentrically
heliocentricism
heliocentricity
heliochrome
heliochromic
heliochromoscope
heliochromotype
heliochromy
helioculture
heliodon
heliodor
helioelectric
helioengraving
heliofugal
Heliogabalize
Heliogabalus
heliogram
heliograph
heliographer
heliographic
heliographical
heliographically
heliography
heliogravure
helioid
heliolater
heliolatrous
heliolatry
heliolite
Heliolites
heliolithic
Heliolitidae
heliologist
heliology
heliometer
heliometric
heliometrical
heliometrically
heliometry
heliomicrometer
Helion
heliophilia
heliophiliac
heliophilous
heliophobe
heliophobia
heliophobic
heliophobous
heliophotography
heliophyllite
heliophyte
Heliopora
Helioporidae
Heliopsis
heliopticon
Heliornis
Heliornithes
Heliornithidae
Helios
helioscope
helioscopic
helioscopy
heliosis
heliostat
heliostatic
heliotactic
heliotaxis
heliotherapy
heliothermometer
Heliothis
heliotrope
heliotroper
Heliotropiaceae
heliotropian
heliotropic
heliotropical
heliotropically
heliotropine
heliotropism
Heliotropium
heliotropy
heliotype
heliotypic
heliotypically
heliotypography
heliotypy
Heliozoa
heliozoan
heliozoic
heliport
Helipterum
helispheric
helispherical
helium
helix
helizitic
hell
Helladian
Helladic
Helladotherium
hellandite
hellanodic
hellbender
hellborn
hellbox
hellbred
hellbroth
hellcat
helldog
helleboraceous
helleboraster
hellebore
helleborein
helleboric
helleborin
Helleborine
helleborism
Helleborus
Hellelt
Hellen
Hellene
Hellenian
Hellenic
Hellenically
Hellenicism
Hellenism
Hellenist
Hellenistic
Hellenistical
Hellenistically
Hellenisticism
Hellenization
Hellenize
Hellenizer
Hellenocentric
Hellenophile
heller
helleri
Hellespont
Hellespontine
hellgrammite
hellhag
hellhole
hellhound
hellicat
hellier
hellion
hellish
hellishly
hellishness
hellkite
hellness
hello
hellroot
hellship
helluo
hellward
hellweed
helly
helm
helmage
helmed
helmet
helmeted
helmetlike
helmetmaker
helmetmaking
Helmholtzian
helminth
helminthagogic
helminthagogue
Helminthes
helminthiasis
helminthic
helminthism
helminthite
Helminthocladiaceae
helminthoid
helminthologic
helminthological
helminthologist
helminthology
helminthosporiose
Helminthosporium
helminthosporoid
helminthous
helmless
helmsman
helmsmanship
helobious
heloderm
Heloderma
Helodermatidae
helodermatoid
helodermatous
helodes
heloe
heloma
Helonias
helonin
helosis
Helot
helotage
helotism
helotize
helotomy
helotry
help
helpable
helper
helpful
helpfully
helpfulness
helping
helpingly
helpless
helplessly
helplessness
helply
helpmate
helpmeet
helpsome
helpworthy
helsingkite
helve
helvell
Helvella
Helvellaceae
helvellaceous
Helvellales
helvellic
helver
Helvetia
Helvetian
Helvetic
Helvetii
Helvidian
helvite
hem
hemabarometer
hemachate
hemachrome
hemachrosis
hemacite
hemad
hemadrometer
hemadrometry
hemadromograph
hemadromometer
hemadynameter
hemadynamic
hemadynamics
hemadynamometer
hemafibrite
hemagglutinate
hemagglutination
hemagglutinative
hemagglutinin
hemagogic
hemagogue
hemal
hemalbumen
hemamoeba
hemangioma
hemangiomatosis
hemangiosarcoma
hemaphein
hemapod
hemapodous
hemapoiesis
hemapoietic
hemapophyseal
hemapophysial
hemapophysis
hemarthrosis
hemase
hemaspectroscope
hemastatics
hematachometer
hematachometry
hematal
hematein
hematemesis
hematemetic
hematencephalon
hematherapy
hematherm
hemathermal
hemathermous
hemathidrosis
hematic
hematid
hematidrosis
hematimeter
hematin
hematinic
hematinometer
hematinometric
hematinuria
hematite
hematitic
hematobic
hematobious
hematobium
hematoblast
hematobranchiate
hematocatharsis
hematocathartic
hematocele
hematochezia
hematochrome
hematochyluria
hematoclasia
hematoclasis
hematocolpus
hematocrit
hematocryal
hematocrystallin
hematocyanin
hematocyst
hematocystis
hematocyte
hematocytoblast
hematocytogenesis
hematocytometer
hematocytotripsis
hematocytozoon
hematocyturia
hematodynamics
hematodynamometer
hematodystrophy
hematogen
hematogenesis
hematogenetic
hematogenic
hematogenous
hematoglobulin
hematography
hematohidrosis
hematoid
hematoidin
hematolin
hematolite
hematological
hematologist
hematology
hematolymphangioma
hematolysis
hematolytic
hematoma
hematomancy
hematometer
hematometra
hematometry
hematomphalocele
hematomyelia
hematomyelitis
hematonephrosis
hematonic
hematopathology
hematopericardium
hematopexis
hematophobia
hematophyte
hematoplast
hematoplastic
hematopoiesis
hematopoietic
hematoporphyrin
hematoporphyrinuria
hematorrhachis
hematorrhea
hematosalpinx
hematoscope
hematoscopy
hematose
hematosepsis
hematosin
hematosis
hematospectrophotometer
hematospectroscope
hematospermatocele
hematospermia
hematostibiite
hematotherapy
hematothermal
hematothorax
hematoxic
hematozoal
hematozoan
hematozoic
hematozoon
hematozymosis
hematozymotic
hematuresis
hematuria
hematuric
hemautogram
hemautograph
hemautographic
hemautography
heme
hemellitene
hemellitic
hemelytral
hemelytron
hemen
hemera
hemeralope
hemeralopia
hemeralopic
Hemerobaptism
Hemerobaptist
Hemerobian
Hemerobiid
Hemerobiidae
Hemerobius
Hemerocallis
hemerologium
hemerology
hemerythrin
hemiablepsia
hemiacetal
hemiachromatopsia
hemiageusia
hemiageustia
hemialbumin
hemialbumose
hemialbumosuria
hemialgia
hemiamaurosis
hemiamb
hemiamblyopia
hemiamyosthenia
hemianacusia
hemianalgesia
hemianatropous
hemianesthesia
hemianopia
hemianopic
hemianopsia
hemianoptic
hemianosmia
hemiapraxia
Hemiascales
Hemiasci
Hemiascomycetes
hemiasynergia
hemiataxia
hemiataxy
hemiathetosis
hemiatrophy
hemiazygous
Hemibasidiales
Hemibasidii
Hemibasidiomycetes
hemibasidium
hemibathybian
hemibenthic
hemibenthonic
hemibranch
hemibranchiate
Hemibranchii
hemic
hemicanities
hemicardia
hemicardiac
hemicarp
hemicatalepsy
hemicataleptic
hemicellulose
hemicentrum
hemicephalous
hemicerebrum
Hemichorda
hemichordate
hemichorea
hemichromatopsia
hemicircle
hemicircular
hemiclastic
hemicollin
hemicrane
hemicrania
hemicranic
hemicrany
hemicrystalline
hemicycle
hemicyclic
hemicyclium
hemicylindrical
hemidactylous
Hemidactylus
hemidemisemiquaver
hemidiapente
hemidiaphoresis
hemiditone
hemidomatic
hemidome
hemidrachm
hemidysergia
hemidysesthesia
hemidystrophy
hemiekton
hemielliptic
hemiepilepsy
hemifacial
hemiform
Hemigale
Hemigalus
Hemiganus
hemigastrectomy
hemigeusia
hemiglossal
hemiglossitis
hemiglyph
hemignathous
hemihdry
hemihedral
hemihedrally
hemihedric
hemihedrism
hemihedron
hemiholohedral
hemihydrate
hemihydrated
hemihydrosis
hemihypalgesia
hemihyperesthesia
hemihyperidrosis
hemihypertonia
hemihypertrophy
hemihypesthesia
hemihypoesthesia
hemihypotonia
hemikaryon
hemikaryotic
hemilaminectomy
hemilaryngectomy
Hemileia
hemilethargy
hemiligulate
hemilingual
hemimellitene
hemimellitic
hemimelus
Hemimeridae
Hemimerus
Hemimetabola
hemimetabole
hemimetabolic
hemimetabolism
hemimetabolous
hemimetaboly
hemimetamorphic
hemimetamorphosis
hemimetamorphous
hemimorph
hemimorphic
hemimorphism
hemimorphite
hemimorphy
Hemimyaria
hemin
hemina
hemine
heminee
hemineurasthenia
hemiobol
hemiolia
hemiolic
hemionus
hemiope
hemiopia
hemiopic
hemiorthotype
hemiparalysis
hemiparanesthesia
hemiparaplegia
hemiparasite
hemiparasitic
hemiparasitism
hemiparesis
hemiparesthesia
hemiparetic
hemipenis
hemipeptone
hemiphrase
hemipic
hemipinnate
hemiplane
hemiplankton
hemiplegia
hemiplegic
hemiplegy
hemipodan
hemipode
Hemipodii
Hemipodius
hemiprism
hemiprismatic
hemiprotein
hemipter
Hemiptera
hemipteral
hemipteran
hemipteroid
hemipterological
hemipterology
hemipteron
hemipterous
hemipyramid
hemiquinonoid
hemiramph
Hemiramphidae
Hemiramphinae
hemiramphine
Hemiramphus
hemisaprophyte
hemisaprophytic
hemiscotosis
hemisect
hemisection
hemispasm
hemispheral
hemisphere
hemisphered
hemispherical
hemispherically
hemispheroid
hemispheroidal
hemispherule
hemistater
hemistich
hemistichal
hemistrumectomy
hemisymmetrical
hemisymmetry
hemisystole
hemiterata
hemiteratic
hemiteratics
hemiteria
hemiterpene
hemitery
hemithyroidectomy
hemitone
hemitremor
hemitrichous
hemitriglyph
hemitropal
hemitrope
hemitropic
hemitropism
hemitropous
hemitropy
hemitype
hemitypic
hemivagotony
heml
hemlock
hemmel
hemmer
hemoalkalimeter
hemoblast
hemochromatosis
hemochrome
hemochromogen
hemochromometer
hemochromometry
hemoclasia
hemoclasis
hemoclastic
hemocoel
hemocoele
hemocoelic
hemocoelom
hemoconcentration
hemoconia
hemoconiosis
hemocry
hemocrystallin
hemoculture
hemocyanin
hemocyte
hemocytoblast
hemocytogenesis
hemocytolysis
hemocytometer
hemocytotripsis
hemocytozoon
hemocyturia
hemodiagnosis
hemodilution
hemodrometer
hemodrometry
hemodromograph
hemodromometer
hemodynameter
hemodynamic
hemodynamics
hemodystrophy
hemoerythrin
hemoflagellate
hemofuscin
hemogastric
hemogenesis
hemogenetic
hemogenic
hemogenous
hemoglobic
hemoglobin
hemoglobinemia
hemoglobiniferous
hemoglobinocholia
hemoglobinometer
hemoglobinophilic
hemoglobinous
hemoglobinuria
hemoglobinuric
hemoglobulin
hemogram
hemogregarine
hemoid
hemokonia
hemokoniosis
hemol
hemoleucocyte
hemoleucocytic
hemologist
hemology
hemolymph
hemolymphatic
hemolysin
hemolysis
hemolytic
hemolyze
hemomanometer
hemometer
hemometry
hemonephrosis
hemopathology
hemopathy
hemopericardium
hemoperitoneum
hemopexis
hemophage
hemophagia
hemophagocyte
hemophagocytosis
hemophagous
hemophagy
hemophile
Hemophileae
hemophilia
hemophiliac
hemophilic
Hemophilus
hemophobia
hemophthalmia
hemophthisis
hemopiezometer
hemoplasmodium
hemoplastic
hemopneumothorax
hemopod
hemopoiesis
hemopoietic
hemoproctia
hemoptoe
hemoptysis
hemopyrrole
hemorrhage
hemorrhagic
hemorrhagin
hemorrhea
hemorrhodin
hemorrhoid
hemorrhoidal
hemorrhoidectomy
hemosalpinx
hemoscope
hemoscopy
hemosiderin
hemosiderosis
hemospasia
hemospastic
hemospermia
hemosporid
hemosporidian
hemostasia
hemostasis
hemostat
hemostatic
hemotachometer
hemotherapeutics
hemotherapy
hemothorax
hemotoxic
hemotoxin
hemotrophe
hemotropic
hemozoon
hemp
hempbush
hempen
hemplike
hempseed
hempstring
hempweed
hempwort
hempy
hemstitch
hemstitcher
hen
henad
henbane
henbill
henbit
hence
henceforth
henceforward
henceforwards
henchboy
henchman
henchmanship
hencoop
hencote
hend
hendecacolic
hendecagon
hendecagonal
hendecahedron
hendecane
hendecasemic
hendecasyllabic
hendecasyllable
hendecatoic
hendecoic
hendecyl
hendiadys
hendly
hendness
heneicosane
henequen
henfish
henhearted
henhouse
henhussy
henism
henlike
henmoldy
henna
Hennebique
hennery
hennin
hennish
henny
henogeny
henotheism
henotheist
henotheistic
henotic
henpeck
henpen
Henrician
Henrietta
henroost
Henry
henry
hent
Hentenian
henter
hentriacontane
henware
henwife
henwise
henwoodite
henyard
heortological
heortologion
heortology
hep
hepar
heparin
heparinize
hepatalgia
hepatatrophia
hepatatrophy
hepatauxe
hepatectomy
hepatic
Hepatica
hepatica
Hepaticae
hepatical
hepaticoduodenostomy
hepaticoenterostomy
hepaticogastrostomy
hepaticologist
hepaticology
hepaticopulmonary
hepaticostomy
hepaticotomy
hepatite
hepatitis
hepatization
hepatize
hepatocele
hepatocirrhosis
hepatocolic
hepatocystic
hepatoduodenal
hepatoduodenostomy
hepatodynia
hepatodysentery
hepatoenteric
hepatoflavin
hepatogastric
hepatogenic
hepatogenous
hepatography
hepatoid
hepatolenticular
hepatolith
hepatolithiasis
hepatolithic
hepatological
hepatologist
hepatology
hepatolysis
hepatolytic
hepatoma
hepatomalacia
hepatomegalia
hepatomegaly
hepatomelanosis
hepatonephric
hepatopathy
hepatoperitonitis
hepatopexia
hepatopexy
hepatophlebitis
hepatophlebotomy
hepatophyma
hepatopneumonic
hepatoportal
hepatoptosia
hepatoptosis
hepatopulmonary
hepatorenal
hepatorrhagia
hepatorrhaphy
hepatorrhea
hepatorrhexis
hepatorrhoea
hepatoscopy
hepatostomy
hepatotherapy
hepatotomy
hepatotoxemia
hepatoumbilical
hepcat
Hephaesteum
Hephaestian
Hephaestic
Hephaestus
hephthemimer
hephthemimeral
hepialid
Hepialidae
Hepialus
heppen
hepper
heptacapsular
heptace
heptachord
heptachronous
heptacolic
heptacosane
heptad
heptadecane
heptadecyl
heptaglot
heptagon
heptagonal
heptagynous
heptahedral
heptahedrical
heptahedron
heptahexahedral
heptahydrate
heptahydrated
heptahydric
heptahydroxy
heptal
heptameride
Heptameron
heptamerous
heptameter
heptamethylene
heptametrical
heptanaphthene
Heptanchus
heptandrous
heptane
Heptanesian
heptangular
heptanoic
heptanone
heptapetalous
heptaphyllous
heptaploid
heptaploidy
heptapodic
heptapody
heptarch
heptarchal
heptarchic
heptarchical
heptarchist
heptarchy
heptasemic
heptasepalous
heptaspermous
heptastich
heptastrophic
heptastylar
heptastyle
heptasulphide
heptasyllabic
Heptateuch
heptatomic
heptatonic
Heptatrema
heptavalent
heptene
hepteris
heptine
heptite
heptitol
heptoic
heptorite
heptose
heptoxide
Heptranchias
heptyl
heptylene
heptylic
heptyne
her
Heraclean
Heracleidan
Heracleonite
Heracleopolitan
Heracleopolite
Heracleum
Heraclid
Heraclidae
Heraclidan
Heraclitean
Heracliteanism
Heraclitic
Heraclitical
Heraclitism
Herakles
herald
heraldess
heraldic
heraldical
heraldically
heraldist
heraldize
heraldress
heraldry
heraldship
herapathite
Herat
Herb
herb
herbaceous
herbaceously
herbage
herbaged
herbager
herbagious
herbal
herbalism
herbalist
herbalize
herbane
herbaria
herbarial
herbarian
herbarism
herbarist
herbarium
herbarize
Herbartian
Herbartianism
herbary
Herbert
herbescent
herbicidal
herbicide
herbicolous
herbiferous
herbish
herbist
Herbivora
herbivore
herbivority
herbivorous
herbless
herblet
herblike
herbman
herborist
herborization
herborize
herborizer
herbose
herbosity
herbous
herbwife
herbwoman
herby
hercogamous
hercogamy
Herculanean
Herculanensian
Herculanian
Herculean
Hercules
Herculid
Hercynian
hercynite
herd
herdbook
herdboy
herder
herderite
herdic
herding
herdship
herdsman
herdswoman
herdwick
here
hereabout
hereadays
hereafter
hereafterward
hereamong
hereat
hereaway
hereaways
herebefore
hereby
heredipetous
heredipety
hereditability
hereditable
hereditably
hereditament
hereditarian
hereditarianism
hereditarily
hereditariness
hereditarist
hereditary
hereditation
hereditative
hereditism
hereditist
hereditivity
heredity
heredium
heredofamilial
heredolues
heredoluetic
heredosyphilis
heredosyphilitic
heredosyphilogy
heredotuberculosis
Hereford
herefrom
heregeld
herein
hereinabove
hereinafter
hereinbefore
hereinto
herem
hereness
hereniging
hereof
hereon
hereright
Herero
heresiarch
heresimach
heresiographer
heresiography
heresiologer
heresiologist
heresiology
heresy
heresyphobia
heresyproof
heretic
heretical
heretically
hereticalness
hereticate
heretication
hereticator
hereticide
hereticize
hereto
heretoch
heretofore
heretoforetime
heretoga
heretrix
hereunder
hereunto
hereupon
hereward
herewith
herewithal
herile
heriot
heriotable
herisson
heritability
heritable
heritably
heritage
heritance
Heritiera
heritor
heritress
heritrix
herl
herling
herma
hermaean
hermaic
Herman
hermaphrodite
hermaphroditic
hermaphroditical
hermaphroditically
hermaphroditish
hermaphroditism
hermaphroditize
Hermaphroditus
hermeneut
hermeneutic
hermeneutical
hermeneutically
hermeneutics
hermeneutist
Hermes
Hermesian
Hermesianism
Hermetic
hermetic
hermetical
hermetically
hermeticism
Hermetics
Hermetism
Hermetist
hermidin
Herminone
Hermione
Hermit
hermit
hermitage
hermitary
hermitess
hermitic
hermitical
hermitically
hermitish
hermitism
hermitize
hermitry
hermitship
Hermo
hermodact
hermodactyl
Hermogenian
hermoglyphic
hermoglyphist
hermokopid
hern
Hernandia
Hernandiaceae
hernandiaceous
hernanesell
hernani
hernant
herne
hernia
hernial
Herniaria
herniarin
herniary
herniate
herniated
herniation
hernioenterotomy
hernioid
herniology
herniopuncture
herniorrhaphy
herniotome
herniotomist
herniotomy
hero
heroarchy
Herodian
herodian
Herodianic
Herodii
Herodiones
herodionine
heroess
herohead
herohood
heroic
heroical
heroically
heroicalness
heroicity
heroicly
heroicness
heroicomic
heroicomical
heroid
Heroides
heroify
Heroin
heroin
heroine
heroineship
heroinism
heroinize
heroism
heroistic
heroization
heroize
herolike
heromonger
heron
heroner
heronite
heronry
heroogony
heroologist
heroology
Herophile
Herophilist
heroship
herotheism
herpes
Herpestes
Herpestinae
herpestine
herpetic
herpetiform
herpetism
herpetography
herpetoid
herpetologic
herpetological
herpetologically
herpetologist
herpetology
herpetomonad
Herpetomonas
herpetophobia
herpetotomist
herpetotomy
herpolhode
Herpotrichia
herrengrundite
Herrenvolk
herring
herringbone
herringer
Herrnhuter
hers
Herschelian
herschelite
herse
hersed
herself
hership
hersir
hertz
hertzian
Heruli
Herulian
Hervati
Herve
Herzegovinian
Hesiodic
Hesione
Hesionidae
hesitance
hesitancy
hesitant
hesitantly
hesitate
hesitater
hesitating
hesitatingly
hesitatingness
hesitation
hesitative
hesitatively
hesitatory
Hesper
Hespera
Hesperia
Hesperian
Hesperic
Hesperid
hesperid
hesperidate
hesperidene
hesperideous
Hesperides
Hesperidian
hesperidin
hesperidium
hesperiid
Hesperiidae
hesperinon
Hesperis
hesperitin
Hesperornis
Hesperornithes
hesperornithid
Hesperornithiformes
hesperornithoid
Hesperus
Hessian
hessite
hessonite
hest
Hester
hestern
hesternal
Hesther
hesthogenous
Hesychasm
Hesychast
hesychastic
het
hetaera
hetaeria
hetaeric
hetaerism
Hetaerist
hetaerist
hetaeristic
hetaerocracy
hetaerolite
hetaery
heteradenia
heteradenic
heterakid
Heterakis
Heteralocha
heterandrous
heterandry
heteratomic
heterauxesis
heteraxial
heteric
heterically
hetericism
hetericist
heterism
heterization
heterize
hetero
heteroagglutinin
heteroalbumose
heteroauxin
heteroblastic
heteroblastically
heteroblasty
heterocarpism
heterocarpous
Heterocarpus
heterocaseose
heterocellular
heterocentric
heterocephalous
Heterocera
heterocerc
heterocercal
heterocercality
heterocercy
heterocerous
heterochiral
heterochlamydeous
Heterochloridales
heterochromatic
heterochromatin
heterochromatism
heterochromatization
heterochromatized
heterochrome
heterochromia
heterochromic
heterochromosome
heterochromous
heterochromy
heterochronic
heterochronism
heterochronistic
heterochronous
heterochrony
heterochrosis
heterochthon
heterochthonous
heterocline
heteroclinous
heteroclital
heteroclite
heteroclitica
heteroclitous
Heterocoela
heterocoelous
Heterocotylea
heterocycle
heterocyclic
heterocyst
heterocystous
heterodactyl
Heterodactylae
heterodactylous
Heterodera
Heterodon
heterodont
Heterodonta
Heterodontidae
heterodontism
heterodontoid
Heterodontus
heterodox
heterodoxal
heterodoxical
heterodoxly
heterodoxness
heterodoxy
heterodromous
heterodromy
heterodyne
heteroecious
heteroeciously
heteroeciousness
heteroecism
heteroecismal
heteroecy
heteroepic
heteroepy
heteroerotic
heteroerotism
heterofermentative
heterofertilization
heterogalactic
heterogamete
heterogametic
heterogametism
heterogamety
heterogamic
heterogamous
heterogamy
heterogangliate
heterogen
heterogene
heterogeneal
heterogenean
heterogeneity
heterogeneous
heterogeneously
heterogeneousness
heterogenesis
heterogenetic
heterogenic
heterogenicity
heterogenist
heterogenous
heterogeny
heteroglobulose
heterognath
Heterognathi
heterogone
heterogonism
heterogonous
heterogonously
heterogony
heterograft
heterographic
heterographical
heterography
Heterogyna
heterogynal
heterogynous
heteroicous
heteroimmune
heteroinfection
heteroinoculable
heteroinoculation
heterointoxication
heterokaryon
heterokaryosis
heterokaryotic
heterokinesis
heterokinetic
Heterokontae
heterokontan
heterolalia
heterolateral
heterolecithal
heterolith
heterolobous
heterologic
heterological
heterologically
heterologous
heterology
heterolysin
heterolysis
heterolytic
heteromallous
heteromastigate
heteromastigote
Heteromeles
Heteromera
heteromeral
Heteromeran
Heteromeri
heteromeric
heteromerous
Heterometabola
heterometabole
heterometabolic
heterometabolism
heterometabolous
heterometaboly
heterometric
Heteromi
Heteromita
Heteromorpha
Heteromorphae
heteromorphic
heteromorphism
heteromorphite
heteromorphosis
heteromorphous
heteromorphy
Heteromya
Heteromyaria
heteromyarian
Heteromyidae
Heteromys
heteronereid
heteronereis
Heteroneura
heteronomous
heteronomously
heteronomy
heteronuclear
heteronym
heteronymic
heteronymous
heteronymously
heteronymy
heteroousia
Heteroousian
heteroousian
Heteroousiast
heteroousious
heteropathic
heteropathy
heteropelmous
heteropetalous
Heterophaga
Heterophagi
heterophagous
heterophasia
heterophemism
heterophemist
heterophemistic
heterophemize
heterophemy
heterophile
heterophoria
heterophoric
heterophylesis
heterophyletic
heterophyllous
heterophylly
heterophyly
heterophyte
heterophytic
Heteropia
Heteropidae
heteroplasia
heteroplasm
heteroplastic
heteroplasty
heteroploid
heteroploidy
heteropod
Heteropoda
heteropodal
heteropodous
heteropolar
heteropolarity
heteropoly
heteroproteide
heteroproteose
heteropter
Heteroptera
heteropterous
heteroptics
heteropycnosis
Heterorhachis
heteroscope
heteroscopy
heterosexual
heterosexuality
heteroside
Heterosiphonales
heterosis
Heterosomata
Heterosomati
heterosomatous
heterosome
Heterosomi
heterosomous
Heterosporeae
heterosporic
Heterosporium
heterosporous
heterospory
heterostatic
heterostemonous
Heterostraca
heterostracan
Heterostraci
heterostrophic
heterostrophous
heterostrophy
heterostyled
heterostylism
heterostylous
heterostyly
heterosuggestion
heterosyllabic
heterotactic
heterotactous
heterotaxia
heterotaxic
heterotaxis
heterotaxy
heterotelic
heterothallic
heterothallism
heterothermal
heterothermic
heterotic
heterotopia
heterotopic
heterotopism
heterotopous
heterotopy
heterotransplant
heterotransplantation
heterotrich
Heterotricha
Heterotrichales
Heterotrichida
heterotrichosis
heterotrichous
heterotropal
heterotroph
heterotrophic
heterotrophy
heterotropia
heterotropic
heterotropous
heterotype
heterotypic
heterotypical
heteroxanthine
heteroxenous
heterozetesis
heterozygosis
heterozygosity
heterozygote
heterozygotic
heterozygous
heterozygousness
hething
hetman
hetmanate
hetmanship
hetter
hetterly
Hettie
Hetty
heuau
Heuchera
heugh
heulandite
heumite
heuretic
heuristic
heuristically
Hevea
hevi
hew
hewable
hewel
hewer
hewettite
hewhall
hewn
hewt
hex
hexa
hexabasic
Hexabiblos
hexabiose
hexabromide
hexacanth
hexacanthous
hexacapsular
hexacarbon
hexace
hexachloride
hexachlorocyclohexane
hexachloroethane
hexachord
hexachronous
hexacid
hexacolic
Hexacoralla
hexacorallan
Hexacorallia
hexacosane
hexacosihedroid
hexact
hexactinal
hexactine
hexactinellid
Hexactinellida
hexactinellidan
hexactinelline
hexactinian
hexacyclic
hexad
hexadactyle
hexadactylic
hexadactylism
hexadactylous
hexadactyly
hexadecahedroid
hexadecane
hexadecanoic
hexadecene
hexadecyl
hexadic
hexadiene
hexadiyne
hexafoil
hexaglot
hexagon
hexagonal
hexagonally
hexagonial
hexagonical
hexagonous
hexagram
Hexagrammidae
hexagrammoid
Hexagrammos
hexagyn
Hexagynia
hexagynian
hexagynous
hexahedral
hexahedron
hexahydrate
hexahydrated
hexahydric
hexahydride
hexahydrite
hexahydrobenzene
hexahydroxy
hexakisoctahedron
hexakistetrahedron
hexameral
hexameric
hexamerism
hexameron
hexamerous
hexameter
hexamethylenamine
hexamethylene
hexamethylenetetramine
hexametral
hexametric
hexametrical
hexametrist
hexametrize
hexametrographer
Hexamita
hexamitiasis
hexammine
hexammino
hexanaphthene
Hexanchidae
Hexanchus
Hexandria
hexandric
hexandrous
hexandry
hexane
hexanedione
hexangular
hexangularly
hexanitrate
hexanitrodiphenylamine
hexapartite
hexaped
hexapetaloid
hexapetaloideous
hexapetalous
hexaphyllous
hexapla
hexaplar
hexaplarian
hexaplaric
hexaploid
hexaploidy
hexapod
Hexapoda
hexapodal
hexapodan
hexapodous
hexapody
hexapterous
hexaradial
hexarch
hexarchy
hexaseme
hexasemic
hexasepalous
hexaspermous
hexastemonous
hexaster
hexastich
hexastichic
hexastichon
hexastichous
hexastichy
hexastigm
hexastylar
hexastyle
hexastylos
hexasulphide
hexasyllabic
hexatetrahedron
Hexateuch
Hexateuchal
hexathlon
hexatomic
hexatriacontane
hexatriose
hexavalent
hexecontane
hexenbesen
hexene
hexer
hexerei
hexeris
hexestrol
hexicological
hexicology
hexine
hexiological
hexiology
hexis
hexitol
hexoctahedral
hexoctahedron
hexode
hexoestrol
hexogen
hexoic
hexokinase
hexone
hexonic
hexosamine
hexosaminic
hexosan
hexose
hexosediphosphoric
hexosemonophosphoric
hexosephosphatase
hexosephosphoric
hexoylene
hexpartite
hexyl
hexylene
hexylic
hexylresorcinol
hexyne
hey
heyday
Hezron
Hezronites
hi
hia
Hianakoto
hiant
hiatal
hiate
hiation
hiatus
Hibbertia
hibbin
hibernacle
hibernacular
hibernaculum
hibernal
hibernate
hibernation
hibernator
Hibernia
Hibernian
Hibernianism
Hibernic
Hibernical
Hibernically
Hibernicism
Hibernicize
Hibernization
Hibernize
Hibernologist
Hibernology
Hibiscus
Hibito
Hibitos
Hibunci
hic
hicatee
hiccup
hick
hickey
hickory
Hicksite
hickwall
Hicoria
hidable
hidage
hidalgism
hidalgo
hidalgoism
hidated
hidation
Hidatsa
hidden
hiddenite
hiddenly
hiddenmost
hiddenness
hide
hideaway
hidebind
hidebound
hideboundness
hided
hideland
hideless
hideling
hideosity
hideous
hideously
hideousness
hider
hidling
hidlings
hidradenitis
hidrocystoma
hidromancy
hidropoiesis
hidrosis
hidrotic
hie
hieder
hielaman
hield
hielmite
hiemal
hiemation
Hienz
Hieracian
Hieracium
hieracosphinx
hierapicra
hierarch
hierarchal
hierarchic
hierarchical
hierarchically
hierarchism
hierarchist
hierarchize
hierarchy
hieratic
hieratical
hieratically
hieraticism
hieratite
Hierochloe
hierocracy
hierocratic
hierocratical
hierodule
hierodulic
Hierofalco
hierogamy
hieroglyph
hieroglypher
hieroglyphic
hieroglyphical
hieroglyphically
hieroglyphist
hieroglyphize
hieroglyphology
hieroglyphy
hierogram
hierogrammat
hierogrammate
hierogrammateus
hierogrammatic
hierogrammatical
hierogrammatist
hierograph
hierographer
hierographic
hierographical
hierography
hierolatry
hierologic
hierological
hierologist
hierology
hieromachy
hieromancy
hieromnemon
hieromonach
hieron
Hieronymic
Hieronymite
hieropathic
hierophancy
hierophant
hierophantes
hierophantic
hierophantically
hierophanticly
hieros
hieroscopy
Hierosolymitan
Hierosolymite
hierurgical
hierurgy
hifalutin
higdon
higgaion
higginsite
higgle
higglehaggle
higgler
higglery
high
highball
highbelia
highbinder
highborn
highboy
highbred
higher
highermost
highest
highfalutin
highfaluting
highfalutinism
highflying
highhanded
highhandedly
highhandedness
highhearted
highheartedly
highheartedness
highish
highjack
highjacker
highland
highlander
highlandish
Highlandman
Highlandry
highlight
highliving
highly
highman
highmoor
highmost
highness
highroad
hight
hightoby
hightop
highway
highwayman
higuero
hijack
hike
hiker
Hilaria
hilarious
hilariously
hilariousness
hilarity
Hilary
Hilarymas
Hilarytide
hilasmic
hilch
Hilda
Hildebrand
Hildebrandian
Hildebrandic
Hildebrandine
Hildebrandism
Hildebrandist
Hildebrandslied
Hildegarde
hilding
hiliferous
hill
Hillary
hillberry
hillbilly
hillculture
hillebrandite
Hillel
hiller
hillet
Hillhousia
hilliness
hillman
hillock
hillocked
hillocky
hillsale
hillsalesman
hillside
hillsman
hilltop
hilltrot
hillward
hillwoman
hilly
hilsa
hilt
hiltless
hilum
hilus
him
Hima
Himalaya
Himalayan
Himantopus
himation
Himawan
himp
himself
himward
himwards
Himyaric
Himyarite
Himyaritic
hin
hinau
Hinayana
hinch
hind
hindberry
hindbrain
hindcast
hinddeck
hinder
hinderance
hinderer
hinderest
hinderful
hinderfully
hinderingly
hinderlands
hinderlings
hinderlins
hinderly
hinderment
hindermost
hindersome
hindhand
hindhead
Hindi
hindmost
hindquarter
hindrance
hindsaddle
hindsight
Hindu
Hinduism
Hinduize
Hindustani
hindward
hing
hinge
hingecorner
hingeflower
hingeless
hingelike
hinger
hingeways
hingle
hinney
hinnible
Hinnites
hinny
hinoid
hinoideous
hinoki
hinsdalite
hint
hintedly
hinter
hinterland
hintingly
hintproof
hintzeite
Hiodon
hiodont
Hiodontidae
hiortdahlite
hip
hipbone
hipe
hiper
hiphalt
hipless
hipmold
Hippa
hippalectryon
hipparch
Hipparion
Hippeastrum
hipped
Hippelates
hippen
Hippia
hippian
hippiater
hippiatric
hippiatrical
hippiatrics
hippiatrist
hippiatry
hippic
Hippidae
Hippidion
Hippidium
hipping
hippish
hipple
hippo
Hippobosca
hippoboscid
Hippoboscidae
hippocamp
hippocampal
hippocampi
hippocampine
hippocampus
Hippocastanaceae
hippocastanaceous
hippocaust
hippocentaur
hippocentauric
hippocerf
hippocoprosterol
hippocras
Hippocratea
Hippocrateaceae
hippocrateaceous
Hippocratian
Hippocratic
Hippocratical
Hippocratism
Hippocrene
Hippocrenian
hippocrepian
hippocrepiform
Hippodamia
hippodamous
hippodrome
hippodromic
hippodromist
hippogastronomy
Hippoglosinae
Hippoglossidae
Hippoglossus
hippogriff
hippogriffin
hippoid
hippolite
hippolith
hippological
hippologist
hippology
Hippolytan
Hippolyte
Hippolytidae
Hippolytus
hippomachy
hippomancy
hippomanes
Hippomedon
hippomelanin
Hippomenes
hippometer
hippometric
hippometry
Hipponactean
hipponosological
hipponosology
hippopathological
hippopathology
hippophagi
hippophagism
hippophagist
hippophagistical
hippophagous
hippophagy
hippophile
hippophobia
hippopod
hippopotami
hippopotamian
hippopotamic
Hippopotamidae
hippopotamine
hippopotamoid
hippopotamus
Hipposelinum
hippotigrine
Hippotigris
hippotomical
hippotomist
hippotomy
hippotragine
Hippotragus
hippurate
hippuric
hippurid
Hippuridaceae
Hippuris
hippurite
Hippurites
hippuritic
Hippuritidae
hippuritoid
hippus
hippy
hipshot
hipwort
hirable
hiragana
Hiram
Hiramite
hircarra
hircine
hircinous
hircocerf
hircocervus
hircosity
hire
hired
hireless
hireling
hireman
Hiren
hirer
hirmologion
hirmos
Hirneola
hiro
Hirofumi
hirondelle
Hirotoshi
Hiroyuki
hirple
hirrient
hirse
hirsel
hirsle
hirsute
hirsuteness
hirsuties
hirsutism
hirsutulous
Hirtella
hirtellous
Hirudin
hirudine
Hirudinea
hirudinean
hirudiniculture
Hirudinidae
hirudinize
hirudinoid
Hirudo
hirundine
Hirundinidae
hirundinous
Hirundo
his
hish
hisingerite
hisn
Hispa
Hispania
Hispanic
Hispanicism
Hispanicize
hispanidad
Hispaniolate
Hispaniolize
Hispanist
Hispanize
Hispanophile
Hispanophobe
hispid
hispidity
hispidulate
hispidulous
Hispinae
hiss
hisser
hissing
hissingly
hissproof
hist
histaminase
histamine
histaminic
histidine
histie
histiocyte
histiocytic
histioid
histiology
Histiophoridae
Histiophorus
histoblast
histochemic
histochemical
histochemistry
histoclastic
histocyte
histodiagnosis
histodialysis
histodialytic
histogen
histogenesis
histogenetic
histogenetically
histogenic
histogenous
histogeny
histogram
histographer
histographic
histographical
histography
histoid
histologic
histological
histologically
histologist
histology
histolysis
histolytic
histometabasis
histomorphological
histomorphologically
histomorphology
histon
histonal
histone
histonomy
histopathologic
histopathological
histopathologist
histopathology
histophyly
histophysiological
histophysiology
Histoplasma
histoplasmin
histoplasmosis
historial
historian
historiated
historic
historical
historically
historicalness
historician
historicism
historicity
historicize
historicocabbalistical
historicocritical
historicocultural
historicodogmatic
historicogeographical
historicophilosophica
historicophysical
historicopolitical
historicoprophetic
historicoreligious
historics
historicus
historied
historier
historiette
historify
historiograph
historiographer
historiographership
historiographic
historiographical
historiographically
historiography
historiological
historiology
historiometric
historiometry
historionomer
historious
historism
historize
history
histotherapist
histotherapy
histotome
histotomy
histotrophic
histotrophy
histotropic
histozoic
histozyme
histrio
Histriobdella
Histriomastix
histrion
histrionic
histrionical
histrionically
histrionicism
histrionism
hit
hitch
hitcher
hitchhike
hitchhiker
hitchily
hitchiness
Hitchiti
hitchproof
hitchy
hithe
hither
hithermost
hitherto
hitherward
Hitlerism
Hitlerite
hitless
Hitoshi
hittable
hitter
Hittite
Hittitics
Hittitology
Hittology
hive
hiveless
hiver
hives
hiveward
Hivite
hizz
Hler
Hlidhskjalf
Hlithskjalf
Hlorrithi
Ho
ho
hoar
hoard
hoarder
hoarding
hoardward
hoarfrost
hoarhead
hoarheaded
hoarhound
hoarily
hoariness
hoarish
hoarness
hoarse
hoarsely
hoarsen
hoarseness
hoarstone
hoarwort
hoary
hoaryheaded
hoast
hoastman
hoatzin
hoax
hoaxee
hoaxer
hoaxproof
hob
hobber
Hobbesian
hobbet
Hobbian
hobbil
Hobbism
Hobbist
Hobbistical
hobble
hobblebush
hobbledehoy
hobbledehoydom
hobbledehoyhood
hobbledehoyish
hobbledehoyishness
hobbledehoyism
hobbledygee
hobbler
hobbling
hobblingly
hobbly
hobby
hobbyhorse
hobbyhorsical
hobbyhorsically
hobbyism
hobbyist
hobbyless
hobgoblin
hoblike
hobnail
hobnailed
hobnailer
hobnob
hobo
hoboism
Hobomoco
hobthrush
hocco
Hochelaga
Hochheimer
hock
Hockday
hockelty
hocker
hocket
hockey
hockshin
Hocktide
hocky
hocus
hod
hodden
hodder
hoddle
hoddy
hodening
hodful
hodgepodge
Hodgkin
hodgkinsonite
hodiernal
hodman
hodmandod
hodograph
hodometer
hodometrical
hoe
hoecake
hoedown
hoeful
hoer
hoernesite
Hoffmannist
Hoffmannite
hog
hoga
hogan
Hogarthian
hogback
hogbush
hogfish
hogframe
hogged
hogger
hoggerel
hoggery
hogget
hoggie
hoggin
hoggish
hoggishly
hoggishness
hoggism
hoggy
hogherd
hoghide
hoghood
hoglike
hogling
hogmace
hogmanay
Hogni
hognose
hognut
hogpen
hogreeve
hogrophyte
hogshead
hogship
hogshouther
hogskin
hogsty
hogward
hogwash
hogweed
hogwort
hogyard
Hohe
Hohenzollern
Hohenzollernism
Hohn
Hohokam
hoi
hoick
hoin
hoise
hoist
hoistaway
hoister
hoisting
hoistman
hoistway
hoit
hoju
Hokan
hokey
hokeypokey
hokum
holagogue
holarctic
holard
holarthritic
holarthritis
holaspidean
holcad
holcodont
Holconoti
Holcus
hold
holdable
holdall
holdback
holden
holdenite
holder
holdership
holdfast
holdfastness
holding
holdingly
holdout
holdover
holdsman
holdup
hole
holeable
Holectypina
holectypoid
holeless
holeman
holeproof
holer
holethnic
holethnos
holewort
holey
holia
holiday
holidayer
holidayism
holidaymaker
holidaymaking
holily
holiness
holing
holinight
holism
holistic
holistically
holl
holla
hollaite
Holland
hollandaise
Hollander
Hollandish
hollandite
Hollands
Hollantide
holler
hollin
holliper
hollo
hollock
hollong
hollow
hollower
hollowfaced
hollowfoot
hollowhearted
hollowheartedness
hollowly
hollowness
holluschick
Holly
holly
hollyhock
Hollywood
Hollywooder
Hollywoodize
holm
holmberry
holmgang
holmia
holmic
holmium
holmos
holobaptist
holobenthic
holoblastic
holoblastically
holobranch
holocaine
holocarpic
holocarpous
holocaust
holocaustal
holocaustic
Holocene
holocentrid
Holocentridae
holocentroid
Holocentrus
Holocephala
holocephalan
Holocephali
holocephalian
holocephalous
Holochoanites
holochoanitic
holochoanoid
Holochoanoida
holochoanoidal
holochordate
holochroal
holoclastic
holocrine
holocryptic
holocrystalline
holodactylic
holodedron
Holodiscus
hologamous
hologamy
hologastrula
hologastrular
Holognatha
holognathous
hologonidium
holograph
holographic
holographical
holohedral
holohedric
holohedrism
holohemihedral
holohyaline
holomastigote
Holometabola
holometabole
holometabolian
holometabolic
holometabolism
holometabolous
holometaboly
holometer
holomorph
holomorphic
holomorphism
holomorphosis
holomorphy
Holomyaria
holomyarian
Holomyarii
holoparasite
holoparasitic
Holophane
holophane
holophotal
holophote
holophotometer
holophrase
holophrasis
holophrasm
holophrastic
holophyte
holophytic
holoplankton
holoplanktonic
holoplexia
holopneustic
holoproteide
holoptic
holoptychian
holoptychiid
Holoptychiidae
Holoptychius
holoquinoid
holoquinoidal
holoquinonic
holoquinonoid
holorhinal
holosaprophyte
holosaprophytic
holosericeous
holoside
holosiderite
Holosiphona
holosiphonate
Holosomata
holosomatous
holospondaic
holostean
Holostei
holosteous
holosteric
Holosteum
Holostomata
holostomate
holostomatous
holostome
holostomous
holostylic
holosymmetric
holosymmetrical
holosymmetry
holosystematic
holosystolic
holothecal
holothoracic
Holothuria
holothurian
Holothuridea
holothurioid
Holothurioidea
holotonia
holotonic
holotony
holotrich
Holotricha
holotrichal
Holotrichida
holotrichous
holotype
holour
holozoic
Holstein
holster
holstered
holt
holy
holyday
holyokeite
holystone
holytide
homage
homageable
homager
Homalocenchrus
homalogonatous
homalographic
homaloid
homaloidal
Homalonotus
Homalopsinae
Homaloptera
Homalopterous
homalosternal
Homalosternii
Homam
Homaridae
homarine
homaroid
Homarus
homatomic
homaxial
homaxonial
homaxonic
Homburg
home
homebody
homeborn
homebound
homebred
homecomer
homecraft
homecroft
homecrofter
homecrofting
homefarer
homefelt
homegoer
homekeeper
homekeeping
homeland
homelander
homeless
homelessly
homelessness
homelet
homelike
homelikeness
homelily
homeliness
homeling
homely
homelyn
homemade
homemaker
homemaking
homeoblastic
homeochromatic
homeochromatism
homeochronous
homeocrystalline
homeogenic
homeogenous
homeoid
homeoidal
homeoidality
homeokinesis
homeokinetic
homeomerous
homeomorph
homeomorphic
homeomorphism
homeomorphous
homeomorphy
homeopath
homeopathic
homeopathically
homeopathician
homeopathicity
homeopathist
homeopathy
homeophony
homeoplasia
homeoplastic
homeoplasy
homeopolar
homeosis
homeostasis
homeostatic
homeotic
homeotransplant
homeotransplantation
homeotype
homeotypic
homeotypical
homeowner
homeozoic
Homer
homer
Homerian
Homeric
Homerical
Homerically
Homerid
Homeridae
Homeridian
Homerist
Homerologist
Homerology
Homeromastix
homeseeker
homesick
homesickly
homesickness
homesite
homesome
homespun
homestall
homestead
homesteader
homester
homestretch
homeward
homewardly
homework
homeworker
homewort
homey
homeyness
homicidal
homicidally
homicide
homicidious
homiculture
homilete
homiletic
homiletical
homiletically
homiletics
homiliarium
homiliary
homilist
homilite
homilize
homily
hominal
hominess
Hominian
hominid
Hominidae
hominiform
hominify
hominine
hominisection
hominivorous
hominoid
hominy
homish
homishness
homo
homoanisaldehyde
homoanisic
homoarecoline
homobaric
homoblastic
homoblasty
homocarpous
homocategoric
homocentric
homocentrical
homocentrically
homocerc
homocercal
homocercality
homocercy
homocerebrin
homochiral
homochlamydeous
homochromatic
homochromatism
homochrome
homochromic
homochromosome
homochromous
homochromy
homochronous
homoclinal
homocline
Homocoela
homocoelous
homocreosol
homocyclic
homodermic
homodermy
homodont
homodontism
homodox
homodoxian
homodromal
homodrome
homodromous
homodromy
homodynamic
homodynamous
homodynamy
homodyne
Homoean
Homoeanism
homoecious
homoeoarchy
homoeoblastic
homoeochromatic
homoeochronous
homoeocrystalline
homoeogenic
homoeogenous
homoeography
homoeokinesis
homoeomerae
Homoeomeri
homoeomeria
homoeomerian
homoeomerianism
homoeomeric
homoeomerical
homoeomerous
homoeomery
homoeomorph
homoeomorphic
homoeomorphism
homoeomorphous
homoeomorphy
homoeopath
homoeopathic
homoeopathically
homoeopathician
homoeopathicity
homoeopathist
homoeopathy
homoeophony
homoeophyllous
homoeoplasia
homoeoplastic
homoeoplasy
homoeopolar
homoeosis
homoeotel
homoeoteleutic
homoeoteleuton
homoeotic
homoeotopy
homoeotype
homoeotypic
homoeotypical
homoeozoic
homoerotic
homoerotism
homofermentative
homogametic
homogamic
homogamous
homogamy
homogangliate
homogen
homogenate
homogene
homogeneal
homogenealness
homogeneate
homogeneity
homogeneization
homogeneize
homogeneous
homogeneously
homogeneousness
homogenesis
homogenetic
homogenetical
homogenic
homogenization
homogenize
homogenizer
homogenous
homogentisic
homogeny
homoglot
homogone
homogonous
homogonously
homogony
homograft
homograph
homographic
homography
homohedral
homoiotherm
homoiothermal
homoiothermic
homoiothermism
homoiothermous
homoiousia
Homoiousian
homoiousian
Homoiousianism
homoiousious
homolateral
homolecithal
homolegalis
homologate
homologation
homologic
homological
homologically
homologist
homologize
homologizer
homologon
homologoumena
homologous
homolographic
homolography
homologue
homology
homolosine
homolysin
homolysis
homomallous
homomeral
homomerous
homometrical
homometrically
homomorph
Homomorpha
homomorphic
homomorphism
homomorphosis
homomorphous
homomorphy
Homoneura
homonomous
homonomy
homonuclear
homonym
homonymic
homonymous
homonymously
homonymy
homoousia
Homoousian
Homoousianism
Homoousianist
Homoousiast
Homoousion
homoousious
homopathy
homoperiodic
homopetalous
homophene
homophenous
homophone
homophonic
homophonous
homophony
homophthalic
homophylic
homophyllous
homophyly
homopiperonyl
homoplasis
homoplasmic
homoplasmy
homoplast
homoplastic
homoplasy
homopolar
homopolarity
homopolic
homopter
Homoptera
homopteran
homopteron
homopterous
Homorelaps
homorganic
homoseismal
homosexual
homosexualism
homosexualist
homosexuality
homosporous
homospory
Homosteus
homostyled
homostylic
homostylism
homostylous
homostyly
homosystemic
homotactic
homotatic
homotaxeous
homotaxia
homotaxial
homotaxially
homotaxic
homotaxis
homotaxy
homothallic
homothallism
homothetic
homothety
homotonic
homotonous
homotonously
homotony
homotopic
homotransplant
homotransplantation
homotropal
homotropous
homotypal
homotype
homotypic
homotypical
homotypy
homovanillic
homovanillin
homoveratric
homoveratrole
homozygosis
homozygosity
homozygote
homozygous
homozygousness
homrai
homuncle
homuncular
homunculus
homy
Hon
honda
hondo
Honduran
Honduranean
Honduranian
Hondurean
Hondurian
hone
honest
honestly
honestness
honestone
honesty
honewort
honey
honeybee
honeyberry
honeybind
honeyblob
honeybloom
honeycomb
honeycombed
honeydew
honeydewed
honeydrop
honeyed
honeyedly
honeyedness
honeyfall
honeyflower
honeyfogle
honeyful
honeyhearted
honeyless
honeylike
honeylipped
honeymoon
honeymooner
honeymoonlight
honeymoonshine
honeymoonstruck
honeymoony
honeymouthed
honeypod
honeypot
honeystone
honeysuck
honeysucker
honeysuckle
honeysuckled
honeysweet
honeyware
Honeywood
honeywood
honeywort
hong
honied
honily
honk
honker
honor
Honora
honorability
honorable
honorableness
honorableship
honorably
honorance
honoraria
honorarily
honorarium
honorary
honoree
honorer
honoress
honorific
honorifically
honorless
honorous
honorsman
honorworthy
hontish
hontous
Honzo
hooch
hoochinoo
hood
hoodcap
hooded
hoodedness
hoodful
hoodie
hoodless
hoodlike
hoodlum
hoodlumish
hoodlumism
hoodlumize
hoodman
hoodmold
hoodoo
hoodsheaf
hoodshy
hoodshyness
hoodwink
hoodwinkable
hoodwinker
hoodwise
hoodwort
hooey
hoof
hoofbeat
hoofbound
hoofed
hoofer
hoofiness
hoofish
hoofless
hooflet
hooflike
hoofmark
hoofprint
hoofrot
hoofs
hoofworm
hoofy
hook
hookah
hookaroon
hooked
hookedness
hookedwise
hooker
Hookera
hookerman
hookers
hookheal
hookish
hookless
hooklet
hooklike
hookmaker
hookmaking
hookman
hooknose
hooksmith
hooktip
hookum
hookup
hookweed
hookwise
hookworm
hookwormer
hookwormy
hooky
hooligan
hooliganism
hooliganize
hoolock
hooly
hoon
hoonoomaun
hoop
hooped
hooper
hooping
hoopla
hoople
hoopless
hooplike
hoopmaker
hoopman
hoopoe
hoopstick
hoopwood
hoose
hoosegow
hoosh
Hoosier
Hoosierdom
Hoosierese
Hoosierize
hoot
hootay
hooter
hootingly
hoove
hooven
Hooverism
Hooverize
hoovey
hop
hopbine
hopbush
Hopcalite
hopcrease
hope
hoped
hopeful
hopefully
hopefulness
hopeite
hopeless
hopelessly
hopelessness
hoper
Hopi
hopi
hopingly
Hopkinsian
Hopkinsianism
Hopkinsonian
hoplite
hoplitic
hoplitodromos
Hoplocephalus
hoplology
hoplomachic
hoplomachist
hoplomachos
hoplomachy
Hoplonemertea
hoplonemertean
hoplonemertine
Hoplonemertini
hopoff
hopped
hopper
hopperburn
hopperdozer
hopperette
hoppergrass
hopperings
hopperman
hoppers
hoppestere
hoppet
hoppingly
hoppity
hopple
hoppy
hopscotch
hopscotcher
hoptoad
hopvine
hopyard
hora
horal
horary
Horatian
Horatio
Horatius
horbachite
hordarian
hordary
horde
hordeaceous
hordeiform
hordein
hordenine
Hordeum
horehound
Horim
horismology
horizometer
horizon
horizonless
horizontal
horizontalism
horizontality
horizontalization
horizontalize
horizontally
horizontalness
horizontic
horizontical
horizontically
horizonward
horme
hormic
hormigo
hormion
hormist
hormogon
Hormogonales
Hormogoneae
Hormogoneales
hormogonium
hormogonous
hormonal
hormone
hormonic
hormonize
hormonogenesis
hormonogenic
hormonology
hormonopoiesis
hormonopoietic
hormos
horn
hornbeam
hornbill
hornblende
hornblendic
hornblendite
hornblendophyre
hornblower
hornbook
horned
hornedness
horner
hornerah
hornet
hornety
hornfair
hornfels
hornfish
hornful
horngeld
Hornie
hornify
hornily
horniness
horning
hornish
hornist
hornito
hornless
hornlessness
hornlet
hornlike
hornotine
hornpipe
hornplant
hornsman
hornstay
hornstone
hornswoggle
horntail
hornthumb
horntip
hornwood
hornwork
hornworm
hornwort
horny
hornyhanded
hornyhead
horograph
horographer
horography
horokaka
horologe
horologer
horologic
horological
horologically
horologiography
horologist
horologium
horologue
horology
horometrical
horometry
Horonite
horopito
horopter
horopteric
horoptery
horoscopal
horoscope
horoscoper
horoscopic
horoscopical
horoscopist
horoscopy
Horouta
horrendous
horrendously
horrent
horrescent
horreum
horribility
horrible
horribleness
horribly
horrid
horridity
horridly
horridness
horrific
horrifically
horrification
horrify
horripilant
horripilate
horripilation
horrisonant
horror
horrorful
horrorish
horrorist
horrorize
horrormonger
horrormongering
horrorous
horrorsome
horse
horseback
horsebacker
horseboy
horsebreaker
horsecar
horsecloth
horsecraft
horsedom
horsefair
horsefettler
horsefight
horsefish
horseflesh
horsefly
horsefoot
horsegate
horsehair
horsehaired
horsehead
horseherd
horsehide
horsehood
horsehoof
horsejockey
horsekeeper
horselaugh
horselaugher
horselaughter
horseleech
horseless
horselike
horseload
horseman
horsemanship
horsemastership
horsemint
horsemonger
horseplay
horseplayful
horsepond
horsepower
horsepox
horser
horseshoe
horseshoer
horsetail
horsetongue
Horsetown
horsetree
horseway
horseweed
horsewhip
horsewhipper
horsewoman
horsewomanship
horsewood
horsfordite
horsify
horsily
horsiness
horsing
Horst
horst
horsy
horsyism
hortation
hortative
hortatively
hortator
hortatorily
hortatory
Hortense
Hortensia
hortensial
Hortensian
hortensian
horticultural
horticulturally
horticulture
horticulturist
hortite
hortonolite
hortulan
Horvatian
hory
Hosackia
hosanna
hose
hosed
hosel
hoseless
hoselike
hoseman
hosier
hosiery
hosiomartyr
hospice
hospitable
hospitableness
hospitably
hospitage
hospital
hospitalary
hospitaler
hospitalism
hospitality
hospitalization
hospitalize
hospitant
hospitate
hospitation
hospitator
hospitious
hospitium
hospitize
hospodar
hospodariat
hospodariate
host
Hosta
hostage
hostager
hostageship
hostel
hosteler
hostelry
hoster
hostess
hostie
hostile
hostilely
hostileness
hostility
hostilize
hosting
hostler
hostlership
hostlerwife
hostless
hostly
hostry
hostship
hot
hotbed
hotblood
hotbox
hotbrained
hotch
hotchpot
hotchpotch
hotchpotchly
hotel
hoteldom
hotelhood
hotelier
hotelization
hotelize
hotelkeeper
hotelless
hotelward
hotfoot
hothead
hotheaded
hotheadedly
hotheadedness
hothearted
hotheartedly
hotheartedness
hothouse
hoti
hotly
hotmouthed
hotness
hotspur
hotspurred
Hotta
Hottentot
Hottentotese
Hottentotic
Hottentotish
Hottentotism
hotter
hottery
hottish
Hottonia
houbara
Houdan
hough
houghband
hougher
houghite
houghmagandy
Houghton
hounce
hound
hounder
houndfish
hounding
houndish
houndlike
houndman
houndsbane
houndsberry
houndshark
houndy
houppelande
hour
hourful
hourglass
houri
hourless
hourly
housage
housal
Housatonic
house
houseball
houseboat
houseboating
housebote
housebound
houseboy
housebreak
housebreaker
housebreaking
housebroke
housebroken
housebug
housebuilder
housebuilding
housecarl
housecoat
housecraft
housefast
housefather
housefly
houseful
housefurnishings
household
householder
householdership
householding
householdry
housekeep
housekeeper
housekeeperlike
housekeeperly
housekeeping
housel
houseleek
houseless
houselessness
houselet
houseline
houseling
housemaid
housemaidenly
housemaiding
housemaidy
houseman
housemaster
housemastership
housemate
housemating
houseminder
housemistress
housemother
housemotherly
houseowner
houser
houseridden
houseroom
housesmith
housetop
houseward
housewares
housewarm
housewarmer
housewarming
housewear
housewife
housewifeliness
housewifely
housewifery
housewifeship
housewifish
housewive
housework
housewright
housing
Houstonia
housty
housy
houtou
houvari
Hova
hove
hovedance
hovel
hoveler
hoven
Hovenia
hover
hoverer
hovering
hoveringly
hoverly
how
howadji
Howard
howardite
howbeit
howdah
howder
howdie
howdy
howe
Howea
howel
however
howff
howish
howitzer
howk
howkit
howl
howler
howlet
howling
howlingly
howlite
howso
howsoever
howsomever
hox
hoy
Hoya
hoyden
hoydenhood
hoydenish
hoydenism
hoyle
hoyman
Hrimfaxi
Hrothgar
Hsi
Hsuan
Hu
huaca
huaco
huajillo
huamuchil
huantajayite
huaracho
Huari
huarizo
Huashi
Huastec
Huastecan
Huave
Huavean
hub
hubb
hubba
hubber
Hubbite
hubble
hubbly
hubbub
hubbuboo
hubby
Hubert
hubmaker
hubmaking
hubnerite
hubristic
hubshi
huccatoon
huchen
Huchnom
hucho
huck
huckaback
huckle
huckleback
hucklebacked
huckleberry
hucklebone
huckmuck
huckster
hucksterage
hucksterer
hucksteress
hucksterize
huckstery
hud
huddle
huddledom
huddlement
huddler
huddling
huddlingly
huddock
huddroun
huddup
Hudibras
Hudibrastic
Hudibrastically
Hudsonia
Hudsonian
hudsonite
hue
hued
hueful
hueless
huelessness
huer
Huey
huff
huffier
huffily
huffiness
huffingly
huffish
huffishly
huffishness
huffle
huffler
huffy
hug
huge
Hugelia
hugelite
hugely
hugeness
hugeous
hugeously
hugeousness
huggable
hugger
huggermugger
huggermuggery
Huggin
hugging
huggingly
huggle
Hugh
Hughes
Hughoc
Hugo
Hugoesque
hugsome
Huguenot
Huguenotic
Huguenotism
huh
Hui
huia
huipil
huisache
huiscoyol
huitain
Huk
Hukbalahap
huke
hula
Huldah
huldee
hulk
hulkage
hulking
hulky
hull
hullabaloo
huller
hullock
hulloo
hulotheism
Hulsean
hulsite
hulster
hulu
hulver
hulverhead
hulverheaded
hum
Huma
human
humane
humanely
humaneness
humanhood
humanics
humanification
humaniform
humaniformian
humanify
humanish
humanism
humanist
humanistic
humanistical
humanistically
humanitarian
humanitarianism
humanitarianist
humanitarianize
humanitary
humanitian
humanity
humanitymonger
humanization
humanize
humanizer
humankind
humanlike
humanly
humanness
humanoid
humate
humble
humblebee
humblehearted
humblemouthed
humbleness
humbler
humblie
humblingly
humbly
humbo
humboldtilite
humboldtine
humboldtite
humbug
humbugability
humbugable
humbugger
humbuggery
humbuggism
humbuzz
humdinger
humdrum
humdrumminess
humdrummish
humdrummishness
humdudgeon
Hume
Humean
humect
humectant
humectate
humectation
humective
humeral
humeri
humeroabdominal
humerocubital
humerodigital
humerodorsal
humerometacarpal
humeroradial
humeroscapular
humeroulnar
humerus
humet
humetty
humhum
humic
humicubation
humid
humidate
humidification
humidifier
humidify
humidistat
humidity
humidityproof
humidly
humidness
humidor
humific
humification
humifuse
humify
humiliant
humiliate
humiliating
humiliatingly
humiliation
humiliative
humiliator
humiliatory
humilific
humilitude
humility
humin
Humiria
Humiriaceae
Humiriaceous
Humism
Humist
humistratous
humite
humlie
hummel
hummeler
hummer
hummie
humming
hummingbird
hummock
hummocky
humor
humoral
humoralism
humoralist
humoralistic
humoresque
humoresquely
humorful
humorific
humorism
humorist
humoristic
humoristical
humorize
humorless
humorlessness
humorology
humorous
humorously
humorousness
humorproof
humorsome
humorsomely
humorsomeness
humourful
humous
hump
humpback
humpbacked
humped
humph
Humphrey
humpiness
humpless
humpty
humpy
humstrum
humulene
humulone
Humulus
humus
humuslike
Hun
Hunanese
hunch
Hunchakist
hunchback
hunchbacked
hunchet
hunchy
hundi
hundred
hundredal
hundredary
hundreder
hundredfold
hundredman
hundredpenny
hundredth
hundredweight
hundredwork
hung
Hungaria
Hungarian
hungarite
hunger
hungerer
hungeringly
hungerless
hungerly
hungerproof
hungerweed
hungrify
hungrily
hungriness
hungry
hunh
hunk
Hunker
hunker
Hunkerism
hunkerous
hunkerousness
hunkers
hunkies
Hunkpapa
hunks
hunky
Hunlike
Hunnian
Hunnic
Hunnican
Hunnish
Hunnishness
hunt
huntable
huntedly
Hunter
Hunterian
hunterlike
huntilite
hunting
huntress
huntsman
huntsmanship
huntswoman
Hunyak
hup
Hupa
hupaithric
Hura
hura
hurcheon
hurdies
hurdis
hurdle
hurdleman
hurdler
hurdlewise
hurds
hure
hureaulite
hureek
Hurf
hurgila
hurkle
hurl
hurlbarrow
hurled
hurler
hurley
hurleyhouse
hurling
hurlock
hurly
Huron
huron
Huronian
hurr
hurrah
Hurri
Hurrian
hurricane
hurricanize
hurricano
hurried
hurriedly
hurriedness
hurrier
hurrisome
hurrock
hurroo
hurroosh
hurry
hurryingly
hurryproof
hursinghar
hurst
hurt
hurtable
hurted
hurter
hurtful
hurtfully
hurtfulness
hurting
hurtingest
hurtle
hurtleberry
hurtless
hurtlessly
hurtlessness
hurtlingly
hurtsome
hurty
husband
husbandable
husbandage
husbander
husbandfield
husbandhood
husbandland
husbandless
husbandlike
husbandliness
husbandly
husbandman
husbandress
husbandry
husbandship
huse
hush
hushable
hushaby
hushcloth
hushedly
husheen
hushel
husher
hushful
hushfully
hushing
hushingly
hushion
husho
husk
huskanaw
husked
huskened
husker
huskershredder
huskily
huskiness
husking
huskroot
huskwort
Husky
husky
huso
huspil
huss
hussar
Hussite
Hussitism
hussy
hussydom
hussyness
husting
hustle
hustlecap
hustlement
hustler
hut
hutch
hutcher
hutchet
Hutchinsonian
Hutchinsonianism
hutchinsonite
Huterian
huthold
hutholder
hutia
hutkeeper
hutlet
hutment
Hutsulian
Hutterites
Huttonian
Huttonianism
huttoning
huttonweed
hutukhtu
huvelyk
Huxleian
Huygenian
huzoor
Huzvaresh
huzz
huzza
huzzard
Hwa
Hy
hyacinth
Hyacinthia
hyacinthian
hyacinthine
Hyacinthus
Hyades
hyaena
Hyaenanche
Hyaenarctos
Hyaenidae
Hyaenodon
hyaenodont
hyaenodontoid
Hyakume
hyalescence
hyalescent
hyaline
hyalinization
hyalinize
hyalinocrystalline
hyalinosis
hyalite
hyalitis
hyaloandesite
hyalobasalt
hyalocrystalline
hyalodacite
hyalogen
hyalograph
hyalographer
hyalography
hyaloid
hyaloiditis
hyaloliparite
hyalolith
hyalomelan
hyalomucoid
Hyalonema
hyalophagia
hyalophane
hyalophyre
hyalopilitic
hyaloplasm
hyaloplasma
hyaloplasmic
hyalopsite
hyalopterous
hyalosiderite
Hyalospongia
hyalotekite
hyalotype
hyaluronic
hyaluronidase
Hybanthus
Hybla
Hyblaea
Hyblaean
Hyblan
hybodont
Hybodus
hybosis
hybrid
hybridal
hybridation
hybridism
hybridist
hybridity
hybridizable
hybridization
hybridize
hybridizer
hybridous
hydantoate
hydantoic
hydantoin
hydathode
hydatid
hydatidiform
hydatidinous
hydatidocele
hydatiform
hydatigenous
Hydatina
hydatogenesis
hydatogenic
hydatogenous
hydatoid
hydatomorphic
hydatomorphism
hydatopneumatic
hydatopneumatolytic
hydatopyrogenic
hydatoscopy
Hydnaceae
hydnaceous
hydnocarpate
hydnocarpic
Hydnocarpus
hydnoid
Hydnora
Hydnoraceae
hydnoraceous
Hydnum
Hydra
hydracetin
Hydrachna
hydrachnid
Hydrachnidae
hydracid
hydracoral
hydracrylate
hydracrylic
Hydractinia
hydractinian
Hydradephaga
hydradephagan
hydradephagous
hydragogue
hydragogy
hydramine
hydramnion
hydramnios
Hydrangea
Hydrangeaceae
hydrangeaceous
hydrant
hydranth
hydrarch
hydrargillite
hydrargyrate
hydrargyria
hydrargyriasis
hydrargyric
hydrargyrism
hydrargyrosis
hydrargyrum
hydrarthrosis
hydrarthrus
hydrastine
Hydrastis
hydrate
hydrated
hydration
hydrator
hydratropic
hydraucone
hydraulic
hydraulically
hydraulician
hydraulicity
hydraulicked
hydraulicon
hydraulics
hydraulist
hydraulus
hydrazide
hydrazidine
hydrazimethylene
hydrazine
hydrazino
hydrazo
hydrazoate
hydrazobenzene
hydrazoic
hydrazone
hydrazyl
hydremia
hydremic
hydrencephalocele
hydrencephaloid
hydrencephalus
hydria
hydriatric
hydriatrist
hydriatry
hydric
hydrically
Hydrid
hydride
hydriform
hydrindene
hydriodate
hydriodic
hydriodide
hydriotaphia
Hydriote
hydro
hydroa
hydroadipsia
hydroaeric
hydroalcoholic
hydroaromatic
hydroatmospheric
hydroaviation
hydrobarometer
Hydrobates
Hydrobatidae
hydrobenzoin
hydrobilirubin
hydrobiological
hydrobiologist
hydrobiology
hydrobiosis
hydrobiplane
hydrobomb
hydroboracite
hydroborofluoric
hydrobranchiate
hydrobromate
hydrobromic
hydrobromide
hydrocarbide
hydrocarbon
hydrocarbonaceous
hydrocarbonate
hydrocarbonic
hydrocarbonous
hydrocarbostyril
hydrocardia
Hydrocaryaceae
hydrocaryaceous
hydrocatalysis
hydrocauline
hydrocaulus
hydrocele
hydrocellulose
hydrocephalic
hydrocephalocele
hydrocephaloid
hydrocephalous
hydrocephalus
hydrocephaly
hydroceramic
hydrocerussite
Hydrocharidaceae
hydrocharidaceous
Hydrocharis
Hydrocharitaceae
hydrocharitaceous
Hydrochelidon
hydrochemical
hydrochemistry
hydrochlorate
hydrochlorauric
hydrochloric
hydrochloride
hydrochlorplatinic
hydrochlorplatinous
Hydrochoerus
hydrocholecystis
hydrocinchonine
hydrocinnamic
hydrocirsocele
hydrocladium
hydroclastic
Hydrocleis
hydroclimate
hydrocobalticyanic
hydrocoele
hydrocollidine
hydroconion
Hydrocorallia
Hydrocorallinae
hydrocoralline
Hydrocores
Hydrocorisae
hydrocorisan
hydrocotarnine
Hydrocotyle
hydrocoumaric
hydrocupreine
hydrocyanate
hydrocyanic
hydrocyanide
hydrocycle
hydrocyclic
hydrocyclist
Hydrocyon
hydrocyst
hydrocystic
Hydrodamalidae
Hydrodamalis
Hydrodictyaceae
Hydrodictyon
hydrodrome
Hydrodromica
hydrodromican
hydrodynamic
hydrodynamical
hydrodynamics
hydrodynamometer
hydroeconomics
hydroelectric
hydroelectricity
hydroelectrization
hydroergotinine
hydroextract
hydroextractor
hydroferricyanic
hydroferrocyanate
hydroferrocyanic
hydrofluate
hydrofluoboric
hydrofluoric
hydrofluorid
hydrofluoride
hydrofluosilicate
hydrofluosilicic
hydrofluozirconic
hydrofoil
hydroforming
hydrofranklinite
hydrofuge
hydrogalvanic
hydrogel
hydrogen
hydrogenase
hydrogenate
hydrogenation
hydrogenator
hydrogenic
hydrogenide
hydrogenium
hydrogenization
hydrogenize
hydrogenolysis
Hydrogenomonas
hydrogenous
hydrogeological
hydrogeology
hydroglider
hydrognosy
hydrogode
hydrograph
hydrographer
hydrographic
hydrographical
hydrographically
hydrography
hydrogymnastics
hydrohalide
hydrohematite
hydrohemothorax
hydroid
Hydroida
Hydroidea
hydroidean
hydroiodic
hydrokinetic
hydrokinetical
hydrokinetics
hydrol
hydrolase
hydrolatry
Hydrolea
Hydroleaceae
hydrolize
hydrologic
hydrological
hydrologically
hydrologist
hydrology
hydrolysis
hydrolyst
hydrolyte
hydrolytic
hydrolyzable
hydrolyzate
hydrolyzation
hydrolyze
hydromagnesite
hydromancer
hydromancy
hydromania
hydromaniac
hydromantic
hydromantical
hydromantically
hydrome
hydromechanical
hydromechanics
hydromedusa
Hydromedusae
hydromedusan
hydromedusoid
hydromel
hydromeningitis
hydromeningocele
hydrometallurgical
hydrometallurgically
hydrometallurgy
hydrometamorphism
hydrometeor
hydrometeorological
hydrometeorology
hydrometer
hydrometra
hydrometric
hydrometrical
hydrometrid
Hydrometridae
hydrometry
hydromica
hydromicaceous
hydromonoplane
hydromorph
hydromorphic
hydromorphous
hydromorphy
hydromotor
hydromyelia
hydromyelocele
hydromyoma
Hydromys
hydrone
hydronegative
hydronephelite
hydronephrosis
hydronephrotic
hydronitric
hydronitroprussic
hydronitrous
hydronium
hydroparacoumaric
Hydroparastatae
hydropath
hydropathic
hydropathical
hydropathist
hydropathy
hydropericarditis
hydropericardium
hydroperiod
hydroperitoneum
hydroperitonitis
hydroperoxide
hydrophane
hydrophanous
hydrophid
Hydrophidae
hydrophil
hydrophile
hydrophilic
hydrophilid
Hydrophilidae
hydrophilism
hydrophilite
hydrophiloid
hydrophilous
hydrophily
Hydrophinae
Hydrophis
hydrophobe
hydrophobia
hydrophobic
hydrophobical
hydrophobist
hydrophobophobia
hydrophobous
hydrophoby
hydrophoid
hydrophone
Hydrophora
hydrophoran
hydrophore
hydrophoria
hydrophorous
hydrophthalmia
hydrophthalmos
hydrophthalmus
hydrophylacium
hydrophyll
Hydrophyllaceae
hydrophyllaceous
hydrophylliaceous
hydrophyllium
Hydrophyllum
hydrophysometra
hydrophyte
hydrophytic
hydrophytism
hydrophyton
hydrophytous
hydropic
hydropical
hydropically
hydropigenous
hydroplane
hydroplanula
hydroplatinocyanic
hydroplutonic
hydropneumatic
hydropneumatosis
hydropneumopericardium
hydropneumothorax
hydropolyp
hydroponic
hydroponicist
hydroponics
hydroponist
hydropositive
hydropot
Hydropotes
hydropropulsion
hydrops
hydropsy
Hydropterideae
hydroptic
hydropult
hydropultic
hydroquinine
hydroquinol
hydroquinoline
hydroquinone
hydrorachis
hydrorhiza
hydrorhizal
hydrorrhachis
hydrorrhachitis
hydrorrhea
hydrorrhoea
hydrorubber
hydrosalpinx
hydrosalt
hydrosarcocele
hydroscope
hydroscopic
hydroscopical
hydroscopicity
hydroscopist
hydroselenic
hydroselenide
hydroselenuret
hydroseparation
hydrosilicate
hydrosilicon
hydrosol
hydrosomal
hydrosomatous
hydrosome
hydrosorbic
hydrosphere
hydrospire
hydrospiric
hydrostat
hydrostatic
hydrostatical
hydrostatically
hydrostatician
hydrostatics
hydrostome
hydrosulphate
hydrosulphide
hydrosulphite
hydrosulphocyanic
hydrosulphurated
hydrosulphuret
hydrosulphureted
hydrosulphuric
hydrosulphurous
hydrosulphuryl
hydrotachymeter
hydrotactic
hydrotalcite
hydrotasimeter
hydrotaxis
hydrotechnic
hydrotechnical
hydrotechnologist
hydrotechny
hydroterpene
hydrotheca
hydrothecal
hydrotherapeutic
hydrotherapeutics
hydrotherapy
hydrothermal
hydrothoracic
hydrothorax
hydrotic
hydrotical
hydrotimeter
hydrotimetric
hydrotimetry
hydrotomy
hydrotropic
hydrotropism
hydroturbine
hydrotype
hydrous
hydrovane
hydroxamic
hydroxamino
hydroxide
hydroximic
hydroxy
hydroxyacetic
hydroxyanthraquinone
hydroxybutyricacid
hydroxyketone
hydroxyl
hydroxylactone
hydroxylamine
hydroxylate
hydroxylation
hydroxylic
hydroxylization
hydroxylize
hydrozincite
Hydrozoa
hydrozoal
hydrozoan
hydrozoic
hydrozoon
hydrula
Hydruntine
Hydrurus
Hydrus
hydurilate
hydurilic
hyena
hyenadog
hyenanchin
hyenic
hyeniform
hyenine
hyenoid
hyetal
hyetograph
hyetographic
hyetographical
hyetographically
hyetography
hyetological
hyetology
hyetometer
hyetometrograph
Hygeia
Hygeian
hygeiolatry
hygeist
hygeistic
hygeology
hygiantic
hygiantics
hygiastic
hygiastics
hygieist
hygienal
hygiene
hygienic
hygienical
hygienically
hygienics
hygienist
hygienization
hygienize
hygiologist
hygiology
hygric
hygrine
hygroblepharic
hygrodeik
hygroexpansivity
hygrograph
hygrology
hygroma
hygromatous
hygrometer
hygrometric
hygrometrical
hygrometrically
hygrometry
hygrophaneity
hygrophanous
hygrophilous
hygrophobia
hygrophthalmic
hygrophyte
hygrophytic
hygroplasm
hygroplasma
hygroscope
hygroscopic
hygroscopical
hygroscopically
hygroscopicity
hygroscopy
hygrostat
hygrostatics
hygrostomia
hygrothermal
hygrothermograph
hying
hyke
Hyla
hylactic
hylactism
hylarchic
hylarchical
hyle
hyleg
hylegiacal
hylic
hylicism
hylicist
Hylidae
hylism
hylist
Hyllus
Hylobates
hylobatian
hylobatic
hylobatine
Hylocereus
Hylocichla
Hylocomium
Hylodes
hylogenesis
hylogeny
hyloid
hylology
hylomorphic
hylomorphical
hylomorphism
hylomorphist
hylomorphous
Hylomys
hylopathism
hylopathist
hylopathy
hylophagous
hylotheism
hylotheist
hylotheistic
hylotheistical
hylotomous
hylozoic
hylozoism
hylozoist
hylozoistic
hylozoistically
hymen
Hymenaea
Hymenaeus
Hymenaic
hymenal
hymeneal
hymeneally
hymeneals
hymenean
hymenial
hymenic
hymenicolar
hymeniferous
hymeniophore
hymenium
Hymenocallis
Hymenochaete
Hymenogaster
Hymenogastraceae
hymenogeny
hymenoid
Hymenolepis
hymenomycetal
hymenomycete
Hymenomycetes
hymenomycetoid
hymenomycetous
hymenophore
hymenophorum
Hymenophyllaceae
hymenophyllaceous
Hymenophyllites
Hymenophyllum
hymenopter
Hymenoptera
hymenopteran
hymenopterist
hymenopterological
hymenopterologist
hymenopterology
hymenopteron
hymenopterous
hymenotomy
Hymettian
Hymettic
hymn
hymnal
hymnarium
hymnary
hymnbook
hymner
hymnic
hymnist
hymnless
hymnlike
hymnode
hymnodical
hymnodist
hymnody
hymnographer
hymnography
hymnologic
hymnological
hymnologically
hymnologist
hymnology
hymnwise
hynde
hyne
hyobranchial
hyocholalic
hyocholic
hyoepiglottic
hyoepiglottidean
hyoglossal
hyoglossus
hyoglycocholic
hyoid
hyoidal
hyoidan
hyoideal
hyoidean
hyoides
Hyolithes
hyolithid
Hyolithidae
hyolithoid
hyomandibula
hyomandibular
hyomental
hyoplastral
hyoplastron
hyoscapular
hyoscine
hyoscyamine
Hyoscyamus
hyosternal
hyosternum
hyostylic
hyostyly
hyothere
Hyotherium
hyothyreoid
hyothyroid
hyp
hypabyssal
hypaethral
hypaethron
hypaethros
hypaethrum
hypalgesia
hypalgia
hypalgic
hypallactic
hypallage
hypanthial
hypanthium
hypantrum
Hypapante
hypapophysial
hypapophysis
hyparterial
hypaspist
hypate
hypaton
hypautomorphic
hypaxial
Hypenantron
hyper
hyperabelian
hyperabsorption
hyperaccurate
hyperacid
hyperacidaminuria
hyperacidity
hyperacoustics
hyperaction
hyperactive
hyperactivity
hyperacuity
hyperacusia
hyperacusis
hyperacute
hyperacuteness
hyperadenosis
hyperadiposis
hyperadiposity
hyperadrenalemia
hyperaeolism
hyperalbuminosis
hyperalgebra
hyperalgesia
hyperalgesic
hyperalgesis
hyperalgetic
hyperalimentation
hyperalkalinity
hyperaltruism
hyperaminoacidemia
hyperanabolic
hyperanarchy
hyperangelical
hyperaphia
hyperaphic
hyperapophyseal
hyperapophysial
hyperapophysis
hyperarchaeological
hyperarchepiscopal
hyperazotemia
hyperbarbarous
hyperbatic
hyperbatically
hyperbaton
hyperbola
hyperbolaeon
hyperbole
hyperbolic
hyperbolically
hyperbolicly
hyperbolism
hyperbolize
hyperboloid
hyperboloidal
hyperboreal
Hyperborean
hyperborean
hyperbrachycephal
hyperbrachycephalic
hyperbrachycephaly
hyperbrachycranial
hyperbrachyskelic
hyperbranchia
hyperbrutal
hyperbulia
hypercalcemia
hypercarbamidemia
hypercarbureted
hypercarburetted
hypercarnal
hypercatalectic
hypercatalexis
hypercatharsis
hypercathartic
hypercathexis
hypercenosis
hyperchamaerrhine
hyperchlorhydria
hyperchloric
hypercholesterinemia
hypercholesterolemia
hypercholia
hypercivilization
hypercivilized
hyperclassical
hyperclimax
hypercoagulability
hypercoagulable
hypercomplex
hypercomposite
hyperconcentration
hypercone
hyperconfident
hyperconformist
hyperconscientious
hyperconscientiousness
hyperconscious
hyperconsciousness
hyperconservatism
hyperconstitutional
hypercoracoid
hypercorrect
hypercorrection
hypercorrectness
hypercosmic
hypercreaturely
hypercritic
hypercritical
hypercritically
hypercriticism
hypercriticize
hypercryalgesia
hypercube
hypercyanotic
hypercycle
hypercylinder
hyperdactyl
hyperdactylia
hyperdactyly
hyperdeify
hyperdelicacy
hyperdelicate
hyperdemocracy
hyperdemocratic
hyperdeterminant
hyperdiabolical
hyperdialectism
hyperdiapason
hyperdiapente
hyperdiastole
hyperdiatessaron
hyperdiazeuxis
hyperdicrotic
hyperdicrotism
hyperdicrotous
hyperdimensional
hyperdimensionality
hyperdissyllable
hyperdistention
hyperditone
hyperdivision
hyperdolichocephal
hyperdolichocephalic
hyperdolichocephaly
hyperdolichocranial
hyperdoricism
hyperdulia
hyperdulic
hyperdulical
hyperelegant
hyperelliptic
hyperemesis
hyperemetic
hyperemia
hyperemic
hyperemotivity
hyperemphasize
hyperenthusiasm
hypereosinophilia
hyperephidrosis
hyperequatorial
hypererethism
hyperessence
hyperesthesia
hyperesthetic
hyperethical
hypereuryprosopic
hypereutectic
hypereutectoid
hyperexaltation
hyperexcitability
hyperexcitable
hyperexcitement
hyperexcursive
hyperexophoria
hyperextend
hyperextension
hyperfastidious
hyperfederalist
hyperfine
hyperflexion
hyperfocal
hyperfunction
hyperfunctional
hyperfunctioning
hypergalactia
hypergamous
hypergamy
hypergenesis
hypergenetic
hypergeometric
hypergeometrical
hypergeometry
hypergeusia
hypergeustia
hyperglycemia
hyperglycemic
hyperglycorrhachia
hyperglycosuria
hypergoddess
hypergol
hypergolic
Hypergon
hypergrammatical
hyperhedonia
hyperhemoglobinemia
hyperhilarious
hyperhypocrisy
Hypericaceae
hypericaceous
Hypericales
hypericin
hypericism
Hypericum
hypericum
hyperidealistic
hyperideation
hyperimmune
hyperimmunity
hyperimmunization
hyperimmunize
hyperingenuity
hyperinosis
hyperinotic
hyperinsulinization
hyperinsulinize
hyperintellectual
hyperintelligence
hyperinvolution
hyperirritability
hyperirritable
hyperisotonic
hyperite
hyperkeratosis
hyperkinesia
hyperkinesis
hyperkinetic
hyperlactation
hyperleptoprosopic
hyperleucocytosis
hyperlipemia
hyperlipoidemia
hyperlithuria
hyperlogical
hyperlustrous
hypermagical
hypermakroskelic
hypermedication
hypermenorrhea
hypermetabolism
hypermetamorphic
hypermetamorphism
hypermetamorphosis
hypermetamorphotic
hypermetaphorical
hypermetaphysical
hypermetaplasia
hypermeter
hypermetric
hypermetrical
hypermetron
hypermetrope
hypermetropia
hypermetropic
hypermetropical
hypermetropy
hypermiraculous
hypermixolydian
hypermnesia
hypermnesic
hypermnesis
hypermnestic
hypermodest
hypermonosyllable
hypermoral
hypermorph
hypermorphism
hypermorphosis
hypermotile
hypermotility
hypermyotonia
hypermyotrophy
hypermyriorama
hypermystical
hypernatural
hypernephroma
hyperneuria
hyperneurotic
hypernic
hypernitrogenous
hypernomian
hypernomic
hypernormal
hypernote
hypernutrition
Hyperoartia
hyperoartian
hyperobtrusive
hyperodontogeny
Hyperoodon
hyperoon
hyperope
hyperopia
hyperopic
hyperorganic
hyperorthognathic
hyperorthognathous
hyperorthognathy
hyperosmia
hyperosmic
hyperostosis
hyperostotic
hyperothodox
hyperothodoxy
Hyperotreta
hyperotretan
Hyperotreti
hyperotretous
hyperoxidation
hyperoxide
hyperoxygenate
hyperoxygenation
hyperoxygenize
hyperpanegyric
hyperparasite
hyperparasitic
hyperparasitism
hyperparasitize
hyperparoxysm
hyperpathetic
hyperpatriotic
hyperpencil
hyperpepsinia
hyperper
hyperperistalsis
hyperperistaltic
hyperpersonal
hyperphalangeal
hyperphalangism
hyperpharyngeal
hyperphenomena
hyperphoria
hyperphoric
hyperphosphorescence
hyperphysical
hyperphysically
hyperphysics
hyperpiesia
hyperpiesis
hyperpietic
hyperpietist
hyperpigmentation
hyperpigmented
hyperpinealism
hyperpituitarism
hyperplagiarism
hyperplane
hyperplasia
hyperplasic
hyperplastic
hyperplatyrrhine
hyperploid
hyperploidy
hyperpnea
hyperpnoea
hyperpolysyllabic
hyperpredator
hyperprism
hyperproduction
hyperprognathous
hyperprophetical
hyperprosexia
hyperpulmonary
hyperpure
hyperpurist
hyperpyramid
hyperpyretic
hyperpyrexia
hyperpyrexial
hyperquadric
hyperrational
hyperreactive
hyperrealize
hyperresonance
hyperresonant
hyperreverential
hyperrhythmical
hyperridiculous
hyperritualism
hypersacerdotal
hypersaintly
hypersalivation
hypersceptical
hyperscholastic
hyperscrupulosity
hypersecretion
hypersensibility
hypersensitive
hypersensitiveness
hypersensitivity
hypersensitization
hypersensitize
hypersensual
hypersensualism
hypersensuous
hypersentimental
hypersolid
hypersomnia
hypersonic
hypersophisticated
hyperspace
hyperspatial
hyperspeculative
hypersphere
hyperspherical
hyperspiritualizing
hypersplenia
hypersplenism
hypersthene
hypersthenia
hypersthenic
hypersthenite
hyperstoic
hyperstrophic
hypersubtlety
hypersuggestibility
hypersuperlative
hypersurface
hypersusceptibility
hypersusceptible
hypersystole
hypersystolic
hypertechnical
hypertelic
hypertely
hypertense
hypertensin
hypertension
hypertensive
hyperterrestrial
hypertetrahedron
hyperthermal
hyperthermalgesia
hyperthermesthesia
hyperthermia
hyperthermic
hyperthermy
hyperthesis
hyperthetic
hyperthetical
hyperthyreosis
hyperthyroid
hyperthyroidism
hyperthyroidization
hyperthyroidize
hypertonia
hypertonic
hypertonicity
hypertonus
hypertorrid
hypertoxic
hypertoxicity
hypertragical
hypertragically
hypertranscendent
hypertrichosis
hypertridimensional
hypertrophic
hypertrophied
hypertrophous
hypertrophy
hypertropia
hypertropical
hypertype
hypertypic
hypertypical
hyperurbanism
hyperuresis
hypervascular
hypervascularity
hypervenosity
hyperventilate
hyperventilation
hypervigilant
hyperviscosity
hypervitalization
hypervitalize
hypervitaminosis
hypervolume
hyperwrought
hypesthesia
hypesthesic
hypethral
hypha
Hyphaene
hyphaeresis
hyphal
hyphedonia
hyphema
hyphen
hyphenate
hyphenated
hyphenation
hyphenic
hyphenism
hyphenization
hyphenize
hypho
hyphodrome
Hyphomycetales
hyphomycete
Hyphomycetes
hyphomycetic
hyphomycetous
hyphomycosis
hypidiomorphic
hypidiomorphically
hypinosis
hypinotic
Hypnaceae
hypnaceous
hypnagogic
hypnesthesis
hypnesthetic
hypnoanalysis
hypnobate
hypnocyst
hypnody
hypnoetic
hypnogenesis
hypnogenetic
hypnoid
hypnoidal
hypnoidization
hypnoidize
hypnologic
hypnological
hypnologist
hypnology
hypnone
hypnophobia
hypnophobic
hypnophoby
hypnopompic
Hypnos
hypnoses
hypnosis
hypnosperm
hypnosporangium
hypnospore
hypnosporic
hypnotherapy
hypnotic
hypnotically
hypnotism
hypnotist
hypnotistic
hypnotizability
hypnotizable
hypnotization
hypnotize
hypnotizer
hypnotoid
hypnotoxin
Hypnum
hypo
hypoacid
hypoacidity
hypoactive
hypoactivity
hypoadenia
hypoadrenia
hypoaeolian
hypoalimentation
hypoalkaline
hypoalkalinity
hypoaminoacidemia
hypoantimonate
hypoazoturia
hypobasal
hypobatholithic
hypobenthonic
hypobenthos
hypoblast
hypoblastic
hypobole
hypobranchial
hypobranchiate
hypobromite
hypobromous
hypobulia
hypobulic
hypocalcemia
hypocarp
hypocarpium
hypocarpogean
hypocatharsis
hypocathartic
hypocathexis
hypocaust
hypocentrum
hypocephalus
Hypochaeris
hypochil
hypochilium
hypochlorhydria
hypochlorhydric
hypochloric
hypochlorite
hypochlorous
hypochloruria
Hypochnaceae
hypochnose
Hypochnus
hypochondria
hypochondriac
hypochondriacal
hypochondriacally
hypochondriacism
hypochondrial
hypochondriasis
hypochondriast
hypochondrium
hypochondry
hypochordal
hypochromia
hypochrosis
hypochylia
hypocist
hypocleidian
hypocleidium
hypocoelom
hypocondylar
hypocone
hypoconid
hypoconule
hypoconulid
hypocoracoid
hypocorism
hypocoristic
hypocoristical
hypocoristically
hypocotyl
hypocotyleal
hypocotyledonary
hypocotyledonous
hypocotylous
hypocrater
hypocrateriform
hypocraterimorphous
Hypocreaceae
hypocreaceous
Hypocreales
hypocrisis
hypocrisy
hypocrital
hypocrite
hypocritic
hypocritical
hypocritically
hypocrize
hypocrystalline
hypocycloid
hypocycloidal
hypocystotomy
hypocytosis
hypodactylum
hypoderm
hypoderma
hypodermal
hypodermatic
hypodermatically
hypodermatoclysis
hypodermatomy
Hypodermella
hypodermic
hypodermically
hypodermis
hypodermoclysis
hypodermosis
hypodermous
hypodiapason
hypodiapente
hypodiastole
hypodiatessaron
hypodiazeuxis
hypodicrotic
hypodicrotous
hypoditone
hypodorian
hypodynamia
hypodynamic
hypoeliminator
hypoendocrinism
hypoeosinophilia
hypoeutectic
hypoeutectoid
hypofunction
hypogastric
hypogastrium
hypogastrocele
hypogeal
hypogean
hypogee
hypogeic
hypogeiody
hypogene
hypogenesis
hypogenetic
hypogenic
hypogenous
hypogeocarpous
hypogeous
hypogeum
hypogeusia
hypoglobulia
hypoglossal
hypoglossitis
hypoglossus
hypoglottis
hypoglycemia
hypoglycemic
hypognathism
hypognathous
hypogonation
hypogynic
hypogynium
hypogynous
hypogyny
hypohalous
hypohemia
hypohidrosis
Hypohippus
hypohyal
hypohyaline
hypoid
hypoiodite
hypoiodous
hypoionian
hypoischium
hypoisotonic
hypokeimenometry
hypokinesia
hypokinesis
hypokinetic
hypokoristikon
hypolemniscus
hypoleptically
hypoleucocytosis
hypolimnion
hypolocrian
hypolydian
hypomania
hypomanic
hypomelancholia
hypomeral
hypomere
hypomeron
hypometropia
hypomixolydian
hypomnematic
hypomnesis
hypomochlion
hypomorph
hypomotility
hypomyotonia
hyponastic
hyponastically
hyponasty
hyponeuria
hyponitric
hyponitrite
hyponitrous
hyponoetic
hyponoia
hyponome
hyponomic
hyponychial
hyponychium
hyponym
hyponymic
hyponymous
Hypoparia
hypopepsia
hypopepsinia
hypopepsy
hypopetalous
hypopetaly
hypophalangism
hypophamin
hypophamine
hypophare
hypopharyngeal
hypopharynx
hypophloeodal
hypophloeodic
hypophloeous
hypophonic
hypophonous
hypophora
hypophoria
hypophosphate
hypophosphite
hypophosphoric
hypophosphorous
hypophrenia
hypophrenic
hypophrenosis
hypophrygian
hypophyge
hypophyll
hypophyllium
hypophyllous
hypophyllum
hypophyse
hypophyseal
hypophysectomize
hypophysectomy
hypophyseoprivic
hypophyseoprivous
hypophysial
hypophysical
hypophysics
hypophysis
hypopial
hypopinealism
hypopituitarism
Hypopitys
hypoplankton
hypoplanktonic
hypoplasia
hypoplastic
hypoplastral
hypoplastron
hypoplasty
hypoplasy
hypoploid
hypoploidy
hypopodium
hypopraxia
hypoprosexia
hypopselaphesia
hypopteral
hypopteron
hypoptilar
hypoptilum
hypoptosis
hypoptyalism
hypopus
hypopygial
hypopygidium
hypopygium
hypopyon
hyporadial
hyporadiolus
hyporadius
hyporchema
hyporchematic
hyporcheme
hyporchesis
hyporhachidian
hyporhachis
hyporhined
hyporit
hyporrhythmic
hyposcenium
hyposcleral
hyposcope
hyposecretion
hyposensitization
hyposensitize
hyposkeletal
hyposmia
hypospadiac
hypospadias
hyposphene
hypospray
hypostase
hypostasis
hypostasization
hypostasize
hypostasy
hypostatic
hypostatical
hypostatically
hypostatization
hypostatize
hyposternal
hyposternum
hyposthenia
hyposthenic
hyposthenuria
hypostigma
hypostilbite
hypostoma
Hypostomata
hypostomatic
hypostomatous
hypostome
hypostomial
Hypostomides
hypostomous
hypostrophe
hypostyle
hypostypsis
hypostyptic
hyposulphite
hyposulphurous
hyposuprarenalism
hyposyllogistic
hyposynaphe
hyposynergia
hyposystole
hypotactic
hypotarsal
hypotarsus
hypotaxia
hypotaxic
hypotaxis
hypotension
hypotensive
hypotensor
hypotenusal
hypotenuse
hypothalamic
hypothalamus
hypothalline
hypothallus
hypothec
hypotheca
hypothecal
hypothecary
hypothecate
hypothecation
hypothecative
hypothecator
hypothecatory
hypothecial
hypothecium
hypothenal
hypothenar
Hypotheria
hypothermal
hypothermia
hypothermic
hypothermy
hypotheses
hypothesis
hypothesist
hypothesize
hypothesizer
hypothetic
hypothetical
hypothetically
hypothetics
hypothetist
hypothetize
hypothetizer
hypothyreosis
hypothyroid
hypothyroidism
hypotonia
hypotonic
hypotonicity
hypotonus
hypotony
hypotoxic
hypotoxicity
hypotrachelium
Hypotremata
hypotrich
Hypotricha
Hypotrichida
hypotrichosis
hypotrichous
hypotrochanteric
hypotrochoid
hypotrochoidal
hypotrophic
hypotrophy
hypotympanic
hypotypic
hypotypical
hypotyposis
hypovalve
hypovanadate
hypovanadic
hypovanadious
hypovanadous
hypovitaminosis
hypoxanthic
hypoxanthine
Hypoxis
Hypoxylon
hypozeugma
hypozeuxis
Hypozoa
hypozoan
hypozoic
hyppish
hypsibrachycephalic
hypsibrachycephalism
hypsibrachycephaly
hypsicephalic
hypsicephaly
hypsidolichocephalic
hypsidolichocephalism
hypsidolichocephaly
hypsiliform
hypsiloid
Hypsilophodon
hypsilophodont
hypsilophodontid
Hypsilophodontidae
hypsilophodontoid
Hypsiprymninae
Hypsiprymnodontinae
Hypsiprymnus
Hypsistarian
hypsistenocephalic
hypsistenocephalism
hypsistenocephaly
hypsobathymetric
hypsocephalous
hypsochrome
hypsochromic
hypsochromy
hypsodont
hypsodontism
hypsodonty
hypsographic
hypsographical
hypsography
hypsoisotherm
hypsometer
hypsometric
hypsometrical
hypsometrically
hypsometrist
hypsometry
hypsophobia
hypsophonous
hypsophyll
hypsophyllar
hypsophyllary
hypsophyllous
hypsophyllum
hypsothermometer
hypural
hyraces
hyraceum
Hyrachyus
hyracid
Hyracidae
hyraciform
Hyracina
Hyracodon
hyracodont
hyracodontid
Hyracodontidae
hyracodontoid
hyracoid
Hyracoidea
hyracoidean
hyracothere
hyracotherian
Hyracotheriinae
Hyracotherium
hyrax
Hyrcan
Hyrcanian
hyson
hyssop
Hyssopus
hystazarin
hysteralgia
hysteralgic
hysteranthous
hysterectomy
hysterelcosis
hysteresial
hysteresis
hysteretic
hysteretically
hysteria
hysteriac
Hysteriales
hysteric
hysterical
hysterically
hystericky
hysterics
hysteriform
hysterioid
Hysterocarpus
hysterocatalepsy
hysterocele
hysterocleisis
hysterocrystalline
hysterocystic
hysterodynia
hysterogen
hysterogenetic
hysterogenic
hysterogenous
hysterogeny
hysteroid
hysterolaparotomy
hysterolith
hysterolithiasis
hysterology
hysterolysis
hysteromania
hysterometer
hysterometry
hysteromorphous
hysteromyoma
hysteromyomectomy
hysteron
hysteroneurasthenia
hysteropathy
hysteropexia
hysteropexy
hysterophore
Hysterophyta
hysterophytal
hysterophyte
hysteroproterize
hysteroptosia
hysteroptosis
hysterorrhaphy
hysterorrhexis
hysteroscope
hysterosis
hysterotome
hysterotomy
hysterotraumatism
hystriciasis
hystricid
Hystricidae
Hystricinae
hystricine
hystricism
hystricismus
hystricoid
hystricomorph
Hystricomorpha
hystricomorphic
hystricomorphous
Hystrix
I
i
Iacchic
Iacchos
Iacchus
Iachimo
iamatology
iamb
Iambe
iambelegus
iambi
iambic
iambically
iambist
iambize
iambographer
iambus
Ian
Ianthina
ianthine
ianthinite
Ianus
iao
Iapetus
Iapyges
Iapygian
Iapygii
iatraliptic
iatraliptics
iatric
iatrical
iatrochemic
iatrochemical
iatrochemist
iatrochemistry
iatrological
iatrology
iatromathematical
iatromathematician
iatromathematics
iatromechanical
iatromechanist
iatrophysical
iatrophysicist
iatrophysics
iatrotechnics
iba
Ibad
Ibadite
Iban
Ibanag
Iberes
Iberi
Iberia
Iberian
Iberic
Iberis
Iberism
iberite
ibex
ibices
ibid
Ibididae
Ibidinae
ibidine
Ibidium
Ibilao
ibis
ibisbill
Ibo
ibolium
ibota
Ibsenian
Ibsenic
Ibsenish
Ibsenism
Ibsenite
Ibycter
Ibycus
Icacinaceae
icacinaceous
icaco
Icacorea
Icaria
Icarian
Icarianism
Icarus
ice
iceberg
iceblink
iceboat
icebone
icebound
icebox
icebreaker
icecap
icecraft
iced
icefall
icefish
icehouse
Iceland
iceland
Icelander
Icelandian
Icelandic
iceleaf
iceless
Icelidae
icelike
iceman
Iceni
icequake
iceroot
Icerya
icework
ich
Ichneumia
ichneumon
ichneumoned
Ichneumones
ichneumonid
Ichneumonidae
ichneumonidan
Ichneumonides
ichneumoniform
ichneumonized
ichneumonoid
Ichneumonoidea
ichneumonology
ichneumous
ichneutic
ichnite
ichnographic
ichnographical
ichnographically
ichnography
ichnolite
ichnolithology
ichnolitic
ichnological
ichnology
ichnomancy
icho
ichoglan
ichor
ichorous
ichorrhea
ichorrhemia
ichthulin
ichthulinic
ichthus
ichthyal
ichthyic
ichthyism
ichthyismus
ichthyization
ichthyized
ichthyobatrachian
Ichthyocephali
ichthyocephalous
ichthyocol
ichthyocolla
ichthyocoprolite
Ichthyodea
Ichthyodectidae
ichthyodian
ichthyodont
ichthyodorulite
ichthyofauna
ichthyoform
ichthyographer
ichthyographia
ichthyographic
ichthyography
ichthyoid
ichthyoidal
Ichthyoidea
Ichthyol
ichthyolatrous
ichthyolatry
ichthyolite
ichthyolitic
ichthyologic
ichthyological
ichthyologically
ichthyologist
ichthyology
ichthyomancy
ichthyomantic
Ichthyomorpha
ichthyomorphic
ichthyomorphous
ichthyonomy
ichthyopaleontology
ichthyophagan
ichthyophagi
ichthyophagian
ichthyophagist
ichthyophagize
ichthyophagous
ichthyophagy
ichthyophile
ichthyophobia
ichthyophthalmite
ichthyophthiriasis
ichthyopolism
ichthyopolist
ichthyopsid
Ichthyopsida
ichthyopsidan
Ichthyopterygia
ichthyopterygian
ichthyopterygium
Ichthyornis
Ichthyornithes
ichthyornithic
Ichthyornithidae
Ichthyornithiformes
ichthyornithoid
ichthyosaur
Ichthyosauria
ichthyosaurian
ichthyosaurid
Ichthyosauridae
ichthyosauroid
Ichthyosaurus
ichthyosis
ichthyosism
ichthyotic
Ichthyotomi
ichthyotomist
ichthyotomous
ichthyotomy
ichthyotoxin
ichthyotoxism
ichthytaxidermy
ichu
icica
icicle
icicled
icily
iciness
icing
icon
Iconian
iconic
iconical
iconism
iconoclasm
iconoclast
iconoclastic
iconoclastically
iconoclasticism
iconodule
iconodulic
iconodulist
iconoduly
iconograph
iconographer
iconographic
iconographical
iconographist
iconography
iconolater
iconolatrous
iconolatry
iconological
iconologist
iconology
iconomachal
iconomachist
iconomachy
iconomania
iconomatic
iconomatically
iconomaticism
iconomatography
iconometer
iconometric
iconometrical
iconometrically
iconometry
iconophile
iconophilism
iconophilist
iconophily
iconoplast
iconoscope
iconostas
iconostasion
iconostasis
iconotype
icosahedral
Icosandria
icosasemic
icosian
icositetrahedron
icosteid
Icosteidae
icosteine
Icosteus
icotype
icteric
icterical
Icteridae
icterine
icteritious
icterode
icterogenetic
icterogenic
icterogenous
icterohematuria
icteroid
icterus
ictic
Ictonyx
ictuate
ictus
icy
id
Ida
Idaean
Idaho
Idahoan
Idaic
idalia
Idalian
idant
iddat
Iddio
ide
idea
ideaed
ideaful
ideagenous
ideal
idealess
idealism
idealist
idealistic
idealistical
idealistically
ideality
idealization
idealize
idealizer
idealless
ideally
idealness
ideamonger
Idean
ideate
ideation
ideational
ideationally
ideative
ideist
idempotent
identic
identical
identicalism
identically
identicalness
identifiable
identifiableness
identification
identifier
identify
identism
identity
ideogenetic
ideogenical
ideogenous
ideogeny
ideoglyph
ideogram
ideogrammic
ideograph
ideographic
ideographical
ideographically
ideography
ideolatry
ideologic
ideological
ideologically
ideologist
ideologize
ideologue
ideology
ideomotion
ideomotor
ideophone
ideophonetics
ideophonous
ideoplastia
ideoplastic
ideoplastics
ideoplasty
ideopraxist
ides
idgah
idiasm
idic
idiobiology
idioblast
idioblastic
idiochromatic
idiochromatin
idiochromosome
idiocrasis
idiocrasy
idiocratic
idiocratical
idiocy
idiocyclophanous
idioelectric
idioelectrical
Idiogastra
idiogenesis
idiogenetic
idiogenous
idioglossia
idioglottic
idiograph
idiographic
idiographical
idiohypnotism
idiolalia
idiolatry
idiologism
idiolysin
idiom
idiomatic
idiomatical
idiomatically
idiomaticalness
idiomelon
idiometer
idiomography
idiomology
idiomorphic
idiomorphically
idiomorphism
idiomorphous
idiomuscular
idiopathetic
idiopathic
idiopathical
idiopathically
idiopathy
idiophanism
idiophanous
idiophonic
idioplasm
idioplasmatic
idioplasmic
idiopsychological
idiopsychology
idioreflex
idiorepulsive
idioretinal
idiorrhythmic
Idiosepiidae
Idiosepion
idiosome
idiospasm
idiospastic
idiostatic
idiosyncrasy
idiosyncratic
idiosyncratical
idiosyncratically
idiot
idiotcy
idiothalamous
idiothermous
idiothermy
idiotic
idiotical
idiotically
idioticalness
idioticon
idiotish
idiotism
idiotize
idiotropian
idiotry
idiotype
idiotypic
Idism
Idist
Idistic
idite
iditol
idle
idleful
idleheaded
idlehood
idleman
idlement
idleness
idler
idleset
idleship
idlety
idlish
idly
Ido
idocrase
Idoism
Idoist
Idoistic
idol
idola
idolaster
idolater
idolatress
idolatric
idolatrize
idolatrizer
idolatrous
idolatrously
idolatrousness
idolatry
idolify
idolism
idolist
idolistic
idolization
idolize
idolizer
idoloclast
idoloclastic
idolodulia
idolographical
idololatrical
idololatry
idolomancy
idolomania
idolothyte
idolothytic
idolous
idolum
Idomeneus
idoneal
idoneity
idoneous
idoneousness
idorgan
idosaccharic
idose
Idotea
Idoteidae
Idothea
Idotheidae
idrialin
idrialine
idrialite
Idrisid
Idrisite
idryl
Idumaean
idyl
idyler
idylism
idylist
idylize
idyllian
idyllic
idyllical
idyllically
idyllicism
ie
Ierne
if
ife
iffy
Ifugao
Igara
Igbira
Igdyr
igelstromite
igloo
Iglulirmiut
ignatia
Ignatian
Ignatianist
Ignatius
ignavia
igneoaqueous
igneous
ignescent
ignicolist
igniferous
igniferousness
igniform
ignifuge
ignify
ignigenous
ignipotent
ignipuncture
ignitability
ignite
igniter
ignitibility
ignitible
ignition
ignitive
ignitor
ignitron
ignivomous
ignivomousness
ignobility
ignoble
ignobleness
ignoblesse
ignobly
ignominious
ignominiously
ignominiousness
ignominy
ignorable
ignoramus
ignorance
ignorant
Ignorantine
ignorantism
ignorantist
ignorantly
ignorantness
ignoration
ignore
ignorement
ignorer
ignote
Igorot
iguana
Iguania
iguanian
iguanid
Iguanidae
iguaniform
Iguanodon
iguanodont
Iguanodontia
Iguanodontidae
iguanodontoid
Iguanodontoidea
iguanoid
Iguvine
ihi
Ihlat
ihleite
ihram
iiwi
ijma
Ijo
ijolite
Ijore
ijussite
ikat
Ike
ikey
ikeyness
Ikhwan
ikona
ikra
Ila
ileac
ileectomy
ileitis
ileocaecal
ileocaecum
ileocolic
ileocolitis
ileocolostomy
ileocolotomy
ileon
ileosigmoidostomy
ileostomy
ileotomy
ilesite
ileum
ileus
ilex
ilia
Iliac
iliac
iliacus
Iliad
Iliadic
Iliadist
Iliadize
iliahi
ilial
Ilian
iliau
Ilicaceae
ilicaceous
ilicic
ilicin
ilima
iliocaudal
iliocaudalis
iliococcygeal
iliococcygeus
iliococcygian
iliocostal
iliocostalis
iliodorsal
iliofemoral
iliohypogastric
ilioinguinal
ilioischiac
ilioischiatic
iliolumbar
iliopectineal
iliopelvic
ilioperoneal
iliopsoas
iliopsoatic
iliopubic
iliosacral
iliosciatic
ilioscrotal
iliospinal
iliotibial
iliotrochanteric
Ilissus
ilium
ilk
ilka
ilkane
ill
illaborate
illachrymable
illachrymableness
Illaenus
Illano
Illanun
illapsable
illapse
illapsive
illaqueate
illaqueation
illation
illative
illatively
illaudable
illaudably
illaudation
illaudatory
Illecebraceae
illecebrous
illeck
illegal
illegality
illegalize
illegally
illegalness
illegibility
illegible
illegibleness
illegibly
illegitimacy
illegitimate
illegitimately
illegitimateness
illegitimation
illegitimatize
illeism
illeist
illess
illfare
illguide
illiberal
illiberalism
illiberality
illiberalize
illiberally
illiberalness
illicit
illicitly
illicitness
Illicium
illimitability
illimitable
illimitableness
illimitably
illimitate
illimitation
illimited
illimitedly
illimitedness
illinition
illinium
Illinoian
Illinois
Illinoisan
Illinoisian
Illipe
illipene
illiquation
illiquid
illiquidity
illiquidly
illish
illision
illiteracy
illiteral
illiterate
illiterately
illiterateness
illiterature
illium
illness
illocal
illocality
illocally
illogic
illogical
illogicality
illogically
illogicalness
illogician
illogicity
Illoricata
illoricate
illoricated
illoyal
illoyalty
illth
illucidate
illucidation
illucidative
illude
illudedly
illuder
illume
illumer
illuminability
illuminable
illuminance
illuminant
illuminate
illuminated
illuminati
illuminating
illuminatingly
illumination
illuminational
illuminatism
illuminatist
illuminative
illuminato
illuminator
illuminatory
illuminatus
illumine
illuminee
illuminer
Illuminism
illuminist
Illuministic
Illuminize
illuminometer
illuminous
illupi
illure
illurement
illusible
illusion
illusionable
illusional
illusionary
illusioned
illusionism
illusionist
illusionistic
illusive
illusively
illusiveness
illusor
illusorily
illusoriness
illusory
illustrable
illustratable
illustrate
illustration
illustrational
illustrative
illustratively
illustrator
illustratory
illustratress
illustre
illustricity
illustrious
illustriously
illustriousness
illutate
illutation
illuvial
illuviate
illuviation
illy
Illyrian
Illyric
ilmenite
ilmenitite
ilmenorutile
Ilocano
Ilokano
Iloko
Ilongot
ilot
Ilpirra
ilvaite
Ilya
Ilysanthes
Ilysia
Ilysiidae
ilysioid
Ima
image
imageable
imageless
imager
imagerial
imagerially
imagery
imaginability
imaginable
imaginableness
imaginably
imaginal
imaginant
imaginarily
imaginariness
imaginary
imaginate
imagination
imaginational
imaginationalism
imaginative
imaginatively
imaginativeness
imaginator
imagine
imaginer
imagines
imaginist
imaginous
imagism
imagist
imagistic
imago
imam
imamah
imamate
imambarah
imamic
imamship
Imantophyllum
imaret
imbalance
imban
imband
imbannered
imbarge
imbark
imbarn
imbased
imbastardize
imbat
imbauba
imbe
imbecile
imbecilely
imbecilic
imbecilitate
imbecility
imbed
imbellious
imber
imbibe
imbiber
imbibition
imbibitional
imbibitory
imbirussu
imbitter
imbitterment
imbolish
imbondo
imbonity
imbordure
imborsation
imbosom
imbower
imbreathe
imbreviate
imbrex
imbricate
imbricated
imbricately
imbrication
imbricative
imbroglio
imbrue
imbruement
imbrute
imbrutement
imbue
imbuement
imburse
imbursement
Imer
Imerina
Imeritian
imi
imidazole
imidazolyl
imide
imidic
imidogen
iminazole
imine
imino
iminohydrin
imitability
imitable
imitableness
imitancy
imitant
imitate
imitatee
imitation
imitational
imitationist
imitative
imitatively
imitativeness
imitator
imitatorship
imitatress
imitatrix
immaculacy
immaculance
immaculate
immaculately
immaculateness
immalleable
immanacle
immanation
immane
immanely
immanence
immanency
immaneness
immanent
immanental
immanentism
immanentist
immanently
Immanes
immanifest
immanifestness
immanity
immantle
Immanuel
immarble
immarcescible
immarcescibly
immarcibleness
immarginate
immask
immatchable
immaterial
immaterialism
immaterialist
immateriality
immaterialize
immaterially
immaterialness
immaterials
immateriate
immatriculate
immatriculation
immature
immatured
immaturely
immatureness
immaturity
immeability
immeasurability
immeasurable
immeasurableness
immeasurably
immeasured
immechanical
immechanically
immediacy
immedial
immediate
immediately
immediateness
immediatism
immediatist
immedicable
immedicableness
immedicably
immelodious
immember
immemorable
immemorial
immemorially
immense
immensely
immenseness
immensity
immensive
immensurability
immensurable
immensurableness
immensurate
immerd
immerge
immergence
immergent
immerit
immerited
immeritorious
immeritoriously
immeritous
immerse
immersement
immersible
immersion
immersionism
immersionist
immersive
immethodic
immethodical
immethodically
immethodicalness
immethodize
immetrical
immetrically
immetricalness
immew
immi
immigrant
immigrate
immigration
immigrator
immigratory
imminence
imminency
imminent
imminently
imminentness
immingle
imminution
immiscibility
immiscible
immiscibly
immission
immit
immitigability
immitigable
immitigably
immix
immixable
immixture
immobile
immobility
immobilization
immobilize
immoderacy
immoderate
immoderately
immoderateness
immoderation
immodest
immodestly
immodesty
immodulated
immolate
immolation
immolator
immoment
immomentous
immonastered
immoral
immoralism
immoralist
immorality
immoralize
immorally
immorigerous
immorigerousness
immortability
immortable
immortal
immortalism
immortalist
immortality
immortalizable
immortalization
immortalize
immortalizer
immortally
immortalness
immortalship
immortelle
immortification
immortified
immotile
immotioned
immotive
immound
immovability
immovable
immovableness
immovably
immund
immundity
immune
immunist
immunity
immunization
immunize
immunochemistry
immunogen
immunogenetic
immunogenetics
immunogenic
immunogenically
immunogenicity
immunologic
immunological
immunologically
immunologist
immunology
immunoreaction
immunotoxin
immuration
immure
immurement
immusical
immusically
immutability
immutable
immutableness
immutably
immutation
immute
immutilate
immutual
Imogen
Imolinda
imonium
imp
impacability
impacable
impack
impackment
impact
impacted
impaction
impactionize
impactment
impactual
impages
impaint
impair
impairable
impairer
impairment
impala
impalace
impalatable
impale
impalement
impaler
impall
impalm
impalpability
impalpable
impalpably
impalsy
impaludism
impanate
impanation
impanator
impane
impanel
impanelment
impapase
impapyrate
impar
imparadise
imparalleled
imparasitic
impardonable
impardonably
imparidigitate
imparipinnate
imparisyllabic
imparity
impark
imparkation
imparl
imparlance
imparsonee
impart
impartable
impartance
impartation
imparter
impartial
impartialism
impartialist
impartiality
impartially
impartialness
impartibilibly
impartibility
impartible
impartibly
imparticipable
impartite
impartive
impartivity
impartment
impassability
impassable
impassableness
impassably
impasse
impassibilibly
impassibility
impassible
impassibleness
impassion
impassionable
impassionate
impassionately
impassioned
impassionedly
impassionedness
impassionment
impassive
impassively
impassiveness
impassivity
impastation
impaste
impasto
impasture
impaternate
impatible
impatience
impatiency
Impatiens
impatient
Impatientaceae
impatientaceous
impatiently
impatientness
impatronize
impave
impavid
impavidity
impavidly
impawn
impayable
impeach
impeachability
impeachable
impeacher
impeachment
impearl
impeccability
impeccable
impeccably
impeccance
impeccancy
impeccant
impectinate
impecuniary
impecuniosity
impecunious
impecuniously
impecuniousness
impedance
impede
impeder
impedibility
impedible
impedient
impediment
impedimenta
impedimental
impedimentary
impeding
impedingly
impedite
impedition
impeditive
impedometer
impeevish
impel
impellent
impeller
impen
impend
impendence
impendency
impendent
impending
impenetrability
impenetrable
impenetrableness
impenetrably
impenetrate
impenetration
impenetrative
impenitence
impenitent
impenitently
impenitentness
impenitible
impenitibleness
impennate
Impennes
impent
imperance
imperant
Imperata
imperate
imperation
imperatival
imperative
imperatively
imperativeness
imperator
imperatorial
imperatorially
imperatorian
imperatorious
imperatorship
imperatory
imperatrix
imperceivable
imperceivableness
imperceivably
imperceived
imperceiverant
imperceptibility
imperceptible
imperceptibleness
imperceptibly
imperception
imperceptive
imperceptiveness
imperceptivity
impercipience
impercipient
imperence
imperent
imperfect
imperfected
imperfectibility
imperfectible
imperfection
imperfectious
imperfective
imperfectly
imperfectness
imperforable
Imperforata
imperforate
imperforated
imperforation
imperformable
imperia
imperial
imperialin
imperialine
imperialism
imperialist
imperialistic
imperialistically
imperiality
imperialization
imperialize
imperially
imperialness
imperialty
imperil
imperilment
imperious
imperiously
imperiousness
imperish
imperishability
imperishable
imperishableness
imperishably
imperite
imperium
impermanence
impermanency
impermanent
impermanently
impermeability
impermeabilization
impermeabilize
impermeable
impermeableness
impermeably
impermeated
impermeator
impermissible
impermutable
imperscriptible
imperscrutable
impersonable
impersonal
impersonality
impersonalization
impersonalize
impersonally
impersonate
impersonation
impersonative
impersonator
impersonatress
impersonatrix
impersonification
impersonify
impersonization
impersonize
imperspicuity
imperspicuous
imperspirability
imperspirable
impersuadable
impersuadableness
impersuasibility
impersuasible
impersuasibleness
impersuasibly
impertinacy
impertinence
impertinency
impertinent
impertinently
impertinentness
impertransible
imperturbability
imperturbable
imperturbableness
imperturbably
imperturbation
imperturbed
imperverse
impervertible
impervestigable
imperviability
imperviable
imperviableness
impervial
impervious
imperviously
imperviousness
impest
impestation
impester
impeticos
impetiginous
impetigo
impetition
impetrate
impetration
impetrative
impetrator
impetratory
impetre
impetulant
impetulantly
impetuosity
impetuous
impetuously
impetuousness
impetus
Impeyan
imphee
impi
impicture
impierceable
impiety
impignorate
impignoration
impinge
impingement
impingence
impingent
impinger
impinguate
impious
impiously
impiousness
impish
impishly
impishness
impiteous
impitiably
implacability
implacable
implacableness
implacably
implacement
implacental
Implacentalia
implacentate
implant
implantation
implanter
implastic
implasticity
implate
implausibility
implausible
implausibleness
implausibly
impleach
implead
impleadable
impleader
impledge
implement
implemental
implementation
implementiferous
implete
impletion
impletive
implex
impliable
implial
implicant
implicate
implicately
implicateness
implication
implicational
implicative
implicatively
implicatory
implicit
implicitly
implicitness
impliedly
impliedness
impling
implode
implodent
implorable
imploration
implorator
imploratory
implore
implorer
imploring
imploringly
imploringness
implosion
implosive
implosively
implume
implumed
implunge
impluvium
imply
impocket
impofo
impoison
impoisoner
impolarizable
impolicy
impolished
impolite
impolitely
impoliteness
impolitic
impolitical
impolitically
impoliticalness
impoliticly
impoliticness
impollute
imponderabilia
imponderability
imponderable
imponderableness
imponderably
imponderous
impone
imponent
impoor
impopular
impopularly
imporosity
imporous
import
importability
importable
importableness
importably
importance
importancy
important
importantly
importation
importer
importless
importment
importraiture
importray
importunacy
importunance
importunate
importunately
importunateness
importunator
importune
importunely
importunement
importuner
importunity
imposable
imposableness
imposal
impose
imposement
imposer
imposing
imposingly
imposingness
imposition
impositional
impositive
impossibilification
impossibilism
impossibilist
impossibilitate
impossibility
impossible
impossibleness
impossibly
impost
imposter
imposterous
impostor
impostorism
impostorship
impostress
impostrix
impostrous
impostumate
impostumation
impostume
imposture
imposturism
imposturous
imposure
impot
impotable
impotence
impotency
impotent
impotently
impotentness
impound
impoundable
impoundage
impounder
impoundment
impoverish
impoverisher
impoverishment
impracticability
impracticable
impracticableness
impracticably
impractical
impracticality
impracticalness
imprecant
imprecate
imprecation
imprecator
imprecatorily
imprecatory
imprecise
imprecisely
imprecision
impredicability
impredicable
impreg
impregn
impregnability
impregnable
impregnableness
impregnably
impregnant
impregnate
impregnation
impregnative
impregnator
impregnatory
imprejudice
impremeditate
impreparation
impresa
impresario
imprescience
imprescribable
imprescriptibility
imprescriptible
imprescriptibly
imprese
impress
impressable
impressedly
impresser
impressibility
impressible
impressibleness
impressibly
impression
impressionability
impressionable
impressionableness
impressionably
impressional
impressionalist
impressionality
impressionally
impressionary
impressionism
impressionist
impressionistic
impressionistically
impressionless
impressive
impressively
impressiveness
impressment
impressor
impressure
imprest
imprestable
impreventability
impreventable
imprevisibility
imprevisible
imprevision
imprimatur
imprime
imprimitive
imprimitivity
imprint
imprinter
imprison
imprisonable
imprisoner
imprisonment
improbability
improbabilize
improbable
improbableness
improbably
improbation
improbative
improbatory
improbity
improcreant
improcurability
improcurable
improducible
improficience
improficiency
improgressive
improgressively
improgressiveness
improlificical
impromptitude
impromptu
impromptuary
impromptuist
improof
improper
improperation
improperly
improperness
impropriate
impropriation
impropriator
impropriatrix
impropriety
improvability
improvable
improvableness
improvably
improve
improvement
improver
improvership
improvidence
improvident
improvidentially
improvidently
improving
improvingly
improvisate
improvisation
improvisational
improvisator
improvisatorial
improvisatorially
improvisatorize
improvisatory
improvise
improvisedly
improviser
improvision
improviso
improvisor
imprudence
imprudency
imprudent
imprudential
imprudently
imprudentness
impship
impuberal
impuberate
impuberty
impubic
impudence
impudency
impudent
impudently
impudentness
impudicity
impugn
impugnability
impugnable
impugnation
impugner
impugnment
impuissance
impuissant
impulse
impulsion
impulsive
impulsively
impulsiveness
impulsivity
impulsory
impunctate
impunctual
impunctuality
impunely
impunible
impunibly
impunity
impure
impurely
impureness
impuritan
impuritanism
impurity
imputability
imputable
imputableness
imputably
imputation
imputative
imputatively
imputativeness
impute
imputedly
imputer
imputrescence
imputrescibility
imputrescible
imputrid
impy
imshi
imsonic
imu
in
inability
inabordable
inabstinence
inaccentuated
inaccentuation
inacceptable
inaccessibility
inaccessible
inaccessibleness
inaccessibly
inaccordance
inaccordancy
inaccordant
inaccordantly
inaccuracy
inaccurate
inaccurately
inaccurateness
inachid
Inachidae
inachoid
Inachus
inacquaintance
inacquiescent
inactinic
inaction
inactionist
inactivate
inactivation
inactive
inactively
inactiveness
inactivity
inactuate
inactuation
inadaptability
inadaptable
inadaptation
inadaptive
inadept
inadequacy
inadequate
inadequately
inadequateness
inadequation
inadequative
inadequatively
inadherent
inadhesion
inadhesive
inadjustability
inadjustable
inadmissibility
inadmissible
inadmissibly
inadventurous
inadvertence
inadvertency
inadvertent
inadvertently
inadvisability
inadvisable
inadvisableness
inadvisedly
inaesthetic
inaffability
inaffable
inaffectation
inagglutinability
inagglutinable
inaggressive
inagile
inaidable
inaja
inalacrity
inalienability
inalienable
inalienableness
inalienably
inalimental
inalterability
inalterable
inalterableness
inalterably
inamissibility
inamissible
inamissibleness
inamorata
inamorate
inamoration
inamorato
inamovability
inamovable
inane
inanely
inanga
inangulate
inanimadvertence
inanimate
inanimated
inanimately
inanimateness
inanimation
inanition
inanity
inantherate
inapathy
inapostate
inapparent
inappealable
inappeasable
inappellability
inappellable
inappendiculate
inapperceptible
inappertinent
inappetence
inappetency
inappetent
inappetible
inapplicability
inapplicable
inapplicableness
inapplicably
inapplication
inapposite
inappositely
inappositeness
inappreciable
inappreciably
inappreciation
inappreciative
inappreciatively
inappreciativeness
inapprehensible
inapprehension
inapprehensive
inapprehensiveness
inapproachability
inapproachable
inapproachably
inappropriable
inappropriableness
inappropriate
inappropriately
inappropriateness
inapt
inaptitude
inaptly
inaptness
inaqueous
inarable
inarch
inarculum
inarguable
inarguably
inarm
inarticulacy
Inarticulata
inarticulate
inarticulated
inarticulately
inarticulateness
inarticulation
inartificial
inartificiality
inartificially
inartificialness
inartistic
inartistical
inartisticality
inartistically
inasmuch
inassimilable
inassimilation
inassuageable
inattackable
inattention
inattentive
inattentively
inattentiveness
inaudibility
inaudible
inaudibleness
inaudibly
inaugur
inaugural
inaugurate
inauguration
inaugurative
inaugurator
inauguratory
inaugurer
inaurate
inauration
inauspicious
inauspiciously
inauspiciousness
inauthentic
inauthenticity
inauthoritative
inauthoritativeness
inaxon
inbe
inbeaming
inbearing
inbeing
inbending
inbent
inbirth
inblow
inblowing
inblown
inboard
inbond
inborn
inbound
inbread
inbreak
inbreaking
inbreathe
inbreather
inbred
inbreed
inbring
inbringer
inbuilt
inburning
inburnt
inburst
inby
Inca
Incaic
incalculability
incalculable
incalculableness
incalculably
incalescence
incalescency
incalescent
incaliculate
incalver
incalving
incameration
Incan
incandent
incandesce
incandescence
incandescency
incandescent
incandescently
incanous
incantation
incantational
incantator
incantatory
incanton
incapability
incapable
incapableness
incapably
incapacious
incapaciousness
incapacitate
incapacitation
incapacity
incapsulate
incapsulation
incaptivate
incarcerate
incarceration
incarcerator
incardinate
incardination
Incarial
incarmined
incarn
incarnadine
incarnant
incarnate
incarnation
incarnational
incarnationist
incarnative
Incarvillea
incase
incasement
incast
incatenate
incatenation
incaution
incautious
incautiously
incautiousness
incavate
incavated
incavation
incavern
incedingly
incelebrity
incendiarism
incendiary
incendivity
incensation
incense
incenseless
incensement
incensory
incensurable
incensurably
incenter
incentive
incentively
incentor
incept
inception
inceptive
inceptively
inceptor
inceration
incertitude
incessable
incessably
incessancy
incessant
incessantly
incessantness
incest
incestuous
incestuously
incestuousness
inch
inched
inchmeal
inchoacy
inchoant
inchoate
inchoately
inchoateness
inchoation
inchoative
inchpin
inchworm
incide
incidence
incident
incidental
incidentalist
incidentally
incidentalness
incidentless
incidently
incinerable
incinerate
incineration
incinerator
incipience
incipient
incipiently
incircumscription
incircumspect
incircumspection
incircumspectly
incircumspectness
incisal
incise
incisely
incisiform
incision
incisive
incisively
incisiveness
incisor
incisorial
incisory
incisure
incitability
incitable
incitant
incitation
incite
incitement
inciter
incitingly
incitive
incitress
incivic
incivility
incivilization
incivism
inclemency
inclement
inclemently
inclementness
inclinable
inclinableness
inclination
inclinational
inclinator
inclinatorily
inclinatorium
inclinatory
incline
incliner
inclinograph
inclinometer
inclip
inclose
inclosure
includable
include
included
includedness
includer
inclusa
incluse
inclusion
inclusionist
inclusive
inclusively
inclusiveness
inclusory
incoagulable
incoalescence
incoercible
incog
incogent
incogitability
incogitable
incogitancy
incogitant
incogitantly
incogitative
incognita
incognitive
incognito
incognizability
incognizable
incognizance
incognizant
incognoscent
incognoscibility
incognoscible
incoherence
incoherency
incoherent
incoherentific
incoherently
incoherentness
incohering
incohesion
incohesive
incoincidence
incoincident
incombustibility
incombustible
incombustibleness
incombustibly
incombustion
income
incomeless
incomer
incoming
incommensurability
incommensurable
incommensurableness
incommensurably
incommensurate
incommensurately
incommensurateness
incommiscibility
incommiscible
incommodate
incommodation
incommode
incommodement
incommodious
incommodiously
incommodiousness
incommodity
incommunicability
incommunicable
incommunicableness
incommunicably
incommunicado
incommunicative
incommunicatively
incommunicativeness
incommutability
incommutable
incommutableness
incommutably
incompact
incompactly
incompactness
incomparability
incomparable
incomparableness
incomparably
incompassionate
incompassionately
incompassionateness
incompatibility
incompatible
incompatibleness
incompatibly
incompendious
incompensated
incompensation
incompetence
incompetency
incompetent
incompetently
incompetentness
incompletability
incompletable
incompletableness
incomplete
incompleted
incompletely
incompleteness
incompletion
incomplex
incompliance
incompliancy
incompliant
incompliantly
incomplicate
incomplying
incomposed
incomposedly
incomposedness
incomposite
incompossibility
incompossible
incomprehended
incomprehending
incomprehendingly
incomprehensibility
incomprehensible
incomprehensibleness
incomprehensibly
incomprehension
incomprehensive
incomprehensively
incomprehensiveness
incompressibility
incompressible
incompressibleness
incompressibly
incomputable
inconcealable
inconceivability
inconceivable
inconceivableness
inconceivably
inconcinnate
inconcinnately
inconcinnity
inconcinnous
inconcludent
inconcluding
inconclusion
inconclusive
inconclusively
inconclusiveness
inconcrete
inconcurrent
inconcurring
incondensability
incondensable
incondensibility
incondensible
incondite
inconditionate
inconditioned
inconducive
inconfirm
inconformable
inconformably
inconformity
inconfused
inconfusedly
inconfusion
inconfutable
inconfutably
incongealable
incongealableness
incongenerous
incongenial
incongeniality
inconglomerate
incongruence
incongruent
incongruently
incongruity
incongruous
incongruously
incongruousness
inconjoinable
inconnected
inconnectedness
inconnu
inconscience
inconscient
inconsciently
inconscious
inconsciously
inconsecutive
inconsecutively
inconsecutiveness
inconsequence
inconsequent
inconsequential
inconsequentiality
inconsequentially
inconsequently
inconsequentness
inconsiderable
inconsiderableness
inconsiderably
inconsiderate
inconsiderately
inconsiderateness
inconsideration
inconsidered
inconsistence
inconsistency
inconsistent
inconsistently
inconsistentness
inconsolability
inconsolable
inconsolableness
inconsolably
inconsolate
inconsolately
inconsonance
inconsonant
inconsonantly
inconspicuous
inconspicuously
inconspicuousness
inconstancy
inconstant
inconstantly
inconstantness
inconstruable
inconsultable
inconsumable
inconsumably
inconsumed
incontaminable
incontaminate
incontaminateness
incontemptible
incontestability
incontestable
incontestableness
incontestably
incontinence
incontinency
incontinent
incontinently
incontinuity
incontinuous
incontracted
incontractile
incontraction
incontrollable
incontrollably
incontrolled
incontrovertibility
incontrovertible
incontrovertibleness
incontrovertibly
inconvenience
inconveniency
inconvenient
inconveniently
inconvenientness
inconversable
inconversant
inconversibility
inconvertibility
inconvertible
inconvertibleness
inconvertibly
inconvinced
inconvincedly
inconvincibility
inconvincible
inconvincibly
incopresentability
incopresentable
incoronate
incoronated
incoronation
incorporable
incorporate
incorporated
incorporatedness
incorporation
incorporative
incorporator
incorporeal
incorporealism
incorporealist
incorporeality
incorporealize
incorporeally
incorporeity
incorporeous
incorpse
incorrect
incorrection
incorrectly
incorrectness
incorrespondence
incorrespondency
incorrespondent
incorresponding
incorrigibility
incorrigible
incorrigibleness
incorrigibly
incorrodable
incorrodible
incorrosive
incorrupt
incorrupted
incorruptibility
Incorruptible
incorruptible
incorruptibleness
incorruptibly
incorruption
incorruptly
incorruptness
incourteous
incourteously
incrash
incrassate
incrassated
incrassation
incrassative
increasable
increasableness
increase
increasedly
increaseful
increasement
increaser
increasing
increasingly
increate
increately
increative
incredibility
incredible
incredibleness
incredibly
increditable
incredited
incredulity
incredulous
incredulously
incredulousness
increep
incremate
incremation
increment
incremental
incrementation
increpate
increpation
increscence
increscent
increst
incretion
incretionary
incretory
incriminate
incrimination
incriminator
incriminatory
incross
incrossbred
incrossing
incrotchet
incruent
incruental
incruentous
incrust
incrustant
Incrustata
incrustate
incrustation
incrustator
incrustive
incrustment
incrystal
incrystallizable
incubate
incubation
incubational
incubative
incubator
incubatorium
incubatory
incubi
incubous
incubus
incudal
incudate
incudectomy
incudes
incudomalleal
incudostapedial
inculcate
inculcation
inculcative
inculcator
inculcatory
inculpability
inculpable
inculpableness
inculpably
inculpate
inculpation
inculpative
inculpatory
incult
incultivation
inculture
incumbence
incumbency
incumbent
incumbentess
incumbently
incumber
incumberment
incumbrance
incumbrancer
incunable
incunabula
incunabular
incunabulist
incunabulum
incuneation
incur
incurability
incurable
incurableness
incurably
incuriosity
incurious
incuriously
incuriousness
incurrable
incurrence
incurrent
incurse
incursion
incursionist
incursive
incurvate
incurvation
incurvature
incurve
incus
incuse
incut
incutting
Ind
indaba
indaconitine
indagate
indagation
indagative
indagator
indagatory
indamine
indan
indane
Indanthrene
indanthrene
indart
indazin
indazine
indazol
indazole
inde
indebt
indebted
indebtedness
indebtment
indecence
indecency
indecent
indecently
indecentness
Indecidua
indeciduate
indeciduous
indecipherability
indecipherable
indecipherableness
indecipherably
indecision
indecisive
indecisively
indecisiveness
indeclinable
indeclinableness
indeclinably
indecomponible
indecomposable
indecomposableness
indecorous
indecorously
indecorousness
indecorum
indeed
indeedy
indefaceable
indefatigability
indefatigable
indefatigableness
indefatigably
indefeasibility
indefeasible
indefeasibleness
indefeasibly
indefeatable
indefectibility
indefectible
indefectibly
indefective
indefensibility
indefensible
indefensibleness
indefensibly
indefensive
indeficiency
indeficient
indeficiently
indefinable
indefinableness
indefinably
indefinite
indefinitely
indefiniteness
indefinitive
indefinitively
indefinitiveness
indefinitude
indefinity
indeflectible
indefluent
indeformable
indehiscence
indehiscent
indelectable
indelegability
indelegable
indeliberate
indeliberately
indeliberateness
indeliberation
indelibility
indelible
indelibleness
indelibly
indelicacy
indelicate
indelicately
indelicateness
indemnification
indemnificator
indemnificatory
indemnifier
indemnify
indemnitee
indemnitor
indemnity
indemnization
indemoniate
indemonstrability
indemonstrable
indemonstrableness
indemonstrably
indene
indent
indentation
indented
indentedly
indentee
indenter
indention
indentment
indentor
indenture
indentured
indentureship
indentwise
independable
independence
independency
independent
independentism
independently
Independista
indeposable
indeprehensible
indeprivability
indeprivable
inderivative
indescribability
indescribable
indescribableness
indescribably
indescript
indescriptive
indesert
indesignate
indesirable
indestructibility
indestructible
indestructibleness
indestructibly
indetectable
indeterminable
indeterminableness
indeterminably
indeterminacy
indeterminate
indeterminately
indeterminateness
indetermination
indeterminative
indetermined
indeterminism
indeterminist
indeterministic
indevirginate
indevoted
indevotion
indevotional
indevout
indevoutly
indevoutness
index
indexed
indexer
indexical
indexically
indexing
indexless
indexlessness
indexterity
India
indiadem
Indiaman
Indian
Indiana
indianaite
Indianan
Indianeer
Indianesque
Indianhood
Indianian
Indianism
Indianist
indianite
indianization
indianize
Indic
indic
indicable
indican
indicant
indicanuria
indicate
indication
indicative
indicatively
indicator
Indicatoridae
Indicatorinae
indicatory
indicatrix
indices
indicia
indicial
indicible
indicium
indicolite
indict
indictable
indictably
indictee
indicter
indiction
indictional
indictive
indictment
indictor
Indies
indiferous
indifference
indifferency
indifferent
indifferential
indifferentism
indifferentist
indifferentistic
indifferently
indigena
indigenal
indigenate
indigence
indigency
indigene
indigeneity
Indigenismo
indigenist
indigenity
indigenous
indigenously
indigenousness
indigent
indigently
indigested
indigestedness
indigestibility
indigestible
indigestibleness
indigestibly
indigestion
indigestive
indigitamenta
indigitate
indigitation
indign
indignance
indignancy
indignant
indignantly
indignation
indignatory
indignify
indignity
indignly
indigo
indigoberry
Indigofera
indigoferous
indigoid
indigotic
indigotin
indigotindisulphonic
indiguria
indimensible
indimensional
indiminishable
indimple
indirect
indirected
indirection
indirectly
indirectness
indirubin
indiscernibility
indiscernible
indiscernibleness
indiscernibly
indiscerptibility
indiscerptible
indiscerptibleness
indiscerptibly
indisciplinable
indiscipline
indisciplined
indiscoverable
indiscoverably
indiscovered
indiscreet
indiscreetly
indiscreetness
indiscrete
indiscretely
indiscretion
indiscretionary
indiscriminate
indiscriminated
indiscriminately
indiscriminateness
indiscriminating
indiscriminatingly
indiscrimination
indiscriminative
indiscriminatively
indiscriminatory
indiscussable
indiscussible
indispellable
indispensability
indispensable
indispensableness
indispensably
indispose
indisposed
indisposedness
indisposition
indisputability
indisputable
indisputableness
indisputably
indissipable
indissociable
indissolubility
indissoluble
indissolubleness
indissolubly
indissolute
indissolvability
indissolvable
indissolvableness
indissolvably
indissuadable
indissuadably
indistinct
indistinction
indistinctive
indistinctively
indistinctiveness
indistinctly
indistinctness
indistinguishability
indistinguishable
indistinguishableness
indistinguishably
indistinguished
indistortable
indistributable
indisturbable
indisturbance
indisturbed
indite
inditement
inditer
indium
indivertible
indivertibly
individable
individua
individual
individualism
individualist
individualistic
individualistically
individuality
individualization
individualize
individualizer
individualizingly
individually
individuate
individuation
individuative
individuator
individuity
individuum
indivinable
indivisibility
indivisible
indivisibleness
indivisibly
indivision
indocibility
indocible
indocibleness
indocile
indocility
indoctrinate
indoctrination
indoctrinator
indoctrine
indoctrinization
indoctrinize
Indogaea
Indogaean
indogen
indogenide
indole
indolence
indolent
indolently
indoles
indoline
Indologian
Indologist
Indologue
Indology
indoloid
indolyl
indomitability
indomitable
indomitableness
indomitably
Indone
Indonesian
indoor
indoors
indophenin
indophenol
Indophile
Indophilism
Indophilist
indorsation
indorse
indoxyl
indoxylic
indoxylsulphuric
Indra
indraft
indraught
indrawal
indrawing
indrawn
indri
Indris
indubious
indubiously
indubitable
indubitableness
indubitably
indubitatively
induce
induced
inducedly
inducement
inducer
induciae
inducible
inducive
induct
inductance
inductee
inducteous
inductile
inductility
induction
inductional
inductionally
inductionless
inductive
inductively
inductiveness
inductivity
inductometer
inductophone
inductor
inductorium
inductory
inductoscope
indue
induement
indulge
indulgeable
indulgement
indulgence
indulgenced
indulgency
indulgent
indulgential
indulgentially
indulgently
indulgentness
indulger
indulging
indulgingly
induline
indult
indulto
indument
indumentum
induna
induplicate
induplication
induplicative
indurable
indurate
induration
indurative
indurite
Indus
indusial
indusiate
indusiated
indusiform
indusioid
indusium
industrial
industrialism
industrialist
industrialization
industrialize
industrially
industrialness
industrious
industriously
industriousness
industrochemical
industry
induviae
induvial
induviate
indwell
indweller
indy
indyl
indylic
inearth
inebriacy
inebriant
inebriate
inebriation
inebriative
inebriety
inebrious
ineconomic
ineconomy
inedibility
inedible
inedited
Ineducabilia
ineducabilian
ineducability
ineducable
ineducation
ineffability
ineffable
ineffableness
ineffably
ineffaceability
ineffaceable
ineffaceably
ineffectible
ineffectibly
ineffective
ineffectively
ineffectiveness
ineffectual
ineffectuality
ineffectually
ineffectualness
ineffervescence
ineffervescent
ineffervescibility
ineffervescible
inefficacious
inefficaciously
inefficaciousness
inefficacity
inefficacy
inefficience
inefficiency
inefficient
inefficiently
ineffulgent
inelaborate
inelaborated
inelaborately
inelastic
inelasticate
inelasticity
inelegance
inelegancy
inelegant
inelegantly
ineligibility
ineligible
ineligibleness
ineligibly
ineliminable
ineloquence
ineloquent
ineloquently
ineluctability
ineluctable
ineluctably
ineludible
ineludibly
inembryonate
inemendable
inemotivity
inemulous
inenarrable
inenergetic
inenubilable
inenucleable
inept
ineptitude
ineptly
ineptness
inequable
inequal
inequalitarian
inequality
inequally
inequalness
inequation
inequiaxial
inequicostate
inequidistant
inequigranular
inequilateral
inequilibrium
inequilobate
inequilobed
inequipotential
inequipotentiality
inequitable
inequitableness
inequitably
inequity
inequivalent
inequivalve
inequivalvular
ineradicable
ineradicableness
ineradicably
inerasable
inerasableness
inerasably
inerasible
Ineri
inerm
Inermes
Inermi
Inermia
inermous
inerrability
inerrable
inerrableness
inerrably
inerrancy
inerrant
inerrantly
inerratic
inerring
inerringly
inerroneous
inert
inertance
inertia
inertial
inertion
inertly
inertness
inerubescent
inerudite
ineruditely
inerudition
inescapable
inescapableness
inescapably
inesculent
inescutcheon
inesite
inessential
inessentiality
inestimability
inestimable
inestimableness
inestimably
inestivation
inethical
ineunt
ineuphonious
inevadible
inevadibly
inevaporable
inevasible
inevidence
inevident
inevitability
inevitable
inevitableness
inevitably
inexact
inexacting
inexactitude
inexactly
inexactness
inexcellence
inexcitability
inexcitable
inexclusive
inexclusively
inexcommunicable
inexcusability
inexcusable
inexcusableness
inexcusably
inexecutable
inexecution
inexertion
inexhausted
inexhaustedly
inexhaustibility
inexhaustible
inexhaustibleness
inexhaustibly
inexhaustive
inexhaustively
inexigible
inexist
inexistence
inexistency
inexistent
inexorability
inexorable
inexorableness
inexorably
inexpansible
inexpansive
inexpectancy
inexpectant
inexpectation
inexpected
inexpectedly
inexpectedness
inexpedience
inexpediency
inexpedient
inexpediently
inexpensive
inexpensively
inexpensiveness
inexperience
inexperienced
inexpert
inexpertly
inexpertness
inexpiable
inexpiableness
inexpiably
inexpiate
inexplainable
inexplicability
inexplicable
inexplicableness
inexplicables
inexplicably
inexplicit
inexplicitly
inexplicitness
inexplorable
inexplosive
inexportable
inexposable
inexposure
inexpress
inexpressibility
inexpressible
inexpressibleness
inexpressibles
inexpressibly
inexpressive
inexpressively
inexpressiveness
inexpugnability
inexpugnable
inexpugnableness
inexpugnably
inexpungeable
inexpungible
inextant
inextended
inextensibility
inextensible
inextensile
inextension
inextensional
inextensive
inexterminable
inextinct
inextinguishable
inextinguishably
inextirpable
inextirpableness
inextricability
inextricable
inextricableness
inextricably
Inez
inface
infall
infallibilism
infallibilist
infallibility
infallible
infallibleness
infallibly
infalling
infalsificable
infame
infamiliar
infamiliarity
infamize
infamonize
infamous
infamously
infamousness
infamy
infancy
infand
infandous
infang
infanglement
infangthief
infant
infanta
infantado
infante
infanthood
infanticidal
infanticide
infantile
infantilism
infantility
infantine
infantlike
infantry
infantryman
infarct
infarctate
infarcted
infarction
infare
infatuate
infatuatedly
infatuation
infatuator
infaust
infeasibility
infeasible
infeasibleness
infect
infectant
infected
infectedness
infecter
infectible
infection
infectionist
infectious
infectiously
infectiousness
infective
infectiveness
infectivity
infector
infectress
infectuous
infecund
infecundity
infeed
infeft
infeftment
infelicific
infelicitous
infelicitously
infelicitousness
infelicity
infelonious
infelt
infeminine
infer
inferable
inference
inferent
inferential
inferentialism
inferentialist
inferentially
inferior
inferiorism
inferiority
inferiorize
inferiorly
infern
infernal
infernalism
infernality
infernalize
infernally
infernalry
infernalship
inferno
inferoanterior
inferobranchiate
inferofrontal
inferolateral
inferomedian
inferoposterior
inferrer
inferribility
inferrible
inferringly
infertile
infertilely
infertileness
infertility
infest
infestant
infestation
infester
infestive
infestivity
infestment
infeudation
infibulate
infibulation
inficete
infidel
infidelic
infidelical
infidelism
infidelistic
infidelity
infidelize
infidelly
infield
infielder
infieldsman
infighter
infighting
infill
infilling
infilm
infilter
infiltrate
infiltration
infiltrative
infinitant
infinitarily
infinitary
infinitate
infinitation
infinite
infinitely
infiniteness
infinitesimal
infinitesimalism
infinitesimality
infinitesimally
infinitesimalness
infiniteth
infinitieth
infinitival
infinitivally
infinitive
infinitively
infinitize
infinitude
infinituple
infinity
infirm
infirmarer
infirmaress
infirmarian
infirmary
infirmate
infirmation
infirmative
infirmity
infirmly
infirmness
infissile
infit
infitter
infix
infixion
inflame
inflamed
inflamedly
inflamedness
inflamer
inflaming
inflamingly
inflammability
inflammable
inflammableness
inflammably
inflammation
inflammative
inflammatorily
inflammatory
inflatable
inflate
inflated
inflatedly
inflatedness
inflater
inflatile
inflatingly
inflation
inflationary
inflationism
inflationist
inflative
inflatus
inflect
inflected
inflectedness
inflection
inflectional
inflectionally
inflectionless
inflective
inflector
inflex
inflexed
inflexibility
inflexible
inflexibleness
inflexibly
inflexive
inflict
inflictable
inflicter
infliction
inflictive
inflood
inflorescence
inflorescent
inflow
inflowering
influence
influenceable
influencer
influencive
influent
influential
influentiality
influentially
influenza
influenzal
influenzic
influx
influxable
influxible
influxibly
influxion
influxionism
infold
infolder
infolding
infoldment
infoliate
inform
informable
informal
informality
informalize
informally
informant
information
informational
informative
informatively
informatory
informed
informedly
informer
informidable
informingly
informity
infortiate
infortitude
infortunate
infortunately
infortunateness
infortune
infra
infrabasal
infrabestial
infrabranchial
infrabuccal
infracanthal
infracaudal
infracelestial
infracentral
infracephalic
infraclavicle
infraclavicular
infraclusion
infraconscious
infracortical
infracostal
infracostalis
infracotyloid
infract
infractible
infraction
infractor
infradentary
infradiaphragmatic
infragenual
infraglacial
infraglenoid
infraglottic
infragrant
infragular
infrahuman
infrahyoid
infralabial
infralapsarian
infralapsarianism
infralinear
infralittoral
inframammary
inframammillary
inframandibular
inframarginal
inframaxillary
inframedian
inframercurial
inframercurian
inframolecular
inframontane
inframundane
infranatural
infranaturalism
infrangibility
infrangible
infrangibleness
infrangibly
infranodal
infranuclear
infraoccipital
infraocclusion
infraocular
infraoral
infraorbital
infraordinary
infrapapillary
infrapatellar
infraperipherial
infrapose
infraposition
infraprotein
infrapubian
infraradular
infrared
infrarenal
infrarenally
infrarimal
infrascapular
infrascapularis
infrascientific
infraspinal
infraspinate
infraspinatus
infraspinous
infrastapedial
infrasternal
infrastigmatal
infrastipular
infrastructure
infrasutral
infratemporal
infraterrene
infraterritorial
infrathoracic
infratonsillar
infratracheal
infratrochanteric
infratrochlear
infratubal
infraturbinal
infravaginal
infraventral
infrequency
infrequent
infrequently
infrigidate
infrigidation
infrigidative
infringe
infringement
infringer
infringible
infructiferous
infructuose
infructuosity
infructuous
infructuously
infrugal
infrustrable
infrustrably
infula
infumate
infumated
infumation
infundibular
Infundibulata
infundibulate
infundibuliform
infundibulum
infuriate
infuriately
infuriatingly
infuriation
infuscate
infuscation
infuse
infusedly
infuser
infusibility
infusible
infusibleness
infusile
infusion
infusionism
infusionist
infusive
Infusoria
infusorial
infusorian
infusoriform
infusorioid
infusorium
infusory
Ing
ing
Inga
Ingaevones
Ingaevonic
ingallantry
ingate
ingather
ingatherer
ingathering
ingeldable
ingeminate
ingemination
ingenerability
ingenerable
ingenerably
ingenerate
ingenerately
ingeneration
ingenerative
ingeniosity
ingenious
ingeniously
ingeniousness
ingenit
ingenue
ingenuity
ingenuous
ingenuously
ingenuousness
Inger
ingerminate
ingest
ingesta
ingestible
ingestion
ingestive
Inghamite
Inghilois
ingiver
ingiving
ingle
inglenook
ingleside
inglobate
inglobe
inglorious
ingloriously
ingloriousness
inglutition
ingluvial
ingluvies
ingluviitis
ingoing
Ingomar
ingot
ingotman
ingraft
ingrain
ingrained
ingrainedly
ingrainedness
Ingram
ingrammaticism
ingrandize
ingrate
ingrateful
ingratefully
ingratefulness
ingrately
ingratiate
ingratiating
ingratiatingly
ingratiation
ingratiatory
ingratitude
ingravescent
ingravidate
ingravidation
ingredient
ingress
ingression
ingressive
ingressiveness
ingross
ingrow
ingrown
ingrownness
ingrowth
inguen
inguinal
inguinoabdominal
inguinocrural
inguinocutaneous
inguinodynia
inguinolabial
inguinoscrotal
Inguklimiut
ingulf
ingulfment
ingurgitate
ingurgitation
Ingush
inhabit
inhabitability
inhabitable
inhabitancy
inhabitant
inhabitation
inhabitative
inhabitativeness
inhabited
inhabitedness
inhabiter
inhabitiveness
inhabitress
inhalant
inhalation
inhalator
inhale
inhalement
inhalent
inhaler
inharmonic
inharmonical
inharmonious
inharmoniously
inharmoniousness
inharmony
inhaul
inhauler
inhaust
inhaustion
inhearse
inheaven
inhere
inherence
inherency
inherent
inherently
inherit
inheritability
inheritable
inheritableness
inheritably
inheritage
inheritance
inheritor
inheritress
inheritrice
inheritrix
inhesion
inhiate
inhibit
inhibitable
inhibiter
inhibition
inhibitionist
inhibitive
inhibitor
inhibitory
inhomogeneity
inhomogeneous
inhomogeneously
inhospitable
inhospitableness
inhospitably
inhospitality
inhuman
inhumane
inhumanely
inhumanism
inhumanity
inhumanize
inhumanly
inhumanness
inhumate
inhumation
inhumationist
inhume
inhumer
inhumorous
inhumorously
Inia
inial
inidoneity
inidoneous
Inigo
inimicable
inimical
inimicality
inimically
inimicalness
inimitability
inimitable
inimitableness
inimitably
iniome
Iniomi
iniomous
inion
iniquitable
iniquitably
iniquitous
iniquitously
iniquitousness
iniquity
inirritability
inirritable
inirritant
inirritative
inissuable
initial
initialer
initialist
initialize
initially
initiant
initiary
initiate
initiation
initiative
initiatively
initiator
initiatorily
initiatory
initiatress
initiatrix
initis
initive
inject
injectable
injection
injector
injelly
injudicial
injudicially
injudicious
injudiciously
injudiciousness
Injun
injunct
injunction
injunctive
injunctively
injurable
injure
injured
injuredly
injuredness
injurer
injurious
injuriously
injuriousness
injury
injustice
ink
inkberry
inkbush
inken
inker
Inkerman
inket
inkfish
inkholder
inkhorn
inkhornism
inkhornist
inkhornize
inkhornizer
inkindle
inkiness
inkish
inkle
inkless
inklike
inkling
inkmaker
inkmaking
inknot
inkosi
inkpot
Inkra
inkroot
inks
inkshed
inkslinger
inkslinging
inkstain
inkstand
inkstandish
inkstone
inkweed
inkwell
inkwood
inkwriter
inky
inlagation
inlaid
inlaik
inlake
inland
inlander
inlandish
inlaut
inlaw
inlawry
inlay
inlayer
inlaying
inleague
inleak
inleakage
inlet
inlier
inlook
inlooker
inly
inlying
inmate
inmeats
inmixture
inmost
inn
innascibility
innascible
innate
innately
innateness
innatism
innative
innatural
innaturality
innaturally
inneity
inner
innerly
innermore
innermost
innermostly
innerness
innervate
innervation
innervational
innerve
inness
innest
innet
innholder
inning
inninmorite
Innisfail
innkeeper
innless
innocence
innocency
innocent
innocently
innocentness
innocuity
innocuous
innocuously
innocuousness
innominable
innominables
innominata
innominate
innominatum
innovant
innovate
innovation
innovational
innovationist
innovative
innovator
innovatory
innoxious
innoxiously
innoxiousness
innuendo
Innuit
innumerability
innumerable
innumerableness
innumerably
innumerous
innutrient
innutrition
innutritious
innutritive
innyard
Ino
inobedience
inobedient
inobediently
inoblast
inobnoxious
inobscurable
inobservable
inobservance
inobservancy
inobservant
inobservantly
inobservantness
inobservation
inobtainable
inobtrusive
inobtrusively
inobtrusiveness
inobvious
Inocarpus
inoccupation
Inoceramus
inochondritis
inochondroma
inoculability
inoculable
inoculant
inocular
inoculate
inoculation
inoculative
inoculator
inoculum
inocystoma
inocyte
Inodes
inodorous
inodorously
inodorousness
inoepithelioma
inoffending
inoffensive
inoffensively
inoffensiveness
inofficial
inofficially
inofficiosity
inofficious
inofficiously
inofficiousness
inogen
inogenesis
inogenic
inogenous
inoglia
inohymenitic
inolith
inoma
inominous
inomyoma
inomyositis
inomyxoma
inone
inoneuroma
inoperable
inoperative
inoperativeness
inopercular
Inoperculata
inoperculate
inopinable
inopinate
inopinately
inopine
inopportune
inopportunely
inopportuneness
inopportunism
inopportunist
inopportunity
inoppressive
inoppugnable
inopulent
inorb
inorderly
inordinacy
inordinary
inordinate
inordinately
inordinateness
inorganic
inorganical
inorganically
inorganizable
inorganization
inorganized
inoriginate
inornate
inosclerosis
inoscopy
inosculate
inosculation
inosic
inosin
inosinic
inosite
inositol
inostensible
inostensibly
inotropic
inower
inoxidability
inoxidable
inoxidizable
inoxidize
inparabola
inpardonable
inpatient
inpayment
inpensioner
inphase
inpolygon
inpolyhedron
inport
inpour
inpush
input
inquaintance
inquartation
inquest
inquestual
inquiet
inquietation
inquietly
inquietness
inquietude
Inquilinae
inquiline
inquilinism
inquilinity
inquilinous
inquinate
inquination
inquirable
inquirant
inquiration
inquire
inquirendo
inquirent
inquirer
inquiring
inquiringly
inquiry
inquisite
inquisition
inquisitional
inquisitionist
inquisitive
inquisitively
inquisitiveness
inquisitor
inquisitorial
inquisitorially
inquisitorialness
inquisitorious
inquisitorship
inquisitory
inquisitress
inquisitrix
inquisiturient
inradius
inreality
inrigged
inrigger
inrighted
inring
inro
inroad
inroader
inroll
inrooted
inrub
inrun
inrunning
inruption
inrush
insack
insagacity
insalivate
insalivation
insalubrious
insalubrity
insalutary
insalvability
insalvable
insane
insanely
insaneness
insanify
insanitariness
insanitary
insanitation
insanity
insapiency
insapient
insatiability
insatiable
insatiableness
insatiably
insatiate
insatiated
insatiately
insatiateness
insatiety
insatisfaction
insatisfactorily
insaturable
inscenation
inscibile
inscience
inscient
inscribable
inscribableness
inscribe
inscriber
inscript
inscriptible
inscription
inscriptional
inscriptioned
inscriptionist
inscriptionless
inscriptive
inscriptively
inscriptured
inscroll
inscrutability
inscrutable
inscrutableness
inscrutables
inscrutably
insculp
insculpture
insea
inseam
insect
Insecta
insectan
insectarium
insectary
insectean
insected
insecticidal
insecticide
insectiferous
insectiform
insectifuge
insectile
insectine
insection
insectival
Insectivora
insectivore
insectivorous
insectlike
insectmonger
insectologer
insectologist
insectology
insectproof
insecure
insecurely
insecureness
insecurity
insee
inseer
inselberg
inseminate
insemination
insenescible
insensate
insensately
insensateness
insense
insensibility
insensibilization
insensibilize
insensibilizer
insensible
insensibleness
insensibly
insensitive
insensitiveness
insensitivity
insensuous
insentience
insentiency
insentient
inseparability
inseparable
inseparableness
inseparably
inseparate
inseparately
insequent
insert
insertable
inserted
inserter
insertion
insertional
insertive
inserviceable
insessor
Insessores
insessorial
inset
insetter
inseverable
inseverably
inshave
insheathe
inshell
inshining
inship
inshoe
inshoot
inshore
inside
insider
insidiosity
insidious
insidiously
insidiousness
insight
insightful
insigne
insignia
insignificance
insignificancy
insignificant
insignificantly
insimplicity
insincere
insincerely
insincerity
insinking
insinuant
insinuate
insinuating
insinuatingly
insinuation
insinuative
insinuatively
insinuativeness
insinuator
insinuatory
insinuendo
insipid
insipidity
insipidly
insipidness
insipience
insipient
insipiently
insist
insistence
insistency
insistent
insistently
insister
insistingly
insistive
insititious
insnare
insnarement
insnarer
insobriety
insociability
insociable
insociableness
insociably
insocial
insocially
insofar
insolate
insolation
insole
insolence
insolency
insolent
insolently
insolentness
insolid
insolidity
insolubility
insoluble
insolubleness
insolubly
insolvability
insolvable
insolvably
insolvence
insolvency
insolvent
insomnia
insomniac
insomnious
insomnolence
insomnolency
insomnolent
insomuch
insonorous
insooth
insorb
insorbent
insouciance
insouciant
insouciantly
insoul
inspan
inspeak
inspect
inspectability
inspectable
inspectingly
inspection
inspectional
inspectioneer
inspective
inspector
inspectoral
inspectorate
inspectorial
inspectorship
inspectress
inspectrix
inspheration
insphere
inspirability
inspirable
inspirant
inspiration
inspirational
inspirationalism
inspirationally
inspirationist
inspirative
inspirator
inspiratory
inspiratrix
inspire
inspired
inspiredly
inspirer
inspiring
inspiringly
inspirit
inspiriter
inspiriting
inspiritingly
inspiritment
inspirometer
inspissant
inspissate
inspissation
inspissator
inspissosis
inspoke
inspoken
inspreith
instability
instable
install
installant
installation
installer
installment
instance
instancy
instanding
instant
instantaneity
instantaneous
instantaneously
instantaneousness
instanter
instantial
instantly
instantness
instar
instate
instatement
instaurate
instauration
instaurator
instead
instealing
insteam
insteep
instellation
instep
instigant
instigate
instigatingly
instigation
instigative
instigator
instigatrix
instill
instillation
instillator
instillatory
instiller
instillment
instinct
instinctive
instinctively
instinctivist
instinctivity
instinctual
instipulate
institor
institorial
institorian
institory
institute
instituter
institution
institutional
institutionalism
institutionalist
institutionality
institutionalization
institutionalize
institutionally
institutionary
institutionize
institutive
institutively
institutor
institutress
institutrix
instonement
instratified
instreaming
instrengthen
instressed
instroke
instruct
instructed
instructedly
instructedness
instructer
instructible
instruction
instructional
instructionary
instructive
instructively
instructiveness
instructor
instructorship
instructress
instrument
instrumental
instrumentalism
instrumentalist
instrumentality
instrumentalize
instrumentally
instrumentary
instrumentate
instrumentation
instrumentative
instrumentist
instrumentman
insuavity
insubduable
insubjection
insubmergible
insubmersible
insubmission
insubmissive
insubordinate
insubordinately
insubordinateness
insubordination
insubstantial
insubstantiality
insubstantiate
insubstantiation
insubvertible
insuccess
insuccessful
insucken
insuetude
insufferable
insufferableness
insufferably
insufficience
insufficiency
insufficient
insufficiently
insufflate
insufflation
insufflator
insula
insulance
insulant
insular
insularism
insularity
insularize
insularly
insulary
insulate
insulated
insulating
insulation
insulator
insulin
insulize
insulse
insulsity
insult
insultable
insultant
insultation
insulter
insulting
insultingly
insultproof
insunk
insuperability
insuperable
insuperableness
insuperably
insupportable
insupportableness
insupportably
insupposable
insuppressible
insuppressibly
insuppressive
insurability
insurable
insurance
insurant
insure
insured
insurer
insurge
insurgence
insurgency
insurgent
insurgentism
insurgescence
insurmountability
insurmountable
insurmountableness
insurmountably
insurpassable
insurrect
insurrection
insurrectional
insurrectionally
insurrectionary
insurrectionism
insurrectionist
insurrectionize
insurrectory
insusceptibility
insusceptible
insusceptibly
insusceptive
inswamp
inswarming
insweeping
inswell
inswept
inswing
inswinger
intabulate
intact
intactile
intactly
intactness
intagliated
intagliation
intaglio
intagliotype
intake
intaker
intangibility
intangible
intangibleness
intangibly
intarissable
intarsia
intarsiate
intarsist
intastable
intaxable
intechnicality
integer
integrability
integrable
integral
integrality
integralization
integralize
integrally
integrand
integrant
integraph
integrate
integration
integrative
integrator
integrifolious
integrious
integriously
integripalliate
integrity
integrodifferential
integropallial
Integropallialia
Integropalliata
integropalliate
integument
integumental
integumentary
integumentation
inteind
intellect
intellectation
intellected
intellectible
intellection
intellective
intellectively
intellectual
intellectualism
intellectualist
intellectualistic
intellectualistically
intellectuality
intellectualization
intellectualize
intellectualizer
intellectually
intellectualness
intelligence
intelligenced
intelligencer
intelligency
intelligent
intelligential
intelligently
intelligentsia
intelligibility
intelligible
intelligibleness
intelligibly
intelligize
intemerate
intemerately
intemerateness
intemeration
intemperable
intemperably
intemperament
intemperance
intemperate
intemperately
intemperateness
intemperature
intempestive
intempestively
intempestivity
intemporal
intemporally
intenability
intenable
intenancy
intend
intendance
intendancy
intendant
intendantism
intendantship
intended
intendedly
intendedness
intendence
intender
intendible
intending
intendingly
intendit
intendment
intenerate
inteneration
intenible
intensate
intensation
intensative
intense
intensely
intenseness
intensification
intensifier
intensify
intension
intensional
intensionally
intensitive
intensity
intensive
intensively
intensiveness
intent
intention
intentional
intentionalism
intentionality
intentionally
intentioned
intentionless
intentive
intentively
intentiveness
intently
intentness
inter
interabsorption
interacademic
interaccessory
interaccuse
interacinar
interacinous
interact
interaction
interactional
interactionism
interactionist
interactive
interactivity
interadaptation
interadditive
interadventual
interaffiliation
interagency
interagent
interagglutinate
interagglutination
interagree
interagreement
interalar
interallied
interally
interalveolar
interambulacral
interambulacrum
interamnian
interangular
interanimate
interannular
interantagonism
interantennal
interantennary
interapophyseal
interapplication
interarboration
interarch
interarcualis
interarmy
interarticular
interartistic
interarytenoid
interassociation
interassure
interasteroidal
interastral
interatomic
interatrial
interattrition
interaulic
interaural
interauricular
interavailability
interavailable
interaxal
interaxial
interaxillary
interaxis
interbalance
interbanded
interbank
interbedded
interbelligerent
interblend
interbody
interbonding
interborough
interbourse
interbrachial
interbrain
interbranch
interbranchial
interbreath
interbreed
interbrigade
interbring
interbronchial
intercadence
intercadent
intercalare
intercalarily
intercalarium
intercalary
intercalate
intercalation
intercalative
intercalatory
intercale
intercalm
intercanal
intercanalicular
intercapillary
intercardinal
intercarotid
intercarpal
intercarpellary
intercarrier
intercartilaginous
intercaste
intercatenated
intercausative
intercavernous
intercede
interceder
intercellular
intercensal
intercentral
intercentrum
intercept
intercepter
intercepting
interception
interceptive
interceptor
interceptress
intercerebral
intercession
intercessional
intercessionary
intercessionment
intercessive
intercessor
intercessorial
intercessory
interchaff
interchange
interchangeability
interchangeable
interchangeableness
interchangeably
interchanger
interchapter
intercharge
interchase
intercheck
interchoke
interchondral
interchurch
Intercidona
interciliary
intercilium
intercircle
intercirculate
intercirculation
intercision
intercitizenship
intercity
intercivic
intercivilization
interclash
interclasp
interclass
interclavicle
interclavicular
interclerical
intercloud
interclub
intercoastal
intercoccygeal
intercoccygean
intercohesion
intercollege
intercollegian
intercollegiate
intercolline
intercolonial
intercolonially
intercolonization
intercolumn
intercolumnal
intercolumnar
intercolumniation
intercom
intercombat
intercombination
intercombine
intercome
intercommission
intercommon
intercommonable
intercommonage
intercommoner
intercommunal
intercommune
intercommuner
intercommunicability
intercommunicable
intercommunicate
intercommunication
intercommunicative
intercommunicator
intercommunion
intercommunity
intercompany
intercomparable
intercompare
intercomparison
intercomplexity
intercomplimentary
interconal
interconciliary
intercondenser
intercondylar
intercondylic
intercondyloid
interconfessional
interconfound
interconnect
interconnection
intercontinental
intercontorted
intercontradiction
intercontradictory
interconversion
interconvertibility
interconvertible
interconvertibly
intercooler
intercooling
intercoracoid
intercorporate
intercorpuscular
intercorrelate
intercorrelation
intercortical
intercosmic
intercosmically
intercostal
intercostally
intercostobrachial
intercostohumeral
intercotylar
intercounty
intercourse
intercoxal
intercranial
intercreate
intercrescence
intercrinal
intercrop
intercross
intercrural
intercrust
intercrystalline
intercrystallization
intercrystallize
intercultural
interculture
intercurl
intercurrence
intercurrent
intercurrently
intercursation
intercuspidal
intercutaneous
intercystic
interdash
interdebate
interdenominational
interdental
interdentally
interdentil
interdepartmental
interdepartmentally
interdepend
interdependable
interdependence
interdependency
interdependent
interdependently
interderivative
interdespise
interdestructive
interdestructiveness
interdetermination
interdetermine
interdevour
interdict
interdiction
interdictive
interdictor
interdictory
interdictum
interdifferentiation
interdiffuse
interdiffusion
interdiffusive
interdiffusiveness
interdigital
interdigitate
interdigitation
interdine
interdiscal
interdispensation
interdistinguish
interdistrict
interdivision
interdome
interdorsal
interdrink
intereat
interelectrode
interelectrodic
interempire
interenjoy
interentangle
interentanglement
interepidemic
interepimeral
interepithelial
interequinoctial
interessee
interest
interested
interestedly
interestedness
interester
interesting
interestingly
interestingness
interestless
interestuarine
interface
interfacial
interfactional
interfamily
interfascicular
interfault
interfector
interfederation
interfemoral
interfenestral
interfenestration
interferant
interfere
interference
interferent
interferential
interferer
interfering
interferingly
interferingness
interferometer
interferometry
interferric
interfertile
interfertility
interfibrillar
interfibrillary
interfibrous
interfilamentar
interfilamentary
interfilamentous
interfilar
interfiltrate
interfinger
interflange
interflashing
interflow
interfluence
interfluent
interfluminal
interfluous
interfluve
interfluvial
interflux
interfold
interfoliaceous
interfoliar
interfoliate
interfollicular
interforce
interfraternal
interfraternity
interfret
interfretted
interfriction
interfrontal
interfruitful
interfulgent
interfuse
interfusion
interganglionic
intergenerant
intergenerating
intergeneration
intergential
intergesture
intergilt
interglacial
interglandular
interglobular
interglyph
intergossip
intergovernmental
intergradation
intergrade
intergradient
intergraft
intergranular
intergrapple
intergrave
intergroupal
intergrow
intergrown
intergrowth
intergular
intergyral
interhabitation
interhemal
interhemispheric
interhostile
interhuman
interhyal
interhybridize
interim
interimist
interimistic
interimistical
interimistically
interimperial
interincorporation
interindependence
interindicate
interindividual
interinfluence
interinhibition
interinhibitive
interinsert
interinsular
interinsurance
interinsurer
interinvolve
interionic
interior
interiority
interiorize
interiorly
interiorness
interirrigation
interisland
interjacence
interjacency
interjacent
interjaculate
interjaculatory
interjangle
interjealousy
interject
interjection
interjectional
interjectionalize
interjectionally
interjectionary
interjectionize
interjectiveness
interjector
interjectorily
interjectory
interjectural
interjoin
interjoist
interjudgment
interjunction
interkinesis
interkinetic
interknit
interknot
interknow
interknowledge
interlaboratory
interlace
interlaced
interlacedly
interlacement
interlacery
interlacustrine
interlaid
interlake
interlamellar
interlamellation
interlaminar
interlaminate
interlamination
interlanguage
interlap
interlapse
interlard
interlardation
interlardment
interlatitudinal
interlaudation
interlay
interleaf
interleague
interleave
interleaver
interlibel
interlibrary
interlie
interligamentary
interligamentous
interlight
interlimitation
interline
interlineal
interlineally
interlinear
interlinearily
interlinearly
interlineary
interlineate
interlineation
interlinement
interliner
Interlingua
interlingual
interlinguist
interlinguistic
interlining
interlink
interloan
interlobar
interlobate
interlobular
interlocal
interlocally
interlocate
interlocation
interlock
interlocker
interlocular
interloculus
interlocution
interlocutive
interlocutor
interlocutorily
interlocutory
interlocutress
interlocutrice
interlocutrix
interloop
interlope
interloper
interlot
interlucation
interlucent
interlude
interluder
interludial
interlunar
interlunation
interlying
intermalleolar
intermammary
intermammillary
intermandibular
intermanorial
intermarginal
intermarine
intermarriage
intermarriageable
intermarry
intermason
intermastoid
intermat
intermatch
intermaxilla
intermaxillar
intermaxillary
intermaze
intermeasurable
intermeasure
intermeddle
intermeddlement
intermeddler
intermeddlesome
intermeddlesomeness
intermeddling
intermeddlingly
intermediacy
intermediae
intermedial
intermediary
intermediate
intermediately
intermediateness
intermediation
intermediator
intermediatory
intermedium
intermedius
intermeet
intermelt
intermembral
intermembranous
intermeningeal
intermenstrual
intermenstruum
interment
intermental
intermention
intermercurial
intermesenterial
intermesenteric
intermesh
intermessage
intermessenger
intermetacarpal
intermetallic
intermetameric
intermetatarsal
intermew
intermewed
intermewer
intermezzo
intermigration
interminability
interminable
interminableness
interminably
interminant
interminate
intermine
intermingle
intermingledom
interminglement
interminister
interministerial
interministerium
intermission
intermissive
intermit
intermitted
intermittedly
intermittence
intermittency
intermittent
intermittently
intermitter
intermitting
intermittingly
intermix
intermixedly
intermixtly
intermixture
intermobility
intermodification
intermodillion
intermodulation
intermolar
intermolecular
intermomentary
intermontane
intermorainic
intermotion
intermountain
intermundane
intermundial
intermundian
intermundium
intermunicipal
intermunicipality
intermural
intermuscular
intermutation
intermutual
intermutually
intermutule
intern
internal
internality
internalization
internalize
internally
internalness
internals
internarial
internasal
internation
international
internationalism
internationalist
internationality
internationalization
internationalize
internationally
interneciary
internecinal
internecine
internecion
internecive
internee
internetted
interneural
interneuronic
internidal
internist
internment
internobasal
internodal
internode
internodial
internodian
internodium
internodular
internship
internuclear
internuncial
internunciary
internunciatory
internuncio
internuncioship
internuncius
internuptial
interobjective
interoceanic
interoceptive
interoceptor
interocular
interoffice
interolivary
interopercle
interopercular
interoperculum
interoptic
interorbital
interorbitally
interoscillate
interosculant
interosculate
interosculation
interosseal
interosseous
interownership
interpage
interpalatine
interpalpebral
interpapillary
interparenchymal
interparental
interparenthetical
interparenthetically
interparietal
interparietale
interparliament
interparliamentary
interparoxysmal
interparty
interpause
interpave
interpeal
interpectoral
interpeduncular
interpel
interpellant
interpellate
interpellation
interpellator
interpenetrable
interpenetrant
interpenetrate
interpenetration
interpenetrative
interpenetratively
interpermeate
interpersonal
interpervade
interpetaloid
interpetiolar
interpetiolary
interphalangeal
interphase
interphone
interpiece
interpilaster
interpilastering
interplacental
interplait
interplanetary
interplant
interplanting
interplay
interplea
interplead
interpleader
interpledge
interpleural
interplical
interplicate
interplication
interplight
interpoint
interpolable
interpolar
interpolary
interpolate
interpolater
interpolation
interpolative
interpolatively
interpolator
interpole
interpolitical
interpolity
interpollinate
interpolymer
interpone
interportal
interposable
interposal
interpose
interposer
interposing
interposingly
interposition
interposure
interpour
interprater
interpressure
interpret
interpretability
interpretable
interpretableness
interpretably
interpretament
interpretation
interpretational
interpretative
interpretatively
interpreter
interpretership
interpretive
interpretively
interpretorial
interpretress
interprismatic
interproduce
interprofessional
interproglottidal
interproportional
interprotoplasmic
interprovincial
interproximal
interproximate
interpterygoid
interpubic
interpulmonary
interpunct
interpunction
interpunctuate
interpunctuation
interpupillary
interquarrel
interquarter
interrace
interracial
interracialism
interradial
interradially
interradiate
interradiation
interradium
interradius
interrailway
interramal
interramicorn
interramification
interreceive
interreflection
interregal
interregimental
interregional
interregna
interregnal
interregnum
interreign
interrelate
interrelated
interrelatedly
interrelatedness
interrelation
interrelationship
interreligious
interrenal
interrenalism
interrepellent
interrepulsion
interrer
interresponsibility
interresponsible
interreticular
interreticulation
interrex
interrhyme
interright
interriven
interroad
interrogability
interrogable
interrogant
interrogate
interrogatedness
interrogatee
interrogatingly
interrogation
interrogational
interrogative
interrogatively
interrogator
interrogatorily
interrogatory
interrogatrix
interrogee
interroom
interrule
interrun
interrupt
interrupted
interruptedly
interruptedness
interrupter
interruptible
interrupting
interruptingly
interruption
interruptive
interruptively
interruptor
interruptory
intersale
intersalute
interscapilium
interscapular
interscapulum
interscene
interscholastic
interschool
interscience
interscribe
interscription
interseaboard
interseamed
intersect
intersectant
intersection
intersectional
intersegmental
interseminal
intersentimental
interseptal
intersertal
intersesamoid
intersession
intersessional
interset
intersex
intersexual
intersexualism
intersexuality
intershade
intershifting
intershock
intershoot
intershop
intersidereal
intersituate
intersocial
intersocietal
intersociety
intersole
intersolubility
intersoluble
intersomnial
intersomnious
intersonant
intersow
interspace
interspatial
interspatially
interspeaker
interspecial
interspecific
interspersal
intersperse
interspersedly
interspersion
interspheral
intersphere
interspicular
interspinal
interspinalis
interspinous
interspiral
interspiration
intersporal
intersprinkle
intersqueeze
interstadial
interstage
interstaminal
interstapedial
interstate
interstation
interstellar
interstellary
intersterile
intersterility
intersternal
interstice
intersticed
interstimulate
interstimulation
interstitial
interstitially
interstitious
interstratification
interstratify
interstreak
interstream
interstreet
interstrial
interstriation
interstrive
intersubjective
intersubsistence
intersubstitution
intersuperciliary
intersusceptation
intersystem
intersystematical
intertalk
intertangle
intertanglement
intertarsal
interteam
intertentacular
intertergal
interterminal
interterritorial
intertessellation
intertexture
interthing
interthreaded
interthronging
intertidal
intertie
intertill
intertillage
intertinge
intertissued
intertone
intertongue
intertonic
intertouch
intertown
intertrabecular
intertrace
intertrade
intertrading
intertraffic
intertragian
intertransformability
intertransformable
intertransmissible
intertransmission
intertranspicuous
intertransversal
intertransversalis
intertransversary
intertransverse
intertrappean
intertribal
intertriginous
intertriglyph
intertrigo
intertrinitarian
intertrochanteric
intertropic
intertropical
intertropics
intertrude
intertuberal
intertubercular
intertubular
intertwin
intertwine
intertwinement
intertwining
intertwiningly
intertwist
intertwistingly
Intertype
interungular
interungulate
interunion
interuniversity
interurban
interureteric
intervaginal
interval
intervale
intervalley
intervallic
intervallum
intervalvular
intervarietal
intervary
intervascular
intervein
interveinal
intervenant
intervene
intervener
intervenience
interveniency
intervenient
intervenium
intervention
interventional
interventionism
interventionist
interventive
interventor
interventral
interventralia
interventricular
intervenular
interverbal
interversion
intervert
intervertebra
intervertebral
intervertebrally
intervesicular
interview
interviewable
interviewee
interviewer
intervillous
intervisibility
intervisible
intervisit
intervisitation
intervital
intervocal
intervocalic
intervolute
intervolution
intervolve
interwar
interweave
interweavement
interweaver
interweaving
interweavingly
interwed
interweld
interwhiff
interwhile
interwhistle
interwind
interwish
interword
interwork
interworks
interworld
interworry
interwound
interwove
interwoven
interwovenly
interwrap
interwreathe
interwrought
interxylary
interzonal
interzone
interzooecial
interzygapophysial
intestable
intestacy
intestate
intestation
intestinal
intestinally
intestine
intestineness
intestiniform
intestinovesical
intext
intextine
intexture
inthrall
inthrallment
inthrong
inthronistic
inthronization
inthronize
inthrow
inthrust
intil
intima
intimacy
intimal
intimate
intimately
intimateness
intimater
intimation
intimidate
intimidation
intimidator
intimidatory
intimidity
intimity
intinction
intine
intitule
into
intoed
intolerability
intolerable
intolerableness
intolerably
intolerance
intolerancy
intolerant
intolerantly
intolerantness
intolerated
intolerating
intoleration
intonable
intonate
intonation
intonator
intone
intonement
intoner
intoothed
intorsion
intort
intortillage
intown
intoxation
intoxicable
intoxicant
intoxicate
intoxicated
intoxicatedly
intoxicatedness
intoxicating
intoxicatingly
intoxication
intoxicative
intoxicator
intrabiontic
intrabranchial
intrabred
intrabronchial
intrabuccal
intracalicular
intracanalicular
intracanonical
intracapsular
intracardiac
intracardial
intracarpal
intracarpellary
intracartilaginous
intracellular
intracellularly
intracephalic
intracerebellar
intracerebral
intracerebrally
intracervical
intrachordal
intracistern
intracity
intraclitelline
intracloacal
intracoastal
intracoelomic
intracolic
intracollegiate
intracommunication
intracompany
intracontinental
intracorporeal
intracorpuscular
intracortical
intracosmic
intracosmical
intracosmically
intracostal
intracranial
intracranially
intractability
intractable
intractableness
intractably
intractile
intracutaneous
intracystic
intrada
intradepartmental
intradermal
intradermally
intradermic
intradermically
intradermo
intradistrict
intradivisional
intrados
intraduodenal
intradural
intraecclesiastical
intraepiphyseal
intraepithelial
intrafactory
intrafascicular
intrafissural
intrafistular
intrafoliaceous
intraformational
intrafusal
intragastric
intragemmal
intraglacial
intraglandular
intraglobular
intragroup
intragroupal
intragyral
intrahepatic
intrahyoid
intraimperial
intrait
intrajugular
intralamellar
intralaryngeal
intralaryngeally
intraleukocytic
intraligamentary
intraligamentous
intralingual
intralobar
intralobular
intralocular
intralogical
intralumbar
intramammary
intramarginal
intramastoid
intramatrical
intramatrically
intramedullary
intramembranous
intrameningeal
intramental
intrametropolitan
intramolecular
intramontane
intramorainic
intramundane
intramural
intramuralism
intramuscular
intramuscularly
intramyocardial
intranarial
intranasal
intranatal
intranational
intraneous
intraneural
intranidal
intranquil
intranquillity
intranscalency
intranscalent
intransferable
intransformable
intransfusible
intransgressible
intransient
intransigency
intransigent
intransigentism
intransigentist
intransigently
intransitable
intransitive
intransitively
intransitiveness
intransitivity
intranslatable
intransmissible
intransmutability
intransmutable
intransparency
intransparent
intrant
intranuclear
intraoctave
intraocular
intraoral
intraorbital
intraorganization
intraossal
intraosseous
intraosteal
intraovarian
intrapair
intraparenchymatous
intraparietal
intraparochial
intraparty
intrapelvic
intrapericardiac
intrapericardial
intraperineal
intraperiosteal
intraperitoneal
intraperitoneally
intrapetiolar
intraphilosophic
intrapial
intraplacental
intraplant
intrapleural
intrapolar
intrapontine
intraprostatic
intraprotoplasmic
intrapsychic
intrapsychical
intrapsychically
intrapulmonary
intrapyretic
intrarachidian
intrarectal
intrarelation
intrarenal
intraretinal
intrarhachidian
intraschool
intrascrotal
intrasegmental
intraselection
intrasellar
intraseminal
intraseptal
intraserous
intrashop
intraspecific
intraspinal
intrastate
intrastromal
intrasusception
intrasynovial
intratarsal
intratelluric
intraterritorial
intratesticular
intrathecal
intrathoracic
intrathyroid
intratomic
intratonsillar
intratrabecular
intratracheal
intratracheally
intratropical
intratubal
intratubular
intratympanic
intravaginal
intravalvular
intravasation
intravascular
intravenous
intravenously
intraventricular
intraverbal
intraversable
intravertebral
intravertebrally
intravesical
intravital
intravitelline
intravitreous
intraxylary
intreat
intrench
intrenchant
intrencher
intrenchment
intrepid
intrepidity
intrepidly
intrepidness
intricacy
intricate
intricately
intricateness
intrication
intrigant
intrigue
intrigueproof
intriguer
intriguery
intriguess
intriguing
intriguingly
intrine
intrinse
intrinsic
intrinsical
intrinsicality
intrinsically
intrinsicalness
introactive
introceptive
introconversion
introconvertibility
introconvertible
introdden
introduce
introducee
introducement
introducer
introducible
introduction
introductive
introductively
introductor
introductorily
introductoriness
introductory
introductress
introflex
introflexion
introgression
introgressive
introinflection
introit
introitus
introject
introjection
introjective
intromissibility
intromissible
intromission
intromissive
intromit
intromittence
intromittent
intromitter
intropression
intropulsive
introreception
introrsal
introrse
introrsely
introsensible
introsentient
introspect
introspectable
introspection
introspectional
introspectionism
introspectionist
introspective
introspectively
introspectiveness
introspectivism
introspectivist
introspector
introsuction
introsuscept
introsusception
introthoracic
introtraction
introvenient
introverse
introversibility
introversible
introversion
introversive
introversively
introvert
introverted
introvertive
introvision
introvolution
intrudance
intrude
intruder
intruding
intrudingly
intrudress
intruse
intrusion
intrusional
intrusionism
intrusionist
intrusive
intrusively
intrusiveness
intrust
intubate
intubation
intubationist
intubator
intube
intue
intuent
intuicity
intuit
intuitable
intuition
intuitional
intuitionalism
intuitionalist
intuitionally
intuitionism
intuitionist
intuitionistic
intuitionless
intuitive
intuitively
intuitiveness
intuitivism
intuitivist
intumesce
intumescence
intumescent
inturbidate
inturn
inturned
inturning
intussuscept
intussusception
intussusceptive
intwist
inula
inulaceous
inulase
inulin
inuloid
inumbrate
inumbration
inunct
inunction
inunctum
inunctuosity
inunctuous
inundable
inundant
inundate
inundation
inundator
inundatory
inunderstandable
inurbane
inurbanely
inurbaneness
inurbanity
inure
inured
inuredness
inurement
inurn
inusitate
inusitateness
inusitation
inustion
inutile
inutilely
inutility
inutilized
inutterable
invaccinate
invaccination
invadable
invade
invader
invaginable
invaginate
invagination
invalescence
invalid
invalidate
invalidation
invalidator
invalidcy
invalidhood
invalidish
invalidism
invalidity
invalidly
invalidness
invalidship
invalorous
invaluable
invaluableness
invaluably
invalued
Invar
invariability
invariable
invariableness
invariably
invariance
invariancy
invariant
invariantive
invariantively
invariantly
invaried
invasion
invasionist
invasive
invecked
invected
invection
invective
invectively
invectiveness
invectivist
invector
inveigh
inveigher
inveigle
inveiglement
inveigler
inveil
invein
invendibility
invendible
invendibleness
invenient
invent
inventable
inventary
inventer
inventful
inventibility
inventible
inventibleness
invention
inventional
inventionless
inventive
inventively
inventiveness
inventor
inventoriable
inventorial
inventorially
inventory
inventress
inventurous
inveracious
inveracity
inverisimilitude
inverity
inverminate
invermination
invernacular
Inverness
inversable
inversatile
inverse
inversed
inversedly
inversely
inversion
inversionist
inversive
invert
invertase
invertebracy
invertebral
Invertebrata
invertebrate
invertebrated
inverted
invertedly
invertend
inverter
invertibility
invertible
invertile
invertin
invertive
invertor
invest
investable
investible
investigable
investigatable
investigate
investigating
investigatingly
investigation
investigational
investigative
investigator
investigatorial
investigatory
investitive
investitor
investiture
investment
investor
inveteracy
inveterate
inveterately
inveterateness
inviability
invictive
invidious
invidiously
invidiousness
invigilance
invigilancy
invigilation
invigilator
invigor
invigorant
invigorate
invigorating
invigoratingly
invigoratingness
invigoration
invigorative
invigoratively
invigorator
invinate
invination
invincibility
invincible
invincibleness
invincibly
inviolability
inviolable
inviolableness
inviolably
inviolacy
inviolate
inviolated
inviolately
inviolateness
invirile
invirility
invirtuate
inviscate
inviscation
inviscid
inviscidity
invised
invisibility
invisible
invisibleness
invisibly
invitable
invital
invitant
invitation
invitational
invitatory
invite
invitee
invitement
inviter
invitiate
inviting
invitingly
invitingness
invitress
invitrifiable
invivid
invocable
invocant
invocate
invocation
invocative
invocator
invocatory
invoice
invoke
invoker
involatile
involatility
involucel
involucellate
involucellated
involucral
involucrate
involucre
involucred
involucriform
involucrum
involuntarily
involuntariness
involuntary
involute
involuted
involutedly
involutely
involution
involutional
involutionary
involutorial
involutory
involve
involved
involvedly
involvedness
involvement
involvent
involver
invulnerability
invulnerable
invulnerableness
invulnerably
invultuation
inwale
inwall
inwandering
inward
inwardly
inwardness
inwards
inweave
inwedged
inweed
inweight
inwick
inwind
inwit
inwith
inwood
inwork
inworn
inwound
inwoven
inwrap
inwrapment
inwreathe
inwrit
inwrought
inyoite
inyoke
Io
io
Iodamoeba
iodate
iodation
iodhydrate
iodhydric
iodhydrin
iodic
iodide
iodiferous
iodinate
iodination
iodine
iodinium
iodinophil
iodinophilic
iodinophilous
iodism
iodite
iodization
iodize
iodizer
iodo
iodobehenate
iodobenzene
iodobromite
iodocasein
iodochloride
iodochromate
iodocresol
iododerma
iodoethane
iodoform
iodogallicin
iodohydrate
iodohydric
iodohydrin
iodol
iodomercurate
iodomercuriate
iodomethane
iodometric
iodometrical
iodometry
iodonium
iodopsin
iodoso
iodosobenzene
iodospongin
iodotannic
iodotherapy
iodothyrin
iodous
iodoxy
iodoxybenzene
iodyrite
iolite
ion
Ione
Ioni
Ionian
Ionic
ionic
Ionicism
Ionicization
Ionicize
Ionidium
Ionism
Ionist
ionium
ionizable
Ionization
ionization
Ionize
ionize
ionizer
ionogen
ionogenic
ionone
Ionornis
ionosphere
ionospheric
Ionoxalis
iontophoresis
Ioskeha
iota
iotacism
iotacismus
iotacist
iotization
iotize
Iowa
Iowan
Ipalnemohuani
ipecac
ipecacuanha
ipecacuanhic
Iphimedia
Iphis
ipid
Ipidae
ipil
ipomea
Ipomoea
ipomoein
ipseand
ipsedixitish
ipsedixitism
ipsedixitist
ipseity
ipsilateral
Ira
iracund
iracundity
iracundulous
irade
Iran
Irani
Iranian
Iranic
Iranism
Iranist
Iranize
Iraq
Iraqi
Iraqian
irascent
irascibility
irascible
irascibleness
irascibly
irate
irately
ire
ireful
irefully
irefulness
Irelander
ireless
Irena
irenarch
Irene
irene
irenic
irenical
irenically
irenicism
irenicist
irenicon
irenics
irenicum
Iresine
Irfan
Irgun
Irgunist
irian
Iriartea
Iriarteaceae
Iricism
Iricize
irid
Iridaceae
iridaceous
iridadenosis
iridal
iridalgia
iridate
iridauxesis
iridectome
iridectomize
iridectomy
iridectropium
iridemia
iridencleisis
iridentropium
irideous
irideremia
irides
iridesce
iridescence
iridescency
iridescent
iridescently
iridial
iridian
iridiate
iridic
iridical
iridin
iridine
iridiocyte
iridiophore
iridioplatinum
iridious
iridite
iridium
iridization
iridize
iridoavulsion
iridocapsulitis
iridocele
iridoceratitic
iridochoroiditis
iridocoloboma
iridoconstrictor
iridocyclitis
iridocyte
iridodesis
iridodiagnosis
iridodialysis
iridodonesis
iridokinesia
iridomalacia
iridomotor
Iridomyrmex
iridoncus
iridoparalysis
iridophore
iridoplegia
iridoptosis
iridopupillary
iridorhexis
iridosclerotomy
iridosmine
iridosmium
iridotasis
iridotome
iridotomy
iris
irisated
irisation
iriscope
irised
Irish
Irisher
Irishian
Irishism
Irishize
Irishly
Irishman
Irishness
Irishry
Irishwoman
Irishy
irisin
irislike
irisroot
iritic
iritis
irk
irksome
irksomely
irksomeness
Irma
Iroha
irok
iroko
iron
ironback
ironbark
ironbound
ironbush
ironclad
irone
ironer
ironfisted
ironflower
ironhanded
ironhandedly
ironhandedness
ironhard
ironhead
ironheaded
ironhearted
ironheartedly
ironheartedness
ironical
ironically
ironicalness
ironice
ironish
ironism
ironist
ironize
ironless
ironlike
ironly
ironmaker
ironmaking
ironman
ironmaster
ironmonger
ironmongering
ironmongery
ironness
ironshod
ironshot
ironside
ironsided
ironsides
ironsmith
ironstone
ironware
ironweed
ironwood
ironwork
ironworked
ironworker
ironworking
ironworks
ironwort
irony
Iroquoian
Iroquois
Irpex
irradiance
irradiancy
irradiant
irradiate
irradiated
irradiatingly
irradiation
irradiative
irradiator
irradicable
irradicate
irrarefiable
irrationability
irrationable
irrationably
irrational
irrationalism
irrationalist
irrationalistic
irrationality
irrationalize
irrationally
irrationalness
irreality
irrealizable
irrebuttable
irreceptive
irreceptivity
irreciprocal
irreciprocity
irreclaimability
irreclaimable
irreclaimableness
irreclaimably
irreclaimed
irrecognition
irrecognizability
irrecognizable
irrecognizably
irrecognizant
irrecollection
irreconcilability
irreconcilable
irreconcilableness
irreconcilably
irreconcile
irreconcilement
irreconciliability
irreconciliable
irreconciliableness
irreconciliably
irreconciliation
irrecordable
irrecoverable
irrecoverableness
irrecoverably
irrecusable
irrecusably
irredeemability
irredeemable
irredeemableness
irredeemably
irredeemed
irredenta
irredential
Irredentism
Irredentist
irredressibility
irredressible
irredressibly
irreducibility
irreducible
irreducibleness
irreducibly
irreductibility
irreductible
irreduction
irreferable
irreflection
irreflective
irreflectively
irreflectiveness
irreflexive
irreformability
irreformable
irrefragability
irrefragable
irrefragableness
irrefragably
irrefrangibility
irrefrangible
irrefrangibleness
irrefrangibly
irrefusable
irrefutability
irrefutable
irrefutableness
irrefutably
irregardless
irregeneracy
irregenerate
irregeneration
irregular
irregularism
irregularist
irregularity
irregularize
irregularly
irregularness
irregulate
irregulated
irregulation
irrelate
irrelated
irrelation
irrelative
irrelatively
irrelativeness
irrelevance
irrelevancy
irrelevant
irrelevantly
irreliability
irrelievable
irreligion
irreligionism
irreligionist
irreligionize
irreligiosity
irreligious
irreligiously
irreligiousness
irreluctant
irremeable
irremeably
irremediable
irremediableness
irremediably
irrememberable
irremissibility
irremissible
irremissibleness
irremissibly
irremission
irremissive
irremovability
irremovable
irremovableness
irremovably
irremunerable
irrenderable
irrenewable
irrenunciable
irrepair
irrepairable
irreparability
irreparable
irreparableness
irreparably
irrepassable
irrepealability
irrepealable
irrepealableness
irrepealably
irrepentance
irrepentant
irrepentantly
irreplaceable
irreplaceably
irrepleviable
irreplevisable
irreportable
irreprehensible
irreprehensibleness
irreprehensibly
irrepresentable
irrepresentableness
irrepressibility
irrepressible
irrepressibleness
irrepressibly
irrepressive
irreproachability
irreproachable
irreproachableness
irreproachably
irreproducible
irreproductive
irreprovable
irreprovableness
irreprovably
irreptitious
irrepublican
irresilient
irresistance
irresistibility
irresistible
irresistibleness
irresistibly
irresoluble
irresolubleness
irresolute
irresolutely
irresoluteness
irresolution
irresolvability
irresolvable
irresolvableness
irresolved
irresolvedly
irresonance
irresonant
irrespectability
irrespectable
irrespectful
irrespective
irrespectively
irrespirable
irrespondence
irresponsibility
irresponsible
irresponsibleness
irresponsibly
irresponsive
irresponsiveness
irrestrainable
irrestrainably
irrestrictive
irresultive
irresuscitable
irresuscitably
irretention
irretentive
irretentiveness
irreticence
irreticent
irretraceable
irretraceably
irretractable
irretractile
irretrievability
irretrievable
irretrievableness
irretrievably
irrevealable
irrevealably
irreverence
irreverend
irreverendly
irreverent
irreverential
irreverentialism
irreverentially
irreverently
irreversibility
irreversible
irreversibleness
irreversibly
irrevertible
irreviewable
irrevisable
irrevocability
irrevocable
irrevocableness
irrevocably
irrevoluble
irrigable
irrigably
irrigant
irrigate
irrigation
irrigational
irrigationist
irrigative
irrigator
irrigatorial
irrigatory
irriguous
irriguousness
irrision
irrisor
Irrisoridae
irrisory
irritability
irritable
irritableness
irritably
irritament
irritancy
irritant
irritate
irritatedly
irritating
irritatingly
irritation
irritative
irritativeness
irritator
irritatory
Irritila
irritomotile
irritomotility
irrorate
irrotational
irrotationally
irrubrical
irrupt
irruptible
irruption
irruptive
irruptively
Irvin
Irving
Irvingesque
Irvingiana
Irvingism
Irvingite
Irwin
is
Isaac
Isabel
isabelina
isabelita
Isabella
Isabelle
Isabelline
isabnormal
isaconitine
isacoustic
isadelphous
Isadora
isagoge
isagogic
isagogical
isagogically
isagogics
isagon
Isaiah
Isaian
isallobar
isallotherm
isamine
Isander
isandrous
isanemone
isanomal
isanomalous
isanthous
isapostolic
Isaria
isarioid
isatate
isatic
isatide
isatin
isatinic
Isatis
isatogen
isatogenic
Isaurian
Isawa
isazoxy
isba
Iscariot
Iscariotic
Iscariotical
Iscariotism
ischemia
ischemic
ischiac
ischiadic
ischiadicus
ischial
ischialgia
ischialgic
ischiatic
ischidrosis
ischioanal
ischiobulbar
ischiocapsular
ischiocaudal
ischiocavernosus
ischiocavernous
ischiocele
ischiocerite
ischiococcygeal
ischiofemoral
ischiofibular
ischioiliac
ischioneuralgia
ischioperineal
ischiopodite
ischiopubic
ischiopubis
ischiorectal
ischiorrhogic
ischiosacral
ischiotibial
ischiovaginal
ischiovertebral
ischium
ischocholia
ischuretic
ischuria
ischury
Ischyodus
Isegrim
isenergic
isentropic
isepiptesial
isepiptesis
iserine
iserite
isethionate
isethionic
Iseum
Isfahan
Ishmael
Ishmaelite
Ishmaelitic
Ishmaelitish
Ishmaelitism
ishpingo
ishshakku
Isiac
Isiacal
Isidae
isidiiferous
isidioid
isidiophorous
isidiose
isidium
isidoid
Isidore
Isidorian
Isidoric
Isinai
isindazole
isinglass
Isis
Islam
Islamic
Islamism
Islamist
Islamistic
Islamite
Islamitic
Islamitish
Islamization
Islamize
island
islander
islandhood
islandic
islandish
islandless
islandlike
islandman
islandress
islandry
islandy
islay
isle
isleless
islesman
islet
Isleta
isleted
isleward
islot
ism
Ismaelism
Ismaelite
Ismaelitic
Ismaelitical
Ismaelitish
Ismaili
Ismailian
Ismailite
ismal
ismatic
ismatical
ismaticalness
ismdom
ismy
Isnardia
iso
isoabnormal
isoagglutination
isoagglutinative
isoagglutinin
isoagglutinogen
isoalantolactone
isoallyl
isoamarine
isoamide
isoamyl
isoamylamine
isoamylene
isoamylethyl
isoamylidene
isoantibody
isoantigen
isoapiole
isoasparagine
isoaurore
isobar
isobarbaloin
isobarbituric
isobare
isobaric
isobarism
isobarometric
isobase
isobath
isobathic
isobathytherm
isobathythermal
isobathythermic
isobenzofuran
isobilateral
isobilianic
isobiogenetic
isoborneol
isobornyl
isobront
isobronton
isobutane
isobutyl
isobutylene
isobutyraldehyde
isobutyrate
isobutyric
isobutyryl
isocamphor
isocamphoric
isocaproic
isocarbostyril
Isocardia
Isocardiidae
isocarpic
isocarpous
isocellular
isocephalic
isocephalism
isocephalous
isocephaly
isocercal
isocercy
isochasm
isochasmic
isocheim
isocheimal
isocheimenal
isocheimic
isocheimonal
isochlor
isochlorophyll
isochlorophyllin
isocholanic
isocholesterin
isocholesterol
isochor
isochoric
isochromatic
isochronal
isochronally
isochrone
isochronic
isochronical
isochronism
isochronize
isochronon
isochronous
isochronously
isochroous
isocinchomeronic
isocinchonine
isocitric
isoclasite
isoclimatic
isoclinal
isocline
isoclinic
isocodeine
isocola
isocolic
isocolon
isocoria
isocorybulbin
isocorybulbine
isocorydine
isocoumarin
isocracy
isocrat
isocratic
isocreosol
isocrotonic
isocrymal
isocryme
isocrymic
isocyanate
isocyanic
isocyanide
isocyanine
isocyano
isocyanogen
isocyanurate
isocyanuric
isocyclic
isocymene
isocytic
isodactylism
isodactylous
isodiabatic
isodialuric
isodiametric
isodiametrical
isodiazo
isodiazotate
isodimorphic
isodimorphism
isodimorphous
isodomic
isodomous
isodomum
isodont
isodontous
isodrome
isodulcite
isodurene
isodynamia
isodynamic
isodynamical
isoelectric
isoelectrically
isoelectronic
isoelemicin
isoemodin
isoenergetic
isoerucic
Isoetaceae
Isoetales
Isoetes
isoeugenol
isoflavone
isoflor
isogamete
isogametic
isogametism
isogamic
isogamous
isogamy
isogen
isogenesis
isogenetic
isogenic
isogenotype
isogenotypic
isogenous
isogeny
isogeotherm
isogeothermal
isogeothermic
isogloss
isoglossal
isognathism
isognathous
isogon
isogonal
isogonality
isogonally
isogonic
isogoniostat
isogonism
isograft
isogram
isograph
isographic
isographical
isographically
isography
isogynous
isohaline
isohalsine
isohel
isohemopyrrole
isoheptane
isohesperidin
isohexyl
isohydric
isohydrocyanic
isohydrosorbic
isohyet
isohyetal
isoimmune
isoimmunity
isoimmunization
isoimmunize
isoindazole
isoindigotin
isoindole
isoionone
isokeraunic
isokeraunographic
isokeraunophonic
Isokontae
isokontan
isokurtic
isolability
isolable
isolapachol
isolate
isolated
isolatedly
isolating
isolation
isolationism
isolationist
isolative
Isolde
isolecithal
isoleucine
isolichenin
isolinolenic
isologous
isologue
isology
Isoloma
isolysin
isolysis
isomagnetic
isomaltose
isomastigate
isomelamine
isomenthone
isomer
Isomera
isomere
isomeric
isomerical
isomerically
isomeride
isomerism
isomerization
isomerize
isomeromorphism
isomerous
isomery
isometric
isometrical
isometrically
isometrograph
isometropia
isometry
isomorph
isomorphic
isomorphism
isomorphous
Isomyaria
isomyarian
isoneph
isonephelic
isonergic
isonicotinic
isonitramine
isonitrile
isonitroso
isonomic
isonomous
isonomy
isonuclear
isonym
isonymic
isonymy
isooleic
isoosmosis
isopachous
isopag
isoparaffin
isopectic
isopelletierin
isopelletierine
isopentane
isoperimeter
isoperimetric
isoperimetrical
isoperimetry
isopetalous
isophanal
isophane
isophasal
isophene
isophenomenal
isophoria
isophorone
isophthalic
isophthalyl
isophyllous
isophylly
isopicramic
isopiestic
isopiestically
isopilocarpine
isoplere
isopleth
Isopleura
isopleural
isopleuran
isopleurous
isopod
Isopoda
isopodan
isopodiform
isopodimorphous
isopodous
isopogonous
isopolite
isopolitical
isopolity
isopoly
isoprene
isopropenyl
isopropyl
isopropylacetic
isopropylamine
isopsephic
isopsephism
Isoptera
isopterous
isoptic
isopulegone
isopurpurin
isopycnic
isopyre
isopyromucic
isopyrrole
isoquercitrin
isoquinine
isoquinoline
isorcinol
isorhamnose
isorhodeose
isorithm
isorosindone
isorrhythmic
isorropic
isosaccharic
isosaccharin
isoscele
isosceles
isoscope
isoseismal
isoseismic
isoseismical
isoseist
isoserine
isosmotic
Isospondyli
isospondylous
isospore
isosporic
isosporous
isospory
isostasist
isostasy
isostatic
isostatical
isostatically
isostemonous
isostemony
isostere
isosteric
isosterism
isostrychnine
isosuccinic
isosulphide
isosulphocyanate
isosulphocyanic
isosultam
isotac
isoteles
isotely
isotheral
isothere
isotherm
isothermal
isothermally
isothermic
isothermical
isothermobath
isothermobathic
isothermous
isotherombrose
isothiocyanates
isothiocyanic
isothiocyano
isothujone
isotimal
isotome
isotomous
isotonia
isotonic
isotonicity
isotony
isotope
isotopic
isotopism
isotopy
isotrehalose
Isotria
isotrimorphic
isotrimorphism
isotrimorphous
isotron
isotrope
isotropic
isotropism
isotropous
isotropy
isotype
isotypic
isotypical
isovalerate
isovalerianate
isovalerianic
isovaleric
isovalerone
isovaline
isovanillic
isovoluminal
isoxanthine
isoxazine
isoxazole
isoxime
isoxylene
isoyohimbine
isozooid
ispaghul
ispravnik
Israel
Israeli
Israelite
Israeliteship
Israelitic
Israelitish
Israelitism
Israelitize
issanguila
Issedoi
Issedones
issei
issite
issuable
issuably
issuance
issuant
issue
issueless
issuer
issuing
ist
isthmi
Isthmia
isthmial
isthmian
isthmiate
isthmic
isthmoid
isthmus
istiophorid
Istiophoridae
Istiophorus
istle
istoke
Istrian
Istvaeones
isuret
isuretine
Isuridae
isuroid
Isurus
Iswara
it
Ita
itabirite
itacism
itacist
itacistic
itacolumite
itaconate
itaconic
Itala
Itali
Italian
Italianate
Italianately
Italianation
Italianesque
Italianish
Italianism
Italianist
Italianity
Italianization
Italianize
Italianizer
Italianly
Italic
Italical
Italically
Italican
Italicanist
Italici
Italicism
italicization
italicize
italics
Italiote
italite
Italomania
Italon
Italophile
itamalate
itamalic
itatartaric
itatartrate
Itaves
itch
itchiness
itching
itchingly
itchless
itchproof
itchreed
itchweed
itchy
itcze
Itea
Iteaceae
Itelmes
item
iteming
itemization
itemize
itemizer
itemy
Iten
Itenean
iter
iterable
iterance
iterancy
iterant
iterate
iteration
iterative
iteratively
iterativeness
Ithaca
Ithacan
Ithacensian
ithagine
Ithaginis
ither
Ithiel
ithomiid
Ithomiidae
Ithomiinae
ithyphallic
Ithyphallus
ithyphyllous
itineracy
itinerancy
itinerant
itinerantly
itinerarian
Itinerarium
itinerary
itinerate
itineration
itmo
Ito
Itoism
Itoist
Itoland
Itonama
Itonaman
Itonia
itonidid
Itonididae
itoubou
its
itself
Ituraean
iturite
Itylus
Itys
Itza
itzebu
iva
Ivan
ivied
ivin
ivoried
ivorine
ivoriness
ivorist
ivory
ivorylike
ivorytype
ivorywood
ivy
ivybells
ivyberry
ivyflower
ivylike
ivyweed
ivywood
ivywort
iwa
iwaiwa
iwis
Ixia
Ixiaceae
Ixiama
Ixil
Ixion
Ixionian
Ixodes
ixodian
ixodic
ixodid
Ixodidae
Ixora
iyo
Izar
izar
izard
Izcateco
Izchak
Izdubar
izle
izote
iztle
Izumi
izzard
Izzy
J
j
Jaalin
jab
Jabarite
jabbed
jabber
jabberer
jabbering
jabberingly
jabberment
Jabberwock
jabberwockian
Jabberwocky
jabbing
jabbingly
jabble
jabers
jabia
jabiru
jaborandi
jaborine
jabot
jaboticaba
jabul
jacal
Jacaltec
Jacalteca
jacamar
Jacamaralcyon
jacameropine
Jacamerops
jacami
jacamin
Jacana
jacana
Jacanidae
Jacaranda
jacare
jacate
jacchus
jacent
jacinth
jacinthe
Jack
jack
jackal
jackanapes
jackanapish
jackaroo
jackass
jackassery
jackassification
jackassism
jackassness
jackbird
jackbox
jackboy
jackdaw
jackeen
jacker
jacket
jacketed
jacketing
jacketless
jacketwise
jackety
jackfish
jackhammer
jackknife
jackleg
jackman
jacko
jackpudding
jackpuddinghood
jackrod
jacksaw
jackscrew
jackshaft
jackshay
jacksnipe
Jackson
Jacksonia
Jacksonian
Jacksonite
jackstay
jackstone
jackstraw
jacktan
jackweed
jackwood
Jacky
Jackye
Jacob
jacobaea
jacobaean
Jacobean
Jacobian
Jacobic
Jacobin
Jacobinia
Jacobinic
Jacobinical
Jacobinically
Jacobinism
Jacobinization
Jacobinize
Jacobite
Jacobitely
Jacobitiana
Jacobitic
Jacobitical
Jacobitically
Jacobitish
Jacobitishly
Jacobitism
jacobsite
Jacobson
jacobus
jacoby
jaconet
Jacqueminot
Jacques
jactance
jactancy
jactant
jactation
jactitate
jactitation
jacu
jacuaru
jaculate
jaculation
jaculative
jaculator
jaculatorial
jaculatory
jaculiferous
Jacunda
jacutinga
jadder
jade
jaded
jadedly
jadedness
jadeite
jadery
jadesheen
jadeship
jadestone
jadish
jadishly
jadishness
jady
jaeger
jag
Jaga
Jagannath
Jagannatha
jagat
Jagatai
Jagataic
Jagath
jager
jagged
jaggedly
jaggedness
jagger
jaggery
jaggy
jagir
jagirdar
jagla
jagless
jagong
jagrata
jagua
jaguar
jaguarete
Jahve
Jahvist
Jahvistic
jail
jailage
jailbird
jaildom
jailer
jaileress
jailering
jailership
jailhouse
jailish
jailkeeper
jaillike
jailmate
jailward
jailyard
Jaime
Jain
Jaina
Jainism
Jainist
Jaipuri
jajman
Jake
jake
jakes
jako
Jakob
Jakun
Jalalaean
jalap
jalapa
jalapin
jalkar
jalloped
jalopy
jalouse
jalousie
jalousied
jalpaite
Jam
jam
jama
Jamaica
Jamaican
jaman
jamb
jambalaya
jambeau
jambo
jambolan
jambone
jambool
jamboree
Jambos
jambosa
jambstone
jamdani
James
Jamesian
Jamesina
jamesonite
jami
Jamie
jamlike
jammedness
jammer
jammy
Jamnia
jampan
jampani
jamrosade
jamwood
Jan
janapa
janapan
Jane
jane
Janet
jangada
Janghey
jangkar
jangle
jangler
jangly
Janice
janiceps
Janiculan
Janiculum
Janiform
janissary
janitor
janitorial
janitorship
janitress
janitrix
Janizarian
Janizary
jank
janker
jann
jannock
Janos
Jansenism
Jansenist
Jansenistic
Jansenistical
Jansenize
Janthina
Janthinidae
jantu
janua
Januarius
January
Janus
Januslike
jaob
Jap
jap
japaconine
japaconitine
Japan
japan
Japanee
Japanese
Japanesque
Japanesquely
Japanesquery
Japanesy
Japanicize
Japanism
Japanization
Japanize
japanned
Japanner
japanner
japannery
Japannish
Japanolatry
Japanologist
Japanology
Japanophile
Japanophobe
Japanophobia
jape
japer
japery
Japetus
Japheth
Japhetic
Japhetide
Japhetite
japing
japingly
japish
japishly
japishness
Japonic
japonica
Japonically
Japonicize
Japonism
Japonize
Japonizer
Japygidae
japygoid
Japyx
Jaqueline
Jaquesian
jaquima
jar
jara
jaragua
jararaca
jararacussu
jarbird
jarble
jarbot
jardiniere
Jared
jarfly
jarful
jarg
jargon
jargonal
jargoneer
jargonelle
jargoner
jargonesque
jargonic
jargonish
jargonist
jargonistic
jargonium
jargonization
jargonize
jarkman
Jarl
jarl
jarldom
jarless
jarlship
Jarmo
jarnut
jarool
jarosite
jarra
jarrah
jarring
jarringly
jarringness
jarry
jarvey
Jarvis
jasey
jaseyed
Jasione
Jasminaceae
jasmine
jasmined
jasminewood
Jasminum
jasmone
Jason
jaspachate
jaspagate
Jasper
jasper
jasperated
jaspered
jasperize
jasperoid
jaspery
jaspidean
jaspideous
jaspilite
jaspis
jaspoid
jasponyx
jaspopal
jass
jassid
Jassidae
jassoid
Jat
jatamansi
Jateorhiza
jateorhizine
jatha
jati
Jatki
Jatni
jato
Jatropha
jatrophic
jatrorrhizine
Jatulian
jaudie
jauk
jaun
jaunce
jaunder
jaundice
jaundiceroot
jaunt
jauntie
jauntily
jauntiness
jauntingly
jaunty
jaup
Java
Javahai
javali
Javan
Javanee
Javanese
javelin
javelina
javeline
javelineer
javer
Javitero
jaw
jawab
jawbation
jawbone
jawbreaker
jawbreaking
jawbreakingly
jawed
jawfall
jawfallen
jawfish
jawfoot
jawfooted
jawless
jawsmith
jawy
Jay
jay
Jayant
Jayesh
jayhawk
jayhawker
jaypie
jaywalk
jaywalker
jazerant
Jazyges
jazz
jazzer
jazzily
jazziness
jazzy
jealous
jealously
jealousness
jealousy
Jeames
Jean
jean
Jean-Christophe
Jean-Pierre
Jeanette
Jeanie
Jeanne
Jeannette
Jeannie
Jeanpaulia
jeans
Jeany
Jebus
Jebusi
Jebusite
Jebusitic
Jebusitical
Jebusitish
jecoral
jecorin
jecorize
jed
jedcock
jedding
jeddock
jeel
jeep
jeer
jeerer
jeering
jeeringly
jeerproof
jeery
jeewhillijers
jeewhillikens
Jef
Jeff
jeff
jefferisite
Jeffersonia
Jeffersonian
Jeffersonianism
jeffersonite
Jeffery
Jeffie
Jeffrey
Jehovah
Jehovic
Jehovism
Jehovist
Jehovistic
jehu
jehup
jejunal
jejunator
jejune
jejunely
jejuneness
jejunitis
jejunity
jejunoduodenal
jejunoileitis
jejunostomy
jejunotomy
jejunum
jelab
jelerang
jelick
jell
jellica
jellico
jellied
jelliedness
jellification
jellify
jellily
jelloid
jelly
jellydom
jellyfish
jellyleaf
jellylike
Jelske
jelutong
Jem
jemadar
Jemez
Jemima
jemmily
jemminess
Jemmy
jemmy
Jenine
jenkin
jenna
jennerization
jennerize
jennet
jenneting
Jennie
jennier
Jennifer
Jenny
jenny
Jenson
jentacular
jeofail
jeopard
jeoparder
jeopardize
jeopardous
jeopardously
jeopardousness
jeopardy
jequirity
Jerahmeel
Jerahmeelites
Jerald
jerboa
jereed
jeremejevite
jeremiad
Jeremiah
Jeremian
Jeremianic
Jeremias
Jeremy
jerez
jerib
jerk
jerker
jerkily
jerkin
jerkined
jerkiness
jerkingly
jerkish
jerksome
jerkwater
jerky
jerl
jerm
jermonal
Jeroboam
Jerome
Jeromian
Jeronymite
jerque
jerquer
Jerrie
Jerry
jerry
jerryism
Jersey
jersey
Jerseyan
jerseyed
Jerseyite
Jerseyman
jert
Jerusalem
jervia
jervina
jervine
Jesper
Jess
jess
jessakeed
jessamine
jessamy
jessant
Jesse
Jessean
jessed
Jessica
Jessie
jessur
jest
jestbook
jestee
jester
jestful
jesting
jestingly
jestingstock
jestmonger
jestproof
jestwise
jestword
Jesu
Jesuate
Jesuit
Jesuited
Jesuitess
Jesuitic
Jesuitical
Jesuitically
Jesuitish
Jesuitism
Jesuitist
Jesuitize
Jesuitocracy
Jesuitry
Jesus
jet
jetbead
jete
Jethro
Jethronian
jetsam
jettage
jetted
jetter
jettied
jettiness
jettingly
jettison
jetton
jetty
jettyhead
jettywise
jetware
Jew
jewbird
jewbush
Jewdom
jewel
jeweler
jewelhouse
jeweling
jewelless
jewellike
jewelry
jewelsmith
jewelweed
jewely
Jewess
jewfish
Jewhood
Jewish
Jewishly
Jewishness
Jewism
Jewless
Jewlike
Jewling
Jewry
Jewship
Jewstone
Jewy
jezail
Jezebel
Jezebelian
Jezebelish
jezekite
jeziah
Jezreelite
jharal
jheel
jhool
jhow
Jhuria
Ji
Jianyun
jib
jibbah
jibber
jibbings
jibby
jibe
jibhead
jibi
jibman
jiboa
jibstay
jicama
Jicaque
Jicaquean
jicara
Jicarilla
jiff
jiffle
jiffy
jig
jigamaree
jigger
jiggerer
jiggerman
jiggers
jigget
jiggety
jigginess
jiggish
jiggle
jiggly
jiggumbob
jiggy
jiglike
jigman
jihad
jikungu
Jill
jillet
jillflirt
jilt
jiltee
jilter
jiltish
Jim
jimbang
jimberjaw
jimberjawed
jimjam
Jimmy
jimmy
jimp
jimply
jimpness
jimpricute
jimsedge
Jin
jina
jincamas
Jincan
Jinchao
jing
jingal
Jingbai
jingbang
jingle
jingled
jinglejangle
jingler
jinglet
jingling
jinglingly
jingly
jingo
jingodom
jingoish
jingoism
jingoist
jingoistic
jinja
jinjili
jink
jinker
jinket
jinkle
jinks
jinn
jinnestan
jinni
jinniwink
jinniyeh
Jinny
jinny
jinriki
jinrikiman
jinrikisha
jinshang
jinx
jipijapa
jipper
jiqui
jirble
jirga
Jiri
jirkinet
Jisheng
Jitendra
jiti
jitneur
jitneuse
jitney
jitneyman
jitro
jitter
jitterbug
jitters
jittery
jiva
Jivaran
Jivaro
Jivaroan
jive
jixie
Jo
jo
Joachim
Joachimite
Joan
Joanna
Joanne
Joannite
joaquinite
Job
job
jobade
jobarbe
jobation
jobber
jobbernowl
jobbernowlism
jobbery
jobbet
jobbing
jobbish
jobble
jobholder
jobless
joblessness
jobman
jobmaster
jobmistress
jobmonger
jobo
jobsmith
Jocasta
Jocelin
Joceline
Jocelyn
joch
Jochen
Jock
jock
jocker
jockey
jockeydom
jockeyish
jockeyism
jockeylike
jockeyship
jocko
jockteleg
jocoque
jocose
jocosely
jocoseness
jocoseriosity
jocoserious
jocosity
jocote
jocu
jocular
jocularity
jocularly
jocularness
joculator
jocum
jocuma
jocund
jocundity
jocundly
jocundness
jodel
jodelr
jodhpurs
Jodo
Joe
joe
joebush
Joel
joewood
Joey
joey
jog
jogger
joggle
joggler
jogglety
jogglework
joggly
jogtrottism
Johan
Johann
Johanna
Johannean
Johannes
johannes
Johannine
Johannisberger
Johannist
Johannite
johannite
John
Johnadreams
Johnathan
Johnian
johnin
Johnnie
Johnny
johnnycake
johnnydom
Johnsmas
Johnsonese
Johnsonian
Johnsoniana
Johnsonianism
Johnsonianly
Johnsonism
johnstrupite
join
joinable
joinant
joinder
joiner
joinery
joining
joiningly
joint
jointage
jointed
jointedly
jointedness
jointer
jointing
jointist
jointless
jointly
jointress
jointure
jointureless
jointuress
jointweed
jointworm
jointy
joist
joisting
joistless
jojoba
joke
jokeless
jokelet
jokeproof
joker
jokesmith
jokesome
jokesomeness
jokester
jokingly
jokish
jokist
jokul
joky
joll
jolleyman
jollier
jollification
jollify
jollily
jolliness
jollity
jollop
jolloped
jolly
jollytail
Joloano
jolt
jolter
jolterhead
jolterheaded
jolterheadedness
jolthead
joltiness
jolting
joltingly
joltless
joltproof
jolty
Jon
Jonah
Jonahesque
Jonahism
Jonas
Jonathan
Jonathanization
Jones
Jonesian
Jong
jonglery
jongleur
Joni
jonque
jonquil
jonquille
Jonsonian
Jonval
jonvalization
jonvalize
jookerie
joola
joom
Joon
Jophiel
Jordan
jordan
Jordanian
jordanite
joree
Jorge
Jorist
jorum
Jos
Jose
josefite
joseite
Joseph
Josepha
Josephine
Josephinism
josephinite
Josephism
Josephite
Josh
josh
josher
joshi
Joshua
Josiah
josie
Josip
joskin
joss
jossakeed
josser
jostle
jostlement
jostler
jot
jota
jotation
jotisi
Jotnian
jotter
jotting
jotty
joubarb
Joubert
joug
jough
jouk
joukerypawkery
joule
joulean
joulemeter
jounce
journal
journalese
journalish
journalism
journalist
journalistic
journalistically
journalization
journalize
journalizer
journey
journeycake
journeyer
journeying
journeyman
journeywoman
journeywork
journeyworker
jours
joust
jouster
Jova
Jove
Jovial
jovial
jovialist
jovialistic
joviality
jovialize
jovially
jovialness
jovialty
Jovian
Jovianly
Jovicentric
Jovicentrical
Jovicentrically
jovilabe
Joviniamish
Jovinian
Jovinianist
Jovite
jow
jowar
jowari
jowel
jower
jowery
jowl
jowler
jowlish
jowlop
jowly
jowpy
jowser
jowter
joy
joyance
joyancy
joyant
Joyce
joyful
joyfully
joyfulness
joyhop
joyleaf
joyless
joylessly
joylessness
joylet
joyous
joyously
joyousness
joyproof
joysome
joyweed
Jozy
Ju
Juan
Juang
juba
jubate
jubbah
jubbe
jube
juberous
jubilance
jubilancy
jubilant
jubilantly
jubilarian
jubilate
jubilatio
jubilation
jubilatory
jubilean
jubilee
jubilist
jubilization
jubilize
jubilus
juck
juckies
Jucuna
jucundity
jud
Judaeomancy
Judaeophile
Judaeophilism
Judaeophobe
Judaeophobia
Judah
Judahite
Judaic
Judaica
Judaical
Judaically
Judaism
Judaist
Judaistic
Judaistically
Judaization
Judaize
Judaizer
Judas
Judaslike
judcock
Jude
Judean
judex
Judge
judge
judgeable
judgelike
judger
judgeship
judgingly
judgmatic
judgmatical
judgmatically
judgment
Judica
judicable
judicate
judication
judicative
judicator
judicatorial
judicatory
judicature
judices
judiciable
judicial
judiciality
judicialize
judicially
judicialness
judiciarily
judiciary
judicious
judiciously
judiciousness
Judith
judo
Judophobism
Judy
Juergen
jufti
jug
Juga
jugal
jugale
Jugatae
jugate
jugated
jugation
juger
jugerum
jugful
jugger
Juggernaut
juggernaut
Juggernautish
juggins
juggle
jugglement
juggler
jugglery
juggling
jugglingly
Juglandaceae
juglandaceous
Juglandales
juglandin
Juglans
juglone
jugular
Jugulares
jugulary
jugulate
jugulum
jugum
Jugurthine
Juha
juice
juiceful
juiceless
juicily
juiciness
juicy
jujitsu
juju
jujube
jujuism
jujuist
juke
jukebox
Jule
julep
Jules
Juletta
Julia
Julian
Juliana
Juliane
Julianist
Julianto
julid
Julidae
julidan
Julie
Julien
julienite
julienne
Juliet
Julietta
julio
Julius
juloid
Juloidea
juloidian
julole
julolidin
julolidine
julolin
juloline
Julus
July
Julyflower
Jumada
Jumana
jumart
jumba
jumble
jumblement
jumbler
jumblingly
jumbly
jumbo
jumboesque
jumboism
jumbuck
jumby
jumelle
jument
jumentous
jumfru
jumillite
jumma
jump
jumpable
jumper
jumperism
jumpiness
jumpingly
jumpness
jumprock
jumpseed
jumpsome
jumpy
Jun
Juncaceae
juncaceous
Juncaginaceae
juncaginaceous
juncagineous
junciform
juncite
Junco
Juncoides
juncous
junction
junctional
junctive
juncture
Juncus
June
june
Juneberry
Junebud
junectomy
Juneflower
Jungermannia
Jungermanniaceae
jungermanniaceous
Jungermanniales
jungle
jungled
jungleside
junglewards
junglewood
jungli
jungly
juniata
junior
juniorate
juniority
juniorship
juniper
Juniperaceae
Juniperus
Junius
junk
junkboard
Junker
junker
Junkerdom
junkerdom
junkerish
Junkerism
junkerism
junket
junketer
junketing
junking
junkman
Juno
Junoesque
Junonia
Junonian
junt
junta
junto
jupati
jupe
Jupiter
jupon
Jur
Jura
jural
jurally
jurament
juramentado
juramental
juramentally
juramentum
Jurane
jurant
jurara
Jurassic
jurat
juration
jurative
jurator
juratorial
juratory
jure
jurel
Jurevis
Juri
juridic
juridical
juridically
juring
jurisconsult
jurisdiction
jurisdictional
jurisdictionalism
jurisdictionally
jurisdictive
jurisprudence
jurisprudent
jurisprudential
jurisprudentialist
jurisprudentially
jurist
juristic
juristical
juristically
juror
jurupaite
jury
juryless
juryman
jurywoman
jusquaboutisme
jusquaboutist
jussel
Jussi
Jussiaea
Jussiaean
Jussieuan
jussion
jussive
jussory
just
justen
justice
justicehood
justiceless
justicelike
justicer
justiceship
justiceweed
Justicia
justiciability
justiciable
justicial
justiciar
justiciarship
justiciary
justiciaryship
justicies
justifiability
justifiable
justifiableness
justifiably
justification
justificative
justificator
justificatory
justifier
justify
justifying
justifyingly
Justin
Justina
Justine
Justinian
Justinianian
Justinianist
justly
justment
justness
justo
Justus
jut
Jute
jute
Jutic
Jutish
jutka
Jutlander
Jutlandish
jutting
juttingly
jutty
Juturna
Juvavian
juvenal
Juvenalian
juvenate
juvenescence
juvenescent
juvenile
juvenilely
juvenileness
juvenilify
juvenilism
juvenility
juvenilize
Juventas
juventude
Juverna
juvia
juvite
juxtalittoral
juxtamarine
juxtapose
juxtaposit
juxtaposition
juxtapositional
juxtapositive
juxtapyloric
juxtaspinal
juxtaterrestrial
juxtatropical
Juyas
Juza
Jwahar
Jynginae
jyngine
Jynx
jynx
K
k
ka
Kababish
Kabaka
kabaragoya
Kabard
Kabardian
kabaya
Kabbeljaws
kabel
kaberu
kabiet
Kabirpanthi
Kabistan
Kabonga
kabuki
Kabuli
Kabyle
Kachari
Kachin
kachin
Kadaga
Kadarite
kadaya
Kadayan
Kaddish
kadein
kadikane
kadischi
Kadmi
kados
Kadu
kaempferol
Kaf
Kafa
kaferita
Kaffir
kaffir
kaffiyeh
Kaffraria
Kaffrarian
Kafir
kafir
Kafiri
kafirin
kafiz
Kafka
Kafkaesque
kafta
kago
kagu
kaha
kahar
kahau
kahikatea
kahili
kahu
kahuna
kai
Kaibab
Kaibartha
kaid
kaik
kaikara
kaikawaka
kail
kailyard
kailyarder
kailyardism
Kaimo
Kainah
kainga
kainite
kainsi
kainyn
kairine
kairoline
kaiser
kaiserdom
kaiserism
kaisership
kaitaka
Kaithi
kaiwhiria
kaiwi
Kaj
Kajar
kajawah
kajugaru
kaka
Kakan
kakapo
kakar
kakarali
kakariki
Kakatoe
Kakatoidae
kakawahie
kaki
kakidrosis
kakistocracy
kakkak
kakke
kakortokite
kala
kaladana
kalamalo
kalamansanai
Kalamian
Kalanchoe
Kalandariyah
Kalang
Kalapooian
kalashnikov
kalasie
Kaldani
kale
kaleidophon
kaleidophone
kaleidoscope
kaleidoscopic
kaleidoscopical
kaleidoscopically
Kalekah
kalema
Kalendae
kalends
kalewife
kaleyard
kali
kalian
Kaliana
kaliborite
kalidium
kaliform
kaligenous
Kalinga
kalinite
kaliophilite
kalipaya
Kalispel
kalium
kallah
kallege
kallilite
Kallima
kallitype
Kalmarian
Kalmia
Kalmuck
kalo
kalogeros
kalokagathia
kalon
kalong
kalpis
kalsomine
kalsominer
kalumpang
kalumpit
Kalwar
kalymmaukion
kalymmocyte
kamachile
kamacite
kamahi
kamala
kamaloka
kamansi
kamao
Kamares
kamarezite
kamarupa
kamarupic
kamas
Kamasin
Kamass
kamassi
Kamba
kambal
kamboh
Kamchadal
Kamchatkan
kame
kameeldoorn
kameelthorn
Kamel
kamelaukion
kamerad
kamias
kamichi
kamik
kamikaze
Kamiya
kammalan
kammererite
kamperite
kampong
kamptomorph
kan
kana
kanae
kanagi
Kanaka
kanap
kanara
Kanarese
kanari
kanat
Kanauji
Kanawari
Kanawha
kanchil
kande
Kandelia
kandol
kaneh
kanephore
kanephoros
Kaneshite
Kanesian
kang
kanga
kangani
kangaroo
kangarooer
Kangli
Kanji
Kankanai
kankie
kannume
kanoon
Kanred
kans
Kansa
Kansan
kantele
kanteletar
kanten
Kanthan
Kantian
Kantianism
Kantism
Kantist
Kanuri
Kanwar
kaoliang
kaolin
kaolinate
kaolinic
kaolinite
kaolinization
kaolinize
kapa
kapai
kapeika
kapok
kapp
kappa
kappe
kappland
kapur
kaput
Karabagh
karagan
Karaism
Karaite
Karaitism
karaka
Karakatchan
Karakul
karakul
Karamojo
karamu
karaoke
Karatas
karate
Karaya
karaya
karbi
karch
kareao
kareeta
Karel
karela
Karelian
Karen
Karharbari
Kari
karite
Karl
Karling
Karluk
karma
Karmathian
karmic
karmouth
karo
kaross
karou
karree
karri
Karroo
karroo
karrusel
karsha
Karshuni
Karst
karst
karstenite
karstic
kartel
Karthli
kartometer
kartos
Kartvel
Kartvelian
karwar
Karwinskia
karyaster
karyenchyma
karyochrome
karyochylema
karyogamic
karyogamy
karyokinesis
karyokinetic
karyologic
karyological
karyologically
karyology
karyolymph
Karyolysidae
karyolysis
Karyolysus
karyolytic
karyomere
karyomerite
karyomicrosome
karyomitoic
karyomitome
karyomiton
karyomitosis
karyomitotic
karyon
karyoplasm
karyoplasma
karyoplasmatic
karyoplasmic
karyopyknosis
karyorrhexis
karyoschisis
karyosome
karyotin
karyotype
kasa
kasbah
kasbeke
kascamiol
Kasha
Kashan
kasher
kashga
kashi
kashima
Kashmiri
Kashmirian
Kashoubish
kashruth
Kashube
Kashubian
Kashyapa
kasida
Kasikumuk
Kaska
Kaskaskia
kasm
kasolite
kassabah
Kassak
Kassite
kassu
kastura
Kasubian
kat
Katabanian
katabasis
katabatic
katabella
katabolic
katabolically
katabolism
katabolite
katabolize
katabothron
katachromasis
katacrotic
katacrotism
katagenesis
katagenetic
katakana
katakinesis
katakinetic
katakinetomer
katakinetomeric
katakiribori
katalase
katalysis
katalyst
katalytic
katalyze
katamorphism
kataphoresis
kataphoretic
kataphoric
kataphrenia
kataplasia
kataplectic
kataplexy
katar
katastate
katastatic
katathermometer
katatonia
katatonic
katatype
katchung
katcina
Kate
kath
Katha
katha
kathal
Katharina
Katharine
katharometer
katharsis
kathartic
kathemoglobin
kathenotheism
Kathleen
kathodic
Kathopanishad
Kathryn
Kathy
Katie
Katik
Katinka
katipo
Katipunan
Katipuneros
katmon
katogle
Katrine
Katrinka
katsup
Katsuwonidae
katuka
Katukina
katun
katurai
Katy
katydid
Kauravas
kauri
kava
kavaic
kavass
Kavi
Kaw
kawaka
Kawchodinne
kawika
Kay
kay
kayak
kayaker
Kayan
Kayasth
Kayastha
kayles
kayo
Kayvan
Kazak
kazi
kazoo
Kazuhiro
kea
keach
keacorn
Keatsian
keawe
keb
kebab
kebbie
kebbuck
kechel
keck
keckle
keckling
kecksy
kecky
ked
Kedar
Kedarite
keddah
kedge
kedger
kedgeree
kedlock
Kedushshah
Kee
keech
keek
keeker
keel
keelage
keelbill
keelblock
keelboat
keelboatman
keeled
keeler
keelfat
keelhale
keelhaul
keelie
keeling
keelivine
keelless
keelman
keelrake
keelson
keen
keena
keened
keener
keenly
keenness
keep
keepable
keeper
keeperess
keepering
keeperless
keepership
keeping
keepsake
keepsaky
keepworthy
keerogue
Kees
keeshond
keest
keet
keeve
Keewatin
kef
keffel
kefir
kefiric
Kefti
Keftian
Keftiu
keg
kegler
kehaya
kehillah
kehoeite
Keid
keilhauite
keita
Keith
keitloa
Kekchi
kekotene
kekuna
kelchin
keld
Kele
kele
kelebe
kelectome
keleh
kelek
kelep
Kelima
kelk
kell
kella
kellion
kellupweed
Kelly
kelly
keloid
keloidal
kelp
kelper
kelpfish
kelpie
kelpware
kelpwort
kelpy
kelt
kelter
Keltoi
kelty
Kelvin
kelvin
kelyphite
Kemal
Kemalism
Kemalist
kemb
kemp
kemperyman
kempite
kemple
kempster
kempt
kempy
Ken
ken
kenaf
Kenai
kenareh
kench
kend
kendir
kendyr
Kenelm
Kenipsim
kenlore
kenmark
Kenn
Kennebec
kennebecker
kennebunker
Kennedya
kennel
kennelly
kennelman
kenner
Kenneth
kenning
kenningwort
kenno
keno
kenogenesis
kenogenetic
kenogenetically
kenogeny
kenosis
kenotic
kenoticism
kenoticist
kenotism
kenotist
kenotoxin
kenotron
Kenseikai
kensington
Kensitite
kenspac
kenspeck
kenspeckle
Kent
kent
kentallenite
Kentia
Kenticism
Kentish
Kentishman
kentledge
Kenton
kentrogon
kentrolite
Kentuckian
Kentucky
kenyte
kep
kepi
Keplerian
kept
Ker
keracele
keralite
kerana
keraphyllocele
keraphyllous
kerasin
kerasine
kerat
keratalgia
keratectasia
keratectomy
Keraterpeton
keratin
keratinization
keratinize
keratinoid
keratinose
keratinous
keratitis
keratoangioma
keratocele
keratocentesis
keratoconjunctivitis
keratoconus
keratocricoid
keratode
keratodermia
keratogenic
keratogenous
keratoglobus
keratoglossus
keratohelcosis
keratohyal
keratoid
Keratoidea
keratoiritis
Keratol
keratoleukoma
keratolysis
keratolytic
keratoma
keratomalacia
keratome
keratometer
keratometry
keratomycosis
keratoncus
keratonosus
keratonyxis
keratophyre
keratoplastic
keratoplasty
keratorrhexis
keratoscope
keratoscopy
keratose
keratosis
keratotome
keratotomy
keratto
keraulophon
keraulophone
Keraunia
keraunion
keraunograph
keraunographic
keraunography
keraunophone
keraunophonic
keraunoscopia
keraunoscopy
kerbstone
kerchief
kerchiefed
kerchoo
kerchug
kerchunk
kerectomy
kerel
Keres
Keresan
Kerewa
kerf
kerflap
kerflop
kerflummox
Kerite
Kermanji
Kermanshah
kermes
kermesic
kermesite
kermis
kern
kernel
kerneled
kernelless
kernelly
kerner
kernetty
kernish
kernite
kernos
kerogen
kerosene
kerplunk
Kerri
Kerria
kerrie
kerrikerri
kerril
kerrite
Kerry
kerry
kersantite
kersey
kerseymere
kerslam
kerslosh
kersmash
kerugma
kerwham
kerygma
kerygmatic
kerykeion
kerystic
kerystics
Keryx
kesslerman
kestrel
ket
keta
ketal
ketapang
ketazine
ketch
ketchcraft
ketchup
ketembilla
keten
ketene
ketimide
ketimine
ketipate
ketipic
keto
ketogen
ketogenesis
ketogenic
ketoheptose
ketohexose
ketoketene
ketol
ketole
ketolysis
ketolytic
ketone
ketonemia
ketonic
ketonimid
ketonimide
ketonimin
ketonimine
ketonization
ketonize
ketonuria
ketose
ketoside
ketosis
ketosuccinic
ketoxime
kette
ketting
kettle
kettlecase
kettledrum
kettledrummer
kettleful
kettlemaker
kettlemaking
kettler
ketty
Ketu
ketuba
ketupa
ketyl
keup
Keuper
keurboom
kevalin
Kevan
kevel
kevelhead
Kevin
kevutzah
Kevyn
Keweenawan
keweenawite
kewpie
kex
kexy
key
keyage
keyboard
keyed
keyhole
keyless
keylet
keylock
Keynesian
Keynesianism
keynote
keynoter
keyseater
keyserlick
keysmith
keystone
keystoned
Keystoner
keyway
Kha
khaddar
khadi
khagiarite
khahoon
khaiki
khair
khaja
khajur
khakanship
khaki
khakied
Khaldian
khalifa
Khalifat
Khalkha
khalsa
Khami
khamsin
Khamti
khan
khanate
khanda
khandait
khanjar
khanjee
khankah
khansamah
khanum
khar
kharaj
Kharia
Kharijite
Kharoshthi
kharouba
kharroubah
Khartoumer
kharua
Kharwar
Khasa
Khasi
khass
khat
khatib
khatri
Khatti
Khattish
Khaya
Khazar
Khazarian
khediva
khedival
khedivate
khedive
khediviah
khedivial
khediviate
khepesh
Kherwari
Kherwarian
khet
Khevzur
khidmatgar
Khila
khilat
khir
khirka
Khitan
Khivan
Khlysti
Khmer
Khoja
khoja
khoka
Khokani
Khond
Khorassan
khot
Khotan
Khotana
Khowar
khu
Khuai
khubber
khula
khuskhus
Khussak
khutbah
khutuktu
Khuzi
khvat
Khwarazmian
kiack
kiaki
kialee
kiang
Kiangan
kiaugh
kibber
kibble
kibbler
kibblerman
kibe
kibei
kibitka
kibitz
kibitzer
kiblah
kibosh
kiby
kick
kickable
Kickapoo
kickback
kickee
kicker
kicking
kickish
kickless
kickoff
kickout
kickseys
kickshaw
kickup
Kidder
kidder
Kidderminster
kiddier
kiddish
kiddush
kiddushin
kiddy
kidhood
kidlet
kidling
kidnap
kidnapee
kidnaper
kidney
kidneyroot
kidneywort
Kids
kidskin
kidsman
kiefekil
Kieffer
kiekie
kiel
kier
Kieran
kieselguhr
kieserite
kiestless
kieye
Kiho
kikar
Kikatsik
kikawaeo
kike
Kiki
kiki
Kikki
Kikongo
kiku
kikuel
kikumon
Kikuyu
kil
kiladja
kilah
kilampere
kilan
kilbrickenite
kildee
kilderkin
kileh
kilerg
kiley
Kilhamite
kilhig
kiliare
kilim
kill
killable
killadar
Killarney
killas
killcalf
killcrop
killcu
killdeer
killeekillee
killeen
killer
killick
killifish
killing
killingly
killingness
killinite
killogie
killweed
killwort
killy
Kilmarnock
kiln
kilneye
kilnhole
kilnman
kilnrib
kilo
kiloampere
kilobar
kilocalorie
kilocycle
kilodyne
kilogauss
kilogram
kilojoule
kiloliter
kilolumen
kilometer
kilometrage
kilometric
kilometrical
kiloparsec
kilostere
kiloton
kilovar
kilovolt
kilowatt
kilp
kilt
kilter
kiltie
kilting
Kiluba
Kim
kim
kimbang
kimberlin
kimberlite
Kimberly
Kimbundu
Kimeridgian
kimigayo
Kimmo
kimnel
kimono
kimonoed
kin
kina
kinaesthesia
kinaesthesis
kinah
kinase
kinbote
Kinch
kinch
kinchin
kinchinmort
kincob
kind
kindergarten
kindergartener
kindergartening
kindergartner
Kinderhook
kindheart
kindhearted
kindheartedly
kindheartedness
kindle
kindler
kindlesome
kindlily
kindliness
kindling
kindly
kindness
kindred
kindredless
kindredly
kindredness
kindredship
kinematic
kinematical
kinematically
kinematics
kinematograph
kinemometer
kineplasty
kinepox
kinesalgia
kinescope
kinesiatric
kinesiatrics
kinesic
kinesics
kinesimeter
kinesiologic
kinesiological
kinesiology
kinesiometer
kinesis
kinesitherapy
kinesodic
kinesthesia
kinesthesis
kinesthetic
kinetic
kinetical
kinetically
kinetics
kinetochore
kinetogenesis
kinetogenetic
kinetogenetically
kinetogenic
kinetogram
kinetograph
kinetographer
kinetographic
kinetography
kinetomer
kinetomeric
kinetonema
kinetonucleus
kinetophone
kinetophonograph
kinetoplast
kinetoscope
kinetoscopic
King
king
kingbird
kingbolt
kingcob
kingcraft
kingcup
kingdom
kingdomed
kingdomful
kingdomless
kingdomship
kingfish
kingfisher
kinghead
kinghood
kinghunter
kingless
kinglessness
kinglet
kinglihood
kinglike
kinglily
kingliness
kingling
kingly
kingmaker
kingmaking
kingpiece
kingpin
kingrow
kingship
kingsman
Kingu
kingweed
kingwood
Kinipetu
kink
kinkable
kinkaider
kinkajou
kinkcough
kinkhab
kinkhost
kinkily
kinkiness
kinkle
kinkled
kinkly
kinksbush
kinky
kinless
kinnikinnick
kino
kinofluous
kinology
kinoplasm
kinoplasmic
Kinorhyncha
kinospore
Kinosternidae
Kinosternon
kinotannic
kinsfolk
kinship
kinsman
kinsmanly
kinsmanship
kinspeople
kinswoman
kintar
Kintyre
kioea
Kioko
kiosk
kiotome
Kiowa
Kiowan
Kioway
kip
kipage
Kipchak
kipe
Kiplingese
Kiplingism
kippeen
kipper
kipperer
kippy
kipsey
kipskin
Kiranti
Kirghiz
Kirghizean
kiri
Kirillitsa
kirimon
Kirk
kirk
kirker
kirkify
kirking
kirkinhead
kirklike
kirkman
kirktown
kirkward
kirkyard
Kirman
kirmew
kirn
kirombo
kirsch
Kirsten
Kirsty
kirtle
kirtled
Kirundi
kirve
kirver
kischen
kish
Kishambala
kishen
kishon
kishy
kiskatom
Kislev
kismet
kismetic
kisra
kiss
kissability
kissable
kissableness
kissage
kissar
kisser
kissing
kissingly
kissproof
kisswise
kissy
kist
kistful
kiswa
Kiswahili
Kit
kit
kitab
kitabis
Kitalpha
Kitamat
Kitan
kitar
kitcat
kitchen
kitchendom
kitchener
kitchenette
kitchenful
kitchenless
kitchenmaid
kitchenman
kitchenry
kitchenward
kitchenwards
kitchenware
kitchenwife
kitcheny
kite
kiteflier
kiteflying
kith
kithe
kithless
kitish
Kitkahaxki
Kitkehahki
kitling
Kitlope
Kittatinny
kittel
kitten
kittendom
kittenhearted
kittenhood
kittenish
kittenishly
kittenishness
kittenless
kittenship
kitter
kittereen
kitthoge
kittiwake
kittle
kittlepins
kittles
kittlish
kittly
kittock
kittul
Kitty
kitty
kittysol
Kitunahan
kiva
kiver
kivikivi
kivu
Kiwai
Kiwanian
Kiwanis
kiwi
kiwikiwi
kiyas
kiyi
Kizil
Kizilbash
Kjeldahl
kjeldahlization
kjeldahlize
klafter
klaftern
klam
Klamath
Klan
Klanism
Klansman
Klanswoman
klaprotholite
Klaskino
Klaudia
Klaus
klavern
Klaxon
klaxon
Klebsiella
kleeneboc
Kleinian
Kleistian
klendusic
klendusity
klendusive
klepht
klephtic
klephtism
kleptic
kleptistic
kleptomania
kleptomaniac
kleptomanist
kleptophobia
klicket
Klikitat
Kling
Klingsor
klip
klipbok
klipdachs
klipdas
klipfish
klippe
klippen
klipspringer
klister
klockmannite
klom
Klondike
Klondiker
klootchman
klop
klops
klosh
Kluxer
klystron
kmet
knab
knabble
knack
knackebrod
knacker
knackery
knacky
knag
knagged
knaggy
knap
knapbottle
knape
knappan
Knapper
knapper
knappish
knappishly
knapsack
knapsacked
knapsacking
knapweed
knar
knark
knarred
knarry
Knautia
knave
knavery
knaveship
knavess
knavish
knavishly
knavishness
knawel
knead
kneadability
kneadable
kneader
kneading
kneadingly
knebelite
knee
kneebrush
kneecap
kneed
kneehole
kneel
kneeler
kneelet
kneeling
kneelingly
kneepad
kneepan
kneepiece
kneestone
Kneiffia
Kneippism
knell
knelt
Knesset
knet
knew
knez
knezi
kniaz
kniazi
knick
knicker
Knickerbocker
knickerbockered
knickerbockers
knickered
knickers
knickknack
knickknackatory
knickknacked
knickknackery
knickknacket
knickknackish
knickknacky
knickpoint
knife
knifeboard
knifeful
knifeless
knifelike
knifeman
knifeproof
knifer
knifesmith
knifeway
knight
knightage
knightess
knighthead
knighthood
Knightia
knightless
knightlihood
knightlike
knightliness
knightling
knightly
knightship
knightswort
Kniphofia
Knisteneaux
knit
knitback
knitch
knitted
knitter
knitting
knittle
knitwear
knitweed
knitwork
knived
knivey
knob
knobbed
knobber
knobbiness
knobble
knobbler
knobbly
knobby
knobkerrie
knoblike
knobstick
knobstone
knobular
knobweed
knobwood
knock
knockabout
knockdown
knockemdown
knocker
knocking
knockless
knockoff
knockout
knockstone
knockup
knoll
knoller
knolly
knop
knopite
knopped
knopper
knoppy
knopweed
knorhaan
Knorria
knosp
knosped
Knossian
knot
knotberry
knotgrass
knothole
knothorn
knotless
knotlike
knotroot
knotted
knotter
knottily
knottiness
knotting
knotty
knotweed
knotwork
knotwort
knout
know
knowability
knowable
knowableness
knowe
knower
knowing
knowingly
knowingness
knowledge
knowledgeable
knowledgeableness
knowledgeably
knowledged
knowledgeless
knowledgement
knowledging
known
knowperts
Knoxian
Knoxville
knoxvillite
knub
knubbly
knubby
knublet
knuckle
knucklebone
knuckled
knuckler
knuckling
knuckly
knuclesome
Knudsen
knur
knurl
knurled
knurling
knurly
Knut
knut
Knute
knutty
knyaz
knyazi
Ko
ko
koa
koae
koala
koali
Koasati
kob
koban
kobellite
kobi
kobird
kobold
kobong
kobu
Kobus
Koch
Kochab
Kochia
kochliarion
koda
Kodagu
Kodak
kodak
kodaker
kodakist
kodakry
Kodashim
kodro
kodurite
Koeberlinia
Koeberliniaceae
koeberliniaceous
koechlinite
Koeksotenok
koel
Koellia
Koelreuteria
koenenite
Koeri
koff
koft
koftgar
koftgari
koggelmannetje
Kogia
Kohathite
Koheleth
kohemp
Kohen
Kohistani
Kohl
kohl
Kohlan
kohlrabi
kohua
koi
Koiari
Koibal
koil
koila
koilanaglyphic
koilon
koimesis
Koine
koine
koinon
koinonia
Koipato
Koitapu
kojang
Kojiki
kokako
kokam
kokan
kokerboom
kokil
kokio
koklas
koklass
Koko
koko
kokoon
Kokoona
kokoromiko
kokowai
kokra
koksaghyz
koku
kokum
kokumin
kokumingun
Kol
kola
kolach
Kolarian
Koldaji
kolea
koleroga
kolhoz
Koli
kolinski
kolinsky
Kolis
kolkhos
kolkhoz
Kolkka
kollast
kollaster
koller
kollergang
kolo
kolobion
kolobus
kolokolo
kolsun
koltunna
koltunnor
Koluschan
Kolush
Komati
komatik
kombu
Kome
Komi
kominuter
kommetje
kommos
komondor
kompeni
Komsomol
kon
kona
konak
Konariot
Konde
Kongo
Kongoese
Kongolese
kongoni
kongsbergite
kongu
Konia
Koniaga
Koniga
konimeter
koninckite
konini
koniology
koniscope
konjak
Konkani
Konomihu
Konrad
konstantin
Konstantinos
kontakion
Konyak
kooka
kookaburra
kookeree
kookery
kookri
koolah
kooletah
kooliman
koolokamba
Koolooly
koombar
koomkie
Koorg
kootcha
Kootenay
kop
Kopagmiut
kopeck
koph
kopi
koppa
koppen
koppite
Koprino
kor
Kora
kora
koradji
Korah
Korahite
Korahitic
korait
korakan
Koran
Korana
Koranic
Koranist
korari
Kore
kore
Korean
korec
koreci
Koreish
Koreishite
korero
Koreshan
Koreshanity
kori
korimako
korin
Kornephorus
kornerupine
kornskeppa
kornskeppur
korntonde
korntonder
korntunna
korntunnur
Koroa
koromika
koromiko
korona
korova
korrel
korrigum
korumburra
koruna
Korwa
Kory
Koryak
korymboi
korymbos
korzec
kos
Kosalan
Koschei
kosher
Kosimo
kosin
kosmokrator
Koso
kosong
kosotoxin
Kossaean
Kossean
Kosteletzkya
koswite
Kota
kotal
Kotar
koto
Kotoko
kotschubeite
kottigite
kotuku
kotukutuku
kotwal
kotwalee
kotyle
kotylos
kou
koulan
Koungmiut
kouza
kovil
Kowagmiut
kowhai
kowtow
koyan
kozo
Kpuesi
Kra
kra
kraal
kraft
Krag
kragerite
krageroite
krait
kraken
krakowiak
kral
Krama
krama
Krameria
Krameriaceae
krameriaceous
kran
krantzite
Krapina
kras
krasis
kratogen
kratogenic
Kraunhia
kraurite
kraurosis
kraurotic
krausen
krausite
kraut
kreis
Kreistag
kreistle
kreittonite
krelos
kremersite
kremlin
krems
kreng
krennerite
Krepi
kreplech
kreutzer
kriegspiel
krieker
Krigia
krimmer
krina
Kriophoros
Kris
Krishna
Krishnaism
Krishnaist
Krishnaite
Krishnaitic
Kristen
Kristi
Kristian
Kristin
Kristinaux
krisuvigite
kritarchy
Krithia
Kriton
kritrima
krobyloi
krobylos
krocket
krohnkite
krome
kromeski
kromogram
kromskop
krona
krone
kronen
kroner
Kronion
kronor
kronur
Kroo
kroon
krosa
krouchka
kroushka
Kru
Krugerism
Krugerite
Kruman
krummhorn
kryokonite
krypsis
kryptic
krypticism
kryptocyanine
kryptol
kryptomere
krypton
Krzysztof
Kshatriya
Kshatriyahood
Kua
Kuan
kuan
Kuar
Kuba
kuba
Kubachi
Kubanka
kubba
Kubera
kubuklion
Kuchean
kuchen
kudize
kudos
Kudrun
kudu
kudzu
Kuehneola
kuei
Kufic
kuge
kugel
Kuhnia
Kui
kuichua
Kuki
kukoline
kukri
kuku
kukui
Kukulcan
kukupa
Kukuruku
kula
kulack
Kulah
kulah
kulaite
kulak
kulakism
Kulanapan
kulang
Kuldip
Kuli
kulimit
kulkarni
kullaite
Kullani
kulm
kulmet
Kulturkampf
Kulturkreis
Kuman
kumbi
kumhar
kumiss
kummel
Kumni
kumquat
kumrah
Kumyk
kunai
Kunbi
Kundry
Kuneste
kung
kunk
kunkur
Kunmiut
kunzite
Kuomintang
kupfernickel
kupfferite
kuphar
kupper
Kuranko
kurbash
kurchicine
kurchine
Kurd
Kurdish
Kurdistan
kurgan
Kuri
Kurilian
Kurku
kurmburra
Kurmi
Kuroshio
kurrajong
Kurt
kurtosis
Kuruba
Kurukh
kuruma
kurumaya
Kurumba
kurung
kurus
kurvey
kurveyor
kusa
kusam
Kusan
kusha
Kushshu
kusimansel
kuskite
kuskos
kuskus
Kuskwogmiut
Kustenau
kusti
Kusum
kusum
kutcha
Kutchin
Kutenai
kuttab
kuttar
kuttaur
kuvasz
Kuvera
kvass
kvint
kvinter
Kwakiutl
kwamme
kwan
Kwannon
Kwapa
kwarta
kwarterka
kwazoku
kyack
kyah
kyar
kyat
kyaung
Kybele
Kyklopes
Kyklops
kyl
Kyle
kyle
kylite
kylix
Kylo
kymation
kymatology
kymbalon
kymogram
kymograph
kymographic
kynurenic
kynurine
kyphoscoliosis
kyphoscoliotic
Kyphosidae
kyphosis
kyphotic
Kyrie
kyrine
kyschtymite
kyte
Kyu
Kyung
Kyurin
Kyurinish
L
l
la
laager
laang
lab
Laban
labara
labarum
labba
labber
labdacism
labdacismus
labdanum
labefact
labefactation
labefaction
labefy
label
labeler
labella
labellate
labeller
labelloid
labellum
labia
labial
labialism
labialismus
labiality
labialization
labialize
labially
Labiatae
labiate
labiated
labidophorous
Labidura
Labiduridae
labiella
labile
lability
labilization
labilize
labioalveolar
labiocervical
labiodental
labioglossal
labioglossolaryngeal
labioglossopharyngeal
labiograph
labioguttural
labiolingual
labiomancy
labiomental
labionasal
labiopalatal
labiopalatalize
labiopalatine
labiopharyngeal
labioplasty
labiose
labiotenaculum
labiovelar
labioversion
labis
labium
lablab
labor
laborability
laborable
laborage
laborant
laboratorial
laboratorian
laboratory
labordom
labored
laboredly
laboredness
laborer
laboress
laborhood
laboring
laboringly
laborious
laboriously
laboriousness
laborism
laborist
laborite
laborless
laborous
laborously
laborousness
laborsaving
laborsome
laborsomely
laborsomeness
Laboulbenia
Laboulbeniaceae
laboulbeniaceous
Laboulbeniales
labour
labra
Labrador
Labradorean
labradorite
labradoritic
labral
labret
labretifery
Labridae
labroid
Labroidea
labrosaurid
labrosauroid
Labrosaurus
labrose
labrum
Labrus
labrusca
labrys
Laburnum
labyrinth
labyrinthal
labyrinthally
labyrinthian
labyrinthibranch
labyrinthibranchiate
Labyrinthibranchii
labyrinthic
labyrinthical
labyrinthically
Labyrinthici
labyrinthiform
labyrinthine
labyrinthitis
Labyrinthodon
labyrinthodont
Labyrinthodonta
labyrinthodontian
labyrinthodontid
labyrinthodontoid
Labyrinthula
Labyrinthulidae
lac
lacca
laccaic
laccainic
laccase
laccol
laccolith
laccolithic
laccolitic
lace
lacebark
laced
Lacedaemonian
laceflower
laceleaf
laceless
lacelike
lacemaker
lacemaking
laceman
lacepiece
lacepod
lacer
lacerability
lacerable
lacerant
lacerate
lacerated
lacerately
laceration
lacerative
Lacerta
Lacertae
lacertian
Lacertid
Lacertidae
lacertiform
Lacertilia
lacertilian
lacertiloid
lacertine
lacertoid
lacertose
lacery
lacet
lacewing
lacewoman
lacewood
lacework
laceworker
laceybark
lache
Lachenalia
laches
Lachesis
Lachnanthes
Lachnosterna
lachryma
lachrymae
lachrymaeform
lachrymal
lachrymally
lachrymalness
lachrymary
lachrymation
lachrymator
lachrymatory
lachrymiform
lachrymist
lachrymogenic
lachrymonasal
lachrymosal
lachrymose
lachrymosely
lachrymosity
lachrymous
lachsa
lacily
Lacinaria
laciness
lacing
lacinia
laciniate
laciniated
laciniation
laciniform
laciniola
laciniolate
laciniose
lacinula
lacinulate
lacinulose
lacis
lack
lackadaisical
lackadaisicality
lackadaisically
lackadaisicalness
lackadaisy
lackaday
lacker
lackey
lackeydom
lackeyed
lackeyism
lackeyship
lackland
lackluster
lacklusterness
lacklustrous
lacksense
lackwit
lackwittedly
lackwittedness
lacmoid
lacmus
Laconian
Laconic
laconic
laconica
laconically
laconicalness
laconicism
laconicum
laconism
laconize
laconizer
Lacosomatidae
lacquer
lacquerer
lacquering
lacquerist
lacroixite
lacrosse
lacrosser
lacrym
lactagogue
lactalbumin
lactam
lactamide
lactant
lactarene
lactarious
lactarium
Lactarius
lactary
lactase
lactate
lactation
lactational
lacteal
lactean
lactenin
lacteous
lactesce
lactescence
lactescency
lactescent
lactic
lacticinia
lactid
lactide
lactiferous
lactiferousness
lactific
lactifical
lactification
lactiflorous
lactifluous
lactiform
lactifuge
lactify
lactigenic
lactigenous
lactigerous
lactim
lactimide
lactinate
lactivorous
lacto
lactobacilli
Lactobacillus
lactobacillus
lactobutyrometer
lactocele
lactochrome
lactocitrate
lactodensimeter
lactoflavin
lactoglobulin
lactoid
lactol
lactometer
lactone
lactonic
lactonization
lactonize
lactophosphate
lactoproteid
lactoprotein
lactoscope
lactose
lactoside
lactosuria
lactothermometer
lactotoxin
lactovegetarian
Lactuca
lactucarium
lactucerin
lactucin
lactucol
lactucon
lactyl
lacuna
lacunae
lacunal
lacunar
lacunaria
lacunary
lacune
lacunose
lacunosity
lacunule
lacunulose
lacuscular
lacustral
lacustrian
lacustrine
lacwork
lacy
lad
Ladakhi
ladakin
ladanigerous
ladanum
ladder
laddered
laddering
ladderlike
ladderway
ladderwise
laddery
laddess
laddie
laddikie
laddish
laddock
lade
lademan
laden
lader
ladhood
ladies
ladify
Ladik
Ladin
lading
Ladino
ladkin
ladle
ladleful
ladler
ladlewood
ladrone
ladronism
ladronize
lady
ladybird
ladybug
ladyclock
ladydom
ladyfinger
ladyfish
ladyfly
ladyfy
ladyhood
ladyish
ladyism
ladykin
ladykind
ladyless
ladylike
ladylikely
ladylikeness
ladyling
ladylintywhite
ladylove
ladyly
ladyship
Ladytide
Laelia
laemodipod
Laemodipoda
laemodipodan
laemodipodiform
laemodipodous
laemoparalysis
laemostenosis
laeotropic
laeotropism
Laestrygones
laet
laeti
laetic
Laevigrada
laevoduction
laevogyrate
laevogyre
laevogyrous
laevolactic
laevorotation
laevorotatory
laevotartaric
laevoversion
lafayette
Lafite
lag
lagan
lagarto
lagen
lagena
Lagenaria
lagend
lageniform
lager
Lagerstroemia
Lagetta
lagetto
laggar
laggard
laggardism
laggardly
laggardness
lagged
laggen
lagger
laggin
lagging
laglast
lagna
lagniappe
lagomorph
Lagomorpha
lagomorphic
lagomorphous
Lagomyidae
lagonite
lagoon
lagoonal
lagoonside
lagophthalmos
lagopode
lagopodous
lagopous
Lagopus
Lagorchestes
lagostoma
Lagostomus
Lagothrix
Lagrangian
Lagthing
Lagting
Laguncularia
Lagunero
Lagurus
lagwort
Lahnda
Lahontan
Lahuli
Lai
lai
Laibach
laic
laical
laicality
laically
laich
laicism
laicity
laicization
laicize
laicizer
laid
laigh
lain
laine
laiose
lair
lairage
laird
lairdess
lairdie
lairdly
lairdocracy
lairdship
lairless
lairman
lairstone
lairy
laitance
laity
Lak
lak
lakarpite
lakatoi
lake
lakeland
lakelander
lakeless
lakelet
lakelike
lakemanship
laker
lakeside
lakeward
lakeweed
lakie
laking
lakish
lakishness
lakism
lakist
Lakota
Lakshmi
laky
lalang
lall
Lallan
Lalland
lallation
lalling
lalo
laloneurosis
lalopathy
lalophobia
laloplegia
lam
lama
lamaic
Lamaism
Lamaist
Lamaistic
Lamaite
Lamanism
Lamanite
Lamano
lamantin
lamany
Lamarckia
Lamarckian
Lamarckianism
Lamarckism
lamasary
lamasery
lamastery
lamb
Lamba
lamba
Lambadi
lambale
lambaste
lambda
lambdacism
lambdoid
lambdoidal
lambeau
lambency
lambent
lambently
lamber
Lambert
lambert
lambhood
lambie
lambiness
lambish
lambkill
lambkin
Lamblia
lambliasis
lamblike
lambling
lambly
lamboys
lambrequin
lambsdown
lambskin
lambsuccory
lamby
lame
lamedh
lameduck
lamel
lamella
lamellar
Lamellaria
Lamellariidae
lamellarly
lamellary
lamellate
lamellated
lamellately
lamellation
lamellibranch
Lamellibranchia
Lamellibranchiata
lamellibranchiate
lamellicorn
lamellicornate
Lamellicornes
Lamellicornia
lamellicornous
lamelliferous
lamelliform
lamellirostral
lamellirostrate
Lamellirostres
lamelloid
lamellose
lamellosity
lamellule
lamely
lameness
lament
lamentable
lamentableness
lamentably
lamentation
lamentational
lamentatory
lamented
lamentedly
lamenter
lamentful
lamenting
lamentingly
lamentive
lamentory
lamester
lamestery
lameter
lametta
lamia
Lamiaceae
lamiaceous
lamiger
lamiid
Lamiidae
Lamiides
Lamiinae
lamin
lamina
laminability
laminable
laminae
laminar
Laminaria
Laminariaceae
laminariaceous
Laminariales
laminarian
laminarin
laminarioid
laminarite
laminary
laminate
laminated
lamination
laminboard
laminectomy
laminiferous
laminiform
laminiplantar
laminiplantation
laminitis
laminose
laminous
lamish
Lamista
lamiter
Lamium
Lammas
lammas
Lammastide
lammer
lammergeier
lammock
lammy
Lamna
lamnectomy
lamnid
Lamnidae
lamnoid
lamp
lampad
lampadary
lampadedromy
lampadephore
lampadephoria
lampadite
lampas
lampatia
lampblack
lamper
lampern
lampers
lampflower
lampfly
lampful
lamphole
lamping
lampion
lampist
lampistry
lampless
lamplet
lamplight
lamplighted
lamplighter
lamplit
lampmaker
lampmaking
lampman
Lampong
lampoon
lampooner
lampoonery
lampoonist
lamppost
lamprey
Lampridae
lamprophony
lamprophyre
lamprophyric
lamprotype
Lampsilis
Lampsilus
lampstand
lampwick
lampyrid
Lampyridae
lampyrine
Lampyris
Lamus
Lamut
lamziekte
lan
Lana
lanameter
Lanao
Lanarkia
lanarkite
lanas
lanate
lanated
lanaz
Lancaster
Lancasterian
Lancastrian
Lance
lance
lanced
lancegay
lancelet
lancelike
lancely
lanceman
lanceolar
lanceolate
lanceolated
lanceolately
lanceolation
lancepesade
lancepod
lanceproof
lancer
lances
lancet
lanceted
lanceteer
lancewood
lancha
lanciers
lanciferous
lanciform
lancinate
lancination
land
landamman
landau
landaulet
landaulette
landblink
landbook
landdrost
landed
lander
landesite
landfall
landfast
landflood
landgafol
landgravate
landgrave
landgraveship
landgravess
landgraviate
landgravine
landholder
landholdership
landholding
landimere
landing
landlady
landladydom
landladyhood
landladyish
landladyship
landless
landlessness
landlike
landline
landlock
landlocked
landlook
landlooker
landloper
landlord
landlordism
landlordly
landlordry
landlordship
landlouper
landlouping
landlubber
landlubberish
landlubberly
landlubbing
landman
landmark
Landmarker
landmil
landmonger
landocracy
landocrat
Landolphia
landolphia
landowner
landownership
landowning
landplane
landraker
landreeve
landright
landsale
landscape
landscapist
landshard
landship
landsick
landside
landskip
landslide
landslip
Landsmaal
landsman
landspout
landspringy
Landsting
landstorm
Landsturm
Landuman
landwaiter
landward
landwash
landways
Landwehr
landwhin
landwire
landwrack
lane
lanete
laneway
laney
langaha
langarai
langbanite
langbeinite
langca
Langhian
langi
langite
langlauf
langlaufer
langle
Lango
Langobard
Langobardic
langoon
langooty
langrage
langsat
Langsdorffia
langsettle
Langshan
langspiel
langsyne
language
languaged
languageless
langued
Languedocian
languescent
languet
languid
languidly
languidness
languish
languisher
languishing
languishingly
languishment
languor
languorous
languorously
langur
laniariform
laniary
laniate
laniferous
lanific
laniflorous
laniform
lanigerous
Laniidae
laniiform
Laniinae
lanioid
lanista
Lanital
Lanius
lank
lanket
lankily
lankiness
lankish
lankly
lankness
lanky
lanner
lanneret
Lanny
lanolin
lanose
lanosity
lansat
lansdowne
lanseh
lansfordite
lansknecht
lanson
lansquenet
lant
lantaca
Lantana
lanterloo
lantern
lanternflower
lanternist
lanternleaf
lanternman
lanthana
lanthanide
lanthanite
Lanthanotidae
Lanthanotus
lanthanum
lanthopine
lantum
lanuginose
lanuginous
lanuginousness
lanugo
lanum
Lanuvian
lanx
lanyard
Lao
Laodicean
Laodiceanism
Laotian
lap
lapacho
lapachol
lapactic
Lapageria
laparectomy
laparocele
laparocholecystotomy
laparocolectomy
laparocolostomy
laparocolotomy
laparocolpohysterotomy
laparocolpotomy
laparocystectomy
laparocystotomy
laparoelytrotomy
laparoenterostomy
laparoenterotomy
laparogastroscopy
laparogastrotomy
laparohepatotomy
laparohysterectomy
laparohysteropexy
laparohysterotomy
laparoileotomy
laparomyitis
laparomyomectomy
laparomyomotomy
laparonephrectomy
laparonephrotomy
laparorrhaphy
laparosalpingectomy
laparosalpingotomy
laparoscopy
laparosplenectomy
laparosplenotomy
laparostict
Laparosticti
laparothoracoscopy
laparotome
laparotomist
laparotomize
laparotomy
laparotrachelotomy
lapboard
lapcock
Lapeirousia
lapel
lapeler
lapelled
lapful
lapicide
lapidarian
lapidarist
lapidary
lapidate
lapidation
lapidator
lapideon
lapideous
lapidescent
lapidicolous
lapidific
lapidification
lapidify
lapidist
lapidity
lapidose
lapilliform
lapillo
lapillus
Lapith
Lapithae
Lapithaean
Laplacian
Lapland
Laplander
Laplandian
Laplandic
Laplandish
lapon
Laportea
Lapp
Lappa
lappaceous
lappage
lapped
lapper
lappet
lappeted
Lappic
lapping
Lappish
Lapponese
Lapponian
Lappula
lapsability
lapsable
Lapsana
lapsation
lapse
lapsed
lapser
lapsi
lapsing
lapsingly
lapstone
lapstreak
lapstreaked
lapstreaker
Laputa
Laputan
laputically
lapwing
lapwork
laquear
laquearian
laqueus
Lar
lar
Laralia
Laramide
Laramie
larboard
larbolins
larbowlines
larcener
larcenic
larcenish
larcenist
larcenous
larcenously
larceny
larch
larchen
lard
lardacein
lardaceous
larder
larderellite
larderer
larderful
larderlike
lardiform
lardite
Lardizabalaceae
lardizabalaceous
lardon
lardworm
lardy
lareabell
Larentiidae
large
largebrained
largehanded
largehearted
largeheartedness
largely
largemouth
largemouthed
largen
largeness
largess
larghetto
largifical
largish
largition
largitional
largo
Lari
lari
Laria
lariat
larick
larid
Laridae
laridine
larigo
larigot
lariid
Lariidae
larin
Larinae
larine
larithmics
Larix
larixin
lark
larker
larkiness
larking
larkingly
larkish
larkishness
larklike
larkling
larksome
larkspur
larky
larmier
larmoyant
Larnaudian
larnax
laroid
larrigan
larrikin
larrikinalian
larrikiness
larrikinism
larriman
larrup
Larry
larry
Lars
larsenite
Larunda
Larus
larva
Larvacea
larvae
larval
Larvalia
larvarium
larvate
larve
larvicidal
larvicide
larvicolous
larviform
larvigerous
larvikite
larviparous
larviposit
larviposition
larvivorous
larvule
laryngal
laryngalgia
laryngeal
laryngeally
laryngean
laryngeating
laryngectomy
laryngemphraxis
laryngendoscope
larynges
laryngic
laryngismal
laryngismus
laryngitic
laryngitis
laryngocele
laryngocentesis
laryngofission
laryngofissure
laryngograph
laryngography
laryngological
laryngologist
laryngology
laryngometry
laryngoparalysis
laryngopathy
laryngopharyngeal
laryngopharyngitis
laryngophony
laryngophthisis
laryngoplasty
laryngoplegia
laryngorrhagia
laryngorrhea
laryngoscleroma
laryngoscope
laryngoscopic
laryngoscopical
laryngoscopist
laryngoscopy
laryngospasm
laryngostasis
laryngostenosis
laryngostomy
laryngostroboscope
laryngotome
laryngotomy
laryngotracheal
laryngotracheitis
laryngotracheoscopy
laryngotracheotomy
laryngotyphoid
laryngovestibulitis
larynx
las
lasa
lasarwort
lascar
lascivious
lasciviously
lasciviousness
laser
Laserpitium
laserwort
lash
lasher
lashingly
lashless
lashlite
Lasi
lasianthous
Lasiocampa
lasiocampid
Lasiocampidae
Lasiocampoidea
lasiocarpous
Lasius
lask
lasket
Laspeyresia
laspring
lasque
lass
lasset
lassie
lassiehood
lassieish
lassitude
lasslorn
lasso
lassock
lassoer
last
lastage
laster
lasting
lastingly
lastingness
lastly
lastness
lastre
lastspring
lasty
lat
lata
latah
Latakia
Latania
Latax
latch
latcher
latchet
latching
latchkey
latchless
latchman
latchstring
late
latebra
latebricole
latecomer
latecoming
lated
lateen
lateener
lately
laten
latence
latency
lateness
latensification
latent
latentize
latently
latentness
later
latera
laterad
lateral
lateralis
laterality
lateralization
lateralize
laterally
Lateran
latericumbent
lateriflexion
laterifloral
lateriflorous
laterifolious
Laterigradae
laterigrade
laterinerved
laterite
lateritic
lateritious
lateriversion
laterization
lateroabdominal
lateroanterior
laterocaudal
laterocervical
laterodeviation
laterodorsal
lateroduction
lateroflexion
lateromarginal
lateronuchal
lateroposition
lateroposterior
lateropulsion
laterostigmatal
laterostigmatic
laterotemporal
laterotorsion
lateroventral
lateroversion
latescence
latescent
latesome
latest
latewhile
latex
latexosis
lath
lathe
lathee
latheman
lathen
lather
latherability
latherable
lathereeve
latherer
latherin
latheron
latherwort
lathery
lathesman
lathhouse
lathing
Lathraea
lathwork
lathy
lathyric
lathyrism
Lathyrus
Latian
latibulize
latices
laticiferous
laticlave
laticostate
latidentate
latifundian
latifundium
latigo
Latimeria
Latin
Latinate
Latiner
Latinesque
Latinian
Latinic
Latiniform
Latinism
latinism
Latinist
Latinistic
Latinistical
Latinitaster
Latinity
Latinization
Latinize
Latinizer
Latinless
Latinus
lation
latipennate
latiplantar
latirostral
Latirostres
latirostrous
Latirus
latisept
latiseptal
latiseptate
latish
latisternal
latitancy
latitant
latitat
latite
latitude
latitudinal
latitudinally
latitudinarian
latitudinarianisn
latitudinary
latitudinous
latomy
Latona
Latonian
Latooka
latrant
latration
latreutic
latria
Latrididae
latrine
Latris
latro
latrobe
latrobite
latrocinium
Latrodectus
latron
latten
lattener
latter
latterkin
latterly
lattermath
lattermost
latterness
lattice
latticed
latticewise
latticework
latticing
latticinio
Latuka
latus
Latvian
lauan
laubanite
laud
laudability
laudable
laudableness
laudably
laudanidine
laudanin
laudanine
laudanosine
laudanum
laudation
laudative
laudator
laudatorily
laudatory
lauder
Laudian
Laudianism
laudification
Laudism
Laudist
laudist
laugh
laughable
laughableness
laughably
laughee
laugher
laughful
laughing
laughingly
laughingstock
laughsome
laughter
laughterful
laughterless
laughworthy
laughy
lauia
laumonite
laumontite
laun
launce
launch
launcher
launchful
launchways
laund
launder
launderability
launderable
launderer
laundry
laundrymaid
laundryman
laundryowner
laundrywoman
laur
Laura
laura
Lauraceae
lauraceous
lauraldehyde
laurate
laurdalite
laureate
laureated
laureateship
laureation
Laurel
laurel
laureled
laurellike
laurelship
laurelwood
Laurence
Laurencia
Laurent
Laurentian
Laurentide
laureole
Laurianne
lauric
Laurie
laurin
laurinoxylon
laurionite
laurite
Laurocerasus
laurone
laurotetanine
Laurus
laurustine
laurustinus
laurvikite
lauryl
lautarite
lautitious
lava
lavable
lavabo
lavacre
lavage
lavaliere
lavalike
Lavandula
lavanga
lavant
lavaret
Lavatera
lavatic
lavation
lavational
lavatorial
lavatory
lave
laveer
Lavehr
lavement
lavender
lavenite
laver
Laverania
laverock
laverwort
lavialite
lavic
Lavinia
lavish
lavisher
lavishing
lavishingly
lavishly
lavishment
lavishness
lavolta
lavrovite
law
lawbook
lawbreaker
lawbreaking
lawcraft
lawful
lawfully
lawfulness
lawgiver
lawgiving
lawing
lawish
lawk
lawlants
lawless
lawlessly
lawlessness
lawlike
lawmaker
lawmaking
lawman
lawmonger
lawn
lawned
lawner
lawnlet
lawnlike
lawny
lawproof
Lawrence
lawrencite
Lawrie
lawrightman
Lawson
Lawsoneve
Lawsonia
lawsonite
lawsuit
lawsuiting
lawter
Lawton
lawyer
lawyeress
lawyerism
lawyerlike
lawyerling
lawyerly
lawyership
lawyery
lawzy
lax
laxate
laxation
laxative
laxatively
laxativeness
laxiflorous
laxifoliate
laxifolious
laxism
laxist
laxity
laxly
laxness
lay
layaway
layback
layboy
layer
layerage
layered
layery
layette
Layia
laying
layland
layman
laymanship
layne
layoff
layout
layover
layship
laystall
laystow
laywoman
Laz
lazar
lazaret
lazaretto
Lazarist
lazarlike
lazarly
lazarole
Lazarus
laze
lazily
laziness
lazule
lazuli
lazuline
lazulite
lazulitic
lazurite
lazy
lazybird
lazybones
lazyboots
lazyhood
lazyish
lazylegs
lazyship
lazzarone
lazzaroni
Lea
lea
leach
leacher
leachman
leachy
Lead
lead
leadable
leadableness
leadage
leadback
leaded
leaden
leadenhearted
leadenheartedness
leadenly
leadenness
leadenpated
leader
leaderess
leaderette
leaderless
leadership
leadhillite
leadin
leadiness
leading
leadingly
leadless
leadman
leadoff
leadout
leadproof
Leads
leadsman
leadstone
leadway
leadwood
leadwork
leadwort
leady
leaf
leafage
leafboy
leafcup
leafdom
leafed
leafen
leafer
leafery
leafgirl
leafit
leafless
leaflessness
leaflet
leafleteer
leaflike
leafstalk
leafwork
leafy
league
leaguelong
leaguer
Leah
leak
leakage
leakance
leaker
leakiness
leakless
leakproof
leaky
leal
lealand
leally
lealness
lealty
leam
leamer
lean
Leander
leaner
leaning
leanish
leanly
leanness
leant
leap
leapable
leaper
leapfrog
leapfrogger
leapfrogging
leaping
leapingly
leapt
Lear
lear
Learchus
learn
learnable
learned
learnedly
learnedness
learner
learnership
learning
learnt
Learoyd
leasable
lease
leasehold
leaseholder
leaseholding
leaseless
leasemonger
leaser
leash
leashless
leasing
leasow
least
leastways
leastwise
leat
leath
leather
leatherback
leatherbark
leatherboard
leatherbush
leathercoat
leathercraft
leatherer
Leatherette
leatherfish
leatherflower
leatherhead
leatherine
leatheriness
leathering
leatherize
leatherjacket
leatherleaf
leatherlike
leathermaker
leathermaking
leathern
leatherneck
Leatheroid
leatherroot
leatherside
Leatherstocking
leatherware
leatherwing
leatherwood
leatherwork
leatherworker
leatherworking
leathery
leathwake
leatman
leave
leaved
leaveless
leavelooker
leaven
leavening
leavenish
leavenless
leavenous
leaver
leaverwood
leaves
leaving
leavy
leawill
leban
Lebanese
lebbek
lebensraum
Lebistes
lebrancho
lecama
lecaniid
Lecaniinae
lecanine
Lecanium
lecanomancer
lecanomancy
lecanomantic
Lecanora
Lecanoraceae
lecanoraceous
lecanorine
lecanoroid
lecanoscopic
lecanoscopy
lech
Lechea
lecher
lecherous
lecherously
lecherousness
lechery
lechriodont
Lechriodonta
lechuguilla
lechwe
Lecidea
Lecideaceae
lecideaceous
lecideiform
lecideine
lecidioid
lecithal
lecithalbumin
lecithality
lecithin
lecithinase
lecithoblast
lecithoprotein
leck
lecker
lecontite
lecotropal
lectern
lection
lectionary
lectisternium
lector
lectorate
lectorial
lectorship
lectotype
lectress
lectrice
lectual
lecture
lecturee
lectureproof
lecturer
lectureship
lecturess
lecturette
lecyth
lecythid
Lecythidaceae
lecythidaceous
Lecythis
lecythoid
lecythus
led
Leda
lede
leden
lederite
ledge
ledged
ledgeless
ledger
ledgerdom
ledging
ledgment
ledgy
Ledidae
ledol
Ledum
Lee
lee
leeangle
leeboard
leech
leecheater
leecher
leechery
leeches
leechkin
leechlike
leechwort
leed
leefang
leeftail
leek
leekish
leeky
leep
leepit
leer
leerily
leeringly
leerish
leerness
leeroway
Leersia
leery
lees
leet
leetman
leewan
leeward
leewardly
leewardmost
leewardness
leeway
leewill
left
leftish
leftism
leftist
leftments
leftmost
leftness
leftover
leftward
leftwardly
leftwards
leg
legacy
legal
legalese
legalism
legalist
legalistic
legalistically
legality
legalization
legalize
legally
legalness
legantine
legatary
legate
legatee
legateship
legatine
legation
legationary
legative
legato
legator
legatorial
legend
legenda
legendarian
legendary
legendic
legendist
legendless
Legendrian
legendry
leger
legerdemain
legerdemainist
legerity
leges
legged
legger
legginess
legging
legginged
leggy
leghorn
legibility
legible
legibleness
legibly
legific
legion
legionary
legioned
legioner
legionnaire
legionry
legislate
legislation
legislational
legislativ
legislative
legislatively
legislator
legislatorial
legislatorially
legislatorship
legislatress
legislature
legist
legit
legitim
legitimacy
legitimate
legitimately
legitimateness
legitimation
legitimatist
legitimatize
legitimism
legitimist
legitimistic
legitimity
legitimization
legitimize
leglen
legless
leglessness
leglet
leglike
legman
legoa
legpiece
legpull
legpuller
legpulling
legrope
legua
leguan
Leguatia
leguleian
leguleious
legume
legumelin
legumen
legumin
leguminiform
Leguminosae
leguminose
leguminous
Lehi
lehr
lehrbachite
lehrman
lehua
lei
Leibnitzian
Leibnitzianism
Leicester
Leif
Leigh
leighton
Leila
leimtype
leiocephalous
leiocome
leiodermatous
leiodermia
leiomyofibroma
leiomyoma
leiomyomatous
leiomyosarcoma
leiophyllous
Leiophyllum
Leiothrix
Leiotrichan
Leiotriches
Leiotrichi
Leiotrichidae
Leiotrichinae
leiotrichine
leiotrichous
leiotrichy
leiotropic
Leipoa
Leishmania
leishmaniasis
Leisten
leister
leisterer
leisurable
leisurably
leisure
leisured
leisureful
leisureless
leisureliness
leisurely
leisureness
Leith
leitmotiv
Leitneria
Leitneriaceae
leitneriaceous
Leitneriales
lek
lekach
lekane
lekha
Lelia
Lemaireocereus
leman
Lemanea
Lemaneaceae
lemel
lemma
lemmata
lemming
lemmitis
lemmoblastic
lemmocyte
Lemmus
Lemna
Lemnaceae
lemnaceous
lemnad
Lemnian
lemniscate
lemniscatic
lemniscus
lemography
lemology
lemon
lemonade
Lemonias
Lemoniidae
Lemoniinae
lemonish
lemonlike
lemonweed
lemonwood
lemony
Lemosi
Lemovices
lempira
Lemuel
lemur
lemures
Lemuria
Lemurian
lemurian
lemurid
Lemuridae
lemuriform
Lemurinae
lemurine
lemuroid
Lemuroidea
Len
Lena
lenad
Lenaea
Lenaean
Lenaeum
Lenaeus
Lenape
lenard
Lenca
Lencan
lench
lend
lendable
lendee
lender
Lendu
lene
length
lengthen
lengthener
lengther
lengthful
lengthily
lengthiness
lengthsman
lengthsome
lengthsomeness
lengthways
lengthwise
lengthy
lenience
leniency
lenient
leniently
lenify
Leninism
Leninist
Leninite
lenis
lenitic
lenitive
lenitively
lenitiveness
lenitude
lenity
lennilite
Lennoaceae
lennoaceous
lennow
Lenny
leno
Lenora
lens
lensed
lensless
lenslike
Lent
lent
Lenten
Lententide
lenth
lenthways
Lentibulariaceae
lentibulariaceous
lenticel
lenticellate
lenticle
lenticonus
lenticula
lenticular
lenticulare
lenticularis
lenticularly
lenticulate
lenticulated
lenticule
lenticulostriate
lenticulothalamic
lentiform
lentigerous
lentiginous
lentigo
lentil
Lentilla
lentisc
lentiscine
lentisco
lentiscus
lentisk
lentitude
lentitudinous
lento
lentoid
lentor
lentous
lenvoi
lenvoy
Lenzites
Leo
Leon
Leonard
Leonardesque
Leonato
leoncito
Leonese
leonhardite
Leonid
Leonine
leonine
leoninely
leonines
Leonis
Leonist
leonite
Leonnoys
Leonora
Leonotis
leontiasis
Leontocebus
leontocephalous
Leontodon
Leontopodium
Leonurus
leopard
leoparde
leopardess
leopardine
leopardite
leopardwood
Leopold
Leopoldinia
leopoldite
Leora
leotard
lepa
Lepadidae
lepadoid
Lepanto
lepargylic
Lepargyraea
Lepas
Lepcha
leper
leperdom
lepered
lepidene
lepidine
Lepidium
lepidoblastic
Lepidodendraceae
lepidodendraceous
lepidodendrid
lepidodendroid
Lepidodendron
lepidoid
Lepidoidei
lepidolite
lepidomelane
Lepidophloios
lepidophyllous
Lepidophyllum
lepidophyte
lepidophytic
lepidoporphyrin
lepidopter
Lepidoptera
lepidopteral
lepidopteran
lepidopterid
lepidopterist
lepidopterological
lepidopterologist
lepidopterology
lepidopteron
lepidopterous
Lepidosauria
lepidosaurian
Lepidosiren
Lepidosirenidae
lepidosirenoid
lepidosis
Lepidosperma
Lepidospermae
Lepidosphes
Lepidostei
lepidosteoid
Lepidosteus
Lepidostrobus
lepidote
Lepidotes
lepidotic
Lepidotus
Lepidurus
Lepilemur
Lepiota
Lepisma
Lepismatidae
Lepismidae
lepismoid
Lepisosteidae
Lepisosteus
lepocyte
Lepomis
leporid
Leporidae
leporide
leporiform
leporine
Leporis
Lepospondyli
lepospondylous
Leposternidae
Leposternon
lepothrix
lepra
Lepralia
lepralian
leprechaun
lepric
leproid
leprologic
leprologist
leprology
leproma
lepromatous
leprosarium
leprose
leprosery
leprosied
leprosis
leprosity
leprosy
leprous
leprously
leprousness
Leptamnium
Leptandra
leptandrin
leptid
Leptidae
leptiform
Leptilon
leptinolite
Leptinotarsa
leptite
Leptocardia
leptocardian
Leptocardii
leptocentric
leptocephalan
leptocephali
leptocephalia
leptocephalic
leptocephalid
Leptocephalidae
leptocephaloid
leptocephalous
Leptocephalus
leptocephalus
leptocephaly
leptocercal
leptochlorite
leptochroa
leptochrous
leptoclase
leptodactyl
Leptodactylidae
leptodactylous
Leptodactylus
leptodermatous
leptodermous
Leptodora
Leptodoridae
Leptogenesis
leptokurtic
Leptolepidae
Leptolepis
Leptolinae
leptomatic
leptome
Leptomedusae
leptomedusan
leptomeningeal
leptomeninges
leptomeningitis
leptomeninx
leptometer
leptomonad
Leptomonas
Lepton
lepton
leptonecrosis
leptonema
leptopellic
Leptophis
leptophyllous
leptoprosope
leptoprosopic
leptoprosopous
leptoprosopy
Leptoptilus
Leptorchis
leptorrhin
leptorrhine
leptorrhinian
leptorrhinism
leptosome
leptosperm
Leptospermum
Leptosphaeria
Leptospira
leptospirosis
leptosporangiate
Leptostraca
leptostracan
leptostracous
Leptostromataceae
Leptosyne
leptotene
Leptothrix
Leptotrichia
Leptotyphlopidae
Leptotyphlops
leptus
leptynite
Lepus
Ler
Lernaea
Lernaeacea
Lernaean
Lernaeidae
lernaeiform
lernaeoid
Lernaeoides
lerot
lerp
lerret
Lerwa
Les
Lesath
Lesbia
Lesbian
Lesbianism
lesche
Lesgh
lesion
lesional
lesiy
Leskea
Leskeaceae
leskeaceous
Lesleya
Leslie
Lespedeza
Lesquerella
less
lessee
lesseeship
lessen
lessener
lesser
lessive
lessn
lessness
lesson
lessor
lest
Lester
lestiwarite
lestobiosis
lestobiotic
Lestodon
Lestosaurus
lestrad
Lestrigon
Lestrigonian
let
letch
letchy
letdown
lete
lethal
lethality
lethalize
lethally
lethargic
lethargical
lethargically
lethargicalness
lethargize
lethargus
lethargy
Lethe
Lethean
lethiferous
Lethocerus
lethologica
Letitia
Leto
letoff
Lett
lettable
letten
letter
lettered
letterer
letteret
lettergram
letterhead
letterin
lettering
letterleaf
letterless
letterpress
letterspace
letterweight
letterwood
Lettic
Lettice
Lettish
lettrin
lettsomite
lettuce
Letty
letup
leu
Leucadendron
Leucadian
leucaemia
leucaemic
Leucaena
leucaethiop
leucaethiopic
leucaniline
leucanthous
leucaugite
leucaurin
leucemia
leucemic
Leucetta
leuch
leuchaemia
leuchemia
leuchtenbergite
Leucichthys
Leucifer
Leuciferidae
leucine
Leucippus
leucism
leucite
leucitic
leucitis
leucitite
leucitohedron
leucitoid
Leuckartia
Leuckartiidae
leuco
leucobasalt
leucoblast
leucoblastic
Leucobryaceae
Leucobryum
leucocarpous
leucochalcite
leucocholic
leucocholy
leucochroic
leucocidic
leucocidin
leucocism
leucocrate
leucocratic
Leucocrinum
leucocyan
leucocytal
leucocyte
leucocythemia
leucocythemic
leucocytic
leucocytoblast
leucocytogenesis
leucocytoid
leucocytology
leucocytolysin
leucocytolysis
leucocytolytic
leucocytometer
leucocytopenia
leucocytopenic
leucocytoplania
leucocytopoiesis
leucocytosis
leucocytotherapy
leucocytotic
Leucocytozoon
leucoderma
leucodermatous
leucodermic
leucoencephalitis
leucogenic
leucoid
leucoindigo
leucoindigotin
Leucojaceae
Leucojum
leucolytic
leucoma
leucomaine
leucomatous
leucomelanic
leucomelanous
leucon
Leuconostoc
leucopenia
leucopenic
leucophane
leucophanite
leucophoenicite
leucophore
leucophyllous
leucophyre
leucoplakia
leucoplakial
leucoplast
leucoplastid
leucopoiesis
leucopoietic
leucopyrite
leucoquinizarin
leucorrhea
leucorrheal
leucoryx
leucosis
Leucosolenia
Leucosoleniidae
leucospermous
leucosphenite
leucosphere
leucospheric
leucostasis
Leucosticte
leucosyenite
leucotactic
Leucothea
Leucothoe
leucotic
leucotome
leucotomy
leucotoxic
leucous
leucoxene
leucyl
leud
leuk
leukemia
leukemic
leukocidic
leukocidin
leukosis
leukotic
leuma
Leung
lev
Levana
levance
Levant
levant
Levanter
levanter
Levantine
levator
levee
level
leveler
levelheaded
levelheadedly
levelheadedness
leveling
levelish
levelism
levelly
levelman
levelness
lever
leverage
leverer
leveret
leverman
levers
leverwood
Levi
leviable
leviathan
levier
levigable
levigate
levigation
levigator
levin
levining
levir
levirate
leviratical
leviration
Levis
Levisticum
levitant
levitate
levitation
levitational
levitative
levitator
Levite
Levitical
Leviticalism
Leviticality
Levitically
Leviticalness
Leviticism
Leviticus
Levitism
levity
levo
levoduction
levogyrate
levogyre
levogyrous
levolactic
levolimonene
levorotation
levorotatory
levotartaric
levoversion
levulic
levulin
levulinic
levulose
levulosuria
levy
levyist
levynite
Lew
lew
Lewanna
lewd
lewdly
lewdness
Lewie
Lewis
lewis
Lewisia
Lewisian
lewisite
lewisson
lewth
Lex
lexia
lexical
lexicalic
lexicality
lexicographer
lexicographian
lexicographic
lexicographical
lexicographically
lexicographist
lexicography
lexicologic
lexicological
lexicologist
lexicology
lexicon
lexiconist
lexiconize
lexigraphic
lexigraphical
lexigraphically
lexigraphy
lexiphanic
lexiphanicism
ley
leyland
leysing
Lezghian
lherzite
lherzolite
Lhota
li
liability
liable
liableness
liaison
liana
liang
liar
liard
Lias
Liassic
Liatris
libament
libaniferous
libanophorous
libanotophorous
libant
libate
libation
libationary
libationer
libatory
libber
libbet
libbra
Libby
libel
libelant
libelee
libeler
libelist
libellary
libellate
Libellula
libellulid
Libellulidae
libelluloid
libelous
libelously
Liber
liber
liberal
Liberalia
liberalism
liberalist
liberalistic
liberality
liberalization
liberalize
liberalizer
liberally
liberalness
liberate
liberation
liberationism
liberationist
liberative
liberator
liberatory
liberatress
Liberia
Liberian
liberomotor
libertarian
libertarianism
Libertas
liberticidal
liberticide
libertinage
libertine
libertinism
liberty
libertyless
libethenite
libidibi
libidinal
libidinally
libidinosity
libidinous
libidinously
libidinousness
libido
Libitina
libken
Libocedrus
Libra
libra
libral
librarian
librarianess
librarianship
librarious
librarius
library
libraryless
librate
libration
libratory
libretti
librettist
libretto
Librid
libriform
libroplast
Libyan
Libytheidae
Libytheinae
Licania
licareol
licca
licensable
license
licensed
licensee
licenseless
licenser
licensor
licensure
licentiate
licentiateship
licentiation
licentious
licentiously
licentiousness
lich
licham
lichanos
lichen
lichenaceous
lichened
Lichenes
licheniasis
lichenic
lichenicolous
licheniform
lichenin
lichenism
lichenist
lichenivorous
lichenization
lichenize
lichenlike
lichenographer
lichenographic
lichenographical
lichenographist
lichenography
lichenoid
lichenologic
lichenological
lichenologist
lichenology
Lichenopora
Lichenoporidae
lichenose
licheny
lichi
Lichnophora
Lichnophoridae
Licinian
licit
licitation
licitly
licitness
lick
licker
lickerish
lickerishly
lickerishness
licking
lickpenny
lickspit
lickspittle
lickspittling
licorice
licorn
licorne
lictor
lictorian
Licuala
lid
Lida
lidded
lidder
Lide
lidflower
lidgate
lidless
lie
liebenerite
Liebfraumilch
liebigite
lied
lief
liege
liegedom
liegeful
liegefully
liegeless
liegely
liegeman
lieger
lien
lienal
lienculus
lienee
lienic
lienitis
lienocele
lienogastric
lienointestinal
lienomalacia
lienomedullary
lienomyelogenous
lienopancreatic
lienor
lienorenal
lienotoxin
lienteria
lienteric
lientery
lieproof
lieprooflier
lieproofliest
lier
lierne
lierre
liesh
liespfund
lieu
lieue
lieutenancy
lieutenant
lieutenantry
lieutenantship
Lievaart
lieve
lievrite
Lif
life
lifeblood
lifeboat
lifeboatman
lifeday
lifedrop
lifeful
lifefully
lifefulness
lifeguard
lifehold
lifeholder
lifeless
lifelessly
lifelessness
lifelet
lifelike
lifelikeness
lifeline
lifelong
lifer
liferent
liferenter
liferentrix
liferoot
lifesaver
lifesaving
lifesome
lifesomely
lifesomeness
lifespring
lifetime
lifeward
lifework
lifey
lifo
lift
liftable
lifter
lifting
liftless
liftman
ligable
ligament
ligamental
ligamentary
ligamentous
ligamentously
ligamentum
ligas
ligate
ligation
ligator
ligature
ligeance
ligger
light
lightable
lightboat
lightbrained
lighten
lightener
lightening
lighter
lighterage
lighterful
lighterman
lightface
lightful
lightfulness
lighthead
lightheaded
lightheadedly
lightheadedness
lighthearted
lightheartedly
lightheartedness
lighthouse
lighthouseman
lighting
lightish
lightkeeper
lightless
lightlessness
lightly
lightman
lightmanship
lightmouthed
lightness
lightning
lightninglike
lightningproof
lightproof
lightroom
lightscot
lightship
lightsman
lightsome
lightsomely
lightsomeness
lighttight
lightwards
lightweight
lightwood
lightwort
lignaloes
lignatile
ligne
ligneous
lignescent
lignicole
lignicoline
lignicolous
ligniferous
lignification
ligniform
lignify
lignin
ligninsulphonate
ligniperdous
lignite
lignitic
lignitiferous
lignitize
lignivorous
lignocellulose
lignoceric
lignography
lignone
lignose
lignosity
lignosulphite
lignosulphonate
lignum
ligroine
ligula
ligular
Ligularia
ligulate
ligulated
ligule
Liguliflorae
liguliflorous
liguliform
ligulin
liguloid
Liguorian
ligure
Ligurian
ligurite
ligurition
Ligusticum
ligustrin
Ligustrum
Ligyda
Ligydidae
Lihyanite
liin
lija
likability
likable
likableness
like
likelihead
likelihood
likeliness
likely
liken
likeness
liker
likesome
likeways
likewise
likin
liking
liknon
Lila
lilac
lilaceous
lilacin
lilacky
lilacthroat
lilactide
Lilaeopsis
lile
Liliaceae
liliaceous
Liliales
Lilian
lilied
liliform
Liliiflorae
Lilith
Lilium
lill
lillianite
lillibullero
Lilliput
Lilliputian
Lilliputianize
lilt
liltingly
liltingness
lily
lilyfy
lilyhanded
lilylike
lilywood
lilywort
lim
Lima
Limacea
limacel
limaceous
Limacidae
limaciform
Limacina
limacine
limacinid
Limacinidae
limacoid
limacon
limaille
liman
limation
Limawood
Limax
limb
limbal
limbat
limbate
limbation
limbeck
limbed
limber
limberham
limberly
limberness
limbers
limbic
limbie
limbiferous
limbless
limbmeal
limbo
limboinfantum
limbous
Limbu
Limburger
limburgite
limbus
limby
lime
limeade
Limean
limeberry
limebush
limehouse
limekiln
limeless
limelight
limelighter
limelike
limeman
limen
limequat
limer
Limerick
limes
limestone
limetta
limettin
limewash
limewater
limewort
limey
Limicolae
limicoline
limicolous
Limidae
liminal
liminary
liminess
liming
limit
limitable
limitableness
limital
limitarian
limitary
limitate
limitation
limitative
limitatively
limited
limitedly
limitedness
limiter
limiting
limitive
limitless
limitlessly
limitlessness
limitrophe
limivorous
limma
limmer
limmock
limmu
limn
limnanth
Limnanthaceae
limnanthaceous
Limnanthemum
Limnanthes
limner
limnery
limnetic
Limnetis
limniad
limnimeter
limnimetric
limnite
limnobiologic
limnobiological
limnobiologically
limnobiology
limnobios
Limnobium
Limnocnida
limnograph
limnologic
limnological
limnologically
limnologist
limnology
limnometer
limnophile
limnophilid
Limnophilidae
limnophilous
limnoplankton
Limnorchis
Limnoria
Limnoriidae
limnorioid
Limodorum
limoid
limonene
limoniad
limonin
limonite
limonitic
limonitization
limonium
Limosa
limose
Limosella
Limosi
limous
limousine
limp
limper
limpet
limphault
limpid
limpidity
limpidly
limpidness
limpily
limpin
limpiness
limping
limpingly
limpingness
limpish
limpkin
limply
limpness
limpsy
limpwort
limpy
limsy
limu
limulid
Limulidae
limuloid
Limuloidea
Limulus
limurite
limy
Lin
lin
Lina
lina
linable
Linaceae
linaceous
linaga
linage
linaloa
linalol
linalool
linamarin
Linanthus
Linaria
linarite
linch
linchbolt
linchet
linchpin
linchpinned
lincloth
Lincoln
Lincolnian
Lincolniana
Lincolnlike
linctus
Linda
lindackerite
lindane
linden
Linder
linder
Lindera
Lindleyan
lindo
lindoite
Lindsay
Lindsey
line
linea
lineage
lineaged
lineal
lineality
lineally
lineament
lineamental
lineamentation
lineameter
linear
linearifolius
linearity
linearization
linearize
linearly
lineate
lineated
lineation
lineature
linecut
lined
lineiform
lineless
linelet
lineman
linen
Linene
linenette
linenize
linenizer
linenman
lineocircular
lineograph
lineolate
lineolated
liner
linesman
Linet
linewalker
linework
ling
linga
Lingayat
lingberry
lingbird
linge
lingel
lingenberry
linger
lingerer
lingerie
lingo
lingonberry
Lingoum
lingtow
lingtowman
lingua
linguacious
linguaciousness
linguadental
linguaeform
lingual
linguale
linguality
lingualize
lingually
linguanasal
Linguata
Linguatula
Linguatulida
Linguatulina
linguatuline
linguatuloid
linguet
linguidental
linguiform
linguipotence
linguist
linguister
linguistic
linguistical
linguistically
linguistician
linguistics
linguistry
lingula
lingulate
lingulated
Lingulella
lingulid
Lingulidae
linguliferous
linguliform
linguloid
linguodental
linguodistal
linguogingival
linguopalatal
linguopapillitis
linguoversion
lingwort
lingy
linha
linhay
linie
liniment
linin
lininess
lining
linitis
liniya
linja
linje
link
linkable
linkage
linkboy
linked
linkedness
linker
linking
linkman
links
linksmith
linkwork
linky
Linley
linn
Linnaea
Linnaean
Linnaeanism
linnaeite
Linne
linnet
lino
linolate
linoleic
linolein
linolenate
linolenic
linolenin
linoleum
linolic
linolin
linometer
linon
Linopteris
Linos
Linotype
linotype
linotyper
linotypist
linous
linoxin
linoxyn
linpin
Linsang
linseed
linsey
linstock
lint
lintel
linteled
linteling
linten
linter
lintern
lintie
lintless
lintonite
lintseed
lintwhite
linty
Linum
Linus
linwood
liny
Linyphia
Linyphiidae
liodermia
liomyofibroma
liomyoma
lion
lioncel
Lionel
lionel
lionesque
lioness
lionet
lionheart
lionhearted
lionheartedness
lionhood
lionism
lionizable
lionization
lionize
lionizer
lionlike
lionly
lionproof
lionship
Liothrix
Liotrichi
Liotrichidae
liotrichine
lip
lipa
lipacidemia
lipaciduria
Lipan
Liparian
liparian
liparid
Liparidae
Liparididae
Liparis
liparite
liparocele
liparoid
liparomphalus
liparous
lipase
lipectomy
lipemia
Lipeurus
lipide
lipin
lipless
liplet
liplike
lipoblast
lipoblastoma
Lipobranchia
lipocaic
lipocardiac
lipocele
lipoceratous
lipocere
lipochondroma
lipochrome
lipochromogen
lipoclasis
lipoclastic
lipocyte
lipodystrophia
lipodystrophy
lipoferous
lipofibroma
lipogenesis
lipogenetic
lipogenic
lipogenous
lipogram
lipogrammatic
lipogrammatism
lipogrammatist
lipography
lipohemia
lipoid
lipoidal
lipoidemia
lipoidic
lipolysis
lipolytic
lipoma
lipomata
lipomatosis
lipomatous
lipometabolic
lipometabolism
lipomorph
lipomyoma
lipomyxoma
lipopexia
lipophagic
lipophore
lipopod
Lipopoda
lipoprotein
liposarcoma
liposis
liposome
lipostomy
lipothymial
lipothymic
lipothymy
lipotrophic
lipotrophy
lipotropic
lipotropy
lipotype
Lipotyphla
lipovaccine
lipoxenous
lipoxeny
lipped
lippen
lipper
lipperings
Lippia
lippiness
lipping
lippitude
lippitudo
lippy
lipsanographer
lipsanotheca
lipstick
lipuria
lipwork
liquable
liquamen
liquate
liquation
liquefacient
liquefaction
liquefactive
liquefiable
liquefier
liquefy
liquesce
liquescence
liquescency
liquescent
liqueur
liquid
liquidable
Liquidambar
liquidamber
liquidate
liquidation
liquidator
liquidatorship
liquidity
liquidize
liquidizer
liquidless
liquidly
liquidness
liquidogenic
liquidogenous
liquidy
liquiform
liquor
liquorer
liquorish
liquorishly
liquorishness
liquorist
liquorless
lira
lirate
liration
lire
lirella
lirellate
lirelliform
lirelline
lirellous
Liriodendron
liripipe
liroconite
lis
Lisa
Lisbon
Lise
lisere
Lisette
lish
lisk
Lisle
lisle
lisp
lisper
lispingly
lispund
liss
Lissamphibia
lissamphibian
Lissencephala
lissencephalic
lissencephalous
Lissoflagellata
lissoflagellate
lissom
lissome
lissomely
lissomeness
lissotrichan
Lissotriches
lissotrichous
lissotrichy
List
list
listable
listed
listedness
listel
listen
listener
listening
lister
Listera
listerellosis
Listeria
Listerian
Listerine
Listerism
Listerize
listing
listless
listlessly
listlessness
listred
listwork
Lisuarte
lit
litaneutical
litany
litanywise
litas
litation
litch
litchi
lite
liter
literacy
literaily
literal
literalism
literalist
literalistic
literality
literalization
literalize
literalizer
literally
literalminded
literalmindedness
literalness
literarian
literariness
literary
literaryism
literate
literati
literation
literatist
literato
literator
literature
literatus
literose
literosity
lith
lithagogue
lithangiuria
lithanthrax
litharge
lithe
lithectasy
lithectomy
lithely
lithemia
lithemic
litheness
lithesome
lithesomeness
lithi
lithia
lithiasis
lithiastic
lithiate
lithic
lithifaction
lithification
lithify
lithite
lithium
litho
lithobiid
Lithobiidae
lithobioid
Lithobius
Lithocarpus
lithocenosis
lithochemistry
lithochromatic
lithochromatics
lithochromatographic
lithochromatography
lithochromography
lithochromy
lithoclase
lithoclast
lithoclastic
lithoclasty
lithoculture
lithocyst
lithocystotomy
Lithodes
lithodesma
lithodialysis
lithodid
Lithodidae
lithodomous
Lithodomus
lithofracteur
lithofractor
lithogenesis
lithogenetic
lithogenous
lithogeny
lithoglyph
lithoglypher
lithoglyphic
lithoglyptic
lithoglyptics
lithograph
lithographer
lithographic
lithographical
lithographically
lithographize
lithography
lithogravure
lithoid
lithoidite
litholabe
litholapaxy
litholatrous
litholatry
lithologic
lithological
lithologically
lithologist
lithology
litholysis
litholyte
litholytic
lithomancy
lithomarge
lithometer
lithonephria
lithonephritis
lithonephrotomy
lithontriptic
lithontriptist
lithontriptor
lithopedion
lithopedium
lithophagous
lithophane
lithophanic
lithophany
lithophilous
lithophone
lithophotography
lithophotogravure
lithophthisis
lithophyl
lithophyllous
lithophysa
lithophysal
lithophyte
lithophytic
lithophytous
lithopone
lithoprint
lithoscope
lithosian
lithosiid
Lithosiidae
Lithosiinae
lithosis
lithosol
lithosperm
lithospermon
lithospermous
Lithospermum
lithosphere
lithotint
lithotome
lithotomic
lithotomical
lithotomist
lithotomize
lithotomous
lithotomy
lithotony
lithotresis
lithotripsy
lithotriptor
lithotrite
lithotritic
lithotritist
lithotrity
lithotype
lithotypic
lithotypy
lithous
lithoxyl
lithsman
Lithuanian
Lithuanic
lithuresis
lithuria
lithy
liticontestation
litigable
litigant
litigate
litigation
litigationist
litigator
litigatory
litigiosity
litigious
litigiously
litigiousness
Litiopa
litiscontest
litiscontestation
litiscontestational
litmus
Litopterna
Litorina
Litorinidae
litorinoid
litotes
litra
Litsea
litster
litten
litter
litterateur
litterer
littermate
littery
little
littleleaf
littleneck
littleness
littlewale
littling
littlish
littoral
Littorella
littress
lituiform
lituite
Lituites
Lituitidae
Lituola
lituoline
lituoloid
liturate
liturgical
liturgically
liturgician
liturgics
liturgiological
liturgiologist
liturgiology
liturgism
liturgist
liturgistic
liturgistical
liturgize
liturgy
litus
lituus
Litvak
Lityerses
litz
Liukiu
Liv
livability
livable
livableness
live
liveborn
lived
livedo
livelihood
livelily
liveliness
livelong
lively
liven
liveness
liver
liverance
liverberry
livered
liverhearted
liverheartedness
liveried
liverish
liverishness
liverleaf
liverless
Liverpudlian
liverwort
liverwurst
livery
liverydom
liveryless
liveryman
livestock
Livian
livid
lividity
lividly
lividness
livier
living
livingless
livingly
livingness
livingstoneite
Livish
Livistona
Livonian
livor
livre
liwan
lixive
lixivial
lixiviate
lixiviation
lixiviator
lixivious
lixivium
Liyuan
Liz
Liza
lizard
lizardtail
Lizzie
llama
Llanberisslate
Llandeilo
Llandovery
llano
llautu
Lleu
Llew
Lloyd
Lludd
llyn
Lo
lo
Loa
loa
loach
load
loadage
loaded
loaden
loader
loading
loadless
loadpenny
loadsome
loadstone
loaf
loafer
loaferdom
loaferish
loafing
loafingly
loaflet
loaghtan
loam
loamily
loaminess
loaming
loamless
Loammi
loamy
loan
loanable
loaner
loanin
loanmonger
loanword
Loasa
Loasaceae
loasaceous
loath
loathe
loather
loathful
loathfully
loathfulness
loathing
loathingly
loathliness
loathly
loathness
loathsome
loathsomely
loathsomeness
Loatuko
loave
lob
Lobachevskian
lobal
Lobale
lobar
Lobaria
Lobata
Lobatae
lobate
lobated
lobately
lobation
lobber
lobbish
lobby
lobbyer
lobbyism
lobbyist
lobbyman
lobcock
lobe
lobectomy
lobed
lobefoot
lobefooted
lobeless
lobelet
Lobelia
Lobeliaceae
lobeliaceous
lobelin
lobeline
lobellated
lobfig
lobiform
lobigerous
lobing
lobiped
loblolly
lobo
lobola
lobopodium
Lobosa
lobose
lobotomy
lobscourse
lobscouse
lobscouser
lobster
lobstering
lobsterish
lobsterlike
lobsterproof
lobtail
lobular
Lobularia
lobularly
lobulate
lobulated
lobulation
lobule
lobulette
lobulose
lobulous
lobworm
loca
locable
local
locale
localism
localist
localistic
locality
localizable
localization
localize
localizer
locally
localness
locanda
Locarnist
Locarnite
Locarnize
Locarno
locate
location
locational
locative
locator
locellate
locellus
loch
lochage
lochan
lochetic
lochia
lochial
lochiocolpos
lochiocyte
lochiometra
lochiometritis
lochiopyra
lochiorrhagia
lochiorrhea
lochioschesis
Lochlin
lochometritis
lochoperitonitis
lochopyra
lochus
lochy
loci
lociation
lock
lockable
lockage
Lockatong
lockbox
locked
locker
lockerman
locket
lockful
lockhole
Lockian
Lockianism
locking
lockjaw
lockless
locklet
lockmaker
lockmaking
lockman
lockout
lockpin
Lockport
lockram
locksman
locksmith
locksmithery
locksmithing
lockspit
lockup
lockwork
locky
loco
locodescriptive
locofoco
Locofocoism
locoism
locomobile
locomobility
locomote
locomotility
locomotion
locomotive
locomotively
locomotiveman
locomotiveness
locomotivity
locomotor
locomotory
locomutation
locoweed
Locrian
Locrine
loculament
loculamentose
loculamentous
locular
loculate
loculated
loculation
locule
loculicidal
loculicidally
loculose
loculus
locum
locus
locust
locusta
locustal
locustberry
locustelle
locustid
Locustidae
locusting
locustlike
locution
locutor
locutorship
locutory
lod
Loddigesia
lode
lodemanage
lodesman
lodestar
lodestone
lodestuff
lodge
lodgeable
lodged
lodgeful
lodgeman
lodgepole
lodger
lodgerdom
lodging
lodginghouse
lodgings
lodgment
Lodha
lodicule
Lodoicea
Lodowic
Lodowick
Lodur
Loegria
loess
loessal
loessial
loessic
loessland
loessoid
lof
lofstelle
loft
lofter
loftily
loftiness
lofting
loftless
loftman
loftsman
lofty
log
loganberry
Logania
Loganiaceae
loganiaceous
loganin
logaoedic
logarithm
logarithmal
logarithmetic
logarithmetical
logarithmetically
logarithmic
logarithmical
logarithmically
logarithmomancy
logbook
logcock
loge
logeion
logeum
loggat
logged
logger
loggerhead
loggerheaded
loggia
loggin
logging
loggish
loghead
logheaded
logia
logic
logical
logicalist
logicality
logicalization
logicalize
logically
logicalness
logicaster
logician
logicism
logicist
logicity
logicize
logicless
logie
login
logion
logistic
logistical
logistician
logistics
logium
loglet
loglike
logman
logocracy
logodaedaly
logogogue
logogram
logogrammatic
logograph
logographer
logographic
logographical
logographically
logography
logogriph
logogriphic
logoi
logolatry
logology
logomach
logomacher
logomachic
logomachical
logomachist
logomachize
logomachy
logomancy
logomania
logomaniac
logometer
logometric
logometrical
logometrically
logopedia
logopedics
logorrhea
logos
logothete
logotype
logotypy
Logres
Logria
Logris
logroll
logroller
logrolling
logway
logwise
logwood
logwork
logy
lohan
Lohana
Lohar
lohoch
loimic
loimography
loimology
loin
loincloth
loined
loir
Lois
Loiseleuria
loiter
loiterer
loiteringly
loiteringness
loka
lokao
lokaose
lokapala
loke
loket
lokiec
Lokindra
Lokman
Lola
Loliginidae
Loligo
Lolium
loll
Lollard
Lollardian
Lollardism
Lollardist
Lollardize
Lollardlike
Lollardry
Lollardy
loller
lollingite
lollingly
lollipop
lollop
lollopy
lolly
Lolo
loma
lomastome
lomatine
lomatinous
Lomatium
Lombard
lombard
Lombardeer
Lombardesque
Lombardian
Lombardic
lomboy
Lombrosian
loment
lomentaceous
Lomentaria
lomentariaceous
lomentum
lomita
lommock
Lonchocarpus
Lonchopteridae
Londinensian
Londoner
Londonese
Londonesque
Londonian
Londonish
Londonism
Londonization
Londonize
Londony
Londres
lone
lonelihood
lonelily
loneliness
lonely
loneness
lonesome
lonesomely
lonesomeness
long
longa
longan
longanimity
longanimous
Longaville
longbeak
longbeard
longboat
longbow
longcloth
longe
longear
longer
longeval
longevity
longevous
longfelt
longfin
longful
longhair
longhand
longhead
longheaded
longheadedly
longheadedness
longhorn
longicaudal
longicaudate
longicone
longicorn
Longicornia
longilateral
longilingual
longiloquence
longimanous
longimetric
longimetry
longing
longingly
longingness
Longinian
longinquity
longipennate
longipennine
longirostral
longirostrate
longirostrine
Longirostrines
longisection
longish
longitude
longitudinal
longitudinally
longjaw
longleaf
longlegs
longly
longmouthed
longness
Longobard
Longobardi
Longobardian
Longobardic
longs
longshanks
longshore
longshoreman
longsome
longsomely
longsomeness
longspun
longspur
longtail
longue
longulite
longway
longways
longwise
longwool
longwork
longwort
Lonhyn
Lonicera
Lonk
lonquhard
lontar
loo
looby
lood
loof
loofah
loofie
loofness
look
looker
looking
lookout
lookum
loom
loomer
loomery
looming
loon
loonery
looney
loony
loop
looper
loopful
loophole
looping
loopist
looplet
looplike
loopy
loose
loosely
loosemouthed
loosen
loosener
looseness
looser
loosestrife
loosing
loosish
loot
lootable
looten
looter
lootie
lootiewallah
lootsman
lop
lope
loper
Lopezia
lophiid
Lophiidae
lophine
Lophiodon
lophiodont
Lophiodontidae
lophiodontoid
Lophiola
Lophiomyidae
Lophiomyinae
Lophiomys
lophiostomate
lophiostomous
lophobranch
lophobranchiate
Lophobranchii
lophocalthrops
lophocercal
Lophocome
Lophocomi
Lophodermium
lophodont
Lophophora
lophophoral
lophophore
Lophophorinae
lophophorine
Lophophorus
lophophytosis
Lophopoda
Lophornis
Lophortyx
lophosteon
lophotriaene
lophotrichic
lophotrichous
Lophura
lopolith
loppard
lopper
loppet
lopping
loppy
lopseed
lopsided
lopsidedly
lopsidedness
lopstick
loquacious
loquaciously
loquaciousness
loquacity
loquat
loquence
loquent
loquently
Lora
lora
loral
loran
lorandite
loranskite
Loranthaceae
loranthaceous
Loranthus
lorarius
lorate
lorcha
Lord
lord
lording
lordkin
lordless
lordlet
lordlike
lordlily
lordliness
lordling
lordly
lordolatry
lordosis
lordotic
lordship
lordwood
lordy
lore
loreal
lored
loreless
Loren
Lorenzan
lorenzenite
Lorenzo
Lorettine
lorettoite
lorgnette
Lori
lori
loric
lorica
loricarian
Loricariidae
loricarioid
Loricata
loricate
Loricati
lorication
loricoid
Lorien
lorikeet
lorilet
lorimer
loriot
loris
Lorius
lormery
lorn
lornness
loro
Lorraine
Lorrainer
Lorrainese
lorriker
lorry
lors
lorum
lory
losable
losableness
lose
losel
loselism
losenger
loser
losh
losing
loss
lossenite
lossless
lossproof
lost
lostling
lostness
Lot
lot
Lota
lota
lotase
lote
lotebush
Lotharingian
lotic
lotiform
lotion
lotment
Lotophagi
lotophagous
lotophagously
lotrite
lots
Lotta
Lotte
lotter
lottery
Lottie
lotto
Lotuko
lotus
lotusin
lotuslike
Lou
louch
louchettes
loud
louden
loudering
loudish
loudly
loudmouthed
loudness
louey
lough
lougheen
Louie
Louiqa
Louis
Louisa
Louise
Louisiana
Louisianian
louisine
louk
Loukas
loukoum
loulu
lounder
lounderer
lounge
lounger
lounging
loungingly
loungy
Loup
loup
loupe
lour
lourdy
louse
louseberry
lousewort
lousily
lousiness
louster
lousy
lout
louter
louther
loutish
loutishly
loutishness
loutrophoros
louty
louvar
louver
louvered
louvering
louverwork
Louvre
lovability
lovable
lovableness
lovably
lovage
love
lovebird
loveflower
loveful
lovelass
loveless
lovelessly
lovelessness
lovelihead
lovelily
loveliness
loveling
lovelock
lovelorn
lovelornness
lovely
loveman
lovemate
lovemonger
loveproof
lover
loverdom
lovered
loverhood
lovering
loverless
loverliness
loverly
lovership
loverwise
lovesick
lovesickness
lovesome
lovesomely
lovesomeness
loveworth
loveworthy
loving
lovingly
lovingness
low
lowa
lowan
lowbell
lowborn
lowboy
lowbred
lowdah
lowder
loweite
Lowell
lower
lowerable
lowerclassman
lowerer
lowering
loweringly
loweringness
lowermost
lowery
lowigite
lowish
lowishly
lowishness
lowland
lowlander
lowlily
lowliness
lowly
lowmen
lowmost
lown
lowness
lownly
lowth
Lowville
lowwood
lowy
lox
loxia
loxic
Loxiinae
loxoclase
loxocosm
loxodograph
Loxodon
loxodont
Loxodonta
loxodontous
loxodrome
loxodromic
loxodromical
loxodromically
loxodromics
loxodromism
Loxolophodon
loxolophodont
Loxomma
loxophthalmus
Loxosoma
Loxosomidae
loxotic
loxotomy
loy
loyal
loyalism
loyalist
loyalize
loyally
loyalness
loyalty
Loyd
Loyolism
Loyolite
lozenge
lozenged
lozenger
lozengeways
lozengewise
lozengy
Lu
Luba
lubber
lubbercock
Lubberland
lubberlike
lubberliness
lubberly
lube
lubra
lubric
lubricant
lubricate
lubrication
lubricational
lubricative
lubricator
lubricatory
lubricious
lubricity
lubricous
lubrifaction
lubrification
lubrify
lubritorian
lubritorium
Luc
Lucan
Lucania
lucanid
Lucanidae
Lucanus
lucarne
Lucayan
lucban
Lucchese
luce
lucence
lucency
lucent
Lucentio
lucently
Luceres
lucern
lucernal
Lucernaria
lucernarian
Lucernariidae
lucerne
lucet
Luchuan
Lucia
Lucian
Luciana
lucible
lucid
lucida
lucidity
lucidly
lucidness
lucifee
Lucifer
luciferase
Luciferian
Luciferidae
luciferin
luciferoid
luciferous
luciferously
luciferousness
lucific
luciform
lucifugal
lucifugous
lucigen
Lucile
Lucilia
lucimeter
Lucina
Lucinacea
Lucinda
Lucinidae
lucinoid
Lucite
Lucius
lucivee
luck
lucken
luckful
luckie
luckily
luckiness
luckless
lucklessly
lucklessness
Lucknow
lucky
lucration
lucrative
lucratively
lucrativeness
lucre
Lucrece
Lucretia
Lucretian
Lucretius
lucriferous
lucriferousness
lucrific
lucrify
Lucrine
luctation
luctiferous
luctiferousness
lucubrate
lucubration
lucubrator
lucubratory
lucule
luculent
luculently
Lucullan
lucullite
Lucuma
lucumia
Lucumo
lucumony
Lucy
lucy
ludden
Luddism
Luddite
Ludditism
ludefisk
Ludgate
Ludgathian
Ludgatian
Ludian
ludibrious
ludibry
ludicropathetic
ludicroserious
ludicrosity
ludicrosplenetic
ludicrous
ludicrously
ludicrousness
ludification
ludlamite
Ludlovian
Ludlow
ludo
Ludolphian
Ludwig
ludwigite
lue
Luella
lues
luetic
luetically
lufberry
lufbery
luff
Luffa
Lug
lug
Luganda
luge
luger
luggage
luggageless
luggar
lugged
lugger
luggie
Luggnagg
lugmark
Lugnas
lugsail
lugsome
lugubriosity
lugubrious
lugubriously
lugubriousness
lugworm
luhinga
Lui
Luian
Luigi
luigino
Luis
Luiseno
Luite
lujaurite
Lukas
Luke
luke
lukely
lukeness
lukewarm
lukewarmish
lukewarmly
lukewarmness
lukewarmth
Lula
lulab
lull
lullaby
luller
Lullian
lulliloo
lullingly
Lulu
lulu
Lum
lum
lumachel
lumbaginous
lumbago
lumbang
lumbar
lumbarization
lumbayao
lumber
lumberdar
lumberdom
lumberer
lumbering
lumberingly
lumberingness
lumberjack
lumberless
lumberly
lumberman
lumbersome
lumberyard
lumbocolostomy
lumbocolotomy
lumbocostal
lumbodorsal
lumbodynia
lumbosacral
lumbovertebral
lumbrical
lumbricalis
Lumbricidae
lumbriciform
lumbricine
lumbricoid
lumbricosis
Lumbricus
lumbrous
lumen
luminaire
Luminal
luminal
luminance
luminant
luminarious
luminarism
luminarist
luminary
luminate
lumination
luminative
luminator
lumine
luminesce
luminescence
luminescent
luminiferous
luminificent
luminism
luminist
luminologist
luminometer
luminosity
luminous
luminously
luminousness
lummox
lummy
lump
lumper
lumpet
lumpfish
lumpily
lumpiness
lumping
lumpingly
lumpish
lumpishly
lumpishness
lumpkin
lumpman
lumpsucker
lumpy
luna
lunacy
lunambulism
lunar
lunare
Lunaria
lunarian
lunarist
lunarium
lunary
lunate
lunatellus
lunately
lunatic
lunatically
lunation
lunatize
lunatum
lunch
luncheon
luncheoner
luncheonette
luncheonless
luncher
lunchroom
Lunda
Lundinarium
lundress
lundyfoot
lune
Lunel
lunes
lunette
lung
lunge
lunged
lungeous
lunger
lungfish
lungflower
lungful
lungi
lungie
lungis
lungless
lungmotor
lungsick
lungworm
lungwort
lungy
lunicurrent
luniform
lunisolar
lunistice
lunistitial
lunitidal
Lunka
lunkhead
lunn
lunoid
lunt
lunula
lunular
Lunularia
lunulate
lunulated
lunule
lunulet
lunulite
Lunulites
Luo
lupanarian
lupanine
lupe
lupeol
lupeose
Lupercal
Lupercalia
Lupercalian
Luperci
lupetidine
lupicide
Lupid
lupiform
lupinaster
lupine
lupinin
lupinine
lupinosis
lupinous
Lupinus
lupis
lupoid
lupous
lupulic
lupulin
lupuline
lupulinic
lupulinous
lupulinum
lupulus
lupus
lupuserythematosus
Lur
lura
lural
lurch
lurcher
lurchingfully
lurchingly
lurchline
lurdan
lurdanism
lure
lureful
lurement
lurer
luresome
lurg
lurgworm
Luri
lurid
luridity
luridly
luridness
luringly
lurk
lurker
lurkingly
lurkingness
lurky
lurrier
lurry
Lusatian
Luscinia
luscious
lusciously
lusciousness
lush
Lushai
lushburg
Lushei
lusher
lushly
lushness
lushy
Lusiad
Lusian
Lusitania
Lusitanian
lusk
lusky
lusory
lust
luster
lusterer
lusterless
lusterware
lustful
lustfully
lustfulness
lustihead
lustily
lustiness
lustless
lustra
lustral
lustrant
lustrate
lustration
lustrative
lustratory
lustreless
lustrical
lustrification
lustrify
lustrine
lustring
lustrous
lustrously
lustrousness
lustrum
lusty
lut
lutaceous
lutanist
lutany
Lutao
lutation
Lutayo
lute
luteal
lutecia
lutecium
lutein
luteinization
luteinize
lutelet
lutemaker
lutemaking
luteo
luteocobaltic
luteofulvous
luteofuscescent
luteofuscous
luteolin
luteolous
luteoma
luteorufescent
luteous
luteovirescent
luter
lutescent
lutestring
Lutetia
Lutetian
lutetium
luteway
lutfisk
Luther
Lutheran
Lutheranic
Lutheranism
Lutheranize
Lutheranizer
Lutherism
Lutherist
luthern
luthier
lutianid
Lutianidae
lutianoid
Lutianus
lutidine
lutidinic
luting
lutist
Lutjanidae
Lutjanus
lutose
Lutra
Lutraria
Lutreola
lutrin
Lutrinae
lutrine
lutulence
lutulent
Luvaridae
Luvian
Luvish
Luwian
lux
luxate
luxation
luxe
Luxemburger
Luxemburgian
luxulianite
luxuriance
luxuriancy
luxuriant
luxuriantly
luxuriantness
luxuriate
luxuriation
luxurious
luxuriously
luxuriousness
luxurist
luxury
luxus
Luzula
Lwo
ly
lyam
lyard
Lyas
Lycaena
lycaenid
Lycaenidae
lycanthrope
lycanthropia
lycanthropic
lycanthropist
lycanthropize
lycanthropous
lycanthropy
lyceal
lyceum
Lychnic
Lychnis
lychnomancy
lychnoscope
lychnoscopic
Lycian
lycid
Lycidae
Lycium
Lycodes
Lycodidae
lycodoid
lycopene
Lycoperdaceae
lycoperdaceous
Lycoperdales
lycoperdoid
Lycoperdon
lycoperdon
Lycopersicon
lycopin
lycopod
lycopode
Lycopodiaceae
lycopodiaceous
Lycopodiales
Lycopodium
Lycopsida
Lycopsis
Lycopus
lycorine
Lycosa
lycosid
Lycosidae
lyctid
Lyctidae
Lyctus
Lycus
lyddite
Lydia
Lydian
lydite
lye
Lyencephala
lyencephalous
lyery
lygaeid
Lygaeidae
Lygeum
Lygodium
Lygosoma
lying
lyingly
Lymantria
lymantriid
Lymantriidae
lymhpangiophlebitis
Lymnaea
lymnaean
lymnaeid
Lymnaeidae
lymph
lymphad
lymphadenectasia
lymphadenectasis
lymphadenia
lymphadenitis
lymphadenoid
lymphadenoma
lymphadenopathy
lymphadenosis
lymphaemia
lymphagogue
lymphangeitis
lymphangial
lymphangiectasis
lymphangiectatic
lymphangiectodes
lymphangiitis
lymphangioendothelioma
lymphangiofibroma
lymphangiology
lymphangioma
lymphangiomatous
lymphangioplasty
lymphangiosarcoma
lymphangiotomy
lymphangitic
lymphangitis
lymphatic
lymphatical
lymphation
lymphatism
lymphatitis
lymphatolysin
lymphatolysis
lymphatolytic
lymphectasia
lymphedema
lymphemia
lymphenteritis
lymphoblast
lymphoblastic
lymphoblastoma
lymphoblastosis
lymphocele
lymphocyst
lymphocystosis
lymphocyte
lymphocythemia
lymphocytic
lymphocytoma
lymphocytomatosis
lymphocytosis
lymphocytotic
lymphocytotoxin
lymphodermia
lymphoduct
lymphogenic
lymphogenous
lymphoglandula
lymphogranuloma
lymphoid
lymphoidectomy
lymphology
lymphoma
lymphomatosis
lymphomatous
lymphomonocyte
lymphomyxoma
lymphopathy
lymphopenia
lymphopenial
lymphopoiesis
lymphopoietic
lymphoprotease
lymphorrhage
lymphorrhagia
lymphorrhagic
lymphorrhea
lymphosarcoma
lymphosarcomatosis
lymphosarcomatous
lymphosporidiosis
lymphostasis
lymphotaxis
lymphotome
lymphotomy
lymphotoxemia
lymphotoxin
lymphotrophic
lymphotrophy
lymphous
lymphuria
lymphy
lyncean
Lynceus
lynch
lynchable
lyncher
Lyncid
lyncine
Lyndon
Lynette
Lyngbyaceae
Lyngbyeae
Lynn
Lynne
Lynnette
lynnhaven
lynx
Lyomeri
lyomerous
Lyon
Lyonese
Lyonetia
lyonetiid
Lyonetiidae
Lyonnais
lyonnaise
Lyonnesse
lyophile
lyophilization
lyophilize
lyophobe
Lyopoma
Lyopomata
lyopomatous
lyotrope
lypemania
Lyperosia
lypothymia
lyra
Lyraid
lyrate
lyrated
lyrately
lyraway
lyre
lyrebird
lyreflower
lyreman
lyretail
lyric
lyrical
lyrically
lyricalness
lyrichord
lyricism
lyricist
lyricize
Lyrid
lyriform
lyrism
lyrist
Lyrurus
lys
Lysander
lysate
lyse
Lysenkoism
lysidine
lysigenic
lysigenous
lysigenously
Lysiloma
Lysimachia
Lysimachus
lysimeter
lysin
lysine
lysis
Lysistrata
lysogen
lysogenesis
lysogenetic
lysogenic
lysozyme
lyssa
lyssic
lyssophobia
lyterian
Lythraceae
lythraceous
Lythrum
lytic
lytta
lyxose
M
m
Ma
ma
maam
maamselle
Maarten
Mab
Maba
Mabel
Mabellona
mabi
Mabinogion
mabolo
Mac
mac
macaasim
macabre
macabresque
Macaca
macaco
Macacus
macadam
Macadamia
macadamite
macadamization
macadamize
macadamizer
Macaglia
macan
macana
Macanese
macao
macaque
Macaranga
Macarani
Macareus
macarism
macarize
macaroni
macaronic
macaronical
macaronically
macaronicism
macaronism
macaroon
Macartney
Macassar
Macassarese
macaw
Macbeth
Maccabaeus
Maccabean
Maccabees
maccaboy
macco
maccoboy
Macduff
mace
macedoine
Macedon
Macedonian
Macedonic
macehead
maceman
macer
macerate
macerater
maceration
Macflecknoe
machairodont
Machairodontidae
Machairodontinae
Machairodus
machan
machar
machete
Machetes
machi
Machiavel
Machiavellian
Machiavellianism
Machiavellianly
Machiavellic
Machiavellism
machiavellist
Machiavellistic
machicolate
machicolation
machicoulis
Machicui
machila
Machilidae
Machilis
machin
machinability
machinable
machinal
machinate
machination
machinator
machine
machineful
machineless
machinelike
machinely
machineman
machinemonger
machiner
machinery
machinification
machinify
machinism
machinist
machinization
machinize
machinoclast
machinofacture
machinotechnique
machinule
Machogo
machopolyp
machree
macies
Macigno
macilence
macilency
macilent
mack
mackenboy
mackerel
mackereler
mackereling
Mackinaw
mackins
mackintosh
mackintoshite
mackle
macklike
macle
Macleaya
macled
Maclura
Maclurea
maclurin
Macmillanite
maco
Macon
maconite
Macracanthorhynchus
macracanthrorhynchiasis
macradenous
macrame
macrander
macrandrous
macrauchene
Macrauchenia
macraucheniid
Macraucheniidae
macraucheniiform
macrauchenioid
macrencephalic
macrencephalous
macro
macroanalysis
macroanalyst
macroanalytical
macrobacterium
macrobian
macrobiosis
macrobiote
macrobiotic
macrobiotics
Macrobiotus
macroblast
macrobrachia
macrocarpous
Macrocentrinae
Macrocentrus
macrocephalia
macrocephalic
macrocephalism
macrocephalous
macrocephalus
macrocephaly
macrochaeta
macrocheilia
Macrochelys
macrochemical
macrochemically
macrochemistry
Macrochira
macrochiran
Macrochires
macrochiria
Macrochiroptera
macrochiropteran
macrocladous
macroclimate
macroclimatic
macrococcus
macrocoly
macroconidial
macroconidium
macroconjugant
macrocornea
macrocosm
macrocosmic
macrocosmical
macrocosmology
macrocosmos
macrocrystalline
macrocyst
Macrocystis
macrocyte
macrocythemia
macrocytic
macrocytosis
macrodactyl
macrodactylia
macrodactylic
macrodactylism
macrodactylous
macrodactyly
macrodiagonal
macrodomatic
macrodome
macrodont
macrodontia
macrodontism
macroelement
macroergate
macroevolution
macrofarad
macrogamete
macrogametocyte
macrogamy
macrogastria
macroglossate
macroglossia
macrognathic
macrognathism
macrognathous
macrogonidium
macrograph
macrographic
macrography
macrolepidoptera
macrolepidopterous
macrology
macromandibular
macromania
macromastia
macromazia
macromelia
macromeral
macromere
macromeric
macromerite
macromeritic
macromesentery
macrometer
macromethod
macromolecule
macromyelon
macromyelonal
macron
macronuclear
macronucleus
macronutrient
macropetalous
macrophage
macrophagocyte
macrophagus
Macrophoma
macrophotograph
macrophotography
macrophyllous
macrophysics
macropia
macropinacoid
macropinacoidal
macroplankton
macroplasia
macroplastia
macropleural
macropodia
Macropodidae
Macropodinae
macropodine
macropodous
macroprism
macroprosopia
macropsia
macropteran
macropterous
Macropus
Macropygia
macropyramid
macroreaction
Macrorhamphosidae
Macrorhamphosus
macrorhinia
Macrorhinus
macroscelia
Macroscelides
macroscian
macroscopic
macroscopical
macroscopically
macroseism
macroseismic
macroseismograph
macrosepalous
macroseptum
macrosmatic
macrosomatia
macrosomatous
macrosomia
macrosplanchnic
macrosporange
macrosporangium
macrospore
macrosporic
Macrosporium
macrosporophore
macrosporophyl
macrosporophyll
Macrostachya
macrostomatous
macrostomia
macrostructural
macrostructure
macrostylospore
macrostylous
macrosymbiont
macrothere
Macrotheriidae
macrotherioid
Macrotherium
macrotherm
macrotia
macrotin
Macrotolagus
macrotome
macrotone
macrotous
macrourid
Macrouridae
Macrourus
Macrozamia
macrozoogonidium
macrozoospore
Macrura
macrural
macruran
macruroid
macrurous
mactation
Mactra
Mactridae
mactroid
macuca
macula
macular
maculate
maculated
maculation
macule
maculicole
maculicolous
maculiferous
maculocerebral
maculopapular
maculose
Macusi
macuta
mad
Madagascan
Madagascar
Madagascarian
Madagass
madam
madame
madapollam
madarosis
madarotic
madbrain
madbrained
madcap
madden
maddening
maddeningly
maddeningness
madder
madderish
madderwort
madding
maddingly
maddish
maddle
made
Madecase
madefaction
madefy
Madegassy
Madeira
Madeiran
Madeline
madeline
Madelon
madescent
Madge
madhouse
madhuca
Madhva
Madi
Madia
madid
madidans
Madiga
madisterium
madling
madly
madman
madnep
madness
mado
Madoc
Madonna
Madonnahood
Madonnaish
Madonnalike
madoqua
Madotheca
madrague
Madras
madrasah
Madrasi
madreperl
Madrepora
Madreporacea
madreporacean
Madreporaria
madreporarian
madrepore
madreporian
madreporic
madreporiform
madreporite
madreporitic
Madrid
madrier
madrigal
madrigaler
madrigaletto
madrigalian
madrigalist
Madrilene
Madrilenian
madrona
madship
madstone
Madurese
maduro
madweed
madwoman
madwort
mae
Maeandra
Maeandrina
maeandrine
maeandriniform
maeandrinoid
maeandroid
Maecenas
Maecenasship
maegbote
Maelstrom
Maemacterion
maenad
maenadic
maenadism
maenaite
Maenalus
Maenidae
Maeonian
Maeonides
maestri
maestro
maffia
maffick
mafficker
maffle
mafflin
mafic
mafoo
mafura
mag
Maga
Magadhi
magadis
magadize
Magahi
Magalensia
magani
magas
magazinable
magazinage
magazine
magazinelet
magaziner
magazinette
magazinish
magazinism
magazinist
magaziny
Magdalen
Magdalene
Magdalenian
mage
Magellan
Magellanian
Magellanic
magenta
magged
Maggie
maggle
maggot
maggotiness
maggotpie
maggoty
Maggy
Magh
Maghi
Maghrib
Maghribi
Magi
magi
Magian
Magianism
magic
magical
magicalize
magically
magicdom
magician
magicianship
magicked
magicking
Magindanao
magiric
magirics
magirist
magiristic
magirological
magirologist
magirology
Magism
magister
magisterial
magisteriality
magisterially
magisterialness
magistery
magistracy
magistral
magistrality
magistrally
magistrand
magistrant
magistrate
magistrateship
magistratic
magistratical
magistratically
magistrative
magistrature
Maglemose
Maglemosean
Maglemosian
magma
magmatic
magnanimity
magnanimous
magnanimously
magnanimousness
magnascope
magnascopic
magnate
magnecrystallic
magnelectric
magneoptic
magnes
magnesia
magnesial
magnesian
magnesic
magnesioferrite
magnesite
magnesium
magnet
magneta
magnetic
magnetical
magnetically
magneticalness
magnetician
magnetics
magnetiferous
magnetification
magnetify
magnetimeter
magnetism
magnetist
magnetite
magnetitic
magnetizability
magnetizable
magnetization
magnetize
magnetizer
magneto
magnetobell
magnetochemical
magnetochemistry
magnetod
magnetodynamo
magnetoelectric
magnetoelectrical
magnetoelectricity
magnetogenerator
magnetogram
magnetograph
magnetographic
magnetoid
magnetomachine
magnetometer
magnetometric
magnetometrical
magnetometrically
magnetometry
magnetomotive
magnetomotor
magneton
magnetooptic
magnetooptical
magnetooptics
magnetophone
magnetophonograph
magnetoplumbite
magnetoprinter
magnetoscope
magnetostriction
magnetotelegraph
magnetotelephone
magnetotherapy
magnetotransmitter
magnetron
magnicaudate
magnicaudatous
magnifiable
magnific
magnifical
magnifically
Magnificat
magnification
magnificative
magnifice
magnificence
magnificent
magnificently
magnificentness
magnifico
magnifier
magnify
magniloquence
magniloquent
magniloquently
magniloquy
magnipotence
magnipotent
magnirostrate
magnisonant
magnitude
magnitudinous
magnochromite
magnoferrite
Magnolia
magnolia
Magnoliaceae
magnoliaceous
magnum
Magnus
Magog
magot
magpie
magpied
magpieish
magsman
maguari
maguey
Magyar
Magyaran
Magyarism
Magyarization
Magyarize
Mah
maha
mahaleb
mahalla
mahant
mahar
maharaja
maharajrana
maharana
maharanee
maharani
maharao
Maharashtri
maharawal
maharawat
mahatma
mahatmaism
Mahayana
Mahayanism
Mahayanist
Mahayanistic
Mahdi
Mahdian
Mahdiship
Mahdism
Mahdist
Mahesh
Mahi
Mahican
mahmal
Mahmoud
mahmudi
mahoe
mahoganize
mahogany
mahoitre
maholi
maholtine
Mahomet
Mahometry
mahone
Mahonia
Mahori
Mahound
mahout
Mahra
Mahran
Mahri
mahseer
mahua
mahuang
Maia
Maiacca
Maianthemum
maid
Maida
maidan
maiden
maidenhair
maidenhead
maidenhood
maidenish
maidenism
maidenlike
maidenliness
maidenly
maidenship
maidenweed
maidhood
Maidie
maidish
maidism
maidkin
maidlike
maidling
maidservant
Maidu
maidy
maiefic
maieutic
maieutical
maieutics
maigre
maiid
Maiidae
mail
mailable
mailbag
mailbox
mailclad
mailed
mailer
mailguard
mailie
maillechort
mailless
mailman
mailplane
maim
maimed
maimedly
maimedness
maimer
maimon
Maimonidean
Maimonist
main
Mainan
Maine
mainferre
mainlander
mainly
mainmast
mainmortable
mainour
mainpast
mainpernable
mainpernor
mainpin
mainport
mainpost
mainprise
mains
mainsail
mainsheet
mainspring
mainstay
Mainstreeter
Mainstreetism
maint
maintain
maintainable
maintainableness
maintainer
maintainment
maintainor
maintenance
Maintenon
maintop
maintopman
maioid
Maioidea
maioidean
Maioli
Maiongkong
Maipure
mairatour
maire
maisonette
Maithili
maitlandite
Maitreya
Maius
maize
maizebird
maizenic
maizer
Maja
Majagga
majagua
Majesta
majestic
majestical
majestically
majesticalness
majesticness
majestious
majesty
majestyship
Majlis
majo
majolica
majolist
majoon
Major
major
majorate
majoration
Majorcan
majorette
Majorism
Majorist
Majoristic
majority
majorize
majorship
majuscular
majuscule
makable
Makah
Makaraka
Makari
Makassar
make
makebate
makedom
makefast
maker
makeress
makership
makeshift
makeshiftiness
makeshiftness
makeshifty
makeweight
makhzan
maki
makimono
making
makluk
mako
Makonde
makroskelic
Maku
Makua
makuk
mal
mala
malaanonang
Malabar
Malabarese
malabathrum
malacanthid
Malacanthidae
malacanthine
Malacanthus
Malacca
Malaccan
malaccident
Malaceae
malaceous
malachite
malacia
Malaclemys
Malaclypse
Malacobdella
Malacocotylea
malacoderm
Malacodermatidae
malacodermatous
Malacodermidae
malacodermous
malacoid
malacolite
malacological
malacologist
malacology
malacon
malacophilous
malacophonous
malacophyllous
malacopod
Malacopoda
malacopodous
malacopterygian
Malacopterygii
malacopterygious
Malacoscolices
Malacoscolicine
Malacosoma
Malacostraca
malacostracan
malacostracology
malacostracous
malactic
maladaptation
maladdress
maladive
maladjust
maladjusted
maladjustive
maladjustment
maladminister
maladministration
maladministrator
maladroit
maladroitly
maladroitness
maladventure
malady
Malaga
Malagasy
Malagigi
malagma
malaguena
malahack
malaise
malakin
malalignment
malambo
malandered
malanders
malandrous
malanga
malapaho
malapert
malapertly
malapertness
malapi
malapplication
malappointment
malappropriate
malappropriation
malaprop
malapropian
malapropish
malapropism
malapropoism
malapropos
Malapterurus
malar
malaria
malarial
malariaproof
malarin
malarioid
malariologist
malariology
malarious
malarkey
malaroma
malarrangement
malasapsap
malassimilation
malassociation
malate
malati
malattress
malax
malaxable
malaxage
malaxate
malaxation
malaxator
malaxerman
Malaxis
Malay
Malayalam
Malayalim
Malayan
Malayic
Malayize
Malayoid
Malaysian
malbehavior
malbrouck
malchite
Malchus
Malcolm
malconceived
malconduct
malconformation
malconstruction
malcontent
malcontented
malcontentedly
malcontentedness
malcontentism
malcontently
malcontentment
malconvenance
malcreated
malcultivation
maldeveloped
maldevelopment
maldigestion
maldirection
maldistribution
Maldivian
maldonite
malduck
Male
male
malease
maleate
Malebolge
Malebolgian
Malebolgic
Malebranchism
Malecite
maledicent
maledict
malediction
maledictive
maledictory
maleducation
malefaction
malefactor
malefactory
malefactress
malefical
malefically
maleficence
maleficent
maleficial
maleficiate
maleficiation
maleic
maleinoid
malella
Malemute
maleness
malengine
maleo
maleruption
Malesherbia
Malesherbiaceae
malesherbiaceous
malevolence
malevolency
malevolent
malevolently
malexecution
malfeasance
malfeasant
malfed
malformation
malformed
malfortune
malfunction
malgovernment
malgrace
malguzar
malguzari
malhonest
malhygiene
mali
malic
malice
maliceful
maliceproof
malicho
malicious
maliciously
maliciousness
malicorium
malidentification
maliferous
maliform
malign
malignance
malignancy
malignant
malignantly
malignation
maligner
malignify
malignity
malignly
malignment
malik
malikadna
malikala
malikana
Maliki
Malikite
maline
malines
malinfluence
malinger
malingerer
malingery
Malinois
malinowskite
malinstitution
malinstruction
malintent
malism
malison
malist
malistic
malkin
Malkite
mall
malladrite
mallangong
mallard
mallardite
malleability
malleabilization
malleable
malleableize
malleableized
malleableness
malleablize
malleal
mallear
malleate
malleation
mallee
Malleifera
malleiferous
malleiform
mallein
malleinization
malleinize
mallemaroking
mallemuck
malleoincudal
malleolable
malleolar
malleolus
mallet
malleus
Malling
Mallophaga
mallophagan
mallophagous
malloseismic
Mallotus
mallow
mallowwort
Malloy
mallum
mallus
malm
Malmaison
malmignatte
malmsey
malmstone
malmy
malnourished
malnourishment
malnutrite
malnutrition
malo
malobservance
malobservation
maloccluded
malocclusion
malodor
malodorant
malodorous
malodorously
malodorousness
malojilla
malonate
malonic
malonyl
malonylurea
Malope
maloperation
malorganization
malorganized
malouah
malpais
Malpighia
Malpighiaceae
malpighiaceous
Malpighian
malplaced
malpoise
malposed
malposition
malpractice
malpractioner
malpraxis
malpresentation
malproportion
malproportioned
malpropriety
malpublication
malreasoning
malrotation
malshapen
malt
maltable
maltase
malter
Maltese
maltha
Malthe
malthouse
Malthusian
Malthusianism
Malthusiast
maltiness
malting
maltman
Malto
maltobiose
maltodextrin
maltodextrine
maltolte
maltose
maltreat
maltreatment
maltreator
maltster
malturned
maltworm
malty
malunion
Malurinae
malurine
Malurus
Malus
Malva
Malvaceae
malvaceous
Malvales
malvasia
malvasian
Malvastrum
malversation
malverse
malvoisie
malvolition
Mam
mamba
mambo
mameliere
mamelonation
mameluco
Mameluke
Mamercus
Mamers
Mamertine
Mamie
Mamilius
mamlatdar
mamma
mammal
mammalgia
Mammalia
mammalian
mammaliferous
mammality
mammalogical
mammalogist
mammalogy
mammary
mammate
Mammea
mammectomy
mammee
mammer
Mammifera
mammiferous
mammiform
mammilla
mammillaplasty
mammillar
Mammillaria
mammillary
mammillate
mammillated
mammillation
mammilliform
mammilloid
mammitis
mammock
mammogen
mammogenic
mammogenically
mammon
mammondom
mammoniacal
mammonish
mammonism
mammonist
mammonistic
mammonite
mammonitish
mammonization
mammonize
mammonolatry
Mammonteus
mammoth
mammothrept
mammula
mammular
Mammut
Mammutidae
mammy
mamo
man
mana
Manabozho
manacle
Manacus
manage
manageability
manageable
manageableness
manageably
managee
manageless
management
managemental
manager
managerdom
manageress
managerial
managerially
managership
managery
manaism
manakin
manal
manas
Manasquan
manatee
Manatidae
manatine
manatoid
Manatus
manavel
manavelins
Manavendra
manbird
manbot
manche
Manchester
Manchesterdom
Manchesterism
Manchesterist
Manchestrian
manchet
manchineel
Manchu
Manchurian
mancinism
mancipable
mancipant
mancipate
mancipation
mancipative
mancipatory
mancipee
mancipium
manciple
mancipleship
mancipular
mancono
Mancunian
mancus
mand
Mandaean
Mandaeism
Mandaic
Mandaite
mandala
Mandalay
mandament
mandamus
Mandan
mandant
mandarah
mandarin
mandarinate
mandarindom
mandariness
mandarinic
mandarinism
mandarinize
mandarinship
mandatary
mandate
mandatee
mandation
mandative
mandator
mandatorily
mandatory
mandatum
Mande
mandelate
mandelic
mandible
mandibula
mandibular
mandibulary
Mandibulata
mandibulate
mandibulated
mandibuliform
mandibulohyoid
mandibulomaxillary
mandibulopharyngeal
mandibulosuspensorial
mandil
mandilion
Mandingan
Mandingo
mandola
mandolin
mandolinist
mandolute
mandom
mandora
mandore
mandra
mandragora
mandrake
mandrel
mandriarch
mandrill
mandrin
mandruka
mandua
manducable
manducate
manducation
manducatory
mandyas
mane
maned
manege
manei
maneless
manent
manerial
manes
manesheet
maness
Manetti
Manettia
maneuver
maneuverability
maneuverable
maneuverer
maneuvrability
maneuvrable
maney
Manfred
Manfreda
manful
manfully
manfulness
mang
manga
mangabeira
mangabey
mangal
manganapatite
manganate
manganblende
manganbrucite
manganeisen
manganese
manganesian
manganetic
manganhedenbergite
manganic
manganiferous
manganite
manganium
manganize
Manganja
manganocalcite
manganocolumbite
manganophyllite
manganosiderite
manganosite
manganostibiite
manganotantalite
manganous
manganpectolite
Mangar
Mangbattu
mange
mangeao
mangel
mangelin
manger
mangerite
mangi
Mangifera
mangily
manginess
mangle
mangleman
mangler
mangling
manglingly
mango
mangona
mangonel
mangonism
mangonization
mangonize
mangosteen
mangrass
mangrate
mangrove
Mangue
mangue
mangy
Mangyan
manhandle
Manhattan
Manhattanite
Manhattanize
manhead
manhole
manhood
mani
mania
maniable
maniac
maniacal
maniacally
manic
Manicaria
manicate
Manichaean
Manichaeanism
Manichaeanize
Manichaeism
Manichaeist
Manichee
manichord
manicole
manicure
manicurist
manid
Manidae
manienie
manifest
manifestable
manifestant
manifestation
manifestational
manifestationist
manifestative
manifestatively
manifested
manifestedness
manifester
manifestive
manifestly
manifestness
manifesto
manifold
manifolder
manifoldly
manifoldness
manifoldwise
maniform
manify
Manihot
manikin
manikinism
Manila
manila
manilla
manille
manioc
maniple
manipulable
manipular
manipulatable
manipulate
manipulation
manipulative
manipulatively
manipulator
manipulatory
Manipuri
Manis
manism
manist
manistic
manito
Manitoban
manitrunk
maniu
Manius
Maniva
manjak
Manjeri
mank
mankeeper
mankin
mankind
manless
manlessly
manlessness
manlet
manlihood
manlike
manlikely
manlikeness
manlily
manliness
manling
manly
Mann
manna
mannan
mannequin
manner
mannerable
mannered
mannerhood
mannering
mannerism
mannerist
manneristic
manneristical
manneristically
mannerize
mannerless
mannerlessness
mannerliness
mannerly
manners
mannersome
manness
Mannheimar
mannide
mannie
manniferous
mannify
mannikinism
manning
mannish
mannishly
mannishness
mannite
mannitic
mannitol
mannitose
mannoheptite
mannoheptitol
mannoheptose
mannoketoheptose
mannonic
mannosan
mannose
Manny
manny
mano
Manobo
manoc
manograph
Manolis
manometer
manometric
manometrical
manometry
manomin
manor
manorial
manorialism
manorialize
manorship
manoscope
manostat
manostatic
manque
manred
manrent
manroot
manrope
Mans
mansard
mansarded
manscape
manse
manservant
manship
mansion
mansional
mansionary
mansioned
mansioneer
mansionry
manslaughter
manslaughterer
manslaughtering
manslaughterous
manslayer
manslaying
manso
mansonry
manstealer
manstealing
manstopper
manstopping
mansuete
mansuetely
mansuetude
mant
manta
mantal
manteau
mantel
mantelet
manteline
mantelletta
mantellone
mantelpiece
mantelshelf
manteltree
manter
mantes
mantevil
mantic
manticism
manticore
mantid
Mantidae
mantilla
Mantinean
mantis
Mantisia
Mantispa
mantispid
Mantispidae
mantissa
mantistic
mantle
mantled
mantlet
mantling
Manto
manto
Mantodea
mantoid
Mantoidea
mantologist
mantology
mantra
mantrap
mantua
mantuamaker
mantuamaking
Mantuan
Mantzu
manual
manualii
manualism
manualist
manualiter
manually
manuao
manubrial
manubriated
manubrium
manucaption
manucaptor
manucapture
manucode
Manucodia
manucodiata
manuduce
manuduction
manuductor
manuductory
Manuel
manufactory
manufacturable
manufactural
manufacture
manufacturer
manufacturess
manuka
manul
manuma
manumea
manumisable
manumission
manumissive
manumit
manumitter
manumotive
manurable
manurage
manurance
manure
manureless
manurer
manurial
manurially
manus
manuscript
manuscriptal
manuscription
manuscriptural
manusina
manustupration
manutagi
Manvantara
manward
manwards
manway
manweed
manwise
Manx
Manxman
Manxwoman
many
manyberry
Manyema
manyfold
manyness
manyplies
manyroot
manyways
manywhere
manywise
manzana
manzanilla
manzanillo
manzanita
Manzas
manzil
mao
maomao
Maori
Maoridom
Maoriland
Maorilander
map
mapach
mapau
maphrian
mapland
maple
maplebush
mapo
mappable
mapper
Mappila
mappist
mappy
Mapuche
mapwise
maquahuitl
maquette
maqui
Maquiritare
maquis
Mar
mar
Mara
marabotin
marabou
Marabout
marabuto
maraca
Maracaibo
maracan
maracock
marae
Maragato
marajuana
marakapas
maral
maranatha
marang
Maranha
Maranham
Maranhao
Maranta
Marantaceae
marantaceous
marantic
marara
mararie
marasca
maraschino
marasmic
Marasmius
marasmoid
marasmous
marasmus
Maratha
Marathi
marathon
marathoner
Marathonian
Maratism
Maratist
Marattia
Marattiaceae
marattiaceous
Marattiales
maraud
marauder
maravedi
Maravi
marbelize
marble
marbled
marblehead
marbleheader
marblehearted
marbleization
marbleize
marbleizer
marblelike
marbleness
marbler
marbles
marblewood
marbling
marblish
marbly
marbrinus
Marc
marc
Marcan
marcantant
marcasite
marcasitic
marcasitical
Marcel
marcel
marceline
Marcella
marcella
marceller
Marcellian
Marcellianism
marcello
marcescence
marcescent
Marcgravia
Marcgraviaceae
marcgraviaceous
March
march
Marchantia
Marchantiaceae
marchantiaceous
Marchantiales
marcher
marchetto
marchioness
marchite
marchland
marchman
Marchmont
marchpane
Marci
Marcia
marcid
Marcionism
Marcionist
Marcionite
Marcionitic
Marcionitish
Marcionitism
Marcite
Marco
marco
Marcobrunner
Marcomanni
Marconi
marconi
marconigram
marconigraph
marconigraphy
marcor
Marcos
Marcosian
marcottage
mardy
mare
mareblob
Mareca
marechal
Marehan
Marek
marekanite
maremma
maremmatic
maremmese
marengo
marennin
Mareotic
Mareotid
Marfik
marfire
margarate
Margarelon
Margaret
margaric
margarin
margarine
margarita
margaritaceous
margarite
margaritiferous
margaritomancy
Margarodes
margarodid
Margarodinae
margarodite
Margaropus
margarosanite
margay
marge
margeline
margent
Margery
Margie
margin
marginal
marginalia
marginality
marginalize
marginally
marginate
marginated
margination
margined
Marginella
Marginellidae
marginelliform
marginiform
margining
marginirostral
marginoplasty
margosa
Margot
margravate
margrave
margravely
margravial
margraviate
margravine
Marguerite
marguerite
marhala
Marheshvan
Mari
Maria
maria
marialite
Mariamman
Marian
Mariana
Marianic
Marianne
Marianolatrist
Marianolatry
maricolous
marid
Marie
mariengroschen
marigenous
marigold
marigram
marigraph
marigraphic
marijuana
marikina
Marilla
Marilyn
marimba
marimonda
marina
marinade
marinate
marinated
marine
mariner
marinheiro
marinist
marinorama
Mario
mariola
Mariolater
Mariolatrous
Mariolatry
Mariology
Marion
marionette
Mariou
Mariposan
mariposite
maris
marish
marishness
Marist
maritage
marital
maritality
maritally
mariticidal
mariticide
Maritime
maritime
maritorious
mariupolite
marjoram
Marjorie
Mark
mark
marka
Markab
markdown
Markeb
marked
markedly
markedness
marker
market
marketability
marketable
marketableness
marketably
marketeer
marketer
marketing
marketman
marketstead
marketwise
markfieldite
Markgenossenschaft
markhor
marking
markka
markless
markman
markmoot
Marko
markshot
marksman
marksmanly
marksmanship
markswoman
markup
Markus
markweed
markworthy
marl
Marla
marlaceous
marlberry
marled
Marlena
marler
marli
marlin
marline
marlinespike
marlite
marlitic
marllike
marlock
Marlovian
Marlowesque
Marlowish
Marlowism
marlpit
marly
marm
marmalade
marmalady
Marmar
marmarization
marmarize
marmarosis
marmatite
marmelos
marmennill
marmit
marmite
marmolite
marmoraceous
marmorate
marmorated
marmoration
marmoreal
marmoreally
marmorean
marmoric
Marmosa
marmose
marmoset
marmot
Marmota
Marnix
maro
marocain
marok
Maronian
Maronist
Maronite
maroon
marooner
maroquin
Marpessa
marplot
marplotry
marque
marquee
Marquesan
marquess
marquetry
marquis
marquisal
marquisate
marquisdom
marquise
marquisette
marquisina
marquisotte
marquisship
marquito
marranism
marranize
marrano
marree
Marrella
marrer
marriable
marriage
marriageability
marriageable
marriageableness
marriageproof
married
marrier
marron
marrot
marrow
marrowbone
marrowed
marrowfat
marrowish
marrowless
marrowlike
marrowsky
marrowskyer
marrowy
Marrubium
Marrucinian
marry
marryer
marrying
marrymuffe
Mars
Marsala
Marsdenia
marseilles
Marsh
marsh
Marsha
marshal
marshalate
marshalcy
marshaler
marshaless
Marshall
marshalman
marshalment
Marshalsea
marshalship
marshberry
marshbuck
marshfire
marshflower
marshiness
marshite
marshland
marshlander
marshlike
marshlocks
marshman
marshwort
marshy
Marsi
Marsian
Marsilea
Marsileaceae
marsileaceous
Marsilia
Marsiliaceae
marsipobranch
Marsipobranchia
Marsipobranchiata
marsipobranchiate
Marsipobranchii
marsoon
Marspiter
Marssonia
Marssonina
marsupial
Marsupialia
marsupialian
marsupialization
marsupialize
marsupian
Marsupiata
marsupiate
marsupium
Mart
mart
martagon
martel
marteline
martellate
martellato
marten
martensite
martensitic
Martes
martext
Martha
martial
martialism
Martialist
martiality
martialization
martialize
martially
martialness
Martian
Martin
martin
martinet
martineta
martinetish
martinetishness
martinetism
martinetship
Martinez
martingale
martinico
Martinism
Martinist
Martinmas
martinoe
martite
Martius
martlet
Martu
Marty
Martyn
Martynia
Martyniaceae
martyniaceous
martyr
martyrdom
martyress
martyrium
martyrization
martyrize
martyrizer
martyrlike
martyrly
martyrolatry
martyrologic
martyrological
martyrologist
martyrologistic
martyrologium
martyrology
martyrship
martyry
maru
marvel
marvelment
marvelous
marvelously
marvelousness
marvelry
marver
Marvin
Marwari
Marxian
Marxianism
Marxism
Marxist
Mary
mary
marybud
Maryland
Marylander
Marylandian
Marymass
marysole
marzipan
mas
masa
Masai
Masanao
Masanobu
masaridid
Masarididae
Masaridinae
Masaris
mascagnine
mascagnite
mascally
mascara
mascaron
mascled
mascleless
mascot
mascotism
mascotry
Mascouten
mascularity
masculate
masculation
masculine
masculinely
masculineness
masculinism
masculinist
masculinity
masculinization
masculinize
masculist
masculofeminine
masculonucleus
masculy
masdeu
Masdevallia
mash
masha
mashal
mashallah
mashelton
masher
mashie
mashing
mashman
Mashona
Mashpee
mashru
mashy
masjid
mask
masked
Maskegon
maskelynite
masker
maskette
maskflower
Maskins
masklike
Maskoi
maskoid
maslin
masochism
masochist
masochistic
Mason
mason
masoned
masoner
masonic
Masonite
masonite
masonry
masonwork
masooka
masoola
Masora
Masorete
Masoreth
Masoretic
Maspiter
masque
masquer
masquerade
masquerader
Mass
mass
massa
massacre
massacrer
massage
massager
massageuse
massagist
Massalia
Massalian
massaranduba
massasauga
masse
massebah
massecuite
massedly
massedness
Massekhoth
massel
masser
masseter
masseteric
masseur
masseuse
massicot
massier
massiest
massif
Massilia
Massilian
massily
massiness
massive
massively
massiveness
massivity
masskanne
massless
masslike
Massmonger
massotherapy
massoy
massula
massy
mast
mastaba
mastadenitis
mastadenoma
mastage
mastalgia
mastatrophia
mastatrophy
mastauxe
mastax
mastectomy
masted
master
masterable
masterate
masterdom
masterer
masterful
masterfully
masterfulness
masterhood
masterless
masterlessness
masterlike
masterlily
masterliness
masterling
masterly
masterman
mastermind
masterous
masterpiece
masterproof
mastership
masterwork
masterwort
mastery
mastful
masthead
masthelcosis
mastic
masticability
masticable
masticate
mastication
masticator
masticatory
mastiche
masticic
Masticura
masticurous
mastiff
Mastigamoeba
mastigate
mastigium
mastigobranchia
mastigobranchial
Mastigophora
mastigophoran
mastigophoric
mastigophorous
mastigopod
Mastigopoda
mastigopodous
mastigote
mastigure
masting
mastitis
mastless
mastlike
mastman
mastocarcinoma
mastoccipital
mastochondroma
mastochondrosis
mastodon
mastodonsaurian
Mastodonsaurus
mastodont
mastodontic
Mastodontidae
mastodontine
mastodontoid
mastodynia
mastoid
mastoidal
mastoidale
mastoideal
mastoidean
mastoidectomy
mastoideocentesis
mastoideosquamous
mastoiditis
mastoidohumeral
mastoidohumeralis
mastoidotomy
mastological
mastologist
mastology
mastomenia
mastoncus
mastooccipital
mastoparietal
mastopathy
mastopexy
mastoplastia
mastorrhagia
mastoscirrhus
mastosquamose
mastotomy
mastotympanic
masturbate
masturbation
masturbational
masturbator
masturbatory
mastwood
masty
masu
Masulipatam
masurium
Mat
mat
Matabele
Matacan
matachin
matachina
mataco
matadero
matador
mataeological
mataeologue
mataeology
Matagalpa
Matagalpan
matagory
matagouri
matai
matajuelo
matalan
matamata
matamoro
matanza
matapan
matapi
Matar
matara
Matatua
Matawan
matax
matboard
match
matchable
matchableness
matchably
matchboard
matchboarding
matchbook
matchbox
matchcloth
matchcoat
matcher
matching
matchless
matchlessly
matchlessness
matchlock
matchmaker
matchmaking
matchmark
Matchotic
matchsafe
matchstick
matchwood
matchy
mate
mategriffon
matehood
mateless
matelessness
matelote
mately
mater
materfamilias
material
materialism
materialist
materialistic
materialistical
materialistically
materiality
materialization
materialize
materializee
materializer
materially
materialman
materialness
materiate
materiation
materiel
maternal
maternality
maternalize
maternally
maternalness
maternity
maternology
mateship
matey
matezite
matfelon
matgrass
math
mathematic
mathematical
mathematically
mathematicals
mathematician
mathematicize
mathematics
mathematize
mathemeg
mathes
mathesis
mathetic
Mathurin
matico
matildite
matin
matinal
matinee
mating
matins
matipo
matka
matless
matlockite
matlow
matmaker
matmaking
matra
matral
Matralia
matranee
matrass
matreed
matriarch
matriarchal
matriarchalism
matriarchate
matriarchic
matriarchist
matriarchy
matric
matrical
Matricaria
matrices
matricidal
matricide
matricula
matriculable
matriculant
matricular
matriculate
matriculation
matriculator
matriculatory
Matrigan
matriheritage
matriherital
matrilineal
matrilineally
matrilinear
matrilinearism
matriliny
matrilocal
matrimonial
matrimonially
matrimonious
matrimoniously
matrimony
matriotism
matripotestal
matris
matrix
matroclinic
matroclinous
matrocliny
matron
matronage
matronal
Matronalia
matronhood
matronism
matronize
matronlike
matronliness
matronly
matronship
matronymic
matross
Mats
matsu
matsuri
Matt
matta
mattamore
Mattapony
mattaro
mattboard
matte
matted
mattedly
mattedness
matter
matterate
matterative
matterful
matterfulness
matterless
mattery
Matteuccia
Matthaean
Matthew
Matthias
Matthieu
Matthiola
Matti
matti
matting
mattock
mattoid
mattoir
mattress
mattulla
Matty
maturable
maturate
maturation
maturative
mature
maturely
maturement
matureness
maturer
maturescence
maturescent
maturing
maturish
maturity
matutinal
matutinally
matutinary
matutine
matutinely
matweed
maty
matzo
matzoon
matzos
matzoth
mau
maucherite
Maud
maud
maudle
maudlin
maudlinism
maudlinize
maudlinly
maudlinwort
mauger
maugh
Maugis
maul
Maulawiyah
mauler
mauley
mauling
maulstick
Maumee
maumet
maumetry
Maun
maun
maund
maunder
maunderer
maundful
maundy
maunge
Maurandia
Maureen
Mauretanian
Mauri
Maurice
Maurist
Mauritia
Mauritian
Mauser
mausolea
mausoleal
mausolean
mausoleum
mauther
mauve
mauveine
mauvette
mauvine
maux
maverick
mavis
Mavortian
mavournin
mavrodaphne
maw
mawbound
mawk
mawkish
mawkishly
mawkishness
mawky
mawp
Max
maxilla
maxillar
maxillary
maxilliferous
maxilliform
maxilliped
maxillipedary
maxillodental
maxillofacial
maxillojugal
maxillolabial
maxillomandibular
maxillopalatal
maxillopalatine
maxillopharyngeal
maxillopremaxillary
maxilloturbinal
maxillozygomatic
maxim
maxima
maximal
Maximalism
Maximalist
maximally
maximate
maximation
maximed
maximist
maximistic
maximite
maximization
maximize
maximizer
Maximon
maximum
maximus
maxixe
maxwell
May
may
Maya
maya
Mayaca
Mayacaceae
mayacaceous
Mayan
Mayance
Mayathan
maybe
Maybird
Maybloom
maybush
Maycock
maycock
Mayda
mayday
Mayer
Mayey
Mayeye
Mayfair
mayfish
Mayflower
Mayfowl
mayhap
mayhappen
mayhem
Maying
Maylike
maynt
Mayo
Mayologist
mayonnaise
mayor
mayoral
mayoralty
mayoress
mayorship
Mayoruna
Maypole
Maypoling
maypop
maysin
mayten
Maytenus
Maythorn
Maytide
Maytime
mayweed
Maywings
Maywort
maza
mazalgia
Mazama
mazame
Mazanderani
mazapilite
mazard
mazarine
Mazatec
Mazateco
Mazda
Mazdaism
Mazdaist
Mazdakean
Mazdakite
Mazdean
maze
mazed
mazedly
mazedness
mazeful
mazement
mazer
Mazhabi
mazic
mazily
maziness
mazocacothesis
mazodynia
mazolysis
mazolytic
mazopathia
mazopathic
mazopexy
Mazovian
mazuca
mazuma
Mazur
Mazurian
mazurka
mazut
mazy
mazzard
Mazzinian
Mazzinianism
Mazzinist
mbalolo
Mbaya
mbori
Mbuba
Mbunda
Mcintosh
Mckay
Mdewakanton
me
meable
meaching
mead
meader
meadow
meadowbur
meadowed
meadower
meadowing
meadowink
meadowland
meadowless
meadowsweet
meadowwort
meadowy
meadsman
meager
meagerly
meagerness
meagre
meak
meal
mealable
mealberry
mealer
mealies
mealily
mealiness
mealless
mealman
mealmonger
mealmouth
mealmouthed
mealproof
mealtime
mealy
mealymouth
mealymouthed
mealymouthedly
mealymouthedness
mealywing
mean
meander
meanderingly
meandrine
meandriniform
meandrite
meandrous
meaned
meaner
meaning
meaningful
meaningfully
meaningless
meaninglessly
meaninglessness
meaningly
meaningness
meanish
meanly
meanness
meant
Meantes
meantone
meanwhile
mease
measle
measled
measledness
measles
measlesproof
measly
measondue
measurability
measurable
measurableness
measurably
measuration
measure
measured
measuredly
measuredness
measureless
measurelessly
measurelessness
measurely
measurement
measurer
measuring
meat
meatal
meatbird
meatcutter
meated
meathook
meatily
meatiness
meatless
meatman
meatometer
meatorrhaphy
meatoscope
meatoscopy
meatotome
meatotomy
meatus
meatworks
meaty
Mebsuta
Mecaptera
mecate
Mecca
Meccan
Meccano
Meccawee
Mechael
mechanal
mechanality
mechanalize
mechanic
mechanical
mechanicalism
mechanicalist
mechanicality
mechanicalization
mechanicalize
mechanically
mechanicalness
mechanician
mechanicochemical
mechanicocorpuscular
mechanicointellectual
mechanicotherapy
mechanics
mechanism
mechanist
mechanistic
mechanistically
mechanization
mechanize
mechanizer
mechanolater
mechanology
mechanomorphic
mechanomorphism
mechanotherapeutic
mechanotherapeutics
mechanotherapist
mechanotherapy
Mechir
Mechitaristican
Mechlin
mechoacan
meckelectomy
Meckelian
Mecklenburgian
mecodont
Mecodonta
mecometer
mecometry
mecon
meconic
meconidium
meconin
meconioid
meconium
meconology
meconophagism
meconophagist
Mecoptera
mecopteran
mecopteron
mecopterous
medal
medaled
medalet
medalist
medalize
medallary
medallic
medallically
medallion
medallionist
meddle
meddlecome
meddlement
meddler
meddlesome
meddlesomely
meddlesomeness
meddling
meddlingly
Mede
Medellin
Medeola
Media
media
mediacid
mediacy
mediad
mediaevalize
mediaevally
medial
medialization
medialize
medialkaline
medially
Median
median
medianic
medianimic
medianimity
medianism
medianity
medianly
mediant
mediastinal
mediastine
mediastinitis
mediastinotomy
mediastinum
mediate
mediately
mediateness
mediating
mediatingly
mediation
mediative
mediatization
mediatize
mediator
mediatorial
mediatorialism
mediatorially
mediatorship
mediatory
mediatress
mediatrice
mediatrix
Medic
medic
medicable
Medicago
medical
medically
medicament
medicamental
medicamentally
medicamentary
medicamentation
medicamentous
medicaster
medicate
medication
medicative
medicator
medicatory
Medicean
Medici
medicinable
medicinableness
medicinal
medicinally
medicinalness
medicine
medicinelike
medicinemonger
mediciner
medico
medicobotanical
medicochirurgic
medicochirurgical
medicodental
medicolegal
medicolegally
medicomania
medicomechanic
medicomechanical
medicomoral
medicophysical
medicopsychological
medicopsychology
medicostatistic
medicosurgical
medicotopographic
medicozoologic
mediety
Medieval
medieval
medievalism
medievalist
medievalistic
medievalize
medievally
medifixed
mediglacial
medimn
medimno
medimnos
medimnus
Medina
Medinilla
medino
medio
medioanterior
mediocarpal
medioccipital
mediocre
mediocrist
mediocrity
mediocubital
mediodepressed
mediodigital
mediodorsal
mediodorsally
mediofrontal
mediolateral
mediopalatal
mediopalatine
mediopassive
mediopectoral
medioperforate
mediopontine
medioposterior
mediosilicic
mediostapedial
mediotarsal
medioventral
medisance
medisect
medisection
Medish
Medism
meditant
meditate
meditating
meditatingly
meditation
meditationist
meditatist
meditative
meditatively
meditativeness
meditator
mediterranean
Mediterraneanism
Mediterraneanization
Mediterraneanize
mediterraneous
medithorax
Meditrinalia
meditullium
medium
mediumism
mediumistic
mediumization
mediumize
mediumship
medius
Medize
Medizer
medjidie
medlar
medley
Medoc
medregal
medrick
medrinaque
medulla
medullar
medullary
medullate
medullated
medullation
medullispinal
medullitis
medullization
medullose
Medusa
Medusaean
medusal
medusalike
medusan
medusiferous
medusiform
medusoid
meebos
meece
meed
meedless
Meehan
meek
meeken
meekhearted
meekheartedness
meekling
meekly
meekness
Meekoceras
Meeks
meered
meerkat
meerschaum
meese
meet
meetable
meeten
meeter
meeterly
meethelp
meethelper
meeting
meetinger
meetinghouse
meetly
meetness
Meg
megabar
megacephalia
megacephalic
megacephaly
megacerine
Megaceros
megacerotine
Megachile
megachilid
Megachilidae
Megachiroptera
megachiropteran
megachiropterous
megacolon
megacosm
megacoulomb
megacycle
megadont
Megadrili
megadynamics
megadyne
Megaera
megaerg
megafarad
megafog
megagamete
megagametophyte
megajoule
megakaryocyte
Megalactractus
Megaladapis
Megalaema
Megalaemidae
Megalania
megaleme
Megalensian
megalerg
Megalesia
Megalesian
megalesthete
megalethoscope
Megalichthyidae
Megalichthys
megalith
megalithic
Megalobatrachus
megaloblast
megaloblastic
megalocardia
megalocarpous
megalocephalia
megalocephalic
megalocephalous
megalocephaly
Megaloceros
megalochirous
megalocornea
megalocyte
megalocytosis
megalodactylia
megalodactylism
megalodactylous
Megalodon
megalodont
megalodontia
Megalodontidae
megaloenteron
megalogastria
megaloglossia
megalograph
megalography
megalohepatia
megalokaryocyte
megalomania
megalomaniac
megalomaniacal
megalomelia
Megalonychidae
Megalonyx
megalopa
megalopenis
megalophonic
megalophonous
megalophthalmus
megalopia
megalopic
Megalopidae
Megalopinae
megalopine
megaloplastocyte
megalopolis
megalopolitan
megalopolitanism
megalopore
megalops
megalopsia
Megaloptera
Megalopyge
Megalopygidae
Megalornis
Megalornithidae
megalosaur
megalosaurian
Megalosauridae
megalosauroid
Megalosaurus
megaloscope
megaloscopy
megalosphere
megalospheric
megalosplenia
megalosyndactyly
megaloureter
Megaluridae
Megamastictora
megamastictoral
megamere
megameter
megampere
Meganeura
Meganthropus
meganucleus
megaparsec
megaphone
megaphonic
megaphotographic
megaphotography
megaphyllous
Megaphyton
megapod
megapode
Megapodidae
Megapodiidae
Megapodius
megaprosopous
Megaptera
Megapterinae
megapterine
Megarensian
Megarhinus
Megarhyssa
Megarian
Megarianism
Megaric
megaron
megasclere
megascleric
megasclerous
megasclerum
megascope
megascopic
megascopical
megascopically
megaseism
megaseismic
megaseme
Megasoma
megasporange
megasporangium
megaspore
megasporic
megasporophyll
megasynthetic
megathere
megatherian
Megatheriidae
megatherine
megatherioid
Megatherium
megatherm
megathermic
megatheroid
megaton
megatype
megatypy
megavolt
megawatt
megaweber
megazooid
megazoospore
megerg
Meggy
megilp
megmho
megohm
megohmit
megohmmeter
megophthalmus
megotalc
Megrel
Megrez
megrim
megrimish
mehalla
mehari
meharist
Mehelya
mehmandar
Mehrdad
mehtar
mehtarship
Meibomia
Meibomian
meile
mein
meinie
meio
meiobar
meionite
meiophylly
meiosis
meiotaxy
meiotic
Meissa
Meistersinger
meith
Meithei
meizoseismal
meizoseismic
mejorana
Mekbuda
Mekhitarist
mekometer
mel
mela
melaconite
melada
meladiorite
melagabbro
melagra
melagranite
Melaleuca
melalgia
melam
melamed
melamine
melampodium
Melampsora
Melampsoraceae
Melampus
melampyritol
Melampyrum
melanagogal
melanagogue
melancholia
melancholiac
melancholic
melancholically
melancholily
melancholiness
melancholious
melancholiously
melancholiousness
melancholish
melancholist
melancholize
melancholomaniac
melancholy
melancholyish
Melanchthonian
Melanconiaceae
melanconiaceous
Melanconiales
Melanconium
melanemia
melanemic
Melanesian
melange
melanger
melangeur
Melania
melanian
melanic
melaniferous
Melaniidae
melanilin
melaniline
melanin
Melanippe
Melanippus
melanism
melanistic
melanite
melanitic
melanize
melano
melanoblast
melanocarcinoma
melanocerite
Melanochroi
Melanochroid
melanochroite
melanochroous
melanocomous
melanocrate
melanocratic
melanocyte
Melanodendron
melanoderma
melanodermia
melanodermic
Melanogaster
melanogen
Melanoi
melanoid
melanoidin
melanoma
melanopathia
melanopathy
melanophore
melanoplakia
Melanoplus
melanorrhagia
melanorrhea
Melanorrhoea
melanosarcoma
melanosarcomatosis
melanoscope
melanose
melanosed
melanosis
melanosity
melanospermous
melanotekite
melanotic
melanotrichous
melanous
melanterite
Melanthaceae
melanthaceous
Melanthium
melanure
melanuresis
melanuria
melanuric
melaphyre
Melas
melasma
melasmic
melassigenic
Melastoma
Melastomaceae
melastomaceous
melastomad
melatope
melaxuma
Melburnian
Melcarth
melch
Melchite
Melchora
meld
melder
meldometer
meldrop
mele
Meleager
Meleagridae
Meleagrina
Meleagrinae
meleagrine
Meleagris
melebiose
melee
melena
melene
melenic
Meles
Meletian
Meletski
melezitase
melezitose
Melia
Meliaceae
meliaceous
Meliadus
Melian
Melianthaceae
melianthaceous
Melianthus
meliatin
melibiose
melic
Melica
Melicent
melicera
meliceric
meliceris
melicerous
Melicerta
Melicertidae
melichrous
melicitose
Melicocca
melicraton
melilite
melilitite
melilot
Melilotus
Melinae
Melinda
meline
Melinis
melinite
Meliola
meliorability
meliorable
meliorant
meliorate
meliorater
melioration
meliorative
meliorator
meliorism
meliorist
melioristic
meliority
meliphagan
Meliphagidae
meliphagidan
meliphagous
meliphanite
Melipona
Meliponinae
meliponine
melisma
melismatic
melismatics
Melissa
melissyl
melissylic
Melitaea
melitemia
melithemia
melitis
melitose
melitriose
melittologist
melittology
melituria
melituric
mell
mellaginous
mellate
mellay
melleous
meller
Mellifera
melliferous
mellificate
mellification
mellifluence
mellifluent
mellifluently
mellifluous
mellifluously
mellifluousness
mellimide
mellisonant
mellisugent
mellit
mellitate
mellite
mellitic
Mellivora
Mellivorinae
mellivorous
mellon
mellonides
mellophone
mellow
mellowly
mellowness
mellowy
mellsman
Melocactus
melocoton
melodeon
melodia
melodial
melodially
melodic
melodica
melodically
melodicon
melodics
melodiograph
melodion
melodious
melodiously
melodiousness
melodism
melodist
melodize
melodizer
melodram
melodrama
melodramatic
melodramatical
melodramatically
melodramaticism
melodramatics
melodramatist
melodramatize
melodrame
melody
melodyless
meloe
melogram
Melogrammataceae
melograph
melographic
meloid
Meloidae
melologue
Melolontha
Melolonthidae
melolonthidan
Melolonthides
Melolonthinae
melolonthine
melomane
melomania
melomaniac
melomanic
melon
meloncus
Melonechinus
melongena
melongrower
melonist
melonite
Melonites
melonlike
melonmonger
melonry
melophone
melophonic
melophonist
melopiano
meloplast
meloplastic
meloplasty
melopoeia
melopoeic
melos
melosa
Melospiza
Melothria
melotragedy
melotragic
melotrope
melt
meltability
meltable
meltage
melted
meltedness
melteigite
melter
melters
melting
meltingly
meltingness
melton
Meltonian
Melungeon
Melursus
mem
member
membered
memberless
membership
membracid
Membracidae
membracine
membral
membrally
membrana
membranaceous
membranaceously
membranate
membrane
membraned
membraneless
membranelike
membranelle
membraneous
membraniferous
membraniform
membranin
Membranipora
Membraniporidae
membranocalcareous
membranocartilaginous
membranocoriaceous
membranocorneous
membranogenic
membranoid
membranology
membranonervous
membranosis
membranous
membranously
membranula
membranule
membretto
memento
meminna
Memnon
Memnonian
Memnonium
memo
memoir
memoirism
memoirist
memorabilia
memorability
memorable
memorableness
memorably
memoranda
memorandist
memorandize
memorandum
memorative
memoria
memorial
memorialist
memorialization
memorialize
memorializer
memorially
memoried
memorious
memorist
memorizable
memorization
memorize
memorizer
memory
memoryless
Memphian
Memphite
men
menaccanite
menaccanitic
menace
menaceable
menaceful
menacement
menacer
menacing
menacingly
menacme
menadione
menage
menagerie
menagerist
menald
Menangkabau
menarche
Menaspis
mend
mendable
mendacious
mendaciously
mendaciousness
mendacity
Mendaite
Mende
mendee
Mendelian
Mendelianism
Mendelianist
Mendelism
Mendelist
Mendelize
Mendelssohnian
Mendelssohnic
mendelyeevite
mender
Mendi
mendicancy
mendicant
mendicate
mendication
mendicity
mending
mendipite
mendole
mendozite
mends
meneghinite
menfolk
Menfra
meng
Mengwe
menhaden
menhir
menial
menialism
meniality
menially
Menic
menilite
meningeal
meninges
meningic
meningina
meningism
meningitic
meningitis
meningocele
meningocephalitis
meningocerebritis
meningococcal
meningococcemia
meningococcic
meningococcus
meningocortical
meningoencephalitis
meningoencephalocele
meningomalacia
meningomyclitic
meningomyelitis
meningomyelocele
meningomyelorrhaphy
meningorachidian
meningoradicular
meningorhachidian
meningorrhagia
meningorrhea
meningorrhoea
meningosis
meningospinal
meningotyphoid
meninting
meninx
meniscal
meniscate
menisciform
meniscitis
meniscoid
meniscoidal
Meniscotheriidae
Meniscotherium
meniscus
menisperm
Menispermaceae
menispermaceous
menispermine
Menispermum
Menkalinan
Menkar
Menkib
menkind
mennom
Mennonist
Mennonite
Menobranchidae
Menobranchus
menognath
menognathous
menologium
menology
menometastasis
Menominee
menopausal
menopause
menopausic
menophania
menoplania
Menopoma
Menorah
Menorhyncha
menorhynchous
menorrhagia
menorrhagic
menorrhagy
menorrhea
menorrheic
menorrhoea
menorrhoeic
menoschesis
menoschetic
menosepsis
menostasia
menostasis
menostatic
menostaxis
Menotyphla
menotyphlic
menoxenia
mensa
mensal
mensalize
mense
menseful
menseless
menses
Menshevik
Menshevism
Menshevist
mensk
menstrual
menstruant
menstruate
menstruation
menstruous
menstruousness
menstruum
mensual
mensurability
mensurable
mensurableness
mensurably
mensural
mensuralist
mensurate
mensuration
mensurational
mensurative
Ment
mentagra
mental
mentalis
mentalism
mentalist
mentalistic
mentality
mentalization
mentalize
mentally
mentary
mentation
Mentha
Menthaceae
menthaceous
menthadiene
menthane
menthene
menthenol
menthenone
menthol
mentholated
menthone
menthyl
menticide
menticultural
menticulture
mentiferous
mentiform
mentigerous
mentimeter
mentimutation
mention
mentionability
mentionable
mentionless
mentoanterior
mentobregmatic
mentocondylial
mentohyoid
mentolabial
mentomeckelian
mentonniere
mentoposterior
mentor
mentorial
mentorism
mentorship
mentum
Mentzelia
menu
Menura
Menurae
Menuridae
meny
Menyanthaceae
Menyanthaceous
Menyanthes
menyie
menzie
Menziesia
Meo
Mephisto
Mephistophelean
Mephistopheleanly
Mephistopheles
Mephistophelic
Mephistophelistic
mephitic
mephitical
Mephitinae
mephitine
mephitis
mephitism
Mer
Merak
meralgia
meraline
Merat
Meratia
merbaby
mercal
mercantile
mercantilely
mercantilism
mercantilist
mercantilistic
mercantility
mercaptal
mercaptan
mercaptides
mercaptids
mercapto
mercaptol
mercaptole
Mercator
Mercatorial
mercatorial
Mercedarian
Mercedes
Mercedinus
Mercedonius
mercenarily
mercenariness
mercenary
mercer
merceress
mercerization
mercerize
mercerizer
mercership
mercery
merch
merchandisable
merchandise
merchandiser
merchant
merchantable
merchantableness
merchanter
merchanthood
merchantish
merchantlike
merchantly
merchantman
merchantry
merchantship
merchet
Mercian
merciful
mercifully
mercifulness
merciless
mercilessly
mercilessness
merciment
mercurate
mercuration
Mercurean
mercurial
Mercurialis
mercurialism
mercuriality
mercurialization
mercurialize
mercurially
mercurialness
mercuriamines
mercuriammonium
Mercurian
mercuriate
mercuric
mercuride
mercurification
mercurify
Mercurius
mercurization
mercurize
Mercurochrome
mercurophen
mercurous
Mercury
mercy
mercyproof
merdivorous
mere
Meredithian
merel
merely
merenchyma
merenchymatous
meresman
merestone
meretricious
meretriciously
meretriciousness
meretrix
merfold
merfolk
merganser
merge
mergence
merger
mergh
Merginae
Mergulus
Mergus
meriah
mericarp
merice
Merida
meridian
Meridion
Meridionaceae
Meridional
meridional
meridionality
meridionally
meril
meringue
meringued
Merino
Meriones
meriquinoid
meriquinoidal
meriquinone
meriquinonic
meriquinonoid
merism
merismatic
merismoid
merist
meristele
meristelic
meristem
meristematic
meristematically
meristic
meristically
meristogenous
merit
meritable
merited
meritedly
meriter
meritful
meritless
meritmonger
meritmongering
meritmongery
meritorious
meritoriously
meritoriousness
merk
merkhet
merkin
merl
merle
merlette
merlin
merlon
Merlucciidae
Merluccius
mermaid
mermaiden
merman
Mermis
mermithaner
mermithergate
Mermithidae
mermithization
mermithized
mermithogyne
Mermnad
Mermnadae
mermother
mero
meroblastic
meroblastically
merocele
merocelic
merocerite
meroceritic
merocrystalline
merocyte
Merodach
merogamy
merogastrula
merogenesis
merogenetic
merogenic
merognathite
merogonic
merogony
merohedral
merohedric
merohedrism
meroistic
Meroitic
meromorphic
Meromyaria
meromyarian
merop
Merope
Meropes
meropia
Meropidae
meropidan
meroplankton
meroplanktonic
meropodite
meropoditic
Merops
merorganization
merorganize
meros
merosomal
Merosomata
merosomatous
merosome
merosthenic
Merostomata
merostomatous
merostome
merostomous
merosymmetrical
merosymmetry
merosystematic
merotomize
merotomy
merotropism
merotropy
Merovingian
meroxene
Merozoa
merozoite
merpeople
merribauks
merribush
Merril
merriless
merrily
merriment
merriness
merrow
merry
merrymake
merrymaker
merrymaking
merryman
merrymeeting
merrythought
merrytrotter
merrywing
merse
Mertensia
Merton
Merula
meruline
merulioid
Merulius
merveileux
merwinite
merwoman
Merychippus
merycism
merycismus
Merycoidodon
Merycoidodontidae
Merycopotamidae
Merycopotamus
Mes
mesa
mesabite
mesaconate
mesaconic
mesad
Mesadenia
mesadenia
mesail
mesal
mesalike
mesally
mesameboid
mesange
mesaortitis
mesaraic
mesaraical
mesarch
mesarteritic
mesarteritis
Mesartim
mesaticephal
mesaticephali
mesaticephalic
mesaticephalism
mesaticephalous
mesaticephaly
mesatipellic
mesatipelvic
mesatiskelic
mesaxonic
mescal
Mescalero
mescaline
mescalism
mesdames
mese
mesectoderm
mesem
Mesembryanthemaceae
Mesembryanthemum
mesembryo
mesembryonic
mesencephalic
mesencephalon
mesenchyma
mesenchymal
mesenchymatal
mesenchymatic
mesenchymatous
mesenchyme
mesendoderm
mesenna
mesenterial
mesenteric
mesenterical
mesenterically
mesenteriform
mesenteriolum
mesenteritic
mesenteritis
mesenteron
mesenteronic
mesentery
mesentoderm
mesepimeral
mesepimeron
mesepisternal
mesepisternum
mesepithelial
mesepithelium
mesethmoid
mesethmoidal
mesh
Meshech
meshed
meshrabiyeh
meshwork
meshy
mesiad
mesial
mesially
mesian
mesic
mesically
mesilla
mesiobuccal
mesiocervical
mesioclusion
mesiodistal
mesiodistally
mesiogingival
mesioincisal
mesiolabial
mesiolingual
mesion
mesioocclusal
mesiopulpal
mesioversion
Mesitae
Mesites
Mesitidae
mesitite
mesityl
mesitylene
mesitylenic
mesmerian
mesmeric
mesmerical
mesmerically
mesmerism
mesmerist
mesmerite
mesmerizability
mesmerizable
mesmerization
mesmerize
mesmerizee
mesmerizer
mesmeromania
mesmeromaniac
mesnality
mesnalty
mesne
meso
mesoappendicitis
mesoappendix
mesoarial
mesoarium
mesobar
mesobenthos
mesoblast
mesoblastema
mesoblastemic
mesoblastic
mesobranchial
mesobregmate
mesocaecal
mesocaecum
mesocardia
mesocardium
mesocarp
mesocentrous
mesocephal
mesocephalic
mesocephalism
mesocephalon
mesocephalous
mesocephaly
mesochilium
mesochondrium
mesochroic
mesocoele
mesocoelian
mesocoelic
mesocolic
mesocolon
mesocoracoid
mesocranial
mesocratic
mesocuneiform
mesode
mesoderm
mesodermal
mesodermic
Mesodesma
Mesodesmatidae
Mesodesmidae
Mesodevonian
Mesodevonic
mesodic
mesodisilicic
mesodont
Mesoenatides
mesofurca
mesofurcal
mesogaster
mesogastral
mesogastric
mesogastrium
mesogloea
mesogloeal
mesognathic
mesognathion
mesognathism
mesognathous
mesognathy
mesogyrate
mesohepar
Mesohippus
mesokurtic
mesolabe
mesole
mesolecithal
mesolimnion
mesolite
mesolithic
mesologic
mesological
mesology
mesomere
mesomeric
mesomerism
mesometral
mesometric
mesometrium
mesomorph
mesomorphic
mesomorphous
mesomorphy
Mesomyodi
mesomyodian
mesomyodous
meson
mesonasal
Mesonemertini
mesonephric
mesonephridium
mesonephritic
mesonephros
mesonic
mesonotal
mesonotum
Mesonychidae
Mesonyx
mesoparapteral
mesoparapteron
mesopectus
mesoperiodic
mesopetalum
mesophile
mesophilic
mesophilous
mesophragm
mesophragma
mesophragmal
mesophryon
mesophyll
mesophyllous
mesophyllum
mesophyte
mesophytic
mesophytism
mesopic
mesoplankton
mesoplanktonic
mesoplast
mesoplastic
mesoplastral
mesoplastron
mesopleural
mesopleuron
Mesoplodon
mesoplodont
mesopodial
mesopodiale
mesopodium
mesopotamia
Mesopotamian
mesopotamic
mesoprescutal
mesoprescutum
mesoprosopic
mesopterygial
mesopterygium
mesopterygoid
mesorchial
mesorchium
Mesore
mesorectal
mesorectum
Mesoreodon
mesorrhin
mesorrhinal
mesorrhinian
mesorrhinism
mesorrhinium
mesorrhiny
mesosalpinx
mesosaur
Mesosauria
Mesosaurus
mesoscapula
mesoscapular
mesoscutal
mesoscutellar
mesoscutellum
mesoscutum
mesoseismal
mesoseme
mesosiderite
mesosigmoid
mesoskelic
mesosoma
mesosomatic
mesosome
mesosperm
mesospore
mesosporic
mesosporium
mesostasis
mesosternal
mesosternebra
mesosternebral
mesosternum
mesostethium
Mesostoma
Mesostomatidae
mesostomid
mesostyle
mesostylous
Mesosuchia
mesosuchian
Mesotaeniaceae
Mesotaeniales
mesotarsal
mesotartaric
Mesothelae
mesothelial
mesothelium
mesotherm
mesothermal
mesothesis
mesothet
mesothetic
mesothetical
mesothoracic
mesothoracotheca
mesothorax
mesothorium
mesotonic
mesotroch
mesotrocha
mesotrochal
mesotrochous
mesotron
mesotropic
mesotympanic
mesotype
mesovarian
mesovarium
mesoventral
mesoventrally
mesoxalate
mesoxalic
mesoxalyl
Mesozoa
mesozoan
Mesozoic
mespil
Mespilus
Mespot
mesquite
Mesropian
mess
message
messagery
Messalian
messaline
messan
Messapian
messe
messelite
messenger
messengership
messer
messet
Messiah
Messiahship
Messianic
Messianically
messianically
Messianism
Messianist
Messianize
Messias
messieurs
messily
messin
Messines
Messinese
messiness
messing
messman
messmate
messor
messroom
messrs
messtin
messuage
messy
mestee
mester
mestiza
mestizo
mestome
Mesua
Mesvinian
mesymnion
met
meta
metabasis
metabasite
metabatic
metabiological
metabiology
metabiosis
metabiotic
metabiotically
metabismuthic
metabisulphite
metabletic
Metabola
metabola
metabole
Metabolia
metabolian
metabolic
metabolism
metabolite
metabolizable
metabolize
metabolon
metabolous
metaboly
metaborate
metaboric
metabranchial
metabrushite
metabular
metacarpal
metacarpale
metacarpophalangeal
metacarpus
metacenter
metacentral
metacentric
metacentricity
metachemic
metachemistry
Metachlamydeae
metachlamydeous
metachromasis
metachromatic
metachromatin
metachromatinic
metachromatism
metachrome
metachronism
metachrosis
metacinnabarite
metacism
metacismus
metaclase
metacneme
metacoele
metacoelia
metaconal
metacone
metaconid
metaconule
metacoracoid
metacrasis
metacresol
metacromial
metacromion
metacryst
metacyclic
metacymene
metad
metadiabase
metadiazine
metadiorite
metadiscoidal
metadromous
metafluidal
metaformaldehyde
metafulminuric
metagalactic
metagalaxy
metagaster
metagastric
metagastrula
metage
Metageitnion
metagelatin
metagenesis
metagenetic
metagenetically
metagenic
metageometer
metageometrical
metageometry
metagnath
metagnathism
metagnathous
metagnomy
metagnostic
metagnosticism
metagram
metagrammatism
metagrammatize
metagraphic
metagraphy
metahewettite
metahydroxide
metaigneous
metainfective
metakinesis
metakinetic
metal
metalammonium
metalanguage
metalbumin
metalcraft
metaldehyde
metalepsis
metaleptic
metaleptical
metaleptically
metaler
metaline
metalined
metaling
metalinguistic
metalinguistics
metalism
metalist
metalization
metalize
metallary
metalleity
metallic
metallical
metallically
metallicity
metallicize
metallicly
metallics
metallide
metallifacture
metalliferous
metallification
metalliform
metallify
metallik
metalline
metallism
metallization
metallize
metallochrome
metallochromy
metallogenetic
metallogenic
metallogeny
metallograph
metallographer
metallographic
metallographical
metallographist
metallography
metalloid
metalloidal
metallometer
metallophone
metalloplastic
metallorganic
metallotherapeutic
metallotherapy
metallurgic
metallurgical
metallurgically
metallurgist
metallurgy
metalmonger
metalogic
metalogical
metaloph
metalorganic
metaloscope
metaloscopy
metaluminate
metaluminic
metalware
metalwork
metalworker
metalworking
metalworks
metamathematical
metamathematics
metamer
metameral
metamere
metameric
metamerically
metameride
metamerism
metamerization
metamerized
metamerous
metamery
metamorphic
metamorphism
metamorphize
metamorphopsia
metamorphopsy
metamorphosable
metamorphose
metamorphoser
metamorphoses
metamorphosian
metamorphosic
metamorphosical
metamorphosis
metamorphostical
metamorphotic
metamorphous
metamorphy
Metamynodon
metanalysis
metanauplius
Metanemertini
metanephric
metanephritic
metanephron
metanephros
metanepionic
metanilic
metanitroaniline
metanomen
metanotal
metanotum
metantimonate
metantimonic
metantimonious
metantimonite
metantimonous
metanym
metaorganism
metaparapteral
metaparapteron
metapectic
metapectus
metapepsis
metapeptone
metaperiodic
metaphase
metaphenomenal
metaphenomenon
metaphenylene
metaphenylenediamin
metaphenylenediamine
metaphloem
metaphonical
metaphonize
metaphony
metaphor
metaphoric
metaphorical
metaphorically
metaphoricalness
metaphorist
metaphorize
metaphosphate
metaphosphoric
metaphosphorous
metaphragm
metaphragmal
metaphrase
metaphrasis
metaphrast
metaphrastic
metaphrastical
metaphrastically
metaphyseal
metaphysic
metaphysical
metaphysically
metaphysician
metaphysicianism
metaphysicist
metaphysicize
metaphysicous
metaphysics
metaphysis
metaphyte
metaphytic
metaphyton
metaplasia
metaplasis
metaplasm
metaplasmic
metaplast
metaplastic
metapleural
metapleure
metapleuron
metaplumbate
metaplumbic
metapneumonic
metapneustic
metapodial
metapodiale
metapodium
metapolitic
metapolitical
metapolitician
metapolitics
metapophyseal
metapophysial
metapophysis
metapore
metapostscutellar
metapostscutellum
metaprescutal
metaprescutum
metaprotein
metapsychic
metapsychical
metapsychics
metapsychism
metapsychist
metapsychological
metapsychology
metapsychosis
metapterygial
metapterygium
metapterygoid
metarabic
metarhyolite
metarossite
metarsenic
metarsenious
metarsenite
metasaccharinic
metascutal
metascutellar
metascutellum
metascutum
metasedimentary
metasilicate
metasilicic
metasoma
metasomal
metasomasis
metasomatic
metasomatism
metasomatosis
metasome
metasperm
Metaspermae
metaspermic
metaspermous
metastability
metastable
metastannate
metastannic
metastasis
metastasize
metastatic
metastatical
metastatically
metasternal
metasternum
metasthenic
metastibnite
metastigmate
metastoma
metastome
metastrophe
metastrophic
metastyle
metatantalic
metatarsal
metatarsale
metatarse
metatarsophalangeal
metatarsus
metatatic
metatatically
metataxic
metate
metathalamus
metatheology
Metatheria
metatherian
metatheses
metathesis
metathetic
metathetical
metathetically
metathoracic
metathorax
metatitanate
metatitanic
metatoluic
metatoluidine
metatracheal
metatrophic
metatungstic
metatype
metatypic
Metaurus
metavanadate
metavanadic
metavauxite
metavoltine
metaxenia
metaxite
metaxylem
metaxylene
metayer
Metazoa
metazoal
metazoan
metazoea
metazoic
metazoon
mete
metel
metempiric
metempirical
metempirically
metempiricism
metempiricist
metempirics
metempsychic
metempsychosal
metempsychose
metempsychoses
metempsychosical
metempsychosis
metempsychosize
metemptosis
metencephalic
metencephalon
metensarcosis
metensomatosis
metenteron
metenteronic
meteogram
meteograph
meteor
meteorgraph
meteoric
meteorical
meteorically
meteorism
meteorist
meteoristic
meteorital
meteorite
meteoritic
meteoritics
meteorization
meteorize
meteorlike
meteorogram
meteorograph
meteorographic
meteorography
meteoroid
meteoroidal
meteorolite
meteorolitic
meteorologic
meteorological
meteorologically
meteorologist
meteorology
meteorometer
meteoroscope
meteoroscopy
meteorous
metepencephalic
metepencephalon
metepimeral
metepimeron
metepisternal
metepisternum
meter
meterage
metergram
meterless
meterman
metership
metestick
metewand
meteyard
methacrylate
methacrylic
methadone
methanal
methanate
methane
methanoic
methanolysis
methanometer
metheglin
methemoglobin
methemoglobinemia
methemoglobinuria
methenamine
methene
methenyl
mether
methid
methide
methine
methinks
methiodide
methionic
methionine
methobromide
method
methodaster
methodeutic
methodic
methodical
methodically
methodicalness
methodics
methodism
Methodist
methodist
Methodistic
Methodistically
Methodisty
methodization
Methodize
methodize
methodizer
methodless
methodological
methodologically
methodologist
methodology
Methody
methought
methoxide
methoxychlor
methoxyl
methronic
Methuselah
methyl
methylacetanilide
methylal
methylamine
methylaniline
methylanthracene
methylate
methylation
methylator
methylcholanthrene
methylene
methylenimine
methylenitan
methylethylacetic
methylglycine
methylglycocoll
methylglyoxal
methylic
methylmalonic
methylnaphthalene
methylol
methylolurea
methylosis
methylotic
methylpentose
methylpentoses
methylpropane
methylsulfanol
metic
meticulosity
meticulous
meticulously
meticulousness
metier
Metin
metis
Metoac
metochous
metochy
metoestrous
metoestrum
Metol
metonym
metonymic
metonymical
metonymically
metonymous
metonymously
metonymy
metope
Metopias
metopic
metopion
metopism
Metopoceros
metopomancy
metopon
metoposcopic
metoposcopical
metoposcopist
metoposcopy
metosteal
metosteon
metoxazine
metoxenous
metoxeny
metra
metralgia
metranate
metranemia
metratonia
Metrazol
metrectasia
metrectatic
metrectomy
metrectopia
metrectopic
metrectopy
metreless
metreship
metreta
metrete
metretes
metria
metric
metrical
metrically
metrician
metricism
metricist
metricize
metrics
Metridium
metrification
metrifier
metrify
metriocephalic
metrist
metritis
metrocampsis
metrocarat
metrocarcinoma
metrocele
metroclyst
metrocolpocele
metrocracy
metrocratic
metrocystosis
metrodynia
metrofibroma
metrological
metrologist
metrologue
metrology
metrolymphangitis
metromalacia
metromalacoma
metromalacosis
metromania
metromaniac
metromaniacal
metrometer
metroneuria
metronome
metronomic
metronomical
metronomically
metronymic
metronymy
metroparalysis
metropathia
metropathic
metropathy
metroperitonitis
metrophlebitis
metrophotography
metropole
metropolis
metropolitan
metropolitanate
metropolitancy
metropolitanism
metropolitanize
metropolitanship
metropolite
metropolitic
metropolitical
metropolitically
metroptosia
metroptosis
metroradioscope
metrorrhagia
metrorrhagic
metrorrhea
metrorrhexis
metrorthosis
metrosalpingitis
metrosalpinx
metroscirrhus
metroscope
metroscopy
Metrosideros
metrostaxis
metrostenosis
metrosteresis
metrostyle
metrosynizesis
metrotherapist
metrotherapy
metrotome
metrotomy
Metroxylon
mettar
mettle
mettled
mettlesome
mettlesomely
mettlesomeness
metusia
metze
Meum
meuse
meute
Mev
mew
meward
mewer
mewl
mewler
Mexica
Mexican
Mexicanize
Mexitl
Mexitli
meyerhofferite
mezcal
Mezentian
Mezentism
Mezentius
mezereon
mezereum
mezuzah
mezzanine
mezzo
mezzograph
mezzotint
mezzotinter
mezzotinto
mho
mhometer
mi
Miami
miamia
mian
Miao
Miaotse
Miaotze
miaow
miaower
Miaplacidus
miargyrite
miarolitic
mias
miaskite
miasm
miasma
miasmal
miasmata
miasmatic
miasmatical
miasmatically
miasmatize
miasmatology
miasmatous
miasmic
miasmology
miasmous
Miastor
miaul
miauler
mib
mica
micaceous
micacious
micacite
Micah
micasization
micasize
micate
mication
Micawberish
Micawberism
mice
micellar
micelle
Michabo
Michabou
Michael
Michaelites
Michaelmas
Michaelmastide
miche
Micheal
Michel
Michelangelesque
Michelangelism
Michelia
Michelle
micher
Michiel
Michigamea
Michigan
michigan
Michigander
Michiganite
miching
Michoacan
Michoacano
micht
Mick
mick
Mickey
mickle
Micky
Micmac
mico
miconcave
Miconia
micramock
Micrampelis
micranatomy
micrander
micrandrous
micraner
micranthropos
Micraster
micrencephalia
micrencephalic
micrencephalous
micrencephalus
micrencephaly
micrergate
micresthete
micrify
micro
microammeter
microampere
microanalysis
microanalyst
microanalytical
microangstrom
microapparatus
microbal
microbalance
microbar
microbarograph
microbattery
microbe
microbeless
microbeproof
microbial
microbian
microbic
microbicidal
microbicide
microbiologic
microbiological
microbiologically
microbiologist
microbiology
microbion
microbiosis
microbiota
microbiotic
microbious
microbism
microbium
microblast
microblepharia
microblepharism
microblephary
microbrachia
microbrachius
microburet
microburette
microburner
microcaltrop
microcardia
microcardius
microcarpous
Microcebus
microcellular
microcentrosome
microcentrum
microcephal
microcephalia
microcephalic
microcephalism
microcephalous
microcephalus
microcephaly
microceratous
microchaeta
microcharacter
microcheilia
microcheiria
microchemic
microchemical
microchemically
microchemistry
microchiria
Microchiroptera
microchiropteran
microchiropterous
microchromosome
microchronometer
microcinema
microcinematograph
microcinematographic
microcinematography
Microcitrus
microclastic
microclimate
microclimatic
microclimatologic
microclimatological
microclimatology
microcline
microcnemia
microcoat
micrococcal
Micrococceae
Micrococcus
microcoleoptera
microcolon
microcolorimeter
microcolorimetric
microcolorimetrically
microcolorimetry
microcolumnar
microcombustion
microconidial
microconidium
microconjugant
Microconodon
microconstituent
microcopy
microcoria
microcosm
microcosmal
microcosmian
microcosmic
microcosmical
microcosmography
microcosmology
microcosmos
microcosmus
microcoulomb
microcranous
microcrith
microcryptocrystalline
microcrystal
microcrystalline
microcrystallogeny
microcrystallography
microcrystalloscopy
microcurie
Microcyprini
microcyst
microcyte
microcythemia
microcytosis
microdactylia
microdactylism
microdactylous
microdentism
microdentous
microdetection
microdetector
microdetermination
microdiactine
microdissection
microdistillation
microdont
microdontism
microdontous
microdose
microdrawing
Microdrili
microdrive
microelectrode
microelectrolysis
microelectroscope
microelement
microerg
microestimation
microeutaxitic
microevolution
microexamination
microfarad
microfauna
microfelsite
microfelsitic
microfilaria
microfilm
microflora
microfluidal
microfoliation
microfossil
microfungus
microfurnace
Microgadus
microgalvanometer
microgamete
microgametocyte
microgametophyte
microgamy
Microgaster
microgastria
Microgastrinae
microgastrine
microgeological
microgeologist
microgeology
microgilbert
microglia
microglossia
micrognathia
micrognathic
micrognathous
microgonidial
microgonidium
microgram
microgramme
microgranite
microgranitic
microgranitoid
microgranular
microgranulitic
micrograph
micrographer
micrographic
micrographical
micrographically
micrographist
micrography
micrograver
microgravimetric
microgroove
microgyne
microgyria
microhenry
microhepatia
microhistochemical
microhistology
microhm
microhmmeter
Microhymenoptera
microhymenopteron
microinjection
microjoule
microlepidopter
microlepidoptera
microlepidopteran
microlepidopterist
microlepidopteron
microlepidopterous
microleukoblast
microlevel
microlite
microliter
microlith
microlithic
microlitic
micrologic
micrological
micrologically
micrologist
micrologue
micrology
microlux
micromania
micromaniac
micromanipulation
micromanipulator
micromanometer
Micromastictora
micromazia
micromeasurement
micromechanics
micromelia
micromelic
micromelus
micromembrane
micromeral
micromere
Micromeria
micromeric
micromerism
micromeritic
micromeritics
micromesentery
micrometallographer
micrometallography
micrometallurgy
micrometer
micromethod
micrometrical
micrometrically
micrometry
micromicrofarad
micromicron
micromil
micromillimeter
micromineralogical
micromineralogy
micromorph
micromotion
micromotoscope
micromyelia
micromyeloblast
micron
Micronesian
micronization
micronize
micronometer
micronuclear
micronucleus
micronutrient
microorganic
microorganism
microorganismal
micropaleontology
micropantograph
microparasite
microparasitic
micropathological
micropathologist
micropathology
micropegmatite
micropegmatitic
micropenis
microperthite
microperthitic
micropetalous
micropetrography
micropetrologist
micropetrology
microphage
microphagocyte
microphagous
microphagy
microphakia
microphallus
microphone
microphonic
microphonics
microphonograph
microphot
microphotograph
microphotographic
microphotography
microphotometer
microphotoscope
microphthalmia
microphthalmic
microphthalmos
microphthalmus
microphyllous
microphysical
microphysics
microphysiography
microphytal
microphyte
microphytic
microphytology
micropia
micropin
micropipette
microplakite
microplankton
microplastocyte
microplastometer
micropodal
Micropodi
micropodia
Micropodidae
Micropodiformes
micropoecilitic
micropoicilitic
micropoikilitic
micropolariscope
micropolarization
micropore
microporosity
microporous
microporphyritic
microprint
microprojector
micropsia
micropsy
micropterism
micropterous
Micropterus
micropterygid
Micropterygidae
micropterygious
Micropterygoidea
Micropteryx
Micropus
micropylar
micropyle
micropyrometer
microradiometer
microreaction
microrefractometer
microrhabdus
microrheometer
microrheometric
microrheometrical
Microrhopias
Microsauria
microsaurian
microsclere
microsclerous
microsclerum
microscopal
microscope
microscopial
microscopic
microscopical
microscopically
microscopics
Microscopid
microscopist
Microscopium
microscopize
microscopy
microsecond
microsection
microseism
microseismic
microseismical
microseismograph
microseismology
microseismometer
microseismometrograph
microseismometry
microseme
microseptum
microsmatic
microsmatism
microsoma
microsomatous
microsome
microsomia
microsommite
Microsorex
microspecies
microspectroscope
microspectroscopic
microspectroscopy
Microspermae
microspermous
Microsphaera
microsphaeric
microsphere
microspheric
microspherulitic
microsplanchnic
microsplenia
microsplenic
microsporange
microsporangium
microspore
microsporiasis
microsporic
Microsporidia
microsporidian
Microsporon
microsporophore
microsporophyll
microsporosis
microsporous
Microsporum
microstat
microsthene
Microsthenes
microsthenic
microstomatous
microstome
microstomia
microstomous
microstructural
microstructure
Microstylis
microstylospore
microstylous
microsublimation
microtasimeter
microtechnic
microtechnique
microtelephone
microtelephonic
Microthelyphonida
microtheos
microtherm
microthermic
microthorax
Microthyriaceae
microtia
Microtinae
microtine
microtitration
microtome
microtomic
microtomical
microtomist
microtomy
microtone
Microtus
microtypal
microtype
microtypical
microvolt
microvolume
microvolumetric
microwatt
microwave
microweber
microzoa
microzoal
microzoan
microzoaria
microzoarian
microzoary
microzoic
microzone
microzooid
microzoology
microzoon
microzoospore
microzyma
microzyme
microzymian
micrurgic
micrurgical
micrurgist
micrurgy
Micrurus
miction
micturate
micturition
mid
midafternoon
midautumn
midaxillary
midbrain
midday
midden
middenstead
middle
middlebreaker
middlebuster
middleman
middlemanism
middlemanship
middlemost
middler
middlesplitter
middlewards
middleway
middleweight
middlewoman
middling
middlingish
middlingly
middlingness
middlings
middorsal
middy
mide
Mider
midevening
midewiwin
midfacial
midforenoon
midfrontal
midge
midget
midgety
midgy
midheaven
Midianite
Midianitish
Mididae
midiron
midland
Midlander
Midlandize
midlandward
midlatitude
midleg
midlenting
midmain
midmandibular
midmonth
midmonthly
midmorn
midmorning
midmost
midnight
midnightly
midnoon
midparent
midparentage
midparental
midpit
midrange
midrash
midrashic
midrib
midribbed
midriff
mids
midseason
midsentence
midship
midshipman
midshipmanship
midshipmite
midships
midspace
midst
midstory
midstout
midstream
midstreet
midstroke
midstyled
midsummer
midsummerish
midsummery
midtap
midvein
midverse
midward
midwatch
midway
midweek
midweekly
Midwest
Midwestern
Midwesterner
midwestward
midwife
midwifery
midwinter
midwinterly
midwintry
midwise
midyear
Miek
mien
miersite
Miescherian
miff
miffiness
miffy
mig
might
mightily
mightiness
mightless
mightnt
mighty
mightyhearted
mightyship
miglio
migmatite
migniardise
mignon
mignonette
mignonne
mignonness
Migonitis
migraine
migrainoid
migrainous
migrant
migrate
migration
migrational
migrationist
migrative
migrator
migratorial
migratory
Miguel
miharaite
mihrab
mijakite
mijl
mikado
mikadoate
mikadoism
Mikael
Mikania
Mikasuki
Mike
mike
Mikey
Miki
mikie
Mikir
Mil
mil
mila
milady
milammeter
Milan
Milanese
Milanion
milarite
milch
milcher
milchy
mild
milden
milder
mildew
mildewer
mildewy
mildhearted
mildheartedness
mildish
mildly
mildness
Mildred
mile
mileage
Miledh
milepost
miler
Miles
Milesian
milesima
Milesius
milestone
mileway
milfoil
milha
miliaceous
miliarensis
miliaria
miliarium
miliary
Milicent
milieu
Miliola
milioliform
milioline
miliolite
miliolitic
militancy
militant
militantly
militantness
militarily
militariness
militarism
militarist
militaristic
militaristically
militarization
militarize
military
militaryism
militaryment
militaster
militate
militation
militia
militiaman
militiate
milium
milk
milkbush
milken
milker
milkeress
milkfish
milkgrass
milkhouse
milkily
milkiness
milking
milkless
milklike
milkmaid
milkman
milkness
milkshed
milkshop
milksick
milksop
milksopism
milksoppery
milksopping
milksoppish
milksoppy
milkstone
milkweed
milkwood
milkwort
milky
mill
Milla
milla
millable
millage
millboard
millclapper
millcourse
milldam
mille
milled
millefiori
milleflorous
millefoliate
millenarian
millenarianism
millenarist
millenary
millennia
millennial
millennialism
millennialist
millennially
millennian
millenniarism
millenniary
millennium
millepede
Millepora
millepore
milleporiform
milleporine
milleporite
milleporous
millepunctate
miller
milleress
millering
Millerism
Millerite
millerite
millerole
millesimal
millesimally
millet
Millettia
millfeed
millful
millhouse
milliad
milliammeter
milliamp
milliampere
milliamperemeter
milliangstrom
milliard
milliardaire
milliare
milliarium
milliary
millibar
millicron
millicurie
Millie
millieme
milliequivalent
millifarad
millifold
milliform
milligal
milligrade
milligram
milligramage
millihenry
millilambert
millile
milliliter
millilux
millimeter
millimicron
millimolar
millimole
millincost
milline
milliner
millinerial
millinering
millinery
milling
Millingtonia
millinormal
millinormality
millioctave
millioersted
million
millionaire
millionairedom
millionairess
millionairish
millionairism
millionary
millioned
millioner
millionfold
millionism
millionist
millionize
millionocracy
millions
millionth
milliphot
millipoise
millisecond
millistere
Millite
millithrum
millivolt
millivoltmeter
millman
millocracy
millocrat
millocratism
millosevichite
millowner
millpond
millpool
millpost
millrace
millrynd
millsite
millstock
millstone
millstream
milltail
millward
millwork
millworker
millwright
millwrighting
Milly
Milner
milner
Milo
milo
milord
milpa
milreis
milsey
milsie
milt
milter
miltlike
Miltonia
Miltonian
Miltonic
Miltonically
Miltonism
Miltonist
Miltonize
Miltos
miltsick
miltwaste
milty
Milvago
Milvinae
milvine
milvinous
Milvus
milzbrand
mim
mima
mimbar
mimble
Mimbreno
Mime
mime
mimeo
mimeograph
mimeographic
mimeographically
mimeographist
mimer
mimesis
mimester
mimetene
mimetesite
mimetic
mimetical
mimetically
mimetism
mimetite
Mimi
mimiambi
mimiambic
mimiambics
mimic
mimical
mimically
mimicism
mimicker
mimicry
Mimidae
Miminae
mimine
miminypiminy
mimly
mimmation
mimmest
mimmock
mimmocking
mimmocky
mimmood
mimmoud
mimmouthed
mimmouthedness
mimodrama
mimographer
mimography
mimologist
Mimosa
Mimosaceae
mimosaceous
mimosis
mimosite
mimotype
mimotypic
mimp
Mimpei
mimsey
Mimulus
Mimus
Mimusops
min
Mina
mina
minable
minacious
minaciously
minaciousness
minacity
Minaean
Minahassa
Minahassan
Minahassian
minar
minaret
minareted
minargent
minasragrite
minatorial
minatorially
minatorily
minatory
minaway
mince
mincemeat
mincer
minchery
minchiate
mincing
mincingly
mincingness
Mincopi
Mincopie
mind
minded
Mindel
Mindelian
minder
Mindererus
mindful
mindfully
mindfulness
minding
mindless
mindlessly
mindlessness
mindsight
mine
mineowner
miner
mineragraphic
mineragraphy
mineraiogic
mineral
mineralizable
mineralization
mineralize
mineralizer
mineralogical
mineralogically
mineralogist
mineralogize
mineralogy
Minerva
minerval
Minervan
Minervic
minery
mines
minette
mineworker
Ming
ming
minge
mingelen
mingle
mingleable
mingledly
minglement
mingler
minglingly
Mingo
Mingrelian
minguetite
mingwort
mingy
minhag
minhah
miniaceous
miniate
miniator
miniature
miniaturist
minibus
minicam
minicamera
Miniconjou
minienize
minification
minify
minikin
minikinly
minim
minima
minimacid
minimal
minimalism
Minimalist
minimalkaline
minimally
minimetric
minimifidian
minimifidianism
minimism
minimistic
Minimite
minimitude
minimization
minimize
minimizer
minimum
minimus
minimuscular
mining
minion
minionette
minionism
minionly
minionship
minish
minisher
minishment
minister
ministeriable
ministerial
ministerialism
ministerialist
ministeriality
ministerially
ministerialness
ministerium
ministership
ministrable
ministrant
ministration
ministrative
ministrator
ministrer
ministress
ministry
ministryship
minitant
Minitari
minium
miniver
minivet
mink
minkery
minkish
Minkopi
Minnehaha
minnesinger
minnesong
Minnesotan
Minnetaree
Minnie
minnie
minniebush
minning
minnow
minny
mino
Minoan
minoize
minometer
minor
minorage
minorate
minoration
Minorca
Minorcan
Minoress
minoress
Minorist
Minorite
minority
minorship
Minos
minot
Minotaur
Minseito
minsitive
minster
minsteryard
minstrel
minstreless
minstrelship
minstrelsy
mint
mintage
Mintaka
mintbush
minter
mintmaker
mintmaking
mintman
mintmaster
minty
minuend
minuet
minuetic
minuetish
minus
minuscular
minuscule
minutary
minutation
minute
minutely
minuteman
minuteness
minuter
minuthesis
minutia
minutiae
minutial
minutiose
minutiously
minutissimic
minverite
minx
minxish
minxishly
minxishness
minxship
miny
Minyadidae
Minyae
Minyan
minyan
Minyas
miocardia
Miocene
Miocenic
Miohippus
miolithic
mioplasmia
miothermic
miqra
miquelet
mir
Mira
Mirabel
Mirabell
mirabiliary
Mirabilis
mirabilite
Mirac
Mirach
mirach
miracidial
miracidium
miracle
miraclemonger
miraclemongering
miraclist
miraculist
miraculize
miraculosity
miraculous
miraculously
miraculousness
mirador
mirage
miragy
Mirak
Miramolin
Mirana
Miranda
mirandous
Miranha
Miranhan
mirate
mirbane
mird
mirdaha
mire
mirepoix
Mirfak
Miriam
Miriamne
mirid
Miridae
mirific
miriness
mirish
mirk
mirkiness
mirksome
mirliton
Miro
miro
Mirounga
mirror
mirrored
mirrorize
mirrorlike
mirrorscope
mirrory
mirth
mirthful
mirthfully
mirthfulness
mirthless
mirthlessly
mirthlessness
mirthsome
mirthsomeness
miry
miryachit
mirza
misaccent
misaccentuation
misachievement
misacknowledge
misact
misadapt
misadaptation
misadd
misaddress
misadjust
misadmeasurement
misadministration
misadvantage
misadventure
misadventurer
misadventurous
misadventurously
misadvertence
misadvice
misadvise
misadvised
misadvisedly
misadvisedness
misaffected
misaffection
misaffirm
misagent
misaim
misalienate
misalignment
misallegation
misallege
misalliance
misallotment
misallowance
misally
misalphabetize
misalter
misanalyze
misandry
misanswer
misanthrope
misanthropia
misanthropic
misanthropical
misanthropically
misanthropism
misanthropist
misanthropize
misanthropy
misapparel
misappear
misappearance
misappellation
misapplication
misapplier
misapply
misappoint
misappointment
misappraise
misappraisement
misappreciate
misappreciation
misappreciative
misapprehend
misapprehendingly
misapprehensible
misapprehension
misapprehensive
misapprehensively
misapprehensiveness
misappropriate
misappropriately
misappropriation
misarchism
misarchist
misarrange
misarrangement
misarray
misascribe
misascription
misasperse
misassay
misassent
misassert
misassign
misassociate
misassociation
misatone
misattend
misattribute
misattribution
misaunter
misauthorization
misauthorize
misaward
misbandage
misbaptize
misbecome
misbecoming
misbecomingly
misbecomingness
misbefitting
misbeget
misbegin
misbegotten
misbehave
misbehavior
misbeholden
misbelief
misbelieve
misbeliever
misbelievingly
misbelove
misbeseem
misbestow
misbestowal
misbetide
misbias
misbill
misbind
misbirth
misbode
misborn
misbrand
misbuild
misbusy
miscalculate
miscalculation
miscalculator
miscall
miscaller
miscanonize
miscarriage
miscarriageable
miscarry
miscast
miscasualty
misceability
miscegenate
miscegenation
miscegenationist
miscegenator
miscegenetic
miscegine
miscellanarian
miscellanea
miscellaneity
miscellaneous
miscellaneously
miscellaneousness
miscellanist
miscellany
mischallenge
mischance
mischanceful
mischancy
mischaracterization
mischaracterize
mischarge
mischief
mischiefful
mischieve
mischievous
mischievously
mischievousness
mischio
mischoice
mischoose
mischristen
miscibility
miscible
miscipher
misclaim
misclaiming
misclass
misclassification
misclassify
miscognizant
miscoin
miscoinage
miscollocation
miscolor
miscoloration
miscommand
miscommit
miscommunicate
miscompare
miscomplacence
miscomplain
miscomplaint
miscompose
miscomprehend
miscomprehension
miscomputation
miscompute
misconceive
misconceiver
misconception
misconclusion
miscondition
misconduct
misconfer
misconfidence
misconfident
misconfiguration
misconjecture
misconjugate
misconjugation
misconjunction
misconsecrate
misconsequence
misconstitutional
misconstruable
misconstruct
misconstruction
misconstructive
misconstrue
misconstruer
miscontinuance
misconvenient
misconvey
miscook
miscookery
miscorrect
miscorrection
miscounsel
miscount
miscovet
miscreancy
miscreant
miscreate
miscreation
miscreative
miscreator
miscredited
miscredulity
miscreed
miscript
miscrop
miscue
miscultivated
misculture
miscurvature
miscut
misdate
misdateful
misdaub
misdeal
misdealer
misdecide
misdecision
misdeclaration
misdeclare
misdeed
misdeem
misdeemful
misdefine
misdeformed
misdeliver
misdelivery
misdemean
misdemeanant
misdemeanist
misdemeanor
misdentition
misderivation
misderive
misdescribe
misdescriber
misdescription
misdescriptive
misdesire
misdetermine
misdevise
misdevoted
misdevotion
misdiet
misdirect
misdirection
misdispose
misdisposition
misdistinguish
misdistribute
misdistribution
misdivide
misdivision
misdo
misdoer
misdoing
misdoubt
misdower
misdraw
misdread
misdrive
mise
misease
misecclesiastic
misedit
miseducate
miseducation
miseducative
miseffect
misemphasis
misemphasize
misemploy
misemployment
misencourage
misendeavor
misenforce
misengrave
misenite
misenjoy
misenroll
misentitle
misenunciation
Misenus
miser
miserabilism
miserabilist
miserabilistic
miserability
miserable
miserableness
miserably
miserdom
miserected
Miserere
miserhood
misericord
Misericordia
miserism
miserliness
miserly
misery
misesteem
misestimate
misestimation
misexample
misexecute
misexecution
misexpectation
misexpend
misexpenditure
misexplain
misexplanation
misexplication
misexposition
misexpound
misexpress
misexpression
misexpressive
misfaith
misfare
misfashion
misfather
misfault
misfeasance
misfeasor
misfeature
misfield
misfigure
misfile
misfire
misfit
misfond
misform
misformation
misfortunate
misfortunately
misfortune
misfortuned
misfortuner
misframe
misgauge
misgesture
misgive
misgiving
misgivingly
misgo
misgotten
misgovern
misgovernance
misgovernment
misgovernor
misgracious
misgraft
misgrave
misground
misgrow
misgrown
misgrowth
misguess
misguggle
misguidance
misguide
misguided
misguidedly
misguidedness
misguider
misguiding
misguidingly
mishandle
mishap
mishappen
Mishikhwutmetunne
mishmash
mishmee
Mishmi
Mishnah
Mishnaic
Mishnic
Mishnical
Mishongnovi
misidentification
misidentify
Misima
misimagination
misimagine
misimpression
misimprove
misimprovement
misimputation
misimpute
misincensed
misincite
misinclination
misincline
misinfer
misinference
misinflame
misinform
misinformant
misinformation
misinformer
misingenuity
misinspired
misinstruct
misinstruction
misinstructive
misintelligence
misintelligible
misintend
misintention
misinter
misinterment
misinterpret
misinterpretable
misinterpretation
misinterpreter
misintimation
misjoin
misjoinder
misjudge
misjudgement
misjudger
misjudgingly
misjudgment
miskeep
misken
miskenning
miskill
miskindle
misknow
misknowledge
misky
mislabel
mislabor
mislanguage
mislay
mislayer
mislead
misleadable
misleader
misleading
misleadingly
misleadingness
mislear
misleared
mislearn
misled
mislest
mislight
mislike
misliken
mislikeness
misliker
mislikingly
mislippen
mislive
mislocate
mislocation
mislodge
mismade
mismake
mismanage
mismanageable
mismanagement
mismanager
mismarriage
mismarry
mismatch
mismatchment
mismate
mismeasure
mismeasurement
mismenstruation
misminded
mismingle
mismotion
mismove
misname
misnarrate
misnatured
misnavigation
Misniac
misnomed
misnomer
misnumber
misnurture
misnutrition
misobedience
misobey
misobservance
misobserve
misocapnic
misocapnist
misocatholic
misoccupy
misogallic
misogamic
misogamist
misogamy
misogyne
misogynic
misogynical
misogynism
misogynist
misogynistic
misogynistical
misogynous
misogyny
misohellene
misologist
misology
misomath
misoneism
misoneist
misoneistic
misopaterist
misopedia
misopedism
misopedist
misopinion
misopolemical
misorder
misordination
misorganization
misorganize
misoscopist
misosophist
misosophy
misotheism
misotheist
misotheistic
misotramontanism
misotyranny
misoxene
misoxeny
mispage
mispagination
mispaint
misparse
mispart
mispassion
mispatch
mispay
misperceive
misperception
misperform
misperformance
mispersuade
misperuse
misphrase
mispick
mispickel
misplace
misplacement
misplant
misplay
misplead
mispleading
misplease
mispoint
mispoise
mispolicy
misposition
mispossessed
mispractice
mispraise
misprejudiced
misprincipled
misprint
misprisal
misprision
misprize
misprizer
misproceeding
misproduce
misprofess
misprofessor
mispronounce
mispronouncement
mispronunciation
misproportion
misproposal
mispropose
misproud
misprovide
misprovidence
misprovoke
mispunctuate
mispunctuation
mispurchase
mispursuit
misput
misqualify
misquality
misquotation
misquote
misquoter
misraise
misrate
misread
misreader
misrealize
misreason
misreceive
misrecital
misrecite
misreckon
misrecognition
misrecognize
misrecollect
misrefer
misreference
misreflect
misreform
misregulate
misrehearsal
misrehearse
misrelate
misrelation
misreliance
misremember
misremembrance
misrender
misrepeat
misreport
misreporter
misreposed
misrepresent
misrepresentation
misrepresentative
misrepresenter
misreprint
misrepute
misresemblance
misresolved
misresult
misreward
misrhyme
misrhymer
misrule
miss
missable
missal
missay
missayer
misseem
missel
missemblance
missentence
misserve
misservice
misset
misshape
misshapen
misshapenly
misshapenness
misshood
missible
missile
missileproof
missiness
missing
missingly
mission
missional
missionarize
missionary
missionaryship
missioner
missionize
missionizer
missis
Missisauga
missish
missishness
Mississippi
Mississippian
missive
missmark
missment
Missouri
Missourian
Missourianism
missourite
misspeak
misspeech
misspell
misspelling
misspend
misspender
misstate
misstatement
misstater
misstay
misstep
missuade
missuggestion
missummation
missuppose
missy
missyish
missyllabication
missyllabify
mist
mistakable
mistakableness
mistakably
mistake
mistakeful
mistaken
mistakenly
mistakenness
mistakeproof
mistaker
mistaking
mistakingly
mistassini
mistaught
mistbow
misteach
misteacher
misted
mistell
mistempered
mistend
mistendency
Mister
mister
misterm
mistetch
mistfall
mistflower
mistful
misthink
misthought
misthread
misthrift
misthrive
misthrow
mistic
mistide
mistify
mistigris
mistily
mistime
mistiness
mistitle
mistle
mistless
mistletoe
mistone
mistonusk
mistook
mistouch
mistradition
mistrain
mistral
mistranscribe
mistranscript
mistranscription
mistranslate
mistranslation
mistreat
mistreatment
mistress
mistressdom
mistresshood
mistressless
mistressly
mistrial
mistrist
mistrust
mistruster
mistrustful
mistrustfully
mistrustfulness
mistrusting
mistrustingly
mistrustless
mistry
mistryst
misturn
mistutor
misty
mistyish
misunderstand
misunderstandable
misunderstander
misunderstanding
misunderstandingly
misunderstood
misunderstoodness
misura
misusage
misuse
misuseful
misusement
misuser
misusurped
misvaluation
misvalue
misventure
misventurous
misvouch
miswed
miswisdom
miswish
misword
misworship
misworshiper
misworshipper
miswrite
misyoke
miszealous
Mitakshara
Mitanni
Mitannian
Mitannish
mitapsis
Mitch
mitchboard
Mitchell
Mitchella
mite
Mitella
miteproof
miter
mitered
miterer
miterflower
miterwort
Mithra
Mithraea
Mithraeum
Mithraic
Mithraicism
Mithraicist
Mithraicize
Mithraism
Mithraist
Mithraistic
Mithraitic
Mithraize
Mithras
Mithratic
Mithriac
mithridate
Mithridatic
mithridatic
mithridatism
mithridatize
miticidal
miticide
mitigable
mitigant
mitigate
mitigatedly
mitigation
mitigative
mitigator
mitigatory
mitis
mitochondria
mitochondrial
mitogenetic
mitome
mitosis
mitosome
mitotic
mitotically
Mitra
mitra
mitrailleuse
mitral
mitrate
mitre
mitrer
Mitridae
mitriform
Mitsukurina
Mitsukurinidae
mitsumata
mitt
mittelhand
Mittelmeer
mitten
mittened
mittimus
mitty
Mitu
Mitua
mity
miurus
mix
mixable
mixableness
mixblood
Mixe
mixed
mixedly
mixedness
mixen
mixer
mixeress
mixhill
mixible
mixite
mixobarbaric
mixochromosome
Mixodectes
Mixodectidae
mixolydian
mixoploid
mixoploidy
Mixosaurus
mixotrophic
Mixtec
Mixtecan
mixtiform
mixtilineal
mixtilion
mixtion
mixture
mixy
Mizar
mizmaze
Mizpah
Mizraim
mizzen
mizzenmast
mizzenmastman
mizzentopman
mizzle
mizzler
mizzly
mizzonite
mizzy
mlechchha
mneme
mnemic
Mnemiopsis
mnemonic
mnemonical
mnemonicalist
mnemonically
mnemonicon
mnemonics
mnemonism
mnemonist
mnemonization
mnemonize
Mnemosyne
mnemotechnic
mnemotechnical
mnemotechnics
mnemotechnist
mnemotechny
mnesic
mnestic
Mnevis
Mniaceae
mniaceous
mnioid
Mniotiltidae
Mnium
Mo
mo
Moabite
Moabitess
Moabitic
Moabitish
moan
moanful
moanfully
moanification
moaning
moaningly
moanless
Moaria
Moarian
moat
Moattalite
mob
mobable
mobbable
mobber
mobbish
mobbishly
mobbishness
mobbism
mobbist
mobby
mobcap
mobed
mobile
Mobilian
mobilianer
mobiliary
mobility
mobilizable
mobilization
mobilize
mobilometer
moble
moblike
mobocracy
mobocrat
mobocratic
mobocratical
mobolatry
mobproof
mobship
mobsman
mobster
Mobula
Mobulidae
moccasin
Mocha
mocha
Mochica
mochras
mock
mockable
mockado
mockbird
mocker
mockernut
mockery
mockful
mockfully
mockground
mockingbird
mockingstock
mocmain
Mocoa
Mocoan
mocomoco
mocuck
Mod
modal
modalism
modalist
modalistic
modality
modalize
modally
mode
model
modeler
modeless
modelessness
modeling
modelist
modeller
modelmaker
modelmaking
modena
Modenese
moderant
moderantism
moderantist
moderate
moderately
moderateness
moderation
moderationist
moderatism
moderatist
moderato
moderator
moderatorship
moderatrix
Modern
modern
moderner
modernicide
modernish
modernism
modernist
modernistic
modernity
modernizable
modernization
modernize
modernizer
modernly
modernness
modest
modestly
modestness
modesty
modiation
modicity
modicum
modifiability
modifiable
modifiableness
modifiably
modificability
modificable
modification
modificationist
modificative
modificator
modificatory
modifier
modify
modillion
modiolar
Modiolus
modiolus
modish
modishly
modishness
modist
modiste
modistry
modius
Modoc
Modred
modulability
modulant
modular
modulate
modulation
modulative
modulator
modulatory
module
Modulidae
modulo
modulus
modumite
Moe
Moed
Moehringia
moellon
moerithere
moeritherian
Moeritheriidae
Moeritherium
mofette
moff
mofussil
mofussilite
mog
mogador
mogadore
mogdad
moggan
moggy
Moghan
mogigraphia
mogigraphic
mogigraphy
mogilalia
mogilalism
mogiphonia
mogitocia
mogo
mogographia
Mogollon
Mograbi
Mogrebbin
moguey
Mogul
mogulship
Moguntine
moha
mohabat
mohair
Mohammad
Mohammedan
Mohammedanism
Mohammedanization
Mohammedanize
Mohammedism
Mohammedist
Mohammedization
Mohammedize
mohar
Mohave
Mohawk
Mohawkian
mohawkite
Mohegan
mohel
Mohican
Mohineyam
mohnseed
moho
Mohock
Mohockism
mohr
Mohrodendron
mohur
Moi
moider
moidore
moieter
moiety
moil
moiler
moiles
moiley
moiling
moilingly
moilsome
moineau
Moingwena
moio
Moira
moire
moirette
moise
Moism
moissanite
moist
moisten
moistener
moistful
moistify
moistish
moistishness
moistless
moistly
moistness
moisture
moistureless
moistureproof
moisty
moit
moity
mojarra
Mojo
mojo
mokaddam
moke
moki
mokihana
moko
moksha
mokum
moky
Mola
mola
molal
Molala
molality
molar
molariform
molarimeter
molarity
molary
Molasse
molasses
molassied
molassy
molave
mold
moldability
moldable
moldableness
Moldavian
moldavite
moldboard
molder
moldery
moldiness
molding
moldmade
moldproof
moldwarp
moldy
Mole
mole
molecast
molecula
molecular
molecularist
molecularity
molecularly
molecule
molehead
moleheap
molehill
molehillish
molehilly
moleism
molelike
molendinar
molendinary
molengraaffite
moleproof
moler
moleskin
molest
molestation
molester
molestful
molestfully
Molge
Molgula
Molidae
molimen
moliminous
molinary
moline
Molinia
Molinism
Molinist
Molinistic
molka
Moll
molland
Mollberg
molle
mollescence
mollescent
molleton
mollichop
mollicrush
mollie
mollienisia
mollient
molliently
mollifiable
mollification
mollifiedly
mollifier
mollify
mollifying
mollifyingly
mollifyingness
molligrant
molligrubs
mollipilose
Mollisiaceae
mollisiose
mollities
mollitious
mollitude
Molluginaceae
Mollugo
Mollusca
molluscan
molluscivorous
molluscoid
Molluscoida
molluscoidal
molluscoidan
Molluscoidea
molluscoidean
molluscous
molluscousness
molluscum
mollusk
Molly
molly
mollycoddle
mollycoddler
mollycoddling
mollycosset
mollycot
mollyhawk
molman
Moloch
Molochize
Molochship
moloid
moloker
molompi
molosse
Molossian
molossic
Molossidae
molossine
molossoid
molossus
Molothrus
molpe
molrooken
molt
molten
moltenly
molter
Molucca
Moluccan
Moluccella
Moluche
moly
molybdate
molybdena
molybdenic
molybdeniferous
molybdenite
molybdenous
molybdenum
molybdic
molybdite
molybdocardialgia
molybdocolic
molybdodyspepsia
molybdomancy
molybdomenite
molybdonosus
molybdoparesis
molybdophyllite
molybdosis
molybdous
molysite
mombin
momble
Mombottu
mome
moment
momenta
momental
momentally
momentaneall
momentaneity
momentaneous
momentaneously
momentaneousness
momentarily
momentariness
momentary
momently
momentous
momentously
momentousness
momentum
momiology
momism
momme
mommet
mommy
momo
Momordica
Momotidae
Momotinae
Momotus
Momus
Mon
mon
mona
Monacan
monacanthid
Monacanthidae
monacanthine
monacanthous
Monacha
monachal
monachate
Monachi
monachism
monachist
monachization
monachize
monactin
monactine
monactinellid
monactinellidan
monad
monadelph
Monadelphia
monadelphian
monadelphous
monadic
monadical
monadically
monadiform
monadigerous
Monadina
monadism
monadistic
monadnock
monadology
monaene
monal
monamniotic
Monanday
monander
Monandria
monandrian
monandric
monandrous
monandry
monanthous
monapsal
monarch
monarchal
monarchally
monarchess
monarchial
monarchian
monarchianism
monarchianist
monarchianistic
monarchic
monarchical
monarchically
monarchism
monarchist
monarchistic
monarchize
monarchizer
monarchlike
monarchomachic
monarchomachist
monarchy
Monarda
Monardella
monarthritis
monarticular
monas
Monasa
Monascidiae
monascidian
monase
monaster
monasterial
monasterially
monastery
monastic
monastical
monastically
monasticism
monasticize
monatomic
monatomicity
monatomism
monaulos
monaural
monaxial
monaxile
monaxon
monaxonial
monaxonic
Monaxonida
monazine
monazite
Monbuttu
monchiquite
Monday
Mondayish
Mondayishness
Mondayland
mone
Monegasque
Monel
monel
monembryary
monembryonic
monembryony
monepic
monepiscopacy
monepiscopal
moner
Monera
moneral
moneran
monergic
monergism
monergist
monergistic
moneric
moneron
Monerozoa
monerozoan
monerozoic
monerula
Moneses
monesia
monetarily
monetary
monetite
monetization
monetize
money
moneyage
moneybag
moneybags
moneyed
moneyer
moneyflower
moneygrub
moneygrubber
moneygrubbing
moneylender
moneylending
moneyless
moneymonger
moneymongering
moneysaving
moneywise
moneywort
mong
mongcorn
monger
mongering
mongery
Monghol
Mongholian
Mongibel
mongler
Mongo
Mongol
Mongolian
Mongolianism
Mongolic
Mongolioid
Mongolish
Mongolism
Mongolization
Mongolize
Mongoloid
mongoose
Mongoyo
mongrel
mongreldom
mongrelish
mongrelism
mongrelity
mongrelization
mongrelize
mongrelly
mongrelness
mongst
monheimite
monial
Monias
Monica
moniker
monilated
monilethrix
Monilia
Moniliaceae
moniliaceous
Moniliales
monilicorn
moniliform
moniliformly
monilioid
moniment
Monimia
Monimiaceae
monimiaceous
monimolite
monimostylic
monism
monist
monistic
monistical
monistically
monition
monitive
monitor
monitorial
monitorially
monitorish
monitorship
monitory
monitress
monitrix
monk
monkbird
monkcraft
monkdom
monkery
monkess
monkey
monkeyboard
monkeyface
monkeyfy
monkeyhood
monkeyish
monkeyishly
monkeyishness
monkeylike
monkeynut
monkeypod
monkeypot
monkeyry
monkeyshine
monkeytail
monkfish
monkflower
monkhood
monkish
monkishly
monkishness
monkism
monklike
monkliness
monkly
monkmonger
monkship
monkshood
Monmouth
monmouthite
monny
Mono
mono
monoacetate
monoacetin
monoacid
monoacidic
monoamide
monoamine
monoamino
monoammonium
monoazo
monobacillary
monobase
monobasic
monobasicity
monoblastic
monoblepsia
monoblepsis
monobloc
monobranchiate
monobromacetone
monobromated
monobromide
monobrominated
monobromination
monobromized
monobromoacetanilide
monobromoacetone
monobutyrin
monocalcium
monocarbide
monocarbonate
monocarbonic
monocarboxylic
monocardian
monocarp
monocarpal
monocarpellary
monocarpian
monocarpic
monocarpous
monocellular
monocentric
monocentrid
Monocentridae
Monocentris
monocentroid
monocephalous
monocercous
monoceros
monocerous
monochasial
monochasium
Monochlamydeae
monochlamydeous
monochlor
monochloracetic
monochloranthracene
monochlorbenzene
monochloride
monochlorinated
monochlorination
monochloro
monochloroacetic
monochlorobenzene
monochloromethane
monochoanitic
monochord
monochordist
monochordize
monochroic
monochromasy
monochromat
monochromate
monochromatic
monochromatically
monochromatism
monochromator
monochrome
monochromic
monochromical
monochromically
monochromist
monochromous
monochromy
monochronic
monochronous
monociliated
monocle
monocled
monocleid
monoclinal
monoclinally
monocline
monoclinian
monoclinic
monoclinism
monoclinometric
monoclinous
Monoclonius
Monocoelia
monocoelian
monocoelic
Monocondyla
monocondylar
monocondylian
monocondylic
monocondylous
monocormic
monocot
monocotyledon
Monocotyledones
monocotyledonous
monocracy
monocrat
monocratic
monocrotic
monocrotism
monocular
monocularity
monocularly
monoculate
monocule
monoculist
monoculous
monocultural
monoculture
monoculus
monocyanogen
monocycle
monocyclic
Monocyclica
monocystic
Monocystidae
Monocystidea
Monocystis
monocyte
monocytic
monocytopoiesis
monodactyl
monodactylate
monodactyle
monodactylism
monodactylous
monodactyly
monodelph
Monodelphia
monodelphian
monodelphic
monodelphous
monodermic
monodic
monodically
monodimetric
monodist
monodize
monodomous
Monodon
monodont
Monodonta
monodontal
monodram
monodrama
monodramatic
monodramatist
monodromic
monodromy
monody
monodynamic
monodynamism
Monoecia
monoecian
monoecious
monoeciously
monoeciousness
monoecism
monoeidic
monoestrous
monoethanolamine
monoethylamine
monofilament
monofilm
monoflagellate
monoformin
monogamian
monogamic
monogamist
monogamistic
monogamous
monogamously
monogamousness
monogamy
monoganglionic
monogastric
monogene
Monogenea
monogeneity
monogeneous
monogenesis
monogenesist
monogenesy
monogenetic
Monogenetica
monogenic
monogenism
monogenist
monogenistic
monogenous
monogeny
monoglot
monoglycerid
monoglyceride
monogoneutic
monogonoporic
monogonoporous
monogony
monogram
monogrammatic
monogrammatical
monogrammed
monogrammic
monograph
monographer
monographic
monographical
monographically
monographist
monography
monograptid
Monograptidae
Monograptus
monogynic
monogynious
monogynist
monogynoecial
monogynous
monogyny
monohybrid
monohydrate
monohydrated
monohydric
monohydrogen
monohydroxy
monoicous
monoid
monoketone
monolater
monolatrist
monolatrous
monolatry
monolayer
monoline
monolingual
monolinguist
monoliteral
monolith
monolithal
monolithic
monolobular
monolocular
monologian
monologic
monological
monologist
monologize
monologue
monologuist
monology
monomachist
monomachy
monomania
monomaniac
monomaniacal
monomastigate
monomeniscous
monomer
monomeric
monomerous
monometallic
monometallism
monometallist
monometer
monomethyl
monomethylated
monomethylic
monometric
monometrical
monomial
monomict
monomineral
monomineralic
monomolecular
monomolybdate
Monomorium
monomorphic
monomorphism
monomorphous
Monomya
Monomyaria
monomyarian
mononaphthalene
mononch
Mononchus
mononeural
Monongahela
mononitrate
mononitrated
mononitration
mononitride
mononitrobenzene
mononomial
mononomian
monont
mononuclear
mononucleated
mononucleosis
mononychous
mononym
mononymic
mononymization
mononymize
mononymy
monoousian
monoousious
monoparental
monoparesis
monoparesthesia
monopathic
monopathy
monopectinate
monopersonal
monopersulfuric
monopersulphuric
Monopetalae
monopetalous
monophagism
monophagous
monophagy
monophase
monophasia
monophasic
monophobia
monophone
monophonic
monophonous
monophony
monophotal
monophote
monophthalmic
monophthalmus
monophthong
monophthongal
monophthongization
monophthongize
monophyletic
monophyleticism
monophylite
monophyllous
monophyodont
monophyodontism
Monophysite
Monophysitic
Monophysitical
Monophysitism
monopitch
monoplacula
monoplacular
monoplaculate
monoplane
monoplanist
monoplasmatic
monoplast
monoplastic
monoplegia
monoplegic
Monopneumoa
monopneumonian
monopneumonous
monopode
monopodial
monopodially
monopodic
monopodium
monopodous
monopody
monopolar
monopolaric
monopolarity
monopole
monopolism
monopolist
monopolistic
monopolistically
monopolitical
monopolizable
monopolization
monopolize
monopolizer
monopolous
monopoly
monopolylogist
monopolylogue
monopotassium
monoprionid
monoprionidian
monopsonistic
monopsony
monopsychism
monopteral
Monopteridae
monopteroid
monopteron
monopteros
monopterous
monoptic
monoptical
monoptote
monoptotic
Monopylaea
Monopylaria
monopylean
monopyrenous
monorail
monorailroad
monorailway
monorchid
monorchidism
monorchis
monorchism
monorganic
Monorhina
monorhinal
monorhine
monorhyme
monorhymed
monorhythmic
monosaccharide
monosaccharose
monoschemic
monoscope
monose
monosemic
monosepalous
monoservice
monosilane
monosilicate
monosilicic
monosiphonic
monosiphonous
monosodium
monosomatic
monosomatous
monosome
monosomic
monosperm
monospermal
monospermic
monospermous
monospermy
monospherical
monospondylic
monosporangium
monospore
monospored
monosporiferous
monosporous
monostele
monostelic
monostelous
monostely
monostich
monostichous
Monostomata
Monostomatidae
monostomatous
monostome
Monostomidae
monostomous
Monostomum
monostromatic
monostrophe
monostrophic
monostrophics
monostylous
monosubstituted
monosubstitution
monosulfone
monosulfonic
monosulphide
monosulphone
monosulphonic
monosyllabic
monosyllabical
monosyllabically
monosyllabism
monosyllabize
monosyllable
monosymmetric
monosymmetrical
monosymmetrically
monosymmetry
monosynthetic
monotelephone
monotelephonic
monotellurite
Monothalama
monothalamian
monothalamous
monothecal
monotheism
monotheist
monotheistic
monotheistical
monotheistically
Monothelete
Monotheletian
Monotheletic
Monotheletism
monothelious
Monothelism
Monothelitic
Monothelitism
monothetic
monotic
monotint
Monotocardia
monotocardiac
monotocardian
monotocous
monotomous
monotone
monotonic
monotonical
monotonically
monotonist
monotonize
monotonous
monotonously
monotonousness
monotony
monotremal
Monotremata
monotremate
monotrematous
monotreme
monotremous
monotrichous
monotriglyph
monotriglyphic
Monotrocha
monotrochal
monotrochian
monotrochous
Monotropa
Monotropaceae
monotropaceous
monotrophic
monotropic
Monotropsis
monotropy
monotypal
monotype
monotypic
monotypical
monotypous
monoureide
monovalence
monovalency
monovalent
monovariant
monoverticillate
monovoltine
monovular
monoxenous
monoxide
monoxime
monoxyle
monoxylic
monoxylon
monoxylous
Monozoa
monozoan
monozoic
monozygotic
Monroeism
Monroeist
monrolite
monseigneur
monsieur
monsieurship
monsignor
monsignorial
Monsoni
monsoon
monsoonal
monsoonish
monsoonishly
monster
Monstera
monsterhood
monsterlike
monstership
monstrance
monstrate
monstration
monstrator
monstricide
monstriferous
monstrification
monstrify
monstrosity
monstrous
monstrously
monstrousness
Mont
montage
Montagnac
Montagnais
Montana
montana
Montanan
montane
montanic
montanin
Montanism
Montanist
Montanistic
Montanistical
montanite
Montanize
montant
Montargis
Montauk
montbretia
monte
montebrasite
monteith
montem
Montenegrin
Montepulciano
Monterey
Montes
Montesco
Montesinos
Montessorian
Montessorianism
Montezuma
montgolfier
month
monthly
monthon
Montia
monticellite
monticle
monticoline
monticulate
monticule
Monticulipora
Monticuliporidae
monticuliporidean
monticuliporoid
monticulose
monticulous
monticulus
montiform
montigeneous
montilla
montjoy
montmartrite
Montmorency
montmorilonite
monton
Montrachet
montroydite
Montu
monture
Monty
Monumbo
monument
monumental
monumentalism
monumentality
monumentalization
monumentalize
monumentally
monumentary
monumentless
monumentlike
monzodiorite
monzogabbro
monzonite
monzonitic
moo
Mooachaht
mooch
moocha
moocher
moochulka
mood
mooder
moodily
moodiness
moodish
moodishly
moodishness
moodle
moody
mooing
mool
moolet
moolings
mools
moolum
moon
moonack
moonbeam
moonbill
moonblink
mooncalf
mooncreeper
moondown
moondrop
mooned
mooner
moonery
mooneye
moonface
moonfaced
moonfall
moonfish
moonflower
moonglade
moonglow
moonhead
moonily
mooniness
mooning
moonish
moonite
moonja
moonjah
moonless
moonlet
moonlight
moonlighted
moonlighter
moonlighting
moonlighty
moonlike
moonlikeness
moonlit
moonlitten
moonman
moonpath
moonpenny
moonproof
moonraker
moonraking
moonrise
moonsail
moonscape
moonseed
moonset
moonshade
moonshine
moonshiner
moonshining
moonshiny
moonsick
moonsickness
moonstone
moontide
moonwalker
moonwalking
moonward
moonwards
moonway
moonwort
moony
moop
Moor
moor
moorage
moorball
moorband
moorberry
moorbird
moorburn
moorburner
moorburning
Moore
moorflower
moorfowl
mooring
Moorish
moorish
moorishly
moorishness
moorland
moorlander
Moorman
moorman
moorn
moorpan
moors
Moorship
moorsman
moorstone
moortetter
moorup
moorwort
moory
moosa
moose
mooseberry
moosebird
moosebush
moosecall
mooseflower
moosehood
moosemise
moosetongue
moosewob
moosewood
moosey
moost
moot
mootable
mooter
mooth
mooting
mootman
mootstead
mootworthy
mop
Mopan
mopane
mopboard
mope
moper
moph
mophead
mopheaded
moping
mopingly
mopish
mopishly
mopishness
mopla
mopper
moppet
moppy
mopstick
mopsy
mopus
Moquelumnan
moquette
Moqui
mor
mora
Moraceae
moraceous
Moraea
morainal
moraine
morainic
moral
morale
moralism
moralist
moralistic
moralistically
morality
moralization
moralize
moralizer
moralizingly
moralless
morally
moralness
morals
Moran
morass
morassic
morassweed
morassy
morat
morate
moration
moratoria
moratorium
moratory
Moravian
Moravianism
Moravianized
Moravid
moravite
moray
morbid
morbidity
morbidize
morbidly
morbidness
morbiferal
morbiferous
morbific
morbifical
morbifically
morbify
morbility
morbillary
morbilli
morbilliform
morbillous
morcellate
morcellated
morcellation
Morchella
Morcote
mordacious
mordaciously
mordacity
mordancy
mordant
mordantly
Mordella
mordellid
Mordellidae
mordelloid
mordenite
mordent
mordicate
mordication
mordicative
mordore
Mordv
Mordva
Mordvin
Mordvinian
more
moreen
morefold
moreish
morel
morella
morello
morencite
moreness
morenita
morenosite
Moreote
moreover
morepork
mores
Moresque
morfrey
morg
morga
Morgan
morgan
Morgana
morganatic
morganatical
morganatically
morganic
morganite
morganize
morgay
morgen
morgengift
morgenstern
morglay
morgue
moribund
moribundity
moribundly
moric
moriche
moriform
morigerate
morigeration
morigerous
morigerously
morigerousness
morillon
morin
Morinaceae
Morinda
morindin
morindone
morinel
Moringa
Moringaceae
moringaceous
moringad
Moringua
moringuid
Moringuidae
moringuoid
morion
Moriori
Moriscan
Morisco
Morisonian
Morisonianism
morkin
morlop
mormaor
mormaordom
mormaorship
mormo
Mormon
mormon
Mormondom
Mormoness
Mormonism
Mormonist
Mormonite
Mormonweed
Mormoops
mormyr
mormyre
mormyrian
mormyrid
Mormyridae
mormyroid
Mormyrus
morn
morne
morned
morning
morningless
morningly
mornings
morningtide
morningward
mornless
mornlike
morntime
mornward
Moro
moro
moroc
Moroccan
Morocco
morocco
morocota
morological
morologically
morologist
morology
moromancy
moron
moroncy
morong
moronic
Moronidae
moronism
moronity
moronry
Moropus
morosaurian
morosauroid
Morosaurus
morose
morosely
moroseness
morosis
morosity
moroxite
morph
morphallaxis
morphea
Morphean
morpheme
morphemic
morphemics
morphetic
Morpheus
morphew
morphia
morphiate
morphic
morphically
morphinate
morphine
morphinic
morphinism
morphinist
morphinization
morphinize
morphinomania
morphinomaniac
morphiomania
morphiomaniac
Morpho
morphogenesis
morphogenetic
morphogenic
morphogeny
morphographer
morphographic
morphographical
morphographist
morphography
morpholine
morphologic
morphological
morphologically
morphologist
morphology
morphometrical
morphometry
morphon
morphonomic
morphonomy
morphophonemic
morphophonemically
morphophonemics
morphophyly
morphoplasm
morphoplasmic
morphosis
morphotic
morphotropic
morphotropism
morphotropy
morphous
Morrenian
Morrhua
morrhuate
morrhuine
morricer
Morris
morris
Morrisean
morrow
morrowing
morrowless
morrowmass
morrowspeech
morrowtide
morsal
Morse
morse
morsel
morselization
morselize
morsing
morsure
mort
mortacious
mortal
mortalism
mortalist
mortality
mortalize
mortally
mortalness
mortalwise
mortar
mortarboard
mortarize
mortarless
mortarlike
mortarware
mortary
mortbell
mortcloth
mortersheen
mortgage
mortgageable
mortgagee
mortgagor
morth
morthwyrtha
mortician
mortier
mortiferous
mortiferously
mortiferousness
mortific
mortification
mortified
mortifiedly
mortifiedness
mortifier
mortify
mortifying
mortifyingly
Mortimer
mortise
mortiser
mortling
mortmain
mortmainer
Morton
mortuarian
mortuary
mortuous
morula
morular
morulation
morule
moruloid
Morus
morvin
morwong
Mosaic
mosaic
Mosaical
mosaical
mosaically
mosaicism
mosaicist
Mosaicity
Mosaism
Mosaist
mosaist
mosandrite
mosasaur
Mosasauri
Mosasauria
mosasaurian
mosasaurid
Mosasauridae
mosasauroid
Mosasaurus
Mosatenan
moschate
moschatel
moschatelline
Moschi
Moschidae
moschiferous
Moschinae
moschine
Moschus
Moscow
Mose
Moselle
Moses
mosesite
Mosetena
mosette
mosey
Mosgu
moskeneer
mosker
Moslem
Moslemah
Moslemic
Moslemin
Moslemism
Moslemite
Moslemize
moslings
mosque
mosquelet
mosquish
mosquital
Mosquito
mosquito
mosquitobill
mosquitocidal
mosquitocide
mosquitoey
mosquitoish
mosquitoproof
moss
mossback
mossberry
mossbunker
mossed
mosser
mossery
mossful
mosshead
Mossi
mossiness
mossless
mosslike
mosstrooper
mosstroopery
mosstrooping
mosswort
mossy
mossyback
most
moste
Mosting
mostlike
mostlings
mostly
mostness
Mosul
Mosur
mot
Motacilla
motacillid
Motacillidae
Motacillinae
motacilline
motatorious
motatory
Motazilite
mote
moted
motel
moteless
moter
motet
motettist
motey
moth
mothed
mother
motherdom
mothered
motherer
mothergate
motherhood
motheriness
mothering
motherkin
motherland
motherless
motherlessness
motherlike
motherliness
motherling
motherly
mothership
mothersome
motherward
motherwise
motherwort
mothery
mothless
mothlike
mothproof
mothworm
mothy
motif
motific
motile
motility
motion
motionable
motional
motionless
motionlessly
motionlessness
motitation
motivate
motivation
motivational
motive
motiveless
motivelessly
motivelessness
motiveness
motivity
motley
motleyness
motmot
motofacient
motograph
motographic
motomagnetic
motoneuron
motophone
motor
motorable
motorboat
motorboatman
motorbus
motorcab
motorcade
motorcar
motorcycle
motorcyclist
motordom
motordrome
motored
motorial
motoric
motoring
motorism
motorist
motorium
motorization
motorize
motorless
motorman
motorneer
motorphobe
motorphobia
motorphobiac
motorway
motory
Motozintlec
Motozintleca
motricity
Mott
mott
motte
mottle
mottled
mottledness
mottlement
mottler
mottling
motto
mottoed
mottoless
mottolike
mottramite
motyka
mou
moucharaby
mouchardism
mouche
mouchrabieh
moud
moudie
moudieman
moudy
mouflon
Mougeotia
Mougeotiaceae
mouillation
mouille
mouillure
moujik
moul
mould
moulded
moule
moulin
moulinage
moulinet
moulleen
moulrush
mouls
moulter
mouly
mound
moundiness
moundlet
moundwork
moundy
mount
mountable
mountably
mountain
mountained
mountaineer
mountainet
mountainette
mountainless
mountainlike
mountainous
mountainously
mountainousness
mountainside
mountaintop
mountainward
mountainwards
mountainy
mountant
mountebank
mountebankery
mountebankish
mountebankism
mountebankly
mounted
mounter
Mountie
mounting
mountingly
mountlet
mounture
moup
mourn
mourner
mourneress
mournful
mournfully
mournfulness
mourning
mourningly
mournival
mournsome
mouse
mousebane
mousebird
mousefish
mousehawk
mousehole
mousehound
Mouseion
mousekin
mouselet
mouselike
mouseproof
mouser
mousery
mouseship
mousetail
mousetrap
mouseweb
mousey
mousily
mousiness
mousing
mousingly
mousle
mousmee
Mousoni
mousquetaire
mousse
Mousterian
moustoc
mousy
mout
moutan
mouth
mouthable
mouthbreeder
mouthed
mouther
mouthful
mouthily
mouthiness
mouthing
mouthingly
mouthishly
mouthless
mouthlike
mouthpiece
mouthroot
mouthwash
mouthwise
mouthy
mouton
moutonnee
mouzah
mouzouna
movability
movable
movableness
movably
movant
move
moveability
moveableness
moveably
moveless
movelessly
movelessness
movement
mover
movie
moviedom
movieize
movieland
moving
movingly
movingness
mow
mowable
mowana
mowburn
mowburnt
mowch
mowcht
mower
mowha
mowie
mowing
mowland
mown
mowra
mowrah
mowse
mowstead
mowt
mowth
moxa
moxieberry
Moxo
moy
moyen
moyenless
moyenne
moyite
moyle
moyo
Mozambican
mozambique
Mozarab
Mozarabian
Mozarabic
Mozartean
mozemize
mozing
mozzetta
Mpangwe
Mpondo
mpret
Mr
Mrs
Mru
mu
muang
mubarat
mucago
mucaro
mucedin
mucedinaceous
mucedine
mucedinous
much
muchfold
muchly
muchness
mucic
mucid
mucidness
muciferous
mucific
muciform
mucigen
mucigenous
mucilage
mucilaginous
mucilaginously
mucilaginousness
mucin
mucinogen
mucinoid
mucinous
muciparous
mucivore
mucivorous
muck
muckender
Mucker
mucker
muckerish
muckerism
mucket
muckiness
muckite
muckle
muckluck
muckman
muckment
muckmidden
muckna
muckrake
muckraker
mucksweat
mucksy
muckthrift
muckweed
muckworm
mucky
mucluc
mucocele
mucocellulose
mucocellulosic
mucocutaneous
mucodermal
mucofibrous
mucoflocculent
mucoid
mucomembranous
muconic
mucoprotein
mucopurulent
mucopus
mucor
Mucoraceae
mucoraceous
Mucorales
mucorine
mucorioid
mucormycosis
mucorrhea
mucosa
mucosal
mucosanguineous
mucose
mucoserous
mucosity
mucosocalcareous
mucosogranular
mucosopurulent
mucososaccharine
mucous
mucousness
mucro
mucronate
mucronately
mucronation
mucrones
mucroniferous
mucroniform
mucronulate
mucronulatous
muculent
Mucuna
mucus
mucusin
mud
mudar
mudbank
mudcap
mudd
mudde
mudden
muddify
muddily
muddiness
mudding
muddish
muddle
muddlebrained
muddledom
muddlehead
muddleheaded
muddleheadedness
muddlement
muddleproof
muddler
muddlesome
muddlingly
muddy
muddybrained
muddybreast
muddyheaded
mudee
Mudejar
mudfish
mudflow
mudguard
mudhead
mudhole
mudhopper
mudir
mudiria
mudland
mudlark
mudlarker
mudless
mudproof
mudra
mudsill
mudskipper
mudslinger
mudslinging
mudspate
mudstain
mudstone
mudsucker
mudtrack
mudweed
mudwort
Muehlenbeckia
muermo
muezzin
muff
muffed
muffet
muffetee
muffin
muffineer
muffish
muffishness
muffle
muffled
muffleman
muffler
mufflin
muffy
mufti
mufty
mug
muga
mugearite
mugful
mugg
mugger
mugget
muggily
mugginess
muggins
muggish
muggles
Muggletonian
Muggletonianism
muggy
mughouse
mugience
mugiency
mugient
Mugil
Mugilidae
mugiliform
mugiloid
mugweed
mugwort
mugwump
mugwumpery
mugwumpian
mugwumpism
muhammadi
Muharram
Muhlenbergia
muid
Muilla
muir
muirburn
muircock
muirfowl
muishond
muist
mujtahid
Mukden
mukluk
Mukri
muktar
muktatma
mukti
mulaprakriti
mulatta
mulatto
mulattoism
mulattress
mulberry
mulch
mulcher
Mulciber
Mulcibirian
mulct
mulctable
mulctary
mulctation
mulctative
mulctatory
mulctuary
mulder
mule
muleback
mulefoot
mulefooted
muleman
muleta
muleteer
muletress
muletta
mulewort
muley
mulga
muliebral
muliebria
muliebrile
muliebrity
muliebrous
mulier
mulierine
mulierose
mulierosity
mulish
mulishly
mulishness
mulism
mulita
mulk
mull
mulla
mullah
mullar
mullein
mullenize
muller
Mullerian
mullet
mulletry
mullets
mulley
mullid
Mullidae
mulligan
mulligatawny
mulligrubs
mullion
mullite
mullock
mullocker
mullocky
mulloid
mulloway
mulmul
mulse
mulsify
mult
multangular
multangularly
multangularness
multangulous
multangulum
Multani
multanimous
multarticulate
multeity
multiangular
multiareolate
multiarticular
multiarticulate
multiarticulated
multiaxial
multiblade
multibladed
multibranched
multibranchiate
multibreak
multicamerate
multicapitate
multicapsular
multicarinate
multicarinated
multicellular
multicentral
multicentric
multicharge
multichord
multichrome
multiciliate
multiciliated
multicipital
multicircuit
multicoccous
multicoil
multicolor
multicolored
multicolorous
multicomponent
multiconductor
multiconstant
multicore
multicorneal
multicostate
multicourse
multicrystalline
multicuspid
multicuspidate
multicycle
multicylinder
multicylindered
multidentate
multidenticulate
multidenticulated
multidigitate
multidimensional
multidirectional
multidisperse
multiengine
multiengined
multiexhaust
multifaced
multifaceted
multifactorial
multifamilial
multifarious
multifariously
multifariousness
multiferous
multifetation
multifibered
multifid
multifidly
multifidous
multifidus
multifilament
multifistular
multiflagellate
multiflagellated
multiflash
multiflorous
multiflow
multiflue
multifocal
multifoil
multifoiled
multifold
multifoliate
multifoliolate
multiform
multiformed
multiformity
multifurcate
multiganglionic
multigap
multigranulate
multigranulated
Multigraph
multigraph
multigrapher
multiguttulate
multigyrate
multihead
multihearth
multihued
multijet
multijugate
multijugous
multilaciniate
multilamellar
multilamellate
multilamellous
multilaminar
multilaminate
multilaminated
multilateral
multilaterally
multilighted
multilineal
multilinear
multilingual
multilinguist
multilirate
multiliteral
multilobar
multilobate
multilobe
multilobed
multilobular
multilobulate
multilobulated
multilocation
multilocular
multiloculate
multiloculated
multiloquence
multiloquent
multiloquious
multiloquous
multiloquy
multimacular
multimammate
multimarble
multimascular
multimedial
multimetalic
multimetallism
multimetallist
multimillion
multimillionaire
multimodal
multimodality
multimolecular
multimotor
multimotored
multinational
multinervate
multinervose
multinodal
multinodate
multinodous
multinodular
multinomial
multinominal
multinominous
multinuclear
multinucleate
multinucleated
multinucleolar
multinucleolate
multinucleolated
multiovular
multiovulate
multipara
multiparient
multiparity
multiparous
multipartisan
multipartite
multiped
multiperforate
multiperforated
multipersonal
multiphase
multiphaser
multiphotography
multipinnate
multiplane
multiple
multiplepoinding
multiplet
multiplex
multipliable
multipliableness
multiplicability
multiplicable
multiplicand
multiplicate
multiplication
multiplicational
multiplicative
multiplicatively
multiplicator
multiplicity
multiplier
multiply
multiplying
multipointed
multipolar
multipole
multiported
multipotent
multipresence
multipresent
multiradial
multiradiate
multiradiated
multiradicate
multiradicular
multiramified
multiramose
multiramous
multirate
multireflex
multirooted
multirotation
multirotatory
multisaccate
multisacculate
multisacculated
multiscience
multiseated
multisect
multisector
multisegmental
multisegmentate
multisegmented
multisensual
multiseptate
multiserial
multiserially
multiseriate
multishot
multisiliquous
multisonous
multispeed
multispermous
multispicular
multispiculate
multispindle
multispinous
multispiral
multispired
multistage
multistaminate
multistoried
multistory
multistratified
multistratous
multistriate
multisulcate
multisulcated
multisyllabic
multisyllability
multisyllable
multitarian
multitentaculate
multitheism
multithreaded
multititular
multitoed
multitoned
multitube
Multituberculata
multituberculate
multituberculated
multituberculism
multituberculy
multitubular
multitude
multitudinal
multitudinary
multitudinism
multitudinist
multitudinistic
multitudinosity
multitudinous
multitudinously
multitudinousness
multiturn
multivagant
multivalence
multivalency
multivalent
multivalve
multivalved
multivalvular
multivane
multivariant
multivarious
multiversant
multiverse
multivibrator
multivincular
multivious
multivocal
multivocalness
multivoiced
multivolent
multivoltine
multivolumed
multivorous
multocular
multum
multungulate
multure
multurer
mum
mumble
mumblebee
mumblement
mumbler
mumbling
mumblingly
mummer
mummery
mummichog
mummick
mummied
mummification
mummiform
mummify
mumming
mummy
mummydom
mummyhood
mummylike
mumness
mump
mumper
mumphead
mumpish
mumpishly
mumpishness
mumps
mumpsimus
mumruffin
mun
Munandi
Muncerian
munch
Munchausenism
Munchausenize
muncheel
muncher
munchet
mund
Munda
mundane
mundanely
mundaneness
mundanism
mundanity
Mundari
mundatory
mundic
mundificant
mundification
mundifier
mundify
mundil
mundivagant
mundle
mung
munga
munge
mungey
mungo
mungofa
munguba
mungy
Munia
Munich
Munichism
municipal
municipalism
municipalist
municipality
municipalization
municipalize
municipalizer
municipally
municipium
munific
munificence
munificency
munificent
munificently
munificentness
muniment
munition
munitionary
munitioneer
munitioner
munitions
munity
munj
munjeet
munjistin
munnion
Munnopsidae
Munnopsis
Munsee
munshi
munt
Muntiacus
muntin
Muntingia
muntjac
Munychia
Munychian
Munychion
Muong
Muphrid
Mura
mura
Muradiyah
Muraena
Muraenidae
muraenoid
murage
mural
muraled
muralist
murally
Muran
Muranese
murasakite
Murat
Muratorian
murchy
murder
murderer
murderess
murdering
murderingly
murderish
murderment
murderous
murderously
murderousness
murdrum
mure
murenger
murex
murexan
murexide
murga
murgavi
murgeon
muriate
muriated
muriatic
muricate
muricid
Muricidae
muriciform
muricine
muricoid
muriculate
murid
Muridae
muridism
Muriel
muriform
muriformly
Murillo
Murinae
murine
murinus
muriti
murium
murk
murkily
murkiness
murkish
murkly
murkness
murksome
murky
murlin
murly
Murmi
murmur
murmuration
murmurator
murmurer
murmuring
murmuringly
murmurish
murmurless
murmurlessly
murmurous
murmurously
muromontite
Murph
murphy
murra
murrain
Murray
Murraya
murre
murrelet
murrey
murrhine
murrina
murrnong
murshid
Murthy
murumuru
Murut
muruxi
murva
murza
Murzim
Mus
Musa
Musaceae
musaceous
Musaeus
musal
Musales
Musalmani
musang
musar
Musca
muscade
muscadel
muscadine
Muscadinia
muscardine
Muscardinidae
Muscardinus
Muscari
muscariform
muscarine
muscat
muscatel
muscatorium
Musci
Muscicapa
Muscicapidae
muscicapine
muscicide
muscicole
muscicoline
muscicolous
muscid
Muscidae
musciform
Muscinae
muscle
muscled
muscleless
musclelike
muscling
muscly
Muscogee
muscoid
Muscoidea
muscologic
muscological
muscologist
muscology
muscone
muscose
muscoseness
muscosity
muscot
muscovadite
muscovado
Muscovi
Muscovite
muscovite
Muscovitic
muscovitization
muscovitize
muscovy
muscular
muscularity
muscularize
muscularly
musculation
musculature
muscule
musculin
musculoarterial
musculocellular
musculocutaneous
musculodermic
musculoelastic
musculofibrous
musculointestinal
musculoligamentous
musculomembranous
musculopallial
musculophrenic
musculospinal
musculospiral
musculotegumentary
musculotendinous
Muse
muse
mused
museful
musefully
museist
museless
muselike
museographist
museography
museologist
museology
muser
musery
musette
museum
museumize
Musgu
mush
musha
mushaa
Mushabbihite
mushed
musher
mushhead
mushheaded
mushheadedness
mushily
mushiness
mushla
mushmelon
mushrebiyeh
mushroom
mushroomer
mushroomic
mushroomlike
mushroomy
mushru
mushy
music
musical
musicale
musicality
musicalization
musicalize
musically
musicalness
musicate
musician
musiciana
musicianer
musicianly
musicianship
musicker
musicless
musiclike
musicmonger
musico
musicoartistic
musicodramatic
musicofanatic
musicographer
musicography
musicological
musicologist
musicologue
musicology
musicomania
musicomechanical
musicophilosophical
musicophobia
musicophysical
musicopoetic
musicotherapy
musicproof
musie
musily
musimon
musing
musingly
musk
muskat
muskeg
muskeggy
muskellunge
musket
musketade
musketeer
musketlike
musketoon
musketproof
musketry
muskflower
Muskhogean
muskie
muskiness
muskish
musklike
muskmelon
Muskogee
muskrat
muskroot
Muskwaki
muskwood
musky
muslin
muslined
muslinet
musnud
Musophaga
Musophagi
Musophagidae
musophagine
musquash
musquashroot
musquashweed
musquaspen
musquaw
musrol
muss
mussable
mussably
Mussaenda
mussal
mussalchee
mussel
musseled
musseler
mussily
mussiness
mussitate
mussitation
mussuk
Mussulman
Mussulmanic
Mussulmanish
Mussulmanism
Mussulwoman
mussurana
mussy
must
mustache
mustached
mustachial
mustachio
mustachioed
mustafina
Mustahfiz
mustang
mustanger
mustard
mustarder
mustee
Mustela
mustelid
Mustelidae
musteline
mustelinous
musteloid
Mustelus
muster
musterable
musterdevillers
musterer
mustermaster
mustify
mustily
mustiness
mustnt
musty
muta
Mutabilia
mutability
mutable
mutableness
mutably
mutafacient
mutage
mutagenic
mutant
mutarotate
mutarotation
mutase
mutate
mutation
mutational
mutationally
mutationism
mutationist
mutative
mutatory
mutawalli
Mutazala
mutch
mute
mutedly
mutely
muteness
Muter
mutesarif
mutescence
mutessarifat
muth
muthmannite
muthmassel
mutic
muticous
mutilate
mutilation
mutilative
mutilator
mutilatory
Mutilla
mutillid
Mutillidae
mutilous
mutineer
mutinous
mutinously
mutinousness
mutiny
Mutisia
Mutisiaceae
mutism
mutist
mutistic
mutive
mutivity
mutoscope
mutoscopic
mutsje
mutsuddy
mutt
mutter
mutterer
muttering
mutteringly
mutton
muttonbird
muttonchop
muttonfish
muttonhead
muttonheaded
muttonhood
muttonmonger
muttonwood
muttony
mutual
mutualism
mutualist
mutualistic
mutuality
mutualization
mutualize
mutually
mutualness
mutuary
mutuatitious
mutulary
mutule
mutuum
mux
Muysca
muyusa
muzhik
Muzo
muzz
muzzily
muzziness
muzzle
muzzler
muzzlewood
muzzy
Mwa
my
Mya
Myacea
myal
myalgia
myalgic
myalism
myall
Myaria
myarian
myasthenia
myasthenic
myatonia
myatonic
myatony
myatrophy
mycele
mycelia
mycelial
mycelian
mycelioid
mycelium
myceloid
Mycenaean
Mycetes
mycetism
mycetocyte
mycetogenesis
mycetogenetic
mycetogenic
mycetogenous
mycetoid
mycetological
mycetology
mycetoma
mycetomatous
Mycetophagidae
mycetophagous
mycetophilid
Mycetophilidae
mycetous
Mycetozoa
mycetozoan
mycetozoon
Mycobacteria
Mycobacteriaceae
Mycobacterium
mycocecidium
mycocyte
mycoderm
mycoderma
mycodermatoid
mycodermatous
mycodermic
mycodermitis
mycodesmoid
mycodomatium
mycogastritis
Mycogone
mycohaemia
mycohemia
mycoid
mycologic
mycological
mycologically
mycologist
mycologize
mycology
mycomycete
Mycomycetes
mycomycetous
mycomyringitis
mycophagist
mycophagous
mycophagy
mycophyte
Mycoplana
mycoplasm
mycoplasmic
mycoprotein
mycorhiza
mycorhizal
mycorrhizal
mycose
mycosin
mycosis
mycosozin
Mycosphaerella
Mycosphaerellaceae
mycosterol
mycosymbiosis
mycotic
mycotrophic
Mycteria
mycteric
mycterism
Myctodera
myctophid
Myctophidae
Myctophum
Mydaidae
mydaleine
mydatoxine
Mydaus
mydine
mydriasine
mydriasis
mydriatic
mydriatine
myectomize
myectomy
myectopia
myectopy
myelalgia
myelapoplexy
myelasthenia
myelatrophy
myelauxe
myelemia
myelencephalic
myelencephalon
myelencephalous
myelic
myelin
myelinate
myelinated
myelination
myelinic
myelinization
myelinogenesis
myelinogenetic
myelinogeny
myelitic
myelitis
myeloblast
myeloblastic
myelobrachium
myelocele
myelocerebellar
myelocoele
myelocyst
myelocystic
myelocystocele
myelocyte
myelocythaemia
myelocythemia
myelocytic
myelocytosis
myelodiastasis
myeloencephalitis
myeloganglitis
myelogenesis
myelogenetic
myelogenous
myelogonium
myeloic
myeloid
myelolymphangioma
myelolymphocyte
myeloma
myelomalacia
myelomatoid
myelomatosis
myelomenia
myelomeningitis
myelomeningocele
myelomere
myelon
myelonal
myeloneuritis
myelonic
myeloparalysis
myelopathic
myelopathy
myelopetal
myelophthisis
myeloplast
myeloplastic
myeloplax
myeloplegia
myelopoiesis
myelopoietic
myelorrhagia
myelorrhaphy
myelosarcoma
myelosclerosis
myelospasm
myelospongium
myelosyphilis
myelosyphilosis
myelosyringosis
myelotherapy
Myelozoa
myelozoan
myentasis
myenteric
myenteron
myesthesia
mygale
mygalid
mygaloid
Myiarchus
myiasis
myiferous
myiodesopsia
myiosis
myitis
mykiss
myliobatid
Myliobatidae
myliobatine
myliobatoid
Mylodon
mylodont
Mylodontidae
mylohyoid
mylohyoidean
mylonite
mylonitic
Mymar
mymarid
Mymaridae
myna
Mynheer
mynpacht
mynpachtbrief
myoalbumin
myoalbumose
myoatrophy
myoblast
myoblastic
myocardiac
myocardial
myocardiogram
myocardiograph
myocarditic
myocarditis
myocardium
myocele
myocellulitis
myoclonic
myoclonus
myocoele
myocoelom
myocolpitis
myocomma
myocyte
myodegeneration
Myodes
myodiastasis
myodynamia
myodynamic
myodynamics
myodynamiometer
myodynamometer
myoedema
myoelectric
myoendocarditis
myoepicardial
myoepithelial
myofibril
myofibroma
myogen
myogenesis
myogenetic
myogenic
myogenous
myoglobin
myoglobulin
myogram
myograph
myographer
myographic
myographical
myographist
myography
myohematin
myoid
myoidema
myokinesis
myolemma
myolipoma
myoliposis
myologic
myological
myologist
myology
myolysis
myoma
myomalacia
myomancy
myomantic
myomatous
myomectomy
myomelanosis
myomere
myometritis
myometrium
myomohysterectomy
myomorph
Myomorpha
myomorphic
myomotomy
myoneme
myoneural
myoneuralgia
myoneurasthenia
myoneure
myoneuroma
myoneurosis
myonosus
myopachynsis
myoparalysis
myoparesis
myopathia
myopathic
myopathy
myope
myoperitonitis
myophan
myophore
myophorous
myophysical
myophysics
myopia
myopic
myopical
myopically
myoplasm
myoplastic
myoplasty
myopolar
Myoporaceae
myoporaceous
myoporad
Myoporum
myoproteid
myoprotein
myoproteose
myops
myopy
myorrhaphy
myorrhexis
myosalpingitis
myosarcoma
myosarcomatous
myosclerosis
myoscope
myoseptum
myosin
myosinogen
myosinose
myosis
myositic
myositis
myosote
Myosotis
myospasm
myospasmia
Myosurus
myosuture
myosynizesis
myotacismus
Myotalpa
Myotalpinae
myotasis
myotenotomy
myothermic
myotic
myotome
myotomic
myotomy
myotonia
myotonic
myotonus
myotony
myotrophy
myowun
Myoxidae
myoxine
Myoxus
Myra
myrabalanus
myrabolam
myrcene
Myrcia
myrcia
myriacanthous
myriacoulomb
myriad
myriaded
myriadfold
myriadly
myriadth
myriagram
myriagramme
myrialiter
myrialitre
myriameter
myriametre
Myrianida
myriapod
Myriapoda
myriapodan
myriapodous
myriarch
myriarchy
myriare
Myrica
myrica
Myricaceae
myricaceous
Myricales
myricetin
myricin
Myrick
myricyl
myricylic
Myrientomata
myringa
myringectomy
myringitis
myringodectomy
myringodermatitis
myringomycosis
myringoplasty
myringotome
myringotomy
myriological
myriologist
myriologue
myriophyllite
myriophyllous
Myriophyllum
Myriopoda
myriopodous
myriorama
myrioscope
myriosporous
myriotheism
Myriotrichia
Myriotrichiaceae
myriotrichiaceous
myristate
myristic
Myristica
myristica
Myristicaceae
myristicaceous
Myristicivora
myristicivorous
myristin
myristone
Myrmecia
Myrmecobiinae
myrmecobine
Myrmecobius
myrmecochorous
myrmecochory
myrmecoid
myrmecoidy
myrmecological
myrmecologist
myrmecology
Myrmecophaga
Myrmecophagidae
myrmecophagine
myrmecophagoid
myrmecophagous
myrmecophile
myrmecophilism
myrmecophilous
myrmecophily
myrmecophobic
myrmecophyte
myrmecophytic
myrmekite
Myrmeleon
Myrmeleonidae
Myrmeleontidae
Myrmica
myrmicid
Myrmicidae
myrmicine
myrmicoid
Myrmidon
Myrmidonian
myrmotherine
myrobalan
Myron
myron
myronate
myronic
myrosin
myrosinase
Myrothamnaceae
myrothamnaceous
Myrothamnus
Myroxylon
myrrh
myrrhed
myrrhic
myrrhine
Myrrhis
myrrhol
myrrhophore
myrrhy
Myrsinaceae
myrsinaceous
myrsinad
Myrsiphyllum
Myrtaceae
myrtaceous
myrtal
Myrtales
myrtiform
Myrtilus
myrtle
myrtleberry
myrtlelike
myrtol
Myrtus
mysel
myself
mysell
Mysian
mysid
Mysidacea
Mysidae
mysidean
Mysis
mysogynism
mysoid
mysophobia
Mysore
mysosophist
mysost
myst
mystacial
Mystacocete
Mystacoceti
mystagogic
mystagogical
mystagogically
mystagogue
mystagogy
mystax
mysterial
mysteriarch
mysteriosophic
mysteriosophy
mysterious
mysteriously
mysteriousness
mysterize
mystery
mystes
mystic
mystical
mysticality
mystically
mysticalness
Mysticete
mysticete
Mysticeti
mysticetous
mysticism
mysticity
mysticize
mysticly
mystific
mystifically
mystification
mystificator
mystificatory
mystifiedly
mystifier
mystify
mystifyingly
mytacism
myth
mythical
mythicalism
mythicality
mythically
mythicalness
mythicism
mythicist
mythicize
mythicizer
mythification
mythify
mythism
mythist
mythize
mythland
mythmaker
mythmaking
mythoclast
mythoclastic
mythogenesis
mythogonic
mythogony
mythographer
mythographist
mythography
mythogreen
mythoheroic
mythohistoric
mythologema
mythologer
mythological
mythologically
mythologist
mythologize
mythologizer
mythologue
mythology
mythomania
mythomaniac
mythometer
mythonomy
mythopastoral
mythopoeic
mythopoeism
mythopoeist
mythopoem
mythopoesis
mythopoesy
mythopoet
mythopoetic
mythopoetize
mythopoetry
mythos
mythus
Mytilacea
mytilacean
mytilaceous
Mytiliaspis
mytilid
Mytilidae
mytiliform
mytiloid
mytilotoxine
Mytilus
myxa
myxadenitis
myxadenoma
myxaemia
myxamoeba
myxangitis
myxasthenia
myxedema
myxedematoid
myxedematous
myxedemic
myxemia
Myxine
Myxinidae
myxinoid
Myxinoidei
myxo
Myxobacteria
Myxobacteriaceae
myxobacteriaceous
Myxobacteriales
myxoblastoma
myxochondroma
myxochondrosarcoma
Myxococcus
myxocystoma
myxocyte
myxoenchondroma
myxofibroma
myxofibrosarcoma
myxoflagellate
myxogaster
Myxogasteres
Myxogastrales
Myxogastres
myxogastric
myxogastrous
myxoglioma
myxoid
myxoinoma
myxolipoma
myxoma
myxomatosis
myxomatous
Myxomycetales
myxomycete
Myxomycetes
myxomycetous
myxomyoma
myxoneuroma
myxopapilloma
Myxophyceae
myxophycean
Myxophyta
myxopod
Myxopoda
myxopodan
myxopodium
myxopodous
myxopoiesis
myxorrhea
myxosarcoma
Myxospongiae
myxospongian
Myxospongida
myxospore
Myxosporidia
myxosporidian
Myxosporidiida
Myxosporium
myxosporous
Myxothallophyta
myxotheca
Myzodendraceae
myzodendraceous
Myzodendron
Myzomyia
myzont
Myzontes
Myzostoma
Myzostomata
myzostomatous
myzostome
myzostomid
Myzostomida
Myzostomidae
myzostomidan
myzostomous
N
n
na
naa
naam
Naaman
Naassenes
nab
nabak
Nabal
Nabalism
Nabalite
Nabalitic
Nabaloi
Nabalus
Nabataean
Nabatean
Nabathaean
Nabathean
Nabathite
nabber
Nabby
nabk
nabla
nable
nabob
nabobery
nabobess
nabobical
nabobish
nabobishly
nabobism
nabobry
nabobship
Nabothian
nabs
Nabu
nacarat
nacarine
nace
nacelle
nach
nachani
Nachitoch
Nachitoches
Nachschlag
Nacionalista
nacket
nacre
nacred
nacreous
nacrine
nacrite
nacrous
nacry
nadder
Nadeem
nadir
nadiral
nadorite
nae
naebody
naegate
naegates
nael
Naemorhedinae
naemorhedine
Naemorhedus
naether
naething
nag
Naga
naga
nagaika
nagana
nagara
Nagari
nagatelite
nagger
naggin
nagging
naggingly
naggingness
naggish
naggle
naggly
naggy
naght
nagkassar
nagmaal
nagman
nagnag
nagnail
nagor
nagsman
nagster
nagual
nagualism
nagualist
nagyagite
Nahanarvali
Nahane
Nahani
Naharvali
Nahor
Nahua
Nahuan
Nahuatl
Nahuatlac
Nahuatlan
Nahuatleca
Nahuatlecan
Nahum
naiad
Naiadaceae
naiadaceous
Naiadales
Naiades
naiant
Naias
naid
naif
naifly
naig
naigie
naik
nail
nailbin
nailbrush
nailer
naileress
nailery
nailhead
nailing
nailless
naillike
nailprint
nailproof
nailrod
nailshop
nailsick
nailsmith
nailwort
naily
Naim
nain
nainsel
nainsook
naio
naipkin
Nair
nairy
nais
naish
naissance
naissant
naither
naive
naively
naiveness
naivete
naivety
Naja
nak
nake
naked
nakedish
nakedize
nakedly
nakedness
nakedweed
nakedwood
naker
nakhlite
nakhod
nakhoda
Nakir
nako
Nakomgilisala
nakong
nakoo
Nakula
Nalita
nallah
nam
Nama
namability
namable
Namaqua
namaqua
Namaquan
namaycush
namaz
namazlik
Nambe
namda
name
nameability
nameable
nameboard
nameless
namelessly
namelessness
nameling
namely
namer
namesake
naming
nammad
Nan
nan
Nana
nana
Nanaimo
nanawood
Nance
Nancy
nancy
Nanda
Nandi
nandi
Nandina
nandine
nandow
nandu
nane
nanes
nanga
nanism
nanization
nankeen
Nankin
nankin
Nanking
Nankingese
nannander
nannandrium
nannandrous
Nannette
nannoplankton
Nanny
nanny
nannyberry
nannybush
nanocephalia
nanocephalic
nanocephalism
nanocephalous
nanocephalus
nanocephaly
nanoid
nanomelia
nanomelous
nanomelus
nanosoma
nanosomia
nanosomus
nanpie
nant
Nanticoke
nantle
nantokite
Nantz
naological
naology
naometry
Naomi
Naos
naos
Naosaurus
Naoto
nap
napa
Napaea
Napaean
napal
napalm
nape
napead
napecrest
napellus
naperer
napery
naphtha
naphthacene
naphthalate
naphthalene
naphthaleneacetic
naphthalenesulphonic
naphthalenic
naphthalenoid
naphthalic
naphthalidine
naphthalin
naphthaline
naphthalization
naphthalize
naphthalol
naphthamine
naphthanthracene
naphthene
naphthenic
naphthinduline
naphthionate
naphtho
naphthoic
naphthol
naphtholate
naphtholize
naphtholsulphonate
naphtholsulphonic
naphthoquinone
naphthoresorcinol
naphthosalol
naphthous
naphthoxide
naphthyl
naphthylamine
naphthylaminesulphonic
naphthylene
naphthylic
naphtol
Napierian
napiform
napkin
napkining
napless
naplessness
Napoleon
napoleon
Napoleonana
Napoleonic
Napoleonically
Napoleonism
Napoleonist
Napoleonistic
napoleonite
Napoleonize
napoo
nappe
napped
napper
nappiness
napping
nappishness
nappy
naprapath
naprapathy
napron
napthionic
napu
nar
Narcaciontes
Narcaciontidae
narceine
narcism
Narciss
Narcissan
narcissi
Narcissine
narcissism
narcissist
narcissistic
Narcissus
narcist
narcistic
narcoanalysis
narcoanesthesia
Narcobatidae
Narcobatoidea
Narcobatus
narcohypnia
narcohypnosis
narcolepsy
narcoleptic
narcoma
narcomania
narcomaniac
narcomaniacal
narcomatous
Narcomedusae
narcomedusan
narcose
narcosis
narcostimulant
narcosynthesis
narcotherapy
narcotia
narcotic
narcotical
narcotically
narcoticalness
narcoticism
narcoticness
narcotina
narcotine
narcotinic
narcotism
narcotist
narcotization
narcotize
narcous
nard
nardine
nardoo
Nardus
Naren
Narendra
nares
Naresh
narghile
nargil
narial
naric
narica
naricorn
nariform
narine
naringenin
naringin
nark
narky
narr
narra
Narraganset
narras
narratable
narrate
narrater
narration
narrational
narrative
narratively
narrator
narratory
narratress
narratrix
narrawood
narrow
narrower
narrowhearted
narrowheartedness
narrowingness
narrowish
narrowly
narrowness
narrowy
narsarsukite
narsinga
narthecal
Narthecium
narthex
narwhal
narwhalian
nary
nasab
nasal
Nasalis
nasalis
nasalism
nasality
nasalization
nasalize
nasally
nasalward
nasalwards
nasard
Nascan
Nascapi
nascence
nascency
nascent
nasch
naseberry
nasethmoid
nash
nashgab
nashgob
Nashim
Nashira
Nashua
nasi
nasial
nasicorn
Nasicornia
nasicornous
Nasiei
nasiform
nasilabial
nasillate
nasillation
nasioalveolar
nasiobregmatic
nasioinial
nasiomental
nasion
nasitis
Naskhi
nasoalveola
nasoantral
nasobasilar
nasobronchial
nasobuccal
nasoccipital
nasociliary
nasoethmoidal
nasofrontal
nasolabial
nasolachrymal
nasological
nasologist
nasology
nasomalar
nasomaxillary
nasonite
nasoorbital
nasopalatal
nasopalatine
nasopharyngeal
nasopharyngitis
nasopharynx
nasoprognathic
nasoprognathism
nasorostral
nasoscope
nasoseptal
nasosinuitis
nasosinusitis
nasosubnasal
nasoturbinal
nasrol
Nassa
Nassau
Nassellaria
nassellarian
Nassidae
nassology
nast
nastaliq
nastic
nastika
nastily
nastiness
nasturtion
nasturtium
nasty
Nasua
nasus
nasute
nasuteness
nasutiform
nasutus
nat
natability
nataka
Natal
natal
Natalia
Natalian
Natalie
natality
nataloin
natals
natant
natantly
Nataraja
natation
natational
natator
natatorial
natatorious
natatorium
natatory
natch
natchbone
Natchez
Natchezan
Natchitoches
natchnee
Nate
nates
Nathan
Nathanael
Nathaniel
nathe
nather
nathless
Natica
Naticidae
naticiform
naticine
Natick
naticoid
natiform
natimortality
nation
national
nationalism
nationalist
nationalistic
nationalistically
nationality
nationalization
nationalize
nationalizer
nationally
nationalness
nationalty
nationhood
nationless
nationwide
native
natively
nativeness
nativism
nativist
nativistic
nativity
natr
Natraj
Natricinae
natricine
natrium
Natrix
natrochalcite
natrojarosite
natrolite
natron
Natt
natter
nattered
natteredness
natterjack
nattily
nattiness
nattle
natty
natuary
natural
naturalesque
naturalism
naturalist
naturalistic
naturalistically
naturality
naturalization
naturalize
naturalizer
naturally
naturalness
nature
naturecraft
naturelike
naturing
naturism
naturist
naturistic
naturistically
naturize
naturopath
naturopathic
naturopathist
naturopathy
naucrar
naucrary
naufragous
nauger
naught
naughtily
naughtiness
naughty
naujaite
naumachia
naumachy
naumannite
Naumburgia
naumk
naumkeag
naumkeager
naunt
nauntle
naupathia
nauplial
naupliiform
nauplioid
nauplius
nauropometer
nauscopy
nausea
nauseant
nauseaproof
nauseate
nauseatingly
nauseation
nauseous
nauseously
nauseousness
Nauset
naut
nautch
nauther
nautic
nautical
nauticality
nautically
nautics
nautiform
Nautilacea
nautilacean
nautilicone
nautiliform
nautilite
nautiloid
Nautiloidea
nautiloidean
nautilus
Navaho
Navajo
naval
navalese
navalism
navalist
navalistic
navalistically
navally
navar
navarch
navarchy
Navarrese
Navarrian
nave
navel
naveled
navellike
navelwort
navet
navette
navew
navicella
navicert
navicula
Naviculaceae
naviculaeform
navicular
naviculare
naviculoid
naviform
navigability
navigable
navigableness
navigably
navigant
navigate
navigation
navigational
navigator
navigerous
navipendular
navipendulum
navite
navvy
navy
naw
nawab
nawabship
nawt
nay
Nayar
Nayarit
Nayarita
nayaur
naysay
naysayer
nayward
nayword
Nazarate
Nazarean
Nazarene
Nazarenism
Nazarite
Nazariteship
Nazaritic
Nazaritish
Nazaritism
naze
Nazerini
Nazi
Nazify
Naziism
nazim
nazir
Nazirate
Nazirite
Naziritic
Nazism
ne
nea
Neal
neal
neallotype
Neanderthal
Neanderthaler
Neanderthaloid
neanic
neanthropic
neap
neaped
Neapolitan
nearable
nearabout
nearabouts
nearaivays
nearaway
nearby
Nearctic
Nearctica
nearest
nearish
nearly
nearmost
nearness
nearsighted
nearsightedly
nearsightedness
nearthrosis
neat
neaten
neath
neatherd
neatherdess
neathmost
neatify
neatly
neatness
neb
neback
Nebaioth
Nebalia
Nebaliacea
nebalian
Nebaliidae
nebalioid
nebbed
nebbuck
nebbuk
nebby
nebel
nebelist
nebenkern
Nebiim
Nebraskan
nebris
nebula
nebulae
nebular
nebularization
nebularize
nebulated
nebulation
nebule
nebulescent
nebuliferous
nebulite
nebulium
nebulization
nebulize
nebulizer
nebulose
nebulosity
nebulous
nebulously
nebulousness
Necator
necessar
necessarian
necessarianism
necessarily
necessariness
necessary
necessism
necessist
necessitarian
necessitarianism
necessitate
necessitatedly
necessitatingly
necessitation
necessitative
necessitous
necessitously
necessitousness
necessitude
necessity
neck
neckar
neckatee
neckband
neckcloth
necked
necker
neckercher
neckerchief
neckful
neckguard
necking
neckinger
necklace
necklaced
necklaceweed
neckless
necklet
necklike
neckline
neckmold
neckpiece
neckstock
necktie
necktieless
neckward
neckwear
neckweed
neckyoke
necrectomy
necremia
necrobacillary
necrobacillosis
necrobiosis
necrobiotic
necrogenic
necrogenous
necrographer
necrolatry
necrologic
necrological
necrologically
necrologist
necrologue
necrology
necromancer
necromancing
necromancy
necromantic
necromantically
necromorphous
necronite
necropathy
Necrophaga
necrophagan
necrophagous
necrophile
necrophilia
necrophilic
necrophilism
necrophilistic
necrophilous
necrophily
necrophobia
necrophobic
Necrophorus
necropoleis
necropoles
necropolis
necropolitan
necropsy
necroscopic
necroscopical
necroscopy
necrose
necrosis
necrotic
necrotization
necrotize
necrotomic
necrotomist
necrotomy
necrotype
necrotypic
Nectandra
nectar
nectareal
nectarean
nectared
nectareous
nectareously
nectareousness
nectarial
nectarian
nectaried
nectariferous
nectarine
Nectarinia
Nectariniidae
nectarious
nectarium
nectarivorous
nectarize
nectarlike
nectarous
nectary
nectiferous
nectocalycine
nectocalyx
Nectonema
nectophore
nectopod
Nectria
nectriaceous
Nectrioidaceae
Necturidae
Necturus
Ned
nedder
neddy
Nederlands
nee
neebor
neebour
need
needer
needfire
needful
needfully
needfulness
needgates
needham
needily
neediness
needing
needle
needlebill
needlebook
needlebush
needlecase
needled
needlefish
needleful
needlelike
needlemaker
needlemaking
needleman
needlemonger
needleproof
needler
needles
needless
needlessly
needlessness
needlestone
needlewoman
needlewood
needlework
needleworked
needleworker
needling
needly
needments
needs
needsome
needy
neeger
neeld
neele
neelghan
neem
neencephalic
neencephalon
Neengatu
neep
neepour
neer
neese
neet
neetup
neeze
nef
nefandous
nefandousness
nefarious
nefariously
nefariousness
nefast
neffy
neftgil
negate
negatedness
negation
negationalist
negationist
negative
negatively
negativeness
negativer
negativism
negativist
negativistic
negativity
negator
negatory
negatron
neger
neginoth
neglect
neglectable
neglectedly
neglectedness
neglecter
neglectful
neglectfully
neglectfulness
neglectingly
neglection
neglective
neglectively
neglector
neglectproof
negligee
negligence
negligency
negligent
negligently
negligibility
negligible
negligibleness
negligibly
negotiability
negotiable
negotiant
negotiate
negotiation
negotiator
negotiatory
negotiatress
negotiatrix
Negress
negrillo
negrine
Negritian
Negritic
Negritize
Negrito
Negritoid
Negro
negro
negrodom
Negrofy
negrohead
negrohood
Negroid
Negroidal
negroish
Negroism
Negroization
Negroize
negrolike
Negroloid
Negrophil
Negrophile
Negrophilism
Negrophilist
Negrophobe
Negrophobia
Negrophobiac
Negrophobist
Negrotic
Negundo
Negus
negus
Nehantic
Nehemiah
nehiloth
nei
neif
neigh
neighbor
neighbored
neighborer
neighboress
neighborhood
neighboring
neighborless
neighborlike
neighborliness
neighborly
neighborship
neighborstained
neighbourless
neighbourlike
neighbourship
neigher
Neil
Neillia
neiper
Neisseria
Neisserieae
neist
neither
Nejd
Nejdi
Nekkar
nekton
nektonic
Nelken
Nell
Nellie
Nelly
nelson
nelsonite
nelumbian
Nelumbium
Nelumbo
Nelumbonaceae
nema
nemaline
Nemalion
Nemalionaceae
Nemalionales
nemalite
Nemastomaceae
Nematelmia
nematelminth
Nematelminthes
nemathece
nemathecial
nemathecium
Nemathelmia
nemathelminth
Nemathelminthes
nematic
nematoblast
nematoblastic
Nematocera
nematoceran
nematocerous
nematocide
nematocyst
nematocystic
Nematoda
nematode
nematodiasis
nematogene
nematogenic
nematogenous
nematognath
Nematognathi
nematognathous
nematogone
nematogonous
nematoid
Nematoidea
nematoidean
nematologist
nematology
Nematomorpha
nematophyton
Nematospora
nematozooid
Nembutal
Nemean
Nemertea
nemertean
Nemertina
nemertine
Nemertinea
nemertinean
Nemertini
nemertoid
nemeses
Nemesia
nemesic
Nemesis
Nemichthyidae
Nemichthys
Nemocera
nemoceran
nemocerous
Nemopanthus
Nemophila
nemophilist
nemophilous
nemophily
nemoral
Nemorensian
nemoricole
Nengahiba
nenta
nenuphar
neo
neoacademic
neoanthropic
Neoarctic
neoarsphenamine
Neobalaena
Neobeckia
neoblastic
neobotanist
neobotany
Neocene
Neoceratodus
neocerotic
neoclassic
neoclassicism
neoclassicist
Neocomian
neocosmic
neocracy
neocriticism
neocyanine
neocyte
neocytosis
neodamode
neodidymium
neodymium
Neofabraea
neofetal
neofetus
Neofiber
neoformation
neoformative
Neogaea
Neogaean
neogamous
neogamy
Neogene
neogenesis
neogenetic
Neognathae
neognathic
neognathous
neogrammarian
neogrammatical
neographic
neohexane
Neohipparion
neoholmia
neoholmium
neoimpressionism
neoimpressionist
neolalia
neolater
neolatry
neolith
neolithic
neologian
neologianism
neologic
neological
neologically
neologism
neologist
neologistic
neologistical
neologization
neologize
neology
neomedievalism
neomenia
neomenian
Neomeniidae
neomiracle
neomodal
neomorph
Neomorpha
neomorphic
neomorphism
Neomylodon
neon
neonatal
neonate
neonatus
neonomian
neonomianism
neontology
neonychium
neopagan
neopaganism
neopaganize
Neopaleozoic
neopallial
neopallium
neoparaffin
neophilism
neophilological
neophilologist
neophobia
neophobic
neophrastic
Neophron
neophyte
neophytic
neophytish
neophytism
Neopieris
neoplasia
neoplasm
neoplasma
neoplasmata
neoplastic
neoplasticism
neoplasty
Neoplatonic
Neoplatonician
Neoplatonism
Neoplatonist
neoprene
neorama
neorealism
Neornithes
neornithic
Neosalvarsan
Neosorex
Neosporidia
neossin
neossology
neossoptile
neostriatum
neostyle
neoteinia
neoteinic
neotenia
neotenic
neoteny
neoteric
neoterically
neoterism
neoterist
neoteristic
neoterize
neothalamus
Neotoma
Neotragus
Neotremata
Neotropic
Neotropical
neotype
neovitalism
neovolcanic
Neowashingtonia
neoytterbium
neoza
Neozoic
Nep
nep
Nepa
Nepal
Nepalese
Nepali
Nepenthaceae
nepenthaceous
nepenthe
nepenthean
Nepenthes
nepenthes
neper
Neperian
Nepeta
nephalism
nephalist
Nephele
nephele
nepheligenous
nepheline
nephelinic
nephelinite
nephelinitic
nephelinitoid
nephelite
Nephelium
nephelognosy
nepheloid
nephelometer
nephelometric
nephelometrical
nephelometrically
nephelometry
nephelorometer
nepheloscope
nephesh
nephew
nephewship
Nephila
Nephilinae
Nephite
nephogram
nephograph
nephological
nephologist
nephology
nephoscope
nephradenoma
nephralgia
nephralgic
nephrapostasis
nephratonia
nephrauxe
nephrectasia
nephrectasis
nephrectomize
nephrectomy
nephrelcosis
nephremia
nephremphraxis
nephria
nephric
nephridia
nephridial
nephridiopore
nephridium
nephrism
nephrite
nephritic
nephritical
nephritis
nephroabdominal
nephrocardiac
nephrocele
nephrocoele
nephrocolic
nephrocolopexy
nephrocoloptosis
nephrocystitis
nephrocystosis
nephrocyte
nephrodinic
Nephrodium
nephroerysipelas
nephrogastric
nephrogenetic
nephrogenic
nephrogenous
nephrogonaduct
nephrohydrosis
nephrohypertrophy
nephroid
Nephrolepis
nephrolith
nephrolithic
nephrolithotomy
nephrologist
nephrology
nephrolysin
nephrolysis
nephrolytic
nephromalacia
nephromegaly
nephromere
nephron
nephroncus
nephroparalysis
nephropathic
nephropathy
nephropexy
nephrophthisis
nephropore
Nephrops
Nephropsidae
nephroptosia
nephroptosis
nephropyelitis
nephropyeloplasty
nephropyosis
nephrorrhagia
nephrorrhaphy
nephros
nephrosclerosis
nephrosis
nephrostoma
nephrostome
nephrostomial
nephrostomous
nephrostomy
nephrotome
nephrotomize
nephrotomy
nephrotoxic
nephrotoxicity
nephrotoxin
nephrotuberculosis
nephrotyphoid
nephrotyphus
nephrozymosis
Nepidae
nepionic
nepman
nepotal
nepote
nepotic
nepotious
nepotism
nepotist
nepotistical
nepouite
Neptune
Neptunean
Neptunian
neptunism
neptunist
neptunium
Nereid
Nereidae
nereidiform
Nereidiformia
Nereis
nereite
Nereocystis
Neri
Nerine
nerine
Nerita
neritic
Neritidae
Neritina
neritoid
Nerium
Neroic
Neronian
Neronic
Neronize
nerterology
Nerthridae
Nerthrus
nerval
nervate
nervation
nervature
nerve
nerveless
nervelessly
nervelessness
nervelet
nerveproof
nerver
nerveroot
nervid
nerviduct
Nervii
nervily
nervimotion
nervimotor
nervimuscular
nervine
nerviness
nerving
nervish
nervism
nervomuscular
nervosanguineous
nervose
nervosism
nervosity
nervous
nervously
nervousness
nervular
nervule
nervulet
nervulose
nervuration
nervure
nervy
nescience
nescient
nese
nesh
neshly
neshness
Nesiot
nesiote
Neskhi
Neslia
Nesogaea
Nesogaean
Nesokia
Nesonetta
Nesotragus
Nespelim
nesquehonite
ness
nesslerization
Nesslerize
nesslerize
nest
nestable
nestage
nester
nestful
nestiatria
nestitherapy
nestle
nestler
nestlike
nestling
Nestor
Nestorian
Nestorianism
Nestorianize
Nestorianizer
nestorine
nesty
Net
net
netball
netbraider
netbush
netcha
Netchilik
nete
neter
netful
neth
netheist
nether
Netherlander
Netherlandian
Netherlandic
Netherlandish
nethermore
nethermost
netherstock
netherstone
netherward
netherwards
Nethinim
neti
netleaf
netlike
netmaker
netmaking
netman
netmonger
netop
netsman
netsuke
nettable
Nettapus
netted
netter
Nettie
netting
Nettion
nettle
nettlebed
nettlebird
nettlefire
nettlefish
nettlefoot
nettlelike
nettlemonger
nettler
nettlesome
nettlewort
nettling
nettly
Netty
netty
netwise
network
Neudeckian
neugroschen
neuma
neumatic
neumatize
neume
neumic
neurad
neuradynamia
neural
neurale
neuralgia
neuralgiac
neuralgic
neuralgiform
neuralgy
neuralist
neurapophyseal
neurapophysial
neurapophysis
neurarthropathy
neurasthenia
neurasthenic
neurasthenical
neurasthenically
neurataxia
neurataxy
neuration
neuratrophia
neuratrophic
neuratrophy
neuraxial
neuraxis
neuraxon
neuraxone
neurectasia
neurectasis
neurectasy
neurectome
neurectomic
neurectomy
neurectopia
neurectopy
neurenteric
neurepithelium
neurergic
neurexairesis
neurhypnology
neurhypnotist
neuriatry
neuric
neurilema
neurilematic
neurilemma
neurilemmal
neurilemmatic
neurilemmatous
neurilemmitis
neurility
neurin
neurine
neurinoma
neurism
neurite
neuritic
neuritis
neuroanatomical
neuroanatomy
neurobiotactic
neurobiotaxis
neuroblast
neuroblastic
neuroblastoma
neurocanal
neurocardiac
neurocele
neurocentral
neurocentrum
neurochemistry
neurochitin
neurochondrite
neurochord
neurochorioretinitis
neurocirculatory
neurocity
neuroclonic
neurocoele
neurocoelian
neurocyte
neurocytoma
neurodegenerative
neurodendrite
neurodendron
neurodermatitis
neurodermatosis
neurodermitis
neurodiagnosis
neurodynamic
neurodynia
neuroepidermal
neuroepithelial
neuroepithelium
neurofibril
neurofibrilla
neurofibrillae
neurofibrillar
neurofibroma
neurofibromatosis
neurofil
neuroganglion
neurogastralgia
neurogastric
neurogenesis
neurogenetic
neurogenic
neurogenous
neuroglandular
neuroglia
neurogliac
neuroglial
neurogliar
neuroglic
neuroglioma
neurogliosis
neurogram
neurogrammic
neurographic
neurography
neurohistology
neurohumor
neurohumoral
neurohypnology
neurohypnotic
neurohypnotism
neurohypophysis
neuroid
neurokeratin
neurokyme
neurological
neurologist
neurologize
neurology
neurolymph
neurolysis
neurolytic
neuroma
neuromalacia
neuromalakia
neuromast
neuromastic
neuromatosis
neuromatous
neuromere
neuromerism
neuromerous
neuromimesis
neuromimetic
neuromotor
neuromuscular
neuromusculature
neuromyelitis
neuromyic
neuron
neuronal
neurone
neuronic
neuronism
neuronist
neuronophagia
neuronophagy
neuronym
neuronymy
neuroparalysis
neuroparalytic
neuropath
neuropathic
neuropathical
neuropathically
neuropathist
neuropathological
neuropathologist
neuropathology
neuropathy
Neurope
neurophagy
neurophil
neurophile
neurophilic
neurophysiological
neurophysiology
neuropile
neuroplasm
neuroplasmic
neuroplasty
neuroplexus
neuropodial
neuropodium
neuropodous
neuropore
neuropsychiatric
neuropsychiatrist
neuropsychiatry
neuropsychic
neuropsychological
neuropsychologist
neuropsychology
neuropsychopathic
neuropsychopathy
neuropsychosis
neuropter
Neuroptera
neuropteran
Neuropteris
neuropterist
neuropteroid
Neuropteroidea
neuropterological
neuropterology
neuropteron
neuropterous
neuroretinitis
neurorrhaphy
Neurorthoptera
neurorthopteran
neurorthopterous
neurosal
neurosarcoma
neurosclerosis
neuroses
neurosis
neuroskeletal
neuroskeleton
neurosome
neurospasm
neurospongium
neurosthenia
neurosurgeon
neurosurgery
neurosurgical
neurosuture
neurosynapse
neurosyphilis
neurotendinous
neurotension
neurotherapeutics
neurotherapist
neurotherapy
neurothlipsis
neurotic
neurotically
neuroticism
neuroticize
neurotization
neurotome
neurotomical
neurotomist
neurotomize
neurotomy
neurotonic
neurotoxia
neurotoxic
neurotoxin
neurotripsy
neurotrophic
neurotrophy
neurotropic
neurotropism
neurovaccination
neurovaccine
neurovascular
neurovisceral
neurula
neurypnological
neurypnologist
neurypnology
Neustrian
neuter
neuterdom
neuterlike
neuterly
neuterness
neutral
neutralism
neutralist
neutrality
neutralization
neutralize
neutralizer
neutrally
neutralness
neutrino
neutroceptive
neutroceptor
neutroclusion
Neutrodyne
neutrologistic
neutron
neutropassive
neutrophile
neutrophilia
neutrophilic
neutrophilous
Nevada
Nevadan
nevadite
neve
nevel
never
neverland
nevermore
nevertheless
Neville
nevo
nevoid
Nevome
nevoy
nevus
nevyanskite
new
Newar
Newari
newberyite
newcal
Newcastle
newcome
newcomer
newel
newelty
newfangle
newfangled
newfangledism
newfangledly
newfangledness
newfanglement
Newfoundland
Newfoundlander
Newichawanoc
newing
newings
newish
newlandite
newly
newlywed
Newmanism
Newmanite
Newmanize
newmarket
newness
Newport
news
newsbill
newsboard
newsboat
newsboy
newscast
newscaster
newscasting
newsful
newsiness
newsless
newslessness
newsletter
newsman
newsmonger
newsmongering
newsmongery
newspaper
newspaperdom
newspaperese
newspaperish
newspaperized
newspaperman
newspaperwoman
newspapery
newsprint
newsreader
newsreel
newsroom
newssheet
newsstand
newsteller
newsworthiness
newsworthy
newsy
newt
newtake
newton
Newtonian
Newtonianism
Newtonic
Newtonist
newtonite
nexal
next
nextly
nextness
nexum
nexus
neyanda
ngai
ngaio
ngapi
Ngoko
Nguyen
Nhan
Nheengatu
ni
niacin
Niagara
Niagaran
Niall
Niantic
Nias
Niasese
niata
nib
nibbana
nibbed
nibber
nibble
nibbler
nibblingly
nibby
niblick
niblike
nibong
nibs
nibsome
Nicaean
Nicaragua
Nicaraguan
Nicarao
niccolic
niccoliferous
niccolite
niccolous
Nice
nice
niceish
niceling
nicely
Nicene
niceness
Nicenian
Nicenist
nicesome
nicetish
nicety
Nichael
niche
nichelino
nicher
Nicholas
Nici
Nick
nick
nickel
nickelage
nickelic
nickeliferous
nickeline
nickeling
nickelization
nickelize
nickellike
nickelodeon
nickelous
nickeltype
nicker
nickerpecker
nickey
Nickie
Nickieben
nicking
nickle
nickname
nicknameable
nicknamee
nicknameless
nicknamer
Nickneven
nickstick
nicky
Nicobar
Nicobarese
Nicodemite
Nicodemus
Nicol
Nicolaitan
Nicolaitanism
Nicolas
nicolayite
Nicolette
Nicolo
nicolo
Nicomachean
nicotia
nicotian
Nicotiana
nicotianin
nicotic
nicotinamide
nicotine
nicotinean
nicotined
nicotineless
nicotinian
nicotinic
nicotinism
nicotinize
nicotism
nicotize
nictate
nictation
nictitant
nictitate
nictitation
nid
nidal
nidamental
nidana
nidation
nidatory
niddering
niddick
niddle
nide
nidge
nidget
nidgety
nidi
nidicolous
nidificant
nidificate
nidification
nidificational
nidifugous
nidify
niding
nidologist
nidology
nidor
nidorosity
nidorous
nidorulent
nidulant
Nidularia
Nidulariaceae
nidulariaceous
Nidulariales
nidulate
nidulation
nidulus
nidus
niece
nieceless
nieceship
niellated
nielled
niellist
niello
Niels
niepa
Nierembergia
Niersteiner
Nietzschean
Nietzscheanism
Nietzscheism
nieve
nieveta
nievling
nife
nifesima
niffer
nific
nifle
nifling
nifty
nig
Nigel
Nigella
Nigerian
niggard
niggardize
niggardliness
niggardling
niggardly
niggardness
nigger
niggerdom
niggerfish
niggergoose
niggerhead
niggerish
niggerism
niggerling
niggertoe
niggerweed
niggery
niggle
niggler
niggling
nigglingly
niggly
nigh
nighly
nighness
night
nightcap
nightcapped
nightcaps
nightchurr
nightdress
nighted
nightfall
nightfish
nightflit
nightfowl
nightgown
nighthawk
nightie
nightingale
nightingalize
nightjar
nightless
nightlessness
nightlike
nightlong
nightly
nightman
nightmare
nightmarish
nightmarishly
nightmary
nights
nightshade
nightshine
nightshirt
nightstock
nightstool
nighttide
nighttime
nightwalker
nightwalking
nightward
nightwards
nightwear
nightwork
nightworker
nignay
nignye
nigori
nigranilin
nigraniline
nigre
nigrescence
nigrescent
nigresceous
nigrescite
nigrification
nigrified
nigrify
nigrine
Nigritian
nigrities
nigritude
nigritudinous
nigrosine
nigrous
nigua
Nihal
nihilianism
nihilianistic
nihilification
nihilify
nihilism
nihilist
nihilistic
nihilitic
nihility
nikau
Nikeno
nikethamide
Nikko
niklesite
Nikolai
nil
Nile
nilgai
Nilometer
Nilometric
Niloscope
Nilot
Nilotic
Nilous
nilpotent
Nils
nim
nimb
nimbated
nimbed
nimbi
nimbiferous
nimbification
nimble
nimblebrained
nimbleness
nimbly
nimbose
nimbosity
nimbus
nimbused
nimiety
niminy
nimious
Nimkish
nimmer
Nimrod
Nimrodian
Nimrodic
Nimrodical
Nimrodize
nimshi
Nina
nincom
nincompoop
nincompoopery
nincompoophood
nincompoopish
nine
ninebark
ninefold
nineholes
ninepegs
ninepence
ninepenny
ninepin
ninepins
ninescore
nineted
nineteen
nineteenfold
nineteenth
nineteenthly
ninetieth
ninety
ninetyfold
ninetyish
ninetyknot
Ninevite
Ninevitical
Ninevitish
Ning
Ningpo
Ninja
ninny
ninnyhammer
ninnyish
ninnyism
ninnyship
ninnywatch
Ninon
ninon
Ninox
ninth
ninthly
nintu
ninut
niobate
Niobe
Niobean
niobic
Niobid
Niobite
niobite
niobium
niobous
niog
niota
Nip
nip
nipa
nipcheese
niphablepsia
niphotyphlosis
Nipissing
Nipmuc
nipper
nipperkin
nippers
nippily
nippiness
nipping
nippingly
nippitate
nipple
nippleless
nipplewort
Nipponese
Nipponism
nipponium
Nipponize
nippy
nipter
Niquiran
nirles
nirmanakaya
nirvana
nirvanic
Nisaean
Nisan
nisei
Nishada
nishiki
nisnas
nispero
Nisqualli
nisse
nisus
nit
nitch
nitchevo
Nitella
nitency
nitently
niter
niterbush
nitered
nither
nithing
nitid
nitidous
nitidulid
Nitidulidae
nito
niton
nitramine
nitramino
nitranilic
nitraniline
nitrate
nitratine
nitration
nitrator
Nitrian
nitriary
nitric
nitridation
nitride
nitriding
nitridization
nitridize
nitrifaction
nitriferous
nitrifiable
nitrification
nitrifier
nitrify
nitrile
Nitriot
nitrite
nitro
nitroalizarin
nitroamine
nitroaniline
Nitrobacter
nitrobacteria
Nitrobacteriaceae
Nitrobacterieae
nitrobarite
nitrobenzene
nitrobenzol
nitrobenzole
nitrocalcite
nitrocellulose
nitrocellulosic
nitrochloroform
nitrocotton
nitroform
nitrogelatin
nitrogen
nitrogenate
nitrogenation
nitrogenic
nitrogenization
nitrogenize
nitrogenous
nitroglycerin
nitrohydrochloric
nitrolamine
nitrolic
nitrolime
nitromagnesite
nitrometer
nitrometric
nitromuriate
nitromuriatic
nitronaphthalene
nitroparaffin
nitrophenol
nitrophilous
nitrophyte
nitrophytic
nitroprussiate
nitroprussic
nitroprusside
nitrosamine
nitrosate
nitrosification
nitrosify
nitrosite
nitrosobacteria
nitrosochloride
Nitrosococcus
Nitrosomonas
nitrososulphuric
nitrostarch
nitrosulphate
nitrosulphonic
nitrosulphuric
nitrosyl
nitrosylsulphuric
nitrotoluene
nitrous
nitroxyl
nitryl
nitter
nitty
nitwit
Nitzschia
Nitzschiaceae
Niuan
Niue
nival
nivation
nivellate
nivellation
nivellator
nivellization
nivenite
niveous
nivicolous
nivosity
nix
nixie
niyoga
Nizam
nizam
nizamate
nizamut
nizy
njave
No
no
noa
Noachian
Noachic
Noachical
Noachite
Noah
Noahic
Noam
nob
nobber
nobbily
nobble
nobbler
nobbut
nobby
nobiliary
nobilify
nobilitate
nobilitation
nobility
noble
noblehearted
nobleheartedly
nobleheartedness
nobleman
noblemanly
nobleness
noblesse
noblewoman
nobley
nobly
nobody
nobodyness
nobs
nocake
Nocardia
nocardiosis
nocent
nocerite
nociassociation
nociceptive
nociceptor
nociperception
nociperceptive
nock
nocket
nocktat
noctambulant
noctambulation
noctambule
noctambulism
noctambulist
noctambulistic
noctambulous
Nocten
noctidial
noctidiurnal
noctiferous
noctiflorous
Noctilio
Noctilionidae
Noctiluca
noctiluca
noctilucal
noctilucan
noctilucence
noctilucent
Noctilucidae
noctilucin
noctilucine
noctilucous
noctiluminous
noctipotent
noctivagant
noctivagation
noctivagous
noctograph
noctovision
Noctuae
noctuid
Noctuidae
noctuiform
noctule
nocturia
nocturn
nocturnal
nocturnally
nocturne
nocuity
nocuous
nocuously
nocuousness
nod
nodal
nodality
nodated
nodder
nodding
noddingly
noddle
noddy
node
noded
nodi
nodiak
nodical
nodicorn
nodiferous
nodiflorous
nodiform
Nodosaria
nodosarian
nodosariform
nodosarine
nodose
nodosity
nodous
nodular
nodulate
nodulated
nodulation
nodule
noduled
nodulize
nodulose
nodulous
nodulus
nodus
noegenesis
noegenetic
Noel
noel
noematachograph
noematachometer
noematachometic
Noemi
Noetic
noetic
noetics
nog
nogada
Nogai
nogal
noggen
noggin
nogging
noghead
nogheaded
nohow
Nohuntsik
noibwood
noil
noilage
noiler
noily
noint
nointment
noir
noise
noiseful
noisefully
noiseless
noiselessly
noiselessness
noisemaker
noisemaking
noiseproof
noisette
noisily
noisiness
noisome
noisomely
noisomeness
noisy
nokta
Nolascan
nolition
Noll
noll
nolle
nolleity
nollepros
nolo
noma
nomad
nomadian
nomadic
nomadical
nomadically
Nomadidae
nomadism
nomadization
nomadize
nomancy
nomarch
nomarchy
Nomarthra
nomarthral
nombril
nome
Nomeidae
nomenclate
nomenclative
nomenclator
nomenclatorial
nomenclatorship
nomenclatory
nomenclatural
nomenclature
nomenclaturist
Nomeus
nomial
nomic
nomina
nominable
nominal
nominalism
nominalist
nominalistic
nominality
nominally
nominate
nominated
nominately
nomination
nominatival
nominative
nominatively
nominator
nominatrix
nominature
nominee
nomineeism
nominy
nomism
nomisma
nomismata
nomistic
nomocanon
nomocracy
nomogenist
nomogenous
nomogeny
nomogram
nomograph
nomographer
nomographic
nomographical
nomographically
nomography
nomological
nomologist
nomology
nomopelmous
nomophylax
nomophyllous
nomos
nomotheism
nomothete
nomothetes
nomothetic
nomothetical
non
Nona
nonabandonment
nonabdication
nonabiding
nonability
nonabjuration
nonabjurer
nonabolition
nonabridgment
nonabsentation
nonabsolute
nonabsolution
nonabsorbable
nonabsorbent
nonabsorptive
nonabstainer
nonabstaining
nonabstemious
nonabstention
nonabstract
nonacademic
nonacceding
nonacceleration
nonaccent
nonacceptance
nonacceptant
nonacceptation
nonaccess
nonaccession
nonaccessory
nonaccidental
nonaccompaniment
nonaccompanying
nonaccomplishment
nonaccredited
nonaccretion
nonachievement
nonacid
nonacknowledgment
nonacosane
nonacoustic
nonacquaintance
nonacquiescence
nonacquiescent
nonacquisitive
nonacquittal
nonact
nonactinic
nonaction
nonactionable
nonactive
nonactuality
nonaculeate
nonacute
nonadditive
nonadecane
nonadherence
nonadherent
nonadhesion
nonadhesive
nonadjacent
nonadjectival
nonadjournment
nonadjustable
nonadjustive
nonadjustment
nonadministrative
nonadmiring
nonadmission
nonadmitted
nonadoption
Nonadorantes
nonadornment
nonadult
nonadvancement
nonadvantageous
nonadventitious
nonadventurous
nonadverbial
nonadvertence
nonadvertency
nonadvocate
nonaerating
nonaerobiotic
nonaesthetic
nonaffection
nonaffiliated
nonaffirmation
nonage
nonagenarian
nonagency
nonagent
nonagesimal
nonagglutinative
nonagglutinator
nonaggression
nonaggressive
nonagon
nonagrarian
nonagreement
nonagricultural
nonahydrate
nonaid
nonair
nonalarmist
nonalcohol
nonalcoholic
nonalgebraic
nonalienating
nonalienation
nonalignment
nonalkaloidal
nonallegation
nonallegorical
nonalliterated
nonalliterative
nonallotment
nonalluvial
nonalphabetic
nonaltruistic
nonaluminous
nonamalgamable
nonamendable
nonamino
nonamotion
nonamphibious
nonamputation
nonanalogy
nonanalytical
nonanalyzable
nonanalyzed
nonanaphoric
nonanaphthene
nonanatomical
nonancestral
nonane
nonanesthetized
nonangelic
nonangling
nonanimal
nonannexation
nonannouncement
nonannuitant
nonannulment
nonanoic
nonanonymity
nonanswer
nonantagonistic
nonanticipative
nonantigenic
nonapologetic
nonapostatizing
nonapostolic
nonapparent
nonappealable
nonappearance
nonappearer
nonappearing
nonappellate
nonappendicular
nonapplication
nonapply
nonappointment
nonapportionable
nonapposable
nonappraisal
nonappreciation
nonapprehension
nonappropriation
nonapproval
nonaqueous
nonarbitrable
nonarcing
nonargentiferous
nonaristocratic
nonarithmetical
nonarmament
nonarmigerous
nonaromatic
nonarraignment
nonarrival
nonarsenical
nonarterial
nonartesian
nonarticulated
nonarticulation
nonartistic
nonary
nonascendancy
nonascertainable
nonascertaining
nonascetic
nonascription
nonaseptic
nonaspersion
nonasphalt
nonaspirate
nonaspiring
nonassault
nonassent
nonassentation
nonassented
nonassenting
nonassertion
nonassertive
nonassessable
nonassessment
nonassignable
nonassignment
nonassimilable
nonassimilating
nonassimilation
nonassistance
nonassistive
nonassociable
nonassortment
nonassurance
nonasthmatic
nonastronomical
nonathletic
nonatmospheric
nonatonement
nonattached
nonattachment
nonattainment
nonattendance
nonattendant
nonattention
nonattestation
nonattribution
nonattributive
nonaugmentative
nonauricular
nonauriferous
nonauthentication
nonauthoritative
nonautomatic
nonautomotive
nonavoidance
nonaxiomatic
nonazotized
nonbachelor
nonbacterial
nonbailable
nonballoting
nonbanishment
nonbankable
nonbarbarous
nonbaronial
nonbase
nonbasement
nonbasic
nonbasing
nonbathing
nonbearded
nonbearing
nonbeing
nonbeliever
nonbelieving
nonbelligerent
nonbending
nonbenevolent
nonbetrayal
nonbeverage
nonbilabiate
nonbilious
nonbinomial
nonbiological
nonbitter
nonbituminous
nonblack
nonblameless
nonbleeding
nonblended
nonblockaded
nonblocking
nonblooded
nonblooming
nonbodily
nonbookish
nonborrower
nonbotanical
nonbourgeois
nonbranded
nonbreakable
nonbreeder
nonbreeding
nonbroodiness
nonbroody
nonbrowsing
nonbudding
nonbulbous
nonbulkhead
nonbureaucratic
nonburgage
nonburgess
nonburnable
nonburning
nonbursting
nonbusiness
nonbuying
noncabinet
noncaffeine
noncaking
Noncalcarea
noncalcareous
noncalcified
noncallability
noncallable
noncancellable
noncannibalistic
noncanonical
noncanonization
noncanvassing
noncapillarity
noncapillary
noncapital
noncapitalist
noncapitalistic
noncapitulation
noncapsizable
noncapture
noncarbonate
noncareer
noncarnivorous
noncarrier
noncartelized
noncaste
noncastigation
noncataloguer
noncatarrhal
noncatechizable
noncategorical
noncathedral
noncatholicity
noncausality
noncausation
nonce
noncelebration
noncelestial
noncellular
noncellulosic
noncensored
noncensorious
noncensus
noncentral
noncereal
noncerebral
nonceremonial
noncertain
noncertainty
noncertified
nonchafing
nonchalance
nonchalant
nonchalantly
nonchalantness
nonchalky
nonchallenger
nonchampion
nonchangeable
nonchanging
noncharacteristic
nonchargeable
nonchastisement
nonchastity
nonchemical
nonchemist
nonchivalrous
nonchokable
nonchokebore
nonchronological
nonchurch
nonchurched
nonchurchgoer
nonciliate
noncircuit
noncircuital
noncircular
noncirculation
noncitation
noncitizen
noncivilized
nonclaim
nonclaimable
nonclassable
nonclassical
nonclassifiable
nonclassification
nonclastic
nonclearance
noncleistogamic
nonclergyable
nonclerical
nonclimbable
nonclinical
nonclose
nonclosure
nonclotting
noncoagulability
noncoagulable
noncoagulation
noncoalescing
noncock
noncoercion
noncoercive
noncognate
noncognition
noncognitive
noncognizable
noncognizance
noncoherent
noncohesion
noncohesive
noncoinage
noncoincidence
noncoincident
noncoincidental
noncoking
noncollaboration
noncollaborative
noncollapsible
noncollectable
noncollection
noncollegiate
noncollinear
noncolloid
noncollusion
noncollusive
noncolonial
noncoloring
noncom
noncombat
noncombatant
noncombination
noncombining
noncombustible
noncombustion
noncome
noncoming
noncommemoration
noncommencement
noncommendable
noncommensurable
noncommercial
noncommissioned
noncommittal
noncommittalism
noncommittally
noncommittalness
noncommonable
noncommorancy
noncommunal
noncommunicable
noncommunicant
noncommunicating
noncommunication
noncommunion
noncommunist
noncommunistic
noncommutative
noncompearance
noncompensating
noncompensation
noncompetency
noncompetent
noncompeting
noncompetitive
noncompetitively
noncomplaisance
noncompletion
noncompliance
noncomplicity
noncomplying
noncomposite
noncompoundable
noncompounder
noncomprehension
noncompressible
noncompression
noncompulsion
noncomputation
noncon
nonconcealment
nonconceiving
nonconcentration
nonconception
nonconcern
nonconcession
nonconciliating
nonconcludency
nonconcludent
nonconcluding
nonconclusion
nonconcordant
nonconcur
nonconcurrence
nonconcurrency
nonconcurrent
noncondensable
noncondensation
noncondensible
noncondensing
noncondimental
nonconditioned
noncondonation
nonconducive
nonconductibility
nonconductible
nonconducting
nonconduction
nonconductive
nonconductor
nonconfederate
nonconferrable
nonconfession
nonconficient
nonconfident
nonconfidential
nonconfinement
nonconfirmation
nonconfirmative
nonconfiscable
nonconfiscation
nonconfitent
nonconflicting
nonconform
nonconformable
nonconformably
nonconformance
nonconformer
nonconforming
nonconformism
nonconformist
nonconformistical
nonconformistically
nonconformitant
nonconformity
nonconfutation
noncongealing
noncongenital
noncongestion
noncongratulatory
noncongruent
nonconjectural
nonconjugal
nonconjugate
nonconjunction
nonconnection
nonconnective
nonconnivance
nonconnotative
nonconnubial
nonconscientious
nonconscious
nonconscription
nonconsecration
nonconsecutive
nonconsent
nonconsenting
nonconsequence
nonconsequent
nonconservation
nonconservative
nonconserving
nonconsideration
nonconsignment
nonconsistorial
nonconsoling
nonconsonant
nonconsorting
nonconspirator
nonconspiring
nonconstituent
nonconstitutional
nonconstraint
nonconstruable
nonconstruction
nonconstructive
nonconsular
nonconsultative
nonconsumable
nonconsumption
noncontact
noncontagion
noncontagionist
noncontagious
noncontagiousness
noncontamination
noncontemplative
noncontending
noncontent
noncontention
noncontentious
noncontentiously
nonconterminous
noncontiguity
noncontiguous
noncontinental
noncontingent
noncontinuance
noncontinuation
noncontinuous
noncontraband
noncontraction
noncontradiction
noncontradictory
noncontributing
noncontribution
noncontributor
noncontributory
noncontrivance
noncontrolled
noncontrolling
noncontroversial
nonconvective
nonconvenable
nonconventional
nonconvergent
nonconversable
nonconversant
nonconversational
nonconversion
nonconvertible
nonconveyance
nonconviction
nonconvivial
noncoplanar
noncopying
noncoring
noncorporate
noncorporeality
noncorpuscular
noncorrection
noncorrective
noncorrelation
noncorrespondence
noncorrespondent
noncorresponding
noncorroboration
noncorroborative
noncorrodible
noncorroding
noncorrosive
noncorruption
noncortical
noncosmic
noncosmopolitism
noncostraight
noncottager
noncotyledonous
noncounty
noncranking
noncreation
noncreative
noncredence
noncredent
noncredibility
noncredible
noncreditor
noncreeping
noncrenate
noncretaceous
noncriminal
noncriminality
noncrinoid
noncritical
noncrucial
noncruciform
noncrusading
noncrushability
noncrushable
noncrustaceous
noncrystalline
noncrystallizable
noncrystallized
noncrystallizing
nonculmination
nonculpable
noncultivated
noncultivation
nonculture
noncumulative
noncurantist
noncurling
noncurrency
noncurrent
noncursive
noncurtailment
noncuspidate
noncustomary
noncutting
noncyclic
noncyclical
nonda
nondamageable
nondamnation
nondancer
nondangerous
nondatival
nondealer
nondebtor
nondecadence
nondecadent
nondecalcified
nondecane
nondecasyllabic
nondecatoic
nondecaying
nondeceivable
nondeception
nondeceptive
Nondeciduata
nondeciduate
nondeciduous
nondecision
nondeclarant
nondeclaration
nondeclarer
nondecomposition
nondecoration
nondedication
nondeduction
nondefalcation
nondefamatory
nondefaulting
nondefection
nondefendant
nondefense
nondefensive
nondeference
nondeferential
nondefiance
nondefilement
nondefining
nondefinition
nondefinitive
nondeforestation
nondegenerate
nondegeneration
nondegerming
nondegradation
nondegreased
nondehiscent
nondeist
nondelegable
nondelegate
nondelegation
nondeleterious
nondeliberate
nondeliberation
nondelineation
nondeliquescent
nondelirious
nondeliverance
nondelivery
nondemand
nondemise
nondemobilization
nondemocratic
nondemonstration
nondendroid
nondenial
nondenominational
nondenominationalism
nondense
nondenumerable
nondenunciation
nondepartmental
nondeparture
nondependence
nondependent
nondepletion
nondeportation
nondeported
nondeposition
nondepositor
nondepravity
nondepreciating
nondepressed
nondepression
nondeprivable
nonderivable
nonderivative
nonderogatory
nondescript
nondesecration
nondesignate
nondesigned
nondesire
nondesirous
nondesisting
nondespotic
nondesquamative
nondestructive
nondesulphurized
nondetachable
nondetailed
nondetention
nondetermination
nondeterminist
nondeterrent
nondetest
nondetonating
nondetrimental
nondevelopable
nondevelopment
nondeviation
nondevotional
nondexterous
nondiabetic
nondiabolic
nondiagnosis
nondiagonal
nondiagrammatic
nondialectal
nondialectical
nondialyzing
nondiametral
nondiastatic
nondiathermanous
nondiazotizable
nondichogamous
nondichogamy
nondichotomous
nondictation
nondictatorial
nondictionary
nondidactic
nondieting
nondifferentation
nondifferentiable
nondiffractive
nondiffusing
nondigestion
nondilatable
nondilution
nondiocesan
nondiphtheritic
nondiphthongal
nondiplomatic
nondipterous
nondirection
nondirectional
nondisagreement
nondisappearing
nondisarmament
nondisbursed
nondiscernment
nondischarging
nondisciplinary
nondisclaim
nondisclosure
nondiscontinuance
nondiscordant
nondiscountable
nondiscovery
nondiscretionary
nondiscrimination
nondiscriminatory
nondiscussion
nondisestablishment
nondisfigurement
nondisfranchised
nondisingenuous
nondisintegration
nondisinterested
nondisjunct
nondisjunction
nondisjunctional
nondisjunctive
nondismemberment
nondismissal
nondisparaging
nondisparate
nondispensation
nondispersal
nondispersion
nondisposal
nondisqualifying
nondissenting
nondissolution
nondistant
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nonspecified
nonspectacular
nonspectral
nonspeculation
nonspeculative
nonspherical
nonspill
nonspillable
nonspinning
nonspinose
nonspiny
nonspiral
nonspirit
nonspiritual
nonspirituous
nonspontaneous
nonspored
nonsporeformer
nonsporeforming
nonsporting
nonspottable
nonsprouting
nonstainable
nonstaining
nonstampable
nonstandard
nonstandardized
nonstanzaic
nonstaple
nonstarch
nonstarter
nonstarting
nonstatement
nonstatic
nonstationary
nonstatistical
nonstatutory
nonstellar
nonsticky
nonstimulant
nonstipulation
nonstock
nonstooping
nonstop
nonstrategic
nonstress
nonstretchable
nonstretchy
nonstriated
nonstriker
nonstriking
nonstriped
nonstructural
nonstudent
nonstudious
nonstylized
nonsubject
nonsubjective
nonsubmission
nonsubmissive
nonsubordination
nonsubscriber
nonsubscribing
nonsubscription
nonsubsiding
nonsubsidy
nonsubsistence
nonsubstantial
nonsubstantialism
nonsubstantialist
nonsubstantiality
nonsubstantiation
nonsubstantive
nonsubstitution
nonsubtraction
nonsuccess
nonsuccessful
nonsuccession
nonsuccessive
nonsuccour
nonsuction
nonsuctorial
nonsufferance
nonsuffrage
nonsugar
nonsuggestion
nonsuit
nonsulphurous
nonsummons
nonsupplication
nonsupport
nonsupporter
nonsupporting
nonsuppositional
nonsuppressed
nonsuppression
nonsuppurative
nonsurface
nonsurgical
nonsurrender
nonsurvival
nonsurvivor
nonsuspect
nonsustaining
nonsustenance
nonswearer
nonswearing
nonsweating
nonswimmer
nonswimming
nonsyllabic
nonsyllabicness
nonsyllogistic
nonsyllogizing
nonsymbiotic
nonsymbiotically
nonsymbolic
nonsymmetrical
nonsympathetic
nonsympathizer
nonsympathy
nonsymphonic
nonsymptomatic
nonsynchronous
nonsyndicate
nonsynodic
nonsynonymous
nonsyntactic
nonsyntactical
nonsynthesized
nonsyntonic
nonsystematic
nontabular
nontactical
nontan
nontangential
nontannic
nontannin
nontariff
nontarnishable
nontarnishing
nontautomeric
nontautomerizable
nontax
nontaxability
nontaxable
nontaxonomic
nonteachable
nonteacher
nonteaching
nontechnical
nontechnological
nonteetotaler
nontelegraphic
nonteleological
nontelephonic
nontemporal
nontemporizing
nontenant
nontenure
nontenurial
nonterm
nonterminating
nonterrestrial
nonterritorial
nonterritoriality
nontestamentary
nontextual
nontheatrical
nontheistic
nonthematic
nontheological
nontheosophical
nontherapeutic
nonthinker
nonthinking
nonthoracic
nonthoroughfare
nonthreaded
nontidal
nontillable
nontimbered
nontitaniferous
nontitular
nontolerated
nontopographical
nontourist
nontoxic
nontraction
nontrade
nontrader
nontrading
nontraditional
nontragic
nontrailing
nontransferability
nontransferable
nontransgression
nontransient
nontransitional
nontranslocation
nontransmission
nontransparency
nontransparent
nontransportation
nontransposing
nontransposition
nontraveler
nontraveling
nontreasonable
nontreated
nontreatment
nontreaty
nontrespass
nontrial
nontribal
nontribesman
nontributary
nontrier
nontrigonometrical
nontronite
nontropical
nontrunked
nontruth
nontuberculous
nontuned
nonturbinated
nontutorial
nontyphoidal
nontypical
nontypicalness
nontypographical
nontyrannical
nonubiquitous
nonulcerous
nonultrafilterable
nonumbilical
nonumbilicate
nonumbrellaed
nonunanimous
nonuncial
nonundergraduate
nonunderstandable
nonunderstanding
nonunderstandingly
nonunderstood
nonundulatory
nonuniform
nonuniformist
nonuniformitarian
nonuniformity
nonuniformly
nonunion
nonunionism
nonunionist
nonunique
nonunison
nonunited
nonuniversal
nonuniversity
nonupholstered
nonuple
nonuplet
nonupright
nonurban
nonurgent
nonusage
nonuse
nonuser
nonusing
nonusurping
nonuterine
nonutile
nonutilitarian
nonutility
nonutilized
nonutterance
nonvacant
nonvaccination
nonvacuous
nonvaginal
nonvalent
nonvalidity
nonvaluation
nonvalve
nonvanishing
nonvariable
nonvariant
nonvariation
nonvascular
nonvassal
nonvegetative
nonvenereal
nonvenomous
nonvenous
nonventilation
nonverbal
nonverdict
nonverminous
nonvernacular
nonvertebral
nonvertical
nonvertically
nonvesicular
nonvesting
nonvesture
nonveteran
nonveterinary
nonviable
nonvibratile
nonvibration
nonvibrator
nonvibratory
nonvicarious
nonvictory
nonvillager
nonvillainous
nonvindication
nonvinous
nonvintage
nonviolation
nonviolence
nonvirginal
nonvirile
nonvirtue
nonvirtuous
nonvirulent
nonviruliferous
nonvisaed
nonvisceral
nonviscid
nonviscous
nonvisional
nonvisitation
nonvisiting
nonvisual
nonvisualized
nonvital
nonvitreous
nonvitrified
nonviviparous
nonvocal
nonvocalic
nonvocational
nonvolant
nonvolatile
nonvolatilized
nonvolcanic
nonvolition
nonvoluntary
nonvortical
nonvortically
nonvoter
nonvoting
nonvulcanizable
nonvulvar
nonwalking
nonwar
nonwasting
nonwatertight
nonweakness
nonwestern
nonwetted
nonwhite
nonwinged
nonwoody
nonworker
nonworking
nonworship
nonwrinkleable
nonya
nonyielding
nonyl
nonylene
nonylenic
nonylic
nonzealous
nonzero
nonzodiacal
nonzonal
nonzonate
nonzoological
noodle
noodledom
noodleism
nook
nooked
nookery
nooking
nooklet
nooklike
nooky
noological
noologist
noology
noometry
noon
noonday
noonflower
nooning
noonlight
noonlit
noonstead
noontide
noontime
noonwards
noop
nooscopic
noose
nooser
Nootka
nopal
Nopalea
nopalry
nope
nopinene
nor
Nora
Norah
norard
norate
noration
norbergite
Norbert
Norbertine
norcamphane
nordcaper
nordenskioldine
Nordic
Nordicism
Nordicist
Nordicity
Nordicization
Nordicize
nordmarkite
noreast
noreaster
norelin
Norfolk
Norfolkian
norgine
nori
noria
Noric
norie
norimon
norite
norland
norlander
norlandism
norleucine
Norm
norm
Norma
norma
normal
normalcy
normalism
normalist
normality
normalization
normalize
normalizer
normally
normalness
Norman
Normanesque
Normanish
Normanism
Normanist
Normanization
Normanize
Normanizer
Normanly
Normannic
normated
normative
normatively
normativeness
normless
normoblast
normoblastic
normocyte
normocytic
normotensive
Norn
Norna
nornicotine
nornorwest
noropianic
norpinic
Norridgewock
Norroway
Norroy
Norse
norsel
Norseland
norseler
Norseman
Norsk
north
northbound
northeast
northeaster
northeasterly
northeastern
northeasternmost
northeastward
northeastwardly
northeastwards
norther
northerliness
northerly
northern
northerner
northernize
northernly
northernmost
northernness
northest
northfieldite
northing
northland
northlander
northlight
Northman
northmost
northness
Northumber
Northumbrian
northupite
northward
northwardly
northwards
northwest
northwester
northwesterly
northwestern
northwestward
northwestwardly
northwestwards
Norumbega
norward
norwards
Norway
Norwegian
norwest
norwester
norwestward
Nosairi
Nosairian
nosarian
nose
nosean
noseanite
noseband
nosebanded
nosebleed
nosebone
noseburn
nosed
nosegay
nosegaylike
noseherb
nosehole
noseless
noselessly
noselessness
noselike
noselite
Nosema
Nosematidae
nosepiece
nosepinch
noser
nosesmart
nosethirl
nosetiology
nosewards
nosewheel
nosewise
nosey
nosine
nosing
nosism
nosocomial
nosocomium
nosogenesis
nosogenetic
nosogenic
nosogeny
nosogeography
nosographer
nosographic
nosographical
nosographically
nosography
nosohaemia
nosohemia
nosological
nosologically
nosologist
nosology
nosomania
nosomycosis
nosonomy
nosophobia
nosophyte
nosopoetic
nosopoietic
nosotaxy
nosotrophy
nostalgia
nostalgic
nostalgically
nostalgy
nostic
Nostoc
Nostocaceae
nostocaceous
nostochine
nostologic
nostology
nostomania
Nostradamus
nostrificate
nostrification
nostril
nostriled
nostrility
nostrilsome
nostrum
nostrummonger
nostrummongership
nostrummongery
Nosu
nosy
not
notabilia
notability
notable
notableness
notably
notacanthid
Notacanthidae
notacanthoid
notacanthous
Notacanthus
notaeal
notaeum
notal
notalgia
notalgic
Notalia
notan
notandum
notanencephalia
notarial
notarially
notariate
notarikon
notarize
notary
notaryship
notate
notation
notational
notative
notator
notch
notchboard
notched
notchel
notcher
notchful
notching
notchweed
notchwing
notchy
note
notebook
notecase
noted
notedly
notedness
notehead
noteholder
notekin
Notelaea
noteless
notelessly
notelessness
notelet
notencephalocele
notencephalus
noter
notewise
noteworthily
noteworthiness
noteworthy
notharctid
Notharctidae
Notharctus
nother
nothing
nothingarian
nothingarianism
nothingism
nothingist
nothingize
nothingless
nothingly
nothingness
nothingology
Nothofagus
Notholaena
nothosaur
Nothosauri
nothosaurian
Nothosauridae
Nothosaurus
nothous
notice
noticeability
noticeable
noticeably
noticer
Notidani
notidanian
notidanid
Notidanidae
notidanidan
notidanoid
Notidanus
notifiable
notification
notified
notifier
notify
notifyee
notion
notionable
notional
notionalist
notionality
notionally
notionalness
notionary
notionate
notioned
notionist
notionless
Notiosorex
notitia
Notkerian
notocentrous
notocentrum
notochord
notochordal
notodontian
notodontid
Notodontidae
notodontoid
Notogaea
Notogaeal
Notogaean
Notogaeic
notommatid
Notommatidae
Notonecta
notonectal
notonectid
Notonectidae
notopodial
notopodium
notopterid
Notopteridae
notopteroid
Notopterus
notorhizal
Notorhynchus
notoriety
notorious
notoriously
notoriousness
Notornis
Notoryctes
Notostraca
Nototherium
Nototrema
nototribe
notour
notourly
Notropis
notself
Nottoway
notum
Notungulata
notungulate
Notus
notwithstanding
Nou
nougat
nougatine
nought
noumeaite
noumeite
noumenal
noumenalism
noumenalist
noumenality
noumenalize
noumenally
noumenism
noumenon
noun
nounal
nounally
nounize
nounless
noup
nourice
nourish
nourishable
nourisher
nourishing
nourishingly
nourishment
nouriture
nous
nouther
nova
novaculite
novalia
Novanglian
Novanglican
novantique
novarsenobenzene
novate
Novatian
Novatianism
Novatianist
novation
novative
novator
novatory
novatrix
novcic
novel
novelcraft
noveldom
novelese
novelesque
novelet
novelette
noveletter
novelettish
novelettist
noveletty
novelish
novelism
novelist
novelistic
novelistically
novelization
novelize
novella
novelless
novellike
novelly
novelmongering
novelness
novelry
novelty
novelwright
novem
novemarticulate
November
Novemberish
novemcostate
novemdigitate
novemfid
novemlobate
novemnervate
novemperfoliate
novena
novenary
novendial
novene
novennial
novercal
Novial
novice
novicehood
novicelike
noviceship
noviciate
novilunar
novitial
novitiate
novitiateship
novitiation
novity
Novo
Novocain
novodamus
Novorolsky
now
nowaday
nowadays
nowanights
noway
noways
nowed
nowel
nowhat
nowhen
nowhence
nowhere
nowhereness
nowheres
nowhit
nowhither
nowise
nowness
Nowroze
nowt
nowy
noxa
noxal
noxally
noxious
noxiously
noxiousness
noy
noyade
noyau
Nozi
nozzle
nozzler
nth
nu
nuance
nub
Nuba
nubbin
nubble
nubbling
nubbly
nubby
nubecula
nubia
Nubian
nubiferous
nubiform
nubigenous
nubilate
nubilation
nubile
nubility
nubilous
Nubilum
nucal
nucament
nucamentaceous
nucellar
nucellus
nucha
nuchal
nuchalgia
nuciculture
nuciferous
nuciform
nucin
nucivorous
nucleal
nuclear
nucleary
nuclease
nucleate
nucleation
nucleator
nuclei
nucleiferous
nucleiform
nuclein
nucleinase
nucleoalbumin
nucleoalbuminuria
nucleofugal
nucleohistone
nucleohyaloplasm
nucleohyaloplasma
nucleoid
nucleoidioplasma
nucleolar
nucleolated
nucleole
nucleoli
nucleolinus
nucleolocentrosome
nucleoloid
nucleolus
nucleolysis
nucleomicrosome
nucleon
nucleone
nucleonics
nucleopetal
nucleoplasm
nucleoplasmatic
nucleoplasmic
nucleoprotein
nucleoside
nucleotide
nucleus
nuclide
nuclidic
Nucula
Nuculacea
nuculanium
nucule
nuculid
Nuculidae
nuculiform
nuculoid
Nuda
nudate
nudation
Nudd
nuddle
nude
nudely
nudeness
Nudens
nudge
nudger
nudibranch
Nudibranchia
nudibranchian
nudibranchiate
nudicaudate
nudicaul
nudifier
nudiflorous
nudiped
nudish
nudism
nudist
nuditarian
nudity
nugacious
nugaciousness
nugacity
nugator
nugatoriness
nugatory
nuggar
nugget
nuggety
nugify
nugilogue
Nugumiut
nuisance
nuisancer
nuke
Nukuhivan
nul
null
nullable
nullah
nullibicity
nullibility
nullibiquitous
nullibist
nullification
nullificationist
nullificator
nullifidian
nullifier
nullify
nullipara
nulliparity
nulliparous
nullipennate
Nullipennes
nulliplex
nullipore
nulliporous
nullism
nullisome
nullisomic
nullity
nulliverse
nullo
Numa
Numantine
numb
number
numberable
numberer
numberful
numberless
numberous
numbersome
numbfish
numbing
numbingly
numble
numbles
numbly
numbness
numda
numdah
numen
Numenius
numerable
numerableness
numerably
numeral
numerant
numerary
numerate
numeration
numerative
numerator
numerical
numerically
numericalness
numerist
numero
numerology
numerose
numerosity
numerous
numerously
numerousness
Numida
Numidae
Numidian
Numididae
Numidinae
numinism
numinous
numinously
numismatic
numismatical
numismatically
numismatician
numismatics
numismatist
numismatography
numismatologist
numismatology
nummary
nummi
nummiform
nummular
Nummularia
nummulary
nummulated
nummulation
nummuline
Nummulinidae
nummulite
Nummulites
nummulitic
Nummulitidae
nummulitoid
nummuloidal
nummus
numskull
numskulled
numskulledness
numskullery
numskullism
numud
nun
nunatak
nunbird
nunch
nuncheon
nunciate
nunciative
nunciatory
nunciature
nuncio
nuncioship
nuncle
nuncupate
nuncupation
nuncupative
nuncupatively
nundinal
nundination
nundine
nunhood
Nunki
nunky
nunlet
nunlike
nunnari
nunnated
nunnation
nunnery
nunni
nunnify
nunnish
nunnishness
nunship
Nupe
Nuphar
nuptial
nuptiality
nuptialize
nuptially
nuptials
nuque
nuraghe
nurhag
nurly
nursable
nurse
nursedom
nursegirl
nursehound
nursekeeper
nursekin
nurselet
nurselike
nursemaid
nurser
nursery
nurserydom
nurseryful
nurserymaid
nurseryman
nursetender
nursing
nursingly
nursle
nursling
nursy
nurturable
nurtural
nurture
nurtureless
nurturer
nurtureship
Nusairis
Nusakan
nusfiah
nut
nutant
nutarian
nutate
nutation
nutational
nutbreaker
nutcake
nutcrack
nutcracker
nutcrackers
nutcrackery
nutgall
nuthatch
nuthook
nutjobber
nutlet
nutlike
nutmeg
nutmegged
nutmeggy
nutpecker
nutpick
nutramin
nutria
nutrice
nutricial
nutricism
nutrient
nutrify
nutriment
nutrimental
nutritial
nutrition
nutritional
nutritionally
nutritionist
nutritious
nutritiously
nutritiousness
nutritive
nutritively
nutritiveness
nutritory
nutseed
nutshell
Nuttallia
nuttalliasis
nuttalliosis
nutted
nutter
nuttery
nuttily
nuttiness
nutting
nuttish
nuttishness
nutty
nuzzer
nuzzerana
nuzzle
Nyamwezi
Nyanja
nyanza
Nyaya
nychthemer
nychthemeral
nychthemeron
Nyctaginaceae
nyctaginaceous
Nyctaginia
nyctalope
nyctalopia
nyctalopic
nyctalopy
Nyctanthes
Nyctea
Nyctereutes
nycteribiid
Nycteribiidae
Nycteridae
nycterine
Nycteris
Nycticorax
Nyctimene
nyctinastic
nyctinasty
nyctipelagic
Nyctipithecinae
nyctipithecine
Nyctipithecus
nyctitropic
nyctitropism
nyctophobia
nycturia
Nydia
nye
nylast
nylon
nymil
nymph
nympha
nymphae
Nymphaea
Nymphaeaceae
nymphaeaceous
nymphaeum
nymphal
nymphalid
Nymphalidae
Nymphalinae
nymphaline
nympheal
nymphean
nymphet
nymphic
nymphical
nymphid
nymphine
Nymphipara
nymphiparous
nymphish
nymphitis
nymphlike
nymphlin
nymphly
Nymphoides
nympholepsia
nympholepsy
nympholept
nympholeptic
nymphomania
nymphomaniac
nymphomaniacal
Nymphonacea
nymphosis
nymphotomy
nymphwise
Nyoro
Nyroca
Nyssa
Nyssaceae
nystagmic
nystagmus
nyxis
O
o
oadal
oaf
oafdom
oafish
oafishly
oafishness
oak
oakberry
Oakboy
oaken
oakenshaw
Oakesia
oaklet
oaklike
oakling
oaktongue
oakum
oakweb
oakwood
oaky
oam
Oannes
oar
oarage
oarcock
oared
oarfish
oarhole
oarial
oarialgia
oaric
oariocele
oariopathic
oariopathy
oariotomy
oaritic
oaritis
oarium
oarless
oarlike
oarlock
oarlop
oarman
oarsman
oarsmanship
oarswoman
oarweed
oary
oasal
oasean
oases
oasis
oasitic
oast
oasthouse
oat
oatbin
oatcake
oatear
oaten
oatenmeal
oatfowl
oath
oathay
oathed
oathful
oathlet
oathworthy
oatland
oatlike
oatmeal
oatseed
oaty
Obadiah
obambulate
obambulation
obambulatory
oban
Obbenite
obbligato
obclavate
obclude
obcompressed
obconical
obcordate
obcordiform
obcuneate
obdeltoid
obdiplostemonous
obdiplostemony
obdormition
obduction
obduracy
obdurate
obdurately
obdurateness
obduration
obe
obeah
obeahism
obeche
obedience
obediency
obedient
obediential
obedientially
obedientialness
obedientiar
obedientiary
obediently
obeisance
obeisant
obeisantly
obeism
obelia
obeliac
obelial
obelion
obeliscal
obeliscar
obelisk
obeliskoid
obelism
obelize
obelus
Oberon
obese
obesely
obeseness
obesity
obex
obey
obeyable
obeyer
obeyingly
obfuscable
obfuscate
obfuscation
obfuscator
obfuscity
obfuscous
obi
Obidicut
obispo
obit
obitual
obituarian
obituarily
obituarist
obituarize
obituary
object
objectable
objectation
objectative
objectee
objecthood
objectification
objectify
objection
objectionability
objectionable
objectionableness
objectionably
objectional
objectioner
objectionist
objectival
objectivate
objectivation
objective
objectively
objectiveness
objectivism
objectivist
objectivistic
objectivity
objectivize
objectization
objectize
objectless
objectlessly
objectlessness
objector
objicient
objuration
objure
objurgate
objurgation
objurgative
objurgatively
objurgator
objurgatorily
objurgatory
objurgatrix
oblanceolate
oblate
oblately
oblateness
oblation
oblational
oblationary
oblatory
oblectate
oblectation
obley
obligable
obligancy
obligant
obligate
obligation
obligational
obligative
obligativeness
obligator
obligatorily
obligatoriness
obligatory
obligatum
oblige
obliged
obligedly
obligedness
obligee
obligement
obliger
obliging
obligingly
obligingness
obligistic
obligor
obliquangular
obliquate
obliquation
oblique
obliquely
obliqueness
obliquitous
obliquity
obliquus
obliterable
obliterate
obliteration
obliterative
obliterator
oblivescence
oblivial
obliviality
oblivion
oblivionate
oblivionist
oblivionize
oblivious
obliviously
obliviousness
obliviscence
obliviscible
oblocutor
oblong
oblongatal
oblongated
oblongish
oblongitude
oblongitudinal
oblongly
oblongness
obloquial
obloquious
obloquy
obmutescence
obmutescent
obnebulate
obnounce
obnoxiety
obnoxious
obnoxiously
obnoxiousness
obnubilate
obnubilation
obnunciation
oboe
oboist
obol
Obolaria
obolary
obole
obolet
obolus
obomegoid
Obongo
oboval
obovate
obovoid
obpyramidal
obpyriform
Obrazil
obreption
obreptitious
obreptitiously
obrogate
obrogation
obrotund
obscene
obscenely
obsceneness
obscenity
obscurancy
obscurant
obscurantic
obscurantism
obscurantist
obscuration
obscurative
obscure
obscuredly
obscurely
obscurement
obscureness
obscurer
obscurism
obscurist
obscurity
obsecrate
obsecration
obsecrationary
obsecratory
obsede
obsequence
obsequent
obsequial
obsequience
obsequiosity
obsequious
obsequiously
obsequiousness
obsequity
obsequium
obsequy
observability
observable
observableness
observably
observance
observancy
observandum
observant
Observantine
Observantist
observantly
observantness
observation
observational
observationalism
observationally
observative
observatorial
observatory
observe
observedly
observer
observership
observing
observingly
obsess
obsessingly
obsession
obsessional
obsessionist
obsessive
obsessor
obsidian
obsidianite
obsidional
obsidionary
obsidious
obsignate
obsignation
obsignatory
obsolesce
obsolescence
obsolescent
obsolescently
obsolete
obsoletely
obsoleteness
obsoletion
obsoletism
obstacle
obstetric
obstetrical
obstetrically
obstetricate
obstetrication
obstetrician
obstetrics
obstetricy
obstetrist
obstetrix
obstinacious
obstinacy
obstinance
obstinate
obstinately
obstinateness
obstination
obstinative
obstipation
obstreperate
obstreperosity
obstreperous
obstreperously
obstreperousness
obstriction
obstringe
obstruct
obstructant
obstructedly
obstructer
obstructingly
obstruction
obstructionism
obstructionist
obstructive
obstructively
obstructiveness
obstructivism
obstructivity
obstructor
obstruent
obstupefy
obtain
obtainable
obtainal
obtainance
obtainer
obtainment
obtect
obtected
obtemper
obtemperate
obtenebrate
obtenebration
obtention
obtest
obtestation
obtriangular
obtrude
obtruder
obtruncate
obtruncation
obtruncator
obtrusion
obtrusionist
obtrusive
obtrusively
obtrusiveness
obtund
obtundent
obtunder
obtundity
obturate
obturation
obturator
obturatory
obturbinate
obtusangular
obtuse
obtusely
obtuseness
obtusifid
obtusifolious
obtusilingual
obtusilobous
obtusion
obtusipennate
obtusirostrate
obtusish
obtusity
obumbrant
obumbrate
obumbration
obvallate
obvelation
obvention
obverse
obversely
obversion
obvert
obvertend
obviable
obviate
obviation
obviative
obviator
obvious
obviously
obviousness
obvolute
obvoluted
obvolution
obvolutive
obvolve
obvolvent
ocarina
Occamism
Occamist
Occamistic
Occamite
occamy
occasion
occasionable
occasional
occasionalism
occasionalist
occasionalistic
occasionality
occasionally
occasionalness
occasionary
occasioner
occasionless
occasive
occident
occidental
Occidentalism
Occidentalist
occidentality
Occidentalization
Occidentalize
occidentally
occiduous
occipital
occipitalis
occipitally
occipitoanterior
occipitoatlantal
occipitoatloid
occipitoaxial
occipitoaxoid
occipitobasilar
occipitobregmatic
occipitocalcarine
occipitocervical
occipitofacial
occipitofrontal
occipitofrontalis
occipitohyoid
occipitoiliac
occipitomastoid
occipitomental
occipitonasal
occipitonuchal
occipitootic
occipitoparietal
occipitoposterior
occipitoscapular
occipitosphenoid
occipitosphenoidal
occipitotemporal
occipitothalamic
occiput
occitone
occlude
occludent
occlusal
occluse
occlusion
occlusive
occlusiveness
occlusocervical
occlusocervically
occlusogingival
occlusometer
occlusor
occult
occultate
occultation
occulter
occulting
occultism
occultist
occultly
occultness
occupable
occupance
occupancy
occupant
occupation
occupational
occupationalist
occupationally
occupationless
occupative
occupiable
occupier
occupy
occur
occurrence
occurrent
occursive
ocean
oceaned
oceanet
oceanful
Oceanian
oceanic
Oceanican
oceanity
oceanographer
oceanographic
oceanographical
oceanographically
oceanographist
oceanography
oceanology
oceanophyte
oceanside
oceanward
oceanwards
oceanways
oceanwise
ocellar
ocellary
ocellate
ocellated
ocellation
ocelli
ocellicyst
ocellicystic
ocelliferous
ocelliform
ocelligerous
ocellus
oceloid
ocelot
och
ochava
ochavo
ocher
ocherish
ocherous
ochery
ochidore
ochlesis
ochlesitic
ochletic
ochlocracy
ochlocrat
ochlocratic
ochlocratical
ochlocratically
ochlophobia
ochlophobist
Ochna
Ochnaceae
ochnaceous
ochone
Ochotona
Ochotonidae
Ochozoma
ochraceous
Ochrana
ochrea
ochreate
ochreous
ochro
ochrocarpous
ochroid
ochroleucous
ochrolite
Ochroma
ochronosis
ochronosus
ochronotic
ochrous
ocht
Ocimum
ock
oclock
Ocneria
ocote
Ocotea
ocotillo
ocque
ocracy
ocrea
ocreaceous
Ocreatae
ocreate
ocreated
octachloride
octachord
octachordal
octachronous
Octacnemus
octacolic
octactinal
octactine
Octactiniae
octactinian
octad
octadecahydrate
octadecane
octadecanoic
octadecyl
octadic
octadrachm
octaemeron
octaeteric
octaeterid
octagon
octagonal
octagonally
octahedral
octahedric
octahedrical
octahedrite
octahedroid
octahedron
octahedrous
octahydrate
octahydrated
octakishexahedron
octamerism
octamerous
octameter
octan
octanaphthene
Octandria
octandrian
octandrious
octane
octangle
octangular
octangularness
Octans
octant
octantal
octapla
octaploid
octaploidic
octaploidy
octapodic
octapody
octarch
octarchy
octarius
octarticulate
octary
octasemic
octastich
octastichon
octastrophic
octastyle
octastylos
octateuch
octaval
octavalent
octavarium
octave
Octavia
Octavian
octavic
octavina
Octavius
octavo
octenary
octene
octennial
octennially
octet
octic
octillion
octillionth
octine
octingentenary
octoad
octoalloy
octoate
octobass
October
octobrachiate
Octobrist
octocentenary
octocentennial
octochord
Octocoralla
octocorallan
Octocorallia
octocoralline
octocotyloid
octodactyl
octodactyle
octodactylous
octodecimal
octodecimo
octodentate
octodianome
Octodon
octodont
Octodontidae
Octodontinae
octoechos
octofid
octofoil
octofoiled
octogamy
octogenarian
octogenarianism
octogenary
octogild
octoglot
Octogynia
octogynian
octogynious
octogynous
octoic
octoid
octolateral
octolocular
octomeral
octomerous
octometer
octonal
octonare
octonarian
octonarius
octonary
octonematous
octonion
octonocular
octoon
octopartite
octopean
octoped
octopede
octopetalous
octophthalmous
octophyllous
octopi
octopine
octoploid
octoploidic
octoploidy
octopod
Octopoda
octopodan
octopodes
octopodous
octopolar
octopus
octoradial
octoradiate
octoradiated
octoreme
octoroon
octose
octosepalous
octospermous
octospore
octosporous
octostichous
octosyllabic
octosyllable
octovalent
octoyl
octroi
octroy
octuor
octuple
octuplet
octuplex
octuplicate
octuplication
octuply
octyl
octylene
octyne
ocuby
ocular
ocularist
ocularly
oculary
oculate
oculated
oculauditory
oculiferous
oculiform
oculigerous
Oculina
oculinid
Oculinidae
oculinoid
oculist
oculistic
oculocephalic
oculofacial
oculofrontal
oculomotor
oculomotory
oculonasal
oculopalpebral
oculopupillary
oculospinal
oculozygomatic
oculus
ocydrome
ocydromine
Ocydromus
Ocypete
Ocypoda
ocypodan
Ocypode
ocypodian
Ocypodidae
ocypodoid
Ocyroe
Ocyroidae
Od
od
oda
Odacidae
odacoid
odal
odalborn
odalisk
odalisque
odaller
odalman
odalwoman
Odax
odd
oddish
oddity
oddlegs
oddly
oddman
oddment
oddments
oddness
Odds
odds
Oddsbud
oddsman
ode
odel
odelet
Odelsthing
Odelsting
odeon
odeum
odic
odically
Odin
Odinian
Odinic
Odinism
Odinist
odinite
Odinitic
odiometer
odious
odiously
odiousness
odist
odium
odiumproof
Odobenidae
Odobenus
Odocoileus
odograph
odology
odometer
odometrical
odometry
Odonata
odontagra
odontalgia
odontalgic
Odontaspidae
Odontaspididae
Odontaspis
odontatrophia
odontatrophy
odontexesis
odontiasis
odontic
odontist
odontitis
odontoblast
odontoblastic
odontocele
Odontocete
odontocete
Odontoceti
odontocetous
odontochirurgic
odontoclasis
odontoclast
odontodynia
odontogen
odontogenesis
odontogenic
odontogeny
Odontoglossae
odontoglossal
odontoglossate
Odontoglossum
Odontognathae
odontognathic
odontognathous
odontograph
odontographic
odontography
odontohyperesthesia
odontoid
Odontolcae
odontolcate
odontolcous
odontolite
odontolith
odontological
odontologist
odontology
odontoloxia
odontoma
odontomous
odontonecrosis
odontoneuralgia
odontonosology
odontopathy
odontophoral
odontophore
Odontophoridae
Odontophorinae
odontophorine
odontophorous
Odontophorus
odontoplast
odontoplerosis
Odontopteris
Odontopteryx
odontorhynchous
Odontormae
Odontornithes
odontornithic
odontorrhagia
odontorthosis
odontoschism
odontoscope
odontosis
odontostomatous
odontostomous
Odontosyllis
odontotechny
odontotherapia
odontotherapy
odontotomy
Odontotormae
odontotripsis
odontotrypy
odoom
odophone
odor
odorant
odorate
odorator
odored
odorful
odoriferant
odoriferosity
odoriferous
odoriferously
odoriferousness
odorific
odorimeter
odorimetry
odoriphore
odorivector
odorize
odorless
odorometer
odorosity
odorous
odorously
odorousness
odorproof
Odostemon
Ods
odso
odum
odyl
odylic
odylism
odylist
odylization
odylize
Odynerus
Odyssean
Odyssey
Odz
Odzookers
Odzooks
oe
Oecanthus
oecist
oecodomic
oecodomical
oecoparasite
oecoparasitism
oecophobia
oecumenian
oecumenic
oecumenical
oecumenicalism
oecumenicity
oecus
oedemerid
Oedemeridae
oedicnemine
Oedicnemus
Oedipal
Oedipean
Oedipus
Oedogoniaceae
oedogoniaceous
Oedogoniales
Oedogonium
oenanthaldehyde
oenanthate
Oenanthe
oenanthic
oenanthol
oenanthole
oenanthyl
oenanthylate
oenanthylic
oenin
Oenocarpus
oenochoe
oenocyte
oenocytic
oenolin
oenological
oenologist
oenology
oenomancy
Oenomaus
oenomel
oenometer
oenophilist
oenophobist
oenopoetic
Oenothera
Oenotheraceae
oenotheraceous
Oenotrian
oer
oersted
oes
oesophageal
oesophagi
oesophagismus
oesophagostomiasis
Oesophagostomum
oesophagus
oestradiol
Oestrelata
oestrian
oestriasis
oestrid
Oestridae
oestrin
oestriol
oestroid
oestrous
oestrual
oestruate
oestruation
oestrum
oestrus
of
Ofer
off
offal
offaling
offbeat
offcast
offcome
offcut
offend
offendable
offendant
offended
offendedly
offendedness
offender
offendible
offendress
offense
offenseful
offenseless
offenselessly
offenseproof
offensible
offensive
offensively
offensiveness
offer
offerable
offeree
offerer
offering
offeror
offertorial
offertory
offgoing
offgrade
offhand
offhanded
offhandedly
offhandedness
office
officeholder
officeless
officer
officerage
officeress
officerhood
officerial
officerism
officerless
officership
official
officialdom
officialese
officialism
officiality
officialization
officialize
officially
officialty
officiant
officiary
officiate
officiation
officiator
officinal
officinally
officious
officiously
officiousness
offing
offish
offishly
offishness
offlet
offlook
offprint
offsaddle
offscape
offscour
offscourer
offscouring
offscum
offset
offshoot
offshore
offsider
offspring
offtake
offtype
offuscate
offuscation
offward
offwards
oflete
Ofo
oft
often
oftenness
oftens
oftentime
oftentimes
ofter
oftest
oftly
oftness
ofttime
ofttimes
oftwhiles
Og
ogaire
Ogallala
ogam
ogamic
Ogboni
Ogcocephalidae
Ogcocephalus
ogdoad
ogdoas
ogee
ogeed
ogganition
ogham
oghamic
Oghuz
ogival
ogive
ogived
Oglala
ogle
ogler
ogmic
Ogor
Ogpu
ogre
ogreish
ogreishly
ogreism
ogress
ogrish
ogrism
ogtiern
ogum
Ogygia
Ogygian
oh
ohelo
ohia
Ohio
Ohioan
ohm
ohmage
ohmic
ohmmeter
oho
ohoy
oidioid
oidiomycosis
oidiomycotic
Oidium
oii
oikology
oikoplast
oil
oilberry
oilbird
oilcan
oilcloth
oilcoat
oilcup
oildom
oiled
oiler
oilery
oilfish
oilhole
oilily
oiliness
oilless
oillessness
oillet
oillike
oilman
oilmonger
oilmongery
oilometer
oilpaper
oilproof
oilproofing
oilseed
oilskin
oilskinned
oilstock
oilstone
oilstove
oiltight
oiltightness
oilway
oily
oilyish
oime
oinochoe
oinology
oinomancy
oinomania
oinomel
oint
ointment
Oireachtas
oisin
oisivity
oitava
oiticica
Ojibwa
Ojibway
Ok
oka
okapi
Okapia
okee
okenite
oket
oki
okia
Okie
Okinagan
Oklafalaya
Oklahannali
Oklahoma
Oklahoman
okoniosis
okonite
okra
okrug
okshoofd
okthabah
Okuari
okupukupu
Olacaceae
olacaceous
Olaf
olam
olamic
Olax
Olcha
Olchi
Old
old
olden
Oldenburg
older
oldermost
oldfangled
oldfangledness
Oldfieldia
Oldhamia
oldhamite
oldhearted
oldish
oldland
oldness
oldster
oldwife
Ole
Olea
Oleaceae
oleaceous
Oleacina
Oleacinidae
oleaginous
oleaginousness
oleana
oleander
oleandrin
Olearia
olease
oleaster
oleate
olecranal
olecranarthritis
olecranial
olecranian
olecranoid
olecranon
olefiant
olefin
olefine
olefinic
Oleg
oleic
oleiferous
olein
olena
olenellidian
Olenellus
olenid
Olenidae
olenidian
olent
Olenus
oleo
oleocalcareous
oleocellosis
oleocyst
oleoduct
oleograph
oleographer
oleographic
oleography
oleomargaric
oleomargarine
oleometer
oleoptene
oleorefractometer
oleoresin
oleoresinous
oleosaccharum
oleose
oleosity
oleostearate
oleostearin
oleothorax
oleous
Oleraceae
oleraceous
olericultural
olericulturally
olericulture
Oleron
Olethreutes
olethreutid
Olethreutidae
olfact
olfactible
olfaction
olfactive
olfactology
olfactometer
olfactometric
olfactometry
olfactor
olfactorily
olfactory
olfacty
Olga
oliban
olibanum
olid
oligacanthous
oligaemia
oligandrous
oliganthous
oligarch
oligarchal
oligarchic
oligarchical
oligarchically
oligarchism
oligarchist
oligarchize
oligarchy
oligemia
oligidria
oligist
oligistic
oligistical
oligocarpous
Oligocene
Oligochaeta
oligochaete
oligochaetous
oligochete
oligocholia
oligochrome
oligochromemia
oligochronometer
oligochylia
oligoclase
oligoclasite
oligocystic
oligocythemia
oligocythemic
oligodactylia
oligodendroglia
oligodendroglioma
oligodipsia
oligodontous
oligodynamic
oligogalactia
oligohemia
oligohydramnios
oligolactia
oligomenorrhea
oligomerous
oligomery
oligometochia
oligometochic
Oligomyodae
oligomyodian
oligomyoid
Oligonephria
oligonephric
oligonephrous
oligonite
oligopepsia
oligopetalous
oligophagous
oligophosphaturia
oligophrenia
oligophrenic
oligophyllous
oligoplasmia
oligopnea
oligopolistic
oligopoly
oligoprothesy
oligoprothetic
oligopsonistic
oligopsony
oligopsychia
oligopyrene
oligorhizous
oligosepalous
oligosialia
oligosideric
oligosiderite
oligosite
oligospermia
oligospermous
oligostemonous
oligosyllabic
oligosyllable
oligosynthetic
oligotokous
oligotrichia
oligotrophic
oligotrophy
oligotropic
oliguresis
oliguretic
oliguria
Olinia
Oliniaceae
oliniaceous
olio
oliphant
oliprance
olitory
Oliva
oliva
olivaceous
olivary
Olive
olive
Olivean
olived
Olivella
oliveness
olivenite
Oliver
Oliverian
oliverman
oliversmith
olivescent
olivet
Olivetan
Olivette
olivewood
Olivia
Olividae
Olivier
oliviferous
oliviform
olivil
olivile
olivilin
olivine
olivinefels
olivinic
olivinite
olivinitic
olla
ollamh
ollapod
ollenite
Ollie
ollock
olm
Olneya
Olof
ological
ologist
ologistic
ology
olomao
olona
Olonets
Olonetsian
Olonetsish
Olor
oloroso
olpe
Olpidiaster
Olpidium
Olson
oltonde
oltunna
olycook
olykoek
Olympia
Olympiad
Olympiadic
Olympian
Olympianism
Olympianize
Olympianly
Olympianwise
Olympic
Olympicly
Olympicness
Olympieion
Olympionic
Olympus
Olynthiac
Olynthian
Olynthus
om
omadhaun
omagra
Omagua
Omaha
omalgia
Oman
Omani
omao
Omar
omarthritis
omasitis
omasum
omber
ombrette
ombrifuge
ombrograph
ombrological
ombrology
ombrometer
ombrophile
ombrophilic
ombrophilous
ombrophily
ombrophobe
ombrophobous
ombrophoby
ombrophyte
ombudsman
ombudsmanship
omega
omegoid
omelet
omelette
omen
omened
omenology
omental
omentectomy
omentitis
omentocele
omentofixation
omentopexy
omentoplasty
omentorrhaphy
omentosplenopexy
omentotomy
omentulum
omentum
omer
omicron
omina
ominous
ominously
ominousness
omissible
omission
omissive
omissively
omit
omitis
omittable
omitter
omlah
Ommastrephes
Ommastrephidae
ommateal
ommateum
ommatidial
ommatidium
ommatophore
ommatophorous
Ommiad
Ommiades
omneity
omniactive
omniactuality
omniana
omniarch
omnibenevolence
omnibenevolent
omnibus
omnibusman
omnicausality
omnicompetence
omnicompetent
omnicorporeal
omnicredulity
omnicredulous
omnidenominational
omnierudite
omniessence
omnifacial
omnifarious
omnifariously
omnifariousness
omniferous
omnific
omnificent
omnifidel
omniform
omniformal
omniformity
omnify
omnigenous
omnigerent
omnigraph
omnihuman
omnihumanity
omnilegent
omnilingual
omniloquent
omnilucent
omnimental
omnimeter
omnimode
omnimodous
omninescience
omninescient
omniparent
omniparient
omniparity
omniparous
omnipatient
omnipercipience
omnipercipiency
omnipercipient
omniperfect
omnipotence
omnipotency
omnipotent
omnipotentiality
omnipotently
omnipregnant
omnipresence
omnipresent
omnipresently
omniprevalence
omniprevalent
omniproduction
omniprudent
omnirange
omniregency
omnirepresentative
omnirepresentativeness
omnirevealing
omniscience
omnisciency
omniscient
omnisciently
omniscope
omniscribent
omniscriptive
omnisentience
omnisentient
omnisignificance
omnisignificant
omnispective
omnist
omnisufficiency
omnisufficient
omnitemporal
omnitenent
omnitolerant
omnitonal
omnitonality
omnitonic
omnitude
omnium
omnivagant
omnivalence
omnivalent
omnivalous
omnivarious
omnividence
omnivident
omnivision
omnivolent
Omnivora
omnivoracious
omnivoracity
omnivorant
omnivore
omnivorous
omnivorously
omnivorousness
omodynia
omohyoid
omoideum
omophagia
omophagist
omophagous
omophagy
omophorion
omoplate
omoplatoscopy
omostegite
omosternal
omosternum
omphacine
omphacite
omphalectomy
omphalic
omphalism
omphalitis
omphalocele
omphalode
omphalodium
omphalogenous
omphaloid
omphaloma
omphalomesaraic
omphalomesenteric
omphaloncus
omphalopagus
omphalophlebitis
omphalopsychic
omphalopsychite
omphalorrhagia
omphalorrhea
omphalorrhexis
omphalos
omphalosite
omphaloskepsis
omphalospinous
omphalotomy
omphalotripsy
omphalus
on
Ona
ona
onager
Onagra
onagra
Onagraceae
onagraceous
Onan
onanism
onanist
onanistic
onca
once
oncetta
Onchidiidae
Onchidium
Onchocerca
onchocerciasis
onchocercosis
oncia
Oncidium
oncin
oncograph
oncography
oncologic
oncological
oncology
oncome
oncometer
oncometric
oncometry
oncoming
Oncorhynchus
oncosimeter
oncosis
oncosphere
oncost
oncostman
oncotomy
ondagram
ondagraph
ondameter
ondascope
ondatra
ondine
ondogram
ondograph
ondometer
ondoscope
ondy
one
oneanother
oneberry
onefold
onefoldness
onegite
onehearted
onehow
Oneida
oneiric
oneirocrit
oneirocritic
oneirocritical
oneirocritically
oneirocriticism
oneirocritics
oneirodynia
oneirologist
oneirology
oneiromancer
oneiromancy
oneiroscopic
oneiroscopist
oneiroscopy
oneirotic
oneism
onement
oneness
oner
onerary
onerative
onerosity
onerous
onerously
onerousness
onery
oneself
onesigned
onetime
onewhere
oneyer
onfall
onflemed
onflow
onflowing
ongaro
ongoing
onhanger
onicolo
oniomania
oniomaniac
onion
onionet
onionized
onionlike
onionpeel
onionskin
oniony
onirotic
Oniscidae
onisciform
oniscoid
Oniscoidea
oniscoidean
Oniscus
onium
onkilonite
onkos
onlay
onlepy
onliest
onliness
onlook
onlooker
onlooking
only
onmarch
Onmun
Onobrychis
onocentaur
Onoclea
onofrite
Onohippidium
onolatry
onomancy
onomantia
onomastic
onomasticon
onomatologist
onomatology
onomatomania
onomatope
onomatoplasm
onomatopoeia
onomatopoeial
onomatopoeian
onomatopoeic
onomatopoeical
onomatopoeically
onomatopoesis
onomatopoesy
onomatopoetic
onomatopoetically
onomatopy
onomatous
onomomancy
Onondaga
Onondagan
Ononis
Onopordon
Onosmodium
onrush
onrushing
ons
onset
onsetter
onshore
onside
onsight
onslaught
onstand
onstanding
onstead
onsweep
onsweeping
ontal
Ontarian
Ontaric
onto
ontocycle
ontocyclic
ontogenal
ontogenesis
ontogenetic
ontogenetical
ontogenetically
ontogenic
ontogenically
ontogenist
ontogeny
ontography
ontologic
ontological
ontologically
ontologism
ontologist
ontologistic
ontologize
ontology
ontosophy
onus
onwaiting
onward
onwardly
onwardness
onwards
onycha
onychatrophia
onychauxis
onychia
onychin
onychitis
onychium
onychogryposis
onychoid
onycholysis
onychomalacia
onychomancy
onychomycosis
onychonosus
onychopathic
onychopathology
onychopathy
onychophagist
onychophagy
Onychophora
onychophoran
onychophorous
onychophyma
onychoptosis
onychorrhexis
onychoschizia
onychosis
onychotrophy
onym
onymal
onymancy
onymatic
onymity
onymize
onymous
onymy
onyx
onyxis
onyxitis
onza
ooangium
ooblast
ooblastic
oocyesis
oocyst
Oocystaceae
oocystaceous
oocystic
Oocystis
oocyte
oodles
ooecial
ooecium
oofbird
ooftish
oofy
oogamete
oogamous
oogamy
oogenesis
oogenetic
oogeny
ooglea
oogone
oogonial
oogoniophore
oogonium
oograph
ooid
ooidal
ookinesis
ookinete
ookinetic
oolak
oolemma
oolite
oolitic
oolly
oologic
oological
oologically
oologist
oologize
oology
oolong
oomancy
oomantia
oometer
oometric
oometry
oomycete
Oomycetes
oomycetous
oons
oont
oopak
oophoralgia
oophorauxe
oophore
oophorectomy
oophoreocele
oophorhysterectomy
oophoric
oophoridium
oophoritis
oophoroepilepsy
oophoroma
oophoromalacia
oophoromania
oophoron
oophoropexy
oophororrhaphy
oophorosalpingectomy
oophorostomy
oophorotomy
oophyte
oophytic
ooplasm
ooplasmic
ooplast
oopod
oopodal
ooporphyrin
oorali
oord
ooscope
ooscopy
oosperm
oosphere
oosporange
oosporangium
oospore
Oosporeae
oosporic
oosporiferous
oosporous
oostegite
oostegitic
ootheca
oothecal
ootid
ootocoid
Ootocoidea
ootocoidean
ootocous
ootype
ooze
oozily
ooziness
oozooid
oozy
opacate
opacification
opacifier
opacify
opacite
opacity
opacous
opacousness
opah
opal
opaled
opalesce
opalescence
opalescent
opalesque
Opalina
opaline
opalinid
Opalinidae
opalinine
opalish
opalize
opaloid
opaque
opaquely
opaqueness
Opata
opdalite
ope
Opegrapha
opeidoscope
opelet
open
openable
openband
openbeak
openbill
opencast
opener
openhanded
openhandedly
openhandedness
openhead
openhearted
openheartedly
openheartedness
opening
openly
openmouthed
openmouthedly
openmouthedness
openness
openside
openwork
opera
operability
operabily
operable
operae
operagoer
operalogue
operameter
operance
operancy
operand
operant
operatable
operate
operatee
operatic
operatical
operatically
operating
operation
operational
operationalism
operationalist
operationism
operationist
operative
operatively
operativeness
operativity
operatize
operator
operatory
operatrix
opercle
opercled
opercula
opercular
Operculata
operculate
operculated
operculiferous
operculiform
operculigenous
operculigerous
operculum
operetta
operette
operettist
operose
operosely
operoseness
operosity
Ophelia
ophelimity
Ophian
ophiasis
ophic
ophicalcite
Ophicephalidae
ophicephaloid
Ophicephalus
Ophichthyidae
ophichthyoid
ophicleide
ophicleidean
ophicleidist
Ophidia
ophidian
Ophidiidae
Ophidiobatrachia
ophidioid
Ophidion
ophidiophobia
ophidious
ophidologist
ophidology
Ophiobatrachia
Ophiobolus
Ophioglossaceae
ophioglossaceous
Ophioglossales
Ophioglossum
ophiography
ophioid
ophiolater
ophiolatrous
ophiolatry
ophiolite
ophiolitic
ophiologic
ophiological
ophiologist
ophiology
ophiomancy
ophiomorph
Ophiomorpha
ophiomorphic
ophiomorphous
Ophion
ophionid
Ophioninae
ophionine
ophiophagous
ophiophilism
ophiophilist
ophiophobe
ophiophobia
ophiophoby
ophiopluteus
Ophiosaurus
ophiostaphyle
ophiouride
Ophis
Ophisaurus
Ophism
Ophite
ophite
Ophitic
ophitic
Ophitism
Ophiuchid
Ophiuchus
ophiuran
ophiurid
Ophiurida
ophiuroid
Ophiuroidea
ophiuroidean
ophryon
Ophrys
ophthalaiater
ophthalmagra
ophthalmalgia
ophthalmalgic
ophthalmatrophia
ophthalmectomy
ophthalmencephalon
ophthalmetrical
ophthalmia
ophthalmiac
ophthalmiatrics
ophthalmic
ophthalmious
ophthalmist
ophthalmite
ophthalmitic
ophthalmitis
ophthalmoblennorrhea
ophthalmocarcinoma
ophthalmocele
ophthalmocopia
ophthalmodiagnosis
ophthalmodiastimeter
ophthalmodynamometer
ophthalmodynia
ophthalmography
ophthalmoleucoscope
ophthalmolith
ophthalmologic
ophthalmological
ophthalmologist
ophthalmology
ophthalmomalacia
ophthalmometer
ophthalmometric
ophthalmometry
ophthalmomycosis
ophthalmomyositis
ophthalmomyotomy
ophthalmoneuritis
ophthalmopathy
ophthalmophlebotomy
ophthalmophore
ophthalmophorous
ophthalmophthisis
ophthalmoplasty
ophthalmoplegia
ophthalmoplegic
ophthalmopod
ophthalmoptosis
ophthalmorrhagia
ophthalmorrhea
ophthalmorrhexis
Ophthalmosaurus
ophthalmoscope
ophthalmoscopic
ophthalmoscopical
ophthalmoscopist
ophthalmoscopy
ophthalmostasis
ophthalmostat
ophthalmostatometer
ophthalmothermometer
ophthalmotomy
ophthalmotonometer
ophthalmotonometry
ophthalmotrope
ophthalmotropometer
ophthalmy
opianic
opianyl
opiate
opiateproof
opiatic
Opiconsivia
opificer
opiism
Opilia
Opiliaceae
opiliaceous
Opiliones
Opilionina
opilionine
Opilonea
Opimian
opinability
opinable
opinably
opinant
opination
opinative
opinatively
opinator
opine
opiner
opiniaster
opiniastre
opiniastrety
opiniastrous
opiniater
opiniative
opiniatively
opiniativeness
opiniatreness
opiniatrety
opinion
opinionable
opinionaire
opinional
opinionate
opinionated
opinionatedly
opinionatedness
opinionately
opinionative
opinionatively
opinionativeness
opinioned
opinionedness
opinionist
opiomania
opiomaniac
opiophagism
opiophagy
opiparous
opisometer
opisthenar
opisthion
opisthobranch
Opisthobranchia
opisthobranchiate
Opisthocoelia
opisthocoelian
opisthocoelous
opisthocome
Opisthocomi
Opisthocomidae
opisthocomine
opisthocomous
opisthodetic
opisthodome
opisthodomos
opisthodomus
opisthodont
opisthogastric
Opisthoglossa
opisthoglossal
opisthoglossate
opisthoglyph
Opisthoglypha
opisthoglyphic
opisthoglyphous
Opisthognathidae
opisthognathism
opisthognathous
opisthograph
opisthographal
opisthographic
opisthographical
opisthography
opisthogyrate
opisthogyrous
Opisthoparia
opisthoparian
opisthophagic
opisthoporeia
opisthorchiasis
Opisthorchis
opisthosomal
Opisthothelae
opisthotic
opisthotonic
opisthotonoid
opisthotonos
opisthotonus
opium
opiumism
opobalsam
opodeldoc
opodidymus
opodymus
opopanax
Oporto
opossum
opotherapy
Oppian
oppidan
oppilate
oppilation
oppilative
opponency
opponent
opportune
opportuneless
opportunely
opportuneness
opportunism
opportunist
opportunistic
opportunistically
opportunity
opposability
opposable
oppose
opposed
opposeless
opposer
opposing
opposingly
opposit
opposite
oppositely
oppositeness
oppositiflorous
oppositifolious
opposition
oppositional
oppositionary
oppositionism
oppositionist
oppositionless
oppositious
oppositipetalous
oppositipinnate
oppositipolar
oppositisepalous
oppositive
oppositively
oppositiveness
opposure
oppress
oppressed
oppressible
oppression
oppressionist
oppressive
oppressively
oppressiveness
oppressor
opprobriate
opprobrious
opprobriously
opprobriousness
opprobrium
opprobry
oppugn
oppugnacy
oppugnance
oppugnancy
oppugnant
oppugnate
oppugnation
oppugner
opsigamy
opsimath
opsimathy
opsiometer
opsisform
opsistype
opsonic
opsoniferous
opsonification
opsonify
opsonin
opsonist
opsonium
opsonization
opsonize
opsonogen
opsonoid
opsonology
opsonometry
opsonophilia
opsonophilic
opsonophoric
opsonotherapy
opsy
opt
optable
optableness
optably
optant
optate
optation
optative
optatively
opthalmophorium
opthalmoplegy
opthalmothermometer
optic
optical
optically
optician
opticist
opticity
opticochemical
opticociliary
opticon
opticopapillary
opticopupillary
optics
optigraph
optimacy
optimal
optimate
optimates
optime
optimism
optimist
optimistic
optimistical
optimistically
optimity
optimization
optimize
optimum
option
optional
optionality
optionalize
optionally
optionary
optionee
optionor
optive
optoblast
optogram
optography
optological
optologist
optology
optomeninx
optometer
optometrical
optometrist
optometry
optophone
optotechnics
optotype
Opulaster
opulence
opulency
opulent
opulently
opulus
Opuntia
Opuntiaceae
Opuntiales
opuntioid
opus
opuscular
opuscule
opusculum
oquassa
or
ora
orabassu
orach
oracle
oracular
oracularity
oracularly
oracularness
oraculate
oraculous
oraculously
oraculousness
oraculum
orad
orage
oragious
Orakzai
oral
oraler
oralism
oralist
orality
oralization
oralize
orally
oralogist
oralogy
Orang
orang
orange
orangeade
orangebird
Orangeism
Orangeist
orangeleaf
Orangeman
orangeman
oranger
orangeroot
orangery
orangewoman
orangewood
orangey
orangism
orangist
orangite
orangize
orangutan
orant
Oraon
orarian
orarion
orarium
orary
orate
oration
orational
orationer
orator
oratorial
oratorially
Oratorian
oratorian
Oratorianism
Oratorianize
oratoric
oratorical
oratorically
oratorio
oratorize
oratorlike
oratorship
oratory
oratress
oratrix
orb
orbed
orbic
orbical
Orbicella
orbicle
orbicular
orbicularis
orbicularity
orbicularly
orbicularness
orbiculate
orbiculated
orbiculately
orbiculation
orbiculatocordate
orbiculatoelliptical
Orbiculoidea
orbific
Orbilian
Orbilius
orbit
orbital
orbitale
orbitar
orbitary
orbite
orbitelar
Orbitelariae
orbitelarian
orbitele
orbitelous
orbitofrontal
Orbitoides
Orbitolina
orbitolite
Orbitolites
orbitomalar
orbitomaxillary
orbitonasal
orbitopalpebral
orbitosphenoid
orbitosphenoidal
orbitostat
orbitotomy
orbitozygomatic
orbless
orblet
Orbulina
orby
orc
Orca
Orcadian
orcanet
orcein
orchamus
orchard
orcharding
orchardist
orchardman
orchat
orchel
orchella
orchesis
orchesography
orchester
Orchestia
orchestian
orchestic
orchestiid
Orchestiidae
orchestra
orchestral
orchestraless
orchestrally
orchestrate
orchestrater
orchestration
orchestrator
orchestre
orchestric
orchestrina
orchestrion
orchialgia
orchic
orchichorea
orchid
Orchidaceae
orchidacean
orchidaceous
Orchidales
orchidalgia
orchidectomy
orchideous
orchideously
orchidist
orchiditis
orchidocele
orchidocelioplasty
orchidologist
orchidology
orchidomania
orchidopexy
orchidoplasty
orchidoptosis
orchidorrhaphy
orchidotherapy
orchidotomy
orchiectomy
orchiencephaloma
orchiepididymitis
orchil
orchilla
orchilytic
orchiocatabasis
orchiocele
orchiodynia
orchiomyeloma
orchioncus
orchioneuralgia
orchiopexy
orchioplasty
orchiorrhaphy
orchioscheocele
orchioscirrhus
orchiotomy
Orchis
orchitic
orchitis
orchotomy
orcin
orcinol
Orcinus
ordain
ordainable
ordainer
ordainment
ordanchite
ordeal
order
orderable
ordered
orderedness
orderer
orderless
orderliness
orderly
ordinable
ordinal
ordinally
ordinance
ordinand
ordinant
ordinar
ordinarily
ordinariness
ordinarius
ordinary
ordinaryship
ordinate
ordinately
ordination
ordinative
ordinatomaculate
ordinator
ordinee
ordines
ordnance
ordonnance
ordonnant
ordosite
Ordovian
Ordovices
Ordovician
ordu
ordure
ordurous
ore
oread
Oreamnos
Oreas
orecchion
orectic
orective
oreillet
orellin
oreman
orenda
orendite
Oreocarya
Oreodon
oreodont
Oreodontidae
oreodontine
oreodontoid
Oreodoxa
Oreophasinae
oreophasine
Oreophasis
Oreortyx
oreotragine
Oreotragus
Oreotrochilus
Orestean
Oresteia
oreweed
orewood
orexis
orf
orfgild
organ
organal
organbird
organdy
organella
organelle
organer
organette
organic
organical
organically
organicalness
organicism
organicismal
organicist
organicistic
organicity
organific
organing
organism
organismal
organismic
organist
organistic
organistrum
organistship
organity
organizability
organizable
organization
organizational
organizationally
organizationist
organizatory
organize
organized
organizer
organless
organoantimony
organoarsenic
organobismuth
organoboron
organochordium
organogel
organogen
organogenesis
organogenetic
organogenic
organogenist
organogeny
organogold
organographic
organographical
organographist
organography
organoid
organoiron
organolead
organoleptic
organolithium
organologic
organological
organologist
organology
organomagnesium
organomercury
organometallic
organon
organonomic
organonomy
organonym
organonymal
organonymic
organonymy
organopathy
organophil
organophile
organophilic
organophone
organophonic
organophyly
organoplastic
organoscopy
organosilicon
organosilver
organosodium
organosol
organotherapy
organotin
organotrophic
organotropic
organotropically
organotropism
organotropy
organozinc
organry
organule
organum
organzine
orgasm
orgasmic
orgastic
orgeat
orgia
orgiac
orgiacs
orgiasm
orgiast
orgiastic
orgiastical
orgic
orgue
orguinette
orgulous
orgulously
orgy
orgyia
Orias
Oribatidae
oribi
orichalceous
orichalch
orichalcum
oriconic
oricycle
oriel
oriency
orient
Oriental
oriental
Orientalia
orientalism
orientalist
orientality
orientalization
orientalize
orientally
Orientalogy
orientate
orientation
orientative
orientator
orientite
orientization
orientize
oriently
orientness
orifacial
orifice
orificial
oriflamb
oriflamme
oriform
origan
origanized
Origanum
Origenian
Origenic
Origenical
Origenism
Origenist
Origenistic
Origenize
origin
originable
original
originalist
originality
originally
originalness
originant
originarily
originary
originate
origination
originative
originatively
originator
originatress
originist
orignal
orihon
orihyperbola
orillion
orillon
orinasal
orinasality
oriole
Oriolidae
Oriolus
Orion
Oriskanian
orismologic
orismological
orismology
orison
orisphere
oristic
Oriya
Orkhon
Orkneyan
Orlando
orle
orlean
Orleanism
Orleanist
Orleanistic
Orleans
orlet
orleways
orlewise
orlo
orlop
Ormazd
ormer
ormolu
Ormond
orna
ornament
ornamental
ornamentalism
ornamentalist
ornamentality
ornamentalize
ornamentally
ornamentary
ornamentation
ornamenter
ornamentist
ornate
ornately
ornateness
ornation
ornature
orneriness
ornery
ornis
orniscopic
orniscopist
orniscopy
ornithic
ornithichnite
ornithine
Ornithischia
ornithischian
ornithivorous
ornithobiographical
ornithobiography
ornithocephalic
Ornithocephalidae
ornithocephalous
Ornithocephalus
ornithocoprolite
ornithocopros
ornithodelph
Ornithodelphia
ornithodelphian
ornithodelphic
ornithodelphous
Ornithodoros
Ornithogaea
Ornithogaean
Ornithogalum
ornithogeographic
ornithogeographical
ornithography
ornithoid
Ornitholestes
ornitholite
ornitholitic
ornithologic
ornithological
ornithologically
ornithologist
ornithology
ornithomancy
ornithomantia
ornithomantic
ornithomantist
Ornithomimidae
Ornithomimus
ornithomorph
ornithomorphic
ornithomyzous
ornithon
Ornithopappi
ornithophile
ornithophilist
ornithophilite
ornithophilous
ornithophily
ornithopod
Ornithopoda
ornithopter
Ornithoptera
Ornithopteris
Ornithorhynchidae
ornithorhynchous
Ornithorhynchus
ornithosaur
Ornithosauria
ornithosaurian
Ornithoscelida
ornithoscelidan
ornithoscopic
ornithoscopist
ornithoscopy
ornithosis
ornithotomical
ornithotomist
ornithotomy
ornithotrophy
Ornithurae
ornithuric
ornithurous
ornoite
oroanal
Orobanchaceae
orobanchaceous
Orobanche
orobancheous
orobathymetric
Orobatoidea
Orochon
orocratic
orodiagnosis
orogen
orogenesis
orogenesy
orogenetic
orogenic
orogeny
orograph
orographic
orographical
orographically
orography
oroheliograph
Orohippus
orohydrographic
orohydrographical
orohydrography
oroide
orolingual
orological
orologist
orology
orometer
orometric
orometry
Oromo
oronasal
oronoco
Orontium
oropharyngeal
oropharynx
orotherapy
Orotinan
orotund
orotundity
orphan
orphancy
orphandom
orphange
orphanhood
orphanism
orphanize
orphanry
orphanship
orpharion
Orphean
Orpheist
orpheon
orpheonist
orpheum
Orpheus
Orphic
Orphical
Orphically
Orphicism
Orphism
Orphize
orphrey
orphreyed
orpiment
orpine
Orpington
orrery
orrhoid
orrhology
orrhotherapy
orris
orrisroot
orseille
orseilline
orsel
orselle
orseller
orsellic
orsellinate
orsellinic
Orson
ort
ortalid
Ortalidae
ortalidian
Ortalis
ortet
Orthagoriscus
orthal
orthantimonic
Ortheris
orthian
orthic
orthicon
orthid
Orthidae
Orthis
orthite
orthitic
ortho
orthoarsenite
orthoaxis
orthobenzoquinone
orthobiosis
orthoborate
orthobrachycephalic
orthocarbonic
orthocarpous
Orthocarpus
orthocenter
orthocentric
orthocephalic
orthocephalous
orthocephaly
orthoceracone
Orthoceran
Orthoceras
Orthoceratidae
orthoceratite
orthoceratitic
orthoceratoid
orthochlorite
orthochromatic
orthochromatize
orthoclase
orthoclasite
orthoclastic
orthocoumaric
orthocresol
orthocymene
orthodiaene
orthodiagonal
orthodiagram
orthodiagraph
orthodiagraphic
orthodiagraphy
orthodiazin
orthodiazine
orthodolichocephalic
orthodomatic
orthodome
orthodontia
orthodontic
orthodontics
orthodontist
orthodox
orthodoxal
orthodoxality
orthodoxally
orthodoxian
orthodoxical
orthodoxically
orthodoxism
orthodoxist
orthodoxly
orthodoxness
orthodoxy
orthodromic
orthodromics
orthodromy
orthoepic
orthoepical
orthoepically
orthoepist
orthoepistic
orthoepy
orthoformic
orthogamous
orthogamy
orthogenesis
orthogenetic
orthogenic
orthognathic
orthognathism
orthognathous
orthognathus
orthognathy
orthogneiss
orthogonal
orthogonality
orthogonally
orthogonial
orthograde
orthogranite
orthograph
orthographer
orthographic
orthographical
orthographically
orthographist
orthographize
orthography
orthohydrogen
orthologer
orthologian
orthological
orthology
orthometopic
orthometric
orthometry
Orthonectida
orthonitroaniline
orthopath
orthopathic
orthopathically
orthopathy
orthopedia
orthopedic
orthopedical
orthopedically
orthopedics
orthopedist
orthopedy
orthophenylene
orthophonic
orthophony
orthophoria
orthophoric
orthophosphate
orthophosphoric
orthophyre
orthophyric
orthopinacoid
orthopinacoidal
orthoplastic
orthoplasy
orthoplumbate
orthopnea
orthopneic
orthopod
Orthopoda
orthopraxis
orthopraxy
orthoprism
orthopsychiatric
orthopsychiatrical
orthopsychiatrist
orthopsychiatry
orthopter
Orthoptera
orthopteral
orthopteran
orthopterist
orthopteroid
Orthopteroidea
orthopterological
orthopterologist
orthopterology
orthopteron
orthopterous
orthoptic
orthopyramid
orthopyroxene
orthoquinone
orthorhombic
Orthorrhapha
orthorrhaphous
orthorrhaphy
orthoscope
orthoscopic
orthose
orthosemidin
orthosemidine
orthosilicate
orthosilicic
orthosis
orthosite
orthosomatic
orthospermous
orthostatic
orthostichous
orthostichy
orthostyle
orthosubstituted
orthosymmetric
orthosymmetrical
orthosymmetrically
orthosymmetry
orthotactic
orthotectic
orthotic
orthotolidin
orthotolidine
orthotoluic
orthotoluidin
orthotoluidine
orthotomic
orthotomous
orthotone
orthotonesis
orthotonic
orthotonus
orthotropal
orthotropic
orthotropism
orthotropous
orthotropy
orthotype
orthotypous
orthovanadate
orthovanadic
orthoveratraldehyde
orthoveratric
orthoxazin
orthoxazine
orthoxylene
orthron
ortiga
ortive
Ortol
ortolan
Ortrud
ortstein
ortygan
Ortygian
Ortyginae
ortygine
Ortyx
Orunchun
orvietan
orvietite
Orvieto
Orville
ory
Orycteropodidae
Orycteropus
oryctics
oryctognostic
oryctognostical
oryctognostically
oryctognosy
Oryctolagus
oryssid
Oryssidae
Oryssus
Oryx
Oryza
oryzenin
oryzivorous
Oryzomys
Oryzopsis
Oryzorictes
Oryzorictinae
Os
os
Osage
osamin
osamine
osazone
Osc
Oscan
Oscar
Oscarella
Oscarellidae
oscella
oscheal
oscheitis
oscheocarcinoma
oscheocele
oscheolith
oscheoma
oscheoncus
oscheoplasty
Oschophoria
oscillance
oscillancy
oscillant
Oscillaria
Oscillariaceae
oscillariaceous
oscillate
oscillating
oscillation
oscillative
oscillatively
oscillator
Oscillatoria
Oscillatoriaceae
oscillatoriaceous
oscillatorian
oscillatory
oscillogram
oscillograph
oscillographic
oscillography
oscillometer
oscillometric
oscillometry
oscilloscope
oscin
oscine
Oscines
oscinian
Oscinidae
oscinine
Oscinis
oscitance
oscitancy
oscitant
oscitantly
oscitate
oscitation
oscnode
osculable
osculant
oscular
oscularity
osculate
osculation
osculatory
osculatrix
oscule
osculiferous
osculum
oscurrantist
ose
osela
oshac
Osiandrian
oside
osier
osiered
osierlike
osiery
Osirian
Osiride
Osiridean
Osirification
Osirify
Osiris
Osirism
Oskar
Osmanie
Osmanli
Osmanthus
osmate
osmatic
osmatism
osmazomatic
osmazomatous
osmazome
Osmeridae
Osmerus
osmesis
osmeterium
osmetic
osmic
osmidrosis
osmin
osmina
osmious
osmiridium
osmium
osmodysphoria
osmogene
osmograph
osmolagnia
osmology
osmometer
osmometric
osmometry
Osmond
osmondite
osmophore
osmoregulation
Osmorhiza
osmoscope
osmose
osmosis
osmotactic
osmotaxis
osmotherapy
osmotic
osmotically
osmous
osmund
Osmunda
Osmundaceae
osmundaceous
osmundine
Osnaburg
Osnappar
osoberry
osone
osophy
osotriazine
osotriazole
osphradial
osphradium
osphresiolagnia
osphresiologic
osphresiologist
osphresiology
osphresiometer
osphresiometry
osphresiophilia
osphresis
osphretic
Osphromenidae
osphyalgia
osphyalgic
osphyarthritis
osphyitis
osphyocele
osphyomelitis
osprey
ossal
ossarium
ossature
osse
ossein
osselet
ossements
osseoalbuminoid
osseoaponeurotic
osseocartilaginous
osseofibrous
osseomucoid
osseous
osseously
Osset
Ossetian
Ossetic
Ossetine
Ossetish
Ossian
Ossianesque
Ossianic
Ossianism
Ossianize
ossicle
ossicular
ossiculate
ossicule
ossiculectomy
ossiculotomy
ossiculum
ossiferous
ossific
ossification
ossified
ossifier
ossifluence
ossifluent
ossiform
ossifrage
ossifrangent
ossify
ossivorous
ossuarium
ossuary
ossypite
ostalgia
Ostara
ostariophysan
Ostariophyseae
Ostariophysi
ostariophysial
ostariophysous
ostarthritis
osteal
ostealgia
osteanabrosis
osteanagenesis
ostearthritis
ostearthrotomy
ostectomy
osteectomy
osteectopia
osteectopy
Osteichthyes
ostein
osteitic
osteitis
ostemia
ostempyesis
ostensibility
ostensible
ostensibly
ostension
ostensive
ostensively
ostensorium
ostensory
ostent
ostentate
ostentation
ostentatious
ostentatiously
ostentatiousness
ostentive
ostentous
osteoaneurysm
osteoarthritis
osteoarthropathy
osteoarthrotomy
osteoblast
osteoblastic
osteoblastoma
osteocachetic
osteocarcinoma
osteocartilaginous
osteocele
osteocephaloma
osteochondritis
osteochondrofibroma
osteochondroma
osteochondromatous
osteochondropathy
osteochondrophyte
osteochondrosarcoma
osteochondrous
osteoclasia
osteoclasis
osteoclast
osteoclastic
osteoclasty
osteocolla
osteocomma
osteocranium
osteocystoma
osteodentin
osteodentinal
osteodentine
osteoderm
osteodermal
osteodermatous
osteodermia
osteodermis
osteodiastasis
osteodynia
osteodystrophy
osteoencephaloma
osteoenchondroma
osteoepiphysis
osteofibroma
osteofibrous
osteogangrene
osteogen
osteogenesis
osteogenetic
osteogenic
osteogenist
osteogenous
osteogeny
osteoglossid
Osteoglossidae
osteoglossoid
Osteoglossum
osteographer
osteography
osteohalisteresis
osteoid
Osteolepidae
Osteolepis
osteolite
osteologer
osteologic
osteological
osteologically
osteologist
osteology
osteolysis
osteolytic
osteoma
osteomalacia
osteomalacial
osteomalacic
osteomancy
osteomanty
osteomatoid
osteomere
osteometric
osteometrical
osteometry
osteomyelitis
osteoncus
osteonecrosis
osteoneuralgia
osteopaedion
osteopath
osteopathic
osteopathically
osteopathist
osteopathy
osteopedion
osteoperiosteal
osteoperiostitis
osteopetrosis
osteophage
osteophagia
osteophlebitis
osteophone
osteophony
osteophore
osteophyma
osteophyte
osteophytic
osteoplaque
osteoplast
osteoplastic
osteoplasty
osteoporosis
osteoporotic
osteorrhaphy
osteosarcoma
osteosarcomatous
osteosclerosis
osteoscope
osteosis
osteosteatoma
osteostixis
osteostomatous
osteostomous
osteostracan
Osteostraci
osteosuture
osteosynovitis
osteosynthesis
osteothrombosis
osteotome
osteotomist
osteotomy
osteotribe
osteotrite
osteotrophic
osteotrophy
Ostertagia
ostial
ostiary
ostiate
Ostic
ostiolar
ostiolate
ostiole
ostitis
ostium
ostleress
Ostmannic
ostmark
Ostmen
ostosis
Ostracea
ostracean
ostraceous
Ostraciidae
ostracine
ostracioid
Ostracion
ostracism
ostracizable
ostracization
ostracize
ostracizer
ostracod
Ostracoda
ostracode
ostracoderm
Ostracodermi
ostracodous
ostracoid
Ostracoidea
ostracon
ostracophore
Ostracophori
ostracophorous
ostracum
Ostraeacea
ostraite
Ostrea
ostreaceous
ostreger
ostreicultural
ostreiculture
ostreiculturist
Ostreidae
ostreiform
ostreodynamometer
ostreoid
ostreophage
ostreophagist
ostreophagous
ostrich
ostrichlike
Ostrogoth
Ostrogothian
Ostrogothic
Ostrya
Ostyak
Oswald
Oswegan
otacoustic
otacousticon
Otaheitan
otalgia
otalgic
otalgy
Otaria
otarian
Otariidae
Otariinae
otariine
otarine
otarioid
otary
otate
otectomy
otelcosis
Otello
Othake
othelcosis
Othello
othematoma
othemorrhea
otheoscope
other
otherdom
otherest
othergates
otherguess
otherhow
otherism
otherist
otherness
othersome
othertime
otherwards
otherwhence
otherwhere
otherwhereness
otherwheres
otherwhile
otherwhiles
otherwhither
otherwise
otherwiseness
otherworld
otherworldliness
otherworldly
otherworldness
Othin
Othinism
othmany
Othonna
othygroma
otiant
otiatric
otiatrics
otiatry
otic
oticodinia
Otidae
Otides
Otididae
otidiform
otidine
Otidiphaps
otidium
otiorhynchid
Otiorhynchidae
Otiorhynchinae
otiose
otiosely
otioseness
otiosity
Otis
otitic
otitis
otkon
Oto
otoantritis
otoblennorrhea
otocariasis
otocephalic
otocephaly
otocerebritis
otocleisis
otoconial
otoconite
otoconium
otocrane
otocranial
otocranic
otocranium
Otocyon
otocyst
otocystic
otodynia
otodynic
otoencephalitis
otogenic
otogenous
otographical
otography
Otogyps
otohemineurasthenia
otolaryngologic
otolaryngologist
otolaryngology
otolite
otolith
Otolithidae
Otolithus
otolitic
otological
otologist
otology
Otomaco
otomassage
Otomi
Otomian
Otomitlan
otomucormycosis
otomyces
otomycosis
otonecrectomy
otoneuralgia
otoneurasthenia
otopathic
otopathy
otopharyngeal
otophone
otopiesis
otoplastic
otoplasty
otopolypus
otopyorrhea
otopyosis
otorhinolaryngologic
otorhinolaryngologist
otorhinolaryngology
otorrhagia
otorrhea
otorrhoea
otosalpinx
otosclerosis
otoscope
otoscopic
otoscopy
otosis
otosphenal
otosteal
otosteon
ototomy
Otozoum
ottajanite
ottar
ottavarima
Ottawa
otter
otterer
otterhound
ottinger
ottingkar
Otto
otto
Ottoman
Ottomanean
Ottomanic
Ottomanism
Ottomanization
Ottomanize
Ottomanlike
Ottomite
ottrelife
Ottweilian
Otuquian
oturia
Otus
Otyak
ouabain
ouabaio
ouabe
ouachitite
ouakari
ouananiche
oubliette
ouch
Oudemian
oudenarde
Oudenodon
oudenodont
ouenite
ouf
ough
ought
oughtness
oughtnt
Ouija
ouistiti
oukia
oulap
ounce
ounds
ouphe
ouphish
our
Ouranos
ourie
ouroub
Ourouparia
ours
ourself
ourselves
oust
ouster
out
outact
outadmiral
Outagami
outage
outambush
outarde
outargue
outask
outawe
outbabble
outback
outbacker
outbake
outbalance
outban
outbanter
outbar
outbargain
outbark
outbawl
outbeam
outbear
outbearing
outbeg
outbeggar
outbelch
outbellow
outbent
outbetter
outbid
outbidder
outbirth
outblacken
outblaze
outbleat
outbleed
outbless
outbloom
outblossom
outblot
outblow
outblowing
outblown
outbluff
outblunder
outblush
outbluster
outboard
outboast
outbolting
outbond
outbook
outborn
outborough
outbound
outboundaries
outbounds
outbow
outbowed
outbowl
outbox
outbrag
outbranch
outbranching
outbrave
outbray
outbrazen
outbreak
outbreaker
outbreaking
outbreath
outbreathe
outbreather
outbred
outbreed
outbreeding
outbribe
outbridge
outbring
outbrother
outbud
outbuild
outbuilding
outbulge
outbulk
outbully
outburn
outburst
outbustle
outbuy
outbuzz
outby
outcant
outcaper
outcarol
outcarry
outcase
outcast
outcaste
outcasting
outcastness
outcavil
outchamber
outcharm
outchase
outchatter
outcheat
outchide
outcity
outclamor
outclass
outclerk
outclimb
outcome
outcomer
outcoming
outcompass
outcomplete
outcompliment
outcorner
outcountry
outcourt
outcrawl
outcricket
outcrier
outcrop
outcropper
outcross
outcrossing
outcrow
outcrowd
outcry
outcull
outcure
outcurse
outcurve
outcut
outdaciousness
outdance
outdare
outdate
outdated
outdazzle
outdevil
outdispatch
outdistance
outdistrict
outdo
outdodge
outdoer
outdoor
outdoorness
outdoors
outdoorsman
outdraft
outdragon
outdraw
outdream
outdress
outdrink
outdrive
outdure
outdwell
outdweller
outdwelling
outeat
outecho
outed
outedge
outen
outer
outerly
outermost
outerness
outerwear
outeye
outeyed
outfable
outface
outfall
outfame
outfangthief
outfast
outfawn
outfeast
outfeat
outfeeding
outfence
outferret
outfiction
outfield
outfielder
outfieldsman
outfight
outfighter
outfighting
outfigure
outfish
outfit
outfitter
outflame
outflank
outflanker
outflanking
outflare
outflash
outflatter
outfling
outfloat
outflourish
outflow
outflue
outflung
outflunky
outflush
outflux
outfly
outfold
outfool
outfoot
outform
outfort
outfreeman
outfront
outfroth
outfrown
outgabble
outgain
outgallop
outgamble
outgame
outgang
outgarment
outgarth
outgas
outgate
outgauge
outgaze
outgeneral
outgive
outgiving
outglad
outglare
outgleam
outglitter
outgloom
outglow
outgnaw
outgo
outgoer
outgoing
outgoingness
outgone
outgreen
outgrin
outground
outgrow
outgrowing
outgrowth
outguard
outguess
outgun
outgush
outhammer
outhasten
outhaul
outhauler
outhear
outheart
outhector
outheel
outher
outhire
outhiss
outhit
outhold
outhorror
outhouse
outhousing
outhowl
outhue
outhumor
outhunt
outhurl
outhut
outhymn
outhyperbolize
outimage
outing
outinvent
outish
outissue
outjazz
outjest
outjet
outjetting
outjinx
outjockey
outjourney
outjuggle
outjump
outjut
outkeeper
outkick
outkill
outking
outkiss
outkitchen
outknave
outknee
outlabor
outlaid
outlance
outland
outlander
outlandish
outlandishlike
outlandishly
outlandishness
outlash
outlast
outlaugh
outlaunch
outlaw
outlawry
outlay
outlean
outleap
outlearn
outlegend
outlength
outlengthen
outler
outlet
outlie
outlier
outlighten
outlimb
outlimn
outline
outlinear
outlined
outlineless
outliner
outlinger
outlip
outlipped
outlive
outliver
outlodging
outlook
outlooker
outlord
outlove
outlung
outluster
outly
outlying
outmagic
outmalaprop
outman
outmaneuver
outmantle
outmarch
outmarriage
outmarry
outmaster
outmatch
outmate
outmeasure
outmerchant
outmiracle
outmode
outmoded
outmost
outmount
outmouth
outmove
outname
outness
outnight
outnoise
outnook
outnumber
outoffice
outoven
outpace
outpage
outpaint
outparagon
outparamour
outparish
outpart
outpass
outpassion
outpath
outpatient
outpay
outpayment
outpeal
outpeep
outpeer
outpension
outpensioner
outpeople
outperform
outpick
outpicket
outpipe
outpitch
outpity
outplace
outplan
outplay
outplayed
outplease
outplod
outplot
outpocketing
outpoint
outpoise
outpoison
outpoll
outpomp
outpop
outpopulate
outporch
outport
outporter
outportion
outpost
outpouching
outpour
outpourer
outpouring
outpractice
outpraise
outpray
outpreach
outpreen
outprice
outprodigy
outproduce
outpromise
outpry
outpull
outpupil
outpurl
outpurse
outpush
output
outputter
outquaff
outquarters
outqueen
outquestion
outquibble
outquote
outrace
outrage
outrageous
outrageously
outrageousness
outrageproof
outrager
outraging
outrail
outrance
outrange
outrank
outrant
outrap
outrate
outraught
outrave
outray
outre
outreach
outread
outreason
outreckon
outredden
outrede
outreign
outrelief
outremer
outreness
outrhyme
outrick
outride
outrider
outriding
outrig
outrigger
outriggered
outriggerless
outrigging
outright
outrightly
outrightness
outring
outrival
outroar
outrogue
outroll
outromance
outrooper
outroot
outrove
outrow
outroyal
outrun
outrunner
outrush
outsail
outsaint
outsally
outsatisfy
outsavor
outsay
outscent
outscold
outscore
outscorn
outscour
outscouring
outscream
outsea
outseam
outsearch
outsee
outseek
outsell
outsentry
outsert
outservant
outset
outsetting
outsettlement
outsettler
outshadow
outshake
outshame
outshape
outsharp
outsharpen
outsheathe
outshift
outshine
outshiner
outshoot
outshot
outshoulder
outshout
outshove
outshow
outshower
outshriek
outshrill
outshut
outside
outsided
outsidedness
outsideness
outsider
outsift
outsigh
outsight
outsin
outsing
outsit
outsize
outsized
outskill
outskip
outskirmish
outskirmisher
outskirt
outskirter
outslander
outslang
outsleep
outslide
outslink
outsmart
outsmell
outsmile
outsnatch
outsnore
outsoar
outsole
outsoler
outsonnet
outsophisticate
outsound
outspan
outsparkle
outspeak
outspeaker
outspeech
outspeed
outspell
outspend
outspent
outspill
outspin
outspirit
outspit
outsplendor
outspoken
outspokenly
outspokenness
outsport
outspout
outspread
outspring
outsprint
outspue
outspurn
outspurt
outstagger
outstair
outstand
outstander
outstanding
outstandingly
outstandingness
outstare
outstart
outstarter
outstartle
outstate
outstation
outstatistic
outstature
outstay
outsteal
outsteam
outstep
outsting
outstink
outstood
outstorm
outstrain
outstream
outstreet
outstretch
outstretcher
outstride
outstrike
outstrip
outstrive
outstroke
outstrut
outstudent
outstudy
outstunt
outsubtle
outsuck
outsucken
outsuffer
outsuitor
outsulk
outsum
outsuperstition
outswagger
outswarm
outswear
outsweep
outsweeping
outsweeten
outswell
outswift
outswim
outswindle
outswing
outswirl
outtaken
outtalent
outtalk
outtask
outtaste
outtear
outtease
outtell
outthieve
outthink
outthreaten
outthrob
outthrough
outthrow
outthrust
outthruster
outthunder
outthwack
outtinkle
outtire
outtoil
outtongue
outtop
outtower
outtrade
outtrail
outtravel
outtrick
outtrot
outtrump
outturn
outturned
outtyrannize
outusure
outvalue
outvanish
outvaunt
outvelvet
outvenom
outvictor
outvie
outvier
outvigil
outvillage
outvillain
outvociferate
outvoice
outvote
outvoter
outvoyage
outwait
outwake
outwale
outwalk
outwall
outwallop
outwander
outwar
outwarble
outward
outwardly
outwardmost
outwardness
outwards
outwash
outwaste
outwatch
outwater
outwave
outwealth
outweapon
outwear
outweary
outweave
outweed
outweep
outweigh
outweight
outwell
outwent
outwhirl
outwick
outwile
outwill
outwind
outwindow
outwing
outwish
outwit
outwith
outwittal
outwitter
outwoe
outwoman
outwood
outword
outwore
outwork
outworker
outworld
outworn
outworth
outwrangle
outwrench
outwrest
outwrestle
outwriggle
outwring
outwrite
outwrought
outyard
outyell
outyelp
outyield
outzany
ouzel
Ova
ova
Ovaherero
oval
ovalbumin
ovalescent
ovaliform
ovalish
ovalization
ovalize
ovally
ovalness
ovaloid
ovalwise
Ovambo
Ovampo
Ovangangela
ovant
ovarial
ovarian
ovarin
ovarioabdominal
ovariocele
ovariocentesis
ovariocyesis
ovariodysneuria
ovariohysterectomy
ovariole
ovariolumbar
ovariorrhexis
ovariosalpingectomy
ovariosteresis
ovariostomy
ovariotomist
ovariotomize
ovariotomy
ovariotubal
ovarious
ovaritis
ovarium
ovary
ovate
ovateconical
ovated
ovately
ovation
ovational
ovationary
ovatoacuminate
ovatoconical
ovatocordate
ovatocylindraceous
ovatodeltoid
ovatoellipsoidal
ovatoglobose
ovatolanceolate
ovatooblong
ovatoorbicular
ovatopyriform
ovatoquadrangular
ovatorotundate
ovatoserrate
ovatotriangular
oven
ovenbird
ovenful
ovenlike
ovenly
ovenman
ovenpeel
ovenstone
ovenware
ovenwise
over
overability
overable
overabound
overabsorb
overabstain
overabstemious
overabstemiousness
overabundance
overabundant
overabundantly
overabuse
overaccentuate
overaccumulate
overaccumulation
overaccuracy
overaccurate
overaccurately
overact
overaction
overactive
overactiveness
overactivity
overacute
overaddiction
overadvance
overadvice
overaffect
overaffirmation
overafflict
overaffliction
overage
overageness
overaggravate
overaggravation
overagitate
overagonize
overall
overalled
overalls
overambitioned
overambitious
overambling
overanalyze
overangelic
overannotate
overanswer
overanxiety
overanxious
overanxiously
overappareled
overappraisal
overappraise
overapprehended
overapprehension
overapprehensive
overapt
overarch
overargue
overarm
overartificial
overartificiality
overassail
overassert
overassertion
overassertive
overassertively
overassertiveness
overassess
overassessment
overassumption
overattached
overattachment
overattention
overattentive
overattentively
overawe
overawful
overawn
overawning
overbake
overbalance
overballast
overbalm
overbanded
overbandy
overbank
overbanked
overbark
overbarren
overbarrenness
overbase
overbaseness
overbashful
overbashfully
overbashfulness
overbattle
overbear
overbearance
overbearer
overbearing
overbearingly
overbearingness
overbeat
overbeating
overbeetling
overbelief
overbend
overbepatched
overberg
overbet
overbias
overbid
overbig
overbigness
overbillow
overbit
overbite
overbitten
overbitter
overbitterly
overbitterness
overblack
overblame
overblaze
overbleach
overblessed
overblessedness
overblind
overblindly
overblithe
overbloom
overblouse
overblow
overblowing
overblown
overboard
overboast
overboastful
overbodice
overboding
overbody
overboil
overbold
overboldly
overboldness
overbook
overbookish
overbooming
overborne
overborrow
overbought
overbound
overbounteous
overbounteously
overbounteousness
overbow
overbowed
overbowl
overbrace
overbragging
overbrained
overbranch
overbrave
overbravely
overbravery
overbray
overbreak
overbreathe
overbred
overbreed
overbribe
overbridge
overbright
overbrightly
overbrightness
overbrilliancy
overbrilliant
overbrilliantly
overbrim
overbrimmingly
overbroaden
overbroil
overbrood
overbrow
overbrown
overbrowse
overbrush
overbrutal
overbrutality
overbrutalize
overbrutally
overbubbling
overbuild
overbuilt
overbulk
overbulky
overbumptious
overburden
overburdeningly
overburdensome
overburn
overburned
overburningly
overburnt
overburst
overburthen
overbusily
overbusiness
overbusy
overbuy
overby
overcall
overcanny
overcanopy
overcap
overcapable
overcapably
overcapacity
overcape
overcapitalization
overcapitalize
overcaptious
overcaptiously
overcaptiousness
overcard
overcare
overcareful
overcarefully
overcareless
overcarelessly
overcarelessness
overcaring
overcarking
overcarry
overcast
overcasting
overcasual
overcasually
overcatch
overcaution
overcautious
overcautiously
overcautiousness
overcentralization
overcentralize
overcertification
overcertify
overchafe
overchannel
overchant
overcharge
overchargement
overcharger
overcharitable
overcharitably
overcharity
overchase
overcheap
overcheaply
overcheapness
overcheck
overcherish
overchidden
overchief
overchildish
overchildishness
overchill
overchlorinate
overchoke
overchrome
overchurch
overcirculate
overcircumspect
overcircumspection
overcivil
overcivility
overcivilization
overcivilize
overclaim
overclamor
overclasp
overclean
overcleanly
overcleanness
overcleave
overclever
overcleverness
overclimb
overcloak
overclog
overclose
overclosely
overcloseness
overclothe
overclothes
overcloud
overcloy
overcluster
overcoached
overcoat
overcoated
overcoating
overcoil
overcold
overcoldly
overcollar
overcolor
overcomable
overcome
overcomer
overcomingly
overcommand
overcommend
overcommon
overcommonly
overcommonness
overcompensate
overcompensation
overcompensatory
overcompetition
overcompetitive
overcomplacency
overcomplacent
overcomplacently
overcomplete
overcomplex
overcomplexity
overcompliant
overcompound
overconcentrate
overconcentration
overconcern
overconcerned
overcondensation
overcondense
overconfidence
overconfident
overconfidently
overconfute
overconquer
overconscientious
overconscious
overconsciously
overconsciousness
overconservatism
overconservative
overconservatively
overconsiderate
overconsiderately
overconsideration
overconsume
overconsumption
overcontented
overcontentedly
overcontentment
overcontract
overcontraction
overcontribute
overcontribution
overcook
overcool
overcoolly
overcopious
overcopiously
overcopiousness
overcorned
overcorrect
overcorrection
overcorrupt
overcorruption
overcorruptly
overcostly
overcount
overcourteous
overcourtesy
overcover
overcovetous
overcovetousness
overcow
overcoy
overcoyness
overcram
overcredit
overcredulity
overcredulous
overcredulously
overcreed
overcreep
overcritical
overcritically
overcriticalness
overcriticism
overcriticize
overcrop
overcross
overcrow
overcrowd
overcrowded
overcrowdedly
overcrowdedness
overcrown
overcrust
overcry
overcull
overcultivate
overcultivation
overculture
overcultured
overcumber
overcunning
overcunningly
overcunningness
overcup
overcured
overcurious
overcuriously
overcuriousness
overcurl
overcurrency
overcurrent
overcurtain
overcustom
overcut
overcutter
overcutting
overdaintily
overdaintiness
overdainty
overdamn
overdance
overdangle
overdare
overdaringly
overdarken
overdash
overdazed
overdazzle
overdeal
overdear
overdearly
overdearness
overdeck
overdecorate
overdecoration
overdecorative
overdeeming
overdeep
overdeepen
overdeeply
overdeliberate
overdeliberation
overdelicacy
overdelicate
overdelicately
overdelicious
overdeliciously
overdelighted
overdelightedly
overdemand
overdemocracy
overdepress
overdepressive
overdescant
overdesire
overdesirous
overdesirousness
overdestructive
overdestructively
overdestructiveness
overdetermination
overdetermined
overdevelop
overdevelopment
overdevoted
overdevotedly
overdevotion
overdiffuse
overdiffusely
overdiffuseness
overdigest
overdignified
overdignifiedly
overdignifiedness
overdignify
overdignity
overdiligence
overdiligent
overdiligently
overdilute
overdilution
overdischarge
overdiscipline
overdiscount
overdiscourage
overdiscouragement
overdistance
overdistant
overdistantly
overdistantness
overdistempered
overdistention
overdiverse
overdiversely
overdiversification
overdiversify
overdiversity
overdo
overdoctrinize
overdoer
overdogmatic
overdogmatically
overdogmatism
overdome
overdominate
overdone
overdoor
overdosage
overdose
overdoubt
overdoze
overdraft
overdrain
overdrainage
overdramatic
overdramatically
overdrape
overdrapery
overdraw
overdrawer
overdream
overdrench
overdress
overdrifted
overdrink
overdrip
overdrive
overdriven
overdroop
overdrowsed
overdry
overdubbed
overdue
overdunged
overdure
overdust
overdye
overeager
overeagerly
overeagerness
overearnest
overearnestly
overearnestness
overeasily
overeasiness
overeasy
overeat
overeaten
overedge
overedit
overeducate
overeducated
overeducation
overeducative
overeffort
overegg
overelaborate
overelaborately
overelaboration
overelate
overelegance
overelegancy
overelegant
overelegantly
overelliptical
overembellish
overembellishment
overembroider
overemotional
overemotionality
overemotionalize
overemphasis
overemphasize
overemphatic
overemphatically
overemphaticness
overempired
overemptiness
overempty
overenter
overenthusiasm
overenthusiastic
overentreat
overentry
overequal
overestimate
overestimation
overexcelling
overexcitability
overexcitable
overexcitably
overexcite
overexcitement
overexercise
overexert
overexerted
overexertedly
overexertedness
overexertion
overexpand
overexpansion
overexpansive
overexpect
overexpectant
overexpectantly
overexpenditure
overexpert
overexplain
overexplanation
overexpose
overexposure
overexpress
overexquisite
overexquisitely
overextend
overextension
overextensive
overextreme
overexuberant
overeye
overeyebrowed
overface
overfacile
overfacilely
overfacility
overfactious
overfactiousness
overfag
overfagged
overfaint
overfaith
overfaithful
overfaithfully
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overfamed
overfamiliar
overfamiliarity
overfamiliarly
overfamous
overfanciful
overfancy
overfar
overfast
overfastidious
overfastidiously
overfastidiousness
overfasting
overfat
overfatigue
overfatten
overfavor
overfavorable
overfavorably
overfear
overfearful
overfearfully
overfearfulness
overfeast
overfeatured
overfed
overfee
overfeed
overfeel
overfellowlike
overfellowly
overfelon
overfeminine
overfeminize
overfertile
overfertility
overfestoon
overfew
overfierce
overfierceness
overfile
overfill
overfilm
overfine
overfinished
overfish
overfit
overfix
overflatten
overfleece
overfleshed
overflexion
overfling
overfloat
overflog
overflood
overflorid
overfloridness
overflourish
overflow
overflowable
overflower
overflowing
overflowingly
overflowingness
overflown
overfluency
overfluent
overfluently
overflush
overflutter
overfly
overfold
overfond
overfondle
overfondly
overfondness
overfoolish
overfoolishly
overfoolishness
overfoot
overforce
overforged
overformed
overforward
overforwardly
overforwardness
overfought
overfoul
overfoully
overfrail
overfrailty
overfranchised
overfrank
overfrankly
overfrankness
overfraught
overfree
overfreedom
overfreely
overfreight
overfrequency
overfrequent
overfrequently
overfret
overfrieze
overfrighted
overfrighten
overfroth
overfrown
overfrozen
overfruited
overfruitful
overfull
overfullness
overfunctioning
overfurnish
overgaiter
overgalled
overgamble
overgang
overgarment
overgarrison
overgaze
overgeneral
overgeneralize
overgenerally
overgenerosity
overgenerous
overgenerously
overgenial
overgeniality
overgentle
overgently
overget
overgifted
overgild
overgilted
overgird
overgirded
overgirdle
overglad
overgladly
overglance
overglass
overglaze
overglide
overglint
overgloom
overgloominess
overgloomy
overglorious
overgloss
overglut
overgo
overgoad
overgod
overgodliness
overgodly
overgood
overgorge
overgovern
overgovernment
overgown
overgrace
overgracious
overgrade
overgrain
overgrainer
overgrasping
overgrateful
overgratefully
overgratification
overgratify
overgratitude
overgraze
overgreasiness
overgreasy
overgreat
overgreatly
overgreatness
overgreed
overgreedily
overgreediness
overgreedy
overgrieve
overgrievous
overgrind
overgross
overgrossly
overgrossness
overground
overgrow
overgrown
overgrowth
overguilty
overgun
overhair
overhalf
overhand
overhanded
overhandicap
overhandle
overhang
overhappy
overharass
overhard
overharden
overhardness
overhardy
overharsh
overharshly
overharshness
overhaste
overhasten
overhastily
overhastiness
overhasty
overhate
overhatted
overhaughty
overhaul
overhauler
overhead
overheadiness
overheadman
overheady
overheap
overhear
overhearer
overheartily
overhearty
overheat
overheatedly
overheave
overheaviness
overheavy
overheight
overheighten
overheinous
overheld
overhelp
overhelpful
overhigh
overhighly
overhill
overhit
overholiness
overhollow
overholy
overhomeliness
overhomely
overhonest
overhonestly
overhonesty
overhonor
overhorse
overhot
overhotly
overhour
overhouse
overhover
overhuge
overhuman
overhumanity
overhumanize
overhung
overhunt
overhurl
overhurriedly
overhurry
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overhysterical
overidealism
overidealistic
overidle
overidly
overillustrate
overillustration
overimaginative
overimaginativeness
overimitate
overimitation
overimitative
overimitatively
overimport
overimportation
overimpress
overimpressible
overinclinable
overinclination
overinclined
overincrust
overincurious
overindividualism
overindividualistic
overindulge
overindulgence
overindulgent
overindulgently
overindustrialization
overindustrialize
overinflate
overinflation
overinflative
overinfluence
overinfluential
overinform
overink
overinsist
overinsistence
overinsistent
overinsistently
overinsolence
overinsolent
overinsolently
overinstruct
overinstruction
overinsurance
overinsure
overintellectual
overintellectuality
overintense
overintensely
overintensification
overintensity
overinterest
overinterested
overinterestedness
overinventoried
overinvest
overinvestment
overiodize
overirrigate
overirrigation
overissue
overitching
overjacket
overjade
overjaded
overjawed
overjealous
overjealously
overjealousness
overjob
overjocular
overjoy
overjoyful
overjoyfully
overjoyous
overjudge
overjudging
overjudgment
overjudicious
overjump
overjust
overjutting
overkeen
overkeenness
overkeep
overkick
overkind
overkindly
overkindness
overking
overknavery
overknee
overknow
overknowing
overlabor
overlace
overlactation
overlade
overlaid
overlain
overland
Overlander
overlander
overlanguaged
overlap
overlard
overlarge
overlargely
overlargeness
overlascivious
overlast
overlate
overlaudation
overlaudatory
overlaugh
overlaunch
overlave
overlavish
overlavishly
overlax
overlaxative
overlaxly
overlaxness
overlay
overlayer
overlead
overleaf
overlean
overleap
overlearn
overlearned
overlearnedly
overlearnedness
overleather
overleave
overleaven
overleer
overleg
overlegislation
overleisured
overlength
overlettered
overlewd
overlewdly
overlewdness
overliberal
overliberality
overliberally
overlicentious
overlick
overlie
overlier
overlift
overlight
overlighted
overlightheaded
overlightly
overlightsome
overliking
overline
overling
overlinger
overlinked
overlip
overlipping
overlisted
overlisten
overliterary
overlittle
overlive
overliveliness
overlively
overliver
overload
overloath
overlock
overlocker
overlofty
overlogical
overlogically
overlong
overlook
overlooker
overloose
overlord
overlordship
overloud
overloup
overlove
overlover
overlow
overlowness
overloyal
overloyally
overloyalty
overlubricatio
overluscious
overlush
overlustiness
overlusty
overluxuriance
overluxuriant
overluxurious
overly
overlying
overmagnify
overmagnitude
overmajority
overmalapert
overman
overmantel
overmantle
overmany
overmarch
overmark
overmarking
overmarl
overmask
overmast
overmaster
overmasterful
overmasterfully
overmasterfulness
overmastering
overmasteringly
overmatch
overmatter
overmature
overmaturity
overmean
overmeanly
overmeanness
overmeasure
overmeddle
overmeek
overmeekly
overmeekness
overmellow
overmellowness
overmelodied
overmelt
overmerciful
overmercifulness
overmerit
overmerrily
overmerry
overmettled
overmickle
overmighty
overmild
overmill
overminute
overminutely
overminuteness
overmix
overmoccasin
overmodest
overmodestly
overmodesty
overmodulation
overmoist
overmoisten
overmoisture
overmortgage
overmoss
overmost
overmotor
overmount
overmounts
overmourn
overmournful
overmournfully
overmuch
overmuchness
overmultiplication
overmultiply
overmultitude
overname
overnarrow
overnarrowly
overnationalization
overnear
overneat
overneatness
overneglect
overnegligence
overnegligent
overnervous
overnervously
overnervousness
overnet
overnew
overnice
overnicely
overniceness
overnicety
overnigh
overnight
overnimble
overnipping
overnoise
overnotable
overnourish
overnoveled
overnumber
overnumerous
overnumerousness
overnurse
overobedience
overobedient
overobediently
overobese
overobjectify
overoblige
overobsequious
overobsequiously
overobsequiousness
overoffend
overoffensive
overofficered
overofficious
overorder
overornamented
overpained
overpainful
overpainfully
overpainfulness
overpaint
overpamper
overpart
overparted
overpartial
overpartiality
overpartially
overparticular
overparticularly
overpass
overpassionate
overpassionately
overpassionateness
overpast
overpatient
overpatriotic
overpay
overpayment
overpeer
overpending
overpensive
overpensiveness
overpeople
overpepper
overperemptory
overpersuade
overpersuasion
overpert
overpessimism
overpessimistic
overpet
overphysic
overpick
overpicture
overpinching
overpitch
overpitched
overpiteous
overplace
overplaced
overplacement
overplain
overplant
overplausible
overplay
overplease
overplenitude
overplenteous
overplenteously
overplentiful
overplenty
overplot
overplow
overplumb
overplume
overplump
overplumpness
overplus
overply
overpointed
overpoise
overpole
overpolemical
overpolish
overpolitic
overponderous
overpopular
overpopularity
overpopularly
overpopulate
overpopulation
overpopulous
overpopulousness
overpositive
overpossess
overpot
overpotent
overpotential
overpour
overpower
overpowerful
overpowering
overpoweringly
overpoweringness
overpraise
overpray
overpreach
overprecise
overpreciseness
overpreface
overpregnant
overpreoccupation
overpreoccupy
overpress
overpressure
overpresumption
overpresumptuous
overprice
overprick
overprint
overprize
overprizer
overprocrastination
overproduce
overproduction
overproductive
overproficient
overprolific
overprolix
overprominence
overprominent
overprominently
overpromise
overprompt
overpromptly
overpromptness
overprone
overproneness
overpronounced
overproof
overproportion
overproportionate
overproportionated
overproportionately
overproportioned
overprosperity
overprosperous
overprotect
overprotract
overprotraction
overproud
overproudly
overprove
overprovender
overprovide
overprovident
overprovidently
overprovision
overprovocation
overprovoke
overprune
overpublic
overpublicity
overpuff
overpuissant
overpunish
overpunishment
overpurchase
overquantity
overquarter
overquell
overquick
overquickly
overquiet
overquietly
overquietness
overrace
overrack
overrake
overrange
overrank
overrankness
overrapture
overrapturize
overrash
overrashly
overrashness
overrate
overrational
overrationalize
overravish
overreach
overreacher
overreaching
overreachingly
overreachingness
overread
overreader
overreadily
overreadiness
overready
overrealism
overrealistic
overreckon
overrecord
overrefine
overrefined
overrefinement
overreflection
overreflective
overregister
overregistration
overregular
overregularity
overregularly
overregulate
overregulation
overrelax
overreliance
overreliant
overreligion
overreligious
overremiss
overremissly
overremissness
overrennet
overrent
overreplete
overrepletion
overrepresent
overrepresentation
overrepresentative
overreserved
overresolute
overresolutely
overrestore
overrestrain
overretention
overreward
overrich
overriches
overrichness
override
overrife
overrigged
overright
overrighteous
overrighteously
overrighteousness
overrigid
overrigidity
overrigidly
overrigorous
overrigorously
overrim
overriot
overripe
overripely
overripen
overripeness
overrise
overroast
overroll
overroof
overrooted
overrough
overroughly
overroughness
overroyal
overrude
overrudely
overrudeness
overruff
overrule
overruler
overruling
overrulingly
overrun
overrunner
overrunning
overrunningly
overrush
overrusset
overrust
oversad
oversadly
oversadness
oversaid
oversail
oversale
oversaliva
oversalt
oversalty
oversand
oversanded
oversanguine
oversanguinely
oversapless
oversated
oversatisfy
oversaturate
oversaturation
oversauce
oversauciness
oversaucy
oversave
overscare
overscatter
overscented
oversceptical
overscepticism
overscore
overscour
overscratch
overscrawl
overscream
overscribble
overscrub
overscruple
overscrupulosity
overscrupulous
overscrupulously
overscrupulousness
overscurf
overscutched
oversea
overseal
overseam
overseamer
oversearch
overseas
overseason
overseasoned
overseated
oversecure
oversecurely
oversecurity
oversee
overseed
overseen
overseer
overseerism
overseership
overseethe
oversell
oversend
oversensible
oversensibly
oversensitive
oversensitively
oversensitiveness
oversententious
oversentimental
oversentimentalism
oversentimentalize
oversentimentally
overserious
overseriously
overseriousness
overservice
overservile
overservility
overset
oversetter
oversettle
oversettled
oversevere
overseverely
overseverity
oversew
overshade
overshadow
overshadower
overshadowing
overshadowingly
overshadowment
overshake
oversharp
oversharpness
overshave
oversheet
overshelving
overshepherd
overshine
overshirt
overshoe
overshoot
overshort
overshorten
overshortly
overshot
overshoulder
overshowered
overshrink
overshroud
oversick
overside
oversight
oversilence
oversilent
oversilver
oversimple
oversimplicity
oversimplification
oversimplify
oversimply
oversize
oversized
overskim
overskip
overskipper
overskirt
overslack
overslander
overslaugh
overslavish
overslavishly
oversleep
oversleeve
overslide
overslight
overslip
overslope
overslow
overslowly
overslowness
overslur
oversmall
oversman
oversmite
oversmitten
oversmoke
oversmooth
oversmoothly
oversmoothness
oversnow
oversoak
oversoar
oversock
oversoft
oversoftly
oversoftness
oversold
oversolemn
oversolemnity
oversolemnly
oversolicitous
oversolicitously
oversolicitousness
oversoon
oversoothing
oversophisticated
oversophistication
oversorrow
oversorrowed
oversot
oversoul
oversound
oversour
oversourly
oversourness
oversow
overspacious
overspaciousness
overspan
overspangled
oversparing
oversparingly
oversparingness
oversparred
overspatter
overspeak
overspecialization
overspecialize
overspeculate
overspeculation
overspeculative
overspeech
overspeed
overspeedily
overspeedy
overspend
overspill
overspin
oversplash
overspread
overspring
oversprinkle
oversprung
overspun
oversqueak
oversqueamish
oversqueamishness
overstaff
overstaid
overstain
overstale
overstalled
overstand
overstaring
overstate
overstately
overstatement
overstay
overstayal
oversteadfast
oversteadfastness
oversteady
overstep
overstiff
overstiffness
overstifle
overstimulate
overstimulation
overstimulative
overstir
overstitch
overstock
overstoop
overstoping
overstore
overstory
overstout
overstoutly
overstowage
overstowed
overstrain
overstrait
overstraiten
overstraitly
overstraitness
overstream
overstrength
overstress
overstretch
overstrew
overstrict
overstrictly
overstrictness
overstride
overstrident
overstridently
overstrike
overstring
overstriving
overstrong
overstrongly
overstrung
overstud
overstudied
overstudious
overstudiously
overstudiousness
overstudy
overstuff
oversublime
oversubscribe
oversubscriber
oversubscription
oversubtile
oversubtle
oversubtlety
oversubtly
oversufficiency
oversufficient
oversufficiently
oversuperstitious
oversupply
oversure
oversurety
oversurge
oversurviving
oversusceptibility
oversusceptible
oversuspicious
oversuspiciously
overswarm
overswarth
oversway
oversweated
oversweep
oversweet
oversweeten
oversweetly
oversweetness
overswell
overswift
overswim
overswimmer
overswing
overswinging
overswirling
oversystematic
oversystematically
oversystematize
overt
overtakable
overtake
overtaker
overtalk
overtalkative
overtalkativeness
overtalker
overtame
overtamely
overtameness
overtapped
overtare
overtariff
overtarry
overtart
overtask
overtax
overtaxation
overteach
overtechnical
overtechnicality
overtedious
overtediously
overteem
overtell
overtempt
overtenacious
overtender
overtenderly
overtenderness
overtense
overtensely
overtenseness
overtension
overterrible
overtest
overthick
overthin
overthink
overthought
overthoughtful
overthriftily
overthriftiness
overthrifty
overthrong
overthrow
overthrowable
overthrowal
overthrower
overthrust
overthwart
overthwartly
overthwartness
overthwartways
overthwartwise
overtide
overtight
overtightly
overtill
overtimbered
overtime
overtimer
overtimorous
overtimorously
overtimorousness
overtinseled
overtint
overtip
overtipple
overtire
overtiredness
overtitle
overtly
overtness
overtoe
overtoil
overtoise
overtone
overtongued
overtop
overtopple
overtorture
overtower
overtrace
overtrack
overtrade
overtrader
overtrailed
overtrain
overtrample
overtravel
overtread
overtreatment
overtrick
overtrim
overtrouble
overtrue
overtrump
overtrust
overtrustful
overtruthful
overtruthfully
overtumble
overture
overturn
overturnable
overturner
overtutor
overtwine
overtwist
overtype
overuberous
overunionized
overunsuitable
overurbanization
overurge
overuse
overusual
overusually
overvaliant
overvaluable
overvaluation
overvalue
overvariety
overvault
overvehemence
overvehement
overveil
overventilate
overventilation
overventuresome
overventurous
overview
overvoltage
overvote
overwade
overwages
overwake
overwalk
overwander
overward
overwash
overwasted
overwatch
overwatcher
overwater
overwave
overway
overwealth
overwealthy
overweaponed
overwear
overweary
overweather
overweave
overweb
overween
overweener
overweening
overweeningly
overweeningness
overweep
overweigh
overweight
overweightage
overwell
overwelt
overwet
overwetness
overwheel
overwhelm
overwhelmer
overwhelming
overwhelmingly
overwhelmingness
overwhipped
overwhirl
overwhisper
overwide
overwild
overwilily
overwilling
overwillingly
overwily
overwin
overwind
overwing
overwinter
overwiped
overwisdom
overwise
overwisely
overwithered
overwoman
overwomanize
overwomanly
overwood
overwooded
overwoody
overword
overwork
overworld
overworn
overworry
overworship
overwound
overwove
overwoven
overwrap
overwrest
overwrested
overwrestle
overwrite
overwroth
overwrought
overyear
overyoung
overyouthful
overzeal
overzealous
overzealously
overzealousness
ovest
ovey
Ovibos
Ovibovinae
ovibovine
ovicapsular
ovicapsule
ovicell
ovicellular
ovicidal
ovicide
ovicular
oviculated
oviculum
ovicyst
ovicystic
Ovidae
Ovidian
oviducal
oviduct
oviductal
oviferous
ovification
oviform
ovigenesis
ovigenetic
ovigenic
ovigenous
ovigerm
ovigerous
ovile
Ovillus
Ovinae
ovine
ovinia
ovipara
oviparal
oviparity
oviparous
oviparously
oviparousness
oviposit
oviposition
ovipositor
Ovis
ovisac
oviscapt
ovism
ovispermary
ovispermiduct
ovist
ovistic
ovivorous
ovocyte
ovoelliptic
ovoflavin
ovogenesis
ovogenetic
ovogenous
ovogonium
ovoid
ovoidal
ovolemma
ovolo
ovological
ovologist
ovology
ovolytic
ovomucoid
ovoplasm
ovoplasmic
ovopyriform
ovorhomboid
ovorhomboidal
ovotesticular
ovotestis
ovovitellin
Ovovivipara
ovoviviparism
ovoviviparity
ovoviviparous
ovoviviparously
ovoviviparousness
Ovula
ovular
ovularian
ovulary
ovulate
ovulation
ovule
ovuliferous
ovuligerous
ovulist
ovum
ow
owd
owe
owelty
Owen
Owenia
Owenian
Owenism
Owenist
Owenite
Owenize
ower
owerance
owerby
owercome
owergang
owerloup
owertaen
owerword
owght
owing
owk
owl
owldom
owler
owlery
owlet
Owlglass
owlhead
owling
owlish
owlishly
owlishness
owlism
owllight
owllike
Owlspiegle
owly
own
owner
ownerless
ownership
ownhood
ownness
ownself
ownwayish
owregane
owrehip
owrelay
owse
owsen
owser
owtchah
owyheeite
ox
oxacid
oxadiazole
oxalacetic
oxalaldehyde
oxalamid
oxalamide
oxalan
oxalate
oxaldehyde
oxalemia
oxalic
Oxalidaceae
oxalidaceous
Oxalis
oxalite
oxalodiacetic
oxalonitril
oxalonitrile
oxaluramid
oxaluramide
oxalurate
oxaluria
oxaluric
oxalyl
oxalylurea
oxamate
oxamethane
oxamic
oxamid
oxamide
oxamidine
oxammite
oxan
oxanate
oxane
oxanic
oxanilate
oxanilic
oxanilide
oxazine
oxazole
oxbane
oxberry
oxbird
oxbiter
oxblood
oxbow
oxboy
oxbrake
oxcart
oxcheek
oxdiacetic
oxdiazole
oxea
oxeate
oxen
oxeote
oxer
oxetone
oxeye
oxfly
Oxford
Oxfordian
Oxfordism
Oxfordist
oxgang
oxgoad
oxharrow
oxhead
oxheal
oxheart
oxhide
oxhoft
oxhorn
oxhouse
oxhuvud
oxidability
oxidable
oxidant
oxidase
oxidate
oxidation
oxidational
oxidative
oxidator
oxide
oxidic
oxidimetric
oxidimetry
oxidizability
oxidizable
oxidization
oxidize
oxidizement
oxidizer
oxidizing
oxidoreductase
oxidoreduction
oxidulated
oximate
oximation
oxime
oxland
oxlike
oxlip
oxman
oxmanship
oxoindoline
Oxonian
oxonic
oxonium
Oxonolatry
oxozone
oxozonide
oxpecker
oxphony
oxreim
oxshoe
oxskin
oxtail
oxter
oxtongue
oxwort
oxy
oxyacanthine
oxyacanthous
oxyacetylene
oxyacid
Oxyaena
Oxyaenidae
oxyaldehyde
oxyamine
oxyanthracene
oxyanthraquinone
oxyaphia
oxyaster
oxybaphon
Oxybaphus
oxybenzaldehyde
oxybenzene
oxybenzoic
oxybenzyl
oxyberberine
oxyblepsia
oxybromide
oxybutyria
oxybutyric
oxycalcium
oxycalorimeter
oxycamphor
oxycaproic
oxycarbonate
oxycellulose
oxycephalic
oxycephalism
oxycephalous
oxycephaly
oxychlorate
oxychloric
oxychloride
oxycholesterol
oxychromatic
oxychromatin
oxychromatinic
oxycinnamic
oxycobaltammine
Oxycoccus
oxycopaivic
oxycoumarin
oxycrate
oxycyanide
oxydactyl
Oxydendrum
oxydiact
oxyesthesia
oxyether
oxyethyl
oxyfatty
oxyfluoride
oxygas
oxygen
oxygenant
oxygenate
oxygenation
oxygenator
oxygenerator
oxygenic
oxygenicity
oxygenium
oxygenizable
oxygenize
oxygenizement
oxygenizer
oxygenous
oxygeusia
oxygnathous
oxyhalide
oxyhaloid
oxyhematin
oxyhemocyanin
oxyhemoglobin
oxyhexactine
oxyhexaster
oxyhydrate
oxyhydric
oxyhydrogen
oxyiodide
oxyketone
oxyl
Oxylabracidae
Oxylabrax
oxyluciferin
oxyluminescence
oxyluminescent
oxymandelic
oxymel
oxymethylene
oxymoron
oxymuriate
oxymuriatic
oxynaphthoic
oxynaphtoquinone
oxynarcotine
oxyneurin
oxyneurine
oxynitrate
oxyntic
oxyophitic
oxyopia
Oxyopidae
oxyosphresia
oxypetalous
oxyphenol
oxyphenyl
oxyphile
oxyphilic
oxyphilous
oxyphonia
oxyphosphate
oxyphthalic
oxyphyllous
oxyphyte
oxypicric
Oxypolis
oxyproline
oxypropionic
oxypurine
oxypycnos
oxyquinaseptol
oxyquinoline
oxyquinone
oxyrhine
oxyrhinous
oxyrhynch
oxyrhynchous
oxyrhynchus
Oxyrrhyncha
oxyrrhynchid
oxysalicylic
oxysalt
oxystearic
Oxystomata
oxystomatous
oxystome
oxysulphate
oxysulphide
oxyterpene
oxytocia
oxytocic
oxytocin
oxytocous
oxytoluene
oxytoluic
oxytone
oxytonesis
oxytonical
oxytonize
Oxytricha
Oxytropis
oxytylotate
oxytylote
oxyuriasis
oxyuricide
Oxyuridae
oxyurous
oxywelding
Oyana
oyapock
oyer
oyster
oysterage
oysterbird
oystered
oysterer
oysterfish
oystergreen
oysterhood
oysterhouse
oystering
oysterish
oysterishness
oysterlike
oysterling
oysterman
oysterous
oysterroot
oysterseed
oystershell
oysterwife
oysterwoman
Ozan
Ozark
ozarkite
ozena
Ozias
ozobrome
ozocerite
ozokerit
ozokerite
ozonate
ozonation
ozonator
ozone
ozoned
ozonic
ozonide
ozoniferous
ozonification
ozonify
Ozonium
ozonization
ozonize
ozonizer
ozonometer
ozonometry
ozonoscope
ozonoscopic
ozonous
ozophen
ozophene
ozostomia
ozotype
P
p
pa
paal
paar
paauw
Paba
pabble
Pablo
pablo
pabouch
pabular
pabulary
pabulation
pabulatory
pabulous
pabulum
pac
paca
pacable
Pacaguara
pacate
pacation
pacative
pacay
pacaya
Paccanarist
Pacchionian
Pace
pace
paceboard
paced
pacemaker
pacemaking
pacer
pachak
pachisi
pachnolite
pachometer
Pachomian
Pachons
Pacht
pachyacria
pachyaemia
pachyblepharon
pachycarpous
pachycephal
pachycephalia
pachycephalic
pachycephalous
pachycephaly
pachychilia
pachycholia
pachychymia
pachycladous
pachydactyl
pachydactylous
pachydactyly
pachyderm
pachyderma
pachydermal
Pachydermata
pachydermatocele
pachydermatoid
pachydermatosis
pachydermatous
pachydermatously
pachydermia
pachydermial
pachydermic
pachydermoid
pachydermous
pachyemia
pachyglossal
pachyglossate
pachyglossia
pachyglossous
pachyhaemia
pachyhaemic
pachyhaemous
pachyhematous
pachyhemia
pachyhymenia
pachyhymenic
Pachylophus
pachylosis
Pachyma
pachymenia
pachymenic
pachymeningitic
pachymeningitis
pachymeninx
pachymeter
pachynathous
pachynema
pachynsis
pachyntic
pachyodont
pachyotia
pachyotous
pachyperitonitis
pachyphyllous
pachypleuritic
pachypod
pachypodous
pachypterous
Pachyrhizus
pachyrhynchous
pachysalpingitis
Pachysandra
pachysaurian
pachysomia
pachysomous
pachystichous
Pachystima
pachytene
pachytrichous
Pachytylus
pachyvaginitis
pacifiable
pacific
pacifical
pacifically
pacificate
pacification
pacificator
pacificatory
pacificism
pacificist
pacificity
pacifier
pacifism
pacifist
pacifistic
pacifistically
pacify
pacifyingly
Pacinian
pack
packable
package
packbuilder
packcloth
packer
packery
packet
packhouse
packless
packly
packmaker
packmaking
packman
packmanship
packness
packsack
packsaddle
packstaff
packthread
packwall
packwaller
packware
packway
paco
Pacolet
pacouryuva
pact
paction
pactional
pactionally
Pactolian
Pactolus
pad
padcloth
Padda
padder
padding
paddle
paddlecock
paddled
paddlefish
paddlelike
paddler
paddlewood
paddling
paddock
paddockride
paddockstone
paddockstool
Paddy
paddy
paddybird
Paddyism
paddymelon
Paddywack
paddywatch
Paddywhack
paddywhack
padella
padfoot
padge
Padina
padishah
padle
padlike
padlock
padmasana
padmelon
padnag
padpiece
Padraic
Padraig
padre
padroadist
padroado
padronism
padstone
padtree
Paduan
Paduanism
paduasoy
Padus
paean
paeanism
paeanize
paedarchy
paedatrophia
paedatrophy
paediatry
paedogenesis
paedogenetic
paedometer
paedometrical
paedomorphic
paedomorphism
paedonymic
paedonymy
paedopsychologist
paedotribe
paedotrophic
paedotrophist
paedotrophy
paegel
paegle
Paelignian
paenula
paeon
Paeonia
Paeoniaceae
Paeonian
paeonic
paetrick
paga
pagan
Paganalia
Paganalian
pagandom
paganic
paganical
paganically
paganish
paganishly
paganism
paganist
paganistic
paganity
paganization
paganize
paganizer
paganly
paganry
pagatpat
Page
page
pageant
pageanted
pageanteer
pageantic
pageantry
pagedom
pageful
pagehood
pageless
pagelike
pager
pageship
pagina
paginal
paginary
paginate
pagination
pagiopod
Pagiopoda
pagoda
pagodalike
pagodite
pagoscope
pagrus
Paguma
pagurian
pagurid
Paguridae
Paguridea
pagurine
Pagurinea
paguroid
Paguroidea
Pagurus
pagus
pah
paha
Pahareen
Pahari
Paharia
pahi
Pahlavi
pahlavi
pahmi
paho
pahoehoe
Pahouin
pahutan
Paiconeca
paideutic
paideutics
paidological
paidologist
paidology
paidonosology
paigle
paik
pail
pailful
paillasse
paillette
pailletted
pailou
paimaneh
pain
pained
painful
painfully
painfulness
paining
painingly
painkiller
painless
painlessly
painlessness
painproof
painstaker
painstaking
painstakingly
painstakingness
painsworthy
paint
paintability
paintable
paintableness
paintably
paintbox
paintbrush
painted
paintedness
painter
painterish
painterlike
painterly
paintership
paintiness
painting
paintingness
paintless
paintpot
paintproof
paintress
paintrix
paintroot
painty
paip
pair
paired
pairedness
pairer
pairment
pairwise
pais
paisa
paisanite
Paisley
Paiute
paiwari
pajahuello
pajama
pajamaed
pajock
Pajonism
Pakawa
Pakawan
pakchoi
pakeha
Pakhpuluk
Pakhtun
Pakistani
paktong
pal
Pala
palace
palaced
palacelike
palaceous
palaceward
palacewards
paladin
palaeanthropic
Palaearctic
Palaeechini
palaeechinoid
Palaeechinoidea
palaeechinoidean
palaeentomology
palaeethnologic
palaeethnological
palaeethnologist
palaeethnology
Palaeeudyptes
Palaeic
palaeichthyan
Palaeichthyes
palaeichthyic
Palaemon
palaemonid
Palaemonidae
palaemonoid
palaeoalchemical
palaeoanthropic
palaeoanthropography
palaeoanthropology
Palaeoanthropus
palaeoatavism
palaeoatavistic
palaeobiogeography
palaeobiologist
palaeobiology
palaeobotanic
palaeobotanical
palaeobotanically
palaeobotanist
palaeobotany
Palaeocarida
palaeoceanography
Palaeocene
palaeochorology
palaeoclimatic
palaeoclimatology
Palaeoconcha
palaeocosmic
palaeocosmology
Palaeocrinoidea
palaeocrystal
palaeocrystallic
palaeocrystalline
palaeocrystic
palaeocyclic
palaeodendrologic
palaeodendrological
palaeodendrologically
palaeodendrologist
palaeodendrology
Palaeodictyoptera
palaeodictyopteran
palaeodictyopteron
palaeodictyopterous
palaeoencephalon
palaeoeremology
palaeoethnic
palaeoethnologic
palaeoethnological
palaeoethnologist
palaeoethnology
palaeofauna
Palaeogaea
Palaeogaean
palaeogene
palaeogenesis
palaeogenetic
palaeogeographic
palaeogeography
palaeoglaciology
palaeoglyph
Palaeognathae
palaeognathic
palaeognathous
palaeograph
palaeographer
palaeographic
palaeographical
palaeographically
palaeographist
palaeography
palaeoherpetologist
palaeoherpetology
palaeohistology
palaeohydrography
palaeolatry
palaeolimnology
palaeolith
palaeolithic
palaeolithical
palaeolithist
palaeolithoid
palaeolithy
palaeological
palaeologist
palaeology
Palaeomastodon
palaeometallic
palaeometeorological
palaeometeorology
Palaeonemertea
palaeonemertean
palaeonemertine
Palaeonemertinea
Palaeonemertini
palaeoniscid
Palaeoniscidae
palaeoniscoid
Palaeoniscum
Palaeoniscus
palaeontographic
palaeontographical
palaeontography
palaeopathology
palaeopedology
palaeophile
palaeophilist
Palaeophis
palaeophysiography
palaeophysiology
palaeophytic
palaeophytological
palaeophytologist
palaeophytology
palaeoplain
palaeopotamology
palaeopsychic
palaeopsychological
palaeopsychology
palaeoptychology
Palaeornis
Palaeornithinae
palaeornithine
palaeornithological
palaeornithology
palaeosaur
Palaeosaurus
palaeosophy
Palaeospondylus
Palaeostraca
palaeostracan
palaeostriatal
palaeostriatum
palaeostylic
palaeostyly
palaeotechnic
palaeothalamus
Palaeothentes
Palaeothentidae
palaeothere
palaeotherian
Palaeotheriidae
palaeotheriodont
palaeotherioid
Palaeotherium
palaeotheroid
Palaeotropical
palaeotype
palaeotypic
palaeotypical
palaeotypically
palaeotypographical
palaeotypographist
palaeotypography
palaeovolcanic
Palaeozoic
palaeozoological
palaeozoologist
palaeozoology
palaestra
palaestral
palaestrian
palaestric
palaestrics
palaetiological
palaetiologist
palaetiology
palafitte
palagonite
palagonitic
Palaic
Palaihnihan
palaiotype
palaite
palama
palamate
palame
Palamedea
palamedean
Palamedeidae
Palamite
Palamitism
palampore
palander
palanka
palankeen
palanquin
palapalai
Palapteryx
Palaquium
palar
palas
palatability
palatable
palatableness
palatably
palatal
palatalism
palatality
palatalization
palatalize
palate
palated
palateful
palatefulness
palateless
palatelike
palatial
palatially
palatialness
palatian
palatic
palatinal
palatinate
palatine
palatineship
Palatinian
palatinite
palation
palatist
palatitis
palative
palatization
palatize
palatoalveolar
palatodental
palatoglossal
palatoglossus
palatognathous
palatogram
palatograph
palatography
palatomaxillary
palatometer
palatonasal
palatopharyngeal
palatopharyngeus
palatoplasty
palatoplegia
palatopterygoid
palatoquadrate
palatorrhaphy
palatoschisis
Palatua
Palau
Palaung
palaver
palaverer
palaverist
palaverment
palaverous
palay
palazzi
palberry
palch
pale
palea
paleaceous
paleanthropic
Palearctic
paleate
palebelly
palebuck
palechinoid
paled
paledness
paleencephalon
paleentomology
paleethnographer
paleethnologic
paleethnological
paleethnologist
paleethnology
paleface
palehearted
paleichthyologic
paleichthyologist
paleichthyology
paleiform
palely
Paleman
paleness
Palenque
paleoalchemical
paleoandesite
paleoanthropic
paleoanthropography
paleoanthropological
paleoanthropologist
paleoanthropology
Paleoanthropus
paleoatavism
paleoatavistic
paleobiogeography
paleobiologist
paleobiology
paleobotanic
paleobotanical
paleobotanically
paleobotanist
paleobotany
paleoceanography
Paleocene
paleochorology
paleoclimatic
paleoclimatologist
paleoclimatology
Paleoconcha
paleocosmic
paleocosmology
paleocrystal
paleocrystallic
paleocrystalline
paleocrystic
paleocyclic
paleodendrologic
paleodendrological
paleodendrologically
paleodendrologist
paleodendrology
paleoecologist
paleoecology
paleoencephalon
paleoeremology
paleoethnic
paleoethnography
paleoethnologic
paleoethnological
paleoethnologist
paleoethnology
paleofauna
Paleogene
paleogenesis
paleogenetic
paleogeographic
paleogeography
paleoglaciology
paleoglyph
paleograph
paleographer
paleographic
paleographical
paleographically
paleographist
paleography
paleoherpetologist
paleoherpetology
paleohistology
paleohydrography
paleoichthyology
paleokinetic
paleola
paleolate
paleolatry
paleolimnology
paleolith
paleolithic
paleolithical
paleolithist
paleolithoid
paleolithy
paleological
paleologist
paleology
paleomammalogy
paleometallic
paleometeorological
paleometeorology
paleontographic
paleontographical
paleontography
paleontologic
paleontological
paleontologically
paleontologist
paleontology
paleopathology
paleopedology
paleophysiography
paleophysiology
paleophytic
paleophytological
paleophytologist
paleophytology
paleopicrite
paleoplain
paleopotamoloy
paleopsychic
paleopsychological
paleopsychology
paleornithological
paleornithology
paleostriatal
paleostriatum
paleostylic
paleostyly
paleotechnic
paleothalamus
paleothermal
paleothermic
Paleotropical
paleovolcanic
paleoytterbium
Paleozoic
paleozoological
paleozoologist
paleozoology
paler
Palermitan
Palermo
Pales
Palesman
Palestinian
palestra
palestral
palestrian
palestric
palet
paletiology
paletot
palette
paletz
palewise
palfrey
palfreyed
palgat
Pali
pali
Palicourea
palification
paliform
paligorskite
palikar
palikarism
palikinesia
palila
palilalia
Palilia
Palilicium
palillogia
palilogetic
palilogy
palimbacchic
palimbacchius
palimpsest
palimpsestic
palinal
palindrome
palindromic
palindromical
palindromically
palindromist
paling
palingenesia
palingenesian
palingenesis
palingenesist
palingenesy
palingenetic
palingenetically
palingenic
palingenist
palingeny
palinode
palinodial
palinodic
palinodist
palinody
palinurid
Palinuridae
palinuroid
Palinurus
paliphrasia
palirrhea
palisade
palisading
palisado
palisander
palisfy
palish
palistrophia
Paliurus
palkee
pall
palla
palladammine
Palladia
palladia
Palladian
Palladianism
palladic
palladiferous
palladinize
palladion
palladious
Palladium
palladium
palladiumize
palladize
palladodiammine
palladosammine
palladous
pallae
pallah
pallall
pallanesthesia
Pallas
pallasite
pallbearer
palled
pallescence
pallescent
pallesthesia
pallet
palleting
palletize
pallette
pallholder
palli
pallial
palliard
palliasse
Palliata
palliata
palliate
palliation
palliative
palliatively
palliator
palliatory
pallid
pallidiflorous
pallidipalpate
palliditarsate
pallidity
pallidiventrate
pallidly
pallidness
palliness
Palliobranchiata
palliobranchiate
palliocardiac
pallioessexite
pallion
palliopedal
palliostratus
pallium
Palliyan
pallograph
pallographic
pallometric
pallone
pallor
Pallu
Palluites
pallwise
pally
palm
palma
Palmaceae
palmaceous
palmad
Palmae
palmanesthesia
palmar
palmarian
palmary
palmate
palmated
palmately
palmatifid
palmatiform
palmatilobate
palmatilobed
palmation
palmatiparted
palmatipartite
palmatisect
palmatisected
palmature
palmcrist
palmed
Palmella
Palmellaceae
palmellaceous
palmelloid
palmer
palmerite
palmery
palmesthesia
palmette
palmetto
palmetum
palmful
palmicolous
palmiferous
palmification
palmiform
palmigrade
palmilobate
palmilobated
palmilobed
palminervate
palminerved
palmiped
Palmipedes
palmipes
palmist
palmister
palmistry
palmitate
palmite
palmitic
palmitin
palmitinic
palmito
palmitoleic
palmitone
palmiveined
palmivorous
palmlike
palmo
palmodic
palmoscopy
palmospasmus
palmula
palmus
palmwise
palmwood
palmy
palmyra
Palmyrene
Palmyrenian
palolo
palombino
palometa
palomino
palosapis
palouser
paloverde
palp
palpability
palpable
palpableness
palpably
palpacle
palpal
palpate
palpation
palpatory
palpebra
palpebral
palpebrate
palpebration
palpebritis
palped
palpi
palpicorn
Palpicornia
palpifer
palpiferous
palpiform
palpiger
palpigerous
palpitant
palpitate
palpitatingly
palpitation
palpless
palpocil
palpon
palpulus
palpus
palsgrave
palsgravine
palsied
palsification
palstave
palster
palsy
palsylike
palsywort
palt
Palta
palter
palterer
palterly
paltrily
paltriness
paltry
paludal
paludament
paludamentum
paludial
paludian
paludic
Paludicella
Paludicolae
paludicole
paludicoline
paludicolous
paludiferous
Paludina
paludinal
paludine
paludinous
paludism
paludose
paludous
paludrin
paludrine
palule
palulus
Palus
palus
palustral
palustrian
palustrine
paly
palynology
Pam
pam
pambanmanche
Pamela
pament
pameroon
Pamir
Pamiri
Pamirian
Pamlico
pamment
Pampanga
Pampangan
Pampango
pampas
pampean
pamper
pampered
pamperedly
pamperedness
pamperer
pamperize
pampero
pamphagous
pampharmacon
Pamphiliidae
Pamphilius
pamphlet
pamphletage
pamphletary
pamphleteer
pamphleter
pamphletful
pamphletic
pamphletical
pamphletize
pamphletwise
pamphysical
pamphysicism
pampilion
pampiniform
pampinocele
pamplegia
pampootee
pampootie
pampre
pamprodactyl
pamprodactylism
pamprodactylous
pampsychism
pampsychist
Pamunkey
Pan
pan
panace
Panacea
panacea
panacean
panaceist
panache
panached
panachure
panada
panade
Panagia
panagiarion
Panak
Panaka
panama
Panamaian
Panaman
Panamanian
Panamano
Panamic
Panamint
Panamist
panapospory
panarchic
panarchy
panaris
panaritium
panarteritis
panarthritis
panary
panatela
Panathenaea
Panathenaean
Panathenaic
panatrophy
panautomorphic
panax
Panayan
Panayano
panbabylonian
panbabylonism
Panboeotian
pancake
pancarditis
panchama
panchayat
pancheon
panchion
panchromatic
panchromatism
panchromatization
panchromatize
panchway
panclastic
panconciliatory
pancosmic
pancosmism
pancosmist
pancratian
pancratiast
pancratiastic
pancratic
pancratical
pancratically
pancration
pancratism
pancratist
pancratium
pancreas
pancreatalgia
pancreatectomize
pancreatectomy
pancreatemphraxis
pancreathelcosis
pancreatic
pancreaticoduodenal
pancreaticoduodenostomy
pancreaticogastrostomy
pancreaticosplenic
pancreatin
pancreatism
pancreatitic
pancreatitis
pancreatization
pancreatize
pancreatoduodenectomy
pancreatoenterostomy
pancreatogenic
pancreatogenous
pancreatoid
pancreatolipase
pancreatolith
pancreatomy
pancreatoncus
pancreatopathy
pancreatorrhagia
pancreatotomy
pancreectomy
pancreozymin
pancyclopedic
pand
panda
pandal
pandan
Pandanaceae
pandanaceous
Pandanales
Pandanus
pandaram
Pandarctos
pandaric
Pandarus
pandation
Pandean
pandect
Pandectist
pandemia
pandemian
pandemic
pandemicity
pandemoniac
Pandemoniacal
Pandemonian
pandemonic
pandemonism
Pandemonium
pandemonium
Pandemos
pandemy
pandenominational
pander
panderage
panderer
panderess
panderism
panderize
panderly
Panderma
pandermite
panderous
pandership
pandestruction
pandiabolism
pandiculation
Pandion
Pandionidae
pandita
pandle
pandlewhew
Pandora
pandora
Pandorea
Pandoridae
Pandorina
Pandosto
pandour
pandowdy
pandrop
pandura
pandurate
pandurated
panduriform
pandy
pane
panecclesiastical
paned
panegoism
panegoist
panegyric
panegyrical
panegyrically
panegyricize
panegyricon
panegyricum
panegyris
panegyrist
panegyrize
panegyrizer
panegyry
paneity
panel
panela
panelation
paneler
paneless
paneling
panelist
panellation
panelling
panelwise
panelwork
panentheism
panesthesia
panesthetic
paneulogism
panfil
panfish
panful
pang
Pangaea
pangamic
pangamous
pangamously
pangamy
pangane
Pangasinan
pangen
pangene
pangenesis
pangenetic
pangenetically
pangenic
pangful
pangi
Pangium
pangless
panglessly
panglima
Pangloss
Panglossian
Panglossic
pangolin
pangrammatist
Pangwe
panhandle
panhandler
panharmonic
panharmonicon
panhead
panheaded
Panhellenic
Panhellenios
Panhellenism
Panhellenist
Panhellenium
panhidrosis
panhuman
panhygrous
panhyperemia
panhysterectomy
Pani
panic
panical
panically
panicful
panichthyophagous
panicked
panicky
panicle
panicled
paniclike
panicmonger
panicmongering
paniconograph
paniconographic
paniconography
Panicularia
paniculate
paniculated
paniculately
paniculitis
Panicum
panidiomorphic
panidrosis
panification
panimmunity
Paninean
Panionia
Panionian
Panionic
Paniquita
Paniquitan
panisc
panisca
paniscus
panisic
panivorous
Panjabi
panjandrum
pank
pankin
pankration
panleucopenia
panlogical
panlogism
panlogistical
panman
panmelodicon
panmelodion
panmerism
panmeristic
panmixia
panmixy
panmnesia
panmug
panmyelophthisis
Panna
pannade
pannage
pannam
pannationalism
panne
pannel
panner
pannery
panneuritic
panneuritis
pannicle
pannicular
pannier
panniered
pannierman
pannikin
panning
Pannonian
Pannonic
pannose
pannosely
pannum
pannus
pannuscorium
Panoan
panocha
panoche
panococo
panoistic
panomphaic
panomphean
panomphic
panophobia
panophthalmia
panophthalmitis
panoplied
panoplist
panoply
panoptic
panoptical
panopticon
panoram
panorama
panoramic
panoramical
panoramically
panoramist
panornithic
Panorpa
Panorpatae
panorpian
panorpid
Panorpidae
Panos
panosteitis
panostitis
panotitis
panotype
panouchi
panpathy
panpharmacon
panphenomenalism
panphobia
Panpipe
panplegia
panpneumatism
panpolism
panpsychic
panpsychism
panpsychist
panpsychistic
panscientist
pansciolism
pansciolist
pansclerosis
pansclerotic
panse
pansexism
pansexual
pansexualism
pansexualist
pansexuality
pansexualize
panshard
panside
pansideman
pansied
pansinuitis
pansinusitis
pansmith
pansophic
pansophical
pansophically
pansophism
pansophist
pansophy
panspermatism
panspermatist
panspermia
panspermic
panspermism
panspermist
panspermy
pansphygmograph
panstereorama
pansy
pansylike
pant
pantachromatic
pantacosm
pantagamy
pantagogue
pantagraph
pantagraphic
pantagraphical
Pantagruel
Pantagruelian
Pantagruelic
Pantagruelically
Pantagrueline
pantagruelion
Pantagruelism
Pantagruelist
Pantagruelistic
Pantagruelistical
Pantagruelize
pantaleon
pantaletless
pantalets
pantaletted
pantalgia
pantalon
Pantalone
pantaloon
pantalooned
pantaloonery
pantaloons
pantameter
pantamorph
pantamorphia
pantamorphic
pantanemone
pantanencephalia
pantanencephalic
pantaphobia
pantarbe
pantarchy
pantas
pantascope
pantascopic
Pantastomatida
Pantastomina
pantatrophia
pantatrophy
pantatype
pantechnic
pantechnicon
pantelegraph
pantelegraphy
panteleologism
pantelephone
pantelephonic
Pantelis
pantellerite
panter
panterer
Pantheian
pantheic
pantheism
pantheist
pantheistic
pantheistical
pantheistically
panthelematism
panthelism
pantheologist
pantheology
pantheon
pantheonic
pantheonization
pantheonize
panther
pantheress
pantherine
pantherish
pantherlike
pantherwood
pantheum
pantie
panties
pantile
pantiled
pantiling
panting
pantingly
pantisocracy
pantisocrat
pantisocratic
pantisocratical
pantisocratist
pantle
pantler
panto
pantochrome
pantochromic
pantochromism
pantochronometer
Pantocrator
pantod
Pantodon
Pantodontidae
pantoffle
pantofle
pantoganglitis
pantogelastic
pantoglossical
pantoglot
pantoglottism
pantograph
pantographer
pantographic
pantographical
pantographically
pantography
pantoiatrical
pantologic
pantological
pantologist
pantology
pantomancer
pantometer
pantometric
pantometrical
pantometry
pantomime
pantomimic
pantomimical
pantomimically
pantomimicry
pantomimish
pantomimist
pantomimus
pantomnesia
pantomnesic
pantomorph
pantomorphia
pantomorphic
panton
pantoon
pantopelagian
pantophagic
pantophagist
pantophagous
pantophagy
pantophile
pantophobia
pantophobic
pantophobous
pantoplethora
pantopod
Pantopoda
pantopragmatic
pantopterous
pantoscope
pantoscopic
pantosophy
Pantostomata
pantostomate
pantostomatous
pantostome
pantotactic
pantothenate
pantothenic
Pantotheria
pantotherian
pantotype
pantoum
pantropic
pantropical
pantry
pantryman
pantrywoman
pants
pantun
panty
pantywaist
panung
panurgic
panurgy
panyar
Panzer
panzoism
panzootia
panzootic
panzooty
Paola
paolo
paon
pap
papa
papability
papable
papabot
papacy
papagallo
Papago
papain
papal
papalism
papalist
papalistic
papalization
papalize
papalizer
papally
papalty
papane
papaphobia
papaphobist
papaprelatical
papaprelatist
paparchical
paparchy
papaship
Papaver
Papaveraceae
papaveraceous
Papaverales
papaverine
papaverous
papaw
papaya
Papayaceae
papayaceous
papayotin
papboat
pape
papelonne
paper
paperback
paperbark
paperboard
papered
paperer
paperful
paperiness
papering
paperlike
papermaker
papermaking
papermouth
papern
papershell
paperweight
papery
papess
papeterie
papey
Paphian
Paphiopedilum
Papiamento
papicolar
papicolist
Papilio
Papilionaceae
papilionaceous
Papiliones
papilionid
Papilionidae
Papilionides
Papilioninae
papilionine
papilionoid
Papilionoidea
papilla
papillae
papillar
papillary
papillate
papillated
papillectomy
papilledema
papilliferous
papilliform
papillitis
papilloadenocystoma
papillocarcinoma
papilloedema
papilloma
papillomatosis
papillomatous
papillon
papilloretinitis
papillosarcoma
papillose
papillosity
papillote
papillous
papillulate
papillule
Papinachois
Papio
papion
papish
papisher
papism
Papist
papist
papistic
papistical
papistically
papistlike
papistly
papistry
papize
papless
papmeat
papolater
papolatrous
papolatry
papoose
papooseroot
Pappea
pappescent
pappi
pappiferous
pappiform
pappose
pappox
pappus
pappy
papreg
paprica
paprika
Papuan
papula
papular
papulate
papulated
papulation
papule
papuliferous
papuloerythematous
papulopustular
papulopustule
papulose
papulosquamous
papulous
papulovesicular
papyr
papyraceous
papyral
papyrean
papyri
papyrian
papyrin
papyrine
papyritious
papyrocracy
papyrograph
papyrographer
papyrographic
papyrography
papyrological
papyrologist
papyrology
papyrophobia
papyroplastics
papyrotamia
papyrotint
papyrotype
papyrus
Paque
paquet
par
para
paraaminobenzoic
parabanate
parabanic
parabaptism
parabaptization
parabasal
parabasic
parabasis
parabema
parabematic
parabenzoquinone
parabiosis
parabiotic
parablast
parablastic
parable
parablepsia
parablepsis
parablepsy
parableptic
parabola
parabolanus
parabolic
parabolical
parabolicalism
parabolically
parabolicness
paraboliform
parabolist
parabolization
parabolize
parabolizer
paraboloid
paraboloidal
parabomb
parabotulism
parabranchia
parabranchial
parabranchiate
parabulia
parabulic
paracanthosis
paracarmine
paracasein
paracaseinate
Paracelsian
Paracelsianism
Paracelsic
Paracelsist
Paracelsistic
Paracelsus
paracentesis
paracentral
paracentric
paracentrical
paracephalus
paracerebellar
paracetaldehyde
parachaplain
paracholia
parachor
parachordal
parachrea
parachroia
parachroma
parachromatism
parachromatophorous
parachromatopsia
parachromatosis
parachrome
parachromoparous
parachromophoric
parachromophorous
parachronism
parachronistic
parachrose
parachute
parachutic
parachutism
parachutist
paraclete
paracmasis
paracme
paracoele
paracoelian
paracolitis
paracolon
paracolpitis
paracolpium
paracondyloid
paracone
paraconic
paraconid
paraconscious
paracorolla
paracotoin
paracoumaric
paracresol
Paracress
paracusia
paracusic
paracyanogen
paracyesis
paracymene
paracystic
paracystitis
paracystium
parade
paradeful
paradeless
paradelike
paradenitis
paradental
paradentitis
paradentium
parader
paraderm
paradiastole
paradiazine
paradichlorbenzene
paradichlorbenzol
paradichlorobenzene
paradichlorobenzol
paradidymal
paradidymis
paradigm
paradigmatic
paradigmatical
paradigmatically
paradigmatize
parading
paradingly
paradiplomatic
paradisaic
paradisaically
paradisal
paradise
Paradisea
paradisean
Paradiseidae
Paradiseinae
Paradisia
paradisiac
paradisiacal
paradisiacally
paradisial
paradisian
paradisic
paradisical
parado
paradoctor
parados
paradoses
paradox
paradoxal
paradoxer
paradoxial
paradoxic
paradoxical
paradoxicalism
paradoxicality
paradoxically
paradoxicalness
paradoxician
Paradoxides
paradoxidian
paradoxism
paradoxist
paradoxographer
paradoxographical
paradoxology
paradoxure
Paradoxurinae
paradoxurine
Paradoxurus
paradoxy
paradromic
paraenesis
paraenesize
paraenetic
paraenetical
paraengineer
paraffin
paraffine
paraffiner
paraffinic
paraffinize
paraffinoid
paraffiny
paraffle
parafle
parafloccular
paraflocculus
paraform
paraformaldehyde
parafunction
paragammacism
paraganglion
paragaster
paragastral
paragastric
paragastrula
paragastrular
parage
paragenesia
paragenesis
paragenetic
paragenic
paragerontic
parageusia
parageusic
parageusis
paragglutination
paraglenal
paraglobin
paraglobulin
paraglossa
paraglossal
paraglossate
paraglossia
paraglycogen
paragnath
paragnathism
paragnathous
paragnathus
paragneiss
paragnosia
paragoge
paragogic
paragogical
paragogically
paragogize
paragon
paragonimiasis
Paragonimus
paragonite
paragonitic
paragonless
paragram
paragrammatist
paragraph
paragrapher
paragraphia
paragraphic
paragraphical
paragraphically
paragraphism
paragraphist
paragraphistical
paragraphize
Paraguay
Paraguayan
parah
paraheliotropic
paraheliotropism
parahematin
parahemoglobin
parahepatic
Parahippus
parahopeite
parahormone
parahydrogen
paraiba
Paraiyan
parakeet
parakeratosis
parakilya
parakinesia
parakinetic
paralactate
paralalia
paralambdacism
paralambdacismus
paralaurionite
paraldehyde
parale
paralectotype
paraleipsis
paralepsis
paralexia
paralexic
paralgesia
paralgesic
paralinin
paralipomena
Paralipomenon
paralipsis
paralitical
parallactic
parallactical
parallactically
parallax
parallel
parallelable
parallelepiped
parallelepipedal
parallelepipedic
parallelepipedon
parallelepipedonal
paralleler
parallelinervate
parallelinerved
parallelinervous
parallelism
parallelist
parallelistic
parallelith
parallelization
parallelize
parallelizer
parallelless
parallelly
parallelodrome
parallelodromous
parallelogram
parallelogrammatic
parallelogrammatical
parallelogrammic
parallelogrammical
parallelograph
parallelometer
parallelopiped
parallelopipedon
parallelotropic
parallelotropism
parallelwise
parallepipedous
paralogia
paralogical
paralogician
paralogism
paralogist
paralogistic
paralogize
paralogy
paraluminite
paralyses
paralysis
paralytic
paralytical
paralytically
paralyzant
paralyzation
paralyze
paralyzedly
paralyzer
paralyzingly
param
paramagnet
paramagnetic
paramagnetism
paramandelic
paramarine
paramastigate
paramastitis
paramastoid
paramatta
Paramecidae
Paramecium
paramedian
paramelaconite
paramenia
parament
paramere
parameric
parameron
paramese
paramesial
parameter
parametric
parametrical
parametritic
parametritis
parametrium
paramide
paramilitary
paramimia
paramine
paramiographer
paramitome
paramnesia
paramo
Paramoecium
paramorph
paramorphia
paramorphic
paramorphine
paramorphism
paramorphosis
paramorphous
paramount
paramountcy
paramountly
paramountness
paramountship
paramour
paramuthetic
paramyelin
paramylum
paramyoclonus
paramyosinogen
paramyotone
paramyotonia
paranasal
paranatellon
parandrus
paranema
paranematic
paranephric
paranephritic
paranephritis
paranephros
paranepionic
paranete
parang
paranitraniline
paranitrosophenol
paranoia
paranoiac
paranoid
paranoidal
paranoidism
paranomia
paranormal
paranosic
paranthelion
paranthracene
Paranthropus
paranuclear
paranucleate
paranucleic
paranuclein
paranucleinic
paranucleus
paranymph
paranymphal
parao
paraoperation
Parapaguridae
paraparesis
paraparetic
parapathia
parapathy
parapegm
parapegma
paraperiodic
parapet
parapetalous
parapeted
parapetless
paraph
paraphasia
paraphasic
paraphemia
paraphenetidine
paraphenylene
paraphenylenediamine
parapherna
paraphernal
paraphernalia
paraphernalian
paraphia
paraphilia
paraphimosis
paraphonia
paraphonic
paraphototropism
paraphrasable
paraphrase
paraphraser
paraphrasia
paraphrasian
paraphrasis
paraphrasist
paraphrast
paraphraster
paraphrastic
paraphrastical
paraphrastically
paraphrenia
paraphrenic
paraphrenitis
paraphyllium
paraphysate
paraphysical
paraphysiferous
paraphysis
paraplasis
paraplasm
paraplasmic
paraplastic
paraplastin
paraplectic
paraplegia
paraplegic
paraplegy
parapleuritis
parapleurum
parapod
parapodial
parapodium
parapophysial
parapophysis
parapraxia
parapraxis
paraproctitis
paraproctium
paraprostatitis
Parapsida
parapsidal
parapsidan
parapsis
parapsychical
parapsychism
parapsychological
parapsychology
parapsychosis
parapteral
parapteron
parapterum
paraquadrate
paraquinone
Pararctalia
Pararctalian
pararectal
pararek
parareka
pararhotacism
pararosaniline
pararosolic
pararthria
parasaboteur
parasalpingitis
parasang
parascene
parascenium
parasceve
paraschematic
parasecretion
paraselene
paraselenic
parasemidin
parasemidine
parasexuality
parashah
parasigmatism
parasigmatismus
Parasita
parasital
parasitary
parasite
parasitelike
parasitemia
parasitic
Parasitica
parasitical
parasitically
parasiticalness
parasiticidal
parasiticide
Parasitidae
parasitism
parasitize
parasitogenic
parasitoid
parasitoidism
parasitological
parasitologist
parasitology
parasitophobia
parasitosis
parasitotrope
parasitotropic
parasitotropism
parasitotropy
paraskenion
parasol
parasoled
parasolette
paraspecific
parasphenoid
parasphenoidal
paraspotter
paraspy
parastas
parastatic
parastemon
parastemonal
parasternal
parasternum
parastichy
parastyle
parasubphonate
parasubstituted
Parasuchia
parasuchian
parasympathetic
parasympathomimetic
parasynapsis
parasynaptic
parasynaptist
parasyndesis
parasynesis
parasynetic
parasynovitis
parasynthesis
parasynthetic
parasyntheton
parasyphilis
parasyphilitic
parasyphilosis
parasystole
paratactic
paratactical
paratactically
paratartaric
parataxis
parate
paraterminal
Paratheria
paratherian
parathesis
parathetic
parathion
parathormone
parathymic
parathyroid
parathyroidal
parathyroidectomize
parathyroidectomy
parathyroprival
parathyroprivia
parathyroprivic
paratitla
paratitles
paratoloid
paratoluic
paratoluidine
paratomial
paratomium
paratonic
paratonically
paratorium
paratory
paratracheal
paratragedia
paratragoedia
paratransversan
paratrichosis
paratrimma
paratriptic
paratroop
paratrooper
paratrophic
paratrophy
paratuberculin
paratuberculosis
paratuberculous
paratungstate
paratungstic
paratype
paratyphlitis
paratyphoid
paratypic
paratypical
paratypically
paravaginitis
paravail
paravane
paravauxite
paravent
paravertebral
paravesical
paraxial
paraxially
paraxon
paraxonic
paraxylene
Parazoa
parazoan
parazonium
parbake
Parbate
parboil
parbuckle
parcel
parceling
parcellary
parcellate
parcellation
parcelling
parcellization
parcellize
parcelment
parcelwise
parcenary
parcener
parcenership
parch
parchable
parchedly
parchedness
parcheesi
parchemin
parcher
parchesi
parching
parchingly
parchisi
parchment
parchmenter
parchmentize
parchmentlike
parchmenty
parchy
parcidentate
parciloquy
parclose
parcook
pard
pardalote
Pardanthus
pardao
parded
pardesi
pardine
pardner
pardnomastic
pardo
pardon
pardonable
pardonableness
pardonably
pardonee
pardoner
pardoning
pardonless
pardonmonger
pare
paregoric
Pareiasauri
Pareiasauria
pareiasaurian
Pareiasaurus
Pareioplitae
parel
parelectronomic
parelectronomy
parella
paren
parencephalic
parencephalon
parenchym
parenchyma
parenchymal
parenchymatic
parenchymatitis
parenchymatous
parenchymatously
parenchyme
parenchymous
parent
parentage
parental
Parentalia
parentalism
parentality
parentally
parentdom
parentela
parentelic
parenteral
parenterally
parentheses
parenthesis
parenthesize
parenthetic
parenthetical
parentheticality
parenthetically
parentheticalness
parenthood
parenticide
parentless
parentlike
parentship
Pareoean
parepididymal
parepididymis
parepigastric
parer
parerethesis
parergal
parergic
parergon
paresis
paresthesia
paresthesis
paresthetic
parethmoid
paretic
paretically
pareunia
parfait
parfilage
parfleche
parfocal
pargana
pargasite
parge
pargeboard
parget
pargeter
pargeting
pargo
parhelia
parheliacal
parhelic
parhelion
parhomologous
parhomology
parhypate
pari
pariah
pariahdom
pariahism
pariahship
parial
Parian
parian
Pariasauria
Pariasaurus
Paridae
paridigitate
paridrosis
paries
parietal
Parietales
Parietaria
parietary
parietes
parietofrontal
parietojugal
parietomastoid
parietoquadrate
parietosphenoid
parietosphenoidal
parietosplanchnic
parietosquamosal
parietotemporal
parietovaginal
parietovisceral
parify
parigenin
pariglin
Parilia
Parilicium
parilla
parillin
parimutuel
Parinarium
parine
paring
paripinnate
Paris
parish
parished
parishen
parishional
parishionally
parishionate
parishioner
parishionership
Parisian
Parisianism
Parisianization
Parisianize
Parisianly
Parisii
parisis
parisology
parison
parisonic
paristhmic
paristhmion
parisyllabic
parisyllabical
Pariti
Paritium
parity
parivincular
park
parka
parkee
parker
parkin
parking
Parkinsonia
Parkinsonism
parkish
parklike
parkward
parkway
parky
parlamento
parlance
parlando
Parlatoria
parlatory
parlay
parle
parley
parleyer
parliament
parliamental
parliamentarian
parliamentarianism
parliamentarily
parliamentariness
parliamentarism
parliamentarization
parliamentarize
parliamentary
parliamenteer
parliamenteering
parliamenter
parling
parlish
parlor
parlorish
parlormaid
parlous
parlously
parlousness
parly
Parma
parma
parmacety
parmak
Parmelia
Parmeliaceae
parmeliaceous
parmelioid
Parmentiera
Parmesan
Parmese
parnas
Parnassia
Parnassiaceae
parnassiaceous
Parnassian
Parnassianism
Parnassiinae
Parnassism
Parnassus
parnel
Parnellism
Parnellite
parnorpine
paroarion
paroarium
paroccipital
paroch
parochial
parochialic
parochialism
parochialist
parochiality
parochialization
parochialize
parochially
parochialness
parochin
parochine
parochiner
parode
parodiable
parodial
parodic
parodical
parodinia
parodist
parodistic
parodistically
parodize
parodontitis
parodos
parody
parodyproof
paroecious
paroeciously
paroeciousness
paroecism
paroecy
paroemia
paroemiac
paroemiographer
paroemiography
paroemiologist
paroemiology
paroicous
parol
parolable
parole
parolee
parolfactory
paroli
parolist
paromoeon
paromologetic
paromologia
paromology
paromphalocele
paromphalocelic
paronomasia
paronomasial
paronomasian
paronomasiastic
paronomastical
paronomastically
paronychia
paronychial
paronychium
paronym
paronymic
paronymization
paronymize
paronymous
paronymy
paroophoric
paroophoritis
paroophoron
paropsis
paroptesis
paroptic
parorchid
parorchis
parorexia
Parosela
parosmia
parosmic
parosteal
parosteitis
parosteosis
parostosis
parostotic
Parotia
parotic
parotid
parotidean
parotidectomy
parotiditis
parotis
parotitic
parotitis
parotoid
parous
parousia
parousiamania
parovarian
parovariotomy
parovarium
paroxazine
paroxysm
paroxysmal
paroxysmalist
paroxysmally
paroxysmic
paroxysmist
paroxytone
paroxytonic
paroxytonize
parpal
parquet
parquetage
parquetry
parr
Parra
parrel
parrhesia
parrhesiastic
parriable
parricidal
parricidally
parricide
parricided
parricidial
parricidism
Parridae
parrier
parrock
parrot
parroter
parrothood
parrotism
parrotize
parrotlet
parrotlike
parrotry
parrotwise
parroty
parry
parsable
parse
parsec
Parsee
Parseeism
parser
parsettensite
Parsi
Parsic
Parsiism
parsimonious
parsimoniously
parsimoniousness
parsimony
Parsism
parsley
parsleylike
parsleywort
parsnip
parson
parsonage
parsonarchy
parsondom
parsoned
parsonese
parsoness
parsonet
parsonhood
parsonic
parsonical
parsonically
parsoning
parsonish
parsonity
parsonize
parsonlike
parsonly
parsonolatry
parsonology
parsonry
parsonship
Parsonsia
parsonsite
parsony
Part
part
partakable
partake
partaker
partan
partanfull
partanhanded
parted
partedness
parter
parterre
parterred
partheniad
Partheniae
parthenian
parthenic
Parthenium
parthenocarpelly
parthenocarpic
parthenocarpical
parthenocarpically
parthenocarpous
parthenocarpy
Parthenocissus
parthenogenesis
parthenogenetic
parthenogenetically
parthenogenic
parthenogenitive
parthenogenous
parthenogeny
parthenogonidium
Parthenolatry
parthenology
Parthenon
Parthenopaeus
parthenoparous
Parthenope
Parthenopean
Parthenos
parthenosperm
parthenospore
Parthian
partial
partialism
partialist
partialistic
partiality
partialize
partially
partialness
partiary
partible
particate
participability
participable
participance
participancy
participant
participantly
participate
participatingly
participation
participative
participatively
participator
participatory
participatress
participial
participiality
participialize
participially
participle
particle
particled
particular
particularism
particularist
particularistic
particularistically
particularity
particularization
particularize
particularly
particularness
particulate
partigen
partile
partimembered
partimen
partinium
partisan
partisanism
partisanize
partisanship
partite
partition
partitional
partitionary
partitioned
partitioner
partitioning
partitionist
partitionment
partitive
partitively
partitura
partiversal
partivity
partless
partlet
partly
partner
partnerless
partnership
parto
partook
partridge
partridgeberry
partridgelike
partridgewood
partridging
partschinite
parture
parturiate
parturience
parturiency
parturient
parturifacient
parturition
parturitive
party
partyism
partyist
partykin
partyless
partymonger
partyship
Parukutu
parulis
parumbilical
parure
paruria
Parus
parvanimity
parvenu
parvenudom
parvenuism
parvicellular
parviflorous
parvifoliate
parvifolious
parvipotent
parvirostrate
parvis
parviscient
parvitude
parvolin
parvoline
parvule
paryphodrome
pasan
pasang
Pascal
Pasch
Pascha
paschal
paschalist
Paschaltide
paschite
pascoite
pascuage
pascual
pascuous
pasgarde
pash
pasha
pashadom
pashalik
pashaship
pashm
pashmina
Pashto
pasi
pasigraphic
pasigraphical
pasigraphy
pasilaly
Pasitelean
pasmo
Paspalum
pasqueflower
pasquil
pasquilant
pasquiler
pasquilic
Pasquin
pasquin
pasquinade
pasquinader
Pasquinian
Pasquino
pass
passable
passableness
passably
passade
passado
passage
passageable
passageway
Passagian
passalid
Passalidae
Passalus
Passamaquoddy
passant
passback
passbook
Passe
passe
passee
passegarde
passement
passementerie
passen
passenger
Passer
passer
Passeres
passeriform
Passeriformes
Passerina
passerine
passewa
passibility
passible
passibleness
Passiflora
Passifloraceae
passifloraceous
Passiflorales
passimeter
passing
passingly
passingness
passion
passional
passionary
passionate
passionately
passionateness
passionative
passioned
passionflower
passionful
passionfully
passionfulness
Passionist
passionist
passionless
passionlessly
passionlessness
passionlike
passionometer
passionproof
Passiontide
passionwise
passionwort
passir
passival
passivate
passivation
passive
passively
passiveness
passivism
passivist
passivity
passkey
passless
passman
passo
passometer
passout
passover
passoverish
passpenny
passport
passportless
passulate
passulation
passus
passway
passwoman
password
passworts
passymeasure
past
paste
pasteboard
pasteboardy
pasted
pastedness
pastedown
pastel
pastelist
paster
pasterer
pastern
pasterned
pasteur
Pasteurella
Pasteurelleae
pasteurellosis
Pasteurian
pasteurism
pasteurization
pasteurize
pasteurizer
pastiche
pasticheur
pastil
pastile
pastille
pastime
pastimer
Pastinaca
pastiness
pasting
pastness
pastophor
pastophorion
pastophorium
pastophorus
pastor
pastorage
pastoral
pastorale
pastoralism
pastoralist
pastorality
pastoralize
pastorally
pastoralness
pastorate
pastoress
pastorhood
pastorium
pastorize
pastorless
pastorlike
pastorling
pastorly
pastorship
pastose
pastosity
pastrami
pastry
pastryman
pasturability
pasturable
pasturage
pastural
pasture
pastureless
pasturer
pasturewise
pasty
pasul
Pat
pat
pata
pataca
patacao
pataco
patagial
patagiate
patagium
Patagon
patagon
Patagones
Patagonian
pataka
patamar
patao
patapat
pataque
Pataria
Patarin
Patarine
Patarinism
patas
patashte
Patavian
patavinity
patball
patballer
patch
patchable
patcher
patchery
patchily
patchiness
patchleaf
patchless
patchouli
patchwise
patchword
patchwork
patchworky
patchy
pate
patefaction
patefy
patel
patella
patellar
patellaroid
patellate
Patellidae
patellidan
patelliform
patelline
patellofemoral
patelloid
patellula
patellulate
paten
patency
patener
patent
patentability
patentable
patentably
patentee
patently
patentor
pater
patera
patercove
paterfamiliar
paterfamiliarly
paterfamilias
pateriform
paterissa
paternal
paternalism
paternalist
paternalistic
paternalistically
paternality
paternalize
paternally
paternity
paternoster
paternosterer
patesi
patesiate
path
Pathan
pathbreaker
pathed
pathema
pathematic
pathematically
pathematology
pathetic
pathetical
pathetically
patheticalness
patheticate
patheticly
patheticness
pathetism
pathetist
pathetize
pathfarer
pathfinder
pathfinding
pathic
pathicism
pathless
pathlessness
pathlet
pathoanatomical
pathoanatomy
pathobiological
pathobiologist
pathobiology
pathochemistry
pathodontia
pathogen
pathogene
pathogenesis
pathogenesy
pathogenetic
pathogenic
pathogenicity
pathogenous
pathogeny
pathogerm
pathogermic
pathognomic
pathognomical
pathognomonic
pathognomonical
pathognomy
pathognostic
pathographical
pathography
pathologic
pathological
pathologically
pathologicoanatomic
pathologicoanatomical
pathologicoclinical
pathologicohistological
pathologicopsychological
pathologist
pathology
patholysis
patholytic
pathomania
pathometabolism
pathomimesis
pathomimicry
pathoneurosis
pathonomia
pathonomy
pathophobia
pathophoresis
pathophoric
pathophorous
pathoplastic
pathoplastically
pathopoeia
pathopoiesis
pathopoietic
pathopsychology
pathopsychosis
pathoradiography
pathos
pathosocial
Pathrusim
pathway
pathwayed
pathy
patible
patibulary
patibulate
patience
patiency
patient
patientless
patiently
patientness
patina
patinate
patination
patine
patined
patinize
patinous
patio
patisserie
patly
Patmian
Patmos
patness
patnidar
pato
patois
patola
patonce
patria
patrial
patriarch
patriarchal
patriarchalism
patriarchally
patriarchate
patriarchdom
patriarched
patriarchess
patriarchic
patriarchical
patriarchically
patriarchism
patriarchist
patriarchship
patriarchy
Patrice
patrice
Patricia
Patrician
patrician
patricianhood
patricianism
patricianly
patricianship
patriciate
patricidal
patricide
Patricio
Patrick
patrico
patrilineal
patrilineally
patrilinear
patriliny
patrilocal
patrimonial
patrimonially
patrimony
patrin
Patriofelis
patriolatry
patriot
patrioteer
patriotess
patriotic
patriotical
patriotically
patriotics
patriotism
patriotly
patriotship
Patripassian
Patripassianism
Patripassianist
Patripassianly
patrist
patristic
patristical
patristically
patristicalness
patristicism
patristics
patrix
patrizate
patrization
patrocinium
patroclinic
patroclinous
patrocliny
patrogenesis
patrol
patroller
patrollotism
patrolman
patrologic
patrological
patrologist
patrology
patron
patronage
patronal
patronate
patrondom
patroness
patronessship
patronite
patronizable
patronization
patronize
patronizer
patronizing
patronizingly
patronless
patronly
patronomatology
patronship
patronym
patronymic
patronymically
patronymy
patroon
patroonry
patroonship
patruity
Patsy
patta
pattable
patte
pattee
patten
pattened
pattener
patter
patterer
patterist
pattern
patternable
patterned
patterner
patterning
patternize
patternless
patternlike
patternmaker
patternmaking
patternwise
patterny
pattu
Patty
patty
pattypan
patu
patulent
patulous
patulously
patulousness
Patuxent
patwari
Patwin
paty
pau
pauciarticulate
pauciarticulated
paucidentate
pauciflorous
paucifoliate
paucifolious
paucify
paucijugate
paucilocular
pauciloquent
pauciloquently
pauciloquy
paucinervate
paucipinnate
pauciplicate
pauciradiate
pauciradiated
paucispiral
paucispirated
paucity
paughty
paukpan
Paul
Paula
paular
pauldron
Pauliad
Paulian
Paulianist
Pauliccian
Paulicianism
paulie
paulin
Paulina
Pauline
Paulinia
Paulinian
Paulinism
Paulinist
Paulinistic
Paulinistically
Paulinity
Paulinize
Paulinus
Paulism
Paulist
Paulista
Paulite
paulopast
paulopost
paulospore
Paulownia
Paulus
Paumari
paunch
paunched
paunchful
paunchily
paunchiness
paunchy
paup
pauper
pauperage
pauperate
pauperdom
pauperess
pauperism
pauperitic
pauperization
pauperize
pauperizer
Paurometabola
paurometabolic
paurometabolism
paurometabolous
paurometaboly
pauropod
Pauropoda
pauropodous
pausably
pausal
pausation
pause
pauseful
pausefully
pauseless
pauselessly
pausement
pauser
pausingly
paussid
Paussidae
paut
pauxi
pavage
pavan
pavane
pave
pavement
pavemental
paver
pavestone
Pavetta
Pavia
pavid
pavidity
pavier
pavilion
paving
pavior
Paviotso
paviour
pavis
pavisade
pavisado
paviser
pavisor
Pavo
pavonated
pavonazzetto
pavonazzo
Pavoncella
Pavonia
pavonian
pavonine
pavonize
pavy
paw
pawdite
pawer
pawing
pawk
pawkery
pawkily
pawkiness
pawkrie
pawky
pawl
pawn
pawnable
pawnage
pawnbroker
pawnbrokerage
pawnbrokeress
pawnbrokering
pawnbrokery
pawnbroking
Pawnee
pawnee
pawner
pawnie
pawnor
pawnshop
pawpaw
Pawtucket
pax
paxilla
paxillar
paxillary
paxillate
paxilliferous
paxilliform
Paxillosa
paxillose
paxillus
paxiuba
paxwax
pay
payability
payable
payableness
payably
Payagua
Payaguan
payday
payed
payee
payeny
payer
paying
paymaster
paymastership
payment
paymistress
Payni
paynim
paynimhood
paynimry
Paynize
payoff
payong
payor
payroll
paysagist
Pazend
pea
peaberry
peace
peaceable
peaceableness
peaceably
peacebreaker
peacebreaking
peaceful
peacefully
peacefulness
peaceless
peacelessness
peacelike
peacemaker
peacemaking
peaceman
peacemonger
peacemongering
peacetime
peach
peachberry
peachblossom
peachblow
peachen
peacher
peachery
peachick
peachify
peachiness
peachlet
peachlike
peachwood
peachwort
peachy
peacoat
peacock
peacockery
peacockish
peacockishly
peacockishness
peacockism
peacocklike
peacockly
peacockwise
peacocky
peacod
peafowl
peag
peage
peahen
peai
peaiism
peak
peaked
peakedly
peakedness
peaker
peakily
peakiness
peaking
peakish
peakishly
peakishness
peakless
peaklike
peakward
peaky
peakyish
peal
pealike
pean
peanut
pear
pearceite
pearl
pearlberry
pearled
pearler
pearlet
pearlfish
pearlfruit
pearlike
pearlin
pearliness
pearling
pearlish
pearlite
pearlitic
pearlsides
pearlstone
pearlweed
pearlwort
pearly
pearmain
pearmonger
peart
pearten
peartly
peartness
pearwood
peasant
peasantess
peasanthood
peasantism
peasantize
peasantlike
peasantly
peasantry
peasantship
peasecod
peaselike
peasen
peashooter
peason
peastake
peastaking
peastick
peasticking
peastone
peasy
peat
peatery
peathouse
peatman
peatship
peatstack
peatwood
peaty
peavey
peavy
Peba
peba
Peban
pebble
pebbled
pebblehearted
pebblestone
pebbleware
pebbly
pebrine
pebrinous
pecan
peccability
peccable
peccadillo
peccancy
peccant
peccantly
peccantness
peccary
peccation
peccavi
pech
pecht
pecite
peck
pecked
pecker
peckerwood
pecket
peckful
peckhamite
peckiness
peckish
peckishly
peckishness
peckle
peckled
peckly
Pecksniffian
Pecksniffianism
Pecksniffism
pecky
Pecopteris
pecopteroid
Pecora
Pecos
pectase
pectate
pecten
pectic
pectin
Pectinacea
pectinacean
pectinaceous
pectinal
pectinase
pectinate
pectinated
pectinately
pectination
pectinatodenticulate
pectinatofimbricate
pectinatopinnate
pectineal
pectineus
pectinibranch
Pectinibranchia
pectinibranchian
Pectinibranchiata
pectinibranchiate
pectinic
pectinid
Pectinidae
pectiniferous
pectiniform
pectinirostrate
pectinite
pectinogen
pectinoid
pectinose
pectinous
pectizable
pectization
pectize
pectocellulose
pectolite
pectora
pectoral
pectoralgia
pectoralis
pectoralist
pectorally
pectoriloquial
pectoriloquism
pectoriloquous
pectoriloquy
pectosase
pectose
pectosic
pectosinase
pectous
pectunculate
Pectunculus
pectus
peculate
peculation
peculator
peculiar
peculiarism
peculiarity
peculiarize
peculiarly
peculiarness
peculiarsome
peculium
pecuniarily
pecuniary
pecuniosity
pecunious
ped
peda
pedage
pedagog
pedagogal
pedagogic
pedagogical
pedagogically
pedagogics
pedagogism
pedagogist
pedagogue
pedagoguery
pedagoguish
pedagoguism
pedagogy
pedal
pedaler
pedalfer
pedalferic
Pedaliaceae
pedaliaceous
pedalian
pedalier
Pedalion
pedalism
pedalist
pedaliter
pedality
Pedalium
pedanalysis
pedant
pedantesque
pedantess
pedanthood
pedantic
pedantical
pedantically
pedanticalness
pedanticism
pedanticly
pedanticness
pedantism
pedantize
pedantocracy
pedantocrat
pedantocratic
pedantry
pedary
Pedata
pedate
pedated
pedately
pedatifid
pedatiform
pedatilobate
pedatilobed
pedatinerved
pedatipartite
pedatisect
pedatisected
pedatrophia
pedder
peddle
peddler
peddleress
peddlerism
peddlery
peddling
peddlingly
pedee
pedelion
pederast
pederastic
pederastically
pederasty
pedes
pedesis
pedestal
pedestrial
pedestrially
pedestrian
pedestrianate
pedestrianism
pedestrianize
pedetentous
Pedetes
Pedetidae
Pedetinae
pediadontia
pediadontic
pediadontist
pedialgia
Pediastrum
pediatric
pediatrician
pediatrics
pediatrist
pediatry
pedicab
pedicel
pediceled
pedicellar
pedicellaria
pedicellate
pedicellated
pedicellation
pedicelled
pedicelliform
Pedicellina
pedicellus
pedicle
pedicular
Pedicularia
Pedicularis
pediculate
pediculated
Pediculati
pedicule
Pediculi
pediculicidal
pediculicide
pediculid
Pediculidae
Pediculina
pediculine
pediculofrontal
pediculoid
pediculoparietal
pediculophobia
pediculosis
pediculous
Pediculus
pedicure
pedicurism
pedicurist
pediferous
pediform
pedigerous
pedigraic
pedigree
pedigreeless
pediluvium
Pedimana
pedimanous
pediment
pedimental
pedimented
pedimentum
Pedioecetes
pedion
pedionomite
Pedionomus
pedipalp
pedipalpal
pedipalpate
Pedipalpi
Pedipalpida
pedipalpous
pedipalpus
pedipulate
pedipulation
pedipulator
pedlar
pedlary
pedobaptism
pedobaptist
pedocal
pedocalcic
pedodontia
pedodontic
pedodontist
pedodontology
pedograph
pedological
pedologist
pedologistical
pedologistically
pedology
pedometer
pedometric
pedometrical
pedometrically
pedometrician
pedometrist
pedomorphic
pedomorphism
pedomotive
pedomotor
pedophilia
pedophilic
pedotribe
pedotrophic
pedotrophist
pedotrophy
pedrail
pedregal
pedrero
Pedro
pedro
pedule
pedum
peduncle
peduncled
peduncular
Pedunculata
pedunculate
pedunculated
pedunculation
pedunculus
pee
peed
peek
peekaboo
peel
peelable
peele
peeled
peeledness
peeler
peelhouse
peeling
Peelism
Peelite
peelman
peen
peenge
peeoy
peep
peeper
peepeye
peephole
peepy
peer
peerage
peerdom
peeress
peerhood
peerie
peeringly
peerless
peerlessly
peerlessness
peerling
peerly
peership
peery
peesash
peesoreh
peesweep
peetweet
peeve
peeved
peevedly
peevedness
peever
peevish
peevishly
peevishness
peewee
Peg
peg
pega
pegall
peganite
Peganum
Pegasean
Pegasian
Pegasid
pegasid
Pegasidae
pegasoid
Pegasus
pegboard
pegbox
pegged
pegger
pegging
peggle
Peggy
peggy
pegless
peglet
peglike
pegman
pegmatite
pegmatitic
pegmatization
pegmatize
pegmatoid
pegmatophyre
pegology
pegomancy
Peguan
pegwood
Pehlevi
peho
Pehuenche
peignoir
peine
peirameter
peirastic
peirastically
peisage
peise
peiser
Peitho
peixere
pejorate
pejoration
pejorationist
pejorative
pejoratively
pejorism
pejorist
pejority
pekan
Pekin
pekin
Peking
Pekingese
pekoe
peladic
pelage
pelagial
Pelagian
pelagian
Pelagianism
Pelagianize
Pelagianizer
pelagic
Pelagothuria
pelamyd
pelanos
Pelargi
pelargic
Pelargikon
pelargomorph
Pelargomorphae
pelargomorphic
pelargonate
pelargonic
pelargonidin
pelargonin
pelargonium
Pelasgi
Pelasgian
Pelasgic
Pelasgikon
Pelasgoi
Pele
pelean
pelecan
Pelecani
Pelecanidae
Pelecaniformes
Pelecanoides
Pelecanoidinae
Pelecanus
pelecypod
Pelecypoda
pelecypodous
pelelith
pelerine
Peleus
Pelew
pelf
Pelias
pelican
pelicanry
pelick
pelicometer
Pelides
Pelidnota
pelike
peliom
pelioma
peliosis
pelisse
pelite
pelitic
pell
Pellaea
pellage
pellagra
pellagragenic
pellagrin
pellagrose
pellagrous
pellar
pellard
pellas
pellate
pellation
peller
pellet
pelleted
pelletierine
pelletlike
pellety
Pellian
pellicle
pellicula
pellicular
pellicularia
pelliculate
pellicule
pellile
pellitory
pellmell
pellock
pellotine
pellucent
pellucid
pellucidity
pellucidly
pellucidness
Pelmanism
Pelmanist
Pelmanize
pelmatic
pelmatogram
Pelmatozoa
pelmatozoan
pelmatozoic
pelmet
Pelobates
pelobatid
Pelobatidae
pelobatoid
Pelodytes
pelodytid
Pelodytidae
pelodytoid
Pelomedusa
pelomedusid
Pelomedusidae
pelomedusoid
Pelomyxa
pelon
Pelopaeus
Pelopid
Pelopidae
Peloponnesian
Pelops
peloria
pelorian
peloriate
peloric
pelorism
pelorization
pelorize
pelorus
pelota
pelotherapy
peloton
pelt
pelta
Peltandra
peltast
peltate
peltated
peltately
peltatifid
peltation
peltatodigitate
pelter
pelterer
peltiferous
peltifolious
peltiform
Peltigera
Peltigeraceae
peltigerine
peltigerous
peltinerved
pelting
peltingly
peltless
peltmonger
Peltogaster
peltry
pelu
peludo
Pelusios
pelveoperitonitis
pelves
Pelvetia
pelvic
pelviform
pelvigraph
pelvigraphy
pelvimeter
pelvimetry
pelviolithotomy
pelvioperitonitis
pelvioplasty
pelvioradiography
pelvioscopy
pelviotomy
pelviperitonitis
pelvirectal
pelvis
pelvisacral
pelvisternal
pelvisternum
pelycogram
pelycography
pelycology
pelycometer
pelycometry
pelycosaur
Pelycosauria
pelycosaurian
pembina
Pembroke
pemican
pemmican
pemmicanization
pemmicanize
pemphigoid
pemphigous
pemphigus
pen
penacute
Penaea
Penaeaceae
penaeaceous
penal
penalist
penality
penalizable
penalization
penalize
penally
penalty
penance
penanceless
penang
penannular
penates
penbard
pencatite
pence
pencel
penceless
penchant
penchute
pencil
penciled
penciler
penciliform
penciling
pencilled
penciller
pencillike
pencilling
pencilry
pencilwood
pencraft
pend
penda
pendant
pendanted
pendanting
pendantlike
pendecagon
pendeloque
pendency
pendent
pendentive
pendently
pendicle
pendicler
pending
pendle
pendom
pendragon
pendragonish
pendragonship
pendulant
pendular
pendulate
pendulation
pendule
penduline
pendulosity
pendulous
pendulously
pendulousness
pendulum
pendulumlike
Penelope
Penelopean
Penelophon
Penelopinae
penelopine
peneplain
peneplanation
peneplane
peneseismic
penetrability
penetrable
penetrableness
penetrably
penetral
penetralia
penetralian
penetrance
penetrancy
penetrant
penetrate
penetrating
penetratingly
penetratingness
penetration
penetrative
penetratively
penetrativeness
penetrativity
penetrator
penetrology
penetrometer
penfieldite
penfold
penful
penghulu
pengo
penguin
penguinery
penhead
penholder
penial
penicillate
penicillated
penicillately
penicillation
penicilliform
penicillin
Penicillium
penide
penile
peninsula
peninsular
peninsularism
peninsularity
peninsulate
penintime
peninvariant
penis
penistone
penitence
penitencer
penitent
Penitentes
penitential
penitentially
penitentiary
penitentiaryship
penitently
penk
penkeeper
penknife
penlike
penmaker
penmaking
penman
penmanship
penmaster
penna
pennaceous
Pennacook
pennae
pennage
Pennales
pennant
Pennaria
Pennariidae
Pennatae
pennate
pennated
pennatifid
pennatilobate
pennatipartite
pennatisect
pennatisected
Pennatula
Pennatulacea
pennatulacean
pennatulaceous
pennatularian
pennatulid
Pennatulidae
pennatuloid
penneech
penneeck
penner
pennet
penni
pennia
pennied
penniferous
penniform
pennigerous
penniless
pennilessly
pennilessness
pennill
penninervate
penninerved
penning
penninite
pennipotent
Pennisetum
penniveined
pennon
pennoned
pennopluma
pennoplume
pennorth
Pennsylvania
Pennsylvanian
Penny
penny
pennybird
pennycress
pennyearth
pennyflower
pennyhole
pennyleaf
pennyrot
pennyroyal
pennysiller
pennystone
pennyweight
pennywinkle
pennywort
pennyworth
Penobscot
penologic
penological
penologist
penology
penorcon
penrack
penroseite
Pensacola
penscript
penseful
pensefulness
penship
pensile
pensileness
pensility
pension
pensionable
pensionably
pensionary
pensioner
pensionership
pensionless
pensive
pensived
pensively
pensiveness
penster
penstick
penstock
pensum
pensy
pent
penta
pentabasic
pentabromide
pentacapsular
pentacarbon
pentacarbonyl
pentacarpellary
pentace
pentacetate
pentachenium
pentachloride
pentachord
pentachromic
pentacid
pentacle
pentacoccous
pentacontane
pentacosane
Pentacrinidae
pentacrinite
pentacrinoid
Pentacrinus
pentacron
pentacrostic
pentactinal
pentactine
pentacular
pentacyanic
pentacyclic
pentad
pentadactyl
Pentadactyla
pentadactylate
pentadactyle
pentadactylism
pentadactyloid
pentadecagon
pentadecahydrate
pentadecahydrated
pentadecane
pentadecatoic
pentadecoic
pentadecyl
pentadecylic
pentadelphous
pentadicity
pentadiene
pentadodecahedron
pentadrachm
pentadrachma
pentaerythrite
pentaerythritol
pentafid
pentafluoride
pentagamist
pentaglossal
pentaglot
pentaglottical
pentagon
pentagonal
pentagonally
pentagonohedron
pentagonoid
pentagram
pentagrammatic
pentagyn
Pentagynia
pentagynian
pentagynous
pentahalide
pentahedral
pentahedrical
pentahedroid
pentahedron
pentahedrous
pentahexahedral
pentahexahedron
pentahydrate
pentahydrated
pentahydric
pentahydroxy
pentail
pentaiodide
pentalobate
pentalogue
pentalogy
pentalpha
Pentamera
pentameral
pentameran
pentamerid
Pentameridae
pentamerism
pentameroid
pentamerous
Pentamerus
pentameter
pentamethylene
pentamethylenediamine
pentametrist
pentametrize
pentander
Pentandria
pentandrian
pentandrous
pentane
pentanedione
pentangle
pentangular
pentanitrate
pentanoic
pentanolide
pentanone
pentapetalous
Pentaphylacaceae
pentaphylacaceous
Pentaphylax
pentaphyllous
pentaploid
pentaploidic
pentaploidy
pentapody
pentapolis
pentapolitan
pentapterous
pentaptote
pentaptych
pentaquine
pentarch
pentarchical
pentarchy
pentasepalous
pentasilicate
pentaspermous
pentaspheric
pentaspherical
pentastich
pentastichous
pentastichy
pentastome
Pentastomida
pentastomoid
pentastomous
Pentastomum
pentastyle
pentastylos
pentasulphide
pentasyllabic
pentasyllabism
pentasyllable
Pentateuch
Pentateuchal
pentateuchal
pentathionate
pentathionic
pentathlete
pentathlon
pentathlos
pentatomic
pentatomid
Pentatomidae
Pentatomoidea
pentatone
pentatonic
pentatriacontane
pentavalence
pentavalency
pentavalent
penteconter
pentecontoglossal
Pentecost
Pentecostal
pentecostal
pentecostalism
pentecostalist
pentecostarion
pentecoster
pentecostys
Pentelic
Pentelican
pentene
penteteric
penthemimer
penthemimeral
penthemimeris
Penthestes
penthiophen
penthiophene
Penthoraceae
Penthorum
penthouse
penthouselike
penthrit
penthrite
pentimento
pentine
pentiodide
pentit
pentite
pentitol
pentlandite
pentobarbital
pentode
pentoic
pentol
pentosan
pentosane
pentose
pentoside
pentosuria
pentoxide
pentremital
pentremite
Pentremites
Pentremitidae
pentrit
pentrite
pentrough
Pentstemon
pentstock
penttail
pentyl
pentylene
pentylic
pentylidene
pentyne
Pentzia
penuchi
penult
penultima
penultimate
penultimatum
penumbra
penumbrae
penumbral
penumbrous
penurious
penuriously
penuriousness
penury
Penutian
penwiper
penwoman
penwomanship
penworker
penwright
peon
peonage
peonism
peony
people
peopledom
peoplehood
peopleize
peopleless
peopler
peoplet
peoplish
Peoria
Peorian
peotomy
pep
peperine
peperino
Peperomia
pepful
Pephredo
pepinella
pepino
peplos
peplosed
peplum
peplus
pepo
peponida
peponium
pepper
pepperbox
peppercorn
peppercornish
peppercorny
pepperer
peppergrass
pepperidge
pepperily
pepperiness
pepperish
pepperishly
peppermint
pepperoni
pepperproof
pepperroot
pepperweed
pepperwood
pepperwort
peppery
peppily
peppin
peppiness
peppy
pepsin
pepsinate
pepsinhydrochloric
pepsiniferous
pepsinogen
pepsinogenic
pepsinogenous
pepsis
peptic
peptical
pepticity
peptidase
peptide
peptizable
peptization
peptize
peptizer
peptogaster
peptogenic
peptogenous
peptogeny
peptohydrochloric
peptolysis
peptolytic
peptonaemia
peptonate
peptone
peptonemia
peptonic
peptonization
peptonize
peptonizer
peptonoid
peptonuria
peptotoxine
Pepysian
Pequot
Per
per
Peracarida
peracephalus
peracetate
peracetic
peracid
peracidite
peract
peracute
peradventure
peragrate
peragration
Perakim
peramble
perambulant
perambulate
perambulation
perambulator
perambulatory
Perameles
Peramelidae
perameline
perameloid
Peramium
Peratae
Perates
perbend
perborate
perborax
perbromide
Perca
percale
percaline
percarbide
percarbonate
percarbonic
perceivability
perceivable
perceivableness
perceivably
perceivance
perceivancy
perceive
perceivedly
perceivedness
perceiver
perceiving
perceivingness
percent
percentable
percentably
percentage
percentaged
percental
percentile
percentual
percept
perceptibility
perceptible
perceptibleness
perceptibly
perception
perceptional
perceptionalism
perceptionism
perceptive
perceptively
perceptiveness
perceptivity
perceptual
perceptually
Percesoces
percesocine
Perceval
perch
percha
perchable
perchance
percher
Percheron
perchlorate
perchlorethane
perchlorethylene
perchloric
perchloride
perchlorinate
perchlorination
perchloroethane
perchloroethylene
perchromate
perchromic
percid
Percidae
perciform
Perciformes
percipience
percipiency
percipient
Percival
perclose
percnosome
percoct
percoid
Percoidea
percoidean
percolable
percolate
percolation
percolative
percolator
percomorph
Percomorphi
percomorphous
percompound
percontation
percontatorial
percribrate
percribration
percrystallization
perculsion
perculsive
percur
percurration
percurrent
percursory
percuss
percussion
percussional
percussioner
percussionist
percussionize
percussive
percussively
percussiveness
percussor
percutaneous
percutaneously
percutient
Percy
percylite
Perdicinae
perdicine
perdition
perditionable
Perdix
perdricide
perdu
perduellion
perdurability
perdurable
perdurableness
perdurably
perdurance
perdurant
perdure
perduring
perduringly
Perean
peregrin
peregrina
peregrinate
peregrination
peregrinator
peregrinatory
peregrine
peregrinity
peregrinoid
pereion
pereiopod
pereira
pereirine
peremptorily
peremptoriness
peremptory
perendinant
perendinate
perendination
perendure
perennate
perennation
perennial
perenniality
perennialize
perennially
perennibranch
Perennibranchiata
perennibranchiate
perequitate
peres
Pereskia
perezone
perfect
perfectation
perfected
perfectedly
perfecter
perfecti
perfectibilian
perfectibilism
perfectibilist
perfectibilitarian
perfectibility
perfectible
perfecting
perfection
perfectionate
perfectionation
perfectionator
perfectioner
perfectionism
perfectionist
perfectionistic
perfectionize
perfectionizement
perfectionizer
perfectionment
perfectism
perfectist
perfective
perfectively
perfectiveness
perfectivity
perfectivize
perfectly
perfectness
perfecto
perfector
perfectuation
perfervent
perfervid
perfervidity
perfervidly
perfervidness
perfervor
perfervour
perfidious
perfidiously
perfidiousness
perfidy
perfilograph
perflate
perflation
perfluent
perfoliate
perfoliation
perforable
perforant
Perforata
perforate
perforated
perforation
perforationproof
perforative
perforator
perforatorium
perforatory
perforce
perforcedly
perform
performable
performance
performant
performative
performer
perfrication
perfumatory
perfume
perfumed
perfumeless
perfumer
perfumeress
perfumery
perfumy
perfunctionary
perfunctorily
perfunctoriness
perfunctorious
perfunctoriously
perfunctorize
perfunctory
perfuncturate
perfusate
perfuse
perfusion
perfusive
Pergamene
pergameneous
Pergamenian
pergamentaceous
Pergamic
pergamyn
pergola
perhalide
perhalogen
perhaps
perhazard
perhorresce
perhydroanthracene
perhydrogenate
perhydrogenation
perhydrogenize
peri
periacinal
periacinous
periactus
periadenitis
periamygdalitis
perianal
periangiocholitis
periangioma
periangitis
perianth
perianthial
perianthium
periaortic
periaortitis
periapical
periappendicitis
periappendicular
periapt
Periarctic
periareum
periarterial
periarteritis
periarthric
periarthritis
periarticular
periaster
periastral
periastron
periastrum
periatrial
periauricular
periaxial
periaxillary
periaxonal
periblast
periblastic
periblastula
periblem
peribolos
peribolus
peribranchial
peribronchial
peribronchiolar
peribronchiolitis
peribronchitis
peribulbar
peribursal
pericaecal
pericaecitis
pericanalicular
pericapsular
pericardia
pericardiac
pericardiacophrenic
pericardial
pericardicentesis
pericardiectomy
pericardiocentesis
pericardiolysis
pericardiomediastinitis
pericardiophrenic
pericardiopleural
pericardiorrhaphy
pericardiosymphysis
pericardiotomy
pericarditic
pericarditis
pericardium
pericardotomy
pericarp
pericarpial
pericarpic
pericarpium
pericarpoidal
pericecal
pericecitis
pericellular
pericemental
pericementitis
pericementoclasia
pericementum
pericenter
pericentral
pericentric
pericephalic
pericerebral
perichaete
perichaetial
perichaetium
perichete
pericholangitis
pericholecystitis
perichondral
perichondrial
perichondritis
perichondrium
perichord
perichordal
perichoresis
perichorioidal
perichoroidal
perichylous
pericladium
periclase
periclasia
periclasite
periclaustral
Periclean
Pericles
periclinal
periclinally
pericline
periclinium
periclitate
periclitation
pericolitis
pericolpitis
periconchal
periconchitis
pericopal
pericope
pericopic
pericorneal
pericowperitis
pericoxitis
pericranial
pericranitis
pericranium
pericristate
Pericu
periculant
pericycle
pericycloid
pericyclone
pericyclonic
pericystic
pericystitis
pericystium
pericytial
peridendritic
peridental
peridentium
peridentoclasia
periderm
peridermal
peridermic
Peridermium
peridesm
peridesmic
peridesmitis
peridesmium
peridial
peridiastole
peridiastolic
perididymis
perididymitis
peridiiform
Peridineae
Peridiniaceae
peridiniaceous
peridinial
Peridiniales
peridinian
peridinid
Peridinidae
Peridinieae
Peridiniidae
Peridinium
peridiole
peridiolum
peridium
peridot
peridotic
peridotite
peridotitic
periductal
periegesis
periegetic
perielesis
periencephalitis
perienteric
perienteritis
perienteron
periependymal
periesophageal
periesophagitis
perifistular
perifoliary
perifollicular
perifolliculitis
perigangliitis
periganglionic
perigastric
perigastritis
perigastrula
perigastrular
perigastrulation
perigeal
perigee
perigemmal
perigenesis
perigenital
perigeum
periglandular
perigloea
periglottic
periglottis
perignathic
perigon
perigonadial
perigonal
perigone
perigonial
perigonium
perigraph
perigraphic
perigynial
perigynium
perigynous
perigyny
perihelial
perihelian
perihelion
perihelium
perihepatic
perihepatitis
perihermenial
perihernial
perihysteric
perijejunitis
perijove
perikaryon
perikronion
peril
perilabyrinth
perilabyrinthitis
perilaryngeal
perilaryngitis
perilenticular
periligamentous
Perilla
perilless
perilobar
perilous
perilously
perilousness
perilsome
perilymph
perilymphangial
perilymphangitis
perilymphatic
perimartium
perimastitis
perimedullary
perimeningitis
perimeter
perimeterless
perimetral
perimetric
perimetrical
perimetrically
perimetritic
perimetritis
perimetrium
perimetry
perimorph
perimorphic
perimorphism
perimorphous
perimyelitis
perimysial
perimysium
perine
perineal
perineocele
perineoplastic
perineoplasty
perineorrhaphy
perineoscrotal
perineostomy
perineosynthesis
perineotomy
perineovaginal
perineovulvar
perinephral
perinephrial
perinephric
perinephritic
perinephritis
perinephrium
perineptunium
perineum
perineural
perineurial
perineuritis
perineurium
perinium
perinuclear
periocular
period
periodate
periodic
periodical
periodicalism
periodicalist
periodicalize
periodically
periodicalness
periodicity
periodide
periodize
periodogram
periodograph
periodology
periodontal
periodontia
periodontic
periodontist
periodontitis
periodontium
periodontoclasia
periodontologist
periodontology
periodontum
periodoscope
perioeci
perioecians
perioecic
perioecid
perioecus
perioesophageal
perioikoi
periomphalic
perionychia
perionychium
perionyx
perionyxis
perioophoritis
periophthalmic
periophthalmitis
periople
perioplic
perioptic
perioptometry
perioral
periorbit
periorbita
periorbital
periorchitis
periost
periostea
periosteal
periosteitis
periosteoalveolar
periosteoma
periosteomedullitis
periosteomyelitis
periosteophyte
periosteorrhaphy
periosteotome
periosteotomy
periosteous
periosteum
periostitic
periostitis
periostoma
periostosis
periostotomy
periostracal
periostracum
periotic
periovular
peripachymeningitis
peripancreatic
peripancreatitis
peripapillary
Peripatetic
peripatetic
peripatetical
peripatetically
peripateticate
Peripateticism
Peripatidae
Peripatidea
peripatize
peripatoid
Peripatopsidae
Peripatopsis
Peripatus
peripenial
peripericarditis
peripetalous
peripetasma
peripeteia
peripetia
peripety
periphacitis
peripharyngeal
peripherad
peripheral
peripherally
peripherial
peripheric
peripherical
peripherically
peripherocentral
peripheroceptor
peripheromittor
peripheroneural
peripherophose
periphery
periphlebitic
periphlebitis
periphractic
periphrase
periphrases
periphrasis
periphrastic
periphrastical
periphrastically
periphraxy
periphyllum
periphyse
periphysis
Periplaneta
periplasm
periplast
periplastic
periplegmatic
peripleural
peripleuritis
Periploca
periplus
peripneumonia
peripneumonic
peripneumony
peripneustic
peripolar
peripolygonal
periportal
periproct
periproctal
periproctitis
periproctous
periprostatic
periprostatitis
peripteral
peripterous
periptery
peripylephlebitis
peripyloric
perique
perirectal
perirectitis
perirenal
perisalpingitis
perisarc
perisarcal
perisarcous
perisaturnium
periscian
periscians
periscii
perisclerotic
periscopal
periscope
periscopic
periscopical
periscopism
perish
perishability
perishable
perishableness
perishably
perished
perishing
perishingly
perishless
perishment
perisigmoiditis
perisinuitis
perisinuous
perisinusitis
perisoma
perisomal
perisomatic
perisome
perisomial
perisperm
perispermal
perispermatitis
perispermic
perisphere
perispheric
perispherical
perisphinctean
Perisphinctes
Perisphinctidae
perisphinctoid
perisplanchnic
perisplanchnitis
perisplenetic
perisplenic
perisplenitis
perispome
perispomenon
perispondylic
perispondylitis
perispore
Perisporiaceae
perisporiaceous
Perisporiales
perissad
perissodactyl
Perissodactyla
perissodactylate
perissodactyle
perissodactylic
perissodactylism
perissodactylous
perissologic
perissological
perissology
perissosyllabic
peristalith
peristalsis
peristaltic
peristaltically
peristaphyline
peristaphylitis
peristele
peristerite
peristeromorph
Peristeromorphae
peristeromorphic
peristeromorphous
peristeronic
peristerophily
peristeropod
peristeropodan
peristeropode
Peristeropodes
peristeropodous
peristethium
peristole
peristoma
peristomal
peristomatic
peristome
peristomial
peristomium
peristrephic
peristrephical
peristrumitis
peristrumous
peristylar
peristyle
peristylium
peristylos
peristylum
perisynovial
perisystole
perisystolic
perit
perite
peritectic
peritendineum
peritenon
perithece
perithecial
perithecium
perithelial
perithelioma
perithelium
perithoracic
perithyreoiditis
perithyroiditis
peritomize
peritomous
peritomy
peritoneal
peritonealgia
peritoneally
peritoneocentesis
peritoneoclysis
peritoneomuscular
peritoneopathy
peritoneopericardial
peritoneopexy
peritoneoplasty
peritoneoscope
peritoneoscopy
peritoneotomy
peritoneum
peritonism
peritonital
peritonitic
peritonitis
peritonsillar
peritonsillitis
peritracheal
peritrema
peritrematous
peritreme
peritrich
Peritricha
peritrichan
peritrichic
peritrichous
peritrichously
peritroch
peritrochal
peritrochanteric
peritrochium
peritrochoid
peritropal
peritrophic
peritropous
perityphlic
perityphlitic
perityphlitis
periumbilical
periungual
periuranium
periureteric
periureteritis
periurethral
periurethritis
periuterine
periuvular
perivaginal
perivaginitis
perivascular
perivasculitis
perivenous
perivertebral
perivesical
perivisceral
perivisceritis
perivitellin
perivitelline
periwig
periwigpated
periwinkle
periwinkled
periwinkler
perizonium
perjink
perjinkety
perjinkities
perjinkly
perjure
perjured
perjuredly
perjuredness
perjurer
perjuress
perjurious
perjuriously
perjuriousness
perjurous
perjury
perjurymonger
perjurymongering
perk
perkily
Perkin
perkin
perkiness
perking
perkingly
perkish
perknite
perky
Perla
perlaceous
Perlaria
perle
perlection
perlid
Perlidae
perligenous
perlingual
perlingually
perlite
perlitic
perloir
perlustrate
perlustration
perlustrator
perm
permafrost
Permalloy
permalloy
permanence
permanency
permanent
permanently
permanentness
permanganate
permanganic
permansive
permeability
permeable
permeableness
permeably
permeameter
permeance
permeant
permeate
permeation
permeative
permeator
Permiak
Permian
permillage
permirific
permissibility
permissible
permissibleness
permissibly
permission
permissioned
permissive
permissively
permissiveness
permissory
permit
permittable
permitted
permittedly
permittee
permitter
permittivity
permixture
Permocarboniferous
permonosulphuric
permoralize
permutability
permutable
permutableness
permutably
permutate
permutation
permutational
permutationist
permutator
permutatorial
permutatory
permute
permuter
pern
pernancy
pernasal
pernavigate
Pernettia
pernicious
perniciously
perniciousness
pernicketiness
pernickety
pernine
Pernis
pernitrate
pernitric
pernoctation
pernor
pernyi
peroba
perobrachius
perocephalus
perochirus
perodactylus
Perodipus
Perognathinae
Perognathus
Peromedusae
Peromela
peromelous
peromelus
Peromyscus
peronate
peroneal
peroneocalcaneal
peroneotarsal
peroneotibial
peronial
peronium
Peronospora
Peronosporaceae
peronosporaceous
Peronosporales
peropod
Peropoda
peropodous
peropus
peroral
perorally
perorate
peroration
perorational
perorative
perorator
peroratorical
peroratorically
peroratory
perosis
perosmate
perosmic
perosomus
perotic
perovskite
peroxidase
peroxidate
peroxidation
peroxide
peroxidic
peroxidize
peroxidizement
peroxy
peroxyl
perozonid
perozonide
perpend
perpendicular
perpendicularity
perpendicularly
perpera
perperfect
perpetrable
perpetrate
perpetration
perpetrator
perpetratress
perpetratrix
perpetuable
perpetual
perpetualism
perpetualist
perpetuality
perpetually
perpetualness
perpetuana
perpetuance
perpetuant
perpetuate
perpetuation
perpetuator
perpetuity
perplantar
perplex
perplexable
perplexed
perplexedly
perplexedness
perplexer
perplexing
perplexingly
perplexity
perplexment
perplication
perquadrat
perquest
perquisite
perquisition
perquisitor
perradial
perradially
perradiate
perradius
perridiculous
perrier
Perrinist
perron
perruche
perrukery
perruthenate
perruthenic
Perry
perry
perryman
Persae
persalt
perscent
perscribe
perscrutate
perscrutation
perscrutator
perse
Persea
persecute
persecutee
persecuting
persecutingly
persecution
persecutional
persecutive
persecutiveness
persecutor
persecutory
persecutress
persecutrix
Perseid
perseite
perseitol
perseity
persentiscency
Persephassa
Persephone
Persepolitan
perseverance
perseverant
perseverate
perseveration
persevere
persevering
perseveringly
Persian
Persianist
Persianization
Persianize
Persic
Persicaria
persicary
Persicize
persico
persicot
persienne
persiennes
persiflage
persiflate
persilicic
persimmon
Persis
persis
Persism
persist
persistence
persistency
persistent
persistently
persister
persisting
persistingly
persistive
persistively
persistiveness
persnickety
person
persona
personable
personableness
personably
personage
personal
personalia
personalism
personalist
personalistic
personality
personalization
personalize
personally
personalness
personalty
personate
personately
personating
personation
personative
personator
personed
personeity
personifiable
personifiant
personification
personificative
personificator
personifier
personify
personization
personize
personnel
personship
perspection
perspective
perspectived
perspectiveless
perspectively
perspectivity
perspectograph
perspectometer
perspicacious
perspicaciously
perspicaciousness
perspicacity
perspicuity
perspicuous
perspicuously
perspicuousness
perspirability
perspirable
perspirant
perspirate
perspiration
perspirative
perspiratory
perspire
perspiringly
perspiry
perstringe
perstringement
persuadability
persuadable
persuadableness
persuadably
persuade
persuaded
persuadedly
persuadedness
persuader
persuadingly
persuasibility
persuasible
persuasibleness
persuasibly
persuasion
persuasive
persuasively
persuasiveness
persuasory
persulphate
persulphide
persulphocyanate
persulphocyanic
persulphuric
persymmetric
persymmetrical
pert
pertain
pertaining
pertainment
perten
perthiocyanate
perthiocyanic
perthiotophyre
perthite
perthitic
perthitically
perthosite
pertinacious
pertinaciously
pertinaciousness
pertinacity
pertinence
pertinency
pertinent
pertinently
pertinentness
pertish
pertly
pertness
perturb
perturbability
perturbable
perturbance
perturbancy
perturbant
perturbate
perturbation
perturbational
perturbatious
perturbative
perturbator
perturbatory
perturbatress
perturbatrix
perturbed
perturbedly
perturbedness
perturber
perturbing
perturbingly
perturbment
Pertusaria
Pertusariaceae
pertuse
pertused
pertusion
pertussal
pertussis
perty
Peru
Perugian
Peruginesque
peruke
perukeless
perukier
perukiership
perula
Perularia
perulate
perule
Perun
perusable
perusal
peruse
peruser
Peruvian
Peruvianize
pervade
pervadence
pervader
pervading
pervadingly
pervadingness
pervagate
pervagation
pervalvar
pervasion
pervasive
pervasively
pervasiveness
perverse
perversely
perverseness
perversion
perversity
perversive
pervert
perverted
pervertedly
pervertedness
perverter
pervertibility
pervertible
pervertibly
pervertive
perviability
perviable
pervicacious
pervicaciously
pervicaciousness
pervicacity
pervigilium
pervious
perviously
perviousness
pervulgate
pervulgation
perwitsky
pes
pesa
Pesach
pesade
pesage
Pesah
peseta
peshkar
peshkash
peshwa
peshwaship
peskily
peskiness
pesky
peso
pess
pessary
pessimal
pessimism
pessimist
pessimistic
pessimistically
pessimize
pessimum
pessomancy
pessoner
pessular
pessulus
pest
Pestalozzian
Pestalozzianism
peste
pester
pesterer
pesteringly
pesterment
pesterous
pestersome
pestful
pesthole
pesthouse
pesticidal
pesticide
pestiduct
pestiferous
pestiferously
pestiferousness
pestifugous
pestify
pestilence
pestilenceweed
pestilencewort
pestilent
pestilential
pestilentially
pestilentialness
pestilently
pestle
pestological
pestologist
pestology
pestproof
pet
petal
petalage
petaled
Petalia
petaliferous
petaliform
Petaliidae
petaline
petalism
petalite
petalled
petalless
petallike
petalocerous
petalodic
petalodont
petalodontid
Petalodontidae
petalodontoid
Petalodus
petalody
petaloid
petaloidal
petaloideous
petalomania
petalon
Petalostemon
petalous
petalwise
petaly
petard
petardeer
petardier
petary
Petasites
petasos
petasus
petaurine
petaurist
Petaurista
Petauristidae
Petauroides
Petaurus
petchary
petcock
Pete
pete
peteca
petechiae
petechial
petechiate
peteman
Peter
peter
Peterkin
Peterloo
peterman
peternet
petersham
peterwort
petful
petiolar
petiolary
Petiolata
petiolate
petiolated
petiole
petioled
Petioliventres
petiolular
petiolulate
petiolule
petiolus
petit
petite
petiteness
petitgrain
petition
petitionable
petitional
petitionarily
petitionary
petitionee
petitioner
petitionist
petitionproof
petitor
petitory
Petiveria
Petiveriaceae
petkin
petling
peto
Petr
Petrarchal
Petrarchan
Petrarchesque
Petrarchian
Petrarchianism
Petrarchism
Petrarchist
Petrarchistic
Petrarchistical
Petrarchize
petrary
petre
Petrea
petrean
petreity
petrel
petrescence
petrescent
Petricola
Petricolidae
petricolous
petrie
petrifaction
petrifactive
petrifiable
petrific
petrificant
petrificate
petrification
petrified
petrifier
petrify
Petrine
Petrinism
Petrinist
Petrinize
petrissage
Petrobium
Petrobrusian
petrochemical
petrochemistry
Petrogale
petrogenesis
petrogenic
petrogeny
petroglyph
petroglyphic
petroglyphy
petrograph
petrographer
petrographic
petrographical
petrographically
petrography
petrohyoid
petrol
petrolage
petrolatum
petrolean
petrolene
petroleous
petroleum
petrolic
petroliferous
petrolific
petrolist
petrolithic
petrolization
petrolize
petrologic
petrological
petrologically
petromastoid
Petromyzon
Petromyzonidae
petromyzont
Petromyzontes
Petromyzontidae
petromyzontoid
petronel
petronella
petropharyngeal
petrophilous
petrosa
petrosal
Petroselinum
petrosilex
petrosiliceous
petrosilicious
petrosphenoid
petrosphenoidal
petrosphere
petrosquamosal
petrosquamous
petrostearin
petrostearine
petrosum
petrotympanic
petrous
petroxolin
pettable
petted
pettedly
pettedness
petter
pettichaps
petticoat
petticoated
petticoaterie
petticoatery
petticoatism
petticoatless
petticoaty
pettifog
pettifogger
pettifoggery
pettifogging
pettifogulize
pettifogulizer
pettily
pettiness
pettingly
pettish
pettitoes
pettle
petty
pettyfog
petulance
petulancy
petulant
petulantly
petune
Petunia
petuntse
petwood
petzite
Peucedanum
Peucetii
peucites
peuhl
Peul
Peumus
Peutingerian
pew
pewage
pewdom
pewee
pewfellow
pewful
pewholder
pewing
pewit
pewless
pewmate
pewter
pewterer
pewterwort
pewtery
pewy
Peyerian
peyote
peyotl
peyton
peytrel
pezantic
Peziza
Pezizaceae
pezizaceous
pezizaeform
Pezizales
peziziform
pezizoid
pezograph
Pezophaps
Pfaffian
pfeffernuss
Pfeifferella
pfennig
pfui
pfund
Phaca
Phacelia
phacelite
phacella
Phacidiaceae
Phacidiales
phacitis
phacoanaphylaxis
phacocele
phacochere
phacocherine
phacochoere
phacochoerid
phacochoerine
phacochoeroid
Phacochoerus
phacocyst
phacocystectomy
phacocystitis
phacoglaucoma
phacoid
phacoidal
phacoidoscope
phacolite
phacolith
phacolysis
phacomalacia
phacometer
phacopid
Phacopidae
Phacops
phacosclerosis
phacoscope
phacotherapy
Phaeacian
Phaedo
phaeism
phaenantherous
phaenanthery
phaenogam
Phaenogamia
phaenogamian
phaenogamic
phaenogamous
phaenogenesis
phaenogenetic
phaenological
phaenology
phaenomenal
phaenomenism
phaenomenon
phaenozygous
phaeochrous
Phaeodaria
phaeodarian
phaeophore
Phaeophyceae
phaeophycean
phaeophyceous
phaeophyll
Phaeophyta
phaeophytin
phaeoplast
Phaeosporales
phaeospore
Phaeosporeae
phaeosporous
Phaet
Phaethon
Phaethonic
Phaethontes
Phaethontic
Phaethontidae
Phaethusa
phaeton
phage
phagedena
phagedenic
phagedenical
phagedenous
Phagineae
phagocytable
phagocytal
phagocyte
phagocyter
phagocytic
phagocytism
phagocytize
phagocytoblast
phagocytolysis
phagocytolytic
phagocytose
phagocytosis
phagodynamometer
phagolysis
phagolytic
phagomania
phainolion
Phainopepla
Phajus
Phalacrocoracidae
phalacrocoracine
Phalacrocorax
phalacrosis
Phalaecean
Phalaecian
Phalaenae
Phalaenidae
phalaenopsid
Phalaenopsis
phalangal
phalange
phalangeal
phalangean
phalanger
Phalangeridae
Phalangerinae
phalangerine
phalanges
phalangette
phalangian
phalangic
phalangid
Phalangida
phalangidan
Phalangidea
phalangidean
Phalangides
phalangiform
Phalangigrada
phalangigrade
phalangigrady
phalangiid
Phalangiidae
phalangist
Phalangista
Phalangistidae
phalangistine
phalangite
phalangitic
phalangitis
Phalangium
phalangologist
phalangology
phalansterial
phalansterian
phalansterianism
phalansteric
phalansterism
phalansterist
phalanstery
phalanx
phalanxed
phalarica
Phalaris
Phalarism
phalarope
Phalaropodidae
phalera
phalerate
phalerated
Phaleucian
Phallaceae
phallaceous
Phallales
phallalgia
phallaneurysm
phallephoric
phallic
phallical
phallicism
phallicist
phallin
phallism
phallist
phallitis
phallocrypsis
phallodynia
phalloid
phalloncus
phalloplasty
phallorrhagia
phallus
Phanar
Phanariot
Phanariote
phanatron
phaneric
phanerite
Phanerocarpae
Phanerocarpous
Phanerocephala
phanerocephalous
phanerocodonic
phanerocryst
phanerocrystalline
phanerogam
Phanerogamia
phanerogamian
phanerogamic
phanerogamous
phanerogamy
phanerogenetic
phanerogenic
Phaneroglossa
phaneroglossal
phaneroglossate
phaneromania
phaneromere
phaneromerous
phaneroscope
phanerosis
phanerozoic
phanerozonate
Phanerozonia
phanic
phano
phansigar
phantascope
phantasia
Phantasiast
Phantasiastic
phantasist
phantasize
phantasm
phantasma
phantasmagoria
phantasmagorial
phantasmagorially
phantasmagorian
phantasmagoric
phantasmagorical
phantasmagorist
phantasmagory
phantasmal
phantasmalian
phantasmality
phantasmally
phantasmascope
phantasmata
Phantasmatic
phantasmatic
phantasmatical
phantasmatically
phantasmatography
phantasmic
phantasmical
phantasmically
Phantasmist
phantasmogenesis
phantasmogenetic
phantasmograph
phantasmological
phantasmology
phantast
phantasy
phantom
phantomatic
phantomic
phantomical
phantomically
Phantomist
phantomize
phantomizer
phantomland
phantomlike
phantomnation
phantomry
phantomship
phantomy
phantoplex
phantoscope
Pharaoh
Pharaonic
Pharaonical
Pharbitis
phare
Phareodus
Pharian
Pharisaean
Pharisaic
pharisaical
pharisaically
pharisaicalness
Pharisaism
Pharisaist
Pharisean
Pharisee
pharisee
Phariseeism
pharmacal
pharmaceutic
pharmaceutical
pharmaceutically
pharmaceutics
pharmaceutist
pharmacic
pharmacist
pharmacite
pharmacodiagnosis
pharmacodynamic
pharmacodynamical
pharmacodynamics
pharmacoendocrinology
pharmacognosia
pharmacognosis
pharmacognosist
pharmacognostical
pharmacognostically
pharmacognostics
pharmacognosy
pharmacography
pharmacolite
pharmacologia
pharmacologic
pharmacological
pharmacologically
pharmacologist
pharmacology
pharmacomania
pharmacomaniac
pharmacomaniacal
pharmacometer
pharmacopedia
pharmacopedic
pharmacopedics
pharmacopeia
pharmacopeial
pharmacopeian
pharmacophobia
pharmacopoeia
pharmacopoeial
pharmacopoeian
pharmacopoeist
pharmacopolist
pharmacoposia
pharmacopsychology
pharmacosiderite
pharmacotherapy
pharmacy
pharmakos
pharmic
pharmuthi
pharology
Pharomacrus
pharos
Pharsalian
pharyngal
pharyngalgia
pharyngalgic
pharyngeal
pharyngectomy
pharyngemphraxis
pharynges
pharyngic
pharyngismus
pharyngitic
pharyngitis
pharyngoamygdalitis
pharyngobranch
pharyngobranchial
pharyngobranchiate
Pharyngobranchii
pharyngocele
pharyngoceratosis
pharyngodynia
pharyngoepiglottic
pharyngoepiglottidean
pharyngoesophageal
pharyngoglossal
pharyngoglossus
pharyngognath
Pharyngognathi
pharyngognathous
pharyngographic
pharyngography
pharyngokeratosis
pharyngolaryngeal
pharyngolaryngitis
pharyngolith
pharyngological
pharyngology
pharyngomaxillary
pharyngomycosis
pharyngonasal
pharyngopalatine
pharyngopalatinus
pharyngoparalysis
pharyngopathy
pharyngoplasty
pharyngoplegia
pharyngoplegic
pharyngoplegy
pharyngopleural
Pharyngopneusta
pharyngopneustal
pharyngorhinitis
pharyngorhinoscopy
pharyngoscleroma
pharyngoscope
pharyngoscopy
pharyngospasm
pharyngotherapy
pharyngotomy
pharyngotonsillitis
pharyngotyphoid
pharyngoxerosis
pharynogotome
pharynx
Phascaceae
phascaceous
Phascogale
Phascolarctinae
Phascolarctos
phascolome
Phascolomyidae
Phascolomys
Phascolonus
Phascum
phase
phaseal
phaseless
phaselin
phasemeter
phasemy
Phaseolaceae
phaseolin
phaseolous
phaseolunatin
Phaseolus
phaseometer
phases
Phasianella
Phasianellidae
phasianic
phasianid
Phasianidae
Phasianinae
phasianine
phasianoid
Phasianus
phasic
Phasiron
phasis
phasm
phasma
phasmatid
Phasmatida
Phasmatidae
Phasmatodea
phasmatoid
Phasmatoidea
phasmatrope
phasmid
Phasmida
Phasmidae
phasmoid
phasogeneous
phasotropy
pheal
pheasant
pheasantry
pheasantwood
Phebe
Phecda
Phegopteris
Pheidole
phellandrene
phellem
Phellodendron
phelloderm
phellodermal
phellogen
phellogenetic
phellogenic
phellonic
phelloplastic
phelloplastics
phelonion
phemic
Phemie
phenacaine
phenacetin
phenaceturic
phenacite
Phenacodontidae
Phenacodus
phenacyl
phenakism
phenakistoscope
Phenalgin
phenanthrene
phenanthridine
phenanthridone
phenanthrol
phenanthroline
phenarsine
phenate
phenazine
phenazone
phene
phenegol
phenene
phenethyl
phenetidine
phenetole
phengite
phengitical
phenic
phenicate
phenicious
phenicopter
phenin
phenmiazine
phenobarbital
phenocoll
phenocopy
phenocryst
phenocrystalline
phenogenesis
phenogenetic
phenol
phenolate
phenolic
phenolization
phenolize
phenological
phenologically
phenologist
phenology
phenoloid
phenolphthalein
phenolsulphonate
phenolsulphonephthalein
phenolsulphonic
phenomena
phenomenal
phenomenalism
phenomenalist
phenomenalistic
phenomenalistically
phenomenality
phenomenalization
phenomenalize
phenomenally
phenomenic
phenomenical
phenomenism
phenomenist
phenomenistic
phenomenize
phenomenological
phenomenologically
phenomenology
phenomenon
phenoplast
phenoplastic
phenoquinone
phenosafranine
phenosal
phenospermic
phenospermy
phenothiazine
phenotype
phenotypic
phenotypical
phenotypically
phenoxazine
phenoxid
phenoxide
phenozygous
Pheny
phenyl
phenylacetaldehyde
phenylacetamide
phenylacetic
phenylalanine
phenylamide
phenylamine
phenylate
phenylation
phenylboric
phenylcarbamic
phenylcarbimide
phenylene
phenylenediamine
phenylethylene
phenylglycine
phenylglycolic
phenylglyoxylic
phenylhydrazine
phenylhydrazone
phenylic
phenylmethane
pheon
pheophyl
pheophyll
pheophytin
Pherecratean
Pherecratian
Pherecratic
Pherephatta
pheretrer
Pherkad
Pherophatta
Phersephatta
Phersephoneia
phew
phi
phial
phiale
phialful
phialide
phialine
phiallike
phialophore
phialospore
Phidiac
Phidian
Phigalian
Phil
Philadelphian
Philadelphianism
philadelphite
Philadelphus
philadelphy
philalethist
philamot
Philander
philander
philanderer
philanthid
Philanthidae
philanthrope
philanthropian
philanthropic
philanthropical
philanthropically
philanthropinism
philanthropinist
Philanthropinum
philanthropism
philanthropist
philanthropistic
philanthropize
philanthropy
Philanthus
philantomba
philarchaist
philaristocracy
philatelic
philatelical
philatelically
philatelism
philatelist
philatelistic
philately
Philathea
philathletic
philematology
Philepitta
Philepittidae
Philesia
Philetaerus
philharmonic
philhellene
philhellenic
philhellenism
philhellenist
philhippic
philhymnic
philiater
Philip
Philippa
Philippan
Philippe
Philippian
Philippic
philippicize
Philippine
Philippines
Philippism
Philippist
Philippistic
Philippizate
philippize
philippizer
philippus
Philistia
Philistian
Philistine
Philistinely
Philistinian
Philistinic
Philistinish
Philistinism
Philistinize
Phill
philliloo
Phillip
phillipsine
phillipsite
Phillis
Phillyrea
phillyrin
philobiblian
philobiblic
philobiblical
philobiblist
philobotanic
philobotanist
philobrutish
philocalic
philocalist
philocaly
philocathartic
philocatholic
philocomal
Philoctetes
philocubist
philocynic
philocynical
philocynicism
philocyny
philodemic
Philodendron
philodespot
philodestructiveness
Philodina
Philodinidae
philodox
philodoxer
philodoxical
philodramatic
philodramatist
philofelist
philofelon
philogarlic
philogastric
philogeant
philogenitive
philogenitiveness
philograph
philographic
philogynaecic
philogynist
philogynous
philogyny
Philohela
philohellenian
philokleptic
philoleucosis
philologaster
philologastry
philologer
philologian
philologic
philological
philologically
philologist
philologistic
philologize
philologue
philology
Philomachus
philomath
philomathematic
philomathematical
philomathic
philomathical
philomathy
philomel
Philomela
philomelanist
philomuse
philomusical
philomystic
philonatural
philoneism
Philonian
Philonic
Philonism
Philonist
philonium
philonoist
philopagan
philopater
philopatrian
philopena
philophilosophos
philopig
philoplutonic
philopoet
philopogon
philopolemic
philopolemical
philopornist
philoprogeneity
philoprogenitive
philoprogenitiveness
philopterid
Philopteridae
philopublican
philoradical
philorchidaceous
philornithic
philorthodox
philosoph
philosophaster
philosophastering
philosophastry
philosophedom
philosopheme
philosopher
philosopheress
philosophership
philosophic
philosophical
philosophically
philosophicalness
philosophicide
philosophicohistorical
philosophicojuristic
philosophicolegal
philosophicoreligious
philosophicotheological
philosophism
philosophist
philosophister
philosophistic
philosophistical
philosophization
philosophize
philosophizer
philosophling
philosophobia
philosophocracy
philosophuncule
philosophunculist
philosophy
philotadpole
philotechnic
philotechnical
philotechnist
philothaumaturgic
philotheism
philotheist
philotheistic
philotheosophical
philotherian
philotherianism
Philotria
Philoxenian
philoxygenous
philozoic
philozoist
philozoonist
philter
philterer
philterproof
philtra
philtrum
Philydraceae
philydraceous
Philyra
phimosed
phimosis
phimotic
Phineas
Phiomia
Phiroze
phit
phiz
phizes
phizog
phlebalgia
phlebangioma
phlebarteriectasia
phlebarteriodialysis
phlebectasia
phlebectasis
phlebectasy
phlebectomy
phlebectopia
phlebectopy
phlebemphraxis
phlebenteric
phlebenterism
phlebitic
phlebitis
Phlebodium
phlebogram
phlebograph
phlebographical
phlebography
phleboid
phleboidal
phlebolite
phlebolith
phlebolithiasis
phlebolithic
phlebolitic
phlebological
phlebology
phlebometritis
phlebopexy
phleboplasty
phleborrhage
phleborrhagia
phleborrhaphy
phleborrhexis
phlebosclerosis
phlebosclerotic
phlebostasia
phlebostasis
phlebostenosis
phlebostrepsis
phlebothrombosis
phlebotome
phlebotomic
phlebotomical
phlebotomically
phlebotomist
phlebotomization
phlebotomize
Phlebotomus
phlebotomus
phlebotomy
Phlegethon
Phlegethontal
Phlegethontic
phlegm
phlegma
phlegmagogue
phlegmasia
phlegmatic
phlegmatical
phlegmatically
phlegmaticalness
phlegmaticly
phlegmaticness
phlegmatism
phlegmatist
phlegmatous
phlegmless
phlegmon
phlegmonic
phlegmonoid
phlegmonous
phlegmy
Phleum
phlobaphene
phlobatannin
phloem
phloeophagous
phloeoterma
phlogisma
phlogistian
phlogistic
phlogistical
phlogisticate
phlogistication
phlogiston
phlogistonism
phlogistonist
phlogogenetic
phlogogenic
phlogogenous
phlogopite
phlogosed
Phlomis
phloretic
phloroglucic
phloroglucin
phlorone
phloxin
pho
phobiac
phobic
phobism
phobist
phobophobia
Phobos
phoby
phoca
phocacean
phocaceous
Phocaean
Phocaena
Phocaenina
phocaenine
phocal
Phocean
phocenate
phocenic
phocenin
Phocian
phocid
Phocidae
phociform
Phocinae
phocine
phocodont
Phocodontia
phocodontic
Phocoena
phocoid
phocomelia
phocomelous
phocomelus
Phoebe
phoebe
Phoebean
Phoenicaceae
phoenicaceous
Phoenicales
phoenicean
Phoenician
Phoenicianism
Phoenicid
phoenicite
Phoenicize
phoenicochroite
Phoenicopteridae
Phoenicopteriformes
phoenicopteroid
Phoenicopteroideae
phoenicopterous
Phoenicopterus
Phoeniculidae
Phoeniculus
phoenicurous
phoenigm
Phoenix
phoenix
phoenixity
phoenixlike
phoh
pholad
Pholadacea
pholadian
pholadid
Pholadidae
Pholadinea
pholadoid
Pholas
pholcid
Pholcidae
pholcoid
Pholcus
pholido
pholidolite
pholidosis
Pholidota
pholidote
Pholiota
Phoma
Phomopsis
phon
phonal
phonasthenia
phonate
phonation
phonatory
phonautogram
phonautograph
phonautographic
phonautographically
phone
phoneidoscope
phoneidoscopic
Phonelescope
phoneme
phonemic
phonemics
phonendoscope
phonesis
phonestheme
phonetic
phonetical
phonetically
phonetician
phoneticism
phoneticist
phoneticization
phoneticize
phoneticogrammatical
phoneticohieroglyphic
phonetics
phonetism
phonetist
phonetization
phonetize
phoniatrics
phoniatry
phonic
phonics
phonikon
phonism
phono
phonocamptic
phonocinematograph
phonodeik
phonodynamograph
phonoglyph
phonogram
phonogramic
phonogramically
phonogrammatic
phonogrammatical
phonogrammic
phonogrammically
phonograph
phonographer
phonographic
phonographical
phonographically
phonographist
phonography
phonolite
phonolitic
phonologer
phonologic
phonological
phonologically
phonologist
phonology
phonometer
phonometric
phonometry
phonomimic
phonomotor
phonopathy
phonophile
phonophobia
phonophone
phonophore
phonophoric
phonophorous
phonophote
phonophotography
phonophotoscope
phonophotoscopic
phonoplex
phonoscope
phonotelemeter
phonotype
phonotyper
phonotypic
phonotypical
phonotypically
phonotypist
phonotypy
phony
phoo
Phora
Phoradendron
phoranthium
phoresis
phoresy
phoria
phorid
Phoridae
phorminx
Phormium
phorology
phorometer
phorometric
phorometry
phorone
phoronic
phoronid
Phoronida
Phoronidea
Phoronis
phoronomia
phoronomic
phoronomically
phoronomics
phoronomy
Phororhacidae
Phororhacos
phoroscope
phorozooid
phos
phose
phosgene
phosgenic
phosgenite
phosis
phosphagen
phospham
phosphamic
phosphamide
phosphamidic
phosphammonium
phosphatase
phosphate
phosphated
phosphatemia
phosphatese
phosphatic
phosphatide
phosphation
phosphatization
phosphatize
phosphaturia
phosphaturic
phosphene
phosphenyl
phosphide
phosphinate
phosphine
phosphinic
phosphite
phospho
phosphoaminolipide
phosphocarnic
phosphocreatine
phosphoferrite
phosphoglycerate
phosphoglyceric
phosphoglycoprotein
phospholipide
phospholipin
phosphomolybdate
phosphomolybdic
phosphonate
phosphonic
phosphonium
phosphophyllite
phosphoprotein
phosphor
phosphorate
phosphore
phosphoreal
phosphorent
phosphoreous
phosphoresce
phosphorescence
phosphorescent
phosphorescently
phosphoreted
phosphorhidrosis
phosphori
phosphoric
phosphorical
phosphoriferous
phosphorism
phosphorite
phosphoritic
phosphorize
phosphorogen
phosphorogenic
phosphorograph
phosphorographic
phosphorography
phosphoroscope
phosphorous
phosphoruria
phosphorus
phosphoryl
phosphorylase
phosphorylation
phosphosilicate
phosphotartaric
phosphotungstate
phosphotungstic
phosphowolframic
phosphuranylite
phosphuret
phosphuria
phosphyl
phossy
phot
photaesthesia
photaesthesis
photaesthetic
photal
photalgia
photechy
photelectrograph
photeolic
photerythrous
photesthesis
photic
photics
Photinia
Photinian
Photinianism
photism
photistic
photo
photoactinic
photoactivate
photoactivation
photoactive
photoactivity
photoaesthetic
photoalbum
photoalgraphy
photoanamorphosis
photoaquatint
Photobacterium
photobathic
photobiotic
photobromide
photocampsis
photocatalysis
photocatalyst
photocatalytic
photocatalyzer
photocell
photocellulose
photoceptor
photoceramic
photoceramics
photoceramist
photochemic
photochemical
photochemically
photochemigraphy
photochemist
photochemistry
photochloride
photochlorination
photochromascope
photochromatic
photochrome
photochromic
photochromography
photochromolithograph
photochromoscope
photochromotype
photochromotypy
photochromy
photochronograph
photochronographic
photochronographical
photochronographically
photochronography
photocollograph
photocollographic
photocollography
photocollotype
photocombustion
photocompose
photocomposition
photoconductivity
photocopier
photocopy
photocrayon
photocurrent
photodecomposition
photodensitometer
photodermatic
photodermatism
photodisintegration
photodissociation
photodrama
photodramatic
photodramatics
photodramatist
photodramaturgic
photodramaturgy
photodrome
photodromy
photodynamic
photodynamical
photodynamically
photodynamics
photodysphoria
photoelastic
photoelasticity
photoelectric
photoelectrical
photoelectrically
photoelectricity
photoelectron
photoelectrotype
photoemission
photoemissive
photoengrave
photoengraver
photoengraving
photoepinastic
photoepinastically
photoepinasty
photoesthesis
photoesthetic
photoetch
photoetcher
photoetching
photofilm
photofinish
photofinisher
photofinishing
photofloodlamp
photogalvanograph
photogalvanographic
photogalvanography
photogastroscope
photogelatin
photogen
photogene
photogenetic
photogenic
photogenically
photogenous
photoglyph
photoglyphic
photoglyphography
photoglyphy
photoglyptic
photoglyptography
photogram
photogrammeter
photogrammetric
photogrammetrical
photogrammetry
photograph
photographable
photographee
photographer
photographeress
photographess
photographic
photographical
photographically
photographist
photographize
photographometer
photography
photogravure
photogravurist
photogyric
photohalide
photoheliograph
photoheliographic
photoheliography
photoheliometer
photohyponastic
photohyponastically
photohyponasty
photoimpression
photoinactivation
photoinduction
photoinhibition
photointaglio
photoionization
photoisomeric
photoisomerization
photokinesis
photokinetic
photolith
photolitho
photolithograph
photolithographer
photolithographic
photolithography
photologic
photological
photologist
photology
photoluminescence
photoluminescent
photolysis
photolyte
photolytic
photoma
photomacrograph
photomagnetic
photomagnetism
photomap
photomapper
photomechanical
photomechanically
photometeor
photometer
photometric
photometrical
photometrically
photometrician
photometrist
photometrograph
photometry
photomezzotype
photomicrogram
photomicrograph
photomicrographer
photomicrographic
photomicrography
photomicroscope
photomicroscopic
photomicroscopy
photomontage
photomorphosis
photomural
photon
photonastic
photonasty
photonegative
photonephograph
photonephoscope
photoneutron
photonosus
photooxidation
photooxidative
photopathic
photopathy
photoperceptive
photoperimeter
photoperiod
photoperiodic
photoperiodism
photophane
photophile
photophilic
photophilous
photophily
photophobe
photophobia
photophobic
photophobous
photophone
photophonic
photophony
photophore
photophoresis
photophosphorescent
photophygous
photophysical
photophysicist
photopia
photopic
photopile
photopitometer
photoplay
photoplayer
photoplaywright
photopography
photopolarigraph
photopolymerization
photopositive
photoprint
photoprinter
photoprinting
photoprocess
photoptometer
photoradio
photoradiogram
photoreception
photoreceptive
photoreceptor
photoregression
photorelief
photoresistance
photosalt
photosantonic
photoscope
photoscopic
photoscopy
photosculptural
photosculpture
photosensitive
photosensitiveness
photosensitivity
photosensitization
photosensitize
photosensitizer
photosensory
photospectroheliograph
photospectroscope
photospectroscopic
photospectroscopical
photospectroscopy
photosphere
photospheric
photostability
photostable
Photostat
photostat
photostationary
photostereograph
photosurveying
photosyntax
photosynthate
photosynthesis
photosynthesize
photosynthetic
photosynthetically
photosynthometer
phototachometer
phototachometric
phototachometrical
phototachometry
phototactic
phototactically
phototactism
phototaxis
phototaxy
phototechnic
phototelegraph
phototelegraphic
phototelegraphically
phototelegraphy
phototelephone
phototelephony
phototelescope
phototelescopic
phototheodolite
phototherapeutic
phototherapeutics
phototherapic
phototherapist
phototherapy
photothermic
phototonic
phototonus
phototopographic
phototopographical
phototopography
phototrichromatic
phototrope
phototrophic
phototrophy
phototropic
phototropically
phototropism
phototropy
phototube
phototype
phototypic
phototypically
phototypist
phototypographic
phototypography
phototypy
photovisual
photovitrotype
photovoltaic
photoxylography
photozinco
photozincograph
photozincographic
photozincography
photozincotype
photozincotypy
photuria
Phractamphibia
phragma
Phragmidium
Phragmites
phragmocone
phragmoconic
Phragmocyttares
phragmocyttarous
phragmoid
phragmosis
phrasable
phrasal
phrasally
phrase
phraseable
phraseless
phrasemaker
phrasemaking
phraseman
phrasemonger
phrasemongering
phrasemongery
phraseogram
phraseograph
phraseographic
phraseography
phraseological
phraseologically
phraseologist
phraseology
phraser
phrasify
phrasiness
phrasing
phrasy
phrator
phratral
phratria
phratriac
phratrial
phratry
phreatic
phreatophyte
phrenesia
phrenesiac
phrenesis
phrenetic
phrenetically
phreneticness
phrenic
phrenicectomy
phrenicocolic
phrenicocostal
phrenicogastric
phrenicoglottic
phrenicohepatic
phrenicolienal
phrenicopericardiac
phrenicosplenic
phrenicotomy
phrenics
phrenitic
phrenitis
phrenocardia
phrenocardiac
phrenocolic
phrenocostal
phrenodynia
phrenogastric
phrenoglottic
phrenogram
phrenograph
phrenography
phrenohepatic
phrenologer
phrenologic
phrenological
phrenologically
phrenologist
phrenologize
phrenology
phrenomagnetism
phrenomesmerism
phrenopathia
phrenopathic
phrenopathy
phrenopericardiac
phrenoplegia
phrenoplegy
phrenosin
phrenosinic
phrenospasm
phrenosplenic
phronesis
Phronima
Phronimidae
phrontisterion
phrontisterium
phrontistery
Phryganea
phryganeid
Phryganeidae
phryganeoid
Phrygian
Phrygianize
phrygium
Phryma
Phrymaceae
phrymaceous
phrynid
Phrynidae
phrynin
phrynoid
Phrynosoma
phthalacene
phthalan
phthalanilic
phthalate
phthalazin
phthalazine
phthalein
phthaleinometer
phthalic
phthalid
phthalide
phthalimide
phthalin
phthalocyanine
phthalyl
phthanite
Phthartolatrae
phthinoid
phthiocol
phthiriasis
Phthirius
phthirophagous
phthisic
phthisical
phthisicky
phthisiogenesis
phthisiogenetic
phthisiogenic
phthisiologist
phthisiology
phthisiophobia
phthisiotherapeutic
phthisiotherapy
phthisipneumonia
phthisipneumony
phthisis
phthongal
phthongometer
phthor
phthoric
phu
phugoid
phulkari
phulwa
phulwara
phut
Phyciodes
phycite
Phycitidae
phycitol
phycochromaceae
phycochromaceous
phycochrome
Phycochromophyceae
phycochromophyceous
phycocyanin
phycocyanogen
Phycodromidae
phycoerythrin
phycography
phycological
phycologist
phycology
Phycomyces
phycomycete
Phycomycetes
phycomycetous
phycophaein
phycoxanthin
phycoxanthine
phygogalactic
phyla
phylacobiosis
phylacobiotic
phylacteric
phylacterical
phylacteried
phylacterize
phylactery
phylactic
phylactocarp
phylactocarpal
Phylactolaema
Phylactolaemata
phylactolaematous
Phylactolema
Phylactolemata
phylarch
phylarchic
phylarchical
phylarchy
phyle
phylephebic
phylesis
phyletic
phyletically
phyletism
phylic
Phyllachora
Phyllactinia
phyllade
Phyllanthus
phyllary
Phyllaurea
phylliform
phyllin
phylline
Phyllis
phyllite
phyllitic
Phyllitis
Phyllium
phyllobranchia
phyllobranchial
phyllobranchiate
Phyllocactus
phyllocarid
Phyllocarida
phyllocaridan
Phylloceras
phyllocerate
Phylloceratidae
phylloclad
phylloclade
phyllocladioid
phyllocladium
phyllocladous
phyllocyanic
phyllocyanin
phyllocyst
phyllocystic
phyllode
phyllodial
phyllodination
phyllodineous
phyllodiniation
phyllodinous
phyllodium
Phyllodoce
phyllody
phylloerythrin
phyllogenetic
phyllogenous
phylloid
phylloidal
phylloideous
phyllomancy
phyllomania
phyllome
phyllomic
phyllomorph
phyllomorphic
phyllomorphosis
phyllomorphy
Phyllophaga
phyllophagous
phyllophore
phyllophorous
phyllophyllin
phyllophyte
phyllopod
Phyllopoda
phyllopodan
phyllopode
phyllopodiform
phyllopodium
phyllopodous
phylloporphyrin
Phyllopteryx
phylloptosis
phyllopyrrole
phyllorhine
phyllorhinine
phylloscopine
Phylloscopus
phyllosiphonic
phyllosoma
Phyllosomata
phyllosome
Phyllospondyli
phyllospondylous
Phyllostachys
Phyllosticta
Phyllostoma
Phyllostomatidae
Phyllostomatinae
phyllostomatoid
phyllostomatous
phyllostome
Phyllostomidae
Phyllostominae
phyllostomine
phyllostomous
Phyllostomus
phyllotactic
phyllotactical
phyllotaxis
phyllotaxy
phyllous
phylloxanthin
Phylloxera
phylloxeran
phylloxeric
Phylloxeridae
phyllozooid
phylogenetic
phylogenetical
phylogenetically
phylogenic
phylogenist
phylogeny
phylogerontic
phylogerontism
phylography
phylology
phylon
phyloneanic
phylonepionic
phylum
phyma
phymata
phymatic
phymatid
Phymatidae
Phymatodes
phymatoid
phymatorhysin
phymatosis
Phymosia
Physa
physagogue
Physalia
physalian
Physaliidae
Physalis
physalite
Physalospora
Physapoda
Physaria
Physcia
Physciaceae
physcioid
Physcomitrium
Physeter
Physeteridae
Physeterinae
physeterine
physeteroid
Physeteroidea
physharmonica
physianthropy
physiatric
physiatrical
physiatrics
physic
physical
physicalism
physicalist
physicalistic
physicalistically
physicality
physically
physicalness
physician
physicianary
physiciancy
physicianed
physicianer
physicianess
physicianless
physicianly
physicianship
physicism
physicist
physicked
physicker
physicking
physicky
physicoastronomical
physicobiological
physicochemic
physicochemical
physicochemically
physicochemist
physicochemistry
physicogeographical
physicologic
physicological
physicomathematical
physicomathematics
physicomechanical
physicomedical
physicomental
physicomorph
physicomorphic
physicomorphism
physicooptics
physicophilosophical
physicophilosophy
physicophysiological
physicopsychical
physicosocial
physicotheological
physicotheologist
physicotheology
physicotherapeutic
physicotherapeutics
physicotherapy
physics
Physidae
physiform
physiochemical
physiochemically
physiocracy
physiocrat
physiocratic
physiocratism
physiocratist
physiogenesis
physiogenetic
physiogenic
physiogeny
physiognomic
physiognomical
physiognomically
physiognomics
physiognomist
physiognomize
physiognomonic
physiognomonical
physiognomy
physiogony
physiographer
physiographic
physiographical
physiographically
physiography
physiolater
physiolatrous
physiolatry
physiologer
physiologian
physiological
physiologically
physiologicoanatomic
physiologist
physiologize
physiologue
physiologus
physiology
physiopathological
physiophilist
physiophilosopher
physiophilosophical
physiophilosophy
physiopsychic
physiopsychical
physiopsychological
physiopsychology
physiosociological
physiosophic
physiosophy
physiotherapeutic
physiotherapeutical
physiotherapeutics
physiotherapist
physiotherapy
physiotype
physiotypy
physique
physiqued
physitheism
physitheistic
physitism
physiurgic
physiurgy
physocarpous
Physocarpus
physocele
physoclist
Physoclisti
physoclistic
physoclistous
Physoderma
physogastric
physogastrism
physogastry
physometra
Physonectae
physonectous
Physophorae
physophoran
physophore
physophorous
physopod
Physopoda
physopodan
Physostegia
Physostigma
physostigmine
physostomatous
physostome
Physostomi
physostomous
phytalbumose
phytase
Phytelephas
Phyteus
phytic
phytiferous
phytiform
phytin
phytivorous
phytobacteriology
phytobezoar
phytobiological
phytobiology
phytochemical
phytochemistry
phytochlorin
phytocidal
phytodynamics
phytoecological
phytoecologist
phytoecology
Phytoflagellata
phytogamy
phytogenesis
phytogenetic
phytogenetical
phytogenetically
phytogenic
phytogenous
phytogeny
phytogeographer
phytogeographic
phytogeographical
phytogeographically
phytogeography
phytoglobulin
phytograph
phytographer
phytographic
phytographical
phytographist
phytography
phytohormone
phytoid
phytol
Phytolacca
Phytolaccaceae
phytolaccaceous
phytolatrous
phytolatry
phytolithological
phytolithologist
phytolithology
phytologic
phytological
phytologically
phytologist
phytology
phytoma
Phytomastigina
Phytomastigoda
phytome
phytomer
phytometer
phytometric
phytometry
phytomonad
Phytomonadida
Phytomonadina
Phytomonas
phytomorphic
phytomorphology
phytomorphosis
phyton
phytonic
phytonomy
phytooecology
phytopaleontologic
phytopaleontological
phytopaleontologist
phytopaleontology
phytoparasite
phytopathogen
phytopathogenic
phytopathologic
phytopathological
phytopathologist
phytopathology
Phytophaga
phytophagan
phytophagic
Phytophagineae
phytophagous
phytophagy
phytopharmacologic
phytopharmacology
phytophenological
phytophenology
phytophil
phytophilous
Phytophthora
phytophylogenetic
phytophylogenic
phytophylogeny
phytophysiological
phytophysiology
phytoplankton
phytopsyche
phytoptid
Phytoptidae
phytoptose
phytoptosis
Phytoptus
phytorhodin
phytosaur
Phytosauria
phytosaurian
phytoserologic
phytoserological
phytoserologically
phytoserology
phytosis
phytosociologic
phytosociological
phytosociologically
phytosociologist
phytosociology
phytosterin
phytosterol
phytostrote
phytosynthesis
phytotaxonomy
phytotechny
phytoteratologic
phytoteratological
phytoteratologist
phytoteratology
Phytotoma
Phytotomidae
phytotomist
phytotomy
phytotopographical
phytotopography
phytotoxic
phytotoxin
phytovitellin
Phytozoa
phytozoan
Phytozoaria
phytozoon
phytyl
pi
Pia
pia
piaba
piacaba
piacle
piacular
piacularity
piacularly
piacularness
piaculum
piaffe
piaffer
pial
pialyn
pian
pianette
pianic
pianino
pianism
pianissimo
pianist
pianiste
pianistic
pianistically
Piankashaw
piannet
piano
pianoforte
pianofortist
pianograph
Pianokoto
Pianola
pianola
pianolist
pianologue
piarhemia
piarhemic
Piarist
Piaroa
Piaroan
Piaropus
Piarroan
piassava
Piast
piaster
piastre
piation
piazine
piazza
piazzaed
piazzaless
piazzalike
piazzian
pibcorn
piblokto
pibroch
pic
Pica
pica
picador
picadura
Picae
pical
picamar
picara
Picard
picarel
picaresque
Picariae
picarian
Picarii
picaro
picaroon
picary
picayune
picayunish
picayunishly
picayunishness
piccadill
piccadilly
piccalilli
piccolo
piccoloist
pice
Picea
Picene
picene
Picenian
piceoferruginous
piceotestaceous
piceous
piceworth
pichi
pichiciago
pichuric
pichurim
Pici
Picidae
piciform
Piciformes
Picinae
picine
pick
pickaback
pickable
pickableness
pickage
pickaninny
pickaroon
pickaway
pickax
picked
pickedly
pickedness
pickee
pickeer
picker
pickerel
pickerelweed
pickering
pickeringite
pickery
picket
picketboat
picketeer
picketer
pickfork
pickietar
pickings
pickle
picklelike
pickleman
pickler
pickleweed
pickleworm
picklock
pickman
pickmaw
picknick
picknicker
pickover
pickpocket
pickpocketism
pickpocketry
pickpole
pickpurse
pickshaft
picksman
picksmith
picksome
picksomeness
pickthank
pickthankly
pickthankness
pickthatch
picktooth
pickup
pickwick
Pickwickian
Pickwickianism
Pickwickianly
pickwork
picky
picnic
picnicker
picnickery
Picnickian
picnickish
picnicky
pico
picofarad
picoid
picoline
picolinic
picot
picotah
picotee
picotite
picqueter
picra
picramic
Picramnia
picrasmin
picrate
picrated
picric
Picris
picrite
picrocarmine
Picrodendraceae
Picrodendron
picroerythrin
picrol
picrolite
picromerite
picropodophyllin
picrorhiza
picrorhizin
picrotin
picrotoxic
picrotoxin
picrotoxinin
picryl
Pict
pict
pictarnie
Pictavi
Pictish
Pictland
pictogram
pictograph
pictographic
pictographically
pictography
Pictones
pictoradiogram
pictorial
pictorialism
pictorialist
pictorialization
pictorialize
pictorially
pictorialness
pictoric
pictorical
pictorically
picturability
picturable
picturableness
picturably
pictural
picture
picturecraft
pictured
picturedom
picturedrome
pictureful
pictureless
picturelike
picturely
picturemaker
picturemaking
picturer
picturesque
picturesquely
picturesqueness
picturesquish
picturization
picturize
pictury
picucule
picuda
picudilla
picudo
picul
piculet
piculule
Picumninae
Picumnus
Picunche
Picuris
Picus
pidan
piddle
piddler
piddling
piddock
pidgin
pidjajap
pie
piebald
piebaldism
piebaldly
piebaldness
piece
pieceable
pieceless
piecemaker
piecemeal
piecemealwise
piecen
piecener
piecer
piecette
piecewise
piecework
pieceworker
piecing
piecrust
pied
piedfort
piedly
piedmont
piedmontal
Piedmontese
piedmontite
piedness
Piegan
piehouse
pieless
pielet
pielum
piemag
pieman
piemarker
pien
pienanny
piend
piepan
pieplant
piepoudre
piepowder
pieprint
pier
pierage
Piercarlo
Pierce
pierce
pierceable
pierced
piercel
pierceless
piercent
piercer
piercing
piercingly
piercingness
pierdrop
Pierette
pierhead
Pierian
pierid
Pieridae
Pierides
Pieridinae
pieridine
Pierinae
pierine
Pieris
pierless
pierlike
Pierre
Pierrot
pierrot
pierrotic
pieshop
Piet
piet
pietas
Piete
Pieter
pietic
pietism
Pietist
pietist
pietistic
pietistical
pietistically
pietose
piety
piewife
piewipe
piewoman
piezo
piezochemical
piezochemistry
piezocrystallization
piezoelectric
piezoelectrically
piezoelectricity
piezometer
piezometric
piezometrical
piezometry
piff
piffle
piffler
pifine
pig
pigbelly
pigdan
pigdom
pigeon
pigeonable
pigeonberry
pigeoneer
pigeoner
pigeonfoot
pigeongram
pigeonhearted
pigeonhole
pigeonholer
pigeonman
pigeonry
pigeontail
pigeonweed
pigeonwing
pigeonwood
pigface
pigfish
pigflower
pigfoot
pigful
piggery
piggin
pigging
piggish
piggishly
piggishness
piggle
piggy
pighead
pigheaded
pigheadedly
pigheadedness
pigherd
pightle
pigless
piglet
pigling
piglinghood
pigly
pigmaker
pigmaking
pigman
pigment
pigmental
pigmentally
pigmentary
pigmentation
pigmentize
pigmentolysis
pigmentophage
pigmentose
Pigmy
pignolia
pignon
pignorate
pignoration
pignoratitious
pignorative
pignus
pignut
pigpen
pigritude
pigroot
pigsconce
pigskin
pigsney
pigstick
pigsticker
pigsty
pigtail
pigwash
pigweed
pigwidgeon
pigyard
piitis
pik
pika
pike
piked
pikel
pikelet
pikeman
pikemonger
piker
pikestaff
piketail
pikey
piki
piking
pikle
piky
pilage
pilandite
pilapil
Pilar
pilar
pilary
pilaster
pilastered
pilastering
pilastrade
pilastraded
pilastric
Pilate
Pilatian
pilau
pilaued
pilch
pilchard
pilcher
pilcorn
pilcrow
pile
Pilea
pileata
pileate
pileated
piled
pileiform
pileolated
pileolus
pileorhiza
pileorhize
pileous
piler
piles
pileus
pileweed
pilework
pileworm
pilewort
pilfer
pilferage
pilferer
pilfering
pilferingly
pilferment
pilgarlic
pilgarlicky
pilger
pilgrim
pilgrimage
pilgrimager
pilgrimatic
pilgrimatical
pilgrimdom
pilgrimer
pilgrimess
pilgrimism
pilgrimize
pilgrimlike
pilgrimwise
pili
pilidium
pilifer
piliferous
piliform
piligan
piliganine
piligerous
pilikai
pililloo
pilimiction
pilin
piline
piling
pilipilula
pilkins
pill
pillage
pillageable
pillagee
pillager
pillar
pillared
pillaret
pillaring
pillarist
pillarize
pillarlet
pillarlike
pillarwise
pillary
pillas
pillbox
pilled
pilledness
pillet
pilleus
pillion
pilliver
pilliwinks
pillmaker
pillmaking
pillmonger
pillorization
pillorize
pillory
pillow
pillowcase
pillowing
pillowless
pillowmade
pillowwork
pillowy
pillworm
pillwort
pilm
pilmy
Pilobolus
pilocarpidine
pilocarpine
Pilocarpus
Pilocereus
pilocystic
piloerection
pilomotor
pilon
pilonidal
pilori
pilose
pilosebaceous
pilosine
pilosis
pilosism
pilosity
Pilot
pilot
pilotage
pilotaxitic
pilotee
pilothouse
piloting
pilotism
pilotless
pilotman
pilotry
pilotship
pilotweed
pilous
Pilpai
Pilpay
pilpul
pilpulist
pilpulistic
piltock
pilula
pilular
Pilularia
pilule
pilulist
pilulous
pilum
Pilumnus
pilus
pilwillet
pily
Pim
Pima
Piman
pimaric
pimelate
Pimelea
pimelic
pimelite
pimelitis
Pimenta
pimento
pimenton
pimgenet
pimienta
pimiento
pimlico
pimola
pimp
pimperlimpimp
pimpernel
pimpery
Pimpinella
pimping
pimpish
Pimpla
pimple
pimpleback
pimpled
pimpleproof
Pimplinae
pimpliness
pimplo
pimploe
pimplous
pimply
pimpship
pin
pina
Pinaceae
pinaceous
pinaces
pinachrome
pinacle
Pinacoceras
Pinacoceratidae
pinacocytal
pinacocyte
pinacoid
pinacoidal
pinacol
pinacolate
pinacolic
pinacolin
pinacone
pinacoteca
pinaculum
Pinacyanol
pinafore
pinakiolite
pinakoidal
pinakotheke
Pinal
Pinaleno
Pinales
pinang
pinaster
pinatype
pinaverdol
pinax
pinball
pinbefore
pinbone
pinbush
pincase
pincement
pincer
pincerlike
pincers
pincerweed
pinch
pinchable
pinchback
pinchbeck
pinchbelly
pinchcock
pinchcommons
pinchcrust
pinche
pinched
pinchedly
pinchedness
pinchem
pincher
pinchfist
pinchfisted
pinchgut
pinching
pinchingly
pinchpenny
Pincian
Pinckneya
pincoffin
pincpinc
Pinctada
pincushion
pincushiony
pind
pinda
Pindari
Pindaric
pindarical
pindarically
Pindarism
Pindarist
Pindarize
Pindarus
pinder
pindling
pindy
pine
pineal
pinealism
pinealoma
pineapple
pined
pinedrops
pineland
pinene
piner
pinery
pinesap
pinetum
pineweed
pinewoods
piney
pinfall
pinfeather
pinfeathered
pinfeatherer
pinfeathery
pinfish
pinfold
Ping
ping
pingle
pingler
pingue
pinguecula
pinguedinous
pinguefaction
pinguefy
pinguescence
pinguescent
Pinguicula
pinguicula
Pinguiculaceae
pinguiculaceous
pinguid
pinguidity
pinguiferous
pinguin
pinguinitescent
pinguite
pinguitude
pinguitudinous
pinhead
pinheaded
pinheadedness
pinhold
pinhole
pinhook
pinic
pinicoline
pinicolous
piniferous
piniform
pining
piningly
pinion
pinioned
pinionless
pinionlike
pinipicrin
pinitannic
pinite
pinitol
pinivorous
pinjane
pinjra
pink
pinkberry
pinked
pinkeen
pinken
pinker
Pinkerton
Pinkertonism
pinkeye
pinkfish
pinkie
pinkify
pinkily
pinkiness
pinking
pinkish
pinkishness
pinkly
pinkness
pinkroot
pinksome
Pinkster
pinkweed
pinkwood
pinkwort
pinky
pinless
pinlock
pinmaker
Pinna
pinna
pinnace
pinnacle
pinnaclet
pinnae
pinnaglobin
pinnal
pinnate
pinnated
pinnatedly
pinnately
pinnatifid
pinnatifidly
pinnatilobate
pinnatilobed
pinnation
pinnatipartite
pinnatiped
pinnatisect
pinnatisected
pinnatodentate
pinnatopectinate
pinnatulate
pinned
pinnel
pinner
pinnet
Pinnidae
pinniferous
pinniform
pinnigerous
Pinnigrada
pinnigrade
pinninervate
pinninerved
pinning
pinningly
pinniped
Pinnipedia
pinnipedian
pinnisect
pinnisected
pinnitarsal
pinnitentaculate
pinniwinkis
pinnock
pinnoite
pinnotere
pinnothere
Pinnotheres
pinnotherian
Pinnotheridae
pinnula
pinnular
pinnulate
pinnulated
pinnule
pinnulet
pinny
pino
pinochle
pinocytosis
pinole
pinoleum
pinolia
pinolin
pinon
pinonic
pinpillow
pinpoint
pinprick
pinproof
pinrail
pinrowed
pinscher
pinsons
pint
pinta
pintadera
pintado
pintadoite
pintail
pintano
pinte
pintle
pinto
pintura
pinulus
Pinus
pinweed
pinwing
pinwork
pinworm
piny
pinyl
pinyon
pioneer
pioneerdom
pioneership
pionnotes
pioscope
pioted
piotine
Piotr
piotty
pioury
pious
piously
piousness
Pioxe
pip
pipa
pipage
pipal
pipe
pipeage
pipecoline
pipecolinic
piped
pipefish
pipeful
pipelayer
pipeless
pipelike
pipeline
pipeman
pipemouth
Piper
piper
Piperaceae
piperaceous
Piperales
piperate
piperazin
piperazine
piperic
piperide
piperideine
piperidge
piperidide
piperidine
piperine
piperitious
piperitone
piperly
piperno
piperoid
piperonal
piperonyl
pipery
piperylene
pipestapple
pipestem
pipestone
pipet
pipette
pipewalker
pipewood
pipework
pipewort
pipi
Pipidae
Pipil
Pipile
Pipilo
piping
pipingly
pipingness
pipiri
pipistrel
pipistrelle
Pipistrellus
pipit
pipkin
pipkinet
pipless
pipped
pipper
pippin
pippiner
pippinface
pippy
Pipra
Pipridae
Piprinae
piprine
piproid
pipsissewa
Piptadenia
Piptomeris
pipunculid
Pipunculidae
pipy
piquable
piquance
piquancy
piquant
piquantly
piquantness
pique
piquet
piquia
piqure
pir
piracy
piragua
Piranga
piranha
pirate
piratelike
piratery
piratess
piratical
piratically
piratism
piratize
piraty
Pirene
Piricularia
pirijiri
piripiri
piririgua
pirl
pirn
pirner
pirnie
pirny
Piro
pirogue
pirol
piroplasm
Piroplasma
piroplasmosis
pirouette
pirouetter
pirouettist
pirr
pirraura
pirrmaw
pirssonite
Pisaca
pisaca
pisachee
Pisan
pisang
pisanite
Pisauridae
pisay
piscary
Piscataqua
Piscataway
piscation
piscatology
piscator
piscatorial
piscatorialist
piscatorially
piscatorian
piscatorious
piscatory
Pisces
piscian
piscicapture
piscicapturist
piscicolous
piscicultural
pisciculturally
pisciculture
pisciculturist
Piscid
Piscidia
piscifauna
pisciferous
pisciform
piscina
piscinal
piscine
piscinity
Piscis
piscivorous
pisco
pise
pish
pishaug
pishogue
Pishquow
pishu
Pisidium
pisiform
Pisistratean
Pisistratidae
pisk
pisky
pismire
pismirism
piso
pisolite
pisolitic
Pisonia
piss
pissabed
pissant
pist
pistache
pistachio
Pistacia
pistacite
pistareen
Pistia
pistic
pistil
pistillaceous
pistillar
pistillary
pistillate
pistillid
pistilliferous
pistilliform
pistilligerous
pistilline
pistillode
pistillody
pistilloid
pistilogy
pistle
Pistoiese
pistol
pistole
pistoleer
pistolet
pistolgram
pistolgraph
pistollike
pistolography
pistology
pistolproof
pistolwise
piston
pistonhead
pistonlike
pistrix
Pisum
pit
pita
Pitahauerat
Pitahauirata
pitahaya
pitanga
pitangua
pitapat
pitapatation
pitarah
pitau
Pitawas
pitaya
pitayita
Pitcairnia
pitch
pitchable
pitchblende
pitcher
pitchered
pitcherful
pitcherlike
pitcherman
pitchfork
pitchhole
pitchi
pitchiness
pitching
pitchlike
pitchman
pitchometer
pitchout
pitchpike
pitchpole
pitchpoll
pitchstone
pitchwork
pitchy
piteous
piteously
piteousness
pitfall
pith
pithecan
pithecanthrope
pithecanthropic
pithecanthropid
Pithecanthropidae
pithecanthropoid
Pithecanthropus
Pithecia
pithecian
Pitheciinae
pitheciine
pithecism
pithecoid
Pithecolobium
pithecological
pithecometric
pithecomorphic
pithecomorphism
pithful
pithily
pithiness
pithless
pithlessly
Pithoegia
Pithoigia
pithole
pithos
pithsome
pithwork
pithy
pitiability
pitiable
pitiableness
pitiably
pitiedly
pitiedness
pitier
pitiful
pitifully
pitifulness
pitikins
pitiless
pitilessly
pitilessness
pitless
pitlike
pitmaker
pitmaking
pitman
pitmark
pitmirk
pitometer
pitpan
pitpit
pitside
Pitta
pittacal
pittance
pittancer
pitted
pitter
pitticite
Pittidae
pittine
pitting
Pittism
Pittite
pittite
pittoid
Pittosporaceae
pittosporaceous
pittospore
Pittosporum
Pittsburgher
pituital
pituitary
pituite
pituitous
pituitousness
Pituitrin
pituri
pitwood
pitwork
pitwright
pity
pitying
pityingly
Pitylus
pityocampa
pityproof
pityriasic
pityriasis
Pityrogramma
pityroid
piuri
piuricapsular
pivalic
pivot
pivotal
pivotally
pivoter
pix
pixie
pixilated
pixilation
pixy
pize
pizza
pizzeria
pizzicato
pizzle
placability
placable
placableness
placably
Placaean
placard
placardeer
placarder
placate
placater
placation
placative
placatively
placatory
placcate
place
placeable
Placean
placebo
placeful
placeless
placelessly
placemaker
placemaking
placeman
placemanship
placement
placemonger
placemongering
placenta
placental
Placentalia
placentalian
placentary
placentate
placentation
placentiferous
placentiform
placentigerous
placentitis
placentoid
placentoma
placer
placet
placewoman
placid
placidity
placidly
placidness
placitum
plack
placket
plackless
placochromatic
placode
placoderm
placodermal
placodermatous
Placodermi
placodermoid
placodont
Placodontia
Placodus
placoganoid
placoganoidean
Placoganoidei
placoid
placoidal
placoidean
Placoidei
Placoides
Placophora
placophoran
placoplast
placula
placuntitis
placuntoma
Placus
pladaroma
pladarosis
plaga
plagal
plagate
plage
Plagianthus
plagiaplite
plagiarical
plagiarism
plagiarist
plagiaristic
plagiaristically
plagiarization
plagiarize
plagiarizer
plagiary
plagihedral
plagiocephalic
plagiocephalism
plagiocephaly
Plagiochila
plagioclase
plagioclasite
plagioclastic
plagioclinal
plagiodont
plagiograph
plagioliparite
plagionite
plagiopatagium
plagiophyre
Plagiostomata
plagiostomatous
plagiostome
Plagiostomi
plagiostomous
plagiotropic
plagiotropically
plagiotropism
plagiotropous
plagium
plagose
plagosity
plague
plagued
plagueful
plagueless
plagueproof
plaguer
plaguesome
plaguesomeness
plaguily
plaguy
plaice
plaid
plaided
plaidie
plaiding
plaidman
plaidy
plain
plainback
plainbacks
plainer
plainful
plainhearted
plainish
plainly
plainness
plainscraft
plainsfolk
plainsman
plainsoled
plainstones
plainswoman
plaint
plaintail
plaintiff
plaintiffship
plaintile
plaintive
plaintively
plaintiveness
plaintless
plainward
plaister
plait
plaited
plaiter
plaiting
plaitless
plaitwork
plak
plakat
plan
planable
planaea
planar
Planaria
planarian
Planarida
planaridan
planariform
planarioid
planarity
planate
planation
planch
plancheite
plancher
planchet
planchette
planching
planchment
plancier
Planckian
plandok
plane
planeness
planer
Planera
planet
planeta
planetable
planetabler
planetal
planetaria
planetarian
planetarily
planetarium
planetary
planeted
planetesimal
planeticose
planeting
planetist
planetkin
planetless
planetlike
planetogeny
planetography
planetoid
planetoidal
planetologic
planetologist
planetology
planetule
planform
planful
planfully
planfulness
plang
plangency
plangent
plangently
plangor
plangorous
planicaudate
planicipital
planidorsate
planifolious
planiform
planigraph
planilla
planimetric
planimetrical
planimetry
planineter
planipennate
Planipennia
planipennine
planipetalous
planiphyllous
planirostral
planirostrate
planiscope
planiscopic
planish
planisher
planispheral
planisphere
planispheric
planispherical
planispiral
planity
plank
plankage
plankbuilt
planker
planking
plankless
planklike
planksheer
plankter
planktologist
planktology
plankton
planktonic
planktont
plankways
plankwise
planky
planless
planlessly
planlessness
planner
planoblast
planoblastic
Planococcus
planoconical
planocylindric
planoferrite
planogamete
planograph
planographic
planographist
planography
planohorizontal
planolindrical
planometer
planometry
planomiller
planoorbicular
Planorbidae
planorbiform
planorbine
Planorbis
planorboid
planorotund
Planosarcina
planosol
planosome
planospiral
planospore
planosubulate
plant
planta
plantable
plantad
Plantae
plantage
Plantaginaceae
plantaginaceous
Plantaginales
plantagineous
Plantago
plantain
plantal
plantar
plantaris
plantarium
plantation
plantationlike
plantdom
planter
planterdom
planterly
plantership
Plantigrada
plantigrade
plantigrady
planting
plantivorous
plantless
plantlet
plantlike
plantling
plantocracy
plantsman
plantula
plantular
plantule
planula
planulan
planular
planulate
planuliform
planuloid
Planuloidea
planuria
planury
planxty
plap
plappert
plaque
plaquette
plash
plasher
plashet
plashingly
plashment
plashy
plasm
plasma
plasmagene
plasmapheresis
plasmase
plasmatic
plasmatical
plasmation
plasmatoparous
plasmatorrhexis
plasmic
plasmocyte
plasmocytoma
plasmode
plasmodesm
plasmodesma
plasmodesmal
plasmodesmic
plasmodesmus
plasmodia
plasmodial
plasmodiate
plasmodic
plasmodiocarp
plasmodiocarpous
Plasmodiophora
Plasmodiophoraceae
Plasmodiophorales
plasmodium
plasmogen
plasmolysis
plasmolytic
plasmolytically
plasmolyzability
plasmolyzable
plasmolyze
plasmoma
Plasmon
Plasmopara
plasmophagous
plasmophagy
plasmoptysis
plasmosoma
plasmosome
plasmotomy
plasome
plass
plasson
plastein
plaster
plasterbill
plasterboard
plasterer
plasteriness
plastering
plasterlike
plasterwise
plasterwork
plastery
Plastic
plastic
plastically
plasticimeter
Plasticine
plasticine
plasticism
plasticity
plasticization
plasticize
plasticizer
plasticly
plastics
plastid
plastidium
plastidome
Plastidozoa
plastidular
plastidule
plastify
plastin
plastinoid
plastisol
plastochondria
plastochron
plastochrone
plastodynamia
plastodynamic
plastogamic
plastogamy
plastogene
plastomere
plastometer
plastosome
plastotype
plastral
plastron
plastrum
plat
Plataean
Platalea
Plataleidae
plataleiform
Plataleinae
plataleine
platan
Platanaceae
platanaceous
platane
platanist
Platanista
Platanistidae
platano
Platanus
platband
platch
plate
platea
plateasm
plateau
plateaux
plated
plateful
plateholder
plateiasmus
platelayer
plateless
platelet
platelike
platemaker
platemaking
plateman
platen
plater
platerer
plateresque
platery
plateway
platework
plateworker
platform
platformally
platformed
platformer
platformish
platformism
platformist
platformistic
platformless
platformy
platic
platicly
platilla
platina
platinamine
platinammine
platinate
Platine
plating
platinic
platinichloric
platinichloride
platiniferous
platiniridium
platinite
platinization
platinize
platinochloric
platinochloride
platinocyanic
platinocyanide
platinoid
platinotype
platinous
platinum
platinumsmith
platitude
platitudinal
platitudinarian
platitudinarianism
platitudinism
platitudinist
platitudinization
platitudinize
platitudinizer
platitudinous
platitudinously
platitudinousness
Platoda
platode
Platodes
platoid
Platonesque
platonesque
Platonian
Platonic
Platonical
Platonically
Platonicalness
Platonician
Platonicism
Platonism
Platonist
Platonistic
Platonization
Platonize
Platonizer
platoon
platopic
platosamine
platosammine
Platt
Plattdeutsch
platted
platten
platter
platterface
platterful
platting
plattnerite
platty
platurous
platy
platybasic
platybrachycephalic
platybrachycephalous
platybregmatic
platycarpous
Platycarpus
Platycarya
platycelian
platycelous
platycephalic
Platycephalidae
platycephalism
platycephaloid
platycephalous
Platycephalus
platycephaly
Platycercinae
platycercine
Platycercus
Platycerium
platycheiria
platycnemia
platycnemic
Platycodon
platycoria
platycrania
platycranial
Platyctenea
platycyrtean
platydactyl
platydactyle
platydactylous
platydolichocephalic
platydolichocephalous
platyfish
platyglossal
platyglossate
platyglossia
Platyhelmia
platyhelminth
Platyhelminthes
platyhelminthic
platyhieric
platykurtic
platylobate
platymeria
platymeric
platymery
platymesaticephalic
platymesocephalic
platymeter
platymyoid
platynite
platynotal
platyodont
platyope
platyopia
platyopic
platypellic
platypetalous
platyphyllous
platypod
Platypoda
platypodia
platypodous
Platyptera
platypus
platypygous
Platyrhina
Platyrhini
platyrhynchous
platyrrhin
Platyrrhina
platyrrhine
Platyrrhini
platyrrhinian
platyrrhinic
platyrrhinism
platyrrhiny
platysma
platysmamyoides
platysomid
Platysomidae
Platysomus
platystaphyline
Platystemon
platystencephalia
platystencephalic
platystencephalism
platystencephaly
platysternal
Platysternidae
Platystomidae
platystomous
platytrope
platytropy
plaud
plaudation
plaudit
plaudite
plauditor
plauditory
plauenite
plausibility
plausible
plausibleness
plausibly
plausive
plaustral
Plautine
Plautus
play
playa
playability
playable
playback
playbill
playbook
playbox
playboy
playboyism
playbroker
playcraft
playcraftsman
playday
playdown
player
playerdom
playeress
playfellow
playfellowship
playfield
playfolk
playful
playfully
playfulness
playgoer
playgoing
playground
playhouse
playingly
playless
playlet
playlike
playmaker
playmaking
playman
playmare
playmate
playmonger
playmongering
playock
playpen
playreader
playroom
playscript
playsome
playsomely
playsomeness
playstead
plaything
playtime
playward
playwoman
playwork
playwright
playwrightess
playwrighting
playwrightry
playwriter
playwriting
plaza
plazolite
plea
pleach
pleached
pleacher
plead
pleadable
pleadableness
pleader
pleading
pleadingly
pleadingness
pleaproof
pleasable
pleasableness
pleasance
pleasant
pleasantable
pleasantish
pleasantly
pleasantness
pleasantry
pleasantsome
please
pleasedly
pleasedness
pleaseman
pleaser
pleaship
pleasing
pleasingly
pleasingness
pleasurability
pleasurable
pleasurableness
pleasurably
pleasure
pleasureful
pleasurehood
pleasureless
pleasurelessly
pleasureman
pleasurement
pleasuremonger
pleasureproof
pleasurer
pleasuring
pleasurist
pleasurous
pleat
pleater
pleatless
pleb
plebe
plebeian
plebeiance
plebeianize
plebeianly
plebeianness
plebeity
plebianism
plebicolar
plebicolist
plebificate
plebification
plebify
plebiscitarian
plebiscitarism
plebiscitary
plebiscite
plebiscitic
plebiscitum
plebs
pleck
Plecoptera
plecopteran
plecopterid
plecopterous
Plecotinae
plecotine
Plecotus
plectognath
Plectognathi
plectognathic
plectognathous
plectopter
plectopteran
plectopterous
plectospondyl
Plectospondyli
plectospondylous
plectre
plectridial
plectridium
plectron
plectrum
pled
pledge
pledgeable
pledgee
pledgeless
pledgeor
pledger
pledgeshop
pledget
pledgor
Plegadis
plegaphonia
plegometer
Pleiades
pleiobar
pleiochromia
pleiochromic
pleiomastia
pleiomazia
pleiomerous
pleiomery
pleion
Pleione
pleionian
pleiophyllous
pleiophylly
pleiotaxis
pleiotropic
pleiotropically
pleiotropism
Pleistocene
Pleistocenic
pleistoseist
plemochoe
plemyrameter
plenarily
plenariness
plenarium
plenarty
plenary
plenicorn
pleniloquence
plenilunal
plenilunar
plenilunary
plenilune
plenipo
plenipotence
plenipotent
plenipotential
plenipotentiality
plenipotentiarily
plenipotentiarize
Plenipotentiary
plenipotentiary
plenipotentiaryship
plenish
plenishing
plenishment
plenism
plenist
plenitide
plenitude
plenitudinous
plenshing
plenteous
plenteously
plenteousness
plentiful
plentifully
plentifulness
plentify
plenty
plenum
pleny
pleochroic
pleochroism
pleochroitic
pleochromatic
pleochromatism
pleochroous
pleocrystalline
pleodont
pleomastia
pleomastic
pleomazia
pleometrosis
pleometrotic
pleomorph
pleomorphic
pleomorphism
pleomorphist
pleomorphous
pleomorphy
pleon
pleonal
pleonasm
pleonast
pleonaste
pleonastic
pleonastical
pleonastically
pleonectic
pleonexia
pleonic
pleophyletic
pleopod
pleopodite
Pleospora
Pleosporaceae
plerergate
plerocercoid
pleroma
pleromatic
plerome
pleromorph
plerophoric
plerophory
plerosis
plerotic
Plesianthropus
plesiobiosis
plesiobiotic
plesiomorphic
plesiomorphism
plesiomorphous
plesiosaur
Plesiosauri
Plesiosauria
plesiosaurian
plesiosauroid
Plesiosaurus
plesiotype
plessigraph
plessimeter
plessimetric
plessimetry
plessor
Plethodon
plethodontid
Plethodontidae
plethora
plethoretic
plethoretical
plethoric
plethorical
plethorically
plethorous
plethory
plethysmograph
plethysmographic
plethysmographically
plethysmography
pleura
Pleuracanthea
Pleuracanthidae
Pleuracanthini
pleuracanthoid
Pleuracanthus
pleural
pleuralgia
pleuralgic
pleurapophysial
pleurapophysis
pleurectomy
pleurenchyma
pleurenchymatous
pleuric
pleuriseptate
pleurisy
pleurite
pleuritic
pleuritical
pleuritically
pleuritis
Pleurobrachia
Pleurobrachiidae
pleurobranch
pleurobranchia
pleurobranchial
pleurobranchiate
pleurobronchitis
Pleurocapsa
Pleurocapsaceae
pleurocapsaceous
pleurocarp
Pleurocarpi
pleurocarpous
pleurocele
pleurocentesis
pleurocentral
pleurocentrum
Pleurocera
pleurocerebral
Pleuroceridae
pleuroceroid
Pleurococcaceae
pleurococcaceous
Pleurococcus
Pleurodelidae
Pleurodira
pleurodiran
pleurodire
pleurodirous
pleurodiscous
pleurodont
pleurodynia
pleurodynic
pleurogenic
pleurogenous
pleurohepatitis
pleuroid
pleurolith
pleurolysis
pleuron
Pleuronectes
pleuronectid
Pleuronectidae
pleuronectoid
Pleuronema
pleuropedal
pleuropericardial
pleuropericarditis
pleuroperitonaeal
pleuroperitoneal
pleuroperitoneum
pleuropneumonia
pleuropneumonic
pleuropodium
pleuropterygian
Pleuropterygii
pleuropulmonary
pleurorrhea
Pleurosaurus
Pleurosigma
pleurospasm
pleurosteal
Pleurosteon
pleurostict
Pleurosticti
Pleurostigma
pleurothotonic
pleurothotonus
Pleurotoma
Pleurotomaria
Pleurotomariidae
pleurotomarioid
Pleurotomidae
pleurotomine
pleurotomoid
pleurotomy
pleurotonic
pleurotonus
Pleurotremata
pleurotribal
pleurotribe
pleurotropous
Pleurotus
pleurotyphoid
pleurovisceral
pleurum
pleuston
pleustonic
plew
plex
plexal
plexicose
plexiform
pleximeter
pleximetric
pleximetry
plexodont
plexometer
plexor
plexure
plexus
pliability
pliable
pliableness
pliably
pliancy
pliant
pliantly
pliantness
plica
plicable
plical
plicate
plicated
plicately
plicateness
plicater
plicatile
plication
plicative
plicatocontorted
plicatocristate
plicatolacunose
plicatolobate
plicatopapillose
plicator
plicatoundulate
plicatulate
plicature
pliciferous
pliciform
plied
plier
plies
pliers
plight
plighted
plighter
plim
plimsoll
Plinian
plinth
plinther
plinthiform
plinthless
plinthlike
Pliny
Plinyism
Pliocene
Pliohippus
Pliopithecus
pliosaur
pliosaurian
Pliosauridae
Pliosaurus
pliothermic
Pliotron
pliskie
plisky
ploat
ploce
Ploceidae
ploceiform
Ploceinae
Ploceus
plock
plod
plodder
plodderly
plodding
ploddingly
ploddingness
plodge
Ploima
ploimate
plomb
plook
plop
ploration
ploratory
plosion
plosive
plot
plote
plotful
Plotinian
Plotinic
Plotinical
Plotinism
Plotinist
Plotinize
plotless
plotlessness
plotproof
plottage
plotted
plotter
plottery
plotting
plottingly
plotty
plough
ploughmanship
ploughtail
plouk
plouked
plouky
plounce
plousiocracy
plout
Plouteneion
plouter
plover
ploverlike
plovery
plow
plowable
plowbote
plowboy
plower
plowfish
plowfoot
plowgang
plowgate
plowgraith
plowhead
plowing
plowjogger
plowland
plowlight
plowline
plowmaker
plowman
plowmanship
plowmell
plowpoint
Plowrightia
plowshare
plowshoe
plowstaff
plowstilt
plowtail
plowwise
plowwoman
plowwright
ploy
ployment
Pluchea
pluck
pluckage
plucked
pluckedness
plucker
Pluckerian
pluckily
pluckiness
pluckless
plucklessness
plucky
plud
pluff
pluffer
pluffy
plug
plugboard
plugdrawer
pluggable
plugged
plugger
plugging
pluggingly
pluggy
plughole
plugless
pluglike
plugman
plugtray
plugtree
plum
pluma
plumaceous
plumach
plumade
plumage
plumaged
plumagery
plumasite
plumate
Plumatella
plumatellid
Plumatellidae
plumatelloid
plumb
plumbable
plumbage
Plumbaginaceae
plumbaginaceous
plumbagine
plumbaginous
plumbago
plumbate
plumbean
plumbeous
plumber
plumbership
plumbery
plumbet
plumbic
plumbiferous
plumbing
plumbism
plumbisolvent
plumbite
plumbless
plumbness
plumbog
plumbojarosite
plumboniobate
plumbosolvency
plumbosolvent
plumbous
plumbum
plumcot
plumdamas
plumdamis
plume
plumed
plumeless
plumelet
plumelike
plumemaker
plumemaking
plumeopicean
plumeous
plumer
plumery
plumet
plumette
plumicorn
plumier
Plumiera
plumieride
plumification
plumiform
plumiformly
plumify
plumigerous
pluminess
plumiped
plumipede
plumist
plumless
plumlet
plumlike
plummer
plummet
plummeted
plummetless
plummy
plumose
plumosely
plumoseness
plumosity
plumous
plump
plumpen
plumper
plumping
plumpish
plumply
plumpness
plumps
plumpy
plumula
plumulaceous
plumular
Plumularia
plumularian
Plumulariidae
plumulate
plumule
plumuliform
plumulose
plumy
plunder
plunderable
plunderage
plunderbund
plunderer
plunderess
plundering
plunderingly
plunderless
plunderous
plunderproof
plunge
plunger
plunging
plungingly
plunk
plunther
plup
plupatriotic
pluperfect
pluperfectly
pluperfectness
plural
pluralism
pluralist
pluralistic
pluralistically
plurality
pluralization
pluralize
pluralizer
plurally
plurative
plurennial
pluriaxial
pluricarinate
pluricarpellary
pluricellular
pluricentral
pluricipital
pluricuspid
pluricuspidate
pluridentate
pluries
plurifacial
plurifetation
plurification
pluriflagellate
pluriflorous
plurifoliate
plurifoliolate
plurify
pluriglandular
pluriguttulate
plurilateral
plurilingual
plurilingualism
plurilingualist
plurilocular
plurimammate
plurinominal
plurinucleate
pluripara
pluriparity
pluriparous
pluripartite
pluripetalous
pluripotence
pluripotent
pluripresence
pluriseptate
pluriserial
pluriseriate
pluriseriated
plurisetose
plurispiral
plurisporous
plurisyllabic
plurisyllable
plurivalent
plurivalve
plurivorous
plurivory
plus
plush
plushed
plushette
plushily
plushiness
plushlike
plushy
Plusia
Plusiinae
plusquamperfect
plussage
Plutarchian
Plutarchic
Plutarchical
Plutarchically
plutarchy
pluteal
plutean
pluteiform
Plutella
pluteus
Pluto
plutocracy
plutocrat
plutocratic
plutocratical
plutocratically
plutolatry
plutological
plutologist
plutology
plutomania
Plutonian
plutonian
plutonic
Plutonion
plutonism
plutonist
plutonite
Plutonium
plutonium
plutonometamorphism
plutonomic
plutonomist
plutonomy
pluvial
pluvialiform
pluvialine
Pluvialis
pluvian
pluvine
pluviograph
pluviographic
pluviographical
pluviography
pluviometer
pluviometric
pluviometrical
pluviometrically
pluviometry
pluvioscope
pluviose
pluviosity
pluvious
ply
plyer
plying
plyingly
Plymouth
Plymouthism
Plymouthist
Plymouthite
Plynlymmon
plywood
pneodynamics
pneograph
pneomanometer
pneometer
pneometry
pneophore
pneoscope
pneuma
pneumarthrosis
pneumathaemia
pneumatic
pneumatical
pneumatically
pneumaticity
pneumatics
pneumatism
pneumatist
pneumatize
pneumatized
pneumatocardia
pneumatocele
pneumatochemical
pneumatochemistry
pneumatocyst
pneumatocystic
pneumatode
pneumatogenic
pneumatogenous
pneumatogram
pneumatograph
pneumatographer
pneumatographic
pneumatography
pneumatolitic
pneumatologic
pneumatological
pneumatologist
pneumatology
pneumatolysis
pneumatolytic
Pneumatomachian
Pneumatomachist
Pneumatomachy
pneumatometer
pneumatometry
pneumatomorphic
pneumatonomy
pneumatophany
pneumatophilosophy
pneumatophobia
pneumatophonic
pneumatophony
pneumatophore
pneumatophorous
pneumatorrhachis
pneumatoscope
pneumatosic
pneumatosis
pneumatotactic
pneumatotherapeutics
pneumatotherapy
Pneumatria
pneumaturia
pneumectomy
pneumobacillus
Pneumobranchia
Pneumobranchiata
pneumocele
pneumocentesis
pneumochirurgia
pneumococcal
pneumococcemia
pneumococcic
pneumococcous
pneumococcus
pneumoconiosis
pneumoderma
pneumodynamic
pneumodynamics
pneumoencephalitis
pneumoenteritis
pneumogastric
pneumogram
pneumograph
pneumographic
pneumography
pneumohemothorax
pneumohydropericardium
pneumohydrothorax
pneumolith
pneumolithiasis
pneumological
pneumology
pneumolysis
pneumomalacia
pneumomassage
Pneumometer
pneumomycosis
pneumonalgia
pneumonectasia
pneumonectomy
pneumonedema
pneumonia
pneumonic
pneumonitic
pneumonitis
pneumonocace
pneumonocarcinoma
pneumonocele
pneumonocentesis
pneumonocirrhosis
pneumonoconiosis
pneumonodynia
pneumonoenteritis
pneumonoerysipelas
pneumonographic
pneumonography
pneumonokoniosis
pneumonolith
pneumonolithiasis
pneumonolysis
pneumonomelanosis
pneumonometer
pneumonomycosis
pneumonoparesis
pneumonopathy
pneumonopexy
pneumonophorous
pneumonophthisis
pneumonopleuritis
pneumonorrhagia
pneumonorrhaphy
pneumonosis
pneumonotherapy
pneumonotomy
pneumony
pneumopericardium
pneumoperitoneum
pneumoperitonitis
pneumopexy
pneumopleuritis
pneumopyothorax
pneumorrachis
pneumorrhachis
pneumorrhagia
pneumotactic
pneumotherapeutics
pneumotherapy
pneumothorax
pneumotomy
pneumotoxin
pneumotropic
pneumotropism
pneumotyphoid
pneumotyphus
pneumoventriculography
Po
po
Poa
Poaceae
poaceous
poach
poachable
poacher
poachiness
poachy
Poales
poalike
pob
pobby
Poblacht
poblacion
pobs
pochade
pochard
pochay
poche
pochette
pocilliform
pock
pocket
pocketable
pocketableness
pocketbook
pocketed
pocketer
pocketful
pocketing
pocketknife
pocketless
pocketlike
pockety
pockhouse
pockily
pockiness
pockmanteau
pockmantie
pockmark
pockweed
pockwood
pocky
poco
pococurante
pococuranteism
pococurantic
pococurantish
pococurantism
pococurantist
pocosin
poculary
poculation
poculent
poculiform
pod
podagra
podagral
podagric
podagrical
podagrous
podal
podalgia
podalic
Podaliriidae
Podalirius
Podarge
Podargidae
Podarginae
podargine
podargue
Podargus
podarthral
podarthritis
podarthrum
podatus
Podaxonia
podaxonial
podded
podder
poddidge
poddish
poddle
poddy
podelcoma
podeon
podesta
podesterate
podetiiform
podetium
podex
podge
podger
podgily
podginess
podgy
podial
podiatrist
podiatry
podical
Podiceps
podices
Podicipedidae
podilegous
podite
poditic
poditti
podium
podler
podley
podlike
podobranch
podobranchia
podobranchial
podobranchiate
podocarp
Podocarpaceae
Podocarpineae
podocarpous
Podocarpus
podocephalous
pododerm
pododynia
podogyn
podogyne
podogynium
Podolian
podolite
podology
podomancy
podomere
podometer
podometry
Podophrya
Podophryidae
Podophthalma
Podophthalmata
podophthalmate
podophthalmatous
Podophthalmia
podophthalmian
podophthalmic
podophthalmite
podophthalmitic
podophthalmous
Podophyllaceae
podophyllic
podophyllin
podophyllotoxin
podophyllous
Podophyllum
podophyllum
podoscaph
podoscapher
podoscopy
Podosomata
podosomatous
podosperm
Podosphaera
Podostemaceae
podostemaceous
podostemad
Podostemon
Podostemonaceae
podostemonaceous
Podostomata
podostomatous
podotheca
podothecal
Podozamites
Podsnap
Podsnappery
podsol
podsolic
podsolization
podsolize
Podunk
Podura
poduran
podurid
Poduridae
podware
podzol
podzolic
podzolization
podzolize
poe
Poecile
Poeciliidae
poecilitic
Poecilocyttares
poecilocyttarous
poecilogonous
poecilogony
poecilomere
poecilonym
poecilonymic
poecilonymy
poecilopod
Poecilopoda
poecilopodous
poem
poematic
poemet
poemlet
Poephaga
poephagous
Poephagus
poesie
poesiless
poesis
poesy
poet
poetaster
poetastering
poetasterism
poetastery
poetastress
poetastric
poetastrical
poetastry
poetcraft
poetdom
poetesque
poetess
poethood
poetic
poetical
poeticality
poetically
poeticalness
poeticism
poeticize
poeticness
poetics
poeticule
poetito
poetization
poetize
poetizer
poetless
poetlike
poetling
poetly
poetomachia
poetress
poetry
poetryless
poetship
poetwise
pogamoggan
pogge
poggy
Pogo
Pogonatum
Pogonia
pogoniasis
pogoniate
pogonion
pogonip
pogoniris
pogonite
pogonological
pogonologist
pogonology
pogonotomy
pogonotrophy
pogrom
pogromist
pogromize
pogy
poh
poha
pohickory
pohna
pohutukawa
poi
Poiana
Poictesme
poietic
poignance
poignancy
poignant
poignantly
poignet
poikilitic
poikiloblast
poikiloblastic
poikilocyte
poikilocythemia
poikilocytosis
poikilotherm
poikilothermic
poikilothermism
poil
poilu
poimenic
poimenics
Poinciana
poind
poindable
poinder
poinding
Poinsettia
point
pointable
pointage
pointed
pointedly
pointedness
pointel
pointer
pointful
pointfully
pointfulness
pointillism
pointillist
pointing
pointingly
pointless
pointlessly
pointlessness
pointlet
pointleted
pointmaker
pointman
pointment
pointrel
pointsman
pointswoman
pointways
pointwise
pointy
poisable
poise
poised
poiser
poison
poisonable
poisonful
poisonfully
poisoning
poisonless
poisonlessness
poisonmaker
poisonous
poisonously
poisonousness
poisonproof
poisonweed
poisonwood
poitrail
poitrel
poivrade
pokable
Pokan
Pokanoket
poke
pokeberry
poked
pokeful
pokeloken
pokeout
poker
pokerish
pokerishly
pokerishness
pokeroot
pokeweed
pokey
pokily
pokiness
poking
Pokom
Pokomam
Pokomo
pokomoo
Pokonchi
pokunt
poky
pol
Polab
Polabian
Polabish
polacca
Polack
polack
polacre
Polander
Polanisia
polar
polaric
Polarid
polarigraphic
polarimeter
polarimetric
polarimetry
Polaris
polariscope
polariscopic
polariscopically
polariscopist
polariscopy
polaristic
polaristrobometer
polarity
polarizability
polarizable
polarization
polarize
polarizer
polarly
polarogram
polarograph
polarographic
polarographically
polarography
Polaroid
polarward
polaxis
poldavis
poldavy
polder
polderboy
polderman
Pole
pole
polearm
poleax
poleaxe
poleaxer
poleburn
polecat
polehead
poleless
poleman
polemarch
polemic
polemical
polemically
polemician
polemicist
polemics
polemist
polemize
Polemoniaceae
polemoniaceous
Polemoniales
Polemonium
polemoscope
polenta
poler
polesetter
Polesian
polesman
polestar
poleward
polewards
poley
poliad
poliadic
Polian
polianite
Polianthes
police
policed
policedom
policeless
policeman
policemanish
policemanism
policemanlike
policemanship
policewoman
Polichinelle
policial
policize
policizer
policlinic
policy
policyholder
poliencephalitis
poliencephalomyelitis
poligar
poligarship
poligraphical
Polinices
polio
polioencephalitis
polioencephalomyelitis
poliomyelitis
poliomyelopathy
polioneuromere
poliorcetic
poliorcetics
poliosis
polis
Polish
polish
polishable
polished
polishedly
polishedness
polisher
polishment
polisman
polissoir
Polistes
politarch
politarchic
Politbureau
Politburo
polite
politeful
politely
politeness
politesse
politic
political
politicalism
politicalize
politically
politicaster
politician
politicious
politicist
politicize
politicizer
politicly
politico
politicomania
politicophobia
politics
politied
Politique
politist
politize
polity
politzerization
politzerize
polk
polka
Poll
poll
pollable
pollack
polladz
pollage
pollakiuria
pollam
pollan
pollarchy
pollard
pollbook
polled
pollen
pollened
polleniferous
pollenigerous
pollenite
pollenivorous
pollenless
pollenlike
pollenproof
pollent
poller
polleten
pollex
pollical
pollicar
pollicate
pollicitation
pollinar
pollinarium
pollinate
pollination
pollinator
pollinctor
pollincture
polling
pollinia
pollinic
pollinical
polliniferous
pollinigerous
pollinium
pollinivorous
pollinization
pollinize
pollinizer
pollinodial
pollinodium
pollinoid
pollinose
pollinosis
polliwig
polliwog
pollock
polloi
pollster
pollucite
pollutant
pollute
polluted
pollutedly
pollutedness
polluter
polluting
pollutingly
pollution
Pollux
pollux
Polly
Pollyanna
Pollyannish
pollywog
polo
poloconic
polocyte
poloist
polonaise
Polonese
Polonia
Polonial
Polonian
Polonism
polonium
Polonius
Polonization
Polonize
polony
polos
polska
polt
poltergeist
poltfoot
poltfooted
poltina
poltinnik
poltophagic
poltophagist
poltophagy
poltroon
poltroonery
poltroonish
poltroonishly
poltroonism
poluphloisboic
poluphloisboiotatotic
poluphloisboiotic
polverine
poly
polyacanthus
polyacid
polyacoustic
polyacoustics
polyact
polyactinal
polyactine
Polyactinia
polyad
polyadelph
Polyadelphia
polyadelphian
polyadelphous
polyadenia
polyadenitis
polyadenoma
polyadenous
polyadic
polyaffectioned
polyalcohol
polyamide
polyamylose
Polyandria
polyandria
polyandrian
polyandrianism
polyandric
polyandrious
polyandrism
polyandrist
polyandrium
polyandrous
polyandry
Polyangium
polyangular
polyantha
polyanthous
polyanthus
polyanthy
polyarch
polyarchal
polyarchical
polyarchist
polyarchy
polyarteritis
polyarthric
polyarthritic
polyarthritis
polyarthrous
polyarticular
polyatomic
polyatomicity
polyautographic
polyautography
polyaxial
polyaxon
polyaxone
polyaxonic
polybasic
polybasicity
polybasite
polyblast
Polyborinae
polyborine
Polyborus
polybranch
Polybranchia
polybranchian
Polybranchiata
polybranchiate
polybromid
polybromide
polybunous
polybuny
polybuttoned
polycarboxylic
Polycarp
polycarpellary
polycarpic
Polycarpon
polycarpous
polycarpy
polycellular
polycentral
polycentric
polycephalic
polycephalous
polycephaly
Polychaeta
polychaete
polychaetous
polychasial
polychasium
polychloride
polychoerany
polychord
polychotomous
polychotomy
polychrest
polychrestic
polychrestical
polychresty
polychroic
polychroism
polychromasia
polychromate
polychromatic
polychromatism
polychromatist
polychromatize
polychromatophil
polychromatophile
polychromatophilia
polychromatophilic
polychrome
polychromia
polychromic
polychromism
polychromize
polychromous
polychromy
polychronious
polyciliate
polycitral
polyclad
Polycladida
polycladine
polycladose
polycladous
polyclady
Polycletan
polyclinic
polyclona
polycoccous
Polycodium
polyconic
polycormic
polycotyl
polycotyledon
polycotyledonary
polycotyledonous
polycotyledony
polycotylous
polycotyly
polycracy
polycrase
polycratic
polycrotic
polycrotism
polycrystalline
polyctenid
Polyctenidae
polycttarian
polycyanide
polycyclic
polycycly
polycyesis
polycystic
polycythemia
polycythemic
Polycyttaria
polydactyl
polydactyle
polydactylism
polydactylous
Polydactylus
polydactyly
polydaemoniac
polydaemonism
polydaemonist
polydaemonistic
polydemic
polydenominational
polydental
polydermous
polydermy
polydigital
polydimensional
polydipsia
polydisperse
polydomous
polydymite
polydynamic
polyeidic
polyeidism
polyembryonate
polyembryonic
polyembryony
polyemia
polyemic
polyenzymatic
polyergic
Polyergus
polyester
polyesthesia
polyesthetic
polyethnic
polyethylene
polyfenestral
polyflorous
polyfoil
polyfold
Polygala
Polygalaceae
polygalaceous
polygalic
polygam
Polygamia
polygamian
polygamic
polygamical
polygamically
polygamist
polygamistic
polygamize
polygamodioecious
polygamous
polygamously
polygamy
polyganglionic
polygastric
polygene
polygenesic
polygenesis
polygenesist
polygenetic
polygenetically
polygenic
polygenism
polygenist
polygenistic
polygenous
polygeny
polyglandular
polyglobulia
polyglobulism
polyglossary
polyglot
polyglotry
polyglottal
polyglottally
polyglotted
polyglotter
polyglottery
polyglottic
polyglottically
polyglottism
polyglottist
polyglottonic
polyglottous
polyglotwise
polyglycerol
polygon
Polygonaceae
polygonaceous
polygonal
Polygonales
polygonally
Polygonatum
Polygonella
polygoneutic
polygoneutism
Polygonia
polygonic
polygonically
polygonoid
polygonous
Polygonum
polygony
Polygordius
polygram
polygrammatic
polygraph
polygrapher
polygraphic
polygraphy
polygroove
polygrooved
polygyn
polygynaiky
Polygynia
polygynian
polygynic
polygynious
polygynist
polygynoecial
polygynous
polygyny
polygyral
polygyria
polyhaemia
polyhaemic
polyhalide
polyhalite
polyhalogen
polyharmonic
polyharmony
polyhedral
polyhedric
polyhedrical
polyhedroid
polyhedron
polyhedrosis
polyhedrous
polyhemia
polyhidrosis
polyhistor
polyhistorian
polyhistoric
polyhistory
polyhybrid
polyhydric
polyhydroxy
polyideic
polyideism
polyidrosis
polyiodide
polykaryocyte
polylaminated
polylemma
polylepidous
polylinguist
polylith
polylithic
polylobular
polylogy
polyloquent
polymagnet
polymastia
polymastic
Polymastiga
polymastigate
Polymastigida
Polymastigina
polymastigous
polymastism
Polymastodon
polymastodont
polymasty
polymath
polymathic
polymathist
polymathy
polymazia
polymelia
polymelian
polymely
polymer
polymere
polymeria
polymeric
polymeride
polymerism
polymerization
polymerize
polymerous
polymetallism
polymetameric
polymeter
polymethylene
polymetochia
polymetochic
polymicrian
polymicrobial
polymicrobic
polymicroscope
polymignite
Polymixia
polymixiid
Polymixiidae
Polymnestor
Polymnia
polymnite
polymolecular
polymolybdate
polymorph
Polymorpha
polymorphean
polymorphic
polymorphism
polymorphistic
polymorphonuclear
polymorphonucleate
polymorphosis
polymorphous
polymorphy
Polymyaria
polymyarian
Polymyarii
Polymyodi
polymyodian
polymyodous
polymyoid
polymyositis
polymythic
polymythy
polynaphthene
polynemid
Polynemidae
polynemoid
Polynemus
Polynesian
polynesic
polyneural
polyneuric
polyneuritic
polyneuritis
polyneuropathy
polynodal
Polynoe
polynoid
Polynoidae
polynome
polynomial
polynomialism
polynomialist
polynomic
polynucleal
polynuclear
polynucleate
polynucleated
polynucleolar
polynucleosis
Polyodon
polyodont
polyodontal
polyodontia
Polyodontidae
polyodontoid
polyoecious
polyoeciously
polyoeciousness
polyoecism
polyoecy
polyoicous
polyommatous
polyonomous
polyonomy
polyonychia
polyonym
polyonymal
polyonymic
polyonymist
polyonymous
polyonymy
polyophthalmic
polyopia
polyopic
polyopsia
polyopsy
polyorama
polyorchidism
polyorchism
polyorganic
polyose
polyoxide
polyoxymethylene
polyp
polypage
polypaged
polypapilloma
polyparasitic
polyparasitism
polyparesis
polyparia
polyparian
polyparium
polyparous
polypary
polypean
polyped
Polypedates
polypeptide
polypetal
Polypetalae
polypetalous
Polyphaga
polyphage
polyphagia
polyphagian
polyphagic
polyphagist
polyphagous
polyphagy
polyphalangism
polypharmacal
polypharmacist
polypharmacon
polypharmacy
polypharmic
polyphasal
polyphase
polyphaser
Polypheme
polyphemian
polyphemic
polyphemous
polyphenol
polyphloesboean
polyphloisboioism
polyphloisboism
polyphobia
polyphobic
polyphone
polyphoned
polyphonia
polyphonic
polyphonical
polyphonism
polyphonist
polyphonium
polyphonous
polyphony
polyphore
polyphosphoric
polyphotal
polyphote
polyphylesis
polyphyletic
polyphyletically
polyphylety
polyphylline
polyphyllous
polyphylly
polyphylogeny
polyphyly
polyphyodont
Polypi
polypi
polypian
polypide
polypidom
Polypifera
polypiferous
polypigerous
polypinnate
polypite
Polyplacophora
polyplacophoran
polyplacophore
polyplacophorous
polyplastic
Polyplectron
polyplegia
polyplegic
polyploid
polyploidic
polyploidy
polypnoea
polypnoeic
polypod
Polypoda
polypodia
Polypodiaceae
polypodiaceous
Polypodium
polypodous
polypody
polypoid
polypoidal
Polypomorpha
polypomorphic
Polyporaceae
polyporaceous
polypore
polyporite
polyporoid
polyporous
Polyporus
polypose
polyposis
polypotome
polypous
polypragmacy
polypragmatic
polypragmatical
polypragmatically
polypragmatism
polypragmatist
polypragmaty
polypragmist
polypragmon
polypragmonic
polypragmonist
polyprene
polyprism
polyprismatic
polyprothetic
polyprotodont
Polyprotodontia
polypseudonymous
polypsychic
polypsychical
polypsychism
polypterid
Polypteridae
polypteroid
Polypterus
polyptote
polyptoton
polyptych
polypus
polyrhizal
polyrhizous
polyrhythmic
polyrhythmical
polysaccharide
polysaccharose
Polysaccum
polysalicylide
polysarcia
polysarcous
polyschematic
polyschematist
polyscope
polyscopic
polysemant
polysemantic
polysemeia
polysemia
polysemous
polysemy
polysensuous
polysensuousness
polysepalous
polyseptate
polyserositis
polysided
polysidedness
polysilicate
polysilicic
Polysiphonia
polysiphonic
polysiphonous
polysomatic
polysomatous
polysomaty
polysomia
polysomic
polysomitic
polysomous
polysomy
polyspast
polyspaston
polyspermal
polyspermatous
polyspermia
polyspermic
polyspermous
polyspermy
polyspondylic
polyspondylous
polyspondyly
Polyspora
polysporangium
polyspore
polyspored
polysporic
polysporous
polystachyous
polystaurion
polystele
polystelic
polystemonous
polystichoid
polystichous
Polystichum
Polystictus
Polystomata
Polystomatidae
polystomatous
polystome
Polystomea
Polystomella
Polystomidae
polystomium
polystylar
polystyle
polystylous
polystyrene
polysulphide
polysulphuration
polysulphurization
polysyllabic
polysyllabical
polysyllabically
polysyllabicism
polysyllabicity
polysyllabism
polysyllable
polysyllogism
polysyllogistic
polysymmetrical
polysymmetrically
polysymmetry
polysyndetic
polysyndetically
polysyndeton
polysynthesis
polysynthesism
polysynthetic
polysynthetical
polysynthetically
polysyntheticism
polysynthetism
polysynthetize
polytechnic
polytechnical
polytechnics
polytechnist
polyterpene
Polythalamia
polythalamian
polythalamic
polythalamous
polythecial
polytheism
polytheist
polytheistic
polytheistical
polytheistically
polytheize
polythelia
polythelism
polythely
polythene
polythionic
polytitanic
polytocous
polytokous
polytoky
polytomous
polytomy
polytonal
polytonalism
polytonality
polytone
polytonic
polytony
polytope
polytopic
polytopical
Polytrichaceae
polytrichaceous
polytrichia
polytrichous
Polytrichum
polytrochal
polytrochous
polytrope
polytrophic
polytropic
polytungstate
polytungstic
polytype
polytypic
polytypical
polytypy
polyuresis
polyuria
polyuric
polyvalence
polyvalent
polyvinyl
polyvinylidene
polyvirulent
polyvoltine
Polyzoa
polyzoal
polyzoan
polyzoarial
polyzoarium
polyzoary
polyzoic
polyzoism
polyzonal
polyzooid
polyzoon
polzenite
pom
pomace
Pomaceae
pomacentrid
Pomacentridae
pomacentroid
Pomacentrus
pomaceous
pomade
Pomaderris
Pomak
pomander
pomane
pomarine
pomarium
pomate
pomato
pomatomid
Pomatomidae
Pomatomus
pomatorhine
pomatum
pombe
pombo
pome
pomegranate
pomelo
Pomeranian
pomeridian
pomerium
pomewater
pomey
pomfret
pomiculture
pomiculturist
pomiferous
pomiform
pomivorous
Pommard
pomme
pommee
pommel
pommeled
pommeler
pommet
pommey
pommy
Pomo
pomological
pomologically
pomologist
pomology
Pomona
pomonal
pomonic
pomp
pompa
Pompadour
pompadour
pompal
pompano
Pompeian
Pompeii
pompelmous
Pompey
pompey
pompholix
pompholygous
pompholyx
pomphus
pompier
pompilid
Pompilidae
pompiloid
Pompilus
pompion
pompist
pompless
pompoleon
pompon
pomposity
pompous
pompously
pompousness
pompster
Pomptine
pomster
pon
Ponca
ponce
ponceau
poncelet
poncho
ponchoed
Poncirus
pond
pondage
pondbush
ponder
ponderability
ponderable
ponderableness
ponderal
ponderance
ponderancy
ponderant
ponderary
ponderate
ponderation
ponderative
ponderer
pondering
ponderingly
ponderling
ponderment
ponderomotive
ponderosapine
ponderosity
ponderous
ponderously
ponderousness
pondfish
pondful
pondgrass
pondlet
pondman
Pondo
pondok
pondokkie
Pondomisi
pondside
pondus
pondweed
pondwort
pondy
pone
ponent
Ponera
Poneramoeba
ponerid
Poneridae
Ponerinae
ponerine
poneroid
ponerology
poney
pong
ponga
pongee
Pongidae
Pongo
poniard
ponica
ponier
ponja
pont
Pontac
Pontacq
pontage
pontal
Pontederia
Pontederiaceae
pontederiaceous
pontee
pontes
pontianak
Pontic
pontic
ponticello
ponticular
ponticulus
pontifex
pontiff
pontific
pontifical
pontificalia
pontificalibus
pontificality
pontifically
pontificate
pontification
pontifices
pontificial
pontificially
pontificious
pontify
pontil
pontile
pontin
Pontine
pontine
pontist
pontlevis
ponto
Pontocaspian
pontocerebellar
ponton
pontonier
pontoon
pontooneer
pontooner
pontooning
Pontus
pontvolant
pony
ponzite
pooa
pooch
pooder
poodle
poodledom
poodleish
poodleship
poof
poogye
pooh
poohpoohist
pook
pooka
pookaun
pookoo
pool
pooler
pooli
poolroom
poolroot
poolside
poolwort
pooly
poon
poonac
poonga
poonghie
poop
pooped
poophyte
poophytic
poor
poorhouse
poorish
poorliness
poorling
poorly
poorlyish
poormaster
poorness
poorweed
poorwill
poot
Pop
pop
popadam
popal
popcorn
popdock
pope
Popean
popedom
popeholy
popehood
popeism
popeler
popeless
popelike
popeline
popely
popery
popeship
popess
popeye
popeyed
popglove
popgun
popgunner
popgunnery
Popian
popify
popinac
popinjay
Popish
popish
popishly
popishness
popjoy
poplar
poplared
Poplilia
poplin
poplinette
popliteal
popliteus
poplolly
Popocracy
Popocrat
Popolari
Popoloco
popomastic
popover
Popovets
poppa
poppability
poppable
poppean
poppel
popper
poppet
poppethead
poppied
poppin
popple
popply
poppy
poppycock
poppycockish
poppyfish
poppyhead
poppylike
poppywort
popshop
populace
popular
popularism
Popularist
popularity
popularization
popularize
popularizer
popularly
popularness
populate
population
populational
populationist
populationistic
populationless
populator
populicide
populin
Populism
Populist
Populistic
populous
populously
populousness
Populus
popweed
poral
porbeagle
porcate
porcated
porcelain
porcelainization
porcelainize
porcelainlike
porcelainous
porcelaneous
porcelanic
porcelanite
porcelanous
Porcellana
porcellanian
porcellanid
Porcellanidae
porcellanize
porch
porched
porching
porchless
porchlike
porcine
Porcula
porcupine
porcupinish
pore
pored
porelike
Porella
porencephalia
porencephalic
porencephalitis
porencephalon
porencephalous
porencephalus
porencephaly
porer
porge
porger
porgy
Poria
poricidal
Porifera
poriferal
poriferan
poriferous
poriform
porimania
poriness
poring
poringly
poriomanic
porism
porismatic
porismatical
porismatically
poristic
poristical
porite
Porites
Poritidae
poritoid
pork
porkburger
porker
porkery
porket
porkfish
porkish
porkless
porkling
porkman
Porkopolis
porkpie
porkwood
porky
pornerastic
pornocracy
pornocrat
pornograph
pornographer
pornographic
pornographically
pornographist
pornography
pornological
Porocephalus
porodine
porodite
porogam
porogamic
porogamous
porogamy
porokaiwhiria
porokeratosis
Porokoto
poroma
porometer
porophyllous
poroplastic
poroporo
pororoca
poros
poroscope
poroscopic
poroscopy
porose
poroseness
porosimeter
porosis
porosity
porotic
porotype
porous
porously
porousness
porpentine
porphine
Porphyra
Porphyraceae
porphyraceous
porphyratin
Porphyrean
porphyria
Porphyrian
porphyrian
Porphyrianist
porphyrin
porphyrine
porphyrinuria
Porphyrio
porphyrion
porphyrite
porphyritic
porphyroblast
porphyroblastic
porphyrogene
porphyrogenite
porphyrogenitic
porphyrogenitism
porphyrogeniture
porphyrogenitus
porphyroid
porphyrophore
porphyrous
porphyry
Porpita
porpitoid
porpoise
porpoiselike
porporate
porr
porraceous
porrect
porrection
porrectus
porret
porridge
porridgelike
porridgy
porriginous
porrigo
Porrima
porringer
porriwiggle
porry
port
porta
portability
portable
portableness
portably
portage
portague
portahepatis
portail
portal
portaled
portalled
portalless
portamento
portance
portass
portatile
portative
portcrayon
portcullis
porteacid
ported
porteligature
portend
portendance
portendment
Porteno
portension
portent
portention
portentosity
portentous
portentously
portentousness
porteous
porter
porterage
Porteranthus
porteress
porterhouse
porterlike
porterly
portership
portfire
portfolio
portglaive
portglave
portgrave
Porthetria
Portheus
porthole
porthook
porthors
porthouse
Portia
portia
portico
porticoed
portiere
portiered
portifory
portify
portio
portiomollis
portion
portionable
portional
portionally
portioner
portionist
portionize
portionless
portitor
Portlandian
portlast
portless
portlet
portligature
portlily
portliness
portly
portman
portmanmote
portmanteau
portmanteaux
portmantle
portmantologism
portment
portmoot
porto
portoise
portolan
portolano
Portor
portrait
portraitist
portraitlike
portraiture
portray
portrayable
portrayal
portrayer
portrayist
portrayment
portreeve
portreeveship
portress
portside
portsider
portsman
portuary
portugais
Portugal
Portugalism
Portugee
Portuguese
Portulaca
Portulacaceae
portulacaceous
Portulacaria
portulan
Portunalia
portunian
Portunidae
Portunus
portway
porty
porule
porulose
porulous
porus
porwigle
pory
Porzana
posadaship
posca
pose
Poseidon
Poseidonian
posement
poser
poseur
posey
posh
posing
posingly
posit
position
positional
positioned
positioner
positionless
positival
positive
positively
positiveness
positivism
positivist
positivistic
positivistically
positivity
positivize
positor
positron
positum
positure
Posnanian
posnet
posole
posologic
posological
posologist
posology
pospolite
poss
posse
posseman
possess
possessable
possessed
possessedly
possessedness
possessing
possessingly
possessingness
possession
possessional
possessionalism
possessionalist
possessionary
possessionate
possessioned
possessioner
possessionist
possessionless
possessionlessness
possessival
possessive
possessively
possessiveness
possessor
possessoress
possessorial
possessoriness
possessorship
possessory
posset
possibilism
possibilist
possibilitate
possibility
possible
possibleness
possibly
possum
possumwood
post
postabdomen
postabdominal
postable
postabortal
postacetabular
postadjunct
postage
postal
postallantoic
postally
postalveolar
postament
postamniotic
postanal
postanesthetic
postantennal
postaortic
postapoplectic
postappendicular
postarterial
postarthritic
postarticular
postarytenoid
postaspirate
postaspirated
postasthmatic
postatrial
postauditory
postauricular
postaxiad
postaxial
postaxially
postaxillary
postbag
postbaptismal
postbox
postboy
postbrachial
postbrachium
postbranchial
postbreakfast
postbronchial
postbuccal
postbulbar
postbursal
postcaecal
postcalcaneal
postcalcarine
postcanonical
postcardiac
postcardinal
postcarnate
postcarotid
postcart
postcartilaginous
postcatarrhal
postcava
postcaval
postcecal
postcenal
postcentral
postcentrum
postcephalic
postcerebellar
postcerebral
postcesarean
postcibal
postclassic
postclassical
postclassicism
postclavicle
postclavicula
postclavicular
postclimax
postclitellian
postclival
postcolon
postcolonial
postcolumellar
postcomitial
postcommissural
postcommissure
postcommunicant
Postcommunion
postconceptive
postcondylar
postconfinement
postconnubial
postconsonantal
postcontact
postcontract
postconvalescent
postconvulsive
postcordial
postcornu
postcosmic
postcostal
postcoxal
postcritical
postcrural
postcubital
postdate
postdental
postdepressive
postdetermined
postdevelopmental
postdiagnostic
postdiaphragmatic
postdiastolic
postdicrotic
postdigestive
postdigital
postdiluvial
postdiluvian
postdiphtheric
postdiphtheritic
postdisapproved
postdisseizin
postdisseizor
postdoctoral
postdoctorate
postdural
postdysenteric
posted
posteen
postelection
postelementary
postembryonal
postembryonic
postemporal
postencephalitic
postencephalon
postenteral
postentry
postepileptic
poster
posterette
posteriad
posterial
posterior
posterioric
posteriorically
posterioristic
posterioristically
posteriority
posteriorly
posteriormost
posteriors
posteriorums
posterish
posterishness
posterist
posterity
posterize
postern
posteroclusion
posterodorsad
posterodorsal
posterodorsally
posteroexternal
posteroinferior
posterointernal
posterolateral
posteromedial
posteromedian
posteromesial
posteroparietal
posterosuperior
posterotemporal
posteroterminal
posteroventral
posteruptive
postesophageal
posteternity
postethmoid
postexilian
postexilic
postexist
postexistence
postexistency
postexistent
postface
postfact
postfebrile
postfemoral
postfetal
postfix
postfixal
postfixation
postfixed
postfixial
postflection
postflexion
postform
postfoveal
postfrontal
postfurca
postfurcal
postganglionic
postgangrenal
postgastric
postgeminum
postgenial
postgeniture
postglacial
postglenoid
postglenoidal
postgonorrheic
postgracile
postgraduate
postgrippal
posthabit
posthaste
posthemiplegic
posthemorrhagic
posthepatic
posthetomist
posthetomy
posthexaplaric
posthippocampal
posthitis
postholder
posthole
posthouse
posthumeral
posthumous
posthumously
posthumousness
posthumus
posthyoid
posthypnotic
posthypnotically
posthypophyseal
posthypophysis
posthysterical
postic
postical
postically
posticous
posticteric
posticum
postil
postilion
postilioned
postillate
postillation
postillator
postimpressionism
postimpressionist
postimpressionistic
postinfective
postinfluenzal
posting
postingly
postintestinal
postique
postischial
postjacent
postjugular
postlabial
postlachrymal
postlaryngeal
postlegitimation
postlenticular
postless
postlike
postliminary
postliminiary
postliminious
postliminium
postliminous
postliminy
postloitic
postloral
postlude
postludium
postluetic
postmalarial
postmamillary
postmammary
postman
postmandibular
postmaniacal
postmarital
postmark
postmarriage
postmaster
postmasterlike
postmastership
postmastoid
postmaturity
postmaxillary
postmaximal
postmeatal
postmedia
postmedial
postmedian
postmediastinal
postmediastinum
postmedullary
postmeiotic
postmeningeal
postmenstrual
postmental
postmeridian
postmeridional
postmesenteric
postmillenarian
postmillenarianism
postmillennial
postmillennialism
postmillennialist
postmillennian
postmineral
postmistress
postmortal
postmortuary
postmundane
postmuscular
postmutative
postmycotic
postmyxedematous
postnarial
postnaris
postnasal
postnatal
postnate
postnati
postnecrotic
postnephritic
postneural
postneuralgic
postneuritic
postneurotic
postnodular
postnominal
postnotum
postnuptial
postnuptially
postobituary
postocular
postolivary
postomental
postoperative
postoptic
postoral
postorbital
postordination
postorgastic
postosseous
postotic
postpagan
postpaid
postpalatal
postpalatine
postpalpebral
postpaludal
postparalytic
postparietal
postparotid
postparotitic
postparoxysmal
postparturient
postpatellar
postpathological
postpericardial
postpharyngeal
postphlogistic
postphragma
postphrenic
postphthisic
postpituitary
postplace
postplegic
postpneumonic
postponable
postpone
postponement
postponence
postponer
postpontile
postpose
postposited
postposition
postpositional
postpositive
postpositively
postprandial
postprandially
postpredicament
postprophesy
postprostate
postpubertal
postpubescent
postpubic
postpubis
postpuerperal
postpulmonary
postpupillary
postpycnotic
postpyloric
postpyramidal
postpyretic
postrachitic
postramus
postrectal
postreduction
postremogeniture
postremote
postrenal
postresurrection
postresurrectional
postretinal
postrheumatic
postrhinal
postrider
postrorse
postrostral
postrubeolar
postsaccular
postsacral
postscalenus
postscapula
postscapular
postscapularis
postscarlatinal
postscenium
postscorbutic
postscribe
postscript
postscriptum
postscutellar
postscutellum
postseason
postsigmoid
postsign
postspasmodic
postsphenoid
postsphenoidal
postsphygmic
postspinous
postsplenial
postsplenic
poststernal
poststertorous
postsuppurative
postsurgical
postsynaptic
postsynsacral
postsyphilitic
postsystolic
posttabetic
posttarsal
posttetanic
postthalamic
postthoracic
postthyroidal
posttibial
posttonic
posttoxic
posttracheal
posttrapezoid
posttraumatic
posttreaty
posttubercular
posttussive
posttympanic
posttyphoid
postulancy
postulant
postulantship
postulata
postulate
postulation
postulational
postulator
postulatory
postulatum
postulnar
postumbilical
postumbonal
postural
posture
posturer
postureteric
posturist
posturize
postuterine
postvaccinal
postvaricellar
postvarioloid
postvelar
postvenereal
postvenous
postverbal
Postverta
postvertebral
postvesical
postvide
postvocalic
postwar
postward
postwise
postwoman
postxyphoid
postyard
postzygapophysial
postzygapophysis
posy
pot
potability
potable
potableness
potagerie
potagery
potamic
Potamobiidae
Potamochoerus
Potamogale
Potamogalidae
Potamogeton
Potamogetonaceae
potamogetonaceous
potamological
potamologist
potamology
potamometer
Potamonidae
potamophilous
potamoplankton
potash
potashery
potass
potassa
potassamide
potassic
potassiferous
potassium
potate
potation
potative
potato
potatoes
potator
potatory
Potawatami
Potawatomi
potbank
potbellied
potbelly
potboil
potboiler
potboy
potboydom
potch
potcher
potcherman
potcrook
potdar
pote
potecary
poteen
potence
potency
potent
potentacy
potentate
potential
potentiality
potentialization
potentialize
potentially
potentialness
potentiate
potentiation
Potentilla
potentiometer
potentiometric
potentize
potently
potentness
poter
Poterium
potestal
potestas
potestate
potestative
poteye
potful
potgirl
potgun
pothanger
pothead
pothecary
potheen
pother
potherb
potherment
pothery
pothole
pothook
pothookery
Pothos
pothouse
pothousey
pothunt
pothunter
pothunting
poticary
potichomania
potichomanist
potifer
Potiguara
potion
potlatch
potleg
potlicker
potlid
potlike
potluck
potmaker
potmaking
potman
potomania
potomato
potometer
potong
potoo
Potoroinae
potoroo
Potorous
potpie
potpourri
potrack
potsherd
potshoot
potshooter
potstick
potstone
pott
pottage
pottagy
pottah
potted
potter
potterer
potteress
potteringly
pottery
Pottiaceae
potting
pottinger
pottle
pottled
potto
potty
potwaller
potwalling
potware
potwhisky
potwork
potwort
pouce
poucer
poucey
pouch
pouched
pouchful
pouchless
pouchlike
pouchy
poudrette
pouf
poulaine
poulard
poulardize
poulp
poulpe
poult
poulter
poulterer
poulteress
poultice
poulticewise
poultry
poultrydom
poultryist
poultryless
poultrylike
poultryman
poultryproof
pounamu
pounce
pounced
pouncer
pouncet
pouncing
pouncingly
pound
poundage
poundal
poundcake
pounder
pounding
poundkeeper
poundless
poundlike
poundman
poundmaster
poundmeal
poundstone
poundworth
pour
pourer
pourie
pouring
pouringly
pourparler
pourparley
pourpiece
pourpoint
pourpointer
pouser
poussette
pout
pouter
poutful
pouting
poutingly
pouty
poverish
poverishment
poverty
povertyweed
Povindah
pow
powder
powderable
powdered
powderer
powderiness
powdering
powderization
powderize
powderizer
powderlike
powderman
powdery
powdike
powdry
powellite
power
powerboat
powered
powerful
powerfully
powerfulness
powerhouse
powerless
powerlessly
powerlessness
powermonger
Powhatan
powitch
powldoody
pownie
powsoddy
powsowdy
powwow
powwower
powwowism
pox
poxy
poy
poyou
pozzolanic
pozzuolana
pozzuolanic
praam
prabble
prabhu
practic
practicability
practicable
practicableness
practicably
practical
practicalism
practicalist
practicality
practicalization
practicalize
practicalizer
practically
practicalness
practicant
practice
practiced
practicedness
practicer
practician
practicianism
practicum
practitional
practitioner
practitionery
prad
Pradeep
pradhana
praeabdomen
praeacetabular
praeanal
praecava
praecipe
praecipuum
praecoces
praecocial
praecognitum
praecoracoid
praecordia
praecordial
praecordium
praecornu
praecox
praecuneus
praedial
praedialist
praediality
praeesophageal
praefect
praefectorial
praefectus
praefervid
praefloration
praefoliation
praehallux
praelabrum
praelection
praelector
praelectorship
praelectress
praeludium
praemaxilla
praemolar
praemunire
praenarial
Praenestine
Praenestinian
praeneural
praenomen
praenomina
praenominal
praeoperculum
praepositor
praepostor
praepostorial
praepubis
praepuce
praescutum
Praesepe
praesertim
Praesian
praesidium
praesphenoid
praesternal
praesternum
praestomium
praesystolic
praetaxation
praetexta
praetor
praetorial
Praetorian
praetorian
praetorianism
praetorium
praetorship
praezygapophysis
pragmatic
pragmatica
pragmatical
pragmaticality
pragmatically
pragmaticalness
pragmaticism
pragmatics
pragmatism
pragmatist
pragmatistic
pragmatize
pragmatizer
prairie
prairiecraft
prairied
prairiedom
prairielike
prairieweed
prairillon
praisable
praisableness
praisably
praise
praiseful
praisefully
praisefulness
praiseless
praiseproof
praiser
praiseworthy
praising
praisingly
praisworthily
praisworthiness
Prajapati
prajna
Prakash
Prakrit
prakriti
Prakritic
Prakritize
praline
pralltriller
pram
Pramnian
prana
prance
pranceful
prancer
prancing
prancingly
prancy
prandial
prandially
prank
pranked
pranker
prankful
prankfulness
pranking
prankingly
prankish
prankishly
prankishness
prankle
pranksome
pranksomeness
prankster
pranky
prase
praseocobaltic
praseodidymium
praseodymia
praseodymium
praseolite
prasine
prasinous
prasoid
prasophagous
prasophagy
prastha
prat
pratal
Pratap
Pratapwant
prate
prateful
pratement
pratensian
Prater
prater
pratey
pratfall
pratiloma
Pratincola
pratincole
pratincoline
pratincolous
prating
pratingly
pratique
pratiyasamutpada
Pratt
prattfall
prattle
prattlement
prattler
prattling
prattlingly
prattly
prau
Pravin
pravity
prawn
prawner
prawny
Praxean
Praxeanist
praxinoscope
praxiology
praxis
Praxitelean
pray
praya
prayer
prayerful
prayerfully
prayerfulness
prayerless
prayerlessly
prayerlessness
prayermaker
prayermaking
prayerwise
prayful
praying
prayingly
prayingwise
preabdomen
preabsorb
preabsorbent
preabstract
preabundance
preabundant
preabundantly
preaccept
preacceptance
preaccess
preaccessible
preaccidental
preaccidentally
preaccommodate
preaccommodating
preaccommodatingly
preaccommodation
preaccomplish
preaccomplishment
preaccord
preaccordance
preaccount
preaccounting
preaccredit
preaccumulate
preaccumulation
preaccusation
preaccuse
preaccustom
preaccustomed
preacetabular
preach
preachable
preacher
preacherdom
preacheress
preacherize
preacherless
preacherling
preachership
preachieved
preachification
preachify
preachily
preachiness
preaching
preachingly
preachman
preachment
preachy
preacid
preacidity
preacidly
preacidness
preacknowledge
preacknowledgment
preacquaint
preacquaintance
preacquire
preacquired
preacquit
preacquittal
preact
preaction
preactive
preactively
preactivity
preacute
preacutely
preacuteness
preadamic
preadamite
preadamitic
preadamitical
preadamitism
preadapt
preadaptable
preadaptation
preaddition
preadditional
preaddress
preadequacy
preadequate
preadequately
preadhere
preadherence
preadherent
preadjectival
preadjective
preadjourn
preadjournment
preadjunct
preadjust
preadjustable
preadjustment
preadministration
preadministrative
preadministrator
preadmire
preadmirer
preadmission
preadmit
preadmonish
preadmonition
preadolescent
preadopt
preadoption
preadoration
preadore
preadorn
preadornment
preadult
preadulthood
preadvance
preadvancement
preadventure
preadvertency
preadvertent
preadvertise
preadvertisement
preadvice
preadvisable
preadvise
preadviser
preadvisory
preadvocacy
preadvocate
preaestival
preaffect
preaffection
preaffidavit
preaffiliate
preaffiliation
preaffirm
preaffirmation
preaffirmative
preafflict
preaffliction
preafternoon
preaged
preaggravate
preaggravation
preaggression
preaggressive
preagitate
preagitation
preagonal
preagony
preagree
preagreement
preagricultural
preagriculture
prealarm
prealcohol
prealcoholic
prealgebra
prealgebraic
prealkalic
preallable
preallably
preallegation
preallege
prealliance
preallied
preallot
preallotment
preallow
preallowable
preallowably
preallowance
preallude
preallusion
preally
prealphabet
prealphabetical
prealtar
prealteration
prealveolar
preamalgamation
preambassadorial
preambition
preambitious
preamble
preambled
preambling
preambular
preambulary
preambulate
preambulation
preambulatory
preanal
preanaphoral
preanesthetic
preanimism
preannex
preannounce
preannouncement
preannouncer
preantepenult
preantepenultimate
preanterior
preanticipate
preantiquity
preantiseptic
preaortic
preappearance
preapperception
preapplication
preappoint
preappointment
preapprehension
preapprise
preapprobation
preapproval
preapprove
preaptitude
prearm
prearrange
prearrangement
prearrest
prearrestment
prearticulate
preartistic
preascertain
preascertainment
preascitic
preaseptic
preassigned
preassume
preassurance
preassure
preataxic
preattachment
preattune
preaudience
preauditory
preaver
preavowal
preaxiad
preaxial
preaxially
prebachelor
prebacillary
prebake
prebalance
preballot
preballoting
prebankruptcy
prebaptismal
prebaptize
prebarbaric
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premuddle
premultiplication
premultiplier
premultiply
premundane
premunicipal
premunition
premunitory
premusical
premuster
premutative
premutiny
premycotic
premyelocyte
premythical
prename
Prenanthes
prenares
prenarial
prenaris
prenasal
prenatal
prenatalist
prenatally
prenational
prenative
prenatural
prenaval
prender
prendre
prenebular
prenecessitate
preneglect
preneglectful
prenegligence
prenegligent
prenegotiate
prenegotiation
preneolithic
prenephritic
preneural
preneuralgic
prenight
prenoble
prenodal
prenominal
prenominate
prenomination
prenominical
prenotation
prenotice
prenotification
prenotify
prenotion
prentice
prenticeship
prenumber
prenumbering
prenuncial
prenuptial
prenursery
preobedience
preobedient
preobject
preobjection
preobjective
preobligate
preobligation
preoblige
preobservance
preobservation
preobservational
preobserve
preobstruct
preobstruction
preobtain
preobtainable
preobtrude
preobtrusion
preobtrusive
preobviate
preobvious
preobviously
preobviousness
preoccasioned
preoccipital
preocclusion
preoccultation
preoccupancy
preoccupant
preoccupate
preoccupation
preoccupative
preoccupied
preoccupiedly
preoccupiedness
preoccupier
preoccupy
preoccur
preoccurrence
preoceanic
preocular
preodorous
preoffend
preoffense
preoffensive
preoffensively
preoffensiveness
preoffer
preoffering
preofficial
preofficially
preominate
preomission
preomit
preopen
preopening
preoperate
preoperation
preoperative
preoperatively
preoperator
preopercle
preopercular
preoperculum
preopinion
preopinionated
preoppose
preopposition
preoppress
preoppression
preoppressor
preoptic
preoptimistic
preoption
preoral
preorally
preorbital
preordain
preorder
preordination
preorganic
preorganization
preorganize
preoriginal
preoriginally
preornamental
preoutfit
preoutline
preoverthrow
prep
prepainful
prepalatal
prepalatine
prepaleolithic
prepanic
preparable
preparation
preparationist
preparative
preparatively
preparator
preparatorily
preparatory
prepardon
prepare
prepared
preparedly
preparedness
preparement
preparental
preparer
preparietal
preparingly
preparliamentary
preparoccipital
preparoxysmal
prepartake
preparticipation
prepartisan
prepartition
prepartnership
prepatellar
prepatent
prepatriotic
prepave
prepavement
prepay
prepayable
prepayment
prepeduncle
prepenetrate
prepenetration
prepenial
prepense
prepensely
prepeople
preperceive
preperception
preperceptive
preperitoneal
prepersuade
prepersuasion
prepersuasive
preperusal
preperuse
prepetition
prephragma
prephthisical
prepigmental
prepink
prepious
prepituitary
preplace
preplacement
preplacental
preplan
preplant
prepledge
preplot
prepoetic
prepoetical
prepoison
prepolice
prepolish
prepolitic
prepolitical
prepolitically
prepollence
prepollency
prepollent
prepollex
preponder
preponderance
preponderancy
preponderant
preponderantly
preponderate
preponderately
preponderating
preponderatingly
preponderation
preponderous
preponderously
prepontile
prepontine
preportray
preportrayal
prepose
preposition
prepositional
prepositionally
prepositive
prepositively
prepositor
prepositorial
prepositure
prepossess
prepossessed
prepossessing
prepossessingly
prepossessingness
prepossession
prepossessionary
prepossessor
preposterous
preposterously
preposterousness
prepostorship
prepotence
prepotency
prepotent
prepotential
prepotently
prepractical
prepractice
preprandial
prepreference
prepreparation
preprice
preprimary
preprimer
preprimitive
preprint
preprofess
preprofessional
preprohibition
prepromise
prepromote
prepromotion
prepronounce
prepronouncement
preprophetic
preprostatic
preprove
preprovide
preprovision
preprovocation
preprovoke
preprudent
preprudently
prepsychological
prepsychology
prepuberal
prepubertal
prepuberty
prepubescent
prepubic
prepubis
prepublication
prepublish
prepuce
prepunctual
prepunish
prepunishment
prepupa
prepupal
prepurchase
prepurchaser
prepurpose
preputial
preputium
prepyloric
prepyramidal
prequalification
prequalify
prequarantine
prequestion
prequotation
prequote
preracing
preradio
prerailroad
prerailroadite
prerailway
preramus
prerational
prereadiness
preready
prerealization
prerealize
prerebellion
prereceipt
prereceive
prereceiver
prerecital
prerecite
prereckon
prereckoning
prerecognition
prerecognize
prerecommend
prerecommendation
prereconcile
prereconcilement
prereconciliation
prerectal
preredeem
preredemption
prereduction
prerefer
prereference
prerefine
prerefinement
prereform
prereformation
prereformatory
prerefusal
prerefuse
preregal
preregister
preregistration
preregulate
preregulation
prereject
prerejection
prerejoice
prerelate
prerelation
prerelationship
prerelease
prereligious
prereluctation
preremit
preremittance
preremorse
preremote
preremoval
preremove
preremunerate
preremuneration
prerenal
prerent
prerental
prereport
prerepresent
prerepresentation
prereption
prerepublican
prerequest
prerequire
prerequirement
prerequisite
prerequisition
preresemblance
preresemble
preresolve
preresort
prerespectability
prerespectable
prerespiration
prerespire
preresponsibility
preresponsible
prerestoration
prerestrain
prerestraint
prerestrict
prerestriction
prereturn
prereveal
prerevelation
prerevenge
prereversal
prereverse
prereview
prerevise
prerevision
prerevival
prerevolutionary
prerheumatic
prerich
prerighteous
prerighteously
prerighteousness
prerogatival
prerogative
prerogatived
prerogatively
prerogativity
prerolandic
preromantic
preromanticism
preroute
preroutine
preroyal
preroyally
preroyalty
prerupt
preruption
presacral
presacrifice
presacrificial
presage
presageful
presagefully
presager
presagient
presaging
presagingly
presalvation
presanctification
presanctified
presanctify
presanguine
presanitary
presartorial
presatisfaction
presatisfactory
presatisfy
presavage
presavagery
presay
presbyacousia
presbyacusia
presbycousis
presbycusis
presbyope
presbyophrenia
presbyophrenic
presbyopia
presbyopic
presbyopy
presbyte
presbyter
presbyteral
presbyterate
presbyterated
presbyteress
presbyteria
presbyterial
presbyterially
Presbyterian
Presbyterianism
Presbyterianize
Presbyterianly
presbyterium
presbytership
presbytery
presbytia
presbytic
Presbytinae
Presbytis
presbytism
prescapula
prescapular
prescapularis
prescholastic
preschool
prescience
prescient
prescientific
presciently
prescind
prescindent
prescission
prescored
prescout
prescribable
prescribe
prescriber
prescript
prescriptibility
prescriptible
prescription
prescriptionist
prescriptive
prescriptively
prescriptiveness
prescriptorial
prescrive
prescutal
prescutum
preseal
presearch
preseason
preseasonal
presecular
presecure
presee
preselect
presell
preseminal
preseminary
presence
presenced
presenceless
presenile
presenility
presensation
presension
present
presentability
presentable
presentableness
presentably
presental
presentation
presentational
presentationism
presentationist
presentative
presentatively
presentee
presentence
presenter
presential
presentiality
presentially
presentialness
presentient
presentiment
presentimental
presentist
presentive
presentively
presentiveness
presently
presentment
presentness
presentor
preseparate
preseparation
preseparator
preservability
preservable
preserval
preservation
preservationist
preservative
preservatize
preservatory
preserve
preserver
preserveress
preses
presession
preset
presettle
presettlement
presexual
preshadow
preshape
preshare
presharpen
preshelter
preship
preshipment
preshortage
preshorten
preshow
preside
presidence
presidencia
presidency
president
presidente
presidentess
presidential
presidentially
presidentiary
presidentship
presider
presidial
presidially
presidiary
presidio
presidium
presift
presign
presignal
presignificance
presignificancy
presignificant
presignification
presignificative
presignificator
presignify
presimian
preslavery
Presley
presmooth
presocial
presocialism
presocialist
presolar
presolicit
presolicitation
presolution
presolve
presophomore
presound
prespecialist
prespecialize
prespecific
prespecifically
prespecification
prespecify
prespeculate
prespeculation
presphenoid
presphenoidal
presphygmic
prespinal
prespinous
prespiracular
presplendor
presplenomegalic
prespoil
prespontaneity
prespontaneous
prespontaneously
prespread
presprinkle
prespur
press
pressable
pressboard
pressdom
pressel
presser
pressfat
pressful
pressgang
pressible
pressing
pressingly
pressingness
pression
pressive
pressman
pressmanship
pressmark
pressor
presspack
pressroom
pressurage
pressural
pressure
pressureless
pressureproof
pressurize
pressurizer
presswoman
presswork
pressworker
prest
prestabilism
prestability
prestable
prestamp
prestandard
prestandardization
prestandardize
prestant
prestate
prestation
prestatistical
presteam
presteel
prester
presternal
presternum
prestidigital
prestidigitate
prestidigitation
prestidigitator
prestidigitatorial
prestige
prestigiate
prestigiation
prestigiator
prestigious
prestigiously
prestigiousness
prestimulate
prestimulation
prestimulus
prestissimo
presto
prestock
prestomial
prestomium
prestorage
prestore
prestraighten
prestrain
prestrengthen
prestress
prestretch
prestricken
prestruggle
prestubborn
prestudious
prestudiously
prestudiousness
prestudy
presubdue
presubiculum
presubject
presubjection
presubmission
presubmit
presubordinate
presubordination
presubscribe
presubscriber
presubscription
presubsist
presubsistence
presubsistent
presubstantial
presubstitute
presubstitution
presuccess
presuccessful
presuccessfully
presuffer
presuffering
presufficiency
presufficient
presufficiently
presuffrage
presuggest
presuggestion
presuggestive
presuitability
presuitable
presuitably
presumable
presumably
presume
presumedly
presumer
presuming
presumption
presumptious
presumptiously
presumptive
presumptively
presumptuous
presumptuously
presumptuousness
presuperficial
presuperficiality
presuperficially
presuperfluity
presuperfluous
presuperfluously
presuperintendence
presuperintendency
presupervise
presupervision
presupervisor
presupplemental
presupplementary
presupplicate
presupplication
presupply
presupport
presupposal
presuppose
presupposition
presuppositionless
presuppress
presuppression
presuppurative
presupremacy
presupreme
presurgery
presurgical
presurmise
presurprisal
presurprise
presurrender
presurround
presurvey
presusceptibility
presusceptible
presuspect
presuspend
presuspension
presuspicion
presuspicious
presuspiciously
presuspiciousness
presustain
presutural
preswallow
presylvian
presympathize
presympathy
presymphonic
presymphony
presymphysial
presymptom
presymptomatic
presynapsis
presynaptic
presystematic
presystematically
presystole
presystolic
pretabulate
pretabulation
pretan
pretangible
pretangibly
pretannage
pretardily
pretardiness
pretardy
pretariff
pretaste
preteach
pretechnical
pretechnically
pretelegraph
pretelegraphic
pretelephone
pretelephonic
pretell
pretemperate
pretemperately
pretemporal
pretend
pretendant
pretended
pretendedly
pretender
Pretenderism
pretendership
pretendingly
pretendingness
pretense
pretenseful
pretenseless
pretension
pretensional
pretensionless
pretensive
pretensively
pretensiveness
pretentative
pretentious
pretentiously
pretentiousness
pretercanine
preterchristian
preterconventional
preterdetermined
preterdeterminedly
preterdiplomatic
preterdiplomatically
preterequine
preteressential
pretergress
pretergression
preterhuman
preterience
preterient
preterintentional
preterist
preterit
preteriteness
preterition
preteritive
preteritness
preterlabent
preterlegal
preterlethal
preterminal
pretermission
pretermit
pretermitter
preternative
preternatural
preternaturalism
preternaturalist
preternaturality
preternaturally
preternaturalness
preternormal
preternotorious
preternuptial
preterpluperfect
preterpolitical
preterrational
preterregular
preterrestrial
preterritorial
preterroyal
preterscriptural
preterseasonable
pretersensual
pretervection
pretest
pretestify
pretestimony
pretext
pretexted
pretextuous
pretheological
prethoracic
prethoughtful
prethoughtfully
prethoughtfulness
prethreaten
prethrill
prethrust
pretibial
pretimeliness
pretimely
pretincture
pretire
pretoken
pretone
pretonic
pretorial
pretorship
pretorsional
pretorture
pretournament
pretrace
pretracheal
pretraditional
pretrain
pretraining
pretransact
pretransaction
pretranscribe
pretranscription
pretranslate
pretranslation
pretransmission
pretransmit
pretransport
pretransportation
pretravel
pretreat
pretreatment
pretreaty
pretrematic
pretribal
pretry
prettification
prettifier
prettify
prettikin
prettily
prettiness
pretty
prettyface
prettyish
prettyism
pretubercular
pretuberculous
pretympanic
pretyphoid
pretypify
pretypographical
pretyrannical
pretyranny
pretzel
preultimate
preultimately
preumbonal
preunderstand
preundertake
preunion
preunite
preutilizable
preutilization
preutilize
prevacate
prevacation
prevaccinate
prevaccination
prevail
prevailance
prevailer
prevailingly
prevailingness
prevailment
prevalence
prevalency
prevalent
prevalently
prevalentness
prevalescence
prevalescent
prevalid
prevalidity
prevalidly
prevaluation
prevalue
prevariation
prevaricate
prevarication
prevaricator
prevaricatory
prevascular
prevegetation
prevelar
prevenance
prevenancy
prevene
prevenience
prevenient
preveniently
prevent
preventability
preventable
preventative
preventer
preventible
preventingly
prevention
preventionism
preventionist
preventive
preventively
preventiveness
preventorium
preventure
preverb
preverbal
preverification
preverify
prevernal
preversion
prevertebral
prevesical
preveto
previctorious
previde
previdence
preview
previgilance
previgilant
previgilantly
previolate
previolation
previous
previously
previousness
previse
previsibility
previsible
previsibly
prevision
previsional
previsit
previsitor
previsive
previsor
prevocal
prevocalic
prevocally
prevocational
prevogue
prevoid
prevoidance
prevolitional
prevolunteer
prevomer
prevotal
prevote
prevoyance
prevoyant
prevue
prewar
prewarn
prewarrant
prewash
preweigh
prewelcome
prewhip
prewilling
prewillingly
prewillingness
prewire
prewireless
prewitness
prewonder
prewonderment
preworldliness
preworldly
preworship
preworthily
preworthiness
preworthy
prewound
prewrap
prexy
prey
preyer
preyful
preyingly
preyouthful
prezonal
prezone
prezygapophysial
prezygapophysis
prezygomatic
Pria
priacanthid
Priacanthidae
priacanthine
Priacanthus
Priapean
Priapic
priapism
Priapulacea
priapulid
Priapulida
Priapulidae
priapuloid
Priapuloidea
Priapulus
Priapus
Priapusian
Price
price
priceable
priceably
priced
priceite
priceless
pricelessness
pricer
prich
prick
prickant
pricked
pricker
pricket
prickfoot
pricking
prickingly
prickish
prickle
prickleback
prickled
pricklefish
prickless
prickliness
prickling
pricklingly
pricklouse
prickly
pricklyback
prickmadam
prickmedainty
prickproof
pricks
prickseam
prickshot
prickspur
pricktimber
prickwood
pricky
pride
prideful
pridefully
pridefulness
prideless
pridelessly
prideling
prideweed
pridian
priding
pridingly
pridy
pried
prier
priest
priestal
priestcap
priestcraft
priestdom
priesteen
priestery
priestess
priestfish
priesthood
priestianity
priestish
priestism
priestless
priestlet
priestlike
priestliness
priestling
priestly
priestship
priestshire
prig
prigdom
prigger
priggery
priggess
priggish
priggishly
priggishness
priggism
prighood
prigman
prill
prillion
prim
prima
primacy
primage
primal
primality
primar
primarian
primaried
primarily
primariness
primary
primatal
primate
Primates
primateship
primatial
primatic
primatical
primavera
primaveral
prime
primegilt
primely
primeness
primer
primero
primerole
primeval
primevalism
primevally
primeverose
primevity
primevous
primevrin
Primianist
primigene
primigenial
primigenian
primigenious
primigenous
primigravida
primine
priming
primipara
primiparity
primiparous
primipilar
primitiae
primitial
primitias
primitive
primitively
primitivism
primitivist
primitivistic
primitivity
primly
primness
primogenetrix
primogenial
primogenital
primogenitary
primogenitive
primogenitor
primogeniture
primogenitureship
primogenous
primoprime
primoprimitive
primordality
primordia
primordial
primordialism
primordially
primordiate
primordium
primosity
primost
primp
primrose
primrosed
primrosetide
primrosetime
primrosy
primsie
Primula
primula
Primulaceae
primulaceous
Primulales
primulaverin
primulaveroside
primulic
primuline
Primulinus
Primus
primus
primwort
primy
prince
princeage
princecraft
princedom
princehood
Princeite
princekin
princeless
princelet
princelike
princeliness
princeling
princely
princeps
princeship
princess
princessdom
princesse
princesslike
princessly
princewood
princified
princify
principal
principality
principally
principalness
principalship
principate
Principes
principes
principia
principiant
principiate
principiation
principium
principle
principulus
princock
princox
prine
pringle
prink
prinker
prinkle
prinky
print
printability
printable
printableness
printed
printer
printerdom
printerlike
printery
printing
printless
printline
printscript
printworks
Priodon
priodont
Priodontes
prion
prionid
Prionidae
Prioninae
prionine
Prionodesmacea
prionodesmacean
prionodesmaceous
prionodesmatic
Prionodon
prionodont
Prionopinae
prionopine
Prionops
Prionus
prior
prioracy
prioral
priorate
prioress
prioristic
prioristically
priorite
priority
priorly
priorship
priory
prisable
prisage
prisal
priscan
Priscian
Priscianist
Priscilla
Priscillian
Priscillianism
Priscillianist
prism
prismal
prismatic
prismatical
prismatically
prismatization
prismatize
prismatoid
prismatoidal
prismed
prismoid
prismoidal
prismy
prisometer
prison
prisonable
prisondom
prisoner
prisonful
prisonlike
prisonment
prisonous
priss
prissily
prissiness
prissy
pristane
pristine
Pristipomatidae
Pristipomidae
Pristis
Pristodus
pritch
Pritchardia
pritchel
prithee
prius
privacity
privacy
privant
private
privateer
privateersman
privately
privateness
privation
privative
privatively
privativeness
privet
privilege
privileged
privileger
privily
priviness
privity
privy
prizable
prize
prizeable
prizeholder
prizeman
prizer
prizery
prizetaker
prizeworthy
pro
proa
proabolitionist
proabsolutism
proabsolutist
proabstinence
proacademic
proacceptance
proacquisition
proacquittal
proaction
proactor
proaddition
proadjournment
proadministration
proadmission
proadoption
proadvertising
proaesthetic
proaggressionist
proagitation
proagrarian
proagreement
proagricultural
proagule
proairesis
proairplane
proal
proalcoholism
proalien
proalliance
proallotment
proalteration
proamateur
proambient
proamendment
proamnion
proamniotic
proamusement
proanaphora
proanaphoral
proanarchic
proangiosperm
proangiospermic
proangiospermous
proanimistic
proannexation
proannexationist
proantarctic
proanthropos
proapostolic
proappointment
proapportionment
proappreciation
proappropriation
proapproval
proaquatic
proarbitration
proarbitrationist
proarchery
proarctic
proaristocratic
proarmy
Proarthri
proassessment
proassociation
proatheist
proatheistic
proathletic
proatlas
proattack
proattendance
proauction
proaudience
proaulion
proauthor
proauthority
proautomobile
proavian
proaviation
Proavis
proaward
prob
probabiliorism
probabiliorist
probabilism
probabilist
probabilistic
probability
probabilize
probabl
probable
probableness
probably
probachelor
probal
proballoon
probang
probanishment
probankruptcy
probant
probargaining
probaseball
probasketball
probate
probathing
probatical
probation
probational
probationary
probationer
probationerhood
probationership
probationism
probationist
probationship
probative
probatively
probator
probatory
probattle
probattleship
probe
probeable
probeer
prober
probetting
probiology
probituminous
probity
problem
problematic
problematical
problematically
problematist
problematize
problemdom
problemist
problemistic
problemize
problemwise
problockade
probonding
probonus
proborrowing
proboscidal
proboscidate
Proboscidea
proboscidean
proboscideous
proboscides
proboscidial
proboscidian
proboscidiferous
proboscidiform
probosciform
probosciformed
Probosciger
proboscis
proboscislike
probouleutic
proboulevard
probowling
proboxing
proboycott
probrick
probridge
probroadcasting
probudget
probudgeting
probuilding
probusiness
probuying
procacious
procaciously
procacity
procaine
procambial
procambium
procanal
procancellation
procapital
procapitalism
procapitalist
procarnival
procarp
procarpium
procarrier
procatalectic
procatalepsis
procatarctic
procatarxis
procathedral
Procavia
Procaviidae
procedendo
procedural
procedure
proceed
proceeder
proceeding
proceeds
proceleusmatic
Procellaria
procellarian
procellarid
Procellariidae
Procellariiformes
procellariine
procellas
procello
procellose
procellous
procensorship
procensure
procentralization
procephalic
procercoid
procereal
procerebral
procerebrum
proceremonial
proceremonialism
proceremonialist
proceres
procerite
proceritic
procerity
procerus
process
processal
procession
processional
processionalist
processionally
processionary
processioner
processionist
processionize
processionwise
processive
processor
processual
procharity
prochein
prochemical
prochlorite
prochondral
prochoos
prochordal
prochorion
prochorionic
prochromosome
prochronic
prochronism
prochronize
prochurch
prochurchian
procidence
procident
procidentia
procivic
procivilian
procivism
proclaim
proclaimable
proclaimant
proclaimer
proclaiming
proclaimingly
proclamation
proclamator
proclamatory
proclassic
proclassical
proclergy
proclerical
proclericalism
procline
proclisis
proclitic
proclive
proclivitous
proclivity
proclivous
proclivousness
Procne
procnemial
Procoelia
procoelia
procoelian
procoelous
procoercive
procollectivistic
procollegiate
procombat
procombination
procomedy
procommemoration
procomment
procommercial
procommission
procommittee
procommunal
procommunism
procommunist
procommutation
procompensation
procompetition
procompromise
procompulsion
proconcentration
proconcession
proconciliation
procondemnation
proconfederationist
proconference
proconfession
proconfessionist
proconfiscation
proconformity
Proconnesian
proconquest
proconscription
proconscriptive
proconservation
proconservationist
proconsolidation
proconstitutional
proconstitutionalism
proconsul
proconsular
proconsulary
proconsulate
proconsulship
proconsultation
procontinuation
proconvention
proconventional
proconviction
procoracoid
procoracoidal
procorporation
procosmetic
procosmopolitan
procotton
procourt
procrastinate
procrastinating
procrastinatingly
procrastination
procrastinative
procrastinatively
procrastinator
procrastinatory
procreant
procreate
procreation
procreative
procreativeness
procreator
procreatory
procreatress
procreatrix
procremation
Procris
procritic
procritique
Procrustean
Procrusteanism
Procrusteanize
Procrustes
procrypsis
procryptic
procryptically
proctal
proctalgia
proctalgy
proctatresia
proctatresy
proctectasia
proctectomy
procteurynter
proctitis
proctocele
proctoclysis
proctocolitis
proctocolonoscopy
proctocystoplasty
proctocystotomy
proctodaeal
proctodaeum
proctodynia
proctoelytroplastic
proctologic
proctological
proctologist
proctology
proctoparalysis
proctoplastic
proctoplasty
proctoplegia
proctopolypus
proctoptoma
proctoptosis
proctor
proctorage
proctoral
proctorial
proctorially
proctorical
proctorization
proctorize
proctorling
proctorrhagia
proctorrhaphy
proctorrhea
proctorship
proctoscope
proctoscopic
proctoscopy
proctosigmoidectomy
proctosigmoiditis
proctospasm
proctostenosis
proctostomy
proctotome
proctotomy
proctotresia
proctotrypid
Proctotrypidae
proctotrypoid
Proctotrypoidea
proctovalvotomy
Proculian
procumbent
procurable
procuracy
procural
procurance
procurate
procuration
procurative
procurator
procuratorate
procuratorial
procuratorship
procuratory
procuratrix
procure
procurement
procurer
procuress
procurrent
procursive
procurvation
procurved
Procyon
Procyonidae
procyoniform
Procyoniformia
Procyoninae
procyonine
proczarist
prod
prodatary
prodder
proddle
prodecoration
prodefault
prodefiance
prodelay
prodelision
prodemocratic
Prodenia
prodenominational
prodentine
prodeportation
prodespotic
prodespotism
prodialogue
prodigal
prodigalish
prodigalism
prodigality
prodigalize
prodigally
prodigiosity
prodigious
prodigiously
prodigiousness
prodigus
prodigy
prodisarmament
prodisplay
prodissoconch
prodissolution
prodistribution
prodition
proditorious
proditoriously
prodivision
prodivorce
prodproof
prodramatic
prodroma
prodromal
prodromatic
prodromatically
prodrome
prodromic
prodromous
prodromus
producal
produce
produceable
produceableness
produced
producent
producer
producership
producibility
producible
producibleness
product
producted
productibility
productible
productid
Productidae
productile
production
productional
productionist
productive
productively
productiveness
productivity
productoid
productor
productory
productress
Productus
proecclesiastical
proeconomy
proeducation
proeducational
proegumenal
proelectric
proelectrical
proelectrification
proelectrocution
proelimination
proem
proembryo
proembryonic
proemial
proemium
proemployee
proemptosis
proenforcement
proenlargement
proenzym
proenzyme
proepimeron
proepiscopist
proepisternum
proequality
proethical
proethnic
proethnically
proetid
Proetidae
Proetus
proevolution
proevolutionist
proexamination
proexecutive
proexemption
proexercise
proexperiment
proexpert
proexporting
proexposure
proextension
proextravagance
prof
profaculty
profanable
profanableness
profanably
profanation
profanatory
profanchise
profane
profanely
profanement
profaneness
profaner
profanism
profanity
profanize
profarmer
profection
profectional
profectitious
profederation
profeminism
profeminist
proferment
profert
profess
professable
professed
professedly
profession
professional
professionalism
professionalist
professionality
professionalization
professionalize
professionally
professionist
professionize
professionless
professive
professively
professor
professorate
professordom
professoress
professorial
professorialism
professorially
professoriate
professorlike
professorling
professorship
professory
proffer
profferer
proficience
proficiency
proficient
proficiently
proficientness
profiction
proficuous
proficuously
profile
profiler
profilist
profilograph
profit
profitability
profitable
profitableness
profitably
profiteer
profiteering
profiter
profiting
profitless
profitlessly
profitlessness
profitmonger
profitmongering
profitproof
proflated
proflavine
profligacy
profligate
profligately
profligateness
profligation
proflogger
profluence
profluent
profluvious
profluvium
proforeign
profound
profoundly
profoundness
profraternity
profugate
profulgent
profunda
profundity
profuse
profusely
profuseness
profusion
profusive
profusively
profusiveness
prog
progambling
progamete
progamic
proganosaur
Proganosauria
progenerate
progeneration
progenerative
progenital
progenitive
progenitiveness
progenitor
progenitorial
progenitorship
progenitress
progenitrix
progeniture
progenity
progeny
progeotropic
progeotropism
progeria
progermination
progestational
progesterone
progestin
progger
proglottic
proglottid
proglottidean
proglottis
prognathi
prognathic
prognathism
prognathous
prognathy
progne
prognose
prognosis
prognostic
prognosticable
prognostically
prognosticate
prognostication
prognosticative
prognosticator
prognosticatory
progoneate
progospel
progovernment
program
programist
programistic
programma
programmar
programmatic
programmatically
programmatist
programmer
progrede
progrediency
progredient
progress
progresser
progression
progressional
progressionally
progressionary
progressionism
progressionist
progressism
progressist
progressive
progressively
progressiveness
progressivism
progressivist
progressivity
progressor
proguardian
Progymnasium
progymnosperm
progymnospermic
progymnospermous
progypsy
prohaste
prohibit
prohibiter
prohibition
prohibitionary
prohibitionism
prohibitionist
prohibitive
prohibitively
prohibitiveness
prohibitor
prohibitorily
prohibitory
proholiday
prohostility
prohuman
prohumanistic
prohydrotropic
prohydrotropism
proidealistic
proimmunity
proinclusion
proincrease
proindemnity
proindustrial
proinjunction
proinnovationist
proinquiry
proinsurance
prointervention
proinvestment
proirrigation
projacient
project
projectable
projectedly
projectile
projecting
projectingly
projection
projectional
projectionist
projective
projectively
projectivity
projector
projectress
projectrix
projecture
projicience
projicient
projiciently
projournalistic
projudicial
proke
prokeimenon
proker
prokindergarten
proklausis
prolabium
prolabor
prolacrosse
prolactin
prolamin
prolan
prolapse
prolapsus
prolarva
prolarval
prolate
prolately
prolateness
prolation
prolative
prolatively
proleague
proleaguer
prolectite
proleg
prolegate
prolegislative
prolegomena
prolegomenal
prolegomenary
prolegomenist
prolegomenon
prolegomenous
proleniency
prolepsis
proleptic
proleptical
proleptically
proleptics
proletairism
proletarian
proletarianism
proletarianization
proletarianize
proletarianly
proletarianness
proletariat
proletariatism
proletarization
proletarize
proletary
proletcult
proleucocyte
proleukocyte
prolicense
prolicidal
prolicide
proliferant
proliferate
proliferation
proliferative
proliferous
proliferously
prolific
prolificacy
prolifical
prolifically
prolificalness
prolificate
prolification
prolificity
prolificly
prolificness
prolificy
prolify
proligerous
proline
proliquor
proliterary
proliturgical
proliturgist
prolix
prolixity
prolixly
prolixness
prolocution
prolocutor
prolocutorship
prolocutress
prolocutrix
prologist
prologize
prologizer
prologos
prologue
prologuelike
prologuer
prologuist
prologuize
prologuizer
prologus
prolong
prolongable
prolongableness
prolongably
prolongate
prolongation
prolonge
prolonger
prolongment
prolusion
prolusionize
prolusory
prolyl
promachinery
promachos
promagisterial
promagistracy
promagistrate
promajority
promammal
Promammalia
promammalian
promarriage
promatrimonial
promatrimonialist
promaximum
promemorial
promenade
promenader
promenaderess
promercantile
promercy
promerger
promeristem
promerit
promeritor
Promethea
Promethean
Prometheus
promethium
promic
promilitarism
promilitarist
promilitary
prominence
prominency
prominent
prominently
prominimum
proministry
prominority
promisable
promiscuity
promiscuous
promiscuously
promiscuousness
promise
promisee
promiseful
promiseless
promisemonger
promiseproof
promiser
promising
promisingly
promisingness
promisor
promissionary
promissive
promissor
promissorily
promissory
promitosis
promittor
promnesia
promoderation
promoderationist
promodernist
promodernistic
promonarchic
promonarchical
promonarchicalness
promonarchist
promonopolist
promonopoly
promontoried
promontory
promoral
promorph
promorphological
promorphologically
promorphologist
promorphology
promotable
promote
promotement
promoter
promotion
promotional
promotive
promotiveness
promotor
promotorial
promotress
promotrix
promovable
promovent
prompt
promptbook
prompter
promptitude
promptive
promptly
promptness
promptress
promptuary
prompture
promulgate
promulgation
promulgator
promulge
promulger
promuscidate
promuscis
promycelial
promycelium
promythic
pronaos
pronate
pronation
pronational
pronationalism
pronationalist
pronationalistic
pronative
pronatoflexor
pronator
pronaval
pronavy
prone
pronegotiation
pronegro
pronegroism
pronely
proneness
pronephric
pronephridiostome
pronephron
pronephros
proneur
prong
prongbuck
pronged
pronger
pronghorn
pronglike
pronic
pronograde
pronominal
pronominalize
pronominally
pronomination
pronotal
pronotum
pronoun
pronounal
pronounce
pronounceable
pronounced
pronouncedly
pronouncement
pronounceness
pronouncer
pronpl
pronto
Pronuba
pronuba
pronubial
pronuclear
pronucleus
pronumber
pronunciability
pronunciable
pronuncial
pronunciamento
pronunciation
pronunciative
pronunciator
pronunciatory
pronymph
pronymphal
proo
prooemiac
prooemion
prooemium
proof
proofer
proofful
proofing
proofless
prooflessly
proofness
proofread
proofreader
proofreading
proofroom
proofy
prop
propadiene
propaedeutic
propaedeutical
propaedeutics
propagability
propagable
propagableness
propagand
propaganda
propagandic
propagandism
propagandist
propagandistic
propagandistically
propagandize
propagate
propagation
propagational
propagative
propagator
propagatory
propagatress
propago
propagulum
propale
propalinal
propane
propanedicarboxylic
propanol
propanone
propapist
proparasceve
propargyl
propargylic
Proparia
proparian
proparliamental
proparoxytone
proparoxytonic
proparticipation
propatagial
propatagian
propatagium
propatriotic
propatriotism
propatronage
propayment
propellable
propellant
propellent
propeller
propelment
propend
propendent
propene
propenoic
propense
propensely
propenseness
propension
propensitude
propensity
propenyl
propenylic
proper
properispome
properispomenon
properitoneal
properly
properness
propertied
property
propertyless
propertyship
propessimism
propessimist
prophase
prophasis
prophecy
prophecymonger
prophesiable
prophesier
prophesy
prophet
prophetess
prophethood
prophetic
prophetical
propheticality
prophetically
propheticalness
propheticism
propheticly
prophetism
prophetize
prophetless
prophetlike
prophetry
prophetship
prophilosophical
prophloem
prophoric
prophototropic
prophototropism
prophylactic
prophylactical
prophylactically
prophylaxis
prophylaxy
prophyll
prophyllum
propination
propine
propinoic
propinquant
propinque
propinquity
propinquous
propiolaldehyde
propiolate
propiolic
propionate
propione
Propionibacterieae
Propionibacterium
propionic
propionitril
propionitrile
propionyl
Propithecus
propitiable
propitial
propitiate
propitiatingly
propitiation
propitiative
propitiator
propitiatorily
propitiatory
propitious
propitiously
propitiousness
proplasm
proplasma
proplastic
propless
propleural
propleuron
proplex
proplexus
Propliopithecus
propodeal
propodeon
propodeum
propodial
propodiale
propodite
propoditic
propodium
propolis
propolitical
propolization
propolize
propone
proponement
proponent
proponer
propons
Propontic
propooling
propopery
proportion
proportionability
proportionable
proportionableness
proportionably
proportional
proportionalism
proportionality
proportionally
proportionate
proportionately
proportionateness
proportioned
proportioner
proportionless
proportionment
proposable
proposal
proposant
propose
proposer
proposition
propositional
propositionally
propositionize
propositus
propound
propounder
propoundment
propoxy
proppage
propper
propraetor
propraetorial
propraetorian
proprecedent
propriation
proprietage
proprietarian
proprietariat
proprietarily
proprietary
proprietor
proprietorial
proprietorially
proprietorship
proprietory
proprietous
proprietress
proprietrix
propriety
proprioception
proprioceptive
proprioceptor
propriospinal
proprium
proprivilege
proproctor
proprofit
proprovincial
proprovost
props
propterygial
propterygium
proptosed
proptosis
propublication
propublicity
propugnacled
propugnaculum
propugnation
propugnator
propugner
propulsation
propulsatory
propulsion
propulsity
propulsive
propulsor
propulsory
propunishment
propupa
propupal
propurchase
Propus
propwood
propygidium
propyl
propylacetic
propylaeum
propylamine
propylation
propylene
propylic
propylidene
propylite
propylitic
propylitization
propylon
propyne
propynoic
proquaestor
proracing
prorailroad
prorata
proratable
prorate
proration
prore
proreader
prorealism
prorealist
prorealistic
proreality
prorean
prorebate
prorebel
prorecall
proreciprocation
prorecognition
proreconciliation
prorector
prorectorate
proredemption
proreduction
proreferendum
proreform
proreformist
proregent
prorelease
Proreptilia
proreptilian
proreption
prorepublican
proresearch
proreservationist
proresignation
prorestoration
prorestriction
prorevision
prorevisionist
prorevolution
prorevolutionary
prorevolutionist
prorhinal
Prorhipidoglossomorpha
proritual
proritualistic
prorogate
prorogation
prorogator
prorogue
proroguer
proromance
proromantic
proromanticism
proroyal
proroyalty
prorrhesis
prorsad
prorsal
proruption
prosabbath
prosabbatical
prosacral
prosaic
prosaical
prosaically
prosaicalness
prosaicism
prosaicness
prosaism
prosaist
prosar
Prosarthri
prosateur
proscapula
proscapular
proscenium
proscholastic
proschool
proscientific
proscolecine
proscolex
proscribable
proscribe
proscriber
proscript
proscription
proscriptional
proscriptionist
proscriptive
proscriptively
proscriptiveness
proscutellar
proscutellum
proscynemata
prose
prosecrecy
prosecretin
prosect
prosection
prosector
prosectorial
prosectorium
prosectorship
prosecutable
prosecute
prosecution
prosecutor
prosecutrix
proselenic
proselike
proselyte
proselyter
proselytical
proselytingly
proselytism
proselytist
proselytistic
proselytization
proselytize
proselytizer
proseman
proseminar
proseminary
proseminate
prosemination
prosencephalic
prosencephalon
prosenchyma
prosenchymatous
proseneschal
proser
Proserpinaca
prosethmoid
proseucha
proseuche
prosification
prosifier
prosify
prosiliency
prosilient
prosiliently
prosilverite
prosily
Prosimiae
prosimian
prosiness
prosing
prosingly
prosiphon
prosiphonal
prosiphonate
prosish
prosist
proslambanomenos
proslave
proslaver
proslavery
proslaveryism
prosneusis
proso
prosobranch
Prosobranchia
Prosobranchiata
prosobranchiate
prosocele
prosodal
prosode
prosodemic
prosodetic
prosodiac
prosodiacal
prosodiacally
prosodial
prosodially
prosodian
prosodic
prosodical
prosodically
prosodion
prosodist
prosodus
prosody
prosogaster
prosogyrate
prosogyrous
prosoma
prosomal
prosomatic
prosonomasia
prosopalgia
prosopalgic
prosopantritis
prosopectasia
prosophist
prosopic
prosopically
Prosopis
prosopite
Prosopium
prosoplasia
prosopography
prosopon
prosoponeuralgia
prosopoplegia
prosopoplegic
prosopopoeia
prosopopoeial
prosoposchisis
prosopospasm
prosopotocia
prosopyl
prosopyle
prosorus
prospect
prospection
prospective
prospectively
prospectiveness
prospectless
prospector
prospectus
prospectusless
prospeculation
prosper
prosperation
prosperity
prosperous
prosperously
prosperousness
prospicience
prosporangium
prosport
pross
prossy
prostatauxe
prostate
prostatectomy
prostatelcosis
prostatic
prostaticovesical
prostatism
prostatitic
prostatitis
prostatocystitis
prostatocystotomy
prostatodynia
prostatolith
prostatomegaly
prostatometer
prostatomyomectomy
prostatorrhea
prostatorrhoea
prostatotomy
prostatovesical
prostatovesiculectomy
prostatovesiculitis
prostemmate
prostemmatic
prosternal
prosternate
prosternum
prostheca
prosthenic
prosthesis
prosthetic
prosthetically
prosthetics
prosthetist
prosthion
prosthionic
prosthodontia
prosthodontist
Prostigmin
prostitute
prostitutely
prostitution
prostitutor
prostomial
prostomiate
prostomium
prostrate
prostration
prostrative
prostrator
prostrike
prostyle
prostylos
prosubmission
prosubscription
prosubstantive
prosubstitution
prosuffrage
prosupervision
prosupport
prosurgical
prosurrender
prosy
prosyllogism
prosyndicalism
prosyndicalist
protactic
protactinium
protagon
protagonism
protagonist
Protagorean
Protagoreanism
protalbumose
protamine
protandric
protandrism
protandrous
protandrously
protandry
protanomal
protanomalous
protanope
protanopia
protanopic
protargentum
protargin
Protargol
protariff
protarsal
protarsus
protasis
protaspis
protatic
protatically
protax
protaxation
protaxial
protaxis
prote
Protea
protea
Proteaceae
proteaceous
protead
protean
proteanly
proteanwise
protease
protechnical
protect
protectant
protectible
protecting
protectingly
protectingness
protection
protectional
protectionate
protectionism
protectionist
protectionize
protectionship
protective
protectively
protectiveness
Protectograph
protector
protectoral
protectorate
protectorial
protectorian
protectorless
protectorship
protectory
protectress
protectrix
protege
protegee
protegulum
proteic
Proteida
Proteidae
proteide
proteidean
proteidogenous
proteiform
protein
proteinaceous
proteinase
proteinic
proteinochromogen
proteinous
proteinuria
Proteles
Protelidae
Protelytroptera
protelytropteran
protelytropteron
protelytropterous
protemperance
protempirical
protemporaneous
protend
protension
protensity
protensive
protensively
proteoclastic
proteogenous
proteolysis
proteolytic
proteopectic
proteopexic
proteopexis
proteopexy
proteosaurid
Proteosauridae
Proteosaurus
proteose
Proteosoma
proteosomal
proteosome
proteosuria
protephemeroid
Protephemeroidea
proterandrous
proterandrousness
proterandry
proteranthous
proterobase
proteroglyph
Proteroglypha
proteroglyphic
proteroglyphous
proterogynous
proterogyny
proterothesis
proterotype
Proterozoic
protervity
protest
protestable
protestancy
protestant
Protestantish
Protestantishly
protestantism
Protestantize
Protestantlike
Protestantly
protestation
protestator
protestatory
protester
protestingly
protestive
protestor
protetrarch
Proteus
protevangel
protevangelion
protevangelium
protext
prothalamia
prothalamion
prothalamium
prothallia
prothallial
prothallic
prothalline
prothallium
prothalloid
prothallus
protheatrical
protheca
prothesis
prothetic
prothetical
prothetically
prothonotarial
prothonotariat
prothonotary
prothonotaryship
prothoracic
prothorax
prothrift
prothrombin
prothrombogen
prothyl
prothysteron
protide
protiodide
protist
Protista
protistan
protistic
protistological
protistologist
protistology
protiston
Protium
protium
proto
protoactinium
protoalbumose
protoamphibian
protoanthropic
protoapostate
protoarchitect
Protoascales
Protoascomycetes
protobacco
Protobasidii
Protobasidiomycetes
protobasidiomycetous
protobasidium
protobishop
protoblast
protoblastic
protoblattoid
Protoblattoidea
Protobranchia
Protobranchiata
protobranchiate
protocalcium
protocanonical
Protocaris
protocaseose
protocatechualdehyde
protocatechuic
Protoceras
Protoceratidae
Protoceratops
protocercal
protocerebral
protocerebrum
protochemist
protochemistry
protochloride
protochlorophyll
Protochorda
Protochordata
protochordate
protochromium
protochronicler
protocitizen
protoclastic
protocneme
Protococcaceae
protococcaceous
protococcal
Protococcales
protococcoid
Protococcus
protocol
protocolar
protocolary
Protocoleoptera
protocoleopteran
protocoleopteron
protocoleopterous
protocolist
protocolization
protocolize
protoconch
protoconchal
protocone
protoconid
protoconule
protoconulid
protocopper
protocorm
protodeacon
protoderm
protodevil
Protodonata
protodonatan
protodonate
protodont
Protodonta
protodramatic
protodynastic
protoelastose
protoepiphyte
protoforaminifer
protoforester
protogaster
protogelatose
protogenal
protogenes
protogenesis
protogenetic
protogenic
protogenist
Protogeometric
protogine
protoglobulose
protogod
protogonous
protogospel
protograph
protogynous
protogyny
protohematoblast
Protohemiptera
protohemipteran
protohemipteron
protohemipterous
protoheresiarch
Protohippus
protohistorian
protohistoric
protohistory
protohomo
protohuman
Protohydra
protohydrogen
Protohymenoptera
protohymenopteran
protohymenopteron
protohymenopterous
protoiron
protoleration
protoleucocyte
protoleukocyte
protolithic
protoliturgic
protolog
protologist
protoloph
protoma
protomagister
protomagnate
protomagnesium
protomala
protomalal
protomalar
protomammal
protomammalian
protomanganese
protomartyr
Protomastigida
protome
protomeristem
protomerite
protomeritic
protometal
protometallic
protometaphrast
Protominobacter
Protomonadina
protomonostelic
protomorph
protomorphic
Protomycetales
protomyosinose
proton
protone
protonegroid
protonema
protonemal
protonematal
protonematoid
protoneme
Protonemertini
protonephridial
protonephridium
protonephros
protoneuron
protoneurone
protonic
protonickel
protonitrate
protonotater
protonym
protonymph
protonymphal
protopapas
protopappas
protoparent
protopathia
protopathic
protopathy
protopatriarchal
protopatrician
protopattern
protopectin
protopectinase
protopepsia
Protoperlaria
protoperlarian
protophilosophic
protophloem
protophyll
Protophyta
protophyte
protophytic
protopin
protopine
protoplasm
protoplasma
protoplasmal
protoplasmatic
protoplasmic
protoplast
protoplastic
protopod
protopodial
protopodite
protopoditic
protopoetic
protopope
protoporphyrin
protopragmatic
protopresbyter
protopresbytery
protoprism
protoproteose
protoprotestant
protopteran
Protopteridae
protopteridophyte
protopterous
Protopterus
protopyramid
protore
protorebel
protoreligious
protoreptilian
Protorohippus
protorosaur
Protorosauria
protorosaurian
Protorosauridae
protorosauroid
Protorosaurus
Protorthoptera
protorthopteran
protorthopteron
protorthopterous
protosalt
protosaurian
protoscientific
Protoselachii
protosilicate
protosilicon
protosinner
Protosiphon
Protosiphonaceae
protosiphonaceous
protosocial
protosolution
protospasm
Protosphargis
Protospondyli
protospore
Protostega
Protostegidae
protostele
protostelic
protostome
protostrontium
protosulphate
protosulphide
protosyntonose
prototaxites
prototheca
protothecal
prototheme
protothere
Prototheria
prototherian
prototitanium
Prototracheata
prototraitor
prototroch
prototrochal
prototrophic
prototypal
prototype
prototypic
prototypical
prototypically
prototypographer
prototyrant
protovanadium
protoveratrine
protovertebra
protovertebral
protovestiary
protovillain
protovum
protoxide
protoxylem
Protozoa
protozoacidal
protozoacide
protozoal
protozoan
protozoea
protozoean
protozoiasis
protozoic
protozoological
protozoologist
protozoology
protozoon
protozoonal
Protracheata
protracheate
protract
protracted
protractedly
protractedness
protracter
protractible
protractile
protractility
protraction
protractive
protractor
protrade
protradition
protraditional
protragedy
protragical
protragie
protransfer
protranslation
protransubstantiation
protravel
protreasurer
protreaty
Protremata
protreptic
protreptical
protriaene
protropical
protrudable
protrude
protrudent
protrusible
protrusile
protrusion
protrusive
protrusively
protrusiveness
protuberance
protuberancy
protuberant
protuberantial
protuberantly
protuberantness
protuberate
protuberosity
protuberous
Protura
proturan
protutor
protutory
protyl
protyle
Protylopus
protype
proudful
proudhearted
proudish
proudishly
proudling
proudly
proudness
prouniformity
prounion
prounionist
prouniversity
proustite
provability
provable
provableness
provably
provaccinist
provand
provant
provascular
prove
provect
provection
proved
proveditor
provedly
provedor
provedore
proven
provenance
Provencal
Provencalize
Provence
Provencial
provender
provenience
provenient
provenly
proventricular
proventricule
proventriculus
prover
proverb
proverbial
proverbialism
proverbialist
proverbialize
proverbially
proverbic
proverbiologist
proverbiology
proverbize
proverblike
provicar
provicariate
providable
providance
provide
provided
providence
provident
providential
providentialism
providentially
providently
providentness
provider
providing
providore
providoring
province
provincial
provincialate
provincialism
provincialist
provinciality
provincialization
provincialize
provincially
provincialship
provinciate
provinculum
provine
proving
provingly
provision
provisional
provisionality
provisionally
provisionalness
provisionary
provisioner
provisioneress
provisionless
provisionment
provisive
proviso
provisor
provisorily
provisorship
provisory
provitamin
provivisection
provivisectionist
provocant
provocation
provocational
provocative
provocatively
provocativeness
provocator
provocatory
provokable
provoke
provokee
provoker
provoking
provokingly
provokingness
provolunteering
provost
provostal
provostess
provostorial
provostry
provostship
prow
prowar
prowarden
prowaterpower
prowed
prowersite
prowess
prowessed
prowessful
prowl
prowler
prowling
prowlingly
proxenet
proxenete
proxenetism
proxenos
proxenus
proxeny
proxically
proximad
proximal
proximally
proximate
proximately
proximateness
proximation
proximity
proximo
proximobuccal
proximolabial
proximolingual
proxy
proxyship
proxysm
prozone
prozoning
prozygapophysis
prozymite
prude
prudelike
prudely
Prudence
prudence
prudent
prudential
prudentialism
prudentialist
prudentiality
prudentially
prudentialness
prudently
prudery
prudish
prudishly
prudishness
prudist
prudity
Prudy
Prue
pruh
pruinate
pruinescence
pruinose
pruinous
prulaurasin
prunable
prunableness
prunably
Prunaceae
prunase
prunasin
prune
prunell
Prunella
prunella
prunelle
Prunellidae
prunello
pruner
prunetin
prunetol
pruniferous
pruniform
pruning
prunitrin
prunt
prunted
Prunus
prurience
pruriency
prurient
pruriently
pruriginous
prurigo
pruriousness
pruritic
pruritus
prusiano
Prussian
Prussianism
Prussianization
Prussianize
Prussianizer
prussiate
prussic
Prussification
Prussify
prut
prutah
pry
pryer
prying
pryingly
pryingness
pryler
pryproof
pryse
prytaneum
prytanis
prytanize
prytany
psalis
psalm
psalmic
psalmist
psalmister
psalmistry
psalmless
psalmodial
psalmodic
psalmodical
psalmodist
psalmodize
psalmody
psalmograph
psalmographer
psalmography
psalmy
psaloid
psalter
psalterial
psalterian
psalterion
psalterist
psalterium
psaltery
psaltes
psaltress
psammite
psammitic
psammocarcinoma
psammocharid
Psammocharidae
psammogenous
psammolithic
psammologist
psammology
psammoma
psammophile
psammophilous
Psammophis
psammophyte
psammophytic
psammosarcoma
psammotherapy
psammous
Psaronius
pschent
Psedera
Pselaphidae
Pselaphus
psellism
psellismus
psephism
psephisma
psephite
psephitic
psephomancy
Psephurus
Psetta
pseudaconine
pseudaconitine
pseudacusis
pseudalveolar
pseudambulacral
pseudambulacrum
pseudamoeboid
pseudamphora
pseudandry
pseudangina
pseudankylosis
pseudaphia
pseudaposematic
pseudaposporous
pseudapospory
pseudapostle
pseudarachnidan
pseudarthrosis
pseudataxic
pseudatoll
pseudaxine
pseudaxis
Pseudechis
pseudelephant
pseudelminth
pseudelytron
pseudembryo
pseudembryonic
pseudencephalic
pseudencephalus
pseudepigraph
pseudepigrapha
pseudepigraphal
pseudepigraphic
pseudepigraphical
pseudepigraphous
pseudepigraphy
pseudepiploic
pseudepiploon
pseudepiscopacy
pseudepiscopy
pseudepisematic
pseudesthesia
pseudhalteres
pseudhemal
pseudimaginal
pseudimago
pseudisodomum
pseudo
pseudoacaccia
pseudoacademic
pseudoacademical
pseudoaccidental
pseudoacid
pseudoaconitine
pseudoacromegaly
pseudoadiabatic
pseudoaesthetic
pseudoaffectionate
pseudoalkaloid
pseudoalum
pseudoalveolar
pseudoamateurish
pseudoamatory
pseudoanaphylactic
pseudoanaphylaxis
pseudoanatomic
pseudoanatomical
pseudoancestral
pseudoanemia
pseudoanemic
pseudoangelic
pseudoangina
pseudoankylosis
pseudoanthorine
pseudoanthropoid
pseudoanthropological
pseudoanthropology
pseudoantique
pseudoapologetic
pseudoapoplectic
pseudoapoplexy
pseudoappendicitis
pseudoaquatic
pseudoarchaic
pseudoarchaism
pseudoarchaist
pseudoaristocratic
pseudoarthrosis
pseudoarticulation
pseudoartistic
pseudoascetic
pseudoastringent
pseudoasymmetrical
pseudoasymmetry
pseudoataxia
pseudobacterium
pseudobasidium
pseudobenevolent
pseudobenthonic
pseudobenthos
pseudobinary
pseudobiological
pseudoblepsia
pseudoblepsis
pseudobrachial
pseudobrachium
pseudobranch
pseudobranchia
pseudobranchial
pseudobranchiate
Pseudobranchus
pseudobrookite
pseudobrotherly
pseudobulb
pseudobulbar
pseudobulbil
pseudobulbous
pseudobutylene
pseudocandid
pseudocapitulum
pseudocarbamide
pseudocarcinoid
pseudocarp
pseudocarpous
pseudocartilaginous
pseudocele
pseudocelian
pseudocelic
pseudocellus
pseudocentric
pseudocentrous
pseudocentrum
Pseudoceratites
pseudoceratitic
pseudocercaria
pseudoceryl
pseudocharitable
pseudochemical
pseudochina
pseudochromesthesia
pseudochromia
pseudochromosome
pseudochronism
pseudochronologist
pseudochrysalis
pseudochrysolite
pseudochylous
pseudocirrhosis
pseudoclassic
pseudoclassical
pseudoclassicism
pseudoclerical
Pseudococcinae
Pseudococcus
pseudococtate
pseudocollegiate
pseudocolumella
pseudocolumellar
pseudocommissure
pseudocommisural
pseudocompetitive
pseudoconcha
pseudoconclude
pseudocone
pseudoconglomerate
pseudoconglomeration
pseudoconhydrine
pseudoconjugation
pseudoconservative
pseudocorneous
pseudocortex
pseudocosta
pseudocotyledon
pseudocotyledonal
pseudocritical
pseudocroup
pseudocrystalline
pseudocubic
pseudocultivated
pseudocultural
pseudocumene
pseudocumenyl
pseudocumidine
pseudocumyl
pseudocyclosis
pseudocyesis
pseudocyst
pseudodeltidium
pseudodementia
pseudodemocratic
pseudoderm
pseudodermic
pseudodiagnosis
pseudodiastolic
pseudodiphtheria
pseudodiphtheritic
pseudodipteral
pseudodipterally
pseudodipteros
pseudodont
pseudodox
pseudodoxal
pseudodoxy
pseudodramatic
pseudodysentery
pseudoedema
pseudoelectoral
pseudoembryo
pseudoembryonic
pseudoemotional
pseudoencephalitic
pseudoenthusiastic
pseudoephedrine
pseudoepiscopal
pseudoequalitarian
pseudoerotic
pseudoeroticism
pseudoerysipelas
pseudoerysipelatous
pseudoerythrin
pseudoethical
pseudoetymological
pseudoeugenics
pseudoevangelical
pseudofamous
pseudofarcy
pseudofeminine
pseudofever
pseudofeverish
pseudofilaria
pseudofilarian
pseudofinal
pseudofluctuation
pseudofluorescence
pseudofoliaceous
pseudoform
pseudofossil
pseudogalena
pseudoganglion
pseudogaseous
pseudogaster
pseudogastrula
pseudogeneral
pseudogeneric
pseudogenerous
pseudogenteel
pseudogenus
pseudogeometry
pseudogermanic
pseudogeusia
pseudogeustia
pseudoglanders
pseudoglioma
pseudoglobulin
pseudoglottis
pseudograph
pseudographeme
pseudographer
pseudographia
pseudographize
pseudography
pseudograsserie
Pseudogryphus
pseudogyne
pseudogynous
pseudogyny
pseudogyrate
pseudohallucination
pseudohallucinatory
pseudohalogen
pseudohemal
pseudohermaphrodite
pseudohermaphroditic
pseudohermaphroditism
pseudoheroic
pseudohexagonal
pseudohistoric
pseudohistorical
pseudoholoptic
pseudohuman
pseudohydrophobia
pseudohyoscyamine
pseudohypertrophic
pseudohypertrophy
pseudoidentical
pseudoimpartial
pseudoindependent
pseudoinfluenza
pseudoinsane
pseudoinsoluble
pseudoisatin
pseudoism
pseudoisomer
pseudoisomeric
pseudoisomerism
pseudoisotropy
pseudojervine
pseudolabial
pseudolabium
pseudolalia
Pseudolamellibranchia
Pseudolamellibranchiata
pseudolamellibranchiate
pseudolaminated
Pseudolarix
pseudolateral
pseudolatry
pseudolegal
pseudolegendary
pseudoleucite
pseudoleucocyte
pseudoleukemia
pseudoleukemic
pseudoliberal
pseudolichen
pseudolinguistic
pseudoliterary
pseudolobar
pseudological
pseudologically
pseudologist
pseudologue
pseudology
pseudolunule
pseudomalachite
pseudomalaria
pseudomancy
pseudomania
pseudomaniac
pseudomantic
pseudomantist
pseudomasculine
pseudomedical
pseudomedieval
pseudomelanosis
pseudomembrane
pseudomembranous
pseudomeningitis
pseudomenstruation
pseudomer
pseudomeric
pseudomerism
pseudomery
pseudometallic
pseudometameric
pseudometamerism
pseudomica
pseudomilitarist
pseudomilitaristic
pseudomilitary
pseudoministerial
pseudomiraculous
pseudomitotic
pseudomnesia
pseudomodern
pseudomodest
Pseudomonas
pseudomonastic
pseudomonoclinic
pseudomonocotyledonous
pseudomonocyclic
pseudomonotropy
pseudomoral
pseudomorph
pseudomorphia
pseudomorphic
pseudomorphine
pseudomorphism
pseudomorphose
pseudomorphosis
pseudomorphous
pseudomorula
pseudomorular
pseudomucin
pseudomucoid
pseudomultilocular
pseudomultiseptate
pseudomythical
pseudonarcotic
pseudonational
pseudonavicella
pseudonavicellar
pseudonavicula
pseudonavicular
pseudoneuropter
Pseudoneuroptera
pseudoneuropteran
pseudoneuropterous
pseudonitrole
pseudonitrosite
pseudonuclein
pseudonucleolus
pseudonychium
pseudonym
pseudonymal
pseudonymic
pseudonymity
pseudonymous
pseudonymously
pseudonymousness
pseudonymuncle
pseudonymuncule
pseudopapaverine
pseudoparalysis
pseudoparalytic
pseudoparaplegia
pseudoparasitic
pseudoparasitism
pseudoparenchyma
pseudoparenchymatous
pseudoparenchyme
pseudoparesis
pseudoparthenogenesis
pseudopatriotic
pseudopediform
pseudopelletierine
pseudopercular
pseudoperculate
pseudoperculum
pseudoperianth
pseudoperidium
pseudoperiodic
pseudoperipteral
pseudopermanent
pseudoperoxide
pseudoperspective
Pseudopeziza
pseudophallic
pseudophellandrene
pseudophenanthrene
pseudophenanthroline
pseudophenocryst
pseudophilanthropic
pseudophilosophical
Pseudophoenix
pseudopionnotes
pseudopious
pseudoplasm
pseudoplasma
pseudoplasmodium
pseudopneumonia
pseudopod
pseudopodal
pseudopodia
pseudopodial
pseudopodian
pseudopodiospore
pseudopodium
pseudopoetic
pseudopoetical
pseudopolitic
pseudopolitical
pseudopopular
pseudopore
pseudoporphyritic
pseudopregnancy
pseudopregnant
pseudopriestly
pseudoprimitive
pseudoprimitivism
pseudoprincely
pseudoproboscis
pseudoprofessional
pseudoprofessorial
pseudoprophetic
pseudoprophetical
pseudoprosperous
pseudopsia
pseudopsychological
pseudoptics
pseudoptosis
pseudopupa
pseudopupal
pseudopurpurin
pseudopyriform
pseudoquinol
pseudorabies
pseudoracemic
pseudoracemism
pseudoramose
pseudoramulus
pseudorealistic
pseudoreduction
pseudoreformed
pseudoregal
pseudoreligious
pseudoreminiscence
pseudorganic
pseudorheumatic
pseudorhombohedral
pseudoromantic
pseudorunic
pseudosacred
pseudosacrilegious
pseudosalt
pseudosatirical
pseudoscarlatina
Pseudoscarus
pseudoscholarly
pseudoscholastic
pseudoscientific
Pseudoscines
pseudoscinine
pseudosclerosis
pseudoscope
pseudoscopic
pseudoscopically
pseudoscopy
pseudoscorpion
Pseudoscorpiones
Pseudoscorpionida
pseudoscutum
pseudosematic
pseudosensational
pseudoseptate
pseudoservile
pseudosessile
pseudosiphonal
pseudosiphuncal
pseudoskeletal
pseudoskeleton
pseudoskink
pseudosmia
pseudosocial
pseudosocialistic
pseudosolution
pseudosoph
pseudosopher
pseudosophical
pseudosophist
pseudosophy
pseudospectral
pseudosperm
pseudospermic
pseudospermium
pseudospermous
pseudosphere
pseudospherical
pseudospiracle
pseudospiritual
pseudosporangium
pseudospore
pseudosquamate
pseudostalactite
pseudostalactitical
pseudostalagmite
pseudostalagmitical
pseudostereoscope
pseudostereoscopic
pseudostereoscopism
pseudostigma
pseudostigmatic
pseudostoma
pseudostomatous
pseudostomous
pseudostratum
pseudosubtle
Pseudosuchia
pseudosuchian
pseudosweating
pseudosyllogism
pseudosymmetric
pseudosymmetrical
pseudosymmetry
pseudosymptomatic
pseudosyphilis
pseudosyphilitic
pseudotabes
pseudotachylite
pseudotetanus
pseudotetragonal
Pseudotetramera
pseudotetrameral
pseudotetramerous
pseudotrachea
pseudotracheal
pseudotribal
pseudotributary
Pseudotrimera
pseudotrimeral
pseudotrimerous
pseudotropine
Pseudotsuga
pseudotubercular
pseudotuberculosis
pseudotuberculous
pseudoturbinal
pseudotyphoid
pseudoval
pseudovarian
pseudovary
pseudovelar
pseudovelum
pseudoventricle
pseudoviaduct
pseudoviperine
pseudoviscosity
pseudoviscous
pseudovolcanic
pseudovolcano
pseudovum
pseudowhorl
pseudoxanthine
pseudoyohimbine
pseudozealot
pseudozoea
pseudozoogloeal
psha
Pshav
pshaw
psi
Psidium
psilanthropic
psilanthropism
psilanthropist
psilanthropy
psiloceran
Psiloceras
psiloceratan
psiloceratid
Psiloceratidae
psiloi
psilology
psilomelane
psilomelanic
Psilophytales
psilophyte
Psilophyton
psilosis
psilosopher
psilosophy
Psilotaceae
psilotaceous
psilothrum
psilotic
Psilotum
psithurism
Psithyrus
psittaceous
psittaceously
Psittaci
Psittacidae
Psittaciformes
Psittacinae
psittacine
psittacinite
psittacism
psittacistic
Psittacomorphae
psittacomorphic
psittacosis
Psittacus
psoadic
psoas
psoatic
psocid
Psocidae
psocine
psoitis
psomophagic
psomophagist
psomophagy
psora
Psoralea
psoriasic
psoriasiform
psoriasis
psoriatic
psoriatiform
psoric
psoroid
Psorophora
psorophthalmia
psorophthalmic
Psoroptes
psoroptic
psorosis
psorosperm
psorospermial
psorospermiasis
psorospermic
psorospermiform
psorospermosis
psorous
pssimistical
pst
psych
psychagogic
psychagogos
psychagogue
psychagogy
psychal
psychalgia
psychanalysis
psychanalysist
psychanalytic
psychasthenia
psychasthenic
Psyche
psyche
Psychean
psycheometry
psychesthesia
psychesthetic
psychiasis
psychiater
psychiatria
psychiatric
psychiatrical
psychiatrically
psychiatrist
psychiatrize
psychiatry
psychic
psychical
psychically
Psychichthys
psychicism
psychicist
psychics
psychid
Psychidae
psychism
psychist
psychoanalysis
psychoanalyst
psychoanalytic
psychoanalytical
psychoanalytically
psychoanalyze
psychoanalyzer
psychoautomatic
psychobiochemistry
psychobiologic
psychobiological
psychobiology
psychobiotic
psychocatharsis
psychoclinic
psychoclinical
psychoclinicist
Psychoda
psychodiagnostics
Psychodidae
psychodispositional
psychodrama
psychodynamic
psychodynamics
psychoeducational
psychoepilepsy
psychoethical
psychofugal
psychogalvanic
psychogalvanometer
psychogenesis
psychogenetic
psychogenetical
psychogenetically
psychogenetics
psychogenic
psychogeny
psychognosis
psychognostic
psychognosy
psychogonic
psychogonical
psychogony
psychogram
psychograph
psychographer
psychographic
psychographist
psychography
psychoid
psychokinesia
psychokinesis
psychokinetic
psychokyme
psycholepsy
psycholeptic
psychologer
psychologian
psychologic
psychological
psychologically
psychologics
psychologism
psychologist
psychologize
psychologue
psychology
psychomachy
psychomancy
psychomantic
psychometer
psychometric
psychometrical
psychometrically
psychometrician
psychometrics
psychometrist
psychometrize
psychometry
psychomonism
psychomoral
psychomorphic
psychomorphism
psychomotility
psychomotor
psychon
psychoneural
psychoneurological
psychoneurosis
psychoneurotic
psychonomic
psychonomics
psychonomy
psychony
psychoorganic
psychopannychian
psychopannychism
psychopannychist
psychopannychistic
psychopannychy
psychopanychite
psychopath
psychopathia
psychopathic
psychopathist
psychopathologic
psychopathological
psychopathologist
psychopathy
psychopetal
psychophobia
psychophysic
psychophysical
psychophysically
psychophysicist
psychophysics
psychophysiologic
psychophysiological
psychophysiologically
psychophysiologist
psychophysiology
psychoplasm
psychopomp
psychopompos
psychorealism
psychorealist
psychorealistic
psychoreflex
psychorhythm
psychorhythmia
psychorhythmic
psychorhythmical
psychorhythmically
psychorrhagic
psychorrhagy
psychosarcous
psychosensorial
psychosensory
psychoses
psychosexual
psychosexuality
psychosexually
psychosis
psychosocial
psychosomatic
psychosomatics
psychosome
psychosophy
psychostasy
psychostatic
psychostatical
psychostatically
psychostatics
psychosurgeon
psychosurgery
psychosynthesis
psychosynthetic
psychotaxis
psychotechnical
psychotechnician
psychotechnics
psychotechnological
psychotechnology
psychotheism
psychotherapeutic
psychotherapeutical
psychotherapeutics
psychotherapeutist
psychotherapist
psychotherapy
psychotic
Psychotria
psychotrine
psychovital
Psychozoic
psychroesthesia
psychrograph
psychrometer
psychrometric
psychrometrical
psychrometry
psychrophile
psychrophilic
psychrophobia
psychrophore
psychrophyte
psychurgy
psykter
Psylla
psylla
psyllid
Psyllidae
psyllium
ptarmic
Ptarmica
ptarmical
ptarmigan
Ptelea
Ptenoglossa
ptenoglossate
Pteranodon
pteranodont
Pteranodontidae
pteraspid
Pteraspidae
Pteraspis
ptereal
pterergate
Pterian
pteric
Pterichthyodes
Pterichthys
pterideous
pteridium
pteridography
pteridoid
pteridological
pteridologist
pteridology
pteridophilism
pteridophilist
pteridophilistic
Pteridophyta
pteridophyte
pteridophytic
pteridophytous
pteridosperm
Pteridospermae
Pteridospermaphyta
pteridospermaphytic
pteridospermous
pterion
Pteris
Pterobranchia
pterobranchiate
pterocarpous
Pterocarpus
Pterocarya
Pterocaulon
Pterocera
Pteroceras
Pterocles
Pterocletes
Pteroclidae
Pteroclomorphae
pteroclomorphic
pterodactyl
Pterodactyli
pterodactylian
pterodactylic
pterodactylid
Pterodactylidae
pterodactyloid
pterodactylous
Pterodactylus
pterographer
pterographic
pterographical
pterography
pteroid
pteroma
pteromalid
Pteromalidae
Pteromys
pteropaedes
pteropaedic
pteropegal
pteropegous
pteropegum
pterophorid
Pterophoridae
Pterophorus
Pterophryne
pteropid
Pteropidae
pteropine
pteropod
Pteropoda
pteropodal
pteropodan
pteropodial
Pteropodidae
pteropodium
pteropodous
Pteropsida
Pteropus
pterosaur
Pterosauri
Pterosauria
pterosaurian
pterospermous
Pterospora
Pterostemon
Pterostemonaceae
pterostigma
pterostigmal
pterostigmatic
pterostigmatical
pterotheca
pterothorax
pterotic
pteroylglutamic
pterygial
pterygiophore
pterygium
pterygobranchiate
pterygode
pterygodum
Pterygogenea
pterygoid
pterygoidal
pterygoidean
pterygomalar
pterygomandibular
pterygomaxillary
pterygopalatal
pterygopalatine
pterygopharyngeal
pterygopharyngean
pterygophore
pterygopodium
pterygoquadrate
pterygosphenoid
pterygospinous
pterygostaphyline
Pterygota
pterygote
pterygotous
pterygotrabecular
Pterygotus
pteryla
pterylographic
pterylographical
pterylography
pterylological
pterylology
pterylosis
Ptilichthyidae
Ptiliidae
Ptilimnium
ptilinal
ptilinum
Ptilocercus
Ptilonorhynchidae
Ptilonorhynchinae
ptilopaedes
ptilopaedic
ptilosis
Ptilota
ptinid
Ptinidae
ptinoid
Ptinus
ptisan
ptochocracy
ptochogony
ptochology
Ptolemaean
Ptolemaian
Ptolemaic
Ptolemaical
Ptolemaism
Ptolemaist
Ptolemean
Ptolemy
ptomain
ptomaine
ptomainic
ptomatropine
ptosis
ptotic
ptyalagogic
ptyalagogue
ptyalectasis
ptyalin
ptyalism
ptyalize
ptyalocele
ptyalogenic
ptyalolith
ptyalolithiasis
ptyalorrhea
Ptychoparia
ptychoparid
ptychopariid
ptychopterygial
ptychopterygium
Ptychosperma
ptysmagogue
ptyxis
pu
pua
puan
pub
pubal
pubble
puberal
pubertal
pubertic
puberty
puberulent
puberulous
pubes
pubescence
pubescency
pubescent
pubian
pubic
pubigerous
pubiotomy
pubis
public
Publican
publican
publicanism
publication
publichearted
publicheartedness
publicism
publicist
publicity
publicize
publicly
publicness
Publilian
publish
publishable
publisher
publisheress
publishership
publishment
pubococcygeal
pubofemoral
puboiliac
puboischiac
puboischial
puboischiatic
puboprostatic
puborectalis
pubotibial
pubourethral
pubovesical
Puccinia
Pucciniaceae
pucciniaceous
puccinoid
puccoon
puce
pucelage
pucellas
pucelle
Puchanahua
pucherite
puchero
puck
pucka
puckball
pucker
puckerbush
puckerel
puckerer
puckermouth
puckery
puckfist
puckish
puckishly
puckishness
puckle
pucklike
puckling
puckneedle
puckrel
puckster
pud
puddee
puddening
pudder
pudding
puddingberry
puddinghead
puddingheaded
puddinghouse
puddinglike
puddingwife
puddingy
puddle
puddled
puddlelike
puddler
puddling
puddly
puddock
puddy
pudency
pudenda
pudendal
pudendous
pudendum
pudent
pudge
pudgily
pudginess
pudgy
pudiano
pudibund
pudibundity
pudic
pudical
pudicitia
pudicity
pudsey
pudsy
Pudu
pudu
pueblito
Pueblo
pueblo
Puebloan
puebloization
puebloize
Puelche
Puelchean
Pueraria
puerer
puericulture
puerile
puerilely
puerileness
puerilism
puerility
puerman
puerpera
puerperal
puerperalism
puerperant
puerperium
puerperous
puerpery
puff
puffback
puffball
puffbird
puffed
puffer
puffery
puffily
puffin
puffiness
puffinet
puffing
puffingly
Puffinus
pufflet
puffwig
puffy
pug
pugged
pugger
puggi
pugginess
pugging
puggish
puggle
puggree
puggy
pugh
pugil
pugilant
pugilism
pugilist
pugilistic
pugilistical
pugilistically
puglianite
pugman
pugmill
pugmiller
pugnacious
pugnaciously
pugnaciousness
pugnacity
Puinavi
Puinavian
Puinavis
puisne
puissance
puissant
puissantly
puissantness
puist
puistie
puja
Pujunan
puka
pukatea
pukateine
puke
pukeko
puker
pukeweed
Pukhtun
pukish
pukishness
pukras
puku
puky
pul
pulahan
pulahanism
pulasan
pulaskite
Pulaya
Pulayan
pulchrify
pulchritude
pulchritudinous
pule
pulegol
pulegone
puler
Pulex
pulghere
puli
Pulian
pulicarious
pulicat
pulicene
pulicid
Pulicidae
pulicidal
pulicide
pulicine
pulicoid
pulicose
pulicosity
pulicous
puling
pulingly
pulish
pulk
pulka
pull
pullable
pullback
pullboat
pulldevil
pulldoo
pulldown
pulldrive
pullen
puller
pullery
pullet
pulley
pulleyless
pulli
Pullman
Pullmanize
pullorum
pullulant
pullulate
pullulation
pullus
pulmobranchia
pulmobranchial
pulmobranchiate
pulmocardiac
pulmocutaneous
pulmogastric
pulmometer
pulmometry
pulmonal
pulmonar
Pulmonaria
pulmonarian
pulmonary
Pulmonata
pulmonate
pulmonated
pulmonectomy
pulmonic
pulmonifer
Pulmonifera
pulmoniferous
pulmonitis
Pulmotor
pulmotracheal
Pulmotrachearia
pulmotracheary
pulmotracheate
pulp
pulpaceous
pulpal
pulpalgia
pulpamenta
pulpboard
pulpectomy
pulpefaction
pulper
pulpifier
pulpify
pulpily
pulpiness
pulpit
pulpital
pulpitarian
pulpiteer
pulpiter
pulpitful
pulpitic
pulpitical
pulpitically
pulpitis
pulpitish
pulpitism
pulpitize
pulpitless
pulpitly
pulpitolatry
pulpitry
pulpless
pulplike
pulpotomy
pulpous
pulpousness
pulpstone
pulpwood
pulpy
pulque
pulsant
pulsatance
pulsate
pulsatile
pulsatility
Pulsatilla
pulsation
pulsational
pulsative
pulsatively
pulsator
pulsatory
pulse
pulseless
pulselessly
pulselessness
pulselike
pulsellum
pulsidge
pulsific
pulsimeter
pulsion
pulsive
pulsojet
pulsometer
pultaceous
pulton
pulu
pulveraceous
pulverant
pulverate
pulveration
pulvereous
pulverin
pulverizable
pulverizate
pulverization
pulverizator
pulverize
pulverizer
pulverous
pulverulence
pulverulent
pulverulently
pulvic
pulvil
pulvillar
pulvilliform
pulvillus
pulvinar
Pulvinaria
pulvinarian
pulvinate
pulvinated
pulvinately
pulvination
pulvinic
pulviniform
pulvino
pulvinule
pulvinulus
pulvinus
pulviplume
pulwar
puly
puma
Pume
pumicate
pumice
pumiced
pumiceous
pumicer
pumiciform
pumicose
pummel
pummice
pump
pumpable
pumpage
pumpellyite
pumper
pumpernickel
pumpkin
pumpkinification
pumpkinify
pumpkinish
pumpkinity
pumple
pumpless
pumplike
pumpman
pumpsman
pumpwright
pun
puna
punaise
punalua
punaluan
Punan
punatoo
punch
punchable
punchboard
puncheon
puncher
punchinello
punching
punchless
punchlike
punchproof
punchy
punct
punctal
punctate
punctated
punctation
punctator
puncticular
puncticulate
puncticulose
punctiform
punctiliar
punctilio
punctiliomonger
punctiliosity
punctilious
punctiliously
punctiliousness
punctist
punctographic
punctual
punctualist
punctuality
punctually
punctualness
punctuate
punctuation
punctuational
punctuationist
punctuative
punctuator
punctuist
punctulate
punctulated
punctulation
punctule
punctulum
punctum
puncturation
puncture
punctured
punctureless
punctureproof
puncturer
pundigrion
pundit
pundita
punditic
punditically
punditry
pundonor
pundum
puneca
pung
punga
pungapung
pungar
pungence
pungency
pungent
pungently
punger
pungey
pungi
pungle
pungled
Punic
Punica
Punicaceae
punicaceous
puniceous
punicial
punicin
punicine
punily
puniness
punish
punishability
punishable
punishableness
punishably
punisher
punishment
punishmentproof
punition
punitional
punitionally
punitive
punitively
punitiveness
punitory
Punjabi
punjum
punk
punkah
punketto
punkie
punkwood
punky
punless
punlet
punnable
punnage
punner
punnet
punnic
punnical
punnigram
punningly
punnology
Puno
punproof
punster
punstress
punt
punta
puntabout
puntal
puntel
punter
punti
puntil
puntist
Puntlatsh
punto
puntout
puntsman
punty
puny
punyish
punyism
pup
pupa
pupahood
pupal
puparial
puparium
pupate
pupation
pupelo
Pupidae
pupiferous
pupiform
pupigenous
pupigerous
pupil
pupilability
pupilage
pupilar
pupilate
pupildom
pupiled
pupilize
pupillarity
pupillary
pupilless
Pupillidae
pupillometer
pupillometry
pupilloscope
pupilloscoptic
pupilloscopy
Pupipara
pupiparous
Pupivora
pupivore
pupivorous
pupoid
puppet
puppetdom
puppeteer
puppethood
puppetish
puppetism
puppetize
puppetlike
puppetly
puppetman
puppetmaster
puppetry
puppify
puppily
Puppis
puppy
puppydom
puppyfish
puppyfoot
puppyhood
puppyish
puppyism
puppylike
puppysnatch
pupulo
Pupuluca
pupunha
Puquina
Puquinan
pur
purana
puranic
puraque
Purasati
Purbeck
Purbeckian
purblind
purblindly
purblindness
purchasability
purchasable
purchase
purchaser
purchasery
purdah
purdy
pure
pureblood
purebred
pured
puree
purehearted
purely
pureness
purer
purfle
purfled
purfler
purfling
purfly
purga
purgation
purgative
purgatively
purgatorial
purgatorian
purgatory
purge
purgeable
purger
purgery
purging
purificant
purification
purificative
purificator
purificatory
purifier
puriform
purify
purine
puriri
purism
purist
puristic
puristical
Puritan
puritandom
Puritaness
puritanic
puritanical
puritanically
puritanicalness
Puritanism
puritanism
Puritanize
Puritanizer
puritanlike
Puritanly
puritano
purity
Purkinje
Purkinjean
purl
purler
purlhouse
purlicue
purlieu
purlieuman
purlin
purlman
purloin
purloiner
purohepatitis
purolymph
puromucous
purpart
purparty
purple
purplelip
purplely
purpleness
purplescent
purplewood
purplewort
purplish
purplishness
purply
purport
purportless
purpose
purposedly
purposeful
purposefully
purposefulness
purposeless
purposelessly
purposelessness
purposelike
purposely
purposer
purposive
purposively
purposiveness
purposivism
purposivist
purposivistic
purpresture
purpura
purpuraceous
purpurate
purpure
purpureal
purpurean
purpureous
purpurescent
purpuric
purpuriferous
purpuriform
purpurigenous
purpurin
purpurine
purpuriparous
purpurite
purpurize
purpurogallin
purpurogenous
purpuroid
purpuroxanthin
purr
purre
purree
purreic
purrel
purrer
purring
purringly
purrone
purry
purse
pursed
purseful
purseless
purselike
purser
pursership
Purshia
pursily
pursiness
purslane
purslet
pursley
pursuable
pursual
pursuance
pursuant
pursuantly
pursue
pursuer
pursuit
pursuitmeter
pursuivant
pursy
purtenance
Puru
Puruha
purulence
purulency
purulent
purulently
puruloid
Purupuru
purusha
purushartha
purvey
purveyable
purveyal
purveyance
purveyancer
purveyor
purveyoress
purview
purvoe
purwannah
pus
Puschkinia
Puseyism
Puseyistical
Puseyite
push
pushball
pushcart
pusher
pushful
pushfully
pushfulness
pushing
pushingly
pushingness
pushmobile
pushover
pushpin
Pushtu
pushwainling
pusillanimity
pusillanimous
pusillanimously
pusillanimousness
puss
pusscat
pussley
pusslike
pussy
pussycat
pussyfoot
pussyfooted
pussyfooter
pussyfooting
pussyfootism
pussytoe
pustulant
pustular
pustulate
pustulated
pustulation
pustulatous
pustule
pustuled
pustulelike
pustuliform
pustulose
pustulous
put
putage
putamen
putaminous
putanism
putation
putationary
putative
putatively
putback
putchen
putcher
puteal
putelee
puther
puthery
putid
putidly
putidness
putlog
putois
Putorius
putredinal
putredinous
putrefacient
putrefactible
putrefaction
putrefactive
putrefactiveness
putrefiable
putrefier
putrefy
putresce
putrescence
putrescency
putrescent
putrescibility
putrescible
putrescine
putricide
putrid
putridity
putridly
putridness
putrifacted
putriform
putrilage
putrilaginous
putrilaginously
putschism
putschist
putt
puttee
putter
putterer
putteringly
puttier
puttock
putty
puttyblower
puttyhead
puttyhearted
puttylike
puttyroot
puttywork
puture
puxy
Puya
Puyallup
puzzle
puzzleation
puzzled
puzzledly
puzzledness
puzzledom
puzzlehead
puzzleheaded
puzzleheadedly
puzzleheadedness
puzzleman
puzzlement
puzzlepate
puzzlepated
puzzlepatedness
puzzler
puzzling
puzzlingly
puzzlingness
pya
pyal
pyarthrosis
pyche
Pycnanthemum
pycnia
pycnial
pycnid
pycnidia
pycnidial
pycnidiophore
pycnidiospore
pycnidium
pycniospore
pycnite
pycnium
Pycnocoma
pycnoconidium
pycnodont
Pycnodonti
Pycnodontidae
pycnodontoid
Pycnodus
pycnogonid
Pycnogonida
pycnogonidium
pycnogonoid
pycnometer
pycnometochia
pycnometochic
pycnomorphic
pycnomorphous
Pycnonotidae
Pycnonotinae
pycnonotine
Pycnonotus
pycnosis
pycnospore
pycnosporic
pycnostyle
pycnotic
pyelectasis
pyelic
pyelitic
pyelitis
pyelocystitis
pyelogram
pyelograph
pyelographic
pyelography
pyelolithotomy
pyelometry
pyelonephritic
pyelonephritis
pyelonephrosis
pyeloplasty
pyeloscopy
pyelotomy
pyeloureterogram
pyemesis
pyemia
pyemic
pygal
pygalgia
pygarg
pygargus
pygidial
pygidid
Pygididae
Pygidium
pygidium
pygmaean
Pygmalion
pygmoid
Pygmy
pygmy
pygmydom
pygmyhood
pygmyish
pygmyism
pygmyship
pygmyweed
Pygobranchia
Pygobranchiata
pygobranchiate
pygofer
pygopagus
pygopod
Pygopodes
Pygopodidae
pygopodine
pygopodous
Pygopus
pygostyle
pygostyled
pygostylous
pyic
pyin
pyjama
pyjamaed
pyke
pyknatom
pyknic
pyknotic
pyla
Pylades
pylagore
pylangial
pylangium
pylar
pylephlebitic
pylephlebitis
pylethrombophlebitis
pylethrombosis
pylic
pylon
pyloralgia
pylorectomy
pyloric
pyloristenosis
pyloritis
pylorocleisis
pylorodilator
pylorogastrectomy
pyloroplasty
pyloroptosis
pyloroschesis
pyloroscirrhus
pyloroscopy
pylorospasm
pylorostenosis
pylorostomy
pylorus
pyobacillosis
pyocele
pyoctanin
pyocyanase
pyocyanin
pyocyst
pyocyte
pyodermatitis
pyodermatosis
pyodermia
pyodermic
pyogenesis
pyogenetic
pyogenic
pyogenin
pyogenous
pyohemothorax
pyoid
pyolabyrinthitis
pyolymph
pyometra
pyometritis
pyonephritis
pyonephrosis
pyonephrotic
pyopericarditis
pyopericardium
pyoperitoneum
pyoperitonitis
pyophagia
pyophthalmia
pyophylactic
pyoplania
pyopneumocholecystitis
pyopneumocyst
pyopneumopericardium
pyopneumoperitoneum
pyopneumoperitonitis
pyopneumothorax
pyopoiesis
pyopoietic
pyoptysis
pyorrhea
pyorrheal
pyorrheic
pyosalpingitis
pyosalpinx
pyosepticemia
pyosepticemic
pyosis
pyospermia
pyotherapy
pyothorax
pyotoxinemia
pyoureter
pyovesiculosis
pyoxanthose
pyr
pyracanth
Pyracantha
Pyraceae
pyracene
pyral
Pyrales
pyralid
Pyralidae
pyralidan
pyralidid
Pyralididae
pyralidiform
Pyralidoidea
pyralis
pyraloid
Pyrameis
pyramid
pyramidaire
pyramidal
pyramidale
pyramidalis
Pyramidalism
Pyramidalist
pyramidally
pyramidate
Pyramidella
pyramidellid
Pyramidellidae
pyramider
pyramides
pyramidia
pyramidic
pyramidical
pyramidically
pyramidicalness
pyramidion
Pyramidist
pyramidize
pyramidlike
pyramidoattenuate
pyramidoidal
pyramidologist
pyramidoprismatic
pyramidwise
pyramoidal
pyran
pyranometer
pyranyl
pyrargyrite
Pyrausta
Pyraustinae
pyrazine
pyrazole
pyrazoline
pyrazolone
pyrazolyl
pyre
pyrectic
pyrena
pyrene
Pyrenean
pyrenematous
pyrenic
pyrenin
pyrenocarp
pyrenocarpic
pyrenocarpous
Pyrenochaeta
pyrenodean
pyrenodeine
pyrenodeous
pyrenoid
pyrenolichen
Pyrenomycetales
pyrenomycete
Pyrenomycetes
Pyrenomycetineae
pyrenomycetous
Pyrenopeziza
pyrethrin
Pyrethrum
pyrethrum
pyretic
pyreticosis
pyretogenesis
pyretogenetic
pyretogenic
pyretogenous
pyretography
pyretology
pyretolysis
pyretotherapy
pyrewinkes
Pyrex
pyrex
pyrexia
pyrexial
pyrexic
pyrexical
pyrgeometer
pyrgocephalic
pyrgocephaly
pyrgoidal
pyrgologist
pyrgom
pyrheliometer
pyrheliometric
pyrheliometry
pyrheliophor
pyribole
pyridazine
pyridic
pyridine
pyridinium
pyridinize
pyridone
pyridoxine
pyridyl
pyriform
pyriformis
pyrimidine
pyrimidyl
pyritaceous
pyrite
pyrites
pyritic
pyritical
pyritiferous
pyritization
pyritize
pyritohedral
pyritohedron
pyritoid
pyritology
pyritous
pyro
pyroacetic
pyroacid
pyroantimonate
pyroantimonic
pyroarsenate
pyroarsenic
pyroarsenious
pyroarsenite
pyrobelonite
pyrobituminous
pyroborate
pyroboric
pyrocatechin
pyrocatechinol
pyrocatechol
pyrocatechuic
pyrocellulose
pyrochemical
pyrochemically
pyrochlore
pyrochromate
pyrochromic
pyrocinchonic
pyrocitric
pyroclastic
pyrocoll
pyrocollodion
pyrocomenic
pyrocondensation
pyroconductivity
pyrocotton
pyrocrystalline
Pyrocystis
Pyrodine
pyroelectric
pyroelectricity
pyrogallate
pyrogallic
pyrogallol
pyrogen
pyrogenation
pyrogenesia
pyrogenesis
pyrogenetic
pyrogenetically
pyrogenic
pyrogenous
pyroglutamic
pyrognomic
pyrognostic
pyrognostics
pyrograph
pyrographer
pyrographic
pyrography
pyrogravure
pyroguaiacin
pyroheliometer
pyroid
Pyrola
Pyrolaceae
pyrolaceous
pyrolater
pyrolatry
pyroligneous
pyrolignic
pyrolignite
pyrolignous
pyrolite
pyrollogical
pyrologist
pyrology
pyrolusite
pyrolysis
pyrolytic
pyrolyze
pyromachy
pyromagnetic
pyromancer
pyromancy
pyromania
pyromaniac
pyromaniacal
pyromantic
pyromeconic
pyromellitic
pyrometallurgy
pyrometamorphic
pyrometamorphism
pyrometer
pyrometric
pyrometrical
pyrometrically
pyrometry
Pyromorphidae
pyromorphism
pyromorphite
pyromorphous
pyromotor
pyromucate
pyromucic
pyromucyl
pyronaphtha
pyrone
Pyronema
pyronine
pyronomics
pyronyxis
pyrope
pyropen
pyrophanite
pyrophanous
pyrophile
pyrophilous
pyrophobia
pyrophone
pyrophoric
pyrophorous
pyrophorus
pyrophosphate
pyrophosphoric
pyrophosphorous
pyrophotograph
pyrophotography
pyrophotometer
pyrophyllite
pyrophysalite
pyropuncture
pyropus
pyroracemate
pyroracemic
pyroscope
pyroscopy
pyrosis
pyrosmalite
Pyrosoma
Pyrosomatidae
pyrosome
Pyrosomidae
pyrosomoid
pyrosphere
pyrostat
pyrostereotype
pyrostilpnite
pyrosulphate
pyrosulphite
pyrosulphuric
pyrosulphuryl
pyrotantalate
pyrotartaric
pyrotartrate
pyrotechnian
pyrotechnic
pyrotechnical
pyrotechnically
pyrotechnician
pyrotechnics
pyrotechnist
pyrotechny
pyroterebic
pyrotheology
Pyrotheria
Pyrotherium
pyrotic
pyrotoxin
pyrotritaric
pyrotritartric
pyrouric
pyrovanadate
pyrovanadic
pyroxanthin
pyroxene
pyroxenic
pyroxenite
pyroxmangite
pyroxonium
pyroxyle
pyroxylene
pyroxylic
pyroxylin
Pyrrhic
pyrrhic
pyrrhichian
pyrrhichius
pyrrhicist
Pyrrhocoridae
Pyrrhonean
Pyrrhonian
Pyrrhonic
Pyrrhonism
Pyrrhonist
Pyrrhonistic
Pyrrhonize
pyrrhotine
pyrrhotism
pyrrhotist
pyrrhotite
pyrrhous
Pyrrhuloxia
Pyrrhus
pyrrodiazole
pyrrol
pyrrole
pyrrolic
pyrrolidine
pyrrolidone
pyrrolidyl
pyrroline
pyrrolylene
pyrrophyllin
pyrroporphyrin
pyrrotriazole
pyrroyl
pyrryl
pyrrylene
Pyrula
Pyrularia
pyruline
pyruloid
Pyrus
pyruvaldehyde
pyruvate
pyruvic
pyruvil
pyruvyl
pyrylium
Pythagorean
Pythagoreanism
Pythagoreanize
Pythagoreanly
Pythagoric
Pythagorical
Pythagorically
Pythagorism
Pythagorist
Pythagorize
Pythagorizer
Pythia
Pythiaceae
Pythiacystis
Pythiad
Pythiambic
Pythian
Pythic
Pythios
Pythium
Pythius
pythogenesis
pythogenetic
pythogenic
pythogenous
python
pythoness
pythonic
pythonical
pythonid
Pythonidae
pythoniform
Pythoninae
pythonine
pythonism
Pythonissa
pythonist
pythonize
pythonoid
pythonomorph
Pythonomorpha
pythonomorphic
pythonomorphous
pyuria
pyvuril
pyx
Pyxidanthera
pyxidate
pyxides
pyxidium
pyxie
Pyxis
pyxis
Q
q
qasida
qere
qeri
qintar
Qoheleth
qoph
qua
quab
quabird
quachil
quack
quackery
quackhood
quackish
quackishly
quackishness
quackism
quackle
quacksalver
quackster
quacky
quad
quadded
quaddle
Quader
Quadi
quadmeter
quadra
quadrable
quadragenarian
quadragenarious
Quadragesima
quadragesimal
quadragintesimal
quadral
quadrangle
quadrangled
quadrangular
quadrangularly
quadrangularness
quadrangulate
quadrans
quadrant
quadrantal
quadrantes
Quadrantid
quadrantile
quadrantlike
quadrantly
quadrat
quadrate
quadrated
quadrateness
quadratic
quadratical
quadratically
quadratics
Quadratifera
quadratiferous
quadratojugal
quadratomandibular
quadratosquamosal
quadratrix
quadratum
quadrature
quadratus
quadrauricular
quadrennia
quadrennial
quadrennially
quadrennium
quadriad
quadrialate
quadriannulate
quadriarticulate
quadriarticulated
quadribasic
quadric
quadricapsular
quadricapsulate
quadricarinate
quadricellular
quadricentennial
quadriceps
quadrichord
quadriciliate
quadricinium
quadricipital
quadricone
quadricorn
quadricornous
quadricostate
quadricotyledonous
quadricovariant
quadricrescentic
quadricrescentoid
quadricuspid
quadricuspidal
quadricuspidate
quadricycle
quadricycler
quadricyclist
quadridentate
quadridentated
quadriderivative
quadridigitate
quadriennial
quadriennium
quadrienniumutile
quadrifarious
quadrifariously
quadrifid
quadrifilar
quadrifocal
quadrifoil
quadrifoliate
quadrifoliolate
quadrifolious
quadrifolium
quadriform
quadrifrons
quadrifrontal
quadrifurcate
quadrifurcated
quadrifurcation
quadriga
quadrigabled
quadrigamist
quadrigate
quadrigatus
quadrigeminal
quadrigeminate
quadrigeminous
quadrigeminum
quadrigenarious
quadriglandular
quadrihybrid
quadrijugal
quadrijugate
quadrijugous
quadrilaminar
quadrilaminate
quadrilateral
quadrilaterally
quadrilateralness
quadrilingual
quadriliteral
quadrille
quadrilled
quadrillion
quadrillionth
quadrilobate
quadrilobed
quadrilocular
quadriloculate
quadrilogue
quadrilogy
quadrimembral
quadrimetallic
quadrimolecular
quadrimum
quadrinodal
quadrinomial
quadrinomical
quadrinominal
quadrinucleate
quadrioxalate
quadriparous
quadripartite
quadripartitely
quadripartition
quadripennate
quadriphosphate
quadriphyllous
quadripinnate
quadriplanar
quadriplegia
quadriplicate
quadriplicated
quadripolar
quadripole
quadriportico
quadriporticus
quadripulmonary
quadriquadric
quadriradiate
quadrireme
quadrisect
quadrisection
quadriseptate
quadriserial
quadrisetose
quadrispiral
quadristearate
quadrisulcate
quadrisulcated
quadrisulphide
quadrisyllabic
quadrisyllabical
quadrisyllable
quadrisyllabous
quadriternate
quadritubercular
quadrituberculate
quadriurate
quadrivalence
quadrivalency
quadrivalent
quadrivalently
quadrivalve
quadrivalvular
quadrivial
quadrivious
quadrivium
quadrivoltine
quadroon
quadrual
Quadrula
quadrum
Quadrumana
quadrumanal
quadrumane
quadrumanous
quadruped
quadrupedal
quadrupedan
quadrupedant
quadrupedantic
quadrupedantical
quadrupedate
quadrupedation
quadrupedism
quadrupedous
quadruplane
quadruplator
quadruple
quadrupleness
quadruplet
quadruplex
quadruplicate
quadruplication
quadruplicature
quadruplicity
quadruply
quadrupole
quaedam
Quaequae
quaesitum
quaestor
quaestorial
quaestorian
quaestorship
quaestuary
quaff
quaffer
quaffingly
quag
quagga
quagginess
quaggle
quaggy
quagmire
quagmiry
quahog
quail
quailberry
quailery
quailhead
quaillike
quaily
quaint
quaintance
quaintise
quaintish
quaintly
quaintness
Quaitso
quake
quakeful
quakeproof
Quaker
quaker
quakerbird
Quakerdom
Quakeress
Quakeric
Quakerish
Quakerishly
Quakerishness
Quakerism
Quakerization
Quakerize
Quakerlet
Quakerlike
Quakerly
Quakership
Quakery
quaketail
quakiness
quaking
quakingly
quaky
quale
qualifiable
qualification
qualificative
qualificator
qualificatory
qualified
qualifiedly
qualifiedness
qualifier
qualify
qualifyingly
qualimeter
qualitative
qualitatively
qualitied
quality
qualityless
qualityship
qualm
qualminess
qualmish
qualmishly
qualmishness
qualmproof
qualmy
qualmyish
qualtagh
Quamasia
Quamoclit
quan
quandary
quandong
quandy
quannet
quant
quanta
quantic
quantical
quantifiable
quantifiably
quantification
quantifier
quantify
quantimeter
quantitate
quantitative
quantitatively
quantitativeness
quantitied
quantitive
quantitively
quantity
quantivalence
quantivalency
quantivalent
quantization
quantize
quantometer
quantulum
quantum
Quapaw
quaquaversal
quaquaversally
quar
quarantinable
quarantine
quarantiner
quaranty
quardeel
quare
quarenden
quarender
quarentene
quark
quarl
quarle
quarred
quarrel
quarreled
quarreler
quarreling
quarrelingly
quarrelproof
quarrelsome
quarrelsomely
quarrelsomeness
quarriable
quarried
quarrier
quarry
quarryable
quarrying
quarryman
quarrystone
quart
quartan
quartane
quartation
quartenylic
quarter
quarterage
quarterback
quarterdeckish
quartered
quarterer
quartering
quarterization
quarterland
quarterly
quarterman
quartermaster
quartermasterlike
quartermastership
quartern
quarterpace
quarters
quartersaw
quartersawed
quarterspace
quarterstaff
quarterstetch
quartet
quartette
quartetto
quartful
quartic
quartile
quartine
quartiparous
quarto
Quartodeciman
quartodecimanism
quartole
quartz
quartzic
quartziferous
quartzite
quartzitic
quartzless
quartzoid
quartzose
quartzous
quartzy
quash
Quashee
quashey
quashy
quasi
quasijudicial
Quasimodo
quasky
quassation
quassative
Quassia
quassiin
quassin
quat
quata
quatch
quatercentenary
quatern
quaternal
quaternarian
quaternarius
quaternary
quaternate
quaternion
quaternionic
quaternionist
quaternitarian
quaternity
quaters
quatertenses
quatorzain
quatorze
quatrain
quatral
quatrayle
quatre
quatrefeuille
quatrefoil
quatrefoiled
quatrefoliated
quatrible
quatrin
quatrino
quatrocentism
quatrocentist
quatrocento
Quatsino
quattie
quattrini
quatuor
quatuorvirate
quauk
quave
quaver
quaverer
quavering
quaveringly
quaverous
quavery
quaverymavery
quaw
quawk
quay
quayage
quayful
quaylike
quayman
quayside
quaysider
qubba
queach
queachy
queak
queal
quean
queanish
queasily
queasiness
queasom
queasy
quebrachamine
quebrachine
quebrachitol
quebracho
quebradilla
Quechua
Quechuan
quedful
queechy
queen
queencake
queencraft
queencup
queendom
queenfish
queenhood
queening
queenite
queenless
queenlet
queenlike
queenliness
queenly
queenright
queenroot
queensberry
queenship
queenweed
queenwood
queer
queerer
queerish
queerishness
queerity
queerly
queerness
queersome
queery
queest
queesting
queet
queeve
quegh
quei
queintise
quelch
Quelea
quell
queller
quemado
queme
quemeful
quemefully
quemely
quench
quenchable
quenchableness
quencher
quenchless
quenchlessly
quenchlessness
quenelle
quenselite
quercetagetin
quercetic
quercetin
quercetum
quercic
Querciflorae
quercimeritrin
quercin
quercine
quercinic
quercitannic
quercitannin
quercite
quercitin
quercitol
quercitrin
quercitron
quercivorous
Quercus
Querecho
Querendi
Querendy
querent
Queres
querier
queriman
querimonious
querimoniously
querimoniousness
querimony
querist
querken
querl
quern
quernal
Quernales
quernstone
querulent
querulential
querulist
querulity
querulosity
querulous
querulously
querulousness
query
querying
queryingly
queryist
quesited
quesitive
quest
quester
questeur
questful
questingly
question
questionability
questionable
questionableness
questionably
questionary
questionee
questioner
questioningly
questionist
questionless
questionlessly
questionnaire
questionous
questionwise
questman
questor
questorial
questorship
quet
quetch
quetenite
quetzal
queue
quey
Quiangan
quiapo
quib
quibble
quibbleproof
quibbler
quibblingly
quiblet
quica
Quiche
quick
quickbeam
quickborn
quicken
quickenance
quickenbeam
quickener
quickfoot
quickhatch
quickhearted
quickie
quicklime
quickly
quickness
quicksand
quicksandy
quickset
quicksilver
quicksilvering
quicksilverish
quicksilverishness
quicksilvery
quickstep
quickthorn
quickwork
quid
Quidae
quiddative
quidder
Quiddist
quiddit
quidditative
quidditatively
quiddity
quiddle
quiddler
quidnunc
quiesce
quiescence
quiescency
quiescent
quiescently
quiet
quietable
quieten
quietener
quieter
quieting
quietism
quietist
quietistic
quietive
quietlike
quietly
quietness
quietsome
quietude
quietus
quiff
quiffing
Quiina
Quiinaceae
quiinaceous
quila
quiles
Quileute
quilkin
quill
Quillagua
quillai
quillaic
Quillaja
quillaja
quillback
quilled
quiller
quillet
quilleted
quillfish
quilling
quilltail
quillwork
quillwort
quilly
quilt
quilted
quilter
quilting
Quimbaya
Quimper
quin
quina
quinacrine
Quinaielt
quinaldic
quinaldine
quinaldinic
quinaldinium
quinaldyl
quinamicine
quinamidine
quinamine
quinanisole
quinaquina
quinarian
quinarius
quinary
quinate
quinatoxine
Quinault
quinazoline
quinazolyl
quince
quincentenary
quincentennial
quincewort
quinch
quincubital
quincubitalism
quincuncial
quincuncially
quincunx
quincunxial
quindecad
quindecagon
quindecangle
quindecasyllabic
quindecemvir
quindecemvirate
quindecennial
quindecim
quindecima
quindecylic
quindene
quinetum
quingentenary
quinhydrone
quinia
quinible
quinic
quinicine
quinidia
quinidine
quinin
quinina
quinine
quininiazation
quininic
quininism
quininize
quiniretin
quinisext
quinisextine
quinism
quinite
quinitol
quinizarin
quinize
quink
quinnat
quinnet
Quinnipiac
quinoa
quinocarbonium
quinoform
quinogen
quinoid
quinoidal
quinoidation
quinoidine
quinol
quinoline
quinolinic
quinolinium
quinolinyl
quinologist
quinology
quinolyl
quinometry
quinone
quinonediimine
quinonic
quinonimine
quinonization
quinonize
quinonoid
quinonyl
quinopyrin
quinotannic
quinotoxine
quinova
quinovatannic
quinovate
quinovic
quinovin
quinovose
quinoxaline
quinoxalyl
quinoyl
quinquagenarian
quinquagenary
Quinquagesima
quinquagesimal
quinquarticular
Quinquatria
Quinquatrus
quinquecapsular
quinquecostate
quinquedentate
quinquedentated
quinquefarious
quinquefid
quinquefoliate
quinquefoliated
quinquefoliolate
quinquegrade
quinquejugous
quinquelateral
quinqueliteral
quinquelobate
quinquelobated
quinquelobed
quinquelocular
quinqueloculine
quinquenary
quinquenerval
quinquenerved
quinquennalia
quinquennia
quinquenniad
quinquennial
quinquennialist
quinquennially
quinquennium
quinquepartite
quinquepedal
quinquepedalian
quinquepetaloid
quinquepunctal
quinquepunctate
quinqueradial
quinqueradiate
quinquereme
quinquertium
quinquesect
quinquesection
quinqueseptate
quinqueserial
quinqueseriate
quinquesyllabic
quinquesyllable
quinquetubercular
quinquetuberculate
quinquevalence
quinquevalency
quinquevalent
quinquevalve
quinquevalvous
quinquevalvular
quinqueverbal
quinqueverbial
quinquevir
quinquevirate
quinquiliteral
quinquina
quinquino
quinse
quinsied
quinsy
quinsyberry
quinsywort
quint
quintad
quintadena
quintadene
quintain
quintal
quintan
quintant
quintary
quintato
quinte
quintelement
quintennial
quinternion
quinteron
quinteroon
quintessence
quintessential
quintessentiality
quintessentially
quintessentiate
quintet
quintette
quintetto
quintic
quintile
Quintilis
Quintillian
quintillion
quintillionth
Quintin
quintin
quintiped
Quintius
quinto
quintocubital
quintocubitalism
quintole
quinton
quintroon
quintuple
quintuplet
quintuplicate
quintuplication
quintuplinerved
quintupliribbed
quintus
quinuclidine
quinyl
quinze
quinzieme
quip
quipful
quipo
quipper
quippish
quippishness
quippy
quipsome
quipsomeness
quipster
quipu
quira
quire
quirewise
Quirinal
Quirinalia
quirinca
quiritarian
quiritary
Quirite
Quirites
quirk
quirkiness
quirkish
quirksey
quirksome
quirky
quirl
quirquincho
quirt
quis
quisby
quiscos
quisle
quisling
Quisqualis
quisqueite
quisquilian
quisquiliary
quisquilious
quisquous
quisutsch
quit
quitch
quitclaim
quite
Quitemoca
Quiteno
quitrent
quits
quittable
quittance
quitted
quitter
quittor
Quitu
quiver
quivered
quiverer
quiverful
quivering
quiveringly
quiverish
quiverleaf
quivery
Quixote
quixotic
quixotical
quixotically
quixotism
quixotize
quixotry
quiz
quizzability
quizzable
quizzacious
quizzatorial
quizzee
quizzer
quizzery
quizzical
quizzicality
quizzically
quizzicalness
quizzification
quizzify
quizziness
quizzingly
quizzish
quizzism
quizzity
quizzy
Qung
quo
quod
quoddies
quoddity
quodlibet
quodlibetal
quodlibetarian
quodlibetary
quodlibetic
quodlibetical
quodlibetically
quoilers
quoin
quoined
quoining
quoit
quoiter
quoitlike
quoits
quondam
quondamly
quondamship
quoniam
quop
Quoratean
quorum
quot
quota
quotability
quotable
quotableness
quotably
quotation
quotational
quotationally
quotationist
quotative
quote
quotee
quoteless
quotennial
quoter
quoteworthy
quoth
quotha
quotidian
quotidianly
quotidianness
quotient
quotiety
quotingly
quotity
quotlibet
quotum
Qurti
R
r
ra
raad
Raanan
raash
Rab
rab
raband
rabanna
rabat
rabatine
rabatte
rabattement
rabbanist
rabbanite
rabbet
rabbeting
rabbi
rabbin
rabbinate
rabbindom
Rabbinic
rabbinic
Rabbinica
rabbinical
rabbinically
rabbinism
rabbinist
rabbinistic
rabbinistical
rabbinite
rabbinize
rabbinship
rabbiship
rabbit
rabbitberry
rabbiter
rabbithearted
rabbitlike
rabbitmouth
rabbitproof
rabbitroot
rabbitry
rabbitskin
rabbitweed
rabbitwise
rabbitwood
rabbity
rabble
rabblelike
rabblement
rabbleproof
rabbler
rabblesome
rabboni
rabbonim
Rabelaisian
Rabelaisianism
Rabelaism
Rabi
rabic
rabid
rabidity
rabidly
rabidness
rabies
rabietic
rabific
rabiform
rabigenic
Rabin
rabinet
rabirubia
rabitic
rabulistic
rabulous
raccoon
raccoonberry
raccroc
race
raceabout
racebrood
racecourse
racegoer
racegoing
racelike
racemate
racemation
raceme
racemed
racemic
racemiferous
racemiform
racemism
racemization
racemize
racemocarbonate
racemocarbonic
racemomethylate
racemose
racemosely
racemous
racemously
racemule
racemulose
racer
raceway
rach
rache
Rachel
rachial
rachialgia
rachialgic
rachianalgesia
Rachianectes
rachianesthesia
rachicentesis
rachides
rachidial
rachidian
rachiform
Rachiglossa
rachiglossate
rachigraph
rachilla
rachiocentesis
rachiococainize
rachiocyphosis
rachiodont
rachiodynia
rachiometer
rachiomyelitis
rachioparalysis
rachioplegia
rachioscoliosis
rachiotome
rachiotomy
rachipagus
rachis
rachischisis
rachitic
rachitis
rachitism
rachitogenic
rachitome
rachitomous
rachitomy
Rachycentridae
Rachycentron
racial
racialism
racialist
raciality
racialization
racialize
racially
racily
raciness
racing
racinglike
racism
racist
rack
rackabones
rackan
rackboard
racker
racket
racketeer
racketeering
racketer
racketing
racketlike
racketproof
racketry
rackett
rackettail
rackety
rackful
racking
rackingly
rackle
rackless
rackmaster
rackproof
rackrentable
rackway
rackwork
racloir
racon
raconteur
racoon
Racovian
racy
rad
rada
radar
radarman
radarscope
raddle
raddleman
raddlings
radectomy
Radek
radiability
radiable
radial
radiale
radialia
radiality
radialization
radialize
radially
radian
radiance
radiancy
radiant
radiantly
Radiata
radiate
radiated
radiately
radiateness
radiatics
radiatiform
radiation
radiational
radiative
radiatopatent
radiatoporose
radiatoporous
radiator
radiatory
radiatostriate
radiatosulcate
radiature
radical
radicalism
radicality
radicalization
radicalize
radically
radicalness
radicand
radicant
radicate
radicated
radicating
radication
radicel
radices
radicicola
radicicolous
radiciferous
radiciflorous
radiciform
radicivorous
radicle
radicolous
radicose
Radicula
radicular
radicule
radiculectomy
radiculitis
radiculose
radiectomy
radiescent
radiferous
radii
radio
radioacoustics
radioactinium
radioactivate
radioactive
radioactively
radioactivity
radioamplifier
radioanaphylaxis
radioautograph
radioautographic
radioautography
radiobicipital
radiobroadcast
radiobroadcaster
radiobroadcasting
radiobserver
radiocarbon
radiocarpal
radiocast
radiocaster
radiochemical
radiochemistry
radiocinematograph
radioconductor
radiode
radiodermatitis
radiodetector
radiodiagnosis
radiodigital
radiodontia
radiodontic
radiodontist
radiodynamic
radiodynamics
radioelement
radiogenic
radiogoniometer
radiogoniometric
radiogoniometry
radiogram
radiograph
radiographer
radiographic
radiographical
radiographically
radiography
radiohumeral
radioisotope
Radiolaria
radiolarian
radiolead
radiolite
Radiolites
radiolitic
Radiolitidae
radiolocation
radiolocator
radiologic
radiological
radiologist
radiology
radiolucency
radiolucent
radioluminescence
radioluminescent
radioman
radiomedial
radiometallography
radiometeorograph
radiometer
radiometric
radiometrically
radiometry
radiomicrometer
radiomovies
radiomuscular
radionecrosis
radioneuritis
radionics
radiopacity
radiopalmar
radiopaque
radiopelvimetry
radiophare
radiophone
radiophonic
radiophony
radiophosphorus
radiophotograph
radiophotography
radiopraxis
radioscope
radioscopic
radioscopical
radioscopy
radiosensibility
radiosensitive
radiosensitivity
radiosonde
radiosonic
radiostereoscopy
radiosurgery
radiosurgical
radiosymmetrical
radiotechnology
radiotelegram
radiotelegraph
radiotelegraphic
radiotelegraphy
radiotelephone
radiotelephonic
radiotelephony
radioteria
radiothallium
radiotherapeutic
radiotherapeutics
radiotherapeutist
radiotherapist
radiotherapy
radiothermy
radiothorium
radiotoxemia
radiotransparency
radiotransparent
radiotrician
Radiotron
radiotropic
radiotropism
radiovision
radish
radishlike
radium
radiumization
radiumize
radiumlike
radiumproof
radiumtherapy
radius
radix
radknight
radman
radome
radon
radsimir
radula
radulate
raduliferous
raduliform
Rafael
Rafe
raff
Raffaelesque
raffe
raffee
raffery
raffia
raffinase
raffinate
raffing
raffinose
raffish
raffishly
raffishness
raffle
raffler
Rafflesia
rafflesia
Rafflesiaceae
rafflesiaceous
Rafik
raft
raftage
rafter
raftiness
raftlike
raftman
raftsman
rafty
rag
raga
ragabash
ragabrash
ragamuffin
ragamuffinism
ragamuffinly
rage
rageful
ragefully
rageless
rageous
rageously
rageousness
rageproof
rager
ragesome
ragfish
ragged
raggedly
raggedness
raggedy
raggee
ragger
raggery
raggety
raggil
raggily
ragging
raggle
raggled
raggy
raghouse
Raghu
raging
ragingly
raglan
raglanite
raglet
raglin
ragman
Ragnar
ragout
ragpicker
ragseller
ragshag
ragsorter
ragstone
ragtag
ragtime
ragtimer
ragtimey
ragule
raguly
ragweed
ragwort
rah
Rahanwin
rahdar
rahdaree
Rahul
Raia
raia
Raiae
raid
raider
raidproof
Raif
Raiidae
raiiform
rail
railage
railbird
railer
railhead
railing
railingly
raillery
railless
raillike
railly
railman
railroad
railroadana
railroader
railroadiana
railroading
railroadish
railroadship
railway
railwaydom
railwayless
Raimannia
raiment
raimentless
rain
rainband
rainbird
rainbound
rainbow
rainbowlike
rainbowweed
rainbowy
rainburst
raincoat
raindrop
Rainer
rainer
rainfall
rainfowl
rainful
rainily
raininess
rainless
rainlessness
rainlight
rainproof
rainproofer
rainspout
rainstorm
raintight
rainwash
rainworm
rainy
raioid
Rais
rais
raisable
raise
raised
raiseman
raiser
raisin
raising
raisiny
Raj
raj
Raja
raja
Rajah
rajah
Rajarshi
rajaship
Rajasthani
rajbansi
Rajeev
Rajendra
Rajesh
Rajidae
Rajiv
Rajput
rakan
rake
rakeage
rakeful
rakehell
rakehellish
rakehelly
raker
rakery
rakesteel
rakestele
rakh
Rakhal
raki
rakily
raking
rakish
rakishly
rakishness
rakit
rakshasa
raku
Ralf
rallentando
ralliance
Rallidae
rallier
ralliform
Rallinae
ralline
Rallus
rally
Ralph
ralph
ralstonite
Ram
ram
Rama
ramada
Ramadoss
ramage
Ramaism
Ramaite
ramal
Raman
Ramanan
ramanas
ramarama
ramass
ramate
rambeh
ramberge
ramble
rambler
rambling
ramblingly
ramblingness
Rambo
rambong
rambooze
Rambouillet
rambunctious
rambutan
ramdohrite
rame
rameal
Ramean
ramed
ramekin
ramellose
rament
ramentaceous
ramental
ramentiferous
ramentum
rameous
ramequin
Rameses
Rameseum
Ramesh
Ramessid
Ramesside
ramet
ramex
ramfeezled
ramgunshoch
ramhead
ramhood
rami
ramicorn
ramie
ramiferous
ramificate
ramification
ramified
ramiflorous
ramiform
ramify
ramigerous
Ramillie
Ramillied
ramiparous
Ramiro
ramisection
ramisectomy
Ramism
Ramist
Ramistical
ramlike
ramline
rammack
rammel
rammelsbergite
rammer
rammerman
rammish
rammishly
rammishness
rammy
Ramneek
Ramnenses
Ramnes
Ramon
Ramona
Ramoosii
ramose
ramosely
ramosity
ramosopalmate
ramosopinnate
ramososubdivided
ramous
ramp
rampacious
rampaciously
rampage
rampageous
rampageously
rampageousness
rampager
rampagious
rampancy
rampant
rampantly
rampart
ramped
ramper
Ramphastidae
Ramphastides
Ramphastos
rampick
rampike
ramping
rampingly
rampion
rampire
rampler
ramplor
rampsman
ramrace
ramrod
ramroddy
ramscallion
ramsch
Ramsey
ramshackle
ramshackled
ramshackleness
ramshackly
ramson
ramstam
ramtil
ramular
ramule
ramuliferous
ramulose
ramulous
ramulus
ramus
ramuscule
Ramusi
Ran
ran
Rana
rana
ranal
Ranales
ranarian
ranarium
Ranatra
rance
rancel
rancellor
rancelman
rancer
rancescent
ranch
ranche
rancher
rancheria
ranchero
ranchless
ranchman
rancho
ranchwoman
rancid
rancidification
rancidify
rancidity
rancidly
rancidness
rancor
rancorous
rancorously
rancorousness
rancorproof
Rand
rand
Randal
Randall
Randallite
randan
randannite
Randell
randem
rander
Randia
randing
randir
Randite
randle
Randolph
random
randomish
randomization
randomize
randomly
randomness
randomwise
Randy
randy
rane
Ranella
Ranere
rang
rangatira
range
ranged
rangeless
rangeman
ranger
rangership
rangework
rangey
Rangifer
rangiferine
ranginess
ranging
rangle
rangler
rangy
rani
ranid
Ranidae
raniferous
raniform
Ranina
Raninae
ranine
raninian
ranivorous
Ranjit
rank
ranked
ranker
rankish
rankle
rankless
ranklingly
rankly
rankness
ranksman
rankwise
rann
rannel
rannigal
ranny
Ranquel
ransack
ransacker
ransackle
ransel
ranselman
ransom
ransomable
ransomer
ransomfree
ransomless
ranstead
rant
rantan
rantankerous
rantepole
ranter
Ranterism
ranting
rantingly
rantipole
rantock
ranty
ranula
ranular
Ranunculaceae
ranunculaceous
Ranunculales
ranunculi
Ranunculus
Ranzania
Raoulia
rap
Rapaces
rapaceus
rapacious
rapaciously
rapaciousness
rapacity
rapakivi
Rapallo
Rapanea
Rapateaceae
rapateaceous
rape
rapeful
raper
rapeseed
Raphael
Raphaelesque
Raphaelic
Raphaelism
Raphaelite
Raphaelitism
raphania
Raphanus
raphany
raphe
Raphia
raphide
raphides
raphidiferous
raphidiid
Raphidiidae
Raphidodea
Raphidoidea
Raphiolepis
raphis
rapic
rapid
rapidity
rapidly
rapidness
rapier
rapiered
rapillo
rapine
rapiner
raping
rapinic
rapist
raploch
rappage
rapparee
rappe
rappel
rapper
rapping
Rappist
rappist
Rappite
rapport
rapscallion
rapscallionism
rapscallionly
rapscallionry
rapt
raptatorial
raptatory
raptly
raptness
raptor
Raptores
raptorial
raptorious
raptril
rapture
raptured
raptureless
rapturist
rapturize
rapturous
rapturously
rapturousness
raptury
raptus
rare
rarebit
rarefaction
rarefactional
rarefactive
rarefiable
rarefication
rarefier
rarefy
rarely
rareness
rareripe
Rareyfy
rariconstant
rarish
rarity
Rarotongan
ras
rasa
Rasalas
Rasalhague
rasamala
rasant
rascacio
rascal
rascaldom
rascaless
rascalion
rascalism
rascality
rascalize
rascallike
rascallion
rascally
rascalry
rascalship
rasceta
rascette
rase
rasen
Rasenna
raser
rasgado
rash
rasher
rashful
rashing
rashlike
rashly
rashness
Rashti
rasion
Raskolnik
Rasores
rasorial
rasp
raspatorium
raspatory
raspberriade
raspberry
raspberrylike
rasped
rasper
rasping
raspingly
raspingness
raspings
raspish
raspite
raspy
rasse
Rasselas
rassle
Rastaban
raster
rastik
rastle
Rastus
rasure
rat
rata
ratability
ratable
ratableness
ratably
ratafee
ratafia
ratal
ratanhia
rataplan
ratbite
ratcatcher
ratcatching
ratch
ratchel
ratchelly
ratcher
ratchet
ratchetlike
ratchety
ratching
ratchment
rate
rated
ratel
rateless
ratement
ratepayer
ratepaying
rater
ratfish
rath
rathe
rathed
rathely
ratheness
rather
ratherest
ratheripe
ratherish
ratherly
rathest
rathite
Rathnakumar
rathole
rathskeller
raticidal
raticide
ratification
ratificationist
ratifier
ratify
ratihabition
ratine
rating
ratio
ratiocinant
ratiocinate
ratiocination
ratiocinative
ratiocinator
ratiocinatory
ratiometer
ration
rationable
rationably
rational
rationale
rationalism
rationalist
rationalistic
rationalistical
rationalistically
rationalisticism
rationality
rationalizable
rationalization
rationalize
rationalizer
rationally
rationalness
rationate
rationless
rationment
Ratitae
ratite
ratitous
ratlike
ratline
ratliner
ratoon
ratooner
ratproof
ratsbane
ratskeller
rattage
rattail
rattan
ratteen
ratten
rattener
ratter
rattery
ratti
rattinet
rattish
rattle
rattlebag
rattlebones
rattlebox
rattlebrain
rattlebrained
rattlebush
rattled
rattlehead
rattleheaded
rattlejack
rattlemouse
rattlenut
rattlepate
rattlepated
rattlepod
rattleproof
rattler
rattleran
rattleroot
rattlertree
rattles
rattleskull
rattleskulled
rattlesnake
rattlesome
rattletrap
rattleweed
rattlewort
rattling
rattlingly
rattlingness
rattly
ratton
rattoner
rattrap
Rattus
ratty
ratwa
ratwood
raucid
raucidity
raucity
raucous
raucously
raucousness
raught
raugrave
rauk
raukle
Raul
rauli
raun
raunge
raupo
rauque
Rauraci
Raurici
Rauwolfia
ravage
ravagement
ravager
rave
ravehook
raveinelike
ravel
raveler
ravelin
raveling
ravelly
ravelment
ravelproof
raven
Ravenala
ravendom
ravenduck
Ravenelia
ravener
ravenhood
ravening
ravenish
ravenlike
ravenous
ravenously
ravenousness
ravenry
ravens
Ravensara
ravensara
ravenstone
ravenwise
raver
Ravi
ravigote
ravin
ravinate
Ravindran
Ravindranath
ravine
ravined
ravinement
raviney
raving
ravingly
ravioli
ravish
ravishedly
ravisher
ravishing
ravishingly
ravishment
ravison
ravissant
raw
rawboned
rawbones
rawhead
rawhide
rawhider
rawish
rawishness
rawness
rax
Ray
ray
raya
rayage
Rayan
rayed
rayful
rayless
raylessness
raylet
Raymond
rayon
rayonnance
rayonnant
raze
razee
razer
razoo
razor
razorable
razorback
razorbill
razoredge
razorless
razormaker
razormaking
razorman
razorstrop
Razoumofskya
razz
razzia
razzly
re
rea
reaal
reabandon
reabolish
reabolition
reabridge
reabsence
reabsent
reabsolve
reabsorb
reabsorption
reabuse
reacceptance
reaccess
reaccession
reacclimatization
reacclimatize
reaccommodate
reaccompany
reaccomplish
reaccomplishment
reaccord
reaccost
reaccount
reaccredit
reaccrue
reaccumulate
reaccumulation
reaccusation
reaccuse
reaccustom
reacetylation
reach
reachable
reacher
reachieve
reachievement
reaching
reachless
reachy
reacidification
reacidify
reacknowledge
reacknowledgment
reacquaint
reacquaintance
reacquire
reacquisition
react
reactance
reactant
reaction
reactional
reactionally
reactionariness
reactionarism
reactionarist
reactionary
reactionaryism
reactionism
reactionist
reactivate
reactivation
reactive
reactively
reactiveness
reactivity
reactological
reactology
reactor
reactualization
reactualize
reactuate
read
readability
readable
readableness
readably
readapt
readaptability
readaptable
readaptation
readaptive
readaptiveness
readd
readdition
readdress
reader
readerdom
readership
readhere
readhesion
readily
readiness
reading
readingdom
readjourn
readjournment
readjudicate
readjust
readjustable
readjuster
readjustment
readmeasurement
readminister
readmiration
readmire
readmission
readmit
readmittance
readopt
readoption
readorn
readvance
readvancement
readvent
readventure
readvertency
readvertise
readvertisement
readvise
readvocate
ready
reaeration
reaffect
reaffection
reaffiliate
reaffiliation
reaffirm
reaffirmance
reaffirmation
reaffirmer
reafflict
reafford
reafforest
reafforestation
reaffusion
reagency
reagent
reaggravate
reaggravation
reaggregate
reaggregation
reaggressive
reagin
reagitate
reagitation
reagree
reagreement
reak
Real
real
realarm
reales
realest
realgar
realienate
realienation
realign
realignment
realism
realist
realistic
realistically
realisticize
reality
realive
realizability
realizable
realizableness
realizably
realization
realize
realizer
realizing
realizingly
reallegation
reallege
reallegorize
realliance
reallocate
reallocation
reallot
reallotment
reallow
reallowance
reallude
reallusion
really
realm
realmless
realmlet
realness
realter
realteration
realtor
realty
ream
reamage
reamalgamate
reamalgamation
reamass
reambitious
reamend
reamendment
reamer
reamerer
reaminess
reamputation
reamuse
reamy
reanalysis
reanalyze
reanchor
reanimalize
reanimate
reanimation
reanneal
reannex
reannexation
reannotate
reannounce
reannouncement
reannoy
reannoyance
reanoint
reanswer
reanvil
reanxiety
reap
reapable
reapdole
reaper
reapologize
reapology
reapparel
reapparition
reappeal
reappear
reappearance
reappease
reapplaud
reapplause
reappliance
reapplicant
reapplication
reapplier
reapply
reappoint
reappointment
reapportion
reapportionment
reapposition
reappraisal
reappraise
reappraisement
reappreciate
reappreciation
reapprehend
reapprehension
reapproach
reapprobation
reappropriate
reappropriation
reapproval
reapprove
rear
rearbitrate
rearbitration
rearer
reargue
reargument
rearhorse
rearisal
rearise
rearling
rearm
rearmament
rearmost
rearousal
rearouse
rearrange
rearrangeable
rearrangement
rearranger
rearray
rearrest
rearrival
rearrive
rearward
rearwardly
rearwardness
rearwards
reascend
reascendancy
reascendant
reascendency
reascendent
reascension
reascensional
reascent
reascertain
reascertainment
reashlar
reasiness
reask
reason
reasonability
reasonable
reasonableness
reasonably
reasoned
reasonedly
reasoner
reasoning
reasoningly
reasonless
reasonlessly
reasonlessness
reasonproof
reaspire
reassail
reassault
reassay
reassemblage
reassemble
reassembly
reassent
reassert
reassertion
reassertor
reassess
reassessment
reasseverate
reassign
reassignation
reassignment
reassimilate
reassimilation
reassist
reassistance
reassociate
reassociation
reassort
reassortment
reassume
reassumption
reassurance
reassure
reassured
reassuredly
reassurement
reassurer
reassuring
reassuringly
reastiness
reastonish
reastonishment
reastray
reasty
reasy
reattach
reattachment
reattack
reattain
reattainment
reattempt
reattend
reattendance
reattention
reattentive
reattest
reattire
reattract
reattraction
reattribute
reattribution
reatus
reaudit
reauthenticate
reauthentication
reauthorization
reauthorize
reavail
reavailable
reave
reaver
reavoid
reavoidance
reavouch
reavow
reawait
reawake
reawaken
reawakening
reawakenment
reaward
reaware
reb
rebab
reback
rebag
rebait
rebake
rebalance
rebale
reballast
reballot
reban
rebandage
rebanish
rebanishment
rebankrupt
rebankruptcy
rebaptism
rebaptismal
rebaptization
rebaptize
rebaptizer
rebar
rebarbarization
rebarbarize
rebarbative
rebargain
rebase
rebasis
rebatable
rebate
rebateable
rebatement
rebater
rebathe
rebato
rebawl
rebeamer
rebear
rebeat
rebeautify
rebec
Rebecca
Rebeccaism
Rebeccaites
rebeck
rebecome
rebed
rebeg
rebeget
rebeggar
rebegin
rebeginner
rebeginning
rebeguile
rebehold
Rebekah
rebel
rebeldom
rebelief
rebelieve
rebeller
rebellike
rebellion
rebellious
rebelliously
rebelliousness
rebellow
rebelly
rebelong
rebelove
rebelproof
rebemire
rebend
rebenediction
rebenefit
rebeset
rebesiege
rebestow
rebestowal
rebetake
rebetray
rebewail
rebia
rebias
rebid
rebill
rebillet
rebilling
rebind
rebirth
rebite
reblade
reblame
reblast
rebleach
reblend
rebless
reblock
rebloom
reblossom
reblot
reblow
reblue
rebluff
reblunder
reboant
reboantic
reboard
reboast
rebob
reboil
reboiler
reboise
reboisement
rebold
rebolt
rebone
rebook
rebop
rebore
reborn
reborrow
rebottle
Reboulia
rebounce
rebound
reboundable
rebounder
reboundingness
rebourbonize
rebox
rebrace
rebraid
rebranch
rebrand
rebrandish
rebreathe
rebreed
rebrew
rebribe
rebrick
rebridge
rebring
rebringer
rebroach
rebroadcast
rebronze
rebrown
rebrush
rebrutalize
rebubble
rebuckle
rebud
rebudget
rebuff
rebuffable
rebuffably
rebuffet
rebuffproof
rebuild
rebuilder
rebuilt
rebukable
rebuke
rebukeable
rebukeful
rebukefully
rebukefulness
rebukeproof
rebuker
rebukingly
rebulk
rebunch
rebundle
rebunker
rebuoy
rebuoyage
reburden
reburgeon
reburial
reburn
reburnish
reburst
rebury
rebus
rebush
rebusy
rebut
rebute
rebutment
rebuttable
rebuttal
rebutter
rebutton
rebuy
recable
recadency
recage
recalcination
recalcine
recalcitrance
recalcitrant
recalcitrate
recalcitration
recalculate
recalculation
recalesce
recalescence
recalescent
recalibrate
recalibration
recalk
recall
recallable
recallist
recallment
recampaign
recancel
recancellation
recandescence
recandidacy
recant
recantation
recanter
recantingly
recanvas
recap
recapacitate
recapitalization
recapitalize
recapitulate
recapitulation
recapitulationist
recapitulative
recapitulator
recapitulatory
recappable
recapper
recaption
recaptivate
recaptivation
recaptor
recapture
recapturer
recarbon
recarbonate
recarbonation
recarbonization
recarbonize
recarbonizer
recarburization
recarburize
recarburizer
recarnify
recarpet
recarriage
recarrier
recarry
recart
recarve
recase
recash
recasket
recast
recaster
recasting
recatalogue
recatch
recaulescence
recausticize
recce
recco
reccy
recede
recedence
recedent
receder
receipt
receiptable
receiptless
receiptor
receipts
receivability
receivable
receivables
receivablness
receival
receive
received
receivedness
receiver
receivership
recelebrate
recelebration
recement
recementation
recency
recense
recension
recensionist
recensor
recensure
recensus
recent
recenter
recently
recentness
recentralization
recentralize
recentre
recept
receptacle
receptacular
receptaculite
Receptaculites
receptaculitid
Receptaculitidae
receptaculitoid
receptaculum
receptant
receptibility
receptible
reception
receptionism
receptionist
receptitious
receptive
receptively
receptiveness
receptivity
receptor
receptoral
receptorial
receptual
receptually
recercelee
recertificate
recertify
recess
recesser
recession
recessional
recessionary
recessive
recessively
recessiveness
recesslike
recessor
Rechabite
Rechabitism
rechafe
rechain
rechal
rechallenge
rechamber
rechange
rechant
rechaos
rechar
recharge
recharter
rechase
rechaser
rechasten
rechaw
recheat
recheck
recheer
recherche
rechew
rechip
rechisel
rechoose
rechristen
rechuck
rechurn
recidivation
recidive
recidivism
recidivist
recidivistic
recidivity
recidivous
recipe
recipiangle
recipience
recipiency
recipiend
recipiendary
recipient
recipiomotor
reciprocable
reciprocal
reciprocality
reciprocalize
reciprocally
reciprocalness
reciprocate
reciprocation
reciprocative
reciprocator
reciprocatory
reciprocitarian
reciprocity
recircle
recirculate
recirculation
recision
recission
recissory
recitable
recital
recitalist
recitatif
recitation
recitationalism
recitationist
recitative
recitatively
recitativical
recitativo
recite
recitement
reciter
recivilization
recivilize
reck
reckla
reckless
recklessly
recklessness
reckling
reckon
reckonable
reckoner
reckoning
reclaim
reclaimable
reclaimableness
reclaimably
reclaimant
reclaimer
reclaimless
reclaimment
reclama
reclamation
reclang
reclasp
reclass
reclassification
reclassify
reclean
recleaner
recleanse
reclear
reclearance
reclimb
reclinable
reclinate
reclinated
reclination
recline
recliner
reclose
reclothe
reclothing
recluse
reclusely
recluseness
reclusery
reclusion
reclusive
reclusiveness
reclusory
recoach
recoagulation
recoal
recoast
recoat
recock
recoct
recoction
recode
recodification
recodify
recogitate
recogitation
recognition
recognitive
recognitor
recognitory
recognizability
recognizable
recognizably
recognizance
recognizant
recognize
recognizedly
recognizee
recognizer
recognizingly
recognizor
recognosce
recohabitation
recoil
recoiler
recoilingly
recoilment
recoin
recoinage
recoiner
recoke
recollapse
recollate
recollation
Recollect
recollectable
recollected
recollectedly
recollectedness
recollectible
recollection
recollective
recollectively
recollectiveness
Recollet
recolonization
recolonize
recolor
recomb
recombination
recombine
recomember
recomfort
recommand
recommence
recommencement
recommencer
recommend
recommendability
recommendable
recommendableness
recommendably
recommendation
recommendatory
recommendee
recommender
recommission
recommit
recommitment
recommittal
recommunicate
recommunion
recompact
recompare
recomparison
recompass
recompel
recompensable
recompensate
recompensation
recompense
recompenser
recompensive
recompete
recompetition
recompetitor
recompilation
recompile
recompilement
recomplain
recomplaint
recomplete
recompletion
recompliance
recomplicate
recomplication
recomply
recompose
recomposer
recomposition
recompound
recomprehend
recomprehension
recompress
recompression
recomputation
recompute
recon
reconceal
reconcealment
reconcede
reconceive
reconcentrate
reconcentration
reconception
reconcert
reconcession
reconcilability
reconcilable
reconcilableness
reconcilably
reconcile
reconcilee
reconcileless
reconcilement
reconciler
reconciliability
reconciliable
reconciliate
reconciliation
reconciliative
reconciliator
reconciliatory
reconciling
reconcilingly
reconclude
reconclusion
reconcoct
reconcrete
reconcur
recondemn
recondemnation
recondensation
recondense
recondite
reconditely
reconditeness
recondition
recondole
reconduct
reconduction
reconfer
reconfess
reconfide
reconfine
reconfinement
reconfirm
reconfirmation
reconfiscate
reconfiscation
reconform
reconfound
reconfront
reconfuse
reconfusion
recongeal
recongelation
recongest
recongestion
recongratulate
recongratulation
reconjoin
reconjunction
reconnaissance
reconnect
reconnection
reconnoissance
reconnoiter
reconnoiterer
reconnoiteringly
reconnoitre
reconnoitrer
reconnoitringly
reconquer
reconqueror
reconquest
reconsecrate
reconsecration
reconsent
reconsider
reconsideration
reconsign
reconsignment
reconsole
reconsolidate
reconsolidation
reconstituent
reconstitute
reconstitution
reconstruct
reconstructed
reconstruction
reconstructional
reconstructionary
reconstructionist
reconstructive
reconstructiveness
reconstructor
reconstrue
reconsult
reconsultation
recontact
recontemplate
recontemplation
recontend
recontest
recontinuance
recontinue
recontract
recontraction
recontrast
recontribute
recontribution
recontrivance
recontrive
recontrol
reconvalesce
reconvalescence
reconvalescent
reconvene
reconvention
reconventional
reconverge
reconverse
reconversion
reconvert
reconvertible
reconvey
reconveyance
reconvict
reconviction
reconvince
reconvoke
recook
recool
recooper
recopper
recopy
recopyright
record
recordable
recordant
recordation
recordative
recordatively
recordatory
recordedly
recorder
recordership
recording
recordist
recordless
recork
recorporification
recorporify
recorrect
recorrection
recorrupt
recorruption
recostume
recounsel
recount
recountable
recountal
recountenance
recounter
recountless
recoup
recoupable
recouper
recouple
recoupment
recourse
recover
recoverability
recoverable
recoverableness
recoverance
recoveree
recoverer
recoveringly
recoverless
recoveror
recovery
recramp
recrank
recrate
recreance
recreancy
recreant
recreantly
recreantness
recrease
recreate
recreation
recreational
recreationist
recreative
recreatively
recreativeness
recreator
recreatory
recredit
recrement
recremental
recrementitial
recrementitious
recrescence
recrew
recriminate
recrimination
recriminative
recriminator
recriminatory
recriticize
recroon
recrop
recross
recrowd
recrown
recrucify
recrudency
recrudesce
recrudescence
recrudescency
recrudescent
recruit
recruitable
recruitage
recruital
recruitee
recruiter
recruithood
recruiting
recruitment
recruity
recrush
recrusher
recrystallization
recrystallize
rect
recta
rectal
rectalgia
rectally
rectangle
rectangled
rectangular
rectangularity
rectangularly
rectangularness
rectangulate
rectangulometer
rectectomy
recti
rectifiable
rectification
rectificative
rectificator
rectificatory
rectified
rectifier
rectify
rectigrade
Rectigraph
rectilineal
rectilineally
rectilinear
rectilinearism
rectilinearity
rectilinearly
rectilinearness
rectilineation
rectinerved
rection
rectipetality
rectirostral
rectischiac
rectiserial
rectitic
rectitis
rectitude
rectitudinous
recto
rectoabdominal
rectocele
rectoclysis
rectococcygeal
rectococcygeus
rectocolitic
rectocolonic
rectocystotomy
rectogenital
rectopexy
rectoplasty
rector
rectoral
rectorate
rectoress
rectorial
rectorrhaphy
rectorship
rectory
rectoscope
rectoscopy
rectosigmoid
rectostenosis
rectostomy
rectotome
rectotomy
rectovaginal
rectovesical
rectress
rectricial
rectrix
rectum
rectus
recubant
recubate
recultivate
recultivation
recumbence
recumbency
recumbent
recumbently
recuperability
recuperance
recuperate
recuperation
recuperative
recuperativeness
recuperator
recuperatory
recur
recure
recureful
recureless
recurl
recurrence
recurrency
recurrent
recurrently
recurrer
recurring
recurringly
recurse
recursion
recursive
recurtain
recurvant
recurvate
recurvation
recurvature
recurve
Recurvirostra
recurvirostral
Recurvirostridae
recurvopatent
recurvoternate
recurvous
recusance
recusancy
recusant
recusation
recusative
recusator
recuse
recushion
recussion
recut
recycle
Red
red
redact
redaction
redactional
redactor
redactorial
redamage
redamnation
redan
redare
redargue
redargution
redargutive
redargutory
redarken
redarn
redart
redate
redaub
redawn
redback
redbait
redbeard
redbelly
redberry
redbill
redbird
redbone
redbreast
redbrush
redbuck
redbud
redcap
redcoat
redd
redden
reddendo
reddendum
reddening
redder
redding
reddingite
reddish
reddishness
reddition
reddleman
reddock
reddsman
reddy
rede
redeal
redebate
redebit
redeceive
redecide
redecimate
redecision
redeck
redeclaration
redeclare
redecline
redecorate
redecoration
redecrease
redecussate
rededicate
rededication
rededicatory
rededuct
rededuction
redeed
redeem
redeemability
redeemable
redeemableness
redeemably
redeemer
redeemeress
redeemership
redeemless
redefault
redefeat
redefecate
redefer
redefiance
redefine
redefinition
redeflect
redefy
redeify
redelay
redelegate
redelegation
redeliberate
redeliberation
redeliver
redeliverance
redeliverer
redelivery
redemand
redemandable
redemise
redemolish
redemonstrate
redemonstration
redemptible
Redemptine
redemption
redemptional
redemptioner
Redemptionist
redemptionless
redemptive
redemptively
redemptor
redemptorial
Redemptorist
redemptory
redemptress
redemptrice
redenigrate
redeny
redepend
redeploy
redeployment
redeposit
redeposition
redepreciate
redepreciation
redeprive
rederivation
redescend
redescent
redescribe
redescription
redesertion
redeserve
redesign
redesignate
redesignation
redesire
redesirous
redesman
redespise
redetect
redetention
redetermination
redetermine
redevelop
redeveloper
redevelopment
redevise
redevote
redevotion
redeye
redfin
redfinch
redfish
redfoot
redhead
redheaded
redheadedly
redheadedness
redhearted
redhibition
redhibitory
redhoop
redia
redictate
redictation
redient
redifferentiate
redifferentiation
redig
redigest
redigestion
rediminish
redingote
redintegrate
redintegration
redintegrative
redintegrator
redip
redipper
redirect
redirection
redisable
redisappear
redisburse
redisbursement
redischarge
rediscipline
rediscount
rediscourage
rediscover
rediscoverer
rediscovery
rediscuss
rediscussion
redisembark
redismiss
redispatch
redispel
redisperse
redisplay
redispose
redisposition
redispute
redissect
redissection
redisseise
redisseisin
redisseisor
redisseize
redisseizin
redisseizor
redissoluble
redissolution
redissolvable
redissolve
redistend
redistill
redistillation
redistiller
redistinguish
redistrain
redistrainer
redistribute
redistributer
redistribution
redistributive
redistributor
redistributory
redistrict
redisturb
redive
rediversion
redivert
redivertible
redivide
redivision
redivive
redivivous
redivivus
redivorce
redivorcement
redivulge
redivulgence
redjacket
redknees
redleg
redlegs
redly
redmouth
redness
redo
redock
redocket
redolence
redolency
redolent
redolently
redominate
redondilla
redoom
redouble
redoublement
redoubler
redoubling
redoubt
redoubtable
redoubtableness
redoubtably
redoubted
redound
redowa
redox
redpoll
redraft
redrag
redrape
redraw
redrawer
redream
redredge
redress
redressable
redressal
redresser
redressible
redressive
redressless
redressment
redressor
redrill
redrive
redroot
redry
redsear
redshank
redshirt
redskin
redstart
redstreak
redtab
redtail
redthroat
redtop
redub
redubber
reduce
reduceable
reduceableness
reduced
reducement
reducent
reducer
reducibility
reducible
reducibleness
reducibly
reducing
reduct
reductant
reductase
reductibility
reduction
reductional
reductionism
reductionist
reductionistic
reductive
reductively
reductor
reductorial
redue
Redunca
redundance
redundancy
redundant
redundantly
reduplicate
reduplication
reduplicative
reduplicatively
reduplicatory
reduplicature
reduviid
Reduviidae
reduvioid
Reduvius
redux
redward
redware
redweed
redwing
redwithe
redwood
redye
Ree
ree
reechy
reed
reedbird
reedbuck
reedbush
reeded
reeden
reeder
reediemadeasy
reedily
reediness
reeding
reedish
reedition
reedless
reedlike
reedling
reedmaker
reedmaking
reedman
reedplot
reedwork
reedy
reef
reefable
reefer
reefing
reefy
reek
reeker
reekingly
reeky
reel
reelable
reeled
reeler
reelingly
reelrall
reem
reeming
reemish
reen
reenge
reeper
Rees
reese
reeshle
reesk
reesle
reest
reester
reestle
reesty
reet
reetam
reetle
reeve
reeveland
reeveship
ref
reface
refacilitate
refall
refallow
refan
refascinate
refascination
refashion
refashioner
refashionment
refasten
refathered
refavor
refect
refection
refectionary
refectioner
refective
refectorarian
refectorary
refectorer
refectorial
refectorian
refectory
refederate
refeed
refeel
refeign
refel
refence
refer
referable
referee
reference
referenda
referendal
referendary
referendaryship
referendum
referent
referential
referentially
referently
referment
referral
referrer
referrible
referribleness
refertilization
refertilize
refetch
refight
refigure
refill
refillable
refilm
refilter
refinable
refinage
refinance
refind
refine
refined
refinedly
refinedness
refinement
refiner
refinery
refinger
refining
refiningly
refinish
refire
refit
refitment
refix
refixation
refixture
reflag
reflagellate
reflame
reflash
reflate
reflation
reflationism
reflect
reflectance
reflected
reflectedly
reflectedness
reflectent
reflecter
reflectibility
reflectible
reflecting
reflectingly
reflection
reflectional
reflectionist
reflectionless
reflective
reflectively
reflectiveness
reflectivity
reflectometer
reflectometry
reflector
reflectoscope
refledge
reflee
reflex
reflexed
reflexibility
reflexible
reflexism
reflexive
reflexively
reflexiveness
reflexivity
reflexly
reflexness
reflexogenous
reflexological
reflexologist
reflexology
refling
refloat
refloatation
reflog
reflood
refloor
reflorescence
reflorescent
reflourish
reflourishment
reflow
reflower
refluctuation
refluence
refluency
refluent
reflush
reflux
refluxed
refly
refocillate
refocillation
refocus
refold
refoment
refont
refool
refoot
reforbid
reforce
reford
reforecast
reforest
reforestation
reforestization
reforestize
reforestment
reforfeit
reforfeiture
reforge
reforger
reforget
reforgive
reform
reformability
reformable
reformableness
reformado
reformandum
Reformati
reformation
reformational
reformationary
reformationist
reformative
reformatively
reformatness
reformatory
reformed
reformedly
reformer
reformeress
reformingly
reformism
reformist
reformistic
reformproof
reformulate
reformulation
reforsake
refortification
refortify
reforward
refound
refoundation
refounder
refract
refractable
refracted
refractedly
refractedness
refractile
refractility
refracting
refraction
refractional
refractionate
refractionist
refractive
refractively
refractiveness
refractivity
refractometer
refractometric
refractometry
refractor
refractorily
refractoriness
refractory
refracture
refragability
refragable
refragableness
refrain
refrainer
refrainment
reframe
refrangent
refrangibility
refrangible
refrangibleness
refreeze
refrenation
refrenzy
refresh
refreshant
refreshen
refreshener
refresher
refreshful
refreshfully
refreshing
refreshingly
refreshingness
refreshment
refrigerant
refrigerate
refrigerating
refrigeration
refrigerative
refrigerator
refrigeratory
refrighten
refringence
refringency
refringent
refront
refrustrate
reft
refuel
refueling
refuge
refugee
refugeeism
refugeeship
refulge
refulgence
refulgency
refulgent
refulgently
refulgentness
refunction
refund
refunder
refundment
refurbish
refurbishment
refurl
refurnish
refurnishment
refusable
refusal
refuse
refuser
refusing
refusingly
refusion
refusive
refutability
refutable
refutably
refutal
refutation
refutative
refutatory
refute
refuter
reg
regain
regainable
regainer
regainment
regal
regale
Regalecidae
Regalecus
regalement
regaler
regalia
regalian
regalism
regalist
regality
regalize
regallop
regally
regalness
regalvanization
regalvanize
regard
regardable
regardance
regardancy
regardant
regarder
regardful
regardfully
regardfulness
regarding
regardless
regardlessly
regardlessness
regarment
regarnish
regarrison
regather
regatta
regauge
regelate
regelation
regency
regeneracy
regenerance
regenerant
regenerate
regenerateness
regeneration
regenerative
regeneratively
regenerator
regeneratory
regeneratress
regeneratrix
regenesis
regent
regental
regentess
regentship
regerminate
regermination
reges
reget
Regga
Reggie
regia
regicidal
regicide
regicidism
regift
regifuge
regild
regill
regime
regimen
regimenal
regiment
regimental
regimentaled
regimentalled
regimentally
regimentals
regimentary
regimentation
regiminal
regin
reginal
Reginald
region
regional
regionalism
regionalist
regionalistic
regionalization
regionalize
regionally
regionary
regioned
register
registered
registerer
registership
registrability
registrable
registral
registrant
registrar
registrarship
registrary
registrate
registration
registrational
registrationist
registrator
registrer
registry
regive
regladden
reglair
reglaze
regle
reglement
reglementary
reglementation
reglementist
reglet
reglorified
regloss
reglove
reglow
reglue
regma
regmacarp
regnal
regnancy
regnant
regnerable
regolith
regorge
regovern
regradation
regrade
regraduate
regraduation
regraft
regrant
regrasp
regrass
regrate
regrater
regratification
regratify
regrating
regratingly
regrator
regratress
regravel
regrede
regreen
regreet
regress
regression
regressionist
regressive
regressively
regressiveness
regressivity
regressor
regret
regretful
regretfully
regretfulness
regretless
regrettable
regrettableness
regrettably
regretter
regrettingly
regrind
regrinder
regrip
regroup
regroupment
regrow
regrowth
reguarantee
reguard
reguardant
reguide
regula
regulable
regular
Regulares
Regularia
regularity
regularization
regularize
regularizer
regularly
regularness
regulatable
regulate
regulated
regulation
regulationist
regulative
regulatively
regulator
regulatorship
regulatory
regulatress
regulatris
reguli
reguline
regulize
Regulus
regulus
regur
regurge
regurgitant
regurgitate
regurgitation
regush
reh
rehabilitate
rehabilitation
rehabilitative
rehair
rehale
rehallow
rehammer
rehandicap
rehandle
rehandler
rehandling
rehang
rehappen
reharden
reharm
reharmonize
reharness
reharrow
reharvest
rehash
rehaul
rehazard
rehead
reheal
reheap
rehear
rehearing
rehearsal
rehearse
rehearser
rehearten
reheat
reheater
Reheboth
rehedge
reheel
reheighten
Rehoboam
Rehoboth
Rehobothan
rehoe
rehoist
rehollow
rehonor
rehonour
rehood
rehook
rehoop
rehouse
rehumanize
rehumble
rehumiliate
rehumiliation
rehung
rehybridize
rehydrate
rehydration
rehypothecate
rehypothecation
rehypothecator
reichsgulden
Reichsland
Reichslander
reichsmark
reichspfennig
reichstaler
Reid
reidentification
reidentify
reif
reification
reify
reign
reignite
reignition
reignore
reillume
reilluminate
reillumination
reillumine
reillustrate
reillustration
reim
reimage
reimagination
reimagine
reimbark
reimbarkation
reimbibe
reimbody
reimbursable
reimburse
reimbursement
reimburser
reimbush
reimbushment
reimkennar
reimmerge
reimmerse
reimmersion
reimmigrant
reimmigration
reimpact
reimpark
reimpart
reimpatriate
reimpatriation
reimpel
reimplant
reimplantation
reimply
reimport
reimportation
reimportune
reimpose
reimposition
reimposure
reimpregnate
reimpress
reimpression
reimprint
reimprison
reimprisonment
reimprove
reimprovement
reimpulse
rein
reina
reinability
reinaugurate
reinauguration
reincapable
reincarnadine
reincarnate
reincarnation
reincarnationism
reincarnationist
reincense
reincentive
reincidence
reincidency
reincite
reinclination
reincline
reinclude
reinclusion
reincorporate
reincorporation
reincrease
reincrudate
reincrudation
reinculcate
reincur
reindebted
reindebtedness
reindeer
reindependence
reindicate
reindication
reindict
reindictment
reindifferent
reindorse
reinduce
reinducement
reindue
reindulge
reindulgence
Reiner
reinette
reinfect
reinfection
reinfectious
reinfer
reinfest
reinfestation
reinflame
reinflate
reinflation
reinflict
reinfliction
reinfluence
reinforce
reinforcement
reinforcer
reinform
reinfuse
reinfusion
reingraft
reingratiate
reingress
reinhabit
reinhabitation
Reinhard
reinherit
reinitiate
reinitiation
reinject
reinjure
reinless
reinoculate
reinoculation
reinquire
reinquiry
reins
reinsane
reinsanity
reinscribe
reinsert
reinsertion
reinsist
reinsman
reinspect
reinspection
reinspector
reinsphere
reinspiration
reinspire
reinspirit
reinstall
reinstallation
reinstallment
reinstalment
reinstate
reinstatement
reinstation
reinstator
reinstauration
reinstil
reinstill
reinstitute
reinstitution
reinstruct
reinstruction
reinsult
reinsurance
reinsure
reinsurer
reintegrate
reintegration
reintend
reinter
reintercede
reintercession
reinterchange
reinterest
reinterfere
reinterference
reinterment
reinterpret
reinterpretation
reinterrogate
reinterrogation
reinterrupt
reinterruption
reintervene
reintervention
reinterview
reinthrone
reintimate
reintimation
reintitule
reintrench
reintroduce
reintroduction
reintrude
reintrusion
reintuition
reintuitive
reinvade
reinvasion
reinvent
reinvention
reinventor
reinversion
reinvert
reinvest
reinvestigate
reinvestigation
reinvestiture
reinvestment
reinvigorate
reinvigoration
reinvitation
reinvite
reinvoice
reinvolve
Reinwardtia
reirrigate
reirrigation
reis
reisolation
reissuable
reissue
reissuement
reissuer
reit
reitbok
reitbuck
reitemize
reiter
reiterable
reiterance
reiterant
reiterate
reiterated
reiteratedly
reiteratedness
reiteration
reiterative
reiteratively
reiver
rejail
Rejang
reject
rejectable
rejectableness
rejectage
rejectamenta
rejecter
rejectingly
rejection
rejective
rejectment
rejector
rejerk
rejoice
rejoiceful
rejoicement
rejoicer
rejoicing
rejoicingly
rejoin
rejoinder
rejolt
rejourney
rejudge
rejumble
rejunction
rejustification
rejustify
rejuvenant
rejuvenate
rejuvenation
rejuvenative
rejuvenator
rejuvenesce
rejuvenescence
rejuvenescent
rejuvenize
Reki
rekick
rekill
rekindle
rekindlement
rekindler
reking
rekiss
reknit
reknow
rel
relabel
relace
relacquer
relade
reladen
relais
relament
relamp
reland
relap
relapper
relapsable
relapse
relapseproof
relapser
relapsing
relast
relaster
relata
relatability
relatable
relatch
relate
related
relatedness
relater
relatinization
relation
relational
relationality
relationally
relationary
relationism
relationist
relationless
relationship
relatival
relative
relatively
relativeness
relativism
relativist
relativistic
relativity
relativization
relativize
relator
relatrix
relatum
relaunch
relax
relaxable
relaxant
relaxation
relaxative
relaxatory
relaxed
relaxedly
relaxedness
relaxer
relay
relayman
relbun
relead
releap
relearn
releasable
release
releasee
releasement
releaser
releasor
releather
relection
relegable
relegate
relegation
relend
relent
relenting
relentingly
relentless
relentlessly
relentlessness
relentment
relessee
relessor
relet
reletter
relevance
relevancy
relevant
relevantly
relevate
relevation
relevator
relevel
relevy
reliability
reliable
reliableness
reliably
reliance
reliant
reliantly
reliberate
relic
relicary
relicense
relick
reliclike
relicmonger
relict
relicted
reliction
relief
reliefless
relier
relievable
relieve
relieved
relievedly
reliever
relieving
relievingly
relievo
relift
religate
religation
relight
relightable
relighten
relightener
relighter
religion
religionary
religionate
religioner
religionism
religionist
religionistic
religionize
religionless
religiose
religiosity
religious
religiously
religiousness
relime
relimit
relimitation
reline
reliner
relink
relinquent
relinquish
relinquisher
relinquishment
reliquaire
reliquary
reliquefy
reliquiae
reliquian
reliquidate
reliquidation
reliquism
relish
relishable
relisher
relishing
relishingly
relishsome
relishy
relist
relisten
relitigate
relive
Rellyan
Rellyanism
Rellyanite
reload
reloan
relocable
relocate
relocation
relocator
relock
relodge
relook
relose
relost
relot
relove
relower
relucent
reluct
reluctance
reluctancy
reluctant
reluctantly
reluctate
reluctation
reluctivity
relume
relumine
rely
remade
remagnetization
remagnetize
remagnification
remagnify
remail
remain
remainder
remainderman
remaindership
remainer
remains
remaintain
remaintenance
remake
remaker
reman
remanage
remanagement
remanation
remancipate
remancipation
remand
remandment
remanence
remanency
remanent
remanet
remanipulate
remanipulation
remantle
remanufacture
remanure
remap
remarch
remargin
remark
remarkability
remarkable
remarkableness
remarkably
remarkedly
remarker
remarket
remarque
remarriage
remarry
remarshal
remask
remass
remast
remasticate
remastication
rematch
rematerialize
remble
Rembrandt
Rembrandtesque
Rembrandtish
Rembrandtism
remeant
remeasure
remeasurement
remede
remediable
remediableness
remediably
remedial
remedially
remediation
remediless
remedilessly
remedilessness
remeditate
remeditation
remedy
remeet
remelt
remember
rememberability
rememberable
rememberably
rememberer
remembrance
remembrancer
remembrancership
rememorize
remenace
remend
remerge
remetal
remex
Remi
remica
remicate
remication
remicle
remiform
remigate
remigation
remiges
remigial
remigrant
remigrate
remigration
Remijia
remilitarization
remilitarize
remill
remimic
remind
remindal
reminder
remindful
remindingly
remineralization
remineralize
remingle
reminisce
reminiscence
reminiscenceful
reminiscencer
reminiscency
reminiscent
reminiscential
reminiscentially
reminiscently
reminiscer
reminiscitory
remint
remiped
remirror
remise
remisrepresent
remisrepresentation
remiss
remissful
remissibility
remissible
remissibleness
remission
remissive
remissively
remissiveness
remissly
remissness
remissory
remisunderstand
remit
remitment
remittable
remittal
remittance
remittancer
remittee
remittence
remittency
remittent
remittently
remitter
remittitur
remittor
remix
remixture
remnant
remnantal
remobilization
remobilize
Remoboth
remock
remodel
remodeler
remodeller
remodelment
remodification
remodify
remolade
remold
remollient
remonetization
remonetize
remonstrance
remonstrant
remonstrantly
remonstrate
remonstrating
remonstratingly
remonstration
remonstrative
remonstratively
remonstrator
remonstratory
remontado
remontant
remontoir
remop
remora
remord
remorse
remorseful
remorsefully
remorsefulness
remorseless
remorselessly
remorselessness
remorseproof
remortgage
remote
remotely
remoteness
remotion
remotive
remould
remount
removability
removable
removableness
removably
removal
remove
removed
removedly
removedness
removement
remover
removing
remultiplication
remultiply
remunerability
remunerable
remunerably
remunerate
remuneration
remunerative
remuneratively
remunerativeness
remunerator
remuneratory
remurmur
Remus
remuster
remutation
renable
renably
renail
Renaissance
renaissance
Renaissancist
Renaissant
renal
rename
Renardine
renascence
renascency
renascent
renascible
renascibleness
renature
renavigate
renavigation
rencontre
rencounter
renculus
rend
render
renderable
renderer
rendering
renderset
rendezvous
rendibility
rendible
rendition
rendlewood
rendrock
rendzina
reneague
Renealmia
renecessitate
reneg
renegade
renegadism
renegado
renegation
renege
reneger
reneglect
renegotiable
renegotiate
renegotiation
renegotiations
renegue
renerve
renes
renet
renew
renewability
renewable
renewably
renewal
renewedly
renewedness
renewer
renewment
renicardiac
renickel
renidification
renidify
reniform
Renilla
Renillidae
renin
renipericardial
reniportal
renipuncture
renish
renishly
renitence
renitency
renitent
renk
renky
renne
rennet
renneting
rennin
renniogen
renocutaneous
renogastric
renography
renointestinal
renominate
renomination
renopericardial
renopulmonary
renormalize
renotation
renotice
renotification
renotify
renounce
renounceable
renouncement
renouncer
renourish
renovate
renovater
renovatingly
renovation
renovative
renovator
renovatory
renovize
renown
renowned
renownedly
renownedness
renowner
renownful
renownless
rensselaerite
rent
rentability
rentable
rentage
rental
rentaler
rentaller
rented
rentee
renter
rentless
rentrant
rentrayeuse
Renu
renumber
renumerate
renumeration
renunciable
renunciance
renunciant
renunciate
renunciation
renunciative
renunciator
renunciatory
renunculus
renverse
renvoi
renvoy
reobject
reobjectivization
reobjectivize
reobligate
reobligation
reoblige
reobscure
reobservation
reobserve
reobtain
reobtainable
reobtainment
reoccasion
reoccupation
reoccupy
reoccur
reoccurrence
reoffend
reoffense
reoffer
reoffset
reoil
reometer
reomission
reomit
reopen
reoperate
reoperation
reoppose
reopposition
reoppress
reoppression
reorchestrate
reordain
reorder
reordinate
reordination
reorganization
reorganizationist
reorganize
reorganizer
reorient
reorientation
reornament
reoutfit
reoutline
reoutput
reoutrage
reovercharge
reoverflow
reovertake
reoverwork
reown
reoxidation
reoxidize
reoxygenate
reoxygenize
rep
repace
repacification
repacify
repack
repackage
repacker
repaganization
repaganize
repaganizer
repage
repaint
repair
repairable
repairableness
repairer
repairman
repale
repand
repandly
repandodentate
repandodenticulate
repandolobate
repandous
repandousness
repanel
repaper
reparability
reparable
reparably
reparagraph
reparate
reparation
reparative
reparatory
repark
repartable
repartake
repartee
reparticipate
reparticipation
repartition
repartitionable
repass
repassable
repassage
repasser
repast
repaste
repasture
repatch
repatency
repatent
repatriable
repatriate
repatriation
repatronize
repattern
repave
repavement
repawn
repay
repayable
repayal
repaying
repayment
repeal
repealability
repealable
repealableness
repealer
repealist
repealless
repeat
repeatability
repeatable
repeatal
repeated
repeatedly
repeater
repeg
repel
repellance
repellant
repellence
repellency
repellent
repellently
repeller
repelling
repellingly
repellingness
repen
repenetrate
repension
repent
repentable
repentance
repentant
repentantly
repenter
repentingly
repeople
reperceive
repercept
reperception
repercolation
repercuss
repercussion
repercussive
repercussively
repercussiveness
repercutient
reperform
reperformance
reperfume
reperible
repermission
repermit
reperplex
repersonalization
repersonalize
repersuade
repersuasion
repertoire
repertorial
repertorily
repertorium
repertory
reperusal
reperuse
repetend
repetition
repetitional
repetitionary
repetitious
repetitiously
repetitiousness
repetitive
repetitively
repetitiveness
repetitory
repetticoat
repew
Rephael
rephase
rephonate
rephosphorization
rephosphorize
rephotograph
rephrase
repic
repick
repicture
repiece
repile
repin
repine
repineful
repinement
repiner
repiningly
repipe
repique
repitch
repkie
replace
replaceability
replaceable
replacement
replacer
replait
replan
replane
replant
replantable
replantation
replanter
replaster
replate
replay
replead
repleader
repleat
repledge
repledger
replenish
replenisher
replenishingly
replenishment
replete
repletely
repleteness
repletion
repletive
repletively
repletory
repleviable
replevin
replevisable
replevisor
replevy
repliant
replica
replicate
replicated
replicatile
replication
replicative
replicatively
replicatory
replier
replight
replod
replot
replotment
replotter
replough
replow
replum
replume
replunder
replunge
reply
replyingly
repocket
repoint
repolish
repoll
repollute
repolon
repolymerization
repolymerize
reponder
repone
repope
repopulate
repopulation
report
reportable
reportage
reportedly
reporter
reporteress
reporterism
reportership
reportingly
reportion
reportorial
reportorially
reposal
repose
reposed
reposedly
reposedness
reposeful
reposefully
reposefulness
reposer
reposit
repositary
reposition
repositor
repository
repossess
repossession
repossessor
repost
repostpone
repot
repound
repour
repowder
repp
repped
repractice
repray
repreach
reprecipitate
reprecipitation
repredict
reprefer
reprehend
reprehendable
reprehendatory
reprehender
reprehensibility
reprehensible
reprehensibleness
reprehensibly
reprehension
reprehensive
reprehensively
reprehensory
repreparation
reprepare
represcribe
represent
representability
representable
representamen
representant
representation
representational
representationalism
representationalist
representationary
representationism
representationist
representative
representatively
representativeness
representativeship
representativity
representer
representment
represide
repress
repressed
repressedly
represser
repressible
repressibly
repression
repressionary
repressionist
repressive
repressively
repressiveness
repressment
repressor
repressory
repressure
reprice
reprieval
reprieve
repriever
reprimand
reprimander
reprimanding
reprimandingly
reprime
reprimer
reprint
reprinter
reprisal
reprisalist
reprise
repristinate
repristination
reprivatization
reprivatize
reprivilege
reproach
reproachable
reproachableness
reproachably
reproacher
reproachful
reproachfully
reproachfulness
reproachingly
reproachless
reproachlessness
reprobacy
reprobance
reprobate
reprobateness
reprobater
reprobation
reprobationary
reprobationer
reprobative
reprobatively
reprobator
reprobatory
reproceed
reprocess
reproclaim
reproclamation
reprocurable
reprocure
reproduce
reproduceable
reproducer
reproducibility
reproducible
reproduction
reproductionist
reproductive
reproductively
reproductiveness
reproductivity
reproductory
reprofane
reprofess
reprohibit
repromise
repromulgate
repromulgation
repronounce
repronunciation
reproof
reproofless
repropagate
repropitiate
repropitiation
reproportion
reproposal
repropose
reprosecute
reprosecution
reprosper
reprotect
reprotection
reprotest
reprovable
reprovableness
reprovably
reproval
reprove
reprover
reprovide
reprovingly
reprovision
reprovocation
reprovoke
reprune
reps
reptant
reptatorial
reptatory
reptile
reptiledom
reptilelike
reptilferous
Reptilia
reptilian
reptiliary
reptiliform
reptilious
reptiliousness
reptilism
reptility
reptilivorous
reptiloid
republic
republican
republicanism
republicanization
republicanize
republicanizer
republication
republish
republisher
republishment
repuddle
repudiable
repudiate
repudiation
repudiationist
repudiative
repudiator
repudiatory
repuff
repugn
repugnable
repugnance
repugnancy
repugnant
repugnantly
repugnantness
repugnate
repugnatorial
repugner
repullulate
repullulation
repullulative
repullulescent
repulpit
repulse
repulseless
repulseproof
repulser
repulsion
repulsive
repulsively
repulsiveness
repulsory
repulverize
repump
repunish
repunishment
repurchase
repurchaser
repurge
repurification
repurify
repurple
repurpose
repursue
repursuit
reputability
reputable
reputableness
reputably
reputation
reputationless
reputative
reputatively
repute
reputed
reputedly
reputeless
requalification
requalify
requarantine
requeen
requench
request
requester
requestion
requiem
Requienia
requiescence
requin
requirable
require
requirement
requirer
requisite
requisitely
requisiteness
requisition
requisitionary
requisitioner
requisitionist
requisitor
requisitorial
requisitory
requit
requitable
requital
requitative
requite
requiteful
requitement
requiter
requiz
requotation
requote
rerack
reracker
reradiation
rerail
reraise
rerake
rerank
rerate
reread
rereader
rerebrace
reredos
reree
rereel
rereeve
rerefief
reregister
reregistration
reregulate
reregulation
rereign
reremouse
rerent
rerental
reresupper
rerig
rering
rerise
rerival
rerivet
rerob
rerobe
reroll
reroof
reroot
rerope
reroute
rerow
reroyalize
rerub
rerummage
rerun
resaca
resack
resacrifice
resaddle
resail
resalable
resale
resalt
resalutation
resalute
resalvage
resample
resanctify
resanction
resatisfaction
resatisfy
resaw
resawer
resawyer
resay
resazurin
rescan
reschedule
rescind
rescindable
rescinder
rescindment
rescissible
rescission
rescissory
rescore
rescramble
rescratch
rescribe
rescript
rescription
rescriptive
rescriptively
rescrub
rescuable
rescue
rescueless
rescuer
reseal
reseam
research
researcher
researchful
researchist
reseat
resecrete
resecretion
resect
resection
resectional
Reseda
reseda
Resedaceae
resedaceous
resee
reseed
reseek
resegment
resegmentation
reseise
reseiser
reseize
reseizer
reseizure
reselect
reselection
reself
resell
reseller
resemblable
resemblance
resemblant
resemble
resembler
resemblingly
reseminate
resend
resene
resensation
resensitization
resensitize
resent
resentationally
resentence
resenter
resentful
resentfullness
resentfully
resentience
resentingly
resentless
resentment
resepulcher
resequent
resequester
resequestration
reserene
reservable
reserval
reservation
reservationist
reservatory
reserve
reserved
reservedly
reservedness
reservee
reserveful
reserveless
reserver
reservery
reservice
reservist
reservoir
reservor
reset
resettable
resetter
resettle
resettlement
resever
resew
resex
resh
reshake
reshape
reshare
resharpen
reshave
reshear
reshearer
resheathe
reshelve
reshift
reshine
reshingle
reship
reshipment
reshipper
reshoe
reshoot
reshoulder
reshovel
reshower
reshrine
reshuffle
reshun
reshunt
reshut
reshuttle
resiccate
reside
residence
residencer
residency
resident
residental
residenter
residential
residentiality
residentially
residentiary
residentiaryship
residentship
resider
residua
residual
residuary
residuation
residue
residuent
residuous
residuum
resift
resigh
resign
resignal
resignatary
resignation
resignationism
resigned
resignedly
resignedness
resignee
resigner
resignful
resignment
resile
resilement
resilial
resiliate
resilience
resiliency
resilient
resilifer
resiliometer
resilition
resilium
resilver
resin
resina
resinaceous
resinate
resinbush
resiner
resinfiable
resing
resinic
resiniferous
resinification
resinifluous
resiniform
resinify
resinize
resink
resinlike
resinoelectric
resinoextractive
resinogenous
resinoid
resinol
resinolic
resinophore
resinosis
resinous
resinously
resinousness
resinovitreous
resiny
resipiscence
resipiscent
resist
resistability
resistable
resistableness
resistance
resistant
resistantly
resister
resistful
resistibility
resistible
resistibleness
resistibly
resisting
resistingly
resistive
resistively
resistiveness
resistivity
resistless
resistlessly
resistlessness
resistor
resitting
resize
resizer
resketch
reskin
reslash
reslate
reslay
reslide
reslot
resmell
resmelt
resmile
resmooth
resnap
resnatch
resnatron
resnub
resoak
resoap
resoften
resoil
resojourn
resolder
resole
resolemnize
resolicit
resolidification
resolidify
resolubility
resoluble
resolubleness
resolute
resolutely
resoluteness
resolution
resolutioner
resolutionist
resolutory
resolvability
resolvable
resolvableness
resolvancy
resolve
resolved
resolvedly
resolvedness
resolvent
resolver
resolvible
resonance
resonancy
resonant
resonantly
resonate
resonator
resonatory
resoothe
resorb
resorbence
resorbent
resorcin
resorcine
resorcinism
resorcinol
resorcinolphthalein
resorcinum
resorcylic
resorption
resorptive
resort
resorter
resorufin
resought
resound
resounder
resounding
resoundingly
resource
resourceful
resourcefully
resourcefulness
resourceless
resourcelessness
resoutive
resow
resp
respace
respade
respan
respangle
resparkle
respeak
respect
respectability
respectabilize
respectable
respectableness
respectably
respectant
respecter
respectful
respectfully
respectfulness
respecting
respective
respectively
respectiveness
respectless
respectlessly
respectlessness
respectworthy
respell
respersive
respin
respirability
respirable
respirableness
respiration
respirational
respirative
respirator
respiratored
respiratorium
respiratory
respire
respirit
respirometer
respite
respiteless
resplend
resplendence
resplendency
resplendent
resplendently
resplice
resplit
respoke
respond
responde
respondence
respondency
respondent
respondentia
responder
responsal
responsary
response
responseless
responser
responsibility
responsible
responsibleness
responsibly
responsion
responsive
responsively
responsiveness
responsivity
responsorial
responsory
respot
respray
respread
respring
resprout
respue
resquare
resqueak
ressaidar
ressala
ressaldar
ressaut
rest
restable
restack
restaff
restain
restainable
restake
restamp
restandardization
restandardize
restant
restart
restate
restatement
restaur
restaurant
restaurate
restaurateur
restauration
restbalk
resteal
resteel
resteep
restem
restep
rester
resterilize
restes
restful
restfully
restfulness
restharrow
resthouse
Restiaceae
restiaceous
restiad
restibrachium
restiff
restiffen
restiffener
restiffness
restifle
restiform
restigmatize
restimulate
restimulation
resting
restingly
Restio
Restionaceae
restionaceous
restipulate
restipulation
restipulatory
restir
restis
restitch
restitute
restitution
restitutionism
restitutionist
restitutive
restitutor
restitutory
restive
restively
restiveness
restless
restlessly
restlessness
restock
restopper
restorable
restorableness
restoral
restoration
restorationer
restorationism
restorationist
restorative
restoratively
restorativeness
restorator
restoratory
restore
restorer
restow
restowal
restproof
restraighten
restrain
restrainability
restrained
restrainedly
restrainedness
restrainer
restraining
restrainingly
restraint
restraintful
restrap
restratification
restream
restrengthen
restress
restretch
restrict
restricted
restrictedly
restrictedness
restriction
restrictionary
restrictionist
restrictive
restrictively
restrictiveness
restrike
restring
restringe
restringency
restringent
restrip
restrive
restroke
restudy
restuff
restward
restwards
resty
restyle
resubject
resubjection
resubjugate
resublimation
resublime
resubmerge
resubmission
resubmit
resubordinate
resubscribe
resubscriber
resubscription
resubstitute
resubstitution
resucceed
resuck
resudation
resue
resuffer
resufferance
resuggest
resuggestion
resuing
resuit
result
resultance
resultancy
resultant
resultantly
resultative
resultful
resultfully
resulting
resultingly
resultive
resultless
resultlessly
resultlessness
resumability
resumable
resume
resumer
resummon
resummons
resumption
resumptive
resumptively
resun
resup
resuperheat
resupervise
resupinate
resupinated
resupination
resupine
resupply
resupport
resuppose
resupposition
resuppress
resuppression
resurface
resurge
resurgence
resurgency
resurgent
resurprise
resurrect
resurrectible
resurrection
resurrectional
resurrectionary
resurrectioner
resurrectioning
resurrectionism
resurrectionist
resurrectionize
resurrective
resurrector
resurrender
resurround
resurvey
resuscitable
resuscitant
resuscitate
resuscitation
resuscitative
resuscitator
resuspect
resuspend
resuspension
reswage
reswallow
resward
reswarm
reswear
resweat
resweep
reswell
reswill
reswim
resyllabification
resymbolization
resymbolize
resynthesis
resynthesize
ret
retable
retack
retackle
retag
retail
retailer
retailment
retailor
retain
retainability
retainable
retainableness
retainal
retainder
retainer
retainership
retaining
retake
retaker
retaliate
retaliation
retaliationist
retaliative
retaliator
retaliatory
retalk
retama
retame
retan
retanner
retape
retard
retardance
retardant
retardate
retardation
retardative
retardatory
retarded
retardence
retardent
retarder
retarding
retardingly
retardive
retardment
retardure
retare
retariff
retaste
retation
retattle
retax
retaxation
retch
reteach
retecious
retelegraph
retelephone
retell
retelling
retem
retemper
retempt
retemptation
retenant
retender
retene
retent
retention
retentionist
retentive
retentively
retentiveness
retentivity
retentor
Retepora
retepore
Reteporidae
retest
retexture
rethank
rethatch
rethaw
rethe
retheness
rethicken
rethink
rethrash
rethread
rethreaten
rethresh
rethresher
rethrill
rethrive
rethrone
rethrow
rethrust
rethunder
retia
retial
Retiariae
retiarian
retiarius
retiary
reticella
reticello
reticence
reticency
reticent
reticently
reticket
reticle
reticula
reticular
Reticularia
reticularian
reticularly
reticulary
reticulate
reticulated
reticulately
reticulation
reticulatocoalescent
reticulatogranulate
reticulatoramose
reticulatovenose
reticule
reticuled
reticulin
reticulitis
reticulocyte
reticulocytosis
reticuloramose
Reticulosa
reticulose
reticulovenose
reticulum
retie
retier
retiform
retighten
retile
retill
retimber
retime
retin
retina
retinacular
retinaculate
retinaculum
retinal
retinalite
retinasphalt
retinasphaltum
retincture
retinene
retinerved
retinian
retinispora
retinite
retinitis
retinize
retinker
retinoblastoma
retinochorioid
retinochorioidal
retinochorioiditis
retinoid
retinol
retinopapilitis
retinophoral
retinophore
retinoscope
retinoscopic
retinoscopically
retinoscopist
retinoscopy
Retinospora
retinue
retinula
retinular
retinule
retip
retiracied
retiracy
retirade
retiral
retire
retired
retiredly
retiredness
retirement
retirer
retiring
retiringly
retiringness
retistene
retoast
retold
retolerate
retoleration
retomb
retonation
retook
retool
retooth
retoother
retort
retortable
retorted
retorter
retortion
retortive
retorture
retoss
retotal
retouch
retoucher
retouching
retouchment
retour
retourable
retrace
retraceable
retracement
retrack
retract
retractability
retractable
retractation
retracted
retractibility
retractible
retractile
retractility
retraction
retractive
retractively
retractiveness
retractor
retrad
retrade
retradition
retrahent
retrain
retral
retrally
retramp
retrample
retranquilize
retranscribe
retranscription
retransfer
retransference
retransfigure
retransform
retransformation
retransfuse
retransit
retranslate
retranslation
retransmission
retransmissive
retransmit
retransmute
retransplant
retransport
retransportation
retravel
retraverse
retraxit
retread
retreat
retreatal
retreatant
retreater
retreatful
retreating
retreatingness
retreative
retreatment
retree
retrench
retrenchable
retrencher
retrenchment
retrial
retribute
retribution
retributive
retributively
retributor
retributory
retricked
retrievability
retrievable
retrievableness
retrievably
retrieval
retrieve
retrieveless
retrievement
retriever
retrieverish
retrim
retrimmer
retrip
retroact
retroaction
retroactive
retroactively
retroactivity
retroalveolar
retroauricular
retrobronchial
retrobuccal
retrobulbar
retrocaecal
retrocardiac
retrocecal
retrocede
retrocedence
retrocedent
retrocervical
retrocession
retrocessional
retrocessionist
retrocessive
retrochoir
retroclavicular
retroclusion
retrocognition
retrocognitive
retrocolic
retroconsciousness
retrocopulant
retrocopulation
retrocostal
retrocouple
retrocoupler
retrocurved
retrodate
retrodeviation
retrodisplacement
retroduction
retrodural
retroesophageal
retroflected
retroflection
retroflex
retroflexed
retroflexion
retroflux
retroform
retrofract
retrofracted
retrofrontal
retrogastric
retrogenerative
retrogradation
retrogradatory
retrograde
retrogradely
retrogradient
retrogradingly
retrogradism
retrogradist
retrogress
retrogression
retrogressionist
retrogressive
retrogressively
retrohepatic
retroinfection
retroinsular
retroiridian
retroject
retrojection
retrojugular
retrolabyrinthine
retrolaryngeal
retrolingual
retrolocation
retromammary
retromammillary
retromandibular
retromastoid
retromaxillary
retromigration
retromingent
retromingently
retromorphosed
retromorphosis
retronasal
retroperitoneal
retroperitoneally
retropharyngeal
retropharyngitis
retroplacental
retroplexed
retroposed
retroposition
retropresbyteral
retropubic
retropulmonary
retropulsion
retropulsive
retroreception
retrorectal
retroreflective
retrorenal
retrorse
retrorsely
retroserrate
retroserrulate
retrospect
retrospection
retrospective
retrospectively
retrospectiveness
retrospectivity
retrosplenic
retrostalsis
retrostaltic
retrosternal
retrosusception
retrot
retrotarsal
retrotemporal
retrothyroid
retrotracheal
retrotransfer
retrotransference
retrotympanic
retrousse
retrovaccinate
retrovaccination
retrovaccine
retroverse
retroversion
retrovert
retrovision
retroxiphoid
retrude
retrue
retrusible
retrusion
retrust
retry
retted
retter
rettery
retting
rettory
retube
retuck
retumble
retumescence
retune
returban
returf
returfer
return
returnability
returnable
returned
returner
returnless
returnlessly
retuse
retwine
retwist
retying
retype
retzian
Reub
Reuben
Reubenites
Reuchlinian
Reuchlinism
Reuel
reundercut
reundergo
reundertake
reundulate
reundulation
reune
reunfold
reunification
reunify
reunion
reunionism
reunionist
reunionistic
reunitable
reunite
reunitedly
reuniter
reunition
reunitive
reunpack
reuphold
reupholster
reuplift
reurge
reuse
reutilization
reutilize
reutter
reutterance
rev
revacate
revaccinate
revaccination
revalenta
revalescence
revalescent
revalidate
revalidation
revalorization
revalorize
revaluate
revaluation
revalue
revamp
revamper
revampment
revaporization
revaporize
revarnish
revary
reve
reveal
revealability
revealable
revealableness
revealed
revealedly
revealer
revealing
revealingly
revealingness
revealment
revegetate
revegetation
revehent
reveil
reveille
revel
revelability
revelant
revelation
revelational
revelationer
revelationist
revelationize
revelative
revelator
revelatory
reveler
revellent
revelly
revelment
revelrout
revelry
revenant
revend
revender
revendicate
revendication
reveneer
revenge
revengeable
revengeful
revengefully
revengefulness
revengeless
revengement
revenger
revengingly
revent
reventilate
reventure
revenual
revenue
revenued
revenuer
rever
reverable
reverb
reverbatory
reverberant
reverberate
reverberation
reverberative
reverberator
reverberatory
reverbrate
reverdure
revere
revered
reverence
reverencer
reverend
reverendly
reverendship
reverent
reverential
reverentiality
reverentially
reverentialness
reverently
reverentness
reverer
reverie
reverification
reverify
reverist
revers
reversability
reversable
reversal
reverse
reversed
reversedly
reverseful
reverseless
reversely
reversement
reverser
reverseways
reversewise
reversi
reversibility
reversible
reversibleness
reversibly
reversification
reversifier
reversify
reversing
reversingly
reversion
reversionable
reversional
reversionally
reversionary
reversioner
reversionist
reversis
reversist
reversive
reverso
revert
revertal
reverter
revertibility
revertible
revertive
revertively
revery
revest
revestiary
revestry
revet
revete
revetement
revetment
revibrate
revibration
revibrational
revictorious
revictory
revictual
revictualment
revie
review
reviewability
reviewable
reviewage
reviewal
reviewer
revieweress
reviewish
reviewless
revigorate
revigoration
revile
revilement
reviler
reviling
revilingly
revindicate
revindication
reviolate
reviolation
revirescence
revirescent
Revisable
revisable
revisableness
revisal
revise
Revised
revisee
reviser
revisership
revisible
revision
revisional
revisionary
revisionism
revisionist
revisit
revisitant
revisitation
revisor
revisory
revisualization
revisualize
revitalization
revitalize
revitalizer
revivability
revivable
revivably
revival
revivalism
revivalist
revivalistic
revivalize
revivatory
revive
revivement
reviver
revivification
revivifier
revivify
reviving
revivingly
reviviscence
reviviscency
reviviscent
reviviscible
revivor
revocability
revocable
revocableness
revocably
revocation
revocative
revocatory
revoice
revokable
revoke
revokement
revoker
revokingly
revolant
revolatilize
revolt
revolter
revolting
revoltingly
revoltress
revolubility
revoluble
revolubly
revolunteer
revolute
revoluted
revolution
revolutional
revolutionally
revolutionarily
revolutionariness
revolutionary
revolutioneering
revolutioner
revolutionism
revolutionist
revolutionize
revolutionizement
revolutionizer
revolvable
revolvably
revolve
revolvement
revolvency
revolver
revolving
revolvingly
revomit
revote
revue
revuette
revuist
revulsed
revulsion
revulsionary
revulsive
revulsively
rewade
rewager
rewake
rewaken
rewall
rewallow
reward
rewardable
rewardableness
rewardably
rewardedly
rewarder
rewardful
rewardfulness
rewarding
rewardingly
rewardless
rewardproof
rewarehouse
rewarm
rewarn
rewash
rewater
rewave
rewax
rewaybill
rewayle
reweaken
rewear
reweave
rewed
reweigh
reweigher
reweight
rewelcome
reweld
rewend
rewet
rewhelp
rewhirl
rewhisper
rewhiten
rewiden
rewin
rewind
rewinder
rewirable
rewire
rewish
rewithdraw
rewithdrawal
rewood
reword
rework
reworked
rewound
rewove
rewoven
rewrap
rewrite
rewriter
Rex
rex
rexen
reyield
Reynard
Reynold
reyoke
reyouth
rezbanyite
rhabdite
rhabditiform
Rhabditis
rhabdium
Rhabdocarpum
Rhabdocoela
rhabdocoelan
rhabdocoele
Rhabdocoelida
rhabdocoelidan
rhabdocoelous
rhabdoid
rhabdoidal
rhabdolith
rhabdom
rhabdomal
rhabdomancer
rhabdomancy
rhabdomantic
rhabdomantist
Rhabdomonas
rhabdomyoma
rhabdomyosarcoma
rhabdomysarcoma
rhabdophane
rhabdophanite
Rhabdophora
rhabdophoran
Rhabdopleura
rhabdopod
rhabdos
rhabdosome
rhabdosophy
rhabdosphere
rhabdus
Rhacianectes
Rhacomitrium
Rhacophorus
Rhadamanthine
Rhadamanthus
Rhadamanthys
Rhaetian
Rhaetic
rhagades
rhagadiform
rhagiocrin
rhagionid
Rhagionidae
rhagite
Rhagodia
rhagon
rhagonate
rhagose
rhamn
Rhamnaceae
rhamnaceous
rhamnal
Rhamnales
rhamnetin
rhamninase
rhamninose
rhamnite
rhamnitol
rhamnohexite
rhamnohexitol
rhamnohexose
rhamnonic
rhamnose
rhamnoside
Rhamnus
rhamphoid
Rhamphorhynchus
Rhamphosuchus
rhamphotheca
Rhapidophyllum
Rhapis
rhapontic
rhaponticin
rhapontin
rhapsode
rhapsodic
rhapsodical
rhapsodically
rhapsodie
rhapsodism
rhapsodist
rhapsodistic
rhapsodize
rhapsodomancy
rhapsody
Rhaptopetalaceae
rhason
rhasophore
rhatania
rhatany
rhe
Rhea
rhea
rheadine
Rheae
rhebok
rhebosis
rheeboc
rheebok
rheen
rhegmatype
rhegmatypy
Rhegnopteri
rheic
Rheidae
Rheiformes
rhein
rheinic
rhema
rhematic
rhematology
rheme
Rhemish
Rhemist
Rhenish
rhenium
rheobase
rheocrat
rheologist
rheology
rheometer
rheometric
rheometry
rheophile
rheophore
rheophoric
rheoplankton
rheoscope
rheoscopic
rheostat
rheostatic
rheostatics
rheotactic
rheotan
rheotaxis
rheotome
rheotrope
rheotropic
rheotropism
rhesian
rhesus
rhetor
rhetoric
rhetorical
rhetorically
rhetoricalness
rhetoricals
rhetorician
rhetorize
Rheum
rheum
rheumarthritis
rheumatalgia
rheumatic
rheumatical
rheumatically
rheumaticky
rheumatism
rheumatismal
rheumatismoid
rheumative
rheumatiz
rheumatize
rheumatoid
rheumatoidal
rheumatoidally
rheumed
rheumic
rheumily
rheuminess
rheumy
Rhexia
rhexis
rhigolene
rhigosis
rhigotic
Rhina
rhinal
rhinalgia
Rhinanthaceae
Rhinanthus
rhinarium
rhincospasm
rhine
Rhineland
Rhinelander
rhinencephalic
rhinencephalon
rhinencephalous
rhinenchysis
Rhineodon
Rhineodontidae
rhinestone
Rhineura
rhineurynter
Rhinidae
rhinion
rhinitis
rhino
Rhinobatidae
Rhinobatus
rhinobyon
rhinocaul
rhinocele
rhinocelian
rhinocerial
rhinocerian
rhinocerine
rhinoceroid
rhinoceros
rhinoceroslike
rhinocerotic
Rhinocerotidae
rhinocerotiform
rhinocerotine
rhinocerotoid
rhinochiloplasty
Rhinoderma
rhinodynia
rhinogenous
rhinolalia
rhinolaryngology
rhinolaryngoscope
rhinolite
rhinolith
rhinolithic
rhinological
rhinologist
rhinology
rhinolophid
Rhinolophidae
rhinolophine
rhinopharyngeal
rhinopharyngitis
rhinopharynx
Rhinophidae
Rhinophis
rhinophonia
rhinophore
rhinophyma
rhinoplastic
rhinoplasty
rhinopolypus
Rhinoptera
Rhinopteridae
rhinorrhagia
rhinorrhea
rhinorrheal
rhinoscleroma
rhinoscope
rhinoscopic
rhinoscopy
rhinosporidiosis
Rhinosporidium
rhinotheca
rhinothecal
Rhinthonic
Rhinthonica
rhipidate
rhipidion
Rhipidistia
rhipidistian
rhipidium
Rhipidoglossa
rhipidoglossal
rhipidoglossate
Rhipidoptera
rhipidopterous
rhipiphorid
Rhipiphoridae
Rhipiptera
rhipipteran
rhipipterous
Rhipsalis
Rhiptoglossa
rhizanthous
rhizautoicous
Rhizina
Rhizinaceae
rhizine
rhizinous
Rhizobium
rhizocarp
Rhizocarpeae
rhizocarpean
rhizocarpian
rhizocarpic
rhizocarpous
rhizocaul
rhizocaulus
Rhizocephala
rhizocephalan
rhizocephalous
rhizocorm
Rhizoctonia
rhizoctoniose
rhizodermis
Rhizodus
Rhizoflagellata
rhizoflagellate
rhizogen
rhizogenetic
rhizogenic
rhizogenous
rhizoid
rhizoidal
rhizoma
rhizomatic
rhizomatous
rhizome
rhizomelic
rhizomic
rhizomorph
rhizomorphic
rhizomorphoid
rhizomorphous
rhizoneure
rhizophagous
rhizophilous
Rhizophora
Rhizophoraceae
rhizophoraceous
rhizophore
rhizophorous
rhizophyte
rhizoplast
rhizopod
Rhizopoda
rhizopodal
rhizopodan
rhizopodist
rhizopodous
Rhizopogon
Rhizopus
rhizosphere
Rhizostomae
Rhizostomata
rhizostomatous
rhizostome
rhizostomous
Rhizota
rhizotaxis
rhizotaxy
rhizote
rhizotic
rhizotomi
rhizotomy
rho
Rhoda
rhodaline
Rhodamine
rhodamine
rhodanate
Rhodanian
rhodanic
rhodanine
rhodanthe
rhodeose
Rhodes
Rhodesian
Rhodesoid
rhodeswood
Rhodian
rhodic
rhoding
rhodinol
rhodite
rhodium
rhodizite
rhodizonic
Rhodobacteriaceae
Rhodobacterioideae
rhodochrosite
Rhodococcus
Rhodocystis
rhodocyte
rhododendron
rhodolite
Rhodomelaceae
rhodomelaceous
rhodonite
Rhodope
rhodophane
Rhodophyceae
rhodophyceous
rhodophyll
Rhodophyllidaceae
Rhodophyta
rhodoplast
rhodopsin
Rhodora
Rhodoraceae
rhodorhiza
rhodosperm
Rhodospermeae
rhodospermin
rhodospermous
Rhodospirillum
Rhodothece
Rhodotypos
Rhodymenia
Rhodymeniaceae
rhodymeniaceous
Rhodymeniales
Rhoeadales
Rhoecus
Rhoeo
rhomb
rhombencephalon
rhombenporphyr
rhombic
rhombical
rhombiform
rhomboclase
rhomboganoid
Rhomboganoidei
rhombogene
rhombogenic
rhombogenous
rhombohedra
rhombohedral
rhombohedrally
rhombohedric
rhombohedron
rhomboid
rhomboidal
rhomboidally
rhomboideus
rhomboidly
rhomboquadratic
rhomborectangular
rhombos
rhombovate
Rhombozoa
rhombus
rhonchal
rhonchial
rhonchus
Rhonda
rhopalic
rhopalism
rhopalium
Rhopalocera
rhopaloceral
rhopalocerous
Rhopalura
rhotacism
rhotacismus
rhotacistic
rhotacize
rhubarb
rhubarby
rhumb
rhumba
rhumbatron
Rhus
rhyacolite
rhyme
rhymeless
rhymelet
rhymemaker
rhymemaking
rhymeproof
rhymer
rhymery
rhymester
rhymewise
rhymic
rhymist
rhymy
Rhynchobdellae
Rhynchobdellida
Rhynchocephala
Rhynchocephali
Rhynchocephalia
rhynchocephalian
rhynchocephalic
rhynchocephalous
Rhynchocoela
rhynchocoelan
rhynchocoelic
rhynchocoelous
rhyncholite
Rhynchonella
Rhynchonellacea
Rhynchonellidae
rhynchonelloid
Rhynchophora
rhynchophoran
rhynchophore
rhynchophorous
Rhynchopinae
Rhynchops
Rhynchosia
Rhynchospora
Rhynchota
rhynchotal
rhynchote
rhynchotous
rhynconellid
Rhyncostomi
Rhynia
Rhyniaceae
Rhynocheti
Rhynsburger
rhyobasalt
rhyodacite
rhyolite
rhyolitic
rhyotaxitic
rhyparographer
rhyparographic
rhyparographist
rhyparography
rhypography
rhyptic
rhyptical
rhysimeter
Rhyssa
rhythm
rhythmal
rhythmic
rhythmical
rhythmicality
rhythmically
rhythmicity
rhythmicize
rhythmics
rhythmist
rhythmizable
rhythmization
rhythmize
rhythmless
rhythmometer
rhythmopoeia
rhythmproof
Rhytidodon
rhytidome
rhytidosis
Rhytina
Rhytisma
rhyton
ria
rial
riancy
riant
riantly
riata
rib
ribald
ribaldish
ribaldly
ribaldrous
ribaldry
riband
Ribandism
Ribandist
ribandlike
ribandmaker
ribandry
ribat
ribaudequin
ribaudred
ribband
ribbandry
ribbed
ribber
ribbet
ribbidge
ribbing
ribble
ribbon
ribbonback
ribboner
ribbonfish
Ribbonism
ribbonlike
ribbonmaker
Ribbonman
ribbonry
ribbonweed
ribbonwood
ribbony
ribby
ribe
Ribes
Ribhus
ribless
riblet
riblike
riboflavin
ribonic
ribonuclease
ribonucleic
ribose
ribroast
ribroaster
ribroasting
ribskin
ribspare
Ribston
ribwork
ribwort
Ric
Ricardian
Ricardianism
Ricardo
Riccia
Ricciaceae
ricciaceous
Ricciales
rice
ricebird
riceland
ricer
ricey
Rich
rich
Richard
Richardia
Richardsonia
richdom
Richebourg
richellite
richen
riches
richesse
richling
richly
Richmond
Richmondena
richness
richt
richterite
richweed
ricin
ricine
ricinelaidic
ricinelaidinic
ricinic
ricinine
ricininic
ricinium
ricinoleate
ricinoleic
ricinolein
ricinolic
Ricinulei
Ricinus
ricinus
Rick
rick
rickardite
ricker
ricketily
ricketiness
ricketish
rickets
Rickettsia
rickettsial
Rickettsiales
rickettsialpox
rickety
rickey
rickle
rickmatic
rickrack
ricksha
rickshaw
rickstaddle
rickstand
rickstick
Ricky
rickyard
ricochet
ricolettaite
ricrac
rictal
rictus
rid
ridable
ridableness
ridably
riddam
riddance
riddel
ridden
ridder
ridding
riddle
riddlemeree
riddler
riddling
riddlingly
riddlings
ride
rideable
rideau
riden
rident
rider
ridered
rideress
riderless
ridge
ridgeband
ridgeboard
ridgebone
ridged
ridgel
ridgelet
ridgelike
ridgeling
ridgepiece
ridgeplate
ridgepole
ridgepoled
ridger
ridgerope
ridgetree
ridgeway
ridgewise
ridgil
ridging
ridgingly
ridgling
ridgy
ridibund
ridicule
ridiculer
ridiculize
ridiculosity
ridiculous
ridiculously
ridiculousness
riding
ridingman
ridotto
rie
riebeckite
riem
Riemannean
Riemannian
riempie
rier
Riesling
rife
rifely
rifeness
Riff
riff
Riffi
Riffian
riffle
riffler
riffraff
Rifi
Rifian
rifle
riflebird
rifledom
rifleman
riflemanship
rifleproof
rifler
riflery
rifleshot
rifling
rift
rifter
riftless
rifty
rig
rigadoon
rigamajig
rigamarole
rigation
rigbane
Rigel
Rigelian
rigescence
rigescent
riggald
rigger
rigging
riggish
riggite
riggot
right
rightabout
righten
righteous
righteously
righteousness
righter
rightful
rightfully
rightfulness
rightheaded
righthearted
rightist
rightle
rightless
rightlessness
rightly
rightmost
rightness
righto
rightship
rightward
rightwardly
rightwards
righty
rigid
rigidify
rigidist
rigidity
rigidly
rigidness
rigidulous
rigling
rigmaree
rigmarole
rigmarolery
rigmarolic
rigmarolish
rigmarolishly
rignum
rigol
rigolette
rigor
rigorism
rigorist
rigoristic
rigorous
rigorously
rigorousness
rigsby
rigsdaler
Rigsmaal
Rigsmal
rigwiddie
rigwiddy
Rik
Rikari
rikisha
rikk
riksha
rikshaw
Riksmaal
Riksmal
rilawa
rile
riley
rill
rillet
rillett
rillette
rillock
rillstone
rilly
rim
rima
rimal
rimate
rimbase
rime
rimeless
rimer
rimester
rimfire
rimiform
rimland
rimless
rimmaker
rimmaking
rimmed
rimmer
rimose
rimosely
rimosity
rimous
rimpi
rimple
rimption
rimrock
rimu
rimula
rimulose
rimy
Rinaldo
rinceau
rinch
rincon
Rind
rind
Rinde
rinded
rinderpest
rindle
rindless
rindy
rine
ring
ringable
Ringatu
ringbark
ringbarker
ringbill
ringbird
ringbolt
ringbone
ringboned
ringcraft
ringdove
ringe
ringed
ringent
ringer
ringeye
ringgiver
ringgiving
ringgoer
ringhals
ringhead
ringiness
ringing
ringingly
ringingness
ringite
ringle
ringlead
ringleader
ringleaderless
ringleadership
ringless
ringlet
ringleted
ringlety
ringlike
ringmaker
ringmaking
ringman
ringmaster
ringneck
ringsail
ringside
ringsider
ringster
ringtail
ringtaw
ringtime
ringtoss
ringwalk
ringwall
ringwise
ringworm
ringy
rink
rinka
rinker
rinkite
rinncefada
rinneite
rinner
rinsable
rinse
rinser
rinsing
rinthereout
rintherout
Rio
rio
riot
rioter
rioting
riotingly
riotist
riotistic
riotocracy
riotous
riotously
riotousness
riotproof
riotry
rip
ripa
ripal
riparial
riparian
Riparii
riparious
ripcord
ripe
ripelike
ripely
ripen
ripener
ripeness
ripening
ripeningly
riper
ripgut
ripicolous
ripidolite
ripienist
ripieno
ripier
ripost
riposte
rippable
ripper
ripperman
rippet
rippier
ripping
rippingly
rippingness
rippit
ripple
rippleless
rippler
ripplet
rippling
ripplingly
ripply
rippon
riprap
riprapping
ripsack
ripsaw
ripsnorter
ripsnorting
Ripuarian
ripup
riroriro
risala
risberm
rise
risen
riser
rishi
rishtadar
risibility
risible
risibleness
risibles
risibly
rising
risk
risker
riskful
riskfulness
riskily
riskiness
riskish
riskless
riskproof
risky
risorial
risorius
risp
risper
risque
risquee
Riss
rissel
risser
Rissian
rissle
Rissoa
rissoid
Rissoidae
rist
ristori
rit
Rita
rita
Ritalynne
ritardando
Ritchey
rite
riteless
ritelessness
ritling
ritornel
ritornelle
ritornello
Ritschlian
Ritschlianism
rittingerite
ritual
ritualism
ritualist
ritualistic
ritualistically
rituality
ritualize
ritualless
ritually
ritzy
riva
rivage
rival
rivalable
rivaless
rivalism
rivality
rivalize
rivalless
rivalrous
rivalry
rivalship
rive
rivel
rivell
riven
river
riverain
riverbank
riverbush
riverdamp
rivered
riverhead
riverhood
riverine
riverish
riverless
riverlet
riverlike
riverling
riverly
riverman
riverscape
riverside
riversider
riverward
riverwards
riverwash
riverway
riverweed
riverwise
rivery
rivet
riveter
rivethead
riveting
rivetless
rivetlike
Rivina
riving
rivingly
Rivinian
rivose
Rivularia
Rivulariaceae
rivulariaceous
rivulation
rivulet
rivulose
rix
rixatrix
rixy
riyal
riziform
rizzar
rizzle
rizzom
rizzomed
rizzonite
Ro
roach
roachback
road
roadability
roadable
roadbed
roadblock
roadbook
roadcraft
roaded
roader
roadfellow
roadhead
roadhouse
roading
roadite
roadless
roadlessness
roadlike
roadman
roadmaster
roadside
roadsider
roadsman
roadstead
roadster
roadstone
roadtrack
roadway
roadweed
roadwise
roadworthiness
roadworthy
roam
roamage
roamer
roaming
roamingly
roan
roanoke
roar
roarer
roaring
roaringly
roast
roastable
roaster
roasting
roastingly
Rob
rob
robalito
robalo
roband
robber
robberproof
robbery
Robbin
robbin
robbing
robe
robeless
Robenhausian
rober
roberd
Roberdsman
Robert
Roberta
Roberto
Robigalia
Robigus
Robin
robin
robinet
robing
Robinia
robinin
robinoside
roble
robomb
roborant
roborate
roboration
roborative
roborean
roboreous
robot
robotesque
robotian
robotism
robotistic
robotization
robotize
robotlike
robotry
robur
roburite
robust
robustful
robustfully
robustfulness
robustic
robusticity
robustious
robustiously
robustiousness
robustity
robustly
robustness
roc
rocambole
Roccella
Roccellaceae
roccellic
roccellin
roccelline
Rochea
rochelime
Rochelle
rocher
rochet
rocheted
rock
rockable
rockably
rockaby
rockabye
rockallite
Rockaway
rockaway
rockbell
rockberry
rockbird
rockborn
rockbrush
rockcist
rockcraft
rockelay
rocker
rockery
rocket
rocketeer
rocketer
rocketlike
rocketor
rocketry
rockety
rockfall
rockfish
rockfoil
rockhair
rockhearted
Rockies
rockiness
rocking
rockingly
rockish
rocklay
rockless
rocklet
rocklike
rockling
rockman
rockrose
rockshaft
rockslide
rockstaff
rocktree
rockward
rockwards
rockweed
rockwood
rockwork
rocky
rococo
Rocouyenne
rocta
Rod
rod
rodd
roddikin
roddin
rodding
rode
Rodent
rodent
Rodentia
rodential
rodentially
rodentian
rodenticidal
rodenticide
rodentproof
rodeo
Roderic
Roderick
rodge
Rodger
rodham
Rodinal
Rodinesque
roding
rodingite
rodknight
rodless
rodlet
rodlike
rodmaker
rodman
Rodney
rodney
Rodolph
Rodolphus
rodomont
rodomontade
rodomontadist
rodomontador
rodsman
rodster
rodwood
roe
roeblingite
roebuck
roed
roelike
roentgen
roentgenism
roentgenization
roentgenize
roentgenogram
roentgenograph
roentgenographic
roentgenographically
roentgenography
roentgenologic
roentgenological
roentgenologically
roentgenologist
roentgenology
roentgenometer
roentgenometry
roentgenoscope
roentgenoscopic
roentgenoscopy
roentgenotherapy
roentgentherapy
roer
roestone
roey
rog
rogan
rogation
Rogationtide
rogative
rogatory
Roger
roger
Rogero
rogersite
roggle
Rogue
rogue
roguedom
rogueling
roguery
rogueship
roguing
roguish
roguishly
roguishness
rohan
Rohilla
rohob
rohun
rohuna
roi
roid
roil
roily
Roist
roister
roisterer
roistering
roisteringly
roisterly
roisterous
roisterously
roit
Rok
roka
roke
rokeage
rokee
rokelay
roker
rokey
roky
Roland
Rolandic
role
roleo
Rolf
Rolfe
roll
rollable
rollback
rolled
rollejee
roller
rollerer
rollermaker
rollermaking
rollerman
rollerskater
rollerskating
rolley
rolleyway
rolleywayman
rolliche
rollichie
rollick
rollicker
rollicking
rollickingly
rollickingness
rollicksome
rollicksomeness
rollicky
rolling
rollingly
Rollinia
rollix
rollmop
Rollo
rollock
rollway
roloway
Romaean
Romagnese
Romagnol
Romagnole
Romaic
romaika
Romain
romaine
Romaji
romal
Roman
Romance
romance
romancealist
romancean
romanceful
romanceish
romanceishness
romanceless
romancelet
romancelike
romancemonger
romanceproof
romancer
romanceress
romancical
romancing
romancist
romancy
Romandom
Romane
Romanes
Romanese
Romanesque
Romanhood
Romanian
Romanic
Romaniform
Romanish
Romanism
Romanist
Romanistic
Romanite
Romanity
romanium
Romanization
Romanize
Romanizer
Romanly
Romansch
Romansh
romantic
romantical
romanticalism
romanticality
romantically
romanticalness
romanticism
romanticist
romanticistic
romanticity
romanticize
romanticly
romanticness
romantism
romantist
Romany
romanza
romaunt
rombos
rombowline
Rome
romeite
Romeo
romerillo
romero
Romescot
Romeshot
Romeward
Romewards
Romic
Romipetal
Romish
Romishly
Romishness
rommack
Rommany
Romney
Romneya
romp
romper
romping
rompingly
rompish
rompishly
rompishness
rompu
rompy
Romulian
Romulus
Ron
Ronald
roncador
Roncaglian
roncet
ronco
rond
rondache
rondacher
rondawel
ronde
rondeau
rondel
rondelet
Rondeletia
rondelier
rondelle
rondellier
rondino
rondle
rondo
rondoletto
rondure
rone
Rong
Ronga
rongeur
Ronni
ronquil
Ronsardian
Ronsardism
Ronsardist
Ronsardize
Ronsdorfer
Ronsdorfian
rontgen
ronyon
rood
roodebok
roodle
roodstone
roof
roofage
roofer
roofing
roofless
rooflet
rooflike
roofman
rooftree
roofward
roofwise
roofy
rooibok
rooinek
rook
rooker
rookeried
rookery
rookie
rookish
rooklet
rooklike
rooky
rool
room
roomage
roomed
roomer
roomful
roomie
roomily
roominess
roomkeeper
roomless
roomlet
roommate
roomstead
roomth
roomthily
roomthiness
roomthy
roomward
roomy
roon
roorback
roosa
Roosevelt
Rooseveltian
roost
roosted
rooster
roosterfish
roosterhood
roosterless
roosters
roostership
Root
root
rootage
rootcap
rooted
rootedly
rootedness
rooter
rootery
rootfast
rootfastness
roothold
rootiness
rootle
rootless
rootlessness
rootlet
rootlike
rootling
rootstalk
rootstock
rootwalt
rootward
rootwise
rootworm
rooty
roove
ropable
rope
ropeable
ropeband
ropebark
ropedance
ropedancer
ropedancing
ropelayer
ropelaying
ropelike
ropemaker
ropemaking
ropeman
roper
roperipe
ropery
ropes
ropesmith
ropetrick
ropewalk
ropewalker
ropeway
ropework
ropily
ropiness
roping
ropish
ropishness
ropp
ropy
roque
roquelaure
roquer
roquet
roquette
roquist
roral
roratorio
Rori
roric
Roridula
Roridulaceae
roriferous
rorifluent
Roripa
Rorippa
roritorious
rorqual
rorty
rorulent
rory
Rosa
Rosabel
Rosabella
Rosaceae
rosacean
rosaceous
rosal
Rosales
Rosalia
Rosalie
Rosalind
Rosaline
Rosamond
rosanilin
rosaniline
rosarian
rosario
rosarium
rosaruby
rosary
rosated
Roschach
roscherite
roscid
roscoelite
rose
roseal
roseate
roseately
rosebay
rosebud
rosebush
rosed
rosedrop
rosefish
rosehead
rosehill
rosehiller
roseine
rosel
roseless
roselet
roselike
roselite
rosella
rosellate
roselle
Rosellinia
rosemary
Rosenbergia
rosenbuschite
roseola
roseolar
roseoliform
roseolous
roseous
roseroot
rosery
roset
rosetan
rosetangle
rosetime
Rosetta
rosette
rosetted
rosetty
rosetum
rosety
roseways
rosewise
rosewood
rosewort
Rosicrucian
Rosicrucianism
rosied
rosier
rosieresite
rosilla
rosillo
rosily
rosin
rosinate
rosinduline
Rosine
rosiness
rosinous
rosinweed
rosinwood
rosiny
rosland
rosmarine
Rosmarinus
Rosminian
Rosminianism
rosoli
rosolic
rosolio
rosolite
rosorial
Ross
ross
rosser
rossite
rostel
rostellar
Rostellaria
rostellarian
rostellate
rostelliform
rostellum
roster
rostra
rostral
rostrally
rostrate
rostrated
rostriferous
rostriform
rostroantennary
rostrobranchial
rostrocarinate
rostrocaudal
rostroid
rostrolateral
rostrular
rostrulate
rostrulum
rostrum
rosular
rosulate
rosy
rot
rota
rotacism
Rotal
rotal
Rotala
Rotalia
rotalian
rotaliform
rotaliiform
rotaman
rotameter
rotan
Rotanev
rotang
Rotarian
Rotarianism
rotarianize
Rotary
rotary
rotascope
rotatable
rotate
rotated
rotating
rotation
rotational
rotative
rotatively
rotativism
rotatodentate
rotatoplane
rotator
Rotatoria
rotatorian
rotatory
rotch
rote
rotella
rotenone
roter
rotge
rotgut
rother
rothermuck
rotifer
Rotifera
rotiferal
rotiferan
rotiferous
rotiform
rotisserie
roto
rotograph
rotogravure
rotor
rotorcraft
rotproof
Rotse
rottan
rotten
rottenish
rottenly
rottenness
rottenstone
rotter
rotting
rottle
rottlera
rottlerin
rottock
rottolo
rotula
rotulad
rotular
rotulet
rotulian
rotuliform
rotulus
rotund
rotunda
rotundate
rotundifoliate
rotundifolious
rotundiform
rotundify
rotundity
rotundly
rotundness
rotundo
rotundotetragonal
roub
roucou
roud
roue
rouelle
rouge
rougeau
rougeberry
rougelike
rougemontite
rougeot
rough
roughage
roughcast
roughcaster
roughdraft
roughdraw
roughdress
roughdry
roughen
roughener
rougher
roughet
roughhearted
roughheartedness
roughhew
roughhewer
roughhewn
roughhouse
roughhouser
roughhousing
roughhousy
roughie
roughing
roughings
roughish
roughishly
roughishness
roughleg
roughly
roughness
roughometer
roughride
roughrider
roughroot
roughscuff
roughsetter
roughshod
roughslant
roughsome
roughstring
roughstuff
roughtail
roughtailed
roughwork
roughwrought
roughy
rougy
rouille
rouky
roulade
rouleau
roulette
Rouman
Roumeliote
roun
rounce
rounceval
rouncy
round
roundabout
roundaboutly
roundaboutness
rounded
roundedly
roundedness
roundel
roundelay
roundeleer
rounder
roundfish
roundhead
roundheaded
roundheadedness
roundhouse
rounding
roundish
roundishness
roundlet
roundline
roundly
roundmouthed
roundness
roundnose
roundnosed
roundridge
roundseam
roundsman
roundtail
roundtop
roundtree
roundup
roundwise
roundwood
roundworm
roundy
roup
rouper
roupet
roupily
roupingwife
roupit
roupy
rouse
rouseabout
rousedness
rousement
rouser
rousing
rousingly
Rousseau
Rousseauan
Rousseauism
Rousseauist
Rousseauistic
Rousseauite
Roussellian
roussette
Roussillon
roust
roustabout
rouster
rousting
rout
route
router
routh
routhercock
routhie
routhiness
routhy
routinary
routine
routineer
routinely
routing
routinish
routinism
routinist
routinization
routinize
routivarite
routous
routously
rouvillite
rove
rover
rovet
rovetto
roving
rovingly
rovingness
row
rowable
rowan
rowanberry
rowboat
rowdily
rowdiness
rowdy
rowdydow
rowdydowdy
rowdyish
rowdyishly
rowdyishness
rowdyism
rowdyproof
rowed
rowel
rowelhead
rowen
Rowena
rower
rowet
rowiness
rowing
Rowland
rowlandite
Rowleian
rowlet
Rowley
Rowleyan
rowlock
rowport
rowty
rowy
rox
Roxana
Roxane
Roxanne
Roxburgh
Roxburghiaceae
Roxbury
Roxie
Roxolani
Roxy
roxy
Roy
royal
royale
royalet
royalism
royalist
royalization
royalize
royally
royalty
Royena
royet
royetness
royetous
royetously
Roystonea
royt
rozum
Rua
ruach
ruana
rub
rubasse
rubato
rubbed
rubber
rubberer
rubberize
rubberless
rubberneck
rubbernecker
rubbernose
rubbers
rubberstone
rubberwise
rubbery
rubbing
rubbingstone
rubbish
rubbishing
rubbishingly
rubbishly
rubbishry
rubbishy
rubble
rubbler
rubblestone
rubblework
rubbly
rubdown
Rube
rubedinous
rubedity
rubefacient
rubefaction
rubelet
rubella
rubelle
rubellite
rubellosis
Rubensian
rubeola
rubeolar
rubeoloid
ruberythric
ruberythrinic
rubescence
rubescent
Rubia
Rubiaceae
rubiaceous
Rubiales
rubianic
rubiate
rubiator
rubican
rubicelle
Rubicola
Rubicon
rubiconed
rubicund
rubicundity
rubidic
rubidine
rubidium
rubied
rubific
rubification
rubificative
rubify
rubiginous
rubijervine
rubine
rubineous
rubious
ruble
rublis
rubor
rubric
rubrica
rubrical
rubricality
rubrically
rubricate
rubrication
rubricator
rubrician
rubricism
rubricist
rubricity
rubricize
rubricose
rubrific
rubrification
rubrify
rubrisher
rubrospinal
rubstone
Rubus
ruby
rubylike
rubytail
rubythroat
rubywise
rucervine
Rucervus
Ruchbah
ruche
ruching
ruck
rucker
ruckle
ruckling
rucksack
rucksey
ruckus
rucky
ructation
ruction
rud
rudas
Rudbeckia
rudd
rudder
rudderhead
rudderhole
rudderless
rudderlike
rudderpost
rudderstock
ruddied
ruddily
ruddiness
ruddle
ruddleman
ruddock
ruddy
ruddyish
rude
rudely
rudeness
rudented
rudenture
ruderal
rudesby
Rudesheimer
rudge
rudiment
rudimental
rudimentarily
rudimentariness
rudimentary
rudimentation
rudish
Rudista
Rudistae
rudistan
rudistid
rudity
Rudmasday
Rudolf
Rudolph
Rudolphus
Rudy
rue
rueful
ruefully
ruefulness
ruelike
ruelle
Ruellia
ruen
ruer
ruesome
ruesomeness
ruewort
rufescence
rufescent
ruff
ruffable
ruffed
ruffer
ruffian
ruffianage
ruffiandom
ruffianhood
ruffianish
ruffianism
ruffianize
ruffianlike
ruffianly
ruffiano
ruffin
ruffle
ruffled
ruffleless
rufflement
ruffler
rufflike
ruffliness
ruffling
ruffly
ruficarpous
ruficaudate
ruficoccin
ruficornate
rufigallic
rufoferruginous
rufofulvous
rufofuscous
rufopiceous
rufotestaceous
rufous
rufter
rufulous
Rufus
rufus
rug
ruga
rugate
Rugbeian
Rugby
rugged
ruggedly
ruggedness
Rugger
rugging
ruggle
ruggy
rugheaded
ruglike
rugmaker
rugmaking
Rugosa
rugosa
rugose
rugosely
rugosity
rugous
rugulose
ruin
ruinable
ruinate
ruination
ruinatious
ruinator
ruined
ruiner
ruing
ruiniform
ruinlike
ruinous
ruinously
ruinousness
ruinproof
Rukbat
rukh
rulable
Rulander
rule
ruledom
ruleless
rulemonger
ruler
rulership
ruling
rulingly
rull
ruller
rullion
Rum
rum
rumal
Ruman
Rumanian
rumbelow
rumble
rumblegarie
rumblegumption
rumblement
rumbler
rumbling
rumblingly
rumbly
rumbo
rumbooze
rumbowline
rumbowling
rumbullion
rumbumptious
rumbustical
rumbustious
rumbustiousness
rumchunder
Rumelian
rumen
rumenitis
rumenocentesis
rumenotomy
Rumex
rumfustian
rumgumption
rumgumptious
ruminal
ruminant
Ruminantia
ruminantly
ruminate
ruminating
ruminatingly
rumination
ruminative
ruminatively
ruminator
rumkin
rumless
rumly
rummage
rummager
rummagy
rummer
rummily
rumminess
rummish
rummy
rumness
rumney
rumor
rumorer
rumormonger
rumorous
rumorproof
rumourmonger
rump
rumpad
rumpadder
rumpade
Rumper
rumple
rumpless
rumply
rumpscuttle
rumpuncheon
rumpus
rumrunner
rumrunning
rumshop
rumswizzle
rumtytoo
run
runabout
runagate
runaround
runaway
runback
runboard
runby
runch
runchweed
runcinate
rundale
Rundi
rundle
rundlet
rune
runecraft
runed
runefolk
runeless
runelike
runer
runesmith
runestaff
runeword
runfish
rung
runghead
rungless
runholder
runic
runically
runiform
runite
runkeeper
runkle
runkly
runless
runlet
runman
runnable
runnel
runner
runnet
running
runningly
runny
runoff
runologist
runology
runout
runover
runproof
runrig
runround
runt
runted
runtee
runtiness
runtish
runtishly
runtishness
runty
runway
rupa
rupee
Rupert
rupestral
rupestrian
rupestrine
rupia
rupiah
rupial
Rupicapra
Rupicaprinae
rupicaprine
Rupicola
Rupicolinae
rupicoline
rupicolous
rupie
rupitic
Ruppia
ruptile
ruption
ruptive
ruptuary
rupturable
rupture
ruptured
rupturewort
rural
ruralism
ruralist
ruralite
rurality
ruralization
ruralize
rurally
ruralness
rurban
ruridecanal
rurigenous
Ruritania
Ruritanian
ruru
Rus
Rusa
Ruscus
ruse
rush
rushbush
rushed
rushen
rusher
rushiness
rushing
rushingly
rushingness
rushland
rushlight
rushlighted
rushlike
rushlit
rushy
Rusin
rusine
rusk
ruskin
Ruskinian
rusky
rusma
rusot
ruspone
Russ
russel
Russelia
Russell
Russellite
Russene
russet
russeting
russetish
russetlike
russety
Russia
russia
Russian
Russianism
Russianist
Russianization
Russianize
Russification
Russificator
Russifier
Russify
Russine
Russism
Russniak
Russolatrous
Russolatry
Russomania
Russomaniac
Russomaniacal
Russophile
Russophilism
Russophilist
Russophobe
Russophobia
Russophobiac
Russophobism
Russophobist
russud
Russula
rust
rustable
rustful
rustic
rustical
rustically
rusticalness
rusticate
rustication
rusticator
rusticial
rusticism
rusticity
rusticize
rusticly
rusticness
rusticoat
rustily
rustiness
rustle
rustler
rustless
rustling
rustlingly
rustlingness
rustly
rustproof
rustre
rustred
Rusty
rusty
rustyback
rustyish
ruswut
rut
Ruta
rutabaga
Rutaceae
rutaceous
rutaecarpine
rutate
rutch
rutelian
Rutelinae
Ruth
ruth
ruthenate
Ruthene
Ruthenian
ruthenic
ruthenious
ruthenium
ruthenous
ruther
rutherford
rutherfordine
rutherfordite
ruthful
ruthfully
ruthfulness
ruthless
ruthlessly
ruthlessness
rutic
rutidosis
rutilant
rutilated
rutile
rutilous
rutin
rutinose
Rutiodon
ruttee
rutter
ruttiness
ruttish
ruttishly
ruttishness
rutty
Rutuli
rutyl
rutylene
ruvid
rux
rvulsant
ryal
ryania
rybat
ryder
rye
ryen
Rymandra
ryme
Rynchospora
rynchosporous
rynd
rynt
ryot
ryotwar
ryotwari
rype
rypeck
rytidosis
Rytina
Ryukyu
S
s
sa
saa
Saad
Saan
Saarbrucken
sab
Saba
sabadilla
sabadine
sabadinine
Sabaean
Sabaeanism
Sabaeism
sabaigrass
Sabaism
Sabaist
Sabal
Sabalaceae
sabalo
Saban
sabanut
Sabaoth
Sabathikos
Sabazian
Sabazianism
Sabazios
sabbat
Sabbatarian
Sabbatarianism
Sabbatary
Sabbatean
Sabbath
sabbath
Sabbathaian
Sabbathaic
Sabbathaist
Sabbathbreaker
Sabbathbreaking
Sabbathism
Sabbathize
Sabbathkeeper
Sabbathkeeping
Sabbathless
Sabbathlike
Sabbathly
Sabbatia
sabbatia
Sabbatian
Sabbatic
sabbatic
Sabbatical
sabbatical
Sabbatically
Sabbaticalness
sabbatine
sabbatism
Sabbatist
Sabbatization
Sabbatize
sabbaton
sabbitha
sabdariffa
sabe
sabeca
Sabella
sabella
sabellan
Sabellaria
sabellarian
Sabelli
Sabellian
Sabellianism
Sabellianize
sabellid
Sabellidae
sabelloid
saber
saberbill
sabered
saberleg
saberlike
saberproof
sabertooth
saberwing
Sabia
Sabiaceae
sabiaceous
Sabian
Sabianism
sabicu
Sabik
Sabina
sabina
Sabine
sabine
Sabinian
sabino
Sabir
sable
sablefish
sableness
sably
sabora
saboraim
sabot
sabotage
saboted
saboteur
sabotine
Sabra
sabra
sabretache
Sabrina
Sabromin
sabromin
Sabuja
sabuline
sabulite
sabulose
sabulosity
sabulous
sabulum
saburra
saburral
saburration
sabutan
sabzi
Sac
sac
Sacae
sacalait
sacaline
sacaton
sacatra
sacbrood
saccade
saccadic
Saccammina
saccate
saccated
Saccha
saccharamide
saccharase
saccharate
saccharated
saccharephidrosis
saccharic
saccharide
sacchariferous
saccharification
saccharifier
saccharify
saccharilla
saccharimeter
saccharimetric
saccharimetrical
saccharimetry
saccharin
saccharinate
saccharinated
saccharine
saccharineish
saccharinely
saccharinic
saccharinity
saccharization
saccharize
saccharobacillus
saccharobiose
saccharobutyric
saccharoceptive
saccharoceptor
saccharochemotropic
saccharocolloid
saccharofarinaceous
saccharogalactorrhea
saccharogenic
saccharohumic
saccharoid
saccharoidal
saccharolactonic
saccharolytic
saccharometabolic
saccharometabolism
saccharometer
saccharometric
saccharometry
saccharomucilaginous
Saccharomyces
saccharomyces
Saccharomycetaceae
saccharomycetaceous
Saccharomycetales
saccharomycete
Saccharomycetes
saccharomycetic
saccharomycosis
saccharon
saccharonate
saccharone
saccharonic
saccharophylly
saccharorrhea
saccharoscope
saccharose
saccharostarchy
saccharosuria
saccharotriose
saccharous
saccharulmic
saccharulmin
Saccharum
saccharum
saccharuria
sacciferous
sacciform
Saccobranchiata
saccobranchiate
Saccobranchus
saccoderm
Saccolabium
saccolabium
saccomyian
saccomyid
Saccomyidae
Saccomyina
saccomyine
saccomyoid
Saccomyoidea
saccomyoidean
Saccomys
Saccopharyngidae
Saccopharynx
Saccorhiza
saccos
saccular
sacculate
sacculated
sacculation
saccule
Sacculina
sacculoutricular
sacculus
saccus
sacellum
sacerdocy
sacerdotage
sacerdotal
sacerdotalism
sacerdotalist
sacerdotalize
sacerdotally
sacerdotical
sacerdotism
sachamaker
sachem
sachemdom
sachemic
sachemship
sachet
Sacheverell
Sacian
sack
sackage
sackamaker
sackbag
sackbut
sackcloth
sackclothed
sackdoudle
sacked
sacken
sacker
sackful
sacking
sackless
sacklike
sackmaker
sackmaking
sackman
sacktime
saclike
saco
sacope
sacque
sacra
sacrad
sacral
sacralgia
sacralization
sacrament
sacramental
sacramentalism
sacramentalist
sacramentality
sacramentally
sacramentalness
Sacramentarian
sacramentarian
sacramentarianism
sacramentarist
Sacramentary
sacramentary
sacramenter
sacramentism
sacramentize
Sacramento
sacramentum
sacraria
sacrarial
sacrarium
sacrectomy
sacred
sacredly
sacredness
sacrificable
sacrificant
Sacrificati
sacrification
sacrificator
sacrificatory
sacrificature
sacrifice
sacrificer
sacrificial
sacrificially
sacrificing
sacrilege
sacrileger
sacrilegious
sacrilegiously
sacrilegiousness
sacrilegist
sacrilumbal
sacrilumbalis
sacring
Sacripant
sacrist
sacristan
sacristy
sacro
sacrocaudal
sacrococcygeal
sacrococcygean
sacrococcygeus
sacrococcyx
sacrocostal
sacrocotyloid
sacrocotyloidean
sacrocoxalgia
sacrocoxitis
sacrodorsal
sacrodynia
sacrofemoral
sacroiliac
sacroinguinal
sacroischiac
sacroischiadic
sacroischiatic
sacrolumbal
sacrolumbalis
sacrolumbar
sacropectineal
sacroperineal
sacropictorial
sacroposterior
sacropubic
sacrorectal
sacrosanct
sacrosanctity
sacrosanctness
sacrosciatic
sacrosecular
sacrospinal
sacrospinalis
sacrospinous
sacrotomy
sacrotuberous
sacrovertebral
sacrum
sad
Sadachbia
Sadalmelik
Sadalsuud
sadden
saddening
saddeningly
saddik
saddirham
saddish
saddle
saddleback
saddlebag
saddlebow
saddlecloth
saddled
saddleleaf
saddleless
saddlelike
saddlenose
saddler
saddlery
saddlesick
saddlesore
saddlesoreness
saddlestead
saddletree
saddlewise
saddling
Sadducaic
Sadducean
Sadducee
Sadduceeism
Sadduceeist
Sadducism
Sadducize
sade
sadh
sadhe
sadhearted
sadhu
sadic
Sadie
sadiron
sadism
sadist
sadistic
sadistically
Sadite
sadly
sadness
sado
sadomasochism
Sadr
sadr
saecula
saeculum
Saeima
saernaite
saeter
saeume
Safar
safari
Safavi
Safawid
safe
safeblower
safeblowing
safebreaker
safebreaking
safecracking
safeguard
safeguarder
safehold
safekeeper
safekeeping
safelight
safely
safemaker
safemaking
safen
safener
safeness
safety
Saffarian
Saffarid
saffian
safflor
safflorite
safflow
safflower
saffron
saffroned
saffrontree
saffronwood
saffrony
Safi
Safine
Safini
safranin
safranine
safranophile
safrole
saft
sag
saga
sagaciate
sagacious
sagaciously
sagaciousness
sagacity
Sagai
sagaie
sagaman
sagamite
sagamore
sagapenum
sagathy
sage
sagebrush
sagebrusher
sagebush
sageleaf
sagely
sagene
sageness
sagenite
sagenitic
Sageretia
sagerose
sageship
sagewood
sagger
sagging
saggon
saggy
saghavart
Sagina
saginate
sagination
saging
Sagitarii
sagitta
sagittal
sagittally
Sagittaria
Sagittariid
Sagittarius
sagittarius
Sagittary
sagittary
sagittate
Sagittid
sagittiferous
sagittiform
sagittocyst
sagittoid
sagless
sago
sagoin
sagolike
Sagra
saguaro
Saguerus
sagum
saguran
sagvandite
sagwire
sagy
sah
Sahadeva
Sahaptin
Sahara
Saharan
Saharian
Saharic
sahh
sahib
Sahibah
Sahidic
sahme
Saho
sahoukar
sahukar
sai
saic
said
Saidi
Saify
saiga
Saiid
sail
sailable
sailage
sailboat
sailcloth
sailed
sailer
sailfish
sailflying
sailing
sailingly
sailless
sailmaker
sailmaking
sailor
sailoring
sailorizing
sailorless
sailorlike
sailorly
sailorman
sailorproof
sailplane
sailship
sailsman
saily
saim
saimiri
saimy
sain
Sainfoin
saint
saintdom
sainted
saintess
sainthood
saintish
saintism
saintless
saintlike
saintlily
saintliness
saintling
saintly
saintologist
saintology
Saintpaulia
saintship
saip
Saiph
sair
sairly
sairve
sairy
Saite
saithe
Saitic
Saiva
Saivism
saj
sajou
Sak
Saka
Sakai
Sakalava
sake
sakeber
sakeen
Sakel
Sakelarides
Sakell
Sakellaridis
saker
sakeret
Sakha
saki
sakieh
Sakkara
Saktism
sakulya
Sakyamuni
Sal
sal
salaam
salaamlike
salability
salable
salableness
salably
salaceta
salacious
salaciously
salaciousness
salacity
salacot
salad
salading
salago
salagrama
salal
salamandarin
salamander
salamanderlike
Salamandra
salamandrian
Salamandridae
salamandriform
Salamandrina
salamandrine
salamandroid
salambao
Salaminian
salamo
salampore
salangane
salangid
Salangidae
Salar
salar
salariat
salaried
salary
salaryless
salat
salay
sale
salegoer
salele
salema
salenixon
salep
saleratus
saleroom
salesclerk
Salesian
saleslady
salesman
salesmanship
salespeople
salesperson
salesroom
saleswoman
salework
saleyard
salfern
Salian
Saliaric
Salic
salic
Salicaceae
salicaceous
Salicales
Salicariaceae
salicetum
salicin
salicional
salicorn
Salicornia
salicyl
salicylal
salicylaldehyde
salicylamide
salicylanilide
salicylase
salicylate
salicylic
salicylide
salicylidene
salicylism
salicylize
salicylous
salicyluric
salicylyl
salience
salient
Salientia
salientian
saliently
saliferous
salifiable
salification
salify
saligenin
saligot
salimeter
salimetry
Salina
salina
Salinan
salination
saline
Salinella
salinelle
salineness
saliniferous
salinification
saliniform
salinity
salinize
salinometer
salinometry
salinosulphureous
salinoterreous
Salisburia
Salish
Salishan
salite
salited
Saliva
saliva
salival
Salivan
salivant
salivary
salivate
salivation
salivator
salivatory
salivous
Salix
salix
salle
sallee
salleeman
sallenders
sallet
sallier
salloo
sallow
sallowish
sallowness
sallowy
Sally
sally
Sallybloom
sallyman
sallywood
Salm
salma
salmagundi
salmiac
salmine
salmis
Salmo
Salmon
salmon
salmonberry
Salmonella
salmonella
salmonellae
salmonellosis
salmonet
salmonid
Salmonidae
salmoniform
salmonlike
salmonoid
Salmonoidea
Salmonoidei
salmonsite
salmwood
salnatron
Salol
salol
Salome
salometer
salometry
salomon
Salomonia
Salomonian
Salomonic
salon
saloon
saloonist
saloonkeeper
saloop
Salopian
salopian
salp
Salpa
salpa
salpacean
salpian
salpicon
Salpidae
salpiform
Salpiglossis
salpiglossis
salpingectomy
salpingemphraxis
salpinges
salpingian
salpingion
salpingitic
salpingitis
salpingocatheterism
salpingocele
salpingocyesis
salpingomalleus
salpingonasal
salpingopalatal
salpingopalatine
salpingoperitonitis
salpingopexy
salpingopharyngeal
salpingopharyngeus
salpingopterygoid
salpingorrhaphy
salpingoscope
salpingostaphyline
salpingostenochoria
salpingostomatomy
salpingostomy
salpingotomy
salpinx
salpoid
salse
salsifis
salsify
salsilla
Salsola
Salsolaceae
salsolaceous
salsuginous
salt
salta
saltant
saltarella
saltarello
saltary
saltate
saltation
saltativeness
Saltator
saltator
Saltatoria
saltatorial
saltatorian
saltatoric
saltatorious
saltatory
saltbush
saltcat
saltcatch
saltcellar
salted
saltee
salten
salter
saltern
saltery
saltfat
saltfoot
salthouse
saltier
saltierra
saltierwise
Saltigradae
saltigrade
saltimbanco
saltimbank
saltimbankery
saltine
saltiness
salting
saltish
saltishly
saltishness
saltless
saltlessness
saltly
saltmaker
saltmaking
saltman
saltmouth
saltness
saltometer
saltorel
saltpan
saltpeter
saltpetrous
saltpond
saltspoon
saltspoonful
saltsprinkler
saltus
saltweed
saltwife
saltworker
saltworks
saltwort
salty
salubrify
salubrious
salubriously
salubriousness
salubrity
saluki
salung
salutarily
salutariness
salutary
salutation
salutational
salutationless
salutatious
salutatorian
salutatorily
salutatorium
salutatory
salute
saluter
salutiferous
salutiferously
Salva
salvability
salvable
salvableness
salvably
Salvadora
salvadora
Salvadoraceae
salvadoraceous
Salvadoran
Salvadorian
salvage
salvageable
salvagee
salvageproof
salvager
salvaging
Salvarsan
salvarsan
salvatella
salvation
salvational
salvationism
salvationist
salvatory
salve
salveline
Salvelinus
salver
salverform
Salvia
salvianin
salvific
salvifical
salvifically
Salvinia
Salviniaceae
salviniaceous
Salviniales
salviol
salvo
salvor
salvy
Salwey
salzfelle
Sam
sam
Samadera
samadh
samadhi
samaj
Samal
saman
Samandura
Samani
Samanid
Samantha
samara
samaria
samariform
Samaritan
Samaritaness
Samaritanism
samarium
Samarkand
samaroid
samarra
samarskite
Samas
samba
Sambal
sambal
sambaqui
sambar
Sambara
Sambathe
sambhogakaya
Sambo
sambo
Sambucaceae
Sambucus
sambuk
sambuke
sambunigrin
Samburu
same
samekh
samel
sameliness
samely
samen
sameness
samesome
Samgarnebo
samh
Samhain
samhita
Samian
samiel
Samir
samiresite
samiri
samisen
Samish
samite
samkara
samlet
sammel
sammer
sammier
Sammy
sammy
Samnani
Samnite
Samoan
Samogitian
samogonka
Samolus
Samosatenian
samothere
Samotherium
Samothracian
samovar
Samoyed
Samoyedic
samp
sampaguita
sampaloc
sampan
samphire
sampi
sample
sampleman
sampler
samplery
sampling
Sampsaean
Samsam
samsara
samshu
Samsien
samskara
Samson
samson
Samsoness
Samsonian
Samsonic
Samsonistic
samsonite
Samucan
Samucu
Samuel
samurai
Samydaceae
San
san
sanability
sanable
sanableness
sanai
Sanand
sanative
sanativeness
sanatoria
sanatorium
sanatory
Sanballat
sanbenito
Sanche
sancho
sanct
sancta
sanctanimity
sanctifiable
sanctifiableness
sanctifiably
sanctificate
sanctification
sanctified
sanctifiedly
sanctifier
sanctify
sanctifyingly
sanctilogy
sanctiloquent
sanctimonial
sanctimonious
sanctimoniously
sanctimoniousness
sanctimony
sanction
sanctionable
sanctionary
sanctionative
sanctioner
sanctionist
sanctionless
sanctionment
sanctitude
sanctity
sanctologist
Sanctology
sanctorium
sanctuaried
sanctuarize
sanctuary
sanctum
Sanctus
Sancy
sancyite
sand
sandak
sandal
sandaled
sandaliform
sandaling
sandalwood
sandalwort
sandan
sandarac
sandaracin
sandastros
Sandawe
sandbag
sandbagger
sandbank
sandbin
sandblast
sandboard
sandbox
sandboy
sandbur
sandclub
sandculture
sanded
Sandeep
Sandemanian
Sandemanianism
Sandemanism
Sander
sander
sanderling
sanders
sandfish
sandflower
sandglass
sandheat
sandhi
sandiferous
sandiness
sanding
Sandip
sandiver
sandix
sandlapper
sandless
sandlike
sandling
sandman
sandnatter
sandnecker
sandpaper
sandpaperer
sandpeep
sandpiper
sandproof
Sandra
sandrock
sandspit
sandspur
sandstay
sandstone
sandstorm
sandust
sandweed
sandweld
sandwich
sandwood
sandworm
sandwort
Sandy
sandy
sandyish
sane
sanely
saneness
Sanetch
Sanford
Sanforized
sang
sanga
Sangamon
sangar
sangaree
sangei
sanger
sangerbund
sangerfest
Sanggil
sangha
Sangho
Sangir
Sangirese
sanglant
sangley
Sangraal
sangreeroot
sangrel
sangsue
sanguicolous
sanguifacient
sanguiferous
sanguification
sanguifier
sanguifluous
sanguimotor
sanguimotory
sanguinaceous
Sanguinaria
sanguinarily
sanguinariness
sanguinary
sanguine
sanguineless
sanguinely
sanguineness
sanguineobilious
sanguineophlegmatic
sanguineous
sanguineousness
sanguineovascular
sanguinicolous
sanguiniferous
sanguinification
sanguinism
sanguinity
sanguinivorous
sanguinocholeric
sanguinolency
sanguinolent
sanguinopoietic
sanguinous
Sanguisorba
Sanguisorbaceae
sanguisuge
sanguisugent
sanguisugous
sanguivorous
Sanhedrim
Sanhedrin
Sanhedrist
Sanhita
sanicle
Sanicula
sanidine
sanidinic
sanidinite
sanies
sanification
sanify
sanious
sanipractic
sanitarian
sanitarily
sanitarist
sanitarium
sanitary
sanitate
sanitation
sanitationist
sanitist
sanitize
Sanity
sanity
sanjak
sanjakate
sanjakbeg
sanjakship
Sanjay
Sanjeev
Sanjib
sank
sankha
Sankhya
sannaite
Sannoisian
sannup
sannyasi
sannyasin
sanopurulent
sanoserous
Sanpoil
sans
Sansar
sansei
Sansevieria
sanshach
sansi
Sanskrit
Sanskritic
Sanskritist
Sanskritization
Sanskritize
sant
Santa
Santal
santal
Santalaceae
santalaceous
Santalales
Santali
santalic
santalin
santalol
Santalum
santalwood
santapee
Santee
santene
Santiago
santimi
santims
santir
Santo
Santolina
santon
santonica
santonin
santoninic
santorinite
Santos
sanukite
Sanvitalia
Sanyakoan
sao
Saoshyant
sap
sapa
sapajou
sapan
sapanwood
sapbush
sapek
Saperda
sapful
Sapharensian
saphead
sapheaded
sapheadedness
saphena
saphenal
saphenous
saphie
sapid
sapidity
sapidless
sapidness
sapience
sapiency
sapient
sapiential
sapientially
sapientize
sapiently
sapin
sapinda
Sapindaceae
sapindaceous
Sapindales
sapindaship
Sapindus
Sapium
sapiutan
saple
sapless
saplessness
sapling
saplinghood
sapo
sapodilla
sapogenin
saponaceous
saponaceousness
saponacity
Saponaria
saponarin
saponary
Saponi
saponifiable
saponification
saponifier
saponify
saponin
saponite
sapophoric
sapor
saporific
saporosity
saporous
Sapota
sapota
Sapotaceae
sapotaceous
sapote
sapotilha
sapotilla
sapotoxin
sappanwood
sappare
sapper
Sapphic
sapphic
sapphire
sapphireberry
sapphired
sapphirewing
sapphiric
sapphirine
Sapphism
Sapphist
Sappho
sappiness
sapping
sapples
sappy
sapremia
sapremic
saprine
saprocoll
saprodil
saprodontia
saprogenic
saprogenous
Saprolegnia
Saprolegniaceae
saprolegniaceous
Saprolegniales
saprolegnious
saprolite
saprolitic
sapropel
sapropelic
sapropelite
saprophagan
saprophagous
saprophile
saprophilous
saprophyte
saprophytic
saprophytically
saprophytism
saprostomous
saprozoic
sapsago
sapskull
sapsuck
sapsucker
sapucaia
sapucainha
sapwood
sapwort
Saqib
sar
Sara
saraad
sarabacan
Sarabaite
saraband
Saracen
Saracenian
Saracenic
Saracenical
Saracenism
Saracenlike
Sarada
saraf
Sarah
Sarakolet
Sarakolle
Saramaccaner
Saran
sarangi
sarangousty
Saratoga
Saratogan
Saravan
Sarawakese
sarawakite
Sarawan
sarbacane
sarbican
sarcasm
sarcasmproof
sarcast
sarcastic
sarcastical
sarcastically
sarcasticalness
sarcasticness
sarcelle
sarcenet
sarcilis
Sarcina
sarcine
sarcitis
sarcle
sarcler
sarcoadenoma
Sarcobatus
sarcoblast
sarcocarcinoma
sarcocarp
sarcocele
Sarcococca
Sarcocolla
sarcocollin
sarcocyst
Sarcocystidea
sarcocystidean
sarcocystidian
Sarcocystis
sarcocystoid
sarcocyte
sarcode
sarcoderm
Sarcodes
sarcodic
sarcodictyum
Sarcodina
sarcodous
sarcoenchondroma
sarcogenic
sarcogenous
sarcoglia
Sarcogyps
sarcoid
sarcolactic
sarcolemma
sarcolemmic
sarcolemmous
sarcoline
sarcolite
sarcologic
sarcological
sarcologist
sarcology
sarcolysis
sarcolyte
sarcolytic
sarcoma
sarcomatoid
sarcomatosis
sarcomatous
sarcomere
Sarcophaga
sarcophagal
sarcophagi
sarcophagic
sarcophagid
Sarcophagidae
sarcophagine
sarcophagize
sarcophagous
sarcophagus
sarcophagy
sarcophile
sarcophilous
Sarcophilus
sarcoplasm
sarcoplasma
sarcoplasmatic
sarcoplasmic
sarcoplast
sarcoplastic
sarcopoietic
Sarcopsylla
Sarcopsyllidae
Sarcoptes
sarcoptic
sarcoptid
Sarcoptidae
Sarcorhamphus
sarcosepsis
sarcosepta
sarcoseptum
sarcosine
sarcosis
sarcosoma
sarcosperm
sarcosporid
Sarcosporida
Sarcosporidia
sarcosporidial
sarcosporidian
sarcosporidiosis
sarcostosis
sarcostyle
sarcotheca
sarcotherapeutics
sarcotherapy
sarcotic
sarcous
Sarcura
Sard
sard
sardachate
Sardanapalian
Sardanapalus
sardel
Sardian
sardine
sardinewise
Sardinian
sardius
Sardoin
sardonic
sardonical
sardonically
sardonicism
sardonyx
sare
sargasso
Sargassum
sargassum
sargo
Sargonic
Sargonid
Sargonide
sargus
sari
sarif
Sarigue
sarigue
sarinda
sarip
sark
sarkar
sarkful
sarkical
sarkine
sarking
sarkinite
sarkit
sarkless
sarlak
sarlyk
Sarmatian
Sarmatic
sarmatier
sarment
sarmenta
sarmentaceous
sarmentiferous
sarmentose
sarmentous
sarmentum
sarna
sarod
saron
sarong
saronic
saronide
saros
Sarothamnus
Sarothra
sarothrum
sarpler
sarpo
sarra
Sarracenia
sarracenia
Sarraceniaceae
sarraceniaceous
sarracenial
Sarraceniales
sarraf
sarrazin
sarrusophone
sarrusophonist
sarsa
sarsaparilla
sarsaparillin
Sarsar
Sarsechim
sarsen
sarsenet
Sarsi
Sart
sart
sartage
sartain
Sartish
sartor
sartoriad
sartorial
sartorially
sartorian
sartorite
sartorius
Saruk
sarus
Sarvarthasiddha
sarwan
Sarzan
sasa
sasan
sasani
sasanqua
sash
sashay
sashery
sashing
sashless
sasin
sasine
saskatoon
sassaby
sassafac
sassafrack
sassafras
Sassak
Sassan
Sassanian
Sassanid
Sassanidae
Sassanide
Sassenach
sassolite
sassy
sassywood
Sastean
sat
satable
Satan
satan
Satanael
Satanas
satang
satanic
satanical
satanically
satanicalness
Satanism
Satanist
satanist
Satanistic
Satanity
satanize
Satanology
Satanophany
Satanophil
Satanophobia
Satanship
satara
satchel
satcheled
sate
sateen
sateenwood
sateless
satelles
satellitarian
satellite
satellited
satellitesimal
satellitian
satellitic
satellitious
satellitium
satellitoid
satellitory
satelloid
satiability
satiable
satiableness
satiably
satiate
satiation
Satieno
satient
satiety
satin
satinbush
satine
satined
satinette
satinfin
satinflower
satinite
satinity
satinize
satinleaf
satinlike
satinpod
satinwood
satiny
satire
satireproof
satiric
satirical
satirically
satiricalness
satirist
satirizable
satirize
satirizer
satisdation
satisdiction
satisfaction
satisfactional
satisfactionist
satisfactionless
satisfactive
satisfactorily
satisfactoriness
satisfactorious
satisfactory
satisfiable
satisfice
satisfied
satisfiedly
satisfiedness
satisfier
satisfy
satisfying
satisfyingly
satisfyingness
satispassion
satlijk
Satrae
satrap
satrapal
satrapess
satrapic
satrapical
satrapy
satron
Satsuma
sattle
sattva
satura
saturability
saturable
saturant
saturate
saturated
saturater
saturation
saturator
Saturday
Satureia
Saturn
Saturnal
Saturnale
Saturnalia
saturnalia
Saturnalian
saturnalian
Saturnia
Saturnian
saturnian
Saturnicentric
saturniid
Saturniidae
Saturnine
saturnine
saturninely
saturnineness
saturninity
saturnism
saturnity
saturnize
Saturnus
satyagrahi
satyashodak
satyr
satyresque
satyress
satyriasis
satyric
Satyridae
Satyrinae
satyrine
satyrion
satyrism
satyrlike
satyromaniac
sauce
sauceboat
saucebox
saucedish
sauceless
sauceline
saucemaker
saucemaking
sauceman
saucepan
sauceplate
saucer
saucerful
saucerleaf
saucerless
saucerlike
saucily
sauciness
saucy
Sauerbraten
sauerkraut
sauf
sauger
saugh
saughen
Saul
sauld
saulie
sault
saulter
Saulteur
saum
saumon
saumont
Saumur
Saumya
sauna
saunders
saunderswood
saunter
saunterer
sauntering
saunteringly
sauqui
saur
Saura
Sauraseni
Saurauia
Saurauiaceae
saurel
Sauria
saurian
sauriasis
sauriosis
Saurischia
saurischian
Sauroctonos
saurodont
Saurodontidae
Saurognathae
saurognathism
saurognathous
Sauromatian
saurophagous
sauropod
Sauropoda
sauropodous
sauropsid
Sauropsida
sauropsidan
sauropsidian
Sauropterygia
sauropterygian
Saurornithes
saurornithic
Saururaceae
saururaceous
Saururae
saururan
saururous
Saururus
saury
sausage
sausagelike
sausinger
Saussurea
saussurite
saussuritic
saussuritization
saussuritize
saut
saute
sauterelle
sauterne
sauternes
sauteur
sauty
Sauvagesia
sauve
sauvegarde
savable
savableness
savacu
savage
savagedom
savagely
savageness
savagerous
savagery
savagess
savagism
savagize
savanilla
savanna
Savannah
savant
Savara
savarin
savation
save
saved
saveloy
saver
Savery
savin
saving
savingly
savingness
savior
savioress
saviorhood
saviorship
Saviour
Savitar
Savitri
savola
Savonarolist
Savonnerie
savor
savored
savorer
savorily
savoriness
savoringly
savorless
savorous
savorsome
savory
savour
savoy
Savoyard
savoyed
savoying
savssat
savvy
saw
sawah
Sawaiori
sawali
Sawan
sawarra
sawback
sawbelly
sawbill
sawbones
sawbuck
sawbwa
sawder
sawdust
sawdustish
sawdustlike
sawdusty
sawed
sawer
sawfish
sawfly
sawhorse
sawing
sawish
sawlike
sawmaker
sawmaking
sawman
sawmill
sawmiller
sawmilling
sawmon
sawmont
sawn
Sawney
sawney
sawsetter
sawsharper
sawsmith
sawt
sawway
sawworker
sawwort
sawyer
sax
saxatile
saxboard
saxcornet
Saxe
saxhorn
Saxicava
saxicavous
Saxicola
saxicole
Saxicolidae
Saxicolinae
saxicoline
saxicolous
Saxifraga
Saxifragaceae
saxifragaceous
saxifragant
saxifrage
saxifragous
saxifrax
saxigenous
Saxish
Saxon
Saxondom
Saxonian
Saxonic
Saxonical
Saxonically
Saxonish
Saxonism
Saxonist
saxonite
Saxonization
Saxonize
Saxonly
Saxony
saxophone
saxophonist
saxotromba
saxpence
saxten
saxtie
saxtuba
say
saya
sayability
sayable
sayableness
Sayal
sayer
sayette
sayid
saying
sazen
Sbaikian
sblood
sbodikins
scab
scabbard
scabbardless
scabbed
scabbedness
scabbery
scabbily
scabbiness
scabble
scabbler
scabbling
scabby
scabellum
scaberulous
scabid
scabies
scabietic
scabinus
Scabiosa
scabiosity
scabious
scabish
scabland
scabrate
scabrescent
scabrid
scabridity
scabridulous
scabrities
scabriusculose
scabriusculous
scabrosely
scabrous
scabrously
scabrousness
scabwort
scacchic
scacchite
scad
scaddle
scads
Scaean
scaff
scaffer
scaffery
scaffie
scaffle
scaffold
scaffoldage
scaffolder
scaffolding
scaglia
scagliola
scagliolist
scala
scalable
scalableness
scalably
scalage
scalar
scalare
Scalaria
scalarian
scalariform
Scalariidae
scalarwise
scalation
scalawag
scalawaggery
scalawaggy
scald
scaldberry
scalded
scalder
scaldfish
scaldic
scalding
scaldweed
scaldy
scale
scaleback
scalebark
scaleboard
scaled
scaledrake
scalefish
scaleful
scaleless
scalelet
scalelike
scaleman
scalena
scalene
scalenohedral
scalenohedron
scalenon
scalenous
scalenum
scalenus
scalepan
scaleproof
scaler
scales
scalesman
scalesmith
scaletail
scalewing
scalewise
scalework
scalewort
scaliger
scaliness
scaling
scall
scalled
scallion
scallola
scallom
scallop
scalloper
scalloping
scallopwise
scalma
scaloni
Scalops
Scalopus
scalp
scalpeen
scalpel
scalpellar
scalpellic
scalpellum
scalpellus
scalper
scalping
scalpless
scalpriform
scalprum
scalpture
scalt
scaly
scalytail
scam
scamander
Scamandrius
scamble
scambler
scambling
scamell
scamler
scamles
scammoniate
scammonin
scammony
scammonyroot
scamp
scampavia
scamper
scamperer
scamphood
scamping
scampingly
scampish
scampishly
scampishness
scampsman
scan
scandal
scandalization
scandalize
scandalizer
scandalmonger
scandalmongering
scandalmongery
scandalmonging
scandalous
scandalously
scandalousness
scandalproof
scandaroon
scandent
scandia
Scandian
scandic
scandicus
Scandinavia
Scandinavian
Scandinavianism
scandium
Scandix
Scania
Scanian
Scanic
scanmag
scannable
scanner
scanning
scanningly
scansion
scansionist
Scansores
scansorial
scansorious
scant
scanties
scantily
scantiness
scantity
scantle
scantling
scantlinged
scantly
scantness
scanty
scap
scape
scapegallows
scapegoat
scapegoatism
scapegrace
scapel
scapeless
scapement
scapethrift
scapha
Scaphander
Scaphandridae
scaphion
Scaphiopodidae
Scaphiopus
scaphism
scaphite
Scaphites
Scaphitidae
scaphitoid
scaphocephalic
scaphocephalism
scaphocephalous
scaphocephalus
scaphocephaly
scaphocerite
scaphoceritic
scaphognathite
scaphognathitic
scaphoid
scapholunar
scaphopod
Scaphopoda
scaphopodous
scapiform
scapigerous
scapoid
scapolite
scapolitization
scapose
scapple
scappler
scapula
scapulalgia
scapular
scapulare
scapulary
scapulated
scapulectomy
scapulet
scapulimancy
scapuloaxillary
scapulobrachial
scapuloclavicular
scapulocoracoid
scapulodynia
scapulohumeral
scapulopexy
scapuloradial
scapulospinal
scapulothoracic
scapuloulnar
scapulovertebral
scapus
scar
scarab
scarabaean
scarabaei
scarabaeid
Scarabaeidae
scarabaeidoid
scarabaeiform
Scarabaeinae
scarabaeoid
scarabaeus
scarabee
scaraboid
Scaramouch
scaramouch
scarce
scarcelins
scarcely
scarcement
scarcen
scarceness
scarcity
scare
scarebabe
scarecrow
scarecrowish
scarecrowy
scareful
scarehead
scaremonger
scaremongering
scareproof
scarer
scaresome
scarf
scarface
scarfed
scarfer
scarflike
scarfpin
scarfskin
scarfwise
scarfy
scarid
Scaridae
scarification
scarificator
scarifier
scarify
scarily
scariose
scarious
scarlatina
scarlatinal
scarlatiniform
scarlatinoid
scarlatinous
scarless
scarlet
scarletberry
scarletseed
scarlety
scarman
scarn
scaroid
scarp
scarpines
scarping
scarpment
scarproof
scarred
scarrer
scarring
scarry
scart
scarth
Scarus
scarus
scarved
scary
scase
scasely
scat
scatch
scathe
scatheful
scatheless
scathelessly
scathing
scathingly
Scaticook
scatland
scatologia
scatologic
scatological
scatology
scatomancy
scatophagid
Scatophagidae
scatophagoid
scatophagous
scatophagy
scatoscopy
scatter
scatterable
scatteration
scatteraway
scatterbrain
scatterbrained
scatterbrains
scattered
scatteredly
scatteredness
scatterer
scattergood
scattering
scatteringly
scatterling
scattermouch
scattery
scatty
scatula
scaturient
scaul
scaum
scaup
scauper
scaur
scaurie
scaut
scavage
scavel
scavenage
scavenge
scavenger
scavengerism
scavengership
scavengery
scavenging
scaw
scawd
scawl
scazon
scazontic
sceat
scelalgia
scelerat
scelidosaur
scelidosaurian
scelidosauroid
Scelidosaurus
Scelidotherium
Sceliphron
sceloncus
Sceloporus
scelotyrbe
scena
scenario
scenarioist
scenarioization
scenarioize
scenarist
scenarization
scenarize
scenary
scend
scene
scenecraft
Scenedesmus
sceneful
sceneman
scenery
sceneshifter
scenewright
scenic
scenical
scenically
scenist
scenite
scenograph
scenographer
scenographic
scenographical
scenographically
scenography
Scenopinidae
scent
scented
scenter
scentful
scenting
scentless
scentlessness
scentproof
scentwood
scepsis
scepter
scepterdom
sceptered
scepterless
sceptic
sceptral
sceptropherous
sceptrosophy
sceptry
scerne
sceuophorion
sceuophylacium
sceuophylax
schaapsteker
Schaefferia
schairerite
schalmei
schalmey
schalstein
schanz
schapbachite
schappe
schapped
schapping
scharf
Scharlachberger
schatchen
Scheat
Schedar
schediasm
schediastic
Schedius
schedular
schedulate
schedule
schedulize
scheelite
scheffel
schefferite
schelling
Schellingian
Schellingianism
Schellingism
schelly
scheltopusik
schema
schemata
schematic
schematically
schematism
schematist
schematization
schematize
schematizer
schematogram
schematograph
schematologetically
schematomancy
schematonics
scheme
schemeful
schemeless
schemer
schemery
scheming
schemingly
schemist
schemy
schene
schepel
schepen
scherm
scherzando
scherzi
scherzo
schesis
Scheuchzeria
Scheuchzeriaceae
scheuchzeriaceous
schiavone
Schiedam
schiffli
schiller
schillerfels
schillerization
schillerize
schilling
schimmel
schindylesis
schindyletic
Schinus
schipperke
Schisandra
Schisandraceae
schism
schisma
schismatic
schismatical
schismatically
schismaticalness
schismatism
schismatist
schismatize
schismic
schismless
schist
schistaceous
schistic
schistocelia
schistocephalus
Schistocerca
schistocoelia
schistocormia
schistocormus
schistocyte
schistocytosis
schistoglossia
schistoid
schistomelia
schistomelus
schistoprosopia
schistoprosopus
schistorrhachis
schistoscope
schistose
schistosity
Schistosoma
schistosome
schistosomia
schistosomiasis
schistosomus
schistosternia
schistothorax
schistous
schistus
Schizaea
Schizaeaceae
schizaeaceous
Schizanthus
schizanthus
schizaxon
schizocarp
schizocarpic
schizocarpous
schizochroal
schizocoele
schizocoelic
schizocoelous
schizocyte
schizocytosis
schizodinic
schizogamy
schizogenesis
schizogenetic
schizogenetically
schizogenic
schizogenous
schizogenously
schizognath
Schizognathae
schizognathism
schizognathous
schizogonic
schizogony
Schizogregarinae
schizogregarine
Schizogregarinida
schizoid
schizoidism
Schizolaenaceae
schizolaenaceous
schizolite
schizolysigenous
Schizomeria
schizomycete
Schizomycetes
schizomycetic
schizomycetous
schizomycosis
Schizonemertea
schizonemertean
schizonemertine
Schizoneura
Schizonotus
schizont
schizopelmous
Schizopetalon
schizophasia
Schizophragma
schizophrene
schizophrenia
schizophreniac
schizophrenic
Schizophyceae
Schizophyllum
Schizophyta
schizophyte
schizophytic
schizopod
Schizopoda
schizopodal
schizopodous
schizorhinal
schizospore
schizostele
schizostelic
schizostely
schizothecal
schizothoracic
schizothyme
schizothymia
schizothymic
schizotrichia
Schizotrypanum
schiztic
Schlauraffenland
Schleichera
schlemiel
schlemihl
schlenter
schlieren
schlieric
schloop
Schmalkaldic
schmaltz
schmelz
schmelze
schnabel
Schnabelkanne
schnapper
schnapps
schnauzer
schneider
Schneiderian
schnitzel
schnorchel
schnorkel
schnorrer
scho
schochat
schochet
schoenobatic
schoenobatist
Schoenocaulon
Schoenus
schoenus
Schoharie
schola
scholae
scholaptitude
scholar
scholarch
scholardom
scholarian
scholarism
scholarless
scholarlike
scholarliness
scholarly
scholarship
scholasm
scholastic
scholastical
scholastically
scholasticate
scholasticism
scholasticly
scholia
scholiast
scholiastic
scholion
scholium
Schomburgkia
schone
schonfelsite
Schoodic
School
school
schoolable
schoolbag
schoolbook
schoolbookish
schoolboy
schoolboydom
schoolboyhood
schoolboyish
schoolboyishly
schoolboyishness
schoolboyism
schoolbutter
schoolcraft
schooldame
schooldom
schooled
schoolery
schoolfellow
schoolfellowship
schoolful
schoolgirl
schoolgirlhood
schoolgirlish
schoolgirlishly
schoolgirlishness
schoolgirlism
schoolgirly
schoolgoing
schoolhouse
schooling
schoolingly
schoolish
schoolkeeper
schoolkeeping
schoolless
schoollike
schoolmaam
schoolmaamish
schoolmaid
schoolman
schoolmaster
schoolmasterhood
schoolmastering
schoolmasterish
schoolmasterishly
schoolmasterishness
schoolmasterism
schoolmasterly
schoolmastership
schoolmastery
schoolmate
schoolmiss
schoolmistress
schoolmistressy
schoolroom
schoolteacher
schoolteacherish
schoolteacherly
schoolteachery
schoolteaching
schooltide
schooltime
schoolward
schoolwork
schoolyard
schoon
schooner
Schopenhauereanism
Schopenhauerian
Schopenhauerism
schoppen
schorenbergite
schorl
schorlaceous
schorlomite
schorlous
schorly
schottische
schottish
schout
schraubthaler
Schrebera
schreiner
schreinerize
schriesheimite
Schrund
schtoff
schuh
schuhe
schuit
schule
schultenite
schungite
schuss
schute
schwa
schwabacher
Schwalbea
schwarz
Schwarzian
schweizer
schweizerkase
Schwendenerian
Schwenkfelder
Schwenkfeldian
Sciadopitys
Sciaena
sciaenid
Sciaenidae
sciaeniform
Sciaeniformes
sciaenoid
scialytic
sciamachy
Scian
sciapod
sciapodous
Sciara
sciarid
Sciaridae
Sciarinae
sciatheric
sciatherical
sciatherically
sciatic
sciatica
sciatical
sciatically
sciaticky
scibile
science
scienced
scient
sciential
scientician
scientific
scientifical
scientifically
scientificalness
scientificogeographical
scientificohistorical
scientificophilosophical
scientificopoetic
scientificoreligious
scientificoromantic
scientintically
scientism
Scientist
scientist
scientistic
scientistically
scientize
scientolism
scilicet
Scilla
scillain
scillipicrin
Scillitan
scillitin
scillitoxin
Scillonian
scimitar
scimitared
scimitarpod
scincid
Scincidae
scincidoid
scinciform
scincoid
scincoidian
Scincomorpha
Scincus
scind
sciniph
scintilla
scintillant
scintillantly
scintillate
scintillating
scintillatingly
scintillation
scintillator
scintillescent
scintillize
scintillometer
scintilloscope
scintillose
scintillously
scintle
scintler
scintling
sciograph
sciographic
sciography
sciolism
sciolist
sciolistic
sciolous
sciomachiology
sciomachy
sciomancy
sciomantic
scion
sciophilous
sciophyte
scioptic
sciopticon
scioptics
scioptric
sciosophist
sciosophy
Sciot
scioterical
scioterique
sciotheism
sciotheric
sciotherical
sciotherically
scious
scirenga
Scirophoria
Scirophorion
Scirpus
scirrhi
scirrhogastria
scirrhoid
scirrhoma
scirrhosis
scirrhous
scirrhus
scirrosity
scirtopod
Scirtopoda
scirtopodous
scissel
scissible
scissile
scission
scissiparity
scissor
scissorbill
scissorbird
scissorer
scissoring
scissorium
scissorlike
scissorlikeness
scissors
scissorsbird
scissorsmith
scissorstail
scissortail
scissorwise
scissura
scissure
Scissurella
scissurellid
Scissurellidae
Scitaminales
Scitamineae
sciurid
Sciuridae
sciurine
sciuroid
sciuromorph
Sciuromorpha
sciuromorphic
Sciuropterus
Sciurus
sclaff
sclate
sclater
Sclav
Sclavonian
sclaw
scler
sclera
scleral
scleranth
Scleranthaceae
Scleranthus
scleratogenous
sclere
sclerectasia
sclerectomy
scleredema
sclereid
sclerema
sclerencephalia
sclerenchyma
sclerenchymatous
sclerenchyme
sclererythrin
scleretinite
Scleria
scleriasis
sclerification
sclerify
sclerite
scleritic
scleritis
sclerized
sclerobase
sclerobasic
scleroblast
scleroblastema
scleroblastemic
scleroblastic
sclerocauly
sclerochorioiditis
sclerochoroiditis
scleroconjunctival
scleroconjunctivitis
sclerocornea
sclerocorneal
sclerodactylia
sclerodactyly
scleroderm
Scleroderma
scleroderma
Sclerodermaceae
Sclerodermata
Sclerodermatales
sclerodermatitis
sclerodermatous
Sclerodermi
sclerodermia
sclerodermic
sclerodermite
sclerodermitic
sclerodermitis
sclerodermous
sclerogen
Sclerogeni
sclerogenoid
sclerogenous
scleroid
scleroiritis
sclerokeratitis
sclerokeratoiritis
scleroma
scleromata
scleromeninx
scleromere
sclerometer
sclerometric
scleronychia
scleronyxis
Scleropages
Scleroparei
sclerophthalmia
sclerophyll
sclerophyllous
sclerophylly
scleroprotein
sclerosal
sclerosarcoma
Scleroscope
scleroscope
sclerose
sclerosed
scleroseptum
sclerosis
scleroskeletal
scleroskeleton
Sclerospora
sclerostenosis
Sclerostoma
sclerostomiasis
sclerotal
sclerote
sclerotia
sclerotial
sclerotic
sclerotica
sclerotical
scleroticectomy
scleroticochorioiditis
scleroticochoroiditis
scleroticonyxis
scleroticotomy
Sclerotinia
sclerotinial
sclerotiniose
sclerotioid
sclerotitic
sclerotitis
sclerotium
sclerotized
sclerotoid
sclerotome
sclerotomic
sclerotomy
sclerous
scleroxanthin
sclerozone
scliff
sclim
sclimb
scoad
scob
scobby
scobicular
scobiform
scobs
scoff
scoffer
scoffery
scoffing
scoffingly
scoffingstock
scofflaw
scog
scoggan
scogger
scoggin
scogginism
scogginist
scoinson
scoke
scolb
scold
scoldable
scoldenore
scolder
scolding
scoldingly
scoleces
scoleciasis
scolecid
Scolecida
scoleciform
scolecite
scolecoid
scolecology
scolecophagous
scolecospore
scoleryng
scolex
Scolia
scolia
scolices
scoliid
Scoliidae
scoliograptic
scoliokyposis
scoliometer
scolion
scoliorachitic
scoliosis
scoliotic
scoliotone
scolite
scollop
scolog
scolopaceous
Scolopacidae
scolopacine
Scolopax
Scolopendra
scolopendra
Scolopendrella
Scolopendrellidae
scolopendrelloid
scolopendrid
Scolopendridae
scolopendriform
scolopendrine
Scolopendrium
scolopendroid
scolophore
scolopophore
Scolymus
scolytid
Scolytidae
scolytoid
Scolytus
Scomber
scomberoid
Scombresocidae
Scombresox
scombrid
Scombridae
scombriform
Scombriformes
scombrine
scombroid
Scombroidea
scombroidean
scombrone
sconce
sconcer
sconcheon
sconcible
scone
scoon
scoop
scooped
scooper
scoopful
scooping
scoopingly
scoot
scooter
scopa
scoparin
scoparius
scopate
scope
scopeless
scopelid
Scopelidae
scopeliform
scopelism
scopeloid
Scopelus
scopet
scopic
Scopidae
scopiferous
scopiform
scopiformly
scopine
scopiped
scopola
scopolamine
scopoleine
scopoletin
scopoline
scopperil
scops
scoptical
scoptically
scoptophilia
scoptophiliac
scoptophilic
scoptophobia
scopula
Scopularia
scopularian
scopulate
scopuliferous
scopuliform
scopuliped
Scopulipedes
scopulite
scopulous
scopulousness
Scopus
scorbute
scorbutic
scorbutical
scorbutically
scorbutize
scorbutus
scorch
scorched
scorcher
scorching
scorchingly
scorchingness
scorchproof
score
scoreboard
scorebook
scored
scorekeeper
scorekeeping
scoreless
scorer
scoria
scoriac
scoriaceous
scoriae
scorification
scorifier
scoriform
scorify
scoring
scorious
scorn
scorned
scorner
scornful
scornfully
scornfulness
scorningly
scornproof
scorny
scorodite
Scorpaena
scorpaenid
Scorpaenidae
scorpaenoid
scorpene
scorper
Scorpidae
Scorpididae
Scorpii
Scorpiid
Scorpio
scorpioid
scorpioidal
Scorpioidea
scorpion
Scorpiones
scorpionic
scorpionid
Scorpionida
Scorpionidea
Scorpionis
scorpionweed
scorpionwort
Scorpiurus
Scorpius
scorse
scortation
scortatory
Scorzonera
Scot
scot
scotale
Scotch
scotch
scotcher
Scotchery
Scotchification
Scotchify
Scotchiness
scotching
Scotchman
scotchman
Scotchness
Scotchwoman
Scotchy
scote
scoter
scoterythrous
Scotia
scotia
Scotic
scotino
Scotism
Scotist
Scotistic
Scotistical
Scotize
Scotlandwards
scotodinia
scotogram
scotograph
scotographic
scotography
scotoma
scotomata
scotomatic
scotomatical
scotomatous
scotomia
scotomic
scotomy
scotophobia
scotopia
scotopic
scotoscope
scotosis
Scots
Scotsman
Scotswoman
Scott
Scotticism
Scotticize
Scottie
Scottification
Scottify
Scottish
Scottisher
Scottishly
Scottishman
Scottishness
Scotty
scouch
scouk
scoundrel
scoundreldom
scoundrelish
scoundrelism
scoundrelly
scoundrelship
scoup
scour
scourage
scoured
scourer
scouress
scourfish
scourge
scourger
scourging
scourgingly
scouriness
scouring
scourings
scourway
scourweed
scourwort
scoury
scouse
scout
scoutcraft
scoutdom
scouter
scouth
scouther
scouthood
scouting
scoutingly
scoutish
scoutmaster
scoutwatch
scove
scovel
scovillite
scovy
scow
scowbank
scowbanker
scowder
scowl
scowler
scowlful
scowling
scowlingly
scowlproof
scowman
scrab
scrabble
scrabbled
scrabbler
scrabe
scrae
scraffle
scrag
scragged
scraggedly
scraggedness
scragger
scraggily
scragginess
scragging
scraggled
scraggling
scraggly
scraggy
scraily
scram
scramasax
scramble
scramblement
scrambler
scrambling
scramblingly
scrambly
scrampum
scran
scranch
scrank
scranky
scrannel
scranning
scranny
scrap
scrapable
scrapbook
scrape
scrapeage
scraped
scrapepenny
scraper
scrapie
scraping
scrapingly
scrapler
scraplet
scrapling
scrapman
scrapmonger
scrappage
scrapped
scrapper
scrappet
scrappily
scrappiness
scrapping
scrappingly
scrapple
scrappler
scrappy
scrapworks
scrapy
scrat
scratch
scratchable
scratchably
scratchback
scratchboard
scratchbrush
scratchcard
scratchcarding
scratchcat
scratcher
scratches
scratchification
scratchiness
scratching
scratchingly
scratchless
scratchlike
scratchman
scratchproof
scratchweed
scratchwork
scratchy
scrath
scratter
scrattle
scrattling
scrauch
scrauchle
scraunch
scraw
scrawk
scrawl
scrawler
scrawliness
scrawly
scrawm
scrawnily
scrawniness
scrawny
scray
scraze
screak
screaking
screaky
scream
screamer
screaminess
screaming
screamingly
screamproof
screamy
scree
screech
screechbird
screecher
screechily
screechiness
screeching
screechingly
screechy
screed
screek
screel
screeman
screen
screenable
screenage
screencraft
screendom
screened
screener
screening
screenless
screenlike
screenman
screenplay
screensman
screenwise
screenwork
screenwriter
screeny
screet
screeve
screeved
screever
screich
screigh
screve
screver
screw
screwable
screwage
screwball
screwbarrel
screwdrive
screwdriver
screwed
screwer
screwhead
screwiness
screwing
screwish
screwless
screwlike
screwman
screwmatics
screwship
screwsman
screwstem
screwstock
screwwise
screwworm
screwy
scribable
scribacious
scribaciousness
scribal
scribatious
scribatiousness
scribblage
scribblative
scribblatory
scribble
scribbleable
scribbled
scribbledom
scribbleism
scribblemania
scribblement
scribbleomania
scribbler
scribbling
scribblingly
scribbly
scribe
scriber
scribeship
scribing
scribism
scribophilous
scride
scrieve
scriever
scriggle
scriggler
scriggly
scrike
scrim
scrime
scrimer
scrimmage
scrimmager
scrimp
scrimped
scrimpily
scrimpiness
scrimpingly
scrimply
scrimpness
scrimption
scrimpy
scrimshander
scrimshandy
scrimshank
scrimshanker
scrimshaw
scrimshon
scrimshorn
scrin
scrinch
scrine
scringe
scriniary
scrip
scripee
scripless
scrippage
script
scription
scriptitious
scriptitiously
scriptitory
scriptive
scriptor
scriptorial
scriptorium
scriptory
scriptural
Scripturalism
scripturalism
Scripturalist
scripturalist
Scripturality
scripturality
scripturalize
scripturally
scripturalness
Scripturarian
Scripture
scripture
Scriptured
scriptured
Scriptureless
scripturiency
scripturient
Scripturism
scripturism
Scripturist
scripula
scripulum
scritch
scritoire
scrivaille
scrive
scrivello
scriven
scrivener
scrivenership
scrivenery
scrivening
scrivenly
scriver
scrob
scrobble
scrobe
scrobicula
scrobicular
scrobiculate
scrobiculated
scrobicule
scrobiculus
scrobis
scrod
scrodgill
scroff
scrofula
scrofularoot
scrofulaweed
scrofulide
scrofulism
scrofulitic
scrofuloderm
scrofuloderma
scrofulorachitic
scrofulosis
scrofulotuberculous
scrofulous
scrofulously
scrofulousness
scrog
scroggy
scrolar
scroll
scrolled
scrollery
scrollhead
scrollwise
scrollwork
scrolly
scronach
scroo
scrooch
scrooge
scroop
Scrophularia
Scrophulariaceae
scrophulariaceous
scrota
scrotal
scrotectomy
scrotiform
scrotitis
scrotocele
scrotofemoral
scrotum
scrouge
scrouger
scrounge
scrounger
scrounging
scrout
scrow
scroyle
scrub
scrubbable
scrubbed
scrubber
scrubbery
scrubbily
scrubbiness
scrubbird
scrubbly
scrubboard
scrubby
scrubgrass
scrubland
scrubwood
scruf
scruff
scruffle
scruffman
scruffy
scruft
scrum
scrummage
scrummager
scrump
scrumple
scrumption
scrumptious
scrumptiously
scrumptiousness
scrunch
scrunchy
scrunge
scrunger
scrunt
scruple
scrupleless
scrupler
scruplesome
scruplesomeness
scrupula
scrupular
scrupuli
scrupulist
scrupulosity
scrupulous
scrupulously
scrupulousness
scrupulum
scrupulus
scrush
scrutability
scrutable
scrutate
scrutation
scrutator
scrutatory
scrutinant
scrutinate
scrutineer
scrutinization
scrutinize
scrutinizer
scrutinizingly
scrutinous
scrutinously
scrutiny
scruto
scrutoire
scruze
scry
scryer
scud
scuddaler
scuddawn
scudder
scuddick
scuddle
scuddy
scudi
scudler
scudo
scuff
scuffed
scuffer
scuffle
scuffler
scufflingly
scuffly
scuffy
scuft
scufter
scug
scuggery
sculch
sculduddery
scull
sculler
scullery
scullful
scullion
scullionish
scullionize
scullionship
scullog
sculp
sculper
sculpin
sculpt
sculptile
sculptitory
sculptograph
sculptography
Sculptor
sculptor
Sculptorid
sculptress
sculptural
sculpturally
sculpturation
sculpture
sculptured
sculpturer
sculpturesque
sculpturesquely
sculpturesqueness
sculpturing
sculsh
scum
scumber
scumble
scumbling
scumboard
scumfish
scumless
scumlike
scummed
scummer
scumming
scummy
scumproof
scun
scuncheon
scunder
scunner
scup
scupful
scuppaug
scupper
scuppernong
scuppet
scuppler
scur
scurdy
scurf
scurfer
scurfily
scurfiness
scurflike
scurfy
scurrier
scurrile
scurrilist
scurrility
scurrilize
scurrilous
scurrilously
scurrilousness
scurry
scurvied
scurvily
scurviness
scurvish
scurvy
scurvyweed
scusation
scuse
scut
scuta
scutage
scutal
scutate
scutated
scutatiform
scutation
scutch
scutcheon
scutcheoned
scutcheonless
scutcheonlike
scutcheonwise
scutcher
scutching
scute
scutel
scutella
scutellae
scutellar
Scutellaria
scutellarin
scutellate
scutellated
scutellation
scutellerid
Scutelleridae
scutelliform
scutelligerous
scutelliplantar
scutelliplantation
scutellum
scutibranch
Scutibranchia
scutibranchian
scutibranchiate
scutifer
scutiferous
scutiform
scutiger
Scutigera
scutigeral
Scutigeridae
scutigerous
scutiped
scutter
scuttle
scuttlebutt
scuttleful
scuttleman
scuttler
scuttling
scuttock
scutty
scutula
scutular
scutulate
scutulated
scutulum
Scutum
scutum
scybala
scybalous
scybalum
scye
scyelite
Scyld
Scylla
Scyllaea
Scyllaeidae
scyllarian
Scyllaridae
scyllaroid
Scyllarus
Scyllidae
Scylliidae
scyllioid
Scylliorhinidae
scylliorhinoid
Scylliorhinus
scyllite
scyllitol
Scyllium
scypha
scyphae
scyphate
scyphi
scyphiferous
scyphiform
scyphiphorous
scyphistoma
scyphistomae
scyphistomoid
scyphistomous
scyphoi
scyphomancy
Scyphomedusae
scyphomedusan
scyphomedusoid
scyphophore
Scyphophori
scyphophorous
scyphopolyp
scyphose
scyphostoma
Scyphozoa
scyphozoan
scyphula
scyphulus
scyphus
scyt
scytale
Scyth
scythe
scytheless
scythelike
scytheman
scythesmith
scythestone
scythework
Scythian
Scythic
Scythize
scytitis
scytoblastema
scytodepsic
Scytonema
Scytonemataceae
scytonemataceous
scytonematoid
scytonematous
Scytopetalaceae
scytopetalaceous
Scytopetalum
sdeath
sdrucciola
se
sea
seabeach
seabeard
Seabee
seaberry
seaboard
seaborderer
seabound
seacannie
seacatch
seacoast
seaconny
seacraft
seacrafty
seacunny
seadog
seadrome
seafardinger
seafare
seafarer
seafaring
seaflood
seaflower
seafolk
Seaforthia
seafowl
Seaghan
seagirt
seagoer
seagoing
seah
seahound
seak
seal
sealable
sealant
sealch
sealed
sealer
sealery
sealess
sealet
sealette
sealflower
sealike
sealine
sealing
sealless
seallike
sealskin
sealwort
Sealyham
seam
seaman
seamancraft
seamanite
seamanlike
seamanly
seamanship
seamark
Seamas
seambiter
seamed
seamer
seaminess
seaming
seamless
seamlessly
seamlessness
seamlet
seamlike
seamost
seamrend
seamrog
seamster
seamstress
Seamus
seamy
Sean
seance
seapiece
seaplane
seaport
seaquake
sear
searce
searcer
search
searchable
searchableness
searchant
searcher
searcheress
searcherlike
searchership
searchful
searching
searchingly
searchingness
searchless
searchlight
searchment
searcloth
seared
searedness
searer
searing
searlesite
searness
seary
Seasan
seascape
seascapist
seascout
seascouting
seashine
seashore
seasick
seasickness
seaside
seasider
season
seasonable
seasonableness
seasonably
seasonal
seasonality
seasonally
seasonalness
seasoned
seasonedly
seasoner
seasoning
seasoninglike
seasonless
seastrand
seastroke
seat
seatang
seated
seater
seathe
seating
seatless
seatrain
seatron
seatsman
seatwork
seave
seavy
seawant
seaward
seawardly
seaware
seaway
seaweed
seaweedy
seawife
seawoman
seaworn
seaworthiness
seaworthy
seax
Seba
sebacate
sebaceous
sebacic
sebait
Sebastian
sebastianite
Sebastichthys
Sebastodes
sebate
sebesten
sebiferous
sebific
sebilla
sebiparous
sebkha
sebolith
seborrhagia
seborrhea
seborrheal
seborrheic
seborrhoic
Sebright
sebum
sebundy
sec
secability
secable
Secale
secalin
secaline
secalose
Secamone
secancy
secant
secantly
secateur
secede
Seceder
seceder
secern
secernent
secernment
secesh
secesher
Secessia
Secession
secession
Secessional
secessional
secessionalist
Secessiondom
secessioner
secessionism
secessionist
sech
Sechium
Sechuana
seck
Seckel
seclude
secluded
secludedly
secludedness
secluding
secluse
seclusion
seclusionist
seclusive
seclusively
seclusiveness
secodont
secohm
secohmmeter
second
secondar
secondarily
secondariness
secondary
seconde
seconder
secondhand
secondhanded
secondhandedly
secondhandedness
secondly
secondment
secondness
secos
secpar
secque
secre
secrecy
secret
secreta
secretage
secretagogue
secretarial
secretarian
Secretariat
secretariat
secretariate
secretary
secretaryship
secrete
secretin
secretion
secretional
secretionary
secretitious
secretive
secretively
secretiveness
secretly
secretmonger
secretness
secreto
secretomotor
secretor
secretory
secretum
sect
sectarial
sectarian
sectarianism
sectarianize
sectarianly
sectarism
sectarist
sectary
sectator
sectile
sectility
section
sectional
sectionalism
sectionalist
sectionality
sectionalization
sectionalize
sectionally
sectionary
sectionist
sectionize
sectioplanography
sectism
sectist
sectiuncle
sective
sector
sectoral
sectored
sectorial
sectroid
sectwise
secular
secularism
secularist
secularistic
secularity
secularization
secularize
secularizer
secularly
secularness
secund
secundate
secundation
secundiflorous
secundigravida
secundine
secundipara
secundiparity
secundiparous
secundly
secundogeniture
secundoprimary
secundus
securable
securance
secure
securely
securement
secureness
securer
securicornate
securifer
Securifera
securiferous
securiform
Securigera
securigerous
securitan
security
Sedaceae
Sedan
sedan
Sedang
sedanier
Sedat
sedate
sedately
sedateness
sedation
sedative
sedent
Sedentaria
sedentarily
sedentariness
sedentary
sedentation
Seder
sederunt
sedge
sedged
sedgelike
sedging
sedgy
sedigitate
sedigitated
sedile
sedilia
sediment
sedimental
sedimentarily
sedimentary
sedimentate
sedimentation
sedimentous
sedimetric
sedimetrical
sedition
seditionary
seditionist
seditious
seditiously
seditiousness
sedjadeh
Sedovic
seduce
seduceable
seducee
seducement
seducer
seducible
seducing
seducingly
seducive
seduct
seduction
seductionist
seductive
seductively
seductiveness
seductress
sedulity
sedulous
sedulously
sedulousness
Sedum
sedum
see
seeable
seeableness
Seebeck
seecatch
seech
seed
seedage
seedbed
seedbird
seedbox
seedcake
seedcase
seedeater
seeded
Seeder
seeder
seedful
seedgall
seedily
seediness
seedkin
seedless
seedlessness
seedlet
seedlike
seedling
seedlip
seedman
seedness
seedsman
seedstalk
seedtime
seedy
seege
seeing
seeingly
seeingness
seek
seeker
Seekerism
seeking
seel
seelful
seely
seem
seemable
seemably
seemer
seeming
seemingly
seemingness
seemless
seemlihead
seemlily
seemliness
seemly
seen
seenie
Seenu
seep
seepage
seeped
seepweed
seepy
seer
seerband
seercraft
seeress
seerfish
seerhand
seerhood
seerlike
seerpaw
seership
seersucker
seesaw
seesawiness
seesee
seethe
seething
seethingly
seetulputty
Sefekhet
seg
seggar
seggard
segged
seggrom
Seginus
segment
segmental
segmentally
segmentary
segmentate
segmentation
segmented
sego
segol
segolate
segreant
segregable
segregant
segregate
segregateness
segregation
segregational
segregationist
segregative
segregator
Sehyo
seiche
Seid
Seidel
seidel
Seidlitz
seigneur
seigneurage
seigneuress
seigneurial
seigneury
seignior
seigniorage
seignioral
seignioralty
seigniorial
seigniority
seigniorship
seigniory
seignorage
seignoral
seignorial
seignorize
seignory
seilenoi
seilenos
seine
seiner
seirospore
seirosporic
seise
seism
seismal
seismatical
seismetic
seismic
seismically
seismicity
seismism
seismochronograph
seismogram
seismograph
seismographer
seismographic
seismographical
seismography
seismologic
seismological
seismologically
seismologist
seismologue
seismology
seismometer
seismometric
seismometrical
seismometrograph
seismometry
seismomicrophone
seismoscope
seismoscopic
seismotectonic
seismotherapy
seismotic
seit
seity
Seiurus
Seiyuhonto
Seiyukai
seizable
seize
seizer
seizin
seizing
seizor
seizure
sejant
sejoin
sejoined
sejugate
sejugous
sejunct
sejunctive
sejunctively
sejunctly
Sekane
Sekani
Sekar
Seker
Sekhwan
sekos
selachian
Selachii
selachoid
Selachoidei
Selachostome
Selachostomi
selachostomous
seladang
Selaginaceae
Selaginella
Selaginellaceae
selaginellaceous
selagite
Selago
selah
selamin
selamlik
selbergite
Selbornian
seldom
seldomcy
seldomer
seldomly
seldomness
seldor
seldseen
sele
select
selectable
selected
selectedly
selectee
selection
selectionism
selectionist
selective
selectively
selectiveness
selectivity
selectly
selectman
selectness
selector
Selena
selenate
Selene
selenian
seleniate
selenic
Selenicereus
selenide
Selenidera
seleniferous
selenigenous
selenion
selenious
Selenipedium
selenite
selenitic
selenitical
selenitiferous
selenitish
selenium
seleniuret
selenobismuthite
selenocentric
selenodont
Selenodonta
selenodonty
selenograph
selenographer
selenographic
selenographical
selenographically
selenographist
selenography
selenolatry
selenological
selenologist
selenology
selenomancy
selenoscope
selenosis
selenotropic
selenotropism
selenotropy
selensilver
selensulphur
Seleucian
Seleucid
Seleucidae
Seleucidan
Seleucidean
Seleucidian
Seleucidic
self
selfcide
selfdom
selfful
selffulness
selfheal
selfhood
selfish
selfishly
selfishness
selfism
selfist
selfless
selflessly
selflessness
selfly
selfness
selfpreservatory
selfsame
selfsameness
selfward
selfwards
selictar
seligmannite
selihoth
Selina
Selinuntine
selion
Seljuk
Seljukian
sell
sella
sellable
sellably
sellaite
sellar
sellate
sellenders
seller
Selli
sellie
selliform
selling
sellout
selly
selsoviet
selsyn
selt
Selter
Seltzer
seltzogene
Selung
selva
selvage
selvaged
selvagee
selvedge
selzogene
Semaeostomae
Semaeostomata
Semang
semanteme
semantic
semantical
semantically
semantician
semanticist
semantics
semantological
semantology
semantron
semaphore
semaphoric
semaphorical
semaphorically
semaphorist
semarum
semasiological
semasiologically
semasiologist
semasiology
semateme
sematic
sematographic
sematography
sematology
sematrope
semball
semblable
semblably
semblance
semblant
semblative
semble
seme
Semecarpus
semeed
semeia
semeiography
semeiologic
semeiological
semeiologist
semeiology
semeion
semeiotic
semeiotical
semeiotics
semelfactive
semelincident
semen
semence
Semeostoma
semese
semester
semestral
semestrial
semi
semiabstracted
semiaccomplishment
semiacid
semiacidified
semiacquaintance
semiadherent
semiadjectively
semiadnate
semiaerial
semiaffectionate
semiagricultural
Semiahmoo
semialbinism
semialcoholic
semialien
semiallegiance
semialpine
semialuminous
semiamplexicaul
semiamplitude
semianarchist
semianatomical
semianatropal
semianatropous
semiangle
semiangular
semianimal
semianimate
semianimated
semiannealed
semiannual
semiannually
semiannular
semianthracite
semiantiministerial
semiantique
semiape
semiaperiodic
semiaperture
semiappressed
semiaquatic
semiarborescent
semiarc
semiarch
semiarchitectural
semiarid
semiaridity
semiarticulate
semiasphaltic
semiatheist
semiattached
semiautomatic
semiautomatically
semiautonomous
semiaxis
semibacchanalian
semibachelor
semibald
semibalked
semiball
semiballoon
semiband
semibarbarian
semibarbarianism
semibarbaric
semibarbarism
semibarbarous
semibaronial
semibarren
semibase
semibasement
semibastion
semibay
semibeam
semibejan
semibelted
semibifid
semibituminous
semibleached
semiblind
semiblunt
semibody
semiboiled
semibolshevist
semibolshevized
semibouffant
semibourgeois
semibreve
semibull
semiburrowing
semic
semicadence
semicalcareous
semicalcined
semicallipygian
semicanal
semicanalis
semicannibalic
semicantilever
semicarbazide
semicarbazone
semicarbonate
semicarbonize
semicardinal
semicartilaginous
semicastrate
semicastration
semicatholicism
semicaudate
semicelestial
semicell
semicellulose
semicentenarian
semicentenary
semicentennial
semicentury
semichannel
semichaotic
semichemical
semicheviot
semichevron
semichiffon
semichivalrous
semichoric
semichorus
semichrome
semicircle
semicircled
semicircular
semicircularity
semicircularly
semicircularness
semicircumference
semicircumferentor
semicircumvolution
semicirque
semicitizen
semicivilization
semicivilized
semiclassic
semiclassical
semiclause
semicleric
semiclerical
semiclimber
semiclimbing
semiclose
semiclosed
semiclosure
semicoagulated
semicoke
semicollapsible
semicollar
semicollegiate
semicolloid
semicolloquial
semicolon
semicolonial
semicolumn
semicolumnar
semicoma
semicomatose
semicombined
semicombust
semicomic
semicomical
semicommercial
semicompact
semicompacted
semicomplete
semicomplicated
semiconceal
semiconcrete
semiconducting
semiconductor
semicone
semiconfident
semiconfinement
semiconfluent
semiconformist
semiconformity
semiconic
semiconical
semiconnate
semiconnection
semiconoidal
semiconscious
semiconsciously
semiconsciousness
semiconservative
semiconsonant
semiconsonantal
semiconspicuous
semicontinent
semicontinuum
semicontraction
semicontradiction
semiconvergence
semiconvergent
semiconversion
semiconvert
semicordate
semicordated
semicoriaceous
semicorneous
semicoronate
semicoronated
semicoronet
semicostal
semicostiferous
semicotton
semicotyle
semicounterarch
semicountry
semicrepe
semicrescentic
semicretin
semicretinism
semicriminal
semicroma
semicrome
semicrustaceous
semicrystallinc
semicubical
semicubit
semicup
semicupium
semicupola
semicured
semicurl
semicursive
semicurvilinear
semicyclic
semicycloid
semicylinder
semicylindric
semicylindrical
semicynical
semidaily
semidangerous
semidark
semidarkness
semidead
semideaf
semidecay
semidecussation
semidefinite
semideific
semideification
semideistical
semideity
semidelight
semidelirious
semideltaic
semidemented
semidenatured
semidependence
semidependent
semideponent
semidesert
semidestructive
semidetached
semidetachment
semideveloped
semidiagrammatic
semidiameter
semidiapason
semidiapente
semidiaphaneity
semidiaphanous
semidiatessaron
semidifference
semidigested
semidigitigrade
semidigression
semidilapidation
semidine
semidirect
semidisabled
semidisk
semiditone
semidiurnal
semidivided
semidivine
semidocumentary
semidodecagon
semidole
semidome
semidomed
semidomestic
semidomesticated
semidomestication
semidomical
semidormant
semidouble
semidrachm
semidramatic
semidress
semidressy
semidried
semidry
semidrying
semiductile
semidull
semiduplex
semiduration
semieducated
semieffigy
semiegg
semiegret
semielastic
semielision
semiellipse
semiellipsis
semiellipsoidal
semielliptic
semielliptical
semienclosed
semiengaged
semiequitant
semierect
semieremitical
semiessay
semiexecutive
semiexpanded
semiexplanation
semiexposed
semiexternal
semiextinct
semiextinction
semifable
semifabulous
semifailure
semifamine
semifascia
semifasciated
semifashion
semifast
semifatalistic
semiferal
semiferous
semifeudal
semifeudalism
semifib
semifiction
semifictional
semifigurative
semifigure
semifinal
semifinalist
semifine
semifinish
semifinished
semifiscal
semifistular
semifit
semifitting
semifixed
semiflashproof
semiflex
semiflexed
semiflexible
semiflexion
semiflexure
semiflint
semifloating
semifloret
semifloscular
semifloscule
semiflosculose
semiflosculous
semifluctuant
semifluctuating
semifluid
semifluidic
semifluidity
semifoaming
semiforbidding
semiforeign
semiform
semiformal
semiformed
semifossil
semifossilized
semifrantic
semifriable
semifrontier
semifuddle
semifunctional
semifused
semifusion
semify
semigala
semigelatinous
semigentleman
semigenuflection
semigirder
semiglaze
semiglazed
semiglobe
semiglobose
semiglobular
semiglobularly
semiglorious
semiglutin
semigod
semigovernmental
semigrainy
semigranitic
semigranulate
semigravel
semigroove
semihand
semihard
semiharden
semihardy
semihastate
semihepatization
semiherbaceous
semiheterocercal
semihexagon
semihexagonal
semihiant
semihiatus
semihibernation
semihigh
semihistorical
semihobo
semihonor
semihoral
semihorny
semihostile
semihot
semihuman
semihumanitarian
semihumanized
semihumbug
semihumorous
semihumorously
semihyaline
semihydrate
semihydrobenzoinic
semihyperbola
semihyperbolic
semihyperbolical
semijealousy
semijubilee
semijudicial
semijuridical
semilanceolate
semilatent
semilatus
semileafless
semilegendary
semilegislative
semilens
semilenticular
semilethal
semiliberal
semilichen
semiligneous
semilimber
semilined
semiliquid
semiliquidity
semiliterate
semilocular
semilogarithmic
semilogical
semilong
semilooper
semiloose
semiloyalty
semilucent
semilunar
semilunare
semilunary
semilunate
semilunation
semilune
semiluxation
semiluxury
semimachine
semimade
semimadman
semimagical
semimagnetic
semimajor
semimalignant
semimanufacture
semimanufactured
semimarine
semimarking
semimathematical
semimature
semimechanical
semimedicinal
semimember
semimembranosus
semimembranous
semimenstrual
semimercerized
semimessianic
semimetal
semimetallic
semimetamorphosis
semimicrochemical
semimild
semimilitary
semimill
semimineral
semimineralized
semiminim
semiminor
semimolecule
semimonastic
semimonitor
semimonopoly
semimonster
semimonthly
semimoron
semimucous
semimute
semimystic
semimystical
semimythical
seminaked
seminal
seminality
seminally
seminaphthalidine
seminaphthylamine
seminar
seminarcosis
seminarial
seminarian
seminarianism
seminarist
seminaristic
seminarize
seminary
seminasal
seminase
seminatant
seminate
semination
seminationalization
seminative
seminebulous
seminecessary
seminegro
seminervous
seminiferal
seminiferous
seminific
seminifical
seminification
seminist
seminium
seminivorous
seminocturnal
Seminole
seminoma
seminomad
seminomadic
seminomata
seminonconformist
seminonflammable
seminonsensical
seminormal
seminose
seminovel
seminovelty
seminude
seminudity
seminule
seminuliferous
seminuria
seminvariant
seminvariantive
semioblivion
semioblivious
semiobscurity
semioccasional
semioccasionally
semiocclusive
semioctagonal
semiofficial
semiofficially
semiography
Semionotidae
Semionotus
semiopacity
semiopacous
semiopal
semiopalescent
semiopaque
semiopened
semiorb
semiorbicular
semiorbicularis
semiorbiculate
semiordinate
semiorganized
semioriental
semioscillation
semiosseous
semiostracism
semiotic
semiotician
semioval
semiovaloid
semiovate
semioviparous
semiovoid
semiovoidal
semioxidated
semioxidized
semioxygenated
semioxygenized
semipagan
semipalmate
semipalmated
semipalmation
semipanic
semipapal
semipapist
semiparallel
semiparalysis
semiparameter
semiparasitic
semiparasitism
semipaste
semipastoral
semipasty
semipause
semipeace
semipectinate
semipectinated
semipectoral
semiped
semipedal
semipellucid
semipellucidity
semipendent
semipenniform
semiperfect
semiperimeter
semiperimetry
semiperiphery
semipermanent
semipermeability
semipermeable
semiperoid
semiperspicuous
semipertinent
semipervious
semipetaloid
semipetrified
semiphase
semiphilologist
semiphilosophic
semiphilosophical
semiphlogisticated
semiphonotypy
semiphosphorescent
semipinacolic
semipinacolin
semipinnate
semipiscine
semiplantigrade
semiplastic
semiplumaceous
semiplume
semipolar
semipolitical
semipolitician
semipoor
semipopish
semipopular
semiporcelain
semiporous
semiporphyritic
semiportable
semipostal
semipractical
semiprecious
semipreservation
semiprimigenous
semiprivacy
semiprivate
semipro
semiprofane
semiprofessional
semiprofessionalized
semipronation
semiprone
semipronominal
semiproof
semiproselyte
semiprosthetic
semiprostrate
semiprotectorate
semiproven
semipublic
semipupa
semipurulent
semiputrid
semipyramidal
semipyramidical
semipyritic
semiquadrangle
semiquadrantly
semiquadrate
semiquantitative
semiquantitatively
semiquartile
semiquaver
semiquietism
semiquietist
semiquinquefid
semiquintile
semiquote
semiradial
semiradiate
Semiramis
Semiramize
semirapacious
semirare
semirattlesnake
semiraw
semirebellion
semirecondite
semirecumbent
semirefined
semireflex
semiregular
semirelief
semireligious
semireniform
semirepublican
semiresinous
semiresolute
semirespectability
semirespectable
semireticulate
semiretirement
semiretractile
semireverberatory
semirevolute
semirevolution
semirevolutionist
semirhythm
semiriddle
semirigid
semiring
semiroll
semirotary
semirotating
semirotative
semirotatory
semirotund
semirotunda
semiround
semiroyal
semiruin
semirural
semirustic
semis
semisacerdotal
semisacred
semisagittate
semisaint
semisaline
semisaltire
semisaprophyte
semisaprophytic
semisarcodic
semisatiric
semisaturation
semisavage
semisavagedom
semisavagery
semiscenic
semischolastic
semiscientific
semiseafaring
semisecondary
semisecrecy
semisecret
semisection
semisedentary
semisegment
semisensuous
semisentient
semisentimental
semiseparatist
semiseptate
semiserf
semiserious
semiseriously
semiseriousness
semiservile
semisevere
semiseverely
semiseverity
semisextile
semishady
semishaft
semisheer
semishirker
semishrub
semishrubby
semisightseeing
semisilica
semisimious
semisimple
semisingle
semisixth
semiskilled
semislave
semismelting
semismile
semisocial
semisocialism
semisociative
semisocinian
semisoft
semisolemn
semisolemnity
semisolemnly
semisolid
semisolute
semisomnambulistic
semisomnolence
semisomnous
semisopor
semisovereignty
semispan
semispeculation
semisphere
semispheric
semispherical
semispheroidal
semispinalis
semispiral
semispiritous
semispontaneity
semispontaneous
semispontaneously
semispontaneousness
semisport
semisporting
semisquare
semistagnation
semistaminate
semistarvation
semistarved
semistate
semisteel
semistiff
semistill
semistock
semistory
semistratified
semistriate
semistriated
semistuporous
semisubterranean
semisuburban
semisuccess
semisuccessful
semisuccessfully
semisucculent
semisupernatural
semisupinated
semisupination
semisupine
semisuspension
semisymmetric
semita
semitact
semitae
semitailored
semital
semitandem
semitangent
semitaur
Semite
semitechnical
semiteetotal
semitelic
semitendinosus
semitendinous
semiterete
semiterrestrial
semitertian
semitesseral
semitessular
semitheological
semithoroughfare
Semitic
Semiticism
Semiticize
Semitics
semitime
Semitism
Semitist
Semitization
Semitize
semitonal
semitonally
semitone
semitonic
semitonically
semitontine
semitorpid
semitour
semitrailer
semitrained
semitransept
semitranslucent
semitransparency
semitransparent
semitransverse
semitreasonable
semitrimmed
semitropic
semitropical
semitropics
semitruth
semituberous
semitubular
semiuncial
semiundressed
semiuniversalist
semiupright
semiurban
semiurn
semivalvate
semivault
semivector
semivegetable
semivertebral
semiverticillate
semivibration
semivirtue
semiviscid
semivital
semivitreous
semivitrification
semivitrified
semivocal
semivocalic
semivolatile
semivolcanic
semivoluntary
semivowel
semivulcanized
semiwaking
semiwarfare
semiweekly
semiwild
semiwoody
semiyearly
semmet
semmit
Semnae
Semnones
Semnopithecinae
semnopithecine
Semnopithecus
semola
semolella
semolina
semological
semology
Semostomae
semostomeous
semostomous
semperannual
sempergreen
semperidentical
semperjuvenescent
sempervirent
sempervirid
Sempervivum
sempitern
sempiternal
sempiternally
sempiternity
sempiternize
sempiternous
sempstrywork
semsem
semuncia
semuncial
sen
Senaah
senaite
senam
senarian
senarius
senarmontite
senary
senate
senator
senatorial
senatorially
senatorian
senatorship
senatory
senatress
senatrices
senatrix
sence
Senci
sencion
send
sendable
sendal
sendee
sender
sending
Seneca
Senecan
Senecio
senecioid
senecionine
senectitude
senectude
senectuous
senega
Senegal
Senegalese
Senegambian
senegin
senesce
senescence
senescent
seneschal
seneschally
seneschalship
seneschalsy
seneschalty
sengreen
senicide
Senijextee
senile
senilely
senilism
senility
senilize
senior
seniority
seniorship
Senlac
Senna
senna
sennegrass
sennet
sennight
sennit
sennite
senocular
Senones
Senonian
sensa
sensable
sensal
sensate
sensation
sensational
sensationalism
sensationalist
sensationalistic
sensationalize
sensationally
sensationary
sensationish
sensationism
sensationist
sensationistic
sensationless
sensatorial
sensatory
sense
sensed
senseful
senseless
senselessly
senselessness
sensibilia
sensibilisin
sensibilitist
sensibilitous
sensibility
sensibilium
sensibilization
sensibilize
sensible
sensibleness
sensibly
sensical
sensifacient
sensiferous
sensific
sensificatory
sensifics
sensify
sensigenous
sensile
sensilia
sensilla
sensillum
sension
sensism
sensist
sensistic
sensitive
sensitively
sensitiveness
sensitivity
sensitization
sensitize
sensitizer
sensitometer
sensitometric
sensitometry
sensitory
sensive
sensize
senso
sensomobile
sensomobility
sensomotor
sensoparalysis
sensor
sensoria
sensorial
sensoriglandular
sensorimotor
sensorimuscular
sensorium
sensorivascular
sensorivasomotor
sensorivolitional
sensory
sensual
sensualism
sensualist
sensualistic
sensuality
sensualization
sensualize
sensually
sensualness
sensuism
sensuist
sensum
sensuosity
sensuous
sensuously
sensuousness
sensyne
sent
sentence
sentencer
sentential
sententially
sententiarian
sententiarist
sententiary
sententiosity
sententious
sententiously
sententiousness
sentience
sentiendum
sentient
sentiently
sentiment
sentimental
sentimentalism
sentimentalist
sentimentality
sentimentalization
sentimentalize
sentimentalizer
sentimentally
sentimenter
sentimentless
sentinel
sentinellike
sentinelship
sentinelwise
sentisection
sentition
sentry
Senusi
Senusian
Senusism
sepad
sepal
sepaled
sepaline
sepalled
sepalody
sepaloid
separability
separable
separableness
separably
separata
separate
separatedly
separately
separateness
separates
separatical
separating
separation
separationism
separationist
separatism
separatist
separatistic
separative
separatively
separativeness
separator
separatory
separatress
separatrix
separatum
Sepharad
Sephardi
Sephardic
Sephardim
Sepharvites
sephen
sephiric
sephirothic
sepia
sepiaceous
sepialike
sepian
sepiarian
sepiary
sepic
sepicolous
Sepiidae
sepiment
sepioid
Sepioidea
Sepiola
Sepiolidae
sepiolite
sepion
sepiost
sepiostaire
sepium
sepone
sepoy
seppuku
seps
Sepsidae
sepsine
sepsis
Sept
sept
septa
septal
septan
septane
septangle
septangled
septangular
septangularness
septarian
septariate
septarium
septate
septated
septation
septatoarticulate
septavalent
septave
septcentenary
septectomy
September
Septemberer
Septemberism
Septemberist
Septembral
Septembrian
Septembrist
Septembrize
Septembrizer
septemdecenary
septemfid
septemfluous
septemfoliate
septemfoliolate
septemia
septempartite
septemplicate
septemvious
septemvir
septemvirate
septemviri
septenar
septenarian
septenarius
septenary
septenate
septendecennial
septendecimal
septennary
septennate
septenniad
septennial
septennialist
septenniality
septennially
septennium
septenous
Septentrio
Septentrion
septentrional
septentrionality
septentrionally
septentrionate
septentrionic
septerium
septet
septfoil
Septi
Septibranchia
Septibranchiata
septic
septical
septically
septicemia
septicemic
septicidal
septicidally
septicity
septicization
septicolored
septicopyemia
septicopyemic
septier
septifarious
septiferous
septifluous
septifolious
septiform
septifragal
septifragally
septilateral
septile
septillion
septillionth
septimal
septimanal
septimanarian
septime
septimetritis
septimole
septinsular
septipartite
septisyllabic
septisyllable
septivalent
septleva
Septobasidium
septocosta
septocylindrical
Septocylindrium
septodiarrhea
septogerm
Septogloeum
septoic
septole
septomarginal
septomaxillary
septonasal
Septoria
septotomy
septship
septuagenarian
septuagenarianism
septuagenary
septuagesima
Septuagint
septuagint
Septuagintal
septulate
septulum
septum
septuncial
septuor
septuple
septuplet
septuplicate
septuplication
sepulcher
sepulchral
sepulchralize
sepulchrally
sepulchrous
sepultural
sepulture
sequa
sequacious
sequaciously
sequaciousness
sequacity
Sequan
Sequani
Sequanian
sequel
sequela
sequelae
sequelant
sequence
sequencer
sequency
sequent
sequential
sequentiality
sequentially
sequently
sequest
sequester
sequestered
sequesterment
sequestra
sequestrable
sequestral
sequestrate
sequestration
sequestrator
sequestratrices
sequestratrix
sequestrectomy
sequestrotomy
sequestrum
sequin
sequitur
Sequoia
ser
sera
serab
Serabend
seragli
seraglio
serai
serail
seral
seralbumin
seralbuminous
serang
serape
Serapea
Serapeum
seraph
seraphic
seraphical
seraphically
seraphicalness
seraphicism
seraphicness
seraphim
seraphina
seraphine
seraphism
seraphlike
seraphtide
Serapias
Serapic
Serapis
Serapist
serasker
seraskerate
seraskier
seraskierat
serau
seraw
Serb
Serbdom
Serbian
Serbize
Serbonian
Serbophile
Serbophobe
sercial
serdab
Serdar
Sere
sere
Serean
sereh
Serena
serenade
serenader
serenata
serenate
Serendib
serendibite
serendipity
serendite
serene
serenely
sereneness
serenify
serenissime
serenissimi
serenissimo
serenity
serenize
Serenoa
Serer
Seres
sereward
serf
serfage
serfdom
serfhood
serfish
serfishly
serfishness
serfism
serflike
serfship
Serge
serge
sergeancy
Sergeant
sergeant
sergeantcy
sergeantess
sergeantry
sergeantship
sergeanty
sergedesoy
Sergei
serger
sergette
serging
Sergio
Sergiu
Sergius
serglobulin
Seri
serial
serialist
seriality
serialization
serialize
serially
Serian
seriary
seriate
seriately
seriatim
seriation
Seric
Sericana
sericate
sericated
sericea
sericeotomentose
sericeous
sericicultural
sericiculture
sericiculturist
sericin
sericipary
sericite
sericitic
sericitization
Sericocarpus
sericteria
sericterium
serictery
sericultural
sericulture
sericulturist
seriema
series
serif
serific
Seriform
serigraph
serigrapher
serigraphy
serimeter
serin
serine
serinette
seringa
seringal
seringhi
Serinus
serio
seriocomedy
seriocomic
seriocomical
seriocomically
seriogrotesque
Seriola
Seriolidae
serioline
serioludicrous
seriopantomimic
serioridiculous
seriosity
serious
seriously
seriousness
seripositor
Serjania
serjeant
serment
sermo
sermocination
sermocinatrix
sermon
sermoneer
sermoner
sermonesque
sermonet
sermonettino
sermonic
sermonically
sermonics
sermonish
sermonism
sermonist
sermonize
sermonizer
sermonless
sermonoid
sermonolatry
sermonology
sermonproof
sermonwise
sermuncle
sernamby
sero
seroalbumin
seroalbuminuria
seroanaphylaxis
serobiological
serocolitis
serocyst
serocystic
serodermatosis
serodermitis
serodiagnosis
serodiagnostic
seroenteritis
seroenzyme
serofibrinous
serofibrous
serofluid
serogelatinous
serohemorrhagic
serohepatitis
seroimmunity
serolactescent
serolemma
serolin
serolipase
serologic
serological
serologically
serologist
serology
seromaniac
seromembranous
seromucous
seromuscular
seron
seronegative
seronegativity
seroon
seroot
seroperitoneum
serophthisis
serophysiology
seroplastic
seropneumothorax
seropositive
seroprevention
seroprognosis
seroprophylaxis
seroprotease
seropuriform
seropurulent
seropus
seroreaction
serosa
serosanguineous
serosanguinolent
seroscopy
serositis
serosity
serosynovial
serosynovitis
serotherapeutic
serotherapeutics
serotherapist
serotherapy
serotina
serotinal
serotine
serotinous
serotoxin
serous
serousness
serovaccine
serow
serozyme
Serpari
serpedinous
Serpens
Serpent
serpent
serpentaria
Serpentarian
Serpentarii
serpentarium
Serpentarius
serpentary
serpentcleide
serpenteau
Serpentes
serpentess
Serpentian
serpenticidal
serpenticide
Serpentid
serpentiferous
serpentiform
serpentina
serpentine
serpentinely
Serpentinian
serpentinic
serpentiningly
serpentinization
serpentinize
serpentinoid
serpentinous
Serpentis
serpentivorous
serpentize
serpentlike
serpently
serpentoid
serpentry
serpentwood
serphid
Serphidae
serphoid
Serphoidea
serpierite
serpiginous
serpiginously
serpigo
serpivolant
serpolet
Serpula
serpula
Serpulae
serpulae
serpulan
serpulid
Serpulidae
serpulidan
serpuline
serpulite
serpulitic
serpuloid
serra
serradella
serrage
serran
serrana
serranid
Serranidae
Serrano
serrano
serranoid
Serranus
Serrasalmo
serrate
serrated
serratic
serratiform
serratile
serration
serratirostral
serratocrenate
serratodentate
serratodenticulate
serratoglandulous
serratospinose
serrature
serricorn
Serricornia
Serridentines
Serridentinus
serried
serriedly
serriedness
Serrifera
serriferous
serriform
serriped
serrirostrate
serrulate
serrulated
serrulation
serry
sert
serta
Sertularia
sertularian
Sertulariidae
sertularioid
sertule
sertulum
sertum
serum
serumal
serut
servable
servage
serval
servaline
servant
servantcy
servantdom
servantess
servantless
servantlike
servantry
servantship
servation
serve
servente
serventism
server
servery
servet
Servetian
Servetianism
Servian
service
serviceability
serviceable
serviceableness
serviceably
serviceberry
serviceless
servicelessness
serviceman
Servidor
servidor
servient
serviential
serviette
servile
servilely
servileness
servilism
servility
servilize
serving
servingman
servist
Servite
servitor
servitorial
servitorship
servitress
servitrix
servitude
serviture
Servius
servo
servomechanism
servomotor
servulate
serwamby
sesame
sesamoid
sesamoidal
sesamoiditis
Sesamum
Sesban
Sesbania
sescuple
Seseli
Seshat
Sesia
Sesiidae
sesma
sesqui
sesquialter
sesquialtera
sesquialteral
sesquialteran
sesquialterous
sesquibasic
sesquicarbonate
sesquicentennial
sesquichloride
sesquiduplicate
sesquihydrate
sesquihydrated
sesquinona
sesquinonal
sesquioctava
sesquioctaval
sesquioxide
sesquipedal
sesquipedalian
sesquipedalianism
sesquipedality
sesquiplicate
sesquiquadrate
sesquiquarta
sesquiquartal
sesquiquartile
sesquiquinta
sesquiquintal
sesquiquintile
sesquisalt
sesquiseptimal
sesquisextal
sesquisilicate
sesquisquare
sesquisulphate
sesquisulphide
sesquisulphuret
sesquiterpene
sesquitertia
sesquitertial
sesquitertian
sesquitertianal
sess
sessile
sessility
Sessiliventres
session
sessional
sessionary
sessions
sesterce
sestertium
sestet
sesti
sestiad
Sestian
sestina
sestine
sestole
sestuor
Sesuto
Sesuvium
set
seta
setaceous
setaceously
setae
setal
Setaria
setarious
setback
setbolt
setdown
setfast
Seth
seth
sethead
Sethian
Sethic
Sethite
Setibo
setier
Setifera
setiferous
setiform
setigerous
setiparous
setirostral
setline
setness
setoff
seton
Setophaga
Setophaginae
setophagine
setose
setous
setout
setover
setscrew
setsman
sett
settable
settaine
settee
setter
settergrass
setterwort
setting
settle
settleable
settled
settledly
settledness
settlement
settler
settlerdom
settling
settlings
settlor
settsman
setula
setule
setuliform
setulose
setulous
setup
setwall
setwise
setwork
seugh
Sevastopol
seven
sevenbark
sevener
sevenfold
sevenfolded
sevenfoldness
sevennight
sevenpence
sevenpenny
sevenscore
seventeen
seventeenfold
seventeenth
seventeenthly
seventh
seventhly
seventieth
seventy
seventyfold
sever
severable
several
severalfold
severality
severalize
severally
severalness
severalth
severalty
severance
severation
severe
severedly
severely
severeness
severer
Severian
severingly
severish
severity
severization
severize
severy
Sevillian
sew
sewable
sewage
sewan
sewed
sewellel
sewen
sewer
sewerage
sewered
sewerless
sewerlike
sewerman
sewery
sewing
sewless
sewn
sewround
sex
sexadecimal
sexagenarian
sexagenarianism
sexagenary
Sexagesima
sexagesimal
sexagesimally
sexagesimals
sexagonal
sexangle
sexangled
sexangular
sexangularly
sexannulate
sexarticulate
sexcentenary
sexcuspidate
sexdigital
sexdigitate
sexdigitated
sexdigitism
sexed
sexenary
sexennial
sexennially
sexennium
sexern
sexfarious
sexfid
sexfoil
sexhood
sexifid
sexillion
sexiped
sexipolar
sexisyllabic
sexisyllable
sexitubercular
sexivalence
sexivalency
sexivalent
sexless
sexlessly
sexlessness
sexlike
sexlocular
sexly
sexological
sexologist
sexology
sexpartite
sexradiate
sext
sextactic
sextain
sextan
sextans
Sextant
sextant
sextantal
sextar
sextarii
sextarius
sextary
sextennial
sextern
sextet
sextic
sextile
Sextilis
sextillion
sextillionth
sextipara
sextipartite
sextipartition
sextiply
sextipolar
sexto
sextodecimo
sextole
sextolet
sexton
sextoness
sextonship
sextry
sextubercular
sextuberculate
sextula
sextulary
sextumvirate
sextuple
sextuplet
sextuplex
sextuplicate
sextuply
sexual
sexuale
sexualism
sexualist
sexuality
sexualization
sexualize
sexually
sexuous
sexupara
sexuparous
sexy
sey
seybertite
Seymeria
Seymour
sfoot
Sgad
sgraffiato
sgraffito
sh
sha
shaatnez
shab
Shaban
shabash
Shabbath
shabbed
shabbify
shabbily
shabbiness
shabble
shabby
shabbyish
shabrack
shabunder
Shabuoth
shachle
shachly
shack
shackanite
shackatory
shackbolt
shackland
shackle
shacklebone
shackledom
shackler
shacklewise
shackling
shackly
shacky
shad
shadbelly
shadberry
shadbird
shadbush
shadchan
shaddock
shade
shaded
shadeful
shadeless
shadelessness
shader
shadetail
shadflower
shadily
shadine
shadiness
shading
shadkan
shadoof
Shadow
shadow
shadowable
shadowbox
shadowboxing
shadowed
shadower
shadowfoot
shadowgram
shadowgraph
shadowgraphic
shadowgraphist
shadowgraphy
shadowily
shadowiness
shadowing
shadowishly
shadowist
shadowland
shadowless
shadowlessness
shadowlike
shadowly
shadowy
shadrach
shady
shaffle
Shafiite
shaft
shafted
shafter
shaftfoot
shafting
shaftless
shaftlike
shaftman
shaftment
shaftsman
shaftway
shafty
shag
shaganappi
shagbag
shagbark
shagged
shaggedness
shaggily
shagginess
shaggy
Shagia
shaglet
shaglike
shagpate
shagrag
shagreen
shagreened
shagroon
shagtail
shah
Shahaptian
shaharith
shahdom
shahi
Shahid
shahin
shahzada
Shai
Shaigia
shaikh
Shaikiyeh
shaitan
Shaiva
Shaivism
Shaka
shakable
shake
shakeable
shakebly
shakedown
shakefork
shaken
shakenly
shakeout
shakeproof
Shaker
shaker
shakerag
Shakerdom
Shakeress
Shakerism
Shakerlike
shakers
shakescene
Shakespearean
Shakespeareana
Shakespeareanism
Shakespeareanly
Shakespearize
Shakespearolater
Shakespearolatry
shakha
Shakil
shakily
shakiness
shaking
shakingly
shako
shaksheer
Shakta
Shakti
shakti
Shaktism
shaku
shaky
Shakyamuni
Shalako
shale
shalelike
shaleman
shall
shallal
shallon
shalloon
shallop
shallopy
shallot
shallow
shallowbrained
shallowhearted
shallowish
shallowist
shallowly
shallowness
shallowpate
shallowpated
shallows
shallowy
shallu
shalom
shalt
shalwar
shaly
Sham
sham
shama
shamable
shamableness
shamably
shamal
shamalo
shaman
shamaness
shamanic
shamanism
shamanist
shamanistic
shamanize
shamateur
shamba
Shambala
shamble
shambling
shamblingly
shambrier
Shambu
shame
shameable
shamed
shameface
shamefaced
shamefacedly
shamefacedness
shamefast
shamefastly
shamefastness
shameful
shamefully
shamefulness
shameless
shamelessly
shamelessness
shameproof
shamer
shamesick
shameworthy
shamianah
Shamim
shamir
Shammar
shammed
shammer
shammick
shamming
shammish
shammock
shammocking
shammocky
shammy
shampoo
shampooer
shamrock
shamroot
shamsheer
Shan
shan
shanachas
shanachie
Shandean
shandry
shandrydan
Shandy
shandy
shandygaff
Shandyism
Shane
Shang
Shangalla
shangan
Shanghai
shanghai
shanghaier
shank
Shankar
shanked
shanker
shankings
shankpiece
shanksman
shanna
Shannon
shanny
shansa
shant
Shantung
shanty
shantylike
shantyman
shantytown
shap
shapable
Shape
shape
shaped
shapeful
shapeless
shapelessly
shapelessness
shapeliness
shapely
shapen
shaper
shapeshifter
shapesmith
shaping
shapingly
shapometer
shaps
Shaptan
shapy
sharable
Sharada
Sharan
shard
Shardana
sharded
shardy
share
shareable
sharebone
sharebroker
sharecrop
sharecropper
shareholder
shareholdership
shareman
sharepenny
sharer
shareship
sharesman
sharewort
Sharezer
shargar
Shari
Sharia
Sharira
shark
sharkful
sharkish
sharklet
sharklike
sharkship
sharkskin
sharky
sharn
sharnbud
sharny
Sharon
sharp
sharpen
sharpener
sharper
sharpie
sharpish
sharply
sharpness
sharps
sharpsaw
sharpshin
sharpshod
sharpshooter
sharpshooting
sharptail
sharpware
sharpy
Sharra
sharrag
sharry
Shasta
shastaite
Shastan
shaster
shastra
shastraik
shastri
shastrik
shat
shatan
shathmont
Shatter
shatter
shatterbrain
shatterbrained
shatterer
shatterheaded
shattering
shatteringly
shatterment
shatterpated
shatterproof
shatterwit
shattery
shattuckite
shauchle
shaugh
shaul
Shaula
shaup
shauri
shauwe
shavable
shave
shaveable
shaved
shavee
shaveling
shaven
shaver
shavery
Shavese
shavester
shavetail
shaveweed
Shavian
Shaviana
Shavianism
shaving
shavings
Shaw
shaw
Shawanese
Shawano
shawl
shawled
shawling
shawlless
shawllike
shawlwise
shawm
Shawn
Shawnee
shawneewood
shawny
Shawwal
shawy
shay
Shaysite
she
shea
sheading
sheaf
sheafage
sheaflike
sheafripe
sheafy
sheal
shealing
Shean
shear
shearbill
sheard
shearer
sheargrass
shearhog
shearing
shearless
shearling
shearman
shearmouse
shears
shearsman
sheartail
shearwater
shearwaters
sheat
sheatfish
sheath
sheathbill
sheathe
sheathed
sheather
sheathery
sheathing
sheathless
sheathlike
sheathy
sheave
sheaved
sheaveless
sheaveman
shebang
Shebat
shebeen
shebeener
Shechem
Shechemites
shed
shedded
shedder
shedding
sheder
shedhand
shedlike
shedman
shedwise
shee
sheely
sheen
sheenful
sheenless
sheenly
sheeny
sheep
sheepback
sheepberry
sheepbine
sheepbiter
sheepbiting
sheepcote
sheepcrook
sheepfaced
sheepfacedly
sheepfacedness
sheepfold
sheepfoot
sheepgate
sheephead
sheepheaded
sheephearted
sheepherder
sheepherding
sheephook
sheephouse
sheepify
sheepish
sheepishly
sheepishness
sheepkeeper
sheepkeeping
sheepkill
sheepless
sheeplet
sheeplike
sheepling
sheepman
sheepmaster
sheepmonger
sheepnose
sheepnut
sheeppen
sheepshank
sheepshead
sheepsheadism
sheepshear
sheepshearer
sheepshearing
sheepshed
sheepskin
sheepsplit
sheepsteal
sheepstealer
sheepstealing
sheepwalk
sheepwalker
sheepweed
sheepy
sheer
sheered
sheering
sheerly
sheerness
sheet
sheetage
sheeted
sheeter
sheetflood
sheetful
sheeting
sheetless
sheetlet
sheetlike
sheetling
sheetways
sheetwise
sheetwork
sheetwriting
sheety
Sheffield
shehitah
sheik
sheikdom
sheikhlike
sheikhly
sheiklike
sheikly
Sheila
shekel
Shekinah
Shel
shela
sheld
sheldapple
shelder
sheldfowl
sheldrake
shelduck
shelf
shelfback
shelffellow
shelfful
shelflist
shelfmate
shelfpiece
shelfroom
shelfworn
shelfy
shell
shellac
shellacker
shellacking
shellapple
shellback
shellblow
shellblowing
shellbound
shellburst
shellcracker
shelleater
shelled
sheller
Shelleyan
Shelleyana
shellfire
shellfish
shellfishery
shellflower
shellful
shellhead
shelliness
shelling
shellman
shellmonger
shellproof
shellshake
shellum
shellwork
shellworker
shelly
shellycoat
shelta
shelter
shelterage
sheltered
shelterer
shelteringly
shelterless
shelterlessness
shelterwood
sheltery
sheltron
shelty
shelve
shelver
shelving
shelvingly
shelvingness
shelvy
Shelyak
Shemaka
sheminith
Shemite
Shemitic
Shemitish
Shemu
Shen
shenanigan
shend
sheng
Shenshai
Sheol
sheolic
shepherd
shepherdage
shepherddom
shepherdess
shepherdhood
Shepherdia
shepherdish
shepherdism
shepherdize
shepherdless
shepherdlike
shepherdling
shepherdly
shepherdry
sheppeck
sheppey
shepstare
sher
Sherani
Sherardia
sherardize
sherardizer
Sheratan
Sheraton
sherbacha
sherbet
sherbetlee
sherbetzide
sheriat
sherif
sherifa
sherifate
sheriff
sheriffalty
sheriffdom
sheriffess
sheriffhood
sheriffry
sheriffship
sheriffwick
sherifi
sherifian
sherify
sheristadar
Sheriyat
sherlock
Sherman
Sherpa
Sherramoor
Sherri
sherry
Sherrymoor
sherryvallies
Shesha
sheth
Shetland
Shetlander
Shetlandic
sheugh
sheva
shevel
sheveled
shevri
shewa
shewbread
shewel
sheyle
shi
Shiah
shibah
shibar
shibboleth
shibbolethic
shibuichi
shice
shicer
shicker
shickered
shide
shied
shiel
shield
shieldable
shieldboard
shielddrake
shielded
shielder
shieldflower
shielding
shieldless
shieldlessly
shieldlessness
shieldlike
shieldling
shieldmaker
shieldmay
shieldtail
shieling
shier
shies
shiest
shift
shiftable
shiftage
shifter
shiftful
shiftfulness
shiftily
shiftiness
shifting
shiftingly
shiftingness
shiftless
shiftlessly
shiftlessness
shifty
Shigella
shiggaion
shigram
shih
Shiism
Shiite
Shiitic
Shik
shikar
shikara
shikargah
shikari
shikasta
shikimi
shikimic
shikimole
shikimotoxin
shikken
shiko
shikra
shilf
shilfa
Shilh
Shilha
shill
shilla
shillaber
shillelagh
shillet
shillety
shillhouse
shillibeer
shilling
shillingless
shillingsworth
shilloo
Shilluh
Shilluk
Shiloh
shilpit
shim
shimal
Shimei
shimmer
shimmering
shimmeringly
shimmery
shimmy
Shimonoseki
shimose
shimper
shin
Shina
shinaniging
shinarump
shinbone
shindig
shindle
shindy
shine
shineless
shiner
shingle
shingled
shingler
shingles
shinglewise
shinglewood
shingling
shingly
shinily
shininess
shining
shiningly
shiningness
shinleaf
Shinnecock
shinner
shinnery
shinning
shinny
shinplaster
shintiyan
Shinto
Shintoism
Shintoist
Shintoistic
Shintoize
shinty
Shinwari
shinwood
shiny
shinza
ship
shipboard
shipbound
shipboy
shipbreaking
shipbroken
shipbuilder
shipbuilding
shipcraft
shipentine
shipful
shipkeeper
shiplap
shipless
shiplessly
shiplet
shipload
shipman
shipmanship
shipmast
shipmaster
shipmate
shipmatish
shipment
shipowner
shipowning
shippable
shippage
shipped
shipper
shipping
shipplane
shippo
shippon
shippy
shipshape
shipshapely
shipside
shipsmith
shipward
shipwards
shipway
shipwork
shipworm
shipwreck
shipwrecky
shipwright
shipwrightery
shipwrightry
shipyard
shirakashi
shirallee
Shiraz
shire
shirehouse
shireman
shirewick
shirk
shirker
shirky
shirl
shirlcock
Shirley
shirpit
shirr
shirring
shirt
shirtband
shirtiness
shirting
shirtless
shirtlessness
shirtlike
shirtmaker
shirtmaking
shirtman
shirttail
shirtwaist
shirty
Shirvan
shish
shisham
shisn
shita
shitepoke
shither
shittah
shittim
shittimwood
shiv
Shivaism
Shivaist
Shivaistic
Shivaite
shivaree
shive
shiver
shivereens
shiverer
shivering
shiveringly
shiverproof
shiversome
shiverweed
shivery
shivey
shivoo
shivy
shivzoku
Shkupetar
Shlu
Shluh
Sho
sho
Shoa
shoad
shoader
shoal
shoalbrain
shoaler
shoaliness
shoalness
shoalwise
shoaly
shoat
shock
shockability
shockable
shockedness
shocker
shockheaded
shocking
shockingly
shockingness
shocklike
shockproof
shod
shodden
shoddily
shoddiness
shoddy
shoddydom
shoddyism
shoddyite
shoddylike
shoddyward
shoddywards
shode
shoder
shoe
shoebill
shoebinder
shoebindery
shoebinding
shoebird
shoeblack
shoeboy
shoebrush
shoecraft
shoeflower
shoehorn
shoeing
shoeingsmith
shoelace
shoeless
shoemaker
shoemaking
shoeman
shoepack
shoer
shoescraper
shoeshine
shoeshop
shoesmith
shoestring
shoewoman
shoful
shog
shogaol
shoggie
shoggle
shoggly
shogi
shogun
shogunal
shogunate
shohet
shoji
Shojo
shola
shole
Shona
shone
shoneen
shonkinite
shoo
shood
shoofa
shoofly
shooi
shook
shool
shooldarry
shooler
shoop
shoopiltie
shoor
shoot
shootable
shootboard
shootee
shooter
shoother
shooting
shootist
shootman
shop
shopboard
shopbook
shopboy
shopbreaker
shopbreaking
shopfolk
shopful
shopgirl
shopgirlish
shophar
shopkeeper
shopkeeperess
shopkeeperish
shopkeeperism
shopkeepery
shopkeeping
shopland
shoplet
shoplifter
shoplifting
shoplike
shopmaid
shopman
shopmark
shopmate
shopocracy
shopocrat
shoppe
shopper
shopping
shoppish
shoppishness
shoppy
shopster
shoptalk
shopwalker
shopwear
shopwife
shopwindow
shopwoman
shopwork
shopworker
shopworn
shoq
Shor
shor
shoran
shore
Shorea
shoreberry
shorebush
shored
shoregoing
shoreland
shoreless
shoreman
shorer
shoreside
shoresman
shoreward
shorewards
shoreweed
shoreyer
shoring
shorling
shorn
short
shortage
shortbread
shortcake
shortchange
shortchanger
shortclothes
shortcoat
shortcomer
shortcoming
shorten
shortener
shortening
shorter
shortfall
shorthand
shorthanded
shorthandedness
shorthander
shorthead
shorthorn
Shortia
shortish
shortly
shortness
shorts
shortschat
shortsighted
shortsightedly
shortsightedness
shortsome
shortstaff
shortstop
shorttail
Shortzy
Shoshonean
shoshonite
shot
shotbush
shote
shotgun
shotless
shotlike
shotmaker
shotman
shotproof
shotsman
shotstar
shott
shotted
shotten
shotter
shotty
Shotweld
shou
should
shoulder
shouldered
shoulderer
shoulderette
shouldering
shouldna
shouldnt
shoupeltin
shout
shouter
shouting
shoutingly
shoval
shove
shovegroat
shovel
shovelard
shovelbill
shovelboard
shovelfish
shovelful
shovelhead
shovelmaker
shovelman
shovelnose
shovelweed
shover
show
showable
showance
showbird
showboard
showboat
showboater
showboating
showcase
showdom
showdown
shower
showerer
showerful
showeriness
showerless
showerlike
showerproof
showery
showily
showiness
showing
showish
showless
showman
showmanism
showmanry
showmanship
shown
showpiece
showroom
showup
showworthy
showy
showyard
shoya
shrab
shraddha
shradh
shraf
shrag
shram
shrank
shrap
shrapnel
shrave
shravey
shreadhead
shred
shredcock
shredder
shredding
shreddy
shredless
shredlike
Shree
shree
shreeve
shrend
shrew
shrewd
shrewdish
shrewdly
shrewdness
shrewdom
shrewdy
shrewish
shrewishly
shrewishness
shrewlike
shrewly
shrewmouse
shrewstruck
shriek
shrieker
shriekery
shriekily
shriekiness
shriekingly
shriekproof
shrieky
shrieval
shrievalty
shrift
shrike
shrill
shrilling
shrillish
shrillness
shrilly
shrimp
shrimper
shrimpfish
shrimpi
shrimpish
shrimpishness
shrimplike
shrimpy
shrinal
Shrine
shrine
shrineless
shrinelet
shrinelike
Shriner
shrink
shrinkable
shrinkage
shrinkageproof
shrinker
shrinkhead
shrinking
shrinkingly
shrinkproof
shrinky
shrip
shrite
shrive
shrivel
shriven
shriver
shriving
shroff
shrog
Shropshire
shroud
shrouded
shrouding
shroudless
shroudlike
shroudy
Shrove
shrove
shrover
Shrovetide
shrub
shrubbed
shrubbery
shrubbiness
shrubbish
shrubby
shrubland
shrubless
shrublet
shrublike
shrubwood
shruff
shrug
shruggingly
shrunk
shrunken
shrups
Shtokavski
shtreimel
Shu
shuba
shubunkin
shuck
shucker
shucking
shuckins
shuckpen
shucks
shudder
shudderful
shudderiness
shudderingly
shuddersome
shuddery
shuff
shuffle
shuffleboard
shufflecap
shuffler
shufflewing
shuffling
shufflingly
shug
Shuhali
Shukria
Shukulumbwe
shul
Shulamite
shuler
shulwaurs
shumac
shun
Shunammite
shune
shunless
shunnable
shunner
shunt
shunter
shunting
shure
shurf
shush
shusher
Shuswap
shut
shutdown
shutness
shutoff
Shutoku
shutout
shuttance
shutten
shutter
shuttering
shutterless
shutterwise
shutting
shuttle
shuttlecock
shuttleheaded
shuttlelike
shuttlewise
Shuvra
shwanpan
shy
Shyam
shydepoke
shyer
shyish
Shylock
Shylockism
shyly
shyness
shyster
si
Sia
siak
sial
sialaden
sialadenitis
sialadenoncus
sialagogic
sialagogue
sialagoguic
sialemesis
Sialia
sialic
sialid
Sialidae
sialidan
Sialis
sialoangitis
sialogenous
sialoid
sialolith
sialolithiasis
sialology
sialorrhea
sialoschesis
sialosemeiology
sialosis
sialostenosis
sialosyrinx
sialozemia
Siam
siamang
Siamese
sib
Sibbaldus
sibbed
sibbens
sibber
sibboleth
sibby
Siberian
Siberic
siberite
sibilance
sibilancy
sibilant
sibilantly
sibilate
sibilatingly
sibilator
sibilatory
sibilous
sibilus
Sibiric
sibling
sibness
sibrede
sibship
sibyl
sibylesque
sibylic
sibylism
sibylla
sibylline
sibyllist
sic
Sicambri
Sicambrian
Sicana
Sicani
Sicanian
sicarian
sicarious
sicarius
sicca
siccaneous
siccant
siccate
siccation
siccative
siccimeter
siccity
sice
Sicel
Siceliot
Sicilian
sicilian
siciliana
Sicilianism
sicilica
sicilicum
sicilienne
sicinnian
sick
sickbed
sicken
sickener
sickening
sickeningly
sicker
sickerly
sickerness
sickhearted
sickish
sickishly
sickishness
sickle
sicklebill
sickled
sicklelike
sickleman
sicklemia
sicklemic
sicklepod
sickler
sicklerite
sickless
sickleweed
sicklewise
sicklewort
sicklied
sicklily
sickliness
sickling
sickly
sickness
sicknessproof
sickroom
sicsac
sicula
sicular
Siculi
Siculian
Sicyonian
Sicyonic
Sicyos
Sid
Sida
Sidalcea
sidder
Siddha
Siddhanta
Siddhartha
Siddhi
siddur
side
sideage
sidearm
sideboard
sidebone
sidebones
sideburns
sidecar
sidecarist
sidecheck
sided
sidedness
sideflash
sidehead
sidehill
sidekicker
sidelang
sideless
sideline
sideling
sidelings
sidelingwise
sidelong
sidenote
sidepiece
sider
sideral
sideration
siderealize
sidereally
siderean
siderin
siderism
siderite
sideritic
Sideritis
siderognost
siderographic
siderographical
siderographist
siderography
siderolite
siderology
sideromagnetic
sideromancy
sideromelane
sideronatrite
sideronym
sideroscope
siderose
siderosis
siderostat
siderostatic
siderotechny
siderous
Sideroxylon
sidership
siderurgical
siderurgy
sides
sidesaddle
sideshake
sideslip
sidesman
sidesplitter
sidesplitting
sidesplittingly
sidesway
sideswipe
sideswiper
sidetrack
sidewalk
sideward
sidewards
sideway
sideways
sidewinder
sidewipe
sidewiper
sidewise
sidhe
sidi
siding
sidle
sidler
sidling
sidlingly
Sidney
Sidonian
Sidrach
sidth
sidy
sie
siege
siegeable
siegecraft
siegenite
sieger
siegework
Siegfried
Sieglingia
Siegmund
Siegurd
Siena
Sienese
sienna
sier
siering
sierozem
Sierra
sierra
sierran
siesta
siestaland
Sieva
sieve
sieveful
sievelike
siever
Sieversia
sievings
sievy
sifac
sifaka
Sifatite
sife
siffilate
siffle
sifflement
sifflet
sifflot
sift
siftage
sifted
sifter
sifting
sig
Siganidae
Siganus
sigatoka
Sigaultian
sigger
sigh
sigher
sighful
sighfully
sighing
sighingly
sighingness
sighless
sighlike
sight
sightable
sighted
sighten
sightening
sighter
sightful
sightfulness
sighthole
sighting
sightless
sightlessly
sightlessness
sightlily
sightliness
sightly
sightproof
sightworthiness
sightworthy
sighty
sigil
sigilative
Sigillaria
Sigillariaceae
sigillariaceous
sigillarian
sigillarid
sigillarioid
sigillarist
sigillaroid
sigillary
sigillate
sigillated
sigillation
sigillistic
sigillographer
sigillographical
sigillography
sigillum
sigla
siglarian
siglos
Sigma
sigma
sigmaspire
sigmate
sigmatic
sigmation
sigmatism
sigmodont
Sigmodontes
sigmoid
sigmoidal
sigmoidally
sigmoidectomy
sigmoiditis
sigmoidopexy
sigmoidoproctostomy
sigmoidorectostomy
sigmoidoscope
sigmoidoscopy
sigmoidostomy
Sigmund
sign
signable
signal
signalee
signaler
signalese
signaletic
signaletics
signalism
signalist
signality
signalize
signally
signalman
signalment
signary
signatary
signate
signation
signator
signatory
signatural
signature
signatureless
signaturist
signboard
signee
signer
signet
signetwise
signifer
signifiable
significal
significance
significancy
significant
significantly
significantness
significate
signification
significatist
significative
significatively
significativeness
significator
significatory
significatrix
significature
significavit
significian
significs
signifier
signify
signior
signiorship
signist
signless
signlike
signman
signorial
signorship
signory
signpost
signum
signwriter
Sigurd
Sihasapa
Sika
sika
sikar
sikatch
sike
sikerly
sikerness
siket
Sikh
sikhara
Sikhism
sikhra
Sikinnis
Sikkimese
Siksika
sil
silage
silaginoid
silane
Silas
silbergroschen
silcrete
sile
silen
Silenaceae
silenaceous
Silenales
silence
silenced
silencer
silency
Silene
sileni
silenic
silent
silential
silentiary
silentious
silentish
silently
silentness
silenus
silesia
Silesian
Siletz
silex
silexite
silhouette
silhouettist
silhouettograph
silica
silicam
silicane
silicate
silication
silicatization
Silicea
silicean
siliceocalcareous
siliceofelspathic
siliceofluoric
siliceous
silicic
silicicalcareous
silicicolous
silicide
silicidize
siliciferous
silicification
silicifluoric
silicifluoride
silicify
siliciophite
silicious
Silicispongiae
silicium
siliciuretted
silicize
silicle
silico
silicoacetic
silicoalkaline
silicoaluminate
silicoarsenide
silicocalcareous
silicochloroform
silicocyanide
silicoethane
silicoferruginous
Silicoflagellata
Silicoflagellatae
silicoflagellate
Silicoflagellidae
silicofluoric
silicofluoride
silicohydrocarbon
Silicoidea
silicomagnesian
silicomanganese
silicomethane
silicon
silicone
siliconize
silicononane
silicopropane
silicosis
Silicospongiae
silicotalcose
silicotic
silicotitanate
silicotungstate
silicotungstic
silicula
silicular
silicule
siliculose
siliculous
silicyl
Silipan
siliqua
siliquaceous
siliquae
Siliquaria
Siliquariidae
silique
siliquiferous
siliquiform
siliquose
siliquous
silk
silkalene
silkaline
silked
silken
silker
silkflower
silkgrower
silkie
silkily
silkiness
silklike
silkman
silkness
silksman
silktail
silkweed
silkwoman
silkwood
silkwork
silkworks
silkworm
silky
sill
sillabub
silladar
Sillaginidae
Sillago
sillandar
sillar
siller
Sillery
sillibouk
sillikin
sillily
sillimanite
silliness
sillock
sillograph
sillographer
sillographist
sillometer
sillon
silly
sillyhood
sillyhow
sillyish
sillyism
sillyton
silo
siloist
Silpha
silphid
Silphidae
silphium
silt
siltage
siltation
silting
siltlike
silty
silundum
Silures
Silurian
Siluric
silurid
Siluridae
Siluridan
siluroid
Siluroidei
Silurus
silva
silvan
silvanity
silvanry
Silvanus
silvendy
silver
silverback
silverbeater
silverbelly
silverberry
silverbill
silverboom
silverbush
silvered
silverer
silvereye
silverfin
silverfish
silverhead
silverily
silveriness
silvering
silverish
silverite
silverize
silverizer
silverleaf
silverless
silverlike
silverling
silverly
silvern
silverness
silverpoint
silverrod
silverside
silversides
silverskin
silversmith
silversmithing
silverspot
silvertail
silvertip
silvertop
silvervine
silverware
silverweed
silverwing
silverwood
silverwork
silverworker
silvery
Silvester
Silvia
silvical
silvicolous
silvics
silvicultural
silviculturally
silviculture
silviculturist
Silvius
Silybum
silyl
Sim
sima
Simaba
simal
simar
Simarouba
Simaroubaceae
simaroubaceous
simball
simbil
simblin
simblot
Simblum
sime
Simeon
Simeonism
Simeonite
Simia
simiad
simial
simian
simianity
simiesque
Simiidae
Simiinae
similar
similarity
similarize
similarly
similative
simile
similimum
similiter
similitive
similitude
similitudinize
simility
similize
similor
simioid
simious
simiousness
simity
simkin
simlin
simling
simmer
simmeringly
simmon
simnel
simnelwise
simoleon
Simon
simoniac
simoniacal
simoniacally
Simonian
Simonianism
simonious
simonism
Simonist
simonist
simony
simool
simoom
simoon
Simosaurus
simous
simp
simpai
simper
simperer
simperingly
simple
simplehearted
simpleheartedly
simpleheartedness
simpleness
simpler
simpleton
simpletonian
simpletonianism
simpletonic
simpletonish
simpletonism
simplex
simplexed
simplexity
simplicident
Simplicidentata
simplicidentate
simplicist
simplicitarian
simplicity
simplicize
simplification
simplificative
simplificator
simplified
simplifiedly
simplifier
simplify
simplism
simplist
simplistic
simply
simsim
simson
simulacra
simulacral
simulacre
simulacrize
simulacrum
simulance
simulant
simular
simulate
simulation
simulative
simulatively
simulator
simulatory
simulcast
simuler
simuliid
Simuliidae
simulioid
Simulium
simultaneity
simultaneous
simultaneously
simultaneousness
sin
sina
Sinae
Sinaean
Sinaic
sinaite
Sinaitic
sinal
sinalbin
Sinaloa
sinamay
sinamine
sinapate
sinapic
sinapine
sinapinic
Sinapis
sinapis
sinapism
sinapize
sinapoline
sinarchism
sinarchist
sinarquism
sinarquist
sinarquista
sinawa
sincaline
since
sincere
sincerely
sincereness
sincerity
sincipital
sinciput
sind
sinder
Sindhi
sindle
sindoc
sindon
sindry
sine
sinecural
sinecure
sinecureship
sinecurism
sinecurist
Sinesian
sinew
sinewed
sinewiness
sinewless
sinewous
sinewy
sinfonia
sinfonie
sinfonietta
sinful
sinfully
sinfulness
sing
singability
singable
singableness
singally
singarip
singe
singed
singeing
singeingly
singer
singey
Singfo
singh
Singhalese
singillatim
singing
singingly
singkamas
single
singlebar
singled
singlehanded
singlehandedly
singlehandedness
singlehearted
singleheartedly
singleheartedness
singlehood
singleness
singler
singles
singlestick
singlesticker
singlet
singleton
singletree
singlings
singly
Singpho
Singsing
singsong
singsongy
Singspiel
singspiel
singstress
singular
singularism
singularist
singularity
singularization
singularize
singularly
singularness
singult
singultous
singultus
sinh
Sinhalese
Sinian
Sinic
Sinicism
Sinicization
Sinicize
Sinico
Sinification
Sinify
sinigrin
sinigrinase
sinigrosid
sinigroside
Sinisian
Sinism
sinister
sinisterly
sinisterness
sinisterwise
sinistrad
sinistral
sinistrality
sinistrally
sinistration
sinistrin
sinistrocerebral
sinistrocular
sinistrodextral
sinistrogyrate
sinistrogyration
sinistrogyric
sinistromanual
sinistrorsal
sinistrorsally
sinistrorse
sinistrous
sinistrously
sinistruous
Sinite
Sinitic
sink
sinkable
sinkage
sinker
sinkerless
sinkfield
sinkhead
sinkhole
sinking
Sinkiuse
sinkless
sinklike
sinkroom
sinkstone
sinky
sinless
sinlessly
sinlessness
sinlike
sinnable
sinnableness
sinnen
sinner
sinneress
sinnership
sinnet
Sinningia
sinningly
sinningness
sinoatrial
sinoauricular
Sinogram
sinoidal
Sinolog
Sinologer
Sinological
Sinologist
Sinologue
Sinology
sinomenine
Sinonism
Sinophile
Sinophilism
sinopia
Sinopic
sinopite
sinople
sinproof
Sinsiga
sinsion
sinsring
sinsyne
sinter
Sinto
sintoc
Sintoism
Sintoist
Sintsink
Sintu
sinuate
sinuated
sinuatedentate
sinuately
sinuation
sinuatocontorted
sinuatodentate
sinuatodentated
sinuatopinnatifid
sinuatoserrated
sinuatoundulate
sinuatrial
sinuauricular
sinuitis
sinuose
sinuosely
sinuosity
sinuous
sinuously
sinuousness
Sinupallia
sinupallial
Sinupallialia
Sinupalliata
sinupalliate
sinus
sinusal
sinusitis
sinuslike
sinusoid
sinusoidal
sinusoidally
sinuventricular
sinward
siol
Sion
sion
Sionite
Siouan
Sioux
sip
sipage
sipe
siper
siphoid
siphon
siphonaceous
siphonage
siphonal
Siphonales
Siphonaptera
siphonapterous
Siphonaria
siphonariid
Siphonariidae
Siphonata
siphonate
Siphoneae
siphoneous
siphonet
siphonia
siphonial
Siphoniata
siphonic
Siphonifera
siphoniferous
siphoniform
siphonium
siphonless
siphonlike
Siphonobranchiata
siphonobranchiate
Siphonocladales
Siphonocladiales
siphonogam
Siphonogama
siphonogamic
siphonogamous
siphonogamy
siphonoglyph
siphonoglyphe
siphonognathid
Siphonognathidae
siphonognathous
Siphonognathus
Siphonophora
siphonophoran
siphonophore
siphonophorous
siphonoplax
siphonopore
siphonorhinal
siphonorhine
siphonosome
siphonostele
siphonostelic
siphonostely
Siphonostoma
Siphonostomata
siphonostomatous
siphonostome
siphonostomous
siphonozooid
siphonula
siphorhinal
siphorhinian
siphosome
siphuncle
siphuncled
siphuncular
Siphunculata
siphunculate
siphunculated
Sipibo
sipid
sipidity
Siping
siping
sipling
sipper
sippet
sippingly
sippio
Sipunculacea
sipunculacean
sipunculid
Sipunculida
sipunculoid
Sipunculoidea
Sipunculus
sipylite
Sir
sir
sircar
sirdar
sirdarship
sire
Siredon
sireless
siren
sirene
Sirenia
sirenian
sirenic
sirenical
sirenically
Sirenidae
sirening
sirenize
sirenlike
sirenoid
Sirenoidea
Sirenoidei
sireny
sireship
siress
sirgang
Sirian
sirian
Sirianian
siriasis
siricid
Siricidae
Siricoidea
sirih
siriometer
Sirione
siris
Sirius
sirkeer
sirki
sirky
sirloin
sirloiny
Sirmian
Sirmuellera
siroc
sirocco
siroccoish
siroccoishly
sirpea
sirple
sirpoon
sirrah
sirree
sirship
siruaballi
siruelas
sirup
siruped
siruper
sirupy
Siryan
Sis
sis
sisal
siscowet
sise
sisel
siserara
siserary
siserskite
sish
sisham
sisi
siskin
Sisley
sismotherapy
siss
Sisseton
sissification
sissify
sissiness
sissoo
Sissu
sissy
sissyish
sissyism
sist
Sistani
sister
sisterhood
sisterin
sistering
sisterize
sisterless
sisterlike
sisterliness
sisterly
sistern
Sistine
sistle
sistomensin
sistrum
Sistrurus
Sisymbrium
Sisyphean
Sisyphian
Sisyphides
Sisyphism
Sisyphist
Sisyphus
Sisyrinchium
sisyrinchium
sit
Sita
sitao
sitar
sitatunga
sitch
site
sitfast
sith
sithcund
sithe
sithement
sithence
sithens
sitient
sitio
sitiology
sitiomania
sitiophobia
Sitka
Sitkan
sitology
sitomania
Sitophilus
sitophobia
sitophobic
sitosterin
sitosterol
sitotoxism
Sitta
sittee
sitten
sitter
Sittidae
Sittinae
sittine
sitting
sittringy
situal
situate
situated
situation
situational
situla
situlae
situs
Sium
Siusi
Siuslaw
Siva
siva
Sivaism
Sivaist
Sivaistic
Sivaite
Sivan
Sivapithecus
sivathere
Sivatheriidae
Sivatheriinae
sivatherioid
Sivatherium
siver
sivvens
Siwan
Siwash
siwash
six
sixain
sixer
sixfoil
sixfold
sixhaend
sixhynde
sixpence
sixpenny
sixpennyworth
sixscore
sixsome
sixte
sixteen
sixteener
sixteenfold
sixteenmo
sixteenth
sixteenthly
sixth
sixthet
sixthly
sixtieth
Sixtowns
Sixtus
sixty
sixtyfold
sixtypenny
sizable
sizableness
sizably
sizal
sizar
sizarship
size
sizeable
sizeableness
sized
sizeman
sizer
sizes
siziness
sizing
sizy
sizygia
sizygium
sizz
sizzard
sizzing
sizzle
sizzling
sizzlingly
Sjaak
sjambok
Sjouke
skaddle
skaff
skaffie
skag
skaillie
skainsmate
skair
skaitbird
skal
skalawag
skaldship
skance
Skanda
skandhas
skart
skasely
Skat
skat
skate
skateable
skater
skatikas
skatiku
skating
skatist
skatole
skatosine
skatoxyl
skaw
skean
skeanockle
skedaddle
skedaddler
skedge
skedgewith
skedlock
skee
skeed
skeeg
skeel
skeeling
skeely
skeen
skeenyie
skeer
skeered
skeery
skeesicks
skeet
Skeeter
skeeter
skeezix
Skef
skeg
skegger
skeif
skeigh
skeily
skein
skeiner
skeipp
skel
skelder
skelderdrake
skeldrake
skeletal
skeletin
skeletogenous
skeletogeny
skeletomuscular
skeleton
skeletonian
skeletonic
skeletonization
skeletonize
skeletonizer
skeletonless
skeletonweed
skeletony
skelf
skelgoose
skelic
skell
skellat
skeller
skelloch
skellum
skelly
skelp
skelper
skelpin
skelping
skelter
Skeltonian
Skeltonic
Skeltonical
Skeltonics
skemmel
skemp
sken
skene
skeo
skeough
skep
skepful
skeppist
skeppund
skeptic
skeptical
skeptically
skepticalness
skepticism
skepticize
sker
skere
skerret
skerrick
skerry
sketch
sketchability
sketchable
sketchbook
sketchee
sketcher
sketchily
sketchiness
sketching
sketchingly
sketchist
sketchlike
sketchy
skete
sketiotai
skeuomorph
skeuomorphic
skevish
skew
skewback
skewbacked
skewbald
skewed
skewer
skewerer
skewerwood
skewings
skewl
skewly
skewness
skewwhiff
skewwise
skewy
skey
skeyting
ski
skiagram
skiagraph
skiagrapher
skiagraphic
skiagraphical
skiagraphically
skiagraphy
skiameter
skiametry
skiapod
skiapodous
skiascope
skiascopy
skibby
skibslast
skice
skid
skidded
skidder
skidding
skiddingly
skiddoo
skiddy
Skidi
skidpan
skidproof
skidway
skied
skieppe
skiepper
skier
skies
skiff
skiffless
skiffling
skift
skiing
skijore
skijorer
skijoring
skil
skilder
skildfel
skilfish
skill
skillagalee
skilled
skillenton
skillessness
skillet
skillful
skillfully
skillfulness
skilligalee
skilling
skillion
skilly
skilpot
skilts
skim
skimback
skime
skimmed
skimmer
skimmerton
Skimmia
skimming
skimmingly
skimmington
skimmity
skimp
skimpily
skimpiness
skimpingly
skimpy
skin
skinbound
skinch
skinflint
skinflintily
skinflintiness
skinflinty
skinful
skink
skinker
skinking
skinkle
skinless
skinlike
skinned
skinner
skinnery
skinniness
skinning
skinny
skintight
skinworm
skiogram
skiograph
skiophyte
Skip
skip
skipbrain
Skipetar
skipjack
skipjackly
skipkennel
skipman
skippable
skippel
skipper
skippered
skippership
skippery
skippet
skipping
skippingly
skipple
skippund
skippy
skiptail
skirl
skirlcock
skirling
skirmish
skirmisher
skirmishing
skirmishingly
skirp
skirr
skirreh
skirret
skirt
skirtboard
skirted
skirter
skirting
skirtingly
skirtless
skirtlike
skirty
skirwhit
skirwort
skit
skite
skiter
skither
Skitswish
Skittaget
Skittagetan
skitter
skittish
skittishly
skittishness
skittle
skittled
skittler
skittles
skitty
skittyboot
skiv
skive
skiver
skiverwood
skiving
skivvies
sklate
sklater
sklent
skleropelite
sklinter
skoal
Skodaic
skogbolite
Skoinolon
skokiaan
Skokomish
skomerite
skoo
skookum
Skopets
skoptsy
skout
skraeling
skraigh
skrike
skrimshander
skrupul
skua
skulduggery
skulk
skulker
skulking
skulkingly
skull
skullbanker
skullcap
skulled
skullery
skullfish
skullful
skully
skulp
skun
skunk
skunkbill
skunkbush
skunkdom
skunkery
skunkhead
skunkish
skunklet
skunktop
skunkweed
skunky
Skupshtina
skuse
skutterudite
sky
skybal
skycraft
Skye
skyey
skyful
skyish
skylark
skylarker
skyless
skylight
skylike
skylook
skyman
skyphoi
skyphos
skyplast
skyre
skyrgaliard
skyrocket
skyrockety
skysail
skyscape
skyscraper
skyscraping
skyshine
skyugle
skyward
skywards
skyway
skywrite
skywriter
skywriting
sla
slab
slabbed
slabber
slabberer
slabbery
slabbiness
slabbing
slabby
slabman
slabness
slabstone
slack
slackage
slacked
slacken
slackener
slacker
slackerism
slacking
slackingly
slackly
slackness
slad
sladang
slade
slae
slag
slaggability
slaggable
slagger
slagging
slaggy
slagless
slaglessness
slagman
slain
slainte
slaister
slaistery
slait
slake
slakeable
slakeless
slaker
slaking
slaky
slam
slammakin
slammerkin
slammock
slammocking
slammocky
slamp
slampamp
slampant
slander
slanderer
slanderful
slanderfully
slandering
slanderingly
slanderous
slanderously
slanderousness
slanderproof
slane
slang
slangily
slanginess
slangish
slangishly
slangism
slangkop
slangous
slangster
slanguage
slangular
slangy
slank
slant
slantindicular
slantindicularly
slanting
slantingly
slantingways
slantly
slantways
slantwise
slap
slapdash
slapdashery
slape
slaphappy
slapjack
slapper
slapping
slapstick
slapsticky
slare
slart
slarth
Slartibartfast
slash
slashed
slasher
slashing
slashingly
slashy
slat
slatch
slate
slateful
slatelike
slatemaker
slatemaking
slater
slateworks
slateyard
slath
slather
slatify
slatiness
slating
slatish
slatted
slatter
slattern
slatternish
slatternliness
slatternly
slatternness
slattery
slatting
slaty
slaughter
slaughterer
slaughterhouse
slaughteringly
slaughterman
slaughterous
slaughterously
slaughteryard
slaum
Slav
Slavdom
Slave
slave
slaveborn
slaved
slaveholder
slaveholding
slaveland
slaveless
slavelet
slavelike
slaveling
slavemonger
slaveowner
slaveownership
slavepen
slaver
slaverer
slavering
slaveringly
slavery
Slavey
slavey
Slavi
Slavian
Slavic
Slavicism
Slavicize
Slavification
Slavify
slavikite
slaving
Slavish
slavish
slavishly
slavishness
Slavism
Slavist
Slavistic
Slavization
Slavize
slavocracy
slavocrat
slavocratic
Slavonian
Slavonianize
Slavonic
Slavonically
Slavonicize
Slavonish
Slavonism
Slavonization
Slavonize
Slavophile
Slavophilism
Slavophobe
Slavophobist
slaw
slay
slayable
slayer
slaying
sleathy
sleave
sleaved
sleaziness
sleazy
Sleb
sleck
sled
sledded
sledder
sledding
sledful
sledge
sledgeless
sledgemeter
sledger
sledging
sledlike
slee
sleech
sleechy
sleek
sleeken
sleeker
sleeking
sleekit
sleekly
sleekness
sleeky
sleep
sleeper
sleepered
sleepful
sleepfulness
sleepify
sleepily
sleepiness
sleeping
sleepingly
sleepland
sleepless
sleeplessly
sleeplessness
sleeplike
sleepmarken
sleepproof
sleepry
sleepwaker
sleepwaking
sleepwalk
sleepwalker
sleepwalking
sleepward
sleepwort
sleepy
sleepyhead
sleer
sleet
sleetiness
sleeting
sleetproof
sleety
sleeve
sleeveband
sleeveboard
sleeved
sleeveen
sleevefish
sleeveful
sleeveless
sleevelessness
sleevelet
sleevelike
sleever
sleigh
sleigher
sleighing
sleight
sleightful
sleighty
slendang
slender
slenderish
slenderize
slenderly
slenderness
slent
slepez
slept
slete
sleuth
sleuthdog
sleuthful
sleuthhound
sleuthlike
slew
slewed
slewer
slewing
sley
sleyer
slice
sliceable
sliced
slicer
slich
slicht
slicing
slicingly
slick
slicken
slickens
slickenside
slicker
slickered
slickery
slicking
slickly
slickness
slid
slidable
slidableness
slidably
slidage
slidden
slidder
sliddery
slide
slideable
slideableness
slideably
slided
slidehead
slideman
slideproof
slider
slideway
sliding
slidingly
slidingness
slidometer
slifter
slight
slighted
slighter
slightily
slightiness
slighting
slightingly
slightish
slightly
slightness
slighty
slim
slime
slimeman
slimer
slimily
sliminess
slimish
slimishness
slimly
slimmish
slimness
slimpsy
slimsy
slimy
sline
sling
slingball
slinge
slinger
slinging
slingshot
slingsman
slingstone
slink
slinker
slinkily
slinkiness
slinking
slinkingly
slinkskin
slinkweed
slinky
slip
slipback
slipband
slipboard
slipbody
slipcase
slipcoach
slipcoat
slipe
slipgibbet
sliphorn
sliphouse
slipknot
slipless
slipman
slipover
slippage
slipped
slipper
slippered
slipperflower
slipperily
slipperiness
slipperlike
slipperweed
slipperwort
slippery
slipperyback
slipperyroot
slippiness
slipping
slippingly
slipproof
slippy
slipshod
slipshoddiness
slipshoddy
slipshodness
slipshoe
slipslap
slipslop
slipsloppish
slipsloppism
slipsole
slipstep
slipstring
sliptopped
slipway
slirt
slish
slit
slitch
slite
slither
slithering
slitheroo
slithers
slithery
slithy
slitless
slitlike
slitshell
slitted
slitter
slitting
slitty
slitwise
slive
sliver
sliverer
sliverlike
sliverproof
slivery
sliving
slivovitz
sloan
Sloanea
slob
slobber
slobberchops
slobberer
slobbers
slobbery
slobby
slock
slocken
slod
slodder
slodge
slodger
sloe
sloeberry
sloebush
sloetree
slog
slogan
sloganeer
sloganize
slogger
slogging
slogwood
sloka
sloke
slommock
slon
slone
slonk
sloo
sloom
sloomy
sloop
sloopman
sloosh
slop
slopdash
slope
sloped
slopely
slopeness
sloper
slopeways
slopewise
sloping
slopingly
slopingness
slopmaker
slopmaking
sloppage
slopped
sloppery
sloppily
sloppiness
slopping
sloppy
slops
slopseller
slopselling
slopshop
slopstone
slopwork
slopworker
slopy
slorp
slosh
slosher
sloshily
sloshiness
sloshy
slot
slote
sloted
sloth
slothful
slothfully
slothfulness
slothound
slotted
slotter
slottery
slotting
slotwise
slouch
sloucher
slouchily
slouchiness
slouching
slouchingly
slouchy
slough
sloughiness
sloughy
slour
sloush
Slovak
Slovakian
Slovakish
sloven
Slovene
Slovenian
Slovenish
slovenlike
slovenliness
slovenly
slovenwood
Slovintzi
slow
slowbellied
slowbelly
slowdown
slowgoing
slowheaded
slowhearted
slowheartedness
slowhound
slowish
slowly
slowmouthed
slowpoke
slowrie
slows
slowworm
sloyd
slub
slubber
slubberdegullion
slubberer
slubbering
slubberingly
slubberly
slubbery
slubbing
slubby
slud
sludder
sluddery
sludge
sludged
sludger
sludgy
slue
sluer
slug
slugabed
sluggard
sluggarding
sluggardize
sluggardliness
sluggardly
sluggardness
sluggardry
slugged
slugger
slugging
sluggingly
sluggish
sluggishly
sluggishness
sluggy
sluglike
slugwood
sluice
sluicelike
sluicer
sluiceway
sluicing
sluicy
sluig
sluit
slum
slumber
slumberer
slumberful
slumbering
slumberingly
slumberland
slumberless
slumberous
slumberously
slumberousness
slumberproof
slumbersome
slumbery
slumbrous
slumdom
slumgullion
slumgum
slumland
slummage
slummer
slumminess
slumming
slummock
slummocky
slummy
slump
slumpproof
slumproof
slumpwork
slumpy
slumward
slumwise
slung
slungbody
slunge
slunk
slunken
slur
slurbow
slurp
slurry
slush
slusher
slushily
slushiness
slushy
slut
slutch
slutchy
sluther
sluthood
slutter
sluttery
sluttikin
sluttish
sluttishly
sluttishness
slutty
sly
slyboots
slyish
slyly
slyness
slype
sma
smachrie
smack
smackee
smacker
smackful
smacking
smackingly
smacksman
smaik
Smalcaldian
Smalcaldic
small
smallage
smallclothes
smallcoal
smallen
smaller
smallhearted
smallholder
smalling
smallish
smallmouth
smallmouthed
smallness
smallpox
smalls
smallsword
smalltime
smallware
smally
smalm
smalt
smalter
smaltine
smaltite
smalts
smaragd
smaragdine
smaragdite
smaragdus
smarm
smarmy
smart
smarten
smarting
smartingly
smartish
smartism
smartless
smartly
smartness
smartweed
smarty
smash
smashable
smashage
smashboard
smasher
smashery
smashing
smashingly
smashment
smashup
smatter
smatterer
smattering
smatteringly
smattery
smaze
smear
smearcase
smeared
smearer
smeariness
smearless
smeary
smectic
smectis
smectite
Smectymnuan
Smectymnuus
smeddum
smee
smeech
smeek
smeeky
smeer
smeeth
smegma
smell
smellable
smellage
smelled
smeller
smellful
smellfungi
smellfungus
smelliness
smelling
smellproof
smellsome
smelly
smelt
smelter
smelterman
smeltery
smeltman
smeth
smethe
smeuse
smew
smich
smicker
smicket
smiddie
smiddum
smidge
smidgen
smifligate
smifligation
smiggins
Smilacaceae
smilacaceous
Smilaceae
smilaceous
smilacin
Smilacina
Smilax
smilax
smile
smileable
smileage
smileful
smilefulness
smileless
smilelessly
smilelessness
smilemaker
smilemaking
smileproof
smiler
smilet
smiling
smilingly
smilingness
Smilodon
smily
Smintheus
Sminthian
sminthurid
Sminthuridae
Sminthurus
smirch
smircher
smirchless
smirchy
smiris
smirk
smirker
smirking
smirkingly
smirkish
smirkle
smirkly
smirky
smirtle
smit
smitch
smite
smiter
smith
smitham
smithcraft
smither
smithereens
smithery
Smithian
Smithianism
smithing
smithite
Smithsonian
smithsonite
smithwork
smithy
smithydander
smiting
smitten
smitting
smock
smocker
smockface
smocking
smockless
smocklike
smog
smokables
smoke
smokeable
smokebox
smokebush
smoked
smokefarthings
smokehouse
smokejack
smokeless
smokelessly
smokelessness
smokelike
smokeproof
smoker
smokery
smokestack
smokestone
smoketight
smokewood
smokily
smokiness
smoking
smokish
smoky
smokyseeming
smolder
smolderingness
smolt
smooch
smoochy
smoodge
smoodger
smook
smoorich
Smoos
smoot
smooth
smoothable
smoothback
smoothbore
smoothbored
smoothcoat
smoothen
smoother
smoothification
smoothify
smoothing
smoothingly
smoothish
smoothly
smoothmouthed
smoothness
smoothpate
smopple
smore
smorgasbord
smote
smother
smotherable
smotheration
smothered
smotherer
smotheriness
smothering
smotheringly
smothery
smotter
smouch
smoucher
smous
smouse
smouser
smout
smriti
smudge
smudged
smudgedly
smudgeless
smudgeproof
smudger
smudgily
smudginess
smudgy
smug
smuggery
smuggish
smuggishly
smuggishness
smuggle
smuggleable
smuggler
smugglery
smuggling
smugism
smugly
smugness
smuisty
smur
smurr
smurry
smuse
smush
smut
smutch
smutchin
smutchless
smutchy
smutproof
smutted
smutter
smuttily
smuttiness
smutty
Smyrna
Smyrnaite
Smyrnean
Smyrniot
Smyrniote
smyth
smytrie
snab
snabbie
snabble
snack
snackle
snackman
snaff
snaffle
snaffles
snafu
snag
snagbush
snagged
snagger
snaggled
snaggletooth
snaggy
snagrel
snail
snaileater
snailery
snailfish
snailflower
snailish
snailishly
snaillike
snails
snaily
snaith
snake
snakebark
snakeberry
snakebird
snakebite
snakefish
snakeflower
snakehead
snakeholing
snakeleaf
snakeless
snakelet
snakelike
snakeling
snakemouth
snakeneck
snakeology
snakephobia
snakepiece
snakepipe
snakeproof
snaker
snakeroot
snakery
snakeship
snakeskin
snakestone
snakeweed
snakewise
snakewood
snakeworm
snakewort
snakily
snakiness
snaking
snakish
snaky
snap
snapback
snapbag
snapberry
snapdragon
snape
snaper
snaphead
snapholder
snapjack
snapless
snappable
snapped
snapper
snappily
snappiness
snapping
snappingly
snappish
snappishly
snappishness
snapps
snappy
snaps
snapsack
snapshot
snapshotter
snapweed
snapwood
snapwort
snapy
snare
snareless
snarer
snaringly
snark
snarl
snarler
snarleyyow
snarlingly
snarlish
snarly
snary
snaste
snatch
snatchable
snatched
snatcher
snatchily
snatching
snatchingly
snatchproof
snatchy
snath
snathe
snavel
snavvle
snaw
snead
sneak
sneaker
sneakiness
sneaking
sneakingly
sneakingness
sneakish
sneakishly
sneakishness
sneaksby
sneaksman
sneaky
sneap
sneath
sneathe
sneb
sneck
sneckdraw
sneckdrawing
sneckdrawn
snecker
snecket
sned
snee
sneer
sneerer
sneerful
sneerfulness
sneering
sneeringly
sneerless
sneery
sneesh
sneeshing
sneest
sneesty
sneeze
sneezeless
sneezeproof
sneezer
sneezeweed
sneezewood
sneezewort
sneezing
sneezy
snell
snelly
Snemovna
snerp
snew
snib
snibble
snibbled
snibbler
snibel
snicher
snick
snickdraw
snickdrawing
snicker
snickering
snickeringly
snickersnee
snicket
snickey
snickle
sniddle
snide
snideness
sniff
sniffer
sniffily
sniffiness
sniffing
sniffingly
sniffish
sniffishness
sniffle
sniffler
sniffly
sniffy
snift
snifter
snifty
snig
snigger
sniggerer
sniggering
sniggle
sniggler
sniggoringly
snip
snipe
snipebill
snipefish
snipelike
sniper
sniperscope
sniping
snipish
snipjack
snipnose
snipocracy
snipper
snippersnapper
snipperty
snippet
snippetiness
snippety
snippiness
snipping
snippish
snippy
snipsnapsnorum
sniptious
snipy
snirl
snirt
snirtle
snitch
snitcher
snite
snithe
snithy
snittle
snivel
sniveled
sniveler
sniveling
snively
snivy
snob
snobber
snobbery
snobbess
snobbing
snobbish
snobbishly
snobbishness
snobbism
snobby
snobdom
snobling
snobocracy
snobocrat
snobographer
snobography
snobologist
snobonomer
snobscat
snocher
snock
snocker
snod
snodly
snoek
snoeking
snog
snoga
Snohomish
snoke
Snonowas
snood
snooded
snooding
snook
snooker
snookered
snoop
snooper
snooperscope
snoopy
snoose
snoot
snootily
snootiness
snooty
snoove
snooze
snoozer
snooziness
snoozle
snoozy
snop
Snoqualmie
Snoquamish
snore
snoreless
snorer
snoring
snoringly
snork
snorkel
snorker
snort
snorter
snorting
snortingly
snortle
snorty
snot
snotter
snottily
snottiness
snotty
snouch
snout
snouted
snouter
snoutish
snoutless
snoutlike
snouty
Snow
snow
Snowball
snowball
snowbank
snowbell
snowberg
snowberry
snowbird
snowblink
snowbound
snowbreak
snowbush
snowcap
snowcraft
Snowdonian
snowdrift
snowdrop
snowfall
snowflake
snowflight
snowflower
snowfowl
snowhammer
snowhouse
snowie
snowily
snowiness
snowish
snowk
snowl
snowland
snowless
snowlike
snowmanship
snowmobile
snowplow
snowproof
snowscape
snowshade
snowshed
snowshine
snowshoe
snowshoed
snowshoeing
snowshoer
snowslide
snowslip
snowstorm
snowsuit
snowworm
snowy
snozzle
snub
snubbable
snubbed
snubbee
snubber
snubbiness
snubbing
snubbingly
snubbish
snubbishly
snubbishness
snubby
snubproof
snuck
snudge
snuff
snuffbox
snuffboxer
snuffcolored
snuffer
snuffers
snuffiness
snuffing
snuffingly
snuffish
snuffle
snuffler
snuffles
snuffless
snuffliness
snuffling
snufflingly
snuffly
snuffman
snuffy
snug
snugger
snuggery
snuggish
snuggle
snugify
snugly
snugness
snum
snup
snupper
snur
snurl
snurly
snurp
snurt
snuzzle
sny
snying
so
soak
soakage
soakaway
soaked
soaken
soaker
soaking
soakingly
soakman
soaky
soally
soam
soap
soapbark
soapberry
soapbox
soapboxer
soapbubbly
soapbush
soaper
soapery
soapfish
soapily
soapiness
soaplees
soapless
soaplike
soapmaker
soapmaking
soapmonger
soaprock
soaproot
soapstone
soapsud
soapsuddy
soapsuds
soapsudsy
soapweed
soapwood
soapwort
soapy
soar
soarability
soarable
soarer
soaring
soaringly
soary
sob
sobber
sobbing
sobbingly
sobby
sobeit
sober
soberer
sobering
soberingly
soberize
soberlike
soberly
soberness
sobersault
sobersided
sobersides
soberwise
sobful
soboles
soboliferous
sobproof
Sobralia
sobralite
Sobranje
sobrevest
sobriety
sobriquet
sobriquetical
soc
socage
socager
soccer
soccerist
soccerite
soce
socht
sociability
sociable
sociableness
sociably
social
Sociales
socialism
socialist
socialistic
socialite
sociality
socializable
socialization
socialize
socializer
socially
socialness
sociation
sociative
societal
societally
societarian
societarianism
societary
societified
societism
societist
societologist
societology
society
societyish
societyless
socii
Socinian
Socinianism
Socinianistic
Socinianize
sociobiological
sociocentric
sociocracy
sociocrat
sociocratic
sociocultural
sociodrama
sociodramatic
socioeconomic
socioeducational
sociogenesis
sociogenetic
sociogeny
sociography
sociolatry
sociolegal
sociologian
sociologic
sociological
sociologically
sociologism
sociologist
sociologistic
sociologize
sociologizer
sociologizing
sociology
sociomedical
sociometric
sociometry
socionomic
socionomics
socionomy
sociophagous
sociopolitical
socioreligious
socioromantic
sociostatic
sociotechnical
socius
sock
sockdolager
socker
socket
socketful
socketless
sockeye
sockless
socklessness
sockmaker
sockmaking
socky
socle
socman
socmanry
soco
Socorrito
Socotran
Socotri
Socotrine
Socratean
Socratic
Socratical
Socratically
Socraticism
Socratism
Socratist
Socratize
sod
soda
sodaclase
sodaic
sodaless
sodalist
sodalite
sodalithite
sodality
sodamide
sodbuster
sodded
sodden
soddenly
soddenness
sodding
soddite
soddy
sodic
sodio
sodioaluminic
sodioaurous
sodiocitrate
sodiohydric
sodioplatinic
sodiosalicylate
sodiotartrate
sodium
sodless
sodoku
Sodom
sodomic
Sodomist
Sodomite
sodomitess
sodomitic
sodomitical
sodomitically
Sodomitish
sodomy
sodwork
sody
soe
soekoe
soever
sofa
sofane
sofar
soffit
Sofia
Sofoklis
Sofronia
soft
softa
softball
softbrained
soften
softener
softening
softhead
softheaded
softhearted
softheartedly
softheartedness
softhorn
softish
softling
softly
softner
softness
softship
softtack
softwood
softy
sog
Soga
Sogdian
Sogdianese
Sogdianian
Sogdoite
soger
soget
soggarth
soggendalite
soggily
sogginess
sogging
soggy
soh
soho
Soiesette
soiesette
soil
soilage
soiled
soiling
soilless
soilproof
soilure
soily
soiree
soixantine
Soja
soja
sojourn
sojourner
sojourney
sojournment
sok
soka
soke
sokeman
sokemanemot
sokemanry
soken
Sokoki
Sokotri
Sokulk
Sol
sol
sola
solace
solaceful
solacement
solaceproof
solacer
solacious
solaciously
solaciousness
solan
Solanaceae
solanaceous
solanal
Solanales
solander
solaneine
solaneous
solanidine
solanine
Solanum
solanum
solar
solarism
solarist
solaristic
solaristically
solaristics
Solarium
solarium
solarization
solarize
solarometer
solate
solatia
solation
solatium
solay
sold
soldado
Soldan
soldan
soldanel
Soldanella
soldanelle
soldanrie
solder
solderer
soldering
solderless
soldi
soldier
soldierbird
soldierbush
soldierdom
soldieress
soldierfish
soldierhearted
soldierhood
soldiering
soldierize
soldierlike
soldierliness
soldierly
soldierproof
soldiership
soldierwise
soldierwood
soldiery
soldo
sole
Solea
solea
soleas
solecism
solecist
solecistic
solecistical
solecistically
solecize
solecizer
Soleidae
soleiform
soleil
soleless
solely
solemn
solemncholy
solemnify
solemnitude
solemnity
solemnization
solemnize
solemnizer
solemnly
solemnness
Solen
solen
solenacean
solenaceous
soleness
solenette
solenial
Solenidae
solenite
solenitis
solenium
solenoconch
Solenoconcha
solenocyte
Solenodon
solenodont
Solenodontidae
solenogaster
Solenogastres
solenoglyph
Solenoglypha
solenoglyphic
solenoid
solenoidal
solenoidally
Solenopsis
solenostele
solenostelic
solenostomid
Solenostomidae
solenostomoid
solenostomous
Solenostomus
solent
solentine
solepiece
soleplate
soleprint
soler
Solera
soles
soleus
soleyn
solfataric
solfeggio
solferino
soli
soliative
solicit
solicitant
solicitation
solicitationism
solicited
solicitee
soliciter
soliciting
solicitor
solicitorship
solicitous
solicitously
solicitousness
solicitress
solicitrix
solicitude
solicitudinous
solid
Solidago
solidago
solidaric
solidarily
solidarism
solidarist
solidaristic
solidarity
solidarize
solidary
solidate
solidi
solidifiability
solidifiable
solidifiableness
solidification
solidifier
solidiform
solidify
solidish
solidism
solidist
solidistic
solidity
solidly
solidness
solidum
Solidungula
solidungular
solidungulate
solidus
solifidian
solifidianism
solifluction
solifluctional
soliform
Solifugae
solifuge
solifugean
solifugid
solifugous
soliloquacious
soliloquist
soliloquium
soliloquize
soliloquizer
soliloquizing
soliloquizingly
soliloquy
solilunar
Solio
solio
soliped
solipedal
solipedous
solipsism
solipsismal
solipsist
solipsistic
solist
solitaire
solitarian
solitarily
solitariness
solitary
soliterraneous
solitidal
solitude
solitudinarian
solitudinize
solitudinous
solivagant
solivagous
sollar
solleret
Sollya
solmizate
solmization
solo
solod
solodi
solodization
solodize
soloecophanes
soloist
Solomon
Solomonian
Solomonic
Solomonical
Solomonitic
Solon
solon
solonchak
solonetz
solonetzic
solonetzicity
Solonian
Solonic
solonist
soloth
solotink
solotnik
solpugid
Solpugida
Solpugidea
Solpugides
solstice
solsticion
solstitia
solstitial
solstitially
solstitium
solubility
solubilization
solubilize
soluble
solubleness
solubly
solum
solute
solution
solutional
solutioner
solutionist
solutize
solutizer
Solutrean
solvability
solvable
solvableness
solvate
solvation
solve
solvement
solvency
solvend
solvent
solvently
solventproof
solver
solvolysis
solvolytic
solvolyze
solvsbergite
Solyma
Solymaean
soma
somacule
Somal
somal
Somali
somaplasm
Somaschian
somasthenia
somata
somatasthenia
Somateria
somatic
somatical
somatically
somaticosplanchnic
somaticovisceral
somatics
somatism
somatist
somatization
somatochrome
somatocyst
somatocystic
somatoderm
somatogenetic
somatogenic
somatognosis
somatognostic
somatologic
somatological
somatologically
somatologist
somatology
somatome
somatomic
somatophyte
somatophytic
somatoplasm
somatopleural
somatopleure
somatopleuric
somatopsychic
somatosplanchnic
somatotonia
somatotonic
somatotropic
somatotropically
somatotropism
somatotype
somatotyper
somatotypy
somatous
somber
somberish
somberly
somberness
sombre
sombrerite
sombrero
sombreroed
sombrous
sombrously
sombrousness
some
somebody
someday
somedeal
somegate
somehow
someone
somepart
someplace
somers
somersault
somerset
Somersetian
somervillite
somesthesia
somesthesis
somesthetic
something
somethingness
sometime
sometimes
someway
someways
somewhat
somewhatly
somewhatness
somewhen
somewhence
somewhere
somewheres
somewhile
somewhiles
somewhither
somewhy
somewise
somital
somite
somitic
somma
sommaite
sommelier
somnambulance
somnambulancy
somnambulant
somnambular
somnambulary
somnambulate
somnambulation
somnambulator
somnambule
somnambulency
somnambulic
somnambulically
somnambulism
somnambulist
somnambulistic
somnambulize
somnambulous
somnial
somniative
somnifacient
somniferous
somniferously
somnific
somnifuge
somnify
somniloquacious
somniloquence
somniloquent
somniloquism
somniloquist
somniloquize
somniloquous
somniloquy
Somniosus
somnipathist
somnipathy
somnivolency
somnivolent
somnolence
somnolency
somnolent
somnolently
somnolescence
somnolescent
somnolism
somnolize
somnopathy
somnorific
somnus
sompay
sompne
sompner
Son
son
sonable
sonance
sonancy
sonant
sonantal
sonantic
sonantina
sonantized
sonar
sonata
sonatina
sonation
Sonchus
sond
sondation
sondeli
Sonderbund
sonderclass
Sondergotter
Sondylomorum
soneri
song
songbird
songbook
songcraft
songfest
songful
songfully
songfulness
Songhai
Songish
songish
songland
songle
songless
songlessly
songlessness
songlet
songlike
songman
Songo
Songoi
songster
songstress
songworthy
songwright
songy
sonhood
sonic
soniferous
sonification
soniou
Sonja
sonk
sonless
sonlike
sonlikeness
sonly
Sonneratia
Sonneratiaceae
sonneratiaceous
sonnet
sonnetary
sonneteer
sonneteeress
sonnetic
sonneting
sonnetish
sonnetist
sonnetize
sonnetlike
sonnetwise
sonnikins
Sonny
sonny
sonobuoy
sonometer
Sonoran
sonorant
sonorescence
sonorescent
sonoric
sonoriferous
sonoriferously
sonorific
sonority
sonorophone
sonorosity
sonorous
sonorously
sonorousness
Sonrai
sons
sonship
sonsy
sontag
soodle
soodly
Soohong
sook
Sooke
sooky
sool
sooloos
soon
sooner
soonish
soonly
Soorah
soorawn
soord
soorkee
Soot
soot
sooter
sooterkin
sooth
soothe
soother
sootherer
soothful
soothing
soothingly
soothingness
soothless
soothsay
soothsayer
soothsayership
soothsaying
sootily
sootiness
sootless
sootlike
sootproof
sooty
sootylike
sop
sope
soph
Sopheric
Sopherim
Sophia
sophia
Sophian
sophic
sophical
sophically
sophiologic
sophiology
sophism
Sophist
sophister
sophistic
sophistical
sophistically
sophisticalness
sophisticant
sophisticate
sophisticated
sophistication
sophisticative
sophisticator
sophisticism
Sophistress
sophistress
sophistry
Sophoclean
sophomore
sophomoric
sophomorical
sophomorically
Sophora
sophoria
Sophronia
sophronize
Sophy
sophy
sopite
sopition
sopor
soporiferous
soporiferously
soporiferousness
soporific
soporifical
soporifically
soporose
sopper
soppiness
sopping
soppy
soprani
sopranino
sopranist
soprano
sora
Sorabian
sorage
soral
Sorb
sorb
Sorbaria
sorbate
sorbefacient
sorbent
Sorbian
sorbic
sorbile
sorbin
sorbinose
Sorbish
sorbite
sorbitic
sorbitize
sorbitol
Sorbonic
Sorbonical
Sorbonist
Sorbonne
sorbose
sorboside
Sorbus
sorbus
sorcer
sorcerer
sorceress
sorcering
sorcerous
sorcerously
sorcery
sorchin
sorda
Sordaria
Sordariaceae
sordawalite
sordellina
Sordello
sordes
sordid
sordidity
sordidly
sordidness
sordine
sordino
sordor
sore
soredia
soredial
sorediate
sorediferous
sorediform
soredioid
soredium
soree
sorefalcon
sorefoot
sorehawk
sorehead
soreheaded
soreheadedly
soreheadedness
sorehearted
sorehon
sorely
sorema
soreness
Sorex
sorgho
Sorghum
sorghum
sorgo
sori
soricid
Soricidae
soricident
Soricinae
soricine
soricoid
Soricoidea
soriferous
sorite
sorites
soritical
sorn
sornare
sornari
sorner
sorning
soroban
Soroptimist
sororal
sororate
sororial
sororially
sororicidal
sororicide
sorority
sororize
sorose
sorosis
sorosphere
Sorosporella
Sorosporium
sorption
sorra
Sorrel
sorrel
sorrento
sorrily
sorriness
sorroa
sorrow
sorrower
sorrowful
sorrowfully
sorrowfulness
sorrowing
sorrowingly
sorrowless
sorrowproof
sorrowy
sorry
sorryhearted
sorryish
sort
sortable
sortably
sortal
sortation
sorted
sorter
sortie
sortilege
sortileger
sortilegic
sortilegious
sortilegus
sortilegy
sortiment
sortition
sortly
sorty
sorus
sorva
sory
sosh
soshed
Sosia
soso
sosoish
Sospita
soss
sossle
sostenuto
sot
Sotadean
Sotadic
Soter
Soteres
soterial
soteriologic
soteriological
soteriology
Sothiac
Sothiacal
Sothic
Sothis
Sotho
sotie
Sotik
sotnia
sotnik
sotol
sots
sottage
sotted
sotter
sottish
sottishly
sottishness
sou
souari
soubise
soubrette
soubrettish
soucar
souchet
Souchong
souchong
souchy
soud
soudagur
souffle
souffleed
sough
sougher
soughing
sought
Souhegan
soul
soulack
soulcake
souled
Souletin
soulful
soulfully
soulfulness
soulical
soulish
soulless
soullessly
soullessness
soullike
Soulmass
soulsaving
soulward
souly
soum
soumansite
soumarque
sound
soundable
soundage
soundboard
sounder
soundful
soundheaded
soundheadedness
soundhearted
soundheartednes
sounding
soundingly
soundingness
soundless
soundlessly
soundlessness
soundly
soundness
soundproof
soundproofing
soup
soupbone
soupcon
souper
souple
soupless
souplike
soupspoon
soupy
sour
sourbelly
sourberry
sourbread
sourbush
sourcake
source
sourceful
sourcefulness
sourceless
sourcrout
sourdeline
sourdine
soured
souredness
souren
sourer
sourhearted
souring
sourish
sourishly
sourishness
sourjack
sourling
sourly
sourness
sourock
soursop
sourtop
sourweed
sourwood
soury
sousaphone
sousaphonist
souse
souser
souslik
soutane
souter
souterrain
South
south
southard
southbound
Southcottian
Southdown
southeast
southeaster
southeasterly
southeastern
southeasternmost
southeastward
southeastwardly
southeastwards
souther
southerland
southerliness
southerly
southermost
southern
Southerner
southerner
southernism
southernize
southernliness
southernly
southernmost
southernness
southernwood
southing
southland
southlander
southmost
southness
southpaw
Southron
southron
Southronie
Southumbrian
southward
southwardly
southwards
southwest
southwester
southwesterly
southwestern
Southwesterner
southwesternmost
southwestward
southwestwardly
souvenir
souverain
souwester
sov
sovereign
sovereigness
sovereignly
sovereignness
sovereignship
sovereignty
soviet
sovietdom
sovietic
sovietism
sovietist
sovietization
sovietize
sovite
sovkhose
sovkhoz
sovran
sovranty
sow
sowable
sowan
sowans
sowar
sowarry
sowback
sowbacked
sowbane
sowbelly
sowbread
sowdones
sowel
sowens
sower
sowfoot
sowing
sowins
sowl
sowle
sowlike
sowlth
sown
sowse
sowt
sowte
Soxhlet
soy
soya
soybean
Soyot
sozin
sozolic
sozzle
sozzly
spa
Space
space
spaceband
spaced
spaceful
spaceless
spacer
spacesaving
spaceship
spaciness
spacing
spaciosity
spaciotemporal
spacious
spaciously
spaciousness
spack
spacy
spad
spade
spadebone
spaded
spadefish
spadefoot
spadeful
spadelike
spademan
spader
spadesman
spadewise
spadework
spadger
spadiceous
spadices
spadicifloral
spadiciflorous
spadiciform
spadicose
spadilla
spadille
spading
spadix
spadone
spadonic
spadonism
spadrone
spadroon
spae
spaebook
spaecraft
spaedom
spaeman
spaer
spaewife
spaewoman
spaework
spaewright
spaghetti
Spagnuoli
spagyric
spagyrical
spagyrically
spagyrist
spahi
spaid
spaik
spairge
spak
Spalacidae
spalacine
Spalax
spald
spalder
spalding
spale
spall
spallation
spaller
spalling
spalpeen
spalt
span
spancel
spandle
spandrel
spandy
spane
spanemia
spanemy
spang
spanghew
spangle
spangled
spangler
spanglet
spangly
spangolite
Spaniard
Spaniardization
Spaniardize
Spaniardo
spaniel
spaniellike
spanielship
spaning
Spaniol
Spaniolate
Spanioli
Spaniolize
spanipelagic
Spanish
Spanishize
Spanishly
spank
spanker
spankily
spanking
spankingly
spanky
spanless
spann
spannel
spanner
spannerman
spanopnoea
spanpiece
spantoon
spanule
spanworm
Spar
spar
sparable
sparada
sparadrap
sparagrass
sparagus
Sparassis
sparassodont
Sparassodonta
Sparaxis
sparaxis
sparch
spare
spareable
spareless
sparely
spareness
sparer
sparerib
sparesome
Sparganiaceae
Sparganium
sparganium
sparganosis
sparganum
sparge
sparger
spargosis
sparhawk
sparid
Sparidae
sparing
sparingly
sparingness
spark
sparkback
sparked
sparker
sparkiness
sparking
sparkish
sparkishly
sparkishness
sparkle
sparkleberry
sparkler
sparkless
sparklessly
sparklet
sparklike
sparkliness
sparkling
sparklingly
sparklingness
sparkly
sparkproof
sparks
sparky
sparlike
sparling
sparm
Sparmannia
Sparnacian
sparoid
sparpiece
sparred
sparrer
sparring
sparringly
sparrow
sparrowbill
sparrowcide
sparrowdom
sparrowgrass
sparrowish
sparrowless
sparrowlike
sparrowtail
sparrowtongue
sparrowwort
sparrowy
sparry
sparse
sparsedly
sparsely
sparsile
sparsioplast
sparsity
spart
Spartacan
Spartacide
Spartacism
Spartacist
spartacist
Spartan
Spartanhood
Spartanic
Spartanically
Spartanism
Spartanize
Spartanlike
Spartanly
sparteine
sparterie
sparth
Spartiate
Spartina
Spartium
spartle
Sparus
sparver
spary
spasm
spasmatic
spasmatical
spasmatomancy
spasmed
spasmic
spasmodic
spasmodical
spasmodically
spasmodicalness
spasmodism
spasmodist
spasmolytic
spasmophilia
spasmophilic
spasmotin
spasmotoxin
spasmous
Spass
spastic
spastically
spasticity
spat
spatalamancy
Spatangida
Spatangina
spatangoid
Spatangoida
Spatangoidea
spatangoidean
Spatangus
spatchcock
spate
spatha
spathaceous
spathal
spathe
spathed
spatheful
spathic
Spathiflorae
spathilae
spathilla
spathose
spathous
spathulate
Spathyema
spatial
spatiality
spatialization
spatialize
spatially
spatiate
spatiation
spatilomancy
spatiotemporal
spatling
spatted
spatter
spatterdashed
spatterdasher
spatterdock
spattering
spatteringly
spatterproof
spatterwork
spatting
spattle
spattlehoe
Spatula
spatula
spatulamancy
spatular
spatulate
spatulation
spatule
spatuliform
spatulose
spave
spaver
spavie
spavied
spaviet
spavin
spavindy
spavined
spawn
spawneater
spawner
spawning
spawny
spay
spayad
spayard
spaying
speak
speakable
speakableness
speakably
speaker
speakeress
speakership
speakhouse
speakies
speaking
speakingly
speakingness
speakless
speaklessly
speal
spealbone
spean
spear
spearcast
spearer
spearfish
spearflower
spearhead
spearing
spearman
spearmanship
spearmint
spearproof
spearsman
spearwood
spearwort
speary
spec
specchie
spece
special
specialism
specialist
specialistic
speciality
specialization
specialize
specialized
specializer
specially
specialness
specialty
speciation
specie
species
speciestaler
specifiable
specific
specifical
specificality
specifically
specificalness
specificate
specification
specificative
specificatively
specificity
specificize
specificly
specificness
specifier
specifist
specify
specillum
specimen
specimenize
speciology
speciosity
specious
speciously
speciousness
speck
specked
speckedness
speckfall
speckiness
specking
speckle
specklebelly
specklebreast
speckled
speckledbill
speckledness
speckless
specklessly
specklessness
speckling
speckly
speckproof
specks
specksioneer
specky
specs
spectacle
spectacled
spectacleless
spectaclelike
spectaclemaker
spectaclemaking
spectacles
spectacular
spectacularism
spectacularity
spectacularly
spectator
spectatordom
spectatorial
spectatorship
spectatory
spectatress
spectatrix
specter
spectered
specterlike
spectra
spectral
spectralism
spectrality
spectrally
spectralness
spectrobolograph
spectrobolographic
spectrobolometer
spectrobolometric
spectrochemical
spectrochemistry
spectrocolorimetry
spectrocomparator
spectroelectric
spectrogram
spectrograph
spectrographic
spectrographically
spectrography
spectroheliogram
spectroheliograph
spectroheliographic
spectrohelioscope
spectrological
spectrologically
spectrology
spectrometer
spectrometric
spectrometry
spectromicroscope
spectromicroscopical
spectrophobia
spectrophone
spectrophonic
spectrophotoelectric
spectrophotograph
spectrophotography
spectrophotometer
spectrophotometric
spectrophotometry
spectropolarimeter
spectropolariscope
spectropyrheliometer
spectropyrometer
spectroradiometer
spectroradiometric
spectroradiometry
spectroscope
spectroscopic
spectroscopically
spectroscopist
spectroscopy
spectrotelescope
spectrous
spectrum
spectry
specula
specular
Specularia
specularly
speculate
speculation
speculatist
speculative
speculatively
speculativeness
speculativism
speculator
speculatory
speculatrices
speculatrix
speculist
speculum
specus
sped
speech
speechcraft
speecher
speechful
speechfulness
speechification
speechifier
speechify
speeching
speechless
speechlessly
speechlessness
speechlore
speechmaker
speechmaking
speechment
speed
speedaway
speedboat
speedboating
speedboatman
speeder
speedful
speedfully
speedfulness
speedily
speediness
speeding
speedingly
speedless
speedometer
speedster
speedway
speedwell
speedy
speel
speelken
speelless
speen
speer
speering
speerity
speiskobalt
speiss
spekboom
spelaean
spelder
spelding
speldring
speleological
speleologist
speleology
spelk
spell
spellable
spellbind
spellbinder
spellbinding
spellbound
spellcraft
spelldown
speller
spellful
spelling
spellingdown
spellingly
spellmonger
spellproof
spellword
spellwork
spelt
spelter
spelterman
speltoid
speltz
speluncar
speluncean
spelunk
spelunker
spence
Spencean
Spencer
spencer
Spencerian
Spencerianism
Spencerism
spencerite
spend
spendable
spender
spendful
spendible
spending
spendless
spendthrift
spendthrifty
Spenerism
spense
Spenserian
spent
speos
Speotyto
sperable
Speranza
sperate
Spergula
Spergularia
sperity
sperket
sperling
sperm
sperma
spermaceti
spermacetilike
spermaduct
spermalist
Spermaphyta
spermaphyte
spermaphytic
spermarium
spermary
spermashion
spermatangium
spermatheca
spermathecal
spermatic
spermatically
spermatid
spermatiferous
spermatin
spermatiogenous
spermation
spermatiophore
spermatism
spermatist
spermatitis
spermatium
spermatize
spermatoblast
spermatoblastic
spermatocele
spermatocyst
spermatocystic
spermatocystitis
spermatocytal
spermatocyte
spermatogemma
spermatogenesis
spermatogenetic
spermatogenic
spermatogenous
spermatogeny
spermatogonial
spermatogonium
spermatoid
spermatolysis
spermatolytic
spermatophoral
spermatophore
spermatophorous
Spermatophyta
spermatophyte
spermatophytic
spermatoplasm
spermatoplasmic
spermatoplast
spermatorrhea
spermatospore
spermatotheca
spermatova
spermatovum
spermatoxin
spermatozoa
spermatozoal
spermatozoan
spermatozoic
spermatozoid
spermatozoon
spermaturia
spermic
spermidine
spermiducal
spermiduct
spermigerous
spermine
spermiogenesis
spermism
spermist
spermoblast
spermoblastic
spermocarp
spermocenter
spermoderm
spermoduct
spermogenesis
spermogenous
spermogone
spermogoniferous
spermogonium
spermogonous
spermologer
spermological
spermologist
spermology
spermolysis
spermolytic
spermophile
spermophiline
Spermophilus
spermophore
spermophorium
Spermophyta
spermophyte
spermophytic
spermosphere
spermotheca
spermotoxin
spermous
spermoviduct
spermy
speronara
speronaro
sperone
sperrylite
spessartite
spet
spetch
spetrophoby
speuchan
spew
spewer
spewiness
spewing
spewy
spex
sphacel
Sphacelaria
Sphacelariaceae
sphacelariaceous
Sphacelariales
sphacelate
sphacelated
sphacelation
sphacelia
sphacelial
sphacelism
sphaceloderma
Sphaceloma
sphacelotoxin
sphacelous
sphacelus
Sphaeralcea
sphaeraphides
Sphaerella
sphaerenchyma
Sphaeriaceae
sphaeriaceous
Sphaeriales
sphaeridia
sphaeridial
sphaeridium
Sphaeriidae
Sphaerioidaceae
sphaeristerium
sphaerite
Sphaerium
sphaeroblast
Sphaerobolaceae
Sphaerobolus
Sphaerocarpaceae
Sphaerocarpales
Sphaerocarpus
sphaerocobaltite
Sphaerococcaceae
sphaerococcaceous
Sphaerococcus
sphaerolite
sphaerolitic
Sphaeroma
Sphaeromidae
Sphaerophoraceae
Sphaerophorus
Sphaeropsidaceae
Sphaeropsidales
Sphaeropsis
sphaerosiderite
sphaerosome
sphaerospore
Sphaerostilbe
Sphaerotheca
Sphaerotilus
sphagion
Sphagnaceae
sphagnaceous
Sphagnales
sphagnicolous
sphagnologist
sphagnology
sphagnous
Sphagnum
sphagnum
Sphakiot
sphalerite
Sphargis
sphecid
Sphecidae
Sphecina
Sphecoidea
spheges
sphegid
Sphegidae
Sphegoidea
sphendone
sphene
sphenethmoid
sphenethmoidal
sphenic
sphenion
Sphenisci
Spheniscidae
Sphenisciformes
spheniscine
spheniscomorph
Spheniscomorphae
spheniscomorphic
Spheniscus
sphenobasilar
sphenobasilic
sphenocephalia
sphenocephalic
sphenocephalous
sphenocephaly
Sphenodon
sphenodon
sphenodont
Sphenodontia
Sphenodontidae
sphenoethmoid
sphenoethmoidal
sphenofrontal
sphenogram
sphenographic
sphenographist
sphenography
sphenoid
sphenoidal
sphenoiditis
sphenolith
sphenomalar
sphenomandibular
sphenomaxillary
sphenopalatine
sphenoparietal
sphenopetrosal
Sphenophorus
Sphenophyllaceae
sphenophyllaceous
Sphenophyllales
Sphenophyllum
Sphenopteris
sphenosquamosal
sphenotemporal
sphenotic
sphenotribe
sphenotripsy
sphenoturbinal
sphenovomerine
sphenozygomatic
spherable
spheral
spherality
spheraster
spheration
sphere
sphereless
spheric
spherical
sphericality
spherically
sphericalness
sphericist
sphericity
sphericle
sphericocylindrical
sphericotetrahedral
sphericotriangular
spherics
spheriform
spherify
spheroconic
spherocrystal
spherograph
spheroidal
spheroidally
spheroidic
spheroidical
spheroidically
spheroidicity
spheroidism
spheroidity
spheroidize
spheromere
spherometer
spheroquartic
spherula
spherular
spherulate
spherule
spherulite
spherulitic
spherulitize
sphery
spheterize
Sphex
sphexide
sphincter
sphincteral
sphincteralgia
sphincterate
sphincterectomy
sphincterial
sphincteric
sphincterismus
sphincteroscope
sphincteroscopy
sphincterotomy
sphindid
Sphindidae
Sphindus
sphingal
sphinges
sphingid
Sphingidae
sphingiform
sphingine
sphingoid
sphingometer
sphingomyelin
sphingosine
Sphingurinae
Sphingurus
sphinx
sphinxian
sphinxianness
sphinxlike
Sphoeroides
sphragide
sphragistic
sphragistics
sphygmia
sphygmic
sphygmochronograph
sphygmodic
sphygmogram
sphygmograph
sphygmographic
sphygmography
sphygmoid
sphygmology
sphygmomanometer
sphygmomanometric
sphygmomanometry
sphygmometer
sphygmometric
sphygmophone
sphygmophonic
sphygmoscope
sphygmus
Sphyraena
sphyraenid
Sphyraenidae
sphyraenoid
Sphyrapicus
Sphyrna
Sphyrnidae
Spica
spica
spical
spicant
Spicaria
spicate
spicated
spiccato
spice
spiceable
spiceberry
spicebush
spicecake
spiced
spiceful
spicehouse
spiceland
spiceless
spicelike
spicer
spicery
spicewood
spiciferous
spiciform
spicigerous
spicilege
spicily
spiciness
spicing
spick
spicket
spickle
spicknel
spicose
spicosity
spicous
spicousness
spicula
spiculae
spicular
spiculate
spiculated
spiculation
spicule
spiculiferous
spiculiform
spiculigenous
spiculigerous
spiculofiber
spiculose
spiculous
spiculum
spiculumamoris
spicy
spider
spidered
spiderflower
spiderish
spiderless
spiderlike
spiderling
spiderly
spiderweb
spiderwork
spiderwort
spidery
spidger
spied
spiegel
spiegeleisen
spiel
spieler
spier
spiff
spiffed
spiffily
spiffiness
spiffing
spiffy
spiflicate
spiflicated
spiflication
spig
Spigelia
Spigeliaceae
Spigelian
spiggoty
spignet
spigot
Spike
spike
spikebill
spiked
spikedness
spikefish
spikehorn
spikelet
spikelike
spikenard
spiker
spiketail
spiketop
spikeweed
spikewise
spikily
spikiness
spiking
spiky
Spilanthes
spile
spilehole
spiler
spileworm
spilikin
spiling
spilite
spilitic
spill
spillage
spiller
spillet
spillproof
spillway
spilly
Spilogale
spiloma
spilosite
spilt
spilth
spilus
spin
spina
spinacene
spinaceous
spinach
spinachlike
Spinacia
spinae
spinage
spinal
spinales
spinalis
spinally
spinate
spinder
spindlage
spindle
spindleage
spindled
spindleful
spindlehead
spindlelegs
spindlelike
spindler
spindleshanks
spindletail
spindlewise
spindlewood
spindleworm
spindliness
spindling
spindly
spindrift
spine
spinebill
spinebone
spined
spinel
spineless
spinelessly
spinelessness
spinelet
spinelike
spinescence
spinescent
spinet
spinetail
spingel
spinibulbar
spinicarpous
spinicerebellar
spinidentate
spiniferous
Spinifex
spinifex
spiniform
spinifugal
spinigerous
spinigrade
spininess
spinipetal
spinitis
spinituberculate
spink
spinnable
spinnaker
spinner
spinneret
spinnerular
spinnerule
spinnery
spinney
spinning
spinningly
spinobulbar
spinocarpous
spinocerebellar
spinogalvanization
spinoglenoid
spinoid
spinomuscular
spinoneural
spinoperipheral
spinose
spinosely
spinoseness
spinosity
spinosodentate
spinosodenticulate
spinosotubercular
spinosotuberculate
spinosympathetic
spinotectal
spinothalamic
spinotuberculous
spinous
spinousness
Spinozism
Spinozist
Spinozistic
spinster
spinsterdom
spinsterhood
spinsterial
spinsterish
spinsterishly
spinsterism
spinsterlike
spinsterly
spinsterous
spinstership
spinstress
spintext
spinthariscope
spinthariscopic
spintherism
spinulate
spinulation
spinule
spinulescent
spinuliferous
spinuliform
Spinulosa
spinulose
spinulosely
spinulosociliate
spinulosodentate
spinulosodenticulate
spinulosogranulate
spinulososerrate
spinulous
spiny
spionid
Spionidae
Spioniformia
spiracle
spiracula
spiracular
spiraculate
spiraculiferous
spiraculiform
spiraculum
Spiraea
Spiraeaceae
spiral
spirale
spiraled
spiraliform
spiralism
spirality
spiralization
spiralize
spirally
spiraloid
spiraltail
spiralwise
spiran
spirant
Spiranthes
spiranthic
spiranthy
spirantic
spirantize
spiraster
spirate
spirated
spiration
spire
spirea
spired
spiregrass
spireless
spirelet
spireme
spirepole
spireward
spirewise
spiricle
Spirifer
Spirifera
Spiriferacea
spiriferid
Spiriferidae
spiriferoid
spiriferous
spiriform
spirignath
spirignathous
spirilla
Spirillaceae
spirillaceous
spirillar
spirillolysis
spirillosis
spirillotropic
spirillotropism
spirillum
spiring
spirit
spiritally
spiritdom
spirited
spiritedly
spiritedness
spiriter
spiritful
spiritfully
spiritfulness
spirithood
spiriting
spiritism
spiritist
spiritistic
spiritize
spiritland
spiritleaf
spiritless
spiritlessly
spiritlessness
spiritlike
spiritmonger
spiritous
spiritrompe
spiritsome
spiritual
spiritualism
spiritualist
spiritualistic
spiritualistically
spirituality
spiritualization
spiritualize
spiritualizer
spiritually
spiritualness
spiritualship
spiritualty
spirituosity
spirituous
spirituously
spirituousness
spiritus
spiritweed
spirity
spirivalve
spirket
spirketing
spirling
spiro
Spirobranchia
Spirobranchiata
spirobranchiate
Spirochaeta
Spirochaetaceae
spirochaetal
Spirochaetales
Spirochaete
spirochetal
spirochete
spirochetemia
spirochetic
spirocheticidal
spirocheticide
spirochetosis
spirochetotic
Spirodela
spirogram
spirograph
spirographidin
spirographin
Spirographis
Spirogyra
spiroid
spiroloculine
spirometer
spirometric
spirometrical
spirometry
Spironema
spiropentane
Spirophyton
Spirorbis
spiroscope
Spirosoma
spirous
spirt
Spirula
spirulate
spiry
spise
spissated
spissitude
Spisula
spit
spital
spitball
spitballer
spitbox
spitchcock
spite
spiteful
spitefully
spitefulness
spiteless
spiteproof
spitfire
spitful
spithamai
spithame
spitish
spitpoison
spitscocked
spitstick
spitted
spitten
spitter
spitting
spittle
spittlefork
spittlestaff
spittoon
spitz
Spitzenburg
spitzkop
spiv
spivery
Spizella
spizzerinctum
Splachnaceae
splachnaceous
splachnoid
Splachnum
splacknuck
splairge
splanchnapophysial
splanchnapophysis
splanchnectopia
splanchnemphraxis
splanchnesthesia
splanchnesthetic
splanchnic
splanchnoblast
splanchnocoele
splanchnoderm
splanchnodiastasis
splanchnodynia
splanchnographer
splanchnographical
splanchnography
splanchnolith
splanchnological
splanchnologist
splanchnology
splanchnomegalia
splanchnomegaly
splanchnopathy
splanchnopleural
splanchnopleure
splanchnopleuric
splanchnoptosia
splanchnoptosis
splanchnosclerosis
splanchnoscopy
splanchnoskeletal
splanchnoskeleton
splanchnosomatic
splanchnotomical
splanchnotomy
splanchnotribe
splash
splashboard
splashed
splasher
splashiness
splashing
splashingly
splashproof
splashy
splat
splatch
splatcher
splatchy
splathering
splatter
splatterdash
splatterdock
splatterer
splatterfaced
splatterwork
splay
splayed
splayer
splayfoot
splayfooted
splaymouth
splaymouthed
spleen
spleenful
spleenfully
spleenish
spleenishly
spleenishness
spleenless
spleenwort
spleeny
spleet
spleetnew
splenadenoma
splenalgia
splenalgic
splenalgy
splenatrophia
splenatrophy
splenauxe
splenculus
splendacious
splendaciously
splendaciousness
splendent
splendently
splender
splendescent
splendid
splendidly
splendidness
splendiferous
splendiferously
splendiferousness
splendor
splendorous
splendorproof
splendourproof
splenectama
splenectasis
splenectomist
splenectomize
splenectomy
splenectopia
splenectopy
splenelcosis
splenemia
splenemphraxis
spleneolus
splenepatitis
splenetic
splenetical
splenetically
splenetive
splenial
splenic
splenical
splenicterus
splenification
spleniform
splenitis
splenitive
splenium
splenius
splenization
splenoblast
splenocele
splenoceratosis
splenocleisis
splenocolic
splenocyte
splenodiagnosis
splenodynia
splenography
splenohemia
splenoid
splenolaparotomy
splenology
splenolymph
splenolymphatic
splenolysin
splenolysis
splenoma
splenomalacia
splenomedullary
splenomegalia
splenomegalic
splenomegaly
splenomyelogenous
splenoncus
splenonephric
splenopancreatic
splenoparectama
splenoparectasis
splenopathy
splenopexia
splenopexis
splenopexy
splenophrenic
splenopneumonia
splenoptosia
splenoptosis
splenorrhagia
splenorrhaphy
splenotomy
splenotoxin
splenotyphoid
splenulus
splenunculus
splet
spleuchan
splice
spliceable
splicer
splicing
splinder
spline
splineway
splint
splintage
splinter
splinterd
splinterless
splinternew
splinterproof
splintery
splintwood
splinty
split
splitbeak
splitfinger
splitfruit
splitmouth
splitnew
splitsaw
splittail
splitten
splitter
splitting
splitworm
splodge
splodgy
splore
splosh
splotch
splotchily
splotchiness
splotchy
splother
splunge
splurge
splurgily
splurgy
splurt
spluther
splutter
splutterer
spoach
Spock
spode
spodiosite
spodium
spodogenic
spodogenous
spodomancy
spodomantic
spodumene
spoffish
spoffle
spoffy
spogel
spoil
spoilable
spoilage
spoilation
spoiled
spoiler
spoilfive
spoilful
spoiling
spoilless
spoilment
spoilsman
spoilsmonger
spoilsport
spoilt
Spokan
spoke
spokeless
spoken
spokeshave
spokesman
spokesmanship
spokester
spokeswoman
spokeswomanship
spokewise
spoky
spole
spolia
spoliarium
spoliary
spoliate
spoliation
spoliator
spoliatory
spolium
spondaic
spondaical
spondaize
spondean
spondee
spondiac
Spondiaceae
Spondias
spondulics
spondyl
spondylalgia
spondylarthritis
spondylarthrocace
spondylexarthrosis
spondylic
spondylid
Spondylidae
spondylioid
spondylitic
spondylitis
spondylium
spondylizema
spondylocace
Spondylocladium
spondylodiagnosis
spondylodidymia
spondylodymus
spondyloid
spondylolisthesis
spondylolisthetic
spondylopathy
spondylopyosis
spondyloschisis
spondylosis
spondylosyndesis
spondylotherapeutics
spondylotherapist
spondylotherapy
spondylotomy
spondylous
Spondylus
spondylus
spong
sponge
spongecake
sponged
spongeful
spongeless
spongelet
spongelike
spongeous
spongeproof
sponger
spongewood
Spongiae
spongian
spongicolous
spongiculture
Spongida
spongiferous
spongiform
Spongiidae
Spongilla
spongillid
Spongillidae
spongilline
spongily
spongin
sponginblast
sponginblastic
sponginess
sponging
spongingly
spongioblast
spongioblastoma
spongiocyte
spongiolin
spongiopilin
spongioplasm
spongioplasmic
spongiose
spongiosity
spongiousness
Spongiozoa
spongiozoon
spongoblast
spongoblastic
spongoid
spongology
spongophore
Spongospora
spongy
sponsal
sponsalia
sponsibility
sponsible
sponsing
sponsion
sponsional
sponson
sponsor
sponsorial
sponsorship
sponspeck
spontaneity
spontaneous
spontaneously
spontaneousness
spontoon
spoof
spoofer
spoofery
spoofish
spook
spookdom
spookery
spookily
spookiness
spookish
spookism
spookist
spookological
spookologist
spookology
spooky
spool
spooler
spoolful
spoollike
spoolwood
spoom
spoon
spoonbill
spoondrift
spooner
spoonerism
spooneyism
spooneyly
spooneyness
spoonflower
spoonful
spoonhutch
spoonily
spooniness
spooning
spoonism
spoonless
spoonlike
spoonmaker
spoonmaking
spoonways
spoonwood
spoony
spoonyism
spoor
spoorer
spoot
spor
sporabola
sporaceous
sporades
sporadial
sporadic
sporadical
sporadically
sporadicalness
sporadicity
sporadism
sporadosiderite
sporal
sporange
sporangia
sporangial
sporangidium
sporangiferous
sporangiform
sporangioid
sporangiola
sporangiole
sporangiolum
sporangiophore
sporangiospore
sporangite
Sporangites
sporangium
sporation
spore
spored
sporeformer
sporeforming
sporeling
sporicide
sporid
sporidesm
sporidia
sporidial
sporidiferous
sporidiole
sporidiolum
sporidium
sporiferous
sporification
sporiparity
sporiparous
sporoblast
Sporobolus
sporocarp
sporocarpium
Sporochnaceae
Sporochnus
sporocyst
sporocystic
sporocystid
sporocyte
sporodochia
sporodochium
sporoduct
sporogenesis
sporogenic
sporogenous
sporogeny
sporogone
sporogonial
sporogonic
sporogonium
sporogony
sporoid
sporologist
sporomycosis
sporont
sporophore
sporophoric
sporophorous
sporophydium
sporophyll
sporophyllary
sporophyllum
sporophyte
sporophytic
sporoplasm
sporosac
sporostegium
sporostrote
sporotrichosis
sporotrichotic
Sporotrichum
sporous
Sporozoa
sporozoal
sporozoan
sporozoic
sporozoite
sporozoon
sporran
sport
sportability
sportable
sportance
sporter
sportful
sportfully
sportfulness
sportily
sportiness
sporting
sportingly
sportive
sportively
sportiveness
sportless
sportling
sportly
sports
sportsman
sportsmanlike
sportsmanliness
sportsmanly
sportsmanship
sportsome
sportswear
sportswoman
sportswomanly
sportswomanship
sportula
sportulae
sporty
sporular
sporulate
sporulation
sporule
sporuliferous
sporuloid
sposh
sposhy
spot
spotless
spotlessly
spotlessness
spotlight
spotlighter
spotlike
spotrump
spotsman
spottable
spotted
spottedly
spottedness
spotteldy
spotter
spottily
spottiness
spotting
spottle
spotty
spoucher
spousage
spousal
spousally
spouse
spousehood
spouseless
spousy
spout
spouter
spoutiness
spouting
spoutless
spoutlike
spoutman
spouty
sprachle
sprack
sprackish
sprackle
sprackly
sprackness
sprad
spraddle
sprag
spragger
spraggly
spraich
sprain
spraint
spraints
sprang
sprangle
sprangly
sprank
sprat
spratter
spratty
sprauchle
sprawl
sprawler
sprawling
sprawlingly
sprawly
spray
sprayboard
sprayer
sprayey
sprayful
sprayfully
sprayless
spraylike
sprayproof
spread
spreadation
spreadboard
spreaded
spreader
spreadhead
spreading
spreadingly
spreadingness
spreadover
spready
spreaghery
spreath
spreckle
spree
spreeuw
Sprekelia
spreng
sprent
spret
sprew
sprewl
spridhogue
spried
sprier
spriest
sprig
sprigged
sprigger
spriggy
sprightful
sprightfully
sprightfulness
sprightlily
sprightliness
sprightly
sprighty
spriglet
sprigtail
Spring
spring
springal
springald
springboard
springbok
springbuck
springe
springer
springerle
springfinger
springfish
springful
springhaas
springhalt
springhead
springhouse
springily
springiness
springing
springingly
springle
springless
springlet
springlike
springly
springmaker
springmaking
springtail
springtide
springtime
springtrap
springwood
springworm
springwort
springwurzel
springy
sprink
sprinkle
sprinkled
sprinkleproof
sprinkler
sprinklered
sprinkling
sprint
sprinter
sprit
sprite
spritehood
spritsail
sprittail
sprittie
spritty
sproat
sprocket
sprod
sprogue
sproil
sprong
sprose
sprottle
sprout
sproutage
sprouter
sproutful
sprouting
sproutland
sproutling
sprowsy
spruce
sprucely
spruceness
sprucery
sprucification
sprucify
sprue
spruer
sprug
spruiker
spruit
sprung
sprunny
sprunt
spruntly
spry
spryly
spryness
spud
Spudboy
spudder
spuddle
spuddy
spuffle
spug
spuilyie
spuilzie
spuke
spume
spumescence
spumescent
spumiferous
spumification
spumiform
spumone
spumose
spumous
spumy
spun
spung
spunk
spunkie
spunkily
spunkiness
spunkless
spunky
spunny
spur
spurflower
spurgall
spurge
spurgewort
spuriae
spuriosity
spurious
spuriously
spuriousness
Spurius
spurl
spurless
spurlet
spurlike
spurling
spurmaker
spurmoney
spurn
spurner
spurnpoint
spurnwater
spurproof
spurred
spurrer
spurrial
spurrier
spurrings
spurrite
spurry
spurt
spurter
spurtive
spurtively
spurtle
spurway
spurwing
spurwinged
spurwort
sput
sputa
sputative
sputter
sputterer
sputtering
sputteringly
sputtery
sputum
sputumary
sputumose
sputumous
Spy
spy
spyboat
spydom
spyer
spyfault
spyglass
spyhole
spyism
spyproof
Spyros
spyship
spytower
squab
squabash
squabasher
squabbed
squabbish
squabble
squabbler
squabbling
squabblingly
squabbly
squabby
squacco
squad
squaddy
squadrate
squadrism
squadron
squadrone
squadroned
squail
squailer
squalene
Squali
squalid
Squalida
Squalidae
squalidity
squalidly
squalidness
squaliform
squall
squaller
squallery
squallish
squally
squalm
Squalodon
squalodont
Squalodontidae
squaloid
Squaloidei
squalor
Squalus
squam
squama
squamaceous
squamae
Squamariaceae
Squamata
squamate
squamated
squamatine
squamation
squamatogranulous
squamatotuberculate
squame
squamella
squamellate
squamelliferous
squamelliform
squameous
squamiferous
squamiform
squamify
squamigerous
squamipennate
Squamipennes
squamipinnate
Squamipinnes
squamocellular
squamoepithelial
squamoid
squamomastoid
squamoparietal
squamopetrosal
squamosa
squamosal
squamose
squamosely
squamoseness
squamosis
squamosity
squamosodentated
squamosoimbricated
squamosomaxillary
squamosoparietal
squamosoradiate
squamosotemporal
squamosozygomatic
squamosphenoid
squamosphenoidal
squamotemporal
squamous
squamously
squamousness
squamozygomatic
Squamscot
squamula
squamulae
squamulate
squamulation
squamule
squamuliform
squamulose
squander
squanderer
squanderingly
squandermania
squandermaniac
squantum
squarable
square
squareage
squarecap
squared
squaredly
squareface
squareflipper
squarehead
squarelike
squarely
squareman
squaremouth
squareness
squarer
squaretail
squarewise
squaring
squarish
squarishly
squark
squarrose
squarrosely
squarrous
squarrulose
squarson
squarsonry
squary
squash
squashberry
squasher
squashily
squashiness
squashy
squat
Squatarola
squatarole
Squatina
squatina
squatinid
Squatinidae
squatinoid
Squatinoidei
squatly
squatment
squatmore
squatness
squattage
squatted
squatter
squatterarchy
squatterdom
squatterproof
squattily
squattiness
squatting
squattingly
squattish
squattocracy
squattocratic
squatty
squatwise
squaw
squawberry
squawbush
squawdom
squawfish
squawflower
squawk
squawker
squawkie
squawking
squawkingly
squawky
Squawmish
squawroot
Squawtits
squawweed
Squaxon
squdge
squdgy
squeak
squeaker
squeakery
squeakily
squeakiness
squeaking
squeakingly
squeaklet
squeakproof
squeaky
squeakyish
squeal
squeald
squealer
squealing
squeam
squeamish
squeamishly
squeamishness
squeamous
squeamy
Squedunk
squeege
squeegee
squeezability
squeezable
squeezableness
squeezably
squeeze
squeezeman
squeezer
squeezing
squeezingly
squeezy
squelch
squelcher
squelchily
squelchiness
squelching
squelchingly
squelchingness
squelchy
squench
squencher
squeteague
squib
squibber
squibbery
squibbish
squiblet
squibling
squid
squiddle
squidge
squidgereen
squidgy
squiffed
squiffer
squiffy
squiggle
squiggly
squilgee
squilgeer
Squill
Squilla
squilla
squillagee
squillery
squillian
squillid
Squillidae
squilloid
Squilloidea
squimmidge
squin
squinance
squinancy
squinch
squinny
squinsy
squint
squinted
squinter
squinting
squintingly
squintingness
squintly
squintness
squinty
squirage
squiralty
squire
squirearch
squirearchal
squirearchical
squirearchy
squiredom
squireen
squirehood
squireless
squirelet
squirelike
squireling
squirely
squireocracy
squireship
squiress
squiret
squirewise
squirish
squirism
squirk
squirm
squirminess
squirming
squirmingly
squirmy
squirr
squirrel
squirrelfish
squirrelian
squirreline
squirrelish
squirrellike
squirrelproof
squirreltail
squirt
squirter
squirtiness
squirting
squirtingly
squirtish
squirty
squish
squishy
squit
squitch
squitchy
squitter
squoze
squush
squushy
sraddha
sramana
Sri
sri
Sridhar
Sridharan
Srikanth
Srinivas
Srinivasan
Sriram
Srivatsan
sruti
Ssi
ssu
st
staab
Staatsrat
stab
stabber
stabbing
stabbingly
stabile
stabilify
stabilist
stabilitate
stability
stabilization
stabilizator
stabilize
stabilizer
stable
stableboy
stableful
stablekeeper
stablelike
stableman
stableness
stabler
stablestand
stableward
stablewards
stabling
stablishment
stably
staboy
stabproof
stabulate
stabulation
stabwort
staccato
Stacey
stacher
stachydrin
stachydrine
stachyose
Stachys
stachys
Stachytarpheta
Stachyuraceae
stachyuraceous
Stachyurus
stack
stackage
stackencloud
stacker
stackfreed
stackful
stackgarth
Stackhousia
Stackhousiaceae
stackhousiaceous
stackless
stackman
stackstand
stackyard
stacte
stactometer
Stacy
stadda
staddle
staddling
stade
stadholder
stadholderate
stadholdership
stadhouse
stadia
stadic
stadimeter
stadiometer
stadion
stadium
stafette
staff
staffed
staffelite
staffer
staffless
staffman
stag
stagbush
stage
stageability
stageable
stageableness
stageably
stagecoach
stagecoaching
stagecraft
staged
stagedom
stagehand
stagehouse
stageland
stagelike
stageman
stager
stagery
stagese
stagewise
stageworthy
stagewright
staggard
staggart
staggarth
Stagger
stagger
staggerbush
staggerer
staggering
staggeringly
staggers
staggerweed
staggerwort
staggery
staggie
staggy
staghead
staghorn
staghound
staghunt
staghunter
staghunting
stagiary
stagily
staginess
staging
Stagirite
Stagiritic
staglike
stagmometer
stagnance
stagnancy
stagnant
stagnantly
stagnantness
stagnate
stagnation
stagnatory
stagnature
stagnicolous
stagnize
stagnum
Stagonospora
stagskin
stagworm
stagy
Stahlhelm
Stahlhelmer
Stahlhelmist
Stahlian
Stahlianism
Stahlism
staia
staid
staidly
staidness
stain
stainability
stainable
stainableness
stainably
stainer
stainful
stainierite
staining
stainless
stainlessly
stainlessness
stainproof
staio
stair
stairbeak
stairbuilder
stairbuilding
staircase
staired
stairhead
stairless
stairlike
stairstep
stairway
stairwise
stairwork
stairy
staith
staithman
staiver
stake
stakehead
stakeholder
stakemaster
staker
stakerope
Stakhanovism
Stakhanovite
stalactic
stalactical
stalactiform
stalactital
stalactite
stalactited
stalactitic
stalactitical
stalactitically
stalactitiform
stalactitious
stalagma
stalagmite
stalagmitic
stalagmitical
stalagmitically
stalagmometer
stalagmometric
stalagmometry
stale
stalely
stalemate
staleness
staling
Stalinism
Stalinist
Stalinite
stalk
stalkable
stalked
stalker
stalkily
stalkiness
stalking
stalkingly
stalkless
stalklet
stalklike
stalko
stalky
stall
stallage
stallar
stallboard
stallenger
staller
stallership
stalling
stallion
stallionize
stallman
stallment
stalwart
stalwartism
stalwartize
stalwartly
stalwartness
stam
stambha
stambouline
stamen
stamened
stamin
stamina
staminal
staminate
stamineal
stamineous
staminiferous
staminigerous
staminode
staminodium
staminody
stammel
stammer
stammerer
stammering
stammeringly
stammeringness
stammerwort
stamnos
stamp
stampable
stampage
stampedable
stampede
stampeder
stampedingly
stampee
stamper
stampery
stamphead
Stampian
stamping
stample
stampless
stampman
stampsman
stampweed
Stan
stance
stanch
stanchable
stanchel
stancheled
stancher
stanchion
stanchless
stanchly
stanchness
stand
standage
standard
standardbred
standardizable
standardization
standardize
standardized
standardizer
standardwise
standee
standel
standelwelks
standelwort
stander
standergrass
standerwort
standfast
standing
standish
standoff
standoffish
standoffishness
standout
standpat
standpatism
standpatter
standpipe
standpoint
standpost
standstill
stane
stanechat
stang
Stangeria
stanhope
Stanhopea
stanine
Stanislaw
stanjen
stank
stankie
Stanley
Stanly
stannane
stannary
stannate
stannator
stannel
stanner
stannery
stannic
stannide
stanniferous
stannite
stanno
stannotype
stannous
stannoxyl
stannum
stannyl
stanza
stanzaed
stanzaic
stanzaical
stanzaically
stanze
stap
stapedectomy
stapedial
stapediform
stapediovestibular
stapedius
Stapelia
stapelia
stapes
staphisagria
staphyle
Staphylea
Staphyleaceae
staphyleaceous
staphylectomy
staphyledema
staphylematoma
staphylic
staphyline
staphylinic
staphylinid
Staphylinidae
staphylinideous
Staphylinoidea
Staphylinus
staphylion
staphylitis
staphyloangina
staphylococcal
staphylococci
staphylococcic
Staphylococcus
staphylococcus
staphylodermatitis
staphylodialysis
staphyloedema
staphylohemia
staphylolysin
staphyloma
staphylomatic
staphylomatous
staphylomycosis
staphyloncus
staphyloplastic
staphyloplasty
staphyloptosia
staphyloptosis
staphyloraphic
staphylorrhaphic
staphylorrhaphy
staphyloschisis
staphylosis
staphylotome
staphylotomy
staphylotoxin
staple
stapled
stapler
staplewise
stapling
Star
star
starblind
starbloom
starboard
starbolins
starbright
Starbuck
starch
starchboard
starched
starchedly
starchedness
starcher
starchflower
starchily
starchiness
starchless
starchlike
starchly
starchmaker
starchmaking
starchman
starchness
starchroot
starchworks
starchwort
starchy
starcraft
stardom
stare
staree
starer
starets
starfish
starflower
starfruit
starful
stargaze
stargazer
stargazing
staring
staringly
stark
starken
starkly
starkness
starky
starless
starlessly
starlessness
starlet
starlight
starlighted
starlights
starlike
starling
starlit
starlite
starlitten
starmonger
starn
starnel
starnie
starnose
Staroobriadtsi
starost
starosta
starosty
starred
starrily
starriness
starring
starringly
starry
starshake
starshine
starship
starshoot
starshot
starstone
starstroke
start
starter
startful
startfulness
starthroat
starting
startingly
startish
startle
startler
startling
startlingly
startlingness
startlish
startlishness
startly
startor
starty
starvation
starve
starveacre
starved
starvedly
starveling
starver
starvy
starward
starwise
starworm
starwort
stary
stases
stash
stashie
stasidion
stasimetric
stasimon
stasimorphy
stasiphobia
stasis
stassfurtite
statable
statal
statant
statcoulomb
State
state
statecraft
stated
statedly
stateful
statefully
statefulness
statehood
Statehouse
stateless
statelet
statelich
statelily
stateliness
stately
statement
statemonger
statequake
stater
stateroom
statesboy
stateside
statesider
statesman
statesmanese
statesmanlike
statesmanly
statesmanship
statesmonger
stateswoman
stateway
statfarad
stathmoi
stathmos
static
statical
statically
Statice
staticproof
statics
station
stational
stationarily
stationariness
stationary
stationer
stationery
stationman
stationmaster
statiscope
statism
statist
statistic
statistical
statistically
statistician
statisticize
statistics
statistology
stative
statoblast
statocracy
statocyst
statolatry
statolith
statolithic
statometer
stator
statoreceptor
statorhab
statoscope
statospore
statuarism
statuarist
statuary
statue
statuecraft
statued
statueless
statuelike
statuesque
statuesquely
statuesqueness
statuette
stature
statured
status
statutable
statutableness
statutably
statutary
statute
statutorily
statutory
statvolt
staucher
stauk
staumer
staun
staunch
staunchable
staunchly
staunchness
staup
stauracin
stauraxonia
stauraxonial
staurion
staurolatry
staurolite
staurolitic
staurology
Stauromedusae
stauromedusan
stauropegial
stauropegion
stauroscope
stauroscopic
stauroscopically
staurotide
stauter
stave
staveable
staveless
staver
stavers
staverwort
stavesacre
stavewise
stavewood
staving
stavrite
staw
stawn
staxis
stay
stayable
stayed
stayer
staylace
stayless
staylessness
staymaker
staymaking
staynil
stays
staysail
stayship
stchi
stead
steadfast
steadfastly
steadfastness
steadier
steadily
steadiment
steadiness
steading
steadman
steady
steadying
steadyingly
steadyish
steak
steal
stealability
stealable
stealage
stealed
stealer
stealing
stealingly
stealth
stealthful
stealthfully
stealthily
stealthiness
stealthless
stealthlike
stealthwise
stealthy
stealy
steam
steamboat
steamboating
steamboatman
steamcar
steamer
steamerful
steamerless
steamerload
steamily
steaminess
steaming
steamless
steamlike
steampipe
steamproof
steamship
steamtight
steamtightness
steamy
stean
steaning
steapsin
stearate
stearic
steariform
stearin
stearolactone
stearone
stearoptene
stearrhea
stearyl
steatin
steatite
steatitic
steatocele
steatogenous
steatolysis
steatolytic
steatoma
steatomatous
steatopathic
steatopyga
steatopygia
steatopygic
steatopygous
Steatornis
Steatornithes
Steatornithidae
steatorrhea
steatosis
stech
stechados
steckling
steddle
Stedman
steed
steedless
steedlike
steek
steekkan
steekkannen
steel
Steelboy
steeler
steelhead
steelhearted
steelification
steelify
steeliness
steeling
steelless
steellike
steelmaker
steelmaking
steelproof
steelware
steelwork
steelworker
steelworks
steely
steelyard
Steen
steen
steenboc
steenbock
steenbok
Steenie
steenkirk
steenstrupine
steenth
steep
steepdown
steepen
steeper
steepgrass
steepish
steeple
steeplebush
steeplechase
steeplechaser
steeplechasing
steepled
steepleless
steeplelike
steepletop
steeply
steepness
steepweed
steepwort
steepy
steer
steerability
steerable
steerage
steerageway
steerer
steering
steeringly
steerling
steerman
steermanship
steersman
steerswoman
steeve
steevely
steever
steeving
Stefan
steg
steganogram
steganographical
steganographist
steganography
Steganophthalmata
steganophthalmate
steganophthalmatous
Steganophthalmia
steganopod
steganopodan
Steganopodes
steganopodous
stegnosis
stegnotic
stegocarpous
Stegocephalia
stegocephalian
stegocephalous
Stegodon
stegodont
stegodontine
Stegomus
Stegomyia
stegosaur
Stegosauria
stegosaurian
stegosauroid
Stegosaurus
steid
steigh
Stein
stein
Steinberger
steinbok
Steinerian
steinful
steinkirk
Steironema
stekan
stela
stelae
stelai
stelar
stele
stell
Stella
stella
stellar
Stellaria
stellary
stellate
stellated
stellately
stellature
stelleridean
stellerine
stelliferous
stellification
stelliform
stellify
stelling
stellionate
stelliscript
Stellite
stellite
stellular
stellularly
stellulate
stelography
stem
stema
stemhead
stemless
stemlet
stemlike
stemma
stemmata
stemmatiform
stemmatous
stemmed
stemmer
stemmery
stemming
stemmy
Stemona
Stemonaceae
stemonaceous
stemple
stempost
stemson
stemwards
stemware
sten
stenar
stench
stenchel
stenchful
stenching
stenchion
stenchy
stencil
stenciler
stencilmaker
stencilmaking
stend
steng
stengah
stenion
steno
stenobathic
stenobenthic
stenobragmatic
stenobregma
stenocardia
stenocardiac
Stenocarpus
stenocephalia
stenocephalic
stenocephalous
stenocephaly
stenochoria
stenochrome
stenochromy
stenocoriasis
stenocranial
stenocrotaphia
Stenofiber
stenog
stenogastric
stenogastry
Stenoglossa
stenograph
stenographer
stenographic
stenographical
stenographically
stenographist
stenography
stenohaline
stenometer
stenopaic
Stenopelmatidae
stenopetalous
stenophile
Stenophragma
stenophyllous
stenorhyncous
stenosed
stenosepalous
stenosis
stenosphere
stenostomatous
stenostomia
Stenotaphrum
stenotelegraphy
stenothermal
stenothorax
stenotic
stenotype
stenotypic
stenotypist
stenotypy
stent
stenter
stenterer
stenton
Stentor
stentorian
stentorianly
stentorine
stentorious
stentoriously
stentoriousness
stentoronic
stentorophonic
stentrel
step
stepaunt
stepbairn
stepbrother
stepbrotherhood
stepchild
stepdame
stepdaughter
stepfather
stepfatherhood
stepfatherly
stepgrandchild
stepgrandfather
stepgrandmother
stepgrandson
Stephan
Stephana
stephane
stephanial
Stephanian
stephanic
Stephanie
stephanion
stephanite
Stephanoceros
Stephanokontae
stephanome
stephanos
Stephanotis
stephanotis
Stephanurus
Stephe
Stephen
stepladder
stepless
steplike
stepminnie
stepmother
stepmotherhood
stepmotherless
stepmotherliness
stepmotherly
stepnephew
stepniece
stepparent
steppe
stepped
steppeland
stepper
stepping
steppingstone
steprelation
steprelationship
stepsire
stepsister
stepson
stepstone
stept
stepuncle
stepway
stepwise
steradian
stercobilin
stercolin
stercophagic
stercophagous
stercoraceous
stercoral
Stercoranism
Stercoranist
Stercorariidae
Stercorariinae
stercorarious
Stercorarius
stercorary
stercorate
stercoration
stercorean
stercoremia
stercoreous
Stercorianism
stercoricolous
Stercorist
stercorite
stercorol
stercorous
stercovorous
Sterculia
Sterculiaceae
sterculiaceous
sterculiad
stere
stereagnosis
Sterelmintha
sterelminthic
sterelminthous
stereo
stereobate
stereobatic
stereoblastula
stereocamera
stereocampimeter
stereochemic
stereochemical
stereochemically
stereochemistry
stereochromatic
stereochromatically
stereochrome
stereochromic
stereochromically
stereochromy
stereocomparagraph
stereocomparator
stereoelectric
stereofluoroscopic
stereofluoroscopy
stereogastrula
stereognosis
stereognostic
stereogoniometer
stereogram
stereograph
stereographer
stereographic
stereographical
stereographically
stereography
stereoisomer
stereoisomeric
stereoisomerical
stereoisomeride
stereoisomerism
stereomatrix
stereome
stereomer
stereomeric
stereomerical
stereomerism
stereometer
stereometric
stereometrical
stereometrically
stereometry
stereomicrometer
stereomonoscope
stereoneural
stereophantascope
stereophonic
stereophony
stereophotogrammetry
stereophotograph
stereophotographic
stereophotography
stereophotomicrograph
stereophotomicrography
stereophysics
stereopicture
stereoplanigraph
stereoplanula
stereoplasm
stereoplasma
stereoplasmic
stereopsis
stereoptician
stereopticon
stereoradiograph
stereoradiography
Stereornithes
stereornithic
stereoroentgenogram
stereoroentgenography
stereoscope
stereoscopic
stereoscopically
stereoscopism
stereoscopist
stereoscopy
Stereospondyli
stereospondylous
stereostatic
stereostatics
stereotactic
stereotactically
stereotaxis
stereotelemeter
stereotelescope
stereotomic
stereotomical
stereotomist
stereotomy
stereotropic
stereotropism
stereotypable
stereotype
stereotyped
stereotyper
stereotypery
stereotypic
stereotypical
stereotyping
stereotypist
stereotypographer
stereotypography
stereotypy
Stereum
sterhydraulic
steri
steric
sterically
sterics
steride
sterigma
sterigmata
sterigmatic
sterile
sterilely
sterileness
sterilisable
sterility
sterilizability
sterilizable
sterilization
sterilize
sterilizer
sterin
sterk
sterlet
Sterling
sterling
sterlingly
sterlingness
Stern
stern
Sterna
sterna
sternad
sternage
sternal
sternalis
sternbergite
sterncastle
sterneber
sternebra
sternebrae
sternebral
sterned
sternforemost
Sterninae
sternite
sternitic
sternly
sternman
sternmost
sternness
Sterno
sternoclavicular
sternocleidomastoid
sternoclidomastoid
sternocoracoid
sternocostal
sternofacial
sternofacialis
sternoglossal
sternohumeral
sternohyoid
sternohyoidean
sternomancy
sternomastoid
sternomaxillary
sternonuchal
sternopericardiac
sternopericardial
sternoscapular
sternothere
Sternotherus
sternothyroid
sternotracheal
sternotribe
sternovertebral
sternoxiphoid
sternpost
sternson
sternum
sternutation
sternutative
sternutator
sternutatory
sternward
sternway
sternways
sternworks
stero
steroid
sterol
Sterope
sterrinck
stert
stertor
stertorious
stertoriously
stertoriousness
stertorous
stertorously
stertorousness
sterve
Stesichorean
stet
stetch
stetharteritis
stethogoniometer
stethograph
stethographic
stethokyrtograph
stethometer
stethometric
stethometry
stethoparalysis
stethophone
stethophonometer
stethoscope
stethoscopic
stethoscopical
stethoscopically
stethoscopist
stethoscopy
stethospasm
Stevan
Steve
stevedorage
stevedore
stevedoring
stevel
Steven
steven
Stevensonian
Stevensoniana
Stevia
stevia
stew
stewable
steward
stewardess
stewardly
stewardry
stewardship
Stewart
Stewartia
stewartry
stewarty
stewed
stewpan
stewpond
stewpot
stewy
stey
sthenia
sthenic
sthenochire
stib
stibbler
stibblerig
stibethyl
stibial
stibialism
stibiate
stibiated
stibic
stibiconite
stibine
stibious
stibium
stibnite
stibonium
sticcado
stich
sticharion
sticheron
stichic
stichically
stichid
stichidium
stichomancy
stichometric
stichometrical
stichometrically
stichometry
stichomythic
stichomythy
stick
stickability
stickable
stickadore
stickadove
stickage
stickball
sticked
sticker
stickers
stickfast
stickful
stickily
stickiness
sticking
stickit
stickle
stickleaf
stickleback
stickler
stickless
sticklike
stickling
stickly
stickpin
sticks
stickseed
sticksmanship
sticktail
sticktight
stickum
stickwater
stickweed
stickwork
sticky
Sticta
Stictaceae
Stictidaceae
stictiform
Stictis
stid
stiddy
stife
stiff
stiffen
stiffener
stiffening
stiffhearted
stiffish
stiffleg
stifflike
stiffly
stiffneck
stiffness
stiffrump
stifftail
stifle
stifledly
stifler
stifling
stiflingly
stigma
stigmai
stigmal
stigmaria
stigmarian
stigmarioid
stigmasterol
stigmata
stigmatal
stigmatic
stigmatical
stigmatically
stigmaticalness
stigmatiferous
stigmatiform
stigmatism
stigmatist
stigmatization
stigmatize
stigmatizer
stigmatoid
stigmatose
stigme
stigmeology
stigmonose
stigonomancy
Stikine
Stilbaceae
Stilbella
stilbene
stilbestrol
stilbite
stilboestrol
Stilbum
stile
stileman
stilet
stiletto
stilettolike
still
stillage
stillatitious
stillatory
stillbirth
stillborn
stiller
stillhouse
stillicide
stillicidium
stilliform
stilling
Stillingia
stillion
stillish
stillman
stillness
stillroom
stillstand
Stillwater
stilly
Stilophora
Stilophoraceae
stilpnomelane
stilpnosiderite
stilt
stiltbird
stilted
stilter
stiltify
stiltiness
stiltish
stiltlike
Stilton
stilty
stim
stime
stimpart
stimpert
stimulability
stimulable
stimulance
stimulancy
stimulant
stimulate
stimulatingly
stimulation
stimulative
stimulator
stimulatory
stimulatress
stimulatrix
stimuli
stimulogenous
stimulus
stimy
stine
sting
stingaree
stingareeing
stingbull
stinge
stinger
stingfish
stingily
stinginess
stinging
stingingly
stingingness
stingless
stingo
stingproof
stingray
stingtail
stingy
stink
stinkard
stinkardly
stinkball
stinkberry
stinkbird
stinkbug
stinkbush
stinkdamp
stinker
stinkhorn
stinking
stinkingly
stinkingness
stinkpot
stinkstone
stinkweed
stinkwood
stinkwort
stint
stinted
stintedly
stintedness
stinter
stintingly
stintless
stinty
stion
stionic
Stipa
stipe
stiped
stipel
stipellate
stipend
stipendial
stipendiarian
stipendiary
stipendiate
stipendium
stipendless
stipes
stipiform
stipitate
stipitiform
stipiture
Stipiturus
stippen
stipple
stippled
stippler
stippling
stipply
stipula
stipulable
stipulaceous
stipulae
stipular
stipulary
stipulate
stipulation
stipulator
stipulatory
stipule
stipuled
stipuliferous
stipuliform
stir
stirabout
stirk
stirless
stirlessly
stirlessness
stirp
stirpicultural
stirpiculture
stirpiculturist
stirps
stirra
stirrable
stirrage
stirrer
stirring
stirringly
stirrup
stirrupless
stirruplike
stirrupwise
stitch
stitchbird
stitchdown
stitcher
stitchery
stitching
stitchlike
stitchwhile
stitchwork
stitchwort
stite
stith
stithy
stive
stiver
stivy
Stizolobium
stoa
stoach
stoat
stoater
stob
stocah
stoccado
stoccata
stochastic
stochastical
stochastically
stock
stockade
stockannet
stockbow
stockbreeder
stockbreeding
Stockbridge
stockbroker
stockbrokerage
stockbroking
stockcar
stocker
stockfather
stockfish
stockholder
stockholding
stockhouse
stockily
stockiness
stockinet
stocking
stockinger
stockingless
stockish
stockishly
stockishness
stockjobber
stockjobbery
stockjobbing
stockjudging
stockkeeper
stockkeeping
stockless
stocklike
stockmaker
stockmaking
stockman
stockowner
stockpile
stockpot
stockproof
stockrider
stockriding
stocks
stockstone
stocktaker
stocktaking
Stockton
stockwork
stockwright
stocky
stockyard
stod
stodge
stodger
stodgery
stodgily
stodginess
stodgy
stoechas
stoep
stof
stoff
stog
stoga
stogie
stogy
Stoic
stoic
stoical
stoically
stoicalness
stoicharion
stoichiological
stoichiology
stoichiometric
stoichiometrical
stoichiometrically
stoichiometry
Stoicism
stoicism
Stokavci
Stokavian
Stokavski
stoke
stokehold
stokehole
stoker
stokerless
Stokesia
stokesite
stola
stolae
stole
stoled
stolelike
stolen
stolenly
stolenness
stolenwise
stolewise
stolid
stolidity
stolidly
stolidness
stolist
stolkjaerre
stollen
stolon
stolonate
stoloniferous
stoloniferously
stolonlike
stolzite
stoma
stomacace
stomach
stomachable
stomachal
stomacher
stomachful
stomachfully
stomachfulness
stomachic
stomachically
stomachicness
stomaching
stomachless
stomachlessness
stomachy
stomapod
Stomapoda
stomapodiform
stomapodous
stomata
stomatal
stomatalgia
stomate
stomatic
stomatiferous
stomatitic
stomatitis
stomatocace
Stomatoda
stomatodaeal
stomatodaeum
stomatode
stomatodeum
stomatodynia
stomatogastric
stomatograph
stomatography
stomatolalia
stomatologic
stomatological
stomatologist
stomatology
stomatomalacia
stomatomenia
stomatomy
stomatomycosis
stomatonecrosis
stomatopathy
Stomatophora
stomatophorous
stomatoplastic
stomatoplasty
stomatopod
Stomatopoda
stomatopodous
stomatorrhagia
stomatoscope
stomatoscopy
stomatose
stomatosepsis
stomatotomy
stomatotyphus
stomatous
stomenorrhagia
stomium
stomodaea
stomodaeal
stomodaeum
Stomoisia
stomoxys
stomp
stomper
stonable
stond
Stone
stone
stoneable
stonebird
stonebiter
stoneboat
stonebow
stonebrash
stonebreak
stonebrood
stonecast
stonechat
stonecraft
stonecrop
stonecutter
stoned
stonedamp
stonefish
stonegale
stonegall
stonehand
stonehatch
stonehead
stonehearted
Stonehenge
stonelayer
stonelaying
stoneless
stonelessness
stonelike
stoneman
stonemason
stonemasonry
stonen
stonepecker
stoner
stoneroot
stoneseed
stoneshot
stonesmatch
stonesmich
stonesmitch
stonesmith
stonewall
stonewaller
stonewally
stoneware
stoneweed
stonewise
stonewood
stonework
stoneworker
stonewort
stoneyard
stong
stonied
stonifiable
stonify
stonily
stoniness
stoning
stonish
stonishment
stonker
stony
stonyhearted
stonyheartedly
stonyheartedness
stood
stooded
stooden
stoof
stooge
stook
stooker
stookie
stool
stoolball
stoollike
stoon
stoond
stoop
stooper
stoopgallant
stooping
stoopingly
stoory
stoot
stoothing
stop
stopa
stopback
stopblock
stopboard
stopcock
stope
stoper
stopgap
stophound
stoping
stopless
stoplessness
stopover
stoppability
stoppable
stoppableness
stoppably
stoppage
stopped
stopper
stopperless
stoppeur
stopping
stoppit
stopple
stopwater
stopwork
storable
storage
storax
store
storeen
storehouse
storehouseman
storekeep
storekeeper
storekeeping
storeman
storer
storeroom
storeship
storesman
storge
storiate
storiation
storied
storier
storiette
storify
storiological
storiologist
storiology
stork
storken
storkish
storklike
storkling
storkwise
storm
stormable
Stormberg
stormbird
stormbound
stormcock
stormer
stormful
stormfully
stormfulness
stormily
storminess
storming
stormingly
stormish
stormless
stormlessness
stormlike
stormproof
stormward
stormwind
stormwise
stormy
Storting
story
storybook
storyless
storymaker
storymonger
storyteller
storytelling
storywise
storywork
stosh
stoss
stosston
stot
stotinka
stotter
stotterel
stoun
stound
stoundmeal
stoup
stoupful
stour
stouring
stourliness
stourness
stoury
stoush
stout
stouten
stouth
stouthearted
stoutheartedly
stoutheartedness
stoutish
stoutly
stoutness
stoutwood
stouty
stove
stovebrush
stoveful
stovehouse
stoveless
stovemaker
stovemaking
stoveman
stoven
stovepipe
stover
stovewood
stow
stowable
stowage
stowaway
stowbord
stowbordman
stowce
stowdown
stower
stowing
stownlins
stowwood
stra
strabism
strabismal
strabismally
strabismic
strabismical
strabismometer
strabismometry
strabismus
strabometer
strabometry
strabotome
strabotomy
strack
strackling
stract
Strad
strad
stradametrical
straddle
straddleback
straddlebug
straddler
straddleways
straddlewise
straddling
straddlingly
strade
stradine
stradiot
Stradivari
Stradivarius
stradl
stradld
stradlings
strae
strafe
strafer
Straffordian
strag
straggle
straggler
straggling
stragglingly
straggly
stragular
stragulum
straight
straightabout
straightaway
straightedge
straighten
straightener
straightforward
straightforwardly
straightforwardness
straightforwards
straighthead
straightish
straightly
straightness
straighttail
straightup
straightwards
straightway
straightways
straightwise
straik
strain
strainable
strainableness
strainably
strained
strainedly
strainedness
strainer
strainerman
straining
strainingly
strainless
strainlessly
strainproof
strainslip
straint
strait
straiten
straitlacedness
straitlacing
straitly
straitness
straitsman
straitwork
Straka
strake
straked
straky
stram
stramash
stramazon
stramineous
stramineously
strammel
strammer
stramonium
stramony
stramp
strand
strandage
strander
stranding
strandless
strandward
strang
strange
strangeling
strangely
strangeness
stranger
strangerdom
strangerhood
strangerlike
strangership
strangerwise
strangle
strangleable
stranglement
strangler
strangles
strangletare
strangleweed
strangling
stranglingly
strangulable
strangulate
strangulation
strangulative
strangulatory
strangullion
strangurious
strangury
stranner
strany
strap
straphang
straphanger
straphead
strapless
straplike
strappable
strappado
strappan
strapped
strapper
strapping
strapple
strapwork
strapwort
strass
strata
stratagem
stratagematic
stratagematical
stratagematically
stratagematist
stratagemical
stratagemically
stratal
stratameter
stratege
strategetic
strategetics
strategi
strategian
strategic
strategical
strategically
strategics
strategist
strategize
strategos
strategy
Stratfordian
strath
strathspey
strati
stratic
straticulate
straticulation
stratification
stratified
stratiform
stratify
stratigrapher
stratigraphic
stratigraphical
stratigraphically
stratigraphist
stratigraphy
Stratiomyiidae
Stratiotes
stratlin
stratochamber
stratocracy
stratocrat
stratocratic
stratographic
stratographical
stratographically
stratography
stratonic
Stratonical
stratopedarch
stratoplane
stratose
stratosphere
stratospheric
stratospherical
stratotrainer
stratous
stratum
stratus
straucht
strauchten
stravage
strave
straw
strawberry
strawberrylike
strawbill
strawboard
strawbreadth
strawen
strawer
strawflower
strawfork
strawless
strawlike
strawman
strawmote
strawsmall
strawsmear
strawstack
strawstacker
strawwalker
strawwork
strawworm
strawy
strawyard
stray
strayaway
strayer
strayling
stre
streahte
streak
streaked
streakedly
streakedness
streaker
streakily
streakiness
streaklike
streakwise
streaky
stream
streamer
streamful
streamhead
streaminess
streaming
streamingly
streamless
streamlet
streamlike
streamline
streamlined
streamliner
streamling
streamside
streamward
streamway
streamwort
streamy
streck
streckly
stree
streek
streel
streeler
streen
streep
street
streetage
streetcar
streetful
streetless
streetlet
streetlike
streets
streetside
streetwalker
streetwalking
streetward
streetway
streetwise
streite
streke
Strelitz
Strelitzi
strelitzi
Strelitzia
Streltzi
streltzi
stremma
stremmatograph
streng
strengite
strength
strengthen
strengthener
strengthening
strengtheningly
strengthful
strengthfulness
strengthily
strengthless
strengthlessly
strengthlessness
strengthy
strent
strenth
strenuity
strenuosity
strenuous
strenuously
strenuousness
strepen
strepent
strepera
streperous
strephonade
strephosymbolia
strepitant
strepitantly
strepitation
strepitous
strepor
Strepsiceros
strepsiceros
strepsinema
Strepsiptera
strepsipteral
strepsipteran
strepsipteron
strepsipterous
strepsis
strepsitene
streptaster
streptobacilli
streptobacillus
Streptocarpus
streptococcal
streptococci
streptococcic
Streptococcus
streptococcus
streptolysin
Streptomyces
streptomycin
Streptoneura
streptoneural
streptoneurous
streptosepticemia
streptothricial
streptothricin
streptothricosis
Streptothrix
streptotrichal
streptotrichosis
stress
stresser
stressful
stressfully
stressless
stresslessness
stret
stretch
stretchable
stretchberry
stretcher
stretcherman
stretchiness
stretchneck
stretchproof
stretchy
stretman
strette
stretti
stretto
strew
strewage
strewer
strewment
strewn
strey
streyne
stria
striae
strial
Striaria
Striariaceae
striatal
striate
striated
striation
striatum
striature
strich
striche
strick
stricken
strickenly
strickenness
stricker
strickle
strickler
strickless
strict
striction
strictish
strictly
strictness
stricture
strictured
strid
stridden
striddle
stride
strideleg
stridelegs
stridence
stridency
strident
stridently
strider
strideways
stridhan
stridhana
stridhanum
stridingly
stridling
stridlins
stridor
stridulant
stridulate
stridulation
stridulator
stridulatory
stridulent
stridulous
stridulously
stridulousness
strife
strifeful
strifeless
strifemaker
strifemaking
strifemonger
strifeproof
striffen
strig
Striga
striga
strigae
strigal
strigate
Striges
striggle
stright
Strigidae
Strigiformes
strigil
strigilate
strigilation
strigilator
strigiles
strigilis
strigillose
strigilous
Striginae
strigine
strigose
strigous
strigovite
Strigula
Strigulaceae
strigulose
strike
strikeboat
strikebreaker
strikebreaking
strikeless
striker
striking
strikingly
strikingness
strind
string
stringboard
stringcourse
stringed
stringency
stringene
stringent
stringently
stringentness
stringer
stringful
stringhalt
stringhalted
stringhaltedness
stringiness
stringing
stringless
stringlike
stringmaker
stringmaking
stringman
stringpiece
stringsman
stringways
stringwood
stringy
stringybark
strinkle
striola
striolae
striolate
striolated
striolet
strip
stripe
striped
stripeless
striper
striplet
stripling
strippage
stripped
stripper
stripping
strippit
strippler
stript
stripy
strit
strive
strived
striven
striver
striving
strivingly
Strix
strix
stroam
strobic
strobila
strobilaceous
strobilae
strobilate
strobilation
strobile
strobili
strobiliferous
strobiliform
strobiline
strobilization
strobiloid
Strobilomyces
Strobilophyta
strobilus
stroboscope
stroboscopic
stroboscopical
stroboscopy
strobotron
strockle
stroddle
strode
stroil
stroke
stroker
strokesman
stroking
stroky
strold
stroll
strolld
stroller
strom
stroma
stromal
stromata
Stromateidae
stromateoid
stromatic
stromatiform
stromatology
Stromatopora
Stromatoporidae
stromatoporoid
Stromatoporoidea
stromatous
stromb
Strombidae
strombiform
strombite
stromboid
strombolian
strombuliferous
strombuliform
Strombus
strome
stromeyerite
stromming
strone
strong
strongback
strongbark
strongbox
strongbrained
strongfully
stronghand
stronghead
strongheadedly
strongheadedness
stronghearted
stronghold
strongish
stronglike
strongly
strongness
strongylate
strongyle
strongyliasis
strongylid
Strongylidae
strongylidosis
strongyloid
Strongyloides
strongyloidosis
strongylon
Strongyloplasmata
Strongylosis
strongylosis
Strongylus
strontia
strontian
strontianiferous
strontianite
strontic
strontion
strontitic
strontium
strook
strooken
stroot
strop
strophaic
strophanhin
Strophanthus
Stropharia
strophe
strophic
strophical
strophically
strophiolate
strophiolated
strophiole
strophoid
Strophomena
Strophomenacea
strophomenid
Strophomenidae
strophomenoid
strophosis
strophotaxis
strophulus
stropper
stroppings
stroth
stroud
strouding
strounge
stroup
strouthiocamel
strouthiocamelian
strouthocamelian
strove
strow
strowd
strown
stroy
stroyer
stroygood
strub
strubbly
struck
strucken
structural
structuralism
structuralist
structuralization
structuralize
structurally
structuration
structure
structured
structureless
structurely
structurist
strudel
strue
struggle
struggler
struggling
strugglingly
Struldbrug
Struldbruggian
Struldbruggism
strum
struma
strumae
strumatic
strumaticness
strumectomy
Strumella
strumiferous
strumiform
strumiprivic
strumiprivous
strumitis
strummer
strumose
strumous
strumousness
strumpet
strumpetlike
strumpetry
strumstrum
strumulose
strung
strunt
strut
struth
struthian
struthiform
Struthio
struthioid
Struthiomimus
Struthiones
Struthionidae
struthioniform
Struthioniformes
Struthiopteris
struthious
struthonine
strutter
strutting
struttingly
struv
struvite
strych
strychnia
strychnic
strychnin
strychnine
strychninic
strychninism
strychninization
strychninize
strychnize
strychnol
Strychnos
Strymon
Stu
Stuart
Stuartia
stub
stubachite
stubb
stubbed
stubbedness
stubber
stubbiness
stubble
stubbleberry
stubbled
stubbleward
stubbly
stubborn
stubbornhearted
stubbornly
stubbornness
stubboy
stubby
stubchen
stuber
stuboy
stubrunner
stucco
stuccoer
stuccowork
stuccoworker
stuccoyer
stuck
stuckling
stucturelessness
stud
studbook
studder
studdie
studding
studdle
stude
student
studenthood
studentless
studentlike
studentry
studentship
studerite
studfish
studflower
studhorse
studia
studiable
studied
studiedly
studiedness
studier
studio
studious
studiously
studiousness
Studite
Studium
studium
studwork
study
stue
stuff
stuffed
stuffender
stuffer
stuffgownsman
stuffily
stuffiness
stuffing
stuffy
stug
stuggy
stuiver
stull
stuller
stulm
stultification
stultifier
stultify
stultiloquence
stultiloquently
stultiloquious
stultioquy
stultloquent
stum
stumble
stumbler
stumbling
stumblingly
stumbly
stumer
stummer
stummy
stump
stumpage
stumper
stumpily
stumpiness
stumpish
stumpless
stumplike
stumpling
stumpnose
stumpwise
stumpy
stun
Stundism
Stundist
stung
stunk
stunkard
stunner
stunning
stunningly
stunpoll
stunsail
stunsle
stunt
stunted
stuntedly
stuntedness
stunter
stuntiness
stuntness
stunty
stupa
stupe
stupefacient
stupefaction
stupefactive
stupefactiveness
stupefied
stupefiedness
stupefier
stupefy
stupend
stupendly
stupendous
stupendously
stupendousness
stupent
stupeous
stupex
stupid
stupidhead
stupidish
stupidity
stupidly
stupidness
stupor
stuporific
stuporose
stuporous
stupose
stupp
stuprate
stupration
stuprum
stupulose
sturdied
sturdily
sturdiness
sturdy
sturdyhearted
sturgeon
sturine
Sturiones
sturionine
sturk
Sturmian
Sturnella
Sturnidae
sturniform
Sturninae
sturnine
sturnoid
Sturnus
sturt
sturtan
sturtin
sturtion
sturtite
stuss
stut
stutter
stutterer
stuttering
stutteringly
sty
styan
styca
styceric
stycerin
stycerinol
stychomythia
styful
styfziekte
Stygial
Stygian
stylar
Stylaster
Stylasteridae
stylate
style
stylebook
styledom
styleless
stylelessness
stylelike
styler
stylet
stylewort
Stylidiaceae
stylidiaceous
Stylidium
styliferous
styliform
styline
styling
stylish
stylishly
stylishness
stylist
stylistic
stylistical
stylistically
stylistics
stylite
stylitic
stylitism
stylization
stylize
stylizer
stylo
styloauricularis
stylobate
Stylochus
styloglossal
styloglossus
stylogonidium
stylograph
stylographic
stylographical
stylographically
stylography
stylohyal
stylohyoid
stylohyoidean
stylohyoideus
styloid
stylolite
stylolitic
stylomandibular
stylomastoid
stylomaxillary
stylometer
Stylommatophora
stylommatophorous
stylomyloid
Stylonurus
Stylonychia
stylopharyngeal
stylopharyngeus
stylopid
Stylopidae
stylopization
stylopized
stylopod
stylopodium
Stylops
stylops
Stylosanthes
stylospore
stylosporous
stylostegium
stylotypite
stylus
stymie
Stymphalian
Stymphalid
Stymphalides
Styphelia
styphnate
styphnic
stypsis
styptic
styptical
stypticalness
stypticity
stypticness
Styracaceae
styracaceous
styracin
Styrax
styrax
styrene
Styrian
styrogallol
styrol
styrolene
styrone
styryl
styrylic
stythe
styward
Styx
Styxian
suability
suable
suably
suade
Suaeda
suaharo
Sualocin
Suanitian
suant
suantly
suasible
suasion
suasionist
suasive
suasively
suasiveness
suasory
suavastika
suave
suavely
suaveness
suaveolent
suavify
suaviloquence
suaviloquent
suavity
sub
subabbot
subabdominal
subability
subabsolute
subacademic
subaccount
subacetate
subacid
subacidity
subacidly
subacidness
subacidulous
subacrid
subacrodrome
subacromial
subact
subacuminate
subacute
subacutely
subadditive
subadjacent
subadjutor
subadministrate
subadministration
subadministrator
subadult
subaduncate
subaerate
subaeration
subaerial
subaerially
subaetheric
subaffluent
subage
subagency
subagent
subaggregate
subah
subahdar
subahdary
subahship
subaid
Subakhmimic
subalary
subalate
subalgebra
subalkaline
suballiance
subalmoner
subalpine
subaltern
subalternant
subalternate
subalternately
subalternating
subalternation
subalternity
subanal
subandean
subangled
subangular
subangulate
subangulated
subanniversary
subantarctic
subantichrist
subantique
Subanun
subapical
subaponeurotic
subapostolic
subapparent
subappearance
subappressed
subapprobation
subapterous
subaquatic
subaquean
subaqueous
subarachnoid
subarachnoidal
subarachnoidean
subarboraceous
subarboreal
subarborescent
subarch
subarchesporial
subarchitect
subarctic
subarcuate
subarcuated
subarcuation
subarea
subareolar
subareolet
Subarian
subarmor
subarouse
subarrhation
subartesian
subarticle
subarytenoid
subascending
subassemblage
subassembly
subassociation
subastragalar
subastragaloid
subastral
subastringent
subatom
subatomic
subattenuate
subattenuated
subattorney
subaud
subaudible
subaudition
subauditionist
subauditor
subauditur
subaural
subauricular
subautomatic
subaverage
subaxillar
subaxillary
subbailie
subbailiff
subbailiwick
subballast
subband
subbank
subbasal
subbasaltic
subbase
subbasement
subbass
subbeadle
subbeau
subbias
subbifid
subbing
subbituminous
subbookkeeper
subboreal
subbourdon
subbrachycephalic
subbrachycephaly
subbrachyskelic
subbranch
subbranched
subbranchial
subbreed
subbrigade
subbrigadier
subbroker
subbromid
subbromide
subbronchial
subbureau
subcaecal
subcalcareous
subcalcarine
subcaliber
subcallosal
subcampanulate
subcancellate
subcandid
subcantor
subcapsular
subcaptain
subcaption
subcarbide
subcarbonate
Subcarboniferous
subcarbureted
subcarburetted
subcardinal
subcarinate
subcartilaginous
subcase
subcash
subcashier
subcasino
subcast
subcaste
subcategory
subcaudal
subcaudate
subcaulescent
subcause
subcavate
subcavity
subcelestial
subcell
subcellar
subcenter
subcentral
subcentrally
subchairman
subchamberer
subchancel
subchanter
subchapter
subchaser
subchela
subchelate
subcheliform
subchief
subchloride
subchondral
subchordal
subchorioid
subchorioidal
subchorionic
subchoroid
subchoroidal
subcinctorium
subcineritious
subcingulum
subcircuit
subcircular
subcision
subcity
subclaim
Subclamatores
subclan
subclass
subclassify
subclause
subclavate
subclavia
subclavian
subclavicular
subclavioaxillary
subclaviojugular
subclavius
subclerk
subclimate
subclimax
subclinical
subclover
subcoastal
subcollateral
subcollector
subcollegiate
subcolumnar
subcommander
subcommendation
subcommended
subcommissary
subcommissaryship
subcommission
subcommissioner
subcommit
subcommittee
subcompany
subcompensate
subcompensation
subcompressed
subconcave
subconcession
subconcessionaire
subconchoidal
subconference
subconformable
subconical
subconjunctival
subconjunctively
subconnate
subconnect
subconnivent
subconscience
subconscious
subconsciously
subconsciousness
subconservator
subconsideration
subconstable
subconstellation
subconsul
subcontained
subcontest
subcontiguous
subcontinent
subcontinental
subcontinual
subcontinued
subcontinuous
subcontract
subcontracted
subcontractor
subcontraoctave
subcontrariety
subcontrarily
subcontrary
subcontrol
subconvex
subconvolute
subcool
subcoracoid
subcordate
subcordiform
subcoriaceous
subcorneous
subcorporation
subcortex
subcortical
subcortically
subcorymbose
subcosta
subcostal
subcostalis
subcouncil
subcranial
subcreative
subcreek
subcrenate
subcrepitant
subcrepitation
subcrescentic
subcrest
subcriminal
subcrossing
subcrureal
subcrureus
subcrust
subcrustaceous
subcrustal
subcrystalline
subcubical
subcuboidal
subcultrate
subcultural
subculture
subcurate
subcurator
subcuratorship
subcurrent
subcutaneous
subcutaneously
subcutaneousness
subcuticular
subcutis
subcyaneous
subcyanide
subcylindric
subcylindrical
subdatary
subdate
subdeacon
subdeaconate
subdeaconess
subdeaconry
subdeaconship
subdealer
subdean
subdeanery
subdeb
subdebutante
subdecanal
subdecimal
subdecuple
subdeducible
subdefinition
subdelegate
subdelegation
subdelirium
subdeltaic
subdeltoid
subdeltoidal
subdemonstrate
subdemonstration
subdenomination
subdentate
subdentated
subdented
subdenticulate
subdepartment
subdeposit
subdepository
subdepot
subdepressed
subdeputy
subderivative
subdermal
subdeterminant
subdevil
subdiaconal
subdiaconate
subdial
subdialect
subdialectal
subdialectally
subdiapason
subdiapente
subdiaphragmatic
subdichotomize
subdichotomous
subdichotomously
subdichotomy
subdie
subdilated
subdirector
subdiscoidal
subdisjunctive
subdistich
subdistichous
subdistinction
subdistinguish
subdistinguished
subdistrict
subdititious
subdititiously
subdivecious
subdiversify
subdividable
subdivide
subdivider
subdividing
subdividingly
subdivine
subdivisible
subdivision
subdivisional
subdivisive
subdoctor
subdolent
subdolichocephalic
subdolichocephaly
subdolous
subdolously
subdolousness
subdominant
subdorsal
subdorsally
subdouble
subdrain
subdrainage
subdrill
subdruid
subduable
subduableness
subduably
subdual
subduce
subduct
subduction
subdue
subdued
subduedly
subduedness
subduement
subduer
subduing
subduingly
subduple
subduplicate
subdural
subdurally
subecho
subectodermal
subedit
subeditor
subeditorial
subeditorship
subeffective
subelection
subelectron
subelement
subelementary
subelliptic
subelliptical
subelongate
subemarginate
subencephalon
subencephaltic
subendocardial
subendorse
subendorsement
subendothelial
subendymal
subenfeoff
subengineer
subentire
subentitle
subentry
subepidermal
subepiglottic
subepithelial
subepoch
subequal
subequality
subequally
subequatorial
subequilateral
subequivalve
suber
suberane
suberate
suberect
subereous
suberic
suberiferous
suberification
suberiform
suberin
suberinization
suberinize
Suberites
Suberitidae
suberization
suberize
suberone
suberose
suberous
subescheator
subesophageal
subessential
subetheric
subexaminer
subexcitation
subexcite
subexecutor
subexternal
subface
subfacies
subfactor
subfactorial
subfactory
subfalcate
subfalcial
subfalciform
subfamily
subfascial
subfastigiate
subfebrile
subferryman
subfestive
subfeu
subfeudation
subfeudatory
subfibrous
subfief
subfigure
subfissure
subfix
subflavor
subflexuose
subfloor
subflooring
subflora
subflush
subfluvial
subfocal
subfoliar
subforeman
subform
subformation
subfossil
subfossorial
subfoundation
subfraction
subframe
subfreshman
subfrontal
subfulgent
subfumigation
subfumose
subfunctional
subfusc
subfuscous
subfusiform
subfusk
subgalea
subgallate
subganger
subgape
subgelatinous
subgeneric
subgenerical
subgenerically
subgeniculate
subgenital
subgens
subgenual
subgenus
subgeometric
subget
subgit
subglabrous
subglacial
subglacially
subglenoid
subglobose
subglobosely
subglobular
subglobulose
subglossal
subglossitis
subglottic
subglumaceous
subgod
subgoverness
subgovernor
subgrade
subgranular
subgrin
subgroup
subgular
subgwely
subgyre
subgyrus
subhalid
subhalide
subhall
subharmonic
subhastation
subhatchery
subhead
subheading
subheadquarters
subheadwaiter
subhealth
subhedral
subhemispherical
subhepatic
subherd
subhero
subhexagonal
subhirsute
subhooked
subhorizontal
subhornblendic
subhouse
subhuman
subhumid
subhyaline
subhyaloid
subhymenial
subhymenium
subhyoid
subhyoidean
subhypothesis
subhysteria
subicle
subicteric
subicular
subiculum
subidar
subidea
subideal
subimaginal
subimago
subimbricate
subimbricated
subimposed
subimpressed
subincandescent
subincident
subincise
subincision
subincomplete
subindex
subindicate
subindication
subindicative
subindices
subindividual
subinduce
subinfer
subinfeud
subinfeudate
subinfeudation
subinfeudatory
subinflammation
subinflammatory
subinform
subingression
subinguinal
subinitial
subinoculate
subinoculation
subinsert
subinsertion
subinspector
subinspectorship
subintegumental
subintellection
subintelligential
subintelligitur
subintent
subintention
subintercessor
subinternal
subinterval
subintestinal
subintroduce
subintroduction
subintroductory
subinvoluted
subinvolution
subiodide
subirrigate
subirrigation
subitane
subitaneous
subitem
Subiya
subjacency
subjacent
subjacently
subjack
subject
subjectability
subjectable
subjectdom
subjected
subjectedly
subjectedness
subjecthood
subjectibility
subjectible
subjectification
subjectify
subjectile
subjection
subjectional
subjectist
subjective
subjectively
subjectiveness
subjectivism
subjectivist
subjectivistic
subjectivistically
subjectivity
subjectivize
subjectivoidealistic
subjectless
subjectlike
subjectness
subjectship
subjee
subjicible
subjoin
subjoinder
subjoint
subjudge
subjudiciary
subjugable
subjugal
subjugate
subjugation
subjugator
subjugular
subjunct
subjunction
subjunctive
subjunctively
subjunior
subking
subkingdom
sublabial
sublaciniate
sublacustrine
sublanate
sublanceolate
sublanguage
sublapsarian
sublapsarianism
sublapsary
sublaryngeal
sublate
sublateral
sublation
sublative
subleader
sublease
sublecturer
sublegislation
sublegislature
sublenticular
sublessee
sublessor
sublet
sublethal
sublettable
subletter
sublevaminous
sublevate
sublevation
sublevel
sublibrarian
sublicense
sublicensee
sublid
sublieutenancy
sublieutenant
subligation
sublighted
sublimable
sublimableness
sublimant
sublimate
sublimation
sublimational
sublimationist
sublimator
sublimatory
sublime
sublimed
sublimely
sublimeness
sublimer
subliminal
subliminally
sublimish
sublimitation
sublimity
sublimize
sublinear
sublineation
sublingua
sublinguae
sublingual
sublinguate
sublittoral
sublobular
sublong
subloral
subloreal
sublot
sublumbar
sublunar
sublunary
sublunate
sublustrous
subluxate
subluxation
submaid
submain
submakroskelic
submammary
subman
submanager
submania
submanic
submanor
submarginal
submarginally
submarginate
submargined
submarine
submariner
submarinism
submarinist
submarshal
submaster
submaxilla
submaxillary
submaximal
submeaning
submedial
submedian
submediant
submediation
submediocre
submeeting
submember
submembranaceous
submembranous
submeningeal
submental
submentum
submerge
submerged
submergement
submergence
submergibility
submergible
submerse
submersed
submersibility
submersible
submersion
submetallic
submeter
submetering
submicron
submicroscopic
submicroscopically
submiliary
submind
subminimal
subminister
submiss
submissible
submission
submissionist
submissive
submissively
submissiveness
submissly
submissness
submit
submittal
submittance
submitter
submittingly
submolecule
submonition
submontagne
submontane
submontanely
submontaneous
submorphous
submortgage
submotive
submountain
submucosa
submucosal
submucous
submucronate
submultiple
submundane
submuriate
submuscular
Submytilacea
subnarcotic
subnasal
subnascent
subnatural
subnect
subnervian
subness
subneural
subnex
subnitrate
subnitrated
subniveal
subnivean
subnormal
subnormality
subnotation
subnote
subnotochordal
subnubilar
subnucleus
subnude
subnumber
subnuvolar
suboblique
subobscure
subobscurely
subobtuse
suboccipital
subocean
suboceanic
suboctave
suboctile
suboctuple
subocular
suboesophageal
suboffice
subofficer
subofficial
subolive
subopaque
subopercle
subopercular
suboperculum
subopposite
suboptic
suboptimal
suboptimum
suboral
suborbicular
suborbiculate
suborbiculated
suborbital
suborbitar
suborbitary
subordain
suborder
subordinacy
subordinal
subordinary
subordinate
subordinately
subordinateness
subordinating
subordinatingly
subordination
subordinationism
subordinationist
subordinative
suborganic
suborn
subornation
subornative
suborner
Suboscines
suboval
subovate
subovated
suboverseer
subovoid
suboxidation
suboxide
subpackage
subpagoda
subpallial
subpalmate
subpanel
subparagraph
subparallel
subpart
subpartition
subpartitioned
subpartitionment
subparty
subpass
subpassage
subpastor
subpatron
subpattern
subpavement
subpectinate
subpectoral
subpeduncle
subpeduncular
subpedunculate
subpellucid
subpeltate
subpeltated
subpentagonal
subpentangular
subpericardial
subperiod
subperiosteal
subperiosteally
subperitoneal
subperitoneally
subpermanent
subpermanently
subperpendicular
subpetiolar
subpetiolate
subpharyngeal
subphosphate
subphratry
subphrenic
subphylar
subphylum
subpial
subpilose
subpimp
subpiston
subplacenta
subplant
subplantigrade
subplat
subpleural
subplinth
subplot
subplow
subpodophyllous
subpoena
subpoenal
subpolar
subpolygonal
subpool
subpopular
subpopulation
subporphyritic
subport
subpostmaster
subpostmastership
subpostscript
subpotency
subpotent
subpreceptor
subpreceptorial
subpredicate
subpredication
subprefect
subprefectorial
subprefecture
subprehensile
subpress
subprimary
subprincipal
subprior
subprioress
subproblem
subproctor
subproduct
subprofessional
subprofessor
subprofessoriate
subprofitable
subproportional
subprotector
subprovince
subprovincial
subpubescent
subpubic
subpulmonary
subpulverizer
subpunch
subpunctuation
subpurchaser
subpurlin
subputation
subpyramidal
subpyriform
subquadrangular
subquadrate
subquality
subquestion
subquinquefid
subquintuple
Subra
subrace
subradial
subradiance
subradiate
subradical
subradius
subradular
subrailway
subrameal
subramose
subramous
subrange
subrational
subreader
subreason
subrebellion
subrectangular
subrector
subreference
subregent
subregion
subregional
subregular
subreguli
subregulus
subrelation
subreligion
subreniform
subrent
subrepand
subrepent
subreport
subreptary
subreption
subreptitious
subreputable
subresin
subretinal
subrhombic
subrhomboid
subrhomboidal
subrictal
subrident
subridently
subrigid
subrision
subrisive
subrisory
subrogate
subrogation
subroot
subrostral
subround
subrule
subruler
subsacral
subsale
subsaline
subsalt
subsample
subsartorial
subsatiric
subsatirical
subsaturated
subsaturation
subscapular
subscapularis
subscapulary
subschedule
subscheme
subschool
subscience
subscleral
subsclerotic
subscribable
subscribe
subscriber
subscribership
subscript
subscription
subscriptionist
subscriptive
subscriptively
subscripture
subscrive
subscriver
subsea
subsecive
subsecretarial
subsecretary
subsect
subsection
subsecurity
subsecute
subsecutive
subsegment
subsemifusa
subsemitone
subsensation
subsensible
subsensual
subsensuous
subsept
subseptuple
subsequence
subsequency
subsequent
subsequential
subsequentially
subsequently
subsequentness
subseries
subserosa
subserous
subserrate
subserve
subserviate
subservience
subserviency
subservient
subserviently
subservientness
subsessile
subset
subsewer
subsextuple
subshaft
subsheriff
subshire
subshrub
subshrubby
subside
subsidence
subsidency
subsident
subsider
subsidiarie
subsidiarily
subsidiariness
subsidiary
subsiding
subsidist
subsidizable
subsidization
subsidize
subsidizer
subsidy
subsilicate
subsilicic
subsill
subsimilation
subsimious
subsimple
subsinuous
subsist
subsistence
subsistency
subsistent
subsistential
subsistingly
subsizar
subsizarship
subsmile
subsneer
subsocial
subsoil
subsoiler
subsolar
subsolid
subsonic
subsorter
subsovereign
subspace
subspatulate
subspecialist
subspecialize
subspecialty
subspecies
subspecific
subspecifically
subsphenoidal
subsphere
subspherical
subspherically
subspinous
subspiral
subspontaneous
subsquadron
substage
substalagmite
substalagmitic
substance
substanceless
substanch
substandard
substandardize
substant
substantiability
substantial
substantialia
substantialism
substantialist
substantiality
substantialize
substantially
substantialness
substantiate
substantiation
substantiative
substantiator
substantify
substantious
substantival
substantivally
substantive
substantively
substantiveness
substantivity
substantivize
substantize
substation
substernal
substituent
substitutable
substitute
substituted
substituter
substituting
substitutingly
substitution
substitutional
substitutionally
substitutionary
substitutive
substitutively
substock
substoreroom
substory
substract
substraction
substratal
substrate
substrati
substrative
substrator
substratose
substratosphere
substratospheric
substratum
substriate
substruct
substruction
substructional
substructural
substructure
substylar
substyle
subsulfid
subsulfide
subsulphate
subsulphid
subsulphide
subsult
subsultive
subsultorily
subsultorious
subsultory
subsultus
subsumable
subsume
subsumption
subsumptive
subsuperficial
subsurety
subsurface
subsyndicate
subsynod
subsynodical
subsystem
subtack
subtacksman
subtangent
subtarget
subtartarean
subtectal
subtegminal
subtegulaneous
subtemperate
subtenancy
subtenant
subtend
subtense
subtenure
subtepid
subteraqueous
subterbrutish
subtercelestial
subterconscious
subtercutaneous
subterethereal
subterfluent
subterfluous
subterfuge
subterhuman
subterjacent
subtermarine
subterminal
subternatural
subterpose
subterposition
subterrane
subterraneal
subterranean
subterraneanize
subterraneanly
subterraneous
subterraneously
subterraneousness
subterranity
subterraqueous
subterrene
subterrestrial
subterritorial
subterritory
subtersensual
subtersensuous
subtersuperlative
subtersurface
subtertian
subtext
subthalamic
subthalamus
subthoracic
subthrill
subtile
subtilely
subtileness
subtilin
subtilism
subtilist
subtility
subtilization
subtilize
subtilizer
subtill
subtillage
subtilty
subtitle
subtitular
subtle
subtleness
subtlety
subtlist
subtly
subtone
subtonic
subtorrid
subtotal
subtotem
subtower
subtract
subtracter
subtraction
subtractive
subtrahend
subtranslucent
subtransparent
subtransverse
subtrapezoidal
subtread
subtreasurer
subtreasurership
subtreasury
subtrench
subtriangular
subtriangulate
subtribal
subtribe
subtribual
subtrifid
subtrigonal
subtrihedral
subtriplicate
subtriplicated
subtriquetrous
subtrist
subtrochanteric
subtrochlear
subtropic
subtropical
subtropics
subtrousers
subtrude
subtruncate
subtrunk
subtuberant
subtunic
subtunnel
subturbary
subturriculate
subturriculated
subtutor
subtwined
subtype
subtypical
subulate
subulated
subulicorn
Subulicornia
subuliform
subultimate
subumbellate
subumbonal
subumbral
subumbrella
subumbrellar
subuncinate
subunequal
subungual
subunguial
Subungulata
subungulate
subunit
subuniverse
suburb
suburban
suburbandom
suburbanhood
suburbanism
suburbanite
suburbanity
suburbanization
suburbanize
suburbanly
suburbed
suburbia
suburbican
suburbicarian
suburbicary
suburethral
subursine
subvaginal
subvaluation
subvarietal
subvariety
subvassal
subvassalage
subvein
subvendee
subvene
subvention
subventionary
subventioned
subventionize
subventitious
subventive
subventral
subventricose
subvermiform
subversal
subverse
subversed
subversion
subversionary
subversive
subversivism
subvert
subvertebral
subverter
subvertible
subvertical
subverticillate
subvesicular
subvestment
subvicar
subvicarship
subvillain
subvirate
subvirile
subvisible
subvitalized
subvitreous
subvocal
subvola
subwarden
subwater
subway
subwealthy
subweight
subwink
subworker
subworkman
subzonal
subzone
subzygomatic
succade
succedanea
succedaneous
succedaneum
succedent
succeed
succeedable
succeeder
succeeding
succeedingly
succent
succentor
succenturiate
succenturiation
success
successful
successfully
successfulness
succession
successional
successionally
successionist
successionless
successive
successively
successiveness
successivity
successless
successlessly
successlessness
successor
successoral
successorship
successory
succi
succin
succinamate
succinamic
succinamide
succinanil
succinate
succinct
succinctly
succinctness
succinctorium
succinctory
succincture
succinic
succiniferous
succinimide
succinite
succinoresinol
succinosulphuric
succinous
succinyl
Succisa
succise
succivorous
succor
succorable
succorer
succorful
succorless
succorrhea
succory
succotash
succourful
succourless
succous
succub
succuba
succubae
succube
succubine
succubous
succubus
succula
succulence
succulency
succulent
succulently
succulentness
succulous
succumb
succumbence
succumbency
succumbent
succumber
succursal
succuss
succussation
succussatory
succussion
succussive
such
suchlike
suchness
Suchos
suchwise
sucivilized
suck
suckable
suckabob
suckage
suckauhock
sucken
suckener
sucker
suckerel
suckerfish
suckerlike
suckfish
suckhole
sucking
suckle
suckler
suckless
suckling
suckstone
suclat
sucramine
sucrate
sucre
sucroacid
sucrose
suction
suctional
Suctoria
suctorial
suctorian
suctorious
sucupira
sucuri
sucuriu
sucuruju
sud
sudadero
sudamen
sudamina
sudaminal
Sudan
Sudanese
Sudani
Sudanian
Sudanic
sudarium
sudary
sudate
sudation
sudatorium
sudatory
Sudburian
sudburite
sudd
sudden
suddenly
suddenness
suddenty
Sudder
sudder
suddle
suddy
Sudic
sudiform
sudoral
sudoresis
sudoric
sudoriferous
sudoriferousness
sudorific
sudoriparous
sudorous
Sudra
suds
sudsman
sudsy
Sue
sue
Suecism
suede
suer
Suerre
Suessiones
suet
suety
Sueve
Suevi
Suevian
Suevic
Sufeism
suff
suffect
suffection
suffer
sufferable
sufferableness
sufferably
sufferance
sufferer
suffering
sufferingly
suffete
suffice
sufficeable
sufficer
sufficiency
sufficient
sufficiently
sufficientness
sufficing
sufficingly
sufficingness
suffiction
suffix
suffixal
suffixation
suffixion
suffixment
sufflaminate
sufflamination
sufflate
sufflation
sufflue
suffocate
suffocating
suffocatingly
suffocation
suffocative
Suffolk
suffragan
suffraganal
suffraganate
suffragancy
suffraganeous
suffragatory
suffrage
suffragette
suffragettism
suffragial
suffragism
suffragist
suffragistic
suffragistically
suffragitis
suffrago
suffrutescent
suffrutex
suffruticose
suffruticous
suffruticulose
suffumigate
suffumigation
suffusable
suffuse
suffused
suffusedly
suffusion
suffusive
Sufi
Sufiism
Sufiistic
Sufism
Sufistic
sugamo
sugan
sugar
sugarberry
sugarbird
sugarbush
sugared
sugarelly
sugarer
sugarhouse
sugariness
sugarless
sugarlike
sugarplum
sugarsweet
sugarworks
sugary
sugent
sugescent
suggest
suggestable
suggestedness
suggester
suggestibility
suggestible
suggestibleness
suggestibly
suggesting
suggestingly
suggestion
suggestionability
suggestionable
suggestionism
suggestionist
suggestionize
suggestive
suggestively
suggestiveness
suggestivity
suggestment
suggestress
suggestum
suggillate
suggillation
sugh
sugi
Sugih
suguaro
suhuaro
Sui
suicidal
suicidalism
suicidally
suicidalwise
suicide
suicidical
suicidism
suicidist
suid
Suidae
suidian
suiform
suilline
suimate
Suina
suine
suing
suingly
suint
Suiogoth
Suiogothic
Suiones
suisimilar
suist
suit
suitability
suitable
suitableness
suitably
suitcase
suite
suithold
suiting
suitor
suitoress
suitorship
suity
suji
Suk
Sukey
sukiyaki
sukkenye
Suku
Sula
Sulaba
Sulafat
Sulaib
sulbasutra
sulcal
sulcalization
sulcalize
sulcar
sulcate
sulcated
sulcation
sulcatoareolate
sulcatocostate
sulcatorimose
sulciform
sulcomarginal
sulcular
sulculate
sulculus
sulcus
suld
sulea
sulfa
sulfacid
sulfadiazine
sulfaguanidine
sulfamate
sulfamerazin
sulfamerazine
sulfamethazine
sulfamethylthiazole
sulfamic
sulfamidate
sulfamide
sulfamidic
sulfamine
sulfaminic
sulfamyl
sulfanilamide
sulfanilic
sulfanilylguanidine
sulfantimonide
sulfapyrazine
sulfapyridine
sulfaquinoxaline
sulfarsenide
sulfarsenite
sulfarseniuret
sulfarsphenamine
Sulfasuxidine
sulfatase
sulfathiazole
sulfatic
sulfatize
sulfato
sulfazide
sulfhydrate
sulfhydric
sulfhydryl
sulfindigotate
sulfindigotic
sulfindylic
sulfion
sulfionide
sulfoacid
sulfoamide
sulfobenzide
sulfobenzoate
sulfobenzoic
sulfobismuthite
sulfoborite
sulfocarbamide
sulfocarbimide
sulfocarbolate
sulfocarbolic
sulfochloride
sulfocyan
sulfocyanide
sulfofication
sulfogermanate
sulfohalite
sulfohydrate
sulfoindigotate
sulfoleic
sulfolysis
sulfomethylic
sulfonamic
sulfonamide
sulfonate
sulfonation
sulfonator
sulfonephthalein
sulfonethylmethane
sulfonic
sulfonium
sulfonmethane
sulfonyl
sulfophthalein
sulfopurpurate
sulfopurpuric
sulforicinate
sulforicinic
sulforicinoleate
sulforicinoleic
sulfoselenide
sulfosilicide
sulfostannide
sulfotelluride
sulfourea
sulfovinate
sulfovinic
sulfowolframic
sulfoxide
sulfoxism
sulfoxylate
sulfoxylic
sulfurage
sulfuran
sulfurate
sulfuration
sulfurator
sulfurea
sulfureous
sulfureously
sulfureousness
sulfuret
sulfuric
sulfurization
sulfurize
sulfurosyl
sulfurous
sulfury
sulfuryl
Sulidae
Sulides
Suliote
sulk
sulka
sulker
sulkily
sulkiness
sulky
sulkylike
sull
sulla
sullage
Sullan
sullen
sullenhearted
sullenly
sullenness
sulliable
sullow
sully
sulpha
sulphacid
sulphaldehyde
sulphamate
sulphamic
sulphamidate
sulphamide
sulphamidic
sulphamine
sulphaminic
sulphamino
sulphammonium
sulphamyl
sulphanilate
sulphanilic
sulphantimonate
sulphantimonial
sulphantimonic
sulphantimonide
sulphantimonious
sulphantimonite
sulpharsenate
sulpharseniate
sulpharsenic
sulpharsenide
sulpharsenious
sulpharsenite
sulpharseniuret
sulpharsphenamine
sulphatase
sulphate
sulphated
sulphatic
sulphation
sulphatization
sulphatize
sulphato
sulphatoacetic
sulphatocarbonic
sulphazide
sulphazotize
sulphbismuthite
sulphethylate
sulphethylic
sulphhemoglobin
sulphichthyolate
sulphidation
sulphide
sulphidic
sulphidize
sulphimide
sulphinate
sulphindigotate
sulphine
sulphinic
sulphinide
sulphinyl
sulphitation
sulphite
sulphitic
sulphmethemoglobin
sulpho
sulphoacetic
sulphoamid
sulphoamide
sulphoantimonate
sulphoantimonic
sulphoantimonious
sulphoantimonite
sulphoarsenic
sulphoarsenious
sulphoarsenite
sulphoazotize
sulphobenzide
sulphobenzoate
sulphobenzoic
sulphobismuthite
sulphoborite
sulphobutyric
sulphocarbamic
sulphocarbamide
sulphocarbanilide
sulphocarbimide
sulphocarbolate
sulphocarbolic
sulphocarbonate
sulphocarbonic
sulphochloride
sulphochromic
sulphocinnamic
sulphocyan
sulphocyanate
sulphocyanic
sulphocyanide
sulphocyanogen
sulphodichloramine
sulphofication
sulphofy
sulphogallic
sulphogel
sulphogermanate
sulphogermanic
sulphohalite
sulphohaloid
sulphohydrate
sulphoichthyolate
sulphoichthyolic
sulphoindigotate
sulphoindigotic
sulpholeate
sulpholeic
sulpholipin
sulpholysis
sulphonal
sulphonalism
sulphonamic
sulphonamide
sulphonamido
sulphonamine
sulphonaphthoic
sulphonate
sulphonated
sulphonation
sulphonator
sulphoncyanine
sulphone
sulphonephthalein
sulphonethylmethane
sulphonic
sulphonium
sulphonmethane
sulphonphthalein
sulphonyl
sulphoparaldehyde
sulphophosphate
sulphophosphite
sulphophosphoric
sulphophosphorous
sulphophthalein
sulphophthalic
sulphopropionic
sulphoproteid
sulphopupuric
sulphopurpurate
sulphoricinate
sulphoricinic
sulphoricinoleate
sulphoricinoleic
sulphosalicylic
sulphoselenide
sulphoselenium
sulphosilicide
sulphosol
sulphostannate
sulphostannic
sulphostannide
sulphostannite
sulphostannous
sulphosuccinic
sulphosulphurous
sulphotannic
sulphotelluride
sulphoterephthalic
sulphothionyl
sulphotoluic
sulphotungstate
sulphotungstic
sulphourea
sulphovanadate
sulphovinate
sulphovinic
sulphowolframic
sulphoxide
sulphoxism
sulphoxylate
sulphoxylic
sulphoxyphosphate
sulphozincate
sulphur
sulphurage
sulphuran
sulphurate
sulphuration
sulphurator
sulphurea
sulphurean
sulphureity
sulphureonitrous
sulphureosaline
sulphureosuffused
sulphureous
sulphureously
sulphureousness
sulphureovirescent
sulphuret
sulphureted
sulphuric
sulphuriferous
sulphurity
sulphurization
sulphurize
sulphurless
sulphurlike
sulphurosyl
sulphurous
sulphurously
sulphurousness
sulphurproof
sulphurweed
sulphurwort
sulphury
sulphuryl
sulphydrate
sulphydric
sulphydryl
Sulpician
sultam
sultan
sultana
sultanaship
sultanate
sultane
sultanesque
sultaness
sultanian
sultanic
sultanin
sultanism
sultanist
sultanize
sultanlike
sultanry
sultanship
sultone
sultrily
sultriness
sultry
Sulu
Suluan
sulung
sulvanite
sulvasutra
sum
sumac
Sumak
Sumass
Sumatra
sumatra
Sumatran
sumbul
sumbulic
Sumdum
Sumerian
Sumerology
Sumitro
sumless
sumlessness
summability
summable
summage
summand
summar
summarily
summariness
summarist
summarization
summarize
summarizer
summary
summate
summation
summational
summative
summatory
summed
summer
summerbird
summercastle
summerer
summerhead
summeriness
summering
summerings
summerish
summerite
summerize
summerland
summerlay
summerless
summerlike
summerliness
summerling
summerly
summerproof
summertide
summertime
summertree
summerward
summerwood
summery
summist
summit
summital
summitless
summity
summon
summonable
summoner
summoningly
summons
summula
summulist
summut
sumner
Sumo
sump
sumpage
sumper
sumph
sumphish
sumphishly
sumphishness
sumphy
sumpit
sumpitan
sumple
sumpman
sumpsimus
sumpter
sumption
sumptuary
sumptuosity
sumptuous
sumptuously
sumptuousness
sun
sunbeam
sunbeamed
sunbeamy
sunberry
sunbird
sunblink
sunbonnet
sunbonneted
sunbow
sunbreak
sunburn
sunburned
sunburnedness
sunburnproof
sunburnt
sunburntness
sunburst
suncherchor
suncup
sundae
Sundanese
Sundanesian
sundang
Sundar
Sundaresan
sundari
Sunday
Sundayfied
Sundayish
Sundayism
Sundaylike
Sundayness
Sundayproof
sundek
sunder
sunderable
sunderance
sunderer
sunderment
sunderwise
sundew
sundial
sundik
sundog
sundown
sundowner
sundowning
sundra
sundri
sundries
sundriesman
sundrily
sundriness
sundrops
sundry
sundryman
sune
sunfall
sunfast
sunfish
sunfisher
sunfishery
sunflower
Sung
sung
sungha
sunglade
sunglass
sunglo
sunglow
Sunil
sunk
sunken
sunket
sunkland
sunlamp
sunland
sunless
sunlessly
sunlessness
sunlet
sunlight
sunlighted
sunlike
sunlit
sunn
Sunna
Sunni
Sunniah
sunnily
sunniness
Sunnism
Sunnite
sunnud
sunny
sunnyhearted
sunnyheartedness
sunproof
sunquake
sunray
sunrise
sunrising
sunroom
sunscald
sunset
sunsetting
sunsetty
sunshade
sunshine
sunshineless
sunshining
sunshiny
sunsmit
sunsmitten
sunspot
sunspotted
sunspottedness
sunspottery
sunspotty
sunsquall
sunstone
sunstricken
sunstroke
sunt
sunup
sunward
sunwards
sunway
sunways
sunweed
sunwise
sunyie
Suomi
Suomic
suovetaurilia
sup
supa
Supai
supari
supawn
supe
supellex
super
superabduction
superabhor
superability
superable
superableness
superably
superabnormal
superabominable
superabomination
superabound
superabstract
superabsurd
superabundance
superabundancy
superabundant
superabundantly
superaccession
superaccessory
superaccommodating
superaccomplished
superaccrue
superaccumulate
superaccumulation
superaccurate
superacetate
superachievement
superacid
superacidulated
superacknowledgment
superacquisition
superacromial
superactive
superactivity
superacute
superadaptable
superadd
superaddition
superadditional
superadequate
superadequately
superadjacent
superadministration
superadmirable
superadmiration
superadorn
superadornment
superaerial
superaesthetical
superaffiliation
superaffiuence
superagency
superaggravation
superagitation
superagrarian
superalbal
superalbuminosis
superalimentation
superalkaline
superalkalinity
superallowance
superaltar
superaltern
superambitious
superambulacral
superanal
superangelic
superangelical
superanimal
superannuate
superannuation
superannuitant
superannuity
superapology
superappreciation
superaqueous
superarbiter
superarbitrary
superarctic
superarduous
superarrogant
superarseniate
superartificial
superartificially
superaspiration
superassertion
superassociate
superassume
superastonish
superastonishment
superattachment
superattainable
superattendant
superattraction
superattractive
superauditor
superaural
superaverage
superavit
superaward
superaxillary
superazotation
superb
superbelief
superbeloved
superbenefit
superbenevolent
superbenign
superbias
superbious
superbity
superblessed
superblunder
superbly
superbness
superbold
superborrow
superbrain
superbrave
superbrute
superbuild
superbungalow
superbusy
supercabinet
supercalender
supercallosal
supercandid
supercanine
supercanonical
supercanonization
supercanopy
supercapable
supercaption
supercarbonate
supercarbonization
supercarbonize
supercarbureted
supercargo
supercargoship
supercarpal
supercatastrophe
supercatholic
supercausal
supercaution
supercelestial
supercensure
supercentral
supercentrifuge
supercerebellar
supercerebral
superceremonious
supercharge
supercharged
supercharger
superchemical
superchivalrous
superciliary
superciliosity
supercilious
superciliously
superciliousness
supercilium
supercivil
supercivilization
supercivilized
superclaim
superclass
superclassified
supercloth
supercoincidence
supercolossal
supercolumnar
supercolumniation
supercombination
supercombing
supercommendation
supercommentary
supercommentator
supercommercial
supercompetition
supercomplete
supercomplex
supercomprehension
supercompression
superconception
superconductive
superconductivity
superconductor
superconfident
superconfirmation
superconformable
superconformist
superconformity
superconfusion
supercongestion
superconscious
superconsciousness
superconsecrated
superconsequency
superconservative
superconstitutional
supercontest
supercontribution
supercontrol
supercool
supercordial
supercorporation
supercow
supercredit
supercrescence
supercrescent
supercrime
supercritic
supercritical
supercrowned
supercrust
supercube
supercultivated
supercurious
supercycle
supercynical
superdainty
superdanger
superdebt
superdeclamatory
superdecoration
superdeficit
superdeity
superdejection
superdelegate
superdelicate
superdemand
superdemocratic
superdemonic
superdemonstration
superdensity
superdeposit
superdesirous
superdevelopment
superdevilish
superdevotion
superdiabolical
superdiabolically
superdicrotic
superdifficult
superdiplomacy
superdirection
superdiscount
superdistention
superdistribution
superdividend
superdivine
superdivision
superdoctor
superdominant
superdomineering
superdonation
superdose
superdramatist
superdreadnought
superdubious
superduplication
superdural
superdying
superearthly
supereconomy
superedification
superedify
supereducation
supereffective
supereffluence
supereffluently
superego
superelaborate
superelastic
superelated
superelegance
superelementary
superelevated
superelevation
supereligible
supereloquent
supereminence
supereminency
supereminent
supereminently
superemphasis
superemphasize
superendorse
superendorsement
superendow
superenergetic
superenforcement
superengrave
superenrollment
superepic
superepoch
superequivalent
supererogant
supererogantly
supererogate
supererogation
supererogative
supererogator
supererogatorily
supererogatory
superespecial
superessential
superessentially
superestablish
superestablishment
supereternity
superether
superethical
superethmoidal
superevangelical
superevident
superexacting
superexalt
superexaltation
superexaminer
superexceed
superexceeding
superexcellence
superexcellency
superexcellent
superexcellently
superexceptional
superexcitation
superexcited
superexcitement
superexcrescence
superexert
superexertion
superexiguity
superexist
superexistent
superexpand
superexpansion
superexpectation
superexpenditure
superexplicit
superexport
superexpressive
superexquisite
superexquisitely
superexquisiteness
superextend
superextension
superextol
superextreme
superfamily
superfantastic
superfarm
superfat
superfecundation
superfecundity
superfee
superfeminine
superfervent
superfetate
superfetation
superfeudation
superfibrination
superficial
superficialism
superficialist
superficiality
superficialize
superficially
superficialness
superficiary
superficies
superfidel
superfinance
superfine
superfinical
superfinish
superfinite
superfissure
superfit
superfix
superfleet
superflexion
superfluent
superfluid
superfluitance
superfluity
superfluous
superfluously
superfluousness
superflux
superfoliaceous
superfoliation
superfolly
superformal
superformation
superformidable
superfortunate
superfriendly
superfrontal
superfructified
superfulfill
superfulfillment
superfunction
superfunctional
superfuse
superfusibility
superfusible
superfusion
supergaiety
supergallant
supergene
supergeneric
supergenerosity
supergenerous
supergenual
supergiant
superglacial
superglorious
superglottal
supergoddess
supergoodness
supergovern
supergovernment
supergraduate
supergrant
supergratification
supergratify
supergravitate
supergravitation
superguarantee
supergun
superhandsome
superhearty
superheat
superheater
superheresy
superhero
superheroic
superhet
superheterodyne
superhighway
superhirudine
superhistoric
superhistorical
superhive
superhuman
superhumanity
superhumanize
superhumanly
superhumanness
superhumeral
superhypocrite
superideal
superignorant
superillustrate
superillustration
superimpend
superimpending
superimpersonal
superimply
superimportant
superimposable
superimpose
superimposed
superimposition
superimposure
superimpregnated
superimpregnation
superimprobable
superimproved
superincentive
superinclination
superinclusive
superincomprehensible
superincrease
superincumbence
superincumbency
superincumbent
superincumbently
superindependent
superindiction
superindifference
superindifferent
superindignant
superindividual
superindividualism
superindividualist
superinduce
superinducement
superinduct
superinduction
superindulgence
superindulgent
superindustrious
superindustry
superinenarrable
superinfection
superinfer
superinference
superinfeudation
superinfinite
superinfinitely
superinfirmity
superinfluence
superinformal
superinfuse
superinfusion
superingenious
superingenuity
superinitiative
superinjustice
superinnocent
superinquisitive
superinsaniated
superinscription
superinsist
superinsistence
superinsistent
superinstitute
superinstitution
superintellectual
superintend
superintendence
superintendency
superintendent
superintendential
superintendentship
superintender
superintense
superintolerable
superinundation
superior
superioress
superiority
superiorly
superiorness
superiorship
superirritability
superius
superjacent
superjudicial
superjurisdiction
superjustification
superknowledge
superlabial
superlaborious
superlactation
superlapsarian
superlaryngeal
superlation
superlative
superlatively
superlativeness
superlenient
superlie
superlikelihood
superline
superlocal
superlogical
superloyal
superlucky
superlunary
superlunatical
superluxurious
supermagnificent
supermagnificently
supermalate
superman
supermanhood
supermanifest
supermanism
supermanliness
supermanly
supermannish
supermarginal
supermarine
supermarket
supermarvelous
supermasculine
supermaterial
supermathematical
supermaxilla
supermaxillary
supermechanical
supermedial
supermedicine
supermediocre
supermental
supermentality
supermetropolitan
supermilitary
supermishap
supermixture
supermodest
supermoisten
supermolten
supermoral
supermorose
supermunicipal
supermuscan
supermystery
supernacular
supernaculum
supernal
supernalize
supernally
supernatant
supernatation
supernation
supernational
supernationalism
supernatural
supernaturaldom
supernaturalism
supernaturalist
supernaturality
supernaturalize
supernaturally
supernaturalness
supernature
supernecessity
supernegligent
supernormal
supernormally
supernormalness
supernotable
supernova
supernumeral
supernumerariness
supernumerary
supernumeraryship
supernumerous
supernutrition
superoanterior
superobedience
superobedient
superobese
superobject
superobjection
superobjectionable
superobligation
superobstinate
superoccipital
superoctave
superocular
superodorsal
superoexternal
superoffensive
superofficious
superofficiousness
superofrontal
superointernal
superolateral
superomedial
superoposterior
superopposition
superoptimal
superoptimist
superoratorical
superorbital
superordain
superorder
superordinal
superordinary
superordinate
superordination
superorganic
superorganism
superorganization
superorganize
superornament
superornamental
superosculate
superoutput
superoxalate
superoxide
superoxygenate
superoxygenation
superparamount
superparasite
superparasitic
superparasitism
superparliamentary
superpassage
superpatient
superpatriotic
superpatriotism
superperfect
superperfection
superperson
superpersonal
superpersonalism
superpetrosal
superphlogisticate
superphlogistication
superphosphate
superphysical
superpigmentation
superpious
superplausible
superplease
superplus
superpolite
superpolitic
superponderance
superponderancy
superponderant
superpopulation
superposable
superpose
superposed
superposition
superpositive
superpower
superpowered
superpraise
superprecarious
superprecise
superprelatical
superpreparation
superprinting
superprobability
superproduce
superproduction
superproportion
superprosperous
superpublicity
superpure
superpurgation
superquadrupetal
superqualify
superquote
superradical
superrational
superrationally
superreaction
superrealism
superrealist
superrefine
superrefined
superrefinement
superreflection
superreform
superreformation
superregal
superregeneration
superregenerative
superregistration
superregulation
superreliance
superremuneration
superrenal
superrequirement
superrespectable
superresponsible
superrestriction
superreward
superrheumatized
superrighteous
superromantic
superroyal
supersacerdotal
supersacral
supersacred
supersacrifice
supersafe
supersagacious
supersaint
supersaintly
supersalesman
supersaliency
supersalient
supersalt
supersanction
supersanguine
supersanity
supersarcastic
supersatisfaction
supersatisfy
supersaturate
supersaturation
superscandal
superscholarly
superscientific
superscribe
superscript
superscription
superscrive
superseaman
supersecret
supersecretion
supersecular
supersecure
supersedable
supersede
supersedeas
supersedence
superseder
supersedure
superselect
superseminate
supersemination
superseminator
supersensible
supersensibly
supersensitive
supersensitiveness
supersensitization
supersensory
supersensual
supersensualism
supersensualist
supersensualistic
supersensuality
supersensually
supersensuous
supersensuousness
supersentimental
superseptal
superseptuaginarian
superseraphical
superserious
superservice
superserviceable
superserviceableness
superserviceably
supersesquitertial
supersession
supersessive
supersevere
supershipment
supersignificant
supersilent
supersimplicity
supersimplify
supersincerity
supersingular
supersistent
supersize
supersmart
supersocial
supersoil
supersolar
supersolemn
supersolemness
supersolemnity
supersolemnly
supersolicit
supersolicitation
supersolid
supersonant
supersonic
supersovereign
supersovereignty
superspecialize
superspecies
superspecification
supersphenoid
supersphenoidal
superspinous
superspiritual
superspirituality
supersquamosal
superstage
superstamp
superstandard
superstate
superstatesman
superstimulate
superstimulation
superstition
superstitionist
superstitionless
superstitious
superstitiously
superstitiousness
superstoical
superstrain
superstrata
superstratum
superstrenuous
superstrict
superstrong
superstruct
superstruction
superstructor
superstructory
superstructural
superstructure
superstuff
superstylish
supersublimated
supersuborder
supersubsist
supersubstantial
supersubstantiality
supersubstantiate
supersubtilized
supersubtle
supersufficiency
supersufficient
supersulcus
supersulphate
supersulphuret
supersulphureted
supersulphurize
supersuperabundance
supersuperabundant
supersuperabundantly
supersuperb
supersuperior
supersupremacy
supersupreme
supersurprise
supersuspicious
supersweet
supersympathy
supersyndicate
supersystem
supertare
supertartrate
supertax
supertaxation
supertemporal
supertempt
supertemptation
supertension
superterranean
superterraneous
superterrene
superterrestrial
superthankful
superthorough
superthyroidism
supertoleration
supertonic
supertotal
supertower
supertragic
supertragical
supertrain
supertramp
supertranscendent
supertranscendently
supertreason
supertrivial
supertuchun
supertunic
supertutelary
superugly
superultrafrostified
superunfit
superunit
superunity
superuniversal
superuniverse
superurgent
supervalue
supervast
supervene
supervenience
supervenient
supervenosity
supervention
supervestment
supervexation
supervictorious
supervigilant
supervigorous
supervirulent
supervisal
supervisance
supervise
supervision
supervisionary
supervisive
supervisor
supervisorial
supervisorship
supervisory
supervisual
supervisure
supervital
supervive
supervolition
supervoluminous
supervolute
superwager
superwealthy
superweening
superwise
superwoman
superworldly
superwrought
superyacht
superzealous
supinate
supination
supinator
supine
supinely
supineness
suppedaneum
supper
suppering
supperless
suppertime
supperwards
supping
supplace
supplant
supplantation
supplanter
supplantment
supple
supplejack
supplely
supplement
supplemental
supplementally
supplementarily
supplementary
supplementation
supplementer
suppleness
suppletion
suppletive
suppletively
suppletorily
suppletory
suppliable
supplial
suppliance
suppliancy
suppliant
suppliantly
suppliantness
supplicancy
supplicant
supplicantly
supplicat
supplicate
supplicating
supplicatingly
supplication
supplicationer
supplicative
supplicator
supplicatory
supplicavit
supplice
supplier
suppling
supply
support
supportability
supportable
supportableness
supportably
supportance
supporter
supportful
supporting
supportingly
supportive
supportless
supportlessly
supportress
supposable
supposableness
supposably
supposal
suppose
supposed
supposedly
supposer
supposing
supposition
suppositional
suppositionally
suppositionary
suppositionless
suppositious
supposititious
supposititiously
supposititiousness
suppositive
suppositively
suppository
suppositum
suppost
suppress
suppressal
suppressed
suppressedly
suppresser
suppressible
suppression
suppressionist
suppressive
suppressively
suppressor
supprise
suppurant
suppurate
suppuration
suppurative
suppuratory
suprabasidorsal
suprabranchial
suprabuccal
supracaecal
supracargo
supracaudal
supracensorious
supracentenarian
suprachorioid
suprachorioidal
suprachorioidea
suprachoroid
suprachoroidal
suprachoroidea
supraciliary
supraclavicle
supraclavicular
supraclusion
supracommissure
supraconduction
supraconductor
supracondylar
supracondyloid
supraconscious
supraconsciousness
supracoralline
supracostal
supracoxal
supracranial
supracretaceous
supradecompound
supradental
supradorsal
supradural
suprafeminine
suprafine
suprafoliaceous
suprafoliar
supraglacial
supraglenoid
supraglottic
supragovernmental
suprahepatic
suprahistorical
suprahuman
suprahumanity
suprahyoid
suprailiac
suprailium
supraintellectual
suprainterdorsal
suprajural
supralabial
supralapsarian
supralapsarianism
supralateral
supralegal
supraliminal
supraliminally
supralineal
supralinear
supralocal
supralocally
supraloral
supralunar
supralunary
supramammary
supramarginal
supramarine
supramastoid
supramaxilla
supramaxillary
supramaximal
suprameatal
supramechanical
supramedial
supramental
supramolecular
supramoral
supramortal
supramundane
supranasal
supranational
supranatural
supranaturalism
supranaturalist
supranaturalistic
supranature
supranervian
supraneural
supranormal
supranuclear
supraoccipital
supraocclusion
supraocular
supraoesophagal
supraoesophageal
supraoptimal
supraoptional
supraoral
supraorbital
supraorbitar
supraordinary
supraordinate
supraordination
suprapapillary
suprapedal
suprapharyngeal
supraposition
supraprotest
suprapubian
suprapubic
suprapygal
supraquantivalence
supraquantivalent
suprarational
suprarationalism
suprarationality
suprarenal
suprarenalectomize
suprarenalectomy
suprarenalin
suprarenine
suprarimal
suprasaturate
suprascapula
suprascapular
suprascapulary
suprascript
suprasegmental
suprasensible
suprasensitive
suprasensual
suprasensuous
supraseptal
suprasolar
suprasoriferous
suprasphanoidal
supraspinal
supraspinate
supraspinatus
supraspinous
suprasquamosal
suprastandard
suprastapedial
suprastate
suprasternal
suprastigmal
suprasubtle
supratemporal
supraterraneous
supraterrestrial
suprathoracic
supratonsillar
supratrochlear
supratropical
supratympanic
supravaginal
supraventricular
supraversion
supravital
supraworld
supremacy
suprematism
supreme
supremely
supremeness
supremity
sur
sura
suraddition
surah
surahi
sural
suralimentation
suranal
surangular
surat
surbase
surbased
surbasement
surbate
surbater
surbed
surcease
surcharge
surcharger
surcingle
surcoat
surcrue
surculi
surculigerous
surculose
surculous
surculus
surd
surdation
surdeline
surdent
surdimutism
surdity
surdomute
sure
surely
sureness
sures
Suresh
surette
surety
suretyship
surexcitation
surf
surface
surfaced
surfacedly
surfaceless
surfacely
surfaceman
surfacer
surfacing
surfactant
surfacy
surfbird
surfboard
surfboarding
surfboat
surfboatman
surfeit
surfeiter
surfer
surficial
surfle
surflike
surfman
surfmanship
surfrappe
surfuse
surfusion
surfy
surge
surgeful
surgeless
surgent
surgeon
surgeoncy
surgeoness
surgeonfish
surgeonless
surgeonship
surgeproof
surgerize
surgery
surgical
surgically
surginess
surging
surgy
Suriana
Surianaceae
Suricata
suricate
suriga
Surinam
surinamine
surlily
surliness
surly
surma
surmark
surmaster
surmisable
surmisal
surmisant
surmise
surmised
surmisedly
surmiser
surmount
surmountable
surmountableness
surmountal
surmounted
surmounter
surmullet
surname
surnamer
surnap
surnay
surnominal
surpass
surpassable
surpasser
surpassing
surpassingly
surpassingness
surpeopled
surplice
surpliced
surplicewise
surplician
surplus
surplusage
surpreciation
surprint
surprisable
surprisal
surprise
surprisedly
surprisement
surpriseproof
surpriser
surprising
surprisingly
surprisingness
surquedry
surquidry
surquidy
surra
surrealism
surrealist
surrealistic
surrealistically
surrebound
surrebut
surrebuttal
surrebutter
surrection
surrejoin
surrejoinder
surrenal
surrender
surrenderee
surrenderer
surrenderor
surreption
surreptitious
surreptitiously
surreptitiousness
surreverence
surreverently
surrey
surrogacy
surrogate
surrogateship
surrogation
surrosion
surround
surrounded
surroundedly
surrounder
surrounding
surroundings
sursaturation
sursolid
sursumduction
sursumvergence
sursumversion
surtax
surtout
surturbrand
surveillance
surveillant
survey
surveyable
surveyage
surveyal
surveyance
surveying
surveyor
surveyorship
survigrous
survivability
survivable
survival
survivalism
survivalist
survivance
survivancy
survive
surviver
surviving
survivor
survivoress
survivorship
Surya
Sus
Susan
Susanchite
Susanna
Susanne
susannite
suscept
susceptance
susceptibility
susceptible
susceptibleness
susceptibly
susception
susceptive
susceptiveness
susceptivity
susceptor
suscitate
suscitation
susi
Susian
Susianian
Susie
suslik
susotoxin
suspect
suspectable
suspected
suspectedness
suspecter
suspectful
suspectfulness
suspectible
suspectless
suspector
suspend
suspended
suspender
suspenderless
suspenders
suspendibility
suspendible
suspensation
suspense
suspenseful
suspensely
suspensibility
suspensible
suspension
suspensive
suspensively
suspensiveness
suspensoid
suspensor
suspensorial
suspensorium
suspensory
suspercollate
suspicion
suspicionable
suspicional
suspicionful
suspicionless
suspicious
suspiciously
suspiciousness
suspiration
suspiratious
suspirative
suspire
suspirious
Susquehanna
Sussex
sussexite
Sussexman
sussultatory
sussultorial
sustain
sustainable
sustained
sustainer
sustaining
sustainingly
sustainment
sustanedly
sustenance
sustenanceless
sustentacula
sustentacular
sustentaculum
sustentation
sustentational
sustentative
sustentator
sustention
sustentive
sustentor
Susu
susu
Susuhunan
Susuidae
Susumu
susurr
susurrant
susurrate
susurration
susurringly
susurrous
susurrus
Sutaio
suterbery
suther
Sutherlandia
sutile
sutler
sutlerage
sutleress
sutlership
sutlery
Suto
sutor
sutorial
sutorian
sutorious
sutra
Suttapitaka
suttee
sutteeism
sutten
suttin
suttle
Sutu
sutural
suturally
suturation
suture
Suu
suum
Suwandi
suwarro
suwe
Suyog
suz
Suzan
Suzanne
suzerain
suzeraine
suzerainship
suzerainty
Suzy
Svan
Svanetian
Svanish
Svante
Svantovit
svarabhakti
svarabhaktic
Svarloka
svelte
Svetambara
sviatonosite
swa
Swab
swab
swabber
swabberly
swabble
Swabian
swack
swacken
swacking
swad
swaddle
swaddlebill
swaddler
swaddling
swaddy
Swadeshi
Swadeshism
swag
swagbellied
swagbelly
swage
swager
swagger
swaggerer
swaggering
swaggeringly
swaggie
swaggy
swaglike
swagman
swagsman
Swahilese
Swahili
Swahilian
Swahilize
swaimous
swain
swainish
swainishness
swainship
Swainsona
swainsona
swaird
swale
swaler
swaling
swalingly
swallet
swallo
swallow
swallowable
swallower
swallowlike
swallowling
swallowpipe
swallowtail
swallowwort
swam
swami
swamp
swampable
swampberry
swamper
swampish
swampishness
swampland
swampside
swampweed
swampwood
swampy
Swamy
swan
swandown
swanflower
swang
swangy
swanherd
swanhood
swanimote
swank
swanker
swankily
swankiness
swanking
swanky
swanlike
swanmark
swanmarker
swanmarking
swanneck
swannecked
swanner
swannery
swannish
swanny
swanskin
Swantevit
swanweed
swanwort
swap
swape
swapper
swapping
swaraj
swarajism
swarajist
swarbie
sward
swardy
sware
swarf
swarfer
swarm
swarmer
swarming
swarmy
swarry
swart
swartback
swarth
swarthily
swarthiness
swarthness
swarthy
swartish
swartly
swartness
swartrutter
swartrutting
swarty
Swartzbois
Swartzia
swarve
swash
swashbuckle
swashbuckler
swashbucklerdom
swashbucklering
swashbucklery
swashbuckling
swasher
swashing
swashway
swashwork
swashy
swastika
swastikaed
Swat
swat
swatch
Swatchel
swatcher
swatchway
swath
swathable
swathband
swathe
swatheable
swather
swathy
Swati
Swatow
swatter
swattle
swaver
sway
swayable
swayed
swayer
swayful
swaying
swayingly
swayless
Swazi
Swaziland
sweal
sweamish
swear
swearer
swearingly
swearword
sweat
sweatband
sweatbox
sweated
sweater
sweatful
sweath
sweatily
sweatiness
sweating
sweatless
sweatproof
sweatshop
sweatweed
sweaty
Swede
Swedenborgian
Swedenborgianism
Swedenborgism
swedge
Swedish
sweeny
sweep
sweepable
sweepage
sweepback
sweepboard
sweepdom
sweeper
sweeperess
sweepforward
sweeping
sweepingly
sweepingness
sweepings
sweepstake
sweepwasher
sweepwashings
sweepy
sweer
sweered
sweet
sweetberry
sweetbread
sweetbrier
sweetbriery
sweeten
sweetener
sweetening
sweetfish
sweetful
sweetheart
sweetheartdom
sweethearted
sweetheartedness
sweethearting
sweetheartship
sweetie
sweeting
sweetish
sweetishly
sweetishness
sweetleaf
sweetless
sweetlike
sweetling
sweetly
sweetmaker
sweetmeat
sweetmouthed
sweetness
sweetroot
sweetshop
sweetsome
sweetsop
sweetwater
sweetweed
sweetwood
sweetwort
sweety
swego
swelchie
swell
swellage
swelldom
swelldoodle
swelled
sweller
swellfish
swelling
swellish
swellishness
swellmobsman
swellness
swelltoad
swelly
swelp
swelt
swelter
sweltering
swelteringly
swelth
sweltry
swelty
swep
swept
swerd
Swertia
swerve
swerveless
swerver
swervily
swick
swidge
Swietenia
swift
swiften
swifter
swiftfoot
swiftlet
swiftlike
swiftness
swifty
swig
swigger
swiggle
swile
swill
swillbowl
swiller
swilltub
swim
swimmable
swimmer
swimmeret
swimmily
swimminess
swimming
swimmingly
swimmingness
swimmist
swimmy
swimsuit
swimy
Swinburnesque
Swinburnian
swindle
swindleable
swindledom
swindler
swindlership
swindlery
swindling
swindlingly
swine
swinebread
swinecote
swinehead
swineherd
swineherdship
swinehood
swinehull
swinelike
swinely
swinepipe
swinery
swinestone
swinesty
swiney
swing
swingable
swingback
swingdevil
swingdingle
swinge
swingeing
swinger
swinging
swingingly
Swingism
swingle
swinglebar
swingletail
swingletree
swingstock
swingtree
swingy
swinish
swinishly
swinishness
swink
swinney
swipe
swiper
swipes
swiple
swipper
swipy
swird
swire
swirl
swirlingly
swirly
swirring
swish
swisher
swishing
swishingly
swishy
Swiss
swiss
Swissess
swissing
switch
switchback
switchbacker
switchboard
switched
switchel
switcher
switchgear
switching
switchkeeper
switchlike
switchman
switchy
switchyard
swith
swithe
swithen
swither
Swithin
Switzer
Switzeress
swivel
swiveled
swiveleye
swiveleyed
swivellike
swivet
swivetty
swiz
swizzle
swizzler
swob
swollen
swollenly
swollenness
swom
swonken
swoon
swooned
swooning
swooningly
swoony
swoop
swooper
swoosh
sword
swordbill
swordcraft
swordfish
swordfisherman
swordfishery
swordfishing
swordick
swording
swordless
swordlet
swordlike
swordmaker
swordmaking
swordman
swordmanship
swordplay
swordplayer
swordproof
swordsman
swordsmanship
swordsmith
swordster
swordstick
swordswoman
swordtail
swordweed
swore
sworn
swosh
swot
swotter
swounds
swow
swum
swung
swungen
swure
syagush
sybarism
sybarist
Sybarital
Sybaritan
Sybarite
Sybaritic
Sybaritical
Sybaritically
Sybaritish
sybaritism
Sybil
sybotic
sybotism
sycamine
sycamore
syce
sycee
sychnocarpous
sycock
sycoma
sycomancy
Sycon
Syconaria
syconarian
syconate
Sycones
syconid
Syconidae
syconium
syconoid
syconus
sycophancy
sycophant
sycophantic
sycophantical
sycophantically
sycophantish
sycophantishly
sycophantism
sycophantize
sycophantry
sycosiform
sycosis
Syd
Sydneian
Sydneyite
sye
Syed
syenite
syenitic
syenodiorite
syenogabbro
sylid
syllab
syllabarium
syllabary
syllabatim
syllabation
syllabe
syllabi
syllabic
syllabical
syllabically
syllabicate
syllabication
syllabicness
syllabification
syllabify
syllabism
syllabize
syllable
syllabled
syllabus
syllepsis
sylleptic
sylleptical
sylleptically
Syllidae
syllidian
Syllis
sylloge
syllogism
syllogist
syllogistic
syllogistical
syllogistically
syllogistics
syllogization
syllogize
syllogizer
sylph
sylphic
sylphid
sylphidine
sylphish
sylphize
sylphlike
Sylphon
sylphy
sylva
sylvae
sylvage
Sylvan
sylvan
sylvanesque
sylvanite
sylvanitic
sylvanity
sylvanize
sylvanly
sylvanry
sylvate
sylvatic
Sylvester
sylvester
sylvestral
sylvestrene
Sylvestrian
sylvestrian
Sylvestrine
Sylvia
Sylvian
sylvic
Sylvicolidae
sylvicoline
Sylviidae
Sylviinae
sylviine
sylvine
sylvinite
sylvite
symbasic
symbasical
symbasically
symbasis
symbiogenesis
symbiogenetic
symbiogenetically
symbion
symbiont
symbiontic
symbionticism
symbiosis
symbiot
symbiote
symbiotic
symbiotically
symbiotics
symbiotism
symbiotrophic
symblepharon
symbol
symbolaeography
symbolater
symbolatrous
symbolatry
symbolic
symbolical
symbolically
symbolicalness
symbolicly
symbolics
symbolism
symbolist
symbolistic
symbolistical
symbolistically
symbolization
symbolize
symbolizer
symbolofideism
symbological
symbologist
symbolography
symbology
symbololatry
symbolology
symbolry
symbouleutic
symbranch
Symbranchia
symbranchiate
symbranchoid
symbranchous
symmachy
symmedian
symmelia
symmelian
symmelus
symmetalism
symmetral
symmetric
symmetrical
symmetricality
symmetrically
symmetricalness
symmetrist
symmetrization
symmetrize
symmetroid
symmetrophobia
symmetry
symmorphic
symmorphism
sympalmograph
sympathectomize
sympathectomy
sympathetectomy
sympathetic
sympathetical
sympathetically
sympatheticism
sympatheticity
sympatheticness
sympatheticotonia
sympatheticotonic
sympathetoblast
sympathicoblast
sympathicotonia
sympathicotonic
sympathicotripsy
sympathism
sympathist
sympathize
sympathizer
sympathizing
sympathizingly
sympathoblast
sympatholysis
sympatholytic
sympathomimetic
sympathy
sympatric
sympatry
Sympetalae
sympetalous
Symphalangus
symphenomena
symphenomenal
symphile
symphilic
symphilism
symphilous
symphily
symphogenous
symphonetic
symphonia
symphonic
symphonically
symphonion
symphonious
symphoniously
symphonist
symphonize
symphonous
symphony
Symphoricarpos
symphoricarpous
symphrase
symphronistic
symphyantherous
symphycarpous
Symphyla
symphylan
symphyllous
symphylous
symphynote
symphyogenesis
symphyogenetic
symphyostemonous
symphyseal
symphyseotomy
symphysial
symphysian
symphysic
symphysion
symphysiotomy
symphysis
symphysodactylia
symphysotomy
symphysy
Symphyta
symphytic
symphytically
symphytism
symphytize
Symphytum
sympiesometer
symplasm
symplectic
Symplegades
symplesite
Symplocaceae
symplocaceous
Symplocarpus
symploce
Symplocos
sympode
sympodia
sympodial
sympodially
sympodium
sympolity
symposia
symposiac
symposiacal
symposial
symposiarch
symposiast
symposiastic
symposion
symposium
symptom
symptomatic
symptomatical
symptomatically
symptomatics
symptomatize
symptomatography
symptomatological
symptomatologically
symptomatology
symptomical
symptomize
symptomless
symptosis
symtomology
synacme
synacmic
synacmy
synactic
synadelphite
synaeresis
synagogal
synagogian
synagogical
synagogism
synagogist
synagogue
synalgia
synalgic
synallactic
synallagmatic
synaloepha
synanastomosis
synange
synangia
synangial
synangic
synangium
synanthema
synantherological
synantherologist
synantherology
synantherous
synanthesis
synanthetic
synanthic
synanthous
synanthrose
synanthy
synaphea
synaposematic
synapse
synapses
Synapsida
synapsidan
synapsis
synaptai
synaptase
synapte
synaptene
Synaptera
synapterous
synaptic
synaptical
synaptically
synapticula
synapticulae
synapticular
synapticulate
synapticulum
Synaptosauria
synaptychus
synarchical
synarchism
synarchy
synarmogoid
Synarmogoidea
synarquism
synartesis
synartete
synartetic
synarthrodia
synarthrodial
synarthrodially
synarthrosis
Synascidiae
synascidian
synastry
synaxar
synaxarion
synaxarist
synaxarium
synaxary
synaxis
sync
Syncarida
syncarp
syncarpia
syncarpium
syncarpous
syncarpy
syncategorematic
syncategorematical
syncategorematically
syncategoreme
syncephalic
syncephalus
syncerebral
syncerebrum
synch
synchitic
synchondoses
synchondrosial
synchondrosially
synchondrosis
synchondrotomy
synchoresis
synchro
synchroflash
synchromesh
synchronal
synchrone
synchronic
synchronical
synchronically
synchronism
synchronistic
synchronistical
synchronistically
synchronizable
synchronization
synchronize
synchronized
synchronizer
synchronograph
synchronological
synchronology
synchronous
synchronously
synchronousness
synchrony
synchroscope
synchrotron
synchysis
Synchytriaceae
Synchytrium
syncladous
synclastic
synclinal
synclinally
syncline
synclinical
synclinore
synclinorial
synclinorian
synclinorium
synclitic
syncliticism
synclitism
syncoelom
syncopal
syncopate
syncopated
syncopation
syncopator
syncope
syncopic
syncopism
syncopist
syncopize
syncotyledonous
syncracy
syncraniate
syncranterian
syncranteric
syncrasy
syncretic
syncretical
syncreticism
syncretion
syncretism
syncretist
syncretistic
syncretistical
syncretize
syncrisis
Syncrypta
syncryptic
syncytia
syncytial
syncytioma
syncytiomata
syncytium
syndactyl
syndactylia
syndactylic
syndactylism
syndactylous
syndactyly
syndectomy
synderesis
syndesis
syndesmectopia
syndesmitis
syndesmography
syndesmology
syndesmoma
Syndesmon
syndesmoplasty
syndesmorrhaphy
syndesmosis
syndesmotic
syndesmotomy
syndetic
syndetical
syndetically
syndic
syndical
syndicalism
syndicalist
syndicalistic
syndicalize
syndicate
syndicateer
syndication
syndicator
syndicship
syndoc
syndrome
syndromic
syndyasmian
Syndyoceras
syne
synecdoche
synecdochic
synecdochical
synecdochically
synecdochism
synechia
synechiological
synechiology
synechological
synechology
synechotomy
synechthran
synechthry
synecology
synecphonesis
synectic
synecticity
Synedra
synedral
Synedria
synedria
synedrial
synedrian
Synedrion
synedrion
Synedrium
synedrium
synedrous
syneidesis
synema
synemmenon
synenergistic
synenergistical
synenergistically
synentognath
Synentognathi
synentognathous
syneresis
synergastic
synergetic
synergia
synergic
synergically
synergid
synergidae
synergidal
synergism
synergist
synergistic
synergistical
synergistically
synergize
synergy
synerize
synesis
synesthesia
synesthetic
synethnic
syngamic
syngamous
syngamy
Syngenesia
syngenesian
syngenesious
syngenesis
syngenetic
syngenic
syngenism
syngenite
Syngnatha
Syngnathi
syngnathid
Syngnathidae
syngnathoid
syngnathous
Syngnathus
syngraph
synizesis
synkaryon
synkatathesis
synkinesia
synkinesis
synkinetic
synneurosis
synneusis
synochoid
synochus
synocreate
synod
synodal
synodalian
synodalist
synodally
synodical
synodically
synodist
synodite
synodontid
Synodontidae
synodontoid
synodsman
Synodus
synoecete
synoeciosis
synoecious
synoeciously
synoeciousness
synoecism
synoecize
synoecy
synoicous
synomosy
synonym
synonymatic
synonymic
synonymical
synonymicon
synonymics
synonymist
synonymity
synonymize
synonymous
synonymously
synonymousness
synonymy
synophthalmus
synopses
synopsis
synopsize
synopsy
synoptic
synoptical
synoptically
Synoptist
synoptist
Synoptistic
synorchidism
synorchism
synorthographic
synosteology
synosteosis
synostose
synostosis
synostotic
synostotical
synostotically
synousiacs
synovectomy
synovia
synovial
synovially
synoviparous
synovitic
synovitis
synpelmous
synrhabdosome
synsacral
synsacrum
synsepalous
synspermous
synsporous
syntactic
syntactical
syntactically
syntactician
syntactics
syntagma
syntan
syntasis
syntax
syntaxis
syntaxist
syntechnic
syntectic
syntelome
syntenosis
synteresis
syntexis
syntheme
synthermal
syntheses
synthesis
synthesism
synthesist
synthesization
synthesize
synthesizer
synthete
synthetic
synthetical
synthetically
syntheticism
synthetism
synthetist
synthetization
synthetize
synthetizer
synthol
synthroni
synthronoi
synthronos
synthronus
syntomia
syntomy
syntone
syntonic
syntonical
syntonically
syntonin
syntonization
syntonize
syntonizer
syntonolydian
syntonous
syntony
syntripsis
syntrope
syntrophic
syntropic
syntropical
syntropy
syntype
syntypic
syntypicism
Synura
synusia
synusiast
syodicon
sypher
syphilide
syphilidography
syphilidologist
syphiliphobia
syphilis
syphilitic
syphilitically
syphilization
syphilize
syphiloderm
syphilodermatous
syphilogenesis
syphilogeny
syphilographer
syphilography
syphiloid
syphilologist
syphilology
syphiloma
syphilomatous
syphilophobe
syphilophobia
syphilophobic
syphilopsychosis
syphilosis
syphilous
Syracusan
syre
Syriac
Syriacism
Syriacist
Syrian
Syrianic
Syrianism
Syrianize
Syriarch
Syriasm
syringa
syringadenous
syringe
syringeal
syringeful
syringes
syringin
syringitis
syringium
syringocoele
syringomyelia
syringomyelic
syringotome
syringotomy
syrinx
Syriologist
Syrma
syrma
Syrmian
Syrnium
Syrophoenician
syrphian
syrphid
Syrphidae
syrt
syrtic
Syrtis
syrup
syruped
syruper
syruplike
syrupy
Syryenian
syssarcosis
syssel
sysselman
syssiderite
syssitia
syssition
systaltic
systasis
systatic
system
systematic
systematical
systematicality
systematically
systematician
systematicness
systematics
systematism
systematist
systematization
systematize
systematizer
systematology
systemed
systemic
systemically
systemist
systemizable
systemization
systemize
systemizer
systemless
systemproof
systemwise
systilius
systolated
systole
systolic
systyle
systylous
Syun
syzygetic
syzygetically
syzygial
syzygium
syzygy
szaibelyite
Szekler
szlachta
szopelka
T
t
ta
taa
Taal
Taalbond
taar
Tab
tab
tabacin
tabacosis
tabacum
tabanid
Tabanidae
tabaniform
tabanuco
Tabanus
tabard
tabarded
tabaret
Tabasco
tabasheer
tabashir
tabaxir
tabbarea
tabber
tabbinet
Tabby
tabby
Tabebuia
tabefaction
tabefy
tabella
Tabellaria
Tabellariaceae
tabellion
taberdar
taberna
tabernacle
tabernacler
tabernacular
Tabernaemontana
tabernariae
tabes
tabescence
tabescent
tabet
tabetic
tabetiform
tabetless
tabic
tabid
tabidly
tabidness
tabific
tabifical
tabinet
Tabira
Tabitha
tabitude
tabla
tablature
table
tableau
tableaux
tablecloth
tableclothwise
tableclothy
tabled
tablefellow
tablefellowship
tableful
tableity
tableland
tableless
tablelike
tablemaid
tablemaker
tablemaking
tableman
tablemate
tabler
tables
tablespoon
tablespoonful
tablet
tabletary
tableware
tablewise
tabling
tablinum
Tabloid
tabloid
tabog
taboo
tabooism
tabooist
taboot
taboparalysis
taboparesis
taboparetic
tabophobia
tabor
taborer
taboret
taborin
Taborite
tabour
tabourer
tabouret
tabret
Tabriz
tabu
tabula
tabulable
tabular
tabulare
tabularium
tabularization
tabularize
tabularly
tabulary
Tabulata
tabulate
tabulated
tabulation
tabulator
tabulatory
tabule
tabuliform
tabut
tacahout
tacamahac
Tacana
Tacanan
Tacca
Taccaceae
taccaceous
taccada
tach
Tachardia
Tachardiinae
tache
tacheless
tacheography
tacheometer
tacheometric
tacheometry
tacheture
tachhydrite
tachibana
Tachina
Tachinaria
tachinarian
tachinid
Tachinidae
tachiol
tachistoscope
tachistoscopic
tachogram
tachograph
tachometer
tachometry
tachoscope
tachycardia
tachycardiac
tachygen
tachygenesis
tachygenetic
tachygenic
tachyglossal
tachyglossate
Tachyglossidae
Tachyglossus
tachygraph
tachygrapher
tachygraphic
tachygraphical
tachygraphically
tachygraphist
tachygraphometer
tachygraphometry
tachygraphy
tachyhydrite
tachyiatry
tachylalia
tachylite
tachylyte
tachylytic
tachymeter
tachymetric
tachymetry
tachyphagia
tachyphasia
tachyphemia
tachyphrasia
tachyphrenia
tachypnea
tachyscope
tachyseism
tachysterol
tachysystole
tachythanatous
tachytomy
tachytype
tacit
Tacitean
tacitly
tacitness
taciturn
taciturnist
taciturnity
taciturnly
tack
tacker
tacket
tackety
tackey
tackiness
tacking
tackingly
tackle
tackled
tackleless
tackleman
tackler
tackless
tackling
tackproof
tacksman
tacky
taclocus
tacmahack
tacnode
Taconian
Taconic
taconite
tacso
Tacsonia
tact
tactable
tactful
tactfully
tactfulness
tactic
tactical
tactically
tactician
tactics
tactile
tactilist
tactility
tactilogical
tactinvariant
taction
tactite
tactive
tactless
tactlessly
tactlessness
tactometer
tactor
tactosol
tactual
tactualist
tactuality
tactually
tactus
tacuacine
Taculli
Tad
tad
tade
Tadjik
Tadousac
tadpole
tadpoledom
tadpolehood
tadpolelike
tadpolism
tae
tael
taen
taenia
taeniacidal
taeniacide
Taeniada
taeniafuge
taenial
taenian
taeniasis
Taeniata
taeniate
taenicide
Taenidia
taenidium
taeniform
taenifuge
taeniiform
Taeniobranchia
taeniobranchiate
Taeniodonta
Taeniodontia
Taeniodontidae
Taenioglossa
taenioglossate
taenioid
taeniosome
Taeniosomi
taeniosomous
taenite
taennin
Taetsia
taffarel
tafferel
taffeta
taffety
taffle
taffrail
Taffy
taffy
taffylike
taffymaker
taffymaking
taffywise
tafia
tafinagh
taft
tafwiz
tag
Tagabilis
Tagakaolo
Tagal
Tagala
Tagalize
Tagalo
Tagalog
tagasaste
Tagassu
Tagassuidae
tagatose
Tagaur
Tagbanua
tagboard
Tagetes
tagetol
tagetone
tagged
tagger
taggle
taggy
Taghlik
tagilite
Tagish
taglet
Tagliacotian
Tagliacozzian
taglike
taglock
tagrag
tagraggery
tagsore
tagtail
tagua
taguan
Tagula
tagwerk
taha
Tahami
taheen
tahil
tahin
Tahiti
Tahitian
tahkhana
Tahltan
tahr
tahseeldar
tahsil
tahsildar
Tahsin
tahua
Tai
tai
taiaha
taich
taiga
taigle
taiglesome
taihoa
taikhana
tail
tailage
tailband
tailboard
tailed
tailender
tailer
tailet
tailfirst
tailflower
tailforemost
tailge
tailhead
tailing
tailings
taille
tailless
taillessly
taillessness
taillie
taillight
taillike
tailor
tailorage
tailorbird
tailorcraft
tailordom
tailoress
tailorhood
tailoring
tailorism
tailorization
tailorize
tailorless
tailorlike
tailorly
tailorman
tailorship
tailorwise
tailory
tailpiece
tailpin
tailpipe
tailrace
tailsman
tailstock
Tailte
tailward
tailwards
tailwise
taily
tailzee
tailzie
taimen
taimyrite
tain
Tainan
Taino
taint
taintable
taintless
taintlessly
taintlessness
taintment
taintor
taintproof
tainture
taintworm
Tainui
taipan
Taipi
Taiping
taipo
tairge
tairger
tairn
taisch
taise
Taisho
taissle
taistrel
taistril
Tait
tait
taiver
taivers
taivert
Taiwanhemp
Taiyal
taj
Tajik
takable
takamaka
Takao
takar
Takayuki
take
takedown
takedownable
takeful
Takelma
taken
taker
Takeuchi
Takhaar
Takhtadjy
Takilman
takin
taking
takingly
takingness
takings
Takitumu
takosis
takt
Taku
taky
takyr
Tal
tal
tala
talabon
talahib
Talaing
talaje
talak
talalgia
Talamanca
Talamancan
talanton
talao
talapoin
talar
talari
talaria
talaric
talayot
talbot
talbotype
talc
talcer
Talcher
talcky
talclike
talcochlorite
talcoid
talcomicaceous
talcose
talcous
talcum
tald
tale
talebearer
talebearing
talebook
talecarrier
talecarrying
taled
taleful
Talegallinae
Talegallus
talemaster
talemonger
talemongering
talent
talented
talentless
talepyet
taler
tales
talesman
taleteller
taletelling
tali
Taliacotian
taliage
taliation
taliera
taligrade
Talinum
talion
talionic
talipat
taliped
talipedic
talipes
talipomanus
talipot
talis
talisay
Talishi
talisman
talismanic
talismanical
talismanically
talismanist
talite
Talitha
talitol
talk
talkability
talkable
talkathon
talkative
talkatively
talkativeness
talker
talkfest
talkful
talkie
talkiness
talking
talkworthy
talky
tall
tallage
tallageability
tallageable
tallboy
tallegalane
taller
tallero
talles
tallet
talliable
talliage
talliar
talliate
tallier
tallis
tallish
tallit
tallith
tallness
talloel
tallote
tallow
tallowberry
tallower
tallowiness
tallowing
tallowish
tallowlike
tallowmaker
tallowmaking
tallowman
tallowroot
tallowweed
tallowwood
tallowy
tallwood
tally
tallyho
tallyman
tallymanship
tallywag
tallywalka
tallywoman
talma
talmouse
Talmud
Talmudic
Talmudical
Talmudism
Talmudist
Talmudistic
Talmudistical
Talmudization
Talmudize
talocalcaneal
talocalcanean
talocrural
talofibular
talon
talonavicular
taloned
talonic
talonid
taloscaphoid
talose
talotibial
Talpa
talpacoti
talpatate
talpetate
talpicide
talpid
Talpidae
talpiform
talpify
talpine
talpoid
talthib
Taltushtuntude
Taluche
Taluhet
taluk
taluka
talukdar
talukdari
talus
taluto
talwar
talwood
Talyshin
tam
Tama
tamability
tamable
tamableness
tamably
Tamaceae
Tamachek
tamacoare
tamale
Tamanac
Tamanaca
Tamanaco
tamandu
tamandua
tamanoas
tamanoir
tamanowus
tamanu
Tamara
tamara
tamarack
tamaraite
tamarao
Tamaricaceae
tamaricaceous
tamarin
tamarind
Tamarindus
tamarisk
Tamarix
Tamaroa
tamas
tamasha
Tamashek
Tamaulipecan
tambac
tambaroora
tamber
tambo
tamboo
Tambookie
tambookie
tambor
Tambouki
tambour
tamboura
tambourer
tambouret
tambourgi
tambourin
tambourinade
tambourine
tambourist
tambreet
Tambuki
tamburan
tamburello
Tame
tame
tamehearted
tameheartedness
tamein
tameless
tamelessly
tamelessness
tamely
tameness
tamer
Tamerlanism
Tamias
tamidine
Tamil
Tamilian
Tamilic
tamis
tamise
tamlung
Tammanial
Tammanize
Tammany
Tammanyism
Tammanyite
Tammanyize
tammie
tammock
Tammy
tammy
Tamonea
Tamoyo
tamp
tampala
tampan
tampang
tamper
tamperer
tamperproof
tampin
tamping
tampion
tampioned
tampon
tamponade
tamponage
tamponment
tampoon
Tamul
Tamulian
Tamulic
Tamus
Tamworth
Tamzine
tan
tana
tanacetin
tanacetone
Tanacetum
tanacetyl
tanach
tanager
Tanagra
Tanagraean
Tanagridae
tanagrine
tanagroid
Tanaidacea
tanaist
tanak
Tanaka
Tanala
tanan
tanbark
tanbur
tancel
Tanchelmian
tanchoir
tandan
tandem
tandemer
tandemist
tandemize
tandemwise
tandle
tandour
Tandy
tane
tanekaha
Tang
tang
tanga
Tangaloa
tangalung
tangantangan
Tangaridae
Tangaroa
Tangaroan
tanged
tangeite
tangelo
tangence
tangency
tangent
tangental
tangentally
tangential
tangentiality
tangentially
tangently
tanger
Tangerine
tangfish
tangham
tanghan
tanghin
Tanghinia
tanghinin
tangi
tangibile
tangibility
tangible
tangibleness
tangibly
tangie
Tangier
tangilin
Tangipahoa
tangka
tanglad
tangle
tangleberry
tanglefish
tanglefoot
tanglement
tangleproof
tangler
tangleroot
tanglesome
tangless
tanglewrack
tangling
tanglingly
tangly
tango
tangoreceptor
tangram
tangs
tangue
tanguile
tangum
tangun
Tangut
tangy
tanh
tanha
tanhouse
tania
tanica
tanier
tanist
tanistic
tanistry
tanistship
Tanite
Tanitic
tanjib
tanjong
tank
tanka
tankage
tankah
tankard
tanked
tanker
tankerabogus
tankert
tankette
tankful
tankle
tankless
tanklike
tankmaker
tankmaking
tankman
tankodrome
tankroom
tankwise
tanling
tannable
tannage
tannaic
tannaim
tannaitic
tannalbin
tannase
tannate
tanned
tanner
tannery
tannic
tannide
tanniferous
tannin
tannined
tanning
tanninlike
tannocaffeic
tannogallate
tannogallic
tannogelatin
tannogen
tannoid
tannometer
tannyl
Tano
tanoa
Tanoan
tanproof
tanquam
Tanquelinian
tanquen
tanrec
tanstuff
tansy
tantadlin
tantafflin
tantalate
Tantalean
Tantalian
Tantalic
tantalic
tantaliferous
tantalifluoride
tantalite
tantalization
tantalize
tantalizer
tantalizingly
tantalizingness
tantalofluoride
tantalum
Tantalus
tantamount
tantara
tantarabobus
tantarara
tanti
tantivy
tantle
Tantony
tantra
tantric
tantrik
tantrism
tantrist
tantrum
tantum
tanwood
tanworks
Tanya
tanyard
Tanyoan
Tanystomata
tanystomatous
tanystome
tanzeb
tanzib
Tanzine
tanzy
Tao
tao
Taoism
Taoist
Taoistic
Taonurus
Taos
taotai
taoyin
tap
Tapa
tapa
Tapachula
Tapachulteca
tapacolo
tapaculo
Tapacura
tapadera
tapadero
Tapajo
tapalo
tapamaker
tapamaking
tapas
tapasvi
Tape
tape
Tapeats
tapeinocephalic
tapeinocephalism
tapeinocephaly
tapeless
tapelike
tapeline
tapemaker
tapemaking
tapeman
tapen
taper
taperbearer
tapered
taperer
tapering
taperingly
taperly
tapermaker
tapermaking
taperness
taperwise
tapesium
tapestring
tapestry
tapestrylike
tapet
tapetal
tapete
tapeti
tapetless
tapetum
tapework
tapeworm
taphephobia
taphole
taphouse
Taphria
Taphrina
Taphrinaceae
tapia
Tapijulapane
tapinceophalism
tapinocephalic
tapinocephaly
Tapinoma
tapinophobia
tapinophoby
tapinosis
tapioca
tapir
Tapiridae
tapiridian
tapirine
Tapiro
tapiroid
Tapirus
tapis
tapism
tapist
taplash
taplet
Tapleyism
tapmost
tapnet
tapoa
Taposa
tapoun
tappa
tappable
tappableness
tappall
tappaul
tappen
tapper
tapperer
Tappertitian
tappet
tappietoorie
tapping
tappoon
Taprobane
taproom
taproot
taprooted
taps
tapster
tapsterlike
tapsterly
tapstress
tapu
tapul
Tapuya
Tapuyan
Tapuyo
taqua
tar
tara
tarabooka
taraf
tarafdar
tarage
Tarahumar
Tarahumara
Tarahumare
Tarahumari
Tarai
tarairi
tarakihi
Taraktogenos
taramellite
Taramembe
Taranchi
tarand
Tarandean
Tarandian
tarantara
tarantass
tarantella
tarantism
tarantist
tarantula
tarantular
tarantulary
tarantulated
tarantulid
Tarantulidae
tarantulism
tarantulite
tarantulous
tarapatch
taraph
tarapin
Tarapon
Tarasc
Tarascan
Tarasco
tarassis
tarata
taratah
taratantara
taratantarize
tarau
taraxacerin
taraxacin
Taraxacum
Tarazed
tarbadillo
tarbet
tarboard
tarbogan
tarboggin
tarboosh
tarbooshed
tarboy
tarbrush
tarbush
tarbuttite
Tardenoisian
Tardigrada
tardigrade
tardigradous
tardily
tardiness
tarditude
tardive
tardle
tardy
tare
tarea
tarefa
tarefitch
tarentala
tarente
Tarentine
tarentism
tarentola
tarepatch
Tareq
tarfa
tarflower
targe
targeman
targer
target
targeted
targeteer
targetlike
targetman
Targum
Targumic
Targumical
Targumist
Targumistic
Targumize
Tarheel
Tarheeler
tarhood
tari
Tariana
tarie
tariff
tariffable
tariffication
tariffism
tariffist
tariffite
tariffize
tariffless
tarin
Tariri
tariric
taririnic
tarish
Tarkalani
Tarkani
tarkashi
tarkeean
tarkhan
tarlatan
tarlataned
tarletan
tarlike
tarltonize
Tarmac
tarmac
tarman
Tarmi
tarmined
tarn
tarnal
tarnally
tarnation
tarnish
tarnishable
tarnisher
tarnishment
tarnishproof
tarnlike
tarnside
taro
taroc
tarocco
tarok
taropatch
tarot
tarp
tarpan
tarpaulin
tarpaulinmaker
Tarpeia
Tarpeian
tarpon
tarpot
tarpum
Tarquin
Tarquinish
tarr
tarrack
tarradiddle
tarradiddler
tarragon
tarragona
tarras
tarrass
Tarrateen
Tarratine
tarred
tarrer
tarri
tarriance
tarrie
tarrier
tarrify
tarrily
tarriness
tarrish
tarrock
tarrow
tarry
tarrying
tarryingly
tarryingness
tars
tarsadenitis
tarsal
tarsale
tarsalgia
tarse
tarsectomy
tarsectopia
tarsi
tarsia
tarsier
Tarsiidae
tarsioid
Tarsipedidae
Tarsipedinae
Tarsipes
tarsitis
Tarsius
tarsochiloplasty
tarsoclasis
tarsomalacia
tarsome
tarsometatarsal
tarsometatarsus
tarsonemid
Tarsonemidae
Tarsonemus
tarsophalangeal
tarsophyma
tarsoplasia
tarsoplasty
tarsoptosis
tarsorrhaphy
tarsotarsal
tarsotibal
tarsotomy
tarsus
tart
tartago
Tartan
tartan
tartana
tartane
Tartar
tartar
tartarated
Tartarean
Tartareous
tartareous
tartaret
Tartarian
Tartaric
tartaric
Tartarin
tartarish
Tartarism
Tartarization
tartarization
Tartarize
tartarize
Tartarized
Tartarlike
tartarly
Tartarology
tartarous
tartarproof
tartarum
Tartarus
Tartary
tartemorion
tarten
tartish
tartishly
tartle
tartlet
tartly
tartness
tartramate
tartramic
tartramide
tartrate
tartrated
tartratoferric
tartrazine
tartrazinic
tartro
tartronate
tartronic
tartronyl
tartronylurea
tartrous
tartryl
tartrylic
Tartufe
tartufery
tartufian
tartufish
tartufishly
tartufism
tartwoman
Taruma
Tarumari
tarve
Tarvia
tarweed
tarwhine
tarwood
tarworks
taryard
Taryba
Tarzan
Tarzanish
tasajo
tascal
tasco
taseometer
tash
tasheriff
tashie
tashlik
Tashnagist
Tashnakist
tashreef
tashrif
Tasian
tasimeter
tasimetric
tasimetry
task
taskage
tasker
taskit
taskless
tasklike
taskmaster
taskmastership
taskmistress
tasksetter
tasksetting
taskwork
taslet
Tasmanian
tasmanite
Tass
tass
tassago
tassah
tassal
tassard
tasse
tassel
tasseler
tasselet
tasselfish
tassellus
tasselmaker
tasselmaking
tassely
tasser
tasset
tassie
tassoo
tastable
tastableness
tastably
taste
tasteable
tasteableness
tasteably
tasted
tasteful
tastefully
tastefulness
tastekin
tasteless
tastelessly
tastelessness
tasten
taster
tastily
tastiness
tasting
tastingly
tasty
tasu
Tat
tat
Tatar
Tatarian
Tataric
Tatarization
Tatarize
Tatary
tataupa
tatbeb
tatchy
tate
tater
Tates
tath
Tatian
Tatianist
tatie
tatinek
tatler
tatou
tatouay
tatpurusha
Tatsanottine
tatsman
tatta
tatter
tatterdemalion
tatterdemalionism
tatterdemalionry
tattered
tatteredly
tatteredness
tatterly
tatterwallop
tattery
tatther
tattied
tatting
tattle
tattlement
tattler
tattlery
tattletale
tattling
tattlingly
tattoo
tattooage
tattooer
tattooing
tattooist
tattooment
tattva
tatty
Tatu
tatu
tatukira
Tatusia
Tatusiidae
tau
Taube
Tauchnitz
taught
taula
Tauli
taum
taun
Taungthu
taunt
taunter
taunting
tauntingly
tauntingness
Taunton
tauntress
taupe
taupo
taupou
taur
tauranga
taurean
Tauri
Taurian
taurian
Tauric
tauric
tauricide
tauricornous
Taurid
Tauridian
tauriferous
tauriform
taurine
Taurini
taurite
taurobolium
tauroboly
taurocephalous
taurocholate
taurocholic
taurocol
taurocolla
Tauroctonus
taurodont
tauroesque
taurokathapsia
taurolatry
tauromachian
tauromachic
tauromachy
tauromorphic
tauromorphous
taurophile
taurophobe
Tauropolos
Taurotragus
Taurus
tauryl
taut
tautaug
tauted
tautegorical
tautegory
tauten
tautirite
tautit
tautly
tautness
tautochrone
tautochronism
tautochronous
tautog
tautologic
tautological
tautologically
tautologicalness
tautologism
tautologist
tautologize
tautologizer
tautologous
tautologously
tautology
tautomer
tautomeral
tautomeric
tautomerism
tautomerizable
tautomerization
tautomerize
tautomery
tautometer
tautometric
tautometrical
tautomorphous
tautonym
tautonymic
tautonymy
tautoousian
tautoousious
tautophonic
tautophonical
tautophony
tautopodic
tautopody
tautosyllabic
tautotype
tautourea
tautousian
tautousious
tautozonal
tautozonality
tav
Tavast
Tavastian
Tave
tave
tavell
taver
tavern
taverner
tavernize
tavernless
tavernlike
tavernly
tavernous
tavernry
tavernwards
tavers
tavert
Tavghi
tavistockite
tavola
tavolatite
Tavy
taw
tawa
tawdered
tawdrily
tawdriness
tawdry
tawer
tawery
Tawgi
tawie
tawite
tawkee
tawkin
tawn
tawney
tawnily
tawniness
tawnle
tawny
tawpi
tawpie
taws
tawse
tawtie
tax
taxability
taxable
taxableness
taxably
Taxaceae
taxaceous
taxameter
taxaspidean
taxation
taxational
taxative
taxatively
taxator
taxeater
taxeating
taxed
taxeme
taxemic
taxeopod
Taxeopoda
taxeopodous
taxeopody
taxer
taxgatherer
taxgathering
taxi
taxiable
taxiarch
taxiauto
taxibus
taxicab
Taxidea
taxidermal
taxidermic
taxidermist
taxidermize
taxidermy
taximan
taximeter
taximetered
taxine
taxing
taxingly
taxinomic
taxinomist
taxinomy
taxiplane
taxis
taxite
taxitic
taxless
taxlessly
taxlessness
taxman
Taxodiaceae
Taxodium
taxodont
taxology
taxometer
taxon
taxonomer
taxonomic
taxonomical
taxonomically
taxonomist
taxonomy
taxor
taxpaid
taxpayer
taxpaying
Taxus
taxwax
taxy
tay
Tayassu
Tayassuidae
tayer
Taygeta
tayir
Taylor
Taylorism
Taylorite
taylorite
Taylorize
tayra
Tayrona
taysaam
tazia
Tcawi
tch
tchai
tcharik
tchast
tche
tcheirek
Tcheka
Tcherkess
tchervonets
tchervonetz
Tchetchentsish
Tchetnitsi
Tchi
tchick
tchu
Tchwi
tck
Td
te
tea
teaberry
teaboard
teabox
teaboy
teacake
teacart
teach
teachability
teachable
teachableness
teachably
teache
teacher
teacherage
teacherdom
teacheress
teacherhood
teacherless
teacherlike
teacherly
teachership
teachery
teaching
teachingly
teachless
teachment
teachy
teacup
teacupful
tead
teadish
teaer
teaey
teagardeny
teagle
Teague
Teagueland
Teaguelander
teahouse
teaish
teaism
teak
teakettle
teakwood
teal
tealeafy
tealery
tealess
teallite
team
teamaker
teamaking
teaman
teameo
teamer
teaming
teamland
teamless
teamman
teammate
teamsman
teamster
teamwise
teamwork
tean
teanal
teap
teapot
teapotful
teapottykin
teapoy
tear
tearable
tearableness
tearably
tearage
tearcat
teardown
teardrop
tearer
tearful
tearfully
tearfulness
tearing
tearless
tearlessly
tearlessness
tearlet
tearlike
tearoom
tearpit
tearproof
tearstain
teart
tearthroat
tearthumb
teary
teasable
teasableness
teasably
tease
teaseable
teaseableness
teaseably
teasehole
teasel
teaseler
teaseller
teasellike
teaselwort
teasement
teaser
teashop
teasiness
teasing
teasingly
teasler
teaspoon
teaspoonful
teasy
teat
teataster
teated
teatfish
teathe
teather
teatime
teatlike
teatling
teatman
teaty
teave
teaware
teaze
teazer
tebbet
Tebet
Tebeth
Tebu
tec
Teca
teca
tecali
Tech
tech
techily
techiness
technetium
technic
technica
technical
technicalism
technicalist
technicality
technicalize
technically
technicalness
technician
technicism
technicist
technicological
technicology
Technicolor
technicon
technics
techniphone
technique
techniquer
technism
technist
technocausis
technochemical
technochemistry
technocracy
technocrat
technocratic
technographer
technographic
technographical
technographically
technography
technolithic
technologic
technological
technologically
technologist
technologue
technology
technonomic
technonomy
technopsychology
techous
techy
teck
Tecla
tecnoctonia
tecnology
Teco
Tecoma
tecomin
tecon
Tecpanec
tectal
tectibranch
Tectibranchia
tectibranchian
Tectibranchiata
tectibranchiate
tectiform
tectocephalic
tectocephaly
tectological
tectology
Tectona
tectonic
tectonics
tectorial
tectorium
Tectosages
tectosphere
tectospinal
Tectospondyli
tectospondylic
tectospondylous
tectrices
tectricial
tectum
tecum
tecuma
Tecuna
Ted
ted
Teda
tedder
Teddy
tedescan
tedge
tediosity
tedious
tediously
tediousness
tediousome
tedisome
tedium
tee
teedle
teel
teem
teemer
teemful
teemfulness
teeming
teemingly
teemingness
teemless
teems
teen
teenage
teenet
teens
teensy
teenty
teeny
teer
teerer
teest
Teeswater
teet
teetaller
teetan
teeter
teeterboard
teeterer
teetertail
teeth
teethache
teethbrush
teethe
teethful
teethily
teething
teethless
teethlike
teethridge
teethy
teeting
teetotal
teetotaler
teetotalism
teetotalist
teetotally
teetotum
teetotumism
teetotumize
teetotumwise
teety
teevee
teewhaap
teff
teg
Tegean
Tegeticula
tegmen
tegmental
tegmentum
tegmina
tegminal
Tegmine
tegua
teguexin
Teguima
tegula
tegular
tegularly
tegulated
tegumen
tegument
tegumental
tegumentary
tegumentum
tegurium
Teheran
tehseel
tehseeldar
tehsil
tehsildar
Tehuantepecan
Tehueco
Tehuelche
Tehuelchean
Tehuelet
Teian
teicher
teiglech
Teiidae
teil
teind
teindable
teinder
teinland
teinoscope
teioid
Teiresias
Tejon
tejon
teju
tekiah
Tekintsi
Tekke
tekke
tekken
Tekkintzi
teknonymous
teknonymy
tektite
tekya
telacoustic
telakucha
telamon
telang
telangiectasia
telangiectasis
telangiectasy
telangiectatic
telangiosis
Telanthera
telar
telarian
telary
telautogram
telautograph
telautographic
telautographist
telautography
telautomatic
telautomatically
telautomatics
Telchines
Telchinic
tele
teleanemograph
teleangiectasia
telebarograph
telebarometer
telecast
telecaster
telechemic
telechirograph
telecinematography
telecode
telecommunication
telecryptograph
telectroscope
teledendrion
teledendrite
teledendron
teledu
telega
telegenic
Telegn
telegnosis
telegnostic
telegonic
telegonous
telegony
telegram
telegrammatic
telegrammic
telegraph
telegraphee
telegrapheme
telegrapher
telegraphese
telegraphic
telegraphical
telegraphically
telegraphist
telegraphone
telegraphophone
telegraphoscope
telegraphy
Telegu
telehydrobarometer
Telei
Teleia
teleianthous
teleiosis
telekinematography
telekinesis
telekinetic
telelectric
telelectrograph
telelectroscope
telemanometer
Telemark
telemark
Telembi
telemechanic
telemechanics
telemechanism
telemetacarpal
telemeteorograph
telemeteorographic
telemeteorography
telemeter
telemetric
telemetrical
telemetrist
telemetrograph
telemetrographic
telemetrography
telemetry
telemotor
telencephal
telencephalic
telencephalon
telenergic
telenergy
teleneurite
teleneuron
Telenget
telengiscope
Telenomus
teleobjective
Teleocephali
teleocephalous
Teleoceras
Teleodesmacea
teleodesmacean
teleodesmaceous
teleodont
teleologic
teleological
teleologically
teleologism
teleologist
teleology
teleometer
teleophobia
teleophore
teleophyte
teleoptile
teleorganic
teleoroentgenogram
teleoroentgenography
teleosaur
teleosaurian
Teleosauridae
Teleosaurus
teleost
teleostean
Teleostei
teleosteous
teleostomate
teleostome
Teleostomi
teleostomian
teleostomous
teleotemporal
teleotrocha
teleozoic
teleozoon
telepathic
telepathically
telepathist
telepathize
telepathy
telepheme
telephone
telephoner
telephonic
telephonical
telephonically
telephonist
telephonograph
telephonographic
telephony
telephote
telephoto
telephotograph
telephotographic
telephotography
Telephus
telepicture
teleplasm
teleplasmic
teleplastic
telepost
teleprinter
teleradiophone
teleran
telergic
telergical
telergically
telergy
telescope
telescopic
telescopical
telescopically
telescopiform
telescopist
Telescopium
telescopy
telescriptor
teleseism
teleseismic
teleseismology
teleseme
telesia
telesis
telesmeter
telesomatic
telespectroscope
telestereograph
telestereography
telestereoscope
telesterion
telesthesia
telesthetic
telestial
telestic
telestich
teletactile
teletactor
teletape
teletherapy
telethermogram
telethermograph
telethermometer
telethermometry
telethon
teletopometer
teletranscription
Teletype
teletype
teletyper
teletypesetter
teletypewriter
teletyping
Teleut
teleuto
teleutoform
teleutosorus
teleutospore
teleutosporic
teleutosporiferous
teleview
televiewer
televise
television
televisional
televisionary
televisor
televisual
televocal
televox
telewriter
Telfairia
telfairic
telfer
telferage
telford
telfordize
telharmonic
telharmonium
telharmony
teli
telial
telic
telical
telically
teliferous
Telinga
teliosorus
teliospore
teliosporic
teliosporiferous
teliostage
telium
tell
tellable
tellach
tellee
teller
tellership
telligraph
Tellima
Tellina
Tellinacea
tellinacean
tellinaceous
telling
tellingly
Tellinidae
tellinoid
tellsome
tellt
telltale
telltalely
telltruth
tellural
tellurate
telluret
tellureted
tellurethyl
telluretted
tellurhydric
tellurian
telluric
telluride
telluriferous
tellurion
tellurism
tellurist
tellurite
tellurium
tellurize
telluronium
tellurous
telmatological
telmatology
teloblast
teloblastic
telocentric
telodendrion
telodendron
telodynamic
telokinesis
telolecithal
telolemma
telome
telomic
telomitic
telonism
Teloogoo
Telopea
telophase
telophragma
telopsis
teloptic
telosynapsis
telosynaptic
telosynaptist
teloteropathic
teloteropathically
teloteropathy
Telotremata
telotrematous
telotroch
telotrocha
telotrochal
telotrochous
telotrophic
telotype
telpath
telpher
telpherage
telpherman
telpherway
telson
telsonic
telt
Telugu
telurgy
telyn
Tema
temacha
temalacatl
Teman
teman
Temanite
tembe
temblor
Tembu
temenos
temerarious
temerariously
temerariousness
temeritous
temerity
temerous
temerously
temerousness
temiak
temin
Temiskaming
Temne
Temnospondyli
temnospondylous
temp
Tempe
Tempean
temper
tempera
temperability
temperable
temperably
temperality
temperament
temperamental
temperamentalist
temperamentally
temperamented
temperance
temperate
temperately
temperateness
temperative
temperature
tempered
temperedly
temperedness
temperer
temperish
temperless
tempersome
tempery
tempest
tempestical
tempestive
tempestively
tempestivity
tempestuous
tempestuously
tempestuousness
tempesty
tempi
Templar
templar
templardom
templarism
templarlike
templarlikeness
templary
template
templater
temple
templed
templeful
templeless
templelike
templet
Templetonia
templeward
templize
tempo
tempora
temporal
temporale
temporalism
temporalist
temporality
temporalize
temporally
temporalness
temporalty
temporaneous
temporaneously
temporaneousness
temporarily
temporariness
temporary
temporator
temporization
temporizer
temporizing
temporizingly
temporoalar
temporoauricular
temporocentral
temporocerebellar
temporofacial
temporofrontal
temporohyoid
temporomalar
temporomandibular
temporomastoid
temporomaxillary
temporooccipital
temporoparietal
temporopontine
temporosphenoid
temporosphenoidal
temporozygomatic
tempre
temprely
tempt
temptability
temptable
temptableness
temptation
temptational
temptationless
temptatious
temptatory
tempter
tempting
temptingly
temptingness
temptress
Tempyo
temse
temser
temulence
temulency
temulent
temulentive
temulently
ten
tenability
tenable
tenableness
tenably
tenace
tenacious
tenaciously
tenaciousness
tenacity
tenaculum
tenai
tenaille
tenaillon
Tenaktak
tenancy
tenant
tenantable
tenantableness
tenanter
tenantism
tenantless
tenantlike
tenantry
tenantship
tench
tenchweed
Tencteri
tend
tendance
tendant
tendence
tendency
tendent
tendential
tendentious
tendentiously
tendentiousness
tender
tenderability
tenderable
tenderably
tenderee
tenderer
tenderfoot
tenderfootish
tenderful
tenderfully
tenderheart
tenderhearted
tenderheartedly
tenderheartedness
tenderish
tenderize
tenderling
tenderloin
tenderly
tenderness
tenderometer
tendersome
tendinal
tending
tendingly
tendinitis
tendinous
tendinousness
tendomucoid
tendon
tendonous
tendoplasty
tendosynovitis
tendotome
tendotomy
tendour
tendovaginal
tendovaginitis
tendresse
tendril
tendriled
tendriliferous
tendrillar
tendrilly
tendrilous
tendron
tenebra
Tenebrae
tenebricose
tenebrific
tenebrificate
Tenebrio
tenebrionid
Tenebrionidae
tenebrious
tenebriously
tenebrity
tenebrose
tenebrosity
tenebrous
tenebrously
tenebrousness
tenectomy
tenement
tenemental
tenementary
tenementer
tenementization
tenementize
tenendas
tenendum
tenent
teneral
Teneriffe
tenesmic
tenesmus
tenet
tenfold
tenfoldness
teng
tengere
tengerite
Tenggerese
tengu
teniacidal
teniacide
tenible
Tenino
tenio
tenline
tenmantale
tennantite
tenne
tenner
Tennessean
tennis
tennisdom
tennisy
Tennysonian
Tennysonianism
Tenochtitlan
tenodesis
tenodynia
tenography
tenology
tenomyoplasty
tenomyotomy
tenon
tenonectomy
tenoner
Tenonian
tenonitis
tenonostosis
tenontagra
tenontitis
tenontodynia
tenontography
tenontolemmitis
tenontology
tenontomyoplasty
tenontomyotomy
tenontophyma
tenontoplasty
tenontothecitis
tenontotomy
tenophony
tenophyte
tenoplastic
tenoplasty
tenor
tenorist
tenorister
tenorite
tenorless
tenoroon
tenorrhaphy
tenositis
tenostosis
tenosuture
tenotome
tenotomist
tenotomize
tenotomy
tenovaginitis
tenpence
tenpenny
tenpin
tenrec
Tenrecidae
tense
tenseless
tenselessness
tensely
tenseness
tensibility
tensible
tensibleness
tensibly
tensify
tensile
tensilely
tensileness
tensility
tensimeter
tensiometer
tension
tensional
tensionless
tensity
tensive
tenson
tensor
tent
tentability
tentable
tentacle
tentacled
tentaclelike
tentacula
tentacular
Tentaculata
tentaculate
tentaculated
Tentaculifera
tentaculite
Tentaculites
Tentaculitidae
tentaculocyst
tentaculoid
tentaculum
tentage
tentamen
tentation
tentative
tentatively
tentativeness
tented
tenter
tenterbelly
tenterer
tenterhook
tentful
tenth
tenthly
tenthmeter
tenthredinid
Tenthredinidae
tenthredinoid
Tenthredinoidea
Tenthredo
tentiform
tentigo
tentillum
tention
tentless
tentlet
tentlike
tentmaker
tentmaking
tentmate
tentorial
tentorium
tenture
tentwards
tentwise
tentwork
tentwort
tenty
tenuate
tenues
tenuicostate
tenuifasciate
tenuiflorous
tenuifolious
tenuious
tenuiroster
tenuirostral
tenuirostrate
Tenuirostres
tenuis
tenuistriate
tenuity
tenuous
tenuously
tenuousness
tenure
tenurial
tenurially
teocalli
teopan
teosinte
Teotihuacan
tepache
tepal
Tepanec
Tepecano
tepee
tepefaction
tepefy
Tepehua
Tepehuane
tepetate
Tephillah
tephillin
tephramancy
tephrite
tephritic
tephroite
tephromalacia
tephromyelitic
Tephrosia
tephrosis
tepid
tepidarium
tepidity
tepidly
tepidness
tepomporize
teponaztli
tepor
tequila
Tequistlateca
Tequistlatecan
tera
teraglin
terakihi
teramorphous
terap
teraphim
teras
teratical
teratism
teratoblastoma
teratogenesis
teratogenetic
teratogenic
teratogenous
teratogeny
teratoid
teratological
teratologist
teratology
teratoma
teratomatous
teratoscopy
teratosis
terbia
terbic
terbium
tercel
tercelet
tercentenarian
tercentenarize
tercentenary
tercentennial
tercer
terceron
tercet
terchloride
tercia
tercine
tercio
terdiurnal
terebate
terebella
terebellid
Terebellidae
terebelloid
terebellum
terebene
terebenic
terebenthene
terebic
terebilic
terebinic
terebinth
Terebinthaceae
terebinthial
terebinthian
terebinthic
terebinthina
terebinthinate
terebinthine
terebinthinous
Terebinthus
terebra
terebral
terebrant
Terebrantia
terebrate
terebration
Terebratula
terebratular
terebratulid
Terebratulidae
terebratuliform
terebratuline
terebratulite
terebratuloid
Terebridae
Teredinidae
teredo
terek
Terence
Terentian
terephthalate
terephthalic
Teresa
Teresian
Teresina
terete
teretial
tereticaudate
teretifolious
teretipronator
teretiscapular
teretiscapularis
teretish
tereu
Tereus
terfez
Terfezia
Terfeziaceae
tergal
tergant
tergeminate
tergeminous
tergiferous
tergite
tergitic
tergiversant
tergiversate
tergiversation
tergiversator
tergiversatory
tergiverse
tergolateral
tergum
Teri
Teriann
terlinguaite
term
terma
termagancy
Termagant
termagant
termagantish
termagantism
termagantly
termage
termatic
termen
termer
Termes
termillenary
termin
terminability
terminable
terminableness
terminably
terminal
Terminalia
Terminaliaceae
terminalization
terminalized
terminally
terminant
terminate
termination
terminational
terminative
terminatively
terminator
terminatory
termine
terminer
termini
terminine
terminism
terminist
terministic
terminize
termino
terminological
terminologically
terminologist
terminology
terminus
termital
termitarium
termitary
termite
termitic
termitid
Termitidae
termitophagous
termitophile
termitophilous
termless
termlessly
termlessness
termly
termolecular
termon
termor
termtime
tern
terna
ternal
ternar
ternariant
ternarious
ternary
ternate
ternately
ternatipinnate
ternatisect
ternatopinnate
terne
terneplate
ternery
ternion
ternize
ternlet
Ternstroemia
Ternstroemiaceae
teroxide
terp
terpadiene
terpane
terpene
terpeneless
terphenyl
terpilene
terpin
terpine
terpinene
terpineol
terpinol
terpinolene
terpodion
Terpsichore
terpsichoreal
terpsichoreally
Terpsichorean
terpsichorean
Terraba
terrace
terraceous
terracer
terracette
terracewards
terracewise
terracework
terraciform
terracing
terraculture
terraefilial
terraefilian
terrage
terrain
terral
terramara
terramare
Terrance
terrane
terranean
terraneous
Terrapene
terrapin
terraquean
terraqueous
terraqueousness
terrar
terrarium
terrazzo
terrella
terremotive
Terrence
terrene
terrenely
terreneness
terreplein
terrestrial
terrestrialism
terrestriality
terrestrialize
terrestrially
terrestrialness
terrestricity
terrestrious
terret
terreted
Terri
terribility
terrible
terribleness
terribly
terricole
terricoline
terricolous
terrier
terrierlike
terrific
terrifical
terrifically
terrification
terrificly
terrificness
terrifiedly
terrifier
terrify
terrifying
terrifyingly
terrigenous
terrine
Territelae
territelarian
territorial
territorialism
territorialist
territoriality
territorialization
territorialize
territorially
territorian
territoried
territory
terron
terror
terrorful
terrorific
terrorism
terrorist
terroristic
terroristical
terrorization
terrorize
terrorizer
terrorless
terrorproof
terrorsome
Terry
terry
terse
tersely
terseness
tersion
tersulphate
tersulphide
tersulphuret
tertenant
tertia
tertial
tertian
tertiana
tertianship
tertiarian
tertiary
tertiate
tertius
terton
tertrinal
Tertullianism
Tertullianist
teruncius
terutero
Teruyuki
tervalence
tervalency
tervalent
tervariant
tervee
terzetto
terzina
terzo
tesack
tesarovitch
teschenite
teschermacherite
teskere
teskeria
Tess
tessara
tessarace
tessaraconter
tessaradecad
tessaraglot
tessaraphthong
tessarescaedecahedron
tessel
tessella
tessellar
tessellate
tessellated
tessellation
tessera
tesseract
tesseradecade
tesseraic
tesseral
Tesserants
tesserarian
tesserate
tesserated
tesseratomic
tesseratomy
tessular
test
testa
testable
Testacea
testacean
testaceography
testaceology
testaceous
testaceousness
testacy
testament
testamental
testamentally
testamentalness
testamentarily
testamentary
testamentate
testamentation
testamentum
testamur
testar
testata
testate
testation
testator
testatorship
testatory
testatrices
testatrix
testatum
teste
tested
testee
tester
testes
testibrachial
testibrachium
testicardinate
testicardine
Testicardines
testicle
testicond
testicular
testiculate
testiculated
testiere
testificate
testification
testificator
testificatory
testifier
testify
testily
testimonial
testimonialist
testimonialization
testimonialize
testimonializer
testimonium
testimony
testiness
testing
testingly
testis
teston
testone
testoon
testor
testosterone
testril
testudinal
Testudinaria
testudinarious
Testudinata
testudinate
testudinated
testudineal
testudineous
Testudinidae
testudinous
testudo
testy
Tesuque
tetanic
tetanical
tetanically
tetaniform
tetanigenous
tetanilla
tetanine
tetanism
tetanization
tetanize
tetanoid
tetanolysin
tetanomotor
tetanospasmin
tetanotoxin
tetanus
tetany
tetarcone
tetarconid
tetard
tetartemorion
tetartocone
tetartoconid
tetartohedral
tetartohedrally
tetartohedrism
tetartohedron
tetartoid
tetartosymmetry
tetch
tetchy
tete
tetel
teterrimous
teth
tethelin
tether
tetherball
tethery
tethydan
Tethys
Teton
tetra
tetraamylose
tetrabasic
tetrabasicity
Tetrabelodon
tetrabelodont
tetrabiblos
tetraborate
tetraboric
tetrabrach
tetrabranch
Tetrabranchia
tetrabranchiate
tetrabromid
tetrabromide
tetrabromo
tetrabromoethane
tetracadactylity
tetracarboxylate
tetracarboxylic
tetracarpellary
tetraceratous
tetracerous
Tetracerus
tetrachical
tetrachlorid
tetrachloride
tetrachloro
tetrachloroethane
tetrachloroethylene
tetrachloromethane
tetrachord
tetrachordal
tetrachordon
tetrachoric
tetrachotomous
tetrachromatic
tetrachromic
tetrachronous
tetracid
tetracoccous
tetracoccus
tetracolic
tetracolon
tetracoral
Tetracoralla
tetracoralline
tetracosane
tetract
tetractinal
tetractine
tetractinellid
Tetractinellida
tetractinellidan
tetractinelline
tetractinose
tetracyclic
tetrad
tetradactyl
tetradactylous
tetradactyly
tetradarchy
tetradecane
tetradecanoic
tetradecapod
Tetradecapoda
tetradecapodan
tetradecapodous
tetradecyl
Tetradesmus
tetradiapason
tetradic
Tetradite
tetradrachma
tetradrachmal
tetradrachmon
tetradymite
Tetradynamia
tetradynamian
tetradynamious
tetradynamous
tetraedron
tetraedrum
tetraethylsilane
tetrafluoride
tetrafolious
tetragamy
tetragenous
tetraglot
tetraglottic
tetragon
tetragonal
tetragonally
tetragonalness
Tetragonia
Tetragoniaceae
tetragonidium
tetragonous
tetragonus
tetragram
tetragrammatic
Tetragrammaton
tetragrammatonic
tetragyn
Tetragynia
tetragynian
tetragynous
tetrahedral
tetrahedrally
tetrahedric
tetrahedrite
tetrahedroid
tetrahedron
tetrahexahedral
tetrahexahedron
tetrahydrate
tetrahydrated
tetrahydric
tetrahydride
tetrahydro
tetrahydroxy
tetraiodid
tetraiodide
tetraiodo
tetraiodophenolphthalein
tetrakaidecahedron
tetraketone
tetrakisazo
tetrakishexahedron
tetralemma
Tetralin
tetralogic
tetralogue
tetralogy
tetralophodont
tetramastia
tetramastigote
Tetramera
tetrameral
tetrameralian
tetrameric
tetramerism
tetramerous
tetrameter
tetramethyl
tetramethylammonium
tetramethylene
tetramethylium
tetramin
tetramine
tetrammine
tetramorph
tetramorphic
tetramorphism
tetramorphous
tetrander
Tetrandria
tetrandrian
tetrandrous
tetrane
tetranitrate
tetranitro
tetranitroaniline
tetranuclear
Tetranychus
Tetrao
Tetraodon
tetraodont
Tetraodontidae
tetraonid
Tetraonidae
Tetraoninae
tetraonine
Tetrapanax
tetrapartite
tetrapetalous
tetraphalangeate
tetrapharmacal
tetrapharmacon
tetraphenol
tetraphony
tetraphosphate
tetraphyllous
tetrapla
tetraplegia
tetrapleuron
tetraploid
tetraploidic
tetraploidy
tetraplous
Tetrapneumona
Tetrapneumones
tetrapneumonian
tetrapneumonous
tetrapod
Tetrapoda
tetrapodic
tetrapody
tetrapolar
tetrapolis
tetrapolitan
tetrapous
tetraprostyle
tetrapteran
tetrapteron
tetrapterous
tetraptote
Tetrapturus
tetraptych
tetrapylon
tetrapyramid
tetrapyrenous
tetraquetrous
tetrarch
tetrarchate
tetrarchic
tetrarchy
tetrasaccharide
tetrasalicylide
tetraselenodont
tetraseme
tetrasemic
tetrasepalous
tetraskelion
tetrasome
tetrasomic
tetrasomy
tetraspermal
tetraspermatous
tetraspermous
tetraspheric
tetrasporange
tetrasporangiate
tetrasporangium
tetraspore
tetrasporic
tetrasporiferous
tetrasporous
tetraster
tetrastich
tetrastichal
tetrastichic
Tetrastichidae
tetrastichous
Tetrastichus
tetrastoon
tetrastyle
tetrastylic
tetrastylos
tetrastylous
tetrasubstituted
tetrasubstitution
tetrasulphide
tetrasyllabic
tetrasyllable
tetrasymmetry
tetrathecal
tetratheism
tetratheite
tetrathionates
tetrathionic
tetratomic
tetratone
tetravalence
tetravalency
tetravalent
tetraxial
tetraxon
Tetraxonia
tetraxonian
tetraxonid
Tetraxonida
tetrazane
tetrazene
tetrazin
tetrazine
tetrazo
tetrazole
tetrazolium
tetrazolyl
tetrazone
tetrazotization
tetrazotize
tetrazyl
tetremimeral
tetrevangelium
tetric
tetrical
tetricity
tetricous
tetrigid
Tetrigidae
tetriodide
Tetrix
tetrobol
tetrobolon
tetrode
Tetrodon
tetrodont
Tetrodontidae
tetrole
tetrolic
tetronic
tetronymal
tetrose
tetroxalate
tetroxide
tetrsyllabical
tetryl
tetrylene
tetter
tetterish
tetterous
tetterwort
tettery
Tettigidae
tettigoniid
Tettigoniidae
tettix
Tetum
Teucer
Teucri
Teucrian
teucrin
Teucrium
teufit
teuk
Teutolatry
Teutomania
Teutomaniac
Teuton
Teutondom
Teutonesque
Teutonia
Teutonic
Teutonically
Teutonicism
Teutonism
Teutonist
Teutonity
Teutonization
Teutonize
Teutonomania
Teutonophobe
Teutonophobia
Teutophil
Teutophile
Teutophilism
Teutophobe
Teutophobia
Teutophobism
teviss
tew
Tewa
tewel
tewer
tewit
tewly
tewsome
Texan
Texas
Texcocan
texguino
text
textarian
textbook
textbookless
textiferous
textile
textilist
textlet
textman
textorial
textrine
textual
textualism
textualist
textuality
textually
textuarist
textuary
textural
texturally
texture
textureless
tez
Tezcatlipoca
Tezcatzoncatl
Tezcucan
tezkere
th
tha
thack
thacker
Thackerayan
Thackerayana
Thackerayesque
thackless
Thad
Thai
Thais
thakur
thakurate
thalamencephalic
thalamencephalon
thalami
thalamic
Thalamiflorae
thalamifloral
thalamiflorous
thalamite
thalamium
thalamocele
thalamocoele
thalamocortical
thalamocrural
thalamolenticular
thalamomammillary
thalamopeduncular
Thalamophora
thalamotegmental
thalamotomy
thalamus
Thalarctos
thalassal
Thalassarctos
thalassian
thalassic
thalassinid
Thalassinidea
thalassinidian
thalassinoid
thalassiophyte
thalassiophytous
thalasso
Thalassochelys
thalassocracy
thalassocrat
thalassographer
thalassographic
thalassographical
thalassography
thalassometer
thalassophilous
thalassophobia
thalassotherapy
thalattology
thalenite
thaler
Thalesia
Thalesian
Thalessa
Thalia
Thaliacea
thaliacean
Thalian
Thaliard
Thalictrum
thalli
thallic
thalliferous
thalliform
thalline
thallious
thallium
thallochlore
thallodal
thallogen
thallogenic
thallogenous
thalloid
thallome
Thallophyta
thallophyte
thallophytic
thallose
thallous
thallus
thalposis
thalpotic
thalthan
thameng
Thamesis
Thamnidium
thamnium
thamnophile
Thamnophilinae
thamnophiline
Thamnophilus
Thamnophis
Thamudean
Thamudene
Thamudic
thamuria
Thamus
Thamyras
than
thana
thanadar
thanage
thanan
thanatism
thanatist
thanatobiologic
thanatognomonic
thanatographer
thanatography
thanatoid
thanatological
thanatologist
thanatology
thanatomantic
thanatometer
thanatophidia
thanatophidian
thanatophobe
thanatophobia
thanatophobiac
thanatophoby
thanatopsis
Thanatos
thanatosis
thanatotic
thanatousia
thane
thanedom
thanehood
thaneland
thaneship
thank
thankee
thanker
thankful
thankfully
thankfulness
thankless
thanklessly
thanklessness
thanks
thanksgiver
thanksgiving
thankworthily
thankworthiness
thankworthy
thapes
Thapsia
thapsia
thar
Tharen
tharf
tharfcake
Thargelion
tharginyah
tharm
Thasian
Thaspium
that
thatch
thatcher
thatching
thatchless
thatchwood
thatchwork
thatchy
thatn
thatness
thats
thaught
Thaumantian
Thaumantias
thaumasite
thaumatogeny
thaumatography
thaumatolatry
thaumatology
thaumatrope
thaumatropical
thaumaturge
thaumaturgia
thaumaturgic
thaumaturgical
thaumaturgics
thaumaturgism
thaumaturgist
thaumaturgy
thaumoscopic
thave
thaw
thawer
thawless
thawn
thawy
The
the
Thea
Theaceae
theaceous
theah
theandric
theanthropic
theanthropical
theanthropism
theanthropist
theanthropology
theanthropophagy
theanthropos
theanthroposophy
theanthropy
thearchic
thearchy
theasum
theat
theater
theatergoer
theatergoing
theaterless
theaterlike
theaterward
theaterwards
theaterwise
Theatine
theatral
theatric
theatricable
theatrical
theatricalism
theatricality
theatricalization
theatricalize
theatrically
theatricalness
theatricals
theatrician
theatricism
theatricize
theatrics
theatrize
theatrocracy
theatrograph
theatromania
theatromaniac
theatron
theatrophile
theatrophobia
theatrophone
theatrophonic
theatropolis
theatroscope
theatry
theave
theb
Thebaic
Thebaid
thebaine
Thebais
thebaism
Theban
Thebesian
theca
thecae
thecal
Thecamoebae
thecaphore
thecasporal
thecaspore
thecaspored
thecasporous
Thecata
thecate
thecia
thecitis
thecium
Thecla
thecla
theclan
thecodont
thecoglossate
thecoid
Thecoidea
Thecophora
Thecosomata
thecosomatous
thee
theek
theeker
theelin
theelol
Theemim
theer
theet
theetsee
theezan
theft
theftbote
theftdom
theftless
theftproof
theftuous
theftuously
thegether
thegidder
thegither
thegn
thegndom
thegnhood
thegnland
thegnlike
thegnly
thegnship
thegnworthy
theiform
Theileria
theine
theinism
their
theirn
theirs
theirselves
theirsens
theism
theist
theistic
theistical
theistically
thelalgia
Thelemite
thelemite
Thelephora
Thelephoraceae
Theligonaceae
theligonaceous
Theligonum
thelitis
thelium
Thelodontidae
Thelodus
theloncus
thelorrhagia
Thelphusa
thelphusian
Thelphusidae
thelyblast
thelyblastic
thelyotokous
thelyotoky
Thelyphonidae
Thelyphonus
thelyplasty
thelytocia
thelytoky
thelytonic
them
thema
themata
thematic
thematical
thematically
thematist
theme
themeless
themelet
themer
Themis
themis
Themistian
themsel
themselves
then
thenabouts
thenadays
thenal
thenar
thenardite
thence
thenceafter
thenceforth
thenceforward
thenceforwards
thencefrom
thenceward
thenness
Theo
theoanthropomorphic
theoanthropomorphism
theoastrological
Theobald
Theobroma
theobromic
theobromine
theocentric
theocentricism
theocentrism
theochristic
theocollectivism
theocollectivist
theocracy
theocrasia
theocrasical
theocrasy
theocrat
theocratic
theocratical
theocratically
theocratist
Theocritan
Theocritean
theodemocracy
theodicaea
theodicean
theodicy
theodidact
theodolite
theodolitic
Theodora
Theodore
Theodoric
Theodosia
Theodosian
Theodotian
theodrama
theody
theogamy
theogeological
theognostic
theogonal
theogonic
theogonism
theogonist
theogony
theohuman
theokrasia
theoktonic
theoktony
theolatrous
theolatry
theolepsy
theoleptic
theologal
theologaster
theologastric
theologate
theologeion
theologer
theologi
theologian
theologic
theological
theologically
theologician
theologicoastronomical
theologicoethical
theologicohistorical
theologicometaphysical
theologicomilitary
theologicomoral
theologiconatural
theologicopolitical
theologics
theologism
theologist
theologium
theologization
theologize
theologizer
theologoumena
theologoumenon
theologue
theologus
theology
theomachia
theomachist
theomachy
theomammomist
theomancy
theomania
theomaniac
theomantic
theomastix
theomicrist
theomisanthropist
theomorphic
theomorphism
theomorphize
theomythologer
theomythology
theonomy
theopantism
Theopaschist
Theopaschitally
Theopaschite
Theopaschitic
Theopaschitism
theopathetic
theopathic
theopathy
theophagic
theophagite
theophagous
theophagy
Theophania
theophania
theophanic
theophanism
theophanous
theophany
Theophila
theophilanthrope
theophilanthropic
theophilanthropism
theophilanthropist
theophilanthropy
theophile
theophilist
theophilosophic
Theophilus
theophobia
theophoric
theophorous
Theophrastaceae
theophrastaceous
Theophrastan
Theophrastean
theophylline
theophysical
theopneust
theopneusted
theopneustia
theopneustic
theopneusty
theopolitician
theopolitics
theopolity
theopsychism
theorbist
theorbo
theorem
theorematic
theorematical
theorematically
theorematist
theoremic
theoretic
theoretical
theoreticalism
theoretically
theoretician
theoreticopractical
theoretics
theoria
theoriai
theoric
theorical
theorically
theorician
theoricon
theorics
theorism
theorist
theorization
theorize
theorizer
theorum
theory
theoryless
theorymonger
theosoph
theosopheme
theosophic
theosophical
theosophically
theosophism
theosophist
theosophistic
theosophistical
theosophize
theosophy
theotechnic
theotechnist
theotechny
theoteleological
theoteleology
theotherapy
Theotokos
theow
theowdom
theowman
Theraean
theralite
therapeusis
Therapeutae
Therapeutic
therapeutic
therapeutical
therapeutically
therapeutics
therapeutism
therapeutist
Theraphosa
theraphose
theraphosid
Theraphosidae
theraphosoid
therapist
therapsid
Therapsida
therapy
therblig
there
thereabouts
thereabove
thereacross
thereafter
thereafterward
thereagainst
thereamong
thereamongst
thereanent
thereanents
therearound
thereas
thereat
thereaway
thereaways
therebeside
therebesides
therebetween
thereby
thereckly
therefor
therefore
therefrom
therehence
therein
thereinafter
thereinbefore
thereinto
therence
thereness
thereof
thereoid
thereologist
thereology
thereon
thereout
thereover
thereright
theres
Theresa
therese
therethrough
theretill
thereto
theretofore
theretoward
thereunder
thereuntil
thereunto
thereup
thereupon
Thereva
therevid
Therevidae
therewhile
therewith
therewithal
therewithin
Theria
theriac
theriaca
theriacal
therial
therianthropic
therianthropism
theriatrics
theridiid
Theridiidae
Theridion
theriodic
theriodont
Theriodonta
Theriodontia
theriolatry
theriomancy
theriomaniac
theriomimicry
theriomorph
theriomorphic
theriomorphism
theriomorphosis
theriomorphous
theriotheism
theriotrophical
theriozoic
therm
thermacogenesis
thermae
thermal
thermalgesia
thermality
thermally
thermanalgesia
thermanesthesia
thermantic
thermantidote
thermatologic
thermatologist
thermatology
thermesthesia
thermesthesiometer
thermetograph
thermetrograph
thermic
thermically
Thermidorian
thermion
thermionic
thermionically
thermionics
thermistor
Thermit
thermit
thermite
thermo
thermoammeter
thermoanalgesia
thermoanesthesia
thermobarograph
thermobarometer
thermobattery
thermocautery
thermochemic
thermochemical
thermochemically
thermochemist
thermochemistry
thermochroic
thermochrosy
thermocline
thermocouple
thermocurrent
thermodiffusion
thermoduric
thermodynamic
thermodynamical
thermodynamically
thermodynamician
thermodynamicist
thermodynamics
thermodynamist
thermoelectric
thermoelectrical
thermoelectrically
thermoelectricity
thermoelectrometer
thermoelectromotive
thermoelement
thermoesthesia
thermoexcitory
thermogalvanometer
thermogen
thermogenerator
thermogenesis
thermogenetic
thermogenic
thermogenous
thermogeny
thermogeographical
thermogeography
thermogram
thermograph
thermography
thermohyperesthesia
thermojunction
thermokinematics
thermolabile
thermolability
thermological
thermology
thermoluminescence
thermoluminescent
thermolysis
thermolytic
thermolyze
thermomagnetic
thermomagnetism
thermometamorphic
thermometamorphism
thermometer
thermometerize
thermometric
thermometrical
thermometrically
thermometrograph
thermometry
thermomotive
thermomotor
thermomultiplier
thermonastic
thermonasty
thermonatrite
thermoneurosis
thermoneutrality
thermonous
thermonuclear
thermopair
thermopalpation
thermopenetration
thermoperiod
thermoperiodic
thermoperiodicity
thermoperiodism
thermophile
thermophilic
thermophilous
thermophobous
thermophone
thermophore
thermophosphor
thermophosphorescence
thermopile
thermoplastic
thermoplasticity
thermoplegia
thermopleion
thermopolymerization
thermopolypnea
thermopolypneic
Thermopsis
thermoradiotherapy
thermoreduction
thermoregulation
thermoregulator
thermoresistance
thermoresistant
thermos
thermoscope
thermoscopic
thermoscopical
thermoscopically
thermosetting
thermosiphon
thermostability
thermostable
thermostat
thermostatic
thermostatically
thermostatics
thermostimulation
thermosynthesis
thermosystaltic
thermosystaltism
thermotactic
thermotank
thermotaxic
thermotaxis
thermotelephone
thermotensile
thermotension
thermotherapeutics
thermotherapy
thermotic
thermotical
thermotically
thermotics
thermotropic
thermotropism
thermotropy
thermotype
thermotypic
thermotypy
thermovoltaic
therodont
theroid
therolatry
therologic
therological
therologist
therology
Theromora
Theromores
theromorph
Theromorpha
theromorphia
theromorphic
theromorphism
theromorphological
theromorphology
theromorphous
Theron
theropod
Theropoda
theropodous
thersitean
Thersites
thersitical
thesauri
thesaurus
these
Thesean
theses
Theseum
Theseus
thesial
thesicle
thesis
Thesium
Thesmophoria
Thesmophorian
Thesmophoric
thesmothetae
thesmothete
thesmothetes
thesocyte
Thespesia
Thespesius
Thespian
Thessalian
Thessalonian
thestreen
theta
thetch
thetic
thetical
thetically
thetics
thetin
thetine
Thetis
theurgic
theurgical
theurgically
theurgist
theurgy
Thevetia
thevetin
thew
thewed
thewless
thewness
thewy
they
theyll
theyre
thiacetic
thiadiazole
thialdine
thiamide
thiamin
thiamine
thianthrene
thiasi
thiasine
thiasite
thiasoi
thiasos
thiasote
thiasus
thiazine
thiazole
thiazoline
thick
thickbrained
thicken
thickener
thickening
thicket
thicketed
thicketful
thickety
thickhead
thickheaded
thickheadedly
thickheadedness
thickish
thickleaf
thicklips
thickly
thickneck
thickness
thicknessing
thickset
thickskin
thickskull
thickskulled
thickwind
thickwit
thief
thiefcraft
thiefdom
thiefland
thiefmaker
thiefmaking
thiefproof
thieftaker
thiefwise
Thielavia
Thielaviopsis
thienone
thienyl
Thierry
thievable
thieve
thieveless
thiever
thievery
thieving
thievingly
thievish
thievishly
thievishness
thig
thigger
thigging
thigh
thighbone
thighed
thight
thightness
thigmonegative
thigmopositive
thigmotactic
thigmotactically
thigmotaxis
thigmotropic
thigmotropically
thigmotropism
Thilanottine
thilk
thill
thiller
thilly
thimber
thimble
thimbleberry
thimbled
thimbleflower
thimbleful
thimblelike
thimblemaker
thimblemaking
thimbleman
thimblerig
thimblerigger
thimbleriggery
thimblerigging
thimbleweed
thin
thinbrained
thine
thing
thingal
thingamabob
thinghood
thinginess
thingish
thingless
thinglet
thinglike
thinglikeness
thingliness
thingly
thingman
thingness
thingstead
thingum
thingumajig
thingumbob
thingummy
thingy
Think
think
thinkable
thinkableness
thinkably
thinker
thinkful
thinking
thinkingly
thinkingpart
thinkling
thinly
thinner
thinness
thinning
thinnish
Thinocoridae
Thinocorus
thinolite
thio
thioacetal
thioacetic
thioalcohol
thioaldehyde
thioamide
thioantimonate
thioantimoniate
thioantimonious
thioantimonite
thioarsenate
thioarseniate
thioarsenic
thioarsenious
thioarsenite
Thiobacillus
Thiobacteria
thiobacteria
Thiobacteriales
thiobismuthite
thiocarbamic
thiocarbamide
thiocarbamyl
thiocarbanilide
thiocarbimide
thiocarbonate
thiocarbonic
thiocarbonyl
thiochloride
thiochrome
thiocresol
thiocyanate
thiocyanation
thiocyanic
thiocyanide
thiocyano
thiocyanogen
thiodiazole
thiodiphenylamine
thiofuran
thiofurane
thiofurfuran
thiofurfurane
thiogycolic
thiohydrate
thiohydrolysis
thiohydrolyze
thioindigo
thioketone
thiol
thiolacetic
thiolactic
thiolic
thionamic
thionaphthene
thionate
thionation
thioneine
thionic
thionine
thionitrite
thionium
thionobenzoic
thionthiolic
thionurate
thionyl
thionylamine
thiophen
thiophene
thiophenic
thiophenol
thiophosgene
thiophosphate
thiophosphite
thiophosphoric
thiophosphoryl
thiophthene
thiopyran
thioresorcinol
thiosinamine
Thiospira
thiostannate
thiostannic
thiostannite
thiostannous
thiosulphate
thiosulphonic
thiosulphuric
Thiothrix
thiotolene
thiotungstate
thiotungstic
thiouracil
thiourea
thiourethan
thiourethane
thioxene
thiozone
thiozonide
thir
third
thirdborough
thirdings
thirdling
thirdly
thirdness
thirdsman
thirl
thirlage
thirling
thirst
thirster
thirstful
thirstily
thirstiness
thirsting
thirstingly
thirstland
thirstle
thirstless
thirstlessness
thirstproof
thirsty
thirt
thirteen
thirteener
thirteenfold
thirteenth
thirteenthly
thirtieth
thirty
thirtyfold
thirtyish
this
thishow
thislike
thisn
thisness
thissen
thistle
thistlebird
thistled
thistledown
thistlelike
thistleproof
thistlery
thistlish
thistly
thiswise
thither
thitherto
thitherward
thitsiol
thiuram
thivel
thixle
thixolabile
thixotropic
thixotropy
Thlaspi
Thlingchadinne
Thlinget
thlipsis
Tho
tho
thob
thocht
thof
thoft
thoftfellow
thoke
thokish
thole
tholeiite
tholepin
tholi
tholoi
tholos
tholus
Thomaean
Thomas
Thomasa
Thomasine
thomasing
Thomasite
thomisid
Thomisidae
Thomism
Thomist
Thomistic
Thomistical
Thomite
Thomomys
thomsenolite
Thomsonian
Thomsonianism
thomsonite
thon
thonder
Thondracians
Thondraki
Thondrakians
thone
thong
Thonga
thonged
thongman
thongy
thoo
thooid
thoom
thoracalgia
thoracaorta
thoracectomy
thoracentesis
thoraces
thoracic
Thoracica
thoracical
thoracicoabdominal
thoracicoacromial
thoracicohumeral
thoracicolumbar
thoraciform
thoracispinal
thoracoabdominal
thoracoacromial
thoracobronchotomy
thoracoceloschisis
thoracocentesis
thoracocyllosis
thoracocyrtosis
thoracodelphus
thoracodidymus
thoracodorsal
thoracodynia
thoracogastroschisis
thoracograph
thoracohumeral
thoracolumbar
thoracolysis
thoracomelus
thoracometer
thoracometry
thoracomyodynia
thoracopagus
thoracoplasty
thoracoschisis
thoracoscope
thoracoscopy
Thoracostei
thoracostenosis
thoracostomy
Thoracostraca
thoracostracan
thoracostracous
thoracotomy
thoral
thorascope
thorax
thore
thoria
thorianite
thoriate
thoric
thoriferous
thorina
thorite
thorium
thorn
thornback
thornbill
thornbush
thorned
thornen
thornhead
thornily
thorniness
thornless
thornlessness
thornlet
thornlike
thornproof
thornstone
thorntail
thorny
thoro
thorocopagous
thorogummite
thoron
thorough
Thoroughbred
thoroughbred
thoroughbredness
thoroughfare
thoroughfarer
thoroughfaresome
thoroughfoot
thoroughgoing
thoroughgoingly
thoroughgoingness
thoroughgrowth
thoroughly
thoroughness
thoroughpaced
thoroughpin
thoroughsped
thoroughstem
thoroughstitch
thoroughstitched
thoroughwax
thoroughwort
thorp
thort
thorter
thortveitite
Thos
Those
those
thou
though
thought
thoughted
thoughten
thoughtful
thoughtfully
thoughtfulness
thoughtkin
thoughtless
thoughtlessly
thoughtlessness
thoughtlet
thoughtness
thoughtsick
thoughty
thousand
thousandfold
thousandfoldly
thousandth
thousandweight
thouse
thow
thowel
thowless
thowt
Thraces
Thracian
thrack
thraep
thrail
thrain
thrall
thrallborn
thralldom
thram
thrammle
thrang
thrangity
thranite
thranitic
thrap
thrapple
thrash
thrashel
thrasher
thrasherman
thrashing
thrasonic
thrasonical
thrasonically
thrast
Thraupidae
thrave
thraver
thraw
thrawcrook
thrawn
thrawneen
Thrax
thread
threadbare
threadbareness
threadbarity
threaded
threaden
threader
threadfin
threadfish
threadflower
threadfoot
threadiness
threadle
threadless
threadlet
threadlike
threadmaker
threadmaking
threadway
threadweed
threadworm
thready
threap
threaper
threat
threaten
threatenable
threatener
threatening
threateningly
threatful
threatfully
threatless
threatproof
three
threefold
threefolded
threefoldedness
threefoldly
threefoldness
threeling
threeness
threepence
threepenny
threepennyworth
threescore
threesome
thremmatology
threne
threnetic
threnetical
threnode
threnodial
threnodian
threnodic
threnodical
threnodist
threnody
threnos
threonin
threonine
threose
threpsology
threptic
thresh
threshel
thresher
thresherman
threshingtime
threshold
Threskiornithidae
Threskiornithinae
threw
thribble
thrice
thricecock
thridacium
thrift
thriftbox
thriftily
thriftiness
thriftless
thriftlessly
thriftlessness
thriftlike
thrifty
thrill
thriller
thrillful
thrillfully
thrilling
thrillingly
thrillingness
thrillproof
thrillsome
thrilly
thrimble
thrimp
Thrinax
thring
thrinter
thrioboly
thrip
thripel
Thripidae
thripple
thrips
thrive
thriveless
thriven
thriver
thriving
thrivingly
thrivingness
thro
throat
throatal
throatband
throated
throatful
throatily
throatiness
throating
throatlash
throatlatch
throatless
throatlet
throatroot
throatstrap
throatwort
throaty
throb
throbber
throbbingly
throbless
throck
throdden
throddy
throe
thrombase
thrombin
thromboangiitis
thromboarteritis
thrombocyst
thrombocyte
thrombocytopenia
thrombogen
thrombogenic
thromboid
thrombokinase
thrombolymphangitis
thrombopenia
thrombophlebitis
thromboplastic
thromboplastin
thrombose
thrombosis
thrombostasis
thrombotic
thrombus
thronal
throne
thronedom
throneless
thronelet
thronelike
throneward
throng
thronger
throngful
throngingly
thronize
thropple
throstle
throstlelike
throttle
throttler
throttling
throttlingly
throu
throuch
throucht
through
throughbear
throughbred
throughcome
throughgang
throughganging
throughgoing
throughgrow
throughknow
throughout
throughput
throve
throw
throwaway
throwback
throwdown
thrower
throwing
thrown
throwoff
throwout
throwster
throwwort
thrum
thrummer
thrummers
thrummy
thrumwort
thrush
thrushel
thrushlike
thrushy
thrust
thruster
thrustful
thrustfulness
thrusting
thrustings
thrutch
thrutchings
Thruthvang
thruv
thrymsa
Thryonomys
Thuan
Thuban
Thucydidean
thud
thudding
thuddingly
thug
thugdom
thuggee
thuggeeism
thuggery
thuggess
thuggish
thuggism
Thuidium
Thuja
thujene
thujin
thujone
Thujopsis
thujyl
Thule
thulia
thulir
thulite
thulium
thulr
thuluth
thumb
thumbbird
thumbed
thumber
thumbkin
thumble
thumbless
thumblike
thumbmark
thumbnail
thumbpiece
thumbprint
thumbrope
thumbscrew
thumbstall
thumbstring
thumbtack
thumby
thumlungur
thump
thumper
thumping
thumpingly
Thunar
Thunbergia
thunbergilene
thunder
thunderation
thunderball
thunderbearer
thunderbearing
thunderbird
thunderblast
thunderbolt
thunderburst
thunderclap
thundercloud
thundercrack
thunderer
thunderfish
thunderflower
thunderful
thunderhead
thunderheaded
thundering
thunderingly
thunderless
thunderlike
thunderous
thunderously
thunderousness
thunderpeal
thunderplump
thunderproof
thundershower
thundersmite
thundersquall
thunderstick
thunderstone
thunderstorm
thunderstrike
thunderstroke
thunderstruck
thunderwood
thunderworm
thunderwort
thundery
thundrous
thundrously
thung
thunge
Thunnidae
Thunnus
Thunor
thuoc
Thurberia
thurible
thuribuler
thuribulum
thurifer
thuriferous
thurificate
thurificati
thurification
thurify
Thuringian
thuringite
Thurio
thurl
thurm
thurmus
Thurnia
Thurniaceae
thurrock
Thursday
thurse
thurt
thus
thusgate
Thushi
thusly
thusness
thuswise
thutter
Thuyopsis
thwack
thwacker
thwacking
thwackingly
thwackstave
thwaite
thwart
thwartedly
thwarteous
thwarter
thwarting
thwartingly
thwartly
thwartman
thwartness
thwartover
thwartsaw
thwartship
thwartships
thwartways
thwartwise
thwite
thwittle
thy
Thyestean
Thyestes
thyine
thylacine
thylacitis
Thylacoleo
Thylacynus
thymacetin
Thymallidae
Thymallus
thymate
thyme
thymectomize
thymectomy
thymegol
Thymelaea
Thymelaeaceae
thymelaeaceous
Thymelaeales
thymelcosis
thymele
thymelic
thymelical
thymelici
thymene
thymetic
thymic
thymicolymphatic
thymine
thymiosis
thymitis
thymocyte
thymogenic
thymol
thymolate
thymolize
thymolphthalein
thymolsulphonephthalein
thymoma
thymonucleic
thymopathy
thymoprivic
thymoprivous
thymopsyche
thymoquinone
thymotactic
thymotic
Thymus
thymus
thymy
thymyl
thymylic
thynnid
Thynnidae
Thyraden
thyratron
thyreoadenitis
thyreoantitoxin
thyreoarytenoid
thyreoarytenoideus
thyreocervical
thyreocolloid
Thyreocoridae
thyreoepiglottic
thyreogenic
thyreogenous
thyreoglobulin
thyreoglossal
thyreohyal
thyreohyoid
thyreoid
thyreoidal
thyreoideal
thyreoidean
thyreoidectomy
thyreoiditis
thyreoitis
thyreolingual
thyreoprotein
thyreosis
thyreotomy
thyreotoxicosis
thyreotropic
thyridial
Thyrididae
thyridium
Thyris
thyrisiferous
thyroadenitis
thyroantitoxin
thyroarytenoid
thyroarytenoideus
thyrocardiac
thyrocele
thyrocervical
thyrocolloid
thyrocricoid
thyroepiglottic
thyroepiglottidean
thyrogenic
thyroglobulin
thyroglossal
thyrohyal
thyrohyoid
thyrohyoidean
thyroid
thyroidal
thyroidea
thyroideal
thyroidean
thyroidectomize
thyroidectomy
thyroidism
thyroiditis
thyroidization
thyroidless
thyroidotomy
thyroiodin
thyrolingual
thyronine
thyroparathyroidectomize
thyroparathyroidectomy
thyroprival
thyroprivia
thyroprivic
thyroprivous
thyroprotein
Thyrostraca
thyrostracan
thyrotherapy
thyrotomy
thyrotoxic
thyrotoxicosis
thyrotropic
thyroxine
thyrse
thyrsiflorous
thyrsiform
thyrsoid
thyrsoidal
thyrsus
Thysanocarpus
thysanopter
Thysanoptera
thysanopteran
thysanopteron
thysanopterous
Thysanoura
thysanouran
thysanourous
Thysanura
thysanuran
thysanurian
thysanuriform
thysanurous
thysel
thyself
thysen
Ti
ti
Tiahuanacan
Tiam
tiang
tiao
tiar
tiara
tiaralike
tiarella
Tiatinagua
tib
Tibbie
Tibbu
tibby
Tiberian
Tiberine
Tiberius
tibet
Tibetan
tibey
tibia
tibiad
tibiae
tibial
tibiale
tibicinist
tibiocalcanean
tibiofemoral
tibiofibula
tibiofibular
tibiometatarsal
tibionavicular
tibiopopliteal
tibioscaphoid
tibiotarsal
tibiotarsus
Tibouchina
tibourbou
tiburon
Tiburtine
tic
tical
ticca
tice
ticement
ticer
Tichodroma
tichodrome
tichorrhine
tick
tickbean
tickbird
tickeater
ticked
ticken
ticker
ticket
ticketer
ticketing
ticketless
ticketmonger
tickey
tickicide
tickie
ticking
tickle
tickleback
ticklebrain
tickled
ticklely
ticklenburg
tickleness
tickleproof
tickler
ticklesome
tickless
tickleweed
tickling
ticklingly
ticklish
ticklishly
ticklishness
tickly
tickney
tickproof
tickseed
tickseeded
ticktack
ticktacker
ticktacktoe
ticktick
ticktock
tickweed
ticky
ticul
Ticuna
Ticunan
tid
tidal
tidally
tidbit
tiddle
tiddledywinks
tiddler
tiddley
tiddling
tiddlywink
tiddlywinking
tiddy
tide
tided
tideful
tidehead
tideland
tideless
tidelessness
tidelike
tidely
tidemaker
tidemaking
tidemark
tiderace
tidesman
tidesurveyor
Tideswell
tidewaiter
tidewaitership
tideward
tidewater
tideway
tidiable
tidily
tidiness
tiding
tidingless
tidings
tidley
tidological
tidology
tidy
tidyism
tidytips
tie
tieback
tied
Tiefenthal
tiemaker
tiemaking
tiemannite
tien
tiepin
tier
tierce
tierced
tierceron
tiered
tierer
tierlike
tiersman
tietick
tiewig
tiewigged
tiff
tiffany
tiffanyite
tiffie
tiffin
tiffish
tiffle
tiffy
tifinagh
tift
tifter
tig
tige
tigella
tigellate
tigelle
tigellum
tigellus
tiger
tigerbird
tigereye
tigerflower
tigerfoot
tigerhearted
tigerhood
tigerish
tigerishly
tigerishness
tigerism
tigerkin
tigerlike
tigerling
tigerly
tigernut
tigerproof
tigerwood
tigery
Tigger
tigger
tight
tighten
tightener
tightfisted
tightish
tightly
tightness
tightrope
tights
tightwad
tightwire
tiglaldehyde
tiglic
tiglinic
tignum
Tigrai
Tigre
Tigrean
tigress
tigresslike
Tigridia
Tigrina
tigrine
Tigris
tigroid
tigrolysis
tigrolytic
tigtag
Tigua
Tigurine
Tiki
tikitiki
tikka
tikker
tiklin
tikolosh
tikor
tikur
til
tilaite
tilaka
tilasite
tilbury
Tilda
tilde
tile
tiled
tilefish
tilelike
tilemaker
tilemaking
tiler
tileroot
tilery
tileseed
tilestone
tileways
tilework
tileworks
tilewright
tileyard
Tilia
Tiliaceae
tiliaceous
tilikum
tiling
till
tillable
Tillaea
Tillaeastrum
tillage
Tillamook
Tillandsia
tiller
tillering
tillerless
tillerman
Tilletia
Tilletiaceae
tilletiaceous
tilley
tillite
tillodont
Tillodontia
Tillodontidae
tillot
tillotter
tilly
tilmus
tilpah
Tilsit
tilt
tiltable
tiltboard
tilter
tilth
tilting
tiltlike
tiltmaker
tiltmaking
tiltup
tilty
tiltyard
tilyer
Tim
timable
Timaeus
Timalia
Timaliidae
Timaliinae
timaliine
timaline
Timani
timar
timarau
timawa
timazite
timbal
timbale
timbang
timbe
timber
timbered
timberer
timberhead
timbering
timberjack
timberland
timberless
timberlike
timberling
timberman
timbermonger
timbern
timbersome
timbertuned
timberwood
timberwork
timberwright
timbery
timberyard
Timbira
timbo
timbre
timbrel
timbreled
timbreler
timbrologist
timbrology
timbromania
timbromaniac
timbromanist
timbrophilic
timbrophilism
timbrophilist
timbrophily
time
timeable
timecard
timed
timeful
timefully
timefulness
timekeep
timekeeper
timekeepership
timeless
timelessly
timelessness
Timelia
Timeliidae
timeliine
timelily
timeliness
timeling
timely
timenoguy
timeous
timeously
timepiece
timepleaser
timeproof
timer
times
timesaver
timesaving
timeserver
timeserving
timeservingness
timetable
timetaker
timetaking
timeward
timework
timeworker
timeworn
Timias
timid
timidity
timidly
timidness
timing
timish
timist
Timne
Timo
timocracy
timocratic
timocratical
Timon
timon
timoneer
Timonian
Timonism
Timonist
Timonize
timor
Timorese
timorous
timorously
timorousness
Timote
Timotean
Timothean
Timothy
timothy
timpani
timpanist
timpano
Timucua
Timucuan
Timuquan
Timuquanan
tin
Tina
Tinamidae
tinamine
tinamou
tinampipi
tincal
tinchel
tinchill
tinclad
tinct
tinction
tinctorial
tinctorially
tinctorious
tinctumutation
tincture
tind
tindal
tindalo
tinder
tinderbox
tindered
tinderish
tinderlike
tinderous
tindery
tine
tinea
tineal
tinean
tined
tinegrass
tineid
Tineidae
Tineina
tineine
tineman
tineoid
Tineoidea
tinetare
tinety
tineweed
tinful
Ting
ting
tinge
tinged
tinger
Tinggian
tingi
tingibility
tingible
tingid
Tingidae
Tingis
tingitid
Tingitidae
tinglass
tingle
tingler
tingletangle
tingling
tinglingly
tinglish
tingly
tingtang
tinguaite
tinguaitic
Tinguian
tinguy
tinhorn
tinhouse
tinily
tininess
tining
tink
tinker
tinkerbird
tinkerdom
tinkerer
tinkerlike
tinkerly
tinkershire
tinkershue
tinkerwise
tinkle
tinkler
tinklerman
tinkling
tinklingly
tinkly
tinlet
tinlike
tinman
Tinne
tinned
tinner
tinnery
tinnet
Tinni
tinnified
tinnily
tinniness
tinning
tinnitus
tinnock
tinny
Tino
Tinoceras
tinosa
tinsel
tinsellike
tinselly
tinselmaker
tinselmaking
tinselry
tinselweaver
tinselwork
tinsman
tinsmith
tinsmithing
tinsmithy
tinstone
tinstuff
tint
tinta
tintage
tintamarre
tintarron
tinted
tinter
tintie
tintiness
tinting
tintingly
tintinnabula
tintinnabulant
tintinnabular
tintinnabulary
tintinnabulate
tintinnabulation
tintinnabulatory
tintinnabulism
tintinnabulist
tintinnabulous
tintinnabulum
tintist
tintless
tintometer
tintometric
tintometry
tinty
tintype
tintyper
tinwald
tinware
tinwoman
tinwork
tinworker
tinworking
tiny
tinzenite
Tionontates
Tionontati
Tiou
tip
tipburn
tipcart
tipcat
tipe
tipful
tiphead
Tiphia
Tiphiidae
tipiti
tiple
tipless
tiplet
tipman
tipmost
tiponi
tippable
tipped
tippee
tipper
tippet
tipping
tipple
tippleman
tippler
tipply
tipproof
tippy
tipsification
tipsifier
tipsify
tipsily
tipsiness
tipstaff
tipster
tipstock
tipsy
tiptail
tipteerer
tiptilt
tiptoe
tiptoeing
tiptoeingly
tiptop
tiptopness
tiptopper
tiptoppish
tiptoppishness
tiptopsome
Tipula
Tipularia
tipulid
Tipulidae
tipuloid
Tipuloidea
tipup
Tipura
tirade
tiralee
tire
tired
tiredly
tiredness
tiredom
tirehouse
tireless
tirelessly
tirelessness
tiremaid
tiremaker
tiremaking
tireman
tirer
tireroom
tiresmith
tiresome
tiresomely
tiresomeness
tiresomeweed
tirewoman
Tirhutia
tiriba
tiring
tiringly
tirl
tirma
tirocinium
Tirolean
Tirolese
Tironian
tirr
tirralirra
tirret
Tirribi
tirrivee
tirrlie
tirrwirr
tirthankara
Tirurai
tirve
tirwit
tisane
tisar
Tishiya
Tishri
Tisiphone
tissual
tissue
tissued
tissueless
tissuelike
tissuey
tisswood
tiswin
tit
Titan
titanate
titanaugite
Titanesque
Titaness
titania
Titanian
Titanic
titanic
Titanical
Titanically
Titanichthyidae
Titanichthys
titaniferous
titanifluoride
Titanism
titanite
titanitic
titanium
Titanlike
titano
titanocolumbate
titanocyanide
titanofluoride
Titanolater
Titanolatry
Titanomachia
Titanomachy
titanomagnetite
titanoniobate
titanosaur
Titanosaurus
titanosilicate
titanothere
Titanotheridae
Titanotherium
titanous
titanyl
titar
titbit
titbitty
tite
titer
titeration
titfish
tithable
tithal
tithe
tithebook
titheless
tithemonger
tithepayer
tither
titheright
tithing
tithingman
tithingpenny
tithonic
tithonicity
tithonographic
tithonometer
Tithymalopsis
Tithymalus
titi
Titian
titian
Titianesque
Titianic
titien
Tities
titilate
titillability
titillant
titillater
titillating
titillatingly
titillation
titillative
titillator
titillatory
titivate
titivation
titivator
titlark
title
titleboard
titled
titledom
titleholder
titleless
titleproof
titler
titleship
titlike
titling
titlist
titmal
titman
Titmarsh
Titmarshian
titmouse
Titoism
Titoist
titoki
titrable
titratable
titrate
titration
titre
titrimetric
titrimetry
titter
titterel
titterer
tittering
titteringly
tittery
tittie
tittle
tittlebat
tittler
tittup
tittupy
titty
tittymouse
titubancy
titubant
titubantly
titubate
titubation
titular
titularity
titularly
titulary
titulation
titule
titulus
Titurel
Titus
tiver
Tivoli
tivoli
tivy
Tiwaz
tiza
tizeur
tizzy
tjanting
tji
tjosite
tlaco
Tlakluit
Tlapallan
Tlascalan
Tlingit
tmema
Tmesipteris
tmesis
to
toa
toad
toadback
toadeat
toadeater
toader
toadery
toadess
toadfish
toadflax
toadflower
toadhead
toadier
toadish
toadless
toadlet
toadlike
toadlikeness
toadling
toadpipe
toadroot
toadship
toadstone
toadstool
toadstoollike
toadwise
toady
toadyish
toadyism
toadyship
Toag
toast
toastable
toastee
toaster
toastiness
toastmaster
toastmastery
toastmistress
toasty
toat
toatoa
Toba
tobacco
tobaccofied
tobaccoism
tobaccoite
tobaccoless
tobaccolike
tobaccoman
tobacconalian
tobacconist
tobacconistical
tobacconize
tobaccophil
tobaccoroot
tobaccoweed
tobaccowood
tobaccoy
tobe
Tobiah
Tobias
Tobikhar
tobine
tobira
toboggan
tobogganeer
tobogganer
tobogganist
Toby
toby
tobyman
tocalote
toccata
Tocharese
Tocharian
Tocharic
Tocharish
tocher
tocherless
tock
toco
Tocobaga
tocodynamometer
tocogenetic
tocogony
tocokinin
tocological
tocologist
tocology
tocome
tocometer
tocopherol
tocororo
tocsin
tocusso
Tod
tod
Toda
today
todayish
Todd
todder
toddick
toddite
toddle
toddlekins
toddler
toddy
toddyize
toddyman
tode
Todea
Todidae
Todus
tody
toe
toeboard
toecap
toecapped
toed
toeless
toelike
toellite
toenail
toeplate
Toerless
toernebohmite
toetoe
toff
toffee
toffeeman
toffing
toffish
toffy
toffyman
Tofieldia
Toft
toft
tofter
toftman
toftstead
tofu
tog
toga
togaed
togalike
togata
togate
togated
togawise
together
togetherhood
togetheriness
togetherness
toggel
toggery
toggle
toggler
togless
togs
togt
togue
toher
toheroa
toho
Tohome
tohubohu
tohunga
toi
toil
toiled
toiler
toilet
toileted
toiletry
toilette
toiletted
toiletware
toilful
toilfully
toilinet
toiling
toilingly
toilless
toillessness
toilsome
toilsomely
toilsomeness
toilworn
toise
toit
toitish
toity
Tokay
tokay
toke
Tokelau
token
tokened
tokenless
toko
tokology
tokonoma
tokopat
tol
tolamine
tolan
tolane
tolbooth
told
toldo
tole
Toledan
Toledo
Toledoan
tolerability
tolerable
tolerableness
tolerablish
tolerably
tolerance
tolerancy
Tolerant
tolerant
tolerantism
tolerantly
tolerate
toleration
tolerationism
tolerationist
tolerative
tolerator
tolerism
Toletan
tolfraedic
tolguacha
tolidine
tolite
toll
tollable
tollage
tollbooth
Tollefsen
toller
tollery
tollgate
tollgatherer
tollhouse
tolliker
tolling
tollkeeper
tollman
tollmaster
tollpenny
tolltaker
tolly
Tolowa
tolpatch
tolpatchery
tolsester
tolsey
Tolstoyan
Tolstoyism
Tolstoyist
tolt
Toltec
Toltecan
tolter
tolu
tolualdehyde
toluate
toluene
toluic
toluide
toluidide
toluidine
toluidino
toluido
Toluifera
tolunitrile
toluol
toluquinaldine
tolusafranine
toluyl
toluylene
toluylenediamine
toluylic
tolyl
tolylene
tolylenediamine
Tolypeutes
tolypeutine
Tom
Toma
tomahawk
tomahawker
tomalley
toman
Tomas
tomatillo
tomato
tomb
tombac
tombal
tombe
tombic
tombless
tomblet
tomblike
tombola
tombolo
tomboy
tomboyful
tomboyish
tomboyishly
tomboyishness
tomboyism
tombstone
tomcat
tomcod
tome
tomeful
tomelet
toment
tomentose
tomentous
tomentulose
tomentum
tomfool
tomfoolery
tomfoolish
tomfoolishness
tomial
tomin
tomish
Tomistoma
tomium
tomjohn
Tomkin
tomkin
Tommer
Tomming
Tommy
tommy
tommybag
tommycod
tommyrot
tomnoddy
tomnoup
tomogram
tomographic
tomography
Tomopteridae
Tomopteris
tomorn
tomorrow
tomorrower
tomorrowing
tomorrowness
tomosis
Tompion
tompiper
tompon
tomtate
tomtit
Tomtitmouse
ton
tonal
tonalamatl
tonalist
tonalite
tonalitive
tonality
tonally
tonant
tonation
tondino
tone
toned
toneless
tonelessly
tonelessness
toneme
toneproof
toner
tonetic
tonetically
tonetician
tonetics
tong
Tonga
tonga
Tongan
Tongas
tonger
tongkang
tongman
Tongrian
tongs
tongsman
tongue
tonguecraft
tongued
tonguedoughty
tonguefence
tonguefencer
tongueflower
tongueful
tongueless
tonguelet
tonguelike
tongueman
tonguemanship
tongueplay
tongueproof
tonguer
tongueshot
tonguesman
tonguesore
tonguester
tonguetip
tonguey
tonguiness
tonguing
tonic
tonically
tonicity
tonicize
tonicobalsamic
tonicoclonic
tonicostimulant
tonify
tonight
Tonikan
tonish
tonishly
tonishness
tonite
tonitrocirrus
tonitruant
tonitruone
tonitruous
tonjon
tonk
Tonkawa
Tonkawan
tonkin
Tonkinese
tonlet
Tonna
tonnage
tonneau
tonneaued
tonner
tonnish
tonnishly
tonnishness
tonoclonic
tonogram
tonograph
tonological
tonology
tonometer
tonometric
tonometry
tonophant
tonoplast
tonoscope
tonotactic
tonotaxis
tonous
tonsbergite
tonsil
tonsilectomy
tonsilitic
tonsillar
tonsillary
tonsillectome
tonsillectomic
tonsillectomize
tonsillectomy
tonsillith
tonsillitic
tonsillitis
tonsillolith
tonsillotome
tonsillotomy
tonsilomycosis
tonsor
tonsorial
tonsurate
tonsure
tonsured
tontine
tontiner
Tonto
tonus
Tony
tony
tonyhoop
too
toodle
toodleloodle
took
tooken
tool
toolbox
toolbuilder
toolbuilding
tooler
toolhead
toolholder
toolholding
tooling
toolless
toolmaker
toolmaking
toolman
toolmark
toolmarking
toolplate
toolroom
toolsetter
toolslide
toolsmith
toolstock
toolstone
toom
toomly
toon
Toona
toonwood
toop
toorie
toorock
tooroo
toosh
toot
tooter
tooth
toothache
toothaching
toothachy
toothbill
toothbrush
toothbrushy
toothchiseled
toothcomb
toothcup
toothdrawer
toothdrawing
toothed
toother
toothflower
toothful
toothill
toothing
toothless
toothlessly
toothlessness
toothlet
toothleted
toothlike
toothpick
toothplate
toothproof
toothsome
toothsomely
toothsomeness
toothstick
toothwash
toothwork
toothwort
toothy
tootle
tootler
tootlish
tootsy
toozle
toozoo
top
topalgia
toparch
toparchia
toparchical
toparchy
topass
Topatopa
topaz
topazfels
topazine
topazite
topazolite
topazy
topcap
topcast
topchrome
topcoat
topcoating
tope
topectomy
topee
topeewallah
topeng
topepo
toper
toperdom
topesthesia
topflight
topfull
topgallant
toph
tophaceous
tophaike
Tophet
tophetic
tophetize
tophus
tophyperidrosis
topi
topia
topiarian
topiarist
topiarius
topiary
topic
topical
topicality
topically
topinambou
Topinish
topknot
topknotted
topless
toplighted
toplike
topline
toploftical
toploftily
toploftiness
toplofty
topmaker
topmaking
topman
topmast
topmost
topmostly
topnotch
topnotcher
topo
topoalgia
topochemical
topognosia
topognosis
topograph
topographer
topographic
topographical
topographically
topographics
topographist
topographize
topographometric
topography
topolatry
topologic
topological
topologist
topology
toponarcosis
toponym
toponymal
toponymic
toponymical
toponymics
toponymist
toponymy
topophobia
topophone
topotactic
topotaxis
topotype
topotypic
topotypical
topped
topper
toppiece
topping
toppingly
toppingness
topple
toppler
topply
toppy
toprail
toprope
tops
topsail
topsailite
topside
topsl
topsman
topsoil
topstone
topswarm
Topsy
topsyturn
toptail
topwise
toque
Tor
tor
tora
torah
Toraja
toral
toran
torbanite
torbanitic
torbernite
torc
torcel
torch
torchbearer
torchbearing
torcher
torchless
torchlight
torchlighted
torchlike
torchman
torchon
torchweed
torchwood
torchwort
torcular
torculus
tordrillite
tore
toreador
tored
Torenia
torero
toreumatography
toreumatology
toreutic
toreutics
torfaceous
torfel
torgoch
Torgot
toric
Toriest
Torified
torii
Torilis
Torinese
Toriness
torma
tormen
torment
tormenta
tormentable
tormentation
tormentative
tormented
tormentedly
tormentful
tormentil
tormentilla
tormenting
tormentingly
tormentingness
tormentive
tormentor
tormentous
tormentress
tormentry
tormentum
tormina
torminal
torminous
tormodont
torn
tornachile
tornade
tornadic
tornado
tornadoesque
tornadoproof
tornal
tornaria
tornarian
tornese
torney
tornillo
Tornit
tornote
tornus
toro
toroid
toroidal
torolillo
Toromona
Torontonian
tororokombu
Torosaurus
torose
torosity
torotoro
torous
torpedineer
Torpedinidae
torpedinous
torpedo
torpedoer
torpedoist
torpedolike
torpedoplane
torpedoproof
torpent
torpescence
torpescent
torpid
torpidity
torpidly
torpidness
torpify
torpitude
torpor
torporific
torporize
torquate
torquated
torque
torqued
torques
torrefaction
torrefication
torrefy
torrent
torrentful
torrentfulness
torrential
torrentiality
torrentially
torrentine
torrentless
torrentlike
torrentuous
torrentwise
Torreya
Torricellian
torrid
torridity
torridly
torridness
Torridonian
Torrubia
torsade
torse
torsel
torsibility
torsigraph
torsile
torsimeter
torsiogram
torsiograph
torsiometer
torsion
torsional
torsionally
torsioning
torsionless
torsive
torsk
torso
torsoclusion
torsometer
torsoocclusion
Torsten
tort
torta
torteau
torticollar
torticollis
torticone
tortile
tortility
tortilla
tortille
tortious
tortiously
tortive
tortoise
tortoiselike
Tortonian
tortrices
tortricid
Tortricidae
Tortricina
tortricine
tortricoid
Tortricoidea
Tortrix
tortula
Tortulaceae
tortulaceous
tortulous
tortuose
tortuosity
tortuous
tortuously
tortuousness
torturable
torturableness
torture
tortured
torturedly
tortureproof
torturer
torturesome
torturing
torturingly
torturous
torturously
toru
torula
torulaceous
torulaform
toruliform
torulin
toruloid
torulose
torulosis
torulous
torulus
torus
torve
torvid
torvity
torvous
Tory
tory
Torydom
Toryess
Toryfication
Toryfy
toryhillite
Toryish
Toryism
Toryistic
Toryize
Toryship
toryweed
tosaphist
tosaphoth
toscanite
Tosephta
Tosephtas
tosh
toshakhana
tosher
toshery
toshly
toshnail
toshy
tosily
Tosk
Toskish
toss
tosser
tossicated
tossily
tossing
tossingly
tossment
tosspot
tossup
tossy
tost
tosticate
tostication
toston
tosy
tot
total
totalitarian
totalitarianism
totality
totalization
totalizator
totalize
totalizer
totally
totalness
totanine
Totanus
totaquin
totaquina
totaquine
totara
totchka
tote
toteload
totem
totemic
totemically
totemism
totemist
totemistic
totemite
totemization
totemy
toter
tother
totient
Totipalmatae
totipalmate
totipalmation
totipotence
totipotency
totipotent
totipotential
totipotentiality
totitive
toto
Totonac
Totonacan
Totonaco
totora
Totoro
totquot
totter
totterer
tottergrass
tottering
totteringly
totterish
tottery
Tottie
totting
tottle
tottlish
totty
tottyhead
totuava
totum
toty
totyman
tou
toucan
toucanet
Toucanid
touch
touchable
touchableness
touchback
touchbell
touchbox
touchdown
touched
touchedness
toucher
touchhole
touchily
touchiness
touching
touchingly
touchingness
touchless
touchline
touchous
touchpan
touchpiece
touchstone
touchwood
touchy
Toufic
toug
tough
toughen
toughener
toughhead
toughhearted
toughish
toughly
toughness
tought
tould
toumnah
Tounatea
toup
toupee
toupeed
toupet
tour
touraco
tourbillion
tourer
tourette
touring
tourism
tourist
touristdom
touristic
touristproof
touristry
touristship
touristy
tourize
tourmaline
tourmalinic
tourmaliniferous
tourmalinization
tourmalinize
tourmalite
tourn
tournament
tournamental
tournant
tournasin
tournay
tournee
Tournefortia
Tournefortian
tourney
tourneyer
tourniquet
tourte
tousche
touse
touser
tousle
tously
tousy
tout
touter
Tovah
tovar
Tovaria
Tovariaceae
tovariaceous
tovarish
tow
towable
towage
towai
towan
toward
towardliness
towardly
towardness
towards
towboat
towcock
towd
towel
towelette
toweling
towelry
tower
towered
towering
toweringly
towerless
towerlet
towerlike
towerman
towerproof
towerwise
towerwork
towerwort
towery
towght
towhead
towheaded
towhee
towing
towkay
towlike
towline
towmast
town
towned
townee
towner
townet
townfaring
townfolk
townful
towngate
townhood
townify
towniness
townish
townishly
townishness
townist
townland
townless
townlet
townlike
townling
townly
townman
townsboy
townscape
Townsendia
Townsendite
townsfellow
townsfolk
township
townside
townsite
townsman
townspeople
townswoman
townward
townwards
townwear
towny
towpath
towrope
towser
towy
tox
toxa
toxalbumic
toxalbumin
toxalbumose
toxamin
toxanemia
toxaphene
toxcatl
toxemia
toxemic
toxic
toxicaemia
toxical
toxically
toxicant
toxicarol
toxication
toxicemia
toxicity
toxicodendrol
Toxicodendron
toxicoderma
toxicodermatitis
toxicodermatosis
toxicodermia
toxicodermitis
toxicogenic
toxicognath
toxicohaemia
toxicohemia
toxicoid
toxicologic
toxicological
toxicologically
toxicologist
toxicology
toxicomania
toxicopathic
toxicopathy
toxicophagous
toxicophagy
toxicophidia
toxicophobia
toxicosis
toxicotraumatic
toxicum
toxidermic
toxidermitis
toxifer
Toxifera
toxiferous
toxigenic
toxihaemia
toxihemia
toxiinfection
toxiinfectious
toxin
toxinemia
toxinfection
toxinfectious
toxinosis
toxiphobia
toxiphobiac
toxiphoric
toxitabellae
toxity
Toxodon
toxodont
Toxodontia
toxogenesis
Toxoglossa
toxoglossate
toxoid
toxology
toxolysis
toxon
toxone
toxonosis
toxophil
toxophile
toxophilism
toxophilite
toxophilitic
toxophilitism
toxophilous
toxophily
toxophoric
toxophorous
toxoplasmosis
toxosis
toxosozin
Toxostoma
toxotae
Toxotes
Toxotidae
Toxylon
toy
toydom
toyer
toyful
toyfulness
toyhouse
toying
toyingly
toyish
toyishly
toyishness
toyland
toyless
toylike
toymaker
toymaking
toyman
toyon
toyshop
toysome
toytown
toywoman
toywort
toze
tozee
tozer
tra
trabacolo
trabal
trabant
trabascolo
trabea
trabeae
trabeatae
trabeated
trabeation
trabecula
trabecular
trabecularism
trabeculate
trabeculated
trabeculation
trabecule
trabuch
trabucho
Tracaulon
trace
traceability
traceable
traceableness
traceably
traceless
tracelessly
tracer
traceried
tracery
Tracey
trachea
tracheaectasy
tracheal
trachealgia
trachealis
trachean
Trachearia
trachearian
tracheary
Tracheata
tracheate
tracheation
tracheid
tracheidal
tracheitis
trachelagra
trachelate
trachelectomopexia
trachelectomy
trachelismus
trachelitis
trachelium
tracheloacromialis
trachelobregmatic
tracheloclavicular
trachelocyllosis
trachelodynia
trachelology
trachelomastoid
trachelopexia
tracheloplasty
trachelorrhaphy
tracheloscapular
Trachelospermum
trachelotomy
trachenchyma
tracheobronchial
tracheobronchitis
tracheocele
tracheochromatic
tracheoesophageal
tracheofissure
tracheolar
tracheolaryngeal
tracheolaryngotomy
tracheole
tracheolingual
tracheopathia
tracheopathy
tracheopharyngeal
Tracheophonae
tracheophone
tracheophonesis
tracheophonine
tracheophony
tracheoplasty
tracheopyosis
tracheorrhagia
tracheoschisis
tracheoscopic
tracheoscopist
tracheoscopy
tracheostenosis
tracheostomy
tracheotome
tracheotomist
tracheotomize
tracheotomy
Trachinidae
trachinoid
Trachinus
trachitis
trachle
Trachodon
trachodont
trachodontid
Trachodontidae
Trachoma
trachomatous
Trachomedusae
trachomedusan
trachyandesite
trachybasalt
trachycarpous
Trachycarpus
trachychromatic
trachydolerite
trachyglossate
Trachylinae
trachyline
Trachymedusae
trachymedusan
trachyphonia
trachyphonous
Trachypteridae
trachypteroid
Trachypterus
trachyspermous
trachyte
trachytic
trachytoid
tracing
tracingly
track
trackable
trackage
trackbarrow
tracked
tracker
trackhound
trackingscout
tracklayer
tracklaying
trackless
tracklessly
tracklessness
trackman
trackmanship
trackmaster
trackscout
trackshifter
tracksick
trackside
trackwalker
trackway
trackwork
tract
tractability
tractable
tractableness
tractably
tractarian
Tractarianism
tractarianize
tractate
tractator
tractatule
tractellate
tractellum
tractiferous
tractile
tractility
traction
tractional
tractioneering
Tractite
tractlet
tractor
tractoration
tractorism
tractorist
tractorization
tractorize
tractory
tractrix
Tracy
tradable
tradal
trade
tradecraft
tradeful
tradeless
trademaster
trader
tradership
Tradescantia
tradesfolk
tradesman
tradesmanlike
tradesmanship
tradesmanwise
tradespeople
tradesperson
tradeswoman
tradiment
trading
tradite
tradition
traditional
traditionalism
traditionalist
traditionalistic
traditionality
traditionalize
traditionally
traditionarily
traditionary
traditionate
traditionately
traditioner
traditionism
traditionist
traditionitis
traditionize
traditionless
traditionmonger
traditious
traditive
traditor
traditores
traditorship
traduce
traducement
traducent
traducer
traducian
traducianism
traducianist
traducianistic
traducible
traducing
traducingly
traduction
traductionist
trady
traffic
trafficability
trafficable
trafficableness
trafficless
trafficway
trafflicker
trafflike
trag
tragacanth
tragacantha
tragacanthin
tragal
Tragasol
tragedial
tragedian
tragedianess
tragedical
tragedienne
tragedietta
tragedist
tragedization
tragedize
tragedy
tragelaph
tragelaphine
Tragelaphus
tragi
tragic
tragical
tragicality
tragically
tragicalness
tragicaster
tragicize
tragicly
tragicness
tragicofarcical
tragicoheroicomic
tragicolored
tragicomedian
tragicomedy
tragicomic
tragicomical
tragicomicality
tragicomically
tragicomipastoral
tragicoromantic
tragicose
tragopan
Tragopogon
Tragulidae
Tragulina
traguline
traguloid
Traguloidea
Tragulus
tragus
trah
traheen
traik
trail
trailer
trailery
trailiness
trailing
trailingly
trailless
trailmaker
trailmaking
trailman
trailside
trailsman
traily
train
trainable
trainage
trainagraph
trainband
trainbearer
trainbolt
trainboy
trained
trainee
trainer
trainful
training
trainless
trainload
trainman
trainmaster
trainsick
trainster
traintime
trainway
trainy
traipse
trait
traitless
traitor
traitorhood
traitorism
traitorize
traitorlike
traitorling
traitorous
traitorously
traitorousness
traitorship
traitorwise
traitress
traject
trajectile
trajection
trajectitious
trajectory
trajet
tralatician
tralaticiary
tralatition
tralatitious
tralatitiously
tralira
Trallian
tram
trama
tramal
tramcar
trame
Trametes
tramful
tramless
tramline
tramman
trammel
trammeled
trammeler
trammelhead
trammeling
trammelingly
trammelled
trammellingly
trammer
tramming
trammon
tramontane
tramp
trampage
trampdom
tramper
trampess
tramphood
trampish
trampishly
trampism
trample
trampler
tramplike
trampolin
trampoline
trampoose
trampot
tramroad
tramsmith
tramway
tramwayman
tramyard
Tran
trance
tranced
trancedly
tranceful
trancelike
tranchefer
tranchet
trancoidal
traneen
trank
tranka
tranker
trankum
tranky
tranquil
tranquility
tranquilization
tranquilize
tranquilizer
tranquilizing
tranquilizingly
tranquillity
tranquillization
tranquillize
tranquilly
tranquilness
transaccidentation
transact
transaction
transactional
transactionally
transactioneer
transactor
transalpine
transalpinely
transalpiner
transamination
transanimate
transanimation
transannular
transapical
transappalachian
transaquatic
transarctic
transatlantic
transatlantically
transatlantican
transatlanticism
transaudient
transbaikal
transbaikalian
transbay
transboard
transborder
transcalency
transcalent
transcalescency
transcalescent
Transcaucasian
transceiver
transcend
transcendence
transcendency
transcendent
transcendental
transcendentalism
transcendentalist
transcendentalistic
transcendentality
transcendentalize
transcendentally
transcendently
transcendentness
transcendible
transcending
transcendingly
transcendingness
transcension
transchannel
transcolor
transcoloration
transconductance
transcondylar
transcondyloid
transconscious
transcontinental
transcorporate
transcorporeal
transcortical
transcreate
transcribable
transcribble
transcribbler
transcribe
transcriber
transcript
transcription
transcriptional
transcriptionally
transcriptitious
transcriptive
transcriptively
transcriptural
transcrystalline
transcurrent
transcurrently
transcurvation
transdermic
transdesert
transdialect
transdiaphragmatic
transdiurnal
transducer
transduction
transect
transection
transelement
transelementate
transelementation
transempirical
transenna
transept
transeptal
transeptally
transequatorial
transessentiate
transeunt
transexperiential
transfashion
transfeature
transfer
transferability
transferable
transferableness
transferably
transferal
transferee
transference
transferent
transferential
transferography
transferor
transferotype
transferred
transferrer
transferribility
transferring
transferror
transferrotype
transfigurate
transfiguration
transfigurative
transfigure
transfigurement
transfiltration
transfinite
transfix
transfixation
transfixion
transfixture
transfluent
transfluvial
transflux
transforation
transform
transformability
transformable
transformance
transformation
transformationist
transformative
transformator
transformer
transforming
transformingly
transformism
transformist
transformistic
transfrontal
transfrontier
transfuge
transfugitive
transfuse
transfuser
transfusible
transfusion
transfusionist
transfusive
transfusively
transgredient
transgress
transgressible
transgressing
transgressingly
transgression
transgressional
transgressive
transgressively
transgressor
transhape
transhuman
transhumanate
transhumanation
transhumance
transhumanize
transhumant
transience
transiency
transient
transiently
transientness
transigence
transigent
transiliac
transilience
transiliency
transilient
transilluminate
transillumination
transilluminator
transimpression
transincorporation
transindividual
transinsular
transire
transischiac
transisthmian
transistor
transit
transitable
transiter
transition
transitional
transitionally
transitionalness
transitionary
transitionist
transitival
transitive
transitively
transitiveness
transitivism
transitivity
transitman
transitorily
transitoriness
transitory
transitus
Transjordanian
translade
translatable
translatableness
translate
translater
translation
translational
translationally
translative
translator
translatorese
translatorial
translatorship
translatory
translatress
translatrix
translay
transleithan
transletter
translinguate
transliterate
transliteration
transliterator
translocalization
translocate
translocation
translocatory
translucence
translucency
translucent
translucently
translucid
transmarginal
transmarine
transmaterial
transmateriation
transmedial
transmedian
transmental
transmentation
transmeridional
transmethylation
transmigrant
transmigrate
transmigration
transmigrationism
transmigrationist
transmigrative
transmigratively
transmigrator
transmigratory
transmissibility
transmissible
transmission
transmissional
transmissionist
transmissive
transmissively
transmissiveness
transmissivity
transmissometer
transmissory
transmit
transmittable
transmittal
transmittance
transmittancy
transmittant
transmitter
transmittible
transmogrification
transmogrifier
transmogrify
transmold
transmontane
transmorphism
transmundane
transmural
transmuscle
transmutability
transmutable
transmutableness
transmutably
transmutation
transmutational
transmutationist
transmutative
transmutatory
transmute
transmuter
transmuting
transmutive
transmutual
transnatation
transnational
transnatural
transnaturation
transnature
transnihilation
transnormal
transocean
transoceanic
transocular
transom
transomed
transonic
transorbital
transpacific
transpadane
transpalatine
transpalmar
transpanamic
transparence
transparency
transparent
transparentize
transparently
transparentness
transparietal
transparish
transpeciate
transpeciation
transpeer
transpenetrable
transpeninsular
transperitoneal
transperitoneally
transpersonal
transphenomenal
transphysical
transpicuity
transpicuous
transpicuously
transpierce
transpirability
transpirable
transpiration
transpirative
transpiratory
transpire
transpirometer
transplace
transplant
transplantability
transplantable
transplantar
transplantation
transplantee
transplanter
transplendency
transplendent
transplendently
transpleural
transpleurally
transpolar
transponibility
transponible
transpontine
transport
transportability
transportable
transportableness
transportal
transportance
transportation
transportational
transportationist
transportative
transported
transportedly
transportedness
transportee
transporter
transporting
transportingly
transportive
transportment
transposability
transposable
transposableness
transposal
transpose
transposer
transposition
transpositional
transpositive
transpositively
transpositor
transpository
transpour
transprint
transprocess
transprose
transproser
transpulmonary
transpyloric
transradiable
transrational
transreal
transrectification
transrhenane
transrhodanian
transriverine
transsegmental
transsensual
transseptal
transsepulchral
transshape
transshift
transship
transshipment
transsolid
transstellar
transsubjective
transtemporal
Transteverine
transthalamic
transthoracic
transubstantial
transubstantially
transubstantiate
transubstantiation
transubstantiationalist
transubstantiationite
transubstantiative
transubstantiatively
transubstantiatory
transudate
transudation
transudative
transudatory
transude
transumpt
transumption
transumptive
transuranian
transuranic
transuranium
transuterine
transvaal
Transvaaler
Transvaalian
transvaluate
transvaluation
transvalue
transvasate
transvasation
transvase
transvectant
transvection
transvenom
transverbate
transverbation
transverberate
transverberation
transversal
transversale
transversalis
transversality
transversally
transversan
transversary
transverse
transversely
transverseness
transverser
transversion
transversive
transversocubital
transversomedial
transversospinal
transversovertical
transversum
transversus
transvert
transverter
transvest
transvestism
transvestite
transvestitism
transvolation
transwritten
Transylvanian
trant
tranter
trantlum
Tranzschelia
trap
Trapa
Trapaceae
trapaceous
trapball
trapes
trapezate
trapeze
trapezia
trapezial
trapezian
trapeziform
trapezing
trapeziometacarpal
trapezist
trapezium
trapezius
trapezohedral
trapezohedron
trapezoid
trapezoidal
trapezoidiform
trapfall
traphole
trapiferous
traplight
traplike
trapmaker
trapmaking
trappean
trapped
trapper
trapperlike
trappiness
trapping
trappingly
Trappist
trappist
Trappistine
trappoid
trappose
trappous
trappy
traprock
traps
trapshoot
trapshooter
trapshooting
trapstick
trapunto
trasformism
trash
trashery
trashify
trashily
trashiness
traship
trashless
trashrack
trashy
trass
Trastevere
Trasteverine
trasy
traulism
trauma
traumasthenia
traumatic
traumatically
traumaticin
traumaticine
traumatism
traumatize
traumatology
traumatonesis
traumatopnea
traumatopyra
traumatosis
traumatotactic
traumatotaxis
traumatropic
traumatropism
Trautvetteria
travail
travale
travally
travated
trave
travel
travelability
travelable
traveldom
traveled
traveler
traveleress
travelerlike
traveling
travellability
travellable
travelled
traveller
travelogue
traveloguer
traveltime
traversable
traversal
traversary
traverse
traversed
traversely
traverser
traversewise
traversework
traversing
traversion
travertin
travertine
travestier
travestiment
travesty
Travis
travis
travois
travoy
trawl
trawlboat
trawler
trawlerman
trawlnet
tray
trayful
traylike
treacher
treacherous
treacherously
treacherousness
treachery
treacle
treaclelike
treaclewort
treacliness
treacly
tread
treadboard
treader
treading
treadle
treadler
treadmill
treadwheel
treason
treasonable
treasonableness
treasonably
treasonful
treasonish
treasonist
treasonless
treasonmonger
treasonous
treasonously
treasonproof
treasurable
treasure
treasureless
treasurer
treasurership
treasuress
treasurous
treasury
treasuryship
treat
treatable
treatableness
treatably
treatee
treater
treating
treatise
treatiser
treatment
treator
treaty
treatyist
treatyite
treatyless
Trebellian
treble
trebleness
trebletree
trebly
trebuchet
trecentist
trechmannite
treckschuyt
Treculia
treddle
tredecile
tredille
tree
treebeard
treebine
treed
treefish
treeful
treehair
treehood
treeify
treeiness
treeless
treelessness
treelet
treelike
treeling
treemaker
treemaking
treeman
treen
treenail
treescape
treeship
treespeeler
treetop
treeward
treewards
treey
tref
trefgordd
trefle
trefoil
trefoiled
trefoillike
trefoilwise
tregadyne
tregerg
tregohm
trehala
trehalase
trehalose
treillage
trek
trekker
trekometer
trekpath
trellis
trellised
trellislike
trelliswork
Trema
Tremandra
Tremandraceae
tremandraceous
Trematoda
trematode
Trematodea
Trematodes
trematoid
Trematosaurus
tremble
tremblement
trembler
trembling
tremblingly
tremblingness
tremblor
trembly
Tremella
Tremellaceae
tremellaceous
Tremellales
tremelliform
tremelline
tremellineous
tremelloid
tremellose
tremendous
tremendously
tremendousness
tremetol
tremie
tremolando
tremolant
tremolist
tremolite
tremolitic
tremolo
tremor
tremorless
tremorlessly
tremulant
tremulate
tremulation
tremulous
tremulously
tremulousness
trenail
trench
trenchancy
trenchant
trenchantly
trenchantness
trenchboard
trenched
trencher
trencherless
trencherlike
trenchermaker
trenchermaking
trencherman
trencherside
trencherwise
trencherwoman
trenchful
trenchlet
trenchlike
trenchmaster
trenchmore
trenchward
trenchwise
trenchwork
trend
trendle
Trent
trental
Trentepohlia
Trentepohliaceae
trentepohliaceous
Trentine
Trenton
trepan
trepanation
trepang
trepanize
trepanner
trepanning
trepanningly
trephination
trephine
trephiner
trephocyte
trephone
trepid
trepidancy
trepidant
trepidate
trepidation
trepidatory
trepidity
trepidly
trepidness
Treponema
treponematous
treponemiasis
treponemiatic
treponemicidal
treponemicide
Trepostomata
trepostomatous
Treron
Treronidae
Treroninae
tresaiel
trespass
trespassage
trespasser
trespassory
tress
tressed
tressful
tressilate
tressilation
tressless
tresslet
tresslike
tresson
tressour
tressure
tressured
tressy
trest
trestle
trestletree
trestlewise
trestlework
trestling
tret
trevally
trevet
Trevor
trews
trewsman
Trey
trey
tri
triable
triableness
triace
triacetamide
triacetate
triacetonamine
triachenium
triacid
triacontaeterid
triacontane
triaconter
triact
triactinal
triactine
triad
triadelphous
Triadenum
triadic
triadical
triadically
triadism
triadist
triaene
triaenose
triage
triagonal
triakisicosahedral
triakisicosahedron
triakisoctahedral
triakisoctahedrid
triakisoctahedron
triakistetrahedral
triakistetrahedron
trial
trialate
trialism
trialist
triality
trialogue
triamid
triamide
triamine
triamino
triammonium
triamylose
triander
Triandria
triandrian
triandrous
triangle
triangled
triangler
triangleways
trianglewise
trianglework
Triangula
triangular
triangularity
triangularly
triangulate
triangulately
triangulation
triangulator
Triangulid
trianguloid
triangulopyramidal
triangulotriangular
Triangulum
triannual
triannulate
Trianon
Triantaphyllos
triantelope
trianthous
triapsal
triapsidal
triarch
triarchate
triarchy
triarctic
triarcuated
triareal
triarii
Triarthrus
triarticulate
Trias
Triassic
triaster
triatic
Triatoma
triatomic
triatomicity
triaxial
triaxon
triaxonian
triazane
triazin
triazine
triazo
triazoic
triazole
triazolic
tribade
tribadism
tribady
tribal
tribalism
tribalist
tribally
tribarred
tribase
tribasic
tribasicity
tribasilar
tribble
tribe
tribeless
tribelet
tribelike
tribesfolk
tribeship
tribesman
tribesmanship
tribespeople
tribeswoman
triblastic
triblet
triboelectric
triboelectricity
tribofluorescence
tribofluorescent
Tribolium
triboluminescence
triboluminescent
tribometer
Tribonema
Tribonemaceae
tribophosphorescence
tribophosphorescent
tribophosphoroscope
triborough
tribrac
tribrach
tribrachial
tribrachic
tribracteate
tribracteolate
tribromacetic
tribromide
tribromoethanol
tribromophenol
tribromphenate
tribromphenol
tribual
tribually
tribular
tribulate
tribulation
tribuloid
Tribulus
tribuna
tribunal
tribunate
tribune
tribuneship
tribunitial
tribunitian
tribunitiary
tribunitive
tributable
tributarily
tributariness
tributary
tribute
tributer
tributist
tributorian
tributyrin
trica
tricae
tricalcic
tricalcium
tricapsular
tricar
tricarballylic
tricarbimide
tricarbon
tricarboxylic
tricarinate
tricarinated
tricarpellary
tricarpellate
tricarpous
tricaudal
tricaudate
trice
tricellular
tricenarious
tricenarium
tricenary
tricennial
tricentenarian
tricentenary
tricentennial
tricentral
tricephal
tricephalic
tricephalous
tricephalus
triceps
Triceratops
triceria
tricerion
tricerium
trichatrophia
trichauxis
Trichechidae
trichechine
trichechodont
Trichechus
trichevron
trichi
trichia
trichiasis
Trichilia
Trichina
trichina
trichinae
trichinal
Trichinella
trichiniasis
trichiniferous
trichinization
trichinize
trichinoid
trichinopoly
trichinoscope
trichinoscopy
trichinosed
trichinosis
trichinotic
trichinous
trichite
trichitic
trichitis
trichiurid
Trichiuridae
trichiuroid
Trichiurus
trichloride
trichlormethane
trichloro
trichloroacetic
trichloroethylene
trichloromethane
trichloromethyl
trichobacteria
trichobezoar
trichoblast
trichobranchia
trichobranchiate
trichocarpous
trichocephaliasis
Trichocephalus
trichoclasia
trichoclasis
trichocyst
trichocystic
trichode
Trichoderma
Trichodesmium
Trichodontidae
trichoepithelioma
trichogen
trichogenous
trichoglossia
Trichoglossidae
Trichoglossinae
trichoglossine
Trichogramma
Trichogrammatidae
trichogyne
trichogynial
trichogynic
trichoid
Tricholaena
trichological
trichologist
trichology
Tricholoma
trichoma
Trichomanes
trichomaphyte
trichomatose
trichomatosis
trichomatous
trichome
trichomic
trichomonad
Trichomonadidae
Trichomonas
trichomoniasis
trichomycosis
trichonosus
trichopathic
trichopathy
trichophore
trichophoric
trichophyllous
trichophyte
trichophytia
trichophytic
Trichophyton
trichophytosis
Trichoplax
trichopore
trichopter
Trichoptera
trichoptera
trichopteran
trichopteron
trichopterous
trichopterygid
Trichopterygidae
trichord
trichorrhea
trichorrhexic
trichorrhexis
Trichosanthes
trichoschisis
trichosis
trichosporange
trichosporangial
trichosporangium
Trichosporum
trichostasis
Trichostema
trichostrongyle
trichostrongylid
Trichostrongylus
trichothallic
trichotillomania
trichotomic
trichotomism
trichotomist
trichotomize
trichotomous
trichotomously
trichotomy
trichroic
trichroism
trichromat
trichromate
trichromatic
trichromatism
trichromatist
trichrome
trichromic
trichronous
trichuriasis
Trichuris
trichy
Tricia
tricinium
tricipital
tricircular
trick
tricker
trickery
trickful
trickily
trickiness
tricking
trickingly
trickish
trickishly
trickishness
trickle
trickless
tricklet
tricklike
trickling
tricklingly
trickly
trickment
trickproof
tricksical
tricksily
tricksiness
tricksome
trickster
trickstering
trickstress
tricksy
tricktrack
tricky
triclad
Tricladida
triclinate
triclinia
triclinial
tricliniarch
tricliniary
triclinic
triclinium
triclinohedric
tricoccose
tricoccous
tricolette
tricolic
tricolon
tricolor
tricolored
tricolumnar
tricompound
triconch
Triconodon
triconodont
Triconodonta
triconodontid
triconodontoid
triconodonty
triconsonantal
triconsonantalism
tricophorous
tricorn
tricornered
tricornute
tricorporal
tricorporate
tricoryphean
tricosane
tricosanone
tricostate
tricosyl
tricosylic
tricot
tricotine
tricotyledonous
tricresol
tricrotic
tricrotism
tricrotous
tricrural
tricurvate
tricuspal
tricuspid
tricuspidal
tricuspidate
tricuspidated
tricussate
tricyanide
tricycle
tricyclene
tricycler
tricyclic
tricyclist
Tricyrtis
Tridacna
Tridacnidae
tridactyl
tridactylous
tridaily
triddler
tridecane
tridecene
tridecilateral
tridecoic
tridecyl
tridecylene
tridecylic
trident
tridental
tridentate
tridentated
tridentiferous
Tridentine
Tridentinian
tridepside
tridermic
tridiametral
tridiapason
tridigitate
tridimensional
tridimensionality
tridimensioned
tridiurnal
tridominium
tridrachm
triduan
triduum
tridymite
tridynamous
tried
triedly
trielaidin
triene
triennial
trienniality
triennially
triennium
triens
triental
Trientalis
triequal
trier
trierarch
trierarchal
trierarchic
trierarchy
trierucin
trieteric
trieterics
triethanolamine
triethyl
triethylamine
triethylstibine
trifa
trifacial
trifarious
trifasciated
triferous
trifid
trifilar
trifistulary
triflagellate
trifle
trifledom
trifler
triflet
trifling
triflingly
triflingness
trifloral
triflorate
triflorous
trifluoride
trifocal
trifoil
trifold
trifoliate
trifoliated
trifoliolate
trifoliosis
Trifolium
trifolium
trifoly
triforial
triforium
triform
triformed
triformin
triformity
triformous
trifoveolate
trifuran
trifurcal
trifurcate
trifurcation
trig
trigamist
trigamous
trigamy
trigeminal
trigeminous
trigeneric
trigesimal
trigger
triggered
triggerfish
triggerless
trigintal
trigintennial
Trigla
triglandular
triglid
Triglidae
triglochid
Triglochin
triglochin
triglot
trigly
triglyceride
triglyceryl
triglyph
triglyphal
triglyphed
triglyphic
triglyphical
trigness
trigon
Trigona
trigonal
trigonally
trigone
Trigonella
trigonelline
trigoneutic
trigoneutism
Trigonia
Trigoniaceae
trigoniacean
trigoniaceous
trigonic
trigonid
Trigoniidae
trigonite
trigonitis
trigonocephalic
trigonocephalous
Trigonocephalus
trigonocephaly
trigonocerous
trigonododecahedron
trigonodont
trigonoid
trigonometer
trigonometric
trigonometrical
trigonometrician
trigonometry
trigonon
trigonotype
trigonous
trigonum
trigram
trigrammatic
trigrammatism
trigrammic
trigraph
trigraphic
triguttulate
trigyn
Trigynia
trigynian
trigynous
trihalide
trihedral
trihedron
trihemeral
trihemimer
trihemimeral
trihemimeris
trihemiobol
trihemiobolion
trihemitetartemorion
trihoral
trihourly
trihybrid
trihydrate
trihydrated
trihydric
trihydride
trihydrol
trihydroxy
trihypostatic
trijugate
trijugous
trijunction
trikaya
trike
triker
trikeria
trikerion
triketo
triketone
trikir
trilabe
trilabiate
trilamellar
trilamellated
trilaminar
trilaminate
trilarcenous
trilateral
trilaterality
trilaterally
trilateralness
trilaurin
trilby
trilemma
trilinear
trilineate
trilineated
trilingual
trilinguar
trilinolate
trilinoleate
trilinolenate
trilinolenin
Trilisa
trilit
trilite
triliteral
triliteralism
triliterality
triliterally
triliteralness
trilith
trilithic
trilithon
trill
trillachan
trillet
trilli
Trilliaceae
trilliaceous
trillibub
trilliin
trilling
trillion
trillionaire
trillionize
trillionth
Trillium
trillium
trillo
trilobate
trilobated
trilobation
trilobe
trilobed
Trilobita
trilobite
trilobitic
trilocular
triloculate
trilogic
trilogical
trilogist
trilogy
Trilophodon
trilophodont
triluminar
triluminous
trim
trimacer
trimacular
trimargarate
trimargarin
trimastigate
trimellitic
trimembral
trimensual
trimer
Trimera
trimercuric
Trimeresurus
trimeric
trimeride
trimerite
trimerization
trimerous
trimesic
trimesinic
trimesitic
trimesitinic
trimester
trimestral
trimestrial
trimesyl
trimetalism
trimetallic
trimeter
trimethoxy
trimethyl
trimethylacetic
trimethylamine
trimethylbenzene
trimethylene
trimethylmethane
trimethylstibine
trimetric
trimetrical
trimetrogon
trimly
trimmer
trimming
trimmingly
trimness
trimodal
trimodality
trimolecular
trimonthly
trimoric
trimorph
trimorphic
trimorphism
trimorphous
trimotor
trimotored
trimstone
trimtram
trimuscular
trimyristate
trimyristin
trin
Trinacrian
trinal
trinality
trinalize
trinary
trinational
trindle
trine
trinely
trinervate
trinerve
trinerved
trineural
Tringa
tringine
tringle
tringoid
Trinidadian
trinidado
Trinil
Trinitarian
trinitarian
Trinitarianism
trinitrate
trinitration
trinitride
trinitrin
trinitro
trinitrocarbolic
trinitrocellulose
trinitrocresol
trinitroglycerin
trinitromethane
trinitrophenol
trinitroresorcin
trinitrotoluene
trinitroxylene
trinitroxylol
Trinity
trinity
trinityhood
trink
trinkerman
trinket
trinketer
trinketry
trinkety
trinkle
trinklement
trinklet
trinkums
Trinobantes
trinoctial
trinodal
trinode
trinodine
trinol
trinomial
trinomialism
trinomialist
trinomiality
trinomially
trinopticon
Trinorantum
Trinovant
Trinovantes
trintle
trinucleate
Trinucleus
Trio
trio
triobol
triobolon
trioctile
triocular
triode
triodia
triodion
Triodon
Triodontes
Triodontidae
triodontoid
Triodontoidea
Triodontoidei
Triodontophorus
Trioecia
trioecious
trioeciously
trioecism
triolcous
triole
trioleate
triolefin
trioleic
triolein
triolet
triology
Trionychidae
trionychoid
Trionychoideachid
trionychoidean
trionym
trionymal
Trionyx
trioperculate
Triopidae
Triops
trior
triorchis
triorchism
triorthogonal
triose
Triosteum
triovulate
trioxazine
trioxide
trioxymethylene
triozonide
trip
tripal
tripaleolate
tripalmitate
tripalmitin
tripara
tripart
triparted
tripartedly
tripartible
tripartient
tripartite
tripartitely
tripartition
tripaschal
tripe
tripedal
tripel
tripelike
tripeman
tripemonger
tripennate
tripenny
tripeptide
tripersonal
tripersonalism
tripersonalist
tripersonality
tripersonally
tripery
tripeshop
tripestone
tripetaloid
tripetalous
tripewife
tripewoman
triphammer
triphane
triphase
triphaser
Triphasia
triphasic
triphenyl
triphenylamine
triphenylated
triphenylcarbinol
triphenylmethane
triphenylmethyl
triphenylphosphine
triphibian
triphibious
triphony
Triphora
triphthong
triphyletic
triphyline
triphylite
triphyllous
Triphysite
tripinnate
tripinnated
tripinnately
tripinnatifid
tripinnatisect
Tripitaka
triplane
Triplaris
triplasian
triplasic
triple
tripleback
triplefold
triplegia
tripleness
triplet
tripletail
tripletree
triplewise
triplex
triplexity
triplicate
triplication
triplicative
triplicature
Triplice
Triplicist
triplicity
triplicostate
tripliform
triplinerved
tripling
triplite
triploblastic
triplocaulescent
triplocaulous
Triplochitonaceae
triploid
triploidic
triploidite
triploidy
triplopia
triplopy
triplum
triplumbic
triply
tripmadam
tripod
tripodal
tripodial
tripodian
tripodic
tripodical
tripody
tripointed
tripolar
tripoli
Tripoline
tripoline
Tripolitan
tripolite
tripos
tripotassium
trippant
tripper
trippet
tripping
trippingly
trippingness
trippist
tripple
trippler
Tripsacum
tripsill
tripsis
tripsome
tripsomely
triptane
tripterous
triptote
triptych
triptyque
tripudial
tripudiant
tripudiary
tripudiate
tripudiation
tripudist
tripudium
tripunctal
tripunctate
tripy
Tripylaea
tripylaean
Tripylarian
tripylarian
tripyrenous
triquadrantal
triquetra
triquetral
triquetric
triquetrous
triquetrously
triquetrum
triquinate
triquinoyl
triradial
triradially
triradiate
triradiated
triradiately
triradiation
Triratna
trirectangular
triregnum
trireme
trirhombohedral
trirhomboidal
triricinolein
trisaccharide
trisaccharose
trisacramentarian
Trisagion
trisalt
trisazo
trisceptral
trisect
trisected
trisection
trisector
trisectrix
triseme
trisemic
trisensory
trisepalous
triseptate
triserial
triserially
triseriate
triseriatim
trisetose
Trisetum
trishna
trisilane
trisilicane
trisilicate
trisilicic
trisinuate
trisinuated
triskele
triskelion
trismegist
trismegistic
trismic
trismus
trisoctahedral
trisoctahedron
trisodium
trisome
trisomic
trisomy
trisonant
Trisotropis
trispast
trispaston
trispermous
trispinose
trisplanchnic
trisporic
trisporous
trisquare
trist
tristachyous
Tristam
Tristan
Tristania
tristate
tristearate
tristearin
tristeness
tristetrahedron
tristeza
tristful
tristfully
tristfulness
tristich
Tristichaceae
tristichic
tristichous
tristigmatic
tristigmatose
tristiloquy
tristisonous
Tristram
tristylous
trisubstituted
trisubstitution
trisul
trisula
trisulcate
trisulcated
trisulphate
trisulphide
trisulphone
trisulphonic
trisulphoxide
trisylabic
trisyllabical
trisyllabically
trisyllabism
trisyllabity
trisyllable
tritactic
tritagonist
tritangent
tritangential
tritanope
tritanopia
tritanopic
tritaph
trite
Triteleia
tritely
tritemorion
tritencephalon
triteness
triternate
triternately
triterpene
tritetartemorion
tritheism
tritheist
tritheistic
tritheistical
tritheite
tritheocracy
trithing
trithioaldehyde
trithiocarbonate
trithiocarbonic
trithionate
trithionic
Trithrinax
tritical
triticality
tritically
triticalness
triticeous
triticeum
triticin
triticism
triticoid
Triticum
triticum
tritish
tritium
tritocerebral
tritocerebrum
tritocone
tritoconid
Tritogeneia
tritolo
Tritoma
tritomite
Triton
triton
tritonal
tritonality
tritone
Tritoness
Tritonia
Tritonic
Tritonidae
tritonoid
tritonous
tritonymph
tritonymphal
tritopatores
tritopine
tritor
tritoral
tritorium
tritoxide
tritozooid
tritriacontane
trittichan
tritubercular
Trituberculata
trituberculism
trituberculy
triturable
tritural
triturate
trituration
triturator
triturature
triturium
Triturus
trityl
Tritylodon
Triumfetta
Triumph
triumph
triumphal
triumphance
triumphancy
triumphant
triumphantly
triumphator
triumpher
triumphing
triumphwise
triumvir
triumviral
triumvirate
triumviri
triumvirship
triunal
triune
triungulin
triunification
triunion
triunitarian
triunity
triunsaturated
triurid
Triuridaceae
Triuridales
Triuris
trivalence
trivalency
trivalent
trivalerin
trivalve
trivalvular
trivant
trivantly
trivariant
triverbal
triverbial
trivet
trivetwise
trivia
trivial
trivialism
trivialist
triviality
trivialize
trivially
trivialness
trivirga
trivirgate
trivium
trivoltine
trivvet
triweekly
Trix
Trixie
Trixy
trizoic
trizomal
trizonal
trizone
Trizonia
Troad
troat
troca
trocaical
trocar
Trochaic
trochaic
trochaicality
trochal
trochalopod
Trochalopoda
trochalopodous
trochanter
trochanteric
trochanterion
trochantin
trochantinian
trochart
trochate
troche
trocheameter
trochee
trocheeize
trochelminth
Trochelminthes
trochi
trochid
Trochidae
trochiferous
trochiform
Trochila
Trochili
trochili
trochilic
trochilics
trochilidae
trochilidine
trochilidist
trochiline
trochilopodous
Trochilus
trochilus
troching
trochiscation
trochiscus
trochite
trochitic
Trochius
trochlea
trochlear
trochleariform
trochlearis
trochleary
trochleate
trochleiform
trochocephalia
trochocephalic
trochocephalus
trochocephaly
Trochodendraceae
trochodendraceous
Trochodendron
trochoid
trochoidal
trochoidally
trochoides
trochometer
trochophore
Trochosphaera
Trochosphaerida
trochosphere
trochospherical
Trochozoa
trochozoic
trochozoon
Trochus
trochus
trock
troco
troctolite
trod
trodden
trode
troegerite
Troezenian
troft
trog
trogger
troggin
troglodytal
troglodyte
Troglodytes
troglodytic
troglodytical
Troglodytidae
Troglodytinae
troglodytish
troglodytism
trogon
Trogones
Trogonidae
Trogoniformes
trogonoid
trogs
trogue
Troiades
Troic
troika
troilite
Trojan
troke
troker
troll
trolldom
trolleite
troller
trolley
trolleyer
trolleyful
trolleyman
trollflower
trollimog
trolling
Trollius
trollman
trollol
trollop
Trollopean
Trollopeanism
trollopish
trollops
trollopy
trolly
tromba
trombe
trombiculid
trombidiasis
Trombidiidae
Trombidium
trombone
trombonist
trombony
trommel
tromometer
tromometric
tromometrical
tromometry
tromp
trompe
trompil
trompillo
tromple
tron
trona
tronador
tronage
tronc
trondhjemite
trone
troner
troolie
troop
trooper
trooperess
troopfowl
troopship
troopwise
troostite
troostitic
troot
tropacocaine
tropaeolaceae
tropaeolaceous
tropaeolin
Tropaeolum
tropaion
tropal
troparia
troparion
tropary
tropate
trope
tropeic
tropeine
troper
tropesis
trophaea
trophaeum
trophal
trophallactic
trophallaxis
trophectoderm
trophedema
trophema
trophesial
trophesy
trophi
trophic
trophical
trophically
trophicity
trophied
Trophis
trophism
trophobiont
trophobiosis
trophobiotic
trophoblast
trophoblastic
trophochromatin
trophocyte
trophoderm
trophodisc
trophodynamic
trophodynamics
trophogenesis
trophogenic
trophogeny
trophology
trophonema
trophoneurosis
trophoneurotic
Trophonian
trophonucleus
trophopathy
trophophore
trophophorous
trophophyte
trophoplasm
trophoplasmatic
trophoplasmic
trophoplast
trophosomal
trophosome
trophosperm
trophosphere
trophospongia
trophospongial
trophospongium
trophospore
trophotaxis
trophotherapy
trophothylax
trophotropic
trophotropism
trophozoite
trophozooid
trophy
trophyless
trophywort
tropic
tropical
Tropicalia
Tropicalian
tropicality
tropicalization
tropicalize
tropically
tropicopolitan
tropidine
Tropidoleptus
tropine
tropism
tropismatic
tropist
tropistic
tropocaine
tropologic
tropological
tropologically
tropologize
tropology
tropometer
tropopause
tropophil
tropophilous
tropophyte
tropophytic
troposphere
tropostereoscope
tropoyl
troptometer
tropyl
trostera
trot
trotcozy
troth
trothful
trothless
trothlike
trothplight
trotlet
trotline
trotol
trotter
trottie
trottles
trottoir
trottoired
trotty
trotyl
troubadour
troubadourish
troubadourism
troubadourist
trouble
troubledly
troubledness
troublemaker
troublemaking
troublement
troubleproof
troubler
troublesome
troublesomely
troublesomeness
troubling
troublingly
troublous
troublously
troublousness
troubly
trough
troughful
troughing
troughlike
troughster
troughway
troughwise
troughy
trounce
trouncer
troupand
troupe
trouper
troupial
trouse
trouser
trouserdom
trousered
trouserettes
trouserian
trousering
trouserless
trousers
trousseau
trousseaux
trout
troutbird
trouter
troutflower
troutful
troutiness
troutless
troutlet
troutlike
trouty
trouvere
trouveur
trove
troveless
trover
trow
trowel
trowelbeak
troweler
trowelful
trowelman
trowing
trowlesworthite
trowman
trowth
Troy
troy
Troynovant
Troytown
truancy
truandise
truant
truantcy
truantism
truantlike
truantly
truantness
truantry
truantship
trub
trubu
truce
trucebreaker
trucebreaking
truceless
trucemaker
trucemaking
trucial
trucidation
truck
truckage
trucker
truckful
trucking
truckle
truckler
trucklike
truckling
trucklingly
truckload
truckman
truckmaster
trucks
truckster
truckway
truculence
truculency
truculent
truculental
truculently
truculentness
truddo
trudellite
trudge
trudgen
trudger
Trudy
true
trueborn
truebred
truehearted
trueheartedly
trueheartedness
truelike
truelove
trueness
truepenny
truer
truff
truffle
truffled
trufflelike
truffler
trufflesque
trug
truish
truism
truismatic
truistic
truistical
trull
Trullan
truller
trullization
trullo
truly
trumbash
trummel
trump
trumper
trumperiness
trumpery
trumpet
trumpetbush
trumpeter
trumpeting
trumpetless
trumpetlike
trumpetry
trumpetweed
trumpetwood
trumpety
trumph
trumpie
trumpless
trumplike
trun
truncage
truncal
truncate
truncated
Truncatella
Truncatellidae
truncately
truncation
truncator
truncatorotund
truncatosinuate
truncature
trunch
trunched
truncheon
truncheoned
truncher
trunchman
trundle
trundlehead
trundler
trundleshot
trundletail
trundling
trunk
trunkback
trunked
trunkfish
trunkful
trunking
trunkless
trunkmaker
trunknose
trunkway
trunkwork
trunnel
trunnion
trunnioned
trunnionless
trush
trusion
truss
trussed
trussell
trusser
trussing
trussmaker
trussmaking
trusswork
trust
trustability
trustable
trustableness
trustably
trustee
trusteeism
trusteeship
trusten
truster
trustful
trustfully
trustfulness
trustification
trustify
trustihood
trustily
trustiness
trusting
trustingly
trustingness
trustle
trustless
trustlessly
trustlessness
trustman
trustmonger
trustwoman
trustworthily
trustworthiness
trustworthy
trusty
truth
truthable
truthful
truthfully
truthfulness
truthify
truthiness
truthless
truthlessly
truthlessness
truthlike
truthlikeness
truthsman
truthteller
truthtelling
truthy
Trutta
truttaceous
truvat
truxillic
truxilline
try
trygon
Trygonidae
tryhouse
Trying
trying
tryingly
tryingness
tryma
tryout
tryp
trypa
trypan
trypaneid
Trypaneidae
trypanocidal
trypanocide
trypanolysin
trypanolysis
trypanolytic
Trypanosoma
trypanosoma
trypanosomacidal
trypanosomacide
trypanosomal
trypanosomatic
Trypanosomatidae
trypanosomatosis
trypanosomatous
trypanosome
trypanosomiasis
trypanosomic
Tryparsamide
Trypeta
trypetid
Trypetidae
Tryphena
Tryphosa
trypiate
trypograph
trypographic
trypsin
trypsinize
trypsinogen
tryptase
tryptic
tryptogen
tryptone
tryptonize
tryptophan
trysail
tryst
tryster
trysting
tryt
tryworks
tsadik
tsamba
tsantsa
tsar
tsardom
tsarevitch
tsarina
tsaritza
tsarship
tsatlee
Tsattine
tscharik
tscheffkinite
Tscherkess
tsere
tsessebe
tsetse
Tshi
tsia
Tsiltaden
Tsimshian
tsine
tsingtauite
tsiology
Tsoneca
Tsonecan
tst
tsuba
tsubo
Tsuga
Tsuma
tsumebite
tsun
tsunami
tsungtu
Tsutsutsi
tu
tua
Tualati
Tuamotu
Tuamotuan
Tuan
tuan
Tuareg
tuarn
tuart
tuatara
tuatera
tuath
tub
Tuba
tuba
tubae
tubage
tubal
tubaphone
tubar
tubate
tubatoxin
Tubatulabal
tubba
tubbable
tubbal
tubbeck
tubber
tubbie
tubbiness
tubbing
tubbish
tubboe
tubby
tube
tubeflower
tubeform
tubeful
tubehead
tubehearted
tubeless
tubelet
tubelike
tubemaker
tubemaking
tubeman
tuber
Tuberaceae
tuberaceous
Tuberales
tuberation
tubercle
tubercled
tuberclelike
tubercula
tubercular
Tubercularia
Tuberculariaceae
tuberculariaceous
tubercularization
tubercularize
tubercularly
tubercularness
tuberculate
tuberculated
tuberculatedly
tuberculately
tuberculation
tuberculatogibbous
tuberculatonodose
tuberculatoradiate
tuberculatospinous
tubercule
tuberculed
tuberculid
tuberculide
tuberculiferous
tuberculiform
tuberculin
tuberculinic
tuberculinization
tuberculinize
tuberculization
tuberculize
tuberculocele
tuberculocidin
tuberculoderma
tuberculoid
tuberculoma
tuberculomania
tuberculomata
tuberculophobia
tuberculoprotein
tuberculose
tuberculosectorial
tuberculosed
tuberculosis
tuberculotherapist
tuberculotherapy
tuberculotoxin
tuberculotrophic
tuberculous
tuberculously
tuberculousness
tuberculum
tuberiferous
tuberiform
tuberin
tuberization
tuberize
tuberless
tuberoid
tuberose
tuberosity
tuberous
tuberously
tuberousness
tubesmith
tubework
tubeworks
tubfish
tubful
tubicen
tubicinate
tubicination
Tubicola
Tubicolae
tubicolar
tubicolous
tubicorn
tubicornous
tubifacient
tubifer
tubiferous
Tubifex
Tubificidae
Tubiflorales
tubiflorous
tubiform
tubig
tubik
tubilingual
Tubinares
tubinarial
tubinarine
tubing
Tubingen
tubiparous
Tubipora
tubipore
tubiporid
Tubiporidae
tubiporoid
tubiporous
tublet
tublike
tubmaker
tubmaking
tubman
tuboabdominal
tubocurarine
tubolabellate
tuboligamentous
tuboovarial
tuboovarian
tuboperitoneal
tuborrhea
tubotympanal
tubovaginal
tubular
Tubularia
tubularia
Tubulariae
tubularian
Tubularida
tubularidan
Tubulariidae
tubularity
tubularly
tubulate
tubulated
tubulation
tubulator
tubulature
tubule
tubulet
tubuli
tubulibranch
tubulibranchian
Tubulibranchiata
tubulibranchiate
Tubulidentata
tubulidentate
Tubulifera
tubuliferan
tubuliferous
tubulifloral
tubuliflorous
tubuliform
Tubulipora
tubulipore
tubuliporid
Tubuliporidae
tubuliporoid
tubulization
tubulodermoid
tubuloracemose
tubulosaccular
tubulose
tubulostriato
tubulous
tubulously
tubulousness
tubulure
tubulus
tubwoman
Tucana
Tucanae
tucandera
Tucano
tuchit
tuchun
tuchunate
tuchunism
tuchunize
tuck
Tuckahoe
tuckahoe
tucker
tuckermanity
tucket
tucking
tuckner
tuckshop
tucktoo
tucky
tucum
tucuma
tucuman
Tucuna
tudel
Tudesque
Tudor
Tudoresque
tue
tueiron
Tuesday
tufa
tufaceous
tufalike
tufan
tuff
tuffaceous
tuffet
tuffing
tuft
tuftaffeta
tufted
tufter
tufthunter
tufthunting
tuftily
tufting
tuftlet
tufty
tug
tugboat
tugboatman
tugger
tuggery
tugging
tuggingly
tughra
tugless
tuglike
tugman
tugrik
tugui
tugurium
tui
tuik
tuille
tuillette
tuilyie
tuism
tuition
tuitional
tuitionary
tuitive
tuke
tukra
Tukuler
Tukulor
tula
Tulalip
tulare
tularemia
tulasi
Tulbaghia
tulchan
tulchin
tule
tuliac
tulip
Tulipa
tulipflower
tulipiferous
tulipist
tuliplike
tulipomania
tulipomaniac
tulipwood
tulipy
tulisan
Tulkepaia
tulle
Tullian
tullibee
Tulostoma
tulsi
Tulu
tulwar
tum
tumasha
tumatakuru
tumatukuru
tumbak
tumbester
tumble
tumblebug
tumbled
tumbledung
tumbler
tumblerful
tumblerlike
tumblerwise
tumbleweed
tumblification
tumbling
tumblingly
tumbly
Tumboa
tumbrel
tume
tumefacient
tumefaction
tumefy
tumescence
tumescent
tumid
tumidity
tumidly
tumidness
Tumion
tummals
tummel
tummer
tummock
tummy
tumor
tumored
tumorlike
tumorous
tump
tumpline
tumtum
tumular
tumulary
tumulate
tumulation
tumuli
tumulose
tumulosity
tumulous
tumult
tumultuarily
tumultuariness
tumultuary
tumultuate
tumultuation
tumultuous
tumultuously
tumultuousness
tumulus
Tumupasa
tun
Tuna
tuna
tunable
tunableness
tunably
tunbellied
tunbelly
tunca
tund
tundagslatta
tunder
tundish
tundra
tundun
tune
Tunebo
tuned
tuneful
tunefully
tunefulness
tuneless
tunelessly
tunelessness
tunemaker
tunemaking
tuner
tunesome
tunester
tunful
tung
Tunga
Tungan
tungate
tungo
tungstate
tungsten
tungstenic
tungsteniferous
tungstenite
tungstic
tungstite
tungstosilicate
tungstosilicic
Tungus
Tungusian
Tungusic
tunhoof
tunic
Tunica
Tunican
tunicary
Tunicata
tunicate
tunicated
tunicin
tunicked
tunicle
tunicless
tuniness
tuning
tunish
Tunisian
tunist
tunk
Tunker
tunket
tunlike
tunmoot
tunna
tunnel
tunneled
tunneler
tunneling
tunnelist
tunnelite
tunnellike
tunnelly
tunnelmaker
tunnelmaking
tunnelman
tunnelway
tunner
tunnery
Tunnit
tunnland
tunnor
tunny
tuno
tunu
tuny
tup
Tupaia
Tupaiidae
tupakihi
tupanship
tupara
tupek
tupelo
Tupi
Tupian
tupik
Tupinamba
Tupinaqui
tupman
tuppence
tuppenny
Tupperian
Tupperish
Tupperism
Tupperize
tupuna
tuque
tur
turacin
Turacus
Turanian
Turanianism
Turanism
turanose
turb
turban
turbaned
turbanesque
turbanette
turbanless
turbanlike
turbantop
turbanwise
turbary
turbeh
Turbellaria
turbellarian
turbellariform
turbescency
turbid
turbidimeter
turbidimetric
turbidimetry
turbidity
turbidly
turbidness
turbinaceous
turbinage
turbinal
turbinate
turbinated
turbination
turbinatoconcave
turbinatocylindrical
turbinatoglobose
turbinatostipitate
turbine
turbinectomy
turbined
turbinelike
Turbinella
Turbinellidae
turbinelloid
turbiner
turbines
Turbinidae
turbiniform
turbinoid
turbinotome
turbinotomy
turbit
turbith
turbitteen
Turbo
turbo
turboalternator
turboblower
turbocompressor
turbodynamo
turboexciter
turbofan
turbogenerator
turbomachine
turbomotor
turbopump
turbosupercharge
turbosupercharger
turbot
turbotlike
turboventilator
turbulence
turbulency
turbulent
turbulently
turbulentness
Turcian
Turcic
Turcification
Turcism
Turcize
Turco
turco
Turcoman
Turcophilism
turcopole
turcopolier
turd
Turdetan
Turdidae
turdiform
Turdinae
turdine
turdoid
Turdus
tureen
tureenful
turf
turfage
turfdom
turfed
turfen
turfiness
turfing
turfite
turfless
turflike
turfman
turfwise
turfy
turgency
turgent
turgently
turgesce
turgescence
turgescency
turgescent
turgescible
turgid
turgidity
turgidly
turgidness
turgite
turgoid
turgor
turgy
Turi
turicata
turio
turion
turioniferous
turjaite
turjite
Turk
turk
Turkana
Turkdom
Turkeer
turken
Turkery
Turkess
Turkey
turkey
turkeyback
turkeyberry
turkeybush
Turkeydom
turkeyfoot
Turkeyism
turkeylike
Turki
Turkic
Turkicize
Turkification
Turkify
turkis
Turkish
Turkishly
Turkishness
Turkism
Turkize
turkle
Turklike
Turkman
Turkmen
Turkmenian
Turkologist
Turkology
Turkoman
Turkomania
Turkomanic
Turkomanize
Turkophil
Turkophile
Turkophilia
Turkophilism
Turkophobe
Turkophobist
turlough
Turlupin
turm
turma
turment
turmeric
turmit
turmoil
turmoiler
turn
turnable
turnabout
turnagain
turnaround
turnaway
turnback
turnbout
turnbuckle
turncap
turncoat
turncoatism
turncock
turndown
turndun
turned
turnel
turner
Turnera
Turneraceae
turneraceous
Turneresque
Turnerian
Turnerism
turnerite
turnery
turney
turngate
turnhall
Turnhalle
Turnices
Turnicidae
turnicine
Turnicomorphae
turnicomorphic
turning
turningness
turnip
turniplike
turnipweed
turnipwise
turnipwood
turnipy
Turnix
turnix
turnkey
turnoff
turnout
turnover
turnpike
turnpiker
turnpin
turnplate
turnplow
turnrow
turns
turnscrew
turnsheet
turnskin
turnsole
turnspit
turnstile
turnstone
turntable
turntail
turnup
turnwrest
turnwrist
Turonian
turp
turpantineweed
turpentine
turpentineweed
turpentinic
turpeth
turpethin
turpid
turpidly
turpitude
turps
turquoise
turquoiseberry
turquoiselike
turr
turret
turreted
turrethead
turretlike
turrical
turricle
turricula
turriculae
turricular
turriculate
turriferous
turriform
turrigerous
Turrilepas
turrilite
Turrilites
turriliticone
Turrilitidae
Turritella
turritella
turritellid
Turritellidae
turritelloid
turse
Tursenoi
Tursha
tursio
Tursiops
Turtan
turtle
turtleback
turtlebloom
turtledom
turtledove
turtlehead
turtleize
turtlelike
turtler
turtlet
turtling
turtosa
tururi
turus
Turveydrop
Turveydropdom
Turveydropian
turwar
Tusayan
Tuscan
Tuscanism
Tuscanize
Tuscanlike
Tuscany
Tuscarora
tusche
Tusculan
Tush
tush
tushed
Tushepaw
tusher
tushery
tusk
tuskar
tusked
Tuskegee
tusker
tuskish
tuskless
tusklike
tuskwise
tusky
tussah
tussal
tusser
tussicular
Tussilago
tussis
tussive
tussle
tussock
tussocked
tussocker
tussocky
tussore
tussur
tut
tutania
tutball
tute
tutee
tutela
tutelage
tutelar
tutelary
Tutelo
tutenag
tuth
tutin
tutiorism
tutiorist
tutly
tutman
tutor
tutorage
tutorer
tutoress
tutorhood
tutorial
tutorially
tutoriate
tutorism
tutorization
tutorize
tutorless
tutorly
tutorship
tutory
tutoyer
tutress
tutrice
tutrix
tuts
tutsan
tutster
tutti
tuttiman
tutty
tutu
tutulus
Tututni
tutwork
tutworker
tutworkman
tuwi
tux
tuxedo
tuyere
Tuyuneiri
tuza
Tuzla
tuzzle
twa
Twaddell
twaddle
twaddledom
twaddleize
twaddlement
twaddlemonger
twaddler
twaddlesome
twaddling
twaddlingly
twaddly
twaddy
twae
twaesome
twafauld
twagger
twain
twaite
twal
twale
twalpenny
twalpennyworth
twalt
Twana
twang
twanger
twanginess
twangle
twangler
twangy
twank
twanker
twanking
twankingly
twankle
twanky
twant
twarly
twas
twasome
twat
twatchel
twatterlight
twattle
twattler
twattling
tway
twayblade
twazzy
tweag
tweak
tweaker
tweaky
twee
tweed
tweeded
tweedle
tweedledee
tweedledum
tweedy
tweeg
tweel
tween
tweenlight
tweeny
tweesh
tweesht
tweest
tweet
tweeter
tweeze
tweezer
tweezers
tweil
twelfhynde
twelfhyndeman
twelfth
twelfthly
Twelfthtide
twelve
twelvefold
twelvehynde
twelvehyndeman
twelvemo
twelvemonth
twelvepence
twelvepenny
twelvescore
twentieth
twentiethly
twenty
twentyfold
twentymo
twere
twerp
Twi
twibil
twibilled
twice
twicer
twicet
twichild
twick
twiddle
twiddler
twiddling
twiddly
twifoil
twifold
twifoldly
twig
twigful
twigged
twiggen
twigger
twiggy
twigless
twiglet
twiglike
twigsome
twigwithy
twilight
twilightless
twilightlike
twilighty
twilit
twill
twilled
twiller
twilling
twilly
twilt
twin
twinable
twinberry
twinborn
twindle
twine
twineable
twinebush
twineless
twinelike
twinemaker
twinemaking
twiner
twinflower
twinfold
twinge
twingle
twinhood
twiningly
twinism
twink
twinkle
twinkledum
twinkleproof
twinkler
twinkles
twinkless
twinkling
twinklingly
twinkly
twinleaf
twinlike
twinling
twinly
twinned
twinner
twinness
twinning
twinship
twinsomeness
twinter
twiny
twire
twirk
twirl
twirler
twirligig
twirly
twiscar
twisel
twist
twistable
twisted
twistedly
twistened
twister
twisterer
twistical
twistification
twistily
twistiness
twisting
twistingly
twistiways
twistiwise
twistle
twistless
twisty
twit
twitch
twitchel
twitcheling
twitcher
twitchet
twitchety
twitchfire
twitchily
twitchiness
twitchingly
twitchy
twite
twitlark
twitten
twitter
twitteration
twitterboned
twitterer
twittering
twitteringly
twitterly
twittery
twittingly
twitty
twixt
twixtbrain
twizzened
twizzle
two
twodecker
twofold
twofoldly
twofoldness
twoling
twoness
twopence
twopenny
twosome
twyblade
twyhynde
Tybalt
Tyburn
Tyburnian
Tyche
tychism
tychite
Tychonian
Tychonic
tychoparthenogenesis
tychopotamic
tycoon
tycoonate
tyddyn
tydie
tye
tyee
tyg
Tyigh
tying
tyke
tyken
tykhana
tyking
tylarus
tyleberry
Tylenchus
Tyler
Tylerism
Tylerite
Tylerize
tylion
tyloma
tylopod
Tylopoda
tylopodous
Tylosaurus
tylose
tylosis
tylosteresis
Tylostoma
Tylostomaceae
tylostylar
tylostyle
tylostylote
tylostylus
Tylosurus
tylotate
tylote
tylotic
tylotoxea
tylotoxeate
tylotus
tylus
tymbalon
tymp
tympan
tympana
tympanal
tympanectomy
tympani
tympanic
tympanichord
tympanichordal
tympanicity
tympaniform
tympaning
tympanism
tympanist
tympanites
tympanitic
tympanitis
tympanocervical
tympanohyal
tympanomalleal
tympanomandibular
tympanomastoid
tympanomaxillary
tympanon
tympanoperiotic
tympanosis
tympanosquamosal
tympanostapedial
tympanotemporal
tympanotomy
Tympanuchus
tympanum
tympany
tynd
Tyndallization
Tyndallize
tyndallmeter
Tynwald
typal
typarchical
type
typecast
Typees
typeholder
typer
typescript
typeset
typesetter
typesetting
typewrite
typewriter
typewriting
Typha
Typhaceae
typhaceous
typhemia
typhia
typhic
typhinia
typhization
typhlatonia
typhlatony
typhlectasis
typhlectomy
typhlenteritis
typhlitic
typhlitis
typhloalbuminuria
typhlocele
typhloempyema
typhloenteritis
typhlohepatitis
typhlolexia
typhlolithiasis
typhlology
typhlomegaly
Typhlomolge
typhlon
typhlopexia
typhlopexy
typhlophile
typhlopid
Typhlopidae
Typhlops
typhloptosis
typhlosis
typhlosolar
typhlosole
typhlostenosis
typhlostomy
typhlotomy
typhobacillosis
Typhoean
typhoemia
typhogenic
typhoid
typhoidal
typhoidin
typhoidlike
typholysin
typhomalaria
typhomalarial
typhomania
typhonia
Typhonian
Typhonic
typhonic
typhoon
typhoonish
typhopneumonia
typhose
typhosepsis
typhosis
typhotoxine
typhous
Typhula
typhus
typic
typica
typical
typicality
typically
typicalness
typicon
typicum
typification
typifier
typify
typist
typo
typobar
typocosmy
typographer
typographia
typographic
typographical
typographically
typographist
typography
typolithographic
typolithography
typologic
typological
typologically
typologist
typology
typomania
typometry
typonym
typonymal
typonymic
typonymous
typophile
typorama
typoscript
typotelegraph
typotelegraphy
typothere
Typotheria
Typotheriidae
typothetae
typp
typtological
typtologist
typtology
typy
tyramine
tyranness
Tyranni
tyrannial
tyrannic
tyrannical
tyrannically
tyrannicalness
tyrannicidal
tyrannicide
tyrannicly
Tyrannidae
Tyrannides
Tyranninae
tyrannine
tyrannism
tyrannize
tyrannizer
tyrannizing
tyrannizingly
tyrannoid
tyrannophobia
tyrannosaur
Tyrannosaurus
tyrannous
tyrannously
tyrannousness
Tyrannus
tyranny
tyrant
tyrantcraft
tyrantlike
tyrantship
tyre
tyremesis
Tyrian
tyriasis
tyro
tyrocidin
tyrocidine
tyroglyphid
Tyroglyphidae
Tyroglyphus
Tyrolean
Tyrolese
Tyrolienne
tyrolite
tyrology
tyroma
tyromancy
tyromatous
tyrone
tyronic
tyronism
tyrosinase
tyrosine
tyrosinuria
tyrosyl
tyrotoxicon
tyrotoxine
Tyrr
Tyrrhene
Tyrrheni
Tyrrhenian
Tyrsenoi
Tyrtaean
tysonite
tyste
tyt
Tyto
Tytonidae
Tzaam
Tzapotec
tzaritza
Tzendal
Tzental
tzolkin
tzontle
Tzotzil
Tzutuhil
U
u
uang
Uaraycu
Uarekena
Uaupe
uayeb
Ubbenite
Ubbonite
uberant
uberous
uberously
uberousness
uberty
ubi
ubication
ubiety
Ubii
Ubiquarian
ubiquarian
ubiquious
Ubiquist
ubiquit
Ubiquitarian
ubiquitarian
Ubiquitarianism
ubiquitariness
ubiquitary
Ubiquitism
Ubiquitist
ubiquitous
ubiquitously
ubiquitousness
ubiquity
ubussu
Uca
Ucal
Ucayale
Uchean
Uchee
uckia
Ud
udal
udaler
udaller
udalman
udasi
udder
uddered
udderful
udderless
udderlike
udell
Udi
Udic
Udish
udo
Udolphoish
udometer
udometric
udometry
udomograph
Uds
Ueueteotl
ug
Ugandan
Ugarono
ugh
uglification
uglifier
uglify
uglily
ugliness
uglisome
ugly
Ugrian
Ugric
Ugroid
ugsome
ugsomely
ugsomeness
uhlan
uhllo
uhtensang
uhtsong
Uigur
Uigurian
Uiguric
uily
uinal
Uinta
uintaite
uintathere
Uintatheriidae
Uintatherium
uintjie
Uirina
Uitotan
uitspan
uji
ukase
uke
ukiyoye
Ukrainer
Ukrainian
ukulele
ula
ulatrophia
ulcer
ulcerable
ulcerate
ulceration
ulcerative
ulcered
ulceromembranous
ulcerous
ulcerously
ulcerousness
ulcery
ulcuscle
ulcuscule
ule
ulema
ulemorrhagia
ulerythema
uletic
Ulex
ulex
ulexine
ulexite
Ulidia
Ulidian
uliginose
uliginous
ulitis
ull
ulla
ullage
ullaged
ullagone
uller
ulling
ullmannite
ulluco
Ulmaceae
ulmaceous
Ulmaria
ulmic
ulmin
ulminic
ulmo
ulmous
Ulmus
ulna
ulnad
ulnae
ulnar
ulnare
ulnaria
ulnocarpal
ulnocondylar
ulnometacarpal
ulnoradial
uloborid
Uloboridae
Uloborus
ulocarcinoma
uloid
Ulonata
uloncus
Ulophocinae
ulorrhagia
ulorrhagy
ulorrhea
Ulothrix
Ulotrichaceae
ulotrichaceous
Ulotrichales
ulotrichan
Ulotriches
Ulotrichi
ulotrichous
ulotrichy
ulrichite
ulster
ulstered
ulsterette
Ulsterian
ulstering
Ulsterite
Ulsterman
ulterior
ulteriorly
ultima
ultimacy
ultimata
ultimate
ultimately
ultimateness
ultimation
ultimatum
ultimity
ultimo
ultimobranchial
ultimogenitary
ultimogeniture
ultimum
Ultonian
ultra
ultrabasic
ultrabasite
ultrabelieving
ultrabenevolent
ultrabrachycephalic
ultrabrachycephaly
ultrabrilliant
ultracentenarian
ultracentenarianism
ultracentralizer
ultracentrifuge
ultraceremonious
ultrachurchism
ultracivil
ultracomplex
ultraconcomitant
ultracondenser
ultraconfident
ultraconscientious
ultraconservatism
ultraconservative
ultracordial
ultracosmopolitan
ultracredulous
ultracrepidarian
ultracrepidarianism
ultracrepidate
ultracritical
ultradandyism
ultradeclamatory
ultrademocratic
ultradespotic
ultradignified
ultradiscipline
ultradolichocephalic
ultradolichocephaly
ultradolichocranial
ultraeducationist
ultraeligible
ultraelliptic
ultraemphasis
ultraenergetic
ultraenforcement
ultraenthusiasm
ultraenthusiastic
ultraepiscopal
ultraevangelical
ultraexcessive
ultraexclusive
ultraexpeditious
ultrafantastic
ultrafashionable
ultrafastidious
ultrafederalist
ultrafeudal
ultrafidian
ultrafidianism
ultrafilter
ultrafilterability
ultrafilterable
ultrafiltrate
ultrafiltration
ultraformal
ultrafrivolous
ultragallant
ultragaseous
ultragenteel
ultragood
ultragrave
ultraheroic
ultrahonorable
ultrahuman
ultraimperialism
ultraimperialist
ultraimpersonal
ultrainclusive
ultraindifferent
ultraindulgent
ultraingenious
ultrainsistent
ultraintimate
ultrainvolved
ultraism
ultraist
ultraistic
ultralaborious
ultralegality
ultralenient
ultraliberal
ultraliberalism
ultralogical
ultraloyal
ultraluxurious
ultramarine
ultramaternal
ultramaximal
ultramelancholy
ultramicrochemical
ultramicrochemist
ultramicrochemistry
ultramicrometer
ultramicron
ultramicroscope
ultramicroscopic
ultramicroscopical
ultramicroscopy
ultraminute
ultramoderate
ultramodern
ultramodernism
ultramodernist
ultramodernistic
ultramodest
ultramontane
ultramontanism
ultramontanist
ultramorose
ultramulish
ultramundane
ultranational
ultranationalism
ultranationalist
ultranatural
ultranegligent
ultranice
ultranonsensical
ultraobscure
ultraobstinate
ultraofficious
ultraoptimistic
ultraornate
ultraorthodox
ultraorthodoxy
ultraoutrageous
ultrapapist
ultraparallel
ultraperfect
ultrapersuasive
ultraphotomicrograph
ultrapious
ultraplanetary
ultraplausible
ultrapopish
ultraproud
ultraprudent
ultraradical
ultraradicalism
ultrarapid
ultrareactionary
ultrared
ultrarefined
ultrarefinement
ultrareligious
ultraremuneration
ultrarepublican
ultrarevolutionary
ultrarevolutionist
ultraritualism
ultraromantic
ultraroyalism
ultraroyalist
ultrasanguine
ultrascholastic
ultraselect
ultraservile
ultrasevere
ultrashrewd
ultrasimian
ultrasolemn
ultrasonic
ultrasonics
ultraspartan
ultraspecialization
ultraspiritualism
ultrasplendid
ultrastandardization
ultrastellar
ultrasterile
ultrastrenuous
ultrastrict
ultrasubtle
ultrasystematic
ultratechnical
ultratense
ultraterrene
ultraterrestrial
ultratotal
ultratrivial
ultratropical
ultraugly
ultrauncommon
ultraurgent
ultravicious
ultraviolent
ultraviolet
ultravirtuous
ultravirus
ultravisible
ultrawealthy
ultrawise
ultrayoung
ultrazealous
ultrazodiacal
ultroneous
ultroneously
ultroneousness
ulu
Ulua
ulua
uluhi
ululant
ululate
ululation
ululative
ululatory
ululu
Ulva
Ulvaceae
ulvaceous
Ulvales
Ulvan
Ulyssean
Ulysses
um
umangite
Umatilla
Umaua
umbeclad
umbel
umbeled
umbella
Umbellales
umbellar
umbellate
umbellated
umbellately
umbellet
umbellic
umbellifer
Umbelliferae
umbelliferone
umbelliferous
umbelliflorous
umbelliform
umbelloid
Umbellula
Umbellularia
umbellulate
umbellule
Umbellulidae
umbelluliferous
umbelwort
umber
umbethink
umbilectomy
umbilic
umbilical
umbilically
umbilicar
Umbilicaria
umbilicate
umbilicated
umbilication
umbilici
umbiliciform
umbilicus
umbiliform
umbilroot
umble
umbo
umbolateral
umbonal
umbonate
umbonated
umbonation
umbone
umbones
umbonial
umbonic
umbonulate
umbonule
Umbra
umbra
umbracious
umbraciousness
umbraculate
umbraculiferous
umbraculiform
umbraculum
umbrae
umbrage
umbrageous
umbrageously
umbrageousness
umbral
umbrally
umbratile
umbrel
umbrella
umbrellaed
umbrellaless
umbrellalike
umbrellawise
umbrellawort
umbrette
Umbrian
Umbriel
umbriferous
umbriferously
umbriferousness
umbril
umbrine
umbrose
umbrosity
umbrous
Umbundu
ume
umiak
umiri
umlaut
ump
umph
umpirage
umpire
umpirer
umpireship
umpiress
umpirism
Umpqua
umpteen
umpteenth
umptekite
umptieth
umpty
umquhile
umu
un
Una
unabandoned
unabased
unabasedly
unabashable
unabashed
unabashedly
unabatable
unabated
unabatedly
unabating
unabatingly
unabbreviated
unabetted
unabettedness
unabhorred
unabiding
unabidingly
unabidingness
unability
unabject
unabjured
unable
unableness
unably
unabolishable
unabolished
unabraded
unabrased
unabridgable
unabridged
unabrogated
unabrupt
unabsent
unabsolute
unabsolvable
unabsolved
unabsolvedness
unabsorb
unabsorbable
unabsorbed
unabsorbent
unabstract
unabsurd
unabundance
unabundant
unabundantly
unabused
unacademic
unacademical
unaccelerated
unaccent
unaccented
unaccentuated
unaccept
unacceptability
unacceptable
unacceptableness
unacceptably
unacceptance
unacceptant
unaccepted
unaccessibility
unaccessible
unaccessibleness
unaccessibly
unaccessional
unaccessory
unaccidental
unaccidentally
unaccidented
unacclimated
unacclimation
unacclimatization
unacclimatized
unaccommodable
unaccommodated
unaccommodatedness
unaccommodating
unaccommodatingly
unaccommodatingness
unaccompanable
unaccompanied
unaccompanying
unaccomplishable
unaccomplished
unaccomplishedness
unaccord
unaccordable
unaccordance
unaccordant
unaccorded
unaccording
unaccordingly
unaccostable
unaccosted
unaccountability
unaccountable
unaccountableness
unaccountably
unaccounted
unaccoutered
unaccoutred
unaccreditated
unaccredited
unaccrued
unaccumulable
unaccumulate
unaccumulated
unaccumulation
unaccuracy
unaccurate
unaccurately
unaccurateness
unaccursed
unaccusable
unaccusably
unaccuse
unaccusing
unaccustom
unaccustomed
unaccustomedly
unaccustomedness
unachievable
unachieved
unaching
unacidulated
unacknowledged
unacknowledgedness
unacknowledging
unacknowledgment
unacoustic
unacquaint
unacquaintable
unacquaintance
unacquainted
unacquaintedly
unacquaintedness
unacquiescent
unacquirable
unacquirableness
unacquirably
unacquired
unacquit
unacquittable
unacquitted
unacquittedness
unact
unactability
unactable
unacted
unacting
unactinic
unaction
unactivated
unactive
unactively
unactiveness
unactivity
unactorlike
unactual
unactuality
unactually
unactuated
unacute
unacutely
unadapt
unadaptability
unadaptable
unadaptableness
unadaptably
unadapted
unadaptedly
unadaptedness
unadaptive
unadd
unaddable
unadded
unaddicted
unaddictedness
unadditional
unaddress
unaddressed
unadequate
unadequately
unadequateness
unadherence
unadherent
unadherently
unadhesive
unadjacent
unadjacently
unadjectived
unadjourned
unadjournment
unadjudged
unadjust
unadjustably
unadjusted
unadjustment
unadministered
unadmirable
unadmire
unadmired
unadmiring
unadmissible
unadmissibly
unadmission
unadmittable
unadmittableness
unadmittably
unadmitted
unadmittedly
unadmitting
unadmonished
unadopt
unadoptable
unadoptably
unadopted
unadoption
unadorable
unadoration
unadored
unadoring
unadorn
unadornable
unadorned
unadornedly
unadornedness
unadornment
unadult
unadulterate
unadulterated
unadulteratedly
unadulteratedness
unadulterately
unadulterous
unadulterously
unadvanced
unadvancedly
unadvancedness
unadvancement
unadvancing
unadvantaged
unadvantageous
unadventured
unadventuring
unadventurous
unadventurously
unadverse
unadversely
unadverseness
unadvertency
unadvertised
unadvertisement
unadvertising
unadvisability
unadvisable
unadvisableness
unadvisably
unadvised
unadvisedly
unadvisedness
unadvocated
unaerated
unaesthetic
unaesthetical
unafeard
unafeared
unaffable
unaffably
unaffected
unaffectedly
unaffectedness
unaffecting
unaffectionate
unaffectionately
unaffectioned
unaffianced
unaffied
unaffiliated
unaffiliation
unaffirmation
unaffirmed
unaffixed
unafflicted
unafflictedly
unafflicting
unaffliction
unaffordable
unafforded
unaffranchised
unaffrighted
unaffrightedly
unaffronted
unafire
unafloat
unaflow
unafraid
unaged
unaggravated
unaggravating
unaggregated
unaggression
unaggressive
unaggressively
unaggressiveness
unaghast
unagile
unagility
unaging
unagitated
unagitatedly
unagitatedness
unagitation
unagonize
unagrarian
unagreeable
unagreeableness
unagreeably
unagreed
unagreeing
unagreement
unagricultural
unaidable
unaided
unaidedly
unaiding
unailing
unaimed
unaiming
unaired
unaisled
Unakhotana
unakin
unakite
unal
Unalachtigo
unalarm
unalarmed
unalarming
Unalaska
unalcoholized
unaldermanly
unalert
unalertly
unalertness
unalgebraical
unalienable
unalienableness
unalienably
unalienated
unalignable
unaligned
unalike
unalimentary
unalist
unalive
unallayable
unallayably
unallayed
unalleged
unallegorical
unalleviably
unalleviated
unalleviation
unalliable
unallied
unalliedly
unalliedness
unallotment
unallotted
unallow
unallowable
unallowed
unallowedly
unallowing
unalloyed
unallurable
unallured
unalluring
unalluringly
unalmsed
unalone
unaloud
unalphabeted
unalphabetic
unalphabetical
unalterability
unalterable
unalterableness
unalterably
unalteration
unaltered
unaltering
unalternated
unamalgamable
unamalgamated
unamalgamating
unamassed
unamazed
unamazedly
unambiguity
unambiguous
unambiguously
unambiguousness
unambition
unambitious
unambitiously
unambitiousness
unambrosial
unambush
unamenability
unamenable
unamenableness
unamenably
unamend
unamendable
unamended
unamendedly
unamending
unamendment
unamerced
Unami
unamiability
unamiable
unamiableness
unamiably
unamicable
unamicably
unamiss
unamo
unamortization
unamortized
unample
unamplifiable
unamplified
unamply
unamputated
unamusable
unamusably
unamused
unamusement
unamusing
unamusingly
unamusive
unanalogical
unanalogous
unanalogously
unanalogousness
unanalytic
unanalytical
unanalyzable
unanalyzed
unanalyzing
unanatomizable
unanatomized
unancestored
unancestried
unanchor
unanchored
unanchylosed
unancient
unaneled
unangelic
unangelical
unangrily
unangry
unangular
unanimalized
unanimate
unanimated
unanimatedly
unanimatedness
unanimately
unanimism
unanimist
unanimistic
unanimistically
unanimity
unanimous
unanimously
unanimousness
unannealed
unannex
unannexed
unannexedly
unannexedness
unannihilable
unannihilated
unannotated
unannounced
unannoyed
unannoying
unannullable
unannulled
unanointed
unanswerability
unanswerable
unanswerableness
unanswerably
unanswered
unanswering
unantagonistic
unantagonizable
unantagonized
unantagonizing
unanticipated
unanticipating
unanticipatingly
unanticipation
unanticipative
unantiquated
unantiquatedness
unantique
unantiquity
unanxiety
unanxious
unanxiously
unanxiousness
unapart
unapocryphal
unapologetic
unapologizing
unapostatized
unapostolic
unapostolical
unapostolically
unapostrophized
unappalled
unappareled
unapparent
unapparently
unapparentness
unappealable
unappealableness
unappealably
unappealed
unappealing
unappeasable
unappeasableness
unappeasably
unappeased
unappeasedly
unappeasedness
unappendaged
unapperceived
unappertaining
unappetizing
unapplauded
unapplauding
unapplausive
unappliable
unappliableness
unappliably
unapplianced
unapplicable
unapplicableness
unapplicably
unapplied
unapplying
unappoint
unappointable
unappointableness
unappointed
unapportioned
unapposite
unappositely
unappraised
unappreciable
unappreciableness
unappreciably
unappreciated
unappreciating
unappreciation
unappreciative
unappreciatively
unappreciativeness
unapprehendable
unapprehendableness
unapprehendably
unapprehended
unapprehending
unapprehensible
unapprehensibleness
unapprehension
unapprehensive
unapprehensively
unapprehensiveness
unapprenticed
unapprised
unapprisedly
unapprisedness
unapproachability
unapproachable
unapproachableness
unapproached
unapproaching
unapprobation
unappropriable
unappropriate
unappropriated
unappropriately
unappropriateness
unappropriation
unapprovable
unapprovableness
unapprovably
unapproved
unapproving
unapprovingly
unapproximate
unapproximately
unaproned
unapropos
unapt
unaptitude
unaptly
unaptness
unarbitrarily
unarbitrariness
unarbitrary
unarbitrated
unarch
unarchdeacon
unarched
unarchitectural
unarduous
unarguable
unarguableness
unarguably
unargued
unarguing
unargumentative
unargumentatively
unarisen
unarising
unaristocratic
unaristocratically
unarithmetical
unarithmetically
unark
unarm
unarmed
unarmedly
unarmedness
unarmored
unarmorial
unaromatized
unarousable
unaroused
unarousing
unarraignable
unarraigned
unarranged
unarray
unarrayed
unarrestable
unarrested
unarresting
unarrival
unarrived
unarriving
unarrogance
unarrogant
unarrogating
unarted
unartful
unartfully
unartfulness
unarticled
unarticulate
unarticulated
unartificial
unartificiality
unartificially
unartistic
unartistical
unartistically
unartistlike
unary
unascendable
unascendableness
unascended
unascertainable
unascertainableness
unascertainably
unascertained
unashamed
unashamedly
unashamedness
unasinous
unaskable
unasked
unasking
unasleep
unaspersed
unasphalted
unaspirated
unaspiring
unaspiringly
unaspiringness
unassailable
unassailableness
unassailably
unassailed
unassailing
unassassinated
unassaultable
unassaulted
unassayed
unassaying
unassembled
unassented
unassenting
unasserted
unassertive
unassertiveness
unassessable
unassessableness
unassessed
unassibilated
unassiduous
unassignable
unassignably
unassigned
unassimilable
unassimilated
unassimilating
unassimilative
unassisted
unassisting
unassociable
unassociably
unassociated
unassociative
unassociativeness
unassoiled
unassorted
unassuageable
unassuaged
unassuaging
unassuetude
unassumable
unassumed
unassuming
unassumingly
unassumingness
unassured
unassuredly
unassuredness
unassuring
unasterisk
unastonish
unastonished
unastonishment
unastray
unathirst
unathletically
unatmospheric
unatonable
unatoned
unatoning
unattach
unattachable
unattached
unattackable
unattackableness
unattackably
unattacked
unattainability
unattainable
unattainableness
unattainably
unattained
unattaining
unattainment
unattaint
unattainted
unattaintedly
unattempered
unattemptable
unattempted
unattempting
unattendance
unattendant
unattended
unattentive
unattenuated
unattested
unattestedness
unattire
unattired
unattractable
unattractableness
unattracted
unattracting
unattractive
unattractively
unattractiveness
unattributable
unattributed
unattuned
unau
unauctioned
unaudible
unaudibleness
unaudibly
unaudienced
unaudited
unaugmentable
unaugmented
unauspicious
unauspiciously
unauspiciousness
unaustere
unauthentic
unauthentical
unauthentically
unauthenticated
unauthenticity
unauthorish
unauthoritative
unauthoritatively
unauthoritativeness
unauthoritied
unauthoritiveness
unauthorizable
unauthorize
unauthorized
unauthorizedly
unauthorizedness
unautomatic
unautumnal
unavailability
unavailable
unavailableness
unavailably
unavailed
unavailful
unavailing
unavailingly
unavengeable
unavenged
unavenging
unavenued
unaveraged
unaverred
unaverted
unavertible
unavertibleness
unavertibly
unavian
unavoidable
unavoidableness
unavoidably
unavoidal
unavoided
unavoiding
unavouchable
unavouchableness
unavouchably
unavouched
unavowable
unavowableness
unavowably
unavowed
unavowedly
unawakable
unawakableness
unawake
unawaked
unawakened
unawakenedness
unawakening
unawaking
unawardable
unawardableness
unawardably
unawarded
unaware
unawared
unawaredly
unawareness
unawares
unaway
unawed
unawful
unawfully
unawkward
unawned
unaxled
unazotized
unbackboarded
unbacked
unbackward
unbadged
unbaffled
unbaffling
unbag
unbagged
unbailable
unbailableness
unbailed
unbain
unbait
unbaited
unbaized
unbaked
unbalance
unbalanceable
unbalanceably
unbalanced
unbalancement
unbalancing
unbalconied
unbale
unbalked
unballast
unballasted
unballoted
unbandage
unbandaged
unbanded
unbanished
unbank
unbankable
unbankableness
unbankably
unbanked
unbankrupt
unbannered
unbaptize
unbaptized
unbar
unbarb
unbarbarize
unbarbarous
unbarbed
unbarbered
unbare
unbargained
unbark
unbarking
unbaronet
unbarrable
unbarred
unbarrel
unbarreled
unbarren
unbarrenness
unbarricade
unbarricaded
unbarricadoed
unbase
unbased
unbasedness
unbashful
unbashfully
unbashfulness
unbasket
unbastardized
unbaste
unbasted
unbastilled
unbastinadoed
unbated
unbathed
unbating
unbatted
unbatten
unbatterable
unbattered
unbattling
unbay
unbe
unbeached
unbeaconed
unbeaded
unbear
unbearable
unbearableness
unbearably
unbeard
unbearded
unbearing
unbeast
unbeatable
unbeatableness
unbeatably
unbeaten
unbeaued
unbeauteous
unbeauteously
unbeauteousness
unbeautified
unbeautiful
unbeautifully
unbeautifulness
unbeautify
unbeavered
unbeclogged
unbeclouded
unbecome
unbecoming
unbecomingly
unbecomingness
unbed
unbedabbled
unbedaggled
unbedashed
unbedaubed
unbedded
unbedecked
unbedewed
unbedimmed
unbedinned
unbedizened
unbedraggled
unbefit
unbefitting
unbefittingly
unbefittingness
unbefool
unbefriend
unbefriended
unbefringed
unbeget
unbeggar
unbegged
unbegilt
unbeginning
unbeginningly
unbeginningness
unbegirded
unbegirt
unbegot
unbegotten
unbegottenly
unbegottenness
unbegreased
unbegrimed
unbegrudged
unbeguile
unbeguiled
unbeguileful
unbegun
unbehaving
unbeheaded
unbeheld
unbeholdable
unbeholden
unbeholdenness
unbeholding
unbehoveful
unbehoving
unbeing
unbejuggled
unbeknown
unbeknownst
unbelied
unbelief
unbeliefful
unbelieffulness
unbelievability
unbelievable
unbelievableness
unbelievably
unbelieve
unbelieved
unbeliever
unbelieving
unbelievingly
unbelievingness
unbell
unbellicose
unbelligerent
unbelonging
unbeloved
unbelt
unbemoaned
unbemourned
unbench
unbend
unbendable
unbendableness
unbendably
unbended
unbending
unbendingly
unbendingness
unbendsome
unbeneficed
unbeneficent
unbeneficial
unbenefitable
unbenefited
unbenefiting
unbenetted
unbenevolence
unbenevolent
unbenevolently
unbenight
unbenighted
unbenign
unbenignant
unbenignantly
unbenignity
unbenignly
unbent
unbenumb
unbenumbed
unbequeathable
unbequeathed
unbereaved
unbereft
unberouged
unberth
unberufen
unbeseem
unbeseeming
unbeseemingly
unbeseemingness
unbeseemly
unbeset
unbesieged
unbesmeared
unbesmirched
unbesmutted
unbesot
unbesought
unbespeak
unbespoke
unbespoken
unbesprinkled
unbestarred
unbestowed
unbet
unbeteared
unbethink
unbethought
unbetide
unbetoken
unbetray
unbetrayed
unbetraying
unbetrothed
unbetterable
unbettered
unbeveled
unbewailed
unbewailing
unbewilder
unbewildered
unbewilled
unbewitch
unbewitched
unbewitching
unbewrayed
unbewritten
unbias
unbiasable
unbiased
unbiasedly
unbiasedness
unbibulous
unbickered
unbickering
unbid
unbidable
unbiddable
unbidden
unbigged
unbigoted
unbilled
unbillet
unbilleted
unbind
unbindable
unbinding
unbiographical
unbiological
unbirdlike
unbirdlimed
unbirdly
unbirthday
unbishop
unbishoply
unbit
unbiting
unbitt
unbitted
unbitten
unbitter
unblacked
unblackened
unblade
unblamable
unblamableness
unblamably
unblamed
unblaming
unblanched
unblanketed
unblasphemed
unblasted
unblazoned
unbleached
unbleaching
unbled
unbleeding
unblemishable
unblemished
unblemishedness
unblemishing
unblenched
unblenching
unblenchingly
unblendable
unblended
unblent
unbless
unblessed
unblessedness
unblest
unblighted
unblightedly
unblightedness
unblind
unblindfold
unblinking
unblinkingly
unbliss
unblissful
unblistered
unblithe
unblithely
unblock
unblockaded
unblocked
unblooded
unbloodied
unbloodily
unbloodiness
unbloody
unbloom
unbloomed
unblooming
unblossomed
unblossoming
unblotted
unbloused
unblown
unblued
unbluestockingish
unbluffed
unbluffing
unblunder
unblundered
unblundering
unblunted
unblurred
unblush
unblushing
unblushingly
unblushingness
unboarded
unboasted
unboastful
unboastfully
unboasting
unboat
unbodied
unbodiliness
unbodily
unboding
unbodkined
unbody
unbodylike
unbog
unboggy
unbohemianize
unboiled
unboisterous
unbokel
unbold
unbolden
unboldly
unboldness
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unbolster
unbolstered
unbolt
unbolted
unbombast
unbondable
unbondableness
unbonded
unbone
unboned
unbonnet
unbonneted
unbonny
unbooked
unbookish
unbooklearned
unboot
unbooted
unboraxed
unborder
unbordered
unbored
unboring
unborn
unborne
unborough
unborrowed
unborrowing
unbosom
unbosomer
unbossed
unbotanical
unbothered
unbothering
unbottle
unbottom
unbottomed
unbought
unbound
unboundable
unboundableness
unboundably
unbounded
unboundedly
unboundedness
unboundless
unbounteous
unbountiful
unbountifully
unbountifulness
unbow
unbowable
unbowdlerized
unbowed
unbowel
unboweled
unbowered
unbowing
unbowingness
unbowled
unbowsome
unbox
unboxed
unboy
unboyish
unboylike
unbrace
unbraced
unbracedness
unbracelet
unbraceleted
unbracing
unbragged
unbragging
unbraid
unbraided
unbrailed
unbrained
unbran
unbranched
unbranching
unbrand
unbranded
unbrandied
unbrave
unbraved
unbravely
unbraze
unbreachable
unbreached
unbreaded
unbreakable
unbreakableness
unbreakably
unbreakfasted
unbreaking
unbreast
unbreath
unbreathable
unbreathableness
unbreathed
unbreathing
unbred
unbreech
unbreeched
unbreezy
unbrent
unbrewed
unbribable
unbribableness
unbribably
unbribed
unbribing
unbrick
unbridegroomlike
unbridgeable
unbridged
unbridle
unbridled
unbridledly
unbridledness
unbridling
unbrief
unbriefed
unbriefly
unbright
unbrightened
unbrilliant
unbrimming
unbrined
unbrittle
unbroached
unbroad
unbroadcasted
unbroidered
unbroiled
unbroke
unbroken
unbrokenly
unbrokenness
unbronzed
unbrooch
unbrooded
unbrookable
unbrookably
unbrothered
unbrotherlike
unbrotherliness
unbrotherly
unbrought
unbrown
unbrowned
unbruised
unbrushed
unbrutalize
unbrutalized
unbrute
unbrutelike
unbrutify
unbrutize
unbuckle
unbuckramed
unbud
unbudded
unbudgeability
unbudgeable
unbudgeableness
unbudgeably
unbudged
unbudgeted
unbudging
unbuffed
unbuffered
unbuffeted
unbuild
unbuilded
unbuilt
unbulky
unbulled
unbulletined
unbumped
unbumptious
unbunched
unbundle
unbundled
unbung
unbungling
unbuoyant
unbuoyed
unburden
unburdened
unburdenment
unburdensome
unburdensomeness
unburgessed
unburiable
unburial
unburied
unburlesqued
unburly
unburn
unburnable
unburned
unburning
unburnished
unburnt
unburrow
unburrowed
unburst
unburstable
unburstableness
unburthen
unbury
unbush
unbusied
unbusily
unbusiness
unbusinesslike
unbusk
unbuskin
unbuskined
unbustling
unbusy
unbutchered
unbutcherlike
unbuttered
unbutton
unbuttoned
unbuttonment
unbuttressed
unbuxom
unbuxomly
unbuxomness
unbuyable
unbuyableness
unbuying
unca
uncabined
uncabled
uncadenced
uncage
uncaged
uncake
uncalcareous
uncalcified
uncalcined
uncalculable
uncalculableness
uncalculably
uncalculated
uncalculating
uncalculatingly
uncalendered
uncalk
uncalked
uncall
uncalled
uncallow
uncallower
uncalm
uncalmed
uncalmly
uncalumniated
uncambered
uncamerated
uncamouflaged
uncanceled
uncancellable
uncancelled
uncandid
uncandidly
uncandidness
uncandied
uncandor
uncaned
uncankered
uncanned
uncannily
uncanniness
uncanny
uncanonic
uncanonical
uncanonically
uncanonicalness
uncanonize
uncanonized
uncanopied
uncantoned
uncantonized
uncanvassably
uncanvassed
uncap
uncapable
uncapableness
uncapably
uncapacious
uncapacitate
uncaparisoned
uncapitalized
uncapped
uncapper
uncapsizable
uncapsized
uncaptained
uncaptioned
uncaptious
uncaptiously
uncaptivate
uncaptivated
uncaptivating
uncaptived
uncapturable
uncaptured
uncarbonated
uncarboned
uncarbureted
uncarded
uncardinal
uncardinally
uncareful
uncarefully
uncarefulness
uncaressed
uncargoed
Uncaria
uncaricatured
uncaring
uncarnate
uncarnivorous
uncaroled
uncarpentered
uncarpeted
uncarriageable
uncarried
uncart
uncarted
uncartooned
uncarved
uncase
uncased
uncasemated
uncask
uncasked
uncasketed
uncasque
uncassock
uncast
uncaste
uncastigated
uncastle
uncastled
uncastrated
uncasual
uncatalogued
uncatchable
uncate
uncatechised
uncatechisedness
uncatechized
uncatechizedness
uncategorized
uncathedraled
uncatholcity
uncatholic
uncatholical
uncatholicalness
uncatholicize
uncatholicly
uncaucusable
uncaught
uncausatively
uncaused
uncauterized
uncautious
uncautiously
uncautiousness
uncavalier
uncavalierly
uncave
unceasable
unceased
unceasing
unceasingly
unceasingness
unceded
unceiled
unceilinged
uncelebrated
uncelebrating
uncelestial
uncelestialized
uncellar
uncement
uncemented
uncementing
uncensorable
uncensored
uncensorious
uncensoriously
uncensoriousness
uncensurable
uncensured
uncensuring
uncenter
uncentered
uncentral
uncentrality
uncentrally
uncentred
uncentury
uncereclothed
unceremented
unceremonial
unceremonious
unceremoniously
unceremoniousness
uncertain
uncertainly
uncertainness
uncertainty
uncertifiable
uncertifiableness
uncertificated
uncertified
uncertifying
uncertitude
uncessant
uncessantly
uncessantness
unchafed
unchain
unchainable
unchained
unchair
unchaired
unchalked
unchallengeable
unchallengeableness
unchallengeably
unchallenged
unchallenging
unchambered
unchamfered
unchampioned
unchance
unchancellor
unchancy
unchange
unchangeability
unchangeable
unchangeableness
unchangeably
unchanged
unchangedness
unchangeful
unchangefulness
unchanging
unchangingly
unchangingness
unchanneled
unchannelled
unchanted
unchaperoned
unchaplain
unchapleted
unchapter
unchaptered
uncharacter
uncharactered
uncharacteristic
uncharacteristically
uncharacterized
uncharge
unchargeable
uncharged
uncharging
uncharily
unchariness
unchariot
uncharitable
uncharitableness
uncharitably
uncharity
uncharm
uncharmable
uncharmed
uncharming
uncharnel
uncharred
uncharted
unchartered
unchary
unchased
unchaste
unchastely
unchastened
unchasteness
unchastisable
unchastised
unchastising
unchastity
unchatteled
unchauffeured
unchawed
uncheat
uncheated
uncheating
uncheck
uncheckable
unchecked
uncheckered
uncheerable
uncheered
uncheerful
uncheerfully
uncheerfulness
uncheerily
uncheeriness
uncheering
uncheery
unchemical
unchemically
uncherished
uncherishing
unchested
unchevroned
unchewable
unchewableness
unchewed
unchid
unchidden
unchided
unchiding
unchidingly
unchild
unchildish
unchildishly
unchildishness
unchildlike
unchilled
unchiming
unchinked
unchipped
unchiseled
unchiselled
unchivalric
unchivalrous
unchivalrously
unchivalrousness
unchivalry
unchloridized
unchoicely
unchokable
unchoked
uncholeric
unchoosable
unchopped
unchoral
unchorded
unchosen
unchrisom
unchristen
unchristened
unchristian
unchristianity
unchristianize
unchristianized
unchristianlike
unchristianly
unchristianness
unchronicled
unchronological
unchronologically
unchurch
unchurched
unchurchlike
unchurchly
unchurn
unci
uncia
uncial
uncialize
uncially
uncicatrized
unciferous
unciform
unciliated
uncinal
Uncinaria
uncinariasis
uncinariatic
Uncinata
uncinate
uncinated
uncinatum
uncinch
uncinct
uncinctured
uncini
Uncinula
uncinus
uncipher
uncircular
uncircularized
uncirculated
uncircumcised
uncircumcisedness
uncircumcision
uncircumlocutory
uncircumscribable
uncircumscribed
uncircumscribedness
uncircumscript
uncircumscriptible
uncircumscription
uncircumspect
uncircumspection
uncircumspectly
uncircumspectness
uncircumstanced
uncircumstantial
uncirostrate
uncite
uncited
uncitied
uncitizen
uncitizenlike
uncitizenly
uncity
uncivic
uncivil
uncivilish
uncivility
uncivilizable
uncivilization
uncivilize
uncivilized
uncivilizedly
uncivilizedness
uncivilly
uncivilness
unclad
unclaimed
unclaiming
unclamorous
unclamp
unclamped
unclarified
unclarifying
unclarity
unclashing
unclasp
unclasped
unclassable
unclassableness
unclassably
unclassed
unclassible
unclassical
unclassically
unclassifiable
unclassifiableness
unclassification
unclassified
unclassify
unclassifying
unclawed
unclay
unclayed
uncle
unclead
unclean
uncleanable
uncleaned
uncleanlily
uncleanliness
uncleanly
uncleanness
uncleansable
uncleanse
uncleansed
uncleansedness
unclear
uncleared
unclearing
uncleavable
uncleave
uncledom
uncleft
unclehood
unclement
unclemently
unclementness
unclench
unclergy
unclergyable
unclerical
unclericalize
unclerically
unclericalness
unclerklike
unclerkly
uncleship
unclever
uncleverly
uncleverness
unclew
unclick
uncliented
unclify
unclimaxed
unclimb
unclimbable
unclimbableness
unclimbably
unclimbed
unclimbing
unclinch
uncling
unclinical
unclip
unclipped
unclipper
uncloak
uncloakable
uncloaked
unclog
unclogged
uncloister
uncloistered
uncloistral
unclosable
unclose
unclosed
uncloseted
unclothe
unclothed
unclothedly
unclothedness
unclotted
uncloud
unclouded
uncloudedly
uncloudedness
uncloudy
unclout
uncloven
uncloyable
uncloyed
uncloying
unclub
unclubbable
unclubby
unclustered
unclustering
unclutch
unclutchable
unclutched
unclutter
uncluttered
unco
uncoach
uncoachable
uncoachableness
uncoached
uncoacted
uncoagulable
uncoagulated
uncoagulating
uncoat
uncoated
uncoatedness
uncoaxable
uncoaxed
uncoaxing
uncock
uncocked
uncockneyfy
uncocted
uncodded
uncoddled
uncoded
uncodified
uncoerced
uncoffer
uncoffin
uncoffined
uncoffle
uncogent
uncogged
uncogitable
uncognizable
uncognizant
uncognized
uncognoscibility
uncognoscible
uncoguidism
uncoherent
uncoherently
uncoherentness
uncohesive
uncoif
uncoifed
uncoil
uncoiled
uncoin
uncoined
uncoked
uncoking
uncollapsed
uncollapsible
uncollar
uncollared
uncollated
uncollatedness
uncollected
uncollectedly
uncollectedness
uncollectible
uncollectibleness
uncollectibly
uncolleged
uncollegian
uncollegiate
uncolloquial
uncolloquially
uncolonellike
uncolonial
uncolonize
uncolonized
uncolorable
uncolorably
uncolored
uncoloredly
uncoloredness
uncoloured
uncolouredly
uncolouredness
uncolt
uncoly
uncombable
uncombatable
uncombated
uncombed
uncombinable
uncombinableness
uncombinably
uncombine
uncombined
uncombining
uncombiningness
uncombustible
uncome
uncomelily
uncomeliness
uncomely
uncomfort
uncomfortable
uncomfortableness
uncomfortably
uncomforted
uncomforting
uncomfy
uncomic
uncommanded
uncommandedness
uncommanderlike
uncommemorated
uncommenced
uncommendable
uncommendableness
uncommendably
uncommended
uncommensurability
uncommensurable
uncommensurableness
uncommensurate
uncommented
uncommenting
uncommerciable
uncommercial
uncommercially
uncommercialness
uncommingled
uncomminuted
uncommiserated
uncommiserating
uncommissioned
uncommitted
uncommitting
uncommixed
uncommodious
uncommodiously
uncommodiousness
uncommon
uncommonable
uncommonly
uncommonness
uncommonplace
uncommunicable
uncommunicableness
uncommunicably
uncommunicated
uncommunicating
uncommunicative
uncommunicatively
uncommunicativeness
uncommutable
uncommutative
uncommuted
uncompact
uncompacted
Uncompahgre
uncompahgrite
uncompaniable
uncompanied
uncompanioned
uncomparable
uncomparably
uncompared
uncompass
uncompassable
uncompassed
uncompassion
uncompassionate
uncompassionated
uncompassionately
uncompassionateness
uncompassionating
uncompassioned
uncompatible
uncompatibly
uncompellable
uncompelled
uncompelling
uncompensable
uncompensated
uncompetent
uncompetitive
uncompiled
uncomplacent
uncomplained
uncomplaining
uncomplainingly
uncomplainingness
uncomplaint
uncomplaisance
uncomplaisant
uncomplaisantly
uncomplemental
uncompletable
uncomplete
uncompleted
uncompletely
uncompleteness
uncomplex
uncompliability
uncompliable
uncompliableness
uncompliance
uncompliant
uncomplicated
uncomplimentary
uncomplimented
uncomplimenting
uncomplying
uncomposable
uncomposeable
uncomposed
uncompoundable
uncompounded
uncompoundedly
uncompoundedness
uncompounding
uncomprehended
uncomprehending
uncomprehendingly
uncomprehendingness
uncomprehensible
uncomprehension
uncomprehensive
uncomprehensively
uncomprehensiveness
uncompressed
uncompressible
uncomprised
uncomprising
uncomprisingly
uncompromised
uncompromising
uncompromisingly
uncompromisingness
uncompulsive
uncompulsory
uncomputable
uncomputableness
uncomputably
uncomputed
uncomraded
unconcatenated
unconcatenating
unconcealable
unconcealableness
unconcealably
unconcealed
unconcealing
unconcealingly
unconcealment
unconceded
unconceited
unconceivable
unconceivableness
unconceivably
unconceived
unconceiving
unconcern
unconcerned
unconcernedly
unconcernedness
unconcerning
unconcernment
unconcertable
unconcerted
unconcertedly
unconcertedness
unconcessible
unconciliable
unconciliated
unconciliatedness
unconciliating
unconciliatory
unconcludable
unconcluded
unconcluding
unconcludingness
unconclusive
unconclusively
unconclusiveness
unconcocted
unconcordant
unconcrete
unconcreted
unconcurrent
unconcurring
uncondemnable
uncondemned
uncondensable
uncondensableness
uncondensed
uncondensing
uncondescending
uncondescension
uncondition
unconditional
unconditionality
unconditionally
unconditionalness
unconditionate
unconditionated
unconditionately
unconditioned
unconditionedly
unconditionedness
uncondoled
uncondoling
unconducing
unconducive
unconduciveness
unconducted
unconductive
unconductiveness
unconfected
unconfederated
unconferred
unconfess
unconfessed
unconfessing
unconfided
unconfidence
unconfident
unconfidential
unconfidentialness
unconfidently
unconfiding
unconfinable
unconfine
unconfined
unconfinedly
unconfinedness
unconfinement
unconfining
unconfirm
unconfirmative
unconfirmed
unconfirming
unconfiscable
unconfiscated
unconflicting
unconflictingly
unconflictingness
unconformability
unconformable
unconformableness
unconformably
unconformed
unconformedly
unconforming
unconformist
unconformity
unconfound
unconfounded
unconfoundedly
unconfrontable
unconfronted
unconfusable
unconfusably
unconfused
unconfusedly
unconfutable
unconfuted
unconfuting
uncongeal
uncongealable
uncongealed
uncongenial
uncongeniality
uncongenially
uncongested
unconglobated
unconglomerated
unconglutinated
uncongratulate
uncongratulated
uncongratulating
uncongregated
uncongregational
uncongressional
uncongruous
unconjecturable
unconjectured
unconjoined
unconjugal
unconjugated
unconjunctive
unconjured
unconnected
unconnectedly
unconnectedness
unconned
unconnived
unconniving
unconquerable
unconquerableness
unconquerably
unconquered
unconscienced
unconscient
unconscientious
unconscientiously
unconscientiousness
unconscionable
unconscionableness
unconscionably
unconscious
unconsciously
unconsciousness
unconsecrate
unconsecrated
unconsecratedly
unconsecratedness
unconsecration
unconsecutive
unconsent
unconsentaneous
unconsented
unconsenting
unconsequential
unconsequentially
unconsequentialness
unconservable
unconservative
unconserved
unconserving
unconsiderable
unconsiderate
unconsiderately
unconsiderateness
unconsidered
unconsideredly
unconsideredness
unconsidering
unconsideringly
unconsignable
unconsigned
unconsistent
unconsociable
unconsociated
unconsolable
unconsolably
unconsolatory
unconsoled
unconsolidated
unconsolidating
unconsolidation
unconsoling
unconsonancy
unconsonant
unconsonantly
unconsonous
unconspicuous
unconspicuously
unconspicuousness
unconspired
unconspiring
unconspiringly
unconspiringness
unconstancy
unconstant
unconstantly
unconstantness
unconstellated
unconstipated
unconstituted
unconstitutional
unconstitutionalism
unconstitutionality
unconstitutionally
unconstrainable
unconstrained
unconstrainedly
unconstrainedness
unconstraining
unconstraint
unconstricted
unconstruable
unconstructed
unconstructive
unconstructural
unconstrued
unconsular
unconsult
unconsultable
unconsulted
unconsulting
unconsumable
unconsumed
unconsuming
unconsummate
unconsummated
unconsumptive
uncontagious
uncontainable
uncontainableness
uncontainably
uncontained
uncontaminable
uncontaminate
uncontaminated
uncontemned
uncontemnedly
uncontemplated
uncontemporaneous
uncontemporary
uncontemptuous
uncontended
uncontending
uncontent
uncontentable
uncontented
uncontentedly
uncontentedness
uncontenting
uncontentingness
uncontentious
uncontentiously
uncontentiousness
uncontestable
uncontestableness
uncontestably
uncontested
uncontestedly
uncontestedness
uncontinence
uncontinent
uncontinental
uncontinented
uncontinently
uncontinual
uncontinued
uncontinuous
uncontorted
uncontract
uncontracted
uncontractedness
uncontractile
uncontradictable
uncontradictableness
uncontradictably
uncontradicted
uncontradictedly
uncontradictious
uncontradictory
uncontrastable
uncontrasted
uncontrasting
uncontributed
uncontributing
uncontributory
uncontrite
uncontrived
uncontriving
uncontrol
uncontrollability
uncontrollable
uncontrollableness
uncontrollably
uncontrolled
uncontrolledly
uncontrolledness
uncontrolling
uncontroversial
uncontroversially
uncontrovertable
uncontrovertableness
uncontrovertably
uncontroverted
uncontrovertedly
uncontrovertible
uncontrovertibleness
uncontrovertibly
unconvenable
unconvened
unconvenience
unconvenient
unconveniently
unconventional
unconventionalism
unconventionality
unconventionalize
unconventionally
unconventioned
unconversable
unconversableness
unconversably
unconversant
unconversational
unconversion
unconvert
unconverted
unconvertedly
unconvertedness
unconvertibility
unconvertible
unconveyable
unconveyed
unconvicted
unconvicting
unconvince
unconvinced
unconvincedly
unconvincedness
unconvincibility
unconvincible
unconvincing
unconvincingly
unconvincingness
unconvoluted
unconvoyed
unconvulsed
uncookable
uncooked
uncooled
uncoop
uncooped
uncoopered
uncooping
uncope
uncopiable
uncopied
uncopious
uncopyrighted
uncoquettish
uncoquettishly
uncord
uncorded
uncordial
uncordiality
uncordially
uncording
uncore
uncored
uncork
uncorked
uncorker
uncorking
uncorned
uncorner
uncoronated
uncoroneted
uncorporal
uncorpulent
uncorrect
uncorrectable
uncorrected
uncorrectible
uncorrectly
uncorrectness
uncorrelated
uncorrespondency
uncorrespondent
uncorresponding
uncorrigible
uncorrigibleness
uncorrigibly
uncorroborated
uncorroded
uncorrugated
uncorrupt
uncorrupted
uncorruptedly
uncorruptedness
uncorruptibility
uncorruptible
uncorruptibleness
uncorruptibly
uncorrupting
uncorruption
uncorruptive
uncorruptly
uncorruptness
uncorseted
uncosseted
uncost
uncostliness
uncostly
uncostumed
uncottoned
uncouch
uncouched
uncouching
uncounselable
uncounseled
uncounsellable
uncounselled
uncountable
uncountableness
uncountably
uncounted
uncountenanced
uncounteracted
uncounterbalanced
uncounterfeit
uncounterfeited
uncountermandable
uncountermanded
uncountervailed
uncountess
uncountrified
uncouple
uncoupled
uncoupler
uncourageous
uncoursed
uncourted
uncourteous
uncourteously
uncourteousness
uncourtierlike
uncourting
uncourtlike
uncourtliness
uncourtly
uncous
uncousinly
uncouth
uncouthie
uncouthly
uncouthness
uncouthsome
uncovenant
uncovenanted
uncover
uncoverable
uncovered
uncoveredly
uncoveted
uncoveting
uncovetingly
uncovetous
uncowed
uncowl
uncoy
uncracked
uncradled
uncraftily
uncraftiness
uncrafty
uncram
uncramp
uncramped
uncrampedness
uncranked
uncrannied
uncrated
uncravatted
uncraven
uncraving
uncravingly
uncrazed
uncream
uncreased
uncreatability
uncreatable
uncreatableness
uncreate
uncreated
uncreatedness
uncreating
uncreation
uncreative
uncreativeness
uncreaturely
uncredentialed
uncredentialled
uncredibility
uncredible
uncredibly
uncreditable
uncreditableness
uncreditably
uncredited
uncrediting
uncredulous
uncreeping
uncreosoted
uncrest
uncrested
uncrevassed
uncrib
uncried
uncrime
uncriminal
uncriminally
uncrinkle
uncrinkled
uncrinkling
uncrippled
uncrisp
uncritical
uncritically
uncriticisable
uncriticised
uncriticising
uncriticisingly
uncriticism
uncriticizable
uncriticized
uncriticizing
uncriticizingly
uncrochety
uncrook
uncrooked
uncrooking
uncropped
uncropt
uncross
uncrossable
uncrossableness
uncrossed
uncrossexaminable
uncrossexamined
uncrossly
uncrowded
uncrown
uncrowned
uncrowning
uncrucified
uncrudded
uncrude
uncruel
uncrumbled
uncrumple
uncrumpling
uncrushable
uncrushed
uncrusted
uncrying
uncrystaled
uncrystalled
uncrystalline
uncrystallizability
uncrystallizable
uncrystallized
unction
unctional
unctioneer
unctionless
unctious
unctiousness
unctorium
unctuose
unctuosity
unctuous
unctuously
unctuousness
uncubbed
uncubic
uncuckold
uncuckolded
uncudgelled
uncuffed
uncular
unculled
uncultivability
uncultivable
uncultivate
uncultivated
uncultivation
unculturable
unculture
uncultured
uncumber
uncumbered
uncumbrous
uncunning
uncunningly
uncunningness
uncupped
uncurable
uncurableness
uncurably
uncurb
uncurbable
uncurbed
uncurbedly
uncurbing
uncurd
uncurdled
uncurdling
uncured
uncurious
uncuriously
uncurl
uncurled
uncurling
uncurrent
uncurrently
uncurrentness
uncurricularized
uncurried
uncurse
uncursed
uncursing
uncurst
uncurtailed
uncurtain
uncurtained
uncus
uncushioned
uncusped
uncustomable
uncustomarily
uncustomariness
uncustomary
uncustomed
uncut
uncuth
uncuticulate
uncuttable
uncynical
uncynically
uncypress
undabbled
undaggled
undaily
undaintiness
undainty
undallying
undam
undamageable
undamaged
undamaging
undamasked
undammed
undamming
undamn
undamped
undancing
undandiacal
undandled
undangered
undangerous
undangerousness
undared
undaring
undark
undarken
undarkened
undarned
undashed
undatable
undate
undateable
undated
undatedness
undaub
undaubed
undaughter
undaughterliness
undaughterly
undauntable
undaunted
undauntedly
undauntedness
undaunting
undawned
undawning
undazed
undazing
undazzle
undazzled
undazzling
unde
undead
undeadened
undeaf
undealable
undealt
undean
undear
undebarred
undebased
undebatable
undebated
undebating
undebauched
undebilitated
undebilitating
undecagon
undecanaphthene
undecane
undecatoic
undecayable
undecayableness
undecayed
undecayedness
undecaying
undeceased
undeceitful
undeceivable
undeceivableness
undeceivably
undeceive
undeceived
undeceiver
undeceiving
undecency
undecennary
undecennial
undecent
undecently
undeception
undeceptious
undeceptitious
undeceptive
undecidable
undecide
undecided
undecidedly
undecidedness
undeciding
undecimal
undeciman
undecimole
undecipher
undecipherability
undecipherable
undecipherably
undeciphered
undecision
undecisive
undecisively
undecisiveness
undeck
undecked
undeclaimed
undeclaiming
undeclamatory
undeclarable
undeclare
undeclared
undeclinable
undeclinableness
undeclinably
undeclined
undeclining
undecocted
undecoic
undecolic
undecomposable
undecomposed
undecompounded
undecorated
undecorative
undecorous
undecorously
undecorousness
undecorticated
undecoyed
undecreased
undecreasing
undecree
undecreed
undecried
undecyl
undecylenic
undecylic
undedicate
undedicated
undeducible
undeducted
undeeded
undeemed
undeemous
undeemously
undeep
undefaceable
undefaced
undefalcated
undefamed
undefaming
undefatigable
undefaulted
undefaulting
undefeasible
undefeat
undefeatable
undefeated
undefeatedly
undefeatedness
undefecated
undefectible
undefective
undefectiveness
undefendable
undefendableness
undefendably
undefended
undefending
undefense
undefensed
undefensible
undeferential
undeferentially
undeferred
undefiant
undeficient
undefied
undefilable
undefiled
undefiledly
undefiledness
undefinable
undefinableness
undefinably
undefine
undefined
undefinedly
undefinedness
undeflected
undeflowered
undeformed
undeformedness
undefrauded
undefrayed
undeft
undegeneracy
undegenerate
undegenerated
undegenerating
undegraded
undegrading
undeification
undeified
undeify
undeistical
undejected
undelated
undelayable
undelayed
undelayedly
undelaying
undelayingly
undelectable
undelectably
undelegated
undeleted
undeliberate
undeliberated
undeliberately
undeliberateness
undeliberating
undeliberatingly
undeliberative
undeliberativeness
undelible
undelicious
undelight
undelighted
undelightful
undelightfully
undelightfulness
undelighting
undelightsome
undelimited
undelineated
undeliverable
undeliverableness
undelivered
undelivery
undeludable
undelude
undeluded
undeluding
undeluged
undelusive
undelusively
undelve
undelved
undelylene
undemagnetizable
undemanded
undemised
undemocratic
undemocratically
undemocratize
undemolishable
undemolished
undemonstrable
undemonstrably
undemonstratable
undemonstrated
undemonstrative
undemonstratively
undemonstrativeness
undemure
undemurring
unden
undeniable
undeniableness
undeniably
undenied
undeniedly
undenizened
undenominated
undenominational
undenominationalism
undenominationalist
undenominationalize
undenominationally
undenoted
undenounced
undenuded
undepartableness
undepartably
undeparted
undeparting
undependable
undependableness
undependably
undependent
undepending
undephlegmated
undepicted
undepleted
undeplored
undeported
undeposable
undeposed
undeposited
undepraved
undepravedness
undeprecated
undepreciated
undepressed
undepressible
undepressing
undeprivable
undeprived
undepurated
undeputed
under
underabyss
underaccident
underaccommodated
underact
underacted
underacting
underaction
underactor
underadjustment
underadmiral
underadventurer
underage
underagency
underagent
underagitation
underaid
underaim
underair
underalderman
underanged
underarch
underargue
underarm
underaverage
underback
underbailiff
underbake
underbalance
underballast
underbank
underbarber
underbarring
underbasal
underbeadle
underbeak
underbeam
underbear
underbearer
underbearing
underbeat
underbeaten
underbed
underbelly
underbeveling
underbid
underbidder
underbill
underbillow
underbishop
underbishopric
underbit
underbite
underbitted
underbitten
underboard
underboated
underbodice
underbody
underboil
underboom
underborn
underborne
underbottom
underbough
underbought
underbound
underbowed
underbowser
underbox
underboy
underbrace
underbraced
underbranch
underbreath
underbreathing
underbred
underbreeding
underbrew
underbridge
underbrigadier
underbright
underbrim
underbrush
underbubble
underbud
underbuild
underbuilder
underbuilding
underbuoy
underburn
underburned
underburnt
underbursar
underbury
underbush
underbutler
underbuy
undercanopy
undercanvass
undercap
undercapitaled
undercapitalization
undercapitalize
undercaptain
undercarder
undercarriage
undercarry
undercarter
undercarve
undercarved
undercase
undercasing
undercast
undercause
underceiling
undercellar
undercellarer
underchamber
underchamberlain
underchancellor
underchanter
underchap
undercharge
undercharged
underchief
underchime
underchin
underchord
underchurched
undercircle
undercitizen
underclad
underclass
underclassman
underclay
underclearer
underclerk
underclerkship
undercliff
underclift
undercloak
undercloth
underclothe
underclothed
underclothes
underclothing
underclub
underclutch
undercoachman
undercoat
undercoated
undercoater
undercoating
undercollector
undercolor
undercolored
undercoloring
undercommander
undercomment
undercompounded
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undercondition
underconsciousness
underconstable
underconsume
underconsumption
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undercool
undercooper
undercorrect
undercountenance
undercourse
undercourtier
undercover
undercovering
undercovert
undercrawl
undercreep
undercrest
undercrier
undercroft
undercrop
undercrust
undercry
undercrypt
undercup
undercurl
undercurrent
undercurve
undercut
undercutter
undercutting
underdauber
underdeacon
underdead
underdebauchee
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underdevelopment
underdevil
underdialogue
underdig
underdip
underdish
underdistinction
underdistributor
underditch
underdive
underdo
underdoctor
underdoer
underdog
underdoing
underdone
underdose
underdot
underdown
underdraft
underdrag
underdrain
underdrainage
underdrainer
underdraught
underdraw
underdrawers
underdrawn
underdress
underdressed
underdrift
underdrive
underdriven
underdrudgery
underdrumming
underdry
underdunged
underearth
undereat
undereaten
underedge
undereducated
underemployment
underengraver
underenter
underer
underescheator
underestimate
underestimation
underexcited
underexercise
underexpose
underexposure
undereye
underface
underfaction
underfactor
underfaculty
underfalconer
underfall
underfarmer
underfeathering
underfeature
underfed
underfeed
underfeeder
underfeeling
underfeet
underfellow
underfiend
underfill
underfilling
underfinance
underfind
underfire
underfitting
underflame
underflannel
underfleece
underflood
underfloor
underflooring
underflow
underfold
underfolded
underfong
underfoot
underfootage
underfootman
underforebody
underform
underfortify
underframe
underframework
underframing
underfreight
underfrequency
underfringe
underfrock
underfur
underfurnish
underfurnisher
underfurrow
undergabble
undergamekeeper
undergaoler
undergarb
undergardener
undergarment
undergarnish
undergauge
undergear
undergeneral
undergentleman
undergird
undergirder
undergirding
undergirdle
undergirth
underglaze
undergloom
underglow
undergnaw
undergo
undergod
undergoer
undergoing
undergore
undergoverness
undergovernment
undergovernor
undergown
undergrad
undergrade
undergraduate
undergraduatedom
undergraduateness
undergraduateship
undergraduatish
undergraduette
undergraining
undergrass
undergreen
undergrieve
undergroan
underground
undergrounder
undergroundling
undergrove
undergrow
undergrowl
undergrown
undergrowth
undergrub
underguard
underguardian
undergunner
underhabit
underhammer
underhand
underhanded
underhandedly
underhandedness
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underhanging
underhangman
underhatch
underhead
underheat
underheaven
underhelp
underhew
underhid
underhill
underhint
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underhive
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underhole
underhonest
underhorse
underhorsed
underhousemaid
underhum
underhung
underided
underinstrument
underisive
underissue
underivable
underivative
underived
underivedly
underivedness
underjacket
underjailer
underjanitor
underjaw
underjawed
underjobbing
underjudge
underjungle
underkeel
underkeeper
underkind
underking
underkingdom
underlaborer
underlaid
underlain
underland
underlanguaged
underlap
underlapper
underlash
underlaundress
underlawyer
underlay
underlayer
underlaying
underleaf
underlease
underleather
underlegate
underlessee
underlet
underletter
underlevel
underlever
underlid
underlie
underlier
underlieutenant
underlife
underlift
underlight
underliking
underlimbed
underlimit
underline
underlineation
underlineman
underlinement
underlinen
underliner
underling
underlining
underlip
underlive
underload
underlock
underlodging
underloft
underlook
underlooker
underlout
underlunged
underly
underlye
underlying
undermade
undermaid
undermaker
underman
undermanager
undermanned
undermanning
undermark
undermarshal
undermarshalman
undermasted
undermaster
undermatch
undermatched
undermate
undermath
undermeal
undermeaning
undermeasure
undermediator
undermelody
undermentioned
undermiller
undermimic
underminable
undermine
underminer
undermining
underminingly
underminister
underministry
undermist
undermoated
undermoney
undermoral
undermost
undermotion
undermount
undermountain
undermusic
undermuslin
undern
undername
undernatural
underneath
underness
underniceness
undernote
undernoted
undernourish
undernourished
undernourishment
undernsong
underntide
underntime
undernurse
undernutrition
underoccupied
underofficer
underofficered
underofficial
underogating
underogatory
underopinion
underorb
underorganization
underorseman
underoverlooker
underoxidize
underpacking
underpaid
underpain
underpainting
underpan
underpants
underparticipation
underpartner
underpass
underpassion
underpay
underpayment
underpeep
underpeer
underpen
underpeopled
underpetticoat
underpetticoated
underpick
underpier
underpilaster
underpile
underpin
underpinner
underpinning
underpitch
underpitched
underplain
underplan
underplant
underplate
underplay
underplot
underplotter
underply
underpoint
underpole
underpopulate
underpopulation
underporch
underporter
underpose
underpossessor
underpot
underpower
underpraise
underprefect
underprentice
underpresence
underpresser
underpressure
underprice
underpriest
underprincipal
underprint
underprior
underprivileged
underprize
underproduce
underproduction
underproductive
underproficient
underprompt
underprompter
underproof
underprop
underproportion
underproportioned
underproposition
underpropped
underpropper
underpropping
underprospect
underpry
underpuke
underqualified
underqueen
underquote
underranger
underrate
underratement
underrating
underreach
underread
underreader
underrealize
underrealm
underream
underreamer
underreceiver
underreckon
underrecompense
underregion
underregistration
underrent
underrented
underrenting
underrepresent
underrepresentation
underrespected
underriddle
underriding
underrigged
underring
underripe
underripened
underriver
underroarer
underroast
underrobe
underrogue
underroll
underroller
underroof
underroom
underroot
underrooted
underrower
underrule
underruler
underrun
underrunning
undersacristan
undersailed
undersally
undersap
undersatisfaction
undersaturate
undersaturation
undersavior
undersaw
undersawyer
underscale
underscheme
underschool
underscoop
underscore
underscribe
underscript
underscrub
underscrupulous
undersea
underseam
underseaman
undersearch
underseas
underseated
undersecretary
undersecretaryship
undersect
undersee
underseeded
underseedman
undersell
underseller
underselling
undersense
undersequence
underservant
underserve
underservice
underset
undersetter
undersetting
undersettle
undersettler
undersettling
undersexton
undershapen
undersharp
undersheathing
undershepherd
undersheriff
undersheriffry
undersheriffship
undersheriffwick
undershield
undershine
undershining
undershire
undershirt
undershoe
undershoot
undershore
undershorten
undershot
undershrievalty
undershrieve
undershrievery
undershrub
undershrubbiness
undershrubby
undershunter
undershut
underside
undersight
undersighted
undersign
undersignalman
undersigner
undersill
undersinging
undersitter
undersize
undersized
underskin
underskirt
undersky
undersleep
undersleeve
underslip
underslope
undersluice
underslung
undersneer
undersociety
undersoil
undersole
undersomething
undersong
undersorcerer
undersort
undersoul
undersound
undersovereign
undersow
underspar
undersparred
underspecies
underspecified
underspend
undersphere
underspin
underspinner
undersplice
underspore
underspread
underspring
undersprout
underspurleather
undersquare
understaff
understage
understain
understairs
understamp
understand
understandability
understandable
understandableness
understandably
understander
understanding
understandingly
understandingness
understate
understatement
understay
understeer
understem
understep
understeward
understewardship
understimulus
understock
understocking
understood
understory
understrain
understrap
understrapper
understrapping
understratum
understream
understress
understrew
understride
understriding
understrife
understrike
understring
understroke
understrung
understudy
understuff
understuffing
undersuck
undersuggestion
undersuit
undersupply
undersupport
undersurface
underswain
underswamp
undersward
underswearer
undersweat
undersweep
underswell
undertakable
undertake
undertakement
undertaker
undertakerish
undertakerlike
undertakerly
undertakery
undertaking
undertakingly
undertalk
undertapster
undertaxed
underteacher
underteamed
underteller
undertenancy
undertenant
undertenter
undertenure
underterrestrial
undertest
underthane
underthaw
underthief
underthing
underthink
underthirst
underthought
underthroating
underthrob
underthrust
undertide
undertided
undertie
undertime
undertimed
undertint
undertitle
undertone
undertoned
undertook
undertow
undertrader
undertrained
undertread
undertreasurer
undertreat
undertribe
undertrick
undertrodden
undertruck
undertrump
undertruss
undertub
undertune
undertunic
underturf
underturn
underturnkey
undertutor
undertwig
undertype
undertyrant
underusher
undervaluation
undervalue
undervaluement
undervaluer
undervaluing
undervaluinglike
undervaluingly
undervalve
undervassal
undervaulted
undervaulting
undervegetation
underventilation
underverse
undervest
undervicar
underviewer
undervillain
undervinedresser
undervitalized
undervocabularied
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undervoltage
underwage
underwaist
underwaistcoat
underwalk
underward
underwarden
underwarmth
underwarp
underwash
underwatch
underwatcher
underwater
underwave
underway
underweapon
underwear
underweft
underweigh
underweight
underweighted
underwent
underwheel
underwhistle
underwind
underwing
underwit
underwitch
underwitted
underwood
underwooded
underwork
underworker
underworking
underworkman
underworld
underwrap
underwrite
underwriter
underwriting
underwrought
underyield
underyoke
underzeal
underzealot
undescendable
undescended
undescendible
undescribable
undescribably
undescribed
undescried
undescript
undescriptive
undescrying
undesert
undeserted
undeserting
undeserve
undeserved
undeservedly
undeservedness
undeserver
undeserving
undeservingly
undeservingness
undesign
undesignated
undesigned
undesignedly
undesignedness
undesigning
undesigningly
undesigningness
undesirability
undesirable
undesirableness
undesirably
undesire
undesired
undesiredly
undesiring
undesirous
undesirously
undesirousness
undesisting
undespaired
undespairing
undespairingly
undespatched
undespised
undespising
undespoiled
undespondent
undespondently
undesponding
undespotic
undestined
undestroyable
undestroyed
undestructible
undestructive
undetachable
undetached
undetailed
undetainable
undetained
undetectable
undetected
undetectible
undeteriorated
undeteriorating
undeterminable
undeterminate
undetermination
undetermined
undetermining
undeterred
undeterring
undetested
undetesting
undethronable
undethroned
undetracting
undetractingly
undetrimental
undevelopable
undeveloped
undeveloping
undeviated
undeviating
undeviatingly
undevil
undevious
undeviously
undevisable
undevised
undevoted
undevotion
undevotional
undevoured
undevout
undevoutly
undevoutness
undewed
undewy
undexterous
undexterously
undextrous
undextrously
undiademed
undiagnosable
undiagnosed
undialed
undialyzed
undiametric
undiamonded
undiapered
undiaphanous
undiatonic
undichotomous
undictated
undid
undidactic
undies
undieted
undifferenced
undifferent
undifferential
undifferentiated
undifficult
undiffident
undiffracted
undiffused
undiffusible
undiffusive
undig
undigenous
undigest
undigestable
undigested
undigestible
undigesting
undigestion
undigged
undight
undighted
undigitated
undignified
undignifiedly
undignifiedness
undignify
undiked
undilapidated
undilatable
undilated
undilatory
undiligent
undiligently
undilute
undiluted
undilution
undiluvial
undim
undimensioned
undimerous
undimidiate
undiminishable
undiminishableness
undiminishably
undiminished
undiminishing
undiminutive
undimmed
undimpled
Undine
undine
undined
undinted
undiocesed
undiphthongize
undiplomaed
undiplomatic
undipped
undirect
undirected
undirectional
undirectly
undirectness
undirk
undisabled
undisadvantageous
undisagreeable
undisappearing
undisappointable
undisappointed
undisappointing
undisarmed
undisastrous
undisbanded
undisbarred
undisburdened
undisbursed
undiscardable
undiscarded
undiscerned
undiscernedly
undiscernible
undiscernibleness
undiscernibly
undiscerning
undiscerningly
undischargeable
undischarged
undiscipled
undisciplinable
undiscipline
undisciplined
undisciplinedness
undisclaimed
undisclosed
undiscolored
undiscomfitable
undiscomfited
undiscomposed
undisconcerted
undisconnected
undiscontinued
undiscordant
undiscording
undiscounted
undiscourageable
undiscouraged
undiscouraging
undiscoursed
undiscoverable
undiscoverableness
undiscoverably
undiscovered
undiscreditable
undiscredited
undiscreet
undiscreetly
undiscreetness
undiscretion
undiscriminated
undiscriminating
undiscriminatingly
undiscriminatingness
undiscriminative
undiscursive
undiscussable
undiscussed
undisdained
undisdaining
undiseased
undisestablished
undisfigured
undisfranchised
undisfulfilled
undisgorged
undisgraced
undisguisable
undisguise
undisguised
undisguisedly
undisguisedness
undisgusted
undisheartened
undished
undisheveled
undishonored
undisillusioned
undisinfected
undisinheritable
undisinherited
undisintegrated
undisinterested
undisjoined
undisjointed
undisliked
undislocated
undislodgeable
undislodged
undismantled
undismay
undismayable
undismayed
undismayedly
undismembered
undismissed
undismounted
undisobedient
undisobeyed
undisobliging
undisordered
undisorderly
undisorganized
undisowned
undisowning
undisparaged
undisparity
undispassionate
undispatchable
undispatched
undispatching
undispellable
undispelled
undispensable
undispensed
undispensing
undispersed
undispersing
undisplaced
undisplanted
undisplay
undisplayable
undisplayed
undisplaying
undispleased
undispose
undisposed
undisposedness
undisprivacied
undisprovable
undisproved
undisproving
undisputable
undisputableness
undisputably
undisputatious
undisputatiously
undisputed
undisputedly
undisputedness
undisputing
undisqualifiable
undisqualified
undisquieted
undisreputable
undisrobed
undisrupted
undissected
undissembled
undissembledness
undissembling
undissemblingly
undisseminated
undissenting
undissevered
undissimulated
undissipated
undissociated
undissoluble
undissolute
undissolvable
undissolved
undissolving
undissonant
undissuadable
undissuadably
undissuade
undistanced
undistant
undistantly
undistasted
undistasteful
undistempered
undistend
undistended
undistilled
undistinct
undistinctive
undistinctly
undistinctness
undistinguish
undistinguishable
undistinguishableness
undistinguishably
undistinguished
undistinguishing
undistinguishingly
undistorted
undistorting
undistracted
undistractedly
undistractedness
undistracting
undistractingly
undistrained
undistraught
undistress
undistressed
undistributed
undistrusted
undistrustful
undisturbable
undisturbance
undisturbed
undisturbedly
undisturbedness
undisturbing
undisturbingly
unditched
undithyrambic
undittoed
undiuretic
undiurnal
undivable
undivergent
undiverging
undiverse
undiversified
undiverted
undivertible
undivertibly
undiverting
undivested
undivestedly
undividable
undividableness
undividably
undivided
undividedly
undividedness
undividing
undivinable
undivined
undivinelike
undivinely
undivining
undivisible
undivisive
undivorceable
undivorced
undivorcedness
undivorcing
undivulged
undivulging
undizened
undizzied
undo
undoable
undock
undocked
undoctor
undoctored
undoctrinal
undoctrined
undocumentary
undocumented
undocumentedness
undodged
undoer
undoffed
undog
undogmatic
undogmatical
undoing
undoingness
undolled
undolorous
undomed
undomestic
undomesticate
undomesticated
undomestication
undomicilable
undomiciled
undominated
undomineering
undominical
undominoed
undon
undonated
undonating
undone
undoneness
undonkey
undonnish
undoomed
undoped
undormant
undose
undosed
undoting
undotted
undouble
undoubled
undoubtable
undoubtableness
undoubtably
undoubted
undoubtedly
undoubtedness
undoubtful
undoubtfully
undoubtfulness
undoubting
undoubtingly
undoubtingness
undouched
undoughty
undovelike
undoweled
undowered
undowned
undowny
undrab
undraftable
undrafted
undrag
undragoned
undragooned
undrainable
undrained
undramatic
undramatical
undramatically
undramatizable
undramatized
undrape
undraped
undraperied
undraw
undrawable
undrawn
undreaded
undreadful
undreadfully
undreading
undreamed
undreaming
undreamlike
undreamt
undreamy
undredged
undreggy
undrenched
undress
undressed
undried
undrillable
undrilled
undrinkable
undrinkableness
undrinkably
undrinking
undripping
undrivable
undrivableness
undriven
undronelike
undrooping
undropped
undropsical
undrossy
undrowned
undrubbed
undrugged
undrunk
undrunken
undry
undryable
undrying
undualize
undub
undubbed
undubitable
undubitably
unducal
unduchess
undue
unduelling
undueness
undug
unduke
undulant
undular
undularly
undulatance
undulate
undulated
undulately
undulating
undulatingly
undulation
undulationist
undulative
undulatory
undull
undulled
undullness
unduloid
undulose
undulous
unduly
undumped
unduncelike
undunged
undupable
unduped
unduplicability
unduplicable
unduplicity
undurable
undurableness
undurably
undust
undusted
unduteous
undutiable
undutiful
undutifully
undutifulness
unduty
undwarfed
undwelt
undwindling
undy
undye
undyeable
undyed
undying
undyingly
undyingness
uneager
uneagerly
uneagerness
uneagled
unearly
unearned
unearnest
unearth
unearthed
unearthliness
unearthly
unease
uneaseful
uneasefulness
uneasily
uneasiness
uneastern
uneasy
uneatable
uneatableness
uneaten
uneath
uneating
unebbed
unebbing
unebriate
uneccentric
unecclesiastical
unechoed
unechoing
uneclectic
uneclipsed
uneconomic
uneconomical
uneconomically
uneconomicalness
uneconomizing
unecstatic
unedge
unedged
unedible
unedibleness
unedibly
unedified
unedifying
uneditable
unedited
uneducable
uneducableness
uneducably
uneducate
uneducated
uneducatedly
uneducatedness
uneducative
uneduced
uneffaceable
uneffaceably
uneffaced
uneffected
uneffectible
uneffective
uneffectless
uneffectual
uneffectually
uneffectualness
uneffectuated
uneffeminate
uneffeminated
uneffervescent
uneffete
unefficacious
unefficient
uneffigiated
uneffused
uneffusing
uneffusive
unegoist
unegoistical
unegoistically
unegregious
unejaculated
unejected
unelaborate
unelaborated
unelaborately
unelaborateness
unelapsed
unelastic
unelasticity
unelated
unelating
unelbowed
unelderly
unelect
unelectable
unelected
unelective
unelectric
unelectrical
unelectrified
unelectrify
unelectrifying
unelectrized
unelectronic
uneleemosynary
unelegant
unelegantly
unelegantness
unelemental
unelementary
unelevated
unelicited
unelided
unelidible
uneligibility
uneligible
uneligibly
uneliminated
unelongated
uneloped
uneloping
uneloquent
uneloquently
unelucidated
unelucidating
uneluded
unelusive
unemaciated
unemancipable
unemancipated
unemasculated
unembalmed
unembanked
unembarrassed
unembarrassedly
unembarrassedness
unembarrassing
unembarrassment
unembased
unembattled
unembayed
unembellished
unembezzled
unembittered
unemblazoned
unembodied
unembodiment
unembossed
unembowelled
unembowered
unembraceable
unembraced
unembroidered
unembroiled
unembryonic
unemendable
unemended
unemerged
unemerging
unemigrating
uneminent
uneminently
unemitted
unemolumentary
unemolumented
unemotional
unemotionalism
unemotionally
unemotionalness
unemotioned
unempaneled
unemphatic
unemphatical
unemphatically
unempirical
unempirically
unemploy
unemployability
unemployable
unemployableness
unemployably
unemployed
unemployment
unempoisoned
unempowered
unempt
unemptiable
unemptied
unempty
unemulative
unemulous
unemulsified
unenabled
unenacted
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ungrappled
ungrappler
ungrasp
ungraspable
ungrasped
ungrasping
ungrassed
ungrassy
ungrated
ungrateful
ungratefully
ungratefulness
ungratifiable
ungratified
ungratifying
ungrating
ungrave
ungraved
ungraveled
ungravelly
ungravely
ungraven
ungrayed
ungrazed
ungreased
ungreat
ungreatly
ungreatness
ungreeable
ungreedy
ungreen
ungreenable
ungreened
ungreeted
ungregarious
ungrieve
ungrieved
ungrieving
ungrilled
ungrimed
ungrindable
ungrip
ungripe
ungrizzled
ungroaning
ungroined
ungroomed
ungrooved
ungropeable
ungross
ungrotesque
unground
ungroundable
ungroundably
ungrounded
ungroundedly
ungroundedness
ungroupable
ungrouped
ungrow
ungrowing
ungrown
ungrubbed
ungrudged
ungrudging
ungrudgingly
ungrudgingness
ungruesome
ungruff
ungrumbling
ungual
unguaranteed
unguard
unguardable
unguarded
unguardedly
unguardedness
ungueal
unguent
unguentaria
unguentarium
unguentary
unguentiferous
unguentous
unguentum
unguerdoned
ungues
unguessable
unguessableness
unguessed
unguical
unguicorn
unguicular
Unguiculata
unguiculate
unguiculated
unguidable
unguidableness
unguidably
unguided
unguidedly
unguiferous
unguiform
unguiled
unguileful
unguilefully
unguilefulness
unguillotined
unguiltily
unguiltiness
unguilty
unguinal
unguinous
unguirostral
unguis
ungula
ungulae
ungular
Ungulata
ungulate
ungulated
unguled
unguligrade
ungull
ungulous
ungulp
ungum
ungummed
ungushing
ungutted
unguttural
unguyed
unguzzled
ungymnastic
ungypsylike
ungyve
ungyved
unhabit
unhabitable
unhabitableness
unhabited
unhabitual
unhabitually
unhabituate
unhabituated
unhacked
unhackled
unhackneyed
unhackneyedness
unhad
unhaft
unhafted
unhaggled
unhaggling
unhailable
unhailed
unhair
unhaired
unhairer
unhairily
unhairiness
unhairing
unhairy
unhallooed
unhallow
unhallowed
unhallowedness
unhaloed
unhalsed
unhalted
unhalter
unhaltered
unhalting
unhalved
unhammered
unhamper
unhampered
unhand
unhandcuff
unhandcuffed
unhandicapped
unhandily
unhandiness
unhandled
unhandseled
unhandsome
unhandsomely
unhandsomeness
unhandy
unhang
unhanged
unhap
unhappen
unhappily
unhappiness
unhappy
unharangued
unharassed
unharbor
unharbored
unhard
unharden
unhardenable
unhardened
unhardihood
unhardily
unhardiness
unhardness
unhardy
unharked
unharmable
unharmed
unharmful
unharmfully
unharming
unharmonic
unharmonical
unharmonious
unharmoniously
unharmoniousness
unharmonize
unharmonized
unharmony
unharness
unharnessed
unharped
unharried
unharrowed
unharsh
unharvested
unhashed
unhasp
unhasped
unhaste
unhasted
unhastened
unhastily
unhastiness
unhasting
unhasty
unhat
unhatchability
unhatchable
unhatched
unhatcheled
unhate
unhated
unhateful
unhating
unhatingly
unhatted
unhauled
unhaunt
unhaunted
unhave
unhawked
unhayed
unhazarded
unhazarding
unhazardous
unhazardousness
unhazed
unhead
unheaded
unheader
unheady
unheal
unhealable
unhealableness
unhealably
unhealed
unhealing
unhealth
unhealthful
unhealthfully
unhealthfulness
unhealthily
unhealthiness
unhealthsome
unhealthsomeness
unhealthy
unheaped
unhearable
unheard
unhearing
unhearsed
unheart
unhearten
unheartsome
unhearty
unheatable
unheated
unheathen
unheaved
unheaven
unheavenly
unheavily
unheaviness
unheavy
unhectored
unhedge
unhedged
unheed
unheeded
unheededly
unheedful
unheedfully
unheedfulness
unheeding
unheedingly
unheedy
unheeled
unheelpieced
unhefted
unheightened
unheired
unheld
unhele
unheler
unhelm
unhelmed
unhelmet
unhelmeted
unhelpable
unhelpableness
unhelped
unhelpful
unhelpfully
unhelpfulness
unhelping
unhelved
unhemmed
unheppen
unheralded
unheraldic
unherd
unherded
unhereditary
unheretical
unheritable
unhermetic
unhero
unheroic
unheroical
unheroically
unheroism
unheroize
unherolike
unhesitant
unhesitating
unhesitatingly
unhesitatingness
unheuristic
unhewable
unhewed
unhewn
unhex
unhid
unhidable
unhidableness
unhidably
unhidated
unhidden
unhide
unhidebound
unhideous
unhieratic
unhigh
unhilarious
unhinderable
unhinderably
unhindered
unhindering
unhinge
unhingement
unhinted
unhipped
unhired
unhissed
unhistoric
unhistorical
unhistorically
unhistory
unhistrionic
unhit
unhitch
unhitched
unhittable
unhive
unhoard
unhoarded
unhoarding
unhoary
unhoaxed
unhobble
unhocked
unhoed
unhogged
unhoist
unhoisted
unhold
unholiday
unholily
unholiness
unhollow
unhollowed
unholy
unhome
unhomelike
unhomelikeness
unhomeliness
unhomely
unhomish
unhomogeneity
unhomogeneous
unhomogeneously
unhomologous
unhoned
unhonest
unhonestly
unhoneyed
unhonied
unhonorable
unhonorably
unhonored
unhonoured
unhood
unhooded
unhoodwink
unhoodwinked
unhoofed
unhook
unhooked
unhoop
unhooped
unhooper
unhooted
unhoped
unhopedly
unhopedness
unhopeful
unhopefully
unhopefulness
unhoping
unhopingly
unhopped
unhoppled
unhorizoned
unhorizontal
unhorned
unhorny
unhoroscopic
unhorse
unhose
unhosed
unhospitable
unhospitableness
unhospitably
unhostile
unhostilely
unhostileness
unhostility
unhot
unhoundlike
unhouse
unhoused
unhouseled
unhouselike
unhousewifely
unhuddle
unhugged
unhull
unhulled
unhuman
unhumanize
unhumanized
unhumanly
unhumanness
unhumble
unhumbled
unhumbledness
unhumbleness
unhumbly
unhumbugged
unhumid
unhumiliated
unhumored
unhumorous
unhumorously
unhumorousness
unhumoured
unhung
unhuntable
unhunted
unhurdled
unhurled
unhurried
unhurriedly
unhurriedness
unhurrying
unhurryingly
unhurt
unhurted
unhurtful
unhurtfully
unhurtfulness
unhurting
unhusbanded
unhusbandly
unhushable
unhushed
unhushing
unhusk
unhusked
unhustled
unhustling
unhutched
unhuzzaed
unhydraulic
unhydrolyzed
unhygienic
unhygienically
unhygrometric
unhymeneal
unhymned
unhyphenated
unhyphened
unhypnotic
unhypnotizable
unhypnotize
unhypocritical
unhypocritically
unhypothecated
unhypothetical
unhysterical
uniambic
uniambically
uniangulate
uniarticular
uniarticulate
Uniat
uniat
Uniate
uniate
uniauriculate
uniauriculated
uniaxal
uniaxally
uniaxial
uniaxially
unibasal
unibivalent
unible
unibracteate
unibracteolate
unibranchiate
unicalcarate
unicameral
unicameralism
unicameralist
unicamerate
unicapsular
unicarinate
unicarinated
unice
uniced
unicell
unicellate
unicelled
unicellular
unicellularity
unicentral
unichord
uniciliate
unicism
unicist
unicity
uniclinal
unicolor
unicolorate
unicolored
unicolorous
uniconstant
unicorn
unicorneal
unicornic
unicornlike
unicornous
unicornuted
unicostate
unicotyledonous
unicum
unicursal
unicursality
unicursally
unicuspid
unicuspidate
unicycle
unicyclist
unidactyl
unidactyle
unidactylous
unideaed
unideal
unidealism
unidealist
unidealistic
unidealized
unidentate
unidentated
unidenticulate
unidentifiable
unidentifiableness
unidentifiably
unidentified
unidentifiedly
unidentifying
unideographic
unidextral
unidextrality
unidigitate
unidimensional
unidiomatic
unidiomatically
unidirect
unidirected
unidirection
unidirectional
unidle
unidleness
unidly
unidolatrous
unidolized
unidyllic
unie
uniembryonate
uniequivalent
uniface
unifaced
unifacial
unifactorial
unifarious
unifiable
unific
unification
unificationist
unificator
unified
unifiedly
unifiedness
unifier
unifilar
uniflagellate
unifloral
uniflorate
uniflorous
uniflow
uniflowered
unifocal
unifoliar
unifoliate
unifoliolate
Unifolium
uniform
uniformal
uniformalization
uniformalize
uniformally
uniformation
uniformed
uniformist
uniformitarian
uniformitarianism
uniformity
uniformization
uniformize
uniformless
uniformly
uniformness
unify
unigenesis
unigenetic
unigenist
unigenistic
unigenital
unigeniture
unigenous
uniglandular
uniglobular
unignitable
unignited
unignitible
unignominious
unignorant
unignored
unigravida
uniguttulate
unijugate
unijugous
unilabiate
unilabiated
unilamellar
unilamellate
unilaminar
unilaminate
unilateral
unilateralism
unilateralist
unilaterality
unilateralization
unilateralize
unilaterally
unilinear
unilingual
unilingualism
uniliteral
unilludedly
unillumed
unilluminated
unilluminating
unillumination
unillumined
unillusioned
unillusory
unillustrated
unillustrative
unillustrious
unilobal
unilobar
unilobate
unilobe
unilobed
unilobular
unilocular
unilocularity
uniloculate
unimacular
unimaged
unimaginable
unimaginableness
unimaginably
unimaginary
unimaginative
unimaginatively
unimaginativeness
unimagine
unimagined
unimanual
unimbanked
unimbellished
unimbezzled
unimbibed
unimbibing
unimbittered
unimbodied
unimboldened
unimbordered
unimbosomed
unimbowed
unimbowered
unimbroiled
unimbrowned
unimbrued
unimbued
unimedial
unimitable
unimitableness
unimitably
unimitated
unimitating
unimitative
unimmaculate
unimmanent
unimmediate
unimmerged
unimmergible
unimmersed
unimmigrating
unimmolated
unimmortal
unimmortalize
unimmortalized
unimmovable
unimmured
unimodal
unimodality
unimodular
unimolecular
unimolecularity
unimpair
unimpairable
unimpaired
unimpartable
unimparted
unimpartial
unimpassionate
unimpassioned
unimpassionedly
unimpassionedness
unimpatient
unimpawned
unimpeachability
unimpeachable
unimpeachableness
unimpeachably
unimpeached
unimpearled
unimped
unimpeded
unimpededly
unimpedible
unimpedness
unimpelled
unimpenetrable
unimperative
unimperial
unimperialistic
unimperious
unimpertinent
unimpinging
unimplanted
unimplicable
unimplicate
unimplicated
unimplicit
unimplicitly
unimplied
unimplorable
unimplored
unimpoisoned
unimportance
unimportant
unimportantly
unimported
unimporting
unimportunate
unimportunately
unimportuned
unimposed
unimposedly
unimposing
unimpostrous
unimpounded
unimpoverished
unimpowered
unimprecated
unimpregnable
unimpregnate
unimpregnated
unimpressed
unimpressibility
unimpressible
unimpressibleness
unimpressibly
unimpressionability
unimpressionable
unimpressive
unimpressively
unimpressiveness
unimprinted
unimprison
unimprisonable
unimprisoned
unimpropriated
unimprovable
unimprovableness
unimprovably
unimproved
unimprovedly
unimprovedness
unimprovement
unimproving
unimprovised
unimpugnable
unimpugned
unimpulsive
unimpurpled
unimputable
unimputed
unimucronate
unimultiplex
unimuscular
uninaugurated
unincantoned
unincarcerated
unincarnate
unincarnated
unincensed
uninchoative
unincidental
unincised
unincisive
unincited
uninclinable
uninclined
uninclining
uninclosed
uninclosedness
unincludable
unincluded
uninclusive
uninclusiveness
uninconvenienced
unincorporate
unincorporated
unincorporatedly
unincorporatedness
unincreasable
unincreased
unincreasing
unincubated
uninculcated
unincumbered
unindebted
unindebtedly
unindebtedness
unindemnified
unindentable
unindented
unindentured
unindexed
unindicable
unindicated
unindicative
unindictable
unindicted
unindifference
unindifferency
unindifferent
unindifferently
unindigent
unindignant
unindividual
unindividualize
unindividualized
unindividuated
unindorsed
uninduced
uninductive
unindulged
unindulgent
unindulgently
unindurated
unindustrial
unindustrialized
unindustrious
unindustriously
unindwellable
uninebriated
uninebriating
uninervate
uninerved
uninfallibility
uninfallible
uninfatuated
uninfectable
uninfected
uninfectious
uninfectiousness
uninfeft
uninferred
uninfested
uninfiltrated
uninfinite
uninfiniteness
uninfixed
uninflamed
uninflammability
uninflammable
uninflated
uninflected
uninflectedness
uninflicted
uninfluenceable
uninfluenced
uninfluencing
uninfluencive
uninfluential
uninfluentiality
uninfolded
uninformed
uninforming
uninfracted
uninfringeable
uninfringed
uninfringible
uninfuriated
uninfused
uningenious
uningeniously
uningeniousness
uningenuity
uningenuous
uningenuously
uningenuousness
uningested
uningrafted
uningrained
uninhabitability
uninhabitable
uninhabitableness
uninhabitably
uninhabited
uninhabitedness
uninhaled
uninheritability
uninheritable
uninherited
uninhibited
uninhibitive
uninhumed
uninimical
uniniquitous
uninitialed
uninitialled
uninitiate
uninitiated
uninitiatedness
uninitiation
uninjectable
uninjected
uninjurable
uninjured
uninjuredness
uninjuring
uninjurious
uninjuriously
uninjuriousness
uninked
uninlaid
uninn
uninnate
uninnocence
uninnocent
uninnocently
uninnocuous
uninnovating
uninoculable
uninoculated
uninodal
uninominal
uninquired
uninquiring
uninquisitive
uninquisitively
uninquisitiveness
uninquisitorial
uninsane
uninsatiable
uninscribed
uninserted
uninshrined
uninsinuated
uninsistent
uninsolvent
uninspected
uninspirable
uninspired
uninspiring
uninspiringly
uninspirited
uninspissated
uninstalled
uninstanced
uninstated
uninstigated
uninstilled
uninstituted
uninstructed
uninstructedly
uninstructedness
uninstructible
uninstructing
uninstructive
uninstructively
uninstructiveness
uninstrumental
uninsular
uninsulate
uninsulated
uninsultable
uninsulted
uninsulting
uninsurability
uninsurable
uninsured
unintegrated
unintellective
unintellectual
unintellectualism
unintellectuality
unintellectually
unintelligence
unintelligent
unintelligently
unintelligentsia
unintelligibility
unintelligible
unintelligibleness
unintelligibly
unintended
unintendedly
unintensive
unintent
unintentional
unintentionality
unintentionally
unintentionalness
unintently
unintentness
unintercalated
unintercepted
uninterchangeable
uninterdicted
uninterested
uninterestedly
uninterestedness
uninteresting
uninterestingly
uninterestingness
uninterferedwith
uninterjected
uninterlaced
uninterlarded
uninterleave
uninterleaved
uninterlined
uninterlinked
uninterlocked
unintermarrying
unintermediate
unintermingled
unintermission
unintermissive
unintermitted
unintermittedly
unintermittedness
unintermittent
unintermitting
unintermittingly
unintermittingness
unintermixed
uninternational
uninterpleaded
uninterpolated
uninterposed
uninterposing
uninterpretable
uninterpreted
uninterred
uninterrogable
uninterrogated
uninterrupted
uninterruptedly
uninterruptedness
uninterruptible
uninterruptibleness
uninterrupting
uninterruption
unintersected
uninterspersed
unintervening
uninterviewed
unintervolved
uninterwoven
uninthroned
unintimate
unintimated
unintimidated
unintitled
unintombed
unintoned
unintoxicated
unintoxicatedness
unintoxicating
unintrenchable
unintrenched
unintricate
unintrigued
unintriguing
unintroduced
unintroducible
unintroitive
unintromitted
unintrospective
unintruded
unintruding
unintrusive
unintrusively
unintrusted
unintuitive
unintwined
uninuclear
uninucleate
uninucleated
uninundated
uninured
uninurned
uninvadable
uninvaded
uninvaginated
uninvalidated
uninveighing
uninveigled
uninvented
uninventful
uninventibleness
uninventive
uninventively
uninventiveness
uninverted
uninvested
uninvestigable
uninvestigated
uninvestigating
uninvestigative
uninvidious
uninvidiously
uninvigorated
uninvincible
uninvite
uninvited
uninvitedly
uninviting
uninvoiced
uninvoked
uninvolved
uninweaved
uninwoven
uninwrapped
uninwreathed
Unio
unio
uniocular
unioid
Uniola
union
unioned
unionic
unionid
Unionidae
unioniform
unionism
unionist
unionistic
unionization
unionize
unionoid
unioval
uniovular
uniovulate
unipara
uniparental
uniparient
uniparous
unipartite
uniped
unipeltate
uniperiodic
unipersonal
unipersonalist
unipersonality
unipetalous
uniphase
uniphaser
uniphonous
uniplanar
uniplicate
unipod
unipolar
unipolarity
uniporous
unipotence
unipotent
unipotential
unipulse
uniquantic
unique
uniquely
uniqueness
uniquity
uniradial
uniradiate
uniradiated
uniradical
uniramose
uniramous
unirascible
unireme
unirenic
unirhyme
uniridescent
unironed
unironical
unirradiated
unirrigated
unirritable
unirritant
unirritated
unirritatedly
unirritating
unisepalous
uniseptate
uniserial
uniserially
uniseriate
uniseriately
uniserrate
uniserrulate
unisexed
unisexual
unisexuality
unisexually
unisilicate
unisoil
unisolable
unisolate
unisolated
unisomeric
unisometrical
unisomorphic
unison
unisonal
unisonally
unisonance
unisonant
unisonous
unisotropic
unisparker
unispiculate
unispinose
unispiral
unissuable
unissued
unistylist
unisulcate
unit
unitage
unital
unitalicized
Unitarian
unitarian
Unitarianism
Unitarianize
unitarily
unitariness
unitarism
unitarist
unitary
unite
uniteability
uniteable
uniteably
united
unitedly
unitedness
unitemized
unitentacular
uniter
uniting
unitingly
unition
unitism
unitistic
unitive
unitively
unitiveness
unitize
unitooth
unitrivalent
unitrope
unituberculate
unitude
unity
uniunguiculate
uniungulate
univalence
univalency
univalent
univalvate
univalve
univalvular
univariant
univerbal
universal
universalia
Universalian
Universalism
universalism
Universalist
universalist
Universalistic
universalistic
universality
universalization
universalize
universalizer
universally
universalness
universanimous
universe
universeful
universitarian
universitarianism
universitary
universitize
university
universityless
universitylike
universityship
universological
universologist
universology
univied
univocability
univocacy
univocal
univocalized
univocally
univocity
univoltine
univorous
unjacketed
unjaded
unjagged
unjailed
unjam
unjapanned
unjarred
unjarring
unjaundiced
unjaunty
unjealous
unjealoused
unjellied
unjesting
unjesuited
unjesuitical
unjesuitically
unjewel
unjeweled
unjewelled
Unjewish
unjilted
unjocose
unjocund
unjogged
unjogging
unjoin
unjoinable
unjoint
unjointed
unjointedness
unjointured
unjoking
unjokingly
unjolly
unjolted
unjostled
unjournalized
unjovial
unjovially
unjoyed
unjoyful
unjoyfully
unjoyfulness
unjoyous
unjoyously
unjoyousness
unjudgable
unjudge
unjudged
unjudgelike
unjudging
unjudicable
unjudicial
unjudicially
unjudicious
unjudiciously
unjudiciousness
unjuggled
unjuiced
unjuicy
unjumbled
unjumpable
unjust
unjustice
unjusticiable
unjustifiable
unjustifiableness
unjustifiably
unjustified
unjustifiedly
unjustifiedness
unjustify
unjustled
unjustly
unjustness
unjuvenile
unkaiserlike
unkamed
unked
unkeeled
unkembed
unkempt
unkemptly
unkemptness
unken
unkenned
unkennedness
unkennel
unkenneled
unkenning
unkensome
unkept
unkerchiefed
unket
unkey
unkeyed
unkicked
unkid
unkill
unkillability
unkillable
unkilled
unkilling
unkilned
unkin
unkind
unkindhearted
unkindled
unkindledness
unkindlily
unkindliness
unkindling
unkindly
unkindness
unkindred
unkindredly
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Unona
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unresistantly
unresisted
unresistedly
unresistedness
unresistible
unresistibleness
unresistibly
unresisting
unresistingly
unresistingness
unresolute
unresolvable
unresolve
unresolved
unresolvedly
unresolvedness
unresolving
unresonant
unresounded
unresounding
unresourceful
unresourcefulness
unrespect
unrespectability
unrespectable
unrespected
unrespectful
unrespectfully
unrespectfulness
unrespective
unrespectively
unrespectiveness
unrespirable
unrespired
unrespited
unresplendent
unresponding
unresponsible
unresponsibleness
unresponsive
unresponsively
unresponsiveness
unrest
unrestable
unrested
unrestful
unrestfully
unrestfulness
unresting
unrestingly
unrestingness
unrestorable
unrestored
unrestrainable
unrestrainably
unrestrained
unrestrainedly
unrestrainedness
unrestraint
unrestrictable
unrestricted
unrestrictedly
unrestrictedness
unrestrictive
unresty
unresultive
unresumed
unresumptive
unretainable
unretained
unretaliated
unretaliating
unretardable
unretarded
unretentive
unreticent
unretinued
unretired
unretiring
unretorted
unretouched
unretractable
unretracted
unretreating
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unretrenched
unretrievable
unretrieved
unretrievingly
unretted
unreturnable
unreturnably
unreturned
unreturning
unreturningly
unrevealable
unrevealed
unrevealedness
unrevealing
unrevealingly
unrevelationize
unrevenged
unrevengeful
unrevengefulness
unrevenging
unrevengingly
unrevenue
unrevenued
unreverberated
unrevered
unreverence
unreverenced
unreverend
unreverendly
unreverent
unreverential
unreverently
unreverentness
unreversable
unreversed
unreversible
unreverted
unrevertible
unreverting
unrevested
unrevetted
unreviewable
unreviewed
unreviled
unrevised
unrevivable
unrevived
unrevocable
unrevocableness
unrevocably
unrevoked
unrevolted
unrevolting
unrevolutionary
unrevolutionized
unrevolved
unrevolving
unrewardable
unrewarded
unrewardedly
unrewarding
unreworded
unrhetorical
unrhetorically
unrhetoricalness
unrhyme
unrhymed
unrhythmic
unrhythmical
unrhythmically
unribbed
unribboned
unrich
unriched
unricht
unricked
unrid
unridable
unridableness
unridably
unridden
unriddle
unriddleable
unriddled
unriddler
unriddling
unride
unridely
unridered
unridged
unridiculed
unridiculous
unrife
unriffled
unrifled
unrifted
unrig
unrigged
unrigging
unright
unrightable
unrighted
unrighteous
unrighteously
unrighteousness
unrightful
unrightfully
unrightfulness
unrightly
unrightwise
unrigid
unrigorous
unrimpled
unrind
unring
unringable
unringed
unringing
unrinsed
unrioted
unrioting
unriotous
unrip
unripe
unriped
unripely
unripened
unripeness
unripening
unrippable
unripped
unripping
unrippled
unrippling
unripplingly
unrisen
unrising
unriskable
unrisked
unrisky
unritual
unritualistic
unrivalable
unrivaled
unrivaledly
unrivaledness
unrived
unriven
unrivet
unriveted
unriveting
unroaded
unroadworthy
unroaming
unroast
unroasted
unrobbed
unrobe
unrobed
unrobust
unrocked
unrococo
unrodded
unroiled
unroll
unrollable
unrolled
unroller
unrolling
unrollment
unromantic
unromantical
unromantically
unromanticalness
unromanticized
unroof
unroofed
unroofing
unroomy
unroost
unroosted
unroosting
unroot
unrooted
unrooting
unrope
unroped
unrosed
unrosined
unrostrated
unrotated
unrotating
unroted
unrotted
unrotten
unrotund
unrouged
unrough
unroughened
unround
unrounded
unrounding
unrousable
unroused
unroutable
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unrove
unroved
unroving
unrow
unrowed
unroweled
unroyal
unroyalist
unroyalized
unroyally
unroyalness
Unrra
unrubbed
unrubbish
unrubified
unrubrical
unrubricated
unruddered
unruddled
unrueful
unruffable
unruffed
unruffle
unruffled
unruffling
unrugged
unruinable
unruinated
unruined
unrulable
unrulableness
unrule
unruled
unruledly
unruledness
unruleful
unrulily
unruliness
unruly
unruminated
unruminating
unruminatingly
unrummaged
unrumored
unrumple
unrumpled
unrun
unrung
unruptured
unrural
unrushed
Unrussian
unrust
unrusted
unrustic
unrusticated
unrustling
unruth
unsabbatical
unsabered
unsabled
unsabred
unsaccharic
unsacerdotal
unsacerdotally
unsack
unsacked
unsacramental
unsacramentally
unsacramentarian
unsacred
unsacredly
unsacrificeable
unsacrificeably
unsacrificed
unsacrificial
unsacrificing
unsacrilegious
unsad
unsadden
unsaddened
unsaddle
unsaddled
unsaddling
unsafe
unsafeguarded
unsafely
unsafeness
unsafety
unsagacious
unsage
unsagging
unsaid
unsailable
unsailed
unsailorlike
unsaint
unsainted
unsaintlike
unsaintly
unsalability
unsalable
unsalableness
unsalably
unsalaried
unsalesmanlike
unsaline
unsalivated
unsallying
unsalmonlike
unsalt
unsaltable
unsaltatory
unsalted
unsalubrious
unsalutary
unsaluted
unsaluting
unsalvability
unsalvable
unsalvableness
unsalvaged
unsalved
unsampled
unsanctification
unsanctified
unsanctifiedly
unsanctifiedness
unsanctify
unsanctifying
unsanctimonious
unsanctimoniously
unsanctimoniousness
unsanction
unsanctionable
unsanctioned
unsanctioning
unsanctitude
unsanctity
unsanctuaried
unsandaled
unsanded
unsane
unsanguinary
unsanguine
unsanguinely
unsanguineness
unsanguineous
unsanguineously
unsanitariness
unsanitary
unsanitated
unsanitation
unsanity
unsaponifiable
unsaponified
unsapped
unsappy
unsarcastic
unsardonic
unsartorial
unsash
unsashed
unsatable
unsatanic
unsated
unsatedly
unsatedness
unsatiability
unsatiable
unsatiableness
unsatiably
unsatiate
unsatiated
unsatiating
unsatin
unsatire
unsatirical
unsatirically
unsatirize
unsatirized
unsatisfaction
unsatisfactorily
unsatisfactoriness
unsatisfactory
unsatisfiable
unsatisfiableness
unsatisfiably
unsatisfied
unsatisfiedly
unsatisfiedness
unsatisfying
unsatisfyingly
unsatisfyingness
unsaturable
unsaturated
unsaturatedly
unsaturatedness
unsaturation
unsatyrlike
unsauced
unsaurian
unsavable
unsaveable
unsaved
unsaving
unsavored
unsavoredly
unsavoredness
unsavorily
unsavoriness
unsavory
unsawed
unsawn
unsay
unsayability
unsayable
unscabbard
unscabbarded
unscabbed
unscaffolded
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unscalableness
unscalably
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unscaled
unscaledness
unscalloped
unscaly
unscamped
unscandalize
unscandalized
unscandalous
unscannable
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unscanted
unscanty
unscarb
unscarce
unscared
unscarfed
unscarified
unscarred
unscathed
unscathedly
unscathedness
unscattered
unscavengered
unscenic
unscent
unscented
unscepter
unsceptered
unsceptical
unsceptre
unsceptred
unscheduled
unschematic
unschematized
unscholar
unscholarlike
unscholarly
unscholastic
unschool
unschooled
unschooledly
unschooledness
unscienced
unscientific
unscientifical
unscientifically
unscintillating
unscioned
unscissored
unscoffed
unscoffing
unscolded
unsconced
unscooped
unscorched
unscored
unscorified
unscoring
unscorned
unscornful
unscornfully
unscornfulness
unscotch
unscotched
unscottify
unscoured
unscourged
unscowling
unscramble
unscrambling
unscraped
unscratchable
unscratched
unscratching
unscratchingly
unscrawled
unscreen
unscreenable
unscreenably
unscreened
unscrew
unscrewable
unscrewed
unscrewing
unscribal
unscribbled
unscribed
unscrimped
unscriptural
unscripturally
unscripturalness
unscrubbed
unscrupled
unscrupulosity
unscrupulous
unscrupulously
unscrupulousness
unscrutable
unscrutinized
unscrutinizing
unscrutinizingly
unsculptural
unsculptured
unscummed
unscutcheoned
unseafaring
unseal
unsealable
unsealed
unsealer
unsealing
unseam
unseamanlike
unseamanship
unseamed
unseaming
unsearchable
unsearchableness
unsearchably
unsearched
unsearcherlike
unsearching
unseared
unseason
unseasonable
unseasonableness
unseasonably
unseasoned
unseat
unseated
unseaworthiness
unseaworthy
unseceding
unsecluded
unseclusive
unseconded
unsecrecy
unsecret
unsecretarylike
unsecreted
unsecreting
unsecretly
unsecretness
unsectarian
unsectarianism
unsectarianize
unsectional
unsecular
unsecularize
unsecularized
unsecure
unsecured
unsecuredly
unsecuredness
unsecurely
unsecureness
unsecurity
unsedate
unsedentary
unseditious
unseduce
unseduced
unseducible
unseductive
unsedulous
unsee
unseeable
unseeded
unseeing
unseeingly
unseeking
unseeming
unseemingly
unseemlily
unseemliness
unseemly
unseen
unseethed
unsegmented
unsegregable
unsegregated
unsegregatedness
unseignorial
unseismic
unseizable
unseized
unseldom
unselect
unselected
unselecting
unselective
unself
unselfish
unselfishly
unselfishness
unselflike
unselfness
unselling
unsenatorial
unsenescent
unsensational
unsense
unsensed
unsensibility
unsensible
unsensibleness
unsensibly
unsensitive
unsensitize
unsensitized
unsensory
unsensual
unsensualize
unsensualized
unsensually
unsensuous
unsensuousness
unsent
unsentenced
unsententious
unsentient
unsentimental
unsentimentalist
unsentimentality
unsentimentalize
unsentimentally
unsentineled
unsentinelled
unseparable
unseparableness
unseparably
unseparate
unseparated
unseptate
unseptated
unsepulcher
unsepulchered
unsepulchral
unsepulchre
unsepulchred
unsepultured
unsequenced
unsequential
unsequestered
unseraphical
unserenaded
unserene
unserflike
unserious
unseriousness
unserrated
unserried
unservable
unserved
unserviceability
unserviceable
unserviceableness
unserviceably
unservicelike
unservile
unsesquipedalian
unset
unsetting
unsettle
unsettleable
unsettled
unsettledness
unsettlement
unsettling
unseverable
unseverableness
unsevere
unsevered
unseveredly
unseveredness
unsew
unsewed
unsewered
unsewing
unsewn
unsex
unsexed
unsexing
unsexlike
unsexual
unshackle
unshackled
unshackling
unshade
unshaded
unshadow
unshadowable
unshadowed
unshady
unshafted
unshakable
unshakably
unshakeable
unshakeably
unshaken
unshakenly
unshakenness
unshaking
unshakingness
unshaled
unshamable
unshamableness
unshamably
unshameable
unshameableness
unshameably
unshamed
unshamefaced
unshamefacedness
unshameful
unshamefully
unshamefulness
unshammed
unshanked
unshapable
unshape
unshapeable
unshaped
unshapedness
unshapeliness
unshapely
unshapen
unshapenly
unshapenness
unsharable
unshared
unsharedness
unsharing
unsharp
unsharped
unsharpen
unsharpened
unsharpening
unsharping
unshattered
unshavable
unshaveable
unshaved
unshavedly
unshavedness
unshaven
unshavenly
unshavenness
unshawl
unsheaf
unsheared
unsheathe
unsheathed
unsheathing
unshed
unsheet
unsheeted
unsheeting
unshell
unshelled
unshelling
unshelterable
unsheltered
unsheltering
unshelve
unshepherded
unshepherding
unsheriff
unshewed
unshieldable
unshielded
unshielding
unshiftable
unshifted
unshiftiness
unshifting
unshifty
unshimmering
unshingled
unshining
unship
unshiplike
unshipment
unshipped
unshipping
unshipshape
unshipwrecked
unshirking
unshirted
unshivered
unshivering
unshockable
unshocked
unshod
unshodden
unshoe
unshoed
unshoeing
unshop
unshore
unshored
unshorn
unshort
unshortened
unshot
unshotted
unshoulder
unshouted
unshouting
unshoved
unshoveled
unshowable
unshowed
unshowmanlike
unshown
unshowy
unshredded
unshrew
unshrewd
unshrewish
unshrill
unshrine
unshrined
unshrinement
unshrink
unshrinkability
unshrinkable
unshrinking
unshrinkingly
unshrived
unshriveled
unshrivelled
unshriven
unshroud
unshrouded
unshrubbed
unshrugging
unshrunk
unshrunken
unshuddering
unshuffle
unshuffled
unshunnable
unshunned
unshunted
unshut
unshutter
unshuttered
unshy
unshyly
unshyness
unsibilant
unsiccated
unsick
unsickened
unsicker
unsickerly
unsickerness
unsickled
unsickly
unsided
unsiding
unsiege
unsifted
unsighing
unsight
unsightable
unsighted
unsighting
unsightliness
unsightly
unsigmatic
unsignable
unsignaled
unsignalized
unsignalled
unsignatured
unsigned
unsigneted
unsignificancy
unsignificant
unsignificantly
unsignificative
unsignified
unsignifying
unsilenceable
unsilenceably
unsilenced
unsilent
unsilentious
unsilently
unsilicified
unsilly
unsilvered
unsimilar
unsimilarity
unsimilarly
unsimple
unsimplicity
unsimplified
unsimplify
unsimulated
unsimultaneous
unsin
unsincere
unsincerely
unsincereness
unsincerity
unsinew
unsinewed
unsinewing
unsinewy
unsinful
unsinfully
unsinfulness
unsing
unsingability
unsingable
unsingableness
unsinged
unsingle
unsingled
unsingleness
unsingular
unsinister
unsinkability
unsinkable
unsinking
unsinnable
unsinning
unsinningness
unsiphon
unsipped
unsister
unsistered
unsisterliness
unsisterly
unsizable
unsizableness
unsizeable
unsizeableness
unsized
unskaithd
unskeptical
unsketchable
unsketched
unskewed
unskewered
unskilful
unskilfully
unskilled
unskilledly
unskilledness
unskillful
unskillfully
unskillfulness
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unskin
unskinned
unskirted
unslack
unslacked
unslackened
unslackening
unslacking
unslagged
unslain
unslakable
unslakeable
unslaked
unslammed
unslandered
unslanderous
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unslate
unslated
unslating
unslaughtered
unslave
unslayable
unsleaved
unsleek
unsleepably
unsleeping
unsleepingly
unsleepy
unsleeve
unsleeved
unslender
unslept
unsliced
unsliding
unslighted
unsling
unslip
unslipped
unslippery
unslipping
unslit
unslockened
unsloped
unslopped
unslot
unslothful
unslothfully
unslothfulness
unslotted
unsloughed
unsloughing
unslow
unsluggish
unsluice
unsluiced
unslumbering
unslumberous
unslumbrous
unslung
unslurred
unsly
unsmacked
unsmart
unsmartly
unsmartness
unsmeared
unsmelled
unsmelling
unsmelted
unsmiled
unsmiling
unsmilingly
unsmilingness
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unsmirking
unsmitten
unsmokable
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unsmokified
unsmoking
unsmoky
unsmooth
unsmoothed
unsmoothly
unsmoothness
unsmote
unsmotherable
unsmothered
unsmudged
unsmuggled
unsmutched
unsmutted
unsmutty
unsnaffled
unsnagged
unsnaggled
unsnaky
unsnap
unsnapped
unsnare
unsnared
unsnarl
unsnatch
unsnatched
unsneck
unsneering
unsnib
unsnipped
unsnobbish
unsnoring
unsnouted
unsnow
unsnubbable
unsnubbed
unsnuffed
unsoaked
unsoaped
unsoarable
unsober
unsoberly
unsoberness
unsobriety
unsociability
unsociable
unsociableness
unsociably
unsocial
unsocialism
unsocialistic
unsociality
unsocializable
unsocialized
unsocially
unsocialness
unsociological
unsocket
unsodden
unsoft
unsoftened
unsoftening
unsoggy
unsoil
unsoiled
unsoiledness
unsolaced
unsolacing
unsolar
unsold
unsolder
unsoldered
unsoldering
unsoldier
unsoldiered
unsoldierlike
unsoldierly
unsole
unsoled
unsolemn
unsolemness
unsolemnize
unsolemnized
unsolemnly
unsolicitated
unsolicited
unsolicitedly
unsolicitous
unsolicitously
unsolicitousness
unsolid
unsolidarity
unsolidifiable
unsolidified
unsolidity
unsolidly
unsolidness
unsolitary
unsolubility
unsoluble
unsolvable
unsolvableness
unsolvably
unsolved
unsomatic
unsomber
unsombre
unsome
unson
unsonable
unsonant
unsonlike
unsonneted
unsonorous
unsonsy
unsoothable
unsoothed
unsoothfast
unsoothing
unsooty
unsophistical
unsophistically
unsophisticate
unsophisticated
unsophisticatedly
unsophisticatedness
unsophistication
unsophomoric
unsordid
unsore
unsorrowed
unsorrowing
unsorry
unsort
unsortable
unsorted
unsorting
unsotted
unsought
unsoul
unsoulful
unsoulfully
unsoulish
unsound
unsoundable
unsoundableness
unsounded
unsounding
unsoundly
unsoundness
unsour
unsoured
unsoused
unsovereign
unsowed
unsown
unspaced
unspacious
unspaded
unspan
unspangled
unspanked
unspanned
unspar
unsparable
unspared
unsparing
unsparingly
unsparingness
unsparkling
unsparred
unsparse
unspatial
unspatiality
unspattered
unspawned
unspayed
unspeak
unspeakability
unspeakable
unspeakableness
unspeakably
unspeaking
unspeared
unspecialized
unspecializing
unspecific
unspecified
unspecifiedly
unspecious
unspecked
unspeckled
unspectacled
unspectacular
unspectacularly
unspecterlike
unspectrelike
unspeculating
unspeculative
unspeculatively
unsped
unspeed
unspeedy
unspeered
unspell
unspellable
unspelled
unspelt
unspendable
unspending
unspent
unspewed
unsphere
unsphered
unsphering
unspiable
unspiced
unspicy
unspied
unspike
unspillable
unspin
unspinsterlike
unspinsterlikeness
unspiral
unspired
unspirit
unspirited
unspiritedly
unspiriting
unspiritual
unspirituality
unspiritualize
unspiritualized
unspiritually
unspiritualness
unspissated
unspit
unspited
unspiteful
unspitted
unsplashed
unsplattered
unsplayed
unspleened
unspleenish
unspleenishly
unsplendid
unspliced
unsplinted
unsplintered
unsplit
unspoil
unspoilable
unspoilableness
unspoilably
unspoiled
unspoken
unspokenly
unsponged
unspongy
unsponsored
unspontaneous
unspontaneously
unspookish
unsported
unsportful
unsporting
unsportive
unsportsmanlike
unsportsmanly
unspot
unspotlighted
unspottable
unspotted
unspottedly
unspottedness
unspoused
unspouselike
unspouted
unsprained
unsprayed
unspread
unsprightliness
unsprightly
unspring
unspringing
unspringlike
unsprinkled
unsprinklered
unsprouted
unsproutful
unsprouting
unspruced
unsprung
unspun
unspurned
unspurred
unspying
unsquandered
unsquarable
unsquare
unsquared
unsquashed
unsqueamish
unsqueezable
unsqueezed
unsquelched
unsquinting
unsquire
unsquired
unsquirelike
unsquirted
unstabbed
unstability
unstable
unstabled
unstableness
unstablished
unstably
unstack
unstacked
unstacker
unstaffed
unstaged
unstaggered
unstaggering
unstagnating
unstagy
unstaid
unstaidly
unstaidness
unstain
unstainable
unstainableness
unstained
unstainedly
unstainedness
unstaled
unstalked
unstalled
unstammering
unstamped
unstampeded
unstanch
unstanchable
unstandard
unstandardized
unstanzaic
unstar
unstarch
unstarched
unstarlike
unstarred
unstarted
unstarting
unstartled
unstarved
unstatable
unstate
unstateable
unstated
unstately
unstatesmanlike
unstatic
unstating
unstation
unstationary
unstationed
unstatistic
unstatistical
unstatued
unstatuesque
unstatutable
unstatutably
unstaunch
unstaunchable
unstaunched
unstavable
unstaveable
unstaved
unstayable
unstayed
unstayedness
unstaying
unsteadfast
unsteadfastly
unsteadfastness
unsteadied
unsteadily
unsteadiness
unsteady
unsteadying
unstealthy
unsteamed
unsteaming
unsteck
unstecked
unsteel
unsteeled
unsteep
unsteeped
unsteepled
unsteered
unstemmable
unstemmed
unstentorian
unstep
unstercorated
unstereotyped
unsterile
unsterilized
unstern
unstethoscoped
unstewardlike
unstewed
unstick
unsticking
unstickingness
unsticky
unstiffen
unstiffened
unstifled
unstigmatized
unstill
unstilled
unstillness
unstilted
unstimulated
unstimulating
unsting
unstinged
unstinging
unstinted
unstintedly
unstinting
unstintingly
unstippled
unstipulated
unstirrable
unstirred
unstirring
unstitch
unstitched
unstitching
unstock
unstocked
unstocking
unstockinged
unstoic
unstoical
unstoically
unstoicize
unstoked
unstoken
unstolen
unstonable
unstone
unstoned
unstoniness
unstony
unstooping
unstop
unstoppable
unstopped
unstopper
unstoppered
unstopple
unstore
unstored
unstoried
unstormed
unstormy
unstout
unstoved
unstow
unstowed
unstraddled
unstrafed
unstraight
unstraightened
unstraightforward
unstraightness
unstrain
unstrained
unstraitened
unstrand
unstranded
unstrange
unstrangered
unstrangled
unstrangulable
unstrap
unstrapped
unstrategic
unstrategically
unstratified
unstraying
unstreaked
unstrength
unstrengthen
unstrengthened
unstrenuous
unstressed
unstressedly
unstressedness
unstretch
unstretched
unstrewed
unstrewn
unstriated
unstricken
unstrictured
unstridulous
unstrike
unstriking
unstring
unstringed
unstringing
unstrip
unstriped
unstripped
unstriving
unstroked
unstrong
unstructural
unstruggling
unstrung
unstubbed
unstubborn
unstuccoed
unstuck
unstudded
unstudied
unstudious
unstuff
unstuffed
unstuffing
unstultified
unstumbling
unstung
unstunned
unstunted
unstupefied
unstupid
unstuttered
unstuttering
unsty
unstyled
unstylish
unstylishly
unstylishness
unsubdivided
unsubduable
unsubduableness
unsubduably
unsubducted
unsubdued
unsubduedly
unsubduedness
unsubject
unsubjectable
unsubjected
unsubjectedness
unsubjection
unsubjective
unsubjectlike
unsubjugate
unsubjugated
unsublimable
unsublimated
unsublimed
unsubmerged
unsubmergible
unsubmerging
unsubmission
unsubmissive
unsubmissively
unsubmissiveness
unsubmitted
unsubmitting
unsubordinate
unsubordinated
unsuborned
unsubpoenaed
unsubscribed
unsubscribing
unsubservient
unsubsided
unsubsidiary
unsubsiding
unsubsidized
unsubstanced
unsubstantial
unsubstantiality
unsubstantialize
unsubstantially
unsubstantialness
unsubstantiate
unsubstantiated
unsubstantiation
unsubstituted
unsubtle
unsubtleness
unsubtlety
unsubtly
unsubtracted
unsubventioned
unsubventionized
unsubversive
unsubvertable
unsubverted
unsubvertive
unsucceedable
unsucceeded
unsucceeding
unsuccess
unsuccessful
unsuccessfully
unsuccessfulness
unsuccessive
unsuccessively
unsuccessiveness
unsuccinct
unsuccorable
unsuccored
unsucculent
unsuccumbing
unsucked
unsuckled
unsued
unsufferable
unsufferableness
unsufferably
unsuffered
unsuffering
unsufficed
unsufficience
unsufficiency
unsufficient
unsufficiently
unsufficing
unsufficingness
unsufflated
unsuffocate
unsuffocated
unsuffocative
unsuffused
unsugared
unsugary
unsuggested
unsuggestedness
unsuggestive
unsuggestiveness
unsuit
unsuitability
unsuitable
unsuitableness
unsuitably
unsuited
unsuiting
unsulky
unsullen
unsulliable
unsullied
unsulliedly
unsulliedness
unsulphonated
unsulphureous
unsulphurized
unsultry
unsummable
unsummarized
unsummed
unsummered
unsummerlike
unsummerly
unsummonable
unsummoned
unsumptuary
unsumptuous
unsun
unsunburned
unsundered
unsung
unsunk
unsunken
unsunned
unsunny
unsuperable
unsuperannuated
unsupercilious
unsuperficial
unsuperfluous
unsuperior
unsuperlative
unsupernatural
unsupernaturalize
unsupernaturalized
unsuperscribed
unsuperseded
unsuperstitious
unsupervised
unsupervisedly
unsupped
unsupplantable
unsupplanted
unsupple
unsuppled
unsupplemented
unsuppliable
unsupplicated
unsupplied
unsupportable
unsupportableness
unsupportably
unsupported
unsupportedly
unsupportedness
unsupporting
unsupposable
unsupposed
unsuppressed
unsuppressible
unsuppressibly
unsuppurated
unsuppurative
unsupreme
unsurcharge
unsurcharged
unsure
unsurfaced
unsurfeited
unsurfeiting
unsurgical
unsurging
unsurmised
unsurmising
unsurmountable
unsurmountableness
unsurmountably
unsurmounted
unsurnamed
unsurpassable
unsurpassableness
unsurpassably
unsurpassed
unsurplice
unsurpliced
unsurprised
unsurprising
unsurrendered
unsurrendering
unsurrounded
unsurveyable
unsurveyed
unsurvived
unsurviving
unsusceptibility
unsusceptible
unsusceptibleness
unsusceptibly
unsusceptive
unsuspectable
unsuspectably
unsuspected
unsuspectedly
unsuspectedness
unsuspectful
unsuspectfulness
unsuspectible
unsuspecting
unsuspectingly
unsuspectingness
unsuspective
unsuspended
unsuspicion
unsuspicious
unsuspiciously
unsuspiciousness
unsustainable
unsustained
unsustaining
unsutured
unswabbed
unswaddle
unswaddled
unswaddling
unswallowable
unswallowed
unswanlike
unswapped
unswarming
unswathable
unswathe
unswathed
unswathing
unswayable
unswayed
unswayedness
unswaying
unswear
unswearing
unsweat
unsweated
unsweating
unsweepable
unsweet
unsweeten
unsweetened
unsweetenedness
unsweetly
unsweetness
unswell
unswelled
unswelling
unsweltered
unswept
unswervable
unswerved
unswerving
unswervingly
unswilled
unswing
unswingled
unswitched
unswivel
unswollen
unswooning
unsworn
unswung
unsyllabic
unsyllabled
unsyllogistical
unsymbolic
unsymbolical
unsymbolically
unsymbolicalness
unsymbolized
unsymmetrical
unsymmetrically
unsymmetricalness
unsymmetrized
unsymmetry
unsympathetic
unsympathetically
unsympathizability
unsympathizable
unsympathized
unsympathizing
unsympathizingly
unsympathy
unsymphonious
unsymptomatic
unsynchronized
unsynchronous
unsyncopated
unsyndicated
unsynonymous
unsyntactical
unsynthetic
unsyringed
unsystematic
unsystematical
unsystematically
unsystematized
unsystematizedly
unsystematizing
unsystemizable
untabernacled
untabled
untabulated
untack
untacked
untacking
untackle
untackled
untactful
untactfully
untactfulness
untagged
untailed
untailorlike
untailorly
untaint
untaintable
untainted
untaintedly
untaintedness
untainting
untakable
untakableness
untakeable
untakeableness
untaken
untaking
untalented
untalkative
untalked
untalking
untall
untallied
untallowed
untamable
untamableness
untame
untamed
untamedly
untamedness
untamely
untameness
untampered
untangential
untangibility
untangible
untangibleness
untangibly
untangle
untangled
untangling
untanned
untantalized
untantalizing
untap
untaped
untapered
untapering
untapestried
untappable
untapped
untar
untarnishable
untarnished
untarred
untarried
untarrying
untartarized
untasked
untasseled
untastable
untaste
untasteable
untasted
untasteful
untastefully
untastefulness
untasting
untasty
untattered
untattooed
untaught
untaughtness
untaunted
untaut
untautological
untawdry
untawed
untax
untaxable
untaxed
untaxing
unteach
unteachable
unteachableness
unteachably
unteacherlike
unteaching
unteam
unteamed
unteaming
untearable
unteased
unteasled
untechnical
untechnicalize
untechnically
untedded
untedious
unteem
unteeming
unteethed
untelegraphed
untell
untellable
untellably
untelling
untemper
untemperamental
untemperate
untemperately
untemperateness
untempered
untempering
untempested
untempestuous
untempled
untemporal
untemporary
untemporizing
untemptability
untemptable
untemptably
untempted
untemptible
untemptibly
untempting
untemptingly
untemptingness
untenability
untenable
untenableness
untenably
untenacious
untenacity
untenant
untenantable
untenantableness
untenanted
untended
untender
untendered
untenderly
untenderness
untenible
untenibleness
untenibly
untense
untent
untentaculate
untented
untentered
untenty
unterminable
unterminableness
unterminably
unterminated
unterminating
unterraced
unterrestrial
unterrible
unterribly
unterrifiable
unterrific
unterrified
unterrifying
unterrorized
untessellated
untestable
untestamentary
untested
untestifying
untether
untethered
untethering
untewed
untextual
unthank
unthanked
unthankful
unthankfully
unthankfulness
unthanking
unthatch
unthatched
unthaw
unthawed
unthawing
untheatric
untheatrical
untheatrically
untheistic
unthematic
untheological
untheologically
untheologize
untheoretic
untheoretical
untheorizable
untherapeutical
unthick
unthicken
unthickened
unthievish
unthink
unthinkability
unthinkable
unthinkableness
unthinkably
unthinker
unthinking
unthinkingly
unthinkingness
unthinned
unthinning
unthirsting
unthirsty
unthistle
untholeable
untholeably
unthorn
unthorny
unthorough
unthought
unthoughted
unthoughtedly
unthoughtful
unthoughtfully
unthoughtfulness
unthoughtlike
unthrall
unthralled
unthrashed
unthread
unthreadable
unthreaded
unthreading
unthreatened
unthreatening
unthreshed
unthrid
unthridden
unthrift
unthriftihood
unthriftily
unthriftiness
unthriftlike
unthrifty
unthrilled
unthrilling
unthriven
unthriving
unthrivingly
unthrivingness
unthrob
unthrone
unthroned
unthronged
unthroning
unthrottled
unthrowable
unthrown
unthrushlike
unthrust
unthumbed
unthumped
unthundered
unthwacked
unthwarted
untiaraed
unticketed
untickled
untidal
untidily
untidiness
untidy
untie
untied
untight
untighten
untightness
until
untile
untiled
untill
untillable
untilled
untilling
untilt
untilted
untilting
untimbered
untimed
untimedness
untimeliness
untimely
untimeous
untimeously
untimesome
untimorous
untin
untinct
untinctured
untine
untinged
untinkered
untinned
untinseled
untinted
untippable
untipped
untippled
untipt
untirability
untirable
untire
untired
untiredly
untiring
untiringly
untissued
untithability
untithable
untithed
untitled
untittering
untitular
unto
untoadying
untoasted
untogaed
untoggle
untoggler
untoiled
untoileted
untoiling
untold
untolerable
untolerableness
untolerably
untolerated
untomb
untombed
untonality
untone
untoned
untongued
untonsured
untooled
untooth
untoothed
untoothsome
untoothsomeness
untop
untopographical
untopped
untopping
untormented
untorn
untorpedoed
untorpid
untorrid
untortuous
untorture
untortured
untossed
untotaled
untotalled
untottering
untouch
untouchability
untouchable
untouchableness
untouchably
untouched
untouchedness
untouching
untough
untoured
untouristed
untoward
untowardliness
untowardly
untowardness
untowered
untown
untownlike
untrace
untraceable
untraceableness
untraceably
untraced
untraceried
untracked
untractability
untractable
untractableness
untractably
untractarian
untractible
untractibleness
untradeable
untraded
untradesmanlike
untrading
untraditional
untraduced
untraffickable
untrafficked
untragic
untragical
untrailed
untrain
untrainable
untrained
untrainedly
untrainedness
untraitored
untraitorous
untrammed
untrammeled
untrammeledness
untramped
untrampled
untrance
untranquil
untranquilized
untranquillize
untranquillized
untransacted
untranscended
untranscendental
untranscribable
untranscribed
untransferable
untransferred
untransfigured
untransfixed
untransformable
untransformed
untransforming
untransfused
untransfusible
untransgressed
untransient
untransitable
untransitive
untransitory
untranslatability
untranslatable
untranslatableness
untranslatably
untranslated
untransmigrated
untransmissible
untransmitted
untransmutable
untransmuted
untransparent
untranspassable
untranspired
untranspiring
untransplanted
untransportable
untransported
untransposed
untransubstantiated
untrappable
untrapped
untrashed
untravelable
untraveled
untraveling
untravellable
untravelling
untraversable
untraversed
untravestied
untreacherous
untread
untreadable
untreading
untreasonable
untreasure
untreasured
untreatable
untreatableness
untreatably
untreated
untreed
untrekked
untrellised
untrembling
untremblingly
untremendous
untremulous
untrenched
untrepanned
untrespassed
untrespassing
untress
untressed
untriable
untribal
untributary
untriced
untrickable
untricked
untried
untrifling
untrig
untrigonometrical
untrill
untrim
untrimmable
untrimmed
untrimmedness
untrinitarian
untripe
untrippable
untripped
untripping
untrite
untriturated
untriumphable
untriumphant
untriumphed
untrochaic
untrod
untrodden
untroddenness
untrolled
untrophied
untropical
untrotted
untroublable
untrouble
untroubled
untroubledly
untroubledness
untroublesome
untroublesomeness
untrounced
untrowed
untruant
untruck
untruckled
untruckling
untrue
untrueness
untruism
untruly
untrumped
untrumpeted
untrumping
untrundled
untrunked
untruss
untrussed
untrusser
untrussing
untrust
untrustably
untrusted
untrustful
untrustiness
untrusting
untrustworthily
untrustworthiness
untrustworthy
untrusty
untruth
untruther
untruthful
untruthfully
untruthfulness
untrying
untubbed
untuck
untucked
untuckered
untucking
untufted
untugged
untumbled
untumefied
untumid
untumultuous
untunable
untunableness
untunably
untune
untuneable
untuneableness
untuneably
untuned
untuneful
untunefully
untunefulness
untuning
untunneled
untupped
unturbaned
unturbid
unturbulent
unturf
unturfed
unturgid
unturn
unturnable
unturned
unturning
unturpentined
unturreted
untusked
untutelar
untutored
untutoredly
untutoredness
untwilled
untwinable
untwine
untwineable
untwined
untwining
untwinkling
untwinned
untwirl
untwirled
untwirling
untwist
untwisted
untwister
untwisting
untwitched
untying
untypical
untypically
untyrannic
untyrannical
untyrantlike
untz
unubiquitous
unugly
unulcerated
unultra
unumpired
ununanimity
ununanimous
ununanimously
ununderstandable
ununderstandably
ununderstanding
ununderstood
unundertaken
unundulatory
Unungun
ununifiable
ununified
ununiform
ununiformed
ununiformity
ununiformly
ununiformness
ununitable
ununitableness
ununitably
ununited
ununiting
ununiversity
ununiversitylike
unupbraiding
unupbraidingly
unupholstered
unupright
unuprightly
unuprightness
unupset
unupsettable
unurban
unurbane
unurged
unurgent
unurging
unurn
unurned
unusable
unusableness
unusably
unuse
unused
unusedness
unuseful
unusefully
unusefulness
unushered
unusual
unusuality
unusually
unusualness
unusurious
unusurped
unusurping
unutilizable
unutterability
unutterable
unutterableness
unutterably
unuttered
unuxorial
unuxorious
unvacant
unvaccinated
unvacillating
unvailable
unvain
unvaleted
unvaletudinary
unvaliant
unvalid
unvalidated
unvalidating
unvalidity
unvalidly
unvalidness
unvalorous
unvaluable
unvaluableness
unvaluably
unvalue
unvalued
unvamped
unvanishing
unvanquishable
unvanquished
unvantaged
unvaporized
unvariable
unvariableness
unvariably
unvariant
unvaried
unvariedly
unvariegated
unvarnished
unvarnishedly
unvarnishedness
unvarying
unvaryingly
unvaryingness
unvascular
unvassal
unvatted
unvaulted
unvaulting
unvaunted
unvaunting
unvauntingly
unveering
unveil
unveiled
unveiledly
unveiledness
unveiler
unveiling
unveilment
unveined
unvelvety
unvendable
unvendableness
unvended
unvendible
unvendibleness
unveneered
unvenerable
unvenerated
unvenereal
unvenged
unveniable
unvenial
unvenom
unvenomed
unvenomous
unventable
unvented
unventilated
unventured
unventurous
unvenued
unveracious
unveracity
unverbalized
unverdant
unverdured
unveridical
unverifiable
unverifiableness
unverifiably
unverified
unverifiedness
unveritable
unverity
unvermiculated
unverminous
unvernicular
unversatile
unversed
unversedly
unversedness
unversified
unvertical
unvessel
unvesseled
unvest
unvested
unvetoed
unvexed
unviable
unvibrated
unvibrating
unvicar
unvicarious
unvicariously
unvicious
unvictimized
unvictorious
unvictualed
unvictualled
unviewable
unviewed
unvigilant
unvigorous
unvigorously
unvilified
unvillaged
unvindicated
unvindictive
unvindictively
unvindictiveness
unvinous
unvintaged
unviolable
unviolated
unviolenced
unviolent
unviolined
unvirgin
unvirginal
unvirginlike
unvirile
unvirility
unvirtue
unvirtuous
unvirtuously
unvirtuousness
unvirulent
unvisible
unvisibleness
unvisibly
unvision
unvisionary
unvisioned
unvisitable
unvisited
unvisor
unvisored
unvisualized
unvital
unvitalized
unvitalness
unvitiated
unvitiatedly
unvitiatedness
unvitrescibility
unvitrescible
unvitrifiable
unvitrified
unvitriolized
unvituperated
unvivacious
unvivid
unvivified
unvizard
unvizarded
unvocal
unvocalized
unvociferous
unvoice
unvoiced
unvoiceful
unvoicing
unvoidable
unvoided
unvolatile
unvolatilize
unvolatilized
unvolcanic
unvolitioned
unvoluminous
unvoluntarily
unvoluntariness
unvoluntary
unvolunteering
unvoluptuous
unvomited
unvoracious
unvote
unvoted
unvoting
unvouched
unvouchedly
unvouchedness
unvouchsafed
unvowed
unvoweled
unvoyageable
unvoyaging
unvulcanized
unvulgar
unvulgarize
unvulgarized
unvulgarly
unvulnerable
unwadable
unwadded
unwadeable
unwaded
unwading
unwafted
unwaged
unwagered
unwaggable
unwaggably
unwagged
unwailed
unwailing
unwainscoted
unwaited
unwaiting
unwaked
unwakeful
unwakefulness
unwakened
unwakening
unwaking
unwalkable
unwalked
unwalking
unwall
unwalled
unwallet
unwallowed
unwan
unwandered
unwandering
unwaning
unwanted
unwanton
unwarbled
unware
unwarely
unwareness
unwarily
unwariness
unwarlike
unwarlikeness
unwarm
unwarmable
unwarmed
unwarming
unwarn
unwarned
unwarnedly
unwarnedness
unwarnished
unwarp
unwarpable
unwarped
unwarping
unwarrant
unwarrantability
unwarrantable
unwarrantableness
unwarrantably
unwarranted
unwarrantedly
unwarrantedness
unwary
unwashable
unwashed
unwashedness
unwassailing
unwastable
unwasted
unwasteful
unwastefully
unwasting
unwastingly
unwatchable
unwatched
unwatchful
unwatchfully
unwatchfulness
unwatching
unwater
unwatered
unwaterlike
unwatermarked
unwatery
unwattled
unwaved
unwaverable
unwavered
unwavering
unwaveringly
unwaving
unwax
unwaxed
unwayed
unwayward
unweaken
unweakened
unweal
unwealsomeness
unwealthy
unweaned
unweapon
unweaponed
unwearable
unweariability
unweariable
unweariableness
unweariably
unwearied
unweariedly
unweariedness
unwearily
unweariness
unwearing
unwearisome
unwearisomeness
unweary
unwearying
unwearyingly
unweathered
unweatherly
unweatherwise
unweave
unweaving
unweb
unwebbed
unwebbing
unwed
unwedded
unweddedly
unweddedness
unwedge
unwedgeable
unwedged
unweeded
unweel
unweelness
unweened
unweeping
unweeting
unweetingly
unweft
unweighable
unweighed
unweighing
unweight
unweighted
unweighty
unwelcome
unwelcomed
unwelcomely
unwelcomeness
unweld
unweldable
unwelded
unwell
unwellness
unwelted
unwept
unwestern
unwesternized
unwet
unwettable
unwetted
unwheedled
unwheel
unwheeled
unwhelmed
unwhelped
unwhetted
unwhig
unwhiglike
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upbeat
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upbelt
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upbind
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upblast
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upblow
upboil
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upbolt
upboost
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upbotch
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upbound
upbrace
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upbreed
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upbrighten
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upbuild
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upbuoyance
upburn
upburst
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upcall
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updive
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upleg
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uprighteously
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Uraniidae
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uranion
uraniscochasma
uraniscoplasty
uraniscoraphy
uraniscorrhaphy
uranism
uranist
uranite
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uranium
uranocircite
uranographer
uranographic
uranographical
uranographist
uranography
uranolatry
uranolite
uranological
uranology
uranometria
uranometrical
uranometry
uranophane
uranophotography
uranoplastic
uranoplasty
uranoplegia
uranorrhaphia
uranorrhaphy
uranoschisis
uranoschism
uranoscope
uranoscopia
uranoscopic
Uranoscopidae
Uranoscopus
uranoscopy
uranospathite
uranosphaerite
uranospinite
uranostaphyloplasty
uranostaphylorrhaphy
uranotantalite
uranothallite
uranothorite
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uraturia
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Urena
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ureosecretory
uresis
uretal
ureter
ureteral
ureteralgia
uretercystoscope
ureterectasia
ureterectasis
ureterectomy
ureteric
ureteritis
ureterocele
ureterocervical
ureterocolostomy
ureterocystanastomosis
ureterocystoscope
ureterocystostomy
ureterodialysis
ureteroenteric
ureteroenterostomy
ureterogenital
ureterogram
ureterograph
ureterography
ureterointestinal
ureterolith
ureterolithiasis
ureterolithic
ureterolithotomy
ureterolysis
ureteronephrectomy
ureterophlegma
ureteroplasty
ureteroproctostomy
ureteropyelitis
ureteropyelogram
ureteropyelography
ureteropyelonephritis
ureteropyelostomy
ureteropyosis
ureteroradiography
ureterorectostomy
ureterorrhagia
ureterorrhaphy
ureterosalpingostomy
ureterosigmoidostomy
ureterostegnosis
ureterostenoma
ureterostenosis
ureterostoma
ureterostomy
ureterotomy
ureterouteral
ureterovaginal
ureterovesical
urethan
urethane
urethra
urethrae
urethragraph
urethral
urethralgia
urethrameter
urethrascope
urethratome
urethratresia
urethrectomy
urethremphraxis
urethreurynter
urethrism
urethritic
urethritis
urethroblennorrhea
urethrobulbar
urethrocele
urethrocystitis
urethrogenital
urethrogram
urethrograph
urethrometer
urethropenile
urethroperineal
urethrophyma
urethroplastic
urethroplasty
urethroprostatic
urethrorectal
urethrorrhagia
urethrorrhaphy
urethrorrhea
urethrorrhoea
urethroscope
urethroscopic
urethroscopical
urethroscopy
urethrosexual
urethrospasm
urethrostaxis
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urethrostomy
urethrotome
urethrotomic
urethrotomy
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urinarium
urinary
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urination
urinative
urinator
urine
urinemia
uriniferous
uriniparous
urinocryoscopy
urinogenital
urinogenitary
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urinologist
urinology
urinomancy
urinometer
urinometric
urinometry
urinoscopic
urinoscopist
urinoscopy
urinose
urinosexual
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urodialysis
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uroleucinic
urolith
urolithiasis
urolithic
urolithology
urologic
urological
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urology
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uromancy
uromantia
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Uromastix
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Uropsilus
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uropygium
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urorrhagia
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uroschesis
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uroseptic
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urosteon
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urotoxicity
urotoxin
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uroxanate
uroxanic
uroxanthin
uroxin
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Urs
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Ursidae
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Ute
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uteri
uterine
uteritis
uteroabdominal
uterocele
uterocervical
uterocystotomy
uterofixation
uterogestation
uterogram
uterography
uterointestinal
uterolith
uterology
uteromania
uterometer
uteroovarian
uteroparietal
uteropelvic
uteroperitoneal
uteropexia
uteropexy
uteroplacental
uteroplasty
uterosacral
uterosclerosis
uteroscope
uterotomy
uterotonic
uterotubal
uterovaginal
uteroventral
uterovesical
uterus
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Utraquism
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utricul
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Utricularia
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utriculiferous
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utriculoplastic
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utriculose
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Vaalpens
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Vaccaria
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vaccinee
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Vacciniaceae
vacciniaceous
vaccinial
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Vaccinium
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vaccinoid
vaccinophobia
vaccinotherapy
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Vachellia
vachette
vacillancy
vacillant
vacillate
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vacillator
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Vadim
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vadimony
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vagile
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vaginal
vaginalectomy
vaginaless
vaginalitis
vaginant
vaginate
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vaginectomy
vaginervose
Vaginicola
vaginicoline
vaginicolous
vaginiferous
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vaginismus
vaginitis
vaginoabdominal
vaginocele
vaginodynia
vaginofixation
vaginolabial
vaginometer
vaginomycosis
vaginoperineal
vaginoperitoneal
vaginopexy
vaginoplasty
vaginoscope
vaginoscopy
vaginotome
vaginotomy
vaginovesical
vaginovulvar
vaginula
vaginulate
vaginule
vagitus
Vagnera
vagoaccessorius
vagodepressor
vagoglossopharyngeal
vagogram
vagolysis
vagosympathetic
vagotomize
vagotomy
vagotonia
vagotonic
vagotropic
vagotropism
vagrance
vagrancy
vagrant
vagrantism
vagrantize
vagrantlike
vagrantly
vagrantness
vagrate
vagrom
vague
vaguely
vagueness
vaguish
vaguity
vagulous
vagus
vahine
Vai
Vaidic
vail
vailable
vain
vainful
vainglorious
vaingloriously
vaingloriousness
vainglory
vainly
vainness
vair
vairagi
vaire
vairy
Vaishnava
Vaishnavism
vaivode
vajra
vajrasana
vakass
vakia
vakil
vakkaliga
Val
valance
valanced
valanche
valbellite
vale
valediction
valedictorian
valedictorily
valedictory
valence
Valencia
Valencian
valencianite
Valenciennes
valency
valent
Valentide
Valentin
Valentine
valentine
Valentinian
Valentinianism
valentinite
valeral
valeraldehyde
valeramide
valerate
Valeria
valerian
Valeriana
Valerianaceae
valerianaceous
Valerianales
valerianate
Valerianella
Valerianoides
valeric
Valerie
valerin
valerolactone
valerone
valeryl
valerylene
valet
valeta
valetage
valetdom
valethood
valetism
valetry
valetudinarian
valetudinarianism
valetudinariness
valetudinarist
valetudinarium
valetudinary
valeur
valeward
valgoid
valgus
valhall
Valhalla
Vali
vali
valiance
valiancy
valiant
valiantly
valiantness
valid
validate
validation
validatory
validification
validity
validly
validness
valine
valise
valiseful
valiship
Valkyr
Valkyria
Valkyrian
Valkyrie
vall
vallancy
vallar
vallary
vallate
vallated
vallation
vallecula
vallecular
valleculate
vallevarite
valley
valleyful
valleyite
valleylet
valleylike
valleyward
valleywise
vallicula
vallicular
vallidom
vallis
Valliscaulian
Vallisneria
Vallisneriaceae
vallisneriaceous
Vallombrosan
Vallota
vallum
Valmy
Valois
valonia
Valoniaceae
valoniaceous
valor
valorization
valorize
valorous
valorously
valorousness
Valsa
Valsaceae
Valsalvan
valse
valsoid
valuable
valuableness
valuably
valuate
valuation
valuational
valuator
value
valued
valueless
valuelessness
valuer
valuta
valva
valval
Valvata
valvate
Valvatidae
valve
valved
valveless
valvelet
valvelike
valveman
valviferous
valviform
valvotomy
valvula
valvular
valvulate
valvule
valvulitis
valvulotome
valvulotomy
valyl
valylene
vambrace
vambraced
vamfont
vammazsa
vamoose
vamp
vamped
vamper
vamphorn
vampire
vampireproof
vampiric
vampirish
vampirism
vampirize
vamplate
vampproof
Vampyrella
Vampyrellidae
Vampyrum
Van
van
vanadate
vanadiate
vanadic
vanadiferous
vanadinite
vanadium
vanadosilicate
vanadous
vanadyl
Vanaheim
vanaprastha
Vance
vancourier
Vancouveria
Vanda
Vandal
Vandalic
vandalish
vandalism
vandalistic
vandalization
vandalize
vandalroot
Vandemonian
Vandemonianism
Vandiemenian
Vandyke
vane
vaned
vaneless
vanelike
Vanellus
Vanessa
vanessian
vanfoss
vang
vangee
vangeli
vanglo
vanguard
Vanguardist
Vangueria
vanilla
vanillal
vanillaldehyde
vanillate
vanille
vanillery
vanillic
vanillin
vanillinic
vanillism
vanilloes
vanillon
vanilloyl
vanillyl
Vanir
vanish
vanisher
vanishing
vanishingly
vanishment
Vanist
vanitarianism
vanitied
vanity
vanjarrah
vanman
vanmost
Vannai
vanner
vannerman
vannet
Vannic
vanquish
vanquishable
vanquisher
vanquishment
vansire
vantage
vantageless
vantbrace
vantbrass
vanward
vapid
vapidism
vapidity
vapidly
vapidness
vapocauterization
vapographic
vapography
vapor
vaporability
vaporable
vaporarium
vaporary
vaporate
vapored
vaporer
vaporescence
vaporescent
vaporiferous
vaporiferousness
vaporific
vaporiform
vaporimeter
vaporing
vaporingly
vaporish
vaporishness
vaporium
vaporizable
vaporization
vaporize
vaporizer
vaporless
vaporlike
vaporograph
vaporographic
vaporose
vaporoseness
vaporosity
vaporous
vaporously
vaporousness
vaportight
vapory
vapulary
vapulate
vapulation
vapulatory
vara
varahan
varan
Varanger
Varangi
Varangian
varanid
Varanidae
Varanoid
Varanus
Varda
vardapet
vardy
vare
varec
vareheaded
vareuse
vargueno
vari
variability
variable
variableness
variably
Variag
variance
variancy
variant
variate
variation
variational
variationist
variatious
variative
variatively
variator
varical
varicated
varication
varicella
varicellar
varicellate
varicellation
varicelliform
varicelloid
varicellous
varices
variciform
varicoblepharon
varicocele
varicoid
varicolored
varicolorous
varicose
varicosed
varicoseness
varicosis
varicosity
varicotomy
varicula
varied
variedly
variegate
variegated
variegation
variegator
varier
varietal
varietally
varietism
varietist
variety
variform
variformed
variformity
variformly
varigradation
variocoupler
variola
variolar
Variolaria
variolate
variolation
variole
variolic
varioliform
variolite
variolitic
variolitization
variolization
varioloid
variolous
variolovaccine
variolovaccinia
variometer
variorum
variotinted
various
variously
variousness
variscite
varisse
varix
varlet
varletaille
varletess
varletry
varletto
varment
varna
varnashrama
varnish
varnished
varnisher
varnishing
varnishlike
varnishment
varnishy
varnpliktige
varnsingite
Varolian
Varronia
Varronian
varsha
varsity
Varsovian
varsoviana
Varuna
varus
varve
varved
vary
varyingly
vas
Vasa
vasa
vasal
Vascons
vascular
vascularity
vascularization
vascularize
vascularly
vasculated
vasculature
vasculiferous
vasculiform
vasculitis
vasculogenesis
vasculolymphatic
vasculomotor
vasculose
vasculum
vase
vasectomize
vasectomy
vaseful
vaselet
vaselike
Vaseline
vasemaker
vasemaking
vasewise
vasework
vashegyite
vasicentric
vasicine
vasifactive
vasiferous
vasiform
vasoconstricting
vasoconstriction
vasoconstrictive
vasoconstrictor
vasocorona
vasodentinal
vasodentine
vasodilatation
vasodilatin
vasodilating
vasodilation
vasodilator
vasoepididymostomy
vasofactive
vasoformative
vasoganglion
vasohypertonic
vasohypotonic
vasoinhibitor
vasoinhibitory
vasoligation
vasoligature
vasomotion
vasomotor
vasomotorial
vasomotoric
vasomotory
vasoneurosis
vasoparesis
vasopressor
vasopuncture
vasoreflex
vasorrhaphy
vasosection
vasospasm
vasospastic
vasostimulant
vasostomy
vasotomy
vasotonic
vasotribe
vasotripsy
vasotrophic
vasovesiculectomy
vasquine
vassal
vassalage
vassaldom
vassaless
vassalic
vassalism
vassality
vassalize
vassalless
vassalry
vassalship
Vassos
vast
vastate
vastation
vastidity
vastily
vastiness
vastitude
vastity
vastly
vastness
vasty
vasu
Vasudeva
Vasundhara
vat
Vateria
vatful
vatic
vatically
Vatican
vaticanal
vaticanic
vaticanical
Vaticanism
Vaticanist
Vaticanization
Vaticanize
vaticide
vaticinal
vaticinant
vaticinate
vaticination
vaticinator
vaticinatory
vaticinatress
vaticinatrix
vatmaker
vatmaking
vatman
Vatteluttu
vatter
vau
Vaucheria
Vaucheriaceae
vaucheriaceous
vaudeville
vaudevillian
vaudevillist
Vaudism
Vaudois
vaudy
Vaughn
vaugnerite
vault
vaulted
vaultedly
vaulter
vaulting
vaultlike
vaulty
vaunt
vauntage
vaunted
vaunter
vauntery
vauntful
vauntiness
vaunting
vauntingly
vauntmure
vaunty
vauquelinite
Vauxhall
Vauxhallian
vauxite
vavasor
vavasory
vaward
Vayu
Vazimba
Veadar
veal
vealer
vealiness
veallike
vealskin
vealy
vectigal
vection
vectis
vectograph
vectographic
vector
vectorial
vectorially
vecture
Veda
Vedaic
Vedaism
Vedalia
vedana
Vedanga
Vedanta
Vedantic
Vedantism
Vedantist
Vedda
Veddoid
vedette
Vedic
vedika
Vediovis
Vedism
Vedist
vedro
Veduis
veduis
vee
veen
veep
veer
veerable
veeringly
veery
Vega
vegasite
vegeculture
vegetability
vegetable
vegetablelike
vegetablewise
vegetablize
vegetably
vegetal
vegetalcule
vegetality
vegetant
vegetarian
vegetarianism
vegetate
vegetation
vegetational
vegetationless
vegetative
vegetatively
vegetativeness
vegete
vegeteness
vegetism
vegetive
vegetivorous
vegetoalkali
vegetoalkaline
vegetoalkaloid
vegetoanimal
vegetobituminous
vegetocarbonaceous
vegetomineral
vehemence
vehemency
vehement
vehemently
vehicle
vehicular
vehicularly
vehiculary
vehiculate
vehiculation
vehiculatory
Vehmic
vei
veigle
veil
veiled
veiledly
veiledness
veiler
veiling
veilless
veillike
veilmaker
veilmaking
Veiltail
veily
vein
veinage
veinal
veinbanding
veined
veiner
veinery
veininess
veining
veinless
veinlet
veinous
veinstone
veinstuff
veinule
veinulet
veinwise
veinwork
veiny
Vejoces
vejoces
Vejovis
Vejoz
vela
velal
velamen
velamentous
velamentum
velar
velardenite
velaric
velarium
velarize
velary
velate
velated
velation
velatura
Velchanos
veldcraft
veldman
veldschoen
veldt
veldtschoen
Velella
velellidous
velic
veliferous
veliform
veliger
veligerous
Velika
velitation
vell
vellala
velleda
velleity
vellicate
vellication
vellicative
vellinch
vellon
vellosine
Vellozia
Velloziaceae
velloziaceous
vellum
vellumy
velo
velociman
velocimeter
velocious
velociously
velocipedal
velocipede
velocipedean
velocipedic
velocitous
velocity
velodrome
velometer
velours
veloutine
velte
velum
velumen
velure
Velutina
velutinous
velveret
velvet
velvetbreast
velveted
velveteen
velveteened
velvetiness
velveting
velvetleaf
velvetlike
velvetry
velvetseed
velvetweed
velvetwork
velvety
venada
venal
venality
venalization
venalize
venally
venalness
Venantes
venanzite
venatic
venatical
venatically
venation
venational
venator
venatorial
venatorious
venatory
vencola
Vend
vend
vendace
Vendean
vendee
vender
vendetta
vendettist
vendibility
vendible
vendibleness
vendibly
vendicate
Vendidad
vending
venditate
venditation
vendition
venditor
vendor
vendue
Vened
Venedotian
veneer
veneerer
veneering
venefical
veneficious
veneficness
veneficous
venenate
venenation
venene
veneniferous
venenific
venenosalivary
venenous
venenousness
venepuncture
venerability
venerable
venerableness
venerably
Veneracea
veneracean
veneraceous
veneral
Veneralia
venerance
venerant
venerate
veneration
venerational
venerative
veneratively
venerativeness
venerator
venereal
venerealness
venereologist
venereology
venerer
Veneres
venerial
Veneridae
veneriform
venery
venesect
venesection
venesector
venesia
Venetes
Veneti
Venetian
Venetianed
Venetic
venezolano
Venezuelan
vengeable
vengeance
vengeant
vengeful
vengefully
vengefulness
vengeously
venger
venial
veniality
venially
venialness
Venice
venie
venin
veniplex
venipuncture
venireman
venison
venisonivorous
venisonlike
venisuture
Venite
Venizelist
Venkata
vennel
venner
venoatrial
venoauricular
venom
venomed
venomer
venomization
venomize
venomly
venomness
venomosalivary
venomous
venomously
venomousness
venomproof
venomsome
venomy
venosal
venosclerosis
venose
venosinal
venosity
venostasis
venous
venously
venousness
vent
ventage
ventail
venter
Ventersdorp
venthole
ventiduct
ventifact
ventil
ventilable
ventilagin
ventilate
ventilating
ventilation
ventilative
ventilator
ventilatory
ventless
ventometer
ventose
ventoseness
ventosity
ventpiece
ventrad
ventral
ventrally
ventralmost
ventralward
ventric
ventricle
ventricolumna
ventricolumnar
ventricornu
ventricornual
ventricose
ventricoseness
ventricosity
ventricous
ventricular
ventricularis
ventriculite
Ventriculites
ventriculitic
Ventriculitidae
ventriculogram
ventriculography
ventriculoscopy
ventriculose
ventriculous
ventriculus
ventricumbent
ventriduct
ventrifixation
ventrilateral
ventrilocution
ventriloqual
ventriloqually
ventriloque
ventriloquial
ventriloquially
ventriloquism
ventriloquist
ventriloquistic
ventriloquize
ventriloquous
ventriloquously
ventriloquy
ventrimesal
ventrimeson
ventrine
ventripotency
ventripotent
ventripotential
ventripyramid
ventroaxial
ventroaxillary
ventrocaudal
ventrocystorrhaphy
ventrodorsad
ventrodorsal
ventrodorsally
ventrofixation
ventrohysteropexy
ventroinguinal
ventrolateral
ventrolaterally
ventromedial
ventromedian
ventromesal
ventromesial
ventromyel
ventroposterior
ventroptosia
ventroptosis
ventroscopy
ventrose
ventrosity
ventrosuspension
ventrotomy
venture
venturer
venturesome
venturesomely
venturesomeness
Venturia
venturine
venturous
venturously
venturousness
venue
venula
venular
venule
venulose
Venus
Venusian
venust
Venutian
venville
Veps
Vepse
Vepsish
vera
veracious
veraciously
veraciousness
veracity
veranda
verandaed
verascope
veratral
veratralbine
veratraldehyde
veratrate
veratria
veratric
veratridine
veratrine
veratrinize
veratrize
veratroidine
veratrole
veratroyl
Veratrum
veratryl
veratrylidene
verb
verbal
verbalism
verbalist
verbality
verbalization
verbalize
verbalizer
verbally
verbarian
verbarium
verbasco
verbascose
Verbascum
verbate
verbatim
verbena
Verbenaceae
verbenaceous
verbenalike
verbenalin
Verbenarius
verbenate
verbene
verbenone
verberate
verberation
verberative
Verbesina
verbiage
verbicide
verbiculture
verbid
verbification
verbify
verbigerate
verbigeration
verbigerative
verbile
verbless
verbolatry
verbomania
verbomaniac
verbomotor
verbose
verbosely
verboseness
verbosity
verbous
verby
verchok
verd
verdancy
verdant
verdantly
verdantness
verdea
verdelho
verderer
verderership
verdet
verdict
verdigris
verdigrisy
verdin
verditer
verdoy
verdugoship
verdun
verdure
verdured
verdureless
verdurous
verdurousness
verecund
verecundity
verecundness
verek
veretilliform
Veretillum
veretillum
verge
vergeboard
vergence
vergency
vergent
vergentness
verger
vergeress
vergerism
vergerless
vergership
vergery
vergi
vergiform
Vergilianism
verglas
vergobret
veri
veridic
veridical
veridicality
veridically
veridicalness
veridicous
veridity
verifiability
verifiable
verifiableness
verifiably
verificate
verification
verificative
verificatory
verifier
verify
verily
verine
verisimilar
verisimilarly
verisimilitude
verisimilitudinous
verisimility
verism
verist
veristic
veritability
veritable
veritableness
veritably
verite
veritism
veritist
veritistic
verity
verjuice
vermeil
vermeologist
vermeology
Vermes
vermetid
Vermetidae
vermetidae
Vermetus
vermian
vermicelli
vermicidal
vermicide
vermicious
vermicle
vermicular
Vermicularia
vermicularly
vermiculate
vermiculated
vermiculation
vermicule
vermiculite
vermiculose
vermiculosity
vermiculous
vermiform
Vermiformia
vermiformis
vermiformity
vermiformous
vermifugal
vermifuge
vermifugous
vermigerous
vermigrade
Vermilingues
Vermilinguia
vermilinguial
vermilion
vermilionette
vermilionize
vermin
verminal
verminate
vermination
verminer
verminicidal
verminicide
verminiferous
verminlike
verminly
verminosis
verminous
verminously
verminousness
verminproof
verminy
vermiparous
vermiparousness
vermis
vermivorous
vermivorousness
vermix
Vermont
Vermonter
Vermontese
vermorel
vermouth
Vern
vernacle
vernacular
vernacularism
vernacularist
vernacularity
vernacularization
vernacularize
vernacularly
vernacularness
vernaculate
vernal
vernality
vernalization
vernalize
vernally
vernant
vernation
vernicose
vernier
vernile
vernility
vernin
vernine
vernition
Vernon
Vernonia
vernoniaceous
Vernonieae
vernonin
Verona
Veronal
veronalism
Veronese
Veronica
Veronicella
Veronicellidae
Verpa
verre
verrel
verriculate
verriculated
verricule
verruca
verrucano
Verrucaria
Verrucariaceae
verrucariaceous
verrucarioid
verrucated
verruciferous
verruciform
verrucose
verrucoseness
verrucosis
verrucosity
verrucous
verruculose
verruga
versability
versable
versableness
versal
versant
versate
versatile
versatilely
versatileness
versatility
versation
versative
verse
versecraft
versed
verseless
verselet
versemaker
versemaking
verseman
versemanship
versemonger
versemongering
versemongery
verser
versesmith
verset
versette
verseward
versewright
versicle
versicler
versicolor
versicolorate
versicolored
versicolorous
versicular
versicule
versifiable
versifiaster
versification
versificator
versificatory
versificatrix
versifier
versiform
versify
versiloquy
versine
version
versional
versioner
versionist
versionize
versipel
verso
versor
verst
versta
versual
versus
vert
vertebra
vertebrae
vertebral
vertebraless
vertebrally
Vertebraria
vertebrarium
vertebrarterial
Vertebrata
vertebrate
vertebrated
vertebration
vertebre
vertebrectomy
vertebriform
vertebroarterial
vertebrobasilar
vertebrochondral
vertebrocostal
vertebrodymus
vertebrofemoral
vertebroiliac
vertebromammary
vertebrosacral
vertebrosternal
vertex
vertibility
vertible
vertibleness
vertical
verticalism
verticality
vertically
verticalness
vertices
verticil
verticillary
verticillaster
verticillastrate
verticillate
verticillated
verticillately
verticillation
verticilliaceous
verticilliose
Verticillium
verticillus
verticity
verticomental
verticordious
vertiginate
vertigines
vertiginous
vertigo
vertilinear
vertimeter
Vertumnus
Verulamian
veruled
verumontanum
vervain
vervainlike
verve
vervecine
vervel
verveled
vervelle
vervenia
vervet
very
Vesalian
vesania
vesanic
vesbite
vesicae
vesical
vesicant
vesicate
vesication
vesicatory
vesicle
vesicoabdominal
vesicocavernous
vesicocele
vesicocervical
vesicoclysis
vesicofixation
vesicointestinal
vesicoprostatic
vesicopubic
vesicorectal
vesicosigmoid
vesicospinal
vesicotomy
vesicovaginal
vesicular
Vesicularia
vesicularly
vesiculary
vesiculase
Vesiculata
Vesiculatae
vesiculate
vesiculation
vesicule
vesiculectomy
vesiculiferous
vesiculiform
vesiculigerous
vesiculitis
vesiculobronchial
vesiculocavernous
vesiculopustular
vesiculose
vesiculotomy
vesiculotubular
vesiculotympanic
vesiculotympanitic
vesiculous
vesiculus
vesicupapular
veskit
Vespa
vespacide
vespal
vesper
vesperal
vesperian
vespering
vespers
vespertide
vespertilian
Vespertilio
vespertilio
Vespertiliones
vespertilionid
Vespertilionidae
Vespertilioninae
vespertilionine
vespertinal
vespertine
vespery
vespiary
vespid
Vespidae
vespiform
Vespina
vespine
vespoid
Vespoidea
vessel
vesseled
vesselful
vessignon
vest
Vesta
vestal
Vestalia
vestalia
vestalship
Vestas
vestee
vester
vestiarian
vestiarium
vestiary
vestibula
vestibular
vestibulary
vestibulate
vestibule
vestibuled
vestibulospinal
vestibulum
vestige
vestigial
vestigially
Vestigian
vestigiary
vestigium
vestiment
vestimental
vestimentary
vesting
Vestini
Vestinian
vestiture
vestlet
vestment
vestmental
vestmented
vestral
vestralization
vestrical
vestrification
vestrify
vestry
vestrydom
vestryhood
vestryish
vestryism
vestryize
vestryman
vestrymanly
vestrymanship
vestuary
vestural
vesture
vesturer
Vesuvian
vesuvian
vesuvianite
vesuviate
vesuvite
vesuvius
veszelyite
vet
veta
vetanda
vetch
vetchling
vetchy
veteran
veterancy
veteraness
veteranize
veterinarian
veterinarianism
veterinary
vetitive
vetivene
vetivenol
vetiver
Vetiveria
vetiveria
vetivert
vetkousie
veto
vetoer
vetoism
vetoist
vetoistic
vetoistical
vetust
vetusty
veuglaire
veuve
vex
vexable
vexation
vexatious
vexatiously
vexatiousness
vexatory
vexed
vexedly
vexedness
vexer
vexful
vexil
vexillar
vexillarious
vexillary
vexillate
vexillation
vexillum
vexingly
vexingness
vext
via
viability
viable
viaduct
viaggiatory
viagram
viagraph
viajaca
vial
vialful
vialmaker
vialmaking
vialogue
viameter
viand
viander
viatic
viatica
viatical
viaticum
viatometer
viator
viatorial
viatorially
vibetoite
vibex
vibgyor
vibix
vibracular
vibracularium
vibraculoid
vibraculum
vibrance
vibrancy
vibrant
vibrantly
vibraphone
vibrate
vibratile
vibratility
vibrating
vibratingly
vibration
vibrational
vibrationless
vibratiuncle
vibratiunculation
vibrative
vibrato
vibrator
vibratory
Vibrio
vibrioid
vibrion
vibrionic
vibrissa
vibrissae
vibrissal
vibrograph
vibromassage
vibrometer
vibromotive
vibronic
vibrophone
vibroscope
vibroscopic
vibrotherapeutics
viburnic
viburnin
Viburnum
Vic
vicar
vicarage
vicarate
vicaress
vicarial
vicarian
vicarianism
vicariate
vicariateship
vicarious
vicariously
vicariousness
vicarly
vicarship
Vice
vice
vicecomes
vicecomital
vicegeral
vicegerency
vicegerent
vicegerentship
viceless
vicelike
vicenary
vicennial
viceregal
viceregally
vicereine
viceroy
viceroyal
viceroyalty
viceroydom
viceroyship
vicety
viceversally
Vichyite
vichyssoise
Vicia
vicianin
vicianose
vicilin
vicinage
vicinal
vicine
vicinity
viciosity
vicious
viciously
viciousness
vicissitous
vicissitude
vicissitudinary
vicissitudinous
vicissitudinousness
Vick
Vicki
Vickie
Vicky
vicoite
vicontiel
victim
victimhood
victimizable
victimization
victimize
victimizer
victless
Victor
victor
victordom
victorfish
Victoria
Victorian
Victorianism
Victorianize
Victorianly
victoriate
victoriatus
victorine
victorious
victoriously
victoriousness
victorium
victory
victoryless
victress
victrix
Victrola
victrola
victual
victualage
victualer
victualing
victuallership
victualless
victualry
victuals
vicuna
Viddhal
viddui
videndum
video
videogenic
vidette
Vidhyanath
Vidian
vidonia
vidry
Vidua
viduage
vidual
vidually
viduate
viduated
viduation
Viduinae
viduine
viduity
viduous
vidya
vie
vielle
Vienna
Viennese
vier
vierling
viertel
viertelein
Vietminh
Vietnamese
view
viewable
viewably
viewer
viewiness
viewless
viewlessly
viewly
viewpoint
viewsome
viewster
viewworthy
viewy
vifda
viga
vigentennial
vigesimal
vigesimation
vigia
vigil
vigilance
vigilancy
vigilant
vigilante
vigilantism
vigilantly
vigilantness
vigilate
vigilation
vigintiangular
vigneron
vignette
vignetter
vignettist
vignin
vigonia
vigor
vigorist
vigorless
vigorous
vigorously
vigorousness
vihara
vihuela
vijao
Vijay
viking
vikingism
vikinglike
vikingship
vila
vilayet
vile
vilehearted
Vilela
vilely
vileness
Vilhelm
Vili
vilicate
vilification
vilifier
vilify
vilifyingly
vilipend
vilipender
vilipenditory
vility
vill
villa
villadom
villaette
village
villageful
villagehood
villageless
villagelet
villagelike
villageous
villager
villageress
villagery
villaget
villageward
villagey
villagism
villain
villainage
villaindom
villainess
villainist
villainous
villainously
villainousness
villainproof
villainy
villakin
villaless
villalike
villanage
villanella
villanelle
villanette
villanous
villanously
Villanova
Villanovan
villar
villate
villatic
ville
villein
villeinage
villeiness
villeinhold
villenage
villiaumite
villiferous
villiform
villiplacental
Villiplacentalia
villitis
villoid
villose
villosity
villous
villously
villus
vim
vimana
vimen
vimful
Viminal
viminal
vimineous
vina
vinaceous
vinaconic
vinage
vinagron
vinaigrette
vinaigretted
vinaigrier
vinaigrous
vinal
Vinalia
vinasse
vinata
Vince
Vincent
vincent
Vincentian
Vincenzo
Vincetoxicum
vincetoxin
vincibility
vincible
vincibleness
vincibly
vincular
vinculate
vinculation
vinculum
Vindelici
vindemial
vindemiate
vindemiation
vindemiatory
Vindemiatrix
vindex
vindhyan
vindicability
vindicable
vindicableness
vindicably
vindicate
vindication
vindicative
vindicatively
vindicativeness
vindicator
vindicatorily
vindicatorship
vindicatory
vindicatress
vindictive
vindictively
vindictiveness
vindictivolence
vindresser
vine
vinea
vineal
vineatic
vined
vinegar
vinegarer
vinegarette
vinegarish
vinegarist
vinegarroon
vinegarweed
vinegary
vinegerone
vinegrower
vineity
vineland
vineless
vinelet
vinelike
viner
vinery
vinestalk
vinewise
vineyard
Vineyarder
vineyarding
vineyardist
vingerhoed
Vingolf
vinhatico
vinic
vinicultural
viniculture
viniculturist
vinifera
viniferous
vinification
vinificator
Vinland
vinny
vino
vinoacetous
Vinod
vinolence
vinolent
vinologist
vinology
vinometer
vinomethylic
vinose
vinosity
vinosulphureous
vinous
vinously
vinousness
vinquish
vint
vinta
vintage
vintager
vintaging
vintem
vintener
vintlite
vintner
vintneress
vintnership
vintnery
vintress
vintry
viny
vinyl
vinylbenzene
vinylene
vinylic
vinylidene
viol
viola
violability
violable
violableness
violably
Violaceae
violacean
violaceous
violaceously
violal
Violales
violanin
violaquercitrin
violate
violater
violation
violational
violative
violator
violatory
violature
violence
violent
violently
violentness
violer
violescent
violet
violetish
violetlike
violette
violetwise
violety
violin
violina
violine
violinette
violinist
violinistic
violinlike
violinmaker
violinmaking
violist
violmaker
violmaking
violon
violoncellist
violoncello
violone
violotta
violuric
viosterol
Vip
viper
Vipera
viperan
viperess
viperfish
viperian
viperid
Viperidae
viperiform
Viperina
Viperinae
viperine
viperish
viperishly
viperlike
viperling
viperoid
Viperoidea
viperous
viperously
viperousness
vipery
vipolitic
vipresident
viqueen
Vira
viragin
viraginian
viraginity
viraginous
virago
viragoish
viragolike
viragoship
viral
Virales
Virbius
vire
virelay
viremia
viremic
virent
vireo
vireonine
virescence
virescent
virga
virgal
virgate
virgated
virgater
virgation
virgilia
Virgilism
virgin
virginal
Virginale
virginalist
virginality
virginally
virgineous
virginhead
Virginia
Virginian
Virginid
virginitis
virginity
virginityship
virginium
virginlike
virginly
virginship
Virgo
virgula
virgular
Virgularia
virgularian
Virgulariidae
virgulate
virgule
virgultum
virial
viricide
virid
viridene
viridescence
viridescent
viridian
viridigenous
viridine
viridite
viridity
virific
virify
virile
virilely
virileness
virilescence
virilescent
virilify
viriliously
virilism
virilist
virility
viripotent
viritrate
virl
virole
viroled
virological
virologist
virology
viron
virose
virosis
virous
virtu
virtual
virtualism
virtualist
virtuality
virtualize
virtually
virtue
virtued
virtuefy
virtuelessness
virtueproof
virtuless
virtuosa
virtuose
virtuosi
virtuosic
virtuosity
virtuoso
virtuosoship
virtuous
virtuouslike
virtuously
virtuousness
virucidal
virucide
viruela
virulence
virulency
virulent
virulented
virulently
virulentness
viruliferous
virus
viruscidal
viruscide
virusemic
vis
visa
visage
visaged
visagraph
visarga
Visaya
Visayan
viscacha
viscera
visceral
visceralgia
viscerally
viscerate
visceration
visceripericardial
visceroinhibitory
visceromotor
visceroparietal
visceroperitioneal
visceropleural
visceroptosis
visceroptotic
viscerosensory
visceroskeletal
viscerosomatic
viscerotomy
viscerotonia
viscerotonic
viscerotrophic
viscerotropic
viscerous
viscid
viscidity
viscidize
viscidly
viscidness
viscidulous
viscin
viscoidal
viscolize
viscometer
viscometrical
viscometrically
viscometry
viscontal
viscoscope
viscose
viscosimeter
viscosimetry
viscosity
viscount
viscountcy
viscountess
viscountship
viscounty
viscous
viscously
viscousness
viscus
vise
viseman
Vishal
Vishnavite
Vishnu
Vishnuism
Vishnuite
Vishnuvite
visibility
visibilize
visible
visibleness
visibly
visie
Visigoth
Visigothic
visile
vision
visional
visionally
visionarily
visionariness
visionary
visioned
visioner
visionic
visionist
visionize
visionless
visionlike
visionmonger
visionproof
visit
visita
visitable
Visitandine
visitant
visitation
visitational
visitative
visitator
visitatorial
visite
visitee
visiter
visiting
visitment
visitor
visitoress
visitorial
visitorship
visitress
visitrix
visive
visne
vison
visor
visorless
visorlike
vista
vistaed
vistal
vistaless
vistamente
Vistlik
visto
Vistulian
visual
visualist
visuality
visualization
visualize
visualizer
visually
visuoauditory
visuokinesthetic
visuometer
visuopsychic
visuosensory
vita
Vitaceae
Vitaglass
vital
vitalic
vitalism
vitalist
vitalistic
vitalistically
vitality
vitalization
vitalize
vitalizer
vitalizing
vitalizingly
Vitallium
vitally
vitalness
vitals
vitamer
vitameric
vitamin
vitaminic
vitaminize
vitaminology
vitapath
vitapathy
vitaphone
vitascope
vitascopic
vitasti
vitativeness
vitellarian
vitellarium
vitellary
vitellicle
vitelliferous
vitelligenous
vitelligerous
vitellin
vitelline
vitellogene
vitellogenous
vitellose
vitellus
viterbite
Viti
vitiable
vitiate
vitiated
vitiation
vitiator
viticetum
viticulose
viticultural
viticulture
viticulturer
viticulturist
vitiferous
vitiliginous
vitiligo
vitiligoidea
vitiosity
Vitis
vitium
vitochemic
vitochemical
vitrage
vitrail
vitrailed
vitrailist
vitrain
vitraux
vitreal
vitrean
vitrella
vitremyte
vitreodentinal
vitreodentine
vitreoelectric
vitreosity
vitreous
vitreouslike
vitreously
vitreousness
vitrescence
vitrescency
vitrescent
vitrescibility
vitrescible
vitreum
vitric
vitrics
vitrifaction
vitrifacture
vitrifiability
vitrifiable
vitrification
vitriform
vitrify
Vitrina
vitrine
vitrinoid
vitriol
vitriolate
vitriolation
vitriolic
vitrioline
vitriolizable
vitriolization
vitriolize
vitriolizer
vitrite
vitrobasalt
vitrophyre
vitrophyric
vitrotype
vitrous
Vitruvian
Vitruvianism
vitta
vittate
vitular
vituline
vituperable
vituperate
vituperation
vituperative
vituperatively
vituperator
vituperatory
vituperious
viuva
viva
vivacious
vivaciously
vivaciousness
vivacity
vivandiere
vivarium
vivary
vivax
vive
Vivek
vively
vivency
viver
Viverridae
viverriform
Viverrinae
viverrine
vivers
vives
vivianite
vivicremation
vivid
vividialysis
vividiffusion
vividissection
vividity
vividly
vividness
vivific
vivificate
vivification
vivificative
vivificator
vivifier
vivify
viviparism
viviparity
viviparous
viviparously
viviparousness
vivipary
viviperfuse
vivisect
vivisection
vivisectional
vivisectionally
vivisectionist
vivisective
vivisector
vivisectorium
vivisepulture
vixen
vixenish
vixenishly
vixenishness
vixenlike
vixenly
vizard
vizarded
vizardless
vizardlike
vizardmonger
vizier
vizierate
viziercraft
vizierial
viziership
vizircraft
Vlach
Vladimir
Vladislav
vlei
voar
vocability
vocable
vocably
vocabular
vocabularian
vocabularied
vocabulary
vocabulation
vocabulist
vocal
vocalic
vocalion
vocalise
vocalism
vocalist
vocalistic
vocality
vocalization
vocalize
vocalizer
vocaller
vocally
vocalness
vocate
vocation
vocational
vocationalism
vocationalization
vocationalize
vocationally
vocative
vocatively
Vochysiaceae
vochysiaceous
vocicultural
vociferance
vociferant
vociferate
vociferation
vociferative
vociferator
vociferize
vociferosity
vociferous
vociferously
vociferousness
vocification
vocimotor
vocular
vocule
Vod
vodka
voe
voet
voeten
Voetian
vog
vogesite
voglite
vogue
voguey
voguish
Vogul
voice
voiced
voiceful
voicefulness
voiceless
voicelessly
voicelessness
voicelet
voicelike
voicer
voicing
void
voidable
voidableness
voidance
voided
voidee
voider
voiding
voidless
voidly
voidness
voile
voiturette
voivode
voivodeship
vol
volable
volage
Volans
volant
volantly
Volapuk
Volapuker
Volapukism
Volapukist
volar
volata
volatic
volatile
volatilely
volatileness
volatility
volatilizable
volatilization
volatilize
volatilizer
volation
volational
volborthite
Volcae
volcan
Volcanalia
volcanian
volcanic
volcanically
volcanicity
volcanism
volcanist
volcanite
volcanity
volcanization
volcanize
volcano
volcanoism
volcanological
volcanologist
volcanologize
volcanology
Volcanus
vole
volemitol
volency
volent
volently
volery
volet
volhynite
volipresence
volipresent
volitant
volitate
volitation
volitational
volitiency
volitient
volition
volitional
volitionalist
volitionality
volitionally
volitionary
volitionate
volitionless
volitive
volitorial
Volkerwanderung
volley
volleyball
volleyer
volleying
volleyingly
volost
volplane
volplanist
Volsci
Volscian
volsella
volsellum
Volstead
Volsteadism
volt
Volta
voltaelectric
voltaelectricity
voltaelectrometer
voltaelectrometric
voltage
voltagraphy
voltaic
Voltairian
Voltairianize
Voltairish
Voltairism
voltaism
voltaite
voltameter
voltametric
voltammeter
voltaplast
voltatype
voltinism
voltivity
voltize
voltmeter
voltzite
volubilate
volubility
voluble
volubleness
volubly
volucrine
volume
volumed
volumenometer
volumenometry
volumescope
volumeter
volumetric
volumetrical
volumetrically
volumetry
volumette
voluminal
voluminosity
voluminous
voluminously
voluminousness
volumist
volumometer
volumometrical
volumometry
voluntariate
voluntarily
voluntariness
voluntarism
voluntarist
voluntaristic
voluntarity
voluntary
voluntaryism
voluntaryist
voluntative
volunteer
volunteerism
volunteerly
volunteership
volupt
voluptary
voluptas
voluptuarian
voluptuary
voluptuate
voluptuosity
voluptuous
voluptuously
voluptuousness
volupty
Voluspa
voluta
volutate
volutation
volute
voluted
Volutidae
volutiform
volutin
volution
volutoid
volva
volvate
volvelle
volvent
Volvocaceae
volvocaceous
volvulus
vomer
vomerine
vomerobasilar
vomeronasal
vomeropalatine
vomica
vomicine
vomit
vomitable
vomiter
vomiting
vomitingly
vomition
vomitive
vomitiveness
vomito
vomitory
vomiture
vomiturition
vomitus
vomitwort
vondsira
vonsenite
voodoo
voodooism
voodooist
voodooistic
voracious
voraciously
voraciousness
voracity
voraginous
vorago
vorant
vorhand
vorlooper
vorondreo
vorpal
vortex
vortical
vortically
vorticel
Vorticella
vorticellid
Vorticellidae
vortices
vorticial
vorticiform
vorticism
vorticist
vorticity
vorticose
vorticosely
vorticular
vorticularly
vortiginous
Vortumnus
Vosgian
vota
votable
votal
votally
votaress
votarist
votary
votation
Vote
vote
voteen
voteless
voter
voting
Votish
votive
votively
votiveness
votometer
votress
Votyak
vouch
vouchable
vouchee
voucher
voucheress
vouchment
vouchsafe
vouchsafement
vouge
Vougeot
Vouli
voussoir
vow
vowed
vowel
vowelish
vowelism
vowelist
vowelization
vowelize
vowelless
vowellessness
vowellike
vowely
vower
vowess
vowless
vowmaker
vowmaking
voyage
voyageable
voyager
voyance
voyeur
voyeurism
vraic
vraicker
vraicking
vrbaite
vriddhi
vrother
Vu
vug
vuggy
Vulcan
Vulcanalia
Vulcanalial
Vulcanalian
Vulcanian
Vulcanic
vulcanicity
vulcanism
vulcanist
vulcanite
vulcanizable
vulcanizate
vulcanization
vulcanize
vulcanizer
vulcanological
vulcanologist
vulcanology
vulgar
vulgare
vulgarian
vulgarish
vulgarism
vulgarist
vulgarity
vulgarization
vulgarize
vulgarizer
vulgarlike
vulgarly
vulgarness
vulgarwise
Vulgate
vulgate
vulgus
vuln
vulnerability
vulnerable
vulnerableness
vulnerably
vulnerary
vulnerate
vulneration
vulnerative
vulnerose
vulnific
vulnose
Vulpecula
vulpecular
Vulpeculid
Vulpes
vulpic
vulpicidal
vulpicide
vulpicidism
Vulpinae
vulpine
vulpinism
vulpinite
vulsella
vulsellum
vulsinite
Vultur
vulture
vulturelike
vulturewise
Vulturidae
Vulturinae
vulturine
vulturish
vulturism
vulturn
vulturous
vulva
vulval
vulvar
vulvate
vulviform
vulvitis
vulvocrural
vulvouterine
vulvovaginal
vulvovaginitis
vum
vying
vyingly
W
w
Wa
wa
Waac
waag
waapa
waar
Waasi
wab
wabber
wabble
wabbly
wabby
wabe
Wabena
wabeno
Wabi
wabster
Wabuma
Wabunga
Wac
wacago
wace
Wachaga
Wachenheimer
wachna
Wachuset
wack
wacke
wacken
wacker
wackiness
wacky
Waco
wad
waddent
wadder
wadding
waddler
waddlesome
waddling
waddlingly
waddly
waddy
waddywood
Wade
wade
wadeable
wader
wadi
wading
wadingly
wadlike
wadmaker
wadmaking
wadmal
wadmeal
wadna
wadset
wadsetter
wae
waeg
waer
waesome
waesuck
Waf
Wafd
Wafdist
wafer
waferer
waferish
wafermaker
wafermaking
waferwoman
waferwork
wafery
waff
waffle
wafflike
waffly
waft
waftage
wafter
wafture
wafty
wag
Waganda
waganging
wagaun
wagbeard
wage
waged
wagedom
wageless
wagelessness
wagenboom
Wagener
wager
wagerer
wagering
wages
wagesman
wagework
wageworker
wageworking
waggable
waggably
waggel
wagger
waggery
waggie
waggish
waggishly
waggishness
waggle
waggling
wagglingly
waggly
Waggumbura
waggy
waglike
wagling
Wagneresque
Wagnerian
Wagneriana
Wagnerianism
Wagnerism
Wagnerist
Wagnerite
wagnerite
Wagnerize
Wagogo
Wagoma
wagon
wagonable
wagonage
wagoner
wagoness
wagonette
wagonful
wagonload
wagonmaker
wagonmaking
wagonman
wagonry
wagonsmith
wagonway
wagonwayman
wagonwork
wagonwright
wagsome
wagtail
Waguha
wagwag
wagwants
Wagweno
wagwit
wah
Wahabi
Wahabiism
Wahabit
Wahabitism
wahahe
Wahehe
Wahima
wahine
Wahlenbergia
wahoo
wahpekute
Wahpeton
waiata
Waibling
Waicuri
Waicurian
waif
Waiguli
Waiilatpuan
waik
waikly
waikness
wail
Wailaki
wailer
wailful
wailfully
wailingly
wailsome
waily
wain
wainage
wainbote
wainer
wainful
wainman
wainrope
wainscot
wainscoting
wainwright
waipiro
wairch
waird
wairepo
wairsh
waise
waist
waistband
waistcloth
waistcoat
waistcoated
waistcoateer
waistcoathole
waistcoating
waistcoatless
waisted
waister
waisting
waistless
waistline
wait
waiter
waiterage
waiterdom
waiterhood
waitering
waiterlike
waitership
waiting
waitingly
waitress
waivatua
waive
waiver
waivery
waivod
Waiwai
waiwode
wajang
waka
Wakamba
wakan
Wakashan
wake
wakeel
wakeful
wakefully
wakefulness
wakeless
waken
wakener
wakening
waker
wakes
waketime
wakf
Wakhi
wakif
wakiki
waking
wakingly
wakiup
wakken
wakon
wakonda
Wakore
Wakwafi
waky
Walach
Walachian
walahee
Walapai
Walchia
Waldenses
Waldensian
waldflute
waldgrave
waldgravine
Waldheimia
waldhorn
waldmeister
Waldsteinia
wale
waled
walepiece
Waler
waler
walewort
wali
waling
walk
walkable
walkaway
walker
walking
walkist
walkmill
walkmiller
walkout
walkover
walkrife
walkside
walksman
walkway
walkyrie
wall
wallaba
wallaby
Wallach
wallah
wallaroo
Wallawalla
wallbird
wallboard
walled
waller
Wallerian
wallet
walletful
walleye
walleyed
wallflower
wallful
wallhick
walling
wallise
wallless
wallman
Wallon
Wallonian
Walloon
walloon
wallop
walloper
walloping
wallow
wallower
wallowish
wallowishly
wallowishness
wallpaper
wallpapering
wallpiece
Wallsend
wallwise
wallwork
wallwort
wally
walnut
Walpapi
Walpolean
Walpurgis
walpurgite
walrus
walsh
Walt
walt
Walter
walter
walth
Waltonian
waltz
waltzer
waltzlike
walycoat
wamara
wambais
wamble
wambliness
wambling
wamblingly
wambly
Wambuba
Wambugu
Wambutti
wame
wamefou
wamel
wammikin
wamp
Wampanoag
wampee
wample
wampum
wampumpeag
wampus
wamus
wan
Wanapum
wanchancy
wand
wander
wanderable
wanderer
wandering
wanderingly
wanderingness
Wanderjahr
wanderlust
wanderluster
wanderlustful
wanderoo
wandery
wanderyear
wandflower
wandle
wandlike
wandoo
Wandorobo
wandsman
wandy
wane
Waneatta
waned
waneless
wang
wanga
wangala
wangan
Wangara
wangateur
wanghee
wangle
wangler
Wangoni
wangrace
wangtooth
wanhope
wanhorn
wanigan
waning
wankapin
wankle
wankliness
wankly
wanle
wanly
wanner
wanness
wannish
wanny
wanrufe
wansonsy
want
wantage
wanter
wantful
wanthill
wanthrift
wanting
wantingly
wantingness
wantless
wantlessness
wanton
wantoner
wantonlike
wantonly
wantonness
wantwit
wanty
wanwordy
wanworth
wany
Wanyakyusa
Wanyamwezi
Wanyasa
Wanyoro
wap
wapacut
Wapato
wapatoo
wapentake
Wapisiana
wapiti
Wapogoro
Wapokomo
wapp
Wappato
wappenschaw
wappenschawing
wapper
wapping
Wappinger
Wappo
war
warabi
waratah
warble
warbled
warblelike
warbler
warblerlike
warblet
warbling
warblingly
warbly
warch
warcraft
ward
wardable
wardage
wardapet
warday
warded
Warden
warden
wardency
wardenry
wardenship
warder
warderer
wardership
wardholding
warding
wardite
wardless
wardlike
wardmaid
wardman
wardmote
wardress
wardrobe
wardrober
wardroom
wardship
wardsmaid
wardsman
wardswoman
wardwite
wardwoman
ware
Waregga
warehou
warehouse
warehouseage
warehoused
warehouseful
warehouseman
warehouser
wareless
waremaker
waremaking
wareman
wareroom
warf
warfare
warfarer
warfaring
warful
warily
wariness
Waring
waringin
warish
warison
wark
warkamoowee
warl
warless
warlessly
warlike
warlikely
warlikeness
warlock
warluck
warly
warm
warmable
warman
warmed
warmedly
warmer
warmful
warmhearted
warmheartedly
warmheartedness
warmhouse
warming
warmish
warmly
warmness
warmonger
warmongering
warmouth
warmth
warmthless
warmus
warn
warnel
warner
warning
warningly
warningproof
warnish
warnoth
warnt
Warori
warp
warpable
warpage
warped
warper
warping
warplane
warple
warplike
warproof
warpwise
warragal
warrambool
warran
warrand
warrandice
warrant
warrantable
warrantableness
warrantably
warranted
warrantee
warranter
warrantise
warrantless
warrantor
warranty
warratau
Warrau
warree
Warren
warren
warrener
warrenlike
warrer
Warri
warrin
warrior
warrioress
warriorhood
warriorism
warriorlike
warriorship
warriorwise
warrok
Warsaw
warsaw
warse
warsel
warship
warsle
warsler
warst
wart
warted
wartern
wartflower
warth
wartime
wartless
wartlet
wartlike
wartproof
wartweed
wartwort
warty
wartyback
Warua
Warundi
warve
warwards
Warwick
warwickite
warwolf
warworn
wary
was
wasabi
Wasagara
Wasandawi
Wasango
Wasat
Wasatch
Wasco
wase
Wasegua
wasel
wash
washability
washable
washableness
Washaki
washaway
washbasin
washbasket
washboard
washbowl
washbrew
washcloth
washday
washdish
washdown
washed
washen
washer
washerless
washerman
washerwife
washerwoman
washery
washeryman
washhand
washhouse
washin
washiness
washing
Washington
Washingtonia
Washingtonian
Washingtoniana
Washita
washland
washmaid
washman
Washo
Washoan
washoff
washout
washpot
washproof
washrag
washroad
washroom
washshed
washstand
washtail
washtray
washtrough
washtub
washway
washwoman
washwork
washy
Wasir
wasnt
Wasoga
Wasp
wasp
waspen
wasphood
waspily
waspish
waspishly
waspishness
wasplike
waspling
waspnesting
waspy
wassail
wassailer
wassailous
wassailry
wassie
wast
wastable
wastage
waste
wastebasket
wasteboard
wasted
wasteful
wastefully
wastefulness
wastel
wasteland
wastelbread
wasteless
wasteman
wastement
wasteness
wastepaper
wasteproof
waster
wasterful
wasterfully
wasterfulness
wastethrift
wasteword
wasteyard
wasting
wastingly
wastingness
wastland
wastrel
wastrife
wasty
Wasukuma
Waswahili
Wat
wat
Watala
watap
watch
watchable
watchboat
watchcase
watchcry
watchdog
watched
watcher
watchfree
watchful
watchfully
watchfulness
watchglassful
watchhouse
watching
watchingly
watchkeeper
watchless
watchlessness
watchmaker
watchmaking
watchman
watchmanly
watchmanship
watchmate
watchment
watchout
watchtower
watchwise
watchwoman
watchword
watchwork
water
waterage
waterbailage
waterbelly
Waterberg
waterboard
waterbok
waterbosh
waterbrain
waterchat
watercup
waterdoe
waterdrop
watered
waterer
waterfall
waterfinder
waterflood
waterfowl
waterfront
waterhead
waterhorse
waterie
waterily
wateriness
watering
wateringly
wateringman
waterish
waterishly
waterishness
Waterlander
Waterlandian
waterleave
waterless
waterlessly
waterlessness
waterlike
waterline
waterlog
waterlogged
waterloggedness
waterlogger
waterlogging
Waterloo
waterman
watermanship
watermark
watermaster
watermelon
watermonger
waterphone
waterpot
waterproof
waterproofer
waterproofing
waterproofness
waterquake
waterscape
watershed
watershoot
waterside
watersider
waterskin
watersmeet
waterspout
waterstead
watertight
watertightal
watertightness
waterward
waterwards
waterway
waterweed
waterwise
waterwoman
waterwood
waterwork
waterworker
waterworm
waterworn
waterwort
watery
wath
wathstead
Watsonia
watt
wattage
wattape
wattle
wattlebird
wattled
wattless
wattlework
wattling
wattman
wattmeter
Watusi
wauble
wauch
wauchle
waucht
wauf
waugh
waughy
wauken
waukit
waukrife
waul
waumle
wauner
wauns
waup
waur
Waura
wauregan
wauve
wavable
wavably
Wave
wave
waved
waveless
wavelessly
wavelessness
wavelet
wavelike
wavellite
wavemark
wavement
wavemeter
waveproof
waver
waverable
waverer
wavering
waveringly
waveringness
waverous
wavery
waveson
waveward
wavewise
wavey
wavicle
wavily
waviness
waving
wavingly
Wavira
wavy
waw
wawa
wawah
wawaskeesh
wax
waxberry
waxbill
waxbird
waxbush
waxchandler
waxchandlery
waxen
waxer
waxflower
Waxhaw
waxhearted
waxily
waxiness
waxing
waxingly
waxlike
waxmaker
waxmaking
waxman
waxweed
waxwing
waxwork
waxworker
waxworking
waxy
way
wayaka
wayang
Wayao
wayback
wayberry
waybill
waybird
waybook
waybread
waybung
wayfare
wayfarer
wayfaring
wayfaringly
wayfellow
waygang
waygate
waygoing
waygone
waygoose
wayhouse
waying
waylaid
waylaidlessness
waylay
waylayer
wayleave
wayless
waymaker
wayman
waymark
waymate
Wayne
waypost
ways
wayside
waysider
waysliding
waythorn
wayward
waywarden
waywardly
waywardness
waywiser
waywode
waywodeship
wayworn
waywort
wayzgoose
Wazir
we
Wea
weak
weakbrained
weaken
weakener
weakening
weakfish
weakhanded
weakhearted
weakheartedly
weakheartedness
weakish
weakishly
weakishness
weakliness
weakling
weakly
weakmouthed
weakness
weaky
weal
weald
Wealden
wealdsman
wealth
wealthily
wealthiness
wealthless
wealthmaker
wealthmaking
wealthmonger
Wealthy
wealthy
weam
wean
weanable
weanedness
weanel
weaner
weanling
Weanoc
weanyer
Weapemeoc
weapon
weaponed
weaponeer
weaponless
weaponmaker
weaponmaking
weaponproof
weaponry
weaponshaw
weaponshow
weaponshowing
weaponsmith
weaponsmithy
wear
wearability
wearable
wearer
weariable
weariableness
wearied
weariedly
weariedness
wearier
weariful
wearifully
wearifulness
weariless
wearilessly
wearily
weariness
wearing
wearingly
wearish
wearishly
wearishness
wearisome
wearisomely
wearisomeness
wearproof
weary
wearying
wearyingly
weasand
weasel
weaselfish
weasellike
weaselly
weaselship
weaselskin
weaselsnout
weaselwise
weaser
weason
weather
weatherboard
weatherboarding
weatherbreak
weathercock
weathercockish
weathercockism
weathercocky
weathered
weatherer
weatherfish
weatherglass
weathergleam
weatherhead
weatherheaded
weathering
weatherliness
weatherly
weathermaker
weathermaking
weatherman
weathermost
weatherology
weatherproof
weatherproofed
weatherproofing
weatherproofness
weatherward
weatherworn
weathery
weavable
weave
weaveable
weaved
weavement
weaver
weaverbird
weaveress
weaving
weazen
weazened
weazeny
web
webbed
webber
webbing
webby
weber
Weberian
webeye
webfoot
webfooter
webless
weblike
webmaker
webmaking
webster
Websterian
websterite
webwork
webworm
wecht
wed
wedana
wedbed
wedbedrip
wedded
weddedly
weddedness
wedder
wedding
weddinger
wede
wedge
wedgeable
wedgebill
wedged
wedgelike
wedger
wedgewise
Wedgie
wedging
Wedgwood
wedgy
wedlock
Wednesday
wedset
wee
weeble
weed
weeda
weedable
weedage
weeded
weeder
weedery
weedful
weedhook
weediness
weedingtime
weedish
weedless
weedlike
weedling
weedow
weedproof
weedy
week
weekday
weekend
weekender
weekly
weekwam
weel
weelfard
weelfaured
weemen
ween
weendigo
weeness
weening
weenong
weeny
weep
weepable
weeper
weepered
weepful
weeping
weepingly
weeps
weepy
weesh
weeshy
weet
weetbird
weetless
weever
weevil
weeviled
weevillike
weevilproof
weevily
weewow
weeze
weft
weftage
wefted
wefty
Wega
wegenerian
wegotism
wehrlite
Wei
weibyeite
weichselwood
Weierstrassian
Weigela
weigelite
weigh
weighable
weighage
weighbar
weighbauk
weighbridge
weighbridgeman
weighed
weigher
weighership
weighhouse
weighin
weighing
weighman
weighment
weighshaft
weight
weightchaser
weighted
weightedly
weightedness
weightily
weightiness
weighting
weightless
weightlessly
weightlessness
weightometer
weighty
weinbergerite
Weinmannia
weinschenkite
weir
weirangle
weird
weirdful
weirdish
weirdless
weirdlessness
weirdlike
weirdliness
weirdly
weirdness
weirdsome
weirdward
weirdwoman
weiring
weisbachite
weiselbergite
weism
Weismannian
Weismannism
weissite
Weissnichtwo
Weitspekan
wejack
weka
wekau
wekeen
weki
welcome
welcomeless
welcomely
welcomeness
welcomer
welcoming
welcomingly
weld
weldability
weldable
welder
welding
weldless
weldment
weldor
Welf
welfare
welfaring
Welfic
welk
welkin
welkinlike
well
wellat
wellaway
wellborn
wellcurb
wellhead
wellhole
welling
wellington
Wellingtonia
wellish
wellmaker
wellmaking
wellman
wellnear
wellness
wellring
Wellsian
wellside
wellsite
wellspring
wellstead
wellstrand
welly
wellyard
wels
Welsh
welsh
welsher
Welshery
Welshism
Welshland
Welshlike
Welshman
Welshness
Welshry
Welshwoman
Welshy
welsium
welt
welted
welter
welterweight
welting
Welwitschia
wem
wemless
wen
wench
wencher
wenchless
wenchlike
Wenchow
Wenchowese
Wend
wend
wende
Wendell
Wendi
Wendic
Wendish
Wendy
wene
Wenlock
Wenlockian
wennebergite
wennish
wenny
Wenonah
Wenrohronon
went
wentletrap
wenzel
wept
wer
Werchowinci
were
werebear
werecalf
werefolk
werefox
werehyena
werejaguar
wereleopard
werent
weretiger
werewolf
werewolfish
werewolfism
werf
wergil
weri
Werner
Wernerian
Wernerism
wernerite
werowance
wert
Werther
Wertherian
Wertherism
wervel
Wes
wese
weskit
Wesleyan
Wesleyanism
Wesleyism
wesselton
Wessexman
west
westaway
westbound
weste
wester
westering
westerliness
westerly
westermost
western
westerner
westernism
westernization
westernize
westernly
westernmost
westerwards
westfalite
westing
westland
Westlander
westlandways
westmost
westness
Westphalian
Westralian
Westralianism
westward
westwardly
westwardmost
westwards
westy
wet
weta
wetback
wetbird
wetched
wetchet
wether
wetherhog
wetherteg
wetly
wetness
wettability
wettable
wetted
wetter
wetting
wettish
Wetumpka
weve
wevet
Wewenoc
wey
Wezen
Wezn
wha
whabby
whack
whacker
whacking
whacky
whafabout
whale
whaleback
whalebacker
whalebird
whaleboat
whalebone
whaleboned
whaledom
whalehead
whalelike
whaleman
whaler
whaleroad
whalery
whaleship
whaling
whalish
whally
whalm
whalp
whaly
wham
whamble
whame
whammle
whamp
whampee
whample
whan
whand
whang
whangable
whangam
whangdoodle
whangee
whanghee
whank
whap
whappet
whapuka
whapukee
whapuku
whar
whare
whareer
wharf
wharfage
wharfhead
wharfholder
wharfing
wharfinger
wharfland
wharfless
wharfman
wharfmaster
wharfrae
wharfside
wharl
wharp
wharry
whart
wharve
whase
whasle
what
whata
whatabouts
whatever
whatkin
whatlike
whatna
whatness
whatnot
whatreck
whats
whatso
whatsoeer
whatsoever
whatsomever
whatten
whau
whauk
whaup
whaur
whauve
wheal
whealworm
whealy
wheam
wheat
wheatbird
wheatear
wheateared
wheaten
wheatgrower
wheatland
wheatless
wheatlike
wheatstalk
wheatworm
wheaty
whedder
whee
wheedle
wheedler
wheedlesome
wheedling
wheedlingly
wheel
wheelage
wheelband
wheelbarrow
wheelbarrowful
wheelbird
wheelbox
wheeldom
wheeled
wheeler
wheelery
wheelhouse
wheeling
wheelingly
wheelless
wheellike
wheelmaker
wheelmaking
wheelman
wheelrace
wheelroad
wheelsman
wheelsmith
wheelspin
wheelswarf
wheelway
wheelwise
wheelwork
wheelwright
wheelwrighting
wheely
wheem
wheen
wheencat
wheenge
wheep
wheeple
wheer
wheerikins
wheesht
wheetle
wheeze
wheezer
wheezily
wheeziness
wheezingly
wheezle
wheezy
wheft
whein
whekau
wheki
whelk
whelked
whelker
whelklike
whelky
whelm
whelp
whelphood
whelpish
whelpless
whelpling
whelve
whemmel
when
whenabouts
whenas
whence
whenceeer
whenceforth
whenceforward
whencesoeer
whencesoever
whencever
wheneer
whenever
whenness
whenso
whensoever
whensomever
where
whereabout
whereabouts
whereafter
whereanent
whereas
whereat
whereaway
whereby
whereer
wherefor
wherefore
wherefrom
wherein
whereinsoever
whereinto
whereness
whereof
whereon
whereout
whereover
whereso
wheresoeer
wheresoever
wheresomever
wherethrough
wheretill
whereto
wheretoever
wheretosoever
whereunder
whereuntil
whereunto
whereup
whereupon
wherever
wherewith
wherewithal
wherret
wherrit
wherry
wherryman
whet
whether
whetile
whetrock
whetstone
whetter
whew
whewellite
whewer
whewl
whewt
whey
wheybeard
wheyey
wheyeyness
wheyface
wheyfaced
wheyish
wheyishness
wheylike
wheyness
whiba
which
whichever
whichsoever
whichway
whichways
whick
whicken
whicker
whid
whidah
whidder
whiff
whiffenpoof
whiffer
whiffet
whiffle
whiffler
whifflery
whiffletree
whiffling
whifflingly
whiffy
whift
Whig
whig
Whiggamore
whiggamore
Whiggarchy
Whiggery
Whiggess
Whiggification
Whiggify
Whiggish
Whiggishly
Whiggishness
Whiggism
Whiglet
Whigling
whigmaleerie
whigship
whikerby
while
whileen
whilere
whiles
whilie
whilk
Whilkut
whill
whillaballoo
whillaloo
whillilew
whilly
whillywha
whilock
whilom
whils
whilst
whilter
whim
whimberry
whimble
whimbrel
whimling
whimmy
whimper
whimperer
whimpering
whimperingly
whimsey
whimsic
whimsical
whimsicality
whimsically
whimsicalness
whimsied
whimstone
whimwham
whin
whinberry
whinchacker
whinchat
whincheck
whincow
whindle
whine
whiner
whinestone
whing
whinge
whinger
whininess
whiningly
whinnel
whinner
whinnock
whinny
whinstone
whiny
whinyard
whip
whipbelly
whipbird
whipcat
whipcord
whipcordy
whipcrack
whipcracker
whipcraft
whipgraft
whipjack
whipking
whiplash
whiplike
whipmaker
whipmaking
whipman
whipmanship
whipmaster
whippa
whippable
whipparee
whipped
whipper
whippersnapper
whippertail
whippet
whippeter
whippiness
whipping
whippingly
whippletree
whippoorwill
whippost
whippowill
whippy
whipsaw
whipsawyer
whipship
whipsocket
whipstaff
whipstalk
whipstall
whipster
whipstick
whipstitch
whipstock
whipt
whiptail
whiptree
whipwise
whipworm
whir
whirken
whirl
whirlabout
whirlblast
whirlbone
whirlbrain
whirled
whirler
whirley
whirlgig
whirlicane
whirligig
whirlimagig
whirling
whirlingly
whirlmagee
whirlpool
whirlpuff
whirlwig
whirlwind
whirlwindish
whirlwindy
whirly
whirlygigum
whirret
whirrey
whirroo
whirry
whirtle
whish
whisk
whisker
whiskerage
whiskerando
whiskerandoed
whiskered
whiskerer
whiskerette
whiskerless
whiskerlike
whiskery
whiskey
whiskful
whiskied
whiskified
whisking
whiskingly
whisky
whiskyfied
whiskylike
whisp
whisper
whisperable
whisperation
whispered
whisperer
whisperhood
whispering
whisperingly
whisperingness
whisperless
whisperous
whisperously
whisperproof
whispery
whissle
Whisson
whist
whister
whisterpoop
whistle
whistlebelly
whistlefish
whistlelike
whistler
Whistlerian
whistlerism
whistlewing
whistlewood
whistlike
whistling
whistlingly
whistly
whistness
Whistonian
Whit
whit
white
whiteback
whitebait
whitebark
whitebeard
whitebelly
whitebill
whitebird
whiteblaze
whiteblow
whitebottle
Whiteboy
Whiteboyism
whitecap
whitecapper
Whitechapel
whitecoat
whitecomb
whitecorn
whitecup
whited
whiteface
Whitefieldian
Whitefieldism
Whitefieldite
whitefish
whitefisher
whitefishery
Whitefoot
whitefoot
whitefootism
whitehanded
whitehass
whitehawse
whitehead
whiteheart
whitehearted
whitelike
whitely
whiten
whitener
whiteness
whitening
whitenose
whitepot
whiteroot
whiterump
whites
whitesark
whiteseam
whiteshank
whiteside
whitesmith
whitestone
whitetail
whitethorn
whitethroat
whitetip
whitetop
whitevein
whitewall
whitewards
whiteware
whitewash
whitewasher
whiteweed
whitewing
whitewood
whiteworm
whitewort
whitfinch
whither
whitherso
whithersoever
whitherto
whitherward
whiting
whitish
whitishness
whitleather
Whitleyism
whitling
whitlow
whitlowwort
Whitmanese
Whitmanesque
Whitmanism
Whitmanize
Whitmonday
whitneyite
whitrack
whits
whitster
Whitsun
Whitsunday
Whitsuntide
whittaw
whitten
whittener
whitter
whitterick
whittle
whittler
whittling
whittret
whittrick
whity
whiz
whizgig
whizzer
whizzerman
whizziness
whizzing
whizzingly
whizzle
who
whoa
whodunit
whoever
whole
wholehearted
wholeheartedly
wholeheartedness
wholeness
wholesale
wholesalely
wholesaleness
wholesaler
wholesome
wholesomely
wholesomeness
wholewise
wholly
whom
whomble
whomever
whomso
whomsoever
whone
whoo
whoof
whoop
whoopee
whooper
whooping
whoopingly
whooplike
whoops
whoosh
whop
whopper
whopping
whorage
whore
whoredom
whorelike
whoremaster
whoremasterly
whoremastery
whoremonger
whoremonging
whoreship
whoreson
whorish
whorishly
whorishness
whorl
whorled
whorlflower
whorly
whorlywort
whort
whortle
whortleberry
whose
whosen
whosesoever
whosever
whosomever
whosumdever
whud
whuff
whuffle
whulk
whulter
whummle
whun
whunstane
whup
whush
whuskie
whussle
whute
whuther
whutter
whuttering
whuz
why
whyever
whyfor
whyness
whyo
wi
wice
Wichita
wicht
wichtisite
wichtje
wick
wickawee
wicked
wickedish
wickedlike
wickedly
wickedness
wicken
wicker
wickerby
wickerware
wickerwork
wickerworked
wickerworker
wicket
wicketkeep
wicketkeeper
wicketkeeping
wicketwork
wicking
wickiup
wickless
wickup
wicky
wicopy
wid
widbin
widdendream
widder
widdershins
widdifow
widdle
widdy
wide
widegab
widehearted
widely
widemouthed
widen
widener
wideness
widespread
widespreadedly
widespreadly
widespreadness
widewhere
widework
widgeon
widish
widow
widowed
widower
widowered
widowerhood
widowership
widowery
widowhood
widowish
widowlike
widowly
widowman
widowy
width
widthless
widthway
widthways
widthwise
widu
wield
wieldable
wielder
wieldiness
wieldy
wiener
wienerwurst
wienie
wierangle
wiesenboden
wife
wifecarl
wifedom
wifehood
wifeism
wifekin
wifeless
wifelessness
wifelet
wifelike
wifeling
wifelkin
wifely
wifeship
wifeward
wifie
wifiekie
wifish
wifock
wig
wigan
wigdom
wigful
wigged
wiggen
wigger
wiggery
wigging
wiggish
wiggishness
wiggism
wiggle
wiggler
wiggly
wiggy
wight
wightly
wightness
wigless
wiglet
wiglike
wigmaker
wigmaking
wigtail
wigwag
wigwagger
wigwam
wiikite
Wikeno
Wikstroemia
Wilbur
Wilburite
wild
wildbore
wildcat
wildcatter
wildcatting
wildebeest
wilded
wilder
wilderedly
wildering
wilderment
wilderness
wildfire
wildfowl
wildgrave
wilding
wildish
wildishly
wildishness
wildlife
wildlike
wildling
wildly
wildness
wildsome
wildwind
wile
wileful
wileless
wileproof
Wilfred
wilga
wilgers
Wilhelm
Wilhelmina
Wilhelmine
wilily
wiliness
wilk
wilkeite
wilkin
Wilkinson
Will
will
willable
willawa
willed
willedness
willemite
willer
willet
willey
willeyer
willful
willfully
willfulness
William
williamsite
Williamsonia
Williamsoniaceae
Willie
willie
willier
willies
willing
willinghearted
willinghood
willingly
willingness
williwaw
willmaker
willmaking
willness
willock
willow
willowbiter
willowed
willower
willowish
willowlike
willowware
willowweed
willowworm
willowwort
willowy
Willugbaeya
Willy
willy
willyard
willyart
willyer
Wilmer
wilsome
wilsomely
wilsomeness
Wilson
Wilsonian
wilt
wilter
Wilton
wiltproof
Wiltshire
wily
wim
wimberry
wimble
wimblelike
wimbrel
wime
wimick
wimple
wimpleless
wimplelike
Win
win
winberry
wince
wincer
wincey
winch
wincher
Winchester
winchman
wincing
wincingly
Wind
wind
windable
windage
windbag
windbagged
windbaggery
windball
windberry
windbibber
windbore
windbracing
windbreak
Windbreaker
windbreaker
windbroach
windclothes
windcuffer
winddog
winded
windedly
windedness
winder
windermost
Windesheimer
windfall
windfallen
windfanner
windfirm
windfish
windflaw
windflower
windgall
windgalled
windhole
windhover
windigo
windily
windiness
winding
windingly
windingness
windjammer
windjamming
windlass
windlasser
windle
windles
windless
windlessly
windlessness
windlestrae
windlestraw
windlike
windlin
windling
windmill
windmilly
windock
windore
window
windowful
windowless
windowlessness
windowlet
windowlight
windowlike
windowmaker
windowmaking
windowman
windowpane
windowpeeper
windowshut
windowward
windowwards
windowwise
windowy
windpipe
windplayer
windproof
windring
windroad
windroot
windrow
windrower
windscreen
windshield
windshock
Windsor
windsorite
windstorm
windsucker
windtight
windup
windward
windwardly
windwardmost
windwardness
windwards
windway
windwayward
windwaywardly
windy
wine
wineball
wineberry
winebibber
winebibbery
winebibbing
Winebrennerian
wineconner
wined
wineglass
wineglassful
winegrower
winegrowing
winehouse
wineless
winelike
winemay
winepot
winer
winery
Winesap
wineshop
wineskin
winesop
winetaster
winetree
winevat
Winfred
winful
wing
wingable
wingbeat
wingcut
winged
wingedly
wingedness
winger
wingfish
winghanded
wingle
wingless
winglessness
winglet
winglike
wingman
wingmanship
wingpiece
wingpost
wingseed
wingspread
wingstem
wingy
Winifred
winish
wink
winkel
winkelman
winker
winkered
winking
winkingly
winkle
winklehawk
winklehole
winklet
winly
winna
winnable
winnard
Winnebago
Winnecowet
winnel
winnelstrae
winner
Winnie
winning
winningly
winningness
winnings
winninish
Winnipesaukee
winnle
winnonish
winnow
winnower
winnowing
winnowingly
Winona
winrace
winrow
winsome
winsomely
winsomeness
Winston
wint
winter
Winteraceae
winterage
Winteranaceae
winterberry
winterbloom
winterbourne
winterdykes
wintered
winterer
winterfeed
wintergreen
winterhain
wintering
winterish
winterishly
winterishness
winterization
winterize
winterkill
winterkilling
winterless
winterlike
winterliness
winterling
winterly
winterproof
wintersome
wintertide
wintertime
winterward
winterwards
winterweed
wintle
wintrify
wintrily
wintriness
wintrish
wintrous
wintry
Wintun
winy
winze
winzeman
wipe
wiper
wippen
wips
wir
wirable
wirble
wird
wire
wirebar
wirebird
wired
wiredancer
wiredancing
wiredraw
wiredrawer
wiredrawn
wirehair
wireless
wirelessly
wirelessness
wirelike
wiremaker
wiremaking
wireman
wiremonger
Wirephoto
wirepull
wirepuller
wirepulling
wirer
wiresmith
wirespun
wiretail
wireway
wireweed
wirework
wireworker
wireworking
wireworks
wireworm
wirily
wiriness
wiring
wirl
wirling
Wiros
wirr
wirra
wirrah
wirrasthru
wiry
wis
Wisconsinite
wisdom
wisdomful
wisdomless
wisdomproof
wisdomship
wise
wiseacre
wiseacred
wiseacredness
wiseacredom
wiseacreish
wiseacreishness
wiseacreism
wisecrack
wisecracker
wisecrackery
wisehead
wisehearted
wiseheartedly
wiseheimer
wiselike
wiseling
wisely
wiseman
wisen
wiseness
wisenheimer
wisent
wiser
wiseweed
wisewoman
wish
wisha
wishable
wishbone
wished
wishedly
wisher
wishful
wishfully
wishfulness
wishing
wishingly
wishless
wishly
wishmay
wishness
Wishoskan
Wishram
wisht
wishtonwish
Wisigothic
wisket
wiskinky
wisp
wispish
wisplike
wispy
wiss
wisse
wissel
wist
Wistaria
wistaria
wiste
wistened
Wisteria
wisteria
wistful
wistfully
wistfulness
wistit
wistiti
wistless
wistlessness
wistonwish
wit
witan
Witbooi
witch
witchbells
witchcraft
witched
witchedly
witchen
witchering
witchery
witchet
witchetty
witchhood
witching
witchingly
witchleaf
witchlike
witchman
witchmonger
witchuck
witchweed
witchwife
witchwoman
witchwood
witchwork
witchy
witcraft
wite
witeless
witenagemot
witepenny
witess
witful
with
withal
withamite
Withania
withdraught
withdraw
withdrawable
withdrawal
withdrawer
withdrawing
withdrawingness
withdrawment
withdrawn
withdrawnness
withe
withen
wither
witherband
withered
witheredly
witheredness
witherer
withergloom
withering
witheringly
witherite
witherly
withernam
withers
withershins
withertip
witherwards
witherweight
withery
withewood
withheld
withhold
withholdable
withholdal
withholder
withholdment
within
withindoors
withinside
withinsides
withinward
withinwards
withness
witholden
without
withoutdoors
withouten
withoutforth
withoutside
withoutwards
withsave
withstand
withstander
withstandingness
withstay
withstood
withstrain
withvine
withwind
withy
withypot
withywind
witjar
witless
witlessly
witlessness
witlet
witling
witloof
witmonger
witness
witnessable
witnessdom
witnesser
witney
witneyer
Witoto
witship
wittal
wittawer
witteboom
witted
witter
wittering
witticaster
wittichenite
witticism
witticize
wittified
wittily
wittiness
witting
wittingly
wittol
wittolly
witty
Witumki
witwall
witzchoura
wive
wiver
wivern
Wiyat
Wiyot
wiz
wizard
wizardess
wizardism
wizardlike
wizardly
wizardry
wizardship
wizen
wizened
wizenedness
wizier
wizzen
wloka
wo
woad
woader
woadman
woadwaxen
woady
woak
woald
woan
wob
wobbegong
wobble
wobbler
wobbliness
wobbling
wobblingly
wobbly
wobster
wocheinite
Wochua
wod
woddie
wode
Wodenism
wodge
wodgy
woe
woebegone
woebegoneness
woebegonish
woeful
woefully
woefulness
woehlerite
woesome
woevine
woeworn
woffler
woft
wog
wogiet
Wogulian
woibe
wokas
woke
wokowi
wold
woldlike
woldsman
woldy
Wolf
wolf
wolfachite
wolfberry
wolfdom
wolfen
wolfer
Wolffia
Wolffian
Wolffianism
Wolfgang
wolfhood
wolfhound
Wolfian
wolfish
wolfishly
wolfishness
wolfkin
wolfless
wolflike
wolfling
wolfram
wolframate
wolframic
wolframine
wolframinium
wolframite
wolfsbane
wolfsbergite
wolfskin
wolfward
wolfwards
wollastonite
wollomai
wollop
Wolof
wolter
wolve
wolveboon
wolver
wolverine
woman
womanbody
womandom
womanfolk
womanfully
womanhead
womanhearted
womanhood
womanhouse
womanish
womanishly
womanishness
womanism
womanist
womanity
womanization
womanize
womanizer
womankind
womanless
womanlike
womanliness
womanly
womanmuckle
womanness
womanpost
womanproof
womanship
womanways
womanwise
womb
wombat
wombed
womble
wombstone
womby
womenfolk
womenfolks
womenkind
womera
wommerala
won
wonder
wonderberry
wonderbright
wondercraft
wonderer
wonderful
wonderfully
wonderfulness
wondering
wonderingly
wonderland
wonderlandish
wonderless
wonderment
wondermonger
wondermongering
wondersmith
wondersome
wonderstrong
wonderwell
wonderwork
wonderworthy
wondrous
wondrously
wondrousness
wone
wonegan
wong
wonga
Wongara
wongen
wongshy
wongsky
woning
wonky
wonna
wonned
wonner
wonning
wonnot
wont
wonted
wontedly
wontedness
wonting
woo
wooable
wood
woodagate
woodbark
woodbin
woodbind
woodbine
woodbined
woodbound
woodburytype
woodbush
woodchat
woodchuck
woodcock
woodcockize
woodcracker
woodcraft
woodcrafter
woodcraftiness
woodcraftsman
woodcrafty
woodcut
woodcutter
woodcutting
wooded
wooden
woodendite
woodenhead
woodenheaded
woodenheadedness
woodenly
woodenness
woodenware
woodenweary
woodeny
woodfish
woodgeld
woodgrub
woodhack
woodhacker
woodhole
woodhorse
woodhouse
woodhung
woodine
woodiness
wooding
woodish
woodjobber
woodkern
woodknacker
woodland
woodlander
woodless
woodlessness
woodlet
woodlike
woodlocked
woodly
woodman
woodmancraft
woodmanship
woodmonger
woodmote
woodness
woodpeck
woodpecker
woodpenny
woodpile
woodprint
woodranger
woodreeve
woodrick
woodrock
woodroof
woodrow
woodrowel
Woodruff
woodruff
woodsere
woodshed
woodshop
Woodsia
woodside
woodsilver
woodskin
woodsman
woodspite
woodstone
woodsy
woodwall
woodward
Woodwardia
woodwardship
woodware
woodwax
woodwaxen
woodwise
woodwork
woodworker
woodworking
woodworm
woodwose
woodwright
Woody
woody
woodyard
wooer
woof
woofed
woofell
woofer
woofy
woohoo
wooing
wooingly
wool
woold
woolder
woolding
wooled
woolen
woolenet
woolenization
woolenize
wooler
woolert
woolfell
woolgatherer
woolgathering
woolgrower
woolgrowing
woolhead
wooliness
woollike
woolly
woollyhead
woollyish
woolman
woolpack
woolpress
woolsack
woolsey
woolshearer
woolshearing
woolshears
woolshed
woolskin
woolsorter
woolsorting
woolsower
woolstock
woolulose
Woolwa
woolwasher
woolweed
woolwheel
woolwinder
woolwork
woolworker
woolworking
woom
woomer
woomerang
woon
woons
woorali
woorari
woosh
wootz
woozle
woozy
wop
woppish
wops
worble
worcester
word
wordable
wordably
wordage
wordbook
wordbuilding
wordcraft
wordcraftsman
worded
Worden
worder
wordily
wordiness
wording
wordish
wordishly
wordishness
wordle
wordless
wordlessly
wordlessness
wordlike
wordlorist
wordmaker
wordmaking
wordman
wordmanship
wordmonger
wordmongering
wordmongery
wordplay
wordsman
wordsmanship
wordsmith
wordspite
wordster
Wordsworthian
Wordsworthianism
wordy
wore
work
workability
workable
workableness
workaday
workaway
workbag
workbasket
workbench
workbook
workbox
workbrittle
workday
worked
worker
workfellow
workfolk
workfolks
workgirl
workhand
workhouse
workhoused
working
workingly
workingman
workingwoman
workless
worklessness
workloom
workman
workmanlike
workmanlikeness
workmanliness
workmanly
workmanship
workmaster
workmistress
workout
workpan
workpeople
workpiece
workplace
workroom
works
workship
workshop
worksome
workstand
worktable
worktime
workways
workwise
workwoman
workwomanlike
workwomanly
worky
workyard
world
worlded
worldful
worldish
worldless
worldlet
worldlike
worldlily
worldliness
worldling
worldly
worldmaker
worldmaking
worldproof
worldquake
worldward
worldwards
worldway
worldy
worm
wormed
wormer
wormhole
wormholed
wormhood
Wormian
wormil
worming
wormless
wormlike
wormling
wormproof
wormroot
wormseed
wormship
wormweed
wormwood
wormy
worn
wornil
wornness
worral
worriable
worricow
worried
worriedly
worriedness
worrier
worriless
worriment
worrisome
worrisomely
worrisomeness
worrit
worriter
worry
worrying
worryingly
worryproof
worrywart
worse
worsement
worsen
worseness
worsening
worser
worserment
worset
worship
worshipability
worshipable
worshiper
worshipful
worshipfully
worshipfulness
worshipingly
worshipless
worshipworth
worshipworthy
worst
worsted
wort
worth
worthful
worthfulness
worthiest
worthily
worthiness
worthless
worthlessly
worthlessness
worthship
worthward
worthy
wosbird
wot
wote
wots
wottest
wotteth
woubit
wouch
wouf
wough
would
wouldest
wouldnt
wouldst
wound
woundability
woundable
woundableness
wounded
woundedly
wounder
woundily
wounding
woundingly
woundless
wounds
woundwort
woundworth
woundy
wourali
wourari
wournil
wove
woven
Wovoka
wow
wowser
wowserdom
wowserian
wowserish
wowserism
wowsery
wowt
woy
Woyaway
wrack
wracker
wrackful
Wraf
wraggle
wrainbolt
wrainstaff
wrainstave
wraith
wraithe
wraithlike
wraithy
wraitly
wramp
wran
wrang
wrangle
wrangler
wranglership
wranglesome
wranglingly
wrannock
wranny
wrap
wrappage
wrapped
wrapper
wrapperer
wrappering
wrapping
wraprascal
wrasse
wrastle
wrastler
wrath
wrathful
wrathfully
wrathfulness
wrathily
wrathiness
wrathlike
wrathy
wraw
wrawl
wrawler
wraxle
wreak
wreakful
wreakless
wreat
wreath
wreathage
wreathe
wreathed
wreathen
wreather
wreathingly
wreathless
wreathlet
wreathlike
wreathmaker
wreathmaking
wreathwise
wreathwork
wreathwort
wreathy
wreck
wreckage
wrecker
wreckfish
wreckful
wrecking
wrecky
Wren
wren
wrench
wrenched
wrencher
wrenchingly
wrenlet
wrenlike
wrentail
wrest
wrestable
wrester
wresting
wrestingly
wrestle
wrestler
wrestlerlike
wrestling
wretch
wretched
wretchedly
wretchedness
wretchless
wretchlessly
wretchlessness
wretchock
wricht
wrick
wride
wried
wrier
wriest
wrig
wriggle
wriggler
wrigglesome
wrigglingly
wriggly
wright
wrightine
wring
wringbolt
wringer
wringman
wringstaff
wrinkle
wrinkleable
wrinkled
wrinkledness
wrinkledy
wrinkleful
wrinkleless
wrinkleproof
wrinklet
wrinkly
wrist
wristband
wristbone
wristed
wrister
wristfall
wristikin
wristlet
wristlock
wristwork
writ
writability
writable
writation
writative
write
writeable
writee
writer
writeress
writerling
writership
writh
writhe
writhed
writhedly
writhedness
writhen
writheneck
writher
writhing
writhingly
writhy
writing
writinger
writmaker
writmaking
writproof
written
writter
wrive
wrizzled
wro
wrocht
wroke
wroken
wrong
wrongdoer
wrongdoing
wronged
wronger
wrongful
wrongfully
wrongfulness
wronghead
wrongheaded
wrongheadedly
wrongheadedness
wronghearted
wrongheartedly
wrongheartedness
wrongish
wrongless
wronglessly
wrongly
wrongness
wrongous
wrongously
wrongousness
wrongwise
Wronskian
wrossle
wrote
wroth
wrothful
wrothfully
wrothily
wrothiness
wrothly
wrothsome
wrothy
wrought
wrox
wrung
wrungness
wry
wrybill
wryly
wrymouth
wryneck
wryness
wrytail
Wu
Wuchereria
wud
wuddie
wudge
wudu
wugg
wulfenite
wulk
wull
wullawins
wullcat
Wullie
wulliwa
wumble
wumman
wummel
wun
Wundtian
wungee
wunna
wunner
wunsome
wup
wur
wurley
wurmal
Wurmian
wurrus
wurset
wurtzilite
wurtzite
Wurzburger
wurzel
wush
wusp
wuss
wusser
wust
wut
wuther
wuzu
wuzzer
wuzzle
wuzzy
wy
Wyandot
Wyandotte
Wycliffian
Wycliffism
Wycliffist
Wycliffite
wyde
wye
Wyethia
wyke
Wykehamical
Wykehamist
wyle
wyliecoat
wymote
wyn
wynd
wyne
wynkernel
wynn
Wyomingite
wyomingite
wype
wyson
wyss
wyve
wyver
X
x
xanthaline
xanthamic
xanthamide
xanthane
xanthate
xanthation
xanthein
xanthelasma
xanthelasmic
xanthelasmoidea
xanthene
Xanthian
xanthic
xanthide
Xanthidium
xanthin
xanthine
xanthinuria
xanthione
Xanthisma
xanthite
Xanthium
xanthiuria
xanthocarpous
Xanthocephalus
Xanthoceras
Xanthochroi
xanthochroia
Xanthochroic
xanthochroid
xanthochroism
xanthochromia
xanthochromic
xanthochroous
xanthocobaltic
xanthocone
xanthoconite
xanthocreatinine
xanthocyanopsia
xanthocyanopsy
xanthocyanopy
xanthoderm
xanthoderma
xanthodont
xanthodontous
xanthogen
xanthogenamic
xanthogenamide
xanthogenate
xanthogenic
xantholeucophore
xanthoma
xanthomata
xanthomatosis
xanthomatous
Xanthomelanoi
xanthomelanous
xanthometer
Xanthomonas
xanthomyeloma
xanthone
xanthophane
xanthophore
xanthophose
Xanthophyceae
xanthophyll
xanthophyllite
xanthophyllous
Xanthopia
xanthopia
xanthopicrin
xanthopicrite
xanthoproteic
xanthoprotein
xanthoproteinic
xanthopsia
xanthopsin
xanthopsydracia
xanthopterin
xanthopurpurin
xanthorhamnin
Xanthorrhiza
Xanthorrhoea
xanthorrhoea
xanthosiderite
xanthosis
Xanthosoma
xanthospermous
xanthotic
Xanthoura
xanthous
Xanthoxalis
xanthoxenite
xanthoxylin
xanthuria
xanthydrol
xanthyl
xarque
Xaverian
xebec
Xema
xenacanthine
Xenacanthini
xenagogue
xenagogy
Xenarchi
Xenarthra
xenarthral
xenarthrous
xenelasia
xenelasy
xenia
xenial
xenian
Xenicidae
Xenicus
xenium
xenobiosis
xenoblast
Xenocratean
Xenocratic
xenocryst
xenodochium
xenogamous
xenogamy
xenogenesis
xenogenetic
xenogenic
xenogenous
xenogeny
xenolite
xenolith
xenolithic
xenomania
xenomaniac
Xenomi
Xenomorpha
xenomorphic
xenomorphosis
xenon
xenoparasite
xenoparasitism
xenopeltid
Xenopeltidae
Xenophanean
xenophile
xenophilism
xenophobe
xenophobia
xenophobian
xenophobism
xenophoby
Xenophonic
Xenophontean
Xenophontian
Xenophontic
Xenophontine
Xenophora
xenophoran
Xenophoridae
xenophthalmia
xenophya
xenopodid
Xenopodidae
xenopodoid
Xenopsylla
xenopteran
Xenopteri
xenopterygian
Xenopterygii
Xenopus
Xenorhynchus
Xenos
xenosaurid
Xenosauridae
xenosauroid
Xenosaurus
xenotime
Xenurus
xenyl
xenylamine
xerafin
xeransis
Xeranthemum
xeranthemum
xerantic
xerarch
xerasia
Xeres
xeric
xerically
xeriff
xerocline
xeroderma
xerodermatic
xerodermatous
xerodermia
xerodermic
xerogel
xerography
xeroma
xeromata
xeromenia
xeromorph
xeromorphic
xeromorphous
xeromorphy
xeromyron
xeromyrum
xeronate
xeronic
xerophagia
xerophagy
xerophil
xerophile
xerophilous
xerophily
xerophobous
xerophthalmia
xerophthalmos
xerophthalmy
Xerophyllum
xerophyte
xerophytic
xerophytically
xerophytism
xeroprinting
xerosis
xerostoma
xerostomia
xerotes
xerotherm
xerotic
xerotocia
xerotripsis
Xerus
xi
Xicak
Xicaque
Ximenia
Xina
Xinca
Xipe
Xiphias
xiphias
xiphihumeralis
xiphiid
Xiphiidae
xiphiiform
xiphioid
xiphiplastra
xiphiplastral
xiphiplastron
xiphisterna
xiphisternal
xiphisternum
Xiphisura
xiphisuran
Xiphiura
Xiphius
xiphocostal
Xiphodon
Xiphodontidae
xiphodynia
xiphoid
xiphoidal
xiphoidian
xiphopagic
xiphopagous
xiphopagus
xiphophyllous
xiphosterna
xiphosternum
Xiphosura
xiphosuran
xiphosure
Xiphosuridae
xiphosurous
Xiphosurus
xiphuous
Xiphura
Xiphydria
xiphydriid
Xiphydriidae
Xiraxara
Xmas
xoana
xoanon
Xosa
xurel
xyla
xylan
Xylaria
Xylariaceae
xylate
Xyleborus
xylem
xylene
xylenol
xylenyl
xyletic
Xylia
xylic
xylidic
xylidine
Xylina
xylindein
xylinid
xylite
xylitol
xylitone
xylobalsamum
xylocarp
xylocarpous
Xylocopa
xylocopid
Xylocopidae
xylogen
xyloglyphy
xylograph
xylographer
xylographic
xylographical
xylographically
xylography
xyloid
xyloidin
xylol
xylology
xyloma
xylomancy
xylometer
xylon
xylonic
Xylonite
xylonitrile
Xylophaga
xylophagan
xylophage
xylophagid
Xylophagidae
xylophagous
Xylophagus
xylophilous
xylophone
xylophonic
xylophonist
Xylopia
xyloplastic
xylopyrography
xyloquinone
xylorcin
xylorcinol
xylose
xyloside
Xylosma
xylostroma
xylostromata
xylostromatoid
xylotile
xylotomist
xylotomous
xylotomy
Xylotrya
xylotypographic
xylotypography
xyloyl
xylyl
xylylene
xylylic
xyphoid
Xyrichthys
xyrid
Xyridaceae
xyridaceous
Xyridales
Xyris
xyst
xyster
xysti
xystos
xystum
xystus
Y
y
ya
yaba
yabber
yabbi
yabble
yabby
yabu
yacal
yacca
yachan
yacht
yachtdom
yachter
yachting
yachtist
yachtman
yachtmanship
yachtsman
yachtsmanlike
yachtsmanship
yachtswoman
yachty
yad
Yadava
yade
yaff
yaffingale
yaffle
yagger
yaghourt
yagi
Yagnob
yagourundi
Yagua
yagua
yaguarundi
yaguaza
yah
yahan
Yahgan
Yahganan
Yahoo
yahoo
Yahoodom
Yahooish
Yahooism
Yahuna
Yahuskin
Yahweh
Yahwism
Yahwist
Yahwistic
yair
yaird
yaje
yajeine
yajenine
Yajna
Yajnavalkya
yajnopavita
yak
Yaka
Yakala
yakalo
yakamik
Yakan
yakattalo
Yakima
yakin
yakka
yakman
Yakona
Yakonan
Yakut
Yakutat
yalb
Yale
yale
Yalensian
yali
yalla
yallaer
yallow
yam
Yamacraw
Yamamadi
yamamai
yamanai
yamaskite
Yamassee
Yamato
Yamel
yamen
Yameo
yamilke
yammadji
yammer
yamp
yampa
yamph
yamshik
yamstchik
yan
Yana
Yanan
yancopin
yander
yang
yangtao
yank
Yankee
Yankeedom
Yankeefy
Yankeeism
Yankeeist
Yankeeize
Yankeeland
Yankeeness
yanking
Yankton
Yanktonai
yanky
Yannigan
Yao
yaoort
yaourti
yap
yapa
yaply
Yapman
yapness
yapok
yapp
yapped
yapper
yappiness
yapping
yappingly
yappish
yappy
yapster
Yaqui
Yaquina
yar
yarak
yaray
yarb
Yarborough
yard
yardage
yardang
yardarm
yarder
yardful
yarding
yardkeep
yardland
yardman
yardmaster
yardsman
yardstick
yardwand
yare
yareta
yark
Yarkand
yarke
yarl
yarly
yarm
yarn
yarnen
yarner
yarnwindle
yarpha
yarr
yarraman
yarran
yarringle
yarrow
yarth
yarthen
Yaru
Yarura
Yaruran
Yaruro
yarwhelp
yarwhip
yas
yashiro
yashmak
Yasht
Yasna
yat
yataghan
yatalite
yate
yati
Yatigan
yatter
Yatvyag
Yauapery
yaud
yauld
yaupon
yautia
yava
Yavapai
yaw
yawl
yawler
yawlsman
yawmeter
yawn
yawner
yawney
yawnful
yawnfully
yawnily
yawniness
yawning
yawningly
yawnproof
yawnups
yawny
yawp
yawper
yawroot
yaws
yawweed
yawy
yaxche
yaya
Yazdegerdian
Yazoo
ycie
yday
ye
yea
yeah
yealing
yean
yeanling
year
yeara
yearbird
yearbook
yeard
yearday
yearful
yearling
yearlong
yearly
yearn
yearnful
yearnfully
yearnfulness
yearning
yearnling
yearock
yearth
yeast
yeastily
yeastiness
yeasting
yeastlike
yeasty
yeat
yeather
yed
yede
yee
yeel
yeelaman
yees
yegg
yeggman
yeguita
yeld
yeldrin
yeldrock
yelk
yell
yeller
yelling
yelloch
yellow
yellowammer
yellowback
yellowbelly
yellowberry
yellowbill
yellowbird
yellowcrown
yellowcup
yellowfin
yellowfish
yellowhammer
yellowhead
yellowing
yellowish
yellowishness
Yellowknife
yellowlegs
yellowly
yellowness
yellowroot
yellowrump
yellows
yellowseed
yellowshank
yellowshanks
yellowshins
yellowtail
yellowthorn
yellowthroat
yellowtop
yellowware
yellowweed
yellowwood
yellowwort
yellowy
yelm
yelmer
yelp
yelper
yelt
Yemen
Yemeni
Yemenic
Yemenite
yen
yender
Yengee
Yengeese
yeni
Yenisei
Yeniseian
yenite
yentnite
yeo
yeoman
yeomaness
yeomanette
yeomanhood
yeomanlike
yeomanly
yeomanry
yeomanwise
yeorling
yeowoman
yep
yer
Yerava
Yeraver
yerb
yerba
yercum
yerd
yere
yerga
yerk
yern
yerth
yes
yese
Yeshibah
Yeshiva
yeso
yesso
yest
yester
yesterday
yestereve
yestereven
yesterevening
yestermorn
yestermorning
yestern
yesternight
yesternoon
yesterweek
yesteryear
yestreen
yesty
yet
yeta
yetapa
yeth
yether
yetlin
yeuk
yeukieness
yeuky
yeven
yew
yex
yez
Yezdi
Yezidi
yezzy
ygapo
Yid
Yiddish
Yiddisher
Yiddishism
Yiddishist
yield
yieldable
yieldableness
yieldance
yielden
yielder
yielding
yieldingly
yieldingness
yieldy
yigh
Yikirgaulit
Yildun
yill
yilt
Yin
yin
yince
yinst
yip
yird
yirk
yirm
yirmilik
yirn
yirr
yirth
yis
yite
ym
yn
ynambu
yo
yobi
yocco
yochel
yock
yockel
yodel
yodeler
yodelist
yodh
yoe
yoga
yogasana
yogh
yoghurt
yogi
yogin
yogism
yogist
yogoite
yohimbe
yohimbi
yohimbine
yohimbinization
yohimbinize
yoi
yoick
yoicks
yojan
yojana
Yojuane
yok
yoke
yokeable
yokeableness
yokeage
yokefellow
yokel
yokeldom
yokeless
yokelish
yokelism
yokelry
yokemate
yokemating
yoker
yokewise
yokewood
yoking
Yokuts
yoky
yolden
Yoldia
yoldring
yolk
yolked
yolkiness
yolkless
yolky
yom
yomer
Yomud
yon
yoncopin
yond
yonder
Yonkalla
yonner
yonside
yont
yook
yoop
yor
yore
yoretime
york
Yorker
yorker
Yorkish
Yorkist
Yorkshire
Yorkshireism
Yorkshireman
Yoruba
Yoruban
yot
yotacism
yotacize
yote
you
youd
youden
youdendrift
youdith
youff
youl
young
youngberry
younger
younghearted
youngish
younglet
youngling
youngly
youngness
youngster
youngun
younker
youp
your
yourn
yours
yoursel
yourself
yourselves
youse
youth
youthen
youthful
youthfullity
youthfully
youthfulness
youthhead
youthheid
youthhood
youthily
youthless
youthlessness
youthlike
youthlikeness
youthsome
youthtide
youthwort
youthy
youve
youward
youwards
youze
yoven
yow
yowie
yowl
yowler
yowley
yowlring
yowt
yox
yoy
yperite
Yponomeuta
Yponomeutid
Yponomeutidae
ypsiliform
ypsiloid
Ypurinan
Yquem
yr
ytterbia
ytterbic
ytterbium
yttria
yttrialite
yttric
yttriferous
yttrious
yttrium
yttrocerite
yttrocolumbite
yttrocrasite
yttrofluorite
yttrogummite
yttrotantalite
Yuan
yuan
Yuapin
yuca
Yucatec
Yucatecan
Yucateco
Yucca
yucca
Yuchi
yuck
yuckel
yucker
yuckle
yucky
Yuechi
yuft
Yuga
yugada
Yugoslav
Yugoslavian
Yugoslavic
yuh
Yuit
Yukaghir
Yuki
Yukian
yukkel
yulan
yule
yuleblock
yuletide
Yuma
Yuman
yummy
Yun
Yunca
Yuncan
yungan
Yunnanese
Yurak
Yurok
yurt
yurta
Yurucare
Yurucarean
Yurucari
Yurujure
Yuruk
Yuruna
Yurupary
yus
yusdrum
Yustaga
yutu
yuzlik
yuzluk
Yvonne
Z
z
za
Zabaean
zabaglione
Zabaism
Zaberma
zabeta
Zabian
Zabism
zabra
zabti
zabtie
zac
zacate
Zacatec
Zacateco
zacaton
Zach
Zachariah
zachun
zad
Zadokite
zadruga
zaffar
zaffer
zafree
zag
zagged
Zaglossus
zaibatsu
zain
Zaitha
zak
zakkeu
Zaklohpakap
zalambdodont
Zalambdodonta
Zalophus
zaman
zamang
zamarra
zamarro
Zambal
Zambezian
zambo
zamboorak
Zamenis
Zamia
Zamiaceae
Zamicrus
zamindar
zamindari
zamorin
zamouse
Zan
Zanclidae
Zanclodon
Zanclodontidae
Zande
zander
zandmole
zanella
Zaniah
Zannichellia
Zannichelliaceae
Zanonia
zant
zante
Zantedeschia
zantewood
Zanthorrhiza
Zanthoxylaceae
Zanthoxylum
zanthoxylum
Zantiot
zantiote
zany
zanyish
zanyism
zanyship
Zanzalian
zanze
Zanzibari
Zapara
Zaparan
Zaparo
Zaparoan
zapas
zapatero
zaphara
Zaphetic
zaphrentid
Zaphrentidae
Zaphrentis
zaphrentoid
Zapodidae
Zapodinae
Zaporogian
Zaporogue
zapota
Zapotec
Zapotecan
Zapoteco
zaptiah
zaptieh
Zaptoeca
zapupe
Zapus
zaqqum
Zaque
zar
zarabanda
Zaramo
Zarathustrian
Zarathustrianism
Zarathustrism
zaratite
Zardushti
zareba
Zarema
zarf
zarnich
zarp
zarzuela
zat
zati
zattare
Zaurak
Zauschneria
Zavijava
zax
zayat
zayin
Zea
zeal
Zealander
zealful
zealless
zeallessness
zealot
zealotic
zealotical
zealotism
zealotist
zealotry
zealous
zealously
zealousness
zealousy
zealproof
zebra
zebraic
zebralike
zebrass
zebrawood
Zebrina
zebrine
zebrinny
zebroid
zebrula
zebrule
zebu
zebub
Zebulunite
zeburro
zecchini
zecchino
zechin
Zechstein
zed
zedoary
zee
zeed
Zeelander
Zeguha
zehner
Zeidae
zein
zeism
zeist
Zeke
zel
Zelanian
zelator
zelatrice
zelatrix
Zelkova
Zeltinger
zemeism
zemi
zemimdari
zemindar
zemmi
zemni
zemstroist
zemstvo
Zen
Zenaga
Zenaida
Zenaidinae
Zenaidura
zenana
Zend
Zendic
zendician
zendik
zendikite
Zenelophon
zenick
zenith
zenithal
zenithward
zenithwards
Zenobia
zenocentric
zenographic
zenographical
zenography
Zenonian
Zenonic
zenu
Zeoidei
zeolite
zeolitic
zeolitization
zeolitize
zeoscope
Zep
zepharovichite
zephyr
Zephyranthes
zephyrean
zephyrless
zephyrlike
zephyrous
zephyrus
zephyry
Zeppelin
zeppelin
zequin
zer
zerda
Zerma
zermahbub
zero
zeroaxial
zeroize
zerumbet
zest
zestful
zestfully
zestfulness
zesty
zeta
zetacism
zetetic
Zeuctocoelomata
zeuctocoelomatic
zeuctocoelomic
Zeuglodon
zeuglodon
zeuglodont
Zeuglodonta
Zeuglodontia
Zeuglodontidae
zeuglodontoid
zeugma
zeugmatic
zeugmatically
Zeugobranchia
Zeugobranchiata
zeunerite
Zeus
Zeuxian
Zeuzera
zeuzerian
Zeuzeridae
Zhmud
ziamet
ziara
ziarat
zibeline
zibet
zibethone
zibetone
zibetum
ziega
zieger
zietrisikite
ziffs
zig
ziganka
ziggurat
zigzag
zigzagged
zigzaggedly
zigzaggedness
zigzagger
zigzaggery
zigzaggy
zigzagwise
zihar
zikurat
Zilla
zillah
zimarra
zimb
zimbabwe
zimbalon
zimbaloon
zimbi
zimentwater
zimme
Zimmerwaldian
Zimmerwaldist
zimmi
zimmis
zimocca
zinc
Zincalo
zincate
zincic
zincide
zinciferous
zincification
zincify
zincing
zincite
zincize
zincke
zincky
zinco
zincograph
zincographer
zincographic
zincographical
zincography
zincotype
zincous
zincum
zincuret
zinfandel
zing
zingaresca
zingel
zingerone
Zingiber
Zingiberaceae
zingiberaceous
zingiberene
zingiberol
zingiberone
zink
zinkenite
Zinnia
zinnwaldite
zinsang
zinyamunga
Zinzar
Zinziberaceae
zinziberaceous
Zion
Zionism
Zionist
Zionistic
Zionite
Zionless
Zionward
zip
Zipa
ziphian
Ziphiidae
Ziphiinae
ziphioid
Ziphius
Zipper
zipper
zipping
zippingly
zippy
Zips
zira
zirai
Zirak
Zirbanit
zircite
zircofluoride
zircon
zirconate
zirconia
zirconian
zirconic
zirconiferous
zirconifluoride
zirconium
zirconofluoride
zirconoid
zirconyl
Zirian
Zirianian
zirkelite
zither
zitherist
Zizania
Zizia
Zizyphus
zizz
zloty
Zmudz
zo
Zoa
zoa
zoacum
Zoanthacea
zoanthacean
Zoantharia
zoantharian
zoanthid
Zoanthidae
Zoanthidea
zoanthodeme
zoanthodemic
zoanthoid
zoanthropy
Zoanthus
Zoarces
zoarcidae
zoaria
zoarial
Zoarite
zoarium
zobo
zobtenite
zocco
zoccolo
zodiac
zodiacal
zodiophilous
zoea
zoeaform
zoeal
zoeform
zoehemera
zoehemerae
zoetic
zoetrope
zoetropic
zogan
zogo
Zohak
Zoharist
Zoharite
zoiatria
zoiatrics
zoic
zoid
zoidiophilous
zoidogamous
Zoilean
Zoilism
Zoilist
zoisite
zoisitization
zoism
zoist
zoistic
zokor
Zolaesque
Zolaism
Zolaist
Zolaistic
Zolaize
zoll
zolle
Zollernia
zollpfund
zolotink
zolotnik
zombi
zombie
zombiism
zomotherapeutic
zomotherapy
zonal
zonality
zonally
zonar
Zonaria
zonary
zonate
zonated
zonation
zone
zoned
zoneless
zonelet
zonelike
zonesthesia
Zongora
zonic
zoniferous
zoning
zonite
Zonites
zonitid
Zonitidae
Zonitoides
zonochlorite
zonociliate
zonoid
zonolimnetic
zonoplacental
Zonoplacentalia
zonoskeleton
Zonotrichia
Zonta
Zontian
zonular
zonule
zonulet
zonure
zonurid
Zonuridae
zonuroid
Zonurus
zoo
zoobenthos
zooblast
zoocarp
zoocecidium
zoochemical
zoochemistry
zoochemy
Zoochlorella
zoochore
zoocoenocyte
zoocultural
zooculture
zoocurrent
zoocyst
zoocystic
zoocytial
zoocytium
zoodendria
zoodendrium
zoodynamic
zoodynamics
zooecia
zooecial
zooecium
zooerastia
zooerythrin
zoofulvin
zoogamete
zoogamous
zoogamy
zoogene
zoogenesis
zoogenic
zoogenous
zoogeny
zoogeographer
zoogeographic
zoogeographical
zoogeographically
zoogeography
zoogeological
zoogeologist
zoogeology
zoogloea
zoogloeal
zoogloeic
zoogonic
zoogonidium
zoogonous
zoogony
zoograft
zoografting
zoographer
zoographic
zoographical
zoographically
zoographist
zoography
zooid
zooidal
zooidiophilous
zooks
zoolater
zoolatria
zoolatrous
zoolatry
zoolite
zoolith
zoolithic
zoolitic
zoologer
zoologic
zoological
zoologically
zoologicoarchaeologist
zoologicobotanical
zoologist
zoologize
zoology
zoom
zoomagnetic
zoomagnetism
zoomancy
zoomania
zoomantic
zoomantist
Zoomastigina
Zoomastigoda
zoomechanical
zoomechanics
zoomelanin
zoometric
zoometry
zoomimetic
zoomimic
zoomorph
zoomorphic
zoomorphism
zoomorphize
zoomorphy
zoon
zoonal
zoonerythrin
zoonic
zoonist
zoonite
zoonitic
zoonomia
zoonomic
zoonomical
zoonomist
zoonomy
zoonosis
zoonosologist
zoonosology
zoonotic
zoons
zoonule
zoopaleontology
zoopantheon
zooparasite
zooparasitic
zoopathological
zoopathologist
zoopathology
zoopathy
zooperal
zooperist
zoopery
Zoophaga
zoophagan
Zoophagineae
zoophagous
zoopharmacological
zoopharmacy
zoophile
zoophilia
zoophilic
zoophilism
zoophilist
zoophilite
zoophilitic
zoophilous
zoophily
zoophobia
zoophobous
zoophoric
zoophorus
zoophysical
zoophysics
zoophysiology
Zoophyta
zoophytal
zoophyte
zoophytic
zoophytical
zoophytish
zoophytography
zoophytoid
zoophytological
zoophytologist
zoophytology
zooplankton
zooplanktonic
zooplastic
zooplasty
zoopraxiscope
zoopsia
zoopsychological
zoopsychologist
zoopsychology
zooscopic
zooscopy
zoosis
zoosmosis
zoosperm
zoospermatic
zoospermia
zoospermium
zoosphere
zoosporange
zoosporangia
zoosporangial
zoosporangiophore
zoosporangium
zoospore
zoosporic
zoosporiferous
zoosporocyst
zoosporous
zootaxy
zootechnic
zootechnics
zootechny
zooter
zoothecia
zoothecial
zoothecium
zootheism
zootheist
zootheistic
zootherapy
zoothome
zootic
Zootoca
zootomic
zootomical
zootomically
zootomist
zootomy
zoototemism
zootoxin
zootrophic
zootrophy
zootype
zootypic
zooxanthella
zooxanthellae
zooxanthin
zoozoo
zopilote
Zoque
Zoquean
Zoraptera
zorgite
zoril
zorilla
Zorillinae
zorillo
Zoroastrian
Zoroastrianism
Zoroastrism
Zorotypus
zorrillo
zorro
Zosma
zoster
Zostera
Zosteraceae
zosteriform
Zosteropinae
Zosterops
Zouave
zounds
zowie
Zoysia
Zubeneschamali
zuccarino
zucchetto
zucchini
zudda
zugtierlast
zugtierlaster
zuisin
Zuleika
Zulhijjah
Zulinde
Zulkadah
Zulu
Zuludom
Zuluize
zumatic
zumbooruk
Zuni
Zunian
zunyite
zupanate
Zutugil
zuurveldt
zuza
zwanziger
Zwieback
zwieback
Zwinglian
Zwinglianism
Zwinglianist
zwitter
zwitterion
zwitterionic
zyga
zygadenine
Zygadenus
Zygaena
zygaenid
Zygaenidae
zygal
zygantra
zygantrum
zygapophyseal
zygapophysis
zygion
zygite
Zygnema
Zygnemaceae
Zygnemales
Zygnemataceae
zygnemataceous
Zygnematales
zygobranch
Zygobranchia
Zygobranchiata
zygobranchiate
Zygocactus
zygodactyl
Zygodactylae
Zygodactyli
zygodactylic
zygodactylism
zygodactylous
zygodont
zygolabialis
zygoma
zygomata
zygomatic
zygomaticoauricular
zygomaticoauricularis
zygomaticofacial
zygomaticofrontal
zygomaticomaxillary
zygomaticoorbital
zygomaticosphenoid
zygomaticotemporal
zygomaticum
zygomaticus
zygomaxillare
zygomaxillary
zygomorphic
zygomorphism
zygomorphous
zygomycete
Zygomycetes
zygomycetous
zygon
zygoneure
zygophore
zygophoric
Zygophyceae
zygophyceous
Zygophyllaceae
zygophyllaceous
Zygophyllum
zygophyte
zygopleural
Zygoptera
Zygopteraceae
zygopteran
zygopterid
Zygopterides
Zygopteris
zygopteron
zygopterous
Zygosaccharomyces
zygose
zygosis
zygosperm
zygosphenal
zygosphene
zygosphere
zygosporange
zygosporangium
zygospore
zygosporic
zygosporophore
zygostyle
zygotactic
zygotaxis
zygote
zygotene
zygotic
zygotoblast
zygotoid
zygotomere
zygous
zygozoospore
zymase
zyme
zymic
zymin
zymite
zymogen
zymogene
zymogenesis
zymogenic
zymogenous
zymoid
zymologic
zymological
zymologist
zymology
zymolyis
zymolysis
zymolytic
zymome
zymometer
zymomin
zymophore
zymophoric
zymophosphate
zymophyte
zymoplastic
zymoscope
zymosimeter
zymosis
zymosterol
zymosthenic
zymotechnic
zymotechnical
zymotechnics
zymotechny
zymotic
zymotically
zymotize
zymotoxic
zymurgy
Zyrenian
Zyrian
Zyryan
zythem
Zythia
zythum
Zyzomys
Zyzzogeton


================================================
FILE: strings/z_function.py
================================================
"""
https://cp-algorithms.com/string/z-function.html

Z-function or Z algorithm

Efficient algorithm for pattern occurrence in a string

Time Complexity: O(n) - where n is the length of the string

"""


def z_function(input_str: str) -> list[int]:
    """
    For the given string this function computes value for each index,
    which represents the maximal length substring starting from the index
    and is the same as the prefix of the same size

    e.x.  for string 'abab' for second index value would be 2

    For the value of the first element the algorithm always returns 0

    >>> z_function("abracadabra")
    [0, 0, 0, 1, 0, 1, 0, 4, 0, 0, 1]
    >>> z_function("aaaa")
    [0, 3, 2, 1]
    >>> z_function("zxxzxxz")
    [0, 0, 0, 4, 0, 0, 1]
    """
    z_result = [0 for i in range(len(input_str))]

    # initialize interval's left pointer and right pointer
    left_pointer, right_pointer = 0, 0

    for i in range(1, len(input_str)):
        # case when current index is inside the interval
        if i <= right_pointer:
            min_edge = min(right_pointer - i + 1, z_result[i - left_pointer])
            z_result[i] = min_edge

        while go_next(i, z_result, input_str):
            z_result[i] += 1

        # if new index's result gives us more right interval,
        # we've to update left_pointer and right_pointer
        if i + z_result[i] - 1 > right_pointer:
            left_pointer, right_pointer = i, i + z_result[i] - 1

    return z_result


def go_next(i: int, z_result: list[int], s: str) -> bool:
    """
    Check if we have to move forward to the next characters or not
    """
    return i + z_result[i] < len(s) and s[z_result[i]] == s[i + z_result[i]]


def find_pattern(pattern: str, input_str: str) -> int:
    """
    Example of using z-function for pattern occurrence
    Given function returns the number of times 'pattern'
    appears in 'input_str' as a substring

    >>> find_pattern("abr", "abracadabra")
    2
    >>> find_pattern("a", "aaaa")
    4
    >>> find_pattern("xz", "zxxzxxz")
    2
    """
    answer = 0
    # concatenate 'pattern' and 'input_str' and call z_function
    # with concatenated string
    z_result = z_function(pattern + input_str)

    for val in z_result:
        # if value is greater then length of the pattern string
        # that means this index is starting position of substring
        # which is equal to pattern string
        if val >= len(pattern):
            answer += 1

    return answer


if __name__ == "__main__":
    import doctest

    doctest.testmod()


================================================
FILE: web_programming/__init__.py
================================================


================================================
FILE: web_programming/co2_emission.py
================================================
"""
Get CO2 emission data from the UK CarbonIntensity API
"""

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

from datetime import date

import httpx

BASE_URL = "https://api.carbonintensity.org.uk/intensity"


# Emission in the last half hour
def fetch_last_half_hour() -> str:
    last_half_hour = httpx.get(BASE_URL, timeout=10).json()["data"][0]
    return last_half_hour["intensity"]["actual"]


# Emissions in a specific date range
def fetch_from_to(start, end) -> list:
    return httpx.get(f"{BASE_URL}/{start}/{end}", timeout=10).json()["data"]


if __name__ == "__main__":
    for entry in fetch_from_to(start=date(2020, 10, 1), end=date(2020, 10, 3)):
        print("from {from} to {to}: {intensity[actual]}".format(**entry))
    print(f"{fetch_last_half_hour() = }")


================================================
FILE: web_programming/covid_stats_via_xpath.py
================================================
"""
This script demonstrates fetching simple COVID-19 statistics from the
Worldometers archive site using lxml. lxml is chosen over BeautifulSoup
for its speed and convenience in Python web projects (such as Django or
Flask).
"""

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
#     "lxml",
# ]
# ///

from typing import NamedTuple

import httpx
from lxml import html


class CovidData(NamedTuple):
    cases: str
    deaths: str
    recovered: str


def covid_stats(
    url: str = (
        "https://web.archive.org/web/20250825095350/"
        "https://www.worldometers.info/coronavirus/"
    ),
) -> CovidData:
    xpath_str = '//div[@class = "maincounter-number"]/span/text()'
    try:
        response = httpx.get(url, timeout=10).raise_for_status()
    except httpx.TimeoutException:
        print(
            "Request timed out. Please check your network connection "
            "or try again later."
        )
        return CovidData("N/A", "N/A", "N/A")
    except httpx.HTTPStatusError as e:
        print(f"HTTP error occurred: {e}")
        return CovidData("N/A", "N/A", "N/A")
    data = html.fromstring(response.content).xpath(xpath_str)
    if len(data) != 3:
        print("Unexpected data format. The page structure may have changed.")
        data = "N/A", "N/A", "N/A"
    return CovidData(*data)


if __name__ == "__main__":
    fmt = (
        "Total COVID-19 cases in the world: {}\n"
        "Total deaths due to COVID-19 in the world: {}\n"
        "Total COVID-19 patients recovered in the world: {}"
    )
    print(fmt.format(*covid_stats()))


================================================
FILE: web_programming/crawl_google_results.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "beautifulsoup4",
#     "fake-useragent",
#     "httpx",
# ]
# ///

import sys
import webbrowser

import httpx
from bs4 import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
    print("Googling.....")
    url = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
    res = httpx.get(
        url,
        headers={"UserAgent": UserAgent().random},
        timeout=10,
        follow_redirects=True,
    )
    # res.raise_for_status()
    with open("project1a.html", "wb") as out_file:  # only for knowing the class
        for data in res.iter_content(10000):
            out_file.write(data)
    soup = BeautifulSoup(res.text, "html.parser")
    links = list(soup.select(".eZt8xd"))[:5]

    print(len(links))
    for link in links:
        if link.text == "Maps":
            webbrowser.open(link.get("href"))
        else:
            webbrowser.open(f"https://google.com{link.get('href')}")


================================================
FILE: web_programming/crawl_google_scholar_citation.py
================================================
"""
Get the citation from google scholar
using title and year of publication, and volume and pages of journal.
"""

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "beautifulsoup4",
#     "httpx",
# ]
# ///

import httpx
from bs4 import BeautifulSoup


def get_citation(base_url: str, params: dict) -> str:
    """
    Return the citation number.
    """
    soup = BeautifulSoup(
        httpx.get(base_url, params=params, timeout=10).content, "html.parser"
    )
    div = soup.find("div", attrs={"class": "gs_ri"})
    anchors = div.find("div", attrs={"class": "gs_fl"}).find_all("a")
    return anchors[2].get_text()


if __name__ == "__main__":
    params = {
        "title": (
            "Precisely geometry controlled microsupercapacitors for ultrahigh areal "
            "capacitance, volumetric capacitance, and energy density"
        ),
        "journal": "Chem. Mater.",
        "volume": 30,
        "pages": "3979-3990",
        "year": 2018,
        "hl": "en",
    }
    print(get_citation("https://scholar.google.com/scholar_lookup", params=params))


================================================
FILE: web_programming/currency_converter.py
================================================
"""
This is used to convert the currency using the Amdoren Currency API
https://www.amdoren.com
"""

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

import os

import httpx

URL_BASE = "https://www.amdoren.com/api/currency.php"


# Currency and their description
list_of_currencies = """
AED	United Arab Emirates Dirham
AFN	Afghan Afghani
ALL	Albanian Lek
AMD	Armenian Dram
ANG	Netherlands Antillean Guilder
AOA	Angolan Kwanza
ARS	Argentine Peso
AUD	Australian Dollar
AWG	Aruban Florin
AZN	Azerbaijani Manat
BAM	Bosnia & Herzegovina Convertible Mark
BBD	Barbadian Dollar
BDT	Bangladeshi Taka
BGN	Bulgarian Lev
BHD	Bahraini Dinar
BIF	Burundian Franc
BMD	Bermudian Dollar
BND	Brunei Dollar
BOB	Bolivian Boliviano
BRL	Brazilian Real
BSD	Bahamian Dollar
BTN	Bhutanese Ngultrum
BWP	Botswana Pula
BYN	Belarus Ruble
BZD	Belize Dollar
CAD	Canadian Dollar
CDF	Congolese Franc
CHF	Swiss Franc
CLP	Chilean Peso
CNY	Chinese Yuan
COP	Colombian Peso
CRC	Costa Rican Colon
CUC	Cuban Convertible Peso
CVE	Cape Verdean Escudo
CZK	Czech Republic Koruna
DJF	Djiboutian Franc
DKK	Danish Krone
DOP	Dominican Peso
DZD	Algerian Dinar
EGP	Egyptian Pound
ERN	Eritrean Nakfa
ETB	Ethiopian Birr
EUR	Euro
FJD	Fiji Dollar
GBP	British Pound Sterling
GEL	Georgian Lari
GHS	Ghanaian Cedi
GIP	Gibraltar Pound
GMD	Gambian Dalasi
GNF	Guinea Franc
GTQ	Guatemalan Quetzal
GYD	Guyanaese Dollar
HKD	Hong Kong Dollar
HNL	Honduran Lempira
HRK	Croatian Kuna
HTG	Haiti Gourde
HUF	Hungarian Forint
IDR	Indonesian Rupiah
ILS	Israeli Shekel
INR	Indian Rupee
IQD	Iraqi Dinar
IRR	Iranian Rial
ISK	Icelandic Krona
JMD	Jamaican Dollar
JOD	Jordanian Dinar
JPY	Japanese Yen
KES	Kenyan Shilling
KGS	Kyrgystani Som
KHR	Cambodian Riel
KMF	Comorian Franc
KPW	North Korean Won
KRW	South Korean Won
KWD	Kuwaiti Dinar
KYD	Cayman Islands Dollar
KZT	Kazakhstan Tenge
LAK	Laotian Kip
LBP	Lebanese Pound
LKR	Sri Lankan Rupee
LRD	Liberian Dollar
LSL	Lesotho Loti
LYD	Libyan Dinar
MAD	Moroccan Dirham
MDL	Moldovan Leu
MGA	Malagasy Ariary
MKD	Macedonian Denar
MMK	Myanma Kyat
MNT	Mongolian Tugrik
MOP	Macau Pataca
MRO	Mauritanian Ouguiya
MUR	Mauritian Rupee
MVR	Maldivian Rufiyaa
MWK	Malawi Kwacha
MXN	Mexican Peso
MYR	Malaysian Ringgit
MZN	Mozambican Metical
NAD	Namibian Dollar
NGN	Nigerian Naira
NIO	Nicaragua Cordoba
NOK	Norwegian Krone
NPR	Nepalese Rupee
NZD	New Zealand Dollar
OMR	Omani Rial
PAB	Panamanian Balboa
PEN	Peruvian Nuevo Sol
PGK	Papua New Guinean Kina
PHP	Philippine Peso
PKR	Pakistani Rupee
PLN	Polish Zloty
PYG	Paraguayan Guarani
QAR	Qatari Riyal
RON	Romanian Leu
RSD	Serbian Dinar
RUB	Russian Ruble
RWF	Rwanda Franc
SAR	Saudi Riyal
SBD	Solomon Islands Dollar
SCR	Seychellois Rupee
SDG	Sudanese Pound
SEK	Swedish Krona
SGD	Singapore Dollar
SHP	Saint Helena Pound
SLL	Sierra Leonean Leone
SOS	Somali Shilling
SRD	Surinamese Dollar
SSP	South Sudanese Pound
STD	Sao Tome and Principe Dobra
SYP	Syrian Pound
SZL	Swazi Lilangeni
THB	Thai Baht
TJS	Tajikistan Somoni
TMT	Turkmenistani Manat
TND	Tunisian Dinar
TOP	Tonga Paanga
TRY	Turkish Lira
TTD	Trinidad and Tobago Dollar
TWD	New Taiwan Dollar
TZS	Tanzanian Shilling
UAH	Ukrainian Hryvnia
UGX	Ugandan Shilling
USD	United States Dollar
UYU	Uruguayan Peso
UZS	Uzbekistan Som
VEF	Venezuelan Bolivar
VND	Vietnamese Dong
VUV	Vanuatu Vatu
WST	Samoan Tala
XAF	Central African CFA franc
XCD	East Caribbean Dollar
XOF	West African CFA franc
XPF	CFP Franc
YER	Yemeni Rial
ZAR	South African Rand
ZMW	Zambian Kwacha
"""


def convert_currency(
    from_: str = "USD", to: str = "INR", amount: float = 1.0, api_key: str = ""
) -> str:
    """https://www.amdoren.com/currency-api/"""
    # Instead of manually generating parameters
    params = locals()
    # from is a reserved keyword
    params["from"] = params.pop("from_")
    res = httpx.get(URL_BASE, params=params, timeout=10).json()
    return str(res["amount"]) if res["error"] == 0 else res["error_message"]


if __name__ == "__main__":
    TESTING = os.getenv("CI", "")
    API_KEY = os.getenv("AMDOREN_API_KEY", "")

    if not API_KEY and not TESTING:
        raise KeyError(
            "API key must be provided in the 'AMDOREN_API_KEY' environment variable."
        )

    print(
        convert_currency(
            input("Enter from currency: ").strip(),
            input("Enter to currency: ").strip(),
            float(input("Enter the amount: ").strip()),
            API_KEY,
        )
    )


================================================
FILE: web_programming/current_stock_price.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "beautifulsoup4",
#     "httpx",
# ]
# ///

import httpx
from bs4 import BeautifulSoup

"""
Get the HTML code of finance yahoo and select the current qsp-price
Current AAPL stock price is   228.43
Current AMZN stock price is   201.85
Current IBM  stock price is   210.30
Current GOOG stock price is   177.86
Current MSFT stock price is   414.82
Current ORCL stock price is   188.87
"""


def stock_price(symbol: str = "AAPL") -> str:
    """
    >>> stock_price("EEEE")
    'No <fin-streamer> tag with the specified data-testid attribute found.'
    >>> isinstance(float(stock_price("GOOG")),float)
    True
    """
    url = f"https://finance.yahoo.com/quote/{symbol}?p={symbol}"
    yahoo_finance_source = httpx.get(
        url, headers={"USER-AGENT": "Mozilla/5.0"}, timeout=10, follow_redirects=True
    ).text
    soup = BeautifulSoup(yahoo_finance_source, "html.parser")

    if specific_fin_streamer_tag := soup.find("span", {"data-testid": "qsp-price"}):
        return specific_fin_streamer_tag.get_text()
    return "No <fin-streamer> tag with the specified data-testid attribute found."


# Search for the symbol at https://finance.yahoo.com/lookup
if __name__ == "__main__":
    from doctest import testmod

    testmod()

    for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
        print(f"Current {symbol:<4} stock price is {stock_price(symbol):>8}")


================================================
FILE: web_programming/current_weather.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

import httpx

# Put your API key(s) here
OPENWEATHERMAP_API_KEY = ""
WEATHERSTACK_API_KEY = ""

# Define the URL for the APIs with placeholders
OPENWEATHERMAP_URL_BASE = "https://api.openweathermap.org/data/2.5/weather"
WEATHERSTACK_URL_BASE = "http://api.weatherstack.com/current"


def current_weather(location: str) -> list[dict]:
    """
    >>> current_weather("location")
    Traceback (most recent call last):
        ...
    ValueError: No API keys provided or no valid data returned.
    """
    weather_data = []
    if OPENWEATHERMAP_API_KEY:
        params_openweathermap = {"q": location, "appid": OPENWEATHERMAP_API_KEY}
        response_openweathermap = httpx.get(
            OPENWEATHERMAP_URL_BASE, params=params_openweathermap, timeout=10
        )
        weather_data.append({"OpenWeatherMap": response_openweathermap.json()})
    if WEATHERSTACK_API_KEY:
        params_weatherstack = {"query": location, "access_key": WEATHERSTACK_API_KEY}
        response_weatherstack = httpx.get(
            WEATHERSTACK_URL_BASE, params=params_weatherstack, timeout=10
        )
        weather_data.append({"Weatherstack": response_weatherstack.json()})
    if not weather_data:
        raise ValueError("No API keys provided or no valid data returned.")
    return weather_data


if __name__ == "__main__":
    from pprint import pprint

    location = "to be determined..."
    while location:
        location = input("Enter a location (city name or latitude,longitude): ").strip()
        if location:
            try:
                weather_data = current_weather(location)
                for forecast in weather_data:
                    pprint(forecast)
            except ValueError as e:
                print(repr(e))
                location = ""


================================================
FILE: web_programming/daily_horoscope.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "beautifulsoup4",
#     "httpx",
# ]
# ///

import httpx
from bs4 import BeautifulSoup


def horoscope(zodiac_sign: int, day: str) -> str:
    url = (
        "https://www.horoscope.com/us/horoscopes/general/"
        f"horoscope-general-daily-{day}.aspx?sign={zodiac_sign}"
    )
    soup = BeautifulSoup(httpx.get(url, timeout=10).content, "html.parser")
    return soup.find("div", class_="main-horoscope").p.text


if __name__ == "__main__":
    print("Daily Horoscope. \n")
    print(
        "enter your Zodiac sign number:\n",
        "1. Aries\n",
        "2. Taurus\n",
        "3. Gemini\n",
        "4. Cancer\n",
        "5. Leo\n",
        "6. Virgo\n",
        "7. Libra\n",
        "8. Scorpio\n",
        "9. Sagittarius\n",
        "10. Capricorn\n",
        "11. Aquarius\n",
        "12. Pisces\n",
    )
    zodiac_sign = int(input("number> ").strip())
    print("choose some day:\n", "yesterday\n", "today\n", "tomorrow\n")
    day = input("enter the day> ")
    horoscope_text = horoscope(zodiac_sign, day)
    print(horoscope_text)


================================================
FILE: web_programming/download_images_from_google_query.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "beautifulsoup4",
#     "httpx",
# ]
# ///

import json
import os
import re
import sys
import urllib.request

import httpx
from bs4 import BeautifulSoup

headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
    " (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}


def download_images_from_google_query(query: str = "dhaka", max_images: int = 5) -> int:
    """
    Searches google using the provided query term and downloads the images in a folder.

    Args:
         query : The image search term to be provided by the user. Defaults to
        "dhaka".
        image_numbers : [description]. Defaults to 5.

    Returns:
        The number of images successfully downloaded.

    # Comment out slow (4.20s call) doctests
    # >>> download_images_from_google_query()
    5
    # >>> download_images_from_google_query("potato")
    5
    """
    max_images = min(max_images, 50)  # Prevent abuse!
    params = {
        "q": query,
        "tbm": "isch",
        "hl": "en",
        "ijn": "0",
    }

    html = httpx.get(
        "https://www.google.com/search", params=params, headers=headers, timeout=10
    )
    soup = BeautifulSoup(html.text, "html.parser")
    matched_images_data = "".join(
        re.findall(r"AF_initDataCallback\(([^<]+)\);", str(soup.select("script")))
    )

    matched_images_data_fix = json.dumps(matched_images_data)
    matched_images_data_json = json.loads(matched_images_data_fix)

    matched_google_image_data = re.findall(
        r"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",",
        matched_images_data_json,
    )
    if not matched_google_image_data:
        return 0

    removed_matched_google_images_thumbnails = re.sub(
        r"\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]",
        "",
        str(matched_google_image_data),
    )

    matched_google_full_resolution_images = re.findall(
        r"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]",
        removed_matched_google_images_thumbnails,
    )
    for index, fixed_full_res_image in enumerate(matched_google_full_resolution_images):
        if index >= max_images:
            return index
        original_size_img_not_fixed = bytes(fixed_full_res_image, "ascii").decode(
            "unicode-escape"
        )
        original_size_img = bytes(original_size_img_not_fixed, "ascii").decode(
            "unicode-escape"
        )
        opener = urllib.request.build_opener()
        opener.addheaders = [
            (
                "User-Agent",
                "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
                " (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582",
            )
        ]
        urllib.request.install_opener(opener)
        path_name = f"query_{query.replace(' ', '_')}"
        if not os.path.exists(path_name):
            os.makedirs(path_name)
        urllib.request.urlretrieve(  # noqa: S310
            original_size_img, f"{path_name}/original_size_img_{index}.jpg"
        )
    return index


if __name__ == "__main__":
    try:
        image_count = download_images_from_google_query(sys.argv[1])
        print(f"{image_count} images were downloaded to disk.")
    except IndexError:
        print("Please provide a search term.")
        raise


================================================
FILE: web_programming/emails_from_url.py
================================================
"""Get the site emails from URL."""

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

from __future__ import annotations

__author__ = "Muhammad Umer Farooq"
__license__ = "MIT"
__version__ = "1.0.0"
__maintainer__ = "Muhammad Umer Farooq"
__email__ = "contact@muhammadumerfarooq.me"
__status__ = "Alpha"

import re
from html.parser import HTMLParser
from urllib import parse

import httpx


class Parser(HTMLParser):
    def __init__(self, domain: str) -> None:
        super().__init__()
        self.urls: list[str] = []
        self.domain = domain

    def handle_starttag(self, tag: str, attrs: list[tuple[str, str | None]]) -> None:
        """
        This function parse html to take takes url from tags
        """
        # Only parse the 'anchor' tag.
        if tag == "a":
            # Check the list of defined attributes.
            for name, value in attrs:
                # If href is defined, not empty nor # print it and not already in urls.
                if name == "href" and value not in (*self.urls, "", "#"):
                    url = parse.urljoin(self.domain, value)
                    self.urls.append(url)


# Get main domain name (example.com)
def get_domain_name(url: str) -> str:
    """
    This function get the main domain name

    >>> get_domain_name("https://a.b.c.d/e/f?g=h,i=j#k")
    'c.d'
    >>> get_domain_name("Not a URL!")
    ''
    """
    return ".".join(get_sub_domain_name(url).split(".")[-2:])


# Get sub domain name (sub.example.com)
def get_sub_domain_name(url: str) -> str:
    """
    >>> get_sub_domain_name("https://a.b.c.d/e/f?g=h,i=j#k")
    'a.b.c.d'
    >>> get_sub_domain_name("Not a URL!")
    ''
    """
    return parse.urlparse(url).netloc


def emails_from_url(url: str = "https://github.com") -> list[str]:
    """
    This function takes url and return all valid urls
    """
    # Get the base domain from the url
    domain = get_domain_name(url)

    # Initialize the parser
    parser = Parser(domain)

    try:
        # Open URL
        r = httpx.get(url, timeout=10, follow_redirects=True)

        # pass the raw HTML to the parser to get links
        parser.feed(r.text)

        # Get links and loop through
        valid_emails = set()
        for link in parser.urls:
            # open URL.
            # Check if the link is already absolute
            if not link.startswith("http://") and not link.startswith("https://"):
                # Prepend protocol only if link starts with domain, normalize otherwise
                if link.startswith(domain):
                    link = f"https://{link}"
                else:
                    link = parse.urljoin(f"https://{domain}", link)
            try:
                read = httpx.get(link, timeout=10, follow_redirects=True)
                # Get the valid email.
                emails = re.findall("[a-zA-Z0-9]+@" + domain, read.text)
                # If not in list then append it.
                for email in emails:
                    valid_emails.add(email)
            except ValueError:
                pass
    except ValueError:
        raise SystemExit(1)

    # Finally return a sorted list of email addresses with no duplicates.
    return sorted(valid_emails)


if __name__ == "__main__":
    emails = emails_from_url("https://github.com")
    print(f"{len(emails)} emails found:")
    print("\n".join(sorted(emails)))


================================================
FILE: web_programming/fetch_anime_and_play.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "beautifulsoup4",
#     "fake-useragent",
#     "httpx",
# ]
# ///

import httpx
from bs4 import BeautifulSoup, NavigableString, Tag
from fake_useragent import UserAgent

BASE_URL = "https://ww7.gogoanime2.org"


def search_scraper(anime_name: str) -> list:
    """[summary]

    Take an url and
    return list of anime after scraping the site.

    >>> type(search_scraper("demon_slayer"))
    <class 'list'>

    Args:
        anime_name (str): [Name of anime]

    Raises:
        e: [Raises exception on failure]

    Returns:
        [list]: [List of animes]
    """

    # concat the name to form the search url.
    search_url = f"{BASE_URL}/search?keyword={anime_name}"

    response = httpx.get(
        search_url, headers={"UserAgent": UserAgent().chrome}, timeout=10
    )  # request the url.

    # Is the response ok?
    response.raise_for_status()

    # parse with soup.
    soup = BeautifulSoup(response.text, "html.parser")

    # get list of anime
    anime_ul = soup.find("ul", {"class": "items"})
    if anime_ul is None or isinstance(anime_ul, NavigableString):
        msg = f"Could not find and anime with name {anime_name}"
        raise ValueError(msg)
    anime_li = anime_ul.children

    # for each anime, insert to list. the name and url.
    anime_list = []
    for anime in anime_li:
        if isinstance(anime, Tag):
            anime_url = anime.find("a")
            if anime_url is None or isinstance(anime_url, NavigableString):
                continue
            anime_title = anime.find("a")
            if anime_title is None or isinstance(anime_title, NavigableString):
                continue

            anime_list.append({"title": anime_title["title"], "url": anime_url["href"]})

    return anime_list


def search_anime_episode_list(episode_endpoint: str) -> list:
    """[summary]

    Take an url and
    return list of episodes after scraping the site
    for an url.

    >>> type(search_anime_episode_list("/anime/kimetsu-no-yaiba"))
    <class 'list'>

    Args:
        episode_endpoint (str): [Endpoint of episode]

    Raises:
        e: [description]

    Returns:
        [list]: [List of episodes]
    """

    request_url = f"{BASE_URL}{episode_endpoint}"

    response = httpx.get(
        url=request_url, headers={"UserAgent": UserAgent().chrome}, timeout=10
    )
    response.raise_for_status()

    soup = BeautifulSoup(response.text, "html.parser")

    # With this id. get the episode list.
    episode_page_ul = soup.find("ul", {"id": "episode_related"})
    if episode_page_ul is None or isinstance(episode_page_ul, NavigableString):
        msg = f"Could not find any anime eposiodes with name {anime_name}"
        raise ValueError(msg)
    episode_page_li = episode_page_ul.children

    episode_list = []
    for episode in episode_page_li:
        if isinstance(episode, Tag):
            url = episode.find("a")
            if url is None or isinstance(url, NavigableString):
                continue
            title = episode.find("div", {"class": "name"})
            if title is None or isinstance(title, NavigableString):
                continue

            episode_list.append(
                {"title": title.text.replace(" ", ""), "url": url["href"]}
            )

    return episode_list


def get_anime_episode(episode_endpoint: str) -> list:
    """[summary]

    Get click url and download url from episode url

    >>> type(get_anime_episode("/watch/kimetsu-no-yaiba/1"))
    <class 'list'>

    Args:
        episode_endpoint (str): [Endpoint of episode]

    Raises:
        e: [description]

    Returns:
        [list]: [List of download and watch url]
    """

    episode_page_url = f"{BASE_URL}{episode_endpoint}"

    response = httpx.get(
        url=episode_page_url, headers={"User-Agent": UserAgent().chrome}, timeout=10
    )
    response.raise_for_status()

    soup = BeautifulSoup(response.text, "html.parser")

    url = soup.find("iframe", {"id": "playerframe"})
    if url is None or isinstance(url, NavigableString):
        msg = f"Could not find url and download url from {episode_endpoint}"
        raise RuntimeError(msg)

    episode_url = url["src"]
    if not isinstance(episode_url, str):
        msg = f"Could not find url and download url from {episode_endpoint}"
        raise RuntimeError(msg)
    download_url = episode_url.replace("/embed/", "/playlist/") + ".m3u8"

    return [f"{BASE_URL}{episode_url}", f"{BASE_URL}{download_url}"]


if __name__ == "__main__":
    anime_name = input("Enter anime name: ").strip()
    anime_list = search_scraper(anime_name)
    print("\n")

    if len(anime_list) == 0:
        print("No anime found with this name")
    else:
        print(f"Found {len(anime_list)} results: ")
        for i, anime in enumerate(anime_list):
            anime_title = anime["title"]
            print(f"{i + 1}. {anime_title}")

        anime_choice = int(input("\nPlease choose from the following list: ").strip())
        chosen_anime = anime_list[anime_choice - 1]
        print(f"You chose {chosen_anime['title']}. Searching for episodes...")

        episode_list = search_anime_episode_list(chosen_anime["url"])
        if len(episode_list) == 0:
            print("No episode found for this anime")
        else:
            print(f"Found {len(episode_list)} results: ")
            for i, episode in enumerate(episode_list):
                print(f"{i + 1}. {episode['title']}")

            episode_choice = int(input("\nChoose an episode by serial no: ").strip())
            chosen_episode = episode_list[episode_choice - 1]
            print(f"You chose {chosen_episode['title']}. Searching...")

            episode_url, download_url = get_anime_episode(chosen_episode["url"])
            print(f"\nTo watch, ctrl+click on {episode_url}.")
            print(f"To download, ctrl+click on {download_url}.")


================================================
FILE: web_programming/fetch_bbc_news.py
================================================
# Created by sarathkaul on 12/11/19

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

import httpx

_NEWS_API = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="


def fetch_bbc_news(bbc_news_api_key: str) -> None:
    # fetching a list of articles in json format
    bbc_news_page = httpx.get(_NEWS_API + bbc_news_api_key, timeout=10).json()
    # each article in the list is a dict
    for i, article in enumerate(bbc_news_page["articles"], 1):
        print(f"{i}.) {article['title']}")


if __name__ == "__main__":
    fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")


================================================
FILE: web_programming/fetch_github_info.py
================================================
#!/usr/bin/env python3
"""
Created by sarathkaul on 14/11/19
Updated by lawric1 on 24/11/20

Authentication will be made via access token.
To generate your personal access token visit https://github.com/settings/tokens.

NOTE:
Never hardcode any credential information in the code. Always use an environment
file to store the private information and use the `os` module to get the information
during runtime.

Create a ".env" file in the root directory and write these two lines in that file
with your token::

#!/usr/bin/env bash
export USER_TOKEN=""
"""

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

from __future__ import annotations

import os
from typing import Any

import httpx

BASE_URL = "https://api.github.com"

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
AUTHENTICATED_USER_ENDPOINT = BASE_URL + "/user"

# https://github.com/settings/tokens
USER_TOKEN = os.environ.get("USER_TOKEN", "")


def fetch_github_info(auth_token: str) -> dict[Any, Any]:
    """
    Fetch GitHub info of a user using the httpx module
    """
    headers = {
        "Authorization": f"token {auth_token}",
        "Accept": "application/vnd.github.v3+json",
    }
    return httpx.get(AUTHENTICATED_USER_ENDPOINT, headers=headers, timeout=10).json()


if __name__ == "__main__":  # pragma: no cover
    if USER_TOKEN:
        for key, value in fetch_github_info(USER_TOKEN).items():
            print(f"{key}: {value}")
    else:
        raise ValueError("'USER_TOKEN' field cannot be empty.")


================================================
FILE: web_programming/fetch_jobs.py
================================================
"""
Scraping jobs given job title and location from indeed website
"""

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "beautifulsoup4",
#     "httpx",
# ]
# ///

from __future__ import annotations

from collections.abc import Generator

import httpx
from bs4 import BeautifulSoup

url = "https://www.indeed.co.in/jobs?q=mobile+app+development&l="


def fetch_jobs(location: str = "mumbai") -> Generator[tuple[str, str]]:
    soup = BeautifulSoup(httpx.get(url + location, timeout=10).content, "html.parser")
    # This attribute finds out all the specifics listed in a job
    for job in soup.find_all("div", attrs={"data-tn-component": "organicJob"}):
        job_title = job.find("a", attrs={"data-tn-element": "jobTitle"}).text.strip()
        company_name = job.find("span", {"class": "company"}).text.strip()
        yield job_title, company_name


if __name__ == "__main__":
    for i, job in enumerate(fetch_jobs("Bangalore"), 1):
        print(f"Job {i:>2} is {job[0]} at {job[1]}")


================================================
FILE: web_programming/fetch_quotes.py
================================================
"""
This file fetches quotes from the " ZenQuotes API ".
It does not require any API key as it uses free tier.

For more details and premium features visit:
    https://zenquotes.io/
"""

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

import pprint

import httpx

API_ENDPOINT_URL = "https://zenquotes.io/api"


def quote_of_the_day() -> list:
    return httpx.get(API_ENDPOINT_URL + "/today", timeout=10).json()


def random_quotes() -> list:
    return httpx.get(API_ENDPOINT_URL + "/random", timeout=10).json()


if __name__ == "__main__":
    """
    response object has all the info with the quote
    To retrieve the actual quote access the response.json() object as below
    response.json() is a list of json object
        response.json()[0]['q'] = actual quote.
        response.json()[0]['a'] = author name.
        response.json()[0]['h'] = in html format.
    """
    response = random_quotes()
    pprint.pprint(response)


================================================
FILE: web_programming/fetch_well_rx_price.py
================================================
"""

Scrape the price and pharmacy name for a prescription drug from rx site
after providing the drug name and zipcode.

"""

import httpx
from bs4 import BeautifulSoup

BASE_URL = "https://www.wellrx.com/prescriptions/{}/{}/?freshSearch=true"


def fetch_pharmacy_and_price_list(drug_name: str, zip_code: str) -> list | None:
    """[summary]

    This function will take input of drug name and zipcode,
    then request to the BASE_URL site.
    Get the page data and scrape it to generate the
    list of the lowest prices for the prescription drug.

    Args:
        drug_name (str): [Drug name]
        zip_code(str): [Zip code]

    Returns:
        list: [List of pharmacy name and price]

    >>> print(fetch_pharmacy_and_price_list(None, None))
    None
    >>> print(fetch_pharmacy_and_price_list(None, 30303))
    None
    >>> print(fetch_pharmacy_and_price_list("eliquis", None))
    None
    """

    try:
        # Has user provided both inputs?
        if not drug_name or not zip_code:
            return None

        request_url = BASE_URL.format(drug_name, zip_code)
        response = httpx.get(request_url, timeout=10).raise_for_status()

        # Scrape the data using bs4
        soup = BeautifulSoup(response.text, "html.parser")

        # This list will store the name and price.
        pharmacy_price_list = []

        # Fetch all the grids that contain the items.
        grid_list = soup.find_all("div", {"class": "grid-x pharmCard"})
        if grid_list and len(grid_list) > 0:
            for grid in grid_list:
                # Get the pharmacy price.
                pharmacy_name = grid.find("p", {"class": "list-title"}).text

                # Get the price of the drug.
                price = grid.find("span", {"p", "price price-large"}).text

                pharmacy_price_list.append(
                    {
                        "pharmacy_name": pharmacy_name,
                        "price": price,
                    }
                )

        return pharmacy_price_list

    except (httpx.HTTPError, ValueError):
        return None


if __name__ == "__main__":
    # Enter a drug name and a zip code
    drug_name = input("Enter drug name: ").strip()
    zip_code = input("Enter zip code: ").strip()

    pharmacy_price_list: list | None = fetch_pharmacy_and_price_list(
        drug_name, zip_code
    )

    if pharmacy_price_list:
        print(f"\nSearch results for {drug_name} at location {zip_code}:")
        for pharmacy_price in pharmacy_price_list:
            name = pharmacy_price["pharmacy_name"]
            price = pharmacy_price["price"]

            print(f"Pharmacy: {name} Price: {price}")
    else:
        print(f"No results found for {drug_name}")


================================================
FILE: web_programming/get_amazon_product_data.py
================================================
"""
This file provides a function which will take a product name as input from the user,
and fetch from Amazon information about products of this name or category.  The product
information will include title, URL, price, ratings, and the discount available.
"""

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "beautifulsoup4",
#     "httpx",
#     "pandas",
# ]
# ///

from itertools import zip_longest

import httpx
from bs4 import BeautifulSoup
from pandas import DataFrame


def get_amazon_product_data(product: str = "laptop") -> DataFrame:
    """
    Take a product name or category as input and return product information from Amazon
    including title, URL, price, ratings, and the discount available.
    """
    url = f"https://www.amazon.in/laptop/s?k={product}"
    header = {
        "User-Agent": (
            "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36"
            "(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36"
        ),
        "Accept-Language": "en-US, en;q=0.5",
    }
    soup = BeautifulSoup(
        httpx.get(url, headers=header, timeout=10).text, features="lxml"
    )
    # Initialize a Pandas dataframe with the column titles
    data_frame = DataFrame(
        columns=[
            "Product Title",
            "Product Link",
            "Current Price of the product",
            "Product Rating",
            "MRP of the product",
            "Discount",
        ]
    )
    # Loop through each entry and store them in the dataframe
    for item, _ in zip_longest(
        soup.find_all(
            "div",
            attrs={"class": "s-result-item", "data-component-type": "s-search-result"},
        ),
        soup.find_all("div", attrs={"class": "a-row a-size-base a-color-base"}),
    ):
        try:
            product_title = item.h2.text
            product_link = "https://www.amazon.in/" + item.h2.a["href"]
            product_price = item.find("span", attrs={"class": "a-offscreen"}).text
            try:
                product_rating = item.find("span", attrs={"class": "a-icon-alt"}).text
            except AttributeError:
                product_rating = "Not available"
            try:
                product_mrp = (
                    "₹"
                    + item.find(
                        "span", attrs={"class": "a-price a-text-price"}
                    ).text.split("₹")[1]
                )
            except AttributeError:
                product_mrp = ""
            try:
                discount = float(
                    (
                        (
                            float(product_mrp.strip("₹").replace(",", ""))
                            - float(product_price.strip("₹").replace(",", ""))
                        )
                        / float(product_mrp.strip("₹").replace(",", ""))
                    )
                    * 100
                )
            except ValueError:
                discount = float("nan")
        except AttributeError:
            continue
        data_frame.loc[str(len(data_frame.index))] = [
            product_title,
            product_link,
            product_price,
            product_rating,
            product_mrp,
            discount,
        ]
    data_frame.loc[
        data_frame["Current Price of the product"] > data_frame["MRP of the product"],
        "MRP of the product",
    ] = " "
    data_frame.loc[
        data_frame["Current Price of the product"] > data_frame["MRP of the product"],
        "Discount",
    ] = " "
    data_frame.index += 1
    return data_frame


if __name__ == "__main__":
    product = "headphones"
    get_amazon_product_data(product).to_csv(f"Amazon Product Data for {product}.csv")


================================================
FILE: web_programming/get_imdb_top_250_movies_csv.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "beautifulsoup4",
#     "httpx",
# ]
# ///

from __future__ import annotations

import csv

import httpx
from bs4 import BeautifulSoup


def get_imdb_top_250_movies(url: str = "") -> dict[str, float]:
    url = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
    soup = BeautifulSoup(httpx.get(url, timeout=10).text, "html.parser")
    titles = soup.find_all("h3", class_="ipc-title__text")
    ratings = soup.find_all("span", class_="ipc-rating-star--rating")
    return {
        title.a.text: float(rating.strong.text)
        for title, rating in zip(titles, ratings)
    }


def write_movies(filename: str = "IMDb_Top_250_Movies.csv") -> None:
    movies = get_imdb_top_250_movies()
    with open(filename, "w", newline="") as out_file:
        writer = csv.writer(out_file)
        writer.writerow(["Movie title", "IMDb rating"])
        for title, rating in movies.items():
            writer.writerow([title, rating])


if __name__ == "__main__":
    write_movies()


================================================
FILE: web_programming/get_imdbtop.py.DISABLED
================================================
import bs4
import requests


def get_movie_data_from_soup(soup: bs4.element.ResultSet) -> dict[str, str]:
    return {
        "name": soup.h3.a.text,
        "genre": soup.find("span", class_="genre").text.strip(),
        "rating": soup.strong.text,
        "page_link": f"https://www.imdb.com{soup.a.get('href')}",
    }


def get_imdb_top_movies(num_movies: int = 5) -> tuple:
    """Get the top num_movies most highly rated movies from IMDB and
    return a tuple of dicts describing each movie's name, genre, rating, and URL.

    Args:
        num_movies: The number of movies to get. Defaults to 5.

    Returns:
        A list of tuples containing information about the top n movies.

    >>> len(get_imdb_top_movies(5))
    5
    >>> len(get_imdb_top_movies(-3))
    0
    >>> len(get_imdb_top_movies(4.99999))
    4
    """
    num_movies = int(float(num_movies))
    if num_movies < 1:
        return ()
    base_url = (
        "https://www.imdb.com/search/title?title_type="
        f"feature&sort=num_votes,desc&count={num_movies}"
    )
    source = bs4.BeautifulSoup(requests.get(base_url).content, "html.parser")
    return tuple(
        get_movie_data_from_soup(movie)
        for movie in source.find_all("div", class_="lister-item mode-advanced")
    )


if __name__ == "__main__":
    import json

    num_movies = int(input("How many movies would you like to see? "))
    print(
        ", ".join(
            json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
        )
    )


================================================
FILE: web_programming/get_ip_geolocation.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

import httpx


# Function to get geolocation data for an IP address
def get_ip_geolocation(ip_address: str) -> str:
    try:
        # Construct the URL for the IP geolocation API
        url = f"https://ipinfo.io/{ip_address}/json"

        # Send a GET request to the API
        response = httpx.get(url, timeout=10)

        # Check if the HTTP request was successful
        response.raise_for_status()

        # Parse the response as JSON
        data = response.json()

        # Check if city, region, and country information is available
        if "city" in data and "region" in data and "country" in data:
            location = f"Location: {data['city']}, {data['region']}, {data['country']}"
        else:
            location = "Location data not found."

        return location
    except httpx.RequestError as e:
        # Handle network-related exceptions
        return f"Request error: {e}"
    except ValueError as e:
        # Handle JSON parsing errors
        return f"JSON parsing error: {e}"


if __name__ == "__main__":
    # Prompt the user to enter an IP address
    ip_address = input("Enter an IP address: ")

    # Get the geolocation data and print it
    location = get_ip_geolocation(ip_address)
    print(location)


================================================
FILE: web_programming/get_top_billionaires.py
================================================
"""
CAUTION: You may get a json.decoding error.
This works for some of us but fails for others.
"""

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
#     "rich",
# ]
# ///

from datetime import UTC, date, datetime

import httpx
from rich import box
from rich import console as rich_console
from rich import table as rich_table

LIMIT = 10
TODAY = datetime.now(tz=UTC)
API_URL = (
    "https://www.forbes.com/forbesapi/person/rtb/0/position/true.json"
    "?fields=personName,gender,source,countryOfCitizenship,birthDate,finalWorth"
    f"&limit={LIMIT}"
)


def years_old(birth_timestamp: int, today: date | None = None) -> int:
    """
    Calculate the age in years based on the given birth date.  Only the year, month,
    and day are used in the calculation.  The time of day is ignored.

    Args:
        birth_timestamp: The date of birth.
        today: (useful for writing tests) or if None then datetime.date.today().

    Returns:
        int: The age in years.

    Examples:
    >>> today = date(2024, 1, 12)
    >>> years_old(birth_timestamp=datetime(1959, 11, 20).timestamp(), today=today)
    64
    >>> years_old(birth_timestamp=datetime(1970, 2, 13).timestamp(), today=today)
    53
    >>> all(
    ...     years_old(datetime(today.year - i, 1, 12).timestamp(), today=today) == i
    ...     for i in range(1, 111)
    ... )
    True
    """
    today = today or TODAY.date()
    birth_date = datetime.fromtimestamp(birth_timestamp, tz=UTC).date()
    return (today.year - birth_date.year) - (
        (today.month, today.day) < (birth_date.month, birth_date.day)
    )


def get_forbes_real_time_billionaires() -> list[dict[str, int | str]]:
    """
    Get the top 10 real-time billionaires using Forbes API.

    Returns:
        List of top 10 realtime billionaires data.
    """
    response_json = httpx.get(API_URL, timeout=10).json()
    return [
        {
            "Name": person["personName"],
            "Source": person["source"],
            "Country": person["countryOfCitizenship"],
            "Gender": person["gender"],
            "Worth ($)": f"{person['finalWorth'] / 1000:.1f} Billion",
            "Age": str(years_old(person["birthDate"] / 1000)),
        }
        for person in response_json["personList"]["personsLists"]
    ]


def display_billionaires(forbes_billionaires: list[dict[str, int | str]]) -> None:
    """
    Display Forbes real-time billionaires in a rich table.

    Args:
        forbes_billionaires (list): Forbes top 10 real-time billionaires
    """

    table = rich_table.Table(
        title=f"Forbes Top {LIMIT} Real-Time Billionaires at {TODAY:%Y-%m-%d %H:%M}",
        style="green",
        highlight=True,
        box=box.SQUARE,
    )
    for key in forbes_billionaires[0]:
        table.add_column(key)

    for billionaire in forbes_billionaires:
        table.add_row(*billionaire.values())

    rich_console.Console().print(table)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    display_billionaires(get_forbes_real_time_billionaires())


================================================
FILE: web_programming/get_top_hn_posts.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

from __future__ import annotations

import httpx


def get_hackernews_story(story_id: str) -> dict:
    url = f"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
    return httpx.get(url, timeout=10).json()


def hackernews_top_stories(max_stories: int = 10) -> list[dict]:
    """
    Get the top max_stories posts from HackerNews - https://news.ycombinator.com/
    """
    url = "https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
    story_ids = httpx.get(url, timeout=10).json()[:max_stories]
    return [get_hackernews_story(story_id) for story_id in story_ids]


def hackernews_top_stories_as_markdown(max_stories: int = 10) -> str:
    stories = hackernews_top_stories(max_stories)
    return "\n".join("* [{title}]({url})".format(**story) for story in stories)


if __name__ == "__main__":
    print(hackernews_top_stories_as_markdown())


================================================
FILE: web_programming/get_user_tweets.py.DISABLED
================================================
import csv

import tweepy

# Twitter API credentials
consumer_key = ""
consumer_secret = ""
access_key = ""
access_secret = ""


def get_all_tweets(screen_name: str) -> None:
    # authorize twitter, initialize tweepy
    auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
    auth.set_access_token(access_key, access_secret)
    api = tweepy.API(auth)

    # initialize a list to hold all the tweepy Tweets
    alltweets = []

    # make initial request for most recent tweets (200 is the maximum allowed count)
    new_tweets = api.user_timeline(screen_name=screen_name, count=200)

    # save most recent tweets
    alltweets.extend(new_tweets)

    # save the id of the oldest tweet less one
    oldest = alltweets[-1].id - 1

    # keep grabbing tweets until there are no tweets left to grab
    while len(new_tweets) > 0:
        print(f"getting tweets before {oldest}")

        # all subsequent requests use the max_id param to prevent duplicates
        new_tweets = api.user_timeline(
            screen_name=screen_name, count=200, max_id=oldest
        )

        # save most recent tweets
        alltweets.extend(new_tweets)

        # update the id of the oldest tweet less one
        oldest = alltweets[-1].id - 1

        print(f"...{len(alltweets)} tweets downloaded so far")

    # transform the tweepy tweets into a 2D array that will populate the csv
    outtweets = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

    # write the csv
    with open(f"new_{screen_name}_tweets.csv", "w") as f:
        writer = csv.writer(f)
        writer.writerow(["id", "created_at", "text"])
        writer.writerows(outtweets)


if __name__ == "__main__":
    # pass in the username of the account you want to download
    get_all_tweets("FirePing32")


================================================
FILE: web_programming/giphy.py
================================================
#!/usr/bin/env python3

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

import httpx

giphy_api_key = "YOUR API KEY"
# Can be fetched from https://developers.giphy.com/dashboard/


def get_gifs(query: str, api_key: str = giphy_api_key) -> list:
    """
    Get a list of URLs of GIFs based on a given query..
    """
    formatted_query = "+".join(query.split())
    url = f"https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"
    gifs = httpx.get(url, timeout=10).json()["data"]
    return [gif["url"] for gif in gifs]


if __name__ == "__main__":
    print("\n".join(get_gifs("space ship")))


================================================
FILE: web_programming/instagram_crawler.py
================================================
#!/usr/bin/env python3

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "beautifulsoup4",
#     "fake-useragent",
#     "httpx",
# ]
# ///

from __future__ import annotations

import json

import httpx
from bs4 import BeautifulSoup
from fake_useragent import UserAgent

headers = {"UserAgent": UserAgent().random}


def extract_user_profile(script) -> dict:
    """
    May raise json.decoder.JSONDecodeError
    """
    data = script.contents[0]
    info = json.loads(data[data.find('{"config"') : -1])
    return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]


class InstagramUser:
    """
    Class Instagram crawl instagram user information

    Usage: (doctest failing on GitHub Actions)
    # >>> instagram_user = InstagramUser("github")
    # >>> instagram_user.is_verified
    True
    # >>> instagram_user.biography
    'Built for developers.'
    """

    def __init__(self, username):
        self.url = f"https://www.instagram.com/{username}/"
        self.user_data = self.get_json()

    def get_json(self) -> dict:
        """
        Return a dict of user information
        """
        html = httpx.get(self.url, headers=headers, timeout=10).text
        scripts = BeautifulSoup(html, "html.parser").find_all("script")
        try:
            return extract_user_profile(scripts[4])
        except (json.decoder.JSONDecodeError, KeyError):
            return extract_user_profile(scripts[3])

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}('{self.username}')"

    def __str__(self) -> str:
        return f"{self.fullname} ({self.username}) is {self.biography}"

    @property
    def username(self) -> str:
        return self.user_data["username"]

    @property
    def fullname(self) -> str:
        return self.user_data["full_name"]

    @property
    def biography(self) -> str:
        return self.user_data["biography"]

    @property
    def email(self) -> str:
        return self.user_data["business_email"]

    @property
    def website(self) -> str:
        return self.user_data["external_url"]

    @property
    def number_of_followers(self) -> int:
        return self.user_data["edge_followed_by"]["count"]

    @property
    def number_of_followings(self) -> int:
        return self.user_data["edge_follow"]["count"]

    @property
    def number_of_posts(self) -> int:
        return self.user_data["edge_owner_to_timeline_media"]["count"]

    @property
    def profile_picture_url(self) -> str:
        return self.user_data["profile_pic_url_hd"]

    @property
    def is_verified(self) -> bool:
        return self.user_data["is_verified"]

    @property
    def is_private(self) -> bool:
        return self.user_data["is_private"]


def test_instagram_user(username: str = "github") -> None:
    """
    A self running doctest
    >>> test_instagram_user()
    """
    import os

    if os.environ.get("CI"):
        return  # test failing on GitHub Actions
    instagram_user = InstagramUser(username)
    assert instagram_user.user_data
    assert isinstance(instagram_user.user_data, dict)
    assert instagram_user.username == username
    if username != "github":
        return
    assert instagram_user.fullname == "GitHub"
    assert instagram_user.biography == "Built for developers."
    assert instagram_user.number_of_posts > 150
    assert instagram_user.number_of_followers > 120000
    assert instagram_user.number_of_followings > 15
    assert instagram_user.email == "support@github.com"
    assert instagram_user.website == "https://github.com/readme"
    assert instagram_user.profile_picture_url.startswith("https://instagram.")
    assert instagram_user.is_verified is True
    assert instagram_user.is_private is False


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    instagram_user = InstagramUser("github")
    print(instagram_user)
    print(f"{instagram_user.number_of_posts = }")
    print(f"{instagram_user.number_of_followers = }")
    print(f"{instagram_user.number_of_followings = }")
    print(f"{instagram_user.email = }")
    print(f"{instagram_user.website = }")
    print(f"{instagram_user.profile_picture_url = }")
    print(f"{instagram_user.is_verified = }")
    print(f"{instagram_user.is_private = }")


================================================
FILE: web_programming/instagram_pic.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "beautifulsoup4",
#     "httpx",
# ]
# ///

from datetime import UTC, datetime

import httpx
from bs4 import BeautifulSoup


def download_image(url: str) -> str:
    """
    Download an image from a given URL by scraping the 'og:image' meta tag.

    Parameters:
        url: The URL to scrape.

    Returns:
        A message indicating the result of the operation.
    """
    try:
        response = httpx.get(url, timeout=10)
        response.raise_for_status()
    except httpx.RequestError as e:
        return f"An error occurred during the HTTP request to {url}: {e!r}"

    soup = BeautifulSoup(response.text, "html.parser")
    image_meta_tag = soup.find("meta", {"property": "og:image"})
    if not image_meta_tag:
        return "No meta tag with property 'og:image' was found."

    image_url = image_meta_tag.get("content")
    if not image_url:
        return f"Image URL not found in meta tag {image_meta_tag}."

    try:
        image_data = httpx.get(image_url, timeout=10).content
    except httpx.RequestError as e:
        return f"An error occurred during the HTTP request to {image_url}: {e!r}"
    if not image_data:
        return f"Failed to download the image from {image_url}."

    file_name = f"{datetime.now(tz=UTC).astimezone():%Y-%m-%d_%H-%M-%S}.jpg"
    with open(file_name, "wb") as out_file:
        out_file.write(image_data)
    return f"Image downloaded and saved in the file {file_name}"


if __name__ == "__main__":
    url = input("Enter image URL: ").strip() or "https://www.instagram.com"
    print(f"download_image({url}): {download_image(url)}")


================================================
FILE: web_programming/instagram_video.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

from datetime import UTC, datetime

import httpx


def download_video(url: str) -> bytes:
    base_url = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
    video_url = httpx.get(base_url + url, timeout=10)
    return httpx.get(video_url, timeout=10).content


if __name__ == "__main__":
    url = input("Enter Video/IGTV url: ").strip()
    file_name = f"{datetime.now(tz=UTC).astimezone():%Y-%m-%d_%H-%M-%S}.mp4"
    with open(file_name, "wb") as fp:
        fp.write(download_video(url))
    print(f"Done. Video saved to disk as {file_name}.")


================================================
FILE: web_programming/nasa_data.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

import httpx


def get_apod_data(api_key: str) -> dict:
    """
    Get the APOD(Astronomical Picture of the day) data
    Get your API Key from: https://api.nasa.gov/
    """
    url = "https://api.nasa.gov/planetary/apod"
    return httpx.get(url, params={"api_key": api_key}, timeout=10).json()


def save_apod(api_key: str, path: str = ".") -> dict:
    apod_data = get_apod_data(api_key)
    img_url = apod_data["url"]
    img_name = img_url.split("/")[-1]
    response = httpx.get(img_url, timeout=10)

    with open(f"{path}/{img_name}", "wb+") as img_file:
        img_file.write(response.content)
    del response
    return apod_data


def get_archive_data(query: str) -> dict:
    """
    Get the data of a particular query from NASA archives
    """
    url = "https://images-api.nasa.gov/search"
    return httpx.get(url, params={"q": query}, timeout=10).json()


if __name__ == "__main__":
    print(save_apod("YOUR API KEY"))
    apollo_2011_items = get_archive_data("apollo 2011")["collection"]["items"]
    print(apollo_2011_items[0]["data"][0]["description"])


================================================
FILE: web_programming/open_google_results.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "beautifulsoup4",
#     "fake-useragent",
#     "httpx",
# ]
# ///

import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import httpx
from bs4 import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
    query = "%20".join(argv[1:]) if len(argv) > 1 else quote(str(input("Search: ")))

    print("Googling.....")

    url = f"https://www.google.com/search?q={query}&num=100"

    res = httpx.get(
        url,
        headers={"User-Agent": str(UserAgent().random)},
        timeout=10,
    )

    try:
        link = BeautifulSoup(res.text, "html.parser").find("div").find("a").get("href")

    except AttributeError:
        link = parse_qs(
            BeautifulSoup(res.text, "html.parser").find("div").find("a").get("href")
        )["url"][0]

    webbrowser.open(link)


================================================
FILE: web_programming/random_anime_character.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "beautifulsoup4",
#     "fake-useragent",
#     "httpx",
# ]
# ///

import os

import httpx
from bs4 import BeautifulSoup
from fake_useragent import UserAgent

headers = {"UserAgent": UserAgent().random}
URL = "https://www.mywaifulist.moe/random"


def save_image(image_url: str, image_title: str) -> None:
    """
    Saves the image of anime character
    """
    image = httpx.get(image_url, headers=headers, timeout=10)
    with open(image_title, "wb") as file:
        file.write(image.content)


def random_anime_character() -> tuple[str, str, str]:
    """
    Returns the Title, Description, and Image Title of a random anime character .
    """
    soup = BeautifulSoup(
        httpx.get(URL, headers=headers, timeout=10).text, "html.parser"
    )
    title = soup.find("meta", attrs={"property": "og:title"}).attrs["content"]
    image_url = soup.find("meta", attrs={"property": "og:image"}).attrs["content"]
    description = soup.find("p", id="description").get_text()
    _, image_extension = os.path.splitext(os.path.basename(image_url))
    image_title = title.strip().replace(" ", "_")
    image_title = f"{image_title}{image_extension}"
    save_image(image_url, image_title)
    return (title, description, image_title)


if __name__ == "__main__":
    title, desc, image_title = random_anime_character()
    print(f"{title}\n\n{desc}\n\nImage saved : {image_title}")


================================================
FILE: web_programming/recaptcha_verification.py
================================================
"""
Recaptcha is a free captcha service offered by Google in order to secure websites and
forms.  At https://www.google.com/recaptcha/admin/create you can create new recaptcha
keys and see the keys that your have already created.
* Keep in mind that recaptcha doesn't work with localhost
When you create a recaptcha key, your will get two separate keys: ClientKey & SecretKey.
ClientKey should be kept in your site's front end
SecretKey should be kept in your site's  back end

# An example HTML login form with recaptcha tag is shown below

    <form action="" method="post">
        <h2 class="text-center">Log in</h2>
        {% csrf_token %}
        <div class="form-group">
            <input type="text" name="username" required="required">
        </div>
        <div class="form-group">
            <input type="password" name="password" required="required">
        </div>
        <div class="form-group">
            <button type="submit">Log in</button>
        </div>
        <!-- Below is the recaptcha tag of html -->
        <div class="g-recaptcha" data-sitekey="ClientKey"></div>
    </form>

    <!-- Below is the recaptcha script to be kept inside html tag -->
    <script src="https://www.google.com/recaptcha/api.js" async defer></script>

Below a Django function for the views.py file contains a login form for demonstrating
recaptcha verification.
"""

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

import httpx

try:
    from django.contrib.auth import authenticate, login
    from django.shortcuts import redirect, render
except ImportError:
    authenticate = login = render = redirect = print


def login_using_recaptcha(request):
    # Enter your recaptcha secret key here
    secret_key = "secretKey"  # noqa: S105
    url = "https://www.google.com/recaptcha/api/siteverify"

    # when method is not POST, direct user to login page
    if request.method != "POST":
        return render(request, "login.html")

    # from the frontend, get username, password, and client_key
    username = request.POST.get("username")
    password = request.POST.get("password")
    client_key = request.POST.get("g-recaptcha-response")

    # post recaptcha response to Google's recaptcha api
    response = httpx.post(
        url, data={"secret": secret_key, "response": client_key}, timeout=10
    )
    # if the recaptcha api verified our keys
    if response.json().get("success", False):
        # authenticate the user
        user_in_database = authenticate(request, username=username, password=password)
        if user_in_database:
            login(request, user_in_database)
            return redirect("/your-webpage")
    return render(request, "login.html")


================================================
FILE: web_programming/reddit.py
================================================
# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

from __future__ import annotations

import httpx

valid_terms = set(
    """approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)


def get_subreddit_data(
    subreddit: str, limit: int = 1, age: str = "new", wanted_data: list | None = None
) -> dict:
    """
    subreddit : Subreddit to query
    limit : Number of posts to fetch
    age : ["new", "top", "hot"]
    wanted_data : Get only the required data in the list
    """
    wanted_data = wanted_data or []
    if invalid_search_terms := ", ".join(sorted(set(wanted_data) - valid_terms)):
        msg = f"Invalid search term: {invalid_search_terms}"
        raise ValueError(msg)
    response = httpx.get(
        f"https://www.reddit.com/r/{subreddit}/{age}.json?limit={limit}",
        headers={"User-agent": "A random string"},
        timeout=10,
    )
    response.raise_for_status()
    if response.status_code == 429:
        raise httpx.HTTPError(response=response)

    data = response.json()
    if not wanted_data:
        return {id_: data["data"]["children"][id_] for id_ in range(limit)}

    data_dict = {}
    for id_ in range(limit):
        data_dict[id_] = {
            item: data["data"]["children"][id_]["data"][item] for item in wanted_data
        }
    return data_dict


if __name__ == "__main__":
    # If you get Error 429, that means you are rate limited.Try after some time
    print(get_subreddit_data("learnpython", wanted_data=["title", "url", "selftext"]))


================================================
FILE: web_programming/search_books_by_isbn.py
================================================
"""
Get book and author data from https://openlibrary.org

ISBN: https://en.wikipedia.org/wiki/International_Standard_Book_Number
"""

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

from json import JSONDecodeError

import httpx


def get_openlibrary_data(olid: str = "isbn/0140328726") -> dict:
    """
    Given an 'isbn/0140328726', return book data from Open Library as a Python dict.
    Given an '/authors/OL34184A', return authors data as a Python dict.
    This code must work for olids with or without a leading slash ('/').

    # Comment out doctests if they take too long or have results that may change
    # >>> get_openlibrary_data(olid='isbn/0140328726')  # doctest: +ELLIPSIS
    {'publishers': ['Puffin'], 'number_of_pages': 96, 'isbn_10': ['0140328726'], ...
    # >>> get_openlibrary_data(olid='/authors/OL7353617A')  # doctest: +ELLIPSIS
    {'name': 'Adrian Brisku', 'created': {'type': '/type/datetime', ...
    """
    new_olid = olid.strip().strip("/")  # Remove leading/trailing whitespace & slashes
    if new_olid.count("/") != 1:
        msg = f"{olid} is not a valid Open Library olid"
        raise ValueError(msg)
    return httpx.get(
        f"https://openlibrary.org/{new_olid}.json", timeout=10, follow_redirects=True
    ).json()


def summarize_book(ol_book_data: dict) -> dict:
    """
    Given Open Library book data, return a summary as a Python dict.
    """
    desired_keys = {
        "title": "Title",
        "publish_date": "Publish date",
        "authors": "Authors",
        "number_of_pages": "Number of pages",
        "isbn_10": "ISBN (10)",
        "isbn_13": "ISBN (13)",
    }
    data = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
    data["Authors"] = [
        get_openlibrary_data(author["key"])["name"] for author in data["Authors"]
    ]
    for key, value in data.items():
        if isinstance(value, list):
            data[key] = ", ".join(value)
    return data


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    while True:
        isbn = input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
        if isbn.lower() in ("", "q", "quit", "exit", "stop"):
            break

        if len(isbn) not in (10, 13) or not isbn.isdigit():
            print(f"Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.")
            continue

        print(f"\nSearching Open Library for ISBN: {isbn}...\n")

        try:
            book_summary = summarize_book(get_openlibrary_data(f"isbn/{isbn}"))
            print("\n".join(f"{key}: {value}" for key, value in book_summary.items()))
        except JSONDecodeError:
            print(f"Sorry, there are no results for ISBN: {isbn}.")


================================================
FILE: web_programming/slack_message.py
================================================
# Created by sarathkaul on 12/11/19

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "httpx",
# ]
# ///

import httpx


def send_slack_message(message_body: str, slack_url: str) -> None:
    headers = {"Content-Type": "application/json"}
    response = httpx.post(
        slack_url, json={"text": message_body}, headers=headers, timeout=10
    )
    if response.status_code != 200:
        msg = (
            "Request to slack returned an error "
            f"{response.status_code}, the response is:\n{response.text}"
        )
        raise ValueError(msg)


if __name__ == "__main__":
    # Set the slack url to the one provided by Slack when you create the webhook at
    # https://my.slack.com/services/new/incoming-webhook/
    send_slack_message("<YOUR MESSAGE BODY>", "<SLACK CHANNEL URL>")


================================================
FILE: web_programming/test_fetch_github_info.py
================================================
import json

import httpx

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info


def test_fetch_github_info(monkeypatch):
    class FakeResponse:
        def __init__(self, content) -> None:
            assert isinstance(content, (bytes, str))
            self.content = content

        def json(self):
            return json.loads(self.content)

    def mock_response(*args, **kwargs):
        assert args[0] == AUTHENTICATED_USER_ENDPOINT
        assert "Authorization" in kwargs["headers"]
        assert kwargs["headers"]["Authorization"].startswith("token ")
        assert "Accept" in kwargs["headers"]
        return FakeResponse(b'{"login":"test","id":1}')

    monkeypatch.setattr(httpx, "get", mock_response)
    result = fetch_github_info("token")
    assert result["login"] == "test"
    assert result["id"] == 1


================================================
FILE: web_programming/world_covid19_stats.py
================================================
#!/usr/bin/env python3

"""
Provide the current worldwide COVID-19 statistics.
This data is being scrapped from 'https://www.worldometers.info/coronavirus/'.
"""

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "beautifulsoup4",
#     "httpx",
# ]
# ///

import httpx
from bs4 import BeautifulSoup


def world_covid19_stats(
    url: str = "https://www.worldometers.info/coronavirus/",
) -> dict:
    """
    Return a dict of current worldwide COVID-19 statistics
    """
    soup = BeautifulSoup(
        httpx.get(url, timeout=10, follow_redirects=True).text, "html.parser"
    )
    keys = soup.find_all("h1")
    values = soup.find_all("div", {"class": "maincounter-number"})
    keys += soup.find_all("span", {"class": "panel-title"})
    values += soup.find_all("div", {"class": "number-table-main"})
    return {key.text.strip(): value.text.strip() for key, value in zip(keys, values)}


if __name__ == "__main__":
    print("\033[1m COVID-19 Status of the World \033[0m\n")
    print("\n".join(f"{key}\n{value}" for key, value in world_covid19_stats().items()))